From 41246f8f5048253caa6e92066388f0f0d2debffa Mon Sep 17 00:00:00 2001
From: Mauro Donega <mauro.donega@cern.ch>
Date: Mon, 8 Mar 2021 13:26:13 +0100
Subject: [PATCH] exercises

---
 exercises/.DS_Store                           |  Bin 0 -> 8196 bytes
 .../Exercise_3_solutions-checkpoint.ipynb     |  601 +++
 .../Exercise_6_DRAFT-checkpoint.ipynb         | 1801 +++++++++
 .../Solutions_5-checkpoint.ipynb              |  649 ++++
 .../straight_line_fit-checkpoint.ipynb        |  714 ++++
 .../Exercise_1-checkpoint.ipynb               |  572 +++
 exercises/Exercise1/Exercise_1.ipynb          |  572 +++
 exercises/Exercise1/Exercise_1.pdf            |  Bin 0 -> 112058 bytes
 exercises/Exercise1/measurement.txt           |   10 +
 exercises/Exercise2/Exercise_2.ipynb          |  502 +++
 exercises/Exercise2/Exercise_2.pdf            |  Bin 0 -> 36354 bytes
 exercises/Exercise2/MultivariateNormal.png    |  Bin 0 -> 162413 bytes
 .../Exercise_3-checkpoint.ipynb               |  501 +++
 exercises/Exercise3/Exercise_3.ipynb          |  501 +++
 exercises/Exercise3/Exercise_3.pdf            |  Bin 0 -> 54052 bytes
 exercises/Exercise3/data_correlated.txt       | 1000 +++++
 exercises/Exercise3/data_uncorrelated.txt     | 1000 +++++
 exercises/Exercise4/Exercise_4.ipynb          |  436 +++
 exercises/Exercise4/Exercise_4.pdf            |  Bin 0 -> 47863 bytes
 exercises/Exercise5/Exercise_5.ipynb          |  406 ++
 exercises/Exercise5/Exercise_5.pdf            |  Bin 0 -> 67189 bytes
 exercises/Exercise5/data                      |    3 +
 exercises/Exercise5/sample                    |  200 +
 .../Exercise6/Complete_TAVG_complete.txt      | 3259 +++++++++++++++++
 exercises/Exercise6/Exercise_6_Problems.ipynb |  308 ++
 exercises/Exercise6/Exercise_6_Problems.pdf   |  Bin 0 -> 44305 bytes
 exercises/Exercise7/Exercises_7.ipynb         |  362 ++
 exercises/Exercise7/Exercises_7.pdf           |  Bin 0 -> 36618 bytes
 .../Solution_2_v2-checkpoint.ipynb            |  616 ++++
 exercises/Solution2/MultivariateNormal.png    |  Bin 0 -> 162413 bytes
 exercises/Solution2/Solution_2_v2.ipynb       |  616 ++++
 exercises/Solution2/Solution_2_v2.pdf         |  Bin 0 -> 73858 bytes
 .../LeastSquaresFits-checkpoint.ipynb         |  693 ++++
 .../Solutions_5-checkpoint.ipynb              |  693 ++++
 exercises/Solution5/Solutions_5.ipynb         |  693 ++++
 exercises/Solution5/Solutions_5.pdf           |  Bin 0 -> 184354 bytes
 exercises/Solution5/data                      |    3 +
 exercises/Solution5/sample                    |  200 +
 .../Solutions_1-checkpoint.ipynb              |  756 ++++
 exercises/Solutions1/Solutions_1.ipynb        |  756 ++++
 exercises/Solutions1/Solutions_1.pdf          |  Bin 0 -> 148963 bytes
 exercises/Solutions1/exercise-1-histogram.pdf |  Bin 0 -> 12270 bytes
 exercises/Solutions1/exercise-1-plot.pdf      |  Bin 0 -> 16493 bytes
 exercises/Solutions1/measurement.txt          |   10 +
 exercises/Solutions1/measurement_1000toys.txt | 1000 +++++
 .../Solutions_4-checkpoint.ipynb              |  788 ++++
 exercises/Solutions4/Solutions_4.ipynb        |  788 ++++
 exercises/Solutions4/Solutions_4.pdf          |  Bin 0 -> 229332 bytes
 .../Exercise_6_Solutions-checkpoint.ipynb     | 1767 +++++++++
 .../Solutions6/Exercise_6_Solutions.ipynb     | 1767 +++++++++
 exercises/Solutions6/Exercise_6_Solutions.pdf |  Bin 0 -> 270193 bytes
 .../Likelihoods-checkpoint.ipynb              |  457 +++
 .../Solutions_7-checkpoint.ipynb              |  457 +++
 exercises/Solutions7/Solutions_7.ipynb        |  457 +++
 exercises/Solutions7/Solutions_7.pdf          |  Bin 0 -> 125204 bytes
 55 files changed, 25914 insertions(+)
 create mode 100644 exercises/.DS_Store
 create mode 100644 exercises/.ipynb_checkpoints/Exercise_3_solutions-checkpoint.ipynb
 create mode 100644 exercises/.ipynb_checkpoints/Exercise_6_DRAFT-checkpoint.ipynb
 create mode 100644 exercises/.ipynb_checkpoints/Solutions_5-checkpoint.ipynb
 create mode 100644 exercises/.ipynb_checkpoints/straight_line_fit-checkpoint.ipynb
 create mode 100644 exercises/Exercise1/.ipynb_checkpoints/Exercise_1-checkpoint.ipynb
 create mode 100644 exercises/Exercise1/Exercise_1.ipynb
 create mode 100644 exercises/Exercise1/Exercise_1.pdf
 create mode 100644 exercises/Exercise1/measurement.txt
 create mode 100644 exercises/Exercise2/Exercise_2.ipynb
 create mode 100644 exercises/Exercise2/Exercise_2.pdf
 create mode 100644 exercises/Exercise2/MultivariateNormal.png
 create mode 100644 exercises/Exercise3/.ipynb_checkpoints/Exercise_3-checkpoint.ipynb
 create mode 100644 exercises/Exercise3/Exercise_3.ipynb
 create mode 100644 exercises/Exercise3/Exercise_3.pdf
 create mode 100644 exercises/Exercise3/data_correlated.txt
 create mode 100644 exercises/Exercise3/data_uncorrelated.txt
 create mode 100644 exercises/Exercise4/Exercise_4.ipynb
 create mode 100644 exercises/Exercise4/Exercise_4.pdf
 create mode 100644 exercises/Exercise5/Exercise_5.ipynb
 create mode 100644 exercises/Exercise5/Exercise_5.pdf
 create mode 100644 exercises/Exercise5/data
 create mode 100644 exercises/Exercise5/sample
 create mode 100644 exercises/Exercise6/Complete_TAVG_complete.txt
 create mode 100644 exercises/Exercise6/Exercise_6_Problems.ipynb
 create mode 100644 exercises/Exercise6/Exercise_6_Problems.pdf
 create mode 100644 exercises/Exercise7/Exercises_7.ipynb
 create mode 100644 exercises/Exercise7/Exercises_7.pdf
 create mode 100644 exercises/Solution2/.ipynb_checkpoints/Solution_2_v2-checkpoint.ipynb
 create mode 100644 exercises/Solution2/MultivariateNormal.png
 create mode 100644 exercises/Solution2/Solution_2_v2.ipynb
 create mode 100644 exercises/Solution2/Solution_2_v2.pdf
 create mode 100644 exercises/Solution5/.ipynb_checkpoints/LeastSquaresFits-checkpoint.ipynb
 create mode 100644 exercises/Solution5/.ipynb_checkpoints/Solutions_5-checkpoint.ipynb
 create mode 100644 exercises/Solution5/Solutions_5.ipynb
 create mode 100644 exercises/Solution5/Solutions_5.pdf
 create mode 100644 exercises/Solution5/data
 create mode 100644 exercises/Solution5/sample
 create mode 100644 exercises/Solutions1/.ipynb_checkpoints/Solutions_1-checkpoint.ipynb
 create mode 100644 exercises/Solutions1/Solutions_1.ipynb
 create mode 100644 exercises/Solutions1/Solutions_1.pdf
 create mode 100644 exercises/Solutions1/exercise-1-histogram.pdf
 create mode 100644 exercises/Solutions1/exercise-1-plot.pdf
 create mode 100644 exercises/Solutions1/measurement.txt
 create mode 100644 exercises/Solutions1/measurement_1000toys.txt
 create mode 100644 exercises/Solutions4/.ipynb_checkpoints/Solutions_4-checkpoint.ipynb
 create mode 100644 exercises/Solutions4/Solutions_4.ipynb
 create mode 100644 exercises/Solutions4/Solutions_4.pdf
 create mode 100644 exercises/Solutions6/.ipynb_checkpoints/Exercise_6_Solutions-checkpoint.ipynb
 create mode 100644 exercises/Solutions6/Exercise_6_Solutions.ipynb
 create mode 100644 exercises/Solutions6/Exercise_6_Solutions.pdf
 create mode 100644 exercises/Solutions7/.ipynb_checkpoints/Likelihoods-checkpoint.ipynb
 create mode 100644 exercises/Solutions7/.ipynb_checkpoints/Solutions_7-checkpoint.ipynb
 create mode 100644 exercises/Solutions7/Solutions_7.ipynb
 create mode 100644 exercises/Solutions7/Solutions_7.pdf

diff --git a/exercises/.DS_Store b/exercises/.DS_Store
new file mode 100644
index 0000000000000000000000000000000000000000..a99fb84504d9f0ab2d3342ef7196063708a3b066
GIT binary patch
literal 8196
zcmeHM&2G~`5T0#9>j<GnYNhtL7j8kaLz}b*Br7zB-jIyo0I0Q_NVR0VRqQB08sx`-
zH{uO=9GKZ%s)Ah=qX_k9M%wu{yZcRcKij(*2LQxiI`ROv0l>jR*nWtv!l<80$yRL3
zI-)>6zzM{V!5Ad$ltyy{E5Hh{0;~WlzzW<11@O!!bFMh|O)q;^fEBox3ef%_u@L$e
z3xj&=K&Mjx#3nXt!#d^xnj<ax77K%#iZM<0AS_kb6hjy~_DdR$=vyod8afC=2VuWi
z*bGJJZ%3Zg!$J54*|P$yKv@CG-8WzeA24g5{Fc8zfiAeZxR}j*&^t`jNtCDv8T615
zbNz?4zmF2lvNIdyTXdGhS?;>u#Y&_3V0FzQd*r+ePSq^P!+e?z!pR%@^dihpBU^u|
zRB}-dqgU~GcGTW{uF^b=)A2-;<Ixz0ynP*~BQ+bSX*$YuoWOLn$d1}u^Lf|Z?MQFY
zk#o=O?|0;0zqeSloUO;+v%}-}A3x2{7oWf2>d=JUdi2Uolkr~>sx;h%WoX!2N|b)>
zD6P_6;azhFR50%7)h@5JTU(o_*Dy9900jw5A;-6B3Q{}~yS1-kRh0Vo<z{Iv8P3M5
zJ)7z{J4=dF|4FHyPyJ`*@@~L&R^WCh&@j{7ru=_+_51(Zu^65uE5Hi;lLDf75F8AU
z`s)YopHV2+mROFlkkPoppr)YH={TZJ#}P07FvPmVp~Uem76!2e?Z3YWxSX@_`LC}5
IyZfiWPfY-Nr2qf`

literal 0
HcmV?d00001

diff --git a/exercises/.ipynb_checkpoints/Exercise_3_solutions-checkpoint.ipynb b/exercises/.ipynb_checkpoints/Exercise_3_solutions-checkpoint.ipynb
new file mode 100644
index 0000000..3f5e75a
--- /dev/null
+++ b/exercises/.ipynb_checkpoints/Exercise_3_solutions-checkpoint.ipynb
@@ -0,0 +1,601 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Exercise 3\n",
+    "\n",
+    "## 1. Poisson statistics\n",
+    "This exercise is about two variants of a counting experiment: in the first, simpler case, we will see that the observations are well described by a Poisson distribution. In the second case we will have events which are not independent from each other and we will see that the results deviate from a Poisson distribution.\n",
+    "\n",
+    "Consider a beam of particles impinging on a thin target. Most particles will go through the target without interacting, while a few will be absorbed. The target is connected to a detector, which fires a signal when a particle is absorbed by the target. In the first part of the exercise we assume to have a perfect detector: it is able to detect each and every particle hitting the target.\n",
+    "\n",
+    "The experiment consists in counting how many particles are absorbed by the target in a fixed time interval, e.g. 1s. We will repeat the counting *n* times and see how the results are distributed.\n",
+    "\n",
+    "To simulate the setup, assume the following numbers:<br>\n",
+    "- Number of particles arriving at the target per second\n",
+    "- Probability that a particle is absorbed by the target"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "ename": "ImportError",
+     "evalue": "No module named tqdm",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mImportError\u001b[0m                               Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-1-2665767b3ae4>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mmatplotlib\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpyplot\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      6\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mscipy\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstats\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mpoisson\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 7\u001b[0;31m \u001b[0;32mfrom\u001b[0m \u001b[0mtqdm\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mtqdm_notebook\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mtqdm\u001b[0m \u001b[0;31m# provides a nice progress bar during long computations\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      8\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      9\u001b[0m '''\n",
+      "\u001b[0;31mImportError\u001b[0m: No module named tqdm"
+     ]
+    }
+   ],
+   "source": [
+    "'''\n",
+    "Let's start by importing some useful modules and functions...\n",
+    "'''\n",
+    "from numpy.random import rand\n",
+    "import matplotlib.pyplot as plt\n",
+    "from scipy.stats import poisson\n",
+    "from tqdm import tqdm_notebook as tqdm # provides a nice progress bar during long computations\n",
+    "\n",
+    "'''\n",
+    "... and by defining the relevant parameters of the experiment\n",
+    "(feel free to change the values and see how the result changes)\n",
+    "'''\n",
+    "particle_rate = 1e6 # Number of particles arriving at the target per second\n",
+    "delta_t = 1 # duration of one experiment in seconds\n",
+    "absorption_probability = 2e-6 # Probability that a particle is absorbed by the target\n",
+    "n_trials = 200 # How many times you repeat the experiment"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Write a function which decides if a single particle is absorbed by the target (return True) or not (return False).\n",
+    "\n",
+    "Hint: generate a uniformly distributed random number between 0 and 1 with the *rand()* function. Use it do decide if the particle is detected or not, based on the known *absorption_probability*."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "def particle_is_detected(absorption_probability=absorption_probability):\n",
+    "    if rand()<absorption_probability:\n",
+    "        return True\n",
+    "    else:\n",
+    "        return False"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now write a function to simulate the experiment running for a time *delta_t*. It should do the following:\n",
+    "- compute how many particles reach the target during *delta_t* with the known *particle_rate*,\n",
+    "- for each of those check if they get absorbed or not (cf. *particle_is_detected()*),\n",
+    "- return the number of particles which are absorbed by the target during *delta_t*."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "def run_experiment(particle_rate=particle_rate,delta_t=delta_t,absorption_probability=absorption_probability):\n",
+    "    n_particles = int(particle_rate * delta_t)\n",
+    "    counted_particles = 0\n",
+    "    for i in range(n_particles):\n",
+    "        if particle_is_detected(absorption_probability): counted_particles += 1\n",
+    "    return counted_particles"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "You are now ready to run the experiment, i.e. run the function. Do this a few times to get a feeling for the results: is the number of counted particles the same every time or does it change? What kind of result do you expect from the chosen *particle_rate, delta_t* and *absorption_probability*?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "for i in range(10):\n",
+    "    print run_experiment()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Let's now analyse the results more systematically: run the experiment *n_trials* times and save the results in a list or array. Depending on your computer, this might take some time."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "def repeat_experiment(single_experiment=run_experiment,n_trials=n_trials):\n",
+    "    progress_bar = tqdm(total=n_trials, unit=' trials')\n",
+    "    counted_all = []\n",
+    "\n",
+    "    for i in range(n_trials):\n",
+    "        counted_all.append(single_experiment())\n",
+    "        progress_bar.update()\n",
+    "    return counted_all\n",
+    "\n",
+    "counted_all = repeat_experiment()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Before plotting the results in a histogram, let's define the expected Poisson distribution in order to make a comparison. If you are not sure how to do this, have a look at last week's exercise. What is the expected *mu* paramter for the given *particle_rate, delta_t* and *absorption_probability*?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "from numpy import sqrt\n",
+    "mu = particle_rate * delta_t * absorption_probability\n",
+    "n_bins = int(mu+5*sqrt(mu)) if mu>=1 else 5\n",
+    "bins = range(n_bins)\n",
+    "pois_distr = poisson.pmf(bins,mu)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now plot the results of the experiment together with the parent distribution. Again, have a look at last week's exercise if you need help. When plotting the histogram remember to set *density=True* in order to have it normalized to unity for a meaningful comparison with the Poisson distribution (if this option does not work, which might be the case for older versions, try replacing it with *normed=True*)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "plt.hist(counted_all,bins,density=True,rwidth=0.9,align='left',label='Data')\n",
+    "plt.plot(bins,pois_distr,'k+',label='Poisson distribution',ms=8)\n",
+    "plt.xlabel('Counted particles')\n",
+    "plt.legend()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "What do you observe? Is the data from the experiment well described by the Poisson distribution?\n",
+    "\n",
+    "Let's now make a different assumption about the detector: it has no longer perfect efficiency, but whenever it detects a particle it needs some time to process the signal. Durign this time the detector is blind to any particle which might be absorbed by the target. In this way the recorded particles are not independent from each other anymore, and as you will see this will cause the result of the experiment to deviate from a Poisson distribution.\n",
+    "\n",
+    "Modify your implementation of the function *run_experiment()* in order to account for the dead time of the detector. Assume that whenever the detector records a particle it is blind to the next 500000 particles reaching the target."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "def run_experiment_dead_time(particle_rate=particle_rate,delta_t=delta_t,absorption_probability=absorption_probability,dead_time=500000):\n",
+    "    n_particles = int(particle_rate * delta_t)\n",
+    "    counted_particles = 0\n",
+    "    particle_iterator = 0\n",
+    "    while particle_iterator<n_particles:\n",
+    "        if particle_is_detected(absorption_probability):\n",
+    "            counted_particles += 1\n",
+    "            particle_iterator += dead_time\n",
+    "        else:\n",
+    "            particle_iterator += 1\n",
+    "    return counted_particles\n",
+    "\n",
+    "counted_all_dead_time = repeat_experiment(run_experiment_dead_time)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Plot the new results with detector dead time together with the same Poisson distribution from before. What do you observe?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "plt.hist(counted_all_dead_time,bins,density=True,rwidth=0.9,align='left',label='Data')\n",
+    "plt.plot(bins,pois_distr,'k+',label='Poisson distribution',ms=8)\n",
+    "plt.xlabel('Counted particles')\n",
+    "plt.legend()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 2. Correlated variables and error matrix\n",
+    "\n",
+    "In this exercise you will work on a pair of correlated variables, compute the error matrix and visualize the error ellipse. Let's start from the case of two uncorrelated variables, saved in the file *data_uncorrelated.txt*. Have a look at the file: each line represents one measurement, the first number being the value of the *x* variable and the second number the value of the *y* variable."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "from matplotlib import pyplot as plt\n",
+    "\n",
+    "data = np.genfromtxt('data_uncorrelated.txt')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Plot the data. How can you recognise that *x* and *y* are not correlated? Compare the 2D distribution in the *xy* plane and the histograms of the *x* and *y* values. What do you notice about e.g. the range of the axes and the position of the means?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "f, ax = plt.subplots(1,3, figsize=(20, 6))\n",
+    "ax = ax.flatten()\n",
+    "\n",
+    "ax[0].set_xlabel(r'$x$')\n",
+    "ax[0].set_ylabel(r'$y$')\n",
+    "ax[0].axis('equal')\n",
+    "ax[0].plot(data[:,0],data[:,1],'.')\n",
+    "\n",
+    "ax[1].set_xlabel(r'$x$')\n",
+    "ax[1].hist(data[:,0],bins=range(-7,7),rwidth=.9)\n",
+    "\n",
+    "ax[2].set_xlabel(r'$y$')\n",
+    "ax[2].hist(data[:,1],bins=range(-7,7),rwidth=.9)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Compute the covariance matrix. You can either write a function to do this yourself, or use the numpy implementation. Have a look at last week's exercise if you feel lost."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "covmat = np.cov(data[:,0],data[:,1], bias=True)\n",
+    "print covmat"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Compare your result with the error matrix you expect for two uncorrelated variables, cf. slides from Lecture 3: $\\text{diag}(\\sigma_x^2,\\sigma_y^2)$. Is your result compatible with this expression?\n",
+    "<br> We will now compute the eigenvectors and eigenvalues of the covariance matrix and interpret them in terms of the properties of the distributions we just saw. As before, you can compute the values yourself or use the numpy implementation *np.linalg.eig(matrix)*"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "#Use this block to compute eigenvalues and eigenvectors of the covariance matrix and print the result\n",
+    "eigval,eigvec = np.linalg.eig(covmat)\n",
+    "#eigvec0 = eigvec[:,0]\n",
+    "print eigval\n",
+    "print eigvec"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "For this easy case of uncorrelated variables you should recognize the following: the eigenvectors are aligned with the $x$ and $y$ axes and the eigenvalues are the variances of the data along the same axes; this means that the standard deviation in the $x$ and $y$ directions are the square root of the respective eigenvalue. Keep this in mind, as we will later see what changes if the variables are correlated.\n",
+    "\n",
+    "For a visual interpretation do the following: plot again the 2D distribution of the data together with the eigenvectors multiplied by the square root of the corresponding eigenvalue (in this way the length of the vector will be the corresponding standard deviation)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "sigma = np.sqrt(eigval)\n",
+    "print sigma\n",
+    "\n",
+    "plt.plot(data[:,0],data[:,1],'.',zorder=0)\n",
+    "plt.axis('equal')\n",
+    "\n",
+    "#Use the following function to draw the vectors. The options are needed to draw them in the correct size\n",
+    "for i in range(2):\n",
+    "    plt.quiver(sigma[i]*eigvec[0,i],sigma[i]*eigvec[1,i],angles='xy', scale_units='xy', scale=1,zorder=1)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Let's now draw an ellipse with half-axes $\\sigma_x$ and $\\sigma_y$: this is the equivalent of the $1 \\sigma$ interval for a 1D Gaussian distribution. Fill in the values for $\\sigma_x,\\sigma_y$."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "phi = np.linspace(0,2*np.pi)\n",
+    "ellipse_x = sigma[0]*np.cos(phi) # x-coordinates of the points on the ellipse\n",
+    "ellipse_y = sigma[1]*np.sin(phi) # y-coordinates of the points on the ellipse\n",
+    "\n",
+    "plt.plot(ellipse_x,ellipse_y,'r')\n",
+    "plt.axis('equal')\n",
+    "\n",
+    "plt.plot(data[:,0],data[:,1],'.',zorder=0)\n",
+    "center = [np.mean(data[:,0]), np.mean(data[:,1])]\n",
+    "for i in range(2):\n",
+    "    plt.quiver(center[0],center[1],np.sqrt(eigval[i])*eigvec[0,i],np.sqrt(eigval[i])*eigvec[1,i],angles='xy', scale_units='xy', scale=1,zorder=1)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Let us now look at the case of two correlated measurements. Load the data from *data_uncorrelated.txt* and repeat the steps from above up to the drawing of the vectors; do not draw the ellipse yet, we will do that in the next step. You should be able to copy-paste most of the code from the uncorrelated case."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "data = np.genfromtxt('data_correlated.txt')\n",
+    "\n",
+    "covmat = np.cov(data[:,0],data[:,1], bias=True)\n",
+    "eigval,eigvec = np.linalg.eig(covmat)\n",
+    "sigma = np.sqrt(eigval)\n",
+    "\n",
+    "f, ax = plt.subplots(1,3, figsize=(20, 6))\n",
+    "ax = ax.flatten()\n",
+    "\n",
+    "ax[0].set_xlabel(r'$x$')\n",
+    "ax[0].set_ylabel(r'$y$')\n",
+    "ax[0].axis('equal')\n",
+    "ax[0].plot(data[:,0],data[:,1],'.',zorder=0)\n",
+    "for i in range(2):\n",
+    "    ax[0].quiver(sigma[i]*eigvec[0,i],sigma[i]*eigvec[1,i],angles='xy', scale_units='xy', scale=1,zorder=1)\n",
+    "\n",
+    "ax[1].set_xlabel(r'$x$')\n",
+    "ax[1].hist(data[:,0],bins=range(-7,7),rwidth=.9)\n",
+    "\n",
+    "ax[2].set_xlabel(r'$y$')\n",
+    "ax[2].hist(data[:,1],bins=range(-7,7),rwidth=.9)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "You should now see that the eigenvectors are not aligned with the coordinate axes anymore. However, as before, the eigenvectors represent the direction of the largest spread of the data, and the eigenvalues, i.e. the variances, define how large this spread is.\n",
+    "\n",
+    "Let's now draw the ellipse. There are different ways in which this can be done. Let's start by determining the angle of rotation *theta*. *Hint*: take the *x* and *y* components of one of the eigenvectors and use the *math.atan2()* function."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "theta = np.arctan2(eigvec[0,1],eigvec[0,0])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "To draw the rotated ellipse, define the $x$ and $y$ coordinates as before, and than rotate them by multiplying them with a rotation matrix by the angle *theta*."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "phi = np.linspace(0,2*np.pi)\n",
+    "ellipse_x = sigma[0]*np.cos(phi) # x-coordinates of the points on the ellipse\n",
+    "ellipse_y = sigma[1]*np.sin(phi) # y-coordinates of the points on the ellipse\n",
+    "\n",
+    "rotation = np.array([[np.cos(theta),np.sin(theta)],[-np.sin(theta),np.cos(theta)]])\n",
+    "ellipse = np.dot(rotation,[ellipse_x,ellipse_y]) # dot product"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now we are ready to plot everything together."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true,
+    "scrolled": true
+   },
+   "outputs": [],
+   "source": [
+    "plt.plot(data[:,0],data[:,1],'.',zorder=0)\n",
+    "for i in range(2):\n",
+    "    plt.quiver(sigma[i]*eigvec[0,i],sigma[i]*eigvec[1,i],angles='xy', scale_units='xy', scale=1,zorder=1)\n",
+    "plt.plot(ellipse[0,:],ellipse[1,:],'r')\n",
+    "plt.axis('equal')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Bonus\n",
+    "\n",
+    "The data for this exercise has been generated with the following code. Feel free to change the parameters and rerun the exercise."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "\n",
+    "n_samples = 1000\n",
+    "mu = np.array([0.,0.])\n",
+    "var_x = 4.\n",
+    "var_y = 1.\n",
+    "cov_xy = 1.\n",
+    "r = np.array([\n",
+    "        [  var_x, cov_xy,],\n",
+    "        [ cov_xy,  var_y,]\n",
+    "    ])\n",
+    "\n",
+    "y = np.random.multivariate_normal(mu, r, size=n_samples)\n",
+    "\n",
+    "with open('output.txt', 'w') as outfile:\n",
+    "    np.savetxt(outfile, y, fmt='%3.2f')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 3. Computing uncertainties on inefficiencies\n",
+    "\n",
+    "Consider an imperfect particle detector: out of all the particles hitting the detector, a fraction passes through unnoticed. The efficiency of the detector, i.e. the fraction of particles which are detected, is a very important parameter for any experimental setup. Suppose you want to measure the efficiency of a new detector. A possible approach is the following: you shoot $n$ particles on the detector, and count the number of signals $k$ which are recorded. The efficiency is then given by $\\varepsilon = k\\,/\\,n$. What is the uncertainty on this quantity? As a first approach, let's assume that $k$ and $n$ are Poisson distributed (and thus $\\delta k = \\sqrt{k}$ and $\\delta n = \\sqrt{n}$) and that we can apply the standard error propagation formula you saw in lecture 1:\n",
+    "$$ \\delta f = \\sqrt{ \\sum_{i=1}^N \\left(\\left. \\frac{\\partial f}{\\partial x_i}\\right\\vert_{x_i=x_i^0} \\delta x_i \\right)^2}. $$\n",
+    "- Show that this formula yelds the following result:\n",
+    "$$ \\delta \\varepsilon = \\sqrt{\\frac{k}{n^2} + \\frac{k^2}{n^3}}. $$\n",
+    "\n",
+    "What happens to the uncertainty for *extreme* values of $k$, i.e. $k=0$ and $k=n$? Do these results make sense? Remember that the efficiency is by definition a number between 0 and 1.\n",
+    "\n",
+    "The source of the problem is that $k$ and $n$ are not independent (the particles which are recorded are a subset of all particles which hit the detector). A way to handle this is noting that the efficiency measurement is in fact a binomial process with total events $n$ and success probability $\\varepsilon$ (see slides of Lecture 3).\n",
+    "- Using the known variance of the binomial distribution show that in this case the uncertainty is given by \n",
+    "$$ \\delta \\varepsilon = \\sqrt{\\frac{\\varepsilon (1-\\varepsilon)}{n}}.$$\n",
+    "\n",
+    "An equivalent approach is to consider, instead of the total number of particles $n$, the number $n_f$ of particles which fail to be detected. In this approach, $n = k + n_f$ is not fixed anymore and $k$ and $n_f$ are uncorrelated; thus, the standard error formula is valid.\n",
+    "- Show that applying the standard error formula to $\\varepsilon = k\\,/\\,(k+n_f)$ yelds again $$ \\delta \\varepsilon = \\sqrt{\\frac{\\varepsilon (1-\\varepsilon)}{n}}.$$\n",
+    "\n",
+    "Note that when evaluating this formula one has to use the measured (estimated) value of $\\varepsilon$, since the true value is unknown. This is a good approximation for *intermediate* values of $k$, i.e. $\\varepsilon$ not too close to 0 or 1. The exact meaning of *too close* is a matter of judgement - the extreme values $\\varepsilon = 0$ and $\\varepsilon = 1$ are clearly too close, as they yeld 0 uncertainty which is nonsense.\n",
+    "\n",
+    "For a discussion of these and more cases, as well as a more involved approach using Bayes' theorem you can have a look at<br>\n",
+    "https://www-cdf.fnal.gov/physics/statistics/notes/cdf7168_eff_uncertainties.ps and<br>\n",
+    "http://home.fnal.gov/~paterno/images/effic.pdf"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 2",
+   "language": "python",
+   "name": "python2"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 2
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython2",
+   "version": "2.7.12"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/exercises/.ipynb_checkpoints/Exercise_6_DRAFT-checkpoint.ipynb b/exercises/.ipynb_checkpoints/Exercise_6_DRAFT-checkpoint.ipynb
new file mode 100644
index 0000000..ac9e498
--- /dev/null
+++ b/exercises/.ipynb_checkpoints/Exercise_6_DRAFT-checkpoint.ipynb
@@ -0,0 +1,1801 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Exercise 6: Arbitrary distributions, moving averages, and Monte-Carlo\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 262,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "# General imports\n",
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "import datetime\n",
+    "import scipy.stats as stats\n",
+    "import matplotlib.pyplot as plt"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 1. Sampling from an arbitrary distribution\n",
+    "As seen in exercise 4, you can use uniformly distributed random variables, which are in principle themselves simple to generate, to draw samples from the normal distribution via the Box-Muller transform. A more general approach is to sample according to the inverse of the cumulative distribution function (CDF).\n",
+    "\n",
+    "A simple example is to generate numbers from the exponential distribution.\n",
+    "\n",
+    "$$ f(t;\\lambda) = \\lambda e^{-\\lambda t} $$\n",
+    "\n",
+    "* Write the CDF $F(T,\\lambda)$ and find its inverse ($T=...$)\n",
+    "* Write a function to compute this, and compare your result to that from scipy (hint: sometimes called percent-point function or quantile function)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 221,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/Users/matt/anaconda3/lib/python3.6/site-packages/ipykernel_launcher.py:5: RuntimeWarning: divide by zero encountered in log\n",
+      "  \"\"\"\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXQAAAEKCAYAAAACS67iAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nLiISJFToIiJBQoUuIhIk/gPEBfwSjHAFpwAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x1a21f10d68>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Quantile function\n",
+    "def exp_quantile(p, l):\n",
+    "    p[p<0] = 0\n",
+    "    p[p>=1] = 1\n",
+    "    return -np.log(1-p)/l  # scipy equivalent: stats.expon.ppf(p,0,1/l)\n",
+    "\n",
+    "p = np.linspace(0, 1, 100)\n",
+    "l = 0.2\n",
+    "plt.figure()\n",
+    "plt.plot(p, exp_quantile(p, l))\n",
+    "plt.plot(p, stats.expon.ppf(p,0,1/l))\n",
+    "plt.xlabel('x')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "* Now draw N samples from the uniform distribution $[0,1]$. For each sample, calculate $F^{-1}(u,\\lambda)$\n",
+    "* Plot a histogram and compare the distribution of points to the exponential pdf\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 242,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYQAAAD8CAYAAAB3u9PLAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nSUWcvAAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x1a23364358>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "N = 500\n",
+    "l = 0.2\n",
+    "x = np.random.rand(N)\n",
+    "y = exp_quantile(x, l)\n",
+    "q = np.linspace(0, 25, 200)\n",
+    "\n",
+    "plt.hist(y, bins=np.arange(25), normed=True)\n",
+    "plt.plot(q, stats.expon.pdf(q, scale=1/l))\n",
+    "plt.show()\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# 2. Smoothing data\n",
+    "## 2.1 Moving average\n",
+    "The moving average, or rolling mean, is a simple technique which can be used to remove short term or periodic (e.g. seasonal) variations in time series data, for example. It can be viewed as a \"smoothing\", and can ease trend spotting, for instance. One has to be careful when interpreting and using the result; for instance, it is generally improper to fit on such data.\n",
+    "\n",
+    "The simplest moving average can be computed using a \"sliding window\" of length $N$, with all weights equal. For example, for a 3 point moving average, the window would be $\\frac{1}{3}[1,1,1]$.\n",
+    "\n",
+    "* Write a function to compute the $N$ point moving average of a data series"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 270,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "def moving_average(y, length):\n",
+    "    return np.convolve(np.ones(length)/length, y, 'same')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The following line of code loads a dataset (into a ```pandas DataFrame```) containing monthly measurements of variation in the global surface temperature, stretching back as far as 1750. (More data like this can be found on http://berkeleyearth.org)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 258,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style>\n",
+       "    .dataframe thead tr:only-child th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: left;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Year</th>\n",
+       "      <th>Month</th>\n",
+       "      <th>MDiff</th>\n",
+       "      <th>MUnc</th>\n",
+       "      <th>YDiff</th>\n",
+       "      <th>YUnc</th>\n",
+       "      <th>5YDiff</th>\n",
+       "      <th>5YUnc</th>\n",
+       "      <th>10YDiff</th>\n",
+       "      <th>10YUnc</th>\n",
+       "      <th>20YDiff</th>\n",
+       "      <th>20YUnc</th>\n",
+       "      <th>Date</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1750</td>\n",
+       "      <td>1</td>\n",
+       "      <td>-0.121</td>\n",
+       "      <td>4.187</td>\n",
+       "      <td>-0.687</td>\n",
+       "      <td>2.557</td>\n",
+       "      <td>-0.364</td>\n",
+       "      <td>0.897</td>\n",
+       "      <td>-0.160</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1750-01-15</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>1750</td>\n",
+       "      <td>2</td>\n",
+       "      <td>-1.278</td>\n",
+       "      <td>3.177</td>\n",
+       "      <td>-0.691</td>\n",
+       "      <td>1.733</td>\n",
+       "      <td>-0.381</td>\n",
+       "      <td>0.904</td>\n",
+       "      <td>-0.169</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1750-02-15</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>1750</td>\n",
+       "      <td>3</td>\n",
+       "      <td>0.112</td>\n",
+       "      <td>3.550</td>\n",
+       "      <td>-0.721</td>\n",
+       "      <td>1.568</td>\n",
+       "      <td>-0.401</td>\n",
+       "      <td>0.918</td>\n",
+       "      <td>-0.164</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1750-03-15</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>1750</td>\n",
+       "      <td>4</td>\n",
+       "      <td>0.026</td>\n",
+       "      <td>2.862</td>\n",
+       "      <td>-0.734</td>\n",
+       "      <td>1.609</td>\n",
+       "      <td>-0.452</td>\n",
+       "      <td>0.951</td>\n",
+       "      <td>-0.168</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1750-04-15</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>1750</td>\n",
+       "      <td>5</td>\n",
+       "      <td>-1.420</td>\n",
+       "      <td>2.611</td>\n",
+       "      <td>-1.043</td>\n",
+       "      <td>1.553</td>\n",
+       "      <td>-0.439</td>\n",
+       "      <td>1.022</td>\n",
+       "      <td>-0.167</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1750-05-15</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>1750</td>\n",
+       "      <td>6</td>\n",
+       "      <td>-1.029</td>\n",
+       "      <td>3.379</td>\n",
+       "      <td>-1.004</td>\n",
+       "      <td>1.271</td>\n",
+       "      <td>-0.414</td>\n",
+       "      <td>1.060</td>\n",
+       "      <td>-0.176</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1750-06-15</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>1750</td>\n",
+       "      <td>7</td>\n",
+       "      <td>-0.262</td>\n",
+       "      <td>2.722</td>\n",
+       "      <td>-1.049</td>\n",
+       "      <td>1.026</td>\n",
+       "      <td>-0.411</td>\n",
+       "      <td>1.023</td>\n",
+       "      <td>-0.183</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1750-07-15</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>1750</td>\n",
+       "      <td>8</td>\n",
+       "      <td>0.290</td>\n",
+       "      <td>3.219</td>\n",
+       "      <td>-1.137</td>\n",
+       "      <td>0.792</td>\n",
+       "      <td>-0.466</td>\n",
+       "      <td>0.933</td>\n",
+       "      <td>-0.210</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1750-08-15</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>1750</td>\n",
+       "      <td>9</td>\n",
+       "      <td>-0.851</td>\n",
+       "      <td>2.121</td>\n",
+       "      <td>-1.107</td>\n",
+       "      <td>0.775</td>\n",
+       "      <td>-0.375</td>\n",
+       "      <td>0.945</td>\n",
+       "      <td>-0.230</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1750-09-15</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>1750</td>\n",
+       "      <td>10</td>\n",
+       "      <td>-1.448</td>\n",
+       "      <td>3.078</td>\n",
+       "      <td>-1.167</td>\n",
+       "      <td>0.826</td>\n",
+       "      <td>-0.394</td>\n",
+       "      <td>1.023</td>\n",
+       "      <td>-0.211</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1750-10-15</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10</th>\n",
+       "      <td>1750</td>\n",
+       "      <td>11</td>\n",
+       "      <td>-3.518</td>\n",
+       "      <td>1.996</td>\n",
+       "      <td>-1.160</td>\n",
+       "      <td>1.283</td>\n",
+       "      <td>-0.423</td>\n",
+       "      <td>1.094</td>\n",
+       "      <td>-0.226</td>\n",
+       "      <td>0.879</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1750-11-15</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>11</th>\n",
+       "      <td>1750</td>\n",
+       "      <td>12</td>\n",
+       "      <td>-2.538</td>\n",
+       "      <td>4.091</td>\n",
+       "      <td>-1.210</td>\n",
+       "      <td>1.458</td>\n",
+       "      <td>-0.451</td>\n",
+       "      <td>1.143</td>\n",
+       "      <td>-0.250</td>\n",
+       "      <td>0.894</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1750-12-15</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>12</th>\n",
+       "      <td>1751</td>\n",
+       "      <td>1</td>\n",
+       "      <td>-0.659</td>\n",
+       "      <td>3.318</td>\n",
+       "      <td>-1.094</td>\n",
+       "      <td>1.533</td>\n",
+       "      <td>-0.464</td>\n",
+       "      <td>1.148</td>\n",
+       "      <td>-0.258</td>\n",
+       "      <td>0.844</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1751-01-15</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>13</th>\n",
+       "      <td>1751</td>\n",
+       "      <td>2</td>\n",
+       "      <td>-2.341</td>\n",
+       "      <td>4.503</td>\n",
+       "      <td>-1.047</td>\n",
+       "      <td>1.776</td>\n",
+       "      <td>-0.482</td>\n",
+       "      <td>1.131</td>\n",
+       "      <td>-0.231</td>\n",
+       "      <td>0.914</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1751-02-15</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>14</th>\n",
+       "      <td>1751</td>\n",
+       "      <td>3</td>\n",
+       "      <td>0.477</td>\n",
+       "      <td>2.778</td>\n",
+       "      <td>-1.068</td>\n",
+       "      <td>1.673</td>\n",
+       "      <td>-0.488</td>\n",
+       "      <td>1.200</td>\n",
+       "      <td>-0.201</td>\n",
+       "      <td>0.952</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1751-03-15</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>15</th>\n",
+       "      <td>1751</td>\n",
+       "      <td>4</td>\n",
+       "      <td>-0.690</td>\n",
+       "      <td>2.489</td>\n",
+       "      <td>-0.933</td>\n",
+       "      <td>1.504</td>\n",
+       "      <td>-0.492</td>\n",
+       "      <td>1.245</td>\n",
+       "      <td>-0.184</td>\n",
+       "      <td>1.004</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1751-04-15</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>16</th>\n",
+       "      <td>1751</td>\n",
+       "      <td>5</td>\n",
+       "      <td>-1.338</td>\n",
+       "      <td>3.435</td>\n",
+       "      <td>-0.771</td>\n",
+       "      <td>1.606</td>\n",
+       "      <td>-0.486</td>\n",
+       "      <td>1.336</td>\n",
+       "      <td>-0.184</td>\n",
+       "      <td>1.019</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1751-05-15</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>17</th>\n",
+       "      <td>1751</td>\n",
+       "      <td>6</td>\n",
+       "      <td>-1.637</td>\n",
+       "      <td>3.336</td>\n",
+       "      <td>-0.721</td>\n",
+       "      <td>1.085</td>\n",
+       "      <td>-0.539</td>\n",
+       "      <td>1.393</td>\n",
+       "      <td>-0.188</td>\n",
+       "      <td>1.075</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1751-06-15</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>18</th>\n",
+       "      <td>1751</td>\n",
+       "      <td>7</td>\n",
+       "      <td>1.130</td>\n",
+       "      <td>3.753</td>\n",
+       "      <td>-0.876</td>\n",
+       "      <td>1.400</td>\n",
+       "      <td>-0.527</td>\n",
+       "      <td>1.212</td>\n",
+       "      <td>-0.208</td>\n",
+       "      <td>1.084</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1751-07-15</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>19</th>\n",
+       "      <td>1751</td>\n",
+       "      <td>8</td>\n",
+       "      <td>0.858</td>\n",
+       "      <td>2.757</td>\n",
+       "      <td>-0.409</td>\n",
+       "      <td>1.841</td>\n",
+       "      <td>-0.538</td>\n",
+       "      <td>1.097</td>\n",
+       "      <td>-0.221</td>\n",
+       "      <td>1.106</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1751-08-15</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>20</th>\n",
+       "      <td>1751</td>\n",
+       "      <td>9</td>\n",
+       "      <td>-1.098</td>\n",
+       "      <td>2.928</td>\n",
+       "      <td>-0.382</td>\n",
+       "      <td>1.840</td>\n",
+       "      <td>-0.531</td>\n",
+       "      <td>1.123</td>\n",
+       "      <td>-0.225</td>\n",
+       "      <td>1.119</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1751-09-15</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>21</th>\n",
+       "      <td>1751</td>\n",
+       "      <td>10</td>\n",
+       "      <td>0.169</td>\n",
+       "      <td>4.986</td>\n",
+       "      <td>-0.429</td>\n",
+       "      <td>1.791</td>\n",
+       "      <td>-0.446</td>\n",
+       "      <td>1.151</td>\n",
+       "      <td>-0.219</td>\n",
+       "      <td>1.148</td>\n",
+       "      <td>-0.276</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1751-10-15</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>22</th>\n",
+       "      <td>1751</td>\n",
+       "      <td>11</td>\n",
+       "      <td>-1.577</td>\n",
+       "      <td>2.326</td>\n",
+       "      <td>-0.302</td>\n",
+       "      <td>1.688</td>\n",
+       "      <td>-0.437</td>\n",
+       "      <td>1.160</td>\n",
+       "      <td>-0.222</td>\n",
+       "      <td>1.178</td>\n",
+       "      <td>-0.286</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1751-11-15</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>23</th>\n",
+       "      <td>1751</td>\n",
+       "      <td>12</td>\n",
+       "      <td>-1.935</td>\n",
+       "      <td>3.412</td>\n",
+       "      <td>-0.129</td>\n",
+       "      <td>1.784</td>\n",
+       "      <td>-0.426</td>\n",
+       "      <td>1.293</td>\n",
+       "      <td>-0.258</td>\n",
+       "      <td>1.173</td>\n",
+       "      <td>-0.316</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1751-12-15</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>24</th>\n",
+       "      <td>1752</td>\n",
+       "      <td>1</td>\n",
+       "      <td>-2.523</td>\n",
+       "      <td>4.962</td>\n",
+       "      <td>-0.154</td>\n",
+       "      <td>1.757</td>\n",
+       "      <td>-0.431</td>\n",
+       "      <td>1.296</td>\n",
+       "      <td>-0.262</td>\n",
+       "      <td>1.160</td>\n",
+       "      <td>-0.299</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1752-01-15</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25</th>\n",
+       "      <td>1752</td>\n",
+       "      <td>2</td>\n",
+       "      <td>3.263</td>\n",
+       "      <td>4.891</td>\n",
+       "      <td>-0.311</td>\n",
+       "      <td>1.743</td>\n",
+       "      <td>-0.461</td>\n",
+       "      <td>1.061</td>\n",
+       "      <td>-0.216</td>\n",
+       "      <td>1.213</td>\n",
+       "      <td>-0.299</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1752-02-15</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>26</th>\n",
+       "      <td>1752</td>\n",
+       "      <td>3</td>\n",
+       "      <td>0.804</td>\n",
+       "      <td>3.040</td>\n",
+       "      <td>-0.166</td>\n",
+       "      <td>1.570</td>\n",
+       "      <td>-0.480</td>\n",
+       "      <td>1.053</td>\n",
+       "      <td>-0.192</td>\n",
+       "      <td>1.258</td>\n",
+       "      <td>-0.303</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1752-03-15</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>27</th>\n",
+       "      <td>1752</td>\n",
+       "      <td>4</td>\n",
+       "      <td>-1.259</td>\n",
+       "      <td>2.243</td>\n",
+       "      <td>-0.263</td>\n",
+       "      <td>1.645</td>\n",
+       "      <td>-0.447</td>\n",
+       "      <td>1.072</td>\n",
+       "      <td>-0.185</td>\n",
+       "      <td>1.364</td>\n",
+       "      <td>-0.295</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1752-04-15</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>28</th>\n",
+       "      <td>1752</td>\n",
+       "      <td>5</td>\n",
+       "      <td>0.196</td>\n",
+       "      <td>1.576</td>\n",
+       "      <td>-0.090</td>\n",
+       "      <td>1.758</td>\n",
+       "      <td>-0.449</td>\n",
+       "      <td>1.030</td>\n",
+       "      <td>-0.178</td>\n",
+       "      <td>1.431</td>\n",
+       "      <td>-0.293</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1752-05-15</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>29</th>\n",
+       "      <td>1752</td>\n",
+       "      <td>6</td>\n",
+       "      <td>0.434</td>\n",
+       "      <td>3.225</td>\n",
+       "      <td>0.040</td>\n",
+       "      <td>1.815</td>\n",
+       "      <td>-0.390</td>\n",
+       "      <td>1.072</td>\n",
+       "      <td>-0.179</td>\n",
+       "      <td>1.504</td>\n",
+       "      <td>-0.293</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1752-06-15</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3195</th>\n",
+       "      <td>2016</td>\n",
+       "      <td>4</td>\n",
+       "      <td>1.796</td>\n",
+       "      <td>0.111</td>\n",
+       "      <td>1.454</td>\n",
+       "      <td>0.042</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2016-04-15</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3196</th>\n",
+       "      <td>2016</td>\n",
+       "      <td>5</td>\n",
+       "      <td>1.260</td>\n",
+       "      <td>0.112</td>\n",
+       "      <td>1.433</td>\n",
+       "      <td>0.040</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2016-05-15</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3197</th>\n",
+       "      <td>2016</td>\n",
+       "      <td>6</td>\n",
+       "      <td>0.882</td>\n",
+       "      <td>0.078</td>\n",
+       "      <td>1.387</td>\n",
+       "      <td>0.034</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2016-06-15</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3198</th>\n",
+       "      <td>2016</td>\n",
+       "      <td>7</td>\n",
+       "      <td>0.935</td>\n",
+       "      <td>0.046</td>\n",
+       "      <td>1.385</td>\n",
+       "      <td>0.029</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2016-07-15</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3199</th>\n",
+       "      <td>2016</td>\n",
+       "      <td>8</td>\n",
+       "      <td>1.433</td>\n",
+       "      <td>0.102</td>\n",
+       "      <td>1.348</td>\n",
+       "      <td>0.028</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2016-08-15</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3200</th>\n",
+       "      <td>2016</td>\n",
+       "      <td>9</td>\n",
+       "      <td>1.058</td>\n",
+       "      <td>0.082</td>\n",
+       "      <td>1.321</td>\n",
+       "      <td>0.027</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2016-09-15</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3201</th>\n",
+       "      <td>2016</td>\n",
+       "      <td>10</td>\n",
+       "      <td>1.019</td>\n",
+       "      <td>0.062</td>\n",
+       "      <td>1.280</td>\n",
+       "      <td>0.031</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2016-10-15</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3202</th>\n",
+       "      <td>2016</td>\n",
+       "      <td>11</td>\n",
+       "      <td>1.079</td>\n",
+       "      <td>0.095</td>\n",
+       "      <td>1.278</td>\n",
+       "      <td>0.031</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2016-11-15</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3203</th>\n",
+       "      <td>2016</td>\n",
+       "      <td>12</td>\n",
+       "      <td>1.259</td>\n",
+       "      <td>0.077</td>\n",
+       "      <td>1.271</td>\n",
+       "      <td>0.035</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2016-12-15</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3204</th>\n",
+       "      <td>2017</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1.569</td>\n",
+       "      <td>0.082</td>\n",
+       "      <td>1.275</td>\n",
+       "      <td>0.038</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2017-01-15</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3205</th>\n",
+       "      <td>2017</td>\n",
+       "      <td>2</td>\n",
+       "      <td>1.746</td>\n",
+       "      <td>0.062</td>\n",
+       "      <td>1.244</td>\n",
+       "      <td>0.039</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2017-02-15</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3206</th>\n",
+       "      <td>2017</td>\n",
+       "      <td>3</td>\n",
+       "      <td>1.831</td>\n",
+       "      <td>0.052</td>\n",
+       "      <td>1.231</td>\n",
+       "      <td>0.037</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2017-03-15</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3207</th>\n",
+       "      <td>2017</td>\n",
+       "      <td>4</td>\n",
+       "      <td>1.301</td>\n",
+       "      <td>0.144</td>\n",
+       "      <td>1.253</td>\n",
+       "      <td>0.038</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2017-04-15</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3208</th>\n",
+       "      <td>2017</td>\n",
+       "      <td>5</td>\n",
+       "      <td>1.235</td>\n",
+       "      <td>0.132</td>\n",
+       "      <td>1.249</td>\n",
+       "      <td>0.036</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2017-05-15</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3209</th>\n",
+       "      <td>2017</td>\n",
+       "      <td>6</td>\n",
+       "      <td>0.803</td>\n",
+       "      <td>0.089</td>\n",
+       "      <td>1.268</td>\n",
+       "      <td>0.040</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2017-06-15</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3210</th>\n",
+       "      <td>2017</td>\n",
+       "      <td>7</td>\n",
+       "      <td>0.973</td>\n",
+       "      <td>0.079</td>\n",
+       "      <td>1.235</td>\n",
+       "      <td>0.038</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2017-07-15</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3211</th>\n",
+       "      <td>2017</td>\n",
+       "      <td>8</td>\n",
+       "      <td>1.066</td>\n",
+       "      <td>0.086</td>\n",
+       "      <td>1.180</td>\n",
+       "      <td>0.039</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2017-08-15</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3212</th>\n",
+       "      <td>2017</td>\n",
+       "      <td>9</td>\n",
+       "      <td>0.906</td>\n",
+       "      <td>0.093</td>\n",
+       "      <td>1.142</td>\n",
+       "      <td>0.042</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2017-09-15</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3213</th>\n",
+       "      <td>2017</td>\n",
+       "      <td>10</td>\n",
+       "      <td>1.275</td>\n",
+       "      <td>0.048</td>\n",
+       "      <td>1.145</td>\n",
+       "      <td>0.041</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2017-10-15</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3214</th>\n",
+       "      <td>2017</td>\n",
+       "      <td>11</td>\n",
+       "      <td>1.035</td>\n",
+       "      <td>0.080</td>\n",
+       "      <td>1.138</td>\n",
+       "      <td>0.040</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2017-11-15</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3215</th>\n",
+       "      <td>2017</td>\n",
+       "      <td>12</td>\n",
+       "      <td>1.487</td>\n",
+       "      <td>0.073</td>\n",
+       "      <td>1.161</td>\n",
+       "      <td>0.040</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2017-12-15</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3216</th>\n",
+       "      <td>2018</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1.171</td>\n",
+       "      <td>0.093</td>\n",
+       "      <td>1.172</td>\n",
+       "      <td>0.038</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2018-01-15</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3217</th>\n",
+       "      <td>2018</td>\n",
+       "      <td>2</td>\n",
+       "      <td>1.093</td>\n",
+       "      <td>0.102</td>\n",
+       "      <td>1.166</td>\n",
+       "      <td>0.035</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2018-02-15</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3218</th>\n",
+       "      <td>2018</td>\n",
+       "      <td>3</td>\n",
+       "      <td>1.366</td>\n",
+       "      <td>0.091</td>\n",
+       "      <td>1.158</td>\n",
+       "      <td>0.042</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2018-03-15</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3219</th>\n",
+       "      <td>2018</td>\n",
+       "      <td>4</td>\n",
+       "      <td>1.342</td>\n",
+       "      <td>0.112</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2018-04-15</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3220</th>\n",
+       "      <td>2018</td>\n",
+       "      <td>5</td>\n",
+       "      <td>1.147</td>\n",
+       "      <td>0.170</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2018-05-15</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3221</th>\n",
+       "      <td>2018</td>\n",
+       "      <td>6</td>\n",
+       "      <td>1.078</td>\n",
+       "      <td>0.122</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2018-06-15</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3222</th>\n",
+       "      <td>2018</td>\n",
+       "      <td>7</td>\n",
+       "      <td>1.112</td>\n",
+       "      <td>0.039</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2018-07-15</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3223</th>\n",
+       "      <td>2018</td>\n",
+       "      <td>8</td>\n",
+       "      <td>0.991</td>\n",
+       "      <td>0.107</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2018-08-15</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3224</th>\n",
+       "      <td>2018</td>\n",
+       "      <td>9</td>\n",
+       "      <td>0.804</td>\n",
+       "      <td>0.161</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2018-09-15</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>3225 rows × 13 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "      Year  Month  MDiff   MUnc  YDiff   YUnc  5YDiff  5YUnc  10YDiff  10YUnc  \\\n",
+       "0     1750      1 -0.121  4.187 -0.687  2.557  -0.364  0.897   -0.160     NaN   \n",
+       "1     1750      2 -1.278  3.177 -0.691  1.733  -0.381  0.904   -0.169     NaN   \n",
+       "2     1750      3  0.112  3.550 -0.721  1.568  -0.401  0.918   -0.164     NaN   \n",
+       "3     1750      4  0.026  2.862 -0.734  1.609  -0.452  0.951   -0.168     NaN   \n",
+       "4     1750      5 -1.420  2.611 -1.043  1.553  -0.439  1.022   -0.167     NaN   \n",
+       "5     1750      6 -1.029  3.379 -1.004  1.271  -0.414  1.060   -0.176     NaN   \n",
+       "6     1750      7 -0.262  2.722 -1.049  1.026  -0.411  1.023   -0.183     NaN   \n",
+       "7     1750      8  0.290  3.219 -1.137  0.792  -0.466  0.933   -0.210     NaN   \n",
+       "8     1750      9 -0.851  2.121 -1.107  0.775  -0.375  0.945   -0.230     NaN   \n",
+       "9     1750     10 -1.448  3.078 -1.167  0.826  -0.394  1.023   -0.211     NaN   \n",
+       "10    1750     11 -3.518  1.996 -1.160  1.283  -0.423  1.094   -0.226   0.879   \n",
+       "11    1750     12 -2.538  4.091 -1.210  1.458  -0.451  1.143   -0.250   0.894   \n",
+       "12    1751      1 -0.659  3.318 -1.094  1.533  -0.464  1.148   -0.258   0.844   \n",
+       "13    1751      2 -2.341  4.503 -1.047  1.776  -0.482  1.131   -0.231   0.914   \n",
+       "14    1751      3  0.477  2.778 -1.068  1.673  -0.488  1.200   -0.201   0.952   \n",
+       "15    1751      4 -0.690  2.489 -0.933  1.504  -0.492  1.245   -0.184   1.004   \n",
+       "16    1751      5 -1.338  3.435 -0.771  1.606  -0.486  1.336   -0.184   1.019   \n",
+       "17    1751      6 -1.637  3.336 -0.721  1.085  -0.539  1.393   -0.188   1.075   \n",
+       "18    1751      7  1.130  3.753 -0.876  1.400  -0.527  1.212   -0.208   1.084   \n",
+       "19    1751      8  0.858  2.757 -0.409  1.841  -0.538  1.097   -0.221   1.106   \n",
+       "20    1751      9 -1.098  2.928 -0.382  1.840  -0.531  1.123   -0.225   1.119   \n",
+       "21    1751     10  0.169  4.986 -0.429  1.791  -0.446  1.151   -0.219   1.148   \n",
+       "22    1751     11 -1.577  2.326 -0.302  1.688  -0.437  1.160   -0.222   1.178   \n",
+       "23    1751     12 -1.935  3.412 -0.129  1.784  -0.426  1.293   -0.258   1.173   \n",
+       "24    1752      1 -2.523  4.962 -0.154  1.757  -0.431  1.296   -0.262   1.160   \n",
+       "25    1752      2  3.263  4.891 -0.311  1.743  -0.461  1.061   -0.216   1.213   \n",
+       "26    1752      3  0.804  3.040 -0.166  1.570  -0.480  1.053   -0.192   1.258   \n",
+       "27    1752      4 -1.259  2.243 -0.263  1.645  -0.447  1.072   -0.185   1.364   \n",
+       "28    1752      5  0.196  1.576 -0.090  1.758  -0.449  1.030   -0.178   1.431   \n",
+       "29    1752      6  0.434  3.225  0.040  1.815  -0.390  1.072   -0.179   1.504   \n",
+       "...    ...    ...    ...    ...    ...    ...     ...    ...      ...     ...   \n",
+       "3195  2016      4  1.796  0.111  1.454  0.042     NaN    NaN      NaN     NaN   \n",
+       "3196  2016      5  1.260  0.112  1.433  0.040     NaN    NaN      NaN     NaN   \n",
+       "3197  2016      6  0.882  0.078  1.387  0.034     NaN    NaN      NaN     NaN   \n",
+       "3198  2016      7  0.935  0.046  1.385  0.029     NaN    NaN      NaN     NaN   \n",
+       "3199  2016      8  1.433  0.102  1.348  0.028     NaN    NaN      NaN     NaN   \n",
+       "3200  2016      9  1.058  0.082  1.321  0.027     NaN    NaN      NaN     NaN   \n",
+       "3201  2016     10  1.019  0.062  1.280  0.031     NaN    NaN      NaN     NaN   \n",
+       "3202  2016     11  1.079  0.095  1.278  0.031     NaN    NaN      NaN     NaN   \n",
+       "3203  2016     12  1.259  0.077  1.271  0.035     NaN    NaN      NaN     NaN   \n",
+       "3204  2017      1  1.569  0.082  1.275  0.038     NaN    NaN      NaN     NaN   \n",
+       "3205  2017      2  1.746  0.062  1.244  0.039     NaN    NaN      NaN     NaN   \n",
+       "3206  2017      3  1.831  0.052  1.231  0.037     NaN    NaN      NaN     NaN   \n",
+       "3207  2017      4  1.301  0.144  1.253  0.038     NaN    NaN      NaN     NaN   \n",
+       "3208  2017      5  1.235  0.132  1.249  0.036     NaN    NaN      NaN     NaN   \n",
+       "3209  2017      6  0.803  0.089  1.268  0.040     NaN    NaN      NaN     NaN   \n",
+       "3210  2017      7  0.973  0.079  1.235  0.038     NaN    NaN      NaN     NaN   \n",
+       "3211  2017      8  1.066  0.086  1.180  0.039     NaN    NaN      NaN     NaN   \n",
+       "3212  2017      9  0.906  0.093  1.142  0.042     NaN    NaN      NaN     NaN   \n",
+       "3213  2017     10  1.275  0.048  1.145  0.041     NaN    NaN      NaN     NaN   \n",
+       "3214  2017     11  1.035  0.080  1.138  0.040     NaN    NaN      NaN     NaN   \n",
+       "3215  2017     12  1.487  0.073  1.161  0.040     NaN    NaN      NaN     NaN   \n",
+       "3216  2018      1  1.171  0.093  1.172  0.038     NaN    NaN      NaN     NaN   \n",
+       "3217  2018      2  1.093  0.102  1.166  0.035     NaN    NaN      NaN     NaN   \n",
+       "3218  2018      3  1.366  0.091  1.158  0.042     NaN    NaN      NaN     NaN   \n",
+       "3219  2018      4  1.342  0.112    NaN    NaN     NaN    NaN      NaN     NaN   \n",
+       "3220  2018      5  1.147  0.170    NaN    NaN     NaN    NaN      NaN     NaN   \n",
+       "3221  2018      6  1.078  0.122    NaN    NaN     NaN    NaN      NaN     NaN   \n",
+       "3222  2018      7  1.112  0.039    NaN    NaN     NaN    NaN      NaN     NaN   \n",
+       "3223  2018      8  0.991  0.107    NaN    NaN     NaN    NaN      NaN     NaN   \n",
+       "3224  2018      9  0.804  0.161    NaN    NaN     NaN    NaN      NaN     NaN   \n",
+       "\n",
+       "      20YDiff  20YUnc       Date  \n",
+       "0         NaN     NaN 1750-01-15  \n",
+       "1         NaN     NaN 1750-02-15  \n",
+       "2         NaN     NaN 1750-03-15  \n",
+       "3         NaN     NaN 1750-04-15  \n",
+       "4         NaN     NaN 1750-05-15  \n",
+       "5         NaN     NaN 1750-06-15  \n",
+       "6         NaN     NaN 1750-07-15  \n",
+       "7         NaN     NaN 1750-08-15  \n",
+       "8         NaN     NaN 1750-09-15  \n",
+       "9         NaN     NaN 1750-10-15  \n",
+       "10        NaN     NaN 1750-11-15  \n",
+       "11        NaN     NaN 1750-12-15  \n",
+       "12        NaN     NaN 1751-01-15  \n",
+       "13        NaN     NaN 1751-02-15  \n",
+       "14        NaN     NaN 1751-03-15  \n",
+       "15        NaN     NaN 1751-04-15  \n",
+       "16        NaN     NaN 1751-05-15  \n",
+       "17        NaN     NaN 1751-06-15  \n",
+       "18        NaN     NaN 1751-07-15  \n",
+       "19        NaN     NaN 1751-08-15  \n",
+       "20        NaN     NaN 1751-09-15  \n",
+       "21     -0.276     NaN 1751-10-15  \n",
+       "22     -0.286     NaN 1751-11-15  \n",
+       "23     -0.316     NaN 1751-12-15  \n",
+       "24     -0.299     NaN 1752-01-15  \n",
+       "25     -0.299     NaN 1752-02-15  \n",
+       "26     -0.303     NaN 1752-03-15  \n",
+       "27     -0.295     NaN 1752-04-15  \n",
+       "28     -0.293     NaN 1752-05-15  \n",
+       "29     -0.293     NaN 1752-06-15  \n",
+       "...       ...     ...        ...  \n",
+       "3195      NaN     NaN 2016-04-15  \n",
+       "3196      NaN     NaN 2016-05-15  \n",
+       "3197      NaN     NaN 2016-06-15  \n",
+       "3198      NaN     NaN 2016-07-15  \n",
+       "3199      NaN     NaN 2016-08-15  \n",
+       "3200      NaN     NaN 2016-09-15  \n",
+       "3201      NaN     NaN 2016-10-15  \n",
+       "3202      NaN     NaN 2016-11-15  \n",
+       "3203      NaN     NaN 2016-12-15  \n",
+       "3204      NaN     NaN 2017-01-15  \n",
+       "3205      NaN     NaN 2017-02-15  \n",
+       "3206      NaN     NaN 2017-03-15  \n",
+       "3207      NaN     NaN 2017-04-15  \n",
+       "3208      NaN     NaN 2017-05-15  \n",
+       "3209      NaN     NaN 2017-06-15  \n",
+       "3210      NaN     NaN 2017-07-15  \n",
+       "3211      NaN     NaN 2017-08-15  \n",
+       "3212      NaN     NaN 2017-09-15  \n",
+       "3213      NaN     NaN 2017-10-15  \n",
+       "3214      NaN     NaN 2017-11-15  \n",
+       "3215      NaN     NaN 2017-12-15  \n",
+       "3216      NaN     NaN 2018-01-15  \n",
+       "3217      NaN     NaN 2018-02-15  \n",
+       "3218      NaN     NaN 2018-03-15  \n",
+       "3219      NaN     NaN 2018-04-15  \n",
+       "3220      NaN     NaN 2018-05-15  \n",
+       "3221      NaN     NaN 2018-06-15  \n",
+       "3222      NaN     NaN 2018-07-15  \n",
+       "3223      NaN     NaN 2018-08-15  \n",
+       "3224      NaN     NaN 2018-09-15  \n",
+       "\n",
+       "[3225 rows x 13 columns]"
+      ]
+     },
+     "execution_count": 258,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df = pd.read_csv('Material/Complete_TAVG_complete.txt', skipinitialspace=True, delimiter=' ', comment='%')\n",
+    "df['Date'] = df.apply(lambda row: datetime.datetime(\n",
+    "                              int(row['Year']), int(row['Month']), 15), axis=1)\n",
+    "df"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "* Plot the data. To plot the monthly differences, for example, you can directly write ```df2['MDiff'].plot()```"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 261,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.axes._subplots.AxesSubplot at 0x11fa1d6d8>"
+      ]
+     },
+     "execution_count": 261,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAX8AAAEACAYAAABbMHZzAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x1a22bcefd0>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "df.query('Year>1980 & Year<2000').plot(x='Date', y='MDiff')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "* Apply your moving average filter to the monthly data ```MDiff```. Try (for example) 6 months, 5 years, 10 years. Plot these on top of cuts of the original data to compare."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 274,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.axes._subplots.AxesSubplot at 0x1a221296d8>"
+      ]
+     },
+     "execution_count": 274,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXYAAAEACAYAAACnJV25AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n7z7cg5XVWcMAAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x1a227b9588>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "df['6MA'] = moving_average(df['MDiff'], 6)\n",
+    "df['5Y'] = moving_average(df['MDiff'], 60)\n",
+    "df['20Y'] = moving_average(df['MDiff'], 240)\n",
+    "\n",
+    "df.plot(x='Date', y=['6MA', '5Y', '20Y'])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 2.2 Electronic response of RC circuit\n",
+    "\n",
+    "In general, the response of a linearly time invariant system is found to be the convolution of the its impulse response $h(t)$ and the input voltage. Consider a resistor and capacitor connected in series, driven by a time-varying voltage $u(t)$. The impulse response for such a circuit is:\n",
+    "\n",
+    "$$h_c(t) = \\frac{1}{RC} e^{-t/RC} u(t)$$\n",
+    "\n",
+    "* Write a function to calculate the impulse response as a function of time, the resistance, and the capacitance, and input. Take care to normalise the integral.\n",
+    "\n",
+    "* Now consider a noisy sinusoidal input voltage $u_N(t) = u(t) + \\epsilon(t)$, where $\\epsilon$ is a vector comprising samples draw from $N~(0,1)$. Plot the noisy signal and superimpose the clean signal.\n",
+    "\n",
+    "* Calculate the circuit response for your signal and compare the result to the noisy signal and the clean, original signal\n",
+    "\n",
+    "Play with the RC time constant and see the effect on the signal.\n",
+    "\n",
+    "\n",
+    "Note: this first order low pass filter is exactly equivalent to an exponential moving average. The \"memory\" of the output is effectively determined by the time constant.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 287,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Cutoff:  0.0005\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXYAAAELCAYAAADN4q16AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n53A4nCLj/wOEwyFbJGq+LgAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x1a23522550>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXYAAAELCAYAAADN4q16AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x1a22d56240>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "def rc_impulse(t, R, C):\n",
+    "    RC = R*C\n",
+    "    return 1/RC * np.exp(-t/RC)\n",
+    "\n",
+    "def rc_response(t, u, R, C):\n",
+    "    return np.convolve(rc_impulse(t, R, C), u)[:len(t)]*dt\n",
+    "\n",
+    "t = np.linspace(0, 0.1, 5000)\n",
+    "dt = t[1]-t[0]\n",
+    "R = 5e3\n",
+    "C = 100e-9\n",
+    "tc = R*C\n",
+    "\n",
+    "fw = 200\n",
+    "u = np.sin(2*np.pi*fw*t) + np.cos(2*np.pi*0.1*fw*t)\n",
+    "un = u + np.random.randn(len(u))\n",
+    "\n",
+    "print('Cutoff: ', tc)\n",
+    "\n",
+    "plt.figure()\n",
+    "plt.plot(t, un)\n",
+    "plt.plot(t, rc_response(t, un, R, C))\n",
+    "plt.plot(t, u)\n",
+    "plt.xlabel('Time (s)')\n",
+    "\n",
+    "# Try different cutoffs (remove noise, fast ripple, then whole thing)\n",
+    "plt.figure()\n",
+    "plt.plot(t, un)\n",
+    "plt.plot(t, rc_response(t, un, R, C))\n",
+    "plt.plot(t, rc_response(t, un, 20*R, C))\n",
+    "plt.plot(t, rc_response(t, un, 200*R, C))\n",
+    "plt.xlabel('Time (s)')\n",
+    "plt.show()\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 3. Monte Carlo methods\n",
+    "### 3.1. Particle propagation\n",
+    "The elementary process of particle absorption and scattering are random in their nature. Propagatiomn of particles through a slab of material (photons, neutrons, charged particles or any other) with multiple scattering events may be impossible to calculate analytically, but can easily be simulated wiht Monte Carlo methods. The following exercise is a simple example of using random numbers with custom distributions.\n",
+    "\n",
+    "(a) Simulate exponential decay of particle beam intensity in a uniformly absorbing, non-scattering medium (Beer-Lambert-Bouger law). Consider 1-D case. Use uniformly distributed random numbers to simulate absorption event in a slice of material. Plot a histogram of distances travelled before absorption (free paths).\n",
+    "\n",
+    "$I(x) = I_{0}e^{-\\alpha x }$ , where $\\alpha$ is absorption coefficient"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 142,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Generated absorption probability (mean) =  0.18107\n",
+      "Fraction of escaped particles =  0.0\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAY4AAAEWCAYAAABxMXBSAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nrkJggg==\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x1a21e75ef0>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYUAAAEWCAYAAACJ0YulAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nRK5CYII=\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x11fe02be0>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "4.391\n"
+     ]
+    }
+   ],
+   "source": [
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "N_slices = 100 # Slices of material\n",
+    "N_particles = 1000 # Number of particles to simulate\n",
+    "alpha = 0.2 # absorption coefficient\n",
+    "P_abs = 1- np.exp(-alpha) # Absorption probability in a slice\n",
+    "\n",
+    "# Generate N_slices x N_particles matrix of unofrmly distributed random numbers. \n",
+    "# Transform it into a matrix of absorption events, where True = absorption, False = no absorption, \n",
+    "# mean(ABs_events) = P_abs\n",
+    "Abs_events = np.random.uniform(0,1,(N_slices,N_particles)) < P_abs \n",
+    "\n",
+    "\n",
+    "\n",
+    "free_path = np.empty((N_particles,)) # array to store the number of slices propagated before absorption\n",
+    "N_absorbed = np.empty((N_slices,)) # array to store the number of absorbed particles in a slice N\n",
+    "\n",
+    "for i in range(0,N_particles-1):\n",
+    "    idx = np.nonzero(Abs_events[:,i]) # returns an array of indexes of non-zero (non-False) elements\n",
+    "    if np.size(idx)!=0:\n",
+    "        free_path[i] = idx[0][0] # the first index of idx array is the slice number, in which absorption happened \n",
+    "    else:\n",
+    "        free_path[i] = np.inf # some particles may not get absorbed at all in a finite medium\n",
+    "        \n",
+    "for i in range(0,N_slices-1):\n",
+    "    N_absorbed[i] = (np.sum(free_path == i))\n",
+    "    \n",
+    "N_transmitted = np.append([N_particles],[N_particles - np.cumsum(N_absorbed)])\n",
+    "N_escaped_final = np.sum(free_path == np.inf)\n",
+    "\n",
+    "print('Generated absorption probability (mean) = ', np.mean(Abs_events))\n",
+    "print('Fraction of escaped particles = ',N_escaped_final/N_particles)\n",
+    "\n",
+    "x = np.linspace(0,N_slices);\n",
+    "plt.plot(x,N_particles*np.exp(-x*alpha), label = 'Beer-Lambert-Bouguer law') \n",
+    "plt.plot(N_transmitted, label = 'Simulation')\n",
+    "plt.legend()\n",
+    "plt.xlabel('Propagation distance (slice #)')\n",
+    "plt.ylabel('# of transmitted particles')\n",
+    "plt.title('Transmission of %i particles' %N_particles)\n",
+    "plt.show()\n",
+    "#plt.hist(free_path[free_path!=np.inf],30,normed='True')\n",
+    "ax = plt.figure()\n",
+    "plt.hist(free_path,int(N_particles/5),normed='True')\n",
+    "plt.xlabel('Free propagation distance')\n",
+    "plt.ylabel('#')\n",
+    "plt.title('Histogram distribution of free paths')\n",
+    "plt.show()\n",
+    "print(np.mean(free_path))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 3.2. Monte-Carlo integration\n",
+    "\n",
+    "In a so-called ’hit-and-miss’ approach, or ’simple sampling’, one can estimate the integral\n",
+    "of some arbitrary well-behaved function over some interval by scattering many points over\n",
+    "some rectangular area A. The probability of a point landing below the curve is proportional\n",
+    "to the function’s integral.\n",
+    "A classic problem is to determine the value of π.\n",
+    "(a) Uniformly distribute N points over a unit area. Calculate the proportion that are\n",
+    "within the bounds of a quarter circle.\n",
+    "(b) Plot the convergence by subtracting the computed value of pi from the cumulative\n",
+    "average (log-log). Compare this to the expected rate of convergence (1/\n",
+    "√\n",
+    "N).\n",
+    "(c) Add error bars to the plot"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 138,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "# Helper functions...\n",
+    "def mc_integrate_1d(f, dist_x, dist_y, n_iter):\n",
+    "    # Hit and miss version\n",
+    "    #\n",
+    "    # f: function to be evaluated\n",
+    "    # dist_x, dist_y: distributions from which to draw (x,y)\n",
+    "    # Does not handle -ve y\n",
+    "    x = dist_x(n_iter)\n",
+    "    y = dist_y(n_iter)\n",
+    "    h = f(x)\n",
+    "    return np.cumsum(y < f(x)) / np.arange(1,n_iter+1)\n",
+    "\n",
+    "def mc_integrate_1d_2(f, dist_x, n_iter):\n",
+    "    # Sampling\n",
+    "    x = dist_x(n_iter)\n",
+    "    return np.cumsum(f(x))/np.arange(1,n_iter+1)\n",
+    "\n",
+    "def plot_convergence(est, sol):\n",
+    "    x = np.arange(1,len(est)+1)\n",
+    "    plt.figure()\n",
+    "    plt.loglog(x, np.abs(est-sol)/sol, 'b', x, 1/np.sqrt(x), 'r')\n",
+    "    plt.legend(('Result', '1/sqrt(N)'))\n",
+    "    plt.xlabel('N iterations')\n",
+    "    plt.ylabel('Fractional error')\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 140,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Pi estimate: 3.14\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/Users/matt/anaconda3/lib/python3.6/site-packages/ipykernel_launcher.py:12: MatplotlibDeprecationWarning: axes.hold is deprecated.\n",
+      "    See the API Changes document (http://matplotlib.org/api/api_changes.html)\n",
+      "    for more details.\n",
+      "  if sys.path[0] == '':\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXwAAAD8CAYAAAB0IB+mAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nQxrSLkJDwB/SkIY0pF2E/j/kkUfElWwMjQAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x120874cc0>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAagAAAEYCAYAAAAJeGK1AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\ngg==\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x1a21dac6d8>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "N_trials = int(1e3)\n",
+    "p = np.random.uniform(0,1,size=(N_trials, 2))\n",
+    "r = np.sqrt(np.sum(p**2, 1))\n",
+    "\n",
+    "print('Pi estimate:', 4 * np.sum(r<=1) / N_trials)\n",
+    "\n",
+    "sel = (r<=1, r>1)\n",
+    "\n",
+    "def plot_pi(p, r, sel):\n",
+    "    x = np.linspace(0,1,200)\n",
+    "    fh, ax = plt.subplots()\n",
+    "    ax.hold(True)\n",
+    "    ax.scatter(p[sel[0],0], p[sel[0],1], c='r', marker='x')\n",
+    "    ax.scatter(p[sel[1],0], p[sel[1],1], c='b', marker='x')\n",
+    "    ax.plot(x, np.sqrt(1-x**2), 'k', linewidth=2)\n",
+    "    ax.set_xlim([0, 1])\n",
+    "    ax.set_ylim([0, 1])\n",
+    "\n",
+    "if N_trials <= 1e4:\n",
+    "    plot_pi(p,r,sel)\n",
+    "\n",
+    "x = np.arange(1,N_trials+1)\n",
+    "c_est = 4*np.cumsum(sel[0])/x\n",
+    "c_err = np.abs(c_est-np.pi)/np.pi\n",
+    "\n",
+    "# Std: sqrt(1/N(N-1) sum{(x_i-pi)^2})\n",
+    "# If we wanted to use error bars, we would have to downsample.\n",
+    "# Error band of course an option, and should be implemented in plot_convergence.\n",
+    "plot_convergence(c_est, np.pi)\n",
+    "plt.tight_layout()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "collapsed": true
+   },
+   "source": [
+    "## To be removed."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYMAAAEKCAYAAADw2zkCAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nIt0CAAAAAElFTkSuQmCC\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x11f29f4a8>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "df = pd.read_csv('http://swiss-glaciers.glaciology.ethz.ch//download/aletsch.csv', skiprows=11, skipinitialspace=True)\n",
+    "df.plot('reference year', 'Length Change (m)')\n",
+    "plt.show()\n",
+    "\n",
+    "plt.figure()\n",
+    "plt.plot(df['reference year'], df['Length Change (m)'])\n",
+    "plt.plot(df['reference year'], moving_average(df['Length Change (m)'], 5)[2:-2])\n",
+    "plt.plot(df['reference year'], moving_average(df['Length Change (m)'], 11)[5:-5])\n",
+    "plt.plot(df['reference year'], moving_average(df['Length Change (m)'], 101)[50:-50])\n",
+    "plt.show()"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 2",
+   "language": "python",
+   "name": "python2"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 2
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython2",
+   "version": "2.7.12"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/exercises/.ipynb_checkpoints/Solutions_5-checkpoint.ipynb b/exercises/.ipynb_checkpoints/Solutions_5-checkpoint.ipynb
new file mode 100644
index 0000000..42e6aa1
--- /dev/null
+++ b/exercises/.ipynb_checkpoints/Solutions_5-checkpoint.ipynb
@@ -0,0 +1,649 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Solution Exercise 5\n",
+    "This week, we are working on least squares fits and parameter estimation"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "from __future__ import print_function\n",
+    "import numpy as np\n",
+    "%matplotlib inline\n",
+    "import matplotlib.pyplot as plt\n",
+    "from scipy.optimize import curve_fit\n",
+    "from scipy.stats import norm, chi2, lognorm"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Fit a polynomial\n",
+    "We start by fitting a polynomial to a given data set, in particular, a parabola. Compare a linear fit and a cubic fit to our parabolic fit and check the goodness of fits with chi squared distributions. Explore how the different uncertainties affect the outcome and uncertainties of the fit."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "# Create some data distribuited as parabola with normally distributed errors.\n",
+    "def parabola(x, a, b, c):\n",
+    "    return a*x**2 + b*x + c\n",
+    "def error(x, sigma):\n",
+    "    return norm.rvs(0.0, sigma, x.size) \n",
+    "a = -0.1\n",
+    "b = 0\n",
+    "c = 1\n",
+    "x = np.linspace(-5, 5, 21)\n",
+    "sigma_y = 0.0015\n",
+    "delta_y = error(x, sigma_y)\n",
+    "y_true = parabola(x, a, b, c)\n",
+    "y = y_true + delta_y\n",
+    "y_error = sigma_y * np.ones(x.size)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "def fit_polynomial(x, y, degree, weight):\n",
+    "    \"\"\"Fit polynomial of degree to data x, y with y weight = 1/sigma_y\n",
+    "    \n",
+    "    Return fit, covariance matrix, residuals and chi-squared and degrees of freedom.\n",
+    "    \"\"\"\n",
+    "    \n",
+    "    dof = x.shape[0] - degree\n",
+    "    fit, cov = np.polyfit(x, y, degree, w=weight, cov=True)\n",
+    "    residuals = np.sum((y - np.polyval(fit, x))**2 / y_error**2)\n",
+    "    chisq = residuals / (dof)\n",
+    "    return fit, cov, residuals, chisq, dof\n",
+    "    \n",
+    "fit, cov, res, chisq, dof = fit_polynomial(x, y, 2, 1/y_error) # Fit parabola\n",
+    "fit_1, cov_1, res_1, chisq_1, dof_1 = fit_polynomial(x, y, 1, 1/y_error) # Fit line\n",
+    "fit_3, cov_3, res_3, chisq_3, dof_3 = fit_polynomial(x, y, 3, 1/y_error) # Fit cubic\n",
+    "\n",
+    "def evaluate_chisq(chisq, dof):\n",
+    "    return chi2.sf(chisq, dof)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Reduced chi^2:\n",
+      "parabola 0.992095154842\n",
+      "line 311170.605008\n",
+      "cubic 1.00698239496\n"
+     ]
+    }
+   ],
+   "source": [
+    "print('Reduced chi^2:')\n",
+    "print('parabola', chisq)\n",
+    "print('line', chisq_1)\n",
+    "print('cubic', chisq_3)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Chi^2 distributions:\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "(0.99999999927784466, 0.0, 0.99999999635390924)"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "print('Chi^2 distributions:')\n",
+    "evaluate_chisq(chisq, dof), evaluate_chisq(chisq_1, dof_1), evaluate_chisq(chisq_3, dof_3)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Error estimates:\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "(array([  4.34814754e-05,   1.17346291e-04,   5.33940597e-04]),\n",
+       " array([ 0.06541325,  0.19804844]),\n",
+       " array([  1.67125703e-05,   4.40364561e-05,   2.99509423e-04,\n",
+       "          5.40755607e-04]))"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "print('Error estimates:')\n",
+    "np.sqrt(np.diag(cov)), np.sqrt(np.diag(cov_1)), np.sqrt(np.diag(cov_3))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAxgAAAGICAYAAADChEYOAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x1a0bac0b90>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "f, ax = plt.subplots(1, 2, figsize=(10, 5))\n",
+    "ax[0].set_title('fits')\n",
+    "ax[0].errorbar(x, y, yerr=y_error, fmt='k.')\n",
+    "ax[0].plot(x, y_true, 'k-')\n",
+    "ax[0].plot(x, np.polyval(fit, x), label='parabola', color='blue')\n",
+    "ax[0].plot(x, np.polyval(fit_1, x), label='line', color='green')\n",
+    "ax[0].plot(x, np.polyval(fit_3, x), label='cubic', color='red')\n",
+    "\n",
+    "ax[0].legend()\n",
+    "ax[1].plot(y_true - np.polyval(fit, x), '.', color='blue')\n",
+    "ax[1].plot(y_true - np.polyval(fit_1, x), '.', color='green')\n",
+    "ax[1].plot(y_true - np.polyval(fit_3, x), '.', color='red')\n",
+    "ax[1].set_title('residuals')\n",
+    "ax[1].fill_between(ax[1].get_xlim(), -sigma_y, sigma_y, color='grey', alpha=0.4, label=r'$1\\sigma$')\n",
+    "ax[1].legend()\n",
+    "f.tight_layout()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 114,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x182c02487f0>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.figure(figsize=(5, 5))\n",
+    "chisq_arr = np.linspace(0, 100, 1001)\n",
+    "plt.title('p-values')\n",
+    "plt.xlabel(r'$\\chi^2$')\n",
+    "for n in [1, 2, 3, 4, 6, 8, 10, 15, 20, 25, 30, 40, 50]:\n",
+    "    plt.loglog(chisq_arr, chi2.sf(chisq_arr, n))\n",
+    "plt.loglog(chisq_arr, chi2.sf(chisq_arr, dof), 'k-', lw=2, label='dof={0}'.format(dof))\n",
+    "plt.ylim(1e-3, 1.1)\n",
+    "plt.plot(chisq, chi2.sf(chisq, dof), 'o', color='blue', label='parabola')\n",
+    "plt.plot(chisq_1, chi2.sf(chisq_1, dof_1), '^', color='green', label='line')\n",
+    "plt.plot(chisq_3, chi2.sf(chisq_3, dof_3), 'x', color='red', label='cubic', ms=5)\n",
+    "plt.legend()\n",
+    "plt.tight_layout()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We observe that the residuals are not uniformly distributed around zero with the expected variance $\\sigma_y$ in the case of the line fit. This reflected in the $\\chi^2$-distribution. Only in 7% of the cases we would expect to draw data that give a worse fit. Note that overfitting with a cubic is not easily spotted in the residuals."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Fit a nonlinear function\n",
+    "Next, we consider a Gaussian as an example of a nonlinear function. We are measuring some feature which has a Gaussian distribution in $x$. This could be an inhomogeneous spectral line for $x=E$ the energy of emitted photons. We are interested in the resonance frequency and the linewidth, i. e. we want to estimate them form our observations."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 115,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "def gaussian_parent(x, mu, sigma):\n",
+    "    return norm.pdf(x, mu, sigma)    \n",
+    "\n",
+    "def gaussian_sample(mu, sigma, sample_size):\n",
+    "    return norm.rvs(mu, sigma, sample_size)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 116,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x182c044e438>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Create sample\n",
+    "## SAMPLE SIZE\n",
+    "sample_size = 200\n",
+    "##################\n",
+    "\n",
+    "# Prepare fake data\n",
+    "mu = 1540  # True values that we will try to estimate\n",
+    "sigma = 11 # using a least-squares fit\n",
+    "\n",
+    "x_arr = np.linspace(1500, 1600, 101)\n",
+    "bins = 12\n",
+    "sample = gaussian_sample(mu, sigma, sample_size)\n",
+    "hist = np.histogram(sample, bins=bins, range=(1500, 1580))\n",
+    "bin_width = np.diff(hist[1])[0]\n",
+    "normalization = bin_width * sample_size\n",
+    "x = hist[1][:-1]+bin_width/2\n",
+    "y_error_const = 0\n",
+    "y = hist[0]/normalization + gaussian_sample(0, y_error_const, bins)\n",
+    "y_errors = np.sqrt((np.sqrt(hist[0]) / normalization)**2 + y_error_const**2)\n",
+    "\n",
+    "# Plot our fake measurement results\n",
+    "plt.figure(figsize=(5, 4))\n",
+    "plt.xlabel(r'$E$ (meV)')\n",
+    "plt.ylabel('count rate')\n",
+    "plt.plot(x_arr, gaussian_parent(x_arr, mu, sigma), '-', color='black', label='parent')\n",
+    "plt.errorbar(x, y, yerr=y_errors, fmt='.', ms=7, capsize=3, label='sample')\n",
+    "plt.legend()\n",
+    "plt.tight_layout()\n",
+    "\n",
+    "# Save data\n",
+    "data = np.vstack((x, y, y_errors))\n",
+    "np.savetxt('data', data)\n",
+    "np.savetxt('sample', sample)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 117,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "# Load data from disk. Format (3,12): (x, y, y_error) x N \n",
+    "data = np.loadtxt('data')\n",
+    "x = data[0, :]\n",
+    "y = data[1, :]\n",
+    "y_error = data[2, :]\n",
+    "# The sample used to generate\n",
+    "sample = np.loadtxt('sample')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 118,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "# Function we want to fit to our data set\n",
+    "def model_function(x, *args):\n",
+    "    mu, sigma = args[0:2]\n",
+    "    return norm.pdf(x, mu, sigma)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 123,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Fit Results:\n",
+      "mu = 1539.5 +- 0.6\n",
+      "sigma = 10.8 +- 0.5\n",
+      "mu estimator 1538.9 +- 0.8\n",
+      "sigma estimator 11.6\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Perform the fit minimizing least squares\n",
+    "initial_guess = [1545, 9]\n",
+    "p_opt, p_cov = curve_fit(model_function, x, y, p0=initial_guess, sigma=None, absolute_sigma=False, check_finite=True)\n",
+    "p_err = np.sqrt(np.diag(p_cov))\n",
+    "# pcov(absolute_sigma=False) = pcov(absolute_sigma=True) * chisq(popt)/(M-N)\n",
+    "print('Fit Results:')\n",
+    "print('mu = {:1.1f} +- {:1.1f}'.format(p_opt[0], p_err[0]))\n",
+    "print('sigma = {:1.1f} +- {:1.1f}'.format(p_opt[1], p_err[1]))\n",
+    "\n",
+    "print('mu estimator {:1.1f} +- {:1.1f}'.format(np.mean(sample), np.std(sample, ddof=1)/np.sqrt(sample.size)))\n",
+    "print('sigma estimator {:1.1f}'.format(np.std(sample, ddof=1)))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Plot the result"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 124,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x182be9feb00>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "x_arr = np.linspace(1500, 1600, 101)\n",
+    "\n",
+    "plt.figure(figsize=(5, 4))\n",
+    "plt.xlabel(r'$E$ (meV)')\n",
+    "plt.ylabel('count rate')\n",
+    "plt.errorbar(x, y, yerr=y_errors, fmt='.', ms=7, capsize=3, label='sample')\n",
+    "plt.plot(x_arr, model_function(x_arr, *initial_guess), '-', color='grey', label='guess')\n",
+    "plt.plot(x_arr, model_function(x_arr, *p_opt), '-', color='black', label='fit')\n",
+    "plt.legend()\n",
+    "plt.tight_layout()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Biased estimator example?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 133,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Expectation value: 3.648721270700128\n",
+      "Sample mean: 3.1303941671784665\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x182bea6e0f0>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "log_sample = lognorm.rvs(s=0.5, loc=2, scale=1, size=1000)\n",
+    "plt.figure(figsize=(5, 4))\n",
+    "plt.hist(log_sample, bins=16, rwidth=0.85)\n",
+    "plt.tight_layout()\n",
+    "\n",
+    "print('Expectation value:', lognorm.mean(s=1, loc=2, scale=1))\n",
+    "print('Sample mean:', np.mean(log_sample))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Bonus: Errors in x and y\n",
+    "So far, we considered uncertainties only on y. Consider the following data set with errors in both x and y. Try to fit a line to the data below, taking into account both errors. Compare with fits neglecting the x errors or both.  \n",
+    "  \n",
+    "Hint: A detailed solution is already on the moodle. You may chose if you want to try to write your own solution, implement a known solution (see references in solution notebook) or just try it with the scipy package ODR (orthogonal distance regression). \n",
+    "https://docs.scipy.org/doc/scipy/reference/odr.html\n",
+    "  \n",
+    "The solution contains a python implementation of York's equation, comparison with ODR and MC tests.  \n",
+    "Week 3: \"Linear Regression errors x and y\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 122,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x182bec20da0>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Test data\n",
+    "X = np.array([0.0, 0.9, 1.8, 2.6, 3.3, 4.4, 5.2, 6.1, 6.5, 7.4])\n",
+    "Y = np.array([5.9, 5.4, 4.4, 4.6, 3.5, 3.7, 2.8, 2.8, 2.4, 1.5])\n",
+    "wX = np.array([1000, 1000, 500, 800, 200, 80, 60, 20, 1.8, 1])\n",
+    "wY = np.array([1, 1.8, 4, 8, 20, 20, 70, 70, 100, 500])\n",
+    "sigma_x = 1.0/np.sqrt(wX)\n",
+    "sigma_y = 1.0/np.sqrt(wY)\n",
+    "\n",
+    "plt.figure(figsize=(4, 3))\n",
+    "plt.errorbar(X, Y, xerr=sigma_x, yerr=sigma_y, fmt='o', label='oberservations')\n",
+    "plt.legend()\n",
+    "plt.tight_layout()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "hide_input": false,
+  "kernelspec": {
+   "display_name": "Python 2",
+   "language": "python",
+   "name": "python2"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 2
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython2",
+   "version": "2.7.12"
+  },
+  "toc": {
+   "base_numbering": 1,
+   "nav_menu": {},
+   "number_sections": true,
+   "sideBar": true,
+   "skip_h1_title": false,
+   "title_cell": "Table of Contents",
+   "title_sidebar": "Contents",
+   "toc_cell": false,
+   "toc_position": {},
+   "toc_section_display": true,
+   "toc_window_display": true
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/exercises/.ipynb_checkpoints/straight_line_fit-checkpoint.ipynb b/exercises/.ipynb_checkpoints/straight_line_fit-checkpoint.ipynb
new file mode 100644
index 0000000..39ba50b
--- /dev/null
+++ b/exercises/.ipynb_checkpoints/straight_line_fit-checkpoint.ipynb
@@ -0,0 +1,714 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Finding the best line for data with errors in x and y"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We want to find a straight line $y = a + bx$ fitting $N$ data points $X, Y = (X_i, Y_i)$ with errors $(\\sigma(X_i), \\sigma(Y_i))$,  $i=1,...,N$.  \n",
+    "We follow Yorks equations for the case of uncorrelated but non-constant errors (see references below for the inclusion of correlations between errors) which are derived by minimizing $$S = \\sum_i \\omega(X_i)(x_i-X_i)^2 + \\omega(Y_i)(y_i-Y_i)^2,$$\n",
+    "where $(x_i, y_i)$ are the adjusted values that lie on the line and $(\\omega(X_i), \\omega(Y_i)) = (1/\\sigma(X_i)^2, 1/\\sigma(Y_i)^2)$ the weights. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 139,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "%matplotlib inline\n",
+    "import matplotlib.pyplot as plt\n",
+    "import numpy as np\n",
+    "import scipy.odr as so"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## TL;DR: Quick How To"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "You can conveniently use the scipy package for orthogonal distance regression (ODR) to achieve this task.  \n",
+    "Check out https://docs.scipy.org/doc/scipy/reference/odr.html for documentation. Here I provide a short example."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 144,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Beta: [-0.4805337  5.4799117]\n",
+      "Beta Std Error: [ 0.07062028  0.35924657]\n",
+      "Beta Covariance: [[ 0.00336226 -0.01647255]\n",
+      " [-0.01647255  0.08700776]]\n",
+      "Residual Variance: 1.483294149297378\n",
+      "Inverse Condition #: 0.09285611904588402\n",
+      "Reason(s) for Halting:\n",
+      "  Sum of squares convergence\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x1eeb4aaa9b0>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# 0. Test data\n",
+    "X = np.array([0.0, 0.9, 1.8, 2.6, 3.3, 4.4, 5.2, 6.1, 6.5, 7.4])\n",
+    "Y = np.array([5.9, 5.4, 4.4, 4.6, 3.5, 3.7, 2.8, 2.8, 2.4, 1.5])\n",
+    "wX = np.array([1000, 1000, 500, 800, 200, 80, 60, 20, 1.8, 1])\n",
+    "wY = np.array([1, 1.8, 4, 8, 20, 20, 70, 70, 100, 500])\n",
+    "sigma_x = 1.0/np.sqrt(wX)\n",
+    "sigma_y = 1.0/np.sqrt(wY)\n",
+    "\n",
+    "# 1. Define the function you want to fit against.\n",
+    "def f(B, x):\n",
+    "   '''Linear function y = m*x + b'''\n",
+    "   return B[0]*x + B[1]\n",
+    "\n",
+    "# 2. Create a Model.\n",
+    "linear = so.Model(f)\n",
+    "\n",
+    "# 3. Create a Data or RealData instance.\n",
+    "mydata = so.RealData(X, Y, sx=sigma_x, sy=sigma_y) # should provide std errors not var\n",
+    "\n",
+    "# 4. Instantiate ODR with your data, model and initial parameter estimate.\n",
+    "myodr = so.ODR(mydata, linear, beta0=[-0.5, 5.5])\n",
+    "\n",
+    "# 5. Run the fit.\n",
+    "myoutput = myodr.run()\n",
+    "\n",
+    "# 6. Examine output.\n",
+    "myoutput.pprint()\n",
+    "odr_b, odr_a = myoutput.beta\n",
+    "odr_sb, odr_sa = myoutput.sd_beta\n",
+    "\n",
+    "# 7. Plot results\n",
+    "plt.figure(figsize=(6, 4))\n",
+    "plt.errorbar(X, Y, xerr=sigma_x, yerr=sigma_y, fmt='o', label='oberservations')\n",
+    "plt.plot(X, odr_a + odr_b * X, 'g-', label='odr fit')\n",
+    "# rough visualization of error estimates\n",
+    "plt.fill_between(X, odr_a-odr_sa + (odr_b-odr_sb)*X, odr_a+odr_sa + (odr_b+odr_sb)*X, color='g', alpha=0.35)\n",
+    "plt.legend()\n",
+    "plt.tight_layout()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This is all you need. If your interested in more details, go on. You can check our small python implementation for this task and some benchmarks to check its validity below."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Further details\n",
+    "## Our python implementation\n",
+    "Inspired by the references below, we try a naive python implementation to learn how it works."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 145,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "class York_eq_fit():\n",
+    "    debug = False\n",
+    "    \n",
+    "    def __init__(self, X, Y, SX, SY):\n",
+    "        \"\"\"Observed points X,Y  (1d arrays) and standard error SX, SY.\"\"\"\n",
+    "        \n",
+    "        self.X = np.array(X)\n",
+    "        self.Y = np.array(Y)\n",
+    "        self.N = np.size(self.X) # number of obervations\n",
+    "        assert np.size(self.X) == np.size(self.Y), 'X and Y must have the same length'\n",
+    "        if np.size(SX) == 1:\n",
+    "            self.SX = np.ones(self.N) * SX\n",
+    "        else:\n",
+    "            assert np.size(SX) == self.N, 'SX and X must have the same length or SX must be a constant'\n",
+    "            self.SX = SX\n",
+    "        if np.size(SY) == 1:\n",
+    "            self.SY = np.ones(self.N) * SY\n",
+    "        else:\n",
+    "            assert np.size(SY) == self.N, 'SY and Y must have the same length or SX must be a constant'\n",
+    "            self.SY = SY            \n",
+    "        self.wX = 1.0 / self.SX**2  # weights of X observations\n",
+    "        self.wY = 1.0 / self.SY**2  # weights of Y observations\n",
+    "        \n",
+    "        # For later: include correlations (not implemented yet)\n",
+    "        self.alpha = np.sqrt(self.wX * self.wY)\n",
+    "        self.r = 0.0 # correlations between SX and SY\n",
+    "        \n",
+    "    def run(self, rtol=1e-15, atol=1e-10, maxiter=1000):\n",
+    "        \"\"\"Perform fit and return result a,b and error estimates sa, sb\"\"\"\n",
+    "        \n",
+    "        self.guess_b()\n",
+    "        self.update_W()\n",
+    "        self.update_b()\n",
+    "        self.iterate(rtol, atol, maxiter)\n",
+    "        self.evaluate()\n",
+    "        return self.a, self.b, self.sa, self.sb\n",
+    "\n",
+    "    def guess_b(self):\n",
+    "        \"\"\"Find an initial guess for the slope b\"\"\"\n",
+    "        \n",
+    "        self.b, self.a = np.polyfit(self.X, self.Y, 1, w=1/self.SY)\n",
+    "        return self.b\n",
+    "    \n",
+    "    def update_W(self):\n",
+    "        \"\"\"Update W given an estimate for b and knowing weights wX, wY\"\"\"\n",
+    "        \n",
+    "        self.W = self.wX * self.wY / (self.wX + self.b**2 * self.wY - 2 * self.b * self.r * self.alpha)\n",
+    "        # also update quantities that directly depend on W\n",
+    "        self.MX = np.sum(self.W * self.X) / np.sum(self.W) \n",
+    "        self.MY = np.sum(self.W * self.Y) / np.sum(self.W)\n",
+    "        self.U = self.X - self.MX\n",
+    "        self.V = self.Y - self.MY\n",
+    "        beta_correction = (self.b * self.U + self.V) * self.r / self.alpha\n",
+    "        self.beta = self.W * (self.U/self.wY + self.b*self.V/self.wX - beta_correction)\n",
+    "        \n",
+    "    def update_b(self):\n",
+    "        \"\"\"Update the estimate of slope b\"\"\"\n",
+    "        \n",
+    "        self.old_b = self.b\n",
+    "        self.b = np.sum(self.W * self.beta * self.V) / np.sum(self.W * self.beta *self.U)\n",
+    "        \n",
+    "    def iterate(self, rtol=1e-15, atol=1e-10, maxiter=1000):\n",
+    "        self.iterations = 0\n",
+    "        while abs(self.old_b - self.b) / self.b > rtol or abs(self.old_b-self.b) > atol:\n",
+    "            if York_eq_fit.debug:\n",
+    "                print(self.iterations, abs(self.old_b-self.b)/self.b)\n",
+    "            # repeat iteration until estimate for b converges\n",
+    "            self.update_W()\n",
+    "            self.update_b()\n",
+    "            self.iterations += 1\n",
+    "            if self.iterations > maxiter:\n",
+    "                print('Maximum number of iterations reached.')\n",
+    "                break\n",
+    "        if York_eq_fit.debug:\n",
+    "            print('Iteration converged after {0} steps.'.format(self.iterations))\n",
+    "                \n",
+    "    def evaluate(self, prefactor=True):\n",
+    "        \"\"\"Evaluate results for a, b, Sa, Sb\"\"\"\n",
+    "        \n",
+    "        self.update_W()\n",
+    "        self.a = self.MY - self.b * self.MX\n",
+    "        self.x = self.MX + self.beta  # adjusted values x_i\n",
+    "        self.mx = np.sum(self.W * self.x) / np.sum(self.W)\n",
+    "        self.u = self.x - self.mx \n",
+    "        self.sb = np.sqrt(1.0 / (np.sum(self.W * self.u**2)))\n",
+    "        self.sa = np.sqrt(1.0 / np.sum(self.W) + self.mx**2 * self.sb**2)\n",
+    "        self.S = np.sum(self.W * (self.Y - self.X * self.b -self.a)**2)\n",
+    "        if prefactor:\n",
+    "            self.sb = self.sb * np.sqrt(self.S/(self.N-2))\n",
+    "            self.sa = self.sa * np.sqrt(self.S/(self.N-2))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Test if our implementation works"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 146,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "# TEST CASE: Pearsons' data (copied from Reed Am. J. Phys. 60, 1 (1992))\n",
+    "X = np.array([0.0, 0.9, 1.8, 2.6, 3.3, 4.4, 5.2, 6.1, 6.5, 7.4])\n",
+    "Y = np.array([5.9, 5.4, 4.4, 4.6, 3.5, 3.7, 2.8, 2.8, 2.4, 1.5])\n",
+    "wX = np.array([1000, 1000, 500, 800, 200, 80, 60, 20, 1.8, 1])\n",
+    "wY = np.array([1, 1.8, 4, 8, 20, 20, 70, 70, 100, 500])\n",
+    "# weight = 1/errors**2 <-> error = 1/weight**0.5\n",
+    "sigma_x = 1.0/np.sqrt(wX)\n",
+    "sigma_y = 1.0/np.sqrt(wY)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 147,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Offset a, slope b, std a, std b\n",
+      "Result: 5.47991022403 -0.480533407446 0.359246522551 0.0706202695288\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Do the fit with our code\n",
+    "fit = York_eq_fit(X, Y, sigma_x, sigma_y)\n",
+    "a, b, sa, sb = fit.run(rtol=1e-15, atol=1e-15, maxiter=1000)\n",
+    "print('Offset a, slope b, std a, std b')\n",
+    "print('Result:', a, b, sa, sb)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 148,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "# compare this with numpy results for regression of y on x (only errors in y considered!)\n",
+    "np_fit = np.polyfit(X, Y, 1, w=1/np.ones(np.size(X))*sigma_y)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 149,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x1eeb5b1bcc0>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# plot data with results\n",
+    "plt.figure(figsize=(6, 4))\n",
+    "plt.errorbar(X, Y, xerr=sigma_x, yerr=sigma_y, fmt='o', label='oberservations')\n",
+    "plt.plot(X, fit.a+fit.b*X,'r-', label='our fit')\n",
+    "# rough visualization of error estimates\n",
+    "plt.fill_between(X, a-sa + (b-sb)*X, a+sa + (b+sb)*X, color='red', alpha=0.35)\n",
+    "plt.plot(X, np.polyval(np_fit, X), label=r'np fit (neglects $\\sigma_x$)')\n",
+    "\n",
+    "plt.legend()\n",
+    "plt.tight_layout()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We see that it makes quite a difference to neglect the errors in x. Of course it depends on your data set if the errors are marginal or not."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Compare against scipy ODR"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 150,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Beta: [-0.4805337  5.4799117]\n",
+      "Beta Std Error: [ 0.07062028  0.35924657]\n",
+      "Beta Covariance: [[ 0.00336226 -0.01647255]\n",
+      " [-0.01647255  0.08700776]]\n",
+      "Residual Variance: 1.483294149297378\n",
+      "Inverse Condition #: 0.09285611904588402\n",
+      "Reason(s) for Halting:\n",
+      "  Sum of squares convergence\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Lets compare our code against scipy orthogonal distance regression, which should be correct.\n",
+    "import scipy.odr as so\n",
+    "\n",
+    "#1. Define the function you want to fit against.\n",
+    "def f(B, x):\n",
+    "   '''Linear function y = m*x + b'''\n",
+    "   return B[0]*x + B[1]\n",
+    "\n",
+    "#2. Create a Model.\n",
+    "linear = so.Model(f)\n",
+    "\n",
+    "#3. Create a Data or RealData instance.\n",
+    "mydata = so.RealData(X, Y, sx=sigma_x, sy=sigma_y) # should provide std errors not var\n",
+    "\n",
+    "# 4. Instantiate ODR with your data, model and initial parameter estimate.\n",
+    "myodr = so.ODR(mydata, linear, beta0=[-0.5, 5.5])\n",
+    "\n",
+    "# 5. Run the fit.\n",
+    "myoutput = myodr.run()\n",
+    "\n",
+    "# 6. Examine output.\n",
+    "myoutput.pprint()\n",
+    "\n",
+    "odr_b, odr_a = myoutput.beta\n",
+    "odr_sb, odr_sa = myoutput.sd_beta"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 151,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x1eeb4a9c0b8>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Plot ODR fit against our python fit\n",
+    "plt.figure(figsize=(6, 4))\n",
+    "plt.errorbar(X, Y, xerr=sigma_x, yerr=sigma_y, fmt='o', label='oberservations')\n",
+    "plt.plot(X, fit.a+fit.b*X,'r-', label='our fit')\n",
+    "plt.plot(X, odr_a + odr_b * X, 'g--', label='odr fit')\n",
+    "plt.legend()\n",
+    "plt.tight_layout()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Very nice, the parameter estimates fall together perfectly! But what about the quality of error estimates?"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Comparison with a literature result"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 162,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "# literature results (copied from Reed 1992)\n",
+    "m, c = (-0.4805, 5.4799)\n",
+    "S = 11.866\n",
+    "sm_obs, sc_obs = (0.0702, 0.3555)\n",
+    "sm_adj, sc_adj = (0.0706, 0.3592) # these are the relevant ones if I understood correctly"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 163,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(5.4799116973248427,\n",
+       " -0.48053369607371221,\n",
+       " 0.35924657332337567,\n",
+       " 0.070620283604428083)"
+      ]
+     },
+     "execution_count": 163,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# ODR agrees very well!\n",
+    "odr_a, odr_b, odr_sa, odr_sb"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 164,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(1.105, 0.577, 0.35924652255111161, 0.070620269528770943)"
+      ]
+     },
+     "execution_count": 164,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Our naive implementation agrees also! :)\n",
+    "prefactor = (S / np.size(X))\n",
+    "a, b, sa, sb"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Monte Carlo testing\n",
+    "To check the methods, we sample our line many times and fit. By averaging we check if our estimates converge the same values and its sample variances match with the estimated variances."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 165,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "results = []\n",
+    "results_odr = []; S_odr = []\n",
+    "repetitions = 2000\n",
+    "for i in range(repetitions):\n",
+    "    N = 101\n",
+    "    a = 1.105\n",
+    "    b = 0.577\n",
+    "    x = np.linspace(-1, 1, N)\n",
+    "    y = a + b * x\n",
+    "    sigma_x = 0.1\n",
+    "    sigma_y = 0.15\n",
+    "    error_x = np.random.randn(N) * sigma_x\n",
+    "    error_y = np.random.randn(N) * sigma_y\n",
+    "    X = x + error_x\n",
+    "    Y = y + error_x\n",
+    "    fit = York_eq_fit(X, Y, sigma_x, sigma_y)\n",
+    "    result = fit.run()\n",
+    "    results.append(result)\n",
+    "\n",
+    "    mydata = so.RealData(X, Y, sx=sigma_x, sy=sigma_y)\n",
+    "    myodr = so.ODR(mydata, linear, beta0=[0.5, 1.])\n",
+    "    myoutput = myodr.run()\n",
+    "    result_odr = *myoutput.beta, *myoutput.sd_beta\n",
+    "    results_odr.append(result_odr)\n",
+    "    S_odr.append(myoutput.sum_square)\n",
+    "\n",
+    "\n",
+    "results = np.array(results)\n",
+    "results_odr = np.array(results_odr)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 166,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "True values a, b: 1.105 0.577\n",
+      "Average of estimates: 1.10497185974 0.589964877481\n",
+      "Std of estimates: 0.00407683840214 0.00672996955892\n",
+      "Average of estimated std: 0.00413703767094 0.00699568925361\n"
+     ]
+    }
+   ],
+   "source": [
+    "print('True values a, b:', a, b)\n",
+    "print('Average of estimates:', *np.mean(results, axis=0)[0:2]) \n",
+    "print('Std of estimates:', *np.std(results, axis=0)[0:2])\n",
+    "print('Average of estimated std:', *np.mean(results, axis=0)[2:4])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 167,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "True values b, a: 1.105 0.577\n",
+      "Average of estimates: 1.10497186026 0.589964657057\n",
+      "Std of estimates: 0.00407683973494 0.00672996788168\n",
+      "Average of estimated std: 0.00413703813533 0.00699568854485\n"
+     ]
+    }
+   ],
+   "source": [
+    "print('True values b, a:', a, b)\n",
+    "print('Average of estimates:', *np.mean(results_odr, axis=0)[0:2][::-1]) \n",
+    "print('Std of estimates:', *np.std(results_odr, axis=0)[0:2][::-1])\n",
+    "print('Average of estimated std:', *np.mean(results_odr, axis=0)[2:4][::-1])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## References\n",
+    "Derek York: Least-Squares Fitting of a Straight Line, Canadian Journal of Physics, 44 1079 (1966).  \n",
+    "http://www.nrcresearchpress.com/doi/10.1139/p66-090#.W7pY7Wgzb4Y  \n",
+    "\n",
+    "B. Cameron Reed: Linear least-squares fits..  \n",
+    "https://aapt.scitation.org/doi/10.1119/1.15963  \n",
+    "\n",
+    "Reed: Comments on parameter variances...    \n",
+    "https://aapt.scitation.org/doi/10.1119/1.17044  \n",
+    "\n",
+    "Derek York, Norman M. Evensen, Margarita Lopez Martinez and Jonas Basabe Delgado: Unified equations for the slope, intercept, and standard errors of the best straight line, Am. J. Phys. 72 3 (2004).   \n",
+    "https://aapt.scitation.org/doi/10.1119/1.1632486"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "hide_input": false,
+  "kernelspec": {
+   "display_name": "Python 2",
+   "language": "python",
+   "name": "python2"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 2
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython2",
+   "version": "2.7.12"
+  },
+  "toc": {
+   "nav_menu": {},
+   "number_sections": true,
+   "sideBar": true,
+   "skip_h1_title": false,
+   "toc_cell": false,
+   "toc_position": {
+    "height": "658px",
+    "left": "0px",
+    "right": "1388px",
+    "top": "110px",
+    "width": "212px"
+   },
+   "toc_section_display": "block",
+   "toc_window_display": true
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/exercises/Exercise1/.ipynb_checkpoints/Exercise_1-checkpoint.ipynb b/exercises/Exercise1/.ipynb_checkpoints/Exercise_1-checkpoint.ipynb
new file mode 100644
index 0000000..d8992d7
--- /dev/null
+++ b/exercises/Exercise1/.ipynb_checkpoints/Exercise_1-checkpoint.ipynb
@@ -0,0 +1,572 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Exercise 1"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "You **can** use this notebook as template to solve your exercises. The **TODO:** indicates where you should put the missing code. You are also free to start a new notebook from scratch."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# 1. Basic plotting with matplotlib"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "In our first example, we plot a simple curve with matplotlib."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## a) Generating set of data"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "First we need to create an array of our x values for the curve to plot."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Import basic libraries:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from __future__ import print_function\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "%matplotlib inline\n",
+    "# the commands above is needed to have the plots displayed inside this \n",
+    "# notebook instead of in an external window"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "None\n"
+     ]
+    },
+    {
+     "ename": "TypeError",
+     "evalue": "'bool' object is not iterable",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-2-5c30e497ab77>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      8\u001b[0m \u001b[0;31m# test your function, it should print 'True' twice\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      9\u001b[0m print(all(equallySpacedNumbers(2.0,10.0,9) \n\u001b[0;32m---> 10\u001b[0;31m           == [2.,3.,4.,5.,6.,7.,8.,9.,10.]))\n\u001b[0m\u001b[1;32m     11\u001b[0m print(all(abs(equallySpacedNumbers(-1.2,0.2,6) \n\u001b[1;32m     12\u001b[0m               - [-1.2,-0.92,-0.64,-0.36,-0.08,0.2]) < 1e-6))\n",
+      "\u001b[0;31mTypeError\u001b[0m: 'bool' object is not iterable"
+     ]
+    }
+   ],
+   "source": [
+    "def equallySpacedNumbers(start, end, number):\n",
+    "    return # TODO: use numpy to return a 1-d array of equidistant\n",
+    "           # floating point numbers between start and end\n",
+    "\n",
+    "# look at the function output by printing:\n",
+    "print(equallySpacedNumbers(2.0,3.0,4))\n",
+    "\n",
+    "# test your function, it should print 'True' twice\n",
+    "print(all(equallySpacedNumbers(2.0,10.0,9) \n",
+    "          == [2.,3.,4.,5.,6.,7.,8.,9.,10.]))\n",
+    "print(all(abs(equallySpacedNumbers(-1.2,0.2,6) \n",
+    "              - [-1.2,-0.92,-0.64,-0.36,-0.08,0.2]) < 1e-6))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Note that in the second case, we can not make an exact comparison due to rounding errors. Having such test functions is useful in case we want to replace our generator of equally spaces numbers with a different (e.g. faster) version later."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### b) Simple plots"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "As example, we now want to make a plot of the fall time vs. the height of which an apple is dropped. For both x and y we need one-dimensional numpy arrays of the same length.\n",
+    "  \n",
+    "\n",
+    "You find some help on basic plot functionalities here:  \n",
+    "https://matplotlib.org/api/_as_gen/matplotlib.pyplot.plot.html\n",
+    "  \n",
+    "For more special plots, first have a look in the gallery:  \n",
+    "https://matplotlib.org/gallery/index.html  \n",
+    "which already includes many common types of plots."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def Falltime(x, g):\n",
+    "    return np.sqrt(2*x/g)\n",
+    "\n",
+    "# create a dataset\n",
+    "true_g = 9.8\n",
+    "data_x = # TODO: create array of 50 equally spaced points \n",
+    "         #       between 0 and 2.0 (height in m)\n",
+    "data_y = # TODO: compute fall time for all height values\n",
+    "\n",
+    "# the simplest way to plot\n",
+    "# TODO: create plot out of x/y data\n",
+    "\n",
+    "# always label the axes (the '$...$' for latex style)\n",
+    "# hint: use raw strings, e.g. r'height [m]'\n",
+    "# TODO: set labels for x and y-axis\n",
+    "\n",
+    "# make the plot appear\n",
+    "# TODO: draw plot"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### c) Load measurements from text file"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Above plot now shows the prediction of the fall time. To make a comparison with our measurements, we first have to load them from the text file **measurement.txt**.\n",
+    "\n",
+    "Numpy provides a very convenient function for this purpose! look at the Numpy reference:  \n",
+    "https://docs.scipy.org/doc/numpy/reference/generated/numpy.loadtxt.html"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# load data from textfile\n",
+    "#     format: height[m] time[s] height_error[m] time_error[s]\n",
+    "measurements = # TODO: load measurements from measurement.txt\n",
+    "\n",
+    "# look at it\n",
+    "print(\"shape:\", measurements.shape, \"\\n\")\n",
+    "print(\"data:\\n\", measurements, \"\\n\")\n",
+    "print(\"first column:\", measurements[:, 0], \"\\n\")\n",
+    "print(\"last row, first two columns:\", measurements[-1,0:2])\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### d) Plot with error bars"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now we want to plot the measurement data (from the text file) with error bars together with the prediction from theory. In many cases there is a non-negligible uncertainty also on the theoretical prediction. One way of visualizing this is to plot an error band, which in practice can be done by shading the area between two curves.  \n",
+    "In this example, use $\\sigma_g = 0.4 \\frac{\\text{m}}{\\text{s}^2}$ as the uncertainty of $g$.  \n",
+    "  \n",
+    "There are examples of plots with error bars in the gallery linked above. For more detailed options look at the reference here:  \n",
+    "https://matplotlib.org/api/_as_gen/matplotlib.pyplot.errorbar.html"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# create additional dataset for the uncertainty band\n",
+    "# use 0.4 m/s^2 as symmetric uncertainty on g\n",
+    "data_y_m = # TODO: g varied down by the uncertainty\n",
+    "data_y_p = # TODO: g varied up by the uncertainty\n",
+    "\n",
+    "# plot uncertainty band of theory prediction\n",
+    "# TODO: plot filled area between the two curves created \n",
+    "#       above, hint: use plt.fill_between\n",
+    "\n",
+    "# plot mean value on top\n",
+    "# TODO: add curve for nominal value of g in a different \n",
+    "#       color than the uncertainty band\n",
+    "\n",
+    "# TODO: label the axes\n",
+    "\n",
+    "# plot measurement with errors\n",
+    "# TODO: plot measurements loaded from text file on top of \n",
+    "#       the theory curve with circular markers, no line between \n",
+    "#       them and also include errorbars! hint: plt.errorbar\n",
+    "\n",
+    "\n",
+    "# legend\n",
+    "# TODO: create a legend, hint(1): you can give a label to the \n",
+    "#       individual plots with e.g. label='theory' in the creation \n",
+    "#       of the plots above\n",
+    "#       hint(2): use numpoints=1 as argument for plt.legend to \n",
+    "#       have only 1 point of your measurements in the legend\n",
+    "\n",
+    "\n",
+    "# optional: set axis limits\n",
+    "# TODO: set axes limits to [0, 2.0] for x and [0, 0.8] for y-axis\n",
+    "\n",
+    "# optional: grid lines\n",
+    "# TODO: display grid lines\n",
+    "\n",
+    "# save the figure to a pdf file\n",
+    "# TODO: save the plot as \"exercise-1-plot.pdf\"\n",
+    "\n",
+    "# make the plot appear\n",
+    "# TODO: show the plot in the notebook"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### e) Histograms"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "A qualitative way to check compatibility of the measurement points with theory is to make a histogram of the pulls (pulls are defined below in the code). Create the histogram of pulls and overlay the expected pull distribution, which is Gaussian.  \n",
+    "  \n",
+    "Instead of putting the formula for the Gaussian yourself, you can use `scipy.stats.norm.pdf`, see here:\n",
+    "https://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.norm.html"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [],
+   "source": [
+    "import scipy.stats\n",
+    "\n",
+    "# as approximation we ignore the errors on the measured \n",
+    "# height since they are relatively small!\n",
+    "\n",
+    "heights = measurements[:, 0]\n",
+    "times = measurements[:, 1]\n",
+    "time_errors = measurements[:, 3]\n",
+    "predictions = Falltime(heights, true_g)\n",
+    "\n",
+    "# compute pulls\n",
+    "pulls = (times - predictions)/time_errors\n",
+    "\n",
+    "# histogram of pulls\n",
+    "# TODO: create normalized histogram (meaning sum of all bin \n",
+    "#       contents equals 1) with 10 bins\n",
+    "#       hint: use histtype='stepfilled'\n",
+    "\n",
+    "# unit gaussian\n",
+    "x = # TODO: 50 points between -3.0 and 3.0\n",
+    "y = # TODO: unit gaussian, hint: scipy.stats.norm.pdf\n",
+    "plt.plot(x, y, '--', color='r', linewidth=3.0)\n",
+    "\n",
+    "# always label the axes, also for histograms\n",
+    "# TODO: labels\n",
+    "\n",
+    "# annotation\n",
+    "# TODO: create a annotation with text 'unit gaussian' pointing \n",
+    "#       to the curve plotted above.  hint: plt.annotate\n",
+    "\n",
+    "    \n",
+    "# save the figure to a pdf file\n",
+    "# TODO: save as 'exercise-1-histogram.pdf'\n",
+    "\n",
+    "# TODO: show in notebook"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### f) (optional) Creating a text file of toy measurements"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "# create toy experiments instead of real measurements here\n",
+    "\n",
+    "# TODO: create a text file in the same format as the \n",
+    "#       'measurement.txt' with 1000 random toy experiments, \n",
+    "#       assuming the same uncertainties as above\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# 2. Error propagation with Python"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We consider a LC circuit with resonance frequency $\\omega_0 = \\frac{1}{\\sqrt{LC}}$.  \n",
+    "$C = 150 \\pm 8 \\,\\text{pF}$  \n",
+    "$L = 1 \\pm 0.1 \\,\\text{mH}$    \n",
+    "  \n",
+    "What is the resonance frequency and its uncertainty? "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## a) Calculation by hand"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The mean value is computed to:   \n",
+    "  \n",
+    "$\\omega_0 =$  \n",
+    "  \n",
+    "Since the uncertainties for both quantities come from independent electronic components, they can safely be assumed as uncorrelated and one can compute the uncertainty of $\\omega_0$ to   \n",
+    "  \n",
+    "$\\sigma_{\\omega_0} =$  "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## b) Installation of 'uncertainties' package"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "There are packages, which make handling of uncertainties very easy, e.g. the package simply called \"uncertainties\". It is not included in standard packages of Anaconda and therefore has to be installed with:  \n",
+    "`conda install -c conda-forge uncertainties`  \n",
+    "This can take several minutes, since anaconda has to resolve a lot of dependencies.  "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## c) Use of 'uncertainites' package"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Look at the example on the official website on how to use the library:  \n",
+    "https://pythonhosted.org/uncertainties/  \n",
+    "  \n",
+    "Define $L$ and $C$ as `ufloat`s and compute the resonance frequency and print the result.  \n",
+    "How can one obtain the central value and the uncertainty separately from the `ufloat` object?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from uncertainties import ufloat\n",
+    "from uncertainties.umath import *\n",
+    "\n",
+    "# TODO: ..."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## d) (optional) write your own uncertainty package"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We can also try to write our own class for propagating uncertainties. Look at the myufloat class below and add the missing pieces marked with **TODO:**. Then test your **myufloat** class with the LC circuit example from above. It should lead to the same result (up to floating point rounding errors).  \n",
+    "  \n",
+    "Addition and the square root already have been implemented, complete the minimal example by adding subtraction, multiplication and division."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "class myufloat:\n",
+    "    def __init__(self, n, s=0.0):\n",
+    "        self.n = float(n)\n",
+    "        self.s = float(s)\n",
+    "    \n",
+    "    def __add__(self, operand):\n",
+    "        n = self.n + operand.n\n",
+    "        s = np.sqrt(self.s * self.s + operand.s * operand.s)\n",
+    "        return myufloat(n, s)\n",
+    "\n",
+    "    def __sub__(self, operand):\n",
+    "        return # TODO: implement subtraction\n",
+    "    \n",
+    "    def __mul__(self, operand):\n",
+    "        return # TODO: implement multiplication\n",
+    "    \n",
+    "    def __div__(self, operand):\n",
+    "        return # TODO: implement division\n",
+    "    \n",
+    "    # for Python3\n",
+    "    def __truediv__(self, operand):\n",
+    "        return self.__div__(operand)\n",
+    "    \n",
+    "    # used in np.sqrt\n",
+    "    def sqrt(self):\n",
+    "        return myufloat(np.sqrt(self.n), np.abs(0.5/np.sqrt(self.n)*self.s))\n",
+    "    \n",
+    "    def __str__(self):\n",
+    "        return \"%1.2e ± %1.2e\"%(self.n, self.s)\n",
+    "    \n",
+    "    # used for print function\n",
+    "    def __repr__(self):\n",
+    "        return \"%1.2e ± %1.2e\"%(self.n, self.s)\n",
+    "    "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "C = myufloat(150e-12, 8e-12)\n",
+    "L = myufloat(1e-3, 0.1e-3)\n",
+    "\n",
+    "print(myufloat(1.0)/np.sqrt(C*L))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "So the results agree for this case! (If not check your implementation)  \n",
+    "Lets check some other cases:  \n",
+    "create two values with uncertainties:  \n",
+    "  \n",
+    "$a = 1.0 \\pm 0.1$  \n",
+    "$b = 2.0 \\pm 0.05$  \n",
+    "  \n",
+    "and compute the result including uncertainty both with the uncertainties package (ufloat) and your own implementation (using myufloat) of:  \n",
+    "  \n",
+    "$c = \\frac{a+b}{a-b}$  \n",
+    "  \n",
+    "are they the same? If not, why?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "a1 = ufloat(1.0, 0.1)\n",
+    "b1 = ufloat(2.0, 0.05)\n",
+    "\n",
+    "a2 = myufloat(1.0, 0.1)\n",
+    "b2 = myufloat(2.0, 0.05)\n",
+    "\n",
+    "c1 = (a1+b1)/(a1-b1)\n",
+    "c2 = (a2+b2)/(a2-b2)\n",
+    "\n",
+    "print(c1)\n",
+    "print(c2)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "What is happening here?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/exercises/Exercise1/Exercise_1.ipynb b/exercises/Exercise1/Exercise_1.ipynb
new file mode 100644
index 0000000..d8992d7
--- /dev/null
+++ b/exercises/Exercise1/Exercise_1.ipynb
@@ -0,0 +1,572 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Exercise 1"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "You **can** use this notebook as template to solve your exercises. The **TODO:** indicates where you should put the missing code. You are also free to start a new notebook from scratch."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# 1. Basic plotting with matplotlib"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "In our first example, we plot a simple curve with matplotlib."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## a) Generating set of data"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "First we need to create an array of our x values for the curve to plot."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Import basic libraries:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from __future__ import print_function\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "%matplotlib inline\n",
+    "# the commands above is needed to have the plots displayed inside this \n",
+    "# notebook instead of in an external window"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "None\n"
+     ]
+    },
+    {
+     "ename": "TypeError",
+     "evalue": "'bool' object is not iterable",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-2-5c30e497ab77>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      8\u001b[0m \u001b[0;31m# test your function, it should print 'True' twice\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      9\u001b[0m print(all(equallySpacedNumbers(2.0,10.0,9) \n\u001b[0;32m---> 10\u001b[0;31m           == [2.,3.,4.,5.,6.,7.,8.,9.,10.]))\n\u001b[0m\u001b[1;32m     11\u001b[0m print(all(abs(equallySpacedNumbers(-1.2,0.2,6) \n\u001b[1;32m     12\u001b[0m               - [-1.2,-0.92,-0.64,-0.36,-0.08,0.2]) < 1e-6))\n",
+      "\u001b[0;31mTypeError\u001b[0m: 'bool' object is not iterable"
+     ]
+    }
+   ],
+   "source": [
+    "def equallySpacedNumbers(start, end, number):\n",
+    "    return # TODO: use numpy to return a 1-d array of equidistant\n",
+    "           # floating point numbers between start and end\n",
+    "\n",
+    "# look at the function output by printing:\n",
+    "print(equallySpacedNumbers(2.0,3.0,4))\n",
+    "\n",
+    "# test your function, it should print 'True' twice\n",
+    "print(all(equallySpacedNumbers(2.0,10.0,9) \n",
+    "          == [2.,3.,4.,5.,6.,7.,8.,9.,10.]))\n",
+    "print(all(abs(equallySpacedNumbers(-1.2,0.2,6) \n",
+    "              - [-1.2,-0.92,-0.64,-0.36,-0.08,0.2]) < 1e-6))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Note that in the second case, we can not make an exact comparison due to rounding errors. Having such test functions is useful in case we want to replace our generator of equally spaces numbers with a different (e.g. faster) version later."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### b) Simple plots"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "As example, we now want to make a plot of the fall time vs. the height of which an apple is dropped. For both x and y we need one-dimensional numpy arrays of the same length.\n",
+    "  \n",
+    "\n",
+    "You find some help on basic plot functionalities here:  \n",
+    "https://matplotlib.org/api/_as_gen/matplotlib.pyplot.plot.html\n",
+    "  \n",
+    "For more special plots, first have a look in the gallery:  \n",
+    "https://matplotlib.org/gallery/index.html  \n",
+    "which already includes many common types of plots."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def Falltime(x, g):\n",
+    "    return np.sqrt(2*x/g)\n",
+    "\n",
+    "# create a dataset\n",
+    "true_g = 9.8\n",
+    "data_x = # TODO: create array of 50 equally spaced points \n",
+    "         #       between 0 and 2.0 (height in m)\n",
+    "data_y = # TODO: compute fall time for all height values\n",
+    "\n",
+    "# the simplest way to plot\n",
+    "# TODO: create plot out of x/y data\n",
+    "\n",
+    "# always label the axes (the '$...$' for latex style)\n",
+    "# hint: use raw strings, e.g. r'height [m]'\n",
+    "# TODO: set labels for x and y-axis\n",
+    "\n",
+    "# make the plot appear\n",
+    "# TODO: draw plot"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### c) Load measurements from text file"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Above plot now shows the prediction of the fall time. To make a comparison with our measurements, we first have to load them from the text file **measurement.txt**.\n",
+    "\n",
+    "Numpy provides a very convenient function for this purpose! look at the Numpy reference:  \n",
+    "https://docs.scipy.org/doc/numpy/reference/generated/numpy.loadtxt.html"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# load data from textfile\n",
+    "#     format: height[m] time[s] height_error[m] time_error[s]\n",
+    "measurements = # TODO: load measurements from measurement.txt\n",
+    "\n",
+    "# look at it\n",
+    "print(\"shape:\", measurements.shape, \"\\n\")\n",
+    "print(\"data:\\n\", measurements, \"\\n\")\n",
+    "print(\"first column:\", measurements[:, 0], \"\\n\")\n",
+    "print(\"last row, first two columns:\", measurements[-1,0:2])\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### d) Plot with error bars"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now we want to plot the measurement data (from the text file) with error bars together with the prediction from theory. In many cases there is a non-negligible uncertainty also on the theoretical prediction. One way of visualizing this is to plot an error band, which in practice can be done by shading the area between two curves.  \n",
+    "In this example, use $\\sigma_g = 0.4 \\frac{\\text{m}}{\\text{s}^2}$ as the uncertainty of $g$.  \n",
+    "  \n",
+    "There are examples of plots with error bars in the gallery linked above. For more detailed options look at the reference here:  \n",
+    "https://matplotlib.org/api/_as_gen/matplotlib.pyplot.errorbar.html"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# create additional dataset for the uncertainty band\n",
+    "# use 0.4 m/s^2 as symmetric uncertainty on g\n",
+    "data_y_m = # TODO: g varied down by the uncertainty\n",
+    "data_y_p = # TODO: g varied up by the uncertainty\n",
+    "\n",
+    "# plot uncertainty band of theory prediction\n",
+    "# TODO: plot filled area between the two curves created \n",
+    "#       above, hint: use plt.fill_between\n",
+    "\n",
+    "# plot mean value on top\n",
+    "# TODO: add curve for nominal value of g in a different \n",
+    "#       color than the uncertainty band\n",
+    "\n",
+    "# TODO: label the axes\n",
+    "\n",
+    "# plot measurement with errors\n",
+    "# TODO: plot measurements loaded from text file on top of \n",
+    "#       the theory curve with circular markers, no line between \n",
+    "#       them and also include errorbars! hint: plt.errorbar\n",
+    "\n",
+    "\n",
+    "# legend\n",
+    "# TODO: create a legend, hint(1): you can give a label to the \n",
+    "#       individual plots with e.g. label='theory' in the creation \n",
+    "#       of the plots above\n",
+    "#       hint(2): use numpoints=1 as argument for plt.legend to \n",
+    "#       have only 1 point of your measurements in the legend\n",
+    "\n",
+    "\n",
+    "# optional: set axis limits\n",
+    "# TODO: set axes limits to [0, 2.0] for x and [0, 0.8] for y-axis\n",
+    "\n",
+    "# optional: grid lines\n",
+    "# TODO: display grid lines\n",
+    "\n",
+    "# save the figure to a pdf file\n",
+    "# TODO: save the plot as \"exercise-1-plot.pdf\"\n",
+    "\n",
+    "# make the plot appear\n",
+    "# TODO: show the plot in the notebook"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### e) Histograms"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "A qualitative way to check compatibility of the measurement points with theory is to make a histogram of the pulls (pulls are defined below in the code). Create the histogram of pulls and overlay the expected pull distribution, which is Gaussian.  \n",
+    "  \n",
+    "Instead of putting the formula for the Gaussian yourself, you can use `scipy.stats.norm.pdf`, see here:\n",
+    "https://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.norm.html"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [],
+   "source": [
+    "import scipy.stats\n",
+    "\n",
+    "# as approximation we ignore the errors on the measured \n",
+    "# height since they are relatively small!\n",
+    "\n",
+    "heights = measurements[:, 0]\n",
+    "times = measurements[:, 1]\n",
+    "time_errors = measurements[:, 3]\n",
+    "predictions = Falltime(heights, true_g)\n",
+    "\n",
+    "# compute pulls\n",
+    "pulls = (times - predictions)/time_errors\n",
+    "\n",
+    "# histogram of pulls\n",
+    "# TODO: create normalized histogram (meaning sum of all bin \n",
+    "#       contents equals 1) with 10 bins\n",
+    "#       hint: use histtype='stepfilled'\n",
+    "\n",
+    "# unit gaussian\n",
+    "x = # TODO: 50 points between -3.0 and 3.0\n",
+    "y = # TODO: unit gaussian, hint: scipy.stats.norm.pdf\n",
+    "plt.plot(x, y, '--', color='r', linewidth=3.0)\n",
+    "\n",
+    "# always label the axes, also for histograms\n",
+    "# TODO: labels\n",
+    "\n",
+    "# annotation\n",
+    "# TODO: create a annotation with text 'unit gaussian' pointing \n",
+    "#       to the curve plotted above.  hint: plt.annotate\n",
+    "\n",
+    "    \n",
+    "# save the figure to a pdf file\n",
+    "# TODO: save as 'exercise-1-histogram.pdf'\n",
+    "\n",
+    "# TODO: show in notebook"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### f) (optional) Creating a text file of toy measurements"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "# create toy experiments instead of real measurements here\n",
+    "\n",
+    "# TODO: create a text file in the same format as the \n",
+    "#       'measurement.txt' with 1000 random toy experiments, \n",
+    "#       assuming the same uncertainties as above\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# 2. Error propagation with Python"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We consider a LC circuit with resonance frequency $\\omega_0 = \\frac{1}{\\sqrt{LC}}$.  \n",
+    "$C = 150 \\pm 8 \\,\\text{pF}$  \n",
+    "$L = 1 \\pm 0.1 \\,\\text{mH}$    \n",
+    "  \n",
+    "What is the resonance frequency and its uncertainty? "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## a) Calculation by hand"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The mean value is computed to:   \n",
+    "  \n",
+    "$\\omega_0 =$  \n",
+    "  \n",
+    "Since the uncertainties for both quantities come from independent electronic components, they can safely be assumed as uncorrelated and one can compute the uncertainty of $\\omega_0$ to   \n",
+    "  \n",
+    "$\\sigma_{\\omega_0} =$  "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## b) Installation of 'uncertainties' package"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "There are packages, which make handling of uncertainties very easy, e.g. the package simply called \"uncertainties\". It is not included in standard packages of Anaconda and therefore has to be installed with:  \n",
+    "`conda install -c conda-forge uncertainties`  \n",
+    "This can take several minutes, since anaconda has to resolve a lot of dependencies.  "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## c) Use of 'uncertainites' package"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Look at the example on the official website on how to use the library:  \n",
+    "https://pythonhosted.org/uncertainties/  \n",
+    "  \n",
+    "Define $L$ and $C$ as `ufloat`s and compute the resonance frequency and print the result.  \n",
+    "How can one obtain the central value and the uncertainty separately from the `ufloat` object?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from uncertainties import ufloat\n",
+    "from uncertainties.umath import *\n",
+    "\n",
+    "# TODO: ..."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## d) (optional) write your own uncertainty package"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We can also try to write our own class for propagating uncertainties. Look at the myufloat class below and add the missing pieces marked with **TODO:**. Then test your **myufloat** class with the LC circuit example from above. It should lead to the same result (up to floating point rounding errors).  \n",
+    "  \n",
+    "Addition and the square root already have been implemented, complete the minimal example by adding subtraction, multiplication and division."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "class myufloat:\n",
+    "    def __init__(self, n, s=0.0):\n",
+    "        self.n = float(n)\n",
+    "        self.s = float(s)\n",
+    "    \n",
+    "    def __add__(self, operand):\n",
+    "        n = self.n + operand.n\n",
+    "        s = np.sqrt(self.s * self.s + operand.s * operand.s)\n",
+    "        return myufloat(n, s)\n",
+    "\n",
+    "    def __sub__(self, operand):\n",
+    "        return # TODO: implement subtraction\n",
+    "    \n",
+    "    def __mul__(self, operand):\n",
+    "        return # TODO: implement multiplication\n",
+    "    \n",
+    "    def __div__(self, operand):\n",
+    "        return # TODO: implement division\n",
+    "    \n",
+    "    # for Python3\n",
+    "    def __truediv__(self, operand):\n",
+    "        return self.__div__(operand)\n",
+    "    \n",
+    "    # used in np.sqrt\n",
+    "    def sqrt(self):\n",
+    "        return myufloat(np.sqrt(self.n), np.abs(0.5/np.sqrt(self.n)*self.s))\n",
+    "    \n",
+    "    def __str__(self):\n",
+    "        return \"%1.2e ± %1.2e\"%(self.n, self.s)\n",
+    "    \n",
+    "    # used for print function\n",
+    "    def __repr__(self):\n",
+    "        return \"%1.2e ± %1.2e\"%(self.n, self.s)\n",
+    "    "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "C = myufloat(150e-12, 8e-12)\n",
+    "L = myufloat(1e-3, 0.1e-3)\n",
+    "\n",
+    "print(myufloat(1.0)/np.sqrt(C*L))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "So the results agree for this case! (If not check your implementation)  \n",
+    "Lets check some other cases:  \n",
+    "create two values with uncertainties:  \n",
+    "  \n",
+    "$a = 1.0 \\pm 0.1$  \n",
+    "$b = 2.0 \\pm 0.05$  \n",
+    "  \n",
+    "and compute the result including uncertainty both with the uncertainties package (ufloat) and your own implementation (using myufloat) of:  \n",
+    "  \n",
+    "$c = \\frac{a+b}{a-b}$  \n",
+    "  \n",
+    "are they the same? If not, why?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "a1 = ufloat(1.0, 0.1)\n",
+    "b1 = ufloat(2.0, 0.05)\n",
+    "\n",
+    "a2 = myufloat(1.0, 0.1)\n",
+    "b2 = myufloat(2.0, 0.05)\n",
+    "\n",
+    "c1 = (a1+b1)/(a1-b1)\n",
+    "c2 = (a2+b2)/(a2-b2)\n",
+    "\n",
+    "print(c1)\n",
+    "print(c2)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "What is happening here?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/exercises/Exercise1/Exercise_1.pdf b/exercises/Exercise1/Exercise_1.pdf
new file mode 100644
index 0000000000000000000000000000000000000000..69ec1a03254b29b73f1f5a860dd10c65c6c0b5b6
GIT binary patch
literal 112058
zcmbTeb9ANMvn?Fkw%xI9+qP|^V>=z&M#pByR>$brw!eOVI_JA@#yx%R9%JvZ|6Dbz
zYR*|}t>+<?7Z#;qpk;v~ot__9f?{DJpeL|1w1nd3hN6=;u{CoxCtzS_W+(W^2Z~PA
z!rIxyk$_Ir+Q8XF*u==r*aV7~7s|=m(Zs+8%6(-3(<*kA_1f!eu)mOlF2R2M{M^9Z
z4^)dev_;lKTLm1}-higVMufE4@LA8d3oaozo3Sf0D=zWxJ(75i8}O51-p+j61&ef#
z<k_2>9gmB#SI#7iWFknhwrIqN1JcC>%rffz-)3YO+Hkw~AV1`;a>upNFKMgE7`JxV
z#-eEs7Cs(p(w|a>`4IL>tl}@4<X(3|#nyGhy}9YC?hzdB&iB#jLiFcI;weNE>Xk_f
zBocwlj|bd;>Y7}o>c|lF4t1Ar%#`uOt2NF%*cGI97jC|I)!YTzN}0UQo`9REsMw-h
z;}#_hDCv_Jp^>+mIag)K)|e8M3dJ!XCTZ_-8E|A=m+>2r6Uk`c3=o_riV)o>zYBvB
zBgVFqb~5X3ht^F8mjI8R&x|;cF+pZKJepHB%x3AE)HBo@W!^-IghOC--%b<@mBpu(
zaq(1~Jdg-QPZ?XE9B;`}cOD=?my05X`h3yMS`5B#DY`>Yk;yzFGo;c0+DHovBW3Uy
ziJvWB@|(EMt#4X!h6o!xG2zgAZY-+CI?-cuxzLl`X<k36{xZ=Cz9QS0Mcb2Ai?D-@
z@EUe!j8)GZ9l&7w)%WhDdB_^g39U#gBdM6;pnQ&G$n(f1uM<V9_O$N2qOZGqPg@l=
z+>3N@Pz!iA*Z~v1YAY(Lww|i$?zsoPfpUP1Z1khL*zuITH+|)L2tTO^K+m1Nmn(qt
z$F%APy{8PF*2l&432NSi*3XHQaT9W=sZhm_U?9XVQ*OF7sw*f$=RV?FY#Fv1N}dAc
z%Z#sa)CFs4ck7{EW$M<^T+{A4%@@m)GYpa2^FP55SS8iRiRq~4(R(@O5$g^gphgqO
zXOvySh)C33r5r<!?aQcQ$xx?LMrdPw$qY`qU>l>y77{q06{^xDza?9B8gbi4(m0@d
zblc(I&Jmp*byS3wGMNz(fkUD-sJ27BXkx>DT|c&B8Ih-Zt(VX8lr-oqBnf<1w{3)e
z#;Ah#<}2a85G+hb=&D+QS+<3J5J$`}*PUyvEw!-8pzvCN?3kvXq#_0!5=ev#ygN`)
z3MENB^M+5hR?)INi2%UyxExAQh$PSPecNHl{(2-p`T$c1GAGs!ATG?>^#(z>gjR&F
z=Zg-K$CT>O9P#lBwi%uf4jQ8*t|P8R%zi5eV;fAoXJXLJEwbxG%g4=)_F;C_4L`%2
z&BlT=*KS`PluO#P`G}(sd9scIlk*y)02zga2cp)J^`I;*I*BL4$%#|$okK;BvcZ=E
ze_~K2ht4_#rS3M)A`1HlUnr+v9Io-;W8&%M1g0Ur@{K{9;A46@b2^a{86X#-CvaE|
z#Ik_Rn%g{|H>LegsIr~bQfUJo^@(F}O$x}MBxI|SALVZ=icdOPoS4b%w}gbKMK)B8
zknTmbOJ*oqh1b&(ISVI{BBfFQ#}~~VoQE5)@Rh$dLJIMD?uZ-GL{+}J!15?};z{0i
zSYns=l>NLf!#e5bXr2sMzS~|yng6&@QL*WXS^QnMN6mN#;9dxE)vHcCt5S@G0^PIl
zJNB2{ANNne%oFNRh+NjaEXv+aj$t?<yf93^=y=1>H&wW}+RtNmL+ZG~>=Syxqrt-=
zK~Y^GUamkFz7XznnY2xzE=86Xs}BDxZtH(CGBY{ZVp^|iBoio06K!D>v6pvf6Gq0M
z;>dxM!X8m+O<ub?gtP2<7dW1nbCZ#4H^rU>nbMv<xh3Em?*eY;=o{Kj5`1X`T)r2?
zPN9&_Q8=(OK=~Cqua_NwuM>ukKO++ac(_*3%fXJ12fLz(V57Z*%^hj$Be9q0jkS+=
zTx6ekTFN&C{*4BPPmzQW^+Uo7tdE2`y6)_B_5PWH@aXl8fs@{8m>N#2&4ptTT@34X
z2T`hiHx2ONb=wVk$MDX0Jb;J6y4!F(0G5`Ro(HXAiaf56dbE&@%V+H6yxl14^~^%G
zl|CMRdLTU98}ntC@%EPS_LTAVmGPFcojDW!Y7<v^#*F05n2)Mkn1MaK|I~b{I4?QK
z=rX{Qk;8|-&yMGssOAd#Y7Tz(u7N0V&zfprdi`5u<g^;Y9)~n2yZ-auGSDLPJC=6A
zt(E=W)4^oCTZu?(4$p^A$Sv)CaCRZSl{{aE<My=Y{*1lrd)44nZG8V!8N2Q;?8$Sy
z?d1NYV~^+^SfGPZOSheGt#X}s#;?3e$iYdTa#j)MM`A1`10J20%33Sw$q~Hb@?MPZ
z+kkwNX?+t$5c5^w&B-$=v+?lld}ZK*eyXiQQU&7sJ8%@OXxP3%gDcNEq&x=9I=863
zQJWFvp2M8n4r{l}R==Ze$bgdU0B?cA(>s4_zH7UKU~eH}k9yM;<;FU#eMvkU=Xl+h
za|0G><?n)S^I%**hf=BcYWt~9vHBDXELN>S6}<TMyw3?ih_TNWFVJ}Onj8Ls^u_CI
zi^mmP0_;;gl!>kJza;eczrW;{mH87L=3t`#Qyo6ju(4ZZMR?!P+v)^R3Q1jPHwJ$B
zZeUewU>-`N7s3pIk<k=<Tt$St`ten4W2O$Jv9Z&7J(SRIK)z$=>U`@8Zxpvm^!)HA
zbV@egT1RR})NA}fT^ZM%DBGCAb}W+H=o-SiR+KGnZ13jJB`2Ll5BC$%>3ibEC*m*1
z+WPI2Ur1}tZNH{<z?2NNt<5Ywq+W!U9(#GPz-=!fa#aV{Y;{}gS_hxYWY(>0pU}{u
zHYo>B1#}}XZL_ORh2j282&uN{PdMOkF#)~fk#uE=DX`$Oq^bMaRzT#?bYk6AY$55n
zU>e_+mWFyv>H!$3pJgf$xj!nERbD*+R1Cqr=%K-Km=r2mRonOjoDO|yX~#(F{7Y~y
zx(ekW^!K(z4VCLmUAu^M68E{w&ca$L?{w0mkX_J5M}_M7bWE?#RGGjQtJ@qkrOh%{
z_Q$G|=LLEV4OFZ6#RW1=xRm9NXtt*049DuIs<hFdhPpvYLsJ+Gc#ZF><;nFUg})@f
zjMamyV&?q}T3p`E9(zfmh}B#4S~uJ`tBQCHjO2Rk=8s<BYD_g-C<6Pwc=n7faiVb}
zhBY;kq;dzRrLlbT0Od!P{?<cPAV-^EOs>(2(C*5^V~<D>&A~w%LU+HA>CvU3o`T*{
z`0k;C!fI@(7m24iJ_^f70N#TiDH<&cy7c;nS{O)wZfB74M45KWR2tLK-})97v9&Qn
z12H(c9z{Zl$gmhS%{M(uw2t>BjUBoc2Pqi8=8L75=1j^j+hH&@yxc8yP-+Oq^!D~a
zkt%0T;uqQ4FS6=5WLc?ff;Q=OY~c#JeWxr(aE(YWM$T@YQCn~)8C08Wk{MnxNZki@
z--5c%raHLHMkVxCT9TL_<L?ZFu{e1kdUi4Vj5gW03{T0&vx*P*Yv}cFMD0ndVZO7w
z(zI+Vmd3yWr}A!?mM@3B{KS-EM)q;@H;c|wN_OR$=Tp=NeQ58tntY0h(M|@TFW%Y8
zVB_mb%L~*pBX>tIXr82QgSNNG-hKmf#qLaG$vOEU(_LCo+0VE|%~a);AE+Y$vw|12
z0wu6KObU_6oHR9z(W!H)P$OncBHs}A3LrYT8&i_E`~fbcgnBnP2nJ2C9t|3IG#8o=
znd!6WN$P;-(S?XEy#K4fzse#;0vJ$<LNeXb%Ai1sD-cRYN;?7)J0&&{bqqD|Cv2L5
zKzl|U2WK66nnCggOW=W)QjtX`hcAN16tOqCF|^!zbdi%Mjw2l$#;yEtcOIdR@^^Oe
z)36#$BRzP^Yo-07D=}0L`2zAhT*n?sIJ`9lgrB_Va`)pF1Xt^cA`*-kwJe1o5lf<q
zEm&OxA<FVMJ;^!}oH?Lc3|LHubtJ|TDB0)rJ3RJUYp_RAhG--LVtbn_BSw5-8c3lz
zeKov&6c7LeKy!qQTtHa+?MutCPqg1scADE=;%(Ao+RknAgqetRM6dQSkv!}sB87Hy
zNP>AKa6{u6yiVovSYfq_kCBzKnx*d+Bak~OK~geA0lF2H(daVDb2T+im8BpY9iXy&
zN@29bmBzwyP1GxGhg}+zmV`&+WV5g$A4N>Xk-M0aJh&)|UTxY4e1U%n@j2VVm<Z8e
z;Pd$H2$lG57|)iknTB{+?qz^sw>UGsxQAI{<Y>Ej9D%3CPp*kaSL*fN_C3!{Z{>?6
zg=DS5mcbK7D^1xp7N|2cDhX|g`y{(}q4KvuqpTSj%<lx%XhB62mg3a|yAc&XKzNTU
zmR>6)rcrp2q`cR^DpoPg%G;&4C2T`?0GLw}tMTyREc@50BdLVYs7!JNabS{-M_SUR
zWt!y<-f$oAQ{?#?(n68x4CJ~1Noz94jiIvxfx6uG)PSFR;NiXwE&Y1JOkxyhW)-x?
zgLAF)AJ=6guf2berK#8E7CYJ!vnVwH<i@|9JTzu9i{w?Yms}cczEs`7?e0RCQDR|+
zIAJ+Xa66z+l{Mt5;Ec}6<CGB+KRybrP{NKXO!1qDcZC?e3IP|J$>ytHAW#Y^XIx7z
ze$OwURx;BQ@g{<E0lpibh6M%{Q9~Lpz-JOD0e`wl;i-^CO0d$6TqaG>yFcp`rPB#3
z^S6SwS-Hc+qo0HK>>Zy+Y~W#k(4vDFGjr&hNg9Ae7x=)$IJ9Pasm#8~JSp*d8$TpR
z7ukH_dmXEcAJv82&B*s2jIu{j(5a&E;A-c6AnQM~>UvR&yK41E^s&|6UF*v)YeJa-
zR?TH!78E|>1#=+I!DXipoB(S+z#-p}qW#`k5|YWVhnzS`+wHK3(VWVW)(<9Zh)xs!
zc6K=uJ&fBRU1ZKT$x`C&=VLQ$!6*_@lC!T=kyAK)oEw^IEx0M^vJ=LI))zC>rrEs!
z!Mp7<l+gb&POu0LF-?jz84c}^M%^FA_E@6<GphC_@Ag2L$>a;ao2eYKU97H{KxjOe
z=6B05K#L|;78KF$TD@r?vq@4#A*PPE5Da!q7bovT6%N_~swvfFAz2|mst8esrNhGL
zgSx)t#;_#<Or!Y<>&Y8h+gE<QQBW`(3rTcl9qTven{B#@cR!R*l%4$(WoM=T;|cqx
zx|H1xE5dJxOIL+(35xHMarEg#+#>Jm%ulq^!5W0egm249xEy?a%-9R@bnR9=vxMka
zComm(CwF$Ho02q|?smo(HaBa`#$w+`9j%+{)M6SpG~=yA?jx3YUnQ$vJl5CPue1vp
zkBn97GJ?^CZ{&(vG+GSUefPz1KyEChkinPFJ!n4?Ohl2_Wt$2GH|dpT+_KbgCEO*L
z@)ZWlL%!O&r)_N8v-oa|7>m`{6^gk0094HPCozqUCUDTijLO)V`0;GUe$jWO4p5?U
zVClw98s`s701C^Vl{{AyeV83g(KzWO5@@`MBRn=~^Zae5Na%-n$7TNH0L|M<Gf)Z*
zpXk-nK}G5S-l<5BIPrZ|SQU{LB?$F35dDB49Z{9ERdDhSJzW$xDi@joJ$ThaAxrf4
z9mdu3iRJN&B(T63_%Q7;vvVr4PM}6p{DpcxGq}n|njy#D2H>&2B{KtK(t*?W-X_I3
zf1}ik;D!2GYJH>CNP|SJ?uoBQ^|th(T&>x5!3zC?ZY&NU6kxd+!KH@`OG75vXWwGH
z&ixQ?)Bw$wIfFgCkoJ7i!isn5PGgt>m#s2+T@%mv&4qT#PBW|gK)(!`%ga)0Z<=Nc
zr6I-{cOoZh$n)iy&BFM$kcns_84zw!tA=KKCa=p@z<~w?z}huHp@CAB5^oC+-e@z6
zrB9eVkto`DD&c}8Ivk4K#F`f5V--k&K9Yrk9y7}ht0Ig_WUH|x_`6!^s>ro@wjqnn
zRcra&GtSHnV!9nznlHMzcr|OIbqL|_I{tvrY1(bn=DogWK;Bc=#&_uVJn+-9CGwqg
z)rqgD93W}X&}T_A3F&?h<G_d!f@If*D3PG4-0KP7f2gr`_`1**8oc$oC0jF}IkpDC
zV*T7hI)@p65>5C7Sg%$qQDX-wKMS!Dj%^ZHQr0hjnQ2UUL9m+DdU*JNMjQo<ilogj
z;*&@)_HLG2A}}`{x0#4)jLM=!y~?MJWr&$y$%7Vrh?RE80)?QDXHed7Q4jf@2@pF8
z1MltXQJfp}9<lOC-Yj(-#=!IArvg;Gyc)TJrtkCC94;<<K^m|fiE3Vi(j5%>LdhBq
zIRj8it~JD*6|cJa&Np*&wfPTdwNoo3pYveGUTNNgVBe(tPOCL>l^G1AJ3PH=a8g04
zg|Ko_DJ#GERNF&M6DG$vK(L}OH`?YT9lgoIc}z5rfI{F$3{B(_c*<UaCt*~ybQigy
zN5Sg7?rv)dX}IpvMhhyjx@Q=DUzI5kS50U=S@eT{6^-P3*U+d5vAC$bfjI!?<F=}a
zK}2DFrKu3Sr(Ofbb`&S?qP}xY2irxX6;p6s<gXM=RDR-r($>OGnP*@AF*c$Xd>FYg
z`XstbzBk^hMVIgMEy(;KnqoRyQuqxiBA2u@{a$9w*;of%NB3$zZndcl3K#f_5v2)3
z&nZt*BYjD?LfNYseit>yY4H()QIx2N^d<A8gVOzmBwcmUBV0mO$eef}F}OOZ5uk2b
z!*Dp|z{bRqY~-P=DJpBHLxD&4Wdah}K+#XxC14&@Hvm4}?J%0$e+tw&b5Vch8#NYZ
z>oC1wOhfpBDDz7@?h>3g`*Wfg%!sboRGa}BykQ^B4pt=?UW@9maoT90^h3x^F46SE
znT020axEfdD*Dmx+ll98obyZQf}19JI&Xtu%|(!lV$SmDjryr)&3TaB#4sCG9k)fC
ziac%G8oKT2cXsDVF6((2m?{vSYapj)T^dd@n>B@%+X;_Wx??Dmm50d&rURKKd~@Y)
zTtSzr6D0a0XO_&@X(xkf4aq=71TRhH1fxc~uKqT1hj|fbT3I?X|D$N3SY7YZ-IanW
z57R4ji(x&qI<H7<Gh?ZSkXXPdMz(TC_wOss4InF<q2J7YMZz^e7A-{o>P6#=t6b!v
zov*ES*7H5@tc*XhuWeW?SyXOvnT%Dk+EP>Ja;i+mUO7Xy`@$qHgZpjke!fAwM5`d=
zj&6fU<vAa#1&mH~uFgJ-{-Qt99BDZM6e27585#6fXS=G9`eljICX5eqP5eiHaLO$o
zyFU2Ti5ku&=0S=IIqM_a0?63qZuYw`R_J&*(62DR-l8IU_istRxvm?Kth%7!@4Cds
z@|iBNvi|ABIiVr-d*>IS>!QYfM#!WmU!=2<AI@f_@Qjqdw)u=G*Jg!`NUB`8=Is4>
zGA@ytbEOI&n*ichQONG)P6x-8Xhl5Rv-?3>cQ^N%44?3Om?y~%Bd4c&43cl6JLZ+(
zqh=Hyt|SHI5bYiJ)RwOoB5wQFU5@TZ(d3GBoAj%>A31aI1`B4}(b_pkoA4~LIYYK_
zV3T}W$<QN(?)%~pS-4z>m>QOS-V+CU%oe(~RO+0_-v=tGQaqEion)5m7pzYhWN^E-
zg6Hd1D4~`QLAeMQ_k&pIP6Blav0mn3UkouAJ7Drl0K<PtLcO(hQdM3tMH5}ZKoKaf
za@*Y^?C#w0rVSP^((!v{>s<zCXIyox9pzEL3^BMYnBH}0Q&@bf#SSjJ(RDJM&0NOa
z00&=1VaG=Pf+o~VmE)0B@T!mzfeZK8we1v+aRuFaI0F<@HemflL_?a{q8aU`5ig(r
z>O!o^2CEk;AFhyLBw&g6E;c_tlk#3O=PL-->h?qth3fNaE@Y;OhFufK3GZ!>bm+Q6
zx;I6$%a5BqVY$AN;;B{8)evm0=$9re5X5js{M`ZsRwo5qabVMxIG{;z<3dg9(8^53
z91?^1^UPAMb^{zlKFCuqwlxU%iAJ&u0S*-2qRRA_BWHz4?EDx!B`ES_%b!q3N+Q|E
zHd6JSsW(Rv=pA5@>e61=u{|T2wvIs{8*m@u%;hsjcb7G>ZY7TVId>B2169R6*XG;Y
zU44eBoSs3^wKvXouIX_?+>JoK`axROlI=_m{l!#P91YlCOFP?(aNG*$`7K04$+cfg
zc^DYAsUy&&X3?cE5b2}@Si_$yjBg6iV66s^iz~~I9qCwMI)&uUTC$P8$b&92n>4fY
z<8r!auszqW8|@n*FFbA=o|f?1+E=%#R~7BAL(pfPcLcWbL)Y%NLQS=%nX^()`F^*D
zNrR0jhD|=ECU!+4YPuH_Xrd4^@-WLXV7A!x%j@*$$k861<Jvmnh8+-~WPKHE89`Wk
zWNir-l5?L;x%#@_$e44%KisP!SlyF8fU~}1!|w}FFD#(n#e&8*gZAZw=A%Q=C4%?R
zF_y2^d`DP4wv^jMTf}VX;|fL!YzUSO3v1(<s@^5kSi^w%cz*8N8i#e(QQ3QU)6@xz
zeyEntvHoGdrI^U%o>W^4p(d~Q^W9u0Yq3Yw7g`b{$>8fdy~=3&YBl#Q=3@};G6X;P
z6O5$rCN$ZwdUaVs12EFfM*9Ld8-y?I)Ff)4HIg*yM!+C95-z>fj0sf1h@f|z2zErw
zphmDF51spwE~?~29;J_5O58=NS13>Y4{h5=<n@Dffnoguv#6#CYEtnBoOIcBk%mb8
zGd~Ypl~zI@q{O|kU^Ta!5XiEsSCDQ4_`i44KxjK*Q=DG9UyusLMoBc-krz(1tU1_P
zBC`>gLh_P3?~-p+G`mjZ`4sSY`>NTwq&c@`HY}`aG~AH^T=X5UbU;qFe@y{)^pLP8
zE$M9>l?snHwWG&d@s2K*D}7QprH)9DvgDA)3Q(O*!P&?8dhau%#^qY=1<HqwUvwD(
z6?z4mm4%aIK&E$a8P>+XbYUA}oegD2HG8gkH*rY~t3x}jjp7}8WCIw!3)Y(5c*NWS
za8#T=S>zx}ArrFc@Dzh9JwLV_bwGZ>&~z|SsI!vjS-66zN_a^_g#dcMIS9-`{Urh^
z0OJq=zkIVv*YGS}9bv0%sJXUGn7<E)?7@*Dr}^mN^tvZzM;$1w!F!YgGAt*ghOx;g
zRx)BKvrS#!_E#`T;Sbbr)N(68P5_-%Ijaw#L7ruiGI86LK_h0e-x0BchSpGwBnd@p
zUa68o@WO>w39;_2rMrQfQ7UW}hkH=WEik|3X@EI#e1J9mG*q-n0c<oyxUYNk_S*8&
zof7$>bDR&plQbRf)yd0+3yOGE&qhNJELn;VFIxECxwu58YEzr<6JBa|SV`TT3F6TH
z?ZUOH0>)9OCJR5!_5FHjawtE)a#~E|Yhjm|FRP8lltHoDc(2g}+0>Mbp3;M(K@&60
z-dDY?K3d}oU%1I5!M?iFyQ3P-c(rE0GsbI-3qv1=JCIei4m9iy)Y<$qkB6Lq?{kHi
zQu3Sym+5-=*K^SMPS}3Y7yBJ{7CaYrOCfF+c0a~<LRp3&OnZ4$QC+*aqakI8OyVpo
z?L!t8Hu28if$0?_RQ|4L?DT(EG)4w`#y?fGA`Bg;wWceb?tpK+kBLVD0Dy5pri^Lx
zwa8}7-&hsM;bZXaNeYc7h^JoTyg$02se6buM&s8~xa@0}bWR@vL>~~NzqaLW<@7V=
z3oE`_0CI-D-p>v2+0s>SL@*mBQk1JZd!aKVBbC=t6h+7b<`Q{!KdtVCuId3WWf(#o
zAyDRbTvIR^CXjH$Vt^O4=dpk*JrsVd)x?j3#9xn*U=lk}0goCjU(hut6k&@}HdYwV
z9k-doL7ZhE15F$hSV)bvzqFIS5V448ks%R%gF(v_&kIm(;szvZI|&ie*z`*F3Z&-p
zp@+aaUTf&2J?Ub`wLOMwzODA4XY#M!7-csMu#fM*@6)O|ph+P?9_Hv;TY0fH1R|nu
z^O$Rr@oJ4GUB_A7FTHTlV`Tx77jeCtQtAe$pi+@Zk|gFQ$Se|nkCj5TgTWikX!`=a
zQ)czr?s3L8cAsF9;Z5;E%G7MoR=i;E0kTebI;Vg98e~|=bTgE@7d2GiAf}*{uNZ+R
znEDfxIGhbgq>#EcyZ2k7R%IdyBj=8HvtSryJ|h|=(wEqQOw5fnUGFx10-$L1ei2E=
zIK+<B=^(MEt?f5(EY$B$(C(qXByJDo;SqzP8*DX1y7gY6fxjpb(3ULdoa0dLiAy8^
z6&rbI8&HBONOE!y)R9Wzm6#=~0(JeOBy-zEiW(J3?8iL(sdobw)yrD5xYje69FU7j
zC7`{X^rcvRRb3#}5ll6X<af`_FIu_!DM_#n-ndn88wpavQ%Hk(m1AZ_kuX4u`zoe$
z6Y{DM>6;)BOSExx>2wByG-*%nJei(BL8;QU^`^g4N@%<?UQrsj`~R3i!)%z8kOY-H
zT|bEJymwG@eY{ARs_%pU(MtRJw1LcCQa<-UQ8*zQMM24cT^mF(e53_8!f@x<FRPjn
zcblWkSa_?c_9QQZG&9Mxu2n5ZLTKN!bkMSDk6MbI-4|F5#?)=UffFNS_sa6T?@3d$
zlmHfziupp90PQ8~YM^%sg^fdn>{Q&HVh)?`F4b$OfJ1#aN$RA2ij^X3aAz^?XD{mS
z)qYdPX5}=(3J#L!Vh{uN^%&1V#^f5W_LvcW3O@sDjIdNk(?j;qythxO_eMWJb~9}h
z+ws6=>sXK8pO;H<E`P`$IjX&P%deSDKlhZT^>Qbi?>W%T3T+>g7^d<TJxQnv)6KRy
zkv~6;;BEq|n??>b-hmr>$hM!`DQTLOy_j1g0_;7$7?P-LJbZ8<<t;`4Veo!w{0a@x
z5?Y!n84*h}WSv8B^QTTtg+*9^<N8NMno`$wR`4X>vDZz<w8)r{TY_K>)7zzT<tn88
z!jMneg~LHEpC?WRA=RS%l`>}@wKF0G+O7Tya&>Mqw+`bVYj!o^!!)+Q)$%O@<_m|@
z>*45+TEUzWDZ3h~liEzv3_DZnZoqt^Zy9bp=PS&{A17o0#~oNu+?Bte^KuW`_*vZR
z1YjTcsv$o3=CPiu1b0%P(WHvupBut^E0FFVR`A8}bvB5C%)(9K2+g&ab4W$%=(nXI
zDZ*?N{edWEka{m{Vm{zZPJ&j}YVO-kx){ULR^L)bgoqBa@LP<HdV1QfO>!#<d0L!J
znn!D^j}$D_bp$D`_mjSR(%0iPSw6SUg;u5o{xYViJ7g?wi7k7@QTjL^SISeq*F%(K
zlew9RT&Ghkc&hC62X6PQg?`=In6~Qoz*K%Tchy;@lReECYV8ZLO$(}UTs*#a?%X$d
z>Ed%;R8tdU(>~3BbL0_V>+DYF_>sibrK<ClS#@%y(#0}vf&#xiKZ7O7L|9<;m|<|=
zP<HT`89RQVT@T~6KTWn7jd=RR#5vPEw7q=h4U1+{0xee2EpoeA_gHtS4tKTgd{F{M
zMb4Y%fPR4763vkhJTMlK4Q<ENEV_{(T_}ZKbgK{)o7AIC-EF^AtMon3=(2Nk%iC6D
zC@v!p@oP+i0a?n9^7QFDu+8Md*JRfp?HT)T*KAxGQ<yfOsZ5Az*k4KS5Fe@sh40^?
z69+A>M$X_DBh|npe`EmN)xa=?aqa1Xooh}{jcPm^+|&aiO;(d^)*u3m8qVF<&-uTa
zM;y#R)1l9)fB4pb7nybtV!~lz?bfuJwYkb+l+tKEI+U2j)hBoPTz#3nCLJ^N^tOVo
zkx(sP)$@J5!`cjkApAln^i9^z8?#P@5>iFtv0OQ`0;$s6slg|^dUATml^U$X^pcKy
z!stzyp0C?H`a+EAP67<A?HEh@uF1j)8Xh8`)Hu))0A}wy2vsVbeYg`Mbxf+*W!~6*
zI2yat*VPgUv{7;d*FB}D1CDm~Ot4bU4oK`_v&PtR?Lp-(;>e_x!+icQ@{(MhBThOf
zXSbq}^+K?dKwCa6-*KMU=&XT4P6q)+hAzu|_(~J*RM`Z?iFJh)+ji0GY5m*b`DLeI
z#3%{LC>M+T08~>V&>`GS7EvbSt$4YzxZYl5N@79U(Ka8_UDt)oE+oyKrB-_?a+Y4(
z)(3g7TIL%WR_5au(^4pHHBof;Nltk9zy=|wiz-(a+Q3JfbzgSRTd>%d$el#MOQW69
zBlix><38JJ_MqO@CJQcXsojyL{m^`hH*3<=2V6w26hLusA)mv8GcZLLo3eo#dpUIS
zwT%;SUwgC60YFnRjE8(%s8tN;bI-`pThm_Xwu9?m5$ybd?;mDs54eKvWq@>IDNRM*
zO9JR|t1pjeuK>?9u_|eDV8gZK6r-Vaz?a;C{spTCUU)V|L}j;>Qj_+-73dW`Gn&=t
z6BqK?T+PJ#=O?+EtG27`2tE&L=Isy!cElklF0gSaXSR`O(9Uir&CC!e8nQtv2^CU-
zuRfvZ1I5zl$)rP&2q5w#l1$v0985R~gM(H6(eT8tkNEF#Rw$<mYwsThw+F_~_ZwF*
z0SaM4hSH`XcG$ddh_1gZEVa||>udLrHal?iWO!{9fO8Zc7B6Zh_q-s%4c?P=e#Q7y
zH`!AjE-~$RC?|6ov>>RoSXc!4I9<t4?RzLwUIU2bFM|fl#pAMvh)_6Y$zzzLkKc<p
z`(&9OrKbM8bHSchJKbJ)&`CF8%xQGVgt9Za?q<euOx1VVdQL>kyU1rL&I+Or=+&Q)
z7YD~)Wo@o9+=-q!gMetPHO95>iFU1}Q)yH-(w#`<ab*MNjUyf1(aJwHg{&|LIU~k_
zL-d((h6>^_zuX%u468dbf-ygObFSppCFr!VP_Ku3)L$WGJL<I-mgFmt3^W<j9Ui?9
z`I#XjhqmpDlcl=nl&>Q9LaFabbi`tYBB6qw2DiEm7)4S}#LL2x)K`n_q_N5EVz|-G
zK9h=0ylO+DQzm5rs3ULvax%aQ8^42ya3JZ6PC=gY@$AjJ&yY-;d}ADMoOG+;qlAj6
zPFX$6eS*_+rnq@+9Te_tK<@{Qe)sy+KFw1M-Yclm20}bB$L^Uen_0}KyK}jy3xZrk
zs@1=@-mZ#^M#VFEEqMEIA>@)`*1*Pb={L4eUTV+}8TCf$WHfXQ=4jKG@9YO<#zq|3
zGpFS`LGt@p7qfbD(O<B=O}aSYz_FfOi%4|<Zh)_u?TvNXnH+bf?wp=`RnfBZmka1-
zFFP)LEpLSFXrsYt%gF9IQ$&5VLWeWFvw!#0sBKHbVx|5w^_LI|v(a*Pbq=ReiO~`Q
z;oaa9MNQQoUIoazx(<t=8E6a%$lt*QTxVP7>K_Em5duoV?Uob-!oFiOtIVlIbxzu^
zAcHx?h5iuT8_}f~xSZg9<-yD(@(O*n2(<;@h&oa;LD(kDa9=rgRWTjrTx3fTS(%?_
zQq?j2L2VsM?G+hBvly`ma-Ghvi!9y@lFtX-B#zKmC6acNwGN-wj7#J6ShZs^a_$z0
z1I}zVtP`oaz1>fAzt7oBT*_ZkR9m;z(jl+t*vB`7>t;B!5g4MrrYtl*>9u|0QkZJP
z%+Piqu7TFSisN+3GTQ!@0u?22QpB~pB)={NfpE2imJGe09Qi``gppmVu}-=THhSJ8
zJ?g2Oluy`(Ds#<42NWbs)g4yA_+(?P5YVc{J1OjxtakmTgXD|c4DUfRfd8=n`1ROG
zTU|apXx<J~bk<KpNo5}zk`nxK07}xMn#(9^fFfTKO69?EmoMKVOZ@J^!iD_&m|8Z1
z3TVGbk#Ia9hSB6{*cLc?H+19K8B#TLL{`=YopTBSyHqYij)ZHqp4LGDf%8nmO$b%I
zrYpdHUZ9y9{CjiVR5Tu!OBUSH8DXuFntXtur#+5|TtwEa2CK^iyB@XGxze!&lF&&+
z|2D_Owg-<)z1CHW<9Q%L$4KkuR+2HmnuJNVB<Phb=D=pY)-vXfH>^=U5h@}f=@jB!
zkZ!QR*PNqmfd+?(B0t#JxQlt))K_b44u(|SXn?FhpIxqoJ$?DTg6SbaWD&S4SOV#9
zv1%mXuKLA3rt+nOcwhROu~_h~d)(ZkEepEI3oeNJDu96EdNx5w)En4EJ1}%~4UC{J
zcx$7?cy_XVTlI_b18rKeu=PYg!Z-WM!UU3J%C?XXzm=!{uu1Lx?#{L()3&Z}r%w#O
zppp#hqIko^-N+b%_yHn|TrU3!KXd$jN@S*I`sa{H@IT*polx1d>Z3>a`cN}$G%DRu
zDB%keLci3{6Vr#62|uIqWt?h-;q@YgS;4x*Xn;|SuobWMqU(b83^1LTKM=0vjrZsl
zPb3Os8pALo0W*RK>je;su$Z9X05<yjiaT{*nOF;mw(-%qzJ^gnvc)DbFEDqehFTX@
z+rsm>tB+kK_fg?<9+Hi;trai4^!qP%J_7*>{vk!kJ|v8Nym;#V0(?JNuNiT0Fn8}0
zqN?**Y{P-d6ey>!-57r52TuqN54dDK`1KD-RJsC!!0h7`^F8*>l(@N-F?kk=yd)&S
z?6Y^bW%D$&C+w}o`Wq4Yab~E2UCeoCXZ=(g5legLzW3;sRh`u<d{(x`d$OyoBci4S
z-2h!>Oqt(#hBgInWy7q812WBOyD?@_BbwDHzG~#Ta|LgwpdP1!COY*||01Bm16F8v
zJDDJ&lbuPg8*CZMx$sy6-h3`3<Ibo4Wvkfc+HG9^-N~)C(W`jjgjsLb=^=f^V55d?
zM_PUp9uYCV8qdx0$7o(?;SFflR8Q*PQ_S(nrDhJce`WX&Qq06KSKlB{FHhf~OwTG$
zUnft`q9)F;OSKCG2?RQx$2iieEt{?Vyak1({fx(NK<gu?7o3nkt4!-t0$>IzMC*ed
zY%T;Es7@<~4w}*SF`NqrXx*aC(+boIP#jAd`u7Dh&@=qKC>a=8{<tXrv*O=t{k>ua
z<$tf3kr^lgXka2w@jq)05&dh?4*%Ch?*pI0Kl1tIz5-Kl>%#}Y5~xoB_`AS-_+7$d
zyz7_h|8l(Bh1vxIK)RvC`7D~xED-abM?*8h0M$p25_;9`>?IZRi_2d^I?0r39?BHK
z*%5)<#aSLD(>ivc;1&Xx2_y&V+NPT>N6x!9#HM%B>D+^L?_1Fqjvw8XFEu;C<@}yQ
zlX1E}S<dP=Z};rqs#&3%3avJ@SizxKfN|sQ{37_%01%Cds*sLG-I$miaEjC<R;WAa
zBjogRO>${`0GjW<@P8H|>)-Rm`0q3QKd7+aI3B1zeuUthLWJTGL`L-KSBrVcX^3Kk
zhB|~VjSccBmC`Z$3XULSC`efo!XSvoPZ8@6`x8c73SQG@vA{W6KIdRK!5w!;{CPwB
zx38KjrxuBE?Yq;&Zd4b$WK6ucQ(TT>Hw<Gu`$<z9=?STT+Ckv@kTSir00(d|cKqpp
za=8i%b>dCpuC!?Jp=k+{eJTiAJ9+AcjpvecaVM!hO5-ixC{<v1J9fG#8xQqH@#~Mc
zx;KvId8A9q_42Z0+)$psFiV&V&`nZEetZTH+a~}q{A(-j4*>G{7eM6Jh^b^^!`l4y
zgh=105QtlXbCZJA%Zh^Y^-Ua^V-twgH9>Zws`;Ypya!IZJZ?<A9N1*Ul?1bb@)Nhi
zR0(YvW=Tr$dG(U9$6>*kBACI5y9RrrEV{WW8HUM$x7%_(#6A6s3H*own8%|n(&~VQ
zF+N~^L9+OL(FT~0{Ni^LIf$iM==y#*Am!sRhSU5`a6=lpdsRW6K=-Y3e8jZ5Ah8hz
zd#XvQqG8ywBmalvfMSXX?-*lpBDF<D^W2V_y}5maTXC3@@@F7@dNt(F`{X}e&#X4x
z_?x6u=$Tai!BdQ^;1O69sn~)=2~Mq<+^x4iP@^VVZ+ZN-wV$)Qph!iE%Cw(l0VkpO
zyCMY}@&W{?Q%WL%fQ8;Z;9+1sK0f$~F!K7r!9;T*g7ifZ@by8s0^9+J9})WjF$iM`
zu=Im?0`&b5@9gv8WM2c3ef2?z12AwVUt%$X5E$&299umdK^*}Bib{=UK8yMj0cK$R
za{>NO)GB`k&7k_<f`<A>(8m8q(0zXvbPvI?00{66I7$GnG&eutV{bnoiXfgeb1uTY
zegI%_DLA4_X+Iyr6?m{d2r{2M#J<2z$Mc8pcaSY`^Z&%W?xi38Nz4qNY)v!J|373}
zX}VFFzDa(XMe$#W2Kg&yA(H<J8TNOcQO1A&_@GKgNGi47W&vfQ`UTKJ&G6&aYG>0z
z-ThAR<L@s%!Fy=XhhKl|Ka~kskNPuZV)#s%SQ-BJ$^?Q)CJI%&4@DWG9jq-n$lnDR
z2AwQ1x#xJjI}5Ky&$vJP008B5<n>IIh>}$7c*F(D{sqJFsGUL>n+l`f(W8xmp59wu
z|NTb~obU(6qk;H4JkauoUmiSxGQ0qM?%@X@yW#7mUl2wr;UPXmZ6M+_-abA|3xF?h
z;42`QS4%IwGCYd4ew@*N)XV!&rtPy_G5(#p7?}TUAO2^9$nuwZ@uLK7K~e@;bo90R
z+3F%0R{=2uXhCRdge7TeQmllJ%y37DeQ$`XtPprCPUFLe9a2S_kp?r+penCZ!YkY4
zif8_4KJzas&<NK|*MMNQr9GH4kyD~2%V$oiR7rr<A#DM|ix)f1$UV5odIawhKm=f!
zCbC5Ap7G6d&;Ocx!qx+n$w~VJC<8-V@>z_ZIi!DGNhA0Jdj3xjf*=m^ugDV)#8t#W
zi(^yxwd$wsYSWU-(Z1Q|0;F_-r{3PGP*76I?9IAC*{cFv>!9TOdm6TK>!3Vh@4_$f
zm4f$u=&SqobP2@xkNy1iB@P<Ep9~-Lvn79aL!SNrt%ZJbrNVz`g8}^iqz&o6+Q;wz
zP8$%zeiB^t8sKCfgyMsI1PM>z0mdHyL{Inx;!nW(b%4Svfg_$^AmPR^bp{AV@-SR}
z^y7Fx=!w?)V~&b}Nm|37fO+AOw#WwYAGyPh$h^QRLm2#EjR3HJ$ODl5t0f6%Wg0(2
z;xl{2^yk|8-;=iwHJJR*mel{NAx%jS963B3k3xYx{%30o<^cxw0v7TN4D%rnh8TkJ
z4h)KkVT~~i@Cpla4UTb&Pd^_x<BI{L3Dh7N<NxpUHpWuRd=}Ivj*5Zt-zE7!cin%2
zVpgVSR->=|izWY?7Ghxj@3ipdH$lRG2M&jSrQZfm1w}I=0G$VRp$EA700RL;`}P-o
z0;g1vs}Z0B*uV&U@<0az!zT{t|B3RM75)l{L6M&EU*gXA4{;YT`7h4@t6ovYTmJu?
z57T=4yW3z<VgAu>#~&Yjm%o<{K8!&!xOY5)It&V+Pf3CNSt>rWB`p8i!2I8k!#~F-
zP$H*b6rpNCS^{{G=<)xWMEF^M^Mib*)aIO8USTP7lGG+;k4A5t)TRjGihM6Y^4Oe+
z{}aFu@O$tmzDGX#4}X1Egm=OZfEd1E{9oWkFTk(xJuQGidjgOAJis_?^9)ODuR{7E
zeEQn({jM;mFsv}z)!Ewq+5m){d|01_`H2r<{5^>M^YcGfT-MZIJdeOX14)TiJOUat
z4!H)x(l1IVO8+m9nwwu0?q2Ku4X2lJAi;%oP!JkA1Dpx^CCzsFJajkyf?=Ka4rt}k
zy(dP))3j1=fh!6r5&AfYg1kdCP5Q{0+fG5W9`T97Nb`BOias2YzXz{pL=xcPx%bla
zJ3efgK5f2vDUK+|6SPz-hj|q~s}fcs{gR!eeMAITtEK(L@uuPE$4}#xxGq4o6DVN^
zfAsE1z1npDiSXnBu`Tvq4qiY8`@oY=kYW79tuV6xc`w>b*Gl&DBY^#aqy&om9vkNY
zDARZ30cfzr-HQ^Sie^}pj27XV0J}RtgqF<Pm$e2>y;S+#I8y?MRqYg<OY8Mxa_co$
zTR%o5<^`I2)PeWMiy%*##>gB_Y~<0=xdP3XB%Pf~<z<d36;+*IV>c&(qsb{H!w;w%
zD5;y}mcan_ec;Eqx88<?MPNO%V80*kWQY$Ho)!k2&DjJnJ@KTcF)~sMlu70NeWI6e
z5l?Yw6V~yLun{~d#(PEpWx)j0$!`h~2uZw5Y35W2Z2N1El%C{gpI=s3fI){pPcKYE
zl+8LHnt8dJQ+=(GU-k(@4xZ)PUe7U0{l2!)yaQl$yk!19-3?S`gLz?&_2~K12DWs&
zidj;dqHp1kRF}^H|HNc5{x8VSW}3nFcPWBS$7-vm4~pr_#x=$Z$P*&Q*&|YxQ6Qj7
zNB{<~-p@Fq;P;XS?@es3vy00XUd>f6Vtp)o-#4UMl`HW+X_qkhOzl!X1-;DMtoL4#
zR0xjC-h`*pY(`M(-g>tBglTm0T9KJny-3dmqF9L-+raiOY;@AZsjogYo{4~K0n$ib
zFg^w=L^5=Kq~415nwsNOhLZL24K(c>=NS5thTK6WZfsh5aiv5?64c9^8hF2Nid5qZ
zeP`QolK(n5Q(!vJVbnzVMtUWRWp`H9VPC=>WDVu<&CXrUx9>C1KJjUcjDOz0CjY2X
z6wqm|LWmBsoVW*IPRNtvVyMb5se;p^hP8@P&?2kr-Yedcyo7rwQrT>6!qMaQzdHT5
z9H%DbDw5DN(L5E8>!$aDd6TZGA|te7Dt6{}A}g`49@<L#^_=LsT4x@bW6iEkR;6eL
z?3}eMuLveY{l_)+?RYXZgbiK!Em`g5u2U0!fz8uE41YCda{#yqitM&$IQsbJD*lYA
z!M`#wtYvHf2E2QQe@r(>!FNgqhPThTEt>EcO?YCEM`pS%t`AFHz`C<p>2Kofr_JI>
zo}>8e^Y9s3pEy27hJW4m`~y-qnXI)9<c9%}Ck7*-^9Fhkz!6_8O^btwA4Q<d;#AH`
zkHN)i{BmjG4V4oY54g1wlGi5bCU^gIZr#0!i$_@kzFv3lOmhj{nX;xx)8T9QGuMmo
zVuojgvTa{_vpEOe`RATEXj{52^yudIGDyRHNCE?0>GKH#rE+c{XZZ*kV>cESWCwrs
zOre6Gl+4)#m@MitYT$2F*tg$Lf5It)wIYltAH_}PPf$vJ8xI+ER!3E)Ja$4A<f?x=
zFr3VrIY!Z}^C{M0dy-qzK$U~#-Ju%-bN2cSs84<7|APD`YvBUuf0G{)c!HKYa4Y$D
zW}x`r5fKH3%fbyx;Nmj33F1eKZ6RV{LNwA0?jqSeCVaea(aNGPLlL9Y5{5HKTv7c3
z92?&q7G2ank4x!^Z5j8oo4|5!{jqrC1Dt)aNa2U>j)akwM?Jl$37^c68x$j2PN<w9
zBTmXAbat1ZqPJgZh-*a5#Du^tCYQ>=8E|0^>0dw?1Sb*)Ct6B_x;N$3$jWvr{7}#s
z%mI>t1a}FT-Shia%HN(!{c8*IZ?A`7Wcu@2U1pL&vS0o;7<?T>^b=fy`#|a3tSz>v
z>T3TGNr-^IrXgFjCh;RsZs~sDQ`8MuZ<C12!I2+*`+-lPTRG3W?k=OKTti|pC#Z|4
ze4L(n1B!DIRu9dd?!_)rd$4rL%DU#VCSD_UH5YQq)ismq2e6x*)#s(_^z?xA`RY#~
zP~rUKoKoAwM3EjrY0|>HlORmJ&(fHQM73k0%#bAGVQ^&nFR|SGHJxZ=mEAd*NpwZ2
z7D$_t&G3kI@T=0#)0js-W_!P|-z?2{#X<z(>gs2quS@kTPCz*Aj?%vZ+9BAHeTLj8
zewLBx&!<S0=~~$zf0a@9F~U(Yx8ONwPwgy9Ds>W3JRl?)SU*S<41xLyx8^e$w_xfp
z!oVwQ1E|J(Nd4Z+ROH^(a#^xv6`qF9ye8e<%xI)CYDLikdAAt+RGnr2nx#s3tFex>
zQ_)|T8LBQGSPoR)O<H=;O=!MB^vIFClK+gua2tn%;o1j(4g#;6nkIaK;;BOj84i05
zJrjPk=It%O_$7bq#rsQhG0$A59Xv>`4TRPAieZC{OiDUF-YOXb!bxWMd8y`rl=V^{
z7=E+YMhF@m2)p0k3Cu)slHSbyD^RmGvm^0l!hv1JXXt%mo*92{Mf|BZbBSS){lSkQ
z!slpUfifa?sZCI;<{)0G3MCK$O;V~TtjsTgD6H8bwp#EAL8~N;_fAEbo10pa##g}C
z-I}#t)u4LdeB7X6sdSR@EpQxN*~(_k<?fmAM72C?5?#@zOzJ1^@#4XiPWkV9xQ-i#
z8y0UwXQKXf;QHhQ^@zh06r}F;ezuo@Xrt~%mkeCUPmnsp;Q5DJtpVT+266?^>W4^O
zABNKGAE8$d4ru0aV$Z0Zv4_u&!_<sv!2#j|VxqFW6GXnVf_R!9$@ojEN(!H0^odPp
zWchPOSDEbohfBMJ)B-6UAvV_K|F-wuUx7A>EKO)`LKu+y+od(7fJP?rCd7OBc?I7=
zt}%)Xv$tcwW$0bOGTlt13{@agNmy~tGV9G4O2A{~IH*c!&gMU3uk&rS4Q{8KqM{un
z7%AvYpNy>%qn(T?&8k};iYj?T-h}`lxdOuHz9Tb<6WbDTP0Q?pQ}+;1u%a=(H^eak
zGXutW(~}r+24KxYSm0j@?k5PuKzj5;4U+L-gq7l@_n;-_#F-jkhfGU1t?T5XcLl#n
z(Z^>^pT0u8o;O1_cPXs3k?nLc&6YF0{?M89YQk)WIid*OJppi6-9dc^!h;IF{0z{~
z27=#X+@BVm&Lod)KRp8IuVzO?i*%e`CqVJaiEAYdO|)<nae0NxQojTs5SsfLZxnn&
zlL(3*Q4RUIPb7R+8{_s4UwsC6LVL{oL#6OMzrx-MtY#JSz;E5&UCX}2w~=q0m$uJk
zwq0d+^LB5RuJWyY?}GP+S>u~?*Y8mSU<8&`%|)FNCn3hNhyYFSS%z5IMk`8-mawS!
z{cLNz<2d$+La2?i2$DwBa%o6)JfynwrtbpZSJ{?KzvBL=reoN$=MjmSF_M$$fI(OG
zip2#W;j_Hec)TO@6;IQ}PmuckUV^{N0^^?}<ulXy`+x@l;1^109}TbI1t|0HjuW7G
z1a|%R36FDC0NwuY=8VBDG;=(F7qkhn8zybd2|6=v4&)74G6%(Y!Wd|Q(+4*&cJ+zk
zs+qQgenYv%`2lgebQ&m2?!JW2misaX2$mb#0x_*8$SG`sreW#K(&o&xaAbQ+@sX;n
z{$NG6a5^$x+syb9MYSYW2B*-QF!5k?6+=aS;MnigTJ2s@N2@z_*YA`RUOLKCyNq8k
zn2W=p)_rzwoJ!rAZxhgx6d*;0WMe`8-V&tb5qqJnpV$YJjSKJa@)>5IdHjE03;F{C
zCi7bmg8mw+2vJDpF2NY-a(h7O%tRcs!%@gIH86gk8cmyfofgbIVO)bLY9b8!ipmo1
zLf4<3zqbE;-nAwU&K$iePqZXfA&e#Mwr}h+Cp>X{zq=e?4Y|=OQ_a*f%)Hr`+|b?3
zVc*ovfj-V!OHy2Osz=T@C*clHPy5=V+6@$6?uyb{OGb}r1e*vKzl=Q2Sq~R9@V)41
zz5SbS6gc?>NO#pMa^O`>U=qD<YD-y4ncVQ2=XX+@SXkm<h$XGdWjK#DX+AD4oCHGw
zwAVP>&ct^c^z-$jU-3D)P1LDzselgl;CQ{*`5yK*Lbz|T;N5yB{phfpm+-u(p&Xxq
z`q>P?%>2hKYcka>3BrH?cBuhGEA8~xq`I!P^rA+SN&a64O<8OEGuIR^5VcZ>NM=Y^
zXx3i`NS?YRn!A>4EF`cw?aEeXEM=x>x(pVt?IkK?I6OQD-uxzbTxeJqxd%;K@?syR
z(5>&AQ~KwCc`h@=aNr6PKmn4-n%>RQe&n0W9k=cq^P$%@8j0cdGel6LKB-cHNl41p
z&GoB}n98VfR#22vWKoH)*63DtIy)k}P3{d0onFa7Ur7u0^@XjNE1`C>b}DqC>r720
zVb_bnDbPl2`U@#jd4_dljmcgPv}-Nx`|kYob9)3Ht#BXv48%`IgFgo`I)>-3=~I+%
zEV#(R8)OPp-_7Xj7pWlemas%+N+PP<LI6_SKHl`$Ix+hr!^SZT;$TK5&xxpgAEvbE
z6zBzOU3X*mgd68cFCp?#mIodkZq;_;DoI7XOe;m~v~N_KPY17F1dY$`CPJ1In<00}
zqb$G<z$9qmtK_Hr+B<wn;rVr8f}!f-0YWQtp2n)?)fASoKyXR+(Kz1o>#hA+{o=;6
zaCRiIhYgq=8tVlj_D{&%5}6R0I9yMwW@v=WrGhj7!6{m9ZgW}^InKa(S*rG*!Su;s
z!1(*UEq^KxCexjN)f)KkS_Av1-aeq+ckx5~qt<?+{&QDkdw&L~YnZoB(_=fqp)S51
zI0N4xVdV9jr)!<|bkM%DwnAs&QPlYt@4nwJE(j{V>2P`os{k8={fU~4t~glNT9|UM
z2u`i0W=8UpWnFxVhSh2o2Y}y+{QqO^E#s=*w(sFhcXxN!rbCeKPDw!;>5%S{PHB)1
zQ5uwPkPZO>r5gk(L3%%%b3ER2@7d4q>iztA!3W;0HNPvyoO6t^G6S}R((UGKG0uDz
z^Ap(K0@!xa>(?F#sMHdoSYoKx)MI$dWwxL7nY0w4upU&XaskT@8DsaX0yWi)T{sxO
zdI+8^dlPOR<GJ`~1ZT#(4ZzNl$vaIEIfi_#c=!0g?eTYZmab=M_L?^j=|$gPa#1yf
zA@(s2Z18&-2Zr6i0$%ca#&(>32wH*^SO`f5r@*h@CeovS&;{UEa1mCiz9D)MOz_az
zKSQ2I{?~0{5B>O!>@Jp&J7cUo4I%~TNYt}Kj^H(&%m8<`D=sHJ(_=wlO8mF6z(N1g
zVX_nKIV>|MJ6j)<O?2b&?`XR1#xw{zE0Jby;k9Bm^;``W{q=|q^)w=DW$L`dGHp1X
zIo!wkR(rfEJ2U9F9l7$##i>aGQH;{%QK)#k+7rX749c>~ipn^d-lp|Np~^@I7trpJ
z>M4+70Wod(b>OQ!_OH_0)czl!E{t~}$jRxY39>i~N|OQ`n+h)LngxYDg$2cv188<4
zt2zdOKK>gJk6H8SS(HAPTgk3OvdymEI+QX)@wAZf%g4^2%}&KidbVVq+FcoV2V8r0
zS^9WLCwY7;d$nlRK@<3i8R7M$y|8c1Hh?DJl=jXEHWjNFlk6I5u9gHj^>!N-RTAq-
zcz#Thsi1u3odMkZyxJv~`EXvV+halb=#;7)H}=~X+IlTD&$#?rbaP3?+0PM~*sl73
zJ&4Rw0x#eA)*j_LAptxW*G2B26X^pWHSNHe)jtsl_wRt=KhmxK<EkN(U3}5tLpVx#
z3vIVrttcsY+H^up%_rv8a3oP|oWK)AKinHeuh1AG>MUDmm|@lCmem0FXYK7PQIC9e
z>Wu0tE?$+0%onq=ygl=sZ<{E|KY4kOG-Ru%Ilpl9W%VFqX!L+?xOMY+=4;ufZ)2d4
z7&Q`Mv_1+-Y@2=Ub)4MD_DMIp+h1+hpxQewB9e0SuDNZ%N#1?2Q?PgR5Q)IDVJCk*
zz<`jmD^ygrF<u^zpdc<9%D*;;w<aN1IblKIi_-_m4`#s|C;NzzS5Io6M>&y20m#H2
zp)F2g=!84_Lt1o@GKt@)4JpUfICKLELoqDGt_aAxKqxPI6~x?XVYQxyD_EF{<sj3*
zlo>@*u(cw^ccxR-`lt{k6px?W-W^tEsu~mMmo;3OHe}jp?<@_;4<$X#Pw|UskFR}0
zxBOI3;ZDWq<VrGNtOH>%Eth4}r{cluzO-puIATEI9Q3*;g3>;Kz-JHXm{>qc4F7`j
z%L=d^CmD)gz)_#jQFs-8g<@=KcJ(AC{9)@gf`r64H}M1(Dk2kQQ6S1l?1M_WO#H9J
zB3XUYi4T*t+{&_xPx8>cUR%%v;5caWKnfJ34B<CYE{pLRaE5>iZ}NzY*=vxdmjhtm
z<P^;G!CB5tEI4xY&!k>{IYwB4BHt{WvEOD@KXQ}A?(&`e+tM_GX*ER#E3^z(E;NyM
z7vYvPGvRLoDoP(MJku3Tm^bmGdG@Bo;K~!^+vV-yA<g#DN2q>U*z_GkS-1=Agt#<-
z#2(sdvSwgJ<jCA*@QS)Q&kt+AOp2e;7=Ak?rsd&tfyi>1mr8=}1JJ#8SmLAJjNPV0
zV&mtO9wVQ~nX&K}ixx`!Q)t!1w|W(()i*f`Qo=IlRnJebhFMHHG<BZawKE}*-|A=R
z^!J(C>#{PCZOd&P=l0Y@&yp>zV@%mb$3O@u52Qqb=hy2?+J7B)qD6>G$+C0cPQzN5
zlMc!@(oZT?{j*c+rlHw6SzQ*I@R&M56{XXz5m8{kB57`A(?<DPVRF(J-dQ?Pt#3-N
zWoGI41LkjW?Dd%)JZIFL^P%t#=sMb4MKo%fgw~^?*p_t+m(*J<I=g5RA5tcd18A0^
zbZ9%J%^9e$B2*a!a?z_FPAzOJ<ofh|1GeqrzIMa+vD>CTL`D}-O2iUkoC-3x)cT-K
zphrt_Hh}8wK%*jhXg<f!ooMK9x>*&d^-!2HBRIpi#Ms2L8O|LZ<XnI!Z9Z6!vC~>?
zdj;1*88i+lhL94A-ypPFjQznygIhtu53)H&%s{cOZM@Tl3h4Y-ucn~pM1Qxne@RR+
zI>EUXA1h}q#H-4yWO3&iQvv!6Zv#(8#7ANe>r8AJ^KYek_BFXC0t{2VPOO~agQ4ix
z>+n5yZek}GHu+5Yrd+fGRMjh-PUyVtTp{BgTUy5<f+*>Vhc_|yzUxZ!b5@LOz0rho
zb-53WO{vP?sHq-JB{P=VStQ(Mr1mdMZ1#EG)j4f-cvBdsG;oOUBP?K&(h2nNQ#a}G
zL5dBe{NOiIdNyz!35F9~M^eDDAvOjKx<btvS?Gm5EykpMD;69@T@d_&No-<GBooX;
zv&*19Ibw*6X1Mm*8m(VExYe*V+EXq($a&k~&v!x>9rvWd-u3QsZt!Wso-#p>mTF8f
z@yoOAYY&#0y-o4U$6rb&uZ+SQppOBa$UtrqPX1v;OCKp&(LN*_RM-enS@F4b&wgPX
zZs58+Lt|xWXfTo%*`lEem2)3as}Qv<=1D`LM-hstr3LBemyw1rdpSW|+QKUS2N&MS
z9GQNH!-ig@8`^hJ2&;z7kRk;k3E&j`^$XPR=PCksLyy6$Xy<E^lo{Ox%)d4w&ibUV
zLR|GGewQ;3lJ4LUFZa<B?d=kfMv||jvY0mCP0S~nAqLHlel&CHYs5O~S*B@-H@naD
zWib2p7pOB&L8@qP1rkNe=*t%XE<j`Cqr!4HHe(cn$`bv}M_v2r^PsDJO7G6uGPs5U
zXUCf=ehojI6s_ny4fdm<u*WGwuWHv{L@R7YTMgV`VpfONz~Y{meS^*3Z0zQjRWy%)
z6ck91|2N9t(xWx^kUtPi1%jU1zyjy&Ek2<=uc5b*9PUeOI!|r#8FdF7I@c5^289)b
zKC`^j#b(f3Ex9nUj9RW#<+qytkf|b6OH#`w;(ihA-uFJ$?wv3BOE4gFRdf#<rOU0^
zt=c&_yG6=6I1@Z>FKmhX<g@Pq3v(2)zY0V9AY>?LMvy&<XvFauMIWq#y0l<dS#${c
z$&n+MIG)J$Zfg_(9iR7s4#LE0*%Qqaoc5je^6O6Z?_^R)WTTdo(eh4mBems8rwMN4
zP+S=ATvYlfK3Z_Yq!%#&yb`dE3f`04z;I1FRziv#1Wf-s%{l4udq@GUc2HPExJjNO
zwi8$<%V7l%Vi#+0Q#Rm8&b+cP2TWEdeBdv1lvoZ-gzCE;7hhSe`?`P4_Gtb2t`rK{
z7r$3CQm-XM73bx&YxNl4d7jjy>akrWp$+C1o=JFou1eXcOOv$_ndKjARTO#Txq^=9
zBM{RaWPu%bWTaq}7M6Tz)oeTkTCl^ZlaG6JJ#BeC?b2R20RP>(5N!p%C0axvn0sH}
zjmHyOb_9N;eTZPrYERnHOUVTOhDwb9S_z&81yyp$k<vf@^(LNI`6hVt8Jp@KLy8te
z)c@<Lr4blc{Lw6^6P9Z3i=Xg;lA}g_TFjiNkT?(stBG(AETk=76FUmSSFQ1P#ji|C
zzF5z82MGIaUmuzep6~f`mPT#4=}c|H%||_cx=_%7tz-UWed7}C5m_opE&i}UWX=9p
z>+g5RQJZ^rnMKA^X38@-?zYf^LmW`S=ptphp-Yj+MYHPfKYqzGJAN;5E)6oK!C8+@
z&Ke$z3!Y{roK@jtD0Y3$x}Se(F$P}~#A9KzSL-A9G(o70^R;ZsN}g;^9{&wdzSR~Y
zTYxKO$6QYg+5<+J(UaL5*cIjH7~tYXo*ksVCYYE90r>w$=*Bozdc{!TCMb#{HY^=F
zFQ5~oNOaU<>shgx67mXk)uHI%5QXZARQgHjWaTobYn_vj4O<iKFKsgq<~QLUwQf#j
zj<Yy0`&aJ9X2zmbRLqV}u^i|#Q#8)2d6}6pe>S)SL$Z7q50Ac&hnH%uXL1KGSFe0w
zyLVvKkfM}|r_x_*vsxyTgj{U+h&G!au3TdpIev1MBQr$aZDg$W%YJqd9AB-E_WHu{
zo#&0219FsGu#Jk*zD@_junxD>s3l!4t0eLu>{<h++xe!Av(y9y*~*M<a27J$N!OUz
zEZgD~2X%-eq>3ci<^7+b<Nx*0fh&@K(mX_qbmuH7o@~SM5FowRgZt4yxIf*j8Rk1h
z(plOEf;p;-!}f*hCQmhd6Xe~uat~6Uzh}{(NJ5X(-_x09+o_z1Z?<h3Iy?V9brA9x
zqq(V|a%Plh_8E8f)alf4o5M!}hTcPx<}FEn?>S8Gfn)|kt@$36XXtO3Gf5{^&y&M$
zF|X}w;+&kAO?%6_$IPLzzZ}QTQt$6;jrs?X;By+(RSE>R)IPO>{=U9m=RUp^JvJ=5
z#yG$FwiUq;|GI!JGuulxWqFQyl%!>?(Tdq+<ddQ<9&H{H_NY{(Ss_+PZ_Q(BU5=iE
zYY=9#CQ%=n+UY_5!rrp5pU{rtEezUr2nwW)4Jl#xjSECO?moo0(ucslPTLbCFVQ^t
zxxAVm6a@)b@plUn>T{+~N>1N>xFlJpK%FZhJn@~ButPsw^l4f+-?(_Qp84S6(?*fa
zw_By*FKrqkc;Agyoi-MpiOsN836-;JmppA;n@xMirv2rM>&Ji|5473uJYWvrTn{q3
z6E+v}<p|>@OCdqV$}(M-L7yG%(~t2o6(-Zc61bM|<aY2Y^wNpAep(mmRN!Xo`;K06
zi{4Owo72^*0Ttr9L5s!>SZP*sRx_p=3Lp80JB&zolIBFbX5kLPogiHJi9Rv@Z)j7G
zEgwLL9GGwR&&A38>x<K9Lgc;!2)9zBAqA$y%ryb@I3l}V-FE<^!Oa*-<|J@4rlL)@
zu2<SO^aVRBhvORJlT6ju^BrDw&3bQ8278T$Q<Al*E-Oe$J5IS|d6Qck?b_OpOU$on
zhqDjRpS|6D{7|$#&W<0$V{Oi1b6)<uds8REl7X6w>_{X6Mje}GZ>aS(OmPK5tj$1Z
zJyksKj6(4@9>;Oo92e+1<7(ayE9O@SIP{+Nn0M;=G?{A3yEcSf<855l=_nW9lz91`
z;`WvuV^0vkI9xlL^VMhqpYyyOAPp&b&x)OcyqtD3Ao>o@x*SsIAS48wzrmDM9&5X2
zFd!V!pwqrPLR`SnRX7TXgyl@^l43wuFN#AaHe!qK&zh5Ua-;mnA~$r@a$c!?m!|Z7
zH=8<%GmW-nFh&Ktim-G6Y+q#@M=+v@`yfgamxa8Lan`LvWQ_4-G?+Fj_&liqCKV2<
zvt`{6AcMc`hhLpwK`dr-6ce6cQR2Yl)2^#~+|e-tEnBnQnZ_G8>sHhDu-$6*r~yZ}
z0p}xoCX-nwp(ffq5xe5T8pgqqjvk~)K!X2#zs}{*PWa<q4d)p8(SxUqeeuD)OM?lU
zD8a<EEFFwHV6kDI-dKCRcqk!*p%Vu(q>Xq!{_(QoI-sRZVEK&&6^o#4(c*v$-8fo@
z^XI(Agv~3r({Dqc<Gvu+kW@OVZ8f)~+&2g+-+4R=-cvc>@!D`;keZj$E%u(+#nK{E
z*FlqNdP>X8^Slg|zCvb?iWFN!@Jb{wrId-2xDKZ$d(4XP>xfJLL+7i)h91Epr${Hh
zd^%dPfo{Dmu0f+D11DO3`QyV{<hn7%3Eu-jk<A@pS{f&w@7UdPGbTjkauEm9+x;}K
zDuuT}%y!;(Fl5MK^Pv<pS1R)DA86aZUc3wdoSHprgcMu|83Py3uV)Nkmf)YDMRZ7Q
zH?<X@@fD6T@`YH7$t&DQR5{=?F{RD{Krr6+q$fDI{iuoY*PpvX-}okxU4MP|`Lu2Z
zoL=qP6dF}5hCSpzp|`IfT|7D3-qrrtre$Dn`^v#rul!C&bV<~>NPpSLhGs^Fg1CaB
zc$qPQ0Z@!4R!?TaN6nvA4a`z6@|6*!&R-CGA|{L(6j&w7-i7Dn5~|I4otEh9f?q_K
zvl6?q$W`a)eu|blAax_w|7w`}y8Fp^XBo&vVl5Fk#{tBb8{wv*@z3vN40n+ry=eyL
zIlzG6Bz0u?fmp#yW|zg`wj02rlFcooFha^PeuHiMY(iY2M+_HCw$#I2vSg?HW+cYI
zS)s`f{}iqcb8jfjP1^{WxjDH+c32(yu}P;gKGix1{&PZLkZ&l_5@_7q-PyfqC3M@@
z`{fbqdedg<izUW7UFEq?9Tf*CY$#k9*?L^>bQv@kj^AHzb7yQ`WomM<_$vv{tJRo>
zmG+YeV)6%*0FkC)-13BgX#3*iY6uEX7~Ev_nNWB_`r8?bRO7q+ECuDFKH+3^bZdp6
zAB1`{XwHqXxV`XMQ73v<7Gt2LLYzl7ATp!~I(YGYNqeRA{DjCe7Rpo@y-;K<za`-7
z0(bN64nfQXHon57viSFJDE%*-%OHgkV$vb_%N-i~@gH}FmHm$fzHZgYGA0u&5`mmr
zv6xl@m$cf4+EvSkQSmDu`qrM%7jaJbrdrJ&#=f8W(wB{b4rdHU&f&?m`A*D-c)osR
zaNp%ybD_4pe@~&{rB6;W2gSva|3Z?n$dU_X$JTadA4-aCzgQ73c6fkw;v>L`X8b16
zw}^h(+%Y+;0{;^_UasL0R-;{iCk-USQY?0xblZkbUj3Arjx3PzMt=kWb?;u^9-Q8X
z=l%4k`WACLwo5z3x-|X@VT>-_8sEGt6?aAXtHXF6$>y~rz1$&L46=jZn#BpOU;gu^
z{f!=~ecT^K&2Xb<5gWzvm>mGx6#d35X0qo}2=|q993WSqN!xu3)h{%Ts62GpiIOUx
zHW%-GT*udaw-zi813{bO?;mo9HHWJfye4NH+l|SMJ^5=}lE>;=Br+F0*$y2k#`oR%
z2|m<*9`<9o3wqy%soup&hdp15sZ2+uty7LipQWT+9!M0%_d!Zh4fA}JLr3C9T;~m<
zwnTx-4kwEDfl`kKbT}Sabb<bRVlvw^p%-sd!piYBt?Qv;`6TipF#*tkAR2T~Pq=Sy
z+EUQ~;)OdhFJ=~O$`kbzy`YYOV&)VHDlFqT-{D*ze0L-WOAp-0|3?rxe`=2ZJDEH-
zt_C^S1UD?62hn2}tC>UXX(|(9Fy02{^fadO%$(YECb)mPM#IB5U@YrqjoQAsG%T{6
z1zX6d^{DL?y5t!Rny-kLOG?dn#cS@W2V@*cmTUWN`_Xl4kF)J6zJFe4RcX#(*$(?E
z@(d%14L2f)fi>i1kWnG1KTkq~n&Sux^$l^GN62lkQNF4Is@!A(CeMbl!gHuS5Kz=u
zu(_;gr+dDeov$|Spdx&xeKI!uTM=81jY`1l(P)c|L7n!*9Hn?jQGtX+exs-q8~=Bi
zY&OcymaOR;*w_{PzgXnKDaY32f8=Fl9P`~e7nwO0OV^g^sQs_tGoOmi2z;RZD9LCQ
zX^IX*7WUODAn6re-6SJ^-HL9E(cYQMgQ$#3%OQ`F&uSq#b4rI|XubnDmng~5{*exF
zRWjRX38d-5B;FhsMPg|$j!5x@;+mCRoDPmD4wvz93~{-Q1Ptc-WX@|BZ8k=+7p>(~
zKYhs9`Ua{OZ<X89`FH~7`AjtlQcxg(lHXY>BJmH<!KD!?GIdJ(WSVM*PR>gJo;o5n
zJ2+hx0~6bzf{Ei&3!Pwxa3iY88FYsRyw@}K>F1q<RwFc31c;oUURdu)IMcJPnWL^d
zm2@X9xnw6kMy9iW<lC~OS`>YjjkcTQ5nd=VpOlhq*uFydAs{+b#Zra5JGexnd{*EN
zk7zpX!PIGKXs4)AnfQQnXy=!=s8pMc9YI}v4`>slqYovy?<P-GW|RZ<Bo52mKfFEZ
zK;Y|kU@i?SM!N-Q<11P~iV6fY!3ma4{`vFYH_%W0PrcS3ve19&ySSkKp@mffOJ)8~
zEv&>XC;+71*&zz<U83B|o<UE@{t%M@j~%@r4IjcS&iNa9=byvZxgR}P#s8O*mka7Y
z)oC$*jQ;;pr~NVdM6p{C2o?2Q5@iMiir)(h_%VT2If+0>6M%4ca`OIq6aMD}{#4~^
z{G0$E%s(bDdLRD(a{_QMm^T2dfEBRbRvm$QPdrp4-5P*>VzLHESV6$S2!wr*egU9O
z$ATRYlL3l{zY>V0|D=^f8Z)Z32Ma?JVdB~OL*nG=qR0Vhwh-<-PT;R&*T2tJ3#`rh
zmmc?z+5TORdq3HK=yCs{%>sb{N?>sr1|l?7*N>q;;^l-?E98Sv@ZtnpU;gSwiB)-U
zFIys;cgQ5Aa)tJg#8+mfl5s)-vjhH+JUpO+;z%t-d~8BzKhTJN{t30``tu}kd|)Di
zs}^aV^8iQq$x|3h#?i1jvKptd*%$iLr-23TD9zbb2-AHo3P^iNd`KB=%}FLl0KjGd
zSz&8%)`|Ke3kARLckP~W0)Fk!E@a%_F*!Z*(nB(vpwgX=WXOCwFzDax^in;RPZX9<
zMYp*&OL_A?qX(}YYT&zxB&6>Lp>)N``Rj?@&+n%L1WW7Gfi*w0f}CmqaU98NDHry%
zWQH{V#^6Y(dy(3Ue;27i4S1+W@M!@fHc#BtO-!nv0D^_evCqNkI*=v??cJ>`oC)XC
z&+v4CcYhgWbT&PNct1iY4{`GR4@yoR%?Vx}9Uz|ukVorJ)guoaG6^GLM(4&)l_09}
zy{Z-aKg96ihF4%|csMfrAKLJJgbUFDP&McY2mui^4@w5T2gTn~NRsu4hKt6C4cvr)
zLT;afFzzTWmet;ybp5r<CV5z0Abcf$2%zZa?f(mu$Dd#6e^(iA5bcxfVcY}&Le3EO
zNj8Xn*sw&!QKUi|FeJRf_dk4({}+cKGz_LNz_cGjESN~8Is_>JRy17zK)cZ;1(Q&X
zfJ0evMp=L^bpwNSZ@-X!0mbzd;g#hYSGq2@yfZ5w_bY4wD&;yMvl#3TnW`58#FLO8
z5*h*idY$dxzLEK>1k(ZS0SjxyjgY@7!Qmvqo;VnJz_j+#DhYXY8|Jij&A>WnwP|T8
z;xdMNp{MxEuJ-)9P7ng{4=wo1AYf-@{0}|2-~yXGq;CZgF#I}G>gT|9{*c$I|I`cL
zp8+(I5la7Y2BiNVXF&D+*3b2(JU$d<21IivKs#kZzYX~@e#&tLO-SR1gc$xZEB=km
zQLT1A{<m5`<-vU5@pDJ~>j<j>A7RgfiT*wK!BlB~v;F`B29W3-cn%0eA-zAgh_n!6
zGNfTc!U_M&$;+b#gr|0e)BGV0rs2*j3kpsY)M6G)8y-wn6gBJ`h*4aIVH_(y+<mR4
zp_MsQe2Ba9#$=%QumID(%mj0IbGN{xr*mBGJ>~LIZr2@v%5dCdIS?ger1QD>xzi65
z5+hjtHb~Qe004eRJRv>72`*mY!kfsFQ@7E{9<=~y)eALJb(y89VX&1@BajG*#Hy(n
zWKY$7VdKf)yb$GlhPRo#rVBdUcJT3PTMy^=ty!;JKL}sezbdL+-E4X~l{<^^2}4WN
zc~;HuxkdSNpS!y&uTpQ2Q_sc2BiG?k6f^|(3P!`tP`9Dz4_^|>ZU}=SC7P`7YVrw$
zxK)Ut;-C$5ET5QiMmvim5aA3d_BLa~;UgAVk*X2dbK?+l%Q~C(8)Qd{S$_Q1+tjW2
z+>Mp-5nN1x)0@zKbq0H}ju=xOW`Au{cxm`u2I%wrg2N!bvI%ZAtim^1Py`0vHqJF}
z4<=}Cq4ZiJUP1~e1X#lL*ZYOPu$+tkY$o*|bAXvVb@A1w(9KALHgCYK@}C3(+{%Q{
z!usTN@d+-`s~>Y<;cT;vYfxHFbGP=t6>KJvJbcyo%FA_Z9Is8i;yIn;r?aiyl(!;H
zK9vS8v%Ber0X-Clxf~mB-=!HQem<7$+oU;r0Dm(<9X~~&gbGa}J<-Q_q(E9|j+L_^
zOUM+=Rfx$=Wn4qURU=to0H|I|>A~=Ula;>fI_X@+@Bv+K4kc8h2<a1Zp;*e0M}-BN
zeoPm%m}i2!QrAR?l^IeMO~-wy!zGyp)4aDE*A68r=mJUk%MS_2@ca*_7`!PC4xA?U
zpGtci7&uH=7jf*LreE~%_jV5I|Ci}k*wy_Zb{!8FNPEW}07f+oN&`WN@V-)T5f>ri
zH=y|V&JviXxG<Yiw;+Ujm!N=7%1-PbNd;?qKm(*j0SVvyFSa)brr;_4H9IpJN{SIU
zdD<UV6)c1#&bvMUX)wC^yXDP4A@+9)w7_1%ISL>uUCc1WkCF4KTZuv#IV3N6POkqU
zLHK|1T_^|%!S)R%CKnhCdsMJ$!B_)?LZPG))eNa+APiU&Xs8ua(2rC5Mc7kENYjA?
zYy^M3GHNmK2gSPo7)-HdBk%Hno=VksQ_E+b6Q39q&z_zXS65Tha4g}sady&rhg@7E
z&xj<@N^uB&^0aVKf@E>6m7dSfP_P<K)y_can3W$hRLp9pNlaE6jyTR4?8OxXpZrir
z3_-wS;<1>3SZWe^ye|C)(UY5gSE^x)G@Zx2-pL>2E_m9IC`xMh)Y}_sxOjyX#={py
zzXW`&{8WBYS;2@?QG8<^x%<eQt5xnnaZIu|({^GleRM4`4q-SjjN%xk5Yjau00I4Q
z{Z8>c9()aeb%qRJonfX(_ZVC!r@T@bYZ&D!G*UTkL{yO!G%PmW`Q+Pm;wC8ecZhd4
zXxNDfjOR91=OEF#sl5w32eYhsb(<-itej8H(+oLUcC{iPucm+r>B7Dt`V{;$JKIUa
zd9cTUOmyE_Q;I(8!Q1-d96m)HiaHhjjVG|ap{N(Af`Kd8zHW%TE%{$z^YLtYTI9&W
zLvtcPVb>@MCvm3U*n|nfq4A+GUr1YB%o#}Ai-9xf^(f(OjDpTi^a99qqIm&kG9A|#
zHnAda<sOG)1*z{`5~0$#-7tk(C2%--D%nbnhmmu8e=C?IxrHYuiV}yk#30}?F0hlt
zujZxC;=$@UG1M^Q7p8Rlg56*q1{!j}jNCAFA~X^+vJ{+w15|KoM&Ty?KGxd>>L{;u
z`zMy)^lEbtyl(q;&r;aW3{`~=I2sPWRU~^nF+4f9dO!3<M<=9S^^Ky>`}+ExHh+I#
z`ywNe1;#pCKg;oGR9op!FlF}i1eQeBBQlif2Z7f8E~GvA)i04*80Ij!+Fv1H4@CxW
z&pY$@lB(&gcF)fRWdaE-76?ivciZY*uW|=@2PsO64X4Bwjt?#H7GW94Zgsc%`&}$F
zwTpNVn#eZP{h^fA&HEq)3j+A#`i=TS@%SGCI)oz@bg+Of_&c;d%PR`azwO7#Cck=c
zn<Amc^r)4V8J*oUoMKht>s_h$!D^t;bg_<<g8D+CeJFxfBJKm+@3+I2@iyVBNm`++
zT0?}E+(KagK-8(rh$5kqlp8%Wz7oR_(#rO%2O%AH*0!c^M%H4RLIX26RM_@<_4$}<
z%80O_kI|jprIj{)$?(<J%@WOQ<*AH6+&{x<dk91D>~w6)wLK}#Aq_hC^&*O!!+!rL
z3P6R+a0NzDF~}WKcp!jGuHU#fV&gj;pgB>W8*yfD&^b<=qSQCNi1@&S9YE_KR-pMj
z3XIP5QyS9R^~l3y8xyIbYHY%KKcX5Jjo04RQHZq1yvn!ocG}1tc<@Q+rZb;lzN|@$
z&j)Phv249F$u3oBpZ4v{Yc4<MP07Z@*v_JQUB9~wa0S-n0v1yM21w9oW_dhPW4coy
z$mA{Vr$vj1Nxd-4P^IZ>td4t`%lNKsk)oV&T^U)PlI4rmcQx5$3$H%Wj!7Q#$ws}9
z@7}=+oS0#!_{PVm)ySIIkfH+#yZ**b%bED20tD_Df(^=q!J2&H5cz3^BdDK1tDd(h
z=+O|G3!GzKahUlHzj{seq{XF))qKYMagTQ?8=8&6D2`|X<%^W}2InHMB-r&sS)1WL
zSS{SgBJQ$?btJJC;6r`>K=~?oM{LWpZpE5X>MgyL*5tR>?o`^KkL@I6y#1NtGl~kS
z%v_(tQWmDmmsocUnZlW#)g+e+^nb(kiVM4Z{+(jf*eg5D;?AS57GXVOsV(Cno@<xF
z#HBRO4Rz0jsfakjEa7J~itQ)8n*IQzgrE*c!GeTKe?!~w41B_aMZMA>X0W);C3FO?
z6Df<bjFl}~3YoTyQRk0(yZd_KCF8^HR3;swcJYZPda5ULr`t`UqHV1cItU`W^`NEk
zoYp~albV^k{q3z?ZN9<_1S5<!M_qi;r9E(4HTq(7F~xoDUGtZEzZcH0%kTjW(v-UJ
zBj~yrqh%v;PSFNx<G^VIC2MR6R3v58#OIy;Jb3#7i5XibHJy!j(rY+*G5vQKno!BF
zr5Z<Uq%0ZHGNqCevZbT7>g43jQ0SN*rg2R8_azy|yKor$F*(XY3KS$H`Wy1LpZPQx
z@qn?~kF?H=`#-Q+)_=!pgoK13ZXmE4B-+_Y2?9aENQy*&4GUpHO>V|PK|z-o5f@!|
zfkC%0W-u@bmXyI|VB8u2g9#E1lnJDZ;0@~!1*fwCV(hoiRRZaL;37#)w-ZQ{h5%5x
zz%S$d>ZJwtasA`cA}b~wRF1<m2AuRT28So6N>jsOYvHooOCQDGeUe(EG^FFFf<urK
zl*Tplc_$&KbZldOEz&`>Oq!dcA<y8ZHM>D_LMNhg5!{~oR9@N7T10rgyl*YeH?}Wj
zy2iR{_nzb%Ju?cYt^3ACQ{5~6Ad@n75IUi5om}V+yEdDOv&081<!X!G-KWfQ6DAQC
z>{Ka&aqA}U?x-JZ+gmYl*h8f>C`i!TNoQDR<W3U17^J+37$Wy~Rcm%EwBc=9lx9UY
zeNo5P4e*)<4!+@}?bT_7K>}>&eeFsIb<H;$WbG@Fc%LEBG!mV_HCwmPe|`{rm)~1Q
zTl-j9!w=>0ThC}w`_+UY2r(9fPzB`V`}G+24c34Q)~N6+LP&a7h+9alwoUjs$PK~#
zh(dx9$<N%>+}a=NQxwNLnRR_gXKb7v?Gk%<C(60&_E0jGvq~N)UlX$eEz_LJyy_Xg
z5d0}WQoP~<d8qQ(y)9R4GYd{84|zeh48sJ)?cqxPdCS+$9R^7b%N#k8Yf96|va6lC
z%nAlmJU%<h^RDy*__v@*_4P996RER_?*ys%Z;xPY(qi92nl&WE{Tu!tKerP&^^yG7
zb~2+}2K4@0?gO^W{4co=VLAzTOI=WOB3@!J!b(8DiNiGkQIu8Of%CX-aw#AX1G(V7
z_=5_1A_bEjDEC+CWS~?J63`QbfSEbDeqFQkb7pX+I5huC>bU=NX1{P!?18|EDtP;c
z1g(l@mp#Wpjp<3dKMg=IK&W2ud4Lp3_@nxHux15GZVAaLob&hHO#zZPC~!D1Kay|P
z2t+CBpH-}0u)`e;Be;0=|6j%G?7Rp$20vi}qPP=91s&d(-N9CU0oNeP6A%a<Q4a$*
z)zJijc~!LYE(C-CeziUnVU7Yo#VnXteHSl+34X@}BNPOhfuV|ljTAHgVIGN79s!9j
zEeHX5a`OIqCfN6fyD8XetOgtGT%C>6*zyGL-^@+*9pm204r0AUCFB|_FhXDH6u!%?
zIo)*;&vemyK=XRn^eB$+S<X}BZIV><nIOBkr!<eW92Cdje~Btzs2IpA-?5g<ozR9&
zkOojhqX!y%?c=Xw)C7GajRw%5C!6Q+Qq%enMlW`ri+Vc<&yFMtqTc#DA2`KR&<Xev
zLfVHApd_c@uXl=K<2liT!5LmhK4nHY4Uk~M0xnXAHtKJt3oJf7aot#qY^dwC>2sf2
z*HpL0vuMdiOGuAHpJyg>GGwY|R^pcO#WeRS#}Q-_6uFa@7U}TOYrnbl_sNzY<wyBg
z<g#QNhpx4OdWc4e3uCSUMU4he&EZMycu}!T?TH2}9*g}XY$Oyue~`5t6|ww7fj_iA
z#^)_wP(cI9sN<}*;I}iftK{=WXJVCsSAz1T(HeV)D2v?ntk#MY2LUD!7d}J``RjG3
ziisckjAhd@H<b9!HnA*NJ**sR^@IVnNEI?n9JhTjxVlqoFI@62GfezhS5)oi>?ub_
zgU!j~9rnYe+eq8OTkoC6KGu$U8BE04<<Ddf8`z%8Pi5A6yoIwssnFEvIJx_BBtGwU
z*{z>*tbkGR>Ln-eTi~fSpa@5OoSIE)wq~{`vPw9nzDLC#UOY^lqm#~|QYan0%>T2L
zEU3Pj=t|JWg=;ZgiKxg6QFLBoEM53h_l^N^x~6a1u3<!6+)mx1YU%ggj$PGTi@gu6
zCHz<LGOywvL0UwRKqS|%Z<>nn8U+|}u$o2G&KWo=(mjfjzBonK3-&Yjlon$_k;h}x
zl&a^2GG<L65F7xzAtDMoWVgAR*(Pq8WdvP<&I(u0(#edE`-;s(Ee|i6ig>-MG~XB2
z>sMOqC;FKm4leCx+wpF{oBd8uJ9SOMJ~(#q%KTm4(|+JV9=yvXYNr%>`M{%2u?<2K
zDOyUL!Y#A{sN}!`nf!R8Z9a5<vD6#6%)qyGV43NO+qRpas$TgPeoJL!W&MoDeriLF
z)pFC}G6;@j{&m=nMLPYb;%rsr*U9w#pez2;&r#$%2qwAl%aEc52~mRkO}`4R8pb%0
zp~b<{jA@(Jpky2YZmtNO?&e_4zigEC$946*VdK}x`)KT%!t6%S5kKqdZ*-|%VDj(8
zhMSjhtdj~#K8Tie)2X#Fb)6#zo|Zk@VBO2GQ@<9>QB4!sv*V<i->Od?g7;GaOfXRa
zL-kv^U{2Y*tm1+w2CeX@8QGJ7@M^qneeb5lwFhfAS@dpzcY~dVben7rMD0U(rQ~TB
zP$h(V5SI)jWceF?66H9sbD9Ze*s>{BdiR2f2YX(CKYxq$AI@njU=Pg(ePm{2_W*}A
zZI>6MPlj=+*0We6M&BQ`WxRj!<@B{8n_GpI`Y;Mx;X_%kw9TkmCvLjyNAuIz<Zew#
zPKs>#?Nj6NRRl;`*S4|PZ?6a&Jd?a%>!OyXXWUY9b8p0j6}l8j%J2YV#Dq)%<pG3b
zNa-{UD@udGFs<cXk$MQ}XY5T;=DjgU@3*qvZi;^$XhvG6u#*UBtxNS`E!QXU93GD|
z;Z*dgI%L^$yuo+T99Hp%s>yd`_~$y|0zw3czg{`|2`Ioc3@{l3Z0`Ki`4<_wDID{s
zBQ>0vE2KVT!rvN7ot@(V*b_8b?0c7VQ9v(<A`i<1>BA^Z7?^KD7+}JH4h%s&$3VTo
zxCrXSxI{qRr$h)s+5&;V5DN@Dh&4el<{x3;*&nG^im?Nd=8P~QO&Ah3{Ea*KKPIdV
z?*9GR^yh@W=h}$<%NbZH1`^fg-+TUN6oeoOlM7JDB?yMq{1!uoa_}M~$PY8&My^OW
zy&fScfN+lj2)wbn0d;_J7AC1lnJo1INE9LI29y<4I|2fO%O6>mt+N}1JD3aH)%+(p
z@;8J=Kj#Z=ilYf<ruYMAVctVw&Y%2-Kj&KTfASm7Kt%f>u-V138klfHkCbR})7g0w
z2&HgmViM5V382O391uml5vvUk>VQhAH%qV*quUJPVIEvo3z4a%xCoNr61@qO;qvzf
z|GE(kRQbqHDd$7E_2W5|QwkjdNS_-*Wt3C!*At*;W8h++7%IY1L`Xt${Dg3j)iGnU
z=L*8#itjhp6czC<@lFm0lvb7eS_sc?C_$l^MDMOe_SAam%=XT0NLHbWH`>?r`RDeX
z)AMwbUNDwq7sy$<3BB}S3_rz^X6&zD8|9@pQ?SR^&-rAiXP?vm1xM6=6999e4kMgD
zxCHtH1i+D)j{!ZHZ&`3(IorlRI{0iLq_|zoc)8MWa$M1*v!rXG;jd&<RX!@oHxi8M
zl(YgZs`1%Z7*b3iz|h|*fZE^3<laFnLUI+KBL6#V$qb|Ws}+p&zZw#g4WorK{OZ0?
zU47y)9t4X;yRt56E!n$#b>#GO1<P*LIji(xB7_ead^bkD5nxcjuYkaLOQ=t{N4@W{
zLGcEXm?C?J4&x@{&0(FlojPh?)qM{@)zdE1Wtu6CXB(Jn8<6Rf*=F!*Nj`fY_Qp%+
zQo+q`P%b2w3cJ>&j=tg9E{;)O)C2yFia9)u%;csKNlo?R=#o5}oHs*Lo%<ePas%CS
z-J}nP>Z~Dn+uqkY|FL6!$2}74*7~QmBAQF8nJ#|C2g(WiLlk?>pLQlfl=MSjX+>m2
zIzRWC^a<h<7LC3{*XjP?E^s4#?0eejdLNU8)`WVZreI+*oV|;0Z!O`6n62x{MHqJ5
zX<KHLX#&Z(=JulP(lnpx;n&<l>Qy`RrTh3_@EYvU3@ks_nz;uh#Y~kwZw#?!>0auE
z+HZ~hj;4X421l$0=*d#D=iTk?<xS2Z^!Oa2F8V3jnA$hqJ-KS>V<i1_6f^Ff{%sx7
zg1mA9@}3b7#gghP6Kp}-7ipV$rar*i#9OI{fph%*r^Hpq1eXW4A&?dqByjs1cW-e5
zC)n#2tokny%M7R4H>7-wnUH{tWX6P>xIYw@!tA_Dt^NT%u3udMnyxSEjLU%5<;xac
z-G@7n|6QXOyWnazg~k{+`^yWOV%4FKX5G@e#!DPp?~3BHQ;LiiGTg3*mW@TNKAiav
zGwp_}GBG$#^Tn7zVa}7Qxx+F)fXc~%vPFX?VMjz`?It2CK+>j&SCbT`B!AIU<}F2z
zhp7^90T+A;-*1uOvoZv~wKX+XJPPVIN*jEa<j>-}jaNX)RX`V9uvaW?9G)Qm(b;<T
zLr~=%B_!`JE+7PC%lR7y9Dg0n)v*6o_508MbN_$uKf}Rq!NbF!!(fhJ^qq*=Vf39L
zfUi^v6BGC~Z^}E2p6#GU5I~YlG^rh`|DI}B_IyQD4Ry=)2hU?)L;oDoQh<O#xqjoV
z1pBy-`;GKGAQ3_Fou@@~E`XsP4XF1Q%_Gv?9MJXYV)Ba7A2yz-oYVtPF43eBYzcID
z$J%oGg=P67rT3(}iv9L&v4_4}5`X5?^|RQIB4G?2JB$nRDG~vPqN45YxF0e2+l#A@
z$15Bkmo0lX(gY45nT!EPXjo6N;=Ck8gk(nWO4ETw;v+reWq9nWfg<@w7$c}YeHqZM
zb2waXL2H1;?<j&W14NfH1(|LN6!nG8Li%5{v^?ZoN6WXgUTa5R##2R?vxYO7rc;MA
z!Eh7YA$%He`#eSC2a~hy0ik2e1%!lOxqm(7QNCAqfva{VvSid=gG28ALFd?XY)_)5
z@qdWBCTb+rTav(hj!kl~*UetcfmokYuA94W8mN3`Ex#n2Ig>yurM|Uv96Db2DQBsL
z$g_v&jSiECWp5@$<%#^Rl}f8l$#1VLB;l^u+VEa<n6=Q!5dnzN6M*qRvp5aYaHX9^
z-qkKR>+li;f=nolb?Om%Lk@GwPL5Ytq5I(^p(?A772dEYWK<1#(ma&}F-{(CJkCK2
zc&_3R^NG)#&}2>JQs+W6l2e9^TS%n54v5Xt$Oy$&9`^Pk595g{wGY78?ieos0B7zM
z3}N9xeDUWe`SHK=*;B?v?&}V4D|BGHM<>`VSoYqtDr#}1BH-ju_x%G{G7n%#W5G0k
z1!U;<`QH9Hf#lny9Daj08FBco=w2~qsH&E}aXT{f<~D|O0!B=Oa)<MwMYHKiV|+J_
zq8vrZ2T3#^m?)^x<6jiNc`?geeDhV3vNx50G1PD;XbOqVi4E-twgaCTQ&#~KB@DW0
z0)@;;e?^~oMepg#Bg*O*ly4amP}+cbSF5sk*o=CY$)=sg2cJ1GCzuP#+Mt9koE}38
z3IyQG^&5696=O9~VD2*fN+NDI&o$x$j=ZdFV!Ti>l`h<Fy&rGuWWrJOKAM*uq$yHz
z-WMuF=yu-ZHRkLY0ezqP>M6yER+g*=3V~-jz!o;;*rbYoUNw3i=oMrs-G)1w{oXnX
z?mjcjy!n!h&7-N$fa_SDo`g&lXt_@j_!a=_9OeGLLR*GUylQZi=hrLF>tqg>v59g(
zxQQEBe$jecVp*`P6#5#|-&*ulu<#MIV*?8m*CTyK`z?Lac}7rqevumOjDC=!#NhKh
zVn8-tV=n@^x*NnuL4eF$yuV(yvQPN4ZN&h#q9t~P8Hb}Japz}EDYi(I3+mOujX-7u
z`_|A4-fN1Al(2+-5s=hP2CE3;w>pkX1X~sc72dk&4{sU1B{(rWC8;98RbRSo6>d9e
z-`sor5$zE_qwCDl5V&)N^60vS<yDi=fF#>%SqBV>4Qi}iC3CIFH+y32J+I;SWGTRp
z`bLM!aKmiA;DqK&LgP~tYz!oF0Ng+u?gdOvBB_~LZu4mDJuA?|x-8C=b51Y@(44_Y
z_zj>_eeQ*eEHeZ`Z(%eCOjunkWMffj$&}3_$`h>Yhm4BPNe<E$dL2ti;|Gtx`pK=)
zR7Z*<8^812aLh3dc@1gtL4x7GVKbO=Z!-wqkrP<aojiiUu274iw8XE)V*aj1@~;2G
zCBjtQD#97-g8;B9eG`QCJj7XvJZ$<q6DlL|<k))}c$aUCQD4#ypfqEjN{h!l?YGkM
zRsp~76whp=G$5NY`H8_XM5vJEsMV9Xt6q%=P-?yqyt|n#M}D-*e$3$b9uaQ}FI$6A
zOhW=48?Sv5HuI)}32MsZ%ih=t!e@sXQ2i6o9ZDKODRJ5b{P~d~4Ww8=g5SS!Ej|PH
zy}-Peu-r}R4?GXSycZMud888kKb4YTalIT^g@nIt?ejt;Sdl;{{FsmlJ9rE4^t9b;
z%5IFqfW2w8E-EW?mf%KXl4QoUX5(3l+e>4c&xrvm&2#~U7AK2r0j&XF2OSS98>u_i
z-$xBI?77Dn0tDp6IB2*M3sa@4H3V_rD;1DnCMh~GkyaBcc~eq!1n#TWIH>7`8%ZT1
z^G5VVJlrZ!f+2DVLLdSp?jk%CUmc2l+u~%^f%E8Hoy~{OAOaFWvgy~ugwu}IK>T57
zeXaGo_NV^nMjmde%j8565Y)O{oREO|Z!`?-|MJYZcLdreN%w*p?Q4m)YPk1j?0U`G
z3-)KkwdeSJsqgh7kOOFf1KhtjB(tUotxC7B0*OX%ZTQl0>kT=xMQiH495fr{5OxIi
zdOj2I^4xkhJX0}3QdtSyDsM{#mjW3oKk^f3xHcrV=v#hYa0oq#j8H(4%0ZK|RA`!)
z3!9e$zx<eOnK%>_<Eu(`8CcbmQw<~H<fmkkNFsJjaV4v?k3F}BZesIF_mEel$YCUi
z3f0t?1=`QX^86LzWxiHFw7O*Qd83Hm)01cnS)_Q=X}BT;EOQ)2KoeH15S^p-<IJ;A
z2AE8mCvnDsS-QPxY>t-*jo(6RAS_4ls~!KD?r>6aatrVvb0}FjSh-nqQSt#fx&QhP
zypjL<laf#HufoZHzf)t~;Iy<3KJn!vhD7i%)`)Ak>?B$4I1EC;6Ovh)Snj0yiWlKZ
zOJJ%IDqt3+pu@rL%IM3BYpjv1i)^=UgJ!SVd2J8o$Dh4-egDj!-CYt#+`{Sw4QjB2
zDO{HXb!}kzOHN%_Oe`!MY%DA|XXlLW8pJsY!d?q>NwXEkqWu7YvVtdStmK*@rpNx?
zjI?<NVSr9kfUX8UD;*0pbVO9dz|BE~#4z+zHOEHoJO)_n!UP~{gd*PN>htiH<|oIq
zH@C<~LdQ@hL_|zM#;bv~4^YuOf~lcI7(b$I#dS;~MFEu5RGbJu9#SxQZZ1~qV(2}2
zLP0@6RrWvzG0a_i1qa}}qRZb&7#@`0_GGMcJRHie?7_Z=l>{Yw=QLW}Wo%ZSVf8~b
z9@I`4#6}#{$Szq=s+=d*fB-?0f?^ax&(4I7oM!<K5CH*}Fo9v6AcgM?S60*lCy#*B
zgGifSp`@?65-S5yNTFa*DVrs9jo$zO(n27&P--BfukbVpvaA@{F?t8{b7)Kp8bFle
zk*W_N1J43XG*)*fmZ$;lT?V|U#j~P1=AlKh@Lp3f(Jz`Ig-=v2YOaw$OZ!H&LkL7i
z+bhtrLM=CT-SfkebErt)zXjX8x^?c71XI{5fvl8N)YQ~;H1M6mQ0Z04Sx4jFO8m*I
zjb)Bh375sy0uhE_oVxg-P=Z@Jj{@=lsM93yCX<h=^8!IUH>a@BAy8J0IQUMXP2Guk
z0aovOfN9HCHz$)$#+~}G<03%Hlhf~2{072p+3Iqk%N>gW2ao0m3w#U=!ayGHLA9^S
z@_d5;;?7uNYFZMo01#~k?j7Aut_^Dbk*LLu>>K<KaBu@RmH4719W3W}l7J=AwH27V
z3`L4CD>l@XTmC_NDO#!QBa9<Z#$JcST>vU5V>5u}GJpW~Sc1|&!z*AOM8Q2x7I$>L
z9>`EZfX@LC!F>!r0CH~#yv45|A0hg>dAF>?#)``UYf;3qW^5K}M(JdnUPndxW#c@H
zt;4x4mszqi5h#fZe|K63jkxyc)R?ceKKtvMC>fzJT|kD3=jP3+!m}crq07=<DLPsx
zMq;!xnANlxQF#EANh^NNllBfdR74aibzNV?Kt~QlC-Ho7QGRA5_|PXWA73=BuNp@*
z5>oC59d+M8-#Mox6A;Iyedgd>LAkuXQTz-{=NLiac^K@H6y2zaHR*JraM>KcqOn{d
zUP%|t;2R9Jz9Un$&XPucoMN$%C4I;uy64a&)Q~sUQp(fiPF$^H;KkI;-dA=fyAtV_
zblj;SV*Mt!ecfy+^W=*=K9{AqX7OgH=D65tbxUtU=wW?Rtw>u@72(5D!EZKGM}CWO
zS^JU1dR+SK$<tJ#U4Gax{4I_+=PnT}?>Sx56GLEG1|+<by+5b9SUUzJT+P3C<vXDD
zSDWx|)%EW@5S&Di?YW!kD=A2)r&lTY9K+h{Gc5wM0LA?J)hE<KD7Z^aGPCxElS{jC
zMp$xHq{n#rc*-0hB=4zS8w|Jk^e+b!)KNfT3C}*T#@8G|d2MqrNeb^~adz&^Gv`v%
z0!*EG$Id*3cD*&q?m^D2I^gT=hmNYQ+f)(ukq6<X>|6F%|79vq%(KR?)h$=I60Qk-
zihb_VmX9col%Mf`nR_Ka@0R+Wz<KU*0X<4O{(*K7(xL1J27>&=6R7X)uSkty#<wFQ
z7E;^i6y|zbnG(q^4#d^uXWrg@ZlWqz55;J(zwl}-x@%f*dH?ydmy(0&9y*bFtKjb5
zXu#$O?GfRic4v8bw{!PrSGsQ!Ps9A}ng>s@&6b4(Px~5p^wVS%Tg>dgUM>nI4dQ*`
z;oy9&`eK0B)Yvc{XWys-`HKcpyj}9V$K#x34rYy-!s}oz(NFBhk?OntNAp_D)Z10>
z`t!##f$%oOL(Wnh==k9VgQSjj>E)hvEsq$~^wXDw9`D$DJFn3MPHC*K4N}b7&R$Ye
zkO??EC`nx&`o^pkc~{l8r#2ZS6C(LIc$nx>DTR{-t19+#wc~yzAIRNZd9yikclDIy
z&K0(zFxQ)4P85~Z37%5H?s3$+8GsA+_o)ah`Gx&LlzdeWE63xdcoEmgiVV(E^~!7$
zc2$bKz|l&NGefl|=U3`Vp*s1FMmPg38-wYSquZq-6!L@3+=scZ+(uH>TwmjwX>v-o
zFi_~Z4VMf?CAjJ+bN27E=z&fmHwvwCeR*AC5^|(xbNjj2x`!KO$SrH)(F5J2kqCz>
z>RxL284qBrnsppv+jzxK+oUkZy)Y5?Pk;XQH8s^)7H4dJx;MH{Xr7%Q9>&aqUdIWZ
z{Sa4v^twG>`6oZvPr9T~9=3tF@&)FCJ2L5B0k#2CpN=B7j?P{z3aoa2e?(bW`H&Pd
z@;TDOWhGMqnBlP=HJKAX7QgtxK?Ks^<pHbOU}<C+`KhtZFCC4J+By`m79(yg_T%QV
zBdNYfbQR^^XUs-5S%c!vkC>muzFcT$xGp#=eAn`!J^W;C<w+{oI6wY*CM?biT$d!9
z23~P%GL9pH8Vc1>EhP5t+z*eQnkq8G!-t&T66(p01?T8C*;$Ibld#|0OYKAJZJV!{
zI>4ykd)e{H+sIvFwq_sKL)kpF*jiTgTJA6-Mb23@*duU*b-f2Gt4Ajg@ph!{n&~;=
zR0Yi1^Nf<*H*TRNZiF4~r@SaY>(*JM!2Yr&oymcxur;+8$|sK_XI3Mh;_+e<@dOw$
zzp@w@jJ!DBY-|KS)c0hpInvo4@#AMQk2$Z{7xJZ6FAQD`pXHln1bho}$I#3SBw*I9
z(yfo34CrSOWP9K(cd|8gmBqpM$$&8me#~O&3~z+_?dG0vk;j_Z&C&5n-RRpd+;&Ls
zOjo-@;_a6MDCL!AeTg+wNd)S0&g`{pKb&6Cqn^t?WZpE0xT>vC@TvVG6u@lM7^jc&
z7&Z%K`B0;&SnKZAGsK9tX`UHjGoZK0c{*T%XlO>~#33(hZ2NS?O5Fgr@)<s3gQ)hj
z%eB~ULuTPxI}+8CgbHY5Dl)}q^lbYl$-<4ZJe)AqVvNYtgwGPO=^s2?2n)6isT!)~
zxxqj&Z|7RtTa$mhaUf_vA>=$IT1kqe%aaFRDGY`4#rpwT%al>*qc-ccuWJ(qpuMFM
zEqhK<?e6qPYY#UE=(z4S)<(^qm)fR%a?!BV3^h8}=f;XP4BsmdtbMaJ<V(-o+&z3E
zaHWhyoc%P_<?FsUy+kF?iX@KUVQe$!aI_qvJQsouX%|i~;x1sFQ;SR6HH!)6u%NAv
zFR}1MFG7V<A%5YdrTI|}+{`|as|HQja>3GGlex9Jdy~;R6mC|ZOu0GW@i5jHZJ@82
z&xOoL{F%(BV4UKYOV%AS*9TKJwx-|LPK7}6->Il*&8HC;)Ujlv-%2p;cc(Wg1cxEJ
zPDBYBuZ1xsSIP?{i;d~bjSRDyJ-s5ny6N|p^JU<yoCIoGH>iK_7Arek4I_v-|LB*x
z#i@mw)3O{}JIttrHvYY}r!HQs`aAR>xfRZA_av1A(|cZ~$KRH*X}&%<qJgeVB=3wt
zm%%<rF(jnztF##-mS{t`$r$n8-8UBbQhd;81Z&wAX|Nwt%C0!Ee(3fMF*XS$z5gsB
zEQK&~vMMucIChigTR-aK{5*DEOs;{<5ELFcMS|Gs@Tzw*(#;QBEL?3>B4M~Z-oOa*
zj5^;O)9zMy&B4HObT`xsx^^FH%HQz#j50e2Ji7=P-JYT;Ys5-l)m8<n(Q+T!yfi+o
zWuTDjpIcn?jC1x+h-+QTomTRGMe9MQ>K>}cKb$*w4mHDvu1uZ<A|?1*QFYFzqAIBn
zuBk}i-=9+;yybn%jZL*HOIV?t3g9gJlsHRepwGQO%I%r=X5?b&0W+QX7S5XBcZHe!
znGe=<<DL{jeNrF|0goxyL?GEpOyt*Bz--iqcZ1O*oL}^#ndO@6Mve8j11>TMsRmT7
z-wS<y=k8C8qI1sNW%2l2?T*o$?WHXj{oc-L!ZK1P{+9h-PbFtF=j$^qrQ~<6MKyKs
z9QcFBie7UYmls^>2BWrt{~u%L6r>3dW$99vZL`a^(Pdj*wr$(CZQHhO+qVCz?Ty`;
zn24E(-N$^&hs?;#d%u(CT$PY6NqLs4DxH<3=dQ~wmslv{dF^~7PfS*LVJXNk*Fm|Y
zCA=VSJRj_8VV-jAsKjleL*C!k;O>FsUrd@bdWUxk=jh!iL_0AmX!;TL5U_`;63l|s
znBGX-tMr`sc&eZ%gQnxQuzv+&5%fOY?U<L08HGzqZW371BMMfIeGN;5R@E!gD*Pg7
z)wiFKWQT_?g8I0<5JMO~?;@VUPnxv;;7SUB48(m*h>P7`-HrDCL&5&ax%*z#c03BM
z?=ZfcxSk1D|A0sDfKS`BC_J|p#wbH?bX3WLOrgAtM?Brj<C<xCWph}-EO{PyR0xZ+
zc>+;3AjDxG9*qlXU;qaZ!c@p~*?P#xG#|tR4?|jodU*yFN%9vE`hKaM*55r7x@;x+
zn{Gdq`AVIpgrr?$*t-k{>HBChOQ--{lLd`$F&^&{oGIqCT?!YRK4-q`w%M#3D!(Ng
z-DiI<w9{5u7JU&gH*%&h={zR0g}RmWt@%}v%~yA8m0Nj=n1f+FVEm!cD=S8oAC<Jr
zVQsXHot^8mVBEYa+o*pG1!F-$VAzvI;L2cvX6^Qcw**1NqU77t<LTn-DNo*Io~HQ7
zCnu5&vTsdN%qLlW&q<?pbKq_vn&=FitBmuynP}C@XLNGxP(rWDBa}Fo!d_Cn>qawA
zB-Cg=AntiYVqm?E>*1idqf9D4Ecp`pU7D9b<(&eAC%$}j)Uv(pG@6cnw5s<3_b-!B
zOLIZOEaxx0zO`qc1Z?x`SIW(m^7ez{ebVh7c@`0ind|YR%oNE+O3XIi(e>YhO!Mbf
zXoMZZmllQdrWi?J2_KT9qN|DuD|=vA*&;!7qSoCi+j_c42Ey@ETwY>r#t^wI&R-B#
zFOwf33+>LwygS-?HTlAGTJTkHvb9M%yyzD;Qr?5N3#;e1YI}GyFY7&m!@W=5PmN`_
z1ra+!a7Dj`bzN~@L<==(q4L|W7NiH=Z}OJL%|z95?O&t_7lg`5A=8r+a>K3fTE|R<
z%}K=|lP1Pe4^Wv}mZWW6;tQ8A)W-%rQ%g-_*<qGV^H&&Owcz2rhlN;48qYy`bs9Lz
zlQNg|@bbGYL<WgU9<&X-UX9-G8cX0t_2OB_i8z|+tp_Cpf8@bW^9iYnwQC(ZG=q`I
zcA~rwCqBF9QfI{XpjG=joOC+aML-vIP{<}trYH@mB_{D4uWr;PAZiUFo^QS#N{x35
z2xVKR)rVt-)qh2}wVr%#@Ods#DYXa+-Wsn5uOClKY*epFu^?PXQB>B1qJAOvBw}!`
z^5qQTvmD;pIm$$D25>gPv~n02{hgF3;QVw5Zh)vvR$CB2UtpzSu^Il7F^82<3zF@&
z`0>W$L|YM*ykFTf0<Ns`Rgg~wNymOwzb^p5c4vDZ6;WF|sMw)<iv?(PfnHXAM}y!c
zpQT$%sBdPTP_{G_VaE*&stj?H#1@rVip&auzh@Ne7~!iN-$IT~YBT8{v|Fill6Qu6
z=V|+hol{ra&R^O&>mWtHI(R!fC5q+<BS3Q#?64k}Z_jj4rIO~Jx#MxRoS}t?oK*xU
z(QE>R?^wqE$&o0u&dGfkM8!D#0Q6UoeaTF<hwQEBsj9Qbhnvy2tH--k><oQ|i9znz
z2C8HgRXNw$6q4$@B6vgLQ3^&Xsap~1ULLK%vZyjQ8{N2E>!r7;b^V>|d}&Eux&8IK
z$)peHGgP@!>{1shW-S!5Sv<5V1sXh;@!YPO>$oy?xvKG7ytkNXr_k7wcPR+W3utQ%
zud2|YbHUIWwfHim5m~DP!zzI9F;Ei7BSaNDco5)F#e{K?ZcUF0&!CPpH!4CU5kw|8
z3xB5rif8hhGS0LmM9sC*d?(z;#~D~Cw50QtI~iI7-9jPljDV7Q^4;y#{v518CWgED
zD&%WoXi<&3PM9y%UnI=8L&({*`QY&vEjb7&ip-7j`(q^BcqWG4jyQ;UMxFE+uSw>w
zM~5u+Pr4@I^zGSN{XZ|8g#6YiD4sR9FT+coF$B*uqTe4hSIEEFGBbK!_C-!XLPhnd
zRK}rWBk@i477#OwgV)XH#(h+rqg6Cjy71i3<|tOPcVu0rTQ%*$Gfs=PyAjr|Vt0nd
zt3}nGu7iG0TdumDYL!DaW_JJ|;PwU;W*sRwE5Rm;C&n`9^B9-!zTH*4XDUiU8Ir9l
znb{kc5n5+Bgeh4qVSr5p)G5ZglKX?n$O9fmiU!U27pV4TiUQRM1I99om-1u8VB)>8
znbc-_l-&~=+0E7-e0cZv9g&|K$TeF(c=9S;j`R&}EWusF@t7*v7cqF?W9d`HJ>O;Q
z3tSaTT?yR%wF0tuG~Y=x00HZArPiU$v9$zj)w+x*6hvN;(<hoClNL8dy{utYnMbtE
zp;$rQ`u=IXypLDp`Dzp|8rha8OL5WfU)9t6Jk(6uk1cR&0qY^J>L|d}BE3HWc!ygM
z$!`2L^4^Q@8SDnFm{PQ>C#X1E=FN!+=f5C^iQ(1vU<xxNmBRJu2nOYDS1@6!i*K_%
z9)nyuU=$voGH9C0mS3YtzGT-hixtmubz3%Y6uh+;ZoZ2rZe-F!QiHlhKZ*w{?C&B8
zlGOJ<g^o-e?|C0L^EmH&C;CapFh2<_p#R|i{+guYP{U%EO*ss35!5WE?IsCj?38Q9
z=K6F%7WwVYmu>lsf!;iqqdD=*fNtW|q`GWRXE>jBF|wLF8dUQ#x2$?jt+%f1`g)RV
zNRFdXhiQg92V60!UO9CY%sw2X$?MTsUQ5R{d-F>S>oPx8=d_57I*N&FTpCH)x`ZoO
zUTQ{!VD%=69ejV~QMku`m6v<a5kO~lUy{SV)lQZFiCnsTq>P4oHnK64epJgQXZTW2
zI$yd&mD79Q^wwgvyCS(c+Vui^*O^TvJjPLV?m?a(xSEeV-`oc?d}-xGqQqF4EXRFC
z_Pf>=+Vman%g)c{RKEADAgr10GP3*P?%X=oL)9j7w)rITQhwz!)$uEJpSxoY68s{M
z`&PHwi5f5#tqpB~a9^yo)8OWKuegECg#EY|4*77B>_=@3IlLMC-Dw}YqclpnR3&a(
z=MH%RZ|8daO}MjQFAs!E1=FU1tC(vm+j}{(hkDt?zvQ~Mp7rI-oNjZpbOtyD@fJ%J
z{~ix%Wk&i-0pIX;pK4+R_YK-vqpR(iABlk6O+~a0YW`5ok}~SE$l7+`Q*f9GC#+rb
zd3q3$QqV#he()<p&NH_(_J*g~^5%6ZIG>6sC(Zf_T$gW5PWC2MQT_CCY*S`Yn9Kxe
z87+xI%H9h~K9VT~1ARE>qS@7le}JYN5@4?;I{Dz)AZ4RJ0(AfNKDtQo#d1hbv%IQX
zqH~S>@sZwL-kW?_@=_GOlw1Nuy~3n;vK*=DN=B*P#ve^xYoU8WuMy*}rYR@M`^Vdo
zUWMN@Txp?m;3%UUZCRI{3`@$HAUE>0drM0}n*437vDatL0~JKyj%{@g4{1*~e{>Gb
zGV6GH9cmTTL$yWcY=_*bb657Smy=Uw^BTWkHDaaH-zy+d01IHfVka$O0FTcdhCNiI
zI|MWMasG7sv_Dth5$D8WW6(eL@`8pk5L~CCz_1gOOS&_uQ*~S$Q!YH`h>V{FHlY)9
zl5s7sk>9P5ew?NA1Nvxh&E$CF+<#jhnsJiVz@eR`p`@>N#4kf;lNVa1mT26u@MZCv
zoF`q>Ks&~vhus3dF8sP7{b}srhC|U>+F@C*Igt*Ex@(q^V|PmLPv&e;GsF8vPX(#D
z{9fj+$BLFQPQ(}waf(29w?%itB0_v8Ro<+2pAaa0R5Rr&yT!`JJnjWxXLhB?jH)xA
z1$^B=eV(7Z*T`U<hgOkJcS`M+&6H6Pd4=Le5k{fXMJf#y+tXTd$kf!qULcMY`5eWG
zN&w$cvsW~`MmPD+a~-%H&AsbhL_OJea_+lDeUKbyE|c+UWIcV^6e<)9T)fV^!`9J@
zqyDd|9&W6$gNmtBPzZAI`k0CYtpiR=Q&@7jcQDGjjpHYQ@HX@yy^)PYIM}}|qjfv5
zsQGxC@N}6sSFg(#jUb~o#Mh`(ksGWT^sZASxv2P(E?0w;T>Gm*wT^2^lu!Q|M{$>#
z_nGLgjRwjN%vPzK?4x%qI84-Fy=p}!@lbUkDt-UL-*aXB8D?0nU0_=~_oHB<J1`U0
zi+D9G`Qg*4aL)a3o$Os!3h0}XnComr7}lM`Nh}6{jdl;3V`1(=lM7Z}OQ=Wq?!^x<
z!?STDW5RKWt4;1b{}`uZw5Pu`+UAP_=U7UyEv}J%@S21~>$1j2qhcq_;o(?$<mBg~
zNXaWo%nv5hDGgH?H!{*xT=%z*)5(d}XRC7MY2stTcp>b)WKpBW_DwNO#$7JjtbhfE
zZnIN$vw2xbrg2UCM3lB$Aw{pY2{8;$eq*AA7}Z=n?FEbDT>4ys;b}h~n$oMPVvp@V
z9Aq?*zuh1Rlb%}N{nqlYIHRZMka}Gj*`>we%$+ufca6pwYFd@CO8ZfEM0eSor|vp_
zlwv<p&eVHL@|Br|ur5QE3q)G=eifAt)$nN)|5-4d=?1(58^>_%YJL<Z+hQXlIamHh
zEzZ4}<<LdpAiH&MvQy1#SHd{>dYa?k{aO?qBG!<tq4;OA7+xf>1~?mo6aS@`Su;&u
zv7h1G|4z6#ra1k%#39?J(wWjSTurT^>!o@0yKItH!f*PHXMPnF3)ePqP@|-&h_{$k
z7N${}2C5eN?9o*non*JV%N!>|oYWiCga5XI)w}E0`^%&cFz&7!2u{%O9S28(we86i
z)$hs~=h1a(D*n#s*t>h;>~xW%%qm9>TUr(RXDr~|#sLT)$x?R&_Nt1U^wup}IU;+E
z%j-Cx=lFn_18KeQv{^=*x{XD`_RrN(1PrP6#Zq$B4HiA+M;#i~WVF=hz-P^FqrI(V
zkY<tBG?&<-%)&Y$#r$BCl_<O`w7XT;eih2!-J8MKO}um<Kz~C4GILdu!~gz=eGCg4
zB7KC*T3Zcz%mu{*`zPrhe<Fevs3%y3Ydt!M5Ua3j9>2r>x&I@)vVT33#x9d&c=>fy
z2Bx|#wf#~PJz_h#5}wxo1^-FM^{Mk{i@T<lR-=qhibH@;FK$TC1lgvGPgOKGF1dl4
zNv3$C2oDEK@Y;xW6LxgqI9Hah_2FX%;IHuw%KV=&-G3+O|1cd36XSo-xPO?Ak^MhM
z{}t1*|EH+z{{z#xK`1L-F0d_#cqCg0c)$^Ad)(#+ibF~G35dr9!7CPrKk58B7ZLDK
ztMGF=hgM9AVF5<FkHnwte%<=fz5G;N*;v12o}J=;oMGnvn;D5`L|g<b!r8|nFCG<F
z#vqTLS7B>F3JnJ2e=s1BhY5v|N{Jij2Z&CzOCRK!vqM#U;m54NXJ7!2Eca(rFW`fQ
zn!6VU5*`LFNN6ZfY!Dz6fQ3wY&kTEr`&H)Oy(jP2!yo_=9F#B7rmS$&gKtM}3hR>F
zYYW_k$mdrU6_vDS>+08lGX)9{OalC&7sWgU*GU2v1(cRYXoorSN<idx%Vo%L(?CqT
zv$Hdx$KJ#+F(~0Lhqwnf$^jasZ<ZE{0So=6!qf+J1^%g&!2pHPw}ch*J*gMqFyt1<
zs|Q5m=O?5m&ayI$0u}5IRM!bA-Si9;a>H<>Yt-Ef8Q{ks3W|t^4p3|3-{}SEkMGUy
zM}(Z1#*2CiCdmehu>*tEE6XR3y6=Ys=@UPrC&u&_)d%$J=y8Bt`H_Cf;=#_zvw`^4
zqkWenz}@?G8qN)vaU7^c6Za12#s0;EQW0ry?~lV|8L$0X+QVmneA)6A`DR%~3x5#(
z_~~{CBFT^Go$&MMh)g5eN8r)lu<+$j4?EgTLxBa21DX(*lnevWLjYnR2x}axg_HK)
z*6)q()whE4|GuGc^&@G4RVUm+3IPz`iv@P;0Y!z9)6D(w!~JN*hed$o*+GKu0XYVc
zNPOer!3^U5Xc&Ip0>1*QgVsNw`~vdz{&qGERQY$l3%`A5es>NNUszatSX6-cN`9v*
zi2OPRy~99G1cn?J2LUc9C`bUJq=XXK<3IH4yW@BN3aw>ZK>8K;POW{#3CM0(C4js8
z#zN%l<2S_0tD6W0cKe}y`V@d71oK1!@J|9-Cx7IQdkem51%Ga%Sa^`ac3pOQA%9}=
zuE53KzV-EzFT?v~{rRA|Va9$+H2S`=wY@P>Z_ZzKRjpw$x#1?n$*T&2g>NB2Z@+>E
z=dtYwFj2uq1+sn=r*+%bYu*1hxI=`7`*{xP_W<_)F&>1+8bbR-{bN}Cz6mxsGspj`
zA|@z5r#nbSMg#@q>E`DxQP(|E^Bd^9V68l8z{@8Ri;y@<Sig=A$ST?&NGu6rymkZx
znApayi2MWN11AnFtc`?c{s&A%WEhATU+?Ue4*>EL4?46s2+AGTI~*W$cl&&M9@_7v
zhnNuj7gd;U>sZ8XRHT}zv2|`#VIX<A<_xUzn*s{<bxX<Dl(&Fpx*US)W$q%A<UH^F
zA#8jd$mBaPk}>zQ)<H03vFU-4;{tF=6)N~kNLNmWvwtm*f~?fO{OQcOVm4f~3iV~>
zZG~qXy)iYm$YY2-*O%&JK6s~#yb;C@G$~7=p_I`uhvL-v`hc&+;8bfER&%>Fs_><5
z=8k2V4d=)I6BFOHW&vsPZP|63i_Y;4@VUwf3OGL)4esb>O;|=^iE8rUx^gFx2#j(H
z=j56;!?i&RXI_3-0)rnhXob8n=J6~)zFiCky$%mN$cdH(uOMQFCFn7Ks1lQynmB!?
zA!uB)Hp=utBkEjFK)t3>&zvbAPyrq0#F=^1B8eHh9WFHmMO<;E1uW%?CZ}X;p6}DK
z4I}fyqO<~7>>mfdB~%N_(qt-l1U>Esnb9N=1E#%)<_+#2Y^eM~!Xv?pqD}j58ZI~;
zH09qW8-3i0kE@N2&Q&KbWA<~#giud`skfEI1B^{qY<6W08e4|eHK`nZoF$~QZ*VI%
zW9a$leYnOr_z?(}c8V!o<s?je0!VhWvI_c=EM}B#QOF1PI+CwQj`z$Z9k5pe9ac^w
zRT6A-CPG`rN+lsc3Fu^Sb(EMlB1@|@d-azdll$2E`w!)Xqt;Z}doObuy~}}P;ZI~i
zy3E=49i)aWsE*_<TsU=)CuqiEO0M<+UZdo@hdov~Y(6us$~Cbap<%M;InWxdo-ZNI
zk2?amQX`Y$-8-^5OELu2Bete*p)Bx&84ZEB`@>-qC2rwt%denL^Cl~xdfhRL=mxPl
z>@hN>x)LJQv&T19L2a8htk_;IKRWjUB5(pS{meh&qF&t^{Arpa1i35|JmS$;ku0oO
zZUv0WR_K&bhgWOD4gT^=+C}tR%_m-4$U-VVGK+w_s?=j%8BM&%u$er9BivdJWucEw
z4DqjR2qY<?@ztJ*vYU>;(9TRYbcD2J!DmgV!m699q#WTUc{GjT*)GZ^uyTS60HS6q
zie<OLwnof?<TE*v@m;z>5#GlB`OTdXteIH6c9wS|L))-LR&kDlRzvPOHh;9|M>e-H
zFQ~oLAvpj*$V;VVXgYsth3yV6M!J0H4-yZ_v<!nIAaSpVKyS|xnR*CICEaKLWfJp!
z^IiSX=NLo`6XeSoahSKkHr95LC=;wX1b(dPX5MBKyg#Rm+xZf`hOtQBW4rTZAnj}%
zn%)vOdFA7n#CTRd<k<-__*0T(@)5GWi2LLBOs^XS(tkj}I)Ug`u1&tc_ogx>ETWY5
zn-=D{A?Jd%-Qz{%@i?5CX}LvzgTYbR`y)Y3b4Mq^K+b!Dwmm6}>pk8TI$$64vN&1$
zm6NKMt=NQ78v7Q-4Xd(b2IKG)vz#-LAwiCM9qI&bA@)e#sY7v*-`-Bxipo;g&&r<q
zg1H-`!Avr5LrBA?rYp|zo+0-Z%8F5l$dhc)gz(x!%y+x~L4jqFWFb(YG9xCyJD#^z
z?f!7(SM~1cy2eu*EKHIMbMpXkLisISpxc2{PBhIgchVV1csn*bj?Cl>>YSBZsv5G+
z$Jr1m_xM@HF?xUDdH>4{)Nx4>a0%p=(1Y^%5`xQOyiH;t=^jzic>U@SuYEWp6@l_|
zIVG*5*qTtw1axmHrWa39M~8#9YXVMvsp6Bbo2MJmd|#8349SbJ8LPe4R8SaqGe$A|
zWt83HW{uAN)5Y0jD?+P<ac6O|atI#%sN9-!QSBXj)+E!l-U{UmR?y$c-9nH(5cx|4
z^vqrk!`_+Wa&#hJK(ik4*><;InvZK=D^K3ao@Lj#rp&Haqltz%ROZ4KU!u$%$=p0&
z$>IYW8MM0So#&%HdJOAOHx|qN1Hu3*Z}CznF@LQu)r;a%+87Cz@P;A#lA;KnWyO39
z2g*gK>^3gu9-FSf!}%k3&gf{!{W5v^Jm00GzBt8ir;I+s6dI2AW`bkHyEJYGGxKi!
zkHIM4U+I6`DP~Mf>7G_2t_OL5WG}ZW{vdgujf_$~4oBDb*V)*)r%a!x97GXP-h9`C
zmH@?P?MqXpzb!IMRF2jp&AENbbV~>PLpBIavo=RQJ^&SvNB7FmBll72_Ja>kbIol~
z9GyoirkbK2W&5XS4T+u`2Wbl-$4fsWj1lkoEU0y%L%Yn1*L8TGjEV?ct$g6R*WsNj
zO=t7M1Ub=&=s%iSxfz<qzAin-I2Tb$y}Z3Du*c;WfVF9wr48W^4Op?Dc7i@<R1D1a
zxoKSPpBVoo>m!BfZmE(UL0*UR`_xhxIYJWh=tGK)Ut1MvR=m{*t=1j4vT_*@Z%jRi
zykjV7ngQzXB`>ip7^kMXB>DA=z9^rAI^yLykmA;|Z-MEjET&yH2UWO+ifqclz$#w4
zrV^oYgE%H=FJ-q|%Y6m&7sov#U)!|FeIHHMs~^QW1!#xfJHzSrn)U8<l7ux6$FaPv
zcP?3Lj1kttewvsTAJ01Pc%p@|)=ta&zk1qPfXDA6by~~#VR)n|CQL7-6l{Zry1ncD
zQj7TOxXBg0M<jbA(|v<n%YRKqr~{R=S$SgNS~-|Wf+wV0#p59!pRTh+pi?ma?uwE@
zf?TUQ-Zo>+<<tnDr>PVa55C}H(wzIC^*q|Vp7dQkx;rgzV@B35N1)ih?pe>&z_?GJ
zmpjt-UJTex(z)%v^II`%wzL9u4Wj6&0a<?C&NN87N%ykkRKVAYt|E@9@a>-9*e&Ra
zU-+m%2|TO7S^QAYi#EhQ2*h8cH}VFHII(qu4on_R<w$Ybck84R-7)H%Lq1L<ri%=9
z<VimR;7&jpW%ojxmtC={1-U9IiG5~UNg_N!IAKr=_;$QynD9cKTeG<0d?ocvyyZPb
zCtffZ9!MOVzNcFMT{ZfG+~Cg;EvJJBf6~WqA$>6wMocPDe}7#xHh|z6F>NBi$kVcz
zdU2jL^U{Le2&#oaj?sbS2wR!y;UK<Vo)IlO_h#z|CC7Kz0Ym<m8o?dI?S3Px?p%X7
z5cDD}LN{jkSS)DpzD#OUDnt?+A#ZQ~%~pX1ou%f6y$Z-$BQJQ%&EK#qfzmSAfyVMe
zSSO&xuNK9V8hyR=z~SunJ;)iPw~!LTr{cjuN#<T@)D){Q#RuFyJ<m?Uurq0ZxkXKd
zM7qeO6%O&`cW>RaD==q~hM5lnj{o}NZZaStaY3mKsI4*4;Kv$UJaO*MlcxwS26$&B
zFqf6t_~cs}A7^5wzWzxL9-0!*BM=Hcaa>u}L@3BSbnQPz7v0qI*<WOL)@hp2L}x^M
z2{UZ+_V?DF4Z1J26?@er-=#BvQ|he#W@8!O?iLN~ZMc%!b_iKDMHn<c!&HQJFATGQ
zo>d@=>J4$9ReX_JEg8@gOQHEWYrwB0xj94W&`JMoW0w$_1ysqe&CT$@wL4Vckf3B+
zP4^=kR&0ZCli-dLyF1XG$UadO>9raUVS31ZVJgk7SmwculJYd7I_B)cPhh)Q(Ld><
zo{BqBaC_2YiFB#YU!ctv`Gp=Ao#>KzR~h8~R&!YNcWMSyUVQKUSlRDuc8%uhCmkPZ
z5J;YWADv6;q%4V+&hF5Vi65%cFR|01POo~8`W+}$k`an(IbXi~ik`VhB6raOd-3!y
zMPN{?2+KrTdZ>n&U$8mmVb*dBrj|ZDx-DioGm0TQC&e43mEp=X;7x!Wc{xv6vo!_%
zVe!UJ%$-WS%V|2yrc*O``s<14G(XBiAICc&l4slyM?~OQwWgQ-Pc&X|(3^Yxi2tIK
zD<E4dd8#`4iubrd@?v=gf_?-+jKZe6ODm^qQ5<y;{cjS3J>duLk=x_-a|KHO-^6dP
z@x^C&kd#t<k>Zu`TV3~cU&3p%s?YDB{U+|QQ?0~DQ$9zx`<XU7y+4<0(d?Ad#;~^|
zoh+ivN`%eMf5Y=1*ei888<@2^hxr%&5=m1uoXf0XSkr8eR5sP!aZJX-E-aKUS@u<5
zr{uhTrw5RJTDB^$&DSkp2IOoVkdG#{SFEgv)I4dbg`^7yboO9yg6A0Rp%!ExG|c$y
zC-~2{VFrdA^iTw?!Azj58Cz$H^MM0kBJ#)*2juvx7yd1mbiN!U6!W2J+vJ<|p*jSL
zI~|cE2dX7IeS2mo$W2YuGe&W)YrPE9^{V2}*55KjasN0q2=;ASx3kH^me8I|^A<ef
z!x@{^U`>mq`Zwdi(jh|4#{EaCKOrETHoog;QW%-w7DwT2#R0PZxd#CoK6PFF!-baF
z@ZwA><lhLGxK(aw9Oy;wt@y5R(H$x85xRLR26T2izyWdv%`2W`2g_Zn8uV5X0TSHl
zC}%GDa)9N;vyezn0?$wyh8hz^D945LS{p8AeTSJZElzE1PV1YimAQQaL0nDfo@M!#
z$^*?JO1#)CFN|4UDc9a$h6?u#t0zik>|OolDA^&vk;dV;68hp1gf@2K!kdd4^Mta*
z_Lw$yVXH7d%(m0vS3BV7X2$6hDWnarR&%g;Cu))X>V${;V88U|zK?0`3Ht%lG1Z-H
z<yk#Qy<Tf7I-cIvXzRYI*@WNOx%zJ4mF?Cens>Fp_ZPD<rDrITW(;H;PWQp0$3<JY
zBBea*;r(0V$Aqcn)KNiVKHVfrp=s7Ghwj)SlQ(4cq{5azRk+X_h4m11+QOKtiMokh
z+uJi}v${t*Q)}eok#hd5Dl=pV&lWe9rHHw<&yl4(qk@arAN>5kjOCvhJg1fwuZtU7
z?{}5&Gmy0oS2o59Xb;QNcP&1XbT(c2wY#DV>IAYcdVP_d<tA@h&c}sqz_N)uyiVP%
zlw*th@%Y>GY&xF66P`4LNt)U^dM_N^vWJoDzrqT&U1^8~^BN2%DqzS&3L&eGWOzwJ
zgwoR+pRkXzw8D*a6dpCqVMvFFuX24k;~I1GH@}K_{#21}-hG`{B%@abnmHV5bgtz2
zM!Nn>NUUBqLMLe_99NQ!ZfG^vuz(pok&d_-74AtquWrgSrh|Kbnak-1{`Z_}3~mIG
z*>;a}1PBF!U!r?m>vv|C>8TtDDS7lBQ;uF{uHMR)77yu!Xf^f*0#sBnvX}SI-|9bd
z7WwAhxQIk3<_+cm4SE%<pp0sxf_G38N@9{c>|qY2`)iiYrJpR~uHyszV!j@Kwe0D>
zCECC1r`7(#h;nLB)oMb;QZPf(tR6uQsMwHd$I>+yg=2tMTTD`(G-jfU00-H!6rR7{
z%}iOb<%E)s%V~^gZNJ6;p1)%vTO&~?DBPpgMd|OwKgf$%eV$9HLRp-xx>8W{+cWPt
zqnGIBX?OW~^5XLi6QKulWv>ECnxYNvm*2~AxA+)8=i2_ulkPP@wt6)_TCYw2dYvs{
zp%JTgI<|j|u^jpwvCXA_O)P<pn_x1~ED$UsxTWOp+9A5QFCZGTe?B4cz8y#M^zz`7
zh9Eg=;#?B|h<+_D+V|Y=8+Gqb+m0uMsu&b}Y>t2M@TaOcIH|ZuO|4R7d^S0ot2%8w
z2JOit2>nb5mwq%<A*EyG&03j){G=e=u^SO98FheI8ew-CMdX+EuKiojNS~eBBcv)X
zRCfDpAG{h2tc2at1477+O)rdpZMqq?{)DsNQ{VPJX2%y#IT5YJ@$y&edEeRak}f11
zHmP4bbu%}t)=iN8lS{bL{A65r;@3_AhnJHsKLrNUBsDieWUUg9Cu+T{BmE<MrcIL4
zZy)y`RRy_svR~1PRx#!((0U;dUL*aic{qBuML2Kych4X7r5eJr4lM3_P_B2eeCAmD
z1dYYlnefs&REYkB&o;m!3q;`w6gELq<;K+F`XJ{{9@?6QN~-k`iA=cyW(Fo4;8=vN
zNJ`F)bl>vk6vc=K7WdQ5ZFOBJu0stW<X6MMmQddCzqDty8y7Gf!KD^FD9R?0Jz})7
zm=yh49@&@|6*hi{j547~l9S}oskoZe{SnWCicWsHZRLz{wwy)_H|k=v6HZ^{!w^%0
zHGVMgvI@reW0vgV;yj6SEbG{jJ)Tp(12K`jATPX)d+P|SFjj@9=4jOGAgLNY9*uXY
z3xqe6JyE`fJ&cn=9_f31qSj8NLvRvqHF{{JGmlTW`<`xVDh}D%;49@o9r$=0;cb=|
z2b&!)u6r*A@KCqQIrysV&(AL37_qcU@|?kOV`C(R58Yn%wQ9Xp-nb6i&hEmL!9naR
z-5RWWQeM)CJzWxEyg=1T#-T#9KPR^crL#@cm&88ph8!d|xnXXO;HZ*&!N1J3oPz@;
z^@)LMv+zZ2j8m|ahM}<qmzULgV$F^)B+jJrZ9!{O7EXEsw@YI4;^GxgBONEeZ79iH
ziL{>?jczO`sXWaTlMruTsMaFgJ%J`(knD&hQ9ouzw~MIx^Ld3~dAz+mSFEucmPL-a
z?Ml}@?;+=Ka6*F1-hV7%ib53WxpaTzX2`|vo?$r~dOXRt<BsDsRSiKxWmL#X4@;^l
zfV02T?Se9Y(TC}>ZG3+gjs(9Mo2Z+OuQd*IiMtHpcD%)TR2zav<T$WilW_*XT8`h2
zdrrFy(T#PvE)NZt+D>Hp(o~&jBE?c(bn-1b2zOgO>6k}W>A_)hpM2js<K8T)*fHOi
zFo5Zn3weD;&?c>lWzk4!glZedcL=gABd3lM1!EasKU8{3^zc6IXlqtCwCF}iCvAGi
zg&vIFpOa=cbZ~D_l)${faSXeENRNLG!X{nt+<9y+^wx(y7O!zTA*_3!U>GpLS2%q1
z7hCvr(_VN8rZR5LpvxpeI;<2QwbCIcwZ$1lew>QOzs$}H?W`}r)@@z4qD&g@uBbt%
z!7}lbS*~LV*M1}>z5jffZ!&)0gtXh|tscB?ys}(b_vty5V9t7Dn6DsR#R_*StAEJh
zwf!L>{Q+|a(S-@V87zYfwhd>^?2*j<@NLPc_96KONam|a*&rU|y11>X4gaaY?Vq_u
z*>w@mQ^tRPaipq8Yy$EvUvxA)<&&MIxFL^;%5%h@4crIfatS8AzwHYlhwa9jwKB~q
z-8#w~M}4qmi?Xd|%>s~#nrp2r3hY06Sf;+ICModWm$ybN3Lj{UA|w)AyRYLqqJ>$7
z4l?t%uXMm#@1}oVID*xRx)f~lPG~(#WF|u<d+2S^=SGy85PF0bGLoI<ezd6zy}Y*w
zZ$(4Ur(o!)+HIj)KK&k-4<(h*f_$P~Dv-Lxdt^XbTxm}68GoxgM>Kv4IDe{J0sE7E
zyWk5hu4nW>@?bEOuLZB;IYug7c<H+9*Fs3<_2>Y|((w)zx5G1`V-q2*o%Ji&FGPc$
z_lM2!rAWv&VA4d)v{JxCj?VDRp87LyZ8ZPFGIckaIzGhx72>nI8AL=&P8XLXNqsY!
z?E3h4$a3$={m`knM5)Toj59;0LvRC0B}Ew8tj1!XDB^%hzdK}9ld$eLKL7{*l}RtX
zc>$cr1jR3wiFz!vUXsRVXVqQ}v{sQ-!~0yu@e<I(e%JqGea8h;L3?Vl__CJ#Y~>yw
z^?_eMS7ovWV@0n{QE-><(4Rc}H>2&*PDhRE)Z%rcQ*l{4i`cu{y8#Pf;Dh^j$7fEG
zG)#B)Kbhn5$BB-y7fClmKAB$Et26hxc08c-HnBSvU-=gunrHihQ(O8(mkm29+_(gP
zB)Z)7A%mH?7>k+F1Y#PUAa2C<sGi5Vt!b_@iF$)W;>*+(Y6jWa9-4*Kkz}5T(WBEE
zQf6j~!yN0rEAYMQP!cgN6|?h{VTjgL%*0>Tbuu5+%E>|_Pty=RHg{5|VE9jL`w{9E
z5Gp*GV@!bSUlMLH2VG7c;!?sy*XUZg6`N2hC)CR|*VrLWlY_q<Zrwd(mZfFn?IMwZ
zxWtW2&rwt!RB%?Sb8%4OY3qjqGFCk5o?SN2XghY3Bd3{!{>Qk3j``)(bT&FL>7oNL
zLZ!%EoZ;T}4f@Gy!|w8d7I>?yaC5L;GhY{R84B5FA~^L%skO>~(CG6aOj#|}E%p%M
zSUxPLPcienYJ^4aK^?>;lL;|v-y^9=rd_a@#t_PGPTha6wpB7g8nnxuesl$X-%G`h
z)uI}5MkGyu0p8~W8u0&0bA9D5!Bk~h)<|#tZVXhC9HcdH3pnVa!q?3<6`MRTN#Mwe
z_-oQ-^=OGRtlL$tOMf$;y+=Uqwi}YnZ{rq&$(Z(_Aaxx*Jmu8!&(5|7>veQm1S;+C
zMeFq^e7I2F7HCN5gMhP>RMC6hapc(T59lAj{QqE0$i(#D|5pCPi;SHA2^5(KnK+r*
z{sT7tSG>r~&hdY}>;B(((ODUaN7?&TPEl}0GNEIIM&HCL-kWxFO(=rVF<O5rD%~=j
zJf2Ol;rC}swqnC=P!$SMa0L2zjW*;h9`9`L&*o=O?&ted_AckHW6!DA?7Oyv0=1?)
zFYY=X3t1wedl85rXx+e<NK2nU&I36iY`otuM98%$`GdG5mb?JWFaa_w144OFh7x2T
z7+5@k3SjXfa77tVXH)?qL<-C^y?wF4J$+bYHbO)o#o+e?0b*cWU>M*V!W@AEc_YOf
zLiae}q6d2Jnb?QeS%RVkJu-n0TNf;#laY7{VJ<&VUJg{aF;uQ(zt<ojbMq=#J#nCW
zqrM4*Sk8ee;QIGS#W&`O1X!dxLPEe#!H+A0Rp}q)fKCFi10a56_#%)va>z9G4Nx*d
zs3L<fyC`lYB1aC&aPZUTAi{`{0s{d7KaiZGQh|~Kw0Z_Kq$fYrd(;9DRLF4(AP*KG
z&^Zdi6rfTd(0amP$_pev5Jhjurs)<a79hFdxU?xmEW)@tVSZf5#X%PYbyhz?(m@}B
zHPF%q;zdGDnq&cgASSdrCKhlWNMSN;P+)bYaPZbYpkw$9$YLWO?|yb){Cto?g!4d<
zut8}&H-tP8*ui1>gI#u1(8+aw0|=r827XNVK`J9g<?V&zTGCU?p|fDb^jiKHf+PQ+
zg9+s$o@dq{$t?ZcqYz??KfT4iV}2rqr(hDOOPnBsOwoe*SnvB1GV*~D1K3lb04R^t
zim^eV!*ObA@_)XVP|QYx1Q1O{{OWT2AVMHl+s#`|W75P4{p)fvtAQTG!442|v&n%k
zWenK9<vJ4gYDF4=?|p<}pJRyykZ)!u^nbBmsy4N_Ffcf!d_MMA7etAxGsTqSN{XQM
z$(LhtL3l;)B|_VwL&f4jfHSF$qR1-~#uIUX<-Pqm-Mg3POlyS<SB5|x=etWwd+C*-
z@*|G+dwn<lcrB>f<Mq2bLq!z$s&ROX`tlBmEJ6nHSAu*KWJ+H5^7-Kh=wdIgl0$%)
z9xo&M@kq+R$&2Lyv62=${=^J@D3sqRTKRzJb$@f((lJ7U2dKxyn;Ko!OX-ADO2~)n
ziE<C#DeK0SBqR_*KigEFSAIanTg0xM-J!3f8d&1R#EfpA%&3M$NiuJ(lNN062yg!Z
zCVPr7nYL!Xyy(#4lN@HEF@Xj*DWKLMgUd5-&zYq8pj3F324rqldW}hWkDty&t(wYm
zujAQS=v87fV{%x3Dp@8y)&EjNnDQ*39ZF+A{^T(n5qMzJiInD-RzV}?iE`HJ`gvfu
z|8viRh}5dv%ujXrr*fsh7wO>%CMIsun(^%w>GS1$28(%VAyP-lOASJZ`=qxXKPCKQ
z|2X*ND7DG@{9E=>hD*Mf^^Ni1s?#TUF>8P3-K_bW<0d%m+*?#AH9!Y7`IC1Jfnj$W
z8|tYOkB%z!!cquRx#X~Rj6;-oA<9{tR`!|FwW#KWV0&3fsG8r~Z%k(0Le<bfo@q>`
zea0bsD@7s1l`<6dboFq%8`^Znd?8%7HW{N_?bQ3$_>nNA*xo)*lqRIBaDf7V-S}DJ
zx&7gw6yUNW+TnHCGez5y#2@7*JzrM5W4c>ju;@f&%zIn>8d>a}bL&2b=DmTa*9mBx
z7c_Aw*JGQ_5xuIYjoz>ht)6C|COv)GvEz!OsdSyqM7mPngJoAi5sV4iYHU{9(3_X6
zJnr8W)y<tdajoU+;e|iAcsgP3j9Uy`Zad@beaUK;zRduSycY_t?@J6{n5Ra;vGvJl
z&6U7w63)1$!CG;yZrspYGHa}jZ;_KLiMK%49Gk>9MzeT$8TUi_L1?+(M>2q@bj#OT
zS##w@J1n(--cfo=4;D3>4ONIPZe>emdlsPp#@K((q9_&|iG5eEVUZ;^jsJ;TTS=QI
zwx$d1S9w`tsyS|Rcg@e5jCgikO-!+o96p&*lkP!Yyv8_CIVmH;syGt(p3c?MrYn_t
z+R%Y!Doo;G6=-!L21L&~N%St4BAkWj8aob#;?(}0`*vu%mrmm)vwG&@X{oF2CIVUc
zJ+c9rI`FpW&&Ew_ea%2M8JD;d-x$c>!PHsBuQOHT!;piBR~W4pYf6}tdy3PWpRk#D
zeAlewsE9?*%P(}mIEzKcZETFFz@g@GWjch;xm&-b%q$zK2)81t#UjV%+mqB%o-p-9
zBE!7RThowpWDKQ}_zK(dwTd^Lyd0g8!Wec2)O&E3hf^I%yi5&+t%M)8)GDg)gsj15
z)|Ydbm-bfwI#k=A`m=yC-R9iuM!9$1Fy*$}H;7x!g3+LY1CV{5bkMo(xC$!6T7JR9
zg6Sf{c=hF<6{Jh_XhqD`(>r?mfM%z9yJ&s<96WUQm~O=rf5D5KyliSbkC~-6j~*!b
zIfmbueM#GngPfvD%-ww@rTMd9c)nkH^r-b3`dQn$G;5-6-8~v>R_XdokFH{(-laF{
zQrfsmmutQ4P%!ST80Ipuhc9JAR=V1quVR0Hc6v~5&~o-G>zw8OcW$1FX{l>y=5TT0
z0U4VC6d-bbE5Fq?JNnRB)=PKG$l;Rh)94eYGAOGLw$S`>b)6oh6uY$Rv`z6#R^$0$
zlPX=B%0+}Hvpw6whL)SrlMg$0O5sW<ZRL_`MEs<K8Uwi-+!>ckrSd*DqRB?yMKvNO
zg>yK@=tkj9<DYhmVV#*Mx5d}6JWOH5?c}o_H09|*crXw~0<uZn-Fxm6U{hgu+m)zJ
zcf<}+m80IS(?PBX5M?-&I+M6LYntdDvbx;d2UVC+5M{l^-?rVZW%qr+i?cV6*Wtlm
zAU?2VXca0@-uS9-ZRlAjcfTxx@1HSAZ3#1l{;HX-n$5g1c+)|kRN{8qu%%C7+2naC
zwYB1af`Ej%ivzp+ij5h`*WMztMkm6X56#`g(b=x3jw(<5cRjHpdN@i^Q5DZg2)X&y
zsSG`sHu>OMkalTJz5BI|897gxjm5NOe6rt;p;uWvw|Olbt2}o|d}3R;O*WldNFNjB
zRG6EN=xsaB1PNG)bXQ(cf5Jyr-0VFiI}d7fD40e*g~gPJvv^jW8s!4xpq6?v?oC~w
zRE&L}QB|3o-=N&e^3v(To$#LeKUPWcTy&74m30>JXfR+bx3tdGu{V`&)R>>|rHj={
z1Y$c(em86~Rd^}M7Z-pcC^BnS_T=f{v41+8-6^NeK@E?rUKCsEuMd2r{vFxfJuZn{
z;W)}mRGVywQH>ULjMj}~?cYOgEyCF9Mn%`LF2Mw(J@rFcpK*wHDi8t(Bf-Dq`Z_x<
zh-@k&*(XZ1M96iH1Q%L_u7aWS=39;8uMaEEv!LG!N@dSlr1e4@;i38>O||TxDBE_>
zia30Xhzs@Fvfa|b4A{|~&36@euiCrR^ZIs|mVNod_(ELH<hQQDq_#q)6sk$mdCgCu
z_@Jgx+x<3`R4{hmB932B5<JVPay^CtyQS9Frej4zqV<w)dBq(cPnwpqfAB2RRHnW%
zp%Zyvk~6qn>A%Ko_m*eV?YV^<0ed!XmUrCsSe+XI```ljNw_<mcJ4<|3oRJz65Psf
zqq%O`R^X30XMZyWH6N2&2aaqcF&`j|hsSlKTyaJU9(KuLrx{<ZneoYcW?I5nTnx@1
zR=_E_@5o(9!sM3^-8x*(=gP15KWv)?vTGctgf`A({6ax8y3DvWoJ0%ET|Ync$`ax7
zTI2=`bcp`Qz^f^C5LdWxa-w%iub)_bD9VD5&@rRG=Ty^Kp;$*$eI~p&n07eZnCl)?
ztU>|ZGDSPB3Iz15-%zexo$!)VG1I}_sAgS=**zILShQ%?bvc~EbB1PbbjR}#<!WY4
z*)Y#zonH<I@O|8fi(K_c?9!dG0DI3qx;(~M>4MNp)-09fifrbZR+<&^m4vOcU$FC&
zUQOZ00P=}KEI;3yVsd;G2W>9&w_rdSqzb5&6O%bmE>VoR99><cVY2>IwlIe9iMv8T
zN1k_g1STLBB2>s%SW91)kVKrQWw`8n4!X9C)Z<6$(Xg*ni44nad!|A|Yi-vz_@%Tt
zii(SF9Et@u2v5+B6eIhWc0kEu1e4pg@OtP^v*}f(*BCMEyzI>_TV)g9jL;3QTUWcM
zUvjJI(DX`TZev{!aE@-bdGtoqSkW=zblUAZ7_Dp#K>SDu&5SL~h?aY6NG0q$bS4{d
z_V(t(#gjC%VC|f$_+E_FS7^y6ds7b~6*OI2-j*V4@`rk$gK#26dV1P@U%Sp%ymN-E
z-hMN{$wbh%bZx%W>z7mWW6{?&h<86si_w}oMKAx8Ht=$a?_4s}I;p9O303VH4c=+E
zv8h{3e5#(Q_x1CLp4p*{`g1*Y7t0b&jK7cGS!j+rW-*}*ducySC7_=I3x{mA>(m$U
zQ4{r`a2cYSZZ|}_%eoc~@`rMirdN(Olkf1fn7*-ie4Ba6bjrmj9HD8sWy=V{XRg0=
zh(*yJMzL+nE&wdM%W>1t!$&fqyFn0QAZS-<4V@6aoadTt#BVJy^tKf~^v5F{12=Ot
zC0K@O!x?kO*VQRG7+O_w6Y7pXz8`UMVq)8)#`+BmW?4K>ZGW90%aKR0XScKETPo+`
z3e0YnVkqdyX~uiaQ*+=ZeDm<{+|`9m4IMqw6XY6X^Qb45Ak;kkz4*5|{<Vsbykq{g
z1g?_P)%@nU+`hiYs|&y6GG0#4{A)a+xKph;C%V_strSq@b}v$iQB%-UVrQ3ZuDnUk
z*RiQ?+eue3C~ZoaJSRy>|FDP>%EQi8We+d$rNbe2D9zbbqh)wt^2q-X@{|2EnxU5V
zJTja}4K0f-b6MZKs;KQeT&?oqTf8(&r7?Hb|9vm*%SPn{)}hUhp||g-w{33Rz(c5>
zY-dB8<!Jhl2kljT?jAML$*+EYvIc`g&4cH4U80%VL1S-vb+Ri~PMs=ljcG91q{jx#
z-L3hEW>jt~7ukFynX5bfSx?-G@B9@2csc0UzKFl9NTvaSVWh<|mArKqUeN@=Oll~f
z{!iN6|8C^?uXtu-{4W+djO;8-?Eh2s%*esS_J8sl{@>MeRhywxv33(F?q=C<w54$C
zP6JJ}$Dx^U>&=$w2yOe}7HhS)<<FfSZ_lk8UBGiJ^GGAcNrvTg_bcXS913;C98CHe
z7Vz-3)t{#+SZN+0R%193Axq1D0SerIJ@mOPQTlY5xj{JF!${^3jr2{aEg(T2fLQ#<
zfE4l={epk>O-)Zn3&11RH@dnuF)%{_mwg+^FD@(qQjc>H{o+4;J-$u2{-&*iP<;Ee
z0aagLS^Kr*^&<YPhEoX;0vW-BrD>>x0uw_=WGFr$15rV_v-*L}p`V$V!O1(ZHMB5*
zk7;0nT4(z$0V#2d`91s60FAh`kfHxHoebS!Nd2>AgtTjbz}Ebd0qwv7z5cPE2fp%;
zVMCXd_3Z^lmjz-L6Vja9#qzrXLT?#anIZc@e6wV4{!ESQF5KRQ%<S#y)r)YEB_uZ#
zgYiV%vsDpMxc}LMVqymaC~b}()Z*xwey}eL&8)rWrhGpGg5NKHVQH*!0SgGQ&+#`$
z`2nH8kb7DRz4kW$1gvvwd`S(ij}NR|^<vHe<NBL~#E`8BkeBu{_}2fV&-gw=z3Fza
zH?_99{IGA{?3VqE_k$dPHL)2B=$X80E@D2lwxkEsf*kT|icG95!S_u)5xAh3e*QeQ
z1L6JPAujWKhJ4UN388RnTDtl{&7t}$?7%;@{`R~0ku?taaV7rYk9+$G@B5KE0{oEQ
z_^zJ$at{6Y&OZ8vqPw=Vq_8&ue`kWd&2obi!n~a(yzd6US<_T)|H$qC@G6475#avF
zQ794)_68z0uzpg5qkt~@V$x;}(Qf&XMW7QJI{KC8KkrfkEY%uWfw?p^*Fb(M-fszk
zpQfaxf2K<<Z1k;+KWEq0e+GXwvA*9f{Xjp_+lv+xBw!oxoBh1vJnwQ0_f7;VZ(si?
zeiCGlt*3v@4)aqC47k_;$$6N#pabzzqde|{zNR9uQ+K}p<RA13O#L*cFoSVwv;jB$
zv;f%u1&jWS-u9|?{J@D$t*p%)<#rc<F#VgYv-`VrpP;qAvG7n=<%jp)H}iY;xfcfy
z(g_rsq>5$(>k<5KiE&Bbg`GnvnQ-%`XF5@6z_k>PN+k4`#g&KG;y_C~HQbIxAXWyM
zJKx3@hV1q3Yq2@Js7J55(n}E#{UcPDMENK596JRUhdBZW=Cfq{;w(!6Pwk0uYhDva
z+7f~bSyIxo<c=OBIk!6AeoampKwKO8t8NVcTEQzy3CEzGdp@I&;R0H2mI6687oQSB
zEHu>Tc}w8+`f%}NXdv+s`{3<&E;70Q798#^PBed>uGZS&<o+?#7Gyh_8GABX06ejg
z9HC<@E8vR=){*7ySGXlu%;de4w<gcW7};xE2?AnPSryL?8ejS^XyP?nlC#qnTp@)Z
z0@7Y~V3o(q=-H4NRKXJhx4NjW%VNLK$#T7}-x7J%EvyDS?QKaxlpE<qj3w%|;-kBZ
z8FpTl;@>`yj=Tn)4af(tALnVQnl^#xnJU18S5810Ui>P3nRW!vVx}BNK2xTgfl6Vz
z$F}QytcZekkJw;V#k&H$Sj|`V<9rR7xK5kd{jiHdb16HUTAeNx8i_CC($p0*|BOPp
z1mes03^|d`VY$hc<O=}}^=C=1GOgy2_Oz}bjj(MJ3KRR>g7^D}V6+ol1zK^#1U{(w
zS;PjxEa>C*&4FZjeaN)~o;er&12aL$&$7abn*!Ga#$H|LwgVJLZ|j`{p45ZEf|+I&
zs|-S8aI4hlhBovyuuPE!g^y{B!*`B`6|<5?^!I-Jc+Il7)M++Qk;ikCD1|Cs@`#+n
z4bolkhkK>eV+O&dZJ^Ax@NB5lvx`f50q>9jBvY8vkqA&OL7UcM$!<6i+t_3uB@Q}e
z3e&kiA6Y8~%PIy|H~>$ST&RjD6a_m_vd78D{zcWLf?Bh8q$N%T<Cd~ltNdNuzZ8rX
z$R${~OmN?w9xBY{!AHFG%Z3~EnGwP{eHE+0hFMFKMVg4QEo>EnM037QDt^?8w=ieV
z1`eSx&cMv-p+2L|8xa#8!Ty?F2B`z9y1|A23pYT>znDq7%Q2i(G(2Iu|Fc@ph(!Qj
z@fb?X?hZDev>I0Ad#dCqp;_Md0z=F&3<8@vW^(#2pYO_x-|0jL*2RDPQY0ucroni9
ze5mn}K!DvbMPBa9>*%0Iuo9JXZjFb+iY)nomvy?$LTi(Du9PG9PT6)6{Pkh-0ZSsG
z){+L5EqaNMkd#1JHX$OU;VZ6nlYBU$KbkIl(^|g_T7q_8uAT(E*PgkoI#+B~5r+(+
zcHsJiN<px|TYT~9+*L-e<Qh7vtM&!B)4O4?<)=Q6|Mx~Ak~vY5FS%Mw)gk+)L8}Re
z9Y;0!E}f2h{_0kX3T@dU>=d)setcju&V1d@p8NEfC<Pt~^y3oZXuh~a&p#^?X%*2d
z@Gp>dZNQZB7P~ah%ZX*mwejteT@g8CZ*a3JP-*v>Kh7(}1jZ`2B0)BC%auyI+9Vfd
zt;zl&Z;QjDZ#&N0`00zV>>WykSVIr_A44LX>Qb36uAvqh9kD#F-jQPZPbw8hqq4yJ
zZow{<_@=#lXwvDqd<d?WKMux%s3+y!B}TKJjgS~>F~ijy2}d4mczm--FUp;Oq42Ja
zHkn2D)JIh27b9*im%0f;te_RRkrFh`hOe}o4%2ej;J?X#Gz(Wpz31;j%zr*Yb$vo~
zjv{po(NLXc995lvzVeV*?(ZtyypgFn^xs&SCL9Y*CYkf<6%yq{qQpXZQJC+TvSK$e
z&5R(^b3ey~&Z|kSqxnR}M<>~mqpB0>aZ8WcR#U}|!ezI&n$!17_`Eu;VK!;c2mId3
zAAsQPqNDaEZ|Y`^IM53D)3rt}ImCpz%c*()IN~%(&jl8AWz9#zAoHu_WWHZ)$|bTA
za@`&i{70f-m<FL**x5edtq12zXCPV-E;2aVSJ#Id;l^Z?Y!7JhJ%z|e)<^XZmYYv8
z78WB0<Ev4lPvYr}f*PhAW3=&35`~~x@-KsqWlFMnVWtnM#^7gHG~@g}39Wwsm=oWp
zQmzD``FPF=fEB7yZWR!f3MoAmbP-YE^YS(MMcOUraj1u24LhwjQ3E0F-ZUBCH+)_~
z;Ai*Xr2t!gYxoaGysQP8o}zAmy6E?voc7999UFqh{_@vCAe({h{MqSAdp9%7Y%1*(
zyX{thvE<`TNJ3t)JqW8q{;Qj*3dG8QsnD1di4gazI@X{48*f8s5iO6(D{8GEx2YWK
z<-u6?)rl+a$Ajt0R|<Ds6nYB*VC0-Gvk1$$Gn|%KR7u933`P9pH&YIgWK40(q2Va+
zvBpO!u(LD!%w~u2#RUSeMk$KuZP#nNcTsHLVH!q=ul`r#+aY|K;N%ldeD)foI<K{P
z)DqE`G9bIp^f>*EVu@=7q*ZGg=Xj#R{?iSYCJ=AAc7-OdWs6@8UY*oW3WN9WT<y*V
z3>|t)ls7feR^8c!>QP&3&VF4u#GJE-Dcc;=Lr8n&fweGi*EyV)Rs2PSepf%U@u#-8
zi31?XG7<T`73m#vY&@-qlEz^oQ=BO`x<>lgp>;;WV;xecUvS8aq9H7?J8cac&!}--
zW>8&;IAY;iYf)l{V$JtVV2;gdAxRTkhIpZi?<*_AI2&I=4V0(m7LFlLIRZYk>Nx$_
zX2zN(491BfwB@CpP!R*TWsz>ZJE%<#Kf4oTqE>MPoYay49Zd`cD*#i}1y*Li)s~bZ
zxdr0+t4^qeCa}FVVSIvik~F#&`u%DFPiidL!MX!4h?IFI(<04%7Md1J`tI;&UcPED
z%@oxz!~oL0z1#_AZ<p7ze3-@FlBE{VPPqR&t$23bM;>DV5x8qEtgC~5+!aHMe%FQ)
zi3Tl~QTW-?L<=)sa#VijE#}<QH%uhvi<nEQ#p*0I7vlx~{a<b_^gjv=Xdras^H{|@
zyz@2e)D(AJ)49;8Dr)eO`YD9L`n?8oQ-1@u`vzq6tm+lz+RB-cDqWm8k<Z##6I0iB
zrgLfBRe2ro*^*?w5tEP<qohP#^t26se37#a;2Vg}{V@02K**>{u4^mkrCN|pU0}5#
z8+|h)K>9g*L~b%3=dRw^R7U}I?@Dk3T9e5>*(~G^&r&YBZxPMYwllpL7zl!|j9C3O
zZA7HZB$e-Zj8m%|rJpJO$J#)$aT36Rb;lEa&)J70Kk8C)ZRWfbCqUW+Gu+23`bBgn
z9#sG9j<Dd9sF#LPQ8<qZO6X=a(JdTyIMXnP+ssg(^sTr99eps1>racVwRNB)Sgp<5
zqVYTG2j+Uv^j`B)2@!?4RW=jxcqP_B(4zofB!ZR_SyjRUGv=r(zb)mnkA+Ys{pU&y
zGydHgGNV7_FM_rQDA;z*`>w#_7DOXj-<wKh{f`2Di}YdEYXbdq-Q_sP#}fMphC>q*
zuCOZM{so~%9r<}1G<mIXDk_VVWB3H+WL`qn5ay_4CuPZPgOL1dj+Jq?Y<n%PgON{e
z&H%dlr0ZXKWk#IKDCD7^?Bq6yDRgJepuX#du<b-usSyALsTg6=AJ{$75k!VKtS4sd
znDYs68u1m~BhPJc^=tL-SADpK=hsx~)MaeX3c*-suX=zj)x>p^^(-gJBdIw%Den{)
zFja=ZZ>hnvg{omX^~)eZi*Y;RFj6Z5R|=?LyR?rv8CvMVUN+cIe83d=6vMYTB8&YU
z{0z2hrr}^6&8ZwWx>xuu<r~zwU<2w=NgwrcF!wR<{Alt!bHZs%0`VQWJ~U%+EkE}1
z9>T(B7Ib;DaAVR!lb*4AzU$2O?~RjG<vpN2RLbW;h4|FU0OiG^X|Wfw>W~YYS9qj{
z?;T<}vY6kS*{mxiP{uNcP|clS^1-2=-d%M#f;K+`EV7~u{M#RIwzj&9v}V(zEGTt5
zQ4+fv#y$lu#iUyon1Oj7YEyC1>IbT@uzT|YdfifvG#!w~@Ow>s`ylhvyms`lUOwce
z@`C4eCbyPxTsHWpu%xP2!o!}qTO~M5fj21?q1q;1cAoq!4}W|(6d#6L>=`UZ&h}4c
zQZfO_r^frsvpwGWpW>gcn@V3)cx&*TA#piqpLH`7E}#|3JxY2Z>j>INH1K2eyjQs~
z?RDjjf5d*!`%VZhL@&z{(kgc*T#sc(dQ?`+cJUA1Gsw#;jOjHf>vi7petWX*qHk|D
zZs<YRo2O5dVEg_;;&Q<U@ASE0q#*?!LDWYOIT)ULkh0B>4ToAATj!h>R+*)a;UAN;
zPl241h00{ypicl)@0+kB2s^i6_rXU0tsZi~%^b(8J>U(&>*9%isW^9RK4bKz=d|b)
za=usE?R1FOz4Sa;iK?ZlZKh)IHl45o(qrRj^}uzSHd|?W$6X-ZYJH?9A2l|O>8FRR
zOwgzLxjdyy%W$kVm$Se78RR{+CX6>iRa7{6>F<f05Ls>#QhI)8_Z22>cDk7zw5q~I
zg(vc9SbA{R%*5?JNf#+EPR<nX^lcnnbbBot7AR>GHr@NaHnpF#s>2S`4+XzCd{Atc
zc!O2{VTJ4>YyCjUt%JcsphA6n^#aDW-rb4<WyBASLrM%=r_0j6DMcx#5NMcPxYv(f
zmh)v(8;O&lYy;fHGntj-+&4${gu!_~AilC0*!OHAyRh;ASz&8%gTuc7&v{vZ_cM&7
zSWm!QTzd-W)3C*XPxI%8RKQCfunf*@UTsj1TZ#WhJ3^F$cxs#Pv(^aYg5&m0X0A>(
zh`%GW(gk$n)|-YuHZumMa_%H`X-ICCqVe|GnW_A!=s=_Khf9#1jrU}Mdf0MG`8Jpc
zK1}61Xb=tM!jTrC%v}$R`To2~H8CMw!`W1rN6wS_Ub(+<nxxs8ghW4Q()lQj!0HnX
zb#0Bw#>>rft3Rz<)nw=W7!Nhh$!MSQ$8fOnLH$n<?j_`X#~P!TQ2V+C$*Il}8^WmM
z>Q0}P4=M>nweBygS~VFMgQ&!$WEqv|GKFWEhr}!o03km!B+W>EhwFwUk)O{q`F#+&
zhLsKk!g~InYyw3CR<F<B+<$1tU4v>ZunV@x<Q6OeC&6KH!|4o4><AMq$gL^<#bt1K
z?Fm_9gN@p-Z#V9WRq?E45{pj^bT`3Jl;mG(91T#FDt~^VUKx(La^tz7+e;Ksi@af0
zS0kHJZ5e-`ST1I-EL4lxeLjA3aU_~0j}kp8^xShvT)UZ~D7HVqduZ%;#zr?3CPI>l
zpc`^WY`d_Wh9*LWUi!J>e+|GV;%7KO2fLP{1fSi3cAQN*VXI<Y7_d@3lWr9p=nWq7
zXTW^Gf+VGHD&iGT_LrUjW}J*&!bt3!4T?<0P8_&T#io}dii}5LOQ0#$74Jc{ayXh(
z{QSXD%hBYUrtH!AC|s-hKD*WGOA_DC(S?b81>lTDw4S<Elq^P`(d?~bzJXry+QKcc
zRqPPF29u@ryeyMa@SNB7d+uJd?}6I`uF$Yfwfc7~h<+Lwk35zN_+#z`6woh8tZI8*
zi^t=@fy&a3D`Jt8y^bp5Yc{m~JYb>yXoZNfMlrbrxv*l7W8Tu-p8|Fdek04X)GQ~E
zi$KW1+aCTXnbc_V7T{XJG5m^)nU(l^aVJh7^kXs=`S0717PCuE_mK9woD}_`pp%@c
zF(wJIngC9ye(~U!U=}l3TGfp^4%M<`A5uiSV1jz=n11yM$3Xhe)r?GMCEE&S6Y%AF
z|4UK}3Nf|O?9f}I$oer1LGW8)np;L5NCk+}W|3#Li4eNQR4;@^e*oP5s1h2+n;$z^
z{1r4_Je&+lnl=5kn`bow|JN#V3tK-TbrN$#rtb}B!>ln*GdeEqtj(JHI>*HRKmy1r
zf<GAUGXqLC76E|?=9OJfH%?!6rB&}L7nAxT1_B!H*5|x=uXWU;P#2R(X62iTifvkY
zX+&BPFno*z1tv^7JFv3MizNW`$~5cYQx;K%t%f&E6Zv%a4U#y2q_R)3V6&JPbyi|a
zExLYjSyGtsUYR@OyYwl(#5A7&>A>5Jfc*@UO*m$bT43SUpZm7Xky%dI`3+m8%q7WF
z(vKkViFQUgh&MpWFeM|O*%vD)T!Y{LIT^~HS+UxTFY2C`ko1D`*N9+4ojChEuQ}tD
zdsg3C@@Bl)kHRan3t=onRq_Pm%ZP5CqhP754bQJeAwJJGC;H5yId^J6Bq}LGG`SNF
zJjSdC;bfDnpZt`yXA5XSZ$N<>iW02Fl&i#zT$W!Qe6_DROZ3Yybm|id-oo+sQrkFr
zC_x((GwfFAHuqBOc4`Qr!vhwU%Y6BY>E~NKlmbylH~ZnCOJCMs?P|L#sOk!Q;qB5#
zg!=j0tBN1hwNtDf6$8sLQ2Pxm<H+aMDK-`14|3RWK4w(b#;Br)%~ZVJ9KcJMmWp8Y
z5QnalPA;_6+Ipn>FM;z`p0W=*VTC~&(Zar#zA0EpB2dUto4BkfRFJ5q_JjR57mTjK
zdf29=_;8-iEL?jbF15c;s?`JW*6o51Yt1h#;=YaYSmuc2F++ZN3P7)}_*$MU8zq!q
z3J${@3E!tn{S-8%Gey_ekq#j8Q-r}Q4MtQIG>stOXw-wMdwpd2!x&2P1qHgokTinB
ziH>J=JUSxK+WWJz8mQ)t(R{lFj1`_!djkKg5^hp#Q(PEq{;tmNP*an!W}0uaZh9q)
zT<n}3HioUJDfY-{Y&_`jww-y*WE~!m%Ys34)$8kntVHcxI<{FXf`cnMSnn0v-I>pR
zYEbIn{PWRZnaJ4HQ24V@qn|lYFUGTPw}|U0mW&wM*EQSjTv{HV68mht?<aF5oy+B`
z!N8Pe=h(#W_vrWmVMAa4NZ*72vK20)uUYbf+Dq&#RuIvrX?SQ~pSf6jD`c)3Nh``1
z%P(DjPB}QX(L#e(?tOCbgBrvS?M^B@6lZt+R4=v_q4GVH8?&;fGPrMgvm!KujKb`q
zi|s{qJhJxqT9R8r(|Nw9{ystQtw4N9*$%e6@+VO*qB698`_QI}k7{N{g+!jZ)(H1l
zRp-35A7)4YGsJo5_rMo@Sk3#~>Rv=XIj6w1!cx1=Fif$CwRw9vhL@Z)1uxh-pIu3{
zin5ku{EU(Q`D2XE4192IFWnu2@#;ExRgy;T6j|9QQ>4J6lI*LAXC@!9g1zu)1!T!o
z79YVp$q+OMA)H&%hoU2ab^iC&ufrJc>rC8$%w1){@VmfS`#xqW^kxiO_dS+HVFB2>
zgyr2B849h_2ZmlT){B#spkg=A7Bp0$kyvgjc)zQFdp-&|1#BhLc8=YWmr${Of44f0
zDu0*?o*Jl%N~{D)nnR#?33OeVQGm?V7l|pHLw^*gzDqP`wUV}a?RQS0s6qjbtrnaM
z5SicskyUj#e*Z+(_2^EgBAdb|p6r;LG{nOjo<QR*#LE3EWax5q+*EQpLJtELF<jhc
z(K#)q$vRK%NflMVCml;cVT?+4qS$?NtW}HvCldPF6SeJt=W>?LouJ}%`?Us3pYN;s
zntsU}?I-n+c`F>De>j$UbAQnf7@Fe@PE)lEm7d`Vg033+0Ok&_nY0rx=eQpK$BRL@
zoM{GichVZ0j)|fuGD|op>6$5J@&ir^n?Jjbxp0s?D6PJGh6+0gVC|G(I*adzNQ0R!
z-g7-xW!3|hl5V9B93P@yr&jGNwRsKxrxy9TKOFBsHNkw12YBDFt4OGCTlKsM1e_5_
z4G#BL1${0cd<L0q3)8Pg9K9KO`&wBvya%wW!U?#M(Cj#*oAIyBUqiVA>DF3j^&R3<
z=M;AL!;c%c#ZCsyP!cP37O8!QP7qww<QrHvUhXLE*ghghbC-6j<9|N;><Uedg6yDu
zU#js!|1^pz@D+OT7kI9}gfc=?i#Lj}Ues;wm1Vs#gm2%N&(BaRz98b99UZ#-I?08Z
zqi5nH4%S4?B_Zom?g6jksv$~YN~>LhKE!kFe?Iev20alHog=&lmMw=UsL?|!ZM@@)
z>R4?RaV;UJ>AQHk6LZtLLR~lyG)H+R7PBM8w|+&Ya)nx375_Fq%ee&1#Oe5WJKZ)6
z*1%g$buSX$-H6>#2~H3_sz<7Uv*&L<5gMqrSCI>BH|d~CfzxB#v~2D$+KCc*8Gx_<
z6WQb>N|;P1t2)ym9(yWZWD2yq#G=bgg61`iDSV@-hn-6?H<k7eQs$`bhqeEJj85v1
z^;BOlPpm-iQhuX`ee*_;n-@2At!K{dBAWjD-ew^5W|Ueo_^N>d-xe4OmS+8%d8hH$
zy!Ez8HPkT@^l@0ts$Tk>iaplZo!GfQJ*rAKX;6;XaX_cTD};h3K`Tq^jH{yIL7I4?
zRxe<@{n4O@MU8Pj0H38{@0-B%9T2SzM{ZH9eiGKG0^F9P{u?dEZa0qR&S2i1gkBgM
zY`S{8=MUQW5r;q*dG651#HSeu-(hgl9DL+yElK%z>E#-!MC(TPVZ|iuuiMRx=(BWs
zN8?GS1B8pd;`Vc~;7e)OvP;UBBF~Fh2752AoBF#C(->F<D?76HA6JBT-4|<lEpD(@
zV*8?S$$D7y1cpRH40%@I+*2i$6!gdMbe&U1F{8FcQ(_h}soV$5?5iA=q$vFHXzWk9
zK;#DKW_9I-L*zHQCkk`s!L3>8$O~`r63&$9+l)DJGqsEjA!a5t{@b(0Mste$`=mmq
zQ;1x=jZP0`)M=t>>4B?Uk*i&f7I6rVXUtZ4@${vI!>n^~CS=PlL*^*EGU8&$#1!KQ
z!_Cz+&20?M<)|?Jp+5XTbv`?5qF!7@R%`WqhJ>F*5HPi7TG{FP20gW};RR-qe?WBK
zWDPX~GTU6=f2x(Wk5CSwg_4|O2C?n5$fKm>X26PoSHk2)Z<*r`cJd_r3IzQE`yS6Q
zQ!gn&u$=jY2wZpCa$JivSyam9PJ!tgwX_kj_0*p!Gw-z{G%*=MZ>c%QawJA^4F6VV
zP-G4HPW%Z?-%*!58#zzB$pw}}rC*=;rv~y!MB8Bb;8*c2zwe}3)t3f+Io@W!{5(YZ
zE~R5lgliU%wx%4#*SLDlqen{Tk)6m;hT2**zLRDIW8*@-pbbQKoUG9wmBhqu`V?I=
zq<@L7ZKK$&vW8F9Kp1=C>4d@mxcoClgv-hMgm798t;LPmpNa6Dlj_Y9^;28lD;(JN
zXU^-lHJBs4r@$DpA9M<HfC7A3&FQL>9b&a94JZqL@k9MeVsoYf@{l;Y<g3Hvm!*?7
z@<n3GBZ|qE1!$k)-d|QNa5R-ttA$<a?iUjYDbV^|0?h|AsYFJgQqAxT{CcK9MOlP!
z*WL-bEYt>-iQL~3FO^zQXMCpjCg?@Ua;M%@<!QY?#rnIJX^^Btu36V-PW?X!i~S~=
z88^LpN`IWhmHKo9v+6n(&84DgH^o;;ZAYwZK{z^-s}`O0!8Ok+UJH78*38b{edf^%
z%37o-c=k=Za}s+FdlJEF$?(9AespTdD3iLBzH!nu)#+D;B74ve*laSr;9UIj{_b^c
zUhe28luK>AB7Y-WpVC;b*%(jnE{VA$h44+<%?FJFX@g`kI7W~kQPy0fdAV^ipT9AI
zVtZ&4g*4`8c~=C={4WX@t4Vj)D#6`2o4#I5TK&PAXXR}pQcHi*@?4ALT_a#4&d_w-
zpn>E88`mv8nBt*imXrpldYC7$3)jZw5>_Bs>uV2t^ABU_04c;@SN>%g#|g1Z6L_p*
zou2pFUV;SN(d`QU!o3Z&_LDCf946kbSqED|SQ(!ilkh$J@r%lD3{!CER5tdLjfn9P
z$Rn?I{4G=fQuso`KUgsx#@Fin)e9v|#HcM<t2iutP;t3-@ruDx4Go>SeELpbkK>}8
zLy%%bmb)Bit=?QL?q<Oec8?aUgJ5wJPWldgH)*I-$0rL~sSY1DZFCG;ePVzuwS8CP
z@l&Sooz!talp3W?Z9zGeZ1K(^H^VJ681;gQWvU@BldupS#GFJ1((D;W{aqnUo5Y*w
z!}gJFM_;NyCx|1<^yX`^0(`Zmq!mWc<7|_JU}x9Uq`m!(6*I24B|I$Kj&%83TRmIP
zEWYp0l+$9iuq%|$A*sag#c;dKTNo!;#cav_eET>a9%6XRD-|253J@M=ejR3vACPbu
zxUqN>mQ+jDY{kUiY7%wst0eqP5XgVw2hXVJK$9WP(%aas(>)svbw7(2olC>xE;+QP
zZcpSNFqk_vd8L)>X0hs=O)?$MGqfjc;Ew3uLiGAW?Aox92lwJ3(bv$WNE@d@Y{ebv
zC)sp|OYPyzFpTQ23+thsboB5eQf#;t>GCd7tFhgU!Lp174WzA^dryair0>G0y%0ll
zo$8*wR99IJGTC85=c3Td_I?FrMuff3t&0_OHM<iN-LN(P)`qw2c<z20YdqU_dMs9x
zg!cuGG&2V{ppDZDX9&i#(;T~`BtYooCI#zjC%O@EF9A=V29SJ}eU^J4%4CQ=r+?yJ
zm59R}ZG$aY3k$UC{XP=Fh}Fup9iQ>OX08oul>IDxt^fnzT79KhXinauvM@lg>=5@!
z@8mc+_jlb#MqH;<o{_TczUJyEFVus=SS`G-_*{O+tFar4qvTt#K9eP}5TlV@g(k-2
znYgzRKju`|5>+)ICnu<N+g?Q-um32luX7O#%N8RTKx>*ake)l-qWRiI8MU{~$}9g0
zJ<kyJvH0$^OB!EIh<qM=Bkw>CZxmEvNT-oYFSL%9wB<C?5QK>RTl6JKMvZe$^h7wK
zJPON~@^StK6ey{;{eGCbL#Fz*?Ta^;I-w7^Ns9ATVT+z$?!%;<8}?o)ru(NLsHAI^
zPX5p79xix3qJkZcJa&%pE7K32+n?(2O4|*nLM>Q>f?B`hDN@0S>u#*$FG1p9VB)(B
zSf7-kx{!EIR->BYa_(0?sDL9dXI9_LkPgi0bv%eh!sHM+Ip?V{5A?kLKl45AnB<{{
zady<r6;Ae4pXh>%zporRzieJeY1^5eYa*j?50D!affz}#2a->mYgLSp1!c15)3BoM
zQOK{O4JYGCjuKyGrzdC8>ehQ5u1NA;eez^Sf=Qfox#C7Og((=52I97(2pF3!y1pxO
zTUwuB%*OP&Jb$GjDKKS|*3P50BQi*lM;_pvrosN?v{y^Oh&)jPQal{o0(8|d;OK{P
zKn`ba=P~OAw2GUDYZh@4Tz6CzzTZDxm0v4Z5E&L1&R+eC%6_6o;+s8s9{i4V_fDOH
zRgY?CEYt;;kEiu(itFDVq41<zBPMwbBe^RR?#Y_g{eX5Hw;&I?D~H-x@~)kzxi8ys
zSjp5AF1qigV*kzx=L4I>;E)MwmX|8t{qm@-2XC2-e9s=!LOUyfoZ0WkyTAK-UP7+H
z?YAk6zZIjskC)%-BQM)^HLbor<M3qzZtYfRO2QzI`GaYRBzBP&jO!(+=DujOD{?bo
z7TcBPZegB!NYmtxGzk$j)3WhTx)RTCl8d((OjhFs*lzoM)DxIFAv<yx9gTlnGf<35
z2S8~DKf$0-lX4uyTcpf>G-b&+pV((1ea01}yylNA%!~xTecpuEd1*V|`Y{MG+Q6;G
z#g;60ou{d9LL`d?G&7sCaHF7<@u#R~ypk16rQo?C1%LY%3VYYuVbtfrPAY%^g&*)|
zs4or#bB`Urj$+-*flB)@XkO?uqP#!$vEoK}8YB9dkAGEGzTJv-(<c!mJ0iNp@d%FN
zRelKsRN-O=l!3mk+q?n3?OF_PW|nA%msVtqv?L-NfHvkB@4LKGk#qfih{a^qwMvWX
zAIhQfb;E-mpD0>;XO&A(FAFF5(Ti;e+B9xG_Cud1?U@2D>>o8pk<)j;kP%;1G@<C0
zezP&TUJ6}Js^uV{1q4*p*{Lj?R630{{~q;v45*&Bpy~6zesDUmLf|3h@Oxo(SWkle
z(J-tJSibr*m<35#4%;%wEy2jfc$Plei9t!$r&&QD9I&P*Viz8mv%LJHTGrKFN>@5;
zKA_(0+sD4?lg7hye8uiwrO|JO$ltO!>Wo`o_Lk862BI*Xc=O-3o6Ez&m30_`XxWI%
zXwx4f3Y7H3F?)ROus<dBl_TY7WbKw9vI<Z+MqIh`CK(%ZkRT2V1v#ynSRT8DV@6C6
z21OK{UKPTA8!K_-R~&~9c;4$rUOR7+jW`mZ!2gnmY{!bbw0-bOaH`jsuea-BclxMg
z*X=q~qwggtWwI6`wj1iSQ#x(wCo$r=Nz~BlcnCce;8=zuG*Jv3k4mQwUQ1u;qF#zI
z)=|?oaRO4hJO+Pk-lj}4yHb0P)z7ls8pd=Ml1|_|?NyQy$tRC2vj%Rt!LJWRLhuv<
z5!b#aZ7Pch8-+JYGi5+WUYW<(SFw3~p^9UEjwInqxvEHHn{0FNQq6@S{;pugFD!&{
zku%IO0&|t3Myo)ZtULn4I9F@ad?<sGc7*XDDxP@8>`G%d=)a#pFpa3GR$kYgB@Hu?
z{v%zRkiQMMogBX$KqWD#V1(YxzwU=520~EfVM6r~q^XQ;UWl`xRaXr|s2-D$BSd*1
zSq#Ct^6$QqU~FWK2&du}w`F#;o~Y6t`|PI1Rf@sD?S|K<iIO%z;@Cc67L+lJsxCd(
z_dFftUJ6KoRcE!^4zq6CFW9}h#)+5eJp5|kppC_5vX%MWrhs24BF2+mzPm}+<c~i%
z^ku*X?9hDvl$g_shS(4mb(}ATxP<oCZY4vQkCNZ0W>as5tcX9dYxiCIu3r5nbKk|}
zn%AZ)KWCKhc4n-aHyJ4CvL=^2jB-u-_3cxH4Rp(d5pMY5PDoM*@eQcnsO<MmyQSRT
z+&n|RcAh9#zaXQ@+|tXl{-j;LTni!fpdiP9bm8v{wg;0ff!@2#Mn7q|wG2MS$t^LA
zfL~Pviu9C9SrQr&>Iz3}dyzp{w?=VPcDY6&BP|1Yay;vgZH!%LGO=z=!A)d13PQ$%
z`V_i!;A?|+EcMP~G8)2=b8;G#A@XpO27gq>!4{M4-*vYp(=%V9Bd~!<;<Sd8rn<y=
zNND0h0#HXoFd`!{`Wqsk5f!_{CjvK?SVm4w>zOWif0_6Vr4Y)>D$EL<IeIe}IogX^
zL@)u)Q(Bv%)E1}Pepg$dhP#wFcA(LApm_*1;%C^-0U*-C+~bH@e*7BVb+XjqF4<+o
zSHc<0vi#M0Hq!5a9(wH^%n_bP+6tirCTp>8okfBeVJe<DDpa6h7h2u(b)^DI6&>}<
zhx5j~Swf62`(vy?@5$%qW9IeIP>RSrUW0Q^%7U95M>&o+8Arr>Y<CFFg0rK}JbLlN
zIPBn4)V__<1%tLB251_3BpQEf$CzuI^&V&(>m~wkbB99n!XnAyuEwDpB0@TTc<g{8
zdWsuU+oM<h;|d&qdsq6<*?2azSqBzzm>e}MUU`kWJX5Zd&~Hw;q+bBjo$G7M6%d|l
zX=Vi>;PUfr3WEsUfiAmY1Nv()jIC@$AWpC(QaV{U)OA91YJaI)(&`$qA#|1KH)jd8
zF(M608%bmS-)Fwr#EmSIQUN8~cA8e8@v9*k%9l#6W-{RAb`Hj%BsH;=a)qY*EC6t;
zwC0uEn-hNe&GdAgQ{US0Q;EMt=$^*STkT9o)&Cq?2`o$l;2(v6=Ml^iwpYPybcpGn
zu#=)(cvXPnu`uv!p}xJcQ1uVXkstGH{K8{G{F|m6nsOk6jS3&NeaxXh_CmTMce3o%
z7&3T45~W>8JC?|kN@-`LhruP&t(uggU8e0lhtTcnpAJ5}Zh$LCbq#&rcJFNr-;hjG
zB0tahXzsbiK%FS_9pe>ThD12K6ItVXCe&Q>%R&?e(WlL#bw%7&BjTSxk7>Dd9ff#c
z{S}V;saJVZxa0#d9T~~Oeqt(@l@ru@f=Z+upDJTu0D*eby^Os$W4d$69#+YNtlNW9
z{7X#0<Dky{NSgEsFp7}5652y>RUjc#`4*Oo)TDlh=9rm4gp=AGv9skr|B3BlOY>1e
z<!NoB;NbhStP4#)qNNB2W4`hzS#@B?+^z2V&>2v0c@oNP5ubA8PWj#D0Q+h@t*LbG
zd(V1%g&y~<z%-OKmtifF>{3ibmWJ(cAg-NhC&EZFFU+4z*_+}QkLwe`7dS34;)#Uw
z6=W5OZ>I_LV3cdOzd&GAnP8%IiXb2FnbTTi%PIMg&w$mw5PmQrweSe}SyT9YQSQ4x
zZK{0+8)DUmGL4~%zvLMwSW#4=5wg-xCiA@ng-xsSDNF<iQT>;#Cp6$ulKRIFH?3-*
z>fj{7)tIqML}Dm<WfJ<5GqO70GGYr%`!wp`o{aIcxyaJ|tcwUozd=lz>GsK4?wjgo
zu_q!_fQnpejcDt5a_310FtOh)lLzh%&rcTAmiac*_D;}P25$GIb60!mt=ZJirr-~Z
z{rPacG;h(&x_NrCwo0tb?rL5`7u1SXY8oYDy?3{VI^5%bwNKe*$(1|hTOjr_0aqn_
zQnK-kO*;j<NFZRDH4lE@XH8D|JLGz0DvJ=n<mo6eXCmf8v}uVn`UV$XtR4L*3LA=f
zrU%dK6aM;MQNMQk!w3udKE+$(lWH(Y99fGCt-9}N?lUGlmgzM)jH7D{y2XKr$|8#M
zW-BAY)Jso|E{%TXTBJ-B=oqvL(H`>_z|yTXTgtk%)D-Z`ALg#aED@#bEH2^N#TVHE
zGG}yPIri+OV)f%FhA_HPOAKD;vn`uSnB77do?&V-9j2D}m>)GyTJJ1Mb(sgoN^g?2
z-8q#PF>)4K@B3iIT=)7nV+K;h2q%64P3SWN$`1Edfz4x`Lp-$XUrhL|&5}}ZO7oui
z9`ZsaN__QefJ~GSo(t@1?7%Wm2Qe(a2Q%OLI<=tWHa7Kw0sA&PHM0vfeAx)xFNLE-
zaH^6$+h##YZ1*Zf$4V2oJMN>Vp$NBM8ZqQUy8?It37pm|_INvp-fEBX7|eqL=-~IM
z5Bt2SGC$7ZCu<`%D>k|^2PT*<5M0|9z1BsB)eE>N9W1bp^32y(7U!J$uf43XYXy$8
z%kIMJUtb5oxi4BeLjv@4oy!^l1cp^%80yD&A^iuj0LcSB$R!PpFJ(LM6a3MD<sTR>
z;PuVyDJPK;ikN@Wmh036@P0u-<96I24k?ruM3&a?Zmc(SVt`dJeu=+r)n$Ic<6ev<
zz8lx?4j>=*MvpmV=~Q^i{=nBPEwj4mLYo=Ye`B|ZCqhGjw0(%Xg#TO?!CK*#)6QT0
z$w8gJj<VGIqZ%7YPWy1|ZFpMb&4Fk*LED=hQD#s40C;yUf|oh8!s&mAoQK4syhu&l
zI$NABdhim-7c1v#oPInPlZ>TrSUMnLphdT1@<G7{5p{WykV7MzKDQGm#e|`fowoWE
zL>e@rWE(3rsrZ?F#Vaj~f5s(wS}5Tpx1UizKci)7B^~37=EERASBt<RD7}1($tkht
zc7bFpVn5LSS#a|wkMFl1euCnr0tSp%F?n#N)8V_qdh8FNrYbnh$MId(b5`EFL4SEB
zHP<}HK$DH>SCZIgu8QJcY^uBN-DGo{6=us99^fC?T6_WMbcmOsy!AwUtun}xY~%Ps
zT7!A7ZIT2TpIB;n97NT*Q0Z3zwXnEweRT^YiVciIWhirR=0iWn(3a^H=!^U`ybxtV
zEPKUS8F6^})OkF=enxD#xtv7q4+(Ey@BVxbKftt|6xl8oFM3YvaQ2u?e=Q;ZX!qJ>
z#^ua~1A6w5tGZ^Fv$pUfF#df=@G#<b(%y{QW<&k)*KJuNY8=eu5XQ;xf&pGNTTX2!
zi_qvZH=`BuPGJ3N$lNRnRh~&*>|3-DAW0N3HzPj5<|^k|O;0T(mX-)-HJyuTNaM%6
zX)L*a+I0JB$k>1SQ64tx=uR0%;H%)TgX_GuPe*!4DQDeDgZ76l6F!4WF*k)>V$?q}
zsXM8S5LEZmk*JoIxa!-GL>(%>Ac&**K1H!@WIfYx35mk`r!>7s!}YLZ&avSmAy^!1
zUSgtU572Ljx>S=UE<+*wb$9RbEy_SXT5#yDSlpq4)n$U3Zj==9Hn0VaWg7dA&}6{Y
zceF>cx=!ZOG}OZ#WB<>-59Xi)RuHUKHCZ1VXhi+~y(r7<mxbKQ<c-X=`X?OJ<|8u{
z?!z5StWZ9&i5EQP*XwdAC5sUx^DX`<?jgm(nHicN;<RQIKx%!kqN2xcRDjd795q9F
zQA>4V#UzcgFjRjrx~ZLBRsfl`Z<>eOp2SaAm{l^F==YI>H>3<nb?Y(ygaP!qF&CU7
zbTUnSOg`gpZRWAWHQWMj4sy$-FwOKPLmbdUt0Rt^_GVZi#V`23rA1BdjqY*YurJ-q
z3TrW&gR_1g=gO*mQ<?@_hYz?m>^a->IwpF4(6PfNrNU7sEbCT^>+WuNR*m}XHZM|p
zq_izZZkgZ=j4*K&Q@+6d84ghaC`1DL?Qm-13r!j1#O&xdJc;zN-<Y&%v7f`6dvGVR
zWt3bFds&bwJz#fJokM_#HKvA?i}5D2RgiNRg;hFN(r1)p=IY6`O!sF+w-jE)<ktK|
zOHJ4)1*NhrPd8JuVni9=iX|E;9vb?K50)HF(CSlYcqrwU{w2dOw;rPm3FRATMuQ)a
z`AezAGQx1=*G9j{h+6{kwyf;ZrM7?mw7?dPSuzWrDb40r7S)mc11o0FtY4b{WgE_&
zz}&ujgIqR)=)q1L2Bhx#jFKu;&cv%a=#d^@X@A~~$2LFW5K0<y2~pc2{L+owj>^?Q
z!1oRNibL7(vu{LY3`%6j;MNW6bzl73SjvX-%p&+R482g}>kc>zTJg3j(2J_x5vn2l
zfu{)t5{*Zzm~6CZsmvvPcDJalvQbu&S)o;#S9|TGbtlWbFO$EO!!Hq1m%VP2yd6t{
z-&x5XM@m}Ie{`X_=Cyy*Gla{QXlmmSz{Ll4LK0X*+lVUeTA0=S1imy8EfL1va@HS^
z8I3YSDm@lA$#TZjlH+f#yfC2DgLTV|G(RnM)1V^&+rVj@wwi%pAG-KEZ`8Dx2)_O5
znNNd+wR*pbzHUV4;T!&B^31EI{=gP#8D!RJOzO~!1rjj4>^l-(5RJ6~_RztM-1kk#
zB$#Lmy4cUyy_ZM1BrE~Ibuv>Bem!+?d4<GIx#40$O>(i%$X<kXdP?qdTj*y*S2%fY
zq3)q-HDG~bkYV(A)icm!$shg3T{(t28dda5F*Sobp?XdgI0X%>9e0sr57M?4nNjOm
z$|;_Dr3O;g94qcn%5mGK>}DkX-S!OQz6G?<ysoS7p)oiPr?(cr>jm^YOm<heEHFAl
zZ!@7E#;8H)Tb_#8DtIT3z4Xs}RdUL1-sOm`S(F0T`=8&USv*~b{Iv+m6wtkce^lAY
z8%mG2*}SnaarJ-=hW;kDFA&j^kJ}xUoe(XIO0JDnlD-a&{6jO&rdp3{_ETB?QXbEr
zbNsn=pAniu#Cx?<WN-Ai0{$M8*8x(esuVIa=@y_-!y}qtMiY>r&O{zRgM2houcf%2
z{l2D$YgkoEfJTMqp5wivx_Khje~Y&abtV`=n#p7cCs69&4NJ&6y>n%2_x=7ezL&vG
z8E3c6J-TMp06i~ke`JG!ufyFLQJVg6N^DXknOM4<qZ2yqoVK<2E-Jrd0;|RGVg2Ug
zs&6fGlK8IBr|H@v5HGis>>WuV5)<+FD%}&D3=*f}In@Ic<L`$+K=KiK?|zbulW81#
zR4K7{8_!MlddMSsN<<Z9pa(*>Oa;W74Vn58wb{^Bb&FXrB{yT%fy`Ly&wHqTJn&e^
zlAmY0!~lkhr}W`=rLS+x>7l08PEA`LWFjVcuJNG`5aXrowvbZ%Jj1)vXNJD(0OYpc
zX5$WK^D9%u6ivIu6Owt5OM-H*H(<)KMwz*#k=!N(b}<%UPkUfcgw^Z&#OF+nTkL}9
zVlt77LmoZsVDm(C@4~em%lIeNC`@8cC?!rTxkT5Kv1t4)(CP0dib7*SUk~?QcECPm
zjrj#sc6Z{Z)Wk5K?|6Nv)gf`j@6k}&3cH`Q#tJtr31BtTE0uFEzW^-Y%$+m^?-g@P
zt1nwMmyOO_z<xC>#;M@JET+l5jLBY^#R(gD+JXOGBRK0<LwPLYCUbu3i%Z-w>G<RR
zY*Cv-K9-RYE^fq=p&iqzm2nOUdKiMYL}ICG@ZZJe!oQVRQTt_sA)reWX4M0ycp8P(
z2i@KXZmr`WX6h$I`}j~5EI+qYZYw?@j`krcm5ExL=lb~~`h?pQdWEvP?cA<Aj6EG~
z>H1R5o*CJMlCSyeJYbQA8P%Pj%poS}QX`uYprJ1deJ7q)RYS~AAJn8PPVWEON^3Y-
zRq<qod`?PfBZ3JI|LRZ?fhQ8<OJZePa-ANJIYu*GmaItIvW}JMO@7qWZyy>X$@v2M
zk;hjnz?9Lt>O{0L;{@x8gGL+oij%ASCBTR9pk-rt#9}){jv%+U4c9kKWikeg-boq6
z1GuhDJbH_Drf7R`QS0=&w1~eNzUPR;aBUu0NIkzBQm+~hWbdy3lFSHq(Z3Il$3mUu
zCZqL54}rexlyIq3QVm~q#*Ivf>Qp^ia|v3IRN<BXOM{2zXK#1SYR%KrmaI{}9^Ql6
zK`Z@tNKS6}oyb{79O`PPfhvkm>h!byAXI7n;h%nP8zgf%38|(nH)sI1CIq(UXfyWY
zGWu#Df{w-27dT%cs^8z@6l%>3><Ra(Y^bU|os@UNQBo_5;u^O<M!hET>XG(KJWxc7
zq|QlA@2mAt(aMz;)5SjoFZpNjsku<mLxsWtksyD9*f(lO_I796FYYu-kOjbnS~#Eb
z6YJHz@%wQn4yed4gY?Gpi>>NmwARc)k%3CPhRwM5e7B2Mb)YY`iSPNfVwh^N;+;oI
zOM^nSmci=3OgkdPlz4muimzj<ZU4Ly2sR2x#J^O^-pi6!CnksmX_WU}qiz-}Xi|VQ
zS+@H$sgASmW5miiLu0t9b<E-OFuK31+)em<`Wk4fP4Nd)y?S3^MDoCSgGv3y`8F2i
z%s{=^_)>a{7JN|e^_1nzLC`6LrG>~T4*=<@XhMZALw>k8<S~D%Q*;lFnCB@DIUPQR
zIhezd@K?K`jtO9E(zJ9~oBm7}|HhMDrpS@;*2u8n;&<-@vEKW`FOyMCl8XwZwb%8>
zL&SGzsrKUKJZ8kZc?CW<ZvoQ^iFcEB;JGfR!CTUud~=GTiJ%vG&~?|qv&kTHZ1x&H
zfN7N)w^Khm-X$Q__0)gyB7~w$K66`_Z-uN`qkk4r@x3w-YTm91t|PcJ$s~oR96M@a
z%tK3_jn~1r1QxFj!UJ@`I)V3KigHf>8MMgBySz8my!`Izf!dRT;w5-^i}^x0gS1s3
z^@CnayaBc2HbcC?TIU#6v-mc<y}X~I+UTrEwFWD_Ba8d1P<eX2sc8D<^+dflw5+}9
zInxiH_DUr7qVX6FeN}&>hLiVtC(U-uZ&=w-H%*$PLwtS(&BuY`ym2djrV%|ulD(`H
z!<zCgR945`mn_d7o4i)RYR;jz7qL>^pM#>gfmF3`MJV6U{M8aoAE9cM4}tp$TY+)7
z&?{Kz*_x9oRRi(`in3WKE}D_IzIlD!C~pK>&oh^txmgn=%}Y(kC;8n%98JOXhsmIM
z92s1RTkx=LyN{=5w6j=A-}h=mBjbRQ5E5d{3~ym)20`x97Eo{`A2&xx*ipv_YDCRf
zSK7KBUQ!Glf1<6RbYu?fF*dj#@|Ixkg0D!{HTrk&VG+v7_h*ouE#;S3*krl&611k5
zr7*9dip4u=b8)y{^pcxjU0k-`u{Yf#_rdQIh+rjh9mUmlBM!nCB!^0J3;6>O^Puk;
z*x4O$an4~SQ;vkbuEs}DTaR}MwnA}t<4}%=jE;QL%0Ye8i$W_b5qR%D&U}68G*!Wf
zS@s*GUwO?SW%N46`J>o_yecSYZ`h~0lIqQRE!?F9rQ{6twHiM}oxnrVt~59)TeB}2
zFn&ZrVkNz&9Zr&CKy5gddZ*R1KkY>2(%e&TOip`t(<K;ebt0>DQJH$SbY|th*>b_$
zY)j`awC$9YEiVpnab*JTJ$uxW5>Kse8DacAT_^C!@rV0?)y<NfR^Z|g#c}!0W;XX#
zOvNFtuM2G*^7{+=XLqSH^gXRH?O90!xoLWpiM^eH&@}l@uQ>$z+|3S&_PTx1`b&k|
zo^6*r{^|*tjgSKenhYPQcXYPZS+mWhv^@V|K*~_lUJBYTTyWBW$m&ya9ArqOa6Bq?
zjYs(H$&dc85A-C}vp?*+I#VcyO^!D9rsf}uWK>9_-a=<6pFSkE+>>O;6{Kp4sN*%5
zijUbne2$K^c~Mu|XIILIBnSL}!m5a+^ACO9T&2q6J^iitb>i0~A+k0Vu-erprN-`{
zf#0R%WYcwGXZ_b~=l02;(952_(FTP2+h2M_PwCQ?lP&Y^baeqLp~6PO5m!mo<l1>Q
z*RsHCPI`95rBULD%)u|NoD>lLcB-ID(bLBSe|+&~Z!7Vi_F+-_2u((1(8LdwJD;Br
z;lyD5Gu4P;fbUs?rEH>EycM7~`pTrp)t+h$jTmh(h@>E5ro$Sm_V8g!Sq;XIewo6!
z=irkZMsNl25M_4!=_X?1G~C4KGXcr(wA2o`UPvymNm)2)c6h%ir)f|3piOU4?Sdso
zO2iu{BN$p7${%N_gG>3>f8Kn4ZiFIJF~1$AI>yubA(%i*B<Ewb|5Vn_+?doa78A-h
za_Y7NVTpmT5CuX+5%rdW*EOSFfMyZ5S`8j{M>-A|PiuNV3Tm0T(*@brPC3<Bt2p6$
z#5?y*q6d8ny^?0;n7#)*g%H``<J@BB+_(7>GFhv7up!#n`!$KAfG~Mqu4!3?`^5Z%
zKJm3qxZ-nz>o%BSs^Z*CAv4b^66nRdpXyYVhhsG?#W)lNvsnXO_K;&<9+AHkB!GKG
zG_k>+nOiNP;feit#BwbQbHVKWJsf?n?OZRhBy3ur@SW<w;DNe1kAzq0>8EAkn)vd%
zzhd=aBYjmF0^3}{rW<eEL6t7tSxr(<4f-X@l1ocCyt*L7{})jPuK9+=n$I#T^xr(c
z+=AJD$pddceAh)JnLlEl(x4oLvEs|=;y{r_rTE^Aqj2?u(fzP#;`z0O1Qxv8VD6dB
zs7*JiL5M8ci$BOw@$Bu^;W0P{>30~-s4&nD30xw$0>Zk5;y@Nq3=E(O;15l>pZR!n
z7D<t;(41IkcyJhuh;0~{QV>?&5Cf^^4&8gyw?+eYri{<19k`8<u8hxWsN-${i=MDq
zd#_M|6W45GaEPld0@WsHp$^BTsyj*x+&s*g+Gsim(Pk)F(H4(z%WQCwg@3pt0cmw!
z>Pd#SK{oyQHu-P-Bp0zR`N9kG<N=o<?Mj^<f|T1-rubuavfFI0TvZgV_0l|dX+hnl
zkg&z$u+2(mi`Dne?*fJV_I9^a4bkfr3R`HQ#COvD44T(N!Fg|UV;@i3y?3$BZT29k
zD9?z{AAQ>!T{%)O5tEMd4$d2HU79DZrL=*wUJhkMW(b+voLKMCLMv)wWK5_<({SqN
zz^ZS**Aqb$xeG|y05zdNOBfzDw8}t_{dg9$=Zs=tihzwmrl;4{hzHV~s)yJOVEUV^
z(*o0%;Z><lQz;>g2LPk_xOft9t=%je;i_TZR4hG5E{u(NR;xw!(!|RYh(31A>>7^%
zy^3sEv*pb#6&lbTj`<*QYWh5LAv?&Q=tpMvTqs^G3@ubBQt$57t6D#Zv8O|n=`6qW
z@3CtJ2#ZLwZZ8o)r*dQw5A3Tkc2{@W|69hOt$Iv1`~%Q5Us(KF%#TWG4gXy;yo6uq
z`+YIJnG~<ofD}0U2*G_KqZB{x1Co91TiZf(0q)xqeqPpWga>p4xA6tw9eG8O5Vy!3
z?PY`OfP@)sI_k?5g0D)mqvF;$mhig||2ipJv?R-oE<Ip0qE}4DVZ~|f6`iFtH7ZV?
z07rz|&SPTN4gY1+3Uf&F(6?~++!kzw+=r)}#4x;HbR-s{XDh7>>^9$As&0q1T8*~6
z(iX-iMnuk+fsmr8DD(yTx>}>I`tg+PsPPupyVVdVm8a3+@Gdj+4<dN-h1gH!<2Od`
znhz%>NXbUag(#I87tSz-cPOhK9)5SJIXyq!+e&5se+1I>9q7Iyf3w7MLLSD%EUY#W
zpd)X3bh7;x&UC`BVTzzw#dTWvSq}Cj@KHBO7cqjs!&p^`qmZNQUs+KoV`&cr5;SHa
zA>r$a6vW{>gzNk~S1fg8j@LSF80J1tcjfKC<tu@^rZ)=U)(+IWH2i=B3ZWtp?3~lv
z0@R%FrRLOY<hbUcyy5k{*}*ivfrxMiRtQHLKVNqD;`uqL@z*F*D$CHZ?x{<lF`nwQ
zM)jhAtc!%I7<!WN4iI4R#StCGf|+Z->LS|U<;1_9{_vEtGCYPD+v+y0{vl5Ah^q;<
zFHY@FajOD%vS0q?voASO${F0?^C-3HS;G!!ko3_&I_N!@MQSTW8^r>`UC|Dl8M-$r
zHupx<NZyyc`!cvTc5=_4IK$hAzB$;k7{}KRXne;fM)x1KMKxp{E7y}3y=HY`OxS`r
zM_?MEUlE~xEpYxEmNbv}TvK!6nP~aXqphk{W%_w#8zwYc)LX$V2kAIvOrMoCv`}ee
zM2EMArfpITaC;vsJWr19C*x<<{~V&=4DWHdvS7V4D^~dg9C??76Ah$gWim#64vl*3
zyLx|mKqfsg$zY%gZRP4ZMCz{(m=2i{6ZXB;4?S1-y8_3{rdUt#Co{M6v`<LqDb%!V
zY79?-6@;Syjs&+x{R$~79XwO_5Dw=jri|-!0tTFl1;3;74I)~tjNL<!C{dR!;IeJo
zw(YuQ+qP}{mTlX%ZQHi3cl+<?jyHINm$RH_#EINzuWv=KHAkLsa~i=_VaO-<DrkAr
zYennTZo)S<%8aqOY&iog&umdT-9NbZx`U0b!$<!c7z3ta5F8;MwZKh`YR&DW7y~Q(
zs-}{_Vgp;|IxR(xL(L|(U&@Xen8XnfqDnKHqyAWvU!D(MLb~Q8zw?_?ZW!@vpmH)n
zo=*+V{`ib9PhkhR<as-56R&I7uU<s(K|EdTDsW|y<WJ9es3Xzf<%V>*7)&>e47gVV
z#4>qd#bN3c;?Ibq=d8FE9(sDIMuufqfrIl4_=t!_38S!QL(~>$fFe;{xt0~M;ER8D
zal3OQb2np`L5|P7ngiONK#49DitC~vKt`f3jtgo}r(NKNCSkuX=wY$H68*nCA5H=W
z0y{%XC?1~wA$$Jg`TQ?``wzoqWMur`*Z*6?Wn$!HVJG;n=l{d=Y5#A}Co9tCai~S5
z-sW)VAK_!&Y1nxXnHXua>DuGDyV+_NyZK$&`*R<Cf7QH_V^zcYx?z@e|397&c6#LI
zX8Rm7Gs8W=QW7gl02`YCvXqyGr-_#q!Cq}TZc3K+&^XGOp@khelr!Topm|ybfQU>C
zAPo(GsjaQ8x}$%1cTj0!aeD}~{{=l5D=qD|pT-X)Acn_J>FKSknUoVZ!0;|QyArk+
zxRw%k>VJVg*ecR8fOKdj0L)U@O8()21u02gApU=ZbYTe$EX^)JngA&}u-4VFfQ@Nm
zWUjMh$^lJZWC1dN)d8Zfucc{zDW}u-=~LpE0U+#M8CYAsI;WQg*OvD2(Q*6wm&R8X
z*57Zr`xY?uZ$O%y-Zz2Kz5I)_>tia%`9Xd*rmucUkM@jz*#}-~Z|Tztx*MY!>l2HE
zp!e)l|3mtm8v;7Nleab=>#_IkT-g;xbk|?<lYU=*LO-rv0yQ-`fwiT1wtXc|j-luq
zo?qz$KYe9>(x(L_9`~|nXVYF!{YC-e_Kh#$oEaM$U0i*kpU}VXZ@<@a0scxo3xI=T
z&-%G-{$fA#bE<c6WNxG}_AxbnO<P<&u(!4s<I)`Ri;GUoOyT{v>7y3*&-6*>`rag{
z@SA~o)<Y6N_Tt8t^N%RUq$%v`p4?3WsQBeh#{Aih`1%z%_$3GW<&S^;&29VDDf~4F
z|NWi$kLlA~9}m8f@zV);m+J;2fOa=aYV_JG3T#7b!}BY5{}1&UeBzh*!8a(<_V?2w
zG&cKDgCheg`DM`Liqi5-%b?Kgz|aDi@@Mn=e5%!%o*7yiA6b}rs?GUrK@ymq8hYCG
zu0Tr}S#kYk$@%8jE^#IM&HmJj_%WF&qoOAzEEM?c-7Wteq5N6i5nW!~r*HmMh|KC9
z|JD8qBBHUr1-vUUJJ|!6YqYQb!R16V1z^Xny!|CS{BP9<xPwE(6TtZ=WqRn#?*8}i
zy*7ErF9@-bxrP1xw5hBO;9B|zukP34ZPe!K;^;|j)z9q5ZvMCZ=d~;|!!r$Qu!d#>
z`!Q6#*vzDD$>11PCj8>*l_>}|gzL*%nU)Twtp4_1E_UsthQ+%U*2FyH=-v8No5SU8
zEwzLb<9xU&|4Ndzo2KuTwD^WvWIgX~Sken&sO%S+@KBm~<kM2u6<Ry<-@HNJOh{6r
z_X#&a{{g)c`Dq#11)yBZ0Dx<~5V#=MK~oq<jNB0!!cu4c98+}Rev$aN@9b_)e->6`
zmk-az76*ot9$}LHIEiC@cyaE$XZ{WOU^#~OXPqLTUa7E)ckuE-9Ju^FB^GndmE($I
zrX=!DwuCi#<-;qDM<?H(fphPQ^<Lgged{Y(M6q;Q|3S*E`b3AC98hWjXqxT{?(IN_
z(*$E5G^OW`oMq`a5$XkryX-B#tnNy5C--bUTxT}93o!PV@I+<A48)q;r9y%5QiZO!
zneO;;1kUn`m@B+Rl`=i}Yl#-IBu>tf1qM*yjhI!a{3ScS3JvIkjB;7_Yzaj{TZG=I
zzB5_vjE4||=Hd*@FEH=&n=OGPJ|r5P4Oj)|rStv90z|e>7pt?9`v%b`!d{Au<wUe-
zu~Om?XDTwC<_4v1fuEa}pE)ktQ}kVT)Oqb{S{F+&^AI@UUJ`9yzJGO?N#r1AK^O;j
zc&MHvkYPAr@NKcQJu#=BHt~;<W2kPu{|sE6wYi7{zU<HiKlRH1lSMF|+I(~;Nbg`Z
z(uiNfUJaWSvzG(>Omi;=ld?^AX+avyZC=JKb2(mtBH`NI>t@J=3;$nl`!>h1t=t+y
z6)wjGfp<cgyxJ0Pt3ub}A%~U7a)7%Vo1GJR`3X@}n*$ld<{%sPr9}Rege;l|I-gRT
zUEK_l?FmlEv0|hj?t;?j2titweg>zP^v-U)zX3Mbt2-cSFH@6K$$$xZ%08c?5|{`!
zY1N2c_e$lP5Fs9Mlw6;mw&3=d`#L&rDU@n!PD$Iat#hg*hM~rk$kqAoz>DYFOvNrK
zAjD#osj6l>pFh3n(k5;n%huY(sOS1GG^1)$p)HiFR5TA_oR5urSXN^kuC3gK?o&jp
z9d9cLoCs&*<+#qRrGNj*@K@iB*0A|oXmyah+d1fM4XvNV4?+YYAvUzse$PnjAb>3x
zy7EDy7*qM=dsNph1`>DZw~WT2>fw7&?&tZ`ux{Xg>3D*+v;nGSKuCsl_M}vscp^vk
zu51v=6Fv*IQo;i38uv?~?Ra;VH1()WJ!<;%Zx8XMd2d$Y3NnQa3g6RH_jy7;w6@N-
z(m{ufwZd%t+ZVUA!^^E9Ez(A?blk)f>6~+9dA_dF8v(D6l2O{qLLUe=%AKzaURnZH
z)mf0k+p)e0FutglJ&>g^#f6tjm*N$3AIw<KUJ`Io8H~MW7J}Rx9O8x=nh$e`<kPy^
z=raRJ_3HcE*@Dul*xSd6U5fCy>=1#E-!A%hgHm)WrXaj@5pM4EsgV~YmbUH1x*ZiA
zBVptZICkVsKaAM+me284SU-ir$0ZRF-qhiJjb?<Q0DP6OC6~D92JfvRr;O;?t(APS
zOR3mp9FlVl4(a>@!U>bonq7hnN8-;ri8p0(-V$J!60YP4s;jrAb<^JbWRcYn{cdb9
zOqbt1h7ucK+fUbDPS^HI0ls<a-4^`P3RCbYgN}$-;#(<h)2<VXXhdQ1>0B;%+L}q<
zLAX{FSOi#m-6!cv5MBF}V*CB2;%1|TYvlyI8$LbGBK?uZ3x$++C5&lCemb-X5F>J8
z%gtbm2wN~!;x&*7@J0eBrc796nL@PdHG<6hMl7fArQ&>2)sM4wr~C*-{U?)vpMf-(
zE1!OhKk4<D=qU#U`<)Y3S4sE+d#n0F3<))X*ZNr?k!x4514J|1xaFB!YykhluUDCd
z0KBKoMQFD5q|RTx!<xdIiUKjU>%*o=>|1Aq)0}Rei#W`Zo_HsBjW%(3SB-CWik$14
zs`D=(gD7Y9_kpWcw)6Nq0!NkALiDspsLWmZT)7S%5BN*N27Y`%z84Sx5!N@@`3@!|
zb(<*KBi*n<Ft1qClqFWgjc0-#(CY2K!KK8Z-}=mD!Qu5LBlMG8j7KG2jCA}S^ur1D
z-}yc3Ih+h2tHv6l;;ph2MTGw%LnXIC%OM&9cl*>Hzvt>$DYRd{^xE{Rw7vy>JR{=V
zwHtnjL&7}$<2hC%JzTP}dCERhydGIZXiV`WcUo`y6ehkQ2eeh<5Aa_g0B9hq$VRK~
z>Pqk6%oVBMgN==(@q4eHu3t=X)J7&yO03;|lKS*nA2v8=*oYGg0uZ0ax7lVvb9O^m
zqxq-g>tIVrSra71s+&(9E0K>QS$$n~nnte#pMs(3f#4>f@iPl{m4B;`r)J;&#CXY^
z0ARYUq1xD5gQPRBd=7W#;>VxRFJX0l14O?%1^u<I^90j2MOZ3<S`^AdPdBb>Fzvv~
zk5UKSepLdGUKhn?+3=80#48er<9aD(&^+3r2M_E|aO1>&QleRzV5HQ%dlww+$hI6Q
zvkR7gvh1D_UV?fO;=o(P^wz$P-rdf0aYH`)k!a?0I1EsYFv-@Oa3D(3@fy%24GdI;
zfJZ)3WJ<OQ@yb9B>KuWSFw-ny^Hy`^y5pTBVa1>%-bWdH#3>RTDW;P5msS$Fkby#I
z`*o;5IOt96Hq5O$2y+rI%a%rB%kj4XAaq3dlbo_#ZS5n0dvKU#;?^NzJ}sCJnZgsa
ziQgra?}@l1HDSo$+;5|4vZlO80c+K(y4p9bHPt_IhsMHO#Y2-UGug%g$7QjnFj*7c
zgRDm36F+X_TVkTUggt05s-V(%0BGt~A5l9{t(|=uGm0^f-abV=p5I(Le&j_EK<!<p
z1bM!&NCDS;(2UvE#EgxBQYwoR#r*7Y3J58l-6AUlhN~j|Z#krgfHd++)uyyY?f$|~
zHf2t@ghb5@Ot1b>xAR=8>l(<KMj2vu)9isgSFboOw{S4pmsxt3tG4X<BzK|m$WfqR
z%S{M}pWZJ><ToXojSYcEJtk26jJcv;EIQ7~y4*|SvThZ?Zz=3oqktFq#%&Mv6{ti{
z8C`*O<T?YdeYIQSXVR>fyMLYMt@`eiX8}kAFjHVSc@E1GAcKr5^Nu&7zmg%?y0YS4
zzu(_T>JU)7iRBxpy2Kbb&vvQ>h^(pf9!ckgji?z8F8lJ}3N@2K2E-4zr1;<md}$7m
zbx~mq^{P35+*&`yQIs{hC^sUsyz<%27a{xtAZSf7xC1Vr30AhP@KzfFvDBwfHPt4=
zQI!}G|3Q4t?qTPk6K5JKlKaKBYKehlw0J>NDmj}jpr=r;bV<m<rb8>97PrhV?x<$k
zpzs=V)OQ#>JCPw&WGZylzib6RvX{j;X26_)|FOg~$G86CQM@|Psw_lx^0_whyv7={
zkgouNalp$0sYw<<Syq!LI&j8W#c9<8&UpKve@Pj~^)BA^z_zIdK+{(wQCgu#P16$7
zJJ0O9bWv_<5|@wZb(GM`jV9I-F_GZ><B8Ut+8BG$8pDM1a6U(@TQxXD(ZE`3QNCVO
zpbkS7g&^!>{3omzbXVwj^s1{t9}>VZhO0U;rPp6e@ElO+_=B1M$^FQjy1j=AX1b06
z5Fc(%Foj_0r#clXc<R(3uHyjPw-!{LcwZ`AmUdz?)+*UG^5UmNGBc*+??a<+V+JEL
zj%A4?3#{_5obbTf`NdP>VI=Yfo<#kp<SWwDD+wfoN(TbWzH9+&WOMh}csh#UfRQ+j
zhY|D_OV+nfL<WMeO9BrP!s|$opb8e2qjUf*k_EI}G^`Fco#Nysn{a$|ix~$?<F<^@
z+D_N*_L?qtZ9N)I`yWp{85LZq%fHPI`|q?*5YcG0G%>Iw=su_f2eBCXVNbEA8-2Ot
znWnvpv{)16&@B*s-Sj4)ZZ;PjaHL-m#YtL0M=7ccCOyF~2Y$timQnxUPH=9$?1VLR
zKNs+KkL@H@b3tUhh|%8@!ucP@t70oNxqY)D87GjnLuzFFETi#l=?RXqQf(B!YHHwx
z*!|cl&wD>;!dEpsHtyk3=)+9XRzj0#XFRN(u<SpM?kV7Je_i3(VLB$(U3Q+>67m}<
zN+{Hj-*8@P?j`RR)miKT1gXzS71XWuCJfBIU9z-So@$G}RMo<)78Nl&$e+noPLX{7
zsLP=Gk#G;%jfJK_Y?x#(n(;7&)Zox(S><yKiO6^dP5ZB$J(yAJHVru!l>(qXTuD{K
z4_?K<l@I+TKKmQFyvKe1pi)X#i<N_K+1K#Nb=gV|dpDlRD4WZocy%U2px_*0y#vWQ
zVlKx5ObsHni;}$t<L5l6$d;|J=g;2ARVSW4PaeFo$$UA4H;*$;>w)nh!Wgs0lqZiF
z*z?s*qdGfw&E@`)GImzJI^YN>C%z=n=aflrF~(H%uOrGua0Y_bY@14pbjIn=b4O4h
zKD6I}>WXjIL4&>fQe7K$Hz9%owkBJV$_uYw$PeW<J*PQmjPKx~$om5klmO(?aeg*y
z!C`$j$wZO&6Mv_c4kV;+sA>tXT8s7P+A$GNxq54HTe?w2=S-;NLO5JlRDz|>Q!{Md
z+I1Pv0@ub@)|wX(?w)M8X6driq~&d6^jrEs|Cu2lh0=<zR2OQq+nUxnji<7{q|OD#
zzX%}Dg@Hj^ZEKvGgh@aA?Cl@?ks_eZ0kS-u-9&vz+gWSgaxcUN@sfC!h<#*h!LI35
z=7;WwmxrlcUMD1a;XiQEEsQG%$#seeGE0uP$s9+92xLJmDi88vx`%9K`p5(-cG}XY
zCLRW@k~8O0*%NsM)WKg#&}>K@?_;{aA+0}|1Jbq)#14%2g*w_{Sq*p47uL*{;J`;n
zT3i`*bEhkL@DJX;<{?wC;G}|P#Kju<hVoSIsMiPHxS40FuH!N9i{cuHtCCJpEz!d?
zBWuSTE5%y$?B<1R_$`$r)`uXcg*xrj?u4&#r-!g=jI1u5q8&mgC>_N>ev@VM8@Nev
zzc{ky?>plvG97KteJt><`#n72Uqz{fb-HAquD|C8qRwp1&KkAjdgJ!AQ?WlW8zpe$
zHMO5xAgom4sYJi|>Sp)aw)_dcM60ABmg?eJwdj<Fb(yovn^$18<kw*^rwrhIwaR<t
zG}M54W>Ez`b6Mxo!oP4qjm7TT!q#KmpFK;<zz;Pm|MpDLmJ;L&TzgKr+ctvG__;Ll
z*`N59S`%+i_1$-+6|U3bZRq;Kspb1>>~zeF_?gs1N{2i7`|qv)<~aDZeV<47w~LQa
z%|EVBPfWi--PgVq2Eyw?&RWyDyoe@I1s#8Jrt3Qv2MiHxd{@nyN|Tip<FPo?4PxZO
z*m?EC&60aV%RX*OYM2rvs*jIu|K{ngBrl(yEErFzjG?MF!@%Eg=(P_0sAB}4t=Xa2
z;EREDMO-Hr4}&9%-x?DlOD=tiOTFBO;ZA4E7;<~#xD^<04wC{|o=S9u1ReA4$L*)+
zltK=gIt2Jf)UW9?qI;0i&FNC9S&Me=V9Z%n>l^10Hchl=rV~82!(87$Bd|Pp!v8zH
z6S)O)swpEA)Qxp+7n#!%w~%RBMf`j%3hiOtc@j)f+9REbpdS}5w;Y?R<|M%6S$Zra
zl!;HbHUn>FAP3NuW=*|b>|qxn^4!|rXdi4U1|g90I<5N;QK|AwYFIWhV)O81J1Ewn
z8<#9y&`ksnxO&TbT>$%S*m}0w&x+OWN5lpbE}_B&H_~Qc^_gmA%$aO<x{1PHcgI6X
zYBM2U4enoO3-1RU`Y_49Cp0yCl#Co7YM#?%A5ZJiTu)~jq?UPz=t96<cl8yW6)0Xj
z_sZSJry(K@o{h3iaMKz_vC6V5-3^~CaZ^mx<aeSyJ4W?akT+z2<hr6R0jwl=D<k3q
zDEBxH**l=8ywI`HH)0$V4?fx??(y@{_vb8p3JmnIfIRRYpW3Y?JAJUydRIX^&~Q*Z
zc5m?LR@#RopQ7l1@Ch`!8UMVr_1-1%o{R4+sxB|0XIvB*v%9oE*T^k6bs9S?{_VS$
z>;zkaWeCZSrJVfDYN}aN&#+0LKTne&hW|Ow|I9Db<o2Bti`Hv-tO?>0OF@>D;>iEY
z8usPIQU~Kd+DKoG@<-9p!phU|wzjnNF+Kh6;oFd{@$J}GcAyeqiM-fhBDC*NQC64c
zP?v-*7?{dwU`&*qNA6IQIVuB;s$@c7h3n|j!p#X&{=IGmPb$|OSMGc+aagn=)Qf1i
z#a3V3u<)4WuRG*gZb;4DqUMPKvygb9Y}WS5G?!m%8yZg64k-)xG(7`0VWSIr2#Z89
zOI%fz>A1SeahP+7@XKW)`cuo!ifQS1ojJ%e6Zrhq6nFVlxx*F$JatN*IBq)|T;J2g
zAijDZ60Pb13H>$)=6)`Id4fx!t?cNGS6~k+V^`{q2^ErUBm$5jcE|XNs-EtxJu$3P
zFM2wvbtQ><iX4)-3giI((Ps<-rsHb_@B0J`BDihV17qfNkLs!@u8;8*T_`P9JY<{g
z%#s#G>8-7gd#Y>utlq%J64(7B&e+|xgMAev4KRM5JGXrKqN=(jd<)-6Dh3u_z7^$~
zsMg_p$b}rJuuAavl<2dz(q6pxz~+Vmi=JHeak^VDvD@RAebjTL+&TX;hN4_-Pp|!g
zau1PmsYda`AM(;$eY12lKd~hOThO$A#ky-kFTY#Nm^lBMNX67R-FxIKlBtv@%a4e9
zpOB4_?s6r0t|l)jP|H|eqaXF_CXPGald?-K#(}!wz(`-3nRp{@byhxh8_4HWpIOww
zY7;Fh=~FoO7zr03OCJ@=7(&FjH<FwVpIG(41BF3X3}R{@u%PUIOvUMM6eO9frK#i{
zmocDTxS&_`0#q_$CO)bwU*y|sl<Y-`CgAZTCX1C8F`h8<T$oe2JI=$$6<c$TdoWp&
zY{7svuJ`Fv`;xl#nY$aq>-A_1sf{OB=zhVaoGJFXvf_lN)*<>(j&gqvPCk{9Jp%9N
z5-&_}hY^_71>PsiX7|DMoXOK1&?YXrw4xSJET#Kl>t6KF$Rhmm;@hNr6Jm)ORj0Hv
zoF)~mmh-RRh5YWmH)^~;V^5>5iXaE|5{Lz#AG8=HUi-%vPW9sZF=Y@+Esok>t_FeG
zu($n~X{MTCMa6SoFOPY9v~S5L!iW{uVnFdKtQUF{|F}^U!c5AF=OQMs!UA>kIpB`a
z76ZZtq@Gv&umdzsJwveV8Psr^o%?DAn>Lwu^71?|c!i5cqy<C^)@)J<)<1E!NAT#v
zXXULvUW|(0BFZ~%{`gEkiOr!$BI9PY;sK3iFIu8|BW7(=pC#AYhF-I`!b@RJGSab#
z8NVphrEk~l-w=RVeGK>3Chlbmbc;E^bei&74F}1iRw!)-DdC+Ca{y`y3e&T49t=iy
z_0bmdf``%7Lsr`ZFP%3~kwy`Qad8Y;7#!nIp_YLKL_Q1~>OlUI;5dW-?l2RCI9Puz
zEK7yGUZ{*iQ}}u{V4+ajq>Ie<*#l)d`h5{M5L3{ym2C8eD6YS_ZS)t;l7tf<(Hu+Q
zd#iAo-`ifjNI@m9HO_ny2~)H=tvH5i05KK%NzlS4xU;c&{?LZ=n;F65=$wZ?9Zj(L
z+{n||253~JpF{44E(YBSVAT}i1*j^}P?7S67wA}*4e@OmP<Zo^jgVu{BfmMvcVyVc
zJZEL;4aYXSf0*s!K;V@E!6+P5HeEzG=P)ZCn=li;hg`x|07=M-*`nz7Mq8saN(6&3
z^$PsGAq3=!!SqSoj5??vq5PA2Ey}Z>{EZ!Au#M#d!vIJq&F*NXVw$zy)QpXq3;q+}
zgwq85EuA2TJzyjHujhBOA(Uzb#E)Z?NHX{>a_X^=ry`er;uiRfbs1#nc)onFFcBjV
z8%?n(2~ClxEB|b^UnH(_%0a^mC&#4Pg*+CmI#6^^OD&!3hU}R=rPx@%iLKAEe~*K+
z)aj7Rz+E(9PJDoyC87|{OEj6kw>MpY%{lS>KG>_gW9cRpA~p@-t>n$7$qeDNng3h*
z59I*_w3O^epq}ufh3+y8Mp9teKPiH=!n4TD%z8tUFrwir8%*#S+AF^dfsY<E_i}{1
zzhLl__4j0xqau6&pEJ6`YL*f}Eh=WX!o<gKujc4$!bxC8Y=a>cY`y?^=GmD4=Ntmu
z&G(q|cK?X(Fe<g*ci$PK1-F0I<_`~I5<7GSi{y31#`j4D3DdZR?J<yHSRZLl&x1O#
zf;2W<3k;l8yelYiP$Ofd-wYg~B>P{H9i&gdCU0#TA5z?R9(%XymMxg~r-#u-Wy4o#
zLTJC@G(y>jxL(vH+Es<1Flrmkg`JyR{zydIP6D@Q;oCX9-T;~r0Xq5&suapaxxWW4
z6y5Ix1J+^W?=w9t3j>s{U#PoNQCMO}%amOZ_ha_dl<TaGM;V2px+x1p9VC#o!fIMX
z=IhQhV%9<N?kLdrqfQ8Fn2rnCpUb>F9^m0$Isro67EFfsH+zLkZlexY(nB>19MILx
zn%sn{PV&WHB8;%VFtsOnp$c$se+aRBkvbn~F!Rsth{t9pX6p)_&$-?T$6d!m4$vTW
zmegEC7hJt3c@Yb!vbuA`&EaA{lR=anMkIhOsToLDZ?LW~tL5V?`gJyhjV{EwT~-ew
z`%T%u;X7AG3*rgoYd+}$<5L|9B{0vDFJ+4RyHrLO6~;q1n>}G05bB+;bHtco%M;#d
zAc}1y*AGx@1Km}1988M61AVu$;W@@m@uV{jSpUNIHu_-+z)E5TNTSUe7sp8Pcr}{L
zPhd{^u%;I%_qFg!qGw(S;JDfIxYiQmy|{R4t&qpWcVN4p3}HiZ@wFd3;7TLD><9?Z
zSl!4xI*UD`Z-akx##v=V+h1*T33nbwf)Ad^*H3u)UVA7YeGGNsf(M8S#E`iRS3mwH
z0Ns8NRu3s<1$2^8B+wH?M?w<=8HBk<w9W09Vib@ziy4E-ns=X7rKLj$rgrvGgpHqx
zcO^i~+^?f;Da1D8fb7oXG}8Rb3N`l@LOpt0w=UTqeoJC3FPBQ(j8lvCE5K*egy5Kp
zS!V<z0fu#~jm2ZVDZevRK9!LfF8*n5ZbxeB4hSw(o=joCujo3gGcp!+%t>=NV-p!^
zM@g{QszxLI?l$X0^IFgtLL5eJ7V&~MK}<L!=~ouHK+h>*wOu_PY5+hF!e9)=#`>*Q
z*#?y9*foB_UJf~D9dodL0K22-C(H)XqD?fae9OD`PV4*#wGAi(Y0~7B{2bYR{~Vxt
zJp7|w-0p1)t5Te3WPTQ^&RSoCNuT3;vYW^8jU!p^U+<$wPXQZG7MKk;zVqEE$b?)Y
z(Z#8%eV7(UO0AM7bpV)XTro3zH0CHtpP*UZvTTp?_26xbk3exSg6?%Vxz_Q8;L2k}
z!(Ay{;szBfTCAtypV$pIV{v8e*RBg4o#d4|5=IcIZ=ARndPhuhjXZe_GXwo%r^YqK
zOptjJ)~0_r;sv+$W_+5gx$a?B3bt&NA3K4c#7I!w<~;{<_rz&ZDg)aY%z<kqJH+RV
zvpln=n8q5UU-uP4pok!(27io1+iEpeV@8LqXooN9S2#Z%1`-%UrDT}Enosb$pS2RR
z{JI<r#XpR4^NkIhm!83&t*QgmgMY?hedY6ggn6bm=cX;EFiO$~CE6%<o`Db;^<t{K
z^aa*x533jrLK4wg(C@h9^oL8z)2>?GO~r*(xX)*>nMtdu6tRxG@XcSRc_G%F4RhCh
zV`>n*9JHfn$sSw7R{7GVeRq8$F}t0^-<Tp=k^ibI@#kqvv?wOO10eliP8p}%*bV{@
zh-AtRU4JbNG6QT4(I>y#bx3?vGwX`T$2tQY=ZT#3T3pN0pixZE$BLYDQ}%3$E`C+E
zY^bW!!RgCV<Kd=fyf*|3YUSnky?XGFBfYgtQg&+n;^E7LPqflJoa^&FtVe>-s5(cH
zQN;jjjXiw_kc*&VtEgX`rsC{#7PkpS?skI*Mt;2QBEho!79`Ki%c%#NiPP#yk^yKJ
zh;g;m5wOHugwEIY>PGzK@rwc}k7E5v=pmsDR;Q1>`7CR#PULc0Blux}IGU3uqbo^~
z%uCN?FFc1O_>Xr!SAfVJY!0BS@eAEI1*F+xU;x9$#e3FV7o7$ab@A_m6t`U4Y>>R!
zBh}MGP1i1>>nObiETl{)n2v-T@6z9cxb)q$m7yVY(=GaQcgV|13b@=h40+;9bXDc`
zT`J-Mn;2_^mLiaW<d9IcpiN72Ibgky>~H=`N+PQl*|d&c5fbqePHibtgX2gFH%9}C
zQ1x)(xOHZ|8VG&02b)VQbUd7|j&%O->6g6<${Clw;Ghw)D(<J}WMy@~=6lS(Yl&Mg
za=zHqyH=6ehi=!mOk+N;<g$Nir7ZCiF-bLMKtbm34B}SAOU2lO97>kUmDQ<jng%Zf
z-t767U5RUTgmS+uHayd{0Xxxq^~VVJcRlUtT%LgG0C#yPIq(bY4p_e`fgbU{dr1$g
z_LK&mkhBIN0tEV4F{M$ZKk%~c_lu}b1g^6fc*-Rc!IlV&7y}ga8j8?=vZz6VLt;E`
zZO<63xSj`CK#mB~h0KqLEVD=c#h2kMYDpx@aWa88Qym&_a&(*kLIe4T4mi-!8b>+)
zK;*SWiDp$82zWO_{~__+(duVmw3+(#SEgwutw9s`>?FC7px_juD<IfXUN?)G<Mqi3
zBgI%D5mm<S*mVGv{#g@z0eXzFVH0~GC<_dR2P=oSou?S;W2j57R3w-rhN3m>lizw?
z!pt%`#6?+x)UMB(r~)q!Y)JfyBb6rt`m5JgW!U@F{+H9O#)P($+-p9BG6M$1If+kk
zV7fi_#}se-W5zpjObat{VsKMI<Y~t_uTh+1;@0&w_9lYNDI?)COK)>RrbfodMb-+(
z%WFC@CGU|QbVr468wHa2K8bq?z^I6&<!)ntfDV+Zai0$Pbc@R_-T2=!L~aJMql^jp
zkq#cM+ZTUFVr^c^M|c~i=?Rixwy0UN{)7~{X>qDxxP6x<qJH&IHElI383JbEJ`D6d
zWi-|NaaeEr1zvmLp?FGvT_M#TXRem4+-EdD4dieN!%ULUW)D4|a|@G5UBKfW@0Pe&
zJy<&OsiMp<)+P%k01uCzu)g&(gQ}u`|3`q57<jP?wz;}RT(2n{)++o=-3r@EBn>7=
zU_59L%+mMe;iK)EXsMXws#TZcc{0&!*iEDd>fN}LWw#IrbykzuumVJ|IgE{lS9T*2
z;-s3xyoof+g^=CmtO5)&qs_r~RSD05Pr&YFPhHej&((9=O%YQ2xl-fK23VJVmUj}7
z&H{00fP1KXcB*0&>pJi?VTsQySpzh6E52zjhQl#XwXhm{x#zGzVOpm?I-JA~@>K%?
zC0G;a1cT5^7DTR^o%gV6Aj+N7*P6+|zdz0W&lrj1wwj(>P08oC9hN<Ee`Jv1Xh+0S
z>}sE|tP0dC*w1cDRjgX0`uWLz;_w>UBy6flPVvG*rdBG;!wcj^UCx=P?5U2s;F7gO
zJQy$_>Y#SZGo-j(A!UxDCvfd-^ceTh=`QP*Kjf0w`N-Wmiq)OGTXo&%=Lysz!BP(_
zY38@DARbr#R$aE=^kp`9<@ix+KQc@<yXNGUg;Az}+WoR<47h0pgS^!VaP9I&%P+#@
z7@P;(+)erk6T&m6-Ej2}7`;YCd9e~^I3P+cbrV4FUW4|T9h5HB?lZ33bstg0=q6bl
z!Zt^1ZaOhh!qf!nRn$3R<!|mts~?zz?ctm!JtpE6v7yTJUGWJc^zo*bt-C5y+b<eS
zxBqTj`bb2UU1EmA3}j39ou6{C?VY!_*avmw-N8D(oZBv?Xa)O&6iU7H*R$@XST@)8
z?3j%KF&vI|h&eQ~BfJdV?>VMVMlpTC6%c=$(6Nz#!OXpKP5-2-oD+X7Sy9%Kv>se7
zV6@7nosh9!1)yJ0nJA?I%_!BWAiJ^fPJ&}EOl-e3#a6qhRrS9=Tr-7gPNP*FR{+h1
z)g-$e?{JaF!gj6Yj>90D=jPOGwZNS;-po_ktB9hWm+*Mj$hE-Kmz{WAVC#F+e~nNu
zKAul8tIPMtSYc?Bc}4G0=?PmG6c&05NI{r?MIVDzgSd;_cM;IL%A14|&JUh`$n75I
zAwae{^F3+fgdW!k8!RYa#UdIpPK_$n53Tscfj|fObZbBkUdG8FXaX-^5v=A5*KoMY
zA>Ll5(p)S%3s!2P%1>WdvkrYAoVlaVwI>&f;}5AjL5fzf@l`o_NQ}`Z(uvwPOJsUH
z!;m5Svm?suL6fTrr{_kX^LfjKpBarYlssR&h6D1k!RXTj5})WVGNl=^iFhN<cOVjX
z(DhRk9s-KPkH!jlMD2Vm&B~wO(&8-LdGUS+?4m%?K~lzp^)&VcRECE(z|<r}yz_0Z
zGz4OE6585zlux@GVH#R{2fC9<h@v0ublBr@C(l>@rsIqg8#;B&ZcgX-^78!-Os`0`
zTn&!!CprDZc};yRXGy!pEWlAUF08I$a0iC)>CIrAy^W2Vj(b~UcAJ>s3<b<cn9mJ+
zevsVKHc8n&f16C!`y>-xG)=PawXKwpy=Xr^aRe0i*7@78h_QHm1j9;O+zPygy9ce-
zLkrh9?+lQfP|K`&kewWh3T*_R9+x;VifRnWp1K!XO~OK#G=<25m?mZr2}7lGpv8x4
zOH{CK(p_-6DvWI%67zjthzo71Cm@3{MS_*7l+L_WtKT&)FXN^s=WYxAfz65E3lLB2
zhz?$m!TvZ4c>yOMSrbMAK?GH~S)Hari_cI_^3RDsncPSl)QmkFB{vS~7VwpJ7$K1c
zHI&Hg^`073fm}pLEKAt`@f=sj4&c3tWNWV*(;@NK?`~D0O_fwU3JOzVlZ>*tf3gW>
zQwftnu8kBSL=*R(aPMdaWlbhC%ah5S3f|vST$zz)(CZ6UxD@~i+H$#w<**J2^_qPR
zGn^yBXKFaArPMu$z!>3P%LlL%PjAWy9xWKyYO>{3JSE#?fNyG2v<j^vxs`|d$}~p!
z$><si5rqBlUI21N+{wLVp59?&TxXEj;Nz7mzXVRJy;A%vOyL)ZeoK5FCg+~{E1@qy
z?~AUgDK8SRvzHLwD&mssC=-Mz!h5`G8YCZh(!Fiu0*dwbvM~H{%L|ohuN*fS7PtA_
zjXw7BC<p~3a48g9c0M7@MxG<*oN*e#eK9)URo#KxAsjB@3n+K3pu{Ooo?uLnZ%WE?
z-YfzSv9Rh-gC(1D2OW~dbYEK9K4-G-{GaA7eq1SBwFcA&xXH%K52sTVhUx(4R*Wy$
zT#Z>S%0Q<{7uoKoPP0jj!^Z#w(B=6<#^YzwqDZUI8*UI@2xu-kNz86M=J>SNtY)bI
zLnz0ejRFPy9(VDhZM6m;q~r%}MV?Pb^|%K<qR)y;D-tyOt_;r8I=Yv9&$TB4@v*)Y
zTR;J3(OwO|hNC#tRNmf&tocS$Gejm_9I_;ck^}+&FFck8OC^?+e93QtGHVD`a&X8J
z3hw&*qRIKbua{})&6ez^ySwW(NUC(Nx%}?y6nxp*^EDiYF-w#xXX)6#whWVJ<+IxP
zspbUIOmG9|kk41DM3x5^82$GIO2NaB)(4T5i&qLG!HlDLpIIXS+?dyA;=QL3E1a=Q
z(Elnxy(D>gAkC}fp9A*~OW*tJ8q}9X87nT&f5rvTvTan6jXR}Hzu#Ji5bVL7>Q#O5
zFYX`?+qOjS$eq^PxACbTbmySu@kl8S%CbRJ$4Z5FqcY)~sgODuF=8b>yoTbF*u+yK
z!uiP9>I(MwTVh{qbQ|O`3!}n%;}x=Jztmxe7f!jKa_Lq6Vi_}caEe~aK}X+Y56JXr
zQ0B}-$>3_vimty8N4yY)Aq%~LCi6S>SE}FfWp<iivdvF*%B?>$$z|?$E?=$k_t}JU
ze7J{@DGS8I-6Gyn;(D4iipw1&4}d{+$ImSNYqutUTV0~(O5#Uf^_Q|QWV3W1O{~l%
z+~BdTi`@C*$)4c4l7vtbT+gYPE<4~ccoyq|CITw?lgYwAUlfKdfzzdJD7GzJZ+s0T
zHNVM9TQZaBg_YIuN9>kgFsE%Y{ti?n2K|v);e3KRDnWU8H7rm)tW)#kY>=_uju415
zKt~dDl?*>4^l*l<^sV%{E?+Q&C_Z3@Rix*_n@CMOrlgdKtib#mopUZ>nEXZJ%Xmpq
z{O~IHaP=L81||*MSkb^+`$D22;JK!^cu=);!!KF{gaWswjuO!@PHx43^&J7?WfdMY
zAybASln+k|55(~AfEO?Wb%~otN~4hNQ5|$x33=5#OlXoER2pNel%cqnYWwLurFBd0
zF)hX7*Osw!<P3c?K(A%*j?aCWeD^J^`aI?%EA!2eD|OFO<JyRtGsR{is?qjvX;-IE
zaK)x`bWD@p5+YMD`flBdm4NrkpS<gYWS!CCVdQpDwAL5sEOm<$zO0Ls_L<Y864@Tc
z!Xj|ZNHl(jM+0^o5!`k4yBSPnZhJO-+a|J<S8Z=`#7jXdY26C}J)Chy?al4`v>z4Z
zQY9g~u7?x@td;I`Qj%x3Xj0i^UnGZK9~SdOV@!;*<#Dc%f7705)#uDeWn`Xbc1nE4
zr>C2tms}&$&*i2U7~wErapy0L8o5GP!@&vYjEzX2v}$j>%pR1_m{gvigx;OEp?o)S
zyz6(Na-;+&l#bFcPzDG?T{P-=>04p|s86!6fR%=)kj3-ya8bcsB96b0xE_}WR`d4_
zIxVZsw9U+x7IXU+JA+=lVx5h1JkO~L7>{CqA};csW2x@Jt#DKu=OZ*1F;QKDDY}#B
zFn6BY0JguDW&wZ^+gpp#!k34g986Y&b6geny`4APiio<)gka)+Y~i~YPKb7M!g`H*
zNM6Nzkp1#Dy#ygywH!qNI!T)(Qs|u>n;nZE>-+{24B8`064eRjmVal}ujZb|DXmyE
z*eEE`nh+cBL4Bab!Kn$x5>Ruh+&cs81LT+OsdAr3r-Kqh<}abaK)@;)Qmj^DhqS<c
zs#W|sx;E@q4b@xlE?Fg#^0U@)x2R!=q8#Cs4x297gL$;1Z7Cxh%tOy%ritrH^GO$6
z*-1-75L@^<EuZz8tQjrUPhL?4wM2NywsSjX{eFLD+t3V$tVRtc;CxO9MpqLGc#Ez|
z1S#;rZjVFK1T<*Tp`Ur0vHulV)X@gdT3YEn{>y|X=h*bb1fYDr`_H^$-c(uKzRJ+2
z$5cw+=BR5-+F`OpIaYi#$n8}Tx=&9JVvZq^kGbiNZq`EZ{^NM_dW#y9Zv;bOhhl?I
zr5N@jn9g)W%+;r^Bv^lEvWJ%O1gUZ}BRz6FjrTL{OOV4LD1PRk7!yc+B|O&KUN@;_
z1G9F_`q26}ECmO|+nT5-V)K&@FD-wq^*#X7KH;{C@e}1`S{7~CDMVpNu7VbI?$XO_
zBP{SMTacENSKv_;ZTyV=Cl5WkJ?oOOuNdAdk|NT4xL3k5{Kgc1SynEhX>T^WT5_2m
zMc9Hp14r4kU|Q4`+u$`EPIyUg)6OrqjqQX-67%(7-}>{f4GH}{idR5bEmWHKS@09q
zpUh<!tAapwQtb>$u*+GRtgnm9L<)`&`s6+bpoa@yd;Vsa6BLup*NP`MmFi8+({PPi
zlu}{*l|-%R3~=#prQ?D42g3Xe8h1RG(_znPn<s0d8+-;D?6*#%>xMsH%Eoz?+yq1r
zK#HZw6j&>0T(i2+#2)E<Owv7$7->={?U?EAZjgHXQ^U1%SDqz_9g()7lM6r$TyBC?
z{`w6@1`+;{PyUF@%K%VFSGd|Sywg)hk<O$<zVy_PEbvsjk*aqV&qZ&73{-v?ik_-8
zr0hMSFW1T@JZt9MUo3Qga(X-$r_yhwDeWyOgB){mq;ONE11*VJc6^s{ax2EjwLKtG
zs#L{vNcY9g>ML#D8XZ=H!<oea(DU9kjCplfA)sBV;YPU`@jsnzfT_0z*;Ij)jE-A?
z=3sE>R;cs`X__Hoy0@C%$GU_5M8Bzpbb(DjKtnJR&||S4eK|eJa8|!%T7|cz?}coW
z?`!qWPs9(8ZcSwriUH@GmSY{6G4aZ=FB12MlTy0ARj|hP)ZQC_Jk?$D98pWQfnKx*
zSy6jt_#S!qHz;{53pd6uWs){I;e!oOehF)(pNy|L-XA1F!%NE_7X|(Ei>%j<mgz@>
z=r6vfK0}H|?Dowl+feYAnX}8Sh=~EPmkbEIe!m!Xz<3wkhEqJ6iaX4_V0aL~5c9>h
z27&!|5ruMm9fFP+t^CuDk3{aZ<R~(Hjvp8DPD8Nw(#JSeURI+`AZSiN0aqyK|L`i<
zIbNPE>(olwBSYhF<C8U_Y^$)9RyEd3YQ2!RH?8<u3n-s9LUj8(ZXhslBD4a2uU}%q
zn9DHsG0Y+efMoP-w?We9Glb~cR%~<XefO^vd;obi;DMoVm>7N-NmLZD4%F;AM8IyN
z#cdH5ehi2mS4p6dv8X_sPv2vW%++x^`yHY)u~&-7{i8Qr=DMTPz-&%75oDBIh&kk3
z$kN03C!a8BS;$AF3@Y@~od12=gM!{+@;UPWGTscMnc^6z;sU8t#FvVLQIlr&Xo?p+
zaIN%P#pYIKloIIzT~wP=$k%2op9t=gDBQhcfyz{$?=cT%lZFTIheal&GV1E}Ett8g
z>B|Q>qsyjrZ+Ll&*}n_OL9&sT5uS7w7E<!H^Pb*mjSdZAobA77hKXFB$Dh0_hu|x6
zcYuu?p9rR``1uwAWdzp1k{bD3(0iZY_%?vP>49nT=rl^G4=*82rcF_Odw|X~5ms(k
z2&@$>A$w@RP1F`p;5c-1^#|`aZaiT8rn$3M%yw8zLuo?dw>3i`16ZD+Q_ukh+8^A#
zqV*F#=#W;nLu(M_d#=DH?@(yil8Bf7HISp4Ez56sQ&uDsZ<QRzY8Q$Q=Cn{;D{lJn
zghR4j#Cl4KfN0){QR2$&uDYzxhz~qu3dab9!r0EwMU}8KVdgG_0L9U;ita_q>k{0S
zq&}xCBkfKCA<3p!r>Q!df6+|Xl|c6mXBhhLw{93Wo_I$Y*t3ifd|-EHNNCzS-=xmu
zJah>O@ys)!MDAbX`Xaij<w}ld0m(%N0j4Y8sOqca!x|r3l7rgtSEq2w*{g%a&_Q&1
zk+*FH36`wsLigrrZHZ>*g-0MDDI(jMWiOVIj@nZ1SUu%xYEm+83Pe7MynJmm9<A%}
z>OCRtB`)*XSTzG_g9c`SbfA3Mq4nd?e=E%1JY>6!ZaBQYODqYp_%OZZ;(d&3z{`e<
zXeb;b$G_pSS?)}u7YE-tIjL3*r;}iJiXSD|#QU$jgt&#ldykdSEJ34NZE1NL`-#|&
zZt1>baQP5xU6ei{u${0%4~))FH5`A<>vr_7ewqhcfA$a5Ew}A-b3*6BTTzJ|`WD+g
zM)3UWVE}Y9cDw&@G#%nq_gPbKn?>q*EpRdWcSQOnsiU*Z2MS2~I02&n1zkxObiH6#
z%7>Ld<jP=lqd0#lSkfZ-kc_XC4@ObqAE{LL;v;*VH|@G)BY}oBSedK1QIh(F@H7AE
z{>xLEKXANxSc{s%tJ!9sh8RrpDFn{E<=_Gr9IZ$bHI<&6=rw2Eg-EYCs3mSL-ueFg
z)p-B&mD71RWD^5edKG6-jKf&aCY5<qlF0&{K(TKPaRIqSPm9oU#Hjuis~>|qTXQ8d
zruf8t&r-Um57HUMy*O6fWEg9JN%%nujhsd*42oW!&j;<B&yjq!b6A;egfDH-fba~>
zHotvEY}V6Ld7a=;CEywUHx2_;Pg}IAm|`9-{-Z@b6Mt7xzAKH3Y#L=qg`Pu0pC%Tm
z)FgIbFv1WS21k1gNVO<)@+Pg<D-WiE42$j0Tjp4~h@U(cE(a@#t3z{=LTw5dPgrIR
z1n&<IBwbq4K?}ZribnLbF~obP{SL%An!~sWxQo`i)(g=&c**trEfFrF%`a7J+{?7F
zQpz}7dS*e~o((6;EpE)IK+SD7V&=e_hB@w5ZvVy(Otpn&oCU!7_jBpOcjJy5Pi&kC
zT=iQlBaqDf>YdH`*}HxxV>4dd2>&Aj0k-tnu@gOsi3qn~K0;t=Ti?nfiyt@qJ?YOu
zQ}kPs;Ju|M4cAE>@$8=>Ws=Yn)_d}tpn53(^%_lD=E$UeUpa_8puyBFM=HW`obINR
zzJ=$_PvU4grM_FU8wJacv;lKb_WVdmuKPyxq>(R(&it3dHR=)L@WEvPTj<Sbc%J!2
z<rpfnrPX-}zh~$)n~Z-RDE&18v}`(2`f>>)vapwga|e}&cH?0XcvPkQ%I_E6laxJk
zcCD(2q<S$Be>`6snk_=3*sN(^s5|Ixzoes8EXl0;Wu{Z5UWgrtG93i*SY6YQSkL>3
zeZEC{hLd@0LI;qq9az$VFo|xlt6a8T8?4dD=F3I9J1H>_@-C~bSfz>(gb^w<%=Z1(
zg@5zva3gSH%w)h$!Vbia^$|*>mGS`9NUExs24Ce9cFyT+M3X{j&+v|3^-nLu@F`B3
z4G1FH;z?T(DkM*8l&d9Q9(8VA3ux-maf~Rm54%1z%|+asy!4T{N18T8EL}-{t{rh3
zeh%CYFdCn^>joW&(8;XdlZf(@u7S$CCMztJ3XTBH2bp1;5QNNXYeoG=lu9v0KiW57
zOUIg$C3SSf0L4%HPZn4t%i&6whv5kYp(U7o?U|=?rWj<+9*Ti!2kQ{_nM8dOlpgh6
zb66P&NvY;*It*T59pUxkwhQJR(><s+U)93L0w6(?7U_-B(s(DfHdO;bF0Lk%R_@@W
zwf@}%EQ=E6I^vBoR2YTZ+(E<YOIzO_*tpBp95hp!2h<;G44G9tcLGme*XwVTKci4J
zJ_0;&Qk^sEX{ZflAX@oAk9ju!$?q?=;6ClT3CIc(b=d6&x0}WK@~LN0lN=Lj9ll!!
z=6vi4xl<kWfZqdS*gkdK<tflnI4H^?lY0*Gq|@x1h8%2@;}4&4v|X}O=#A4e=Z%5x
z@|E9~axmT7Pg{Km2jhhl;hK;zc7&P1k6fc+bnFtHVou4%WPwaF$7{dEOYONvQG3Z(
z*8nBKw_ThMMWOF#_%VwPWYgcLof_@{Q!y-d(x`+IsO(BOL#BL)Iyy(@Fu85~Xv4W1
zEJ*P73z#B1t0B*$=<4b?g1{8V?BE{)3X`36U~&BTBi*Gkb5-C;wt>mwCZp}b^gr#e
zmFN#1Oc;`Nl?<#dN~RI{5xxz*K;QPX9Y+emIXDQ{AjYu5IXR}-U)hhUjWE81bzNiJ
ztPqdP01T87K&HNvdP@)&9c>Sz0pUrXP);lB6RYm&ta!qA_~P)*wwZ%8+>0LoD&`-A
zM_M(Rr>|R7;c~j!%$U+59b56J`z~BB+ELPSRd@3TkfHsS&%>vIz0_3qUsop7TChvw
z)&L`WUyw)9lK|y>;N2g<=jim^UCBhUI3;)8UPPfN$&%tBcYN5`_Ao}58CQQxds*Y@
z%OP_mG-Ad2CnyjE1pXR`cSy0OWytd$0yj#lxY`}OvwXS8u(_x(tS{gzB=Ao<`4=EM
z_zR~Q`ZEl2%2`FKa2CCZ)7oA$XY&+pqz1r;XeC0EEJ_>OWM&!dRtuk81+FDE8(#&T
ztlHI)_!qhLkgH}F%X#pi8KwcJl@(N>vhvR+Ibb0twgL%dVS_n4W#KK>j+G-47MZ9#
zg(6PKHH}{K-1wiNG*#w2|1=aQTVN}J+8yyHPE;z|LyG*ZxP+r~fL3U_XiPG_lY)6}
zXqgP*+$Bqv$m8OdXSgv)ZyeHt1oywdZQ`};q*Nn#W%dqb3wh*SJHZ2llgRfT-4Yu9
zlDQ72o&9kiMOqsQ7+N?fbQjGhE>lO05D)wmSXC7vy(%8k{&<LxDYEWweLx$__(E$f
zGdiKAxWN?qeHgLPP$PK+9ypmRxrxBMUC6|KT^8$WcQ(YR=A!1#j-MB=+x|O$1mY~V
zEe{^o3gg2>yz%8^eOz%unfh=F65!HXEj${X;!Ac=&VrPlgVMRDOLk?Ko?8ZnGmv!=
zZsdkkWt&2tr5!oZf-am^gqxGgN08p8U*J=#&u{oP_0$|3XTA%0di^js{H^I1Xe)L1
zEEZ-(!GX~s=@B2L@)rTBJLuV3Fa+G2<O&5|F7gCkhZq{xT}h_l??H0FBKPyc5>@6t
z&K1gWp5FJq|EY_RG7KuylfcyV$q}!IJmiVu*`wnaJBFp^rwu)*eoF6X@a7p<hYY!a
zECkcrQ~ldlxdS$w_FH0NLtjF=7$hcn>#y`^`%rb%Pxo~T!-MlXR3#B;{dwnp_hX|P
zKi*J!utdl^?-eW&paDBn4pGMXth?<DX>F`eqUDuyI;6$Yrw++A0r5*L7D)p5cGh}h
zOv-o3(EnlV9e^_l!gb+j^ToDp+uUTsjct6fZQJI?wv&x*ZEV}N{+$2ZbL&>!Q*}?(
z)J)^;o}RAmuI^{LpXUkBYJjJ-9?(EMHLw5Inpv0jrwkiisGxs)ztk}gO{g)!GFxOx
zPq9z|-Y5%xBmzUm4B-l$o~A)`V+|NVS_d}m2rIebv=1cV&__4I{fRS$O{B58t~f+B
z>Pwq(J^sT<CA{yVf{Pmc@*CchHu|ZKp4HVR)rosifd9O8>9d>(NYK=-><8oKDW9)f
zy=@Mtxx&ucK$S1MwNixapql~`9yz}zeZ?TNqa<+n@Sf<$rbZyY#9MpBU_XA_f#Rwg
z&_FP><zE0PU$&NBxL^j`a5w}%E%`4%`OdXG`fV4|8TsIT{rkulvTwBH@|`3C`eh9c
zdc@p$s8Rodj_Dh&{YzTQ(0H5urDnvE)X>KeMT1saI9B$)Px+t69xFn5bv2jq!Z01g
z9@7P{k@V0Z!N1>IjS4V!3OFns?Hdq8|4y4_>kPoGse;WN&zH!^Lh?R-Dm?c`Np?AG
z-gS0IqGl53dB$qi-=hf_NH<G5jxI8>U@@2z?^qlD@F%8FZwe_L8F#T7^j#O00tG{)
zRlPRV-Vx3m*|l=v`JF6HsJhDp<7FMw<TX}Z@Dd<H<be0M`i0qqCUqt-VIDEC?1K29
zhCh2uWbYns=E-QLS~0Tqi`?QL=XO`p<;C`_1ChnAf<-8ddR?>E&r))J2-jm5OJJj0
zc2d^AnR__BV9HhK-OOR>Wk*K8j)~p&Ttm6r;(E1tA3pcV#wr(9Ey}s18A`-sl!GXK
zWgM@6yqwa%5uGY*mUjA*kA4BR&u92J={l3x9IW0yvGAbNBB%8;`}DBgv*gmi;Lp5q
z=1BYhvMZ0-cT?4sU+{)a>B-_Xehri|GPD2fFkdDq>uqFJ6RTd)J`b4`3YMGyqiB*b
z;l$T^EX4E<@=BJk|MYcwzd}*ygBe|LgDehiWqUVjuv8h-<7c5mW0t$L`DayL-suaz
zQUPJ5Q#7`>#u)X8l$p31*vH*ek`AJq)EO)SXfIO6&>mj}Sv=dIT;X$QBFN9jhLM`W
z6?}5?J_}hRdeVg9<GA|(?^R=ZSmahufZMava`k#sU=OwH5||?M#sq%c)K?bU7%@2Q
z+=qcktY=m$bcx_mIoae#!`(a@pz@d|t^T2@)+#uCa4OPHp8dKt(<IN-YIxCkgy?#t
zuk74muq44~(T4Cix_*(8n-jevXSkxID)b)&xu0}n%Y|R9JQ+9g)!%x4ix>}AY%4L(
zRwS-K;v{w>c5{!*`b-^(VnWy>e-i~;seR{l$2L6mCVz6N%G)eFj`czXC1Q<6D%Chr
zUZmVo{U_<gV>ssLBD&LZM;G6jDYyh?E^qH5C7_cd;rnbu^UcDdRs51l9ln5)ehkw-
zP~aG7)uenlCOEbd%LDfJpa6ORhd(J;gI;n^%xbaKz>9a(2yi@QE<Lb0X^IqqBEoOI
z<~`cr<O&Oq@Og6SgKT!FC}dk0fob)}Jp=|Mee~;b-g=LUAndl54{j8bTV3~XD9JUk
zHAHzia_NMXuwVSSG_R9jIABZYQHAF7?5<{ojka(tWkm98OyEkp`Y@d3#;;U(DS0Y%
zTaue4&Yp;0)!Hbq8PX&*FSh7gnj$(|v_~MFo_-flW(IJ!$p}guTAy<AhO?;0P-M(k
zVl%CtI>85k@)-wv7(<L}ujp`#<BZJOrtqp~-Qc>bVwQA_D$$d~7(GkOVAT47I>r(V
zSalk54HW$q<{_YSd{aSh(aK(SJGdt41fnIe%9rtYC7Cp6p%s$i7_W$-JT96;8(SxS
z)(>#YyEo3<+D_=6;Isz9jBFfrO#fJ?T};R2c`0-b)J9WKiN()Sh<Xz$;728c9&>!X
zZE^C_cnTZ(RcgTravFakRU_n=gUIa4C!Nl<9E^=jup|Nh@+W+fQ~abVP1KASBojb@
zFA$_sY8P##)<8JZaP}^f!kgf&ezv|5FTNvd*BvLpv2&7LYX4po3{Qxt-ccQWkiS@N
zP_bW(DQRkX<;zuP0a@Vz1ElR<2uw)Y-6n&>EXR<DF1;*|UG$9#M$pTo_)$kKnM#8+
za~!iFo8X}L-1oQJYWh?3#TaMq>!oKx*MAZ?YOq!EebS1tLoQBTYahM1qJ2S!E1D&f
z!^kCeisqabK%@AtD(g4pZJ8cpK8}{1jkM)ne#;(pSud3GmnUv;4(ha_#~K;zqpFM=
z-1U=JMQA4V`<ReN^v)Q9TyF=GtNjJP4hFd2r5kAdl_)35vqwq$A~WHDNeB(=`k5(U
z82lJ1T#uF>dcAikJ=3yy1`2^ALV<cIub62^_2c6CX<26YAusGYVxM63%arLRvXa^c
z_t@`a#MOc=pYjJ6v&%$@buHJCyP}L`!1O2te=bN}>51&BVLUn?<W;5;N*ORAVX;Ci
zFY6lNZw6}OuW>2V3zwKsVTIFyN;hWN3^6*%H9hWpDzsD#yU@x)v|0aO_0>$wzDb!>
zSwc)W>lli~X|9#JAfXd)%yJ$1AoH+jB0httBdk)_p5<8E`u-x;50Xtf#Z~1#UO)B7
zPc~89JtECPl;7!N#~>58Cz%WiGw&M=af6UZVXq|9oYWxl<mmWv_`2T(sr%BS--;-w
zLOH1-5L<1+v8PJ%wwA&1Qp@1$>=ujFvU1yQGsD{0Fj+@8Qo5}LTw`F0s~?B1&hG|m
zTH>9w&VtpHL516v(`#&Te-5E@?olaj`cEz8?>23jftpU(Z{JOoOt~5XediUdw_9ad
zC`>I8ppy4r@wDcVw(k4Ttm}sWuO<4QpgqYs-C6(2^^{Kd4RKg)+vysSWQ*lcz}Jgs
zoYN2|8{LJUI=b^(rr>byIiFSk(O5mNTh?Rzh<~pX<OdCs6qc=`V!#4!kMC))pfxwH
zv(T#e<D^I?^iuDvhC@bN(V984GD6@eLsbH~qbl7i+?V1=rC1U)$}X|$%Ao|NnbaZu
z`M}LnTl$wgoj8t#D|+Q*z_Q9*m+N6(b_0U@$2agl26{CfhqI54js;@F?~R$LQsvQJ
zaps1HUZOErbpWlOX4z*3ZJxT6ys$>v8HLQ_0GY_BFWG*S!$M_oN9;}96FH`7{jZ3B
z4Mqnnt{L#Er+`}$$(uru1shh347xvn7B3vpfG7}6Fgz6!PaO?%?4h~e5+rXSSjIK4
zP|?`|Dabs3^Cm%|20tsqrsj`*V0<IGw)cv`MLmeAGTVfj+9JtFXHpw0x}kA~a~8LV
z???k~Qpq+x4r3$sk_B5f#Vx=+RzNR<0{}0vP$yweZE((?rIDpwq|MoNsGGmjL0={F
zX{~VAT+46Sy-_0$2_gE={SHJ7&|#zZKGmFZiPyv-=p4W$o~z+*st8-b2Bigl><Vc&
zb7$HAxgIkvhe^)Gj6AmA4JH)6qq=3kXy53vI+*N|#&8#U74_#f!zL%S&KH@RLqvOr
zcs;D`b~|;E+J3BlA;38JgQfvVj2y`_J|2^{yV!-xDvu}vrZ5PPhSTYoO*W{~qcJ9q
z$yOp6?_P2A(f!&*QC#gHMAr@URg+YyAGlqkc_w`^WkG$A9N$zwX3jsLgs@BHr(S6v
zi$wI+HPnQ3vd6qeWes$+dd5u5(WztyOOvyMmO0&gpY!+!4f~*s;epwa+1^1HE>&zw
zhfXOl%WS+dXWjvnT>eK6sZiT}N#sJAdf*vH0z7Ia=&ZMzh?1#vE_RrwJzT%R`$<_S
zOIJuy88DfvDAa;TV@)8~e~cu92H9VmjpHKnf<2xw=9M@vQzsN^dBg$#0sar6O)6gO
zjy%Gl9i?YMlXBV7*tpf-;U5&ke3L@Rwi4#*zjk88ci*6ckQ>Jr_MeKzyGECE#E5!R
zv-?Hond^w+141^Jm;mcBX-t$)WRRAwPq4c`2S4?Q%a134E?py%e+l)=-?NgjFbvS7
zMdWe{2pzWcuHR5byu-wN>7!4mT@;I~K`Z{)^yQQTjVkTRCH_=hql&1FQv_yxZ>FOp
zMgGSwyt9nucz2|eF0Bs<AdBd{k&tTs{A?*D0wT^rn>(E%X09n7dPFn%F<S%)wVENt
zw{MEZf?~Jz&fBCm`q18WsGcxxD&^bqQ1xe!6PNM7^0Id~3rkth)@5c1!DsBoq<S01
z(cU)kGZ!%a0rO>N83GLjvmxTN5CU`kR~72u;-5S3q>T&YKM1f3It+Dk{2}6Qx16Uo
z5#(}{>RcW<!Oqco7H7OB=y5dM&gZDHd>VX3aY2#n;?Ga1!b4k`tI=%vE^(NocV}MO
zY|{``r@s)1Hcb=4zzH>i9mVY+{|iJxo~8AoRsj6ptZYSY@FEPRdUB%FunlOMMe;zy
zdWzf^2IA;4=2IJt;~i-M`U<M;qq*|$${oa8d`^yAS!ccsIuA?>?}6~WuDBCPrykwC
z_Gb;aCWw(z>GOl3-u+s3*{0q<6*3^utqOYsK3v}2R)X4XT8=6YQV~cU0pI*z>NA-1
z1={B4q4*kQm2^<yLKy;WEySkLB_4aH!|0!O+k1MwSqtf=ygZ4tn}{hiWV%om8Nc|x
zB+Tr3e%uqn0*DKhmFZ6Co9sC|N0I3v76r0?HcJx({C)vK1~-O$5zhAq?z&A`8LR??
zV^v8Y+}yBaVkiq>0)jke*z53Hfeq3WdgpPUPZCv6JW!~2)dHWtTy2ZfqxMtxQjv)#
z-35=C{`>+U;y`GG0;}sV+18DySZ#{l{gs|@kcmZBd`=#%H*b6YnN8KI9vKi>h_b-#
zWS1LOO_};Z(?s=ez@C*?&m}^83n%2)-rP|pF{|r`_nXxK0unV=)Teidhx^CiP=noY
zj?G=A#wES==O+i329b1rC_+%>{LuMUnmI3p{#U>95t($6Bb!Z**cXi37L_c87YKLI
z16L_Xkayg7wr2u|TaDQbOniKlC+Y9mWLzDrytJk}7GVI{vtO+IbD1{`o-lb%ev+;V
zrs_Qdf{a@`H+dle?xJaIGXK$eK}{4z9lMm6-q^m+frg+%InmQy<oS=%Kp}G|VVaY2
zvC<GdsDGBxZ<*Q*v}d6%M7IZ37Dj%cPmT?$Hx1LpnFgW;+K$``&!Iek$Fk_KLrOLF
zauwiYrlfd+OnpSE<LyWFm%~shbhON#fGH?-732*aEdzbfk_?fH<s6K`=Q^buCY<(L
zMi?<`=_U|bZkN~!e-&UlZ~)HfRGqA31GzoFNJBYMhYhy5nEUGx<2dU>xb^t5&j9;4
zh(lyXW?$|JJvFs|{umodxfzKwyB0yWC${hARm_qV@aMWy@M>wkj)?;Q35Q1N!tXMH
ze35WwzsNbNCvCmItw2uDrq%v>6rlcufkO{|dzE(zp+Er{-h3$&DLc9WF=Z+U6Rngz
zTCUW_`L_<mk7ClY0<$&hB>Qfphi0y55nFGdvLr-K43-f_uFHN;wOc8k36O645plZ4
zIt+Rl@=WOoU1j)W8U%BA`Xaw;6Z*=sba9J1I>2oV`Ot-^DU7i`rOT49w6{IXU*Hc}
zP+1)M)^Ja=gn2-6mSP<_^cObg4p2OeA#a-8UdpS1wlk6z!)Y&kXk}UgD<2;TlJ!{_
z_;Jqx+K3q_aK|tqPAx?|kpPGUH_I*Oz+}b1rmsyuHylrsbZGB>hq=wKg}jWbQ(DrZ
zpgwXkPhaLuHn=2d?co*@VfnlHgBn7T$4+JzZ<UOwNoG3^jTtxQrHz4+%IXLh>_9~J
zxL~3Vz_j4u1mV&QMnTkT)LM$dbf0f2RN$<3N0}h7(A)n+UE8qu{UK1VOtvtdA^}?J
zEkj^8@P@HsDG%hx*8?eerRH;vt;sofq|6MJ?{fh+QRi)EgEMg562Y;~D}|^|s<Yum
z6JCV5<-~`qL^mEqizXDlRSZV}Oig@Lj5N=96WQ9a{gW8|<C8r&$ypmwhhh$SDb-Mn
z`(dn7m28a#-3b0jbL&#&@WoU`>Lm(WI8*#8@$;z$0^5G8PtK6t?ob<j086If(N%Em
zDDO}{+-Tz!T9?@#61R6W0h+&BaB>j;jVhjEcayT+h{JnW=rc4)6ZOpmg~w2wnElyP
zGbQo}JY}cd9_s~RQNyHDEdpuICpDV0V5rRfb9uK)Ug5U^&96I;ja%B)Zj`aE0{`7V
z#3z6Q&tTx=ff|8(YQ+-(aM=}SpbdMXb8;3riU8dh60kk4FOyEItSXHXqToD%08AzT
zQ%)zBoKg&sp-*pUzNEU~8Dz#9qKrzm^}+GS>EUqm8M^5?Q$-98NSpsXjtrNrXu`#h
zojO8to-#+P#J5^}%4+ORQw6UP@OuWhltF8&j77K()T^Q{E>S@fT9WTXb#+=!cy9qI
z$5iO~EfF}7HYjEKLqq6+3F*jCQ91XlUh)VSmq`3(=Tr-|EP+zlD8f)Vt{jTh%Kn2|
zZNg)Z4aaMJqKQX>Hyo9r=UC$eLT<>cc_dU?C%>A$C!x>5=w89JztdxOMMJ1hAvj`i
zAEe^}+mMpq-i%qnwY?$-6m=Qu5=kZzdE`){jDSM)_r2EJYqio)BJ-86j|%yzL9Gd0
zy6)(qh>l#b2Nr`F8v0lSVs4*`&2?F~r|OShHGDZg)3K4Cg_`XdoqnAjVPYsftN1Zt
zst8O+p{yy99d}LjGoy`#!zAzw6Dd08&ah?LeKHS2iVg2@2(+3c9nnsN%MDhvD2DT5
zcF3=xw{yLYBN<+x-6q5=3;X=<d{*k5n_Rab5(g7A121k2PK-Zaiyt>qLxh<@k*6?a
zpPSh+Iv2z~9^^Fy4kzu{jE$hrO!7wa2Xewvs1ld#_VRu?rM{HK2O`>~jmmhqZXvJk
z#3ROPerzfTMe(Q2;PAq{1R%Fuw3gSrygQwt3K}Hv7kutDE<4p=v5D+noDw;-W<%*m
z+cLyOQa`1wdsZQgoQ2F*utqm9DR&QW0lyp~I2`>0#EYz=fQtQ38+LRTNdRXoY=j@j
zL|A}$sN<*!ZkZ3>i^9kOz8=X{*!M<#nPv?*@opHBp=u{WsHZ4>o?1*-wMVY2FG}tp
zAA+`IZ^o_JMvRPcPv0LgaRGf|LIKI-l#tM;#7@FSFv=pl;W9p7htJpS6RP(Z@SR2*
zQj<J6KT!YF^*1bzEG_mZ+PaLUH0IJ{Ws%;QF=mKx<}-^l)|1I6Ov0v9mQD~G5s+0$
zc0#?aK#de5y?`4ADn$f~Zk#P?Y$?US#h)j~=Mq;}d4=#cfK=ytBLq@%g@J)xwty0R
z1rz-Z<E=`G^^mV}evi)Oij*!8CC_wIpa16oimI&<3OJKA<F}&s&8rCOh|Z6os0H);
zRD1NTrrj8y0WV=60FPA9OFM2bhgbU$xHVwYy&F!fF*XCMhJ|&(n05<gr4@EokL({>
z`ewUf2QlQQ+gr!uV61tmm9aLJ+^(}sMdM^JSZi?8Q9g}3n6XFJW#q)l*MOb*N8$3N
z`O&e2LYc}B!k7%GrmFW`**LI+Y3<HsK&TloiLXQ-CCk$P+?WhmJhjpgndYBE2qK28
z2v#D$z5e1dcu~+-R^SA=<P<Eer1}JJepimvUx4SxF&uXTr{jl&HxON=+M&c1*C)64
z@opcV^cM>0Np$exq5N5~FFhORAj;77+zSRGMc$8DY{9d95>xVWl85dRR;q<c_TO;E
zvNHd-oUtrS|HUB7Ld3?+!u?;q|C2J7os*U0KL}(0CzP>Os_uMA7q66-k8~@2%TMgQ
zE8k4xTO>v%Oi}5~tXPu9quE*V-}8CcbmaQ+-;ZJS<kCOKP+ny_yPrCa?y~QexQ}9w
zUih9KI;*dmysqA|kq-f@QfDG@+47`hV6^m%q$LQoE%p!r+jdA0A#AA}g8|zh`5NKk
z>gt5>KMV{cf<U2QRRCNf#30~QSh=|%acW9R1;~)cqzc4lX)TBtMB@8MsYyw%<G15K
zwy|$1MWP0JI*>t-cYh(F#r%8`MOG&n^u;rS4F;<fkoUuc`|eR%pjoo?(V&JyDUTpV
zRA3Pl9H4ObLH#`-j#yX`Jwk(^x)N+4pTl4v;v_`1dQ}F-VcKn2AW(k7!o5s}SZIKd
zd>>&&5Hz$4#D4v-Ef6fo?Py5i+5v3+cn~ZwIK-fL8b7lqkxvGwU~v@w_i(IuxUd0z
zW_>oe;2%g0VDM3&rKo4%eV=2|{>TO(vysQp!U5pEP4~E@vq%bbP$4}aweon6Y7BBz
zAYlEu+qp=FHYC_E@NYWeM35oZpA}%#AULP+BBI;!a3K<+`=9Jt-LDYuN>4;+Fa)=}
zI4^5wgzP=FSXArMA>z9JX{b@;hmRq|X2!nO=t@DDC`61;q4+3auc)vA`M!t}=S$hh
zlo+4^M99}jAP}cwq?X|WC`w=hpRCEiPgjyJM6%BusP}J<*lyIPE1Zux;!oS@yREWl
zupK+;IKg{Yr0r>5NPUE7=h2>?uU&-O&|c9`ot`az{5)S}bMOa*pum5=p<lU6Vs5DD
z@v4amul+FnSiN@gP)H#^-atBp>~<@C)-I5dsBjQOg~BsF2`Gg$b$?&lrez_*orgZN
zdld;1QGwk)-G;M#Fz7ChjtmY>DBtQ`Eo}J+<$tQTBcY*Pe@Wue$Hn>5`P3bs5Zn97
zo4ccP``rV%$#?5W3HaE>UTS?4Z#d)M^(<kLFyQ+^wGWK<_xC{uo^q!3Xj{5xut<6&
zMr!blB7YDQ4ahzs_A_GPg^8m!eRyAn%YGBXc5dX!<Dtpixxhd#hCOzs=0sK*FrH(x
z*<F|=Ql5ni^OrTyQpc2vG>2?OzU4kn>=p8e7yGDxR5BYofIGK2s2Dc2T##rtvg5Yt
zI8n>UcXU4Ybx0&e>{}F8zM;OZ%MKU*%Ywdp+GiMZK&XfeT;#YcqD<oJmrb3S=LPH{
z2M7-$SBh*Yz_s*nH&I(n$~=%wu*Y(6h_MLZdtqLjdTz{Zv`No&_0em@Iq0f`5BShp
z1`49$?)q=mfafUS0m>lY<_NlXJC0CY6VwfwcRT_~9hM$H&;#=JpMD#iF^U(h&_f4J
zI{gAfCRT~pvZfAHVh7Sy=9{^L6vszSmNA{%TVdd$XKZbSiVu)Za>F@XMc!4;V#Im>
zqn`@vZR4q{DIMVa^Pn4B|3zg0l1uOKx+r3vB`i18b``aWI6LQ&T$oLY6}GcV3yjK@
z>=PVEYd7k9veteL+$zZVixMr==18df#(1Llj&W|*z1mOC+v1WwVB7MZ2*$$3dk<Vs
z-H1fCS*%DWka1mi7T@pFS;TEh+ZPlp&-1$O+4Ev5HBIpRslESaZAE)2Hdsh){mmkt
z?E+;SwuK<Vj2DY6vAQD+WOq>FG{ab3q;N%etr>zVQ}q6kA6Wjk%ia2u>+$rwj$5v}
zP+}XY;AX>pDx(L}#Z|SRp0)&bSK}CrEIWEaST2LM+fs@PE|rXgphuNolR@Tih}ijQ
zpXU6;wwXmC41A-l23F{hmyW-%gSRho_cYe*+_{l<xR1AgDOM1Qogh~w);3)lf|+~Y
z2UD&Qvr}3z5>By~WwVRNDZ0&?`93U*Lys!TM#;d-BzVaxKIpX_YscK_Ffzeac~jHf
za-q6DReg8Sz`BMOz4GP``v?%6CMno()=AK4o3=Q$>C_g%d58?#JwQ$<y)xdH#p2NL
zH5uc0L9rcir!=!`U>#CyTlZKeT_U|;br#8B^ptm%xfJj{dA{F<-YgUw#xJ5}^YjDp
zVSRuufIO29V(cf;+%4j;W$Yk3mboL`MO5YI#Kz!0)PWOdp5}%Lq<}e)rM_a}?U+Ra
z0@IV(xc(%m(yKn>n91wjI6YEp<HPUW`n*Ii8jBH8hDt_ViG`ix?@mv^>W+7m!Im>a
zs!iAh<4o-bG!_0J9w+I%s(-;+Y1Ml$L$k}6&^(ZTW++qjUd>t_XV4*~#`(Q#chyNg
zm(4@_X7eVz<2T$iw%2&vk|knvH($ARNaR$)3YI#eb6*Iq@PWUZ<b7^zYz_=(ix%28
z(}1?x`3);JA@@iE&T8IlB?fN*)$oHX{;5NDf&9+e#2~VaJ|{R9h0|ZE{8whztXsuM
zA$5@@c$E?<Fy6NL{!w`|ixiZSkd7DAV=?SSJZl5`&B<LgZ-is1oi_2bgts$GQPJF1
z4MsDW|L|CG^mmd>+Sk$pB2FN#(b1S5WZFp-VG=YvkS*j@@u1sSQ<~dFQj3vL_G5+E
zo{Esju2uUQSgj%p$Ya5j=nl_!qce1X9!PvDl(OeMZ$ePjbiC-)uSiDfYP)`k7IU_@
zI0Q_ASs{{YhSpq9Xk%wrx+@hCj@0}cctJ0;#1~IQu@uqB<OA~L-lu?CJ!>z9ZC|pK
zrSqW*u_}k&$<kIjVxf)VAVGCPwfbn@d9}xGCHB%{%P*P~=NuG5!=TDjnZf%BLStM}
zZ>7IHYuvRe^bK=X_rf>0JWu7#NRSt(z3xW0_yiFRQpvSvm&6JkKnW7YBuOI_CT%$x
zvhNM$e5H8pSrC4>%<l(BnlwMePc0fC(oWLqE-*}evt2ZD?)DTJ%OFf;f8I?Sqn}T0
zW$Wg$=S}|Uh*v2;Y`20rKE^Jc-*8hsGyPe8Q?Jg5XltFB{MAS{uNhFS;PUZO5Y+}U
zYFW^@$`f5ijPUMw5xQT_(POF{p-87;RetlH@H&DzAAFg$cbQGOpc(ZKZ}4~!19CD!
zUCP7=w7BJLE$2+8j2g#|k`N<sm!RcesPb^kVRO@B2q{K`OO6G;Xn`5awfDYXj>)LL
zqa<Gu@TCoX4+FDB!(Fo}H}&uh80<mH#q=fSn5M>&rhGQ&f5__{e5S&Od=M+g2G)pd
z)CI?xYas1M58q(F)ZPkT)aU9}c*bZP_*47No`;UYej5D`wn+L1?{UgIK!^_2>H@(@
z7q*fkRYE80ynCMlYUj>IE&0e@DVV+t1N)WUb2AA6v&L5jRdMm<b36!G8WtOkvA0)s
z$Jm6MFhQ+u`we^Y&B>xr2Gk11%&wMj36*<EBA6uAbrizuS_R@oIxR#qKmU~Y)>=vI
z6+0}G;$h`FP??bWI~d&`dKH6^yoH6}tw3C05hAFI%h7!MXMKboxA6%r^;Yi`gXGyl
z>>(R1OV@zGVAk2qqKGYP=@@~4#(D}tv5K7tPhh_Ixi4`}(^*%H0#0@_J5>-iDKtO(
zoVsLbCTGh$cMnUbTx{Cp`-I%v9ZlGImzNWU^ER^sbH&^J`^a46{Z0$S0-!W82U`c&
zox*yl*$7p6*~+K-xJU`Efz9@Ag=gZsHk>Zb;Sr_Q`DXs!7iLvH8Qa77x3J~b?XRlT
z%it6a=kUcc1wp`#ZjJMk6;Cg7hNjTq<KlkItL7Z3m$Tm3P*Qo`9{)2QFjVPd`)Fx4
ztIA1w&_44i|G5$?qA0zEZJFxiJUOW%GJ8eo1TX9*nKI!%G$nP7oHm*%K~1i8okg9D
zH3K_YeTEX^Bo#-8;NAjAhiQ$WTm5^pQz-w(X01$&Dlms%j4w=ajyDb3Is+T8Nu5LY
zu<pp_-)x2HWfW%L1DC+Gx74gM2mZE8A0b8AR11N(^Z5bpgLed%?NjGbTRBh8(31L&
z`;jgozxK<?HWHJf%FJN37>v&{Qx<#1A$Be2Y!Dqj&HT-RbdKz}pnA`ao|&%NrwmWx
zflr=R1KU+nvc~iYXt+<MCHU3`(S|Mbg$%L|sHq5AuO{UWg^+P#1gd2ZLfsMu`#zOH
zkF#H=$}<-W0WED&7-j2E!&EEvK4Fjih!Z|>zNidMDAV^8B1`eS*e!$aW$W|#LPY$s
ziCRCYGcqy^mpjgjYP1Ha#B^3JT%%J+J;yT0!+%h0Ipggjv08%-hTkkwRIEbxbDF9I
zmYBN!R?yElD_dMU52dt_kLE{}^0!qQj+N$i2J+lKS*2^v`JPvx8K-UHk>jN~j?--U
zl6`hMQ6L^XjN0wV2yN&E*|A%C7qIy>G;GK3{L3>LT!xG24{WtL&_`M_eTyTi57_0}
zPpzLmA1#vw!6+5L`8_gU-l7&4W(_#g=WGe;_VGYZ>Rq4QmKdzVxob@J$+iEB$%A?G
z*v4RVY1&c9F;}ms#n0saSty=ln9yZ&MP#?Xd>r2T>K@Ld#^y353%5k>(6cX;C?{bf
z$+}9<KUeN?^ixg#8QGrJDAkLdbAQdD1M1hze4%#XVf@F2wd<ppvq?!2fOWgcDr?OG
z)jO!D^0T2Z#1VrhlG-aU4}HlfSrtHXQ!r&xak1e=Ksl;sW9{3f(%X6p$*#dbXIKM$
zlAP$&*muzJ;ZUG7TbVWVnI*R29xL6}a^%rYN>gIEsvJTT@|tknIph(^dlnG|P1G)&
zP>x)uMxiFbXeRMnje=H!)@oY!g*uUPDe>Yr?35~e-wW~9r<sTBra(!@CPYQX*wC^V
zlCJI(cV=8Rj~XO%?3E`?WYtXdo<*3I|4pfjL~^LJgPA0q!{o-1Q1`;)TdPyaoaK$B
z75Y+8=j6lKh4d9l(qbV?S@|hew4LgK!%_Z4sM6`)QJcO0-(_Actl`)lCgc=!1YV{b
z3zA4P(Wa}#I@fYGYCC#x0nd%@0uKKGjAcVMEOx=Ff9;^Za9nZR2<adAm75%sX|4~B
z3GJO!>FV&0aFEhfxtc7)H7_HG6LXD2Crvs@T1=Y5yT;6INiC|31>dtrf5(HDo?)WI
zIn24N;2mTAQle}+tK>vbTY`H9UA$%5t6~fB^aUD8<tY>B|EYV&+PO(eb=DGQF!a6L
zhBOW9-R1HbxC)vdtoEE%6CI`#BsHUF6j4~e*?K4T`(w1^;OmvgI?BH~mXcE8<Fdg`
z!XCjv(mZ8e>Ejbua$f+*6yHIdT`pp5H|`R7+(fw=muYF{dT!P^h?*^wNN&?xw_4}d
zuVrhee}<fT{FIIPLXR;u2Y`}Got{f54Q^j=d~y}w3(rxlL0P)P^XhEq@aw2uilO&a
zb-2UN{SfVn!^wU6OIl-kOplz%Z?VYaJ2vw=&=&0UQn5FukX;o!eLo2x!7nly%Epz>
zZN7SC@PZ@2eHc3?I7>U-7PF}q8x5DWW-UAdHe3Hq6xi6h_&#?2rrZf6=$J5!YeOkD
zDZeQAbTP*QkAF%U#=gP7`YgOxX<rQdbo03vI8vs}R8#T~h-`X~(q3PWNd!N;C|rz8
z(MvDbI`L9tXoBIMu5b`*jE=kBjnu4LI94;MX;@SQGUI0ac^yexS>8!!!S$$Ny4}q?
zTAFyP!?<{pL+fB{*|#yQ<|+w-hv-;kCTr4;)k*sb;P72p!T3-CS_$TOUXyCDgbrFt
zV<YG>ez5WNDiR%{wwPeeo^FguFQg5mPcIetWm2=6p2!7s7e^IUNT--3DQI%F0F(zg
zpeywW;=KCa@m6#N6vJHF*kpAw1Uz!vR?4pX7&g>Mo7PMN7!^&Wb{y>tSBA*Zy;;-o
zK5BlkZ)jaP;`{{|-jyntThLdzJwOl3?yLqDIK`mJK7nBtNq(@PshnwYVnpfv#Om*g
zx%V!1GOsq7z+#J%gM*Q#sz1;BYl?ZcVuLg~digJjEvZ~_0rhM#^OBFXGbzBV>UV9i
zqwaj$iGMG>t4g~Rm+wP3ilnoIY-MH^Tg1VOHs-Eq>F%Z8b%WT(zLd(Sz#0aH7mnnL
zj<4Ot2mFttH?4L{F!_Vq9yi*=5nMbZq=)y`^z^}I57(Pt)da2@w}GPr5d~nZ<CKCy
zQve>$`6reir608zbd-cowP15xb^HaGos`q3SInuys0xG`H>YDjUgcI^Ny5lqnQ=K2
z8xkOo{&NsL<U`Hxk8f_-AM-<FAvF4aY)vsF%BHsu%CJ-At%{GU2Wf{{nj$+l)0eMx
zt}ZFyz+79<h?!XvBd{~_3|YKY-cQ`gcUMDH-57=$Dc2Q17O$uxnH!c#v2R#Wuw?XM
z+~2zR#^V0W*(k&HV;d|Lm5d^SnXAT9*`iuSxbkhUAJuLU(5kh)7;o}D<F(CLVLuQ*
zpA~u|MJb;^>lS?^WElK2pFC1H&t^y>3iVP|jswj)`Z_gb6*jS98Ato|KiEs`fsW|P
zqD_~LX`MN}7fXO}1X$6_kcGZ@J<N_LO^G6t8C|-d!#$x;q-!1F*d?|$J`cBGYr%n`
z--#{m%lI6z)`}0eZ>20c^@DG91_)+=r^B!VPgOF$8i6ITKcYi~<aTT|!`BuY<1x40
z-AhE23R_wlhT@mV7xSLZxn;OVYeu_L<UR)1&D<qSm!o1GBd~#Uzou!W4fl-+lZltI
zHBRa6VDYEsrVdgCCJtqI<?8K@|EW}`d9(|f#q=(&rfvFAw8kZPkoZJvcs_>@VlOIz
zhd!c*Ar#GCF(5S>T_;U!*Cg)k66P5GcuD;QP|UC@w{0tum2GNbx73{*^EmFkWql6)
zlzeGHUzs|lOm5osBm~h+=hH3J&m-c0jDFsk$u3sX{3)Z5hF43A6#{L_g9pvBly<U^
z%4r|VC$#jUT<^4Tkl-B>&wiX={!Vpm?f+t8aV}4F<Y0h7NgwxQQW|E(1)_d8byy-J
z<h=A35BQpQ>LJ$C;Ti4WCC0`#^9&CFw5mB<7Goz=J$}yDd&`zTQaaP?#z;rbgtEzh
z>eJ<7>GhMdqKu}E<6O*}U`)S!N<96B^|A_a5maEN;tjAuasEyGHEwy$Tfs{v^+bDy
zy)2;WJ0YHf<HV$SvA^YIFEj;+6DMdL`8ZY|W_utE1!502$|mAbq<e1ZX<h2~U?fRL
zIO*Z+C`A<bQaye+YBa{IqO)5VO?#^(o|ls;{H*27*$zhDpV<*BKP750rqxaPs8d}-
zde5sYCvqCWk6H5B#8cL7;On8@A!}k2cVLlVj&sFy#91^nTJ$Q%j>8{9zc$J4n-iC6
z3}37`K)UtQk8w1sw(tCmd-QhWCwyUFo=cDCwZ|0THb&O$iO;&}4H^%P<#%r)NSxs4
z{S`V)C3>lXo<O0<y6s9-9tfPclhAsJh+bHGyi^X2znR8*mAxcx-52-KrAvp=&Punu
zA%B2TNqI<19j2?G;cHlY^Reyz9J*c{I`}a=tGJrHss<7g^N84Rc@mM$JBX@$$tyY0
z_sd5xqmxa5>nXI{^ACL#rWaz}v9i>f_k5iQr;#VEav5)%^MgKkCiUOdX*u*2!PRGd
zr!R=?<Zlt9GATl`NkP{AA-l;d^h6)_7A(~V&o5Um(Ujf!+0cr-xPk~U_-Zc`-TH>B
zVzzYtch#Z@HO6lK_CA!`1+ke^YvSP5_*+NZMGIsO)C<bNVi61LtK;myP3zImg8IL^
zbpIQC`~N_<mEG-tM2vEV7D`UmFpR&6n3=voZgF!5M<*gq?*EN&6R~n}asU6Q_WwW$
zty5FW5tp;x=cuM<Z7BKs6ue4NGfl$9050#%>eOnu8lsT}*$v(3OoFo2(Nazgb0Bsc
z3AvO@GZ`Ep?NWTp4-zL%hH1P}KZbjlBvB-vsjQ6Y<i;os?s+?1@fli%8x8solVXIq
zbvoFOSi)f^WjJ|p+I({oZ_|_>xQ0dWC=Cm^3IBxn2@QUhh*l`FalW1?qfP1UdLq1r
zfFA%1a@d}RkU^oOo`P98I2hctVYz$KinJsghg=N>M88=w0NE@;Wq}PuVQ&R<26s=U
zUzfkFaI(-JIgdEq)p*L@UXl#sAgp6~am1HgXkxy!K}LJrJcMg3GByYbwhwVCD4e`c
z_Abk#U&o5rVa9&866&Qhxq9!kfZjlG^J6BEslXS%E6{-gKdHQUkO%1HkbBAwTzAEi
z9@jtyKSG1xu-_!<pgl9Jq(Q(!P2|zW+OPm!{11=%2E(C3Mx519e)4%EI^$Yn1Tg<e
z84waS2-2Sx;S)R~VUVd&jCm|)L=f5oVrELcjhlsXOeB8*J|ZST6EM|CvSjf728fVo
zWHHDB1<FKMq!)2BLFBQ4guTtV*f$D&a1)8iqXl4{T;Tk1;iJGFS|JFm;K3w6Z%MFZ
ztU^q)$ash`q?U~J0h;=XJkGSaT+}qM(>x5!zT@^QEZ@mz#L3K%o8j|J6@Cr5%t3Vl
zL*K8rV_a^N;ROpG1GNT|&TIyT<>8apgvkna_K9Lbf{jAEk37nRQROe60iROLhk7YC
z)rSoBqw`8lwe}lM31bpMq5TIZANC4V&I(a^l}H}Y)4O6Gqq{au9mX^FPW77Qm-_Su
z=MVGAsgj0QB!@O~S4aoOUXh9@k5;RcbI~TgFw(h%QsLd<6N?RPx{4R!ux2qnz;1W9
zd39-LMxH}`rB;<{AYI{x^PRis`Y%uG4Qk2gl|-_!h5FWV2AbDJsps3f*Lsb03k#Ku
z*TM|hYEycD#GDmi6IVO920VpNvwBs9MkqY@r%Pwr{J1l_PG!l%sqJ}r#(POy=@n5a
zp#Q6_obyCxDRhoGaFDa{(;k}U+|jYqcyjm$uG@P@>b2*@O+csN`-zjclydi*&A^sd
zS2?yX^fR)cImX$3nkwpcSa-SFvA$w~SQ9<tX@j}*x|iYA51c;(a7YU$P!K6Sg&KZ6
zJgZ`<S3Yl9hcLDuT>7i(+S4W_Z#_b&4J-IWs>gLh5Z(Z+zJFY&^tB~%iSZ8EI2jha
zMgI!V+g%~>C%QIx#pd4$%xp?r<8~QSC->Ql6Q1O^_e!F=iJz`KSIRUdBDFG{5$7xC
z0Ol^>RxKtiLXD3`H6J?qVNiR1xplu9a98OX-<Qle*8f#{=9+wLWNciBsN1>CbQ;a<
z+`0Al^;dLjf~phRQ1_gLQukyAYFmD1V`=TL-^7KG`@G-{NfUDVky^?ahI<N&sI12)
z<83d>Iki^jbj!eWhOBi7#xfOM7tjeS!wXt#hl#bgZA5s!30gZ$@>bc(ED_=FPcJXe
z3puU-5vut()B}Zxht<1oX|lZ6T$Ed6_)Wfth|aT#v`R?k)IsdzcI@O<da&t<n6aEV
z)8O5*jBMSFe>;F7LzeWGhcCp&@6vMk8Il6#R>~U=bb4BcF7LP?V)pTVf+LPHdLj$d
zupF-TY(|n)4raIV!H{Y0e8Kwu*%enPcW@oEv5vLol}rRAqM4Bw9NMmsm0Oy%N4a45
z*`q&A2_*qdf{Y)I8XixCS}}y0ApsWvfu+pBoOeAC!|fd6jEjY6v}&HAJJC5UVh+hR
z8DqNG{aeZ+r`(lbts;AK_0dnLP%d&t6oGHIrbe(*DH-P>>`Qy{3{6WD(O01=RSAdF
zh#1Hs{^hbNf>&ZM290!XU$-#?;iefQj6<@vx4nW#`jl3L&Yf~NzVNzVugQY{>)l;N
zdGq)5Vk)hac%i|&F|+xU*&2HLNi<0KW5g}$29l5MI+kUl;%roC9Y4Qd4fA1m-4oBP
z)Uc1x-uJROks44+v0;7k6qgT!Gbk^J-|4Du@1FAqJw}!_N6Ps=3C6qR(M-l-y7r{y
zE#*w$Kv3qj$W|Eze&=;7B~xfKP`)f3x$7<`ZS@ABy0TyZ2OdS9B4P;docXYe09-Gx
zGVGm}FQi^RlR@;Pls+eKV1CICN6HrCW)Df{<t>K6rEo-n3h_l>=R`zJu}oNF(Ux{R
z1b58B$UUzzbDFFtI>q~6C?CM9t^oMlzC!Y7=ZO1Q{Yg0Xlq%spa)+e0U?)cdIhe1{
zbM4h(wrKjmZ33cO6hKrEqS{jo=C&u#Cne;+rX{2mo-fa&kVY^LhJu=aU#hh&@51>=
zkC3LW)0W2wz_K#f0NI;?O%@8j>SFgJlrmrIA`ig8u(r=dCpM+=6LSi)c?N@46t+cf
ztXs_uEey>MEe_2C(2GI-q~>EU{*?<LmKI3~qJ!2zsV6fwKiN4J#!6r!Gob2|56V+!
zwl<UEZwai-*RF$WHKgA8LrZ<Kt^!x}N4xr8z}OKCvV5xu#4BUW6r)41!wQ93?e7MD
zbDq(WbM>(aQ;U@PyL6DOkYyQ=s@YX-VL^^N6Y84mZuu1LL&lhdomRs0*4)Huute$P
zH|Qjq=?{4q9pj6iJlUhGy={I`iz*W5baI8kT3_VAZO34q$<@3$*fJ5l0zRK}OPVb8
z100^k`kFk`s#i@Ov{TDtt<0{Fsiq6d_?T!1`mFGWjzZF+Y)r|{r;dMh=_l@abF6cB
z2{E}Iy15qS9=h9r+53*3;ldMl$zQ7cj1dS29=iIZ-w|l|7K3j?T&~ktlt_DM_^Do!
zQLMPkzaN=@12NBy{)`zJhuN3o28l+Y^6pN3-L}&Dg!9*3H;1({ieFwSc-aqpZVmG$
z7Ax`(KnJzMfXjs9D<J-0{W}vn#EaIWA?=7C(T%(Xk~L-$1&&8Q3P9k~|7I>0^}5Tw
zsAI?5E&GL-9a9;Kas5;JBd;5zGMAdmMqCUn`EXR-t4@!ZCZq-xDN;S`gwE7qRC~6a
zWS3jvCts>5zl_`LgD^sa(Nu7W`mrH0Y$vs09;O)Gy+c^AMOfuB@<Tj&o%};D;J<D+
zkl(JI_1QXSLcx;(O+*F72hio&yk>fl;%Tm!0ada02@Pup@)K5DC-E%Q=6#`A)6HWS
z;op}DYa?;)fx{!Y5hJH8ClWYKVrkCBVU<(p-)jkydPt?8i~8duINI16fsCY<P(Wk&
zx~NKpq<Ho*mK1T7q1n=LbsSl2=>mEqb0;8ObXQanb5b4K5J?IRafEt#5S?KeN_kk3
z)bV$K!FNEL3d|3T)h4BQ_tcoDj<Y3m^!TUq{EZV>1+TS_Ut4H8#C01CdZhvYWU42r
zt;lmQAZRHdK<;l4=EjaY{2E4ne5B+H*p?|36)rN*3ze4el`_?y5V<Lv;v=JPA>^xU
z8JwbWDn;IG5%fnEcMQYZf)q6{=V*jGQDAtsGcklEb%h5)?%6{+pb+$oDv*@w3R9Ce
zq<Pp$^w*}%ujHX#dKZXEt6vuf`oBmMaHM!qQrBE2GM;H%3gHrow9<){4n~wuCfFV%
zTOyCO*b0ez0Qp9%dcQ{1j-h!*)5|^6XEZl5XY@Ttu?+hllas1u(V0>=!HdZ)T0*cu
z8e*y?HQKTV*qFmXElyz2b|jW&k$1(vs;E5>nprn_tT&=01I(fDT2xiM!I~4v4Nz5s
zK*F(!ek^$SOAA+?TjBDbEH_$=T>vvjts&MTMLB0gt}t8qizYARW#V<#8K?`W3<XJE
z<Kxc-&~Brxo-RbZigr50c++*$tO73Z_Vzs3HGbDDPS$S_HS$KN$>n@~#_rk5>2@T5
zT{N5;4$8C!a<Mc9*Glfi@Aot8zAcd9TZ#!~E2{}E&P7TqgFjNw!+P^3+~2IUWyn%)
zF*2C2gTE}Dd3-YFMnAVB44bW7qMPB~!q<Fx>P$W_oO?W!9_&zWfGS$Z(b%;}$Pozo
z+&n{&bbear$SlVah-o6pv)yhYun6~fjyRFYri_JkDo&O}o~b01duyqh&PlSbp($+r
z8u0zR)1!>_{dxpGm<DETA1I8^*=X>uAdY8-T^}?N{A3qS&W{k0KiEjgt!v4~-$yG-
zn(XK*eYWsmuHhhWcuF|mh_%h>czaw@v(=m3C~Gjkdq4i0o^_ED%|i1_)9HrcbT*E}
z3Tw7PQQ+v2t3<i2wqUDvk6Cah_67A5{3~8?Z~BGto!obF>srr!Q1BW$hpoY+ciL@o
z`sD@f%Xiy)Xu59i5c54j?#1^xYWgLBvqNM4Jwk6r;iOVPuf<mSG+A$9+}v^K%X6dh
ztQk0}<~6yLI^K4877O2fxD?so(VD?Fp5eQn^L!*5{q|<|J~=(@^L&yj8{KVjxIhqX
zH0e=j)=P^PT+)|wOmR8%Z{?cGp*af&u~?pK5<al6`wH!|QRzi6_a5{8NaV)$#a7<O
zxvnzztQRuTM4%>t97RXhH)Epwg`h#(r>m_Jvv%rhV_KkAzBAv`sj6&BU?L$;^?@+A
zIm2hSLZhi|-S)4b=G=|oOU^_6N~3;9?2j$}kB<epX%2dc@=YV*3?!#jzgcu3DJ*Px
zG<L68C$q_Z7k6)HUy!-#iL4t~VpGAMp{Piwk<?bAzx=2L>%gGZaHVbK&oP7gam^%8
z+s-^zN7K$lj>2bS3zpxO#rqhezY?GmnYBi(%sL~vH!=z4h&%4x*5s&{{<YJPqbIb~
zZ@*|^kbFfu00JosJA`KycwHME#ZIEtuA5y@{t~Q**qFFh`+AtdAUE??t2(#o)#2ZB
z;?e57|IVx=1~nTKW#HWZ$`_>aZk>5@aoW6Gw{m3|+b_UFSZ?D#>*U$O{Z?tVTho@v
zScef9Q&PAA5q}JAwN~xpptZ2&;IPR#4t;ZUk>*<7r7O8{b%c~g_;WCq!nk{$Vs4Y!
zfJ<=hRmPSFVC4B}>O^iK=9*t^zOc4sr}*NwTXM2e-p#zOV|&5i>w<~do@r;Td329)
z>OlCw(d39*KAmeVP}A<$6nr!O;K8ATq^O8+7J_>c0&Hs9SB~Y<F&eszp?Q9yBeZq$
zGO0`*e?>Hz2zk_$Su-lvV&cVoNw;xK--P-?Y|-kH|8LgkY~MS|{<mhiu(OkytpgP`
zqq4b^74ZA1<ZNgGG;;cOi8=rcoWA2^43vTDM6TveW<+M+>v;zUpb3$kfsv(wDUg~`
z-oe(`*$C)BWoK;iZAQ<`z{bSD%uUTG4Rm+4buf0MqK5g;oXl-)ei}FdiKu?^urM)m
zF>$kSe&0EmnQ56=D4CcjzwKmgjsIUJ$_@s0c0gkyMsWixM<5KNyt0@&gSfMmm7#%+
z4bXvzO4-cZk?8yS&+^a_sRA7wzh6zn%D~LV%+Afi!A{S@!0}(@{I>u8HW_mlAklvm
zikisM&cMmc5okc<^1sq?Ft9Q(QNsub{6|gxhtfL>EBpUZfBzT4!TDbR2Q$n6R(&U8
zXZc_F#{U4|@K%<!TxUY+ysAkv@{&+vO|ZfiAtJk<<)F9rpyB=-t`3*^Q2&_nxaCT=
zCUQFl8puD}fyl3+2nI2NLIWY`rwoowq_|BGD8yHRTni!gP9o1909pk{MOC&M2Vj*^
z)t7>l1g9b+1x|jfGD8cU{6%9e9rLbDDmQm@Y*-O!>fZ#DgMRK>*#zDDSZX;mMhtXh
zPoaDp#(o$q_~7+Z?E2doZMHlhQ&7)Fs%RX?LL?SY*t3r(vqiY3BnZKqgUc|#|JKFJ
zda7A;k9Yo44k%(v;xJYj<^(d!Bmc94G|>S1CSL>C%eC<b6YeBm(=<G>SY{S0K|J2*
zre{<d3=X*%nnH`E;*CDfN!m#YGhp!D$CL2Rm$g1KjPGD5{jJWuf&abD<5;)a7jDv=
zO30J^FAoRr8)&Plg6xl^n~wdq7z-6474as99=HDK^8#<n(BS3uhYq&=VQKMUD;3Zt
zth3E7IBQosNFYxi2KjH%qpiGYXI0Gpedznyoysn&&f4?|-UA-eUSjHW?4w#`V`}Cz
zFXC$1dX&>9$&`MN*8HLwI)?U+#&G^bZKcDFVww`K-uNGY7r6^sQuB`5cOCw&$@}W9
zw}MTePc1*VnSIeGujcY5r)a~K%cb#!+YOsuo!)eR7yonoOX-V~L~!EY2E8`5_K%gd
z)^vkko`3HMgaA!nfsV~w|6K=W`@b5Xx+2g7hEdYS80bc%#YDuy#;yaysATT(-K^Qz
zVHnkjw3vxliI~3w6>V*uz5`jlvrznx6ehOczW*2z{=@f^NQ+N|NlctwRD_w0OO#Dm
zn3IcxMU0j0C#wjD2pfyIun4;V(f`}z`#my18`JL*gM*XlKasuvLCYtf*N~4<zye&~
z{VXc236>IQES1-L>niVRG@I)(CpV9mGR9mjB_mg2!*UvqqGdISVm1naHCksX;9XB!
zasWnEynhW(+GfAx++{y?Og~K)Bq`EzNUc!C51K|KV`Cvy1}(=;PAt}Him9UNnLJin
zh2!5C71MU+=3JW=w;YS&kN!4!{U#g1hc+pWbtRc1i%ReE5C0oyXYy)M{r?3g0@(e;
zjYKfhw|6J&NhE0@8;A#qBF)5;c#(}{izsE%2Y8cBq>j{*JtUiCl6ta@Y!zicryuhn
zK_ozwsYhplq=xJxRV0LL7v<rgx9lLvB$`-6nV0oKD5)lKBt}#~lU|4=i6mZBu;?s-
zG>{}wAzyWtLQ+W@*+nWzI>``anbrHOB#Z18722Xig=K;8RuFLoL=J+eeh?i7VthfY
zEqMy!UV`{JkkAGa{XmifNcIOQGa&U1Nc#iQeL#i<WF7?82Ox{9+1?<>3UUWP-dm9G
z1_}~D;WQ{(1jQdfi90Aw24xeV{5`1n0jfMfbqJ{8?Q8k+x_6-75j1px#wF1788m+f
z`*Xp8DbTtM+FW%@a!4-86LqKp3+<htgN^9qr*<(r%(r#35#2AqkyoH+9Q1a8J~KFK
zBCkRJC^(h|j@yBON^oKloaD);0?BW1#tEFQ0_TRn`TJmyExzyzT=D`#+%nt)M%cvB
zbTGzjYz18AzHz=|A{tD#gR9&(#U@_62&VhM^<FSD3}*Q&-1q`+I+LGZJ_s!Ev0JCX
z?T29TFSx_+y_>4rlurssAt@roqVCP;)xDygC+Vt0)Ten}m5TcIQCDRm5lgx%7a95g
zm#-j=q)FuQ0yBft!r+WBI3o;B27{Bq;5;x~L>|>*ay>Dte^Bs~0}5qsWOH<KWnpa!
hWo~3|VrmL=bYXIIcyeWC3NbM;F*7m>B_%~qMhcr2np6M)

literal 0
HcmV?d00001

diff --git a/exercises/Exercise1/measurement.txt b/exercises/Exercise1/measurement.txt
new file mode 100644
index 0000000..1038d5d
--- /dev/null
+++ b/exercises/Exercise1/measurement.txt
@@ -0,0 +1,10 @@
+4.980537739146572718e-01 3.304070957398243524e-01 1.000000000000000021e-02 5.000000000000000278e-02
+6.762361077018429478e-01 2.837307206165508577e-01 1.000000000000000021e-02 5.000000000000000278e-02
+8.052292433523977611e-01 4.407017550224799907e-01 1.000000000000000021e-02 5.000000000000000278e-02
+9.704434451011637597e-01 4.982765800216331642e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.129455114674715155e+00 4.537414756680630545e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.285083611438478268e+00 5.281917212757096802e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.435421444857490014e+00 6.421928523153139778e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.591387685845770950e+00 6.063640103412939464e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.727425218084549075e+00 5.999229259846586837e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.897833779891006545e+00 5.580646104775206506e-01 1.000000000000000021e-02 5.000000000000000278e-02
diff --git a/exercises/Exercise2/Exercise_2.ipynb b/exercises/Exercise2/Exercise_2.ipynb
new file mode 100644
index 0000000..d1f2227
--- /dev/null
+++ b/exercises/Exercise2/Exercise_2.ipynb
@@ -0,0 +1,502 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Exercise 2\n",
+    "General hint: You can always ask for help from within python if you forgot how a certain function works or what the correct ordering of input parameters is. Executing \"some_function?\" spawns the docstring of the function and \"some_function??\" the source code."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import scipy.stats\n",
+    "scipy.stats.kurtosis?"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Probability density function (pdf)\n",
+    "We will look at a few common distributions and investigate their basic properties."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from __future__ import print_function\n",
+    "import numpy as np\n",
+    "%matplotlib inline\n",
+    "import matplotlib.pyplot as plt"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "For your convenience, we define a few pdfs and functions to draw samples from them. Have a look at https://docs.scipy.org/doc/scipy/reference/stats.html for more details. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def gaussian_pdf(x, mu, sigma):\n",
+    "    \"\"\"Gaussian distribution with mean mu and standard deviation sigma\"\"\"\n",
+    "    return scipy.stats.norm.pdf(x, loc=mu, scale=sigma)\n",
+    "\n",
+    "def gaussian_sample(number, mu, sigma):\n",
+    "    \"\"\"Draw samples from a Gaussian distribution\n",
+    "    \n",
+    "    mu: mean\n",
+    "    sigma: standard deviation:\n",
+    "    number: number of samples to be drawn\n",
+    "    \"\"\"\n",
+    "    return scipy.stats.norm.rvs(loc=mu, scale=sigma, size=number)\n",
+    "\n",
+    "def lognormal_pdf(x, mu, sigma):\n",
+    "    return scipy.stats.lognorm.pdf(x, loc=0, scale=1, s=sigma)\n",
+    "\n",
+    "def lognormal_sample(number, mu, sigma):\n",
+    "    return scipy.stats.lognorm.rvs(size=number, loc=0, s=sigma, scale=1)\n",
+    "    \n",
+    "def binomial_pmf(x, n, p):\n",
+    "    return scipy.stats.binom.pmf(x, n, p)\n",
+    "\n",
+    "def binomial_sample(number, n, p):\n",
+    "    return scipy.stats.binom.rvs(n, p, size=number)\n",
+    "\n",
+    "def poisson_pmf(k, mu):\n",
+    "    return scipy.stats.poisson.pmf(k, mu)\n",
+    "\n",
+    "def poisson_sample(number, mu):\n",
+    "    return scipy.stats.poisson.rvs(mu, size=number)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**1a) Generate arrays from the lognormal and poisson pdfs and draw an array of samples from each distribution.**"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Generate arrays for parent pdf and samples\n",
+    "sample_size = 1000\n",
+    "x_float = np.linspace(0, 10, 1000)\n",
+    "x_int = np.arange(0, 30)\n",
+    "mu = 4.0\n",
+    "p = 0.5\n",
+    "sigma = 1\n",
+    "\n",
+    "# Gaussian\n",
+    "g_parent = gaussian_pdf(x_float, mu, sigma)\n",
+    "g_sample = gaussian_sample(sample_size, mu, sigma)\n",
+    "\n",
+    "# Lognormal\n",
+    "# logn_parent = ...\n",
+    "# logn_sample = ...\n",
+    "\n",
+    "# Binomial\n",
+    "bin_pdf = binomial_pmf(x_int, n=int(mu/p), p=p)\n",
+    "bin_sample = binomial_sample(sample_size, n=int(mu/p), p=p)\n",
+    "\n",
+    "# Poisson\n",
+    "#pois_parent = ...\n",
+    "#pois_sample = ..."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**1b) Display your results in axes 1 and 3 in the figure below.**"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 720x432 with 4 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Plot the generated arrays, comparing the parent distribution and a sample\n",
+    "f, ax = plt.subplots(2, 2, figsize=(10, 6))\n",
+    "ax = ax.flatten()\n",
+    "n_bins = 16\n",
+    "\n",
+    "ax[0].set_title(r'Gauss')\n",
+    "ax[0].plot(x_float, g_parent, 'k', label='pdf')\n",
+    "ax[0].hist(g_sample, n_bins, density=True, rwidth=0.9, label='sample', range=(0, 8))\n",
+    "ax[0].set_xlim(0, 8)\n",
+    "ax[0].set_xlabel(r'$x$')\n",
+    "ax[0].legend()\n",
+    "\n",
+    "ax[1].set_title(r'Lognormal')\n",
+    "# ...\n",
+    "\n",
+    "ax[2].set_title('Binomial')\n",
+    "ax[2].plot(x_int, bin_pdf, 'k+', label='pmf', ms=8)\n",
+    "ax[2].hist(bin_sample, n_bins, density=True, rwidth=0.9, label='sample', range=(0, 15), align='mid')\n",
+    "ax[2].set_xlim(0, 15)\n",
+    "ax[2].set_xlabel(r'$k$')\n",
+    "ax[2].legend()\n",
+    "\n",
+    "ax[3].set_title('Poisson')\n",
+    "# ...\n",
+    "\n",
+    "f.tight_layout()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Mean, variance and their estimators\n",
+    "**2a) To familiarize yourself with the properties of the distributions, write a function that calculates the first five moments of a sample as well as the mode and median values. Compare your results with the expected values.**  \n",
+    "Hints: If you like, you can try your own implementations and test them against scipy.stats.  You can find functions in numpy and scipy implementing all tasks. The 0th moment is just the total probability, following the convention in the lecture notes. Sometimes the value 3 is subtracted from kurtosis to shift a normal distribution to zero kurtosis."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def moments(sample):\n",
+    "    pass\n",
+    "\n",
+    "\n",
+    "def mode(sample):\n",
+    "    pass\n",
+    "\n",
+    "\n",
+    "def median(sample):\n",
+    "    pass"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Calculate moments\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "None None\n"
+     ]
+    },
+    {
+     "ename": "NameError",
+     "evalue": "name 'logn_sample' is not defined",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
+      "\u001b[1;32m<ipython-input-9-e9b6f1fa7aea>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[1;32mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmode\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mg_sample\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmedian\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mg_sample\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 2\u001b[1;33m \u001b[1;32mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmode\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mlogn_sample\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmedian\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mlogn_sample\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      3\u001b[0m \u001b[1;32mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmode\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mbin_sample\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmedian\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mbin_sample\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      4\u001b[0m \u001b[1;32mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmode\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mpois_sample\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmedian\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mpois_sample\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;31mNameError\u001b[0m: name 'logn_sample' is not defined"
+     ]
+    }
+   ],
+   "source": [
+    "print(mode(g_sample), median(g_sample))\n",
+    "print(mode(logn_sample), median(logn_sample))\n",
+    "print(mode(bin_sample), median(bin_sample))\n",
+    "print(mode(pois_sample), median(pois_sample))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "What did you expect, knowing the parent distributions? Hint: scipy can also help you here, see for example \"scipy.stats.norm.stats\". You can check wikipedia to quickly recap some analytical results if neccessary.  \n",
+    "https://en.wikipedia.org/wiki/Normal_distribution  \n",
+    "https://en.wikipedia.org/wiki/Log-normal_distribution  \n",
+    "https://en.wikipedia.org/wiki/Binomial_distribution  \n",
+    "https://en.wikipedia.org/wiki/Poisson_distribution  \n",
+    "  "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# ..."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Estimation\n",
+    "Obviously, there is some discrepancy between the expected or \"true\" values from the parent distribution and the calculated sample moments. We would like to work on the inverse problem of guessing the first two moments given only a sample and knowing that the sample was drawn from a normal distribution (but not knowing its \"true\" parameters).  \n",
+    "**2b) Remember how to estimate the mean and variance from a sample.**   \n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "  "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**2c) How to quantify the uncertainty of the estimation of the mean?**    \n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "  "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**2d) Given that it can be very cheap to repeatedly sample a distribution with a computer, try to come up with an alternative approach to estimate the uncertainty of the mean. We will come back to this idea at the end of the course.**"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Multidimensional pdf: covariance and correlation"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Imagine your're an astronomer and are measuring a specific parameter called the \"Clumping factor\". You're interested whether the clumping factor varies with temperature and how. You have 8 measurements with the following values:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "clumping = [0.5, 0.4, 0.3, 0.2, 0.4, 0.3, 0.3, 0.2] \n",
+    "temperature = [2700, 4600, 5120, 5550, 3600, 3990, 4190, 3900] # [K]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**3a) Write a function in python that computes the Covariance and compare the result to a python numpy or scipy function.**  "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**3b) Calculate the correlation coefficient.**  "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**3c) Interpret your results of covariance and correlation coefficient.**  "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "  "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**3d) If the two variables are uncorrelated, does this also mean they are independent of each other?**  "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "  "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Counter example?"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Bonus\n",
+    "### 3D Plots"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Try playing with three dimensional graphs to visualize properties of pdfs with two variables. For example, try visualizing marginal and conditional distributions as was done in lecture 2.\n",
+    "<img src=\"MultivariateNormal.png\" style=\"height:250px\">"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### nbextensions\n",
+    "There are some useful extensions to jupyter notebooks, check https://github.com/ipython-contrib/jupyter_contrib_nbextensions if you are interested. There are features like a table of contents to navigate around in notebooks, line numbering for all code cells and options to collapse certain cells to to keep a better overview.\n",
+    "\n",
+    "conda install -c conda-forge jupyter_contrib_nbextensions\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "hide_input": false,
+  "kernelspec": {
+   "display_name": "Python 2",
+   "language": "python",
+   "name": "python2"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 2
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython2",
+   "version": "2.7.15"
+  },
+  "toc": {
+   "nav_menu": {},
+   "number_sections": true,
+   "sideBar": true,
+   "skip_h1_title": false,
+   "toc_cell": false,
+   "toc_position": {
+    "height": "658px",
+    "left": "0px",
+    "right": "1388px",
+    "top": "110px",
+    "width": "212px"
+   },
+   "toc_section_display": "block",
+   "toc_window_display": true
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/exercises/Exercise2/Exercise_2.pdf b/exercises/Exercise2/Exercise_2.pdf
new file mode 100644
index 0000000000000000000000000000000000000000..a6bcd4a441c142a6fad8798ee9501e717e3b7693
GIT binary patch
literal 36354
zcma&MV{9%^x3*i`)~apWTD5K4w%t{G)wYeNUA1jnPi?o~dy<o!z4Psy>_0PO{+=Vb
zt~tg{p(HNJz{1D@M{#s}^$N#B%uMWPVgtv|&m?JW=VtE0Bxz^tW-e}S>S$)pBxmkm
z>1IXD!pXxXC<y23=3;Ja59hVfs%`I>BZ>08V;D@TKA_UGqrV18FOgJ5Y5hTAZ(dpD
zk~Hk>%gO2pIfMPO>iG=mz+wsAQGJkUZfud4mFsVT#rMNK)PI}64H`~#N#6Oe80Y$w
z#7!=o%DmgT&P#reTH5P6?l(ksvSruXLDU=l9s=Qz%z2P*;0Jh<mqo>3kt|)Jfb**}
z10$r#nw8eA6S+)gz4UQ>N;5&Dy+h{OV0Vv7;LoR{SnDK5NR%Qj2-ed>`_tIO$PfxA
z<ihZPBf(87nap~WBn%sX7G80Puv6aan~!zHfl&PK>w}ckp)=(nFMB9FfMn+RPStc|
zI+W0Eaw96XhE<go!n|(y#Tx$aQwp#Wz%<Rh4ZKKcE9MB^(qFgf|8Q}%JC`K|a$d9B
zdrB;n70A9*5AKtkTzFkizKawgh-n(blX(UbwfijXzzaxC=wzWwZ}!2r<13dB=Vq+~
zH5ZFEaPMgA4)t1!9Hxq)^!46TC+DcTSE3}Z;RviI^tL>>cL)D486s|##k@$kLUfEk
z(Y4d0lyPtqV`Ct708g3Eay31j8|OnCl}(q)(w$VV|EVJFFO4L-kSS%@uwJm3%;e|4
z8~c}|&-vy_5{eC#+}4AH_gDGhmRt&lL`3=eQ9nPu8)@S4TZ~&$lkY3lNiP6lIqbJM
z&x|$O%~Lo5lt(W=rPwQ1%8D?fV~5{!|2~R4DgX)Q5Zs~Dc5TE!&ixzr&iGa9^fpD_
zJ$z;MuW6@q4TskCI!1$y3W(QXRp3uD{sR4);4~{kUfOZ!c<_o|9A@KcXvx&W%o=5y
zbnA}v!*uhKbnNf>mUI2B7{OH)G3kpAX>@^_$C)}Ks<I;OWDdf@vD5yon#SP~?P7To
zQ$s|)kMBmkrdE4f;;Kf$y?{eRo;teQB;-fFId^!z`t9X|XAc_D14z?WYlAfRkFQ$M
z5Ri^A#BjR3q-PJ|44>f>i9ar3uz`>>shQz&NRb;c`UV0FNZ7+sP&-PdTlD=NQ9X!t
zygbW2Ff0LW|FkzSU>WZ*@L+Q((>YtDREYxcGfIw=+Olf4A``r$P_}iyI@}m=SiW^8
z?mKFmnx+??!u*$^r&JQLv~{9}KcZcSIs@xk8d?CCYbBUZ{ey`}>Wq_!LIk1JdE{-w
z-2#4P4(kf#nF;<iO@|P&5dg3lHI5kS7C4>TVjv4R{f8C@sghgaU#v=MG?MN&Vy73;
zzGhUqKh`9VzKy+SCWzo?ka!Q`%jR!Ww{2ODvi{pkjY-xAJriNxb{spwwEH}6<p#pr
zh6lfMt1@!oH8ycC+Dx|Ow%YBN^$hjLJus}hNamzT7t1H#=s5f#(F^I?L=F3I=(cmV
z+r7(LFHFIxr(4}t&*7}jn<a9?ZbW>=dpdK&7(9HajBPigu7=zQKn_XZteDl@v2XrT
z#>MJNMZ<nBr7`LbPkcRVK!ta{%^>`JWK7V+hAu~S;*uQtRO#W*Jil!!_VV`*E1g)&
z=5iFo9otCVwguvPBd~jkzi9L>YNlPo8N&~DaC+k3WsX22!U<~WbXf>EVGV$0Qt!0}
z1=MDum=OGhY!8dX^t$dWHe{D2`x{hZdIp*-GMyVsPHeh6_s3%~oo<5H5H8H5hnifV
zaMR`9$h<8Gc`KbBY8b#$;fXGMXN53fCqbk_H8Z3lMcATWESCo&?a@Z2i9kbdZ{~L<
z;^%_(&1VHXSJZ5Wt)v#}r|}S?)*de&)>-;tgO{8;X{a#m&sR6ru;?0~cU&S-Rn6_~
z8AvaldHc9M>G^1)lOy!gGD3UWAiaQ0G++U@gp7Y9d8;!#{=Q(-&0yfaNXSbh8tLhs
zH+}23Z;5DnNnUMbhB2<3{e@>G1wPef#@jmAi1LyjWr@wJ`^q0av%+0IhnSdCIyWt`
zfXS<SO&{(+nb<prsPWvWovXIzhZOm0DN~NY{VaDPH;m|eXrxHvz>U{k_*#1U6_^nU
zcUFYs-QR4tNZ;JE<w8K5Fz>pgtE;ZDGwv7P91R`sV7tMKw7oJ}>OmRoOnZ993f3Gx
zD`wO-!UMr6?1BZB9a}=itUFWlX0kyX?9gdpQn(~W_!8lw(LAGYz`ABbrgWq5g(EE{
z81aXy15cSe61=n7spR))mU~k+;Zf(iiKQ(Cx)jpAAI4)mC|38_9rtoMGh>TVv6|4`
zpTVzN`Y_uif-#T#JISxO1-^<*pElgUfs%Xlq))TiVDtfI0>*nHp-*V$wzge3a|g5k
zms9_#{vWcju>Y?R$HU6=-w>yx>$u5@5wKg&JQvCHujrOm2J&9Tj)C*i82?@aGXl+!
zb}C&!+F<kRJ?VhrcY3x;NlY0_>8s!SnCm`Iq|RsaKX~$={cz%YXofr_An*SDi{Z^y
z!#cwbKmGMi6Qrbcyi1SQ8sh>zsyMyFGD98{;mS?MbQvGl({yCyE><7E6i>L3I1R=y
zwJd86qS<CS@Nqbra_UQy|DU-lW@1d3Gbe}~zZdi0y*~)vZsw~{4=7&jEJTE&<T-c2
zYu_E!3DYF0u>GXc*1T!3B<zJWzigg%+qBx21G9}7tp#;Xe32PLD({4A1c?Z_rWLu`
zbC^5D6$?`<*fV7>QA@`Q7IGL@3H*KYQjd!{ZY;+#`Xi(k-OQ8;WfdT$qdJ_TY&@wM
z^2=R_?4bVad#`3Ux?zOewo{FB<3h$BoM_K5eX`UE<XXTU6hUh{wiVMn^{bnX_b*$o
z3Bx54R=;~yOnd>|)_lT{@xMSs8$a#bG_q$^ogaLF<P03~DHx^3EQDh-=!c+tsW`~q
zQ)q`&_YEwBQBO8V@m18n<fo_xOl!!9P*OB3uEIrpVV_`0rlIb5{J@BL@~tKZVZ{sU
z>$xp?3BVJ<u8ALBB^kxa*e8j1L7E<3p+IsD@DoSrds&hqpAd;T6CC9Z_x-?uYUV<%
zcX27((56i!uyql)B!cTojFPg|_B>mPEa_R$RIc2#VZ6Vp+XPm6v#|-uFedw^Wr$`_
z>!N9rUZjld!NM$Q6+jM3fDo)%<@{M=L(5dcGgdsk!dWB7oua{dOHfeqlxt)v?6F)p
zI2W)xx>%EP+9n_|#>42Rh#@iz5gI^YFG@#{sFWU1)~r!{N>=9`kRywS2y4AO04S}d
zy>{e7jxT0dev`IHXTO8{OS*8dZgL^HDliQlt+Mi1=IvOQcJ6+=Y=_;veO}XiG1X8<
zV26`c_U!le==bcJhr$Hl%>%FkUQO!XxYHKecX-@IBebr&C*tF^1ysBm-#97BpzK64
zc1Zq<Z2ab;D(Ig|7N34mb@dl`x1~Occc#Wy3rlU|r4A{@5G`Zo4)$SNOV;lj>uP?-
zW-p_3Ie6jRDBlr}7Xi`JCimKk1M6&PjqkgX(AD&=e0bH!LgTS+2b}q{s{0_{`V2@4
zG5?ahbC4R7ie^h`dLF385gX04&X4GrM}(7GAR(26k<T=xRV9=09iOef`quI&e3)!(
zgaz@wLI19N4y4x1KhVVe_a?4KTB|x&xYb*=sruGJlRl3R5+;Gp2}1p%${b5iL~4u-
z2SEaeoe}L{77)V|Y^w_Cw?dTv(|1)~7DC3wskhvZMgI~R@Ie(qMgGucWceygKM<5F
z1kzz9iU)Z-QC8#0a$=hz0TV;{C{du-@m6;3C4END1Qs&mqNo1a(@Ho~NT41f&RPj3
za{_xAf_npAFeTKWDvtKoIF~y@AqM^zI*HcfkRB6W&_SmLe8|pl$c7h)$T$S15u<DS
zDR}%^1H^4nnvp5izoCaxM_?*kl3!wP+G$(ugwMeJ9s54%g@^}T%4YZprDz5-%0qyt
z+%G2<HtlhY<_75$x;%Xea!d!ZI0uRj;^yv}WicwU5@Qq=e^%icc}Z1TqoR+69G{K;
z6B0||G2-r@ZA`UhT#}TRFdTk~G<!NNimi(Ta%l@cMq^kCyGOm^$D0?ucyvh^ziWKg
zy!{(DdUI*U2Kxj${DfL);-gfby^t8b8hW>6%Y@_*bd}8&ucTdg#0K~lzbU-3BEwk+
z?J6#9YHe{dQu0zCrm!GSLh(yF45e$etu@!^Tc`w_&Ggw=uqfcsRY*d7rT7UYqpxeZ
zXHwE~xD;hm7<yoK>o8erH@935md?dl*?O!vAa2zfrobDwUXCb2g(X6kh6M@+L;ztG
zU<H0`!L0WZGAVBB<k+Ei8@<cTb08f9?Y&HdEx&(eA7+XRrIHKmQTdv>={0t#^bCgd
zD%#apCrdUitjI3aE3>6vWFp-Ay}Yk3u#jK<+@d+H;eL1Q<?vXs-g4@{3XH8<$v-}g
z>kOSnT$WS==lwL<MbiicBWdE}IsrO23k7tI-?5h9PO=@%7*esD7Ov$?Yem`CIs~#q
z&CePldXx!uM<D8?@Cg1wUWlsl@gxCffcI!%;%%p}+j~D{<u8|xaC2_vkeiroRL2p|
zog)Y_Dfh<)h>7s$UnknfigrGKdL_r$k-dwxv4I;$E1E(jl%vk?l=yt)PKZ7sMI!hZ
zZfbN_pff~_*!tgO!y062bvVHE1>M`cZyY&e4R@^ClIQ^YMR_;JY-8{<KGq|csjCKm
z=`MrNpj{86Aj_{ipku^F(d#jxiOYVH!DDP*Pu6WNh1<GxWgzLL>(!f?Y)HCNi~(>_
zcmJ>=BErO|{s`wcO;zmqr=sHEAagYe$kE-CJa*0`BP2%dTuGWqY{<6*s~m)^9wx1*
z`z!*U#GyP50_+D!xlD(B4$3qQs~b9mpbia$avD1P6Pzh$o{#pba}3uaYNlK#b^~#d
zky{(yn*3DK%?AZ8W{JL`t)qjm{s)|M{cmv2%Erw8-*B$0>sTy>6|id<+`@pkqLA4I
zw!F|YWyQp{k@lRNq1=s$iqB`q>;>wX{Pv_Pfq7&^5E&BFb?Efb%j;e@Of&kCA1Ofo
zW6VT)FLcideN(P`6WOPPTr5l+4A{&a^+GvZ>sHR_xOHeZ{5*P_1YTU#@8mlQU^IN6
z?gswK(fEM3Bl*Q#m^YeYbVEvtM2d!}WOy1+*cjx@xd*qCK`aRxKMvwPHED4rw0m{W
zYTawN?KJTDwarqwAe#5QAfXmu^e_*{-LWu^GcXSmACedmy5Gx(?ZWzGL>e|1l&8Jt
z%=x5bLki~Zr4EEgQ|7#9ES}--RD9@+pj=qE_xjH#tvE}@^OgEzXn#y_^lBz<3u8Wr
z&B2x=MqIe4@TFdp;W{F0X(*-0BN-nCo;$|g6<nBM1~F)olnZ1@Ok;AZp|3h+kVP?$
z3)}NWTe+QTYqv}ymxHM}Hyat6fUj-H<*jmT%J}zCg!sHvVEZynE=s#T3P?$o-czc5
zc4Vh(F*!ljzUZD#vI=Wcat~ksTh<&XPu@w<qqr%Hd3y7J-tD~JuTrXrM1`s#9llQd
zdE8^(Wj*w-5G{m!+=|E^TqsLA0@fzUG>i|o%H2ksiz2V-sSKk+#u$*c=hJsQbn#RF
z?xhKFe%6r}SM$B5s2r-lul~5)CuD;)n)*-1h0Nh=+E0bfL0)FM{vbBaPoR2O%g_bD
zmQLT9=0U&6evE_@4iWrJ$zqx*51c8JTpN&eh{*mN>kN>F8GYuMX%*Ejc+Y(X0%#IA
zdjMnsmM{EX(=Are!cLJ4?qdeeFen?ZN&VJR_zo^eYjN@lA40+R<L7wN7U~M2r~EZM
zt3;B*8psNtGj{e&4tMG#q{Gz^Nb6cOXht|XxLV%zqDl948u>1bbw!mVd9~xEsB}{p
z8cmhSz+xJNQfwMT2Yo7p6752E&G_PQ@XoQf@HPG1xs-$Ee`*xruNN1?)Mb+KUmQ?J
z_pf>^syf8f1o9hRF1BO?PaEprEg?Y<3J3w6hE&KX_HW-6yV@Z}Avcd+Zwuro&I`BT
zYC}bb&_<KG0YOt`r_e^SqSQS&qKVsC#fyzcLgo_QV|qNi@xy#YhA6TPjmjTYE0E>g
zY5W#d2|=~`NdMBi35c7Wc|b1Sr?ltYvu>IXt~<6F>{&fK;gC+MV%I6y$G%>veoMFA
z#2cc2>RjOO>9x2R_!b1MsaI?F?#HvAPssR35_E0$D>rA2nUuAI(@&F=s`}4N4}FRW
z^i@WU>;rnL*IH>wF2XXFdhubM{bd2$U``t<qa;lVp-@fyka(ookCwz;adivZqZ|t#
zYaFICHzhAgHkp)d^B>GC%xvS~0&bl>bA?|gEq1uaMWUAagWR>ggrw+GgX;_v?f&TR
z6Gs*t`R2=ba`Wno1n8<>2=$Bcom0f2LAeWzH(lHH8jV*OlMT3;|Kyu{E>!!h*XZPd
zNNjx<<ETpGVG2^a3xw749%dot*C5g$53qxiq^wS>PZLK&<g?GicG^X+fIr-m%HC5E
zu5K9m{8(a|{ARjbXpz)-WhbzOm=jt=<UMs&56mt)^TnF9*;PnJlXQH<&6U?6Cr;I1
z3mcc}!dv&#aATYlQKi|L8z4bnvMd&qL|qTrudQS@hg=|STaF-Eal0UE&~`e)KC9hW
ziJMPtkVoH-XFj_JRY#hfjKtT69*k<pWM(Tn2*#6r^bliqy$ldWM18&@<my9@2C%J4
z7(oo07{txW7(tzmeWD(#SDIsAv#iw@O-{@Hr7qL5yGh?6W>vQEER3AEFoU3|e$SNP
zNi&k+JCAGA1}!EJkl4bUB2a|50$^GxS44li`Mqfrr}3;RB+!u$g`E^I@T3d;K2Qp2
zUO=_CjlvRt64+^uRC1h-0%fb{X*eqdWn*dutq2!t79y>;%_2)^=(aa)M!nLM;z**S
z)8oih2x%Ce9W`;t`qZe#b}1t1XOE=%%HQi!0ac5B<+FKBCn7}S#>=303F_W*)s(Wf
z{>58}g#W%dtFvjlg*dd=+jOMHKFeusr>?{oFBOI*1?GcOB#G>k{am}OPeY-qGx)7H
zNW85glA&$Gq}1ui6E;#DPeuy)OC(e;@N2{FO^Ot!=~{ZtaPk-US3Ohqags_lJdkbC
z$7IhnbR-Qw(~rtqkTkebVSY~U3wnoZ^Hu)#FelK~Dtgo5q2~s)YR4((C!Ci_x96f;
z`Pdu!S?sRbytd0JygUFSNcMk<PUynWgkvajX=e%5!T*?i`q(cj2n#N&F}=&sIr>)m
z(LR>SS<6_nTE|u1wAm<mmI~kv-X#(~wwmReg?H5`0OdXWtN{!a6yXbKDKT&M6eXKE
zjAvz-F{Jid0&;7<GPR1$&q-rU3XzAgy{T>qK+w%iz?pU<mm&>%btq*ek*+rB{w{56
z^=T4oSXZ_F=pXZu&l_n)ngt6R>4}lcD8zE78V#+kEq@oL!z$(G(JEJo;<4Q{Zv#PV
zZ*CT~ocHXe*D?t8{sM;<w$#;WaB-|Rc~=|n>F^>uoqYJ?(jO^rb3CO+P+>LS_<$TB
z-Id*uwSSjmRmoFh<0l`(kI2XAVWrIkZ*^Qk`n%^>;uT&>`Xf^_`VQgXE73uKBF9@I
zKDqAFDvGNYi>YidE-X07qu?G>xC24}Zpmgd6Tt-}Q<(o3f4lx>8qrvryeIY<|CT&d
zU*kX4Y6_|4<GL4HmzP}jD1+?JHUs~>xhzxcaN~QDvh#RpeWn%F8XdKP=sXbh?95&D
z<bNZNAm^gLZGQh9nQ<GULAE03nnNvNO|uE1Q{Ju*F@m-^0CJ??+uHoayVnCCFmFad
zSHOb``5wbrO%FU-wmK-=V)(yHqHIoSMhQ=4G6!jwcEr-zjd|1@;T`&G0{isg3NARc
z41a1$9l30O!npqVhL_e9)|kl6@id8AQP~<?$F>js+R`O%^pU+mt1TS@*^vJ1+T=mQ
z+0h}cr$sQy>jBvq_f2JLbA;vZ@O-8Wj|>EldWzM;kq{vm9TXhy`@RhnEnwC8zb%TD
z`F~|m%<SC%)uL*(brTM|5Cd-NVH%L#gMP&^jJJcvK+YUI=3D`w!RcdiMGCnm#BoNS
z-&w7{{iT(kx$o?#oI?TPWzxVDaTRjFYcC9z;3q5=OJv_S=_W%!m*Lhoixz~S>=(P%
zmLrzPkP0e;Ln205?8g(=&4otmB>w*B&PI)MqE}=SS<JU$Y4bA~ztgn<_VEse;$zvi
z%|Wdi03oQkL55Ump`}&Zy_P2SWbs~-a^Pbb<LVcJ_Zdf4B@%g7Wr$m44^4E-vbO{~
zG^ljR&*A;uw<1W7VUST_2_!pS0faTse!|Cl(4WYtT0e~s2!0x3fe5y1Dqp=3foTpX
zCh18>7*e_Zvdw}boQXBT`b9h0#_LPDvBRG&Qy*8vWiNenlRKXG0FDZ4Uq<Vf;U(X>
z_$Z1=E7U@%+NmEM*aDSo)74`iq+6D^{?GBuH++V5o4v~8cC@JwbGyxcd24$I7=rk!
zWsNDgO?v$7*r1j6<Lk%jDC}i8YdBG!eIU~nz$($nwmSEiu)r>_Er<E#=lE;Da{r*+
zFv9pt+<|1oA=nj)1--H%B_OyMx%092$)^mB-#DptFlp^@`=W<?A6;%tjL#O?<a3J*
zcGSmwESZ>R<pXp+jL`H_tuO7goGyt4{%a@(>QA$gcS=aw_g0HVdBipkECTUpaF#6H
z^UHE9O*iH5VtD@Bz`WP9d$Y1z9QfIS(vhb2TW;~y*{EU_2@m47G=~=*>>=A?C?x8J
zGY(jtK1kXw8(k*T`o**Kl0AvzTn|_Kns*n^B71^RC5WJ{9%I@16VQw3e<aTDL&K~D
zL5>#sbuifcT#SGAyr3uPiCv6v9su<?wCv~W8T!`{E)SP$ES!5cy*2sPONx9`7BSfR
zT-Sv~X|*l@vNo585x6`fD!iC}<2@6nF#z=Lsrw=?u$R0_Ob%J`1QHn(cLK~56Akn0
zadIfbLpAXg@9IkKIUeMm`BclQevAG>=W6%<V7^ntPL;7`T}kwi?6fr%8adBLk_(4<
zKeVwBjfYAD+%-tLBG<t<lqh`)_j5?u0Q7!&Fc~6nkq@AI{@2ZMQULMNd!U|@X(7JL
zJv3JXiAx(3!9dOaA0okESzGmQsQ1DzGOI(`xDei1dcwVrCyf3V1<cxY0tFN|X7z2_
z4(sh~7uSJ8L-2o_2pv(1Y@$>NU|5iMrG>8a9@BIGdAI$qsC7p{?q9KkQEh?pC;tG3
zV~!mdGq8zPIz>wHAYo~gVBC+v)QKEMa7M>6q|03kIJ$OaAde(nQWw!uyw2cWja*mh
zJ2d4TTk5~oo>)^knGYeD&l8f#_&TMO<rXWN8$J!#lrCv+xu5}%T4S-4SnBHTm8_$Z
z7u+tOB@VuT-%!-K{)hz0m$Ay}ukNtM8*r2$<g?NJAvux>X#)@lxy>)Z9=16T*{vQ(
zr5mcgHv-NkXWHu`d**C%`cYw}l|4&Kqb95DPmMc7e|<nI1I9ie#c9Z)8)cCo$SVbp
z$^yfO7Hbf<uo1Nkh*GPcPLLAdYfriVB8o85UFe+NSYR^NZd6jK?&X4JP?5i)nC{<y
z`{%1gEwz_X6Ju)bK|fCf(W9EO0Cc-EOktU;%Bs)HWX@&4uMkVHq3VbKj19UA*Y6k2
zwk}F?;i23SzU6p!!==eDT*gi4OsW2J&>u$;A>5OJ(i1!A%tqz^M<Ek8O`h1cYm+V5
zA9{A96h-u9_gt9|I@+<_Rmj3q1rq4^RJYJ-(`^iXi&l=WyVeaiN6ZSch|T6$vJ~^l
z_moU>%~ipA-!h4FTN;aO+P9wSE!Y)I!`7X>FR9{^E;(N6z>byOHLq6ZnbN4oDQKK(
z;qq$ffAe%F|44ImoW#HK7Wq;L5x_cpZ_gM-oamIZ-^jUk%jOgnG|VrU{>hCWi9s**
zN`vGL6+f1LedtHv%)G6ixraa??L&c}bjXG~p^EV7k3e7pi1wcy?N^N$oSY4tj1qZs
zV<0f*Z*6Iwfso-PFocIbawkP&bAmCH1K4pQp@y;9iNOdyHT;%&u`<8s-o>wj`TBv*
zU^Xd~Et!RN=I2e%BHD={=2#dp-4y~Cr_N=H6F(hIqju?KU!W{*4qE%?f{0QdY+k~E
z@QO=JT<qKyh)FEkMa>6=7I6xK8`7^HB#b7jz_LI&!;6)7e;z&aa!w-pC%EJsR~RCp
ztelr9DoTHh0N=(53#B8P>OXg>seL3KfxnMA#i-UW<vuR#3H8E^jLDx?cyNLpW)HIo
zr=Cz|XbofTP+<czK|xiU=?$&@yjn_-OeK$;N>Hj#@%$rS8j&8Gi+W6mUivh<k8bjz
zv&AOx>(u?D9^kjtIz)}K^=<ix1dSzz99Pgvn2X^35Pd=79XFVr9vL6Jrmp_&i9G5q
zWBUPV`=#e!T9<#-@3n3q5lNivDA8%_hO2Hy{jYt8Qt}3DDK24rLe2lyiXtwlH+!%?
zYY#Df)3RW70~O*Q4VqH+)}4o}sUl}iuxnDPb-H^IIUt<WkckE)r?Z=NP=0I=6RNyL
z5PPW~dkelR948M!K2EtC9%@}au0RQEPxnCx2IVVP*MejH9re*P*F4J-cLSD(l5CL{
z$s^J2%d{?J1_PfDA+4x?5y!=~6SY3cTvAS*FH7DZRCQx_mXSR1ZkN~2np<X5KKJ_r
zz8=f`AxB}Qg&}1zM(L%cj+M?nA3T2`kCh5n{s)U-``=guCp+tZm54Z1dB+@P6u^t7
z6T6CWqqfmGi;e49W!kfbdJ<@Jhb{B4MaotEO(u&rnuIb$vcTb+51V61A1ET)tCbS-
zYf8a&Xt!xYw<Tz)bCx+hciS!DJVXgAH)kJA+zZK=wCMFCbN21ao$r6r4VO?6($WCk
z2@@YF&GyU)W$kqGFFZ+l*)il&hWDH6b2>#y(~-Fc96}jZ{eDZsWaYbR0tH48ii8CZ
zoCyzADR@$dn7GSfqY}an(P8TYQ_HzYn<f2{tj^>gh^kSF5W-=zQszwg_U~G2MBZ8&
zlBq+yNX2ALnN03KDidRXJS?R;d+VI5M*D;4mtwShmInP>yO`Tdv<24+GAgR*)=&l_
zJin@^IjL@uWhqlC?P-P6i$mMoi_-Z}#~6ZJwW2n*HhA=g*KLw?3Ax2h6kH_dilLNY
z%0RP-TrD<>N8wj_>gahu-?U^4o6)3}DCc|z7Z*qQ4_ksJd_R`)oQ9fCK4J?8A4bWw
zxWDAb%*MRm`jrB6LuZWb_6@n0hfmZ_CQ{3ayUS^@kk{Kj<V4glZW2SDra-rBz~I2W
zz~h=v!AQ&qD(PhVUheb=30dvl1v7#moi~;s>tC(kj7wJeJptjQy28SR4T}FnF4Os-
z1Cpp5AZ0SOXXBM<{`9k$G;Xx8$h%S--x3aPX`1MaoeyDEAj%@LtHN=#I`mjGq~5Rd
zPzHVB3UIV@EbGt+;@z#_7`?p?EPlEEGY33PKF|-Hy~Y2gxD<u+T*YS5FY!W{3?pu~
zCd{z@_Sr);d>L4SOh7MsZ?olh@hUW>>Z87CGVK;+;tVcrfR?}$_f{T?95f!!uYDF0
z`hu<uh-CS{+nAH%|7&AacM~^nCvzsX{~Ro0|38@+PIhk2|Jv52+OTS>t6hSq<2<!7
z>5Ha{buubXI<+f>tw`$C2M|$G>f%mjFet$gQkj-8DECw(C?_;QQ*1F4C04mQ&@CFe
zwvH0DZ7V=~$IaDW*LQU<&;2%6i@ol<zVI4YX8B*pyWhQmKfXkuEH30EC@|vL8jc?B
zZTaVI<J-%0)MJyHWljOMDWq$$M1HfZ!3}a4T9k2=^d4mNCUmh@<S&G)&$E|-bxnEV
zC}iIcnBS@b=|7KzkwVdxuVVMD48{T>C@3o-cKuW#-68p>(|I4)ZkHw_Q(4U#G@?a=
zX5XN+<2}Q%*s?vv6n$}08nS7uft_Y}-(;eAB`~n*6Y9+r*`+Gcw8FG^DUi|>)tp^E
z++8&(a)ze|l-g8jvV%P#<aPTyBCJHw%~QW=HDxTNJ~51w3r2sW{4iV$vt-H==`3Y6
zXekWP%%hfmM^H%@`<0M<{-c+UVyD+CQ#<IRU@aXInv$(cewR^$i<Y}a$GD#C9^L(*
zIii)8$^=V*K18*fP%l)Y@gFcFp@n@u>+-oe#8C$|pxvogiHM|t6Z$NvAE3&aJvq;u
zkfqd=QCpzKycyM{y-TAJ3+X9pG_<27m*XT5?NFh7`Pu%Um(E<;rJLfCsv>BcEWY;*
zNVaTxhJ%HTA<V1B2>@0GX35YX-e-S(;C=3DKq5=$%}E^4@4u1$9C|{K;BbX_nGV%t
zV4>ciPQv>3|4o|t4`-*rJO@J8<h`Qhz(QassShX)%uNuC#r{x?pw^?bLCE*WFb{c&
zipvp`4`G<lvc!3XW-3e(?~w11o}jo!dk^^?=!dj-hcr-WpjJn74C$Es+85?VQyB6Y
z$XXNbK*5LAJODRAZ_HW~?LgIq^)$hMzLd73G7~4i6P%qz9g+`D`)80yTY>QiQwMV$
z@8v08xIC$GOeR5wUxTf4)7ndONj#ePQ}k}Q@qS;FAu9>s0UlY+pFOkKj&8M$6Y>RI
z;+uYq00bBx(mV}#Y`QZEB{KSWfy)h=3{x9-sv|^%KlQ7$Yj1Y3R&7#W8Pb6?`Fq^^
zlhIYto|N*rN4BPKk!2)9cG}k8nA+j`4POZ&7%?^9yiynQo?-@aVKnoJAi*do=!DO~
zWOTdP4e$gsOfL3`?%duqlYec=`n8MG&99dl`pE$&(BQ&|N^X;a6^j8$(j1A`P+0~k
z;Fb~<h_K^1rdyc^7un>b@D~aj%c5C6hT<Zeu~;s)S*bbO`?`nv%cK%~-o&z3)F-L4
z`1K<r4^^Oh(1@J957Ev-lbQ&*=mdCp^!2c{_4T!t_3)LdQT9rQb9l{rmXii2M``iF
z>AJAtHGw8g7P@*HXr+hGFR>}w_-+?d40LWen-eu%=o|PxTnCFsv-^}=W8HcIy(g0P
zvuWAN9G9u<JGv!oqQB5I23-=>6fbgDfpbS6%O4nd@tiwNyc3YD88fR~T+jZG1+dCN
zl7p>blZD_5&^ts5Z;Z65fP06lc;K)_dCzhK3>G<4f)*w(liNpCdt<PG6+L<~T5cPM
z0)T(&JTyx}+RpNAH{hIG*HeBd8~IZ$NJp7?kvY+!<wPco1Zl+0HruTkBu-32&MAtN
zyF){HS50$st)K!PZBr|tDHY+$oD+^n`KQa=U((8SUc3!yzBc4tM2*ENTqa_Lc`K)M
zON&t-VM`WsQAa>mK#^Jy3U2z7Antdk)czwImo&kzfBb7gp{lN|=X|a^kKd@)iGd$+
zA8Wx8<x_KJ%?xmaC2`>C0hEeSnX0_GNN&UvE9KO~2}ROQiZU*Xc7z6en(X*Y27(x(
z6fIUXT>#-txZDln06P})Q?b4=myADLyyF_svxpdXj8iNYlexBSXWiW`jU_R#$8mN;
zk{it68=K7FiP@CRVD|+uy3>oAE@tlv4DBS~JS1Gt{fdZ01C|)18Tuzy-RgU|=jW({
z@@5y|;oB7D8&%N4ZWm(hkS1FDt;x+NlB)btxo4vA6Sxf$p1t=yzkj^Ol4scACKZm_
zbL`Kd=if|qb2U47>#@p3lNm*@%T3YSPfFZV)A4OZ#CN;ryWOu1^$9FuJwrU5opPI4
z3u%E~QmnfBCik<?+*tnHt-pH19J>Vxtj_5L5CrD%z32LPYvj!D$4bwL`aU}f@fB1(
zHS$hsE?4^NDy}vwcY`Ev@&@l)B&I{%1Z*VZr{K^yDTjS69B?95K%<#yKbUFtKfKh$
zdzjns`4OeVog^P|z42&U-0^^{on&Ni2X3j772+l&1+DM8BQxS;-K=4@zDFtbO_C?B
z2-wu(!DraEgz=1zlN7<n$JrWDZJ(hcawD(|Wv%mL7U{vTrUnp{{@Pth`bmwl7f`xJ
z?G6$eKD^bg^H(F7rok3v7VSK7SLc-sKycs(+XZ(>v(C*B?2yBrGL~WU1Dyu0VQVFE
zgH?M4<F)Vv2~N=D6VY>kThBSEUTNZ!uc3=*_tjp0L%*Y!w@ySR)@H=yrjQsgcC&8L
zb57+k@h(0_8ZIGGhRidVkK<{?#Vih2?h`z&Ih5%O$$20kruTVzR33UC!QJ+$a<x<W
zAw#h()V+0~o=bTR;<!IY-OVcmzjbc?4gje;v(+3*_q46qecN*Hh<iHn58NrNYi`s1
zP3FeKmv0T@<l;zTy@U)-f=ni8q*p5VY=6wM*Vf9fMn;o=T*a*)3lLAO-~^++U0naG
ziNFM?It_ShjT_b{et$fxdosC=Msjm^agIU+#f8A8!7rkXSzEhr5v2}38CdAk?|hSZ
z3gDLBHsEDmcHn|2u|q69%RVdpt<O{iDWzJ<duA(%tfTxKya%Og=1)ZN?SG}{=7aWb
zF&4Z(gcrmeH88b51Vx)^xA<sgR%u*$mXrh0w{-j3GH0eCv5=C&cuwbbtTp(i6oh%1
z`e;sR^rRmQZ>~RGj@)JtrnqNiR^FnaLpA9y)Xh{++g_Jbw2BUxUXCotf8XfLD8B6A
z*+Pui`P*XnpK)!+fgJY*jkkX-8<z|{bebT2c9S1ha-Q&#ndW(R(0l7>;QVJCA9o=)
z6dJLV;yQx2I3W?9tCG@N_43?isGQ6WRDNsHq{vXG>*3vmFR>-LIH%R{Ad$b{a5bLv
zc2Z4S5j;Hb68_3qkUqxg_Bad)p9-8S_#k9%bBjII$`f3Wu6s%H&WHoz!6;WC3J>|-
zD6UhnToVZ}{%F(1F``C@!G)G?QQ>L!ifrq659tr6L;9lx{R;=(mwY5s&4fn5;i!~*
z9;Cb};q*xrbYN<2%FAAIBQLDr)h*`vkJ4uOEVyUX%49RmlHAHUzMH-irVWQVO&Rmb
znawuw($*m7#BEjRJ~XXX=5?L?e2G~}Opz)k-tSwmEQPZ2#4Nihnq~cv6em&ujk)1p
zl3!}6s&ufHe*kZh0#71ZamEeUJ*_hv0vjj<*-B8|Zi_7G>869n$Ln^d#l(*k4?odm
zB;|6Rcs~2$I;Oyfd5DYWAn{2b3#h#UY8|Rm?-!`qZXwZ}ye`#df1HEm@~7IafX|vP
zqK%evw#e8`1+gyJx>&o2CQ@XiXfIB!&%RbX6Z-HDlfn7RyrRP#^~JU=3SsBBt&c5s
zMmdB<os(Sm&K4|GL9ftWgbcX(AtX2kibCb`*~zL|Q?{zznI&PAsG~%vhbQg|d?0XY
z3qvWUEU92hZc2$t!v2rvkfZ>Q?hvWrQ3MP-nmxwL;C@T|kgD=BZK6rC0WQ>Y>|lij
zj>hEDN6XATOq@mD^wgWQh5xLjML)3~*WWHg090FW%W9ZuomY@byUY*0+LjKhD1PD7
z2q*;%fJ6_wvUqMI(DEb@{LPwrdNwj7)vUG`0?DY65(e1_Y{}xF?D*OVp81=_c0)Pw
z80?|X^WuG|_72iEb~&o`bx2~Y?+Pq~!CS`5&PIuzQ5PG5r-NvESXtA=AMc|@s-Sid
zNk5zLg6&jELv;t7Rw?tWaM@~Er<Pj2E?#aBjf0YWTGO@oA5li>oLx^@zTmW%Svy5v
z_O)n=6IrWv2AJ+E2+{*+$-kz3f{fDg%cV=g8RyO2yKB9Po0;LYW2VoOp6po`>|PXY
zKPlPU;{NW&L`Yuyc&Y4q4p7Eme{w(LsVh!`6ZBkuy#mwQqw@%%S6FhQaNXX<Hry(I
z+7O^&E&XByKMrF|_OSe^D7O%MTz0ev38oJbg9yo3LJBcfEXhc$$YkJ=9k=0(E7A*E
z-GnTUEj#X&1b*)qF2ex+Xu=?d;CfiR2Y(%6#Ur#2C>$Q%Y|CF@vkvpGC{@DdlTPkg
z?BBvUf=j8Vv)8gr3xDs%N(`?H=XaU-rsogbq#__{^F2B>;zgLl9zybGsTNAl7c$X|
zjsvNNH>gUa0#Rvb$&ir&9!d?4D7pA8O?GW+EKc;*6gRKQW%}N_4M-MFL%LhGCb#sD
z^~lz+z<B>eCx%LnN=bEeW#Z9|oc00hm~AwzHX#6;dk`W9u!sb!+TYT`Yojj3xExJk
z|LiL1is`K0<V@O1V+n%Y7t@6-mM1$|VA?4TkKgyiw)<-?PX~XMAce>>(bCbxo?dKl
zs{t6_Z?Dtuzg3nMVPJ~DnhW==l#u>OA`nS+zT7AL^g7R|Us{+Y>Kf*rm|vu7!C;Ob
z&JKV2^btp;Y5fuc_TbgU1jUAA`O(IVyX98xjxk-gh|6{E-!HvHJ=NtPuef?OIXf)Z
z{${Bc<876v&5V&gV1|hw<0dJVSJ|%?yVKOtv~KR*nf6{b$}T%1%Qi_2M~Q5u!|`_B
zrG;uGT)gabS?rarYjy`GRLoM^76lfLif`i4=^D5{;*7n{Mepzz%wF&)x`36bT_Z4&
zYh>MKX|L4RFrjwY8pr`D-T6(*4pbJ|r<^;vLi>e!!kP7)_Co9%3m<E+-)kSRnYb|l
z>m-rShH$KlGxq(_!s2N~Lq>JM!EpX{4SgkHMSz`}YZn!%h066dTw3x4iTQ_Qf3PTB
z)zDBvV_HAZR~VCtq?9gn%a2R$7^`UuLk+`gqMJ{L4s2`)_F<axWdarSXPm&h<c|0b
zJDRHfVbpA#WwT6o>_c_~SN|hM+R#XTmN(%^KkC;FvuNux^Ua!O{gQcRfnwN7iq+p$
zmaXSY(0uYr8(ZQF@s$dAd^(?dmoAaVUzaazQ@MoGF6e`tS3z%HVtOmh7hU?esdz4b
zFa&o2{~4a-mi7w2L9`H|iFJ3j7WQ<#4i?QmIH&$bOVEKhg~W!M@7>fmn<jCj9B$#W
za0D)hCmld&r>9`h2OKF5K-ow6$V=n-7F-Ei>E5%TTt)uPi8&w=G)LxY-3g+@DAL&S
z+IE2Ul8lUSSfA?p6IRsIl-$(y{#BYQj7X?UwJ+(Q*Y?lfT$N&XRPtFjGeBf|RU@6|
zi~xLlWM+#q-yigCr{NdpZ~N4`^{aNMox%>-7t6xFwWmSLWA9;1c{`UzLo=foA&2Ff
zOGlFDRWi%#8014$C2g&E_;i$a#C>alw%YdjLXoZ|*42YCyPa}oNMRW2#R0UP^>GKd
z=jKSrhLV-W*>Z`U6eq$dr8M)Yfo*nKzVg}ri}`Y<dCWa<8C1a+r50BtMk&k<%NscQ
zgt1enB3UANTv&yyaah`6q}hUB!hl0R^f=Lw!_(^+zCB^l>L$`&Vo(*9%ZV}5v0}(t
zD=aMnA!D@r6<Ku}U*RJNho_j{3JZ7r6B;;qbb}DF2-&wTQhi2@z0k9X*^VTSfCj35
z-A=F39YkixoY-xfQs_1$2!E)nIR_wp^>jV;)I^z}aN{4}GL;KtJ`%N4i?F<!iGjZ{
zgON7Ag}63o6QZ;)Cu4|6pWB0n0?~bK{n@ua`vW6S<GC^)51zCYh;fHZb_Txv;fUA2
zI@=^u@5enEsH=zD&L3(+shyNObH9gGOd5@=_}Jh&#&H7>zajI8HxM@u0BPCa$9a2T
zblvUx^7_C5%F$A)J-jmN*sHkwzR=Rn*Cj6@n4*&%XG!+I(URN0IrANannvQYncOt7
zmSPI$QVmtJ?3XT@o9<iIy~_C^Mkn|#y8qH4|6^gcg;S-Yb~uziF?l(GR50k)8+r`n
zz6ADp)B-MYI!=@DLu+2Db9VH*0-7@0rgps^t8lt(c#%`nGl7p8PK7OcM7=PkLQP*j
zD5VGs;XGn&ndk$fthC>Vt@;TskFoDM`T~9Bt75g!r1fw5ipucB-Hea^>`rTk{(N=6
zgg%l)Uc7#o24D;iP@hlorz2cRk0gsS+HwdeCBt}0eLBfvufG%&@ZDZtx_LHr$84U7
z_2cvyR46HIb;Eo=S6X)KCkB2RYO3?;ZLc3DyHfgS>HPZDwZT>`2seyJe$}R3!J2Vc
zCiX_|co#NrlFKO?{A%S=E{%!(*KJuNsIEzDu4wMA=0;$B#mEP}O7lMYOYHg&Ke;$b
zJz}M8MWuBGa&6M#yEFZKPFQo(mv^E^tq<r>0$b+C5#@ZEkioY17N<b)T6{@d#UHi+
zLq~lHS`h=hwWyCfO|74mk^vkxlK?Z{4I6l!2H-C7YW;5GCfC?bTI;1BlD1$(j8KB{
zEII~V-tW70Qj-V8doG~(U;7`ysE{|q4JC`Li57Un7JkNEe1w1Pg|+TV#R9VrqZbLE
zJgz<dn=X6~+eGC8(dAE&CjK6HWx12+8?9IemO`ib+jo!Jj`uf2e*B}1AY2pkjgza5
z!Pq^vt06?xw|%y&&0NwO*XKn)b?`Y`rsik#%^V0<pF?5~eG-?O<ofTYE&erhr&L`w
z-}&z5raTzycG?T{R$Y<r*V=`Yv48f<<g;VC`&MtJ#1a0+Nq)xch@)HZ@7=Jxu<jc8
zg=UW>ZE66x2Jd{gPU5cxemKew#Og{xOYgI(XRfkEmfW)g5??;51VSwL0};OA6Mus2
zq8wB_MFf`2GLA_0)t`msXBC~|2k^S$UIK(}731S{dqTeQN~i?GgOEgHdGMiP{=9{k
zK3(M={|<V;{&R@oP%>ELeZa7%f3g`JyD+|wqNp?S2y@%kf&9{w!pN-0i$pI5;CcWT
zWES5as|xCK?#p?DjxS5Rb@upe{aa1&0=K2sMsbJm+YUbwFQaKLPX7k5irP-FWxI-9
z;Ov2^^jF=gDBxta;%;Itk=r~(rgmtvU{ccx0nhnh>*YYuO5QH*Fmq@yjMzaE30_L|
zshr>QRn4&Y5LVamBth4dWAFvgjgSS^K`cr)#s(e#xXvLg;U`V3PBJA?oZ?c{TqJ^l
z5Zs7roc(J(U+S-aM6@i~CIfu7{ie{}?}$Iuj{pVs@Zi?JdusXLnO{JMt*$<go0Lcs
zE4j)T)<}&T2Cs5AXXz;1u1d06ccS(A45)ahOO$G$)HcnwpxEIG>komSN7;?@5|H6x
zr~#m|@=H0}i~iX}Pt(O4LXe@uTT1iL`uV!{_fT#LVppD*#;wyFuy8nipLaZMI(YSx
z`F7+oXUEh|)LP3=&j%yQ&ve?~MD33Og=>Oq%0dmF6t|TXTdwSsWtww8tjKtTkv;~5
zs_mFL=n85_=O@V>*~_SVUOl0--vu~RO*gzWOoC>G8k~et4--TXsZbk#ulP1hB+Zhn
z;LoQD+KL%;TDUrt=TPoYG_PlWaM1=ZI6*tn)|7*f+Ghm>%b7*!u_wx&o~U{nOg7sb
zQ}wsQ)Ss1+gJP8yhvGby#M~?j9aT7oK@yE$$vBr!yxG)>YEszF#Y$A!8YMJGH?Xd*
zGH(7pZSgt52@T4xmfbTc`CG}_Io5KM$b%SOj}cF(#vaxNh-VM@aJp_m29n`+=8DQ$
zS$&rua`tW9dpIY?!AvKdqvM=T$>9}w%SQ)SyAZ?SWj5QOJA-bQBxpYfM9G+D`WE^Y
z#%52f^FkebD*^+N)FWDnef1?0W`a?aE-$s+;XIgSw5p|VZpWkA>)7*pZN>!!EelG6
zn>v5&?<&FQIT3#A)-uS1S)r1fQ<hPAIiYcK@N=*_vHUI)v-6AH$kB<FP_AGZUD7FA
zrO3AbkBy4+Ak?wln4cb5riD~gI5eW<r)x0$PUX15)67z%w%xCgTEWpt(k`q0GaHLt
zIG-a>XIQ;V18Y4_90S`)F*UwwiJ6+Vl%{r?Stn=~c#L(%T6<oy<)zvrEk959-gxbL
zw?T2dul2$4nhnJ|l~^ImPJtO4!Pf!`%Igvfdm~j9bFG_@-eg^hJN${C;-{Kc7drbi
zy{iGWCoj0#G-czbL5GAw0;;Lk&Zww;LZZ?M!T7jM0*Pwirz*#iMyysxt;i_N1U(Nz
zU~|zC=b;O>2ELoRs(}ym%9hPPXisg|4qYvxhCGlcn2{ToDkJRMv~G>ku|J2K^q90M
z3)V})tw{qbXKTpXY>BqEt%Ne}AFhdEJyics8gC#9z*2>R`yz<_kM{VE<+^S?i5_rZ
zDaPpl>ik||0IvS-&jTpBB)l*(epIpbk&tZ3@SzkWQQR{Oyt@HO8l01{NC7N~JUbYJ
zZE7T1a_i*y`|l3#%pTrrJ?7mjj^<4f-3j%Gi}QSZe?pp;c#rkdxA?Bp{?z~QyqB(I
zK>u$2`$w{v#P;#5zvi-N-ZXK4`3G#wH@M}0Sk?TO^!NXIRl~u_`QKwqTCiH^s~dSy
z<9{^fSrzyeH^`UMt4ddjYSk~6Lxxw14r1U)bw$BI!C-Yk)=9G1V1tE&8p3{wq!SlZ
z%ynt#cCX3T+Fz9c0Ils;yPMBp@jrn-Y*w{TZGe}X2O;mV6lR`X@4gq`zGDJZPG?d|
zv}l|@ke>qiU}VR290H8Od2#V-ba0|O*(Gs!<czMH;Vm|a>yI)chjWPbsX+l#(wjk|
zFPe*T^Mem&yMbfA@RuIz0pB6F$M|V6$CqdT^Bsg-ShR$B5tl@U#I68mVE>`(9F{9l
zz}l#ga&}Tpyz=>R0)uvwIAIn^-<^1`Orh#1`50h6QZ1ZP8NXg?ZZ^YFh5E=RO|(wU
z7Q^M|Nn|r$w_Qij@~MmAemMq0!X-?SFcqy*`S&>ZvT_VWu1TF<Rwn(b-VbSlj9iq)
z#d0)k*V}!$`#nsjLUoi%u|&)xb-0Gt9@#2w^5HV?uXlRQizAkCR9GL07QK9GO)7C~
zm7?&g`Jc3CSf5T#9#nyZ--?Nq1>(5|*l9H@z+<b?0(H<;7N9z+kFjAco<KSk?L1R4
zda(+Gql4h$?+SD`7XYa~(<ORk;_qsA9eHu64eYE1%IZ;)lCvdPmcGktGDVFcaq{1`
z7+ucYV03-2RDHu2Ftr*fEWMo0pPF1RniuYHaW3CXEU&K0A_+%AlM;l~*pK%YfyS9u
z8xVH!W0nJ^gtCp8nfaqYSldQoJF+%h_z}{m4r?5&1o;6<Q&kp4JG_(dq&0*;1J}bx
zYj&5Qmm*X6*`d?JC&mwrqYdF}hifUBL?;Qg19|;m4a;lIYp!c}^eDQqwSy~U-S~;e
z@?+0fc63dBh@0_O|3sM$MZ(Y<Zp0H}*hw{(n0VQYhr<Ab@B;DPpUf=kmvn7Xg#E)_
zxJmMQS&#QeVSGI|XW2zry#6+!R440#aak*Txd7Me+$V#F9+h$TqYO5}>^nwG8G!35
zup93>;L(8!@K<oa&IHM)t8l*5W8lWH&NTqx^ey1{3Gy+_c8~vC<V^Z{)}5>CumUVP
zP{uB=h#P~s2YQISU{}S^z}|If^REb51p{!Zf}y-`0I}5jgdPR@(L1K2e070DIUIM7
zhLk{YLTd6qu^6{kiRw(p;r^2Y(NdZPaXRM(CTt%a5LVH|=}vhNkSKgu>^j!bOxcNk
z7N|B+<enapEeh-Q@|)DVO71lky#kiX00VjLp%SuHSfu9q>=A66sbNm1)b*3^cv9Pj
z+qI6UD2JyxBgNT0BRj>CDfEDQS~LE=okkpBp}8_i)v)x1M?Dl>e+<xabM=XV5a!E5
zLKYqXqafvr{m%(RA}xP8zd}f3)q9UR7;DgX9vI(<xHPw-WB=7Q;-01mk_X}^{`ZG;
zr&!{5pliUM((do8@@hlq)BetFNAg?^{TMv&Gyj1H{{YG-!k*?2vt8c^9bR@i&Olj@
z{;JnV&o^F=#gKC&ZV`4kMU`RPvLXU?<1Usy?nw>dLA>bJ!5z)b3`1$(6K;t)ctK$a
z>NHeT|Ehix&E36%CLx)Vb56M6$B_L*O8kAO7wd`t!`VOf=)y(ox@h?++qP}nwr#sc
z*|u%lwrf<4vTfVec~8!lIkQ$ycJ^OrJw0i;akZ&TV1R8+C*lao>dGM@5ub|D0|)Vi
zyi0kfm#CKq#VTagTs63BGuXN&9DEZAEwOX0n324DfILLr@SxZu4Lc`OdD01=hzGBi
z9lIpGT)ixD2wyHlx%cg>{+(G%ZR@gRqL5>$n2(@vSHYmAHSlsl8nB#hYiI9>^y6P=
zIrcMvM$b#tC7xs0`^u}GUl%K62o`&o#hr|0dgBkA&Z*-i@IViHi%lzRO^kk{4_AMT
zfpl>%?BDH~e(yn*ITNNF`8>NIn2IRI=sK@;MRE6eNWwi~PEg}Jd@Rl+Zp`I8-E3`i
zbF%Sx2G}R~;Kh+}8vQJ?ZP+4b_7Us1ch~xo{TiEFE5V*7h1U1?hVf<QRMPMB2V)DZ
z;7%yUqxHvP9(DTNwsnOvkc*2Fa$l1_CjTIvf-T<m!u1=_<%#v+9HGEtbw~{@w38N|
zdIoMN-4m+Ct{|@QEYb<};Dt7rg*KSbL&<v4a82X%`y!gr?}a_e!E#RZTZh7-s}Tpa
z{gC^O(F9CL1fo4%1k}{c=xZG}Hm~GXqSA?kKwVVoo4fy6)z`Zse*NvRfY${&9R}|&
zhJO1uva2YKsC~5Kx(tKt2rS_C#M=|zbC=g0ap6^6(^cJs!T1-#MA%F)wGwK(#U+^t
zy+W`^&Y=FbvQW!KsdXN{3Ttj<jcv_kAJ+Fs&=-MIUiX=2I=U1`&~;`7v$#qz`GM9L
z|KsjASdfaC_)}FRm;*S5&zcwgDukd1l0_nwo`Dl@2jP1kH!tLIy!GyCO(i7`&X7fj
zT@r|=vwSK-Io+vaL>Ua7d6oT8P%kG{4KZEl^1glAP8Z-<0J0Et#PdYFIisC`h%OXQ
zlOW73$x~n*Yo(?^+w8a0Szcy?AavtkRdo*fJ#uP;a95JjAO0J1k&+;ifVus=cQu#$
zI@u&hZ_$8w!$IDriK`15wey4UVVtkuB6Cg`o$e7w1y|oCz(dasbm|$y_eqti(kk9N
z)&QX<?w==^XB$BC`vdxvnhQ#DF43+-Q2r`)r9lUW_AU0uOK6w;lUVT1tcPKPGi`PF
zy$2U$-C$6L?G_$6&*T=mr(>U!N@ef+h6j--(1DYR58k4Pc^&9tP^k^gbK@Hs=2mdj
z>TIifYJJt4A(&t=edDz)-j=hoCbU2O5F;b4Q5=lVl>-@HU{!c)%+S`!TpgGhl)&9z
zxK-1-(;SE*@YRikXt*0&BbaM|$5Tvl`~|OC*@H<E0!!DKN|~S5)2bOI4?pxuYV^4w
zk;U*EQyj1T$?@-iQiDMLWNU57Cn=kwx>Dyt(u^3WIKsjDbWJ`Ll2|e!s2X9;Aw@>y
zZtVH$_@cSsljQi2LoDbOjOBb4h7gAPf^qBopEH`cv@-%=+Q}@<Fx%xLvJ_j8M8d31
zbMgX|TgFu<xd0Ej1q5psIw`ZwbKpoM;{kZi$#mJ263b#L7k4Kx!@T4X;=s~O@C_xi
z_lu4*#GKQ#VJ&<UH%Ej~1rCq|u&41mWfr7J0w%aj+;_sreJdXwFIy$fsKY~E2&xXG
zx~>mCU0QEo+=<Qnoh%KdwW5By8@``qm-$;7#VsuTVH>3_i(%gN_PriZc`WihW89XM
zv@Y92WK2cw{mR2R{yp&Wq7lwm&AMRtS0HeDmlKgvdKkWd$2`bf>|V_Qg+@fBh)EH$
z9H=+uRUgN_$t+=+6mf273ebI3Q455FhHd~GDCe>y?4GoYC6x_CI#vgg!yz`;6Q-Ap
zmmtaJ81mQHE)(Me4#bva`!5*UZS<w=xnQ8=T^gkz_8iI1cFf*ohUH#Yif+S?0ymZ`
zO7SxXqt{PL>gm?CGPJjxXjAS}(kEI=?rX-NZ$?+!8HS80P?OuLoDQ(SCh_U+$-PBd
zlM!$cOnxZhNf8E6GaVH0du6BB&$~??>n#k!aZ4Fo=x^+PIVMYBUi4`O?NNBtXX;I2
zZRl8019D5|0;M?0w4TP_MO<I^B6%N8Oe;x;QnH#QOSBh1-0(0PZYC|=JhRp1TVbvR
z$A*>#H~sNxr|ljOMD{J(N}GMqBa}PX9?5k%+=d1^aT=XqVL82TT={Qu$CTHk#57ze
z2Ig857ojReDj(YR!|u?N04Hm&(5N-Cr)F=kED<^$x-#WEtizcRJhSJtKZ3g0(2siP
zXt<O#g7_3#T83a`y88IB*5yDC;=U+|VFOwv@!k}!Du1(eaP4xvyR3B$Bi=4NL*%kx
zTm`4e8)eo|Tg>!|0vvV-_CHN(NqpGM;L_Lyb^VB3D`9Qr;G>twEXXi!51?5rD^B~7
zk^?p7I461s)Pd5WYGbKpX%?@^Lj1@g!kwY%2%whv{(dV!%O}ACcUKlQ2utXIj({RR
zddk#aJ0fK{zr<XslL76RP*IoJ5t*l&fin>{=7F27P)DaWrmbp+DN>T_$)tbfr`Wo7
zf#x#KWgb5XUq9nviqSbGmqCZDjRjO?RcYHx&K-MP90B<Sf<MpnjzJ{zmGdSBHrW_N
z@20rwH*wKq?G+;~INr4K{9BxCvg0lz#>%j*9N|GeJvFmCwwqh*jjH3<+}5<XL8s*p
zfjucq2PSn2)rFpelIFLxHLr#iWCwj%^D(*M75aD`tTuM`I_b;FTT&6tAT_heqc=z|
zw+I2gQ4uiG&3VdO1>!c{0^AA`dEdKLdDONF%6|03P`5MEhN=f@zoZa;d&o#zIgxos
zSdFar+5)hZH#>WE1vKEO1-YC&zmXiQz#^v9F51WDfjumY7As17ihJ-F>w|ll(#T*~
zEO%(AXt8;P61Elw?;aP(6*aW2NcsX)DxOHqym$!@7Ia=F1|*9gYs2=bSxtq9aRO&R
zD86oK?ihUa(~+h`&&2A2nM{mSg);r6h5NcJMUYT}37FU$OFaoAI@u8bI<c40ZR}`s
zC9i$GlspLJ2*+0vdA8Re*KK@mpmcz4?kovFH$wmn*psH`y`L4U<hUAH-KG#g!S>>&
z4hL)`H29JrDRH!UIPXzJestODQ%M<swG(8}pL+7-To~vc_=(RcotoSDM+29#rfi~_
z$3{iQL{UzTcF7E2W4T-Fv^hu|WZ>b1ltYIHUKb@&8w)u9l6Lze?F3?YQr8VRG3zZp
zGafc5m?dV==eM=XkY41p;`@p<xJ$$;Mqc1z5WM-({qvCLkwrIOM<)Xziz_+4iv1;W
z9t(Xfe#bdD>*o%fLuv=n8gb(9Ba2awnhY%L*sG9<!^zVS=+y=q%K&^_G0p~k8nc#<
zU|W*=$}1KcO3hIZ%>&cu;BDX`(?#%9>J|<|!f{~qPMPDTdlVNCUP+*lcTA>?CP}?y
z{$CMyR*a$sz(0QfiZR@|fHI)wS=N2dn0p8*7d0CXBFnfopV%YNTwJm48WG?wBOkEe
zbjSuquqc%(85NK=d2qnF@!&Q&Ams?sBAR&`^GY6=*6$no0Zu#u4zu-H@!Rj>F!$D|
zLHV?TU6{g8i3>M0r9GqgUlEHke?BrsFnssC<J&aSE|w`X7K}1D?hdZl@f4$;AS6tw
zzODa#(CSW#lMdZ0VYhp83x(=3b3Dmal|B<B<ttOy)WY)lvEq2<&etmqma^JJCkcB2
z_q~^^x^2!gG_<k;;qQ_~%#A*_aI%Tkd#9A34DE*{3pLD-<HKpfJEiM@PF3<0(^$ce
zpd~X+F7sYRg|qr{b1LM%mSdTxV*;W7)kW0}crm3-wKO&)ziSA06UA4hn)dO^UdO(R
zgZmRfYNE-E&8+fVGt1+9fJLycZ*Z7?8nA|mLV0mpPd#exw*8h!hgd!&_ZRZ<wza4?
zo@$Jk$`6$kc%zsObRu+k>@>MdUxHhe!8RCJoB^2Y?<F5l%wKOnH$u-!p*FP`6CtQV
z0X0d^bQk!J0NW0=PzO8!<KZ)zJ86Rik4KPq4$bUFzb!2c!_k$OqiavWcY25{a<$wM
zUxCD9ci6)n_gPk2H?NMgD1#};xGjOqUTA`1Ckol92R@+_yg(-;`R3FJ>A7A54TS8b
z`*S~wf1YD*5p*oh<sJu3^4Y;XP(Sqz;R`i<e}pB@6{7Acu_TLyh9&2Vy`t2vZDxk=
z`o;O=cz>g+iy2_p_3_?K>a1V0YL(0p%4k382D0^ghT=;>82S4sxh1flCm@Fx-`elO
zJ2zSPo9IS&leu)OC=nJ1=%~ZGvQU5lUdcL~BI5wr;#=c!Y!YeI{l_3-BMp5Zn{{dP
z6X;x{mVu|OSmV#SQx<KIMx+<w7dRCk({yY&=bBAkz<QFPiw&b31sjC@L{X#-qS&_k
z<^|6{on0$WyJ2hUH}9DBSQgSL@NglJaLr7$`*LdP4NqaQ6LcN1-{5^>G!K*mMY1Q0
zz!^|7$)MYjU@$)W>xkroNZ<AFAG_s)wsT}Q@i;`$;kD*~;D8S$SmTpsHxOZ<GFM_2
zI_v0~9`gsH$Mu*h1x|l=>ntI?z?v6r$rIwRO_)3#Z8xh1s(qpeI|I09avYLPVHiyV
zQlB>}-gFgPXYOuM=XY$rX@R-A^{I8f?{xqz=;>#l0mI^VfmL8cip+nuHdn<myD8i5
zk$a3`Kioo;LI2hg-~OE|isQ@M?ZR=+U_<X&*%}THH_;fu0k%F45Cbi0K?kGLLgI&$
zA#(TH-;0R6@Mq4lwu-cFh{CVlrVt%OAfU)8uTM-FGwcHAn0OddbKpxUw*mqk=oSq!
ziak6#dUC)C;$~pNq8p<7RZ2(5S3b6`G^tIPy56;&3x_e1kWT`slP2T`8KW%hUh?*n
z;SKWXF5ov99*F%6ORt=26B_eJzn>0aV!*{0$pM>Rsuba51uGy5GB$FT6b`&Gj#J}a
zi(6ZHeAl1Yu44~UF!k`Td3R!9Q+m~8hV0qZRm`03j2Oa8J6;+v5a=qSeGIYxu;2)#
zX0M7xpHl>lz3}ibR~Qa8U4@r8_(kBjS>012>4OZApxs6*UQcyZ?IO3(=_1RJ0aT87
zeRVoILHm`dPHF7-(T|od(9db>8)1N0bu<Z&QwkT{Q72^)6fq6(J^EI?NwKQx3cuR{
zQVZZ+Ga#9-48oyRbszubNkxi<)PJ+>!#Fk>Nq8Ks#8&odls24<&BC_-HDkN+bzM|D
z$DcEI;Y2JT5if@<!>UvuZ5>XnRl9bCz%8N4qNcf^%~kIr@L%hQodig;6c2pLvDu0d
zjavxDF?Cbg;Qcb@<|pM_E7FHZx$4_QiUioxxHtxzw<MIubPJK+wH2h+mT{}J7V_sH
z*@NcX0JR*elej4{Lt}YQdU7Hxd{KRXN{!|%VAr)Yb@z0(T+}yY)m_xp)a`qL*+kDb
z*b6cMq3b|`^P5(oj6LB(q+~d+Ct>Q#o{FAY!SQ%f`6TbqYmER-%a2P>N*T$($AXf+
zigJp!N4bO;W6fpOr47ld?9l$46E$P*_pD~bI_@y+U9q!eQ$N~ipPYIds?JTMGNMvb
z`GGJfESeoz$;N!R_{=uov8A!(&zs(Gd^t09KJckNV*@mC)<{(OV##WS+=ehd<_v_S
zGz`o#qfK=AIpgmig9KKdEm+H!nXA6aSyDsWFgO0C7H^$FJg?hzN8CrA6+!9;U-BA;
zdqaf+EgXu@paZ>R;o7Q|i3|$v+PFMrKjElZvl%7{R_X1g=?GiAwVbkIaDbl3IbNN?
zI5NXJNzAKV$8}-LWFt<!i<8Vb3Y|D<*l2kJkDCc5Qmv|9P3xSGG6Fk}Ih$~)%o)}Q
zQrEO;aCYS1j`WV}zQVt%s50DW6d4aI_G`}GBc!0CFN*)*JN1t&X#eRL^TRz}HHG@c
z;1^&c5@|n?B1e}1lNWEo7i{InXv3;icO3BQ`eH;Oqgpv~*vdr-Qub&ZVVUM?+v*&o
zN|b+T5O|xVN){&{wsMbI`&V1~qP&?x<(SpFV4cm3rfvSkU~&`c@P84W{|6}Z|FQ7Q
z%E`#_KRW`UngC7J)i!<+E*CJ6p?lJdEpPb7zrr!%!EA!4;NWnEBH%(LhvRc$J4l*@
zVo|OMang#B*rQ2=+!YLQ;<4mR$5HdFayM+d*v-?QJNQuv+1`&Q!P8eNEBc=XzaOg`
z<CvhJ;^fI%-0Kcodp*`;))p3{@}oG-7M(YD@v!&m7dO+ZeM6SN<cmLg3N#lsWWg3V
z5hs-g?YhJLEHSi8x*Qjo^NH`yHTJrq;pu)YhOlPLe`F*@uW&Edn|FOA8fnC_&Ik~V
z*eqVII-AYsodQ!8Vp4lsQ<Gm`48>eM91Buk<qc>kY%FYOFvM7UF)i8T8W;arNCdb?
z<fjhJ{`37SUhQT-skdFD-chI3zH-fW>Hqr2<EBV4%qv{6e~MzFY{GoJMlOxff#qci
zdMr_Ey2gy1MF)#vEE|Sh)fv`gykQD+EP}~M-*76UF^0=z@NSAz+)&?C-<US@erj@x
zCT3id2_@5N%GDhG5u!Om>))Dk^ey9L#&Ha2YfsX~pQ4pqEDUovy+ggRSSO1WrprvY
zDRFa@Lm`da`oI4T@T9}Gw?_Jpf<sqcynfWqr`K=wZI%D>z~8;9!eAqYPmVTSe)&G_
zyuTBjPfX?}>@imC%#2NJQ7lzVd1gJUZ-J5V6A}!vY`Lb|W*^yFY3rqpMkf6n{?k39
zLG6|YW<Fs47P=XX;%6T7un1W%`r>?S|L8AmzVGaSyU^mtkIdnHj{)xa@)cVdNokp2
zk-DO4sxEpWTIG=K+X6)5WV+=&$k-wxf>;zc2`IfIY7W78AsEOI7~zhj`Zro<Dg^#z
zjQyqzSEt%T)kP}b?ha^!W$skY6FK?3@F~}JbDC86X~kC4nGSo{639-qd)iGue4L==
z@_5%Y`;O<$>0oPTon~sx;XKE`$sSWE?9OHEzMO>!%^!}&`8CDm^`{yi)$u$nr=dK3
zeA#?|dThFnA~q{;hI`vZRr!#s4y%hs9VIo)$P>$U({*m-CuTxi<<+n>NZ19H-2AB6
z1@w$E4U#l~#J1Kg`k^_#>1{WT+E-@kT&~X-b203rN({O+?v$yLl2qybc2{k_Tvx+C
zBi-+@Y%l9QSwG$%vQ3+rv32)|s<bSg+2E~0DJJF-sECjGwW9q6#U3~urre+0RZl}d
z76EVPXlA#iSQn?2YJ?nl3+CWv&u#8MUIEfo5xUl&K;2zDJ4ERr1j2UcC2;imhTF_@
z+)Kflw?ZCCFT*e`giBOQ@Z1Aa`Hm3P&%{#*2EW(UHgYwgu@{k{Et_H0O*VRB^R=U8
zTSfwsBF+pzS!X_jgbV=U&Xyz}Pd-}>fTv7$1j!sH7ZQ{JxTZvMB2kP_o@q>=&gq8f
zI7adFTca-ZN%XWygh1R{s^O!;6jHWRGVFND=q}u()MUs;oE*>?SHpij5M4wL-)3GO
z=JFS^v`~43Gn0<g(_IbhkG=evSG|07y)g74CgS5KN%VQ|4mR`Bg%`xeP(jk6SrJhZ
z+vop!s0=n!GNT{RpJCOi-L6i{UD-o|ew4pLn=@nX6Mc#pRDED}BBt+8NLNIF!#|un
zBjrwkHqAm|(ry}oigm=6pXcrJJlwD`rGA#HWm7A*YJp{`Bui&6<ikA}S>K>mm#|X;
z$CApF;yH1UR5$y3ENT`Uq@r%29>F%J=JCb&YP)aismmD>!Y&*d@@^|e(jvv3tz{kE
z{9UnhwRESokw9N4r4RZX2wb2XL7IB}SD*87xYk61>u0|-4}pI86gNw@Q%p8;{s8>(
zTRW-x;zgr1LNTqAp|#>5_&>%7u>0%FN+&YytH<hmZU_>_<WftFtmK|BcA@eS%pLHg
z3m`fQK@t^fsyhbXI{NV2=HvCbIMKX2Y_~F2&>OhUp>uC-<xzjq^DUv<>X#`ICHXiT
zF9;jO8h}ANOF6NzxZ{Tx71-^yv8xg^_ZFa+IB4|G_SbfG`~E*kH36dQ06=T-a5nYN
zU2jW$Hmz-VVWA-z99%3^agwdVIu$ALe$d6w2V?rKw4Q)>$tWg9`_nz@UQRE`deQNb
za7yM5%A3*Gp`gNWn23dfDXU`KA+0hpK@JL4HD`z37}zLh9VtQsUa-bn%US$nhH!7B
zPmfR<J@qZR(Ej(@I;T8sd(jXc&I+;%T7cm@5;GEbFZBjxqL8L+GGB-2*;diZx<S<X
zhzZe2uqNy)sF!RS&{gG4bw+WOi%zHWm{t;?=8Z;xhI&O;q+|PEMkfkE?z#2dnhU$&
zz4)_&;UaDMQY4sPx1f#F-FMxh<FgqK`=^8*a}{cQ=9<Fm0AdaC@?0gZ-p~CNA2;aV
zQIPD%L;xmz4|i8(V_Q}2=I`)%N*_ZqMg)bF5a_1_u|I*CfNv;y(s1-6uHG(E?m#<_
zcJ4yLD$ud+?&xB^pObxQuBA3|>9yKqoXyN4o`j_X!jMd#DGRj;AzX)GgsRkzGny#=
zML-rhQnjYBggPKePw=S&4DzC)irNkhrUAvGu=hjQ&P^Y`RX~uO&A!xhp~J>e0Pig`
zShefeb+GQ{@dRbtF|V8F7oLfn+<{J?GUyVJ%@1?<cG;#>y%I#n@x8ChDiIBM^+UD0
ztJu4^GGllET<0qMN4Vg6UeA5Z=8!x-irCvbGbeedV~^v*cDpTgAjmPeW*#^K_g#~^
zUosrdHCuhBK%J%mF-P>BmMsz(4I3KS$reF7q0On~o=+7Xqt9dqRUQ=JsABFfC_t?0
z2+P=DG;_7JbbMyUl^C6i@j4`;@1*OfcWKq?_&Ln}?bvyY^nleSrY`2n<Jr@x2{Ox(
zZ=7=yZEb<@Por}l+?!Pn!>QZuC0`t~rVD`=kVKpyVIPeDcOjHp^D~x)SHdi9j_nF|
zPWr~dP8G3p8R85@(0H5+iu|cbQz>LOzq<SQUXQ&aeOWAr#GU#}I79pk?zucXPuD@z
zC!b!6oEn}qH8hr%EG7}~c`WG|0cMmaRfWpzi8K{>ox?ziZ%0GLOR7GxZ+iS5ZlO)X
zqq9#dfzGQ#(jvM-gPNEKE#_+~Bjf8kDVQW)Ij>@OUkh=_md*ABWrs?SEZF@1Uqo>7
z`R}YSf&1_={XB*gYBnN7A@_M=RzBI4@nH%pU$*P!xY0W#YtNd$gcoQxSia%k^8?Z+
zQJ1v-Z?|5T1XT%>>;3K*xx_onP~u4(pKbG7F2svYYu>svycI)~Qb+DG7&{|G24oX<
zeW}%=B9h)&awO1ry30VFH+E)DGz;IKSsrD8Zd3)atiG+0GKwRfq6SSN>s)$-QbHy!
z0p?s=?+^~BF>_>(Fz&>Kf8xC*ZEpDMruC~!BI33a<b{o>F0I2F!ZqkYZb4dMl-Is=
z^<V%>3_CDZTT!GnM6S#cBThYNfmFHVR5RF6OcnwDe{^EJObC{W$~XE#1AQ2`7BEqX
zNO)3_>^Tw!2Yhx;f6)W(e6qLLsTT69f||v<8g-L2?U!PRW<b)Gx`(BE{<s)2Q<K+b
zbI9DjqoP3wQoe{28yu)J$BjUVa+6>7FzmhB)JN*~e#WrZ#FF^8E3uop6r4mo0djn%
z0>!};QYTqlu<-jQwWp-fKO!W&@T%>(Zm1TO_i-JMF6o^-C1lElzxe=oiy!{PDO@&p
z+znJM(PXj!P55Wh)%8UDBW|8G7t@OEWe+_&AsOGCFbyl3%}{o-*hr2N)ROCcMo~ZC
zZ4J|fwh2Q5ZH!eybwL>?dy1>+>Dp<U+3oUb)BHR*E%L-;tr1(4sl7MTa2fgjh}=01
z*QVS<yGs%IV_b;3(maTK+V^rGB*bH!;rq`o6F#?v^DCFL2(ay4*MlRUxUOK1WlK)P
zFkwAiCQ-p=E`eTZdx+VL0Yvy*h*t-JBs7XVw5EAPF;DeP%0He2GJHU||NOr2J+ULP
zCUesmpgP>8dx-kyyl)n!^*=epwyE2fn{cpUZ&%%pm<!D)Oe35Ul5+8L6Du~10b5`0
zb-vzJ;Ky&$+enhB&Y;%FmaA)u8p~cL#2(0-Y)1|*krVF8^B|=n0%~x;o@%Gv;OvF1
z!DGO+@1k~2-SqRwq6&6IpA>&5aM>%C%bjti@z-NAL(<>)Os~Gr`=j{o{9I&c{&v5K
z3@P11=&V<W%wq2%zrk}TWS35^=-&SBZ=~9!cW-aGZ3af?>yJ2D!}z7IxO?!lFq)NC
z6LZwbs0Gli<;8u54YSzl!MPH5Ur;;{GvzU*W=Q!^FnxPMLtA=2_!W1+Qb3l3(e*p+
z7uyJuNony$E*^{LV^32-Y1sIE0qnY437|1Bgtm%Gb#_r#6;cswCDY{ulKZHNm0OKS
z`N|9lvf_xl&!t{w{?3u#(7#+)Y#T^DCywZfc0Us6jkGg_>9<&3t6Ax?btt2(bn86Y
zGYF*3qg&I3epkJ?88dqv*}@%pkdW807MRmGHm9x6q#Np}DTYQJn#nrrT9bwd3wAbf
zMjkHy-S8ZOmOMG(b1EdW-b(JP7u%VE<~EUcu$8BMsPhNyIJU(m71q}0H5F9m`l}!*
zPd{4;2>9xG_)6ki4=3pg^maT<_KVCnfw8NsZKQKEgM0sUhH<}EU;wVO_SdQwRY&$6
zT%NQU=Dtln75$MOd_Vj<>-YAP_Dswi0;%7&Stj4g_eVG6;s6w2jYAW?58?Q23Hb=I
zbb7&fy-jr;UCB&cDd}Ov`}sI#5<Qx_hgG5MboM~7mcPeVB|K#|>6WpWPR5`+!{6um
zHr44oik&F>2|re;^}19OSY9$XzT;yX7#M%e)}!6+DVa#Iw=L9`1XX-sCHXN9Q+#y0
zRDk-}JOO6+?N9HS!ZDV@0prxH6OUuRvWgB{i#U|D>d1GEaTJDEoU-ecqS$Q8pJ^4L
z0MUNMTI8@ji_9xtnfnY`&)a7x&>uN=<R<cawx70g77s3>;qet7M|3p0UsICF8MGhW
zas*j?;+~(+bLO{Y)xQ)x)<w{#(;crJ1>%pyV)ZQaCk_*uKN5QPO(g1q5Hf5o<W4f~
zm$;h_+c*FQf20Y1Cs{IoOTd~E`xW9<VOp|7c*gzJJ@;zE0RNIS39Qqa)3p8PZq9(0
z`QQX7-7@=rcYMSKUU2k909tw1z%kO{+kcIfvig*5tzuZSeCuQ-J_4AjAy0e?>i1jL
z%XsPY2hf6u7X@Zxp~J&-s)Nwq>wNq{k)CU=0<Ktf@1{o#`{t(`td70&FpC!8zm%od
zcNkAK_h7L7V)R=?e1}fLW`xO>Q!3;O=QBOKGG-MUx>53KHxk=GJ?6{iE;+M{ZyLP=
z`Q1n83nP)(=!2U1>=wITa!daez!1^7uQne$u)M0yjsW@`iCw+?q0PXGv1cG!0Yk5;
zWZuc*FNTG?p9SCI&)^4jT<AUIZ1QNl8P3fxZFq<V-;L6=*n80<C?@l_C<6Qkx#x%U
zMM&6v<214%@9_~Pi_RXnUHLe7>SDXpXaHPLwS`sh6`_#55tG_wlDqqR)~sDo^91+p
zVJSdD!^(NCceb!mCmdavZ3!IgSadXsYo?A0X@D-YITpFBXu6^~cUtey-5MSm9leK3
z3H%m`)>TaP4~M;<Q;h*`O^k2xr16)e7A|_0Km%jKardrf25Ju`JU*h+Z$4W*YBLM#
zKF0|C8TGmC{u^EApqnDxmSQPuZPWsGr5RkuIlcYDs<{;f-0^2dqGW4>sJJ~6)(*2R
zmRiPG45T7XwDUs)Z9-crV;{te$hpu4f{(>IJ1L-BV3fsLi_HGVUU01?Ike3&0x2>l
z7OFh@Djd5qds}n{^Q56W8(Bc|BC0b*4I7Ax)p8<H>wGh^gC}d!h+_|H*uJeR&<hcm
z+Uo6@yK6*jA)la;;)gOu0NOjMdO7yhdGdA1rPFpWOrDuspq91``(FXdi|Wa$!HC~v
zTh}1GPWT@dFGTkzPKn<dafF{Xc3(-)_GVMu84>hyEPOKw?IR+~i$blY?1z`xFu4|^
zMYkYO_n|Q5Z^~$KQ*11m6D)g+_p#Gerzye^)rAs=xtjb8ENbF#aF3}0>J}WeO1-(E
zXmzYsF%)}Zn%g2!`vZd?hkMU(F!B$xk&^J3StAx3<;w+|8m8^0djUXo?kLoel^?Vz
za121PFSVki)xW&k|Bnwm0DSb5bf=A}Oq=TA@1N1J5Kn@Q9Xh@+6w6G5sCpX?0fY-6
zk0yJ#Kl8kbu28)Xr?9(17snpp-IvxbuY${OB8dga6C&)Cbaav%+^#S6)o9s_3i5Rc
zHx*|&v=Mj&nh0z~D>1bUA=(uM5<kk<Jf$5ato<oyv9Mcc<R($FF#{u-b|p;wx>_04
zpQuYG7+D#vOUK6;$16u^J{}DtN8onf9lxMeP>B}*r#8mH^#6jN!OY0^zpyZRYTl|S
z8u)pF8L-l=&av@}+{t>~&=Li21*y+#A3#bz(QnkPVH(I0P~?P>LCS(DTTFy#3CN1V
zq&=XCbi(UZ-nQ38_%yYLH*F(bn~QaNmnQ8ux!aQj-YAM|<fmIM_9yAy{kyN9eGa54
zs?t)VNtuy2hpTqGZHB{Rv0k};A}>SZcWPdCRj>0n2Q(>fRigi8Gn#%5T+hexzux^}
zf9+s1M3j>DtV~PsWuNQZ4PNRd$B{dDGLoc8U6sveiXz6x$*P~V%!9>CXVht<Vsvrw
z<yS1lnzS0Jg^}uICzejJS=gXU>D7=38kvL}rSfI`mMDdvxW$~av@7K_&KA)&7ynQp
zqZqf7Q699}$!{egV&B8m9O?u`M;5fH+$#M8f-6nM{+b3`Q>Bj57OE{$S7B16Z4HYm
zKB$acBDZE~4c(9?OZX7A2DFAJeJUYKOHY@^DZ(YaCs|LR^uzX7{}ldEd;v<ilu_@*
z2Pj;@eD&B3{)A=5Zj=kMN(B8W;QBe+q4QJ-Q>mR=uvFe~%k8(hI5R946jFsw5=SWF
zqZ~M7$U}rV5@FVGa)ah!j+OWBXH1PwJiY_aKi$FeY(}3U9rI^uStynA&^_a7%dfIM
z*3t7h9K1wIqu$|v7yW_O_fa4=KM?d(fX<&i5aJdXP0H~ZR384X3UvaqJ1oaVUW26A
zkN9=ZIF5YTbxe=vVD84at8HqQp!{NX;LGc9yM^LlgFP)*1J|bUK)}>6tv$ID@(po`
z-t)kj*8>{HR6uy*wKZ!?@l{haI}v~uR})>Tmtf_<dw$^fWx~ZOq#}+F`j0I<wE>cO
zb1|ETYwa@~owYcJh;r^Wlpu6Z8UZ)%rJ%<sff`?kn=1?bJvnF_pz^>H1FOyaPZ}1E
zSdLq!t7_EvP?yBBC6VK$!LR|%PjNW^dd3xlO$6ed?*ouEBQ~qq<EP>i^OCLqNAl0H
z^Y5}w`e9n*&L=(0U^|~>AjKn<2eCCG-L2x|XbluT9UW^L?zCE6_|!uy$BiEEFRa`l
z&|_q%g4MW9P;+kV^EIxA^Q*=RX^%5XUNdC>)1eJyK_}jCOJlL?i<gd&8@^=UUWBHY
z=JqUsv6?YH-^u`|4{z|w#$xp%Veq%`a6=BLI`4J(oPRTD!)MLnV1OLOb2;=@0#?yw
z_B%a1gGlsofs4k2PU9uA1H8Zc_eQLOxBKl&E2;-T3M{h1YeyEImRCO``ST`^7*rQ#
z)z7qVxBz}*9>_b<cAJD31rRzj%y3w#;2&&Wpz%qNb6PaV%f&Cg3a@|hNpcme;aru*
zC|WR1$BRO0Io;6TA@rr`_dT6_a&~-6Yx+u;lIdDXt=3!`_le(4V)f`pJc-^U!roOE
zI_@msa6+7jBW0DXQK}_cs<|PDpik{!KKU?vwn=E>P-Sx}&d?bJ5~Rdjl<qNbday&^
z`Fd;1*~KM|V?Oa-YQ6ie(1`QNSDvWrKIo1IAZDUb|EB{NHzgY^lzq<F(#nNvH!Bwg
zkLYif;1a2$$@~7!>p1czICq3Zmd9%3|M#?>DWFmKjvx$w*N+zrV&sF~v%6AvlsNfH
zmkK(@@R_kzFx{zw>r#YnX|U$Cc^dYutYdH_o2d9%XtsiZ$lh^MwJZr%jfU%6r>ic~
zy_JAo!^tue7S?BbepAo?!x1F<xhWW31!~GvRX#fQ+q&gu&T@M-w^z?X0JHX=uaeqj
zt#u~k^KBk!{J+OsK*#96#<FEeiC-HD2i9yLm8Mv7h?-W;sA?4=5!OnIWZT9i=RFvV
zKh+aE62R;Eg7u396DPUlaj=T9Md!B;ZZFRoSbrKcn+;A#CO6@sOKp51eSVU3?+L5Z
z9qSW?)<|#zK!=1G2NntF1%JkW_eH?gO3@xF>>8b%Htl!&-LE%dv-9ozl8@5V1?wTt
zis~cx-NV_2DP1wAKKdmkXJ#20_t>I)8>Eke>mLW(oeGxOjFl8J_AXnd%VQOK&!9l7
z&u?OG4h+n}U;;SEJ0h8cB6z7vJe3L%5wssr0uvY@{^7*E(3$TB>OB6rF|{@o+($_a
zf}ok|Qf32W^$t$K8@a>s;V+^c`enphV#|iVGJL}+sY8rlUY|UkUc1%uBjh7L3EyFW
zaMK6S88c@Qh0or}dW*mJbZG(P&D4Z=Kv7>-#vHU74rnw@TFf6iS9FnE_<V|UyTt~U
z(z%yM-o--yMyIr)O11!gLUv~&m7N&BPPYK>y`w`)9qIEG%|%er-PFzDacn87Hblv`
zOxT@{nUSlVYtoj%78{?fprvvlWfg7x@Nec`iJF#u04GwwpQ6kMmj<OwYo?Q?w2|#l
z08umgOYWf}Jalre2iblD!#PAWNhogKbe*5f2}))%=gaIHBAzqP_09Hvv_Q;gS@xVy
zuq68g=);J#fhE4Vn*J+>T6q%+`PGmVE9TqvKM=i~(Q{L!>&Mq27^rSXC_HA@J^D@X
zZUuK%M#9P=j}_%x86^s-d8#!>7d+$9T4BdolQ}Z&5rg<p5IOJgvurIoyiglqMY(~@
zdktF8o5xU&dt6rKJ&V6E@MGvlQ!{dl=GlY>>Q<oy%!JkNdtA9Iu8Qret+&XZnD7Pb
zW@?nGx&qZswDQnZBc*DVR%%*k)AC97s{n9yFu5S1<WQ-+iSHNYolmNI1|Bq2*H6+}
zjfCy>#jo>%^pUXhU(oIigWLbhV(~xta{tdwH)f{)1?H;N@N`vK?h+`R-e**^obIk1
z<`NSJr;Z}le=eF5GykWn%{?pkx5SF21#8n!Dw!QGsq8U9-pVRdJ2Xf*6p|7gK!4F4
z)s^Dj>0WrLzb(Px-rJrW6xyD5`~A4lI-|Yroqzk)xBKJU|7{0+e5(Kj79`NqXmyEG
z-A237;@M0p^02i)X4E-vHPN_JDR3XJ@`VEOGa<!$`Pb#=YC6`B>6goQL5~gp^LWuf
zZis?Py1^#xRZ2iOZ>gQ7cUEKkP)UW53>ES$VhQ*Cu}~(xuR%MBYF1ue_8*+5-}!=>
zOj^R}BoWg?f?6RFPO*s!SyRr^QNmeFM?!32wunfZq^T(*muVp>2`M>06*nD`Xc8ha
zem{rl>P#!opRk(UMz__rT!ru-j=5KV`$W@niE<=OmV9K|GT}-A<$5KWO10z_6$j`x
zB|t^6Dk|D^$-9L@8KuC#{460pVLtgrRY<n*ESc$IbmizuvL%<L>ZSaO)k?J`YiqdH
zG;N96a&|?dCGr2?c4AlDrovO{XpPaDy&-a4`l9q%8JbK943$CUp`Sp+2*aO&%38U8
z3nkFKEATZL$`Wcd6mJzLUu5)9oE%NDYtE`tK^0f|SgxOk&UC%xIst1heihRV{2)=r
zTa?%7VZKXiwf8XKkiY}<O)2Iu2__jhGAwx&&L}|5tdX`^kmOXgxv;J|H<ns;=v307
z3R9{$1SILThTgEGeTiu**hnl>TZvn9SbZp%tyvWFR|%okKGga4BEX!=>zq2vAB`>@
zs_&rI@5`vgU@0HWnmva<`UGaT)aJ7=9~RXK`D0vw=3)R0Z4fTX3o9FxJ5#9{0T7Mx
zbu%T?!i|-b^ii0`BN2Z1KIEvKdxTT5-(@LhFHBjH+2f@Kx(SgAm$tD;sp)g-KK3iT
z`XVF{4yu?jj$<8&U<i~)Ery##CP-!3Mj%WfR(vma4q!1^C~>Ql(4xsG+#+2^A)B1L
zka>J2G;2}otR1#o!4PN!-t%QZ1Ai~<*GFlc=sr|ABn?OAIj$qQ8UF6GLJczq_e#GK
z6B07{a`p?W;OHjfBckFWDkj_HTRX)!)i`+UOJ6TI`46BAGM88V>!;?J&2V=pa0uGl
zg5LB7UmHaM`-B8Br?;^qqAWcSE%rU3IC9Y4LOm@JpDf~T`AL`w8^B}WHJ2d~@RY9j
zMkesX*C%_l?&1dw@B4hd&0Jp7+gn>XY2N_6>3$&n&B#a*KXJenv7KMXVr*Z^DW8P_
zGa;L2GCP`0a{J6tQT@0^tQeqPHjzY0HgLvj>Rm9Z<XQ7v{&-*`v9S|)2&=xkO?5Hh
zBuSQ()AFP)7F}Ik8?JaDq>K>t1o`%_+Rq0Ng*@2m`<=*^p51ph&_4AUKEq(%n51qh
z6g2|=r!H~~*gbW~R6Ehr0qoq8^|5~Z)|;N7Q`CFdAbWVGR6?-?ak{8KELPi|;0CF7
z2k`|rI`~Y96s?Hje72gy!U<Rmf9C}0%VaA4H_m_#TTmhU-!wZVLf!nzX=AA290nDe
zHKiI&(zeqK=)fMD2Upy1BIn-ldxQ{-L!m&=;dCBNkno%oO5?AvCp9C(U;iJB7UV`t
zI%v%6`1O&%nikk6S#b>22Kk0;8Xl}9j(Sq5?7H`Rg8OJu1ByZ_4~S@$F&4W}{h4%&
zF^|059bOX4ClKuj<T-wOxj;yPV5%N`Ky$Pu>Htk>yVa4!FbhhrRiPO^>k-lyV_03{
z(T*5|k_CB%Ca+ykD?hjA#7S&+fL8XOZwR3e%#DBwn>IDAQ8Lc~KA_Y&V*Jz4&e>>r
zm6FA?Xg6$66=Mm`{bMcMsKq-W-p7Sz0Y?j?j^@_+gf&oh%K&T{u}wy3l-HJ^>`ywI
z_6io5hjm5PAowCjBO1F~5chI0)h%X%5##Ef=wZH&eb6PstE`ON-_NJ5!b@FKHTn`~
zU(#%>h|a*tP*1JbGJ$8g->`xXdDAx6VylrQZWh5=>4Fx`WL-q^u`VZWh6Oh~QryDW
z;Yo_V1vdKr3Miom7E58&wTm9_D0WXpD5%}HL=6?IqUiZmrcvY3k%yIgASi^ZYD?VC
zSC!5u9m{0I>5}<Ff#P!km+Ir9XB+*@zx-kbf@DMaXR}*KpPd0{c?Ks553z^sANN+D
zqWb2Vyz$~RewieqljLhdMy~i3g6V;)UpD0(Q;Jm-)K7lNVn%&xuOgk2q^vDEAuC@^
zle$JtN!XYO*e^g+oveQ61}-5U>!2n^t}L7dP1#Tb^}1+xI1L_Km-iB{cAaS=1n;!@
zFmv8>6kzNwqwxY!CsDL!b`v45$gS+#8TD&apyKK)+Jk}9vOf$Y;cLpL&)Q{`bNU1S
z1obqFr1)}-Cn&s%%~76RqU9Za6L3P?<yAFwmTQ@Ahag+<o7Zu+<C+agMx*kO_a_*P
z%xzL`q*;*tBFpqJ9Yw{P4h0hu3_TB^|B;~;No*JWLjLZtv11{pP;vd8r!h`m)&)k3
zi4RlMPIY29RPqA<QO<^DRs^4YwCxwS6^u9`j?=S1H~R48wc6Gn^$wPNqkHK0D)}!n
z=Phta@mKNR>&PBI(()+>I&%Ngc`O;TXB3zMab$f!e&XKkZ_fmqU~i*(B@3zzQTT+d
zGMhU`JAvQn!2Pmj2<ZJ`hKZ54i><>(;~L8x+_&Qf>ye{&J@PjrLJ68ACH{LqrFioD
zc=*EC;m8>L{JqBn=jPxqf~~-bP#_pOPnfGo|K#9gK*gWr%0!n%js5Sc!H?MwY6f8z
z7r)y(YToE0f{@e1t88Oktzkg8(cS))fp}{Xn?j|5>Eihh4nZz1cv*dGfCul)>zyw?
z>m&*05B&EvkH8PKzpN|k`ZqHVhUz67z?Ev{r09AoEE3u`ekagj{O)MV0$#8I8<xCw
zUD=2}oV$#tyo*pID;0^0v~$W2<gbIOJC}kg!e$slk-?~e!qT=)OKbB(#;E?hQLO+-
z%&40A(SRjuK-7dCrRBAy&5cR#z#I*}>nMtc8SGe)-=2YwT*%j3?qL<eOQrsQJHAq$
z7i$@Fc!eJ``T^n;eMiH!$qXFmOCLKFdQ|Mil15_yuArErA$w{v|FpPt85Iu;2gCJr
zy-|eN<Nnv~wMB;ZQ?=zwv}Q*ZoHiyI9)Bzt$9#zm$xLqJmcs&+=x$7EEVvfMQZkfy
zDv?1W4+|Rtrd#Foj1XLgO?PI2j@q>Y2~v&;f85#b)J;&v709vE)5Wcnb;ekr`(Onr
zT``m9iRyB5uHwA*Jbwgwp0ExgoUFf?K0E*^@eRoh8pU)O13M*Q_R5{2*Y}h2qrJ?#
z^l73!P?GHg8|H(h-V3N?p@M!5Jv$3NMp$WmM5y$zp|<nY#c31#R4J&O|81&pxclUK
zKt;I)U5jHAe#P2GZ?jzFS#E%J2_3_+fXVuwwE7-qqmWt=C|0vK<#{fF#Qc&zJ0)q9
z0r|yv{p94-<jl1DksEtAc2}>52VJ5Y_q^+jFXg(SB2KDTrR_D5;nU~jCuc*@#DO4d
z|1XjCT`@}C10J69E1U_Dr4T{Si}pz-0btHV9=SiTeB7wXW{F*zc99MwysT}&mhl`I
zyKs8t0N0*((-}!TqcLN~k{txOi8%@G`JG~j2z|i}{9n?`mowi{ppd#UYIJ%uaDi%a
zCPrGI)f^M@E!Hkn8T8-vw#`$;9XUg7x(8l(WoHfS)XvME>XzS0YBke;NkI_{x(O^a
zv%#gq8`{3R*KZhOU-(H03xpas>Ey^z=Q7k)Tn!rC)Y-PRB9w?bM)acu+e6|)HM~SZ
zOeu=Vpi&8BspY6;RPI4x_jVc>oqDQ&(pI_no(Gn)pj&V&CSyuBT01m&>8VcgN^0O*
z%dzX*x37BFd`f<wAQ|OavryrQi|<v%TJ-G|6T<k4cs$=$_@e%9b6LWkM_Ob_fxfG(
zon3lh=S5kV(7U=`r;qGgB={Ngcjs~T7*f3$baK2NVIBSQA^Ui>IsGM4o><3!s$r0y
z#{5V%wHE>{av-L!C(=QtNHjgoi({=O#BL({SM#NMY){#hpVGOqqBU>r-<~<ZYKFr}
zNkwLr2q8oypQ8xSW}PP#U7(o9SFv*V!h#fEQaHmx9rBFN`<?f^Ea7d`>r2StS)Fe$
zqOsYkjwrnf(A!kwqpFiML^nHp7iU0^L$s~8ti-Osx&bSznxNblL?{&>^}0gFLF=>G
z0R`)hQ1RdL>@w+acMN&rmyphWc!Fp}Z_3OW8%l$4zaDp(>N^+0;q~jmokkhtc*G&L
z<*7!YE`+I~?%sOdo)M}bhZALJp>*v^BBnG?>V{pGtLrkcApXEO^o*1#)*7$<=Paey
z^pRv!h5!9-91#v;X5GZgBv3{g+`_S+u&%3b?=IYX>sKYYaHSQJN9lr6bDKTWrO?r}
z6Nb{acpn9@BU;_#be^Bv!mxnHjBa(d5><6YZG^WHqX3{8wk_!ivmNLh_QdOS;qaL|
zz14Io?I$#26-?4DlkQoJAh-fJ&fq!}I$kxG$PQ{K3m!Sp(_;C22x*=>Au^3=&dpND
zW;VR@zYtqvUwP5X^vqTM`#4gSH;8^BHM4RfkNN;|t;>s(g3UiqU|GU9b?gl+7AV{%
zA|?%z7s;v=npfx+-=S^t#Ff&~`f?W|nl%g&`Fb5N@XRzbq;XBYa~8Mfb&0K~@Iy+L
z?pzz$0P9&~9c%zj#18IaeBr2xP`d|57Y+6to?O}_j2W#HLT_n@u8ndMy)u3@AP-uj
z1EG2D6cfK^7|gEPQTuvsJVzRHt=j`3FYg9)38YQhM&vRdaFH{>N$sYePe-$S%p#-*
z1#|38805MCEwVaa?NF=`<{O4h3Dd&|U3_-&IkLCukG}pXUu4EO#<?%M#R;3M<-o2!
zAp{K(AvqLNsMy8CK@ObZ`=2S{d|h)@Wm)G5GsX7zQUFy4yXr#{;argidR>=MQiko*
zv1E-<W|(DS)Qlx&$z@JuU3Y!%zOkdMy9nPavz|S()yj-&Ksb_WR?V2Enm!md0V_I+
zg_UcAmv3;VW?F+{n8<wRx##f8=<LrQT(@h1Qk&4pIsZDhC;a3f2K&OnTY|MW+FPxv
zsa-!pHl&QDFLh`3wQ6{cmPBXd&8|rsJo)nG&VN$SLv`(xs@Qf!s2|i-CzkJ{FaW^N
zx@JVW*{OM#N|j1L=6`K<W1r<q5SiN1c^{{eMYx!5BA!%L2Q8<{A4GjR^=tkTQeUtN
zTXBix;Vt2%V><+AQt(fNm+Ri4KLe2$`t3Z02D`wp$UXsI0AIaDJcO7@qlMRRjUf4Z
z9JQkU`}o*PmbX0q=XG(hA8IFvyu650UWBQg1(aCaCv~_dKfP_G%P{g2U0IE%qF@j8
z7e3Fl&Dxo+#X8`F|6;33NPtnYjf1NY@PD9)6TIF`ZYg*m?$M*PdJXpP+zU$;G*52R
zZgUU=V4fQjP&JYC>rK9tHz3hv0~(YS$2jB#6=4X?MXT16a;!Yd0=mq;(#KDgjJ{bG
z7i%s1+{v%A##N14<q858O`6k37M+!S&L6OS$Me4+9=AXV{}(ai{|#lFh5dh>F*GqX
zG%_+YHBGsTb98lkS$(;Go}QiLV4Z`LmSdQfq6L}A{7x`>Bbs~@i8~MHeMA6H6WH%(
z_?r4%9;Tig7`RhI6!kj*B;JAvmarulx?&g`nwX*@PS@Gr(9%vbNYTwq$;?VE(@?Rq
zF0e2%I?XUt(EgL1rBq^MW@KDh22V<ZmP&<|_kz}soLGN99$n9bj`XC0*7xf3g7$kq
z795lXkNx5eEm?{g$zcusVhv4Lf()HATrDsJe<l2#3ow-9b6p*N^LrQTjN}ruV5$O*
z`~yKQE{?u?YxCCUPy5+dt$XHuWAfQN1hpIg+d;L_+2E`%#UX*CBc-9HW&ss`7h<1J
zrSH4LH+pTA-NlvmTBk!OkgBIy_$s(t2nFyWf0ez?n;KkYybgbCxoungC~ur_Y79Ag
zbd*2Xzc|_FA9M@!KD>(LBV;u4`xV8{bA$2vq<EgX%=B`cR>j{TfDr?dTnqj9Ki`k@
ze~3}yR<^EY&J5zVMy_U}W+o1%W(=}s_7<*|gv`wUEAg$T*lj<|2)p%+hPznUa8;-W
z1{w+qivJ;)>k~jb9~TgTOzL^JyQ&-mizgW*fj?og40jqR8+1Xz$z~k1o&b-zjg2}K
z5>2GeQ4lLFq2AN9&|LMI@*|@i0xh*|7xjGkys+v};N+*ukS$>u=ffGVBWnrB!h;+H
zs@nC{lr;)o4F7}DXL`kB2^)sasn^{|czE%RN2d%jv(Xk)O>jzC&g50eD5;$QHKT**
zyUR@aUH^x%Ol#ZJu*h?lhig~lhHq}_-$LHMCG4TT_A`d42WOEtpG0^5=BRJr2};|e
z|8q`E|HBRZ|5EfB6pXD^T<sX-2|1Yl<LNuQxDv9la{m7z>oc>m|1W27T1(4iQykfE
zwmzdhA(af^0)$zjT(@Av6-TB+Heq9d#wU0rIRpWM3gmkA_3`ZmgiHvz@O0l$YCOGu
zy1K{*3Kj1u`$ij;!6@tX2300HUShrhllhPVPf{j~?!mtpg*EktA01=dXDFNzAvGs$
zm7#3un@<&qeu-U+9Ap)%j#cK{A~fI{z`g)WDhxAcqHhxcJ0FG^|BVezHTB7C2>t16
zz|w%&mnbWwPbkUMk}-mgglKSvBANgbEgb1~h0rKv;*Tl(|0(Ru!l7`q0G=$P5h`Tt
z`<60g8#^_YEZMWK)eI&Kl4YV{2ou8Cl6|R^oh<(_nPg3tA`J~=8&qnT8coBv?!7Pn
z@B4q>z31sXobSAxm*08#odd2H<3~ZkNo>wST<J!?O+mle#E(3Vi#VFWXP}s$>Qa#@
zPpdqm%l`cGqF*^CkR3nih1&|eAn14fvUV&N#d^%55}t^GjN~-;rN=3bOq>^D0eN}+
z%GEIhKA1rkCsnxT^`%dh{{G5K5D+IY<Ps>I3(pO$U^_PHlLP@Vj3`GBwO<=^6E(%*
z7Or6STFxLY`X!GIg49tl!IA)z3c2%%FQkDFuk(kX8TZOp-OYIWOnbbj0=Nx5V5=A4
z&22!&Guk&1WAF~~aYG}6v3=}StCT8h=K{Xd;`S{!2pCyr$gGsRHV`lnt5CNzW~ZF+
z#ix357U($=s7v447(OufS6y$Yp_|2>TSv}NHDhg_@1P<}CKL;=dO5G;><6a1JI-On
z7pb$`)!v>3whJ5D6Q|eQ^lqBdMw}kGg7gkDgWVOf!t+;&ZjVq;VA#CGqOzYBVgb4Q
z;W=-JW46n|M$aUJ9c@HyV;vq?*iE9Id*stA2$o8lS?MpVC?nPHtTqIn=00VRZWJ_4
zjqg-72Q_H6f^uM;RPQV7E^6iog*R@SQSw8hNDD+ghK-%{NHS}5AS5V29(JZL0A|zH
zX!CYNs;*X2h!GN3Tx0nwNL?v@a^al%&^;TGn-g1}7bL&VeJ?wI4j||`FL#r(+X*#?
z#Vo)ZAOp26bV$H2H$WXxOR?tAcBu&(33kU&L&bZoC~NHzp%oaj%%}th0;0ov_UYdK
zkAupLFJj318}CjBp=kZ>d8Bp|3n%%0_!U!<me50$=e}(ngN^`}pnHtv>G}p0U9$z!
zYsV`N_FPvhuW4UiwODXI{&lKy^8$?=re%_ZAg4ODMWklyf5b@KoT6)~>{iPK?me^j
z{P<d>5dd{zRu)x>a47zKqW5V?saMAyaX%N&IobRe${{JYl@|$)(0vGR#GPrcrqQXb
z6l&|*5)7RY7Iy&3VzAMDcZ3;N$FrD21?niTPxV#vfFP=95H|Fzq2<JUefLNcnxba|
z0hP+Mu+q=9$w8xTsI|4)!bz1a@)RXY2baiR*?V>?9q2r;bfhdwyHuFYP8#)Kj-`0?
zo*gH5m%A4amR6B8@lcEQ%^8oRit3BgvnCR4w_Mkwn66Ds2*RC9Y^lZNy3vqJXzOhc
zfKT~WW!<>Gc!g&+;)`gF2SMR-#H5*6p8@i|er`Or4l`|@yogP!RkdkKe&%8Vt<g7q
z36=liE!;6CJTO*ZGu`18k81UcMiv^d6YXJP+M_<5QIWO#`?dEZ6(M(UsY$Dj!7_&@
zj^2wY&fdY*@i7q5#29CKUevvITY}A~civs6WYWi%3C(&i?K7XL8v1t2{d+4~?kD{0
zKqDz62Y;284BHh|X4!tJGygZXQ(+H(d~z~i3Sb5!TS~$34p49Bi?>jfG+B*C?8(5P
zaG1rAv9%p2ahBJo41XNG^CqioccrU<Mj3$v)Xo&23dcD^#vioBg)-*f<K3NuaD=nb
z>y^TCA)e#fQ|_MlNE~gz!D}GsuAMM13VdgAGfOfJSZKGVRsmYM1dT^*_x0}&itSDL
zlB`~>Jt5_1me}$4)owD344E5=S#Q(2jb><))twP(dJ-{OrCIZ6X~Y%swayFN`d>cE
zmY$*tV&%-g>mSWiy!bO?Xa)2Gu{Agi`Dw{U!z()2bL(0zmDZn}wt6rAk=>vujqBpt
z?-<|jV}rWNl=j%guShWyiK3f*F~O?msqnNUc%T2V#0X<p6&$(pTfw|izt9@wl3rY(
zFE45uXd^z7tJ&D@OV7Z%BwlPN9ujw`GuQBt>qXQj4K@i%<A1sm9Csmie0Mx|c2IK2
zb*t!!M@o9k-PsZ5{rvH!?XlhTt_jc1`P_xFj`}WoGl??g{W4TmtERA1h1tEl5ifDS
zeIHWy-eS@B_u6tt727**mRgZ3ze4<Y9S={VngxG&;v*}F@T|}rb)9&&T#0BHA6%W8
z8XhFknZELt_MyLg!KX(*>w{*Q5@T~!7M)Rgwg(J1+esF+lw!?^eR3>}lE~lKbS?gD
zT#;shlgiV0zFv8JBXjwWKoAES8<9W8*UFq$&+>JlZ!eAo-o*{>HWRiDYCH^2UNjK|
zKI3%FyId#M8H1}iT~vWr&j~N=5Z&%hK4>3S^~{cAnMPx$7qog&5e3Gt6`gkh#o>9s
z<;!X6hdLxH>s&-xK!GcIy$7XSzZTs^O4tOv4S|+WL`)~0I7*>+-;~n?SeS!R-yJ8`
zqgPAoT?szh(R<POVM>ScwtTYzr891DU0j@Dh$bI903YN&-OC9uShw2)`%rPNaRg)P
zD~1INd*CFd{_098+uOu8|8E8+!pRL4zsST>X9y4Mb0Y54ikFF(`j$0X8;*TonAbk+
zg>uTY0EL<a&frCk3(3sb*OOmrI@FRIkDHD9rss#rxzPEt6Kg@-_pWn5C4d3kNj$<_
z#lvW>UB0gQ&plFIPXLX;lqt@OQ3*<9L=T41*}wZ^+-E-$6;%-M7gbT1em7@ufNOMW
zK{;J$bUMT#MGvZcct&q{PWXt(Y;!iNVHxYkyBa958e4`NOvsVvWoPB7jdK0kr(G<H
zTYbEraN?N;H05iofB?P8tRzZq<(@U)`d&w$(`y&oyC0ggJ{76F+-_C%=%{FSZBF(9
zyZ!WNPBX-;Z4r^+CN<IRr$U0DQSLz|#qA^^Y}-DmrAkEx^amnv&CHDCc5DDuuSxa|
zNs<#evg`y%@aO-wjwTw{|FPajhy9_DskeF1Th+V==|AFKlrGsB3JtSzczf0O9B#og
z`~COFQV<fUu!a@l^7d&H`Ca0l?avWo%51O5P57nS5W_%;ywv?3snPwqqWd0sQ@%5v
z9}K?HI^8iUJe>`~WY-`3Wm3J2V7T_510{`A){#-zC9kJfRrDkmu6c!U{pI)7U&r?Q
z_p>{H#tLRn#Xc&TTL2*O+5Nb#t2muK{vxSgDgVSjoxiO)|G_^uM;ILJh(tz8{~HV)
z!Iq(Lq_p~fQ&Ufa8@k#zG_^G~uj#1kLe6U*2HgMf?Wpc`?F-j$=xAvf{r?JYuu~8U
z2Gr38yLw(YY_0D7zv%M3*58#(g$Z3-4BLs6C6P&~ldv3b(;RO?=9VxUB=7zM9zUq1
zommB4xr7j=V0R=|<6Z8q^%Iek#4UqU*<1#MbCHz*_LwC8{A_N8m#miO1p88SG4M8T
zXb6gI>-69X`18%sVA_#!vUt^r#OpfAsyc~B-bnKu3zI&k(v@($1jCOxfjM6z!&4Z>
zad()&>%1fi%ubbcuHb;hBm)8wG6<>m7Y6||AF(%QrigDwXBdND+zWZ?kTHoyJ+JlS
sUSD?g?q<GUd^RlEum&3W=PYhT`bR{@M8M!c4J};_ZJ>gJnVmWCUm6%DRR910

literal 0
HcmV?d00001

diff --git a/exercises/Exercise2/MultivariateNormal.png b/exercises/Exercise2/MultivariateNormal.png
new file mode 100644
index 0000000000000000000000000000000000000000..e57714acf424b2972992c62616b188e0806359cf
GIT binary patch
literal 162413
zcmZ_0by$?&_XR4_4Fl3Ogru}|Gt?lB(kVzOjg)jNF|;6pAf-rmO9%sq2oj=nhcwc0
z4<FI*@80LRfA}cl`<^-H?0xoLd+l|I(o|Q%$DzWxapMNQiZV?5#tl^XjT<-purR<U
zxI@ABz+WhC+DdXaWj^|S0zY6oD;v1oxIqrN`h#MVH3VL{LFulb@2=zY$lcSz)%u2-
z-9uL=H}DgK#-m4e53K_>?z-H#afwiY$?AIj`1KmQF2?$>D=}9S(yz_0^l40QO=s=2
zKt*?Hn=%2)Z~dGZUpon@n;v>EjZ>RLX9;RW$D!4$nIBnpv3~fg(u2k(tMX9PcWQlV
z%xzZ0ck9q^%0~aNa<pNLazOg>jB@mYLFJ?V^t%rK{bM7GELE6=QM78Y>VF^h8)_O6
zFu}`J6DqPqTr6X1ri=g2J7mKLv<EfBdEc4g%PXi8>=+?!=DZYm7$ML?P4QDbz8unT
zFuO+lhyOmV10S>U<ygUb{yYaBKSO1LD<*+={=6!WL!rf=WA)!CSD$Eqj7%VA`8z-x
z;-+F!G#lmL$HPf^|6haU1`@L{!LP=O)S{90&xn~2YX^hK-T3<*L?Pd+9a5kD9?`3?
z9b`P-8;~xRznU-d-#C1cd8(<smluc9-Y2UCi>YS*q8pc&7pG35?kkA1-(zM=9{dz|
z;duY%)yW{_G{xn2dXP-aq=?*Vz4ZTa$zxCfb1y}>%y)^lU#%tlqdBZ6GT~}LmP8Az
zw30r<-m%(y6lz&gCicUH(9+h61{x9Pk7A#}6E{QuEkge6Y;S<y_u#QX<I`d$ovVdm
z;r;*ZfL3Hj1Yuvw9V4r6`D(HQe2ltVwbTBIz`S+R_~2D%F|c!5VDcF9q;scRt*Hf>
zK5#Mjl^LWquPoctOTLG>vLP6|gG%&i9GPIMxi|mqTG;mnkD{idxfi<=ZUdr2r1xZ!
zZmXp;-!ZC5ij%(h{@=>}@8dFqv1N5;0q6H)X+$Bz1K|`KKWhiDrM3&Mc0fJi-};CU
zM^aCJz4Ug!vH_08-#uas1n#M`^f`@5C;y&HChe8^VdVd2k0zsRGFD=e_n_FY!g;p6
zut7+|nDpKG`Qh{;*vN_qYhDTn<nJy)Z!!qm&UHrZF1>ORqujP_ZwtX(>>!kOfA9N(
z?9Tu9VY$dr8Wv^aR{!%{$H|6WVD6tGV8^hr|82!h8Gp^9xB6VJ6Xj?no_kCE6;?fv
z@p`uc$$KGf2*f`>g5Ic?#Ja1AYqvA^X%q^!pXlNL`M>v1&rc6i?ML4C48PNvUz;MJ
zm*QF<ElzZ5Js(};rNj%b3;VlCNVF#CQ!fNkbT@dr8W<A^T#{Y-d|$_SQowa-vcV$}
zm_noit<Li#u%)a)6YM12KX2m?lJq@50@E(M_MD0mms`MHxPwn4qM7NtDQeK_-+Vew
zD1FA@)ciXonp#->U~|#|wZojZQ{vxV-<FYNwyIhmE9K#Q@Ig7!SMgmOLVS(u%7f4G
zW+Q%7J2bX@FMk+;j|Wz`_iu%iXrV#vAKaF|Bx^{Y>-wK=)P`>0A_u$a1GqrQNdSu%
zzo~GP9X1P=s{g;GG9U-Cc{%Hq6|p3a4F%=)y5i`!bT6^g1i=x~|932q42m&SEQC@=
zZ=xyq+9S33-$afTYBAXj=7be>Umdq{tiR?$$a0yr7*F|bNg7ny#^ZJfc*$VRf%#gu
zhhkSajB&3MfoUiHZ`z^4!=IhS5V#uLsg73zVf-Cv1{Dj!;Gwqjy$WkETRtKq5M>TM
z3TKm+r6~lHFkh8!RWBf53^+F<`v2YlXECwFq_Kg9Ia1SJhAtyrPSR&zdF2_+8vE+h
zf<61jRI_;hv`Mp1wZ!SK%H7SztzP<oGYiyy;O#7bEf7M;;9*jrPV2o<J@$-hpEPY_
zDDQ>flOMV*r<;wT8=Sk<M9zUX^7NFVGD<M;sgSBX%}Lzvu0oV@+uzW6bVCjemz+BZ
zpIRt`>q9yubWs+uEk&H4S{SIT?M~)2INK5N?aTycgxj=9f3(DeGvNGpD!w4tn&f|5
za}|4d|HW5C8N(J9A+jrmy1&xmD!}Rdbvb4(a4cCsRH$=bA62Vys$CQ`lu_e|bYLvP
zqQVPDc3e+NS^v#&_>l}aX&FARp<-pOi|~R>VYj7wS@jlIr^Wtn9>9LVq!cUChS7<>
zZE3YHu&sE0G#^)zshx9E>jl04k*xp4@$$XIS3QZWee#5o%)qrY^eFy|`hQIUb(H1P
zX|J`-=6H;w;<NLKe6fc$%bmk93a6dIsM?;Tv}yPG*}+ezv=c4~zlUAXF8p(3{x<)|
zmQcr_0^1)|c3<SnflDKvVbYPVp;S9fOJzqvAgfP)RK-s<`(oym$}2p+3bbZ_r|$9s
zL`Y@e26+}8;p&szqzwCxKWm<l`H**hZLGQxBv(vSIs)R+69hG0IO4ClB8`4cH5*Bt
ztT0jXS%#kN_VJ8DU2a6KhGLWCKKWU*d%T=w6alvUe=j)91eNiY9HCUq5=~#8Zv}AY
z4gpt``@4RS4Jko3oCrIFLLCz}L4Zj5XI9xj@8YZA+w#|bom@ygSv$ws_AqXGc8E-;
z`KRZ%D!g_q#~VGJ+NE$wG2-_W&$$z-<0we30v0+hS?ImhujahW7}p*f{_<0%q;H0x
zCkRc}7iY)B_2F6mCn}jfYwUItb>&4YNrn0~kJAKgzsfXttlt)Y`YUqf<;qA=>TrpP
z(W8OPCyAZryjB#~@mv1$O~Nl_79FrL*m6%K8sZ(DgjX47)pHu}9LtVy{Gqo>gx<@q
zJ!uT0NDnrCvI190+MI0c1BuV!|1(Z-r@!2EVG&9156M*%)ZfntXhaygV`){t+w2@p
z2Q*5}cz<P$zgcfNT={(AC2;8-l<SiWeIJAp%Pbf`b%i>Jxc?eg3&gXDdbh=m+UXiz
zj;qrVdh73`hi_^Oy^#&cbzSV`cbpJeCz8((;+W!TJ!1sscZ2$Xfo5?3_i!8};Tv$}
z><^~RVjQLM85AXnDah(1%RKrTr)h4UrtM(&U*EL=N_&_H0K(D#0wJ2_)GNM+;m^)?
zy46v5w1QF}4dtp_Wi4@+7q;k`xyzZp>Q@=w-uVhL@%)`SG5V|q>5ETwu?lgYM~jWF
zjzf~q@3s2&%BbfoswqgOIEguXxNLgPt<b5R&n=f1z6D>wdBFSUBrsAlzu(DK4r5;y
z8EB=Ri7b`SCmaIc&;ww_R@1F09AtOnRtqNytGc^sjXuWiQWTA%9WRB%U$<k#ksDnb
zE#};6J~aJ&d+ed^D~mfWgO~wlI}xAL@2Zk<>Cddmfz;6XH#HDxot><gRy-c(KVB`!
zf;5;HLB#?;$^#F0ow^6IN)-Vg1>~>K09z>EoK19U+6l+a112i)h-^cA{QKPi{y>l#
za@#>&)A$(jbM4ph2#X!7fax%Du6*ZZcL-k$7W&~!WOm5Z@;$Rofwnj5r1suBVww0D
z9nyctk$e7oeXK7K1vP`}$Hn>S{N}~ejUOym4p=+o)5qS*!h!gC<*d=P;-lD%ot%Zu
zFx#nxo<ud%3G+y|zovkG$`EzS1%Nwe{ekh)GsL#WT)_FsZ<2=Cp`kq0JTT-?z1wnO
z$=f7@H<5bHJ|5m6e$A&70cZT{o}CP$7l|5D@yC11-HX^~Eq<p~06OiC+hno0?_S2z
ziZ6WAkoM?&IoGy>GfY`<FKuq21G`lKcsTCgTm_;9*p`3LJMCt{niol7g!}%A)>VcC
zhyeZX>Aq(%-xV`d?Vz_h_%;H!Qh)+N$u^rE-JOjRkj|gkM)YlO{CY=`BAV3>(=E*s
zd!Gfk13Ju=tr#}>EZWh$4<|iMrhb4R@j(YKoZ_#E5$p_K%j~}09<Q)|QIwr%`^KNB
zV%hg_(}OM)hE7L%GV?JA72Bxr*4E&1Rsi?mly9lATmM~f%uqcMVE&d*)v2#52Rl^M
zuX2&vnDquA8F;!55_Q2ofl!kR1}jM!0^xzAhW<gR=^ek5{do1)VxpyxV_y&gh5q8t
zTx<!&tK99>a@@7nm3y-tztMe_QSFsb@>a_!x5cT&@$TXf036;2<2JP3_jH}#1S-Wm
z!UQ|`dBEoHmI@Qu&bVjrGOG7Af4spw99vciT!Z=XC5@Onl9(B%1mcrY7zq1@35qWj
zB+<P9bGI(@$a+zSYYcqU1(1U=)}TXa^AcpF`Qz8Oh+iPJGhboWSbmBj6lS}%;deuo
zHX3?mqW~`m|Fw^xEEaKf0#=)9W^UY|u=9+}y9dSTO!vV0p3q2meO+|EUu<|k|6ZCU
zttBr^^zU1EDAmGpJDSJvO*?)H2yN(hmpU2!+k2h3y2NP$X3uuQ&bB)UN3}5I_Xn*1
zuF#wp4R^sIhF;qA3STk7Fy!&BlRIK&rt=iQRF|RA(}Rsl0&JoM`zMnPBO~#Q5`0>1
z&EGI112lEm<IDX{pRRt*4jC@e(@5bmm^(aO5uR%F-g~4M*9+D}_9sGyqhVr<gL9Uv
zl6YJF<^&N1?_&?O6rm?SL_XERKJ_MZE=I9tI?JJpdv4xa;R|{{sb}WL0)Ui@j7L(T
zbeJt1fOKns!iDPb!m<9%Fpv$Ok{@PR^Vkk<O!*Pi!@rQSARU+tj=<0%s~bP6-NOBI
zNb;g@=InP-J1K*>{d%|oWKyo{EyE^|+v0spY=y-<5fSR)$|u67v49Y0&tp&xon~=8
zX9&ezL2VZyzQc<~a1vwLyw8N=4Qx<x9X*(DAby7vv7{b;;nMtl0`@k~V||n#pr3WC
z5NPL&bFlZl`7QALg4>rrr-^8b!86=Y2>@|IDuZA)t;iYxDfs<P-2Zz)=5d%j_HLH{
z<+&#~uh7oxZ|M{j@m9<~YQv;Y8hopSd4~(9Jq&?T&))cSqas#_A|bFFQ#PX6_YmVg
z3SwK|6`R=fs?PMNY9q3|j!$zmM0ud#?^!Qx<=$H!kj}f24@yue<S{jfY)$(^s$X7E
zb2&5YfUgkI;Eo^#Q?B~AM7j94R@*nAFBbb!``W{BU)L$*SPo=L#zvOvmosK7+_^DF
z2daZALB|PwP_>d(4OG*xgg~4AP96noOSYT<bp)?)`(_w_ZX5Mod*-4C#qjU`fkeZl
zW@ZgfH>fl>-gu$m1P&(~0(id&p~U0E`{SPSG=b@y%|5>q-su$FKC|<$lYM#1qtNGY
z^Uid@rC<9&2Y{}Npt^s0?Ch0#1%O7cPk$upGf?+ySKHrToo)?~sYdXlwjcSQtg?E#
zW+E*K_EG83)}}(mFJEj;)jWBH>j_GmX~rLfk)dBP_rt=x6fr*gb>0Wg$*dGhQx38c
z|8?moPjln$hXJY6&+#eFl*-7Ed6joYwI!#H@<&03h|UW!k2R5FPkGCbuLJpNuX-y7
zq}Fl90ABw57ZoCj7_0y`jRzT_=ht{;u{`4q<eCz_AG3zkaqhG8-(#at>{tNskpcq}
zOdx{@^!SHBv99-$f*%dW#1`1obGO$id^?1wwki{4*7<B26n``Ab9l2Ta~y2bv#a>a
z6EoYvME6`fKi~znA-u1h!14w~CxsbBOTIn1$&B~*W7}*mA&Mr7kCc4RJh!GcnsEDH
z38#K~LH?5hpM;@cSkwMzO)+jnOuk2GIq8R-Zy54T*QbsMmBAw5>}Y3qM9(bEhM~92
zbIbG?FdWtZwLp7tfuqGl(@7eFPypdIXs-<Xs070B<f%Ni1Y7hVKu`RSgBb^L76yF}
zez5~a!E+90Fc=_gBq-<>Upck7<bp_;i+}9^c;-`0-r7YE?yQ1D+CEkoB}8Nzw*1Dl
z*=HA+4C8zcN}tSeKLNn+#*AE<z~z~%fK{=WeVd1<T#3#b1}w7BA`p*)J>KHii0d7l
zUpCCsawPX&_uZV;#2w>Nb7lJL_(X9deDiCz{OgKwfCJtxHDE1fv$z)CLRVnD_mTl{
zGM-&7WhlpTCeE7^^|FL%bM@%!l-gdYd}7wUq^ZKzky0PbTBqJIe|gE*vCx5!A^jJM
zTVaI;L6xEfMNDtW|3Z;NMUm$lc-{#zf5*FPyTVYmNfGP8vs9e+_lLJBdCdbQK%m~;
z>8AfF|CvE#{Bk}{+St~(4!1g?a_IeiPVZmk-8meoT}1vfTDkhXblMl7vc6tL;YV;7
znXj0rrkyTozQk!+I*h4Y5KHf;aGTf@@`ZflEO2N56*uEAs6(Vi6Ue|<oJs?qt+Fw1
z7V(SK(c)M4vZN-(I?ai$O`sb%5yYD5)&6=j@dh343>uY&zG7YxKHVFc2cV)YNFdwX
z7S4nY#F05VJ&-_+!=n=;AlfGDy4i%DTclUIxw`=l<{uga$`QzF0cw&GmWC?V4=BMw
z!4wr6l+b8Wtk;shHG5p>@9oL$ouwzp4^!#lg9Y~*5uq?v6XiU>lYHaObpe=yPasw_
zH%-(n|M67Q`0biDhHLm80@Zm_xQ>ixS}d-ea_x26L4%25_03<@4O9j4&jS#jp~v{3
zi7mVRDj&CMfoi!NWC6A7qtSk2?vjsrtk{VB{_a%xXH-{E#E9E{eG~Kngt(H;nA2=2
z-3S8Op?(X-&(TEfUtWu|J_DSAfLA!NZFzeMhWxK<{7HmCWcs@umq!_}Ida-bg4!Rk
zVCUT5fBRWthpK%^zuU+1aciP}uj;bf_I7S8nFG{rf-smi6eC}0n>hTX(3A8;>RQYE
z9ZV-FqnrUx`Suv!Ya0~Ge%GFE@ypXGP_F<OzpjooI#1MXNo1tTtG+<<##Zmj;M=S7
z_n-(#R2j$+-wM;3t#%kQjG%yAbFJaLH?(D^ZmLXC(CpkuI5_>i&YH}rm%^@-|6Tq7
z1xAI{=P+K8hubPhVM!cH+;Z^4VfU*%VNXGpzdfy`GS*+DOL=p_36z5+7jdt7Pcfi&
z^WAZ&Aa-@$+KXl-f+IzEy^nNcUsCzWO>ZB1-N2Lxz3+cC8*y9HLOHko@n2>TF9=n1
zHHw5)to?Bg`sEF11n}1%@$<CePxV1b%!hNmXlF8=)L-gev{1Y7P83trg|Z%bRly40
zhh2EyV9dp2JyVy5Su|WHjDwQ*e%3mrg3?j-s$A_!VBrgpj&^{l61i>PG)ZXoRZwp1
z&`Ph^af7#iijwa~ytzJkXeeyyS`PUSyJPvCrX<!8XgBFlU8}EXwedQahjNqRu7`ja
zN-1qQfv518aZfgSrUs#6<{P=OPoWsl2s>twh_$Vgt?Pr3*91y@e!C$w7G)ZY48&H8
zIMk%^xx_`C^4?NG?W2Aew&3n@jUt65`Sn3%2t4w8{=_x-GkYV{rR>R1p}Q1~@7s>*
zsN$wsCeD4`oEhM8k_RuDq8F?wCHzPTMd1Lz<rxQ@<GT(>dLj$52<}^+MO8Vq-uohb
zS(cGK8d>`E(=A576X%!a58H?;-2k6^MY$sk5W_9jePjkrUgbq;Nmw6m<eCx`EPD+@
zZiN~$#t~@AP;vn<r{hj$L0DSB6u>XVMRT`?&-dUS`~CNY8E)!iEcX=h?xh9v<)um$
zSlOmt++%h!jmPC7Wl?9Wca2&=LS2{A=qhvz5y-s(9mclyZM<^KKNbvu$}>2j7;KZ9
zJ<}rNHnyd;6hWt<(~Q(R<UF2x!850Leuuc+b}-d-y;vdnXklzr*~s_F+r<gD!ycx%
zNIXhLe)B-(t*K0Bak<@#y{rHURdSOBsHzK5n5{DJwMU+zVgK-o#;R_|y5~M~hk-uN
zu-e|mL=Rm~d6Rf50XEQ#jfN$rX<G|e+FgKezGp;PfC3%n*jQYikeOP&@%5?x7t!U&
zgzOGDVzl?yxLNPnF|PU-LKw|{e4mMDl(FaQ?hf}hbVi<5t_rt;-v!X8*O7gj<SLVc
zuM-^%Us%(K!ojm>|Il)_t9n)3&K*c$)2>f8)&gc&2^4Y<N!m1KWEp9aGbAG)F?uI4
zgoRRrx{`P4){WWPzLaxl?bOL@NN(2ah)Cx~s=KhRn4c^I($n`<bXB|;;(ZtQ2D<wI
z`r9|4PnGnI?cH5ea{rQncu)-cBCe5ZE*rl&DJiPDWG>iw^BURu&w;i`DhO45Rd(M#
ze0+2hW&99;%^pxi7Guow12UuGO&KpHC*Z1|elD82W5zvHGC83A=A$l)3NEuzP3yvJ
zgu<hKM(May8s=(yNygu6Bc2H>Ah4wr8`bvqCgWN!ecm|{|CvF)nq~tlY0_p6@Q+V5
zg|G|?spAb2UVA{6OkLnt$VDg7T+$gx&3r?o%OAyW7|e|q+%{|*@ITv88n3cTq^6F-
zX=2?+nNkRlDEUmiG!TwYoiWCpDf7}1#NCB}%Tu$@qgs}QrKTJ4CsLPZrOXba;><Q*
zMzvec9MhyG*NMeS2K-8kvZ^gV;S`*xnA};IJ3a7Ib8Kz?fJgIPNyy3cP9m$**MQ4K
z32j84X|qIi3SPKv_;p&srf{TaUdYWQzYTLec^fCSrXEd5gm6;UM^q}FzX42Wy^lcD
za}X}yYfT#a?61tG?lN`SBN5Ocz-iSb4%?``1SzR+@+a}zAKy<z7R$R~+>Lm$;Tp`o
z2M)RLWThnL^V_F~Sr@0EBT{+AkO6PL6xkA2h;&;(vManGyT9vRFyT4P<a)g5wqF@*
zFP<$;?tNeoPaj~l@ta1u>_V<W?Wlw3&UM^k4ochreJYW>lKq8fzF^+y2txWzJSq9P
zo2AO7rp?CsODDhAL4Ax+?tZZgD3?BUY5s#W;Yf+9$K%>p>>Mh4uZ%72&=lQb!)k(n
zjh*+!ad*;i?#1IB<~i9$016T%;C^{BK{vq$(}L>U<eg^rb7N;&U!?}btz|c!`p&nA
zh-Cl~v#i=fwcwrktW`FnL?-{Hhu;8C=GbHwY}RCuE064$224ZydvBLDRcV(5nm*IJ
zn;}#(6WtfXvAy3==#-l<LXEuFF<BVvJewRyU!4Q~>D#&A68>I<cT&8+a3}_sjYO<P
zhXgFDLAl;7poF;~TH@UOp{P6ClN|SHze7<%=(;V4`X(rr?MdWZFYi>_kEGCurn9U3
zn28)Ru!)*@`U=?@rHtT7xUFWdlFDr|QO8S>gK`~;nP;@x<rm_VdS-h1+fWZXNi^z6
z7WB~-&+A;~jlWaK(UKiJRJQu{bNSs1w4nME`*;j|n3(&KaY<`Sbd~L({?je>EH*y?
z-oI3Ot>&e|!*D$NShJqxpgnH=oOSt5;@~?Pi>i1s8*OTP*ky{I*}0LBlT`NB^wXiY
zR;E7kRezh1pg4d7Zla`bgpn2cK-Q5eVGDnlF*mS;m_?p(KGh6nKXwp0vDnAD`$e45
zZP}mua6(6s>aP7Sfe-vFT5DsaSuN68N!%uepo72`LEzmKKJCw`-FjgL&mmKef^7AE
z&3<S-R?<JZ6JGd-eUxtxWVT4^!y*^RYxo@UmA$<msTQ>;_koB>Zn=&Sh84}Gfcf%m
zDC|U=I{LuD=Jo84hs8@&KS(^*pIh}XYUf4*(9XEG<i~xB!z*&gsWnd{iKuaPys`k~
zso%S6MWqd8qYt|;)YF}YtxrteKk$3-MWXV}gx5m@N(nE-ub;JB3tJR;gMV6-;h`v>
zw9LF_(J2LO5uZL%<7p8ncNsY~FOs4|%gfQ4xKS|)+}H8f)wyIvV<2?#eLRQ5Qa|dn
z7W#OkQ;i(inPR95wzL{Rm-lr<ny(T{Tv=n%_sZorf(}9|Uqs%%s42+ReaD)?(3^6n
z{C5Wa&Yut!9@@6M*p~-biH2k8`J`TcR0;M4ehakt&rXq!4E%xGvw(CugHU0iE$C=+
z+JI`Lq~$?Qu%Q^}A$aU@QRI+C%B`3#%ZZcon9}I@1!3knjFmVxXG>e6b6F?ej!I%@
z(ysY{9Lmeg0(-Ko-QaExTm3|CmIO*21JE9L`^_KJPw``>;@m9oLiBXMh=ja+*GZ2&
z54G37_I@Ok)<<!?d59(bC#*v-D9S0SfXkcY<JW_t7!IQHVvO*VGn6TO1*6~x8`Z(q
zwDby&UttaCY)p-y=A>xmSiUnHq38v5fxD(XCb6TE35W7fCPmS`=M<mc-!Gc*A;JCR
zjvXQ2`sjyX{O+Q{7fHQbONDr~R}z=fZjZX6hCs_g{nUgp%i{;~mD6)lu1MXZhuT#u
zcR@>A>7Bn&Csy{Yt#pS7o$&4czh&D;nO-t^^WBA>2YXByWzMs5<T#ysI5nZd;Y?u>
zX1*JD0hU*7KiIil{T@4uO3d9>&Ue(3IOBIT6^nLuuyQO7qvs?Gza^Tf+`#3)%bofp
zb`r0ZNr3Is?N?rt?=3|^m?hvI$&t4btoyq1PJD@2uqr+K@oV(Hn{Qt?Oyn^>M%~WD
zR&OS`drUvS2<T<q`A2CavdY8D3eqwu*lns-@kE=ir9Qrb#C~Nv$(UeC?@_TK_&JhL
zsMYb--4mqz5Df1oDN~teH=*#Jbv9YYqt@XjnFe>|WDehjrae=HTo_%vMaLtY)wl1|
zn2^K+qVZ$K@<)Ft2qNK+A|1*dffW=7Ur`4>18cL#N$f`ADnv8$=6G!yCrX}n-k55v
zdGfQNriYs85f;`qetutrN2aEDq;}9vMtXm*wJo)*O=&H;-?7hR<QBM_w&mEhazgiN
za?D>s&Q{h-<CKdH*IATeKe_hZ5eJ2jghgP`WH)vJH&*=fyUDWhRRhHTW#MoU6zZd^
z0^xg^h1S+=B&Kr(=-244elLIfIZc2yXDoQ|-VwEhXo^4z#Yf(52U#YK&qN}T=0=r@
zxi%W{2`maFb#l4Zs_{ubD1*F`nmm$D4mLPbja{QAT)Jr;)uvhl0^V<hkV;-QMdZHr
z&R2dV^v*YF=;i_z-z`!#>=!~5!GCNLUe~tN|0)mUJ9QQZru3R{M6ix=G`+H2?p_sd
z0+3M5hN5Yz3Uo?uGcxYVyr{{z)&5!2?sPVSK8GVB-a*#3gZe!dH2cl3yi}jH+mhRX
z5h8if3Alp$v0`wZ=KX}Tb+gN%fdE!Dp395#Zg3Er;Ym+bY92@AnM%S%or&fz{fJez
z0ne$j*qdxk+VcQI9`Ogv5eYK{odLSWt*0aVL67CPWzug6&)=dV1KyS>AT{dt?lVMF
zbXCC!PUojqQI}wX3kc?{x3evDNaZ*>Nn?F;L3ZeuEa_>JZxQsdnvAacKnVZ^oG(<x
z%gufSqEg&x{07Ee|FQ#kVaC#3;)JohKBJR!+Uh$g!RQLDN<e&Yx%I;35KtOo?7R9!
z_12M&*Z7t{5Hu(n6IJozT5pP;2zJsU0>a<O4+leE&=$RU`W2+84B<Atm-p*RF*}a?
zcv|tqy}OE3l+6KdG8PTCZR?C8Sxk2*i3bj-g+cJlY|8IY&wGLR3c%N&KaDd!+?*_E
z<za$h6GyyqZ0L!Jsselt0Z`3gxE_puT8I|7T}LD#VZ7SZZ;plgCkR<$WDw1>w_{<~
zbh^EnN!q_+CZ+sN^wsYLqb{}r)=U{xVUzEk<^-q9f=uix>X3G4h33QhJA=x6iQmII
z7KGi=qhA~CzDXs{eR`p?IbnnJ7`v}MR0VTgnqbv4J${8p{S?N=mtyFzsVE7z?n_Bl
z(JfO$rOVkbwCa0WoRAw8%Vy%8hfHE`YjipwU?dPhmbnsy%Us)1fB_a_WrA0Ji}L|&
z-w<od>#9{zsF$l^#4M{!qNrRb|Eg)4BqsX_!ZW(<3P#O~Xa!~88<L8^8g%wTocZLm
zb=<-~I-1v5oW5+4<<M(U`B597^qg$+P3Hm=yK!Cfvz<>8SzEYCY**5sVRe^m&sE|)
zzf=F@5|dXw1~!w0WBi|p^BhWukAGFpObXA(|4wiJ3PYj5yZA#CKp%aKVwmMpOBZzc
zgoz)IUQfSZcJGx?|Lcqh2!dF76-c_YlPQHL@KzrV<+{L=N>an|#d`x$7(FLkl)0PT
z3njK?ti8{F6NqK$<@&6|$BV&9>A4N73MGyg5;x8J^dfhGgo%SzTqyAcPm5OT!{;sC
z^IATeqH&MCdB0@q3m`b1h7043fNCn>I$Q}bWT0D%N4xFH;l-n83P#68(D|@UhHiYe
z1T1<igRsNsXHtCpa6C{}bR%0Y?(1^5HZ>=)xft&*OLNCbx5(dCr(solCB@eNN}9Vb
zmB$mF($gHg@EYAv>0Q9(MS{4eBfEat8<LutH=K7tpT2arlcaxP)W|96aO%}uZu{>i
zt|RvuO&Xk)6OK=pG$T4_u#5`B{PA}F_P?R>GFo`VsJm5w$f6qBV$8))vtSM+G2m@8
zN8+_y_#{CxZ@P9-##C>lJ|2f<`cvgKHUk-yJYc?WYnPfz>RDQV<`b(tS?kFfC%Z{Q
z)if+>x&ZkYuo-z*V3pyMp5xb8+SUeCgzr|f7TKM2Mkd9`MsB%n`(Z2spt$1y!%KwQ
z8X~T=StYj#p5Gs2;Crlm>J6BS&&2r;gC0bF{^+TYxL*Z52%152z^Cgq?>o~O!`%7=
zm~%fLZN{L|mh^X_1tkfH7P#U?615`{rNnog%9W#?x1bFm=sgpJxxXwdVnG1EGt;aj
zN`&4RT{sm(DR8^rLC$u5r~6e8t>*-rz@vMEOIkA>MefV58FdOEnKttyM3jF51`MC~
z0g*$YX>-FB1FmI~h~X+@i-QqQxtWGJK!t5{A~<TVBMTv<5t&h+CaEtWJN&XYFWMz1
z$&QAp?|?;bO@~QBv+uq+VL&DHm<=$uFVcN4)`}$I!4KH9PGoV&ByKyks^M1zByD>6
zSK16bMT-h9A9;V@jge)41Wb;&Y;E#R?F3^YtA_esdJ_!0XE#ndlToDH{W$dsT!?Ed
zY&Lp7$TkajOhB#uhZvL}4(!JX%eBe!8)YX^s4NfLkw=b$!a2mziiJ_cgI?4%SHQ3p
z0K1nB2PTbAd$wmgObQ{|vr#y^nBM*p-L|_qN9{QZFD48H{LhZwaO&~j4hQT3;F>EQ
ze=mdDZypzwc6!HSIW%7Q#8)vsdWZX<$;)L-39Cl8P*ax9_g8tKBz(96j!VQMx7C|;
zoLX{8r>#(DGV(riJ-3Tw)+-y$RA7l0(<7TKpRbebPZZ3-K@gBW&uNb2q$_>jka)O0
zHjo<;a-;oCp{EXL%@m3S9by{QIwk|EUj;DvOx#BFDu{xvy^cf(zDLp!s;R{&m8(tg
z#;pS%ww!+X2Ip*WtQMgR3eO`yXKtK&vKOkELNfEBiC9XN?BHVP@nA2t?|Sc&Zq%Mv
zxGoBLpY3#ja*;9Bn9&FSjBbES@LX<+X=rwE2}KY_cU{`x1?ng@sZew&$yNJy->CD%
z>;Fs@66!Fk(D`xKI#Pj?q4x^LCLp~akXvkdJ7-?8JDS6Z86uk@((@>xWBspvjeC<s
z-@{g&*zdzZLwY!Xd=7AL^u4W@=T3SFD1#}Dn+=@iaYOyW(_DZ?^YB}F5$z+lMF(u%
z*NA8ZdY)bqjVeSCYC(_N!1*0?+~-ML>_gQ>toL|HXGDFMoj(*yhkyOszDE-A`WLF~
z0u@gmK&Bt$@mx?NrRDIVH@phR6|J&&Z`cja`PMI`W4mrt<K`L(_uk39I8B4X3#XtL
zfoqfZ^-Nem2hQX2;8xBr7Wn01z~!=p-W3^c?B`+n&g>G!ZtBdTCv{*RMgDtR4rrPD
zVF2XyWeahqDC`Y7<=Si8gpunE`#{OmyL$Wa-Uq%i<ZQX7f_zoT*J+?InP&eO-w0V}
zu>K&Ad6()-8Y8)>zIvvA!wX1K$Xp+BhC$9k@moL_k+ZGV|L{(Iwph%<3XfF8qufm~
zeriC+t~ta1q(7PY&QOhBZ}OhkM*=#$fXi9YY0!ltSyf)Z3Wi-*X8N*qRAKT;adfo%
zX!i16vyvXm1|?>#QvK5`Rbk(}U5OtRBvV-M!ca8%uT)Pand*v7c|`b2N>#}5#OZH^
z#F=UF3F3#zL$yv1&sQ%5OQkF6@_@ieHCrw$*X~<B_lN#5pR5eFa%ns|31xU{OPinG
zTSeM^AL=y^4TLJUDEG+w^E==2Un877S3zSh7f{dt=|(H8lxhBin$TO33|>(Q?>?~*
zFu)4~Ed+rswNrdMU5<>1KQgxrJ@jS*jGakt?2E|53`m?dQ29`cx-Q62ZQ-iNYJ&pc
z9OAYOVR22;BxycB*yVSb6ESGLpthT8sy24{q~CmSCxLSe<~<)XVzH&fqqR2G+{o$w
z<bPitIsr{po%7_1n6fx}n*?La4kOAZRA1qvVBb{9`b4r##W45rW$e@HF3n)y?v=@A
zU(boL9E&eZ?EO@TbZ2x6!@|P&ERr4?)|C7>X|no7viJ;9D11Jd&7=VKu2`ycn<x)(
z)cM{q?}%AoS7OC?m%h3Wicd`6-#@)y>h~U!*E^KgSLuWQi@v*kG|sDo%}D0E7vsRr
zUlX#CrPT4Xi`jgkVZ~PKe;E>wl`agza|+*PruR}>;rlcaN08_hW4?293$sj(Tq4dd
zW<m&QC=JWZ+w3k0qk<W>#gW}gI7BSkAGjNkOp!#6IcT$#!j8u2V=oifpiA4O0q3d!
z(3u=w4(r+vg*uqA+Pwb#+}P9PE+BMD;EKYEBQKRh{dkD4le{ibYfR@FIGXb(3o*I#
zpp(!8aG~8C8a6=_CL$Mr^S-r_B0wA=SD_ys9J1P+bqO%Yk&fj(9UU_ZpdA?#^LQ^3
zwXpzuXEX4Xgk_EGd|CR^Wr6k15hEO^fHF@57|9(*c8vpS9=CF8t=)-}GE`8E@sSsL
z{GCfF7G~P=+LvKC!O-&d0g>4st_X{x1*GFEWXP$l{L-W;n3ofW1oE7C%R;dwkWWEN
zZTJJ08gx0@hK}&A`%RR?=BFEmdOElo@;Si>IC2Qo=5wZn=yuO`ALygzjQEDTq1qf4
zYB7f4zR2+9e$uqi@B6yx$kH}y2yoD|2Z7X1O<Lguv}>c-X3*YMViu8F|6;3wSesv=
z?yD7QQ_tKYb&DRzHh2b%#*ZpL1jYU?kWq=0N5D)`=ZOErR9SQ!t>FO*Y5%65H26EX
z>gud&=?i%8fZ|HTx(LW&#Nrr5RX{7+^`og?3De;e=M}CQDS9znB5C^kR-<~4q7hK6
ze<fnk-4fvq**5k+UOYKJY{;o>ISAR0ge-F}7>s;ag+4nwUU@scVnaSA?a;H;s?;LG
z^*+m3_%O{DQ(*^)`GML#{LRfRoPr*TL9$X>3FqKuAiGJ`q`8w#E3pi^6mTG`n#AVM
zjdF(Jag~gjE9>$qr4><pjmPPqEJw9mz&2?7#jjB^RFD-5f^}&ks6_L<5w|{w!M%4E
z;z2fr119mAkN*%8{6;~uX>bw;OTt~wC*cc7Ekm)V&vZ|Pcj+XXtV7d(wvY#08xzY5
z=es#EFK=}%g6C^VDYl)QM2^;5Mefb4vQ}G4+)#sA6-MEhT#u4O1hW7cUKHMv{PLIA
zlhB*qoY{u-O<l*UZX`Sr%8R{pIl3QSB6C$pAJg)9PI<qsa-4jCt+)~iF||f&ML}2=
z0LA`Pj=qJ!>kJAvALSYx*jqAGOU)3yWHD(F9KGx6IUI|6pnL#tPc-OO<KsEFE)9ry
z!{0^7SD)BM;>X!A6!gF2!WaWGa;|#UMc44iTM~j4In;&pQ$XsRARXYJCiKKmxh2Gy
zVr?j!BZ9e+Ji{!TeRu8!Ba7;DW0x?bjz-k8Lt5zL`NjNKa_d^~5+Bd?;@U@spWARF
zr;_zD;YnhijvqpKe4-#zFnf%g*hr*`Jx)~k%5~b2VX%0uGjt_0sA`L~MZrGXmwj(Z
zz+B0M+vM_=W~xf1#^LANjXEKf@qJILpF$eXR3J|;4qoA%&Y;$-#zX*B(Ss7*5KhoY
z9|HD@1YL!$2?2C+lKUSS?|M0WFsEU4HKk)+-bcB~8dY+JZs#=@#hMEmAy;~Ppnu&f
z16Lc=d4oEN0JXg)GQkjV?lHW0TNZ((Q>2YPx8yyL#6G`xvfiPTRQOiVqCHe_tHUfu
z&?Xisc^H}DY5I$Z<>a|sbr{YqSDe7Nnxnj(q<^5%&6~K=uNe4NqKu^@rEEbTwdwa4
z@rr2&XCg<8AQ2zki;Fc*QQg)SnwCS;w2P0ZL&iTNeq0RSW`9LR#yUK6>@dzR`eEzR
z`p|Lpc&$h~PZ57LFCdmuKv7KccM>VLc4rUIyxns&gzQpgk1D@)K|r8{SFPhDO_Z9I
zw=;njxC^s{w_uA+7(=Ql2SafN2*1F~HyC>q#r5WZ$UGjqX;Ym*r%}yg25MpMWQ&fA
z2XZd$-e&HU3DW_GO_({l?{$wNwI8jq<uSwEnY5P)rB<MDQP(roOdpPhD9C)^*M%!2
zP&~izfzt9?02+>GKKg5X=%9zyx-%5pHG$i4>et|$-|3U%lBZ87pB;?vrW<l3SD6%U
zh(5uTaA7K_mUdlAih`$@e#5W+8Hl2-ho4sBciPLN5;fzN!kui?mzwHhA9+TaeKSRQ
zW=<tHGJhoY1e4-JDMvO7Ia56D7deQi<kncJ*#vn;42BoBA_!J_#`Uh+%|7@n>(y14
z;_&&dZ!DghsE*!R+$>XUt38R`VKHp+8Y(Navq2XVN<J`b2DY(!H(*BjUw;)<D8HoL
zm1|ZpT5Y-sMAfqi??M*xQYB8R$xYcvl9`kBIP;Ll%c@fn-psWZrz6Jo3j?a$#Pw&c
zai*XxoH=*<#8=wRcP_&%H?rmM(Rpi|y}OfHxm@hL<j~fU>x4-CVi$Fu|F`^IozUmr
zbF!TjjK2`f)>Hw@Hc|I7G><yIGvNuuCst$2GSlEWC4@c7$Co3a<U#z34rk}zV)Ig6
zp@o{Xp6wQW&OEONW>RonTw^n+ILlS-D|KCdO>K2?RxG9-#}<^vVcnbLf=QRn44)E%
z3r{$Q;8E~?B(^bQK?P)<NQJn&NHW{TPa9Ydk=P$FlWR;m5w57JR?l^O$tr<NU#d#7
zj~sa)MS90)NWMz(r|<Eu%H?QrUJ4Zpda_KjcJ+IjXNM;7K(o@>P2V>SOg9L^5(OR0
zlPvr7zSF8F_a7>z3+{CG7w+sFzxM1^BRAv1|52Uz1j?~My;2!Q%CHs`FMr<$Xo%^N
z^K!XiXqUy5`$U&O7OxFt74-lSad#3vo~YA{8niMAeq{SI$7<DDbU9=9Dkb#*dF3LI
zb0rx$oyS#;daxIF#C(1~MV$XrZ>;tOcZ0HH!|HACy?$Z8@O2EiBrNE#s<cWHQcKK>
z_aF)qq26*}EN}G(6gcC2mtZFo@mL$y?XdaId@PMOAF5_-);KDUK)wX3;cxP*j&*H`
zzWWP_jO(L4WkA24ONBe>dys^x!ff^SU_Qd{1c^}PN%jWGa<*~nz{*5=AE6wE1G>Yn
zchj;Q8rfCJ?A~NG;@@KLWDEvE+NUD&)<6dBFKHABDL<sp$Y7wx!?+9#^}ph*9H~QD
znF7sa@AalM-yfSVoftS`TV!_}7df1mURZjSl?YGnRVg;Sn;Tam9I80w-0^#;{9(5s
z?puryHsatk9y6({+J27%8uywCn0kR71OpbhaIf3I-K7FPNwRHa9<!E-k?cyE{FOh|
zX9v4^<$P5<K2LS0J0wYE#cAYyGH#^`+l=hVh3`Q8qn$ZMJ+tD<ZJN0I!K6A<O^m`*
zZ2YddN<G}~0>Z%+7M9Xh)6A_ARsZQ)jRX62Y4^ej_w`~|watlL<F|?xHanIBHi^qs
zR#64oxm1)n%<wN_eey|FcfW3pKV5!JmqTV%QKes+UE|M#ks&>hqNCX!q1pbEgpnt;
zjpzl#K0;KM!qR^BCG)Y<@LkKwFNYnJ8QrEi?tOP^>q0nlXbr^l=_C{$#j(jAo2Di&
z@_TO500<q{XVU;~dc9`lEan~j3}GQq_W^RNF%!?%O+ebg-T75d<Pifb%9u0v%Ybx1
zdQHMm%@aSrOYW%AlhFwQ%veD)g*xbmEAa#1ft0&qX&pc%_a})u<FG|Xh!MWhwcnk7
z94<eCED(aVk7yZID!SW{K?m6CF;|CFC8yrXhQPYwE|n}fu(z7_VvS2NI*OWo8x!T#
z4YO<q<r*aRqTRQC>PZ<?DHG)5)ql<EK|mSdB?~n_7xuUeBw#8(YaWkFj1s+iPi5S8
zb5_Q9-K!b1DBhaaiUtuSJmn?d;JLL)0Fgt91R?J-5#047OB3BwRZ2(~`kgCN!KLM9
zI|m!sswk0n)kKP4p6!`jXd2dQ4RS1fWEptHI+Wkfs-3G0WjCm(ffT%k<Zyox+#dQC
zfKVJA8~*6_@dcrQVY|E_?AD)@rXC_}Dpq`1z(V?cA=BS*AsE-Hn2MU7b&>Q*?<>97
z)!SPVvQpb$8n+rk9q<Z3*~__Aua++C#NE1bdyQ<aswMT?l10bDp6lX5zT3cWA{HLm
z)2#r>%)A?DXaE2#tQF^y8QL7%P@*!Dd(9(IE+o-a)R`GVBti9H04`eWy=3O!esd+l
zh8w3hbi~U9vEs~A_IQ|O(no5apLjVt4o;tIpB!GYT9Pa6cM!gaRT<clb8LdKdu~ps
zH;C)f<t^;aEU==h(n*K{294|TSc9ZFYYH^{O;sXZDB$h)0QLE5j^L1X=l<rql((dQ
z|6w|WSK<_94~2V_Dd`Z83>G`i&-ckR&1bI1qN-%8K3_z&Pnz~}e}$3veyw@3NYtHX
zN2JswNR)SB-*YcRWN?HmdntDM_x+0_c`15HL&t^-A3MaISC9MR<zb~A>`Tc!JaS(k
zJC}$o-I9=HwG~W@L<;6%OSl!~x;bh{HMow+&3p@zUZ~&T{*Huv1>BhuSJAMNA#FX~
z6dz-ygbpb0jJ?0#7lu5>aro_%MCUa_W>7Yx>OGII5oAF1gCD`{WJuKcVy?<GPP;A=
z;$TvS^W}E8=k!M6Pe-GI!^-<nL(?TK+EuZrNNxuw#bjBiW{r&!!GAy>g4fwWg4w^+
zt>~zX8TvC@=@m~Fmlo4JNV2)(A%wm2CXMh2J+oxWq6aEIvpYmALjDqPAXqLJz{>QR
z!?s~KdNGj775{3pNrx)w4(Cs*yAXC=q?`M*rL;`f-#(H#V@y7hJ#9;8;Ko=(Mg{2O
zBE8|;Z1>WTVOk;hcuFS}s9V0fpCP1PobOl_UKhg-KA@w5%Q|ijf)&#(c)#ca_KEU(
zlby64o~hCde0l3WY7m<LnktzZJ7)pEIpi!i3{Xd~i~YBg5U>8bjFmRsTl#^_#A{PU
zdORjmdgBXN0{^)-GX)k2qiOoL-tnELMk{}h{YXytUn^^j<xGd)9?b3b<&B+g^{G!b
z5zt9;1RN7{#3!(wAGTiNXR!6ZgbF8fvf>Zl{83{Wh6MahB4?QA>aR-4Il=`vOBl*U
zi)SgW@0E02CzAtoi>>-n`dN$;l`{>9aw06rvmdl77S`wp29lUTD$<OiXljr^RGyK;
zYHaI>cH+AHg%-|BrCRS=i#**LaE@;quk=j`1-<u#7b)SduF79<sj9Df%yhlw#U*a|
z%Mfy0G8)~S{kKyC1oitvt=8M-n_A3teV#5WH&$V`Hb8|VB-;K*S&WU5<cWnUb5nw8
zmTwB5rh~U625M#aN?1v(_n~)&M@m!9oq(X5LmE9w44!nPIl1AzYJlLvH>^&068cLk
zl8k5Q6L}T~m&>5SuIKWX3XPtjnevBumN8V^F221<?3<_TLf!+Che#?3>ol|}6$pYJ
z^jugeop>qQQAcNsjf7Xlv^92@nib%w7uwHBcQVo#I^Sg|C-CshGQ(*)&A(P&T{2O6
zPR_F_5z~giJhW^JR)8lV>(^LpvYPeS#K#(Il_j1#?Peg|9uC|->?bn)17komk+)y&
ztX`vK2Dg{c$-QtB>gJfw-I;i_KD=x?SbF6u5~#oPhA3}9+B0epe{C%FiS+rxG1Llx
z&u_X%Lbtfs&JAl*(g!42i%tCvgjm8tvKw^s;J0iLM8a7HWkCV^gGwbrea23sbwEQd
zD&#nki?)Q$!vD#=H+AG7=G8@SP_80=+z%7i<`m~Eia^$jyLn%`e(#I8iG{rxG?bDF
zp2eock$booSu;J+JocU?;kjl}8-o461w_i1#S<GUK%C3&yU#VmLg*^)7)d~8RA!$h
z2oxxfyyq5Zn4W3VZn{UQN50^E1MUU|<!Rp=U(e%88I)rwRkHEYu_n!*O!clRO6QaK
zswo+|BSy7Pi*-wM*==D_<yZIpY(n$yHw_U1@liLyc=^K@G*XWTSRf3mbaOHm1`^wC
zU7yn`7eJR#d&n)ETW8Gf)mBOoXw%(RgXrn89S_k*(d5xd`iKxy?;ft8nh|4x6@5!$
zOCCD4d-Ei<ukXFyz`SkT?XTUTtZvG;I9QassM_fklx{ctuwwf!gF*?~Fb-GV#x<xd
z+afr3%4Z>u?gA;9E=&GHDz5~r+C3lJ3Y%1tWX`@FgG!q572#)fq^%b|-Wwx&lU*9q
z5YH?+N_-A2?Suv(8{E3dtu%Zj0>I&m<*d{$pGo>P52ryPlb^ME#?Wr!9OYhv6ZNgq
zIaETVi9Le3Ld>YF&CQ(4ybQ)g^ZPrWFxjP)%RT?`a<!9vZ><J*JErxq2#u-`-vYvw
z7qC>(+aJ=JAOuNrd}Dwg;FgbQ$y>-tvg&bd=GGIKOt@cRwbqU?dmH|(K*RI7U!-W+
zQeIdzao+05_~p>)Z06HO(*E0OBFxbxE*}ji>)mYhI>jlxvs+FG*-rp3zWU58>+2r(
zpX>ib5i%nL$96)st@C`1cS+4dR)<xXJsk;Edb6fKxjy#VxT}Oyd$ALNM<bzsaX&vg
z@%VN(=J-!=z2~-C2r614+ekNH0h5m0UQ!A8P5Y>3QVA-*U%iyfx3Ytk7*~`Ui^kEA
z^6TF*viyYf{J}9=vVoVGEW6D{DZzl|0j`CXXga~H%7KjZtr(dN9`)@lf5CgEYrhjn
zIUgwXCi}vH42-_3@B8K`U6;LL9%3H-^Ap`(v8TT%_&o;CPYK_HXveKLpgQ`YD9=_)
z>!E@6ofax~Z|5luFGnB0Wa6Cttjja`Bm~SR?S)VTj>69@pfXjm5-QMg;1QS^9~!&T
zXCRUKjQ>?)@-)FG5_PkDGamv?#NNE&e5G3SK%HW?jtb&ib?!VLQ?avAyZFv=a^WKV
z?xN0JQU-}dr$)6YZOdT2m~=jHCCX*i#ty0vi%Mtq!oi;3hIgg3&J)z<n9JJj4D0NM
zCDd?ViDm6atd5h|8P89mNZDrM+qEG_uOThZM~eDP4ksSc<uL?*O$TO&gLZBRWN3w)
z*ERlFTmBa!;TYYXMf&~x;o2E_->1}x4_)&{+BLr(q)DEZmazxj0lzoFe$5zab7wH|
z%g%cXW&t6&TPZl?8{c;VaV7iG_IQ*MY8+We*Y~Gmk|ewU6KoAB542-=xI04^ryWZZ
z!{RhG$t|p!`p}^EOJ%MS?sl2$8--~ag26Dp(IbNPEdr%){oUAqfEk3kFC|LQ;i<Od
zW|>g?7#(2uSy;?Ke?U9eN+nIuBABF{O|K+lkIe=Cc~um$zWPMHjS(JNt~vW!q8!`-
zzq(hH2kw+@E}Rtcv^c-o9lGma^?9Ox7qyELBZf+JbBeq1-h)ZM+KGbPwN`K@pEHEn
znrpK7D0mYvYu*a2yP%1E?8=NR&`2c_bBvSPjeMqEh!7m*`)O)aEP$iDDPTIB-889y
z;P7<I)}Qr~pm3lV^BNlxm*7QvIN|*sW#&e=%S2_tmNzB)f1o}zlnva<j9{A#2za<U
z`lNP7rge=&<7hHRK}ZO%?Yd!R8KMUC#u^-5=Xo2=oTW+6Z*d?4HtUtRzg>3de>@6d
z+wshGL=~;a#n5Wk%+}=Uv<3Gk08aL}>M&XXX3(xRUbD<5>sI|7&hdk-4WTEOC0g<+
z=(C+!W$Mb*hp?drC?3P|b{~)R9#DZRop4PLj@~O?=-#X|A(M2@y2tUus6=f3Fp)8m
zx&Cdikd6AY(U&T^0Iyp2TCm_0?ThjZEjM*zo45PL(o1tp`_;mi7g<^5%iGnGT88<0
zQPjaTfXP{c>d|L@W{QzRRd#ea!^K7q4Vrywi>N<&6(mh;&k9LhuQ!|mEt}@?vy30%
zr_A)FpMZuxBgga}`Pjk_VymzHUdz_0JwlV#o;E#<O5AlaHJ)qCeJNb=PANR8iuM!}
zKz`MyWI2CZ11pC#vO$wK5`s<xg;UUi4rL0s-)h96SMtm3o|x5Y7`at3<YT%|93guf
zj^Y%WRlM(<*mfa>9G<mU_oIQ7ja!_(oa2?Mb~p^2Dn2=mCI(H;Ei^xyWTmL9bR5~Z
z6IkPba*6e>O0G5wp+P&YEHY&zB<9IB*H**2P0#Db*BRcA?gC`uy?eGtN9T96ElbI7
z75OTaGy@~P@NMX_B!KGLW#+O4fy+f1ut$+Yl><U4)efGj9pRbfv3t+H^HS6I5DWW0
z4gHfwF!+=_RTAX+raJCmHURQh&xID%TW@!0s~qEbO)L$2QIeqh=QJJr74Dy>&p8)>
z7+4{}+s^a>Q_yxi-@`7EG^5>6M%ko79oP!(Cu(*Iqc<`r@9r0+^eOD&0*NnuvrkWS
zd)T6z-xeKzmR@nME;)o+BT^0ALoktr$z!2Z4z`#cew80m=yvd<Eq#(aEd8yi{@L+H
zEo;D&jsdYLDWcWW29f#r34A<d-8T0^)mK6}^Qlc7>qGioRd$R(=n*3IGh*R6=W~?;
z^>0=?mRn6{eItC9rrhc$7VsQ27tsX8J9VIVef~C~)f?#j;XqAtbq)4+TG3TN9k&6B
zkYSye-*ldhXUtuqzuT1JYIfpjei4(>(u+91=`#~T`*XjR4@)TV>X%&TbZ8E*DB~6l
z*FDma?`KaTsfpYA4qdk&P0DO&ywLmU0Z5*{(c(qlitNPgT1b#1Tp7x?z$Ba0M3>jv
zZma|fYwu05-KBm`pkeZ0t&R?&M=kRrd<U&G?HTcDo|Yf4@V<E>uxc<D3TbzrFec3o
zitYA$LDnOzlaCxe-AYZ}AHP2|Y8EpoX`e**Z8^1Rd56dMGA8CjYfe{*h(Z;Y!3DO4
z&#|=zHzp0|l<vh_6q;OTFW!KQXPyc4Ttre)BnIBGpk)wuBH_>%aN(B7WhBHj;=THt
zfteLf&ej&*nHy&QZpx*s^G6yPzkCu%=v=tun-BPxD1Yi1&qn3Pl9frzKOkSVdZ9Z-
zhf@r$!;ZA9K`8Hkq4@9jxqy>~Jx}ok853@F;kz}}tUmdCB1vEOyXYG<n_9#0Y4BSm
z?j)7Xs8GeT#yta>q{6)eL-ghUL(_T3Q~m$%zo_hW>^+ZjkXiQTSchzpO-hIm*_6HK
zG0G?h*@VoD>=}`fy?6HBzo+--`}^PR#&OPhJs;2Oab5STZ|+8Qhp1am0;xRZDTyRY
z%VGR`Dgq@?qK8^ugY9g%h4QUaY&%<sSoH6XYr;R{(v0u=@!#)LVs1b1zsP5N(Z$E7
z7}xqz?cNJ1xfYJ!XICfAHE*rYke$ZM@z7-&A5$WbGiY_KzsBfRsO2Lly`!#?jdnV2
zxM}P<8uMz|!*2hvkJK94!Rfv@{PS44<BfUBA1t^4yO@(A=M{&E4)8LpIpYT04<vL|
ziI)p<k2XrAyVTgOKcINlaNg@ObCSv`Y8nF(jJ^$Des(?f=U9m~HE?(ts=eAD<tjbF
zeYEMdzo#rb(yQJx5`HQ2MFV6`*SC4K1*20EVg*s^Psn*4|2KiN;UDMUJ*k3Z2uXS>
zCjiB85SrXInQY2=G4chND%CH1>J>S{AO&uLd@>YR_*8R3$A>Mu{@g2fRN9$5)-pqe
ziA^_8ti20qU28e@mO8X^tf)(ZTY@B$-v0Uek+4<OirD`gSgvYTBV4?`z7I*Md&Jua
z{{iq7F(r+LVtG!Jh{>#;*XQ6V*u?r~C8}bd)Rt7dJTQ(xxN*+kuZ>~5fzskmjMGPI
zN5_CTamzMr>p<9fiuuf<58+FDTDmSEcaLQ35$8<4X;K)=It<e$g^m&DydE#F#bm^e
z<t0fn_dxCb4C%>oIrw!GUu93bnKsGo9n?6z4A(2=5pY6V_bJgFieKEf!@PPPD*3;$
zUe4Sq_Vob!%4XZZoOJZ&*QSdY>7_p^3E3OP)kuCQ*2!`ohfX5=YP_mA_f(lvSp{WG
zpR>PgLJ!5q3U4~z9OIo{_|(jV)U6AFYb~d<R0AmkiF&%8Qg`tIE883FG%T;Z2kDIw
z1WtRw^V|k=%#rH`RDlqFAtHmmB+LhzAi|vZ{&AOq4`)jjRRGm+0xSZ!!c{pZ9Q#F`
zKhsn#m%mJWmZRExSJ3!oxa^(3&OAQb3;DoZI)1D81SpN-SHd#2I4B`e9=Bqs26{}Z
zB}?%yxt%;;!}UXI<;Gk#q-ye44HR~hKOE$b6mHF$`5(@q%Ou)&5jZWh#SG9<dEZ={
zKOGaDKhjSQx2hGuMmHfwKL=T0ZAL<kRh01k6}h&#r1dmQa(5epcKZMJ3TT41N@3gK
zE~xo~M~#wNPRiXYU*cGEKE<a|qR1k(pU)p0^e6bxi-!{v&a*1A3dTU@-^8-252&QP
z3{Z*b1Jdv5!<IAU@9GRgA@g^&ScLHvI3m+R7<~;!wJZiTysK>!b#+-+N`QkeQ8!E}
zs6e0B>AsuuDusUJy_1$NvKcq35B4khE|njwT#Kf&r(v?jZeEFhsLbW}#}0yikd!}2
z{H@d`XwC&RQ8e3iO7x(@go9Sr@hF(-MpF3Oe&1X6EDL&CN--ZPv-QH-<+E4Gtum~W
z>nV54r27Ha)>395g<l?ogQ&_`3d8U-u)OO`8zM=7=Vjg4=WpPy_WsXgCL%i`458*O
zt%bJ+W8Oh<>tMK`&5*(BXQeN9NU%cVlTVf4L);x5!#MhQvGUUUqb=No<g7k`PZk=_
zJR$Q+4`8O!S1sSfMrI*88e2n_`JobHX>X&|4j3}cJoDjpDhEDb$1kS>g2nU#{8;MC
zSVRo}bhqP4zk!SLPGdqiIQUI$6^e|@S`OrKvm19?I4ZNVU2F#SUJH~z)=~M%#oP2x
z0yeBzyIr?AB^c)?+ho<1U`afGrgaDQW+vh4#tovfQBa&Ib#NEZ#Riy(?VBs^NvjrA
zKD5r@uw4p2+~V?&!u=?Kz!K@5e=VO(%%QWQi*}i`ukI>;u}+$^WJwqscX-F<!`c^T
zn!F8q(ZnTZQ`Zqab(i4EmSmEQXE6{2&VFg{ziyku-|~a=TH)_xP=DWc<+6I8miBeb
zc33k4ZwA^bROnmgHxI<~Y0MIPASIOp5KRgWULai1@Aw&q?~I2)kjZ0f^_UYx)Xn#~
z2zZb-gZFTrmUg~2H1}1?rMR3T>P|3`fZ=`IOZg;ZqH`jxR*zB9ju+ZUxVGf=Go|?c
zo=<vF6_ZmcOO99c;j`-D?D_e5jIkHrI3+Fd`N+c=JT6QoNh*(LMy;K0imQ9Po_BOf
z(PL%ys4C$!Dd6!~l=>b~bboTqP4l~U%4K-q7ofJTo9u#CC9t>yjSD9~SV~;(31W3G
zyk6gfbDRDH=4gIW-Z<W70uWBtl8Hqo^K6ZZhpH$7w|3Gl&smSr?Esu9>Aovn+1jNu
z;V>F!X8u{0AVy4ozND85?>~u?_+!|r5UXJkx0-TPmED+nhTZ~+a`Y3nGBX?#BPhn$
z`d7&hCc0taz1PqfmZ)ouD0mPMCa!wj=JnJV`YCkL*$)P67?>nfug*Ol@^594J}d9y
zZ&+C$DNq3yr<@>~C-REXC|}G6%A!z}KqiQ7mFJPIQoImbq2vo?*C8(&Q?Z9)hmlUR
zbK_QOo|QimGwF1+>%12bANigKzb0^6<4#5_nRP~g?ro<O9#VBTo3wD_I6H`lri}X}
zC@Dv5QGy^|jG3(H$R&5Cz!u>R5KeQ;uuehoY7GB->LbMvGxTr#nl$GSw_olRlQuD3
zczfg7N@~fR>lX&7#Ln&ZmM!$Ru35MpxECStVK5}|<tu8evYN4*N5|@kpEQ2{FSyA(
z#l%Aw@R7wkuAn$pi;3ZPz78xokM<El#fWy=yHp>unIuPv@rY}dwfOfB*1KyQSn_?$
zO9&iu6c<T6twXHjgoaL~;Gd_gUhh86<i)*}HaD!)iZ`xPqyDHU7s2df-p*~jrd7fE
zE?gqpih)gZl`c+vhs-CBg-qf&6G4G2GCIj|S^KR<&}X4tGG&oW{P_~nn{?F0lWN^9
z{jKDYHH>pUCr$pPOx@U+Sp?txJ+?gw29Ll>_d=KTeQ6)l>aIxYW>%y-g$RD#$|pNx
zRx&E|y|^$Tek^AcK&EYB-~1bA$i%~z(mqX7LYQ-=k0$>+D7q*!_(JcD*lGjey9=`G
zP`CdbXLp!rV+zXpSBUN6GudpiqUBFb)B<NXGRI35TNX{~l;`nyQ89V+fuRA66J_}%
zIpwze@rU7gcjs}_8o%m4Vt)#Z9zjj(71LLJkIFYT3Uj(YS|hmwdB0yNG*#NL`mMbj
z|1OWkQ4qM7VDsm-K!nnJOnI`L>OYYh9S>YxhRY=lzr6nk)~;uy6moLJ%x+en6FjcR
ztuqXmape@vP3a}iLwt|e$E&vV1)s)<$J}Q&HAM%uMzmBqO4^>S?MiP6>`}?!@@11g
zo5Cdibh(tEAx8eQ{VuXLH&9&#Huru8cL1Y&xFkIU?z%?ojSh6`YrOYtKX~SaZr_I^
zNO{WbY*~{l>Gcj)JeHBK=&L<i!~%ray%q%s**ztDVyCY%WXP=hbl3nLI207n^kl{s
z<3E$1{JozYKXHBm+xk>k0(0Qs8m(gv6<fmslsG;8pSsGfdJhBF^+rKKv<pBo{2(2@
z4LLUx*hbN*!Pq&Th7LWb?5aE3;=20mrT#QO?vYkhw#M)`u@tpCi~5588;K7&f=2F~
zTWuTDnz2OlHJyVLb0nnikL}8wN`aBSy0PPjz9A`Zu`qWAUPIk%FSi(I2#*_Cd!-Y{
z-zr32%W8cL<DyQke*y)}FU&|a8bqjp$-~vTAhgWglR=HCB>)yg*H|poLv)HaJn1O1
zkB`mW%G_v$LpW26G`?OPg~?41$uS|i@g$gu>75fbyLw;ILr43+$38>1WhPRl`3b&0
zCCc<(PV9=t+>fmFJH9Xo`>9N*d;mOI(+pMb_epU6N4*O^?YKO1u5ovw{wltz^P;DV
zWdrLhY#7L?sL=6Fz7PMtsHK=nX1-!Ydf$Yz@nb17oU|xL%jy=teBz<CV}sQ}FMGq5
z&*UA)Ch|>V^2bEo+Q8G;DNDz&9q>#_fK65<aw{j?T{v?sve%D4bUOYgAb0UkHd5EV
zp`;F}E^$+z3s*;Bg#13;W6>-0#3o&?iVs<^_$F+(5=Z)3u$Q#E&S?>se(cv$5T$9P
zN*3SHPg*6LP<xJeyF81&$86Fnx%KYJ-#+_JY=o-hg+em!g+}W$(6Izqw^8D#b00<<
zD|TrZcEPpe>gqiXR(M)0MheI${5DN+nHJ;ud*T%)a++iR=V->hD-zVK(-v%b|2Zpm
zQG<qb;$jePH*eirhoN6?vuFk1rfK~(6L2X_cZ!U?n@PSNM6E!pRiK&aPa0wVnnhlH
zegfk@ZdU_vVznwo#&V0l3+?^c!o*^1)la9f>(`&AUU!aVUkK7z$MIzEU&t*WF$$sl
z>Ow_kymm*={*6kB7ZuHiMpvYtN6zRf?lEqaTCQerQJOk_SmHG{nDN;XjB!^)olwUl
z&gaOthp})ry;ZPP$!{T62(JaH3VPh3Ch3eypK{+T<oGv>tZsbkJMNAtpJS<rsP`g?
z=;qw_9h#;S3YT1i7xQt+wdCCIyz%G}Lk?TAw4q|38}8q`Swe)RePs}P<$iMu=EB`M
z$JOe`)>`na*&(DC6Uht(@Gs?Lo?kAKMqC6mbW);H-8#Un{y!os^Br33{r<GyZ^B%J
zY!*r!7z*Wtx0{CRE|6~hL=H8wLSI>NGU#l&140U-<9UIKDtS5=Py&Y+xDG2U7*15#
zZ_45N1gpvN(F(=~)H-(~wtBCVP1tF_zcFc~?5z5T&l`^Qdc@=AIQ31>*aXbZZWLuI
zwoUWOtn#V<nQpq+zFMj2eo}Oh>mXAnIa61{AoN#|K&I$CRCVTog&bBJYEK{<?C*PW
zzy&GU<Xza|_LV=#1qO6kz!qe}nKlBtbFD?fAenA-+27P1y98eSW~w%2BWhJG@l@h>
zz#ahbmu<qAy9O8g_0}l(eDMa@5#j}u80<-H{YX2vX64X*RYsd|wjWk9E>wd5@jv-2
znE3sZu={Cz_sO0w;JEj|H9KEYSL)SkLE0u|r!=cxmz%l$5}I&;n;5dgyLTz6<FIo5
zlxNt#|GE$#MBFYJ?}A9S(v&qYrA<)NQaww<PpC=#8dxlK(g37WT40so2A2uw%xk}e
z^6wgGQPe}-zoi52^PdC-?3R%>rNOKWS^D2Kp4xh8PacRmcM18ZD^Wa*ZHmPm^^Mm1
zXikSALhQ)5!6z2=kep*7j->i~Z{*#0&3bnnQLsq|NK-GX9@I&WUg4AYN2^-;qxj8j
zOFpy?=)m020P+^hy`^syP$E(_6_mjaNZ_#osUi{ZQqKB1f8lFwOx0G19|FH+aGICH
zJ20=_Y|vcQlEHU>7`qG_d+c|wm)rbt-cuf-@+UxZboE-PSv{TRH~h~k!hBEW3P=9q
zQl~qqoy@)W1j&X-tsi{82T{s7$P7gIqXE<v_+~sA&TA@Aqoxi>?$)phgzrtQGvecx
zk~CkS!{8$&CX>_xfrIAQzfOeTsCFKaKvg%v4oYDn7et)UvPl_U{iNC}SWGVuE!2X)
z%DEg)XuZGGCFnvDNx@(ABqeesvMoQw7&p0CQnK%@j1d|wSXSW-%hG)85LC}L++J?2
z!E@}Wi$wi}U7gdv%OJ8UCe^!)=6fUo?+UyqymLCJw73L7P{SVMnkqe;`+uLuz->HL
zK%yoW5`_uYi6QD?qxiyFb%4rgqE4&@VLSbkf@$g=1~J`yCMIJ5D{VM7ggLi}Ef;3X
z0fA0OLvmRHsaN<k!Fl<bHB0fy@11LfCa)x8SG^at`#swGYgq)YCf84WZ+v3#?&7b?
z-d}vblimKDo+aZ3fY1{2M%9EVxu5=@JC_(Bi2rS1lX%7%+rtZf`unt#Gi(;W(h}7K
za>?M6Be7|3*gee)Sn+Rhv(#$<7&5+LBT@|$LN6V`D{cNLI{erYvVL_M(Q0eueK4dd
zz+tUEo-~y%!)2g}xEbg+&c(zQ4*m@|I?o4M%Y7UE=`5PN$1n_mRacHhd?&>J5M4z$
zalX<6oy2*9FC)R0^!``pM@$R%9;}4mpA`02IlB2Owm%vEcrXb(4_<X6CCE#ceQW@`
zB4}cex|FXyTM-a&S+f&s^VPHzkjWi}Lt%D_Z~`rX$LcC$(wpivZ&5>}Z|6MW>QDMv
zO&NONC@y^-HcP^8U`Ubz9N5^+2%n2<$D5m@vDuBGS%sYwt?rK%WhEZ=Ir1T}9KV}J
z3v!ywQiGeg(P*U@ypWczCh3!@x7Um{?NtBYcgS$3-YO0CRbLv8K?%bU)ncXKu(NK^
zD}rADh^-JnO1dJQ*_oD?VcM+7%k-$Uz?aG-rddm{phKdepNBCgtAlDNKC73cU)Nyo
z=q2FAl^WM?!H&26MRK}stN3`{u`(6I*wJE4|G`4LDZOwZim5EKYWM<>qO<W-BkP<{
zG>45hm-AT9sy!>cCu=Jr8%{0r;{*#IOYcZL^E_KFmmO~r_PpuJ7w(UfFW9Vj7TbL;
zT6Ra|-Et4`EWZ^h(=C5loYVRx)2dfU&w2U$Y1GP=dL+}}V|!w+=EBH5^}KRvDoM{2
zNJ1fC!4TRSFb)YWzK4W1Ev3F0RsQkr=&-_8)c>1w{zhqXN?-y!2TIg$2x6b|)#_!V
zTGF8bIhtf?PgGD$)<8-{(Thu%Ugqn>D95q*tzB)$zwQ5+6<ASc>}79JdvpC`%23@e
zFl&_3QhJJZ=0aw4!{F}Xy;6vfY&~wxe`9bL6JMz4lQ1PW&p!WzekmV`M1GD96=j28
zpUVBV3~jPHE?mLZoOCFEJ&q0x!MBl+Ke9rr6x@a)`HrRY3wVQ{vQR+C-Q1qm)-S!X
zZTK7$C+D@VLV~SR^*ZBPeSe{Q+RdwcK#P9Z_fluIHwEZL3jsvPXIK4r2g?aya_IRn
zAslxM=MUlkr2d2O`qyRfzbHBm+R7%zQcYPT7fzGXPDKbLC+B=eQtZLZ*oCL&#he9H
zjsB<+#w9u%cc<qK!c<EmOj{H4!M}Lz9I3cR1Fv)C=LzX?t2v)}%66`o`qMMwd5ah@
z{o4jkd8Y%4tSLs|=ESa@54J<ORE_vbEZLH{Z3dsuNqrPQ(E<4qr+n?4a3lHm$?(iJ
zYMzlF-$Nm@vac9CR6t$<T#Jv!en=_5eS!ngDll#M6koYBeWQcb$7N&oeT~`UY?&H~
z2vMDFEQM!!woU7Z*!q+SJNjU+&b?53%oiQn82Z8ph#G(g@Zh~xdyBBknkJYj<onpI
zJL$3Sw~1K0Wov(Rs8V*?nk`qDaM{|xY3L19!tuYSMFSwTy$YUybvQlyJb&)>%djDJ
z!Qf9qHY{VazCXN!3*XntDrc?_EPYK?KjInW)K6l3K$PS*<7f7Xv#g+Csr=Km!yfG$
zlpL77c8R27mHRU=ZM&FWT#iIim&aJjbjud!142WA@5IUM=9SCHyK2L4#f5qamA3BM
zuh)oP-~|l-^a8ZS{M-Ro&pZCidqP*k9f=PJUgpnFDD%{RizoarG2yw%_ZRLGk<NA2
z(GnDq2$w9p5;QHhmR_)BpO4%-9#0dkNbd~3{0$PJwfpznIqUr&+Az~0!j5hc`0BMW
zcAUmF-+F6V%s99M{ozs6i&_oOafW)Os0r%dZB^ff>eKgOqI$u<{Ut!UEVs^Q9}CyN
zo$0@aht-|ee9C^$^DM2}5o?<D0a^BP>DxaBD#RD&0g_<#h1|!YC>qK!6Tb$v6h7uJ
zt7&O)pD&7%!ScE4=`O7%n=N)-e~>$8ovd1QWv`!|sS%*0QEb<FK6_Y-A;&QXEBPN>
zPRo0(?2;aqul}oeu@Xdy6ufft@|mqZH!;#Lj<*Kh5|D=NO>GTPRkS8DHNlrzI>r1L
z(0S-qy<TI`WS<(T-C9&+sC}b@adH(+pWzg!cVESJVwki=W$z0x;woQX?%S1FmOuO4
zJOGfUB>0L~J{a!rHmD{qdwKT*r2a_zES0v4yWe{?_HW9=Ac^NnWn*}p>Fj_gK7!|5
zTJX{g<+U@-W9H+kM#UM+DQ<?OiRpxMFbmiYTW~ymoy%i(k)s?($}=KR`w}0v-=`Pp
zjg5D&O*|y)1DwQDA0#Z5++#77;s|4g0OoP(<YFHV-h}!2>_$pYskB*{p^p>bdGB<h
zTWnbQ2ls-;pGa<B^G?a_zA5O%Kbe%Fh?Y?H|2<aj?83G35`Mi$Oe6N;PNlEiwUh{t
zJTxDzXNO`^uK*&%wOy0xn^eHN-7NXR(~1B@VyBWsaGBuI$zqhp#GGbqBi9;=ik=FX
z1Y7a1PtrLPa-Sp!4M_8v6`O9A`QlV)J-2>0Rx-2@uev1fLt*#*$?vMuPj(0zAu^kB
z?FXELazZst$LnQ&50cHse*S$<$75KdnV~%-X55`rO2eIR5#TVaA$1TrWm56gIQPU!
z@%Z1qOvTH6Ns4-!5bYmTT~{X&5kC8#9bhGEwWj&-aoimZ#e{*62BusXwM0YG%D+Ik
zV@qt?EP>X+-Jtx17t*20Co6iIbf|LEDQpmP<qM|s+3{qCfB9<qDzyU;R{=D4y{XqX
zKuuUMib=^m865;C!8HIzVWlNFV{@Gw;K}}|ByytN7t)!O_g6vrUJlBmL=Jv$je8{I
z$tvVv2P*un=F+WcH(5R2dMOn|6F9{$MB;5;YN<wCo{^Ev-!m%tU#R54O>?m7{WI#i
z&3l$<!E{tLGX{8qztidT<CjK#1+gc-+tZSv5&tAJp=t43=EBFAd&($m*|Y+-FoJKD
zzWpt1^B8@r3Z74v0wuF&9~nn|m2V@~a)*VrG5k3$s!~OT)_)D2;m_#KR?(dzd-jd#
zSKp1vg!ST)Xnm=oZtDXARIl+;{K}ui`A%h>0NW!J$B<4`8y0Vq@wG<jys14ASn|Fp
zfF5BvJ*Sh0m7;Mi{6*GNW;0K&!yI-iRtJ&;?ne5Y_la9u)*9atrJ&0DE<?skL_d-9
z_Dgbn4$VX{?<+5*{iTnb(?1wpGGC+}!^VH(;M9Sja06tMr9C_he&g2uQpXY`HlstC
zI`L)Q_q$<P!Ek74Rv>eqXR^lFlE8}|5geOiJgH<sJAgst@ut2P0iTDSUtjEKTodY`
z$dKoP`aE%_4ODis%I)FRH4q#{R103q?u_RqH)V$Cqu`c=DA3{HOe+kNt&!JywO5J#
z^1+g$<2i$>ppM9uT>qmfS4uQ}8rdk|V}d;sH9akX(wM=sr8tnbbEr{|=F0Er^quBs
zzW)INjn_;d1*z50+Ep^jfOfNyY<E`VYnQ2k!}UMu&3zmv$M%y`=lp(W&Nf4t0TS74
zFVUt44p-y+`zfO(<dwE`&(4iL|Djz?hb;ro(Fw|hflIfGHTCJ2_aSp~N#7WrMJNa6
zuFv|72^kXPi*xD4qiwyH{|xlRJ<63hXu&JL*|iwBU*54hlYLV2_2ydV;!2#J(jJcj
z6@H%1I?Q5{s7v)OpvnZ?i8`77*1qj!Zm5z3KJjMjj5yNswDe7RX}7_R5pC6|UxX$<
zdXDd8`*MhFr9bK>s5QVmOId0&cO>=P01G3sE4^SMMvffS+2SRkCYsGe;1S`?HgA?5
zz9apBDA7IKz{+0c;rmup>rC0l4VuM|H24>f`cGAPYRqF?wmfB4hr)DK-*|wVq*~wn
zD=0EB`{dvFpP(5JJ-IWr*YCkPTp60)S`@*giwJuZR%I_~gt3tJngoy1mo<@V7Hwzl
z9vUw!k@AjBR^GYcXN2<SN5Kt)R5RBW_z1fjDlMcVx=pUzvTY%FzJ1|hNR{`jcxZ9Q
z&D^~F)Izx(*WjCI{@358Y837GB!BTeGG4#aauSPf7IXgwzxYk<6m6}a4S899`nJnK
zF%&W&=@AW~6~Avy;);C_e7n|E?>)Ig%LEG=Db#lx8!?W`;LlR*ZbU6U2YIhf7t5b_
zo&g4_nqGi?mQ*QecROPj*1{P8;t$oIbsra2`A%Gq)mFHy{ZTR<kt*I>%Zk6*C|v%S
zY?kCRqp8XE_s}h^CeSp*v`9IELO|o9etq0pmHR4Mm|2lS0ro1A(FAXGbG1>k0HrqK
zl<2*=!WU-UO@(Xq?>QaQrCgqa2&zi%eg{{_qp<O7Pn`sSs(fcV+W?afcKOpesZDTv
zV^iJ4YyJ7^N5t#4A7s3$++Oel`esVA?D&J~H%&+Gk|JCpi;+1<1}^}YQ6If|GW{y@
zJ{s|gX>$aRw|=e*e@2&=DYh$zG(3d!i<@zfKb%>hbH90*J)FX{%KJ=s!jA#{-QH*|
z3~~^7-C7c^#~+}1q5-07bonbkrD!Y=93R3UFp1l2GA#Y!_lIIsmJda0x<w`X9`dpC
z5j)h(KRBW9)yAuf){X>PrAnzB_ClqU%mYdSN+SlrGYT&K@F|a6Yhj7ahcWCASqQty
z`arB8&30apH7Px+2{?xw{t4*EVp(S#?##}{`3iZWa)(%l_NY8V?kd2k8FGLZ{D&rm
z-0K?lk|TGT{g;uSwkBY+6c6D|+SwFuAe=$@ag_F{l?1<!zf?sp_x7GB8?L`)%pW+z
z^_htoE8_UU-+{gQo@%+zW5U||YjJdy+xM$(k=kod*kxI%i_No~;6);L)<n(x9B5Xq
zdLqk<UgX$nMnXV$`TC<r$5{fUyqH~=QzwT2y`p-kl^Hpv!#Ln@k|MIH%wbMk$V8I~
zLh9R^ACB56W6QoCrdX%=!a>M9PReJ(hVka(#!e?hYe5{?Q-r0KsVt@3J2iovXASYJ
zg~{)@bdNM%b<lf{l$seu9fCmUc_8+^(TS-1c5QYM;^8#_h5l|oIZ9p;qX@ntGUzrI
zg(AKPVntGzDU}2~o>(7WrG;BP=fz)N#fWl?G)q+Z?{bmZca!!!_n=RIVpM~|0^zuZ
zj=S^jZR@Z)JjsG@FQf8u=Ee!M;G2JxqU~q5TO^5K&y2A!up2;`3oKLji?!4j2lreE
zKQGvIhD@lq(`dN^FxG50+Y7T$CA3Q?y_?AM`_IFV{7Gdc)cQUil0|*dY;{fdK!lCl
z^O@aamY2}pZtL8|=dDvFO!uj}EE#ZVs;d4zowIe{e)H{WQj^x`j|+#h(+Ruf6*|%E
zitFp0^VbS01Kqo-r3i^^mDz*Bs+`NP_Uz#J-ovM@FV1%7we_2RRMOZKGWm)<Jzt+l
zDSf524*HAghIWD@=l_-k*dCVZvN|`!=df-&X`QQ8<5+|X<WMP=al+JV=rghbcln1P
zRPdWbrf`CA>FNxiKc)~~6=u8bj5;5_@+OAbO5{|p1c$>(K15VUL*HLR74|>bH4Z`-
zD6SeOklAt?D0oNsbOi{sul1})L1L?ywm!cyv5SI3nY3CG+A!Mul?>v9Gl%fhTh8;!
z#sP}m12ik8GgGXsGvi@&F!P^Ly#VP}7fwmFV1xS|yMTF@mMTIuBP1UyBO>1(enTdf
zE^Rh?ef<OfdJgjp8HH6ZNp<fU0;RB1Cc%2}aPIYSEU+K@?;iLC#>_qQKfMIq7dE~x
zB_KJt<eW9<RCN@hM`9AC*9&hdeSaLSF;jAtc|5r|qN4Z}N|NEJW)NL>?Tudgo-6ey
zXQJ`<aY0ojM?E6-d(?=K*`!&%M-~;40TTHxXrbmYr+YM=$IxPpX4E(>7`^t_c1E?I
z7pK#hz5w#BbH3{l8nW_zUlso&mTEqsO=nC)%#+;Ih>WGOoWB_h57H_#O<Ee{P@MV{
zQ0c53gEDOpc-L6}lvroPO(DO-ByGbCPKW%63?fsgt)*ATm@x8_gVtnr)xYuQmW08F
z;8}Aw>=yW+p4q|ijaMI*{RPkn`oFOWv&P1fP)J6~QaFcF&8v`gFN*W?WZgH0jma~f
zE(*3LAmI0DWgkx~VU(I?u*!!HJQ40DoW8%B-@X?`vrX?Bz~kLYupdv;5lfZ9TXLnH
zgUnGy@e%Vyi>{+xw<bpHyG+rD=%HK6d_DUmLx-X#77jQ#_&zZq>F7{TTLKW(P4D4H
zYvVe&<h(qX!T6%j9N(qV7|!;kb1`b9vi};^E-|bs>KhbXtMR>Nw(s1fnis7*HXID$
z@B=y5qJaz(-_ccHW9dyq7^IxlOzK&P@J0`pLWa+%4k=Fcy{8G)P`pIrdhc4);EpJ(
zDg}pOdG@7ICuEc11naCfPf3!PxUuU~%Uw0LhLK3?4pQc!Yve2z=4*L`<i0`oi0n9H
z7DfKQByLH8#k8{Z(Ph+}rDRMPb7+sm%%ijnY_9liVVj{>97a{yg@2A$KbJj0g=)|H
z<OM>~<$R+{iI+7=pg)Imo$sl?;?)vcO*3-2DlR_g57icFHVkiQH(V`!MKxnkU+!)V
z4s(b9-lbbw$U;c-5?5J@Lj3(2^JagCG6@QqOUwu(P?eJYp*u`M@eU$P=d4`?(lU65
z!f>AxJAJ<s+W)mBusttwIq+Yff7$!7`xubHG`ubx(@61cM~ab7+qId(2hP-$-+8n!
z&(n-+jYX(B81W7hx;Nk!k<LWx)H$a7SS?B-eDC2TZ)0)=i8bUUAkavfP)K&%(Y+5b
zG9ARsQSA}r3Dq;+e~N{%4m}PB2jGQ@qfI*v`i4!1@oIXDZzwl{j6YZJy_vW?6Hrod
z!ZXR5DK*k3w7I#Iv`eZlwtJFZSQ5WJAR%v&>{9yTgD>F&%6n9@xcGwGc8%9lg~Q~w
zjwuK!HlLOcYS};C*<dmCddFdocX`5Vm>zKJd_u{~<;3=MOk!mIJdkU%3V6V<&^FxO
zAEPL%JxY5VF+8f2M1a~2qxj1H?xO(1P}>#YMH}9jXp6ao+M@$E=*K!dlVKVR9A6L8
zFv-m@;2?u}r<Cn^XQ;MSa|Yn_n(4QOU-dD@&MHw|KB~)5vbBGVM*U=fQc#$2x;*^y
z@MkEWITLWM7}W68%=sro&_!_C8h|PoFI^8yzQ7dkG#bY6kbm=hcVE!x&i+NPmU!j{
zwimhxuYxsdB~Km!h%Xqq>eqwlN;=676zPs)5=|IJKKo|=q4$x0O|HUm{Fv501d4AP
zs?GGgBXlSqKq#^U*8c!6T&*;J(#8h+gqrjXp0v=Ag{`hhrk(DA^=H=tw^QE?U1#rS
z`P%!0qj`)n6W%}H-`ze_<m`)%wQ^R#*_x(o_Lrk#>9cR)AmIqR$ArJw8S&k273ZTI
zzm&&gXuJHGsP@3{O;nY(ND(ZsV$tHs#{@iH=BfTdNqKpSDZ85}T4BSIhN|rC%yjP(
z-ZD896E87C_}hx7yU!X4+2o&~MD+P%w3nJw6wea6(t`35&hOU$i_|-|GpqH@{4Y_S
ze;u6+x9B=mMg3(m$OdHEWk`coi)EPtF$+x?B!OZec0>giKW#U*iDFO;DZ#tc#1Y>d
zchFAR%UoIHtCQuH?9lNhk6aIpz_H$S3SWjIi3vKOsY1aIY}IUnp5`c``m)IA(1F!4
zjfcoz6;g9S*Ro@=Cg=3mhdatXz6vO^%B`tq$0U~di|7ie139Q_k7gT<_8lT-#OGgQ
zcHj3d8!S+97>kjW*zsg4yxapptwbfzc;shqK{>+L8rdil&vmKp_!j{>oxSl2Y)XV_
zT}`Tc3zIK)=UO2haM`AF;vD_sen2DPvN0@Jw66r1^inaiXz_uRWVZG4W#_W`ySR)#
zWYUN*F(hc|a&%o~k|=6z?05&p>VoX02*}saP&Gq@iU(>f-Z37Y?zLS!@y7LgjC_;d
zU?*J%v2)TIR(T}HsNpE3-<T0v>eOd6N4YraHV<}|HuZrR`hZW7s^byT{NX!k7`YWf
zkcTELp82_XLfb3rgy=vdf$FkZs!+EDbjdH_x8pBCG({Ec9SleL$D?;RtSwB(!82tx
z8Xy-Jho`$4YxgTpDQuv9ccp@qUx|YX5!Toiu@5ehKa3wlR?7&<V?WK0ho;v*q~-e@
zkzAAQJL-5fHC#fkK`}NUM#VMPNEOlCmAoGuL?1ySDq7i)<F<3=d^8f1A1AIkDQlm%
z6m=_Rq8DEl9Ij&yvPBB*W0`iweEz*Bp$mjq7^MkHpHsI+Z@cN{E>yTR`}H_0?4XoM
z<)M+tcD)d5?iMzq#bu{}7K>7t<uILYkX+vf{=O^c*=u)H9*vCP9pz(c>%PRd{k19+
zRmC}W?qC2vCbAO}q~1%TwIsUzkL8?;gZt^i__L`(K-<UdhR*&zBc$L9)qcDTr?w<j
zb*-TO(y?0zZiBCf`}0~`<17T+3EsqqHZVXtDx@t5IaXzX3HX`pR`hQ+-;VB=p1IEz
zQR)iLw_YbpRfd%0zvp<N+TFU~#v~=^gtHIzctFL?WvQ$lMa)xA78v<5_@R?nuY1mb
z54miV;=jLT#&xcC1y=b<T$)}T)U=gae(SI8ZxYc87hUh~?r>87h3wf$zWY}b-4LgZ
zMv#DgMhie~6T$iOv&PQis-E{7Sa-KjXOtJIO8|eGgvgF{x*8t%Omy&Vl6wr~*_XfE
z-2q~+C3CE{$#vz8H2}epCyQ_AWxw6XU2^S~#~_Skjx@Hm9eWw-d#iJyHg>=_)u68-
z=!kF1U`e|GT9#*-q7}iO=Cl(^@Xe9*oHFu{j#R(^i%X~Tr<RzeXokcnS|Mdh!R^wf
zLv4;CTHjLRS}R6NLR%SjC3qMF&Bk^m4^c)2!PzY7bIidwE;5V=bCUV|DCAFqOAI^T
zvAYAE+zWQL=-T%rh5&itqH&gm7xLobcn7TU#gsGUDwoT98<WyRI|~NQkdx2}T-7qm
z>Mk%54)0zmjIBS1<SX}(!lF5v-HlegoP_mfJ?+xBvr-RfPF`K;l)s$jeDdj^ieNS8
zOekc3j_y72b{Yk^EcLyuZ*l*}Q@aE?ack2YVnL>HYbq?{=MGQ&YE-_M^97;ym)c+Z
zcp)1$cgk21^aJtaB}B)@2Z8$*odWFgiE_95G3|n;MP9(^4UB8Sh`hT&pLe-_CBB!F
z*DH$})uekICCk|Np+Aft))}aBpA{!|K&C8+bFxfVMA-D3rpoc+lTXT75*|zB)Bi5)
zQoUw<_9@2)Ij}=`(MuZiIj93B?wFy|y~Szv^qB{*5bL^uV4SWBf&25K$qMIcout`h
ztVgrj+?OjbsD%gpfqnApfm<3r{dvG}euYWY42SI@;pnK@B%T<>#zy@b8ex^C6Ca_5
zY@PLt3IJ;}i1WqPRko+=Ewgr|)BBdvlMTs~^|a58IdDIO^OwSMSc@QeN;dszqUtyV
zf|8@BhZ4w)N@JH|IZxl*<K|T!{dIH%VQsu^@NTo+>iyQy=ZqE)WA~e9=nAw!UKN6Z
zmob%n)0|{FQfQYR5vO>k?kXZ^`DOnNK4T<1jo%54wn8|W6HJXNC4ZUO9N!%AK$Ype
zA(PsdWK&NL_{MAR2egWyl_>4Y2WE*=ov?n+t(|T8VC70N*QHjp!ZL4ojJb!V+GFxo
zF%+V^jSd|F-%As%-}OAIuQUqIiO;eMW$gO|1^Z6);&^IalabLI`$RU?B+B7eGyk$X
znAMD0sNw*FeRinviWo#7?2ZgQUy08rarSuX71baMp4u1K4c=IF#rg_ZwuS1n3{nq-
z{*HAc1q0{iI-sbN@fiQzYW+*!INBE(D(bj1<>t&E0uW*6-)#(@4qxtuXV(GxJ)B^|
z7?-kZ{YAaTM*%3@BJ^*Zgja`P0q|!;!tY1H`8D}#u$#W9E8FhUB&Ge8@%1KwLWKCG
zKqH7HC_;!lkg>>Sdqd1dvGUd>XknHrHJ|(UB^|2+;0E%vaaa6wWFcg1|7Z@CeN=m<
zrd~=)E+H;tA`vvNj15!U*jxFvy!}b=g+Js=!?_!PZDf%aA>i7G?xki{4OBgBH*a}Q
zs)Owcc~vb_0!#WJ8&REFh7e(1Og~CE;>{S5D61_C8+4==xL58Xx4&`)*cLQs#oZf~
z{<MKE!Ldtt4w9ZiL!~&Y-0Or<?2SC7v&w{i#Y?|lXfi&kVt{9DKd=0yBr`B))J2E(
zj|7~H7iOC^CA&z%x!WU>9XBBtxEYX9s*blsq|i6?!1)In$&w078z+3s_%`8XF9GbN
zC=8pO34jvVOA|UpaXpa3EBla~!V-uIjPo}!;+vv;M)wQr9c<uXu2|VkFSE{cQ$+bI
zqOfOWt|HLjnsb4H$A?FkYubo!xu3xI)IhnL;7eTTl6_z^6QyaryAxQ6bGee*tjI}g
zv+`WhWs#kTI(0TFC9D+AvuTF6swz=gis0@zXxgonAQP9^>7{9bAU|EK@GKTa`)Mj$
zH@qV_9{!9z2jE!hiAsexi85XZ%>&v+*N6jkdbotU1jh4NV$JjD3CBS6w{3xbONDWs
zik}Ypsip*P2g_=8_Ln^L2<A5?CTxth1Nmu%roP(!lmU4E?*sm3h2eSo6x^B>7e-A7
z1omf@4QQ`Cr3H!FxpBAI;z9n&UJ@1-GI!H|y%I{tFC+cTL|y)y(25BDT@>l%MV{B?
zg1EI5uLtM_Q9%{UJWDn-FS_5YgU5ti0^)IZdj*bvgTpRqq%h$a76A>J&N?u7Lt^1t
z^4Z{y=&(lvh`csdI47rIl{JO#L5GkeCRy>`gfRC6r-R-K3XR5y31ndFndQ}GTA-2;
z4uvE;X{scineA+hZ2O8oM3<=}k@>Cphvz!2<|oT>`0PUKZW7tF=Cu|5@#HhH;$7MT
zqPr;U5G9V-He*&~E=vkSj@>Zs%Hxl}BLGB8tW;=o)SnE4aSwNNlUY8vCXFC679<jN
z8GH`4l0#5%1cxZ`qu0Z)Vo=-{pU8NxrMr{t-^E+ho0h+9&|hWnsD86H!>_l}Z}N4k
z25(W`8eaJ`V6pQC^(%F3qp`M|W8y=Wl4R4s1u9i+@JYYD^D-p&wOa?Z*()zyBN#Yp
zMa#X<cJli^EJlks&fOb_#rs!wi+4W%2!{@6Sw>^&9Q8*m-M40bfnu7UNI3~St*Elt
zbZ@Su7_52G>*-HIMM`PHHk1_td;CaO4DX+`sW?>ozD^cSkxJlPP!>Dbz+Q2#;P|~H
z`|qFb1nI}{Cjp~8)LPeacY%I)wCk2Y_V~@rRJU&~{`}lkjrdh@X6ugSuohiEql#fk
zv00O^!ye4^hws%n4-Edo`w_EiTE~3OL^*W7{970Xg%|b?=rC*5&VG9kO|PvN^>eb~
zMZyaer4LHODW^U&662*EZF{<*Zh4Z(+jQoY3|z%9h^&DR?Q;H-8Xt}LysHMP0Oli=
z@Wmzm1X^b2BB*%3@IJfgy34#2U75CPYbGbTM~H7~Rjpris?rf{QnYBB8xQU3tyV`~
zf;#8ecqra_N`tyXec8t<a-{A%<kmz}p{$qg-%%>D$JdQ72^5AIli8nK=U)7_G&#72
z^xPfIXFZo193bJ)QCx~wmFRFg{s%i0CxmlcAp50$G){TF@k!6hANyx%7Q|FGAe*ua
z=Vx3Dg3B7DjDfC%Adt(FKnA`oD=A|b`bHfe(p7=5qXd+Fv|Y{Wvvk)a<@px$6*qGN
z4$Os4JV@{n#eH2%YP!+4=52rH1|ox3sBaa)rTeKifB<G9i4iFzx1?OR^f=zH!aPUl
z8y|wt*&r`E8*+?-w3z|m`E>u6y%SV&pqeewH+N@AV@kzm`<u;h86IH+(I>%cZ7&5c
z>k&435G})WuH<^d;!lz1hdSfQNNi&;87bn-sIL$OGO#j9=G)wU`V9~`0zWTvc|9l^
z@kQh<G0+FiSTL6&9*~|>NsgAA=%Tm%Qe0g1bRy&Ts9N_hml&_FCI<8VzY6s8!A1L5
z6D|$$Us9J_@CITZ5ZH3roGNaNh%<u*Q(G!Zf*9?ijm{j~5duKx#LV^vwwPO2%z*HP
zu^E$cAEpGFI0u~hB(?Dr{<61@m|YAxP-2iPRSo+01Ro2ed@22=^8n<6SYG1KV_rzj
zbTEj;o&5n@|D5+9%39iPn}dx|$}H`%Mf#-~pcr`yhGWSOiwSik@{_=grP9r3vbrAC
z+TU1wYq@je=)@TSYqe8$x}s{n6sKcmuZid$kbO60yf)wZa%<D}SE{N96gjI^yce%(
z6&?RY4D1>Q3Z3e?$OmMB&>Prkq!b1c#Zey|Tv07bsNIw~NsBE@^@aJbH~bG{loW}?
z3Yx{#{l)FXX08yepZyq;fYppct7lzVuIe=r7%A;so5b0m$iAWDZzxXJ%B2A_`F=6G
zoCAS1?X0F9EoJe8zS%<`4LdFXV!716d6Rv8b*jQD?)5GPE)xb}(JKh;`*=_tz3%4P
zXvPalRMF7`J|$K0rAPPCZI@w6Xkc#;ASH~^_S~N{{bu|$Z<k1g<x}b^MZp)c(QL;F
zj^ID5R2KnmMbGivw?zC#Vu9?*r?ro<FP9#0TkOsXDb+B1fCUqJ*hBT|TFv_bd^T=~
ze4E#uDEIEe-%Cu{$&bi6nRq@V;}KB?{vu|0J@n*Ec6#p3uTOeOWrcz^EaEPYt_-6N
zmu*iO*)5pv%dKl!z73`$*+wi#JimCwITlNIdue$Y3h7|Og4Odj_{JzX4n*2?e3gb}
z2kNp$x89|)Bnk^@Mar4R;_*u8_e+VVqIk`WI-;kc_-7v<o&50NbK8DJ%{QkC)$~f6
zPZQ)qtc6x(ou9CtS8z`>>OutNC)uykNK4Y4IxK4Uk^7O+KO~?9igZ%mx{5K|FaW;_
zqPgVF{n6O=2q^j3dhE%uPMdc1!3pqUV~%sl8$cr5wH;fTAa%42q?YjOpxe~X9(7PA
zeMcvno}F~+F%6!X>F{>kTQjKcsX;xflR)de)`a(;K1<g=4Q4V25QX8UCf(mp4m_tv
zD9S62HWIfd<+xYgqvNG5asih@j2s|ZZ|f&FG0*u)!XS1;nG#gt*Kl5E+wkUYkrgt*
z!Jkt~X?Z%i(ST$3R&XL@qej%k_->q@?tzOTs5ofqev$kn9s=e2>88B5fCu%ve&f=A
z1uZs;u#1uM)bu<47++YO_zF6JC0;VH`h??YQO)7n9-8J^=O1IUg*Z4=zky5h%ljWs
z0~P)AJ(B@M|F0nut=lA3JB%YN*g{ykLBC1qrN#F1+MhmTD7$IwHeai)c83VuG(S3p
z?<@Gjoe10$|2kgdCL+}F-~S@KPc0GgAvQ?HQTf6x!uOh}w2_k*k362DE_FoY#Q048
za}z%OrxHZmh!&C_Sjymr1X@T9w&18}-_}2L1FrtwcLP#ZqY+Kg%s&qWt-_@6LQav7
z7k+29^86#-naGq!2xd3>xdX5HwdY3^uixkR%p}{taX`lE^m+m{p2aayZ|gsqy(w9&
zOuoMKwLbN^*ute;P-fK84yX*Sjhi`#lp?x~l-B+6c(7IIb0SG|TF)h=?WBpd&#GCG
z<Xqu>sY-`PIrW%tA1YJ%4BE(J-D2(uSa#l~gveNFr<s*dv06~XZ%ZFmEV*Cq6vKVE
ziJ=|m_LJES@o6Or;a!T;f15^xG=%#bUZ=edT$k`G%tDEwr?c7){O|1DNK_!$<ks(!
zbIax6#HlY~rka1?U!i{E4wIbuoXAB9_<^Xn(PBHUyPueUrRtA)yiF*2mLTD>_YD%T
zEZ}`gi!>|{MbBodkgirfFaE*+xyM=@Tj9=*G$zV80VM4omC<}Kvsp@0^mp~9+^pkP
zyNP$I+7pGnI_C=!zax1&-WOYEc&i{{*)=lKw%>-A3*KfAa@MeNU8q)vmHE+R&&*S@
zl0xJ0Q{TX4wz5mEE_x5DXL*|9w4Ou;Is}$aH_bC3(Sh^knQkC5Qcr|-`5|TJY*WJ+
zdS{Fhn_7@c0Amy@(vyX}%HgW{DssXHFe*c$4?4tzU#es&5-e~3;HvqY+XhN%jr%bV
zKKSRN2)|gh13Yn#)h0u=*%@bFI^2@5$is#ioLN=A8XQ|8A>XfD!Y$|D-)&|zb01`S
zP-OG9hdT8(pv1v$j^OZozZd?nLf`eF?m+6!sfzG{{Mmjt!*ut%d?&}p^y~V(aw;eh
z(GIGX)A5qFDF0til{om&Z3j#)_&an%&5zHi(2qfHn!Y#zD;J60M2cA-cYWo2SF`_a
z7F(BrdfRUaYy?QjnKWzfNbC~lh`UBtxxnTkPr!m^Z8kYuEUEg<&G>Tk-fhFBat5IP
z0p?`9@EcR54DVlq2qt9TW*8YS5_2Ake0%8S@Y^@9z<yE%ojn;Tg(=rdw01eUF?wPi
zs#q4o@4F`4YajjORsbO*<wK*)=kC30!={vIe56E3{YCtl#??iPFU1dRs8-8?tU87w
z$<R4jxhikze?qM%3j8YTQ0<QbFO2=S0OxUPfbVjsP=h??XG+U+Rd;KVjaJznrsBXM
zmekZ&OmF;a6-?u*9YvQy1@81|Wv}fKdxQ=xJeI~Q#|iroxzv-`J91<(y{qsLB6~zw
z%I_0P(V~iK9NekZyU2{L+Jc1*k?V&>(zwbcai`{PdeUBPp9DZA!j-~Sk&J<y7_G#H
z|9c&w572FbOr;UFs!R_`m@#Dv^`h>+`}Sml;rF`)NKQKmJCc|o6rxBFj1v@_4@=6>
zP9@11z6e~E<>thODALVqgmH?@wcnPp{FV8;0Gg2r&rX<oF!-PQ5Li8IXRQ}yi)HP2
zyp-Nbyu#HX4dofan893Ovswa1gC~D}pOo<KsiCk{0{d=}?>44JB4I;rvp!iTP7%lQ
zTRNdbiTdGcAD!i%mcBIc)_p)oNA{ggrnfSZVNMp-L@M4t8vwtx{#arR%Al|@E&sH2
zsqI(|#tOOn9>Qq1ZEB@ns}JYbEfVVga>{bpII-?!zX8#Uvd^%Y%tHX0h@_1)sApsw
zJ2<mx4jq#IQi^TY{M;EBl6rx#vuJy%nSd*ZM)1-^q}(5{0q6+}=BU7P{$Q%!bq__-
z`-e(k-$I`$GxLcb*{J1t|Bns?8i*V+GFwQ5GcRf|aG@!P8G7taKTXaTX9X&)Q%oD$
zs%}qBnt=YsAqC1Xd}?R8u`ko-m-;=R2ldtcq5%93GGuyWyYlkq{VP-O>gt?tumA1$
z&>!Fy2=j-4e7|cI8qEA0R3A|h?*|N@a>(RXf_9V3Gf-wbK2?yY`{!5H<+NL`K)Cto
z2ghM8+@<e>;Ds?sgk5t1qi>qmc5T1Bl57v+gpC@F6{uRK{!L0NCEl2IWd<m#f4t3{
z(JtN4WRW*-JvUV)#=z#&_}%!6ZJ#W~sQq;hBAAw8{V6Q~A{n_nDdjFUdXrrKLK1|8
zvy<0Z8n^119-BwOZ121|0}nb@XM#R1xrVYup)=}C%iBY#PwksBDO8BCp3zv9<?~|;
z#GF>^tFE<tVxBiGK74Xdp)lz{_!|4VfZce*;m0?owb}(jcI?%g>~#;xNAicw_vi9i
z!M3R_C5<S9Xl%eIXEiPY#>bG%ZM3oyEA1MKwrG4l%;c4>Ec*N(f6>8`z3bZV=3irn
zD~8VKd$R9eWmh-0y#<jb@j&iy3hoU;C3I%=&{XYg7pN{-$evB+o&7p{p|LmqXy7mw
zYPRYWiIuS@JnOwE_mtp6sZZp|Err2VT6=fG2{b<b?|o70WA=RC+!oyXpuxwhBbbpb
zV4(Sqc)LL${AiuEF2U<v$??{U3K~v5qssWnL}Mzga36GAz5v^Y-*g=^XqESiQD0|2
z%EuL`Gp>0n)fNLzc|WoG<8Nc;WUS?JraYF;XoSH!r~ozY`dE6!nGpurNUV=>O`$`S
zOzNuyIYK~@rUU}mL2uUIUPV>ZBp||qLu}Ty_s;6qNE@+882vhl=D*uS1t9KT@Gaif
z(OQrc;QUtt5RL=>7zXn-3%ab!;K%QNH?O$Q2W6NPs*sKsDZ1(j_N6Ozj}aze%wsp;
zA?iS*dNW>j4bFv(r4)sF&fXo%-Nviq$){NC^fD;Tx`0UHr#%uOclP>xih1{Vi2AR-
zsb>f6LUL-O;jaHq^P;yfhw6^<5&3fW39+>Oc<rRF`Wp9xK9AURcUoP+@E_4FJPe+@
zr3dAtya$Kf-RuD3@bB?&J*21z0!PkF@UR9jf}iKAXY@_<@{q2N>j-U@=v8X5#wc;%
zhC=+TSvyZZWSi5aqy&)U$gB{(#nP~S7)WPHCon5YUhOJuFXMeH<98_oa;HfKqz%J)
z(atB#&BJk{-^t8|ISm2W827asFa5ILZF!9(rSJ;cF-Qi%*K>M<2^uf%5s9E5U3<2W
zT!0^|eQYztHm?VLPX50Sz(XaDs-NsJci(>+2kOVA7im~xv0<3;+bCtwg9Iv+eXE`s
z=?UUG0D@8S<UA2L>GDebwOYeiQpGa_BfGxGVk2yT*95kTbCr~L(Dp;!Tp5Z{Hpk#G
zI`x*Nhv`vA!x;z}XoKEfdVY(mgT&MXY%m;gadQKMJh$0(aY>U~54pZETzLBM+qJ0D
z1rXp`NOHV($YrPa3{)xSKfyh}3KHVa%R7trJKsJ^$k7T%(F$|{u{tdB69W;xEI6Kn
zXoc&BFmD2%Mu+OUOXs-kD8IMbB6u6G;@EEqw6R%0ZL9lx5yz^;{<`_uMiy}L>EnL<
z^K=D%U&1GduKf{TBvWkc#vd~JMPjP$$Tx(95Utu+|AwW)FZ&(FMc_l-5+RCsyGC!v
zbRsLA=A`)4ex%Z;$o})BXE+6UgM(FDfKn3vASU6r`gdtfnFIg#$jga_AngV1Mh|CC
zH8dT2kbca^bNcoakr?3+3NH3|u4FjvURNNO(558?Hv(rdSrf9a+BIMquqyTEAgzs5
zt7VQQGO$j-`@xyelD%x?9p+@Rg<CD8&eeK>ges2sl>}ZITc^14M5p5psC~oZP<yNy
zNHHMV7H|aURCWE>O}j04q)!>9jFfOZT#sLG$H|kL{XeGOf*}g1TiaF|siC_WN*d{g
zA!g{1kS=LNLP9!+u0cdfN<@&55RjGzK@b7y4(U$i+dj`Z=l%Wx1H;~Xt#z;Kx_MFj
z&&tQV8r;-C2rNT#zej^#RhSqJJ~sOJ(`PR^v=8kfM(zHmA2n|ssokr!18z9<7DV#m
zr;=(V!94|h(efzqSSy7*@#i**c_<FkM|0tQ!f)**=c1((2hx?f4Nu?C7mOk_zPRsN
zoxI}JRYoykzF&Yk(?j|Hv)VHsxReJdeO^MRgtl29Hs>5z*zatY#`7!mW0(AU1?=75
z+L8C?ASnf6N;R@p$fUVu;%kzKgU6I92SBuW+}v{I@mW%R0r)+n0p9HA^XG3@;?-i(
zIa9@kF8rL$bNT0+W%$gShf^O`PQR1eDcyS^aWTg|Be&`kBK7-RGUT&(%K$RZgWoKD
z`LsUuYlPZ4k?n#@-!j2D_W$ol#E$xgziq0h1>pH9M`mQAW+FmnR*hoWuevA(JcDs8
z<Jb#PTgOY%Aq%ROX~hW+WkA=+zG0>2C(oqQ-R;P+>t~T>B>nLW3XZ%BEZt_{gEnvU
ztRpUAQ9d3D$IQn;dx43!Kb3#>^V|}O$nZ{dEq9rr6>^<^*?bVVGj$TfSu<J7N24oA
zpWuXK`}1`=k^FyOZDcZU@3BVN;8F7JMiPi?emgwrI7?)BXg^l_^sGOelqs*m0$xW_
zB7ZEn63a;})Oq6xdRtoGfTK%q(_WpQ2;AMiZy{=q#4)PFA~={kM4!G#{(dK|6X)=c
zUHN4>ns!cRoWIS0fmIpxfO3T2Yf~%rSsU@*g{|j^gkOds*p}uS|G4Eekl$XtPdzns
zGU_|!&?qx6k{^`fNSY_t^k$$BL+;|SDz|P*ONb48`akdg{~n9kY-m_)zilpUA$HS6
z-DR_njr^tIob${Cd4)*jI%4RvE%QPW=<jeV4{KNJ3StYT#Y@^=D}in)!%DMlY4zb6
zkcDc@5mD6F4=5m27``tQv8Db5Nv6xKv*Zchr8zn`sDA6cYdgK=cs9$nycvgiAJYIu
z&|_u6qu{~u<B`YjUo2|3EpUIs{Ilslh34s3q@H(YcF|hw;)r|_35QF6Sh*l`rrFA`
zER^(NttD!j$E^9(j$!&0=g`0I!lC?9N}&C!X4R!Dm_(*o3+_Lub6ig7fT6GE1$XPH
zk8-MeSBox%<pF$s*F=i7X7{uzby_`^kH*FW><3&?ZfPJf{9}4{MUx>X;@@^M1dzwB
z)-zr5Uph<c?pOJr=*N%#t{Ny*ZdV6>D=@!e;dABD`Ofjr)?V;B@B-{9%-3G?a$!$F
zNOxZUlc<g*J2D|$LmT5IbhW)H1KueRrnwht#h+Ewu2t_N+F$YW&rZDev!535e;eR)
z1;fR>6pUNzZ1q9?%S1ce7QE<rmzOtV1U}9+$pJAI{7E!L=_ZxI#Ot`yYsop`D}O+!
zCv@b2vKW^h$^I}J9)P_s?=jv?!4{Ez)=$sCP8g`SZ<+v|B<ja-!E-@BAuf`%kVd?Z
zd?%*OqKxAEvddTqE6(8?C<QJJ2crt{_iUfD$@ne4f4m6`_kpAmO*%;<%=ib9?{a9V
z**>q{lIIKlr}Bajz;JC}e`654#<DE=Lhv^XLN@QwDm9Mp=614{ighu^8ija;Qa|3z
z1bl!mmQkEF_f_z8yEqj(od9rc#EQLwO~yLLj|K4;ZTlxE2ZxH`=_?=F2p5#{ltD%{
zXFRJ@OaK9R475{5RJF0jDWa`sQF`cWGAs#77Nc&T=P8U}TyK;P4mpYUaVFd+|4Pgk
zfi|^8YhL9HQHd7!%co>WneU~4ZZ5QuG%jN}yZ5W;vBS>(=NKsj$A2`V1xCV}A2#m$
zZae)Grq=JDLgj;SJ=3`#r=gqF4${r1&Y1ZX%;`1Pp_;bY2g*bEfyr4zMaZl$nbUeg
z$BtYZ_fA`V^j-oKPymLu#b7a{M+s=F?}0l+i{(mQ=y{Sc_UIdjk;VtVTafzSRjK|Q
ztwn7<dQ(q4o|bj9(h4aF$HZ&aX$7|d+{qj}-+zNTKjvS$<$YDp%|E`WXB`t(+ch_M
zl%{$FY?u0@3_s31eAarFFz2n$)tYx9-1_3sEQ=`iwQPs%FtaeWM%ZppCkFOHg6gh#
z2vPID<()WO7`m=aDn}&RfRUPhOT>2}QQL%@wE}yd7gTLjan!ga67RBa{>G7m7+|1~
zh&n}C9&5}aeOBwa(`_Mhu^Y*H7pXL1phUd583Xr=`SAU(ubxnzO4PFp{l<Jd4e}S(
zlrE9sO@zc7)Y+75-iI_vr#?KyT)iSLt3bJz!*#|t37z}^hG|d0S52sCCGKHP8XH@$
zQAl^(|I0%G0PYRGWe_<5EY{R{9JaPwtL$y+3By&GjKaG>v<HuhC+j_p)eGpTv~C~V
zTA<nMa9{fM>8?6o8Esejp<#u`3p(qX;7a{}SA?=0cs1WHb)(WLuciD+gu0`Q_ki8m
z9w`u91uMy<LErP?a4yW=T5a~%R%d_E5&h(o9h_RwrhDKZUNl(`6eWdKDS?{?FTubW
z7fz<u*z3|?*65$8h`j;97CJoxb&TysvtXUEDHBQ7(JP_PmpnL>_4Z8A&AgYDKo0hc
zBQ~}~5<%4i>3A?Gj#F~}(+FJ3#v{LIJ{Xb@WKSuf9*`?8SywHL3hR9^;G&igs)NNk
zbX#=(B~P{?8omH|`^F|xb%`hcz3;#WdN+zIxLhZ8wM_vZB%pbe0+!mPsuY-}Kje$a
zi*?>@ZTM@p1&&t-2bsL<{W3+i{?Zu6ECCuoK0Agdobba~l3q-(CXYl(AWo@E-NI5j
z?};%`&J3KviWEAi$_&cVh7{sUZ$CJqyWkZ#Q2nLquR)u{+)x#WL@=!c60SwGCRW$i
zO=cQ1BPxvkMTdHx4(u_{4I5c1qwAva_+HmII{?Kka0#ys)*FG19hJ(acPNcFWa7Fj
z5{0*Fptfic=EniwZiP3if-$k1qsBz*3nYa*qW{j3YY+{+3`Ic}I#uZVpAz}o=&PKI
zmdv?1hZfU~9b8&0PBr;HE2|tmOvK|$(bdczl!nTxrpu^TV%vnP#%j{Q%2fxli&n~!
zHl8rRHUN#%6AA@XqnO>uSKJML7}Xg^*G(bsFa~QB-NOWKQy1`Olwu%{BUjZ=%u1+C
z%))ee;7^*u+)KZ>H>ur9aUh<ML07W<qcnbo%l#W78ABCM7p?dsiZ55%$f>HqVeNOh
z`t}UHxUM2I*>l3(%v0D~oadVs#>re?t&oKO_Xr4-mlrVn^;$8*HRSR;vjffrjexvl
z`(gM5pP`1u<h;YP27q6P&N;uKVh7f?MCZat-Ee2(;s_6t7QyX-1aMHY^i|sQ!~u)8
z66NoS504*G>M0^k#KV{ey_RkS{YVr<XCP7?v#}Ktaf$6kwDioN*f5I`0RHxwKf}K{
z{UO|i#X06cNtesHtoue<!lN+KKsrxEt>~wD{Qq>AKxZbhM)N&&ym<iMsqwuJPb00Y
z{jjBOV>MrO&r5i1A`)lnOTIrgf?ow*+1X829Pc!34nBtph8ruzZBpU=Yof;Ldq@jM
z680eIp=&y2PD;kV-s=|8=`J5omx<?_ScM+DN=Og@N;EclWRq3*<tp(b7*+-Lg2)u(
zflqY@GmcmG6){|!(yPe#V8J~pILE+<k79?@pIjq|`3;gpx4%E!g%TPoX_v&gOaPNX
zZ9JQ2u(Trd2pPte;u)dZ63bI&a*6wQ$qI&nOP1;{8$D@dw+-zM6I*CocQ(Y8HF8tL
z=F-F8U_ibU5MdKR2EjXt?o&K#bM~|7fsRlQ-kjq$V{ko>K=J_4q?yz~O{KyQl@hQi
z!|C#zpnu-h16CBr8vW)oCawA_p67p(9R!HUcx=h3))quE=lVVF>vc+oeTO|Y*b~B=
zB>%1R{QWY)Y&fSvj`Qy~2&5s9-4*6>5<gC%02}_g+VD)D4@MYKoS3AMWkQs%Nh#iB
z3i_R})NuSEPx+rql@}KKqZOq1nlXn`&TS&se3!vyY4Sjly>K%?o$@HnH|G_SmoZTw
z^?2eXtn93Jt=Isu*i5<T1O1<*z=xyUWE|I=eAche_GYJ$dSvJkIX3kbO@BxJ_+eWt
zOYJv)B}T`Q+kuAjkd<rV0XlI7?{4Nqw{4~1X5hyxfN;2XpS5`y3wp};dZ{|JNxoer
zt;2!LZ>qmDLR=M5fi~Oi(s%Xc-T$@rB8ij7Mg}Z+$iCa@+Mu&6$i61&bFGZzsX4lS
z01ognI4M_Q@ru3;^*c>cpa&lv(XV}nOjdkX14G&9GUP0l;P6CKEDMZ%*y?rS0)sfY
z?Oc=t2553oa&kY}GC%G5vY~irqL05qHgU*K9MDJSK3S2)ib#p7gMb{PpDTak5?a&m
z59K+&14eB|;E9UH6L3y8kg3gPDoUiwZ7gsMi<Jod)0u<p@xl8N8$oo=t2o7a-?dME
zitN9QJ@yTV_;lU$()+QaS|Xb?;%eR6?d7)fdTWO7nC~^q4Lw_F)>fGj`tJat80n;g
zE+W#6rW>~|iRk}SxY6sN?3G6!6wiFRk^KzII|NIq1wCza?w{(M&LljtS~k_|4F%x3
ziZ3z2DnJdwftPUo#H%oo^Lb^XUya)42#%EBKc#VrH*GwRZk&(60u}b!Wk3(y=KlYl
z{+NVmKevT#-Z0a4qQ#EzKB@DdCCe+B@Q@#X@0{<gH;<RtD<(4(x_(tcRW_*rZV%77
z!6KzPLy0_z*H=x{A&ZSP<U{JX3)w?VJO8JT^t**IXmeai(IR1g-5FhUA4N!L=-Ne^
z2!OmD=klOPag^FWMkbm12j2mO2SWg6ulxHQY+LebCB7q;<c5gtm07JZekR^|W6ulQ
z{Nxg!`Ty;zbabM9dl$;)EzpXc<Cs>|6Pr@?{UiGI$s__<`Q_3kOWqwcTLa#>H+HFP
zu(~oCN+WVmpSFn+;bR#LS2$w2il1^0>AGnGsYQ<Pdx=M*9qbBxj_~q$N5#Ky4ugO^
ziO|(D8A##ad{l8=0b+m`k<6`698*cWtoCvgVG1-z!@|1YR)Ek8={IqZDVa6W{WI?y
z$;+Qo%ii^pi)psb=|{@TzF*1Ae````AnN}&K4|ce7h9T?3F!9$n|`D%m|Q>+!v<fO
zq%6z@fcAcDDxh3f*J8hgtbaL0IyM5mHieMb)8W_NB}1#kIPzAm#LY3j-yr!+#eN|6
zTv@QP%`b2&2&74m$eRhsDl*@{0w|gZWk@k*dC{5z5R0r|&HLV^NF4;~0R#zuy)(g!
zIKQysp)sS#Hv|mQ(*pr952j(e@KcE<myK8=hW~rTtzaZ$$S0h^d<#mkH+2J+F`F#J
z>{L<5Ys5_#7TT$CkCIkx!Z`gwU`f}DWxmR?y&9kxL7M(C2y=kkvRvAGM5VU0`|CMM
zJBa_(#C6`G4!&dymf@OUaM+wo>Di;nry-p=R_04<GLte%yHOST+k3GP=mxA2IsR{D
z5=@g%o=alejUiASZ9&+@+WBE=tg1eslRcnSCvs0~zn5k;;fMn9CF;4x;u!X>@CN*1
zUi)90*=GE|-)7KeW@b%To(CPr_bDIjS&J+WpTe?Z_Conov|CQo$fgMV>0o7^_Nmmu
zTivh!+Z|%nDvXs<t)wK${g(+4kY+;UO8LHMB0t4FGkCv=a$K@I&vM9n-uu`Y-brW2
z#6N;tURKh>&onS0SOl$lxN1)86dU+yq}A(vM{}Hm2*Bk;asx5-jCllDj&Nj0gxm#b
zfJ#dhiWoYTX-EA0AggT3`rw+PihB%e7k5m2ze%N+PE=hW!w=nDcp~^kOwKM*u*K*9
ztL!wY|7C(&k{t!KBuZ>j`@RsnEwknv$-T=q|2x-YPX;S{{4jaLSrSPui2YljST!k)
z^))Q^<rARokOn-VeJ>lh45u+zdddtu?8Mdz7Jsb>jlR6+mFM5vaw9?WaYo?2qA(zl
zQpQC+;d<%VXm9|mfaU2QeEr(}Ln=V)27BdQ(DnO_9CJ1o!&G+7>6dOv_819bd9>C@
z=@_+pUj|(Ii2av{{@>Rq63d8}89$6OFX{oC+lsk7%d4)4Wi%C>dOA+3Om36!x$AF9
zK5u5cp34+2)rjHT-rx>KJ}{<-F3P)8etiov44@ApaKVAwwY|>X&ARncRH9djWy6Yk
z9BU!uw+FMgd>#$QUKpKgH=c1(S#7s$m}68X=lV$Q>_B|!PmazHMcuyUL1#acUV~>t
ziT>1^RbV0?YT9(q5FVW?U1a^_VQuA)7`wMi*%Aknhar|g&Y}#M*@(-FU~<*6R()5d
zs^9;6=)_7CH-s{Jrd3b$;C3&K@ElsD+$e16e~3M9hTP46DyiTzGv2WK#x2E!NTew+
z$=!D_1s{WHIC-=`TdYd@umhcsIQK7dBS5VHZIzIjpqHsw2HGN2S7)56B!)q>OSx7>
ze637H#jpf32w|fmuEAa_lh6r!qyPaMW9!%K6F#V|i>_v(x`{ubU!^UhoxDj)<a|C-
zGjHZE>MaT@@wFP5U7Aat1!<ecPe-tTNY(|bYLa&uIw^-%Lcd6LJF83b0~?;$_y6~9
zl#!QytDecX4@=X}`T^E5)M{)PO1{iBDf>cGiDVCVa7Gjl0wYLg{;?*0IppuHl#@0=
z9>QYN;R(ePNRqozNd?QB>}nUJ+>)+!g8T>EDGfW1H>1}*G=3uzn~i4zX2(l4JR2Tc
z-x&jB_SVDkD#tHc)*I?iMk)Po-0rS(&#>(c&T|beh6KVKzP;&hQ(~mO{SW?FFbu|q
za5ZpM%V~k>`w{1n_2Vt`l(N~SNe}uc!Z`VclJq{Ha7vA+%7fW5d(m{cO&cnNTl_?+
zux_&~R$KvEygn5N+67tm76rq4YK{cC4Hf4+8Rr<{H)_^vFnrAf-)d13*$Ca7S7=-@
z)Cr{Bax59r47^sYOlH|RCEynh|L3vuFCHPn#^ZWC4i@>xPpn{4y1!>Xr>~8et1X_K
z4<IPNMqf=G>{<eScq-76DnI)C>*m&kXz!47>8$rZgKkw85~wJjr|!QM5QCAz1p$=`
z>r=<%u6h4We`K2bCz4P_tBMI-7Haa9aEC+(Zvpy(I4cM=LFR!*nv2!4ofG=k<V|G~
zsopT=wtM%F^O9@@Xm7E*(|jW9QHS0CsAJ@#sNUM2y2nf33tADVW-i@kt;LhJ*-5@8
znBA`N-F0?w6z8!_EY)9XB7s7({dlnBds0U}{^DapUwu7%?1|GCxGAyQRb8$<j7t(p
zG8MWe07ou!+Tc3L=SHP-uCx~A(b=Skx^buRX5W7NGC<?zg~)tGFYJKjJvyd^Y9u7-
z*7b3oZ?10vBb04JMIsdEk$>&eZ=P71EYbL52Wp?kjl#%CMA$|yt%8^q!vkXy(28h;
z665SxCUZ?{Y@%2>|LLJmzC%^3r4cFA$BsV~ACZ<sU|#_3<tSijloWe!U-%FaH|+|g
zin_sKpI+Nk)_A_}Sw?r^`;(k0Xweyzb`Vd00qXzDGX_>f0f&lz-Ta4-5e=F31Y_rA
z4OLn1WoPu<@||ca_k7&u>tx>YhCC?rZXhl>3`=*C#`)KkHw}>!?uh8_jcBG^q(8&s
zvnx=>Q$-Q}7M~pBzu?k+QyJN1*{!%m52iB?sZedC18Tt!A3R}XoTn;tC;q{-gk!PN
zzU%USy}aTMJtFN7=(5J{Em)W8WiX~q{nNYkO=AHYD{y@<BR9c^X@%zqjnGa<ftBu|
zy`v2v!2ENfZV2o!?67;y7#YC4DRbr@j>_I7oGWoriysF0@9=8dNfZ(i(57KiGTMe6
z2Io-*A@8ZZ-{57?oLo?gg$NSXtm@#YtaUR|={_Kk7|ehrH9NkvoQ->=mfF5Y>9(5I
zPf@%r6y#x&#$YuE29If<-HEM@wt0JTar5AtBb7_tvh=lyb?Zijj|MIM!S;zDHtH=%
zWp|16CQz?B+gS&)(M-bxZBi)-mCfWNG}gckG(PxTuv?4@dl>GFf))n)JyFv_L9_<c
z=%F~HC4-#C2mOi1k8dU3#F9jx0a$#|jW!oF<N$9|H0psr%h1^l)M9Y@xlYHzBabC{
z)jXv$dN7yud?~G5$m)|X?Nz8KdNbge$cdTBWL~fbcYAilr24R4bLzaNA$3Ofa7=bW
z6gp5|$CEMotH4d8(yS=}MDPNivGB}*2FSGdv_{h_t01{}NoK>GtsaD@tcr9zT{s$t
zwsq_?2W<aK>#=#&^Gr@=?X7(1AS(5Fe#riX14p>Dc!4YxAyDIaXL|(L@)9f4Hiq;n
z3T!^{geb-<Oaae&q>)m*A;fcFrV&I?Q$Ah<^4TWTJoOJL4fnzJ{2CB*G@R3PvGCHI
zvyq?Q0`LPXeE(@&L3Q2q6>1qn0{&;3Uj(Uuj4Cvpy|wBbG~VwQo_3(mc?C)z=uC@o
zby{f1n&nFRm^nTzhHNc3!?^tXRrl(HPzaoPKeq$toua)LZbr^u)Jz*5$sSY`-xlA_
z3_8D5o|S?#l=v@<j;2B5=lnNw_w6PYjfaetTjC@<niY@g0obeKV8tO7tH8?j)r6j;
z$yx9S-I%$7w-FgV%2WQJwB9{%YSBAg<MpF~gHbJ%Bw~0Lc!<Af%B23>VZrPtUas{k
zFD&q@TlP+Q`5zHcvFZx7wYX59;J&TB8zH-KCICb1pPP9m8h<|I6mg%M<_8pP(=}QD
zDQmmiATA;mDcwSg*^i%Jjms<rajXOY8J0<H++0XI4&g!F_-yrK9G4=f4TG7d#~rPh
zJ1Po~G8#H6g>(rcE@6U{*l@L|6xFr9+gqF&ArfN#qBW}yw<uMfNjh$Q|DpRN*0_ZP
zsx^<qd#WFs@tvr}5;)6ztgXMaBH(O07J)|twhibmlWIG%L2Hswa#Hc|6Ve*807~Rs
zbtWe<LPhPtpg)<-+BHwY4=ZtcD*gBGtlz3C&SZ;3G$C5WrA=e8c4I+jc__okVtZ0E
z&Ww^vubV#QDlJn>b9h<q?kZV5M;ot39tC2*s06yJw!@=l`yB?T-rG>Mz9g1-${&_x
z?TiPsFQLpg>>8xCUY$ri2+r`&8Fr3!){Gx7+^M>5M@G^)MtPHE&ZhwdJn%d37D$2+
zEs(1JO~}}c8SC0mXt{c>_2lz2N_(xmT|*lZZ&sOV_uRTk&EsZJwc~EP!now5W@1UQ
zg<-^<n4;#D{H^JVs3Rt0*IPd!0krA&jKP=IrHvV(w8_FWetCXhd3Og=X69_&*erQT
zRwE+HfrRD3O}Q`c12JQx_v9+@P&GAm{a0vcdSDGP(*?zADr%HHY*~mgR<dsvV8=#*
z9QT>Z@Vt2-PgWTmG}^>8n`cpZDFoL^VGVc-#O8wLY&lo*R6HwsH-9)87`4ImLpSbO
z*-*~%T>tvs=jE=1i!fR@2?PaS1_q!41e@eMb>5$2`SwQQ+XTAvQr-S{g-S4i3Pr+s
z`Xe&=Ef;bjg}=KuR2_8!PKdHDXzTb}Vpjth^1s#>qt-Ps1VYN>qK+Xa5;Gg5t3BCY
zY}7OaRB%bVX4@qc$Dc;J5CHG!x_7mEBrSx+Q<oW95#g7~nfLo)6+Z~YygzRAE_?tA
znhfs82q5zQQyfg}G&ik;@?3-XuahsHO%R8%wBx~l8&f1LXBAQOc@+}bQipBWQ0smQ
zul;X7XpU`V^>K%v7}BBdy~&SS+qGe?8Hv1a3UK=!Q8zSp_8J3^SBc4^9HTs~;k9N&
z9G`M3J?R74qqPeW`br7?3Qmh{t*kkq@6FjQ0qB~Pp7wSF5U%RV>Ncj^t^iWFe0cvi
zJM(iN0kY8oUkCuR<JRc?;r_g5?s1<O{YZ|2`5R2{{oXbq#D*MpVAUtsa7*w~?7zbk
zULPPp-scf<o=EAzfZp8KO7Spa4S@Kn2ueIwe}$a$&3EWy`jP>W7U3b<YHgU@B!33O
zO~d~MI+MvK7{nnXSZQwxM(>E7JMU`CG9Mag@ilL;_(~3QxfM<wblzhQ6r^cgrqW)s
zTjJH|zLs3R$FawDX=_GHtw~?;u!3=yahtw3a_7mR=aKIl-^7!E*I@7^{ro-RlS8eA
zCsCUs$~KDAzY4o%Tq_rLyiPihZB%S3W<XP;53KT^EQ$ZZG4xe>G%xXc?~Cddx3~oI
zzsUKi8<!*PXXpmy5^{lYHU4Wac0PJ}?&?dkTs6qVF&^pgT#{aUIrV$^>d8i|^f8h{
zjkeJH{1*$@&Brm2CWU$?ikoO^$Hq&lCCgsFpS@(punfK`8S?K4MH<w!dW7pdPAD@T
zma}2)3$<y1HtRipFTW`2GCBOM#F#)Xaa}Oaq?+U9R3jWd_-^3w0TaKP!lu6HKXDhO
z;#N7G;^|O?FNj?{=R2SGWif9?n)zga=QYdTOpQC+dCpS?KT~I`#iC$h(Xpxf6Gr&#
z)1PZrk&afD=O=AoMJknZed63nkGB|GN%M6#Qg0%{&iFxmT2U9Sv$R!=I~{5Jm5M9~
zwuhYWGNgfw6N}GpBpZ3^jeg>gA3n&qFys(WDM5Z#!iJq9T>s%ze$IWe$%{tGzx@vh
z;q^I`5c19H?`W>nR8+6qOdZ#99-|BEOF<X9H7t1)P|t5lBXBf`KSWXH7S^x%tRx}`
zz~RR9at#<HR}F*qEq@2M{Y-_nYJA3|;N^A><=7AGEk|ApS-)z-5roAa`nVPv>!m#^
zBr_}Y4i?Hf%&#Z-X((Y32?qlP8Djx8j9zqd{sP0rN>h$A=c!j5o|~`sb|H1xjK#yP
zbl;;O10Gt)*3pL+GKg(r5VEpq+Kz`wKkq$3T+)EzosQ&Q9@M%qLESPspE*c?-I!M5
zr{<_jADxO!f=-pAi7K;i<SXm9wsjmd;<xa&-urpP1=|y*U6A0t`YF23P<*B6K$$Pb
zIIXrsp=8O_!iMeT;Vpk&5Sg#O_|35S3vEmo!OM~_ljtGb>n}a!TEtGzb`{H!=Mnj9
z>NT@>vj0K6hB7PoD`j%#7P&PxEOse?zfl@9+VtPO&q5zuO|ssvzO2gR+-CQMCH4{j
zC{<q!Lti(AKH^-f{?+qP7tzgv3cE*bf=}N?w=y8#AteeXUYi8WIpdH3rUIaLk>aRj
z<YgYtNjkYTahc=@7=BD*T+Zb$ui&;Jvjae#7Rl-Spf9*0+nadP@1IfGa?HZ@<kXqV
zX#ep*?v3wf%vTRLM;>US#}rQ>5-#5OODiGSZd`cEZ%TlLr;|gS(y6@2E$}^VI!H~4
zi!a0K@stZ;@N3xu+HXe>$k!T^b|Pjd9B<UbQttz!iP&!}@2S>{K_h4Rw@FH`XD-Dn
zin?*RmPI2oD~xNp(#G@`%~pg$A_O}xStwa$&;?S%`l7DF9GGiM``FCIT&KUsqQQpX
zb-sbMlqU27L~7?TbigcV;r{c(;}XDoc+N~jsB1%Xxm8rt{FyA>rIM@IuB<Y6#B@ew
zd$IkM*n&6<=KzP8_F?7QCpcrt_VtbO8%lHww6N`6HrhLOH-6_G>hSZNh7gW~-?=PR
z4Nu@#2-<Ywcb0VoKh9Gc@De$%VnDy%;wPgh`m+(tXelgG6+gBIXet;vd4?5R(r~Fd
z86V&t`3me+B1V?HCL@yS+;d*Z!;e>xk6Zy9<zFLvJqJ(;G=EQi+*vs!6~u^dvmO@a
zAQ|{NBpG^yuCVd8J4F(w$7$ky>cfVtUy`td8Zal9WED5;7HQN{9DmJvMCX}toOkp#
zrMycL9`<DN_*A+I%wAQJ8Cbu@5P1Rbwo){HrjR{1D6}b4cYiUyB7r_tZx0U1Q$&f&
zD^sUjrQop0wuKV(nIXNY8m5wL_s)GS*omRtGb0m|tXVRQag(tJa87>@<ri)jQ2+Ea
z(s%S(Pz@UxS49U^uTJzmKP6s)72W5|PBPXB_ReaLKdd3g7kEtUyfe7R>s5)IDo0EG
zYHakHJnb{r@ZroFykK`&Xt7_#ix<K!x(h?4u-7Fu$>+1#jF~CTF!4upyP+5aBS{IU
zqIL<^1ffqH7lyNh*VEMbo?|-STpZ+AHXH9kO2#-YLcz;ZK6dl~G;&M>=;=>S-hk*@
zVzF)C(b$a@3r343oj1fjEzehQqJv?HS`Xj<+Z&O;7ILud3bw)tD8|}WN<-|7ic@)-
z2aHSkFG=LKn_IjdguO+Jhyxx^Ic3O_@xpEV?qB^X1dDB)1;c$HfH*TBmR|BkW6j!8
z$FQ$;o54QG5V!@i^?qQ;jeWVF`n8#hVM6xOsgJO%G|20q^`hCM+m8t$ZRE^XE%-^0
zh*5Sfh%cU}i3Gy+ge7_bLa2PaUpp$Bof2KmFTh|}Wx)${b?S8dUffoRPu+^4g}+&0
zsrBepZ`yYZr9To(#P9qp&y<qacot;$M_x#h0#w$g2O*1VEVv!}^C9q)4eZksiostF
zQ>dBxoxwDyWq&Fw`{@eiDC3#FGbM4DlHiKk9(96h75!(&F_w@jIsYwlYPTxKom|9A
ziGg0ZRJ+B|iSrN1i+j0tHwOq71f<w(W<>omyf%(!#{GM*T$`H0>#YgunKq4*ppR~E
z8Ug|OP8Bu1^qkz5%!`0Z9z=!`;1aVMyxy)<0e4jdwCp18P0H_p7ly9%J5N0%?bwTu
zli6vmICidR&FT7d`?&(jk1?K`0V)cYRs-7SqSO6(DRV*Wzxm1%7GK?Uf^GShOoXiC
zLqbWYV9h`($>$4d^O^oR8#jChak|AG-{bi)x?^00H8Ofzp0RU0>EYcB&YTjl;4Kbm
zEkBgbQ2dL+?u9M}c|HoJw1fZzm~d;1Nbb0REoF8-F_(oFCwIBpTM?qy=#f^k=hL?P
z5(=+a?&rl$C7p<ous{T{m8;mYgDJF<yn`wEAJ4K8pzq!ckthx`-|I5am^F4&MfV;f
z2aizM!pO0&U1~VWEctKlD!wRy7`Mn^M*6uVVWx|eZWV)jLwdWCt7NJ2(V8w=WD*&8
z>AO{O<`N^mZW{-n5k}^F6voxKawo>3<3E&6PJyb~wV<&^NFbQUcugB3_g;ovE@NNh
zo(kH{L$W(Op5DG?QPuniS~dOizKeUmN0?`bzrkXgb=30FM>^kWk(XIou6-Ips^v2S
zI9uaH`IO#3M;`)Xwe)+SfV?}u%j(KR-9!=DUy>5G8qwa#r*3!0>3BK0Y-=4iDl3l>
zj)f=#hqA(kwwVnMR=LInb{E>hy^qCm`Y!18<`CCH*d98*9KK|ivJkDdCXyz-<HkQw
zG<GylTSa>cgt`RAbq|>XS@OtT0?)4EG9J!*(Mj7OrFEL?yd_RFexYQ(+?u})-P#YU
z_ddQ@P2^mL+s%=XfKEK&K*qz&+2JOJ5LD&#+Q5{e&`*OS66_q*Dm+rH+pEC5<=!wh
zhGt7a|MESrv;&y3>uiIY;A{xnxz{Jn!Vnhrt6IyqSbfW+5+70NZ}(}iHONYPT$K1?
z`C(uN{#wNO_tu#_D#<aMbu-sU%Ppq)_=h&F-H&(Q_!y&GImg1xJ7=iWb#eYepgrp|
z8-RLXntiWHf!WQaj(+NVF_H~2fHVGOrh%PR?PZ<j__>^0rV1cu+>h^-Q{jAI&m;YB
zwpGl;`h1-#lBzHV|Hz+*uWr`;%eNkv(#(mh$+b8P-7e#3&T79tQ@7C5YS@7LnWRtg
zFz;tIdnn(Z3^up7Rv2vXzJwH<tYJukSYhOSbcK;j{(N-}_aE;Pg~xAmDe1%^;m8C(
zo()HjZ&cTh@ECC!wubz+j5f$)tkgndgemd(NfzM@8a%Xl6?kNukvvP#_?~)Z;R5><
zrCuUt9a>RW5A3wNKL;VXefRlgVO$i_2#acZJ@o<oCmtW|h71iELR#MOSjt*j1lGjV
zT9WKli$JV?2)--S{W-2(czR&>A}IBqM3)KtN;4+S;OrASzAj6?+AMk*q<9{VQI^-B
z&H^P$VphxH#r<XQ<GewY!M~d;==mTi4wBY^*$E6k4XP~6ohK`{(=M1a=Pn4u=2Dg+
z1P{e&We@3o`Q-swQCVg8W<fN;dQ=SKD|-KvfEQumm@-XpE(j<f;I>*4S)_ql;$Pzn
z@2^oV5!h;xhCAaBZmE-zQ7;ZtE7^qO;7x*x+8_EA8wqeUufD5Jj$5r+3Uc(DFY7$W
zUT(YD_j-A6Cz&6FE#3&L78`K!>ptO;@I+0?Z0cU^e}PuMBf}oAQ9*vi+?60?)*Zl~
z*vH4dWkZN@)E?kr_LJkZ@4Vf6-C#fC*)5~1XIA#nORr^FyOUil3yP?IK_RI|f;Ue4
ztYulN-NZ_OkeLNsRc~3@Z>43nZM8CWE~$l|CK!A$81OGfN<7tF62=`0ef0dvzXhJp
z)mQ-!K!OO@Hv5_@2cx$Z@6XF1cSxSpnSanb+V2$e-64OL&?PL2k!qD@g+ze36#ip@
z@);~vKo_5?SCH1fkjs~tJN#%I>{f|w5Om&m;i-?(F=zR;nDW|+@#&)7d_p}$QxOX{
zq<e^Pd5DLJ6=XKk$hZyED%-A|!4%AkE#&X5kpHn={s92bcTXBLeFFPH4F5`;&+s4b
z>b;46?MhclZ^Ft-$vLMlYIblQm!7I{hV5<Duik##UgOYhh7OF#z5S?&1zWggYrQ*A
z<6ex4V6xXtyUc#A7;5eN3Gzif{46L<0T4CA^JG3~<aigGZ6T{1zRO;36}dCR>N$8>
ziAlb0%2y&ywD(}4KhT^jA%n1Q1@f!<Mam3xo^TBxX<BhwF;|b@N_WjCTEFDq-;=u6
zML=oB1b{9q>Fl~}Yjqbr<*$_r(L9l8KTGq2oqf{6aR}qyY32S-0K9w`q_TiJ$F{7z
z<8y)E*Z~>+(+{uDb~XnE$vAbG-bo{18-SXoSNkr8(|*WMi$<)na%am>u$TaR8#csU
zEkV{wtvwz|4Q|oEE6?_YRSsS5LO0ViBb=9aarYX((!+fWlcC$>cF7KfD5Y6nE*K5F
zV|n@Bid9IyT?(f{U_avr<C;Qks2GyCyVzs7D`~n|S%XRNJ%rdcfn$L3H3rz3@j^%S
za6ka4<aw7cC_o*gy#`Y#m=OJn0JFhe89TnAnZ0H`?>+eQJ@XK5%R38<JT{vLr_{*g
z?o|+Say{&DsU{(1+`kpxCoLfD^$ei>(h0$>7EQy3y|yZh%B5k!ZgJ6nr}3P@=sfUv
zOpnzTv<RfVRMvVZWxyM$)?MGUAbN&RZbnHH?S{eMY4>GnoSqere{5R&d1O9#Bo|Aw
zR{fDvF}+%Y2I={a+Rr#Un&zA`{O12-X%;WR>B%v_%UHms2AqBX=k4RJWy$ddLX58-
z0#IlV&X^Su<9Zi1UwMtI-{MKdQWSRkc<)Sp<E`HA>9uiP4Prf-u*`Jh3Ewn+e6!ks
z$gtZ!-Dc62+l%GsC@TKau{{4}of0~ovbz?W2|AxtauBGfb%&ShTw@I$FP@l*^)1A?
zYeP%inN2=l;0~9+PEsuzW#z^e5*@^%C!$S49obS_MbbdNkMm#>Yw|)ZD*h-Jhck6Y
zgEPdrNe0Y@vG5^HTZKJo52xG^=gwPax16}f&m<mKALt6LF2^vwh(Y~DqfL*{+(2h2
z7*2AlqTf7lz+@t_;3?ZL%j8)Wr_L}iEmQw=9;nAW^BgW>(ArM)te^G(#-*y4WBpZ!
zq!?`1dqx{`8XE^fDL?5bpdeN_6oO%#y2)6h`gP#buy)0(vDE476N%oPrW-u&@-i(@
z{A4hYHs@FLuimGZY8GfAC^s59kqKx_WNDgt{D=gqsL>k51?Wo8=A@>y9I<)l&WvAM
zMt;=kpwP`w3;ixB-HG!V_L~NO3~y3pLs0LV%T#4OA+9ivQ4B`43I=NnFT^9X>w4sx
znyW<A;Ana$76#`%ouUMrtpdy_Sge;)xMto<_Zk%UW39@z<~MatwLiI5sHJZhTn2$l
za!-*L-5*R5CJ3bymQ_MA9n_s~csj1%*Cp4PlU`stq?3I56uA@4Mhe5V#-yM~(6K}(
z{Yqkk);WML7`6rpiTgsTEJN}46oW<YCP4=Rg+axwJLI+N?1C((Z%;<`KI*bI0U?#i
z@6<zLF7r3Jvw!52A4#m+Mj$NwWkAsCOzaY0RQSMn*^63kzzcQRruJCdVUGAIN!jzh
zSiw=8&geSDrv%HG#koJc?b2Ddo6ai}6UGuMKL=tlI7{ngeH_hnT)Gl-{4Q@1e6?O3
zgQcXw1g|&=qpItS_I;-178K2?U@;vdkO$UeKwo6yc>~rB1~E(7nJH&%8fCWFD9{L2
zO&z@FY4Qr;;5Fs@u_<A+dBT{s)sJkA`qP(}6p9pMVavgZ<b;;TpEfse%}l;iNHSz|
zqB+H9E9I)h8)4_xU0I{i#UVQv|JK(?I3YfauiG`<aT%IpBJ=vR2A*%U{!Vj!!}XV)
zARGK{A~&i_e*aElo=r8LfXlz%@KFGeLC)<aH1#ot%&qqe1{cyNJQH=qf2BiSZl7e^
zqL_wuc1WX$O_GqM3X<O1V$XzXmnQP+1r0E@srw?`N!wI4>7){l86#-~DGs+H%dJFi
zSZ%}3($EDbuZE)~qaGlK<w4HOYh(ENU^Cryicw#)cERC<^!hRr!t^uWm|#d%a3ARm
z1z79RRxj1k^pVZplx4M=?;y0g(+(D=<-<Ur^wyaE<Tqs2fu)3}dOP`K!M>*Ag`;5q
z+&&&G{HqLK5WnbK$tJb~yraXA+oQgH?}W80N1mq?ZqpuzZw(yB6cGU1%G00Lqm%}A
za=Rp*+W6lf$ERy-&4cMzF-w$>Q*?9-OP~_8yjCC&ColauR)&qjZ-H!%PSn*{O-a#2
zDXSFBz$yYkAf8zQ*45F8?0MK#)($qRux?nbT{$QC)Jia5kHUO%zE*4HN=jr$JCZvq
zeo~cpM4G`r%03oHj?dJ$4+LEVeOMIK_S~4`u6IsE#gZ=S9n+egeZpq|KYNc2o_+tk
z@{FX@7ujJL*A3`=g}fuwV-j3jW<%t}EpzwqCdFy0tmX<Up%wy9G#wm^UcAammr&H%
za#(x#mHq*-=<Y98xiW<x1$s~?lt_6+Pf>BC>TTnqBMV}UUbiJBfUX3Qkp@uZjkj!U
zM@qbMH26A}C4hqLK2lxdfH6$mm4~x@RN_vJE8q*Dsc$f7!n1KYjMvHX(}O~gn4{F5
zJzj*xp}byu5k#^cu|J1JCI{DC%&OUzf2kiT_Pw#{uUYL|>-#7-7#@XZx1;@6UHV5d
z)s{5TvAv>#{FO$Y$YN(L(W#W*h+jG-zeONI&?z_xyBb6soDg2B=!bL)3Dx@II^5>X
zbth(@Hfky%@U%C%5W+$rRmw-80KMjx$^V${6{Y1ZaDhm?^;$AExNxcn7fC-FZqBfH
z?j4*dLMS!8Xfps1L`o=kwK%by^file$Z!C>EYMQc4jatO7E}}aA_6av#H_ar2Fomn
zKrB2wTESj^YYZoHXZzG%aW%KGQH?G7wFG@lGJT<1Cu?!<gN1qOs#9w-n_hg0z)_h`
z%+D-Lf;O@|Atr~xf2u!>2e~{VhH9;HSr~&wZ!rsq)>1J_Q|O5jJS`$I?<!m{MOSYm
zXBiTef^yX3Wt~WdP-}%N(LbR`wkmR<Kl_jhuvs#y%sh~1TrSKiqU*#h7V2r2tuAyb
zeu@_-mLWsa%eEyi>Su(4CoT>0JD1xD^BYR5#cDQco~9ZRKj@Lfd4!!J?L~-&o`|yI
z-vK*UL)%$kl+2Zn*JP^^sW*r8$@@E`QDELL7A?414|uJfotTu3bQFYH<BA}_j0Hu|
zpGgIP7Qh|*-F0UF!zzJxl9zinmDTRno0!7Q*m`qCszeSURy!qP?o`5ScLn$MHhpMy
zAEc>r`KSJ3Oef>9fOE^6l5HPq1pd96K{&bOvvDEkmYfeR1f9@voq(7tlR<Y7NJ{aT
z;ve8qHZe!GmS9sj7&PjsGbBcb*J=n%9O0-VO*5(mZKZ{l{L(6$5vR+s)rZ|wx;FRi
zSZ_eekmQBF4Iyc`T8K*e*ia|t_?CfHGP|}>Md+M-oF|FiuyF5Ehv`CzI_2l+r&v_q
zE1pMkSHZo@_{@)AX_~SJHJ_%gTjo4@?YY-nzYjsP2{;_pC%R*zMLs{2mc6=6Pl==r
zP@=g?iY4X1YGM48`TTWc=hnd+8Xk|26{`ORcriv<nE7FeVL_p8DS7u0;9okL;AH*p
zjrz84N@-n>TPcxriu-B6T*O&ib5tY+b}a?8+Xbv3x=UB7k>nm@_QrtjfN$aukFH)$
zmVqp1asyr(O^BSFHBwJc)hxZ(Oz`pSf?e^pn46^|ZC-Ks7%N=0RgvV1E9<oM;-F5d
z-e=-GykV*?=&hR}q9*}tMUJ@5H6C@!+s3wHIy?kTwAv(+;l)Ks1XtKjIspa?mWzYp
zCF!xsCw>*t2pM}$W>S0wB*@ALjq(IXN=2s0``6!Y*AC}IDnlC?yIurbQ2ZyP4+JMg
z-hFpaDG~dEFix98_Np3UO+Z)thDBK=;gr*Rj1T#6D6*0D_4K|So58>}HLbJiTuu+?
z=bX-t`_wFJI7&Zu=-0LLyb#GU5?U3BPKoJjOTNadezK|W^V4<deohfxKPj*BQ3&`b
zS7R|rXUw|z_xfXnZ1t1ij1aXyD_QX+))~>qy(iE5Qx~-t@dz2EdA<bCLqqn7=37=x
zPKKFf!c-84IY$PynNd<~X(9{*&vuE5H-+&k^D#ntd4d!zzo*S&4%~3i!JNLmiE1WV
zVB5^Udu!(Nlc*Bk`s$d4)AWXVk34hf3c?QOR$=l%jiWbYwXaGvffpGK<y&b#Ew`xp
z`J#_5X#{divFHhE*FSD9h|&-b&NMz<J<}?ecbh{>a8^zjn$McfJblNE*9f28WkY);
z^=r|ggp~uyv909BZ}D=k-d>1hL*7j244d1q;<fW9)<Y6f3@YSf!pP9Wn{M$Ue@2|x
zuZOhKe${wCSG{r2vkvT2ZJ)aV=E4UCzGvjmR`z%|nIl5kK(*zFEtT<wRB}>_b1cK4
zi|X)F_vf2ysnl244s=$o9B<w*dqqifVI>i}s?k+HB}Kq-Yy+@Gk&miH327enr*b7=
zfN!#U4o>G#e9v`&hNbWJ&H_{G{*S4z*8wm~qV4jSwqVlH&XxVWFZLS~S)ZOB(aiJo
zZ2FY=EH2#PIpwP^H}_K&Pjj5%33KnG7^qgMR_Hj34NUQ|b%BebX3l47ubFZZ75pa{
zh0S=HBexLJt_HbM7!LcBD?`_#XgQ`ahCD^4u^6R=)Bk><R3B97=7r_V#rsp#aF!O{
zj7?Vkjpp+Q-?-YkjPK_O4V`du-%N+Mt8G)~k4Yqcf80x2+m{Rqoka`UqB5)wNSKig
zy3Tp`0dtl?Hn8y#=<2T%3snEXWegnv;^?o$Z@^xWy(7!gN54O3Q@P;;i}PQ6pM73B
zIsw@h-M?p5A-S)FmH*;KOIfSbyb_=j3FWMb+j%`(wddlNzuoRz%F?DV&PI!kFdUFc
z14(fRo1)otBNJgP?3J3a%Ett7<>E}=ev?Y4Vv32BP!{(J;3%9(vvy<SzQ->Uq8GxR
zbC&gD+oiB=VQlIKUR5w)ap16W7S?u|(0e^tP$jH*$^D^<@>E38^6S}IYMaXT!}^y?
z&C%QOazW-dFRxebG7*O-<|e8}l~1-=q*xvch^BSOE~UH$O1b?d(K(pYa#xS%X71<h
zx$=TPcSYV?OSt3xS^8_=#M}ttMJ`zoa3>L&G-;)WKdM*79B+D6XVZFbp0fEDF%TN%
zmh|`j0Z<_5%-^DqrfKDFR(h4hyU#WBPX7McD;{A!-4A0k^tY(~%~^w7dBr|LU-U|`
zy4&YdDoJf@Sez+cG6c(H@Ea9N&*&9?)$_qc6PSC`Iz<@}YsM?tD_sP$6KG@HvD2~f
zuJkxOC*aXwTT_}A4_m^>#f%Ft08%$Tqqc{wqawxkO2^iaDLPeH8I`;4B6kX=L2iC@
z?eo>kb)T2(S#k4=f2}cRfZu(BI`R=hHDw#df}q1;((0OP{b%6|e8CfipcWz|#1>au
z`9=C7gKju%{^g6Ici*z@ruNN8;=5=1Y2a-ZKb;?&TMRwR>gQf8ulg9=5<E^B(pFOC
za^@4;#-|-%p1SMf53$(X-bsM8k4zZJ20j0VdHPt*67wu$>}8{dWea8UPm&<FDWiZt
zO_sN$g%W{F)h!DrK26Klv}Rdhun4i;*ERWin7lc#GW#JK=Zo9-k>8Y1BT}QREJLU9
zpSS)vV&(a|QuHdR5dLFW^t7k%UQ(B(Wc$M#{)Dn^Vu{|(!wa94)!(E$a~H@7aK@qs
zesnYXG&)!0me#O8Q;B#liGbsp``4Slo;YTQ`dzI%r{j}A`9<Ak*1Aar+!=v)QZ?z4
z6iT1wAH)^D)akzx%Q08DEP}d{QV}FN12vFuG;f?ORZSot!Po43QpwCb-M*D*BRnD1
z6|pH86;pw4>{GHBd_NNKuTiW;KNiE<?9%u%FrwkX3}V~Z7}DWn2asP3I(Z?^3{b7%
zD;Y%zlII#8R-Lh2XuKP`h#ZHcK`v)|t|{G(jymmk@c2H{{$kv8t*iFE*@hw2BH6Sm
zhd|yChGFA<{OD_|pvOOqG=qL3Rx$4{`69M<@Lyg9<`(gH-8%0K8Ce=m8PNwVw_dA{
z%RwYQ9U!HD>{Cm~ZC)KaR~fmDv!TOqe=h2flqkmoHiJnm&GiLiiIYB&E)a|Zfg=!^
z*;&>)5c8$5$iqltU={gcEU}!qxpto|%iR=&NNTlaFN;||t+8eFHjHBM_otI38a{+8
z(}vga{<>J!8F0aL_)}Gc;woGdZToXGAK4C?{%c79;f{w>;3e?Pvg3;qu{JV6OoaiH
zz8{j0&b)%Usr<6<$sxAz0#M$X?`&&u*G{vmxz{59?KC^cuYQb+)J@>PEMc}O_w9}d
zBsf|ITtY*SXFp^YDrmdD8ksTQ>2}q!-CHlWB7r`@4S*gTSsgb)N>HD67rN(RF=q-k
zGA;)v3r9PI;DM5Czp<NjyjdUz4@hlF-$2|%@7ecmQL}LAt825>v)vX{Rh@~zEg4Hs
z-c(j8TsLc3hC%KtuDDMbL4zJo8ePc^spt9ouO)Cf^q<XEW@4ZDgoIYbU4Qt%->*a-
zboA^v_BVU$r5uh*P>_NU_l@7r??&iB3VMx6gHA1t!EGGt^@kxAX(yp=XieBSBzasR
zH^(YL#9iW#!9w#7j%ylU$D9`N4dw0WmLi@v8~GSQE8O~}ni>301DV_Jh}+}PQRbZG
zHw-G9<jtc6G{6>(K<zgc<vemVHlM7V=Vn!n`#Vb-&?owG=ObB3eLas^hv+gj2^3Rz
zBa~cf(kLmI+l3FtWlEBIWl3Vq41Jg1@TJb2PBCWC9%Qc+25@Ewz*tEQb&uJAhk>e~
z?({du(jGG^sdJOSXY~MIUX9>x^N+Ojy@lR(xV%akM+!uuYZvF2l5<XB0hiK27pCG3
z$ffN59=|MaXUyp6S3E_-W$31DVcVYbQqJ>J6x~77t2g9r(u8|4ofU>Fqad+|vJSnt
z)&-GXpPHgf2Sp9<mu>nBJY^L$!e8!;gFY;_{uX^~*0*W5$uG5@(iS{-RTTJT!}RKC
ziqU>EHaG3I-qi9-ngtH&Qo-ftHi60b;uG$7{NQZ=y~7f;uD+RJWO*BV;gr<YrX9e0
z>sIh+cT&gwLqIxyw(xM-Pqzn$^;g|bP1=2113#}y{qpupcU?QXP%)`>NesN0_5E~p
z_|)X04`l0xxJ~Xh14i#kk7qnQA}TKZqbhEKa$O~B1~w1;ExD?`2K8Ff+Wky=y#Gb)
zuJza5%kPv22j%>VmwXf8;Dax2PElUzwdLts2a(HI7%<GAyyv-oo_gDSVe~6&Fn+UL
z`!DYNT#I|{qi5gEX2hQH&wh4Io|RB~a4)9WA<zGvS>RERRv14o@V+v?_L+72JiwYf
zfdUq2mi9jlme&_@KsWcY;Yq`MmT+plR>j$1xp?0K%24y0RRnsFG#sXTH1GGyxhzpB
zS1}}|tH?lzE2q-$HS70Jjt|q<>YJyLdaq;kD(-2c7x+^1hGv@x)RY-14*Nb~`1~H5
z`AL2dyIxgZzcQu2;r{L~WfQ`m^7bbqxq9`_ZJP*)lK3wF0`6n1azY}`D06H-7_4X1
zjz|u{XOwygwj&}Q;BcI=1Z&&L%!v2pF`oBFe&*<J!9B5?S}@mgd|Tg^H)-@TsQ$ar
zi5>snc|FUIr#<aMTMxhM2qH(bue-iH0{yDMmg}8(xy|1@WV@6m;`Rj^0bXglbB)5b
zV!<%GziU_HrCyA`>KxNIRlg+lPv5t-pLY4W_W5V~K*dpQameStpZsGFbg~mq#0JZA
zEi(93g8-hNtUWjT#bC!iy1mAo;PFLe$5+c#`a2!VKd0;E{M#-#<O~l(@_%Gp+wcew
zz9~I+F07h5UC)rY^Xkh#KKh=#exjQ{G-j>}wYYy2b_k5CBg7@Ao{d5qy4NA+`{*NS
zg)@woX>8c{l@&0^Ne4-f%3ce|>5>V^-C=3G##GSF$3?xDB}Rt}T6LY`Bk#i$!g2T{
zum?=!t*n2U-JR@ZMQHx;-B=i?^(t?W6Mk9x%r{E^ar>eKup&OmJIUF6^|{49#`vpN
z^7{GBx=58oMMcxb-cQdj`_1S>2aJE^iKV?Z9zKw+Jf6IChYuQa6yqk<ioSUH-t(^G
z*T8Ikox7PSVP4s3lmh9({fL+en{rd!dt)33bgr)v+8bfDbLRL$?h!v^9lI@FW_;?U
zL1!CaCaMbh{{Rj_@xCo|UH$2@tFJDvivQDywON$7XxJF;(7^inJYzwW?6VQYjYUa+
zi-1V`C)(l1KK8LHfWb^kTmu}aD2#3|XLoTe5Ego$efGKEX6AmH+G$bbiy|Kv`NU!J
z35!oCC(b*uoHqZoa`=+NE9+1O9vCVp!Sa#)&O)(yWUg_~8YXWUnXB6Ik?)-oBQG2#
z-!BAq{CCOxC1ue)i|+3l*kxKUZ$bI-_kUbo{*srMPww`~N-=QJ1s9dQcHgUf{KFqF
zXP$Ou`OY`KQ+9moj^(7|J0Cjz=VSg{-u#9)mqWjHXqi8EWV`DxIOl@$o_D;b?D2^`
z%1zhbG~zyY7F4G;^mlucj*cu;`7@S0qdfeohnHD@npM`laNX+v*-y`otgl%A^zky0
zJIR8e6v)zFBcHhV#7dTqpLcvYZSiUMu{KEpfc*KmKbO<bJiW}DKfMFTP<o00DNvN-
zMTSZvB}%p$y!+knjy3e8veQm)Da$SQ*jR4A8rPN8vTONuBF9Wh+TFf$-a#<JWTeD#
zf;x1IBl|>Y;tA422P0RAY;pP!VFAyH>t}K88`s7&ohZ>nh<Z1fL}{r>4ylZ`tag?z
zyX-(P2bQD3eGg33uiSq7jzozo+}qyvw(6X%>uTpLjRjFcK=<2kzq0PS>jr;&VA*lU
z9V^QcR|aA1m%4*~he-mb2!JBGpU7q1CN6jSPLPY4)@D;VEslJ5WFYj;mV8g-b0a$w
z=!uaxA0j^~GGg@X!OVSb<;eB~tu&ZdZsZl0tXO8<73<4wv&wSwmaF#H*n4zN<g<p!
zr$@dxvNA5bXJNVXwmZxGyXTiO3ESfqEnZZvx%QgMZOWN_)`oM>JGWeP@kQmFbI+;v
z*j#bt6=nA6vxig^+<4QC<+Q(_RxbO;Wg}iwZolL9a{3vkm-8<;f839!K$aYad&c53
z%AUX6vpjP9N0xVg{oUn^g=gH`PiIGVuGGgObJtrj^2#ka<wOKNmPPxYk`TWr@=qe)
z6`8zBvrnE~-tzjllzsQux6HY%xA5}7DWw|g_Eb5-0|7EW4yQ<RA8(eC5T*CL=RL7@
zof_-dTPs|*^2(c+)mJ~Fa^P5Hl~O+XQAeW8ocWI;6uz)FiEcbyQlBVIEJ@lZt{;xz
zETtpc{2|K>B1$ILK2aKjWKsCaS4!Dzvr^VvbHI{x!U^}eg*n>!Je`OV!a^&=Wr1}m
z(^wEC{YrbcN~|JJdCF5Nt=~)z`A$>LDrCRbMEZ31)A%I&Qn*-gjoZDEKNH!Zrbv=K
zDI<xXCP;v@ewxpfKO1?sxx1Ckf4zBGVeb{n%l`hdDv-lS(kUmMQuf+&ukwfA|Dh~e
zIC*n1050kyH<~#!330}hc-bkJl|%PBwEXR;zqMNCklHUr{#4}8G%~p$CvT37NVUn*
zR*^p)`HPXyk9^YwH<fRF{#)gkA089vzPIA?!l3ZlYp=a3Z-nWT$kj-R5{uF)r~IwF
z>s{|I8*Q{+{Qqrb&pl5lZ+T0J{|^Lp`1;o=w)%79R8HLxrNL-*yD)}~)BdE5C*ttg
z$tFp{fB&>ber_5J`}dVWs}U>HX9lnR2k*V0cE6K;AKfQPeWEm?WM2}uwL=dr<&lpZ
zu=~H^4PvF<dy6pK&MXo*L%<4A=KlWozqdSUVWnkrhD)8qjD#q0=U^?e$<lK@PssZ1
zx8J^sMl{pO0c#LTl6DwN!l<t-=JwhxEn`UpPa0x!TtDAyw28*~@3a42uD!1NsrI(W
zH2bVDXGA`8Fe5_tj?n0@*2t;pPq(-#v;LettE_a^O675L9#^)A{ItkG>@y<!KWo5$
zL>85ebx1%5mVkQ(v)|`LW*NA3&aLIEU-@cz<*Qy<e*N2D-`_2+%VbP%yWO@iog6x*
zZ7s1p*o!Fek3MZn#)7$uB7rP($)(gG&od<g<dS(y%02(Or!2g?^S&s3KBpy<339>$
z_14Jb=gEVaWy!f$f=iIc2S&E1>F@LZUS{7uyPPrSjB?iEvnsiE{?i4K)9TbInZFJ>
z<dAA!??rJoS&2-WOU^#~>}o!7Tnfb@qV%nAeXA#;^q0T<rE-IQ$xB`wGR<9mUKPmU
zagQrynPp0O%wz5<-}uIvA<KU(7hd?cGI#C_BK*^3taV3}9x;g3;reakT5*iH!)9?E
zH0WWr>e5b>o*&ne!G`2=_lBWP+7;De@H;=Rk)^%kdO)8jP2L_Qnz3GdTc=E-ZT<3>
zOWAbOfqhBb?M^?v%n!gZH^4Y@WKR(5#VMzpQn}Wd=-Y@=r!*3x^v6H`F^cm4%9p<M
zrHXt>z>WoqyXDfJ$PsxWR}%^F_~j$Rtb%thC83vqlO1;0q1xj%ngdmv=`Y9qrM&t7
zy}5jC|F4xhZ@DjU=Snv}@`Ax^F9{dA!y>;hGB>9BJ|84$IIEC7CCJhW@?|TO<#%4b
zJo^Iy(k?!toD%uOMJJY%=AKkeoF6}zoK#MZ{Flgx*}Z^u@c@u6O|Q$q)HhymV>#!z
zbIP69-Z|nnK~n6U1138Wc=Am7;p#=Ujem2|d*A!sDuQZTGG1u*9aG`R23873NE~+9
zVPiTs1I*^3$vC4-yQ=4S&JDRAPE^8^2J_z{|6<86%2xBXDzokhiCjFZB2D(O31`kN
zk1Rhk^3x*=&;F;##zKyR91}^zwcjNdUs8_y^KljNnCgkA#J>CPTb&o_yfVKF{=n7K
zu_%a=1D}VQi;^h${+r+Y=ITtzfBa)0@W@$b4V<TBDPCroOM@)EuRP-!Pb{x~^-F_T
z9XF%BD8@UD1jltrls2qk^{WTNd2cof&ai%5`^5E^+H==fYKanNImiYV#dTR+FX-XB
z50C3NaV?JPs<<}k6Q!R`TGTr(P_(Gr&!$y!Zq<%E4$RE<C4Kk1mG{K=0x<6$goeh;
zo*+aA=n!ennOfsblmvtjoz0m^owUbGb0SB`3YqR@57KCp#anO;@;p`c42(93WVFsY
z>r`jCjpg_}_l$GP?mO*X-v65Smv8O+t#Zqy1Aa0<=CP4QBjZl@mB^yN?Hw6G>cwSF
zPz9Uxca5A;=|n8s>6)F&2fp}$@`KO+pv=2!U<3UP7u-;e`1%p$Z@>LpDIIhPxBm0i
z@{=QeQjYlH5#^5C@0gVSum?e(IIpDcqB~r&?~&q!(xy{XDwbAcfA`&YujVV)XFl_p
z@~dC{YRuLK;NEk;@|CYlN`x+!Ox6o+IMJ5%n{T_hy!uD4E^B>jt+LHE+f<?9JIO9m
z?_gmE5r1Vc?;rWl$o9TX$HZF#5&!(>KVPj;8F81FgE`Yt750sGN^KS;^U~R8pIQAK
zS9Itsvb4-HSI4rvZ(RRfmRs(>V-5Xy`QsmdTjtE^M=KqVMDdY0ZY-nTMOrhi!{fTS
zMr@Fry^yFLF#qy#{a0MSXl%i8Zqv$3Gf`SSu7!h@b<*xKJ%0biam^Y0{+4~BG&w}c
zVm~d<QL>r;?6XUG^{dMr%Pm*VU1pi`cOd!bqsyz__O|l)O*btM-*Cg2u$QSepIR8k
zk|^n~PKh<c_iXZ=iCQ#nhi`bp8>)lwx;^}EEE2za?aqp~w74oAcieH+d0QX+;0Ny$
zWMs5y!95Gg-!A=IdELifSN>z$|0v)2(|5|%k!jJ9n>;V*L4%q0eYwbOpwi~~jUxk|
z&cgANo0Ef4PmSyVSZ5qL=f=-l7u{L`=DTmYI|?#To)eEfvAkisH<T~${pE7k?d<?s
zAcLKD-l@F&6)&&cg&dIG&76sVAek)8qg7mRi*tzi)?05~MQyydJr^vv*Tpxj$oFaQ
z_&bv2yuD6=NXV@3x(A7HspC*(HrKF#ar5oQTAfy_ZMNB_vLa;JSiGDUI9`e!@3{Kv
ztE)rYfoo*27os^9>6)vrDIfU22g=*u^7eA_StnO}ogN?AIZcm?yy0CNmgO&6zRbET
zq<Y@0YM;}CYS|cIJr$bV{w90#$o6ze#)>vK_FP~7@|UaVt6%-<SF6z0k9o{vDgxap
zbs|b<o_WUL^F=f*G4In|E{N-<xDcij%WA8=IPQOCjFAhMig~%&uH0me{fU<8Oy-Rd
zo};v47g*gsrPsyv+Xl(uj<?-#u5*$8Gp>W<y0rG)YvTH5Tu<wjJ4`pCv`SoO4qB8x
zQg@&0^81^_b<1F*J*`iaCWj~~12>P~{N^{+qC9Pqh<0y$V<{VKP-ce=y*aLDZM0E&
zRS>=}#$@kY16HNSKmPI6#D4P0Cs)ztIuRuxC9isF5+w^fqBPve=QBBxqu>7ax7A`g
zl{Ta6+v!GJfooyWMP(dIE7?_%1z%YIy!FehBWIP(uiCu4J~CG%dy9s1qpSa)%M!P|
z-$(w<U_Nrmk=34$g-aUlT^;!Al~-L^e)+3kmQzkWrQ9>`p8Jf+OhCN3>cb!Ya0T?a
zq;+GJ(1x_=T#iP2xHE;zrkieBxhdIilu1NJ`#a1v<DqcsMEphD(jKe>h*~F^&+l7f
z5Ipl2U?*|uWa)a}``$NhhhGEcENWb}l+_%Z^4u)rP~&#Ed)w-r_ue(<u5$YEr<e1N
zIj=1K=i+ih<ljX;GO`fm2VHzndD1RVDziQpQhj}A`4^)6`H`Ky_3g;^%uTnXZO9JX
zA)NR_ANtTLD(g_^FLfeHXAB;}A<+RirGtY<p7}m&*0pgxCrH_|f{-tLX9k*s4m#+d
z3SV&Dos2~JVx55Fx>cz48ue~4@PgO_e`^q)Z^ZS~4)=etaeo=~M6=&%wWU7l-JmIr
zui@OmpUe07{YS@j>tMq@wNI4#!5q3y07^{OW#yHtnfT1rRx9t03H!ybc}@AqZoAzJ
z7-&de`qGzH3jxjiP(%qK(g#N#dE|XG!gZo_^wCFGt}xGd#xo{uLhPrJCBVQwH5%=@
zC17b!z~C<;e<$+y2J=rN3$wgl<X&9qHi`VE$lT}N6IpbzH%9($<T_YfJZEt^?da3W
zuYdUKa@|$`UbM|`r=NOy`N82o7`6|%h|!Gduk+43?>>J6*ml}!r^<z^o4shXyduci
zyxwVlP`An4f>Z$gb!(}22-spiZ;Prq(!^a#WHlDFw)55~CHCBN&uR}_+u!Y-5oOc9
z$xaq0L{fbaE`)qM5+cc3!8Nwt2l`oi>E}A}%%sd*YJc*RpH#qJ`P9dL&rV5w%;omr
zAk)6rO`3DXoO1YnhnF1>+_Aj%=C_tNM9wq3I%V#t4$u8jWZ}!FTjKS=-A+IX>)b~_
z`q9eOlU1{sx*<xu;T_==k1Ok~mqh8Jvi$P@Rd(3nW%0iAr(GKjm&`%tB6HFSjcB`5
zIgK<tc%-Kco{xG~jr{z5@a$AkuvZ<lK>e;kkd7ZbPxT=)Dm=J-NrS;0&W-DlJ%0BC
zaoru)$@KsbrB9UlsYAMJ{`_)TERg5NgnsQ>Yn27U>#VtExof-a%4H{=RL+Y95irPh
z1jZ00o8gBeN)~CGQ)yM}0El+RL2@s9*~==84N$^L#a#hNnTf7-v{yhGR~oK7(;;#F
z`TEztURffHwWY1!1U$Gl^3{Vmf49d%^e^YiY&e)Vip;gH)1jSfM&7WNeV0q!K9MiE
z>ymQ*73Y_WuD_^Uz3}SFGSxi9^R^prD|>%@@A9Tszp0#h%BlB}n}ZKIxNQB>tt+rQ
zR05C@596-s=4#$VSb({-8c=Q+k8Q+hEM4!?g^q=!9t^?nlq%knR@5w~`DCQ$eyJnn
zj6gJX0;b-|z~zqXow4Y>-enF<YYj`fGnrU%Zs`!OS)I&t-0#LK2uik(d86KL$_Zo|
zm-civ6CjV60`bTmxltd)u~VWTGna3?@y3+}E5U2l^Qx<_Ds%6-KWUtQ=lt@IGyhS}
zKkxi<-Q4S{@pDAvr$*i&va#^6$bM$1dwZE!n2afk0}&yOJrN?OLn24U4Kit66g<Rw
zdCocaxBPTNlo;;}F%j^d_Ov8Q=LJ!EPI=`kUl>I9+_JPCfu{{wW$g{3*qAP9wC5;A
zdRAPAHTEwZ71#F$BVgxe_`&Dndcur6M`>`MQiccrTwE)3`MoE_b@AZ$wy*mrmOfGH
zrw-|an5f?p3+8{VzIypW$j&XRt~%g;ckHqE9xPi=CVu-nSd?fbhqEZrTGFP~C%d#L
zS;#FEG_N+-Kl;&+u7LMU4AuaYA&j5?^ru_heWqeEwpb{>^PTThAYf?<w=6Q(FC)J*
zGK-JBK|4hDbF;|3SdCVX{O-s<h|KzQcx3KHtWeiSzVfUq%YM7=SAP7}AD0EUwujIL
z*njtj-<9wG;QRN91_mH=UDF3`)`NNiYU}+qR6?qL`qQ6QKKgC``2van*3L;oDkdr+
zG4_A5Y>ae&S-R8+@=$+<IwK9c?6OO>Cr|=-y-Pb>bkRlCXWHMHOn>^*pQdDh6Ea~w
z*3WgoU9Tr4m&k0uJh?%rhO&Gikmho(kow(xB_W+i7t+`!2ll}2^Z9+sw-5SunRnBG
z0_gV0T=R^9!w2&ZBRjwA5s~X-N62w!ei?)IKiyhxopS9x+?aD1xVCF^!|#SD-P@K|
zl(N-UNtDhl%PjMZvcU#h1qnN+OsB2TOLH2F17`=VN8ISXJGc#}Zb3RPuCK?nRsX=*
zS+gD%*D-^>iXRy6@Tf?SiR;+GhTOmHp1}@W>QkR6b(d}nGIHAN+2yk_LH}2fqi2Nd
z>=D<^E3H(@fd>wpr__>YU|;;=7k5RJB<<hq1DYaQwk@{Uq5?e#!mD5X>ME?NP^uFd
z)orRInrCeRq)n$ti8O!uce-1XY#={Aa_+65^#;)Hb5>EOJSVObXO)$Iw{m&fO;0P^
zMt<2~7W(^Lk)0z`PxW(&&X|2hdEdL<R}TLA!R4O2@43&|L4xe#15~rEn}oE}=R@_^
zaQ7O6z}s4DtyP_C(@bQPqGQ7iH>`a4n~B>TYX$2|+xbZ>G5&VRC70a$ncpc@hH@cf
z*${+702ueNY;fPzKHVfr2+`rVr6D@?xhCCxX#cgj#9HHEYwv8jD|VAuqkjMU-&bUW
zf{1%DK-`NmNEwyKciZMh0Gul!LYu^GxN%vR16SL<+KSt>IX~}576Jd9qtr?J?7Z{N
ztuB~|t-pPC_s^CauDhY*7?5s^j11YQ^z1vIT~`0&>SflSVr<<uFwPzs`F|tZx5p)K
zei@k=1(DR3h%$NVHQ7I-54A%##|sQK=8`B0iex<<iYV~{{n*D!dHBQ2U_ggY#<fRT
zb=9lNd)`x>;!&oN@nxw@V?mT^xc=>NEvS(szkeG)m~4(R6)Z}t4F+|1)!<$w=C(rz
ztxIj!o`a;S47%)neeh7)d+%9krIq&i$VWbMr%m(xkeB9z_ND?+GBI**Fo{l8H>y)(
zQG5NH-c;VO_S)rlAv>qVb=k7ZmiYmE3U`eT?ba(%YD++p@2#@RDrJKWHmFWvw=mfB
zj4({bStOI4Xn6re3wJN)LXkDr25wR_vE4?J05pebR?*@HGh!s@LPqPdcgP`{ZNx~V
zI<9nFg}B?TbKN>+josEL&wu^%%aOB>ESE;Ua4<V$^UBCWfjWJ6{sre(AoRwYZma;i
zdBd1`=R4n70r!dC1I7X@!s25e``F6z&`c~bK%G#?h)OfDR5;(rJwj)zQ+?e55MTMq
zS60CwSR&My1&hLj#eytz$7@Dg1X7KWbo+CTk_en_B!=b49E`N(Isn{q9ab+y5RlL0
z9>j*VEs2-)#68}b?tjJ{!h)cf=Z88-7a0@%)LtU{`M4odhztj?wTF8UDg?xQ>$`1x
zgAh*b#v0ugAxzt|{PY^RQonAu-F8*b4sD~JqWW>0PI9cA&Gyk|%C2pErk~7h8BnBC
zTC`wMx%}qK%SCrwR8F|$gtGlRwl8bGYt8b&iyv6lip(<0T~7o^hbB7{j5{6+6$@}L
zbFF<=qSFoMuG*ILJB1dnh|vb>NTOsMP>OH|wZ67JmwTP=|J>(FS%3ZcWrY=xrEB7v
zUt$@L@0QYK6zhKKB8>#cjgdv^>4Svmf`&E8bzxlJiR*>E276nYf;lWZ7#i?<gYI{8
zTN-Lq7c==-pO8M4(o`TyK!!*+w4Ni;SWQGaHbB@Dw%DR<&)qIV>1?t|Df{kQipWjl
zNR$8|p{`e7ef0`N01*8&vIH2>jss%T;ef^|(PojIWAwhr9~#V}%ysKZXMfUXBHM6Y
zor`q)SIQTD`i1iALw;T6UNQ2+k68sM4S*-)0s(NyGEg^`Va&TpNCp6ov~+qm_cbIc
z-LUEjIQL!qT973X8|y^gv!C#UCscC)LPddxpnUhc->nEEa$p~kXLNFJ17cW*5G<CB
zW<n|iWZ=%5#0R0uAPeT`kwhg0oR9~EVeh^7zK>O`nUI8o4?eiEt|<=}H<pay)+OtP
z^+S93og1B~qivQmL`5A%-D^fH1ol|nxDGm}u9x*tTiMe_F7xosV~;(ya<?3cARzTf
z8|z#z=Q>?^*_GvhJr5|q{N^vqoD1iaKYjO4<%@@Zu^e#Y0o55~Z;y;D8H2)%zap|A
zDEmgfB62UXtxb}=_L>k*a!Gzh>n5E<iPeqOkz#~vdz(cmlW9K0uWYtiJqlha&w3VF
z=+EzCm5}_zaot*zM?;h%J#Ns@F}raf?KcL4yPaJ7{GzxHiEHzjN|Zi2*5s&uIka<W
zIXJQ{O_g($SnmLYv5eL<mg(34Bb&bPg=OoIiK|vzaeySn;#P(M4kIN>7EJf>{bx1e
zZf8;OS^sqLktZODtA_JB0N1IM01}`{xsc;YofjgX7x}M|zY+O~ky(Zw5V@0k-Gd`P
zE%GxWi^TQx$bSD~Wak$x9L%)Y3l}Ubi{>tJy6lMOtpg7{u+k_4)d&ERVK2;h&DThR
zeJO}go29|riHrbq#(jcb_j5f0{3+e)<2OSj>+^JTd(CTJQ$@(DE9g9L%PqGoE3B|W
z6<h)_WNEf{hP#(&dOio#J6Tu(8S@9Qn^83p3GZg5@{YH^{q2<nCxgi#YwE#L<GC)R
z%y%*>nZF}A2wU^r>V&i?o3gWr4|Pzs_aTkOg7!=cm3j^Zvh@phKhe`tqunWZh39&Q
zXSca!B4+vkVe7@}TrYd`T|+%be<1kgc=9t;Qa62%44yvw^s?jIb}V~*Vvlmm^|zEI
z^MahsomUntUQk(Nxx{k6t6O%BUBMvsi+o+_UYVdy>d~e&_F2kA8<Ez=+L~;P7GGEo
z9gV^5oRY*`&WeYKl0j|R7BB=^dfn^pOO!U>yu^ILV`KWSYvfD)^}$3di84`mCyrz7
z>n4qaC^=+yFuL8J8+iTy;@T#zl?LzIeDHkLi)!~EPyZX&BW9ZG-O~r-s()#Wt}m>(
z;)(~n|NZZuL#t%qEzSFOrUFqi2^-`V@hQ3ACMNp7oOxz>RuG)m#$dZ<oplBdYUsiu
z(F;-1_P~q<Mf4h4Gwv?h21o!HX38EifW^WLppB)A98e0h(ar-LQz-#Sz>)*|Cc110
zjHq=1g{$9BjQpy|TSVqow?gFR0k2%`R*6ho&86-mk?n;zBeKX_vm^UyFU7q>Y5tNj
z_s#%J3r8o%WWsnbzJN#IfL2)>0%i&9n?N{0n?=2sC~)Kv3FCf5^WSM4DiezjH@pm$
zZO;u?FNC3~<N)ZAB9>y-2jw<5aFwgyYaM##GoM)<G>aHfE+Jq4{onsxHrs5oDzX~4
zDBn{*&oO@L&oGBj{IL#YpauJMyqm>`g%#kW4IauW#mmBXkQWJPN3iOyZr<yCEL5!F
z4i;rm5`c!KC;LJl4Hf0ib6CI-2?QoTOY)X;gK}vjmbP^J(MRs-#hqK3Sla+SZJB$L
zl+&}i^)69HZP?9xtuGM(?Ks@?^||?Bzy0>B<ZJfnvj_UC#ZB*&$U=)7pUv?r2*mD@
zZz_W>jq%!fcLuq<Xa4>6LJgOYZt|mClosQe)D<~ed+oI=S$fce9#qMf_OoBtI-7w-
z>gnd27l#bZ8$5(|fGDlDS_#rsVq9`B)MwLjsjQOmAaICrX}+0^lw{4EJRIS#jkm6e
zQlz!x`tP`Y(;!HH9^5|junymQQCvTW>xSBQ99;YVJafhba~SI@I&S&sz-y-Ou<1Va
zgE@4OV)4B)fX_EK+_3yO2H72m(%_;!T<V1=u^iEob00C`Fk|n(|NhhK5Z*CL2qQNo
z^&iSrr`Lptd+wo!9@={5%VZ`%42YyD9mz)cktW3Gw~_aZyjNr!z+W4=&1%G=B>2Lo
zB7b5qI}eFF&n0E}emd=Mr<Kp|`}uOr(Z`INOEa(!xN%==f@F(1FefrwFJu((1^Sm?
ze)+Q7Zo757&lm<+xOb*?#aQQ#BJe_!oM{8DTAn?W`;ceQqO&TeGRbGGKQy@JjokBM
zyl|Hz=k@5B<`FJu#*}hOECI-Qx0X21bFc4Y)Hej>k&k?2wf`z333{%wvCL=>7Ak*p
zC}w^SK|<~j&rS+5?PcE*OIaRH3xuWnY!Wli|Ms`PU6IJ#-^E>#9E|q7IUtm!Pd&Ol
z<B5AEVoV0dGTb>LnEf>z0lX|Smsw*}6g>L`>toserM)BHy!hsF<LNh+AAI2l<xfZb
zsVrQyu;=y>rB1oV>#*QVETHy%aaFXII;3{%t+%d-E&PHkr4pc?uDRyski{PbK>qc8
z$WprCg$S0br*ZhMjJl~0dU5NY?9>fW8ayxc`EdpOn)S~b**Ry>Vzg-&Bx{{PVpKm@
z>AJXnG`N3h**;O~ohUt^A0E7)3fCEv?#?@xMGtyVnY+v~<({?HDobAUqJcviy4dL3
zMwIwix49Ss67F+o9PKXP3>3`7KH)xLF&yhXS!t;PDyLd7hYU0UG>!*}SP=R0$Y(}A
zXE2{1`KZV*j=XB*)grGEnYHNQkvEUL*<ju@vQ6Q~M!qxh?X^5-;hb{yKd!FGhwI8K
zuDn-n5mEq(s}{GbS8V%=a^OJ+-Vd0&pB2K`XdX66YmEQ^`@@X~mWOS(-L~2r(gdl1
zHw6R>3?kuq%9i`p$OLOH(o%Qf(Ko>P3t#xc>ihntJgg6OtBvm~D+^b;>8U4xr*GR_
z_LPxTf%_ZR%lbZ*44B}|w1+?Z;g$A0_ZN9C7ft`K6C&f~wXc0`HGb-QbUce})dMPM
z4|OrubmP+N9`Ev<1nNi;i_Zr>@PT{FChD9vMo80*H04{lwL9`*d?QnUzkM*C)f|`F
zhD@11fnel_i;sCS_q5utg)sVD9ksJ(Dgz=mT%E|M`Ip=&uf9u}@H`eAXD77<;;73g
z+0i!0IO3>&W9bT|&9lFT3pBAPGApq_EUdg4s$T#WR;IKp?Z0^c^0lMBR-U%~)5_jo
z-n+6c^*V1Uk8+LoHiewOjS1sdBuQ&3xglGf*2}A}z9{7EWdV+#H%OLFtIk%MHS6xO
z+;RhCX&Uj1`=zv^PedP#ak;x8O6$gTX2Xgkc*Ap$s2)Ag9yRECcSh}5r^U5$pD6WC
zl=|l=^^;w;rM&Z<r36X3SN-&|mkDn);MhD0qD1Rsa;xuI00`Ve6FOdpE>4$}QOvj_
zbrVu{3NHi|@B>IkySFUm#no*zu0?}cLX2$iwr}X!gZa6UpA?xZU0vHOlHH+^?NK^z
zF#kF7S&_Nt-S0hjEGpkP;2Y)H&wh4w9emKi_sXyh`2fDn^ME7}$vV)SYt)Mt-n}dX
z+ABdbs{$a*B9WGpP6^opXpxR4==MzD8j$B&<vpTtAwd!nz#3z}Q`6#v1Rzj;ayt{U
z-#(V+27G<vxt`G$0k16q(-}58HR4Gu0DbOtE3UX=wI^#h3$yWJ+#rCxf~E|8oqL4*
zKM&OHl$4RBgB4Vt`<^!9Cf2sENI%nla~pJ?5KGPW+izbH3t^v|k@fUFnRw1~o>R3E
z8A4=oFQ0n=ME7yKGIuC1LPZYREDX)L2C3raEXp4X6w4i}v$`v<`jOlELEz0qCOm`1
zLzKHGKJkf_t7clRGZNl}t+Bdn@xV{}0_{0GA+qzy>>o7F9r(P_l8wqEuYP1%{g~Cu
z9_Q{c<@dlUpMEkHSYnandiZ$l+Z^{-Uwv5+r5D5!zCv7^mt~gODhSff@$=I7|Dy7!
zM=gxCciJv9(>8TUls2r98pry4Jg&7yxn*a|#zD1f^@&oSD0NJIqBKOZA@MPfsR+^y
zas4K)KLwEKB{ESPQKAJyhWwwK-9P#<N|);KWl;yZh68LsCjWB4xJ@R2K`t(USX<Px
zW;!YIL6HxP?669QxN@CqbEy-A!QY)><e*sJxubM{$Oy3fy~Dp(0a_Q}nXYGC&V&aZ
zN*kZaJzU!o^m3;HQbn?}F9G?uWej&;8;8J_KFWG%e|vd`>Mulyg-4{ly4x6w1hS``
zE`)3-lHqUW2>W5}V?xr9fTNB&sv?+39V?uW=Rmmr?`8h!H39N0JK6vMMK<(bH!fAk
zuQQ~EvjBSs(xI*_0o(vZ>|+H)o)9PRHFn%*zZVOcJurwVP^`_k)wx%BN3(7rh5Ct0
zqVgbotZ_(gFV7{b4}9PQE0U!T(~UZ<b>vz3+YTfik95W5SCpUs^ylT&Q%)^Q7XDiX
zS($P^cztAt)aE>}bz~OiHf6|ENr)}0@kc-U(fb7CF_w^Qd;ZL$bvIY8kSv!#@dF+(
zt33C)o0SiK@bj@QUsxXTh~JgfR{LJypc~6D=Sug}7^g0Y(t`(G=Jt#0@naa@V2!w5
z7uOdZG1MnYeWKJcO$DMv`@_wF>&bB1;t5MMRL+ce$U~}KpC_-setGdKtCY9A_r2xX
z9)MaKQR2ekcR&HC>~^mN*A8t&Lpj~g0Lo3%fMDBMCQD0Vp|Ix&2pn#svvvR&otFdr
zH4_Vn^F!YJmN$3Y!z8+#(7%E)IEYdtx(7vmNMvqx>qSmiI_Ilw9GQjb$B}L97I@)?
z$W4+2=-qJr4OM@&ZPZPzzB0PhB{kEC1fX)q5T`pLRkZc)^V2-Q0tFlkEsU7F<Rveu
zEDB>8@I{%mB`Zx@Q@E=ksu@Hj-PF9-XF#wp*NB_I4H-dBLM{X?5ZnMDuFq#IJ)Wx_
z5UO+~1ng{32cor`djwoS8tlDrzxmO4NtY`ZYl``x{{Bv5&AXL7-Qd*8GnJvPjZYUt
z?ann0DHml-eZ5B+@-W|~e6SK_unRKc+3KnMh>yNCZz+%8&5hc>lC5Y5_14$khZK5_
z{hZo?TO2~g@`i9AmhKZ8+I_tQ=4fjlGUvJ4$nSaPQ!^>6IvEE|!VbK%rm~W0tGv<@
zjQCQL4d)6t(xhzqM!Wm@fB*M?m6h&8yL{+=9tzy?%oSX3-xHY&-iDFQ5$1zmN4~3!
zA0Z!YJ&i^ERFAT#omo;@Cn-Pb3WWip^xBFjJ@A2RmalwepJ=0)E3dez?7sVF%DU@r
zUDjLgu=2k5-B_Ky)IVac_f*Gm-5wC#B~f}nT$_wzK)4m-TBc8w`b4Q?nhHe8q$jK^
zA9vd0Nl$>0xS_E~7oosop+0ucJ<Aif*rIIxrZ<(Vdhj_11}x^>1h}FgN;aR;WYf~p
z)^;0I0HG3D%Esm?86B{vCLVCJ(jExJ(i-xd_ETRQTe*RLsIf>!SP<II+PU*zca}dL
z{ipJ~-~6uJ^3VH|bwo!{h3z9hFLLt$SY&CR$ifEG8vio#Pb24^qv6V?zi7*Wk+Dw5
zip~ElZ|N=uTm$R0`~W)bF?YJL+`r|S$SDg>9c%;bEL-(GHpU<KKl_wK+*)n5)hf$V
z9gra;+^&$Mx=b6df)4=rTuKoFZg$+f)E%hJc>uZcx#x6pvolX?8{_BK!vvN|S2yGh
z;gkSr8{dW0Zd!}9xBCEY(dulNxA#ZbbCwp?y(aKhSKlM&$RdlN`J7cIgHCu>T32ZN
zSp=F9x{wutAy!^_<tl`~;2ErGxh}C*vDA6Ca<H(GiP5ZE%46KI4pLIEQgw2D2FwMZ
z(Cz{?)Dse?-qx@-B8i0Qhi(Hx^qK%wWzwhC6@g)dW7l3oDIgv4j2N*pAxY+nts?&{
zaxVwdPIf|+%(3QAW3@g{m`_=35v6t4eNeQ`k8*UB%P&8n?6Jou%flY_in7i+UyZeT
zZkgcos+WpHAp*zQJJ@YdkFhv#cDh`BqBM=uR3S<hZNTB2$+mrSX+n~o`OFeC>cHU(
zv3MVU=9%RgFMC;eP5l3w9>U{T_z@+VXIfE&>B&!ia@Xe!)RW1dMM31MrGY4MOR*<m
zX(meJK7J$rCGyWBI~#?SsJ_qWRfGAZkv|vt`gzxvi%z_#{Qf7uFE?M&{aH;(l=Rnl
z5GDIN0Av;kK$1m`Ty(3M>^7O-C@&BbL;!hV8R8mP2gks-d69cp21Kww3E6nT3tmuO
z`qGzHV;RtALGwIyXmgA6ckki)gB+L#kRSxDEtGM$_3#Y)tGK3d`(m+Za~Cw$%<cLP
zkeAezYZy?Ik?MeHKwJMN$VB*9NRWMGa-)qlsw_OiS%I|$z>XNCYhj%zadkwV(&bTk
zyaO?1krRZ06_zFbV;}oiHCN~_miQz}EO7{@HKUhx3us2*l#wDsc@R^53P?Mz3b}LX
z-@3&PQQ}srEt^EC*QCx#h{tL^NBqex@~r$Uu*PjSA<~f|mgOf$PIug2L>^1alJ?Vw
zLn)3B9##o`G*-v6cvtqX@!quRoqhJD<tb0uDgOS(vf66<l@EXT&>;R3<03g0DG$7+
zI2g+)vArZ?J8|4l5j-1x-6u+YqBQl>RB^qdO{GB|PyhR_5+LDlg=Lp5i#OY>+!hPY
zoS3+eKmGKw)eB!(UiaG9_C%B{;((Py@o2he<hfsrM5{<+!v~w@a%phAOZTI0&s><1
zDe%o5*WOXCyZ*Xz{ru}Iw>a)`*GK+m<Ud3fu}*}#_3l}(EO+g4W!W2+EstLC=<1-@
zD<fYO`5%#QiG26%cbBj2^|kV*ZQoQ*`BV4N1k>e6e~ksG13r!SlxJ>s&ItmpktD8X
zKzv&;0ALH)WxeTj93TK(-0XQ_k)d^e+S8s^x$4vdADD-f&%6WxC}Yl1EC%V82kasf
z+-X^t()#54T(iul_Fb_e1N`cXkN|FgY&|MneLl>yp*jU)Fn)|1z!+J2$U`1dp7D%l
zRC|Oxvp$cfo1U_BjbSZgq4T@X%q#i>5n@q6;FKL1V$t%`-w~5dH{G-%l}R4Rg6E~1
zT`D}Xf4x_Ikwvaj86ZOcsecA)Q63gN?Z_IUow&DYTZD@Hvh_*3BA2`Fx@#3Ifoopf
z^-f=TXVW6&bN%OYAPyOGNN>(P+5jnHEflFvyCTWTg9N)z-CUlpzTLVEBPr?5SC?0H
zCo>2kLYeY1lBE9RMPFz;&r;XyzfSw)j0Y~Y*I#>mSuk%wwSS4V8A)0;vU8Yz71_MQ
zRc}EVoi#Vzi&=L%g>BacESi)i+|x(nR+&lrvrMkC$|}{_ODxy*M8EKgS6p0HUU{D&
zOPj_$Pblww?}w_koI7{w2Uar1L}ui6I9dWf(xl&Vj_h^|MV%-`TCLMrNE37>>xSbQ
z{jN`xrg7>Ab7-V=Y>X`c0RQw!L_t*F{HBx#Ke!6uaPh+)R=)WD_m?AMVg6Hqx0k%=
zMV0S<FX!)AxDcgv)>)?lOU_fF?OU4n<@D3or8$?*DTnNFNO}9dZ!hn-_8moj`(U;i
z`vsBjb*{T8u20V@oB!|T<r_DAqcs@AJ#+6VNB{BY^3B7(SuVch;z_BKIfnZZ%LKp#
z=yQ8wbwNUaW<ZHzA&EylQ9iKDu&nkP*a2V!zF8<d2Y^QqSXDaBeFzay?R{<cARP5G
zbJAkMU5!TE9AK|cDo4z@fH88G)|`4$XVw=WnmgN22@qzfdRWX=2n-8?17Y)gp=MG}
zN*i;2p0|=#5(I&3FU_=e5nkQ>NCZ-;pR|KM0{FFsI&()vu#~xt_%)OE5j?@Zr?jji
zK>C~&A?HSYi>v^+KwzGuByp8f7C@S%-7^sqBul$D112Co$hW^EDCz^K*Y~&}4eIXM
zWYFIccVtOBjCB7j`B5g;vijPg&g6s!-a8P{q4x0&l@I~)q&<33WM~U*yvOc)luIue
zh&Je4HRQ<LvRq`5G2a&Xqmj=oqw`5qXN1)F>m;N4k~=EbO4g^bz$E(FdySQiH{Q5v
zr+OlC!H3H>+gun+|Ne39S=L_rgXNv?+%;r$>@DbHo$|afN`v}1nd)hfC_P}1s5~#O
zqXxrwKRK=yCV(8RI2Zx%khp#x*T#LK)F(>!OMRkLO92kbCqG#htiF0VJBZS*aXoD1
zmCNq??_d5Li}AKEe|ZJ&uI|F31dsta7C+j{=RWtjm9_^!7_Wmir&^+obC_n^*(m+g
zo>+>Gk9<sIKM%X$u(HJmw<ybeY?-p`Rm<KlS{<#gzzo|(-u|-f%htPWUB33oua(=c
zxxJ%Yj#aqwktC!d_vg^0KmF-XFUu{rT;<Z$O;kJyz~=%7XtxomW>QyH1hNRIb`$;@
zutIEDH4p<Jo00@sLUMrVI;hRi&yw~Pz+?58M`*>HZnmr&_8GD+D;My{ie~<GFKY!7
zAfQ2=NO>-oK;$IdmW(kLYWsOuUIc|$cinZX@}~6)=+w4cytwX)%qG-$f<^Z@8;X@C
z_xq@$vyrqjcN%5o?sjft^M*F10Md^v1)W0qYj4EV{HI(%E4Mm?S6d=S>Q|RZZiLEf
z&rq8SCSv5uxrG%=8$9Y!kE%d+Js~}apn4#M05}3O)OzP{h%OgPZP3XLRR1AZosKe4
zUu~zZO?NZzAkRY0BZI>Q=BOw4a%<F1E<~ACu9Hh!_CdOfR`X2|LT!c+)=4?b(q|*r
z=bE)6Ct33Mk))h|hFcHFL5|U}O0bUcYu^@k()+hhDMuYu$_rkwpscdW9Tj0(Z@o=I
zUOqo9){dOlrxS)7BuXm`l9pH1!hoL=*LULD-GQ=ktvZI}XthBiwohE&Yn-X{xIR(p
z6Q$P4W5yFDi!)7MEHY)|jmw>DtWow0FtJgPq*==^U-pUx?1U3fEUy?0U)v2)(vHB;
z%U<@f>g*Sr8v(De-g8k;wAnPe7Vd7(*%{9S5Zi0O&0(gW3)Lzkkwu^SS7g>7!5IEG
z@)nUFANh%qA2H_<Wrg!s7_b)2omFXgR~yU@jdbr%7XPGNa_1%GjBCy)=bdw2x#i+p
zY+jo5L<SKK@|Xaaiybg;&H-L&)LA3UKft`j9{B+{I~{@wAZF-l^OyhAqIUvco`V!{
zx63^`LzNl$wm*uNABglGdnSzMXFcm#RakI<8|ec~fc*Nr>33uP!4H0LS!tz}s{M(+
zZ*00(n*gc^m}l5u^pvMOrFt%RLqy3t5u7?1%foV6gwnmnT!hSc*At%bgo<zqGQma8
zo+Y3e0TO(I`x@6gBn+_vthKc^1m=-8MACTGM=Vq7tbbUlfN^EepDbBi?N~B^bwokA
zjdOjWo^^##o=-(t#DeEO7DN9>2y<^$Mo-jdzJr*lqc+oi<U)V9tyA8sPTGQdWj&>v
zB0})k$3C`lw`2jUTZud~Bh{rWkp%>TRj`wMad|^bS=`$8oT;m4sAJuNRqt0;0DbQ7
z+D=`DTZ^^3_M>$0U8LS~I?c7()WOB%$2iRTk<C1?_1-c?{(9tk=x|xo%nwDrZqapR
z?(I?5TW&4$<`2LBscn7KCll#v@0}t#Zoc{Em36Nz>7L;2<)trOSe9RY|FYIv4=D#8
z__;D~Ug@XtStIJMck2&^06(R1xa{$9{V=Yt#kEIVPl@ZHajhNKvO^G}<>Ojs@VU>3
zYwx%YjO#}&@Aze0pAa&<Pn7yZ>3*qCl*)jE@<%>0aIkEEm3x9P{pNrJ%B$b_#<Kn+
zA6aSojz8|W^6~)v-4dmAzi~E4{Q$-BOthr5x<J&tDKN7GPom(^v`&TcOto}*<S$0v
zCGy83?-cn7k!N+ttVi2K-aGQ=Bh&bbT6f1ica)QUdQ!Fb=!#3Pm@@SPY`KEi2b3;y
z6b3f-1NT5Xu<bLhI6xjS3)pk#^7r9f>wrowdjNeWYlb<F3?do8<Z$yLD+vY13M;Ho
zS&=gG9MVCa5g)E}NCqp-o_p?jKTC_cAWQ1Xnt%+b6Y`P!t^iZ+Z3vRZ+8!g%9cs^>
zF=b9k_Zq<3Jj3mZs~$Hhe+L8sX@tx^9?xHY{q-x7;`ub|fW7(v*{mAs!FnRf9kNBy
z!Q$X&x(x#L+}8v%;4YcL80vj4NQ83KU8|JSyrgX2rTp&U>Zk9M5F)^c9IH?6O+heN
z58bOB)ivD)>q&beEb2*i+7ip1F!S1j8&=&SNfybGGG5)oK2$P@lxow_o{5L3Y8OOr
zIBO}Hwl~c|yls2=+GLwGiFItaAR_i6ogVpPkwwd_&qJ??e8s=6D8KpEZ_4NQ{d~FL
z!rooI^r^AX?Saf=kzgCj0CUoCXMO$t_Y2A^Ua@z1-~-o>xnQrNnEfSe>{DAXhe%t*
zwO3rfitDBZp>drW*O77kEUs_Gb!dm{+k@_O?mfNbIp+=D^FQ_{^@BO|iPDgh=VTKl
z3nHLl@|x(qJLd%W`NJnaS^m84y5+hUJWF2q!m{L&OUk^33(L=b@r&~GEw`-52MzNp
zdLT;KwifS+vhL8(+VjyCzIQT`ex=3K4pYIxH5C%e(OHrI78&7jcFFRQ9R#~VWI%+~
z=rNI>8Tsjh`Kgg#5n1pCZZD$UITX@<f_v_|ryTg@1Irs<_r`MEpO5SKZomuB22zLH
zAPpef=h4f4ym~@#SP)n^jGI&zP#_>QfIO>A5{z`C0-WsWqxI)T)ON5gjlZ%1-dtPl
zVF8|L#?v(oh^)IH0q}YD4N%F&t!+Pzaf7V<=YRfZwU{DG^)k?aBO0C~Y8t|TZ~$Nk
zl@QuVbaMQ&VzJ6R;t`Li$WoFVBnI&JIrll`Mkw0Gif1Y_&A;EZXIdy&ZoEgkcsAmx
zjrAQDI&*>z?4o_8OPaFUH^YMES=`^8>89<S1*DwFk+Nvh>jsZRus$F*EHYbcvBkZi
zs1YU>Fnu5jA6K$wz#9Fl{m342E6O3FrY!2F9ITAmoP4D9MVZ%JbIod>C$~Wt(kroI
znVYR0>PsGcAAxO4%1@!>o_a6`iCkz8giL>`k8<Ujgv@!5detpMWLmxS!DwBKl>re)
z_=X#M+%iP~6eI-68!qWfGHFikHbBDFk&z{H(0@c;HL`i>`!{{R?D5Gx%Fa9NTuwjj
z^q%WUM)d)i(^uE^aO6g}iB(SjvD{`*6#e7(HurabBTutu|24+X<>iMz{6&!Nca;@Z
z_`fQ?6C>O-4>milNt-5)>vr$xP*Lv&gDY$|xMzvQ=f)PY)9ZC^T;GrDGjTmTuGRZd
z@A^b(*fbT0Qbxit>9=VL$0J$JE)F2|-d%Pnn>$Q42GLP*Eqv{3A<F6=i~qBp`ONC<
zkUCMy1<b@?ft)JV66MN`*fTyT#}rD!?s8>8Y=rTCN#v(Qepuwx(k>I3#mG-Cci)P9
zM&!Q_=2Iiv8*opHPo2KL=;DjYNhhDw@!TCL0U`_OaFGVN!O^%QI|xO$L>FiT*txiH
z1xw967qcv@i;U80H_PEZ1cX(m2}m;`2!OcfvktHV(B$W)dC|~BFGIS3AMaq@;i6>Y
zf9}6wwE%97U-Mv&8K8~UeUnW#sR)DT<~^BMzPK<chj!KOz`ME%)>0pT<_H8zo%~Ge
z3BYQ;L!=O(`t!{#NLxOKbttWH%ES7@@>aKCWXNy(FAx^xKt@?Q+^fCnE@{3Cu<Bo=
z(*B_&nLs#V?HP!uw#<MDz@&TJtG&0{YO9Jgc`qvxu%uqO--hhefw?v}ry@$+4#@$+
z$>k3z)key%?!c~lS>3qRS*y4W>K|l=Rf}7p{YRdo9}pZsE!_m0q(IxMgF4p%v-Tr%
zF7-g9)LnTbMBD!nlQsh1?f4+m^}fwE(ze>%`#Q;fJ!wZ{!B`rL$Z*O=2CK3yTvV1U
zT2dCyUs$eMbXA!h`L~fp*jy#@mh-kOd!DmrnSJW)a@(!lM=kYi^)v>^kp8n*ak#U<
zMv}Cl{;Ef#9Iic$m3O@3^#S&g|69tV9(6&?VK)haTb}l`fs;cFQ_Ve<PP9l8Ox`DJ
zKd?T9<II`e9)Hqpc<@LMA0$nm8+5@tBCbEib><MH=r3{oVbE&yzk{xI$k96e@Zf!-
z)Ki)YM9BbS4z~fi?HrKtNGD!!LHWo>KU&`Spa+$=#-RFeT&Ki>zvw&PF=JNuh>Vn*
zl-r0B&54c1eb=&n8Y;0MeJ%1{kpYdSOWm^~bHB4udgsW8M836*y=4{n1#Cr}8*Z;0
z0Bv3X{DJar-A4gXRssM8um^61SI)f%z$|ORXd*KK&nySPzYCy6Rseo=Lk@szV4d}s
zb%I4@z4g|s<~U_QngkO7?){x>7q=vU6KU~%))Hg@@HamJ_37S+6s@tw8rA!Ob8`i2
z1PwXxrySixfNM(`y$xu~P50(S1Xmfkr`bcq@>93km`7QAw6A)nOQUC`D`4|{6NCb}
z0{*puw(uTqQTAuq&mr&w7eV`xSa<3{H<U#rPjyD(+6XxE%l#6$^t)$hd-JOD)A*}v
z(~VENBI<xwo@WK{BF5Spz~*|!YRSd18AXko9YRGmdQBObQ9GopD!c!8;}Y5_`JO&O
zSW|f*Aw}FG^&OHp8HxNDA7ooZ%-MgRU4H!iAD62x_;)$~FY@yvvn+7~{!L`Z4s?^q
zq`s?L`>d<vhir^>GG~!#_30)OAS`6>*1r4h5p#!wzb_~oZ~V&u+~*2{TU{@BK?xZs
zF&-!{rp_9p?4m~6cQ;-K8V{Gu@rmQQo!>`tj*@72aXmP$#}4j2+BL4-8rM#7Z5G#~
z<65(Sj#8f}je3AhHc_I<GLcyb$3ipKDcwL@8Q|&o01LZ(;uB??2S2#{JZ8UZS6;c?
zwB?p%F57x-^Ym~;iSNF}+QK>2gB9f2C8;SM3s*RnK$9{cHd7+`-LG9EUvup><*YN$
zDtF#+rw^)hTjUEPpBWkX;pe|bWNvoIk^c+W@SMm$iVSSr9vMM$z~hoKHm-{L8aUSX
zWYqs@`iF~n2dwK?E^SCpw;Dp=R1gHf72pG$>th3HVg;h*Rjyt`E^{$b7FH+k61Ewc
z1Ev9Z@1yNU5?F=+dHZfyAp~=vtvAQ9Isn_kYIEyCdK^dz&?A!w58$4MUvkGXPo~=~
zuxB5Wy(tJ8*E$i}l!412@CWFd)0Dw8I&EB6W@Jp=5Li}W@5`uolJNy(BWeKrlb-aX
zYCb|TSVq*x9P09%Jc9^eNq0W|z#2ut<8#2+_gU8*f-N$q!&aSV#Y!Z=1Q$r|c!B^R
zINS_@eDdL$=0eZQzRr0?U6n~+XcNy)VuU<%EkhLC?_S^6UU|k8IW<=)8#3ze<k8>M
z0g)9|aG7P6srG>RuC>fFwWYEko2-7#2zcIsR5j0|v-WroH$?J-7@Fg9P0|PY)3fXA
zq_Lz--aA~>I?psV$cpyK<a<b+v4ac`my`qH*MI6h7VEY)7qo+Q&-kjWvrl+p`N82o
zxc44b@xvpp6M3b`TSvaA3@A6}+&QO`b;^?Zdf<0NAK4aAgd!!|RG+C!eU2a#KGXjx
zBO^)P(biu>t&zsxEw}tL#>OqNu79aK=s}wW5kD~IkLqZ}vcd|bthH7Nf?i^bmV(nx
z1?LSio#%eBTu(+y97m42RUG#Zob3~(X_011Fb9+7Vc+^zd2xW7EuQzh^7aijD8C4j
zbSE{@^PfKu7qmfkXqONrpoe>mP`_L}k|<daXq+v0Q$75(ex-%y-e@5ni+%<ufKZ#C
zXX?37OBOFFM|}T?vcns9C`bR|Xrb&XFmY&PAmSO39~`-EL3&W+Peiug=;X+v-i>LQ
zV!_Y;rKJXL5sJ3O%i`wVPQ;0fll{6&pXYZ1JiYZ_lVSh<e{FOK1Egzow}SL&5F`~g
zN~DpH5D=*$NXJHpga`sM5D=t8L>%R$rKG!b2uS+feZN2V_dmFPxE|+uzK+*%JP)ZJ
zp%<gum)zP!zD*_5r^12CR-^swar%@HuQXbTci}RGrU@q<nvS{Lb!)QRbc<S5q>E}I
zhbh_3+P0l{S=FRN6#hN4#9sVcBt<IGQ_9_GjZl49)VaT>1$ST1h3ed)I4|#df-z}}
z5OVml;Sn|8F-#bx?nL?5t;#1=teA)Mg+!$k@0WKY=ULt!5t`cYR}<x3wAApXZt{Aq
z6TsjubS=q{m$|+8a!u&;+9zT>NWWak-IG6Q_*qedjk<q8*dSM)nc5)#qTU_~K`a1@
z5ZoVZ*t9E(W~rBMoh}kM<Wo2peLu_i+DtHtJuLg!>DXytBHMWs_crH6gcHcKNU=-`
zt!Q~Ga6h(Ae^Bc+@^WG~%c)w;60i;&Mr|D*Q^-Jrk2?dm-&seJIwq9x)nQe|?hUT{
zu7*@<Gl|gu(TjC`c*wJxrPb}X_!*Iw+F|A#Ftf@_b#6MsxM^?x^XTC8KGV-OS(;Y8
z9!TiaUGH1A@Sv+~ke#3wg+k=DDi3#Prk?uqedtLjDFwouunYBn?=sMI&?eCTF2or>
z&Eu^df%RH2S6rXQ{a0<+M!AB`&cBxDWOMhg{QCqa06+4lGp$Ia&l9fnhr}(wU#RbJ
zHA6UOpRif7Nn7tRcW&u0!gd9Fzuz}JzpZh@DzpBM*4I)>l%Mz3`ej<VsE%CGWm3kF
zUeLAv%oS~W&J&4qOU4yKxGvD2KT{!xkxU^%(EstHuN`+r-y?I?UU!nc@c0E<Jm+Ge
zK!FnXZg?pFow#x)@zuS<j~s3R9v9$t<v~dR1Ua`~nwkq7u*#&l<cxt0fcta|4%9Nx
zj2`*A^|L{$rE0x^@jH017vJ3>sqQeB{det1p2VK&VBkR9_!t`^PMb+vjcK^6<J)0k
z!{#~v<3G-~(D04o^N9+}Vm_g+uOo?5KTk6YdUaS|HtWWs1oyoo)7yY4!W8+jhkEu?
zI|P?Z>_(eSh#hOcU(J7e*|<}bVmkYIeNPlApK%CAc-;Q2?Eiwl0YfESGSd1epDN%Q
zN>(%geqGb@d!5yEr(6Q9tZkqi@Xt^Ye1dp~`q(sOUG1M?u1ofUSM*pocJ^Utm8bPa
zW0U+h)9u09d5j+{LMTW1cr*g+K5@#1mbuG~v<IjI>!y7wmsHP<3Mz?0j0N&#*#hE0
z4Y@eeCocD6m%-A`#pt|_>DkYv6{)5ClwP0Ct7?+n@Gzc!QN{ee-Zz&!6ODq!42bw&
zak}<$4QG3USOvSM7Bg3oLGR<)d3P^%JUZO1_9wji;M!vyFMCX4>HV)SNqCwgB?;zo
zV~y{@;$$4Y+K?mmA&Vke2gKcm#`B$W!gjy>mWuU7)zE);>bE4ujG%YbRq<GS@}}zh
zFkxUK4+NE(bI$N5V}CAw>t**-vQzGS(^;#eT8g<90|rGdy61upn(vT+uZ*ReB8Z4d
zZT540HhU7^cXXVq@noON0e((WO3yCve#pSSl*q0WqTAmPq1q51lor2N(P<#Aub_SH
z=Ikb%qBlmXPp?Vq0J1qJ#~ORfolVvPGNzw3KjXFe4g;9z8o(2v1sWsWuFJ(-X_`G5
z&`-LXl4jmhqSpR+QbOvv)t`s!)-(+2kS*yP;$u7r@?<h3SCIAzLMrr-CyTsS9KOgR
z^8i`P4a+xd$;F}kSKoPF3iN6I^}4VHjQeDWl!e<nm2aKS#szBG`1<h_91zWO|NHyZ
zib<ebto>}_2*S0Nl$6^;GJgrL1N5dIA0FG0p&wJN($gKWI(}In?Zg7<U-I9oPFUPm
zyU3g{dFa8%47<xfD^8R@NCW5NDT*@UO?hDl_2^o?Ag6ZxJQ%|vT(LxRq6(GJr$nYn
z^C<L(b!DiaZ!9q-j5Dnf!KdFl_idge@YrA?c3`@ksYB^GyJ_-*4^Q+aPnFSR*)ogA
z9V*dxXPOjeUSoL1*SMA+a9_<d4%P?Nf2^OKR5+`^Y*Ve>6gf>!URl5Txy#$ErbXHz
z_PHx#KxHBe!;1vlt#wIKlJ$+{zkt(ZnP<-zTx6r%B+E{sp!t}G<i%>zU&Ye?Dd5mM
zw^`qx-@mvOeV3dw0<lJ`r<*AsKdc#XHa|4fG;M1WuD1XQ@$ch}$FgoNh;Ajn+*=%o
z&sAD__uOPyZ!?=<@z{5nwp(Ni_=1q(wzbMeY~WdngTDlNHbr_A27Z%JK6qxa9;=|G
z`dx4Q+kXXj3-uA=(x-4_dmM@HnLB_AdH+l&A+IJAYW$!B#}noOB4Jx5^gh&CcYugz
zo6wKPE~+kw`9b0Hp0gNE3mzVo3!MmywP_tvwjH;WOK8IgPCfZ@6U(PM(v?FTUuOwD
z@%Hfs^dFZ+>3bV`-pH&I_2nk2jS5&Aa@!#3AtTokgnRADfQk2Amg%#YDxT9nxbZ{#
z<E*V5=q^r^2GE%G1d0L$PB&0QmXx8%D6$@l8RVTQMs1u)$^5BD4Z^CW6m-^8U3WHE
zayhbPg=8@$40Z)2QTH9}0P<|elsGKy_}*Gi@~_w*exBOWnGcj_<IK{!VkX=JfTCk9
zo4>XHF6QO_P&KR+Vj$wuJsoGdonyRT?!Mq?c)nE;z!sqnQ!)22Zr&tm1vjE^;;_+{
z$E|9^CCo*i{cC-&pwjZ>4HbS9?z$gq*PK_Y7d_7f`J<1&1%u9sznoJlK0+*;$9eI1
zop1Z4h1%*so3vu>+^VJo19PgWl{7XWAbztDkZ)PW(5pipYitb_ou(J!_KtyiLnE)2
z)_%Ek1cbxiC<x+Prr*w2-*1vibqe*I<pk+|L$h{2sry8l6}JA@X_-$dP~rQ>O!P8F
z%Ybd*gS>9ZanzNwzPt5fVMXkuwsu<Qt%mQ*wn_V~^SAN0*YiV%jH}XD9oycLP%%A_
z4ZQr_zyC$Xo+jS!PeRML)ZJ!3Z-TLLh=}`{@1L^{7WS*A_MHD6TZCxGElU7N;$8O0
zO8G4u+r{eOa1}T$aY$9-%O0v|Xb!xTHCiGs>F4dj4~kjM*1O&>FBjP+f>!<{BTQ}{
zuerp;?l%vtP|kz@RG6SAzk=%ZbeUEzDzP--;m&LPdTA+_W7zrKYqk)2=Hk?R8%vl&
zmZ#-`80aw)#6${Y+AvM?|1-%m*`=V5&IR)H8Gq5tPvgQK8GzSNl!qXARDgER9h_qS
zMbtAUYFLJ4Q8kNWdQW;5@JshQTpoG9)-Imaym9+&f#rqyM8Xi0boG31P^ga&{j?yg
zN#;hMTOH5`hFjVrnLY4c(3@r&l5l;9%qb74xW!GO!K_f*u;NJy0}X!^6<L(h1=)Z_
zwvxky{hCtK$xdn}s8jr@Ex74M>5E^_m=wgOgov3~m>!AR*<anV?ED&b;3cT7eWnsU
zX*x&#VWh?+651M{=qCl5#sulZrwqB@MPE6P3o$EOvg23UcXqh6)5Gf4S}!{dh%X;r
zR9194=u4LBrZ#pe>PCvtj#&uF#GAKm3m``Stpz~MXL>;G=V@O|{)vd`Bn52?Y_sF;
zbGw)c;=W79cLebfjp%!01=EPuUQUvqSuycvxsd!Glv|(BW9Nw}oU+|t=Hi!gspwLN
zJbD{nVme5kbawi?uD5J%=`iA=-T$J7$UoR`O@goB->WOj;?w?Wj*P5>*3F0O?Tc6D
zA(Pc=O#aU-he6#5^^NxsE{ut2&LR9zq~b>VXH6Poxb>r6)WEab-39HlH{Cd=-u>Ux
zO8T!*u`uEmvPOl<$teB#55t>12b=6Z@S^U$hV>8C7;~Rs|5tMCZN8hOXCLSa#FBKL
zd8pmW$}Y&}0EX27eiCp=;bG7Udv)K9*zB7>a^2G{O)xNVJuB_&XU+~t<pn`Bi3?l;
zKa0Nlw~KBqvu*e7xmxSreIkBzn5SO5uH_4IbE+Kwajb}Xla>-3J`gB8$7lc=j8XyU
z6iE(-Da2``sjQE=PRIVZ#YivBx&@l<a`vd28tl??5H*uWhjVv*E~IC{xx0nHd760-
z;3v}M9M9A=wL%ZM@<Rbc9zq{@2AliIPMkK!>^t-Hc;Lp4xV%2w5b2C7iR1pSaP6pF
z-We-b0#1u#<*fwV1_+wEQAVoalrZMHqgU_PP5<RCy+#x>Jd3`r!m#)(^$R(L(@VT+
z48L8&1eKpl9xNH~by%<^9`+<s7m?p1F)vEK-`q<ncHg_^2DY?~F98?&^JwrcA*V^F
z7>-G6%nC(1@KcJo<b4my<c}{^@_qOxShlxt@It##%LjcQUs@y?+L!#$U-Ja=)Ok?#
zr^(scf;f#D^L(_UHam~;d1cq(Xh26sby=4mI{N9q2i;HQCb^yGetg$e<8#{i!Bm*(
z70P^eqPowc59Y=CYulr}8a6xib5A@DhK2)DAWJH5DC%`DbslJEk9p52|B-kxKk%-6
zeqKm~Z8mk}m+IAfFNXTGxcPU{*R4w(RD+z&^Jdr2uDFMoPQA?N%H#B|b=E>$wpB!(
zey`|g=)bRZ-f0-xAo!KK1>*lGx)gZ3euba=BLYS$G6Sb(R5prF<1j_6SCJRQ+1`G)
zyEEPo_&-qKf&?G2G0*rA1dHQ@u||E1C}G5&JCD2mS8>OrN*j=Q%ZweX!{!!1<My?;
z3)ya$liCNcnfmrq|ES8dwy>VqBSPlK=G$I+l5!dNaF3?(bxYy@B@r+J&yl2LeGOck
zH_wF7Pb7(a?5KR~O=&K69<5sx|D6bzzfRKw&m(=7x-ycINHQm12DL}PJOp9vbU-FZ
z5{kRR(0tH)1}C2uWrEb|iL+x2Bwv3(geISEyvB$#U=yLlYlPzazQ2)0b{UV7(a$V7
z9b^VlgIPa#FUi$Nl2i9clnUTM0MW8OrDwkEW2+_AH_H?|FOW0UZ#v;@fJe+{dv?p5
zwsuZ1X@~ohHQb`db(A$zedDZ1HJ(qis79)_v7Skoj~A=ikMG$Z4uDTV=%!R*Tp>=1
ze%$nAwtP~OmdxI{wK0PQfnyOHw(HBKv3qA-o6qEXo`l1!Id^tK2k)g~DMChL)!ddf
z646TGJ+djU)<R5+zNn_M%QfFR2*5;<7d>x4`c>c8JKSWmI@Ai+>)-vv!S5WO#LTSa
z{DOpZ;BJX-Kg#`Z!nJ75b`>QZlfqu9<PqBNu49&Q#zlg|fH&uyVRXXM;KhKPqL5Ib
zZL7se>sy*Y)F16~@wAhUy%$4+r!?pGA<GpH;ZsUaY96aKN*z5w7uMXepjREPCopR4
z*_`%~xeQ&VmJhE8<j_49;)_iNOPW<c#LbV(8=8iGt1>h&i{9+MkwdUG5<Dw91f5gF
z#HU$0Rp*xcK~U74yU-@lTO;bLN}-bvh8}<}nT~ybq8p5k>lU5z{(wTTYQHRslM&rU
z(QB8y2mFx<UQf`U9dZ(0J?bpxJR?CDM*ca$YOYGM4d?lEEpOmkabZ4C(HVprO&HS>
zSNy8^#I{PI55q4K2XL${C*ZPFH_nsWbN(M5jK|e}h<iprxY5+HdO8vcsyNa16<L`j
zDBKI=QMzws#}Er+`I~^>J;!<=HYyUhsZTNwdOF3*7JPKCO}M@v_|?-8ln%L<O-9!R
zrv!Af$&P=^?P91A*KL>v*E({b4QP^eS6?UHp}SGSa`xMp-vH4hu@aN(E43D@cTr49
zbP#Hwlx-I@6G|~g#Ptg~2>z@@cB}GDaKoRvDSri`j!6prw=u6tbf+3$25GQmQblR3
zL4?=Kt8@hAXPh(M6~ccKQ$l{e@+^`S;~vuygv)kVbvE*?RD=zR^bk9mF8*_gq!-Js
zo5AJ+5Fo8fuM#Dn4c+gh->^H-d)KIU5|w0WV+YScxvbr!(5gfUAB4<4P(HIok@<vW
zH97vOySu*}eDykQqWBV}&cx|r>gKHb59)Gq_)ND*AUft<&N<K9^E$<fjPOLT3k>Y9
z0u+<q#Mi#ZQ;Ra4x+0^E&TVEgwN#JUsfh9K)W<o^xa%*`&BnN~A`6e}5xjeM<ig+g
z4ga_<a_$AqmkpnXO0;eEG7e2Kd)Y-q4j&GwOa0p{%L(C4uSInH$a4(Nw(7$7p_&$)
zjJibn&u9(fEa>}H5EjXNl%S&#XUBN^wf~?_v^}iL%0+XdKN)23*jF$t@99xG{ECCm
zFVX|6yE5qEs(r(TO2nRoz2WYenO6DJfiY6K`fG249|ytOKgAoa^u+WcV9p1D?b_F&
zYe#&=oHIq`$d{*~u`hcEPUgSRvw*A`Pg=j0O%of{v!D@@_hD~t{6a!`qp1UxFsxPe
z%|uAOso1+A_BXthd~cM1Kiv9rD;p-9?ph0od*tqFQ{)*8ku6chhmul(zkA{6f90|H
zXB$P5vFXHX2_i?IqQNoY%ycjnB*@3CYusR{Xw_O11|c@v8up7PYlb=4%$*hL#Ji9I
z#E%i-UMqUCmnj!{*W>a<DKPLljYmxe|GM55oGOz6@tec@8}FBOBRfd^Q%)^m<WtFe
znEQa5mAVf>{`_GP1K};VU1)5L*kr2Ygjcoro)x<Ex6?6)uU}zh%uCZ+LU~}~;RY>@
z%9`bA3l$tE(|-%sakEJ17Ok^EF|VO5Fi?1cjSq01cn)slnK;q$$Vonr&Gna=C~^i8
zK0Zt^^5F9}+V~TF7SBjoWl2VyBrZb5>cnX-@4qdy%aynGc<YeW+Lk+1Hyy4$bVT=@
zpyIn2@z&hS<K4x0V7#%=^rU&`t*6JexWe;{>MAJAS>vGxGd}idJ}rJv{Im{VzHZdc
zy1KwMrC8<p^@YxQqG7EtSz&-_$jZKnxR`cr<(S=v!Mkyo`yXc`s{My?sd;9eU%i{J
z<ebkU2Pws%8|v|jzjhiv(||Fi(7exIgC!Jb1|klu3TOyN)&KoSiNXnZM(FFr$8r3M
zzo5uZk3go}uJv=_r~WqH(e*E>zAstJ7V;e>->UboREAECoG}@OY9fOYlR3&`Cua1%
zQ7A(3B<nBe)Y$<yRsg2we@6->cFsyg5Bqw9qGivu101kjbjw!V#(81W@qr_w7C8`j
zNOF0p?F#h1wGpi=Uamn<Bkc~%G2BSLW^os?M9`GVg&-}1w-3rHsD!P=*`@v=q?L7$
z(O?GtBXi^ku=6p-5(nSvVa4io42SFURA)!XaJm0O)Aq1Q*EXjp5si?)$Af>7)Az;<
z&paU_FR%eZ*LAN6@;qlaPL7xx_7U*h|CI0gkJdA_f$HKF@o)e_2ST-9G16GQS=Z+m
zG9J;2GinVx2-)d5&vsh|%(si;kiHZaf`LvUA84TZvs#PF$}}3#$h90DL7etj1SVjf
zyE8+XUqOz)f1PuF1a;r}K_M2vM`3vm;u(evReL#mO~g`ttJR0f)D#}F>cvBQ<GtM7
zvSs!Sjq^q95|0`1MFE_Oma)qx>J7Q|9J7bz``89G*X<SaN{L-ie5NWcTa$W{PXgYb
z@!TEP_;F?&O-9SN8*giEQK3cIhUj7?<eGBQRCbF{GjH6o$PP|>@SnS{eRXTNgBQa+
z|15uUY2W|J74QQ4EK<s|7U9?>GKwqz5i~>IrnpMLcKr8sV@P6FI{NX{v3cPxB6J^=
z?i`!=MbXsa%!e=-FZd5N7y>Vpc);06Fx9zl+&_sF3J1X5y=Z))+@f@!_$JU;6;^B3
zyq(H^D7;zZ@1uCUVllKw@?ppWy~rJsg+1X_6~HI){u9N&!X6E6y^sZ7G!D%A@H-8w
zkxT(gD9b{@LK_o>+SeG0enn~d`SvJzmx;y3J#lJrTHv(|82(ln0vTM%@Oe%TA#e02
z(-H=Je4dCKV53_5k5C!&1t8mvjZlNay#bA<VYT~kYrqXAx3Hh=a(erA<sh&YO6F_Q
z1+v8f!$)2mp>>%upCyRC0IF7EC0wI0qsFENUmdN#DB2iUXk`YHRPrG9z|c@)X_qzr
zX59^kfFV2$6CZJADd~XUM!H*W+62Y+qo|eGIBkk=sEfqRg#e_N<$LO{H(n9_)4!)X
za74q8zMVKHAnQy^Lscg4Xx5-$#9h}l6SuA?ZZVi@%iBFWdSy#S$7l&Xiu$<P7U#Pi
z;O)dskMGZa6Atf3b=-C`p87cWt|seVxN3!u4q<F7tS&yRlJnR3IkC{*RDK9$v^~tU
zP@bQEp}nA2wPbgq&Xao%J$*7ct?BvV#OXJm)lCLWGdXXFocw%TNxk?sQTS^d92M}Y
z9*Sl!u1fR!Qe$F=`5?)k&S6kw$c5fffdz}3lYaVc?Ct#qGrP!At0!`6V_0goy*AS{
z4Kug?ujSQy|5ge7Ca)FcTLF#Cjc=mt^IaNe10BV~b&P=qjho$?t`V&Gzk>v(vohID
zMT!1{ZpXcBLgp)LrDJ_oaZpa}zv-@fNw*%H2qT8`FR0p#PxwbKYny)p2L8ET;;)j7
zm8#B<=s>8VKNA(%3u>3k`1fpPD@9u4`32c-)?k$hRayp4Q4lG)K+5fs+Y94+POetg
z+Y%>>r7FiK6_$PODcD`zpK^E+bRkS#p<!PyX;ZRx{+?3#{Is+qE0^Ht{D)Ma*fs~k
zQs8=cP`7E!n(5MlXE|)Os?5U@dv{)m1Gk~p!G~`Q+;kwSlC*uqcwmY6#~_(6xEPQS
zizRo^*W=pDhm9RdmN2mBt;qc)Mm@&7)iIhu21U`s%XR`a%HYiKl9o2mLi(mZVimj{
zjN9n_Dly7M$eD%aosgjRn;fGcA~f!}Etj34hS!(zKzdJU%=bDKs2zF2mLEA?b$oZ;
zLo^+Kzgs*c{nJWTXdJ3VCGsxI<80c)q3#d0P`&GZ^Ur}PS>_19K88_v<G@vlj#c9s
zN}T_t3yF*-1g?S-3Di>wdus=wkWAcp`HJ^5EFgAsYLgrpp%bs7T=Ni1pI%Ws{9d0q
z#KF&8!DUoo{sx^Z@z|p5ySHk??tyEC)FEX4+8^gD9wLK}vQ=4U?-rsUYZcz_xd=S;
zc$sF6hBx+sAse-u2<Ji_x;zUqZ%surZoQinYi0$ZG!50ste1UZ9(Dri&Xa7LtX)px
z7SHM`d2F1gSblNvH&{*EF5do@Ilys0_~m4U^ZhwzmcP&Ol1{JY-?@eF@qOLEay*<-
zzsdbRe8F=_f#zEGmRD7#{<n<w=_|<wt&f~<q-P9l+#kx`HoHh+mV9Mlth9|~5qmE|
zz*&D}VXm;M>tifslzknZqo!g;NFc3X|0^MQpb0F3(tB^bSKo~>WqD%PdcFYnPp#8Y
z<$yl(es|;<LL58Y=XiYqy=$u$l%x|X*!Cku2W&bvSrOs-?diR*#m~b+`YO20Tsi56
z&R<1+xq6KbP7CvHJ^ZeIG5lf3iuUhg#dBJ{;Q41GZ9R4=>y90K_eVFtu1MPoyl`~1
zX;uZpFYu*`VQT?d&Ryh77&XNU&?n;8@c<;b61czinTr)R9_(eSrv!N6NLfHG$mCGg
zUcl%BvTpW{iC&>CxuQAAT62>N=~I|35RUsLZVAB;2qaP!y73qmrOgvHop6ArVImIL
zLDVsk?e2aLCC`@Tbzymo3n+eqW~XL;kn+!_Dj|dPcmAK;QgjKa?G}pts0QyLRaW-1
zzia44X(Y(u%H70F^_{(aJn2$mV{SJc%T)?FfR*HzIy9M9!0A{X1(e;YQQCNPc%&-n
zNFj}WbTud&2lHjs#_sI%ZAC={@U*LG9kiVCJUx$nK=w#e32l08SPk8cYbd@|xt0#W
z@vmWG&RKW}B;2=)bdZZ4_y(M_K=N%obJ7hRTK{>bFAM|sgNg5lt2A4T{hN)w;762d
zJD6$rwN$+$YaKf3$shRPu+4BzsF~4=fHZY!upCn%@Lg?L$rv`~*$#>4Vqy%6f<nhl
z%-iPb`$d~ISD^!{6=6H$#iHGp-489b6D}5f(7SXR6_jSY<C(XnY7?G%*fuyvK!y)V
zKF-S3QaKt|2Z1uzbEMXnX{BVytmU#LlVPdLcUG(C<b*E%-;5`^DeQ82RVyU7?mSZ`
z<gpMamcAxEBj?DZbem}~ZNJHCMullvb!D>)<l{~eoHFxRFt(K|Nn4C;7!GHC%4#2u
zVNekFk?c9umseS2Kmep>`!so>`XSg9T9N9XU5`Wltmx$k>0UowQ$chUMFgxp5uFxT
zY99~Hq97;g08z|30hE6=#p$Bad-qDUfi~ns<8x~iiUenDNG)+2_3H4K@r4sG-FTvp
zWb|&vKbJ@LJ&^qDH$BIOUScCo{*Vk+G{{6TOtE^u;0HhIsZ-U)<{h>!+VjJT7==Fn
zn|mrg+BMfqB>e#c6|H^x#ysxh8J2grbi<aL?FlTkfPIJrNt+wW1qjC@{ePUkN&OjD
zS6?P7RZ22E7yMaO8kaLD7dOG~dtz%zC_LU-S<vT(QTz=U`oN7XV&BIsAgW>}TyKNd
zTK|OPoEgtS-@$5At_}idt2M2`O*#G)CCFr+2PYA&+{<{@6{HvRk-jKmTu2-?8~;U5
zbxiZ+GvG+vo*a~;?v2hS*Zy+aLXs|1Mzmxp6o8cJ$&V*TD4+xaH62lAy9sKLc+K#A
zghixta&?hRZ@KA(=o_WCO|eGM<}-2cd-i|$s>u<PxS!PrJ29rby+zfM-WOS64<FVo
zxd$BSr^|U~ed12$c8-2k$=#d1X*uaK`qy|``S2USjd`%+oSZL~Rfys7KNxBqFVa|z
zYr>4b>YZIz7I!atG^~Kb2c+S7n`RMn(~O8gBgM*8@4H!h7A`e5(|_WW&g|(GWvkSO
z_0IpL&XEoC2J2iuUenXtXq(r~%(R)yXWzY2{YkMrB!|NrwImo|#De?7LM$di?PVlZ
ztFY$27r6m<Q3{DsAQm~w+OQ|;o>u#FBIn!BDpd3cyCnW;PMiOG<SUg`!7?*HIl?Sv
zn&f{{GxRj7{QV@bKdaZ@<UOp3qo}Ko0BR9Kk~UE#U;B7R)Qe7nWof1MD@FwXRJOf(
zD!>2yBJUvt@$67YCkVb*&G+vXUIzVM)atg-?;W9gVh+)Fo?0)_`Q{s1lEZFFlUdV|
zGqF2#jvTwRtReb^Rz^i2TDqxP0G?Bgh&<|`%^z4Qw<EC@5kNjgvfrr{@-eZkbpK)p
zyjNQLdtcHS+ymyny5Vb7)0q@BTAGqOGjU*##3^Sb8K$WUBh{rpUs2_PHt1BeH{UYF
zML6Y7l5Ty>A!gCM31=8<dt{R4297(JWVRhfu?AYXUTC0T`%!mLQtJ<VvYAT?NGMr}
zACCw`oQvCWXQ7h{HUa$LvRx?%EICVZHr{nY&SN$>vhzqCbqEZ_3w?j0&H#@7^VrjS
zkR)}NON<RzQQWxe|0#=(LW4e2r#*wwIsHrA7oAXF4Q*lgQ@9#LnMpBHs~5r$pni73
zbr3$wLs54)#gJY@I~iA9dVO0E!L(n_a7RNKr58wOs>kJ5P=m#vsmVU170ht6^CPox
z%{kFwI%YV7Q8(r+4v4Uh;-(<z6ZqplTB;#kYL7@r@>_3OZh~}_>*>u|COU7x7vOb!
zd}E98ehaE|cgz(Iaksy?k>_;{l@*7npSG{7@pzYCLDGypCBaS@7VcT3YzZL-$y^@S
zRFEWs;C_Fb1~gtF{`%N&+nt!qpPS=MotN(}yS5L#fXfHmNXx5AY(#w3HPj;T<*2iO
z%!8kQ`=jnyd^#FBDt@TRM6%1e5HNB&JE`)kVpPp5hGO(ByiRh0=oNL=|Fp2|PaJ>E
zzrM)X<X@nhmRf!O5XjpHUwU!F_;7b#-*~(J;k$LC(o0i&Hj6e+IILY2@F^SMrQFKr
zKV%Ui^ICe<Pg%L0ZTbPCC~-}OUZkJlsb})1%m0`b7Ut|GblZZSqepFku0~pVFcIJn
zoSi162K8d;>%7pU-`iAcI*QygK@1lL{@i+UpahsQ7}z6vgyaP%lPAC4f~?yEg-Hb`
zWuM55qB<}P&_U5RH6RX$G#j&mT~R9fq2x{l+dznTN#^iq!!+N_OaVx`J%@y~l4MK{
zGzioK!1Y_sU2T(4VDO|28rR<_o6*j3^lfOl*OCl_C9j4+Q}{vHojx$JHlO~j^@zvJ
zIE_U3?TMMoDEN;z8BC(yF<ySvAL8O~-`Fx1OKPZ2!X;}QiG_@j&9@0Z*<;ll`-46q
ztE98CMjSq+1;^icti1p3+hAtYP1L<9SqBO;y*;=}BOSfV_4wxO4c76GYED^>$2?R!
z(Iu{1ev5B7eo5l2jEV2ffQ+3ACu#2lJS@85YTKag@#N}|w_Dt3N8@Ma4Wy15O8?3T
z<k;b1(RY^>GRfa1ZaQ00gr%Ipv>Tq+;ze!bwrv^?Ld11XbUmzGoHSMwEA9JHF&qs(
z=@wU3WT*YFK28x_I8qywve7E<gjy$Zu&g0l0#85S&r`9{=apP*Q5+%`Tn=-Fvg%dr
z{&W2HS^`IuEBz@_HND4u*8d)&N8a!15&k!*wfgR9UL%b{h6_~tU4!`gPXXryv*J-t
ze#5}nL!8{mTSG#iN6jps$-|mXfQnxIaTL3wpACoH@V?)bBy6x6Y}-+geR+8_R4y~_
z+q{|zpz4T$yvCAFaq4kZ&}}ApHhZL_3WEY~zOO?#|5T9=z3+=>&#p}mB7^n1B=K43
zm&Y5HZp%UbmrCfXYdls+r<VJsjR}k$y6=`s(KCMsWZbG8$Qv{GHo51HXC{^V<MBUW
z>JX%7TR%_;ymk9a13fG<UIO2FvpjMtA_OJBte389bLPAx?s2b(iYOs-Xs~)w{^{_i
zle$4$V7(*Y*?2)-s#d9|rri(necjyp=B!%@sKXNZmQl|?Lx!)?S1r>}p~SR`CZoxH
z3pIlvqNmUba{7H`s+dFa8cb@)HN+ey()&X5qLzA)Vj@&wH^mM7xu@fKy45jh5W)v=
z?E98$#=Qn+A^W}`_gih9tgK0<n!(eg?LBCo_4PM5XA%#PyUrZZiN{1ZcL+X`U$a<*
zK>~B4yEPF_G9))4fm<HyZ|py3(^!sun<lpeEMW>;vU$R;Q>6pQ#$5;dHKQQgVwUpS
z%&mS>>9Mc2pY*)qK=JuR0g+RM1<b+9-ld!fvH|Tje-8!OCDomhCs=SDrnsrf|5>f9
zhXQk}!-zTV++KWLQ|Y*(eq{zr4%SOp>dd@N9!dYzRkLLvt;;#Z{7DScz;xQ07Aw2}
zLU&y9YV>UcPA(_e;wlQ;hRwhD!^7$p$isfFr|euzt?*<avegDYKnymy&RV=k5j)oI
zf*=mR3oeLOX1;VuQ!#UVx|`}_|Gj_Xjl95UIr<?-Yl`st^RibP@BaJmRo!WMnp^>5
z?l_*Zfm_-g&o*42TlLS&D{NH$<Xc^5`<}#M^zwiE?MB|a;s%Eir=6SpBPqMTT7GgQ
z{qiaPtF73@)b!Qa=XcS-CXow#KA}Mv#LaXtP*nL3a;A!Y$b9x29T5Hd4R0h5o|dl|
z5~8t9Z4C%GRCj^$brSm7e98XC25^JiWt{^m;thKkeTO?m?6iQ;7zXJ*7n4FYm=a*+
zz+FO;ji#7FKF8Z$+<{vG>dw)Oq{vdVHXQI|5|Dg;k`eDt3gf%4uT8HEu+IEaoBGfe
z7-a+m!dOdcim;^3Li;a>>tLW9u(q(eDP|a(v`9zEAA1~o42l}773YtSKT8TEu3w74
zt|vFa6ww-V!@v_CZ*lp-bV-Mzs19FsA}rcz$-Wiji_;=SkwJf+PW;DtQ~SLG>2I0P
zm<lBmPg=tssgV8F;GIBR@ZRlOElR#aD$FN{(YW=M@Bs>CfH2t)k|_rnjcXJ%CY@0a
zmiYWTiFYoOfTir!3t==Pli;xn+F4PjY>&rKELo=K@m4GA+k#0V#5nT^WPI&z=s9~1
zEK$rU@p4NKr}L`QhLukAs;YZ8?(DqqLMW*um^E_awq$sh;R)IS|Kf)kaU-(fv6tVd
zToBz*riQm^@LZYg<2koSosFBR0*TbTZC!>w$xc)J;U(cpB>pV-G_ftj&nwh4Z4pez
zA>%7kBCHqv&{iQK@&!15oNs{Vfp+HkcGp1A-d1dGoDaoi(?jgfud8i!{sT$d?a3Kk
zes>Ay72(h#OrAH)5=lz^o2ejZ!cE9jvf5O?_5Sq7*X)N}kzojx+lFdYBA=zwnn`g-
zavev{I+H9<)4pETU#`b=&*PMi(y)4Z*NC-gZF?DiRmyQEzDOB-O?f+#^43$<kNJYR
zI;l+EIln=}pbX#%SRZ!d`(5rxXK<w+6JA_w_OS!XCZn4u=%+ddsQjL`<laktZa;?`
z1`apCkLd(U+H1IwOuZ{Y(n-R=3vqgwN-6j+AEP*>{>`43ro8%v-*^K|tjUo9Fz))n
zD^_HYJPeDJmy7^zH{=$af`Xg{YN$@C>YoAI;$efte|ZY9Bt1CM==dLfX&j(BEJL%T
zy4)7A=`Jf4+OYlvrr^osA1}?zj7{w!2UKMTIVt_{w(3!#q1JF=)J<X?MYt&H_9>s6
z_;YM@D6!kGOX`0qna_$&^k9S)6SuIu3183bEEik&NaA`ZIc@sOJChW#-wz0$Ihc$m
zd&6th$+<-|m{>n@Yn9*QT&eiYBE;0c9p~W7A(?@dP&|%2tt>vN;Fk^>CSff_<Q;zq
z=2@BT|B#iWnbfiE8K|w)Sl`W4J==FE9xzKc!+&_5fT_D=^Ay*@Ix}4I9rioTjgmT}
z2n%ksOq5iM^Yf4Orol(INe2Xm7+PJMw6B{O!Bsa9%YPm;v(N4(c)6>uzES(S&Lxh|
zzu3Tw%Q>4|G;+4giEPvQ_l-TaXGvPS^Kuj-U>OjoKm9c?{h;fjc)cWAjbYWTf`xYT
z!7stS>IU&I=4Q_%X?KdU+x~a%5PUUE_tj3BmM`45M`(J#5dUpeMXucjQP!p))@lJp
znv709!Smq#2E!0@Aj;|MOq16;!KA8}Vei@*t+zni$rJMF#426eBTq!z_ZkgbPj;e~
zJtFB8+D)`0G}j0go3c-io#tz|`JS-N=&srfVoO24H_7A?&l;NzRb$!3ZZ%_R2^*JI
z?n>fb;9)39Bb6sNGHbLPDU<I6L+L=UdR()bo1ubW17(`V5bpiwT)tG=qjf|p;&S9P
z{;w~u4}Q)oXvFUAn%Vcze|M^aHNd4Z$wV~OP>dIb!|X*AJq^8;8PDA&<X727!LY2T
zCSs9+&$^u^tSyPHR(WHk{nh}I=sU9g6spa7@5Y^Djy~8wV2K|5a@bw2Z2SnD8tigY
zPTgaJ?#{ZIE?^Z-%=^oB|H~$~uZ|${IW%AFb?~}eYv;C93>(Fi8b%o-cs?tKP@j+X
z+V#XOIlh$wy!HoH%8fA%YrP+$B9AA#^re`UaC*@3yKilGhGaD@TRwO^DWmyhp|CW5
zF@P$bpMuXdO6Bo~{*2Rpr=IsaOgnj<id9CP=tIcIY#f~y&NNlUkP@WL))e~osf<AJ
zS_%hG`FTb%kMyj@#XBl0<~&m$XQSe6L-j0m+Q?&t#p!SH9HltTm&6r})8Rk$bSEZN
z40Ka+&Wn_<hO=m^Ccmwbg$#Z``*-QAHFmqXAnExZF77Dc_{R?faI+FPMf`>7C#FVP
zUj2iepAgm`L<Z(L$zE?IGkK4NBealX?>PFqK8{Mx`eSHpxJAtFx@4Z*?{<Zz)*Ej}
zU=9UEEBn5CXkdrc9c#PpY$Ue7gREf9sTT&@{>*REKm_f+eR6oW)3AG|b}m5Ve}V`h
z+~eqoUJOrD9emlU$Y_xGMUv8rsc$6`4kU0op8Sxhb>b8Z{WI$FNWAOo*Oluxfcrl<
zC5^`ZZN%TbDu4#qNl-F<!cT#6C<IP+E^M9~SUn63Jezv7DBAsZRBEuphK%XX1?pO;
zLD(>mD*dxlaB@eaa}<Y<D4#k_VfL%H+|NKLkO{P9+VrSDTNPycaxc1WIlQvu=80O_
zm(mcmyMl|jaQ9o6tZXd8>%8O>kAqj0Ylv)PkxG4OO<zvY&?%NxNCI%}G2!t_m5iL|
zu4>Z9Mb{9zgD~cv^6e8zNrN>si4lQ-VK1~~V~6aS|4hVBq&xdLbT@P^O#RK62wycA
zR$BzaG}OC;KIq4W;xAwdNnoh+Z`V$TsKF2Uz{Bvbga?zaOaQGI7mC)D$_F`L*&Lw-
z_m@hS_4GWx`3l)y;s)h8*H>*FsQ2a3+K~ccE+G6q7C)1OTdWFE##csyDmKcGZ34A9
z8naYIdgGc~|JDoF$Ar9hs}^Ei{TbT`Rl>ko1Mm6MS}C|Vy!<1_x7^x7#G4ipk&lPw
z3@W`E;u*_ao;niewzOC!)zEm%flM;pF>x6^F?iKd=dwh%$jA4y*piK@oI3J7C*7?8
z2ht2v<0*<mT|A*5TdNJE*k{uoDp;yPpv(TLDE_(GNp4k$G=&MX&rEl(A-X?`$RK?Q
ze4pxhO!WCjuFTioD(p6x2Z8hz5-rsYua4;<rA4Sahf_KFoA3%Wy}<ta6m0bgx54)U
z`}Cj@MQ8p`(*0rxrZ=HO<Hgs9P+Cbk{EPNDOb+kCJ>M0PJk0L)$$ZU&y8pMjB&Zt5
zBvr@y8J-^O_Q@C!12T8ik+gc>;;8ueY<<YassAtTft3U<^!jKB=h%R|S5HMmK58jh
zL^FEce}}*!@`nlIeDdBU3^7B=`jY>OS8uJ51WO4w4|kRi+pDzCx;0v@@f*;_cOcd_
zwIkftKJY5vj;QR^U*KN?yK7sb$d~4MZO97kta#QkPndg%@|8L3Fj5jqu%=V?3M4a(
z4ANi=9e^>`^@k_j=4tvHkMg^L<fWPS+%tSFp+^}O`?r3~_lxE=^3JigY|AxJ(Ae&=
z4L*nSAla@Ge820CXI@mP%`e!_qmj<)h)4jZvi3j`kv-<WD%33Dkp6LwOXQHu5uuIr
zSxd}{p@KF`?rW^pUJp`VmbddjlTl#XSx5-b)0$uY00eQlVJ6S7m)A1W3OG!W1peXh
z-V7DD#VBG)Jd;$FZ5QAgEz=%SrwxO`AA&^{Jd-wgNoR|{XmwtWsD^x_w-Vleg9~50
z0t&l8J35qqjJ?YxAlgjA;l1U?|NINa-|K_@U%+_j2DgN&i9>jYimIs34bR}0nL0C>
z3<e<4sNhhC`3#?&ljpaIB>B&dgZpBrIEG5pV812|&P0GcH;W+IFXhy)|AaY-@x9}@
za)!@Ux64m`#1^z_hcro?$rRy3G5na4pH)|oXII0F^zp0b2{(7iykq}BO$QT)q1*8_
zty#UF{-NJImu4&`IzE5Vggb3THINGam5-goU8EqclBmVH+t3oShS?%K$#%~D&yE-r
zOLIrn&Oe1OJl+vVBwF3-_|(~6q#>V7<m7;{kP6KlCin${NZzGzQgZM=@0gZ3k{5_;
zE#qPh5asXhC$#+W9F?ma<&|T)uK`5jBUY!tB`h{AGHhwDeB=4+;(^B%Ta~MY!UDrH
znVt(paWns|2$c8_@?4)5c%}=K%z2DD@+5&D$n`0B*QI)%=89Ys9t9#YzaQdi+ytK*
zGY|ASqMMeCU8a?-O(jKvOMMy)W1)}z+DmjKGlIps4Ey4hWm$zzkRbiYThpdO-r3`P
zfO>tSb~!jnO#0Rsq#47p#<|p89evd5hZT^*QKX~j`pC8jm**eqt7Kb3)RulnnH+I2
z0E0C~a_N&N6EN3=d|T_+eh#GjmelD+R7TNIXPPB@6(kd>Be<~eM+A~>$=rtxM)N_@
zhGz7V&J6B42XIq3qY7<Ha~g@?(Z8ontD4y{(Z@gum=q7SfmzKos5i1UtKpW^azcZU
z!BceEj7!S21(QV)CWhp^F%VD>phW+9(kL8}hxA=E3)+vh{VR#@O_3^o(4EPN)Q6@*
zpi*bS&fhkjwL(MUHlYkXYE;ONCl-+uOC`xYtr6fhI%R+a;S}Y>c+{>m@#d=s^ZIG%
zhgY-t$JCWVp0iAOdc62UafzaW(A=<WZ_Ga26Ge?5C@rU*&p0OjiDr~V)|uh!n9|kp
zrQ9HOJXqh8Is0axWkJ2{WA^=n3Osl&{^cRhUE8e=99Nx}y0>-^mbuiS0~PF?ep^eC
zAYK-^akP-NF7t*2@835Ks0kV^Udv`o4&!-&Z4aCbnE2Wr6@atg4v=qnOv^-Hr8)t^
zV#LtG;KtkLR?T706nIH;X>R8v?p4~ye_V?wOBrS-aw?Qeye!3Tti<WcGb>nR+dX74
z$&{Pb3N3IF;zdgxp}TSu=~lK9{A_g9=NhKrt6fZjk|E0b&L;0p#G{X{Ua?gFBapCJ
z0oT%udcB`1c^{qlXa9d6Bf&n@DdvT%M{Qz$CA7#V=dDQVS^wLyB0Bvuo^05d^)T^r
z1}l}IQjIRAJ5xq)X15x8vqM$qyS(6TH-P8Bt=cEYS5R*X>7j75kxa=~`ljO&^uI_Y
z5jXcdL2F3M&|8pb>d#(WuV3nU0rr4w0u}0Ox;_yuzl6H6_2eh8WXtZc2GW@l<rk`9
z#E`(cFXZmBS>1A`@-vl$(Zlb3&)tOcNCeUY4x!w?;`xEcFn$!J%G6<#LR@*`OF;v`
zu~#$p8PreQ<hm$|CAOW|21^u$Xq*_?B!44cdB~{IpLK81pLIfkVge0E>T~;sJmo%>
z^8R|y+@}5q57S0a%$HEO7d&y|)27FIRnDj?wyXh4PR>5W0V_DIlpDI)`!e2L{UG9c
z!%jQ}rp7zrMdYHn2a#do#P5Y*Oa@bbQQkKt3!UY~z0rJ(5;%0_bi6@)ex<q~gUD%t
zG$ea;KL^NUjqFjdL*5JhjIOC~na0O4PtTO`vmzm}NcP!Ja1B#<GvpS2+wBN2UVX6W
z;Y2+9Lj$?Hn#NJhF8hG&&w&PNvWY9}Ltckgfip2|)l4;=KQ*vT-v8T(ryi4CV_v46
z>bN(0Wx2New)rO({uqzV!k3V4DMaFS)n3C;&m!5Fs*G!d837Sne6d$mu=795kBC<0
z-hJXWFfx-6^DLbu;T0`jaDUSffqeV^PMOca7}X190V?s7vX{Z4?NcdMj*M-0@NvBA
z{0*=hV%o+p_}dmDb+7%_+V7rHNkYIkQzm+EvnG(l4>9D3XEQG)$g^RcJ}nQX!-iUn
zt*G<;m*@;iNc4i>)yaX`g8Kk$s%Z*ij3qDGE|k<B3I>8|eLNOGSa_ofch&FC<i;%m
zM3u^t5lArNgZJDHmiB>Tcrd_qs?m?M3~h=}jv-5quJ2juYHEvaXcO%t1!Z5WLWD;L
zQ3;JF&Oh9k$ZK8otsZ}9&8~k@c99cG#{*?#!D0Q$>HOoV{fV?Fk$zKx+4TJUxb@$P
z)hWfcg)J<+vGJhvo?{`3>`pj<xi*)Y4T4Smn~WAK+9Ya0wx4Kgj_nW6j|il^jC0Wq
z8B2T#X^0n*!sk6&XhLsJ%?U|7UZco$3Hhx&#81DA7+mIz&z0BqZGFb$86wYQ$iA|9
zN{vK^Ry0O;>3PBP8>3G6AD$P!EvBw-DNhnWoJLxThP$TSJhPx}AocZh3fEE-rpF3;
zzWFFCi1fp~-JY5{PiW9!&u}}Q<hn_EycQ9zsg@#u&&qCB3NbSdprqdozAEE7kDcdL
zo|PG>{T<bV_CB1%4Z>-kI$`S5d1^49Li(bA4_U`=Rd@UU&6CYM2$`R2Y1(|{(D8Tp
zU%<BjDbLiQfDu~f_ulHx6^(ZqR7<*(J6xPWPg|Ba8CvsHa>UKv`daMClcP^$tqQ7r
z+P2@yTBN^SF63O{UOnw{PGJA3r@kYcM1R%HzvgT_yxDxRR-|}#y!&6hmek`eL?G!a
zPh2L9X_C5*zm^0<x=HrzY@R>>*ZbQ0ck(c@d$=GfZUXe+@^@=M@-6b-#$0J;DUtuw
zI^28GO2tPtSm(2bkjN9*o<@iA7|_=d>5n?{QE3S*k<@Ludcpt5iR0lyFsknx^4S+O
zfK(uX4|5mkMe(<y-n9loIolr$ySb$aK!ozYKE*w<D|u~PO$yrzASN~d-2sgd``f+;
zB9S-!H0a$g>2j1_Pt%+@LA8*6*_<s}nqk~qT8(hq7B&#-CsiZS3^|3jx`YybH9X!<
zZBaQ&s@5NKlbmP;3B4sDc-(m_o(@J^GWL;5_;s?u_ZBnI36>%Kfe>RncQs?(NW*II
zKGCpeKxiz4J%$$aN7;d@kFQN%r>G{=Y`>AGI?M28GxcQQ)a_b3@^o1qQ3T<xkaalB
zy9h6GS*MI^f!5EC6tQM!KK(Q(yS5&RQJx|?Yzo%C;O!fHX`0@|pL&imf_95PD5~;_
zcTG1w)M+?6ynZ_ZnQGJ&QO=WQ8epsD1#7#aY1b`rmy&0E)oV%;DIp>XhKXG|rA;@^
z8ou#p#)p%Bfv)P`&|I-2>`h<oT1b}fd|Mbj#oi0YG^GUMoD!V2Cr-s5Ve3D>PrHE&
zUNjA8Z{V|Y7Te_Im!*yTsEcA=ALr`i_`7d^6hoyk=8BmZ%zU&Emt;Pe82M~Xf>&J_
zFOM#CnGU*1RGRIEXhNiSj9)Hy9tTYpIj0#t4Yi*b%Ov*2j+<?v-A1fAreEN{$4MrJ
zywm<i`)c6U{?yg~i`ohn3MVW@6G}z6tDlP~@0q%)pSHRz#;Ib$c$tPyzi3{Hm_<9T
z6eTb*S{ngGrj-|1Devw1Bb-DWN0`3i=~{qczx^F`yd;{NrPVl9KKXJ3dzdo6LAH3W
z)QrrwC4yQH2GRk*OKiqAWKTii7^IVTRjGkaE=USU3Qg~!8Z;UImcD#Qp9G=TxAEq_
zG18<26*y4<{lJi5Z&*p3@}w4h4t{t6O!tU@2}GJ#@QTdn3%h`{q$~4ytGPSn1}9-<
z$Rw6~s<QooRJkNY1oBtKIV$*YTF*AWBkgidE*by@><r$cA}1>)B=vE_bu)#MOV%Mr
z4))I9+u_LJm*mD=jqmKT3K@yet7Du02r|7nSC4&nKorsCku9e>BAd7k=AY3b@Z+TG
zc`I>p3CpP7Ldy}4(YeVS&vR@x9yCQR+mK`Z%ra%(Dl0U0$7;%=-TC57Eqx6^&SfRb
zluk{S3Ug=PPoswFVTpMU>|>I5^8ixY;dGTrscf++qn^bsxfp{?HEkpI?NDMUAHF`d
ziYmS2Uni9r%=K98dz$tPnSRg}_v>-va~#Vr#oq0Zos8OQTdiHm%E5B(6xyrOaEMNz
z(>*Q67FOP1ahJjSCMRR8PF+u^k2M|P&xTC~jUpg1N*)@bXd&%X$Edvz6UUBzGA2Ky
zT&b(Xa=s^z<!C6xQa$r^TU+3DgB8;1ad`lofwHNY6<nD>m8|XnXm`fQ2%BCROrTG$
z;9oGmVvO1DK`lD1THi<HLIj;XSUMLo{{yZTx;xH$kJ1b*-%96ue0I`o7_#RybdM0)
z{zeV+du()Bobqyg;P(Ze)2DyM@TJ-o{GhB<mt0mfW3p9)gN?03eo{{F6(WgA>Rlt|
z;g2IKmpSGqHZ<1<pVn7GB_%g?ev06g(7XHz{r=Nfd76K6_#4ghrjly^R{RNxxSTIZ
zZBMvMY}&zlp9dIIymh~_?NHyO@O}MT_fBkk^PE383?uH$gHs3lA`RGkJ*Ghs7Y6w6
z2CKPZsE;ID3shU+DL^JEVX^hr`iucgsAu5DKq%rxSpQv+G|{`pDWGO(Qy)}KGDM93
zBI?XlU&Wj^6c7vCUHcqvTL)3Gb~)t}4q>|4v(vdlvFERVkAw{@MrlBcLs^5xoKvzi
zm3>vmBzw53`%Q_X14(^JR_Is*#|3wyk)yRl#mHuSLvMXP(5D~WR>=)bxb!0<>d4YN
zgQvpfFaNNLSy)#C&j)D_mt?mW3`+104%r_*qUQ8poC+j3fd-KjYI@ob9R~)#HT+xJ
z5$<zjmMCrCy_IFwLGocf(Rl52hw%J^#96XW)rXt-*3c%NA@oO$$^nepB=@<sdPZ|{
zSH&TO$EEopxv=iXy_x!oSh3=ZM#i(Ti23!#59ixXYyTflZ{gPT`~L5*D5=q%Bc)rU
zMt3WrG@~S>J4Q%Ij&2YMksjTsh@%8aX_QhBke2@K{rY^5-+!=UySF{B>wcW)<2i%(
zuu@m251U`mU783yZ=f#qvz~OOKte^B&q&<_*|O|p7U;vHDM$X~ZG!rVdf5h|plRw6
z?IAM9xfG)AB124)_MGZ~_4Cs**&}8LiLaq+kKRo@UHl%1(a5@cIW|v46@J0UfmI=O
zj{D?hele?HG+dL#`x15qUx<^zoF8D$Q}Ro45^w4;m}!l4kyzRnrY6lgL29Xexz!>0
z{P(i#FMGTUlcue+c4VEFX!WgAygJI+wZ2-v=IvwGwj45|U22sbDiBGhUi|+_qzt9Z
z4*tPSW<IzCdglv|f{t%D061~iaQ~1c{?*kaz;*5M&0p^!@x|Ql`n}u~dsTNk##=>8
zz0Hh&v%n+Vf|n{m;~(cU>tIV6Pq;Pj?yE65SrO{$kMAzN<S?!E*;kAgg<SmmV+$Tc
zO4Yykq}uiN8;+d|@+Av+mKpl#CkQhuQ463jQZ!B(1TNP~7nKnM4dFRqj02?bc>YGg
z%}#zezQN|Ij{HV_3;>II+!=3ty|Ta8IIz~7EV@=mBN$BNe06QU8oe3FOvnVN@c{6w
zKQO2fvcW^Li@_QH#u{vezuA5TM~KVbBAV~9m977FiszH?O#?nde9cKa$~yJ0o8$qP
z;SX6xAyM#Y;>I>}w3+3Mz%c3G2?-q~q`8WykySC(=g+m5jO8)c%t~*H9?$Bsuvsia
zhuJ|@ZdXhTijUK{ZDwvU7HVDgOjG{yuJZFcclAVP0O~6-0cL#qpV4mYq81$(r)Q06
zdenpvrcV3Isjn937VaRm?Hk{c5FX}J!T{~-XBcVG!QBK;v^V=X((hmcg#l`ppm$;R
z1W@(h*_;Zz4>tQ)Ym5Ht0`5IjR}1IF>^)s6SXi7I^~=|ivw!!UU{TaNq8un_Lwn(r
ziqADHY_4x47252%MQ3~#bCa8q#C!d|qXA9s)JPOD-?rGzd<y!YbTt<8N%bv7vTq4y
z`<DZIzrgYM);8IAz&6!MR5<hRAVcj4^!%SG=eO2_H`3Sg*Vz^y)@oop+}P;L1KAU>
zc}rD&P5$Zs>K1h4#DA!0K6%sLClNdJdiGtPa6Ptqx9y8;We-2#qc&LQh6nk*hb-3W
zpz1e@TJV{fUQyVKg&nc$Cmb$oVdnyCJ--XKUZDiqE~eg&Zd`c*YWwBh|6%Ct1hDv=
z!YTPoph!Nc(}{iPKQMk9K3GBn(74qdggYvnan*Ry_`*|4H2ZPUmB$z`3t!`fzYO-V
zju!7A9l|EMY%nvAv$=WsNVyXi^lTa3LC(wlcVfSqAg1Gmm;29iFb|u4I9lslzK^t)
zsDltCA-;gH0ijKZ;2xE0ObI`T;5~|@G_`=%?$i576|Z|k!HK_2$b;*(`)~=NCTcd#
zT2{L3p1ZN08DeKgX<$;fm!&czl#4abj~6IFENMd^JdfpC<G*tccD);mXSKt($xv3o
z6KSO3Bc0m{|8hKN_bKY565uq08~IV5Ri(j7P=x?rePoxK)14F!8(NuZ*#9|KB@~HO
zcx$kVR_zy{K(mIuGa0R#GHkKC1x}A0TBINbEUs@`5OXD81W3P4w%6(}rd6o)qm*gN
zR4bD-safkC4!GC+%yBk{lP1VGC+ecZBKe@=qLuvs=@`c9>D1j9*iS2s06_&b!aGJH
zcug&T*oKKm{Ob-GMvHC(3p(+>T!&hB?wugL>91=ghG-MNd!9T%C+IMo=$4h=@Ry9d
zx#dK_R=oFJ#RD2WVJ{EZWbVFB#SD6J&ushQXtoc_ms_ibt8UK1WZFK2S4cb9T}*RT
z&oF2BrA)>&ydl({Y0s={>k{40huMYR^44EcVK-ssedn#@t_*exB3rhqqH$>r9&aC>
z|98|9#cfpxj(%h}?ze5VNzCz>es=B)!-8=WEXQfb$LezLLw8R~gqrPbds(^mB6MR{
z0@Kq8R-;ty<x$t4dgf)ilOK!Exg6bm-f9Gs@HK=S?LCBEjx+R`w>?8YHJA2IFPC6f
zIPHkbCbn+14Z4V1wbYY7%k-YD_+}!t>e~=9?0!%GXanDQ-M9qHrm4DvMszj@cmbeE
z&xIx$x7x+5Ir<KzIsQez(k!QVR>t!Mjz%$(EMkL((a0TOAwHQAI}lO}UXDNtCz2-u
zy#ay#XasOaBl5g53@^pyZPt8<^KT&E9M`i@K@$wYJcppg08?7kpS~Q_0TghE^F0kI
zJCOC<_#oo1Olj&Spao;87Ncwun)(#l0qH}Tde{JhyCN!P@ohFAeBaiKBVr$^<sI+e
z>69tX_&|~d7@VZl$q_J7Mh~(Njl}f0#K{IE6l7gA#sZg@SD@{_ieThEo-%(X6UspH
zY_luAoZW6wamgjRMFn`?7gs)2>mxQgA@_+H26;0sF(D?4m6x@T`{~oQ3Vr<eRA}iH
z)yVxBK@I6>krOLqnSc2GeU&RyRGbEytMZzY&d`5?4K_qGrI?Kcf&2L)ia)7HV#rEM
zu3b-6AL3wd&V}}0%RVF}d@G{lh$6?v?01Ds7)#i2_|^KN2e~w{1VwA6%of5$sV%fV
zSquOn-#(<IAL6}aWyoZ>d%5~eAHS+3z27jC)s~V`x;R<HPmG227E^wN?PTtJQngoe
z`BBDvM7BoW6y|guR^8l(5zFlvWvu!`saR_<@;dN+w7+nUv+0}~wYa-{0L0MYp8E1L
zN);CN1QDWkPZe|oDe-;h-Xc?>)gD(Q$n@_+u%}W|g=yB={JoBa&wGQPA_7kp+^<ur
z%f35Gu70o_mfnJy?@RuD^8fG{3bo8a7IMM|eqV$(7K5;Fm0{Db@ygB=)swLyuJ3v2
zNY3>M+}kVL=#3(zBQn>~>keUd7`0;&`0>@&(%QZM!tLS;pYG-{YR|Ucn&onmk3>M>
z-vQ}}{NZ9%kNtH(=6>5kedrmVwY#4P7+}WkcD~>8t6%`=nQyl~&}-qgD=1^11)%c~
zYZ>X3ij<_M19&90|M6M>szmx|j)v}l8-r`KVUC}Hu_z`GewK?R?KGA7W#886&*u2y
zAAv;4ApPI~iyu(67(zHgz`3gvSiI^UEjNlkO6Ced9&}?O17P|iKMvvVHhoH|6mjOv
z2mmj#2})T!RqmjSK4hJcQs8EA`QwT5;r56MO$fmMz+U4egW)lg`#T~!UR?#&>>+T}
zTRAc?WMqwuA%-%|#L<MWK4y8tyTo8xsUlRoM*K49uZ!F|&H&LeEJIhxer+@r_minh
zl`pZ1q^vPrtHSZ@cKACFe0SW3&e^(afV<_o%sS@bRM(PD4NDZ^?RwD2gFu+fBB>z<
z%c5QhUrwLuB$Ru?Nd$qTNIlh?oW*-h-89+ONBZ4oc7tRm9P*|Zqu0xfk(83#f5*Ih
zktXQZMEW3-$_&&g=I`B5fUb!PKhilgA=$*4G!fhcqFPB7N0onrLSe^OB{%aF3%2gM
zrmzXc?aT}#&<`b}mOLjqQc5QNXbT=6ePc1U@R8Sz0a_RmbC=?y7)pA)?N%aY#cZ?W
zQ)Mo}285ZkT7F9MO)h5p54VbZs}70ga8MCBCl#|8%<EIPEOvxf>`E1kW04mcvPzKt
zwN@Wkw%L1k#-Cx_c>3Q+Vqe71IzTnfSBi+LXEAz|E?%#~i(Kf*{gs-G<u!q^;S_Jr
zuXH7O6<)vkBY>TO!|7Si4ghI{MeU-4_WWwp;KZT*Y2I>engnZUsB9(`LYABr;s*S@
zLFHd)LL;nByUUQYqb?sd{Cn&Bt1sCvh5q^bkr;I?$yxQ?967YUHQ_6fIq_j*2{2{*
z6mea%mTl^BJ#w5WP;5jN-PQ&Q1Q3;097M%FK{2_HgEH9syNNf5(|}U^QY&sl=N_zt
z@!%wvFmqNUb-hL{1(HoPV~44l;;*~K{L)BS$+xLLIKYp`aK#9)k;_zPu-7b_1sARp
zz_CBLg4;L--5GH_FyK}p(^S%sWigzxjtBo8?HYLhAs?g?lJa=ga?p9EQ_Lk`FlC6*
zxvI!13@!^$oj7J57`rF)n-lO;>X<>PM*Ats09JHqWHv{{7R#!dGFdgRI`t?4&V#xP
zd@S(756s9HC%ieLXD8j^&0peX6_+x8v=k%OU+Ed1WfHReCE0lJ=p}Q<gCrPTt)V*}
z`US-aldDs<@d}0r*${^G^zoMqW?~<tkJ3SbO^EBW=v0)#koFL|JVmbK?yI42es3+V
zWKM|U8sK7apgAjn2sRYYjE+od6lbf_sAyvc!#n&{sAuA8*<aM!S<q*dsB*&cf1B20
zn;=5<{5z9_auRE?FZv0FmfbXmO!N<3e1`n^SyXLvWm7pF14bn!LSYugDM}hU&WaaL
z$?h163V9$f$1(9#|2PVtE7HhupY6&f7OmzCg=qmt80`%h3KFLlH|0+fJ0Ds;K>6)E
z+>|S+_8YX(eo8--n*T$zXGz-Ru2X}T?ouQr<%&nGYcB<UbDE!$+7pEEx!7Q5p)t=7
znO}atyg7TlD~Q^Fbktns8|Ms_sw;DQ%PMTz{IHQRh#@gcx6_P2vbOvBP$Zjov(jYv
zznH*~cpi@ck{b9-7x3zufcTrUq+AM(;WNgJ1E%Xg#(MEPeGdWC_8_<$1?*g?sl$?m
zaPqRrW_hMUAXiQBQ%OqPc7$jKPL9bM=rIbfWE+U8h5YX%hY))LCpTp}5QWs25-6!e
zH!muqIi>4gSUITtFHH1^SbK=$Lco34X#d@1gZECM%;@aAmE~92y|EGOt*Peo=U!((
zT;$)!sHr~8yMADTA07Aokjc^sl-h>I*_0H}R?r4=bS{Z};#W0X5osz9(C-Gvl0$Lc
zO&**yNM)0Wm5=D7I(9uA`Pnq;MLs0sYl0+Eb<0l#DIXzsnJm5|VF;eRI(Yz$mALSM
zCh?6=5h#;$fMDHeOZ1SH2;kku2^~{<%fC2NvAReWZ)`oVBBw#9O#n~y%=_eg7HKm9
zvgu`cBk~m`<^FLbQxSZl+s($e`8sy3ITNNySVNdth6caqw+kZWD@KEj2I`!+8`bn$
zD(+NG$hiD)?^@ga4IK{~@I(w8M{qAVY!y!dzNHy<qlHhomGIfnIXkQlE%2?sZiraI
zT*+zwOL+*9xsfCS8n6Bf%}mFk#Ndjz)%Y@Li?VveCv`?K?5x{=-v3I0^lX*|ljaqh
z%bLEbt~X3<qg#x6AXq+>zvJTM$8L;Iv1g>#dLlSTtf-WqsU0>A!f)AQaX88gNs{p{
zB|<2bfUaxbVac%Tf<E!N8Pz2=?gQ0Dp9ZbiLX!Xootnx_Cn*#!WF`=077G>kRujau
zoPE2WElGU%Ga1R?iZPPt$I+O-pwrY_X(_sg5xR%q&r*1)aJ0&h>w2USDVz~3WbtT1
z&Fq_gzm|G3Rg6!|mvv0ftx<HTT41<MM1$KS(8MYyfxax*vt$+xnLd+x+7abu*y7wh
zth4Ry3Sxh}l0qj^mCoyMNL{i(?X)ByKW;;7tU1O8&gL4@?+JMbA^r2e{Wg6wqO6rQ
z52Nc#EwRW;2qA9lPo8miwTL(3HweH1D9BQy+CPJC>pSGHVz@JJ!QxipphW0^^S9D!
z2F=sb@U93DhjRpQ`RLgr=ktsEsk^NX1r}54ah9~23|K$s@k1=Ew2$p~A4R=P4BeoU
z$}S1NtlDb$a@PtX0T2Mh3f({oaANnM6)iP;LIWTKiH9r(L_2&A&kN>Ryy|#+yF>4o
zG<M)^%1;jC5P5~`4cP<)i~#wk{(_6h6NS@bG$yrRvO^NOedwtxr$>$hYdggs=%bUq
zo2|`2U~F*Mz@PB5nwRynJGFSaHAJ3u#eizG=iZUvCgjIPR0ZiNL0XF2wG0{|4?^;K
zxb3R0LOCa%hSPg5UUH}{#z{o^5wK~N>f4j{cnj6!5VNW}CPmm#22OxgTYc2f%6h|z
z7vQ^XEoxbWG2KncmH?^Dc&{8jH{ii{7zg6>MeKvuNRGUS)|bU0F~VMKr-HXt%gS=f
zMPx>(vOjBVcvr1-PuPSU*f-#Ue7_AQ)b(U@92%zOKDzHI`v4oq$m0#1Bg#Loa%zD@
zU4#hB8rCeYinc2EI}s98fU|c&ed^gz63B>ID{fOZTRgx2;_5Yqf{?a`;}uEXe0{B$
zM$-Mx2lVEUKH6M=Q#Ax7v*yq#m1AaX<|SK_$ju%T>I{ykN>d)nWJpl#;&DDDy%bS{
zicUOf+Ya%;`aUx$Y<Dwn#&c&&@OjKR6Sv9cU|QI!aXN;BGYh|$1?{l~8ObY!qh_r<
z>Zij`VV)5RGRM;}rzcZuIg!r60e5>}U-UG`qcZdaV=1zDiZRTba3Lpi-B;z-qVZZn
zP6!9I?4YSVGp*;RW-xnK#g$toiRN3q|DGM*>H#7Nmdf;}T|VOA0?D2UXGqPHM7$*v
zD-D*ANu@Ic=~O>t#HArzVq$QAa-O8Roh%}%AAYkj+Hd#dYl3JhBz<Sdaz2J7US_1c
zgujgXb+xYzzjyiJ7vpY6GOn7FopGU)oH3z@)SDqpjhbFmiyI=lLP)hws%+)vU%<`{
zI`G5Kkub35qSIm6<F43u>c!_I2I6nWQFI~M&RIaX&8itN6JS|#DVW@FQNfjuI|XuR
zs&cQOnoIpuv=Mm&5b1O3F|uQIYPlZbOBVFZl0O@P%aA~U9_JP14CIaZb8#Gu%lT%R
zE^P&Z@%XGtAH0F5p!V9V&L2!(#^Hd(r@R-HfaUWf9`z{s(g(lPJD~LUJLy$*J*Euo
zD10!Wx)3SiF2jx~B5ZUb0}Pq5j@fqh<C=rg#0Dh^V|IACJ#ij6<-X{jZ#~E7+$%W8
z>Dn6vAK4Xw#mNBfuZWuLSmO?krgsFfh2Q)#2h(5W)qB!#CtQ~(vf>IrF;`<3X6Urm
zq$|@kaOS~FI;zRF7CCc~37AHF+zuPwB4j}YlEwC^=2~7Aue4aSOh{}n5I1@9<;c62
z7VZbHTW+t0at#hbWBY9OwGtVoUUdNO@uBQb+PAktGBkJ;(xVXbYhR3FCs~y=a-Gsu
z>t8Qlwj}f!91zv*Ghe(A8%%kXstRdhr5P#OKzH8JjSc~#bWFy+Y?{g?=_4@T#oL_&
ze1P*$5yKZVaXR1yd1Zqnq2q=LXszr^&9YndP~<aoLztO;++&W}+^7C)66Z50s<fNn
z`u@EBG%*K>wlr6?%gRfxi*!=^UTKB$EZNb$m2K@3zvedYbnLGd9g4S?B&Ek?Gfr)r
zQ@EQ5+Q^xh^JK56P4Qq`-<$Gzm}<(O0@nkvg5BXRWv*dYpL?tJ1m;^p>;E0)m%Nn&
z=%XR3CpD}LIX#_kWi6v`&}Hdo9=(PCaYbrH0609k0sX3Kd}7{7+>OgVzs!o%o`K4+
zEDKM|vj5tWS18;o0WS+q<nY1f*P$4e6M@=P=&aurRA=&+BiTJgj6Y_#aSGBesZ%pm
zduT2OejC~%42XDR;d3ETAz(lb07G29=>2FTqk!Z8(xjnE^v!^HTb6287`8Zls0<-A
zC{|E0CM;Y?wVi4o=w^WfADMZO5g^_l+^^}-RXniO$l-hzgv>YtlChH3LgAb>RB+Pr
z%jN4{a4C58SckQgUIQ`!Fxwi&dGKiD7~rk(z{V#0uM~a2As*T%RjC+9Igu3qJ)fwG
zD}nR3@(}%JzTPaaZ|RW|1B&27F`u-c2c>F&+qIoqDh%r=wNyRF6M~_z?Zdw4N7XRC
ze9`=g8Cadv_M_X%LVh!>@uoQiI8QgUw910CSbEXWCKid**-Yi?^~f^Ba-rQQeD~7O
zOhb5R`ui4UHW<HRtrsG?f)K6>S&UCO$Lk=gC5pk41912w@5I6`^~)AEP(}TsTpfHd
zF^f`BsgF$0PuEDZ7$cefD)YI5=!oTsEN>X#`=0S_PQ<iAe8I#l@5-au)}X$OkTcSc
z_C2hcnH=~Hj~y&gPqw(PfA=NKbfzZ-Gj9RuCMPt=X_Q|BJ$$aT!}iIHA_UWV1VnuU
zK@qM6LGYk4!XaUYm;e{k4e&y_g~&Dr<yBf|MmtSD`hKH#F~M)-pKx1U0VT~@*vC~@
z*$hGzX@|DyJ7fFygA}J_l#G2d^8=<2(Ks{nS=s$hm}fGs3@UR;yNRa@hb@50L=|uu
zkz@b=l>>7RETRJ9lVn$(R&LuiZX!jA(@H$>=IGToqm*n9c)k-k6`JANG1E5kcXn(>
zx-GdGk2HPx7!3x#R!^_Z$t2vqk|cC}{vWMG?<e~E836EB7WPKqTQP`|?n8Bs_`|Y6
zpd{RU!Dr&(pMX~T#_(fv{_jm_+TYVJ?C3{T3MQ8I6CbCKqqUW<TsPa*Jg@_q^l`oE
z;j?pj=fmUaPvOrzdV>&%{JHe!T&zIKg>s_5n+sg-1mCw`WZ(&DEpH%A07)VAy8Uu&
zLo|B{M?fN<>N3=U?^VZVBD&+y(j|Eom9OmH+v^_B*4l-@lXm;Vlb=Vd^oM-ZY`|({
zg4CI^47it-epTjr0m?Q5KAqe_#pnWsjuTdS@sklpeTV_>jDxR0y+Q3WfR&s9`~Y}N
zlueP%;s-1xlk_{0-&c1f90U$pBxI5u0e(fE%Tf?H&t?BUemma51RRpbL(De!apRk7
z)a19}yhs)}K~#NmUJ|r<t}s1U=^(H|YR(4;3rl7M?ctLk_j_9|N2Z8+i8b~pSeYYr
zl+5~D<OWia4#IewgN#Y*9EOu$cPG%F?SA%Z#<XbS`u-&;efFHAS|w%q(C4iD-H%`s
zg^cPT6z~4))2%nHo71$V@KQF(;;HebaKUuHFBhjW>#057UK`Y!T&KNF!|D0dl785D
zVrOy>o5xU2AJ(cV=paI!=4isab?oM#g!UH7nmEln(gG^!q4c)2gQhBn+WJJ#$ME{9
z{$S$TjQT8KVe=PBgno*&WC~~Xhx>|CW|qlp8?NsRW-<65U6-AFHMAJ7H#&OfhO?U7
zllHU+^ORJBmTF;rJ9L?py%596)!GBZBPu=jgpieBln*ma-KiMr+GUp;$`Kfvlu9mY
zp%i=XZW}yXgImg&)59jKD#HcIj2ik!s}=tLLOwIYo6eEV9aAs-rfdqHbXtG!7dKRK
zNjRakvn}fVfV(w>-Mnh!lBOk7RjNd42kjc;sck|E6Ux6*e_F1UGfdb{8S>x(K^|>#
zP5s`Me6;ARkI3e*?c2-dAUkko?{qH=I>_64`|{p-;q12C1nIyr+`$T7T)-)B()vy*
zpSir(-X%F7h^2j*0Jwi!`=kBf3sBa4ClhY?yXkQ0L1GQt4<hwRLK`%f>_SK?xO#Yx
z(3jT3ilH<@GCuI+&{T}?<N*u`AP-O>Bqx@d0%ZnqO{sp;-57rW`VO=$jx%7Tz<;pc
z`Pe(8S+T?q)FC?0s<fQ}IY#ml%Qo)P-KlMmJV|$P0E~>YAPa&t41oLPjO3=$r6?0h
z><6|=c*EN)3i>&~_cwL&a>3V9Sz{)F6FJ0UpgM!pEeF{+N*nq+yu%Z;@$zO<j*pkO
z6*%gBtl_k;n1T{Tpw3rMFqxgykuS(OCma-ynK`0c%Y*dOEhmioZ@L3I`k8q?HfMfb
zGqdk#<@B26k~b#xQ`V9bBkdRu==gHDXviYDgm$6&d5|5_-oiF8mhMnOaq%*nESq_b
zY0KfaZ)%&8O<Lfb6h}^>ivs$Em70o~1Ny5*aoYqYWH)w(O>QLd7j4No4%;F^#!?*h
zM|L{lRs0w&_zG9Vm~=uZMYN^U={?1zS}=w)NBN9R2C=}nFNa_Za(Fe~8I{B^+NZzG
zL)-(_ti>M9@HC3o1of3g%Rk1A7b$)`J@*|KD;`tUl~U@T{?1o2+7tP`#XMa0lSSFB
zx7?V-)T2F0aZG3|KP+-&RfiB}O}AiWRPQgHqabqp2E~2#r@VsmH;kEL4kt`@H9?VB
zr(8YBMR;fz;G6h{Wg~cCgwi<dx5UU}I!-6Q&ON*RgZ~DoI{2F3A(u6>dioA_AWf}9
zzhs5C7#~j#?_ToQ62>DO!lyV7d4uAr)dG~70dX0vxq1F#rDM7b>*6RAh7i5nyw>|C
zixJ{a$+gJ?y040{_~hR#3`mN<%g^xg`Y56S8THFwfWn0{qX?n!>Dtj-2oA3)s86{}
zvWkC*jMn(|+{4qQPNdK6PAzbXO1Oly<==|F3DfZhOM*afER-hSa}HAO(&0q_@&2TA
z1E85>&IS)akvPA|C>H~^BuJdsT!v93hsyQHC)u3+;3N&u1?z+@5afHCf~`IR)CGW|
zgW5=IsGvzV9eA{D9Xo7I8YT~2W^`A2X>zBnDCLnYgpUxwv0K5B3QIG_F+V?TYS0u1
zBmd)p_F#6-k>N$q;QQ1dK;8*+wrKW44~<I;xUhhe#XuVQp(h(Pzu0b)+sqmteq2D+
z7FYjt9hGaX(5A}u{HNBcYuhTm(L(H29xJpA$$*l%mmiWn--CGDe{Ei6rA4TRpl4VU
zt^f(>;xv(qjuL+M>(wvhZv{Hl97_qP8IQ+)zS}n+OcLSv{z&*B&7In2lOAOl9y1zJ
zo67IG$&g@2EXsu;Flq=LVhhsX)9G5GSB{4|;J=DKdSmIM5Z|=_5?@}Gpz}-~f&b$@
z-}74VHqpkMnc=ww!x*u&HE&#hhIY*Dkk{(dmmb27tuIriD}O$fOR{tr{ZzL%9NHUz
z*x{<9T(CF@Kxs9+mbFYszY}&MZT*_$q!etP=hMHc!bL(NvSP|neL%EXy2xYXLKZEe
z`~Rk&`1B8-){37`VsDNO8XqOe82<Edf_(mXK22-bJ#^-zGAuHwwhTSc+sHyH<qIRd
z*h$@64W5NM=L&R4C0*2tT2f<I$8Iz$f;2N@O33GKFZox1;1CxiF`#0<hdTyhpv3Xm
zr3ndLboAhSOHi@K%L^Xw`Yd=MHA_*3L+KbJ@$8T9hfy$JCqDwgM?hfgX#beBS^RD#
z&Nq<eGd}B!;!-j^$gO~7vGbI9q**)QIW_JaZ@xd|RThv}DbVblnLrxzNy1@O0Xh56
zupknWd6vbngAk-(sh*<kA?`;6e|dJ1EAwRTTuP59y>&6gj6a($CF?SHZw^}idd@1O
z+Wr7XG|q+Z*0!G_(dK{~93y5Bi6n#vb%nX$IUs(Jf6aZK>;FE7IOuOEHPnQ!9Ps0;
zHW(rY0sqB~65F-DRYG@cRZlx7kf#rPd{<w~6JO~E?<DitAH3Q)wAehY3Q40rQ@KhT
zmUZHdw&h`vwsAR;<>0O3L8KHEOR^FkXoGgG%7^s@Gcj!eTg}6)3uEgKG?dh+XQUQ0
zLYTZ`IM~=mG=`uy-dQ0mr+ns4OjA3X))B6F`>uL`t8tdh2hx3M<wV&(<Pf>(057f@
z3;wJUqGS$p{c+%C_G}w7b%E+^@uR~xv{TuX4=7sI=szufWW-b$N{0|^!{R?hsDJIe
zQD8R?ybSH)I^mxH`=F$gqw1!uz8`7TCT^mPqHui6D2gnJKIb+ZXlF|<HxA00zX!Vo
z#iYgwcKnB!%(#p|IX6FDs?u1QdE9*eGQvq3q|pgen{xNEq?j*kofp(j<G7o&EZ!5&
zPUto&mJvf8xVLPH!>LtS_r?s1w=4UB@WeZWIsyIBM~b}el^XUcPM2{^YCQkrsU28=
zb~?v1i&6?Q55nY;u%U49djIr?&Xy5C(fGc*{4KRw3HV~A*ulYzLRGtjuAt@Gq8Ff#
z#B>-jhb6$lDj4!8z(57{vm)mSm!`cCCg+geXbZj)FXCU9MBjmdD9E)aK#Ne$;58J#
z9L{V$=v+evS`2^?0~~Mw4?Cp|1p%*+t^V6nL;D0!+){HlWJIaCH1KJDpbtrnE;*?Q
z%qBY|<HBo}oc5#7o)b~tXa|1<PSiO|F5W>wNEr`nivYQ4doW;Ge!f1OU8wl;<8q6Z
z+mZE!D@z8nJg1?N8Up{_d4wNMKG5qr4d9ubK~$WEO&Lhe5v><SLB1o7)CF%=PBK4V
zxAKWjQKO&dol(rU5@mn$oq%a;sK`NI1-w&!c4y|%Qmriw#r<DcvnS1lgMiF50)p@L
zH9i>mcvLg21Ugl9w+k;$(+C^-8C`po@CU_oR&)5tuG3bLb7Q0`HDmCq{#-i<2Y=Wx
z?7nDcS7?AL*NI>(%v^(;4k6-TZYLn=$g7w{s-iA;N?1dYZfB*InZ%IOG*VV(F_L;i
zmtpZocETd17PTNd*;*{Bktnix=<ZtQ<!U92mpf`+2614m87gvO=pRuMT-xT}>j}<l
zAJS^da4ko>uz0@+ZDJ-qO9=}2vK;tZVdRymg_&M)g!cj$T26C$pCl<Xgl-AE-kjXJ
zq7vxx3(tP-MtLr+LWVm~UjqGt$vN@+W9(FkRuzoZN%{bEHF}n7*Tw6*F5)-X^ZBiv
z+uHw?aC1cmP9=~h8}nBdn$z_Eq}oa|D_^65{mOhI<N0FCLrRHwc`6N{G+@_4&6)VJ
zOL^td0v=<Ma6&wX&Yuw!iX(3Lb^PgDjJj%H<tg<lWf1VxIc=$o3P=IKPy3!=f%f;5
z1CK58ryyyjY(M<L%{3bXxNO8a(%OLY&5fNGfa3X@-nKej^GX=1I)Z^rx<C#E?0&BD
zPw{@<2<Gw7!@=W7o~f&5K~^!OEFkGLPYU3I;DXrzU&a^odkIbX*D4_;rXUDVI)-K)
z<M42z<(t|SM0os@o?OWp$Q)Q~?z;R%E92S&-~rLGjC29W!)6wCB7f!Y<U8E}vVVij
z6#DTKj-&<_ZgSYBBdrk}nuf7_>QU*L&O#=V2ocgqKbFZ9@t|0=R6>g6x9yRJyWI=~
z*#|iE^lg~*NRI8~9!=WU^pD9Xix%_jmbX`|;%6tBP)#gQkJ^F>z>D#cp0fbtR7Tda
zOrNs?FnD&c_~@x$GA3Lx-5!^AM=C1ASphzYV|`;6P2b<yr|0&Vg`aAkocLZ&o8Ze{
z+GRmzrjrfnpyKxNTc6g_Xc58!dcVov_l?9QF(hhqU9o%Z>c^sFv*kwkHnTauJh~wA
zhHqo#lNhQ?;S@4P`!4rlc;%@y`}+pf3b*I_!?4RK?sGP})FB~|%~&g{ak~&cl$ltV
z%*N9@Z@C=0Qx+o};HNcj;jMdlT3;e4bdT+Wn3Hm|l#J(JT#<rZl>Ob6%;O#x+a6D7
ztVd}0za~5ws12KbswusQ2(npEvVB^7`{(*ujtVA`w>w@<A-n7Ef00|mDgtgT;G;w3
zOo$!+v2Sov{>wq7i_f$oFTId%{FPNhQQd>X4k8Of55z0D0TlJG@Gy)*$;o}@q9b&J
zp!cLzFvBwQ8|_UgRlW4n0nE$zG2)lycYD956_%MRek{tDe3(tHr1w%H2k;IsH-hFO
zUtl}K%C*Y8AaiC={(4Cba2a(FlE|xcLHz(=mFQWpP3u`!_^6#Q;OVoM*pS7TN~OyY
zit!A%jI_1MIydV?>Mh!fzJj`dRq!+*Gbpab=W`s?-1ayf6g;57Q@1r!wKKS93$bff
z05BO}?HP3%^a6CU2sFUcBcngGZ#wn>$klPwx@=3SkZKNxDbTS3&G3Rx7~fnKvu)t~
z+xVI_SQiB6n|yye^G%MflYfOxX8+ZTZnN*Fyfh*MYs<B67{<x!<n%$`F0(E(=FYg*
zmBbE|&_U`MoI+k5f)PNYY`>o6sMwa?p5nCk5P2x}nWs)=iAF!`NF(HE3h(f^(+&Bx
zB<m|}NqAOuKIR-b1Y~~|`0(vvFioeSo@lAg9IbnCcF%U+lT4M`H<|`vaiK0wXxvG9
z<68HH_A7-tqme|60sb#S6ETEw0ZJh(dvm~PD6R>~KYKy0ImG8mNRA50;^9FCZkqH?
zMI&2dEeZ%N;>qaV(7xF`ity1Q=k}2qZ4APQ@qF+2;I$?pY4)Kf2Pc$2fn3&>$f(0v
z8$xl^(2XZKt(&v_<ox<?xI?p?6koFkt!L+T1I9F`W;C!oflF&#!f}`*a8Rd}qj)l)
z&)9B^G=_L5DhI(E`;rXRHy#9Tdy$eSxY+mKf9@&8k{Of}nJQV^_xp4$+Ro#063Yp2
zyY~fMn)lcn>QRc2Z+|Wi%VQ<O7St9y+rHkLCJls33sJw8{?lk`!FI|(`M3hFPZgu2
zC$`9k68A!DI4j(W6jM~cdX`ybWie2x)%D0*u50Br>aw|nImj^YQGkZMN<`kfC4K{Z
zmDub`xk|bTA6Nb<ICa_sB6d-Cf_Tw6H-B~7B;9&_Y65B;`Vc{R0F?b_(be3&OWlcB
zIbM^7wjlY=!c}nygB0CGy4-r?oaF+LcP)U;zg|`d*SSgnj|U}zZBbR`=#T*Vp3Ad6
zM3&=Q*UI)`74R`Z?V`X*@vCs}NQfQKWNZ7`(BsH>mrszz0g<1tc$$O(Rfxq9;1iPv
z^#OF6B&F1Bx-8D>0NIu8sV)%4aUlQ7ir$7Q4e`NVwOTz{TS|mR2}gM8ODvq#dgL32
z6+v4T@$jgqIGAa(Q+}jQp@oW&yz$WTOisH<kOL{fiKP~t1Ag~8uQuE>5s1V!$)fwp
zsqi|szvi(`kx>I_F-gG>Yy97_kQC(HsT`kGhgY}TKJz|xkF~lWayJ(0?$3aQ6>(@}
zs>I~;|AwI}@As+7GAARhFt^239#_yKZCBPPh;Ox6)YsRqot<4T*q<C6h5Mam)Rq$#
zg-@nXveGf7Gt#~t0@+118W`jej?ua33Y(y`vjt+lJkG<DYBp;^IF-xp@#SE@tv|;E
zhIn2*@ZOU5A!HxTkJf48c?kRY)12RJ2sGMo^v+`Fn;9;*=oeO(UE36(Tb4Ty-&Is6
z;Yd(Ui7C@Y<H*g^5qi|VT=GKI-o>2d=%O$tY?WpUeU~S;opKJD5z*OpI610=JtCdT
zFHfdW)`^2~EjT_LxxjWeuSdo46L>Hxcdj4$cB*i<cdmGS+0QDAn(ZE6Tm)MGCpJwM
zlt?h*PLpReL<>*O<sl1(2U`itC=dUc1Kz8GVcnTn(xnhRYqAwB@_3bOp4lr*zFw@F
zGW_l&QrWRe^QZR*brv%Qk1`LgM1sjT?&jrJ^i}7+idbj@rU`n~BO)r*lc0B%s7*rb
zy+p@FEo+k)JO42b0O;!Ec|I9Q8&dLE;58sl*;5WL4d@&Cg&Afu`bhcN?rY-p7(NE}
z4^bb(2)<V=RJ!;pjN*Czwpu4i^Jasb&j5}F_)CWc)ED+NnVb{kC>HE_O{h=s|Kb)-
z_rbi1Ns$jJgz*c(b;$g-lN87el8=w5k#LMnuVoi|i!!>K>2%5}J5YXvI|xh0FF2Nb
zi>Ws58ero@*7}RD9<Li8&N;&USL_Cfb){Qilgnx<X{Pi(fNWJ_O^2sH({XdzgU1hn
z_lwV$Hapdd1k2UBJ0h^uey;_-np(y*>6uS?8i5L6U8}6TCmw8&O606yq6?YxfvnpM
z|2~zc7AQy7q8Voe%vP+f^pJQeG6~szpnO?|zp7)yf<aj0Y-@OFTI0R&QYvHmQ}2q1
zG;)=-JF$Bw2jx><$=Z-jqD%DQm-}Q@4%Pn6Rqib%XR_dOOo!S4WW!K0+gLC%^MMPe
zsJ@v`@0UOX9s3u$EdEr$<j`(>Oh}c;i{)0%lju8&ze=~QIGpOmG!-6KjqWU42u{>N
zAJgR}s;CI-?vF@W9$n?|;gEjP?=dl-r!^e<(uy-@$h6_Qbi5zaB(k`#SZmDdMXQuu
zc$1!8ky|_}vubE(aT_xK`R3AR)KLAll0G%wL+bzVy%}E8!q$PL?E&-Xf}z}{NSx2*
z)>6sgp%*Ko89$j65)<6KdUg(ADLmXb&?^I-tA=>gQE!JwD$Y)^Ks$e;{SO|w^yF&c
zZ)_SZPkYIk)-$$ITu@=A-Rt6s-UO54wumkcIN(=%TI!&}btPskek`JB=*SP{q;6$2
z6#E3^g|%OuiD~bKA%J$@L=?d)SA0}pc=D^SaxF&#&_u$4@`*H9SfZKvOj?~snIiB}
z@vHc2xrxV$!7VOfia3?d|A1--Dz&^CP~wJ*=GTq16>5xcfKic}2YTn-fY%xX>pRkz
z!kv5uq>7+3UBb6eq$dw)vOJ)8fI4qjBUpS9U;o+5OyWmQ(u!(nI58YjC)dmRNQ2Hy
zUdtNtU*KhU75X|PcHNOoiF{Vk5*WDER`QyVt>#tG>!$>^+Jq#HHT-__k4l=gW&~L8
z8hR`~rom3w42U^kaRUz;Yl_kkT08@Vt|cX%_XLWDTqQMNz9({`7QIBPsRkr|YKB$X
z46NykJkiXoa@#(~^-Q}EBDNpQ)y%&=vbQGNmcN8fT%c6QR?LFU<O>eJ`XoYMTriNX
z=7f~BfYDKmQgu2DbD2K)t%M6txe#ni502C{TEh;u*jW&%SuC2$m1)V#acXU|kz?s>
zXh^B!btCjLsir!l)?p~JslbOO4{vPmy8G1O_V4TyyGDFAS%jIwHIwuS(FFLom9$LH
zzcmBFj222eFHEBQLg~W)6lqtrDW5I1CJ_*r6O`Ec>go5#5{pQcV*3W-&+E?7ik?kN
zg6NZUX2#19R1VR0{d`>R8uVfrEWMK6I<Sb<I_+Aq<7_|vP)D2X`mnuH+q;tduEZly
zGuJHNu8SYMbikM_(*I1MTRye<zd!F50_SG4MxMOdrLi_HRl@RD<gC~k2BZT#Y-yk-
zo!EF`JJrWY{hD-;<D7-<sGkUl&4zb!QSV@Jtq#kiSZln}Zuiz%t=bo%-xrIFtAeIt
zsdbRunlZ;j#Or5mwj>j(Zk^l<&dZLy1@9B6qUV@9e$7f8rC^i><>imPQ5hV`=Q_mj
zSpIG#UcD8(1ucnH!CuGXiDFkkheFSER0i#eLfm<X{}`ru>qqzRtO`T4o|{o-0DyX%
zFw)>t{urXP(aF{2Xn?wn;DzkJ--opZNNQISK<&6oX7cAGL@duY1UOTZ{^kHg)JuU6
zlAL}jY`J(W9}_JR$7QuL3@atLVYdc6o-9l5VW<X#ww4@U{|<-ej54bx_6q4X){YH4
z3gC)iuVc0-gYumaae_alQ{&Qy*+iOPGe6090RPU&tsiZE+9RGh{`PgEcrS!hK8l4p
zmf#uahLDamJN{BGrNf-j8O4}6OF^QpDQy~vHe*$-tdnN6Vl<kPT&9Ch<y8!Wpb0J6
z4suYF5B$fIYrcd!7k!SU_*;&|N?m67yn`6JziTa<4geCgqwMdOhbp2co$-?CTXgY)
zonD-5Py7#UZsBfFDup^Rm+|2fROYCD4B^qwY~>+2)8{ZZbN$;7?1-@f<z!S+Pwv?h
zy$iNHO>y(H4vaaBmvC5aLHsL*gWRch%Uo@!-OJ7WRL;T9T+&~TZwJ>trZyd{$n33W
zC?8=@tgOPzIYxos(WJ)~T|Og6JH|3H6jgW-sF86bv)uWjm%~r#9Q9(Nwce0=Hp^yl
zU?)>mxwE5)?$R6;^g#CEu@LMczh^#B?40{?*|Hgjdxg+h``!N2g%@>5wy_nM*Gj>|
z1n$R#Mmma5pUA2Z*m~R4#RrMFsZX{2--b||%ZJ{M>v{e|WCa12ON`4yo$mmQLCACG
zHQ7WiDg)8W{yn)v-U1x6%TzN!gw(r-9h~(qhx`Kh?E(nr8V~y_;*-Q)g~omn{YL%m
z{A8A8e3dnko#w&7w=!8va#LR9TzG-i&BvE1e=^ULVbhNu5MbR(O&ak8<NUgXpA%Fy
zx*a#$f<YiNF6YXnb1JW=O87`G_?Kp6Ps>$x|KZ{uirO4)^GV_F&LIFMoK^prE|{UT
z5=Y$v*`M^!p^)>)Ur6*-p_c;CCXGr3oWL3~Bs95sxSR`cJ!~KFCRFmz1e2*CjcO7o
zjWPjCLw;QYyfn$8lQ9T(-sRyIHf`WvBH5|kZYfv_FmyRDlg6MuZsbriCZC#Y3p55i
zY$Lvcb2ld9dxjXRJIuz85CXt`7MoDM{IGiMv$R=d%UYWQQx8F>D*|)069lThIOena
z$5JD*PqO5SL8&?%LtbXlzRrphAKO>^d^5N=?E}%8Rr^=`7Mp|fpo3AmMD4X*{r%Fl
zrCQrv7k5Q1vnD8KHl|i#3KJFOMjNd`A(|k&%ODCm&D((zc{`4E2|1`*4(Ikw9tv)b
z-J4VGz4?4%lwBxe{-U)TjUqMSE>FlB@xhRN&J4GI(^N@kzsry9G;sl=vv<PTWQLNC
z4)cmXk6`Fy1{!7QKK-_f!>6~l*jr5Bfqh&p7;bJ+xRg8>GcTEyyLib;yv^uyXx3Gp
zNBRRaLQl<jI7#j2n&OwjIl3IZ+*Z-d-p?(nJ*9?Hpt<}gdTX(B<?@GIs^|7XNTei-
zcIm!N@UuZ`(@e<UjsNCm5zQ8nPQ#pSg70EYK<%3<ftzJ+ot96Xx<C&kdz~~5c(Ioo
zl556EJ*TU*m^x}aWY^SnvM?bS@HxtO|JPf(619-@VBMBqweE}l;%^iqf6h4T#X;Zl
zGsWX+<K_Lm_h@yvPYQKLJX71@bZ5D2!SHMP926S6xEOzuNr%J$sVo|i)*O+4+EfL~
zg>A;C58OS-<iT1qBFdZ$#&ijSt)pjGupSM#+8`(tDXJ13OUL>|ftse|TQ&&BFH;Mw
zg_0+708-N$M`(Qn=Q3o$>bIFIuNdEpA>(B)70{95K`b^-5eDgxY24*mcUG6}qjCZ1
z@HfGo`UPF2N1#fe^8n2o00&TK%ZS?#m)7O4IUQ25U~5*u@+=gWGQ_D%ZR>NEz#hgS
zNn%hA!?s6c<haacF8}sS9f9=o+jt$P@vg?(|Fy=>rG>zF#4h%-K{QB_NNzghB#EmZ
z#>r1xoB(wuwP9JPkK=K=HS|AwS$s6p1CgGjzG~A97VU@%uf+@YEV(v+vYTD$A$ZX|
z)L4m#n7I&bYDqJk6Bmu9oLj3~!SIp4DM@1&OhB-{9qEi4V+i2qI1g$PXA8?z8hXGd
z?rp1E=85eN&ZrGZ@lww4Sbi4KQPA|L$>zm#(aNwiAyG~0PITv5u8~66>oiuSD=h^W
zLv3@dUM3Ab3tFjI&eWQ9Ad`JtDnutsJB$Yk^+wwkZkcOgEzw@C4!=(<Ojt$;s4FXF
zP(g9n=M_;-MC{dt)tM3ZYiP`1GuCcj8FF&nX_cg+=ya#_(A;%*PxotFz3*8x#jX{}
zK~-SIApHG7bBe0GjA%N-BSL3W4pwkNll`iq<N#~r!(k#{-KOso=VY!~XZ=(fXoX2{
z{DJM5Ob9{`2KKkm3k58HvxKJq-*MIMym+;p=s#uWL5|z!Tq*$R&5tH_qyKCj)6y@C
z|DD3l>r$0JB>)F*4*!H8{UkB3lf^|luae>Xt+n5QowT;-&!B3|D7~&ojc)tzuNdp(
zn+}YHs98FrXhHZM{JoPmg990zCOR#IBx>NJ(q4Y~XdqxF_>8JO?c3_K0fV+Xn@%$9
zaE=+kfCL7DjR0x4a^c*3n5>dwa3&xIPWBUI!^=HsSIg#v<iJrcZYmEW|K&7gB|b60
z__6H3V%n_Vx&1_z0rK%ARP8N^8z^gl3rgGp<2#EEUMVo)Het-Bnj<frDvZLv6s!RO
z%;*2X9>7^>)|VAxFn|esO|{;Wur+&ZR$!u(NE0$<u0h^dM~YOpq4(6&j*}+&*8F4T
z8rzw3I4-BPit}sF)cxawn0OZ-NXtHa{_3+`d5IYM+0uggiUoddo9eOI4{z(N<9CVv
zlV8tzbu@6&lM7qSH<uHa#rKLMl<B?ilL9JQUx&gpGi|ZDT(*y$q<nR9`0qqrlsQ0e
zl(Iwd*pzfIu(IS|Y$Av+x#H(go{M1IL`Lr)%-*_fnV)2ab-xNBgeMy8P-95kAxI)D
zJ(||=3G36VtWlPzA7cC3%ox@v<vbLbEsC2XiF4Y_!BEleR7CVOC0~w+Mqe_G=!;r5
z&21W2;1)jU%}Q<Em3oNe%8)4gL!~cevGF{q{s>K_c(b}0zKI3pWhjG+XG>(HSvTtC
z##-~4=1J!^Yqqqzu|nhXki729s?H-T8)ij@xz}zJ)yg0DmG`U85duo9!#rDOI-2+t
z8;92SU5$S_cY1QcFJE^eQjgo(dnM!*T5FvC0}+#hK=lBFjQ-Cb*=M(3&$vF`Zio=*
z?%j9a3d1%$%A<c*ZY5gZzUX|BkK;zGTL%20^>Jj9?_cp(0ycmLpu|JMmV{0v{f}n8
zMD9KJvE0_$F^Cd~Y#Dzm7enI-JO;G-CW19&*M6>PM|de}=LpYb%dS(=whXWt!NIz&
z#UXaJz|8Y51Fgw{s!`^3MSD``S;b6IpdaGJJ7vUd!V&{q2q>|9iL@Tn4swnbqVv5o
zi74fZte1pOL93H)vacn{Ex_v<gzXHQKzxW>r%E*oT>baYd-2~zO3$$(8x7dRgY@es
zJf2AFbxBf(ie6JG8gh&%{v>wG^Uk6(R8M|}sRL`}?K6fFoT*b6P}2+!>@4q^X#+w`
z$m)Fs>&@=u3puep5b}c7Z@mJ*f6YIbk?y06q)wXeX1Op)0kfL=#ltZO)>^v1tU_~|
zVPACX=-wAf=;Q$-+=pL1IGQ1?r5liw#)`T~e4fj7C@wizcxrOMG-AUmbdF>`Cg-vZ
z#@Ft^VAx;YuWLy{S`dda3rw%!ZjvnBbACpEL%LkKBsh`P-<+D7_pp@p8}2s1KH+I*
zC|dv`_9s$@UGz$XK!FpK$XQbq45Ju{6{p1;B8jT^7)Rpa<9D7AWow1KkZm__?qoPt
zS)cpTtb9GYvM(y$-y{nVJggsTcS@PNf?q0`+x|gI7JN$T{5IB^;$x%fja%G#Yl}+d
z{W6v-;Z?r(&JUSHOy%q9qRU+p`XuT8o_AE}WnYMP(ib`V+JK$1{}XL(539J7LsBfN
zRq4;*q{t!8-vU5!XX*A(tPjo<v}wr*a6cl#k0XAEHRjF*X*VU>$^qe5jDbMPE-xy|
zhv#V+3laXZC@Ifg8R28gtu;Y^K)=&eTZ)Oi`9ImX^T%~zNS-=5(`qXV6X&-sg^>+J
z3H)r22NV(k^WtzKXD}rh2P@UON`2Zc(G=jOk&;zNL!B!^9T%w%q!mr4(nWg}&|7Cy
z)hlU00C@PpLQ0_A>h@dQK4mr#Eia56-b~86Cq?fL&|NWWfohiHrKyCvNmhN+lafQ%
z_ie6Pk%TA!e)xVK7W2ucX3P%@i6U>|!z&wqn9268HO?Q`TMT4XtoZ41Tt^^_uwFqb
z5ij@pA?OAW=3rBCBp=}v_E#FcCBM#Sa6wdWt${jnrI6uM>zXItnc8Ne8+@}gKvYX{
z{&KeN>F;M4qbf_3i+jD>B1^)gI(`*42kj#eh4(8%LaXtr+e`<O+UC~m`#Ua)QDYm*
z?(s%mJ-CNZQ_KpZ)ivYSwcPNsg$>Q>C7_%HW!k{4p5qP=ZkInIK}Vjl;v%ezTFKI*
zpjR?K@eSy^=~_TRW4CV*G6Eh~B#JY!l;jAhdAah`tau_<7Dz&<v+?1^*J^=KQ-&9&
z*%akp0!69EBQUk1K4KCqn~ct1MI$O?8V(nBeg&8uJjpb`XL~})yK8^==3)-~=%3}K
z&)2_J-rC<5ctM3h!!kE=(St3V`!+rFpN3k45`Ws1<Fyu3T&|!@Q{BP?JUc!)=s)CM
zVG&b#Zn)l9)~>o(q{$1uF#wI3KZv&U)4IBip|~U8*eep5V`1?5K)vN^Ez|hy3pM{Q
zk3GfzV+OTYHxfidSjey)o|~<}JDr|+y|=>iFkK6Uj+bUona))K_Z@CvJM~`IxerED
z1p8F!h?-6xJR{NUVn0^7s^1gZ!o{b)@cb^)2(}Q1kp@xc2-RC@;O+C<*;#|uHMPe-
z6Xn?eJ8W2~U#Mz1H@KYdtwZ7s)Ygu|%JImK!ys&RIu;>NPS)HxM$|Fmz`_MK?M{X{
zC&>@Yccy<an7qwg>ZjmYBb(Pl;f%*<?%p%qoS^}@M0%*<fMa{{gwKoH(k9*EOa|x0
zvZ0wcgpX7KX_N=LEw$o@{~t@|;ZF7cKK^r#gJU0i#yPf-O|p-@N8y+mN4AWtkYi+z
zWABlXaL6o&%+RsRsyJpw2q6i{&-eZL{Ryw@y0827yw~GFz#MliJSf>k5m~{r2uNtz
zMHiI1EZ`eL=Z4l*#Lf&VH?l2t+iNEskg@2e-DQ@$cU9K_je0}m*?!{z!*;Gz|GhvI
zxji0?F;`b;dIlWE>P@_g#*3Lv*Q#Kk5e<>JW;n%ZZ<gphtBbdS>RZ;;D6i%;nAmXA
z?_UPYpS8RVl`$Snl!Fi}ts9xw*&$pU+hSXo5$WGgUgbSY@kxU8Tv>in$z8}28-u?k
zTyO!`ndd`Id_mM9v!LKpt&|Kflhrro1a*_YTx#l*h4Y*mA5JiQu{HZTr1N*Y2Rt@{
zoxg-QQJIwz(zjTbI9kYbJn5Y)Li4>ECGa`8D4E4D1->d{F1W}R*(Y(_{>8h`Pkd0M
zBQN>fzv5pf{Ef%I&AvR8Q{zpQIw)|bEf%Vc%w0!IW_#A+9BN8}?r3uyb^DJ?sHBH}
z-z_bj`Hv0gJ>xC$3G{?Xc)_4|W^#h}ZJU9r7=XMASg1e7O9mzKya>E2ys(+E7naEh
zJs-*FW%)|7Qk##4IT$I;h^Cnn!L^>C85X?DNzWbSL4VEae{lNK`~<ZS=z&%+Nt&Cj
z6=VTmXqJ~-0Z%E%bztHC>4giaa+EwE`6Y|ri7k^*<e1WZ0CV&j#;qLsr)tPji-`@E
z!NdjCK0rI3J;M9vG9D5&0a2T3g6^-19DA;~<Q;FwXCRI!3*OOf5#<uXQD-+)Ks8(V
ziwP5_>S7md#*@u4efF37mQubN5mPixuV{We-+k$|I|7P&(k$}z67g;7-bbxBP=$RO
z-Zz>cuP$WyFF1vbb!mrtD9J#wxc}bl{HXfYugDqz6H2X@=ix=ZLA@OJx9i=kjDuO-
zqB_gWuNT!TOU6cRz;f>Z6*<+*7^J3>`r<B;#>fd0ox%v7;D|=MnvnC#O|fUXEu^v^
zj-F)_H`Wfn^4n4>O*J_$VN4l^xn<K1!^X(`H%k&&5RZx%EL}KSFzok3ORXnYhTFSu
z7_8rC=_}UGFQOzX3y^!W4<q3Y7vqNzj?YToYCs1udg7GJ9GmmkJC5mM{#E-oy%6_t
z8xhB)+bz3^W{5Jk#TTOv2RBOM$C@`FQAX~fm8<a3jTNnfC*v`lg~F>bDbTwXBevCI
z<Mkp@-Gc9jd0av}WHR^c$0{GwI!A<!=s39<ztAYau8iD0XAX~VajyFRI5&ahjHrq3
zsFxXA=;Uc%S2Dw)Y*$NUQyA5JpEtfrHOw7>v`(OF;8`zMi7)yS$rL(+ch|M%J9gzU
zeooc1`{MD4XE&$xM1pettKJ#(!b7kd(T%GIP2L7lU_u?p=|Ib)ynIcGWRIKF`ks5*
z(~T)&GWi#JNGZ5KD;|gg#3(>NKcpt`-wM5XC6fIRZgG2BM$vZ?apzv!h^j!UWIdfK
z7W=G5iS(|N6FY{~fSZ}g@4#F7w`xx(T)s~VKx#r1%X*X+w}K#j0%{bw*ApKiR5=Bl
zy|7X~{kS@6n*MWmX{>AMFxZ0gP#c}y?#*Rp!=PZnH1(K{Vd{in1V)tjN3sfAE_s`j
zv}FaIru=0eQ1g7f)Dgw{?E+xZpxrmtw7Bb~JZlsP8X0)r*Hk4#r1M=R;saYQ>Gtgy
zS2z0o>YiuZ0<7gP?z6)s2t(ugkQ`M8)Z#XTQ#DV6wty!X53zFW=gqPT+W4SoY=vfN
zr53FJXZCQ4mmN>CM=AO8SpwG(EpDBATVY}R1P{O+V;?B8*d!SBFuTu`;R~*`vx(5m
zh;8}Z4|>%r*;XqsEmirSj|srNo34ZhCw&T>=F5cP>6WJ+y@jY`6xel@gzS@~{SBkE
z|B>X&SuTlV>RjGUmyy}V;qE{_Mp{<#GGdD6U6<79^m*2AMkV7@@2%pGQ@8?bm-Wfs
zC&dARZ>wvGMmXJ^UNE(({fwjH33#g@>mRY=E@e9UDp+fdV+@rlYgv#dl%o}Z`hesO
z)Knai7xsNk?i`_Ssw#W$xl<W+A}Hwxd)^lD9(;)4cG3G^cXvU69=^}b3}I7GuG9?d
z)Qlraa*uL9e|id+6#{+g^NipcHPBm0E55_J)Ah`E!`{f1>oxcbsQjYl841Hj^-F^;
z%D(*78BqvEH5Opg_f?r0J-P06e0Iy)uFwNL9xxJ7YB`@mxB1N4r97#Nru@B3vKE6c
z=|2F<f==UulfEz&!wmjq?x&9R$v#k)=?K8SobeS32u_Ij%*i)JEQsT;ExUf3QP@@t
z2uCX>KR*_tDc6fxdGZwjfle50v--)HF|ERvwmN#gimfX!2=}?a-G<LqeheMfnG^-B
zwlh1K3HWjYK;_th3#5F2F`a%=i}hbxXa5)gV1J_fW{|e}X!gxT_0mXuuN;v&jCvt+
zx|Z{_aUbD@*VLl*>YCQmFzQ%tCif7l(vQFpC=a6CI`Y~_SG?|OHyxk%(A?A+(Zm_S
zV1WFmJHt8W#&x9oFk?KGKJ`~!W{Jz+Ke~x#h4|Wn`MqZdp3kA&?0rntah<}XUrlAN
zRC|<W*s9XUHyP+AP?TY>gF7Bda$Xs|#0!rJAD4kaxSz^QF|vF}5_2F#Dud?KyYy=u
zroa*%Ht6^2N^?Tvd|hclfu?Jpzn~~Qo`Qo|Ew{j5n{|;uwl5I)&(7|=T@3~AOHT+7
zzj3A?|K8;;Sy1gp>ItwvmL8j_*VJ9HX2%ac8G;T!%RE#hea&t;v-Urfs96S-nC(_f
z^gNZ$w?;5{D{iH>v?KAOufH1G$RmCF_Mm((W;EGD7`H;fRYb<;6fZS9vEzE4<k+@y
zk^EnhE&d=A9n%uQ9pMO)zaPiB=}^qbN=7XoC=FOp#tBg1YBl={H+4nb_wlD08z7xf
zh3`EC@u>2vZVQxyIY0ze7Vhl=!UhJw7BVk3O1eP`2QdNV%4R}{2KvOrDgHjncNv_#
z^ry%9^J2u6qsG?=8*axyLmy+-Zt4sGI!BBrr+GlID2y&7;324UW}jwFeNyr^Ods-=
z(&en=Ntx5oaoUT~p^9jHZI*0Bl%~!CHJ^7T$-?D3OPn3nT_<bG3JrZw7>w3lfhX_w
zcV@XbpQ=}lkgdneHr<9lhqQNI-VuH<^Zeu0@4i>9PkD8Zo_Wu<VFEjE$W#E{g&tAq
zkhn<89+CJURT5m#)7~?<6yC0Lt=XwL#dqvMnc^5(qNH+1g&7-61Y2-1P1sq#SM~UY
zs%***FV%_ii%oE2cewRcl9RUN2^GrJ>6AAT>rgtC<SME*h<D};RoiPUCR`n3+*&r3
z5_*&jY*%*^!$gNjs>#+r+;b+hhM3%BdtO>o?rYmk-T)F!^5iHSxLGntNJKLFMKF)v
zdtgw2VMK{4A;t4VZ)tNh0?;~z`?ndO-7=Y9`HMAlK5{~)UiTj|vtvO?$Ul>p*5E8r
zF$aO({;Oo3-BKw!^<)s*l-ca*wNUdxlTbXxreG1c^M5<v)^&6Z5KMo>0Lb8*mxR=s
zabU1CdJduaUT&WLv6f3~pHa}DB7M<4&}rokeJ(ef<_o9mha04tOly-B7t#0%n7n?i
zkEvO(w@Lbw8b!dg+nCe?vxnvJTWvE620zf$;@Uiht?}7^j5=5U+@X@lQ_6jyYuCZ+
z=UTyl|JXBdlo{zj4<Vg|@~+bSc~cHh9j2gQ6wbb0e4HJ{`;N<D^Ci(wZfh(ufez;G
zS8-0l6s0?uw3wx&H`?DY5{wQ)jRaV|o*o4N-dc%;4_jk+DOa~LHPqc`Orm$e*E*d3
zi8{4hCM4lJ-nKUf4PcNeiIJ6P_|%G!0NS33Jl6RY-hY?mdo7Xb52c>GOY7qT!{maq
zW|k(^*)s-Q-qy@Yhc_HV!2YTU;v+)6U<HaTt=$k;{(U1S39qmJ=Q5#gf)P3M@I3Pf
z-Y?up^Lj<pH{rB-j@P4;(wJ@CwP3m&1Uzfm=}w3VItgF#UUc0+NCF<u8=;K5HrBfF
zg~7E&$HJB*K_6uo?Bu2XkY;*tGzGn+s6DJz8Az*wF(pvz-?p?+$zVDD-gPHe=}_Vs
zhIVs$ih-OpFzcPL@NxC6k^JUIUKpt)l9uW_It8cNq4+|SkA(ePe;4k}MM+@#CUiu~
zD4Xc$7p14#flbvDhtyAxN5|-#mDL`kD&@&e$IJH?eNI_1x}&|t;F%VN^qEsHo9}a+
zd$eOMTNp@$lsRnimx-B`&MK!Q)qM73W0OaW-fGtD()h9H+uv$NDoaIz0x3MBQqC*J
zo(~&So&V<>%N<400`IwPyD=6xD?FDkAtTEtL5Y6M>Nd@q&y&5YZk8_H%zGu@BB)cZ
zS!zBedv5>m;Lr>dJH6nUte6MZdC^ZycfeW`fa{omBsd(J)M%)uH~=&NOrhSHw=M4X
zF?1Mtw@!&LwfzD}{UrShIBNR_VELIO4opQ0_SGmJ=zpHO82<ef<OM)1M8b@&SuI6a
zcGbg30X*yw`Tcw4G`8IIOycE5fgkSvl0#E}g{3cn*;TRGroC`<pkz#TldK@k+3j5J
zh0uRHSg{5A4z1`m%>xQ2Vn;?(xsBv7SPOAN(Czr%%~Hx-vX;T8d6$V8?GtL>um5Ky
zf7#`<<Uc$xu$ju25(u_>w1Z%X@}Rwp^I-R;;B8fH+HJSmZi|O;0Rk6s8U&?QN)aQS
zfKGYP_5~T)t9aX*?g@Otu%xB3#@Lsw$$72284R(v95v|h-2QuCFH+5uZ9P6*oMo?Y
z#0l0El~OI<z_e%D3WbV}Cd?;J_Ja}j;bo`dL?R(Ck6tUX-YMaZ4$m})|HxoO@i(9n
zJUeAWl_mQ(wej8?Nd(tf`Aa@1SaO(quj@kXYpOzDni-(q{4iT{s!qTKv78Ou;S1|Y
zcPOOzMT2wpmL$nwi&(eY{cP~N@(a)K#qg*n2hWR%Gw#G;@Yrt+q@m!Oj>RkOjp8x6
z^dGHzqtjep-;_N?UOWF~oyum))*Dv6;&|U|@ZZo+-^EF^T%vNMvZ1}nS}>sX@yXAg
zL><-V5jt(Z&kTO5;h4-<nP9AY-&MN#*<AX@t1N=m*>&&1ju&p&jqa8BvXt-u+%>!o
z3n#oj&i*4p(?k)T@<y?Q4p2F%%Iz@s(p=NQN(<o8C~bRKxEfnT2_=2<$?1|hx-v3?
zwBwB%QX~a<sy2-w>dUcEoO%J==nw(u4#xzCDz6S!ldkD`w`~LS0WerPlG|`N(-r0*
z{RdCiH!=XY*@tAEg%Od#q=(@Eviz;JmOwd)RjyC81(XtqKTwT9@T*^;);UnlKD(vq
z!huxka;;e?doFkib%Z}_Du(6anwJAcs(yb?D~%GF6}1;(#6DUesns0Z9VE?|4l1oo
zVHF2<5I>>iIz1`|FOOC|hO&WUelG`PK)_8tO6*A@wdrLd5?Co*Z@i327|%L^bJ2EV
zwm%q$Wn~v(f;33N<oYB**l?XUhfO+g*Nw3&9e|m3qgx;_|B*(INKI!ZmG|D#GXXIX
z-o9RCRUfE+%V_k_)150kj1C}Z3Z3Plsgp|f^2i?XpredPOxdu~*jPHWwV<Wl7#Ld6
zjGM^Ofe2%j$CP|-U_OecTatOs*=8}vh?NW?CV^borYUoaL$PnbiG30X3{}&}6@m#W
zWxqIJ`Y@nhN~phu$NZKJg39ww+zqbIA#});ol}Q;@q<lmzVn>KAD$aY!J@7&!=*D&
zITX#<DFN?9{hL);3W7=5)I<97g`0ep2*>iF+$xmRB_YsI*2&v<?MGPDw;H}~EEej~
zYB}~t-?Q(i`|p`tYpe<rcl}f@aT8)G0vCXL6wjY03Ok&5B^?><WG3)dD||rl!GMlH
zg4V9ec%ihwF=vKl>iDM7&$xK)tq*q|ge(^5CbY_~f-)(n`h1RwJ$5&NbcA%@^s3_0
zsbdGzqaRP&)%<6B3($~=SYJRA`a!_DCH1Po%}1tomByE2{9++}e2ZkO$d~A#QIH-{
zEZ__AceHe_;AU1fMLyAn`x%a&FZ2OTqYeQXrKu79#+n@%s@k}XE2Nz}=Dt?t8i2%t
z+Dhwf0e+dc`wE!2erk|pkiyDNM_nOo_4*{*4nsG`D@mfI2X9G9OhMA_^I0DOU48eW
zt4o)QG>GNDJt9eYO3IycSsTu^re-@1-#t{=S=b82$DCOYS_uYlLCeRj9n?O*6tVJl
zW#3_Tafq5O#YTH?yfjkGe&N#%S6D8|Wn$vl*MC&FeRE219C3P|G&Wii_~NtAzo~XA
zQ<9HLDrd;eD6B}5PHxc8uFgzti$M`*xwS>w2U8~^E1Ey?0-i5>j+Lt{?4XRfZItg1
z4%<c({L<JhDk-gByxWcLKmmx#(?0+U42D3N8&a`IB|Jm0LLgozWmHgHv$Hd_ta&E_
zf9qVrNYV14hs`%~zp*|N+a)~qXeYo&1J}#D%^o1bZETM*ZyDEf8P7NFpfJO97_*LB
z^;NmD%nSz<vD1NGhA%HseK*(s_Kta=ieov3x3(*^HcOu&uk*FWmeaOZ6|b(a=CXwU
z%BBwtetRKvz@uU$DKN8r_DeqHA%~>lBO%a4#yqR%rUpm+^nd*!o=jr!cV7qo&vdZp
zWc7oq`~>h%zlJr0^jDNiXH7j%q|$LKHwEM6N=61h>6krhs0iH|%RF_TJ8wJ>2=d$b
zWAt9YR5=y6A_`|DWEX-=k@WJbiIs*CV^sm!N~}!O6y)cVRLkgsU?TTE$Id<EAGD0M
zdtY)_#&f7RiAa<(6UiNFJTuN*7C_}ba$>A#c`H@yR8x6jk{hJxzTp1dN=ee63FA7W
zWKtMK;kGHoR?yq51xy)+j@ejSqXVoX*@AeXQ7=UQ52+lukwp9sG<u*`j<mi93|f(J
z{n7<o<$)%!9#?QM+}VPnS=SzvQr^pGjJ7?;Q&+s-r#)9iB8Re@&y5a0-O=G4qeM@U
zr?EV_EkHq@`AP&Aud@QI#+rt5fhIhnz_Mj)QdJD_kqCV}sU-HVs+E@g1LIAgS$NI;
zHdUXr9i#1M`>%WB@0Hbsx;r1jNKX&p+Nfe~_*t|N`<c9dG{jFxDlMh;B|C5Ek*&kj
z-L;lD=$@E`E$?`K#Ya8Q>^CSOb*2+64|AxtJ(PbhS^j;#x?n0<5*WpLB(){U8nbMg
zerI?Q{q7OVu>oN@rASfr9(_INT%4)F{UUBIutUD`b1aO^)`?K>`F3O>sVR$Sl;dhn
zHvONlWaa}J0%n`HTlUyA<1m3>Oq4-6(RJa>3oO&h8o!>ubK)UYv8%ybN`oR?3}zbs
z78q4{`CsxG=A{oV-E@c=`)<;<#x!^IyPxEp2t(i2gXG(l2C~bZ@kjnD>|@dj$GI6+
znX-xDDAd3G;JmJ|I;qrpVLODIqI(@JslzZRT`&a|;AYJLcuKEmwuUQxsIr|bJHDO3
zD9m22Zj@bXcz^f`Q2-RA5P-$FHHpKGm>7~-9Ui||*bnt6s;2xArRpa?dU#1KPE*zV
zw5bpV=@T?m!6Z<^DH86}{}Oz?4SKTBK<(rH4QM+U@4^UoDDZuKkH`V-6nA7aW?Z^Y
z<i-QSeSP>9@EFD+WXgGf3<cDcT}(-3sLay>=C4@)^cnlO1b)VKnv?i#%&&!tkZ+N%
zvROG70K<W9*W1hBKA4eCsqob4n(V79WDkbUBJs_Y!oRd)-<|tLgCpJUiJwXnXz6k+
zvK7PhR08BRGBI&ayzl$u(ro@R0Bb4KWnJk>w?<P7Dg-m}G9Lw$DLV~0SP>avs@o`U
zaE}m&l}9g}mX0`7td37pU29w>!)V7|RQ!N@k1q4J>2&m{8sPzQ#+x5|m#d%cCja@)
z?4M_$=N&Jd$tu~0aN=mTuU4p2<_{Q-z6a~fW<m?Ct5>F~hLRA+O1OpdxX$M(zD-%@
z9*K6f36Q)CMz~PJu1j@|{10!jO3bGOoHRE(rpPX>zawo{r@T?VHU+tNq6*Z<mA06_
zb<5Yd5qVaG)`}Xu^?y~Q<V9fUGTb8F$kFE~HY#%YH;kXurTdh|Hxm92kGIiSmTd{h
z1pnB1QvSn0KPwUbE}i4)wMGf6CTFVgiQFcm3r#gk0m_s;Ro_kQe+@@1{^WFB0yjCs
zM||MDN+*6^3OUi#kyqZ$#U4Ac_zLb%ipTEPo*VY`6lLAQjcq>xq6q+f&`(uQiZ3aH
zXRJWgfhZZ?Sp~O@cE784^pbgDP{8O5x=f<MuV5cz>L#C~%pVa%Za_6XfT~&V8~1!#
zh(*rDxy0^BJCW%d!P|1ls!D_HlAx-<w=`$-A5xz`U~xdppPd2xRrw^J<KeW@6RJYd
zanP@kKn9{C-4=fp#%Rn2h}BfCk9w!Yn5RLSm5kczr@Ze=n)VE0%LsI^1q;gL;p~)T
zop$9(DZ%-DL~7u3{<&weWjxN$KyQ@kJqm{eMX6c)$FCRh$jqd_w}j?u7WyxX&OF%5
zzu1>h{zU7*Qx|kX+xajhE0!yYmd`aMX!w3}GJ(#xMSIdsxWPCYV4thG8l_@PpmgO|
zI7SD!r%e)qAG^jOxx+UYbRP`20l2kA>{XSxr$?%xv`3UvbzS?RJ+ke_X=6e-IsQ!E
zTK04C-mquZ3%?l0C~>ALclx%%{qrT!UQfl&_%{~+RA-C-8z~GsRXdQ`Oar%>nat8O
z&ZQk_N=^t}Jh=S27&|M()l`#m0qHjkHuuIBFUQ*!B*%eX;^|<ukFDIIKD^^q$#B@O
zoG9`vmO%K>!Z(@=pkgOEL3=KbU%&aOl$2Loxtqwhqn|-r*~VQkqxn`rzJ-TtE4TJ_
z8~a$*<6nWDCPp==TP6o@d=GhaH>U4Oye`sC6&S7W%=80YR{xir9`UPwsc?yE5}ziw
z4sqqS0s*2p3pZ14P)zju>p~g9sef1T)I3iB@CnpwvOWM)R1TpPP%OR^K+ozq#s*Tm
zoZ@Q?E$0#^&IUQCyOWC|0N$N;pQd^A`#8!;(B!$8qWQ&9fHd&DPjEesWyXz2npJl!
zdgHP!IH5`c@!mMbSQ)?{#)GHuZ8QSh>Vsf~h!D{oH-^A(qqP(6)iXnsiH;?&x)R{G
z427cs#P~c3a^GFKFuCc|=sB@N7e@!jH8a7dFuL9Dg}b_xs^fZ_cJFUB7B;%EHd8jW
zhX~Vv@?$@=wpIQHq$p%mURY`LWVzg6x)%$QJ3QtNHKUJz@W-ZNo8HW>yy$MH8hCjv
zgvOAd@WFVd3Pas0Z(E3Q=4}lvu1wkh7~MYWa%IUx>h6SDt(}z@(xIdIYm9j?d}G;7
z**$<=1KgpO=?rcVB`=dTQQqHC!qZ8<Cy&F!^h^8A26D3gW>ERBhM^-5XzJ-B4zb!-
z8sdfVEIcm~q?wKV<MK1zTl8?NtP7dXb-P01{BE=q<K(d@R<Y6?S{yX+EaVPdl7ru8
z9-?8gum12We4RfiiGlt-E<u=LD6V<v-ac;OjwsEtw~xbDcbgTc!ihcSf`a`>K@e8M
zc3+wKW-hh2&3pBDBV&A1UtjXAKbH%xY;@1prX`J~etz=PL0JV_oY_xE_OQ@1HD=^^
z@V}N&VYARwP}9zw(}$(FGG$d4(;qTl01Z5Hl*zjq+U@M#6<7KIMboVx8N3wx2N@*=
zgXZ6^GF}%4xZsWlgoQ2h?74xTp2_T`aKIsb6%?;F8mE(2dbha|qceX?r+{6*#7I#c
zD@}iAr}S@%Om$Sw^LHR?v><}$E)N@%fVGW_D{lQ_hQDhW2n7}c?)6bXKrc%*gWQ{e
zKC8xI)2rvjT7@os5x*M%TYbV5YWA#gW%DI_bgSP58hQ-(@l@W*6{sWUou7oZdq98b
z(?E1;EX;fXU8acfz_z>ujFpl$@z5*lf4=9#_D`&X2sD19S*;mDgldkREU!^pyKEwI
z(GOK0mOwKJXYZq*`-bG<V%vE{!T+dJwx6*b28oFqy+M0unAkk}O3SjrlL)m`QSJX*
z)jMIlAW5@qSn-_Fcg0zhs3+%Z7t}bGF+}T5c1F8)#PK`cKwkO;X)B`D_U_+60!FHr
z=vajF0s>Uc--C}w2wbwi_(aWG&fO5ZKq2Jg8SMQ+g_iRGg-YcxC3h2!s4c<dXIp~l
zbxtA!d22*;HWbh7;Db8(PhG?rTe4jpT-Y<UB^zmGKVQ>AoC_pDA^LLC8!KHIdK#Ln
zr(9+udUgA$qK9Lzo~l;)xivHm!;XeU_HxI<v!fiQ>6U~F*i-HY@`o+NZ+-}I6g4Eq
zgz`-#)z=h9W)BgjWv^eoZQd^Zg!PQxYhvh@Y|*Lq)C@AB%ly@G1Jd7jaUv2iY>(YI
z_JM~Px-2{}o)-_uYA)!Hda8>m{d=FTiPwflP#It9C;2I?<$qk1+A<PmI%^p=^+Fvs
z$A#ba(MrgV00V2a<cBJ$5l*HHLjE4`v*hse(7e2<{lB=DOV^-YW|kC9kQR`-@W{R`
zjOngw0de`kFeDog$sb8k5&Hm%#9P+@xbRWEM12Xw@|6W74(QupSPVC1p5y~Dhj_PU
zrmrz;=c)?6!!A_f*fHf}ZK={I%0i~}cW4vvr-5AF#%D%!prO;r9?nLF<@a-+l+cYD
zS@yvrpw*~fv#~ZNwCeM8u$Ac1>V(aNAs!k|jT?|F{&QU0E+DM?9pU3~(`TO;N>n-U
zvKD#T#?{g{9sMdqj;`E`Kn(P(;*U`R?Da)h%c3oF<u`zC0$hD>%DztO(BDM#vN}oI
z<I<Jc6posRJvGwIKHnqI-<;-9L7&d!uX(Xx)EDo4H!+5m_+L$07m)`tPHVOFZ?>Lq
z>~K}hUuOtv(2uw&@QkjUl8)V4ADGSoOZL-Q$-GHvPr<q_YxU6Ho%(nHup~jfdWfQC
zm1fFR{G7-(P&tKsl#<gnSx6fGSt0wwT}x#PK!CEPQeT>X)bm_TCRWeRNx1p2lu3R7
zkv^}!8;K5(O{vT)u*HY{diYMmjg0B(cob^sE_>RSre_7;-gRY#aaB{8jmmV1TD!j8
z`Ga57WU6lCY>VnDfhi8eO#MQYB3e#YbU+ZpN8Bd_l?H7dG7C*0qSoO@(S0FsYE&)e
zKcv`2ZEgq0CnnHCRfdn^*;=nl-Aug*B$$Bay_#lyYRr||#<upPPOx-#o?VF5*S*N!
zw=eg*9^%%9*@u5bsDjHu2~X%JOM;lA)<(6vk{w*2kd`Rk3?U!)fkKe(9bSOR%TM0=
z=(a=9XC`_La39gKTPP6clPK^190D{=fbK<I0BuYA>DF*Mi9^&5TCY0P0NH4Hha^lJ
zE*Drg3N~@^g{*RXUCZ3G8z#x)w)S;k#)I8*J_OUYJbEH~V^b4M*2jrf{dQ`8;AXkM
z{A3gK`-X}Mcj=P@0SQDMA^~_CPJ}|Hewbj<Gefx^LKtd_0i*j2s>Vd_@_>K8kC?)d
zTh5l`MBEZxpxJj-nx0+lqM?EjjU~k)R52L@mDxAfLb{lz6b>1mzA1L~XpbpDKsSYc
zff^%5YEw<Qu+anGDo@{hKbP0AC!Ub8PZ?jL0f~BPGTK5J>`uRrUrz{!v2ECwU9#iA
zjhT==(w6><oQ(5d&qHs$aH8U?7qQ%}{)<XTE?a5uZKmkRypMWrQ`;QB8b&h2)-t$0
zlq6+~*u~g<107X95T^;bgL7S)i+h%b;mjpIvx;AsXPBC;+2||&XpoR8(a^oHCGKDI
z&z5;Y3j1+O$vM--5`=Egl22vKo=YO|hl0Ofl^ML9aNP0PR;+4+xxDFpa*e+hp^t1_
zdtkV=ytDgHC9`Ea)NSJ<$6EA@1=|k-&G!HG53jPxkd^yykw+&rp~QGEy>>w=7e^4n
zeOk)@>vXo8KdsiPV~g#{KbCX%jFP-x*?5)Yar2lrrwg8q4brvFtf|@$d1&~5`0R?9
z0LzI8+)Sfcjp1mLCf!^#UwI4(%OALydd^Z0-F~A8A?U}Ak^l%}_9gxPu_wdk4)nl#
z6b6eG0l4chdoqEvTMyP{KiEMMGyhVBY`huDjQS23&>@$c0dL)z+Omdlgps1h?Z$&M
z_%5xoX#m7c=(i~j;8;*epOk*|cJos}dtZm7VDb%X6Z$j3*Cku7j7O!f<?zoP-yil4
zdC73Z#x2_^4K4K+fcuosye2Es)mE3hRWZ;%3JRm~wqR^@UDlTU7a0}|^szBNp!zE~
zf0|@()A%X@qLVBg9V-YUhmcPT!R$32bSfiuVxA;G656J9@*o_Y*;?zcCA)7$z5T3r
zt*}e&>sRTnN|(jmp9?+HiVv?12eVewuzr<1wyh5OtNq*w4;bKOe7a>;6BwNVcQ|8X
zqRwv7ANU}xqi@-SPTU?&&L{e2Pdy>lGglfBrrXkYF_fw8=C_=4Dbct^|01inufuOg
znCazuq3Z;kdeUHJgmL;V4@0AQ=4~XYa<bQ6){0!_y@+r3TsidMc2~~(ek#u{uqy>L
zd!u)`<u4qSX`P(C8^^iCfV|0z<x{vj>!|L$fJJ?k(=r$Q<>}{BtaGp2v-ynf?G#n&
z9|6}|fA#y3U?Nk$AO>c&fFwbl&VxLY?;=gE#lYXT&79{V-0B%l1`Q9x1_NHX{pjXe
zQ*TL)xH?Bod^+H<##%l{%amGerUXo8OR_<v{gIPg5hziITIq+sQ~t$Fv9q6j%2$5Z
zu{h$eP0&hHHftBEi{st5k<q=?Njx7ND*LFLab;fv1OIh%MtSe7K{u(ZqTC_lv}^4b
zB`Y=9l)QrBJCM^ydev5RlxRY|m%X3rrrdcGinRs(Gi#rEF!6pqCTO#+wz>b-YjaMp
zZ4jgy55D`8Of&YuU=d6UT@l4dA!kw+8BO^@+l%x}iiUGOr9~@(1~E+B)^XM?^K34-
zQ>8HcY+y4E{Y_wB0hr*s>qy*aM|hoNa|>RF$`?kx{O6>`?Pe`@K=GnZ;1=vpZuHo5
zlTQwpa^5oVSy#xw&`>Jhi>h3oHp!iUW@TI?lbw%ng1eIQLtQ;IL(`9hE7-5*LfG4h
zT{hY@K8UJ8GGjF=xpGI0_4m`T9XUhyky(u|a{>rAzYOjlbbVyU6Six}x<QmoeN^mS
zCW;WLb?f1w!lM$bE973qmY1-f9-kvvAEZ_$Q;F=r*lN_wepz3@S0GsXxLqfZ!M3d_
zj83jf@FgrCKD1!!Sn~Jqz!=VYsyO~Tw9`ruvxfLm*o}ehB@PXCGGuN1`)kA15EyVL
z4f6HLGo1ZMxm&7qXG2TX-My50Wg8-VLGv_MuT;E_^R0xWFvrx&*e0VNeo4U3QfUyl
znfA^`Sc7g=<y%ncRt2(lqE#N6N2<d(;7v#^kcx^POg=T_LiQ1CA|>Z(H>_gzn5sda
ze_dAO|9mXS#OJ!qL({vYdH%O9QTO&i5Ptfpxk8JFfJH;aOS@B!K<Y2Sbe^+PWkE!G
zE%3|2^{I1HJy8>GlaFoIU$diOdTQAqrN?Q1p|A&Z%49B_?|4jSU(fYxrkLzUDJ?Me
zQz$;EsV^iw=&Bn|{U%_}UbjdxA@L$9?iTH%eHKbceyne_i4aR73#|d}%dFj9eey5&
zQtA%{kiWIcA-qH0@xx!$Z^WHYP1V!ecNFboZzJT#h$u1u;jytnMM1q)1N?szaDFt)
zVphPlWb}6d;AnSF2VEJD!DXcGO3D*j66EL)My8_9cC<+<g!ZKp@6htc1^Q8p0-G;_
zhwecFSsrgL6yyY_5K&nEb{P>L2J7?xKu7PSh~b&%qj6f%zxyVhEO+_6Ssy|x#D}S}
zD)M0D#{I20(Cmi@A5w>NrGf!35Nwd%A*yt!2^Neemy<xe4gd=UieWh>2p>~!A$I4m
z$og~jVWchFo(24N0*oRq)Nw=umO=&pyb{r4q<FQwAhV_X!5Y;de@!SmJYr?4&3rJ(
z(!u1E=*!(RpY{c|VR;PQc-}tB!QDL%vy|B5sMQP!pYMPzR@<ld-msq6G@4~|I!(3)
z{~h1Xm580WaJFmK{`LMZt$};vl0Y)q@y<dZNbT!d=|sshQ_4RI2m$<Z1WzpfK3wi!
znjlPlQfKdd2Qu%UAsJePCvUi7@TI8+lw(5oVPh-4ZOYnmJp=mVB4D)ht^I^~EpB5u
z^=;tr?b(NUf`_N!o)(rEzA?7*-m-ryHb>@<tKQbM^|X4pTI_$RyK3?v|8Vbh4R+zS
zc~_l>@=R)bM)48hUIYBHs%TYyPciTxPx7;;kG={|US%{o%QO5h&k%u4X*6kgXsp@n
z*^9(?Rz1x{@xB8@(?q2J$%V#X&R1A10rP!*bM<W0yGxTf*@lI0?rwvr`egmd^ZDYl
zZi%dYj)k9NByOv16NKHem3Rx4AbJRr0jjr*jh?Zhg`Nj@M8sU)(Mnv40efSgfy5;(
zl0M$(S?m@(KJ%&4IpI{#RlMau_t#S)xnLvSB($41JI&x=K0}`H3b65ECLJ`~E^qOn
z?mZI-BfPV8@CC2Ls)q$6|9Zw~%x(BIUO(gL_|`#Ulb&GEYffyex<+~zbFlEUA7IM?
z#kIn)l0E<AV{T}e%3JYR;7D^k*-Gq_tDl@3O-OS#yF;XxExQ%s&Q+POkL`HWQC;M4
zOc{UgtgC+n=#JVK0$dNvdEpaIP>|g2jHsrU<80h%ogkl|zp<FTz31#H0{L{nXytz>
z!kT)TPHq~cVmqTZO?=|6MC(85Ixgq*t;fm}`3&r2<0&GVz+2IOoJioV!*c%4JsmlA
z>bBaRPY<h#JqoTWN4;*#LmpyO#bOC(&BYy@$8p7-G~b-P^8-BRZHt^O<YN;Yl8Sj&
z=93Qn<L<}JN!eN7U*Ap1ibw%u<J4JhJi}JmtiKz!qgyLcp(2`vR_3&_o?U~?xdDC4
z+jW*L`uB+R`WGI#CO3;Six{L@M8qyC@f`AXckNyDtW7e#RN2B`DP9fEFt%PytsoQL
z)D;F?P?!g3rH6&uKAoyO)Kv*tC*6tr?1PukUm|zKwa?_SN7nS?4p<RB6*^VYO)Dj*
z*`W>g8_Ef0&}vncJ~jvEynAfYfrJvp2R9ZC{Q6E^qV~nJIsVsd71HK&vu0-Z6S@AH
zz~01@l;q~4<%&;*ZIg$<@^1W1FjK;d!3AgsG60ehy+phhup*?^fYolx2=7#KbDzWw
z)5i5(b})Z3YrQu@8K*-VqW=yEePH#(wkWRfryEt5<-EXWStdM@u+1$zd#WU`j4@6%
zNt|JfT&<{FN1X32Gbs8CjAq#C`q%q*D|%!vR0hFq$;TFSs5Xlva~tVi38TFm_VFy>
z1;XH0It3%QYVt!7R&Xe9@+B=I4w>6r@kf>EB1@-F#*Qdd-3KEvpj0uR-%gc?2-1gp
z26$qZXw*7?RbnQh-a&a!cEV0;-rmqOuu?s(vsDOxWLF{X&*_6-a}g~DfOS(RT*|HW
zSQfZ2_?OxJvA=)1SjsyY@qfRoM8A)Y8?>o+>iD}%p(~baG0@TarixKp83|}m8Du?g
z=u+W!$)@}b&`;aD?7a*y#y88X_Fy$VOPyjgKUvFI#v(nzM@<hK9-k$B#BqTeKGCWA
zIf`CVizPv(h)?VZ^?H3zTiHBTyKITb>OY6>iU~Rm647aBS4t06rmGYNtvH`O!7ImC
z<#9dB`eKM(kTtJo#!T#|?Y6(~X#4kXuO^T4F_qj--}mUDoN-j-UAZ57!_nIfDgXOJ
zp;vW$H?NI;s;9VdlUe=kA#;tDSqL~_Tyn75B}=G^m+p`35H)I9YOrWOvuscd@3n9D
ztp9C22T=gExO<V(!}P_SB`OoEH}@up(U|uwYp#)7?C0>G^z&GHAMt~Ba2vM@C!5aQ
z2{vyD;FK$*2%_G+<r}#6X3(3c*=Y^d#^a=u={a+IrjtIQyG(|7rzK7QlldVJhWmL>
zOd#5!_Mvn*9vam&%TO7*?XRugFtwiEVF(?#c_^SW3FuY5O5^<5;01M5+GX8Tt87Q(
z<+hKuj;1mO9+t2=&iFp!%gq@8&hqn-0!Y3d(4vae()*##bO#0MuNsky!%{V>effQB
zXkq*Cl)IKMH~x}!9WPUFJ9UVAOgo4x#u-Qb5m67!YjS!nN%HKmSE@=sT9fz&uynF{
zF=4y2D|Ql{z>p_-Eua%Xr0x*j+tbX1>ycPB+6r4Da@ufFVi8*zp)!4VaUb>hDrZ42
z_E|GAp80rBA?1a{sU22vD{*s3NjAC0NkTm+MM%<}GsF5&?j>GNchN9(?NEAZ3k-KT
z3Ory_LFAqb-zjqmPYG<6WTS{3{nzpg3Pp{tKpzh3B0%5z>0FkYAf`+(t&7Uu;rVS^
zsAm~yAT$cx{Az(+hXElZ3`1z#3^zALnpbd}3%wVcVRjTF6)@jh<Rk}SsbsVl6uGa~
z|H)40hg=S}onI8yP{}Qcmpr|$-N~%;e%PLF(ZK74@!e)Ow^@xNp04l|EgXI<qpWmh
za)vYoJ#GwhRn{aQ$~c+%*B*=w+!&DpJMM}aJ;u^%2ql=6lPNwy0wIpJrivQ4*^nk$
zCMCR*30Zio8C!_8QuKvtQfUymTgsownJwzK^`yaRjCB^D&7J~QN|$Hf-E$z@ZyS96
zAcX-6iB<%5sGJxmOxVTu-P{F9;tfBsC2y<!gz{D;23BLZnuTL)*DD<Na}3<)Iid<a
z4(-RUKBvt2g~tjdK#t=FpE?xq9BF}#5$$EMUIQ-~#$q4)7=dL#uVoGdJZbtfhuQp`
z<{TKyRevtUI)TIuyZ06ZHcK<45U$Ogot!R2FOcCG@dCiYzJ^|%ThTFXaLe_EzeSoU
z!uN%^_1v`Pk)Oe2VSd-#;4ou2I@U$)JtXJU-C(@rFmUQQ7RK=V?p}U;$GLc^PL`kK
zBm_pm1YCUI&C2Tb#3ew5&xU<0_VoSjX`84;v!=PiAQIdmLi*Q|46Q0S=Vz*_wKJG5
zATDIK^$#4VBeQN$;FzW_K^Q(fy_I@oqQhvGgn|LLM*r~0Lj>vKcp3Pf2d0eYwY%8C
z?l*dK!7_BxWa1rKH}aVyn%+Wb_Ud_Q{JPELC`MYe^p4}6zEnAJk7TXPOoe`G<`nA2
zFz;bB=boyWV0SW|mY&^iEV_7<ny>Dhs@g0m@BD-3n*kYm@B%C2EVlTXaL#sPQ4p8}
z&5K)z?x{U&;7g*|8}E5zNkYey$guO-t@BQz*wc%Gnwgx>_60$mp*7Oi6el&Tb-DEW
z?@T9#D85Y_Oig5`Jw1K(Bw(&FY`62t`*yji%YS|kpPp9Fi#}2oalSthza4$@_^wpb
zn?;uIy{<IRGuy5vxh_8}w;sOxQ2Jtabxw8XhvMOgW;gK#!+#-1O>;ZCAfBI!zkeX6
zrJ#L)DF0E;qrsS&_i30#@AxWGWnk)~CmnVw$n1G+Vfm_7G94k}^O9A-cwn{4X<8g8
znm>xx@%t2qjXq&@u=Zoc3|p%RBO-nf>B%X`$sv747=ze=0|#uJa{^+c!_onPOadSt
z{}{@g*$=U!TFh`7p_|C^Dywv_iPE0@YcmaumZP|JykqZnK|CLp9-)fXxQ#3y)NkU2
z@y{zEoHz+IV`S67izHS0gwv&9S|@#_TNV}T{6m^kD;Of5r^>EBkW9Ni7T^d_-8p~;
z8NoA4=SzkcAwI~Horz)TZ>qUkI%~3rWjF3A|0-zOi?VOK8Yb68siDjrzQX!=3{vN-
zddO!xmDl>e%+4gs^^lVtM+~<#!|Iri*ObRu{jprbpZ>J6;grp6lg=+n&V>qgcYXX$
z4`nQ{$Qxo-U2`-{c9`a7c`d_ikJYro0HOLWTxIW7Z`f=3;5*2?XWXi>l}{yXHKu~|
z=_6Vip)6GugL^Rjt^2)Dw_drkp#=*`u(ce%xqVY1MW(*`O!ofj7r935t_jAuki4+U
zyS~>Kj#oB3%iH!)wkpcC(_2H;Gk+I)e;X+cN%Vhu*7R6W0BWdVrcz=9DcFM_5y`*D
zTB4>qXog)Li+8;62($ElTPJGS?#~}t;UTPo^Dq2|-<h#IyYMLVU5stax`l@%;AH0V
zWtwdI>SM#iT@oWwc|+}GW28Pw;Y%G-+k5r6t1J-EYM5TswO8B^i#4EOmLas<N-OSZ
zYo_n}pH1r|>I%9i1}FC{zOD^}qzf<fVJtqMX*Ap$>gE#Z-cp~+103PvP%qMoRLPnI
zv}L5-?|132Z?2J-iFfJBu{;N^|Lh3#?r*H9AsE6o=GNJ!z%lw2{rK+3htUt4d7%r~
z#Z2j&q7kFLYUP}iyTIXBe**aIdM)cyU1}<X+SB}g1FiOQ)3J(!(hk__Qm;KupgXqo
zXl`oqKjMDX^7JdIW;s74oyb)KgSn{M1h#eg@T?~$ISzRPIHs|gfAO@1D3@%Yr#WCH
z*6~nnJ%v401+jX~XGP?69|9W7^K&V1&_!5S0WtShw7+OU{<71;{ya_SicDmHoMx&+
zgCfMZEi#=MJV~N?IPolrQ!l(EwtC(pFBD!ByVR9NP{WTRVjo5K9=hf3Q(KA1L?eB|
z1)_g&_>(llnpK(n$3{20!Y8*g;AfK;TP{mT6>y{lUoPV}=Tz>x9_AfHELJF2Eg+q@
z^$~4IN)&T++)t#!eE!~U7cNK%Jau|Hce$jZk7425`@~rux|KF+J{OWtzmkgXC{?=u
zws?oaOw@?nlphQC8eUQMc;p>qk9-Sx7j~z%D8t!vN?Dp@c;>IZhvRFcb9LGLGt)v?
zG6PU2Qfa?qsv{T#=WbL{_9P%b{*c-0Q4G*ay6Vx%YA*lf+mQL(hZoi=82zAxdS~1G
z5;do6-%evO5h_x-v}N9Pv?BM?PIiT_p=We=r*7<0o1r+lI{$H3*J~TGg!cB`AA9`y
z3WKD&PfC$f^IfNetmD~$k8EOAR#p}zRFi4_kIHV5{a4n#^0ub27!aVUAT^IvgZy-G
zQG$s>Q#z8L*qGv$sZC$^V_rSda6hQC%ZTG=fogG#+Vcgzy%er}LaP5K-00zDR%RZi
z51|o)wRxq8?ASQBks7eeq=gavGrYUO6f2J7f5G7hAD@X0`-zUHn~+y~ebhM=ky+0Q
z;^wa|y~j#>^3&Dv7}~l_wszCC5#6JCLDdh(w$6qiWtp5#BhO5FWnG9`c<+F5JjI<%
za1OHLRx49v1WKM~gZ>{bvAHnccz~w@p}KkS;F_bq7bVC0vo)7d?zP$H`0BVVE_sA1
z(w6)MT!-hm?L{$j0@Hx`E}&VQK7s6YLak?BEO6^Xs9L~irrY?QOf6LJ%sUO46RsVJ
zwa_VV{}Glf^Qm)vVLB_hEKF|dfH+Yb1-XD^_=$bJS=u-+Zianid3Ysj+17s=suefZ
z(~_3qbTStGEnH*pifc^^6ER<4Lzf4ToC3K~6{*ZKqgFbb=dFzPER;|bN5A9_ye9p5
zKOZjDOORf+9El*_{eZ^~J(YdjGp4klf_<x_1p2T$n(6ZHst0n`==zXF|8-~i2hdBE
zg!;V;c8Zp;<Hsi`k(3;XR+V2R(-m?dJ;;ZXiE9RklQ)O27G9WNKAJ1;Dyy?zqPmEz
z<DV%BxYuoxF+_G&<0<w|R_V9SwTN@Ja>A=4u2c;M9x~UCP!~1#U*PW#vR$jM`b8Jz
zb!b<;gr`dW0Y-RLzQ!<6H88aP_jhxkyJ0BDp=GlT^)OYeyk1W+bRi`E4o|q<49TX!
z(NPyL=FbX&SyjZYh8Ko&2gd4SvN|v!8D!NCadfeI>Efb;?wzPC8MM~o`(DoCRB#h=
z-t%_OA>+qgNA+@UG+TYCYV!WVyl|$O<RjWY062Gc*!Pd`@(gUirqQLOY-;)Qiz@_r
z=EO|e)M`K|+?PkA+M(`}AK9hds1f^!iG6@?Ef1|NsD()aBpWnC;YMHs8nqQm4^`S@
zY&wD=!cw;~m9j+;nu+VisW6Ei2{sy{?`YJ{(s9r3N!3%WWjW~m<P%}elZ%!YjtM<h
zZghbV!+V-vW`2?6t?B54>*G_89?hwfTOp7<9gT^WB0h3_rDV0Y5#Y0WrzIE*Hzd%L
zHIv_H?C*|tgh)|0M#S0qUxx?x2sUxP`S4~gA+|{+H*(=>H9%k=OJ|;TQ+#Jjaimq6
za$jh5?AB|B+geE_eyNEPdbe(~xj4JriJ)m{4wDH&qgZJ6Dmv~ak8z_eMh1{cyWD+z
z?UKT#t<9^Y+hG_X&*s0&#mFh0s&<cXw@$k$wijU4VymiOsTPE>Fr3<jPt{y_zVnIT
z%josK&`0~$@A<-9BG+0=spcWVf-v)ckL^+d8%kX3gF@!E8mvn=x?g)xSbCS#d0Vv?
zO|K@`EEElQ^!k-W%}1T+zlv%ta~fpbGhp!=FLrHoEjS^RdNKZ&PX0_i!Xwxm{RIma
zIerr$gBei&j&C{9@k@oQG`bbf`4gz5pHY}vS9)Zb8*X)7Tee49?zc;~l^cV|)RyWA
z^k%R>*_c^meU)-ASSNsRvv~H0+baiAOz^}CFQ9Ts5Ee@%lbt*wNr^BHx-E<s@2i^T
z<IDAm2fM;K&yc-f!vzl-_GqNv+L42Ln&K>mV?f-o^k>b~P@P5-FSrE>=h6IAd36KR
zidQQrR}m3ijwiqz|Jk(+HiP^$)fC<FR>3KxplwbJ%-O{|JA}u%{u%<{MjSa@!NkEj
z-`=-lU}+ppA<!mTSq-vJu<=%-sGhKN(!0XomO*Qc5IOCGW>koJIXRiI7^(p>f@c``
zFp*l%TpxYbiV8`W7rgVzi4oZdxmbVhh9rx*NE>Y8&8I;CgYRB_BCSf%M5tK3Er#1(
zA+=I!_vSodtODTl)u}=!<#EyD{(D19lO62Zcj8ZbaQt%4&YR0v%adpp*xUK%r%`|8
z-;YOm!pA42Hil1WmA>`o7;$w}!yAW_o~OD{%Rh>cjGKo#Iql~~w_ZGY_Je2-!#H_L
zNWZobz^8}tVa_?f`82>EK<ZDu9<o@XjJZ;8WIx9|J_OYvFRQF<dYxKaAg3>o`xP$t
z(l;&-F)^PQ=33kGAz2uHmlPWrOML{If?i?<-XpEzmmhj)?sT1fIT?OGWmbtF>!y^^
zjqy_ziP%<ckTJH{H#WcYm=mmJNWFS8m;FdJ)2LyA$Nc^WA3;7g$Oi`V-xF@AAME6c
zy;cXs^*k>9o}~E#dn?vw@7k`5sj}TPYh{tyosSw&c9a!28A%)gE2pD&`=<nP-(mk|
z*y%39QNbxr#Fu>Ya^%0VocsNHLB~8$zy!MGCLcEKbHmduhtu;VbWzhhL$EmJV>BTS
z5pY0qs2bCr#A7_wzO-6~*21Y(Da4Y9??<fC-ozE{8#f$T;?VPWafG<$GTE0{d)|Sw
z0`{|L09lU!fnM5*Cn;#^57fFl-o(|O_J+rx$u~=9L2Gp2A8U2?zF|Nnyf*|45*R=W
zvNE3`LNuZ8m`5CBe)@|h-okK2H^jG1!3|?Sa=&Z(o-N`Ab{puv+|-NvJJRBsf-~D8
za+=ZMDC`b@IeVyv=QVN*r>Xe%nD}7kDz`VlMg0MmigzXyox(!T<Tap7QPtBjpA#6%
z!1C~0(k#44q#UB5WM4a8*{lH=nt}e-f0y0UBI_Sezw%bXkxG3Y`Bd+b5|}Y@9pOq1
z1@ICVI7zg{HHh6LL)(@&r;A@&=I7|OU{61AMTWoO?0mD=!j0TZ&{d+~TG!O=OA<^Q
zIm^YS;xi4B=NSg}T^fA9(n?uovtzY&q&?Z{F%nX;kZPqZ5ybCbAG&w6bvf2bv7$G9
zu3t-6R-}QR4!3>_B}4n0OTR>A_9^O^tts!bpLq8`vgaj-h^xUBo~g*!ljUmwM9nq!
z{?Gz}>m9P4s9yH|9F*6hYRcR@zU~U=!Teyqpe~RdOB>)E!SiN`d`gR)Xq#IYtK>!V
zyUNz<MjCxHAY;Kz<k$(jX+hGd83)?q#ekhTnMzP;CH(pCJT{g0iT{RwNUQUh9s{_r
zGKs#%crA`Xb1kuedG-S5|HsmK$Fudm|G$U|T4L8ujH0$udvA);BC+?bS+!RLu|w^x
zr7dF9CiX5hOGT+sqo`Rkc7NWV@9+O}&L8L8kNdu^*Y$k8j)X{D(}e#r(GYmkPaWJu
zUG&u-sDvc4O)a^NI+gPd{SbFgnZ~xk1SB}K`A1=dp)`Z;eP1i+4yhBuO@#yhaL*7Z
z<8E@!Vw>7Ea=3a4Y(EKvcvA|ntf-<&4(|x85&?otD6hF+Om@&5>QE{7u&X$Y()<9%
zBec{a%^7W>?1^=wmC=~ZxX$=uzLTKiMD9rLc*m|HUtz6}AtHBi)!Q7b9i~e<ZE4$_
zh5|%2j#puKOC3gknQZ))g+}!epG#K3#{(gD18-?79VKi@{Za*9oQe}B(Q<wO1n+qc
za@eVUbcTQn7>3EoNgCi8)H&4<yaurG;f`jpDhi5BO2jkr5GuHXCS$ZXXtgL3%SE5;
zUF_^zU_dYM2~CjuHLf*D`S*<bcT;WlFMx?5Wuiht(OOTazaApE^2sAMCV3*;F^L?}
z{l0N!4X?0XqEg88=9sz$r53L&ZDF;+0w{k?YTdS?lyG~Te<fxh0n%{SrDTD81tzB5
zqCmJALiOyjCq(C-E!Mz(%}XG6jJO7k%nDLMC)EeNE|!N;?fB!LB2R<roKes0Jz4W~
zkL+IN)V>Y+0IDB2=`Ls-nQbJhe~1m(uH~}kQ*xquy!1!DeL1sI@8jHo@z2k0jmFiO
zxv&bipC-E<;w?UsV@#`a=!t#k9m7Y61q=UtIn^j6csBbq`!Vk7_+(@!vV-J*OUgBn
zH~3e`Cbfc`oGVm5(7byg@<~&fl(2-C8F6w=z)xd6y0Z?n4u#jA5Q2COaZ8vD{P5dl
z@=zod85oXk^<+E@$+P1^pp4ms0AN6=Lm(#_O9J<xY6ChUo|Z*16TwyfRw#yASSzBr
zm#T07Iai|_NvEnpv=7mE##PpM2ttwbaUok{$Ilc9#gc>SUtP#{(=0FF0%aZ=KR|R`
z$?prg#=^8mXrhuFk#}_XQ<ET;0J9zrF?H0m*R{77!q+5?Bd5^=EF%B`_+~+loSvd+
zmJAV39g_MJiTkgp^{LeMO!mn6z`$*C^_cBCB<3!`Tp0CQ8iNUm30N_W5O1TH(W0aE
zpt}>Q0}>0%GGZ!KDH~yApjA^VCLLY>-p<Jl6yz9y$7Wy@5rhP{^{!LRE!0-(N7<pt
ze*DIJz%9xGTN5N+ygu)olzRg^8jP1H^*V)LDj$xdVpY$si|$yaOXLtS*}>&jTyB6%
z7s}F+NbQ^`sa(zNpC_Zt{Ata734DnxDh{Cu%pb7_5%$Qz#tCe8`s}w8w7UZdX@gT(
z?Iqyf=S52ftR!xrD}0h&CrX9Bz;PXe!OVWRS$GhDM9n3xkNIPcb3^=6Y(PlMpARG+
zI@c|Jy=i7gbFUhqmAT&)HQWE&?%e-e9Qvv6iJ>@nMT?U=O5S7XK7tpkiJ%)o^S8`@
z2)`WbSc#~qn)_<}rdz)!_FoN?sjLZiMVDg-^GnP|dd|REibO&57~6Jfe#4v*;79gY
z#$l-Mn3;L6rA-Ij|D;Nrj9=YxEb+w4uT^q5P)#aMa+I$qJ>aVR<-ZXF4ius50hM#@
z#@qK1oQNom!q>(9@1aCceyH?aA{F*w!eO;*rhX=+UwoqS09v@@%UAI<Dq!Fjz;U|T
z+sNPX6G>8a&}n7AFUcBh6v`<RBYAd~R3gM5v$^gfDWb5o8Qm6pz>WUGkKZ{ta%hlL
zV4-Ix63Sh-N;nl@F?~7ZuNC1<SP^QdKR!~|ULVD<|3WOEkrU4MSr8HBtvC2ek1(R5
z{#~tr)hR`Q1l+scjVcGL4q-#yi^@D@?Dl@ClI5?*D``EDT9@3k`R3gI_uqZHKg#pa
zKoCcnp7pMwA9P$lu8FRSft)lwW-X(${vpQ8ug7vcAQ+;=eHw4${`7chyog%T+qfxf
zwEhRg-nEU);>+!5?82d%F=EdS;q5SGrnE9&)m&<@4wooT`mB)ecWpPTl;na+POWE1
zH8z_3X^oR5v{-4mI%X9gT`w)_A8VO0@Ub~f9jC_Z80<fOGT0gNCN142_D&Y`v69R@
zNg_GiG3k1Ysb1glQFCnC&`Qq_W{gv?@Z`u!ubIa4y+#W**xNkrVjr*m<nfDBHnnUc
z8*qFiFjwoXGu%f!0#|V@ck8F;a0;tyy8rM~G`DMW<&cdjT5H%ukK;CGp7yyt!K(c(
zlj)_n$P*h^!9~)s$2v42HRhoIbi|pi-tp*H0`a8xi}!heb-WY4jdwwhSTyX7KW4oj
zA^L=YCj(qwkZM{9@Scbsh;U_yQ<ErV;A+gYDaV|{iz+RPg!WG^T}+Y%Uj}HmKK;ez
zLM=wJZIFBg^x(NiFPX0$=USw=3T^U0n84W?O45bs84tT4Ytuy5RBS5j$xqxty2O%{
zYu)>#cJOWak2))NRUibZsBz<X6E=bmCy$H5N?n@C(YhuN9in~{l{RXSL%1dyRyJjo
z(;V-^-f3qqq=0r8Nq++L&1~QwMV0Q6Ui9ac|5Nxvs2Q@~5}u06fO0Cr{v>40KiCp3
zWwM=)FnQEB5Y8b$Q!<LZRILQo;F^87&bCwCCwf`~!oWKm#z5f8K;}KHvN$H=oDJ?G
zuPzP09qiqteLa-TJ$J#M<bplfjwzRIs`<hmcK|KoH~7@85}DuTaB+Q?XEfdSw~U}s
zbO^2H&U?u<2{oJ1N;*OiSB6p&%(1N+ZL)>YN2#SBQw6D-UH!y!v~e#;uGrtRoN4(?
zQSE>9+e<)O!TiE;hpN=>rK7)cI_Qc0SF%6hI&CRueD0=8peNg(Q8z{}W;W7-5u=P&
z4QH8ivZv;=0fQAG5~pE~KhW6(HV4izO$r67W90)!KW3A?=f-gb2SPfC20V#h_?+o!
zdsqwt8!PX*3kHUzv0uLm$822S&1Xz4!;D+D@~S4M#yb|uFQO{rB`$QQO{(*rz8*8(
z8`U-4?U59EYH+hX^y(WULwSV7zf=NeepaEJ=V8pr&hP5|2B74)HRuVJu4_x1al^tv
z`%&}n9fG58IyJA?M`2z4ZP1dq^9$t+IIsT`mL`A*r5oI->bs;jJ6n_ZFu<IUMoiMg
z!L=;v#e{EubW5ik1OuF$k7iQN|D!y1Qe)-)N%-v=WTTEJkj4p1MU}+*5%Ia3?HJ*y
z@<8yFby<Fi(TZTgrTLJ@Z?-K;yVdA2@9cN#JZcR|paItxq|cG}O4~uA*N{4xB9M3(
zBq{D*ndIoR%oh{1EhNt-f)MGOHn>)Hm?zgIBPX%N^c9^cz(2S5^;bIV`5K6?iZ3rF
z{-Ek3?X;){9sn%YGD?Qk6PYXb|5_s1AVsa&_(mttcUUE0E&IhfT6qA#sGNW*r^c`S
z12Wk%r-7#IcU-ovEj{wwX?7fA{8+c)<y0`c>%&~5;&}f|k-gSF*7}>Q;}t7~R8ZX`
z@Gk)wv|G7WRXr~;0_#uJ1@lWV*7-Ld*TJC}p4x{A@C*{i#2`3CfLeTk+wB+eN_2@i
z=Sp3$eiZABq$x1jNAn)6?e_9`!$bLtsGJFEPBFVbsGjts9z4hU8e0mTPy9HAh|f1y
zg(8maQd0>hO??iz=2<$IU1p*}H%@x30fZ_08j&LsRUC=jiIA8mAF%;|1j3*b=s7y?
zoZK8_UtYoQ5FGr3*4H*{PIByHkzJZjl&PrhMa(ma7PfQ!u*!cOJCEH<53a{(?6;r#
z2xWCL)A+??+{cY>kNw<#>W*&ExHxc|&b=IOeXdSE<!AgZbgtZO%0!R8mC`hsV~f*R
zHN6Hs$M4maE~L16*fb|rRZcX(K0#N}I!Z&m4_lUdiEq)5j=A2x$E#inli95@IeNn9
zP4O%z_w&lB!pJtqf204gyZ6DPTm*fp?e(#vQ1~xhKb=9P4B%IaEqwqa+C0yc?*<5X
zm_<V$>(or+qL5u{)}Hd^%6L_1l08Gf1n~P?Y<NTEAlZumRkyop@A_guIlx2#I$TYN
z*E+fW-==Uh^PD2(>$zhUC6EvoxhSX2A-}LICJL<ocQxH<fi8Wo14apDf|_0}T!(a?
zvBy&Y->x@Nz{N}RMJ()$puwm(m8Vp|ELIg6Q~slx@tDXn1z3yR$24M34<y(wMyh@b
z^wu)FIqGMwJ5sme!>EvOSV^uXG%>UMMx_XK|5~%)s-|ztC(U0G;6>kfu`!bAzQu6A
zsUu6tdDU^gT0UeR%AwZoh~q4|lv5}4wX>okvwybuWpd*m@|kv+rNz{|@X8t<cOTMf
zRoBEdGr_YRTUFd~uD`ahl%`Tsi1duV&8fYprBraQt_Rb(8=q4Y+s;1nEI>Rm5jk4%
zYsZ#l_F!<CH;pw=cU<E(M<S3Tkz5%_@L5>r+b@|=9`eF*+8!JYTD^~g>XS95lxe((
z7EF7eJ4m|`57u8PY}?`*JDVd)_MhI7T+$=xl7W&CN8+=amZQ6qXBKkSi#+#^3mH4#
z>|wT2j`5WhAx+BD?`zGZ4LU6(I+q&VHcSOqlQU{M_b%ocE<Ze{3mhG@b%#%5%`YtC
z<a;d(rf033iSZt;)8BW;m^Z4=bT;g?Z^R0Kw=I2z{W3&1L=W{AcG~6K>{9MPEI<Yz
zqqKo-%oq0#^``w2LuJ%dQ-qZpo>ZC}v2-CQRBRhb-pH`7Rt+rd5g9Y{8v2IKO)PuA
zQ?kMMozK2eul)X$15y8C;#EDh1kKjhYpOb^I8+fu(wES+`CCTIsF8&3CFW#xdA$gu
ze$I9+F$U1>S4t!6$vJT>gs9-MZGzXLZM37^*ybDex!Y!j$g7G&UPWjTCaUqJyY8Xx
z&v}WOgkLaIqrJ44IC`j+X$<(fjMUAC#Ejg&2mXMlBia5&J9FC+-x3Eooaz^Y)srFp
z{dGl~L~l8r2CwP1DYHdX6cV37R65?6GdFaRMUPMefm=5<sRQ4yFRWY}v0e2^bq_Nx
zbb+h{Gt#Pe9{#xX`!J}@s1}r0K`WW8=dVz#tEIKvW24Y#Tc8>&XmW=mVPFEEIpVX|
z*hlt&25(}Q%xj(CNIs-qQcdE}B_paYpwzGaMyxw@lgRel52&nw{9vW%X}AdkK02cw
z*J6F`98=G>`<Nb;zSb?r>2ykd_|zu`0FA~a`ETDqRnklxp0QBJWIdiYD4TClQJ~`W
z+qldx-}sD6`r!WkO*Qmg$Im751CfsP>0I+IGdjHewa9zN&S-@*0pcim&mCPeA8)Jd
zSMxUm>8@94BA3L5+>s19`wbs}?@q~v!7<U>EzV0b82t5;ADU}!^N&Ew(Vpy-(Y9bF
z{i65st25TP56zXQrIp$Vy}{zn(;bEq0PTgcjVhDc_!oQK3ZCl9vuB1ijEzp7KckiZ
zG&a^vZ_C`@kTM&`J&Z~9LlBPU%r&Z3Z%9zrtrueVOec$d6{hK;dSm{>Qe>!~q>*qy
zkX<VL8s=Xu-__od-o!%8Y@uv(2&$-l!jM+JR{bf}t7Oy3FyV#lfles}y@Lzdc53dU
zJ!Q=wjHK*tgmQY#4^S=qWTlm%Jl%sWswkiw#Zk+)C1+^X2_m+@dxP6EuF9yQ{6&OK
zcE(XM{lgkni#eB8-5p}zzQV*3-Dj#PpgTQJl;4zC34ofA2qC;B{E`X~&wa%#3JTn3
z60kswQE$Z1*1tNVfPcE-)CFh~mXHWXneQZaFlzQlZO6prx5u+g+Htrb+{+?Mv~BPK
z*Bm>yM<C!OJh=Wlm=m2jP~i9u8VOCu?o-X_P7)v@e@8VFGOn1x@6msj+5j-9xyQ!<
zqx{(ShUCGJfnMiON->_(`&2aKrqsMYrVv+lf5eJTpj-e@{A+f{_B<ko?sjmjR)Ivn
zn0kqi2KW)$d%fg#6<eW|E=DYqiIYLLJ30h*3Rh@ZEq4Co95AB4;DNI<cNN)NCkvOE
zWCs595akAF8fFFb9&>cgVkfTsaWJ7M{YNh}3qw#gBGEo0C6f;u172&z-+(>P&(i&V
zNLnyglLN+Ko0nATGwK;TanzE_A5LtSz)=6+NdIml-f!ALcQ+d`T=;J;kcQ#~p5KHG
z<|Krb>;l&Q0bG3}ZRS5a#@XJNfd?-s`W9ymX}Wxu65+iVS!-T`;6^7Xzk0nBr9=@i
zH|Hj+@M2mPHZ1ozO;$1eeR%lR@p86e!9e>9s^giZHzu`n<;HJz^_PFV2B@sImD+68
za+mh4J{j=y=SQ=Kd~1wWnU#(WmHKDKn8BZ+H5O~MkG66NmHJ=f_obUaYg?;P)8<V(
z65K-<(}jCVKC1&pQWrVw`T_$Vd^Y>K_pPrnRX%@AVP&W3^v3-|>ioa7&q!*;3k_OR
z$$HK(Y|H(-z`M%Sh9Akra16R63@R!acyDEQw88g#Y%c~$m5YAGveboxG)O<Onb|2N
zn4~^a59C2jD|7M|5!pWS7GC5}7RxT>c=3}6AO#?#;t=7~w>U~59#I{f<3!yKS{eA3
zHOf2vL2_xqqTB-Hr?(m)>Y)QC84_GYV4Hc8K-UxMBhMjl>AB=w+<*if?ppt;PvgV)
z>m@~62@r=<qe(m0+c0L@3f+Xy2Z2T<5x{p#^&H6l1IC%}raj?&p=Jr6%yfYz;jP6+
zL~)?~aA4<3I$5q-x(>wn$W0C!hf;(~5I);0b@-wR+6I?2iapCZv)V9a1*q6>g`=}o
zy3*KhGbSFXsJ8P8x-)Y84o79C7r<@SuJ@u!QXb?m<F!9(HlP&tAxT+3SdTQ6X8svP
zW*hf032x8sLj|a0kCt2eql1c@UwVO+2S49#a(4{X6sR7%M^AqVe<U<DueB>U)0jd<
zdF0iR)b6*$GRxynxXiXg?57`O%aRtvMlxz)zU$SOBQcp*)ysB*rJlDcG0@69KVrpE
zPDYE@xB1XDzt_E;g`t9ruM_K-$RqYg3H7@I=nWACzK48&l4ou>|2C5Gk+r%{hZ{>s
zpc<kV-b`<Q^!cND8OB>3HZ!L;KK?pdRPAdr%$iYw9DSXxRAFrIsX>m*xmK6n4t=}1
zB0Yir=*z#**f@4H5Y+S4!;7I@uVPj5|4!9aC404rcMF0QVe+9^&k4+@miLG{Wuomm
z4ymji9rzJxNA!uQ7YXPNOR)G5cgmk{m+X47FWa8yIi0vpVj|l9JRi4Q%X&2|t6=cM
zZ&UWKCLBaF2^8O}7aGiAR*U>)?8X|DiJw4lK3Oy+TWpDKt{eyGdRil-;J)Vqdg26h
zFb)J&haAWyoms*emy8FS;$)2wQgx?iLff$q=_-`lNZ${{OH?=*aUN5bS$>h<B<H_h
z+6FZT)&Xr<5l{E=tlU^4T{4b=gx-}NjxNWclQi;#J4e4eNQHomVw%6cbsD)Nr^&z7
z72j5B6IcXj&Q&G3>Jr@=2dYeDX40jxP3W>h1S9S;u4KijQ6bWZ>EEtUZ^#R1cT7E?
zo1w7pPInZ1U=8~j8h6gGJbv*<7JKJHz@2uODXO?!)4EUpPc~M<bRCWHqdH8ZQzMOU
zq}U0gms87Wo;?H0027zY_kp6Hh3W4*+dv8gJQTbN>!I}7LS=&iVETb^Jo4Xi%AbpY
zF9De?e7`LY3xR^VbOEf_@23S}v{Yc6$g0Dxc?9sDkweEwq5PQM-Jyd*Zs5<k<%^-o
z-|~ZXH<|4YVdsU-J66ZpCtt&u%<bW{oTC0!wc}WZo_cw>mR}5~3$`P>{<CmGbN7Q;
z@?__l{nL=u%^0bm8ca=iaIQgwpQZgu-eB^zhrlkwKHkfH`i6JLG5%OOOYLZGq~*nF
zYrTzs=LfejH7?hVN}uZdHn-x6yp{ULdIfx=XNinQ77HTFvC0g)p3Tnf^<os){*;aH
z3*=8#<V`;1uW^QqFskw;dOSl*nesC1rkQ+dnk66GN}p}w8Y!KybFFRtMDfmo`afmE
zxx06uHxg_l9>o^<^y{ut#uiwMx(GX)k%77RUSV8E`j|rbNVDX%P3Kk5@Sm398r^)W
zI@gO$8un2&*Qm+`)FO<hzcd~J<~-?1>?NLiq?VM`$(FAq;-n72NDfo{4!<SD{pWmD
zxCo8d_JCO@Ut0_#w5R~C&7_u1qi9jK!(<7!ElMgv{!y9koZCcX6SuD!-`B;r2LXBl
z@D~D<XkfpQIufiD5>c@f4XV32&XT4MQraP{5fDdM_3(Bh^7GG?Gcdo-C5JWQhdKU2
ze}-Gn54NEr^7P?#<9|}eAvQo0`$w;OxQSKkDInTok?A8*nVO2c267gpf7U;7X}i$t
zu7#jD5Fd(OAsmSy4MJD>zfwc7rdd)UIOh@3?VL;7WShc6X}@ljFNLXe@jnYYF6G|3
zSWaa44*3=%c8zK)FjNg%U{PdKvkOadYYzLWgYa3QKeSfZVI-~7&e7IUH9bN0A)$S>
zv>(tES9)S2$f>*D(icygn0n&A3vRcIK|_ig*<e(ppR|%hRZP$Zmla%hA;%d{(@q||
zbE=B~xvNe5XzT-jT%VMu^PT2B!_G7)B>BfT+8rqW-FCgagku|pM=Qc*h=8S|VtWva
z4DJ}zxq`jL<ob*)yO9GpKP*%1%z63=nu(a&s0;!3Gy3XfQm$M1ddzR5tWj7|b|LU4
z`Oit5?9ur*nyn)*cIbEpb@hXg$CFA3l%J>D3avbU0pD)=bR9<Z`O{3Z=rPH0)PEHk
zocDwX@r8Q3YoQD%E{<j!+11GS+P!Y8J*>a^h!Do}UWoe#s1BHq_$dQvd*{Y>F{S60
zZ>(1^IsE5$IBN~^X}oR4?N6bI_G}G#6i1c70bWF-NVUMX2OJNmFI<OVvcu7NPN~d#
z+SuJ+JaWN7bu>)X&OI$8geub>tjxkSisE>LNR1)DgDb|SWFLoW|5cPE+Fh{yAbrgb
zeQyA+6!!zdDgN^!2?6m!BI}!kVA@ISQS%#6%B$oOgU!<rmA3Ib^N`8QYFh;INnxU?
zWu)ZY^~4xN-L3*GR(I}|a6}`|u|YZ^&QwLHw6QG1iBwq&igw84Zw?1uTy%#KeN(!7
zoaPVb*1Y#Le#!1cSqA;4g|42KQR2h=WfE)YJ1&G$Iw%}2&R^ss)SPv-D9@-!_@p}~
zF@HN_PF;(&q2~g@GKWW|xI@@$Yy~&7_tilKq`1g~Ok`9u@|o>b8k67PTZiz1HysQW
z>hO_#2_$k`QMaVNs`mlp0)S3egM-G^$NWe5d{<eV_@;P{R@qQI+l9aT<T>NfCVQ+=
z;5LqouzrU;$)n%0R1w<<u~O6{uDJdwtG9s%4Ou5MekDMYfk?U^n928-`B<epBrEAL
zf1#-Z&EI{MeRZ<5@MgkKU%tE)av!xi#yZWOw|)76DRwRj=J^v`>Gt8pZe8VdnHxKr
z$+s-2XQt}C7XFrrl3ti^KD^VAAfBkXQ`V;y9n&~-?rAOayuhC`O+Tc}M)-MxRO;@}
z6=T~@U)THp4UdsV?0>=UvG2LxpOi@m^a$K{lr_sB0}fLJ!?^`H3DUeR=PPB(Mc5#3
zF`-nUTX{+nN_(|=D~<mwZ>i*$8<q0cS5tpVTiL(vLAg0IMJs5uRX*h~&Y2UMumYaK
zA6JLkD+TafLB)os>VVD^>rogUj3+6SXEW<~HU`+@)nf$FUo=OiH0we+kW)D{@U%FM
znbwu?-;Dqhl?UD9(L`x=JiwQBKEP!_L?3E8zRn;=;t2vuC|6owe%Hv9nweIT1V>dy
zgta>Wx%cXg_I~}5uL|djVs^ESYKSH6c4Rr3l@Cn)kvE{T-;tV_b$}1Q%pWN7PHl@P
zeVjG$y|?%>^U}v!Y!UKk-F!m!?VuS~u8MY}o|5<dwKu4}j*+N+h=sIfXsnVUI-htk
zkqZ)pR=C&;p`tO7ELP?1yP^%yT-~}AtLWmqUs8~c2uSZK-+EiZAc8;|cLSg75l@ph
zTeehv)&6U=SO^ygSnW*DQxmOKw7<d;$92i3Hg+9+E)@2-rlVJGRuh+WlwOV~ujt!{
zCgLa)U2=t2ZEiw~>yKe&L&439&}E>*Ubh9bm3z0@FG+nU@Uq?COyK!C3#lmkNqvB#
zmYC59nS*7b!Qo<h$Imv-zw1N8Mal^}SMacni!nxFLjQ_wheC=qIsY(wr(Lk*81-uG
zi#bh%5$+e^cK0Hv^SASY+1k>@vVv9M&1h2E-o`C8tetASJ63sJ<u<=_^Q@V%%4F2x
zb)z5Kjn_0idalAa1mh+a`x>6OV6(hahsVlt)GYwF3mOKjY@=+o)yIazy%?(U<aJ7y
zlORNf=**4gFq;2X9jH894lNfd4<<H~B(vlmrg{6l61IOqo@dm71xOQ;E4yQi-hGmB
z@SAIL>u+{wtz7UKW_A5M`dasLx6!)g)~4Q&*_HEM0*TNi<R<(k^$(#&$O8HBO=s;N
zKn_<Dte5vKkat3Zri^ru?eJzF&*q8vIq*Q>_Qd1jr(GU*-X#i&j?LfBlaCYtb?K_y
zr8J^6>|s1y2|;nG+^u?ZVlq?5DGZ>5Kk5G2sryi}UaDU(b>gerz?N<Mld7CO+b=T9
z;aew;dDTHZJck>>XgYtv_IjmPq?>@NU^73;XE^2|elDNCA$a{~{w5hd)kLBqO8|HJ
zM{rC8Lr<hb{k>b>CJ94v$QR8tOixatywIXdJI~*p+uxT12H`3W8clQwDOALh`6T6V
zfbHhbIejY{?-Vck69!ab?=cGd=m_WzuvbiysC_8%AJ0YdW@8wYXn`yM>eb!FZZ?Pu
zB(Gd3y?yl(%AO{>D!8uPX*qte;uZ(2KV~-Ao(k~JSIs~(Upq<*WZn9Xu}%NB;^{4J
zVdvD9!-QYYnQ|kJdM?~XFD*!KUKeu~4#xdv5LIWn&gbX~M;DLN-nr2G^X-9n?LTI8
zr?SiyKJJ60)itGbXp39O3q8>v>@V~zv9L;$EpS+y+fUP7Ol6_Dgt{Y<*Q1XK<qerM
zv`JiO#mmGtr6A?p`4pVQwRcS&*KIfPTf30gXP?dUKW5?kY02)xXP|q)=utaKmqJni
z+uKpVr`K>II7sO^%&2I}g4#(&e&LU<@#XXB4$J0$6?2qTpYVbqA5J2$;d9RWVUx*{
z^EnIYz}RF;BEsj5!D{|*zY84ERo$r~EH!zLUPgvJf=g8g8}zl8oe4=;eho7pX1kl5
zWk^V1n_*m9!!00@;zDym1bdq-95n)XPRRN>$%y1bsCCe^A;3D%Y)@B!4e+vFzs8b5
z!?hP{<<qUo1Zrprc}KzmxA!3J<BnI;R<?p~k^Y*WfVyE=>7&?ORn109Mb*i$FrU=p
z_CXX$RVTBT&+`)uC!wM{YpInYkjjFAip+0U9l9YD2ZD%p5FH!_^$qTWwAW($2N}!Q
z2f914Y*}dM3onl}?bYQLB*&r6VrXrU5hrQIgu(V?V8*=!0frh!RxnwAOri*vW6;o+
zCMZDOM`y2kgg<?>XZ2zXw825ny8Z4_P3PpN{8m0zP}B?9if`Fa@u2bysd4x`<%}B=
z(?k}D(iiuytnb&nTrjk%D`>E|Sd;;Qw{xqC@O~duQKN6<VRlcynyG7`<jX#N$3F@Q
zyagNT4+?xvPaS-2?-$$7d^}s^_If=7>(aL*bY(vGYC&(xwNqBG{cJMvkC(A*{eRr}
z`^xD=PrqiGH%%7PpDO0U{^$`C4~Izho;zG;$xlsTm;Ti4qfIxmuOD^CUr#+3{cRF+
zG!{BHW2Npo^}}?LA;Xw+S?9rjJIZi$Rx{>#kk9Dfsr{=_)i+h~ag`*uW0~%)FsmCq
zq}n5weAlBndE``YaipFW#KpH%nF0!ef;BkOl=&mo<GngY^rv3z_8MPakXsvkSqcda
z-dv(|44~WYDag)ygj4m3ss!k$H2Zj1FtF87xT(<F(lQFyC=<nye7fX2-~c?Le@1BA
zh?lu)PY5ZBYY#tX$u1vgfgt?d2?-vB&9HN6|Lbc4Ig$RjiA7)(Ot}FhjIQ+j-+=h2
z67U@bo|G6R{hyk5YH6gFy7f}~qlOvzbzu3YLd#iqzRDYTi)Mqa?fQSknk?#WW&onP
ztPz&>h&rEzRTG$nV45(z0UwkrOwYMyL`|Ceiwu@Nlrqfbj`VO{i9)gOApjxT><*@O
zLCS8>agfxt_c~}!*Vbs2zvjz6l?^_nB$JDr?GTuyuub_{YK{8*C^CQ&S+MU6v7=Mg
z*U62x<Ng5SKC44v74)!2AObP=nm=sNW`|6_bx7jgxAr_5yp6|vcD7%ie-d4`Qkb$3
znzAMrSK%M$A>&MHiL3Uv(c=Kn(%_2RGs;+QyaZ7EYBM>0+5+10ATl%0EZrmLg}Ki4
z;WOAcuFU7J)lqa^o*({ss<ZDydease(r+X+0qdT;p3+DrvAO5T?^arzSwPlVl7LWf
z;MSiHlcNDk1ve*yos-<P*6CT5a~qYXw<bz?6%=jKHFUbeyg9Kd7VJ|`-bNZiAnhId
z{Y{N{bh}N2*$1L{>6fz`_RSl|U)WwfG_OT}J<V=>tGE$T)qRwxk~mq#>-`n>TYb^z
ziP42r?@)Axc&~O+G%vBCTTmd9lYlM53ZbA#O2)PhuwZhpK+3;z)oK6GCI@~gZe}Jd
z%Fr~Hd>cMM-2K*pVwHWoXRU4%5#7mSVc9Hfn*2-T<#bK887FWA!Ni1LWXox~0xa5f
zyWH)eSLuK0R!7MkC&}?E9P@i3wBAPiVfG#P`%WM3M_KHYL?$dp!&?*P#TH9=kdf{J
zE9Gd5jrN1`YT+2=v;rfZ50W4k(%%-+p(!Es1mCV1dk2&PMHztieU)tu2XcDg+(UQ{
zt{O+m#wL*XbvufE#wOE-&o1cD`@1k<<M?^fO`sG9wE(%J#jc+-AibZJVo0D~M<ErQ
z$KbA>^t649ilF#pG$14ue<8FMBb(|T0;L%Nv3q)Q50{+R*MP`EnrN%I>jF1r8==p2
z7KHmiFkcPnpFsTHmk<<P-`#GdSj?5S`Cd416G2jpk2ui7@ue%5fYI4&Y}~m7DsZs*
z)Cp9<giVKF`z>^FmotldHQ7d5HjJ{nZ=RV_cmpkAN=q7U_927(W=wG6LGF<qo#VN$
z219`CZSv0iN5BN-D#B)qojePFnn_2=tIZ2c^6_C{xtCNZ{q3I<4t$j8kabAFjU7?~
ze2S}ltu+0lW-i^BpOi@_AL}M-vRhJV32iolKKaWowTgb3wcOFBS-@EyZtj&U_mg-G
zNXF>Q0*jn?X#ezhUePiAfmTC);|T2enk<=2k;-rR3@z^I8*j*e6;2k(bN-ooOwPh{
zriw2?X4*X@_jya>27Vaq^9eIO4PR~Qv04e1E0c}U3Ix%F0@a$nh=oZ%1xoI|SAI*y
zi9*(v=I&|_o@q|wD)N-p4NQ_FI+QkKfY)e7MAM>$Cz@z=#&kfI&~)e#jv2+mg?zs#
zLFx=dgeZjqcLdlvKGSY`03g{SdM<ddu)na&5os~t%i>-y9x?_9RYUpr^v3)(vAA~G
zNn;4nz)5?)8eOrSm+a2$=?P#W5q-iofCLz+{wg{7%#iq3$EIG<^?)?BKfw`Aieg()
zux|J`J0&2Nkng)t`R2(r&t|X)htPa6mk5aY_aZ5to2_4!nonsylqdU1m~*D+t;33(
zisJ<)z#h?saaNJ);bcVAzwTWBI^5zgYgf<L93RSl_?0d-HI#H-(t7bj>Hs<RIH-(D
zS}hjEcceV15es$Wiuo()Hww42(jg?7TTV|-jUk7nfx7UGY7m+MO!%jB7Wn49W})M3
z8<&FWdZW$e0&ZO3&_m~f>rO6pk$(d}LOB+xVIcjH(xAVr+#!&+37fFaaGux~onM!B
z{X)76paC8Ek?u8^dlE(D>)jnx<1t7~@l86aVK3HqeLJAhujeed2t3Svpm{!a!@F8k
zJ-1Z#@RLh+rJm)jId^x#ST&`<hgw{~s=2-L)M47;VrQM}ewe&!LDjW%RUyB{Cv@-Y
z6w6Of5{rKP3BPkE*ic(d`9+8Xqf#!xM%?w}9+~Yp`v!L7#pCm_e<#`Sc!@%^<{yay
zE7kw{{bo6r1o;>81>3@eye~i8Uq<mO9G@^$HxVIX%D>l@`goqab&Do2Qv1bO^35Yb
z9$Zqd1xok{d0eEr0J#OQx0MX=S1QwG<o|-EX<yKe2+-~I9iNuCdfFhLB$7a!#n<QY
z5Wc28z;pV$+VsG^J(Ao_{%es@c@A)x7=s8`&g<{0?-WH}<38_mKAU4lX2yLCq{%{Y
z{LKoB{!SNAp%JJ!!%rAOMNK+0n9`BuVU4icuHk}#{CcjD+j%6Gr0-9q6Ye_{8Fxtl
z$ICH7Rna8i(k1!a$MDB?u|K|D-3mE+T8R=?7X0nm+`S6k>@~`P>TMuqCT-71AfpO=
z4V&d~FDBBAXYc_za63i<2JpadErI|hGBv&e577}?6#V88LVy#o9=0IfQU!pGJ(HU>
zwcvscCeftFv~70ceL9c+3ipSd44;9u?b1+k&Wz+TJ%i2lRt8_2$z7Xos4U&T1X4Ym
zXLxI@r3v9LFTQzL^bvNSefv((>mrU!G;GW~OgLT?ExWy7_S(`t-Lbmp50$j~*94J`
z{jn3H=M^dGf@6!7JSKc=V~nP_5BuI>muO+%?>|G56PNI>!_6*nnb{}kVi@V+3b}l^
zr0ztb@ta%FQIw-E^8w!@9B0jIi=A)_2}h7;ZQe-0LW-F6y2Hn>`tIL5O6pub|0sM5
zmY~>MdK~K*vl2V6YWR)xgnn1q8PTZT$>b>KEMK1Dw-|Km4otvOu0sD~G{2vNK?$oq
zs9X5><HGVCNJ+LCKjP6Iw3?y^NRagF_nn>Xy62o~FS+S970o4-NAd4NmQLAJ<h!#Y
zrI_l?0cNBmNwUk@qa^V9OYkxE*yW^E@Ez)4(n_?l0EMdj8R@G*{fL2P3QAtJceFAO
zE$YHI?T6N5+RhXcjc2U~@8iXUsE`0#OGV(RmeIQqI_SFr9XkT1V0vXU>$36Y#FJ<n
z!6`;lHTuLAqq~SuPhYe!dh90UPHxCdR!&#2x4cUF<;r{e4wo$X4ANF$63OE#GU}L&
z9P@og?nfqkSu76P1sMvQjcoI1Ic~OuDt58Hxnb%)UqXO9fQrwyTV)JWTYr`z5RALG
zVk6*ip!yfOiY`@YLVXt-u%UpuLBDKi>y5A{-m`PjPo@)d6X4|~Bjs^<pjwkJkNwQZ
zd9AUY#M&bdI-|fzxWD~85>AXOl%Dv|LSCrgGhXk6^7fWLa`<=r+cOSmR0iWY@u;XV
z7br$k$WPZi?iff=DMeN23bk{6S%9t9b{>qizqX$ae=gyijkveUlqaM0)8yrNrDbl-
z&e_JaQY6Og=+xZ)^-oViLffSVmio21Xj;`#GS&c@o!f_uJ@x$5oBW^f*Fp&3duT?e
z-}-@MpHioRq(;c@S<ZWy6fisto0BX7j6h`Hyde9K+y36ox{8TFb>gvPTWh6BgCf;i
z;0POfGyz6^2n%=V44GsKk!l1`-MyTYqh2BX4KU4mpi=*jMupU1zUD&JZ1hbW+YFlu
zM-)B5>*8pL9^m;k*AD(TXtkFxQRz%Fea&hi#K%!Y5)g#=lJos?C!Dkb$7agE2Nf?&
zCj+gJW|ZSnZt5H|9|++4T2_@cH;<)Fm`qs9w3c*}1=spFNDl=(Gr+*xT${g%VYbp@
zi#7W(U1xuoZd;DaW6fe%FLWNba^p01l317Ymp*`Edr}?(MBY1GRUqx}S5LmvJI8Sl
zd97)?EW@41vJ!L5$iJP65{?24=Ld<c-y--uK)njexL32ogPu|D849<<)UMxmAp$2F
zTfFY1+P-Yw%#9CssqoGPVT&O;VpNwsE&MehHUep|Jt4HM_^ki^POON*>C?vzF@b)1
zYt*wN@E3rR6}@MB9}YacBapI^Drz$US#xISlrU0#t?~oOFEK7r3XN{MC!-DSY*-(&
z2>;<%uuCuO#S6{BbDpAN+|AA0pFUNCr`$<Mi9wjZ_N^+Mhc5hQEHT9B^l?tkIRBm7
z5_-<i*#2d3hg<FYF=RQb5R@e2ZU*_u`PRc9!rY{3=&UiAvr_~uMIq1`zClG9{qfy@
z65b?Onb$i9^hBd2OM^d3y9lL?Jg|X)sLFKuFY51fzb`9gqd@9WpIJ23&znA}n(Lg(
zpF7cH4;Qx#>d%~|D`nI^j^y*V4nA}jQWql5!%J??^d?1`4l*u}$*eKWCe_hB$aWIq
zMws0$5lhQDuBM;}?Q%;^ALgUyk&3&V_>v+E5b3Ym5E*PlqbC@2vhOFTrlXkG-$iu{
zdt5ESr0+mX0Pf;=7S53`z-=*<q<B_%+<eNr67?hNjKe`~{RzHra==V&LIY044S@R-
zb=wLm+qD(<CxX$u72~wq33#eXDki_4yhtcGfDLnFvJP8H6LBvodrN=RYZN;I>bq_q
zsW?!EUyT*`(C~YT;37$|3iYwr_cwmSt8Q^+(#0UYEH!!t+`TYWG4ArT_C~9&raJ^r
z#`cD;&h(e8CtuCjK6T(%TSJLn+fq6B@C4SwagDNB!FGgwiTou&pS-w!)mbH(UO!zn
z;Zc~l79XaKhx#(y7ED<%T(~rIf^XD78|v_(%F~B0X{iC4+oqjq-0)Y{HI;)oCpJY}
zi+5|b|DE3FfQp?$-n2h@R9SuUD~$5}uu!|eg5QSx`M(tuMGcegH?;Ak+Vt0;W~FKU
zk4$)(KV96FK^*c_l)F~z<UyVVwP~Ryus8O*mHSpZ!eu2H4oVMpIh~(1hAj$;Jj_rj
zqFFJHWBwn*NDO=`M(-o<_JuZARlR~l?N<r$L)t_9KEH_*ml6_^qa7&1f)hk+)>R6c
zNHCf8d|{~iq&oegexIBT19%~%Ex$lX(CIMG^hd^GF!SKxZ`BfIX<nDGNgT@DtsgW&
zWxL&4UsoRYF+?Nf`e$^rBm-ZM1f($EZfI`(h=|W+r`4gJ)8ayCRTt>>;@KHny+SxW
zs0WM6AnX*N5|Bo?5<VsvlBl-a{)mw`B*Yltlgi=~Z+)t_P`dU?Pq3{A%n}7EtN)Ix
zm%O3~Qbt-%1^Api@(D+HIz+_`oHag#`1+sSYcT$NCeCb+dwHOTEW&RUmjRJdD+4db
zLLqzNU$ok!dDUaEx!(I#G$9TMy8eQb-*%OXxJCy^!v=(C;JpJoZ7*U17j^b0pnjrG
zq<&?SME5!8*K26@=w2LmBAwn+;;@Z&dkTVRIaT8R`EE5>G{^nOa6C}T!ubOi%X)0g
zO+n-MA48WchvkCG<k~ri@y}Yf&T1xpe1S3lnoB}uwPmxliC$rusnLcL?@MRsYKCMx
zwj_mqN7l0hSF(Ze^DNn`DJ0t{kv_b~8}mJm=KnsOi=CWS`y8aATdPaifLYiCPM?EP
zB_Gao5|36mznF@pdL73ZX>!WL^fRo|M7&u{qh8>;v(oZk-^riTd@qC4`6I31>&$@D
zH&0rD3-Z<Xd#K^b8Mi0|0px+C+hiP>AIH0#g9`E<G?f;UF1hLBEJ%s5dMGS^i;^_Y
zlq*`7YCG}Vne~9JcICZcLf%#<ev^SP+2z~!%wMlW?l$e$*zbX8dR}Uy2?wHy5zXEH
zq-Th|!vOpKxnEJKZFPB|`9%H#d}}x)UST6|@twhXqI!&aZ`kbAfVv`ZO-nd2Sz7&x
zLkj0r;|Rup@UHc1fLcT{(PTU#3&2~WK7qH&;w2=FDo!VW+48NL4E?U6AZ*4SNOKKt
zFf}h;Aj|lCez9G%iaPPBSS5YlpRvBieO3`qDhy)4j9k~(Y>LpYkI6CNHLA)_>_?J{
zBQCA%`-9^|nD}e>=SLjI8VrU5@`e49Co=q37yC3Ax19*hXCLo<;Bxs$bFc5YBIvts
z?X4wP_3CJ`<}|O~yY||rznMNsIb!jr_2w7WfW<UV>~hxeV4vos-?&0HX&Q7ezw$Iy
zThBN5sOr<J=>pY3)MG7`yC;i%hB`mG-ZmLevwh6F_c~n$;I?sI1|OlNwr4f)h-fgv
z&1|}KTk$!wrmMxP67-Q-w2{eR0fS0k+%VLk$KKcf9rlC^gm8#$iip<HBP)bAPM~pG
zE6SYu<)QZnA^8?J|7`kQa%1!3boD8*ZGks8Qa7o8&c7^akDxS}f|)9!%4mO2-ZC|j
zZ2B+ltyfitwLc1aIsl2??+|qCg)c8hg<{56=G43$jo-2g$JkP_dXhK!gux!b2-1kR
z<8VcZOaUaKQV25e=9yI%by=hYX&OJ5{yz+IXzNS*a?!~bXkK*3ulD&Z9oBampe?G$
zVVNnGu~I!L`#h27R*c&^VN~?o2O)#xgf9ffxRa$J#ICmA{xYgjEQ_osd*%#aF;Y5?
zRkQ>>KcQ7XA0;Q~qif40?(>g4Rv`B0E3I21w>JBL$K<?5^Bw}aPAX++VA!nzI!6J*
zldk=Mzvk8aKD1$k6R*3W78dq{|Ki)55bLmF@IgBNcRDfP{b57xOv=HWX2k;f($bZv
zOLi^UeT?7S%Z-J}H<TSg`iqv$#l|m}D+QlaXfFwlX^nitJbam3IpiJp^yJIrLN)mB
z;ngPJ@~{j1ZP!the~8rf;c)=Vjxo+Tfm{eJ@ZLi4@=+P{1FQe}>oz;|iX*6b`%6H+
zSP3cw(2=vc{P6m>M8UH}htbM!zdH2Puyz)~GY|K5ezLW_3_hp`P}q?J!kC#7y{OGd
zQ^n7?-+(^z_B0g(j0hD!R~OwXsazg3b)?@Eq8&15=b~AQ4+B)t`@#gLh>#xQY*7_K
zvA)7;6vu5#BRIPjSyIIgIJLui*9w^90Ctw*$LcOmoPfj)g31ZwoGg2)_w;Xb98~Gv
zMk$?3y3=H5aoBL$3_gO~Iaj`uMAuz19}?ElmMuZT$5}@+kgUpRIwMgG)QyMT0&#aV
z__r@2d9_erg%A2Kwn7}i-uo9(-gWnUIbV4t&(w5gx8{cXk~<73(?XLl?;#=bqY;fu
zoQBBs@YcIkRMa~G?3X{j`2I_*GG_%o)@v^ACw|kM>a^6kU>`BBKUy4KX9VfT8RXV>
z3^acB_d)~FIN@xC8P?=qCp<g~x#9TyrE<MbOI*8QGm9|DJKCrQz-q7ywM3mjwPCYy
z<tOFov0Jx?2+bzrn22-PW9-hi`f#h1*P+zzpN`UWMfpD$k4;l^GCnww_@AMqgju;&
z_b=7+(gE7iq~=Hwxq=%0cf`$^m>#sTgs6^%^`d#R&$#K^VWQQ^*wmLfo<Zwb;s@3=
zQ>&D6MOGod^%!K<A|uCd1Cj!#UemmWF}rkj6BSfZ6R;U7@w#l}V}&>j5V(GNa{+Tv
zi_~8UIZvE8w(qm00L*c58r`TnW0{jI4W5C;m+ET3c>$sh<}@WXXkc4gEtJ#Zx#Dpg
zY9$0Xl1}$jw4ZkNB<Tb}|3)M=DG)_-)y|-NDEkk;;C4ql{n_5a2J~@~DE`TT@SxK|
zf-CxJL7o~gLP|X#QDq3N9A|hCvH#l+n$ChTaL+AA7f~#1B;8L;7xjr)e_@S{exfU*
z^mj&4t@eBQ7Wu`h8M5?XY9pO`{v=6J%+6!pZ%E9Ut(uarvEL*<T7~Q`VHsN*kBe{l
zn;n%1*lo6B0)hMbWSKQiN&9_Qf|JMfqV)2=c39-e?4>19y^2Pf1Kxf<j)~IsWrMz%
z^@XEgW|;Jjv;8z%?VP-ED?Zb0?_Zei+)=mul&RH)j)Ex&g4m2UzBHPCU!C_@Gaf!}
z`f|F`(!4pY>eObMtzu5MLSpR2(egc!57nEop0zS0=v)(hf6FmGdsnY}nO)GOv1+4&
zS?u0D?=WZW&rVJ5X;_N?2HO*(-cmFk#C22QQ*tyMKj{D&_En!ErX6xQCfcgpjQ$Em
z938r5T34r2ES>y4KJINNX}gV9i3Oi>JPSmAYoJlPBs7&OOYnZwFA`Pg7#-sbu1R4V
zm~U|T&HM%c>XUzpeU^|2@%9_C<D*qo-F#>fJaJ*A64KapN$8-tgSrzTfr2`q1Wk}J
z^k%%~lE5A`xFglLVV<+0$FB5@Z(H?%c|)U@?AIc8q=NW`aMIj8fe^I!d^3=&JqkQN
z*#$RD*T=BexUZLZLezn2*4pGKDo=m0p<GUF;yl<zsG@1VSLiD9tbcWF8Da9TB3{Gj
zGJq0BQo+urZREMOl1*4$8*O5WUCn@}h{(g!rtEzUmbCIB0UcF>ezG2Is>m0OGU3PX
z*hdMMyu0PThC0h{zrnIraFjNT?OI9f`3k5cboe&9H?f3$b{or^s(i@T%g)nWHEApu
zqR`;|GY;3MWRXnd73WN-i>WV@Vy@)58-of|tnbZG7@PC1cfbF?ozcY)v<Hj_WOPH}
zE`N2%Tzfdl5xwj|)WQ?bliw66@dhZ`FV7TGHdku@?dnl;$?bI8uV88rpS}O4d?;K7
zb}>YusBIv3tX90*vjt~?FwP?r%%KrYaTdRTO8^g8EAT@NY<?=qAOeuNBJ?G*QdF>M
z8$wbuew)MN!0pZ14w!!Hk1t&8{W|^pkfMw+H~lUw&%<4bUg8?nKZd8ZOO~UZjEo!{
zM1Xv0AWD(-Du*ur5}C7_mf`QP7Z8iltvtphv0GG6ClFNJr}H}uCr6NX2P$AUuxQY8
z&!}>IUdJ^&u_{00v%Vvh!wF~TjQvZC;d#F(Whcx=ZnV+br2i;na(=3#@djp`<f`qp
zdmF3JH>G=za69#YuVT@1JWMCSF#YvCC+qAM{To4zVGKiq?6U_3LWRja`8Rc-ui;;U
z9X;nC8@YC*DY%bs6x_dY61d9=HKQpv=q+rR4f0W+*00V(`=iy=hl~oDq#?bb#^#^g
zma67_YX!1DwRlz?f8o5z(U-X@do|@KSw3SGDmWw0q{|?CWZSme?IhGq3qmL0{6Xd2
zy|XJ8K1GniXl|V`;E`izpb64lK;mD|XwCoMJV%vj)PKCoWYZChNvBKhk2(!%`SX65
z=_P$))U9KglKe}G?4HzCE}ZGWV){F;;h^YtWO0>MK(cPt{AZn(-OQi9hMI&%USL3j
z3OJc~kX#unIYZ~yy|SVn$hXfuF&yX=n$Y#Nzb^#1kxY4OzmCOVPH4%EEsn@U)kT2i
z{!tH<IZHSs<f*ue@zN@-L1nYAY7yqRM`iQ!zCR&n7o9#f(udMv{$YeUrhLa+?H9ko
zp*|pCcXDN0OJ6<2nA9;hOWh&N8m;Fjgf=YeX$G<3Ez-f8pOXyd|3XQ0|J|CdG{s*h
z3uP$8L4BhR3Xyc_Q1k=foBdK^A!0<b^$^phnH9Htu@4_CHz{VEGlZJs!8?u8>5b6$
zit#F;xPVi&A6JE~h1=N9B8yG3dh~5Bh!s8&-H6gLF(e^>4@D0>%okvA<St?6^sDU@
z&>I-qfL8L^_30%y^HZ5(zVI|VSH6$^W@Nl7!(-0+S!Qjsvub^5sU}bD9^P1Qpfy4P
zKi>3(Af)WvIbU&9@+j1`b{DTRu6P>C<PP3D6&&_%Y`Ve4pRA+VM?F3e_fL9+CHa5d
zEj{hE^2w5-BvEloYCRfYsY{X>N0~Zj`8k#8J_Fp9oB6NvAf97HJo7gN9!AqCU@WMf
zU~gX!X~wWntH*2Yi=}_pODtEno$~X&xGXf?Rb_GITyf>V{nXbFZ1d{fwsNkT+~AEN
zI^Qnmwrd@$qM#+|&7(v`CRRJh2ui1pq{j0{eaKci+r?;KE(F6&*>zck8shSZ6%cd)
zp8uYQjRZd!EW&zzdID0(;$repLy)uclR}g<o?J_$4A2FweEq`oE!#K)%sZqpl;m`e
z^q0jvaBcoR%;W2lW$NEC*0jtdcfQh22f`gvtu!UKo`}XAF+60o_|=?1-33JN)0xm(
zNyq64>|e66wM5F@1S8?$`G!Z5aFMv3*4lx-2gxF-0!X{jgx@31-whs}w36`&h<sMy
zgd~Manw7vL9b?r#0{<UVU%^mS+eE8`lynJ5w{$lgx<Ns@yE`SNyGuHxIdmObx<gVL
zLAtx)ZuI@`y?+4C-uroG)~vN=#!s6?qoSV`MQJ%Nq4a7>+9sjY;k(fnSHT<Fv7H~K
zQQ6ZJM*0jMNHd3u5POk@-_M7!4U0Z42Yd5~+k{GET?Um=rkqnxP^X1~)z2C<Ek7;&
ze=IOz*^kj9rnWD|K^ukq#r!rXs6?pd3kRKo&hTB@_4y=}4}gnh8pWY2CsJR#*cgL+
z`U;2O<538XXC1MB;K!afKg*EvL(xP&FP%(lc&z4AU4b$>dsUQC3v?e&FXwc}jRJlw
zxjm<s<Y?eSQh!!pq9KIS^Y%rc&8M{Fp5{t3oQp>&y0gz}KS+erkmA<eQfg_lRdIgP
zd(_`u)1_X?Pc=<*2~X;S?m&}BvkV;hM#8it4Qc$sbX}`N9U3cFYx6!5jx=Fn;vH9K
z&2Jz0!^=ArpOE(?%(M7(Q9QW50j;FME8`wwG;XL%hd_Tk%WRPnE_@hS6-OV@E!Y?%
z`)<1Ei~~LR4swffZ*n$Btw{WQ(|>+LCUX@>@8n|B1k63p)FM-BeVPra4yJr9%<GBa
z;f)$!><(_$h@u(9j_rwmjA4cG69?~IhfSm^8rML+;2SO2iY15?{KQweYT0Ke7Yfbj
zxVOmKGfR3-*Rm>PnYptEwS7-7bJd+;%$Qb>KDZVyRUP|!ttMX$OcQI+pNY*MF55b1
zlC!iUm;Oj-Q`vrDnMGcStLv3Z8R%nGm(gKlz_N$5l_ApOBe}+hy#=_<dW&gHMnW!+
zywD;jeup_!F@=1;@z6K^i#$nUgWlVMmde6&6kn~Yf(9sIh7g0<(RUCE%H6AK9Ye1_
zLKpVzW7&6q7tboQG(?dBaSB`PmCEaJ2ro9O$VXy!N}0Bj`??uxx<$E6@t9>Nz~-*c
zO6+h^Y5#!F2(;6@_rsCCIQi2hF>q8FG$i^?<8rT(Sq!aUM$#i3q_%D_#DPX3!s=*D
zuK{;akCnq9Qaw(>5B>lzb~>K2<4-yHZ6~2#6hZ~o($hXqD<^<zHby=ASq}^zrvTTY
zRdZtJiJVlu!In?9&dXn>dWRvhhkyrpn?|0@(j`b1?ySCjm#;9ve?F2hm~d0ptd-ZK
zPkT2YDH#78-Q$4l?Au-j$tMM6-!E;~?3dk3GA}1wrxFW!E9gN)9k|#PuC(?1IsAk4
zHMIn0aBB48-d+DN<k;=ZI{tZm-?Fc+*Lzm<EX7Est*CXR>mB3rF%%C&KWqP(4b?<|
zy(RkzaknpMV&Jm4GL0uyz3oEA<F+YfilDOHd#MM8l!IwDZIx77DrUraO3^aD`PcE%
zuFON6(<|va?-|<{cehK>pnlGCp8*XbSRt_5z9R8)rG)u9kt{U!(YAv-9Vq%V9I_Hg
z#v*9mFP>R({+-*L<AzrC{y^Ii-ZPNvk41is0x}Lg446Ou9k63BRD3PC(Pkz#@3~x4
zAY!JdtU-6iwRizt#dCdzdQzoGCkf@(?N{f+q`7waH3N>^PP0xjeN3e$B4iF^N8hil
zp}jxnI`3*RziOnQeI=_x)4xFQqw#0$oOi1RxfeU7oHv;|jq`Tjbu~*cn*|v&yn`>1
zC^l9!f+fqj7ZedV8y1HR!vsD0p?Z;wGIzhd&GLY<f|td}>$IHvX8TLimC!*7EIP|Y
z2HPvs+pB~D^9br$ilJ9C@q!n+i=5DKGZBmxVe|p3%p$sp_Q0TvFGO1C`R8$r$SbdT
z+g11nDjc(I{n#t)II7opq}uOa9eLDWl0>InRU3x~hGOE1LK*t5Qz9hv!3~P7dv>3X
z3uKS+?~XVRRH90|>0MvJkf45+PaZBWYk17R+Al3JqUbBuC?~Ig@VzJ^^T$$?Miy(*
zj2=*%U%ibpg87E^b1r{B<bXCXhKJ~_O0u!O7!oa3!$>y%ZKDO{D53?!8!VL=(;0EB
z4T;`-|4l7-t!iY#evJ7Vr0UU3ykHu*TT%MhYKb8bs*z_v1#;-T{W#OESx>&gO{R0b
z<g&X(ZL2Wec4PYR+TVTynafm-0g84QyR#Z*1$A~g2s>!vxEWXn(7<AcGX=C=q^<v6
zltVj0^%~=^X$ZwzQ&O*Mg%4t(@0DnZbchj6jy3l8Bqfx3HHY3s0%&P+@cN{&J706S
zV{>ebBl?nhqg@o9EsSxs?K{~=*{6)>m|<idF#Sb;NvfueHhNCN_2l@V_NnP{{?ZTd
zLN*Q+mli@e0!`4)Vml23s}{Ee>bjnSxl?^@y`*qO%xu5L8pi<YW(_CV=g@j}V!Vsk
zu9Y0cty~Cg^2Kpb{;fq6r2UpA&Ek<EAB-1AKgZD!P*M=^s#YH_gcqT~hXMm+dM}D1
zqwlNB#&pfdEqMQ@)?$CTL1BuC%X{H4V|g-4rNvZ9)Kxt!HgAxcBkbM5E^Qg7*^FuS
zv}wtLp^A}?&!69tlN@T=2}kYcM;Gf>-RZfQgV-VDV^JdAK32WW;^eOZ%b8^vKW-o5
zY`xP|RxG;XQPcL=(7SQD4YrBM8oz4czoS1vF*G7V!m%G2U1)9E;e>uGA>$vpYkyxj
zosdlKp+`$g25T-;#%M)jbt0+QbiXt~yxsBLX%CrHO}9;ISarQWm=yb1MEl;{J5z#0
z!q5YLI?yS#6aI{{xLr3XGj`YpS{ToIiv}hHjama*iCp*ViLRv>=_`6}C8OG$szfv&
z@5*+ZYdR}o^f`$++8$YJT!Rwo*VR4d8fFD{l(teG5wV*san2Nlkk1%>>ed&p@hl{0
z+E$mU-pu%cL(SGXCHEDF>88l#?W&im>ZI&NesPTdkol@lO5Z1Kjn>%@tO6PMRGkkI
z!oig$Qa6r+zn98>xL7gtwtgQH&r*r9;s2g&fHK$L^TgM)x*a7J6{gfjs2f2{;~=@Z
z#HueUM)3YfxFG9Dy#HiGc07O7Zh^n;*#Ekqgi0X^=>KF1QF8UowHyUY)XW`zs;4V+
zJ<X?5*xf4)<7?hqDw_<Zv-$Q^_nfVSza=++XZIe>-Z%rcS}Q!bhNiZPvtrm5Dkx2z
zeh6_eHX{ZrV(h&P@wkGj9^yCENJ^v!xW5n)N?w?GBe?f46eL#l$8NL>#7yyON>lcd
zUiy_^4i{IkYVfyw<<f>PGd`c{D^<!F?SFoB+PL+c$-2E}+l8F=7j*l2x;GyZH*UJW
z|6^J#R?;Kj@S9u;b1jM^A~X<SKWR^R-;;pILG3ZjOrPL{ThS=5MJI&)mr^`M-sOKu
zmdoua9;h(!MvH|*gb76|x_=kx8GqSV+POY02QSVkbkm7y%BnnPy-z-zI1(_+n!=Mz
z)>Et=2>(b=z11)escpS&i41K+2ndugT3TEp^=0j@J_tkJ=(5n>pNG+yL&FkL8x+|o
zdTt5%>AY!xz@{45@w@6BKhNo)^Ok{GVF&T~^~I1;r7>CUcz%Q33`#M6*Lh}XTcahw
zO3_Oad_JUB(Q348QH|2lRfMb`tL2Y!F4GgbjX3OXrbtk2O*Z}TH;-;Eutg^uFWq@-
z#c_QQ7tA);RQ{}OO=}<%XXiP6J2-jNn%k3W{KsQY4!MpB0Op?%VOE0U!9PS5UjJnB
zIgPNRv?qC=3{_aC52KR|JqwT53lC$+&rknGdD^i!Clixen1-<UV;7~}Iwt1S<}-xf
z#`#&oqEF3iM0icxJzRF>Oyz#D%vZKi(ADa0&ggk)_4<#pqUnA&D15($=p7eK0pycI
zI%@+ZUB>Et2MZN=n13Mcr`|VWimy4dBc*G{FkXyWTN&aKLPL-yVkfEnF#4a-H5Z}S
zWH%|-)!~qSQ!#O&re0F0ikP?PN5hXdZ8xM);=ZL51gMPiL0Aw;m@f61`kEu;xRgx$
zF`n%a5leyg5$#)T^z}%dnjY-9*}3|qmQ{iTQ;K}7{Q4D*h*ogh9WQf5fqwaVnG0ll
zz1>1Ps@hZk5a`07(-R0rAhl>E7P^FPaxAaee#i<!swPwro`EzMrrZ<Bn92?+QK=pt
zl|F73cH{@C>2Q}n`IZ&2J-e7{c32Wu)+>G<RjhEK?CqeSr<b1kY*Sh~s#A|)mM@^m
z!PwA-?aRUW?6b4_1vTb#7wmrVJpQ5aFX*vA+r8Q^rm#g{Qyu#7rs!)&;x~v6fwiu`
zM_jJ%1;nS-+Ru_6bOXcrR@C{qpxem*f`&C4prv)hBrgw7)E~llSy;X0Dv314U>7Hj
zBfa{t#Z_CNU-vkL&X4m(glewFZ@oF9LbgNw8rPy0np3u4(A%1~*0M~kd|HjP*r8YS
zn~xR;tQ8zI6uuRVT4Owa4mDZ>!noJ+ed(hzoIy|$6ar}|Dh|<JO+P7XUt|8}5i0)$
z0`!u6&#XH65`rv?x_@GQZ+Qh1F3YunPE8IZX5}l57PKKWxLf4NGvJcU59VxvI5(AK
zz_8%g2QK_0_B)LZuKjFRorbnPd<n3mVH$-X=-tex-=!u@w*<&eMrUDJUwfKyhRM~b
zVK^%iMC+Y7qtTJMk(NK|*kIc(=UIZq&ILI2JXTYWWwRk`m=-HV+w$2Z3lKs4Ili8q
zi<fzJ+n*1AU)UqU^g&(WQN}~Wvfhnl_N5t(j?o@}cXwnw8?m2#qlxq{7}-I;h8JFU
zRD+7W|Bfw|5BW%{9*WT^U)UNK8n=VMeiCS5i|UOqHEKU!kWvuk+w8!*HDNS~q}XA>
zf<E;l)2yIWnn4?eqjSSk<#4scEQLi(IAbJ?I`f9pfbdg1mZ%{NMKKc=Qa`9}B@`om
zn3n}j_A?uM-+0wKjDvI&9P5LQ155fSsaI~SM`@S$yNVs^1@JpcF5^Pa58lo0WQf|r
zCavU4<l)*#_d_SMtM$59VFz9qilO}<ouq}*%Uevn^92gUB@$)Qu37t5R#$H&@?&_m
zzFDV=Rt>ccHkgd0Y3#-FJLS?i>)lV)_t=uw^({Y%B;J*U>d*7kTS}@P?nndXdK<O+
zM#G4eV%<u4kITt+_4>_Hi7%g+u$I$jijxeYoBV3rYor>G4#@XI_V>(<Z>?2+@m*y(
z1z#3V`#W7-nLQ`B***_b?17`ZYRmuCpgOP8Y-|<l&b*uQ9n8q2z93H%26<vT_QLwP
z>Y&xWBf@uD6_~sv1BSC9xCNA)UmQ5uHH-5<{+fbHVVyS1E%B+cgi|@BQ@J;vQBTRo
zOwm+7#AXz`{v4%$vyhLv>elaVk#NZ+O;FJUtYp7xS-B&EV!`;`BJttJ;!@J_jvc7J
z{`ccnJGB)FT}YF-y1M_JrrT2V2%N!J*b-wnaQAAgc7=&cp2*sXC%&e%=+l136J1O3
zB}IOU!)AD2eeY!~{U5nVoiHK%F@YfdDntx-^!}QoA$88?$(xqzd1@&Y0Tc`739~tx
zdBlBB$Wfs>uuJxjf`4*5e2fpC6ArH`3!s5)HdhK>TY4O{TV@J)_@>?5^?XiyT;C=x
zr3#>+`-zpXU{+>|ip|yBN2Rs=Je%e%!BVX<L!n=yP;}w7H(wBYBF$nTr^(~zU;iVo
zYFbIw1b5EW&zqu8;w)96B4~PXKAhfhssJl7N?8eSHm4Iqa-UtuGs~yBTcV77i|=p5
zC<>SW?Nge57pf-deqEtjHy$tG9ty8*GiQtn3sZ+#CUK)xWT|}y@4&jnAXHNFGmzcy
z#|6@}mjUnfjnkA<OoLA!9ZU+g2xn6km}$UIJuAldVPk&Nikn1-F;^$YDO1kLOn3<1
z1S+cu!%=#l_UKJDnLC+8?_tnv%05BafWDQ&<r~`WCg|j^hfqghbt6*1erR-h4eKU~
z<eR#m!8`zMfP7D8iK^hmOn6^pG__llnU(eBF0<n6wVsdfg24viq)bS8;;g%C)MvXg
zm2wsq89jOWobeJPVE(pleRpY}zq3_yz1|}^xaOCmfN_(RN@5Mty6}A|`Ha%Getis>
z_~^&7`6m&}X{7I<6S+$}oCe!L%U?Dxb5dw0U`Z~>7=VQxTml(6ZPd;)Wsy_PQwNx;
z>cl+`_VVklGh@A1y6Mx#TO!F=6<CeH&dX9N(Jbfnezn69t}(goc4u4O`-0jj*h;-v
zq7qHIfXlCyt@V@2ZxLl>q{E^qUX_L2ysMTB_!)Yaq5k2BdbZRm>0);2ld-y!(5$Lm
zy*U~s6zl#c2hs131x<}xX-zY?f=7&xQ3#X1`%4~Xw3|MER?w^O+8hcnYJUjCsFdgQ
z{FILMr+0{Pu7r<(!Kx6o--_p%rnjJ%Ihy(g8TTN=+4wp2trdADTkp$m(#76zWue-t
zJIAs%M}>2d$|1P)(Cc(;7s<*CAhpJgciYBWxss@6o?6OgF-h;TOXt~S)oe<nZB9$e
zRqAC+sph-skjpz5T?ZU!KQBq?Ya!1jxGXAM1`pj;S4w<9kSNRT33p6N+SA$M4UdiI
zHfJV!>vskEi^dsWy-xVL^^q8%9QU=FDHC2h{&J}olNzz6w;-VTqWL2P2kyBJgd0C(
zxu1nyTqr*~42mWnRpNJkIvqw(5sfw8S=Y^flkg2us6)GczSLD}Cbj@D=JO)dc{H@2
z+#u~16zO^FHya^1N#1aojS@Wsl8exk!E?pM|3<#0N~}S=<&TKDzNCD(<tqz6c0x|G
zWYJmojwQEs(w@Fc=Y9W4xL95OK3yPuGmJpfD7h&sXB@-9>)3O37>DBzbf<;03WHGz
zsta8z%Y}e;IfdXShzc_GTuwoXg_47e1L#p&<R`&&D{FE)G|N64f@J+0y)So37t<Sx
z1x30Qx+)1g(dh(vU3sEBCrzc^?Usg-f=|=^QxIR(>FuYz%RS%FSg><}`?(|)#~<^v
zIcmj?ETWV`Pp@X@CE5PXd=xi%OSjLXS?fo<Sn6s8fx^q{20mbUt8SiOdm=uAa=Sio
z@hWW0tu%Ot2UASGx5E}$U$&$@kK5d`S<?K*{(XWUvBI8=^(>?Aim1*|;a@#)kSad2
zKHSLhL2Roc4{P=rEgp0?rbM5Q=DlUXlTRueK1J_LzvE;Q#N#tP`Pj@5-ESN1qVheF
zX1cG1?cPdib*#X1QO%-BzuFU_nxt?D`C-}xNmC3(iJv0yicZfSCldt|&B9tG-4y*S
z<_pNDO`}V$5IPl+ig6h;^gBVtxl^bn8T&pRrv&<@MdU^_6q1Xx-+F$<cTRMF+b5)W
zf86NXe7v#}wnh-s5jV#3C!-GVq!dQkt#I2YS*5E0%HAiKi%mgETK%YFoq7Q)d7XTl
ztR*V>>-*iwi$(ct#-`u#>}ez?GBO5Uy$N-^pG3A|%{(iFr1hZ4q3@SdDP(Eqj9oT^
zYYTO9>i<p-Az?PI613M6iTA)1G@okX3DZ!r60sqvcw2W=K8;(U!dj5TO-TD4o!>UF
zO@mN9Ot(0#?za+-Z>8nAs{Dryp3*|9srxdC;db4bs}|D^_Bl<b0`W#&cEabi)7`UD
zozFlW#rbP`qZ<|`*<Qk(iP0iZPF2`}%Y`)Sl}xdyb*u~{ZC#~zZ@0rddm-X7nfd^n
z8eb0qsYOF_EE9{o;3BbAzUMaCK=UL$Rp_O#-jc6x#da3hX^v3FG2W0t|I7JuU)6N2
zW436?r;Ay));kZ6qtxw5WkvoZ-}cizg59vvBo|ldavjzBDPmp$hQ?*b-43<q;|o7J
zq1e4B7b&@wM3vesT<`)EaycZIOsZW{mG}Plt@P;g^W#p(lr7(J@~oywx$~yUcLE{f
ziYCtRbZc<=On4O>x{jxU=F?B80@Zo8@2T~5NoyIoXf?XVMTyJtYJoh}*Pc$VQJiu9
z79h^d@NbAbV5-Rm$moo|40sNpwf6>w{sg7LbWawyG*(~DP#4RTkHED}bCXSkuL3(H
z_2}R_W3`A<TK8W#)*hooIX(9CY|k?%iyA8`+2gj&wq02!AKFn(4vkCrJnGL;z+}zi
zO<?W1A?F94=ctqwsmit!%_lc<8SB@=9ZX7J|4ih`7(_q3S|2FwDF9Q)>w86FrZlu0
z`n0CVM$l`*u+Cl1OUQ;Ff1q`Pm6sJ}iY^jzcny;!&pA)gkA!R{pZiIeL%gy>{pWFt
zuJ8FOQ%BY_(7?Uq6yn*Ga-|s{RCMY5c_tRG<jAbFUZa%rFk-3@^=Tu(Vrgau(R)2J
znM_>I4ZMJ?VuapSm>ag(QeoNFx2~dmbyAL6q(!}ymgQmXTs|0zE}O-%Br}w{GG>?h
zX8Bl(f@~LI38yKqCMQx%&Z?|iqo-b>P#(67c93RFlaxG@t&)Fym~HvExuEJ;o#b!Q
z59tz3{<!Pc-zG`LYzr;*O8&Q0(cTd%{00Ube7l*nzRu0Q-ZO8Z*jRT6K{CBnHv<KU
ztad!m#d4vtKk6OE+~N%wjq5WB78FzxlC|ST8+)BTkDs1DIqUK|R+3MNHnu5Od7P}G
z76zd5a3A{qc=EoRtIhanT1ymF$RsEmcU;iMkpI~cP8uDtv%wUc@DytaRp=kB{jea+
zU9pi0mn}_A?C8k5UtBwx;KrZI_;_Bho9L||t+;yYd?z{6r#8y_3wWpdr5d6ws`7_q
z$=U7N+k&bbF5TdnQR;fV&r$bFnY&}$+I;T~Hx5#jLcr-?DLf=RsJ?u0<!r`}ng4P!
z!p6fA;dutme|09urP!@;M{?r&&uRl2sOg2%qn`lRj-{CbG4sJGoVHLZK~eAvW8XYt
zi5xna18Lx|rtK^~xS`1v^x*yv?y06@&|#_$5H`H(SMbrkHGoU+#c$hWX~=7uIX#_`
zT49->Xz9Nuw3L6lSfj$%Vo9%7_khdqA$<2VyQ;`m$?e2@aVF{%kMR18ser5Yt)P8B
zv_(X=b(~Mr5z~}F<MPygTe29EwtVp7kmL@i2Xf6$WeNEcFI!`UW%!vL64(&W|N09n
z7k$Aax-OW-dm=@3ls@=C#`GgSYnpo_<}o)5L+kG@i;~e~nGi*8{_^JGQj4E6xbl*^
zF2)NrmX??raKuNs^14>H&z167R4tx0>_?>z7DFM0BUU!E!F`hyID>8cTJ=Q$K}WbE
zBQl9vh3Ht6Z@ojC>6ydvX_c)!=OclIJC5VE35TJyrq^k&Q)EFcg2nrNG5j$e7=^dR
z`O;b2wIi&uX%>a4ll_8M)$E&z%BD!3`aOL@|B&c!06H28ezZuxQ6Hu#+=9h8`x)cI
z-l80(f}CQyMnoB#O&QXHo9*R5Q@#bKi#rlhYVbK>cM|CRtv|OqTnB2cu}9k_f{hig
z%ZVP|FmKH|%ZSf5E4bV8#O-_C7E7=jlaVna-zuwZXW&;()~A>X%a~et<sr@@9#19J
zn7X57@6cVOVzZkU;rEl)EF27jio?+pmU1iqpc&QD1p9<7zSx0!Y8hBd7*y-$v$F)e
z*J@Dn2dU+<w$F{`eCgYs*RP9qi8BRp4GFm>uCy<C?X}YkH)$s;$Dds8v}X3Kj{Qrk
zJ;+ujolr{@8<&%f_@+DeILpLxyw?tLzio{1&1pMr(#P%9*H5O2YidOCPkrI7YLLA@
z{zB`zhnamN_T>4W+LIFil4S}=9x?FW#byB`<<_+<+I4ulI(_$BuUwU}uY}M1JawcO
zE5rr<?O{G>U$`20-f6tJaYy+*{Z5d)#DX$EOQ}A{R+px2KgX?rDf6+QZUL#x#31dm
zM9l!=r|w;6i6Qx(wS<tR<o4RH9$=yrD)6o5q@X{Y<!tkIl~-gw?jL8_yYc@1iaAM`
zvV5u<?k2Y3roxwF33v6=N3T9xEy|@>F>fl6y-bCoPbzNqvg~SOWdTV@!HTd}bzv8m
zuYiw<Py?4Y-xY8@j^K4^9%k%fRY5S)X4dfs@CN<Kcmh4lm5SZy)Qg&mJc-EOEA7-$
zAagK0+jlle{jRz{YB8g`$I-HmOOk*c`!r55=meRavnqfMM*Um6^TLOsyYcz$`>zu)
zu%hT5!GJl>N4fN(&&@d!BV`}Di_&eI!JEo!iXwiwn%}f>!>}4TzCj37V+)csKO~gL
zF{TX@?tiO|mPTT#wO=ruvzc60T*)M=#DDG8{?d-cq;goRk`M*vdVSwfM)jqqsI(8`
zH`~)=m?mb*dNZ}^p`OYHqU$zu-D26*aVZBCj{8-rnN#WIc9yrTI+&FlHs8f?=HmNG
zWlP!CHlyTmys3`m?8P(6qwj8o+R+e&G=GeyW<0T1UW_Rbr0=qytjBw-wQQV2en#+e
zsc5q0*xgO@uzhB-ymBdx$jUs1vtf={K8F8N%42U;v0yISmzu{L(0?X-?y0uw)<xyz
z72<xtep{PGKGHIJ&C%_STs5M!a)G-QcwZ0xPJg`&Gmcgro)t4d=9u4*-6H_+?JENl
ziNjwh@V$vp89%(y`bsV(Ke%4nkV4uSsypJZkW2`Js(^!sI+|5?;S)pSeHx)R;u58a
zeiYXM$;)8(f_bgU=CdG5NSyq(t5Y9F7>5Ucz?8z7;$%lBrZ7UGm1fzYmylN8F(Kts
zWIC2eH<YveOb>4qL2$7Bs7n2@noX);+D-B1-OqOAdYjT<>pF6@K0Q^S^5D2y9cNlS
z+hjkwaj@9iFH#t8ZI?S@BIC9FmR@&OrWU1DIDKyR*(RN!QCiuS-9*zVg#$3yq3Znb
zyoi&WB5FB~LxKzW0VuS=Jk(99sA8wx@&50MXL^TL<WJsyOtH)j!WpWTvb5Ck*ezg4
zJp$Y};E=77#vYNHIL7K842!FIGh#FYlnYOyf7oRrc<lAQckeifO!o7`_7nnj#zc*`
zaPmdamGYf^1>B--iw#OcQjj^bSW#q5$hy8^<d8sATG=<})ynqMWZ9JC_Nz0iDGSN$
zWTMu~&IdjOvgndDdJ`e=0%HA^o1u<6Szi>$(@iMtT3&_2q9CrtXrZ`iRZk_#q`3c+
zdz(ia@9s89x;mQ%idxA6qWr>{%7!X+VU$Tleo)6@-{Rzz(4ydB<G#=CspJZUjMT$V
zN#e|i-zd&eLT5YVUn;{GfL<=5?Y3I5keLtp&~`Ird7aOP(;wK0>C0ph^zo`%HuV`E
zZAtqUNoERm*P#!j-WZ(X2J7haW`FNmGwf0?1eZhxiM|RHjcNgBAA$yHUh_vPEGbO;
zAhe?%q)F<uYCgW&c~6|7`rNs<n>WP-cuF3<JPtbhC%Y8|GN{^FShQ@2z#NQr@uKu9
z^@^%d>2pQ*=Do<3jltYrRhH1C(1rz&NAJQY{Nah`&rw#|iW`R7QR&$x)?P>FDpRW~
zeG6{i5-3aiWN1431LV`bctb3u90)Bx2so@_?uPja#p!xp%AdthICSzHRCT`rQ&Pt|
z9Ha05TJ<pSn+<RwaiX@0Q)gj}El)dj$R~}CD9d~ssb$+_vO-|ktZw<xp1CYC>O>pc
zeo!-PiYs8u%zUu%{$(majur{*heqfM4oO0n>EhXbyblmu`(EV@5oCk~5R0f$X0r>s
z@JX9vSJEzIBSyAy*++}_v2JRLioh<Ok$clW(ZhWOvjX9__63DKcU_FDl+XfkX&+%!
zl~6@DfqzfwXAFIIt4oLTW1*M5(g35e(x=&)dR>Lp79T9BZzQc`B_8XBm3yFo_C(v+
z^Ceu=kn;Vge|<SS3KkseJYI;)tOhk5+QM$roZ2%ZWX(Tgz|X4Ly|ACilb-Vj^FP`c
zSJ(?Vg%5j9fLm*RGiT5sNjlCL6>)1XfMh8Eg_ovCv`A$?=FPIpji=M9@{)6v5y@~O
zy(qhTw~k#2^-i{D%7=8$&wFlze9=N%Hvpv|U=`V9)y5ww?+;B|&Q-F&Ro38M&cNbd
z-h)uC&cPzm?J}Bmq_^NgAw3!V6#ZM4sWs8IX+7C_7cZqSgj#TE-&4=)sn>aGldB+N
zyqR0z?)dkhYOmuDd@Ka4X52Ay2ZnW3io<!2{c^tG_ZPUWXwd>!zv{NqtO_fAF3Wbc
zR_|05x~5j|tPWYnH~M1~v!5=CoEA<xA^LCbB>r&^uRFsEzufH>jccsRu#CGNMzEK#
zz8(zc0tC~1d9LL;Jz7VL`8YZae~Q|7U|GaGaoJtr-cb|wSe;`OLGJSAz0zF(APyO;
z^^vE()$=uSE{9t;h6K_^ejv}Fx5pqR`Pdk(-*Os6FvX#$n_%nyJNROp>xt*jev;?h
zL(3DF_r@J38vpf<lf}c;#uTrRI<=0c#j%5+qR@r&f_M7zlqOw85|uWOf!J(>hlTcM
z(XV@NKPGeAWvmKh>TIRK>Sm<MWiO3L@XDIe{g}7a&WH%FWo-Hrf7=AA1`J~svpA1&
zZ69-8Qw^ZW1O7&9omZ%z8R4~A%yF@Q?Zc#Sh=A&G9Z#Ct9fWu2;4{&3b7<YL{8|G>
zUH^Du-6j~i)~)jZ?svr1;oedLhmBjV!c(m%Ma;2pu}Vnz^6=HvneIvoypw8C_#(@v
z{e;;;8GJ{rM!$}G?;2J$h{(5!*+8^;4H@TUi>$Mku*RVn6SZGir6u_0A9>LeiL;=!
zCw$M-SDg>f_1XV9r{zh0qzxxstZKKns&nK+ACB@;#H!u>!d6u$6#}V~s2lSNcY}!$
z`vp&XFnK^~QRJ^%!kNiL>q<wzQsCF-LPl=ER%jqeD|+vk<NfhA@~=QB7MkkC)#G2c
z<x&*5{Tv<EbXQs?$=}&G*q@!Ds%cwrQPlmMsOxF5L+f2mn3`A7wzY7O%=UT0vSn;)
zoPW9KKK)wo`oSqx`*N;E`eQqQw^tVW+g_I>GsMq06}!YimA@Xu^SP?c-sB$5#h}DN
z5c6Mj<kQMnYT3>)+&drRv%6cEYfp~QC~KPwV=Nv!Cy(YP6FTmplAf3@ALNyQ{u@3Q
zVBjYWBN&pEzmBo=zrW>Jw5~0<oYi7dJ3c~Kk8i(UJ~l0j2r(>r<1R}+#-t~#aY->f
z7)x8J!@esO8**nqb3S30W(JFmeh}9)%C(v?tLOVjpAlJ?e7|ykB%kth2y~w<yCDy2
zQc+aY&uUtnk8+MfNtTJ8T!qtBd~;p(z6Mk5AWyz=;&T)XB$>ejb!pUM<8G_+{8EA=
zX+qtccU`oZOSZMRJKB7x4pGyzD9FeUjgPc)rS^|M_zI0OaB4PWa&dKl`WE$XI;=y8
zeV-;)=!y!<gv#SW3UkHfpEI)g#(|{gL+RYEMwNQF`2vN<gq>gO{b8e?tcun#LS0}8
zk%#$JIdSw2i;*8i#Kw9acRU(F1CTjQa9O*)@Cl2credM;pZp+Vh%JX<<b<K9s57A4
z)3x9KvqIalte-?jKgM?iF&d*<z}>awfaqPz^N4EDRL9hIB-G~A_@q0juIoMSsl_3B
zCH{BZzTTkyfvimHNrAq5z82QXBo!0>V2|Hu`;!Kf2=*L<;-<_=@ur*jDR#LGLRY^R
zF#YJUR)*f|5*uVq)6+1mluJvrE2!m5w9dYC*f6%#A|Q5RNoBv60t^n$(#*A6iX16v
zBHX(JjcH70ko#SA;hAN`S#2M*9JdGPrYyOYQ(ATCB$}HnD?yzle)HHXnd0iW9eq!?
zFz9m0kE+1Cn}0w`!k8laX#a&6=1$w`AF4&BN2pWq*MN0)qeCOd;VLoM&TtyjmJFC!
z3<t5QKdK{RqZn)K72<y?M?3}9&9FBe7P=fG2hv-~P22;p1Wn^~0e(y70ogG}8^kp;
z?5H4POuA7VX}45n@`|v0<h#-9A)(p3&15Y`tqzS&=<m&|{}2wMdIr3ZzsBly`}L-X
zC+o&RJ+SSU8OVi<x0_WaCKe&zB6HWT7VXB5@IKU#$4vH~8Q9gYnRLad(UQ=(eBsK!
zuyK)6G7yB^MoP$ddn#oF!O#bG#X-^a&=zIH8{tw0Bh=w$V8bhMp=qn?5n*I$Eh#i*
z%PxqbtB#E-1W_ntt?E6j<4%PLJ^Tj$(ig}2uAa5q*Q+=l>1xkr*WHHmHx(=Y*w#Ln
zOjK4#6+BNJ|C1(-AvnUl5xGmJcRQq2X;GBPnSRnjT0mm}<%iWzDN8cP$s$+Ni4eG1
zI46Uz@n=8)eMnN~RgQN-QhrpjmqBr0q*n3i<;P=o$09rJDacw>A1`__Q#%R|TNZiH
zdRmEHCOFDYt%@x45%Zi&l~Hw!B$;k|TRje)LV6w1p+Q-mLq)-mBEqN<k~O&1-)sLu
zBxFAxm`%N98;M2Qna$pvlIPNSdVE7*JyEIY@8|b-ss{+JvpXPJz0_LMLi6PDdR2&;
zy}0AsY14MS*4pDoVUm1-B5~Z7x}CfT(+I6bgm9vEtI9T4P5VyRDbsYD<PE!Es6eL(
zhK7L(W^&pkH|PWDG=q{hzQGwLW@eI+by<^&_Yp2%-|4vj4$`<0d^qV2`7C2Vqi>K>
zH{fmWduhEvIl*9`72eMZm$e(-$0azf@r{35U{PWcak>1TC?cG5TIhGauDS7BqI>lQ
z<9^+4`Ol)RzV^*kZSg1|PVPzy-F+H!=lonVp;xsAKa|vK4^~Q%#TyMW0tl5s+c41?
z5gD=EtdO^!!imA{A%~;Y6)szmv9H7*Fv>*qok?A|=nxyrs>x)Qmep5jmn0Hscw&sJ
zM4@8E&FUsmvviiPmP)hXHdAfwJZ7@DeOzU{dsIdLCU(%AFhe}waVs0r4OBqK<Ds3|
zFoqkutsQx)nBNI0jhg04b*WC5f8lfYriWQ;`2MoGMb;v0Al~`d-QtjuI5#eI^$6>5
zq&s27557;@$HwjiW!4e1Kg?TJc9~+}>Q5jk(mw*KP$gM*K>Eyo-v-)_W$3;VbZC%x
z4V*2}QkH}aGiVG&@MbMq(Tu=68ji@F1kTV&Sxi+68=MJ0DGWzAUERwET@FiOQC8kC
zWLBNe2mIdi(5zlRkncY$=fup~0;oHNo}uaol*Nma>4<1AF;Yxealv5WlWK-4&a0bd
zS$9%_D|2JdeH{DK&-9Mn$Xp{DEt=S~g&daUuCmAtKUee&zb3+%_dlA*f4n1M6_r+#
z*PuYz{bdw0iX&G5G$9)~xI6_1@iNc;dkpw|E{;b_g_1#1P7sTvMUMa&A>#2eA|-6F
z+m|HULcFRDFC_cn4>g0$tvQ!EE<JlVC<X?dm_dCJtZ;`G!~}<e@DY+EO+OXQqQ9l<
z|9U%LQzGCs*XN;k6$7k|wC~HT#j!vrbr^pG3Jer-4rRfqTBWEfHzSFm{T;MO%q#07
zm%6+|#7i#}?zOK69@ze}Ip{HhcjhQ(W?_eg^v2z=wx;pe1BSEF_Ynm})0~DbL^Nm&
zFtRo9Oxxv7%8}+I6Egoj3;S1Kl-RTkU3sF_Cuwp<c_y`z_NCa0j;A}h3c%o^SLgDE
zTNO&0xV6g=%^U{lU@P4&%jm8-muOMyu+p`Dz+p78N4zWKi7LRswdQlS=TX!Ai~{{+
znpH(=0tZc^B|jMv?S#LTz+dKyWCGrn6q8DBKpIzF6%s-sRMR7@XzPNQP^v8Ee(*<5
z9tIyOOAVgOE~NhF?uMa_p4+ku&kR2)7upVzWnjr7=P-TkAAH*(gkoH52Pr?*e-IUv
zgK8i3=^&PFrH{ZagN>CI>er~oK17Q&cJDyUVEXy1>H{F_$TC9FqEMpkNRUj%t7Lec
z4bH=AtcPT}F3^_O41H)~oE!)6iM2E$j3(jV3<Bln12QeeRODyz-sW4CW*g^sa>a`i
zY|@g3lI=pI1IIQjISz*!l=^*yJYFppZKmi6nz17}Q$xOosr|7G=655=h|sdG7EiNj
zn6HT+j3`?MFiXNlEU-I7o#f-c<BPUKosXV^9fKXTIDqPV)9w5xP7<X_--^$lAPQ^G
z_kM9TkLWY9|8$dXme1WTkG{}EW|dIswlgCKiEZ^F$(3b-Jg4+VU(_((q479BKofS!
zvrV}E_hm0Ip?+D2y+DDwW!J@mBsE<gq*p-Zv~%pELLh0T)Gg|v3F{AgSJ3#Kz%cQy
zs~c4)V_EqH3!1@-Hu}woNq|^*xR#xj)h=~1whTfDliB90d21`kBEd4OkD1<A^Km3p
z6P*A1Z%}DJfsD<EtHQq?N<P^LM3jr9N-s)k*ELP?cWFk^Jd<@(3hB!e(*p>kIZl2L
zXb;%YQNO@h2E_J?1yb*1geP5t-r6Y@lgrg~yYNzU#BC~T6PZlL%QB`mELfI-wUso5
zn_ytuni$H6dtK(5^}isA%jP8glb+CaVAo@BX6b@y4jcDQfkSA9@>wD@|1C94F|`Qu
z!)c6fMy($#FXj^cjPJaD0CJ5f|HOO4k1ycCd9S1>xJ3nb4LCd_`R=E^xQeUKPuEi@
zd<U@%2e9k)(~*v@H8s_&Vb{i~F$>cF@6;jmqzwF`;G24Zq+6+GSqFyH4zH0NN{V_$
z!%yfHBvrm7?V0q7W>NT>Z|BvDc4oS*q{42NRWyIFO6P;!THr4urjVeUcz+e7?_H@-
z_l_I70<s={pgGX|tDG4HSI^5KdR{EP{pqr@D2j7|=J?qI0oLlr3VL^cT93`VXJ(4$
zLA9hH^<C*FT=+=X|DuIFiAy<q1HBkvY87^++@otD;1cMnO_S9=#nCX1(^fwnq|QEQ
zJ~fh72PhjSZGVSdEVP@RReKhU2=sUF(P)yXjv@|sN-d8xQZy8sM{CSc2cU*vRob{K
zoDJjhd(8}(>bE@7J#F3Mwck5W2wQ|vCQAX|-IMB*9|)=9`fmZ{2O>8YYYKn3tn?*}
z6&HI%`(wUn(<m?Wd^=vdNJI*172)zj%TJyFq=7|j7JNUy-#}DR(6VcDSur{+meLni
zjr4eaydL-7rTSL6`gGOEvrlajpN|aQ4)_!rYGCM{B43w4Kkhg7pIJ2r0pf=i{6={n
zn&l&(_$Sq;8+y6$TF0s16;lkI%Y88|@-4tr$O4fQGM4^<-$QhjBd{6Rq&EFv_t`pc
z>nOK`uS_ff`F}l&lP~_UWKGKwxpyH-WPXl=>JmyJpopW(e-HLUN|>q`IQi5JbrT{@
zS)qZbTub(%b1_IzG9b%d2u!twb}0llh{M|W#|MYQ`Z-e*5jq?q$~tn;ta+mr@4+Xr
z(EZb!(+ww#H}irsA2GFhkeSeSKL6EI;9(@*1$Gj;EwS|Tl>_BRQ(>sJ$MT1-2xqgt
zMvjA&DXk1UE!ZT#mfFI&ti(wLna`@Y#({)reH#C}MyoRq7?|K6d^x8lM9uO^N{wEL
zWCHoWyBWz0PvAe>^e)L5wLs}n99JhXAiHJou*aQ7stL&9fAaFnB@)%+)cIjjLii?r
zeZBPe%)&P{suo216W@Eb^F8Khh^GoQR*Xf;vVZV_Ry5OzURw!z+5g`=vE?~f@ra`A
zVa)V>y5%{9Kt<}^FU-{t$a0zVvB80@`}|1W!?XK~2<N>ziV+R-#iNg9L(nm9y{KKx
zdA=$NM79i;{z(42OTAFUE{TnQLg(MF-Z3vWx@cK_WK;`F2^)(zVN0%N2}C=h*FNp(
z8i*{CP;Q(tLdd0_z=Rl=O1eIr_Q#Y&ST>e_nzg|@d<o5UN6q_NWQ#>Wg#V@)tA3r4
zviVl6c6We0Ci3FJ?<X(xzYD{QspLrPG{Vw^mM4bDHo`3G6NVn|ZA-tQXg(MRfg|=}
zE<$~tZER1vTCl2o|DItn&02FAQDh!~o7qpd<G!XTy}yIkoaU0v$(1k~sUQLi9ZCvt
zsYICqZgV<r1wd+GdI5U3t@%G3h@wnHFjvb92@8~Xx|?46yf`Fi2V2SyMD#EI#j%Mv
z5IYv1j?eAp-}tkEj3V@KVy&|-1Ei?3KQ}q5uU?Da+1oNAF+%j)%?44Lv6nCrtl1YZ
z=-zVmw1XR0JW{`X@W6+6=A%-`_y$lsdIi{O+AJ+le006kQ3aF;!m1-~n@MU4LXW$O
z=K^0IpRPL;fxD?ANS&D;XHUu8wa@%FQ|Hu@90ggui7Hml0ZL1TmW65}_u?$)Nofs|
zwT17xWBQ1cC|u?eZu2H7){mON>8#aX6Kr9*WXz-B6z_@{X6o?zRjDXDR!U>z2|0FV
z%0R;4kaAj%N*3QHYUHKuKt~Nv({Wqo5V)8Sy={Fj8{ZR8PbKryR&3BanQdu!Nt!ix
z$^V9yF~IMK)ITXUXXD4RZTsz(Y{yM6(<E?BPEqCvZ3Vnm&@lo_RQ&5r63%{fTujPN
z@$Q2YYB$hvAfW;PWY<ZskP$+cJaXIn^_EL}s=i6E!H}>Bbd0tk8SAs=2gh1MM2{u2
z*;T)t<rzJ|Yh|*-6oKx@KJ!_*;H9ZImAyem3GWE#HDheMn4CXb-Fe!UyCy<Jy{`D?
zM#7Bo-&y6h@BijFMC;TRleNTZPJyfI{#&m2<=3k6eXF(qu}(P;EkYfeYEKM&nQ)bF
zu_GK-RV3!J%66453F2XSJ1>G>1Elts6h(KYyv&tqC&!$20-OH}hc1xixm1=Jr+3qZ
zd&d6;irnwtt&0xerts@nlJ=P7UUlDy;_>TxNkh>0TFBrJ+CA`Wz>)6PO0&Br<dE}#
zv%&&;WtXBVA~I3JK1TEI6~8xg2fDjZ?|8vs^w`Uwc=g}5kl(=v@m|0iF-_sXJA@FD
zDfHzg#hYsTK3?+Zo&y)rmXUJeEm&{8)HK7PZ>bWbPw^iHcgP{UJV}&kpcbAFxLI6%
za-CIQ0T@Syc~oF@=$7tf*hJO5Y5L|Wu#;uYnH)_V=GPLZQ>G}@oTgRz`)nMSG+xv|
zpIOvo{iB4oHpTbRYGv3QL^k%j%fJ&&rgS`PvIDidDN_BZ)r=Ut)L)_<TTCExd>skB
zK*KDPLu&xaFYcdB2h{(%Bvzt9NKE=HP?b`~Wk5z8Qx2&Wzz-%icj%Juf4Wp-ljufx
zZdUp}?iZe#XuF{B{E6ikTp`#|2t7>b9M#;}$|oG-WpuRyS4*f84M6c?NGk<nhGNX^
zJfRh6n5I&OD(Uz9@4<J(@_HEF&|=uP0X1gfZc(;idEKN;Q?gWJ0Ym~H!81_MfhtPh
z0i#2|wRt&yB1)eUJ)qeBLqv7`q)lMz{qF5{cJ|X*N=JTq;|lK!%pBY%lV!KEE~92a
z$ip)Kx5Xab`8{tO4YoZ8h8oG65V}z%S%xHFtKWmOsDlE`z(Yw0I|i%;^dWM10Kn~%
zAKTzix(xMjxFA9P$fS<C0O-?n7!h0J_d*G`6<PtP?0>guVo<CE<%mt&QdQ6UQrpcU
z-KJr1K983Wh9a3=Jg^`X1!gaYUH?ncXgM}!eI!L}uJ`paSN{$VE|zM8;sQe!3&7~B
z{NERWlh@-frlp|%?aNl`)Isfd*1Z>y{#@(c=)9eLDH*Kfmz}5Nk8k*dHF9_xTW#gt
ze<JH^v;zFeSwxwMu=KkAv72bHl;^TX2f&rE(uy!n$2wJ%jLt?oL;}-S%KytX(E%50
zhVJ=r$6+*I&>9^*PW(X0TooKIoY*Yc6xB<;K*-k-xYqIfc+B(WyM`A|r$!WEMOckD
z0R9qT;Du@Fm&-x%vq0$4b{j}-0K`$QsAVf^?riM%Lk0nNQW@3^8C+ucNSOb!1Iqnz
zhwt5inNWHFgn+IYcS31WeGI4t7}V@tV?(|i(fJtw;dHFR9V{C{?Bg!^re@zxF$^Dm
z^hITkR*btLok)dTuXs)=@Es<)u6)^kjc)ko?q(xqyxwi0DU?kX!^AJIj1F8S($M|@
zoP?1u`UOJmP>lh~jo7v-JoV@#*&6_nXD1o`e}T|`$)SJKFo=B=j1+amMO0;$;UF;v
z6ZDx~mSwqO&D04fgtyw!NzA`JUla*#QlKrz?!3lAqkKyzHNQ9C1{Dy}WTJ9{Kfq6}
zLLWVO;G}Fr{i_>Q;NoM0KV5O_)7^g23$2w2R2;jVaKt0~?U<|rHKC7da6(c*{Uyf>
z)ZjJPP0>oAIer<<&O9Rc$cZeZ7Y``f4*dj(3=W1ARf&<;#l86HBt&SWLgOVGF0K>s
zz77mmR^XwW!(aC)u3U0hpxsv?VOCm;zMt!O;y6YRvRXu_;UC)RXRraljvRqa-6!6|
zx+xE57F#v$ZlF0K1AK4?7#$MB^a=T)VtarP$wuvJVFom%{bxX%2!qq|nK)`(_0gak
zgq5#WW$H*kKn#mFZBY(rv-mZWK>AV2i$uH%s{H<-R`Er6)8w5G@N5CHhEw>)3yS?!
zIc^d$gD!p4=ilZa*cx{YH*CZQw5e-t<E{e~15*A=Hr49mo3Sj_14{!?-g4oknvkt$
z%YZT;W@sESy9Zub#(0x$buwJvpVt4<gWN>TB<Gn^%98LzrL}uqRXibDV)I(;xTX_r
zz;?Fp(?t-I7ioFh)x!K{u}YAbrjrBpOSd<bOLvqkV-p2K$HN&<G%eV=97*PFg|YUi
zM>xlU^G@UAuz>n&om5_p5LyroI`1g{3OvQmURmYXQ2B0QI%D=Kz~-qr^3RqOWi2=)
z*Mau`v1U2nNd+o$4quQa(yA`ZCa{K*7@mSVCoE~re>i-^J$$xdof{3MX?b9MPPwlv
z_v^%$Uj`usz^OkctVZ>lU1*`Fq0d|Y)Qp_?{>o1-Iq^*yxJHIbdRdjAR6Tjm>;@=w
zF)3)7HShn2B|4KLw(47(S$+_cDk+DS;raSH$g*w>hP!nkP}hAHL4qQauem2gn3I}w
zKD3{=uo;-8Da)J&cFnrX6nit#B?r=AeZc65gq7*So3%^UAGE-l5GQJpAA|-!U$V6(
z?DxR_uMxqI5xRy#&<Y@V<Z4GbwG_Hr=5#%yEb$%uQ4E-2vOtO;V@kugc3<{#I98LI
z^#|Z?5zp{yOSQCF=t<o_J|6oDY58jS6bu8{z<mi0>igeOk5GOfT>p<$=P4t;@R*(U
zK&Zrv`-2dr|C47QTzdv=^O}glKLhQHyy0N+2A5Cn#cW;I<VdrBxAu$7lu}X`fcC5Y
zk95nC6*f{-9*?KE5<a99*X%ud0RAO03xi;u7`)1}_*IiCxeSb|#x;ceO&9@>y<bw|
z5B0Lw(;un?dn+7H@uP0JP}ZYkYhUKtP}9ZED_{iJN^xVt*RlSKSAFl-UmG*ym$`oh
z$zW0|jQoMsh}V!gfxOy_n=oym8pggBqK(l)rq1?qR1okQAPot$T7&QMNvY6neZOzo
z^6HC-Em~>rL5xTj5JR%+fKz0u#)qIrVUL!8O+kzH&huZT63H{oa|gth%m}z(;-_DJ
zCekdj^Yzn==)}49mCnn{FgVfW4GSMI3rS?i;ZC_R!mQgaXLrwrne5W<ak|-k9&WTD
z9#t+7V2zCJqH%(}W7nIC$_l*g)n9(FF?44pDLOa-=<fgZmKdS5-vAtk?D3az&l+rU
zX4jleHLV>$kr}v5#;WJv%}=NaPN&oKWqT8T7(02}et%@ulGi@)6ELEs2Capn;_)Q@
zf$eL~gx`Ise3m5&%3P13VN+Cb*gf|n^d1Z~&2xh$0imC;WUn7>ta04?$Z7Y*LZl!~
z43G7{=Y{_h8n1>{F#GkU00EOK|FB_^5w|Jhh1Gaz1M{(Jh0UFcchcLx$ouAZ<S#%H
z9b<2k{T2qER4IS0`tBor!%{SM5^^MAtloI`%j5~IBq+cCavBSq8WU;>s@EEHldE(N
z2WUg_T_U#KO9*^P3ykWX%Pw=5w;KNaF9a1m7RszZ33g{*ABQ5#(tc%!AdWN>T0nOu
zf=2t<5FKA1ZxJy#A6oi?q>Za_c5gm2Lcp%7^9_&0ILE^1`a&24C}XCmg>DBQ-eY9H
zgP*?g{7VM^_$31M8nqZ0E%7`GrIG`X-uB<WpFV96KYzo~^Ew4Pi02$ZHli<jFcsM!
z-#Y@J9o2;EyMm*hC%~7~T8+J6k_{^;0qEDD#KYrm6h1H=E4=3Zl3MS5l7AoTeOPz=
zsO_wV*=RC_Cv`kW9g1zo;}!e$lGFBfEL4Xd$VTyHOfD9DBm>0t3&?v{R&OY64rC>Q
zxcwpome-E){v{e#ACKIoh=H_n(S^k1Jo=6#dVnA51OU(EIO0qojGhuaI{bUv&}$JC
zlT)AsWT;h9sn%vJ!<dok9B;-k)86+}DstxpN?!Wkh<N|^d?EU81Ai`q_~YNYRsZMP
zy~u}~exb4UN6QBo31S!>7Mys#P@#B{2jIm?E6^!S>=begYoakKdw0KcSycZ-<h7@q
zJ;V`be6a*T@hLq3(pg8b<9tdlyZixF@yhknuU<A_gT{;ULc&~l@>Nsc5B3at53r7U
zxGI-G3uElFzNqPLZTmX6-fW}n%H}5hXiMSn7-pF+s#yd_?%+4{3NZp#>S`@m=>7%D
zDLhpKN)eb(O{R|3Z=qt(mWW@N7=Xu8Ab#ADtX<4yUt^VURRZhs`K4DuwXb*KXEVVj
zf|f)D^a{TjhC8o^QTP3T9a{L8o!;@GHE#p-)x%eeYtl@1d#evH(o}iz*meMNl<fdS
z)jaOQcm7uBZe;!;?nr6sNA5~gWeDA)rCOvqbz-P}1wztH3xrE(*s`1P^M~`unI<p%
zn<fCo{(_zau3rRW)i3O0KP_$D`*#8#i{*JX6L05FkK8)=<b@tDc*;H5=9-b1TA4eJ
z0U6OAD74!QPoVJr(Xd8{{?I9Ws5CC(`AJWkOuni65z`2hfS8x6qq$JcsQ7>Cy7G7^
z!>*k%Ldu#o%P3OW%94<!)lLz{Rzf8El0syNh{VWF%94b!XU&#1YZ8$y`z|5bzw=mn
z-}jHNzxw&j%rnpZ-1j-xxvuM+;qg%73YR2jJ6u03P*``=KQSRt;#5mLNNjOqb#vrT
zg<S4yOsf<5ciXrxL{CS3SG{tBl%g`}d*H|AK1sdMqC<T5MpqC(rXX#i42p8o@4}k+
z6IYd1ybz0o4o%{_>8x7zXas+-)4f6aP}MM*$KHFN(&rMk-uZY#`xO;K;wtm~q32?z
z7PKuSc^fFA-F4Nd+Y|wyF-96;^mtp$a_wL1%V)ob6z>cd+XcuwUwt7Be+dXvrdBs|
zG$r}6u>0o9*jRjS?zrsf#m5eLZHl}j1NXc2tsnLdcxqwxS4WwuOzywHbjIX@$kqn`
z)gA@I6;qWBz>P7b^UrM(5w2|F*4ApLo*!?T8|s|4CR!nYn^!#ie3;Ihzk`*@ZA$CH
zkub}VjYc!+g^rw&V;n2;uW}$#ikGzrU&Tia_EMAWHatXlXv)8-Miy-XQq-0nRmSh%
z`;IyKyOIa6VSTWFJFv#ja{(-xiSd?Ak?S8Ne=?Pxf3qV|sXS}0bglHtI}}cJABo4{
zcW6;D;1V#9sxJ4y5{RkJpDybx9f+@ePs7}Cl~n)I$<r|^_x^;M9U@`NR{AoOwjen>
zaKVsx_q4n}f%Y41CVsCPh{YE;kX*RO7U6#<Oy@Z1JEd#oAKwc$5FHZW{0$3xvm+No
z|0{x{BCWIAfE)Y_F{-f97Qe=Ainwj9^|y>ga#6lSyODR&N<0+gWxaxmr?*o|ps;%~
z3OIHBwo8j^Ur3x(&&*q(j4VY3$-NXyEMFJz_BNavpx8{7**KZpm`=x4k%!pV7Rolb
z3`uoKA&jXJNQLbNtBBkv-y98y0px6+-|N(1qQu5*lKP{f_kHZW19?9-0uNKO*0X^7
zh7j{cgVi5<%yU`xLL}x$v<XvigsJ5KAHL85;{Kzja$Zb2W7?SAHHeoD;@U@Tu_&{(
zAY&utE4@;$Hi~7`m1H0-8!CPb!f5sDJZhh#T;VMW^8yuKXwvKvtGsVo6Hwz?XuqWy
z^Q<d6?vU_3LyMH+GlEWgPZo=>coZZUICXJDn!YD~n|L`L(KqU5buP&28LKIhN(06(
zU3z3>{_F`QPQg`Vn-enpTfwmo{8^(YeiE&~dDTt;6{BQ8gUhKF)VFnfog*8O_%W2(
z3X$QSU;})xJUX9>$R{UkyYSPM?04_`P6TzcRLq>!<}!;|@Exh`hIyf-q3H?w@*I!X
z=VJ<vwTcHdUeNtDIFCvHi%9UqUq^ON5{)G+XW@Vmr##1M>(gw8Fq_Ss7U{K)HEawv
zx`Z@klAlSUwXa}`P~2u$%NEXm_=t7EZQa}6^!5^etx?PT%)~nF-4%zfqdbM1&mAki
z<rM87;Y?dw*`)fR2+@r*e#(xaRz{BMgPP7~++4gpx|alX43)*N&zQKzko#!qo3<He
z7R~zFShP*)`Zc^z=NKxM-1;i*1?sC+``?uvBmPb(o{d7-1Ol~u0Pe~0nm24cgwHXB
zW7f8G3Gc)QJczS1`G})T4jEzFYc8@7$5PEl_}VG_#vHq8Qa-gay^<_|2^&Zq*XJ2K
zMPeou)6Hp}z*b>jd;i%K<3*#M>Ns0Y7JpP}UE33KIw8L><`+tR(reQpV`b6av;<#t
z9#RJ1Fe-TTbg{iOCwkIAv@$A(r=TNc>g=b3!X{U!@{6<8bZPK!zqgDeQH{3ET`J%>
z&((!kDi$0jj5*e@TEpm~?b){^DC1`cxM+eoGAlT)Yf&CdOzys<>T}*sk0z4Bn{~#+
zVD~H{%@1p%FG#3GbSP_IaKybA=0^-);_fOH9oF6v5lv@*@KH=BT)#znMsrKf^SL$q
z1Gn(^KUhTAjqtisQ;|$+eix?ir2PFjZKa?Qcy7?Q?yVR)^Oa(OP?t%_h}qWw;pT`b
zK9VAP{#_i#hOlrVKqZVk4K4`P`quM-7rTU*^^|4N3B1F_2n_>)T9|S5P7|HjS*?MN
zYCI03^s(`q-|N-2{w?X^F;R!kX_LCu6J5lE=UQJ*d*Ljt5%*#@0z;iDtv&hN+dAod
zt8eJKrbY1$bPQv$x@#0;T=>f?S*;f-lDrvjo@1QTvc6FBAyiA({F-?KvCS=xXQ=Rp
z+~g{#-kqjh+!^o6=Lloh=R$4AOn#6}B);c8`X^n~-AEcL28zmuDI7wztPaDvr$>a1
z$vtC5+L6fZz(&U)$d!?S%8x}yc3{N{(gJ;WS+!+mb@hdm7(QEjxGTMTBT{igLnSnn
zO#O1c#F>1#nUI7R(h~foe+SJGBg1?gqDS=o04<SYTgvE-uqmGTU|V+NsEON4OW*uY
z{P{h!#BJVtDT+EE84!W;ODCxC2!SC?s!Vd89C$9u@!=3P_h^i=M-k95+YHXx?z;(y
zR#wV?<R=z)XX3RN<>y&5?&oLUWYW*_w=&C$V1tpI!1}bR`_%ps$e9)^uK6;5Kt|VZ
z@T0%Rr()dSo<4N7PMvAi=#rNMp2AYBlX_~WmxL_fWL2{*<ArUC<eiF;a!|=_uv@9f
zK*66~{jRH^O*Ib4XXuD9{oFOC%PHL`RrsLFH>ftx_iXrE)-Da#ccBLHS6S};)d#bN
zGwW)OHdgg+wGIP)$Fx=^!TwC159oQBX|h<LGruC_YNu{-`vM+2I3}~@eEcCFYttW8
zYDtb`w|R0C^EwJwKCm12-(sd|)(dn_&N7BhJ|#t;(8(mL|EOaObWU!}{Xi!-5j^QZ
zbehrZV_?ynP7YURe~I@`ozQyOEsd92tW{A?N3zLDI#Y%!Ym&~3!zkwgMebfP?Yu8_
zeCJ{e=|?4aU#0PQ<AgQ04l9SgJW5S=kbMXD@Do`J@{!}dkc1b*Z6B{aoyT(hR8V~6
z5gM`cI`!@Ox5>w^*3tU?Wrg4={whTfqoL1uN33LJG`iTnQ2-bN)~&nGbFNuK8SKm|
zN$=YxP&|#d_+Ed4nGC)E#jZ}r=y^OgwHHYWUX|vr^+bm-O@%jV2L(-5b&dSEp|QxJ
zMM|vpZf2C@*Aiw5Mej`*0EIoQU1P2I?6%c0d9ye-r@#xKmL+9Zce4n&$4-{kAK=aU
z)S+h+8<IEffT3nOzf+i^49JF2;&|E>s2rNIsAi}+{y-)%u{bbQuJ6zk3hfD5%CC-+
z%fI$;jS}Uj-z-^Zj)r<6b`kjBB!0Z;L3+?OEOc!?E@ktk2gF|w7ocWh`S;PN!~ART
zAXiw&`&A7&uV2%UEuFsAlR^pFcg#4ZwfEZj6%eGYoWAM<{1JK+rjw(WX3m#(Yqc%h
z{@KQNDqMN%o{lV3_v=37V3P~@AH7(H7&boap@IW#ua&qrW9J_6gz(s%LoNT{_V2n@
zkw-b9>FTE!DN?i9^t*%K?>e=aTNrr|H@QO4zRN;%LtpqUaIW0y1wb1Ox3BibjGU%s
z64+JxiVkKUB!H%)6CttCD4gTb+bkRC1a)36P*G#k5B1X}#Ia?R0#DVG8^+(B%ZP|M
zB-V2p2pc<tkgKuu-l<*d3oUVBKu7DF$!ir@dseX{?Bj<_q)2n#c%VV_Q`RtX2R=}b
z)3&D3z5S+)H`2ZL%Sq`)4;?nx<dYtplblRGCH9NoCn68iaFbm9*75VAGcc4RMhSxj
z60s*ZNr5B3^%X(Eiy!CQ1o?}`<9C9L(+t_NmB5|%L(=2mZgah(WNveEUZ<(u2aoFO
z3*#30%!fvO+VRo?ak7t7y+?b#12IT+4oxHumg=`wnNXil1LUua)(Km59<1`~Qpuq6
zmIFY5fMGL-g(85E*}r=HPHc#F_Q<7=A>{+3W+`Yi-4f2Yyl2CyOCm!7!lU8s8`<Y4
zErcU%B4-L(txM)>3eVmUV>F>e`|Rd$oWMS#s$Od#qg5YoF2t>AdL<jYaSD>Q&i!&#
zoKh*u<o=zi8FcrNdu}tZ=bTk(+{1DKBqSW`5RR`EN*txvRu^=W8V<VD*88A;BXAkM
z_1B39Worgn2j0<%-mZLt#o+eb;h5Hn0hb#3r%JT=%MtORbB>v>?Jm*foI#2Jk#V6R
znyWt!(bCH=@#$1tOCzbdJV<%PCz`x3opLr5F?haefRPi&(!m2TG3YNi8xi32hg(}w
zAbTmiUvyR{EHOk?;$%7)a|%uIqO@daAC6t03MI+UXP82W)1x;AcwRj;a?+z6Zknu=
zN-bH{E6<HB|7;7)WTFM3@OZ6Doe#o!j~#A9H#zyW2-(nTA}ao+_l(@U-F>cev7N-c
z%90+g?+ORi>x(K69n!(4c|_}o9p^JtP?wxWD%MrROcBcQCv)lMpBy*q7Z32RR+!Ta
zI|+r%5p%bz+6p5-=e}J074Y&z?&SMa(+48=pMF;Qa9Vc)N{zw<Iu7|`s;yeuPbuQ1
zk5SAR8iagPIWESK{U%7V!w+hG-WedDCW@vA7Ax*ksrTz?PchW%UUAe3E20;=ksZ(s
zpk%^e&P6-Qx@f|_=F(JQX-2x8f&ig(b&|lADr<OdZ|BQW3*BkJlgy^x+PD`5Rq0nG
ztUR{YJ0)msK1uNPGPE1Nfw_!S*2l?fep=jG(^XA)uH4p|zp9@dbMTu)xzRWa;oIcw
zy);hs&YXxwkQSYvv)h&vITMP+6QaB2q2EIjG5EA0LF^VBWwlM6{!ZU^*dV<zD`)-_
z&2f}yBMkdko06bPxPg<7yTZ6F;N;R7?-TOOgmY%eRW6JBPevqtF-?(uSylW_itCSA
zTDEs2ugTrCS`H#Rs@Xefd{(3TBI)F~iDQ(l+rB$+hJMWaQL*&<ZM5}g^f+*9Eo;Sg
zYOV^=vo_Zq&;BPm;t0e?J-FUWeoC4`=!<5dvkcY-MpoFOs-u3j%)GW!t56`loch}X
z=H-OVjb~M};L|CjwvVKHg1LtxLE*A4?)U{Ut)Yfeg+G0c*iUybf(Q!^2#O@c3lWm^
zC3m)UU1`2#C{WULxjf6W{fE%Illq@^&*&Of;2n(tyeloV$UG(-s~TyZE2{VmDM%NR
zJ;ZNdRy#d|n)&A;a5ZqHv?m5zM{*mBq%o5nm<lU*C5J&}M&Vq614W`N-g*sx6>}?|
zJ$XG-wzWBdl<dni-WtTXa_mlX1_lg<)E$L`ez>2gd?3S;p*=xFbIg*VTTY_u(#ob*
zUAgwGaUv*d9RR+RyTQ_A?{wB!K!@1B`|hL17;3V`-Pu|=Swq_P_zQtZT0*$_pXnx4
zu`v>ExtzuE0Zw>PWzlap3QIMUlar~?s2RWAWQw^)tbLB~@IZ97lWnc!@490-7Tt#Y
zYzZSZVr@){GLKZd=Ppr6iKJ&%9@vhf1Sg&t+lwxXU*wE`!a{`YQp6bQcW+)DUju?u
z9@u47U56&o96ff<EhrnAsdWGWRA0{0jvVQx?->Lce=&yOimCG9jUA>wkaUVEbv&be
z+9+F>livlopC@}?t(BN;zU@K7{$$%Z#8P{<|IvaY#GGZxxAMJ5CJ0+|TbqKtpzv8q
zGOU(>dOH%}sP4Q>N|rRQL1hk|s0t3v;%2Hu(%q3H#IQQMhE6gx%FScROVkAXnTi(a
zd0K?|p$8i{E>K$SE<24KX;|#PMA8r;p(MxQ=IEq0O@+l%r$Wk*3~2(fgE!d(mJjE&
zE8W85BTkc3f8eh{o3Wh-nT{l0F!Jp4^=c`Fv<PFnZdp&A<KAc`b(?64@;P|Z@ynG+
zCdE7fb1yV=O*7Mni%w;-@F6?ididb-eHeqkh?!hNw*}12=0H`pBx15ZBrP@yw<KPo
z6z6IJB-o{52Z>h&@k=#3GQu6r`0Op(X<5vb<QRKJ0&!I~my_B0-}wm~Y0<L0JJ|H>
zEB!%MkpRkWPEm<J@5U+!-wdL`u%CbI3`2zFKZwE~2BJ!Q0+IK1p(bwwPBI(;qK-(O
zZG6~kcw7Ae1W1A+s6g(Z%O|wKL5@QpV=hoxCT&$wjvz07xNoTiH?~_N!kxKaeAEOa
zS=cNbgj^`H5=j`am~DX$5w;}$4tP1>x-%Y)@QOFYT0V$97<QD)JXBMw-r5OaP#q5Z
z0(FTm7V19npVf$1%deDXQ`5vtO@{6!**li6IUWK}*ap<kS}riwNUx7bZU6eXoXqFQ
zUx^K2tv|trucB*LO)u>HOS*%boLb@#t?;`P$g4+2N?D#x7QO@r4q^SlQas{PJ<lN2
zr|?`L>k~|-j!3t{$zob5cXI|B^-=a?92Bo7Ho#ERtQCTE!|{Xlp-b_1yN22bdOtU%
zTpOxP5{Q-@-oDkTjJRf~dmSOCe{fsJOGDoJ7&%_I$fF{0)bXb`<0zVO_2vt?i*Ey}
z0Wt+|yI>GKtn+%eb@5CaSfn_}+2KJo?1c@oMO5V`=myb9QJV|g{wS7JXO7)%@e8|*
zhR_iB&}ACtC6EaA;cT8OThdH^zjTw-a?eC`|GMca!b$R*`Wo5DzM{U8rJtq@nb!1!
zkV?d9P6ps`?wapnJ{0bz2br(T#e-z)gO;lr6m@IZ5JY>o#6hD-sQs62@N(tM)C>T4
zRw<s&RzmoTZ6BqwW4q#{jS81Z{X<M_599MY1p^Q+wB&ZfGI$^B%7<Rayy8EVQS$q<
zhcxd(uNRec#4OxVF2J)SL@e66e7+#7o~84>tqstFWo=0NOZx5p)sFl&_qCbuIlc2?
zl{sESgA)tSTK2=*@cvty08~)YHdPu%eVoLi1yW#GnsC*s1NcXh5h}jRlWSvTb8RjT
zQH&z$;JJ$ad#(xSGm6EtMHYy}Vjt6@_IUNHvb;&VMUv}z=uP?xZvLR5)F6p7x;>+C
z&DYVpc^jN?G(zJFJOX;GLPr&<5JFS=a^kmHo1PY1PZDySA)^z~antVVF1NtLxN?)j
zc{n(t04{w0--V6$&8ybo+q)*I6?jUaMd*b>Ht87+pl+N6B6vsgC9cb>!})X+-mM^T
z1aA`xvA&1K?kQ`+-G=qoHRM&|5wnL`?GYLGN2|3mo55RW+hjYoT{4s>7|g-%+3fAe
zZOcT_&~dGXrsAbSMraQcghtoBg&S9tkGHL+*B(wh(5Bh{px@2do+G;!aY0PLnQ~Q)
zZK8h>Isr7bnqb+wy}(1s9$SM9U?RA!-~^ekjD)KpK@=&!g%nusI+bPBfN3Gr9WZRN
zrnZ5of!orztO7`WnLCCKh!2Vw1!U}En`_?!G#a-l<zK#<??=Zodbo#A`5rx3Ok5_w
zWPHg5nsT4cNLF27v#;Y^Sw(Wprt|olr=yEwGh_>IGv}c6e`N|WU-=I_lz(7~%#ps}
zI|#Cx+fEUJDVW#R+D@gW;gKK|Rv@$Fz1>^5ZG?ft#^@Z%tQPP6rc%@-m(Cv<ubPN-
znB8*C6b-JS<+CzMH5t}Jbdd9io#HkORz!W;3%su_%6n%KM8gn`%nV^vj|J@JV3nZt
zo~Eh@ep+!X39*tQO#$1X_XD3_%0=q^>aO*!Zdc!EhjD=cu-|=6IcU3!ld^<u?7G1z
zfe@qjhv(`0y-#~zISHYe@!z8_hZ;9r02q{9a4Fq+Abk1Boe9bQ6;-E-V!$)HlkUMp
z<n&ni#L1hGiG|O6cOHxV3IyD$8(S412$<)jmYPxOa@-e=npLSgFGKMRg?t>E2?zp>
z*_P{aY@r$vHejxrK*00y#}N2Vv+nKFBGf%11ksBZvnoATHrKxCd<W@H5x&mipWCxR
zlwIYL4Tm?8*))Q)2_l~O^8#hJkA;mZ(R{XKc<Gk2KP*#RoQ4zWXI_3G(D>pTJwS1>
z>Nb8MWv>o{Ql!Np%P08@6rEsjg-V=ZFAf<H`O#L5DoZW3+vu>hA2_O?FLeEs;g#D`
zC+pcuxDsXmPFB^HZKyIx4PU7AA*MWsVS`90o?uXX>0tQZY_&&&bQ1$!RLx}}OS1@X
z4yGc^>9oCDl3?ef^xUK&t-irc<bkx6n1iHYLlIx4LP<1u(RcBLIxqJh2*Y6^GIgfQ
z;4K|3n}O!2E`t`=K$*?ImiOnooE-B~FL5ks>f<}P!}alxTLzC$T-m-#%)hI!z_0y2
z{`lR<f24pRG3Bs>6;J#P(m@9#`g0xDZ^deC6Et5`8^0mEz`Eq+N>@pudy=ux<SQP!
zh6Emd%cPpa{_xwt@a=p4eJ%qZACf|KfhTB#rn+da&8eXAeb+CZBKt9eHg9f$s6Xxg
z0qW3EG(BPwQ|&2_M_94ZWk*z%9w%;80jH17zv&x(#T?Fl<7I`)K)@tSlzVH>=7$v3
zhzqsxj6@k&yT1}sl`IszT(X8%`g<u7iW}ZowkJ4?UZfcFLkXQ|a<<ExLSu^6BY961
z&(|DD29kT0ZSG7y?0((V8nM#guVtiwb!`8IfpfmqE4_2>;S@p7hJrU{0Hr7bS8nM7
z+1&o8rl?<meM&+uXrLzA1g)0+$+uPqxUJ67zPn54mPc$;4wcWG8ty)AwR<7LvlPDa
zTTH;G=QvFTz!)Anp0I487e*fvC2u)(-T8`Wa0mh=>WqTZi!=SVh_uh@OP4>+HKk-h
zz>j;2%#bgfrCb(~2@Ze<D&z)gsp7UE1}?$ydO2RNJYS30hpfw6Ajcm~-Wt7Gw~@v+
z4}0t_)Z9|FdXbD72m#RPivKnTlyF_-fu>+IrFu!{Sa8D|OCO|rY!w9CSLAfr?Rt}o
z{{sR8(YKvblEM@bZMO|6|M=zL7<cLkmT82KU*cH376q*!Uul*2f}6pn8#YZb72e*b
z?p3U|++;wVwIG)G+JOHA7`SW#C8J>T^hX^Ljl<Rlacx|6{Pu#@tKE{_jX`0xoK!k!
zG{QL0(qJfx2{7cPU1vI`HFzMbI#!h-7kXR;Ltd{cJ)%--R2FV}>EEo#P#&8~&$(YN
zSVzE<l+E$ltOlpT3?;!~FtFfTXyLrg!l&-U7hq;`Z5~L5iDLTiy}7wp5v7_uQRLUm
zxF=jlcV~tN#OY3>$|vMD+zvr!OEl+!a~(N61`XXU3a3yuPYd-RY&!^%dOIQ5eXlwu
zbL7YJ7mT_=Ah(!U9O#)Wz<W#TIQaI`MlXKLa2Mry3^#)NxrfeM^9Gu^HCVzwK=<ge
z{fR1Xu$fON<pq~Z{%3z1P@w*>yG{r9b|=PC=JGVgI{wNY^D{&C$A5j&H5)mVqxYSU
z{w;I6J<H{NI($?Fu&2*XmoMB5<Ysuo{=Gx9^9PoH=wSO5clW;7_G@Kd6YT-l|L@Hy
z8-8uavI1ZUc3$!G3b&aI)S!9%*Igcq;_`1~@C?#tJgnxuIsZ(%1o`iwsVQ)<%&3G(
zV?S<6{`>RxD>^evrN1BIN-_3R-+t|Xzinh`i>TOy#M}HjYt=sZr*K8}GU1}3=l=nk
Cd2THL

literal 0
HcmV?d00001

diff --git a/exercises/Exercise3/.ipynb_checkpoints/Exercise_3-checkpoint.ipynb b/exercises/Exercise3/.ipynb_checkpoints/Exercise_3-checkpoint.ipynb
new file mode 100644
index 0000000..94586fc
--- /dev/null
+++ b/exercises/Exercise3/.ipynb_checkpoints/Exercise_3-checkpoint.ipynb
@@ -0,0 +1,501 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Exercise 3\n",
+    "\n",
+    "In these week's exercises you will practice Poissonian statistics, delve deeper into the meaning of the error matrix and see an example of a case in which the standard error propagation formula is not directly applicable.\n",
+    "\n",
+    "For the first exercise you should install the package *tqdm* from the Anaconda navigator. It provides a progress bar, which is nice to have when a long computation is running.\n",
+    "\n",
+    "## 1. Poisson statistics\n",
+    "This exercise is about two variants of a counting experiment: in the first, simpler case, we will see that the observations are well described by a Poisson distribution. In the second case we will have events which are not independent from each other and we will see that the results deviate from a Poisson distribution.\n",
+    "\n",
+    "Consider a beam of particles impinging on a thin target. Most particles will go through the target without interacting, while a few will be absorbed. The target is connected to a detector, which fires a signal when a particle is absorbed by the target. In the first part of the exercise we assume to have a perfect detector: it is able to detect each and every particle hitting the target.\n",
+    "\n",
+    "The experiment consists in counting how many particles are absorbed by the target in a fixed time interval, e.g. 1s. We will repeat the counting *n* times and see how the results are distributed.\n",
+    "\n",
+    "To simulate the setup, assume the following numbers:<br>\n",
+    "- Number of particles arriving at the target per second\n",
+    "- Probability that a particle is absorbed by the target\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "'''\n",
+    "Let's start by importing some useful modules and functions...\n",
+    "'''\n",
+    "from numpy.random import rand\n",
+    "import matplotlib.pyplot as plt\n",
+    "from scipy.stats import poisson\n",
+    "from tqdm import tqdm_notebook as tqdm # Nice progress bar for long computations. Install the tqdm package from the navigator\n",
+    "%matplotlib inline \n",
+    "\n",
+    "'''\n",
+    "... and by defining the relevant parameters of the experiment\n",
+    "(feel free to change the values and see how the result changes)\n",
+    "'''\n",
+    "particle_rate = 1e6 # Number of particles arriving at the target per second\n",
+    "delta_t = 1 # duration of one experiment in seconds\n",
+    "absorption_probability = 2e-6 # Probability that a particle is absorbed by the target\n",
+    "n_trials = 200 # How many times you repeat the experiment"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Write a function which decides if a single particle is absorbed by the target (return True) or not (return False).\n",
+    "\n",
+    "Hint: generate a uniformly distributed random number between 0 and 1 with the *rand()* function. Use it do decide if the particle is detected or not, based on the known *absorption_probability*."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def particle_is_detected(...):\n",
+    "    "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now write a function to simulate the experiment running for a time *delta_t*. It should do the following:\n",
+    "- compute how many particles reach the target during *delta_t* with the known *particle_rate*,\n",
+    "- for each of those check if they get absorbed or not (cf. *particle_is_detected()*),\n",
+    "- return the number of particles which are absorbed by the target during *delta_t*."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def run_experiment(...):\n",
+    "    "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "You are now ready to run the experiment, i.e. run the function. Do this a few times to get a feeling for the results: is the number of counted particles the same every time or does it change? What kind of result do you expect from the chosen *particle_rate, delta_t* and *absorption_probability*?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "for i in range(10):\n",
+    "    print run_experiment()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Let's now analyse the results more systematically: run the experiment *n_trials* times and save the results in a list or array. Depending on your computer, this might take some time."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "progress_bar = tqdm(total=n_trials, unit=' trials')\n",
+    "results = []\n",
+    "for i in range(n_trials):\n",
+    "    results.append(run_experiment(...))\n",
+    "    progress_bar.update()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Before plotting the results in a histogram, let's define the expected Poisson distribution in order to make a comparison. If you are not sure how to do this, have a look at last week's exercise. What is the expected *mu* paramter for the given *particle_rate, delta_t* and *absorption_probability*?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "mu = ..."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now plot the results of the experiment together with the parent distribution. Again, have a look at last week's exercise if you need help. When plotting the histogram remember to set *density=True* in order to have it normalized to unity for a meaningful comparison with the Poisson distribution (if this option does not work, which might be the case for older versions, try replacing it with *normed=True*)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "plt.hist(...) # histogram for the measurements\n",
+    "plt.plot(...) # plot of the Poisson distribution\n",
+    "plt.xlabel('Counted particles')\n",
+    "plt.legend()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "What do you observe? Is the data from the experiment well described by the Poisson distribution?\n",
+    "\n",
+    "Let's now make a different assumption about the detector: it has no longer perfect efficiency, but whenever it detects a particle it needs some time to process the signal. Durign this time the detector is blind to any particle which might be absorbed by the target. In this way the recorded particles are not independent from each other anymore, and as you will see this will cause the result of the experiment to deviate from a Poisson distribution.\n",
+    "\n",
+    "Modify your implementation of the function *run_experiment()* in order to account for the dead time of the detector. Assume that whenever the detector records a particle it is blind to the next 500000 particles reaching the target."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def run_experiment_dead_time(...):\n",
+    "    \n",
+    "# Run the function n_trials times and save the results as before\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Plot the new results with detector dead time together with the same Poisson distribution from before. What do you observe?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "plt.hist(...) # histogram for the measurements\n",
+    "plt.plot(...) # plot of the Poisson distribution\n",
+    "plt.xlabel('Counted particles')\n",
+    "plt.legend()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 2. Correlated variables and error matrix\n",
+    "\n",
+    "In this exercise you will work on a pair of correlated variables, compute the error matrix and visualize the error ellipse. Let's start from the case of two uncorrelated variables, saved in the file *data_uncorrelated.txt*. Have a look at the file: each line represents one measurement, the first number being the value of the *x* variable and the second number the value of the *y* variable."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "from matplotlib import pyplot as plt\n",
+    "\n",
+    "data = np.genfromtxt('data_uncorrelated.txt')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Plot the data. How can you recognise that *x* and *y* are not correlated? Compare the 2D distribution in the *xy* plane and the histograms of the *x* and *y* values. What do you notice about e.g. the range of the axes and the position of the means?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "f, ax = plt.subplots(1,3, figsize=(20, 6))\n",
+    "ax = ax.flatten()\n",
+    "\n",
+    "ax[0].set_xlabel(r'$x$')\n",
+    "ax[0].set_ylabel(r'$y$')\n",
+    "ax[0].axis('equal')\n",
+    "ax[0].plot(data[:,0],data[:,1],'.')\n",
+    "\n",
+    "ax[1].set_xlabel(r'$x$')\n",
+    "ax[1].hist(data[:,0],bins=range(-7,7),rwidth=.9)\n",
+    "\n",
+    "ax[2].set_xlabel(r'$y$')\n",
+    "ax[2].hist(data[:,1],bins=range(-7,7),rwidth=.9)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Compute the covariance matrix. You can either write a function to do this yourself, or use the numpy implementation. Have a look at last week's exercise if you feel lost."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Compare your result with the error matrix you expect for two uncorrelated variables, cf. slides from Lecture 3: $\\text{diag}(\\sigma_x^2,\\sigma_y^2)$. Is your result compatible with this expression?\n",
+    "<br> We will now compute the eigenvectors and eigenvalues of the covariance matrix and interpret them in terms of the properties of the distributions we just saw. As before, you can compute the values yourself or use the numpy implementation *np.linalg.eig(matrix)*"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#Use this block to compute eigenvalues and eigenvectors of the covariance matrix and print the result\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "For this easy case of uncorrelated variables you should recognize the following: the eigenvectors are aligned with the $x$ and $y$ axes and the eigenvalues are the variances of the data along the same axes; this means that the standard deviation in the $x$ and $y$ directions are the square root of the respective eigenvalue. Keep this in mind, as we will later see what changes if the variables are correlated.\n",
+    "\n",
+    "For a visual interpretation do the following: plot again the 2D distribution of the data together with the eigenvectors multiplied by the square root of the corresponding eigenvalue (in this way the length of the vector will be the corresponding standard deviation)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "plt.plot(data[:,0],data[:,1],'.',zorder=0)\n",
+    "plt.axis('equal')\n",
+    "\n",
+    "#Compute the x and y components of the two vectors:\n",
+    "vector1_x = ...\n",
+    "vector1_y = ...\n",
+    "vector2_x = ...\n",
+    "vector2_y = ...\n",
+    "\n",
+    "#Use the following function to draw the vectors. The options are needed to draw them in the correct size\n",
+    "plt.quiver(vector1_x, vector1_y ,angles='xy', scale_units='xy', scale=1,zorder=5)\n",
+    "plt.quiver(vector2_x, vector2_y ,angles='xy', scale_units='xy', scale=1,zorder=5)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Let's now draw an ellipse with half-axes $\\sigma_x$ and $\\sigma_y$: this is the equivalent of the $1 \\sigma$ interval for a 1D Gaussian distribution. Fill in the values for $\\sigma_x,\\sigma_y$."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "sigma_x = ...\n",
+    "sigma_y = ...\n",
+    "\n",
+    "phi = np.linspace(0,2*np.pi)\n",
+    "ellipse_x = sigma_x*np.cos(phi) # x-coordinates of the points on the ellipse\n",
+    "ellipse_y = sigma_y*np.sin(phi) # y-coordinates of the points on the ellipse\n",
+    "\n",
+    "plt.plot(ellipse_x,ellipse_y,'r')\n",
+    "plt.axis('equal')\n",
+    "\n",
+    "plt.plot(data[:,0],data[:,1],'.',zorder=0)\n",
+    "\n",
+    "#Redraw also the vectors (copy-paste from previous block)\n",
+    "plt.quiver(vector1_x, vector1_y ,angles='xy', scale_units='xy', scale=1,zorder=5)\n",
+    "plt.quiver(vector2_x, vector2_y ,angles='xy', scale_units='xy', scale=1,zorder=5)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Let us now look at the case of two correlated measurements. Load the data from *data_uncorrelated.txt* and repeat the steps from above up to the drawing of the vectors; do not draw the ellipse yet, we will do that in the next step. You should be able to copy-paste most of the code from the uncorrelated case."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data_correlated = np.genfromtxt('data_correlated.txt')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "You should now see that the eigenvectors are not aligned with the coordinate axes anymore. However, as before, the eigenvectors represent the direction of the largest spread of the data, and the eigenvalues, i.e. the variances, define how large this spread is.\n",
+    "\n",
+    "Let's now draw the ellipse. There are different ways in which this can be done. Let's start by determining the angle of rotation *theta*. *Hint*: take the *x* and *y* components of one of the eigenvectors and use the *np.arctan2()* function."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "theta = ..."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "To draw the rotated ellipse, define the $x$ and $y$ coordinates as before, and than rotate them by multiplying them with a rotation matrix by the angle *theta*."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "phi = np.linspace(0,2*np.pi)\n",
+    "ellipse_x = sigma[0]*np.cos(phi) # x-coordinates of the points on the ellipse\n",
+    "ellipse_y = sigma[1]*np.sin(phi) # y-coordinates of the points on the ellipse\n",
+    "\n",
+    "rotation = np.array([[np.cos(theta),np.sin(theta)],[-np.sin(theta),np.cos(theta)]])\n",
+    "ellipse = np.dot(rotation,[ellipse_x,ellipse_y]) # dot product"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now we are ready to plot everything together."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "plt.plot(data_correlated[:,0],data_correlated[:,1],'.',zorder=0)\n",
+    "for i in range(2):\n",
+    "    plt.quiver(sigma[i]*eigvec[0,i],sigma[i]*eigvec[1,i],angles='xy', scale_units='xy', scale=1,zorder=5)\n",
+    "plt.plot(ellipse[0,:],ellipse[1,:],'r')\n",
+    "plt.axis('equal')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Bonus\n",
+    "\n",
+    "The data for this exercise has been generated with the following code. Feel free to change the parameters and rerun the exercise."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "\n",
+    "n_samples = 1000\n",
+    "mu = np.array([0.,0.])\n",
+    "var_x = 4.\n",
+    "var_y = 1.\n",
+    "cov_xy = 1.\n",
+    "r = np.array([\n",
+    "        [  var_x, cov_xy,],\n",
+    "        [ cov_xy,  var_y,]\n",
+    "    ])\n",
+    "\n",
+    "y = np.random.multivariate_normal(mu, r, size=n_samples)\n",
+    "\n",
+    "with open('output.txt', 'w') as outfile:\n",
+    "    np.savetxt(outfile, y, fmt='%3.2f')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 3. Computing uncertainties on inefficiencies\n",
+    "\n",
+    "Consider an imperfect particle detector: out of all the particles hitting the detector, a fraction passes through unnoticed. The efficiency of the detector, i.e. the fraction of particles which are detected, is a very important parameter for any experimental setup. Suppose you want to measure the efficiency of a new detector. A possible approach is the following: you shoot $n$ particles on the detector, and count the number of signals $k$ which are recorded. The efficiency is then given by $\\varepsilon = k\\,/\\,n$. What is the uncertainty on this quantity? As a first approach, let's assume that $k$ and $n$ are Poisson distributed (and thus $\\delta k = \\sqrt{k}$ and $\\delta n = \\sqrt{n}$) and that we can apply the standard error propagation formula you saw in lecture 1:\n",
+    "$$ \\delta f = \\sqrt{ \\sum_{i=1}^N \\left(\\left. \\frac{\\partial f}{\\partial x_i}\\right\\vert_{x_i=x_i^0} \\delta x_i \\right)^2}. $$\n",
+    "- Show that this formula yields the following result:\n",
+    "$$ \\delta \\varepsilon = \\sqrt{\\frac{k}{n^2} + \\frac{k^2}{n^3}}. $$\n",
+    "\n",
+    "What happens to the uncertainty for *extreme* values of $k$, i.e. $k=0$ and $k=n$? Do these results make sense? Remember that the efficiency is by definition a number between 0 and 1.\n",
+    "\n",
+    "The source of the problem is that $k$ and $n$ are not independent (the particles which are recorded are a subset of all particles which hit the detector). A way to handle this is noting that the efficiency measurement is in fact a binomial process with total events $n$ and success probability $\\varepsilon$ (see slides of Lecture 3).\n",
+    "- Using the known variance of the binomial distribution show that in this case the uncertainty is given by \n",
+    "$$ \\delta \\varepsilon = \\sqrt{\\frac{\\varepsilon (1-\\varepsilon)}{n}}.$$\n",
+    "\n",
+    "An equivalent approach is to consider, instead of the total number of particles $n$, the number $n_f$ of particles which fail to be detected. In this approach, $n = k + n_f$ is not fixed anymore and $k$ and $n_f$ are uncorrelated; thus, the standard error formula is valid.\n",
+    "- Show that applying the standard error formula to $\\varepsilon = k\\,/\\,(k+n_f)$ yields again $$ \\delta \\varepsilon = \\sqrt{\\frac{\\varepsilon (1-\\varepsilon)}{n}}.$$\n",
+    "\n",
+    "Note that when evaluating this formula one has to use the measured (estimated) value of $\\varepsilon$, since the true value is unknown. This is a good approximation for *intermediate* values of $k$, i.e. $\\varepsilon$ not too close to 0 or 1. The exact meaning of *too close* is a matter of judgement - the extreme values $\\varepsilon = 0$ and $\\varepsilon = 1$ are clearly too close, as they yeld 0 uncertainty which is nonsense."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.7"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/exercises/Exercise3/Exercise_3.ipynb b/exercises/Exercise3/Exercise_3.ipynb
new file mode 100644
index 0000000..94586fc
--- /dev/null
+++ b/exercises/Exercise3/Exercise_3.ipynb
@@ -0,0 +1,501 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Exercise 3\n",
+    "\n",
+    "In these week's exercises you will practice Poissonian statistics, delve deeper into the meaning of the error matrix and see an example of a case in which the standard error propagation formula is not directly applicable.\n",
+    "\n",
+    "For the first exercise you should install the package *tqdm* from the Anaconda navigator. It provides a progress bar, which is nice to have when a long computation is running.\n",
+    "\n",
+    "## 1. Poisson statistics\n",
+    "This exercise is about two variants of a counting experiment: in the first, simpler case, we will see that the observations are well described by a Poisson distribution. In the second case we will have events which are not independent from each other and we will see that the results deviate from a Poisson distribution.\n",
+    "\n",
+    "Consider a beam of particles impinging on a thin target. Most particles will go through the target without interacting, while a few will be absorbed. The target is connected to a detector, which fires a signal when a particle is absorbed by the target. In the first part of the exercise we assume to have a perfect detector: it is able to detect each and every particle hitting the target.\n",
+    "\n",
+    "The experiment consists in counting how many particles are absorbed by the target in a fixed time interval, e.g. 1s. We will repeat the counting *n* times and see how the results are distributed.\n",
+    "\n",
+    "To simulate the setup, assume the following numbers:<br>\n",
+    "- Number of particles arriving at the target per second\n",
+    "- Probability that a particle is absorbed by the target\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "'''\n",
+    "Let's start by importing some useful modules and functions...\n",
+    "'''\n",
+    "from numpy.random import rand\n",
+    "import matplotlib.pyplot as plt\n",
+    "from scipy.stats import poisson\n",
+    "from tqdm import tqdm_notebook as tqdm # Nice progress bar for long computations. Install the tqdm package from the navigator\n",
+    "%matplotlib inline \n",
+    "\n",
+    "'''\n",
+    "... and by defining the relevant parameters of the experiment\n",
+    "(feel free to change the values and see how the result changes)\n",
+    "'''\n",
+    "particle_rate = 1e6 # Number of particles arriving at the target per second\n",
+    "delta_t = 1 # duration of one experiment in seconds\n",
+    "absorption_probability = 2e-6 # Probability that a particle is absorbed by the target\n",
+    "n_trials = 200 # How many times you repeat the experiment"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Write a function which decides if a single particle is absorbed by the target (return True) or not (return False).\n",
+    "\n",
+    "Hint: generate a uniformly distributed random number between 0 and 1 with the *rand()* function. Use it do decide if the particle is detected or not, based on the known *absorption_probability*."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def particle_is_detected(...):\n",
+    "    "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now write a function to simulate the experiment running for a time *delta_t*. It should do the following:\n",
+    "- compute how many particles reach the target during *delta_t* with the known *particle_rate*,\n",
+    "- for each of those check if they get absorbed or not (cf. *particle_is_detected()*),\n",
+    "- return the number of particles which are absorbed by the target during *delta_t*."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def run_experiment(...):\n",
+    "    "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "You are now ready to run the experiment, i.e. run the function. Do this a few times to get a feeling for the results: is the number of counted particles the same every time or does it change? What kind of result do you expect from the chosen *particle_rate, delta_t* and *absorption_probability*?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "for i in range(10):\n",
+    "    print run_experiment()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Let's now analyse the results more systematically: run the experiment *n_trials* times and save the results in a list or array. Depending on your computer, this might take some time."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "progress_bar = tqdm(total=n_trials, unit=' trials')\n",
+    "results = []\n",
+    "for i in range(n_trials):\n",
+    "    results.append(run_experiment(...))\n",
+    "    progress_bar.update()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Before plotting the results in a histogram, let's define the expected Poisson distribution in order to make a comparison. If you are not sure how to do this, have a look at last week's exercise. What is the expected *mu* paramter for the given *particle_rate, delta_t* and *absorption_probability*?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "mu = ..."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now plot the results of the experiment together with the parent distribution. Again, have a look at last week's exercise if you need help. When plotting the histogram remember to set *density=True* in order to have it normalized to unity for a meaningful comparison with the Poisson distribution (if this option does not work, which might be the case for older versions, try replacing it with *normed=True*)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "plt.hist(...) # histogram for the measurements\n",
+    "plt.plot(...) # plot of the Poisson distribution\n",
+    "plt.xlabel('Counted particles')\n",
+    "plt.legend()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "What do you observe? Is the data from the experiment well described by the Poisson distribution?\n",
+    "\n",
+    "Let's now make a different assumption about the detector: it has no longer perfect efficiency, but whenever it detects a particle it needs some time to process the signal. Durign this time the detector is blind to any particle which might be absorbed by the target. In this way the recorded particles are not independent from each other anymore, and as you will see this will cause the result of the experiment to deviate from a Poisson distribution.\n",
+    "\n",
+    "Modify your implementation of the function *run_experiment()* in order to account for the dead time of the detector. Assume that whenever the detector records a particle it is blind to the next 500000 particles reaching the target."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def run_experiment_dead_time(...):\n",
+    "    \n",
+    "# Run the function n_trials times and save the results as before\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Plot the new results with detector dead time together with the same Poisson distribution from before. What do you observe?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "plt.hist(...) # histogram for the measurements\n",
+    "plt.plot(...) # plot of the Poisson distribution\n",
+    "plt.xlabel('Counted particles')\n",
+    "plt.legend()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 2. Correlated variables and error matrix\n",
+    "\n",
+    "In this exercise you will work on a pair of correlated variables, compute the error matrix and visualize the error ellipse. Let's start from the case of two uncorrelated variables, saved in the file *data_uncorrelated.txt*. Have a look at the file: each line represents one measurement, the first number being the value of the *x* variable and the second number the value of the *y* variable."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "from matplotlib import pyplot as plt\n",
+    "\n",
+    "data = np.genfromtxt('data_uncorrelated.txt')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Plot the data. How can you recognise that *x* and *y* are not correlated? Compare the 2D distribution in the *xy* plane and the histograms of the *x* and *y* values. What do you notice about e.g. the range of the axes and the position of the means?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "f, ax = plt.subplots(1,3, figsize=(20, 6))\n",
+    "ax = ax.flatten()\n",
+    "\n",
+    "ax[0].set_xlabel(r'$x$')\n",
+    "ax[0].set_ylabel(r'$y$')\n",
+    "ax[0].axis('equal')\n",
+    "ax[0].plot(data[:,0],data[:,1],'.')\n",
+    "\n",
+    "ax[1].set_xlabel(r'$x$')\n",
+    "ax[1].hist(data[:,0],bins=range(-7,7),rwidth=.9)\n",
+    "\n",
+    "ax[2].set_xlabel(r'$y$')\n",
+    "ax[2].hist(data[:,1],bins=range(-7,7),rwidth=.9)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Compute the covariance matrix. You can either write a function to do this yourself, or use the numpy implementation. Have a look at last week's exercise if you feel lost."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Compare your result with the error matrix you expect for two uncorrelated variables, cf. slides from Lecture 3: $\\text{diag}(\\sigma_x^2,\\sigma_y^2)$. Is your result compatible with this expression?\n",
+    "<br> We will now compute the eigenvectors and eigenvalues of the covariance matrix and interpret them in terms of the properties of the distributions we just saw. As before, you can compute the values yourself or use the numpy implementation *np.linalg.eig(matrix)*"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#Use this block to compute eigenvalues and eigenvectors of the covariance matrix and print the result\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "For this easy case of uncorrelated variables you should recognize the following: the eigenvectors are aligned with the $x$ and $y$ axes and the eigenvalues are the variances of the data along the same axes; this means that the standard deviation in the $x$ and $y$ directions are the square root of the respective eigenvalue. Keep this in mind, as we will later see what changes if the variables are correlated.\n",
+    "\n",
+    "For a visual interpretation do the following: plot again the 2D distribution of the data together with the eigenvectors multiplied by the square root of the corresponding eigenvalue (in this way the length of the vector will be the corresponding standard deviation)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "plt.plot(data[:,0],data[:,1],'.',zorder=0)\n",
+    "plt.axis('equal')\n",
+    "\n",
+    "#Compute the x and y components of the two vectors:\n",
+    "vector1_x = ...\n",
+    "vector1_y = ...\n",
+    "vector2_x = ...\n",
+    "vector2_y = ...\n",
+    "\n",
+    "#Use the following function to draw the vectors. The options are needed to draw them in the correct size\n",
+    "plt.quiver(vector1_x, vector1_y ,angles='xy', scale_units='xy', scale=1,zorder=5)\n",
+    "plt.quiver(vector2_x, vector2_y ,angles='xy', scale_units='xy', scale=1,zorder=5)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Let's now draw an ellipse with half-axes $\\sigma_x$ and $\\sigma_y$: this is the equivalent of the $1 \\sigma$ interval for a 1D Gaussian distribution. Fill in the values for $\\sigma_x,\\sigma_y$."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "sigma_x = ...\n",
+    "sigma_y = ...\n",
+    "\n",
+    "phi = np.linspace(0,2*np.pi)\n",
+    "ellipse_x = sigma_x*np.cos(phi) # x-coordinates of the points on the ellipse\n",
+    "ellipse_y = sigma_y*np.sin(phi) # y-coordinates of the points on the ellipse\n",
+    "\n",
+    "plt.plot(ellipse_x,ellipse_y,'r')\n",
+    "plt.axis('equal')\n",
+    "\n",
+    "plt.plot(data[:,0],data[:,1],'.',zorder=0)\n",
+    "\n",
+    "#Redraw also the vectors (copy-paste from previous block)\n",
+    "plt.quiver(vector1_x, vector1_y ,angles='xy', scale_units='xy', scale=1,zorder=5)\n",
+    "plt.quiver(vector2_x, vector2_y ,angles='xy', scale_units='xy', scale=1,zorder=5)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Let us now look at the case of two correlated measurements. Load the data from *data_uncorrelated.txt* and repeat the steps from above up to the drawing of the vectors; do not draw the ellipse yet, we will do that in the next step. You should be able to copy-paste most of the code from the uncorrelated case."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data_correlated = np.genfromtxt('data_correlated.txt')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "You should now see that the eigenvectors are not aligned with the coordinate axes anymore. However, as before, the eigenvectors represent the direction of the largest spread of the data, and the eigenvalues, i.e. the variances, define how large this spread is.\n",
+    "\n",
+    "Let's now draw the ellipse. There are different ways in which this can be done. Let's start by determining the angle of rotation *theta*. *Hint*: take the *x* and *y* components of one of the eigenvectors and use the *np.arctan2()* function."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "theta = ..."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "To draw the rotated ellipse, define the $x$ and $y$ coordinates as before, and than rotate them by multiplying them with a rotation matrix by the angle *theta*."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "phi = np.linspace(0,2*np.pi)\n",
+    "ellipse_x = sigma[0]*np.cos(phi) # x-coordinates of the points on the ellipse\n",
+    "ellipse_y = sigma[1]*np.sin(phi) # y-coordinates of the points on the ellipse\n",
+    "\n",
+    "rotation = np.array([[np.cos(theta),np.sin(theta)],[-np.sin(theta),np.cos(theta)]])\n",
+    "ellipse = np.dot(rotation,[ellipse_x,ellipse_y]) # dot product"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now we are ready to plot everything together."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "plt.plot(data_correlated[:,0],data_correlated[:,1],'.',zorder=0)\n",
+    "for i in range(2):\n",
+    "    plt.quiver(sigma[i]*eigvec[0,i],sigma[i]*eigvec[1,i],angles='xy', scale_units='xy', scale=1,zorder=5)\n",
+    "plt.plot(ellipse[0,:],ellipse[1,:],'r')\n",
+    "plt.axis('equal')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Bonus\n",
+    "\n",
+    "The data for this exercise has been generated with the following code. Feel free to change the parameters and rerun the exercise."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "\n",
+    "n_samples = 1000\n",
+    "mu = np.array([0.,0.])\n",
+    "var_x = 4.\n",
+    "var_y = 1.\n",
+    "cov_xy = 1.\n",
+    "r = np.array([\n",
+    "        [  var_x, cov_xy,],\n",
+    "        [ cov_xy,  var_y,]\n",
+    "    ])\n",
+    "\n",
+    "y = np.random.multivariate_normal(mu, r, size=n_samples)\n",
+    "\n",
+    "with open('output.txt', 'w') as outfile:\n",
+    "    np.savetxt(outfile, y, fmt='%3.2f')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 3. Computing uncertainties on inefficiencies\n",
+    "\n",
+    "Consider an imperfect particle detector: out of all the particles hitting the detector, a fraction passes through unnoticed. The efficiency of the detector, i.e. the fraction of particles which are detected, is a very important parameter for any experimental setup. Suppose you want to measure the efficiency of a new detector. A possible approach is the following: you shoot $n$ particles on the detector, and count the number of signals $k$ which are recorded. The efficiency is then given by $\\varepsilon = k\\,/\\,n$. What is the uncertainty on this quantity? As a first approach, let's assume that $k$ and $n$ are Poisson distributed (and thus $\\delta k = \\sqrt{k}$ and $\\delta n = \\sqrt{n}$) and that we can apply the standard error propagation formula you saw in lecture 1:\n",
+    "$$ \\delta f = \\sqrt{ \\sum_{i=1}^N \\left(\\left. \\frac{\\partial f}{\\partial x_i}\\right\\vert_{x_i=x_i^0} \\delta x_i \\right)^2}. $$\n",
+    "- Show that this formula yields the following result:\n",
+    "$$ \\delta \\varepsilon = \\sqrt{\\frac{k}{n^2} + \\frac{k^2}{n^3}}. $$\n",
+    "\n",
+    "What happens to the uncertainty for *extreme* values of $k$, i.e. $k=0$ and $k=n$? Do these results make sense? Remember that the efficiency is by definition a number between 0 and 1.\n",
+    "\n",
+    "The source of the problem is that $k$ and $n$ are not independent (the particles which are recorded are a subset of all particles which hit the detector). A way to handle this is noting that the efficiency measurement is in fact a binomial process with total events $n$ and success probability $\\varepsilon$ (see slides of Lecture 3).\n",
+    "- Using the known variance of the binomial distribution show that in this case the uncertainty is given by \n",
+    "$$ \\delta \\varepsilon = \\sqrt{\\frac{\\varepsilon (1-\\varepsilon)}{n}}.$$\n",
+    "\n",
+    "An equivalent approach is to consider, instead of the total number of particles $n$, the number $n_f$ of particles which fail to be detected. In this approach, $n = k + n_f$ is not fixed anymore and $k$ and $n_f$ are uncorrelated; thus, the standard error formula is valid.\n",
+    "- Show that applying the standard error formula to $\\varepsilon = k\\,/\\,(k+n_f)$ yields again $$ \\delta \\varepsilon = \\sqrt{\\frac{\\varepsilon (1-\\varepsilon)}{n}}.$$\n",
+    "\n",
+    "Note that when evaluating this formula one has to use the measured (estimated) value of $\\varepsilon$, since the true value is unknown. This is a good approximation for *intermediate* values of $k$, i.e. $\\varepsilon$ not too close to 0 or 1. The exact meaning of *too close* is a matter of judgement - the extreme values $\\varepsilon = 0$ and $\\varepsilon = 1$ are clearly too close, as they yeld 0 uncertainty which is nonsense."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.7"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/exercises/Exercise3/Exercise_3.pdf b/exercises/Exercise3/Exercise_3.pdf
new file mode 100644
index 0000000000000000000000000000000000000000..129e50268f7befd31bf2e7744c864a5766227ab4
GIT binary patch
literal 54052
zcma&ML$EMR(4~28+qP}nwypQtwr$(CZQHhOW4?}=nCOm~#lNY_TI8Z4Dl(r_CrK4V
z#Aq4mSfNOd?{D6r7#Ro{2<(ikpm=!b#VoCzO&#gQtPPz_MNEzDOibxzOl{4bEeMzx
z*_inFpq!i?O$}|J+&5b^_3e(vVg2sP7!*(({AZkhEyNZ9eJrB7jG|sW-5}<}wuX$X
z<7^B~CCMk`9&e=KD5V{<McKMW%!uJe4(!MAIdM!MzJzE=zLld?hT?Rp;iLL?=zqV1
zw8~X1Op``_TnipWEOKaWt%-DO(ciN4)Asm7U)4&cToyv&PG8%;{1$0Q9sjY;b}|#O
zvdKy5;*tND@2pX$yOq`GQ2oS0(5j-Ldi%*oU4=*mOX{c)qq|K8(x$4EZ*h~BL|+|k
z<`Ye%Ap;kWo7TFQwhorF$a(7rlq*m9;vdqKrNI+=cV2|UCH**)e5l|@xKm6f7H6*3
zeHqO>`Bg^MR#gqEv)p>_q2qSn*_+d8+6s9XZOB5%Zgb9yuB?d!Fg6bpzE_sJ4%f6f
zaJM@VTMJ>EE7mv1gsxU!?|-$ri)_;Pv=A0T8}83D^Lz>`Ga)}ONqbiNjL}1-`mUp2
z@g{Uv56q}y<EQQFuof369=Yv@Wa>87Yz;LWheX<T(hn-CzM%}t0zU09RewE3aP(SE
z8r`UyA~@ta60zH!Cga;XBuOzX6<d7>O6|Dv!j=k>c3>!zbo%(vu#>2%)N{fx!p&x4
z_5B{Z1tgf3jt7QA-Y~68Mo-^m4Y7V(lH&m61-BmO0H3>lw2$jk_tg&6u~dFn0Nf3f
zDm9_Bxxu;LeU*RBj*FO7WZ2~C`cknWHk~s|QTjFniMPVw_UU|-zvIS)5(K#~EHn{W
z0HXpz&Gqmzo*Cj(6}rbX#=3)A>EdS^^NaWt!l!EEQG>L$%iK4}8Dw^%Jmw(lz);rq
z1B;ysXC_QCj_FBxK*X>quKe&}CALEo5QOa(OZQdA@iu&hWxA8^3CX?DAU1*Od12LF
z8Luc;4pQ(4siB7{yzCV;V3r_s=XVEK!i=9kTW_GIhtl>84jcahOFLBnf~F6yRr-tn
zd#MLNc&UWGq4ew{hBLMFhQ$(IE@!3?-07rMGZa>;B2O(HJ2%C!?jIrW6e(eZ3cWmY
z-k~FKj5tLq^dRm(hyg$)e5Sb@qKpr-;UZ$pO1H&b1B;T|5qJsKSCwZTwH{QwXTjw#
z(4ERGv?=FB2%>GIR2MgBV85xSHdy%FX&^bmalcV{&)t(-6BXfspFbbfSSq#kE(ra>
zJp_Y{4uSa&OA!#R1oI<+ItZH^ZS(&`LdHD-5C<3xAEHoQNGf~#DkSR2<>`dx9bNz$
z{M8-1eAglV0N2016oj^UYu{>W<Q09qon#vg5kN!Kef~<<0)rM>33GQ=>vGe$q#4DD
zBNl!E!QZ+U0Ko=Ywu;wqae?k1Nh~KhW_=v!bC6}PjgYi460D@+kU+QyjoL6NkUc*z
zooTuEfB@$>r6i>%1j1AS@!v!3nk3GAw5Evm;OfTtmMYS)5U^ecb})WC0ltk-AEj)F
ze6&d(A5F5+2rAdeeY9S7R=jeg0i1i9bK807-&uMx;>RH_*a@t%U}%Sh#sw)jKWo98
zyZG1;L=MMOQi1cJYyfJO@18wpWGOoD=-1*TDa{`K9Fd7^pRdSOF~zE59O+D7s3=8(
zZOBt`+`qj7CxYwsB(h2*px|F6<47ex0GYs;bv{q^XPg1U)rL9rN*vPQ8$3o&s=zQp
zW<EQXzD)^QhkWe2On<+#p-ZFTUFMi@sfNtms%pZY>*`%yOV^Pw?J%vV&8g3jaAIcR
zGca{SRIRN^{Naq*?`}3~a&$d!Rx`>J@3SB-I9hl@*gLhFIDwp1vY>5X!De>$2ctcY
zo8g{y7yFPg&`vY`5hy_pU3ma-ON2AX5zu!RpT;LnI@bt@$zGAh_-v(>Gp9X2GfY`a
zme_V=)(M#vuZRKLKO(2<q3)xaEcC+)Jpb|toJBI18DVB?2<OwCB4G;3V(JExGDPqD
zK9XtWwpvMydZls@qe#iAQ$cflT+?)7&njRpU}DdE-EuqXY~P1N3m#R28tI#l<}|M#
zm2)9i8|TTo>h0+8XmWH@^@!Bv(IxyF!Rx9ACaz&Q8-|shPS!k-nFPZ9fcey@-d9`D
z^q1#Yyh?>qN-jt2ct2bZxoIHzRIrOh4CDyc3~SsnZi$7>M>xdhW=|8Mek*h$)*vA!
zPgGA}E^k^4U)JnJS_z~6M6>EI=&Yq?6*8aDK__u|BGIxRSIwzwR3w$ta5E$UJyAMI
znaDs^I(wS1m?IhMomPgr6}fa0>mF6t1OopJX$`u|YP_8?BF!W(-o37W$|c|Qc|+rF
zStgP{&CG24vuZy;JWR9$Zi}e3)dkqTr`znowGqH(UHkHEmNa~fnt;{k-Gyo=L5yJ9
z8&yZvJ)0x8b4XMnl05s=yD4f4qL2Q7c$=)Rypq?yq^xmF(Bxfv$GKA{!vbeLaPmdm
zT%7cVk{?r_BfmjP*y<SNUo^Zo{*DYf6JA>FV)OltID^Ula@Ja=bvQ?cQ%aKOSXU>y
z`ngwyDH}co>{zf;n%x^aBmgp_<Mf^ObR{4?`*Wx}x_<&_sThAKVz7RZw-ZfH`BwET
zw5AEK6G&MklBGjIQPos2ggITqX=ei-{PD}g%rYOWD96SQ!Jxk-9Az^>ZWVR86`DP;
z7RHq8y$suL`vrLGN^J<?eU$Nb1pO-2w%_*~@q>P^-wy2u*l9Hsu$YP8{fgY@WQ4X=
z8jHG(=mRg%=x!3hJ*s`^Wl~-DY5!@(O<hteejIsyIq|l)*Ir81S~?)QZy14Izzo+<
zwU8pW|Cu_={CjM<sES6#ntQEKL)~`j(bocgxD(PWJj)Ct8b@zO%)WRALO=5B?H}Pe
zrx${U_3W+~$q0}p-&?L7vG~2&qbPXz*CRbJN^{hw`_P^DchA3UdFKXYYHRZUz{h{l
z|H2hU*8dT=aB{Nz@7z+OePwslj`+9RHz>fL4O8vm{a+<HoWFYU^rDFTmvE?HjTT_N
z)PSJ*DEVl%)7Q(4qg0CHg}gCJhN#a>6gITSQ=aq9h=>gLH%yXnKSUlib3E2H@x~v9
zJfJzs%-EqVI~e{zlQUgUIZ5U=B|o5)ru#cD%fI)-^|7@R+*JSf^R@@Ra{2%=7R{V6
z0vwC)sfTR9;DIxvY(Fq)(y!coOiBDM+!tD*?xu4Ds7|PfBnI5-mpe;@2tK8o<R+?X
zNDFzH;-i+?`Lb4H8yQo}bxCpEuSS1qwSgyp)4gswPL!L;e)iOA+xos3W}`g!<e`YR
z^Sh{vAUU2$fzy5PX(M4gYRYCn^+MC@OI-|+fwJJ=v=h0<V~9cMcC{VJTZXvReGw^I
zaiiDVihRPkCS5u6#?q?2WvIK$b9sBiU7|EvG%Qo?d7eo`zZE4fF|1SqMq_6$gap<H
zT$=cVjf0fYz!3=^eKY&5#GgdN?s4)81{ad@*Sot=FJ{-W)u8vi3(3_2YQzwdZVF|F
z;FJz|F?zQo0xG)z$X>xg6{td_aZ?GJqgBmY7d4U@DcwVn%E4HTLhY$l*jkP!Eqf9~
zmJyJ;pD(E)EIU}H8A*9OgQ6C^X+q|NpWb=vDl1Csw!{)N%Cdg(MajAXi>S^DX>f{0
zi4sNBG)l~8i1rO51&)(ma1SX2RHq+V>BXNzi8)|R1&a`>96c?VfY{wjrRLH^`KF?a
zMoaPNy_QijP)}Bu`Vz`jwPRL`ZIyR*t9cd4#ldSp;R033kG_+MrNF>3aObD?OGl;P
zg*sY4#-ZxH;sdalpAht;RzFwK2<#wlPEyE_OcPZ!km%5bNt0nd;HTB3g@=O6o7Uo!
zxzpxD?YVd>II!h-ZHp^ehOS2E+O!Xu^%5)f)P2KUMI0-y`lFMR?5}9hRSUR%#0O9K
zCqbe)DuSh@<jTR+%|G7ij)cPzE<;tm`0%5#Fz|i+-d!9|*-ej^=;QkbBn!cH{UzJ*
zffo9?o$Bcli0e-6%6Q09#bMa+0K%hY3ymN7TCmry9-v#CO-TW#u>+2dtz(Yu<9cnS
zF$WTL^nR*?T-xp#as^qlZx1j;93i`xr?j4t03J3#Wfll<lV?G1f(AZ^)=`kYG$d?W
z?!wBuWz<9sIn4BryME)Xw|-X!*}oOa9_LE2p6TqRA17vU;;Ed7te9K1ck2r&-o%x|
z2SCK~exbKpcdg4&(iE6u0}yb)OsaGq`U&Bm>VK^gCJD4SmIL0*U(XPQ=5oe0OnPJz
zi!=nZv0B4XYF8;mMmQEsE`)FbY|F<u*u~yNk*-XQ-q>IBBXlLq;GLN})?l}LqUVuj
zci>QXY)__d%wa@?##=kAY%RTN=?;BT690jHLcs!w2VnDpq&cyl?0Kc3M0;%%PhiQY
z23>wx>$%5MKF6fBV4~@9e3VJ-%Npx8Xe2U6t3;+hN1DkmM_j{scGfhKbP#b@K1>>W
z-M9RW3)PW8*)d<arvZ$-Ko1r+tHoj#JCLXnBAW>7mKOVF^LIQQHX9qmS1*r*M%+J$
z<CP_*%@k>0{K37N_GzNF&6KZ=+9qSJazhoq?tN640^S&HMEt2^Fj6#85)XDm*Uqjm
zxC8){{S=EDt+bATR}`!_nFHxdlN#(1q2j(~-LEVIf}vHB2lm|A!cl-aXfu%8UhlR7
zcLtMhLT-aCD3{^2e&cT3i9THnIVl(Oj-~|nNm+Jg2TNC-=0v1^(Upmfgjtk3y*dc<
zi|Ex}P3kHU#_H05O%)AIDNOFB_F4;uBQc!E+)pA-vI5l9o$gXsRF74~+n);|R4~hq
z2Kq;o-D^v`U5q`=^oAm)FK&Cy<E7k>;oo7e%g2rh3_@>i<YdPU8W|+C11Z|R^}NBL
zI?X;9t+hx4k-U$vuvffJehsH35Gl@_Na@%ppzIDex@9(~6u`0yhro#Y)iN{)u-&b*
zOhRlprEfbxnBdI5LiDI%-nA#rYezwDujeoB<!A5+AiXgH**i8`chBX7CJE(KF^7?w
zZf7YU<FUg(g|a<=E2M4~dor+Q_4NS0tD^?wZ`xOg$x8_$I9<t}U6M%Ov|<y;GUIO0
zwIf=x1+plg#R5&<v-p|Ccu}aN(!>!Rj7h)qTkheE^+Rs7M=JWa(XMD*sNsybi1m1|
ztW8bu>Xxt8r7Ro(0{SW5LNj-Ncbs`<uExJHUoQ%PH61+D_$VINW~BN!x;=v#09ilD
z5VpTVAXJ4O8p+U#lol66c!UilVMAp+PTtAFo6v~FZp+6X75@-J6LP=IS&{s<POU`6
zhetAi=G4hh0oc=51YE+&%Xr(HMy9MohaJwo=ZQc&ZM*C`DCzFuJQYfcX;6;Sgr4CB
zJJUpX=dDs-Yv(P#>8I1fCMqScfYbPgur>j)yba!Y7e5;Iw|xp{4-XeIlWT9C+MzP1
z&!5;<Q*~5yeVGZ6WCB^e4D_)5NP@j@#%OTbSU09)TxQB4!n-@VQ=eix``9L854;@#
z9aaxeIS~I7>Lfo-#%Y56o(=z5B5Dqkz!JLgw(vy_gpNlu#DPd{gFl8rJa>~kzBpEi
z+viXo(D#_pFKoVpmETeSjg{ZfIh*RfgQeZKu@%j4O%&#oacwI>za`Yb6f_=hr|mOa
zFP~2DD7K5=5I=jyQGx9A;REU<>>1W%n&ZP$9pzC~F0FVbrTkLy!0x=xAo=?|xYlnn
zGdWQ*I~M2y1o6w6uUj6aJli&1Aqdx1coxW88Z3@L{!7)nxd<AVR?{7q(JaJ_9(6P_
z%o~pYqNwO55(>;1czZK01hHO0_%L!o$zM`e+85rl#zk=)N`R4a%zu3=qFbw}=6*ry
z?PzaonU_lJ=EUAfCTT?L@#LfzLMV9Wp(nBnn^CNI-opx-QrJjG{kwH3+h!64Eju4G
zWFav#&v=pntgC`R?XDRh|DO4O)YDvkOt5K|nCxsWG_S~yB7o_CRI}V@BhivbnWiX4
z--!IWCJT8ZSG+89?Q4+xSx%4|vvw#~5Fnq1KD?JYy00cV`+lnMqUg(-fQ0--kuG)A
z0Gbfk$DxMT_E_{1QNsc9n7yXO+?V(^FzhzVnzUU3+lLbgdq1aZe@TcH^QjKvFIMtz
zaQw5FM(Ww6^bI|KyP_o?c0n23ywl-D+>4P)=#nfTVyGZx;byTJyEm9|Uy4F!L#<wK
zC(Juq6pkUh1wahKmnhz+jP+FPtA%U<&u|Eg4p9c}2MK5h!6+e;AcN{h4C9#;KLLa4
zNg30mIVSf>;3`-O+1P4aIz~Q*839mzB?Dr>p$m21<5YS=@?Ev|#gy4rqN^`9E|VYw
z2?6L_Rt6RS?7<J(0r=03QMGB(kKx{Boau?@g43xE+_!^VWjxf~|7-ITQ-^+J4b(@z
zBw+u%sE|0MIuk?2#LnFHyEapXzYUzq_k3lEYA}ZbnZ3=vu7km$ao2??mF&f5T=FDr
zTy-L8%G<(xvjovHS`6o(b=c+aETnD+VYGC20mWupj>;<x6WUrMR>L*d5`)2d^qrRm
zzqF>Bm@ljk*j%(vq201vO<JwlHpdT4kisk0F%gpc^fGZz&s_hzvB}tv?h&bFBF4O+
zs3T4Nmx;0ePT5xKPRjVLld&EArZ*E$TADZ*-RkG52;C3e>!^&4KdX|NkN?S%CioVF
z^><kq+vqdqrX4(Q{O}y)%*GQR0}BK&T!ol6)1lY4hSAnc^6U|t*acWpxm|(tUgr3S
zl$&;`+{Ng`b-pv+j!p8cu|-O(H`ZU&z244eJ&ZPZ>G@43cs`!kp<3se19p)Y-o_eJ
z-qqq-uXJNnSk8;a9tErCx=-Aj{tju=pUr!WQVCL%uH@&WksOBbn@^Ygnz_t2I;8LD
zu_q|R(n6M<q|{G&&zw_^2l|i<db<Ink#u4`OUoZ-f6U)n-Y<Oe23qL<5BGEY&$yqN
z;eY4;7A>2&qgKS<U46rRa1rQhCL+-uTm%rCMHWac0$8~7|Gq1QT08Uz8;>O(d!N_b
zLWL)jSFc)3bm`nmDQ1T<4yNrnmJjDZ_22Kq=#2pXzM@uKAKv7n?EDU)Z=06oKMt3E
z0>x=y3rApGLoN=NKAYb5!m4{Z5e2Vns6TxwUe_hMtv1R(6@C0%klzd5q=kEKf|!n)
z*otbuJ|oBUT1Lvw83cImRC4T8Hx}#3RZ&7!SgW}7D_MEz^ugx)R6UkdNMCB7N<#*|
zva0Cwd_i|>U;v>>C+1I8KYDz<j!*hjzF-@3hxIgh6^*lg3d&O!=D7OA5pPi?65WR#
zkUTV(uG_eS*^AOvdP~S2B8w|kt6D4h12d5>PELP>S7oL8L{)78^x6dJ-vmV9rR06C
zzKl|;9A6wqINxgZp|PzTQpW}MrSC2TQ!Wt^9b6t$eIv3$Lrs?3rY_m^Q7icyRjh1A
zSyxuOt)FUD7rJn3ZlqYv>d|n-Sw2dqp<G>vjW2%M>kby;`Ho$$0pZFOi2B;t)ZEG;
z_NH1!+uLX!9c5=)3-_wM3q}Reygfa~)!~=7PqV^S=d;>28Y`@rn66nN%ntGsEEm`+
zMKQf^f0Q;6Q<KW9C5k1ND;HfG!LHYYG|?2nk=hB6JSnJ`dr|Nt%3Yy;DP_k7!tApr
z73i+(3@$S>^Mh?fE!O6d2Y`xlpQ}QBTkeSZm1BEDbz_v48K!`0hNe_o?K;=S)WS>u
zy@Fn<mFl^`_Z8To0LA1c9Ysa@u3a+DX)%vOBKRjLo()Vmv^zLHv%aQM|8)WJkDx-5
zN@*!m@7+R$z?h6G7R0S5r^;UJ+)btU#?0^iqO>b2-lvBMLL~l+cstn<EZ4q!ot%Fr
zvILr;U?l0tQH|<Y)x!f5L#>ypQf(ps0U%c8HL$5+Vk*k=50e4AAZRUsj!0I)e^+~%
zU+|E}akm&WJp6$LPHjADOw|fg2rwB9@EQBMudv%V2e1@E9KI7p$P>Z=NJ#ry)qlxe
zF^E;-ZL8H(M1c1Bj;kJUKxY9-=UY6@OT)&R&&a&0hxmw-_ndmtY=8gB({0+FA0^*8
z8Qd5Oejr(VDk=+Om@|)h$`qPE$J<DY3-fRojLBGJf4Vwd_%I#+X1OS;A-wMk4Z_`q
z7>W}XWsEY}4!hjC#?@NRVao>mMP&3DP}&ghdg0<C>*^a%dD6fam)5|5KQ^f=#)+CF
zMdr!IWi^$g*cIdzBJY<>a?Wq83bJHCOr*r<a?YbAUr;=f5F$gW0Q{2z`~1?=u9Rf2
zUjSwL>Fo|G&p?gA1d+DQ^fd1mOX8)%+7evm%H0Y$b*ms?!evh{qAd3BMIa(z`qEas
zxEUBFi|~!BNIjkfD|{M5Vat0B(Btgtd!~fB^hJI|s0A`#`@-kLrRcKEj1F})XHWRq
z3D$Gtp4YZSjjYo&#b{T`LO)tHab=l|Dp)ufISN7;W+wAtWP|81sSBFlIv6e4LcLLG
ziv@Y|z`ubJZwQzq4MHi-!P8reCl$PhBQ_T7nA?b-!4xnC^Ao>{b{a6ke7%qXW1?)e
zCp5fS<jaSoYsXkAooyv+%qQn+AbV`3`uEy0Ta$?>27a0t>P=VVG(U==8y8^(SHwLs
zZc>zmzKpfQk%JpOTaYz&jw4{~%-y4&pJB+i#oR_0Y-~p#oM-3dFg(~nQ#XWV?1sDG
zlt8O`u&ImF&%ga$gn99zp?7%nZxhh(X;W@i#MgL*`xyT|y>Qj6ptIZi()nRw`97F{
zT&WK@g;8dGNO{y_o8MEv_q<}xJBZC}1GiDgX5YZ?%00dmIp4t^Y@CA<6$0d49_b&_
z?C=U~7;yr*%;$r!>RRRavz5W@L%LvY+qIk|SI^SeU1uq9$(;?eefpVkn5qnq0Tf|Z
zH*vVJHhg%4LYebz)1(U{0UN7;b-I#Zw)=k~82m+*^u^@4ed;lj>lqT&b00ynBN-9k
ziwm%}VLfp${l<2Wim3RA$WbWxQwD2HjbukGL4S=L@5w9=_2bVOaR^9o2sCkAGT{zu
z`i6+VnN$#Gt-qkLq*Ob=Azn?>SWAn?`z5%Akt0KhiZ5B^l@xa0A{O&KlcGVvU_gH=
zkhEV2w@TqYSQ0VA3V2ob?s_>QBeUf+n^)sgW$<<cmNXpybMQ+O9}-9oROp?u@)CY&
zVUb=E9`^8pxLPxQy%I&~xCB$+@Gd+>%p7VS-#;N8Fj?eWF<l~GNn|rG-&X5rDIXSb
z)YeGvwoRG~pg6u*S+T!{pVw?;mt|*jnUQodeZ;<-NEgQiMW(H$@tO$^r#7oyv1-!Q
zRYvYe280mW$dJs}BZ`@E-&zV)WY8CB%HSGsn6#@K>ag%+OINi`8Hz9h1RFDdK<HYp
zv~g@;258<9p7CGl{ivZR=&Gvc^bMUFPUu5&;``TTwwZV%8hGdU{7@iAl!s;lJ^58F
z@_GPoZ>xB?@x-CMK)2UI#o@s4++mS4hEDl)^imWA#}N_My~28bvdA<#m)8Qd;nDrF
zb&)m*B$@caU)P(*HO;TU$@=oJQw*@{tmXiY4i440>pYqva3{w`3=rAZ<4nr+rpZEp
z>+8Y*o~K7+*04<Jg{5Tdx&an|$ucpw?@#`dVYmtqb!dThaFmS;4VsqFH2Kl^m!`hY
zsQZz!QqZL4e0|8DY{e44(D-H%b_#w2$}v|*NFZ<qAe*U|%if@g+s@-79YhJ*u!ba4
zq+n!(`mzIr<df@~ZrckBp)U*8LYb6PQgoT}fG9oql~t&qz#3P8_rEnTV_9J@&}VNI
zIDpgyR1<ZR5+ajHG9WKVkeFJurTL~=jNRO<utbLmdHP`Zv(Y!S!Mo@0N4)S^b1X6T
zuGc0&OFr!Q|F(#K$#u~huY)+5kv1BCbQov=v%!|@efHCnF`h@Yj9$JjzZbhVm1J#i
ze!u^e#)YQLq(<0K5I%<7tp|@2V=C4=o!{12bYFQqvLrR2ptps2jq?JAXyn0#bnKgN
ztuW|z(>s%;*RIpj(OH(DJzdir6hAPx*J+1Kx^kz3Wj%D7_hIOe@Lj?9j)k@+e0nYK
zNqAl!XUznNYeYd1qlyA3(6<St28#LP72eJ75y5C8hceJ4L=sDY>(61xj4CgmQyfgA
z)WW8Qgom?kcT3Gs!6>7?&$nM1JIbWla=^2aIxVb4!X3tM4e9^m8P!=^I91*EmdQ`=
zidG^9^*l$izi+5K>DQCR<~>kuVNG2Ow{fhMU0FKDH{r-&R|fyOGs)nN=x167c(2-Q
zzToE>_^);RT>j}{5A<7k2VbgsZfD&YE&rCVh5jG<)^m_LlVgi0*KxWw3vlabQEn59
z1$t)fW?=p<QSS97?dG7L15?*1zL5{gDA)7eM$sWbj)$NHw3~Qc+Xc9FjQ=tl&$=^i
zfaXDSzo6zv)ckInjOg?meBC48jDP?6Ocz|y%&O#@v9?1r;{+R34|JHIV`oFN4eskO
zeJ%n{@#XQ$@n>q>#HmQ3_jqb`^u}X<1r$P0&VAG_=&-+KbWEdn$%E<6XLvVoV`j4i
zMR;!|?MU{@XJ6}d&cZ9t7E)w#a>oHp6n_{6$JvX7T|0_9_m6TE3csQ@I-&dC>Wq%$
za;G^ooXyS3JNgo(Ib8JG(jiZnG_ztSPSI`v#55QQ3+Y?Uwx(b#D{h6a48AbYXHC$Y
zXLUUTyzLxQ%VFQsAf;5!y@yWRVujuYZfdkiVw7h^^#$Iwf(2p3ZJ@!-yo|K;GB!;G
ze*_pSx2dnaDEHdplQ|jI?d-B8EQCWw5k~gh_!}bv;~%CJl|TE4g5vZ^UQt%M*$srb
z60nRpOed0DtQGw|rTqcZ8^p2xf4QBB`G3Og%#2K&|2wz$X#c<5P5@X)>x%iH+et*R
zS)5inlZK{_GIB#Wzo}D)rTM7&=$WIxcMwIgc$Gq{t$y5CDluC`XwE@T!urKC)P(Un
z#4Ne5&y!VW=J(6+^~#%dfKlQQX^xL)9$myCYvvYAh)3Q89~Gzg`uK3V6xKKM`)G(p
zy!!JuhaWUH^9YeiKrsr4aglE(z|Sp3l3>7;|LIH#oJf>d4*;EWDnLd-O1Rfz<?BJ&
z2(G#<&dZ!Qh=2Wy;TxD)L}LhMidAR_=fQ=o6*>>(fGBmB6;&Wy%PB~53i!PCRY!iP
zT?}E&IsJy1OCp>Yw%F6h=<JqWEMX4_8p$C|u4Iy#pFY(Pd9A2hGmf_kA^KXL``FnL
zho+h2<L%}}$(~c_(f<w;&?0p_H-00mw~Ef32!WEsBJh2iq6EYn)`vBZ8W&W80Z9l{
zFRMnka*=?TT^O9?zs!zuBqj<WP6H|K^5$6)15$!vh!iWA%g96(k5X`|Tg@JfI1Goj
z$jQTSE+_2L)z`6e{V4k6%t%iO8bl&jK5H=#BFW$$bmdG#5rb)TP+U?*9wWqQ7M5go
z-s=<hlj~+i#lRTKlCY}bt6#IaCAPDTm@6`qL_J+~W}Reyvdvr9XqMEMpT#ZZ@tl{1
zBEF*5Hg1ud(i$}x;8`?f*}Qd3Yhvby*LKo9LA};iaeY_V_=8=q)@r72!`?!LXLq1D
ztk}}9vZd9+o5K7J)cw!mZR*CVC(M>6=5eJ{fm)R<!?)rTdu&0!$vg;2yY#TUyi#9R
z^vJ4lGsRWv=Tp{Z>Jk(zRFq{778~ntq4oNPWq6@TNfcU(T@5w1zTBJ}m<H35AUpFx
zC}KFFrlw8QIM0nCuhpbP$iA4ZG${imzeT7)HWjhZL$}Jqm>QotZ0DLv8(68FCY-8K
zjFu)o;YGN>iuVd+^rPIIW>iu47+STD^B^uYCnaY$@1SSP)w(mVx2wZASuBc2o6ASW
z9D|t3;KYI(=PDl%2y8DpaCTHMKeGRC9nYm~8L5P+wHIAcAfEy?x9#_p_oZ{2u)rqL
zpCcekj(!Xc9K0yP93&#ytMv|p{aDMj*o2S!%h+iMQMO`i_5l_40l_#H7XedR^I5ru
z9G`n+kyO9o`AO%>AStN(rb(4Ww{a@osIX-`VWr;px4gje*{<g{$J)(9alO{G<;wmp
zyV9TDumQ$-1QT<$k{P(%yJFIOKGRBhtF&6@T~@O}wYJ2w85Y;Updeqk4I_VMU|3O)
z&FV;O(b-PFB?eB(My-W*vu!vhCE^C58iGcyMJ<c0&00~sU%Qp#+!NND$GT;s!e(Wb
z^~dd>HMFr}&dsw*rf8~&pW_c+vR+;0iuTq_t))c+n;JTo@l9V-9{K8q{Fc!w|F_lC
zMa)>lu;y1gbG8Buta(|l@3RQ!AqIC1^;-G(RJ8^X!bVS0pt9Ogt2K$4Fq1OR<tK|V
z7iT!FIsbq=C&o5iEdOBuGKip|#OuY*KXjRPSWsN#U#F=Ej#P}VcKsjeEuWcqT|%7T
zX9blfeH%qD=ACs(adj}PYU*gESK6A*e?m2)VCNHvUz0U?+9#_y+}ST+%U}NX#%mu9
z`G8>=>CC4Hh{X}GSi9Qh)LeEnL@6s?+L`97z}jXncZlkP9(PjK<Y)MzD*UQjMut<V
z7J9T(-|VIn*;G*Wnxcv~kB*SNK}d`2%}uUvo7fvC7uDesMY{t~;rx-_npn3cXUd}Z
z;y6_70m}khJA9YqNJp~P;Ixs%1pW(ICqE7da1+tM9*VSkqt=|KFntA>JUCiwHL7T9
z6B6TL3!3>w^p%d7vj8%t(H4QgOA6-H!c#C_e*Q*_7a_+BQl_Jwf?iK1M&!y$tu3|&
zB&o{GcL;#xXFN^^_|Gh<>W^)~0J7oZ&%r24o?1|rjzUn9)TN@g&5f+v{Vx0)_w#YL
zPiz+TdIeV3$kRO;Dv_9jo^yvOJta3l<u3WDS@9@=mwL;iwD;$CQZ+Vi>~{)G$l!fd
z-8vTJf`Wy$mS^=f#;c?~RP3!9Rjn&h&{9w2IS;MvaW`2y{C=22#@zdBd?d|)cB4J3
ztqRy4$~e4dFKpD-s{6&-v;m$;IP+|J{z3-W1YEl4W~;21Oiju`U%SWP+6P`H|2?0u
zy_FhxT~*o?N#k7xrUF35kRS>$D>Z|_CzpgawKGFM0i<#S8WZ^tJz9MVXD6MyD#wH^
z&}?@sV|^#P6=CNKlw{Y9U)eEuOPPYn5=;cQrokgVs(NF`jrbKR3f}S{+f;5ud+>R1
zF#szrVtc<Jx^Q?37=govg8=dWgs{7p_BOG$*YES-&M+QK%)0EeW;xmVR8V_NF%3ZM
z-1^kNwuYaK?w-^%Tku+NUUPlMl<?7K|Fngz=Yd?7hymDfg=6HK66AY8kS9aX_V_DX
zd`JJQ0@$(K>(@4`CWWl1wlxr8OODYiw>1)sK#^EgRU%L#lI8o??EEd%7v;q(b+&|f
zh2o;LR`JxH;}=kI*#Nd;9Ma^_cI6!K5e(eMYQBGayfYCBUf#^(pYe=O=iSXuhs|-0
zp%iWAGWtGy<j#e24SIpzaE{q*=Gp<k!P6TjUf_~B^;!0vqoB~n+lUr&SX(SJN=N#7
zbZosf61?+OX*8oClB2>Z&b(GY6_6QNDGVAt7m&+<mLv=TIv|Vd`n0g{&O(;%<;YUT
zO3QLs!sWn=09WW^+eu&<1~%^b$WlYXISN=wg|3SjLO$A9GGWR*$)hX(hQM>=u=p;W
z7t_&NQbr+{rF(FJ*{L{uS|%=dbkpp+#%^Keo*j3s8zx-AyW(dD2)7=DwRLNDP9q)i
zu+_DxS`?xHr&DN8P5|Aw9$3V6KMPj1BhLO&N+U*0h$!aAu@ON%Pw?42nYVP@<$TrP
zTHonSQ`j{*YL<U)YV7zuxgnF1*o@yA%cqF9IP}E*H0Fgrh!CZgl4IbvtL-mwR;)Z#
zEA&}Ub$4i}s6Y&lqa&iG4C|o0BNY}ZWfD)(6H#T7P@o=(iYkYS@DNl=hYVAsn-j`Q
zS<+ZVnt12mnqS8<)Y~TGt`|`^d8INW51U*fmd=a7gG|g}7*Z+Fz=Ld<y+;d+_arb|
zhR*G)KQ3gs@o8FYO$6AGD{r~p%_MEP!h1Px-5Sc<ZG~arH{Rgwpx56lMOC)_-;2J0
zN{<abdF5_vA7SN{%~yRgUCRyLCB@b)yc5+H-vtEs)@?!T|4PGt!^n_9G#g+Y%~O4f
z>2fllBe|%GV2he3^%7UiQ$qXb>>1ymea!!)q{h3O=KOBp*5{BV_=f=A*85GZyIl(%
z4T5Tyrx`?x#Bf}z>}QMH8)x9;VRi13UG{tRYPSt5{hxp8R1oNz#!XP**!P19eN$mD
zksM=FPae3)T?&5}?@!nCihzfY3uT{qsJ?dco%4?Y;s&vs6Wo`rZ19vQoIBWbe-4?s
zXBXDbt1#TJUOb-PY^!s&CjMx@e{JX6dk<It^yGlj+^b7S|Iqo)n-sAg6z2&Ui$Kl_
zAe~B(Cn4K!?B4T1-|=`d;=C3mj>G9?Vic$I)7hn0$29HkC;)Nr0+4#`4yWUW0{{7w
zHdliPM?QAb))BT4E*xmJ)wQaJ;#ECrFeqE@Kc6SKFdd}@c?99opMpRu*_PP#Cs{vx
zu6=qq?CbWMKFYh%(WiyZ-NIZ()T{R*3DrZhuBoH*`zq`9hzy?%@4+QW-t9vbf=;op
z2@pR<r^Y#u$d#daFORYL6}ZQ8I3@{W0VOWg?X5*U|I6cTM&FBcBnm>u#O-GSUjF%&
zliSVbxoWGZYtiEc>eMioxJNF9r<1yPjWP&4Ow7CXVri!8Q!lIT-|TtU7B%0!Ivb5g
zN0xd#?1|}W`4RX%la_i1cMf_2@5nvY*7Cl0SGJH2){?+iE#JkXptxn8xLZlzZOhyc
zPKgiR4$9`)K+IyVfz%{q%N(%lX4_xizdV9Y+e%~_e4Al9^YP%`qV<e}En7($7ydcF
zjb-#_@9UXi_GhpUy!OJgC70Yfm-3`r2WSbqjdxz#JnKnqYrfmzwsr4wdWR^R_?LkH
z&boF*dVZ}%c8o|r8x8RW*2rG6F)crT4_$wNA=Fjl|3C7B?SCXcn3x&=5BZ^^ZFe+^
z>U&p*5FaKHz;dw7seMc;u+$6z#d0rghz(sUvL!;IgjmtCqQA$Ct`w%DDIK{@^e-!o
z6Mt?x<6|~tX!<vMK@;R}A;avRKC{NHDeumV|Ch*&g@}yF+4YSDIZ=cxx8|DE(#$RE
z>%&)@7Vqwt^J{B|hN$r4<L9nsY3K=*Wkf<m+n_igGvNEWFrG~6JQhmyg<+3G)$z~J
zqlzh6dNA4oP+VU<42t9QMvsSX?9zxrQL0725?(i&jOP$ncUH;*uFvBA<k>>ELUjQn
zNt7|6)8_H6tMWP2wqmNBAbRo&d0(uig0R74t)({~F-c^&l(hBR&SAYcOj~v$8K4~P
zP+tGlfuJ-tJo;t6VzQYiy2bo`=y02n>>%yWK`Z}B3)Uq9#$AS}0(aZ3uPjUWu~S2`
z@^qq=<~ku)oxXzqJ-fE*P~r0n{>i0*zvfj^Gh|CX%;+rcHSF0*4M*DV)l$3qPoT1Q
zYeb6_@`_vu`2iUSyI)@7a?bdblb0uWcPY+JnjhfhW8%#*<9KeX8HammKFBZAf_3Ey
zR#1Kf@FiPh-mWZSIhGZ1R7d?QP%0F-*eU`vDCNo|%a`X=)1@h;T}h<g&XBY=LpyA*
zUrJ}F_;$LcqI&o!9fNgUY8ztho0odW3V#4JBKbEnAT$uGu;`WM?qCjd9uwUL4C^9R
zghm3VA)?ECAI&aH;q^RMkz~;Z87xsB67oi8eIOzk8L=ZVcyd4_ws=!ymP=%ojLh+Q
zzaj!hP+s;J!qbtcuk;m_NZe=%`Qus5D%{(XnpNZ%wt5-igW-Qgu_<E6A1=T<62}nE
z5D7<oaw5k`u61ONAY8%Y2^s{C_<jZLCsG!|6w&x3T1cY{t>3gYXw)Nx9Wk${MNi%C
zFW_5+??Zc1RDL6*NUsJt$rXL7JR#b3qV1|UaL@(^&RHrKV6}?OdnJ+{&+2ZLdMPFi
zg+QCXY9v{3jj}tuk4eb>(Zg=RZCpaA#Xpl@<Ofca)R5VWzI+wCmD0q91`QaXz*eM5
zIL-TvIbH)kR$u?-a@2UtoFo!nOZryJPi7P4N6zL?+u?;m`0aviq*71jeurb#p}CGt
z{B)~>!_Tq?_hzB%Ua2THFUzGLFw*dX9YAw-%)Xeivlp!_<EFn^bv$NTwf#&UdWvs~
zdJ<25?SfLu><&_ISkoiL9e?ve3Z4$nL#EOW+jrjC@&Z*JEwN)O?;)~vH?O)1VcK0`
zF2y(H#5iU((6OlrqOmPc#5Qw1en_U5y17TLZMcaQRS;P{oH@GN_pgIWh!{<87yib1
zA9mI_(6hB<EUGt+;_yGsM>S?;HMY{`;}%kwgMXCs7Bh&HT%0H0*sBOxBWlD)?RgJ*
zDmf5wX>*shSBVU6_@>2I=Q0zK0`sU&50e#2Yq44m@*aM3#)2IlQgU_d?*o%GmRh&D
zZv&X6s~S)6XQ}tn%}$;u9(@P#k}WUcc`DwE>?vpZQ)M?I#QW2IFFbjqooaO(XL6rd
z2>Q%cq`}Q~QRHlsP9#x-liHd`r{%f*`0WLz_}%qBTHIv`y)-NgkFZPkGfGHNfKgsi
zf`?)RSRKT@w%FVu-hthu?@eDDYe`|w&$O$ns>gZ5jk({qnm0ZP-Q}hTbZ>j&T|&(v
zomPkBsLHTidZ8T5>>+ze=kwP%Ht?1lFD{sfD^BdOaxkBr9LE#AcUU%x;T4A;?cYW=
zSMWG^QO`Az?bSZ9X?+gl)IJsa_LB)a-*=@fkItfaA%3ZASp%g9{4_-e>fPuqnI)a0
z&7x6fbFVj)FJ<>(0BrP<lrI=__=7_0=r=w~d}Yv_HOM>qo-0DBA3rN3RvM+?R4v>;
z9fekWIcgS%=%#Y)%o|+`?Dvrr{u^hJJ|UM=B(>Gk3AdminP4m;BmyLX2%@7=6eGM|
zE<o)7DTc5!@SS<Q#6=N1nm-42&J%1;{7%Rve*!fqfQ)uV@azJC1|h|atf2E>C$G}_
zTk;~{VbMVE;>Ca<65|hlF+lS$cODwQG8?JwUP%Wmsj#g>?MpY@?QEY5K`~ALx^u=Y
zG{c+9SLr2>$nw6Tn%EfP)`8kpsO0MR4on}x`b`B0vD`N*2?L|hu$J%-Jeh5}4|Nw1
z-iJbN0OjBk4g`tFl)D?ZP>?j5B?l`(ByII}L0yhmV1sqf1grz&#C0)poBdBABC)y+
zD(=g9n4zIV4dn=wrf%E3I*_^1?q%(>gt{1=zl_xMbvCP#)Y02)@C<Q3b8tK_{=4O2
zve<YXXN09*xwymVUb!*jaTVlJU<(=#W7^gF?mB9neBDYqv(8hy{@#334C9sh#?SKw
znRJbDlXMy1{nmAt62{b<O!zGcXh>k}3E2!|1VyjsO2?T*(XfV4Ht6_~c6D;#SoUDl
ziMuW-MW=D~dQ^IX61z(1TAmIVY1P!bO>&83P77F8BsaroozsJufCxfePt=Z`J!8Pa
zmW?9Z8p`K?mPCRrP?0ya#S#z3=33va*OM5QF~2o`(N20YOJ+U*VfuY{ZK@qeu%C!n
zEOc{~$6OF<J(;}rNZXFfOM`Hn?-p^4J31p3`YQ0t0gQ@C7t}(a6~(SygQ+yfOVma`
z2`ZUq>=Mj00s;G-<ql{|d1;L)Yq=Y@-rwHDYNTgEH`{s@-R;m!L;v~$GjIW`MX&Q;
zI!E;v0IvZcVPAm~h<6@`5^zMW1_)*|0VQalR09aaZU)jan;_Q#gy5+5U;6U{#W+B6
zs2T=5`)Ly)cS#Rm_@eQbKz_HJiecc0A%r%wY3Bno4-{E$K1m!R<m8>cyDPIl1b(s1
z=16oxxfXf~8+mliNoNUG=F=$ioC8F2`bSj$+}@qhl-bERss~$+Ezj)Lo(yvCbU$v7
ziirF47G@UQlIgY#>|&)S+{Ye-hkq4c@6qE+C*oFdJgOqve;NA%z;rK%5*2e0xUbEq
zlh>l|1d+P6esjbsPIs3T?y(^P7iatpgaHw~>|u5fjs5jIVCPQDHs-R@CqT?WuHzN;
zTUDFhw_4&~YD9af4mNR$0tFXhXY4lfMf|08sL~V6<OuR#^_xuXXYH$iBQSyaVG+Ip
z`0tV90!+p1k%TUQT_gRYR`iSW*Bg%c#38u88iT)RI^Z@M?oG3Sy>{g4;Ybk~Y5eiW
zTR8U+Y(~L;D@<_`E56V$s<csVf$rksv|a9Uby9!nJ;sAgP&EIH+g4e&J{U|d`ga-w
zjm6<33bIYbwS?yfy05{GkjTxWQvx{?Zh$wU=JrKzLlRyY{YsnILiG}4&)4(GRtTEv
z$JosKr2>A8aT9+$cj0e=F%?^m$|RhuV#Bd29OJnpAsrJ^_M`fgu+|aF8n|H$SP+*3
z*x;NM`Gux_B>*0=!tL*amB$+^r^omX51gn@lV&!TxAZat==s!uG8zr$O&fBOx^d)q
zz0ZQ}1`Y?u%(###&&6SWDW2{u@Vbbb?cg;ummO|EzUf9oWRTKc^eBR-q98?bQ$0}O
ziU$og;cuv~_#LQ(;Nd#v*a>@xgoGzM!p@iyEE70oXG{fIkrL6DP(k93U;1vKENRbt
z>La~lO>8QWW+67ljnuxA?#^H;vF~ci-q+&ENmr;$6L>Bzw=E1hUFR_t$;%c=4HwX7
z8?_Pwg-Jcgk~Mj@wvin`k#RAwfrIq4p+f;0UA`Tr?u5aGRWFG)Ma%ymK~gPy`@{rz
zO)ok%&nsFS^JxMa8mt*9-z1?jM!1{9z$5!)9q+nM)Sdg3C`6rHw%GqN`A)VF0M7W9
zGQJC+HfcPjrWm`4taU(-=8Vs~#2^Zn`Qatqu(aAb=2TVcPq@@?b0)X|RVxc8A@Fbd
zgxjhjiJcpnMrZu#9FILNp5k^X#&y!)Z^zQM*=SzgvqGmeNPFxA>_uC}EwcB`xa6c1
zd`KhN`baHp<MWnzc;0(;U0beW>DrOMj&Io@@-YM`fBk@HR->Ff=`T8CZ@v?mxJQ2p
zjj;JRaQ*{+JLx_2ag=a%&HCpyR=kA`dO7xFy!*ii>~@v<SQ5Ga;cfAPB<Z8sjiXg1
zV6b7Uk5dWlUvR$<S6kWxLBoIhTBe=y&*}_<fzocFS`$H|#pf@w?ht`8BvbIWLB{n9
zky2F_3&Yttc)2^Tznmqq5HJTNa2|o=Cf1nWN6f&7tqt&F_&+z}Ybw)>h^QIx1)WB%
zuN7Cfb}%0i^ZxH+AaH8oivz4m(XOXIFb-E=sH+Ppoi>sJw$OoG?e9(h7yKxAwfp}o
za+sO^M<R!X@&E8xcK(}<+8X^|vrz>^6obM(Sw7@zA&AA6%FPs!WUdMDoJ0GP-P}Y-
z9%z(#b$z{aw=|TAg~i-kH(^BdL}^V;P0Vw%Tf2upLNpw|h*2ts`n?*qXnx*J@2B8B
z2-%#s!-q37LfE6at?kdkqqMu#zLI>$TW<LDyS*P<hjVsl*%!ZkdB04aJG-;@(#)sD
z7!O`6Pg?#1hp!tvxaNZ!i1IU~GI4gYR%Y(2b~g`BZEX8*Bdu)vl(`M8DU7y@3^tm6
z?r#rCZ>fep%x3Q&Ud%MgR>M!6Ud(}-wnNP~WwuiqrT)K!b?RrN^^{FqxHFZv{OGK}
zX7UO`=5Yhn-d!{Jb&2|eCPUSaWoiL5-ajWmW5SeN-Sl(}j4|=<53i!>gAqDivAYmk
z3BePYv13tf0pCiK0zBEzua`#OvZVuRzgtIVn-UOX$q93;i}ssm+Lq%tm}XdVTc<0P
zyWW)qLMAe%j~j^)?g(_-?z?7fw$d0a(_4qSs&?!jpHM;c<F8u~Nu}Rz4;6<}X$>r@
zbFI0DnHLegRc^Aq9s209BAN#M83TVk6b}wwljaaM8Ei!LUKCqh!04|cz7<AHOUAx0
zK8iVe8fVX;%yo%9o(0tH-|v0-2MZG9vRsX{=rjU8{ZCiIci$qb#_RXV2YnNAZZ|)*
zrZ;1+N$8kUcBo4mm>=dQO|g)S2{$9=l}tx6oDX{ore>IFwt<86#X?~M1~*SG=SrpT
zC~<Rq`ibV##mLB~GaoM{EDSx|{cExvbb3lOh-9W2sYL=$pg0{TbFPODQb~Rk{y$Z6
zv*&d9MP1z+&3V_fA!bVHKT37IFkB?v7G}q}cZ?!l8`JSNIP1Bsy2qc~?ju?BBn^tw
zrKiEe%#RquPzA3}uN-j0NCU{AfpKK)gj<4+HqFryWsjV2XAwr|X^~yWU!K6^>S}H1
zX^ZzJ>a<ZdS!t@;Xt1y=s6VNy4o-joIu3Mcri!$@A^PuG_`?c(G?bu8N@1vdbycN0
zsM^)1Dv9d9>f|S<z3aOuQ>l7)Pd3_gYoI+fk!bptH_`wzrg*+A9lZs2AD*)CH@b|u
zd-GSi8;h}EB9-P|2~_QK(zLpX$P+Vc`mLtAGsj_X9!nstOu%I~p#J)Iws+qGP^PM^
ztF&tKB@1jG$f-aj>MEV;%fvBYAHaD6r=>`gIb@{NL`|*6tJ3JF&N89eqxVC)9o<Fr
z9`TJBwxN#3TD2O6l#~YYcKUSPNF-?!VMbpPzzlM*Fe<b?MkU@s722LX6-UB)qMwJv
z(VDiAl{0HBizqj@cwg}fw^utqK07GS2MxOmL&9^tH5YEm$1}GI&lX|%uo2te3N60N
z|3>~kdnl4rMu2%?SaAUAlJ3Hhm{?C<Ite<ze{-#~ylACR5Cpm3k<i)J-#m5s0o3Su
z@iE&1;b(Mp+a3tY6&=$sQ?>AA0_pR(smHNuqCtS8LTOdK0EPSklrj=7Bxt{WF8m4N
zF04Yp3lx{GyJLTd4JFHqH*IQ%G*OC~|F)9bRC{etT?rD<FxPJO*FJD9P+COh0}!BH
z2w>=+yMxn-g2oTp`#~2R5S`dwnz}&??@?p6Q}NFg$=Bj?7`!Q9{cX~bU5ktBh$^09
z6*b*Y2-+@C{;X`T(&=OPTAZeE3V{&4na6-ePY#n`Xom*0odG|U%E&%G=-$|lLR;sE
z0Z`k3*S;n61EnEQaBseFV^uf!e6KP#j=MOrm*Uj#TO+Q+!r#UNOjkv}T-b{&U9$>}
z^X7)O&Q!)n<N}2^<ihaMn04q{#5kaBXlJVD$68L}zul@{?h|K8|Ei}-2~F=!RM~D?
zyLSiX7W!3r5Z$fsTxV#kgIOv1i0(8QVUt#6Te$|4gvE-;{gE6c07W1TW<xxAao*{y
z`}7%V1fTt3#K2^93a^2FuZ@BT%Qw<ecji4GJRgWdJ|7H6bWXYm?>l8AycTJ;annt!
zk%I4OT+CGx<>q-8AtY4Eatjk_j+w_)0+mK6Z{F<E2O@%`8d3gD{md(-m;`=Q+Lk9Y
zkt1HH2uuO6Ig@!jD$L(8#aRqdYVo_OJQxSfcM$p48fBFyx$P%M(&F;t%d|)z8g|dw
zIYqwsA{4kbX`dzgDs~SDC-FN&V*b=30=fPK^z%GCQcRZAF`z7(P{wMnGG&=6VzWDy
zik1vERhW+5DifW#KtuLWGu?#IRcfnj76<M4LSW7vAe7MFARzCO10&-^Cij+6@dg4w
zQshEry%H0P6i@#*_y~m6-CBpAB4mx$+|Pdq*BEl24pu2lVrYq_g&*zqQn*;EzLtAN
z+JuD3)16*|X0TB;jn86dM{~`MjvNB(ZqQcx#BZcl0Pzp2%Y^9_6a9%Sb^38Wd`Qf~
zO?`2blejFphyV|ZvvR%5x?E#W@uO1QGKPc{hnpwjr(a+Md5w^_Hy_EACF&3_2}n(X
z8~?#UtfP0}s>VN`PwiHN`#w5fynw7?w&PF;Kky%yRjv?sD!9e|=o07u#o0MFX#zx9
zy34k0+pg-eZFkwWyNtJN+qP}nwr$t+?w6g3o!FSzFZl=ZMr52k=Q+2ELTEsU3Rs0O
z-xrQWDD!s^@Kg5j(xhc_k!?91lOE)7JgsseE<ed6*+E`7LgINH_#3Exnl7yJgoo1^
ze#ywB$*9hCP@BqZzKCu*@|vC5bkI!HR}x}F9TW8lZ;O}>zNJC!BzRRc)Y{Rupm~rd
zbZ^Qbz5dS9TtR$ENeOduj}e6q`i1I394ek@CG4ro1>i7$t!jhB7PF6XqLWmiSygc@
zl{=^Ooh0kLgj1UoT9fy211ksWB+l~Y-FusSz+cV)i0VaH{D3m?*!x4~A<}_uly;b3
zxbL<*tEy`jwVds<RP`h8Kw2WmvX!3B-|U3E&eNUzQ?X>wAS^=iuOl@Wdt&~;(+3F^
zzc^|Ca)U)Zs|YJZe#t~si;aSedky1ivnc29eh1%;RI9-8>{=VHSmCKW+z|u_%bu&Z
zg53-`@`k<j@xnt?-{{elF+9igd}vH{l}sZ>L)|C<;?GbE$gy*V&?7$)J%4jzj&%fA
zI4awYM`woKP3aS<PdN7gPoskTGTyfN);fd4?W{%`jRP5Bg`CMVR{eD#tE=N@>zT&w
zTaflN>1M(u9!xgMf%6bF!z@OjS;MBC&r}Mz-#SifI61kr$IsH>urN5@or7%Gm|xU(
z4Uk4eZxn?<U+?0d)PK5yccIH4<XY$IiR7yqT3#`U31wKqxkvYd7!l)2SaI-%H(r&-
zGYsQ040;;@77Rfa;;iPway%_%Z%YXV>c&PKQThJDrH&|q&@?xdAq;G~ewm_!jB3GW
z-USxY{qpN8u2^x$VQr-^wx(PgxwcXbZ2Gpmbia4$k9r_Iik{KLut2oCtTuaH>>XJ#
zu71eNLXgv<G$^_2itOCH(`*Gp(z2B&rqr%gIVBMO%{XUh;nR}$rJb&BkZnHn7LNdx
zUu2?7CbUg8z}6MZ!>y-V7N_4ye=16i>-UktkHc)%W~`Qj`S>YW8KUx-Y`66pRY}f8
zeL088X~aGqu%#yJTvu?#+Ns$_bouMA?}shU#5l2{lDxflUqfD4=w%I8t7jpdla-@(
z>yr+1V3*iAJn$aw`A<h(Fn2HlL?7`6di6WCUwJb`PpAX%&b<K%V>{w#G~g<XwQ8E9
zV~n>oA+X>H32C>tR;mIjk6b#(>JzX2tIvs!rEWW~H(El6a*Xo|!zad};22Sush|z}
z8_aeQd<ViZkw$yHF+=<&&K3_`xd#1adP*VacNkMZ2f8zHxU(kTHTI;j`!bV2MKfX@
zc?TwV%#HRp#;8&Ql#i9I62Cny4vKyRm%B$&Qf=UhC}%#RVNX6G71C5tQt>4W(HE@h
z4Thnc^5oisIB%_~i*2)g1e{r27N{a@<inU(N>Q?;&zyc=Wcd}(9GZ;3_EZX|rznBH
zWQ{0>c<z5yi<pX3Q%IL!ple3Ty(04(9{&_l>2=N>#XEx|+N~4oBk6LoYGT~<i`)yk
zUmjSSdbM|LWOtp%mXc;~UPj+PN{>95ZVYTHRG7Kb@hf{2*S&<RSTDAu2k8g5H<p~)
z{tj`oaKEZyg5~vWi*7+=i_#h40bDcFZJLxG)}Z-Bcex`*;#@ji^McVP@-7H9ouz3*
zSIL`1g<<+wa_fUa6_-dJoSZi)3ra|Q1|n1u2n*v=k5`Py@^#{KyRzi==diRH{NkFQ
zcD>herK_88+K?GwCmD{uWSLqtlyB@gVjd0+Oiyng;)Y=U{`;5wbiPl&)_998v+e-h
z0Tb(AR?lk3S%I9)Wgp|D=h3}E>Xq><wA5uU$1q{Na516y!Y<*u2BGQZ{CtiJ8?Z;R
zT1dlp?z0Oz*@8<~Nh<GQkn?ozR5a0WvrfLG*lxE=(V$Aw?Dt+c>WeWVCq-bL4VEV<
zvOf<`h*~fYG;6$re&r>{DB%lmHu^qhDKM6{Fem=Xp+H@JK}P%q4kgOr%%4|FZPfpz
ze}tkYeL4mA5N>;F&2JC~VSkXFC|`P~?-rLlBK52eq&G%Io2Cd@bbS=~J{I_0kR|%x
z2(<qZw9dxK@}EtpDizt73`W$>7j=zuu)?7yh(F$^MM_?$c}hi_?R*-2P|khZ0T!-5
zJ~?+JvZN|c3SeUcMusz!UcY^j<uE@v6@lr+qDkKD32*ov`mvT|oE$lk^pxqWl>VXH
zV|ic6K6ib`!Kg0i+$h9KYefz8L?08d0H`h=yG&m>c{yL~l)gfo8*AXKw764cgS0!z
zQ^d&S$%#a#)y}9SYOl#l-1j-q4gTYB)ELojfukLSoB|YAt0G$I@ka28xuK0HC-FQX
z+n~@Sg+v$8U}_>D$qHsN#+PeIGpH@nIGR+)*=P<*^8Qmj^DMgSu{S`0hoYLQ>uIy6
zR>oxEM#BpodUlTb%r_bKRhP7h1X9w3_R(%exmfP=HW*J-^A%AZUyiD<^mJTjgecm|
z=`}k88l9D~z}2;Q8f{G*T6{xgN%D2G;4jT3zYAbv`m*vP-Q($jQYa`vF9QGR=)jl)
zWP@I0X&8~lYTWr`8Nv0@28Lry<e)sqi`Zel7aJJ*()D4W7@ONwyfr0ds-<Ort~$7E
zQ($M%rL6gPBa0c=MtCqZ^P`yMSX19d3!2LXz2e|f&ZWOAgXJCOY<rpGT4NGHV)YRU
z!>{8WJtrVKEZ}?kn$|H(3h)Q6N0=+uZaCA)`=$*)6o4N<B`Oi9hS14<fLS2h!Yy=g
zgn>LzC7+K3r*F)1XqM=D1>Sm)h8DW}SIM7UI^p4>ix}MizV9s$4(--?>D#;72OG5b
zff6v<C8@b$3$-E313#Y!oLVAN%Kv*7+5XckD!Ujud)S*WsQf#ag#RBsP#i2w?Ehsp
zJ)ksD)fa4II2W=?%}<@@QIfFOn6RUy4P_$9uOTD;!cd37Ow^7t_tr8Dzz-}=GBRFn
z{Hni!rUpcaMLBSYMa_w@rVnC%)qitMt!$ormncTQJ$>A`{df^L<EQeUs4Qu#+Noxt
zBaw(B6URxkJEm0yW*xwuJY{5Uf&g!JeI`EJ)u@BF!ypJ84x?;>B5^pd_G!lK8lz`A
zll~CxJkMPE(PPMyLc-ihzdDI*YV0i-;eM0qjS$#1Y9Q?+1%)93>P2$^-6&uDZp)c3
z#Rf<VW|OIHI#{@P=O+g^<T)LG`heTS8$a(*-k^iWXDB52(SDHgAQiwNr~~><$2cfa
z?-})jH#Zo56X{={9vYt1O7Yty1vYpm5lbMQ;}g0;zd+tTByN0o{OdM|ICHc!DdpXr
zBXE>AiA~W@&0&^clkQd_b4#fRK+E=ZPaqywMxy0Sr9w~(0)H=4d39^ck-A1X$44!`
zjSr<d@)gVm+S3bg|LkgErj)_QEgzf#xkK?zOi)5AzK4B+fln|Bm!q-9Kf^q}z$YJ7
zecKyxEaK2RD|&>*sPwp}o!nxOAVW|V35IgyMRjBlP__T-#hMI=frfv=Q6kbg!Z>_L
zc}%-k2#)b8RsSo*=3M=ET>p72r+%G}latWXyvEUd3FXb#fLiqLOJ$Gut-u4cFDU^$
zMq8IoSW0e>pEzRv97!05_$2OEo;g!pq85psU0s?zkt^c0QUD|=<v!THs3EL@WF*-z
z5(APNsBEVU;~>po1*o({_}w6=kem#8F=7ph9innLN4UkH^FEy+`5ikGAXRpf=otA1
z`628j>}?QYFmw>v5UVazL$H$6I!rZ)&Op49d>Qe1P^;hHo-{MeWzf?Qq%O{$oE<46
zoK6Ep-H@hEDQq5TkS;FRJdILRh3Xth7u|<rzY8fCok1%xCNbI?QF_R7%zVsHvhq-U
z{`Xa`r~a+4!vdwN%EsyCoL{6xvkqr6wl*7j6ia90Q;~kvBdc_d_uBW74a3jyE3;3{
zYuEiqg_J9mmu9<)*3Ww7%KkqP+>di_3rd~hI^BI_eWG2wS<qE8oAInjGAcp8;m~)9
z6*Qx*=vW|5^pvaO=Tw|%bU(^hE5;Ys-i%lV3Y_gV&`!qvl$(d$JjIh|Rq?t|_Ah5<
zXDZq8hD7a3;k$|LLaje(*S#6YZ#V_#3ER>I-y})4*bF#dPg(fP<a~BgBB$tEBj&*t
z2@&*Vyq4<V`o%3Iur*rGoEC$4C)Xgm2sf!D>W#7JN);Zg8qeojOK*2QwqKu>eW7+Z
z{F@mcA$d-~!a~4-mO|$jR#xX10T+qP(u<t6^R(|-8$-A~ohRNCHZDXL39*H1$SKVV
zCM{0~`=rarTl^jyz<5U-OcOUDk0IU|ccSwt_As``y3_*NP7Tc`_f@6XuH&>ZQ4wcE
z&F5&fn1@TsUMJ1#>>fT3Ou;QABOB<U9)8FBYwyRyfjNBULa4cO_Od=yhn@TWBNWQM
zk<bPVJ?a-~UA>*$c3|GW9um;SX%W(J>RsM=UjJs9)1qYJW;IjMyEvSlyRbwcuT9_A
z{H!{)U#F{ZZvVg_S&Dwk47@oPq%y>TZeTAAC7+ohVTaPD_Wm*X^QWr5yR5poPEZqr
z@ZTLBNP)XDMaK)C`)#4;Bx*ROk1hkXtE%k8P`}eDjP*)|<aC7*Oq5Lg^~*D~tgx!M
zU^=48osDFeqoKs0QvaEjPZa%NMtGJxQT7lR>S)-xe+9FMmH(Lf(D*9`ndU!cN`ty8
zk2B-sBa;ovMB&VhcltZMN?vUYyg+g_GxfA`jq#9&f)$2`fdD!WS*sCsOE1?ZSnf)}
z+nSa1NH{0)BRZ0!`dJ%^b3~syjBzBjtz)HfHfCpTIG&nZgmVyyU_OK-w+l}Em*cXr
z+HZW+J>`eTB;K@c*@>E|-xYBzAG<9f^1IZ*qcQY%jZcpDQO+*mdla=#@%`8FC^kzc
zf}S5JCvVIe``w8c4+T^}g>uf-U`LrPLT)_{UE)9WInrj>WF{l`+Hx(A{*RY&vd_|)
zm+GTRgo-80Luac&vRoEkQWH^4ghe-7ms_kSa1a}s^)|bTxEdwpD(a$maB_w;xH9}}
z^y2vlg^dGnuXz^<4|G{Y5V;VZBrk6&p~=B%I*wHDDL5tA?R(1S*YoloFc+$|e3ln#
z=S;JwT`Zh@RSHf5x-sc-cnv|IBZKZMlTE_2>%>%ICLCf#RxN)cR3sv+z}1Bfi82%Q
z$OM9+ZL&hzMcPe71T&-OB~&EHfs6}!Y%XOO>!kzVngwS*8JU=K4BY*o?#2DWz&P7y
z2{1%A0WGwMQ(xnR<Oiwfp-pq6rpf+LMtY#+KAP=uy76_o7qB=6p|+B0p4=5qvsb;5
zO!_Rs4Fj3O&yS1fc%bJc2^H=EEgB8!7-N}zMYP?f8yuC?-IhN7esk~^x;o<bzMKdt
z=K^P6m*xv#twKb1p&ahvPinV{HBb1C@+|FM$VsW9vO5I<8|a&z{vMHTXRv-TqjWrl
zH8UH%+Q0&F`xu+<AWWx85IuQNVU2CvW9wkROLDg1V_G(bQegELeqq)zN}pbzV8;^!
zt_#K1bzkn^%@e0=SdFGhCn;*pkC)eAmYl!rylq4&@F$EMmkd3}^dg<$huerD4m#Bv
z5W?ZVec_|TtY$f^LB>$9y4Mj5uv*Wz$EBpq;8^a;NhLsMC-bmLxkhA49A&l$MNPv`
z0{*%{hY}TNfv2KZgh*@G<S)W#S1-8D5kJP>{rd`ljZvT^ZW%B=f64&FVV02H(O{dw
zadf^s#PdgVKYx`K``E{~ckK_7`Yv`3B~%%xR_#xFLK6tKF_t8G>GY*}?rdKVyy3`i
zH-t<)ZK=24Hs2Fqeu(|RbBbhLT(vv5xm_?@X%L(@t!LHCh@(b`B|=8|;8*2_lH|Ns
z=Q)H~&i)fEKP|na&+|Q`b*ig{W-pF%8ieZ3)6lX)s0`~dwAXVh&rTm{@2F08zBWb<
z7!p04ojy^pyaiubYCNmM;DH37=w;aX)Df$z_l-i7{>T0EH4HoN<(>?<CEdEt-!2z;
zyBVESLHTxh7~mb*!qFf{0s3+Iff+W<`B9sbKorCJ$XzVcJao1w0wG5>;3X8GqGF+D
zQeA9@nR?RK1~n;=dJL%b0L1!<ja@5+li%T}(12%k+;X0od^t%D`Q&!9G9pdr@Vc_R
zG+W`dHqa=a&YrH%F<${;2{psAo0a)KO>^Qvb7xG=yHJODB3ac{eg+oL*@$``V4TX>
zSAMD2&?!4-PvUpq-sC+K?7y60{S<f1AZQZJ7M{%YN(OPxjA5WRiMOwfv83#gg%lKe
zi|VK2Ob5rpK*t+P;T+6BPDdu3>dQ-?XWh#%w_Lc<mnw_Y=bw%O)BLIGN!sdib5>#7
z$LC+O;#+k$M^sk6?0yM(9Awu^Cqi7+2nX^V;}mPF4+X}8h{Xs~#jOP5^9SMSm()He
z3VzS8-i$MIQ7NN_zu|y9#~pjg?oWGDee%Zz=)kwn%;F!GJ=aC(!Kr%E{J=sRVd}+^
zjl+|9KZh2in>*tc@y0D_xH)=kHTRfPAqYfPJ@1gUN5*<%N?w0G!V930Y3u}5m|$$&
zt_`Jw;2-<XUG)ibIyC#uKaWhwV1`phx}zl;EKby(6|K}AiT>3K$w?RJc^xVO3HvKR
zb=a$2z|<MzLaoMz&5&`Hzg4ys{&jc^hvU*I5;J!->ueP{b~}gIE;zPVM}@i-DdyzI
z$TqAtKoofl_tafFet7>{f4W_zI<Y6aEB@YPc5-=ojyV7@zyT*B%|QNKN2GjxdgNbh
zmA}16{c!d?>eZI1AtVyL&0&E8Fe_~_dx!L!-gEhMRl`RrKzP+UPQ=3ED26v>Y*LEM
zfr4a?@~5wezezD0u^cpES@0|Y-JPLcVaK)8`p{d4nENQO2d9s5`vg|QRR69jrd>lF
z$IcprL!RRE4*7+E>01m;c9zjJmVOwCYT+EAWCU@&Tk^1=JSxL}oOn*(@4%V7o!H4U
z6$@{$c&>&lhwb%%(0UHSI2GDYe>=NwbQn-)i!88Ooe{D%jfYjPEw9ak0G5k0`2%2w
z<1%TN&%=l=Vk_pYkj=HF7;UPwDrQiN$afJGis+;&9Ni(3lV2-d;|9HpkbkOb=kH{1
zLRfFiF@;y1vI%iU(Mi#QHx|9diG}u`c>Dar)N3EyiuQM4jZakE15ixY2IIf~@D}q{
z=3NR~+&sy5Wj_awl->H!0g3@L7sQ9EB_dQHmy@EJGh(XJlThaWD;8W3dlH5C!qn2m
z)RwulP+3yJgxt80T#RdYV^3JugJzJJ;V=@C6`5=NefRfzb`Xl<l0j}Slb;9VY7KmD
zI*~@(%FP0|En<{ucJrj~YJK30*02F$HID37h#p9_<^<~=r<%_1BJFBFZ0hZh$XPLS
zzFHy6aEPD_Vi(l<H#gUBjM$vg`Oivf3DM<q>0FS92(5X4kX&{Yw}BnFr{o&mh;&!f
z-Ylc<n;~vE02}{IOGh@k!Jz*eT0%;o@7_(AMac{tu&iM-Jt!Y8@+h|uK?z_Ap)}>5
zV@^{gTFJ^y9b@^l@Z)hGd>;S70d`P0(l#p_nw&@$4+Up}<Gw?)5%Bp_$zh5#Ni^UI
z@v|`t>!kjhwi@;XmdpM3#E`kVp&k?dB5_<ZJdCrY=O7K&M??BOzQT7rC`Of7M){J|
zLVyy<0%H^AQuu??o8oR@nm%EZ-+Jkk<iKw&XD9v5k}HUSP$9)#rXHd|Q`dt?3|L2=
z=S_vr@zf2QRf^IccgQi$${`^TeO~vVBk490n9%X8$GMi(QV@AateY=NK93C35fY}K
zUR#<W!%M*3yZ~s+72@Dwzq*nwkK~2I!Y*E&X1sEyZoIBeeM>HV3FMl6vbCa*psFT}
zTqQ&H;NYk&eNV=<5XxO@qhJaw&eKoSF5N&tM>ff7!XYrkM-L#FcGkkFaNWBK%6^RZ
zb@dV?U@ak(HnK0H<ZJ7<pCe=Dhn_W;!bebe`A_3+F?=hdH>yBF&M?HowmG!`h>f_x
zsZk>$7>S);w8o?a(>1KtCyd>g*IjkP8-_ia{vcq^xDlr6YtQWw=(@`nYd`Kl_VhMe
z95hPGulS_kufKY#nzD}?%(Ucw(8OdL{PTk`5@HDg)6rXP#{!_y^wW5s;AQqShyOjX
z#N@1XK-H?N3D6{n2`b#$+x`G5+|H_toq(PEy#m>S6@B}7Vse~G#GnCxq4LT?-x454
zR}3y#vLkPuCE&HjogH5!#9ZoP0cG929;=v3AHimPNKWX5zj}%KnUJ24nUz^Gd^c+}
zIzWeboTF2@ZqX<39kOceLPZb`Uq2FId7n}7zJ3AU2X$VOB>v{G>}*jP<Ojwxgpk5+
z9jU`u{9th-5VPexBfT;{3IbAdjq>js#_XN06;1~YM7&<>KAPToJ-Y>o<-$4JJ;P5G
zygHcwsgDk5s}5mt1z8)#s^5|rI)HIwMz`KG;z{V$+|JW%++$fk9}uFUBhYLTQeq>3
z?<wR1++m1S{|?ec6o~KAP(R$V))h;jIjNm|X&qvv@^hBpR{0|2C1s=IB;%V&MCrw6
zH3kPOXg!A)NHHGp;hxV5*Ex>)>1<G&fUWb&@n9V~RpBRx*C*ZKoD^aTJ(avD=vyzB
z)D&iuI7^$kbi;<PU%dq;HOaWpX>UAeV6_uR>fc}+9G$<W%6;J3g;ANzE>&L*IJ0Qq
zdTUD!w70|eu>5O|NCf!U%(MmgrvdkU<tPXD$lNEWt=-w(GLOj5PAs4H^{DP6${p56
zd3AF0(u;n=_Vf)!R9|Ojk5~MqWo#QF%YZme7X`^1lToY7lQ3gFOv9FGI<=`znxdbW
zs9NOUo2P=hU|AqUOCM5`^=+qDgT|gUXXE9;oVh2kzuu!@D^5f&>mfC?nS7r5wEE=y
z2L2#Fsic}c7S2YcesHvU5=I@>wovNReW}CGQ7b81K_xwYmzXQ<3+R8!o!X%1BOSA0
zzM{VooZy5&?;8A>{fs6mO2XJ1OyfITYN?a|u$P>`(-;JedrD7!?4J5amYn0{C*&DJ
zM5t5py??ln{csv*5$>lDrK7mk5k#NT)fdQ(z=)410-Dyd(m&ZTd!s5H{Rt9xYgB`d
z4TnQ7*8DCKCf-ZnVl8zby2F4PX@O1w+>@xuw#1FVt>f!{gh=Kv6q{uab!t*Bf|00L
zAwULnJyPq2K*3ARVI#}B2n3A8WbLMvLT((+RFhjN!Qjw%-aFp@`Xae}VxLSypSnj8
zTIuNH&GaRBvhK0f1uqK0B?%tROGzU{@CwFI$nL)SFqyTrwV|w`|1Lb8@UA?{3mV8g
ztvuBcSpTX5^R(&>lw`jOTT7~^6fEZzOFkk69|!l5(5Qfu)76Y0c5QSCY9zx0I@2p1
zE2I2=gL(d~d?DK^Lu5HNCAn<7i;ag|?*MYah#YZ0E;W9B{*vqpyyRz`lu2;*I`=sx
zok1OJz)>G#^e)2YM%zkt3JbC|JiW<2v(^2EUyH^r!bPoieX9p`qxAXu!7^vH>|@`=
zThp2!E=&C!{V4z3N0Km9zY+HJFgtc*43W!GPs=(2WX9qddAo>Tv1~W@?c1K1F|fvj
z<4PmjjR;%bsPNRWvHIk{J@JR1wz7%mbPaj2fFH)Rbe!3AFD|3#YQfCN4|g%;6uLJE
zq6j*(>6t6_l%`RBr`9p>kcD%uC{S6f|9q0vC3Sv^trxD;?aD4sQNsCg(yg9vHqTa7
zbr7SJ2yCD_f;T`%Yc}2#$?S!zU|PEXQhFO|erZ>yh8J@wQ!B+IKuDPk!=Pu+u)A6!
z1qKY2I7RPBT+3@ft|`>gf1|pI5$qC>Wrc>9F%6*;>}t;8lDu@Rj%sS6e4#+wz@=RD
z)TgW-HoXSPr<oMYh6eGx@+`!%SUM3v<8}m!WgU#OA>O3ddO3`D{Ii?BVev}d{<W52
zz8EqLwX#{mf0B-aAm*)2BK7?_f}Xf!xb%2b?0dLV(;@fMmt>zT<6<TTB)~P$(O1%o
zR><>5x---=&@YyTvv>ZQPF)F|t5(wC%u*&`ync22)x(x{YNTY)OZXStNi|>o94zZO
zfBmQ7MPAbKC-0c4s$dl?wCo#O(wbNoKMjq1Mifu%mwjPpLw7}6JxKIlP33<bCwI{!
zX)s$j$2G1y*MtQ>1Fl{>O;%UBX2yJh)3C@laN1wn`D_U$tQ<3Uw`f?-OU|x|$*Jd$
z7{Olk7s53Nt233?j|GRm?TluBxSeF~DWf6?3YxzZYn%o3D=IKNi#$u0^bj3=KTsCE
z$Euwm1bF;<kBqFk@oK&%bG;VT(F}qZpdq~=ts3?sQMnRMgQG@5fk}wjh&}7@0M}n=
znaQWU`gW|9wj=<}FK0H#eN-98<>aubyNvm7&pyO&j+SanSF7s=+b#0WKa}3nFyL%E
zrug<O2M$yH=a}`WTk<=?H~i|WEu=EiT1ZVaM6|T@z!>)$J`vTw`?5#ZmFrFvtlZYH
zTIJ??TH&ls0|Ek*1e^L?;MmyUo`~24Qw5?)Q&H+v)p}kQ<Wy(;U*o`>LUwvMq1+(c
z2$~885VaHBSE&x71eO355@?`F?;QIE4`j<FY;}yok*WIs@<r<b=$TJ;@m9;NSGUj_
z$AgX=*@9}bdHebowN98r5oU_s=_0g0lKk*l5)DfN?~1v%G3<#BH7r;0Cv78p6oEiN
znng|UmW^h?uMR(6pP9Vt-6nNxJ?A$YAx{=Qa`pUOaG_ZP_Nz_MDL(qpR$JMXPlP{p
z*NfIx`n<BDBlLPh?>P^pO+a=E`vNx0wJj3s@8JtS<1p5n1(UN54Xm&>0<?K=JJ6SQ
zlI@TBp;rZ+^#s!u$3Ofvv~6E;Q=9#!iVJ+sx7lEp`=6M;=Zh;2B;04$u!Fgp=7Tw>
zbgaJbAbVFc^v9tvcK0amjuZC?B^zHsBcXF$W;eSE-lHw7xu|(zh8x{C;<`KY2>0Lj
z>x8^;KP0y~fuG#<MD0{fFz0#X9i#}7)wOtRd_8?w&?mDkt<PS8@=Dg50c4l*q<ni&
zpw7RvdbODqj3*grtL$!Q1wa>LD6WccNo)jCX*MA7P4u!-6QdEz+%s_&Ncv42IBa_;
zvW;GyD>cN<t0!Fo3}ft|f?pC-n19d<#z-^Ax0Tx|sB>K4R+XAq>`Lq%4l<BaKD${u
zhK*A2Y`I?5Yi7g`Drs_AD`rK$=w-U@%42sqlelSBgC}waILu+O9WE#3>jP)gXR(7^
z;^DwR9cUaW{aICin5S)eW>-#w{#J3ou)JwZ&1%<Qg~)(pCghVHjztSYRN>?jcMs;2
z#~$X-g>%hm&Jn_a{;Y=0!nt1BkxucWg#(B;+FmlM)&*V!Ln3J&yX07dfpBF1M40JM
zZZF#MigtS5JcKfv%Tz}&hpOCAySF$CO4eevmkC$7<Y+En%n&@gBm^(TwWG9eg$&D@
z@N?m7ba?SL86N0hSyw$?M3>5PB6|^4)0FW7BhXLNN1(ZH)K67~8fO)fwP)LFJ=X5*
z6PXR&W*yF({#Y$zx*NI5-5{yyVq+7e;DagEXgmmFQHwDow28lkTkP|ZUa~Z%O_Lrr
zl(+tN`5jNBfC1*eD0NSw?*M8`?<e<a{Xmk9u}Y$7?z7w0`N0phcQ;<kSTrxN4dX4C
zMxc$aAHNO>LAfZ+-{Fe{MbiK>E<^+JGKhz9joO}DP}=<dNrfKJSclCKk?ny`;ZS#b
zx<=Z3C|8s&s)N&#Sw@1#QqWZAZV6cCI;SFAOVi9}BRgQnHB=5n0p6mhgA#<28iM$w
zTpTM#vw#l%c;C!K-V)@~7MB}&kVE}mD|bR;Z!tzU!(O--c7TNKrs5|Zqo1k(^6?cm
z0Km@zN@J@osi;|CQm)mNr!wi54d~@=!9GJcdzv~weN<*F9Ua~{1Re6qv{r~DQbWi@
z_H**Wr6iY4mQB#b)ci=ll6q*a<9J1wh66sg$mo<F_*wmX7_O5KRZ`1kl8)tR<V{TU
ze><&)Jb)D?g<$Vhg1yrAsr&^CA+o7x5`{vf3m%*da&CfQFjMYVCjP>>C0+lwi=i-G
z+0sD?)pC-lX|u7Nv;mDyJc}~4kN3gARnAw9N&;|>E^*t~=*^Tr*r=>yVwk57BQIJ%
z92+soY-8fB*NGZ+oMP~;6_;KqCk<>Nb890h*Uev%TUl6L$UWkGMD#%+pr1ljjN)`4
zD_N`(p*J7;z>o1k&xCNXUedS28k-jasvAI6r@_6LmxYlh+>&<77p^)7v63rltz{ur
zcbm<%qf)d%viiPkH1n&IY*i~+Gs>*VP@f9<ZpfwLUd4odo)O~brE3$FaA=xi2vylK
z+)~m*Nz#)`VrJmGnQtc_FOy;8&d0;%Uz~x{X%6KR9m;I^k3K4ya3`t~O9D$geow6R
zQDK#uGgwQKb{}gj4GTyU^sWmyyAyz&Xez`q7FB1ooRUQkh$@{pd=(7^{c^-_@}s^y
z_ce7&ZY_?s>(EV@-4>|styB+0^?i%0YyJ}7A(zxME>$lc=_;EW1mWU<EIIBNNV{S1
zT@>)i@%?g&p{0LGuw==M9TieJSMbqL4-ktH3`zMx$s1fV{%?#<j{lR2{vR8iEF6sg
z)#y}*(Lga<&JHskE}t`*ypC*=)T(Hb(iPp%wbKR#sH36{!4UhCB0@?M?x8ns5{*d;
z5pLS;6+;Gq3JyW5R;YC>k1uakyJ%N5w{M*{>w3NAANak2+gNS%d<e|^e9C7}vasy9
zbvtc4dCXt|F?q5QAwsTB(X80QLS;2s2#ciZ6vWXiwzDySp0PsvI^?}b_VbAD`dH%b
zB}Lv#`E(|TIel-AeRNzi-1|fjis5~t`Auc^{!1;~C8dTl^t4}yzeW>-2G!)M+*0&W
z1avr=&3HI~tKEH8YJT@|`<O;>({EE|{4cXpQ>OTGF2Xu(J~w({y!ar4wMeW)Udb}5
z!2~BsDve2xO1X?pO7ph8_FumSeae(Zw`+oRn^J{LoRzUb856ckrF9gyS{WN<PD+_G
z74y^pgl45&g#ykJca=f2b(tV}3fAMe1{=DP=^*vfXF!ikjf|uc4(zL5G5hGg(xFy?
z5)sR}v87EVxoTg<$c-v@m142{+};yuXFv?}0#;<mDr(a<<pB0Taxib>93}(Dy(gSy
zC+$Mntz0lYdy@FWSGr>LGPbdpNl4rZN<kvkB6ycrcKFkN!Q(&E4@C*i(q<E3{mX=E
zoYNyhsDL#xbHeKg!n=M%VdBYC_BgE4)_CRReMu}(sUZDJ81~yvu}`}GW?r;eZ1`j%
zMjt8Y&n2ZHJCmDA>@FH{WGu{5kg4<dDQqUO<REB&Om4X3pvu0cAvaSBBym8v$smmg
z<o~uQiG{+O2DxdQvR5U7P%^@%L_q73+#}@66bE(tI0d73rzs0&a0xcdgKsc*Z1;!_
zK(J)+i`8O0Y-P4mdjuT3FQ4n^4upog_<gV48^X{u$s>_H;?@dW3u<=C-SmZ7K(1eX
zB1Fv5(#K?liQny`TgEadr!l}w$PcLj#QsO#h(tkp;=-Cu70rE1z_JSJt8tp@!<$61
zWNpv7v5><$Cj0XWre4;9<g~^4V~3NEnL)JUzx5p69c@U1FwjbAnb5^V=LNYW4u5$=
zinbiGg8U+$N(_x`8Ce5N?82cp5fRi^&b27FwlEuoCjP@kl@+^(k$<upSNEh>j$8Lq
zpYSHaa)<Q}0}?LLZ*-Y-g+(Yx$B%T+g}=78zP_Ngn3NQowf}z3HW;=^k)^y0z7X6$
zKE70V8Wj=Oq`d?YM2Nv|1uc8cgkQ1qtoE8TVf;pZ`hy3x9!Xn7wi@Y@(~#A`_NStN
z$68h)*sp5Vr0BHRlG75e(NJ0lPs&S-&bjuK54n2v=TxYmfd*5qF^dE(uY;(Fw17Bm
z-=$sF_U60hYgSvMs$q(@ct9a{;3X{fnJ{5(9(qY(M5v)^y}M;~%--%$Lx&Qw%X`me
zCTEZL!=#tYHlGwWUj`?ir41}{WYd0biD}jqbQLDll|Dr%cmIe?x>a;j2>gOM>%RTx
zEOeSUXD2WG(Viupgg(X*vm66&{E`|M6ng3(2IEgIy(gmOce$O|-Z)Q5A*$c8>GtQ>
zv?4+G=P^sXeb|&<PVSy53q(-VK=#+k-}i&Q*V`pUrir4v{>UN|(d&6JFP7TJTm8^x
zF#0uQe9|Zg7Z=BdItw@Db3(HA7wrceB-_V6heWo*``5hiQw!8cQ+w8Q$<X{gFTfDE
zRTvwLYaqJoD*8rU3*WK7p*Yc`5kXKwF8z8FjQ~>uQ+&#$m-UqbV4(!<<9pWOLazd(
zAT`F@mY4}V&X=^Cu3A>Nd-P3lq&t1u2i2#wsjV2ovrIcroKve`_P83r6ZA+V0(X_k
z&gM9>Fs~bH#KJ{+Gc;Eft))#8eY=O?iJUyWQ<hFIiyU!XCS&I%z)!{*VEVqP?I4_~
z+a%RB-Uz)S$|!C?dZD;0&?5*m|MG|N;85)ZFezwoWFv0m@ek}~DKRrT^3H8lqW?uB
zw;O5BP9aSA#nY+zkm8#OQ~Ek(&%w<RRB4uo@FgTK1or}YM<w2J1;+^$`e4mf{kvFP
zBQTqaR+)?08v0LEtT9{*xTf=}E@~G!e!k5nCC?K;?gMv48%AMEsC`%3Dd)+Bw(*kF
zd3~BQlfD=k?3Frb7^By+Nvo1}t9Wb)-7W}IVYPXc>X3Zn>Z#c@mmbn9kA5Uhr#<Nm
zB^?PU>5r55@GvwCheo{~T|7vKQtDrlRNE7Wv=0?q=TAz@PyX$^Q_XiWXM9~GbuH`N
z+UE4EWo%sTyDM$;PYkONP64W`QFKS(2`wx5LIcCyPdtJCLwTXUOURm)%q(@Jcmcyk
z8W6AcF88#|Ycfx9anX42(oo)$o(-ex^WY<Txz+d)&>E;Xbz<oVY7(wmeXv8#o11a_
ztw@6@`>8Kq?~EY)!Nc=bvv+QTqIuKa`s+V*ksZ(k1e#zj%MH9sq+CkU5!976b+Uyn
zJxzEhy?PtI+BH?!<9U(q##`ZM$XtVmrI8sZX^8a&xgMj6%X-Zit8Rb%GL8B=WkMdL
zqm_E4QEjwW3BJ@NSr$`NT1(~UI32|5sY#$p2P2mQl`yRxdp0-~f^9^K0alxDwvPlH
za|E8B{C%e9u>@V{Lt6mklN9h0%ZJ6_3z#*|`=GWpt#}xN%fK{JYxWwN&CSf2*#(5_
zmm)xPG442jWSsSNP*=6jlTVd6&xUr$dk6i13G^GNo`+WcFB`c|+bec*LuV&-bsqtR
zl$dHSIVlX;C@riYL9*owF9(eQnVbBX9a*;z55}~~J2^4o{rQLK>U$EfC0M@B0{SXa
z_{3Zq#>?ngMxYFomVWPu3^x=0bZRVM`8O*YS@E<^=%?#NV!GOyny!FT?XAF*B9PgX
zYsTa%oW74WA(^r5oy?Ov{Ql0WA=$FIi%-$c%rF_ZGwP=zs$TK{c6mGKXk&KDC!Af;
zh>Z6XF7wDju=CoCp7vo>u=jZ)Z~Ky*CXRLLJ}|gt*GUp0lUYbv-cSnf71>iMIcGIo
zv@&RYbhgMa287P<>O;oILO0O|)ab8TV@N(HsokUkqkTJ&P_8^u{CJp0%{Xq;BB2iW
zPo~?RFL*u%F1YN72i>WW)XVkJ!@^FDKO}#&y~g({pZn1#tWe*hZdqXyv&-{ZTj3q*
zY7rlsb(=+S4qiK(ZeTM*mNh<LE8~xNl3obhK4&p65fR2;@KJw$*p6!BvL~H#8bTO~
zExe_p>o07InVrb2s9N@I#rLLp<0^TTFSShM>V|*O+-L7%ht5XJ?{G!P2>W_{BL$J?
z?i4^ljfQ@&cZ~38UGfqrXBx<Yu@lr2O`tCzX?9^R>=phknvoDx&6-W$Ox4S9BlKD$
zCvV9mgqtU-CUQDm3f{na->&Y=G@ey{5P%kkwMCcT6uK0T^I0GR--C6+HHDN)P~A%w
zvhhgjDDTtqr^5|-p!@nl{jxDLdji7jSJhVClyHa(+(-Gy$6#2fU>7Ha?{q8cdMQlo
zKJl#;iWI@qtPoZc0R;`h^OT0pF20UlZ?Dfr-oy|+ZC?w#erdcViQj?a#bsK((k~$8
z#p#MqPqm^<#;7x;tVh=<<8<uZG;}z;8!Pql*6J%5A7Bdhv6i`mmRRq=MvL&{FTLgN
z(tm=yCXPdpYA-?jiFv~N>8WUykGPH@kWBs4vSXIYiq7}B@Km);wXa{B9F2=rb*{Zz
z`-Cg+=INE+l`4Qh7N`3K@jl&1lOoYdrcul(FqiK^`G7W^!Jf(SRK;o6%_hJf`z28c
zck_ixAi!x%7w9{2P!zHpFM<4$E))N&EVx)5Bba%3p{|OEGmwC<TO2)EPW1wMVCu49
zZ;7Tg`x|l1F>?I*MxhEzaOvs611ZBTx94Is`1w^7*%o2j9UH|LFJyiwr5El&UM6V5
zqigJH%*)8KMGzSix&dEHOx|O%8Ku~0n`#8q#~eDofQr8q!3>7OZkyK@pW|cT%^W-3
z?*ZKt>W!gwd@9z|4nzN3Z<uc++!<*iP}NUtCjSnPBJ@rrqE!kZqC2d5TPG4__68tc
z>4!EHW1PdiV!W-?d#vF@FwqIIo>M5wwhHvMK6&p_S%;SNK<O(De_hSeBE*4f-s)c8
zSh#pb+s3%~z`t2|dbabqH@--K-4{w)wNEav4fm=>_|=W}hhK<*urJJCe;*T;@Tp*a
zl(lBe>_=BtB@kpbq0<D~tFfyTRj$q)N3!Ph@`jtzA~56<Ti6QhKxBV2Uj3@We@RfR
znc4Kj_1{x(CrCoi|H|+Pe19kS6Mh1PflPJIR+Mok@oYR>d{S6jFaAxj@Wgs-F4qOM
zUpCrW7!@xeQ7Ze03daI%0QB;B_UQkK(o5W9HQ-icFL{)9p*P!0Io@!dn{a+%%R_Y*
zNP-pp81V#KEw!N9=h98ekb`!@QG9Zo-taeSQ<Z~K)MF>Mx`oRi?*<E5j<bC{)K#6a
z4lDhV4l5c`8@y2QVl-*4yX^1{S-(00_LkC4f;+FOvNJv>PKHDOSUGK9*Bxhs6S9M8
zkmty5KY!-{-USUqjZEb?U!yi9_xTAPG>W9{8O<vV-2(2rQRocO*|5zVQwB#NUMrm-
zTTFA>zr^v;;bQ`ho%C}bzPb$^`)LrCA7xR-&`Pii2%3$m*IkOBfbzDEqTbD^J#*I~
z?E|_te(7ap{AJUk#smyuOtAICb-%7vo2HDOrCz!u8v+?io6S&D=UMf<mUa&wjHBU0
zMJ(6?o<{wYl3f`)n@Gcs52QgzK8PZzQh1wUChb;iT18#$x%OeV|9E@(`vU=s^EAdL
zvOf2%kIt~Hj~d41!Gy+vgk7Wn-rvtO9*Na8WySNlI()x)lGZe&71*vcx-%DR@9lY7
zRezdjhxKv$X&1qPYd#(-#rBVF<QLBth5I1+qsAX>4%1sGR)fpjib+i+&3$YRcv*Wj
zPThlHl99q6@MRTNhW|#+a{YhESypDQ|B@-sQup#!R?Y6oBh>+|U_Wj+X}7{AIM@$>
zX0F_&zK=Jf3I_I>Ra$4YnJ<=17hiZxGJAM0^5C#pU_A?L&STKZZT<}iP=yJ|C*&+R
zuv%%k=8V4Rnluq;;4pDr)Ep6L_RE~Hnda)*dGYS)8R4R}{`QU>GDJ%AdbVDRZF1hp
zu8b5aYpm5EmiIEwXje#2)e-+95&J>`w7W5h-JK|wNus9D{B*v|08Zwt&QiXOzZN{7
zyD-`9j;jaFJ4u+Nu8qYm`Av7>Oa5GXV8R$Rcvo_0<kWU+-<{s8N@I#6FzB{h7wF_E
zOGyn`_C>H+Eki~)2lPxrvWKh0E2Zrc9T!NECecufFh3Op$izwHq60w5kjo@$9n{69
zn;+={$h7BQpqj&siB0lUV)LltrGf%j!xc!C!|~A*N0Wfhs{BBO+A1~jlSfU;#lbFA
z1BCpgh>)dHW%DibB;ryv$Q8m8(U6lO-q0of0e2>OGIZ4FrANTwGL%cG6q_tPDWfKq
z{z6n{$26%}!zv*<MQTt<$iZ5@?vtyHZ|Gu8tqNJU74eJ<z#dQy^&0exq;d}{TrPPc
z#mJ?iOXop3#BsSA`AQNnE|L1fi9%Fqfw_v3#zg||`s4}`QZPwFN^zFcwgE{5(-g0B
zWiV*aWwy38-nL8s$}AVD;~Ed=2v}c#%KBxXzB$Z6E&kP0NCXLdAq{8%l53Nvyl}m+
zHzJCRyy}1}5gB@_(t{0P>NaXO>WnYpBLR1Hp>-p*s;k`AAk9b{L3{w4;bumjW`m77
zR%?m|CyZqOtKsKAX^~MrtG=`l6GPYXK`Xr0bj1S!8n`?oTGLWFEhgIp@s{y_z7HN|
z>Xg?~xGXXmFgn9&Ztc*QX*+#49spA4S^AY-d`HjAhaU-iZj~Je8$2EHV+b>PlEh|q
zD)(xY$Y`ins|`ZkxcC8^z1hp1(T&%g4?mkr5!5qDJ!&67-jcl-doj_Se2?FwYw|q<
zX<dc4QuBKs?gW0U><*v((gZJlz&pK(9>sf!*8%T~(>qq!3OC#SRdK4dk$=U{)zm8K
zyo5S$>OSrc8NYtn6+WGhQ0(A-(-~Om(zB#k;j3SxnW8{dl&5GYDh-hsWk#p0IwvMr
zN7YHNbLFTPQ(}w}w|D1#_s#L!lrXcU{o91ehEcl0P#nAC)2r(3<S!1q#^&UEi>*W6
zz3sXu=*ymX3N!9D2T)A0IM15<#v-Jb#otVZ)|OJc($H<STS#xJaB4tYsnG77a_>4T
zw?enCc0;e`)OQo1FBtxnkr8K?ygKxkLU|{iiXlD#Lv!&#h54qKCc)9cyzf(+V2mRl
zHW|~yCzBUndM`RY2{nd8PVFP+&8NWDaP#Wgtb&u~vMD!EnBGu4LDzSD?f^DTQ2ck7
ztYuPlPaD<}@3Awqji{?}LQKc%rb~(LRyAc{=>rW<N6NwVj0;V=jPEHa;pu%x!2xNm
z=8DG(XG2(6Khz1>NhJ!@nWJ*GsI3jDwP~R2!Kh@a7n6Z)zsn$6fGegw|H)um!GWCH
zkF8tL1ZJxV(`mG~lxXZv>I}~@*SQ$%q1Lpc7oLisKS08WFy|ZT(9ud1tP6+<f@AQp
z4hMm;gkJ>P&iaVY!VHJ&372QFZLwMlqkdc*7s)Jz%$T_U-JUJA56ZP93ky#!bm6j*
z^NPl9Jl5B&ply-7iREI9)D5O}mxgEWioqFXtN0fz5~6)@y3mcnP}qi~uj2#VGwX?y
zWqX1#hX-bWct+>fCH3^dv#PBt+sJtA#)WnBZ#p1!ucCsz9oyvs;|LiEXW?GLtHSFx
z2ZS5y6At^u^W}~y*(JFG8;J9h$GD0V9(C>(&Y|Clz3?m4E6f=4a}{e2a<6E1DLq>!
z6yD!VJCwVJ-qYn7cuf;{u6bFb%^Qa-P|-0R`_-;t%(w(boa{1;xMrVWMq+H_@t2Rf
z3zdVT8Qm5$nz1q_n1=0`Plqb1v_nq8fYMM(su@h$g}S!8eETSIAjerPG&r6j6SVQo
zfRUm{Lw9TjNa_4fddv%K9DD*Ec8}-w$YWydC}D^uPW4JC_Mo>d`}`NT8f|XHf~D))
zwQ)3;V(wnqsJ>>E+klOu;lM*}Ubw09>3HKS4`LrTuHE<H<!%-CrW_s4d%2mm>*$49
zIK1X?Zt#{_`v|Rx!UI1R6JfuCpb`WV)#P+`El6#&57v^BHKhb-^pd`{*V(Csu((xl
z*UnexHLh>U%y52=+oOw=Q7?ZHMpN!$1nmI0ZEgj@U`@T8D$0u0H&nUmAHJFv)vOF?
zo^mx+YlaP4+8qe@?s_j?G<dJ~z=An9PdNet0&HJ?e0k$Tr2(w-ZK~zH3&}%_L$6L-
zx!rs}*3}AVKp>et;&O_q>CF_{7Q)Mc^hm?bUQX~rom0XBMJ$(^*{1t%m4UEKDxU8R
z6~ekW0l6rb<`+=#Upc&5RZ^%aJ?8PqZT)yfjeaMsrX^z*aSJi|5xM;pK~uM*!=L)n
zZdNIMD6h}<9+^831#x@b4Jg7RdLLq4xLDAYM+)IiXsUufK?MHU$$JAg-2stvz!qI~
z%Z9-Hyd`qIXpbWfbjnaJG*0T*lZYu$s{fdx^mTX^du6%!iIQQhjDl_c)W7Zr3UCA9
zeWeY?nP}Z|5!+J4J7fv&<>_DfAnQtm_r?YP*z5L>&byr<g~5dtZ_dZQ)4BwEd;v17
z(LFl|ZVRWo=!i^cPbBN1ob1t_{v3ad4}q?b1!_kg21l-Eoz9j5pWGsdTXz-hY-q6k
zPTHc*?nV3AQRhG)ZeRcROIiT0n1e@lY0t)r=>rKsc*)%U7+7)QjP#_%Z&kn*IgDgh
z9Yc-HE4HP}gAnl{M6pW$TsaWP^2#)0yp~+IYTo=2EricSK99I+xsp|r?Nm3Ksv%&S
zI&Xn314>scxDhp#&R18x3vO-k38Jz%m(3cX4~mn={&Mj$shXoY-p{!ObQsvk2@2z;
z<QkteG(=fS@5(P>(~If``i1<7(S?FxAM!UM@$BWfD+EQ{7DbCW-UsGr*Klrq8+GoZ
znjq4l3Pz#{`nc-=x%ZXv_r~Y1Ai_gx<h*t6K=|{%Q;x>797U-MsX}BA7beI%S-Zmo
z+IaE$n$D5X0#$K@sLOPr`0fx&W!!-7WBbJ`hqm-h&X<B3uWfIHZ;*iLT#`tqo&til
zH`*Ix3l$&}_vPdMC9CJinR|muBjehdmWGNq*oI?3OR3DZ^xeEH0>1n6+T%S+&)5Fs
zP(^qw+fXSjxd2=bYGtr9Nc@5{;LJtU!WX1{b6bu+Y;stikbL{=K>grv3%D#?S#Cqv
zq{*5yJ9E-Ht3p(Tkc*-(1Mb!5dwCs*>hfqg8#oDK^6($Zq&@oG3(q82V*MJoU&QVy
zd9ouZzdbeAed{X4QgZTCiOI!|XXD72mKlTHeXstUn74F{A&5Bjj=DFlE;hg0G=ilS
zsSV9|s^G{##Y{~_juhv%JlHsbdl1VKLGB_c*CyBa$CtZ&INVD*hEOCVNha>CXZvTr
zYRKyrMi@{8-u>K?p|9OaU*hKSD1NoCPiZz%nv2wkwvX`ymUxkehK5f#Z|^1#%tG^3
zp@2|8#Bq5_sM<AiPX5O1+h-9Stg)O%PjG8&%3J#v(<|{0M-m@IZ~N=apQVkk@#jhF
zo!*m#e*_fp_=y|Yu2LJgRNY{uaIo$CzSN%iPP-VppaG=XdVeDsOE@^vm~5}iI2U6F
z)(5xN(%F+lCAiM!m`*gcl}$K>S$;I}yHHvn(N%$VddoYg6T`#sn(_6nw5}*wALuQ;
zD;NyfX6ZRenAD&5O4h)<ub?>D(-$qQeEBBDYAz-S_AP1>s2*_YBl-wp3*yPQ`4o!J
zTy}HhRD*axK@sx^a{ZEuRW*d2t}J3#xfRhFGO;&(v$2EM&CVp6nURH$1<Je2wp1K2
zu2=KsVxu)hUoc48q8e~K0?Nqs)nDcG{K=Z^3+xYdGay5CelIeXI1Qj+$np7Ef0OJL
z=0|A|Gpk`vF`F|qrmibXOD_8?f_!NgqMspSj{bCz7eLwD3Q?o$L1YIoKSvBYN-l>m
zIilS=vo`&PykTuwWO_ukVmICkU*;F9!Zs4EPEaeBg;P-N6pmGQs{I_>f*=6q#_F+i
zobmc}x|O;)e0@uykbw6N^vh;xNY^LcpMuBpCPqt^x}jOEsVHBR2HK%ZlX0ebX1b3q
zJE+RD)koMYw`H1lC}$O;^PmcDe}Q&`YU`bXSzp_1@tO+He<hBEspfav8@y!XLa5%W
zDWj0Wy8WPOxQ`fIAD@rJcSLd&x3#c>|B7n<!XvVal~n2}%N|1tT{hU1{SVIGDM-|y
zOBQY0wr$(CZQFM5wr$(C-M!nkZQI><N8C6uGjaZh6BG6Dy?qZ=6)RR{=F0Ssi0-OP
zvsO=+j_+$&#D9eIieN7<^Uyuo-c9<w!`thj&HPgFP?DhE_>ERm(^CggQuv_WL{1Cm
zY-YTupp>O4IfZ&<jugi|!i^mJVp8}sK~qP$jFN1XJlJjY&o>ca)}rFfu$FVa-$1=T
zj4rp^!}Mu^nH^tTEk&`)9%7{^p#M+Z=r!jhH+uF@{Dg3UroR0nz>MfKADjEQ)Q4p*
zp|-L@VqOa|uRNGXPlq!FYOhc1SK9q~ssj%P#P{5~M<><1pe*Bqw-aGhrhJ3P4dboB
zUhsgmvnJ-9lB#8EAqJ}XMU~Y^0P@%(F2~K~!!sF_0`-vgRq7f4BsTJ07yP`a(8rA#
zG?e$|_V&&S*^)HfjYr*QNCc*lM`2wP^x&Kg?DFjM{ozvNA!V7~3}L|&>y0_^5mGK|
zm!i!W{vvFam;scWTQ;ZPiN`60%+UJ7+efP~U^BG}ZUFKdbP~DQ+twtxfy`=c|EnAQ
zU9`uCka=X$g8-(9pw~zMFvn|?REQvCNj#^5U&VePo^1N2t3tH>EdAQ>6>1O*$?d?d
zet-90B-abN3p_nC-{g)m?klfC#hY^A?-~=#fP^)Bx?pVu@2ZBwe3jB=ELef`>VQ<0
zGh1(Vxu42so{wDQOtf_pI7Ec&ys%QP8LUBxD)qAxFVY?~R25Y$7d3HUXKsJPo8A=G
z*Vwl^>ReG%Wr6MAaVb^Xk^o1e){l%rbPK4CAv*jEGEHC-$6y@;$U<7yzgL}QDcZ%J
zB7Yys*dYPgwSy8=EUc}b@p1{h<GXq)V72T~8Lwv{&|uPGDh!;la#~ruusCJlP?wn1
zJzX`>5A^{a+?;+sN%#}oLZgn{SKD-@_Iu~vZ+Ygpu&^d^y~?&NA+t7ZX&FF94}i;R
zlb{tXoG<&_(D6YFp}~lQ?kgZoB2(2UF0BH}gF?-Wr%+g<wGKP&=f;*QqI|c3Vbtf0
zURGK>72be*c8|=k@(_Ycxbdmiqp;+gI&waKRdA*ON~tnsVY}3e!tci%s$)FC-~`nb
zkM`%}NVYsJs)}`T?+orZx<ma~)G7W-szy&lG)ys{){$wR)$o*oVhu#%W1-4F{6dd;
z9_7#{agTqNvzS0kz+8yM1R*R+<dN?vq)LZ3IYYDLgDbaP)>om1jDO1l%Nozg%V=>~
zy}>Hjb1JlwA&0muiu2r<<?UYo%WP55O`Yg|!OA(o+6c`Sax)COVt6F+_zkiq;ZE}d
zGhLiLvGG82XehKNQMAhFs+$i=Y+?K-uRIy$_}WYq>ic*+2r+K^Hk*%6dw=vqqbJ*T
z|Aog!<yri{kr6h={{=F_%Fg~j65?AlJY7&!JL?e2J8c9P*+Lbo9CXBV5D*a&){|BL
zd47`-bP=peENDOH?j+vSaZBDxdriX$s<^GgN`$q8pn`~?_{MTwIGf<?+Rm8Hc((~>
zb3UeWp60mkRLFfRcDVL^+V<_<R^gQT`;(EApQa9;rqbv&9~F!=K|$kVGv7yCH92N*
zr%&a4Gr1p|+1<*oB=+ra8oWHigKv%etNivQ?qsYq-fcUHJ^TXp?mXGEM<+*5r}&u6
zANS@HQmx~rSyW;(^qcJ!g)Sxq?Y2pc2dE!M{NG`A>`yz{;2*qO_Au;>r<Ny}W^_%7
zn$y*WD+PsyFwB1pGXkav%ov!H$Bjc7+nC;?gF9jpn1(VoGk{Lmr*L6rw<dYUWXwt#
z7Sl0Z!pt)=rfAIAn8R*Z<2-XSW@!x2nEK514A2;(GK9<^o02vE(wM6;&?{*he=>5)
z@SiMZ*ZLKGXgaAMvz_|w_$&C!|6T&STtTdiZqHr|v=w1|c<nI*!^2~7LV)cLyu$Et
znw-RM3zxzBvfefr#=&wsO%k`A>~@m;j(@e0xJ$$Bz6{&iORh@q;_<n^{cPHTUK6WD
zb0oA0IP#WGaW2PXu$Xl^H&8hXb2WHf*<e5)($RDmK5uNpF$lF3VOb%pPu1MpNm<0^
zYrzEy!iy+KCDGKWL>%+*xRl;Wm+azIgW_&Xj01&wu%JlXg>_W*%J0_wLV0qwv+;e4
zfb7d|-CO`q%z>5R;yhI{T?~YqFq$IY8fj-IkgyEV8^6lJ<O}O4ovxNYd!gp_I?6^)
zwkNHyueL)gvVuoJ>1$buK{K;aYR=TWOz*;#?d|32b_Mlv^)2d0HRg(@NSgl4%n0!u
zV&xKYroB%;dn%d6sG82p5NzU+lgcMDWz7_~T%Ls*1#$gdZH%FFUqJo@ru)X3<QqMH
z29zJeEWVL?`OAu!kk)Mt-ZcaleudGyMw108J-=`gp=+*1*E1shl3U*1_$Mv9Dga9y
zmSD?oi27Q)x!QjI{wlKfH|a3rN2%O-NeI^p8b7>cd8YEDV6a)vQrJxm<PgvM;)Dzk
zP7C<+knYF?c|fQI;P`{J5SLSf6~leCSYgL!<hh**T>g4|dP~xGY26j9C<DgF39kcu
zOrrQ?>!>$w%-&&#q;J{&5!ea>lo)7uiLd8%2VTE_2yhol<ADt?z<Cy&2arBkF^KP9
zUUSRh(9d-LORmn80<c%tIsjO_ExXv0f3{OJ3=k6vgqG;qXj$bFy|o}%XFTD-8Z3Uc
z?=u6JtK}&C9k0QUrw{sm?*iBoBI;KsD_;KCKc#EcVuwZy^TL6MVmF7~9fpsW09B8c
z@V4(ZLY>t-yR{a~*o^JGO^WUO*%TIZc|2Kh>?0w>M_?}Qee7FoaL<||%!tGhj#_;n
z?08Tn2!4>l8fFMc@fD<aQq%fQv+#PsMgf#oU@1br2gL?~z~rQ0N*|BPyaJvGot^s7
z%wJttL;E1`0~Rzz)H?8>aRW*<)QZTI+LJ3b3bD<8)Ib0!OswQ?2(KJrSo(pBPF9LI
z7!?EjXKU0GXGiavrA9;TW3-l?(h5LQi@^;raTqM&`TpM4>(M<9E;UCNK7A8aRUyia
z4V{i8ci^6E7Cz_&In&<Za7>%D3#%!wIbzkh^aDrkEGV!dpx|l580Z85>I^f$rp4^%
zO~aA`S<1Wq9b}lP5m%3R6+7DHAu*LLt(5`qwd5{dkgoYyTU$||+<%eDw@ws~aZh`?
zyoo_;j=btpVpeDgm;w*`uRox&(yg;R%->Kte-?Fdw>K9HUiW<81iURLElE1spG^!;
zX#^63*dkHbv^JESG_g+htblf3Ms-~R+Ey+ffSel4_x}03qwdG-wA91hY|E7HXbVT>
zFx11&HuVNQWS>u2dYK^8vM7rs@1%c0s4JGJNBIdS{W-K*I*(FViogU)`8tb^DnWes
z15PTV2&xb0=9wbaQ{wU%N@X0gqiV{$X45RA2z#pdR%QH$=c=|d2X<Pv#|-B@drD%1
zfw-%9xZe#|5}(_*yXx_X`=)AacAxN7pFsFclEKt<1SP9i3s_49O`VN>Br%33I0o-d
zxVwWDPf9FvYx)nJj^1}Wz-x5&$5vwE!$M@@1L`G|i<c?t*nF#sZ1SjnLxhEIR(+JT
zR?(&k)+HNX9aYTb&It9wdj=sdwl@j?KK7e_@h8{Iby(co>nm<HL);Oic*@8~>4`;Q
zLf!Gz%>_XF_cgv^5cs}uh_xab_$j?+L;Z>A<^q5X09FYYIM=jP@Auw9CFSfCWj0N9
z2x>s#b*op?=zfVRte!mpP%N3L3|zU2J{>tsukqn=9>XUb9@!hm<|j2bdaK!ClU_hs
zDKl}(``eYI7CinQrBWhuT+(GsA;uhe2y(SZ3;4_WD}a?<wKXycxH48<WL}taZGD6S
z0Uk5R@gRv$PRugN<Qwf3wSkhw<mxo72AkpfTM6=%R}dbH#H4C)W#(IKGfLWlHSicK
z9d>Gt?puV-y#IWV?X9$5f6={ui3j7(L`Og;AO&jOB?1m&kG`-ptj2g58Uv^7AF8F5
z8X(J~rItpvUPzvXPzUYB%O7?CT`o5JzsSmH@~CV>9}1>j)3y&m=stY(!3s$jb=4{r
zlQ+Mn=c-%quU+BxJ#5$DOzFD-*wNZ56yAlrx*kScr*ER!`5~Mr{-myL9a`Ttwx^x^
zols-${e(~kty+|Esw7J>w6GOR@8+9m@r=ad8V?lX_T<v{&gt(b@abJ_$K-);LK-xi
z?TDU`<Tb~99DRLRzZkL8HCaKG?HST#@+WzrF}$60D91Q@U2$e~^!a1ia^e<jxfco9
zBOMvUN;rnHV8$uq0v|~Uk7FbR&It+56_jeG*fsOVvO++hob-M_!aQL_pWy}!GF50J
zu70TUd?uuI)cvRx04jD1>JXD0o@@X7ceZOZ8TygC?sgVAN4*^mjO@hvH%l%Q>E~<l
zpd^|a`Dp&E%4!3!P8LQ&#S}VC97o!H4X=7|^<wYp1?m$pOWqJ0LwNs=d@>x!3z;9@
zY>$QGCn|ZA2UwL=tbnLmoIJHWmeG6N0^J^7@LhPl@^piLqJ^S$RMSCgG@UI^LbK0P
zKTh!r(nEXR<{4t^rTmcPJldToR;<*rFH|BEe{kr_Hx%VIvKSuj3(!!0m;$|rv;eeR
z3$Q)Zt7scDDpaeAMbq;14v-z}sGwO7p;+5*{Z(T3I<KP2xi52mQ6Rufc^CMYsG?`9
z1~D-j2Ub-cQx*XE=#nVi2d^2M{JDG7-0BlORD;awSGGyQM(b-ggNrxZS$=bT!}{lw
zcg3~S`nrcbx-Y!O&fZ>cXI)PQSYE@2g?u-pQ%O~PK-rYKmfQnjCUI(jStE{F6N(*r
zza@Jop5hfG1%r_Y#5W+O+siOAS<a|3kTq~s58y>)+&<yepJiKIs=$E942BBCIZ$~`
zhAYbHDzH1{Js|CfdMqnrub`u!XDh0+L2NnNb-Zx-x?k4rP(v?58c4EK{7~*8$sL1L
zqAfyJpPX}r<{MLypXOI%;Gnc2;_n+w9kEQ}!IPU}<EXsp@@P@ms~O*HPFz{@4X%7q
zP5Jz6xX`yFo_pZJO2(U#oJgDED4o4g=yB_^*ez=yjNs&TrU;eYqXQ`@ju3eEVPV&$
zQFR2_g4(zcuaLJar7gOWI#U(3l;1ti%bAxVJ(5X9>#8xIU-sV9&&D2%1jK?-L^V;O
zu5mdnG}csdU*aNX4zD&RAwLC^@K+ZST~_K8{D32%-b9Xb9Q$*5<8e@L?a-6<q~g(Y
zTLGR>c5OwaWoRCXVwmj%t5As6+>WJFL_kVS_ole?BF9zbeg7TDhyD7=f_-C(F3%)K
zJ#~(S?eAd>(2`J15!(LgxbcGH))Zvwb5?Ft&Rz~q_7_#h>Z0PV;-;Xo$@SoA7em_2
zdo4Kk?1^7mO$dg1LOm;sccCL+zbi@)p?}g}Xx$Oc^zTWUsRYA8D~%E=Dj^Td)zWIz
z_j{I4G2rq947R`TGW<@mRyhqW8;gI;YI>Gv1^O}bgVE1o>|ZmA%IfeixTDI5hQvVC
z^n>{d_upTq3@ESdE3e{dYORit5#X0u3m)Z{X^->`A+=d=010S_fceNd(@*~q3M!+7
z@(dV3U`yrYi}$1e6I3x8Pmy-aJdjN@>-@scTg$cby@$$kAme(*(hF}knOxa@Rp6^H
z60v*w<&!2n>RX+Z4Y4bGwn>t7m=3R`3;521j%(Xb7gZ_%(#<J?#>D<1G2&m48*omB
zqNM?{Sir|$%GFZVO5uXSLIm`QdBqC1<w0I9_e&{NWwGdV2LpQybBJeVq|&idv_58V
zBd0BST3%khr@McZHp1HuNR@oTZ6n{bKs>as^xEtvXH+$52!!!_;$X;z{5?TVp>yKp
zu6fR&LyZtbAzRdsqya^W$|*KPbaP>v<gNJyXWB|4nm!lv1kkUv(Z6~okE)$A8gb*g
z7WKcH8FJ!IUnJ-bI#7_wn|x*I7co;J!zl^BWID-B6=^;d<Prn`YV>yv#68eAMTm2j
zKB<Ct_w9JHW59=CX(PE}1nkaD;&3Hyj!Ac0|9JA2OqvjFkZHu$y4VMv8hSN&sl~vZ
ziHg=-pyYZ!EKzp9sPr)PeJ3}9(_eTVCLig?@E@EO!^u^(rYha*pQwjWuTn`v8Ils=
z;Qi}3ev$5Q>;Anu84-w?+FgnGLwzN4JY*f5<>YHdk1dtkvfO|=zt+$5K-LU52-(^`
zp!Hl=D+WV3qQix0j->M0*}?SpqYB2FWp!=qYFjzaU`&7*t82s)^I{6Mt5pX)Jltws
zftZ{Xu5>qPguxa_Qof9ejZ>km<|}4iioW2l2p`?;FyhD(=E+F=5N_l%x*EB023Je?
zZpLzvN8gR>-HW^-nH=fuG&-a8;V#{^cQ2*~*6F57AA{Zs)-`uY`Jddf4LQl*qsp1X
zrKrq7cw%Kmea6%2D4-_q7q8xZPw5{0UfM@`0QJN~K8ezFr500=?lf8{kK~_dxBm{>
zeP)?*Q*E+?>xUlSB^phl1*`wK%X&58+-+TjM^$-|>>gCYkdtnkP>`=?Bm<3b*|J^V
z){hG?psj+N3gH9F<QutkX^=kzo1eEU7Q(2sKxhQkD0OI5RLiKevXmjAWYV)yW=&Yi
znvAk=-75uQqhQj-gq1!fy|snpB=)4AKlj8Z;izCeS*eam;Ce+<>DpJW77R{a9YcE%
zU<wZ;58gp!2-YEyP<c+t1bi9b$LkjhEjTN$vW5UdQjut*E@>w$3&iYRsW+pkxcysm
zeLz`V8^*50<mfNp!UKEzL7@8%B<s{yO%w5nQXI!}O2sjmgHE|y@7B%~s%RNx`LYn7
znoO}gJOKXVikr#6ibb3{6a14_$!eIi&B1s2mML2!6*5}3Y`I<&xNq8Qi1%d5T>2#f
zBpDc+g%e^Aj%vhpl<MHFj-8_DH<G8TdwwA~Z>d-E4?}&XHwmo~&ky>XYyFihW}vpR
zd9f!F59e+RvXG;hB~;_^QnD$5p-hB6vt-09iJ|c_XT%y`9HRcS=G(B(Gc?}$yb2Pa
zbyAa*tY)0YUu65*Y_81P55J4tTsOt%laOCz8szGzi-6PZ*3#7wkp@*_bhpBVq`Ln0
zfzEH^GQG_K)+=5qUnCbgDUGYZ<6YvCuTE->ufG+nAMK|{pY8YGIySRB^rS({%=kbH
z@aLSby!gLp@W3br_wje@{D5W(f118{aG8VoNI^xW{Cq3QaK7&UMO#10y-YORGw13t
zxg{DH=e<<FuQ}x&=z3!DOJTM)-KpE>n3L`*+J{L4>l6_Ze1hBe@p%>Hczurl?p>3;
z<T*7Y@x1mKY+sH4ExsQ;&)(+caARW+l9QA?N2+Z@1paFl0QPztlkS;yJ-1Yq%%#k^
zOC3QoQdcKeU1*5G@uA1$ab(H_0FMYalu`PZ9U)?WS2Ad;%$!bNHqXxvurEG~&HKm4
zkE!}=&E?0QL*Q{Y0DWr4s)gw*YN^3pU=WM;7;-eSP_%Zeliu<t*+o7-QQnLp+(-hy
z468c#BDeaAt~ic(ZZ8~Q1JAw91htX?^7(mGgD1Mj3F7pCP^ENnaz9agSZqT2UgPi{
zw6|tZ$m1hr;duRvez=KY5MEL3iy=nXQW9nNz0$>f9B`3PK>w@#(i`xO`Djb#4Gi~Z
zxGl~fS5Ax9I^LwLY^$K;qoRim4VmvyNvv<odcdUhBGQO{)fS8;g8{V&RHL=cqVHsj
z?aKrrSENajKP=R}p4vcF$q!00vI+}#9!bEIO%cT+OY?l4RswO3{OsvmZdu$LI=M2%
z&?VNh9z3p~{DHvqy1}Mopj<^v#&O@8sFD6{^k9=t$LkSF)tc6-jiPE{(gV89D(2uA
zeW?z2uK_Xc)Tm-(W#?X4`uO~+ZjCKQyJkUS;*Dq18@lN`{a4qX8t;ov8n|DuqKZaq
zz45;M9N;SKdmzVFq7Z32lkEqY4fC0Wtl2N+j_79GPv|}nyreR)<g1w`+IA7dMRFfQ
zb2f00kUjii`Vtl!;Cy|$Hj)5Gygk91EoAaNLWNzGa(O)t&{I*=%z^6^+p4p7LH|4j
zm&_eKc!j+hzBbP0esjZsehEB_zFFV4r}NuNH-F|9Dj%+u@k)LeHUKIB#uSySNNQf-
zdD1PF;Rwg@t^;s$-6WkYE<p)hSJ9B6@<`FSa8~0bYYh@-FseijP0uC+U*~XlOJN%(
ztbga28>JVi1F84mVG|fu6Cj)e7|<M~gF}+30&>iAJfc^~`w!|eT4dlyvO}V-9M`q?
zh_9-*So%hdEEgA6oCjz%Q`D4-4HYB$<`UXGA>Bw0C^@A;s1aq0IEq+9qRGppuv(yH
z6-wZdh?>3VmCEyC!tj2xht!4Cg|yJ(;G*#2MbuX0YCdXU?HzV+Y+!%x-A<>kzcs~$
zBDkK==J`3@Ir>{Ff4nxBg(fF0ieMutcIM9dXjaJ-bhG;hMH44RbQ{%$$cE-g)|#zR
zj6q$Sr;02nsXfXqo#&9AcGCCo)q&2BL=gX6^W)eE$=qF;g@baTI?ByCZgREU2h#}C
zg)nzXh#23LT$lYt3t)ksTYgW5(r0o{Q!b~q8I|w2lXGXHHS9!Wa!jq|>kdzTTFf8)
z8}?Q^OWzLPs;bgju20=LHC^SJEj0pq<vHnORE=p?HQ;<AHL;v(2^H~R{@2(rJMe8H
zyhrl$(5F9Cd~p<u8M?ld_(HjfW}w71>tc*#)QgnI)GujQ`~HEeckw6CpN>C#+5P~#
zTG%e36zwdH^BT5c3OP!vR)E{mi>}Ji1Ra9%MQ%jWtUlb_kuFGR?r;`G*_YB}PktG9
z0`^wkyvgUVEYRds=PD!YjgAoXaLL_VcB{zTqsv+svIb(yIx&B%8?BbARGm)*@r6$e
zM6iK2*A<IyaCxng<;oa^NtL`>rd@`O4jVq$_J!$8At*dxEo@kcw1kYd;`o2wH}U;p
z!iMZD#2}fknICwm7t<$$O|zqWGym~0o_E||d%~Wo0aGyexo8Fv3)A;OPQc1xFi$X;
zM>tGx@<Fk1C;Dqto8^^wpSNw_;I$Z!6aN9?{%<Mj|If2*Ol<!H;WjogFf=qUF+p*F
zd5DI2S%!0agkfZ<X911S{Ejz!BOH1XPSw0AK%ex-E5$#ck?Z>%s}KF|9;TY??`Kd(
z5Rp3sPe=k0C<Y6EBN*<V8XGILGRil<%FfeP&`Q)Q-pk5M)JiTxP|}Ro(o2R!x~Gwx
z8kZ4M?gwbrz_kzq14bq$Rz}!;jWgYKPo}3pN8-B^J|_>daC5h|^0w495Ed{NFn2bW
zR%aFm_g;?f<aGMBnf&}<lhxul-TM+|hI^L1$y?zK5%w4ej9fo<8U*L+45*I^s&l-6
zpUu6MTU_%?d##^zWR!0@-OnB#S{@qu&b6j*(=kw!loIWl_eTVf0n?TF4}1H+F+^o$
z|DT4aDH`Aos;0etfKE5$1}b#!J+01L&k?q&BBqv9P=y3N!2n7~5<(I}8`nK;zt=r!
zPXb5~JrWQiu%fVQ&mEDb+rG6`pKdyRUCwLkuQ`8avV58C-N>2QnKP1ZzxsZESAW{V
z*$si}%FEAANUhcRyG-tdt<h-pKG&Ys<(>Sql-q;U+nsgxYww><rOCdGAKC|NkaLae
zb~JT9+S;OS5AGxX?h5gFe%c@K0kb>jy(YzN%FFJ@&{JhP+#)aHd-@0p`xc4ltoyt+
zhT^77cirk$SyU&pt~&T@($Ljat^dfw$16+U>~HHoIn3ki0PRGHN_$RvQhQc;T1T}$
zOekK<^6Y@@z;;J;Bkjm`b9?*_c>j4NxHa4<>K^rod(1tWKAJw|75}j(y!G56+JXMZ
zt4DcCd;I&!7$x->XVsXbwTHFmrAJBkb2rlt*pAo^*-p@*hiJE>XnA?#*;f1b;Yo~h
zTprv03uNrUXH;K4%2&)6z5@Nn1=#4YHhCr)FXMU2cozBZ_g`evi!8S7r-A7TZ7e%a
zZz&0fxV^ol5am8JGr#+%nnU|<IhV86$i!}xS=@m|@jD((L^$+PC|1^woX(t1U81mg
z%Q=9`QWB~lnM5|z3^ys&f{jvI_l*HCw_>=8Mi=z<4dHbO`)>NJOE?eUT7r)!eeWw<
zsyrC;q>uJs%95GA^N}DGF4S?8WkbeA8BsVAumfPcKJGmz0(SnA*s3$MdKN8|PAk`x
z+6lvEE!ZQUs!2GDXZg5)=r;rbI)6=U#Ti-sP3suTa-*h{0KJk^j9P`n8Pk-fTW7rY
zZrKvpi@I6Pnxaml%tsqcN1vmMkKycYDugRsh@8CvzUk=hXewYA6y9y7qo3v)0s|>P
ziSaxMu%qo9%Fa`!-Pj9b<Q?Gb7Zm-l@^7N#lL%p4VrF<`YY;Au+%Rr#>amG3R5v?l
z+8qL?{w2eOAoN8zr45H(swWy6Aonff3#gh$olcWBxKVNJ1#)xNMOZ?D@2B6_z3dmt
z-shh1x;=ZgaNu`laiuDZgu^(XTN$tdq#oZvqAcRsz0?&#C9W8(MLbvG_?(9DG0{B=
zaeCgoh?Ygb4ggpgAOd$3(iD5pxG4~{Mvj^incyI7F*xoUm|;v!2cBC&r<*7_ZvM*$
zS64pz``e40WhrF4_4;ToJ&$-Ue<*p(C_f4s#HH<X%ohJK3Gfr1y*vArsCT9_?32t(
zjsLa4cZZNiF~qK1Xgq}viH%*IylOv_hH|g(Wj8rngYp$k$kUf*PBnmhabFO8-$nZs
zq!%%Q5MD2B(F>&^dB2-!PP5fzD(oM%Rv(`L$@`wSQfZGBc=LmRV1CFmk?!ii`MVsk
zFu>@siC2om6_P7Z_b&R~D0}1%A3ORt1u0y`MI{B+43+X>)<)e(__)0qW7(ZJ+Yt3&
zv=UuI<)4QOn?d9>McDETrOk1B)<n{2$`5g>G?-JS%=+wkCE@moyy%Xod*S3#nKnJ`
z(p{p)%PlBOjRCPcYS5ekLUFj>d_}}nQ_F!L01#b3fjq@^6^cB?Fw`Ry9i=(cHsyB_
zlB^X`k|`LeIa|P}Ng#<*CJBrIKg+G;(!-_6E2t>3OA0gm!opFa(eYWr1n!IIvH@_u
zGVlk4h<4^+mB5>5s%nBdVgZLt!0BQWsw54>l~YWo)4*7m!`q`uwHRI7)h%Dw-Z8=Y
zqQl%ItEa}&Y~E=D+?4Sy@5GPv;D$fzl98HHqF{mGgW$cx{-~Zqu@*RUs23mH+$DIR
z(-#yS!-{p07LU>RKy>^Lw0Z%jXr-~N9ASs6*fy$P4;MTO24SMJl91xA#H7GELrc*Y
zjc`D>&?@vEi37Du+C=VSLxuyR=r&E{Eq<W;;&!k{w>z?PKRl@XxVVuyKLCAH52RNS
zDEWR=(2nZ)*e%k&R+%{xkUnUG;0vK>F>+*aNn{XC0eO?e+XOB9P2`vI2uNMO`XCZa
zD<t3IRQM653#$l!$k8)%uoQw$*akQa@Sz$PhpvJoZj#*RYgFLhn0C~r=xxwEm4BMK
zLu%tRu@e~O6$8KdV>D~hm;#+wN2laEH&lEA>Dxix@Nj=pP<Mm=sWHX<z<V5Q_#Vm$
zM|Fniq76pJk^>JK_b^@(7u@R?t=246uUL#(De+`P5H2WEl<TmlcM^;Hia?zB-*S#_
zIy`u$A!3h{1vzU<V-Jr<eno2^X>+5aFgXgj6xUahH?AwmW&I)~s^#e7Ftsw+RtTR?
zzBKi35Po9DaDy#S<~S=)3r%kpqs(UwrC{FZ+$JDuy>VA#WCrpMJFaw#&L@G+`=)E&
zD#i?K7@UHm@j1#{ln4iN5b|67Ce4)CZOZ9Gl-3f;ISs`Ct<Kg$j4@3$#%%)5m_abr
z%63Nu_?Gr(2?rAPa_{sCL32vsEH_7L3*`5CT^f9j3^RR`6VI2WG#8T1I@h!eeHgsB
z+_+>cP(u+Kf`gVKzMoB-PLt`(#^QA|t;0LOf4xQ^z|CVt&_-A}_-UOqvekIOkE|c4
z0dtnOf-{>|P-5^gvb@!B6B$}idOPwVJEQ6p&@vh@k0cmKPW~)|<~GykV4-#Z+X>+>
z(cPC@yy#Y9;UGyz*#(W+)g!04Qzn*@xCHW_NFo9v97)t5GKC-wNepm?nx4=Gn!r{}
zubJ33v(%Wl5-I0&z*)^d`u4p?{)NdNeN?^$_YU$7>LI_UiZwJ;hj2w!g=#fxHiUe<
z!{C_4u{M!{%T6nXRF0KXVCxY!U#&BkCVGb2mPXAgKKRg0`9q~*?u}{j+5)=4H)tQZ
zCF!2rOfOZU&8r(7wo7$yaTZpaCka5mY+<^UA?ch-=V{_Hb?~!9qO$R9r9h~vvMzYY
zvy2yIjv;}S2nBD1JG?4W+#rULL~KN@Mnntk7^?I3SF!&R)ah$AX>hm}Pm{kJmN!pS
zuEU3pD7svAUv)mvxFp_ao=t#Cn{7g14B07c#*n+CE3K<5EKhM1wHe99tE#i4hO{LK
z;5U}@s!^ZrBJUft3EV5`8q)<UE99iCwed?+X(lhi=6VhW*I+M6(C0CQEp8GPPXPG7
zk?KS%rJRJ47J=(7;vjMdD^9z;fJNhC8W#1+Cc&=A3fM+~{)PWV{KJ;R-)zt2w_*j_
z0Z+H~{X?)5sZzxiNq!;ifz12PWTqd&Suty^s1!e+#8}I0iiu+e4+YCSwed?FrX-*9
zk^>?S?f_D~6X=FupPVa!a(mzpCXF03RYUgBZIY55vD_EXPu844y@h0c{T**(G$Cv`
z{mSBaKeja&yGKH-(XHA0RnOJZXqdS%-Xq&rnY#%C1_TIU;KD<5=1RbT0fTLYuC*q-
z#pc7(ZSN;gk$ncv@Ra2B^F4|k=DQ`&D3taniq%KU&CZK|XO?snQdg(x=N_-cCyn34
zvAgpk^4KF&UFK9Bj1f&GKyhm+5e&$-p*z4MRFue0IzOOea@r^A6O9yI3oBLO2Mn><
z=kK(lrubbBvBv}_x!}@u(HaxgTd{+%VMF|PNGPq5J=lv{y06d5D;Y}JPO$rS9o(?c
zI!CdhRO-qcR6J`GEO)ZD&GDZV+nrZD<fAa%0Sw=S53t?!7)r6b>nv$-wYfm~Y8^V_
zvk%rT9g4Ts-)Hrm4-f_OxLz|p?)k=eN<&lPkRL=Ef*>rqyO8IrZLgOdsZ>U_vD{4<
zF@}HR^YU}Dg&<Zbe^I-Rr(vshmhg%g@wx7*?<$_m8>l0wBW6WvIj%^@&y=a~<4ho-
zaXCxm;VkG-y|;O|fEdHM3gNoe<({Bgj#x5kZNxwC9+tR(dT>KiH%@X|Sy!+Mm%IwC
zq9`lWst9tX<50s-WA>zpML{qA%&iz%H8g9Ox>X3yKRtYeDk%gG)&Sfgy?82S(a_nW
zNn@$?7~|$IF6r-2s(alP_o3jSb`bwy3@fPND$g;^Rn<&vds`zf7c)N@U*WXAWfVF0
znt&M)%x_1|81&4sv|SXeyOL!k&@fmF@*eTP+~T}_#S}$2uq6W?6~uBZWNoYdW;u8H
zvEjOXEg!FfsKnX$v@zAn3xR_?_R8S*epBqkg8q2{88HH5fs4_#SB`MeUeZ?4mQ_kZ
zA+d0_Uxxe;#`xlLNj<7+<|-_<9;j`eh;coDb^U{rJaA2r8vx{^K8$`Zwm`02CnEo@
z?OQV!xudz;6}khKa-l1mkkpVgpA<khkJ0^It|~Kp&Ymn|n2ZoK&7^bhf0_z4fWRlv
z|5{jKba=DO7F*6$9C`Nm1fbDnT+?uq`{Vn^2(l=!e$&uRqu7*a+3neF*{vwJnvZsD
zSpQq<Tartr=Z4?2+IJ40v>=9sCo=IPJeW<nYoK?rTIJSmO%qh23a$imtfVuj<7i+w
z;Ap@>yLx*$AhhkNu1r$Mc>`b{(j!2@NPR2^3CWVwQnj{*byE=9iPKa|3OdwpLo-pS
z8-G?~K=zMw5j-6vTnuEMs;&C^(p7sEeFP3y6jrlD>H_JR8^rOKZH@<yVI%uZEN-j6
zP2hBXGnL@^j*G{s<7U|0kJYPnZauwNF}Y#1)Yza!bGjSW<*~NJEo!Gr;94+093)4d
zoxP@u$K5h@+7^l0hD5tci$(z=<W4+EN)2#BKoHB->ecQ6IomYafRS2r{@^a+8H@|e
zK31Z6ExD|>);@Vx$=>!Qpci0C@)}mMA}{9LY$w@TC_j$$JY0<I<JfWpQPxB$(M!wQ
z^<TjcA{^X_oM+K*NA+j|vf3z13u0B^TC=9)BX!>%8*f<2V%G+?Let@>!1nSxB1U3;
zkiqh*b<yl!GbzGWb_(K+f6~D!6I%*NiAS8)G88q~x_94021^^yeZB%-%jil0K(iWI
zQ#%A!`Kqoy&rDU*)C2=la*fl6K|u%DFt09uj!<=7Lc<Ai!uolxX1AP&^%KwsbI6_R
zkA@70VUj-cb2$jHo(wpdoWW3j$D@f2UZsav^0cYoQAJyuP3q=+Zro#{Bf>R(+EBhV
z>|4lJlh6lZhUh_`&4|zd*m}rHZ1>f(7YQ#mVOW-mtfSR)&0ymYN+D%^*;=qv;Yp=w
zvaFd(%%NaX+1Eehr_&#sMdC?(4>t%NEOgNOv}e~|T{ohwsz(gmvIb;|f;pv|1c~>@
zLf|629)pnGC;u{QsO(>?B|(%oOwn50dKzUE%@#FU;8>aN+T2uf^Jj<pt1<@sbLDCO
zEEO`aR<^NY;_e-_`Xmu!m=Ns8y(3i&JXM@o`p;}adAfL7?`n0zTnwkQWV@o2#;0@~
zAvwm}zj@EF*s05#-vYZKWx6}Pum^{{{XN5PU<kJtf&2ka(cc#C8=XPS%=36J7`>1c
znUndmGO5QsCGyt$3g_!`PXIYsK;xcpxbh`NAx>(uC}9Za=eO=TlN;^9tAo_-7~-p>
zAsV477C~b;#r%d?NsBg<a4j^DoG@T3!FIFy)NlJ18+Tv%>sRy=l`+LeVP9J(9+=!v
z$J7OVBK(-t86!und0UMm?)1Vzd&UdwG{p}^v9EfLLEv=BkZ~c?a?(k@ZZE>tqI#@A
z_;orM=)b7nf&G*;uq(A7?pWu5tgZFMLP}gKR7gtchn*53&P=JRGFY9@L#e-zMYNeB
zauk7JHmab6t-L5}P{Bq~{EeD%JxrE9x@N9<(LvnQX972{qoN<X7W|<<BP3GF<Uz<C
zlti_fz5|CU6}<7C@-F3pg=KEN!d(MbOP^RxD+ui~G#5RODRAE|HX~U(Zm2ix<|sh&
zDjXS1CW#ODik$#F{&@blUDt}Y%g!OB_TYQ+CvuDw!$l0K1%%U5XQ!8bqbm(<4Y$ad
zd!?U?0$p7-W+d(vvdKJ0`v)@O)_<>g_u*119q>w5gBvM(a$whleQin>TW=v1(8}v?
zmMH<kKPm6nti3uJ32SEOHZ!BVe}bG+Aa&pKR|ow&Fw_I!?r?AM_Kq0@#yAzI9TS%O
z(UHSJIJD^uR|*Wqlo<norB+T6Xqp$&_BB2)2GwD3tOk#spo4Gx)CZtcC7VRbs+%`{
z8&8~4E17XpNk?W5S>pt)Q(1O$`^VGF;xGlh{#ExGq(?_|V$x@_3$O=kqnD<vDrtnc
zlPJ$5DMz7Kol(aa@=LIe;S)Jk8pNqmXnA^uWXEA|J9+N=ADP`zPc<=A_4ycsRE=TT
zKBRS5wi;pfl}4!UdI)|giPt#bK?mc!#we42SrUcjwf@_SWW<&ocsNW^OIyXeB*4WD
z&i9PCV;+ljhNPNL-G+tjma;l>bEZ}e<E~kvw2M~j$IJlNEN<?M3|m~;oVYjPE`sP#
z_tT%nzS#So9`BUXQe`o3g|HPvojkrM#W~<&f%6)XG<<}0JHrA&-5d4V9g2!zC1)YT
z*t^B>{W~t|qq9y87EeHaCOoxdMS8{?otm8kg^Z4q^(nV%BsG+c^<0kuGTm*@4)~5A
z`Kj@at23mf+o4>-tXCpcuQbDlwUm<tex0cDPbWQ|g)+J;Ny|<pN^;!qb~XC)a4L^E
zu%+84V&7=nXqF+Br3KkC17fyM6fgaL9LsK8zD!GK*MHz#x=;GKKD2z5{rp?MK3RtX
z`S+TF23?*7*9G5TK6+<#Cv@#TF~kzmb}cWe$gz{8%+rwb07YfQ5)(u0t60>qt5oP&
zcYA>Tfz-QrU>`)@!ae&#`$Gvs`@lr%{m8+8h-;yfLN=r-aM5aXwP$9KD^gqQEl4%V
zig|%yT*A5raXSzb_LRzL*W@Cx=W_K(*~`b7UcuMgn$xP~#CnS~SB;RU+bHxxW6s`*
zOKulN3z~S_PB(rSgXX;VZ|{WcboC?3d`Y6gYH6V>lTE@3It{lQu7_OcuOeE_dGPY`
zj#MGeeOt2&C$}K+`#;eeZ`-?#rHkX})9s|Kd!#7B`Y&u*B@EOpN4OvfB!x@k?jHy*
z<v*FW*kcNOZ9n0=isGBSTE--Kq)iE=dsFDg&y~I-c6f!g;cH5(462kimX;LrDoLoi
zEleMw-H0efBtZBW%h<yNnK?YdyGb!8MV<b5IUVT2jL|q%btljwolmk>sRQ3n=93C0
ztA^tDIpjdgBf6F}3au-RFD!44U)T|lUX=WR93FHLM=+Qu=%vQ1UAnVX^Juz;{_XqY
z&bat<S_jQ@;~hpoXfvFSx;Sl+tU1zhg_2;SN6PIAhZ)%$Cgaq8Epy^h&bt7XLvp$C
z!iq~1WN!GZ{6T6=wkHr-UXii`91m>*T>N0NaEt!#i|7xbuw+hvV5hc(fsOia8RBZq
zH(g<OPA;QaMkNHq*d?No#U$#ew!`|<ZS|Hk8!3yfw@znKNy~E8gzjmi%$cA2LFDm`
zkS4;3TPs)PN>Ginx1wOKVxx13_aOQwpNXoWDj>`|W}zHMR#r~b%pD|_78|-MTq}C^
zeO!!A-u{FXQIRM_L%`|6wl<2<jY#u6L=zFkNqs8`NT2rAE9EknjQK&?4wy8z)bvEf
zHx?Ca21~*04;rES(Frv$q37jt@I6c&VFM+Rcaq6}_#IS8K2_=-;5uza`^$Xe3z_v8
z{Rg1>e`9FJ#PUBvzyFH_fQ-F+a9D(MgNFS7Nq~&6|3d=2hjC2a2JiZtYHa)K8KwM7
z#yLX-NyzykXjBCdvHBApCImAT9ZloZ3Fh^8=Dh>7gtS5ws`s6;LevwAFa@;YSTsWb
zkp&YhU-v>_8WTn)I$8$MXO}<KcUP0{iTAU2(Q+Sr*V~^dJk@<k%_YqlLjUYJb8q=}
zS$ie5qt@Hk{qkf76=x8h4Cjn*&OhJW=(YGp5+`Y$8%=G7>hcC)fDW$ruO)wWsgHa5
z-<K{QqAnctM;33q8A%=;UY@Pnju)p%P_vX`m1gX3bU*;JJ1v+0u$liGI|VjI#{Zsz
z=Bu<TjiQK>%iVd|Dp%D3ie^Plw3MjZQ05ge#&QO(o%oX4#O!WHKQ)7U)3ewaAgVCI
zmKmQ+AG(kgkAl)e+ViyKKc@}y1I&E+HkVU!Rg)Gs$$9$O>pa6NVQH_9AheCVdOIbv
z&8NPAzmU~gPSOCa7>D$1DYX<=2*}E{j`rM&egDbpYEQIWM{%)E3<>8(>d~oP$du0G
z?v<QeNkCeaEx38cys_`sQVA`zO=yhq7qXQy*+&~cpkaP~9xWENR-tYtTo>^!j|lTL
zFrksY9<tZk2&^#B@L@$aeVERylbF*q48tJ^W9=9t^q-IlJ!(X&My+1APas2=r8p&s
zR<W74jNoM00g%plF2H+@KnZ9Tb4_{!7ZARQYG0=}z@a|lhHNGf$Pspwy`eeHF{%@Y
zN6fUBidm^rq$8aP;~qo-tmGcDK0Ha~5(wtPi4zRmYFsJ_%MVrhXE%E;Vp4FAGvO_n
zF+JEjRxhnrX~MX8IdC?@`qvpBFw{0D1MY-&+k{$al4Kl%hmgx3t<XDH+1}Bz>Y_gn
zA{MtQ{)?Xk+`$3j0m)BFLq#QcDMiPb^=6~`*NV<xr{ejDy)82?F2u#3%ddVntq}Kp
z{h#A!S*kFK$wL-?VEr2pjvdJEPKd63p9$4D`H^CWkH6o(hhOW@)5YLBK~vm90d7Uq
z<-JO>v?%$4A>Eo+tCk=0PFK!M+DRY1pOw$_k-70wxH(p8GOebdk3H!ONQRDWk*6&$
z?hgbPSfgzny=9o5)jDa7GjZ2u$Gw&8qT6co(0)`=AK+*q@X!B!ZQ1`<vo0L}g9sFj
z)s;{-#QD4HZl2|AJ2NdN>gi?|7{L`6nVujj_kr{|7k^gCDsQ*MmN7cRl7<;W>CaPG
z%+`S0BWrI20y;06$1b=dhJGSC2M((HJ?qnBfn1V>&jZiHk0%u^CPdld?$YA^q*L?a
z?zS8?Qb;+rG_CT}E6FeMB&<@$a>i43%dCbZ^urbPbwlxB?U@h;&5-SgNL-RSun(zd
z^Auqe#J|Fi0{<FfGJsu;bfK-4X&nrvXoDyxR_wun5e}6D$Vnm!<V3iI)M-3WPXZ6Q
zV@#-d!>^@YMiEJmfO$7^;5%An1wu*_7EEU;5V@P`6{6fwPLmBULSp#YWS{k;QS7gf
zdLU$8tsW^3S&*Hkmb5ZBqGUwMHi(?`@8L?aIwq|pG<$Jq`r%hfXX|GDG|LUQx(>`<
z&cN9{mVMS4kp^H5xS4B&%{BLKfk!6TGF7~c!VmUbM&B4oJ;gA=VG+8*Y34sIgP(4E
zxJ9E;TDF8a>(9PJIhI#&5jy)i=Z7haJ~#j52t!lh!gU<rm_7#g{nBe1(@$bRZddQ7
zHlyP%ZV2?3wxZ&Rg&%sCuV*w;-6CBaZiUwLeN;U4&J#}GLWTX9qX#W)yWMEUL~s!)
z{;3{Sht-^*iXvX(!6v09l>|ATLUYWcK7LA4!UD^L*+(!0-05s`p8Z{m?sk;^CPa5~
zh5m8IYM|)Xg!cybH{AR(%+q95ovpq46P=NZy~oCE^;9}nWA%lGzj=LyB`c>5D~nZ|
z((dXTJP7f3??2ql{|!ma$jHI)KWb{CuMM&(&gg9`;3)+?Hk%yrX3^hZN;{BY1Yk{=
zGZD}80|5;B!wtPU^;-AUE^2M{F{x|4i+`iEooO3(+fXoU0x-yb$zTE!AUOI(kVD>(
zgpJIMU?TjtrZdl+h41)(-;c*1z2E)(W*<o-vT*`Q#>;kEZFU+v4osnS$?b|&PpL0e
zGf!>ADrIKLpNNnPh?V^5r2YB2?p6c(WHr46XR<O1`13MernF=bG+TLy)o2+CO$lR%
zt2mA<-a%4C<3U8Hh#w+GhN&Ky-cjFii79dDq1-~%EklgAn?)w6#2?*e_BD*C#V$EQ
zQ4>Zc7o_JOOea&PhmT3EMaRjVtCdB6m4k0lRZpt*QW}WqSK<>cOcdTrY0I+_F)~gZ
z<520t*c{lz6fl~HD=HT*OG;Q-SxKw+Xc0|YmqJTC{uZP*BIy^dRF06I!Gg|9Bw%2J
ziOST(B&f)tq!L9WY-bnlF(04|Pc$1)R1qD3VBfYdYvAp2iwKG)(j0VSo8M7Hm6zYH
zo5(38P!Lacw#5e>##m{IvMDOuVL>6`wlC0ablZWw@=sK-kW)yQTB!<;P7rXHN$^|`
zb$TR|I5R`&g&?o}E~OD=7+S8+Fi%YB9ID>YaGO^bV8nd&!3#+v-DaJmnpfgoI#&+f
z#Uc%nj@3hYStN<q5<)PYf)2`23`6j-B}|*7f5oSpVh}`0Bpe@ISO0zQq-3bQ##mU%
zo00!&f4me`-f;B|>G1rnK5Qx~!UevU*XdEs!MOqpo26=hU9TJfTuE9G5rFl)&_QZB
z3z$T14uY7u$&B+6o=W+kW`IKmezjYAMX~wkUDP0;no`!+ozVNFJ+F8M|H>ky{Ul-S
z-QHxNUtZb)RxYZXRXs{4FB7dteGw+2%#>^qeq|4-{P?^b;Mns9v;J6vmN~V;E|ZpV
zd0>siq)?T!oE2ptZFCzUI}mC>vGn$HW2@RKWkV0GqLk6iIBT-+iE)FEe{K;sVuXu6
zYQGcWeFyCwfo(WS)f@1r!sYtpvXAhziVruO8ESRtWZq1W84cr7S{{8)Ki=JTm7|)H
zu8Y&93Nrp;Jijx<uhZjvYQ?gEb{-CA$FB-#J%Inff;*hA&jTiFFxTZZM+52}qC?Q{
z7O(Y&c1j3Z8L*>&?N?)T8WlgzjDZ<+Z?hXRpU`w_54ZC#9r_h6JIJHGtK(mk3`cPR
z`a*+=wz@w_CYO*Y9>d9{I$Z5IN?)>O&jDd7+p99|>!3q7^|l)bLnyz8(5J5o_FME-
zM_tK{x%J{lI%jX|2)y4NFRV~|hvR71p^X!^O2G2yMwPoKTfZ2U@K^Ig+@LlCqsz_4
z$fkLVxu7;c(ZFH=^S;}0F1zlkGV7Hb?pbwQh!%hNn>MgOMLZ)bRc?FVXqrMNHq?i_
zo)nxg(1(!iv@tkX@sU&|DKf(+^E&veN2_P+jiV~C>iNf)x$Vq##cdSWzQ*^cQFQ??
z$XlLo&j;TePQ`kL;8o6fh~+e>Gn8{v*My9GbC#2~jPs`x`RU8Em=W2ciF%u5?#jP0
z%9I*LpZ`$s{x`}I7G~D}Uhn=-Lw@w=c>f6JzU1}-8_U815V3aj4gLPSe`>RW&jiD7
zg6TiT_#JQlLpVe9Iu~`!51&K>3_PSSi0~eSFXD#;kYFVc=opCrOifG-?r-~8TsWLO
zbu^3v3~bB`3`~6b6*y5i^%RW+8e<|0Dl9GSj0;K(3@i;gy0t`<-^l~IJDeE3v^9(r
zPZqNGbu_h<C<O|e6`hy}MD6?i@Zi)lyk*q$Rp`PkiyQJVl?W4(zjUxv*tmoO+V<6r
z1ll*er2-m`|3r}N>}MxvDQT4uXzwRwrHD7@DFXuoDo{peY-4U<W@0j*{Z<rod%AV2
zn)NT^>22mq`P)0?EW2a-@l@SSef)QQOiguNSC6mFJC<j6w(3_1?F%Y**5mjT=mbB}
zUq9-{?VWI)sjH@|NV~D>-={!xRI{)aaqN1};?g>YguTIgX38>&TrQo}cH`(a!C!}z
zOjZ6YcY(L6%l@D^gTK&^Q$i+leS3F%lCj0psz=A!U0U0j6<q)RAnyfw-A`KHIPK7g
zF>iK=Ga{@iEX<pE)$C@v$<yOc`s=4KB0zg?LD+w|25kQ|e=KHc<6`PWFJ@!tVk%;4
zY;R&pFJo$F?qWf}#KQ64&s|qR);gF0q32Y6%2Aq<jPeOs1fdNLzP04W&O2t@0+<z2
z5&q{hlfwIqa42g$Yt%alWx0XLEAVJYdwmZihi&4}!kiva>#Bt?I%vUZqf6tM07o)X
ztNmYtNK&Wo>Nsv6^>Lgv-}P8w0-s;MNl`XJc-K=GJIy4=p;FouUkXC-u7f%{PVX?5
zVtY?EwbLV1Ckr3uJU=YS%>5b#oC!%W-bgXZCci+1lM{aya-L9}Y!;ttDmGW{@Bg|f
zwmFW!FF666r1X<qlWzOwITGhHlHJ5(b#;}G=A>^tx&FB<&m$*;C1V-1`pJ1YsOIAj
zAn*8}ng)*l+Pwc~Wg7?U|1fEnRkXFxR>kNgmrP=*$#8>h@yOE29$5;ZyX0!(QrA2{
zH~Km=NFWTV?vXB<4r6y8Pjt!4A?vWWJ6*7w+HfLn#5XRKSeqlbgxAd>Km|c;aEAF-
zjq<1ACFexHFQv8NpK;zfJ@(Ywfgi?zfe{+}Q0l+sZujq+{T<Zi-j0bTAE^DiUOH=?
zmNj+=vmQEz%(YNMjvGoBDi|P+_4beo2FF)TXuK+*Z)L32Y!vtWbnAWGU$7bFp7`aM
z5En}_I6sGh4-e(z63tKLHB>Oig*aF+?@9(DJ~%YcKnx#zU>1J_n!w*HIo!V4Q?+`1
z^Eh&F?#h0ObF=RT2jiimlefv+e;s!hSQ_p0lg}|B9%wZ57dIR>%#(4NGY=Pa<c*oz
ztyrmI$ADxW8(AobmyQk<O}vAUZva}-FW8$A4Ygx65HWXiP5k!nX_V-}sEFR)R|BG6
zxK$Ch7A72Ibh)tt+_55{B!t5MSJ-(5HK8?qT%|_@rO2gsR6s)yp@kws=v7K60wEzF
zB@_igRJtI&NC*T0LocEC4kEpTt{_NN6r}g?a6dio``(%RemgU}vtM@3ng8zW+25_+
zcNYC(l&kkvDR(Zhv=3bx?m*`YOOtwQ0UdLoy*ik6g(I&8i9E?d{&;jxJSnk<C%GKv
zGBrJM*!Q&mW3EDTb{X^ZMDYzFmB7|2PVC|L0}vtj*+S@*QnKx9tY=~71V-Ro2kcdM
zN~;*ZexF1-$X0=B=>UCKIg1BMnmRP(+&4n8lyK#O7`+e4=g7v%3h{+Ci-8Sil0Nr|
zrLSsq^_lP%V#7%b*-Py<pBI@GG02Uw+1KXtLatTZKuE0hgRd)Rhrg0c23{X`k*A}T
zH_xa%N$$?1j2fT;)#iumyH#|BEaUBJg`sjk#<1QSUgKjLtOaqyD)RvdfKtWqYuXAc
zQPQ7tck}RO^Akq1)(+4m;5Y9NR~uJlwbD@O+=I$Nt`ba+D6R_nNbWHND!(4@g{%hS
z^*&oGStkWr`ZFOf8uu|ZmDkPWpvkVA3EDeS0n>gc`;#Qa>&J=5jF$BPZB2~yys3d}
zes<Xn)c65DV6iY*nq;3AT5kdvW0i-2w1lMFWGM23Z7{v0H<@l@P1xG69)-`JvTBL|
z22izDO<S((Eh33PVfF@i_kr7kix{=@S#R4w&S5NKP1$fP4sC868IMPGn^c>?gz=M)
ze=EXk{~9ZwL<E$bzq(<OasJPTPF&)@clB>%JW$`>+1T3^sK)~myM)JkdU^9mOG^I>
zMqWZf@_((PoWQ`0zi8g>`L<@f_eI%o`sLQ-q)B027Y#4@WLO++ckm}_gOYM{?E%Cg
zaqFTgARdmnOJj_U7I|C*TRR|*j-vR<mvqd9>X3Z)3{(Xeh8Sp1)sD_RM3qCIL=Tvl
zP_HRI;Lo2Lnfe@uYUgvqtcn`N`G{69REX}}B7RI$OTNPxrjmRU&=Wxetq7x8@yI}^
z!crYNl;3~I>6WhhD%|@-XZmv+M2EFopiAMa41-35_6}Eyovr5)^b%}61x{222w}Pu
zI+fAUuWt&ZOWZbmF--f*vkbFB&0@LOQjuC4@#&Fl-oj1EGolqE4aengA{|~SFE&!A
zbhzyOr0-JS6a0?aRR-F0o%XM}*)iL&3#Rr2g;LfnF6M?;8it?{x4``F0y-KV*H%@A
zEPZJmCfnbIr%`)+=s`Q}#DH(TAsb2-ryY`aMKfl7yGFMe;`RD2`dvuRdC>Elk7Xk@
zB_)clFyCzs%V|BI<c1T2=&mh#$o!(e8z*K!%}aaJ5B$2_Q<=`;&rj2frzz1IQ1k>u
zo?~w~5vYE#&fB6**Vh^7C)*IX|MPuh3BIEq7rBogy|PI4wAjU}wt-MidlF7&iL#od
ze_oXS-8<)}UNci~@**;|B_OFEZUK7ywkAl~$Sp-p9mN`kV>lDs^kH8N-F7!6@dj${
z6y0wrQ5}cb!$N<3OZXxjLcrJfP(RUj-*^%vP-Y@_`eS!1%gua3jRK>jD>T(=MPmkI
zYzjwuwg|oS8FVT>UP&t`&0;t(pk8&6_WE>;W84+&@<?4)*PBr2(wAIAi9>51#%4I8
za7g167Igfoy4+Chsej#*@mGl%$aA$W?XK?@68G~15|inbAK{y`Tp?H`X}qcF3tiQN
zX%!i)U}Bx7+jQo(X!J#@05~cSB#IfVVD*oPvN*wKnIw#<$n{@;F&d|_wKQ?W;~!LR
zJ?e4tH02Dsm1lazkjSaNsi2_5{9`XhQMaeJID&|Z@8G|wmDl^*RZcJQ9tpHGzLBIA
z>>}PuN*MTr!ih~7w2K(dXqgyF3s*~Jn?<gv=JLUXRkNF%Q<rCmPv1Rxc+7;mI2C`>
z&<M{W{;40YYC{DI9`Ru}zCR1DbBe$|+s@CaD~kY@4Xvs4h7o&y+<4ew+}5||GDsL_
zr|oaBE12!J)<B;Q2KI{~DmYUw@f$@Ac7k8JT{Y0VzXQWz4_&7v6H1$Enxt2Bagx|Z
zk2wg`ZOj*P>Z#sP@}bmtXo7Pfq3$yRwzago%y?>M`L)vDTzRz4F?RciTdYc#;(V1R
zc4IOxq*QgV3({x}$eY=_x$n5~$2}n@YFTwhGmCR}@w?g3tGNfi)0KpX&EnPyKzwl3
z<reXDI0$;dr>~b8N#m?+({d|+3EzLaa+j*&UBK0Q1lfov--NkQ`F2nHGYM|zJZ2-c
z(7m?$MLQjhDBP@p?OE~#K1u3&h8+1KhYx<-*@(TMbNU0+XPq!A(*aPVq4U!dxfPk5
z_bDmOL~rMIbfwAzm_p3QC?byMtHp-!)g(G{vp6bv+Bj<OBNHX76mFLM=XW33yz;yu
zOd3jeb>T0c?P+1B*`7FUj*Z)#!o4FY?c_|pYO@9hq{!iGPdI<C(xW}HRA!cqS!{c?
zcx3KqtN}70>I3glDjKmf-MXUEL>~c-q9|siwW@msh0ja69o%4R{p3!NFHDZ8;4`&v
zT^*CIT<n79K?K@{bz!!6L}btqDp>%^cb2(kyu!t+2iF)b-;pmi8DgoNTg6?h7S9<8
z&X=XT54=tbB+YS=QmxjuD0T%;UvNy+?_+e!A)v;${w!{`Ip>l@pOlv5L&d&VC%Jd%
z8$;>9%%%J&<VCjg%z4h>E*E<dX-$a|^76xc{GW^69+3&-75VI`<mM=H&#E)h%n}wP
z9K=qn-0$44sK;lrpXB600CHKZqpdpbk*Dc_C{dWkuO7P1CDsPSY)oiYT*bu;xgj?v
zuOEGiBZ296RK9GS*K)_`J%14P9=Q!?`1Wz;bk6SJCKYVDITrbYzv0PAG`c#&>Zrm}
zqwM%d2D-I*9l<~It-oiyJjXc4_DYAZ*^5z#s#;piVea$9kZn@FCYO>#*=aB|;JqZf
z_ogwZMWk4ehJrjnPSJ<+Hq{S-ERZv+s8Bxt{9kIFUv4b_@>)JZcyh-oy`#M2Q*=7B
zjEGKO*P#nZS@sQ0-!54WF>zfdbkCSN8K`Ng_l=L#%Qfh#c&n|kuS=CxFk)w-!#Y8y
z?|mwn7TM&F6)o~iLNBQ!&l(Mf2e!eGyft6u%%zawE16f6?mHi4%eCmDy*rS8WSJGe
z>F<;0`V$qbaX>JR+xA@;i+oP|as5KPMQ5Je5~K4Q`o;x-(hO;YZ9c(|0ZXuUjQ8ss
z**a^A9Ey}{2-}#8=oKn=Qm0)iBm2V6^^z;2?Wdu<*!fu7v(QcS+{efLvUw3~&aK&y
zHmUpqx$3ZsxqRtwH~IoxaLw}Eo6YjG?*`AFPP4g3Tv`Y-+`2WLqj}HWsj!)Pr;Lgj
z(TA#?`)+M%Wn}k6y4K-Pgoi>C$N9@OQIh^M29`q?xRseiK)d69!*Yox^#vQlV82i>
zsg45$BOA-#yAdmY6@7*|%wZPc=;`cSif!HENT;Q+by#eyr7seFyC<wot~VPu?Zn8y
zcrqFRHr8gCx}0aIZM+&kY{~~TdpM8Fut_iV*h(RlmA|zS)fayGM!IArtWCCOoqls&
z%-M=f{SvI|7Lr!oFl+c0@Zm6=I|g{z7P#l9h~y)2f-B>7s+l$>jYRqX<QR#D;9D=I
z7NoS$gWGGEbR-LrNnP5mw7C1aX6aR^iT|@jv!r<|97lkFLm8ynfB|UZSmh$pl;0?^
zFgl$JzT@H>z^OmDGzN-9EcLI7H8<-bdcD(^%_?4D$FM&#^)2EDK6{-Qe|mM*1kC33
zPh|xDzsktm$PocFa(DOU`MY6n1cbUF+<E?Xx;#FFDZuR|WM#$R4hTC0+!0|f4MNDu
zf+QqlK#ulsX*k$k>Hqhz1{$MYI0C?uKr@*5WoQZOe-mC(692RKyg$<Wpwo^iY<qWd
z1fo^I<}8ZOP3uX!hZ-x%==#*bDJ3)6Od*11=F^Q}3o5y9X6FFIabRt6Amp}SCdRag
zH_DuWl9!Gsc72yX-A@pXN;AwoOgE!~GdCSGc?**kB+2Ip6!KcQ(=1AczO$p^P@Cys
zj;D7|SEl6FjY%Cy#fLnvDV>5IQL9N-snJ?~V9p+)Bpabn9HCbeCMSI&47D^H2|sRn
zWOA~{HZjp`W;sPuCO)U&7rk+CffX_d+)o>yE0{{+iY1Y@j;E22fbVU>#mKw}SF{P&
vTnKbn!i`1(J=KY<_$fu5fMCQw6XxY@=jrY1>4*SaDpoKEASkG={|N9OIFUmO

literal 0
HcmV?d00001

diff --git a/exercises/Exercise3/data_correlated.txt b/exercises/Exercise3/data_correlated.txt
new file mode 100644
index 0000000..ec97968
--- /dev/null
+++ b/exercises/Exercise3/data_correlated.txt
@@ -0,0 +1,1000 @@
+-2.60 -1.80
+0.69 0.78
+0.60 0.65
+0.16 -0.09
+-2.40 0.55
+0.70 1.26
+-3.35 -0.52
+-0.77 2.42
+-1.07 -0.06
+3.40 0.40
+-0.99 -0.65
+1.88 1.19
+0.42 -0.04
+-1.32 -0.78
+2.47 0.98
+0.23 -0.01
+0.38 -0.57
+-3.94 -2.46
+-1.91 -0.58
+-1.90 0.88
+1.18 -0.98
+1.18 1.31
+-5.75 -2.75
+2.01 1.44
+3.64 -0.70
+2.08 0.44
+1.42 -0.42
+-0.59 -1.39
+3.02 0.29
+-2.31 0.16
+-1.65 -1.18
+-1.73 1.57
+-1.08 0.54
+-2.74 -1.75
+-2.51 -1.17
+-2.85 -0.18
+2.43 0.28
+1.50 -0.08
+-0.24 -0.20
+1.01 0.58
+2.76 -0.45
+-2.63 -1.56
+2.07 -0.13
+-4.53 0.02
+1.09 1.09
+-0.90 -1.24
+-0.36 0.23
+-2.40 -0.79
+3.36 1.01
+0.91 0.33
+1.38 1.29
+1.32 0.73
+1.91 0.15
+-0.84 -0.08
+5.01 2.50
+-1.46 -0.55
+-1.66 -1.33
+0.08 -2.14
+-0.05 -0.35
+-0.06 -0.26
+-4.04 -1.57
+2.53 1.77
+2.67 0.11
+-2.19 -0.88
+0.12 0.05
+0.96 -0.10
+-1.42 -0.29
+1.70 0.76
+3.88 0.72
+-0.08 -0.72
+0.25 -0.92
+0.02 -0.17
+0.35 -1.55
+-1.99 -1.53
+-1.56 -1.07
+3.95 -0.71
+-1.63 -0.18
+1.88 -0.10
+1.15 -1.23
+-2.11 -1.75
+-0.83 -0.15
+2.83 2.15
+-3.77 -1.34
+-0.26 0.88
+0.53 0.89
+-1.96 -0.14
+0.76 1.55
+0.66 -0.91
+-2.03 -0.37
+-2.60 -0.67
+5.14 1.39
+2.29 0.76
+-0.83 -0.50
+3.33 2.37
+-2.47 -0.84
+-2.94 -1.30
+-0.76 0.15
+1.17 0.27
+0.13 -0.39
+1.57 0.15
+-1.51 -1.43
+-2.25 -1.11
+-1.75 -0.24
+0.74 -0.28
+2.43 0.53
+-1.98 -0.01
+1.58 0.95
+-0.55 -0.47
+-0.64 -0.40
+1.27 -1.50
+3.91 0.11
+5.26 0.15
+2.47 -0.01
+0.77 0.07
+-0.93 -0.87
+3.00 0.76
+1.66 0.98
+-1.44 -0.82
+-1.98 -0.72
+-1.17 -0.03
+-1.54 0.77
+2.26 0.48
+2.37 1.57
+-2.05 0.08
+-1.00 0.08
+-0.67 -0.81
+-1.08 -0.30
+-2.86 -1.82
+4.24 1.56
+-1.38 -0.58
+-2.47 -0.07
+-0.53 0.35
+2.70 1.52
+1.57 -0.01
+-1.58 -1.59
+-0.61 -1.69
+1.90 -0.06
+-3.08 -0.20
+6.20 1.72
+-1.47 -0.23
+0.52 -1.40
+-1.75 -0.16
+-1.02 -0.98
+2.63 1.05
+1.80 0.98
+1.58 -0.17
+-2.24 -1.58
+-0.66 -0.59
+-1.12 -0.57
+-2.88 -2.18
+1.73 0.75
+-2.69 -1.55
+-0.66 0.42
+-1.99 -0.14
+2.93 1.10
+3.01 0.97
+2.10 0.29
+0.46 0.55
+-0.21 0.90
+2.70 1.52
+1.21 0.31
+-1.48 -1.01
+0.60 -0.57
+-0.90 -0.58
+-0.61 -1.37
+2.21 0.67
+3.23 0.77
+-1.55 -1.05
+-1.13 -1.34
+-0.56 0.91
+0.01 0.16
+2.13 -0.24
+0.03 -0.31
+0.08 -0.37
+4.15 1.66
+1.38 1.73
+3.42 0.57
+0.20 0.58
+-1.02 -0.86
+1.39 0.72
+1.21 -1.31
+-0.63 -0.05
+-0.68 -0.36
+-3.08 0.34
+-0.02 1.08
+-1.95 0.75
+2.96 2.05
+-2.16 0.67
+0.78 -0.96
+-0.10 0.07
+-1.67 -0.14
+-2.77 -1.98
+0.46 -0.28
+1.06 0.15
+-2.31 0.82
+3.91 1.51
+-5.05 -0.50
+0.20 0.77
+-1.61 0.08
+-2.21 0.05
+2.43 0.68
+-2.30 -0.56
+-1.10 -1.12
+-3.97 -0.85
+-1.45 0.83
+0.13 1.83
+-4.04 -0.70
+2.34 -0.52
+4.77 1.26
+-2.92 -2.22
+-2.79 0.72
+-4.31 -0.40
+-2.20 0.12
+-0.58 2.43
+-1.17 -0.70
+-0.61 1.59
+-2.79 0.19
+2.66 0.56
+-0.35 -0.26
+-0.76 0.03
+-2.48 -2.01
+0.42 0.73
+-1.96 -0.13
+-2.22 -0.98
+5.39 0.85
+-0.08 -0.20
+3.85 -0.76
+1.55 0.41
+-2.31 -0.56
+-0.02 0.13
+1.20 0.84
+0.29 1.74
+-0.94 -0.41
+-0.95 0.03
+-1.76 0.36
+-0.95 -0.14
+-0.09 0.40
+-2.65 -1.48
+2.05 -0.32
+1.53 0.11
+-1.11 -0.19
+-1.11 -0.86
+1.23 0.51
+-1.01 -1.14
+-1.25 0.16
+0.88 0.50
+-0.88 1.01
+-2.19 0.29
+-2.13 -0.57
+0.20 0.05
+0.54 0.14
+3.11 1.18
+-0.43 1.11
+-2.50 -1.66
+0.40 -0.18
+2.31 -0.68
+-0.70 -0.22
+-1.30 -2.03
+1.41 0.41
+-1.51 0.34
+-1.16 -1.36
+-0.51 1.51
+2.47 0.01
+-1.73 -0.74
+0.55 -1.68
+-3.14 -0.61
+0.78 -1.58
+4.20 1.30
+0.56 1.04
+1.03 -0.11
+-1.15 -0.21
+-1.22 1.93
+-0.60 0.82
+-0.26 -0.82
+2.25 0.97
+-0.24 0.32
+1.37 0.98
+-1.10 0.56
+-0.39 0.29
+-2.03 1.30
+-1.15 0.49
+0.15 1.36
+-0.92 -1.35
+1.09 0.29
+4.02 -0.34
+-0.19 -1.19
+-1.85 -0.15
+1.67 -0.55
+3.59 1.85
+1.60 -1.14
+3.27 0.58
+0.95 -0.17
+1.58 -0.17
+-0.16 -0.01
+1.42 1.20
+-2.39 -1.42
+-1.75 -0.72
+0.95 0.69
+3.89 0.59
+0.26 -0.79
+-2.71 -0.20
+-0.77 1.08
+-1.67 0.55
+0.42 1.05
+1.14 0.21
+-3.87 -0.92
+-0.16 -0.23
+0.73 -0.32
+0.17 -1.00
+3.44 -0.29
+1.61 0.61
+-1.09 -1.11
+0.12 0.69
+0.10 0.03
+-0.04 0.60
+2.00 0.30
+-2.71 -1.08
+-0.69 1.20
+0.78 0.18
+2.48 0.60
+-3.85 -1.28
+-2.34 0.22
+2.40 0.17
+1.63 0.45
+0.84 1.08
+-4.73 -0.56
+-1.79 -0.45
+3.28 0.79
+5.03 1.43
+-1.40 0.83
+-0.41 -0.85
+1.82 -0.19
+0.80 0.34
+-1.86 -0.81
+-1.26 -0.62
+-0.65 -0.46
+1.25 0.71
+0.31 0.05
+0.30 0.04
+1.35 0.58
+-3.69 -1.32
+1.44 2.29
+-3.42 -0.47
+3.16 0.84
+4.14 0.95
+-1.80 0.38
+-1.33 -0.05
+-0.59 0.99
+-1.46 -1.88
+0.26 -1.50
+-0.63 -0.50
+3.03 0.75
+1.86 1.84
+0.47 0.96
+2.23 0.39
+-0.16 0.46
+1.41 0.06
+-3.78 -1.79
+3.03 0.00
+-1.57 -0.82
+-4.08 -0.31
+0.17 0.12
+0.63 -0.48
+2.57 -0.10
+-3.19 -0.99
+-1.72 -0.79
+0.35 -1.00
+-0.43 1.11
+0.81 1.69
+1.77 -0.32
+3.27 0.76
+0.49 1.46
+0.06 0.07
+-0.69 -1.44
+3.41 1.13
+0.13 -0.63
+0.81 -0.60
+1.68 -0.22
+1.37 -0.89
+-1.59 0.23
+1.74 1.20
+-0.12 0.09
+-3.87 0.94
+-0.40 0.94
+-1.95 -0.31
+3.76 -0.19
+-2.16 -1.64
+-2.03 -1.62
+0.26 -0.96
+4.08 2.29
+-0.69 -0.71
+-0.88 0.28
+1.20 -0.03
+-1.33 0.18
+-0.26 -1.39
+-0.77 0.51
+-0.73 0.05
+-2.54 -0.11
+-0.56 1.47
+3.63 -0.17
+-0.24 -0.42
+1.52 -0.23
+0.16 -0.96
+0.99 -1.61
+1.88 2.02
+-0.47 0.90
+-4.02 -0.42
+-0.07 1.62
+3.17 1.80
+0.91 0.17
+2.74 1.07
+1.74 0.30
+3.71 0.99
+-1.77 -0.27
+-1.44 -0.06
+0.43 0.20
+0.70 1.38
+-0.79 1.10
+-0.99 0.40
+1.76 -0.07
+2.50 -0.28
+1.40 1.96
+4.09 0.21
+3.96 1.39
+1.36 1.73
+-1.92 -1.39
+1.01 -0.45
+1.97 -0.35
+-2.17 -0.95
+-5.85 -1.13
+-1.28 0.91
+1.95 0.67
+2.23 1.08
+-0.52 -0.20
+1.72 0.87
+-1.43 -1.48
+1.84 -0.29
+0.81 -0.65
+3.08 0.99
+2.41 -1.39
+2.90 0.86
+-0.21 -1.27
+-3.98 0.12
+-1.11 -0.60
+-1.24 -0.59
+2.63 0.12
+-2.95 -1.91
+-3.03 -1.16
+0.68 0.50
+1.66 0.35
+-0.17 0.90
+0.27 0.63
+-1.54 -0.24
+-2.55 -0.06
+0.62 -0.30
+0.13 -0.28
+-0.14 0.08
+-0.77 -0.56
+-1.84 0.35
+1.34 1.18
+2.53 0.47
+-1.13 -0.43
+-3.41 -0.81
+0.74 1.74
+-1.92 0.62
+0.26 0.39
+-5.36 -1.29
+1.27 0.66
+-2.04 -0.99
+1.64 0.69
+1.45 0.55
+1.20 0.76
+-4.24 -1.49
+-2.11 -1.19
+1.44 1.97
+-3.68 -2.57
+0.17 -0.53
+1.62 -0.25
+-2.47 -0.29
+-2.17 0.36
+0.01 -0.40
+0.18 -0.28
+-4.08 -0.26
+-0.20 -0.23
+2.48 -0.02
+-1.12 0.06
+-1.13 0.02
+1.56 0.39
+2.57 0.95
+0.09 -0.31
+-2.26 -0.11
+2.87 0.33
+3.24 1.13
+2.41 0.89
+0.95 -1.08
+1.35 0.27
+0.74 -0.35
+-1.68 -0.35
+0.70 -0.88
+1.56 1.28
+-1.71 0.01
+-2.14 -1.17
+0.44 0.90
+0.25 -0.15
+2.12 0.81
+-2.16 -0.67
+1.75 0.44
+-2.07 -1.29
+-3.19 1.27
+-0.04 0.37
+-2.61 -1.98
+1.65 0.59
+-0.26 0.32
+-3.59 -0.44
+-1.45 -0.47
+-3.25 -1.33
+1.86 -0.23
+0.64 -0.14
+1.35 0.41
+4.21 1.25
+-1.23 -0.30
+0.30 1.52
+1.30 0.09
+-0.41 1.56
+0.03 1.85
+-3.14 0.26
+3.32 0.57
+-1.49 -0.55
+-2.74 -0.72
+-1.50 -0.89
+3.98 0.07
+-3.12 0.21
+-1.04 -0.21
+-4.69 -1.80
+2.07 0.21
+0.59 -0.73
+0.70 0.91
+2.06 0.73
+-1.14 -0.48
+3.41 1.67
+-1.01 1.18
+2.93 0.04
+2.75 0.09
+0.07 0.80
+3.30 1.90
+-3.05 -1.12
+1.63 1.83
+2.20 -0.30
+2.50 1.43
+-1.86 0.58
+-0.13 0.12
+-1.49 -1.79
+0.44 -0.45
+0.81 0.38
+-2.40 0.86
+-3.34 0.62
+-0.40 0.05
+0.73 0.71
+0.56 -0.89
+-1.07 1.34
+-0.04 0.76
+-2.47 -0.31
+0.52 1.25
+-1.04 -0.64
+-0.67 1.24
+-1.72 -0.10
+1.02 0.38
+2.21 1.20
+-2.11 1.54
+-0.41 0.19
+0.40 1.07
+-1.73 0.02
+-1.57 0.42
+-0.41 -1.98
+1.21 -0.46
+0.05 -0.31
+-0.36 0.37
+2.90 -0.50
+1.36 1.29
+1.04 2.14
+-0.15 0.59
+0.20 -0.28
+-1.32 -0.69
+-2.39 0.16
+-3.18 0.04
+0.24 0.68
+2.33 0.36
+-4.29 -0.69
+-1.50 -0.99
+-2.41 -0.57
+-2.52 0.05
+-0.66 0.79
+-3.00 0.09
+-0.87 0.75
+3.39 1.30
+0.82 0.27
+-1.26 1.55
+-0.37 2.13
+0.36 -0.02
+1.26 0.37
+-2.67 0.68
+-1.76 -1.37
+-0.62 -1.40
+-1.44 -1.35
+1.08 -0.31
+-3.04 -0.92
+-0.30 -1.69
+-0.34 -0.23
+-1.54 0.74
+-1.37 -0.73
+-0.54 -0.86
+-1.19 1.17
+0.12 0.40
+0.50 0.43
+-0.09 0.30
+-0.82 0.76
+-0.02 0.20
+0.20 -0.22
+3.14 0.77
+1.61 -1.43
+-1.27 -0.47
+-1.24 -0.53
+0.24 1.10
+0.13 0.72
+-3.11 -0.51
+0.56 -0.97
+3.42 1.32
+6.59 1.09
+-2.47 -0.17
+0.62 1.03
+-1.08 -0.37
+-1.73 -0.47
+0.56 -0.26
+0.92 0.57
+-1.73 -2.63
+1.21 0.08
+1.76 -0.92
+-1.69 -1.54
+0.07 0.73
+-3.72 -0.91
+1.09 2.25
+2.54 0.27
+2.73 1.69
+2.69 -0.32
+-0.88 0.36
+-1.86 -0.87
+-0.52 -0.26
+0.41 -1.71
+-0.34 1.00
+-1.09 -0.05
+-0.60 -1.59
+-3.43 -0.70
+0.81 0.30
+1.11 -0.24
+0.96 -0.19
+3.31 0.09
+-2.30 -0.61
+-2.72 -0.71
+-0.00 -0.01
+0.06 -0.61
+0.41 -0.40
+0.12 0.60
+1.49 0.31
+-1.93 -0.01
+-1.11 0.44
+-3.11 -1.78
+1.36 -1.00
+2.95 -0.11
+-0.68 -1.44
+-2.24 -1.01
+-2.47 -0.36
+-2.50 -0.51
+2.61 -0.04
+0.99 1.09
+1.72 1.70
+-2.64 -0.19
+-0.64 -0.95
+-0.22 0.24
+1.49 0.27
+-0.59 -0.36
+-0.79 0.32
+-2.26 -1.78
+-1.03 -1.90
+1.63 0.02
+0.38 1.27
+-1.31 -0.19
+1.16 0.82
+1.62 1.47
+3.77 1.56
+-3.06 0.26
+1.06 -0.76
+1.96 1.37
+-0.32 -0.16
+1.13 -0.19
+-0.13 0.10
+1.45 1.60
+-1.36 -1.53
+0.12 0.29
+-2.63 -1.76
+-0.36 -0.58
+2.16 1.07
+1.01 0.28
+0.59 0.38
+0.01 0.51
+0.17 -2.03
+-1.18 -0.81
+-1.90 -2.37
+1.61 -1.35
+1.51 0.60
+-2.82 -0.84
+1.23 -0.04
+-2.11 -0.63
+1.76 1.24
+2.83 0.22
+0.61 -0.01
+-0.30 0.03
+-1.50 0.64
+-1.36 -0.55
+1.75 0.98
+3.09 0.78
+-2.38 -1.63
+3.75 2.01
+-2.95 -0.82
+0.78 -2.39
+-1.01 -0.03
+-2.19 -0.26
+3.49 -0.12
+0.46 0.83
+3.09 1.29
+-0.78 -1.22
+0.29 1.95
+3.24 1.18
+0.58 2.14
+-0.42 1.95
+3.61 1.25
+0.14 0.09
+-2.80 -0.24
+3.78 2.22
+1.71 0.76
+0.31 -0.21
+0.78 0.14
+1.51 1.88
+-2.78 0.54
+0.54 0.04
+1.25 0.27
+0.62 -0.64
+-1.51 -0.82
+0.84 -1.17
+2.65 1.38
+1.43 -0.95
+0.01 0.11
+-1.18 -0.45
+-2.41 -0.05
+-2.54 -1.70
+-3.34 -1.40
+0.97 -0.51
+0.19 -0.05
+-3.87 -1.46
+1.00 0.14
+2.17 0.02
+3.35 2.00
+1.59 -0.89
+-0.32 1.80
+1.99 -0.76
+0.18 -2.16
+1.11 0.06
+0.53 0.77
+0.11 -1.30
+-1.21 1.35
+1.25 -0.36
+-3.34 -0.50
+3.92 0.60
+-1.09 -0.19
+2.01 1.33
+-0.26 -1.36
+-1.71 -1.25
+-1.68 -1.79
+-1.81 -1.35
+-3.08 -1.77
+-0.54 -0.49
+0.87 0.85
+0.42 -0.18
+2.22 1.96
+-1.18 0.18
+1.38 1.86
+-2.15 -0.96
+2.05 0.87
+0.15 -0.50
+2.58 -0.35
+0.76 0.72
+-1.84 0.55
+-0.20 1.40
+3.17 0.96
+-0.95 -1.90
+3.29 1.12
+-0.90 -1.42
+0.24 0.30
+-1.41 -1.43
+1.11 2.56
+-0.04 0.86
+-0.39 0.11
+-0.30 -0.15
+-3.20 1.08
+-1.42 -0.71
+-0.49 -0.10
+2.76 -0.20
+1.08 0.34
+-2.99 -0.55
+-0.00 1.51
+5.48 1.63
+2.21 0.40
+-0.32 0.03
+3.45 0.55
+-0.70 0.85
+-0.23 0.03
+-5.84 -3.15
+-1.91 -1.30
+1.61 0.60
+1.76 -0.15
+-0.14 -0.89
+3.34 1.95
+-3.76 -2.76
+0.35 -0.61
+3.23 0.99
+-0.07 -0.37
+0.35 1.57
+-0.14 0.54
+1.64 0.85
+0.74 0.45
+-2.10 -1.12
+2.85 -0.62
+0.17 -0.00
+-2.46 -0.64
+-1.56 -0.36
+5.91 0.15
+-1.86 0.67
+2.56 0.52
+2.15 0.30
+-3.17 -0.23
+4.08 0.80
+-3.92 -1.06
+1.29 1.00
+0.76 0.40
+2.13 0.12
+0.53 0.71
+2.40 0.30
+-1.05 -1.04
+0.97 -0.20
+0.07 1.42
+-0.45 0.27
+1.18 -1.10
+-0.42 -0.13
+2.21 0.38
+0.46 -0.33
+1.70 0.37
+0.46 1.05
+0.68 -1.14
+-0.19 0.02
+0.74 -0.92
+-1.91 0.11
+1.68 0.85
+-1.68 -0.52
+0.24 0.06
+1.12 0.98
+-0.31 1.10
+0.56 0.42
+0.04 -0.80
+-1.46 -0.78
+-2.20 -0.98
+-1.20 0.38
+-1.00 0.26
+2.45 1.06
+1.33 -0.76
+-4.51 -0.84
+0.74 0.69
+-0.87 0.17
+-0.71 -1.09
+-1.57 -0.06
+-2.91 -0.75
+-0.58 0.07
+0.23 -1.28
+-1.66 -2.28
+-4.01 0.41
+-1.09 -0.60
+-0.36 -0.50
+-0.23 -1.01
+0.91 0.52
+2.56 0.39
+-1.36 -0.47
+0.59 -1.19
+0.77 0.71
+2.60 1.27
+-0.62 -0.43
+0.91 0.23
+0.58 0.27
+-2.00 -1.07
+3.43 0.62
+-0.98 0.62
+-1.83 -0.51
+0.51 1.31
+-1.72 0.19
+4.03 1.09
+-1.12 1.15
+-2.15 -1.20
+-2.25 -0.15
+-2.53 -0.83
+-1.87 -0.23
+-2.48 -0.81
+0.23 -0.37
+-0.88 -1.21
+-1.86 -0.35
+-3.04 -1.19
+-0.88 1.75
+0.13 -0.03
+-1.47 -0.67
+4.46 2.86
+2.36 2.48
+-1.52 0.13
+-1.27 -0.89
+-0.18 0.85
+-5.73 -0.32
+1.19 1.80
+0.40 0.79
+-2.94 -1.35
+1.96 0.60
+1.75 0.04
+2.63 1.21
+-3.74 -0.08
+1.15 0.03
+0.21 0.09
+-3.61 -1.51
+-1.59 -0.54
+3.45 0.51
+0.62 -1.66
+1.43 0.68
+2.01 0.40
+-2.71 -0.90
+0.91 2.89
+0.07 -0.73
+-4.17 -1.14
+3.68 0.52
+1.63 1.05
+-1.18 -1.37
+0.32 1.22
+3.30 -0.52
+-4.01 -1.85
+5.37 2.31
+0.38 -0.45
+0.43 0.10
+1.78 1.56
+-1.52 -0.50
+3.30 1.78
+0.94 1.99
+-1.62 -0.23
+4.21 1.69
+-0.20 0.58
+-4.08 -1.34
+3.90 1.49
+2.14 1.45
+-0.91 -1.03
+0.46 1.04
+2.16 1.49
+5.94 2.20
+-0.15 -0.16
+-3.82 -1.84
+0.58 -0.89
+2.99 0.29
+0.50 -0.53
+-4.77 -1.35
+-1.34 -0.82
+1.18 -1.23
+0.63 -1.64
+-1.91 -0.53
+0.21 -0.42
+-0.95 -0.21
+2.47 1.18
+-0.48 -0.56
+-2.24 0.29
+0.29 -0.25
+0.83 0.69
+1.63 -0.49
+-3.71 0.35
+1.56 -0.05
+0.12 0.14
+2.96 -0.97
+-0.81 -0.53
+0.96 0.99
+-1.36 -0.80
+0.22 0.72
+-0.65 -2.01
+-0.58 -1.27
+3.47 2.61
+-1.18 -0.30
+0.19 0.02
+1.17 -0.09
+-0.21 -0.40
+1.41 0.11
+1.49 0.29
+-2.86 -0.42
+-0.74 -0.39
diff --git a/exercises/Exercise3/data_uncorrelated.txt b/exercises/Exercise3/data_uncorrelated.txt
new file mode 100644
index 0000000..9b1ca74
--- /dev/null
+++ b/exercises/Exercise3/data_uncorrelated.txt
@@ -0,0 +1,1000 @@
+1.76 0.12
+0.95 0.87
+0.20 -0.09
+-1.17 -0.79
+1.75 0.78
+3.23 0.06
+2.26 1.88
+-0.54 0.42
+-2.66 0.57
+-4.13 -0.52
+3.00 -0.04
+1.53 0.34
+-1.62 -0.11
+0.21 0.46
+-2.85 -0.93
+2.48 1.08
+-2.15 1.27
+-1.24 -1.02
+1.87 0.32
+-0.85 -0.42
+4.31 0.37
+0.21 0.48
+-3.76 -0.22
+2.13 -1.31
+-0.63 0.19
+-1.22 -0.42
+3.43 0.87
+0.29 -0.85
+-2.54 -0.15
+-1.88 -0.46
+0.19 -0.32
+-1.10 -1.50
+1.97 0.48
+0.49 0.81
+0.71 0.58
+3.32 1.01
+-1.08 1.33
+-0.69 0.67
+3.19 1.05
+3.28 -1.30
+-1.02 -0.32
+-0.03 -0.64
+-0.21 -0.26
+-1.56 1.73
+-0.60 0.53
+1.89 -1.10
+-0.36 0.89
+-3.54 0.69
+-4.19 1.97
+-2.36 1.23
+-0.39 -1.68
+0.48 -0.76
+-2.30 0.05
+-2.79 -0.77
+4.45 2.20
+0.06 0.24
+2.10 1.38
+5.72 1.49
+-4.50 0.50
+3.33 2.59
+-0.34 -0.40
+-0.69 -0.07
+-0.64 -0.83
+0.47 -1.86
+-2.78 0.21
+1.13 0.28
+0.16 0.30
+0.07 -2.17
+-0.18 2.18
+0.65 0.33
+-1.40 -0.20
+-0.47 0.05
+-0.67 -0.80
+1.25 1.45
+-1.87 -0.35
+0.26 1.65
+-0.32 -0.14
+5.19 -1.81
+-0.19 -2.16
+-3.80 0.93
+-2.24 0.54
+-1.24 -0.34
+2.23 -1.21
+-1.32 -0.37
+-0.31 -0.63
+1.98 0.42
+-0.62 -1.10
+-1.96 0.08
+2.69 1.06
+-3.16 -1.12
+4.12 -0.05
+0.41 -0.72
+-2.15 -0.53
+2.73 1.06
+-1.26 -2.47
+-1.70 1.10
+-1.53 0.53
+-0.08 0.77
+0.64 -0.30
+-0.84 0.82
+-0.40 -0.42
+3.93 -0.59
+0.40 -0.39
+1.99 0.18
+-1.66 0.47
+-0.23 -1.32
+3.70 0.66
+3.26 0.46
+1.71 -0.06
+0.63 0.06
+-1.05 -1.69
+-1.46 1.22
+4.37 -0.25
+2.05 -1.18
+-0.04 -1.51
+-0.39 1.72
+0.17 -2.05
+1.85 0.18
+1.37 -1.34
+-0.64 1.50
+1.80 -0.65
+-1.83 0.71
+-1.33 -0.66
+2.70 1.23
+0.69 0.68
+-2.62 0.54
+-3.90 -0.15
+-0.60 0.35
+1.01 -0.82
+0.89 0.85
+0.47 -0.37
+0.21 -1.48
+-2.27 -1.07
+1.17 1.25
+-4.57 2.42
+3.24 -1.17
+1.75 1.73
+-1.27 0.51
+3.08 1.25
+-1.25 0.21
+0.22 -0.48
+4.81 0.63
+-0.98 -1.39
+1.76 -1.28
+-0.70 -1.81
+-7.54 0.38
+-0.23 1.30
+0.11 -0.54
+-0.10 1.07
+0.63 -1.85
+-0.17 0.51
+3.20 -0.08
+1.35 0.39
+0.11 -0.26
+0.93 -0.22
+0.21 -0.89
+-1.38 2.78
+-0.72 -0.34
+3.14 -1.47
+2.82 -0.98
+-0.18 -0.72
+-0.19 0.32
+2.06 1.22
+0.75 0.94
+0.95 0.39
+-1.00 -0.28
+6.59 -0.18
+-0.11 -0.02
+-1.69 0.81
+2.96 -1.31
+1.52 -1.62
+4.58 -1.52
+1.99 -0.20
+2.74 0.67
+-1.71 -0.46
+2.07 -0.56
+-0.32 -0.45
+0.76 1.11
+-0.92 2.14
+-0.81 -3.07
+-3.48 -0.27
+-4.54 -0.11
+0.85 1.03
+1.05 -0.75
+-3.04 -0.06
+0.29 -1.18
+1.16 0.02
+-1.66 -0.56
+1.53 -0.57
+2.75 0.31
+-2.17 -1.09
+2.21 1.29
+-2.60 0.59
+-0.47 -1.77
+1.44 0.14
+0.94 -0.76
+-1.29 0.04
+-1.49 -0.16
+0.55 -1.04
+-0.50 0.19
+3.51 0.95
+-2.60 -0.11
+-1.57 -0.44
+3.16 -0.30
+-3.92 -1.47
+-1.35 -0.22
+1.15 -0.64
+1.98 0.73
+1.49 2.04
+1.67 1.44
+0.18 -0.52
+-1.91 -0.83
+-1.97 0.06
+-3.72 -1.64
+-3.94 -0.50
+-1.81 -0.08
+-1.08 0.95
+-2.45 -1.85
+0.51 -0.61
+0.40 -0.34
+2.94 0.56
+-0.68 1.59
+3.89 -0.55
+1.18 -0.42
+-0.24 1.88
+2.08 -0.19
+-1.88 -1.76
+-1.07 -0.08
+3.00 -0.09
+-0.53 0.86
+1.37 0.00
+2.34 -0.87
+-1.54 1.49
+1.46 0.40
+0.82 -0.24
+4.93 -0.15
+0.78 0.10
+-1.04 1.30
+2.12 -2.05
+-1.85 -0.28
+-2.86 -0.24
+-1.99 0.41
+1.46 -1.06
+-0.67 -0.68
+1.33 0.96
+-2.80 -0.12
+-0.08 1.70
+0.02 -0.52
+3.51 1.36
+-1.60 0.16
+-1.62 -0.15
+-0.39 -0.05
+2.34 -0.54
+0.54 -1.78
+-1.44 -0.37
+0.01 -1.14
+1.56 -0.29
+-1.04 -1.48
+2.48 -0.14
+-4.39 0.49
+1.04 0.87
+-0.42 -0.28
+0.65 -0.89
+-2.34 1.34
+1.53 -1.32
+-1.64 1.23
+2.16 -0.25
+2.55 -0.08
+1.03 -2.96
+2.22 -2.19
+-0.13 1.15
+1.06 -0.21
+0.50 1.06
+-2.70 -0.97
+0.78 0.90
+1.23 0.17
+2.16 0.17
+-1.35 -0.51
+-0.12 0.49
+2.03 0.89
+-2.66 1.33
+0.21 1.57
+-0.08 -0.16
+-1.64 -2.06
+0.87 1.57
+0.36 -0.92
+-1.10 0.22
+1.26 1.58
+5.91 1.08
+0.66 0.17
+2.09 0.84
+0.46 0.59
+-1.27 0.19
+0.70 -0.89
+-0.39 -1.95
+3.93 0.65
+1.87 -0.32
+-0.60 0.80
+1.31 -0.23
+1.94 0.60
+3.58 0.58
+-2.44 -0.20
+2.42 0.65
+1.43 -0.37
+-0.60 0.22
+1.77 0.03
+1.80 0.44
+2.21 -0.07
+-1.38 -0.48
+0.26 0.55
+1.21 1.21
+0.15 -0.10
+-1.56 -0.39
+-0.69 -0.40
+1.73 1.91
+1.99 -0.46
+2.42 0.83
+0.54 -0.56
+0.72 -0.83
+-1.47 -0.06
+-3.07 0.05
+3.42 1.94
+0.04 0.13
+1.67 0.58
+2.69 -0.37
+-0.89 -1.03
+0.18 -0.52
+-0.19 0.69
+2.54 0.42
+-0.84 0.60
+-0.54 -0.39
+-0.65 0.46
+-0.73 -1.26
+-1.35 -0.08
+-0.48 -0.23
+-0.27 -0.71
+0.89 -0.26
+-0.61 -0.15
+-1.45 0.20
+0.61 -0.06
+-2.35 -1.74
+-0.75 1.38
+-0.22 1.14
+-1.39 0.04
+-0.98 0.71
+4.08 -0.51
+-1.84 0.46
+-0.73 -1.80
+1.48 0.16
+2.29 -2.57
+-1.51 -0.80
+-1.51 1.21
+0.95 -0.18
+1.01 -1.85
+-1.51 0.78
+1.34 -0.92
+-0.62 -0.07
+0.30 -0.97
+-2.47 -0.20
+0.03 0.24
+0.66 -0.01
+3.40 -1.00
+1.48 0.13
+3.45 -2.07
+-0.21 -0.25
+-0.24 -1.23
+2.01 -1.04
+-1.39 -0.19
+1.54 -0.73
+-1.26 -1.32
+1.70 -0.11
+-1.54 0.44
+-0.58 -0.77
+-3.14 -1.41
+-0.25 0.47
+-2.90 -0.02
+-0.97 -1.02
+2.04 0.61
+-4.06 1.98
+-0.14 0.54
+0.57 -0.08
+0.83 0.74
+-0.03 -2.81
+2.43 1.09
+2.32 -1.77
+-0.58 -0.88
+-1.65 -0.27
+-2.36 0.42
+-3.02 1.05
+1.39 0.18
+-0.63 -0.25
+0.52 -0.12
+0.42 0.33
+0.71 2.43
+0.29 0.76
+-1.18 -0.28
+1.62 -0.70
+-1.98 1.64
+1.63 1.30
+2.69 1.07
+1.57 -2.19
+-1.53 -0.47
+-2.96 0.79
+-0.71 0.40
+0.86 -0.54
+-3.61 0.50
+2.19 -0.86
+-5.64 1.82
+-1.60 -0.29
+1.99 -0.39
+-1.21 -0.97
+2.58 -0.77
+-3.11 1.53
+-0.32 1.23
+0.04 -1.73
+2.75 -0.90
+-0.90 -0.04
+0.28 0.42
+3.49 -1.06
+0.85 0.57
+0.05 0.34
+1.23 1.09
+1.47 2.61
+-0.21 0.51
+-2.34 -2.59
+6.17 -1.34
+1.24 -1.30
+1.25 -0.02
+0.71 -2.75
+-0.37 -0.35
+-2.63 -0.49
+0.69 0.92
+0.11 -0.14
+0.60 -1.84
+0.48 -0.00
+-1.99 0.29
+-0.57 -2.49
+0.73 -0.59
+-0.54 -0.17
+0.29 0.19
+0.66 -0.83
+-4.73 0.84
+1.14 0.24
+-0.96 0.25
+-1.24 -1.76
+-1.33 -0.95
+2.05 -0.36
+0.70 -1.30
+1.19 -0.79
+-4.42 1.59
+-1.02 -0.48
+-2.68 0.55
+1.01 1.62
+-1.38 0.63
+-0.30 -0.05
+0.99 -0.33
+0.71 0.25
+-0.83 1.53
+1.27 0.85
+-2.74 -0.66
+-2.09 -1.36
+-2.30 0.30
+-3.05 -0.02
+2.17 -0.07
+0.39 0.57
+0.02 -0.30
+-1.80 -2.42
+1.32 0.23
+0.32 0.76
+-2.23 2.10
+-1.26 -1.86
+1.72 -0.78
+0.12 -0.64
+4.22 1.69
+0.22 0.63
+0.16 -0.39
+1.89 -0.81
+0.40 1.31
+3.96 -0.31
+1.00 -1.31
+0.60 -0.21
+2.20 -1.14
+1.74 1.14
+-0.22 -1.74
+-0.36 -0.01
+-0.38 1.55
+-2.12 0.64
+-0.71 -0.84
+-1.28 -0.06
+2.71 -0.08
+1.29 0.68
+-1.04 0.87
+-1.45 -1.39
+-1.24 -0.69
+-0.60 -0.52
+-1.62 1.16
+-0.04 1.15
+0.44 0.01
+-0.14 -0.47
+2.81 0.32
+0.47 1.05
+3.15 0.68
+-3.09 -0.10
+-1.29 -1.09
+-0.29 -1.15
+1.06 -1.01
+2.45 -1.05
+-1.12 0.33
+-0.88 0.53
+-0.65 -1.73
+-2.80 0.93
+2.71 0.63
+0.97 -1.04
+-1.08 2.47
+-0.42 -0.02
+-0.14 -0.51
+-1.97 0.18
+-0.74 0.18
+2.21 1.26
+1.80 -1.03
+-2.21 -1.02
+0.86 0.05
+-3.63 -1.80
+0.32 1.02
+3.26 0.64
+-3.14 -1.48
+2.38 -0.84
+-2.76 0.21
+3.48 0.22
+-0.74 1.38
+3.69 -1.30
+-1.18 -0.28
+3.37 0.66
+0.37 0.57
+3.20 -0.69
+0.29 -1.10
+0.26 -0.30
+-1.00 -0.54
+-4.40 -1.06
+-0.93 -0.17
+1.56 -0.92
+3.51 -1.35
+-1.62 0.34
+-0.67 -1.27
+-3.10 1.22
+-1.13 0.22
+1.73 1.71
+-1.63 0.33
+-4.91 -0.26
+0.89 -1.62
+4.67 -0.31
+-1.45 1.09
+-1.32 -0.65
+0.37 -0.40
+-2.06 1.26
+2.24 -0.71
+0.40 1.03
+0.99 0.26
+3.82 -0.16
+0.50 0.71
+0.52 -0.38
+1.03 -0.44
+-1.97 0.06
+1.00 0.34
+3.35 -1.97
+2.08 -0.30
+-0.15 0.70
+-1.37 -1.04
+1.71 0.15
+-2.73 1.13
+-0.89 1.81
+0.39 -2.55
+3.45 0.67
+-0.24 0.97
+0.28 0.32
+-1.09 1.01
+-4.65 -0.28
+2.19 0.22
+-4.27 -0.69
+-0.42 -0.51
+1.36 -0.69
+-0.13 -0.09
+-2.78 -1.37
+2.09 -0.33
+1.88 -0.98
+4.12 1.20
+-1.20 -0.52
+-0.03 -0.59
+-2.99 -1.22
+-2.12 -0.35
+-0.53 0.37
+1.70 -1.47
+0.48 -0.57
+0.55 0.67
+-0.20 -0.85
+2.14 -0.66
+-2.39 -0.08
+1.80 1.51
+0.33 1.79
+1.18 1.08
+5.01 -0.39
+-3.70 -2.04
+1.42 -0.85
+-2.84 0.96
+-0.47 0.15
+-2.95 0.92
+0.92 -0.09
+2.02 -0.56
+2.27 -0.72
+0.19 1.13
+-1.55 -0.23
+1.22 0.40
+1.66 1.47
+-1.79 1.16
+-2.47 1.04
+-1.25 0.50
+-0.86 2.05
+-1.69 -2.21
+-0.77 0.35
+0.75 0.63
+2.05 -0.27
+-2.48 -1.83
+-2.20 0.26
+1.56 1.05
+-0.74 0.06
+-1.65 0.71
+-1.99 0.03
+-0.44 -0.30
+-0.62 -1.30
+2.89 0.86
+-1.64 -0.80
+-1.83 -0.47
+-3.86 -0.95
+-2.17 -0.05
+2.24 -0.37
+3.26 0.68
+2.75 1.75
+-1.74 1.04
+0.69 -1.75
+0.61 0.86
+-0.17 -0.04
+0.31 0.89
+1.39 0.25
+-2.83 -0.80
+0.99 -0.98
+0.01 0.90
+-2.10 0.05
+0.43 -0.84
+2.02 -0.71
+-1.20 -0.50
+-1.15 0.12
+-0.98 -0.21
+-0.13 0.44
+1.00 0.16
+-1.73 -2.06
+-2.46 -0.59
+2.59 0.06
+1.87 -0.85
+0.99 0.19
+-1.65 0.01
+-0.12 0.38
+1.78 1.08
+4.54 -0.18
+-3.86 1.41
+2.44 -0.25
+-0.98 -0.13
+1.75 -0.15
+-0.58 -0.29
+2.00 0.81
+-1.11 -0.83
+0.37 -1.02
+0.86 -0.00
+2.68 -0.74
+0.32 1.48
+-0.56 -0.56
+4.60 -1.40
+4.03 0.18
+0.68 1.45
+0.73 -0.69
+-2.28 -0.77
+-0.87 -0.37
+-2.72 -0.53
+-0.63 -0.12
+-0.64 1.01
+2.97 0.43
+-0.04 0.64
+2.53 0.73
+-0.56 -0.83
+-1.06 -0.94
+-0.13 -1.10
+0.57 -0.54
+1.59 0.25
+0.28 -0.61
+-0.82 -0.35
+-1.70 0.49
+3.03 0.42
+1.94 -1.62
+-1.17 -0.13
+3.05 -0.26
+-1.27 -0.06
+0.67 -0.92
+-3.17 0.45
+-0.23 2.34
+-2.10 1.30
+-1.70 -0.28
+0.06 -0.44
+1.55 0.15
+-2.58 -1.17
+2.04 0.69
+1.19 -0.41
+-1.09 -1.50
+1.05 0.94
+0.65 -1.11
+2.81 -1.01
+4.33 0.31
+-1.20 1.28
+0.57 -0.53
+-1.82 -1.04
+-0.30 0.40
+-0.59 0.99
+0.63 0.38
+1.37 -0.09
+-1.57 1.27
+0.19 0.38
+0.67 0.08
+1.09 -1.63
+0.99 -0.63
+-0.76 -0.89
+0.91 -0.14
+-1.17 -0.11
+1.56 0.36
+-1.97 2.52
+0.99 -1.10
+-3.90 1.16
+0.59 1.33
+-0.17 0.92
+-3.92 0.17
+1.28 -0.34
+1.29 0.86
+0.83 0.38
+-3.58 0.45
+2.07 -0.28
+3.37 -1.72
+-0.15 1.63
+-2.31 1.67
+-1.22 0.39
+3.42 1.59
+0.80 0.24
+4.05 0.06
+0.38 1.73
+-2.29 0.99
+0.44 -0.71
+3.17 -0.93
+-4.89 -0.22
+1.59 -0.78
+1.66 -0.60
+-1.92 -0.14
+0.50 -0.75
+-0.19 0.90
+0.52 0.84
+-0.61 -1.26
+-1.48 -1.08
+1.93 -0.09
+1.89 1.26
+1.07 0.89
+-0.54 -0.17
+2.06 -2.52
+-0.36 0.37
+1.68 0.45
+-2.02 0.25
+-1.87 -0.57
+-1.80 1.15
+-0.51 0.17
+-1.21 1.22
+-4.41 -1.41
+0.87 -0.20
+1.60 -0.24
+-0.85 1.02
+-0.09 0.74
+0.08 -0.07
+-0.40 1.24
+-3.23 0.96
+1.39 1.06
+-1.67 1.16
+0.63 1.61
+-2.65 0.39
+5.49 -0.69
+-1.33 0.18
+-2.61 0.12
+2.20 0.48
+-3.59 -0.36
+-1.18 1.27
+-0.64 0.16
+1.40 -1.05
+-0.72 -1.87
+-2.53 -1.51
+1.80 1.78
+3.45 -1.07
+-0.85 0.56
+-2.17 0.07
+-5.02 -1.38
+-2.43 -2.58
+1.54 -0.77
+1.10 0.83
+0.17 1.01
+-0.40 -0.18
+1.60 0.42
+-0.23 0.35
+3.66 0.88
+-1.02 0.10
+3.64 0.50
+1.24 -0.38
+3.94 -0.13
+-2.88 0.29
+-3.07 -0.60
+-2.83 -0.82
+-1.84 0.79
+-2.16 1.23
+0.75 0.96
+-0.38 -1.16
+-0.26 0.01
+1.16 0.37
+0.34 -0.65
+-0.70 -0.13
+1.21 0.24
+-2.90 -0.62
+4.10 0.15
+1.03 0.86
+-3.41 0.20
+-1.63 -0.17
+1.17 0.92
+0.57 -2.26
+1.84 -1.10
+-0.38 1.20
+0.36 0.35
+0.57 -0.01
+-0.11 -1.42
+-1.19 1.99
+-0.31 -0.85
+2.63 0.60
+1.59 -0.78
+-1.20 -1.04
+-0.72 0.56
+1.50 -1.48
+2.42 0.09
+-0.92 1.76
+-3.70 -1.33
+-0.91 -0.01
+4.87 -0.67
+0.22 -0.18
+2.58 0.15
+-0.43 1.74
+-3.97 0.97
+1.45 -0.07
+-4.17 0.66
+1.32 0.56
+-2.33 -1.15
+1.61 0.18
+-2.79 -1.34
+1.18 0.04
+-1.25 0.19
+-1.83 1.02
+2.07 -1.96
+-3.07 -1.61
+-0.79 0.19
+-4.42 0.54
+-0.93 0.65
+0.46 -0.66
+-3.83 -0.23
+-0.26 1.01
+-3.23 0.61
+0.65 0.25
+-0.36 -0.57
+-0.97 0.97
+-0.59 -1.53
+0.33 0.37
+1.93 -1.33
+-2.08 -0.28
+-0.34 0.72
+-1.84 1.14
+0.03 -0.52
+-0.33 -1.32
+1.22 -0.64
+-0.02 -1.20
+-0.33 -1.34
+0.89 -0.24
+0.36 -0.08
+-2.07 0.27
+5.60 -0.04
+-2.60 -0.44
+-1.15 0.86
+-0.27 -0.87
+-1.33 0.69
+0.67 1.31
+0.53 -0.31
+4.69 -0.93
+0.56 0.00
+-1.93 1.83
+0.13 -1.95
+-2.74 -0.23
+1.81 1.53
+0.37 -0.53
+2.32 0.33
+-1.17 -1.98
+2.28 0.63
+0.51 0.56
+0.09 0.97
+0.70 -0.70
+0.05 0.07
+4.40 0.72
+-0.42 -1.27
+-1.57 -0.07
+5.54 1.01
+0.26 -0.19
+1.26 2.47
+4.06 1.11
+-2.58 1.03
+6.53 -0.91
+-5.01 -0.44
+-0.47 1.08
+2.69 0.99
+-1.44 0.37
+-1.20 -1.32
+-3.54 -1.44
+0.49 -1.73
+3.30 -0.16
+-0.67 -0.13
+1.84 -0.04
+-1.26 -0.52
+0.35 -0.85
+-0.93 -1.89
+0.38 0.91
+-1.99 -0.63
+1.72 -1.12
+1.11 -0.13
+2.81 -0.12
+-0.82 -0.73
+0.04 -0.78
+-1.68 1.02
+0.25 -2.02
+-0.21 0.21
+-0.05 -1.73
+0.15 2.04
+0.49 -0.74
+1.13 -0.83
+-2.10 -1.43
+0.94 1.11
+-0.05 1.12
+-0.43 -1.15
+-2.93 -0.34
+1.68 -0.73
+-2.31 0.37
+3.38 0.78
+-3.43 -0.56
+-0.98 2.49
+-1.03 1.20
+-0.12 1.29
+-3.98 1.04
+0.53 0.35
+0.95 -0.81
+-0.02 -0.28
+1.59 1.90
+-0.82 0.71
+-2.48 -0.79
+-0.14 -0.36
+2.52 0.36
+-0.63 0.18
+0.70 -1.03
+-2.57 -0.19
+3.23 -0.58
+-3.13 -0.44
+0.41 0.09
+-0.86 -0.03
+-0.11 1.07
+0.16 -1.30
+-1.12 -0.22
+0.71 -0.65
+0.55 1.39
+-0.08 0.82
+-0.41 -0.19
+0.74 -0.79
+0.15 -1.28
+3.12 1.40
+0.12 0.11
+1.24 -1.38
+-1.23 -1.58
+-0.07 2.09
+-0.38 1.84
+-2.03 0.81
+-0.93 -0.46
+-0.77 0.48
+0.04 -0.26
+-3.69 -0.38
+-0.11 0.20
+-3.66 0.46
+0.52 -0.02
+0.36 -0.49
+0.58 -0.47
+-2.56 0.38
+-0.18 -1.11
diff --git a/exercises/Exercise4/Exercise_4.ipynb b/exercises/Exercise4/Exercise_4.ipynb
new file mode 100644
index 0000000..704d95b
--- /dev/null
+++ b/exercises/Exercise4/Exercise_4.ipynb
@@ -0,0 +1,436 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Exercise 4\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "from __future__ import print_function  # For Python < 3\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "import scipy.stats as stats\n",
+    "\n",
+    "%matplotlib inline \n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 1. Approximations to the binomial\n",
+    "\n",
+    "For np < 10, large n, the Poisson distribution is a good approximation for the binomial.\n",
+    "\n",
+    "* Show analytically that the binomial distribution converges to the Poisson distribution in the limit of large n. (Hint: $e = \\lim_{n\\to\\infty}(1+\\frac{1}{x})^x$)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "* Keeping $n p$ fixed, plot the binomial probability mass function for an increasing number of observations $n$, comparing in each case to the equivalent Poisson distribution ($\\lambda=n p$). For convenience, you should use the relevant functions in ```scipy.stat```."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "n_trials = [5, 10, 100]\n",
+    "p0 = 0.8\n",
+    "...\n",
+    "\n",
+    "# Plot the PMFs\n",
+    "fh, ax = plt.subplots(1,len(n_trials), sharey=True, figsize=(10,4))\n",
+    "for idx, nt in enumerate(n_trials):\n",
+    "    ...\n",
+    "    ax[idx].bar(...)\n",
+    "    ax[idx].bar(...)\n",
+    "\n",
+    "..."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "For np > 10, n(1-p) > 10, the discrete binomial distribution can be reasonably approximated by the continuous normal distribution.\n",
+    "\n",
+    "* Choose a large n (> 30, with p close to 0.5). To start with, choose n=100 and p=0.45. Plot the binomial pmf, and, with equivalent parameters, the normal pdf \n",
+    "* Calculate the probability that X >= 55 for each. Don't forget to apply the continuity correction\n",
+    "* What happens to the relative difference as n increases?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "n_trials = 100\n",
+    "p0 = 0.45\n",
+    "mu = ...  # Expectation\n",
+    "std = ... # Standard deviation\n",
+    "\n",
+    "#...\n",
+    "\n",
+    "print('Binomial (exact):', ...)\n",
+    "print('Gaussian (approximate):', ...)\n",
+    "\n",
+    "\n",
+    "# Plots\n",
+    "..."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 2. Random walk\n",
+    "\n",
+    "Consider a simple 1D random walk. A person starts at the position $x=0$. With equal probability $p=0.5$, they may take one step forwards or one step backwards, corresponding to a displacement of +1 and -1 respectively.\n",
+    "\n",
+    "* Show that for an N step walk, the expected absolute distance from the starting position is given by $\\sqrt{N}$.\n",
+    "\n",
+    "* Write a function to simulate such a random walk, parameterised by the number of steps. The output should be an array, with the displacement at each step index.\n",
+    "\n",
+    "* Plot a single walk."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def random_walk(n_steps):\n",
+    "    return ...\n",
+    "\n",
+    "n_steps = 100\n",
+    "w = random_walk(n_steps)\n",
+    "\n",
+    "..."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "* Simulate ~1000 random walks of 500 steps.\n",
+    "\n",
+    "* Plot the average distance (rms) of these over the whole set with respect to step index (time). Does the average converge to the expected distance?\n",
+    "\n",
+    "* (Optional) sample and plot the running average to show how the convergence improves with number of walks."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "n_steps = 500\n",
+    "n_walks = 1000\n",
+    "n = np.arange(n_steps) +1\n",
+    "\n",
+    "print('Expected distance for {} steps: {}'.format(...))\n",
+    "\n",
+    "W = []  # Final distance\n",
+    "A = []  # Running average over whole set\n",
+    "T = 0\n",
+    "for idx in range(n_walks):\n",
+    "    ...\n",
+    "\n",
+    "# ...\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "* Now consider the case $p \\ne 0.5$, where the \"person\" is more likely to step in one direction than another. Find again analytically the expectation and the variance for the (rms) distance travelled in terms of $N$ and $p$.\n",
+    "\n",
+    "* Modify the random_walk function to account for the unequal probability between the directions."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "* Run a series of random walks as before, and plot again the histogram of distances travelled. On top of this, plot the Gaussian PDF with the $\\mu$ and $\\sigma$ parameters as determined above."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Plot histogram\n",
+    "n_steps = 2000\n",
+    "n_trials = 5000\n",
+    "p = 0.4\n",
+    "\n",
+    "V = []\n",
+    "for n in range(n_trials):\n",
+    "    ...\n",
+    "    \n",
+    "..."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 3. Small sample sizes: t-distribution\n",
+    "### 3.1 Compare to normal distribution\n",
+    "\n",
+    "Student's t-distributions are interesting for cases where you have few samples and the population variance is unknown, but the underlying distribution of the means can be assumed normal. They are parameterised by the degrees of freedom (\"df\"), which is usually equal the number of samples minus one. As the number of degrees of freedom increases, the t-distribution converges to the normal distribution.\n",
+    "\n",
+    "* Plot the standard t-distribution for several increasing degrees of freedom and compare this to the normal PDF.\n",
+    "* Plot and compare the cumulative distribution functions\n",
+    "* Plot the variance of the t-distribution as a function of degrees of freedom. Compare to the standard normal variance (=1)\n",
+    "* (optional) make a Q-Q plot (see Wiki) and compare the distributions\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "x = np.linspace(-5, 5, 200)\n",
+    "df_all = [1,2,5,10,30]\n",
+    "\n",
+    "fh, ax = plt.subplots(2,2, figsize=(10,8))\n",
+    "\n",
+    "# PDF\n",
+    "for df in df_all:\n",
+    "    ...\n",
+    "    ax[0,0].plot(...)\n",
+    "\n",
+    "# CDF\n",
+    "for df in df_all:\n",
+    "    ...\n",
+    "    ax[1,0].plot(...)\n",
+    "ax[1,0].plot(...)\n",
+    "\n",
+    "# Variance vs degrees of freedom\n",
+    "...\n",
+    "ax[0,1].set_xlabel('DOF')\n",
+    "ax[0,1].set_ylabel('Var(T)')\n",
+    "\n",
+    "# Q-Q plot (optional)\n",
+    "for df in df_all:\n",
+    "    ...\n",
+    "    ax[1,1].plot(...)\n",
+    "\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 3.2 Eggs\n",
+    "An egg producer claims to supply eggs with an average egg weight of 63 g. In a box of 12, the following weights were measured (all in g):\n",
+    "\n",
+    "    62.75, 56.98, 53.30, 62.65, 57.63, 57.23, 56.65, 64.89, 57.87, 60.42, 57.01, 63.65\n",
+    "    \n",
+    "* Calculate the sample mean and (adjusted -- see ```ddof``` option) sample standard deviation.\n",
+    "\n",
+    "* What is the probability of obtaining this average weight or lighter, given the supplier's claim?\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "s = [62.75, 56.98, 53.30, 62.65, 57.63, 57.23, 56.65, 64.89, 57.87, 60.42, 57.01, 63.65]\n",
+    "n_samples = ...\n",
+    "dof = ...  # degrees of freedom\n",
+    "mu_samp = ... # Sampling mean\n",
+    "sig_samp = np.std(s, ddof=1)\n",
+    "mu_claim = ... # Claimed population mean\n",
+    "\n",
+    "...\n",
+    "print('Probability of this sample mean ({:.2f}) against claimed mean ({:.2f}): {:.2f} %'.format(\n",
+    "    ...))\n",
+    "\n",
+    "plt.figure()\n",
+    "...\n",
+    "plt.xlabel('Egg weight (g) (W)')\n",
+    "plt.ylabel('P(W)')\n",
+    "\n",
+    "plt.show()\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "* Within what range would 95% of samples follow? And how would this compare with an equivalent normal distribution?\n",
+    "* Plot again the two distributions, marking the 95% intervals"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "...\n",
+    "print('Normal: 95% of samples between {} and {}'.format(...))\n",
+    "print('T-distribution: 95% of samples between {} and {}'.format(...))\n",
+    "\n",
+    "plt.figure()\n",
+    "...\n",
+    "plt.xlabel('Egg weight (g) (W)')\n",
+    "plt.ylabel('P(W)')\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Bonus\n",
+    "A pair of independent, standard normal random variables can be generated by sampling a uniform distribution. One approach to this is the Box-Muller transform (see https://en.wikipedia.org/wiki/Box%E2%80%93Muller_transform).\n",
+    "\n",
+    "* Generate a long sequence of numbers drawn from U(0,1)\n",
+    "* Use the Box-Muller transform to convert these to normal random variables\n",
+    "* Plot the normal samples on a scatter plot - verify they are not correlated\n",
+    "* Plot the histograms, and superimpose the normal PDF\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "u1 = ...\n",
+    "u2 = ...\n",
+    "n1 = ...\n",
+    "n2 = ...\n",
+    "\n",
+    "...\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Improbable events\n",
+    "In this example, we tabulate the amplitude deviation against the probability, odds (inverse probability), and equivalent timescale (once in 10 thousand years). Modify the code and try with different distributions - especially those which look similar to the normal distribution, but carry a fatter tail."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from IPython.display import display\n",
+    "import pandas as pd\n",
+    "\n",
+    "def format_days(d):\n",
+    "    if d < 365:\n",
+    "        if d > 90:\n",
+    "            return '{:1.0f} months'.format(d/30)\n",
+    "        elif d > 7:\n",
+    "            return '{:1.0f} weeks'.format(d/7)\n",
+    "        else:\n",
+    "            return '{:1.0f} days'.format(d)\n",
+    "    d /= 365\n",
+    "    \n",
+    "    if d > 1e9:\n",
+    "        return '{:1.1f} billion years'.format(d*1e-9)\n",
+    "    elif d > 1e6:\n",
+    "        return '{:1.1f} million years'.format(d*1e-6)\n",
+    "    elif d > 1e3:\n",
+    "        return '{:1.1f} millenia'.format(d*1e-3)\n",
+    "    else:\n",
+    "        return '{:1.1f} years'.format(d)\n",
+    "\n",
+    "\n",
+    "z = np.linspace(0, 10, 500)\n",
+    "\n",
+    "data = []\n",
+    "for n in range(1,8):\n",
+    "    p = 2*(1-stats.norm.cdf(n))\n",
+    "    data.append([n, p, 1/p, format_days(1/p)])\n",
+    "    \n",
+    "display(pd.DataFrame(data, columns=[r'|X| ($\\sigma)$', 'p', '1 in', 'time equivalent']))\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Code to generate \"egg\" distribution"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [],
+   "source": [
+    "# Generate small dataset for T-dist question\n",
+    "# True parameters:\n",
+    "sig = 3\n",
+    "mu = 58\n",
+    "n_samples = 12\n",
+    "\n",
+    "s = stats.norm.rvs(size=n_samples, loc=mu, scale=sig)\n",
+    "print(('{:.2f}, '*n_samples).format(*s)[:-2])\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.6.2"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/exercises/Exercise4/Exercise_4.pdf b/exercises/Exercise4/Exercise_4.pdf
new file mode 100644
index 0000000000000000000000000000000000000000..c25bc6b9ae8dcc015e7756b5e5530b762eea6ca6
GIT binary patch
literal 47863
zcma%?Q;aA;x24;*ZQHhO+qP}nJZ;<Er)}G|ZEMa==3(yK$>e{i%1TvI^;YZ4+Pg><
zM8s$r=~$sikM6GDpct767zpf*te|*!=*28;Tuhzl#cT{+OhrtM?M+PSWlZhNT`UNg
z7#Z35_@JC!oJ<XEp*%KfG%w?e#t?sVdxI#VLR=5!dJELs5|hr@#zhrhoE~kvu?(@}
z6B#@JZ{r?U%RT_@I7W;$tntH2FIS&0E-Ekfqy>Or{3Q?+-=c%33EwV_@5<mw2oMWJ
z?)Dtf5rz><cr>8mijn#cUzFb#rL|}!Q0yAHd=H{$<N39zf>IDvlxZ=eG8jY>Rl?5r
zvrBb=x09f7ke&_jDaaZC-_Gah<bVed4;T9O3?xDBh-g4GX&^_|s_Rz4AZmqn7OV{!
zkox_9cX-ui(NaM{h(l2=6^)}rNMJAcqlc9cF#Rs>=Zo?gi5jkwef``u)#=}^(uVAO
zA2~Ys&>^b+#<x~Y3_{>xj7kcRG!TVZOnqv|%Xca}S1y#5wc{X>A*cecT{+NHnL%(0
z;RMbP2fS&RiURwfW|L7z!-2?HzdM*c?rP6?%pxTPCqFm*8eG2?R6hx$`0=oRHrCJb
zZ`faT+Ln+Ispu#U(XK>sA{hm!{ec^Ov=L6kXvla$O!k7aN6-G8!tGUeM4=`r@}`^-
z=O&qEq|fHjo<&L@WG42NV1bt;A_Skpoy}@nH;W}5P<bXi%}h0lYF720I-YjVPGxeY
z*&46L)9M}<dmY?#BR~<=d73}bY~EJo?a3;xFvnI=5V;R%%luTK5|MC$c8+lWm2v3q
z(rc^fi%_%*_~lSH>SMqRfPeXj>o${xC!l_)mI!|zAiWMq&KbcNCI}rzSIA~WXx3n=
z9_Yo1SA9<cBzAyWW&^d9dhL|I9^|5)5KcQvA1Y1fRsgaxHv@D&I>P}uC?@=#FJWIR
zVfO@zOoGYNtyJQHN&C<+bFG=B`fi-QD4kd#cNWS@oNCBe%R6n{>Htg-HaEzjLGxEv
zs~ZdQk|Orq1z830H)0V=+8}lnt?<YJB3p=fru5rR-!+g#3ztkhIwjjA@$_pYp60WB
zN_O(;S12=P=1IWmv7b1ol4Z(U+$cz))v~Ifn+TpK3;{B4pIR+EYmO)Ex24N853g>}
zqIS=1D2#4so<+c^9q<>pd6@AO-crT|QH?PUSV^eJE^L-&g2Y;Y#Hyy)4tX`k1NDnz
zbY>2yvde*}fas)+cYqN`6-?5!MW;oABTzA<MZVI1lfjw0-3Hn3ACfS*>8YbYudAOl
zz;}bL8DPvMw0TT3Wr9OSyde9UBlJL098v}0Xx^@@Y4e3dFi!&C1%gtAm`?>aW<215
zg98lhCa36xIqt9Pr(Y5F4YgDP6CF%)lt9mHd7H#20(wgTRH0q~TUa&{`AwJ=gCe6`
z2gWKF5t?qiBIC@z<d&1-NM26CJyraomW|iE=Q&BoSvz}z-y|5>cU*b}0b)2s#8^ys
z`OMGB4I%T^407FaQ=ZsuygNCV<<d0wT={doC9Jgi;+%fI$&Q@9N9YKw*}(W@sbr04
zK(i28Ujv`Io|Ls+qChdQWT%tenR3Zh)6?<BOT@JVE)yyDgjhgI(YqWQH<~2S%!!jc
z;Y8FBiz5W+p52YRPExuN!iXGdf^KNIkgoi~I=)!gfZvtylif#lZz?RAM;>NeFEZ)I
zcT&NCP0bfUa!-&{PY2NFl;|~lp};jia|MliLLhU|G<Ei86Q~QFR+fXVv3ac+o{cMa
zGC4NuxrgWYMuThleDAA4ZDz4fa=*~_67$AlQVaXJNez;)9J-o}RD^336IonhsBk6U
zLX0^88B~-dX9MPL+;0=cgSY$PJc`^Qo}{<f4q2IvQ~~C`BmDg*><CKOVi|F)?2{!u
zl1}w;eu$PbSGvi!BYa&)fx=;^j}BzFPfQzfCp?yI@wUkk8sFAv*O{i1d>=<7qkJcq
z=b)WON`kYwD<<eQ!LckeYMrgnM`3K}RyEmyqEYipN|GJ1iB|R|$})HkJ43lsH|Hr|
z_a@GF_4>mC&SLw3sbS@*`{%nL7a{Bb!q7@{`x>9d>VavsY(r@$yqgfo6>y69aTB>o
zI59lDG=wO=maoPYOQn<88;*NB?Zsd`1g%p3br08;-7PWSNv${@C~^Y!l6oxfzK^Er
zrdf;jPv_ESvDB<{3os@z$;uUMUc0JRc@P3V#HU8iLR*Qc3Q<W_XisZwNat1i%uo6Z
zYNBXjJ8NbKt;Q5VdXX^jlo@ec0Uf4<$K?7LBM9Y;p7T&eKhB_CJUH6{y91zQJwfnZ
zqn3_Tb;Y(cpiv}6W5eumw>oEnff(L1WYcxDbJ9SxI#bXugtINr?Xy?c1t5CUW{fS|
z(O`*!Mv3aeRy}e@#Qx&v=g;<4N|7o73i?n>%v`rq+-J~$TEP+Q95v8IZboN@lpbGT
zvQMtBSF>hEd?Z=3oNtG#lJ6+z$_}jVu?dq^w#P2!vhJwNvR3xrPZzo&n$GA>=?`D_
zL#J(dw8hxV`R(z)s~nm3Tc3rkBRS<QGk@`N-2JAir2<fgwsiZpCz3vcIy#Q1&LqN-
zn21Ve5r-A7;B>s#`-%RIgS*+6Q9U;YB($mon)v+X#JabIk6p7C>mSw%&?kD}gNDg~
zA>L|Skxg!zXcJp{%)<Uui4NB}b9Djkz%E!D%mXR*wybK7RPxm%s)-AO49u<ZEr)O-
z*JtcxNL<~#1N+a$W?UA_I-kOw9FQ0^Hy6`pX!t2TmWn&=m8<Ky`5zl6aMX6_T8Ih9
z{SIFe9~}y$G_+>Bq{;?mmKZ^h68EWq;oVEPFi)AN8tqeFjvNPvRxH-&*qv4nG_X*7
zcy4z@FpHZCRDf6P$~!-q$|HatrRBj*=3Rx+>zk-XOvjBdAD%dH*ERayPyS?M|5+$g
zJCpy-6aUnI5eOs4e}W>6oGkwrifG1fvLX20)*;M=k6EPc(^B@aS;#fRTPD*a0Id_u
zZ9Hj3&~PSob$XuL-`*w2a7M`!hEqg6I+}5ykM_3(fMEP25ER~_gD;xieNlNlWPyMb
zfsm8@%>ox8h$`9>l8^kSeWB_oQIluY{Xy$$!5}O`EQY*5=ofrCW){U{=`3Uh%nZR;
zbX1X%LXTy=l8-eXb>)rTve;f8dtdH9OGuFidS)jl<jP$-1d^fSMBf@|@L02F1H7uR
z+O#e6&aG1Y+oW-TN&cjXCZk%nv4_4+AXJ@)A|eE1z(Cp!TQoMlwQtoFYQ)q>#3`hR
z-_1|VBs&0JRThS!2i8D*SiXe?nj*1eZBusorA<F?>GYRzs0=Snm%$a*$5&@)Z8WPr
zH>I)Lf?`DOeP`QgY-`13&N7w@2XT?GPW?b;1dZJ(%MalJ#v(?V#$P-xPS<*M7Wn=Z
z;E41#8BQ;qSm?TmK)n62Z0%kZTtN;r8u3a<-;BYDmUIY-;B$gcG4T28gE)t5bf)&E
zGEO3z@l-#9Mq?B)(iIMAJ-i=8iXmxvmUhW}yde-vwgQHf1B+P{lB7sz92XMtDxhT8
z)$FSE+YNf`t4tap&UaR~;;GMDz0@rEJ=gl{0eOOOX`@h`y*^HkLpWZ<w{@&Reh?hz
zMphIwpqU_Y1Iqhc?yKRxhM(1(QL-%4E4nYUZ5j;j|CO6#$Pk1PcbyCIO%WUF7&!t{
zl@ms0G}OSlrkaX6?T2lfvqjzw$4&-?iT-r+!#wb8rsW2K$wCGHG#_upjo`i3%yV|C
zsYpk-xvty^w=B$MTT3o4ztcbu8_8%i&gTgBpi6^TDw%h4U$G;f1Yr~UJU`<`6-6oa
zc9zeGq}T=S8W0x%jnrTogIssTHG@qAMB2hJj?J@h-_;L{yNS(n#(v&W+th5)4M=rZ
z=`)t*kcTl#&L-X>U0y&er$A$ocVm*5kH8O^>f8J{oi2$Y$;U=jX0T`ie}RCz<-WFp
z=`H8z^iU8&1PM4S?eTkLs+>n{5iU<k6cks)TTr7E151S%-1%jvAS?!Klp?>~5(i`{
zVtYUqA(!vf$5HIiX~qG$E4;2icMbc%H{FXhR<l_R1}J6q{98zKVN?1fsvE(u43D|8
z?vzT+EvmQCUKyMQrgbY(bO+yQRZ`hOgX5a4CVLHl&3#HY#H5qzx{}HyuQ$qP4%6IE
zP;M}|9;}^Y0m}ecAQ+CD(!5I_9s)c`MB+q!HwOjyN_>NcfobyQNaT^h3O(8Q(YgY;
z&Ct30g0c4=1i7}*thi@)F7)#cHX_0p$hg}_x#r!f`clDG8A&p?0(imPD(0kCt`VaK
zO)J4kAhVyU8kJ28u2i<mQ**M?mwl~7{+u5YIa*+`Z5v_y9Tsp)mg{w^+78o2FV#yO
zrlGc@jXDnkaI~T{*X*8Htk9xLhY&PJ*!8BOK5d15z-}0U9x($)FSah0=!V}4Y~)V$
z&+GGbKV;(0;(>920QVC~A1!*Tr?~AG6A;<FG9t)1w>l4$bNN`*{<Kmp5#Qhy5RP%Q
ze)W>eyv)wxT*&U2T8(W4$R{X&Ewek`!rE@!R&Tl6QxzNb>To80q^$zaGHA;$8?bsV
zWz`MxO+Lc8$zYN|I88MFo1bGJm}}hci+~J(qD774C>Cs*fKX1`oyqft(AD&D<tYX;
zLes)$h<#A_y-8>dmY3H2IQC6WMVfS!0ahk|?}&6?KM`}$zC>1u|LDr-dJ)2I+xr1&
zIL9hHz3J6)hS-9r7{5sz@9<Zu%B2EJ)wG4v4$tyidoOg5YGo1|*ZEl^yQd%gvtqta
zP>)t8x?YF9%GLP0KriKT>wPTNDrbbZifS1yNY(&gz#Dz5Ys6%hk~Y=*!mMU<kgNAx
zY-VW?(0s6<JpG<==S2C8sE<_`7uevoeWRG!MF>A7d&SJhhIonmvjN%bDzr~LyW$-{
zM?lMP>;^9e-jw!EI{n;TAIdVQcn#^Ai>3!-@DlYb9CFoh2r@;2El8zOQ+?RtTMPf=
zsxVr>E7pYu8<SOxrqo-(JtHheajN(-C%cKG-Sy@}h3^QW+j+C(K`{Oz$5{e|rq%N2
zM`eB^XdS$BYgX925HItTCHFaRLW8=bFG0+h?Iy=qN1<#WC!Zhw8fd|HK`b^{s8jyF
zK)Qsh>$i-awzhV&qv5Z9zYH&g{Cw?W<KYYpiAYFD6&sw%Vvz&pn0(54PUV93qfnQW
ztbYewRS6sJW5qEBg!OB8SJNw1qhP0gO8a1C>ZVn4&ZZF-)$q@4q%9d48*<ww`?`{@
ze3(^OdP!wXZ8%8($E(WppZE9Wn$4ecC*_6?uYQT2s{mq&r#c&P%_w=qpYDYcjV$YF
zCdgEKEBhiDvKPI4aB62IFOz6043%UG9rVze#K2nQwQ4HdjG8mUQf!xh-fY7O2jYO*
z3i_UdRoS?`9|*LWHN&oNJbzZKZ(l%Dc=VQM&u%jM?blg=O$qs)P((_IuY!{tLXz$v
zZ4cTUc%lQyJ9ZE9Bp{oxg`MY0zQB<fLxj@lAg$fu-ungbM04czKMkIV@jo&6e^~?L
zf6E%Qw*MJCl7DaSz!<NJm~mQOAqoM!Z5`!Jz)O~!qi;lTu+5~kq3w9fk<BaIYwvD?
z@I>*-xK^i_n>xzy`#+n{6Cv}LSqmB9XPd|IJ2Pp|_Hk!^Kgr2pkaEgu8lUzeLM`xQ
z%gm%qmA6yFPxAV>KfzDNw<Aj#`*~f(FIpYE#-vF_0`v~mu=lQp1dy3gEXgWX8pXNF
zn+%$fn9&3g%QYiR7OBvH94;ugpY&Av?Yy4w<<8?vCoUzOmlE64hu6e{%~B+K(2#q9
zKsiJGVhynKK%2@A7=uvVFuI0U-R7-fr-hg`5#f}Vi&_BsVR#*lLY?+7!{Bxg0z6TV
zUv?#YT33ck@PBjGr|WcL&7n*qtE#+?^3u#=&#BHHG=E_QFB-7j9$MGsoQ_zyZ~gt=
zbtO5_LCicxY-L?#SKsGJHPBf#;^8x`RUT;fxyTsx1f9hgSHcU_+xzBO6gg=?+rM)c
zMaO#NPy>;#G4@MLozD+KPQA1aHVc8~RI?O<N;+SYKUGF2Gv}F&0AtQ@glzylT)d^1
zrLG{7DKTD@5_sM}j0CM^AQ`ATCiHlf8AG50H=bF9xT(Wn-BERI7*3c6Pato9WJE8_
zDV-Sb{Z#q}(I9?8R?rpZ2mG@*TQ}NKd!fCga9_cL(ndGHb{AxQ!#=$t+iL3G{^0}{
zBiXq*Exn9x(Cu5H?0F29@l<l2sVK@s<>P^+tdRpzwVX%JTY@FbBAh8#Xa0Uij?=$9
zn;NAkyT=~Z#Jai|#8?0^Ybs+c)$(cN4PLU5eVQdy$C!p(@a76sO`lW?eQ~)(9~}35
zu(M(!Rcus%jqJonEn=C|-@hij46}eA7RaeWnft&9N&;brBr0+5?9877S({|4+_pRc
z0y$PD*+%JDPoveGWWQrNe^cLGn9o{mxQzrxi$F{is$0Dsc>Wi)LOU@WnuePN0$3Ok
z6h&sD20+x$OqNQi?Sz$qmNbMT0r)^WvhaDJYa>0pwL*Vid-=e5H6*%iS-0KSLCeBn
zX2~<Ibi`bldrUQSXtLI7PS9K?5EaU~v9D(+WXl#R|44~MblWA2m$om$EXXQ(N$qCS
zR`Gc<>RMQxHoC?zELcGe>an6Z)Wwj!SszM%m;PM^hpM`E+8w!n;1&tzt)AI8Ehm<V
zQnYoRqXYOT;kvc?t{pX(I?IJr;jmsaj}=ZzlB}6KC2tlFo7nOsb`Zh=EG#$^ijSp%
zB2OT8WDDrQbMUox08p{y9YP(F($7k5c>@RRH5?EaqNvKuyogY)I{tY9H&$~sqf9F5
zxRFyRJ6!t8GlXKa<Ll6Z3j49K4Q-hwO0=bht8KsV>SnSLZ|-@89U6Xi6USj*uP_zm
z0TJD}8A(B4+^`y0ge4xuDoR+jl>_XuUS0($&UUBIa6dQN>00Nm<ud>*hwRgKv7Pc3
ztD^0IWj$b@tu5RL{r2Q~z2H?$X6EcHk2T9}^~yU~3>*AzmBU45Z5#=lA8oX?2I>^P
z_v;!L?xxDlSu64-3octC$8^TX{Zxjlt$SmRej!jlVuya?kzgr&$QKZbs^2tc6z!&+
z$+@$#b;kAtU4aP9GW;cT3U>BH%<3Z^X9AOE@mJ3n=o4XmE+3eLM%uVB-o7?nPFN>h
z&c6ZN0%|9DSV!jtsvd0yaaLD>x9#X&RTtv@;2~X6Oa>P<$VuuP0<yq=MT--Xdkkmu
zG#Wy*5P|UoRmA1lI-+u9oPrWj2Q2dlbXyw2t>uLjBy^Zq1Q=wzf#!QPS`@lZjHQ)O
zb8bDZ2PKqqdemK)(C~XJ_tFK^3&`7Vc9~l!z|C@Un6!W<-tx5DTCd;~shuRM>GDNq
z74$jWQC7j;90|!{;^8uY${D<>7WQK6J~}@wCR++Ci}R+jK?NrXCQO0?xpb~gISXRb
z>S)}a`;44nw{6(2*Y#LN`@76$w+{&{b#iJ+KHSRjiW@6$^qt^Qn!{_HsTbXFQ8V{6
z5*K6bHXBz3-ws|Ia<GZ5tvfDUoEvrmLs^$W8$OfP?=r5c@ur6fG{yi}?3zR#YIh<N
zRdu_aum1X~CL?p`06Q&fW<?P6yU^3uBJQsVa4K=!$}umTf^8h!rAo%|00P*jng14A
zH;-cjrkiQIE&ueR#_sa8?>E-20G4{9ozyRG$HO%+6u@ob!W9f!58!w$ja7IL%~oOP
zr&{y1<0&^64@?lbe-kDCj00}DN?zr7WeWz8G+L=JqMv()MrxZxkkv#2zenvh%L@<#
zqr6_tO5KSvbth+-ReL|Z8Yh64O?)kInB*>TX!i2xb|KGR@$P8ee32j8#CDP82Pr!&
zPect<j?bmpT^axw-U6L=c3FfeQXjS7UW1HTVP!gp>D1(wc<}=WuLB)-o6Uv1g<o5V
zc?oC3)0==7JQl_vXie@yQUC|BR(Ar>r9~}snO_ch>7rIaX!TZEV1ya!#~)3-EbepK
z9*``d!xiDpd92wb0g+Z<>iO5b$ptY?`=Ye=O5RmC_JVX6qD0GmC%182A*&19aq)77
zVh1V~KaI{2>Jxqw2hXi_gNF(pWV-8^UK<Pgy==*(QyZ9FK+p%;@=jqS6|ng!8Eyak
zou%oHf{H=$Mei(o(4O2{sVQtC7~Pt1v;xS0OQ~z*nBK0U8AlxSc-nmS6`IHc{0pXt
znL9T5oyJ9fk+&c%QgRk-=Moe}lIb0b{Ivs9oRFJ?8ApY^!|$u_J1iqOzazA7K2@fQ
z8!CCfK-FQ*$HyzElegt86HGJD{ng&z$zQ-ZY6-jl2ZXTvM-aly&ia2sNQ<_uJ#ic2
z&yGG}J_Ql5dDyKE76*uB67>fCbc@($KO3C2HK{$JbfsF4GuBbBdzwzInd!Dy`y|1A
zq?RKd?6~Kt#mg>cw&Gj-G)(yK`PKe-cbUla&waYvU;XJHAKuTG;0#77nid<k?vQx8
zOZSPHT6FfuUOPHE)Wyf_=+4XDX}=CW;XdX8rtN}`PN>E6>YonhU1HP%w#$U>W)+cE
zf7af!Yt7tLG9b06hMnH&KsFKS#e$8ikwyb1BhJf!#5T<_62;0+$V(^X>xb|IH@-Af
zGL(HaHwAim&q%OT2DEL^$~N?|VlpXr^*6hxffT~bqj??+K@%#p7!onp*ED6W0LaOR
z6<yZ$ZU>k2QCTDr$=`LOcS`-b(MHUps6p!KFscw$668W*S7xsw3gQ+gLK-%M^m4+x
zRYGikwHRb#Rqz=2y+BhD6COl(0HMt509Zs#yGS)IMEHKZy{3G`j_eS~VaT%WK?Ik-
z4@8ZfV%8hZOf$(#__X-Dp8l!5GOD<sA5yN>7X+2t9}JpKh&Yi6IwESkySFQw!mRx{
z7Sb5ZxJ!gqC^V!**z~-Zq(ET}JSJHRcJdw$K#er&s_(r<G{QO-M$Iv($aI{E&7E=g
z<b$SS<!2KY2*W26Nc|#NMf+Mo!j}f7z#0q)0cN@d9o-f<g@-}Z=tW@(sJ-w$W6t*^
zAVy$UGO%yr{!9G7Z~LlGhYeW<Yp}1rf|a0$pp92DQ*Qh`%_1(*$Bwoeef$mpQ}oww
zt*b<Y^-jz~>0a<#`vq`RwIg`Xj0RxV;=`UzD7YNZ1I(FN&9Nii0^Vq20)i;+ChwEe
zD(XS|VC7|?hV>oNY+Ag*#!*Lv5b$K3*!a+FinwCfN3&S$5$9I+Z2g%nmNNL%_vM5E
z@=;hqrE9j<izFiH1I;UwDN%--AA>HN6D+ap)qxfrs<0A_WY2HcmRA*=C%)%bmP&I5
z@}K{1&A=6YLU}>yZ-DTQ8Y|sY20=5HN7MxJj+!gHqAZj)mH*Vhw3kmVY<*#I+SKxn
zI@{QY-BdoeX1A&gv6j2I3Q%Uycd8EnXB{*t0_9nQhhY=w3l8lwaZjF9hlqhucadRJ
z?ApW<#wS0F7P3?no~E!{jXNe3Nhi|T+X}F<z@hu1-c5f$DJb8~d_SG&rHfkbp`hY7
z?R<hyzq4rY&FY}0Wlrj+Qq8JSOTj2m3T8{k=+5VbRza#xkueQKz>2LGh_LAZ6*8b1
z03vKWM}r6))*uj3Lk*Xwzx?XaFy=N1iI>~vk1Lu<?n5y?jV_1y&0Y;+NBHVWA6(=A
z`5v@*LS7N(tWST=oxm@(&Z%S8C2DwFHh8q{IwNj8FO2Uq62VWDMfU|kBmRh}M*G%R
zisl=Tr~81w)>*#(!g}wIe89i?^?K)bx3`GMFO*In;ua<&rXAgRKRgz{^}fs>kGDJB
z+%DL%dw9;wiI8~$Z3&S=%eyijIEUmHi<bQ8)FF}l0zDNWo%u{QVsk5jZ!&ikLMBrt
z{f3|=Iu%z*R{r6D(`w*Ul9hi$)R5_kA44kn^o@y`c}Wfe^G9qq?MEeORIlJ0T}~bx
zauBvq&`A`fMklJmYty$&RCZALDz1>$5ELmIiUGJEE@^SQ0n?d(m#<Pi2IzCnNpO;o
z_}eS;?&s`CnURBDg$l!wbNhn6N7fpB8g6vU@1=2itbEMRYx8aW+_hQbe}7o`L&W}}
zgono~+jB4fFgwhrk9s|1rl(ko2k)m-7XDm?_TwsQC2p!)lqGye|GJre<1@0`FOc!R
zKd=9>AlEtV)Tw9Rn}Yo&MrZpNo4T7xU(a+G7y5fvF;->Xc4oAnw>5Jqhs%!o73r<J
zt*7JLZ4B3=Ma2<&l7$4)Uw)#6l_Zee{n)cAYdOLo-sUU-B9dV&05zZy&dGouWa|AV
zFY^v(C=uV15_%SN&W(dFvPU3O;DW>h<n7U_-xD763?jU~T=-tN$InCWG~A5*GTEV1
z^;o{sl{xI%_Jv`W<_qA({XXyXLV@L$o*uig&;q2@yJd<t5XWmJ<qUoEn6u%eo~S5W
zl5YeM)N5GI6@bmjeQVm1K5EufPoCV~KD{u-Br|4>PUil06emQF388ADw*xmh2DGMg
z_IA?M6JR)LiA%ALe`^Q-!RgRs9of^#ZK?LdQGrUl;dOT9nK;3X_<{{m6WZy$eeF$h
zI-Pw&XXV1~=qvjohK&>XZhJlh_J&7wp!FI6v8F78rt}qMQB^rxo>4>Om6EJ8GmG7b
z!CWnDlrqD`YZ6z?nax(BQxjRcc%S6$7mmC|G`5`cpEmI$4=)18fiq7ti6TD;ffmEy
z-yiZ!&d4nw`O|#OuPgV4AZH^MBRL!yz?iyD9f|A2C1ldqATu1<Tsz!AP2Pf!-xnFE
z+)RZ^j(Oy}(ZiK91;bDSqlB6og$#+Nmy6Q^Z5X!Z!68zr%m{SC<=su}B0xk@L$*|G
zBqB&fu@q^0A!4t8Dgr5*!ZF`bnyn@@LK8Y%vNTPO52>o`-G{oSTiSDgIogR4!o{m=
zqivpC7#7{5x0L%Pa@m^6IMjeVA&YV2Te*P%meXZu`{*h)rvU8*%+i{$fo95Jrz|PT
zo^?`#J)L_vYq8n!{&dys=mJZ91sL?FHcGG;@ysR1vW=C}%9`~<u_!cu*&$>TqMzUT
z@_eEG<4M<u`|GU60$aVxiS_zw&>k7#<TGOTz@X!vKY?3I1BAVpX!V9+Cu#7&O^@|C
zb{J&Q<118P_ap}X9g!;f?^VBx?R$f*J{Q@B0jf?duPRG(G?fQc(;Nhxm~2^pq#|~O
z4>0Qv9m_Na2K&?03b`73)0XO{n|k`t#P!LgnUT$GG+N{Js_e>R5?XUb&Qq}14s&ab
z{-={OhJ)wMlHA5hS%vyCArn+Uq+Nxu4T^#PPSFSc&7TL*UN01?%o>AsPm4g^u!~@&
zjJy*;e<=L7_vfg)V|&XLDvB}Sje!o9OTU%2iAmYzEm?j8UZ_dt$TaYF1Q6GUSIyk&
z&GPpC%TB2Q7m0&XW?tJss40A-L;1)oiVcuf@fIT31d|X<mnmQ(BvRIKjHH=kx8IZ^
z?Pca;-+muoeQe>zspN5n-$VQ#c=SXi%>M_BvHwRf#=!aCU~E`RGyYHn!S7W4%`}AA
z)@v(0J{7@eI7k2pjU@s+D1rQ(WQD7B)s2i>=*ztlp8Hy61=-vy^hBcU@S`Kn&2<^w
z8=vP2^e(tNqR)Ph)?F_^<M(y*HMs40#M}9F+fSSqC!a*3-o5Q*03wv;;lwG5d}sSY
z9)J3$<8$8I8+;64&z8(s3?(sxb0kJl=#Jw=xgvvb<}h%{s5OJKpHmhAx|=gnAz|pE
ziOSx4s=|+i0^r#q1_)2LQt#A9+A7EPrA6aSY@C|KP-mU_6eCpdY~+(i0P#4EQCP%q
zH_pg!qa%TMN?2U6sgF!#I_V>$?3GCWO`Mkq>bL&AOa*hwWy8RR_>e&8jy>S#p(2ld
z2#+F`Q4sME_^d5^8fFXs13b~TwFXE0XJEH)hk1~{+Lpuhy}%NLS%d+f{kmcUgq-5U
zgkWX6m&1tSfN54?Nb|sgUZ5}%h|iT?Ln%J)H3;<Di(X^yRtF4QIZk3=#Ff7YJ(v-9
zBp=#sKvAaR&?XjG0QPLjVAE*2m1k{T%iGuh^-X95Q8`AUSXCckfS(qS4e9Pd;2<9J
z)NjSE$%w5i<UQPWV*>H@vUv@HS@Y&GHCOg6HkW$Ut5DKil@5!yo$j%V#|O5J)AWaZ
zSZX$i@?!%p{Jq|&h&=tcR_YS-2($(389qPmhk`bRF|Y^Kb#Ex#_a)(^5P%g@`m3<o
z-4f0i@2#rZ^76`W*Ua?(88r8h5eIc1WmXgE_F<ts!A|L%uR^dB)y#n+?_#NI!MU^9
zFAp^v8Sz+)b1X`2$-vloi|a1<??#ZBhuf#5_^=8oTg5(nsNA4G91QkJKgK=>PSp4F
zM~S$Dd&Wljj1_2GeyIiqxGEle%;&Ph))k2m%tL?_8LP<SJIY*oYuZ_~vakN8R(_n4
zuRehG9Dfz}eantB<nNFW6ULU_sLWDP=BD1MSe*hFSF=Zmkyw~?^)r&MU9#$Aw*|Zq
z3f&YDk08q8e%9~T<p=>%XhcGCcZv|5TY(1<xy6y({k~rb1DwX!RalN&w6esj;Z%4z
z`k#pu1CjCbkp%CqmfNYwiIBa>48Ec%mnDQ8yZ!WFF%j4J<Zm=V&g4IDowsq%mTZ(c
zNXw@B$+EPP=Hp8PE`OwHk2>3R+5XB_I7R>^!K687D@qC|Fg^X4&y=wkM8Fy!Qf0qr
z_6@kD$WR7n5j&N>NYWk8tx;iFx%BE~cGJvBGX+V?zl3=h;+Zz8M&17<OBQ4l!x^m=
zEZ7a(F)f_M2-M@k<eDzTP>nM!wRII$L>NbJG_l&Wif%8$gbX*xh}+V<-9uY?f-A}=
z29vso6s<KfFOZGQAw9{mX>QPEmSKDO6=ILWVjp1NaLP+}AW>)t88>-ZM{}0t+*q4&
z4+;JDlo(aK7WY!uz6qHl&gVCAm*k;%2?K*9D%iorXjsPq7u@TmjP5=OlC!7~z=NN=
zvYfcfUgrtA)+^Dh5l<?NcDOPhmQn%wLA|b8=P8}C*D5z;Id4r`4dIO?tE<}|bSh)S
zTi}wuRh=i}*D4Lo?M3%8B8E~dtI^0j7w3gd-T9()S3iz=hC)6l`!LlvX+)W`zsf=n
zye5qO(uX4CFZR<?mD**}wL6A+*;I<Qq*Bx?(vK`%I@*(ccl=r8n-#60x9D#^`MWn@
zA5y=}!oJ&TTXph$T|+TP=$)r(lv+}*GHGvjJVgtz+eOOm=#Z??-M7p8VicBpif1XK
zh|mbRRYZH=ec!Q<&uW{Ij$5)80q?Cy16xPY7U)C!(&??p9+p<U7`#ujx>3gs`|>#e
zueUE9VV)I2G2{{jnKpDAJKuB4e{Dc%^n^k4yn;|U+311bx|v)Si{WR7Jg_a7c6cb~
zx55PG%t3-o(?l9K<Y1pwqr3}Jv6g|6fxtG|Y|H&g9SdC4-XO5F{?Wj`72|cI)3k4U
zTB0?3dU4{jV<My&a*vWs8_7&ID7&7P&9&`X+;)}KocIX^v#x52-|lSZ^?L2DKDQe~
z8a0<I;lF7le|{^!>d?A{*L&C3+TY{!Tpa>?ab*ZumXG_*2*rg2bf+gG3o#}wY!KKE
z9SC|s2SO+Ki4YSQ7b3tk)4+ESjlK(tmeGlzk$)q!htv$;k8vgeVRS}cP6rqDe{BUy
z&mAf&6S@EoJsT_kJLR-gOrW#Y)ZxsD?2~)P^<P?lBAN8Ye^uGhl}?(@H$M!_LV*6n
z_^=fQHMsQbM8a2@C5eWl2xQyiW9pZ^bL*@NQdKqG+{I%3O3mDh1g}ZO4fU<&tG?R|
zW@t^vR0B$p9e8yJV|-P$Y}Bh$gXQ}SghffwpM{+Ho(VRvM`H#sP+s9{F#sk{KlW<d
zimdP?i9X&2O#zW^R-HQ*ppG-94mXkZSqkojV!1sgXN<>q@T*d*Vp>knCIn40nHqRe
zvaz94oZH~BkOvsiYzd1>@#JuV2+igbA}Hsmnm7<`LDev|z?0rW0bLT;QGNPjm_05g
z3-{RQQztIlI{DDdhMw-FBn}AX`wnjGEsF^W)_vTwoJYIZ;kPvS6!W;M3rjmW&oViW
z>S`i^X@nzRLu}i?9Iq(zbucNWy*f9_GyLda4vQz>3mm+|E0iB)y%!h%sp)aXRg5v8
z0h35@p_<F5cgcaHG$e;Zjvf0EbPLmIUn91hiNTK+U+xf@+(BF+TuEZPsB?`su;(Km
z=U`rt8?Pg7^Q=`f)8W3xT4_F$ZwpfdG2DQ5)j6f#`v#wF5d-)?p@W&>KY|WMCg%U1
z0Bzb~vm^N4szcZYNC*zm)JbE>;(1BV^|agx15QG)y=<UTjYp};B})HxH*JuzrsP<7
zA|i1oB{*~ZImVfRshIyn#6`UekCOuPkDgy{^pz+1`gx4?Zg0o^cH#57&x*GYlrcOz
z`!XXTp^U+k%_L}?D|)<)zIXLyZs+;+eY)ff(0a3--Er)X7SB|XOrhW{&F-9T-0119
z<zowvfxl0|EG&M2Fgv_lKeCC(Wh1wSTrEyGj<fJ{-U5I<l)CP(#f^e^U4)C@;j;=#
z6mh<B{C?}J2>jDb0r}-otTRrSi&~sN0u08zj1q$7!IgxhTHY_`Dn_i+tWLQcVtta>
zE#+3F>mIR$0&GUM$6AI*w^o(Hqy)v8MI~8&CBgRm;@9NGQIj-Czwx^<J@q;CpnL&K
zfN2=Y-?v!j&?c9Z(eb<IIB6=`Y=C!v$}AO?{aYye3T6OGEavew`LFtaOO|Yop<b+V
zkYPT6hKw8&5#&{T^rmG5M3ig|zY=*$!>5(8&)|KW*dvjfKVJ-TYox?SpvBQe7s5Zd
zPNEhf#Q=onDy>8hHlCmg7ZQ_BScMTmqZ?NJhhV`e0*aC-^~K<x%0z&sn4<uy0Pvv_
zfru{>itxw(@*~fONCeXKBEuNSc!>&|2$w{t+l^481L8ah!kA|PhG>cU-y#CFTqgpJ
zUa$oqr(VA|qSXZ<(NZ{O*a$dvNtna997c^tNY{Q;raV%eN5JSs{F;Yp5`=AB2N{cj
zH)>zCFwqJa*GPN9b^1#Rl`usp${!~RhlN(YHB*=(TGYSo2A%`!XnGk>rc@O*eGKQs
z%SslqlB$S03R*Hwtd@~_rqrag#5J-4xR#3Zq9nA2lu*t<=ZzJZc!cpTj*@F~nh4I+
zx-97~in<7P<@^Jk??#Nh+Oqi*Du?l|E#svoNyw@bMY&KaOprn%?Xz4W<0%9ueHlkv
zgUdJ>&zct~oT(z`z}l9)xCjD&tOwruv7lxJ{-6okEwkN*r3=bQATag7I1vS{EtHI_
zS!JZISOxI3Lx_%9L>h?#>aCEcQ1A%pMGj1rB~tJVJRBjp4k;%hg=zwmKn*@fqL@vS
zD`udQNP$YAgpEj}6afuX#3WF}$fQ$DrO6jEP}g_{h!~o5i8LTl3A#!}3K5&6Q4IMI
z{S(bds&yx-H&k~K{zO#*Q&>u%Mmk|5u%udyd90e5I@}OVK}`1na7<xj6v_GZNTvdx
zBcZrLBcrt_BG8T#Yof#^ak@i3h5sd{DWGIcD&o1w9TbBzn5leM2m_^I=ab(5hF@nZ
zB`5`L4*LoJpkOa6A?mdf53o^BscX^hLSTVt*C=O8WW>VQml>=X^;oSetMX!tmqFO6
z5^p3N>c@EF*@oJ-8?kW>!3;;vwO7|m!#0f@MPP;@m88VGP_N}GGOD<$ZBluCI}EUB
zjAIfnz~~O%c#Yz@I$~1ozI4EZ^QLzM;Nl5Dw4wV@IC0Hi)UM^X?ZV&>vFZ=Mux^W{
z@~)R^ZLbivNxjU4Ff?zX85IFSvIY&)`(rOe$bjSY8;L))rImh$wOhZDT#}*hQQI;-
zkAH@MBsc0zyUTpojF}QjxhV!2KuS++rwh5TbiZB3?=oDn?12G|ux?sy4$-T3-XI(X
z_%2<uVzdP{6MJq#unv`aW4Ql&d$8-wklQ@3zG%B3q}F-1yuxFX@B6p$+X@k@wdyXH
zpKW{^ymnZ7SI)WVPvrHhX8I$ZhZDeyB>H3GwO^A((zy<elT+)-&zT;5G%FyCi9C90
zSDsI}vvoE^|I#-of-&2#L|+`*bjexBKYM{kBeBKzJ06C>?)wF1cyw2!B|x?9&+}JT
zbq;?!q0%zi?RXTY1FB0h+!)!5E{*qe6NhuMw`)&ld|8%KL$>b6wD*2Jmv{>`-6zWo
zu1Pa`WUSMmd-}VAC9^+nnzCl|j6cEXT)t<|?j?>S?1`j<M_+s18F4d+NsS#ZNuq(M
z-}OEPGOxCGm7`eNuZ3_mm~?jTd*+mLIy5YDC<?Px$IQxhW@MdOl|o8I7IUp-2L%a9
zXRc-kSrWZT{-NYpa=O?%w?Ccicr^e1@%t&zTB@=o>kX2RG-6t-L$Z=9h4XPZPZI6B
zVSq7?npa^MW3y3|3@~D19f|Sp=?sHp(u`?y-|lUDuG~@C{8_xc+}ynFOs`+JXMc2m
z-F(S+-eq1>UHM)$+DMP*YGV~paFBN2dert;^4ibzNr*>~==WJWUKidC26r#<wFmLD
zr$vl+rDwQ>&tbLSnj)ngoo>{k=}5O_nqSgR|8&jEmDi4UsoULMjlE4b`4sTUR9owL
zoc6Wf?67M3W7};x0|ruPs)F_4-ex;|BHS7qe4a$@puJ+OgvY=-onL<yN1XOL&kX!t
z#;5r)^udQqmda&drr?6P<372=w(Qhhp1q`lo!m3E0@GKW0-IN8f|ImStGiLm_ROd$
zpuF2eSVCxMZ6YHP=I+ZtQ-N!Uom}Admp+Ja>jYS(efmo|M`s<J4_Cd@+pa-zF5Nx4
zv+8VNMvXMkZQYsyqqXby+On2FI-(bPaaRknIf7Y=gInX5;9=i<kdCV0Dz$@H%l-s@
zF}KbdH*BWs#8jow1fG_`Hf>Pn)m2X9>piC4M!cO4Vjf`DImdCc_7|~Dr1h8avMJz)
zf23VPw2Fa6=(CQ=V1ux1;w1+A@T$V8nYQ&E=?r^Sy`ST5d|1=xwEp(qaK!SXJEaFW
zLUTP~#?U_x^pFmIA`yJ|&8U-JA&qpt<C3%^+SA|Xl^>*H^YMQI3EThC7o+TI<l^aI
zO0V*7Vif-We92*F`|lO18ZSs4<<*YcIL7tP8YStijzue^aIG4=@Rbd%mKlnO2w<uR
zVBvr<nE@EUffN%`LWC2_0D%laK~sln<(wKV>+1>&YwgRqm*#F=ZrwkhI~yEJdc3bW
z?z^A1e%T2vE9_srhQ7O6cM%1`ka+RrFIpFkHqQ?gc$aMLpCKV{Z`1?2zpZpUOX1jG
z3BJJwqjzf!gAi&=A*m0V_X<yhR=>or@g+eGApQ8?Sbp&HT@&5D;(P3jh`nUKhY}EB
z2s}c4lgGXjpJ}g`)#uE+*)f_jnOv?L3wIH36ru(RQf$^YTkTA{9_xpQ>rCxRh!|wq
zA^byQ!UM!Y$A*>Xjn7W)GN>f!pcFz!jV0DwygA!yV`Oy>j|nvi;p0U<LMAu!J%mvF
znN<Vwc%>=wiJ&3k<3f1fgpbhhkLu+p`-T(ejl)uz@iLf%ugZI(4;=iSAnXjXXoT@l
zsuZbjO)>>3pknVK6-Pfzs()n=PtcLBXB$R$!9&6;Ob`|KJ$O*!4U}&7rBWB@r4S2}
z4C8Jl|KN?6Lp>-u`lLef&_KiYF@Fz!7joL>##Q*B7)oVI;gub{y-k?@K$(g}$n%^;
zONxq6f{8|`Q4Ik79`VVdk-|SkKENkZgP%K<e^umNVb=H(5eXfDuZF?TcunXRC`||h
zmHt`=|7NP*C0s>zj_H{G=?(dQ<__BH<_f`(?~?3dHMyY1hsm7Pm#HCAQ_l9-gv1w<
zMKREaBz+4>$d5>mNRKoeau^8oNW~EB65~K3G9nQ~E{d2B$?(YW2*!|>fS@ECd?G_Y
zW}KEB(jGD~g1Tp8jLndnA}~X0ig1r`k6a(}9>O^QHlk~Y&=8^_O+%c7BsT(WNOB<4
ziBJupZpd;VUO~7S(lWwr$XyfgK){Za9Won8z9VWyrZPf3Z4-{r?sDwH+KD6-4yVCK
zVj;1PaFv)%VE$TtAOFe=D~11-50t#i6ADfsElZj)A;^^dNv86pd@E6=7w<mcwmGQp
z*++#75q_=8BrYaasq22onpPzB(p2GhS{c)J;i<tV#Z_gv=sy!O!%?P#^UitoTD#2a
zZa9t|VbnC%$L|y#NJo(rmvAoGom3jVA8CpCwfH(k9zx!6H25vBjj3;puIUx_Tk;(J
zJqu4v_%-VtgYd8aoMTKwNyY*R&JQ{J*Xu8{Cil#?C-a^lM;K)BpcDttlH8}7T`*_6
z%v4Zp@h(pWZ~;JSwGC?XLGPkM+&*5+VD;FC$!HTU#B>XRl}r0qRH$*yO-Biyipgyl
z<(TQt_ejM0p;g>o#%&fM+&v+O6LUtX*eBP6mP}6p8sJSbI?~=(h$rAX3hg*lOe`#H
zS(J@sX_a-2$Yx&&Jk11^A@~)$DnEPQEEsW5TnEuMHZn3UHrXVR)}jf(4)@t)ilBDL
zc%IR*A^u+@n?E?~oZ8}KcR*^}DU^;2Xxzz;&)@ByKe;E(U)Sz4?OW~M=MUfZIy19r
zR;o`0C)3Wm&|0_u{`K@-Z`O$Or!`{F5Bl4m^l`XknuV_kgZd2w68^%Va`cgXI#J)M
zyOH~TUEDy|v@H+I7)WKgIV+v;lsu-zOD$r6dwee0^6^BmElA&ryomZ?JCj~JJ3w#I
z{MGTSy)pB>UDkGVoeDYc!9G$ZQm;%83>8r0uCQ0zoG|;X<14&#`g_9mivU$hpJQ!d
z$#pj#Y$gH70i&a?AJ;|TzxR0`f`34iS#`VYy*ks<fkFd}AHZn|ZJZ`H6SdX&ggX)V
z4N8W8D7eYU*WBODqEr)3h2jh|BCyO@95nGP1pXJeM@rn{fSH}llqSYWle>i^^}O=y
z)4p?dtu~qyGN|s(02`(ho8BZ7v|I+GVc`#{!&u|P9>iFgF!6w}@Z}(OmhH1_?0zeP
z&6@ri)2EzQtV6)Zsck=H0dQ5UZBz7y2kdu!Wgh%wz3p7g`<ZGADd<^ArKGK%t={N>
zJTR2pH%?s8JHytoI*S?>z6k#sGpH#+=+`!;{pSx%3q$?k3GbA^<Z!5O*D_+*OY{`_
z+Mr7y!U8!A7%&5XW36IQ3fxe#fwEO)xZ8JraL3^JnlQ8=V>wujm(-GCu|2TvM?b-u
zffO}J`0!PmN#nX`1C5JWeTK22Y<;79E%yL^4{GM5)tN-x7?w;iZl|X>x0~}>!Ln)~
zkU~%e8b%djW{YaPnz=CgSwGKP$-S{dY|v5Aqy&j3RF_dV5AqyFsi4M|vRh&I3lPHY
z&zf-@3eAIN42T8BaNrW?Y}-H=bllyHx}Liz|E+P;8JXd5!@vr2O?cqjR$^>z^p<YA
z<f34upsfTG110t&lY6>))@7iE%d6S$NTz*9%^$jLC?9EI+X*XI*{%5qmePr7q>M{X
z5<h=0@HZr%-S7L*mD-2zeDtT`8`$dDN(D`xN6PtG{am{%K=KOiXnCR@6VNrZHdE+l
z42!oN%|F37hSI&|fUN@NUWkw@#bn;+YVbmxNKuSboeWglog6AdI#2eDo|#I~{gn5t
z4<zLao6Sq63*6jrB$yO63Xmm2068&j)4G&qW{x0M_;=JQkonIvu|c5>7(ZltduL(2
zx(5yPy9dIEpYT`<>!VL=kJDyi>VPyDk4OfLY9^VPqU)Pacm}|0b{Z+kslymsFq@8A
ze~NM{x9z4)yu2@^;Q%gvDo`T?zEtTV$e#gnQO8}idyP`YSudl4q+6qHC3CWT_kjLx
z-8t@(i)YgZ%~DXj-AR+A*Z>AzECU9gSO&Vw^)pe|y>n~0WY5mD&XxjCl!1MY*zM5e
za(MGP0a(z4p7_0!74MgW_x3R+xOA||9Y6{#-|SRib@Q=yLvg+Ghfv2Wo$<OP{(L1o
z9zkZS`>~i`s=jrgG#{&=rKz{Q+*|+$e`#I2R2d&0)2<qf3{SI*xbOMACA`E+OT@+{
zP8zob$}SCMLl*#u!`#Dj2m$rLtc8b!Q_er%%CX;qmX&W#%EbuRP3dvy0jVub_G*e7
zBBhY~lKHfu-{tSPyZ$)Lgekj|?pX=Xns0k(9=|qQD?5;LNaWmJ_V~2ePa;8Tpda5J
zFjK^$p7;ax+V5-H4?fhrzZzSP+22-iZanUt5J~{Oae<=yyg?hyjaAqetRi||f9sRh
z7|0uno|lnlxUk^gJ9Kf|mn}%1nfOQFfYCZG`=R!W>KS0O8XsQ7pLpv0Jf9Al_at$c
z2tthrM}h16D!K*2hx0w^8ZREk6g54p#k)RfIvFrNnV=8W0DUQjF9o%bqyvxQ1Sc8u
zyfAP_;W1R3!wQjLew+;!wfJ&nEep2nf#`+!*AT4_TU_=T9Yx~K<3bOm{c#UDBnb8R
zB>1g8``~lK-%hZ*rmnW9x&}L-4sWeV0_uG;a7S-TjYJdx6p~JfbyOvlF)}q{qyVNJ
zKKAeRx{Ti7-Q#$E{(?p(-4skPGbSPJ0M*Aj^Am(?QFnR=pWt4{R1;6k%rSE>3l6>K
zP6A>|W-Hu>al*<-LPEpFuR#X40Of!}-yRm!2n(*S%zzdOf*t@+PLRw2pBT#jYofm_
zxCsj82I-BUd6QfOhQ%l$I~B3u2@iHp^mcLhjGHRIN-dMfS#d1rj}9*rZfr>r6rNAa
z6+#%efwipXPwg`Y=FBaOja#sXC-Xjkj;^Ri^)=IzB&I|QAV~nHETn{tD%EQ3-Q>*F
z=W{QB0SUBFl*~-Lh=EFpmFG3CVOl;E*^Yp+-+YzyQ3AJ@;)VLB?#zy}z3rOl4FxCa
zI%##%HtH+h2_9wXXp@R^o~p2}#0{TA#`ka|oB}c{&~BkHw!%B+Js-)Q*#6IbQmnI+
zXr07hzywCBbOS#OYPaCh@d`#Z;Ip^=9(dEL2XO%lV7XvKqEo&12wn^P?qt<Zj=g_b
z-U>?fi*uAN#E=CtR_Nd)a)%J1OF%T<&($ft?uG~!+(~o#pFCr2r7n;uF#MtFC-H$)
z>k;55uu!2rZVM!t#4V6jD!LZqjgpP;9<|T<xCp^NYpK1_I`P_;sF0K~9<b$o<?05@
zllBJF(f~M+H*G#%=M%@@2>`vybFh+o4k#4keT5^3mWw>k+)v!~IbFbgV8BRNj2$@}
zcr}{9XfTD?rIB(nWg@f=7@yd>=61}uLl%A&JJ{+q6*9FEx0^~$C|#g)3d7zrc-CDj
zBm{tKQGyXuB^;e>VNPzmP3Uab$5&imxfE3!KWy8hjuNL=r-DK6>zF(1fx&jnKV1#|
z2Eb+pz?}^a()lRVim@fwyF;Gy7|0xn-pzXqs3c3I7fH|SncPc+<C-aB)MUnzD>U_x
zL)e2(lxsp8F|lEGcbMg?Y8Et#G3&Z>6iYk9EF2yI=j2o>87-1^^Hz<E!}sU=TIPGe
zC}5$9L28YZbz2UQIN+m=S*)E*vYG;-?ahgMDeZL*z3Ge~w_u6}<af)RMn^>THbS6l
zW*ITlGkBO=JGsklY%qcP3%~RWEGMcMazT6!G)<J>o|Zze+yOGP9_K;z3Mpd<BcWs3
zIuT!}2h~qC8??a)^xT#<%s3;?%y7YO-;&#VCmqWvrhx22a4&z>y4@I0|Jv-m`fyL9
z9Yz7-OkC4)M!ijEKb`_Yp%%u^k)V)*CTp4JICdU^K=SJd>w%?5?PqMG{E-hL3vM7N
zD+m~`^%XAzB^1B`W9qk=X|gk0x_Dp}pv)BkDxo9gCT6vh{a=ioL$feS6kV_HcWv9Y
zZQHhO+qP}nwr$(CotMfWNmT}!^iSyObM{)h8<|KiL$C>ee+s~f_vPC~J<+e{wsRZ_
zYgIvy*U#!;fxbq3)!F`0aXsXY_rvxZJU3-_@Ge~UKV>$c2V<E^#Z}A=Gbc(lFRZI^
z2Q1bvzf&uZM?^1-o%=NKTNcoTvkqgDpFiJKf79Xfp_H6>DC8$rv!w>eW~(g^%3cG!
zaB8Rfaj6H!dLFaj&<FF#{p~fm_zkXp6K>slr-oo~ouWAl;C^HH5W#o&SspGD&#sQN
zvv7`+T%j8!5;V{k3q;!vI@KeZF5gws)G~#?VS>gKQ_YS+;A0S@uVPmGLo}{LLPSzP
z+8pW6IsP;2__J5kTFMoly)&rWM(Wrk0<)PTf&H&oz=DT;m>qI=OxOI2Zu*0#v?p5}
z3S2~P(1CF-SQ0#zzC4?U(azovf$tL)s`JkRY}g9T*rL_4jnD9jXtV;m$$@4>$?wR1
zbGBguTrYgXrg$763%nOWJ;bA=!=$6c#4FO;gJeF!MPCv#(vniMflSZCGPr=p+90kR
zD|X~&$}<Sen*Nt9nvKt3`v+*$ml%t4{?CSR{J&%*S6#2X4WL^L$G!U1SN7&12$OVw
zB<ZhGpPmDL#y6~M@3X8x?~5xh$M~&S0Xs94wCEo)09iqWJ(lu7+&(utAaFc1BReSZ
z`@H?Z+&JI|Q60uPNw>|c7!W(|ZE#iJABwL(R7D;*@S8gmck`EmXz&SN#A!eL7kTH*
zV*FBMkU#qYhfQkMm9+)5IHRa0Bv6Kq28kyXF2>iE4*T_QV3IiT43GtX4ct84a+m?U
z^n!L~)(pjB&CROjvi^gg3q=_jZOTibe0kfPdnE;u7@6s)38nItTe!;rSyq&zQw%7-
zHiWf?jD`K9YAH<%MG;faAG(srr&-A*i$2u4JZGJYi%;Ojm^V>f(Y-w|_V0yRJeWNp
z=+C@6J7&fGkO5lgU;(s@{{Ui?4+IR_Pw)UF|L$F3fqAMktop*W$L5ptxz9Zj=uY1k
z%4`bzx*4oz_z+Teq~D+yU6@)`Ny5<88j{xOw0a||!Xk7&prw#0q*6Jn30j2|qRhqz
z#XJfiDH{$ShDWBhOaSJWWnBfpnh-n2iP8G3eJ_%=<g`j!k-ED-hpM95x}sD2kSlz4
z{w*C}q^P*?NrWIXBv&-hofEn_2DtNDQ|?EhvY9*JytT`<@7H-xx6aSKHD8|2-0J+k
zxUAK{g2&7jB<nsi7bt?CP*F*L6?;8FAxkmYL%^q}rIuw*C2c7=blI;)ls2jOX#l8K
zW9K3r{eYklxA<_@gsP77Y3q>!*)myu7Ii>u-vC=lzS<6=F52>;Qgmo4VOS#$%|7Rf
z-{~@2B2PY14{rrY0sRDWYFDg-{!I}4lNX{rnx-?HTLJ2q`DQVFc|WYB{;uA#)`xIU
zD4I2;yJcI!=Ca@bh9DOCGI}z{QwC{4IN({d*YsH0rCXrA_NerKws}@?i;q}37&r73
zbaJ?c0ev0QCoq~r68%{yKl4U?75!f57qgeiwFTG0c+j`k^)zm;4`v&}cis@X4#`40
z%IvuevCb0%Hbns3Z_N*^n<f+`>-Fp1a}V|t(mSYj<>l?gO-(NtexBnyk%UppT;t=l
z0w!nKva%t479@2{s^>dfYs^Fq`FI^GD_<f7%oNiM@hRmPtBpY_fdX_hf7KeHDSn0k
zUOn$ZS##>MszW`!smoxiOnV+y04<|k1MViF4*@xR&}HDa=aIgrXzV8RzR~-5c*RCD
z%x2;8H$Q{7Nq)II&ODkm5+3p?o0hA}>mW%r7O<YsUR$@3q@55q_A*mD>KHG^8=J~2
z!;5n{#cZ-7wzG0{>HsP`&Zip=4J9Q@WS4Z{s06WQjODyIdf{e{GOilF=n-Lb!p^2n
zXKM}(*04RXVt&hb;mjg%jw*JExJ1mVD6ggU>yfbaz;C?efaKlMpay*u1vw3Qh0Rcb
zTOtAtXABv#1JZQ9XrJ5|OV@cz5cjPN9`?NFA(&#8qc@ZrA{E!+x;NIRFNI^sT3=z)
z=|AD6*@KmlZC+Ebmd~H;t`Dq~P-`f5{Lm_K_KCB9w{orkeaan{M)XI5t0u^7Dle$V
zF8~j4`f~mjr^oJKF%g<)Fs4WG+VfpEp~XPSS&!4m*nnb-2EDb8e*7PxF4swcuvS_|
zCN>viBzb6t6mX^3h8}0`XI}@kbszUOU34`K)^h6^ynG_qzR}g=QZOm7Ac^`dyo5#`
z8l+1byBHg}t{~xW{+->Gi+Q#5Us+qWfmO)Gs5{tX4`8|v2?<!MOtwM-5&|L`AvFea
z9%%WWan<n!1y-oj8_n$<n0D~D4PhFe^mVsbyj*~}B?56BRDC;kYVrkivQ1Y<tK(^Q
zaO$wTCG%Ksr2}guVFN9bfs2)<q&3X=tm;VNUX;IyTk#)1`xSO2Ujlm8BFec~vG(ui
z2NFH8HhM!oKZTC2q*u~)dnmRLKnVO97$v@LR86$Z`Vi^Brz>1|_f{}hDtW-F@<~Rs
z13Vl=jx(77HxB*krsRWQbbu=Ci8@dBqsk?Iuk4vP_<2JT%DnUj<n{|C6)Fe2x&*ua
zo@+0t-k^`Mxj(9%Sk9GO$tn{fmhrI$YNNOTxi$6*P;oB5$*O48iB-&8{_CLb%)xl!
zRulCXvhwzc_lWCeoyz9q497u)kcm-s1qF!!^+GpQ*;hSIEa?E+QaqO@IaqYT+Q2yZ
zLyIa3dg?0KNm4Z-5znMKig@8$jp9LJ7|*B5Xlp7rdun^&n*2GPgoU1Fy6F}qZtUOX
ztOvGW_7I<Z?MU{1j@Ek*-wiz0VMOg*0Sk2PNc}lTmUe-ea(?C^95t~sd3M)f13#fy
zg?sCas{yq)leB_IO~9DIQQiLTT<h7_RoZyir=Z*Yhq*I(C39{@i;0cP><po5#IGcC
zucpG}96THV!SYOZ?Scr|ka`{9IJMRs?b?B+P;3-YmnHe@it1`PR;-v#j*!nC?S`A+
zvC6J2&TYzWfkge4G_rMt^O;A_Be^e5+TN`l5+Z*Mj#~#;Yh0akl3w84=H%-|jvjWu
zl@E#tTwXH;bH({xwN*{s(}qmChqGI@(7P@4dF;MRa4bKChumMtpXv7s=qbFSHd{rT
zw}lP6^ZhHXF!{QE2~i(5atcs{U2}Nv$Ai9eam3}ob3enz?#ke)`oVM#|NbK@odz@*
zp79T)uwvmjMWschzXi>}c7K*QaQ1)s>in?ngnplW6=<BzPMS&0oTf{UV>0}9M}_}J
zs;6?(S^hklY)@>BBJ=uU0xXwpTfDR!_{QA0!9JKiMcfX$=!{=kAV|w-qP8*$6-h?+
zXLuIUZOIdv$Q)hOZg|&Ml^jsF^M(1mw%8U75D*RwUJzPGJ3B{1$2;k>`C^OJl^RrI
z`(C5At#kn%h+s^4JE|SeV9{>5Df9C9sR<m|lmn*Uus~Rf5rb2}*$!-<SxD<?TG$65
zFY+>WUq1^~v0v;Gs#HDf-rzJ2XX~}{gi@0VF7>ABk02sd=8E2}5gp!xystSMRLAhX
z49MSz7**<#E?^I$rjK0rjDgx+;afKNbAGYU_;~$Z@|485Sm~1UQT%jXg>zfcK(3mm
z>bE+09J*L<neUx!Yu}bSdXM9*<mTc*0_bJD4+l}C?>+WB$>0G7AM#|Y(zS}kAD7Sl
zMnP3?6^zl>FElleKZAv)`cBsMk_sRFZd&6GCtoDATfW~MPWo@7Yz)p??lbQOf!#jp
zGgRiy7mFj>*>T*=D2p9#=#SmrS-p)Wfp1YQ$bNoC+UC)PZFN5nkk7u_cQ97nZn3T-
z;S{Whq|aCts<+&J>c6ZOf&%$!SOIcF{t({j_<d<M(D$+zU@-Z0Y&8IpeMg;FUw(mR
z71=|3N4@+2{z*$oXLf~m$VHBFhwvi<oe_d#CZ|J1O<{3%Qo;dUhAGV=&CjRcO()s{
z^rHOguv6gRAwRHlb*F{3jp(!!<9X$ZYN*#=N9T05Wb4PpA#_OXOm+6XmxqFsIk~0X
z#Znn}3$m_VldlitT!WO5S?s>)tA5-%kCK36`WV4t6I1}5aEzc{s?|!#sfvmcQ>;`%
zlk9@+^ac(c#2cC2Nx}KICb!a4Cppg(gljbf0T$PMr1|zfMmEg$b65wZa6x;kaJg@_
z&J;5RJQ=o!bFX;Z`xhf9lca$vsSJ9vQ?t<VlHrC63D_G-FMFDuqA3n@A4NPFajm~;
zVRP}q1eadV3A=UegJ7~-2rd1<l3h=n59ExTcjfUW2a?LjWV1B7nk{slI86~&=ZA5w
zn|=usb~+!($G0h)5W$c^cS*}&Q2{-9H#oZ?v{(;OQ>-i2oJUm1aU{AkRB+WWg0V5-
z=02LZZzRgtfS2eL19vAlhQ8J3{l&W!E1-3>le~9=0Nc&ZTyYJSPZcF5H6Hq}^DTJt
zLrNWpGyaidW>Kum1oo7=PHiL$ie?~J!b&iOU>@?5w`6O>6Ne9soXAn+S$ig*bzCP*
zrtt2Ec)a-_3X_FZDIXjRO#I^i3q~VVB~3G74){U7Cd$j>%LAEm5~3`u6mlcOIh*D0
z6fl$Uxd~Z<{<lPC*+a3=48<NU)lqO3v>w2dV{p=NbFH{3TDvfEh2%hnW~XjcJUBtC
zld)R}*lr^}9WD;>f-zPcToOFR$6wb^OWGu?HH~wAc9wzxi;jWC0s6-8@V;Sqe~8+4
zDd}=LJxG2H1Z)QZ3+Xfkb?M(w-_X7Sus>CwUU5rPQSVsxxW-t0ElE8@jlJ)*@JT#k
zN$iEkO26sd{iDTi(NMBFKjDxb%`tpvRzC$vX2Y3P%iii5@Q!yLo3Hi`PnsSD>6X$G
zS>ttp?kqP?G+ouWtX^3|RwZ|KtQ*XfF$yJevQo+!yQ(c6rG(<&Q7T|z^+G~{uM4f{
zLZM+PM{KlMv}6=?Mu>Q8l&Hm2eeuFh9_c`*G$-3ya$qY8)}@ad^E4gS;Ss`d!mn+Z
zMKz4GyciOX7`~lUsT74$6CP<YouJYQ>Eq%k(1;EzBxV1rnOdM=Rto4KIBa8}Tfk6O
zUO26)nq~HJP4L8(X*3TZT=6lH$}9y&qUnr2=EJ+zhODmvXZ64qW1LJe5^pF$g94{&
zj^=*9Z^P;7W-0R>pkmLPq>-yLxub@rX$jrMp|j^}jRCNG>RdCwa`;=rEDr5azT8^K
zuW>-3+5y^kcSi$mXzeW}NKt}HiKbGTVxj`{09s$?^R{Nsg*EK#tEQo6)#$){#y4We
zE7k<NRVWOcID#)!HX)1k9T!|PCK>m%4*3p@(@20%4FCn7cct1_O)-cF$qM|JquZoK
zls+5f(%!fQ`q*Md$P3qMaoJB$-)FOj7K9wzF!KET0c9%N!}}NJrKjEZHWWzrOX?Ed
zvZ<ySl9=`pV)t8TK-bc<{U@0<V$GW67z%SWH$j8Jik&I?YbU{$NcaE9lmCxS$N%p<
znVFfM_5bAEa?~N*kxo`~=Si9Hk+0!%5TpssMfhn^yZ?=s$Q$cTt~MqwHcMnSTO_ek
z*eFT9F;B2bB$Z0Ml~U=PCoZBwHva<(JX91x=%6j%f4S_rM0t_I`8~-B^xv)e`+J{W
z@<{Y`>V3+6>OIT08yx!nVZe<Ovs*{#B(+|vzR@BaEVgV>E?unl^Xw3%IYLGIDAn(+
z;NB}bV#qU(mb*U7x}KXgmHTV{D;xP1W3ZkM0q^iy6~3j-3BFyIc1CIiHT-5khdX-0
z?rw8m^Vs8IGP`YJijOKYoyo+hTre#zKTP(VV1h!UO&&HDzpWNm9e1g0CzgQ!te2}@
zHAgy;_~b~eaqJMTOO`^WY|%#Sq_95AjHx1KQnlHp)gX~eEE8WztY1cNp;T?uMWK>g
zoXATgXQn`WxuHu`mr0Z^QL{*Nb|Ri~MywDnOBt(-PvGWJtvx|Cm1=%W7EX~3<CRk8
zl+cc3mY_?Mc*cyNq)^SFJlZb$qhgluj9?WBT18rb+@zE)TDme+Qr7GN@qpH9cw`mr
zQLH@szEn2XC{)NNGs#?6rp@fK@~ohgAX}JPddbd1?+`9sv{+e$K1Q=Ckx_^d6kn-G
zJiPVDqmf7$lTb3Y?67s#qosN~Oxi|kQLa)ki{7*p&Fj3<D_NUZiR5@=e>bYmCz+UY
zOsGz?_xw2NqmO1Yz<g!C!{`%PZ)Bj;x2mV%uJGGV2l0*U_cuNa1Jy@r;Ex_AH4?Oj
zWQEC!<%tC~U~2GK_qOIhjjZX{?N>FdN;uK9=4l1o3bz>?te;s|wZ>}2ff^&M$61%P
zW<wo@uz_xcl^Q_D9aVbj#Y^Hl4cCkA8{g~S8|0%EN7x>o3MUIMY><nq!D{;ppnyY;
z%j4ALATS;T2V4T4mDA>YxG!!<qQt{~+dt^n2#5vHn;!>)gM0Kj3K28lH3B{_>^ka7
zrc7a!1(EKbM#eFDqELUEzpDlBm(vHq9>NMd3m%1bGH7kY-$^#xz7sA*0rHCK>Z;_L
z69F|QG7GqOC{Qklm+VUM`Pseg<rR^uX74v+^iav=M~9(kgtdLuf!D;K()V97qydN+
zu90CEmz+z^5|T*<>g(6mw*(LnZDVlBht`S79}<jrIoVG)nHO|_fMM-m=|OB>xMzf|
z)zotl{If<I0lPMJ*a)SLSIoYZBxTRT4H|Ph|Kfj#bhNT#gqWa5H$1{aiPtr!&%hGk
zC@&P?QL^PxH!fRGbDWlK$ENl9VY%blC;YLwCvi<AhZe?Y6*jAfqNuAll`-4mUsB-j
z+wF5pK@T2JrPUauHAPbxPdkX7dz#~S$Ni-7PZ~e)WLHziG+9cayPZ2H#|8n(1}77O
z<|PCxdwm(fm9E%43vWa^ZlUVY0p16CI9N<8i)am4zn*!pbcJ0ZDaU!m?F0x{gQIGz
ziHg)9QH5MgOF`#})wJTtpE!66bq%53Xt3!}hfqG5c=_zC?pzi)l+8Ur!UG$mWKVrw
z9SjjP-=qcbT1-Sab<uVWOE;26nY3LmShz+kZbJkB7$HqwXV2PB-35tVD67*Op<(^y
ze$NN!9_uy@lS54|0I}L=xL!oLKBl>5{;VMq6w7F7Up}&RUE7k<3jhJFe$mN|WsK_@
zRgtJ)yZ5(dN1F|B@)JV2SNZc(n-r%R!`yZsWg9{gU;R*~7iI5<jC88$z|RBwdUAB6
zKG4QC{+GPoZw}(hhBlC-4Z@jK%0A_jpJF=Y{dZUgZ%pCXBEq53xNU3%+jY9CIf>6y
zT$YK{fs>g5wr~88u{fjxwB)=JlqP#tlE!5pzpN>T6*x%;u+l=_&}Ae0XFe}5X}@YP
z%Nimt1{Eawd*WF!7~J=sa2i|D9e82Wh#q-XUxGbp8z4+97wwXoiY5aeia3C0`bqV8
z$qJLa5hc*$xyFWF9^>t9PHsH^`JKdyX$go@0LNVT-=|(PcJq3?>8Vg3mV7vIlB@Ct
z@>(&aj`d%AO-o@<3Vs~(s+w~U?%@-Rn1<po@1QTCHB$UfJo@I>@bfrNT*O-t_NYFQ
zx7{b(CZ&S^_;JwfyUEiVW#DkczplamUgJ$n-9`4Ld*hdKAsZ%EDpoL7fHR?uAd$vQ
z&>v@T^y=Oi(4~317JjLm)b@TRRWvsg&Pq;QZJlSpfiCp};||<aVNU80AcuVcX;iy6
zLX^QGbaqeuP(t&Lvc6n&$HwYRfe>@+oEd5T7WTw(6Aq#BJAshwHTA*<Qf9{ft3kq3
ztW%CnU?oxbYg7WvL$M8reHqPl2tP3X=g9G(dzqdzWC;tAXaZbLv!-r}w&>Go5YKYJ
zQ6q+}^TtV>Y!&8I;ed>$VJZH&-6NVa%fN>&0&wM%4J2HyiakI`!~R(Gt#lXa0tcv0
zr+`Js^Hv}a;RDQ6kx{HKl-xw!yITYv1~GMN|7w|5k4AK{R-&2!Ndc748v$IqW5PxH
z<IosP;idO7V}kUfI2y$b3Jo2WLrL+kbF!Ve6wrc^q=o5f>qStts`oi#+7G_p_jMF*
zLYKxjdU@u_xR>VDH?~(dbq_#)hWS=u8UhNj^MNU)8chmFP;As|1X}uMn|}yx`ks4#
zYu{r3_IJ`E)YvU;X8hZI866YQgUf_R&J|zF(BBwAF_4#=_U<QM*sQ%1IV>nZb(tDt
zs3u&_VI8`$WWQ{4k9CCeXXxke5Tg+T;|tTXY3_#e`0OztJW+=G{NthqUc#Z6GBpIS
zTB=zKaI%R*^*9|=oFSo&pUJVIDtH0iXM78rSJXvoY@_XK1QfNHjToXl&zk6J|GK~=
zi<dEm1X$K(sI}T|G!`i9TV;H@_nQ`2zb_nXFXDXo>e0aZ#SVbUH_`UsG1=CiK8~-|
z;Lo&&!l{8(QlCtf4<lNa4U486vulTF!l-G?m$8ju8F-{QEWt^JUu4Pxg%#nGisr+}
z<f$H713wA0B%~$;`1=51M>bGrr}EePkI6CKnFJW4J`4AdR)Q`K(~{nSV$n*;s1}g`
z6XJK>)uF+zxb4<plNGcRceU>(-unMK0sj_p+;Ik$`}rM-qOl?nPq_q@k{*>qvY;;5
zzmqF=6|<oR&$XBw;B7aR(RK6W5(L8F9wd$$l7Fv>$P@2f?dLZ%cedQCdrAz->y9AX
z1^N5ptKuSz@hQYAHrk^i@9XZ)%Wr{)Q;f25k&{L*6s%H<@)0Xrc)}a$hqmso+LpKj
zbeUUEKXC(u1jj_Y#4Z*B9V4-Km0If9VaEeX`O{DeN&|<GTmwHqxb7~!*>#vfx=Y5g
zE@KruXjN1z86f86Y`P&Q=D5J=BEk9XGG+7{Etq%d&<YBUz1|7Y?D#T4CFuRDUpD-h
zbXfu9oGiCB#US&M_!V<k;V~s_60F+%%^+MqyPOWx6@beHEqXm-7(?$TQm{J&&T3ks
zvDgJ-Laq*}q~y8kjym1DRcu%|2P`t3n*h&36cQqBs0u>~UTxMU+C;?o-899R+1M3u
zQ=tbe%-ne?xCy=)jMGuVlkq$FvoI<OR>u=r12)L-`)K{;B4&Z=t_;O~ASk;S895l}
zQ0Ec!)ubj9rc(#Oo*aO9so9~xu+1p{vDV6{6O)5zn(`&}%eU5HdCdhh<uj~u0(Wqa
ztilfP=yf|lV}=4{{q<tY2G0#dJj};^@2*`xp>5KQB9{~umOcrvjvE_p5>BlZD5hg~
zTW6w^Paa_<SuIgMbvCM5mpMW~z6LPEt0gfw>o3?wVhcDiVwgvK;MoNsXG$FLhCPoZ
z^av2<=!a6wCIbI+DV-t$ODQqQZN!Y4QUnVf7zyp=4g7BC>FI`oE&!sSsw7z-7dWU`
zJwH=3M>#-%08XQ8dCo>nBFfdO3Zc)ZaoU+&K^Pb?_^AsIXsd0iX{;>nLdS81zu4v&
z9{UO|`j#SoPzisuB*#mD5K>BdI)Z9L!yg2fhB4K`C9@}iZN++8F)hi+y1puae@TOd
zV>j>!qEbAWgB}#_uBtmazoMBGA#8=<aYsdAc1yn~=hwMYTKcgbieYb0Tua(c^rkO$
zL=y+;9GnJ@Vw1mzjGb*FrTebv8gPP{h5kmKHKqW$^GZLLg71ybjIW|f#I!)7*My`V
z_954p`5{Lb-2%Ay0{C=izt94Q3R!r7P_ziE?5k^Ps-x>=66MZB+#BMtw_&@(Lf;2J
z5_5XMi`4n>^9%@}{RVgSE;+b{O1WT$zCgZiGVNsKzJ@I?$)0U(T@AWL{7ea`Y_t?3
z$=~u?4+B9=ty{BDYP~J>dEA1y-?={Z86%MRJ`(rlI{@U9K|kSKW|Dk9-10MaDYC1)
zXHygtjYZVn0f<$nwFj}Pfmki%0&#nGdC9d(phIYoikDIF4b<@2Iwi%_l-Ulp64w4R
z|4gC1%^=Vb_6=yo!){FW=Y6VU1{84^BPn!`*EHW>&gaA0YDXmg{(fQ9Jx{M)M7CvG
zcw^2!?VvI^eJlL*8+UC8xA`?{?1@fNx*Kwxv>#-90jWci4N+uYfqNyqXnZ=IF*<8-
z;?0K3^MA7QhW&#!qx%leA(-8z>0a&#^ht!77p{g>=?2CNL)w&ubILT{(MA6u{XQRk
zD#rxs>tKrJ4V+w1lt00g=7K!OhWh(8l=)1do(w?g50e)EkDXvKqCP2nN8(LaF|iab
zi>}RNljDlSarJt*656>7!UDl~9@49o?PJU19hLz78vhZB%#uIsLW&e^`2;+U0s2OD
zpa)QKyJQ|}=FR)<0iqGJYqp0BlQxZM#l+c%hH*PMzI^SC9V#T1Ptqx{a#nC9pQho}
zp8;wHD>j=ldc7^}9qyQ5&paIZ3zQS$<9pw`ubhVfHeRt5W9KB1)9C$?U{GJ8@tL}x
z;Js9rZ2=^Y7;hi0DHMm-%h+duRbmtX{94~=4)|6ANV#q~xEtP29Q=wlGk_tXnyCi5
z5w<g1vQVI7&$N|tvN_dlg25|DjgWkt^f2(X(X_3lK$=hZssbCKzXRX?z@#ES&Knw_
z&=R|lPmPL{Q8T*}Zxf-v(8p_l&_kM$8=*se+F;~fr}|S~hRu|e+$kDs#FFBmK+=Zn
z6U3$yv(UcC`cx};;9zFw6AU>4qm_rzoCd*oxeG#aY0(~os(2tj!1B$w?_k&HqT(d=
zp6acw6Rtm;H44>xYJ`O2?0KP5pfYz=Rdr07J3bg-PP#`DSn;o_xW@Nnxh;~s(kWSK
zW#w84nq$1tfAk-eoO-}@3Mpv@I9|a2FG6VdBjSphl$zNxlb;TnP_|W4AOYXGC^!DB
z9*;-Kg!Pw(Hx^2jIkj}^C?LhmDcfW5@TN+bvQN6N)c?15JNmXSE#7ce0S8i2GlC-B
zP*qf1(0*-KpG|pXpQ1*evuz7C@38An3xK-*k7L)awl%pxLpq4iDYk?>_C#r;QM<+f
zjw;YjfeZ9$BfPtL+oz+#S)rGrkGzh)>)xM%3%XPx)i4TbJ;KqfLd$u3QjEPUy%BzP
zj6ECY6s(DAuA;>6cLc~q@!1gz0!7djno45H<hXLC`O>M|r9X!Ab7YR3@eIoqA`jXF
zuO+KfHwvlQ@kZ1}s!AmnCbX%-k6*y6ZR)_p{LVUiWIY36&6x)S#zLi;(N3eZupdKg
zBLh(x3n!nn%HwX1F9jr#qgSW*EJWx1k+6gW(q%*j`pexRAc3(V?%oTem2@lu@(qLd
z)B)&~&U&}E7<y;(?A-lU#T$E3p6=3|qkhA<EvV=fiiB)lDp?_??S%i;R+$b81kfTk
zfh2bZH^4kQ?<?XDgU{6-JNvyqziC+an$|TI4~}e8$Mx(TVxING50FRui{<~ZiedTR
zuHpapDu$hr@&D>OXz_G&S2p>*gCIETMh-H?{?E#;&nKj+_y1RjoKoq}e_o&_Ay@jA
zo8e(by5yC|{0|=lD7?8uCYq$2oP590YiFgUroHp-9{Y9UD_3H5=jZ2k=WO?k_br3o
zZ#vs)_ic;!1P3xa05~ErFt97FI=kh%gv2OyhJxzr<-KCgK2K(nhyNHCJKn3D-ffTH
zmFTMtJnD4!xBi?UkR06$&X-JasNL;oXZtJZ{RTo_5E&I2x;PJ)&-GA>MWsb>p!G8W
z%Bt12st#9!v}T92)(^+;QLlF(uIs8HkW4BUkG^XEWbk9S5YS{e63`0V8lN>F5;2gR
zP}~rnAst;1n&g%+@;^#_XP8GAVu0wD<bBxwkhd@yU0_>|o1a^F9z=*+kXtgE5SJ_$
zKbJU{a1`MvLJw&UehyJ7ykhui7;4zrko1A@0r7#6AuC;In&cGWDFSr3YFN+^p&{Kp
z=6_-9Cc}>Z6I)josU}^8w~BE5pS*fHmp?f^82lPkCwMfjJ~=g=HO05>eiCpRjCa*}
zNg>a%dSh6oj1Hicz*~MzP%kdGgT3edUYIhRN$72d)w)huR2uT1%lc*)_}-4|9MW65
zROyrC9q1eD_807*#&7CD$WY9XU$>mso?D3v(WU5iN8e>Y3EVp_GN1DOd})}v<~TC_
zMS=(s4bk7{{fJnvC-XD<L;7t)RM9C49pX-G1k~&sK4DaNt|0Nm&VigwHa(iGe^M=V
zZ^6B~wrY96e_6CS*fp$WkeQaCFAzI9`oDGq^y1@*POymP*ifTg+OM8<GYIy4#Op#3
zUl(r7XR`CQ@~qvfJFY$eT*%$bY#&nSdDhCNhjRAz5z_$G??p>xd0-F*htSp}npS&6
z)}cHyXPK9vBW1;lq$y=eh@2j$s5r}Vr%^Vx_DOys(6rPVy#nwkCsN+7NX;{ycC6g6
zG1!|sX!k5{);wVQ1d$O5hl$-Llk*X+bCKPX?VtY`&k@t2Vf?(oRaxVzToWs4$(+H$
zVYp;bV}?zE(wo|1u2*p7A1&f{no6h79RV$XuqiI=w6GH9I~}NIbsG43uTZ)rDAOSn
zeUYc-+cquUAD9^~@6z8<!c4af=owWekn*3{usy$p$=@hhC$(q4x!hTp;~~;2VAg=v
z`33bXBUvJtE0?TcQhkCFd8hHjORw;}z?xqo2d?^r2>y)d0GCP_MX35j#_|j1GX4j_
zg`4(vfSGfeX`XN+WzA&Em|UFaYoJ$!@mLVX$&uL4@E$xAzB8szY2JT?|CJvFFl4Bm
zcw7&Wn6nGN@eg<Ti{=l;{n++_hw=-bq#e{~-kbVq=)2hw5Z(h(M(M(=$k%Hjss31e
zQn=C^-vv^Wn@vP85jWmIlKW5Qb6Vd7<7sccp|by7<k}su!7tPmhN)jdh3KKC_WN;T
zqC_~-9hyxwhKPqceoqREbTihvR`pQ+Bc{y24)fy2S!*rNJfX4FFL0f7gK(Wp@$3MU
z^yemzS`t9N2ix{K#ChO>kSb|GR0X0iVG$%^1~lN$%{QV)Zxk^ADy4?78eO%XxJGaj
ze-#G`^{4$Oz>OY5UvJ{Ye^>>zDs~CfFMAVrEprza%#FYneWWc+(hOPYVkuNoET~A)
z%H|XY$iNNuivJvx%t=^UB45%xx$oItYXMCDD-k@|SPm^lI*=eSkLLY~p;)OpZi3(v
zEP%8jQOsN(EgDpS9z$9Hp-g-kC$d5SF(fOPG@edLoK%sl*qQ#HzN`D4?kJ2_=nB49
zU(#w&CT)_KRe(Mz6dBF}wlq-QS7q}?;f|SI$=4BIOD~D3&$7O<U7zME@ZESIOgFhX
zOu`LA7#~u{e5E1}N(?9yDSBf}2_Mc#Uko>O<NuP>odH(fcF_T%8PhIGfq<F3H;^y5
z91QjpGHQvk4N0kG#c9r#;n)IhK(?;5r7G~p=-SQHVbQK+iAj}LtEZ%;+E#6=@sVVe
ztNm7N>~te~B1)y^3l)C=y<Z`>p=}{Yyn%8j>8vAQ#3y*fCwc@l6A<0Ef%7b)4}YJC
zU?ML2H2XZK*HFrJcGVgwCHq|X)6;D<?%5ST7n@9Hq8Drp;XTtv-sJsu-DbAqueLvO
z8=JecvF~;7^B!=xl!FF%cUkLXVs^&g&t*C|Z#vjCZdUgogxsCM|MqLa*V}(|zs>`{
zKGAj%5Hhh+P?H;DkK}C)&UM7Vlq;L+-(nu{fFo}`bxM1<e130y?x^^ibCy?n*+QAI
zTsBKjE-PxYOCLIszPZOAH?tw34Z-Fd6iiE5cvT=$o-*aCdbeJ<lCI)fBVogM&;evR
zN%P1uif^CvmGvr`$@F1Ty%D%A&V4}C^--O9&6~jE^5Fco;dIw-3xpRE%Ih0~BgqF|
zR#%m0tH?500JJ<jGLxasC^Hm43gbh+yNwA)I)tId1fo`3lQrHiCN}GC;-RAB;-j*2
zDN`buq7D_3&uS98cn-yg;L^(QRZ+~KNtJ+Qbzx|O%F+|1Y9{3<<kj~2=*#am|F`ev
z;*1l37horflhx=?gBn*GA;mPaAe`wckE&LL*XezOcK{kU04MTVf^AQy-Rs%l(Pa^W
zXB)sSg+SFV{@DAuzw~%PH$&x>yYWx$i^?@C+p}#YXbj$y+xNW9beMqvWTldmKq?=+
zRrd=p;V$1GI*qZX@Adjnm6k6{#9Wk>O;J0LHfQMP9x!v(3<_0VFh!K|r7Ce6Baw^)
zq>DNX-&~fhg`wMsqC-rOjvth5TzDQYt^!!_l*2hIIwOhYS@zMT9EswRb%sud!{p=`
zMd_Kc1#&VVuoBLa$yqElu5zJ<@D+8W#KexxGZd?K?}c#btJ`it{h5tdGt^aqHhjn*
zAI0uczuF*0E>;UBvHJ?Fyy*hJJPpH*!8$XM1H{G|tkWn#UhCBV4j&{hSM&amX(??+
z-?e{cAk;NU>MRKN{KfO0+PL^36fC0p^dV1eTNrrWzv?_!i>c2fY+h?0bw(Cq-_^Ms
zOJ)W&yEhWCo#Ae9|H!hb?Nf7$_RZTF3FuCtDmlQj2m$Fo<tf!RqF52HZ@V=dhc7Xw
zKh{WUa|7iZMcDfTTzDt*7Z~cCv0b{}h3dW@c~8LS{viSP6Up7ucZoZ{#I!E`h5H9+
zd$Z~qOkwh}!QJ5`@~3cn#2Q`ANR#dkk#ag`zlgp<$*P%?g)1JX!`}zC@hKp*af-g(
zF(|dwE0G=$C>gxxlOQv!!dw_1eozPMP%<yrl#kd0TzDS0HY{JZtkO(XaWI}1H>y+9
z!OWG&i%m0+hU4)!Qa9P%Z{({RZvp68>o>b|<LZFv{s7Nx#}Y*Mk<NTSEZlhC?##8P
z8|%Ke2i>a<itSEsebDL10*7OO`;vtnbM{8#uN9<wTK440HyG9m$=SbZEw)ciFYwL=
zN;b?Ct1QG+T8k?#7x)ZMS!V<evS7#??qzr7=Zsc+qe1`oIcMs8#QvA}M+CNfe%aRf
z-EhF7yQwOK1MKE>O0;e)+zAcjv1Jw>n1_}-H(vuDWo1ePW~pxFE#K@n+_WHU!AHd$
zf-8&0+ml<0*VhFZ=$XTvb!P*D*&G^AC=G*k(-{_LIdG$il{UNOxreGb&GNJSYbWl&
ztKQ1NzGBCmjh`rN-y`ux=zd%34{!kH+UpNu&o11N@;hH_0kk)VGm~@Ep}2*om1`IG
z>Vg`^D~VToFdUMo1*o#c@+WykNljLV#m}iivTjv?BiX2TrLV2yd9)IXicb$`9=zE=
zHaeL8hg6t1<ZWekaReL{&;2HUvgna=C%t*HNzW8&cP8eDikcDzm)}Air7Q<sG)LW_
z!I=9vj--SaaOhVW6CZX3Qu?~?9kNLv=0XO0wR!#=HY40i5xXaZzGtT+ZCGPUDy~=E
zF-Shuu7LF)o3eFvO{%IyWl5bajmo*OCi&>6AV$Fu{XUdCFQ3QJbBxj*4lRr)kS=-v
zLbW{i6I_}s56jDQa*}1-W?4U|3PpoH0$Gc}e>Cah_3QO()K=hUKCN*-`brFgr6%he
zx#<`%Ti1tsU%F8Me@Uu1a5`--p+^h0emjFcNFYO~8$QKbgoQW8Dq}{TR0HKWxAia^
z#{RzsZ7M_emPLO`ktbMv$%YR6_sD4wpoVrzeEsKlQs8D`nTFBx;ccFhO*WV{H+BJ-
z4kXOs(N53TTglg3OV@OJX^Wz1;+FA#y#=RlDDned4P&yMD!TdzLg-5Mu6&q{M80Q2
z2|EwEyE49gj^3i$!ePfA`BOC#^kPyigQ%K~H`&wiu5^7q&5zk>^8J(9wruu)d?0!<
zS00(BSmFq=3g~RZ>36r~AL`IB1&Z_*GUtGIWkZRnFX~J*nk^43MEV%q;^x4Vh3*|m
zcaf%y_8XZmQZfI1&4zFyYb;g!RtR7Eb@k!1tV9^1JAO}$zVCn;>%a+K2G}E5=#Vy#
zEL^mxwqWgwIFy=%*r7`dYrZ(0cMt1msVrU)@ECNBWKW3Q7U2_5BmYJd2y3=5YCW;m
z6Yh6Gj`mbh_hh9aZ-~*BDv79g)FS&@#_fiO(bi?uj?>=R(O;%4d4o1VEP5R-%_{5P
zJ1QEF4pU}tb+{SO8PAFl)xLQ?SMhDDlP_|BB4rYa$*K3BOm<*SG{F@jEj4bb-Div+
zU))!KiNqKN9ms{ek-r=n!r+sAaH*6bf`a|@Js%~i&AB-B^&9qW>vrl!1$ngyjk&jm
z;;uEVs2p)i_BZF|$XCgq>bC`U_cWTm9bysv=A#z{_{!Qwl)zps#en@JJz2=s1PV^S
z?jxU$wLQWn50poW6T{N7Xf7Ks3E~scyiH@ej;NTgYGkWQmVsovaTO<Bgf{T#Hqw{i
zim@APH%Rqxx66|i%>u4<&{h8=;-;abQqKS=pru4To4=r}iH5ucR9?X(*-YCbc2yar
z>P|^8pLk8Q>2VD`9_#NENOz;xeAnVM#b*h^`^e6nette~Md8v4071_L`M8lO{W765
z-ycKRM&KM8z1c%hy<M28&r=iTNxN+3c$rm<<;gBN2Lk+%PKs~1&Qv^IsASj^0$!N$
z$`7aN-p5Q$JCkOn@T0N2OZOzTA}?4XI(SdXz)WJXyZg)fY1AtUfdbZuxtNN?b!^+A
z#CsT{pmUv1>w1siI{(j4M!qF3DcELKJ$NEyvz%F$M3n|++C35NW(X8tvHW4_Et0Sk
zWXDxZn)P$~SM;yePLhqN9s%XRZ6;Syq6|!x|DKW}^-8!<UhlxM*Lq+CH+ev#Vp#JN
zbjQ0b(x;#SyEf=#Uj$Ot>hmRl=Bu^GhzDYTAbV<${q;76Y};lQ^^ao<)E!p4XdmKA
zE3}O{4??!y$lfTrlPNte49E;bw_eA(bN)H@j~VlyK=`%4-G7-^@OsCT!qqlNLo-;N
zqJ2(_cAEghf8Y6WXhWv`X9Y1~*W;OU&-10xM^S%tFuy#?et$_*ewsdlq)BIcDGQSl
z{@1T{fW(Mmj({oX`?eBR4!`t!m?a0s!pgF|zq1l=7@|sjxcNF)9TG>4p4?cP>&Oo0
zCEb4c0&_+DvdO8QR%ddn@LV;rk(jE>oc(o_^NvJj9=fMMM<UW5LE<X|6=a$)(@yA6
zqBlrA@{h?I0@ZW-d_Pt0Xn{ctWqW_yBOdtx!>H8JjQB!$JgXoosBpW(b~ZbwBRcPj
z0<P|@Zi;(_k@QLVQ>{d{LM)d)i1iV`nRI8yZxV(UrW=FfmUq?x^cDx(3eqh$9z*Og
zasAnF84xi<bozc)JdmC4_S@Oc<U;3~DCK%wv_asX$of!!F~LeY{I_zH-sjKf&*l$0
z@Z)iiR@TA6_Xo+kJ;swg;Any%$|%mZT69&NS%*&EoYo|h-GL6*q|Zt1Be@o~S(K1<
zL(_nw4?J#2{gM~{GLk3P{Lw$I_JBcLb1U;4w<xbLvof<VH?D0@WZd9XS2NPcQRDf#
zMDu8(%kT|De2jtWoV_sgJK#9-wz!XUsb)h5@hrp5@f&L`o-UMDj4kOJqGmFdcN6PY
z?Clc(!$wszh0Ng5PsJO|7tI|^rJ4q?RqE*osjYG4@w9#nB!;Iaj3uWaqT8u-`a-$I
zKgRBE;>_)tKO)T!oUTUkI4w~+WMSy^w||T%ucexxt{g?gCGroYq#6r7SB5I9*NhJ8
zmSxtpq-jGaJ8GDG=v?T0>F3lg?wQ}d)o$$uac%!_AVWzWnaH0i6s6<6Gkgp=JqyQ$
zMu)VIXM+=~_#69x<dW8xHKbqiITz4NhK!*{)j+S9t}>^x);4BdMc2cz)aaBUSPC;q
zQlu=?m@lUxQ9u-<oBqp>HnB~5@jb9Q2#+pDhJHb0Y1e9;AC$50Zf3N*%RAH7VmU*;
zw~FSODUa{L4F25*Vnkrw75B(R0=Pm}1X4x`n5?Fp#%V;$+b9_veYTZVRHkzX6ipD@
zl|ccM+?JXTZcN{Aa=ethrx|hVW?XGcq-azHN|rxUX;&^;A7HrlW3<A{nD$Y>Oy)Ea
z_i~}bGSL&-t%%?WtgL^p@WxsK&Ps@5jXJ;2J^A}V2#ki{ckx93wJBUPOIoZRkO0`3
z32noVF+3B|W(tBC%%6@Dq;5Wn5!36#^aHf>q;Ar<gj=AF!C%|KW~?jEjpRR*51Gny
zx3=v+arQ1(tLQJIi72ZZ=@MiS4S?)ckWw}Pzk*p@BNdI5QhzdQ*|(xa(AUe(z?_WT
zKyZ~dG~tOi&=PETxL7{~)3N$aV~2)q+YN8a=*B^D8pz`^nlyV=*T92JG_xU^U?=m|
zG)Os=B{p(m=}uZ$1<C->6z{Jw(Z#w1;2c+;MMGI4K00&a$}!Cc^h5#Yt8M5#ktsBl
zp|jd~k+_DoD%@|QO$^<_nuQespUtet@ZO3lw*nAV-6$I1sv9Z$3?f^&iXAoQU6!+x
zdn27eZ3}~je_Oy7t{Xs~f=Gyf@>5l?HV8?|jUtyT2w}vBGCOdx`S*;l^|l+HLcvdO
zyj#El|FH5s8(__yw4L1~q1oZ)(1TwQRY6=8{X+W$FFn0&!}GQ>o0<a#wiC;c>2t3q
z1LDb9X-fCWHWs@sCdHEgYCUVU0xUdhxD%{6Y{x_(iSlbM7Z+FIBU_y;My{oB?Kj!a
znoOPbOYB_#UR!a3GSO$~S$m-BOdk46y}l8Gi#k3t!Di%0tF+omSr7L3+&x(nXg%MT
zUGobwlo^<r?(yOmxkrLg<6dC7Gy6bPLj+G>Y6nYOe!Dy~D+bO0soq0`>J0t%hsj!F
z<m_L5V6n~R(P~KwwWJtj_d*=xr0cyda$Bdd>9Rea8kN!E^QscP<)3&vXHN(B&_H+N
z-)t<mIN5m^)9lXc9Emmvtsn65#Ym<%(PwRXO-JnjMh6B@6O~jEBwH32M%u6+pB-#R
zMb7!(0P#6oh`%ze!*sEN>NZ_jYj(vNF)Szj?7*du&VTFfsxaei^`A}no)F1fveQb_
z`x$uU5`E=Fz)ul?QF>Q~gXaHuL?kMETxF2S!(qyf0vCu!l}pUlyXBF{;mF{Ydg!(A
z|C|QF6C*9Kj@mocSYdgi%vL7PFi>?+fR9ukb7%V8etR@ObSr5u`s!{+rM}{F&wa^0
z@py*Vl^`=3u^{(j<`FN~D`=~LtpTM98d25G_l2DY0M}~u3la3gTI5K^k*!T3+R@Wa
zp1QW*+_GwP+0ucm@%Fs~1HYZWif3v0lzeH+sACQvgtJG3-ay=+uJ&I!lT^>IOk&c+
z#E??eCvuX*P-OeRp~WndxQOo?zdw2(A?KIrmzWtf0hDh*=e;$JUi!P91DwsZ<`wsm
z#9j5d4ovKGxK_(X=lmW~_^`bNADy%AN#(FRpECPE;_hB*!a6T!(om@`R_&k49@#yz
zv@geuTT1}MWK50BeznR?Ru{VSNu*d-u$Rx!ub&$@N{l80QZzyK@cVANd|=)X$zf@0
z?Ys6<uF7whcw7XM#$(8Wz3OO64a!bsv3Ox_x`DI~so3|JpUL{K{?>&y*Wt{2!$u{v
zroEvoo|5x!4ef@YRLV~4S1Z|@_=QP-`aL9jpm2lw2;8Q!N?4?llEBnUUauKG3<-($
z%5UP^iZeGemm#7*)|n1xaBd@QGxVL{MW#;{b~TbnJd1B=>>3Ck8_^ZHj}2R<NFy%q
z`}@j}7UnAR<~LgXhi93?bM@{$LoQQN+md8|4A|D;jn^e|M|9n#(;ywP->6sp6Qdfj
zws^hpDO_C)jSY?U{H|L*wsgkQ=@$+04$#NHUu{^FC(oYFYkK{<m7h!*7ROo%31<G<
z=L;30X83pPD-y^d*qF4ghf=Vo<WX_~XyH?y>6?)p6$7+w4sqJ&1@%fGS*S$b?p;am
zmR~n6yq+XY(E|fLH8nDh3XwJuQLJrVn*^GiU3Deevw_HjDesE9GxPJ%`(t&h;G4Bf
z@g0VsaCSqDD^rKK7VhT5wk6~^3HRUloXUmSUm;Du7cWswWO$kXpBZe+j8N3~^^_aF
zG}-{p4l?D+P%WJB_O$+PZw6n{>j?FicdMXy=G>PjXcv?foG4k%!t&)dN?CulPJ#yc
zm0<4i#uv8F=*g-!7$-ji8JF%03tonDMzr7Km3X5e@(BCqhZ*C0%kjo5*{2sy-J$kz
z4Zm^C5RU_(oP4z)hbu6If1hM-e!iGTK<J;x_-%qpsQyi%H7VJlcbA<W#K+<GgK6`h
zQ=a*K2sK5<J(fO>wvENh;2r%?OB*$65Xz(V8fR{k=B{^S9`^L3ta*d>t<~(u?NNp!
z_O<axx@mLlcWj9DHTeg6<;aDVCz59j#p$JRt!Mz7eb%%xRq>7g$ua{id&>vT;1+Nl
z&qZQbQX4xfMJRL<;1c1O9V%diXA~CQJ&IaTFs*feBmIf5Z*&gf>)-kN+LE;qRYzTj
zro{B$ia?wXZq2vw>m3cd|K*N9>eNxQ2JC0L4VP9Dy=aA5DnYSAfc$Xqj787Pt5lyN
zgOB`QWj&RJo8it7&QHog+z`DXj8Hw-t>u9==MdEE+!zRRbhiUGN?`$WFD_D}4ti=S
z1Ko(x#ZpvvSNh0($Xb<}!;4<@DZc-xzUnu9RG@|~GEaNlo`#FE8+PEHPv0LFSYg`w
zRMSD)e$8CB&NHC<?D;N0y+d6SWxcPZ+pyvEnX=?j(jsK}5>^UI!;K6VzI!go<rt&=
z$w4I1<5bJ}2EI4yv<bNSNpp%e%v93glfPPpj8>5f{BnA+qA8)9U@#Jtu&+wsEWg?$
z#ta*|D%H?1=y`=MGj7qOQhEznJd9<GI`-;F24AD1V}u=0=i6kN-EO~SjmT*tWHTk8
zP5vZXcq?5(k3+I3CUZt)33kl~kvwQ>nNuJB)-i2{ux)ukl*}E?>1jgKsylL=&&&cq
zv3^Sr>E?XnOYto^H&7hNYb#)`@MU7#LSDi|Iakb1ORz?eXNO)h_dJZ^CO->LQc#0t
zhhB!UY(tg=c_ZYC;z8`pB@KM}IL_-Q05^<$rsZnL<#~lVo5cv@ZCl1+IXN4#);)>t
z=ElC&{l8s9JX#prSZi2o*t_S9&3%J&Xl6Z&z70>Ow;58uq`6yg9AnI__V!J^xq7z*
z!vE$e98dZ#iBx?$qayo`|JK*g2L}uKmxF4{Mo7fbmq<TRCq7h=g(luu5NplU(AM`i
z;moW<NArs}Jh+_*y$GEKZyMhSumFRseS4ubR3lQPC%y!(ML#jG#1ttQ!P^f+PO;h}
zxh>eKt`=}H^DS6RllDB=51h=9&_FsRwUT-ki5SJI7m&`a&)8to`qU&&QW*>D0gilN
z4v5kNI2XAG&#LQzDtu<k)?jWbN-0g0>`cs5>`NL4h&_N>2K~64=Qrdpc#t@@m!Bs2
z_QE*k9Jpm@ogaNdZhrwiUE%_eE7>(W8GLYu-Em<>(^T%RpA=0PTnnlSNyu`ky>MMF
z#bBD*q;14XnlAN?O_%v22B*JKfjNy|g9v$iO>e_0*3+EzQLm&*igC#v%y!A2vpA1q
zi#O@0i_YQM9G0Ush5ETT_tvGxx(>TSr#|1%-bn>>IuIz94nC8?brx^WHuFNE1#;(0
z&9~M&yS-DkwwA}fr7SF&SR*YZgO~`u5}+mj-G}9&`GHSuho8bm=^qSsTwmWd(`mzS
z!&t+=sVHVnUtdzZ$vLBEAVjwt&CI0b3XvgIamqM_e*Sj(1v6@~`H8g3j>tPl|JDQ1
zwHJ+<PtWx+l^DZ-0>>V~!9J2$3p7KTTtka6J9DsSa!n+hTgzdbYS}XQ#=RKzHob#^
zQZl);&HHO3Ws@AxHBaj{*)0B;yvR79WpS2+L?@}&q@o=+I48P6>+=Yp;QN6CsE^MV
z;$m=kG%J#?41nRikZ0Bn6CTo=Vm|2owxy>EwvAH*PpqRqf4}hh{yXH!j{|4$3&xyA
zeT5*8KQT>7Nc!7K1#?|7KdX7qPo$YR|3_!%6s1{^ZR@mc+qPY4R$7&|tJ40{wrx8r
zZQHhOTQ|>rIo-E=oO?#U#Y2q!u=W}eD<bBa-$JDP11tu3>#*P=$|j@&&^;P>-Ns}E
ztA!nNuRjrK&6ou=_Y{|OIWFY#3YzBy`o*aDx!3?rVFj^41QYY$IZt`aQuA4Zx_t0+
zveQs)cs&DK`B#wI5(X+m<9+EPJgBX~-!eV&eWmE3pL;%?2btgRje(~AP+_-aV?dwI
zh=Cqrh)y{iL?hF3#{D~p%cR?nj`H4ra^ls7!0rK|yFjD2OH0(c!B#-w`5NuR^~+>`
z=UcWprekUYPPkq=&yuFz$c4DV!mpm?Ieo!Wp@Q<TYh@-SM=GOCbloKKF_nuEbEM`G
z^I7)H>8S_N$eyTCWfS;pTETWix_7A>Th%j>{S7MxpYkv#?&B+##b*3rKUjkh>=E&;
zae4Nq{1p&#lh0AC6WQdS-#Wffy;5tG{*CJ3AFvAlYt;b<6D!j{Hh?5*xH#e+G!Pni
zJoaH3)Nj@ljmP*ABZ@Fjz%wl`FK^o#j|_?>Em%9*Ty<<*bX-Z7+m3W#YBs{1iCg<a
zlY`+&k_VD8?r3j5ceLA?#NEl?eo977rV2hrm|nVERc}z|QpgubOG_73X|!DrfS(cQ
z)|rp2QW9i;INmgI2YQtBb~K?~rgy(Pp7eLgw-o+f>Q8RQeUhj7dd-x2QdAiU;2lX^
zb&|L_&DLrc5M_9ZNKizTmZlK6p5KcWXvz8R&ZR4}=mKbtHKsitzAcSvTNPZu59lR@
zl;Pl(eeFQp&aZXJ@T=VV$5EDY&+}RK^qb6&h90P(tIBb1$}6IlYL||vP&+}gN45sH
zM!ym~0I~bK$GC^MC%F5iN%2eZi}B0x%XEm+7w8sYD2A7+mypjZSz<S2XbD#qs4140
zvd?RsI+bN;NvQrzoOeIn(JnzbO+ryoQntTLqHY>*J2(xvlAV^z5Mbt%lJ64v!o2dD
z_dKhM4~!RVuKZ~HN{ws#h+317(WCx&z<(?azZ7*z8%?^tI<PA4;JOIY58sa7f?0n!
z)#*aMOwDR;E2~%bJ374CbK_I^Mpy+}fH?VA>1tPRFx2DXxwGf+XjRP13{V>dT@)p1
zCu*~)BH(KD1gQv{1f77IxiFy;guw>~{qm09!WPPYw`%J6;X)yj8o)qdI(9Pmd?Q8b
zNP{hJrb3LoCLjDX@HF_?>c!AC7yYZffTqZpr+eKp$?8m{VO6WG$8aY8NIQc=sQk;h
zZR-M3(Ee7btR&6?RHja|tSWv|3CKM#sgxbY?N_?#ltVOZ*wtG=)qbbZY}k3cbh1>Z
zeEc0Qg8~(tcZW(&!&n0k{}UGHj&K!7v+A|+*7)6+h<g_^0u_2BnMQ~O$l!DkXG8vC
z^v@K?=hW<-K-bmm*&AOd$GiZZ3S;YUGa68I!mK#cLKE;)n@@B;>8$EnT*n+>)n%+%
z!b-_5sBoY3C$OdzR6ZhQPAlO66=a=^KQnDa*;^fN<<*;mQ^4Lx0x_Qjw|Ki(ij)aD
zBtq}B&NbZUKGHY0?aEq%OFN0n3_n-kEq{`#YGj($q)i4HNhJviRwB4Cd1eyaBk&X1
zVtn^igXGUanbvU870CJpXiFiwUsw7;Sb3mYOGIwm>JBPg-9J9|Al=?tcf>j3qD7mK
zKAqjs{vOD_b@k}1xc(U1q<u#XOuk<8^L$!~-qvv}yqzVD$jY)yQ(xfOvTaQ7iLmC=
z{6;@O0MCAk9$pVm&iOcB#8+lM_1-2gW3+<Si<g6~xZRd844fBC9X8r{T5tX}OSg(|
z)1*WsNhLBy(pxBx2yMhEOF!&Vy&*W<8Ckl?gu|Q1GsmGK$h$`2?nm}q1A9N}1rd&K
zC~a_!t8isxJwCY#&NQ##U+d-jexcOkJs67fOX8ajn1UkN{LOU!Wqn`Ax-?|ytAV6M
zRT@Qw&5jl6es_MWndmU8z67lbQA$uiL=Y!hZ$=|ujawdJp>&}sbfdvHDI$)iJfxPX
z$=zs(mUudeF7krn$=RdRew~}s(MH{Zp45|8b~<sFwc4Gy!i96<TIbF!kTw2JAn>d?
zrRvg+8boA&S7DK;4*USL5GF2laVVGQqvq`C=qq>2_Z&s*x|ppn^!`T=WV%*kKuvAJ
z0qfkIi3oOzUO@w;2KCp*<|Hwn&hdRK-?o^Ps>Xn%(2A)_o|0LomJ*jRg|<s#7i2mN
zC75ZToUN+o+n_ZGoID5<S)QtM2aHNd<rsn4HCx&iPEZIb+*M!Td(ZpJl(P{Sz&AP#
zz{=^<EUi^GD}_#MlXP%Z+L?=PTt&Vmqr)4mBjPfR90}we|H;JTK@Pi~DbwPH*g?Dy
zPaskohwe(yNgqwQE1|YO_WZGY2PjKbPoS=Yh>1&*p`XahpdZ|gKKTIZ=)8PR7f}y!
zqz!nke5@zQAxk*X1LcldMHmQ^-@yTp9H-rL+pw;Nb({=q?2XI+ethQn`0(`x2;SZ(
zwbq1`LTdZV`4Qwr?<96=tg}N;6<<)atr+|*lLS}8KZd4HG|<5mZerb&k(h$oAXb19
zL7EerXXy8m44>zL9=l2&fVU``e_7;!CK%r1{d6wb7Q`Dhoka!H$_&`x(IRf(kclrM
z3%5wqyYlzxL!`nCN2!Gi`xZ4wwfA6BLcV~@Su)4?hs-8{&HPdrjhEIzZ|&`*5*_v-
zz0ISte1IP|Z^TVW-6vdn+a1`Wd*+P3gQi!+>fh&@4Iu7AqVHBXxKRW#={bOYE@$vn
z$L63Uf&{kHe<I4Fx$o~_R>u=NmZi%eMqxCO(|1k*o+ucwtW-&3D_SAY`de}Nb_ZO!
z@Qcs@c*_A>@NOVbg9%#_ga=7WZ^~~r4}{!OA!Tm-D_W1*ydR>Fid4=fDDh`!q4PJ9
zb94I=)RrY}ZD`{)dmIT)%!5@-13d~=_li#dnAWyY*Fe?tXs}Ksy#K(WzQkrQFDR}x
zZd{co{^p1W-5Wcc60^~NoC0#i@e-QA9FN9cxw1Gn89V6ucfAKy*PY1Mi@9AM<#cpL
zy_|)jMk3;-Yfgw#CPqwFl7%V^h%ecjK&E`qU(ReTt!ek3qwAh~S-LnWi#{c1RvqZq
z!Jq-Ba7#>1`ubyfo;KeYg1=ev#{mpxQEX2E7hD-aQo4&+R%RG;D3NM3qZ%mZTprB(
zwV6m7-iZL?43@QKZ20vEE!&{ns02{)U69E7*<585nDDtnuse@5#=FR5-@mmQVF!dG
zG$1VEVuS-@lpkq>%HH9{+fsCRN(AL24dm&Wasgu1D0&N{#)@8;>3ioO3X91%rij~S
zfmJ}FP~}rIpqLX5*?KG<2->4^iJm&bOe`;}0llKx+r|{UX?#iCsJytiWADvJ%Fob_
z+7Ria6F2V5z{5gzK@U-BjTa;b98NlW4!33|4)e6=WBS}_UK4-fxJ8GEyEh1!-043_
z2$dY$$1+WN_J{zB8PhEK_-yb*7plIGHXied34QknTI3C(8cfab^7-2Pm)BuG`z*O8
zdK)gEl&=C10uaTYiYJRUU)j%tyiBhkj1Pq?t5#L2wemd0T2-_wH<s5WD-gGa5)c0D
zX*V<L1p&g2?4NYxbq-`|9<l?Jz)LdKR^Y^P6DVY*4rzJPp-kpZV<Hx&YvXrfk0qaD
zrqKf=;eKM`2l%i`4tI>T$HD>8q>tv}H4flLrN(3v@Z0Z5Y9TAtq!ZnWuo8v$9hu~^
zF7J(zeCBrRn^VR<=o&?nxzyU-qq-vMRE&Q4>=5J^@+69+F%>Nf*aj1Epm3w}qw@&$
zBm0p}pBG18Sd^vk7kfgNLnOva^6s$S6tc8GqHJ@PG28$Gv$0UlZC8qUV4&u*zqOOK
zOeEYL43W<OjdlK^%jO`4{jC;z8l8?n;KW2S-1*o>3Mgv4j!x^?uYi2r-kAiJ64U;i
zD<+z>)^rqYApUFrU_yNZFSCJ?0`)DkQ`SK`Xpey5scIC<E-<p;QSIy1^<>({`v~S3
zA|4D09hU026@+QiBsO~&7<`O2a5~8>rv4Q9ko#BEz4OmYIg^RqFNR4`+j#B$t<IbF
zn2-!Hb+Dz0Yf^~Hf%%|S)+93jE(Mpt@Kd&`Qddi1{NHk|^67;^&N0NF$DDbm@@~o_
zV<O302c(*H+UII4>+G!v=Y|f!jY{TosI)V@2hc_!?md`Jlzor^?{p_lDbu-ZG}Ia7
zXfc@^zimMl<7f5LD_1)whHa2mF|;vwF;M;0S`WNr(*nAFGAyU^lX#*6XrsaH41}L7
zayifCk#Y)70%*~Kxn}lCRzo`_WK`Tk*=!|iz0<Va_hJ_VJAZs;7hPvdMh4)`)%DXU
z;pEW@<`jxHA0X?b(KjGj>4eP9vUt9kbe}!5yeIGZJwF1UFt|cO2*77Mx@QiQI*ss4
z2LTH3;_ygFm9<!rG5jubXk8K3pd9&$&6r~fRGGQ+fhs5SPKTYy&O&+CS%bA7FT~nR
z0fPv=Kl6Aa<qhJy@7GPEIAxS%tH|2yl%p?=cJ`3@9^5A{9DQ-8H#NlQW$a5w48#i$
z9iAl$4CbUnwIaGOjvx9j76OuOEwr*h*zoX=O?GC}Hl-0~XV*XQqCI@lANQB7Woxgx
zqxoNAeYOaFn50LPXvAUNJ2smwhP3W@gtU_=0iSPEOt19mfhE+r!koBD&8%NdL3q^G
z1elXUc3Ebxf|-=*D1%K;GA?}{F>h?nvnRL`+<J@D4<(XAG(@Ahl|Cv-#wme5wEMS4
zWi^t=p|8XHKa0mH%}i%lLK~bsiAZdhx>oRH2)5FPOpfr3gIx3m569l4VVf|LpqlMU
zTTpCie!KLX=ht<z<zanr{Bp?Lz=bz899XB3E5+uPToZe>36oo-5vsOZ{~*BK4$SbS
za)|u|*;<RXNcvi9(pziu+W}ZpUqTsYMgJZHf8V-vKkG*ngqY{eu}Ybe<6tBkWthNp
zK48v<v#jVwW&87V)(mOMwOFJ2Ix9{T+Hd060?Hr=_A{A`!Y-+yslr{`A)trvxTj8p
zQaSruh*Lczh|XhF6EC``a4a8(s=+yxrRr+pb2vNGB%6FEWEjD3*pzA=&{0BKF8^fW
z-a3>-sb^-XpWbOC;nCp~S?GIQ74_Q_`j2R2hGU~cyAU5^boIUr%)NkbL*KH&WsRf_
zN=N%eq!ax?4p55>!Zxw%Q%(jOg%xI~wpxeP%2>g1SN9Pm#I+58hR>N*N=#84Ju$u{
zS6LHng4d=0OwufcvYC6m025*AC1V5DWA8)fm~9a{!oi9#Ys0)AC*-Rde4%8IsJ-o~
zI2mE+N2)$fOuB%vf{o)s-fTdD5L}?dYtzRSMz36R?R!ziaR^K+8nX_FJJIxs9O7OZ
z;-2L=BpVhIvY_(X0!OAQ^}MZ;jgl>gbOZ`ge&mmap%g^@eas$KV_*5wUKmrzWi*V`
zTt$@?sA2gt9p_!3;VFlp4g&=)Cq6e4``M*~3qOYY%hp`>af}j08#`FSXo6_Grzhy4
zor@njBqWeivq~+(vYvu{7~x-6yMAMY18-2zy3DpZTkWe_WMvq!NGl`f;4*-E+Eu|C
zE6yDAbt1DKmGSgAnB2cws+AQSAtN}Oz7g#Ulxb0{#IN*QH>4Y+ogvexIMG$mIvV3C
z$EGz4>1bt#Nv~Q$II2toA{;A(!xWE0Pvnom_A|C-h&XHYR`^EbW9f-03!?2pM2#aR
z2+S+A1JouQONl22&np!q<Oa|AfVJ!|dCyObcVKLvK*#wq@B2aHTvZFzxZ#%l%thow
z5<G&5tQ&DpX0wEPwxY-~Y|ss$N#}U5o4QsC3n$Q=s6q3nn9N}#pky>%;@4yPDk857
zc|$a1i<aTVJSeMHO~L0(vc`l#^2~vqWOTL2$`_L}tS_J{8a}ETayVr2;hut88aevj
zN9;(%hN&7VIi=l74v?U{j&sN(jo&3-Qxex@XJovt0|nJMZG%Os?aQ4)k0ihWa%O72
z5I16@H57gKB1F4VPx>ft4_&l*j7APJ!@OC#uW)>!&Z12j`p+MwlFYC8KKxE3SV5V!
zzSBbP^{j|SRQ+`XRx8@jxH(k~EyBE11j|tH(?yo?UjADsc=QR-5q<D05E=;h)uo&o
z4H)chU2GQe%%Izo?kt^`!#e@V?YvO74Rf+S@I2|0MP7ktD64V^q$x2hdD6_>P{kc9
z+X6d4S*~!^FDNn5muxR={k7;ErJ9AB#U(N78VeYaI)V4?6a4H$DR(tb*6#a+M|Ge=
zhDo^EstYy{yj)?~#u1M~K7q}Qr;(HrN|dC<o|+1nj(O$j!AmUY(^O?Eiv6Ow5!O(J
z>LC^K-Fg_R54H<?>;ByvL5KL+AK<>!o&2}BbWq!CiWyJd6H}dI?L!mDDOv7&JCtb=
z0XO18X9acy#(jKO9*_d<&Dr~%`bDlW`AL%xKLe#(8a)jVyxmq_^1N}cd768>95X$%
ze#(R=Krq+d$zupgALZrJ-Pm$#3qz=<ns4M2_?$dvMPHW*>aO0bu%2U-SlU^NycZZW
zQ`F%f{&<^PeL#R+siF%3NM$q(V0}(o#29QE2>ppxk^!mN(C5U#jpUV$RB+IPs-y{@
ztrC4k3<Uow0!CM;35WY+Fr11H0>f>-m71DUB58|jjz#MVlpi8FDc~S6RiC~r;M|eg
zr3SB^#(x|LiK1XrL|>7daB^=kURrcypHX~-z7&h{KAAqBS^=$L<<21IDMP`^qCRqs
zJ{=SG8tfz#!0th<etOXX{%e$DbaHDWDnHC&LDP#=d?R8@k=0`O?!Qb$K%W4^=3&WW
zwe4n}S*a^AYgo&;yIVB%6eY|nUJN{7XsM|fAK^SlQdJElYA-+*U5+|V{96a+N+v@g
z^Ga~d$?-`tt3t0$&YOG&b=nG^Ed07IE~Uc5#n_y(uW5yG>OvE=`bT0(qw{(;Ks1H_
zZ2dO+Mg6)uY}eeo0Kyc+g>Z2n)FJ$ubM;<LS^J<o4Mxa@(Upbn*<V;4>cw><thrfu
zWBDDf$PqIYY@+KE^@%VD6(9~wu+7`7&c49iu_eR-`8#%|xp!!2H#mdJYcN+|*p4NZ
zTsKb11p715*A?&-{DoXWB`&b*>k8iU;LUP&6+I-LC!Se>*e?I~S*NJdCWynq1E2GU
zd2Ff)0yg!ZhtgN&eyJ+m^N!<3k1&d?L^pI<Sl9Vk3X2~$@FR#UZgk2KT%r$?hN;u|
z_K=yx_q;r~h$iu`At7Et&I9etE_5FPshv$!rxqr%BlUh#))?Q0k>+z+?9LaiV~x9k
z<j>3^tzDO>0b0T6USv^^-gw*}6M5P3t*Wx9%Y~+)rE*QeFp@U3hvf-^7g8Oe=mXIL
z#CgrsPO{?DCg(My^|*oftj@p3%s|RG@1JU!7TJ>o&`+VCgb878W!uR6&UQ>(_qZRV
zgA=iW(Mv^E<qw%8-JcFXW6k3#q=cg<heg6G<hLp`7^{8k>z}7zn^L`MSQnP=Tv{|7
zUqHMaL8?geb&b~B6_qU1aKqcO1CQb;>7@Q<bMt(hEdN~UF)$1Sh9};8d@UR_^~&!=
zIGs7|34BgUA7$~&zUqzV&m0IOe9(+q8bO1>-rH_r6To-N2|F@p=1A9aO0Q2R(@un^
zS2c!FbMMd=fb!TjzEnANWx_o1C2*`a*g6}Zcpde2D>@xxH!{xUWhIeZxJbSXRxo(R
zT%lblZ;9S!xxIFe?2{wwr)}WCzQK->C?u0kaa+Xsk(sOZV)^z#3uLp1fcTdQZxb62
z1|JR|ju7qwmFMFU==p-#CgeOCL(7EI$~uk4HtXk$7G$tmTs=MZ@|XyqgmFH80`^7S
zuSJNH+P3;|o+edS>@i%gC#mnp&rmo%kG94>DkbZ<F5t77vEb2aW{KP;US8L^VlxzX
z#&>RAjrB{lU2H-alFxJttyDC-xuu47?*jJ-Y`-2!m(`2MSIHdzoJp&R(Kp$SYk~J;
zkwcwgGtcUq+vjL)Wt?b`vhFT-8PMIe1e{nkILY-m0#kmz;b)~y#~WPb-!8UO!s=is
z7|{^iA!gJM#+3Ef{hBuR>YnDm9q)V>Wa+KdBv)76)8g1g12HSD^{h$bL;?b?(9s_b
ziAp5I5n`Ii@>YIhObe;iXM&e%n#MNRlZ)six<6PFd1EsTh5nVkiO1~R>2(m*If)Q@
zO?z%wSb)hCRgj*S^8B+n;t;q;ESWEF@k^<=+)mT2{gKVT3w()b?ITzZtz<V1hbRCZ
zmxZJVA5JOU98#SkVX}OuXoC3141A1K)Pa1l0(e^=_c6J_bZeUp0K6TlJbR8FpA(oj
zP9kD}(KqJ7?7iStwD$=0wcS7u>1TgAC6aA2CzjU1*}>V4Q@x@73Z`r}E9Rt_VyS?N
z#%@1jpoRSDK65-}fL@AyIbhWIC}FKL!}FHIbisLsBh~3pulR#8SWckl-(2x?{|AkQ
z9L!w*c*F1Yjgg=KiDtv=iFQonN5&A)!YmF+&Ke;4u0=)YhvO@5=!cA1=r!k7)V_4z
zVTuLtVJPcQp~6K~tLm>YBxYeqC<(>kJRS^y`a=rH3AFXhq`F+sSYP+J<i6T`GcGL>
zk27gG$P;juBjn4)bvZ8S375K*ou?5CW}`lh$(JV|Ey(j{H*K#kKSx+{mW-Qi5bVi+
zL*ZK|`N@+MP+qB2Niw43?<lI9kbD3`0WQnLXg0xs)jM32^VT>6-@(t%uX;WKuhevI
z&^EjyNEU0h-Zv*8f$`igLU2vu7h8MA<Og*NCvh*!yBtuyEE+&OjKS?Pb$C~(L~VvD
zD8bKb2SW>q?z-umiI;t#!+mU~59AOV8VHug9y`WNzOhYW2f<2V*bQerMHI?f>=SzO
zjDhk<qL&lkf2OFIjf_rQs60q1i`a%&0{J@4gjN$@1CKyKE*sI~g8?>|S0v~!`n83_
z$z;$yL#N<bgshR$5~YaIDKh%-`vF>@umAIC6Q1BN{FFUX`6-1qvKEH?puSZ8of&b^
z8P9<wW98q+cV@F;>ixL8ANF4z03v~2R^TTxv82S7!%V%z#L~*^p2d0Tv%7{&Wh<NT
zncvT)9gG#6x>*SGzRf50>zKt&ZvayPh%FjI5fj!b?al_|k~4!>hh@y7Xu!7&5Bil-
z!bpr>*SyP3o-i9qg;$;*t}gv7aVeFthvXA^sr;L=vR0()1$g?j$zcdAZTs`M-yJzJ
zF8TW{utqsu29`HDYcUX4*vri5bi}Q4Cw9?Q&q~XD-B!z$ui9emPKzgl_Zruy9<uvi
zvzPNfAzpEFvHf%Qo+;p|qGfBEYZ?snhD~xfw|FyQgu|8=$@N2os-EUcv-cZnot<B9
zY*?6@1+}=e(iQrGgRJRhjS1w`jM@64oq`gY%cQBv$y(u*g+3r(5bCpe*XU*R%pBv_
zzfP?0yVy<cJ@Fo`_puBNz>WQVB3R#gR9bW3)q0k96O8_WDCqB|c&%9_o*&2D$c@!E
zfwRB?iq-=7^*m?5+PqslUbxj*)nvCi$kgbCj=`~EV$g>XHlB|b)}+RgZUZ-#z=Uup
z?40@$(m``;P@OFy!ZdF8wBXYL0yml?27B9}Z|3Fo0daZHM@_4!3KhikoK0jYp@H6x
z6XcyE<y``T=a=&ms5T8pUUEw^aWgB>edmWL4dr)19FaJ5dP))-LP=#opb5YDQj!1#
z+RRWALa^>@#g_vOOh5$w3MLKA>-n?Ck8S}3yvzWY_!R;KL^v^-&V&wB_%<|+P|3{_
zaTo<6j7Axd;!dR+9&tS;p!$<3Ms@n{lLZozC~ewMH>FHSqpuHMPOSGzaUk(T*F|4D
z5wVW}WTRs{(ldBLh-bp75{(!-hxBv|5%`yKH8>62ZJAzxx&Klc%)u?J8t$+(+7~ud
zHI<<wsrqKftGZq{R2Xn{15)=Y)b*42x-L?XfelIcG5fIW-#<mmuO_&2Jsf}4M<ca2
zUd+8e1|$nvyVi9Q<>VtL6g=~<ny>wJ^R7NmpWe8S&PX#)vMg8>l3)cljwkFdGL^sv
zUPG*6;Nr4=>6z*?JTBI~(pTv~<}xBi(x&Mx$oC^B%g~v`8T-<;C-E@t?Kr1Kv<%^S
z753=buCrm@xqEU*N6+wRmtK8-E~QVdqx+Xji*61xnkpMf?Mjoodz6iImwMl230|h3
z?j^C9#f{8Q7trQgrDtTD?S?9o8i!WsbbdaJT7I6lj1kB088TJH?l~W=S+>$1=@MO`
zd3Pe$g{ej7XW8za;z|~jjr3pNAKznV@)x%y-N${BEv;koE-!>UN(JlCJHgB88eZYc
zPO8Mu{W!S^RCwUFHFkFmGd_RmyoY{bMhW4YkzQ+Fbr>meOU`^z6YNkk_B6X!qwWZP
zi$)2p|85|}gMlZt54hHm=xsy5bN5L>cdj!;uWFf!Ph1Jd6!^sru(?@%A6j7?hBUr4
zrpWTX+t*Yg(o{-GAy7Dy7_xV8zQfdbSfQ-tH=X!#xEI|{Q>|M~;lA?belqYxF&B8l
zZO9mH8fheoxX+OuzK^20r8FHKhvH(P*gEq^qF0e;pDLYRhc<m8N}dbP>Lg-M-fRD|
z3uwQj+xg$D75|BwAu}t}KP?uSN?z8eCRkl-=G4}yt+lS-+Zy~#gbyeiR?igh3Iq!k
zRC;*M{obmsbK9BwQiIXPVzf=YS6d_PIgIww8HAA&E>nn~YzP)v7;xZq;Z1ly*pK%R
z0Mb<snJXvSz226fx7*g!6JOWXUHg?k39Bg)RoJfTLOG3^^{*N?qs&dAZ_edNg_kqC
zSq|E*(+RYwJ4)gq21y);!t8;}GTg;PtVf_*i@Jl^rQ=rV&k3;@zAGab9zw+!PZjZf
zYHSrkOwuwwFfkQ#Mt9^6-%Ty0PFIUmYc@cSUmz{EN4S6eDp!s1%3qW2WspzPZW=%G
zN;X&VO*T|CBRewlD!g_nB%eCuZV@FgjTT+KE_WSts&cI+Y!zg-9j2c`S8PfJ&=urz
zU=t{JzJY(f5ktT!SgW%3WIyT-a{Ry{x>uo5sVR|dQ4Q2X0=wn8aHNtFpL~pc7YuT^
zd*QesV(LTf)6hL$HhZV>YIw0y?GmsxrKf7}tn6c)5r(5)RTH1A_N^i0Z%m|8AtJ=0
z*jOowXD2|TSYy~=xq2t41;jy8q!#6|wT$&NM=yzrWq$u9v++9aMB;)f?=dKXa@JCJ
zOAP+-1vuH%td%C6{4(V^^%Wec1v8-rJVSn6sT;$*v$E;a|11h(`*=bT{<**tAY|Sj
z)qK#S5uieH77H)QpQk2HikK=iHeG$YdW(s^A@320u=B{5+iaYE+@_<`%2#q*x;8I-
z9Qr$U5o&P)53aEHM`k<W)zz^}9}rxhJ{Oe{6&?&^Z|2VSor^_9ei5zyhA{YnE3;r)
z0P?dHQgk#<UQr2vGNXHE5^AdtK$}_{7vZ{hiM}B9Aa`OcuJX`Nk^gM0;Hk1u;my#@
zR%Tz`mRdS3*<6j<xLd=zjAjWqvBgy*+u5WpJqNA-N|XFGg4u<7C$n<V(7(n&+ut-D
zpMA33dHm`6=m9J<@c4AGSFJ$6jWA_dG+gU`fs-;;&DJ%ev6VvWvyi49Wz$jcftE$^
z@lr-H6lNu@j2V!>65HcjyRTa^QltaV^IT;klj(0!=e~7g5mun=EnJ2=-GQQ(_B+yy
zA~>HPI^+&ITLN;6<&Xo{;SNRDTF<cAg(?Xnob@P5?|FZy+)OY4r*nyMs2(gG6yAv&
z_ps@FTfcm;$76OC*_G-WwYo*LSfA<4+aVC*;(W2|#6++|T6<@RA!BTm&s2#0c<F2b
zUlTWNM`8`9*|b~Jj1~_6NJd;e0v1PL!^+01K0jgt;h-rDC#MNsdM;bb_M{}0%EhYH
zN?Stn;!ZYAxW8jTHQDZYAO0jLNOag!p2pZ>q^8-@SZW(Sb7=YZ^eGHngKc3BF+R=F
z$1XlG^e{|oFpyd&8sYXv2kA=YdCidt$v)5%*S<@?^Wk!!iX0d=>3EbZ;Mo<gi>l)n
z58334vgbQw3APkZ)9JRoa#hYFJLbkVv&c7QUJg{cVbfu^(yDw4GdW}QT?Ixb_pvJI
zrvw+)<vAkUxnwW2D;Y0`lUzpNO=6oK76+l<GZLOcq?rO<-@AoXe%~C-A4Kb`JLu1P
zm%qGgw~)@){qrp!eMU%xJ|jZwb(BF5dRLOq`Q9PvIU`zi!+)XAUL{rIPM$ZsB@xqe
zShwxAu1S(^J6wd8<Aa{T@K}lt@7PmHO|{uqx~UL0HK;D}2f}{&%@c)agEu%k3w+&o
z^lAp10hF1Llhy4iYJNsczQ6+YIh6dH#r8k(D`DsQCsMjtl#Zmb0p8%2i<#c);k&s?
zg4q#8O37VG2)5>ao>C1W=7Nb;)_REzOLV3M11pa5r>m%xjUn$PhYKnxRS3gtJM=9_
zD-)xi0M+p+;P6Z=ooeHK+im;h=BFVWigf9QOX1McMfua?BYSvZ9)0)xgz-+Nl%VAO
z50z^6W4_`m)<qPNPqoD74aME1Q4~Z>11@d3hy<-*iDJ>(1=2A3&EsuJIyR&PaF-l;
z^UGs%J0va9+HXuql3}ioAj$pfK@QsE*uZa@YcWt_DFWp6A-}(TuJxr-ifGY-eEXq;
z5K&9)P|}hJFkZ7UXl?Wlu*Lbvnp{VSGQE#xQyd*L*V{F80|A@nU1;G*BHWynl$itJ
zCIuPGPztgTBB8OrNEu55ozLRYU%iW}tY6t)kMM)f&V5l;QV4WRRW-a&89_KeocRYB
z9Q7CS#Zn*^c#@Uk-@(|jTe>Z)So*m3a`1UDQ$E;Pz4W6Zv)pPXCrccvzHHl-69j)O
zKxbd&exRgH=I3ARfoa5-s?37NGbLibTzHRZ`AT)muj?0Uke7GzA}D`%UrrX7zPLR<
zp71ZWi}aD$*VtB4@d^C(o4R`z$#12eI*H^u?N@c+0ZqdQ4hXL_oXiM{$P?o0slyx7
zMEUbG0j@wtL-r7?pB<jXqO+tI)a`!%Bwd75{dyPkXtv7aEZNPhVSg6zcWe^>E5+(8
z*v)!LojGHnV+>0(Wg>0mL2NAkwt;^om}4!?wC?U;(0M{F2-o8aIR5SH=-;F!$A6!A
z#VxEIP3#%PtqmMaL`{tBj7=D2O>E5^&52l8|H(Q>=ck-i9}`^1oBHuGb5K0QC;~B)
z5{Pf(+i%ddncBqh>1FXh2ro}E>(hM*D90!KQVQ)A(GBdt&yd{YOhRTA6nGn=Fu4Pp
z8cCPJxaf^ErtFeau5L-6B#>aAUS*8g6Ao?|f)=;4zsm1{aN2kecsv3=)RrWeqk)50
zoe^#`fUm1fcCpNZHqxEx1uB<$Ey#>Kme~tKLQ>khc6>O5dc#PfWUe@=i~rm!5lgL+
zj>Q-Fw3opIsePT<gf%L6#c5>WW0=O7RL8dEmRmkN)f7|Ynb>)XIMy?8Vh{puC#%b_
z05dH%R{thmT>l-f|FsB^otya|&sgNq6AE*`v4#L$l|mbXNQqo*&VmeJB-r4nBXBq5
z%6SlyXp;T@MIwC|O-?6e6GACP*4BL1LXfz7<Ucxw-DdjzDN(;b9xS<VQSEFvgD|-G
zvX98OuHrqJ^QZ!tods~(Ip6iq|Js=ww+HjL-UlN}&;lYWc`}7tanJhmnlvBo(CPr$
zuSYe%M4<G{1$I}8NLLloMK37myv%-r4mu~ddD%EbqlyV*fGT@ak1PC95`vQO|25BO
zAMXpzF#K1Yk8bS@m88}P{v_p?j9=GI5A(!Cld%PTS4<g`;z!2*(T}1nwpe5Xl5QF1
z*_o^4g5icsTg@c?UsR{a{iSD_I-&@Y-nTK)x4Salad1!up>PhwSs4L$8En}|VeK)l
zv2f7w?N}V78f(+4Xw1Rj8NC*wnn>bkFnRpz6~!5e>Z^{}fXd~HK?R#ulZ0?S<-;sE
zD*qbEA=ntBMg!`)_PfieCOxf<qh5OsP@0lZYxk2A=Y|{D>W5;O@D+=kM6Ujg7+u_V
z*Cw>r?XUZcs)_j_L!vR>nmuO`_GWDh8iR>SMh*D%9h(t!Vo*&jIqPwjKa%8!W{889
z?8z*!7LqezI9vKKF*>T@^>fdExLUQbsdught5oeOKWs}k7qyR?;$r+3#Uy*J3dfGp
zJdCQSc~a#{#%ZiE?^+fTCp-m;BMI04l(N#+K%@^LJ`5$MGc-Vr#pEs}K5*rCdn%>o
zX3jhfMm9Yxgug%xia#Xc6{`f>j2JMkjXvHc^%<z;Hv%pNm*h_T{xwCpnErzlWnts~
z#}qX();BQFH#SCtdT@fjcbZ3VhJ|Kkt6>8VO?{6seg1|Q|44cX<{G;S*aDbnYyfl(
z6HWB>kCDM*GvA5+ssf2x`h*0Fz>P;l(Al>_xDQUgw15|qmx{r3zEGEnxxo-8f|nkM
zr3xXlVMFBToC-}~!^uWO$O3zA2_|`MX)@gNzje-8>_BdL09ZnjT;|mrlO3S+jvvyt
z7rx=Oi%Bh29$qeIhnwhly@(We#{$!U95chGf=em9_!V9ZwMm+@OQ0SGgw8<=!HgnL
zmz2TBHcyf^Jj^>b4}wW4zF+)&>sKw0_G93ti3KY4xF49nK&Eh74*wcV=6~3_|BD{Z
zs9<QR>}bO%PsGXk%@MbEa3o@9=l&mv;w=BqnQ21PMtN-z<8xE5c8+l%zGwAm!I>u&
z7?j>d5-KJgGYBzEF`}qZjDF_uacS$Q!dN@W*mCw>p4oM4@+^O&?g%Ou+pL#0moGj}
zDtwS>Vy1r_YBE7TNv4R+;TQ8k${fMjxLb?`hE-3!L2Z*P^<kpH^8*H4lG_w^5;!Ka
zGD;<cV#J~_l8T|P#W*Z<C`{!aI5Lrbq(~>|K!`=DXbi_uO;e%bN`DJkEGYIM=(B0R
zDc==y3PY-y={<vbXIXn_FLOFqx_A*wxamP8)IO_Ypg<Ko&<a{gEV?+H^B^rmo)G3~
zF(?BZFa&8jENCEm^+7BvSH$QFaFJiEu)Jt=f4YHT;hZfGn1Xb0Cb^!ih=zIqL(D>?
zKorA<RDHpHF<6_hAw);emKuddOnqiKKot>p0VEND#2U5h$nH$AQy}tM;GFuIzAnav
z;NAp-fk%TC!49s|2+gc~%#_d`?vqG6;0_SWL+1DtfM;T!kt1?;SLbCO%o*tJY91Jh
zAOyZj9_IO=sNmjdUy}ErD&q7XQ*jN)`~ZSX#@ibcEnSHt3CbDO*Omx*vk@|U)d_=z
zj7>hkr(8C*4s4D{b5drs#zP@S1sdnKKeR?#lhV47fl1#t8YUO8mLwru68V~j(B#s;
zl+f|FE%H=3v3=N7Q}fr=O-~lqx+r73CXP6bPc(M`v!m9$eNFM=ynT)zdx~;nuomLu
zc$yZXitBlba%b==!s9M`1OD-En#Xk^b&3Jd4Yrfy^TuYi9v7IQd2?NMcPVFltUQ-1
z!GIK_=V=-aK#IIwXA6e2(L@XJu6GB8n}gh-;qkboO}xS=vFT|8ywl*okAP|K*g<&Y
z<D~C4Md_AYhmTWH>)_vZEr*=XVC&kaph6NBiWrX6fg8fC>YChgPyI3Y>sySsQ!8}T
zb)LF4EM^BfqXMRPo=N0$!j3?@W5+}9G|^Et=;n6h;f}hqP9ivZh7L1{^Ko>+=On3H
zhpdkhEFTTz;?4sZjI&!OLE7PHHnEme%T(DL{?X$ZN8y?*CQX`(41c2$jlE?eZA(h~
z7xGlq>y3xvrDjm{TS5FX#ht-xWHeY!#8}$0iz<dcWy7yDTfRcv!V0pD!}BV@#Tnxa
zEp*sM33HtE9(PoFba?aE8Z#!ZMFlAc4a8r0HP&=niqu<)M;gf^Z`Xhx$TEw=3uITC
zPK<9ajADj#RuTs?D_v5i2v?I<AV`XQLq4oTdD;%19a^!>#6}%up!H}=pR@}_ITMc)
zQJOG*u?|=jwdyg4hMkho-v$qG(Z?hPE|h<579(*=sdqQ0S?SL1wO~wWEb2dLFYjS^
ztdB7q*q$q~EvwIqT_jjz<BQm!Giss_wT4aD+RAK95!e|#(K|1VRrTARhY7COXtrGt
z?$tloph?nJq*h3v8#yLznvNKJzNE?4NdAebSlks4XQRFw*bLvN@f!LZ9_4k0gbx>c
z4c(yJ)5|p%N`AZO<I*|rkO>V#<o7<$F&s?g>s8#~ebvny7UJJgWF;a63?8^-060tT
z+glZmAdN_>7|Y@-+M&HOcE%;_!tNJ-a8Dfmrv25c5`eT~-zb2Ycq9`8f{|Y!Tl5lO
z`)kaC@;wuA0e^mQhbOrN70n(Q{T6vlF@*?aHbe^tce3fT(u1X6Ay4n~B!-e}xJj}G
zPntj2FW?oTXNXuiM723Gl*SqqQONL|9WxA8r-8~A&be#AmqIgPMg)HWL#`NSkZ3X9
zQmH^OCx*6QI%hq$gq18&8%tTRC=K#XJYEDVNLog*C)B8bY=XMXTOZB!w~$;&+CuOz
zPnh!@3F+LHa{v|lww&Y=5u>0?*a4*vEZMIR8MTnY`ehe3q=~e=s~@c(mhA5AMc1j~
z4-lmu%&LwL`4t6m^1B5K2hy_3jM;GQkU>AnN>6ylIR?M7?KXLbzT!+BOae8k={~`9
zY+CfD$^p+F4{UEW%N%oq)#0ixCT&wXscpIQZBN+|dxG@<Un}c*IrL+>iZl+bv}(U?
z)Lt(Ix{}>xQ)|s^CG?>m{S()6%!BYQGg#l2)q5FrfICQGi;|?ZC(}f`X>H%*+Z`h*
z&@|+kwe!Yy+T#apr*u+f{0(wgGM0prpsHyBo~-D*HEV&~CUsHQ6f}#NM}zyjnU$M5
zPE5Ng#X+(r%}>KGl5BEe%+aA6k~RrL5ysuc(8t-N2cD|0x9nMK`(V-GvEv*pWt2eL
zc<JfsEeUA+OcIIVLC8C3OEdzVq?2>O=DqxQ3l?8Q;X`6_$+upu-Ji`;vBmC{w!OPj
z9CFl->XU|kaS4?I^%Wa_8P>$bj-Wi$O+Q>7Z5`b|4U{ho1UfE+ik*WHkScTb;E!e3
zVKZaO5P4M+-@?LaU~fjP^@}-b*ADw_mrppH8$&ZS4IqPFdfJE8_RvGBw+Mbk=?b>-
zwX~wFrz1GKo1F(}83xa{`evSQIR`xnef!T}(7JP19ee%V#hp%-JJu??3QDmdHBAc%
z#YggRa&lyHhixeS2uZDuT<)CyQMFdXz1clM?Fa8x{kIV`eJ!BU<jb~ZfPOi^W;dt@
zCTs658S^OL>}VwWMN?_hX}h+qw=a3S0+HluL+T!mhZj0fCPa_9N@N}m*XLPINDJc%
zEW~Qaqh}9-C=YnJt?mi)3ZNOfRsHLDd$$vZ;mzCJ(!AnigFYbAVLX5Ow<T$Po0|C*
zr#ENFs_zCnvWV*j&NATr<aqnqkd-2C7s2!<c|BB`Ju0nkEyurse|ko+3uz7hgEBTu
zu@+dDw$bj+i1T^1R_Bi0Q>|jA)${G{;N+lgW+iYNKj8gReTxUntwUT=f7<1^V$1nh
zma0o}Bt4R8Uu&k#;&IRC*MZ&PN{Vi1D~&rNvi*fdRhq<4j;QVMx#a3Cp{0X22JtKa
zu(<Y=F00zvrUpHKBldtL@$ps_?uu`?_E5FOv+deolkknz&xEZVZx09O_t$fMEAj1t
zE=tmn7m8o>bF`!*wUxSrCfNdo;f@0G;a1pQqfmKU1h*_YF|hac*8uu;kH>#4u((<N
zyITFf1(t@Ai7BI!ot-1me?4a@F-qB*+7bQNr0=1qHXk>u5f?i%8#^aE6T2Z3GbcB<
zp%Ir62Q$<6z|@qLQ{ewULx)k>!rcUho0U;roB8|GEIR*V{l@Z-g)#Z`7)viOR?yAW
z2d|1Gaj+x}_hE(+0`$Nld;JvzT_ip&uPI#x9BYJ$`Zi9*N^{u#c+<<o-DT&?c*Oz?
zohd96qH+!NS`92YR}d<fKPZ<HZoVwxmM(S)_4~GRjOzQS8PJ?`+7!Ytt;<mzs;LT;
zr?W7edoJ!P5e#uiA5cIp;kUB~$uuamWFHw^9~7AZ+8`159uu%bjK8La%FU(%?eXo9
zmDEGf(%&jThuZz~K|@(FZZ$hUz`gQ}e8YR$YVIg#^Rqq7z3ZXXhyEM1wH*9|>Ib-d
qF3gc-()cHE{0_<bzy2l%M+19DS9=pv7&cBeW-b_Vaxn#QnEwO(UN;Z`

literal 0
HcmV?d00001

diff --git a/exercises/Exercise5/Exercise_5.ipynb b/exercises/Exercise5/Exercise_5.ipynb
new file mode 100644
index 0000000..f0901bf
--- /dev/null
+++ b/exercises/Exercise5/Exercise_5.ipynb
@@ -0,0 +1,406 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Solution Exercise 5\n",
+    "This week, we are working on least squares fits and parameter estimation"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from __future__ import print_function\n",
+    "import numpy as np\n",
+    "%matplotlib inline\n",
+    "import matplotlib.pyplot as plt\n",
+    "from scipy.optimize import curve_fit\n",
+    "from scipy.stats import norm, chi2, lognorm"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Fit a polynomial\n",
+    "We start by fitting a polynomial to a given data set, in particular, a parabola. Compare a linear fit and a cubic fit to our parabolic fit and check the goodness of fits with chi squared distributions. Explore how the different uncertainties affect the outcome and uncertainties of the fit. \n",
+    "\n",
+    "Hint: You can consider a plot similar to the lecture notes week 5 page 29. and/or plot the residuals for the different cases.\n",
+    "\n",
+    "Extra: Do you see any way to decide wether the data is better described by the parabola or the cubic?\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Create some data distribuited as parabola with normally distributed errors.\n",
+    "def parabola(x, a, b, c):\n",
+    "    return a*x**2 + b*x + c\n",
+    "def error(x, sigma):\n",
+    "    return norm.rvs(0.0, sigma, x.size) \n",
+    "a = -0.1\n",
+    "b = 0\n",
+    "c = 1\n",
+    "sigma_y = 0.0015\n",
+    "\n",
+    "x = np.linspace(0, 1, 21)\n",
+    "y_true = parabola(x, a, b, c)\n",
+    "delta_y = error(x, sigma_y)\n",
+    "y = y_true + delta_y\n",
+    "y_error = sigma_y * np.ones(x.size)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def fit_polynomial(x, y, degree, weight):\n",
+    "    pass"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x2bbf82dc240>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Plot the fit results and their residuals\n",
+    "f, ax = plt.subplots(1, 2, figsize=(10, 5))\n",
+    "ax[0].set_title('fits')\n",
+    "ax[0].errorbar(x, y, yerr=y_error, fmt='k.', label='data')\n",
+    "ax[0].plot(x, y_true, 'k-', label='true')\n",
+    "ax[0].legend()\n",
+    "\n",
+    "\n",
+    "ax[1].set_title('residuals')\n",
+    "ax[1].plot(y_true - y, 'k.', label='data')\n",
+    "f.tight_layout()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Draw the plot form lecture notes week 5, page 29 and plot your chi-squares for the different fits.\n",
+    "pass"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# ..."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Fit a nonlinear function\n",
+    "Next, we consider a Gaussian as an example of a nonlinear function. We are measuring some feature which has a Gaussian distribution in $x$. This could be an inhomogeneous spectral line for $x=E$ the energy of emitted photons. We are interested in the resonance frequency and the linewidth, i. e. we want to estimate them form our observations.   \n",
+    "\n",
+    "Finally, create your own data set and vary the sample size!"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 45,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def gaussian_parent(x, mu, sigma):\n",
+    "    return norm.pdf(x, mu, sigma)    \n",
+    "\n",
+    "def gaussian_sample(mu, sigma, sample_size):\n",
+    "    return norm.rvs(mu, sigma, sample_size)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 46,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Load data from disk. Format (3,12): (x, y, y_error) x N \n",
+    "data = np.loadtxt('data')\n",
+    "x = data[0, :]\n",
+    "y = data[1, :]\n",
+    "y_error = data[2, :]\n",
+    "# The sample used to generate\n",
+    "sample = np.loadtxt('sample')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 47,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Function we want to fit to our data set\n",
+    "def model_function(x, *args):\n",
+    "    pass"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Plot the result"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 48,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x2bbf99d2860>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "x_arr = np.linspace(1500, 1600, 101)\n",
+    "\n",
+    "plt.figure(figsize=(5, 4))\n",
+    "plt.xlabel(r'$E$ (meV)')\n",
+    "plt.ylabel('count rate')\n",
+    "plt.errorbar(x, y, yerr=y_errors, fmt='.', ms=7, capsize=3, label='sample')\n",
+    "plt.legend()\n",
+    "plt.tight_layout()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Biased estimator example\n",
+    "Take a lognormal sample and try to estimate its mean. Try it by fitting a Gaussian - what do you observe?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 44,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x2bbf97e9320>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "log_sample = lognorm.rvs(s=0.5, loc=3, scale=1, size=1000)\n",
+    "\n",
+    "plt.figure(figsize=(5, 4))\n",
+    "h = plt.hist(log_sample, bins=32, rwidth=0.85, normed=True)\n",
+    "x_arr = np.linspace(2, 5, 51)\n",
+    "plt.tight_layout()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Bonus: Errors in x and y\n",
+    "So far, we considered uncertainties only on y. Consider the following data set with errors in both x and y. Try to fit a line to the data below, taking into account both errors. Compare with fits neglecting the x errors or both.  \n",
+    "  \n",
+    "Hint: A detailed solution is already on the moodle. You may chose if you want to try to write your own solution, implement a known solution (see references in solution notebook) or just try it with the scipy package ODR (orthogonal distance regression). \n",
+    "https://docs.scipy.org/doc/scipy/reference/odr.html\n",
+    "  \n",
+    "The solution contains a python implementation of York's equation, comparison with ODR and MC tests.  \n",
+    "Week 3: \"Linear Regression errors x and y\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x2fb04639160>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Test data\n",
+    "X = np.array([0.0, 0.9, 1.8, 2.6, 3.3, 4.4, 5.2, 6.1, 6.5, 7.4])\n",
+    "Y = np.array([5.9, 5.4, 4.4, 4.6, 3.5, 3.7, 2.8, 2.8, 2.4, 1.5])\n",
+    "wX = np.array([1000, 1000, 500, 800, 200, 80, 60, 20, 1.8, 1])\n",
+    "wY = np.array([1, 1.8, 4, 8, 20, 20, 70, 70, 100, 500])\n",
+    "sigma_x = 1.0/np.sqrt(wX)\n",
+    "sigma_y = 1.0/np.sqrt(wY)\n",
+    "\n",
+    "plt.figure(figsize=(4, 3))\n",
+    "plt.errorbar(X, Y, xerr=sigma_x, yerr=sigma_y, fmt='o', label='oberservations')\n",
+    "plt.legend()\n",
+    "plt.tight_layout()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "hide_input": false,
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.6.6"
+  },
+  "toc": {
+   "base_numbering": 1,
+   "nav_menu": {},
+   "number_sections": true,
+   "sideBar": true,
+   "skip_h1_title": false,
+   "title_cell": "Table of Contents",
+   "title_sidebar": "Contents",
+   "toc_cell": false,
+   "toc_position": {
+    "height": "calc(100% - 180px)",
+    "left": "10px",
+    "top": "150px",
+    "width": "225.438px"
+   },
+   "toc_section_display": true,
+   "toc_window_display": true
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/exercises/Exercise5/Exercise_5.pdf b/exercises/Exercise5/Exercise_5.pdf
new file mode 100644
index 0000000000000000000000000000000000000000..b463ebddb4ba075650069371899855d24fc8ef19
GIT binary patch
literal 67189
zcmbsR1yoi2+ck`CKtfPJQfWcDlx~oe?(UZEPC*1gK#=b4?(UNAkOl$ilCJN!_5R26
zp69&dobip}828%j#olXOF|Rr2wYbUUghlD!Ft8$%|2*2iMCK%UO=4qUj?BZuC~9Wu
zXl&0YYN_vNENpCOV`R)IWo&Kg_@3kq2PYFBAF_j^y|KO(vg?mxjqUJ6QLKlh)(!>b
z&sJ-XnFno)u$0XkwvV&T8x@p|$xznIU9g^@uIzF7cx}NF)l_blMFt^u-W->>^>S2>
zF!MNU5c8k}-R=%Y?(c2g%q3p9eu5KzomqKv_CyTpnV_kIXasTj+})19!^L0-J^{yP
zsw=-m&!&e?TYaoIqF6HvLLYir6j#I&wnwu1V<T;3;&#y=s>!to%zYa@PVJaFvD_wS
zULRP7#RrxJw6QzvHI$snrX0{lm}2;e4kvr=y!#x*f`AqDGovrGtef<S+w6?oMR*3w
zo@|m&MF)@iy_)$J_d>~5H)Vv?VDNoTQw|RUtx!zrMR^TEtuI=MWWhM8F|E+v8tKwW
z!E|bD^lXm?Vqw$C$B@~*LtXre+2`L}d(G<@GUU_FF13-nkev7&zs#<Z2uEnwtOU#m
z{q*p@I18^Wj+1B!jUKIDGq1pj6=dm`3+iO}(a=|GdSpbRv2pBH<V#B9U*TV^R&+7d
zvJ%p*_P883sUvuwfw91_PtkCdMBqyW!)Xb+I{8L_BpD)FW!wTH!<jC6ZupR(Niy1K
z#htUub1Q~5QVdt^t~vrTO$#@wg@Z@PW1Fe4k7`X_-WdvzdKofLqZv*a&tBvDeh+Pw
zJf*=NAc7%P=7vF6r(xpbdQBx7H*NusAaGM-(7YgSoP#QkixDb>EE_OS7-gB+$;RV{
zk6Y1oChN42J@xZ<USXk2@^7TFe&M9WomlM^_jeJ5Rd&OUor;8~$wfYEw8Z-|%OWX<
zPH%50tda}E>7(~T7(L5zrhZv%2L_GO)}!lbw+BG1GEmaBl(nKW?`D1?`_4B0`jkTd
z)|Yc2WZz-}g*@EveI%z@i#$p%p~-w+fiq1cRToQKp7on$l=-FZ8lQm)p%@#HB=5r7
zt_Eops)r?EHJ9*~!XhQASE(EW{`EWeWlZ$hO`${Zo&w9NZl-QzEk}c23+pP-Bz5wK
zr%Wp{)Ud8tBlZ;MA`ntCaq~a4HNb0cntid}tY~#!+L%wa(i&=jSI(wp@XgcEpZe@k
z5g9M4Ra>1>m_9{5$0%a(t}{bC+pl5srUPBYkGYi1_dA&^pZd9&b&zyQDU(jjP8jDE
zo^9+WZ;UC!A;u?TH5+(y4k{T%$mNs%LZ64JoG$24qzuvL`uI0xd#*GvM2fmJic6<1
zF}ZjO2SX|*)<v)fN@R~3j%yhy&Z7}XmsQ1EHpJH3Up&e--CEbsC*wGm&vJJpZ#JX$
z{#I1Wv}yPAHn)RuS+Y33N-T-mkMr}SM$zPL`hh`Z0d0Y|T)$&chUoJ9-u%LI-&YFx
zy)A5^y*%~vG~~G&il^Vw<LdX;LXF8HzObggzi9bR?|LH*Us@F}<W$7GCrqw#!%^)p
z#l?7CPxe_-Ck3y-&Oy1WmV&}tK5-Bw*`Epb7`8sM?&aE^UQEiGzgq@-R#S;9>Hd4%
z`l#%(-SG4D#nh7sqBk2(-)Whz@|ATcn4Tyr?PPa8b7__wT3@hlHlk_1^2U23roj0E
z;R#wY*-K4Vx3G_;sqtjb^mF9Y;#a-sIk;7AULIUf%(v3IYS{_Jx__hYzZ6nrgAbC3
z*X51SPHU-AQFWn6rjo!8=a5FzzW<brE20zi-uyz0mpGoLW^FeGzQNE!9T5&gAWby=
z9#dlAc7<pD`o;Y2xX!o0#QF563S<PKa%wg<45#ALLUj$NzJtXIaFm@Y%D(4@Xw*CL
zxF@{fF@Bf0ubzMTwW^4z3o{kAg&r*>T5QT4bX^dLwi8BtLN9jHb|G!U!-4_pi5I?3
z_LjZEsE(f0edo|<Z$;1x%}w4l`W7zD(YmQf5=*TXcAD)W%Ahhd_h*v8v?Sh_K~CQn
zGSAj)DIvnVkIdLeCf6jdR=%L!xMRWy<JQBWa=oN<jLa50gkKv|EWCJ)k$#Y`mwnk!
zY;D=AWD`@PPNXfMrnf6FnR4EeobW7B4L<EOKkZB$_L0+uqsLf^lZu|T2SsC&IU|xY
ztH)b87hi)q-XLiTT7<Te+~*L&wKt`_k&~MCcFzlSK1v^=zN9RiNfLXj6z{1uh(PuI
z2U+UY_a!(z8^(drOT@Adsk8{l&l)x#J;^xhb2+Q$HIfujIavEBOZCJl|3jHqzo1tb
z;+0N#j9_x_P95BPR{<MWLh2nNw2WswD_R&#Y{u_2<D4iiIx}d_Jslxa5hNQ=NypiP
zcR!(2OiiqX_@|C#-oJg4SH5QE6lz3<eL{6mD3&M#Nwwy(pnso?fIqk2637sjBt$w#
zFY2%OZh(~@DZvDrqy_Kw7%bbbp85mgu*~ZD@o>k=@W&ZCEVDyjZ4BHjOyq*D)_r;4
zu6k5`DaHgiSkiC<J>7aX^j^2zskDA3NX(MGq4$W!to1whnEjk}m|uqPN&0v$V}LU9
z=xqC;5<WwkAob;>hEhl-aWaF_+R@~5D?a|&-C!H!;r$^@+{9k<4%hvO(dqCC9$M0a
zFxS~vgt`c}&p$1m+|Rf?z}ZzTP$3&z8~rnG!4Y&ozhU{$q*ijXHD*+kH83|ebYxU?
zGH`^x7PrzjHD**bGXevf{S6c2J7Y7`_l_jYtW1nTHkLN_injWO#*D(o&Sr+jV)puO
zjDlv44syo!LN->mHrB@0j>sI~UFg`C)3>*B0EFQGJb{Gc?<YBsyn!A#Sy?;$`6fAg
zV<R&|M;m(*z!Lty(+Ff1CJy%hrV;y#9%@R)L~X||f-Ef0BowinCY7X!81b13-P);M
zRrOkaUpPAn>MzJnjLXg=|COnrgE1jNO!{VAika1sc}z+xUAXwH0u86VPDe$g{@pv_
z!C`nAKhrkOw4<Gi3$cTrNe-KycRaadWn0*O`p-Z8*FVvq;Y2Q$gj!$JTCX2T<|mCC
zGeKga%D422y{W$xhptE%2XAFtU1b#(;@gj{;=5!GY&|`4bl!v=PVV6!X&#5A;+nEj
zp(pb!?>pYDZ?oh_eD}05+6uF|81eDQKB{(}GUdRn)x5}!VyTs%E)?DTD7^fOVTM0Z
z$H<PLs>*3;Uh;d6+j(zq5jXdY2HVwQIMI>JOYtF&VYkhl5Bu^dpTD`xH)s%0QU>BX
zTdNZAZzk<rjj+<vg)u&nwYPDJvSeMoY@0bWdX|+#FcQvcX-y{Vnv1TfTDcy>&weFh
zNhMF9xx{YO_Bv#p8HIiGS+NO$OVWdqs1dBVd5_(%lvVtb09y&<3MW?X^Q(+_^bk$j
zFmCz_GquBa{2}W)4mS@TDok?)+pcZB)2&;J7h1PMt)p3@1hVh;np+IFQ6y(fUJMtH
z56!HHOJ}<8d&_ZbJe6MXpgZ|dwbD}4y2U9Qzm#A(<+$fmBvSIc`sU=@<AWJ91);EH
zR2vQT$?lD^Vhe}UJG=h&xt2!fFXr~ESEVMcL~)ne9e%^}jG6?gZhS9YZt<<Yt4Q~f
zI_FJf+Q75049|&1CfL(yFyPOUSXmo1RTpj?y6vyKQ{>i%Vi@9bH?tZG8az~|^tO0+
z?6?NDQ`7H}%>6s@aX+48KCY5dVV0Q5AD^6J5%aXw%9T*W?d8Z}2($ZQhyV^)oc<p4
zyzw_AP)umx=y{_kve@=MH;(GGwBc8pU%8m1uINV{oJtpYyxxjQC$786xlSKma^cZQ
z5}~T}-^RlC3KHvT84b;32%l}s)q+@W*n?U4oA=4X?C4Y%SK1yMROtt!KbA-AUW#|0
z9|-!oFsWKWUF`P7onQv0cc@Ydg{Mb8VYhtY0(*0Ll+slZ`ZdKaKOPoVTa&R2j-p69
zC6>oyiExGE^dDv0(Md0+Vat@GP=3B}DuhK5RIp;~7UPwQ$7Gbl>t&kBW(l(!TzTHh
zVPdN#CfSQxZ*;dNTYq>5?m9C1AhmUAr*7fmK0m~8P9oO(P0QNMFL3;VF!m;e+KDw>
zKHQMNf!Cz2wCzpUfY)Ja14m5JZ_(Bq+$~Pk%O!p%JI~rEXzZl43D%$YJ^=@2J8ORq
z{?BM;gRuSywTbN)5Li6u1$p-F7L=focF{^E8vhBVvM#Z3RoJonlU|OPoRZ`%R~z-R
zMZ`ZZt{aerQe8PQd#9df67^OeURIaF+1m}Csh!^he{6>DIBhz7pb9k<-zg|XGq`_R
z0)8pKWapZMqo@4Vi8|DffEi>m>;=JkTtn2~gTL3SE%DzMKH2@|Z@B*4)jx6j_l0ZS
zWDleyY!S_;+b%qQ#@6o0ll2uuq_FA)y_x(fLZ|RflhE_`b#CLdJZ`Ujv<i`PB{Pk9
z)<u5oIIqooe`#KbVZ8(`8nNb2j7^U|+n*Z&Gp%^y+Czqu>vbxGjiMJk2iWGfdjUy9
znrrz3XKLd&$fv)U8lEB(F!tPDiR`SYm5lc*o-QB<3*Lmj={j#{VK^fMKdl%yMjP8D
zH09#t(-dxz^R(<Joh%mbE!g#^d#+(`w&zB$d_arfWKmFZeN9s0wniK-lfe>ZoNr<3
zh5oW*UMdIrqOgT8NR5NSWyM&+f(V4zslEKl-SdG*!htvDz9nOb!3I=yOvBEuzvHdE
z6d}5l8G)pts*~5(5w6(hl*yUkGfE`QKz!T(3E|{_mEIS_9fdzB|0kCJ*M0+yw;neR
z(v=NL@o(P#lfb7=$}@=SCsOk6?@&SUOKP8;y@fr;ZlyK>Ur;eweM4}l-(ExzVraJW
zLtAw<MEW>tnn#p3Z1>|jC*Mds)zF#R$xU!M<{b*C7BgtIVAOfG8UN{65U|_HmEd>!
zyov;p+Og0}aB$eJuwkq3b*3V45v=QWP3$2Doda^)GFSDAf83(FHApfzfBALb-#QRU
zPnH)aZI)fV*q5H{E_ck1W*ld<e|*Yobm_I9e;}rFAJCxDo7Zbs%;g+<;I=?prPIH@
zEV|V!qg~P)xuhB=7CTm~AFT5`ku~n`Ck|26`elrI8w!tqtO^n>Re-iF!;OjJA;-k`
z<GBnmsk>C6iu{oE#;iZc1?@i2S^q9s&}-1b|DTck3v2%l^PdBdwZpLn5CI<Iv2OP0
zacBdAYSEUM9t6F=k#Aw+_4N)kld#i%aiQ<JdFX$j;h0xV)yoT=U|p>O`T<Woa@B_~
z{6To*cmI|2KNs=8w_)5kwCSLk{!=>C(f~^ST|*bn+ZBQT3O`crq=OLXq3@uFtTZBt
zx2u2X25s||fUC01jt->V`yzp{?Zx2UL!T68NHCzH;Wj%&8vA-#=o6VH4tvT6!~Oqm
zw4E2fGl;|Li3U7$^@+4aYmdgdFB+{0MYIV9lr`KWzG=Rdf8Y9M)2H+7c`rr`gU9=S
zLe9~1yr}$VE;!jdpE`R>w2(DIkRokHFrWaU@U^x?bjUtc{Ljlf&LuN%T%L26e;TYr
zFf4J@xKUo?^!XV_34WMJ4R>}uk%R7AGlJ!MXlK#YW3cYF&-|^-610=iT&pjgr67p^
z1IPcXnf_;7|700D&i-q7{VTcui7RMa&|c7>12lZz`xgzDw;Ozl(rnj*k<qNStF4it
zb4A-2KER%qhGl1aAK>SJqN>}t-QkHWAJH!OQ1DXy?aR8`U}O+}EGJA&wR=v=5=;1i
zl@nT;l{yU&>bM)^lHM==PSMy{G(~y7;CGZ}YWxHT;?lq=Mh(*=k2ypDh-k%*vuZb3
z9vfcFq6VzXROu`)RIz~o#}rjnTpmZ>zAK%&Cn6HVCw7Z@_^}GWuAel3J2O()JvC(0
zr`l_Z=r1m2l!s__c%r=sLrUjgNfy&4kldMNZP>Ca9X;%ZTc%r(_y=bXX$Lb0e#LAh
zm3eSFf%L0rsNc>Or%_ccKi6KEG6P-fnLU8RfA9xR8*RpqG2VFKf<YJi|6<Vn(}mCm
zP~QcRbE2elm!W#`ubG#%)qT3usQKq*^#y@VdgIzLEy#elw#Tl2(2BS$r~s!HYit0$
zi_gBwnEe?T3=68R2Pjv#pn?DngZ`_vA*}@`o=k!%YGi#GF8D|bpgj5iYfxUE&$b#d
z$LQd#5n8Ro?3V=L&Kb`JKQR#Z&YCgz*&uhaqf^`-H>E);|1<8)bk8F}`tlvOnXNyn
z{h4*6C41yf)c`ZrHJtYF7EkL`f01!)y3?I`1-LoBSZuJ@{b2Y)GM2AU9O*CIMa6<d
zC}kIqm?ykiI{cf9-(vt%kF^#4Hs8Z@fc%~_$l6_7Q9b2!PE#|8W@<`%$3g=J0L&vB
zoWbkZab&;)vP+WB-aaX}5_8Z^_W~?y`9-gC5gKVj*BN6L%Z%aNA#GR7i~`8bAM!AM
z(AsVFtr1E{BL9T)chR!Mrp^DDZ->R>v7ziO<_X!y?={$?&U~#BIh)`DC71ifisKh2
z^U0K5Lq3f^>3sd?-)zoaJKbOENVshMQ);pLJO6u*H1y_3G0U;C<YnZoA{ac>*;g&n
z&Dq3D*Usfa3~ne-jjLj<n_KG5tg1G!YE6TRPp4eIx$R#Vq)NC<xP3$4Uslt1VV}k<
zyi}YpKm0ooheHvYx9Ojy$9C^&pF10R4de`W(B}p=H_r~&2g@p+QK6YUPQWt?V&iDh
zPJJ-qC)6Lc6<DpxeeE2wK3`9W%yW7~{uD83(a5B-3P+^2N&n&YOr;k!CSTMHEj%+c
zoX5gDWckZW=ZkOKhBqEw*~A0eN$v0da}|$gRc~|R+}7XPxJwG(j|cmd&^(Ua7_P0w
zo#zZMzMW9w?SDvhAG!~XNp-bz<vy)TeV@JRYw*LS8KZ`yWif<)%bdIOXqs{R!jY!_
zjQ;HLH2)*S0j0{{=gJ<|Lw4DXkK-=t%3ARUJ}cjDQ*cxA)468xewePM3Y&5<oDVq|
z#N_|67;=`Rs(fr#K(KLz^Ot1jquQ9JywJg9At;;r^r0M;L#0M@b)nksxBF8>%cjo4
zV-)Uy&M-nc#pdyAxFt6eU(Jz}L_38$!WZ?erRthfy}`T<60^&p8(lQ_lU2#z9t;$3
zL8RyG-dXR^&N+jxmVDUY>p0LU+Vac^mUW;cK=Z6U#D0pn&_rjPjMH1_d8-|XnCk4=
zxzd~KGCRRrD#Pu)k{F;l)LIww6H&xPX<4?=0qro}J?&(1l7IQkfV`2~N$VgtXG+#P
zY{Vf4AI*J2Gr=?-$Ms8L$FT=LJ*M-80q<4;-w<J4>96yviQH;}*FuyDIJaJjo%2KX
z@sxa*_Lb|1RXnTK%SDGfCtd*#9S0lOhr~)f*Tkp&eAA<`ur%{S^vkMWl7H#S9i*9i
zS+*+K>&hQ47@F=McDoOoO*NOw5e+iAN_^GNE?u9mo@3wqngf^68?jqnp&hI2zvIKc
z_HiA*#pO-L)!2bLDz|f)6syGUT8T{MeK-!Tbc^PXf_pvD%$pL{AIuK}{ruCEjr>KH
z>@ojT>|-sy75gjyrQbN=D5Qh0I}WUi9>aAa&Mt@SCly|^D9g?{XgL1SGla_wn~b9J
zdzq`gs9g7kjjBQt1}g74^ah-W8uBhRm#s5(Cqhn>2pKb*9hbA#$qk(GkME<tchBS&
z>Jt6%Vc4<CTSF@vi$AY7h+JG$leN?gNKH`jkahK^B{hz`;;r#7Xznfzq;^<?_h>9{
z+3<Iw-sHFR@F-6{t&R4<`)}6(dQ8p#fT%5dQ~kd<G_3z|X#PJO7X>jvMn!3T2Mdxv
zMvemG{~sO>GStKQU(A^QU)>t!|J|)Ye#82IvujA+Fuh@8`#<~|PfX*MUb}=*fpj^B
z7}AUWZsl@0syg^F0i{RsRP6OMblF-cea-J_Q*fxN^s3>jv_5VaaQaqI5$Mr>(k8D^
z8SCFEC?iQL(tOrHGA=NGYPpq^l%$no7Ma8Red|t4_uKcX<lD%2&#idVl*crTjHU1p
z$jVbZ2?zv&gad*2>cRXyfILEhKm^Lbk7<+=G1}tO(nxj#wc?+oNi?D+CVygC4(0tk
z--_f1AJyk>XmW}mmz|6Ni=Hb8fkaoDbv#N+?}m-u6W+U^7(Qnt$bsA^Qxc>QRi~PH
z(R8gIbmZ($oQ!(w2U04|Zc!1?<K&U2Vi3r@o2`Li3Ou}k+jGDQ!$EP?-|BEiy}XO~
z)M<inb>}P1I4mKXGkfvNAXTVLK!CWeEFTW0qFmLCf8{!*{mhzR&jAVNXx=|g<Dr!u
zVV)BmNwfLZJ5A#06V%R+mBTL>YCOU1oe@^3*(?n^!$5BBPhH|5NlwjCCjE}9j=WY%
zKPa(~-)>3QIh0{2wR>CS3i~eG+@Hv|Mc?@b-XJRkj89PNB0wN*n(xa&VE!E;=z(wl
z)tdbT_d#8zcB69%ow|||77<ZNa7VteGBq{1lks<1zANGQyE^Q;i=z$1wg(-osnb>;
zqt=Tfkuvm~06#xi)tE1zKik=T$IFdT&d!ROj3&qSxJ!Y(AZodVTrP)XxZhud?_Cfj
zA%hI*2thOBjYo%pfibDTQC&nso}C5LVahlit9;gqD{=<AN<4)+s13L5S5)9Kb2?qD
ziOZ<d<aU~qp;9fI%BSmf7JYZy;Ivy~K1IX95vSL#N;b7leC1XKQIg0CMtF)>Wicaj
z<?@D#k+IeDszPnGukYOw2Si7V7^MxxdZ}ry%1lvt0bjf*OHDbOwFE+<1}VGiFj0p2
zr+@@V>fO(Fz!&7fK{hf^Dbf=3-gsR*4kXRj+RT&`2kp;XcLksIwXjiYW##VSI@dji
z_`&;{SGgXqF`EuM?*Cxr;Ha}+YzW0?zn@>ecmCNI@zx(9nafFn?rVI!+uik9wq$H@
zx5;3fypobqsZR4#e74(7_SR$dg0DpVui)YXzGJ4mj+YY6lrNp>?def?icSY<WilO3
z8sWPc+}Fh&dr3<>;O*`1-hhO@#u#!KPMP8=C?zH;n!@ce?EU@Y$B#2Q9>s>eVU#jS
zaeQ9aW0_)JM+Jm6yt6#UZYn1Fb+I&05d7yXr?(xiuC9zw(CO~OgXY(AXKhYOAWZJ(
zd)t;s!Mii1#Qff#T|v*rJ0l}8s+*`1XZ67aV_{+%ewTt_jmZn?;#15D9v@dYR+Pe|
zTfwi#2BlSy+<A_0SfJZ_|6ti%Z4nzCEj}?`hqb1kDH_US#w4F`bYxq-AX$Te_T7;y
zdD7q|VG0bN-s9`@;&5bSoNjC>3e3;=Wn*b?{Virn<_5n61zm3OKe%0;Y>bYMx-5HL
z9QP92%$DgJ8T~w1Y!v(Q{3Yrg3JOZ2<95bTtch91%FiH=E>vcI{!~63In?Y&C=rxh
zGI9hqFP?mokj1ngfAk^8Y$B)G<KkeU-r?Q5pgzp;!IhPjsHmtEUXL2fx$$vvhu$#4
zi5zK3Ny$d1-RV5}OixTYBc=~$oTz3E5_i0-1~nuKzIytSS&#DNV<$fP3URv}RHbqj
z`kf{9_4Nq_qFz-rw{IdVY^Qe?>9xML!iKbGH2gYq4;3Y=2%yb<tj%vGIFGR6Z@GGu
zUTG=6O#ms#iHcws5e^Ct#z54JPC<;2Nl4=LnAUq8WVQrQ2?+=FV?aPPxbAULq{MF&
zCn&hMxT*X;DYq~XUmL1l1?af4YIWY@SrUYTFc1M&1QgTtsXC5(pEK}5Sh%={v*kj3
zw4*PvAP^9cnXCJ?@^6%~4Hat9TpwZi1Z)f-AMoic8J<-7)Wp>GK8*DB%~o5e=2_se
zTQ3wS(-y1Ho+4`7%tI!Px5u*$Xyf>OT83`DZqA){CbyS74zq<JSh#deecfaCA}T>a
zL3Q4D*Py~nG|Qi8SdwvYT$VH*?C52KdbFCascwmNRD)N5hy93z<B#yvo>#5!%Q+gP
z|0xbb*g3IoyNQXL+tpAad$B`LwZ%-=$UrP(DyPGyBYfuA<p5*r?Z|Gvb|ZkPn7nem
zcE85M7R{D3_@ftSy~Vm9djKmESuLhP?}Fk0W%9NQwsMY+Z)d*7>SnKU<os~iXK%K=
zs;UYU01>bI<n<ir^tp$(@niQe0`=`3m<B$$3U9M8O$UM&j@HBZy>IqwbBjGMR|8`_
zXcz2y3DNj2^-}q~x<-Ee`n9s6x8!=65*SPEb@H9k&CQLJccI0rQM1;XRP6~Nm*dIL
z?`!$EI<ESlm_C30EX#W~3Cj8t8Zm<joB3qlruq^r^uD9R9uC)!#+v+sP^Zn-b5p%B
z9<P`B+!Z3dAoe_JZ}Gb6@{Ex8zL{T^;1rT#c|Y2ALhD#0RDE*9l+FKeHDYIHr_kvS
zfM6?MF<1C~9k`*h-I<$<Lz~K`+e*mFuAcs5<Y*m<9n0GXJewV)QWEFo@fR_Yu?*sm
zh16;+<7c}qSQ1LK8~H9<FImzw-0qmNA7fGk)h&P=(rebtA{tzo!nDn_cc}B(+THrV
zH=GW02dbh^%#>)~{K^c^`dV(c-lMAPr96n$cmpE8xw#okLfQhkv~6qTX_DGb4<y1+
ze@RWfeZD_`*mOD^MXlJ?tL9l?v)Dj>uQw1wZ*lj~SUAs*0%3hYcbho-z^Cz6Dunm&
z`!~v3o8^?`WGlAB?)&SjtyE%Wlfj^8WZ(`(I(%{6?Bf+`trt_gueTYr8(1u+i^Rf-
z3}npBci_-?(20l`yd~TWCiCP$spGtL8%g0kAAyCmci{T@`+J=4#iyn+@?wb#?uF?3
z+&PuI;2?T-etL?}=XvRm#z)0(g0KQNqFR>1Z1sa>HxN|I{mB3$z3dzh3K*Ts<w*}E
zin+r%(urIy2c9c!9~K*3Y!>R?P*PHok{bLPO()go`8_H$y>0|i7Su(AJPHxe_qq2_
z3IL$`FynJK1H~FJAwZ)X9UaxxaYVenKHC+;2lM$6N-jal1M1VKgz~FBq4<HQgcy$v
zw}zAJ>+3=HBm*?h691OKW+kyLS6Cr`6kSu$qzeFIN~R^gL8sYWi1zk+w-gbTKn;W@
zLnK7U^SJwsVb5R^Co>mU0xUuWNRUp8r+SvuhABz}O;<rM_NF$RK$+afuV2x(E2b24
zWs0>M&o5dZ?ly-KJ<fKb<Kn0c0%dY5b>iOE!U`9v6f@{FsVOOC#XNtd66f+1(k`eD
zg3ttR1)EmIa;`$;)fX^tmfg0J6?3G--@QY}oTN;9gop^nlL8``wXOy7nW7GxgYlqG
zS33h8?CoP1v=d@t*gP+fy1Ke%XVuVbxB#(hCq*}7Nr1tYN#$z>p#qSwwXp$3oOzp8
zdPYW<!yH8jB<~O<0>f1mL}|K6-Qjqx8w|evovA{FiyJVr1|V7x2s+)-!!O!CI&?fd
zF5~{F+GTIe2GO*lB1<rb0W8qc(`UOwzyMzuD^x87g#^e012Z!-Dd`$une`9%Hy3+z
zWQ*MR5XgfNF^Wsw61gItqs3B_+X(N)f~HM#bYvuxL01sKgUQLs4{(SD#ysRu$et4U
z`MIN`qoRUIwZTY3v~2V}OJ^Wjcu2^6y~Eacwj|<HyjR|D=;@1fTfL*O09{4WBBgz|
z#WZMA8CNo1O{7ume0zBerl$LPD7%89q6o{gcEK(bhUXvL)!CcW(QvFMNGN%gw>zG&
zqvjEM(G^HFpcTZ8Zabc6b0j}TMU{E??(%%zrm~_!CYfsr+#8gG@VG_(5S+^IxK9s3
zagaejqO4L=iof%cTT}$)GFNFTo4`U%NohJ&puE`NgwO44X<}k>x;aG6G}+z4+Hp^v
z$o9D7kw977gPkj}(#6M5pFlCV?o8&tc=3W%8T2+cXy-&WE4&LMG2gEkpDH~%aj>zw
zySm;@JjTv@p7y978wp2e{VXw<)qL{Z&(Ef*Ud}yVUI1{^tg%#Y_4W!U;-zAReW~1s
zgoegQMU^J!JFeyj7VA>PDDc~p`4bUjVkajj-T;pgiCp!re{ZXn>SWU)<$Y|Y#t_(l
zUoN+BaJHSBO7vP!Kte*o+4+2_*#n%y>%6CSULz+$_tmZ6zC!4}1HA+pW)jeJa&tf>
zR8&;nzI_V_#pUt(&Drk7l&Y>g4%Js_)UV_Il}Y-oui+ovqS52@V-pg3fs_DR2kyEK
z04Ruzq>N07YH5mNXyMF_8)T^;hpGN8h16GOP*4TQqM4xnpEDWtbp)VHOiV0$o(}Ep
zS@y$1<PO+z$>my1h7zpiDmG@z4b#)pq2mES-bfOsAya(blQb@a0qt@F_<2SooIE!)
z-rrw?umJpB&a~b;9j}Qr>-gMW+V#Wc(JDWG^6BSKqvLOuFyK35Tl{XB+S}U!a7X0~
znVe4ME0${23vNTIU*m-u4A9flM<1{VzZEF+B|Qmr%*A-d)N*?=ke8QtvFwvt0WqKR
zcH&A_DRjD#5%N75b5o=arj3)LxQgf~K2##D=mwYi6ruC>>Qqrtad$Fb=miWp=Ddx!
zcgxs&tb9KYyiVOQgLEWPSu7kJ0CCgN%%%GJ`c^-xo_S=)#l^|;-#PY2QG;S>z1z2G
zy*-hnc}&Hx`HGx8f<~ZB;0#0TwXv~rHI2KBlKbYA+S`N@KqCP#UK}nz++8fY94_&K
zm3KQ-zJv&vZgZ|K#m`WtdY*(Zw)~nY)ip3!Yx@BAa2)O<rvSwdTX9}WQZlmB)6+!@
zve2L)*Na6LFtjXK@D5ua?)h(5(Uh?N^j%B}ru9QWT3XunM6S>Ec5bm|EfJH^J3+w?
zKsW^TVIVDC$}c5Wi$i`K)^DYR^IcPslN*B`S+hBqEzbl~Ckhv`f?uU}`7&81HYLUT
z_ph<jtr7mCHaL7X%QOloy6|Mo>^6cM0BjP_b1<nBhD^+10UWk@0g{D;f`SkJvs>D_
z1V$dHEO|_D1F#2Q5f%ZqE`TBi@#Y5%2Cy23WwJl-l@U2g7ET4fdxn8=a@2_i#!ti=
z6nFCQ65rNlWMuH&U#<i4Ad|x5_M^&-O`AchzLo|Wxi(||Un<`yq)~}@>fVoK0K^Ps
zz=d98L67$+eX&YnN=ggh-au>6QBZ7VhVw;k{zEs3o<ksL-=BWP#=<g+uW6}{KJy$<
zL^aJY;ly{e04k^TYRG17ZH)-@C*W>NfUO4v1b~bp__y!B>4t&ikxv-tV`5nnafRUh
zd|dstLF?DqHMHjAnS0MX3Sl8DGG%3t;Ny$*mR0mq`P0;57;t^A26VGH4;D4CArO@C
z18jpuxz)X<&vJi+2$?jcQrZFXOQ-svurR>qszfut4-IYbyc76dEI<gMB30{SQ3?v2
zo+IVqX-Z4;)u5f8kmXN~h&b-X)g6p`lc`*&!lLmARdO`9_?dt-0*d5&+iYA)rMItI
zQE(Y}Km_k@E|zPo=KCVZ@*hLmZrVHaCBB+ytOacFAVmmHY1CTZLDk98N;?4R2FLC3
zy}3&0i5Bf0E%P6xQ*1Bkl#0c_QHX^Ryx?};OX75B0Tbo-Z%4B|nYQlEa2oB{;piO!
zJ$RHG`h|wi8RG%z3NK&21eKD)YM~-3+JnY_Tjq9Zk}M;pz9LbOXFSu|-yOgig8WFL
zOh9kkyqc4f6STVL{neJw?Qw7Il6xV5`6&2kqK_O>2r;>qqK*1T_`Z4{+PI!*R~U(h
z;LxwFuM>j=`6HsS*(@bhq+Rpb45zFaI0?iXF{n_gwdDiyCoC-dgqW`hASalt0NycK
z-#UFYJ6ieYtXvDCpHH5@ZhOz7)9T#}sv79byzZydFpvsRf5HI7!-)Bp%Je%y{k7a2
zG-5qEFKU$I@aVtxV>*BX6B&J!%kdXUMR)VX61P#`S3oun7xV~m&S%2De|%z!oACV>
z88Vd~4zGpr?3q3b9<kfcXWJi<*ww_c?b^F{$Lnbx9Whs_wJ8JG1R5eT$7ZSN>i*_X
z@o(2KKYuc=mb69%1wR4{Scs)9%_C)We0+RtY^SrGDbPc$SDT5?o;?GUadnRa(&mSQ
zq9m=RrUvK$6k`B8EGjAjqGQw=22z&R4XbR(f|va{R@Ym-aH>STGN-DFg_}Fk88|1P
zM=`}onVA)hLcV-?5<x#*qCHS72Lo7~YZSdoi57K*2vF&LIHk?!cyq9zJAK9G41ks;
z)uhv`0bfGL&OQ@KpB2$cpDP39er?&75eW%272Q6!y*^PfG1d!p@=}U8P>bTgG$dBX
zT|9G)PP;+LD1?2<Rmdo04VmKGXBaN1bD=Z`V36uW*xue=U40IaY-ALa?nnMUaNr_s
zCr}uhK)fT#C0j1npuCTua?X+k?=cS4CR>Pzih5Pi4cKB<AXl=?PWNL7(jzSbq(jX*
zTYVFgoVYyI(j`FE%u1jru@d2Sx>;?rd=J<uAf@A+&!AzjT?W`WF+N^nv&`pmpmjhA
z3xV<D!^snsjG?;%6S<<I;-GFl45*<Get*u_m6IC<M9l|O?$4h;0h5+VWD8CB^H!>8
z=3JfKdNi#nut>(h5g?2J5RbMN`N##FP*72qfI0;ttS6k<=hx^<WLRk6$Uxfw(Q~*^
ze>$2j6i&=9^A>6dy1t^L+XFv<Hp$v&O6cAHb3Vww&+R(#?o{DStIq?p1hF3dNzjjK
z3l{)pk&%(TL>_y@50{AWFp%1KsT{y>gHj)%JgNdEeYn!@PpbSI3?^_B;1?Gpu@U@V
za&oHQDk;^FsHGwB0Q9$My*mfO=<Me=*`+!=e?N$T@{3T>$~i3!4Vp#a1icRe7eGLP
z8M5Aq#!tX${}T)zDkO-~XPwfSZAXn_6*Kk-6JNT?xMyUdTJ`pTy;5UR$~4)o3d+fS
z%@AbQdbPwcP<1A)U$mu}QlUG$Y5<|Z#>Y<t(#AQFRGDgrV!TQ%je`8uD>~g4PvB|*
z3uzT!WFN}^c2Q0b;0}yKva()@WCv@2^LprYnl6F-)TlHW0vtz*;@fx)dR>S9y#J75
zjHwk4g#yI;lir5}{-1<wmT^cpswG-uyJw=A5tc9^VBW;VJvZY@hh>F{BB_OKhedYU
zn|))}VaaB*+>)&KEbeN2^%iIy;1%w`QH7HKnzw2CT`n**v3rIWDthg^xv`N942^_G
zCIu^1NGpi%!FwU0p`pRSGz<(f($Y}j^9|UL?TExc0{iyoF}&ytL@@iIjw_=8ze4=J
z+?VvPMPdT<J%G>qUEK#R_us#OJJp}z$z~=Xfi`Ua>8^wAvCw@62BbhB1RkrM$*<cm
zoHyp%H=y#WKvjcLCI5oe1SRk^c_=F#7Bt<I1wcP2Zv)oD0+`yM$Q98cN8j{T&wbqq
z0@{QCm&uh)1z<g`?Xq}wxgHMQmO}}p&CiI*99Czjae%cRHLnH+Q4_#@Fa_LpCaFOI
z7NFt8q>+5X-s_z#{H+SX4fyn_1+eF}EnpCM-yVxWPg#*CM!7M05~v#X#+hhg0vIt7
z^hEBz(%bzJ0e!gv^*-6vXw)+d2hl}DF};8<OC6bYv(>yfSOgq`&vLfxGbSZ4xfJol
zP?kuUorxfq3~Fi`WWUND69<KaXx7_r09p!$Qd~lU#9N3!cYDXG-4~XLO>?LGT=+TI
zDgFD~E2uWs&`9XR8R0p70@|S4{zsYCwy!%=Jlqk(aXpxS+O6JqV5B_!&gGW}w@z1&
zgMx-Q3D@*4cVd68l9G*$je+5y>oX?sLjV&>6L1XhH)pY4xP&sb>zURD=lyFCTRC(n
z#1WY-x7-0$MW!I)>B$pn1O+Fa>?w1=jgn$xFZyXq<(~e5LgsiWz$`!kK07u5N(@wX
zCdD5lGh)FT=<m<krjSnfLcn=)bJ&_o_velDnceJmb|<by+R`SV<XfTREi3C|KNN8C
zDrRSA0R;x;-R<oyP|HBYZUElneo()a_XLb9h|AyF5`FdL4KI+X@F+M%x~)2|p3uNS
zB;*n?1hl`1W}bC!48-~XBM3-nP_{eM#rS71O&vx|pRJ@zXCz;y;KI+08Nft~t^cS#
zJvm8WwP17nH45x4Mf5gW-L`+w18bs*3d~2fj?PYyNDtt*0WlpL6Qh9MCbU+zWXTp%
zEPh*MM)p(WgWd<<Q9wQc)4B(gS4vzw9nUxLw|lcp?u2>uT5jB?NV>!?Lm2*N#Kb;p
zU7x|#g?xQu)Q64?TSpJl!q2}v(m-~(5kd;eoR*vW=5&PLXEWXu${T^Lmgomb@qBu-
zTwAK!LIuC3gmfzgBq6va&0Lw}y1F{R5JA5GByF+b4A@64_t(42cLy#D_gGLE%>}$2
zaN!oyk(9wfsR9@XpC~cPW%||W*24y)k9wW$DzJn2A8s9x6^i}*{OBg8i-o68fy2VM
zw>JZmheeNbG*Svx{WFEChc-Sp;M^-&<h4X4{fE(S0Wcsb@MfcFZ+EAQfyz;o<u$IK
z;3^PzgTKn9d(&Z}AHNKrCYpr&!G|ecYT5gyIF7?^t*c1A5=_Uu&v@AX5v7m+ic%yJ
z(af=NxpH7*08v^4<@6#>APFeQ$a!#{K1LmK8Ygsbm+zA(as^jXyNKRqUS0HZzj`I?
z$r*~rT2NOPR{^mlSGH|rk{vaGd3Y7Z3?NZB4(KH@FpUAc^8o82ncHPtzl^I-X{x~@
zfMrM%5>Q#M{UQ`cStJDhRs|3cAf3Bp(g6oyZ2qmFqqBr>)AUxoX&`4gJBlbt1o0*y
zJ4s(XWe|pZ#o3rBqZ{_#b5=hdDDY!YBmmo~?R!A?2-y{|YH}nUT2jP*6fmpPjt>N2
zDu9ki4CqZE5L7^dc;B6=BEt&s{gqxITS++d8kUO<)n#S2pzGOWw`t`_p~+Q#ZM)J2
z{E8CI+JpVt<s7^}Em>{Q9Sm61!+}dHbQIDthlGTTr1FnA8AIPL5Y3&~*w|QX@$vvz
zyk=S)Y%vWSdSJi}u<rgjp$K^7r9jyQ0wOvt$v9Pkm#Nrbl6(#ErL0@P`tTkw^Fu%l
zD~t!O&-ZCV$EYjdXNur_x4?b)_74vq03!gf0ze_rK_Xwg09M|})Ss_jkOyeg)YO#f
zbMMyi@iEw|MD_g2`ilUQpiu?~2LbOzeZll`399@cJgkO2UjUvmYE%VzI6#@wkrLn~
z!1*F0CG80#^jPnC`VU=@PGFe>-WmI{XQk`0K>^G~+#f>l8WmYCWI!4w9k@7PZn~dM
zsUo5gS-yTwg;bD0)79H!$+qjrRYDI7RwMV)1lG<wlV+e9ySuxU;FJwm@I(rz=;@8W
zR3{)iv*13XgLLNut_1`KVCaFsk5PR3Z!!?`X4?GKKVtvWPgPWz0LVio=q_<_aWgYB
zAsRXKI;y{I{hlRQsof2oxYCRv<NgtffO?nR2r#C=x(zs|Q$^~`$gne&@VaKKg60g7
zTy>*bS$=xYppuRO*lHZh*VF@^y8$rR&u(&-AMP)JwVBFf+z*^C<5PLWw6=F*4iV$;
zwZyT{(Ztb?R6$Ma0OK7neQ?#HtPo`*D5X#cK=unD=k0rhv`tOzU~7rkdc5E;a<)A|
z`R2_r7&}UEkb<sEJ{*QmpZ`N0oB|`ws(%2cCM+T%oy2hrL>FG@vJOwQ<_8x7UsAsS
zr-H9J0|NtuqM=}kWZeaEs^2YX1jZjTU`Ak5GnB+Rp{tui{%c|lsZDc%6N~micQ^xq
zoBHwt5SeJtU+b-Q1b|`wbP58&RUS83eNC$NgOZN3b57CM{WIDrAhcjm-wra50N)+4
z+QPw#4Wcw`77`%M@m0oQHuWBIs|ExV*fF?(6aspx@DySju<$m{tHzc?cT*KojSp-s
z5D6Xk?QF1;%K#V&rG{uYkdL7Fh=832>?3%1cmM_IBy0sNktuf_!w3sPPGH&hg*;%V
zN4n2fg2n2GBUl&!l$8({A5%@E7#{b(eW3td3bu%2FO;Hyj6Op^CE##7`3W?Kb-}^c
z!FTR4xkZ7ul%kVJ)`uk-NO?Q>?G7}LY`+6?y6kb-3~Y>(VeUgLd>;DmOGD(tDF&{*
zjBDQ7=Hh_0zI9JLIX%U0ER1-e6RrnSmjx6g3Jx7~aR?MVC7TDi&CyOM?zySLbDfcp
zI$hOJN(9y5(ir^qfhVQC`RHxRKqp310Ke_M_&MnT`_BPOdH;)D1rL<gt$p+oN81Rz
zm@188cl;R816t&4_MxJRDk>CV#hfdZvIr-#>Ugh^&|y59N`i6j&DY$yKN7RhfkCOX
z`ks-7rU1~gHSUw6)lRIe7!<PY?~HpAEFW~eD-l-oKkx3No7<=X9L)18Lxw|m0tWbV
zPJ1IqM;6xn@pta$%=Ln=(puEvYIgH9e!OfNFB1mn<UC34tpmspAn?b>$3VHR5Rv-w
zdsV7fz?&Zh+*IsR<qG=JSS-}p$;b=?I(!Z2g5uM*Gn;8V%T+WZz5}lQcAS+keXuQf
z!33-;at4MHfM-~bd=0MZcB$xbe7V6q19L_=Pxjrrv{(p){M2qn9tfwL7<x?;BO@pi
z1VEpGchn$2bOy6TUM-ZBlXJZ(s0&*9AS66|82~P@*no*Yn;`<^3S&+%^u(zMde3ue
zaJNT#H)HSFENA5u6e8o|-m$KTEWCa~v-Cy*whfbz+gS_v5SrL|2u<B+Jx-!+V05qK
zgHHqudk8olfQ|!lpF?s)kjW>|3^KxlO+e5DR*Rqy0k4sJ>iS0W^$r{oj({(qF!NyT
zF5JZ7{+ntFikB<-U?&8XcrRZ<mlL3i+hKjh1O)}b)!1$hzKD38hX5oZMD}cd9{e$n
zH-H;p&pn=kpvTn#&7Sk+%NICARH+1(d=Ngo&`-~hAdm1g=pZ)clX+u6cL6?d8iLCR
z`U*UjkP;jcf)79*YD=f`4TTaxnV73N!1c8);8xnae%^-zm<EK$9&l>248Z2B|GoxR
z8*74(jTWHufU;zgKu=o0{iFY<fXD?tA8>U5PY2&Y^cV$Y7wE6ScxGTAj+Eg*U=h~=
z9}?Wrkk`_>O?|jZ1-%I9rbcm01?0%13cN=}M`sCcj2IX$AO`t=Z#eV?vuNoIB{?~m
zNb-L!pXf0%vPPrxKInWfT!mR7m%P{Dw-3O=6kz5-fpQ@rsgq!D#2+F(jRs&XnU|K=
zxV7nyNe=hhcR*y%+`TL<>81qeL4cOu$;f~u$l34h36Q{jpVDD{lCtmUkC1$%p&jhm
z^uh=Sh~Qj7At7M+1LY6aDzG~Nv8qQ#FjVnl?^zOuva_?-DE$Zh^k6QPw6qp{Vc--L
z6u_84<PSleYT${IDxCq73ha8lgUyirG=7&uJSnKCr~okp7EU6&%~GOGOMcH;Y&gmZ
zN?om|yu7@W)EAayxesWO8C^X+n_F8R&EfEOY*CC*`Io1^KVK^=A)(RWXy)nJ)UGEt
z)6c#8?xhm7vXa@=Um?iHi9CX{bax$lZ51~JEG|j~lsUn8z_PqGH$l-V0<6V2HURqp
z&}1Ku+_QhS-=NNn*$C|e!Xi6WtjP|p4Opf+l_sJ5*@!o<^d~y>??&g~$+FNVGnPMH
z+kuj+h5@)h$i>3S>I7H?*q|w&S(Nkh8}N$Vt6#+<sZCAVzy%}W43}`n60dz)d?c3-
zTp6&Z0c^!%xy4Hfu51KH82N0xE|SKab0_(w1D&8D1xYEGHq_MAK9`-uOI{b+1u!He
zVBFF6vGA+){HmrC)r4Qk0R9medAxuWT%XP8<|FtDJjOZM)1-J`2+_)#*oID#^SwFX
zDF995-FY{h$duoav*`EI8UjfC9UzWC-f()~UWO5JUwgwFeX4_jEW@J&dQm3<x&Fis
zjw3cc{tAFPSnWX_j~1JwZ!gvmAO+-V91DxHT3Oa!%1B;xzBFb)AZcj)0NfXBzxiE{
z^Z@bOPdYCzBVOn+*~2r9_kU^aOB2s#^#kh00VfUeA_UXd=bJ^jEt_phi2oAAwwo-R
zCu7PStR_x(3QHN(%6$DGa;%?zBbe&|9~nGw6P1u~4YuWf`}TmAfq~EG{uWR)@UYEQ
z2MQxlcc9m-0Tl<T4N#V3K*oVP2)+{R$i@3%flt#$D4JPbR3s%8H?#@v82Z+55~rgE
zEEIK6n{`UZZatVMw;e$?-$_c|Or+jdxg6@yz=2;)>o+ho%*@F6YxQ?Kf^u`M1J4G4
znpGfxez7tR4$gFrH2yZlm_{)hE$uFN@&j0Z=}eHxJT`KOdCb`RD(_h!nqLZif*K_N
zVdyog()b{g0XQfEc1+C7U{zgh^c_P2>a(>eg33+>5Bh)%^50(?7X1C;2>*kpu<)bU
z6sT=*<Of#C44O52V50<{3;2tZxfD`y=!AsLwyPaL(1NT(@n_7u8VJ(<?hssNCZ>OZ
z=k|mtc<hE&wFFo_u>SxraH^xDqk%;ervN49M@j%%Hb+uh9kyh?eFx}5MNDP`D^Uxc
zK?0fyR4>pbuc)Z{OtNH@Fe82n=byPZv;5$K`gfE7B)~Q*)$4o?=)6WSV1qT-pE(v0
z|M=3Ne^>N7>h9#kCmlgCpk42E;t((d8~6!Qh1x{G!&^#vU@QWEln3l@!AQY!mFd#Q
zD-(Zm{!-|JTSo_!c*+BB%X?h-5%iIzc{$buV15H7a09*xQ*4}O$3|YK{S4l6?VU96
zj=*Pw4MwU#f-K1j7Z`p!wpS;{W`bOwRAkmYmGSXuhF(ax%4alzhm-&ufv#W1ShmFr
z%>i|iaDIo}S4%#75tGNFqsDl+=MoYeY+zuJwfzyfWe~J%Tl@ktU`pLxv06$>lVFAb
zjUpr{=-CQ8%%y;&EP;JKpqaD(AmZxk`t~i%AzvCxOqxyWJ|-Px3T$IQiJSuGt{Ut|
z02mY+B-1`6V9JN}#RC+5>MMh+8fa@U8UdC!ixIgAf7pJT5Yu49)rirh|ARyynpsvo
z09<o(b6}4^M@2RI-u$wI;R;1eyS^yuIn3n?26lL*9I%)L)SXf`1sF{1{~9Djyj)yd
z(5D?3sj1WP{tUQzYt{sie=o4V0+cfQ?+?%V$ijjD$WKqd-ycn@)agX=&8#~Zdw~WA
zlt)>${YF37`R3&2Dk9H7Qwau$%1qfW=)h(KdkSNaNNC{!J)ZxGwyyd)=ra;ZR#xR(
zsF8qGvNCOO#3fTQXGj2I_@l-uF)j}JWC=*RsECL>y4!-r6CGJFdfI^_kw?R);hFae
z%*LG>qwkTTp?IKuf)wiF=z)iN3G1tBYaa@xPCA&DA(nQ)v9)Uh1*^OE6A$PuS<p$A
z6FEab6l5nBz#To^{;6SZPP6qEq+2&1JWB(x3$U;Tu$3=>x#ZCN=d;QUd!4~ue2*l5
za`F_~mFx8N244>ywuZqzJ;nS_i%bG@?;IGcIXOAt$%yEGeh7jEOlQ;MZ~yd>qvLOY
zRKTBR(5g!-6_AFqj5<bE*6O07J+LLp!h)E8T==>KHpDU8CnxrSz@y;QeD2E#<~qZh
zH+Dk_tl-H$ro<s&#G|Z2E+c!Qfkh$5XFkjLq|G#jPF(>=QmEJko1q0_0$+tcx`>5;
zwo17VoWY*JVs^6Pk0vp(&;juccXM=6#MPt-{&y`;66UFZImqRCS%!3dQv15ESfC!d
z*Q)bwA^T^i1;<C$Asow~4d{}~-t5bX-VTi4Q{LC(Mof0hj!;|AzQw-<%hw=48R;XV
zM#R4#{aZjmmUAz&eNLDaALIIxB8&6q69G=xT#EqLY*J;gdypWM04#9G{)gK{4t`D)
zQBkJ<;90ig>Z28A_hy~k3A1XAD^zlbr%*|Ehq{(+96k~z#9Y|Y($d?T&x;O%4VvKV
zmTetR;K+~gEY*P>;ex<Gs&Z^sZV0*FI~z!feH?;O&rqdq3d5@JGT5>RRGXs4*R`oD
zfpkI+DB*54jPaPC>!8U|#IK$K3a3Tt6WP&HE1Z4YacvEWX=U`*Fh%(^0~aN3;>rt1
zr)s8eXVps)7*Ot&8-;k%5Gu@eSOL}o7Fsog1u|vG{f9z9h$}O%n&|kq7wjW^LbFLY
zFljpd=H-1uL&}m{bbp^jow{Z;Vjw8~t$s-HIJKGaUk|W;I0}!NGDy$h<|zF0RP0l9
zK9BO2qJoEJssdc3hakn5;MrOWKTliHZ+z;$C)AY=a2{v^P9EVT4HDp?SDRHOBnY%B
zp(_*!<j9bfGc2Hz8*R>Urr=nI#9VzuBYH7jgVPZGdr|JK<jP{HBd_s}`at-n)RUnb
z($krO%re%K40Fy_(`Ju{zEyg?0yB?`xrdW=AEPTL#(Tz4jB}J~tc8ol@iV326b*0X
zXhr>aY{ef(AtkLhT1_jx9%%6{^|wB57t6}KTdm_xB{{eol^62fL(P}t1UUVTjQ!CU
z9p6-q9_G&bxs^L}1&wG7+$0`1S_bZ_FV43zT}ZUJ?P5pI8mTCAx^;AGhx5ae5byEQ
z`G`GE?J;-9Y`z~axJGO~X;xeG;7@I)Qf--OzOl3S;??$%U-(Fe9)2`gQA1;1yH&B@
zw%}K=Ub%B5LtC%}!LD|4EpOsmQv6Q5RM(6;Wp^GwoCxzfxwwImAA2fB@#TlIP}Sv7
znY{-+C!Y+X`xZge>%z0_t09ElRa*PYx%1m(Qng2;<<-Vk*e&w;8<t@WD%eYlJ>ZW3
zn=7QR?yEx3bfPv6FHLF{&5(afxD{sN2hVeUKdnJdbX>dQR@QupU)<4JNm!E0q!`et
zTz$1bRg={}$a}Jz87xHH+C*KCc230P(_2C9b}_HIiFUmr{rfuP#PddB^M?FB=(cg?
z4A%V)yZ+eD{*D-)dQ~dL$%&@<SHPL0q4GX|NU*kSfUb2xux$PvGHpQ?jk|8rzIb6~
z*)YRH;q-%X^O)N%@pqp)R~HYLJ0G{panJJ%BR+%^w~ofrzE0fkX7Ko@@62c~Ec8Jp
zP(1DbIsgsvp9kJa((c$37i*LLmp?n;_|Kmm{NMf;PAvc9f8kWEp&6dZhUI-&nzkeK
znML2L9f4+FvKqG<O}Q2?AC0^4**A{IKOR*GI8)s0dU%pi=vNBlZDPRjrSb!_xwq%I
z#dTKb$iF$LjqwDB+bI-w^X{~TI>zGg!sBp?dq*GZ6;=Dv9Wv3-2DW6=&4$=RxF?(G
ztKa;?e2Wj;&CQ3eNrZ7URXFawo$wH%0*iN2y(_08DCdel5b6hK81Lz5{um4}=Fod-
z(SA>w&-`#`CBLiC{H&p@e=u1=DbPxLJA_4-?X03A|0_M(b`6)Q>)e|G-;Xdt<pyO9
z%zccZZs=@21-iKd!(&15&Lwcy*S$)SNS;!fDmPZVF>pPrA|rmp-`XCD(wkT2p~`wZ
z58G)cmZs=<`(+)6=HM+wFvGPidxFpc(X;W)yH`(0F_Noo{Oq)im%G%MUj1A#VPX+R
zvPk?w{H!A*q(pzxA4V3I;G5Z}k6lzNQMGMqXsB;m<EYUp1LG@_Fh^;yE#d@^-ph;Y
z@`Y{m8SYc!AqI&c7Y8P0yhc)=t+-~<kp6mJ=Rajgs`=Euh(?}&TD=+DdbUjq(eqSx
z%xtmzi>rms$KQVArLl8ql#U+#0mtw*==bI<T!)kC7G0&kz8aczKE*5%3YEq-`x?oy
zCPX$=-oQCdPy`dPsrI>ou0OZR^GNsLx5Cf&j;BcBqO^6<hPl~%8J|Z3Lc+p>Gq6t=
zf3eK}T4j}4_ls^DP2qOVP@hIBg}dX73vw2ORd14L^v9s{nVfVSl-jeLpe!fk7KjqX
z{eP%?>!>)MZEbiUxCD0%?(XjHPO#t-Bsc_j5AIF~?k>SCNN@=jEI0&r`<ndx-gE9f
z?^^Hs$G6t3neM5Y?p;;8c2&=Q_OtafEPScHtTtm&;u7Oh@bqzy57#MBk`SV_-C}cy
zu~{KSCkn}M$uV(MBRF8b;n0F2C`~sV@4_Vx_>ic}&Wn1jl?-j~YP?rp{Ke9&WExMk
z?c6m-aEwysvqnqhL_5jyht!FKbTPlco$d(L;s(8WML{8}i=)n=cWGQR2>68Q68N=d
zI0~qlPz8FylE-1+l+@pq*=j&SzxAcz($J~Kyg6MX%idas>m@__Sd1UUY=R7vlBHv_
zPd#^FrrG@Fh396Pe22OHMB(?krf(}=u<Q=uuc<!|b;yd`n0BOpg@-o;mqatSmD?Qd
zTg%-}Stfk{@>|Tf<A~GcthUE_k~nutqu(~aOGbvy1>Zb@ogVKuh$DZ61~;y=?<R|3
z6hA9t+cB>`ETiLvQ*^a<s?rl;-xx>~x-V`UDK=(X%z}vyd};sUcv#Ukpc6&Fw$t(9
zUddMdoq_hMIfZk>_vz@xb1nQm3cnZKyF0<-z7(gBXd#DmDZ{K8zU0`L>06*e>K@X!
z?3`vFX4Iwbijd?2rG2?ywV;~#g8EkXUQTQJ9K>7|919d>gGnmB3VR{eoolXGfzvN2
z;OVfh=zjZSJ#C|q)AZKU<-X2NAs^sSVNFM_E<*ZL9D`NQ6rwUF;yFuh)<ApbWK$CR
z<6w0U0>RK!p$aqITHlL1|C4wi$JjMp?TkzUNoD#}^`44D{fiOolDrGCZ@Xd0a7>D7
zx3Z-rFDhN--leoF^Knt&v&y~i4OI1+OnLWCUJgRI(Fhr7gV@l)@{>na6-fO3tLPUj
z>BfOEPYPN3sKNJYK0n0UzdFPw-8wjb!E)C4Q4*wibezfw3rdA~ko`Q3tj4y5Y<4^t
z+d<`^^%l-Ik1iJROZw52sJf|TMdjoWAKTnI`SM|-$O@fCL0r32(O_%RYo>HZ+`Fey
z)B`-xB7agZlsn#7jS!aMxrH(sm-q953#be#DJ-)Hd;E4aeG-_}9m;gv6MCKH49GCo
zB@DHN0S@u+v$S2w50(7cv8ZrW4%?jE6Ch*V>3A`MA0H@6*|zeytdm>1D0A&T$vdVS
zX?AYOqeF|<Ed+ok>}qJn=+_Ry&yTq5xhuO&^eJ~5dyZlb=YZQ3l)&}UuN4gKD<H$^
zQ55tqRxrp$_EXD<72yZy7FBBzZL-hB5+5Dav|vsmaa~@e=LN&O&}5EO+(4R9*g$H~
z)_=>SjHBwVD9VPI1}C1i1#jFqdDV_D*3QJ1!10=QQz<2xnf#}OIL{ro&TKj_e*nv>
zr1CoaO;~Ii{^_G*T6(G`)-Bq#X=@$FSZ-KmYJkj~sq}6Y4WD}6ecDhA>t48f%r}B~
zoNun$S+<P>I8aAYEQafhPT5YR@4Wb432e9IBpH-Ic<7QM)-2W>v(rZ-@~RGwXsVC4
zhh;0ip4N%i774(f$W1#)3pyA`JD5Eh|KZ0yD6SK(t|`;qaMski{}m_oMLoyDX_KNl
z>R~mxX!0^|+wwAjTtQ{c1%p=#_ruS*l`TY$@C;X&oJ4md+sk3X3VVhg_k;FCiDc_r
zPp{r~Q~Sfq;hC!kwrP0|IxfU?)0-&6H4%10!!4b$svMhykXqY?nr_d8sjDOq>_%*t
zAwnT+BoiSadeRNBM^n1?LaI+FNF0Ib+bSaQUzYW}49Y?he1IOQS%CTP>n;oPU+eDw
z8qD_`!S_2M6AKs9|4Txq=R5tUgiOHy9!&oX@N-vF+r}9<37J8(Cx^;&W}C?&#t=u5
zCz1T9zNZ|&pbBXz9&d#qjK&!m);lXjZ)3$~xeRH{WTmSTt|Cr~E&fg(JxV(e+e&ST
z<b4_4RIDEi>jrPu+IP(Tt1D>7mDFv%tE{o+?Mx5JZ2}Y#m{)<wdd7i5i6o4k7b9!L
z0Fpg<6~H)MumAqmg=OFg+kP!D*&cw|TqecD&qEK_%DxpEI-J}6>e^~+-@&Z2vH9bv
zST{uoBqJQAJNFkfgF{czbsrl08X=S20qRrtots(dGlPCj-W8Mp{e&;87O~TTUUAH4
z;{}jWg==pGA)!J%iICE<!z;bdMCD8@y~e(NJee7qyO4Y8UOJ8^0scIN_iioR#Thk_
zuN>=sT(7Zugt$qciUTm1FV}`2Apa7#9l`);0wD8WZ-DfFH0Jjs)W4PfR*(F8{f_Ex
zR+p&2+l+rs-tlB+WX{M7yHKLf_RgsGSr!K`?AYKtZx3XHa!>lX5PR<X4OcT3!Ef?@
zUCait1v(Vjothmt!ulPFp_Ll#nEO3$iMnr(#{~1&OXn}Nzg`%E$LVHpju<~~K7#*E
zI{&|Q^|N$GPqB6b9Sx^Q*tZ^@&Zo<~UY0o-#{-&_D@%luFE+I_pT8t*t6mJX4>*|_
zY<#Hst~n-wzFHb@*IFBuVOPf+QK`@_*|O}_%-h~_eS1rv!LZJ~jAWqa8G=Z38~Y{d
z;_hU|(i?`m^C3N}{bJx|1`Fr;CwPh>V-Z{!zkdBf5b@pcUJ&ptH$N+-ck&dWL8hm{
zebq2-^jy+<X>{dTD59?5j{74aT(_p;N?;z6ZMbe1AY-_qOXTN{-OW4SXSF%$(==X*
zd{p;2p84MB-NT=&u-@E*1xR9Cc#Y08cr~1?#;@$?mBKMt9yyG;rW0{vc)Gqv8gXIx
zxh<MafR7Jp<@M)|3y!96GR>FPWlvQ$g9@GmS9*6ps#%!()jpKXF*2Ln`b1Z@p=1BN
z8UjcocgsBaFGp8K?sX#0T#ZL=tVoA%y6D*-#&iXz`&||1GJGNs%JH?!kTw#WOyTCt
z>rW4LPF(F~s<v*K6wSRf7;2}EHy@iSZoe~yw=T*ndS3AQWCmZP1yyF9^-Kr>eeWgQ
z%Uxs#)6R2Kfl2+<z#GM&f{0LNWj5kYO5>pMdhbW;*+V@Gj}Zu{q2YNEb=0xDm0ryw
zE+Kw$WcU82K@Z<(iRcWmU00AVp<TLqE`L7z`Y}kq9iy>-iD-YzgYUeAZDkP>=x-l(
zkB|38kFPy1-uvt?DY|T*JoaC__)UZVS0%S2I4*A%h%Cc(m>>dXBmHbhta(KI$;|ki
zl^3=sO*`)G>v97?_ne6#p|(O=-aWo9Ln6Jl^7PVh^um^gwQ)E6DdXAP(fQXz`lnU+
zCB|=K{8tsM=PUk~xduo|By9i;X-V+4)yCUufe|E8&-#;kt*0&}_Fhdqphy-$UDWgB
z!JweC^Ii#S^trM(_Il~WMQYzI6NYHTSkqr}>}MT)N*BM3c#UbPoTvWPr&gYnag<pE
z#ywQ!<P7YDQ7>-Z5|y)pjzX`c(lQ3~ZJpkvk!m#igpr;~o0*XRUFL3UqRo2-w8Crp
zJq{AVp$J8_2Ih=g-5SbomS|PewVwxUOyzr>Vts7~)LI>MCcd(}`$g%^%l3^y_hM0|
zaqt$NpU=-!(@}1{Sy)`ONoIpPzv8@kov2m``}vsh>6}t*reG~o<!D3T(+~$qdL%Sj
zq798^uttMDKdSrZzIqnk);I>8Y_*>>o;zQH-E(hdZ4S163a5E2Jz#V33jn$t2}fYp
zDrO?5ROhCXJY9rRIQ;$Vj7`4YC7S*oN6+07ox-_BJ@Z{jGzFb!-7>D~ceeJDrJ17R
z?R5Md^)ZQb=+mec@14X_*_xI3#KkkTtax*?m(_YkmxwhC#mc0l6nt3u)@YwgY<^%m
z<3(q_D8QhkS;SWGw)av<YWbS*Gupk}z~U;j1J^~QnYOY10Z&2Ctn_QZbps8{<ETg3
z<eYBaWqHZToyDrA@E*q`^{%#$X?kx4;>pzod-C*I(~?)Ifuw1=W&Hc4p%lmEQ55LP
z9g-ovi&U-x?b+3>m$zv|31^}+0ao5co)&GqduS&bjVSZ{AC=}QN~755eI7(TYy`3j
zY*`(SD>v&GkBXzUzPFqg+=i|U>eH6Gm~e9mx@9L9cac7PKiZ9-$XzdgW1n`2T3e}=
ztX68-auxN}sB}wTy4CTM4H5)8^<oN?YoUNTUL~#RVQq`9a)V{k$$2J$-GgWw?Y85X
zXSu+7YP)ijgQU0B$EZxTdru(?P2<Pwv(vqAlhljk7V-gy@ljXQgI7)+986;+`R~7G
zG%<=n>8sU;WvHtN*p*T(s@;|>+<WRv-z;@3TC(ztrf~Eq$XkdoPO+BgxtIMssJo5k
zSU~=@(u(Zq_74~!huzENcYW|S#jHG(mcmLsRXeEg{v^bmLN2M9#!1o4P~&;X`AV5&
zo|R?WyZI#*bUgP(mAkQzTf3p8%hA$|9Uo>a=||%wBn4}SmJ0$=Go1}Z%s{ZsHw;)w
z!>z+&OC9dI2nJADBm4rk7SqQH@CH=woV?u5Hx1hKg<-?4_gE0nQ)Y#g?mgy>mDnyW
zF+a|o4>?_<vb>aW^Lr%`aGb3(cJCR^+rSb<VLf);&m?L;28Sy`&gI*(@XK9>wch0q
z%mvK4vhicOCkt&VnX!kJF7eE*UGlGtF5c=|e{T{JaI;#}jm6BqCfU@Qy`V?kVC^d?
zPo!@Bc{9Ygks(WAI($r@sSptf;|^K8P_Q`t<*u#HeHO>lDzjSJw87vVQEKE%U}qu%
z)9my3g8t-#&cE3FvHZ39`yaR-e(e$e1Fna^wuf-f+r$4WTo3=)2>x$e53F3Q%>Ud8
z+F0Pv5DuIy^G(MTNlgW5;C{olMPF>ES{6yyqDp#q*B#;tB`o|}D)w%Da(cXdXDY&<
zEkP!ThWgq!l!OYM4Ydpl4z;w2W>Uqtu8vcFbBLq4pibUu;eg*&a=_ljtcKNbYj@;I
zlsbO-tAd%!7Uz?r*`-{AE`vF!Q-+`v1Ofx|^#utDfryDgV#TjCe7*u;vDdF(J2dNZ
z)zT))tc}pt;S}>QVh%lhrl6qU%czOe6#8-Ie&SQDg<$RyB=Rc5J&1!;eY;QOCOa7h
zCJh_3699@C_1)RuSGCiZVL+#4LWG|Y0s<qc4g7!zqWJiDM@PqnXaKlz3q%@-Y845Y
z1Mz-!fSaYiU&0F#0C9^3`GQ0!CfBk&ehzM*&0A|kk<%m(1A#k6&45AP4FGwf)Qi4-
zTgtLa9BPUpn?EN80kP^DZ4_o<1k(TPWOe_o!<$Xubd26-aegW_*p!mkS3Hys`RRSg
zXz1>hi*@Av(LCUD0z%;6g22JRJ|yM%gCvLDTD{B-jc&K<=Wn`Vs~@vQgWqFix&pyO
zZhOUr6qJ+zAifrW3_OQ?04T(a%*?~RJzQMeA+y>%5-q@o2%sG)^lxSXco(od0>0V9
z!$W=mm->wJ000NT%dY*#>^atHerwC@Aye??M?3)GSZZ-D{rC|GO#I#iAOK-uh`}Wl
zqn;dWY;!Z*z0Qp|fM>y5rNa(h78VB}atjFwDPlJ@H8m?M3qW2(>;m_^e8$%T5i5P?
zKukh--#XyW$?`lM1RN#rpJU9k0Sru`n+(Lz+sd}t<>fZOaj&WGz8Qq2KmPp^05y>a
z3p|`pB_$;R-Z>)v_i7$G;NUQe+gJsdG%JA_=?2(sOzf*$u5*Cb8}OI`5H`WfZ^qAK
z2e?|GprHXTH#a{&eK<HhahBUU>|x@1>@&E37YHg`J>47z{A>m~I_Xl-Ctp|r;4=UK
z+6Q~yjAsb?=zHt|5u}Sv%rOqpz97uGnTgxwAC;R91;y1BtsiG*j$B+^!lAkch<rW+
zFirr=hA-%?iw<wp2zcIeyo<X5lsNfgWohXYMcpnyCt?2hzI&2Ok0Du{`4m7>Lba^w
zLW9v&bQC)FwaL@V?lrnLwjS0z0N%j#^z^<HpEqxW$ixd+ehva5-YY&&53jwwfT*{T
z)F)TiHUK{!&3795cn$cTB_t(*KsL(qbYoA?3^?#m2~jV-#mOy><Tj+VTJy1R3uFvo
zW8;m|%J%m_lsXU{$_V(qfq>Zh``hR6=kVu9eg6C79>A;p`O%Fb*#mG;0#K-3z#kVz
zghaqYCX<)~6<B;)?YVTF@W|!5>3jO9pZlamh2A@A?b!vu<m<z}V)R2Z>a|Rdj~@eZ
zs5<SB6Ou#^0DfTodoR-`-}jp3B%As?>{%<U_nUo|CyH{RKI^e{2?{merAi^`ri6WD
zkf+FC;>24y$l|oO&u#$MUNJJVN`ege1NdY+T!TWOP}@e*cQ--h*c9J!0jSn7(&XVF
zj!4QJODn6iv^14nyp;tvWTWOt=+}0#Sn`m$Pvx&a2H2pZyav!;KYrvF>0`+9SL-rc
z8FBrH-{gjGOL+4_ybc!+?;-kgJ`Jonvm9^ptY;1-s*-O8TpkZ89g*ub2FOYS83Y5s
zmLNdLe;j}Vu`bW%2-yyS8=6({%6I(T6#+Q-(5WL545I5wZWx$p>QAsPxL3r!g3iu$
zRx5oM?~q`Sk(DyCJ%ogq&xLcW(vR(tV3dTP%^5Os84%C{0s7;rzZto|-|V;B|6cCP
zQdfO8rk9F?kApT<787Xng&U&#>Dx_?zi$mTZ?-SUn%PAF4D6fHfhYSowBH*Jy8cm0
z;vB*aFV{1VVAdtqWM7cef|si{()c*^uU4p|k(Opolu#uPCq=(iR2S?v2NmfGr(C*E
zMJJu>7H#%{;`D@f?mln6tP~_yVgXqqz&stI<f_qNz(LxyRQfBz9B+t;$ANciy-l$S
z*JaS8!@N-f(>{Mu-nivyV$N*w7DWB4&mR-KF+biqLHT0GDG8@of!S=ket(LJ?J@^Z
z=?klw2QEJ<l7Mzgp1b6^%YXIF?|lyrE=0#fLH@(|2Qcxg`o1)>F+bO`FcSJ?))8zb
z2m^EfX?}&*NkXop(U_DL;<sYpBKF3M(1zSGWc%<r{!FB}AaH+OkdmX&PB}tw5j0Gw
zxDhaxh7-Lx<zV?!B$_Iyh`{M`L5{tj#R+75lP0oI;I+Rr{tJ<}8lk6tmykk2-&ok0
zzrYjQTr)s{6v1*65zt?Pz?9#Lz5sy<vyqJ*^1`D{asD|z$k%hL$cStVahp5ECk7Q?
z=@VEhqxI3VS2|}yv?yWn{fTkocFy2c;S<MfY`qQ7yU}SOgw>Ig<PlUzw_12!cckB|
zd&<a1IWH5Gmmx!_3loRkdhlp2El$_udwaO@&XJOSgY$p?%01ikw+a8ANC@)xrBX^m
zC`G~W-2{4!2m<ztPI)1>H<73tyU}~J)J+ZG6&7>R2I-kZ_&EDVyVga8%J|MgO$1B@
zEzC7LjC;~uRc!D2FE*IViI)fuFSP94hOnV$j*!$?^#lbval@pAAlj~8xBC>U(V)XE
zI2k}@D-GE{G$~Tj`0oY46g_f&$N6>**b!RH2+yMNiz}zB`^%w`gL_9k_X?sbA;IzY
zygwwFO6v7^K$v(`bI}8}Vam(M@4jafoDQbI<iCg^MpeK~b#bPL#4I<j@mXaB#c2vR
z@pNkfT9U5BMeMnhE9)(!MzlEJ7^}3DOWO(OuK|jA9dRX`K&zz+!U$AFdm(LUwta;#
zH6l?3=10a+tBnLhO>Ch7TBU;m>3Fsh`rg5jY;c{!`_@52<buX~L7-#|2)#H^MsOSF
zOFMbT7sSM65iljNV(Xt&H*WZQ4D>TFpLzqYDG3ehd-K9#nGv|<Z+xYsKJz84oozxx
zOn^+8JllUz1TSMV38AJ2kC_Ka6%oTgQU$D?k!vFR%KT&C3_KE5g+Y7PI|$i|e+(F)
z@V%Is2`C#`48+85B0O4rDWLvt7XI7>!4P7}2z&Jpt3S_Z-3l2Y5KmaZ$|TSPa1xF>
zl=;8ZZ@jlN8#|Uj4Y{f>^Z>YMY3WDxRzVcfngCy49MT%nh9Bp`*hk~$pO-)Ej(QdF
zdN~+ptq8u5FbV0#2&9H=%A^z$N=c;6$eyeT=mwM069Oq!J>Zpg6#Q-WpTzdQ`l6^t
zg^Jv)TcJf)^NJ)ZjtipMJLh7Sn~I(o-)zhho;a86TsXzW<HWeZXyWrbZyb>LEpZ8J
zcdkj!2at-|?}iX`4Ba0Pirqu*@9Rq+EDIrzAcSIxM-&4G{_d5{#tRi;5JSF=rx5sW
zr>Mxj@Xw_JFr8mt#@C#H75`y5{%K8>AhBe?>Sfke2_PVpO3`0_7e)gn>tFjxz>chk
zO=Wz?84G{g^a&C$B?`Q>-~X#E`DK><;~Ma)U)k*BpOf2g8oj`P(3M(@`X(gQ8C16%
ziU{%BX$;4?@U@N?bt?)UOz3r-vn6Y?)C)MMz%tqNW-;6%<?dTz6Ft|?3wU2E3^9rC
z&d@5~DT9ph1ALE3N;CIMf7#)E#rd`-%!2)wP@f;WelMp8u5>DToEVEJ9c=mOyN~6o
zl#V?!BYdHHTl<pHWp-uC{dAF*wf1bxdGi6k&9n_^wF|IDd9k!ZTJ|}p=|;eopzi3S
z0#RVu{>vtBFcegei~WyS;%6=gf^(iOpQPQhdm+UBn9xBd+S-}^P7EX>ZKCGwpSjBA
zBfRdecj5bdp5}UXFL774#ezt{Vuv@w`|_4+Yj_}cb;SaE1^BpF3J7k}arYZs8$EbW
z=dJ~A{bofY^v#60M!tW$$dUKB(*a{Yzj6vEcyr#Z<|<h_R>4Mnv>_x?8N93ZDGA&K
zMd`ta11n!%)GL!~A^Tpk@p?sZEU)Ye_OpN0;vxN&j|i9)T>DQ%VExSz61wIF_z2vo
zo0ZBs+33*0x<snMwW)y3{nBv%UIzG_B&h48s@9x)7v|5YL=0i*lRo7>g5AozOJ}g<
zD3L_E;+_(_=pNLd<ZY(5L8*Q>j9RC}{s=aiJbd;UT+grix}~v)DQR6YY`Mtr@{7jU
zutPeGlZ3;ZehTflLvHudy~uTy+$+3u%1xWnq6jAV3<Hg}Tix<5aRc3l;O}NX4E1jt
z_0ZasaMDpEJ~1XNd%k#0^<cVi2hCqOu90)nQ{p92T|_L$9tY0qkd?gjF<vx?`hf}i
z9BkI`5b|nXoVPN?&f7^vA|^n1&ca2H1|=yF)f|0m#_81vZRllCZ%}xXKg^+Ov`Ka`
z!`=^DxR>zc4={F|j1Q99wT@hoAq`4dl6ui+1wU<aNh*jB9(9MAPan$WUwdl~Mbd4X
zHkU(JE8XG^>3O)|n3QRp?}{<138iN*e?GWe^W|_xg2$M*RA?oQUWS$0Wi9ch8F!a1
zCbi0lO|bfO83gw}9>y`(#KyZqBWEm+WgO#+D>JCKKI|XU>$k<cX{M9LcT+vCpm0f_
z(YWPuWHAPgm>3!BC2x7z1NY96ufMO5ZV7^_=Qq9YvQ%WaUdY#u<{Vxgg_o5@-@Ndm
z%pk$QFk}L1{2cMRUV*c)d*;%H+NlIkK%sWNVxb}`BF@=*X>d28PgLLa3jGT_4GdX*
z?pkwgCzlZmt!go10Np@d*DHI{T>qRfzw-tbDQFrX^!9nDp#k=HD;qlb1j0o~gZLMZ
z`K*6E=Kl}a3RYI`zdt<JYunhZOCUdfH!v)w#3(VbTbcWiz^KkRqh45|nd=R!{NqJW
zF8G%9N79E2Jr7qyeBLUK3aK}UcL-yj4@Xi~b%emnh{%-Gp4Q$p6jv~#b{TXKB+s*4
zo1%X9c>~r()r|s60sk;z<4&IUhQ$ZpYw3d><mShLlc^t9t3COULYB>CxlNuPyc?m?
z`iqZF+YK8zCXLevMMYFR{l^Ovm`Y{DQty<}?u@1NA_YF3ER}89qomJ}$Z}q`sXrpI
z42Q5S_6BJyN*)A{?_Q7U5!#OI^|c95YA2aZRX6jcBn#@(PEN0&e8Pvt*AS!Ce;Imx
z(xGziG{0AYOo}?0)^s;aM&eHv&pz!kmki#8kSI%qsJA8bE>r|{_RZVF(sd;{NlJ$B
zT}q>D8lHMrd{S*RdN<LI>@?;r>Xy;hMg$!PHc!rp`jmt$Vi>%NpM%t}XNY-dWJl-?
z;p~;oAg6@QYu3l-zJ8jd6Vswvp;FcL;jPS-QRUYsun;2tiEIIKG5%2BN@+}$8#yn_
z8)nrr%x*Tx%}{+{Md6FvFDFF2`-TG4YE9vYaQO+BI$lnNnB+5}iwWGDIqk9`G<`HF
zez$jKFAYGYI%VsVjS0ob)O2sC(>>)!NTNAg>-CP#dS@F+*ei!k(oO7RudzOcT4{W~
zrJ-%96tyt~18>8(BE{^iD3T21tUE21L{Hy=s><DqO+b7IE|CmR(REu4(o`o4{}guO
zIIWW=A|j^=^QM7Do$E<Ifhu3@TQd$MQw3j(a+S}x3;LIO>HDgc(Qx$^cFh$VOrPjR
zasCLbQcb)3y8~yUbV~7n(pP3SI`jmh--lvQdUZ+T>0bBlGI#TY*R>8}Fi}~>kH%k|
zb??6CzSA9s3R1IiT~QS(F7Ow;J69ROXhvfgO>*Z($XK5n?w9O&_d#UQ{a~HX&Q#by
zvK<wMHJ%4TS**WN89nMzw~&XSsPki98Pq0;?CC?xPP}gHbnFs;Gbl2Ia1ip6Gk*WD
z1j0UKn`?jBEHF=c$y`Q7{&6l+y5X(gy60XTtvPa|zRj5)pX?jcJ*j~iGO8q)Nd5st
znElLrbmR0_F@fi=gsI=YFBC)}C`H^xKA7N872c=f^u$>nk~XjuO&@b1mTw)!Xu->u
zk)^$8_)2j~#6WCa{rV>>0naVF!NX$f9IyPmP=jKlD}FARt*=n<dLT679JjFUlY?W%
zk{{bo-WE~O$T9gNQ3&MtTmA`dU(i9-hYqrRLOuJtE+}#H7t2!iU-=UF)rRFw^?tS@
z9mySLeBc;dsruR`13A~3p}wX|2EIgH5c<LY-Ej))7XA^gD(G^Ov%thd4s0UeX1NbN
zhF|uoz<&E<L+st_x$LR+jD4_*nF}MPG1(7HKYzM|N`Df2h1DjdA4orC9htx%t6#N4
zvvhvb(QR(4t^Ic1fRk8O{O};;*b;ew1SWJT<fm9aSMqpMR_k&4vf74Z^u%@zU1m`X
zl!2Ho|9wsrL6XN}M;ysHK}Jk3uceG=Dny>N%Nk~MeVN@0PxbssNhQcRY(!K#^P?TL
zc49y~jl;51>UY~~xed0Okh9~|{#1(=voDM8x<qJRlTox2a-5plA7%-gim6I--ek(c
z`<BO>)?_LwvZl5MFw;$h68l@4=YcfJk_;lYo$up*Mo>4YzP}|mphCCofc5$bt1x76
zzD&8v+Vq*7x2xX{3=Mrv$w#yK6Q4u135`|k&ErNeL`OlF`9o1k{B44qoP6R!zajSQ
z+5H*P6WAdY5$wM(z#M-W;QyaG*_m0n|C#k@$xd$C8Nc)7$X<wqw+qrlzZ$Q8(rG=Q
zXR0X3ao4@+f*3Dpm!z_Qswd^8HFYwc@Ye=jQrT7oixD}hGDA|^CJH$P$dVuqt%y-+
zc}X<k8&a5)AKv`jvse9B+x?f`f`-P~50{G(D_%Ft@zKZt(~H<pTHqUs3-XOp^Fs~5
zGn#x1{MFU{yYKAz6Bh87lK<|Pe*XMtg{$_v1%3&;o9VH@z3Cm%ffMzEw+G&j3!joJ
zVNP5`lpW^$Tdzwl^fq5pUWT!}y5w(}TK6#mH>hvNJ5;FdYmkMRa*dGVHs>NT(;Y{h
zbu~6yGk!b=^K|AR8cpDgsO`f#QQyZ~ZY#K&q)iUG{?W3Z`{7)`6Qd)Wdr87z*X3ll
zdng2Pb0(w1^LJ<5Kjix*_aCkJ)BFBEHVJj=XEdtz&y@D<WA%g5!)O^kyN)!67Y2Rp
z3*IT8gO1(_6kW|+;x!v5rSKW7@^1JiRkdTxvfG!>FJJFD37`)ohK}J`9josHzkRJ+
z&2YJ1pzthj(4}NpTQizI=QiUC<8vqf)~&z9{k?nt*|+~q_CLh_{U@23fJCIR6D`G<
zzl0+-JdeIURzEB~WJGBlVA(r)K3rkW);jFb6c(1%T1i8Ck$Ao5DsYZ1HaVVN2y|@e
zA!l0nwr0o`t4#+=JJ5rFcHZ;7o@M+m_xwx3e_Ns81o1F>LDvL(=>)z#+^haUS@Frm
zv28fG(rD%BQWBQAV=A)J$iSv!Ld^q{`y|s|c$lrdG@9=Wsl6o=X?Sne_3SZcL35EP
z@x9Bw`}eWwUtQ;JfDmp^`z+!w)%;V=KhYHbCxh(javOY!hr{<FcaG=RAagqW;I$^a
z#4Em>xlG1?IFX%5yZ|`-0pWQsHtF+<i){8Z1C&J~&&3=D^99<1%@;q#$Mps7`glqk
z8jeD%q%^Ehc_jC^xIq$^z#nq5sC^4@;3oVj>p04ftfxc5Z)?53Ow513z1n79dhjmK
zb@#`9d0da8H@VmXSmTd#ozX!9M@bXA{8hvrb6eB);Oj+ZdPuPO6OsMTFrMWo_lp%)
zm;IxM$=RHj3;quATS<k%ie3iP?#rJjHJ5C@J+^jBy4enYfRFnat(J(nw~>DA_}YYV
z<E<<UIptTsbHAw&Jn}+=8auQ*NkOH8x7Gml4axl40?QPk`%0G1)d#}(3_b4ynfD%D
z1UCS&tD2P0x@-s@=kQC$N0*s*sygl6+nm)59jdkt0i=wy;0BI%;et;YOJ(da?p>@^
z2Db~<KJ?8D+&oYF8INfTR`axW!>x|)PK`rP_RzsE2N9EOaI#sC(ltq&>h>Fce8PgD
zTc2OI-i7a|ax4#BMeA@j)rW9>_+C$1VjVwlyMen>nVc29dJ`3m8+k=!9g|_};?lf+
zC9^UxNc3U&!-6MHPofRc;D^E`DI4AS${|<nMCQF|;oK0r*O{RL$~3%fOL%vZKjn8j
zmS^g8Ca>;oN3Rt}mFIW2pa<a6-RuFHP7)Hhl@3xyYleTZu5kXfuKZ8_yXWQS{}28<
zW_C`lzdwQ;E_d0lOQHK3rjbDG*J1f9VU7l1<NF&EBbmS2UTyW?$<2R_K@N*(9B`4-
zQF#fPEz`8^8GE~Sk)Lq}^=5Py6$B0j13`@JD+GGGuzb(j{`vF4)!m~D5M8M*e$5k*
z5e8XJ9ILo-cIJ5Auyy#tzC?72?cuPcB4lwN&Vd56*I!H^Dj|*@Y84j(nGZRn=hc@v
zoF8&@p^yMv5(Q=qy3kPAni=E=f&fQ`5h4Z-2q%B8jk-NuGT)t4Ud_o$-ii9c{!#X4
zB?$0eb^mt$`>%ihfB?T1m-S(kkg;sabj*6V-a%ICg<vk}-1+H_RS@D!(tzFUC@<_=
z(}1msi;-`$*ivz~81g|q_{)b(56E>knGDXnl9B>TIv&38wII(H`s>G5Z1`5BoKX7y
z(;%qiSF6o7SWtlz%hRc|UHD++3dmCuRNyJZf3*SvTx+=^gV7mi$HVH}T`4peQ2Mu4
z{wevN0{`>i&+>m9{P!#Pnpf*^C8^-rZf8{TLGV36*k}1pEUz`N2~FrkE;Tzzk>)JX
z_t(feiY+!c>s>V>5tJgt67X`q>~*Ie#Zy*SpJKx#AnZF95ooEZx*7blm335e_~2PR
zE`~zb%kr7?qO&OB>BN{K#BfUI5MaO5;ZMEz^I&s9+duwcao{|_;HXjq3Jn5Knz8D>
zl+2r`hd2Iz@0))!c>m+5`etk2F1D%4^c-fKefl^h+8M8iyzP9wub0zHFY!~0qN=ZH
z_VjwZ!A5WH;ZSsdM$}|r^eZ?s0#@y(qLLCz+=!RaMsBaN1wM8>G4vld>qh&EG-B5(
zKCYFB`Amz$xoIp05%fBV8ft;idix~kp1^VvVE>(#{Y}s}7`(Do*1xN(W<<cVuKxF7
z`t{AfYPGM!b@}YdHkQ8ea|iud;|+$aont{l@FMs2e020nFmXBLk8qJc_rMbbgb^^9
zSbT++B>wAwI!#TK0u4=jD-b}W9t;zVb-Ek>{;r4To2^i@PUJkZFhLG77G>x}{F4)>
zv&5&Sx(R^x$3KOmdldxUMbOMP%nD4zf2bQVg&?#&miIR>wBIbivmxk=cE=(`KJxne
zfc{f;|J3q7ujL)KB7K8>-y$RTY)2Zf`PLiyvLMkQ-uQQ>0F#?Z_!x=RpBcCd@CAfe
zr%?W%4UfW)9_U#^Ufbu(95g%2o~k15A}Y1dV)2<bilkv`xno>;luy|Xm}<I|Q>fgV
z@{!Sx0VjH;X~TTG`=;S#Pi}nBD{x-_AfJ8m{u{wAmn{X}`iQYZ0k~~H?8hJ7xprf?
zLJ%K`4}Lxnfe4cux&4mx1GIo>zK}=+r6maF_Y2uSFXu?<aS}H4?SyonYEe5*#QU*b
z7#?&7!waj|Q{@b-{WGLv?Brv*?%U^xCG0%ME2RUE9`?3lo*GYSO;R#X<-JdL8Y_kQ
zer_->h(PN`DPcwD@e9uBg>WG)==nwW!61oMX4rS5@A=^#URZm~eKPQhK>0C`{ca4y
zL|(%nge$;nM8!__x+di4wgqXxn_AZw#!bMU0RfUbz7^WYP0{IM`^QaqrH3ES7k_Kr
z<mUV3uwxm_z1G@?AjWSJoU8u5?gH2aB!Z^*(J7tp=l-xlzw78~!y|~>T21|5tc?Fz
zI1xMNKmYR5+K*o3KzM5JAXzB@MUBPL3BJS*zNoI~3C`8?HM9s9N#+W}8jztcoI9u_
zqNWM+lJ;m{LcAJ}xDU&x3g23t@<kE>C@aM}?YL4ZpvMLRO0)ZX&xloJ5c(aph)$3t
z=HKvnjJXkqFufGL{5;N)jVLJ+MAs-^JsVa0a2%m;H{^M3cu$*i0(KgLb7kQVEh6^P
zhHr#xsucfQT#BVNsz#c)>0Uj9r3LKpg>w)d1@+rKg0F1BuNnOcX>nyUohwS;aK)wi
zwoOK&=paA@I&K(J7*eGb6k&Q3S?TYW*9TQtu#qG!ezLVN+PeClTCR#gPyPZU_R~nz
z*Zr}ZXxrVHZ`wQv)2qw-qK*17ZJPw}<ZoW#@Fs9pHKU?<bcAr5B7?UQR8ZaWG)IEt
zND`bP%}KseZgDiCewkMM5)Xe`(u2>^1SzkFXm1`jYbNn*2jU{+&UB5WofhW(NS{it
zz3(!I$+##Sv7-BeVX5FEY;7_eiHMfUMB~?!cjH{E#XG+KXUmn@$fv!kS+Sf+X78&%
z$naHr)4-y}ItiH~9U8eCy)Dc%aw%bSwZs{^)4N^E(I!Tn2iv^br)uVLecy0><#3UQ
zkRc*a{az+hSIE=&y+w-C(Td5An}ka5yOx|bDG;~w+w_`dwS6vftl>$ap&~n9DU#DF
z%(>56*886RhsFwirBtE9n;cHrO*kP=ZuAYX9!}q^bBmz^zhW-dMwm*G>qTN}O^XJz
z?c*9Yx#BaRIpBK^ChF+wAFvZ+g~-v!dt{G)H(jufK3<HBPp?~h&t;nwlU_HU6l6nD
zPPsBLl%Cge-@Gwc>=x~Vv{dUX`JVdyXP6iq?g4Ao+ZgQ?%-Ss@No31wAF8ze!th|>
zg^ib!9wehhCD>*vF>?mD0tccujq1+3b~}!SbITva3D{t?C?#1c8r_jFUa>T#ESvH3
zA*m=SOb^X<cuqTe6)zO$;b*{Oh^bt*{S1kiH7e!6g6?6un6>J<kT$u}c1y1ejA)yf
zx`+K9!cped`C?+r;~suOLX|%|lrnZTX2z9lC$=5-9<F0wJtSnPZBuz5D4V!vjRldV
z2?t5ee!*J4T8o277BRc;*vQQtY*WkDH<CZV0$aE<3Syw9#~bpgF_<*Z8pYcmhSZ-4
zF^IJ53vNHl;OHCuHtmQmSynfALGxi;nn-`8n;MBaM!+HewlahiCiXyGFMUBp{$o+m
zg9xHJNltBR$IuDoEbMy9S7&}XvVLt8^8?Jc81~%-1vPzil_7kF6<MgX4dn&y%R6~i
z;Y+Bg#X|&LDd7vtF=sJ`&Z4FVeT04V?6OD#oTiqx#T8&@tjiXA^PgXZFM@w(=S-Ya
zfSSba4_evozfP{ySpkWDp&}RU)a+8u;8Ghfiij+d2tcp+vfgX~<u9kepC>KtkU^|$
zzefR$QKbYC9RH?6qB29I2@3Oc^*s;!;!qE7?DDxXHRC*d?RN_n4rxg!t}o_eMXxs_
zSh8<Vk>U17Je;5!r4Q(#_wRVH!-bPW2ZXm!eMxs1iSO8X^wMgwg*Z}cv|pO*=b`no
z7|$jw7IaSaD;pNiki%A*^BOInaphLka+qms<uF9Y1wKS2vFhaM?GsGMCHKESHBvbI
z5ZcM?Jab??qHNVdX<-BDTk%-HvPKEfS`JUwZiwY;wU@dW&f}rJ3Smq)W(eLqdQ$gJ
zUA4AEVaKu-)~$4aVtLz5M<uC;zFJJB4qMV(VLadjW-^_kgV_wy9=3ld6JzwLwjS|o
zj4hM;HS8&JhCN3=H7`9!798PVqW<-bpnm(uc~}sqY%LSUEVI2O6l@(-m}Mrqb`0UH
zzgxWFJBee{DeHranVAte8=+Paksk035GI$;W<e!PiD?=wYk(VC^_%&!;Y#<lZ_PEU
z^zj=o{;0u3vZRqXB2`~svYkSNkWNcduofoiR_J>j&7Udi94Z>XAh@}`fNwQFLxFie
z`o0MbDiICo9QEn3+n{@iqrsTPVWQ*R6`ui0Vz1ImdrR<a$PVGUiASq;#|FjEhhYt9
zAVW{oJ+OQot<B{lXoXa$CN7f<%PZsaL)Ry;R@i`l;bUR_KZ&DxMo0b;N5k}cI`aQF
ziOrwy^q<DjaC5Q!^WjTYN=+Gm;N;`Sc<vjR8uSPg82>mYW<x^}<U|Xl2$A>L7PW(@
zvT<}(bl7yE<Af0=RTLyca`0j*n%w@RJQP~~40NOcQm2{Xv*@1!mRTc4$>}nV>|C*)
z+&#G}`?5U(i2-=(V~^WC!5bzxECYKTGr{t9#=sjnY6je7B4z}#iw%-iYu@w`zSA|8
z3427W`MztHLtfp$o#?v<_w87l14)7Y;kMiKrY{Tyb6Y67p5?6>`|r9Sv2_34+mlmR
z-GE1p&bx^gt_&CB9VXyTp$fGj?qscGNv@PlXB6)y&RMl|2g^-AiZ@yG%hu5<<f9jC
z<mtI)&Sn{@f-$^K7Hh3$d<%m$>fE&I56|$g-`0H?D*M|NwBN<*(FH_e={|nj-25_!
zx7j3hWFAVOwUDwH7!mzY<aUqBfQzNNno@jnkEYAUu!W1_G}nLI-Td<3H$nG)qHkQF
z>IK$3`3mZXh=ZCX!{GsOmtq=NUcZR(OV$Ia;rYl2K7ARMLXqxLq*aomDL??OxwU^Q
zEKu-0kc}RugeM|mmb{aVa4BQETRclL_iKob{X`4)QfWoD38D0nIWkEN#T7S$z~x6)
zB6q=nS@ON%<L=;y`X&qAS#AWs#BFVZ|M5N%Pt9dbZ6m7s!N7ee3P?xVO7XCW&UQ}f
zC3H=an+mR#b;**&6>Vn97*(<*-`PBvUXogVhTsywH_lGd<KS*>51VpsdRNi&??yP)
zfZ^!QoSzP7`eMRS=Q6p&)--`QFvcfgnkC@D-yyNFM~Zz4YoLf?lJIEIR+)j<_QJ{3
zYqG~^XZg0k#;(yS^^Y3;Yjt>{A?l?@>2q=#v&)kkHPvpdT;ww636BR%DkCF(%;8}>
zZ2Q~}eJ!XGM0%FvXWK4?uf@$Kl?f6%cp}kR9ql8HP={O5mfX>XoZTA+gCC3(^a^+*
zr`OxKB$zQZ9p27c#9Ga-hv4)emHZsp<V)LSQi-@)$L<qrT^ExvY_XcpnigGO4aVnZ
z5N^|fTDWXeqCa!Nrk0#@Ls59ESY0esPg894@T2^)w8h)}b#ZQYf$ruZQyS@S3M$on
zpxZyqx^Mo{_!$JA%6apWYfhf%zi*XU|1%KyF9Y`<{E76JZG&S0Y}@}85cof=*#8Ow
zXJTRfhaJm&Yb|F<)amg_9LrKNkA6YXxr4(^6_F&N2@T)~u(dBPk~K08gNU`TUVmXf
z&nZkgA#Re`3oSq_)oV4w?hGqi@4r{lRHx?tbR@8!Z)U|{J{swG^H3AZYCe|Xemio%
z_2iSqz5OKSeRE0<4)P}rX8;M2fyBUpH{eAYHgS@XpPvtqv1f<OOiwrIGQ>YUu*Ho|
z3u|kqE;=<5#`fj`^v~HLre<chj^Cn#BqmdNLL(#3IZ50JhZq$=V1+Je99a@zzN3Rs
z1Zx%mIpo&Uoh4c3O%Da$j)p8-O?7p3Nl8g=?knrVk<rnLq9P`D^F0d_U0q!(D=Ta3
zGVj>eBm2OiNpC^hfW}R`6RwdmK!*4FHSVB0lN5u7jt*@m0D690@fjzIT{`7S9ySe=
zCJQ00hKcZ(<Y8iFR`!=Xt6G~cy(fNqh6I+0oMjscH?bIVw@7{yebsq?tPtc`<45#?
zm>9@yz-HJT2*7V2Iyt6iW-zd@wzs#3FQ*C>0ft&2^#N}19C^JX8~NtUW}<WQ(=Bi5
zain#~&5WSet;uy4$s|C~JM99H61vfa!|a}OrmW-{JT~1_@77vt&dm%rSj*wWppHue
z6rRuQe)C#YZ@K^m`5zlu$;qw&*BwBgL%{VeM?Rec;M4<B0KLDvv;o-T01h}HGZB!3
zZ319W2YCBN)7VP^xHZ6T75U+VEx_drunVQ9r9G$S0f?xPhy(!YdZ&pz@tQX8hg*Q<
z>wB}KIgpzI_%in4)B(tqVF6&Z*ZU#>Mx=WH<lW!j4{%uxH}E|G6s-=A0LSfcER6^U
z8ylT=&GWERP)OkNIsA~a90ZuIzjydBLMsAPJOCr`=_HVTBiYzQj1>TS13aB1sMmy8
zgB_?53F6_m{RP?4->)CWhI%3%H@4ly;s8iG0Djh%022*Hzyb0<0cdf6<~Thukvv;G
z3;`f$eoicQvN52?Fb9y}&z0*GmfZn~dD5;ArwYdaPH><j$l|_;x>sO3Zw_IgNGq_I
zjbP7HzYa&(-Q5L<aA`Hllt>7;9mKM*v9JJ!p6X{HHNeIUkVa;*S*jQs=Hhz-IRg`|
zN?e}ly3d<{Oo!j7wgLQj)<J^tk3fJFe)P+i7>Rbblf0Ie*PEY8m_s4FCzkS#4o^Oa
zVchW8EjE-}%QgX&%Kl;qC_;pz0C(Np<sp!7Eit+P0(zd*;0E=>BRe;zrp=eG=gdpo
zjs<*rgjoQ=CqT(WL*xMfzkyEBskdSW=;5lRE`i(*LIOWoPK%3+W3kCA$^9%27wS=m
zo*9h;w#Tx4Dl_xKt(}g(w*mSnlMk@-ht<_g0I>3bXd+n{uj}Jnd=Bq>H*zKtKP0u0
zSo>HLr>|FqPN7$V`1Mj}srvItQshMAvdK&4kcl4MfUsmaKw=^XfX#XDdmuny3FHH?
zkd(11B%lK*`$XBElXa<_JoD`*@&P$VIe;8CIm29Jn8k?FbmXKMtd#~G49W^=WNQlI
z3KV#qoTZK|>&74JLu%+fz<ERgJifGaSR@;eLxqw$i4S1+Q(FL1;vwO2$kkBX<dpKG
z@&lTB=mLe4C#BG2IYlw}0Ax%dfjb0R_aWhl++I{+KPzEti;%}jN*DE`+B852AFt(f
zd7vFeQ$q>xU;-+db%?$>&HX88l4h(!Jgx_zLIwyc_4;4O$)Uz}8ccuokBlJu7_UW!
z3CS}B5Go5vkC0uc#3P=5V#rp;iTMJOJ|)Llku{<qx&`R*Z+eFt+auqR33-v8?ajn>
zNE6420kqBDK)$_1zo-n2%h1<G#f!8<Q+;SSs>O87jEr^w8!gND(8x$ao%|&k1%*t@
zH-G^jNXk|`kq`sq0*H$30hrMMPN_PrY5YM1V9L@m3|<0cq~I_6c)aOOw`Zx?GZ0x6
zecu2i=sOc1L$cA3KC6N`7Yw})lUfzf_a`=tgz%MG#b7vv)8QnEvk8lcC@nAV`?N|Q
zXPYp3?u~6swiuU|_F7&2lWo2qaYtM}E6QLlKnhs`5AMs13L*g`Mg;+{U!NqYC__uY
zAs4uv;Fp$}`VAvA&;tsr59nMne`^gU8FXIgVk=?EbE6w2l1Om>?VKAhWgqZTz(%B%
zN|i$eZ3D=UN|muyT#!2hcMPILBvr2Vi(4<E<QGKellVVFuZ|BweTw-2NuA}bLsQ<z
z4^luW?$d_$k@|^z<cQ=OK!JBIKU+RJ7-Jusw7g5j3-g9H_8i4?UKk{HJNIejsVp+b
z43|j){I`YXkJSeF%vZ|&`K9T8ZbduD+$5b70a-d)+}71(6_r?}^PMCVaU9s+-^)e^
zL!+Z+2^}P%QZQ8++4>1^e5Hb$dkJuate}0RK)@>$;tlz|UX=go3W!Hs<P}JT7sM(~
zmbl6uEi0h;5mZHftNC#}DKEx{J^6E`grVKERl_~cft*u?MD7SMA~}NfQ!G3L@G2>1
zyy<Vi1+8nk9Ge9|FvKK=FMSPBU_r>RpjZ&{V3LvvQoCg2O}a%K?AtLC4-^BtU}$2J
z8+h?cP%MHkiHENglu#_c5ZQ~IFN8oV&}>zq@~u;63)Rn`Jm@}&ezs<v0^u^hto{-M
z%Nc&)_mLD8%@+7gXCNL>n`sRy)C45gBwS?QYx1J>HHZXf>sPrM7*bcW@$c((zZ6Qs
z1~@SZSc(Xl7rO?S)ZxAV3{_|9j&@YH?4ccHr39FQ#I_as*KDCQ=p4%Wse)ZTEZkZu
zmo_Rtb&e5%c@QXAp5quE6@)Ulr6i6M+X;$T3U66OB@ynt_dJIZ*FzrE{_*G{EnkTf
z8ZQ1dw+J36$yb_&j3suX+VhwCkd6V<w}h*n@bv{dUbmt0jt14oB6yTRY9*%Ui&3S>
zSwTa^EWK*K)p3l>)FCY<CE*6&nhlkDGvX-(0U3`1i(ntt%nl3@u=P;kT@+H06#G*}
z-oXjblv^pu!U*|@InbiPg8anEM38;KNSeiSxAd!VA-T{POO?fLBl)r-F2lsjo*SG0
z4sGD7jDVZa_i=i3_*Uu-rOZ%iR7+n^r>gc#pE*eqhWQzikKu&8pS@XXT|P)aA|D!h
z8OI;MQpH(p8V}R%^C^D%*&Y6_lf6}Ue$|36g3#k9>{GEdMr<c+z8kukG;9@91g2FD
z+?ZR|5I1Kuax8w7UywGw{zg#f4+{jkk@aI-#?U}nYN%O77i&K<pk=>w{a34)(9I5s
zJ-=O-Xpo==j*;LQ%*cp=^IOc7+(uuOzJ3_UNG3puC6@bk_grB|SW>I%O1)f+3<mA8
zy(BRd!cgSv#KZf&pHnTjCW~JeUZDogkhkj3$ox`#K<l5FQA?FU*qE_eQQ*W>vF-OS
zs$n`)m{F@?gr2Ymzmg<5gB{nD$;#zm!ZIXBnCf-<Ss3=P%16LWlYn5j>U*esbHqgO
zKu>E!P=6Ep*WR$|)wDH8Rz*eMri6zk6e6i^Ct1*TmXkx=v)mj1q+SMv#P4B3d8bJ{
zvc!9d<km`qMFM=^uzPO+ke)~agNnoO!k7eA3kgj_kb=bTf(bYk121hcDcKvycL8l-
z?s}M#$U#N{1};ouQUN*?ViljndD<+;gv>XcD;Av_jJW>AFF`<2F}m~Po*dm7#!T?^
zAI-vK8k2U$VmMf6roXbEp%V=k1r-SrTzjFTO$x9dLaXh|&okAECowJE!%AVdOz#?#
z8O2s-7Bl(OvFTn@(XqHI*yR%4(y@iJE?okrH<apc3kL%V$phy!BpODr2Y(#3kkdJ&
z!!z5c&Ty%|I{;CWp-@i`7CBD7hswZ&OVND`-HH9nApBt%o=<YfAE7<m-CzBS$Cv*Q
z+{*M1&vJd*u<Zs1dhnGO9GKoT7;3Pq3S<q=x&N*$93urb^ovi*d{7x~BpwVur;<^{
zZ^s>sRo02ajQe;x1WxczDRD3uA*riwNP_aV@UWivoCmPO){g`q7R|Ijr((_dRQbPA
zTaQCOlRTSH_aGZGdOS9~yu4YLQ4+X&XV3XmdhO*+;yZb1gDgJ%ls(F26bD_n^022c
zATHa;=A`Bn`T?7e*BZ<eQrJI2=yDrVZ)EGj#e9Wdt#$8OMpUR!j2l(URp!VGef_bp
zhX+5)T5b?LGyP0c6W_ioplEx{n6WZQ9uZk|8$|{HunMcqu+4be7x}9pcf+WtL8f7^
zozVsEchT#9D2VwsO~{)^MM{-`JKgIBNAUfn@GT!$A1aFc8M0Cy6i$JZgi^|AY1pV(
z7kM7dXYybzh#->y0hX+8r=|^XRy`*Fy!;}s^dNsj4k{+U9d|g1lN{ry!Z*|C+HSH1
z2P9N(+&fwu)N+jc3@l+{5J5v~sdC8y;Hu2*UGj|lKeb#VA{dUtVGZGf>zW%doq7AT
zQY5sB)QrKDr=lQ3$+6)>hs@}NRK*Z?N411jos40r-2AIea>v&Wdl8LCwH%P$fUi^{
z$>D=oF^T()ppfn8w5c+xA<${Mv>cS&jKPf9%(~a2p39R=;LLw7<eSv0LFNJq$qHE7
zs(pcfR7qOK+M^7rEz(_T;o`LV(<1s^kinEUzV^*SAV)A`nbJav$#Y_vPC>H4_A?J-
zn!^w~+&5K(mM=O*#v#>kdkZ-#fc1(YKz^b#Fm@{h8S#XRjLSiclKWyukubIWXsQ}e
z<w}NGcx6QA7##D$KUPVqAEd->(GJ_6S2hUMEOnenhdiqJ<|)#F^Ajrymw_Jy${b@*
zy(zNGJB(55dT`C=%srY(T_v(sxX`qpG(vP;>OyK9nqX;UW?kJ-<;>qzAc*QD)CS)(
z4vFNZW){7uP7zma9UZjyVzokIiLJp(4(g&$1*Bn_nf&miP0ZC$5Ciwhq2B*Yjkv%F
zv_wJ+AD9#{N(gBP6=v<_zveJb38Qc_Lx(=9>8CZVJ60(!wA!W8(j0=|(%DeDsd{m8
zx1-!fl%2?oO{ah-ds-D(|CJJ%yvIj_Y*+<HN?$V4$<`G<Fv?Zvh*$`b{2;-v>QJ^;
zCq=zr*g6@?o22@#IE;Nt*Q$ia&dATAq8EHHq1V3(Aq>)rIt<d#%g8PoO*V0N)<yL0
z>^-M5u2g8MH{MYwgtN{Z(}8{c{p57S<(12Y9ltvXM7<p<<~5ToGhT-u-5otX@c>$Z
zF_Z1O=fO^a){j6si%@t?L6;iZRT>h>hpNlefb&~0L+2^$w7U}Rk?#fzg)eOHR#v`B
zPU#@)?ol*A3d`9=Bwy}EJV1~M*2{?H)zk$HA-`QQ<`+1mP-Oc5I6J2%Q2;18-?44m
zwr$(CZQIrz+qP}nwr$Vs%~N)hP1RQZ#Hs2&{dMaW4~#VxYXHB{W!vAOmC_6jck8v|
zHu+&x`*<Gyv9x1!z<Y7f+xIp31sHjz&{TVU_Owo9AK{~x@D=+6BaT4wB$Rk{X{^qy
zUAArZ*fH&ZILvw}FKmjwSKO4~jdl_uJza=#^kqyjnmW4v@F$K-$G6s@yF3jGA$ykd
zJqAf($7q0Cul}3sinqG#`)G2?5Hqmv(UzF6==wX&P)KNU9prFR_=1A1`&=5xq}M^C
zO`T5JnN*f8XIP`|A^GxgjYQ?UiF`YyCMG*zV{I5CNZIJV^o@iU`?@Xn3$V(RSonWm
zSeE~EVU=7AojvSL=>AuVl|hK^|4kFH{vU2~u?K{^(sIi$1PO;&NMMYZ9C9Z31pZiF
zI3u|UB~7wofTE^kzR*T!i^Qfg?gDG}LKU&hY5O_19De{TzW{%7K0Y9!E(JLyp?3Fy
zOYY9%>yBTpD*W%>_s#20s_*+k<E!qVUG46M)in|RNE{h3V1RK*VfW-lbE6Jhd%HUr
znAazTvaFr8Vu<@=kknc#xND@pj|X7?Myye>YLzi}Yw+cl+Dxx1gdQn4)qdlfSC%W{
z?==&KA5!f>twod#_&&QJKR5_Spd839<PBXK*Y9<@`f$~H<7t&*$Vey^ARv)36e^uA
z^<zZJH!q<UjT#kTa>MzXSTeUd3MQeN=v0c$-$@gM0LS!RLUmfL=K106d5>!A6HPO<
zb<K7~+#4Kfr-&yA)whW(<Q~UjO<t*!TF6w8xNo9I6^d_U1jrOc9<>%hn#aH8LdQLj
zUE;WhRB*>&Cu4%g`vj29n7@B1mKyx+?M#BV<WTbyDUy5o6MVZozqa&>qvJzDEAQOF
zar+Cjl`p`X?b-;L1w&ans#@?G83^aN0#_?l_YBe}@MfQs-bEjFxUr+|RLPecz&)ga
zYe&UsAC=04g1U|Bb*U+t|6$02MVk50Z$h8g8g1NtTw8SNHMl90g%4qV4Psp{{|2FB
zadav%v!CUA2?c53UUEN2aldX|LEy%5gQKP-Mn<C^W@o_#=`FDDAsLLevPySxTC>yz
zst`FfY%otn+mN^*>?Csh@dd*jfJL}PxMie?t0E+Zj1M#hLgA5S5t#3}?*R!2bcuBd
zjfa#ER1R1SIT?b|B&G;=@J|pQ!{3Lrh6E364s;A548*F4RS_y9Sb}-#|JvhYN4N}m
z4n(Z+vm;?apdA1iBGsp_3AQ0>L9iKOwe$t8$&!bg8<OA^ABJNEwt{Q|iv(5%?vBV{
zvRQ2<I(#_1R=H|!<JDsS?g|#$3nP<L66KO`m6eu%%PsTa+&%g6IT;079yCO#Rhelu
zCP^ph<$et}>dsbJk8BRDdsD{x$v$aAcBlW`yid=*cI&?eMnD+;h&6~hehvqt{k|(M
zNtu<7m3qHA#$GdT3N3If=v!p6e8TBCm4)aSyu)aO<Tk#a!b1wF*1|-@oP`<KaZ=Ka
z|C6!c(NWO9eF+LTEA9bA$1)pWP(=Ug`2=f{fXQ?+2(7|mAqa-8d>H|jfJkg>LqxyP
zG?banO%>5y+U+$1ITOOCcqU|~kazGAG`8FaS^y{)EDIwY(Vt+=<gA?BL~Ui9C6dZI
z#(dT-Vije-+wM52yYgWF_%0aibK!!&n)Mb8w-Xo`5KMg3m6U{jl#I-YrYvDL8{<dv
zm>}e_aaG6DG!zI4st^#sRFtfYg#I;EF{*3I_)cs|%51@07A-Ey;j)N^FG-*<77#Ux
zsf(==p$TOST3^CvaKqBd&!`}(pWA0a?e}qI^Ep_^>3_-0FeJsN5NV|$`1jU!`1~xU
zh-T^VxF|v49I+`@+lIV^`((XnOrG5b)uwAx_h|X?6xuDuVtAfvYUkBDY8=33t~B5n
zyC`|3JF_=`{=D#kq!Yuk)5O^iJcBaJ+|Y#e@oq&?@F(q4nI5hac-P5=EB%H?8qxOb
z>~FNKH__?0bVoxirjFA@X~DPttYEA6*D*)Lsfdx;&4_Q|_TC0-luAs$ndtrQICVZ$
zfuQ3(V*<F~7jS;m57ih;i4eq@!VtWKhF&@%3oxGk8TboxTf!D8TK3yP!A;U&>N1Zr
zcmOM~2w#EOAZRjZWp2B}Xp5Li0CH1aagKwFgFC8{`zOTIFE&T)UA*~*_qBnUHNL0Q
z4K;Oy;>d2=Yx~r!NC-e@^;RE*lNUCuRMT31EPjo940aBLP&k(v9a{g7M65#P-`JtV
zy}wyk6`S0c@U-T%vEhQ`p_b;WN(VKBK48w5VnMc16vgnM#8<7x1s>UkE@NN#5VZq{
zW(IaT%>sPhRII7Xa|T`UG3w%Eu$48k*s5bHsVX2K@`1&u=#?CdMqxRd@ewNoY9}{z
z(|7|YkO{XsM?)=@eMB3BJh7Pr%n^}Zld`TWFo8!WVKq@j`lsE2NRCM4xJ9<kH8{tx
z8+lrK9$Tot-u}~hTEZx!P@8hCaQ;VE^VPDhw6c}|x;?Gu&=H}f7gn~Oc#cy}K;y&h
z^lo=Ke_71J+81#4lrR^{YF3tpu%ToODzY8{9k=2}aHX#U>GnT!;J`LJC*cI~p2Tk9
z%Ajh)$DC$Mhxq*7C7kHIg|{{D5zI_FQM(LV>`HyXFuQ#~opwN*VInf+u^fx8#n;lZ
z&JvmpkTt=G`^%OLl|3yzl!>`FTQa$&$A|4D9YemrE1qIFF9^@mF@PXICCL<t%E9Bi
z?@QXb#;2;lk2%G>p@@RV979h6g~`FcDUbHg1bTJh>*RLp1Px#Cm<yQVE*;b}r=?Xv
zjtr)x@MqN%3{0;WyPs<6KwL4e8kiW-gBlIUWQC%f8(p|)&ZYJAgvJT-e7T$d60ui4
zU$O@*p=?nhUb=Y?mmyiPqJX4aB{P#=E#mqMw5>fE{Em)l&JLPDLZo_+M_9KC`I`p)
zp1m~XYn95Ui_CrCP<B`h(_-t2f2PjDK1A%aC_P*ZOlPs!(<yBo(CG;=d;Z+enL`p@
z*LE3R!V+Mj%^Fwh=4NSzV29lhgc%{fvbyE`e1#uQS!`nO=rtW8dMZHTtfzz=FJJ#l
zsMA-}`lQ*mTJ*GiLrQY>UT3_aYKGp-^D;P$Cnz|2G@-RA)avTR2B$F$@y!`)z5N3k
z$uRSV)OtUQT==pIY-@PmAz!Bkj+JZ-Y=XWrWsp$?+80Yzytw;PJV(bJ#tUdlZ8rys
zOLiuC+%=A0o0O#Hc-f`1FK^r3%l$qjwbqbtT4%hLFrg-X(S3GF>$ZfC^|ZdxWr!+g
zelm=@-D>d!i1W}Wo^}%T*h9T+U!ZS9V!Iw@d^TOzwA2+o&Lv1wv_>H4cX8Yl%ZVHC
zas*!awWOQvMC22|+!Y>MY~B6JFNZ(*;Ie)uRxr&BkfX=}GM<XA0&imcjx?Mm)khrm
zmE6WTu$~|b*!uyDU9`NP|9JV^N;W^Cu{I^Y_#ZVA>0TD^VPPFL%&ZgeA|t~q7TCs?
zQyLslEk@--Z$wW1x}J}qxp?gZKR-A0tJ$~-=DAv-lk^1SV4iyj=3vzvIRPi$8l7rb
z51G2o)Is48M{cL=<>u6-WRBUt5H%(y&(|ktAo^v)GY#Lcl1gHbN~$o(AqSF0_EO9h
zjfXHoAg5xNac;4X<=HCGiT(95v*M=;IC^Wkqn=eLSkIT}t-Y=AZqqe@oRp=7`^q#n
zru9r0HFO4Ew-*au8N8Vv4JErlQ~ot&`8t8Grdivw!t2H+&cb@-jzxDn3Kky2T;Kj&
zUzO{q2w}#axuNE0EpsrrmsAJ873FwyAPkF93h;_J2%OK{a!xk(!l_Z#J}asUlX3y`
zp&mr)5X`iRzk|KJ%UnAT4vED_2!W{zfV4ISk!?6$ZTQoO%mc>P;^VpB7zKs^(5o<(
z-YA09{%4bUj-NV*byTNUsu!yB_#93PI%`ZkB>vO6o3jyhCn=g-Lf(?-P>I3;E2Hm6
zay64nI&2@&yL=r_sk&OaD*Sp^w-M6x51a0NOW~zxT#Q2&yeoel12q^J?E*{K)oWwk
z;-|vo54`p55Z6KU#s2EKudWa!by9R&VjVTpG!v6P@^SfTv`nZdy%5n=|46m4;*~H<
zi>)OT+y^pJ))a>-JwHff!$BkBbNYEFbAJRz@){APjmZe0eUN$~W0;_gabf-X98hY$
zfLi`El-U&RfN8=F>)-xCppx7&c!khR-O5GN5=lcP6;81`&=3KGqWBQ&aT+#le<XYE
zLKUq{+X;VgOC2zX(y8wdFrMgs_WqDT+q-!#gCoIzG#-dMhAC!!ub3FqOW_W!aQ~Uq
z$FHG`lWoX8(OKBxIZb(-7Lwr_2qVv70!nyH!4N13>clktzRVZX5Jowd^QIVq?sr1{
zAie)-2@HSfPhr*kqhS~Y5nPQ#Raj%;E1c9@p9xS;D`v^Mp*Q$ILRL?rQI^VO%U3bK
zL&(<WmNTt3<l-h;mTQlG9JrqLqjoUVu=xZoBn?sius-#eEu}u=<<8WC0KIr}JS^1v
zSVxX(YDz6gvoR;cMOD#M(@|PXM67xlm{}0|QJ6MDz(`@t0swnj581h~v<~o?!;~H-
z$V*gEnJ=>H%QBZC5LY1ST%700Q(vP{oY#mUd`<eJr_48LNxF7<)KL8atmPo(VWGUk
zi*Yiq<ZTelLbx~n0=!LL{I_4j!!OW#K7~zOX5f->P(Ad^exwx9Mi@{YfmJeP8u{RC
zoQMoLr0MMZZ^Q-|Fifl&;h0m2w)WDgQNnT>_lb}v@f@2Wk+$aOSdtfhXY(E{3bCZ)
z>gAJ#XE(7<I%;<8yU_|Uo%CT&z@Kn*lbhZvmcPh7Q-?4O7bbk2M6Q^yIK!D_m4-{>
z9Np|y9%MXmd<eI6Q}5j+-(ByME`J>2{j1e24<yB68MQgQoGE<M{v_6yu9XMDHNxtC
z>V~?8*`B6l^d5Gp#xAp`9F+Gyc({?(lH&Em*Xb6m`D=>xAZQA=7ju|!6q|TOVLD00
z^H@zMGovgkHyxh6OTYG|+;>|W)V6_jM_2AmgVI<t{9p}l;*#HZ1RClk!eC&0u;MoK
z=gp>^K|OoK3)P4Dx_#o`zAP!g9o`+J`?=DGV@DYG4xS1R3Uw@hn-0t3xt6x0+rqLX
zK^=e1JKM582qK*MG$Dr&`pJf(OLwx{Vbc7jH=ca4$9ZfiZE<^dLCq^SVcER>@Oq-<
zMn8&eH}5e04V1r^WA^}q<CAMWWie|7a``=a5qVVg?NeA_o(mj_&ly6tluj4PNOV^+
z^+j|%@E?~L&TZ1RiEZHz3b<uL*?YhdbK-qDW`+}vr{EP+nh_}!nz~Q?7--1|s2Lfw
zPyBG0X0UCjZsXP+;)&~4+Y^pJN{s%uCaRE@JA^Wbgovnx<7598@*TKA;A}b;xjnTq
zXV2W(y|=cYS{q1{R@%sZ0CNLE;f@T(9MS^zI<mX0EpsDY6IT{Tsd%te-)Fncyjgs=
zvlD%@BtR-|93JAH72|3wQsR{j&R5P6VbO-T{uQa4B$!(wqk^|~qB9jkcU(2;+T5>V
zdG=IpXZ<RuW+^17rEL%>k=__lZyF{>&~gzonBp;yqn&;V)61WU916ZB5<~r+<OK`2
zW#uWTI-ukQy<EZyIgP|3n7@)YqdU?RJ_Mb`a>J6VUc3uKDSe#o>q!zz9O<Q$3|JGo
z3@SNQ#gfK`@vWa-B$DhSL!M(%Ml&^P-1?&yxWd@gM~Du)y0Bhcg1Ci*cEobZlE=0N
zOIlih-@CEKiJC>>ZgrAI{N=HmPc=n9B*hQL*&S;niimNev%>X|OiS0SgGd~?0e^ZN
z3N<;t<zWhXSwXdcCKv!*0rh|tT?l+??QF4U<GveS%yN<8EHeHMfcP{eu5eE}hvqH;
z8}*>|Xi4=sC%KX?FzdM6+HE0uy$Ad@l+FfujD<cHz-8$4bH}JD!Fs;I1iJN7o18bU
zZ7&;1fp9>wwZzwd@$hG*hRj$!BN$F<L(bO;L!<fm>zY=jBRRPi-1T|$j`9_DP)3ME
zfXv6!=o!(U`ifr9z-bm2^=|iRA0eA70*^R*ZAPzkEOw)5N0?VHGJ9^h_AFXN@II@N
zoeKF;V;0)wBdE)euW%zi9W0F4oF}5h3-6eGQ)e%eI&KYfsEX)vF;c7I1Jcn;-~s6B
znQb~3W71w}fX!n-5c~1=*+ZwX+TT&BCVhbB+o@?`RN@9K*-n2IfWldWGlS|<l8^em
z3tr2E{T!T%or;|;*s`}puomufZlf#Pp;IZ9L_&FVJaX#@{#^VI1lCi&h1JADx=;4!
z0H;!HpfTOjscOp46NXb=^C72p0lbOZH;R_rT{sNXW<$CIE*uF6iU~@LjMrpm048UL
z>PXD)+YVtXO7*h02Odw~0#M+GUDcY!88l4y$<3GS?na*=-WqJC5$nY2)sr5Oox+xs
z+esdp4cMNfiGYO)Rg&Tb9!?QVpGxiWL}vK=^<BALF_^=l7DCQtG)1tln7e1L%;@S_
zUwm6u_AKW@rj?3E06gTyll`<7SDFmL50wD|wT(UZ(PqunVipM0l5u9ELsEZuHsi_u
zH@h)46Y_pe6L4z>yV~&u%~zb*P1YL114Gu(G1214U`G|QOz^}a(Hc@&YnE-ES^VBw
zx?QoCer}17PKtU_fHHI^=Lfl?otUc);L5#{rc#|QZvHk%qRZY!5hwEE-<33%M8QQ7
zv_1cG!TkK8csLPcLN)FEk<E`m+L$=c`qLX2dYCLp9jv3z7FbZd0NS&O%Nld?D$1Fe
zd|v+hCau4kc>YkQ^g)#v9}SgJmzA)%=4WB7Wvj};s<$&L8Q5=^CG|<sy+259-*&V=
z+PGW);?2HnVVv{EKL@uVWZGZ*8-FjY1vcdT%*EEPS*vsHa0CT}Lah$;))`kaYA$D}
z4@yiw*ueI36dRCQNIuHgz$ke7FjSvxJF_~b(j_Fu=Jv2lD#CB&w9m%F+lQ8f{lH63
zb{z*|7OkJAaE!m#Uk0tL+d~N1Toi(kBOf6zE1jl9ja*#=c)@aO!S>8e+?3?Bo_X0J
zQFR<FQHE`}1#)k<yUep&QyZ1P<iKhJD%CnYXT(26wJnIZu<CEFO)g6%hpz@=#Av1R
zOMATZJT!C*?*Vs@^su_+O?X@0O~Ea~hKAb*8_y047%)?QVl!6NN$hvl^vw^b0!Yp0
za!)yKpW&PN8&0?K<G|oLdhI4%_S^9?p^a38AVl8)<Lcn}*uZAQ<EbKYHXIF^=?fML
z3{$xn&tCBQEn;AQ97e62kTj-o5LXG%RAsFiTSeMQY-N5ACs-cjHakB0TTb|Ts=3~%
zRu9Ou+b!E<C+kW<)V5SvM-!I22<~m3R*+JBWCo^^ml-;f2ni7j?#?mnq*;*1)05V{
zDqgT}&Z^mgR>|*Ya|Ugj7@3uet-)=qYijLmAuH7mz8sjx6QjEu+WClG%RK;xjBuy?
z?1`=iTK!r_BCEmRHxmt+D)y%MSRt&$S;!}YH0^uusFCIpw6V<uo@S->%!CxUOuwNW
zzddKOefz0&&|OE3#vWLeznft?ar4xl*(2jf5ib1_dc$O!hEHyV$b)~8GAY;8&S~e9
zT1MZ+k|5qBy9yC5p3UCh?th%VvN%_oZQ5yCUuGw9*B0-A8+bdQ!?GWP>Mr+c*?3P+
zJ$bV(g?_$+<WAN>4|EAwecv@n&$8r#c;12_f313R+65wQ@N7Tuy832f7zX=hmgfyi
z!7w*|3j2OCkW+tM)?)7`&H-vSet2rD)`8}2t`=AYE^VC8T2L%aP?*_#J}TQjypC6_
zg$vuwx9rYbo))S)zm}hEKRzFs+9<HwZI%-?V)+gZ+y#Hv?d-{S4mUCtDE-0>cYCk|
zdGw-gQNQY~i0;V0jQ02d-r>eh)>fKu%>zHO8UTqqrmiY)KS5^X(}eU68+Y*l6%UY5
zHyQX64=CUW;K2%qvshqAP6iGhN@{K<2Km43Cmb-@cJ{YH7HIaA_E%$)79tdo<m!+<
zf#1}thFNYWPt9q8u>PjEa9slJ)a}xFzOk{PHU1zMY9jKEuPgPCmBinJ@2ECb>Z9#?
zft8S&?>d;Qd|tT@k$}ApG>e!w*q|hvp`9wczUR;?;GkyYtq2#D-!q(jp=5-Q;P5>b
zKEXQ^yO7t*EX@1k+X}}aimN@QI^5i)*YEU!Sto_uvXh;Q*1mO1Yml;&3Ai4W`-H)*
zvkIvgYQU?IOo$;RIvF(@2_OOr+$-#7;-u?|GdzkD(?;DG?pidSe=z?^XQYd=b1Sr9
zUb_@j^Z`3~DS19v_zv03-ANLRY9Sp#RBi1%t9}?Z$wxgahIM21wWjK!cPE}x!kib!
zl*P1^q0yoObab$z>zP)lY^9DLc(D|*Qx;m7e|v=Bp-53^)3SF-tsR+E?Qp^V>zcH3
zrnz9z-%U{IRRuHcl0>x;ShHccH_g%hErucE;ch4>3N8M1KoeF}hRh>&f_JDZi*`d`
z&|A)QE3HUSC<{IhT$&~b%>XZ0O|T1vEV_U2P+AnXJ@UYv%ai!N0G5;Hwo5?3sDOfy
zuLwf&A%I^7c}RscxuCg+AM!CidP+xRAR}up;_fn*6O80Nx&`zpiFpCaXTV<`x9XvI
zz!8)-RQxq}NZew|C(#JLChLl_Ep9s%xjcHPO0iNmFJ3-&Q^n|$gPOVzpNpD-eDDpK
zR3xo9zI&V_ur2KwQG@;(7V_S15-KEGl8_am)r<bMZhN<f)@qhwn!X4%=l~u)1&@n*
zoQ$><RA1NLw*BYKLU~I0HAPuHx=pD!(bNE^4hD+8AI&Kf2^ouhovSQj{`l~a4^A@b
z@dEnO9fqks)cgy>rdq|=NYzNy!Fk&&(a*j$*r&jYd`4SYU2ElE-H(`38`prYf~|_K
zjBfLB;e}j-(@f#J^rOClM{{{jYBT!efewheHD+^*tX?8;J)-!G<iuQ?6C)`LBMYZF
zgIEEOw4ql9Ni#rFfx20QmA)BkiMsU=<5G;HMV8s$qa#$))S#jwhk9@~smi}ZlH{%u
zWwNxYO-{<pMzEC=6?1B2sKk<Uz_uhadX<Y!8BfUZLm@lJ$EcW%QIr#ylNXMUw7W*W
zho~40!3RfQLtV*T%#zk@{vNq;ueAU4H)r0UT*kP)QX&BbUAr=)W<Ct$+<vC>QO;4u
zn$YDuBWX8xW3r!wYMPqS+TiEM$1NAo9p|l;h$_y_vLq0RcwY)@4UN27n-pPI(9Zp1
zauBecudX&lF+%w@rP2f=r2r#180V{ZjB7){RL?CX)`fIT*OUp|z1GNN)u@+3L5MI!
zk%<M9Qd#|~2ToPdps4n+t(n^5^if6?d^a|GP{jB~iJW9>X@-c%X=5ejRH{H=NkoC`
zs7Nqn+iN?&Xti{-lhb&F>5%KI3`ZOA&`lvJ&Jwzn(<cds&j7r_6=VJyEz=5@4eL$3
z-odW>L#<E3;2osrqGWr}!L=dGqK2|Qr(%Xv^^5oKDFg_#X2H*7J=o>wcjoH|dFxB<
zE`^v@C=NB@W@$B~-e)d^oC`P4leAdQx;3kbTpE3Hwi2TOAtTT0?;rNbiiH2>Imhz9
zD**n-&p9SGmjA*%W~)ItBOSNo$Y)@{N0tiBq$@}lfCs7y^Yi$R74pMr=J9)g7qd<m
zN@SASB=z*SJf%$KNdMcL-j&g7LYB;200hy*1q4IJdv-wPnCL%!a9J)qeFF<~x&1E1
z&(r;fPNbyxO&|GP+4+5Ldi^FK7>yz_rblnLQF9wkc7(Ck>|z%)WU_E3u{`XXs5Z)G
zXv4Q<f=efW+@$jZCmR7)<n<lZ8>YEmjN<UoLidE>k6?cXG2dhn$7FEbi*tvE%|*%+
zx<eBX1)3Z;teW6W_INq3T#cIQBQfE5l2dPGBxajR(r+xs&E)dw;pA5LFcj+6w5B6c
zU!1=Y2-&ri5MR=~Xk5T}c5m1&Rl<-q>f-dUUaYHF5iT(iu2?IJozZ+S$7tZB;M%cT
zEGLjw?-(x5Dq~eySUx_ZHLqCCr(cm)4zo&iFO{s6*-&^^L)a~tSv*rL2&Zw1<o>m8
zSifuBFjgWfvY>YDmbGcEkU_2H&>>47?Zv7>?c^kxjyz4ehvuc6ux#9HvM53B#MFw`
z?0Mi6?Nl&Iz1om=*R)~TOW~|!)p<BM$<i@uSX*-S`K!}_RI7F=-GbaminTc`R$P=W
zN>H>Z{Wn-^WpK&j16U?wrm8h2hpOIjlJD6@*Gf@wYQ4~_y0FrAgKgc$v(aFs8vV;m
zp<n4D_oyINS<3VMT@IKalRe7a`3lewqh1hI#2TMDn01VtDNY7zA`xMTm@qjnVmvtT
zjmUwGAtg<go1a^9YOpdDi7Grr7`Q&tJwsilieLp%VTiMfFJn#InlOi?1(8xh(<q!G
zIJ1qE3R5V~0{u}FK6)9nGH`ZGZH$jBm*7|8ysYxjazqZj_c(Ult&2M#$KL&b2uX`f
z7iOOnMCKv%=-rqcdAoKF9U4=n)?qKN*G<1~u2dxi7#p^C94+zyUWl#GCEyVf?G_N1
zJF5~rvE0f^qR#mEB6}Zpgg@tW_A!Qk-a*HkN%>F?%h?HeTN_BMjUe_RsS>1{suH$C
z{fQMKRh!OZZmo+_6ndRBv16HwpqjdpG9}Ua!_hmy=ND3h&am;uH7qbzP%3zD&~YQq
z5qpNUEWi_0^9@r?tUMDzZ_Leej<M-2CH32`ALM0|!!FW|vJjuu%tyP0J@=jqQ}59k
zrUei5B_(K9q)b1;_P#*mK+pjo$mTc;_U(rXIa^3TE|2Rx0hL)x0fTDI5*2sqh87nP
za#|z_A>c-_e>@t%MoiO3)T1i1pjP|p79;p^=`-rrGnWyw$>S1n`hdDbY=`+^*A2HU
z6&AfPHzFoZ9^+U;ib~S^Sf&P5|2)Ahw3Sw)iNrH~0=y(+3DxUy=H!Xs0(91V<1@bu
zH>U7g#=HlRgLT5d%e12ZJ$mSd-Y>Xz^h)#^%Othn+vShHGOV<9U>;h-#y+^$#i0~2
z8St!#U;H4~*QD(ezXA&>y9hQJtuC#sF5S*(kFU6b?looQlZTs^)>aA~VI+W>R9tQ6
z#;HwJMd1)tk?^F((tS4cxBm!OetLi|NQYiopwSiznbytmMLueU#$CJRHcc_EsI~(4
zW=el8(prI_8+$G^sVgP~MppIFc!_sOXJhFHz5H$V+S^cW?)Fo%C5bhMepg!7G&7G%
zj%v=)?6wv+yChRg;Cwf^`TgaAunIOYTFt2Dm{8+9HS;IiQG_n$r`5Qw&(QsVDW2yo
z@B|L)o6vmr+dmISsAxXu$&%UeSt2AxYy5aXwyw|hM*^*6<rbY9CDMS_izWkDX*?Du
zL}EQU+k3G&^EQ~%Jd~CCFo>)TR<=+4G(S%+9`KUjv96vce_OeI?mMoAr`hXO{wcrZ
z?}qMKA+L^)dQr-O&H-2?Er$=h!r-h-CT8Qe?lSDysS_~e-NqVgeyrVq0{4@y-NAy#
zEJjIX;xPe$^2e$<9w%RPTjR&&+WC0dJ!L*S&DE)%zmmN<TJEjJz|(})yw5NSC_&UJ
zfK`|=ZHCrKlVK`3=K{Dx7yF3FcZrc6)Sqhp>(oV&UCxj8L-)l8q`Fi-sy3czv}uTy
z@oPAsQ3u)B;>~H0zY8$%aGbtTQG&b;i_3kjf1V?|;4U6RmLAN~ecOQ$_CR#s0+%PE
zR=J#V>E{1*O6G)X%s3l(<T^iLd7oX;Ti%=PI|c&D$nm4B)e@+&_2TJ<2#I*_(DXhD
zzPR*0Z3&*b(!ww@3`s(*bh0bGrRy0GPwi(h6oY~llAIr#6S~^)>2CGNNyc_!EAwFx
zS)*l{+#y7*`~-i$)z+^<-b${qZTIyYqq;Ijeh(omIV;Ik&VN50o&k0g-En%rx3Pw0
zRt_)7d%sg;yvZEZ{af`5;ch2)s@Z=a5E{UF@oH0xJ-A6Ys(W2+18O(c@hF@FSUL`>
zcS5yf6RUs>2vTMPgC#$Pg0#mG;wS~&#Y@%cyUQYHK9fPN^d~94f4|(7<bM6yMArl+
z<P&<W+&i2;{f8qT>r5?_6m{EYVg6%QNscAZr*4>%XU#y5?ixPTk8=_&Pv~qiD^&qV
zXw&NDhn8>akrHyX5;O<1X3pscSuM|*=Hn=c_ReV^=L_OQq0Yl5iyC-tw&#4218)S}
z<`T|^DkBq@ZwB-^uSBG~l$#7I@^iROhghgI#ObKY4`LgpiCg0?ho%dHc1K_DS>z#5
zmPW1QWYsM3d3W`9lZUE^J0mqiLn3D)VfFPwd>{tC+SjAeGcYTIMkzA%=;2>u8$d88
zD!7--%mWFjmg7^Pl@(Pt0Z^b>K6lJ9t47cZ9e4QkdK1o}o(~oIxa`p|Rg?AX-PCYt
zD-6yOuAebXAo9Sh5^PRQnV8I!78>c}5Knb8!b&K^`t>}Ty5GSYD<_(mx`zO-rdU$p
zzt4a2p(+H<W|6N@ti%TIh5@z5fc!fAb1q<b!YBWNgv`kRye_MY7<jmMn534w>o8>U
z=Quann}EO%M-mv9j9$o4$N{lJP<JXi4j839e<VNOp_9mdZ(1|Uu0uYr)!)|AlSl=g
zryyo8&bDW`)4As*Tn-gmn1C<~G%gt+>6ofTfXUW3sX=p;hQ}}n)5YNk47Ku%?#kl^
z?o8dVm#^#NqQVq@%1#1eI!oUVCL$=*Fbi5LNZLlHXcRYAh0tHU?dI|`rI_{Ux#V{q
zqbI4MC1rF;L0y@yZKS)Bw>Gvddn60`CFTb7KP)yUr_B|Ur=STz_6ErvVC-ME{50Bw
zZZy?ktPxW^xAvXtg%miX!Shi<W6SgB&Z#RLmRmhF9bjtdZKdew^?Qq)B%`#s@*}Q6
zJxNHZF>N-9tltr%h?SU{osgX<vOXkosaul~bQF#4MY|n*#MFWju=^D`Ueu&Km}M*>
z8|hGZ%Z3b2a#C@4qvgwUbtFCI&^&Wriys~Yq%dj$l6>+<E~BY8A)splE$A&%3@j=^
zAw{vF?u)~4YGx16VwYQqBC`HP&d457$V?C?0AAeY-9phzd9wbB@JyC|fp*68EnaNn
zk8oH>Fspkhp4p}`_?&a_N}Iz(q`kS2x|Y5Uuib<k*Qc8C@!hus)gcy|1B2!JsBc|%
z1K~mcAn=qortk90KjKd`6!BCHs8&`Q;LHoAIw;$ek6hVQFpORWXlMn19uw!hgQip+
zA|bV4-Yy9O`h10hCccSNi`jto-lh744FS$SQXurDS<gkf_se`sfPQy4An)GqQLshR
z{at{u380AF<(C?bcHEeQ3~PHtETyO+Ry&80?F94un038CqZk%(m$NWPT=e#tePw^T
zPTL7(mA-|G)eAmYVa1M2o4vai8q@}XX7@t?Es|!hP$d=gbcjX%^NC@zDM>wmRcn_t
ze5@q1a$r7c(=h(Dtqk+)rTfj7+#pYJ^D~?Lz$ziv4zoF)WfVX$YW!^uxsXR8P=;42
zlEvj#!>*R~{KL%*GyQp_^u!LC>8FvEKe1Wj?)2+;55O?g!9lonhuVQBbH&LLSo<yh
z^r5h2b@o6eX9y7L8?@b2ApgP07+W!`9Dlf`ICSFF-+CH$jMh*FfR@L{sZN11?JZG!
zG)#0fH{&UWbu-TsQ=3gmN9EQzsf%&rgT|YGcK+*ov7lTGOZ_f-nNdCUNo)gui@CY5
zy-ojTX_&|gFAcNPGF=i#0*1n8RSMD*Zd??=H_C`UT$)XhXcIwW>_=C2$^XxpEkybZ
z$|)s9*dq0Y<bvNi*PIXkI&6$bb4v^5NDMNF$!w=9pjAi}x6hZ;W*VJ%c~rEb_g?Oq
z0Wxy%57sUqH@WbtKJ6}WmHTT$3|yJMO6|#W793+5(K`%>evu(!A{)68em(8D9^=5-
zk;p-}86y~!9AK1SZM1i3AREk&n9m~?hK`9X&sFiqYiq9J%iumWL8B#3P$T0hV~rv^
z30;m7GOGzHGP`3y%(?<!kznVKjHfQ@@o>MH1UrSZ8tiSAVUxy57mdb%DK8XbS@Tgv
z+n)1-phlg#%*_2IpYlz}OQdA2bL05CaXqn#WWk*j_Mu7INJ;>OpIY)423z_hdwrKY
z2+Z7h(b<|F8-}(x<b+xgnc;xiZ`R#9@pcmnt?%MTbPiog*~>^nmfi?SV3x1tVCn=<
z-KLV7+hZ<X4f+!zau776Lv-0plL=Z{cGbC(<7{4QPsjd%7<Be;OdENc?!&({%!%&|
zkXKO?BUHM~2$PdM`Xfr>?R7Qf>thvPG&00}|FoL5&w6$GbCtvDg0@fsS%AYMiaOBt
zKa=3qY2jPhx`S9~buI&c2_vocj7VhUp8IG(Ibb=7XNI$evsHGsfMeBkjcsieONLZm
ziD1xbH(PJQILzg!`EL{8Cuf)a1v_S33rLf+i<pj3b=~IlQ$iy)Ed8Sbt+!%pW6mS0
zATQMLjJMgi^ZYb-0CgW1B{qt+K~f=Nqt0P<)nR$O^zO1qj|*?Rqj$X9#;-Iw8#As<
zhtZhQKJDXQ4^nCVq%9TO+29EAw6ER=6mRqYMg#w+8od9p24?2?udAUU>JaM4YArRO
z9UZ{JMb<Wn5-5cZ7-)jh%w_X`YVyhew(JG8*Yb<kGjP`T{iz$qdTI#;Otc-MB4Zem
zR?6myNs|(U8`F<u9<mu%y>~mO36D%jYx&xAS}#_3HvN8_Y$wr?kVHw7%D32Pbh<q!
zt5|AkF5sUlU7faGK16{%te|I4r05tI^o$pzL=*L?BS>di5x$k4YdIcL(M3<Va$=*I
zXXt%REzr^C1)k}sL}`=iHv<QZPBu2yyL*(o=`;^-FVSq7R%dRSIh@Yg`b^cDVv?$3
zqF!(KU6j@HdSl$iaIw&|)U-02FUUO?cTGb%hfG&>`EK#?u$tpyMt7!Jv9?k>+c&G6
zwOS3jKX<KvYg+~%n=NJP+~AjIzD(AkD~G2XN7C2FtpD1Wv@q#Lv!Sua8XQ4SMjJ?D
zFpXqkOsdl#qDz@_8x7x00gH}H8<R3nj%0WQcqAaCSxiZvz?cFw#(N}b46xJ7PAQsV
zY`cIoCTfVhx*Dv|*Gy@e;xy)XWI7maVZfOJrL;w?Pgxsqr&mXJBM=!$rzJJSG)S%s
z6^a)+iT0vbM(@(YGT*T7OD`NzdSrZIU`_5QRw-$pMz<Ajbs9;J4rNA`fJxV8j!&DW
zWi$JYb1!{$)?1C>HlJa$*=Sc3rxb^gVBqoov@KVvvUFkS4fAEgGw+uy_!U?iJ`#ht
zs%!he#W<6D&8k22w<gaO`U;k?I<<+-Cpggd7(V_aKM9NP4NwtfT470c0=J#s6EM)6
zbReV5p<@GQs2kJ<qLWnlLSYkB`P09d!iyasb3wW;4`<VjQSpL5YfGt{C^y)RxB^_&
z(hAT<JSq$YmUP2lQUw=6HkI-If@~d$1J<Z@pxAkX%?z$Lj($dRXg^pdBC_7DwQxoZ
z!ViB!kh%7m&J6Uxk-Zw2;>n&Tx#-?>enEY+jdU-d9DnnqwtC+9Y#Q7%yf*TP)$X9k
ze+p}$s9}%<3skW3Brt!sXs6WkQQ;_Z*oas+&;1Ee@gQ0IaxSK<WoN^KY=@flRoJ;d
z-)<N->n=1}{ct+%@Vo^t(&H?9TBtxmYNjFUtlWUbses%e(H~_R-1d30eH>3(o-Qt4
z{_FyVa$drcn!!qz&&fd3k)-C^ja~64y&F_Oz7MITl!ScD%i>DYMZy$xMMR-M^iRw7
zZFZUzDeb(0>0>!w#YU#N!|7)DiSCx<Z@kv~$li3{+;>E`&uA(`MmUgEd}Nx>8JigA
zwhuVx6(*+J7@pS`z)n?!UtWvkQV0=IPCt8%DYik+3!WG%sKWj?*%mv;XzZy{91;eZ
zpD`J<vJF_Tow1qx$lIieCLqTD2bdTNKJhRMOET@U;;n#M9E6sVfKwLOMq(phIk{R5
z!PLjlQV>&E!;F8-0f2^Y3Ub;t6#mi}{Orsh)>7H(<x@k3(=Rt8b7#7~-5$OzLrCR#
zsUb9zF}lNodRYFF^BZ3QKgj`73o_f+cTdbrY0OxJu>0vUSP_9NiNc!j&BayFto)yR
zYbZ$8jYRc7G--n=#W+k7Ew-IysNLT05Af1r!4U*NK0!ViX}67%nOe)m$c9zW*7lG-
z-p{4YPoT%week0FK;<I5K>h5j9>qHR<~32XSK8B+k&l<tI@>RdxxNkzIeK*dH(f%A
z&q7NR;Qg`*sbXo($`6UAgR4ZeBw*mtPw;hh4yj@w0^27rLnuLpvot<)evb|!2Cl1e
zuu;w)fsT-LegtwLxbh=Rnvan2<xiIvRaYP^VcG`2I2@%jhitaT{GG(rYWEW(jYiE+
zrg1h$C>eV0T-t0i@j@Sta#bX`5+5Ikx`4Qd^g6SfTr?dy9XkccQd%xahgOl_xp$;+
zvo6RbVvP<A!Y0@r?qTgg)D#iQ!{nlh_MuP+H~*NfDBs#3p#bCZ<C~`fMVx+2<2CE&
z^*ZDb>*xLbaQ?c71Up^2O@cb~G!63TqnSu)bxEZKOfJKhtgY_*A^q9e$J5sO&4TdP
ztLOSkKyX}B#Zgj|bI3^`MxNmA{Vn{sJ$9gAknkD$q$ezgvoh)X@&oR<u-vyb2QFb?
zUIL=E<(acp$G7wtQv2YB{X2*}{QaSt3J~+q3KZmTF^U<kxm_=aNS}}Wj9VCZzk}~5
zA)pY8MvsOqhQl8MZF!&+_l)DH+$_E47tL1|*8^L1MMO_hzTUb&mTjOmF{IptwDvb{
zc8Ba)PqSiiFubCoWZl9(-lP09ijcNA&X0y)>DS^sXLM!j^y0I!;e?tGm6RL+4df$$
zk&2rjQ)JOpTn7g#K(wq~)E$IQeslXn<vJ3~)RuQ-?fpNlI5UgmxEBnWq0Hdh$$Yf$
zIcbo-dnp}MfkJH3D%`svOZ5Qd;{k-_s=`8V2KkT=4{)e#csDg2lG(l!<0kVBE_UIT
z>OS({w!T08f|iC>?jpw8_7Aw9v#Es;v3?IEm{6egqCVyeVNlMhXm&K8=uEsjMW?gk
z0c`>iS61Ic;%mT}hOSqXgT21rdj!}4kwst59}+?4v{jT9<@F^E)uXQq(vZ7oW9kh-
zS7Jz|WPraU6FtA!)59o8ryQL$DBAjRa>=%SLbgE34nC_hK2PVvDzSkg4n@u?WL)LF
zf;6zHH!vNk-XkR{!a|VtKk)e|-6)#j><bXfG=QoNLkU&)WS{=%4)^eL8X6i4lt)Hr
zGXi8fb}}lqwA+4vq&c@_`-@)ZjCgR~)`R3bE*CD<KP4W^aoYxX()@DM5FXifw8{N9
z&uQ?&9KUG7#+EPp12W&=Sg_+%<8Xb+)(#ztGb>Y*`rO#tb2_~DAJ*e;_0~HisEJ0C
zsj{}OL3XBh-Wzn?o<G#$YN|B2{Et9<)<nL`M?*g+ZSVP%>~`c0F&S27Nk<m1$}`u(
zoG2i4{Gn1xJ6@3{hnY+T7~f8WRgaEO(fCPL;dPKIA|fGRt29HA$V1`^H{2nkZYg+J
z+t%r}-r~4^J6zs{_JNy%)r1|m-Q498Q2#oBjtHJ2tRzbRh-($C9>pn#X4>-RIinM4
zP;mL+>*C9;x#LNO;ec{}rO;gGIg6WSI)kE@J*crV`14){G9u*Gndks1mk)yDc%E1F
zEFKbnQ+%`X{8lD!S6|^B#Ep>~fRy)xXBDl1)Mtz3)o8kNKT*4;@=MG{9d|NV5pkhk
zZeZ@W6!289>AACp#z2Ma<&kEH+RPpVt*gVxP!38XYV4TDbCWv_UmBMclRFkFqeD6X
z8Wg!3uRV}v&^yy677~5pKT_w=7oLcLtwf+w<cecvtDw{nDA`YswuSbR;l$riZ9-$o
z;%MthP3s7HBC%TTN^V<ZPnWovDwDcRCs(O_&CF<;vsIzIKPzec+|PZu2`lqdQtxO1
zRO@d;FUv25P?k$R!|L!9b*M0PAd)$62iL?tO~C5BZz^8Ilk8K!Q>o@-4<5S^y+|7z
zGCO~ImZ7C4SV-Atbd}^tu0r!-wp8VaI3WsDNfdJj9$V!iA?Ci@apJ5|Lk|p#wJFS)
z@e(*o!^WX&KHd_PMmyTx*hj)j)CPe%FiJ%7^}=7ju4=L^#hB~6OH-&-nK*!+rz9<7
z4nKOCvkHb`YEK_HU!|<Vst|Z>FH$IHg+@kgunU7X=F0Z36Wzj}3Osz;G!vHT{~`cn
z`~BK-U#IUptMcmbfD?3h3qM`$mc*|Yp3Gd~2agd7@$oDBEx;9>b340BnTbrgRpqQo
zqFhS`H3za?&B}4~8N?r~gwBpna+L-u+1pqL$-~l!aOl|6la-RTQlG}>bNV<Fn5T>X
zHA{5R(nlR7t8Tk_NSXuj2{GH?*vY3ovwl(7(=5XhX?XKXhKEh?D&Y8ux0`|m!s)o-
zbd(Oxq3f`>#1&ia=i*GhG-f`9ENeZW7mqkR@%>dZpt=fwfjgKpne#2(&!Rw*<Dt?o
z#7`vEmnodP_91+yw<rI+!8)Q)nAN;#@UGt-Y-E1+tkv!EJ*`;03>28)$-ZjA9sGbX
z14?fo0bU4JU&~FwO!=F)eFDDqRm|4QxfuxQUH^hk9L3?geBb0}Q&w%=%>&{J|4-1+
z)gnCBmKLByiuKfDJ05|7Zdcbq*r=VU<<aEgqcmeN(&(-cpw#)vXHA&^X}YOg0mqJ+
zPLp<x;o<+)oB0xHgZ8ttwK#rj?t%ByZktwR#_TeSvLk<&eZ_f^`di(({Cho(#kPCt
z@JgQsw94BNisY>N#cMHJ$I-NATw+PYYA3E51jL>d^&L3)Xl=vdOwjq^#h8#bogq6C
z;f+@R=7345;Bqo4YJefDnrPW_<$Bx6wh#DZ5vh9uExcAbc#j-x^|bpPh5M&SYQicC
z-*rCmx)utnlnNA@(Rekk$Zmri)DnIC%3>`HwXo0DA+-A!&K!v`^2K>&Nv#PSmG}db
zQ`h4av&V(zEkor}rFusuuJS2gjp@lw@0bRooL`aRviTY-nD}mL=Vtc4MA|w+@7Z<l
zgxV>MZeV7@-;ri?GOcLs2pk-=mr+K6aI{pD^Tt0VerWpBgNB9+$@k{7w2y0pU+t-V
zdKw~R$E6mqHA^M{SbRJth*_Na@NHZ|!erg$PkgG@Uo~(1OLKQP`#4*_HM$2UoIIL#
zmXhL@CW2de+5Vo7CBr?+ODQdphDda(?XE6Xn>$`7g+03?Jr0kDf#P1fO>%sp@P$c(
z)2DM-GFeD@73Vj586y#dEzlagV|u&1C2x9YFF43gLdhgdNkm=r(SI#NLzx4VYx}j!
zj%_b|d3-xo#-X+KCZM^CsUmdRGe7pHXYWxa7cPJi8%*e>rh^q6K$S!NV>|Etkl%jx
z3y+)@m+(AtBVa-1BuQe!O0pkgYYCbmW?<vVtwc%1oF-Dj*_t>A`KqfTO-)P7&EP_9
z425kP;ab~torkJM6n^4@@KQgaSQc=3@ouq{<}F0uCTLP8N$ZYZ9&^U22)>sdsJ9%E
z*Vbb8$L|!M<2cMjwPQYop_|H!e2P7{bMt}H*fWwD5k$OM;#B31OJDtujlj*LK`;EN
z^HX=Dui{~6vI%=s`W*$J@!3?sgEhd<Ic-E26@S2k_ulHjV}F?|X&?&%pp0|X<Q7Wr
zVz47e1_eIPSqW>D?|8nq%{ne84Yh$0Yv*9Yu5NBozsD01SitIrazASI51zyFy<cx3
z%5UZBhFX94%rT3WsXVQj-`4uJ4sCMbkqiyFf}+X3*c#6%=FT$h<|Wk?%9>nU?46yv
zKi?|<)iC+I<wW6_{E30pV;~;zzae`zFFaN8tNQbmv{Kd3-76RH_qXDO_C<~o(2hkB
zDYR&JemoYd!^-F!{2pC6qSQG8=jy`n1@cqVjip_ohc#XDJwx4>2PtL<ZqHfGhDQvL
zN30nrDs)E3sW?KCZ59LyTgTS-{jB6Csk8JiJJhUEu+x)yRhZv{t74ULNtMJ`XLlWj
zs+d>Rek}Q$SI`<WCctNDk=;J|S!#?VjuH0k1D)UYCrvDh;{atLW<X_Sxj8QEWEf}F
z0)~_>yDr2p>BL6gS4we3d{;`beY6$l)=$hGMluK<_;kd0j0M=tTy^9}0Ny|_{aE;{
z<a5O{APtMR!W*C-g6=!ViGsJ>1Q|qK!s9(e9a76bB;v%^Q$<vQ+&*+iJ<H;aph|{3
zFfwbQ26s0~gN7O03C_>emA??qoKkV~R8h>1pUMPf=>x=AYy^D0#`beCKnoBJQ$$+y
zQ=wGo6YT17#H;`E-=Z|ZxUrefp4aD0DcMIzKj#)(2l!|V^1gxQC(g;YM$hSTNJN00
z;HUw><?mQsQFwMSTqljQkeh=V)TrGgL{$joUg$-Gh)I!gMH0*2U$C}e^@(I%T*Fib
zq6ABWi0Ca6#x5#53>DDP=s6v%D9DGlrCw0W{tnEG{GuRNJiW?|&@r+@BJSEGmA#c(
zFmvUM3Tvn!^8oU-v$}wscFik;*7(N<Yt}-FeP=_rr{^g|zBRd^zsL#c9hu-53fiol
z5VMnBYZags0_^6)`>R&v;Z{X_bYJ{i4t2b+V38HhF>p>3uuBdc);}e*jHGnM%q5VL
z`B1RRN6R{czABsYwRU%|OrD5vPhH}@D|uu*hR|#uof?b)rvx@>$D`{FCkAz~7&pzC
zBBK^|-{f|fHx1)`^XBpDv5n@ErtngXay^;HSh>5S3_-L-z?8lLhacnY?VfG@rL)kA
z=C_s0)gn}Mnv2e=GHoGMp8iz!P`?9yQL~B%xqC^d*_g)czsh8*C90c-6&3e<V3pa!
z<3|=dzGJ|3pmSXbdHLC*;bC^F+N@yjB4NTi`3xPBM0aE_!@^;nc<b9lKYvQ)Q`7=$
z#kF$5T>3m3aCux8x#iucYGbi!UK2S%cKUKQGO~c=EqsUpn*cqgK#Pii!#7c8<&tI6
zD)HIG<&2eGNr%)wR|T5!V~j2#^FSv!qKXhLEFfrU$EgfJl#t+>-_r!0l<3(O(=VoD
zRgsU-dEZ$)Ku%7wo7+7?IF>nvQ#m82M<rd5e$XImKve%5TaM%ZBbvg<!1P~QNOe@L
zl$F#_d-ffi6e3fk<W(=*)9VZCfFt2FF;c4`!{xm3dCOV468WX+0VI%-0Tdi-{FV77
z`6USVarlu1Mn<Enah6PqS!SfAmeOlnuhj9FTxPO4zS5HN2ADN2tD2qMX5L>~UthDS
z3c9+;6U3QmOcz7X*4{X*R%({ZSgaaOMT2P;%M^9uc93=of?Gufpy2@AApvv?H}BDY
z=YLiYc!COq0hGoic`^<(Z@bSm(GrGF6Zwuw5a%V*IUWay2vLQt8lW|cwG(Kv(GVE;
zsH3YC7L0?OHQJn0s7aKPDkqgCO)iwrEAE>27u;oN-xVleR?3RUEg;h=bt)uMZ6!A;
zWu(Km)9_<9+h`qp1jXOz>SDuy(U4?q3V2J5cO)f`i=Uap$t@vKBxv&gD$FZGqe#(|
z9+aspX;S8BidC1g%0m$IC2UCBl)B0X;3kJZ6qqW4m5I(<E5w|oi-nG|%)-wW<_PI>
z=#G8z+)6pK`v`Gsc_%^`zT~^|g`0-YnKBO|ZTHfgPuEcxYe<?%c6K~!D~W;K1;GlO
zz-wfMyAs-!mf9<m$3sV553^9{J5y%6meHp~A)MZM&AdFQGMx!Mm072*<*`mI+Khn0
z(v)0gdU&OVt~zxBV?6w%`DR~+o$jJRq_2rBWDcAHaSNZ8qW?!{XBCuZl)QN)xCVE3
z=i|c(?(VL^-66QUyUT}b2=4Cg?oNQ<uAAAat(vKs{qNS?os0MCbiLhO=jncaNlDXq
z*4#kR<=jIr?(Y8BLuBC2RX10k8QlFQT6-oZ82bb>J)Lp9?3>!22%q)wL?JO9V<?qj
z;2*U&59p?p#Q+@#L7gTd=5K-2_Msp|9aLxX3-cK`ftvGX)^jxLrI0zMkp`XERql?{
z@-L?#e)2dY`s7#BU$B6zxh!7pmB%zJ49#9tj1%`*V!{tKc`md=Q4eyP3J0)qN(-w2
z1?67|B?smwI1Pr;DL9!iGnFyFZ?Fm@>f?@PdGwbNRzT`cQjC5Ty!EAJxPX!UQ2|9T
zY)mdc*S89s#bzHvP4D}$p2l^JJuL8L4-JA3_iT$;wp;F3Sw%AKInYj201*)b<3b9-
zL`rq&#2zN+r2*G1gxsO#QDTCE<%D&h5QmHi?=XM+V`Uvh2asqwJ#72wnsA^XgYXj=
zz{>NgTb%DnO=0lhuPCRvIe7``L>lL-)x+gYAXz!_o82<LmxvUk*;i}&;}nJT6FTVE
zYx3mSuj^spFoGuun=B{rR*yhG_(*1Ds(O@o;HPW{`^{#|8K=P6defv{;<O%xQ}~zX
z%I{8wy9s^S8*xq8mb>sRdmXz>K|B`L<mV!LDGFD>b9?|ocSI@QClb7<JqjOBzgU45
zns@q7{I-oxi4~NZBUNU@5&r_BVT3SPM&fIXC0J}q&Mg76aP1CbrWJOLDvh+U!^t7f
z3$G1yA$+%_S{u@@fZ2OS69)HNXeNkz66Upo%sB=?@k(VDQJd~8;@GW%)f$PhU4{Me
z5OQm1$GZ}s0Gn%-IXFiTZFyS=r;S}4AKK(oImGMpxCO?mqacC6eaVMlSlp0ARZ6>Y
zX+pMIJ;iddsZp!iwnjAc0oBJe-X}sEEcdN~n9I9^)Z(TLQ6Up`+x6^>vPU!Q^D-=N
z%Y$bE86HV5(ak4>fTZ)uh4uyx1}|Mbe`;kp`@S5$!e|L(1KIDZvr%@86tC0@G6yE3
zvfqUtz9b@g(67FO>unFLuRm&xa5qR!Ap6eGE^j(~_k-xcyVfu4`eFuf%9{PsMKr$7
znffSaZm?XgFjVJcV7}N|r%fgN-p)2k>+XAO;uTzxCPp8h`LTS|MH3IEQs?R!s%O-I
zwOk||I#CqrQa7kMTx+oA7#BET2QTT3(J50eN2MXKSQG${A?D}TUhkK$-HvN`XjhxX
zputTO{L`7-;Y`M5F=9+NxIK6xl)ratg%^lZ&$V++a1g;qEFjLHsYx5LfrF3Td_C3w
zx?Ho}asS-*7-3&daKZ@Fw!`%Opz!M<EIh7J>1Q4##j&{s?gL?A>o4t#&>FXXPDh(c
zpM|pYnO=9xBrgtrXAm`_==>(`=3w_7Y)>6i)(+L$7t)(_^2rbgDBjt?JlJ6GUWvIM
zzx?=Dz&Q_WSNj$swaUH1i-7F|`AljKHudA_y(||IpkdoKCtZ~KuX6R^SG$MZq!rOd
z#x7?48Ei){>dm2Xyg=knX2O&3AU$S^LP0q1fYJ;$I<rNEZD+npyi8nmE4xNMu=B(k
zqw1dwS<{T;l;Z2-IujeV5GA065|S}yIzD9zwqikm*LmJm7<Ymba_D>ZD@}LJiX-ZU
zNpfTrP2b*9#rbZ33?)0=2nD_?50h-w@uHg|l-!lctH0H+hKL!A)rVyvcLiotxJfs%
z*ry&Wpqvw)wn@3T9uo_d9sLGE2Ij9%V3?C)+0%VWut;|Se#{a^od-wwhu}F80AJ}m
z@0y?INq()<iyYhchh^5c;G@5rxJlVIxr&Dzlkb8T%6>|6bm=ojyz&LBz1vKTio>|J
z9R8>Jn+P=$E3q;sJG<W=1+mZhXJ<!#Nu{)@3_&+Xt%XOu82zbEfrZ5<_~=wvW$LAQ
zdnu-!X{e02$X%RGXyL$F^vomZ{yys5%%mG@UYvl@&87T{vx$V7hEp!3I?UTc4fieC
zs6O*CSH1GiYmxU)J5x}p11eOCMj@)vz00^L@7H_YQTAQh^H!SYpcOvxB;>Nck~UA6
zO{%yiGJfEc)iZ%VaXe>$h~<X-V^$BhMXkSQ5!(LkO38Sr1c;wz&<Y3{P?AOG!B%Dd
z244{pvS?xC>cNgg<zYMb^rHGAAJ~;dn3SCaGjZ5)rZTJOyRrN@#hP0~F<XxQ2Di-_
zJmmigKI58H`R}a?$3MBZ|Nkq9902xzHX5$h@X%E;Z{s)OCQ>RFo)2Bi4YJiMQMbm{
zu|0EfY?c<ARTf2!_kn{Tg);#cM%$T`_NNG@v;n6e2}fwUP?oD!YtSxb>r}D!(z$dw
zn#w7Dmw)B%<~0zE`jfNuIt}G}{5ZaQ@V@ovk@x76OP(JXNE0A<Whi^T7Nazaz-hNe
zF@-MBX)^m#P?SA^@!9P=IbHG;2K7b`&WAtZEOdU150Uig<=JD)PW1Y*PmI_br<iPb
z%<!4g6{lOyZ2nTioG=bqZKV23P;l5Bdn`|@!(`N%e(?DwaN;hyV8lF?!NYBq%t?<Z
zgCWk_$MGPvDA}gAfrXb1Kx^-Q5IUHJB_SSBd|+mF0O6=_;$$VREU#U}#rSs+ox^E;
zX;G(1{O9J)QPZT3U*nUx)b=|LCDVx97^&r?ceFOi55UfE|KbIwUg=&uD{@RR+mi2P
zqI40X$O+Jjs;KNK@E|iq(1MRD4yOF3!Z*QhqLU>=rANxQ<zfpe3-b%gr)sCJR+NpA
z+H%#PCFQfS`vqt;YxIZ*st9a3TToMC*?QR@Rfc;GOo?x}f^d>2^?_P}KLSJ?l&`dU
z>iow8?4dq5Xy`^T2+*?e-cVj_U)GW_Rgh&{r_R6gXTS6<M3Lf)eE4Yhy3F3?;+C^*
zA*5_J?{sBn^s<eceHf>*GO*aN==y%%ymkw3DY~<I?gBF1albKRZtO4yyzygD6O4ed
z<BQ~p-v%noK7iwpaE@`Yc79$iiL>~#7ii5=m?D6bD{6k=ON1YN?)mBGFJ{Ciu!6Z=
zAQA>0Lk-WIMqlG2>7wW<B{$(LL>EadPPhr$*01^!h=qT=Aw2pitiKxh_k%1tXNavn
zeguM)2Ddq-pvt@%;tgu$V5e@@==O;oWiBBzQBG>{Ezl#y1Bco?5n9r<bG#JD_ydiv
z;IKG&mr-mkWclySP+0V^+!@`eF$7YABloc3cB^jsqwAqynsUY=#kGH`I_1}$;HzmA
zd}HoI@IPkgSNcJZtf;DblAo-!M6ZKVNKONtV<_S%$+|bg06r${4Dr=dgDZ0_vz<+V
zyR?PXE4|VGt1R@jPQ)BvOp|s=$airSjRpQdGoKem{74=q1jR4i6!8M#Q!iOcLwl9(
z0X91`i*O{uy~G0eAut63x?kN!yj1Glquh(#$G^RHzCy1kUR}>my;qC%bRS~69S23~
ziIbBW*#E+*E>b$GKxVB3s}?WRZ)Xu_{+X&~eUSfsVM!U~EEuxH7&30#SGyrz{Xisj
z1rixK<M$BLl@rJm+oMpS2Omy0Z3bXu9U(V0makixgk2LWvj(meZ<Mz1^IQ=hAa@y^
zd&ajCEgV|_<Ku6}=BMWi1+!zG+m}!)omCfQ3x>VW7B-XI-Gl_`><Bcagqg0A>^o0A
z2@Ecn%tG6PUqZDRpS<ZjSQY=(ivO|(LVoDuFD~`=`|Z03Za6c6U?DFcH4!%ELo-kP
zG>PeAG9i5Pnw$n()9n~Q@O)Q^;7)UvoMVHXepW5_2fNG013SGIbT?porauHslcv8V
z<*mWb-eB=5G<AO2fBM*@H%KD<c?{NL188c=NAkB+Za%)4J0J7A9*eC>0Auta7OQhG
zb@!9np9~6P>g5hKx!&p~T?$Km`NC-j;!hOgRfhHJzF*VjKtOAhsw1b74)(XZ^KaHc
zq(g3=9sDb|Zn(DS6=?{wpJX^yc0}2JNh<I9Im-(0hD;y7_#<^otoK0w6GJ^q)jZqP
zm8tIpIYTB<KQrm(jDG!767~@lUX*MJW#Hy<HebOXE0*-KSnva)TPU2!<31<t8j=eF
z#>j4cM;91H7&P1UGnInxz7|Ts9}iyWsH|~mQJ8nW#Aa$b0qepS8A_1;6{h94*X5~S
zffmOd1tP08W8bm9$Mb9{oq+4d;WVeco^w@%@w<)a*=!Yy0J*W!k0+c@J^_&xjhWP}
z)P#)GW%V7x(?{gmnw|Q6UDN)}$GOFAzO5tlx0s%L%x-0_a9>jvTwB9Piy5HQit>!*
zcrvC{nL0h7vt5tZzjyrV_6MhY4+#616q>Omgotmo>SvmRsUm#J;BPLA06zTm!}9Le
zZ=O)K7VPkZgpyk$0S#@?4YK+unp)Ug(T$tYeyYjAv!V0>ZxV09yOW4O%xObMtm+k!
zk!@rjld}zV=wmrVk!C-fJ<B@+ZU4EM^^FQNQeOy_AB?ev*mE?=8qj_sg@X9G8S<eE
zVRdhQ5FcLpR4#sIEcn*F1iSWpUc5GYV4p6ov-=W78_omY*Jho=$Rh4&lQE;s<`+|V
zpi}{-sfqec_|fXaQYxK|^P2W<+Ppo&E2|_%xB1$IvBp<6FGZg+;@Z@-3ZZo3=gh6R
zcoR#p#4=3w7<iMN7I@Eg?nD+^tg2}mCaG1}yJHd3Q*kJretr&5&!Y#PRC99K4E}jS
z=v=)^KR6#AQEs}kH2IvVx|W4fVRAirIa@+~la^0m*neS>UC_2zl9@f2OxRO@r2#3y
z=Hc7qPj4<|Qa}%ahnJPE_aHZQnpC!mdJP|25?sdctBOfmKaVIB6YM@0ouz-T{SL5s
z9T2j7G-S-ik?_k9yZhR}iZfX>dww}{ciEESw7aIpJZVZ?JPoSYvH!j8xxe4i&JO=!
zu0mWAm5&JIp2HSXJ2XkZ1lxvpRkzMM6ZwiJN6_s7AiZ1btR@;J{Px7W9%|6Wu(ND5
z8!WybcQIC;BSIV_!;W2X53)A7!38|@pE!bAOURQN8l|g87soMlibcn))~X3sq7NP+
zoFXS;eA&Ni%MemlVJsA@VXnT`O#lE!3j}@EcDvUnQ5!S_+^PolO3l=HfyOhwJx3PT
zJPJWE(WIWL;iVn^8Jy_!%&kgS<`!$MLgvB{ya}-IRIPm8aF_|bO9lG7egobufs?^N
zQ}tb;yF?4&m~_Vjtoe!#-glhhwB2doDhZU@fUdzECSa%c#Bre3I$Bs(AP914fNl$4
zxYwW{fBdywnX}%#($@sh)Q51yT@RgOR9ku#P_}SdS#-m|icAv19CR7Kqu}?MZ5>M0
zsi##=zbr_av|Zu!*v9*qv*GAKiM7YVhwscF+OG2Agj|&EuaD!uAKEXUb_f8$IxA=y
zec3bq#2&DK&$~(j7-Sc18u4H<XA1J}{UvILdDBD}4aS1TY7GVqxk)PK4J~#%?o5Al
z+m-!pes4VPoy?9Q^dOujyARuMEqYlpna)kfA!oUpKTRWVcUUYuEw*9$b~P$-84EGL
ztzQ?0FbE*tz7(nV4os+-A?_xd@)sZ>n7U&!Cx%&EizH0Z!gALF*4riCUe4-VQ7iLN
zent?Yf}nGRjDb2aR2C+l2xl@kk&g$I1Xu=!HNCwGr(U{YrxG^g$FnE}SxHHCflV&U
zL@c~jdZ{JvJ?Y=rJ(2G`2_0F*CMo~u5nlK%<>7dahA}<&F~#(1KKWhCXuv9wbh`%L
zf8DQ23lc6*Ge@Cb#zI?C=<Ve~tG}JL?Hcninwi}u9qJ|P<asq^AemE!rW6kvH6+`#
zV6<f%VNN{ga70J0w;;-XPul-tEFl1U%rMwXzj_4~F-Sg~u;Yy3aF}jw4gW~)+Bbu3
z%x=7!D)n#>q+gsw+jML$<FBN#PCx+kpWPY{-mW<FhJo8fbY}N8L~ln*M37E~nH}#I
zE&qxawiybC{gu?UJTqKYbG<r8#G+RTBgDcGx++l^c@O={=ahkJqkwUdR%mFzgKg{i
zh{QAheu_r#6@XYi#Am$Qy{q;}QNB<q!%=E-3{j}K(B!0ewqfr4_%6TTIK?h|V;RX+
zYrWmns&lo#5Tms=?iN_QHMeqoy<4JgnEB_Bl0fx@-l`&X*Cat+O$|=j>~%Lf$q!eI
z)W_zB3E5}WFPSBp(KHN<(d<OK=F-;XJd_&Jcptupp_N&I;C4TVOl9h2dXvg||MK{B
zuhy$=!K7knzAGV{M;&>ucNpx+BbCy&7&&N~zc7IcWwm5_p(%`}<R(J4ygvlA7u%y$
zg1WsW_q@hYCTRDrE-z^sbJGE~&|#Zv9MQSx9+mc1B)M?zBIQ3rZIb1N0dXJY#I+B+
zqX>#jxFdb-9@zf!)f!e&#g*>hg)waR&W7MYs}UCb7u3W1uhi3@k+B``nOzvb{1{Ci
z%Fh7<=vfgawC;@3@D*G9#+XE(OG;)z%m=i>seCAl0A!sk>!L%Kbq}tgXi9hL+Uxix
zYG##X3SJCI+IV_KC3V*^S?zd#d2O}Sz&z})^*VY9%vyP2mV0bNjivaA>c_uh?Waga
zk9!)j4$+Y7BK2qKqn4iX=H8eU%F*huQU)}zzfKr%BOEcwc)f3`O$u;gJ>9C)?MKeT
zeKxA8@3j%$=dP*iyc%IZQ)i$khZU}fN+y&R(;}C$V+cc^)25oI9MLojL$OUY60xjW
zx68#a8KASciA01#y2f;A*W*2Ix-PmO_0HJ{tT}b=-*(+zyh9GhfzMmjjO>w+!iCW!
zHaPA5ip+T0nn=)bC&`UZy<#n*MA#1J>$6{u>xwMhMdgEWj*`&u6F;zozTUcBX%x-<
zBI7VaUn_Q*D=2h;w7WmgsN<HvuDT<l5HoCvBO(w#Hff>wB=#1fRz%r8xEENk4=0XE
zArzq5Dv?^ffo$p?1z5A)Tyh(x<&>IN%-_$i$V?U^2o_G}A4P6(nCqdKxh!Z#T=XfY
zCV^1eE}&MVI9{Igqs32(_?m)zFz(pkknO2sc?bI$yDHUG#NAh1M<32PLnp05nY-~*
zvyL)|MWpG#*w^vs#c-yp#u{deO50UI+z~z21~Ts&A#qc40=;!lCPLpp!T_GR`ld@>
z{Tgcpt9mW#Pzub<2t2p+vA-u_i>J!gH3*4(vuC04bdbOTq+O#K^^L6a`-aKpybg*|
zOXcQpyZB3J>{pq#?l!f8#d3B<7XGJ6{nuLJtd~%f>RYHAC!hwb!Gv{evu(9PWe8DO
z<)3Sg`?k=0qU;$M+sLu83IL5cz{};nI(G<?dJiUdz<EB6qE=D0d06E<fkRDC>Ca*=
zW0)$9l9~C~yy>W#M}8pAh<lbSGB%Kv$yMSfS>axF$jL4q^XR=#zvU&V!Zfdg#fF;h
z4Tz>3>`xcLh{i@vl<-WN^}*nEgj9sdA(l=%_piX&jVEv`)Xhxq5M3d!S&{uofLH@f
zw$lx=VupcULTDc9W_=$&XnFTzX9Y5X7*$Ldv?)+<b-tny6RoUMlti~hL8Pd4Shvl6
zLw1^e6=_zYVx^LBOgFfg(tc%4W~;=L`%1@>fv3O>)7i8uVo}jaY_nr1U+P-QAV1W%
z=g8q6v})wWo^y6WW%HBWdeKDm{%|evFDql+kh=a{^o3>}<vq5u6KQU$fD|WGKCXWL
zn}vWJM}c2Ts8oL!Zitf0vrmCc&Ndw3`(=T-QM6-k#HN!+d2)z!;Zj*mk_hVw2|pxT
z+uR<K*r+J+)=z@dz5ME?;Zr*<h@sFRIc%|`qc-2~J~>wAv^rnf_D&<49Rnq)Cwtk9
z{a3#XXL7CRoc*-DXrr?_Y2*+hb~Y|lN>WLJVo=4U$feVf^FcZYP&H7`)le!+urL*c
z0s>sA&c7e&rxBPu3e{9vub{g()DbfE^kh8D@**o={bmH--G6Jjyw(9LX7H47<6&8?
zb1Q(4ZSz?|{%bE*y=>y%rpv_X@!LI6#9F<K4p^+nBYzTVH2>_(3uC;3A(?`Oap+Zl
zTJN7xkA;vp95xJT*1r0i->+&NA-cJhBnweN&ZE%{Tc1|hcDtD30Y;UhvwqRihJ5nA
zJyY0GYGdkSMaBl3pyKk@a~`@uuZN^JjK;!Z<NSRU=7X$Hv1b`FtuO8dU$sx*21Ra$
zd=L3s1C(f{0ruhU@83_g=Kc}Y{0n_T2AhW4lP^r|*xDIvqu-<u-1oGg+*gd8G<9Q!
zzXh3$5FXV5kjE;4hftQcDZC^B9;e<9G+sCzwr0lHZ|u`Gr24Y9iyUqEAV`0>M_06c
z#ROGMOrIZ*+*ooU_%t1V5U3ie0u1bh=+CJjCqusshL<W-=MX{6mp%&*mBaw?-UQ4<
zf+hhzFStRg-zkXYLQB?uvpqGve!^XPL*4y{SEm2QNSTe}U-U3z6GI~-LlcwaD-`6|
zyX)D9>-)*+aZcb2lC&K2q!a_hXvTZI(G%(TlSu6EFuq$9s8oU7Zsxa%&&7Va@t&S5
zHB?dGJ(z^cFM>tv@rEwg-wlmTFi<Cd+FdX(OfpL`PEE*6OD)pVascPpSXdmVn9CT(
zWT$DASO6?6ON&s6sqj)M@baGU`Vph6&xeDn8SoJvwD9_#U7qm1&xeA&f1v|!d?7^(
z(F57QkT+mR`~qyqjNx*=A=D$u=ZwFh+&7oy{wLpOvDOG~L37p$$Ot!RDsgeF?Mv&Y
zE<c9Xu1eh#uM6YX`aZbrxX%{awboxw`cj+{_&QP=YHH?iVOPO+d9?aITi*u%SZ1|x
zr#}AFArVN?(*!*VZiC=pJgFXK|Kv{eF0=dzyKT5^ntv&+9d)b?KDc#|-`hPs+U4(c
z4e;7OkN8HyV&wZNN|fu0;Q2~@KXI1f=`g8Ev_*^{1{T&HO8Fn+_uuT=b8-Jm{BjZv
zWC_FwhTTyvf<1fTZR_wEV^KILNwf>aWr6B+lEF?*l-lUFj}J$&qksM|wR?rgB=BN+
z`nf%{K(19{^rfoDLr7ao12K{wKqxV`bNq{4?3w9L-@PsBvc4;umt*<3Qazg<J?1?7
z9Q|FmluJod0m{|?f#K&~c@y$k{)mIfCUGF8XzX4oaSWvI+UFDDo}QaR7C2n+%sw6?
zDCu};UJdh|epx?TCMqI6zrM()iF;_UE-uxLYC@CwrXcGf#(sFO2s|v7_`QBtbN9X(
z!`$5z<$rp5hM!5v4a%2nhIZp*XAdxhc&qmBR22LA^_(KAP%-`h2cuEk0ZcgGRRmQW
z;>%=CfQap@cJI$6AH96cNX6YRsNpbB13_*BB1$2yBn-5IKN`S0C_>{yDU@u#c&YY)
ztA+BBr5O^Vm}*#n0aU+Bs*U22V2WbMFBrmFpb3&O4~Q`3KJ_P-fomm%6OqHiVaBV<
z9b!^g`=dg7=@4})ogD$wD#^$oX=UX@DflnUZZ+q*C@KVld@{%3gi(bn7#!gGC_yGr
zwtS>N$yn>8?7`rp9i%1wPsMc~H8^e|KgTkYOi61UHP*Q9)8l}c@Vi5kcyHYmsBmxd
zz<k0My`ZO09@@x<4vG7O)D<bhg0T&^D@B1>@f2fo#@C0)Z_RVoYRd$*f0cvg^^`8|
z4ny6kNxX@>$BbG|ww%^x7W{|mm6s6{-wu|kpGVk}V$RU@%h!~f=fr*?+{gIOj^|->
zgN4uO89iFreHFDAoH(1XJ3<6QfDbMMCFe6XHhR6u95!hYuHL<Awpo|+MFBagBuQjq
zBKb#_v<ax&HcO+WShG%QAM_^~=Lp%7U)jx(#|l-ogIYD`yi43Tvq{&$^(+t0&5$52
zyQXM$jNBgdqZW^Xg0ayOyOqbwhRv#<txiX2e_A*>9r!Y3@U)Dd?q*DmF!cMjr<kj@
zYUwkS$FXbjkQj~Kpqb?vr)qqLPv#TS=ll@s-+$~J<QFTHG8G-8`>||SRd1#9w)?M2
zd0UmO2q+oKZ_C8~Cg(Bpn2_MnhbK0PHF#Lcmik~~hH*fNMfnX$+|?_Kl>51snLc!m
zmJ28U_k{wdYk%aGhORYOl~hC4XHfNCTYjNVpwYWgXH1%z?@mL1!|8Ffm<+cNs(YJg
z^#S`iED!cS{L%l-<`CPzE)M;ljC_FP{D5!=H}Cd<1OS4;rBi)gye_`fN8E=2`%!R*
zi0fnp4$}R?>qq`3Qa<c22?|d_2aw>X<?s|ZF~K;RIJn%>l{`p@(f5dm(b=~MbWwnL
zHfaoCB0DA`BjcniMl>QK115p9N)#Xo{T9O;T?U}+j4tD!O%Ss4b}+G!C=G>_6`hy}
zLGSZ^bK}%Gu<^a~t-ys>jxgkAA`vkpe_?N-pl$&Js`;%04XS%!Llx>*)_qTrv#*_y
zrIb}dpuMlOl@ig6QgoBx@hB?`E1<6B{bc8r`?3At=+ZL_7v*!o^yBy2d(%mk$L8&^
zhRgEUr^lqa&a9q3e~nk_TbxFBzo@yeiPgbphe>6?$VBonz1~y5$JAqoB|3K9yL{$U
zSL7MQV;?OaANJAh)tF;#`t<Sc+lS&8Fg9_Q@Bbbh&VS0c|98>=0KoarDvX|DyInsE
z;>JBD!93{KIY{pdWC$cA(TiY?w?D&dtbaHfg~!$QvT`&cp=6*0(Wvnv(s6)n;3+j1
z`}e@rcx0SSJdD2JC{lIK{1|Bo^^Ur^`ij@&4;k%Xc&Sa>$osSVxn=u&M_*OuED4KP
zZ>~5USqqp<LfBrgiftcFS)-u&FgNrrlXG4R#850Qz4ls?{nK|sMrDYpwWjDw;$xap
zR?l)4N$q&JDIHXwZ2-l4jT=je)~1PJfyXv4_qNEzx0#8tx!kb@yuPmH6XwVpCy^)b
z1b6=W$anBj8k>WEzbDpzQm_62W?)wMZl&U4%PddA&i)tC;N<K=0%T?TA7BlvT>si+
zOlsPytPf#*Y#G#UTPZ+W8#J^lmnPw|1OQlr!K5T{`O6+4+St)F675I3FP$k?Q%$&C
zrPf-F4Be+MJpN+vxFx=3J5mW_h04CjT8)~FsP3>GvI#UyU{ApICL0Yw%W9c%KM0$p
znDCpc>tvgy;s6o8geOAfKWSR_dyfml-XS4CB7&m}`{MyfxtI8d;@J#XK@j2QeQD6Z
zXiaI&l16_uSsZXAO?+!y$v)xMp|w!1VNozZnGp$2YmZSJmAfHNRXi3M@W8nW$viR{
zAC8!D>41JECIA7PZwS@O3@j)_0dH%sAtZ$W-l!v(<?4Ct1#y_&yWwixAI=vBTIR3&
zf7K9;U{k22n7Z+Te}*>;^eYY|7m5i8jRz89oduSIEdKmBud@(HYvIqYyAa||VWh;Z
z^s6{K9`i8x&iLk~(LJ|#UPfc%$8ng%R*Dx>N7C@gR!rq?KAyatLH7p;^qulo>OLXv
zO@6g;v9GD3q@5Z?TiROSnS1y9u$<JzD7oH#x1HuNv}nQ`7Cy)O#oo43I8DQ6^87m!
z-N3yJFjp*&sT=nSM5jj(4x&>-IZ#YtLR?Tx;Xr&N&E=Q7n<*V|6e6>utNbbOsoe!x
zEFMW2_5Ah_hEJNMqr9qQk?N()`N%-zbs3txOQUOheU!Rn7Cch%sI7h?7#rvb!$j29
zF+!V`a)o+G$8==pfPu~MUE%keOd|i#toO+u<I6c-`5NU*R7uoSrK7|^C(Ka+$n@kX
zp1`SN%ic(sVL%%5k(flR)fnr$IE5u34jtR{+%7>jic5}1m;#PFvc4m~O1eq5;NCl%
ziNssMW#ZfP#wQJSjT}nbDasy=I~#QH-f)>D+fi=eaB1sV*&}CdS#Vb9m4nESOop4h
zl`ICC`s_hCFEb^8u>nutJ;yHol77i8R6ze0U%L(7aqRC{hOHhcP6zlbu4j5%t7?$h
z4Zf^zkd$gElWs0kUHN!MS;6(`cv4V{S95Z?$7BkI<`L)aO>skO|7MHW%sA8g=1TFY
z`F4q(%gSpby&tFB;zAcs&T{xz?5zF1mcD==-GjmI!s4njPh}lqWuk~}U$y(GnWIZQ
z|2FFCPBfA!qg^(4<XtaCo73`nT+0tbl$m(bi`Z{s^V=W`Np6>>^fnuZKPMQ(Ay4L^
zm~3QMEOZ+?SJb0oBrT$Td(OoMj@RVJ0?72=E^~G347v_^cz4UUQ+=~b*p7WhHrvI7
zYWQmsS=bd^Y^16S>u<c_lp_YY=n6bM6;_uWAkGg3=Ad&@`l`!sj(&xzXU<!2y<kr@
zfBZC8uAzK6B(E#RlF3nB-Gm+6vWUiL6VuA0unPA^qX|eIQ3c+K;*lDOn#S#PkgbQ#
zA{}a{z)L0D_s_W~2YdR*OQ8Gr6thnT38uIprG%;!!fMP`KmX8{Mnif%nS<D))!|gJ
ze(@4ur!h6PR^r*Y#@zXnS6m}A!@)iy@4R6LV*nDN#|&m)zG<^W6x2JdpqS&{E8HxO
zg|~&QIq__$3UuK9B<hy&AY@igp6t9RZ!G%g_OtEfd{Hdy?_Qtgurz3H8g0$W>+opW
zA8dNv(zTa7ZEW{SecW0e@AP|)xtVMq*skfWyZo_iI`wNQHgH|-Ytc01rPH;J5JCa$
zr3xQCk<-gXwKQU8`ZeT>q}Bt=Ax7T(R+dTT)ZRgE7vT2}_*$3p+Q;bZ^YwKl3U=OK
z$2Gpp!QUpAWIoZ-dMDYXT5qC0AWJuQ8J(rqGAV`bmZuL;kJRr!$0`-)>UgJJ<IlN>
z!R~>5m<o?cudT)#?F<|H5?D`LvwUsS8MLNEii=z5Pb4s5!eeQw*bBC;9vIVvH%@vA
zLiuB@i(#>iI`Ox>BhMw^IT-7pR|gjUF5hi+ulOl*y%UCQTpq!Oz*x@wc_C&O;R^n9
zR75X$_G*SS&>Z8r3x2z;{CQ~3Z^KgUl~GJLL&=9EKK*J95`AC1ZlBlZ#e+MboO16Y
z)5iOEB2#>v$Q)J-dPK{%A=INgBa1#FSf9HDugDKJ@vqT+4I~Y=u+1&M@B@1_3hlAK
zpww)|ZD%i@QbQr0APGNDg2B&!`l>Pe^A8$=f%$-y^<nuN;rIBWxb;n6*?l?vSZ*im
zt~}cxU46a3SL3(aDeZCxyMXf>NJ-e_YUqeC)`a=4xVM5VO1{SR;8lkoyZHj@V~<U*
zNzAYAexE~;LQFJPZ?4~KA0MAi8Z{8Gxc$7%mn+S+aWvv2X+InYp7cLhA0IuqS+2ZZ
zccY^E<cP0lE+3F)kFWoe)`1=Cf|QH&QlOY0yvuGajEp&m&3MOyX_`gc-n(2)BVM)J
z?;Wx<e7*WQI;^ojkI>7B3h&0;Vuh&#>K2zYto5>Ste81mrsa2Fw-1;v$CRt5ha4@e
zmO5qWYIn|%DPihP*wT|#xbBW>aKO#*d*!5L$hb|<#=~qSf%Aj-fwD6E?}eK4pC)4e
zccIo)HZ@~bwzqd7`9}#?W|p!uvnTlnkNfXYREN*hgd51h!O03VW8>y9WjA4E=Qd{J
zH03g518|wLn40qczrWC9R<ZOnh38^n*3e-kVIu+P{SSQr;9qBN*=Jgrf{k!OcaL$6
zhbC~0&8v(JhNnp5BZM0WAVS1l=wg%$XDeJ&$!F*r(acHU$qw3Yt$uEJY%xD486^7p
zu_;T5aD{Jjg|KmjA!s(MCRk_*9G(#AHr*&#)({Q+?FQBNDY1-e8n-2xQJRw^?pw@$
zP4l$MylOFd=to(jKj>g<Saxs7>0|F_4x$~g#EIGTLU0(<yBSfZ)&|<H#1eA(s}VHW
z#qPJ0FBy}1E_i-?QnR1kb9hd8u166y^IsHFS542KNS#?zd!m;*VjVkvO>-E3@r`?=
qZwbYBdk}hooH-!b{r5mQyBImS{BSZggXiGj1OnhGDa91T;r|!E64@C5

literal 0
HcmV?d00001

diff --git a/exercises/Exercise5/data b/exercises/Exercise5/data
new file mode 100644
index 0000000..2f9e946
--- /dev/null
+++ b/exercises/Exercise5/data
@@ -0,0 +1,3 @@
+1.503333333333333485e+03 1.510000000000000000e+03 1.516666666666666515e+03 1.523333333333333485e+03 1.530000000000000000e+03 1.536666666666666515e+03 1.543333333333333485e+03 1.550000000000000000e+03 1.556666666666666515e+03 1.563333333333333485e+03 1.570000000000000000e+03 1.576666666666666515e+03
+7.499999999999914504e-04 7.499999999999914504e-04 2.249999999999974243e-03 1.124999999999987295e-02 2.249999999999974590e-02 3.674999999999958161e-02 3.224999999999963313e-02 2.024999999999976819e-02 1.574999999999981970e-02 7.499999999999914721e-03 0.000000000000000000e+00 0.000000000000000000e+00
+7.499999999999914504e-04 7.499999999999914504e-04 1.299038105676643190e-03 2.904737509655529702e-03 4.107919181288699137e-03 5.249999999999940478e-03 4.918078893226444405e-03 3.897114317029929786e-03 3.436931771216840664e-03 2.371708245126257549e-03 0.000000000000000000e+00 0.000000000000000000e+00
diff --git a/exercises/Exercise5/sample b/exercises/Exercise5/sample
new file mode 100644
index 0000000..391dbdd
--- /dev/null
+++ b/exercises/Exercise5/sample
@@ -0,0 +1,200 @@
+1.529870873031946076e+03
+1.533296951267377153e+03
+1.541745745425848781e+03
+1.538050016717445487e+03
+1.531865032820430770e+03
+1.536116733929038674e+03
+1.541835516650040518e+03
+1.528663915804065255e+03
+1.556493680342280641e+03
+1.544858003463946716e+03
+1.563981311086416554e+03
+1.517786003285926654e+03
+1.539421446924835891e+03
+1.546569003219708975e+03
+1.530293311105024713e+03
+1.535341062267174038e+03
+1.528633867031483305e+03
+1.550027656082381782e+03
+1.542119709410979112e+03
+1.563317578184181002e+03
+1.535424894465434591e+03
+1.529016519771415233e+03
+1.542578375596524666e+03
+1.525176109789066004e+03
+1.525888025713215711e+03
+1.547526040919889738e+03
+1.554597173143397640e+03
+1.532986758233224464e+03
+1.549490423726573908e+03
+1.554381314655129245e+03
+1.533749199139399025e+03
+1.538692201330529315e+03
+1.526212081780602830e+03
+1.510966601378296673e+03
+1.506655439306679455e+03
+1.528347221437730013e+03
+1.549659820104275013e+03
+1.537903517987974965e+03
+1.539576339311538959e+03
+1.545561143809418809e+03
+1.536060067205941778e+03
+1.539079943094946429e+03
+1.534329650390569896e+03
+1.526389397502558950e+03
+1.547278557687202465e+03
+1.547261821718864439e+03
+1.540854857618832739e+03
+1.552198078482798337e+03
+1.537595389384989858e+03
+1.535829245465445638e+03
+1.553933558808945008e+03
+1.545263291132967424e+03
+1.525262548008899785e+03
+1.561446177626339932e+03
+1.527275235112886548e+03
+1.534505794152838916e+03
+1.553818080320350418e+03
+1.552786135048861070e+03
+1.521181342877550605e+03
+1.533128916407230236e+03
+1.559447830527483802e+03
+1.535880821169179399e+03
+1.544369273778867182e+03
+1.543844112592955071e+03
+1.544798485413858998e+03
+1.542801575338362909e+03
+1.544564475876326924e+03
+1.533363202877219692e+03
+1.537017339304906272e+03
+1.540082690835609128e+03
+1.546746596680323591e+03
+1.535981445994849764e+03
+1.532652064680736657e+03
+1.532488770341622740e+03
+1.547363078562538021e+03
+1.551072088809281922e+03
+1.552506089057507324e+03
+1.530052907249433247e+03
+1.551155830699945454e+03
+1.551240935499189845e+03
+1.529748434919387819e+03
+1.534420029003830450e+03
+1.544436875384179075e+03
+1.518797161812581408e+03
+1.530604465134051907e+03
+1.564935383735057712e+03
+1.531621539958778385e+03
+1.543508803771411067e+03
+1.547213451982075185e+03
+1.556595013835684313e+03
+1.521089329195507389e+03
+1.558296818121613569e+03
+1.533818004406123237e+03
+1.521157890175715920e+03
+1.534353959596810910e+03
+1.546662138935911116e+03
+1.529386740867591925e+03
+1.529722283547238476e+03
+1.536441157203280682e+03
+1.533257844284850762e+03
+1.532260557242805589e+03
+1.527754699569532022e+03
+1.554814434063591989e+03
+1.546469003345774581e+03
+1.544317917017430091e+03
+1.549100102215363677e+03
+1.558607953665875584e+03
+1.545874293860520993e+03
+1.536133307153057331e+03
+1.528893991553999967e+03
+1.516125854511396938e+03
+1.542938390615595381e+03
+1.535305853633108654e+03
+1.530049826280729349e+03
+1.557103320105787816e+03
+1.534852787831978731e+03
+1.542008020967928815e+03
+1.543419232796401275e+03
+1.554849174080596867e+03
+1.540590424055221320e+03
+1.539602574035891621e+03
+1.565065724074901027e+03
+1.562082569376450010e+03
+1.548806654709629811e+03
+1.540225786626847139e+03
+1.538658933992134280e+03
+1.532963911108989578e+03
+1.536544240961102105e+03
+1.549785313872546567e+03
+1.528769627507651421e+03
+1.553710244082619511e+03
+1.546421621297795809e+03
+1.545523551170422934e+03
+1.552965643828807742e+03
+1.528668339268469254e+03
+1.520307660002598823e+03
+1.553446661904086341e+03
+1.549795791684632832e+03
+1.535093009417762232e+03
+1.550664049291777701e+03
+1.539458535874844983e+03
+1.563461398257525161e+03
+1.533433040907057148e+03
+1.555823819479552640e+03
+1.544338561193084615e+03
+1.533776424634749446e+03
+1.545624387443126125e+03
+1.549012557242275989e+03
+1.536343627685704178e+03
+1.536845362217866295e+03
+1.544325486004079721e+03
+1.521895149574257630e+03
+1.541981954453400931e+03
+1.561155551470342971e+03
+1.532489702192498271e+03
+1.534430701665578908e+03
+1.523057337055363405e+03
+1.539037640394216169e+03
+1.535963371294240687e+03
+1.541732550931476226e+03
+1.535919776740505540e+03
+1.536884796959576761e+03
+1.561398938114921066e+03
+1.537952332038284794e+03
+1.544438201554240322e+03
+1.524312178114626249e+03
+1.555312758040954577e+03
+1.542225934932082737e+03
+1.553485980450914212e+03
+1.550860595357351031e+03
+1.536615361701991787e+03
+1.525319490348989802e+03
+1.542911476349673649e+03
+1.551558322259055331e+03
+1.535003624335907261e+03
+1.548958043379415130e+03
+1.558410392743886405e+03
+1.545868962322799689e+03
+1.539308405570162222e+03
+1.556760869510119619e+03
+1.532280190948667041e+03
+1.535055790738423184e+03
+1.541938014712620770e+03
+1.542880072815748463e+03
+1.543471536247729773e+03
+1.557877314618248420e+03
+1.562502796042364707e+03
+1.524013998614134607e+03
+1.550008172021271776e+03
+1.547759878579495762e+03
+1.545377711047633056e+03
+1.524851118249196588e+03
+1.536107281343830209e+03
+1.539928525673537251e+03
+1.543854005856039976e+03
+1.530490780085420283e+03
+1.536347380266474829e+03
+1.545438346857276201e+03
+1.557946331680182539e+03
+1.536614232988644289e+03
diff --git a/exercises/Exercise6/Complete_TAVG_complete.txt b/exercises/Exercise6/Complete_TAVG_complete.txt
new file mode 100644
index 0000000..754acdd
--- /dev/null
+++ b/exercises/Exercise6/Complete_TAVG_complete.txt
@@ -0,0 +1,3259 @@
+% This file contains a detailed summary of the land-surface average 
+% results produced by the Berkeley Averaging method.  Temperatures are 
+% in Celsius and reported as anomalies relative to the Jan 1951-Dec 1980 
+% average.  Uncertainties represent the 95% confidence interval for 
+% statistical and spatial undersampling effects.
+% 
+% The current dataset presented here is described as: 
+% 
+%   Estimated Global Land-Surface TAVG based on the Complete Berkeley Dataset
+% 
+% 
+% This analysis was run on 21-Oct-2018 08:22:38
+% 
+% Results are based on 38983 time series 
+%   with 16951483 data points
+% 
+% Estimated Jan 1951-Dec 1980 absolute temperature (C): 8.63 +/- 0.11
+% 
+% As Earth's land is not distributed symmetrically about the equator, there
+% exists a mean seasonality to the global land-average.  
+% 
+% Estimated Jan 1951-Dec 1980 monthly absolute temperature:
+%      Jan   Feb   Mar   Apr   May   Jun   Jul   Aug   Sep   Oct   Nov   Dec
+%      2.59  3.21  5.31  8.32 11.33 13.47 14.34 13.87 12.09  9.25  6.09  3.62
+% +/-  0.13  0.12  0.11  0.11  0.12  0.12  0.11  0.11  0.11  0.11  0.12  0.12
+% 
+% For each month, we report the estimated land-surface average for that 
+% month and its uncertainty.  We also report the corresponding values for 
+% year, five-year, ten-year, and twenty-year moving averages CENTERED about 
+% that month (rounding down if the center is in between months).  For example, 
+% the annual average from January to December 1950 is reported at June 1950. 
+% 
+% Year, Month,  Monthly Anomaly, Monthly Unc.,  Annual Anomaly, Annual Unc.,  Five-year Anomaly, Five-year Unc.,  Ten-year Anomaly, Unc.,  Ten-year Anomaly, Unc.
+ Year Month MDiff MUnc  YDiff YUnc  5YDiff 5YUnc  10YDiff 10YUnc  20YDiff 20YUnc
+  1750     1    -0.121  4.187    -0.687  2.557    -0.364  0.897    -0.160    NaN       NaN    NaN
+  1750     2    -1.278  3.177    -0.691  1.733    -0.381  0.904    -0.169    NaN       NaN    NaN
+  1750     3     0.112  3.550    -0.721  1.568    -0.401  0.918    -0.164    NaN       NaN    NaN
+  1750     4     0.026  2.862    -0.734  1.609    -0.452  0.951    -0.168    NaN       NaN    NaN
+  1750     5    -1.420  2.611    -1.043  1.553    -0.439  1.022    -0.167    NaN       NaN    NaN
+  1750     6    -1.029  3.379    -1.004  1.271    -0.414  1.060    -0.176    NaN       NaN    NaN
+  1750     7    -0.262  2.722    -1.049  1.026    -0.411  1.023    -0.183    NaN       NaN    NaN
+  1750     8     0.290  3.219    -1.137  0.792    -0.466  0.933    -0.210    NaN       NaN    NaN
+  1750     9    -0.851  2.121    -1.107  0.775    -0.375  0.945    -0.230    NaN       NaN    NaN
+  1750    10    -1.448  3.078    -1.167  0.826    -0.394  1.023    -0.211    NaN       NaN    NaN
+  1750    11    -3.518  1.996    -1.160  1.283    -0.423  1.094    -0.226  0.879       NaN    NaN
+  1750    12    -2.538  4.091    -1.210  1.458    -0.451  1.143    -0.250  0.894       NaN    NaN
+  1751     1    -0.659  3.318    -1.094  1.533    -0.464  1.148    -0.258  0.844       NaN    NaN
+  1751     2    -2.341  4.503    -1.047  1.776    -0.482  1.131    -0.231  0.914       NaN    NaN
+  1751     3     0.477  2.778    -1.068  1.673    -0.488  1.200    -0.201  0.952       NaN    NaN
+  1751     4    -0.690  2.489    -0.933  1.504    -0.492  1.245    -0.184  1.004       NaN    NaN
+  1751     5    -1.338  3.435    -0.771  1.606    -0.486  1.336    -0.184  1.019       NaN    NaN
+  1751     6    -1.637  3.336    -0.721  1.085    -0.539  1.393    -0.188  1.075       NaN    NaN
+  1751     7     1.130  3.753    -0.876  1.400    -0.527  1.212    -0.208  1.084       NaN    NaN
+  1751     8     0.858  2.757    -0.409  1.841    -0.538  1.097    -0.221  1.106       NaN    NaN
+  1751     9    -1.098  2.928    -0.382  1.840    -0.531  1.123    -0.225  1.119       NaN    NaN
+  1751    10     0.169  4.986    -0.429  1.791    -0.446  1.151    -0.219  1.148    -0.276    NaN
+  1751    11    -1.577  2.326    -0.302  1.688    -0.437  1.160    -0.222  1.178    -0.286    NaN
+  1751    12    -1.935  3.412    -0.129  1.784    -0.426  1.293    -0.258  1.173    -0.316    NaN
+  1752     1    -2.523  4.962    -0.154  1.757    -0.431  1.296    -0.262  1.160    -0.299    NaN
+  1752     2     3.263  4.891    -0.311  1.743    -0.461  1.061    -0.216  1.213    -0.299    NaN
+  1752     3     0.804  3.040    -0.166  1.570    -0.480  1.053    -0.192  1.258    -0.303    NaN
+  1752     4    -1.259  2.243    -0.263  1.645    -0.447  1.072    -0.185  1.364    -0.295    NaN
+  1752     5     0.196  1.576    -0.090  1.758    -0.449  1.030    -0.178  1.431    -0.293    NaN
+  1752     6     0.434  3.225     0.040  1.815    -0.390  1.072    -0.179  1.504    -0.293    NaN
+  1752     7     0.831  1.966     0.222  1.538    -0.386  1.030    -0.171  1.657    -0.298    NaN
+  1752     8    -1.027  1.386    -0.140  1.327    -0.390  0.956    -0.146  1.677    -0.293    NaN
+  1752     9     0.642  2.557    -0.087  1.373    -0.408  0.990    -0.145  1.681    -0.294    NaN
+  1752    10    -0.994  2.816    -0.050  1.361    -0.398  1.019    -0.188  1.640    -0.306    NaN
+  1752    11     0.505  1.992    -0.036  1.516    -0.366  1.108    -0.201  1.701    -0.307    NaN
+  1752    12    -0.383  3.917    -0.084  1.361    -0.334  1.151    -0.204  1.647    -0.321    NaN
+  1753     1    -0.333  3.751    -0.148  1.244    -0.318  1.128    -0.232  1.563    -0.337    NaN
+  1753     2    -1.083  4.958    -0.086  1.230    -0.333  1.064    -0.268  1.563    -0.351    NaN
+  1753     3     1.435  3.525    -0.117  1.036    -0.336  1.142    -0.242  1.567    -0.363    NaN
+  1753     4    -0.809  3.659    -0.057  1.005    -0.266  1.130    -0.255  1.601    -0.365    NaN
+  1753     5     0.359  2.669    -0.158  1.148    -0.216  1.199    -0.292  1.622    -0.368    NaN
+  1753     6    -0.142  2.707    -0.386  0.997    -0.203  1.291    -0.310  1.638    -0.385    NaN
+  1753     7     0.060  1.606    -0.366  0.982    -0.174  1.293    -0.323  1.651    -0.382    NaN
+  1753     8    -0.277  1.721    -0.488  0.994    -0.084  1.443    -0.351  1.655    -0.386    NaN
+  1753     9     0.263  2.096    -0.659  0.937    -0.095  1.425    -0.373  1.701    -0.395    NaN
+  1753    10    -0.272  2.003    -0.223  1.130    -0.054  1.392    -0.415  1.715    -0.393    NaN
+  1753    11    -0.708  3.137    -0.140  1.242    -0.004  1.384    -0.408  1.738    -0.398    NaN
+  1753    12    -3.115  2.735    -0.113  1.281     0.028  1.473    -0.418  1.800    -0.399    NaN
+  1754     1    -0.097  4.243    -0.070  1.369     0.001  1.474    -0.395  1.759    -0.403    NaN
+  1754     2    -2.538  5.537    -0.171  1.410    -0.032  1.497    -0.368  1.771    -0.387    NaN
+  1754     3    -0.625  3.212    -0.325  1.395    -0.021  1.471    -0.361  1.802    -0.384    NaN
+  1754     4     4.427  2.526    -0.248  1.480    -0.039  1.507    -0.371  1.829    -0.393    NaN
+  1754     5     1.356  3.564    -0.183  1.432    -0.037  1.589    -0.392  1.839    -0.398    NaN
+  1754     6     0.179  3.515     0.119  1.456    -0.046  1.613    -0.389  1.868    -0.416    NaN
+  1754     7     0.577  1.705     0.139  1.791    -0.037  1.574    -0.414  1.884    -0.429    NaN
+  1754     8    -1.486  1.583     0.225  1.963    -0.063  1.622    -0.414  1.525    -0.430    NaN
+  1754     9    -1.591  1.746     0.197  2.094    -0.079  1.647    -0.411  1.527    -0.437    NaN
+  1754    10     0.655  2.919    -0.124  1.896    -0.051  1.719    -0.412  1.529    -0.430    NaN
+  1754    11     0.077  2.709    -0.192  1.834    -0.036  1.796    -0.433  1.510    -0.435    NaN
+  1754    12     0.502  3.293    -0.135  1.967    -0.000  1.833    -0.438  1.523    -0.425    NaN
+  1755     1     0.140  5.142    -0.125  1.955     0.022  1.927    -0.467  1.502    -0.416    NaN
+  1755     2    -1.506  4.365    -0.051  2.012     0.088  1.944    -0.455  1.494    -0.414    NaN
+  1755     3    -0.954  4.793    -0.004  2.164     0.111  1.742    -0.479  1.456    -0.402    NaN
+  1755     4     0.578  3.205     0.173  2.225     0.075  1.674    -0.490  1.430    -0.402    NaN
+  1755     5     0.534  2.783     0.122  2.345     0.038  1.707    -0.490  1.459    -0.405    NaN
+  1755     6     0.858  5.570    -0.065  2.160     0.006  1.490    -0.498  1.446    -0.426    NaN
+  1755     7     0.702  3.033     0.010  1.938    -0.053  1.429    -0.522  1.439    -0.434    NaN
+  1755     8    -0.593  1.923     0.391  2.250    -0.071  1.470    -0.537  1.412    -0.446    NaN
+  1755     9    -1.034  2.347     0.456  2.041    -0.109  1.467    -0.547  1.428    -0.454    NaN
+  1755    10     2.787  4.106     0.558  1.946    -0.115  1.469    -0.554  1.415    -0.459    NaN
+  1755    11    -0.542  3.503     0.647  1.848    -0.161  1.442    -0.529  1.458    -0.464  1.277
+  1755    12    -1.743  3.887     0.600  1.741    -0.168  1.447    -0.528  1.490    -0.471  1.281
+  1756     1     1.043  2.940     0.505  1.779    -0.181  1.468    -0.540  1.525    -0.492  1.259
+  1756     2     3.068  4.190     0.458  1.761    -0.220  1.484    -0.525  1.577    -0.487  1.275
+  1756     3    -0.177  4.549     0.510  1.712    -0.258  1.508    -0.522  1.576    -0.468  1.288
+  1756     4     1.804  2.923     0.198  1.747    -0.338  1.499    -0.523  1.558    -0.468  1.315
+  1756     5     1.606  3.980     0.121  1.812    -0.331  1.463    -0.500  1.539    -0.474  1.323
+  1756     6     0.285  4.037     0.063  1.785    -0.297  1.481    -0.463  1.519    -0.478  1.332
+  1756     7    -0.433  1.903    -0.190  1.832    -0.264  1.514    -0.440  1.509    -0.491  1.340
+  1756     8    -1.163  2.242    -0.303  1.931    -0.199  1.560    -0.441  1.506    -0.491  1.344
+  1756     9    -0.408  2.234    -0.301  2.104    -0.191  1.572    -0.434  1.525    -0.489  1.344
+  1756    10    -0.954  3.386    -0.418  2.337    -0.297  1.570    -0.459  1.519    -0.483  1.348
+  1756    11    -1.461  3.777    -0.457  2.640    -0.347  1.563    -0.453  1.535    -0.471  1.343
+  1756    12    -2.440  4.869    -0.268  2.713    -0.352  1.533    -0.483  1.490    -0.481  1.341
+  1757     1    -1.991  3.612    -0.050  3.119    -0.397  1.546    -0.435  1.458    -0.487  1.337
+  1757     2     1.704  5.413     0.291  3.070    -0.366  1.554    -0.452  1.498    -0.470  1.338
+  1757     3    -0.151  4.729     0.494  2.980    -0.342  1.561    -0.469  1.506    -0.450  1.338
+  1757     4     0.406  5.121     0.309  2.602    -0.377  1.546    -0.458  1.501    -0.455  1.360
+  1757     5     1.131  4.566     0.287  2.506    -0.416  1.546    -0.468  1.492    -0.459  1.369
+  1757     6     2.553  5.010     0.299  2.334    -0.486  1.542    -0.481  1.473    -0.468  1.375
+  1757     7     2.181  2.865     0.144  1.935    -0.548  1.532    -0.498  1.465    -0.477  1.413
+  1757     8     2.928  3.741    -0.179  1.632    -0.520  1.559    -0.489  1.468    -0.470  1.418
+  1757     9     2.027  2.197    -0.238  1.492    -0.550  1.506    -0.512  1.310    -0.477  1.423
+  1757    10    -3.170  2.786    -0.368  1.281    -0.581  1.460    -0.526  1.295    -0.485  1.424
+  1757    11    -1.721  3.095    -0.665  1.205    -0.613  1.445    -0.527  1.313    -0.474  1.446
+  1757    12    -2.305  4.124    -0.924  1.136    -0.662  1.424    -0.535  1.136    -0.474  1.442
+  1758     1    -3.844  7.286    -1.167  1.137    -0.725  1.437    -0.548  1.165    -0.491  1.436
+  1758     2    -2.179  3.918    -1.628  1.021    -0.740  1.432    -0.557  1.181    -0.514  1.430
+  1758     3    -0.848  4.045    -1.961  0.960    -0.758  1.410    -0.599  1.166    -0.510  1.429
+  1758     4    -1.158  4.034    -2.123  1.226    -0.842  1.408    -0.602  1.162    -0.515  1.441
+  1758     5    -2.434  2.151    -2.001  1.236    -0.843  1.432    -0.617  1.156    -0.530  1.454
+  1758     6    -0.556  2.631    -1.901  1.290    -0.852  1.382    -0.632  1.153    -0.540  1.462
+  1758     7    -0.735  2.202    -1.422  1.383    -0.906  1.436    -0.618  1.159    -0.543  1.473
+  1758     8    -2.602  1.502    -1.127  1.599    -0.967  1.393    -0.618  1.161    -0.549  1.478
+  1758     9    -1.975  1.825    -1.071  1.736    -0.949  1.377    -0.629  1.174    -0.553  1.499
+  1758    10    -5.106  3.186    -1.134  1.739    -0.993  1.386    -0.650  1.173    -0.557  1.507
+  1758    11    -0.266  2.936    -1.070  1.683    -0.995  1.377    -0.657  1.155    -0.555  1.513
+  1758    12    -1.104  3.054    -1.031  1.786    -0.954  1.322    -0.639  1.163    -0.556  1.534
+  1759     1     1.905  3.048    -1.145  1.935    -0.882  1.294    -0.651  1.176    -0.551  1.510
+  1759     2     1.365  4.971    -0.902  1.772    -0.849  1.278    -0.609  1.182    -0.547  1.510
+  1759     3    -0.174  2.807    -0.749  1.714    -0.847  1.322    -0.606  1.177    -0.545  1.517
+  1759     4    -1.912  4.162    -0.444  1.487    -0.878  1.298    -0.649  1.181    -0.537  1.529
+  1759     5    -1.675  1.517    -0.610  1.567    -0.868  1.281    -0.672  1.189    -0.532  1.533
+  1759     6    -0.088  1.439    -0.825  1.707    -0.919  1.263    -0.692  1.173    -0.523  1.545
+  1759     7    -2.103  1.996    -1.283  1.756    -0.834  1.214    -0.711  1.187    -0.526  1.552
+  1759     8     0.318  2.531    -1.379  1.517    -0.840  1.205    -0.708  1.185    -0.526  1.227
+  1759     9    -0.135  2.151    -1.594  1.423    -0.860  1.193    -0.703  1.173    -0.524  1.227
+  1759    10    -1.452  2.161    -1.546  1.255    -0.865  1.155    -0.698  1.170    -0.534  1.226
+  1759    11    -2.251  3.076    -1.521  1.297    -0.901  1.123    -0.699  1.165    -0.548  1.210
+  1759    12    -3.694  5.478    -1.684  1.371    -0.962  1.107    -0.685  1.146    -0.554  1.206
+  1760     1    -3.586  3.405    -1.768  1.744    -1.019  1.074    -0.673  1.112    -0.555  1.189
+  1760     2     0.211  3.130    -1.919  1.837    -1.066  1.028    -0.659  1.131    -0.547  1.183
+  1760     3    -2.755  2.492    -2.085  1.970    -1.135  1.033    -0.640  1.110    -0.562  1.172
+  1760     4    -1.331  2.319    -2.152  1.952    -1.126  1.045    -0.636  1.091    -0.554  1.161
+  1760     5    -1.369  2.044    -2.012  1.834    -1.092  1.046    -0.643  1.078    -0.552  1.175
+  1760     6    -2.054  2.720    -1.897  1.424    -1.076  1.062    -0.675  1.058    -0.555  1.156
+  1760     7    -3.110  2.134    -1.779  1.568    -1.043  1.108    -0.684  1.043    -0.566  1.158
+  1760     8    -1.495  1.504    -1.844  1.432    -1.043  1.142    -0.682  1.039    -0.574  1.144
+  1760     9    -2.119  1.520    -1.543  1.528    -1.089  1.164    -0.678  1.029    -0.573  1.156
+  1760    10    -2.261  2.222    -1.499  1.617    -1.090  1.127    -0.707  1.024    -0.566  1.153
+  1760    11    -0.566  3.687    -1.261  1.492    -1.074  1.119    -0.703  1.019    -0.551  1.154
+  1760    12    -2.322  2.815    -0.861  1.222    -1.096  1.139    -0.692  1.000    -0.536  1.173
+  1761     1    -2.165  4.523    -0.277  0.968    -1.055  1.139    -0.727  1.017    -0.521  1.171
+  1761     2    -0.574  4.049    -0.086  0.961    -1.015  1.133    -0.742  1.001    -0.528  1.195
+  1761     3     0.856  2.658     0.065  1.179    -1.001  1.138    -0.736  0.982    -0.542  1.187
+  1761     4    -0.795  2.574     0.019  1.174    -0.962  1.121    -0.752  0.983    -0.548  1.178
+  1761     5     1.482  1.394    -0.003  1.019    -0.984  1.101    -0.764  0.986    -0.548  1.180
+  1761     6     2.748  2.215    -0.272  1.160    -0.981  1.082    -0.767  0.992    -0.538  1.186
+  1761     7     3.892  1.844     0.171  0.850    -1.038  1.085    -0.773  1.001    -0.544  1.188
+  1761     8     0.798  1.778     0.328  1.113    -1.019  1.022    -0.761  0.995    -0.543  1.184
+  1761     9    -0.300  3.709     0.148  1.220    -1.021  0.995    -0.753  1.000    -0.539  1.173
+  1761    10    -2.817  1.919     0.223  1.222    -1.002  0.974    -0.748  0.980    -0.533  1.173
+  1761    11    -0.831  2.535     0.011  1.143    -0.996  1.000    -0.720  0.962    -0.531  1.179
+  1761    12    -5.545  2.430    -0.307  1.227    -1.033  1.000    -0.704  0.935    -0.526  1.119
+  1762     1     3.147  3.876    -0.735  1.260    -1.026  1.019    -0.712  0.934    -0.518  1.106
+  1762     2     1.308  6.277    -0.793  1.262    -1.050  1.033    -0.724  0.894    -0.541  1.111
+  1762     3    -1.306  4.391    -0.944  1.051    -1.064  1.018    -0.708  0.861    -0.556  1.100
+  1762     4     0.115  1.958    -0.932  1.025    -1.019  0.999    -0.725  0.844    -0.548  1.089
+  1762     5    -1.063  3.375    -0.834  1.122    -0.982  0.963    -0.739  0.821    -0.555  1.094
+  1762     6    -1.072  2.285    -0.485  1.142    -0.883  0.895    -0.758  0.806    -0.562  1.093
+  1762     7    -1.244  1.532    -0.901  1.371    -0.798  0.823    -0.783  0.796    -0.565  1.093
+  1762     8     0.100  1.615    -1.193  1.211    -0.798  0.834    -0.795  0.779    -0.559  1.090
+  1762     9    -2.105  2.965    -1.384  1.334    -0.730  0.830    -0.809  0.767    -0.567  0.960
+  1762    10    -2.676  1.868    -1.494  1.247    -0.690  0.831    -0.781  0.770    -0.562  0.955
+  1762    11     0.342  3.165    -1.527  1.179    -0.673  0.816    -0.747  0.736    -0.554  0.952
+  1762    12    -1.361  3.485    -1.596  1.208    -0.688  0.804    -0.744  0.733    -0.541  0.833
+  1763     1    -1.834  5.166    -1.350  1.210    -0.644  0.755    -0.750  0.770    -0.553  0.835
+  1763     2    -2.200  3.232    -1.373  1.285    -0.623  0.752    -0.759  0.811    -0.556  0.843
+  1763     3    -3.603  2.918    -1.292  1.251    -0.597  0.753    -0.778  0.829    -0.567  0.837
+  1763     4    -1.205  1.591    -1.299  1.455    -0.573  0.751    -0.774  0.801    -0.558  0.838
+  1763     5    -1.459  1.701    -1.462  1.304    -0.563  0.717    -0.769  0.799    -0.551  0.833
+  1763     6    -1.901  1.858    -1.423  1.261    -0.532  0.728    -0.771  0.793    -0.554  0.831
+  1763     7     1.717  2.174    -1.396  1.187    -0.548  0.712    -0.763  0.789    -0.553  0.835
+  1763     8    -0.184  1.796    -1.004  1.072    -0.517  0.723    -0.746  0.784    -0.552  0.838
+  1763     9    -1.132  1.878    -0.730  1.025    -0.523  0.699    -0.734  0.776    -0.550  0.840
+  1763    10    -2.759  2.909    -0.693  1.106    -0.510  0.691    -0.699  0.757    -0.547  0.838
+  1763    11    -1.612  2.050    -0.681  1.220    -0.533  0.704    -0.702  0.741    -0.554  0.833
+  1763    12    -0.892  2.139    -0.715  1.198    -0.581  0.761    -0.694  0.709    -0.530  0.836
+  1764     1    -1.511  2.793    -0.999  1.470    -0.664  0.804    -0.707  0.681    -0.542  0.842
+  1764     2     2.501  2.844    -1.077  1.371    -0.673  0.810    -0.726  0.665    -0.526  0.850
+  1764     3    -0.311  2.125    -1.065  1.253    -0.658  0.780    -0.728  0.649    -0.523  0.847
+  1764     4    -0.763  2.985    -0.732  0.993    -0.617  0.767    -0.703  0.645    -0.531  0.845
+  1764     5    -1.320  3.841    -0.601  1.005    -0.573  0.746    -0.672  0.643    -0.512  0.843
+  1764     6    -2.306  1.699    -0.339  0.925    -0.489  0.726    -0.656  0.638    -0.505  0.828
+  1764     7    -1.690  3.649    -0.085  0.888    -0.590  0.758    -0.638  0.642    -0.499  0.824
+  1764     8    -1.122  1.050    -0.274  0.856    -0.607  0.684    -0.638  0.635    -0.491  0.823
+  1764     9    -0.979  2.625    -0.141  0.864    -0.557  0.634    -0.638  0.634    -0.487  0.822
+  1764    10     1.232  2.120     0.012  0.952    -0.585  0.640    -0.655  0.636    -0.492  0.822
+  1764    11    -0.037  2.877     0.093  1.115    -0.578  0.642    -0.663  0.631    -0.505  0.814
+  1764    12     2.252  3.532     0.041  0.958    -0.554  0.649    -0.670  0.635    -0.519  0.820
+  1765     1     1.531  6.323     0.143  1.201    -0.548  0.670    -0.643  0.626    -0.513  0.800
+  1765     2     0.233  3.585     0.214  1.175    -0.523  0.672    -0.639  0.623    -0.497  0.801
+  1765     3     1.289  2.555     0.250  1.116    -0.483  0.662    -0.646  0.636    -0.486  0.785
+  1765     4     1.074  1.484     0.081  1.066    -0.435  0.656    -0.618  0.645    -0.492  0.778
+  1765     5    -0.356  1.815     0.085  0.915    -0.403  0.619    -0.614  0.647    -0.497  0.776
+  1765     6    -2.922  4.507    -0.143  0.869    -0.412  0.637    -0.612  0.635    -0.508  0.755
+  1765     7    -0.472  2.773    -0.528  0.971    -0.458  0.585    -0.610  0.661    -0.511  0.758
+  1765     8    -0.272  1.236    -0.440  0.966    -0.475  0.621    -0.611  0.656    -0.506  0.761
+  1765     9    -0.537  1.184    -0.507  0.927    -0.467  0.645    -0.598  0.665    -0.506  0.762
+  1765    10    -0.800  1.624    -0.601  0.939    -0.459  0.621    -0.578  0.663    -0.513  0.756
+  1765    11     0.010  2.110    -0.558  0.939    -0.464  0.620    -0.572  0.650    -0.501  0.726
+  1765    12    -0.479  2.768    -0.328  0.978    -0.446  0.613    -0.545  0.662    -0.501  0.724
+  1766     1    -3.093  4.348    -0.380  1.217    -0.471  0.610    -0.503  0.666    -0.518  0.726
+  1766     2     1.282  3.453    -0.337  1.289    -0.477  0.616    -0.531  0.656    -0.528  0.718
+  1766     3     0.486  2.024    -0.240  1.364    -0.468  0.605    -0.561  0.648    -0.533  0.725
+  1766     4    -0.044  1.940    -0.203  1.302    -0.436  0.624    -0.572  0.647    -0.546  0.725
+  1766     5     0.156  1.320    -0.053  1.195    -0.420  0.625    -0.597  0.653    -0.559  0.751
+  1766     6    -0.160  3.988    -0.056  1.121    -0.407  0.623    -0.614  0.671    -0.558  0.735
+  1766     7    -1.095  2.789    -0.043  1.003    -0.377  0.641    -0.647  0.676    -0.560  0.737
+  1766     8     0.238  1.688    -0.122  0.950    -0.433  0.654    -0.646  0.680    -0.551  0.726
+  1766     9     0.627  1.769    -0.024  1.015    -0.436  0.675    -0.644  0.662    -0.557  0.724
+  1766    10    -0.349  1.852    -0.151  1.035    -0.405  0.706    -0.608  0.666    -0.559  0.719
+  1766    11     1.804  2.605    -0.215  1.052    -0.349  0.766    -0.609  0.676    -0.556  0.703
+  1766    12    -0.511  3.748    -0.172  1.201    -0.280  0.796    -0.569  0.706    -0.541  0.702
+  1767     1    -2.936  5.415    -0.152  1.206    -0.250  0.843    -0.601  0.677    -0.519  0.676
+  1767     2     0.326  5.804    -0.043  1.245    -0.227  0.872    -0.630  0.629    -0.521  0.646
+  1767     3     1.666  4.894    -0.071  1.364    -0.212  0.864    -0.642  0.607    -0.524  0.635
+  1767     4    -1.569  2.900    -0.025  1.388    -0.291  0.861    -0.638  0.607    -0.537  0.620
+  1767     5    -0.613  2.180     0.016  1.706    -0.343  0.844    -0.643  0.632    -0.544  0.603
+  1767     6     0.359  1.992    -0.102  1.702    -0.457  0.801    -0.644  0.638    -0.562  0.597
+  1767     7    -0.856  2.524    -0.237  1.228    -0.488  0.810    -0.632  0.638    -0.581  0.578
+  1767     8     1.551  1.683    -0.533  0.868    -0.481  0.809    -0.629  0.635    -0.599  0.579
+  1767     9     0.288  2.469    -0.934  1.194    -0.563  0.805    -0.623  0.648    -0.612  0.573
+  1767    10     0.207  2.116    -0.863  0.941    -0.547  0.845    -0.598  0.654    -0.609  0.595
+  1767    11     2.290  5.475    -0.958  1.063    -0.554  0.847    -0.581  0.646    -0.605  0.593
+  1767    12    -1.924  4.076    -1.057  0.938    -0.536  0.878    -0.548  0.673    -0.597  0.609
+  1768     1    -4.558  6.926    -0.965  0.839    -0.576  0.818    -0.557  0.628    -0.579  0.642
+  1768     2    -3.225  4.636    -1.141  0.857    -0.599  0.803    -0.556  0.629    -0.563  0.643
+  1768     3    -3.145  3.519    -1.213  0.896    -0.599  0.810    -0.534  0.644    -0.567  0.651
+  1768     4    -0.713  2.192    -1.303  0.988    -0.584  0.809    -0.513  0.651    -0.555  0.653
+  1768     5    -1.754  1.851    -1.549  1.043    -0.581  0.863    -0.484  0.651    -0.547  0.649
+  1768     6    -0.833  2.961    -1.397  0.918    -0.558  0.909    -0.475  0.663    -0.555  0.661
+  1768     7     0.244  3.566    -0.993  1.346    -0.458  0.995    -0.489  0.657    -0.552  0.662
+  1768     8    -0.558  1.662    -0.794  2.078    -0.545  0.974    -0.487  0.665    -0.547  0.660
+  1768     9    -0.579  2.342    -0.573  2.550    -0.600  0.954    -0.471  0.656    -0.535  0.650
+  1768    10    -0.867  2.629    -0.422  2.348    -0.634  0.939    -0.445  0.678    -0.520  0.662
+  1768    11    -0.667  3.610    -0.107  2.523    -0.662  0.900    -0.450  0.671    -0.529  0.658
+  1768    12    -0.095  5.632     0.117  2.451    -0.646  0.950    -0.421  0.684    -0.530  0.662
+  1769     1     0.286  4.759     0.106  2.216    -0.631  0.980    -0.433  0.699    -0.545  0.668
+  1769     2    -0.836  3.088     0.173  2.421    -0.619  0.996    -0.443  0.714    -0.546  0.688
+  1769     3    -0.488  3.926     0.214  2.314    -0.630  1.015    -0.440  0.724    -0.542  0.679
+  1769     4     1.096  2.639    -0.008  2.293    -0.599  1.006    -0.414  0.740    -0.531  0.672
+  1769     5     2.027  2.824    -0.212  2.034    -0.645  1.016    -0.352  0.788    -0.514  0.672
+  1769     6     1.856  1.833    -0.588  1.594    -0.649  1.010    -0.318  0.797    -0.521  0.678
+  1769     7     0.111  3.008    -0.638  1.639    -0.612  0.916    -0.287  0.817    -0.518  0.681
+  1769     8     0.236  3.277    -0.514  1.452    -0.653  0.839    -0.273  0.811    -0.522  0.674
+  1769     9    -0.077  1.529    -0.775  1.528    -0.727  0.828    -0.272  0.799    -0.520  0.670
+  1769    10    -3.538  2.154    -0.698  1.654    -0.690  0.849    -0.287  0.793    -0.515  0.669
+  1769    11    -3.116  2.159    -0.932  1.524    -0.707  0.861    -0.310  0.766    -0.506  0.673
+  1769    12    -4.604  4.736    -1.241  1.369    -0.733  0.840    -0.352  0.775    -0.494  0.699
+  1770     1    -0.318  5.270    -1.489  1.138    -0.717  0.838    -0.353  0.755    -0.480  0.676
+  1770     2     0.651  2.900    -1.649  1.169    -0.734  0.854    -0.335  0.752    -0.479  0.664
+  1770     3    -3.611  3.094    -1.687  1.193    -0.762  0.866    -0.332  0.741    -0.469  0.673
+  1770     4     2.022  4.483    -1.381  0.977    -0.760  0.873    -0.349  0.736    -0.444  0.656
+  1770     5    -0.784  1.711    -1.106  1.023    -0.760  0.860    -0.350  0.732    -0.424  0.635
+  1770     6    -1.855  3.065    -0.649  1.374    -0.683  0.859    -0.341  0.738    -0.414  0.618
+  1770     7    -2.862  5.469    -0.378  1.430    -0.657  0.840    -0.338  0.734    -0.397  0.626
+  1770     8    -1.682  2.988    -0.760  1.467    -0.636  0.867    -0.331  0.741    -0.395  0.616
+  1770     9    -0.541  1.426    -0.694  1.292    -0.602  0.891    -0.334  0.753    -0.397  0.619
+  1770    10     0.132  2.886    -1.035  1.113    -0.567  0.917    -0.318  0.756    -0.386  0.618
+  1770    11     0.185  4.498    -1.097  1.316    -0.505  0.926    -0.299  0.721    -0.383  0.606
+  1770    12     0.885  5.373    -0.879  1.189    -0.504  0.921    -0.310  0.715    -0.380  0.606
+  1771     1     2.930  3.409    -0.654  1.155    -0.506  0.899    -0.309  0.709    -0.370  0.601
+  1771     2    -3.923  3.835    -0.436  1.193    -0.497  0.903    -0.314  0.715    -0.372  0.602
+  1771     3    -2.822  4.510    -0.394  1.204    -0.474  0.874    -0.330  0.737    -0.377  0.599
+  1771     4    -2.078  4.043    -0.277  1.267    -0.454  0.860    -0.340  0.746    -0.380  0.600
+  1771     5    -1.519  6.424    -0.374  1.281    -0.480  0.825    -0.353  0.788    -0.388  0.607
+  1771     6     0.756  1.510    -0.507  1.442    -0.436  0.845    -0.349  0.792    -0.408  0.615
+  1771     7    -0.158  1.747    -0.815  1.689    -0.488  0.886    -0.347  0.800    -0.425  0.616
+  1771     8     0.935  1.219    -0.666  1.814    -0.453  0.892    -0.342  0.790    -0.427  0.620
+  1771     9    -0.041  1.492    -0.661  1.434    -0.443  0.906    -0.361  0.791    -0.433  0.610
+  1771    10     1.534  1.650    -0.433  1.106    -0.423  0.899    -0.371  0.788    -0.424  0.612
+  1771    11    -0.976  3.386    -0.443  0.947    -0.355  0.927    -0.391  0.779    -0.414  0.611
+  1771    12    -0.713  3.034    -0.608  0.972    -0.356  0.909    -0.377  0.772    -0.391  0.623
+  1772     1    -0.762  4.514    -0.583  1.001    -0.324  0.880    -0.325  0.722    -0.413  0.615
+  1772     2    -2.142  4.855    -0.620  0.946    -0.319  0.852    -0.319  0.684    -0.428  0.588
+  1772     3    -2.764  2.759    -0.729  0.905    -0.332  0.835    -0.340  0.676    -0.433  0.577
+  1772     4     0.665  2.391    -0.831  0.935    -0.282  0.832    -0.349  0.682    -0.433  0.576
+  1772     5    -1.641  1.646    -0.556  0.854    -0.278  0.801    -0.348  0.677    -0.428  0.586
+  1772     6    -1.219  2.996    -0.272  0.861    -0.248  0.835    -0.366  0.684    -0.423  0.591
+  1772     7     0.137  2.287    -0.460  0.870    -0.218  0.790    -0.379  0.687    -0.420  0.591
+  1772     8     0.498  2.472    -0.448  0.870    -0.188  0.791    -0.404  0.694    -0.415  0.586
+  1772     9    -1.357  1.955    -0.306  0.818    -0.102  0.770    -0.414  0.690    -0.404  0.594
+  1772    10     0.314  2.013    -0.246  0.853    -0.152  0.757    -0.438  0.707    -0.396  0.582
+  1772    11     2.317  3.529     0.053  0.841    -0.147  0.756    -0.463  0.705    -0.405  0.570
+  1772    12     2.696  3.623     0.088  0.918    -0.145  0.733    -0.449  0.712    -0.403  0.575
+  1773     1    -3.019  2.754     0.087  0.979    -0.100  0.747    -0.408  0.746    -0.409  0.572
+  1773     2    -1.987  3.080     0.045  0.989    -0.062  0.766    -0.366  0.762    -0.393  0.568
+  1773     3    -1.065  2.168     0.228  0.982    -0.069  0.783    -0.356  0.795    -0.382  0.572
+  1773     4     1.382  2.629     0.228  0.953    -0.052  0.802    -0.335  0.802    -0.378  0.567
+  1773     5     1.955  1.570    -0.149  1.203    -0.017  0.711    -0.324  0.799    -0.370  0.563
+  1773     6    -0.803  1.579    -0.162  1.157    -0.062  0.706    -0.338  0.811    -0.358  0.569
+  1773     7     0.126  1.563    -0.148  1.231    -0.159  0.706    -0.341  0.809    -0.370  0.569
+  1773     8    -0.014  1.239     0.122  1.419    -0.083  0.699    -0.348  0.805    -0.370  0.570
+  1773     9     0.841  1.308     0.219  1.511    -0.061  0.711    -0.335  0.786    -0.376  0.564
+  1773    10     0.312  1.809     0.300  1.384    -0.047  0.711    -0.341  0.794    -0.371  0.572
+  1773    11    -2.207  4.538     0.645  1.716    -0.045  0.780    -0.355  0.777    -0.379  0.574
+  1773    12     2.549  4.040     0.860  1.708    -0.051  0.756    -0.367  0.787    -0.379  0.576
+  1774     1    -2.853  5.714     1.020  1.907    -0.063  0.753    -0.382  0.807    -0.374  0.560
+  1774     2     1.246  5.445     1.062  1.858    -0.064  0.728    -0.366  0.837    -0.393  0.554
+  1774     3     0.101  2.696     0.925  1.824    -0.091  0.722    -0.357  0.832    -0.396  0.555
+  1774     4     2.351  2.984     0.854  1.721    -0.143  0.730    -0.358  0.813    -0.395  0.552
+  1774     5     6.106  1.237     0.797  1.746    -0.137  0.691    -0.355  0.802    -0.396  0.558
+  1774     6     1.769  4.148     0.351  1.622    -0.105  0.686    -0.385  0.812    -0.389  0.564
+  1774     7     2.047  4.227     0.711  1.294    -0.038  0.649    -0.399  0.805    -0.381  0.562
+  1774     8     0.498  1.417     0.809  1.020     0.015  0.634    -0.406  0.797    -0.371  0.556
+  1774     9    -0.807  1.521     0.934  0.928     0.048  0.636    -0.403  0.793    -0.375  0.558
+  1774    10    -0.548  3.395     0.657  0.938    -0.009  0.642    -0.375  0.783    -0.389  0.555
+  1774    11    -2.879  3.532     0.108  0.724     0.011  0.619    -0.349  0.789    -0.397  0.553
+  1774    12    -2.810  4.251    -0.186  0.789     0.002  0.641    -0.318  0.822    -0.407  0.542
+  1775     1     1.474  3.493    -0.370  1.045    -0.040  0.645    -0.317  0.783    -0.419  0.545
+  1775     2     2.421  3.155    -0.363  1.080    -0.073  0.650    -0.318  0.767    -0.428  0.546
+  1775     3     1.599  2.118    -0.377  1.154    -0.066  0.631    -0.291  0.768    -0.443  0.538
+  1775     4    -0.975  1.401    -0.234  1.029    -0.115  0.654    -0.269  0.736    -0.455  0.537
+  1775     5    -0.484  2.251     0.199  0.879    -0.167  0.668    -0.235  0.698    -0.460  0.532
+  1775     6    -1.756  2.886     0.282  0.859    -0.216  0.686    -0.217  0.675    -0.447  0.541
+  1775     7    -0.163  1.863    -0.085  0.943    -0.159  0.763    -0.184  0.659    -0.445  0.537
+  1775     8     0.578  1.883    -0.230  0.920    -0.096  0.789    -0.179  0.643    -0.441  0.536
+  1775     9    -0.970  2.526    -0.489  1.053    -0.110  0.833    -0.196  0.641    -0.446  0.535
+  1775    10     1.161  1.979    -0.514  1.093    -0.103  0.819    -0.194  0.648    -0.447  0.535
+  1775    11     2.324  2.493    -0.589  1.344    -0.144  0.799    -0.195  0.638    -0.449  0.535
+  1775    12    -1.819  2.056    -0.408  1.317    -0.171  0.828    -0.214  0.638    -0.455  0.532
+  1776     1    -2.926  3.433    -0.468  1.296    -0.175  0.839    -0.238  0.640    -0.450  0.527
+  1776     2     0.676  3.908    -0.446  1.107    -0.198  0.822    -0.213  0.634    -0.458  0.520
+  1776     3    -1.507  3.665    -0.504  0.998    -0.197  0.805    -0.193  0.623    -0.458  0.522
+  1776     4    -1.269  1.783    -0.730  1.043    -0.229  0.834    -0.187  0.621    -0.455  0.518
+  1776     5    -1.391  1.801    -0.975  1.315    -0.231  0.822    -0.179  0.619    -0.454  0.514
+  1776     6     0.420  1.536    -0.725  1.260    -0.298  0.818    -0.202  0.621    -0.454  0.520
+  1776     7    -0.882  1.911    -0.210  0.901    -0.277  0.826    -0.202  0.620    -0.446  0.517
+  1776     8     0.843  1.869    -0.180  0.767    -0.279  0.897    -0.207  0.624    -0.445  0.514
+  1776     9    -1.669  1.304    -0.120  0.764    -0.270  0.878    -0.222  0.623    -0.448  0.511
+  1776    10    -1.546  2.369    -0.240  0.781    -0.294  0.842    -0.239  0.621    -0.449  0.511
+  1776    11    -0.615  2.675    -0.165  0.975    -0.356  0.832    -0.219  0.605    -0.460  0.503
+  1776    12     1.181  2.433    -0.342  0.821    -0.414  0.880    -0.214  0.600    -0.469  0.499
+  1777     1     3.252  3.821    -0.471  0.854    -0.473  0.931    -0.225  0.614    -0.457  0.485
+  1777     2     1.038  4.842    -0.664  0.782    -0.492  0.925    -0.226  0.608    -0.462  0.476
+  1777     3    -0.790  2.488    -0.603  0.854    -0.474  0.925    -0.225  0.610    -0.471  0.477
+  1777     4    -2.715  2.066    -0.693  0.823    -0.468  0.897    -0.229  0.607    -0.468  0.475
+  1777     5    -0.487  1.608    -0.706  0.793    -0.421  0.930    -0.214  0.603    -0.466  0.475
+  1777     6    -1.708  2.586    -0.825  0.817    -0.389  0.967    -0.202  0.607    -0.464  0.473
+  1777     7    -2.428  1.365    -1.066  1.215    -0.415  0.924    -0.208  0.612    -0.465  0.473
+  1777     8    -1.469  1.312    -1.003  1.461    -0.447  0.893    -0.200  0.607    -0.473  0.473
+  1777     9    -0.935  1.928    -1.093  1.695    -0.480  0.922    -0.186  0.610    -0.481  0.475
+  1777    10    -2.636  3.990    -0.716  1.681    -0.387  0.841    -0.195  0.578    -0.486  0.473
+  1777    11    -0.769  2.457    -0.719  1.711    -0.324  0.763    -0.228  0.571    -0.499  0.473
+  1777    12    -0.237  3.445    -0.781  1.873    -0.289  0.736    -0.258  0.555    -0.494  0.474
+  1778     1     0.348  4.479    -0.584  1.955    -0.269  0.708    -0.260  0.585    -0.486  0.467
+  1778     2     1.797  3.543    -0.579  1.899    -0.296  0.670    -0.231  0.576    -0.471  0.458
+  1778     3    -1.869  4.330    -0.426  1.911    -0.322  0.639    -0.231  0.567    -0.460  0.451
+  1778     4     1.811  1.609    -0.340  1.904    -0.336  0.627    -0.243  0.553    -0.458  0.452
+  1778     5    -0.518  1.320    -0.471  1.685    -0.373  0.678    -0.257  0.544    -0.454  0.451
+  1778     6    -2.454  4.660    -0.575  1.646    -0.366  0.666    -0.241  0.544    -0.449  0.450
+  1778     7    -0.067  2.293    -0.735  1.558    -0.316  0.659    -0.251  0.547    -0.442  0.452
+  1778     8    -1.409  2.457    -0.790  1.936    -0.343  0.649    -0.254  0.540    -0.438  0.454
+  1778     9     0.901  1.780    -0.581  1.636    -0.326  0.613    -0.281  0.537    -0.437  0.453
+  1778    10    -1.608  3.994    -0.657  1.501    -0.327  0.607    -0.297  0.530    -0.432  0.451
+  1778    11    -2.342  2.316    -0.416  1.574    -0.312  0.550    -0.308  0.532    -0.428  0.453
+  1778    12    -1.482  3.119    -0.354  1.427    -0.352  0.569    -0.336  0.525    -0.438  0.458
+  1779     1    -1.569  3.555    -0.473  1.476    -0.341  0.571    -0.315  0.496    -0.435  0.470
+  1779     2     1.142  6.205    -0.407  1.395    -0.350  0.601    -0.343  0.491    -0.436  0.469
+  1779     3     0.630  1.966    -0.460  1.465    -0.353  0.609    -0.353  0.483    -0.438  0.471
+  1779     4     0.901  1.701    -0.343  1.246    -0.336  0.603    -0.376  0.467    -0.446  0.464
+  1779     5     2.375  1.625    -0.152  1.431    -0.301  0.607    -0.441  0.469    -0.455  0.460
+  1779     6    -1.704  2.852    -0.103  1.788    -0.323  0.595    -0.459  0.481    -0.463  0.461
+  1779     7    -1.505  2.302     0.019  1.246    -0.412  0.652    -0.476  0.498    -0.460  0.453
+  1779     8    -0.612  1.170    -0.034  0.760    -0.467  0.674    -0.468  0.502    -0.463  0.457
+  1779     9     0.269  1.373    -0.116  0.845    -0.497  0.671    -0.478  0.505    -0.463  0.457
+  1779    10    -0.204  2.165     0.194  0.806    -0.449  0.655    -0.492  0.488    -0.454  0.452
+  1779    11    -0.052  2.499     0.268  0.964    -0.439  0.673    -0.484  0.489    -0.439  0.450
+  1779    12    -0.899  2.774     0.441  1.244    -0.407  0.657    -0.461  0.466    -0.421  0.443
+  1780     1    -0.104  3.069     0.654  1.445    -0.376  0.661    -0.484  0.478    -0.419  0.447
+  1780     2     0.510  4.361     0.618  1.529    -0.328  0.645    -0.520  0.483    -0.422  0.447
+  1780     3    -0.355  2.580     0.381  1.481    -0.307  0.669    -0.554  0.481    -0.411  0.443
+  1780     4     4.617  3.684     0.426  1.470    -0.275  0.590    -0.562  0.479    -0.425  0.451
+  1780     5     3.260  2.490     0.438  1.567    -0.290  0.569    -0.570  0.471    -0.426  0.450
+  1780     6     0.381  3.147     0.396  2.002    -0.301  0.532    -0.553  0.473    -0.418  0.446
+  1780     7     1.045  1.944     0.412  1.594    -0.360  0.519    -0.552  0.465    -0.410  0.436
+  1780     8    -1.045  2.210     0.293  1.348    -0.366  0.499    -0.552  0.461    -0.404  0.431
+  1780     9    -2.573  2.488     0.281  1.476    -0.351  0.466    -0.557  0.452    -0.405  0.431
+  1780    10     0.344  1.583    -0.217  1.213    -0.384  0.462    -0.575  0.445    -0.408  0.435
+  1780    11     0.091  2.322    -0.527  1.115    -0.369  0.466    -0.599  0.468    -0.411  0.436
+  1780    12    -1.409  2.601    -0.727  1.232    -0.311  0.454    -0.600  0.466    -0.418  0.447
+  1781     1     0.091  4.984    -0.828  1.361    -0.328  0.452    -0.591  0.459    -0.432  0.451
+  1781     2    -0.915  5.994    -0.716  1.437    -0.309  0.453    -0.603  0.453    -0.417  0.446
+  1781     3    -0.508  2.307    -0.657  1.381    -0.366  0.450    -0.586  0.437    -0.408  0.437
+  1781     4    -1.352  1.894    -0.728  1.384    -0.366  0.438    -0.569  0.429    -0.396  0.429
+  1781     5    -0.467  2.205    -0.616  1.315    -0.385  0.439    -0.555  0.404    -0.395  0.417
+  1781     6    -2.011  1.355    -0.509  1.280    -0.374  0.441    -0.560  0.406    -0.400  0.417
+  1781     7    -0.170  1.484    -0.689  1.110    -0.354  0.420    -0.546  0.403    -0.403  0.415
+  1781     8     0.304  1.249    -0.802  0.869    -0.408  0.431    -0.549  0.416    -0.408  0.417
+  1781     9    -1.869  2.779    -0.974  0.923    -0.435  0.422    -0.534  0.420    -0.409  0.416
+  1781    10    -0.508  1.287    -0.847  0.876    -0.458  0.431    -0.527  0.409    -0.420  0.415
+  1781    11     1.431  2.525    -0.801  0.843    -0.526  0.442    -0.529  0.410    -0.416  0.409
+  1781    12    -0.118  3.051    -0.614  0.805    -0.505  0.446    -0.560  0.412    -0.414  0.409
+  1782     1    -2.069  2.800    -0.647  0.767    -0.478  0.463    -0.590  0.447    -0.419  0.405
+  1782     2    -2.274  3.807    -0.555  0.742    -0.444  0.468    -0.604  0.467    -0.409  0.399
+  1782     3    -2.573  3.799    -0.371  0.784    -0.482  0.478    -0.603  0.467    -0.400  0.402
+  1782     4     0.166  1.607    -0.389  0.934    -0.515  0.474    -0.586  0.458    -0.404  0.404
+  1782     5     0.084  1.483    -0.647  1.077    -0.548  0.481    -0.583  0.467    -0.396  0.404
+  1782     6     0.240  1.080    -0.715  1.083    -0.533  0.517    -0.562  0.460    -0.390  0.402
+  1782     7    -0.571  1.506    -0.807  0.945    -0.553  0.494    -0.551  0.462    -0.390  0.399
+  1782     8     1.412  1.805    -0.498  1.067    -0.594  0.483    -0.541  0.465    -0.397  0.402
+  1782     9     0.341  1.601    -0.366  1.167    -0.628  0.474    -0.548  0.480    -0.390  0.404
+  1782    10    -0.728  1.778    -0.391  1.208    -0.736  0.426    -0.535  0.472    -0.396  0.401
+  1782    11    -1.670  4.713    -0.369  1.288    -0.815  0.417    -0.535  0.471    -0.410  0.408
+  1782    12    -0.933  3.216    -0.300  1.233    -0.817  0.426    -0.540  0.460    -0.421  0.407
+  1783     1    -3.171  2.799    -0.347  1.048    -0.835  0.430    -0.563  0.465    -0.413  0.404
+  1783     2     1.444  3.533    -0.487  0.943    -0.809  0.443    -0.575  0.478    -0.398  0.403
+  1783     3    -0.992  2.053    -0.723  0.978    -0.791  0.458    -0.565  0.502    -0.393  0.403
+  1783     4    -0.139  1.641    -0.795  0.754    -0.815  0.447    -0.581  0.517    -0.396  0.402
+  1783     5     0.354  1.861    -0.946  0.785    -0.825  0.453    -0.583  0.511    -0.405  0.400
+  1783     6     1.063  1.920    -0.941  0.853    -0.833  0.456    -0.561  0.502    -0.402  0.397
+  1783     7    -1.132  2.489    -0.705  0.968    -0.865  0.479    -0.544  0.494    -0.402  0.394
+  1783     8    -0.269  0.919    -1.000  0.944    -0.862  0.519    -0.529  0.483    -0.404  0.394
+  1783     9    -2.491  2.322    -1.002  0.886    -0.845  0.522    -0.538  0.485    -0.409  0.394
+  1783    10    -1.596  1.357    -1.028  1.023    -0.810  0.545    -0.523  0.457    -0.413  0.392
+  1783    11    -3.483  2.434    -1.201  0.910    -0.799  0.542    -0.501  0.448    -0.406  0.384
+  1783    12    -0.867  2.498    -1.324  0.871    -0.768  0.538    -0.508  0.441    -0.423  0.390
+  1784     1    -0.339  3.908    -1.222  1.182    -0.751  0.556    -0.487  0.430    -0.406  0.398
+  1784     2    -2.094  3.585    -1.082  1.406    -0.748  0.593    -0.506  0.421    -0.411  0.410
+  1784     3    -1.018  1.965    -1.038  1.225    -0.716  0.631    -0.519  0.418    -0.408  0.412
+  1784     4    -0.458  3.764    -1.090  1.221    -0.718  0.617    -0.533  0.422    -0.415  0.405
+  1784     5    -1.722  1.653    -0.966  1.299    -0.757  0.641    -0.554  0.429    -0.435  0.415
+  1784     6    -0.409  2.147    -0.897  1.459    -0.798  0.634    -0.542  0.427    -0.447  0.421
+  1784     7     0.098  3.259    -0.975  1.066    -0.767  0.661    -0.522  0.430    -0.462  0.430
+  1784     8     1.403  3.627    -0.963  1.026    -0.742  0.680    -0.520  0.431    -0.465  0.430
+  1784     9    -1.959  1.920    -1.078  0.925    -0.709  0.697    -0.524  0.436    -0.459  0.430
+  1784    10    -2.217  1.841    -1.198  0.818    -0.724  0.695    -0.533  0.428    -0.456  0.422
+  1784    11    -2.001  2.343    -1.176  0.860    -0.728  0.725    -0.529  0.422    -0.445  0.420
+  1784    12    -0.039  2.457    -1.118  0.958    -0.718  0.751    -0.523  0.412    -0.434  0.415
+  1785     1    -1.277  3.107    -1.132  0.877    -0.725  0.755    -0.521  0.402    -0.436  0.423
+  1785     2    -1.941  2.414    -1.203  0.812    -0.755  0.752    -0.527  0.399    -0.439  0.431
+  1785     3    -2.408  3.455    -1.166  0.795    -0.789  0.778    -0.531  0.398    -0.442  0.435
+  1785     4    -1.894  1.349    -1.071  0.701    -0.796  0.731    -0.580  0.378    -0.433  0.436
+  1785     5    -1.452  1.507    -0.950  0.751    -0.781  0.696    -0.616  0.369    -0.428  0.431
+  1785     6     0.277  2.149    -1.105  0.743    -0.778  0.687    -0.620  0.368    -0.424  0.425
+  1785     7    -0.066  1.717    -1.150  0.695    -0.766  0.815    -0.635  0.365    -0.421  0.424
+  1785     8     0.552  1.267    -1.051  0.708    -0.784  0.812    -0.628  0.371    -0.423  0.425
+  1785     9    -1.512  1.016    -0.806  0.652    -0.779  0.878    -0.614  0.379    -0.425  0.422
+  1785    10    -1.080  2.088    -0.585  0.721    -0.779  0.907    -0.621  0.380    -0.433  0.420
+  1785    11    -0.548  2.180    -0.446  0.743    -0.797  0.873    -0.628  0.376    -0.444  0.429
+  1785    12    -1.896  1.642    -0.484  0.736    -0.811  0.898    -0.622  0.395    -0.435  0.428
+  1786     1    -1.819  2.535    -0.410  0.773    -0.760  0.929    -0.627  0.417    -0.419  0.423
+  1786     2    -0.754  2.382    -0.411  0.793    -0.748  0.898    -0.621  0.444    -0.420  0.428
+  1786     3     0.528  2.541    -0.281  0.828    -0.711  0.870    -0.622  0.455    -0.422  0.428
+  1786     4     0.760  2.647    -0.247  0.809    -0.680  0.813    -0.605  0.462    -0.419  0.426
+  1786     5     0.212  1.574    -0.273  1.144    -0.617  0.812    -0.612  0.458    -0.414  0.418
+  1786     6    -0.173  1.387    -0.331  1.194    -0.642  0.802    -0.598  0.466    -0.412  0.415
+  1786     7     0.824  1.903    -0.199  1.169    -0.619  0.688    -0.604  0.471    -0.409  0.413
+  1786     8     0.533  2.562    -0.199  1.191    -0.604  0.661    -0.608  0.481    -0.409  0.413
+  1786     9     0.055  2.464    -0.294  1.237    -0.604  0.670    -0.596  0.489    -0.402  0.410
+  1786    10    -0.673  2.024    -0.417  1.083    -0.608  0.637    -0.601  0.493    -0.397  0.404
+  1786    11    -0.859  3.452    -0.446  1.082    -0.581  0.663    -0.613  0.488    -0.396  0.401
+  1786    12    -2.598  2.106    -0.363  1.275    -0.579  0.687    -0.614  0.488    -0.410  0.407
+  1787     1    -0.235  2.256    -0.516  1.211    -0.567  0.652    -0.614  0.490    -0.415  0.418
+  1787     2    -0.746  2.263    -0.592  1.149    -0.596  0.607    -0.591  0.505    -0.409  0.429
+  1787     3    -0.617  1.843    -0.735  1.159    -0.566  0.624    -0.576  0.519    -0.409  0.433
+  1787     4    -0.713  1.155    -0.775  1.494    -0.552  0.622    -0.579  0.521    -0.404  0.432
+  1787     5    -0.141  1.917    -0.768  1.217    -0.511  0.626    -0.579  0.531    -0.404  0.436
+  1787     6     0.829  2.117    -0.616  1.121    -0.512  0.575    -0.578  0.536    -0.395  0.432
+  1787     7    -1.015  2.141    -0.802  1.325    -0.490  0.542    -0.572  0.527    -0.387  0.430
+  1787     8    -0.375  1.582    -0.708  1.456    -0.460  0.533    -0.594  0.511    -0.379  0.428
+  1787     9    -1.673  1.551    -0.715  1.819    -0.433  0.525    -0.594  0.523    -0.374  0.432
+  1787    10    -1.148  3.143    -0.664  2.035    -0.424  0.507    -0.597  0.493    -0.363  0.425
+  1787    11    -0.779  2.276    -0.717  1.841    -0.416  0.488    -0.591  0.475    -0.364  0.422
+  1787    12    -0.773  2.673    -0.767  1.752    -0.423  0.492    -0.583  0.480    -0.364  0.421
+  1788     1    -2.467  4.130    -0.519  1.639    -0.435  0.489    -0.566  0.513    -0.365  0.416
+  1788     2     0.385  3.388    -0.451  1.393    -0.448  0.479    -0.565  0.512    -0.371  0.417
+  1788     3    -0.698  2.137    -0.335  1.295    -0.436  0.464    -0.555  0.516    -0.356  0.415
+  1788     4    -0.109  3.069    -0.218  0.927    -0.427  0.472    -0.549  0.518    -0.366  0.414
+  1788     5    -0.770  1.438    -0.127  0.818    -0.430  0.472    -0.553  0.508    -0.364  0.413
+  1788     6     0.223  1.145    -0.259  0.735    -0.411  0.495    -0.562  0.508    -0.355  0.403
+  1788     7     1.969  1.183     0.029  0.726    -0.389  0.487    -0.553  0.523    -0.354  0.399
+  1788     8     0.436  2.605    -0.101  0.735    -0.380  0.481    -0.555  0.528    -0.345  0.400
+  1788     9    -0.280  1.115    -0.125  0.979    -0.398  0.491    -0.537  0.528    -0.351  0.402
+  1788    10     0.251  4.181    -0.176  1.117    -0.401  0.477    -0.529  0.533    -0.346  0.398
+  1788    11     0.321  2.314    -0.120  0.951    -0.425  0.473    -0.505  0.547    -0.337  0.400
+  1788    12    -2.364  3.041    -0.161  0.975    -0.427  0.489    -0.510  0.562    -0.337  0.403
+  1789     1     0.998  2.088    -0.258  0.918    -0.456  0.479    -0.497  0.517    -0.333  0.401
+  1789     2    -1.182  1.893    -0.321  0.782    -0.469  0.467    -0.479  0.513    -0.342  0.395
+  1789     3    -0.978  1.472    -0.312  0.756    -0.477  0.454    -0.463  0.519    -0.353  0.394
+  1789     4    -0.721  1.463    -0.447  0.606    -0.484  0.474    -0.454  0.497    -0.356  0.398
+  1789     5    -0.106  1.591    -0.438  0.617    -0.470  0.444    -0.429  0.499    -0.366  0.399
+  1789     6    -0.259  0.913    -0.248  0.597    -0.431  0.451    -0.435  0.506    -0.359  0.396
+  1789     7     0.798  1.200    -0.328  0.574    -0.460  0.446    -0.448  0.483    -0.353  0.394
+  1789     8    -0.316  0.795    -0.243  0.573    -0.440  0.462    -0.461  0.468    -0.351  0.396
+  1789     9    -0.176  0.848    -0.227  0.605    -0.443  0.465    -0.440  0.477    -0.353  0.398
+  1789    10    -1.367  0.964    -0.281  0.705    -0.435  0.469    -0.421  0.480    -0.351  0.394
+  1789    11     0.428  1.391    -0.352  0.838    -0.429  0.454    -0.405  0.488    -0.351  0.393
+  1789    12    -0.079  3.133    -0.339  0.855    -0.438  0.444    -0.406  0.483    -0.351  0.386
+  1790     1     0.034  2.746    -0.475  0.840    -0.418  0.414    -0.388  0.487    -0.351  0.394
+  1790     2    -0.158  2.113    -0.464  0.830    -0.433  0.392    -0.357  0.499    -0.354  0.397
+  1790     3    -0.792  1.332    -0.518  0.896    -0.399  0.386    -0.329  0.503    -0.355  0.400
+  1790     4    -1.363  2.086    -0.450  0.923    -0.399  0.374    -0.304  0.508    -0.372  0.400
+  1790     5    -0.963  1.388    -0.546  0.898    -0.401  0.370    -0.286  0.502    -0.387  0.400
+  1790     6    -0.107  0.751    -0.601  0.616    -0.388  0.373    -0.295  0.502    -0.389  0.401
+  1790     7    -0.827  1.100    -0.647  0.572    -0.365  0.356    -0.290  0.502    -0.396  0.399
+  1790     8    -0.191  0.826    -0.648  0.584    -0.347  0.353    -0.294  0.501    -0.392  0.404
+  1790     9    -0.818  1.297    -0.631  0.645    -0.332  0.349    -0.293  0.504    -0.385  0.410
+  1790    10    -0.547  1.122    -0.466  0.671    -0.319  0.356    -0.292  0.513    -0.386  0.411
+  1790    11    -0.726  1.307    -0.488  0.756    -0.309  0.351    -0.290  0.512    -0.388  0.408
+  1790    12    -0.739  3.295    -0.507  0.762    -0.314  0.348    -0.270  0.510    -0.383  0.410
+  1791     1    -0.524  3.621    -0.513  0.765    -0.346  0.344    -0.247  0.507    -0.381  0.422
+  1791     2    -0.167  1.721    -0.516  0.834    -0.362  0.343    -0.237  0.510    -0.374  0.441
+  1791     3    -0.590  1.750    -0.484  0.847    -0.363  0.346    -0.258  0.530    -0.367  0.446
+  1791     4     0.617  1.421    -0.531  0.861    -0.378  0.373    -0.270  0.523    -0.366  0.451
+  1791     5    -1.221  1.462    -0.470  0.954    -0.393  0.393    -0.273  0.519    -0.362  0.446
+  1791     6    -0.334  1.295    -0.429  0.783    -0.378  0.418    -0.263  0.519    -0.356  0.449
+  1791     7    -0.910  1.313    -0.552  0.695    -0.374  0.433    -0.271  0.512    -0.356  0.451
+  1791     8    -0.224  1.023    -0.500  0.776    -0.354  0.448    -0.269  0.492    -0.361  0.454
+  1791     9    -0.428  1.275    -0.516  0.822    -0.322  0.455    -0.270  0.477    -0.355  0.459
+  1791    10    -1.114  1.043    -0.588  0.837    -0.300  0.454    -0.266  0.486    -0.349  0.463
+  1791    11     0.002  1.824    -0.470  0.870    -0.277  0.443    -0.263  0.457    -0.357  0.457
+  1791    12    -0.241  1.540    -0.417  0.844    -0.292  0.444    -0.259  0.465    -0.354  0.459
+  1792     1    -1.999  2.358    -0.326  0.773    -0.329  0.446    -0.240  0.453    -0.339  0.463
+  1792     2     0.454  2.423    -0.415  0.698    -0.327  0.453    -0.214  0.453    -0.327  0.469
+  1792     3    -0.785  1.734    -0.346  0.734    -0.313  0.457    -0.216  0.458    -0.315  0.474
+  1792     4    -0.248  1.281    -0.350  0.779    -0.290  0.459    -0.221  0.463    -0.316  0.475
+  1792     5     0.194  1.407    -0.425  0.859    -0.299  0.472    -0.225  0.456    -0.321  0.477
+  1792     6     0.306  1.456    -0.405  0.765    -0.300  0.509    -0.228  0.450    -0.321  0.477
+  1792     7     0.184  1.490    -0.329  0.841    -0.286  0.533    -0.223  0.441    -0.319  0.476
+  1792     8    -1.293  1.513    -0.241  0.809    -0.254  0.552    -0.217  0.432    -0.326  0.471
+  1792     9     0.402  1.659    -0.160  0.791    -0.226  0.561    -0.201  0.424    -0.327  0.474
+  1792    10    -1.166  1.631    -0.085  0.804    -0.184  0.592    -0.191  0.413    -0.323  0.457
+  1792    11    -0.901  2.801    -0.112  0.768    -0.155  0.609    -0.192  0.408    -0.320  0.445
+  1792    12    -0.004  2.114    -0.145  0.765    -0.167  0.605    -0.188  0.417    -0.319  0.449
+  1793     1    -1.081  2.813    -0.158  0.792    -0.145  0.613    -0.167  0.403    -0.313  0.469
+  1793     2     1.511  3.521    -0.093  0.877    -0.140  0.629    -0.167  0.398    -0.324  0.469
+  1793     3     0.180  2.120    -0.155  0.904    -0.149  0.644    -0.147  0.382    -0.321  0.468
+  1793     4     0.659  1.651    -0.111  0.918    -0.156  0.645    -0.152  0.376    -0.317  0.468
+  1793     5    -0.135  1.307    -0.085  0.843    -0.149  0.656    -0.146  0.378    -0.318  0.463
+  1793     6    -0.086  1.637    -0.209  0.952    -0.130  0.618    -0.150  0.373    -0.321  0.465
+  1793     7     0.025  1.429    -0.016  0.976    -0.104  0.621    -0.165  0.372    -0.315  0.472
+  1793     8    -0.506  1.277    -0.140  0.961    -0.094  0.631    -0.161  0.379    -0.310  0.473
+  1793     9    -0.342  2.182    -0.074  0.983    -0.118  0.647    -0.164  0.381    -0.299  0.471
+  1793    10    -0.644  1.860    -0.078  0.998    -0.140  0.651    -0.170  0.399    -0.289  0.474
+  1793    11    -0.587  1.918     0.038  0.976    -0.121  0.654    -0.173  0.406    -0.275  0.481
+  1793    12    -1.485  2.395    -0.052  0.973    -0.099  0.634    -0.166  0.415    -0.275  0.490
+  1794     1     1.233  2.186    -0.172  0.978    -0.086  0.629    -0.178  0.423    -0.274  0.468
+  1794     2     0.024  2.635    -0.146  0.947    -0.070  0.609    -0.179  0.428    -0.270  0.468
+  1794     3     0.967  2.217    -0.065  0.872    -0.063  0.597    -0.187  0.428    -0.270  0.474
+  1794     4     0.611  1.651    -0.007  0.807    -0.048  0.591    -0.179  0.434    -0.262  0.466
+  1794     5     1.261  1.273     0.030  0.784    -0.057  0.581    -0.178  0.433    -0.252  0.465
+  1794     6    -1.172  1.547     0.141  0.838    -0.087  0.581    -0.177  0.434    -0.249  0.469
+  1794     7    -1.418  2.138     0.113  0.795    -0.021  0.583    -0.184  0.434    -0.245  0.462
+  1794     8    -0.188  1.426     0.257  0.764     0.012  0.576    -0.183  0.435    -0.249  0.452
+  1794     9     0.627  1.401     0.254  0.797     0.012  0.578    -0.182  0.435    -0.238  0.452
+  1794    10     0.048  1.284     0.297  0.803    -0.007  0.588    -0.169  0.436    -0.229  0.451
+  1794    11    -0.138  2.115     0.258  0.807    -0.020  0.584    -0.173  0.441    -0.216  0.456
+  1794    12    -0.152  2.614     0.286  0.825    -0.018  0.582    -0.179  0.450    -0.221  0.455
+  1795     1     0.898  2.367     0.443  0.946    -0.028  0.581    -0.181  0.460    -0.213  0.452
+  1795     2     1.754  4.098     0.470  0.972    -0.001  0.582    -0.181  0.463    -0.207  0.455
+  1795     3     0.924  2.178     0.303  0.951    -0.002  0.568    -0.179  0.472    -0.195  0.458
+  1795     4     1.124  1.464     0.219  0.924     0.018  0.558    -0.164  0.482    -0.187  0.456
+  1795     5     0.797  2.023     0.206  0.952     0.016  0.540    -0.159  0.489    -0.182  0.451
+  1795     6    -0.841  2.170     0.253  0.962     0.013  0.551    -0.158  0.493    -0.180  0.449
+  1795     7     0.467  1.633     0.262  0.939     0.032  0.549    -0.157  0.492    -0.179  0.445
+  1795     8     0.146  1.820     0.151  0.829     0.012  0.547    -0.157  0.496    -0.176  0.440
+  1795     9    -1.376  1.998    -0.094  0.910     0.039  0.547    -0.157  0.500    -0.170  0.437
+  1795    10    -0.962  1.431    -0.245  0.910     0.016  0.549    -0.151  0.500    -0.171  0.441
+  1795    11    -0.295  2.110    -0.317  1.012     0.017  0.542    -0.148  0.502    -0.176  0.439
+  1795    12     0.407  1.997    -0.166  0.845     0.014  0.531    -0.143  0.485    -0.165  0.437
+  1796     1     1.012  2.282    -0.217  0.811     0.017  0.519    -0.134  0.484    -0.156  0.439
+  1796     2     0.415  2.254    -0.167  0.765     0.039  0.509    -0.127  0.492    -0.153  0.441
+  1796     3    -2.011  3.312    -0.053  0.684     0.035  0.500    -0.112  0.491    -0.158  0.444
+  1796     4    -0.687  1.342     0.008  0.696     0.038  0.498    -0.126  0.495    -0.162  0.445
+  1796     5    -0.073  1.255    -0.009  0.674     0.047  0.487    -0.112  0.488    -0.162  0.444
+  1796     6     0.976  1.803    -0.214  0.656     0.045  0.479    -0.115  0.485    -0.166  0.446
+  1796     7    -0.150  1.144    -0.135  0.736     0.018  0.480    -0.109  0.485    -0.171  0.442
+  1796     8     0.755  2.012     0.030  0.779    -0.003  0.482    -0.115  0.481    -0.170  0.435
+  1796     9    -0.013  2.110     0.132  0.881    -0.052  0.471    -0.113  0.484    -0.169  0.428
+  1796    10    -0.227  1.501     0.074  0.906    -0.058  0.486    -0.097  0.489    -0.169  0.433
+  1796    11    -0.502  1.724     0.034  0.983    -0.080  0.495    -0.101  0.485    -0.169  0.428
+  1796    12    -2.047  2.489    -0.011  0.918    -0.062  0.501    -0.093  0.487    -0.156  0.429
+  1797     1     1.954  3.389    -0.035  0.903    -0.039  0.512    -0.065  0.494    -0.162  0.426
+  1797     2     2.397  2.558    -0.071  0.861    -0.039  0.517    -0.064  0.489    -0.159  0.424
+  1797     3    -0.785  2.524    -0.042  0.838    -0.050  0.509    -0.055  0.488    -0.159  0.421
+  1797     4    -1.388  2.030    -0.020  0.849    -0.048  0.509    -0.053  0.491    -0.162  0.419
+  1797     5    -0.554  1.776    -0.060  0.843    -0.046  0.504    -0.063  0.487    -0.170  0.414
+  1797     6     0.436  1.292     0.094  0.762    -0.059  0.506    -0.064  0.479    -0.174  0.412
+  1797     7    -0.435  0.834    -0.064  0.870    -0.076  0.499    -0.066  0.484    -0.171  0.413
+  1797     8     0.324  0.816    -0.239  0.862    -0.109  0.487    -0.059  0.488    -0.171  0.411
+  1797     9     0.334  1.184    -0.025  0.822    -0.132  0.502    -0.061  0.487    -0.161  0.409
+  1797    10     0.038  1.276     0.030  0.779    -0.143  0.493    -0.050  0.480    -0.157  0.402
+  1797    11    -0.988  1.574     0.073  0.742    -0.162  0.496    -0.050  0.472    -0.153  0.399
+  1797    12    -0.193  1.684     0.013  0.709    -0.149  0.500    -0.055  0.477    -0.146  0.401
+  1798     1     0.057  1.902     0.066  0.659    -0.168  0.496    -0.060  0.485    -0.141  0.401
+  1798     2     0.300  2.614     0.107  0.674    -0.174  0.493    -0.083  0.489    -0.147  0.400
+  1798     3     1.772  2.229     0.031  0.710    -0.166  0.490    -0.086  0.489    -0.149  0.399
+  1798     4    -0.723  1.447    -0.009  0.742    -0.146  0.488    -0.085  0.488    -0.152  0.395
+  1798     5    -0.036  1.589     0.068  0.691    -0.147  0.475    -0.083  0.487    -0.157  0.394
+  1798     6    -0.287  1.015    -0.049  0.719    -0.157  0.478    -0.080  0.489    -0.159  0.396
+  1798     7     0.206  1.448    -0.089  0.708    -0.165  0.481    -0.078  0.491    -0.167  0.397
+  1798     8     0.816  1.062    -0.216  0.688    -0.159  0.488    -0.066  0.490    -0.172  0.400
+  1798     9    -0.584  1.838    -0.525  0.627    -0.105  0.483    -0.061  0.489    -0.167  0.403
+  1798    10    -0.435  1.610    -0.445  0.685    -0.113  0.481    -0.048  0.496    -0.172  0.409
+  1798    11    -0.069  2.226    -0.446  0.746    -0.104  0.470    -0.044  0.502    -0.179  0.415
+  1798    12    -1.593  1.561    -0.432  0.811    -0.132  0.476    -0.040  0.510    -0.180  0.421
+  1799     1    -0.431  1.577    -0.449  0.820    -0.131  0.478    -0.051  0.506    -0.193  0.423
+  1799     2    -1.218  2.591    -0.535  0.949    -0.159  0.495    -0.061  0.509    -0.198  0.424
+  1799     3    -1.942  1.834    -0.488  1.056    -0.164  0.516    -0.077  0.521    -0.201  0.426
+  1799     4     0.238  1.952    -0.438  1.122    -0.146  0.535    -0.071  0.525    -0.201  0.428
+  1799     5    -0.050  1.400    -0.437  0.977    -0.146  0.542    -0.075  0.521    -0.209  0.426
+  1799     6    -0.111  1.870    -0.378  1.037    -0.099  0.532    -0.063  0.528    -0.209  0.431
+  1799     7    -0.004  1.260    -0.356  1.095    -0.109  0.564    -0.041  0.540    -0.219  0.437
+  1799     8    -0.210  1.601    -0.270  1.029    -0.139  0.567    -0.036  0.538    -0.221  0.439
+  1799     9    -0.017  1.200    -0.147  1.061    -0.121  0.556    -0.037  0.527    -0.224  0.440
+  1799    10     0.165  1.012    -0.129  0.984    -0.099  0.549    -0.037  0.526    -0.220  0.438
+  1799    11    -0.060  1.578    -0.156  0.969    -0.105  0.545    -0.028  0.539    -0.233  0.440
+  1799    12    -0.881  2.401    -0.147  0.960    -0.110  0.544    -0.035  0.545    -0.238  0.441
+  1800     1    -0.174  1.948    -0.205  0.912    -0.105  0.549    -0.038  0.542    -0.245  0.442
+  1800     2    -0.187  2.312    -0.203  0.875    -0.116  0.553    -0.056  0.536    -0.247  0.446
+  1800     3    -0.467  2.205    -0.277  0.838    -0.119  0.565    -0.060  0.529    -0.245  0.451
+  1800     4     0.457  0.943    -0.271  0.822    -0.117  0.562    -0.070  0.525    -0.247  0.458
+  1800     5    -0.367  1.326    -0.298  0.838    -0.116  0.557    -0.078  0.525    -0.252  0.461
+  1800     6    -0.011  1.463    -0.239  0.712    -0.123  0.551    -0.066  0.519    -0.258  0.465
+  1800     7    -0.694  0.819    -0.180  0.623    -0.152  0.576    -0.068  0.516    -0.261  0.468
+  1800     8    -0.193  0.963    -0.100  0.665    -0.178  0.613    -0.058  0.513    -0.267  0.474
+  1800     9    -0.899  0.855     0.039  0.577    -0.211  0.617    -0.047  0.499    -0.272  0.479
+  1800    10     0.228  1.061    -0.093  0.616    -0.185  0.631    -0.051  0.500    -0.279  0.479
+  1800    11    -0.381  1.736    -0.024  0.583    -0.183  0.640    -0.062  0.500    -0.284  0.477
+  1800    12    -0.171  1.250    -0.081  0.592    -0.174  0.641    -0.060  0.500    -0.284  0.466
+  1801     1     0.537  1.890    -0.032  0.592    -0.173  0.639    -0.064  0.501    -0.287  0.468
+  1801     2     0.774  2.131    -0.095  0.592    -0.171  0.655    -0.069  0.500    -0.296  0.475
+  1801     3     1.202  1.322    -0.045  0.619    -0.156  0.690    -0.057  0.488    -0.300  0.475
+  1801     4    -1.138  1.206     0.009  0.720    -0.134  0.710    -0.054  0.492    -0.310  0.480
+  1801     5     0.465  1.187    -0.004  0.717    -0.136  0.705    -0.051  0.491    -0.310  0.480
+  1801     6    -0.692  1.482     0.074  0.788    -0.125  0.729    -0.068  0.501    -0.313  0.481
+  1801     7    -0.105  1.696     0.145  0.776    -0.119  0.708    -0.071  0.499    -0.318  0.486
+  1801     8    -0.945  1.657     0.129  0.783    -0.118  0.704    -0.071  0.504    -0.319  0.485
+  1801     9    -0.300  1.051     0.055  0.883    -0.102  0.721    -0.068  0.511    -0.324  0.486
+  1801    10     0.868  1.974     0.144  0.913    -0.084  0.709    -0.072  0.520    -0.327  0.488
+  1801    11    -0.534  1.288     0.027  0.948    -0.070  0.682    -0.075  0.524    -0.337  0.486
+  1801    12     0.764  2.017     0.097  0.892    -0.065  0.666    -0.054  0.509    -0.335  0.487
+  1802     1     1.389  1.662     0.095  0.852    -0.044  0.672    -0.085  0.516    -0.332  0.496
+  1802     2     0.590  2.681     0.146  0.817    -0.033  0.659    -0.103  0.512    -0.346  0.499
+  1802     3     0.310  2.501     0.181  0.810    -0.025  0.639    -0.102  0.507    -0.356  0.504
+  1802     4    -0.069  1.735     0.123  0.717    -0.026  0.639    -0.103  0.503    -0.361  0.512
+  1802     5    -0.946  1.096     0.093  0.710    -0.009  0.676    -0.115  0.499    -0.369  0.511
+  1802     6     0.158  2.192    -0.024  0.630    -0.012  0.672    -0.120  0.502    -0.377  0.510
+  1802     7    -0.134  1.050    -0.281  0.702     0.000  0.668    -0.119  0.513    -0.378  0.510
+  1802     8    -0.336  0.990    -0.434  0.846    -0.004  0.690    -0.124  0.514    -0.367  0.509
+  1802     9     0.129  1.046    -0.475  0.839     0.012  0.658    -0.122  0.521    -0.376  0.504
+  1802    10     0.172  1.067    -0.403  0.908     0.002  0.655    -0.123  0.517    -0.377  0.501
+  1802    11    -0.900  1.781    -0.315  0.899     0.007  0.647    -0.113  0.516    -0.380  0.501
+  1802    12    -0.638  1.471    -0.308  0.820     0.017  0.640    -0.104  0.510    -0.387  0.500
+  1803     1    -1.698  2.424    -0.273  0.820     0.033  0.635    -0.116  0.522    -0.397  0.502
+  1803     2    -1.247  3.031    -0.168  0.845     0.058  0.624    -0.127  0.536    -0.407  0.494
+  1803     3    -0.180  2.585    -0.154  0.913     0.072  0.601    -0.151  0.550    -0.424  0.492
+  1803     4     0.793  1.321    -0.095  0.977     0.044  0.616    -0.153  0.551    -0.427  0.488
+  1803     5     0.110  1.068    -0.033  1.028     0.023  0.630    -0.169  0.551    -0.426  0.486
+  1803     6     0.248  0.719    -0.057  1.206     0.037  0.622    -0.168  0.555    -0.428  0.486
+  1803     7     0.285  1.022     0.077  1.020     0.036  0.622    -0.169  0.552    -0.428  0.484
+  1803     8     0.926  1.250     0.085  0.928     0.021  0.622    -0.182  0.561    -0.430  0.485
+  1803     9     0.296  1.742     0.019  1.041    -0.009  0.628    -0.170  0.582    -0.427  0.480
+  1803    10     0.872  2.183     0.063  1.032     0.004  0.643    -0.175  0.590    -0.427  0.478
+  1803    11    -0.151  1.838     0.120  0.939     0.001  0.662    -0.185  0.590    -0.421  0.479
+  1803    12    -0.928  2.965     0.116  0.934    -0.005  0.659    -0.193  0.583    -0.420  0.472
+  1804     1    -0.086  2.272     0.195  0.977    -0.011  0.644    -0.208  0.582    -0.428  0.469
+  1804     2    -1.151  2.214     0.154  0.945     0.016  0.628    -0.216  0.578    -0.434  0.471
+  1804     3    -0.977  2.910     0.168  0.786     0.029  0.616    -0.216  0.576    -0.449  0.476
+  1804     4     1.327  1.359     0.103  0.742     0.002  0.619    -0.223  0.571    -0.457  0.477
+  1804     5     0.791  1.570     0.198  0.831    -0.004  0.613    -0.239  0.561    -0.470  0.479
+  1804     6     0.197  0.807     0.188  0.715    -0.009  0.590    -0.241  0.562    -0.469  0.484
+  1804     7     1.239  1.552     0.239  0.837    -0.060  0.576    -0.255  0.571    -0.470  0.492
+  1804     8     0.436  1.154     0.300  0.873    -0.067  0.560    -0.259  0.569    -0.474  0.497
+  1804     9     0.463  1.566     0.424  0.697    -0.084  0.559    -0.266  0.571    -0.482  0.495
+  1804    10     0.086  1.111     0.300  0.663    -0.107  0.556    -0.271  0.565    -0.488  0.499
+  1804    11     0.996  3.219     0.226  0.688    -0.126  0.550    -0.293  0.569    -0.484  0.501
+  1804    12    -1.056  1.624     0.260  0.753    -0.131  0.548    -0.297  0.565    -0.473  0.502
+  1805     1     0.534  2.137     0.178  0.714    -0.132  0.563    -0.308  0.557    -0.479  0.496
+  1805     2    -0.426  2.556     0.251  0.718    -0.132  0.562    -0.312  0.559    -0.489  0.499
+  1805     3     0.513  1.653     0.211  0.707    -0.124  0.562    -0.312  0.561    -0.500  0.493
+  1805     4    -0.163  1.431     0.080  0.725    -0.129  0.556    -0.331  0.567    -0.512  0.497
+  1805     5    -0.092  1.350    -0.140  0.681    -0.111  0.558    -0.345  0.574    -0.521  0.501
+  1805     6     0.605  1.583     0.003  0.728    -0.085  0.556    -0.358  0.579    -0.528  0.500
+  1805     7     0.254  1.393     0.001  0.723    -0.080  0.550    -0.365  0.584    -0.530  0.502
+  1805     8     1.308  1.356     0.025  0.722    -0.077  0.536    -0.376  0.590    -0.533  0.504
+  1805     9    -0.017  2.100    -0.066  0.667    -0.091  0.557    -0.388  0.597    -0.530  0.502
+  1805    10    -1.481  1.387    -0.082  0.686    -0.120  0.549    -0.407  0.598    -0.534  0.501
+  1805    11    -1.653  1.798    -0.052  0.721    -0.155  0.535    -0.419  0.594    -0.541  0.501
+  1805    12     0.660  1.836    -0.190  0.720    -0.162  0.545    -0.425  0.588    -0.552  0.506
+  1806     1     0.519  1.909    -0.249  0.674    -0.165  0.542    -0.439  0.595    -0.555  0.501
+  1806     2    -0.141  2.167    -0.302  0.656    -0.192  0.544    -0.466  0.609    -0.568  0.503
+  1806     3    -0.582  1.627    -0.264  0.735    -0.183  0.551    -0.488  0.610    -0.565  0.500
+  1806     4    -0.355  1.998    -0.203  0.750    -0.216  0.552    -0.493  0.614    -0.565  0.501
+  1806     5     0.269  1.320    -0.136  0.688    -0.235  0.559    -0.508  0.622    -0.568  0.503
+  1806     6    -1.055  1.496    -0.156  0.687    -0.262  0.531    -0.511  0.623    -0.580  0.508
+  1806     7    -0.453  1.039    -0.338  0.632    -0.297  0.553    -0.527  0.627    -0.588  0.511
+  1806     8     0.671  0.711    -0.314  0.628    -0.315  0.551    -0.523  0.622    -0.596  0.516
+  1806     9     0.442  1.059    -0.322  0.653    -0.330  0.538    -0.534  0.618    -0.603  0.520
+  1806    10    -0.750  1.378    -0.414  0.608    -0.363  0.540    -0.558  0.621    -0.616  0.520
+  1806    11    -0.850  1.363    -0.607  0.590    -0.408  0.547    -0.572  0.619    -0.622  0.516
+  1806    12     0.426  1.551    -0.533  0.578    -0.418  0.567    -0.577  0.612    -0.623  0.512
+  1807     1    -1.665  1.968    -0.513  0.580    -0.466  0.579    -0.600  0.626    -0.634  0.518
+  1807     2     0.151  2.955    -0.597  0.569    -0.485  0.594    -0.629  0.641    -0.642  0.513
+  1807     3    -0.680  2.187    -0.584  0.579    -0.508  0.617    -0.657  0.650    -0.641  0.510
+  1807     4    -1.461  1.305    -0.531  0.515    -0.516  0.616    -0.668  0.667    -0.636  0.507
+  1807     5    -2.048  0.887    -0.443  0.528    -0.577  0.600    -0.675  0.667    -0.641  0.505
+  1807     6    -0.162  1.304    -0.403  0.535    -0.582  0.597    -0.690  0.666    -0.652  0.504
+  1807     7    -0.214  2.592    -0.382  0.650    -0.617  0.587    -0.689  0.661    -0.660  0.506
+  1807     8    -0.346  1.016    -0.480  0.789    -0.621  0.579    -0.676  0.655    -0.664  0.507
+  1807     9     0.606  1.074    -0.510  0.923    -0.636  0.608    -0.692  0.641    -0.679  0.508
+  1807    10    -0.122  1.124    -0.469  0.923    -0.664  0.625    -0.705  0.638    -0.691  0.508
+  1807    11     0.215  1.346    -0.464  0.954    -0.698  0.648    -0.710  0.639    -0.691  0.507
+  1807    12     0.896  1.268    -0.464  1.016    -0.733  0.669    -0.720  0.637    -0.703  0.508
+  1808     1    -1.411  3.120    -0.437  1.004    -0.763  0.691    -0.734  0.629    -0.708  0.507
+  1808     2    -1.027  2.224    -0.468  1.036    -0.810  0.710    -0.732  0.606    -0.719  0.510
+  1808     3    -1.042  2.623    -0.448  1.063    -0.848  0.743    -0.761  0.606    -0.725  0.509
+  1808     4    -0.966  1.195    -0.528  1.146    -0.858  0.736    -0.769  0.601    -0.716  0.508
+  1808     5    -1.980  1.883    -0.655  1.228    -0.862  0.703    -0.769  0.596    -0.717  0.508
+  1808     6    -0.172  1.809    -0.942  1.222    -0.886  0.711    -0.775  0.594    -0.715  0.506
+  1808     7     0.119  1.839    -1.008  1.107    -0.915  0.723    -0.779  0.586    -0.720  0.501
+  1808     8    -0.717  1.399    -1.106  1.142    -0.954  0.754    -0.793  0.586    -0.727  0.500
+  1808     9     0.840  2.287    -1.178  1.080    -0.967  0.750    -0.793  0.572    -0.730  0.504
+  1808    10    -1.081  1.714    -1.151  1.095    -0.990  0.746    -0.805  0.561    -0.739  0.504
+  1808    11    -1.308  2.100    -1.143  1.055    -1.016  0.757    -0.797  0.557    -0.742  0.501
+  1808    12    -2.549  1.548    -1.165  1.029    -1.016  0.770    -0.800  0.539    -0.739  0.501
+  1809     1    -2.203  1.921    -1.309  1.084    -1.044  0.795    -0.805  0.537    -0.742  0.500
+  1809     2    -2.197  2.885    -1.309  1.106    -1.063  0.799    -0.807  0.539    -0.741  0.499
+  1809     3    -1.911  1.571    -1.455  1.075    -1.096  0.799    -0.821  0.544    -0.742  0.494
+  1809     4    -0.639  1.610    -1.399  1.052    -1.117  0.814    -0.843  0.545    -0.748  0.489
+  1809     5    -1.892  1.352    -1.511  1.019    -1.140  0.811    -0.866  0.548    -0.754  0.484
+  1809     6    -0.436  1.825    -1.410  1.032    -1.144  0.831    -0.874  0.554    -0.757  0.481
+  1809     7    -1.603  2.385    -1.359  0.977    -1.139  0.886    -0.900  0.559    -0.762  0.479
+  1809     8    -0.718  1.384    -1.228  0.916    -1.190  0.936    -0.912  0.570    -0.767  0.477
+  1809     9    -0.909  1.252    -1.106  0.984    -1.230  0.976    -0.926  0.572    -0.771  0.475
+  1809    10    -0.418  2.464    -1.204  1.067    -1.230  1.019    -0.940  0.580    -0.774  0.470
+  1809    11    -2.647  1.837    -1.222  1.111    -1.224  1.025    -0.941  0.577    -0.777  0.467
+  1809    12    -1.342  1.559    -1.311  1.142    -1.248  1.027    -0.910  0.576    -0.778  0.467
+  1810     1    -1.583  1.987    -1.305  1.142    -1.246  1.007    -0.919  0.571    -0.785  0.464
+  1810     2    -0.633  2.416    -1.376  1.118    -1.219  0.989    -0.922  0.584    -0.787  0.463
+  1810     3    -0.442  2.338    -1.488  1.128    -1.259  0.955    -0.940  0.581    -0.787  0.460
+  1810     4    -1.815  1.619    -1.629  1.070    -1.280  0.949    -0.953  0.592    -0.787  0.461
+  1810     5    -2.107  1.468    -1.564  0.944    -1.309  0.963    -0.964  0.598    -0.783  0.461
+  1810     6    -1.505  1.913    -1.517  0.913    -1.354  0.949    -0.989  0.603    -0.784  0.459
+  1810     7    -1.534  1.669    -1.487  0.960    -1.387  0.908    -0.992  0.606    -0.784  0.457
+  1810     8    -1.565  1.488    -1.641  1.098    -1.387  0.871    -1.008  0.612    -0.790  0.457
+  1810     9    -2.252  1.524    -1.717  1.033    -1.432  0.859    -1.014  0.617    -0.790  0.455
+  1810    10    -2.109  1.442    -1.714  1.050    -1.418  0.858    -1.017  0.614    -0.797  0.457
+  1810    11    -1.865  1.876    -1.646  1.068    -1.383  0.835    -1.020  0.611    -0.802  0.458
+  1810    12    -0.782  2.040    -1.608  1.065    -1.388  0.813    -1.044  0.624    -0.809  0.459
+  1811     1    -1.226  2.217    -1.656  1.124    -1.392  0.780    -1.046  0.621    -0.809  0.460
+  1811     2    -2.483  3.487    -1.565  1.038    -1.394  0.773    -1.067  0.624    -0.810  0.458
+  1811     3    -1.345  1.624    -1.505  0.951    -1.404  0.746    -1.074  0.625    -0.815  0.458
+  1811     4    -1.782  1.701    -1.500  1.047    -1.395  0.717    -1.076  0.623    -0.812  0.457
+  1811     5    -1.295  1.649    -1.526  1.145    -1.359  0.706    -1.085  0.628    -0.815  0.456
+  1811     6    -1.045  1.719    -1.446  1.209    -1.338  0.688    -1.091  0.625    -0.816  0.454
+  1811     7    -2.112  2.410    -1.458  1.418    -1.312  0.661    -1.105  0.630    -0.821  0.452
+  1811     8    -0.474  0.710    -1.496  1.387    -1.298  0.653    -1.121  0.632    -0.821  0.451
+  1811     9    -1.537  0.917    -1.638  1.487    -1.311  0.675    -1.138  0.629    -0.828  0.450
+  1811    10    -2.038  2.006    -1.611  1.620    -1.322  0.681    -1.160  0.617    -0.834  0.449
+  1811    11    -2.181  1.397    -1.645  1.562    -1.325  0.666    -1.168  0.602    -0.834  0.450
+  1811    12     0.172  1.835    -1.693  1.509    -1.330  0.664    -1.191  0.612    -0.841  0.447
+  1812     1    -1.370  4.544    -1.523  1.356    -1.334  0.659    -1.184  0.610    -0.846  0.448
+  1812     2    -2.939  3.235    -1.376  1.348    -1.338  0.658    -1.180  0.610    -0.844  0.447
+  1812     3    -3.050  2.585    -1.397  1.219    -1.345  0.647    -1.179  0.620    -0.836  0.445
+  1812     4    -1.453  2.772    -1.346  1.064    -1.363  0.650    -1.168  0.625    -0.837  0.445
+  1812     5    -1.706  1.015    -1.288  1.137    -1.305  0.654    -1.166  0.628    -0.836  0.443
+  1812     6    -1.612  1.161    -1.454  1.013    -1.238  0.654    -1.185  0.627    -0.839  0.441
+  1812     7    -0.075  1.021    -1.623  0.795    -1.221  0.643    -1.202  0.623    -0.840  0.440
+  1812     8     1.283  1.189    -1.461  0.640    -1.224  0.668    -1.203  0.618    -0.841  0.440
+  1812     9    -1.789  2.450    -1.520  0.663    -1.243  0.645    -1.235  0.615    -0.847  0.437
+  1812    10    -1.426  1.186    -1.409  0.716    -1.242  0.647    -1.258  0.616    -0.852  0.437
+  1812    11    -1.483  1.858    -1.259  0.762    -1.231  0.636    -1.269  0.619    -0.854  0.436
+  1812    12    -1.819  2.031    -1.163  0.740    -1.246  0.627    -1.301  0.622    -0.862  0.435
+  1813     1    -3.394  1.982    -1.167  0.776    -1.222  0.621    -1.300  0.605    -0.859  0.434
+  1813     2    -1.001  1.991    -1.344  0.729    -1.205  0.622    -1.310  0.602    -0.857  0.430
+  1813     3    -3.757  2.005    -1.172  0.690    -1.181  0.608    -1.299  0.597    -0.858  0.429
+  1813     4    -0.113  1.122    -1.101  0.675    -1.176  0.603    -1.279  0.601    -0.866  0.427
+  1813     5     0.090  1.181    -0.908  0.709    -1.178  0.623    -1.265  0.596    -0.871  0.425
+  1813     6    -0.463  0.928    -0.861  0.696    -1.202  0.629    -1.263  0.590    -0.871  0.424
+  1813     7    -0.127  1.097    -0.635  0.760    -1.178  0.615    -1.272  0.578    -0.873  0.422
+  1813     8    -0.838  0.712    -0.664  0.647    -1.181  0.592    -1.271  0.570    -0.880  0.422
+  1813     9     0.272  2.051    -0.575  0.584    -1.181  0.596    -1.291  0.568    -0.888  0.419
+  1813    10    -0.568  1.178    -0.674  0.590    -1.162  0.604    -1.303  0.555    -0.897  0.415
+  1813    11     0.828  1.545    -0.851  0.646    -1.153  0.613    -1.299  0.555    -0.902  0.412
+  1813    12    -1.256  1.784    -0.875  0.688    -1.166  0.610    -1.285  0.554    -0.903  0.410
+  1814     1    -0.682  2.252    -1.017  0.863    -1.166  0.607    -1.277  0.549    -0.901  0.412
+  1814     2    -1.343  2.383    -1.029  1.001    -1.180  0.607    -1.265  0.546    -0.898  0.411
+  1814     3    -2.688  2.577    -1.162  0.944    -1.180  0.601    -1.268  0.534    -0.894  0.409
+  1814     4    -1.304  1.361    -1.239  1.053    -1.203  0.568    -1.273  0.534    -0.900  0.408
+  1814     5    -2.032  3.355    -1.239  1.075    -1.197  0.534    -1.268  0.527    -0.904  0.408
+  1814     6    -0.746  1.225    -0.912  1.144    -1.239  0.544    -1.272  0.520    -0.907  0.408
+  1814     7    -1.831  3.569    -0.900  1.094    -1.229  0.526    -1.270  0.513    -0.912  0.406
+  1814     8    -0.988  2.411    -0.856  1.276    -1.170  0.504    -1.275  0.505    -0.913  0.406
+  1814     9    -1.319  0.998    -0.766  1.132    -1.129  0.507    -1.276  0.500    -0.917  0.406
+  1814    10    -1.497  1.880    -0.804  1.149    -1.107  0.508    -1.276  0.484    -0.920  0.406
+  1814    11     0.832  3.167    -0.755  1.082    -1.108  0.512    -1.261  0.475    -0.925  0.405
+  1814    12     2.663  3.022    -0.893  1.081    -1.121  0.515    -1.259  0.471    -0.915  0.405
+  1815     1    -0.541  1.712    -0.745  1.056    -1.157  0.531    -1.261  0.463    -0.915  0.405
+  1815     2    -0.815  3.565    -0.712  1.085    -1.187  0.538    -1.262  0.455    -0.915  0.404
+  1815     3    -1.599  2.228    -0.666  1.060    -1.212  0.524    -1.263  0.450    -0.917  0.404
+  1815     4    -1.771  1.868    -0.693  0.952    -1.236  0.519    -1.243  0.446    -0.914  0.404
+  1815     5    -1.444  1.731    -0.927  0.835    -1.230  0.530    -1.221  0.439    -0.915  0.404
+  1815     6    -2.394  1.349    -1.338  0.839    -1.249  0.532    -1.210  0.434    -0.917  0.405
+  1815     7    -0.060  2.329    -1.272  0.805    -1.212  0.514    -1.204  0.424    -0.922  0.402
+  1815     8    -0.589  1.433    -1.426  0.797    -1.233  0.531    -1.204  0.419    -0.930  0.401
+  1815     9    -0.765  1.033    -1.406  0.879    -1.167  0.524    -1.192  0.411    -0.935  0.401
+  1815    10    -1.817  1.975    -1.313  0.892    -1.141  0.528    -1.188  0.415    -0.932  0.401
+  1815    11    -1.986  1.788    -1.255  0.916    -1.148  0.528    -1.184  0.421    -0.923  0.398
+  1815    12    -2.265  2.293    -1.209  0.865    -1.138  0.543    -1.193  0.420    -0.927  0.400
+  1816     1     0.256  2.725    -1.380  0.995    -1.152  0.535    -1.179  0.421    -0.931  0.401
+  1816     2    -2.667  3.313    -1.437  0.996    -1.149  0.524    -1.153  0.412    -0.937  0.399
+  1816     3    -1.364  1.837    -1.504  0.950    -1.179  0.532    -1.143  0.414    -0.936  0.399
+  1816     4    -0.652  1.957    -1.637  0.902    -1.212  0.531    -1.132  0.411    -0.935  0.396
+  1816     5    -0.749  1.971    -1.621  0.739    -1.239  0.553    -1.121  0.404    -0.933  0.395
+  1816     6    -1.842  1.181    -1.627  0.665    -1.231  0.569    -1.121  0.397    -0.930  0.394
+  1816     7    -2.109  2.122    -1.712  0.768    -1.241  0.570    -1.115  0.389    -0.927  0.392
+  1816     8    -1.270  0.811    -1.440  0.535    -1.232  0.583    -1.118  0.392    -0.933  0.393
+  1816     9    -1.569  1.042    -1.377  0.512    -1.225  0.545    -1.122  0.395    -0.938  0.392
+  1816    10    -3.416  3.428    -1.335  0.497    -1.224  0.539    -1.111  0.390    -0.936  0.389
+  1816    11    -1.794  3.542    -1.420  0.468    -1.212  0.505    -1.096  0.395    -0.928  0.388
+  1816    12    -2.340  2.108    -1.465  0.447    -1.215  0.497    -1.105  0.391    -0.923  0.392
+  1817     1    -0.767  2.114    -1.477  0.498    -1.206  0.471    -1.093  0.387    -0.911  0.395
+  1817     2     0.601  3.454    -1.415  0.507    -1.211  0.449    -1.059  0.382    -0.908  0.393
+  1817     3    -0.609  2.758    -1.554  0.572    -1.206  0.451    -1.015  0.376    -0.902  0.397
+  1817     4    -0.143  1.422    -1.511  0.544    -1.188  0.418    -1.005  0.374    -0.891  0.400
+  1817     5    -1.771  1.071    -1.454  0.724    -1.217  0.396    -0.998  0.371    -0.882  0.402
+  1817     6    -2.383  1.343    -1.505  0.677    -1.280  0.393    -0.988  0.367    -0.882  0.400
+  1817     7    -2.258  1.767    -1.542  0.775    -1.301  0.385    -0.990  0.370    -0.881  0.399
+  1817     8    -0.526  1.045    -1.779  0.811    -1.300  0.373    -1.007  0.371    -0.882  0.397
+  1817     9    -3.234  1.492    -1.710  0.745    -1.283  0.380    -1.003  0.367    -0.888  0.396
+  1817    10    -2.896  1.237    -1.579  0.747    -1.244  0.377    -1.000  0.364    -0.888  0.397
+  1817    11    -1.113  1.730    -1.458  0.761    -1.211  0.381    -0.998  0.361    -0.887  0.399
+  1817    12    -2.946  1.900    -1.246  0.855    -1.174  0.379    -1.004  0.354    -0.892  0.399
+  1818     1    -1.213  1.754    -1.141  0.901    -1.186  0.372    -0.984  0.353    -0.894  0.393
+  1818     2    -2.249  2.162    -1.150  0.888    -1.203  0.368    -0.983  0.361    -0.894  0.390
+  1818     3     0.228  2.681    -1.007  0.977    -1.204  0.363    -0.955  0.356    -0.888  0.389
+  1818     4     1.428  3.571    -0.979  0.980    -1.200  0.382    -0.962  0.354    -0.882  0.392
+  1818     5    -0.318  1.832    -0.952  1.120    -1.191  0.369    -0.972  0.352    -0.875  0.389
+  1818     6     0.152  2.449    -0.775  1.160    -1.184  0.363    -0.967  0.355    -0.873  0.385
+  1818     7    -1.000  1.454    -0.778  1.155    -1.181  0.358    -0.967  0.359    -0.873  0.384
+  1818     8    -0.633  0.923    -0.659  1.236    -1.126  0.348    -0.967  0.361    -0.872  0.383
+  1818     9    -1.520  1.542    -0.867  0.868    -1.104  0.347    -0.983  0.365    -0.883  0.382
+  1818    10    -2.559  1.137    -1.090  0.671    -1.102  0.331    -0.989  0.367    -0.880  0.377
+  1818    11    -0.781  3.112    -1.171  0.573    -1.090  0.322    -1.006  0.365    -0.875  0.373
+  1818    12    -0.829  1.694    -1.260  0.474    -1.076  0.320    -1.006  0.373    -0.868  0.373
+  1819     1    -1.242  1.658    -1.288  0.461    -1.064  0.319    -0.998  0.372    -0.861  0.370
+  1819     2    -0.829  2.155    -1.340  0.465    -1.056  0.321    -0.989  0.372    -0.858  0.369
+  1819     3    -2.267  2.926    -1.300  0.442    -1.063  0.324    -0.967  0.367    -0.851  0.369
+  1819     4    -1.244  1.081    -1.123  0.450    -1.019  0.366    -0.957  0.363    -0.851  0.368
+  1819     5    -1.294  1.377    -1.129  0.463    -0.995  0.406    -0.942  0.352    -0.843  0.367
+  1819     6    -0.918  0.776    -1.157  0.472    -0.972  0.393    -0.939  0.347    -0.845  0.363
+  1819     7    -1.326  0.698    -1.204  0.561    -0.957  0.412    -0.924  0.335    -0.839  0.362
+  1819     8    -1.265  0.793    -1.195  0.642    -0.949  0.383    -0.914  0.328    -0.839  0.360
+  1819     9    -1.033  1.346    -1.054  0.599    -0.901  0.355    -0.908  0.331    -0.841  0.357
+  1819    10    -0.433  1.553    -0.906  0.577    -0.903  0.352    -0.901  0.323    -0.841  0.351
+  1819    11    -0.860  1.743    -0.749  0.587    -0.888  0.342    -0.909  0.316    -0.834  0.352
+  1819    12    -1.163  1.656    -0.687  0.623    -0.854  0.338    -0.919  0.313    -0.831  0.349
+  1820     1    -1.801  2.638    -0.642  0.642    -0.823  0.349    -0.912  0.314    -0.822  0.350
+  1820     2    -0.727  2.052    -0.673  0.617    -0.827  0.347    -0.907  0.309    -0.815  0.349
+  1820     3    -0.571  1.175    -0.655  0.676    -0.794  0.362    -0.895  0.310    -0.816  0.346
+  1820     4     0.534  1.369    -0.750  0.587    -0.764  0.369    -0.874  0.304    -0.807  0.342
+  1820     5     0.581  1.130    -0.797  0.554    -0.766  0.351    -0.866  0.301    -0.802  0.340
+  1820     6    -0.173  0.803    -0.855  0.525    -0.760  0.352    -0.845  0.295    -0.799  0.337
+  1820     7    -0.778  0.683    -0.669  0.508    -0.755  0.356    -0.851  0.293    -0.791  0.335
+  1820     8    -1.641  1.139    -0.556  0.534    -0.734  0.348    -0.853  0.289    -0.787  0.333
+  1820     9    -0.820  0.961    -0.512  0.528    -0.743  0.323    -0.856  0.284    -0.784  0.331
+  1820    10    -1.572  1.316    -0.600  0.480    -0.784  0.286    -0.847  0.294    -0.779  0.331
+  1820    11    -1.422  0.932    -0.650  0.443    -0.796  0.274    -0.827  0.291    -0.765  0.334
+  1820    12    -1.855  1.177    -0.721  0.425    -0.796  0.256    -0.810  0.285    -0.757  0.333
+  1821     1     0.420  2.150    -0.770  0.450    -0.783  0.264    -0.816  0.286    -0.753  0.333
+  1821     2     0.632  2.032    -0.703  0.481    -0.786  0.274    -0.806  0.281    -0.746  0.326
+  1821     3    -0.041  1.131    -0.800  0.545    -0.787  0.277    -0.798  0.279    -0.743  0.325
+  1821     4    -0.516  0.749    -0.731  0.508    -0.765  0.286    -0.793  0.271    -0.738  0.325
+  1821     5    -0.029  0.813    -0.641  0.568    -0.774  0.258    -0.782  0.264    -0.734  0.323
+  1821     6    -1.016  0.891    -0.567  0.567    -0.780  0.260    -0.769  0.266    -0.731  0.321
+  1821     7    -1.365  1.185    -0.594  0.548    -0.755  0.260    -0.750  0.264    -0.726  0.317
+  1821     8    -0.846  1.156    -0.553  0.553    -0.747  0.251    -0.745  0.264    -0.731  0.317
+  1821     9    -1.980  1.040    -0.363  0.578    -0.708  0.278    -0.739  0.266    -0.733  0.318
+  1821    10    -0.750  0.824    -0.343  0.605    -0.689  0.278    -0.712  0.287    -0.729  0.314
+  1821    11    -0.340  1.588    -0.409  0.635    -0.672  0.281    -0.688  0.307    -0.726  0.314
+  1821    12    -0.969  1.089    -0.357  0.620    -0.664  0.277    -0.655  0.299    -0.732  0.311
+  1822     1     0.105  1.755    -0.276  0.595    -0.641  0.278    -0.638  0.309    -0.729  0.308
+  1822     2     1.121  1.857    -0.266  0.565    -0.618  0.282    -0.637  0.296    -0.721  0.305
+  1822     3     2.242  1.433    -0.209  0.515    -0.609  0.284    -0.625  0.282    -0.712  0.300
+  1822     4    -0.276  1.314    -0.235  0.501    -0.613  0.297    -0.614  0.280    -0.707  0.296
+  1822     5    -0.829  1.003    -0.313  0.478    -0.601  0.302    -0.597  0.278    -0.703  0.296
+  1822     6    -0.385  0.528    -0.444  0.481    -0.558  0.303    -0.579  0.279    -0.701  0.294
+  1822     7    -0.388  0.719    -0.530  0.503    -0.522  0.323    -0.561  0.282    -0.704  0.295
+  1822     8    -0.731  1.232    -0.704  0.479    -0.514  0.333    -0.561  0.283    -0.716  0.294
+  1822     9    -1.299  1.073    -0.922  0.510    -0.507  0.331    -0.540  0.290    -0.716  0.293
+  1822    10    -1.059  0.804    -0.982  0.600    -0.504  0.320    -0.518  0.295    -0.712  0.292
+  1822    11    -1.275  1.008    -1.000  0.615    -0.520  0.323    -0.504  0.287    -0.712  0.291
+  1822    12    -2.539  1.341    -0.953  0.623    -0.516  0.320    -0.484  0.288    -0.714  0.289
+  1823     1    -0.938  1.848    -0.940  0.631    -0.517  0.316    -0.488  0.289    -0.701  0.286
+  1823     2    -0.960  1.539    -0.948  0.620    -0.502  0.309    -0.478  0.285    -0.698  0.288
+  1823     3    -0.370  1.529    -0.972  0.642    -0.508  0.310    -0.477  0.272    -0.687  0.285
+  1823     4    -0.999  1.771    -0.988  0.702    -0.493  0.306    -0.486  0.259    -0.689  0.284
+  1823     5    -1.050  1.209    -0.992  0.745    -0.463  0.317    -0.484  0.253    -0.692  0.283
+  1823     6     0.178  0.852    -0.875  0.762    -0.436  0.315    -0.483  0.245    -0.692  0.284
+  1823     7    -0.227  1.054    -0.777  0.669    -0.451  0.315    -0.474  0.252    -0.695  0.285
+  1823     8    -0.830  0.984    -0.725  0.677    -0.487  0.315    -0.472  0.256    -0.695  0.285
+  1823     9    -1.584  1.805    -0.692  0.623    -0.493  0.321    -0.476  0.254    -0.700  0.286
+  1823    10    -1.248  1.129    -0.614  0.566    -0.485  0.321    -0.457  0.259    -0.702  0.288
+  1823    11    -1.325  0.941    -0.552  0.563    -0.475  0.327    -0.452  0.246    -0.702  0.286
+  1823    12    -1.141  1.521    -0.603  0.531    -0.462  0.328    -0.451  0.246    -0.698  0.285
+  1824     1     0.242  1.627    -0.579  0.494    -0.436  0.318    -0.445  0.246    -0.704  0.283
+  1824     2    -0.329  1.666    -0.499  0.484    -0.433  0.310    -0.450  0.247    -0.700  0.283
+  1824     3     0.020  1.268    -0.408  0.466    -0.414  0.301    -0.434  0.259    -0.687  0.280
+  1824     4    -0.067  0.914    -0.363  0.491    -0.405  0.300    -0.429  0.257    -0.683  0.278
+  1824     5    -0.301  0.728    -0.262  0.532    -0.380  0.298    -0.419  0.263    -0.674  0.273
+  1824     6    -0.433  0.790    -0.048  0.675    -0.338  0.296    -0.417  0.263    -0.674  0.270
+  1824     7     0.056  0.691    -0.037  0.704    -0.318  0.293    -0.408  0.265    -0.669  0.265
+  1824     8     0.127  1.329    -0.033  0.658    -0.324  0.290    -0.403  0.268    -0.668  0.260
+  1824     9    -0.487  0.987    -0.046  0.617    -0.350  0.289    -0.406  0.267    -0.665  0.261
+  1824    10    -0.708  1.099     0.017  0.605    -0.326  0.287    -0.406  0.276    -0.661  0.257
+  1824    11    -0.107  1.697     0.010  0.614    -0.306  0.290    -0.406  0.285    -0.661  0.257
+  1824    12     1.420  2.819     0.053  0.628    -0.304  0.296    -0.403  0.284    -0.674  0.257
+  1825     1     0.370  2.035    -0.021  0.562    -0.299  0.298    -0.383  0.297    -0.669  0.258
+  1825     2    -0.280  1.406    -0.096  0.542    -0.295  0.302    -0.368  0.304    -0.666  0.257
+  1825     3    -0.137  1.148    -0.150  0.518    -0.286  0.301    -0.369  0.303    -0.663  0.258
+  1825     4     0.691  0.821    -0.150  0.495    -0.273  0.304    -0.370  0.298    -0.658  0.255
+  1825     5    -0.382  0.716    -0.106  0.536    -0.242  0.310    -0.383  0.300    -0.656  0.254
+  1825     6     0.077  0.581    -0.245  0.419    -0.208  0.315    -0.388  0.302    -0.652  0.252
+  1825     7    -0.826  0.951    -0.318  0.414    -0.220  0.315    -0.379  0.307    -0.658  0.254
+  1825     8    -0.769  0.871    -0.418  0.428    -0.223  0.314    -0.370  0.306    -0.663  0.253
+  1825     9    -1.139  1.047    -0.441  0.459    -0.210  0.313    -0.376  0.311    -0.668  0.252
+  1825    10    -0.706  0.825    -0.502  0.480    -0.187  0.318    -0.371  0.307    -0.665  0.256
+  1825    11     0.414  2.285    -0.421  0.479    -0.172  0.317    -0.346  0.307    -0.664  0.253
+  1825    12    -0.245  1.055    -0.449  0.496    -0.170  0.320    -0.322  0.308    -0.663  0.251
+  1826     1    -0.511  1.559    -0.364  0.474    -0.166  0.326    -0.327  0.299    -0.666  0.252
+  1826     2    -1.477  1.769    -0.356  0.457    -0.158  0.328    -0.338  0.288    -0.657  0.251
+  1826     3    -0.404  1.537    -0.331  0.464    -0.164  0.335    -0.343  0.284    -0.651  0.250
+  1826     4    -0.046  0.980    -0.287  0.452    -0.150  0.346    -0.344  0.284    -0.653  0.247
+  1826     5     0.586  0.746    -0.230  0.464    -0.129  0.350    -0.346  0.287    -0.655  0.244
+  1826     6    -0.254  0.755    -0.076  0.466    -0.122  0.360    -0.341  0.292    -0.652  0.245
+  1826     7     0.196  0.560     0.072  0.493    -0.135  0.357    -0.337  0.293    -0.648  0.247
+  1826     8    -0.677  0.656     0.258  0.470    -0.153  0.366    -0.344  0.290    -0.650  0.250
+  1826     9    -0.837  0.530     0.353  0.466    -0.160  0.361    -0.343  0.289    -0.650  0.251
+  1826    10    -0.183  0.700     0.453  0.487    -0.169  0.351    -0.348  0.286    -0.637  0.261
+  1826    11     1.107  0.881     0.431  0.519    -0.165  0.357    -0.357  0.283    -0.638  0.271
+  1826    12     1.595  1.313     0.433  0.527    -0.171  0.363    -0.360  0.280    -0.629  0.268
+  1827     1     1.274  1.839     0.408  0.560    -0.175  0.362    -0.365  0.275    -0.627  0.272
+  1827     2     0.745  1.499     0.425  0.553    -0.189  0.370    -0.384  0.273    -0.631  0.268
+  1827     3     0.736  1.579     0.431  0.548    -0.203  0.373    -0.408  0.268    -0.635  0.261
+  1827     4     1.161  1.224     0.425  0.576    -0.200  0.371    -0.409  0.263    -0.642  0.258
+  1827     5     0.325  0.924     0.377  0.654    -0.212  0.364    -0.408  0.266    -0.641  0.256
+  1827     6    -0.237  1.253     0.206  0.653    -0.249  0.338    -0.414  0.267    -0.637  0.256
+  1827     7    -0.108  1.219    -0.041  0.597    -0.244  0.339    -0.417  0.268    -0.634  0.259
+  1827     8    -0.468  0.766    -0.194  0.597    -0.222  0.348    -0.425  0.271    -0.636  0.260
+  1827     9    -0.764  0.687    -0.223  0.568    -0.231  0.349    -0.428  0.273    -0.628  0.263
+  1827    10    -0.259  1.167    -0.290  0.576    -0.235  0.351    -0.424  0.273    -0.623  0.268
+  1827    11     0.539  1.488    -0.329  0.523    -0.246  0.355    -0.427  0.276    -0.624  0.264
+  1827    12    -0.458  1.496    -0.283  0.493    -0.259  0.363    -0.424  0.279    -0.617  0.266
+  1828     1    -1.700  1.834    -0.274  0.491    -0.240  0.387    -0.419  0.276    -0.623  0.268
+  1828     2    -1.090  1.641    -0.263  0.497    -0.237  0.389    -0.414  0.272    -0.620  0.266
+  1828     3     0.389  1.397    -0.362  0.507    -0.243  0.406    -0.420  0.267    -0.628  0.262
+  1828     4     0.362  0.937    -0.372  0.476    -0.249  0.401    -0.416  0.267    -0.638  0.258
+  1828     5    -0.142  0.926    -0.426  0.446    -0.229  0.380    -0.412  0.267    -0.642  0.256
+  1828     6     0.306  0.887    -0.448  0.496    -0.208  0.390    -0.418  0.268    -0.647  0.253
+  1828     7     0.005  0.897    -0.349  0.554    -0.203  0.381    -0.423  0.272    -0.644  0.258
+  1828     8    -0.337  0.653    -0.379  0.593    -0.189  0.368    -0.423  0.273    -0.644  0.261
+  1828     9    -1.947  0.729    -0.444  0.573    -0.194  0.362    -0.417  0.280    -0.642  0.262
+  1828    10    -0.386  0.723    -0.525  0.513    -0.203  0.362    -0.416  0.287    -0.633  0.263
+  1828    11    -0.099  0.880    -0.517  0.575    -0.218  0.365    -0.398  0.283    -0.633  0.256
+  1828    12    -0.728  1.479    -0.606  0.593    -0.221  0.371    -0.390  0.277    -0.633  0.255
+  1829     1    -0.513  1.672    -0.626  0.575    -0.238  0.379    -0.410  0.269    -0.628  0.254
+  1829     2    -1.448  2.060    -0.653  0.599    -0.254  0.382    -0.411  0.263    -0.623  0.251
+  1829     3    -0.397  1.157    -0.605  0.602    -0.273  0.384    -0.407  0.261    -0.624  0.258
+  1829     4    -0.608  1.056    -0.613  0.631    -0.291  0.377    -0.409  0.258    -0.621  0.258
+  1829     5    -0.042  0.915    -0.674  0.642    -0.333  0.386    -0.406  0.258    -0.619  0.261
+  1829     6    -0.762  1.026    -0.682  0.543    -0.381  0.373    -0.409  0.259    -0.619  0.260
+  1829     7    -0.236  0.784    -0.584  0.566    -0.411  0.360    -0.414  0.259    -0.613  0.260
+  1829     8    -0.662  0.712    -0.374  0.528    -0.443  0.352    -0.421  0.261    -0.607  0.262
+  1829     9    -1.370  0.600    -0.400  0.513    -0.467  0.339    -0.422  0.265    -0.608  0.262
+  1829    10    -0.479  1.129    -0.311  0.540    -0.492  0.328    -0.420  0.261    -0.610  0.267
+  1829    11    -0.832  0.947    -0.394  0.538    -0.509  0.326    -0.414  0.259    -0.614  0.270
+  1829    12    -0.826  1.130    -0.390  0.561    -0.523  0.320    -0.428  0.252    -0.618  0.271
+  1830     1     0.667  1.898    -0.346  0.625    -0.535  0.322    -0.426  0.248    -0.610  0.274
+  1830     2     1.067  1.861    -0.338  0.588    -0.555  0.327    -0.425  0.251    -0.610  0.278
+  1830     3    -0.707  1.252    -0.349  0.617    -0.570  0.333    -0.431  0.251    -0.613  0.277
+  1830     4     0.460  1.003    -0.396  0.586    -0.575  0.321    -0.441  0.255    -0.618  0.276
+  1830     5    -1.032  1.003    -0.195  0.548    -0.611  0.318    -0.446  0.254    -0.621  0.275
+  1830     6    -0.718  1.415    -0.040  0.625    -0.641  0.326    -0.459  0.255    -0.621  0.275
+  1830     7     0.292  1.486    -0.111  0.558    -0.617  0.324    -0.465  0.261    -0.618  0.276
+  1830     8    -0.563  0.905    -0.255  0.519    -0.605  0.311    -0.473  0.264    -0.615  0.276
+  1830     9    -1.510  0.785    -0.254  0.533    -0.629  0.300    -0.480  0.268    -0.616  0.278
+  1830    10    -1.037  0.901    -0.340  0.519    -0.644  0.295    -0.484  0.267    -0.616  0.277
+  1830    11     1.574  1.277    -0.281  0.519    -0.652  0.298    -0.502  0.260    -0.618  0.279
+  1830    12     1.044  1.469    -0.258  0.491    -0.665  0.294    -0.516  0.265    -0.616  0.278
+  1831     1    -0.195  1.881    -0.351  0.499    -0.680  0.297    -0.515  0.267    -0.625  0.275
+  1831     2    -0.652  2.840    -0.443  0.498    -0.689  0.294    -0.507  0.270    -0.629  0.272
+  1831     3    -0.704  1.594    -0.480  0.524    -0.671  0.298    -0.503  0.272    -0.638  0.272
+  1831     4    -0.564  0.841    -0.500  0.514    -0.683  0.300    -0.513  0.277    -0.643  0.272
+  1831     5    -0.326  1.001    -0.746  0.543    -0.667  0.288    -0.527  0.280    -0.645  0.275
+  1831     6    -0.438  1.746    -0.943  0.501    -0.658  0.270    -0.535  0.279    -0.644  0.277
+  1831     7    -0.828  0.877    -0.972  0.501    -0.685  0.256    -0.547  0.283    -0.638  0.277
+  1831     8    -1.663  0.842    -1.013  0.444    -0.668  0.245    -0.555  0.289    -0.636  0.275
+  1831     9    -1.956  0.821    -1.013  0.430    -0.654  0.246    -0.560  0.288    -0.633  0.275
+  1831    10    -1.279  1.057    -0.992  0.450    -0.648  0.248    -0.563  0.287    -0.632  0.275
+  1831    11    -1.382  1.492    -1.026  0.444    -0.648  0.245    -0.588  0.287    -0.636  0.274
+  1831    12    -1.321  1.392    -1.080  0.426    -0.647  0.243    -0.604  0.288    -0.636  0.273
+  1832     1    -0.535  1.830    -1.080  0.418    -0.652  0.243    -0.616  0.287    -0.639  0.273
+  1832     2    -1.151  1.723    -1.081  0.423    -0.653  0.237    -0.625  0.287    -0.645  0.276
+  1832     3    -0.700  1.332    -1.056  0.419    -0.640  0.238    -0.644  0.283    -0.652  0.275
+  1832     4    -0.314  1.467    -0.992  0.412    -0.641  0.236    -0.670  0.282    -0.652  0.271
+  1832     5    -0.736  1.005    -1.012  0.423    -0.616  0.234    -0.686  0.283    -0.652  0.273
+  1832     6    -1.083  0.921    -1.093  0.456    -0.607  0.238    -0.696  0.280    -0.654  0.275
+  1832     7    -0.832  1.442    -1.068  0.478    -0.609  0.238    -0.707  0.280    -0.657  0.276
+  1832     8    -1.668  1.101    -1.002  0.489    -0.628  0.245    -0.712  0.283    -0.657  0.278
+  1832     9    -1.657  0.746    -1.036  0.445    -0.630  0.246    -0.717  0.285    -0.657  0.279
+  1832    10    -0.520  1.261    -1.052  0.449    -0.647  0.251    -0.727  0.290    -0.656  0.280
+  1832    11    -1.621  1.383    -1.044  0.444    -0.647  0.246    -0.745  0.287    -0.655  0.279
+  1832    12    -2.285  1.570    -0.994  0.427    -0.659  0.244    -0.749  0.290    -0.644  0.279
+  1833     1    -0.235  1.574    -0.999  0.423    -0.689  0.243    -0.759  0.299    -0.637  0.281
+  1833     2    -0.364  1.361    -0.931  0.395    -0.709  0.240    -0.763  0.296    -0.630  0.280
+  1833     3    -1.100  1.296    -0.865  0.404    -0.717  0.239    -0.780  0.302    -0.631  0.279
+  1833     4    -0.515  0.789    -0.915  0.411    -0.719  0.242    -0.791  0.306    -0.629  0.279
+  1833     5    -0.631  0.646    -0.709  0.385    -0.774  0.238    -0.801  0.308    -0.630  0.278
+  1833     6    -0.491  0.509    -0.533  0.392    -0.823  0.237    -0.811  0.307    -0.635  0.279
+  1833     7    -0.886  1.015    -0.692  0.399    -0.827  0.247    -0.813  0.306    -0.637  0.281
+  1833     8    -0.859  0.835    -0.697  0.399    -0.825  0.258    -0.815  0.309    -0.636  0.283
+  1833     9    -0.861  0.822    -0.567  0.393    -0.813  0.268    -0.808  0.313    -0.633  0.287
+  1833    10    -1.117  0.793    -0.544  0.435    -0.824  0.279    -0.809  0.310    -0.630  0.291
+  1833    11     0.843  1.308    -0.494  0.468    -0.837  0.282    -0.815  0.305    -0.629  0.291
+  1833    12    -0.174  1.649    -0.515  0.500    -0.850  0.273    -0.815  0.300    -0.623  0.291
+  1834     1    -2.136  1.643    -0.484  0.525    -0.855  0.273    -0.811  0.298    -0.626  0.290
+  1834     2    -0.424  1.407    -0.471  0.552    -0.856  0.282    -0.796  0.291    -0.629  0.288
+  1834     3     0.451  1.236    -0.452  0.575    -0.848  0.281    -0.814  0.293    -0.632  0.287
+  1834     4    -0.231  1.229    -0.405  0.601    -0.834  0.286    -0.812  0.297    -0.636  0.287
+  1834     5    -0.033  0.912    -0.419  0.548    -0.843  0.293    -0.820  0.296    -0.639  0.288
+  1834     6    -0.738  0.687    -0.426  0.517    -0.827  0.299    -0.820  0.293    -0.641  0.289
+  1834     7    -0.520  0.710    -0.200  0.574    -0.820  0.309    -0.818  0.290    -0.644  0.289
+  1834     8    -0.708  0.607    -0.175  0.590    -0.808  0.317    -0.812  0.288    -0.649  0.291
+  1834     9    -0.623  0.689    -0.282  0.575    -0.821  0.310    -0.810  0.291    -0.652  0.294
+  1834    10    -0.552  0.753    -0.307  0.569    -0.848  0.303    -0.813  0.290    -0.651  0.292
+  1834    11     0.674  1.018    -0.390  0.544    -0.862  0.307    -0.822  0.288    -0.657  0.289
+  1834    12    -0.256  1.171    -0.448  0.525    -0.868  0.313    -0.833  0.292    -0.670  0.282
+  1835     1     0.568  1.638    -0.534  0.514    -0.878  0.320    -0.836  0.286    -0.671  0.278
+  1835     2    -0.126  1.520    -0.618  0.513    -0.868  0.330    -0.853  0.289    -0.676  0.279
+  1835     3    -0.828  1.366    -0.735  0.488    -0.863  0.334    -0.858  0.289    -0.678  0.279
+  1835     4    -0.532  0.931    -0.783  0.507    -0.879  0.341    -0.866  0.293    -0.683  0.279
+  1835     5    -1.035  0.638    -0.983  0.471    -0.878  0.339    -0.858  0.291    -0.685  0.279
+  1835     6    -1.423  0.674    -1.122  0.484    -0.858  0.344    -0.855  0.289    -0.688  0.279
+  1835     7    -1.554  0.609    -1.204  0.535    -0.902  0.365    -0.856  0.287    -0.686  0.282
+  1835     8    -1.722  0.628    -1.239  0.525    -0.921  0.365    -0.860  0.288    -0.685  0.284
+  1835     9    -2.026  0.617    -1.167  0.542    -0.930  0.392    -0.856  0.287    -0.684  0.288
+  1835    10    -1.129  1.021    -1.223  0.563    -0.939  0.409    -0.861  0.288    -0.687  0.287
+  1835    11    -1.725  1.745    -1.232  0.581    -0.949  0.404    -0.889  0.291    -0.692  0.281
+  1835    12    -1.927  1.770    -1.215  0.570    -0.956  0.411    -0.910  0.293    -0.698  0.281
+  1836     1    -0.410  1.644    -1.180  0.563    -0.945  0.409    -0.922  0.297    -0.693  0.281
+  1836     2    -0.544  1.435    -1.181  0.632    -0.942  0.425    -0.921  0.307    -0.685  0.282
+  1836     3     0.029  1.187    -1.133  0.613    -0.945  0.432    -0.932  0.314    -0.679  0.283
+  1836     4    -1.205  1.191    -1.074  0.611    -0.936  0.430    -0.942  0.319    -0.683  0.286
+  1836     5    -1.141  0.900    -1.091  0.563    -0.962  0.425    -0.944  0.321    -0.687  0.287
+  1836     6    -1.214  0.774    -0.960  0.568    -0.971  0.426    -0.946  0.317    -0.687  0.285
+  1836     7    -1.139  0.822    -0.939  0.546    -0.937  0.441    -0.940  0.313    -0.686  0.285
+  1836     8    -1.730  1.398    -0.926  0.619    -0.923  0.443    -0.929  0.315    -0.683  0.285
+  1836     9    -1.447  0.876    -1.055  0.576    -0.974  0.442    -0.923  0.319    -0.681  0.285
+  1836    10    -0.427  1.368    -1.115  0.539    -0.977  0.440    -0.917  0.320    -0.685  0.285
+  1836    11    -1.927  1.200    -1.151  0.519    -0.993  0.437    -0.915  0.321    -0.693  0.286
+  1836    12    -0.350  1.442    -1.171  0.516    -0.994  0.434    -0.912  0.322    -0.699  0.283
+  1837     1    -0.163  1.653    -1.193  0.532    -0.985  0.428    -0.913  0.330    -0.704  0.282
+  1837     2    -0.393  1.651    -1.139  0.528    -0.971  0.432    -0.906  0.332    -0.705  0.278
+  1837     3    -1.510  1.562    -1.131  0.549    -0.979  0.432    -0.896  0.332    -0.714  0.275
+  1837     4    -1.925  0.817    -1.221  0.595    -0.985  0.437    -0.895  0.328    -0.720  0.275
+  1837     5    -1.576  1.365    -1.190  0.634    -1.028  0.434    -0.896  0.329    -0.725  0.275
+  1837     6    -1.455  0.918    -1.247  0.646    -1.060  0.430    -0.895  0.333    -0.727  0.271
+  1837     7    -1.407  1.030    -1.473  0.719    -1.064  0.424    -0.897  0.335    -0.728  0.268
+  1837     8    -1.073  1.077    -1.567  0.697    -1.078  0.419    -0.889  0.337    -0.729  0.267
+  1837     9    -1.351  0.590    -1.578  0.790    -1.085  0.415    -0.885  0.341    -0.728  0.268
+  1837    10    -1.507  1.311    -1.505  0.848    -1.084  0.412    -0.888  0.339    -0.730  0.267
+  1837    11    -1.557  1.310    -1.478  0.808    -1.070  0.412    -0.884  0.334    -0.734  0.264
+  1837    12    -1.041  1.835    -1.431  0.809    -1.050  0.412    -0.863  0.328    -0.740  0.262
+  1838     1    -2.870  1.927    -1.335  0.773    -1.023  0.413    -0.856  0.333    -0.741  0.265
+  1838     2    -1.525  1.383    -1.299  0.766    -1.010  0.418    -0.846  0.335    -0.734  0.264
+  1838     3    -1.643  1.923    -1.273  0.824    -0.994  0.419    -0.841  0.341    -0.738  0.264
+  1838     4    -1.040  1.414    -1.195  0.760    -1.003  0.419    -0.841  0.343    -0.740  0.263
+  1838     5    -1.259  0.705    -1.126  0.723    -1.005  0.426    -0.847  0.343    -0.743  0.265
+  1838     6    -0.886  0.694    -1.103  0.636    -0.997  0.426    -0.852  0.346    -0.746  0.265
+  1838     7    -0.261  0.924    -0.871  0.641    -1.017  0.417    -0.851  0.346    -0.747  0.264
+  1838     8    -0.641  0.986    -0.708  0.662    -1.017  0.420    -0.849  0.350    -0.749  0.265
+  1838     9    -1.032  1.129    -0.789  0.634    -1.052  0.415    -0.849  0.351    -0.745  0.266
+  1838    10    -0.578  1.042    -0.736  0.611    -1.059  0.413    -0.844  0.354    -0.746  0.266
+  1838    11    -0.722  1.327    -0.712  0.644    -1.052  0.412    -0.860  0.353    -0.749  0.265
+  1838    12    -0.763  1.209    -0.703  0.618    -1.043  0.414    -0.856  0.356    -0.752  0.263
+  1839     1    -0.091  1.736    -0.680  0.615    -1.024  0.407    -0.843  0.365    -0.752  0.262
+  1839     2     0.436  1.503    -0.616  0.602    -1.002  0.402    -0.847  0.365    -0.749  0.259
+  1839     3    -2.622  1.314    -0.624  0.559    -0.999  0.405    -0.856  0.363    -0.747  0.260
+  1839     4    -0.400  1.128    -0.652  0.546    -1.000  0.399    -0.862  0.363    -0.751  0.264
+  1839     5    -0.968  0.640    -0.751  0.514    -0.988  0.395    -0.872  0.364    -0.756  0.264
+  1839     6    -0.788  0.546    -0.868  0.521    -0.998  0.388    -0.873  0.364    -0.755  0.263
+  1839     7     0.021  0.750    -0.835  0.493    -1.006  0.395    -0.875  0.362    -0.755  0.263
+  1839     8     0.128  0.860    -0.948  0.497    -1.003  0.393    -0.877  0.362    -0.754  0.263
+  1839     9    -1.131  0.981    -0.837  0.500    -0.971  0.397    -0.882  0.361    -0.753  0.264
+  1839    10    -0.911  0.921    -0.844  0.512    -0.941  0.395    -0.881  0.360    -0.752  0.262
+  1839    11    -1.912  1.017    -0.776  0.530    -0.929  0.393    -0.900  0.359    -0.750  0.261
+  1839    12    -2.164  1.137    -0.732  0.541    -0.922  0.396    -0.912  0.354    -0.754  0.262
+  1840     1     0.307  1.428    -0.728  0.509    -0.915  0.391    -0.915  0.353    -0.764  0.260
+  1840     2    -0.929  1.411    -0.817  0.490    -0.910  0.384    -0.927  0.349    -0.769  0.261
+  1840     3    -1.281  1.473    -0.812  0.457    -0.908  0.386    -0.926  0.349    -0.768  0.263
+  1840     4    -0.487  0.803    -0.873  0.434    -0.897  0.375    -0.924  0.343    -0.775  0.265
+  1840     5    -0.149  0.771    -0.866  0.435    -0.890  0.372    -0.923  0.342    -0.777  0.266
+  1840     6    -0.268  0.690    -0.808  0.416    -0.869  0.352    -0.918  0.343    -0.776  0.266
+  1840     7     0.076  0.642    -0.970  0.440    -0.811  0.341    -0.907  0.343    -0.777  0.264
+  1840     8    -0.941  0.914    -0.936  0.454    -0.771  0.344    -0.897  0.344    -0.775  0.265
+  1840     9    -1.078  0.615    -1.001  0.459    -0.753  0.332    -0.888  0.346    -0.772  0.265
+  1840    10    -1.642  0.807    -1.098  0.466    -0.744  0.320    -0.890  0.347    -0.773  0.267
+  1840    11    -1.822  1.127    -1.142  0.489    -0.745  0.325    -0.882  0.348    -0.785  0.268
+  1840    12    -1.470  0.994    -1.175  0.516    -0.749  0.320    -0.881  0.347    -0.794  0.268
+  1841     1    -1.635  1.472    -1.186  0.493    -0.756  0.320    -0.871  0.347    -0.794  0.271
+  1841     2    -0.524  1.778    -1.140  0.466    -0.757  0.314    -0.863  0.348    -0.794  0.278
+  1841     3    -2.062  1.095    -1.155  0.478    -0.754  0.310    -0.855  0.346    -0.796  0.281
+  1841     4    -1.654  1.282    -1.058  0.470    -0.753  0.316    -0.852  0.346    -0.799  0.282
+  1841     5    -0.676  0.788    -1.006  0.429    -0.759  0.318    -0.847  0.345    -0.799  0.284
+  1841     6    -0.662  0.910    -0.963  0.427    -0.741  0.318    -0.838  0.344    -0.798  0.282
+  1841     7    -0.056  0.588    -0.883  0.471    -0.748  0.321    -0.826  0.341    -0.795  0.281
+  1841     8    -0.388  0.694    -0.858  0.487    -0.771  0.322    -0.811  0.336    -0.789  0.282
+  1841     9    -1.261  0.642    -0.650  0.481    -0.738  0.318    -0.802  0.336    -0.783  0.285
+  1841    10    -0.481  0.640    -0.523  0.466    -0.748  0.322    -0.807  0.334    -0.779  0.287
+  1841    11    -1.189  1.084    -0.536  0.449    -0.752  0.326    -0.799  0.335    -0.778  0.289
+  1841    12    -0.958  1.066    -0.568  0.440    -0.753  0.328    -0.795  0.326    -0.775  0.289
+  1842     1    -0.671  1.935    -0.646  0.459    -0.765  0.331    -0.791  0.323    -0.773  0.292
+  1842     2    -0.232  1.521    -0.680  0.447    -0.783  0.330    -0.784  0.316    -0.772  0.291
+  1842     3     0.442  1.028    -0.674  0.446    -0.784  0.326    -0.783  0.317    -0.776  0.289
+  1842     4    -0.131  0.856    -0.705  0.445    -0.778  0.322    -0.770  0.316    -0.778  0.287
+  1842     5    -0.839  0.695    -0.699  0.457    -0.772  0.322    -0.764  0.316    -0.776  0.289
+  1842     6    -1.040  0.873    -0.602  0.440    -0.764  0.317    -0.758  0.317    -0.774  0.290
+  1842     7    -0.998  0.586    -0.495  0.406    -0.766  0.325    -0.750  0.315    -0.772  0.292
+  1842     8    -0.797  0.669    -0.405  0.405    -0.776  0.325    -0.746  0.311    -0.768  0.293
+  1842     9    -1.181  0.650    -0.488  0.404    -0.767  0.329    -0.740  0.312    -0.764  0.295
+  1842    10    -0.861  0.719    -0.521  0.407    -0.764  0.321    -0.732  0.305    -0.764  0.292
+  1842    11    -1.117  0.725    -0.562  0.411    -0.777  0.320    -0.723  0.303    -0.763  0.290
+  1842    12     0.207  1.038    -0.568  0.392    -0.785  0.322    -0.730  0.294    -0.753  0.288
+  1843     1     0.615  1.167    -0.539  0.392    -0.791  0.322    -0.723  0.290    -0.755  0.291
+  1843     2     0.850  1.151    -0.533  0.392    -0.783  0.316    -0.705  0.292    -0.758  0.289
+  1843     3    -0.550  0.956    -0.505  0.391    -0.781  0.317    -0.696  0.286    -0.759  0.291
+  1843     4    -0.528  0.745    -0.476  0.405    -0.776  0.316    -0.689  0.280    -0.760  0.292
+  1843     5    -1.328  0.725    -0.472  0.391    -0.760  0.309    -0.686  0.284    -0.759  0.292
+  1843     6    -1.112  0.480    -0.466  0.421    -0.765  0.309    -0.682  0.284    -0.758  0.295
+  1843     7    -0.652  0.799    -0.558  0.458    -0.724  0.313    -0.681  0.284    -0.754  0.295
+  1843     8    -0.726  0.501    -0.707  0.430    -0.709  0.313    -0.682  0.282    -0.751  0.298
+  1843     9    -0.847  0.691    -0.715  0.436    -0.658  0.311    -0.683  0.279    -0.749  0.298
+  1843    10    -0.513  0.820    -0.753  0.445    -0.645  0.312    -0.683  0.279    -0.748  0.298
+  1843    11    -1.071  0.696    -0.745  0.456    -0.642  0.312    -0.683  0.279    -0.757  0.298
+  1843    12     0.280  1.070    -0.721  0.474    -0.633  0.307    -0.690  0.277    -0.759  0.298
+  1844     1    -0.486  1.358    -0.727  0.466    -0.627  0.308    -0.693  0.276    -0.755  0.299
+  1844     2    -0.944  1.220    -0.746  0.464    -0.620  0.305    -0.702  0.280    -0.759  0.300
+  1844     3    -0.639  1.076    -0.776  0.458    -0.606  0.303    -0.681  0.280    -0.763  0.299
+  1844     4    -0.990  0.754    -0.776  0.431    -0.614  0.306    -0.689  0.282    -0.765  0.299
+  1844     5    -1.229  0.821    -0.819  0.448    -0.610  0.314    -0.692  0.282    -0.766  0.299
+  1844     6    -0.826  0.701    -0.981  0.418    -0.592  0.303    -0.691  0.286    -0.767  0.300
+  1844     7    -0.719  0.629    -0.924  0.399    -0.576  0.298    -0.692  0.288    -0.765  0.299
+  1844     8    -0.959  0.607    -0.971  0.399    -0.565  0.295    -0.697  0.289    -0.762  0.300
+  1844     9    -1.202  0.585    -0.982  0.395    -0.595  0.290    -0.696  0.288    -0.762  0.299
+  1844    10    -0.520  0.702    -0.925  0.373    -0.599  0.292    -0.692  0.286    -0.757  0.300
+  1844    11    -1.589  0.933    -0.900  0.366    -0.599  0.292    -0.678  0.287    -0.762  0.299
+  1844    12    -1.657  1.085    -0.894  0.366    -0.595  0.289    -0.674  0.284    -0.761  0.298
+  1845     1     0.191  1.262    -0.856  0.365    -0.584  0.291    -0.692  0.287    -0.762  0.299
+  1845     2    -1.504  1.197    -0.816  0.366    -0.582  0.290    -0.686  0.289    -0.762  0.296
+  1845     3    -0.772  0.896    -0.794  0.361    -0.572  0.290    -0.679  0.294    -0.761  0.295
+  1845     4    -0.303  0.769    -0.865  0.373    -0.567  0.286    -0.685  0.295    -0.758  0.294
+  1845     5    -0.935  0.622    -0.802  0.368    -0.557  0.286    -0.695  0.294    -0.754  0.295
+  1845     6    -0.749  0.657    -0.813  0.402    -0.591  0.291    -0.697  0.294    -0.750  0.295
+  1845     7    -0.266  0.768    -0.759  0.468    -0.635  0.296    -0.699  0.294    -0.747  0.295
+  1845     8    -0.478  0.690    -0.603  0.448    -0.639  0.299    -0.691  0.292    -0.741  0.295
+  1845     9    -0.932  0.749    -0.457  0.439    -0.639  0.302    -0.688  0.293    -0.737  0.296
+  1845    10    -1.378  0.702    -0.505  0.514    -0.634  0.308    -0.684  0.293    -0.735  0.296
+  1845    11    -0.834  0.912    -0.468  0.517    -0.626  0.311    -0.681  0.291    -0.731  0.298
+  1845    12    -1.779  1.057    -0.414  0.530    -0.615  0.316    -0.678  0.290    -0.730  0.296
+  1846     1     0.838  1.592    -0.369  0.520    -0.606  0.319    -0.665  0.294    -0.728  0.295
+  1846     2     0.369  1.312    -0.327  0.509    -0.606  0.321    -0.667  0.298    -0.729  0.296
+  1846     3     0.973  0.944    -0.281  0.496    -0.611  0.325    -0.660  0.298    -0.733  0.295
+  1846     4    -0.883  1.484    -0.249  0.514    -0.612  0.329    -0.656  0.296    -0.731  0.293
+  1846     5    -0.481  0.763    -0.256  0.549    -0.608  0.328    -0.653  0.298    -0.729  0.292
+  1846     6    -0.104  0.881    -0.096  0.534    -0.638  0.322    -0.651  0.297    -0.724  0.292
+  1846     7     0.268  0.682    -0.146  0.504    -0.639  0.322    -0.650  0.297    -0.720  0.290
+  1846     8     0.031  0.626    -0.139  0.537    -0.633  0.331    -0.650  0.297    -0.717  0.289
+  1846     9    -0.380  0.793    -0.334  0.533    -0.623  0.338    -0.643  0.298    -0.716  0.289
+  1846    10    -0.996  0.949    -0.292  0.557    -0.631  0.339    -0.640  0.301    -0.719  0.287
+  1846    11    -0.915  1.015    -0.319  0.548    -0.631  0.337    -0.641  0.304    -0.714  0.287
+  1846    12     0.134  1.423    -0.379  0.531    -0.628  0.341    -0.637  0.305    -0.714  0.284
+  1847     1     0.242  1.562    -0.430  0.519    -0.619  0.342    -0.633  0.308    -0.716  0.285
+  1847     2     0.454  1.618    -0.486  0.500    -0.610  0.344    -0.638  0.311    -0.718  0.284
+  1847     3    -1.371  1.432    -0.503  0.480    -0.608  0.346    -0.655  0.309    -0.715  0.285
+  1847     4    -0.379  0.834    -0.471  0.489    -0.606  0.346    -0.662  0.310    -0.714  0.285
+  1847     5    -0.805  0.637    -0.435  0.499    -0.584  0.352    -0.657  0.313    -0.713  0.285
+  1847     6    -0.820  0.809    -0.600  0.445    -0.585  0.358    -0.654  0.312    -0.711  0.286
+  1847     7    -0.346  0.864    -0.787  0.421    -0.619  0.363    -0.647  0.312    -0.707  0.285
+  1847     8    -0.636  0.649    -0.774  0.435    -0.596  0.375    -0.647  0.311    -0.705  0.283
+  1847     9    -0.587  0.620    -0.705  0.473    -0.590  0.383    -0.643  0.310    -0.704  0.282
+  1847    10    -0.609  0.591    -0.696  0.497    -0.606  0.389    -0.639  0.309    -0.702  0.279
+  1847    11    -0.487  0.781    -0.699  0.500    -0.613  0.389    -0.642  0.309    -0.699  0.281
+  1847    12    -1.844  0.926    -0.666  0.499    -0.609  0.392    -0.643  0.312    -0.695  0.276
+  1848     1    -2.008  1.245    -0.648  0.495    -0.606  0.387    -0.654  0.315    -0.683  0.274
+  1848     2     0.614  1.467    -0.656  0.497    -0.599  0.385    -0.670  0.310    -0.680  0.274
+  1848     3    -0.544  1.001    -0.703  0.493    -0.595  0.385    -0.676  0.309    -0.675  0.270
+  1848     4    -0.268  0.997    -0.699  0.514    -0.592  0.382    -0.678  0.309    -0.671  0.268
+  1848     5    -0.836  0.814    -0.727  0.500    -0.602  0.377    -0.671  0.311    -0.668  0.269
+  1848     6    -0.429  0.799    -0.700  0.493    -0.591  0.369    -0.664  0.312    -0.665  0.268
+  1848     7    -0.134  0.657    -0.578  0.498    -0.607  0.361    -0.658  0.311    -0.666  0.267
+  1848     8    -0.735  0.658    -0.677  0.505    -0.624  0.367    -0.652  0.312    -0.666  0.266
+  1848     9    -1.148  0.654    -0.639  0.528    -0.661  0.366    -0.650  0.313    -0.666  0.264
+  1848    10    -0.556  0.775    -0.734  0.513    -0.666  0.359    -0.651  0.313    -0.665  0.263
+  1848    11    -0.830  0.827    -0.769  0.491    -0.664  0.357    -0.653  0.312    -0.663  0.263
+  1848    12    -1.518  0.859    -0.789  0.495    -0.668  0.353    -0.661  0.309    -0.664  0.263
+  1849     1    -0.541  1.265    -0.790  0.489    -0.673  0.352    -0.668  0.306    -0.666  0.261
+  1849     2    -0.573  1.243    -0.765  0.490    -0.679  0.355    -0.670  0.308    -0.670  0.261
+  1849     3    -0.086  1.225    -0.760  0.489    -0.680  0.351    -0.670  0.307    -0.660  0.260
+  1849     4    -1.413  1.164    -0.744  0.474    -0.666  0.357    -0.668  0.308    -0.658  0.261
+  1849     5    -1.254  0.646    -0.700  0.494    -0.671  0.353    -0.660  0.308    -0.653  0.261
+  1849     6    -0.669  0.804    -0.715  0.483    -0.682  0.357    -0.661  0.307    -0.650  0.262
+  1849     7    -0.140  0.679    -0.822  0.477    -0.690  0.359    -0.654  0.307    -0.652  0.262
+  1849     8    -0.440  0.670    -0.786  0.470    -0.710  0.361    -0.648  0.307    -0.655  0.261
+  1849     9    -1.088  0.675    -0.817  0.469    -0.715  0.363    -0.642  0.306    -0.654  0.259
+  1849    10    -0.364  0.642    -0.803  0.500    -0.725  0.363    -0.633  0.306    -0.651  0.259
+  1849    11    -0.300  0.961    -0.812  0.500    -0.716  0.369    -0.623  0.307    -0.644  0.259
+  1849    12    -1.696  0.935    -0.800  0.488    -0.712  0.369    -0.609  0.308    -0.637  0.260
+  1850     1    -1.836  1.004    -0.794  0.463    -0.709  0.365    -0.609  0.311    -0.641  0.261
+  1850     2    -0.140  1.355    -0.761  0.454    -0.713  0.364    -0.598  0.309    -0.640  0.261
+  1850     3    -0.454  1.214    -0.729  0.454    -0.715  0.362    -0.596  0.307    -0.642  0.262
+  1850     4    -1.240  0.802    -0.799  0.437    -0.711  0.364    -0.592  0.309    -0.643  0.263
+  1850     5    -1.372  0.679    -0.892  0.404    -0.728  0.366    -0.585  0.311    -0.643  0.262
+  1850     6    -0.516  0.743    -0.844  0.414    -0.695  0.367    -0.583  0.310    -0.642  0.262
+  1850     7    -0.077  0.431    -0.699  0.410    -0.673  0.367    -0.586  0.306    -0.643  0.261
+  1850     8    -0.037  0.563    -0.745  0.394    -0.701  0.353    -0.586  0.304    -0.639  0.261
+  1850     9    -0.709  0.558    -0.812  0.376    -0.713  0.349    -0.586  0.302    -0.635  0.262
+  1850    10    -1.197  0.637    -0.804  0.362    -0.722  0.345    -0.580  0.301    -0.630  0.263
+  1850    11    -1.416  0.934    -0.722  0.374    -0.716  0.344    -0.581  0.303    -0.627  0.264
+  1850    12    -1.126  0.842    -0.708  0.389    -0.714  0.341    -0.579  0.298    -0.630  0.264
+  1851     1    -0.100  0.992    -0.703  0.399    -0.710  0.338    -0.585  0.293    -0.632  0.267
+  1851     2    -0.682  1.007    -0.725  0.417    -0.698  0.337    -0.596  0.296    -0.633  0.269
+  1851     3    -1.257  0.926    -0.704  0.433    -0.688  0.336    -0.612  0.293    -0.628  0.271
+  1851     4    -1.143  0.765    -0.619  0.447    -0.690  0.333    -0.609  0.287    -0.624  0.271
+  1851     5    -0.396  0.836    -0.603  0.470    -0.699  0.330    -0.610  0.283    -0.625  0.271
+  1851     6    -0.342  0.687    -0.552  0.487    -0.684  0.332    -0.609  0.281    -0.623  0.270
+  1851     7    -0.019  0.552    -0.565  0.514    -0.697  0.327    -0.614  0.278    -0.624  0.269
+  1851     8    -0.306  0.794    -0.568  0.489    -0.708  0.321    -0.622  0.278    -0.623  0.268
+  1851     9    -0.456  0.621    -0.602  0.488    -0.716  0.315    -0.629  0.277    -0.620  0.268
+  1851    10    -0.172  0.999    -0.587  0.472    -0.705  0.314    -0.630  0.275    -0.622  0.267
+  1851    11    -1.223  0.717    -0.578  0.482    -0.690  0.316    -0.629  0.274    -0.618  0.269
+  1851    12    -0.515  0.899    -0.600  0.484    -0.693  0.311    -0.632  0.273    -0.616  0.269
+  1852     1    -0.256  1.267    -0.612  0.482    -0.690  0.310    -0.640  0.275    -0.622  0.271
+  1852     2    -0.725  1.012    -0.658  0.456    -0.686  0.309    -0.652  0.278    -0.627  0.271
+  1852     3    -1.664  0.857    -0.677  0.463    -0.677  0.307    -0.648  0.279    -0.633  0.271
+  1852     4    -0.954  0.763    -0.693  0.430    -0.660  0.308    -0.657  0.279    -0.637  0.274
+  1852     5    -0.289  0.876    -0.717  0.440    -0.662  0.305    -0.663  0.279    -0.635  0.274
+  1852     6    -0.604  0.624    -0.663  0.424    -0.634  0.303    -0.663  0.277    -0.633  0.273
+  1852     7    -0.166  0.494    -0.701  0.416    -0.600  0.308    -0.664  0.277    -0.630  0.272
+  1852     8    -0.858  0.517    -0.728  0.385    -0.599  0.295    -0.664  0.276    -0.629  0.271
+  1852     9    -0.687  0.533    -0.697  0.375    -0.602  0.292    -0.668  0.274    -0.628  0.271
+  1852    10    -0.362  0.706    -0.685  0.364    -0.577  0.291    -0.671  0.274    -0.628  0.270
+  1852    11    -1.515  0.754    -0.699  0.343    -0.556  0.294    -0.674  0.278    -0.629  0.270
+  1852    12     0.135  0.927    -0.674  0.336    -0.556  0.291    -0.660  0.278    -0.638  0.273
+  1853     1    -0.718  1.125    -0.651  0.326    -0.566  0.291    -0.643  0.277    -0.639  0.274
+  1853     2    -1.039  1.138    -0.582  0.329    -0.573  0.290    -0.656  0.277    -0.642  0.275
+  1853     3    -1.294  0.732    -0.569  0.334    -0.576  0.290    -0.655  0.275    -0.642  0.276
+  1853     4    -0.815  0.547    -0.594  0.342    -0.568  0.288    -0.653  0.277    -0.641  0.276
+  1853     5    -0.461  0.596    -0.583  0.339    -0.559  0.290    -0.650  0.277    -0.635  0.276
+  1853     6    -0.301  0.671    -0.648  0.340    -0.567  0.287    -0.648  0.274    -0.632  0.278
+  1853     7     0.118  0.784    -0.697  0.339    -0.563  0.283    -0.652  0.273    -0.631  0.277
+  1853     8    -0.039  0.566    -0.713  0.343    -0.567  0.280    -0.651  0.272    -0.631  0.277
+  1853     9    -0.528  0.580    -0.654  0.360    -0.562  0.275    -0.649  0.270    -0.631  0.277
+  1853    10    -0.667  0.567    -0.646  0.367    -0.552  0.273    -0.647  0.269    -0.634  0.275
+  1853    11    -1.373  0.829    -0.638  0.372    -0.557  0.265    -0.642  0.270    -0.632  0.275
+  1853    12    -0.653  0.795    -0.686  0.376    -0.551  0.261    -0.639  0.270    -0.637  0.274
+  1854     1    -1.302  1.057    -0.690  0.367    -0.556  0.259    -0.639  0.269    -0.639  0.273
+  1854     2    -1.233  1.046    -0.702  0.365    -0.566  0.256    -0.638  0.267    -0.638  0.273
+  1854     3    -0.585  0.985    -0.703  0.361    -0.578  0.254    -0.640  0.266    -0.638  0.272
+  1854     4    -0.714  0.596    -0.597  0.361    -0.594  0.251    -0.626  0.264    -0.637  0.272
+  1854     5    -0.367  0.678    -0.514  0.378    -0.587  0.252    -0.614  0.263    -0.633  0.270
+  1854     6    -0.885  0.780    -0.462  0.391    -0.583  0.249    -0.610  0.262    -0.630  0.269
+  1854     7     0.071  0.607    -0.337  0.462    -0.590  0.249    -0.612  0.261    -0.627  0.267
+  1854     8    -0.172  0.572    -0.243  0.454    -0.593  0.252    -0.613  0.259    -0.625  0.267
+  1854     9    -0.551  0.528    -0.247  0.444    -0.581  0.255    -0.612  0.257    -0.622  0.266
+  1854    10     0.611  0.719    -0.165  0.445    -0.590  0.261    -0.611  0.258    -0.622  0.266
+  1854    11    -0.373  0.787    -0.142  0.444    -0.609  0.260    -0.611  0.259    -0.619  0.267
+  1854    12    -0.037  1.180    -0.113  0.421    -0.614  0.256    -0.600  0.261    -0.619  0.269
+  1855     1     0.200  1.478    -0.173  0.427    -0.618  0.257    -0.590  0.262    -0.618  0.269
+  1855     2    -0.101  1.467    -0.199  0.432    -0.615  0.257    -0.594  0.262    -0.616  0.270
+  1855     3    -0.637  0.915    -0.228  0.427    -0.621  0.258    -0.606  0.262    -0.618  0.271
+  1855     4     0.276  0.714    -0.338  0.429    -0.632  0.261    -0.600  0.261    -0.621  0.272
+  1855     5    -0.100  0.757    -0.381  0.421    -0.621  0.268    -0.591  0.261    -0.618  0.272
+  1855     6    -0.535  0.490    -0.509  0.379    -0.625  0.272    -0.588  0.261    -0.617  0.274
+  1855     7    -0.652  0.441    -0.513  0.342    -0.612  0.270    -0.588  0.262    -0.616  0.272
+  1855     8    -0.474  0.477    -0.583  0.320    -0.610  0.272    -0.587  0.263    -0.614  0.271
+  1855     9    -0.910  0.481    -0.610  0.328    -0.596  0.273    -0.583  0.265    -0.611  0.270
+  1855    10    -0.707  0.723    -0.676  0.330    -0.584  0.277    -0.576  0.268    -0.606  0.271
+  1855    11    -0.882  0.637    -0.726  0.336    -0.584  0.280    -0.574  0.270    -0.602  0.273
+  1855    12    -1.579  0.918    -0.680  0.307    -0.583  0.277    -0.582  0.271    -0.598  0.274
+  1856     1     0.150  1.009    -0.652  0.317    -0.594  0.273    -0.598  0.273    -0.600  0.273
+  1856     2    -0.930  1.059    -0.688  0.319    -0.603  0.271    -0.600  0.274    -0.606  0.275
+  1856     3    -0.964  0.978    -0.711  0.325    -0.609  0.269    -0.596  0.276    -0.613  0.276
+  1856     4    -0.517  0.679    -0.748  0.317    -0.605  0.269    -0.593  0.278    -0.612  0.275
+  1856     5    -0.702  0.543    -0.742  0.325    -0.585  0.270    -0.596  0.276    -0.612  0.273
+  1856     6     0.020  0.837    -0.630  0.321    -0.593  0.271    -0.596  0.274    -0.614  0.273
+  1856     7    -0.317  0.683    -0.703  0.312    -0.580  0.270    -0.598  0.272    -0.614  0.273
+  1856     8    -0.902  0.593    -0.699  0.331    -0.567  0.273    -0.596  0.270    -0.615  0.272
+  1856     9    -1.192  0.595    -0.695  0.329    -0.563  0.271    -0.597  0.269    -0.616  0.272
+  1856    10    -1.154  0.544    -0.781  0.327    -0.548  0.272    -0.603  0.267    -0.614  0.273
+  1856    11    -0.801  0.742    -0.841  0.330    -0.539  0.268    -0.594  0.267    -0.611  0.272
+  1856    12    -0.243  0.923    -0.914  0.334    -0.526  0.266    -0.596  0.267    -0.609  0.273
+  1857     1    -0.728  1.071    -0.926  0.328    -0.534  0.267    -0.610  0.270    -0.611  0.275
+  1857     2    -0.875  0.865    -0.906  0.328    -0.541  0.265    -0.616  0.270    -0.616  0.278
+  1857     3    -0.922  0.859    -0.891  0.330    -0.548  0.265    -0.610  0.273    -0.613  0.280
+  1857     4    -1.544  0.603    -0.880  0.326    -0.562  0.265    -0.612  0.278    -0.612  0.281
+  1857     5    -1.429  0.558    -0.886  0.337    -0.560  0.266    -0.613  0.279    -0.612  0.281
+  1857     6    -0.853  0.470    -0.874  0.348    -0.566  0.265    -0.612  0.278    -0.609  0.280
+  1857     7    -0.457  0.632    -0.809  0.354    -0.581  0.261    -0.613  0.277    -0.608  0.279
+  1857     8    -0.666  0.529    -0.815  0.348    -0.589  0.263    -0.611  0.278    -0.606  0.280
+  1857     9    -1.014  0.555    -0.776  0.333    -0.610  0.263    -0.613  0.279    -0.605  0.280
+  1857    10    -1.017  0.521    -0.652  0.345    -0.623  0.265    -0.617  0.280    -0.603  0.282
+  1857    11    -0.880  0.796    -0.573  0.350    -0.626  0.266    -0.616  0.281    -0.597  0.284
+  1857    12    -0.090  0.737    -0.520  0.350    -0.619  0.267    -0.633  0.286    -0.589  0.283
+  1858     1     0.048  1.198    -0.527  0.354    -0.610  0.271    -0.623  0.290    -0.584  0.283
+  1858     2    -0.947  0.807    -0.522  0.344    -0.601  0.275    -0.615  0.295    -0.592  0.283
+  1858     3    -0.455  0.758    -0.513  0.342    -0.589  0.279    -0.609  0.298    -0.593  0.283
+  1858     4    -0.058  0.699    -0.463  0.346    -0.584  0.283    -0.603  0.297    -0.593  0.282
+  1858     5    -0.477  0.715    -0.401  0.335    -0.589  0.286    -0.599  0.296    -0.590  0.282
+  1858     6    -0.220  0.583    -0.490  0.336    -0.597  0.287    -0.600  0.297    -0.589  0.281
+  1858     7    -0.534  0.637    -0.537  0.341    -0.634  0.292    -0.604  0.297    -0.588  0.280
+  1858     8    -0.609  0.640    -0.498  0.340    -0.633  0.299    -0.609  0.296    -0.585  0.279
+  1858     9    -0.905  0.481    -0.488  0.345    -0.630  0.304    -0.613  0.295    -0.583  0.279
+  1858    10    -0.414  0.542    -0.465  0.334    -0.634  0.308    -0.616  0.294    -0.580  0.279
+  1858    11    -0.144  0.733    -0.412  0.330    -0.635  0.311    -0.610  0.293    -0.580  0.279
+  1858    12    -1.158  0.700    -0.403  0.335    -0.642  0.310    -0.612  0.295    -0.575  0.279
+  1859     1    -0.514  0.811    -0.391  0.345    -0.641  0.309    -0.611  0.298    -0.575  0.280
+  1859     2    -0.480  0.775    -0.389  0.331    -0.626  0.309    -0.605  0.299    -0.570  0.278
+  1859     3    -0.336  0.743    -0.394  0.326    -0.617  0.309    -0.606  0.295    -0.575  0.278
+  1859     4     0.220  0.637    -0.380  0.330    -0.612  0.308    -0.607  0.294    -0.571  0.276
+  1859     5     0.161  0.683    -0.388  0.331    -0.602  0.306    -0.605  0.291    -0.566  0.275
+  1859     6    -0.113  0.528    -0.325  0.353    -0.610  0.308    -0.598  0.289    -0.563  0.274
+  1859     7    -0.386  0.646    -0.340  0.365    -0.630  0.316    -0.600  0.289    -0.562  0.274
+  1859     8    -0.593  0.460    -0.351  0.377    -0.640  0.312    -0.602  0.288    -0.560  0.274
+  1859     9    -0.965  0.492    -0.479  0.362    -0.640  0.314    -0.602  0.286    -0.554  0.273
+  1859    10    -0.245  0.572    -0.542  0.369    -0.633  0.320    -0.612  0.287    -0.554  0.274
+  1859    11    -0.233  0.607    -0.576  0.378    -0.616  0.321    -0.616  0.288    -0.554  0.274
+  1859    12    -0.406  1.057    -0.577  0.369    -0.610  0.323    -0.628  0.285    -0.551  0.274
+  1860     1    -0.694  0.926    -0.553  0.369    -0.607  0.320    -0.627  0.280    -0.546  0.275
+  1860     2    -0.606  0.795    -0.500  0.392    -0.607  0.320    -0.635  0.283    -0.550  0.274
+  1860     3    -1.882  0.746    -0.434  0.418    -0.605  0.323    -0.640  0.283    -0.552  0.275
+  1860     4    -0.535  0.648    -0.450  0.442    -0.603  0.323    -0.650  0.284    -0.549  0.274
+  1860     5    -0.241  0.762    -0.527  0.449    -0.611  0.317    -0.652  0.282    -0.543  0.273
+  1860     6    -0.132  0.475    -0.665  0.440    -0.642  0.323    -0.650  0.285    -0.540  0.272
+  1860     7    -0.088  0.573    -0.780  0.466    -0.635  0.331    -0.645  0.284    -0.539  0.271
+  1860     8     0.032  0.693    -0.802  0.490    -0.620  0.339    -0.643  0.283    -0.541  0.270
+  1860     9    -0.171  0.724    -0.710  0.513    -0.621  0.343    -0.637  0.282    -0.538  0.270
+  1860    10    -0.438  0.876    -0.729  0.533    -0.622  0.338    -0.631  0.284    -0.535  0.269
+  1860    11    -1.156  0.745    -0.775  0.562    -0.615  0.334    -0.623  0.285    -0.532  0.269
+  1860    12    -2.061  0.748    -0.796  0.557    -0.617  0.340    -0.617  0.288    -0.533  0.269
+  1861     1    -2.073  1.253    -0.810  0.543    -0.615  0.344    -0.616  0.289    -0.539  0.268
+  1861     2    -0.865  0.948    -0.814  0.529    -0.615  0.345    -0.616  0.293    -0.542  0.267
+  1861     3    -0.782  1.002    -0.850  0.506    -0.616  0.346    -0.615  0.294    -0.540  0.267
+  1861     4    -0.761  0.917    -0.885  0.464    -0.628  0.344    -0.615  0.296    -0.535  0.265
+  1861     5    -0.791  0.662    -0.806  0.434    -0.635  0.341    -0.613  0.295    -0.536  0.262
+  1861     6    -0.387  0.490    -0.694  0.401    -0.631  0.345    -0.618  0.296    -0.536  0.260
+  1861     7    -0.254  0.427    -0.683  0.404    -0.641  0.349    -0.613  0.297    -0.536  0.259
+  1861     8    -0.023  0.549    -0.733  0.363    -0.643  0.350    -0.609  0.296    -0.535  0.257
+  1861     9    -0.594  0.492    -0.747  0.352    -0.648  0.343    -0.602  0.296    -0.535  0.256
+  1861    10    -0.861  0.505    -0.775  0.365    -0.666  0.341    -0.598  0.297    -0.535  0.255
+  1861    11    -0.207  0.683    -0.743  0.365    -0.672  0.340    -0.592  0.298    -0.535  0.256
+  1861    12    -0.713  1.007    -0.753  0.378    -0.670  0.338    -0.586  0.299    -0.536  0.255
+  1862     1    -1.948  1.245    -0.758  0.385    -0.665  0.339    -0.582  0.301    -0.539  0.254
+  1862     2    -1.462  0.838    -0.809  0.382    -0.663  0.340    -0.580  0.302    -0.540  0.255
+  1862     3    -0.948  0.788    -0.833  0.391    -0.657  0.339    -0.579  0.304    -0.535  0.255
+  1862     4    -1.103  1.072    -0.836  0.396    -0.662  0.339    -0.567  0.304    -0.533  0.256
+  1862     5    -0.399  0.673    -0.931  0.402    -0.673  0.342    -0.562  0.303    -0.533  0.255
+  1862     6    -0.510  0.560    -1.036  0.439    -0.690  0.338    -0.556  0.303    -0.533  0.254
+  1862     7    -0.310  0.498    -0.832  0.421    -0.673  0.338    -0.553  0.301    -0.532  0.253
+  1862     8    -0.637  0.537    -0.716  0.475    -0.680  0.337    -0.549  0.302    -0.529  0.253
+  1862     9    -0.888  0.586    -0.678  0.484    -0.670  0.337    -0.542  0.303    -0.527  0.253
+  1862    10    -0.891  0.586    -0.598  0.452    -0.676  0.337    -0.534  0.306    -0.526  0.254
+  1862    11    -1.346  0.615    -0.568  0.428    -0.679  0.336    -0.520  0.306    -0.523  0.255
+  1862    12    -1.976  1.020    -0.555  0.449    -0.682  0.344    -0.519  0.306    -0.529  0.255
+  1863     1     0.495  1.131    -0.566  0.471    -0.681  0.339    -0.526  0.307    -0.529  0.257
+  1863     2    -0.075  1.295    -0.561  0.469    -0.684  0.333    -0.529  0.308    -0.525  0.259
+  1863     3    -0.490  0.878    -0.567  0.461    -0.685  0.327    -0.531  0.310    -0.523  0.259
+  1863     4    -0.140  0.551    -0.587  0.449    -0.678  0.320    -0.533  0.305    -0.523  0.259
+  1863     5    -0.036  0.699    -0.525  0.431    -0.657  0.321    -0.530  0.302    -0.523  0.258
+  1863     6    -0.352  0.729    -0.434  0.422    -0.638  0.324    -0.530  0.303    -0.522  0.257
+  1863     7    -0.445  0.690    -0.572  0.405    -0.597  0.315    -0.524  0.302    -0.523  0.255
+  1863     8    -0.584  0.470    -0.613  0.382    -0.598  0.314    -0.519  0.301    -0.524  0.255
+  1863     9    -0.955  0.475    -0.627  0.336    -0.600  0.308    -0.516  0.301    -0.524  0.254
+  1863    10    -1.127  0.440    -0.683  0.346    -0.596  0.306    -0.512  0.304    -0.521  0.254
+  1863    11    -0.604  0.505    -0.698  0.349    -0.590  0.301    -0.518  0.303    -0.517  0.254
+  1863    12    -0.892  1.042    -0.671  0.337    -0.594  0.303    -0.512  0.304    -0.515  0.255
+  1864     1    -1.151  0.958    -0.641  0.322    -0.585  0.307    -0.511  0.307    -0.510  0.255
+  1864     2    -0.566  0.882    -0.629  0.319    -0.591  0.305    -0.502  0.306    -0.505  0.256
+  1864     3    -0.668  0.679    -0.602  0.316    -0.588  0.305    -0.510  0.307    -0.508  0.255
+  1864     4    -0.808  0.556    -0.551  0.309    -0.585  0.309    -0.515  0.304    -0.506  0.255
+  1864     5    -0.216  0.416    -0.575  0.329    -0.583  0.312    -0.517  0.304    -0.505  0.254
+  1864     6    -0.024  0.482    -0.621  0.303    -0.563  0.313    -0.516  0.305    -0.502  0.252
+  1864     7    -0.090  0.614    -0.500  0.285    -0.534  0.306    -0.512  0.305    -0.503  0.251
+  1864     8    -0.431  0.509    -0.538  0.296    -0.520  0.313    -0.507  0.307    -0.503  0.251
+  1864     9    -0.636  0.569    -0.586  0.313    -0.518  0.313    -0.496  0.307    -0.500  0.250
+  1864    10    -0.515  0.592    -0.596  0.328    -0.501  0.305    -0.497  0.310    -0.503  0.250
+  1864    11    -0.895  0.862    -0.612  0.341    -0.508  0.304    -0.498  0.311    -0.504  0.250
+  1864    12    -1.440  0.602    -0.634  0.357    -0.502  0.302    -0.502  0.309    -0.505  0.250
+  1865     1     0.299  0.866    -0.629  0.350    -0.498  0.303    -0.502  0.308    -0.511  0.247
+  1865     2    -1.026  0.807    -0.608  0.353    -0.491  0.305    -0.507  0.310    -0.514  0.248
+  1865     3    -1.235  0.712    -0.576  0.353    -0.480  0.306    -0.497  0.309    -0.515  0.248
+  1865     4    -0.936  0.756    -0.534  0.368    -0.465  0.311    -0.497  0.307    -0.520  0.247
+  1865     5    -0.407  0.510    -0.449  0.355    -0.429  0.314    -0.496  0.307    -0.522  0.246
+  1865     6    -0.288  0.852    -0.405  0.398    -0.395  0.307    -0.493  0.305    -0.521  0.246
+  1865     7    -0.032  0.467    -0.399  0.407    -0.417  0.302    -0.491  0.301    -0.519  0.245
+  1865     8    -0.175  0.423    -0.391  0.441    -0.437  0.302    -0.495  0.299    -0.519  0.245
+  1865     9    -0.247  0.491    -0.364  0.458    -0.442  0.299    -0.494  0.295    -0.519  0.244
+  1865    10    -0.019  0.638    -0.326  0.472    -0.443  0.295    -0.495  0.288    -0.519  0.244
+  1865    11     0.123  0.741    -0.329  0.474    -0.446  0.293    -0.490  0.287    -0.521  0.243
+  1865    12    -0.912  1.094    -0.357  0.433    -0.443  0.288    -0.485  0.285    -0.519  0.243
+  1866     1     0.383  0.939    -0.331  0.448    -0.432  0.282    -0.479  0.279    -0.522  0.244
+  1866     2    -0.942  1.039    -0.347  0.448    -0.424  0.280    -0.485  0.276    -0.521  0.245
+  1866     3    -0.904  0.939    -0.365  0.443    -0.417  0.278    -0.484  0.272    -0.519  0.244
+  1866     4    -0.477  0.760    -0.418  0.452    -0.396  0.283    -0.477  0.266    -0.518  0.243
+  1866     5    -0.446  0.474    -0.435  0.431    -0.401  0.287    -0.475  0.261    -0.518  0.242
+  1866     6    -0.623  0.544    -0.320  0.392    -0.393  0.282    -0.475  0.261    -0.519  0.242
+  1866     7     0.273  0.549    -0.368  0.401    -0.380  0.286    -0.474  0.259    -0.518  0.241
+  1866     8    -0.367  0.522    -0.340  0.380    -0.361  0.280    -0.473  0.257    -0.516  0.240
+  1866     9    -0.455  0.482    -0.333  0.384    -0.371  0.289    -0.472  0.256    -0.513  0.240
+  1866    10    -0.659  0.695    -0.303  0.361    -0.364  0.284    -0.467  0.256    -0.509  0.239
+  1866    11    -0.080  0.564    -0.332  0.376    -0.362  0.285    -0.475  0.257    -0.510  0.239
+  1866    12     0.462  0.711    -0.293  0.374    -0.362  0.290    -0.476  0.259    -0.515  0.240
+  1867     1    -0.182  0.971    -0.320  0.364    -0.359  0.292    -0.468  0.252    -0.514  0.240
+  1867     2    -0.609  0.888    -0.308  0.383    -0.351  0.297    -0.463  0.255    -0.512  0.240
+  1867     3    -0.818  0.856    -0.291  0.388    -0.336  0.302    -0.460  0.252    -0.508  0.240
+  1867     4    -0.123  0.592    -0.237  0.390    -0.332  0.308    -0.455  0.248    -0.504  0.239
+  1867     5    -0.798  0.525    -0.163  0.398    -0.322  0.308    -0.454  0.245    -0.499  0.238
+  1867     6    -0.154  0.406    -0.195  0.395    -0.314  0.310    -0.454  0.244    -0.496  0.237
+  1867     7    -0.050  0.506    -0.246  0.387    -0.330  0.310    -0.452  0.244    -0.493  0.236
+  1867     8    -0.226  0.555    -0.305  0.400    -0.333  0.312    -0.446  0.244    -0.489  0.236
+  1867     9    -0.249  0.519    -0.299  0.398    -0.325  0.311    -0.441  0.244    -0.484  0.235
+  1867    10    -0.009  0.781    -0.308  0.384    -0.317  0.305    -0.434  0.244    -0.480  0.234
+  1867    11     0.813  0.802    -0.257  0.359    -0.313  0.306    -0.431  0.244    -0.476  0.233
+  1867    12     0.078  0.935    -0.262  0.365    -0.304  0.293    -0.424  0.239    -0.479  0.234
+  1868     1    -0.795  0.793    -0.238  0.353    -0.301  0.293    -0.435  0.239    -0.482  0.235
+  1868     2    -1.320  0.885    -0.225  0.339    -0.305  0.294    -0.435  0.239    -0.476  0.235
+  1868     3    -0.748  0.655    -0.252  0.340    -0.303  0.293    -0.437  0.237    -0.469  0.236
+  1868     4    -0.223  0.503    -0.238  0.339    -0.311  0.287    -0.444  0.239    -0.467  0.234
+  1868     5    -0.194  0.502    -0.384  0.316    -0.322  0.284    -0.447  0.238    -0.466  0.233
+  1868     6    -0.215  0.515    -0.425  0.328    -0.332  0.279    -0.445  0.237    -0.464  0.232
+  1868     7     0.241  0.386    -0.389  0.347    -0.360  0.278    -0.442  0.234    -0.460  0.231
+  1868     8    -0.072  0.416    -0.233  0.298    -0.371  0.273    -0.438  0.232    -0.456  0.230
+  1868     9    -0.566  0.452    -0.274  0.318    -0.367  0.274    -0.436  0.232    -0.452  0.230
+  1868    10     0.155  0.654    -0.289  0.304    -0.359  0.264    -0.427  0.232    -0.450  0.230
+  1868    11    -0.942  0.575    -0.278  0.318    -0.361  0.263    -0.425  0.233    -0.448  0.230
+  1868    12    -0.408  0.776    -0.263  0.338    -0.357  0.260    -0.418  0.231    -0.445  0.230
+  1869     1    -0.370  0.955    -0.276  0.344    -0.362  0.254    -0.408  0.230    -0.444  0.230
+  1869     2     0.560  1.112    -0.263  0.370    -0.356  0.252    -0.405  0.229    -0.444  0.230
+  1869     3    -1.249  0.875    -0.196  0.385    -0.356  0.251    -0.410  0.231    -0.444  0.229
+  1869     4    -0.400  0.562    -0.231  0.385    -0.348  0.247    -0.405  0.231    -0.448  0.228
+  1869     5    -0.063  0.487    -0.180  0.394    -0.367  0.243    -0.404  0.232    -0.451  0.229
+  1869     6    -0.033  0.831    -0.225  0.380    -0.388  0.243    -0.405  0.231    -0.451  0.227
+  1869     7     0.087  0.584    -0.249  0.365    -0.402  0.237    -0.407  0.231    -0.450  0.227
+  1869     8     0.077  0.583    -0.394  0.430    -0.406  0.239    -0.404  0.231    -0.450  0.226
+  1869     9     0.249  0.572    -0.355  0.413    -0.402  0.236    -0.398  0.232    -0.449  0.226
+  1869    10    -0.274  0.660    -0.361  0.427    -0.409  0.236    -0.395  0.233    -0.448  0.226
+  1869    11    -0.331  0.587    -0.366  0.422    -0.400  0.233    -0.391  0.233    -0.449  0.226
+  1869    12    -0.945  0.694    -0.347  0.366    -0.405  0.234    -0.382  0.235    -0.452  0.225
+  1870     1    -0.658  0.839    -0.337  0.340    -0.405  0.232    -0.394  0.233    -0.453  0.225
+  1870     2    -1.184  0.926    -0.380  0.323    -0.402  0.233    -0.393  0.233    -0.455  0.226
+  1870     3    -0.774  0.628    -0.413  0.310    -0.401  0.235    -0.391  0.232    -0.451  0.226
+  1870     4    -0.479  0.534    -0.429  0.276    -0.403  0.232    -0.390  0.229    -0.452  0.225
+  1870     5    -0.118  0.431    -0.448  0.288    -0.432  0.228    -0.392  0.229    -0.453  0.225
+  1870     6     0.198  0.389    -0.494  0.285    -0.454  0.223    -0.392  0.227    -0.453  0.225
+  1870     7     0.202  0.435    -0.549  0.276    -0.453  0.223    -0.394  0.228    -0.454  0.224
+  1870     8    -0.439  0.449    -0.584  0.286    -0.433  0.222    -0.396  0.228    -0.454  0.223
+  1870     9    -0.142  0.556    -0.572  0.300    -0.431  0.220    -0.401  0.227    -0.457  0.221
+  1870    10    -0.471  0.453    -0.534  0.295    -0.444  0.224    -0.406  0.225    -0.457  0.219
+  1870    11    -0.551  0.772    -0.570  0.287    -0.448  0.226    -0.420  0.224    -0.454  0.219
+  1870    12    -1.495  0.506    -0.617  0.286    -0.446  0.228    -0.420  0.220    -0.447  0.219
+  1871     1    -1.330  0.853    -0.639  0.287    -0.452  0.229    -0.429  0.221    -0.440  0.216
+  1871     2    -1.594  0.969    -0.600  0.286    -0.453  0.229    -0.426  0.219    -0.439  0.215
+  1871     3    -0.634  0.621    -0.630  0.282    -0.454  0.231    -0.423  0.216    -0.437  0.214
+  1871     4    -0.019  0.462    -0.606  0.279    -0.458  0.226    -0.421  0.214    -0.434  0.212
+  1871     5    -0.557  0.494    -0.660  0.259    -0.448  0.224    -0.423  0.213    -0.431  0.211
+  1871     6    -0.369  0.517    -0.601  0.263    -0.442  0.224    -0.420  0.212    -0.433  0.210
+  1871     7    -0.054  0.433    -0.574  0.257    -0.437  0.217    -0.423  0.210    -0.432  0.210
+  1871     8     0.023  0.428    -0.515  0.250    -0.449  0.221    -0.423  0.209    -0.433  0.209
+  1871     9    -0.504  0.422    -0.508  0.248    -0.448  0.218    -0.423  0.208    -0.432  0.208
+  1871    10    -0.175  0.431    -0.555  0.249    -0.445  0.220    -0.420  0.206    -0.430  0.208
+  1871    11    -1.202  0.575    -0.528  0.252    -0.446  0.220    -0.428  0.205    -0.433  0.209
+  1871    12    -0.783  0.545    -0.536  0.255    -0.449  0.216    -0.444  0.204    -0.432  0.211
+  1872     1    -1.006  0.982    -0.537  0.259    -0.454  0.214    -0.446  0.203    -0.422  0.209
+  1872     2    -0.893  0.734    -0.542  0.264    -0.457  0.213    -0.444  0.204    -0.416  0.210
+  1872     3    -0.546  0.660    -0.516  0.264    -0.461  0.211    -0.438  0.204    -0.413  0.209
+  1872     4    -0.584  0.493    -0.512  0.266    -0.458  0.209    -0.440  0.203    -0.411  0.206
+  1872     5    -0.237  0.493    -0.488  0.273    -0.460  0.210    -0.436  0.201    -0.412  0.205
+  1872     6    -0.460  0.433    -0.525  0.268    -0.450  0.210    -0.436  0.200    -0.413  0.204
+  1872     7    -0.071  0.495    -0.501  0.287    -0.458  0.206    -0.433  0.199    -0.414  0.204
+  1872     8    -0.030  0.513    -0.436  0.296    -0.453  0.202    -0.429  0.198    -0.413  0.203
+  1872     9    -0.197  0.417    -0.447  0.304    -0.458  0.201    -0.426  0.198    -0.410  0.203
+  1872    10    -0.120  0.439    -0.481  0.321    -0.463  0.201    -0.426  0.195    -0.410  0.202
+  1872    11    -0.914  0.526    -0.495  0.320    -0.472  0.202    -0.432  0.193    -0.409  0.202
+  1872    12    -1.229  0.758    -0.468  0.328    -0.480  0.205    -0.439  0.192    -0.407  0.201
+  1873     1    -0.724  0.813    -0.471  0.324    -0.487  0.205    -0.438  0.192    -0.414  0.200
+  1873     2    -0.115  0.737    -0.479  0.313    -0.488  0.204    -0.424  0.192    -0.419  0.200
+  1873     3    -0.677  0.661    -0.515  0.315    -0.498  0.200    -0.407  0.193    -0.418  0.199
+  1873     4    -0.988  0.665    -0.510  0.312    -0.501  0.201    -0.401  0.193    -0.420  0.200
+  1873     5    -0.406  0.445    -0.466  0.315    -0.517  0.197    -0.402  0.193    -0.422  0.199
+  1873     6    -0.135  0.561    -0.367  0.330    -0.508  0.195    -0.397  0.191    -0.422  0.197
+  1873     7    -0.099  0.477    -0.309  0.305    -0.498  0.197    -0.395  0.191    -0.422  0.196
+  1873     8    -0.128  0.418    -0.314  0.289    -0.481  0.196    -0.392  0.190    -0.422  0.195
+  1873     9    -0.630  0.484    -0.359  0.282    -0.479  0.191    -0.387  0.189    -0.421  0.195
+  1873    10    -0.059  0.442    -0.295  0.258    -0.482  0.194    -0.388  0.187    -0.418  0.195
+  1873    11    -0.388  0.551    -0.272  0.260    -0.485  0.193    -0.378  0.187    -0.420  0.196
+  1873    12    -0.045  0.621    -0.276  0.247    -0.484  0.193    -0.377  0.186    -0.418  0.195
+  1874     1    -0.030  0.582    -0.288  0.244    -0.484  0.194    -0.377  0.184    -0.415  0.195
+  1874     2    -0.167  0.644    -0.284  0.249    -0.491  0.194    -0.386  0.186    -0.415  0.195
+  1874     3    -1.217  0.549    -0.230  0.243    -0.490  0.193    -0.379  0.185    -0.418  0.197
+  1874     4    -0.224  0.424    -0.235  0.251    -0.492  0.193    -0.381  0.185    -0.420  0.196
+  1874     5    -0.135  0.476    -0.240  0.265    -0.489  0.194    -0.385  0.185    -0.424  0.196
+  1874     6    -0.176  0.397    -0.265  0.256    -0.501  0.193    -0.386  0.182    -0.428  0.196
+  1874     7    -0.245  0.400    -0.357  0.254    -0.491  0.194    -0.388  0.181    -0.430  0.195
+  1874     8    -0.081  0.431    -0.416  0.251    -0.483  0.196    -0.393  0.180    -0.431  0.195
+  1874     9     0.020  0.409    -0.402  0.243    -0.474  0.198    -0.401  0.178    -0.432  0.196
+  1874    10    -0.122  0.543    -0.451  0.240    -0.471  0.197    -0.399  0.177    -0.433  0.196
+  1874    11    -0.454  0.532    -0.492  0.240    -0.472  0.196    -0.401  0.177    -0.433  0.195
+  1874    12    -0.346  0.587    -0.504  0.248    -0.466  0.193    -0.402  0.178    -0.429  0.196
+  1875     1    -1.129  0.586    -0.500  0.246    -0.460  0.191    -0.404  0.179    -0.437  0.196
+  1875     2    -0.871  0.660    -0.534  0.250    -0.455  0.190    -0.404  0.181    -0.436  0.197
+  1875     3    -1.049  0.444    -0.601  0.242    -0.451  0.189    -0.405  0.181    -0.434  0.196
+  1875     4    -0.811  0.466    -0.643  0.229    -0.449  0.187    -0.408  0.183    -0.434  0.195
+  1875     5    -0.630  0.470    -0.734  0.217    -0.432  0.189    -0.409  0.184    -0.436  0.195
+  1875     6    -0.322  0.532    -0.784  0.212    -0.425  0.192    -0.413  0.184    -0.438  0.193
+  1875     7    -0.193  0.371    -0.749  0.212    -0.424  0.192    -0.417  0.184    -0.439  0.194
+  1875     8    -0.488  0.432    -0.725  0.203    -0.415  0.193    -0.414  0.184    -0.441  0.194
+  1875     9    -0.782  0.335    -0.681  0.188    -0.382  0.193    -0.419  0.183    -0.443  0.193
+  1875    10    -0.629  0.353    -0.630  0.185    -0.358  0.189    -0.420  0.184    -0.445  0.193
+  1875    11    -1.544  0.449    -0.635  0.181    -0.356  0.187    -0.418  0.186    -0.448  0.192
+  1875    12    -0.952  0.366    -0.638  0.177    -0.349  0.182    -0.410  0.187    -0.445  0.191
+  1876     1    -0.707  0.583    -0.626  0.190    -0.339  0.181    -0.400  0.189    -0.451  0.192
+  1876     2    -0.577  0.502    -0.619  0.188    -0.332  0.179    -0.392  0.189    -0.453  0.191
+  1876     3    -0.520  0.484    -0.588  0.195    -0.319  0.179    -0.390  0.190    -0.454  0.192
+  1876     4    -0.201  0.427    -0.562  0.197    -0.318  0.179    -0.390  0.191    -0.454  0.191
+  1876     5    -0.696  0.442    -0.516  0.202    -0.307  0.179    -0.387  0.191    -0.455  0.191
+  1876     6    -0.357  0.329    -0.563  0.214    -0.312  0.179    -0.390  0.192    -0.456  0.191
+  1876     7    -0.045  0.384    -0.540  0.242    -0.317  0.181    -0.391  0.192    -0.458  0.190
+  1876     8    -0.406  0.413    -0.523  0.250    -0.324  0.180    -0.393  0.191    -0.459  0.189
+  1876     9    -0.415  0.386    -0.484  0.246    -0.310  0.181    -0.392  0.191    -0.459  0.189
+  1876    10    -0.312  0.346    -0.499  0.236    -0.317  0.182    -0.394  0.190    -0.459  0.188
+  1876    11    -0.990  0.425    -0.463  0.236    -0.323  0.182    -0.391  0.192    -0.462  0.188
+  1876    12    -1.521  0.446    -0.444  0.228    -0.324  0.183    -0.388  0.194    -0.465  0.188
+  1877     1    -0.427  0.693    -0.419  0.221    -0.322  0.184    -0.376  0.193    -0.472  0.187
+  1877     2    -0.377  0.593    -0.362  0.219    -0.330  0.184    -0.368  0.194    -0.474  0.187
+  1877     3    -0.049  0.458    -0.323  0.221    -0.341  0.184    -0.366  0.193    -0.474  0.186
+  1877     4    -0.379  0.398    -0.294  0.226    -0.339  0.183    -0.367  0.192    -0.475  0.186
+  1877     5    -0.271  0.446    -0.202  0.236    -0.341  0.184    -0.369  0.191    -0.474  0.186
+  1877     6    -0.128  0.369    -0.145  0.245    -0.354  0.182    -0.372  0.190    -0.477  0.186
+  1877     7     0.265  0.360    -0.166  0.266    -0.350  0.187    -0.377  0.189    -0.478  0.186
+  1877     8     0.276  0.384    -0.096  0.280    -0.356  0.191    -0.379  0.188    -0.480  0.185
+  1877     9     0.054  0.414     0.014  0.290    -0.352  0.194    -0.380  0.187    -0.481  0.185
+  1877    10     0.025  0.457     0.086  0.288    -0.352  0.197    -0.385  0.186    -0.483  0.184
+  1877    11     0.118  0.545     0.083  0.280    -0.347  0.199    -0.387  0.186    -0.489  0.182
+  1877    12    -0.839  0.624     0.119  0.287    -0.347  0.200    -0.389  0.186    -0.493  0.182
+  1878     1    -0.675  0.664     0.137  0.294    -0.347  0.203    -0.392  0.185    -0.493  0.181
+  1878     2     0.467  0.692     0.140  0.299    -0.341  0.204    -0.402  0.184    -0.493  0.180
+  1878     3     1.268  0.515     0.145  0.303    -0.340  0.206    -0.400  0.183    -0.494  0.179
+  1878     4     0.485  0.386     0.144  0.300    -0.339  0.208    -0.397  0.181    -0.495  0.179
+  1878     5    -0.314  0.494     0.156  0.299    -0.318  0.213    -0.398  0.181    -0.498  0.179
+  1878     6     0.305  0.367     0.198  0.308    -0.311  0.217    -0.399  0.180    -0.499  0.179
+  1878     7     0.485  0.435     0.227  0.301    -0.303  0.219    -0.402  0.180    -0.501  0.179
+  1878     8     0.311  0.483     0.138  0.288    -0.304  0.217    -0.406  0.180    -0.502  0.180
+  1878     9     0.109  0.488     0.001  0.284    -0.300  0.219    -0.407  0.179    -0.502  0.180
+  1878    10     0.024  0.427    -0.091  0.282    -0.297  0.218    -0.409  0.179    -0.503  0.179
+  1878    11     0.256  0.500    -0.109  0.276    -0.289  0.218    -0.415  0.178    -0.500  0.179
+  1878    12    -0.339  0.477    -0.152  0.278    -0.296  0.218    -0.418  0.178    -0.501  0.179
+  1879     1    -0.326  0.509    -0.202  0.267    -0.298  0.218    -0.422  0.179    -0.502  0.178
+  1879     2    -0.603  0.498    -0.275  0.263    -0.295  0.216    -0.426  0.180    -0.504  0.179
+  1879     3    -0.365  0.490    -0.338  0.251    -0.295  0.214    -0.427  0.182    -0.499  0.179
+  1879     4    -0.629  0.376    -0.339  0.242    -0.296  0.214    -0.435  0.182    -0.496  0.180
+  1879     5    -0.526  0.432    -0.410  0.236    -0.293  0.218    -0.444  0.181    -0.497  0.179
+  1879     6    -0.207  0.411    -0.476  0.218    -0.274  0.222    -0.450  0.181    -0.497  0.178
+  1879     7    -0.115  0.340    -0.521  0.227    -0.260  0.221    -0.454  0.181    -0.498  0.177
+  1879     8    -0.566  0.406    -0.573  0.246    -0.253  0.223    -0.459  0.181    -0.501  0.177
+  1879     9    -0.646  0.375    -0.610  0.250    -0.258  0.221    -0.465  0.181    -0.504  0.176
+  1879    10     0.005  0.410    -0.628  0.260    -0.263  0.217    -0.470  0.180    -0.505  0.175
+  1879    11    -0.587  0.420    -0.609  0.260    -0.267  0.215    -0.474  0.181    -0.508  0.175
+  1879    12    -1.139  0.491    -0.617  0.262    -0.277  0.216    -0.476  0.180    -0.507  0.176
+  1880     1    -0.867  0.631    -0.627  0.265    -0.294  0.216    -0.479  0.183    -0.507  0.176
+  1880     2    -1.226  0.584    -0.590  0.270    -0.302  0.214    -0.480  0.185    -0.505  0.176
+  1880     3    -0.801  0.489    -0.598  0.276    -0.309  0.211    -0.477  0.186    -0.507  0.176
+  1880     4    -0.849  0.399    -0.645  0.283    -0.322  0.208    -0.477  0.186    -0.507  0.178
+  1880     5    -0.303  0.389    -0.617  0.288    -0.342  0.205    -0.479  0.186    -0.510  0.178
+  1880     6    -0.300  0.347    -0.570  0.298    -0.354  0.206    -0.483  0.186    -0.514  0.179
+  1880     7    -0.236  0.430    -0.514  0.281    -0.361  0.203    -0.485  0.186    -0.516  0.178
+  1880     8    -0.119  0.410    -0.464  0.265    -0.390  0.201    -0.486  0.185    -0.517  0.178
+  1880     9    -0.742  0.400    -0.423  0.257    -0.417  0.199    -0.485  0.186    -0.519  0.177
+  1880    10    -0.556  0.476    -0.355  0.248    -0.436  0.198    -0.483  0.187    -0.517  0.177
+  1880    11    -0.259  0.483    -0.344  0.247    -0.440  0.197    -0.477  0.188    -0.520  0.177
+  1880    12    -0.574  0.611    -0.385  0.240    -0.450  0.198    -0.469  0.190    -0.517  0.177
+  1881     1    -0.187  0.585    -0.382  0.242    -0.464  0.196    -0.473  0.192    -0.515  0.178
+  1881     2    -0.626  0.505    -0.387  0.233    -0.481  0.195    -0.480  0.193    -0.514  0.179
+  1881     3    -0.310  0.585    -0.360  0.225    -0.495  0.193    -0.486  0.196    -0.513  0.178
+  1881     4    -0.033  0.343    -0.346  0.223    -0.500  0.191    -0.488  0.195    -0.516  0.178
+  1881     5    -0.174  0.395    -0.390  0.232    -0.523  0.192    -0.488  0.197    -0.516  0.178
+  1881     6    -0.794  0.402    -0.376  0.234    -0.525  0.190    -0.491  0.197    -0.518  0.179
+  1881     7    -0.200  0.342    -0.327  0.234    -0.526  0.192    -0.492  0.197    -0.519  0.178
+  1881     8    -0.181  0.363    -0.273  0.251    -0.527  0.195    -0.494  0.197    -0.521  0.178
+  1881     9    -0.414  0.328    -0.272  0.245    -0.545  0.197    -0.495  0.196    -0.521  0.178
+  1881    10    -0.390  0.362    -0.327  0.234    -0.553  0.199    -0.498  0.197    -0.522  0.178
+  1881    11    -0.791  0.524    -0.354  0.227    -0.564  0.196    -0.497  0.197    -0.522  0.178
+  1881    12    -0.407  0.625    -0.351  0.226    -0.576  0.196    -0.485  0.198    -0.518  0.178
+  1882     1     0.407  0.465    -0.394  0.214    -0.586  0.196    -0.497  0.197    -0.517  0.178
+  1882     2     0.027  0.552    -0.399  0.211    -0.587  0.194    -0.503  0.196    -0.515  0.178
+  1882     3    -0.306  0.438    -0.393  0.209    -0.590  0.194    -0.509  0.196    -0.517  0.178
+  1882     4    -0.689  0.293    -0.425  0.197    -0.602  0.193    -0.511  0.195    -0.518  0.178
+  1882     5    -0.503  0.350    -0.448  0.175    -0.607  0.194    -0.513  0.195    -0.520  0.179
+  1882     6    -0.759  0.300    -0.542  0.167    -0.598  0.195    -0.518  0.197    -0.519  0.178
+  1882     7    -0.706  0.336    -0.668  0.172    -0.609  0.194    -0.523  0.198    -0.520  0.178
+  1882     8    -0.243  0.389    -0.779  0.164    -0.604  0.194    -0.532  0.197    -0.522  0.177
+  1882     9    -0.343  0.329    -0.782  0.168    -0.602  0.190    -0.536  0.197    -0.523  0.177
+  1882    10    -0.779  0.263    -0.779  0.182    -0.603  0.189    -0.540  0.196    -0.522  0.177
+  1882    11    -1.058  0.450    -0.785  0.185    -0.611  0.188    -0.546  0.195    -0.523  0.177
+  1882    12    -1.545  0.517    -0.742  0.189    -0.620  0.188    -0.546  0.194    -0.522  0.176
+  1883     1    -1.100  0.453    -0.718  0.196    -0.623  0.187    -0.548  0.193    -0.527  0.175
+  1883     2    -1.310  0.531    -0.752  0.195    -0.630  0.185    -0.562  0.190    -0.533  0.175
+  1883     3    -0.340  0.423    -0.787  0.202    -0.629  0.185    -0.581  0.187    -0.532  0.174
+  1883     4    -0.654  0.439    -0.744  0.212    -0.628  0.185    -0.590  0.186    -0.529  0.173
+  1883     5    -0.574  0.351    -0.752  0.223    -0.636  0.184    -0.593  0.186    -0.530  0.173
+  1883     6    -0.244  0.296    -0.657  0.210    -0.627  0.182    -0.601  0.186    -0.532  0.173
+  1883     7    -0.409  0.348    -0.602  0.215    -0.643  0.185    -0.607  0.185    -0.531  0.172
+  1883     8    -0.660  0.343    -0.548  0.217    -0.657  0.188    -0.613  0.186    -0.533  0.172
+  1883     9    -0.756  0.387    -0.636  0.229    -0.671  0.188    -0.617  0.185    -0.531  0.171
+  1883    10    -0.262  0.317    -0.678  0.225    -0.679  0.189    -0.618  0.184    -0.532  0.171
+  1883    11    -1.159  0.357    -0.727  0.215    -0.687  0.190    -0.623  0.184    -0.531  0.170
+  1883    12    -0.408  0.463    -0.784  0.212    -0.686  0.191    -0.624  0.183    -0.534  0.170
+  1884     1    -0.440  0.493    -0.812  0.213    -0.686  0.191    -0.628  0.183    -0.537  0.171
+  1884     2    -0.659  0.492    -0.808  0.216    -0.693  0.193    -0.621  0.184    -0.538  0.172
+  1884     3    -1.399  0.560    -0.812  0.213    -0.694  0.193    -0.618  0.184    -0.534  0.172
+  1884     4    -1.155  0.360    -0.851  0.212    -0.699  0.194    -0.611  0.184    -0.536  0.172
+  1884     5    -1.167  0.351    -0.830  0.219    -0.701  0.191    -0.608  0.183    -0.538  0.172
+  1884     6    -0.929  0.334    -0.843  0.219    -0.697  0.189    -0.608  0.183    -0.539  0.172
+  1884     7    -0.736  0.340    -0.935  0.227    -0.733  0.189    -0.609  0.183    -0.540  0.171
+  1884     8    -0.622  0.413    -0.959  0.239    -0.753  0.187    -0.608  0.183    -0.541  0.171
+  1884     9    -0.796  0.324    -0.896  0.230    -0.761  0.187    -0.607  0.184    -0.543  0.171
+  1884    10    -0.735  0.377    -0.875  0.236    -0.758  0.188    -0.611  0.182    -0.544  0.170
+  1884    11    -0.903  0.444    -0.846  0.244    -0.759  0.189    -0.615  0.182    -0.545  0.170
+  1884    12    -0.567  0.492    -0.837  0.245    -0.758  0.192    -0.611  0.184    -0.545  0.169
+  1885     1    -1.537  0.557    -0.810  0.241    -0.752  0.194    -0.610  0.182    -0.546  0.170
+  1885     2    -0.949  0.607    -0.806  0.233    -0.761  0.195    -0.606  0.180    -0.547  0.170
+  1885     3    -0.641  0.405    -0.793  0.241    -0.763  0.196    -0.609  0.181    -0.546  0.170
+  1885     4    -0.902  0.443    -0.772  0.250    -0.759  0.198    -0.606  0.182    -0.543  0.171
+  1885     5    -0.825  0.365    -0.759  0.243    -0.751  0.197    -0.611  0.182    -0.543  0.171
+  1885     6    -0.817  0.313    -0.715  0.243    -0.739  0.196    -0.615  0.183    -0.543  0.171
+  1885     7    -0.418  0.329    -0.683  0.241    -0.736  0.196    -0.616  0.183    -0.543  0.171
+  1885     8    -0.570  0.354    -0.727  0.235    -0.734  0.195    -0.620  0.183    -0.543  0.171
+  1885     9    -0.647  0.388    -0.770  0.244    -0.745  0.193    -0.619  0.183    -0.541  0.172
+  1885    10    -0.480  0.408    -0.735  0.241    -0.744  0.191    -0.615  0.182    -0.539  0.172
+  1885    11    -0.747  0.340    -0.720  0.248    -0.746  0.192    -0.622  0.180    -0.534  0.172
+  1885    12    -0.031  0.443    -0.715  0.249    -0.753  0.192    -0.623  0.179    -0.531  0.172
+  1886     1    -1.162  0.489    -0.697  0.252    -0.749  0.192    -0.629  0.179    -0.529  0.173
+  1886     2    -1.471  0.511    -0.699  0.260    -0.744  0.195    -0.635  0.179    -0.529  0.172
+  1886     3    -1.162  0.452    -0.687  0.255    -0.739  0.195    -0.637  0.178    -0.532  0.172
+  1886     4    -0.484  0.350    -0.704  0.258    -0.736  0.194    -0.643  0.178    -0.535  0.172
+  1886     5    -0.642  0.412    -0.717  0.261    -0.723  0.195    -0.646  0.177    -0.534  0.172
+  1886     6    -0.754  0.313    -0.726  0.263    -0.724  0.193    -0.645  0.177    -0.534  0.172
+  1886     7    -0.203  0.372    -0.777  0.254    -0.730  0.191    -0.647  0.177    -0.534  0.172
+  1886     8    -0.598  0.378    -0.751  0.248    -0.715  0.191    -0.649  0.177    -0.533  0.171
+  1886     9    -0.499  0.338    -0.719  0.243    -0.692  0.190    -0.649  0.177    -0.533  0.171
+  1886    10    -0.691  0.396    -0.725  0.242    -0.669  0.188    -0.651  0.176    -0.531  0.170
+  1886    11    -0.894  0.404    -0.717  0.241    -0.653  0.187    -0.653  0.175    -0.530  0.170
+  1886    12    -0.141  0.519    -0.710  0.249    -0.641  0.188    -0.649  0.173    -0.525  0.170
+  1887     1    -1.779  0.432    -0.724  0.248    -0.631  0.187    -0.658  0.174    -0.526  0.169
+  1887     2    -1.153  0.438    -0.741  0.247    -0.628  0.189    -0.662  0.173    -0.527  0.168
+  1887     3    -0.777  0.383    -0.735  0.244    -0.624  0.191    -0.667  0.174    -0.531  0.168
+  1887     4    -0.564  0.313    -0.722  0.234    -0.619  0.189    -0.668  0.175    -0.530  0.167
+  1887     5    -0.545  0.396    -0.698  0.227    -0.623  0.189    -0.670  0.176    -0.529  0.167
+  1887     6    -0.671  0.387    -0.756  0.220    -0.624  0.190    -0.665  0.175    -0.529  0.167
+  1887     7    -0.361  0.370    -0.680  0.225    -0.612  0.188    -0.664  0.176    -0.531  0.167
+  1887     8    -0.813  0.403    -0.685  0.214    -0.608  0.184    -0.666  0.177    -0.533  0.166
+  1887     9    -0.417  0.313    -0.705  0.201    -0.615  0.187    -0.666  0.177    -0.534  0.165
+  1887    10    -0.543  0.323    -0.707  0.200    -0.610  0.190    -0.659  0.177    -0.534  0.165
+  1887    11    -0.601  0.367    -0.721  0.191    -0.612  0.192    -0.660  0.176    -0.537  0.165
+  1887    12    -0.833  0.469    -0.718  0.187    -0.609  0.194    -0.654  0.176    -0.535  0.164
+  1888     1    -0.878  0.499    -0.703  0.181    -0.609  0.194    -0.662  0.175    -0.531  0.163
+  1888     2    -1.210  0.357    -0.667  0.184    -0.610  0.196    -0.664  0.175    -0.537  0.161
+  1888     3    -1.015  0.318    -0.668  0.182    -0.608  0.196    -0.664  0.175    -0.549  0.160
+  1888     4    -0.585  0.334    -0.630  0.184    -0.602  0.194    -0.662  0.175    -0.553  0.160
+  1888     5    -0.719  0.363    -0.609  0.188    -0.608  0.192    -0.662  0.175    -0.554  0.160
+  1888     6    -0.626  0.292    -0.579  0.183    -0.620  0.189    -0.664  0.175    -0.555  0.160
+  1888     7    -0.182  0.385    -0.574  0.176    -0.616  0.188    -0.661  0.174    -0.558  0.160
+  1888     8    -0.384  0.423    -0.454  0.182    -0.614  0.185    -0.659  0.175    -0.561  0.160
+  1888     9    -0.434  0.327    -0.370  0.194    -0.603  0.182    -0.656  0.174    -0.563  0.160
+  1888    10    -0.083  0.306    -0.303  0.193    -0.607  0.182    -0.655  0.174    -0.565  0.160
+  1888    11    -0.348  0.370    -0.258  0.180    -0.606  0.179    -0.647  0.174    -0.568  0.159
+  1888    12    -0.479  0.378    -0.225  0.185    -0.604  0.178    -0.649  0.173    -0.568  0.159
+  1889     1    -0.810  0.391    -0.223  0.181    -0.609  0.178    -0.653  0.173    -0.567  0.159
+  1889     2     0.225  0.411    -0.227  0.180    -0.606  0.177    -0.651  0.173    -0.568  0.159
+  1889     3    -0.006  0.370    -0.236  0.185    -0.604  0.177    -0.641  0.173    -0.569  0.159
+  1889     4     0.217  0.302    -0.268  0.175    -0.602  0.173    -0.638  0.171    -0.567  0.159
+  1889     5    -0.175  0.320    -0.333  0.185    -0.604  0.172    -0.631  0.172    -0.566  0.159
+  1889     6    -0.230  0.291    -0.342  0.197    -0.602  0.170    -0.629  0.171    -0.568  0.159
+  1889     7    -0.164  0.324    -0.343  0.198    -0.583  0.169    -0.626  0.171    -0.568  0.159
+  1889     8    -0.425  0.422    -0.421  0.193    -0.571  0.169    -0.624  0.171    -0.566  0.159
+  1889     9    -0.549  0.361    -0.512  0.200    -0.574  0.170    -0.622  0.171    -0.564  0.159
+  1889    10    -0.467  0.257    -0.579  0.220    -0.578  0.172    -0.617  0.170    -0.563  0.159
+  1889    11    -1.128  0.409    -0.641  0.239    -0.580  0.172    -0.615  0.168    -0.559  0.158
+  1889    12    -0.585  0.435    -0.679  0.247    -0.573  0.169    -0.615  0.167    -0.556  0.158
+  1890     1    -0.820  0.385    -0.696  0.248    -0.576  0.168    -0.613  0.166    -0.557  0.158
+  1890     2    -0.715  0.391    -0.713  0.243    -0.570  0.168    -0.615  0.164    -0.553  0.157
+  1890     3    -1.095  0.457    -0.715  0.242    -0.569  0.167    -0.615  0.164    -0.549  0.157
+  1890     4    -0.584  0.485    -0.684  0.250    -0.559  0.165    -0.609  0.165    -0.545  0.156
+  1890     5    -0.926  0.415    -0.685  0.241    -0.569  0.165    -0.606  0.164    -0.544  0.156
+  1890     6    -0.677  0.372    -0.696  0.229    -0.568  0.164    -0.603  0.165    -0.543  0.156
+  1890     7    -0.372  0.334    -0.705  0.232    -0.589  0.162    -0.602  0.165    -0.543  0.156
+  1890     8    -0.631  0.378    -0.756  0.229    -0.594  0.163    -0.600  0.166    -0.543  0.156
+  1890     9    -0.570  0.369    -0.709  0.216    -0.584  0.165    -0.597  0.165    -0.540  0.155
+  1890    10    -0.089  0.263    -0.721  0.203    -0.580  0.166    -0.595  0.164    -0.536  0.154
+  1890    11    -1.141  0.309    -0.691  0.194    -0.578  0.165    -0.591  0.163    -0.536  0.154
+  1890    12    -0.717  0.310    -0.689  0.182    -0.575  0.166    -0.593  0.161    -0.534  0.153
+  1891     1    -0.933  0.387    -0.698  0.182    -0.573  0.164    -0.586  0.161    -0.535  0.153
+  1891     2    -1.330  0.357    -0.681  0.179    -0.574  0.163    -0.578  0.158    -0.533  0.153
+  1891     3    -0.522  0.340    -0.665  0.174    -0.572  0.162    -0.577  0.156    -0.530  0.152
+  1891     4    -0.734  0.315    -0.704  0.166    -0.573  0.163    -0.582  0.157    -0.529  0.152
+  1891     5    -0.564  0.337    -0.695  0.170    -0.572  0.161    -0.580  0.156    -0.529  0.152
+  1891     6    -0.656  0.284    -0.638  0.171    -0.574  0.163    -0.576  0.156    -0.526  0.152
+  1891     7    -0.480  0.341    -0.610  0.173    -0.576  0.165    -0.576  0.155    -0.526  0.152
+  1891     8    -0.430  0.364    -0.537  0.177    -0.586  0.166    -0.573  0.154    -0.525  0.152
+  1891     9    -0.369  0.308    -0.575  0.184    -0.590  0.165    -0.571  0.154    -0.524  0.152
+  1891    10    -0.562  0.244    -0.579  0.188    -0.606  0.164    -0.564  0.152    -0.523  0.151
+  1891    11    -1.033  0.316    -0.591  0.193    -0.610  0.165    -0.563  0.151    -0.521  0.151
+  1891    12    -0.037  0.315    -0.555  0.191    -0.617  0.164    -0.565  0.149    -0.520  0.150
+  1892     1    -0.598  0.378    -0.557  0.190    -0.620  0.163    -0.555  0.149    -0.522  0.150
+  1892     2    -0.445  0.398    -0.560  0.191    -0.620  0.161    -0.552  0.147    -0.520  0.149
+  1892     3    -0.984  0.419    -0.558  0.188    -0.619  0.159    -0.553  0.146    -0.521  0.149
+  1892     4    -0.782  0.328    -0.511  0.187    -0.614  0.160    -0.549  0.147    -0.520  0.149
+  1892     5    -0.702  0.345    -0.525  0.186    -0.607  0.156    -0.544  0.145    -0.519  0.149
+  1892     6    -0.226  0.248    -0.587  0.181    -0.606  0.153    -0.540  0.144    -0.517  0.149
+  1892     7    -0.509  0.323    -0.711  0.173    -0.614  0.153    -0.539  0.143    -0.515  0.149
+  1892     8    -0.458  0.356    -0.803  0.166    -0.622  0.152    -0.534  0.142    -0.515  0.149
+  1892     9    -0.352  0.289    -0.751  0.163    -0.614  0.149    -0.532  0.141    -0.515  0.150
+  1892    10     0.006  0.253    -0.715  0.155    -0.608  0.147    -0.528  0.140    -0.513  0.150
+  1892    11    -1.202  0.392    -0.708  0.151    -0.600  0.144    -0.527  0.141    -0.513  0.150
+  1892    12    -0.777  0.350    -0.728  0.155    -0.596  0.143    -0.523  0.140    -0.510  0.150
+  1893     1    -2.088  0.360    -0.691  0.150    -0.595  0.142    -0.515  0.139    -0.506  0.150
+  1893     2    -1.553  0.330    -0.687  0.152    -0.590  0.142    -0.512  0.139    -0.499  0.150
+  1893     3    -0.364  0.304    -0.687  0.153    -0.587  0.140    -0.516  0.140    -0.499  0.149
+  1893     4    -0.348  0.255    -0.699  0.157    -0.587  0.140    -0.515  0.141    -0.498  0.149
+  1893     5    -0.616  0.314    -0.624  0.158    -0.574  0.140    -0.514  0.141    -0.498  0.149
+  1893     6    -0.468  0.336    -0.606  0.162    -0.566  0.140    -0.509  0.141    -0.500  0.149
+  1893     7    -0.063  0.336    -0.512  0.165    -0.556  0.140    -0.510  0.142    -0.500  0.149
+  1893     8    -0.406  0.363    -0.413  0.169    -0.543  0.139    -0.509  0.142    -0.499  0.149
+  1893     9    -0.351  0.304    -0.405  0.174    -0.552  0.138    -0.508  0.142    -0.498  0.148
+  1893    10    -0.140  0.271    -0.435  0.173    -0.556  0.139    -0.511  0.143    -0.499  0.148
+  1893    11    -0.302  0.255    -0.419  0.169    -0.555  0.139    -0.514  0.143    -0.496  0.148
+  1893    12    -0.568  0.357    -0.434  0.165    -0.548  0.140    -0.511  0.143    -0.498  0.148
+  1894     1    -0.950  0.370    -0.458  0.167    -0.543  0.139    -0.505  0.144    -0.500  0.148
+  1894     2    -0.374  0.401    -0.457  0.166    -0.540  0.139    -0.514  0.143    -0.501  0.147
+  1894     3    -0.261  0.366    -0.471  0.165    -0.539  0.137    -0.520  0.142    -0.498  0.146
+  1894     4    -0.712  0.270    -0.472  0.156    -0.526  0.138    -0.522  0.143    -0.496  0.146
+  1894     5    -0.424  0.303    -0.505  0.151    -0.522  0.138    -0.524  0.144    -0.493  0.146
+  1894     6    -0.648  0.317    -0.503  0.140    -0.527  0.136    -0.527  0.143    -0.491  0.146
+  1894     7    -0.349  0.313    -0.532  0.134    -0.528  0.135    -0.528  0.143    -0.491  0.146
+  1894     8    -0.397  0.370    -0.599  0.128    -0.533  0.132    -0.525  0.143    -0.490  0.146
+  1894     9    -0.517  0.277    -0.632  0.130    -0.532  0.130    -0.520  0.143    -0.490  0.146
+  1894    10    -0.150  0.222    -0.587  0.146    -0.520  0.129    -0.515  0.143    -0.489  0.146
+  1894    11    -0.693  0.287    -0.592  0.148    -0.508  0.127    -0.502  0.142    -0.484  0.145
+  1894    12    -0.546  0.266    -0.573  0.152    -0.508  0.126    -0.502  0.141    -0.483  0.145
+  1895     1    -1.299  0.348    -0.569  0.157    -0.502  0.125    -0.504  0.141    -0.478  0.144
+  1895     2    -1.184  0.329    -0.567  0.161    -0.498  0.123    -0.499  0.142    -0.479  0.144
+  1895     3    -0.656  0.300    -0.553  0.165    -0.496  0.122    -0.489  0.140    -0.479  0.143
+  1895     4    -0.172  0.376    -0.551  0.169    -0.497  0.123    -0.485  0.138    -0.478  0.143
+  1895     5    -0.479  0.326    -0.522  0.173    -0.484  0.126    -0.476  0.137    -0.476  0.143
+  1895     6    -0.429  0.292    -0.496  0.176    -0.478  0.124    -0.472  0.136    -0.474  0.142
+  1895     7    -0.295  0.320    -0.416  0.178    -0.441  0.123    -0.470  0.136    -0.473  0.142
+  1895     8    -0.374  0.390    -0.362  0.170    -0.429  0.123    -0.465  0.135    -0.472  0.142
+  1895     9    -0.347  0.305    -0.395  0.161    -0.449  0.122    -0.461  0.134    -0.471  0.142
+  1895    10    -0.133  0.250    -0.466  0.156    -0.451  0.123    -0.457  0.133    -0.470  0.141
+  1895    11    -0.346  0.259    -0.465  0.155    -0.450  0.123    -0.451  0.133    -0.466  0.141
+  1895    12    -0.226  0.278    -0.449  0.156    -0.443  0.122    -0.445  0.133    -0.467  0.140
+  1896     1    -0.337  0.387    -0.439  0.154    -0.447  0.125    -0.441  0.133    -0.465  0.140
+  1896     2    -0.535  0.269    -0.430  0.148    -0.445  0.127    -0.430  0.132    -0.462  0.139
+  1896     3    -1.056  0.240    -0.424  0.144    -0.444  0.128    -0.423  0.132    -0.459  0.138
+  1896     4    -1.019  0.331    -0.399  0.142    -0.449  0.128    -0.416  0.131    -0.455  0.138
+  1896     5    -0.474  0.317    -0.432  0.154    -0.455  0.130    -0.413  0.132    -0.453  0.137
+  1896     6    -0.239  0.286    -0.443  0.148    -0.449  0.130    -0.408  0.132    -0.451  0.137
+  1896     7    -0.173  0.305    -0.469  0.148    -0.434  0.129    -0.404  0.132    -0.451  0.136
+  1896     8    -0.260  0.351    -0.486  0.148    -0.442  0.128    -0.401  0.132    -0.449  0.136
+  1896     9    -0.275  0.292    -0.478  0.147    -0.450  0.128    -0.399  0.131    -0.448  0.136
+  1896    10     0.166  0.266    -0.398  0.140    -0.439  0.130    -0.396  0.132    -0.445  0.135
+  1896    11    -0.749  0.282    -0.356  0.136    -0.438  0.131    -0.389  0.132    -0.444  0.135
+  1896    12    -0.352  0.248    -0.353  0.131    -0.437  0.132    -0.391  0.131    -0.443  0.135
+  1897     1    -0.653  0.360    -0.352  0.132    -0.435  0.133    -0.385  0.131    -0.439  0.135
+  1897     2    -0.740  0.283    -0.351  0.133    -0.430  0.135    -0.379  0.129    -0.439  0.134
+  1897     3    -0.963  0.235    -0.346  0.134    -0.421  0.135    -0.374  0.128    -0.439  0.134
+  1897     4    -0.055  0.303    -0.362  0.141    -0.417  0.135    -0.371  0.128    -0.439  0.134
+  1897     5     0.035  0.309    -0.336  0.146    -0.397  0.138    -0.368  0.127    -0.441  0.133
+  1897     6    -0.211  0.320    -0.344  0.142    -0.398  0.138    -0.369  0.127    -0.442  0.133
+  1897     7    -0.154  0.371    -0.276  0.144    -0.395  0.138    -0.366  0.126    -0.442  0.132
+  1897     8    -0.251  0.363    -0.285  0.147    -0.376  0.139    -0.365  0.127    -0.441  0.132
+  1897     9    -0.209  0.276    -0.335  0.150    -0.363  0.138    -0.364  0.127    -0.441  0.132
+  1897    10    -0.031  0.289    -0.367  0.154    -0.362  0.137    -0.368  0.127    -0.439  0.131
+  1897    11    -0.438  0.389    -0.416  0.156    -0.352  0.137    -0.365  0.127    -0.440  0.131
+  1897    12    -0.446  0.244    -0.406  0.150    -0.347  0.137    -0.366  0.128    -0.439  0.131
+  1898     1     0.162  0.339    -0.415  0.156    -0.345  0.137    -0.350  0.129    -0.437  0.131
+  1898     2    -0.849  0.313    -0.420  0.167    -0.340  0.137    -0.335  0.129    -0.433  0.131
+  1898     3    -1.561  0.259    -0.429  0.176    -0.336  0.136    -0.334  0.127    -0.434  0.132
+  1898     4    -0.442  0.330    -0.462  0.180    -0.327  0.134    -0.335  0.128    -0.433  0.132
+  1898     5    -0.553  0.343    -0.478  0.178    -0.327  0.135    -0.335  0.128    -0.431  0.131
+  1898     6    -0.088  0.272    -0.459  0.193    -0.323  0.135    -0.336  0.127    -0.429  0.131
+  1898     7    -0.263  0.369    -0.477  0.196    -0.325  0.135    -0.339  0.128    -0.429  0.131
+  1898     8    -0.308  0.402    -0.478  0.191    -0.317  0.135    -0.339  0.127    -0.430  0.131
+  1898     9    -0.324  0.313    -0.408  0.192    -0.295  0.134    -0.341  0.127    -0.429  0.131
+  1898    10    -0.425  0.262    -0.377  0.198    -0.275  0.133    -0.343  0.127    -0.430  0.130
+  1898    11    -0.626  0.324    -0.359  0.198    -0.270  0.133    -0.345  0.128    -0.432  0.130
+  1898    12    -0.223  0.317    -0.404  0.202    -0.268  0.133    -0.347  0.127    -0.432  0.130
+  1899     1    -0.057  0.330    -0.400  0.196    -0.265  0.134    -0.347  0.127    -0.434  0.130
+  1899     2    -0.852  0.285    -0.384  0.197    -0.262  0.135    -0.351  0.126    -0.437  0.130
+  1899     3    -0.724  0.313    -0.356  0.193    -0.259  0.136    -0.355  0.125    -0.439  0.129
+  1899     4    -0.074  0.350    -0.310  0.183    -0.266  0.138    -0.355  0.126    -0.443  0.129
+  1899     5    -0.338  0.338    -0.217  0.174    -0.256  0.138    -0.355  0.126    -0.445  0.130
+  1899     6    -0.627  0.277    -0.250  0.172    -0.255  0.138    -0.354  0.126    -0.447  0.129
+  1899     7    -0.211  0.332    -0.335  0.169    -0.243  0.138    -0.356  0.127    -0.447  0.129
+  1899     8    -0.114  0.403    -0.272  0.180    -0.225  0.138    -0.356  0.127    -0.446  0.129
+  1899     9     0.016  0.285    -0.197  0.176    -0.216  0.138    -0.358  0.127    -0.444  0.128
+  1899    10     0.124  0.261    -0.200  0.172    -0.221  0.139    -0.360  0.128    -0.443  0.129
+  1899    11     0.488  0.263    -0.165  0.175    -0.229  0.140    -0.354  0.129    -0.437  0.128
+  1899    12    -0.621  0.311    -0.124  0.173    -0.230  0.140    -0.352  0.129    -0.437  0.128
+  1900     1    -1.080  0.302    -0.121  0.174    -0.230  0.139    -0.344  0.129    -0.434  0.127
+  1900     2    -0.089  0.397    -0.116  0.169    -0.231  0.141    -0.344  0.129    -0.433  0.127
+  1900     3     0.167  0.248    -0.127  0.163    -0.232  0.143    -0.344  0.129    -0.431  0.126
+  1900     4    -0.109  0.308    -0.103  0.159    -0.239  0.143    -0.347  0.127    -0.430  0.125
+  1900     5     0.087  0.344    -0.171  0.159    -0.246  0.142    -0.346  0.127    -0.427  0.124
+  1900     6    -0.142  0.261    -0.120  0.157    -0.255  0.146    -0.345  0.126    -0.426  0.124
+  1900     7    -0.175  0.337    -0.067  0.156    -0.259  0.146    -0.345  0.126    -0.425  0.124
+  1900     8    -0.043  0.356    -0.066  0.145    -0.240  0.146    -0.344  0.125    -0.423  0.123
+  1900     9    -0.120  0.254    -0.056  0.140    -0.220  0.144    -0.344  0.125    -0.422  0.123
+  1900    10     0.410  0.223    -0.033  0.138    -0.220  0.144    -0.345  0.126    -0.422  0.123
+  1900    11    -0.333  0.263    -0.056  0.138    -0.220  0.143    -0.342  0.126    -0.421  0.123
+  1900    12     0.000  0.288    -0.050  0.141    -0.229  0.142    -0.342  0.126    -0.422  0.123
+  1901     1    -0.452  0.310    -0.038  0.141    -0.231  0.139    -0.343  0.125    -0.422  0.123
+  1901     2    -0.072  0.372    -0.039  0.142    -0.233  0.137    -0.346  0.125    -0.420  0.123
+  1901     3     0.289  0.218    -0.039  0.145    -0.237  0.135    -0.340  0.126    -0.422  0.122
+  1901     4     0.166  0.267    -0.092  0.150    -0.237  0.134    -0.329  0.125    -0.420  0.122
+  1901     5    -0.188  0.328    -0.079  0.155    -0.236  0.133    -0.326  0.125    -0.420  0.122
+  1901     6    -0.076  0.287    -0.101  0.146    -0.245  0.133    -0.325  0.124    -0.419  0.122
+  1901     7    -0.028  0.345    -0.060  0.152    -0.259  0.132    -0.326  0.124    -0.418  0.122
+  1901     8    -0.057  0.392    -0.023  0.148    -0.260  0.132    -0.325  0.124    -0.418  0.121
+  1901     9    -0.117  0.277    -0.085  0.151    -0.261  0.129    -0.326  0.125    -0.418  0.121
+  1901    10    -0.232  0.242    -0.130  0.155    -0.270  0.127    -0.327  0.125    -0.416  0.121
+  1901    11    -0.168  0.311    -0.149  0.156    -0.273  0.128    -0.326  0.126    -0.413  0.120
+  1901    12    -0.272  0.256    -0.165  0.150    -0.271  0.127    -0.322  0.128    -0.413  0.119
+  1902     1     0.048  0.375    -0.176  0.146    -0.276  0.127    -0.323  0.127    -0.412  0.119
+  1902     2     0.370  0.283    -0.195  0.145    -0.282  0.126    -0.326  0.128    -0.410  0.119
+  1902     3    -0.458  0.242    -0.211  0.148    -0.294  0.125    -0.324  0.129    -0.410  0.118
+  1902     4    -0.366  0.309    -0.228  0.149    -0.304  0.126    -0.330  0.128    -0.407  0.117
+  1902     5    -0.420  0.336    -0.286  0.143    -0.311  0.125    -0.338  0.128    -0.404  0.116
+  1902     6    -0.267  0.303    -0.342  0.155    -0.305  0.126    -0.343  0.128    -0.404  0.116
+  1902     7    -0.164  0.327    -0.356  0.153    -0.292  0.126    -0.346  0.128    -0.404  0.116
+  1902     8    -0.286  0.377    -0.362  0.158    -0.312  0.125    -0.348  0.128    -0.406  0.116
+  1902     9    -0.308  0.290    -0.352  0.150    -0.325  0.124    -0.350  0.129    -0.408  0.116
+  1902    10    -0.432  0.244    -0.359  0.150    -0.332  0.123    -0.350  0.128    -0.412  0.116
+  1902    11    -0.867  0.234    -0.369  0.145    -0.339  0.122    -0.353  0.127    -0.409  0.115
+  1902    12    -0.949  0.327    -0.399  0.144    -0.343  0.121    -0.355  0.129    -0.409  0.115
+  1903     1    -0.119  0.294    -0.417  0.143    -0.345  0.121    -0.359  0.128    -0.402  0.116
+  1903     2     0.306  0.317    -0.430  0.141    -0.349  0.120    -0.355  0.129    -0.399  0.115
+  1903     3    -0.345  0.243    -0.454  0.138    -0.352  0.121    -0.351  0.128    -0.401  0.115
+  1903     4    -0.446  0.306    -0.453  0.141    -0.363  0.122    -0.351  0.128    -0.401  0.115
+  1903     5    -0.542  0.290    -0.428  0.146    -0.356  0.123    -0.349  0.128    -0.401  0.115
+  1903     6    -0.624  0.271    -0.410  0.143    -0.361  0.123    -0.350  0.128    -0.401  0.115
+  1903     7    -0.386  0.346    -0.477  0.143    -0.361  0.122    -0.349  0.126    -0.402  0.115
+  1903     8    -0.435  0.365    -0.577  0.144    -0.374  0.122    -0.351  0.125    -0.401  0.116
+  1903     9    -0.601  0.273    -0.612  0.140    -0.385  0.123    -0.349  0.124    -0.401  0.116
+  1903    10    -0.419  0.254    -0.630  0.135    -0.383  0.124    -0.348  0.123    -0.401  0.116
+  1903    11    -0.567  0.270    -0.625  0.140    -0.381  0.123    -0.350  0.122    -0.400  0.116
+  1903    12    -0.734  0.289    -0.615  0.142    -0.382  0.121    -0.353  0.122    -0.396  0.116
+  1904     1    -0.917  0.274    -0.629  0.146    -0.386  0.120    -0.362  0.121    -0.388  0.116
+  1904     2    -0.895  0.251    -0.630  0.150    -0.388  0.120    -0.359  0.122    -0.387  0.116
+  1904     3    -0.773  0.197    -0.640  0.148    -0.392  0.120    -0.359  0.121    -0.387  0.116
+  1904     4    -0.650  0.299    -0.643  0.146    -0.388  0.120    -0.364  0.120    -0.385  0.116
+  1904     5    -0.492  0.324    -0.589  0.142    -0.395  0.120    -0.367  0.120    -0.384  0.116
+  1904     6    -0.498  0.266    -0.553  0.145    -0.389  0.124    -0.366  0.120    -0.381  0.116
+  1904     7    -0.554  0.344    -0.502  0.147    -0.403  0.123    -0.367  0.120    -0.380  0.116
+  1904     8    -0.448  0.376    -0.531  0.149    -0.428  0.125    -0.367  0.118    -0.379  0.116
+  1904     9    -0.716  0.264    -0.517  0.155    -0.432  0.126    -0.368  0.118    -0.377  0.116
+  1904    10    -0.465  0.227    -0.510  0.155    -0.438  0.124    -0.370  0.118    -0.376  0.116
+  1904    11     0.088  0.212    -0.498  0.148    -0.447  0.124    -0.373  0.118    -0.374  0.117
+  1904    12    -0.310  0.287    -0.485  0.146    -0.455  0.124    -0.371  0.119    -0.372  0.117
+  1905     1    -0.302  0.292    -0.463  0.142    -0.462  0.124    -0.365  0.118    -0.368  0.117
+  1905     2    -1.240  0.317    -0.450  0.139    -0.466  0.123    -0.367  0.118    -0.363  0.117
+  1905     3    -0.609  0.243    -0.417  0.140    -0.468  0.123    -0.374  0.118    -0.361  0.116
+  1905     4    -0.569  0.252    -0.398  0.142    -0.461  0.121    -0.375  0.117    -0.358  0.116
+  1905     5    -0.346  0.318    -0.400  0.138    -0.461  0.120    -0.378  0.116    -0.356  0.116
+  1905     6    -0.343  0.256    -0.395  0.137    -0.456  0.120    -0.380  0.117    -0.355  0.116
+  1905     7    -0.292  0.328    -0.411  0.136    -0.459  0.120    -0.379  0.117    -0.354  0.116
+  1905     8    -0.287  0.341    -0.379  0.132    -0.470  0.121    -0.380  0.117    -0.353  0.115
+  1905     9    -0.321  0.265    -0.359  0.133    -0.483  0.122    -0.382  0.117    -0.352  0.115
+  1905    10    -0.240  0.244    -0.287  0.136    -0.483  0.120    -0.388  0.118    -0.353  0.115
+  1905    11     0.068  0.229    -0.264  0.135    -0.478  0.121    -0.391  0.118    -0.351  0.115
+  1905    12    -0.253  0.293    -0.248  0.133    -0.471  0.122    -0.400  0.119    -0.350  0.114
+  1906     1    -0.491  0.281    -0.245  0.131    -0.467  0.122    -0.404  0.118    -0.349  0.114
+  1906     2    -0.858  0.271    -0.236  0.136    -0.468  0.121    -0.410  0.118    -0.348  0.114
+  1906     3    -0.367  0.243    -0.238  0.138    -0.460  0.122    -0.420  0.118    -0.345  0.114
+  1906     4     0.294  0.295    -0.218  0.138    -0.460  0.120    -0.424  0.118    -0.342  0.113
+  1906     5    -0.073  0.298    -0.273  0.151    -0.464  0.119    -0.426  0.117    -0.342  0.113
+  1906     6    -0.151  0.298    -0.245  0.154    -0.461  0.119    -0.430  0.117    -0.343  0.113
+  1906     7    -0.247  0.323    -0.270  0.153    -0.465  0.119    -0.432  0.117    -0.344  0.113
+  1906     8    -0.186  0.359    -0.289  0.162    -0.458  0.119    -0.435  0.116    -0.344  0.113
+  1906     9    -0.342  0.267    -0.318  0.161    -0.457  0.121    -0.438  0.115    -0.344  0.113
+  1906    10     0.002  0.220    -0.403  0.153    -0.458  0.121    -0.436  0.115    -0.346  0.113
+  1906    11    -0.595  0.380    -0.478  0.155    -0.461  0.120    -0.436  0.113    -0.345  0.113
+  1906    12     0.088  0.332    -0.530  0.160    -0.462  0.120    -0.436  0.113    -0.349  0.113
+  1907     1    -0.792  0.269    -0.558  0.161    -0.457  0.119    -0.438  0.113    -0.348  0.113
+  1907     2    -1.090  0.309    -0.585  0.157    -0.451  0.119    -0.441  0.113    -0.348  0.114
+  1907     3    -0.714  0.237    -0.590  0.159    -0.443  0.119    -0.445  0.113    -0.348  0.114
+  1907     4    -0.730  0.274    -0.592  0.153    -0.436  0.119    -0.443  0.112    -0.349  0.114
+  1907     5    -0.965  0.313    -0.614  0.142    -0.436  0.119    -0.440  0.111    -0.353  0.114
+  1907     6    -0.777  0.239    -0.675  0.144    -0.437  0.119    -0.439  0.111    -0.355  0.113
+  1907     7    -0.584  0.306    -0.634  0.146    -0.437  0.119    -0.443  0.111    -0.355  0.113
+  1907     8    -0.508  0.353    -0.574  0.146    -0.422  0.120    -0.447  0.111    -0.355  0.113
+  1907     9    -0.409  0.287    -0.606  0.145    -0.423  0.120    -0.452  0.111    -0.356  0.114
+  1907    10    -0.015  0.207    -0.586  0.141    -0.417  0.120    -0.456  0.110    -0.359  0.114
+  1907    11    -0.861  0.273    -0.522  0.143    -0.417  0.120    -0.454  0.109    -0.358  0.114
+  1907    12    -0.650  0.297    -0.476  0.141    -0.417  0.121    -0.451  0.108    -0.362  0.114
+  1908     1    -0.293  0.259    -0.442  0.141    -0.414  0.121    -0.455  0.108    -0.366  0.114
+  1908     2    -0.367  0.327    -0.441  0.139    -0.412  0.122    -0.464  0.107    -0.365  0.114
+  1908     3    -1.108  0.225    -0.417  0.135    -0.412  0.122    -0.468  0.108    -0.361  0.114
+  1908     4    -0.479  0.249    -0.448  0.136    -0.412  0.122    -0.467  0.108    -0.362  0.114
+  1908     5    -0.203  0.318    -0.444  0.134    -0.427  0.123    -0.467  0.108    -0.363  0.113
+  1908     6    -0.229  0.256    -0.437  0.130    -0.438  0.124    -0.466  0.108    -0.365  0.113
+  1908     7    -0.176  0.314    -0.509  0.128    -0.447  0.123    -0.465  0.109    -0.366  0.112
+  1908     8    -0.488  0.343    -0.519  0.124    -0.445  0.123    -0.463  0.109    -0.367  0.112
+  1908     9    -0.130  0.263    -0.483  0.122    -0.454  0.122    -0.460  0.109    -0.366  0.111
+  1908    10    -0.377  0.212    -0.503  0.126    -0.465  0.121    -0.458  0.109    -0.364  0.111
+  1908    11    -0.823  0.217    -0.544  0.123    -0.472  0.122    -0.454  0.110    -0.363  0.110
+  1908    12    -0.567  0.279    -0.568  0.125    -0.477  0.123    -0.444  0.110    -0.365  0.110
+  1909     1    -1.146  0.269    -0.578  0.127    -0.478  0.123    -0.430  0.110    -0.366  0.110
+  1909     2    -0.496  0.265    -0.546  0.126    -0.482  0.121    -0.423  0.110    -0.364  0.110
+  1909     3    -0.671  0.214    -0.551  0.128    -0.484  0.120    -0.419  0.112    -0.363  0.110
+  1909     4    -0.722  0.267    -0.525  0.132    -0.484  0.119    -0.416  0.111    -0.363  0.109
+  1909     5    -0.698  0.313    -0.446  0.135    -0.477  0.116    -0.412  0.111    -0.363  0.109
+  1909     6    -0.515  0.253    -0.434  0.137    -0.483  0.113    -0.408  0.111    -0.361  0.109
+  1909     7    -0.297  0.318    -0.360  0.138    -0.473  0.112    -0.404  0.111    -0.361  0.109
+  1909     8    -0.095  0.329    -0.351  0.142    -0.454  0.111    -0.401  0.110    -0.361  0.108
+  1909     9    -0.195  0.266    -0.349  0.143    -0.458  0.110    -0.397  0.110    -0.361  0.107
+  1909    10    -0.066  0.200    -0.308  0.142    -0.447  0.110    -0.392  0.110    -0.362  0.107
+  1909    11     0.123  0.229    -0.276  0.140    -0.434  0.109    -0.395  0.111    -0.366  0.107
+  1909    12    -0.424  0.276    -0.263  0.142    -0.422  0.109    -0.393  0.110    -0.365  0.107
+  1910     1    -0.251  0.262    -0.248  0.143    -0.423  0.110    -0.393  0.110    -0.360  0.107
+  1910     2    -0.395  0.297    -0.253  0.146    -0.428  0.109    -0.382  0.110    -0.361  0.106
+  1910     3    -0.646  0.247    -0.265  0.145    -0.436  0.108    -0.378  0.111    -0.362  0.106
+  1910     4    -0.226  0.267    -0.279  0.149    -0.452  0.108    -0.370  0.111    -0.362  0.105
+  1910     5    -0.319  0.301    -0.355  0.152    -0.447  0.107    -0.365  0.111    -0.363  0.105
+  1910     6    -0.359  0.272    -0.401  0.153    -0.446  0.105    -0.364  0.112    -0.364  0.105
+  1910     7    -0.108  0.332    -0.461  0.153    -0.450  0.105    -0.362  0.111    -0.364  0.104
+  1910     8    -0.154  0.335    -0.491  0.145    -0.459  0.103    -0.361  0.112    -0.365  0.104
+  1910     9    -0.348  0.277    -0.514  0.143    -0.453  0.103    -0.360  0.111    -0.366  0.104
+  1910    10    -0.227  0.229    -0.523  0.141    -0.450  0.104    -0.361  0.111    -0.368  0.105
+  1910    11    -0.792  0.272    -0.538  0.143    -0.457  0.103    -0.360  0.110    -0.369  0.105
+  1910    12    -0.971  0.295    -0.547  0.142    -0.462  0.103    -0.359  0.109    -0.373  0.104
+  1911     1    -0.973  0.256    -0.566  0.141    -0.463  0.103    -0.355  0.108    -0.370  0.104
+  1911     2    -0.758  0.234    -0.586  0.136    -0.457  0.103    -0.350  0.109    -0.370  0.104
+  1911     3    -0.926  0.220    -0.596  0.132    -0.460  0.103    -0.350  0.108    -0.372  0.104
+  1911     4    -0.336  0.265    -0.578  0.128    -0.457  0.105    -0.355  0.108    -0.373  0.104
+  1911     5    -0.491  0.308    -0.526  0.119    -0.445  0.106    -0.357  0.107    -0.373  0.104
+  1911     6    -0.469  0.252    -0.467  0.108    -0.427  0.106    -0.361  0.107    -0.372  0.103
+  1911     7    -0.336  0.304    -0.405  0.109    -0.396  0.107    -0.361  0.108    -0.372  0.104
+  1911     8    -0.400  0.322    -0.335  0.113    -0.387  0.108    -0.362  0.107    -0.373  0.103
+  1911     9    -0.466  0.249    -0.337  0.111    -0.381  0.108    -0.363  0.107    -0.374  0.103
+  1911    10    -0.013  0.203    -0.317  0.108    -0.373  0.108    -0.365  0.107    -0.373  0.103
+  1911    11    -0.160  0.189    -0.288  0.106    -0.363  0.108    -0.364  0.106    -0.373  0.103
+  1911    12    -0.268  0.185    -0.254  0.104    -0.354  0.108    -0.375  0.105    -0.372  0.103
+  1912     1    -0.222  0.243    -0.281  0.106    -0.350  0.107    -0.372  0.105    -0.374  0.102
+  1912     2     0.083  0.289    -0.316  0.107    -0.351  0.107    -0.370  0.106    -0.379  0.103
+  1912     3    -0.951  0.239    -0.352  0.107    -0.351  0.106    -0.371  0.106    -0.378  0.102
+  1912     4    -0.100  0.267    -0.428  0.105    -0.347  0.107    -0.369  0.106    -0.376  0.102
+  1912     5    -0.139  0.291    -0.465  0.106    -0.354  0.108    -0.369  0.105    -0.375  0.102
+  1912     6    -0.068  0.230    -0.491  0.110    -0.348  0.107    -0.368  0.105    -0.375  0.102
+  1912     7    -0.656  0.321    -0.519  0.110    -0.349  0.108    -0.363  0.104    -0.375  0.102
+  1912     8    -0.820  0.338    -0.596  0.109    -0.341  0.106    -0.363  0.104    -0.375  0.102
+  1912     9    -0.904  0.270    -0.582  0.111    -0.332  0.107    -0.362  0.104    -0.375  0.102
+  1912    10    -0.926  0.193    -0.600  0.113    -0.322  0.107    -0.368  0.105    -0.375  0.102
+  1912    11    -0.595  0.195    -0.641  0.115    -0.314  0.107    -0.363  0.105    -0.371  0.102
+  1912    12    -0.584  0.236    -0.678  0.117    -0.312  0.107    -0.369  0.104    -0.367  0.101
+  1913     1    -0.554  0.240    -0.641  0.116    -0.310  0.106    -0.372  0.104    -0.367  0.101
+  1913     2    -0.849  0.268    -0.585  0.118    -0.310  0.106    -0.375  0.103    -0.371  0.101
+  1913     3    -0.782  0.208    -0.538  0.120    -0.309  0.105    -0.370  0.104    -0.372  0.101
+  1913     4    -0.318  0.245    -0.476  0.128    -0.309  0.104    -0.372  0.104    -0.372  0.101
+  1913     5    -0.630  0.299    -0.431  0.140    -0.294  0.102    -0.378  0.103    -0.372  0.101
+  1913     6    -0.504  0.265    -0.344  0.144    -0.280  0.100    -0.381  0.103    -0.370  0.101
+  1913     7    -0.218  0.331    -0.235  0.146    -0.264  0.098    -0.383  0.103    -0.371  0.101
+  1913     8    -0.152  0.343    -0.161  0.153    -0.255  0.099    -0.383  0.103    -0.370  0.101
+  1913     9    -0.329  0.275    -0.123  0.155    -0.246  0.100    -0.383  0.103    -0.369  0.100
+  1913    10    -0.182  0.245    -0.117  0.154    -0.245  0.099    -0.379  0.103    -0.366  0.100
+  1913    11    -0.066  0.276    -0.070  0.152    -0.243  0.099    -0.375  0.103    -0.363  0.100
+  1913    12     0.464  0.271    -0.028  0.151    -0.244  0.098    -0.377  0.102    -0.359  0.099
+  1914     1     0.751  0.277    -0.016  0.151    -0.244  0.100    -0.371  0.102    -0.356  0.099
+  1914     2     0.047  0.282    -0.017  0.147    -0.243  0.100    -0.369  0.102    -0.354  0.099
+  1914     3    -0.336  0.241    -0.004  0.142    -0.242  0.100    -0.368  0.103    -0.350  0.099
+  1914     4    -0.239  0.238     0.025  0.144    -0.245  0.100    -0.361  0.103    -0.349  0.099
+  1914     5    -0.070  0.302     0.006  0.141    -0.251  0.102    -0.358  0.103    -0.348  0.099
+  1914     6    -0.001  0.246    -0.037  0.141    -0.268  0.104    -0.356  0.103    -0.346  0.099
+  1914     7    -0.068  0.326    -0.125  0.139    -0.271  0.105    -0.355  0.102    -0.345  0.098
+  1914     8    -0.164  0.330    -0.121  0.133    -0.287  0.106    -0.355  0.102    -0.344  0.098
+  1914     9    -0.169  0.245    -0.104  0.133    -0.285  0.108    -0.354  0.101    -0.342  0.098
+  1914    10     0.165  0.243    -0.051  0.140    -0.291  0.108    -0.353  0.101    -0.340  0.098
+  1914    11    -0.302  0.240    -0.033  0.137    -0.304  0.108    -0.359  0.101    -0.339  0.097
+  1914    12    -0.047  0.280    -0.051  0.139    -0.313  0.108    -0.359  0.100    -0.339  0.097
+  1915     1    -0.308  0.277    -0.046  0.137    -0.304  0.106    -0.355  0.100    -0.339  0.097
+  1915     2     0.091  0.260    -0.045  0.140    -0.297  0.106    -0.355  0.099    -0.336  0.096
+  1915     3    -0.120  0.247    -0.053  0.140    -0.288  0.107    -0.351  0.098    -0.334  0.096
+  1915     4     0.397  0.291    -0.089  0.132    -0.284  0.109    -0.349  0.098    -0.332  0.095
+  1915     5     0.138  0.296    -0.056  0.124    -0.279  0.111    -0.348  0.098    -0.331  0.095
+  1915     6    -0.213  0.252    -0.058  0.118    -0.292  0.111    -0.349  0.097    -0.332  0.095
+  1915     7    -0.008  0.306    -0.036  0.111    -0.294  0.111    -0.349  0.096    -0.332  0.095
+  1915     8    -0.155  0.352    -0.061  0.113    -0.292  0.111    -0.349  0.096    -0.331  0.095
+  1915     9    -0.266  0.241    -0.086  0.113    -0.288  0.113    -0.349  0.095    -0.331  0.095
+  1915    10    -0.269  0.189    -0.140  0.108    -0.294  0.113    -0.349  0.095    -0.330  0.095
+  1915    11     0.098  0.199    -0.183  0.110    -0.298  0.112    -0.347  0.095    -0.329  0.095
+  1915    12    -0.074  0.206    -0.211  0.108    -0.299  0.111    -0.347  0.094    -0.325  0.094
+  1916     1    -0.039  0.236    -0.238  0.113    -0.302  0.111    -0.336  0.094    -0.320  0.094
+  1916     2    -0.206  0.252    -0.250  0.112    -0.308  0.110    -0.330  0.094    -0.314  0.094
+  1916     3    -0.426  0.234    -0.262  0.115    -0.306  0.110    -0.325  0.094    -0.310  0.094
+  1916     4    -0.251  0.242    -0.257  0.118    -0.300  0.108    -0.322  0.094    -0.312  0.094
+  1916     5    -0.377  0.293    -0.306  0.122    -0.306  0.107    -0.319  0.094    -0.313  0.093
+  1916     6    -0.551  0.234    -0.407  0.126    -0.326  0.106    -0.315  0.094    -0.314  0.093
+  1916     7    -0.329  0.323    -0.441  0.133    -0.345  0.105    -0.312  0.094    -0.314  0.093
+  1916     8    -0.302  0.342    -0.495  0.140    -0.352  0.104    -0.311  0.094    -0.313  0.093
+  1916     9    -0.402  0.256    -0.530  0.141    -0.355  0.105    -0.310  0.095    -0.312  0.093
+  1916    10    -0.212  0.206    -0.549  0.140    -0.349  0.105    -0.309  0.095    -0.311  0.093
+  1916    11    -0.494  0.231    -0.594  0.137    -0.354  0.105    -0.310  0.095    -0.308  0.092
+  1916    12    -1.286  0.266    -0.598  0.138    -0.359  0.104    -0.308  0.096    -0.310  0.091
+  1917     1    -0.449  0.363    -0.578  0.132    -0.360  0.104    -0.310  0.095    -0.308  0.091
+  1917     2    -0.848  0.335    -0.586  0.131    -0.358  0.103    -0.318  0.096    -0.304  0.090
+  1917     3    -0.847  0.236    -0.584  0.137    -0.357  0.102    -0.311  0.096    -0.304  0.090
+  1917     4    -0.475  0.247    -0.625  0.141    -0.359  0.101    -0.310  0.096    -0.302  0.090
+  1917     5    -0.918  0.287    -0.607  0.140    -0.364  0.100    -0.310  0.097    -0.299  0.090
+  1917     6    -0.608  0.236    -0.612  0.138    -0.370  0.099    -0.311  0.097    -0.296  0.090
+  1917     7    -0.086  0.299    -0.632  0.133    -0.361  0.099    -0.307  0.097    -0.294  0.090
+  1917     8    -0.401  0.334    -0.624  0.130    -0.370  0.098    -0.303  0.097    -0.293  0.090
+  1917     9    -0.371  0.284    -0.596  0.134    -0.370  0.097    -0.298  0.097    -0.291  0.090
+  1917    10    -0.704  0.241    -0.615  0.133    -0.377  0.095    -0.293  0.098    -0.288  0.090
+  1917    11    -0.280  0.228    -0.610  0.132    -0.383  0.096    -0.288  0.098    -0.284  0.090
+  1917    12    -1.347  0.233    -0.608  0.134    -0.386  0.095    -0.284  0.098    -0.283  0.089
+  1918     1    -0.691  0.285    -0.634  0.139    -0.387  0.095    -0.280  0.098    -0.280  0.089
+  1918     2    -0.755  0.266    -0.641  0.138    -0.388  0.094    -0.278  0.098    -0.278  0.088
+  1918     3    -0.511  0.267    -0.629  0.135    -0.389  0.094    -0.275  0.098    -0.275  0.088
+  1918     4    -0.701  0.237    -0.556  0.128    -0.389  0.095    -0.278  0.098    -0.274  0.088
+  1918     5    -0.855  0.306    -0.565  0.129    -0.400  0.096    -0.276  0.098    -0.273  0.088
+  1918     6    -0.579  0.263    -0.518  0.126    -0.415  0.096    -0.274  0.097    -0.274  0.087
+  1918     7    -0.403  0.336    -0.492  0.125    -0.408  0.096    -0.276  0.097    -0.274  0.087
+  1918     8    -0.490  0.336    -0.457  0.124    -0.405  0.096    -0.277  0.096    -0.273  0.087
+  1918     9    -0.224  0.266    -0.461  0.124    -0.403  0.096    -0.277  0.096    -0.273  0.087
+  1918    10     0.178  0.187    -0.390  0.123    -0.399  0.096    -0.273  0.095    -0.271  0.087
+  1918    11    -0.390  0.226    -0.351  0.121    -0.394  0.095    -0.271  0.094    -0.266  0.087
+  1918    12    -0.782  0.208    -0.325  0.124    -0.386  0.095    -0.273  0.093    -0.263  0.086
+  1919     1    -0.381  0.249    -0.302  0.121    -0.379  0.095    -0.282  0.093    -0.261  0.086
+  1919     2    -0.335  0.238    -0.266  0.117    -0.380  0.095    -0.285  0.092    -0.263  0.086
+  1919     3    -0.558  0.275    -0.260  0.111    -0.377  0.095    -0.281  0.092    -0.261  0.086
+  1919     4     0.155  0.260    -0.269  0.114    -0.374  0.095    -0.283  0.091    -0.259  0.086
+  1919     5    -0.387  0.295    -0.287  0.118    -0.370  0.095    -0.283  0.092    -0.258  0.086
+  1919     6    -0.278  0.262    -0.256  0.119    -0.348  0.094    -0.285  0.091    -0.259  0.085
+  1919     7    -0.119  0.302    -0.202  0.121    -0.349  0.093    -0.285  0.090    -0.259  0.085
+  1919     8    -0.059  0.323    -0.214  0.116    -0.349  0.091    -0.286  0.090    -0.260  0.085
+  1919     9    -0.149  0.240    -0.174  0.109    -0.337  0.090    -0.286  0.090    -0.261  0.085
+  1919    10     0.071  0.201    -0.191  0.106    -0.328  0.090    -0.288  0.089    -0.260  0.085
+  1919    11    -0.612  0.245    -0.176  0.106    -0.317  0.091    -0.284  0.089    -0.260  0.085
+  1919    12    -0.415  0.212    -0.186  0.102    -0.309  0.092    -0.285  0.088    -0.263  0.084
+  1920     1     0.268  0.238    -0.185  0.104    -0.310  0.093    -0.285  0.088    -0.263  0.084
+  1920     2    -0.469  0.249    -0.198  0.105    -0.309  0.093    -0.290  0.087    -0.262  0.084
+  1920     3    -0.087  0.202    -0.212  0.109    -0.309  0.092    -0.291  0.086    -0.259  0.083
+  1920     4    -0.042  0.255    -0.239  0.110    -0.301  0.092    -0.294  0.085    -0.258  0.083
+  1920     5    -0.213  0.295    -0.233  0.111    -0.298  0.092    -0.297  0.085    -0.258  0.083
+  1920     6    -0.390  0.266    -0.280  0.108    -0.276  0.092    -0.299  0.084    -0.257  0.083
+  1920     7    -0.111  0.309    -0.270  0.107    -0.265  0.092    -0.302  0.084    -0.257  0.082
+  1920     8    -0.219  0.326    -0.237  0.110    -0.264  0.091    -0.302  0.084    -0.256  0.082
+  1920     9    -0.312  0.263    -0.252  0.113    -0.263  0.089    -0.301  0.084    -0.256  0.082
+  1920    10    -0.251  0.217    -0.249  0.114    -0.261  0.089    -0.299  0.084    -0.255  0.082
+  1920    11    -0.547  0.270    -0.241  0.110    -0.255  0.090    -0.298  0.084    -0.250  0.081
+  1920    12    -0.970  0.196    -0.211  0.112    -0.249  0.091    -0.291  0.084    -0.246  0.081
+  1921     1     0.378  0.239    -0.198  0.115    -0.249  0.091    -0.284  0.084    -0.241  0.080
+  1921     2    -0.074  0.272    -0.209  0.114    -0.247  0.090    -0.278  0.084    -0.241  0.080
+  1921     3    -0.259  0.240    -0.203  0.118    -0.247  0.089    -0.270  0.084    -0.237  0.080
+  1921     4    -0.008  0.257    -0.182  0.119    -0.246  0.088    -0.269  0.084    -0.237  0.080
+  1921     5    -0.119  0.296    -0.157  0.116    -0.236  0.088    -0.269  0.084    -0.236  0.080
+  1921     6    -0.028  0.240    -0.075  0.118    -0.219  0.089    -0.267  0.083    -0.234  0.080
+  1921     7     0.045  0.329    -0.146  0.118    -0.218  0.089    -0.266  0.083    -0.232  0.080
+  1921     8    -0.350  0.330    -0.210  0.118    -0.217  0.089    -0.264  0.083    -0.229  0.080
+  1921     9    -0.239  0.267    -0.201  0.118    -0.208  0.086    -0.261  0.083    -0.227  0.080
+  1921    10    -0.000  0.223    -0.195  0.118    -0.216  0.085    -0.257  0.083    -0.224  0.080
+  1921    11    -0.249  0.219    -0.205  0.119    -0.213  0.086    -0.252  0.082    -0.223  0.080
+  1921    12     0.007  0.224    -0.216  0.121    -0.211  0.085    -0.244  0.082    -0.221  0.081
+  1922     1    -0.473  0.212    -0.230  0.118    -0.211  0.084    -0.243  0.080    -0.216  0.081
+  1922     2    -0.842  0.264    -0.230  0.118    -0.215  0.084    -0.238  0.079    -0.217  0.081
+  1922     3    -0.152  0.199    -0.242  0.117    -0.215  0.085    -0.236  0.079    -0.215  0.081
+  1922     4     0.065  0.243    -0.262  0.120    -0.218  0.085    -0.234  0.079    -0.213  0.081
+  1922     5    -0.237  0.294    -0.248  0.124    -0.203  0.084    -0.228  0.079    -0.213  0.081
+  1922     6    -0.154  0.242    -0.248  0.124    -0.199  0.083    -0.225  0.080    -0.213  0.081
+  1922     7    -0.121  0.307    -0.216  0.126    -0.209  0.084    -0.225  0.080    -0.211  0.081
+  1922     8    -0.356  0.329    -0.201  0.123    -0.210  0.083    -0.224  0.080    -0.208  0.081
+  1922     9    -0.377  0.261    -0.225  0.124    -0.213  0.082    -0.220  0.079    -0.204  0.080
+  1922    10    -0.250  0.253    -0.279  0.122    -0.211  0.081    -0.209  0.079    -0.198  0.081
+  1922    11    -0.075  0.241    -0.301  0.124    -0.212  0.081    -0.205  0.078    -0.197  0.081
+  1922    12     0.002  0.228    -0.306  0.121    -0.213  0.080    -0.198  0.078    -0.195  0.081
+  1923     1    -0.090  0.238    -0.333  0.120    -0.216  0.080    -0.189  0.077    -0.195  0.080
+  1923     2    -0.651  0.228    -0.331  0.115    -0.215  0.080    -0.181  0.077    -0.194  0.081
+  1923     3    -0.442  0.237    -0.321  0.111    -0.212  0.080    -0.180  0.076    -0.193  0.081
+  1923     4    -0.592  0.241    -0.281  0.103    -0.209  0.080    -0.175  0.076    -0.192  0.081
+  1923     5    -0.491  0.295    -0.256  0.101    -0.196  0.080    -0.169  0.076    -0.191  0.081
+  1923     6    -0.224  0.235    -0.237  0.101    -0.168  0.080    -0.168  0.076    -0.191  0.081
+  1923     7    -0.440  0.308    -0.253  0.101    -0.160  0.080    -0.166  0.076    -0.190  0.080
+  1923     8    -0.336  0.327    -0.225  0.102    -0.151  0.080    -0.164  0.075    -0.191  0.080
+  1923     9    -0.250  0.253    -0.186  0.101    -0.138  0.080    -0.163  0.076    -0.190  0.080
+  1923    10     0.226  0.183    -0.167  0.101    -0.139  0.080    -0.163  0.076    -0.190  0.080
+  1923    11     0.229  0.215    -0.141  0.102    -0.144  0.080    -0.158  0.075    -0.191  0.079
+  1923    12     0.232  0.229    -0.137  0.103    -0.148  0.080    -0.150  0.075    -0.195  0.079
+  1924     1    -0.290  0.230    -0.111  0.099    -0.153  0.079    -0.151  0.074    -0.199  0.078
+  1924     2    -0.311  0.246    -0.106  0.099    -0.148  0.079    -0.157  0.074    -0.197  0.078
+  1924     3     0.028  0.222    -0.100  0.098    -0.145  0.079    -0.154  0.074    -0.198  0.078
+  1924     4    -0.373  0.242    -0.124  0.100    -0.141  0.078    -0.158  0.073    -0.199  0.078
+  1924     5    -0.173  0.298    -0.121  0.098    -0.134  0.078    -0.158  0.074    -0.198  0.078
+  1924     6    -0.182  0.227    -0.156  0.096    -0.140  0.078    -0.161  0.073    -0.198  0.078
+  1924     7    -0.123  0.301    -0.159  0.096    -0.137  0.077    -0.164  0.073    -0.198  0.077
+  1924     8    -0.279  0.325    -0.176  0.092    -0.127  0.076    -0.166  0.073    -0.198  0.077
+  1924     9    -0.174  0.252    -0.201  0.089    -0.135  0.077    -0.167  0.074    -0.199  0.077
+  1924    10    -0.064  0.186    -0.162  0.088    -0.141  0.077    -0.166  0.074    -0.199  0.077
+  1924    11     0.265  0.198    -0.173  0.086    -0.139  0.077    -0.161  0.074    -0.197  0.076
+  1924    12    -0.188  0.203    -0.193  0.085    -0.141  0.077    -0.167  0.074    -0.196  0.076
+  1925     1    -0.330  0.251    -0.209  0.087    -0.141  0.076    -0.171  0.073    -0.196  0.076
+  1925     2    -0.515  0.241    -0.197  0.089    -0.139  0.076    -0.169  0.074    -0.193  0.075
+  1925     3    -0.267  0.190    -0.195  0.092    -0.130  0.076    -0.166  0.074    -0.193  0.075
+  1925     4     0.091  0.244    -0.196  0.093    -0.116  0.075    -0.167  0.074    -0.197  0.075
+  1925     5    -0.300  0.288    -0.200  0.096    -0.112  0.074    -0.168  0.074    -0.199  0.075
+  1925     6    -0.424  0.244    -0.125  0.096    -0.120  0.074    -0.166  0.074    -0.199  0.075
+  1925     7    -0.311  0.301    -0.026  0.096    -0.112  0.073    -0.165  0.074    -0.200  0.074
+  1925     8    -0.138  0.331     0.058  0.100    -0.099  0.073    -0.163  0.074    -0.200  0.074
+  1925     9    -0.151  0.250     0.124  0.107    -0.097  0.073    -0.163  0.074    -0.199  0.074
+  1925    10    -0.076  0.194     0.108  0.108    -0.089  0.073    -0.161  0.074    -0.197  0.074
+  1925    11     0.219  0.220     0.098  0.108    -0.084  0.072    -0.154  0.074    -0.200  0.074
+  1925    12     0.706  0.193     0.111  0.110    -0.087  0.072    -0.145  0.074    -0.201  0.074
+  1926     1     0.863  0.224     0.118  0.111    -0.082  0.072    -0.147  0.073    -0.203  0.074
+  1926     2     0.499  0.257     0.126  0.111    -0.080  0.072    -0.151  0.073    -0.205  0.074
+  1926     3     0.525  0.223     0.133  0.109    -0.078  0.073    -0.150  0.072    -0.205  0.073
+  1926     4    -0.107  0.241     0.161  0.110    -0.079  0.074    -0.151  0.072    -0.205  0.073
+  1926     5    -0.419  0.288     0.155  0.106    -0.079  0.073    -0.153  0.073    -0.204  0.073
+  1926     6    -0.262  0.235     0.068  0.110    -0.080  0.073    -0.153  0.072    -0.202  0.073
+  1926     7    -0.226  0.302    -0.029  0.105    -0.085  0.073    -0.152  0.072    -0.200  0.073
+  1926     8    -0.052  0.337    -0.090  0.099    -0.097  0.072    -0.147  0.072    -0.199  0.073
+  1926     9    -0.062  0.253    -0.189  0.100    -0.101  0.073    -0.144  0.072    -0.198  0.073
+  1926    10     0.253  0.207    -0.201  0.100    -0.099  0.073    -0.139  0.072    -0.197  0.073
+  1926    11     0.147  0.184    -0.181  0.103    -0.104  0.074    -0.136  0.073    -0.194  0.073
+  1926    12    -0.337  0.216    -0.179  0.104    -0.111  0.074    -0.133  0.073    -0.188  0.072
+  1927     1    -0.298  0.196    -0.170  0.102    -0.117  0.075    -0.121  0.073    -0.187  0.072
+  1927     2    -0.233  0.222    -0.184  0.100    -0.117  0.075    -0.117  0.073    -0.182  0.072
+  1927     3    -0.658  0.240    -0.169  0.103    -0.119  0.076    -0.119  0.074    -0.181  0.071
+  1927     4    -0.258  0.239    -0.140  0.103    -0.115  0.076    -0.116  0.074    -0.180  0.071
+  1927     5    -0.173  0.308    -0.137  0.103    -0.119  0.076    -0.116  0.074    -0.176  0.071
+  1927     6    -0.244  0.250    -0.147  0.102    -0.135  0.076    -0.116  0.073    -0.173  0.072
+  1927     7    -0.113  0.300    -0.091  0.104    -0.134  0.076    -0.115  0.073    -0.173  0.072
+  1927     8    -0.222  0.339    -0.060  0.106    -0.127  0.076    -0.114  0.073    -0.171  0.071
+  1927     9     0.116  0.249    -0.035  0.105    -0.120  0.078    -0.109  0.073    -0.169  0.071
+  1927    10     0.599  0.213    -0.021  0.104    -0.124  0.078    -0.104  0.072    -0.164  0.071
+  1927    11     0.187  0.192    -0.022  0.103    -0.124  0.078    -0.106  0.072    -0.162  0.070
+  1927    12    -0.458  0.204    -0.037  0.099    -0.120  0.079    -0.107  0.072    -0.157  0.069
+  1928     1     0.376  0.223    -0.038  0.102    -0.114  0.079    -0.110  0.072    -0.153  0.069
+  1928     2     0.140  0.229    -0.039  0.102    -0.112  0.078    -0.110  0.072    -0.150  0.068
+  1928     3    -0.358  0.225    -0.058  0.103    -0.114  0.078    -0.110  0.072    -0.146  0.068
+  1928     4    -0.090  0.247    -0.094  0.100    -0.114  0.078    -0.107  0.072    -0.141  0.068
+  1928     5    -0.182  0.299    -0.093  0.102    -0.111  0.077    -0.106  0.073    -0.137  0.068
+  1928     6    -0.432  0.236    -0.039  0.104    -0.121  0.077    -0.107  0.073    -0.136  0.068
+  1928     7    -0.119  0.303    -0.117  0.104    -0.134  0.077    -0.105  0.073    -0.134  0.068
+  1928     8    -0.241  0.324    -0.217  0.105    -0.151  0.076    -0.104  0.073    -0.131  0.068
+  1928     9    -0.110  0.260    -0.206  0.106    -0.162  0.075    -0.104  0.073    -0.129  0.068
+  1928    10     0.170  0.191    -0.221  0.106    -0.163  0.076    -0.106  0.073    -0.128  0.068
+  1928    11     0.192  0.181    -0.243  0.107    -0.162  0.076    -0.110  0.073    -0.125  0.067
+  1928    12     0.200  0.214    -0.257  0.109    -0.157  0.076    -0.117  0.073    -0.122  0.067
+  1929     1    -0.569  0.244    -0.286  0.107    -0.150  0.076    -0.116  0.073    -0.119  0.067
+  1929     2    -1.059  0.225    -0.288  0.105    -0.147  0.076    -0.110  0.073    -0.118  0.066
+  1929     3    -0.227  0.242    -0.305  0.103    -0.143  0.077    -0.115  0.073    -0.118  0.066
+  1929     4    -0.261  0.267    -0.305  0.104    -0.137  0.077    -0.115  0.073    -0.118  0.066
+  1929     5    -0.446  0.305    -0.320  0.107    -0.138  0.078    -0.112  0.073    -0.116  0.066
+  1929     6    -0.602  0.238    -0.429  0.102    -0.127  0.078    -0.112  0.073    -0.115  0.066
+  1929     7    -0.467  0.305    -0.405  0.105    -0.106  0.079    -0.111  0.073    -0.115  0.065
+  1929     8    -0.265  0.324    -0.327  0.106    -0.107  0.081    -0.110  0.073    -0.114  0.065
+  1929     9    -0.313  0.259    -0.293  0.104    -0.103  0.080    -0.112  0.073    -0.114  0.066
+  1929    10     0.168  0.207    -0.286  0.102    -0.092  0.081    -0.110  0.073    -0.115  0.066
+  1929    11     0.014  0.213    -0.272  0.101    -0.092  0.080    -0.110  0.073    -0.112  0.065
+  1929    12    -1.113  0.200    -0.238  0.101    -0.090  0.080    -0.107  0.073    -0.106  0.065
+  1930     1    -0.274  0.225    -0.196  0.101    -0.089  0.080    -0.108  0.073    -0.108  0.065
+  1930     2    -0.125  0.241    -0.176  0.102    -0.088  0.080    -0.096  0.072    -0.105  0.065
+  1930     3     0.179  0.225    -0.173  0.102    -0.088  0.080    -0.094  0.073    -0.105  0.065
+  1930     4    -0.172  0.243    -0.190  0.101    -0.092  0.079    -0.100  0.073    -0.103  0.065
+  1930     5    -0.279  0.289    -0.158  0.097    -0.099  0.080    -0.101  0.073    -0.103  0.065
+  1930     6    -0.201  0.236    -0.056  0.098    -0.094  0.080    -0.100  0.073    -0.101  0.065
+  1930     7     0.037  0.301    -0.028  0.096    -0.108  0.081    -0.098  0.073    -0.099  0.065
+  1930     8    -0.023  0.321    -0.061  0.095    -0.121  0.082    -0.098  0.073    -0.099  0.065
+  1930     9    -0.275  0.253    -0.085  0.094    -0.123  0.082    -0.098  0.073    -0.097  0.065
+  1930    10    -0.042  0.193    -0.089  0.096    -0.126  0.082    -0.095  0.073    -0.095  0.065
+  1930    11     0.400  0.195    -0.095  0.095    -0.128  0.082    -0.103  0.073    -0.093  0.065
+  1930    12     0.116  0.193    -0.074  0.095    -0.128  0.083    -0.111  0.073    -0.087  0.065
+  1931     1     0.058  0.206    -0.063  0.098    -0.128  0.082    -0.122  0.073    -0.089  0.065
+  1931     2    -0.526  0.210    -0.047  0.101    -0.127  0.082    -0.132  0.073    -0.087  0.064
+  1931     3    -0.108  0.215    -0.012  0.102    -0.130  0.082    -0.140  0.072    -0.086  0.064
+  1931     4    -0.211  0.251     0.044  0.103    -0.133  0.082    -0.141  0.072    -0.085  0.064
+  1931     5    -0.354  0.285     0.019  0.108    -0.141  0.082    -0.139  0.073    -0.084  0.065
+  1931     6     0.044  0.231     0.037  0.113    -0.154  0.081    -0.138  0.073    -0.084  0.064
+  1931     7     0.176  0.305     0.112  0.116    -0.148  0.082    -0.134  0.072    -0.083  0.064
+  1931     8     0.165  0.331     0.131  0.121    -0.123  0.082    -0.135  0.072    -0.081  0.064
+  1931     9     0.147  0.254     0.106  0.120    -0.129  0.081    -0.135  0.072    -0.081  0.064
+  1931    10     0.634  0.196     0.156  0.120    -0.130  0.081    -0.136  0.073    -0.079  0.064
+  1931    11     0.098  0.231     0.171  0.121    -0.121  0.080    -0.137  0.073    -0.079  0.063
+  1931    12     0.332  0.230     0.154  0.119    -0.113  0.081    -0.132  0.073    -0.079  0.063
+  1932     1     0.954  0.222     0.137  0.117    -0.106  0.081    -0.131  0.073    -0.075  0.063
+  1932     2    -0.296  0.253     0.108  0.115    -0.104  0.082    -0.127  0.073    -0.073  0.063
+  1932     3    -0.411  0.219     0.109  0.112    -0.105  0.081    -0.126  0.072    -0.073  0.063
+  1932     4     0.392  0.249     0.087  0.112    -0.106  0.081    -0.126  0.073    -0.073  0.063
+  1932     5    -0.168  0.299     0.056  0.111    -0.102  0.080    -0.124  0.072    -0.072  0.063
+  1932     6    -0.164  0.231     0.015  0.108    -0.079  0.080    -0.122  0.072    -0.071  0.063
+  1932     7    -0.033  0.296    -0.104  0.111    -0.082  0.079    -0.121  0.072    -0.071  0.062
+  1932     8    -0.177  0.327    -0.130  0.112    -0.066  0.079    -0.119  0.072    -0.070  0.062
+  1932     9     0.161  0.244    -0.138  0.114    -0.068  0.078    -0.118  0.072    -0.068  0.062
+  1932    10     0.368  0.196    -0.191  0.113    -0.076  0.078    -0.119  0.071    -0.066  0.062
+  1932    11    -0.280  0.201    -0.203  0.114    -0.079  0.079    -0.119  0.071    -0.065  0.061
+  1932    12    -0.155  0.198    -0.223  0.116    -0.079  0.079    -0.117  0.071    -0.064  0.061
+  1933     1    -0.476  0.236    -0.234  0.116    -0.081  0.079    -0.118  0.071    -0.064  0.061
+  1933     2    -0.603  0.256    -0.232  0.115    -0.083  0.079    -0.118  0.070    -0.060  0.061
+  1933     3    -0.507  0.245    -0.268  0.116    -0.081  0.079    -0.113  0.069    -0.059  0.060
+  1933     4    -0.249  0.246    -0.303  0.115    -0.077  0.079    -0.108  0.070    -0.056  0.060
+  1933     5    -0.305  0.302    -0.301  0.111    -0.095  0.079    -0.105  0.069    -0.053  0.060
+  1933     6    -0.409  0.235    -0.336  0.110    -0.101  0.079    -0.103  0.070    -0.054  0.060
+  1933     7    -0.161  0.294    -0.313  0.107    -0.109  0.080    -0.102  0.069    -0.052  0.060
+  1933     8    -0.155  0.323    -0.228  0.103    -0.114  0.080    -0.099  0.069    -0.051  0.060
+  1933     9    -0.273  0.244    -0.233  0.100    -0.118  0.079    -0.096  0.069    -0.050  0.060
+  1933    10    -0.054  0.201    -0.242  0.102    -0.119  0.079    -0.093  0.069    -0.048  0.060
+  1933    11    -0.254  0.186    -0.207  0.100    -0.115  0.079    -0.092  0.069    -0.049  0.060
+  1933    12    -0.580  0.210    -0.182  0.104    -0.119  0.079    -0.095  0.069    -0.047  0.060
+  1934     1    -0.194  0.208    -0.173  0.103    -0.118  0.079    -0.087  0.068    -0.042  0.060
+  1934     2     0.418  0.229    -0.172  0.103    -0.123  0.078    -0.079  0.068    -0.040  0.059
+  1934     3    -0.573  0.207    -0.183  0.101    -0.127  0.078    -0.081  0.068    -0.039  0.059
+  1934     4    -0.349  0.262    -0.166  0.101    -0.135  0.078    -0.079  0.067    -0.037  0.059
+  1934     5     0.112  0.286    -0.125  0.101    -0.136  0.078    -0.074  0.067    -0.037  0.059
+  1934     6    -0.114  0.242    -0.053  0.096    -0.138  0.077    -0.069  0.067    -0.036  0.059
+  1934     7    -0.046  0.295    -0.076  0.094    -0.157  0.077    -0.065  0.067    -0.035  0.059
+  1934     8    -0.148  0.321    -0.041  0.093    -0.148  0.075    -0.063  0.067    -0.033  0.058
+  1934     9    -0.396  0.251     0.009  0.093    -0.149  0.075    -0.062  0.067    -0.031  0.059
+  1934    10     0.141  0.194    -0.012  0.092    -0.160  0.075    -0.064  0.067    -0.029  0.059
+  1934    11     0.243  0.194    -0.061  0.094    -0.156  0.074    -0.064  0.066    -0.030  0.059
+  1934    12     0.279  0.198    -0.070  0.093    -0.153  0.075    -0.044  0.067    -0.030  0.059
+  1935     1    -0.471  0.204    -0.073  0.092    -0.153  0.075    -0.045  0.066    -0.029  0.058
+  1935     2     0.842  0.213    -0.073  0.092    -0.149  0.074    -0.042  0.066    -0.030  0.058
+  1935     3     0.031  0.203    -0.052  0.094    -0.147  0.074    -0.044  0.065    -0.029  0.058
+  1935     4    -0.609  0.258    -0.046  0.095    -0.147  0.074    -0.039  0.066    -0.029  0.058
+  1935     5    -0.476  0.290    -0.124  0.094    -0.139  0.073    -0.037  0.065    -0.028  0.058
+  1935     6    -0.219  0.232    -0.169  0.095    -0.140  0.072    -0.035  0.065    -0.027  0.058
+  1935     7    -0.077  0.294    -0.166  0.095    -0.128  0.070    -0.034  0.065    -0.027  0.058
+  1935     8    -0.155  0.325    -0.301  0.095    -0.115  0.069    -0.034  0.065    -0.025  0.058
+  1935     9    -0.148  0.245    -0.333  0.096    -0.102  0.068    -0.031  0.065    -0.024  0.058
+  1935    10     0.219  0.201    -0.304  0.097    -0.089  0.067    -0.029  0.065    -0.022  0.058
+  1935    11    -0.693  0.187    -0.276  0.097    -0.082  0.067    -0.033  0.065    -0.023  0.058
+  1935    12    -0.257  0.199    -0.273  0.098    -0.079  0.067    -0.030  0.066    -0.028  0.058
+  1936     1    -0.434  0.206    -0.250  0.099    -0.075  0.067    -0.030  0.066    -0.030  0.057
+  1936     2    -0.780  0.218    -0.244  0.099    -0.072  0.067    -0.022  0.066    -0.031  0.057
+  1936     3    -0.361  0.218    -0.244  0.098    -0.063  0.066    -0.023  0.066    -0.034  0.057
+  1936     4    -0.258  0.251    -0.249  0.099    -0.054  0.066    -0.019  0.066    -0.032  0.057
+  1936     5    -0.136  0.284    -0.185  0.100    -0.042  0.067    -0.016  0.065    -0.030  0.057
+  1936     6    -0.189  0.235    -0.145  0.102    -0.035  0.066    -0.015  0.065    -0.029  0.057
+  1936     7     0.204  0.300    -0.126  0.102    -0.027  0.065    -0.014  0.065    -0.028  0.056
+  1936     8    -0.082  0.325    -0.038  0.101    -0.035  0.065    -0.014  0.064    -0.028  0.056
+  1936     9    -0.148  0.248    -0.052  0.102    -0.033  0.064    -0.018  0.064    -0.028  0.056
+  1936    10     0.155  0.232    -0.050  0.101    -0.027  0.063    -0.020  0.063    -0.029  0.056
+  1936    11     0.074  0.196    -0.036  0.103    -0.027  0.063    -0.022  0.063    -0.029  0.056
+  1936    12     0.223  0.204    -0.014  0.105    -0.025  0.063    -0.025  0.062    -0.030  0.056
+  1937     1    -0.206  0.210    -0.035  0.103    -0.024  0.062    -0.029  0.062    -0.029  0.056
+  1937     2     0.275  0.203    -0.022  0.101    -0.022  0.062    -0.030  0.061    -0.028  0.056
+  1937     3    -0.521  0.201     0.010  0.100    -0.018  0.062    -0.027  0.061    -0.023  0.055
+  1937     4    -0.240  0.251     0.031  0.097    -0.022  0.062    -0.029  0.060    -0.021  0.055
+  1937     5     0.036  0.289     0.041  0.094    -0.026  0.062    -0.028  0.060    -0.020  0.055
+  1937     6     0.072  0.244     0.005  0.092    -0.010  0.062    -0.026  0.060    -0.019  0.055
+  1937     7    -0.046  0.301     0.042  0.091    -0.007  0.062    -0.027  0.060    -0.019  0.055
+  1937     8     0.071  0.318     0.031  0.091    -0.019  0.062    -0.027  0.059    -0.018  0.054
+  1937     9     0.233  0.245     0.099  0.091    -0.019  0.062    -0.028  0.059    -0.018  0.054
+  1937    10     0.415  0.201     0.163  0.090    -0.003  0.062    -0.028  0.059    -0.018  0.054
+  1937    11     0.188  0.199     0.169  0.089     0.004  0.061    -0.024  0.058    -0.017  0.054
+  1937    12    -0.208  0.193     0.145  0.088     0.009  0.061    -0.020  0.058    -0.015  0.054
+  1938     1     0.237  0.188     0.155  0.087     0.013  0.060    -0.018  0.058    -0.014  0.054
+  1938     2     0.148  0.208     0.152  0.089     0.014  0.060    -0.011  0.057    -0.015  0.053
+  1938     3     0.292  0.194     0.156  0.089     0.019  0.060    -0.009  0.056    -0.015  0.053
+  1938     4     0.531  0.253     0.162  0.090     0.019  0.060    -0.004  0.056    -0.014  0.053
+  1938     5     0.101  0.283     0.181  0.091     0.029  0.060    -0.001  0.055    -0.012  0.053
+  1938     6    -0.211  0.233     0.185  0.092     0.041  0.061    -0.000  0.055    -0.010  0.053
+  1938     7     0.074  0.293     0.189  0.091     0.049  0.061     0.002  0.054    -0.009  0.053
+  1938     8     0.035  0.319     0.175  0.091     0.069  0.060     0.002  0.054    -0.008  0.053
+  1938     9     0.276  0.252     0.114  0.091     0.072  0.061     0.004  0.054    -0.008  0.052
+  1938    10     0.492  0.192     0.067  0.089     0.080  0.061     0.010  0.054    -0.007  0.052
+  1938    11     0.418  0.203     0.070  0.087     0.084  0.061     0.013  0.054    -0.007  0.052
+  1938    12    -0.157  0.200     0.087  0.086     0.089  0.060     0.022  0.054    -0.009  0.052
+  1939     1     0.285  0.181     0.081  0.085     0.091  0.059     0.031  0.053    -0.004  0.052
+  1939     2    -0.027  0.203     0.079  0.084     0.094  0.059     0.031  0.053    -0.001  0.052
+  1939     3    -0.437  0.185     0.039  0.084     0.091  0.058     0.037  0.053    -0.002  0.051
+  1939     4    -0.038  0.238    -0.008  0.084     0.096  0.057     0.040  0.053    -0.000  0.051
+  1939     5     0.144  0.279    -0.041  0.083     0.092  0.057     0.039  0.052     0.001  0.051
+  1939     6    -0.008  0.233     0.075  0.084     0.088  0.056     0.040  0.052     0.003  0.051
+  1939     7    -0.003  0.295     0.025  0.086     0.099  0.056     0.041  0.051     0.004  0.051
+  1939     8     0.011  0.323     0.039  0.086     0.088  0.056     0.043  0.051     0.005  0.051
+  1939     9    -0.203  0.242     0.078  0.088     0.096  0.056     0.049  0.051     0.005  0.051
+  1939    10    -0.072  0.190     0.110  0.088     0.101  0.056     0.052  0.051     0.006  0.051
+  1939    11     0.027  0.180     0.094  0.088     0.101  0.055     0.050  0.051     0.006  0.051
+  1939    12     1.232  0.200     0.100  0.088     0.100  0.055     0.046  0.051     0.010  0.051
+  1940     1    -0.319  0.196     0.116  0.090     0.099  0.055     0.049  0.051     0.008  0.050
+  1940     2     0.144  0.203     0.107  0.091     0.096  0.055     0.037  0.051     0.007  0.050
+  1940     3     0.030  0.196     0.136  0.091     0.092  0.054     0.035  0.051     0.006  0.050
+  1940     4     0.344  0.236     0.157  0.090     0.091  0.054     0.043  0.051     0.005  0.050
+  1940     5    -0.049  0.281     0.150  0.092     0.092  0.054     0.044  0.051     0.006  0.050
+  1940     6     0.065  0.231     0.084  0.092     0.099  0.055     0.045  0.050     0.007  0.049
+  1940     7     0.192  0.306     0.118  0.092     0.091  0.055     0.044  0.050     0.006  0.049
+  1940     8    -0.093  0.323     0.137  0.093     0.093  0.056     0.049  0.050     0.004  0.049
+  1940     9     0.143  0.247     0.122  0.096     0.085  0.055     0.051  0.050     0.005  0.049
+  1940    10     0.184  0.207     0.109  0.098     0.080  0.055     0.051  0.049     0.004  0.049
+  1940    11    -0.061  0.189     0.123  0.099     0.081  0.054     0.057  0.049    -0.000  0.049
+  1940    12     0.438  0.201     0.127  0.097     0.078  0.054     0.054  0.049    -0.001  0.049
+  1941     1     0.086  0.194     0.136  0.096     0.078  0.053     0.061  0.049    -0.003  0.049
+  1941     2     0.374  0.205     0.153  0.094     0.076  0.053     0.069  0.049    -0.005  0.049
+  1941     3    -0.152  0.208     0.116  0.092     0.071  0.053     0.072  0.049    -0.005  0.048
+  1941     4     0.190  0.244     0.137  0.089     0.073  0.053     0.078  0.048    -0.004  0.048
+  1941     5     0.120  0.279     0.127  0.087     0.068  0.053     0.079  0.048    -0.002  0.048
+  1941     6     0.114  0.224     0.091  0.085     0.080  0.054     0.079  0.048    -0.004  0.048
+  1941     7     0.303  0.292     0.122  0.085     0.090  0.054     0.078  0.047    -0.005  0.048
+  1941     8     0.113  0.317     0.060  0.085     0.096  0.054     0.078  0.047    -0.005  0.047
+  1941     9    -0.301  0.233     0.068  0.084     0.106  0.054     0.079  0.047    -0.005  0.047
+  1941    10     0.426  0.173     0.058  0.084     0.108  0.054     0.078  0.047    -0.006  0.047
+  1941    11    -0.177  0.178     0.047  0.083     0.105  0.054     0.078  0.047    -0.006  0.047
+  1941    12     0.014  0.183     0.043  0.083     0.105  0.054     0.072  0.046    -0.004  0.046
+  1942     1     0.451  0.192     0.005  0.083     0.106  0.053     0.074  0.046    -0.006  0.046
+  1942     2    -0.374  0.220    -0.014  0.083     0.108  0.053     0.072  0.046    -0.004  0.046
+  1942     3    -0.050  0.194     0.013  0.083     0.117  0.053     0.080  0.045    -0.004  0.046
+  1942     4     0.074  0.238     0.005  0.082     0.127  0.053     0.085  0.045    -0.005  0.046
+  1942     5    -0.012  0.280     0.042  0.082     0.126  0.053     0.084  0.045    -0.005  0.045
+  1942     6     0.062  0.225     0.061  0.083     0.101  0.053     0.083  0.045    -0.004  0.045
+  1942     7    -0.153  0.293     0.001  0.083     0.105  0.053     0.084  0.045    -0.003  0.045
+  1942     8    -0.116  0.317     0.056  0.082     0.092  0.052     0.083  0.045    -0.002  0.045
+  1942     9     0.017  0.236     0.042  0.081     0.089  0.052     0.081  0.045    -0.002  0.045
+  1942    10     0.340  0.177     0.059  0.081     0.088  0.052     0.084  0.045    -0.004  0.045
+  1942    11     0.257  0.178     0.069  0.080     0.085  0.052     0.085  0.045    -0.006  0.044
+  1942    12     0.242  0.191     0.034  0.080     0.080  0.052     0.086  0.045    -0.005  0.044
+  1943     1    -0.264  0.188     0.054  0.079     0.074  0.051     0.090  0.045    -0.002  0.044
+  1943     2     0.285  0.196     0.054  0.080     0.083  0.051     0.089  0.045     0.002  0.043
+  1943     3    -0.212  0.180     0.050  0.081     0.082  0.051     0.083  0.045     0.006  0.043
+  1943     4     0.269  0.234     0.074  0.082     0.084  0.051     0.080  0.045     0.009  0.043
+  1943     5     0.109  0.276     0.062  0.084     0.084  0.051     0.080  0.045     0.011  0.043
+  1943     6    -0.354  0.228     0.090  0.084     0.068  0.050     0.084  0.045     0.013  0.043
+  1943     7     0.084  0.293     0.185  0.082     0.073  0.050     0.083  0.045     0.014  0.043
+  1943     8    -0.118  0.315     0.189  0.083     0.070  0.049     0.083  0.045     0.015  0.043
+  1943     9    -0.022  0.236     0.221  0.084     0.071  0.049     0.081  0.045     0.017  0.043
+  1943    10     0.625  0.196     0.205  0.085     0.076  0.048     0.080  0.045     0.018  0.042
+  1943    11     0.116  0.185     0.193  0.086     0.074  0.048     0.077  0.044     0.019  0.042
+  1943    12     0.569  0.195     0.219  0.085     0.069  0.048     0.077  0.044     0.023  0.042
+  1944     1     0.880  0.182     0.219  0.086     0.065  0.048     0.080  0.044     0.021  0.042
+  1944     2     0.334  0.210     0.240  0.087     0.062  0.048     0.076  0.044     0.018  0.042
+  1944     3     0.167  0.175     0.269  0.087     0.066  0.048     0.078  0.044     0.020  0.041
+  1944     4     0.077  0.226     0.261  0.086     0.059  0.049     0.078  0.044     0.021  0.041
+  1944     5    -0.034  0.272     0.247  0.086     0.065  0.049     0.077  0.045     0.019  0.041
+  1944     6    -0.034  0.219     0.182  0.085     0.056  0.049     0.074  0.045     0.019  0.041
+  1944     7     0.084  0.291     0.100  0.085     0.048  0.049     0.073  0.045     0.018  0.041
+  1944     8     0.127  0.312     0.020  0.083     0.056  0.048     0.072  0.045     0.019  0.041
+  1944     9     0.331  0.229    -0.008  0.082     0.064  0.048     0.072  0.045     0.020  0.041
+  1944    10     0.525  0.185     0.010  0.081     0.068  0.048     0.075  0.045     0.021  0.041
+  1944    11    -0.048  0.181    -0.009  0.082     0.068  0.048     0.075  0.045     0.022  0.041
+  1944    12    -0.218  0.180    -0.021  0.082     0.066  0.049     0.064  0.044     0.020  0.041
+  1945     1    -0.104  0.188    -0.042  0.082     0.068  0.048     0.061  0.044     0.025  0.041
+  1945     2    -0.617  0.187    -0.019  0.082     0.070  0.049     0.056  0.044     0.022  0.040
+  1945     3    -0.172  0.177    -0.040  0.082     0.070  0.049     0.055  0.044     0.018  0.040
+  1945     4     0.293  0.229    -0.058  0.081     0.077  0.049     0.050  0.044     0.020  0.040
+  1945     5    -0.264  0.276    -0.059  0.081     0.077  0.049     0.050  0.044     0.021  0.040
+  1945     6    -0.180  0.219    -0.083  0.080     0.073  0.049     0.049  0.044     0.022  0.040
+  1945     7    -0.170  0.289    -0.044  0.079     0.090  0.049     0.045  0.044     0.022  0.040
+  1945     8     0.406  0.317     0.026  0.079     0.085  0.048     0.043  0.043     0.024  0.040
+  1945     9     0.084  0.234     0.032  0.079     0.081  0.048     0.040  0.043     0.024  0.039
+  1945    10     0.299  0.173     0.047  0.078     0.079  0.048     0.038  0.043     0.025  0.039
+  1945    11    -0.054  0.179     0.071  0.078     0.080  0.049     0.032  0.043     0.027  0.039
+  1945    12    -0.512  0.181     0.070  0.078     0.089  0.049     0.028  0.043     0.027  0.039
+  1946     1     0.364  0.179     0.088  0.078     0.088  0.049     0.023  0.042     0.028  0.039
+  1946     2     0.229  0.210     0.049  0.078     0.090  0.050     0.013  0.042     0.029  0.039
+  1946     3    -0.106  0.179     0.038  0.079     0.091  0.050     0.012  0.042     0.029  0.038
+  1946     4     0.483  0.222     0.015  0.080     0.086  0.049     0.011  0.042     0.028  0.038
+  1946     5     0.012  0.268     0.030  0.079     0.086  0.048     0.011  0.042     0.026  0.038
+  1946     6    -0.180  0.217     0.031  0.079     0.074  0.048     0.008  0.042     0.026  0.038
+  1946     7     0.042  0.288     0.001  0.079     0.070  0.048     0.004  0.041     0.024  0.038
+  1946     8    -0.065  0.319    -0.013  0.078     0.056  0.048     0.005  0.041     0.021  0.038
+  1946     9    -0.046  0.229     0.033  0.077     0.049  0.048     0.009  0.041     0.021  0.037
+  1946    10     0.023  0.181     0.019  0.078     0.048  0.049     0.009  0.042     0.019  0.037
+  1946    11     0.130  0.173     0.017  0.079     0.048  0.049     0.011  0.042     0.017  0.037
+  1946    12    -0.498  0.180     0.027  0.081     0.044  0.050     0.016  0.042     0.016  0.036
+  1947     1    -0.007  0.177     0.023  0.080     0.040  0.049     0.016  0.042     0.016  0.036
+  1947     2     0.063  0.178     0.024  0.079     0.036  0.049     0.021  0.042     0.014  0.036
+  1947     3     0.452  0.168     0.033  0.079     0.028  0.049     0.018  0.042     0.015  0.036
+  1947     4     0.315  0.223     0.093  0.079     0.024  0.049     0.018  0.042     0.016  0.035
+  1947     5    -0.014  0.269     0.106  0.078     0.025  0.049     0.018  0.042     0.015  0.035
+  1947     6    -0.060  0.260     0.147  0.078     0.026  0.049     0.017  0.042     0.016  0.035
+  1947     7    -0.008  0.296     0.208  0.079     0.017  0.049     0.020  0.042     0.015  0.035
+  1947     8    -0.049  0.307     0.201  0.079     0.019  0.048     0.022  0.042     0.016  0.035
+  1947     9     0.061  0.227     0.128  0.079     0.021  0.048     0.023  0.042     0.015  0.034
+  1947    10     0.737  0.173     0.114  0.081     0.012  0.048     0.019  0.042     0.015  0.034
+  1947    11     0.286  0.175     0.130  0.083     0.016  0.048     0.011  0.042     0.015  0.034
+  1947    12    -0.003  0.181     0.149  0.080     0.017  0.048     0.009  0.041     0.019  0.034
+  1948     1     0.725  0.179     0.153  0.080     0.016  0.048     0.014  0.041     0.022  0.034
+  1948     2    -0.023  0.178     0.158  0.080     0.002  0.048     0.015  0.041     0.023  0.033
+  1948     3    -0.416  0.169     0.155  0.081    -0.001  0.048     0.020  0.041     0.022  0.033
+  1948     4     0.148  0.221     0.121  0.079    -0.008  0.048     0.022  0.041     0.021  0.033
+  1948     5     0.178  0.275     0.104  0.078    -0.019  0.048     0.023  0.042     0.021  0.033
+  1948     6     0.159  0.218     0.093  0.077    -0.012  0.048     0.027  0.042     0.020  0.033
+  1948     7     0.046  0.274     0.084  0.076    -0.026  0.048     0.026  0.042     0.020  0.033
+  1948     8     0.002  0.298     0.047  0.077    -0.045  0.048     0.028  0.042     0.019  0.033
+  1948     9     0.030  0.223     0.060  0.080    -0.047  0.048     0.029  0.042     0.016  0.032
+  1948    10     0.327  0.157     0.049  0.082    -0.054  0.048     0.027  0.041     0.015  0.032
+  1948    11     0.088  0.170     0.031  0.081    -0.053  0.049     0.025  0.041     0.014  0.032
+  1948    12    -0.140  0.186    -0.006  0.085    -0.054  0.048     0.023  0.041     0.015  0.032
+  1949     1     0.618  0.171    -0.024  0.083    -0.056  0.048     0.011  0.041     0.015  0.032
+  1949     2    -0.461  0.171    -0.030  0.083    -0.053  0.047     0.006  0.041     0.016  0.031
+  1949     3    -0.265  0.236    -0.047  0.080    -0.049  0.046     0.003  0.041     0.020  0.031
+  1949     4     0.012  0.250    -0.051  0.082    -0.042  0.046     0.001  0.041     0.022  0.031
+  1949     5    -0.033  0.272    -0.056  0.084    -0.043  0.046    -0.002  0.041     0.021  0.031
+  1949     6    -0.282  0.228    -0.060  0.085    -0.023  0.046    -0.002  0.041     0.022  0.031
+  1949     7    -0.173  0.276    -0.161  0.086    -0.016  0.047    -0.004  0.041     0.022  0.031
+  1949     8    -0.071  0.294    -0.165  0.087    -0.014  0.047    -0.006  0.041     0.022  0.031
+  1949     9    -0.170  0.216    -0.150  0.081    -0.028  0.048    -0.009  0.041     0.022  0.031
+  1949    10     0.272  0.162    -0.168  0.077    -0.031  0.048    -0.011  0.041     0.021  0.031
+  1949    11     0.027  0.173    -0.169  0.076    -0.031  0.048    -0.006  0.041     0.020  0.030
+  1949    12    -0.179  0.181    -0.156  0.074    -0.031  0.047    -0.006  0.041     0.015  0.030
+  1950     1    -0.595  0.176    -0.160  0.075    -0.028  0.047     0.002  0.041     0.017  0.030
+  1950     2    -0.507  0.167    -0.189  0.073    -0.026  0.047     0.006  0.040     0.019  0.030
+  1950     3    -0.085  0.148    -0.186  0.073    -0.025  0.046     0.001  0.040     0.014  0.029
+  1950     4    -0.214  0.194    -0.216  0.073    -0.039  0.046    -0.003  0.040     0.012  0.029
+  1950     5    -0.045  0.227    -0.278  0.073    -0.055  0.046    -0.002  0.040     0.010  0.029
+  1950     6    -0.121  0.185    -0.270  0.072    -0.055  0.046    -0.000  0.040     0.009  0.029
+  1950     7    -0.224  0.240    -0.261  0.070    -0.061  0.046     0.000  0.040     0.008  0.029
+  1950     8    -0.420  0.229    -0.293  0.070    -0.054  0.046    -0.000  0.040     0.008  0.029
+  1950     9    -0.128  0.195    -0.306  0.071    -0.041  0.046    -0.002  0.040     0.007  0.028
+  1950    10    -0.086  0.157    -0.285  0.070    -0.034  0.045    -0.001  0.039     0.007  0.028
+  1950    11    -0.723  0.151    -0.273  0.070    -0.035  0.045    -0.002  0.039     0.006  0.028
+  1950    12    -0.086  0.158    -0.284  0.068    -0.036  0.046    -0.000  0.039     0.007  0.028
+  1951     1    -0.481  0.147    -0.272  0.063    -0.036  0.046    -0.005  0.039     0.008  0.028
+  1951     2    -0.894  0.151    -0.226  0.064    -0.034  0.046    -0.011  0.039     0.008  0.028
+  1951     3    -0.238  0.152    -0.199  0.062    -0.032  0.045    -0.013  0.039     0.010  0.027
+  1951     4     0.036  0.165    -0.158  0.062    -0.032  0.046    -0.021  0.039     0.010  0.027
+  1951     5     0.102  0.231    -0.088  0.061    -0.037  0.046    -0.026  0.039     0.011  0.027
+  1951     6    -0.257  0.175    -0.025  0.062    -0.028  0.046    -0.028  0.039     0.011  0.027
+  1951     7    -0.072  0.243     0.051  0.063    -0.048  0.046    -0.030  0.038     0.010  0.027
+  1951     8     0.128  0.253     0.138  0.065    -0.044  0.046    -0.035  0.038     0.009  0.027
+  1951     9     0.190  0.214     0.125  0.067    -0.042  0.045    -0.037  0.038     0.011  0.027
+  1951    10     0.408  0.143     0.133  0.069    -0.046  0.045    -0.040  0.037     0.009  0.026
+  1951    11     0.112  0.160     0.124  0.071    -0.051  0.045    -0.045  0.037     0.010  0.026
+  1951    12     0.679  0.150     0.141  0.072    -0.048  0.044    -0.041  0.037     0.010  0.026
+  1952     1     0.430  0.143     0.163  0.072    -0.048  0.044    -0.042  0.036     0.009  0.026
+  1952     2     0.143  0.193     0.158  0.071    -0.048  0.044    -0.044  0.036     0.013  0.026
+  1952     3    -0.382  0.179     0.154  0.072    -0.045  0.045    -0.050  0.036     0.014  0.025
+  1952     4     0.129  0.226     0.111  0.073    -0.045  0.045    -0.053  0.036     0.014  0.025
+  1952     5    -0.004  0.268     0.046  0.074    -0.038  0.044    -0.053  0.036     0.013  0.025
+  1952     6    -0.054  0.214    -0.011  0.075    -0.037  0.044    -0.051  0.035     0.013  0.025
+  1952     7     0.187  0.281    -0.018  0.076    -0.014  0.044    -0.053  0.036     0.012  0.024
+  1952     8     0.069  0.306     0.003  0.076    -0.007  0.043    -0.051  0.035     0.012  0.024
+  1952     9     0.144  0.223     0.066  0.075    -0.018  0.043    -0.050  0.036     0.012  0.024
+  1952    10    -0.110  0.169     0.100  0.075    -0.018  0.043    -0.054  0.036     0.011  0.024
+  1952    11    -0.671  0.178     0.114  0.075    -0.020  0.043    -0.055  0.036     0.010  0.024
+  1952    12     0.000  0.177     0.126  0.075    -0.017  0.043    -0.049  0.035     0.010  0.023
+  1953     1     0.343  0.179     0.112  0.077    -0.016  0.043    -0.047  0.034     0.013  0.023
+  1953     2     0.393  0.198     0.120  0.077    -0.002  0.042    -0.043  0.033     0.015  0.023
+  1953     3     0.373  0.163     0.117  0.078    -0.003  0.042    -0.039  0.033     0.015  0.023
+  1953     4     0.542  0.224     0.153  0.077     0.006  0.042    -0.039  0.033     0.013  0.023
+  1953     5     0.156  0.277     0.194  0.078     0.016  0.041    -0.039  0.032     0.011  0.023
+  1953     6     0.100  0.222     0.224  0.078     0.011  0.041    -0.043  0.032     0.012  0.023
+  1953     7     0.014  0.280     0.148  0.076     0.017  0.040    -0.043  0.032     0.012  0.022
+  1953     8     0.163  0.301     0.099  0.075     0.024  0.040    -0.045  0.032     0.014  0.022
+  1953     9     0.110  0.224     0.053  0.075     0.020  0.039    -0.048  0.031     0.016  0.022
+  1953    10     0.326  0.164    -0.008  0.074     0.012  0.039    -0.050  0.031     0.016  0.022
+  1953    11    -0.180  0.174    -0.052  0.073    -0.000  0.038    -0.049  0.031     0.018  0.022
+  1953    12     0.355  0.174    -0.069  0.071    -0.001  0.038    -0.047  0.031     0.016  0.022
+  1954     1    -0.560  0.141    -0.082  0.070    -0.003  0.038    -0.049  0.031     0.013  0.021
+  1954     2    -0.197  0.193    -0.100  0.070    -0.017  0.038    -0.045  0.031     0.011  0.021
+  1954     3    -0.180  0.155    -0.112  0.071    -0.026  0.038    -0.038  0.030     0.008  0.021
+  1954     4    -0.192  0.233    -0.116  0.072    -0.039  0.037    -0.034  0.029     0.006  0.021
+  1954     5    -0.370  0.226    -0.063  0.071    -0.046  0.036    -0.034  0.029     0.004  0.021
+  1954     6    -0.107  0.191    -0.101  0.070    -0.059  0.036    -0.031  0.028     0.004  0.021
+  1954     7    -0.146  0.226     0.011  0.070    -0.068  0.035    -0.029  0.028     0.004  0.021
+  1954     8    -0.053  0.250     0.020  0.067    -0.073  0.034    -0.029  0.027     0.002  0.021
+  1954     9    -0.030  0.204    -0.028  0.070    -0.072  0.033    -0.029  0.028    -0.002  0.021
+  1954    10     0.276  0.150    -0.030  0.065    -0.074  0.033    -0.032  0.027    -0.006  0.021
+  1954    11     0.462  0.142    -0.013  0.061    -0.076  0.033    -0.036  0.027    -0.006  0.021
+  1954    12    -0.103  0.139     0.000  0.061    -0.070  0.033    -0.033  0.027    -0.006  0.021
+  1955     1     0.782  0.137     0.001  0.061    -0.078  0.034    -0.027  0.027    -0.005  0.020
+  1955     2    -0.091  0.142     0.037  0.057    -0.076  0.034    -0.018  0.026    -0.003  0.020
+  1955     3    -0.750  0.125     0.025  0.054    -0.076  0.036    -0.027  0.026    -0.003  0.020
+  1955     4    -0.219  0.151     0.040  0.053    -0.070  0.037    -0.027  0.026    -0.005  0.020
+  1955     5    -0.164  0.187    -0.011  0.052    -0.054  0.037    -0.030  0.025    -0.005  0.020
+  1955     6     0.050  0.133    -0.031  0.051    -0.043  0.036    -0.030  0.025    -0.005  0.020
+  1955     7    -0.142  0.172    -0.110  0.048    -0.033  0.035    -0.029  0.025    -0.005  0.020
+  1955     8     0.378  0.207    -0.142  0.050    -0.032  0.033    -0.027  0.025    -0.008  0.020
+  1955     9    -0.168  0.147    -0.117  0.047    -0.037  0.033    -0.026  0.024    -0.010  0.019
+  1955    10     0.456  0.102    -0.135  0.047    -0.043  0.033    -0.023  0.024    -0.010  0.019
+  1955    11    -0.147  0.119    -0.174  0.042    -0.043  0.032    -0.020  0.023    -0.011  0.019
+  1955    12    -0.346  0.110    -0.206  0.043    -0.050  0.032    -0.014  0.023    -0.008  0.019
+  1956     1    -0.163  0.083    -0.210  0.039    -0.050  0.031    -0.007  0.023    -0.010  0.019
+  1956     2    -0.475  0.070    -0.301  0.034    -0.057  0.031     0.004  0.022    -0.011  0.019
+  1956     3    -0.453  0.102    -0.314  0.032    -0.064  0.030     0.008  0.022    -0.010  0.019
+  1956     4    -0.437  0.083    -0.381  0.035    -0.068  0.030     0.010  0.022    -0.013  0.019
+  1956     5    -0.631  0.129    -0.399  0.041    -0.062  0.030     0.011  0.022    -0.013  0.019
+  1956     6    -0.335  0.116    -0.375  0.041    -0.065  0.031     0.015  0.022    -0.012  0.019
+  1956     7    -0.190  0.077    -0.372  0.039    -0.050  0.030     0.015  0.022    -0.011  0.019
+  1956     8    -0.709  0.121    -0.343  0.039    -0.045  0.029     0.014  0.022    -0.012  0.018
+  1956     9    -0.332  0.074    -0.333  0.039    -0.034  0.030     0.013  0.022    -0.012  0.018
+  1956    10    -0.339  0.106    -0.296  0.050    -0.023  0.029     0.010  0.022    -0.012  0.018
+  1956    11    -0.365  0.136    -0.253  0.044    -0.017  0.029     0.010  0.021    -0.013  0.018
+  1956    12    -0.062  0.091    -0.202  0.047    -0.014  0.029     0.003  0.020    -0.013  0.018
+  1957     1    -0.128  0.070    -0.209  0.065    -0.011  0.028     0.002  0.020    -0.012  0.018
+  1957     2    -0.127  0.108    -0.137  0.083    -0.010  0.027     0.005  0.020    -0.014  0.018
+  1957     3    -0.332  0.063    -0.094  0.095    -0.013  0.027     0.010  0.019    -0.015  0.018
+  1957     4     0.007  0.155    -0.048  0.099    -0.019  0.027     0.010  0.019    -0.016  0.018
+  1957     5    -0.113  0.069     0.006  0.097    -0.033  0.027     0.008  0.019    -0.015  0.018
+  1957     6     0.272  0.174     0.068  0.096    -0.029  0.027     0.008  0.018    -0.016  0.018
+  1957     7    -0.275  0.206     0.157  0.096    -0.041  0.027     0.005  0.018    -0.016  0.018
+  1957     8     0.163  0.223     0.204  0.092    -0.030  0.027     0.003  0.018    -0.017  0.018
+  1957     9     0.176  0.317     0.241  0.094    -0.035  0.027     0.000  0.019    -0.017  0.018
+  1957    10     0.222  0.176     0.254  0.080    -0.036  0.027     0.003  0.019    -0.018  0.018
+  1957    11     0.276  0.143     0.277  0.082    -0.041  0.026     0.010  0.019    -0.019  0.018
+  1957    12     0.679  0.114     0.227  0.068    -0.042  0.026     0.012  0.018    -0.018  0.017
+  1958     1     0.940  0.112     0.254  0.050    -0.043  0.026     0.011  0.018    -0.022  0.017
+  1958     2     0.448  0.145     0.214  0.034    -0.051  0.026     0.014  0.017    -0.022  0.017
+  1958     3     0.105  0.070     0.176  0.033    -0.048  0.025     0.009  0.017    -0.017  0.017
+  1958     4     0.168  0.143     0.167  0.040    -0.052  0.025     0.003  0.018    -0.017  0.017
+  1958     5     0.157  0.116     0.157  0.037    -0.055  0.025    -0.001  0.018    -0.019  0.017
+  1958     6    -0.326  0.099     0.113  0.032    -0.039  0.025    -0.003  0.018    -0.021  0.017
+  1958     7     0.048  0.164     0.063  0.033    -0.031  0.024    -0.002  0.018    -0.023  0.017
+  1958     8    -0.313  0.045     0.034  0.030    -0.015  0.024     0.000  0.018    -0.025  0.017
+  1958     9    -0.278  0.072     0.068  0.028    -0.004  0.024     0.003  0.018    -0.027  0.017
+  1958    10     0.107  0.047     0.093  0.022     0.008  0.023     0.006  0.018    -0.028  0.017
+  1958    11     0.155  0.067     0.078  0.025     0.023  0.021     0.011  0.018    -0.029  0.017
+  1958    12     0.161  0.066     0.111  0.018     0.032  0.021     0.009  0.018    -0.030  0.017
+  1959     1     0.335  0.095     0.112  0.013     0.034  0.022     0.016  0.018    -0.036  0.017
+  1959     2     0.098  0.068     0.135  0.012     0.045  0.023     0.015  0.018    -0.037  0.017
+  1959     3     0.519  0.118     0.143  0.019     0.052  0.022     0.013  0.018    -0.037  0.017
+  1959     4     0.466  0.133     0.124  0.019     0.058  0.022     0.011  0.017    -0.036  0.016
+  1959     5    -0.027  0.066     0.082  0.020     0.066  0.020     0.010  0.017    -0.036  0.016
+  1959     6     0.074  0.053     0.078  0.020     0.064  0.019     0.011  0.018    -0.035  0.016
+  1959     7     0.060  0.076     0.058  0.021     0.073  0.019     0.011  0.018    -0.035  0.016
+  1959     8    -0.040  0.040     0.096  0.017     0.082  0.018     0.010  0.018    -0.035  0.016
+  1959     9    -0.181  0.100    -0.035  0.020     0.091  0.019     0.005  0.018    -0.035  0.016
+  1959    10    -0.120  0.121    -0.095  0.029     0.094  0.017    -0.001  0.018    -0.035  0.016
+  1959    11    -0.356  0.074    -0.133  0.033     0.092  0.015    -0.006  0.018    -0.034  0.016
+  1959    12     0.123  0.072    -0.142  0.035     0.086  0.014    -0.006  0.018    -0.031  0.016
+  1960     1     0.090  0.062    -0.162  0.036     0.087  0.013    -0.011  0.018    -0.027  0.016
+  1960     2     0.552  0.155    -0.165  0.037     0.081  0.014    -0.012  0.017    -0.023  0.015
+  1960     3    -1.053  0.091    -0.153  0.040     0.076  0.020    -0.007  0.017    -0.023  0.015
+  1960     4    -0.256  0.106    -0.125  0.033     0.075  0.024    -0.008  0.018    -0.021  0.015
+  1960     5    -0.475  0.071    -0.119  0.039     0.074  0.026    -0.008  0.019    -0.022  0.015
+  1960     6    -0.030  0.065    -0.078  0.040     0.066  0.025    -0.010  0.019    -0.021  0.015
+  1960     7    -0.182  0.064    -0.060  0.036     0.055  0.024    -0.011  0.019    -0.020  0.015
+  1960     8    -0.083  0.068    -0.068  0.031     0.061  0.024    -0.015  0.019    -0.019  0.015
+  1960     9    -0.030  0.066     0.040  0.031     0.055  0.025    -0.017  0.019    -0.018  0.015
+  1960    10     0.217  0.074     0.084  0.027     0.049  0.025    -0.019  0.019    -0.018  0.015
+  1960    11    -0.284  0.133     0.146  0.027     0.041  0.026    -0.020  0.019    -0.015  0.015
+  1960    12     0.609  0.060     0.165  0.026     0.045  0.027    -0.016  0.019    -0.015  0.014
+  1961     1     0.312  0.060     0.174  0.027     0.046  0.025    -0.016  0.018    -0.012  0.014
+  1961     2     0.453  0.050     0.177  0.030     0.058  0.024    -0.011  0.018    -0.009  0.014
+  1961     3     0.238  0.100     0.189  0.031     0.070  0.024    -0.006  0.018    -0.010  0.014
+  1961     4     0.272  0.130     0.174  0.028     0.079  0.023    -0.004  0.018    -0.010  0.014
+  1961     5     0.273  0.113     0.205  0.022     0.084  0.023     0.000  0.017    -0.011  0.014
+  1961     6     0.192  0.113     0.141  0.020     0.083  0.025     0.003  0.016    -0.012  0.014
+  1961     7    -0.068  0.098     0.146  0.023     0.081  0.024     0.007  0.016    -0.012  0.014
+  1961     8    -0.048  0.117     0.146  0.023     0.076  0.025     0.011  0.016    -0.013  0.014
+  1961     9     0.112  0.075     0.141  0.028     0.059  0.026     0.014  0.017    -0.014  0.014
+  1961    10     0.035  0.145     0.133  0.036     0.044  0.026     0.016  0.017    -0.015  0.015
+  1961    11     0.085  0.059     0.091  0.037     0.037  0.028     0.018  0.017    -0.015  0.014
+  1961    12    -0.159  0.051     0.068  0.042     0.035  0.027     0.016  0.017    -0.016  0.014
+  1962     1     0.375  0.057     0.059  0.041     0.033  0.028     0.018  0.017    -0.021  0.015
+  1962     2     0.452  0.089     0.046  0.040     0.030  0.029     0.016  0.016    -0.025  0.014
+  1962     3     0.183  0.132     0.026  0.058     0.024  0.030     0.019  0.017    -0.023  0.014
+  1962     4     0.173  0.101     0.037  0.060     0.018  0.029     0.020  0.016    -0.024  0.014
+  1962     5    -0.233  0.081     0.044  0.066     0.022  0.030     0.023  0.016    -0.025  0.014
+  1962     6    -0.082  0.169     0.078  0.065     0.017  0.030     0.018  0.016    -0.024  0.014
+  1962     7    -0.179  0.057     0.068  0.071     0.019  0.030     0.020  0.017    -0.026  0.014
+  1962     8    -0.203  0.081     0.096  0.074     0.006  0.027     0.018  0.018    -0.025  0.014
+  1962     9    -0.124  0.178     0.060  0.071     0.021  0.028     0.016  0.020    -0.026  0.014
+  1962    10     0.173  0.140     0.029  0.063     0.020  0.028     0.018  0.022    -0.026  0.014
+  1962    11     0.159  0.093     0.022  0.068     0.025  0.029     0.017  0.022    -0.024  0.014
+  1962    12     0.249  0.071     0.021  0.061     0.023  0.031     0.013  0.021    -0.024  0.014
+  1963     1     0.257  0.118     0.047  0.060     0.021  0.029     0.003  0.021    -0.023  0.014
+  1963     2     0.785  0.119     0.099  0.055     0.021  0.029    -0.001  0.020    -0.022  0.013
+  1963     3    -0.247  0.090     0.146  0.042     0.015  0.028     0.005  0.020    -0.022  0.013
+  1963     4    -0.194  0.085     0.187  0.034     0.015  0.027     0.004  0.021    -0.021  0.013
+  1963     5    -0.322  0.080     0.209  0.030     0.015  0.025     0.000  0.021    -0.020  0.014
+  1963     6    -0.085  0.102     0.197  0.034     0.007  0.025     0.000  0.021    -0.019  0.014
+  1963     7     0.128  0.081     0.195  0.034    -0.001  0.025    -0.003  0.020    -0.019  0.014
+  1963     8     0.416  0.045     0.111  0.032    -0.007  0.026    -0.004  0.020    -0.020  0.014
+  1963     9     0.443  0.112     0.092  0.027    -0.008  0.024    -0.007  0.021    -0.021  0.014
+  1963    10     0.659  0.054     0.071  0.026    -0.016  0.026    -0.006  0.021    -0.020  0.014
+  1963    11     0.434  0.062     0.060  0.024    -0.023  0.025    -0.009  0.021    -0.019  0.014
+  1963    12     0.097  0.094     0.064  0.027    -0.025  0.022    -0.014  0.022    -0.020  0.014
+  1964     1     0.241  0.138     0.047  0.026    -0.020  0.022    -0.022  0.021    -0.019  0.014
+  1964     2    -0.225  0.071    -0.006  0.031    -0.022  0.022    -0.030  0.022    -0.021  0.013
+  1964     3    -0.481  0.075    -0.089  0.027    -0.023  0.024    -0.036  0.022    -0.021  0.014
+  1964     4    -0.440  0.100    -0.181  0.025    -0.026  0.023    -0.038  0.023    -0.020  0.013
+  1964     5    -0.461  0.119    -0.229  0.027    -0.030  0.024    -0.038  0.023    -0.019  0.013
+  1964     6    -0.037  0.081    -0.254  0.029    -0.033  0.024    -0.039  0.023    -0.019  0.014
+  1964     7    -0.067  0.112    -0.254  0.030    -0.037  0.025    -0.041  0.024    -0.019  0.014
+  1964     8    -0.229  0.129    -0.253  0.032    -0.050  0.024    -0.041  0.023    -0.018  0.013
+  1964     9    -0.553  0.050    -0.226  0.033    -0.052  0.022    -0.040  0.024    -0.020  0.014
+  1964    10    -0.443  0.039    -0.219  0.036    -0.054  0.023    -0.038  0.024    -0.022  0.014
+  1964    11    -0.137  0.062    -0.192  0.033    -0.046  0.023    -0.032  0.024    -0.025  0.014
+  1964    12    -0.209  0.045    -0.204  0.047    -0.050  0.022    -0.029  0.024    -0.025  0.014
+  1965     1     0.238  0.071    -0.223  0.036    -0.047  0.023    -0.028  0.023    -0.026  0.014
+  1965     2    -0.206  0.075    -0.212  0.034    -0.046  0.023    -0.028  0.023    -0.025  0.014
+  1965     3    -0.152  0.115    -0.199  0.026    -0.045  0.021    -0.020  0.022    -0.020  0.014
+  1965     4    -0.363  0.098    -0.144  0.024    -0.039  0.020    -0.016  0.021    -0.018  0.014
+  1965     5    -0.130  0.094    -0.154  0.021    -0.040  0.018    -0.013  0.021    -0.016  0.014
+  1965     6    -0.184  0.154    -0.126  0.020    -0.040  0.018    -0.013  0.020    -0.016  0.013
+  1965     7    -0.298  0.143    -0.158  0.022    -0.049  0.018    -0.011  0.020    -0.016  0.013
+  1965     8    -0.097  0.059    -0.137  0.021    -0.062  0.018    -0.012  0.021    -0.019  0.014
+  1965     9    -0.392  0.102    -0.108  0.017    -0.045  0.017    -0.011  0.020    -0.018  0.014
+  1965    10     0.215  0.129    -0.094  0.019    -0.040  0.018    -0.012  0.021    -0.020  0.014
+  1965    11    -0.257  0.062    -0.095  0.031    -0.041  0.017    -0.010  0.020    -0.020  0.014
+  1965    12     0.122  0.073    -0.075  0.047    -0.044  0.015    -0.016  0.020    -0.019  0.014
+  1966     1    -0.143  0.055    -0.031  0.038    -0.053  0.017    -0.016  0.020    -0.017  0.014
+  1966     2     0.049  0.044    -0.036  0.036    -0.066  0.018    -0.023  0.021    -0.016  0.014
+  1966     3     0.191  0.119    -0.001  0.038    -0.083  0.021    -0.027  0.020    -0.017  0.014
+  1966     4    -0.193  0.123    -0.031  0.039    -0.091  0.021    -0.030  0.021    -0.016  0.014
+  1966     5    -0.145  0.092    -0.023  0.041    -0.102  0.021    -0.033  0.022    -0.015  0.015
+  1966     6     0.054  0.135    -0.056  0.038    -0.111  0.021    -0.038  0.021    -0.015  0.015
+  1966     7     0.230  0.102    -0.038  0.033    -0.126  0.022    -0.040  0.021    -0.015  0.014
+  1966     8    -0.150  0.114    -0.069  0.038    -0.136  0.021    -0.040  0.020    -0.014  0.014
+  1966     9     0.028  0.098    -0.078  0.040    -0.132  0.021    -0.040  0.019    -0.014  0.014
+  1966    10    -0.143  0.064    -0.057  0.034    -0.121  0.021    -0.040  0.020    -0.015  0.014
+  1966    11    -0.160  0.098    -0.022  0.036    -0.113  0.021    -0.039  0.020    -0.016  0.015
+  1966    12    -0.284  0.070    -0.054  0.039    -0.113  0.022    -0.036  0.020    -0.015  0.015
+  1967     1     0.082  0.063    -0.073  0.042    -0.114  0.022    -0.045  0.021    -0.015  0.015
+  1967     2    -0.332  0.068    -0.075  0.043    -0.112  0.021    -0.054  0.020    -0.014  0.014
+  1967     3     0.083  0.074    -0.082  0.038    -0.105  0.022    -0.056  0.019    -0.011  0.015
+  1967     4     0.059  0.076    -0.024  0.036    -0.094  0.022    -0.058  0.019    -0.009  0.015
+  1967     5     0.277  0.113    -0.006  0.029    -0.086  0.021    -0.057  0.019    -0.006  0.015
+  1967     6    -0.325  0.101     0.039  0.024    -0.074  0.021    -0.057  0.019    -0.006  0.015
+  1967     7     0.001  0.190     0.009  0.025    -0.074  0.020    -0.056  0.019    -0.004  0.015
+  1967     8    -0.176  0.117     0.037  0.024    -0.062  0.020    -0.052  0.018    -0.004  0.016
+  1967     9    -0.051  0.067     0.096  0.026    -0.061  0.019    -0.053  0.017    -0.005  0.017
+  1967    10     0.541  0.068     0.099  0.025    -0.051  0.018    -0.054  0.017    -0.007  0.017
+  1967    11     0.062  0.076     0.046  0.023    -0.051  0.018    -0.058  0.016    -0.006  0.018
+  1967    12     0.252  0.052     0.050  0.034    -0.048  0.018    -0.059  0.016    -0.008  0.018
+  1968     1    -0.273  0.039     0.016  0.030    -0.042  0.018    -0.057  0.016    -0.012  0.017
+  1968     2    -0.002  0.044    -0.003  0.035    -0.045  0.019    -0.058  0.015    -0.013  0.017
+  1968     3     0.796  0.049    -0.046  0.038    -0.036  0.019    -0.052  0.015    -0.012  0.017
+  1968     4     0.093  0.086    -0.076  0.038    -0.038  0.020    -0.046  0.015    -0.011  0.017
+  1968     5    -0.351  0.113    -0.100  0.037    -0.035  0.021    -0.040  0.015    -0.012  0.018
+  1968     6    -0.282  0.133    -0.155  0.040    -0.039  0.020    -0.036  0.015    -0.011  0.018
+  1968     7    -0.403  0.101    -0.189  0.042    -0.031  0.020    -0.037  0.015    -0.012  0.017
+  1968     8    -0.408  0.068    -0.255  0.048    -0.038  0.020    -0.041  0.016    -0.013  0.017
+  1968     9    -0.569  0.125    -0.342  0.046    -0.047  0.021    -0.044  0.017    -0.012  0.017
+  1968    10     0.185  0.089    -0.331  0.045    -0.044  0.020    -0.047  0.016    -0.012  0.017
+  1968    11    -0.223  0.054    -0.302  0.039    -0.044  0.020    -0.049  0.016    -0.012  0.017
+  1968    12    -0.414  0.043    -0.281  0.040    -0.052  0.021    -0.049  0.016    -0.012  0.018
+  1969     1    -0.676  0.083    -0.261  0.039    -0.059  0.021    -0.054  0.017    -0.013  0.017
+  1969     2    -0.803  0.100    -0.234  0.036    -0.057  0.020    -0.057  0.017    -0.015  0.018
+  1969     3    -0.246  0.094    -0.196  0.031    -0.058  0.018    -0.055  0.017    -0.017  0.018
+  1969     4     0.228  0.076    -0.193  0.032    -0.053  0.018    -0.051  0.017    -0.019  0.018
+  1969     5    -0.004  0.062    -0.149  0.033    -0.047  0.017    -0.048  0.017    -0.019  0.019
+  1969     6    -0.028  0.109    -0.069  0.032    -0.039  0.017    -0.049  0.018    -0.020  0.019
+  1969     7    -0.167  0.106     0.004  0.026    -0.053  0.017    -0.049  0.017    -0.021  0.020
+  1969     8    -0.085  0.064     0.114  0.025    -0.058  0.018    -0.047  0.017    -0.022  0.019
+  1969     9    -0.109  0.077     0.130  0.022    -0.061  0.016    -0.045  0.018    -0.020  0.020
+  1969    10     0.224  0.056     0.130  0.022    -0.062  0.016    -0.043  0.018    -0.019  0.019
+  1969    11     0.305  0.097     0.118  0.023    -0.069  0.016    -0.044  0.017    -0.016  0.019
+  1969    12     0.547  0.066     0.121  0.026    -0.064  0.017    -0.043  0.017    -0.013  0.019
+  1970     1     0.196  0.035     0.137  0.023    -0.066  0.016    -0.041  0.017    -0.012  0.019
+  1970     2     0.511  0.071     0.123  0.026    -0.059  0.016    -0.038  0.017    -0.012  0.019
+  1970     3    -0.052  0.094     0.146  0.024    -0.061  0.015    -0.033  0.017    -0.008  0.019
+  1970     4     0.235  0.118     0.133  0.025    -0.069  0.015    -0.028  0.016    -0.005  0.019
+  1970     5    -0.146  0.067     0.103  0.028    -0.075  0.015    -0.024  0.015    -0.001  0.020
+  1970     6     0.004  0.114     0.046  0.026    -0.077  0.016    -0.022  0.014    -0.000  0.020
+  1970     7     0.025  0.056     0.058  0.023    -0.066  0.015    -0.020  0.015     0.002  0.020
+  1970     8    -0.252  0.118    -0.016  0.027    -0.055  0.015    -0.023  0.015     0.003  0.020
+  1970     9     0.160  0.080    -0.038  0.023    -0.060  0.015    -0.020  0.016     0.004  0.020
+  1970    10     0.074  0.051    -0.059  0.020    -0.051  0.015    -0.021  0.016     0.004  0.020
+  1970    11    -0.060  0.054    -0.059  0.023    -0.039  0.015    -0.021  0.016     0.008  0.020
+  1970    12    -0.138  0.052    -0.094  0.027    -0.029  0.015    -0.022  0.016     0.007  0.020
+  1971     1     0.342  0.057    -0.116  0.028    -0.021  0.015    -0.019  0.016     0.010  0.020
+  1971     2    -0.369  0.105    -0.097  0.019    -0.015  0.015    -0.021  0.016     0.012  0.020
+  1971     3    -0.322  0.056    -0.110  0.018    -0.005  0.013    -0.029  0.017     0.015  0.021
+  1971     4    -0.019  0.071    -0.106  0.022    -0.003  0.012    -0.028  0.016     0.016  0.021
+  1971     5    -0.139  0.085    -0.085  0.023     0.003  0.012    -0.030  0.017     0.016  0.021
+  1971     6    -0.422  0.093    -0.055  0.025     0.013  0.012    -0.034  0.018     0.016  0.021
+  1971     7    -0.236  0.084    -0.146  0.028     0.018  0.012    -0.037  0.016     0.017  0.021
+  1971     8    -0.020  0.082    -0.168  0.025     0.021  0.013    -0.039  0.014     0.020  0.020
+  1971     9    -0.001  0.138    -0.150  0.022     0.023  0.013    -0.042  0.014     0.020  0.021
+  1971    10     0.121  0.091    -0.147  0.023     0.019  0.014    -0.047  0.014     0.020  0.020
+  1971    11     0.191  0.084    -0.151  0.023     0.016  0.014    -0.049  0.014     0.021  0.020
+  1971    12     0.218  0.071    -0.114  0.022     0.014  0.014    -0.047  0.014     0.025  0.020
+  1972     1    -0.750  0.084    -0.107  0.027     0.016  0.013    -0.048  0.014     0.023  0.020
+  1972     2    -0.626  0.064    -0.083  0.027     0.019  0.013    -0.044  0.014     0.021  0.020
+  1972     3    -0.104  0.109    -0.100  0.025     0.014  0.014    -0.041  0.014     0.019  0.019
+  1972     4     0.007  0.070    -0.104  0.025     0.007  0.014    -0.037  0.014     0.019  0.019
+  1972     5    -0.181  0.109    -0.145  0.023    -0.001  0.015    -0.036  0.014     0.019  0.019
+  1972     6     0.017  0.131    -0.152  0.023    -0.012  0.016    -0.030  0.015     0.019  0.019
+  1972     7    -0.147  0.116    -0.056  0.023    -0.008  0.016    -0.028  0.014     0.020  0.019
+  1972     8     0.273  0.075     0.052  0.022    -0.014  0.017    -0.025  0.014     0.021  0.019
+  1972     9    -0.215  0.139     0.102  0.021    -0.006  0.017    -0.027  0.014     0.021  0.018
+  1972    10     0.080  0.108     0.150  0.020    -0.006  0.017    -0.032  0.014     0.020  0.017
+  1972    11    -0.306  0.088     0.200  0.018     0.003  0.016    -0.029  0.014     0.019  0.017
+  1972    12     0.135  0.059     0.224  0.019     0.003  0.015    -0.030  0.015     0.021  0.017
+  1973     1     0.412  0.058     0.243  0.022     0.002  0.015    -0.027  0.015     0.023  0.017
+  1973     2     0.661  0.094     0.216  0.021    -0.001  0.016    -0.025  0.015     0.022  0.017
+  1973     3     0.500  0.035     0.234  0.024    -0.004  0.017    -0.029  0.015     0.025  0.016
+  1973     4     0.588  0.133     0.256  0.015    -0.004  0.016    -0.026  0.015     0.027  0.016
+  1973     5     0.408  0.058     0.293  0.015    -0.007  0.017    -0.023  0.015     0.030  0.016
+  1973     6     0.316  0.054     0.296  0.017    -0.004  0.017    -0.023  0.015     0.030  0.015
+  1973     7     0.079  0.093     0.231  0.020    -0.006  0.018    -0.020  0.016     0.030  0.015
+  1973     8    -0.050  0.133     0.122  0.018    -0.003  0.017    -0.022  0.015     0.030  0.015
+  1973     9    -0.006  0.058     0.069  0.023    -0.011  0.018    -0.018  0.015     0.030  0.015
+  1973    10     0.351  0.085     0.020  0.030    -0.012  0.017    -0.019  0.015     0.028  0.015
+  1973    11     0.132  0.072    -0.028  0.032    -0.016  0.016    -0.015  0.014     0.029  0.014
+  1973    12     0.176  0.036    -0.065  0.035    -0.016  0.017    -0.011  0.014     0.030  0.014
+  1974     1    -0.369  0.058    -0.080  0.029    -0.016  0.014    -0.005  0.014     0.030  0.014
+  1974     2    -0.650  0.104    -0.067  0.025    -0.022  0.012    -0.001  0.014     0.030  0.014
+  1974     3    -0.133  0.118    -0.099  0.030    -0.026  0.014     0.002  0.015     0.034  0.014
+  1974     4    -0.003  0.099    -0.142  0.038    -0.040  0.014     0.001  0.015     0.036  0.014
+  1974     5    -0.163  0.052    -0.169  0.032    -0.051  0.015    -0.001  0.016     0.040  0.013
+  1974     6    -0.132  0.073    -0.192  0.040    -0.054  0.015    -0.001  0.016     0.040  0.013
+  1974     7    -0.100  0.064    -0.127  0.044    -0.044  0.015    -0.002  0.016     0.040  0.013
+  1974     8     0.101  0.110    -0.059  0.050    -0.030  0.015    -0.002  0.016     0.042  0.013
+  1974     9    -0.382  0.101    -0.015  0.044    -0.021  0.017    -0.000  0.016     0.043  0.013
+  1974    10    -0.172  0.070     0.005  0.038    -0.013  0.017     0.000  0.016     0.046  0.013
+  1974    11    -0.195  0.080     0.051  0.038    -0.002  0.019    -0.001  0.016     0.045  0.013
+  1974    12    -0.101  0.111     0.065  0.034     0.003  0.019     0.003  0.016     0.044  0.013
+  1975     1     0.420  0.059     0.071  0.035     0.010  0.020     0.004  0.016     0.045  0.013
+  1975     2     0.166  0.106     0.026  0.036     0.009  0.019     0.003  0.016     0.043  0.013
+  1975     3     0.389  0.041     0.057  0.036     0.008  0.020     0.004  0.017     0.044  0.013
+  1975     4     0.235  0.060     0.075  0.033     0.005  0.019     0.007  0.019     0.047  0.013
+  1975     5     0.388  0.078     0.073  0.037     0.018  0.018     0.011  0.020     0.048  0.012
+  1975     6     0.036  0.127     0.082  0.035     0.017  0.018     0.012  0.021     0.049  0.012
+  1975     7    -0.028  0.088     0.068  0.038     0.012  0.019     0.014  0.021     0.049  0.013
+  1975     8    -0.432  0.138     0.038  0.035     0.004  0.020     0.019  0.022     0.050  0.013
+  1975     9    -0.016  0.085    -0.062  0.033     0.001  0.021     0.019  0.022     0.051  0.014
+  1975    10     0.044  0.093    -0.086  0.032    -0.001  0.022     0.021  0.022     0.050  0.014
+  1975    11    -0.211  0.059    -0.151  0.032    -0.008  0.022     0.026  0.022     0.051  0.014
+  1975    12     0.002  0.088    -0.190  0.032    -0.018  0.022     0.030  0.022     0.051  0.014
+  1976     1     0.248  0.086    -0.205  0.027    -0.019  0.023     0.037  0.022     0.054  0.014
+  1976     2    -0.191  0.065    -0.202  0.029    -0.029  0.022     0.047  0.022     0.057  0.014
+  1976     3    -0.812  0.103    -0.220  0.030    -0.030  0.022     0.057  0.023     0.058  0.014
+  1976     4    -0.051  0.074    -0.287  0.029    -0.035  0.023     0.062  0.023     0.061  0.014
+  1976     5    -0.390  0.183    -0.306  0.029    -0.033  0.022     0.064  0.023     0.063  0.014
+  1976     6    -0.434  0.113    -0.305  0.034    -0.034  0.023     0.070  0.023     0.062  0.015
+  1976     7    -0.207  0.163    -0.336  0.036    -0.027  0.023     0.075  0.023     0.061  0.015
+  1976     8    -0.392  0.212    -0.301  0.040    -0.023  0.024     0.080  0.023     0.062  0.014
+  1976     9    -0.237  0.057    -0.201  0.043    -0.019  0.024     0.080  0.024     0.061  0.014
+  1976    10    -0.763  0.188    -0.154  0.040    -0.017  0.024     0.080  0.023     0.061  0.014
+  1976    11    -0.436  0.092    -0.083  0.047    -0.018  0.026     0.080  0.022     0.061  0.013
+  1976    12     0.020  0.076    -0.017  0.044    -0.017  0.025     0.085  0.022     0.063  0.013
+  1977     1    -0.131  0.084     0.023  0.045    -0.020  0.026     0.091  0.021     0.065  0.013
+  1977     2     0.230  0.073     0.072  0.045    -0.023  0.025     0.097  0.021     0.070  0.013
+  1977     3     0.394  0.115     0.070  0.045    -0.015  0.023     0.094  0.022     0.069  0.013
+  1977     4     0.508  0.071     0.125  0.037    -0.007  0.022     0.095  0.022     0.070  0.013
+  1977     5     0.458  0.157     0.198  0.037    -0.000  0.023     0.096  0.023     0.069  0.012
+  1977     6     0.366  0.058     0.204  0.036     0.017  0.021     0.094  0.022     0.073  0.013
+  1977     7     0.271  0.069     0.222  0.034     0.016  0.021     0.097  0.022     0.075  0.012
+  1977     8     0.199  0.098     0.222  0.035     0.020  0.021     0.094  0.022     0.075  0.012
+  1977     9    -0.266  0.083     0.214  0.029     0.014  0.023     0.095  0.023     0.077  0.012
+  1977    10    -0.100  0.067     0.209  0.027     0.019  0.025     0.094  0.022     0.076  0.012
+  1977    11     0.444  0.075     0.174  0.028     0.019  0.027     0.095  0.022     0.076  0.012
+  1977    12     0.092  0.087     0.120  0.027     0.020  0.030     0.100  0.022     0.078  0.012
+  1978     1     0.084  0.073     0.095  0.030     0.027  0.032     0.103  0.022     0.083  0.012
+  1978     2     0.231  0.072     0.028  0.032     0.038  0.031     0.103  0.022     0.084  0.012
+  1978     3     0.291  0.097     0.042  0.030     0.042  0.032     0.102  0.022     0.083  0.012
+  1978     4     0.451  0.078     0.056  0.034     0.045  0.032     0.100  0.022     0.085  0.012
+  1978     5     0.031  0.092     0.040  0.034     0.058  0.032     0.099  0.021     0.089  0.012
+  1978     6    -0.281  0.057     0.039  0.036     0.064  0.032     0.097  0.020     0.092  0.012
+  1978     7    -0.028  0.142     0.036  0.037     0.080  0.032     0.096  0.019     0.096  0.012
+  1978     8    -0.605  0.083    -0.017  0.037     0.096  0.032     0.100  0.019     0.100  0.012
+  1978     9    -0.099  0.076    -0.032  0.044     0.125  0.032     0.105  0.019     0.105  0.012
+  1978    10     0.070  0.070    -0.060  0.042     0.135  0.033     0.104  0.019     0.107  0.011
+  1978    11     0.253  0.049    -0.080  0.046     0.144  0.034     0.108  0.018     0.109  0.012
+  1978    12     0.082  0.109    -0.063  0.047     0.156  0.033     0.108  0.017     0.114  0.012
+  1979     1     0.044  0.066    -0.082  0.043     0.165  0.034     0.114  0.018     0.117  0.011
+  1979     2    -0.398  0.069    -0.038  0.040     0.182  0.035     0.118  0.018     0.122  0.012
+  1979     3     0.106  0.131    -0.021  0.040     0.187  0.035     0.122  0.017     0.125  0.012
+  1979     4     0.119  0.031    -0.001  0.040     0.201  0.033     0.122  0.017     0.125  0.012
+  1979     5    -0.211  0.170    -0.007  0.041     0.211  0.031     0.129  0.016     0.125  0.011
+  1979     6    -0.079  0.103     0.065  0.036     0.225  0.030     0.129  0.014     0.125  0.012
+  1979     7    -0.258  0.077     0.088  0.033     0.226  0.029     0.130  0.014     0.127  0.012
+  1979     8    -0.075  0.094     0.156  0.031     0.223  0.029     0.130  0.015     0.128  0.012
+  1979     9     0.101  0.055     0.150  0.029     0.209  0.028     0.132  0.015     0.130  0.012
+  1979    10     0.313  0.080     0.188  0.043     0.203  0.028     0.134  0.014     0.131  0.012
+  1979    11     0.182  0.065     0.238  0.044     0.195  0.029     0.134  0.015     0.130  0.012
+  1979    12     0.947  0.063     0.250  0.048     0.185  0.028     0.131  0.015     0.131  0.012
+  1980     1     0.323  0.070     0.302  0.051     0.183  0.027     0.130  0.015     0.133  0.012
+  1980     2     0.416  0.068     0.331  0.061     0.178  0.029     0.124  0.015     0.133  0.013
+  1980     3     0.033  0.162     0.339  0.062     0.181  0.028     0.122  0.015     0.138  0.013
+  1980     4     0.572  0.212     0.335  0.066     0.182  0.029     0.122  0.015     0.141  0.013
+  1980     5     0.392  0.164     0.366  0.069     0.173  0.028     0.120  0.015     0.143  0.013
+  1980     6     0.069  0.098     0.317  0.073     0.183  0.029     0.120  0.017     0.145  0.013
+  1980     7     0.364  0.129     0.388  0.076     0.195  0.028     0.117  0.019     0.147  0.013
+  1980     8     0.274  0.105     0.420  0.076     0.201  0.027     0.122  0.018     0.149  0.012
+  1980     9     0.191  0.048     0.492  0.070     0.202  0.026     0.121  0.019     0.148  0.013
+  1980    10     0.269  0.086     0.494  0.059     0.200  0.025     0.121  0.019     0.150  0.013
+  1980    11     0.559  0.060     0.474  0.052     0.206  0.023     0.122  0.018     0.154  0.013
+  1980    12     0.357  0.067     0.489  0.060     0.211  0.022     0.123  0.017     0.158  0.013
+  1981     1     1.174  0.098     0.488  0.050     0.212  0.019     0.127  0.017     0.159  0.013
+  1981     2     0.793  0.062     0.515  0.048     0.229  0.020     0.135  0.017     0.164  0.013
+  1981     3     0.899  0.114     0.505  0.048     0.240  0.020     0.145  0.017     0.166  0.013
+  1981     4     0.598  0.112     0.492  0.043     0.242  0.019     0.150  0.017     0.170  0.013
+  1981     5     0.156  0.114     0.458  0.039     0.249  0.018     0.155  0.018     0.172  0.013
+  1981     6     0.251  0.132     0.500  0.036     0.251  0.017     0.158  0.017     0.177  0.012
+  1981     7     0.341  0.056     0.396  0.032     0.255  0.017     0.159  0.017     0.181  0.012
+  1981     8     0.601  0.109     0.335  0.031     0.259  0.017     0.163  0.018     0.183  0.013
+  1981     9     0.071  0.070     0.223  0.030     0.263  0.017     0.163  0.018     0.185  0.013
+  1981    10     0.111  0.044     0.183  0.031     0.262  0.016     0.169  0.018     0.186  0.013
+  1981    11     0.155  0.065     0.169  0.028     0.276  0.013     0.172  0.017     0.187  0.013
+  1981    12     0.856  0.087     0.127  0.028     0.275  0.012     0.172  0.016     0.188  0.013
+  1982     1    -0.074  0.060     0.114  0.026     0.280  0.014     0.178  0.015     0.195  0.013
+  1982     2     0.063  0.051     0.055  0.019     0.282  0.015     0.183  0.015     0.201  0.013
+  1982     3    -0.447  0.071     0.043  0.017     0.279  0.016     0.179  0.015     0.203  0.013
+  1982     4     0.125  0.062     0.030  0.017     0.276  0.016     0.177  0.015     0.204  0.013
+  1982     5    -0.014  0.077     0.007  0.018     0.268  0.017     0.175  0.015     0.206  0.013
+  1982     6    -0.252  0.090    -0.008  0.022     0.244  0.017     0.175  0.015     0.206  0.013
+  1982     7     0.178  0.044     0.067  0.021     0.245  0.017     0.177  0.015     0.206  0.013
+  1982     8    -0.098  0.059     0.112  0.017     0.229  0.018     0.176  0.015     0.204  0.013
+  1982     9    -0.082  0.044     0.178  0.014     0.231  0.016     0.181  0.015     0.203  0.013
+  1982    10    -0.036  0.029     0.197  0.016     0.226  0.016     0.184  0.015     0.203  0.013
+  1982    11    -0.131  0.140     0.231  0.018     0.220  0.015     0.180  0.015     0.203  0.013
+  1982    12     0.680  0.095     0.249  0.027     0.220  0.016     0.186  0.015     0.205  0.013
+  1983     1     0.827  0.024     0.237  0.029     0.208  0.015     0.193  0.015     0.206  0.014
+  1983     2     0.599  0.052     0.280  0.031     0.206  0.016     0.193  0.015     0.205  0.013
+  1983     3     0.352  0.051     0.335  0.035     0.200  0.015     0.195  0.015     0.206  0.014
+  1983     4     0.348  0.079     0.356  0.042     0.197  0.015     0.197  0.015     0.205  0.014
+  1983     5     0.392  0.138     0.421  0.042     0.186  0.015     0.201  0.015     0.203  0.014
+  1983     6    -0.030  0.121     0.379  0.050     0.182  0.014     0.208  0.016     0.202  0.014
+  1983     7     0.037  0.082     0.337  0.050     0.174  0.014     0.211  0.016     0.203  0.013
+  1983     8     0.411  0.057     0.274  0.045     0.173  0.013     0.222  0.016     0.203  0.013
+  1983     9     0.577  0.098     0.273  0.038     0.166  0.013     0.227  0.016     0.202  0.013
+  1983    10     0.223  0.110     0.249  0.043     0.164  0.014     0.233  0.017     0.202  0.013
+  1983    11     0.648  0.072     0.269  0.040     0.166  0.014     0.232  0.017     0.200  0.013
+  1983    12     0.175  0.055     0.260  0.044     0.160  0.014     0.238  0.017     0.201  0.013
+  1984     1     0.319  0.061     0.260  0.036     0.153  0.014     0.238  0.016     0.204  0.013
+  1984     2    -0.153  0.057     0.231  0.031     0.145  0.014     0.245  0.016     0.205  0.013
+  1984     3     0.336  0.093     0.174  0.027     0.139  0.014     0.248  0.016     0.207  0.013
+  1984     4     0.063  0.124     0.165  0.024     0.138  0.016     0.250  0.016     0.210  0.013
+  1984     5     0.636  0.105     0.086  0.021     0.132  0.016     0.251  0.015     0.212  0.013
+  1984     6    -0.139  0.182     0.032  0.019     0.120  0.016     0.251  0.016     0.215  0.012
+  1984     7     0.039  0.097     0.037  0.019     0.129  0.016     0.256  0.016     0.217  0.012
+  1984     8     0.056  0.082     0.006  0.018     0.144  0.017     0.258  0.017     0.217  0.012
+  1984     9    -0.106  0.065    -0.011  0.023     0.150  0.017     0.260  0.017     0.220  0.012
+  1984    10     0.112  0.051     0.005  0.022     0.152  0.016     0.261  0.017     0.224  0.012
+  1984    11    -0.294  0.074    -0.042  0.027     0.155  0.016     0.261  0.018     0.228  0.012
+  1984    12    -0.481  0.048    -0.025  0.051     0.166  0.016     0.259  0.018     0.230  0.012
+  1985     1     0.387  0.055    -0.060  0.052     0.172  0.017     0.262  0.018     0.232  0.012
+  1985     2    -0.531  0.070    -0.051  0.049     0.173  0.017     0.262  0.019     0.237  0.012
+  1985     3     0.130  0.043    -0.054  0.046     0.180  0.017     0.273  0.018     0.238  0.012
+  1985     4     0.259  0.106    -0.058  0.049     0.186  0.017     0.275  0.018     0.240  0.012
+  1985     5     0.074  0.052    -0.044  0.047     0.188  0.017     0.276  0.018     0.239  0.012
+  1985     6     0.069  0.196     0.008  0.035     0.190  0.018     0.279  0.018     0.242  0.012
+  1985     7    -0.387  0.124     0.032  0.032     0.191  0.018     0.279  0.018     0.245  0.012
+  1985     8     0.165  0.063     0.140  0.030     0.184  0.018     0.278  0.018     0.250  0.012
+  1985     9    -0.147  0.047     0.167  0.034     0.188  0.018     0.278  0.018     0.252  0.012
+  1985    10     0.068  0.036     0.188  0.031     0.193  0.018     0.280  0.018     0.255  0.012
+  1985    11    -0.126  0.065     0.201  0.036     0.195  0.018     0.282  0.018     0.259  0.012
+  1985    12     0.138  0.149     0.188  0.024     0.205  0.019     0.286  0.019     0.261  0.012
+  1986     1     0.682  0.062     0.216  0.024     0.211  0.021     0.281  0.019     0.261  0.012
+  1986     2     0.769  0.048     0.208  0.023     0.215  0.020     0.282  0.019     0.265  0.012
+  1986     3     0.447  0.084     0.197  0.025     0.215  0.020     0.276  0.019     0.269  0.012
+  1986     4     0.509  0.079     0.195  0.029     0.223  0.020     0.279  0.020     0.271  0.012
+  1986     5     0.237  0.074     0.191  0.033     0.216  0.020     0.280  0.019     0.273  0.012
+  1986     6    -0.084  0.067     0.188  0.029     0.225  0.021     0.284  0.019     0.276  0.012
+  1986     7    -0.059  0.066     0.174  0.030     0.221  0.020     0.287  0.020     0.278  0.012
+  1986     8     0.076  0.097     0.186  0.031     0.230  0.020     0.285  0.019     0.283  0.012
+  1986     9    -0.287  0.101     0.143  0.034     0.232  0.021     0.289  0.019     0.285  0.012
+  1986    10     0.044  0.071     0.120  0.035     0.237  0.020     0.292  0.019     0.289  0.011
+  1986    11    -0.172  0.056     0.114  0.036     0.225  0.020     0.294  0.019     0.293  0.011
+  1986    12     0.101  0.059     0.156  0.034     0.227  0.022     0.290  0.020     0.295  0.011
+  1987     1     0.519  0.068     0.204  0.034     0.231  0.021     0.299  0.020     0.298  0.011
+  1987     2     0.912  0.066     0.198  0.026     0.234  0.022     0.304  0.020     0.299  0.011
+  1987     3    -0.074  0.051     0.251  0.025     0.240  0.022     0.313  0.020     0.301  0.011
+  1987     4     0.242  0.049     0.275  0.025     0.247  0.022     0.314  0.019     0.301  0.011
+  1987     5     0.162  0.072     0.284  0.022     0.254  0.022     0.315  0.019     0.300  0.011
+  1987     6     0.420  0.041     0.345  0.019     0.275  0.023     0.318  0.019     0.301  0.011
+  1987     7     0.518  0.071     0.373  0.017     0.279  0.023     0.314  0.019     0.300  0.011
+  1987     8     0.000  0.084     0.314  0.017     0.295  0.023     0.314  0.019     0.301  0.011
+  1987     9     0.345  0.062     0.370  0.018     0.316  0.023     0.311  0.019     0.304  0.011
+  1987    10     0.336  0.046     0.403  0.023     0.325  0.024     0.312  0.019     0.308  0.011
+  1987    11    -0.067  0.026     0.431  0.027     0.331  0.024     0.311  0.019     0.309  0.011
+  1987    12     0.842  0.055     0.446  0.035     0.337  0.025     0.309  0.019     0.312  0.011
+  1988     1     0.848  0.060     0.435  0.038     0.350  0.025     0.308  0.019     0.315  0.011
+  1988     2     0.206  0.059     0.489  0.038     0.351  0.026     0.308  0.019     0.321  0.011
+  1988     3     0.600  0.080     0.507  0.041     0.355  0.026     0.310  0.019     0.322  0.011
+  1988     4     0.636  0.056     0.540  0.041     0.364  0.026     0.310  0.019     0.325  0.011
+  1988     5     0.500  0.094     0.562  0.042     0.379  0.027     0.307  0.019     0.328  0.011
+  1988     6     0.595  0.121     0.552  0.041     0.390  0.027     0.308  0.019     0.334  0.012
+  1988     7     0.394  0.095     0.490  0.038     0.389  0.027     0.309  0.019     0.338  0.011
+  1988     8     0.639  0.062     0.506  0.040     0.390  0.027     0.305  0.019     0.345  0.012
+  1988     9     0.570  0.047     0.494  0.041     0.387  0.027     0.299  0.019     0.347  0.012
+  1988    10     0.726  0.044     0.471  0.039     0.393  0.028     0.300  0.019     0.350  0.012
+  1988    11     0.195  0.039     0.422  0.031     0.394  0.027     0.292  0.019     0.350  0.012
+  1988    12     0.727  0.042     0.370  0.036     0.408  0.027     0.294  0.019     0.354  0.012
+  1989     1     0.096  0.026     0.359  0.034     0.420  0.027     0.294  0.018     0.358  0.012
+  1989     2     0.400  0.065     0.325  0.029     0.426  0.026     0.292  0.018     0.366  0.011
+  1989     3     0.461  0.075     0.302  0.028     0.440  0.025     0.293  0.018     0.366  0.011
+  1989     4     0.354  0.105     0.285  0.030     0.447  0.025     0.298  0.018     0.367  0.011
+  1989     5    -0.084  0.066     0.276  0.031     0.456  0.025     0.295  0.018     0.370  0.012
+  1989     6    -0.025  0.132     0.280  0.030     0.461  0.025     0.301  0.019     0.372  0.012
+  1989     7     0.256  0.074     0.325  0.034     0.469  0.025     0.304  0.018     0.375  0.012
+  1989     8     0.238  0.088     0.328  0.033     0.465  0.024     0.304  0.018     0.377  0.012
+  1989     9     0.291  0.119     0.405  0.031     0.476  0.024     0.308  0.018     0.379  0.012
+  1989    10     0.516  0.066     0.443  0.036     0.475  0.024     0.313  0.018     0.381  0.012
+  1989    11     0.089  0.049     0.488  0.041     0.476  0.024     0.321  0.018     0.382  0.012
+  1989    12     0.778  0.034     0.524  0.036     0.470  0.024     0.329  0.018     0.382  0.012
+  1990     1     0.637  0.073     0.537  0.036     0.457  0.024     0.334  0.018     0.382  0.012
+  1990     2     0.438  0.083     0.534  0.034     0.455  0.024     0.350  0.018     0.385  0.012
+  1990     3     1.379  0.063     0.518  0.035     0.442  0.024     0.354  0.017     0.388  0.012
+  1990     4     0.817  0.064     0.524  0.032     0.438  0.024     0.357  0.017     0.391  0.013
+  1990     5     0.450  0.067     0.585  0.032     0.435  0.024     0.359  0.017     0.391  0.013
+  1990     6     0.404  0.093     0.585  0.038     0.429  0.025     0.363  0.017     0.393  0.013
+  1990     7     0.421  0.062     0.583  0.037     0.426  0.024     0.373  0.017     0.394  0.013
+  1990     8     0.198  0.068     0.617  0.035     0.431  0.024     0.378  0.017     0.395  0.014
+  1990     9     0.094  0.174     0.522  0.034     0.431  0.024     0.383  0.017     0.396  0.014
+  1990    10     0.593  0.045     0.528  0.032     0.426  0.024     0.389  0.017     0.396  0.014
+  1990    11     0.822  0.084     0.513  0.027     0.420  0.022     0.395  0.017     0.394  0.014
+  1990    12     0.784  0.119     0.545  0.026     0.410  0.021     0.398  0.017     0.394  0.014
+  1991     1     0.609  0.046     0.562  0.032     0.407  0.020     0.395  0.017     0.392  0.014
+  1991     2     0.844  0.070     0.583  0.028     0.396  0.019     0.395  0.017     0.391  0.014
+  1991     3     0.240  0.071     0.622  0.021     0.383  0.019     0.393  0.017     0.391  0.014
+  1991     4     0.885  0.059     0.613  0.022     0.377  0.018     0.391  0.017     0.392  0.014
+  1991     5     0.271  0.112     0.574  0.021     0.368  0.018     0.391  0.017     0.395  0.015
+  1991     6     0.785  0.064     0.543  0.017     0.362  0.017     0.393  0.017     0.396  0.015
+  1991     7     0.632  0.085     0.572  0.017     0.368  0.018     0.398  0.017     0.398  0.015
+  1991     8     0.446  0.081     0.560  0.017     0.354  0.017     0.403  0.017     0.398  0.015
+  1991     9     0.560  0.058     0.588  0.019     0.353  0.017     0.407  0.016     0.400  0.015
+  1991    10     0.487  0.046     0.532  0.014     0.358  0.018     0.409  0.016     0.402  0.015
+  1991    11     0.350  0.079     0.524  0.013     0.365  0.018     0.414  0.016     0.407  0.015
+  1991    12     0.420  0.054     0.464  0.016     0.376  0.017     0.418  0.016     0.407  0.015
+  1992     1     0.956  0.056     0.393  0.015     0.376  0.018     0.419  0.015     0.413  0.015
+  1992     2     0.696  0.076     0.345  0.016     0.374  0.017     0.416  0.015     0.419  0.015
+  1992     3     0.583  0.060     0.260  0.017     0.376  0.017     0.422  0.015     0.427  0.015
+  1992     4     0.210  0.075     0.227  0.016     0.379  0.017     0.424  0.015     0.430  0.015
+  1992     5     0.176  0.044     0.180  0.015     0.389  0.017     0.424  0.015     0.434  0.015
+  1992     6     0.066  0.051     0.182  0.021     0.384  0.017     0.426  0.015     0.437  0.015
+  1992     7    -0.227  0.054     0.159  0.021     0.389  0.017     0.423  0.016     0.440  0.015
+  1992     8    -0.129  0.063     0.146  0.021     0.405  0.016     0.426  0.016     0.444  0.015
+  1992     9    -0.455  0.058     0.146  0.026     0.393  0.016     0.428  0.016     0.447  0.015
+  1992    10     0.094  0.074     0.157  0.027     0.390  0.015     0.432  0.016     0.450  0.015
+  1992    11    -0.218  0.093     0.152  0.025     0.386  0.015     0.439  0.016     0.454  0.015
+  1992    12     0.446  0.157     0.148  0.025     0.390  0.015     0.438  0.015     0.453  0.015
+  1993     1     0.680  0.082     0.184  0.025     0.395  0.015     0.437  0.015     0.455  0.015
+  1993     2     0.534  0.053     0.191  0.024     0.404  0.014     0.449  0.015     0.456  0.015
+  1993     3     0.591  0.071     0.214  0.020     0.410  0.014     0.450  0.015     0.458  0.015
+  1993     4     0.332  0.071     0.235  0.021     0.414  0.014     0.453  0.014     0.460  0.015
+  1993     5     0.115  0.067     0.223  0.022     0.412  0.014     0.456  0.014     0.461  0.014
+  1993     6     0.029  0.054     0.220  0.023     0.407  0.013     0.460  0.014     0.464  0.014
+  1993     7     0.200  0.078     0.198  0.023     0.401  0.013     0.465  0.014     0.466  0.015
+  1993     8    -0.052  0.071     0.119  0.022     0.399  0.013     0.468  0.014     0.468  0.015
+  1993     9    -0.177  0.109     0.103  0.024     0.399  0.014     0.468  0.014     0.468  0.015
+  1993    10     0.350  0.075     0.131  0.028     0.389  0.014     0.467  0.015     0.472  0.015
+  1993    11    -0.362  0.044     0.147  0.026     0.389  0.013     0.468  0.015     0.472  0.014
+  1993    12     0.411  0.058     0.198  0.021     0.378  0.014     0.471  0.014     0.478  0.014
+  1994     1     0.414  0.090     0.206  0.018     0.375  0.013     0.478  0.014     0.480  0.014
+  1994     2    -0.417  0.089     0.216  0.019     0.380  0.014     0.487  0.014     0.486  0.014
+  1994     3     0.398  0.076     0.266  0.020     0.374  0.014     0.484  0.014     0.489  0.014
+  1994     4     0.671  0.082     0.298  0.021     0.370  0.014     0.485  0.014     0.492  0.014
+  1994     5     0.310  0.066     0.385  0.021     0.371  0.014     0.488  0.014     0.490  0.014
+  1994     6     0.638  0.055     0.389  0.021     0.375  0.014     0.493  0.015     0.493  0.014
+  1994     7     0.298  0.077     0.431  0.022     0.369  0.014     0.495  0.015     0.492  0.014
+  1994     8     0.070  0.070     0.586  0.021     0.366  0.014     0.496  0.015     0.493  0.014
+  1994     9     0.419  0.041     0.605  0.019     0.367  0.013     0.499  0.015     0.496  0.015
+  1994    10     0.734  0.082     0.601  0.017     0.373  0.014     0.501  0.015     0.499  0.015
+  1994    11     0.681  0.043     0.593  0.018     0.373  0.015     0.504  0.015     0.505  0.015
+  1994    12     0.460  0.047     0.594  0.022     0.383  0.015     0.505  0.015     0.510  0.015
+  1995     1     0.916  0.061     0.631  0.025     0.389  0.016     0.503  0.015     0.513  0.015
+  1995     2     1.447  0.049     0.688  0.029     0.397  0.016     0.507  0.015     0.518  0.015
+  1995     3     0.624  0.044     0.689  0.028     0.414  0.017     0.503  0.015     0.523  0.015
+  1995     4     0.629  0.070     0.698  0.028     0.427  0.017     0.506  0.015     0.527  0.015
+  1995     5     0.215  0.076     0.696  0.027     0.442  0.018     0.507  0.015     0.530  0.015
+  1995     6     0.645  0.077     0.698  0.026     0.448  0.020     0.508  0.015     0.534  0.016
+  1995     7     0.744  0.076     0.644  0.025     0.449  0.020     0.508  0.016     0.539  0.017
+  1995     8     0.749  0.087     0.586  0.023     0.467  0.020     0.512  0.016     0.541  0.018
+  1995     9     0.439  0.055     0.550  0.022     0.468  0.021     0.515  0.017     0.546  0.018
+  1995    10     0.839  0.072     0.526  0.022     0.479  0.022     0.511  0.017     0.552  0.018
+  1995    11     0.659  0.054     0.527  0.022     0.493  0.022     0.506  0.016     0.558  0.018
+  1995    12     0.486  0.040     0.487  0.024     0.510  0.021     0.501  0.017     0.562  0.017
+  1996     1     0.263  0.046     0.463  0.023     0.522  0.022     0.502  0.016     0.562  0.017
+  1996     2     0.753  0.071     0.460  0.028     0.540  0.022     0.499  0.017     0.564  0.017
+  1996     3     0.192  0.026     0.441  0.031     0.552  0.023     0.505  0.017     0.566  0.017
+  1996     4     0.335  0.077     0.392  0.030     0.558  0.023     0.505  0.017     0.567  0.017
+  1996     5     0.224  0.075     0.372  0.031     0.569  0.023     0.510  0.017     0.568  0.017
+  1996     6     0.173  0.093     0.387  0.029     0.579  0.023     0.508  0.017     0.572  0.017
+  1996     7     0.459  0.057     0.412  0.028     0.588  0.023     0.510  0.018     0.575  0.018
+  1996     8     0.707  0.102     0.394  0.029     0.619  0.023     0.510  0.018     0.579  0.018
+  1996     9     0.210  0.086     0.432  0.031     0.615  0.023     0.510  0.019     0.583  0.018
+  1996    10     0.253  0.058     0.448  0.030     0.612  0.024     0.512  0.019     0.588  0.018
+  1996    11     0.420  0.049     0.447  0.032     0.612  0.024     0.519  0.019     0.592  0.017
+  1996    12     0.665  0.042     0.485  0.030     0.610  0.024     0.523  0.019     0.597  0.017
+  1997     1     0.567  0.044     0.460  0.032     0.613  0.025     0.527  0.019     0.603  0.017
+  1997     2     0.530  0.068     0.430  0.028     0.619  0.025     0.533  0.019     0.604  0.017
+  1997     3     0.656  0.076     0.460  0.033     0.622  0.025     0.540  0.019     0.609  0.017
+  1997     4     0.526  0.055     0.510  0.035     0.622  0.025     0.546  0.019     0.614  0.018
+  1997     5     0.213  0.116     0.534  0.033     0.619  0.026     0.552  0.020     0.618  0.018
+  1997     6     0.627  0.065     0.544  0.035     0.627  0.025     0.556  0.020     0.620  0.018
+  1997     7     0.157  0.095     0.560  0.034     0.617  0.025     0.566  0.020     0.621  0.018
+  1997     8     0.347  0.090     0.649  0.033     0.609  0.025     0.573  0.020     0.625  0.018
+  1997     9     0.566  0.131     0.651  0.035     0.613  0.025     0.584  0.020     0.627  0.018
+  1997    10     0.852  0.047     0.691  0.037     0.622  0.025     0.589  0.020     0.630  0.018
+  1997    11     0.717  0.088     0.751  0.031     0.627  0.026     0.597  0.020     0.635  0.017
+  1997    12     0.779  0.054     0.784  0.031     0.626  0.026     0.597  0.021     0.635  0.017
+  1998     1     0.757  0.046     0.851  0.034     0.621  0.026     0.602  0.021     0.633  0.017
+  1998     2     1.604  0.054     0.905  0.033     0.619  0.026     0.605  0.022     0.633  0.017
+  1998     3     0.678  0.076     0.904  0.032     0.619  0.027     0.606  0.022     0.637  0.017
+  1998     4     1.009  0.085     0.890  0.031     0.608  0.027     0.610  0.022     0.639  0.017
+  1998     5     0.926  0.065     0.856  0.031     0.600  0.027     0.616  0.023     0.640  0.017
+  1998     6     1.027  0.086     0.878  0.030     0.596  0.027     0.620  0.023     0.640  0.017
+  1998     7     0.966  0.088     0.893  0.028     0.604  0.026     0.623  0.024     0.642  0.017
+  1998     8     0.988  0.085     0.881  0.030     0.600  0.026     0.630  0.023     0.641  0.017
+  1998     9     0.559  0.108     0.838  0.030     0.611  0.026     0.638  0.024     0.642  0.017
+  1998    10     0.681  0.054     0.791  0.030     0.620  0.026     0.644  0.025     0.643  0.018
+  1998    11     0.308  0.050     0.741  0.030     0.631  0.027     0.653  0.024     0.647  0.018
+  1998    12     1.047  0.067     0.700  0.032     0.638  0.027     0.662  0.024     0.648  0.018
+  1999     1     0.930  0.073     0.658  0.026     0.644  0.027     0.666  0.024     0.652  0.018
+  1999     2     1.460  0.065     0.614  0.024     0.641  0.026     0.680  0.024     0.654  0.018
+  1999     3     0.163  0.055     0.617  0.018     0.647  0.027     0.685  0.024     0.655  0.018
+  1999     4     0.449  0.053     0.620  0.015     0.654  0.027     0.687  0.025     0.658  0.018
+  1999     5     0.328  0.070     0.635  0.018     0.667  0.027     0.686  0.025     0.662  0.018
+  1999     6     0.530  0.062     0.625  0.016     0.671  0.027     0.684  0.026     0.665  0.019
+  1999     7     0.469  0.048     0.577  0.019     0.685  0.027     0.681  0.027     0.667  0.019
+  1999     8     0.453  0.062     0.536  0.018     0.700  0.028     0.682  0.027     0.670  0.019
+  1999     9     0.601  0.053     0.595  0.018     0.714  0.027     0.683  0.028     0.673  0.020
+  1999    10     0.715  0.049     0.656  0.023     0.720  0.027     0.684  0.029     0.674  0.020
+  1999    11     0.481  0.054     0.670  0.025     0.731  0.027     0.689  0.029     0.678  0.020
+  1999    12     0.930  0.039     0.671  0.027     0.730  0.027     0.690  0.029     0.678  0.020
+  2000     1     0.358  0.064     0.671  0.030     0.744  0.026     0.693  0.029     0.680  0.020
+  2000     2     0.969  0.065     0.687  0.033     0.749  0.026     0.687  0.028     0.683  0.020
+  2000     3     0.866  0.066     0.673  0.035     0.754  0.025     0.692  0.029     0.683  0.020
+  2000     4     1.180  0.085     0.630  0.036     0.751  0.024     0.697  0.029     0.685  0.020
+  2000     5     0.503  0.094     0.605  0.033     0.752  0.024     0.702  0.029     0.688  0.020
+  2000     6     0.541  0.085     0.545  0.035     0.745  0.023     0.705  0.030     0.690  0.020
+  2000     7     0.472  0.061     0.577  0.032     0.756  0.023     0.705  0.030     0.691  0.020
+  2000     8     0.635  0.089     0.538  0.032     0.743  0.024     0.704  0.030     0.694  0.020
+  2000     9     0.436  0.069     0.539  0.030     0.744  0.024     0.710  0.030     0.697  0.021
+  2000    10     0.204  0.078     0.514  0.029     0.740  0.024     0.714  0.030     0.699  0.021
+  2000    11     0.182  0.049     0.547  0.029     0.738  0.025     0.720  0.030     0.702  0.021
+  2000    12     0.212  0.020     0.551  0.029     0.729  0.027     0.726  0.030     0.701  0.021
+  2001     1     0.732  0.066     0.576  0.029     0.723  0.028     0.730  0.030     0.702  0.021
+  2001     2     0.503  0.047     0.567  0.028     0.720  0.026     0.734  0.030     0.700  0.021
+  2001     3     0.881  0.037     0.580  0.031     0.724  0.027     0.740  0.030     0.703  0.021
+  2001     4     0.873  0.085     0.618  0.032     0.731  0.028     0.743  0.030     0.704  0.022
+  2001     5     0.906  0.100     0.705  0.031     0.737  0.027     0.745  0.029     0.705  0.022
+  2001     6     0.592  0.077     0.760  0.028     0.744  0.026     0.751  0.029     0.705  0.022
+  2001     7     0.770  0.072     0.818  0.032     0.744  0.026     0.753  0.030     0.707  0.022
+  2001     8     0.526  0.095     0.898  0.034     0.741  0.027     0.755  0.030     0.710  0.022
+  2001     9     0.591  0.099     0.945  0.033     0.755  0.027     0.760  0.030     0.711  0.022
+  2001    10     0.653  0.058     0.947  0.030     0.762  0.028     0.767  0.030     0.714  0.022
+  2001    11     1.226  0.045     0.945  0.029     0.760  0.029     0.770  0.030     0.716  0.023
+  2001    12     0.878  0.057     0.944  0.028     0.757  0.030     0.776  0.030     0.718  0.023
+  2002     1     1.424  0.058     0.961  0.028     0.748  0.031     0.788  0.030     0.717  0.023
+  2002     2     1.468  0.052     0.972  0.027     0.746  0.031     0.792  0.031     0.716  0.023
+  2002     3     1.440  0.057     0.993  0.024     0.744  0.033     0.796  0.031     0.716  0.023
+  2002     4     0.895  0.063     0.998  0.023     0.747  0.034     0.805  0.031     0.721  0.023
+  2002     5     0.885  0.091     0.961  0.023     0.759  0.034     0.813  0.031     0.725  0.024
+  2002     6     0.580  0.070     0.919  0.024     0.754  0.034     0.814  0.031     0.729  0.024
+  2002     7     0.977  0.054     0.913  0.024     0.768  0.034     0.820  0.030     0.733  0.023
+  2002     8     0.658  0.080     0.862  0.027     0.764  0.035     0.824  0.030     0.737  0.023
+  2002     9     0.841  0.048     0.803  0.028     0.770  0.036     0.826  0.029     0.743  0.023
+  2002    10     0.709  0.065     0.794  0.029     0.771  0.036     0.828  0.029     0.747  0.024
+  2002    11     0.782  0.075     0.786  0.033     0.777  0.036     0.831  0.029     0.752  0.024
+  2002    12     0.375  0.070     0.780  0.038     0.784  0.037     0.832  0.029     0.752  0.024
+  2003     1     1.359  0.066     0.750  0.044     0.790  0.038     0.828  0.029     0.754  0.025
+  2003     2     0.860  0.079     0.760  0.039     0.790  0.038     0.818  0.029     0.756  0.025
+  2003     3     0.724  0.045     0.755  0.049     0.801  0.038     0.825  0.029     0.758  0.025
+  2003     4     0.793  0.051     0.792  0.055     0.820  0.037     0.825  0.029     0.759  0.025
+  2003     5     0.790  0.124     0.778  0.052     0.839  0.038     0.823  0.029     0.763  0.025
+  2003     6     0.508  0.152     0.872  0.053     0.856  0.038     0.820  0.029     0.767  0.025
+  2003     7     0.614  0.130     0.836  0.050     0.855  0.038     0.819  0.029     0.769  0.025
+  2003     8     0.779  0.079     0.870  0.049     0.868  0.038     0.814  0.028     0.773  0.025
+  2003     9     0.781  0.147     0.893  0.052     0.869  0.039     0.816  0.028     0.778  0.025
+  2003    10     1.146  0.106     0.901  0.055     0.866  0.038     0.820  0.028     0.781  0.025
+  2003    11     0.619  0.110     0.849  0.056     0.858  0.037     0.827  0.028     0.788  0.025
+  2003    12     1.500  0.082     0.839  0.055     0.864  0.037     0.824  0.028     0.791  0.025
+  2004     1     0.931  0.074     0.784  0.051     0.862  0.040     0.826  0.029     0.794  0.025
+  2004     2     1.264  0.074     0.742  0.060     0.870  0.040     0.821  0.029     0.797  0.025
+  2004     3     0.996  0.061     0.719  0.056     0.873  0.040     0.827  0.029     0.799  0.025
+  2004     4     0.888  0.093     0.697  0.055     0.880  0.040     0.831  0.029     0.802  0.025
+  2004     5     0.173  0.138     0.747  0.059     0.873  0.040     0.835  0.030     0.805  0.026
+  2004     6     0.387  0.126     0.675  0.061     0.882  0.041     0.837  0.030     0.806  0.026
+  2004     7    -0.052  0.085     0.698  0.063     0.891  0.040     0.840  0.030     0.807  0.026
+  2004     8     0.285  0.104     0.651  0.060     0.884  0.041     0.843  0.030     0.811  0.026
+  2004     9     0.506  0.118     0.670  0.062     0.879  0.042     0.847  0.030     0.814  0.026
+  2004    10     0.874  0.088     0.699  0.059     0.889  0.043     0.848  0.031     0.815  0.026
+  2004    11     1.217  0.065     0.757  0.059     0.894  0.042     0.852  0.031     0.815  0.026
+  2004    12     0.637  0.068     0.805  0.058     0.898  0.041     0.850  0.031     0.818  0.027
+  2005     1     1.211  0.076     0.878  0.060     0.895  0.041     0.857  0.031     0.820  0.027
+  2005     2     0.703  0.049     0.906  0.061     0.899  0.041     0.858  0.031     0.820  0.027
+  2005     3     1.223  0.068     0.956  0.054     0.899  0.041     0.863  0.031     0.823  0.027
+  2005     4     1.238  0.071     0.995  0.050     0.905  0.041     0.864  0.031     0.825  0.027
+  2005     5     0.871  0.114     1.004  0.047     0.909  0.041     0.869  0.031     0.828  0.027
+  2005     6     0.965  0.106     1.055  0.046     0.918  0.041     0.872  0.031     0.829  0.027
+  2005     7     0.817  0.113     1.012  0.046     0.900  0.041     0.875  0.031     0.829  0.027
+  2005     8     0.622  0.101     1.056  0.044     0.892  0.040     0.877  0.031     0.830  0.026
+  2005     9     1.104  0.037     1.036  0.043     0.907  0.040     0.879  0.031     0.832  0.027
+  2005    10     1.342  0.042     0.991  0.037     0.910  0.040     0.888  0.031     0.834  0.027
+  2005    11     1.327  0.079     0.953  0.036     0.908  0.039     0.898  0.031     0.837  0.027
+  2005    12     1.246  0.082     0.953  0.032     0.911  0.038     0.901  0.031     0.843  0.027
+  2006     1     0.699  0.095     0.937  0.032     0.914  0.037     0.901  0.031     0.848  0.028
+  2006     2     1.227  0.088     0.970  0.033     0.909  0.037     0.901  0.031     0.854  0.028
+  2006     3     0.987  0.067     0.942  0.039     0.908  0.036     0.900  0.031     0.862  0.028
+  2006     4     0.702  0.089     0.917  0.039     0.908  0.036     0.902  0.031     0.869  0.028
+  2006     5     0.413  0.088     0.877  0.039     0.917  0.037     0.900  0.031     0.873  0.028
+  2006     6     0.961  0.051     0.888  0.039     0.905  0.037     0.902  0.032     0.876  0.028
+  2006     7     0.624  0.174     0.996  0.037     0.908  0.037     0.905  0.032     0.878  0.028
+  2006     8     1.019  0.114     0.981  0.043     0.902  0.037     0.911  0.032     0.881  0.028
+  2006     9     0.764  0.091     0.995  0.042     0.898  0.036     0.912  0.032     0.884  0.028
+  2006    10     1.047  0.051     1.062  0.051     0.900  0.036     0.916  0.032     0.888  0.028
+  2006    11     0.851  0.054     1.124  0.049     0.910  0.036     0.912  0.033     0.890  0.028
+  2006    12     1.379  0.077     1.111  0.049     0.916  0.036     0.914  0.033     0.893  0.028
+  2007     1     1.993  0.068     1.128  0.041     0.932  0.036     0.907  0.034     0.897  0.028
+  2007     2     1.040  0.095     1.116  0.036     0.941  0.035     0.898  0.034     0.902  0.028
+  2007     3     1.161  0.095     1.123  0.033     0.950  0.035     0.892  0.034     0.907  0.028
+  2007     4     1.499  0.102     1.127  0.033     0.950  0.035     0.896  0.034     0.910  0.028
+  2007     5     1.167  0.066     1.139  0.034     0.945  0.035     0.899  0.034     0.914  0.028
+  2007     6     0.795  0.057     1.100  0.034     0.946  0.034     0.902  0.033     0.915  0.028
+  2007     7     0.834  0.078     0.957  0.034     0.945  0.034     0.900  0.033     0.919  0.028
+  2007     8     0.875  0.054     0.903  0.029     0.953  0.034     0.901  0.033     0.922  0.028
+  2007     9     0.843  0.051     0.939  0.030     0.955  0.033     0.902  0.033     0.923  0.028
+  2007    10     1.094  0.046     0.895  0.027     0.957  0.032     0.905  0.033     0.925  0.028
+  2007    11     1.004  0.073     0.855  0.030     0.960  0.031     0.908  0.033     0.926  0.028
+  2007    12     0.913  0.072     0.845  0.033     0.959  0.031     0.908  0.034     0.929  0.028
+  2008     1     0.268  0.069     0.843  0.035     0.959  0.030     0.906  0.033     0.931  0.028
+  2008     2     0.398  0.059     0.810  0.034     0.964  0.030     0.908  0.033     0.929  0.028
+  2008     3     1.587  0.073     0.799  0.036     0.958  0.030     0.910  0.033     0.931  0.028
+  2008     4     0.970  0.069     0.805  0.035     0.956  0.030     0.909  0.033     0.933  0.028
+  2008     5     0.694  0.072     0.817  0.037     0.957  0.030     0.910  0.033     0.934  0.028
+  2008     6     0.677  0.074     0.805  0.036     0.947  0.030     0.914  0.032     0.934  0.028
+  2008     7     0.809  0.074     0.875  0.038     0.947  0.030     0.915  0.031     0.935  0.028
+  2008     8     0.477  0.065     0.917  0.038     0.935  0.030     0.916  0.031     0.935  0.028
+  2008     9     0.713  0.063     0.851  0.039     0.931  0.030     0.918  0.031     0.936  0.027
+  2008    10     1.164  0.056     0.854  0.037     0.938  0.031     0.917  0.031       NaN    NaN
+  2008    11     1.143  0.070     0.862  0.038     0.942  0.030     0.923  0.031       NaN    NaN
+  2008    12     0.775  0.059     0.866  0.039     0.940  0.031     0.919  0.031       NaN    NaN
+  2009     1     1.099  0.073     0.874  0.039     0.948  0.029     0.921  0.031       NaN    NaN
+  2009     2     0.902  0.061     0.901  0.044     0.951  0.029     0.913  0.031       NaN    NaN
+  2009     3     0.800  0.054     0.930  0.043     0.952  0.029     0.914  0.031       NaN    NaN
+  2009     4     1.005  0.102     0.905  0.041     0.953  0.028     0.916  0.031       NaN    NaN
+  2009     5     0.788  0.098     0.889  0.041     0.951  0.029     0.925  0.030       NaN    NaN
+  2009     6     0.730  0.096     0.882  0.041     0.946  0.030     0.929  0.030       NaN    NaN
+  2009     7     0.899  0.071     0.884  0.036     0.922  0.031     0.934  0.029       NaN    NaN
+  2009     8     0.804  0.065     0.906  0.035     0.912  0.030     0.940  0.029       NaN    NaN
+  2009     9     1.058  0.093     0.956  0.032     0.905  0.029     0.944  0.029       NaN    NaN
+  2009    10     0.864  0.064     0.982  0.031     0.902  0.028     0.946  0.029       NaN    NaN
+  2009    11     0.955  0.075     1.006  0.028     0.903  0.028     0.941  0.029       NaN    NaN
+  2009    12     0.696  0.079     1.021  0.028     0.906  0.028     0.946  0.029       NaN    NaN
+  2010     1     1.117  0.093     1.014  0.029     0.904  0.027     0.947  0.029       NaN    NaN
+  2010     2     1.170  0.104     1.022  0.028     0.903  0.027     0.953  0.030       NaN    NaN
+  2010     3     1.392  0.055     0.997  0.027     0.904  0.027     0.955  0.029       NaN    NaN
+  2010     4     1.326  0.068     1.024  0.026     0.905  0.027     0.952  0.029       NaN    NaN
+  2010     5     1.068  0.065     1.060  0.025     0.906  0.028     0.953  0.029       NaN    NaN
+  2010     6     0.912  0.065     1.057  0.027     0.899  0.028     0.954  0.028       NaN    NaN
+  2010     7     0.820  0.054     1.022  0.026     0.913  0.029     0.953  0.028       NaN    NaN
+  2010     8     0.891  0.084     0.969  0.026     0.923  0.029     0.955  0.028       NaN    NaN
+  2010     9     0.762  0.093     0.916  0.028     0.913  0.029     0.953  0.028       NaN    NaN
+  2010    10     1.190  0.032     0.899  0.029     0.909  0.028     0.955  0.028       NaN    NaN
+  2010    11     1.385  0.055     0.862  0.030     0.912  0.028     0.955  0.028       NaN    NaN
+  2010    12     0.658  0.090     0.858  0.030     0.918  0.027     0.960  0.029       NaN    NaN
+  2011     1     0.694  0.092     0.883  0.031     0.915  0.027     0.967  0.029       NaN    NaN
+  2011     2     0.535  0.077     0.907  0.034     0.922  0.027     0.975  0.030       NaN    NaN
+  2011     3     0.765  0.069     0.911  0.034     0.928  0.027     0.985  0.030       NaN    NaN
+  2011     4     1.123  0.095     0.905  0.033     0.926  0.027     0.994  0.030       NaN    NaN
+  2011     5     0.613  0.100     0.852  0.038     0.929  0.026     1.001  0.030       NaN    NaN
+  2011     6     0.864  0.087     0.884  0.041     0.934  0.027     1.000  0.030       NaN    NaN
+  2011     7     1.121  0.070     0.875  0.044     0.935  0.027     1.003  0.028       NaN    NaN
+  2011     8     1.184  0.093     0.866  0.044     0.925  0.027     1.006  0.028       NaN    NaN
+  2011     9     0.812  0.066     0.861  0.042     0.929  0.027     1.009  0.028       NaN    NaN
+  2011    10     1.122  0.074     0.879  0.041     0.933  0.026     1.009  0.028       NaN    NaN
+  2011    11     0.740  0.112     0.930  0.041     0.940  0.027     1.010  0.028       NaN    NaN
+  2011    12     1.047  0.128     0.940  0.036     0.942  0.026     1.009  0.028       NaN    NaN
+  2012     1     0.587  0.098     0.906  0.030     0.936  0.025     1.006  0.028       NaN    NaN
+  2012     2     0.426  0.062     0.877  0.027     0.940  0.025     1.012  0.027       NaN    NaN
+  2012     3     0.706  0.080     0.884  0.028     0.939  0.026     1.017  0.027       NaN    NaN
+  2012     4     1.331  0.103     0.887  0.032     0.942  0.027     1.016  0.027       NaN    NaN
+  2012     5     1.236  0.135     0.912  0.030     0.937  0.027     1.016  0.027       NaN    NaN
+  2012     6     0.978  0.082     0.865  0.031     0.946  0.027     1.016  0.027       NaN    NaN
+  2012     7     0.717  0.074     0.909  0.029     0.949  0.028     1.017  0.027       NaN    NaN
+  2012     8     0.831  0.071     0.959  0.031     0.954  0.028     1.019  0.028       NaN    NaN
+  2012     9     0.903  0.075     0.980  0.027     0.954  0.028     1.020  0.027       NaN    NaN
+  2012    10     1.147  0.080     0.929  0.023     0.948  0.028     1.021  0.027       NaN    NaN
+  2012    11     1.050  0.103     0.898  0.024     0.947  0.028     1.021  0.027       NaN    NaN
+  2012    12     0.475  0.095     0.905  0.024     0.948  0.028     1.026  0.027       NaN    NaN
+  2013     1     1.124  0.089     0.900  0.025     0.946  0.029     1.034  0.028       NaN    NaN
+  2013     2     1.025  0.061     0.904  0.023     0.946  0.028     1.039  0.028       NaN    NaN
+  2013     3     0.958  0.078     0.918  0.024     0.948  0.028     1.038  0.028       NaN    NaN
+  2013     4     0.717  0.066     0.907  0.022     0.954  0.028     1.041  0.028       NaN    NaN
+  2013     5     0.863  0.142     0.931  0.021     0.953  0.029     1.045  0.028       NaN    NaN
+  2013     6     1.062  0.055     0.983  0.023     0.972  0.029     1.048  0.027       NaN    NaN
+  2013     7     0.652  0.041     0.985  0.022     0.987  0.031     1.050  0.027       NaN    NaN
+  2013     8     0.885  0.064     0.924  0.022     1.015  0.031     1.055  0.027       NaN    NaN
+  2013     9     1.071  0.049     0.932  0.024     1.038  0.031     1.055  0.028       NaN    NaN
+  2013    10     1.014  0.049     0.974  0.025     1.049  0.031       NaN    NaN       NaN    NaN
+  2013    11     1.337  0.116     1.004  0.027     1.060  0.031       NaN    NaN       NaN    NaN
+  2013    12     1.098  0.133     0.987  0.027     1.060  0.030       NaN    NaN       NaN    NaN
+  2014     1     1.151  0.088     0.976  0.025     1.057  0.029       NaN    NaN       NaN    NaN
+  2014     2     0.287  0.077     0.988  0.026     1.061  0.029       NaN    NaN       NaN    NaN
+  2014     3     1.054  0.093     0.982  0.031     1.066  0.029       NaN    NaN       NaN    NaN
+  2014     4     1.229  0.064     0.989  0.035     1.064  0.030       NaN    NaN       NaN    NaN
+  2014     5     1.221  0.103     0.930  0.040     1.069  0.029       NaN    NaN       NaN    NaN
+  2014     6     0.851  0.061     0.940  0.041     1.073  0.028       NaN    NaN       NaN    NaN
+  2014     7     0.527  0.055     0.953  0.042     1.089  0.028       NaN    NaN       NaN    NaN
+  2014     8     1.026  0.124     1.051  0.043     1.111  0.028       NaN    NaN       NaN    NaN
+  2014     9     1.003  0.107     1.081  0.039     1.130  0.028       NaN    NaN       NaN    NaN
+  2014    10     1.093  0.069     1.058  0.040     1.130  0.029       NaN    NaN       NaN    NaN
+  2014    11     0.626  0.096     1.038  0.039     1.130  0.030       NaN    NaN       NaN    NaN
+  2014    12     1.223  0.068     1.052  0.040     1.127  0.030       NaN    NaN       NaN    NaN
+  2015     1     1.307  0.120     1.066  0.047     1.131  0.031       NaN    NaN       NaN    NaN
+  2015     2     1.466  0.108     1.056  0.045     1.135  0.031       NaN    NaN       NaN    NaN
+  2015     3     1.416  0.055     1.046  0.040     1.135  0.031       NaN    NaN       NaN    NaN
+  2015     4     0.947  0.118     1.082  0.038     1.137  0.031       NaN    NaN       NaN    NaN
+  2015     5     0.986  0.088     1.141  0.036     1.137  0.031       NaN    NaN       NaN    NaN
+  2015     6     1.013  0.065     1.190  0.038     1.154  0.031       NaN    NaN       NaN    NaN
+  2015     7     0.702  0.106     1.214  0.042     1.154  0.031       NaN    NaN       NaN    NaN
+  2015     8     0.896  0.073     1.274  0.041     1.156  0.031       NaN    NaN       NaN    NaN
+  2015     9     0.888  0.061     1.336  0.043     1.162  0.031       NaN    NaN       NaN    NaN
+  2015    10     1.529  0.055     1.406  0.044     1.173  0.032       NaN    NaN       NaN    NaN
+  2015    11     1.334  0.063     1.429  0.045     1.178  0.032       NaN    NaN       NaN    NaN
+  2015    12     1.804  0.104     1.418  0.043     1.178  0.032       NaN    NaN       NaN    NaN
+  2016     1     1.597  0.126     1.438  0.037     1.185  0.032       NaN    NaN       NaN    NaN
+  2016     2     2.187  0.075     1.482  0.038     1.187  0.032       NaN    NaN       NaN    NaN
+  2016     3     2.154  0.105     1.497  0.040     1.183  0.033       NaN    NaN       NaN    NaN
+  2016     4     1.796  0.111     1.454  0.042       NaN    NaN       NaN    NaN       NaN    NaN
+  2016     5     1.260  0.112     1.433  0.040       NaN    NaN       NaN    NaN       NaN    NaN
+  2016     6     0.882  0.078     1.387  0.034       NaN    NaN       NaN    NaN       NaN    NaN
+  2016     7     0.935  0.046     1.385  0.029       NaN    NaN       NaN    NaN       NaN    NaN
+  2016     8     1.433  0.102     1.348  0.028       NaN    NaN       NaN    NaN       NaN    NaN
+  2016     9     1.058  0.082     1.321  0.027       NaN    NaN       NaN    NaN       NaN    NaN
+  2016    10     1.019  0.062     1.280  0.031       NaN    NaN       NaN    NaN       NaN    NaN
+  2016    11     1.079  0.095     1.278  0.031       NaN    NaN       NaN    NaN       NaN    NaN
+  2016    12     1.259  0.077     1.271  0.035       NaN    NaN       NaN    NaN       NaN    NaN
+  2017     1     1.569  0.082     1.275  0.038       NaN    NaN       NaN    NaN       NaN    NaN
+  2017     2     1.746  0.062     1.244  0.039       NaN    NaN       NaN    NaN       NaN    NaN
+  2017     3     1.831  0.052     1.231  0.037       NaN    NaN       NaN    NaN       NaN    NaN
+  2017     4     1.301  0.144     1.253  0.038       NaN    NaN       NaN    NaN       NaN    NaN
+  2017     5     1.235  0.132     1.249  0.036       NaN    NaN       NaN    NaN       NaN    NaN
+  2017     6     0.803  0.089     1.268  0.040       NaN    NaN       NaN    NaN       NaN    NaN
+  2017     7     0.973  0.079     1.235  0.038       NaN    NaN       NaN    NaN       NaN    NaN
+  2017     8     1.066  0.086     1.180  0.039       NaN    NaN       NaN    NaN       NaN    NaN
+  2017     9     0.906  0.093     1.142  0.042       NaN    NaN       NaN    NaN       NaN    NaN
+  2017    10     1.275  0.048     1.145  0.041       NaN    NaN       NaN    NaN       NaN    NaN
+  2017    11     1.035  0.080     1.138  0.040       NaN    NaN       NaN    NaN       NaN    NaN
+  2017    12     1.487  0.073     1.161  0.040       NaN    NaN       NaN    NaN       NaN    NaN
+  2018     1     1.171  0.093     1.172  0.038       NaN    NaN       NaN    NaN       NaN    NaN
+  2018     2     1.093  0.102     1.166  0.035       NaN    NaN       NaN    NaN       NaN    NaN
+  2018     3     1.366  0.091     1.158  0.042       NaN    NaN       NaN    NaN       NaN    NaN
+  2018     4     1.342  0.112       NaN    NaN       NaN    NaN       NaN    NaN       NaN    NaN
+  2018     5     1.147  0.170       NaN    NaN       NaN    NaN       NaN    NaN       NaN    NaN
+  2018     6     1.078  0.122       NaN    NaN       NaN    NaN       NaN    NaN       NaN    NaN
+  2018     7     1.112  0.039       NaN    NaN       NaN    NaN       NaN    NaN       NaN    NaN
+  2018     8     0.991  0.107       NaN    NaN       NaN    NaN       NaN    NaN       NaN    NaN
+  2018     9     0.804  0.161       NaN    NaN       NaN    NaN       NaN    NaN       NaN    NaN
diff --git a/exercises/Exercise6/Exercise_6_Problems.ipynb b/exercises/Exercise6/Exercise_6_Problems.ipynb
new file mode 100644
index 0000000..d7404e0
--- /dev/null
+++ b/exercises/Exercise6/Exercise_6_Problems.ipynb
@@ -0,0 +1,308 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Exercise 6: Arbitrary distributions, moving averages, and Monte-Carlo"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "import datetime\n",
+    "import scipy.stats as stats\n",
+    "import matplotlib.pyplot as plt"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 1. Sampling from an arbitrary distribution\n",
+    "As seen in exercise 4, you can use uniformly distributed random variables, which are in principle themselves simple to generate, to draw samples from the normal distribution via the Box-Muller transform. A more general approach is to sample according to the inverse of the cumulative distribution function (CDF).\n",
+    "\n",
+    "A simple example is to generate numbers from the exponential distribution.\n",
+    "\n",
+    "$$ f(t;\\lambda) = \\lambda e^{-\\lambda t} $$\n",
+    "\n",
+    "* Write the CDF $F(T,\\lambda)$ and find its inverse ($T=...$)\n",
+    "* Write a function to compute this, and compare your result to that from scipy (hint: sometimes called percent-point function or quantile function)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Quantile function\n",
+    "def exp_quantile(p, l):\n",
+    "    ...\n",
+    "\n",
+    "p = np.linspace(0, 1, 100)\n",
+    "l = 0.2\n",
+    "..."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "* Now draw N samples from the uniform distribution $[0,1]$. For each sample, calculate $F^{-1}(u,\\lambda)$\n",
+    "* Plot a histogram and compare the distribution of points to the exponential pdf\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "N = 1000\n",
+    "l = 0.2\n",
+    "x = ...\n",
+    "y = ...\n",
+    "\n",
+    "...\n",
+    "\n",
+    "print('Actual lambda:', l)\n",
+    "print('Estimated lambda: ', ...)\n",
+    "\n",
+    "...\n",
+    "\n",
+    "# Check the fit\n",
+    "...\n",
+    "\n",
+    "# Plot histogram, fit, and calculated pdf\n",
+    "...\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# 2. Smoothing data\n",
+    "## 2.1 Moving average\n",
+    "The moving average, or rolling mean, is a simple technique which can be used to remove short term or periodic (e.g. seasonal) variations in time series data, for example. It can be viewed as a \"smoothing\", and can ease trend spotting, for instance. One has to be careful when interpreting and using the result; for instance, it is generally improper to fit on such data.\n",
+    "\n",
+    "The simplest moving average can be computed using a \"sliding window\" of length $N$, with all weights equal. For example, for a 3 point moving average, the window would be $\\frac{1}{3}[1,1,1]$.\n",
+    "\n",
+    "* Write a function to compute the $N$ point moving average of a data series"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def moving_average(y, length):\n",
+    "    ..."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The following line of code loads a dataset (into a ```pandas DataFrame```) containing monthly measurements of variation in the global surface temperature, stretching back as far as 1750. (More data like this can be found on http://berkeleyearth.org)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df = pd.read_csv('Material/Complete_TAVG_complete.txt', skipinitialspace=True, delimiter=' ', comment='%')\n",
+    "df['Date'] = df.apply(lambda row: datetime.datetime(\n",
+    "                              int(row['Year']), int(row['Month']), 15), axis=1)\n",
+    "df"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "* Plot the data. To plot the monthly differences, for example, you can directly write ```df2['MDiff'].plot()```"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# For example...\n",
+    "df.query('Year>1980 & Year<2000').plot(x='Date', y='MDiff')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "* Apply your moving average filter to the monthly data ```MDiff```. Try (for example) 6 months, 5 years, 10 years. Plot these on top of cuts of the original data to compare."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "..."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 2.2 Electronic response of RC circuit\n",
+    "\n",
+    "In general, the response of a linearly time invariant system is found to be the convolution of the its impulse response $h(t)$ and the input voltage. Consider a resistor and capacitor connected in series, driven by a time-varying voltage $u(t)$. The impulse response for such a circuit is:\n",
+    "\n",
+    "$$h_c(t) = \\frac{1}{RC} e^{-t/RC} u(t)$$\n",
+    "\n",
+    "* Write a function to calculate the impulse response as a function of time, the resistance, and the capacitance, and input. Take care to normalise the integral.\n",
+    "\n",
+    "* Now consider a noisy sinusoidal input voltage $u_N(t) = u(t) + \\epsilon(t)$, where $u(t)=sin(2\\pi f_1 t) + cos(2\\pi f_2 t)$, and $\\epsilon$ is a vector comprising samples draw from $N~(0,1)$. $f_1$ should be a lower frequency (~factor 10) than ($f_2$), where the cosine represents a faster ripple riding the fundamental tone. Plot the noisy signal and superimpose the clean signal.\n",
+    "\n",
+    "* Calculate the circuit response for your signal and compare the result to the noisy signal and the clean, original signal\n",
+    "\n",
+    "* Play with the RC time constant and see the effect on the signal. \n",
+    "\n",
+    "Note: this first order low pass filter is exactly equivalent to an exponential moving average. The \"memory\" of the output is effectively determined by the time constant.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def rc_impulse(t, R, C):\n",
+    "    # Impulse response\n",
+    "    ...\n",
+    "\n",
+    "def rc_response(t, u, R, C):\n",
+    "    # Cumulative response\n",
+    "    ...\n",
+    "\n",
+    "t = np.linspace(0, 0.1, 5000)\n",
+    "dt = t[1]-t[0]\n",
+    "R = 5e3\n",
+    "C = 100e-9\n",
+    "tc = R*C\n",
+    "\n",
+    "f1 = 200\n",
+    "f2 = 0.1 * f1\n",
+    "u = ...\n",
+    "un = ...\n",
+    "\n",
+    "print('Cutoff: ', tc)\n",
+    "\n",
+    "...\n",
+    "\n",
+    "# Try different cutoffs (remove noise, fast ripple, then whole thing)\n",
+    "...\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 3. Monte Carlo methods\n",
+    "### 3.1. Particle propagation\n",
+    "The elementary processes of particle absorption and scattering are random in their nature. Propagation of particles through a slab of material with multiple scattering events may be impossible to calculate analytically, but can easily be simulated with Monte Carlo methods.\n",
+    "\n",
+    "* Consider a beam of photons propagating through an absorbing medium with absorption coefficient $\\alpha=0.2$ per unit length. What is the probability of a photon being absorbed in a unit length slab of material?\n",
+    "\n",
+    "* Now take a piece of 1D material made up of 100 slices, each unit length. Starting at x=0, propagate a beam of 1000 photons through the material, slice-by-slice. At each interface, you should \"measure\" each photon to determine whether it has been transmitted or absorbed (hint: uniform distribution, $P(abs)$)\n",
+    "\n",
+    "* Plot the number of photons which are transmitted at the end of each slice, and compare that to the Beer-Lambert-Bouger law\n",
+    "\n",
+    "* Plot a histogram of the distance travelled before absorption for each photon (free paths).\n",
+    "\n",
+    "$I(x) = I_{0}e^{-\\alpha x }$ , where $\\alpha$ is absorption coefficient"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "N_slices = 100 # Slices of material\n",
+    "N_particles = 1000 # Number of particles to simulate\n",
+    "alpha = 0.2 # absorption coefficient\n",
+    "P_abs = ...\n",
+    "\n",
+    "# Generate N_slices x N_particles matrix of uniformly distributed random numbers. \n",
+    "# Transform it into a matrix of absorption events, where True = absorption, False = no absorption, \n",
+    "# mean(Abs_events) = P_abs\n",
+    "Abs_events = ...\n",
+    "\n",
+    "...\n",
+    "\n",
+    "print('Generated absorption probability (mean) = ', np.mean(Abs_events))\n",
+    "print('Fraction of escaped particles = ',N_escaped_final/N_particles)\n",
+    "\n",
+    "# Plots\n",
+    "..."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 3.2. Monte-Carlo integration: estimate $\\pi$\n",
+    "\n",
+    "In a so-called ’hit-and-miss’ approach, or ’simple sampling’, one can estimate the integral\n",
+    "of an arbitrary, well-behaved function over some interval by scattering many points over\n",
+    "some rectangular area A. The probability of a point landing below the curve is proportional\n",
+    "to the function’s integral.\n",
+    "A classic problem is to determine the value of π.\n",
+    "\n",
+    "* Uniformly distribute N points over a unit area. Plot these on top of a unit circle (or quarter circle)\n",
+    "* Calculate the proportion that are within the bounds of your shape for some number of samples N (for large N, it would be unwise to plot)\n",
+    "* Repeat the exercise for increasing N. For each run, you should compute and store the error $\\epsilon = \\bar{\\pi} - \\pi$\n",
+    "* Plot log-log the convergence of your estimate to the actual value (to machine precision) of $\\pi$, i.e. $\\epsilon$ vs the number of points $N$. Compare this to the expected rate of convergence $(1/\\sqrt N)$."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "anaconda-cloud": {},
+  "kernelspec": {
+   "display_name": "Python [Anaconda3]",
+   "language": "python",
+   "name": "Python [Anaconda3]"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.5.5"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/exercises/Exercise6/Exercise_6_Problems.pdf b/exercises/Exercise6/Exercise_6_Problems.pdf
new file mode 100644
index 0000000000000000000000000000000000000000..436d17316d026707b4c652d90dfef572ed3f3591
GIT binary patch
literal 44305
zcma&MQ<E-S)Mc5rZRbhbwr$(CZQHhOn<r1&nNQlb*>6Ym#aB@k9d)x}|A7^Atg$D#
zqNq3>6FnOY`O)3=8w?W*5hIa<u{8`YFN3(1t*e<cgSf4ctC^^oiG!&bgRGgog{vhI
z6DKPNKR=9%tFxJr9gOE@ofg0zAdcjBtATU|3$6+1bN-9w@3`1$8H6QeQ;4X9G>>A2
zS|}M_iuZZT<3pN(;+GOyPE0kM&&Sfz;}Q<-4@Ne4M+i+8Ccvp_AN;yD<d8u|grY>^
z>DY&`7Yr2>!lhdx3bt0$us;z|n}v)`|H%GH;P(rM=%l%gwo#tv*Yg7qL`sDcLI*2}
zgepQdO|=ZIx!?zE0Zrw?;4QNFjFGJZ5jMc1;vmIiC9uLFoQR4>{*e_)+{iVIWy2OT
zWk1D>Sm@c{m6r5e*gDcnVNbZQK&gLdw|U61%g```ZMo+};8^j~-^qSG_ggLP$nr@`
zr+NIWsJ?3MIuV6NAp=H0A)i)!OJo;2hN@9CCJPSuxpK(SIfQ_P5{H(LbRm^W+=Do5
zaAwC$jWCdiPY|ES5~IaI{Cu&`)R6PEsTg(l_q#kf<;TIK{Bw^*x4W#iBu6xS(D;*R
z)v8$uVAAf|UQUQuBt;{o-oRqAL);h76vb1&&(!s^-J>*uqIePwdR(y9^hl}iXLeq|
zx*4o=3YV1i$gGQx$B9!M{aaOhQdGA{fB7Ki&MFd*EF~}I2xOD-e&p`Sh3fbQO)qYE
zMnu$6;j6B<iQm7h%A_cf4C%RNonp%HA)$2HLWPxlWqSTJ&u*oGsitwnF=5Am_|RDU
z;8~#iUD=7ByXFab(^#sEcm1h}>C>tkM_3AiCV>;Om$<d;c~s3S)WS6)_2$rB1*_Md
zW~j-rGQL$-)`#F`!1v)6koc#>7|tzESp}C~n=LxrF5QX~<K!jVD|%x6j^**BX)`;=
z%-XJF9L#>oSC1{{U?CPrN=SHuLMMw5m-8VGk+xSMhZh`u#oChKr}tlm$2jdu3!n<W
zs{5}Drw)wE1C`!TA|p&)4=7za`fJvMWUxddI1HO^b7Rf)GdqpYlWSJC-1zrg&7W_p
z8*Ox(kU^s5=%8}ys9_F)j6Fl`^(6nsXNFfV>yz8-T@A!X1we72qjzbS7D-}fvl?-T
zsSMO0_z|y?@Ql|Sij@`SiJJCWxJETenf_^=SIL%=#$jliK0W-hbOX_@EUGGp7qoI?
z8{&%h>9upu28LC}t_A9o$JeP;=T5rJCs>RVvj5wmGZRZro+uG%$-*0~$?7T0O-qEU
z=cJCyN>*@<OraOZ3V3k08*a%Q#ysP~nxmVVxmm_#>h%?(DMut;0Tf!3X5JIZXyATV
zdwA4l7j5ux@vI)1862<@M2sU0jC455S@{de(smHj+cr+59*{MBG!+7JbQz20^xz05
z^jt-vWUz5l2<yQGsaZTNg7Ijhbo#-t$(zjH<TQkZssJ)86pBu!<kcKYFD8@<b99sq
z&}8QpNwgW`>6C^Q+D5SQ{S(w=I{n*E<ga;hgk1ev%mAgKl6;t;@IhA0(6MVLR|Z2V
zZ#lw&H}z>_C5buwEW#`ot}{OuWUl6_Fo*k;k0jR0tF?if``VMw?$s()LSc$w>{TZ#
z3E22DGIzm&2<7m)7Ex%qt+Aefgg)N@Go=JHxq}w=4wQo#V{&L!7hFV!_aY?u*naco
zvW~^Z_Hnf)(;qq06^E%p294O!YGHQs+V{5YV3c_W*79&E9uuZqor^xD*-caAony0C
zdADP!b#+`o>g_V(8Hjeut{Dv5_(?~58!ke>mmcu+^1kkd1F}FnRc^w~q#L^k!p{83
zBUEXA*-JS4E+PBE2`P1KXbahw#YAW#mDCmJZ}nN^oy={W_XVkQ>B|WU0>2Ke`o>2Z
ziZfa)rFilb6Pjwnt)F*!DOt|dk6)yiW&yTA%$RK?-NE-Scj?IC;}J?E|8kHO^sl($
zU%G9T;PG{+dkdl0h%QkWl9f}Al3m2O^hSz!ME4(NNKDfJO)o}hOen%@^dQIY0s*+q
zgm(FiEIK5}#Pf`D#2AC7QoY-=1RkEuFq;EE)(032<Z$B&`LSU?AgrdrhA8pg6f7Uf
zxlcx~vLa6JXXaSr&+pH#b-RrQJ47`0v!=%H_G8C%N2hF3QKU!Yi`GK=>bW3(IuFrQ
zBH?%3qj-UX>R^bBnj49Z?u#2~y~+9e2PMR@JY9A6_L>f;&s|J$V<c|_J)hX*Pwwp(
z!5m|FMl=c(F?Y-x7?~i^gVNKEjKab&c_?Uxw0b33mTh80`D*6KrbQ{4l?8JZV-I;$
zwbbMV`R-inh(dHCM!)7Y_#`C`GEvj>q;mph<=<iEz20RB`-)<uA)F$KS2PEuU6LTK
zbDa<7fKiL*A$gj`<w$v?6+JgV>30QPjfupR_ww+yua}~;yIotuVh_(QeEtufV#6xr
z*;Zt=#D{v`B_d$0OQFF2r?bW+!Hw|BbfOzk`p(8$>lRl9H$J!A^xd%HaRxMxQfsc$
z{P=Wa!CtFysms+GnlMkhUt&)B^&Wa85<VrBGXAis`t#x6?~skt0*slx>Hn9Y|Goa-
z%w^*IzX@Mv4koVu8NO}WJ_&$M<llh;VaWw`h*g*Z?^3y-ryJ)^5G$WPu^19uFm0Xo
zu*qbN<io3u+eDGS%hH$Y%@|&h#aseOk0*(<AtLx70hOqXZ!)=KtM7oi#}7Ua%6k$N
zrBnA~sPiyZ*OO`)@!9K#l26W0*H4er3Y`eg;_uhn0-lCrZy*^60$5MTOS_n3pum=X
ztR=+0KyZB7TkUB<PvK}QCXs`MM7fxa$?yn*q#@SKWN+okD5khA1!It|&|ma?t9;!S
z20m)PxHg)lUjOLVURAm&p4ZALMj@o6cZG)Pr6t+$X5uRC%zz7*OWCvfe#w<9q5+-d
zPtKefa#VW^hue(%oQ2&B`jC(QifoFtg#uD|O9d<rYyZWW8*_<|e`L6UVyIl=%^1g~
z$!&}j>9t{BDryygPWf~Ok}XE=zF6QPage@hF?W{LOO}IIO|Lc?qtus9PGHNu_bmZ<
zZo%=+3BR3{felS}d{BB;sDjlR1=!8-$CN_Z;*}ABIP1O+7#T>eLF^}{xKAHf8Ufqn
zF-+Ul95CFP)_m$j<^2$fBGgA;<@xZ_3ZaQd-aW?4$&>ei=Hl_yKXn(zdgFkc^v81a
zQyE*@1KXxK9F0HQ+s<rRj>AB~s+>)Zf57cRU0_sGf<iC|F!@zXN3&SQd1n)YX9kO*
zoU%A_Tu{0O>|%;{O5eTNEs{I`b;jdPL%{kLrP8GbvdR>4F(<TJqMZw%8R*o$*(i@+
zijD+{uiGdIE`zR>JK3i-IbHPVh|E~cD~7;0&TbcvA|J!~Q#Vh;fhLp9&SbVSr@qOg
z*?VXO>$x?rADxfv^b)Y28;6u&IK}(Dnj?HlTV?#Au66js$iY&)iAK&1Uc41au|TyZ
z)fE;N(Vt)IeUr>gmwqnX2cF1Ewu942Z+WCHrHk%W`+&vwV53{^l!xIJX0n9$N$g0m
zt^%yCY}9~)Qkx}aNcKjTx!a*hx3>4Id??w+{ru=>%%A!PvW<TQ6Ss3+Kc;C`L#+g}
z0nI}zQhbuifpc6>Gvoxy#l{pl-q5~{<^o5O%`DVK70gP1#i@TjC06*gz$^GWr*jy>
zITu%p@eK{6;RV|rk6miV5O~1jt>t5N5a%g2D^jquPTqQ;9!K!JN2+b^Lg0|-i0o@|
z`eD3x^+apmCGV&(Q|raDyMr?dBs5OY8u=oFM>w;H&e>Ppr_4PrujpQRH%3+6P)-*B
ziaK5CsCKAhf?vJ8j?s%1Rk`CM(m~nY)Ay2macce^4M*5eO{<qJe?ze7tA(e(uP_Ev
zpq1Qk8P~L3c*q33aL&8n@;#C06`>C{8^KD&<6pXC2<us4brLL8U%e<{a~?sa=WqeT
zmBl+4FZxa;z4aRB8C#5d-l`mPKXl(jqiWrd1L&9N_9}**1X`kh*+9v8l~_afR1$nz
z^c?Mr6pz2lydP@4&p?c)=w!$h;epyhiG?j|38Nn*I#^HR8sHkpHslHvI0bpQ1?u42
zk|k4~&HO|HSLMAMl;YZg-Al2apCFcUd{Z|i6EfOv#AKNpl>LIHo)m2#{(j<<Y(S;q
z9;tl73L-0znTH_V<{3YFM62DH)Is~r10SYa;ueZyRMx+K40}iyk00NQT_p_TDeP@b
z3hj&KEbASF8_?Uc5@TNNyQnNgD6+gcYf0c)VK-x*8RAq>r3&#hifsNRpRfZ=ARKn<
zgd%ZZuy0gwU@i-eI&Pmvw#%x6IwII%h$v*t*;1MwTJUgj_UVL%irkLbCi1-Ak6)R$
z6?EtHVfT<)pF;I04;{C%^-Brm6c0(w1B<W%iwlN8PDeAU2L4Vr9|U^KmBD(c1=H0n
z-|3>su&~k`n1$sr@u!w+NX0RDP?KSDva-9ctSGNLLJL^J!4|i`d`gE4fXw<xenF~?
zMOq*KnHkpzFjP8n%RRa7?YQe-AlB+5yT>Di!(1liZEk6`c3A8pTQdsJEJLAz9$8!F
zWs*%C)Uz1tt4^$v57Ziq8I6@}NBTb86x`jzqe($uflbjLskkv|8&Wmoeh1l9(={$R
zFAO5fm%i~eJdXfjO6-=K?eUUN_qWQ^Pbo8$4iuUOoAT?wGAGUH-s$uD{@|w?Ak%ut
z95w5^zYDqS$>JDxfHv?B4$b(W%c;HX$lq<*a9x$ZQ-&Puk#v}2fZjCiIbMfmCFjAt
zjvY>9ibc@M8vQpHb8)!>E=|qJjf?A7jAC}VV0VSsSOfX^Pybi3@~K{)f4Bd0Zmt0H
zWTs|d1yzl<E3&cB-P!8f%7v;a)@~__Qq@n&V=vxXQ?O0N-^kUA{;K8s`b_fXk-x;@
z-#50BMyUVp__T3`RJuJeIgLcYauAsJo&yC+0~pi!!M~MkpKDQ~RlRBxX2Z<5r}cW0
zdv)--1^f7+eo{k*5!ZqgOeb{y3_t%bqiQJ;_j#8?3#h-CLc5Z3QvNrnZ5b$eKz+J?
zy^HuZ9_WDpR>)kHrnRGfc2+WzzoKyVq{~%Vh+yr5Zx%6EOPXyJWUT$;!aSs-D&meD
z)=O~;%#;;=>IKM0REV+h+OGHZ8GPErKhuu$1eKebo;aT4OsXzq=BOEC`*Bgwg>2oL
zTrXIN2XYE$r@8TB(_m&gvgXVdO_p!5@<8=uog2KDshLhjnKnv0R0w)DpK!&T)q!EW
zNuY-QVD+)?O}~&lK~cOw(Wg8rgvr+P7AF-w8#84OM}}jT?io`lE@}-v&|c2F9MDrw
z524?E&<_|dkZoM~l7-c80F!^%DCncNxatzc8Rk>87cNOtEaoJI_<RdeY{<OUU+L;4
z6=;k3%?5s;@Tm6`II?q^<v2g70&>YDp$<1~8&XrRHeJa<N}^Z6+Vyfq@!nxoy)_U7
zA=6awQ9e8|l{$g~30ryC(g`8}l!S5_jcVDZU_L1LZ88Hgbg20XI7G1J#6zy23xC>|
z$O>3esGzbmu{xps5=cD@f=XSt`1f?0RrSrD{`hksDcTgz>}{L!!$3rV@|qP!c}w)(
z{4wYpl)eg2w^Mg}(L9}oB<fbMg|cUy9NWTK-XH%!c-tGuGZ(@YO9W27+b@tLIFN)!
z8(7hqp`JMRak4K8GQ`hP-`;M<C9(<j2UR!hVWy1cCN<_e4V*Brw!;KUxRp;#ai}b6
z1xtFaJl)m#<H|~b<NJ=1O|lzdC6-94&5cc0%Zy#|IdY^-M?j{^^_rGsER{loxE-~F
zi9%=15!nUckn5S}LsYOlm+fPi>lE%1h=G`RTA&0o2??{8;oC$o+wegS$Ga*r*l|Af
zl&+>O)GT9VE%nO`j2$<(Z^2fYoL#Gp3c>xzkKZX6Z8SwyH<5D#?RMGUAa4UIZ6bA@
zn?OS5lT-W5heE5)p=40OlaoICAHs`L7TN@A*-k$aE7?PYbDD(;x|>pnljPy>Bpn-n
z1bj+Y;lj9sUofP>OWlU*hj92UiOz}j5){ZSlun-fxe<vo)zBl&wncq}p}iHtPfe-M
zyc%ZDjnucXfyQ?J3%#UL`6cVxgl}Gp?`LvQR~qX{=rkr~K|C9oNY-j+PdxmA=__6Z
zsBNo3=qZ5%i#$C1TO6aUJr_>5LBMMOp2OY2{|>p#O#fGq%lf|>rao<%gu`*<{!<O4
zS>n_d>lOntpP)X|J+RrK#t9JLbuBQ~m26_+ByE24Vi|v*Y6Df314}Y^jyvx$iPtqX
z1HSI+uAwy`NaU}GkkG*b%E^wQUG*Wv01Dvv#7Hm%bzIK@RS#XTn&;C*qS!lQs?b20
zW=#}=FoAUD+w5*?XP4bccMXj?R*&cLubthj-CoOw{xb{%_21C4y9H%1QsGSfiwc-O
zP#FAE((GFAMop#2h&>cxG@k3Ujwl4`D9g2AsLU*dwA5K_$2+`N(RHjEV$Zp3V(M-u
z?b{uBTiH}%Hu-iJ{n`MpHm~Q1Ji9duBYllIw+#<5y)0yVFE(MSipY{k77^VAV0Lm=
z+V-A)Rg#*VB$V)^Q425$G;$_j+OT`mh)5t4(ZkScVmxxPCQyQ5Dx~m(WrVYGB+A$$
z1S2N1Of)f2(eg~CW}82);w$yH)8vecW(g>C>A8`22+P!~%}lq=NmF8gl@D+(qLU5!
zf3Tmq)ZsLDv|aB1EEm!<N1<4ljXTohh;<y1hebQ>qb;<s9X?GQ{8=7LdJUmZb}?5<
zyo|~7$qX1p@tD5RT$qYgeNAq#DgRzUl^P3%)==Q;gfN^p;zEKUH7UtfYuFVC#CZl1
zsg}4AT@;CZI!XHy5uhO<Wq^$Ckut~zgGG@Ik3*YeHjYx3M-ikB6x$|4L<qGgwv$ys
z3CJ-?a7wG0i?FgKoA6yssNJy%#*Wi^LPJF%VeH9T#gY&Q+X+%i;Hf(+Ozm~-X{2CE
z7*}WIjtwaXVj9K;AP(={$u|V=Qyoo%H7%F2oEmcmI~FH7>z2{Tq=k`^?unp@p<azs
zo_s~3T=h{<9xwO*8ZB~jx}|<F6MXV$LbMoqC7!BPD4e=`Ge(ztnhH)MN1-^<z_G`|
zPauZgtyv_3A)E08jNitDhRH2d6-q|M2d0MS^iSE{g!AMbtE2@@^)j%qtd?`SIYG6i
z5<q4T+s@=j+4eC|GEVYQi$MDh@{x_yPR#%$Pe+#EN2h}n+q4$t_RNF-LQ<pFkOh@E
zwgrYY2Oy?QE1$^Mz#i6gZ<w3yGfQ$DrB$aSG@cg2+Kx~ell70Y@5B~dVy;tTsdw$z
zqvu-TRz;}kwfa1k(pC4@#ki?wYq-xTHO!hi&Z7qh5|rIJS>CH2*Nv`n2q?VUfZT(!
zEihW<_oj*eP3#aCR5hFIXwTCkMZyaY4`>5@j~s$(t_Vq9dWJl{|8+tHD~O;)@8^}e
zTu`BLo~-!-s9+fw&ytTQ3K%ibYhuXchKE(#4qL+zu!JgD`lX>UoAInq=sGp$pvVpy
zzkHh;YXniQs9{Jmy|mA%WUE?sGGTh*V%+c99pY08LA$Kf-~17tR80ldTS99xGVskK
z{lJj$L`YWsK5SqXOU7kUKtsw#&>3@D7S_hiRgpIWUI^Lgz64HQxe+_O!?w29;~aL=
zckqx~$hOwnAO_q>Z)9g&{_$bbz{QV#c4vl^DEvl}PI*^DCur(i-yl^an7H3dFrnQo
zZg`Z8i1rpf0=pn`mNCUs!gD$a6aQ3wEn`|s7E>%BJ4`L)k+t5`DD18^Fg2lv?|ZOe
zsPT1x;0yiTHl3B{9Zjkat-45PnbMW3V9Om&&y4119Z{k86D_8;MGPUp*d#|~V6qXC
zlLJ0DV(U!mV+VD=OA&r1t*OVa>i=6v-jU5VhQK?k*N{e$O|KX@TCJ@H_uv!;_PoXo
z0I99Ua5YlVSWFiiNOkj2y<TS);gB9PIsOB$rUtkz?AyAzNa3bpFjIeLVW7-}QfG_k
zEH-?w*zy9u5JK_YoX1D6VqA3(;oMsDbE&0wzc9vsJhWb*GiLg1e*7oti^cet%)g~j
zjdoMH-9CE4m@x?4D@O&vj@n|)@nYt!Z2Cn!IiuKzo4JXnz4=sP>P*ne;y=l)Pm<6l
zVvlBkD_H^2S04}Y#$ST4LAlZ`cw^qqeRDK1mTVOb>*pV;mLQqk&ce~g9>r%Ir8>)q
z*;D%*o{@(gC<00H3;I_CKkhuI!S6mdt@&cw)J*+Zx2qi+P9Mwx)(qp>P)FbrUkK4V
zJ&ymLNqSUr(El9#cuMQNr*3O?=BMYrYHv_%+oQX9?OMWzh;bDRW3IWCU0Y_}9Jb1o
zkAELKQJ#)Xw2b>Yw4Sf?cfxUl7l)xp0x}8B8X2S~kSFRWG^{|vpDkNRxpL$1zLkLx
z@v<76@H#n3&%mJjc|++t`@l7}Jv|4y6Nty`u9e!UdJa-TQSOIfZJvNQe|F+XvL>SO
z)9|cwZGgAeM-z{Ag~B`%k@fRcUNDw|ycAj5cJ9X_<SWIdXGQL?AmxLWi2bWUgLf<6
zB9zI)F=Qtut5P|2CMHX~`<gewt4@R*t4VVDK#>5AbJ=D!>yGlwK>;aO<G!(Dz{N*n
zDlt%h_p!dmzouPzl5Tf$NED`uH)~_$1PovI3h(Tk^;8b-rFxwJ-5nBFj{>xVry23(
z+zuP>?@z0>M-5W^f65SqM7f@RVri<_+Cz)p?%gkhG4s#(D!;9(kRg+I^;kqs+1|9R
z6)6=`zhFFA!N<h4bLrEvP$v(i01Z^sfr_xL0#q3cn(lpNkml~yXj5_mOz8U<!WGD7
z!Kz>XMo=Dww<Tt#w4sfO!)At#8Pdh{cVy%-_zj9uDZPa;h)KDCTHGFe6ak8s0MEX0
zR%Xhd0iUh_kNyw@vZk<~imrA%!c-Y=G3o@9b(yMjrro-1NcIA20?K%jE;J3x45(D`
za{ucG5Ad!CSGh!=rJLP8DKEoagC;4jrJF6L)F|Gw=#>h2Ka$X05yXgC8WP@wV|*`u
z$}X~7o)#A<tIq{*k)5(tU`zfwgiO?)i@L5gk?)`6<(zzGGgB>UR)*c@TRE6qWwP3o
znRGPY`3>nSEg`-6d~m+QVOn6+%=WT|o#T=J&UyK(pjnBP+CuivJV?L3xcqjQGeVXn
zhU@I&3;JE?uPXnT!hU5^+uz0LxO})7x-8Ra(C>d#CYZ(wk0(OA&w70uzP(;SO?M+)
zgIecvtr9=HKAQ8#5djL$brqcdfzagG8}H3T6P5TWcpKF-{Cv%vGz0{cEo|(E1$m9R
z1O4ilIVrB6dab?7HA-EV7Zh$+-L_No2NqGC-ZCb>&t&nyDW?nH-znWb-jy;}#F&c(
zmcuwQ_$kKV%0iYL7j;F8S(if80%jur>0UA+D{Ezd;=}Jp#V_!C1X2F~0qR))FQJa{
zf11lWZLNgEPGtYN21a&7I#9cK$tZ3JEHIXwY2%R6g}?5J5!YfN=__=^&82Ba{6Bux
znx(QEoBVNHqmL#{Z)*3H4E_dpVTb{5Bp7#V2Bv_qj+(KGTjL=vnKHDP8l6f5Cqn1*
z1M9~cicC%6o!7oVI*p(L?_b*sVuSk7r#?b84Yt&+6Ojemk7Eao0E4e<>rikafnx}E
zq*#ieFf-pS=_NErE)tjB9@T9{^E6<Go^mx0Yswt$6911`#5*txVWb_kpuU6oe>J{2
zYati;l)LUS1U_=<uYNl!N-j3lFC#+ob)V%n&z6Z9`PCe%3l`Q`5B0UTvLbPo-fr`?
z8_M0Rd=VrJJk-@IjU^|XW1^lJWb!?WD#B`zvf-uHJX-L=!c$6;bUt9~V2wRAv8<{)
z8rFQ*oobY=)s;U*crYscHQ~)=S`v^cDf<41Q<rqZsBGG&s*3i2^4ouGuI{B7+Hb&K
zNagzF)wUW`|B%#bE};JuR$o~CHHw?7+VJL343S+)om4YWS4h>*+41XI(Zrp~mJ+<s
zLTHADrYPM{h87SHj-Zt?p?hybdPwkz%#7m9e0giC`gBei!GUBRMu#$mW3Hhqa|FKw
zV9Wr~$&J(Y>YFaJyXV!Gk5|uN{_SMHHqKlx-@<lP1}VoICrF{Pc>s_r^xEuVYC*?X
z+e&t)qy&cvf$*_?%f_JP2_X%Y7Ku$A5V6DePiGNh(nj0C66TKS=0uJSBP<o~h@yD*
zbmLK~9Y1d?28M+SCjMebV}|fOtMXX_!b2DKwd?*><K<T>KH~RBb0;lUS>~ZOG^x|5
zqEa!XSAQ0^)}*pzb(*pl11m_ej)*riNuDz$IgXG2Jiv_qO}tMW2y>E8h&cgX<&EqU
z`evv|EmqJu$)Z~54Pp4D@p~l*bjDq>Un6LJ^^<;5gZ6*G+ei!MGY_+BXbY-xDl(n!
zGk|cF%Pyvjw|aOR6DTMmrPnh)OE8v{az~AGN5$@mQY0u)jt9rbjv>=G9F|8Tk5F4#
z(yS{`=LTLBCwb{d2sJJTuR_t4^r&g}mEWl_hXm-u><n1CuY@lO*bYfy2mFAdBjc6R
z-g{EFv5Yp$A&A)i<E}V}0Kr3BB)n9t(A2EVhgtQq?-{9IboeL4!(VI7U5%yXOMBO*
zF&N6fgaJ?E4sg{WIWND8s0WLHjX&7RwBRkWL~3R{9_ME`-4YyKTu{*UaUt5nA2EC)
zV{ELj8@oK+^MpEuy=1M=aN#En%>rM-nA^<QqcpjL_;mKsRfos%O~kPPjq>i$v(W&z
zvb(**e~-+HC?0K_IQo$cgu1yn-xN9P-H~;Q63bjnUGXVpq(Gva&bGURO9D#9G3PXV
zHN=5R|Ix|6w66)E%$;uhfLJY|2&<Rl>lLnR6bpRs6|0j+EK$djO%eomZ0fpPcE6C2
z2uiA{o|Q#l^(xRv+x}VOO5C&tX4*!@T{fxXk~SSTOXvYEZRpdvu<P!yru?`h&oOzV
zHbcGwpN|KpQaU>2A`xkW({GNE%*IG?K}seMGJ$DiC_9wr5kldgG%oTdu=`_fb#(~=
ztmy<6YN#aL6E47@2zL6=BxfJ=B{mE~m=$U_%n8mLxMW-;(moOK?U}T?@N|d7^IQ)t
zFuUOihZ?LCgg=f4PZ+&b`!F3v4n!>G&3FdJ8uY%Q^b8H6Py{JkG@|6|$O)o-cs*(1
ze1BUoZt5Xniip6#!f!#$u?3_{C49RU<t6J4l}Hm=zU}|nONa@Xi%SLOe`mURdl2q^
z(Zm5e5kT3Ml9$F0J|?k`_V}-e(gFO~-<g(*V>D|IrV8EW`Ras@xq!PQa0Hk+U-%bW
zjlR!75|umd25jJP==6DW3?KR`xV2}%rB7ujm-O`*?e!1`V*xGcvqFghk2NX>p<U@V
zYy({HnOj47`k+3<z(>m`WOay=j6$Hax?S;h#p8e_Kay%MVg<efWHB1lnt_v0^-U5H
za+7k)O)XPG!%c^PvkDU(7v>~IrLPxw6!Fq6!GpW)hG`>$X`u+bV(iF&kntVlEC(|{
zw}jGci?=ze?U$<IQGPd}^<0ag!6l*nZt!Dp&F%PbS#ai_&_IQeM<3{pC=C2%0cOc{
z`dWdzMYM5UVJk~-KP$7IEOV?U;b(Q#t5P6;rbuiab%o39t}zk7<o@URxUt0QOHb{)
zRcafLgRh8W5ejFbo@5U~%fQEA?HgVWRKYi%SH;F}VOB)IENcIR;BmVreS1i69ra3^
zUk^@K)jhfIt~K%{(MfsPsSKpk_Tz!IFnQ_4Nn6n+z=t3;(_wak`~9o6B=OzfFh6i=
z?aGKrm*150+PYpndx?njO8P{AuSx2!Ws<Qysf*BDJll)()j0tb6IWP!ysyYwun6L+
z`z-Ds4BZq1EWaTi@Emz=q=hExIeq;g!3isbRyTQ-D!{9EhM@GOf}ZLNj1|dmrp$!B
z>#z8edHgujQc;74<MO#TcRY7t#YrXTcb`gDnJK4zyH`QgYCbALJWj<y*<i|C3`<^@
zaL8stxs$(>iD`cqoz3a&tGc&%l2bLpTZP6v3&tJ?;-ACZMLhlfUm{|^&Z576`G5;n
zKUA*elPP3#-5t2eOE<FegL2_H{xJgQkD2spt$VgG-kal#>2d*!!a;Ajal?FOWgmSj
z+FzHw?9oX_CXO9G8t}YgwKdyOs^iBuhy~3f*8s^jIH+S0h?R_7X(=Jla27VSo9z}T
zWm(Ie$(Vsda0wTQfOK=|_PDWXh)_vpJa1>&spi1on~jZf66TdaMMGVW(oCHT!XfZr
zYN~E`<8j9}xXeE1=#lhB^j521_A0)9G_l9`0`&4WEZvXTUO!f_L1$-HL-XCfj`Km5
zn>zfnVI?ITNh{^#0I7di`4S`&!Z!*py7oS{>y`7Ou2f{ix*B4;a}1C~S(L_^stLTx
zQ_{nvsWoeqHr@1%O5;hMAs7H|ukKUdrjhOEieja~iTStN`RWlC-_xO2$yo+wzHAIR
zX&0Z&Edzy`&hsnXQyovO*1IB*BOVm|Y+m6JY8%H(6dxL3E2Occe>*}sw+VhX477`8
zyIPfhhQG{B$b=Y^2fv_<V<~R~N{SS@oU9L#n&Qb{PbzRn<tVMSUp<J?q{3^(V0&wq
z9ZxDqfNh_-7lEpFJ@!t8qO;z7sz~$aPrJ_20%ahrK-?3^ig;Ws0l$|DzmVowt&RT&
zn&bGtp*ap__WucUX{tO907j&pPa4iC!Rb3W>$Al|Y;G{Q%evXZNm*J!6m-6Wr`=ak
z<#@D7N)W)_``h-t{%#SbC~^>@SoTmxVXyG6``Jf2iL|4%a<scbr1W&8I6#S9rIqJP
z>LvE?%*YMoN52k}Ar8w7CIp987NXfIU?v?p=}CfNveb%0m7?Cs&Y;t~d-}sM=N(|_
zLj7T)6wnypD&lt*e3d`^z6NiX*ln^Zx|d=TVs%<wSsih)&@Ka@uW9FJ0@-(=6@wa}
zstX{KyV*-5+mMJLN++JOzy;P2(tFzH?JD90yI%-*sZ<cdJl+Xwq0$@J)vZ5Y2D4{O
z`aqS-#QtiIdoEkCswIMYnfZ8oL_)C?HYY-JBfv>?6Z^(<_2A6yK%F#yM<x?Vb}u1r
zWAZB*{}cLog9QuZd{b?H#{zU$8^2laamvp?Vl(2{%K!ILhf4ubrCvCubZ$mmF`9Br
zSLg(YITJUIk$BmpUwpc`5d^mk$=tfSdf5ii9(xrI2S^ar-1tHKL?IYl>FaxjkJxku
zKa=rn7JKb$;91(3dWAw;faMMP5M0fSmumYF!~d`#N`mO9-oMQ${&I^(iwIm-TsX+I
zr@s;;W+<&-yDNkCHlh~p!A2bcxkG@I1}nWd{%p_>HUx4xVxwS3w<>)PY)KV_1~aWd
zgq2-XY%mSdc8v2=ahEFv*7n|7fc3<;xdW2`>HcSh0hGCpm|2IMtRBU%mFA*9CjY7V
z6$cjIZ@aF~%&}S%3}~NI=`XzFsUP7(5=9tdNchF<NRva{jE@i0?`Eg_uE^*5HTuOM
z^JsJ_Q@CF<Mp<CBJb94~P?VW8>7I8QJq+%MQgqn73{G#M(!>HZv=mbr$T-(nD6A@R
z5ftZ%g<O<>$>(5*Se}H;81|-h2wPH&Gea^sM^%>cwytzq0j_hFSFZSl6&5Bc##mO>
ztHBR2oz)`w|GpY*|HEFZxEZ^8Ihrx3{x_IJ{(tI{>|8AW%c_)lL2Ih4z1&7+<}OuR
zN+s}BWtVPh*R2+{wYO_qfQyw<Q%8b|1WqCm#ex`xC#Ho8W>x@!7(sxiNsU#v$y?U7
zsXN59x2x53^w~MQ?i1|1?(h?>^7p-N-~RTWy$^5rH0*cy+g!JKfQ_ClgbETRcGkNC
zdN+;I|CQU#5&#1e>`12_=H#db5q#}^UXcao(Hd67l53t*s+*5Ui@V&q(EUz*tV!>q
z1vg^<g80WT#-A4Cqu!->q6?J&m`gxOAcF`~PNMk!dDFiGd~rSQWCf{B=d#(nNUJ%z
zRxYkF$npm)6pJ~}S-zJbzf*^4pW~7h#1Dv4f~XD!MHe=5J<Py_@lGoczbZjc9?@`c
z_i%I8rOjzx9+4Q-Kq-fMgH7g*@PVf4J4=*hkVw{LQoSmLO@W60Ci+wb_of+1t0j8J
z`*vyH5Nklv^rm_ykxa=TPa{sJL(&L?rdhN7&}&p5_ZJZe8NoPsWlGn>J-|S{o~#_c
z@>dr!dO23YBtRY@(@trXA*lF-QHP8iX_h=zvyHHazk7w=d#_0I(rUc#W$jQmIPT<3
zm>3aE5w-EkBBPIA-m6abrCsa+C0N#C#=sz`A!H%dt3$m1RQa{@s_tJRU*H!iBK|#A
zdS4QFN7eTMgM<`CtW$%Z@vb$%E>{P8n|^13_-(1&BYua{JBoMCXAlbTlRyILQ5sb}
zcvfne=x5LrrX^2@#fO?$$gyol?tw;}+Eo`hLZQoK3JQf6LY_mOLp_AjG3I3o@(gMu
z9UYAUlanRWC&3aHmL>Uvsw^CTEJvzOu1{Ky+JNeS<bdK5*)_^*%zw|`6zCbDDcLJQ
zORA1U6Qwq4Z4BS^UuO(2(k+y<Daet89c6Y@V)TzOOH-I5aXShi@;a1eKfDf_6eUuP
z*FXZf1FQi`9Xc7j0&EwiQ|YPlyo5yD&#YyqJfIbD_9@J<@3a7+6kI9c8shqgP<bNX
z=e=ubjt~cx>ttN>xHIgK9?)+NVBt3jwiSMbwb6g8$eXO!{0d&b73;y2u?p`OV6s2>
z&ImT(9!HZoksdZM*SF7o+APhM3Q2LPkj-@#?$-JcWS3}HZ{JS;j%cS}AExj43bGYu
z*!*rG3?s@}`xt|8br=kZXPm%g+e7sK92OC9HVl5Tb`JtQ7OICv3Qx&k3Dbpzj^9Kx
z8<(d;m5kQ-wFRq$kltz~tnF@KuH<&kQL>shAK`+w79wHp$-vE^?-e6y9`u&h1}&rd
zBz<VXdZO<Lm!I0f?`NCk5-YYQ2QaJPndW|2?W<{e+M@$~)s78&#6!Ir4U~^<1Q8Pl
z39S&fwXv<Vts2%yoBvC$MJa>ww@7mZ-K^&T`jM0#p+iRAPf}WBZB7Evi}Fy!$DWg$
zdD8NdP(LhW(p3{`+97|9-;^on@y)Nf>ZlC9jZ;{6OqBZ<y070GDUq1O*cWu!g~&VV
zoTqsRcZ>8Vyz$=4bb{|5=~MM6e#^<Nt>`GNOt*67>!7iST(Q*{Qcc#Bd(1hsJKX(z
zh`=yN;5q>C4!_R-VOrx}`tbYB2T}@}?026?OBNgiolY#>h=nm~>{dB7Xq#$f^uX+d
zghk1as)tV_ZZtj6(HbJ+C_qw(RoKVLY2kF=lxT%P%ld5W`U^PDdg~3xy#DG2W+w^g
zt!0>_IFA(@`lw{jv(JzSq9(%QW7WdI)hey9Yua2~s;&aTZ8q5659)iDSSEfLbc9)m
zw%4*ZdkJpQR$faSf3sJ58nVsI=uO9+E}9PVY4G=L)NNdsUeGF9u4Y)0ObJ!H4LGTo
zF8Du%gs^m>$ya&$+~DR95Yau@eV_2F7antQFs>L!fP9XX^S^NB(Fi|T_30yeCz@g>
zFE<ZaqO_Bfby{*Dg70F_3@yxsfK&$l!6&z$Sz{CFv5z#=iH-7d)E)L$7|&Xz(g2@*
z%($bb{iMChyL3O>c{NsXBF7(w-G~fpHH2wvH-sU^v=iq#5++X1wyE5kY|Z1g7va{9
z(9MJ#SoMQjqZFm@8jdQX8KPU9aU<l&szOJ1#VD|^x&ZxqouG#?WtcVLEI%1n=9##;
zF!m>P+alt|_u5_K>z@&OTs>x5{<IDEnkya|rgsN7y^FgpxlA}%pEzNS#X*O81py@$
z%SxcA?`zIY-J`xsa{(y;I$tbE-7Tz~`9jo@MCHcxWybkBjz4(|hGTysKH)QaQzl*z
z(^Z1tp$>))o~7qR@xZ`7zwR<rp_1pHz`kv%jgiK3JpM9i;R4U8lRuzn+~0}LS`^`l
z8HN4NXqTBSR`4o;6eIN~6E*&)mnxaB`88uVHY&_s>=Dj8bn0cT_65v&J^^@>w=|71
zX%^Hx&WN3gPj8eK!E_s(_i-3>(lPJMdrYFhhu-AKLXiIetzp4{S;**>fTd_f!LpHd
zj;yRWRVLF(m?3*=c~+*`d5<f7Be?rzhFN(TlQ+e;y{<EcwS|?3j&fFgefBUxp+9#^
zIw@z)^Cf4{G`OzJVYP?<Oywap$MGlTO}eHDAQ?e~P`B#4^K%ImK4T9S26}hkZ&WGI
zphnA(1to9Xf3T!#i_H2J_}~C&goVJE=O=IZ;v?<2P4DeCWU+wVLW|aMePk0Yi9+-j
zOF`~B*`!g{fLiA4p&&bVh5mayS@cWXEFk1`>zHxIkia#a*}nZV62{X|oMASftCg;y
zys+FhfLn<Svh$Ub$GLAxpI3q$oq<2fX<?H4uZ3wAvMnO9@U$bwl2$vR-~2){Uh;LR
z50Pe2?pNI07{??x&VpPRB+#8ejbe)5ia*Eh1`N76vKIr~x6~2Gx!NkwzPYBS`4C6C
ztMxnl4M97WU@>3m6J?kbEqYR~G0yurr%W<(DftaDn<y-YKZ0xFzwg%Xh!*&WJvcX?
zm2-_wCfIcOwBb#hF^?Qd+F<9hxhjOtAlT>nl95l?j@&Mqu$s0!1J^w>Gl9kU=N~)A
z)b90dr(^5acy#4;w2QvkkY+g%v}o~Eptt~aEg?h+f&2P;|H|dU9#8%shme?c*x&ZZ
zx-ojbzVtC<?x<1<@GB>G628ja=?k%3*QYy7omK3d5e)rSkp`k@R6KEZkFhnxmWcCB
zQyC9^IX@*xNq&KLn?AVS;_tjQu8j$>5EKyD6*r~hFj-kGYhW}H6QRGm^*bNc7eu+m
z{d!xk)vg?G<aY2yqaE`uA^3BYd7x&#%Spv?c{WX`IqMssv#kS^-U_t})>rEGcq4V#
zx!8ng*!cOx_X{vi4|F{-TuRd}#es8t!E=0sB?U?;@M9>Hl)SqB%bOF0?n)FA>7J(6
zL{cEoOjJeKHzHj<aB%^x{cTtG-epW=EJdTY4#`7YmN0RnXIRaL@%yQwn^TP<I~Y%e
z*k*0us~g1oerUh1Yy;6tDD#M*FRT<xAfv))>7##RRf#r=uchZ?gT|7Fh){f!M-G)m
zP_h`6dD-@xi*yabFP#XE4y}9X_st>pq`lGU)7@G9oh#o_d?6-C0O)KmHO!|YwfS39
zcsAJN`U<Wb>iFS@%O3IDc;v??&b#vmb<mLTT#Dfy2~ww9M-8=3N8^*hMAJywVok5&
zF%-xvT4~`Bg+FHxehx$89`a-WNE|3e#dgs8BnpUed=%9$BbGaLfxOh7Ase|P<wQq7
zXH@20{UmZ!BAxcj9VUar#+0{m0BcLx-GT6BbTE1&&`wO_>xt+KZUqQO)B(u6&Xy}0
z!#6~In!f!|b4;rQFCD0fPDq5K@%1kfPk!^f5m_<gL7HN;62n-1#@1jyCWtq(zxCJE
z!>L9*5y($rA}WR0A*X>A3_BZ+Le6zHpac+dcyt4*^OQRoxL~*BZY?L(XbQDmSbCRf
zD!mms-aN3hQf_{z6ni2!Q&bsU+T6K#c7vZrv+uSiPP6OexN+9*L`V^uhZUMg8=YXI
zM?|>h5~27_%wK<a6SG7Ou~XRN1^?IgOIK1eH~%32hDS7~3KPD~n<jG*2C~tKCJ~ND
zDX1<-d#=T@8Cl*UZ<0^`J&M_w`M~wO%7+!$$trc7j>JrC({~+WA-s?lhAmlLB(EQ|
zKM=>xDw!*li3|2pbhAej0ud1_pd3WZ3g9ZWS^8uu<=QNL2}Dr1OaqVBocO#!{_)Qd
zM!uYl>{@}XTnMYJJ}Z=^{HB-GJ{+hQI&@h7ZkfyYD7eXvg#2o8ZqUfWC&ImrqE9an
zWM1y$cOtv{O8J{&9-czwGNqvXkKzF25%)OV2qLan)U%Lpbnk4%qye$K%udxd6WD#V
z0^H2Up{M<nljVY6GE-~C4QqMUK7{>LKcRKt9|YXpXpr?7)-A>?p15uCUewz8aFbX)
zp*jPrH$>`O3DwN}B9Igi6=PAcyMQ|5&}d*L&IJaX7AKy9kcFB-R;K5(L_v!&m^|EY
zt7JdU7UEanV}@vt6)QI??mRL!N^)j&ma$SP&BWn2V1wLAUWwl}aT6)L`;GFH@2~!K
zCnBu4vIwgxJZDRH3Iw9t#LvGKc*ae)23gMBaS@)683)2`h#Ay@h_Sj0|MdXV8v6+0
z)ENHW>0B)*O}vM#(M!o}R&Ddup4@VV$TZ?iknlcx^qbzCtQ-w$ptxgFt(Jrj-G*W)
zxhttVsVm(N`P#S>YmaO)hqb+@mC1Fo`~GnHmhf!!$~!@32m2h%e2}dhzad6<Bv9UX
zh8r^^!{c53@<T8SHT+9IdvG-$L~lGz0!VmvR3LbDA~NLPYe0Vg9ddkuEV>qyJ1PQ(
zHbCB}7O-7E2d!tt9Gf{YcO1$^%$}VQ?oq`0K=KO=PC}%RfWxE)oC=kc4LG*va-6bf
z_#j{&kL4oCiTNyq69$Of?#I1k6#4S31jgfI&2p$dw0IK3U}Z{j<k+y}IG+EJ@0rcN
zJ?a$10B#CLa;ax%5vWtN7$Q~iABY+MX+1)fr<EDGjuk&G(W!<VCc+wdC(|C}SuDDq
z&t_}<`Za&cMh>sw3)u&K3@<k1F;*Fx>-2tLXUn?u%<<>xGuhulCv-}s1y2?%QHZm6
zU>$$*l{1QE;-etoavn4#KU9!*S5{Z9hj=Xdml@;1svG9gXm$Gb_J(WH%u23Hc3~us
zp?=DyPT<f+5Vo|eP@~|8T6&O-h0K8rDHcV8?k+ydv-D&7RNy%7G>ZM*ve-uQFgYj_
z0rvU1@a~W6&Y~kF;$W_F6NQN5eryjMo%0vj!I|OjVBKH`dnZ*P%Sq6Qx3G(_o2Hwn
zKN4l-`@dDW6n`KYgz`3uSEe5lKh{w}>Z@reg>5(Lr`8izgfSS?R~~OU2)%4^wKjti
z31E)y$QHVtC8)Kk9~X=M6oSyzqwMu@Kd+|o!dkglIvz%_Cb?67U*E}rgapKspbX0r
zWkLv_$|1y`gZ%GEpEiY_iGwQq4;T~Du#@Q8#Eeo63wrYlF=GgNEQD<Ny5(zd^m;~?
zUM3dfrVfWFlp(PJX@#h)Ht(fBAsn?S%Q4IjP-84lpO4Ot@WXZLM;pM!@`1KDEsxxG
z5ay5IH@|t*TWZNZW_6&iI23?4CU2!^g2Z1jts^A)qeVK%UI1@{=oj3;@bN?ZY<+#S
zO`xr#Q;b|Q+;j&NgmB8)G>J*kj_EYpT9(axjx9@yT6T_#ktJ3;6*7PYK)L_=V{WIP
z%`Xn3J%7=P$AUHCc-us<Z_#>k^e1Y7(s-m_573#>1-(Un;<hT5)9^1u7h3h`WEm@5
zn#iI;4jAz}0)lp?x^Cr!;^<9!K6B_RSg!EjHaQpdlojJG!=*5dCIv*_;P?7lI#X8`
z>YatjO;`!Yj}#&Pa(D8Z*jE+)7hPiSTKzuzLob!P=-NQ}5(dmz3&UQr+F*t$s_J?C
zozgRV<@34uSyJIMS@z9B3~m?SURog(;9{<pv_=Ky;$M$*%e~Ms0p1hSY!32_V~Pkz
z*9ge;7r9=Ky`)UhXr^89JlGQn{?U2m9mLeXhUZ2&ZMqpZGc_?nO#iG1kN1-M3C$t!
zp)vo~Ume`1@j!|^)15vChlc~ts0%W!>^vk&+$VCQ5PD8CHz(?))kTcO<aOBO5XC$_
z{iny%>7?)zhdt`BO0(Tw5;w?>v&2NY9PEk(#Y`P-UF$kN%|!&&fLm=Y4)4R{FhDwo
z>Ynx~o>y*J0&Mj_i%~_<Bdcrnri*s(%jdDzFv2=sn^Kc^$DFli&cUDUf;jzjf(RS4
zF0o^HS>q&y(tu<Jdwx6hE%9kWi8)^ep5VE&vZ4)n&ZK$L&<c+YwtyIf*BF^~uUgPP
zuYsv6$e}`c_?6zilnu1A77&bKNHzpMeK{e$Y*@pfDOr_2{Jo6E6RlmG1bpGfe*FCW
zm<K9c^_m-<<LQ_Ay)Y(_e=8hOGT`KK;V+p{*o>$>I)FS1ciel!#)*ca9(VsC2<DV)
z=@Dc!1ewD1a5rOiu@{-efvoPcXs7h(Bd51Ql3fm#sjr}}WE#(K$`zd#|8rdT<WexG
zoD1@WN&45|R8q#@C~;V&f7I^*DhoP6l7S-zSnUtAnTM%6d0EWveF&bWy&y#sarmcr
z>Fe~1Q|!Scq+u>5<o2F4!nQpRwUWKK7u~lZB<}NLZtIzI>_G06@+8oi;7ZWNZ0q+i
zh16CE{pEKgGuN3gO7~|?IU$+9XDyUD=L)BPYct{N^O<9Gpk=I<PH!bYe@;&Bwd!%k
z1N5)`I>Bkx&K2PignVPK%O~dFn7qu<byoO#f$GJ~n@;eX*vuH2_{Sh1>m7UvU!t6_
z@hyq=f?LjR?I}7WJC)P5$-=>04ZS2YW-MkF7dI`>M*+A#v+FkeICQ&tSPVNMXRa|j
zUNM7<tzO5x33`gGw}O|Rc!PnRO(*?TwliVU{h>y;TUvbqwLj*Vw!AN4aNN}ZfOPbX
z`iR*N|H1e?AH!qSw#PSRm4R*|A7ctX6^?vKu|(xHj=c&!w)In+Z;mo7Tepnw?5%ff
zHsOX-)9F$_X+5EImpbpON{N4G8PzD1!3pxq(W&{67qm0+f{TtkJTfT>If-`_Xvocv
zl)R4X(Oiv14`9+<exMVEs|ALe%Ef22y|b<=U<WMXm%O${d^r2jRny~vV+ee%6Z$5u
zgUry)ec(AJkEUS%v+>M}lw*DN>;7!kF^2u1ClxJ6k;>wuN?lW`nz(XwAiqU~fw6xE
z8vJcp;dj`zZTW~}-_<uycVNe_!($M}B7FNj^<OXi@$XJJAL&h+!1YebhTSK_gOV&M
zoqNEoM9`N&Q&Xy6nmsxnEJsB_toYvNt2vG8CH-B{^P7#~T(0GIzv@)o_hmD(Pj*3)
zm;(KXuOrUB2unc0J-NWz_r0#d!!2Q69?kOzMeJE-nGN=DOg@{<Af)Qs9NT4<PO0Vx
z=TiU*1`mg<79Wp<ObosRE9;L&>}NV!#+MI2JnD{9=v~}z)?NG6{-pRzh`7**-m=RY
z1Y4tYb`Jly+E#9#vjs-+0v_uH;uAzR>mzW$cVEWO{o>`8Y5Z3I!*>>fz<#>7?Q7UA
zBRZk8oEv4<?slVtz*9-Q!x*CTZ|o)!FZ3_t+nnGJX)}E{aT%y}5qAwIW>ml7W_6E$
z*dtnV)+gOgs^1_7(;xQ*<`W8K-ghRj1EAK(ma?MxR1HOCz^1-CTBVN#Ub$1r^==LW
z03^R`=ekon7l-*Cji+8F@U`Z<lRVEk>146d=$y)jh5rEF#}#?_chQCXkO5DQWZ!l*
zC>d1AD^_RlWs0xM^A`$a;Ap+nLof3=-v{53VX)8DggBdsE+Tmvgyk7G03}u>0&Jq1
z&4|jnSAfeeZoEi|V(yphGU(*(gvd6<HWF#HZn6k$QT@Lj03g7=a`iE^uI=u0`F^vg
z0NNd!CDu_XI6cW?GyX;LvXHlay?`3?xUdO&V<BsS2pa4+Gje_gVY;C-(<?YChE!+6
z1NT~6;8AS+iDA?Ou)vD5Y6+Us7k|r>xfsQBnQ`O)q6qOUm8O)QSl##TkU>k0&@M>f
z*1GXgrRv*tn_199J1u>oaQTXR*^&NX?qhs;<Gfr=_h{BwBD{gEgu09CLe);%L<_?4
zNM9eV=D87VHzQ$oT}t==aP|&Cx-ik!<|*5@ZQHhO+qO^He9N|N+qQkmHu{_O{iE;T
zkDlZ-BX&gY%vjIbD?5<_<nQYDPKj(JT&|BF#D6c9VY{hly1LRAQbI>ZO2vYPc}5pW
zM8wX;n9o2t$kaRoLq$f>AK%}2#qn^;p(I(sFNz2gwB_jUQN;IaZ_nNo+Tbt@d%a2T
z_@H>3K6+(@J+rB4WlJ97AHf9_PexKk(8`vFkf7a?73K_N2QR7v7w4n;K^n^5T-+g=
z(N<NTbIb@OF^b&EhMPVekumN?6et?#eAq{b4RzL2n^U(lkZPg_C;*n<;vgl&TP#s-
zr=<OMF0#^&zWHM`vc8Jl()air;4Lf_VVl4|(##ZHntLXO)yJ~F;1Pp)c-|cTPAcL`
zf2r6QP*n`?+zynK)TQj%Fdr_UGoJ!;CBS6GG>j_3`ZT)JjE$}v;2xcV84lUUs5u-T
zJe25)W(GbRv7(@-)jrtuhhVFTkOTh8V^J?=8qH8fRBIEzLwHE6lX*wH03IG+FgT=W
z!{<$Jdh1nE^z3U{H8lAvII{q5P%|bM9}hCrF)Q8GVw&i;)cwRq2`y@!gPsv-g2Ljy
znWL%6EE5M4i9gYt$av<5naZ>iY@Afk&1rsYSFkpPBl5Ho*OXbKZl~YXSKZUd38>l_
zk#|tWC(5EwBeM-?P+*hfaZ#qSL?>7!ol))EoKV<PHT};vYSKyMY)GV^!4A=lkFbFE
zl9AQJO4~;a1b{$$*avI@ClSSLwxxW*@KoGU_uN2(%=kzv{@Njjn`t?dK7oTgWfE+Q
zydSXZ6FZ(iVF7ce1)uhGaau9Py+HE`faV#((_qgN6)TX#(?Eu$^6tH4Tp<`#H_^?m
zwsTnx`Skd=wyg`8l=JP>Pb@{eSYJI&Gg}=ziJx=Zd0(eF>)B+}pR%fgS=$Q`O>oqf
zk**bMqLcyZge*BBDXmcawg=;$OUq4WD6_!FZ5XYPfWS`3Jyl_@=2y>!*c$Sx-v%Z(
zAiGs!z3qG*j(gp)&+uI%wc-dH$4c;HEjcLF8kpPUInk}PhOyW6yYl?`n&$gl3hPm6
z>r(f}Z-RZPXFIH>*sj>Df0sCk-y!*R^B4b9_wn)DBy)y5c_M?$-8R5n{b1c`{O#};
zD5N9K>VJW4|09g#{~ETjvT(BePcEIK38RU!)|wk(Dqgj~T$*ELmt-wdqg*Ry7w>KB
zhvrLoh(dr0MH(bXC^$$H*e#OuA8sS;hGGyxB!i|_Z_sYhuBlmB)%LL=uy)Y#+05;A
zzw*54@R_f??zryRdp|YlCG}a=|NU6&Tipx^#N<jsgaox6U!1>Nktwaw!9${7vpj}w
zVSfUIX~zZ|WFPsZ+{vSQ65xn<l96;P6WoR->i+R~O5Zj({oofzB$4ME-+LUp_jvd8
zr6@hj@(d$5;0Z=V><xw<;0d+~zRiQn=C^@%zkh1B0*Y>?+OWE4_w2da*l4ntGHx{U
zP&bWE<#y9;oF)huSF~=XlA1L2%eReHrdk@KF{5q~r_yO=_B~pxN8CtBsCR2bG;3s*
zt4$0m8?#()XjF8oX4We6)h=BuonUS28#iv5fNs&Rky{0uL_arm2GEOPRgH>IR%@C;
z_aPF>D3y{`xN(1Jm99=4E*?*Kmk5`xO)gI~WV(Xg;{Vnzl3$upS!s9YhHBiYA86<3
zV`P=eFHF^s-Hi;iF<l&;c<<K9$EKTUqxmZr#LAr&b##@cS0%eK_b|)MeMTxxqFU=k
ziRZ?CAJ!-@E)O9tx?LQ%`|N_4T1|3#WeOK9!`)6bIWzEY_$f`PD6?)1wQe4i-F#?V
zx=F`U-|55u^i=PYZPYm@hE00*e7#TvBSrv=^YR>W8N)XOYKnG?Ruc|Jt^jkDU;)^f
zWdspTppcm2?no?+f$tgbDVZWYgYPJSBP}AahGmV3TD4+of+G=)p&C*&|6UJ8q<Y4y
z4PzVAHN<Huh$7-d$_*!X&x<26f8}R;D%1<!jYgbFkiPLAC$l>P9P)3$h!RXB5uSlp
zn5jLNgBFYsWc~JInI1<o#wN#pM4C09M%vGvFh&*d0`3N^5ZvGR7(NoRD1ZL&t@{+-
zf{1{i0J0RD>FYe57MtmWo^`O0K2XK-k%Ea_g>9$V(@)3JO#x9c9ka&ZD^PfPwY`6P
z3$4XdZqW$^o<cQil_yU8?m>L`{6Ih|cN&xv*GwPEcl;@_nOtGCPx#1Mwd@T4vtX$M
zOD)%=l9F1SP|ifl#zgU&@tN5HN5$Q=whF@aZWgQwFVM9(_B)RYdyS*Hm(W$=cWSvN
zhEBCE$+I%|WgduTaVa+tBP>ofRjN?!mTm37+`KIy^aSm2bITfY<Ic2w-e4@^n=%5n
z3ed4NEn8MMxu<YpU$uiaBGC&m=6e5mVa(R=f$8m!$_x@s)^l@bWzBfX$~o|KLGZkQ
z__*JuAyQ3ibaOi3bEIbt85;aG(SP0hnwL6$<i%FVvk_YwQB!i)yWZ~E=qU*g6lR$R
z70s6@b|f_+Y-On-D*6u-E#&E5?6E<XmB;J*`X!mt4WHIY&pQT9(;Gc<&Bdt*=Io;M
zxqGR~MzHE<wB#?I*IKr=0L$e01XwP8dEu<mr;LVlvbU)1C9Ng-N5pS1hQ9bw-x*pM
zSvJROxVuDg&f_Nb*dD1c*dBuRPZ+j-j^Hu8+Ht0N;};<t^aPO;IiGX^EjlU|JHWg6
z>%3ae1^pyjc<2W@I|`?k^adTIIhT!4yoAc-0osjbq)!01{5SZXdeoB=OG<>9KM+z9
zWfDJCi1zG{QbI;!-74GuuF)E?#={}m_1*L8kR!Cm=xC^#tB=I_XCweT#|Ns-1K>YT
zDNq*r7Her|9j*2j9oV;fei$&1s$o=SU|vy!swO~fPpizM`HZF|S;=pBty)|CRwd`}
zdaLH;gBmQZ_so@z&4LsRMX~N7Y<=+9TIJ1ZTRA=plHjotWg`NPslB?*imobVo=ZN@
z8HNIx7cMRn7KdM%W*26E&HA0AIiUqQpl5!!6euOVk>JhncFr?Ku8=zI*R!hJ(>pW%
z`Pt~Q1RYYh6W$=v2Q3u|%KsJoFu3#H@d4pfD!+Pev3IqG)RL#<^Y?5U`v+D2kDQ3#
ze9y`cE^e@w*e1w@e~F@6Bw3n9?S-}qSd))bYgHRPvn3%W5-K*_l7?eYyh1YGs(xA@
znq^(bMoJOK#U&c035)L?uCyMmJV&_mm`A1WgppnQi~31ZVI5C?G%VN`P57I|ut9Sg
zJ?&;5l{~#B2vbp$W1aGtyvsIrRXu+e+CxMnh_A(%WQl>52w2y!C17L}T1F$wr4{?6
zpB{}|5Z`o@a)F^E?4|}^t#8uLg%sXq`EqxOlleCb{8s3`20#j`HJxZijUoaumh>cI
zNjutx5(2#&nFWMxQ+VF*uWS}i+qS-seF;Z+P}yT#zinc{drXQV2-N$Bd@DI5ni=|O
z`$32JFO0Cp0}u17c)*0Si7Z!s8@bkoZoOdlGe`G+k9mf4(PTYK>I4SNRRPYr7t8H-
zEY6e=i#q6aU86*57H;>g7Af=g;``$0q;7hx70hY<yP#LgnIFFgIWB?s^B-$`<(*kP
z?V#NJJQ0g|vxX^Ku&$Dw<ND?MXSX94)|ah$qFDv$t;ewJeq3{^Kv-N*48vc(UW18&
zWl#i_CYYkFwV|qCB$O2e@lAu)tf>R)%Ne3en#`*lLA?zeG*%9FVpZ)J5_vbxeo(X3
zL1FOdbclaq9!U1$$>)D=r^adcS6B7>YJ7YYav)xm4H(AONx||#TF6$Ix02%Y*sZ;M
zReoU&&Ucode=<8iQPM?L)mXRMfZVPfk8!E06{4dSN={0#+B9Vvv5L=npG!pQw*0Iv
zjbzX~r`OqQ8|#JJLMYZ8mp_M$2C!f^W4?Up^C_988KS8)zwHMnYA~2eh$6m#`G~@7
zkjhGJk3x5*gHMO?Ic0NvuJ_F*kT9orYbrBlU?C6W#s(^{N3qSNd_<p>b)#KMr%xPz
zbDpiw)3r&HS;k;<u-9ejG<)6CNI}!wZ1kx*xVI+-bjA_llnCCQNszo@`2vglp6ME#
z!iv_@;XOex!1hv!ZSn`4lQiU#_<FWA?YzS5i0F7|^*nXmv^_LEbZngFDvE1scn=v8
zMP!4d`}h-~$h%~;!>>~HQgixy!FUd@nk-CNvJI?LfOCs1FSCQrU&(6bHTAEHdj(B~
zwJIvBN%N7LHeV$V6r(?V9}VG#lAqkmyVhfWobY<)&phCUmWc(pVMlgikTEo9r4{Iv
z(`%{Cj>bt!)n&YhLYvx3lj?Vy8(sZum_$BrH-f_a2<;qx_;Dvb@XuZC9j+V%=9gC3
zS?7JA=#|DTeon^7pe5-e*8bWV6N|ULys*Rnar@UlSytTa<h;BX##uf*nzF-bcJTM{
zJnIo)FMxpWx6~7#PvkU$CE>90lcfEZ|J7MU+zXddNT+ZrdY)0}5`|+>HY&@>i#(GS
z3s_-=29FLRqtMz-zmo}Ty=;h{<l`|Xd{z>oKwzo1PL~V!R>S2EXz=q6i+{JrbDxw1
zY3IuA%cwg$V%pVOj|$DIpIq2kDauHRv`HJ#h;6R4ef(Nlq?E*N+0XiW2W66dD+BR3
z@ho6v)7p8rY}0sGMBI-28jkL-O1jR<UW5|Y*c^?LgDN3bxk?|$9UgJ(j>pfi0pSPe
zSr{66<;~;>CC5YYJUP#QJ9|IlEFi^KmQi*lZkbjVdyt0g4+?nW_11RA<z2Dx)<Ix?
ze@?8B5^Lxe?=P)w_T$nku{+hEe5$|WgjL6_Dr#@HcLLO-Dpz-`iqe?;miGLD;)cIy
zZlPW!oy@82GW3nsprafLjqLInuiH6PR=cfoaBby-VM}*4;ZOtWq(JZ+I}#v2gv}0v
z(WXz=lWgB;(Bw+ZEzn7i6}cZ8hte+zjcOW1&El9ISvYYcZ=NJ2@b<!Tk1ZTi=tiu|
zSvxdcQo-_OD3^mqlWS4TX3a`jWnk-xZ;@)fAbY+>bRZIp#vQiOVHP-_Fj(n<-sc6p
z+q@*A@GOic8y(AxqKOEctC@BZTc(`+<RP^grJyS}LtmCpr#}ky-lNx;!pO+55}=&q
zHbivJY|YQJ-a-?gsg|eX*NDOCgo^|uuEr9-e~@z={|hT?Q<qLwxlR-IQ;Y2?oluTN
z$_-($0|7z(c2Q8crpFvq(@|NKbxI5$mS~mub#scc_7gqaAA!;9ikJSEb)zFQEV-Q1
z#Oo;~s;Kz3%Tc^<wXvbMr?{aJr{Ls!dj^H!J9&9Wqw7JG@giQ_U4h6Ttw1MFsizUF
z#%XEIvK9SS8BJl7)1YY*M{vr58tJrj={6)ZrQ>@R@vEjz8M)0i-}P&&@r4UD3xwy5
z>j41o?Rw}AFz+-7ri=-d5$jUvQsyza;1EJSmv(8L)f>o{A>}X6;yq9AWq+lxtlYD(
zq#iT$6euKhD10k<8Wz?AY>#L5TT(SBz7`xezZMN+I)Iqof}EozG<$GSqe)PZPM-8!
z^nmbqz{8SW#U#RiKK;y7yx~XsDlRf2{mOhs{`Tp82}k*3@yOm1zcRMDIQ%ZeUhhU8
z3JA(bz`Ja&gMZSNdCIHUMz%KcWWT^)T?j0cWK$fR8f7&eiZ-3L&LC*6;(Zm{j6MC>
ze&|u(01i||{e#*BSWPQJfgW!c14*6g#z~<P^sV<%@&)j>@MB=EX4j%ktwu~+T3u@*
zdnmGGU-ecre`za!!<A2^+wFdHmFbh7hCl1^m~fxuc`45&P!e(Fo`k+N>9%9ASlY34
z_a#^sk}Xf4=N4Qj4PVggqgVr)-Ueq!wpFqvagZ7%pXt4UW3TzW;rkAWW>16`9Exbx
zextz7U$1itT){>Vsuc7r8SN;IU}$?^*CZZpO_gBS1rY$6y)%~8qtyISzk~=UY#E#P
zRwf!tTM{&rG`MjOO(K2-{cce&zyHFfl5b}!;w805_?&<14=GZgZi)x5k-sUBVL`s&
z&kPYOZd1ftshTssn=;G6c=9E@Ordp}XEcGvG@B!f)1P3UZW3JuRs3^mdorB}os8bq
zb@1!8$0s#LZ8xTU#U4<cGY<IzB<2VS2%h%dvd=|{#fnOe`TTP{T@O0TitC#de&P<6
zU{3{wihvF)#@mb8k|ZU|6`nBXn4pcIUf=Gg2M$U4iTf00{TRYvsOOq)HsoRosmp-8
z%YSn4B*?z&1*USfdZGf%ICKZ}pwo{_k`E70rw4_7+098@aNwns78Hsf<>WOle1bzs
zUUn-Bx~<9CWs>`drtpnsf?=)6I#@S3a(Gp4rdAS!@J}p~@XyPuo#6=PYWv1z0EblF
zK`z~hOPE(VZw2db7`%dY`-9FxY^=iRfWo35x4=VjWzBnKZ(wrY5eKL|`Nf&6LX*|H
z*>yLO;ENkx^EW?zVaeYj8+*ytA$AKtK5*CJWAuF4<Tn+gAwxF_IST(rJi|)usw(X3
z9e8sNu9@ZmZBS~QDpHTEOIkHD#bM!IsybGVn5<FM5)3m;RTf2Rm31ZcIS~)_WgQ6H
z^#qixR$=(Jtp>C3js;yC%P8}J?>gWc#Wy%M<ci^KNcpsiDf;CMt>?4j7a<K1xg$Uo
ztlk|R$6R{uFA)7vntwD(>!Vq9g9ZB-0fI2l^qS=Ql7`+vLu2lrB$D<Dq`Y%?i#9X%
z`g<`LXH~LU&QYV9;mpgh(r!DQ@`DO*tw8$%MuO#&9J<jz0HeTUufVS9=d{dhZ?foM
zd%1<Wg}OM6svx;A*mQp#A*^J)dBC2zEyxR==k>V}E4JOsC^K8mbFABo9j{anIf`ZV
z0k2fgDR^`hCbT9c8%9R#Pj-kGK3T|$s#T?Fbe5bNXID#Yr8f=Pr0ZVxnf?j7wD@Ox
z?$=p{Qn+}L69TTYI`^dB$t;t}Jf}p<5SKY^qrw{j4*ulR|Dw6D{lDJ-|7*>Kjg9gD
z&|JbaAv96fUh+z&;|j}Ts$`NlL|D_xnM8|(iom-F+3Ssk6r)syCY94V2_4S~dtS+d
zEUy|sM@E`g$|L78(nVv)$;rJIZeu!*KYu*55FeC(Qo8o0w>!$dXK%Bcm`8_06DLe3
zOWa?o@Ydu#G@Ho#1@_r4HE?(MM11u^p5{)a=U5hDnEx4wCmGU4lK1FD+*tKHSe5=7
zV`>dzeow|0(mbA6et(eb>0{arWK5b~!SC%yXtC7P7#LvJ3-DUzm1)$k(Ar#X)O8E5
zd8ykUsLPmZZ*<+<LqYQFH_c711k|N|PqVSqtb&IwWPMhbg1_l>bog|9cx!@rYTCK(
zS+`7BbrdD9NRPQ?t@8BnJ_fYlwd>;`9-m#vJY(}nv9DPE^@TSvY3{dVYFZwS(<0HC
z<7CDG%uKVJ^E4$p8R>EnxcoK3;$n%xn$2J$!E!N2Wy!=E(q!aiNy8e`WS(Q1WBD}(
z6*nbkWME3pn3zU2r)G%-Omt6nk4~GPGCD9m0>Gw;N7J+hY0c7@rT}Qu(&lI;W9uyN
z)7a*8O>tUtw)u@Y<xg^|Kj`K(D+2&6)8TPUlv43DqAvTVZl$b+w$Y)OGVEK%S{8q6
zWNYqe*3~Bu^^w-5B;Q<KA$d-LT9@@CBfKFD*tHBgMjHYn{=;QSf78Wriqks4XQo9Q
zvbdGFAOEJ+QzDZzOqf{bhzNhS0N<+JmEDL-)S%N<d;UH7kw^Tms#VdlmNy><vd)ZM
zr;{)TxyRA4y_SxJ#~I3pL(I*h*ho!J&Q{J2$2z_5*uN)0oD2t;DxY$SSn6LW2*D!h
zC?dB$1`Yfe87W}6GU`ZL3~RzNS{8!KyjoRreCfGh+#B6nlo7c2TDvXYRR;a--k+Y=
z%`SV$Vu?w$_HtljV^z4Z_%t>&^tAU5sP)aNzOKC88Ug?A?%w=--@a%={Hl>lZQQaJ
z%Xu>oa@W@LE1rT&?=lsPJ`VM$G<{`Of<VawHFj#qFJGH{)$P7%pV^<8g;aZuLHe&R
z%+CDGrN92Iviqsit#VzZ5%oM{6^lefMne}Vsw+W~4C@p3daVvQ+8+Q%__$V9kCgH_
zyB^SOiP*esd$>Q4vQV<f2%Y7HBm*GZ3ZodRt!ml4sy<jXsWY^5T4-y+$&&jtU~l0*
z_AXJ}>E8IxTt9lJ%6fV$2TyAEg9E#X(D5_5Qr7$KBYG7W2d@4D6)s$fC%a+;xDLar
z*E3NJeEonU<D3T5HYuQy%;`H|<_`g$1>|{9{|iB+yw6rr0Sc6;j_u+o!GOjrN~zpG
z!&k>Pc3ZOy9sg88boJE5YAxd&E@SSQ+^PP|xbHrCJ3P8=&4ToUk^Lo$KuoQhkQT=w
zrXhFf$1n9nOyqUILrYHpRnir9*R&(K5hpu^I>r1+Y-k4QK~uy|PESHt?QCSLhXl}@
z3CSD5r?>#-+a(B#(o5G${6J&Vyyh>i;dbwV-^w;hAdpX^lylO7vG)fHhl)fMPbko>
z6<hVCGM<?-&qX1dcp-lzKykmVqzx<a-gHJoJZxy1m5Z=d1f(mLZXV0qFSH2&@E!eX
zsA*`?r-3G>?I095M@8CU?zcSjKEdC<+c+WGVYcH^Q&RETO(e{xhPuw4$e~V3BS<B5
zeY%1q*3vU-fMYSf#ZxdQ;EvHz&!BV-9MlNUSUSs)Rtt%Ercs@pDEB^Yf>%NVgZR?^
zw7AVv^8wulPwtVxFT{CKGVx@F0E2>M(NbVi$Ab|hG-0%3R11v5bOjAc7wN@7;6PxF
z$(707r2`(;${$auARCGUPh(Np(uA`JKCMp}tax$D2NeGBm%%7sFo3nfUj|*v_#r21
z`BlD7r|*j?(<noD?FrrJQzK^CLW&wYX2M-<YBQPqRRu*MV_D^x&uzMf%9Qctc~r>#
z;K`(@FlWw8zwVIB90$61-aozmEsC_p<XaFL-+&42Xcs=r;>Wf7iKJU4v7Bz)$``L7
zYC*z#T7k~XtD&s1ILEILnj8v&z%AUk4BzC8<O7(~nP<xgx|(WwJH&j#3P%32x8sV_
z*PyP)_t_0%T8~}BwnWgRmdUr!VNykx-WEmTJ1x&Vav2+W{pfOqe@_VWS~gDI7J3Rs
z2=Hsy3->?DjPy$VEqHqFaybqhx}h-Jv19}&Tv!dhpU}73Zec=(N{0o<F{h_C2@u;9
zw_Z2M0}PWk0TBv@icuC(M+2090^$}0BOQ}Y5cs&etcSEIAZ(lvXeSYPQ&Uru;m~wr
zD~TyNNhd8<5pWz)<9;uGLip3HRu6G&u4>^`D$@NHl<pr32__r|V02mSiEz0K#cN-4
zcHfCBjC<Jb`H890!$lmU6~O^o9pM)`acN42?JmZV-&s?uJw)LB7u$iaO^wbj<;$b{
zN%NF_Z$0e4T5;r@rwe?nHz--PBRR_`ow3~2r7!|R`{7wHdeJlvYk8a?7#%z=4oED_
zhp}LfV2->U*Ly*@0W<8b&))SXrF<E&Ci90sI|QIOyoHXsM~$NUI_cG13tqL4Pkj!0
z+r476#J7~{>s%X<-9@o_W%54%Cfi;axT~*WhPv%-*R-?OjAm-g*51aF5d6#gMUAVo
zgkmVR68;Nc<cT()=i=1xKD5Ne>T$eqjewYIo==*tuC}E-u^yxW4jCl=@|{dO(3Hk4
zDrcV^9GpYL;o#bw-ut?bb*Odor^XHWyPw1ucc%SvT49J)rohpRBg-{$JjW%`Df>f=
z(9^3X=;dl}3(pnoE?q3+FK<MjqN82~O&sPaDX%t`%wrnQ%+JIPq$=2kzqr3Ptfz&V
zmnhAoqO7kd$K^b=!jpnRObR2G1yK(65TWLyC~vvqGU#9{L~{blJXAcRGl$XFz_k0M
z#e*987C~pd_9#Y>BWNV-uM7W<y+gG|RyG2HQ4s}Isw;78a|hX=g4{W5{gkN*o<5cW
z>6MVEo)Yfi7|+b0Q^(;J_M%bqWSmirzs5K0HmYol!pHGXQC8hp98weri%O01h-cZV
zcOM}U@m5~^MV)#D5jK*lAu`elaprwRsNxPrM_pt93q05OxvrEmy(ZW40!w_U7kqNq
zV11F>FHt)}2TgRsKa?QrWoZcs&t0j#$}y<$`OmO20=P(2-|5La#CBj)?jvZ~!PFk_
zKO}~sPoC&g3(;Z;cAz!zR5aqK2mTMJ@Zxq-xk(|(Ncac)b*%<8&O$<RKh!qte-v>d
zH<!6Se%VE<lQ-w}G19POFwnk!@bV;Sz)mJ4A?1~BDFv0@D?}k0=c9*jU6x@k!)Qn2
z^>C)BOz5RD7vZ#-?#MEO{}e_L={hax{O1omcc6CQ@qV*Xyj2&L6f6pi2BDRMh&c6c
zz!UAl3o3Oz=YMGz<??Z_1|KAt%vh>|6NbE(LN6aqhebX^ca3aS3GJw#qFA3Rzrk0M
zMAJB!9kI}EYkrfz3x_buJxw-~NvXl*F<IW^ZR$3CB8*rzEKD20dhWqq7*c(L_$M(7
z0sIygHuvr7s|7>c)UqMiVk(59P{X$QAk;{EW+oAlk8v~oGXXGI*}-+&Keu!ZaJ8W6
z$mD#)Jf$}Q#+cAj4N9NgnJ=hai|4VPk-Q355kqyu$TP#yclIh`Y#9HI*gZQ2b(dmi
zLJUSySB8<)Ncl-KCShZ>W(3Ox&?UtQArLQBkxwUF#`XI7;b)hPAlHnH%<km0@!W%b
z3~0yzo~9M=kVsp)OOhs!&RD^ekmQ$@AXe^!s>bQfVl27?3I)<9M{_b`!KgNz*n|Xq
zXv_E&B7BP+$eJO0tUDAa-=ciedV!%mV8If8*Bw0#GkF8fAWTinUF{tUeA38y3ItXD
zgej(E>7*onqnTr5pDlWWXniC3p{2xkv1kfcd?Ro(H7Ab>eQYbH8JOmwZEfxbs-|&D
zPB7IdVSNrbpBRQ(`>Y6sXefX;Ad-R=H=NIB>7J`4X+xyRfnnhlrs&ZSBt*h%%dSpR
zuB((Eg^=ltgp9mK=n@VDK&Y$2f)69$Il@M7Kl>n93ft}W7<b+glM5IT;OjJaAx@$1
zozKgkRaVX4@5!7XpwfApM-)vbT#S(wdy4*tL>_^4gSOu;GDi2M-T21@e#(1vVI(^0
z_Busu$hG*FVV0K%z=v%kYR>(l>B7k^q4wYQDuszab*&UGG;UNb))2ig+iBPc+#*cT
ze>4udZ9-b=QF{c;-g7_4)*J}#e#SaKpVl;`v6Ecpns%X}b`O!?o2t3u(|RuG9xQ%F
zM?{k1iPgDShmh@MgtViXT?tVZ4))Ib1L(~p+hC>GC-#q|wSmwRYtLsY+gQ8kR5prR
zLrU(imq=yE`nqC`)%rA!*A$H3A-+HN(3LiHyvKV$u?HIQCyDCtQk4M@*OR{!#ZN%E
za;*JD(AL9Hr0Ra|{F}pS+N=K^7Ax!s6o>_ti9^*zGih}QxQ06fd;J+@_;co(D~Q_I
zmw%$v^bt`oTeb<}=Dax(EZ}=Y?tgzyUss&>uG)uyH^1-o*zlNkJ=A#h?C!XB_$j?d
zZ&4;<h}PVzgQzC5KIpS4e+ys~mhG1h*=oS-GD+OJrKzpdpU8S>hRw)K9nWd{TK@?g
zm+T}PMjDuz#wx<Xd0Aa2WTmMm>Zhoe-$DuSoOQ{PP9;y2+cD-GnP})X{EAtI-(;)I
z;5z7>_;9y?w1Z!WYs39k<!8X};LP5K<sx+v0=2W*hwmXBOs`#YQY9B%?8DJzzL@E-
zKdYmDP&+PH6BwYj4%OcI_2O>gTp2JY*ohrR?%J}$3R7vQFGEY{#gDZ832!9mmB=OE
zemZ%X!tmbVlcSMcD|ytg*VTUL%uj#lP<Pi0hwzReLr9^*{vuL}2yI7|(==|j9y8uQ
zrSJK;{s*3N^q6`p*qw2zR$F?cFK1?T^c-0qy<SVgFilR<n-z*qiT~5kyqB1F?$*}N
zo!uea25j-Lpse47KW1%|o-*?z<-{EQZ0}05Ax9Hv`vE%`?F)|O=dg)VfU;ZTV0rx3
zlAF&@tZN}-{k)qM=|K6$UZ%$$omSJe`gnH{t|7pA)U4zHulsYw9Jiu<_gT^davCZk
z+$fIL=%(WPXFd4ZC(*#gGp&X;+}%{;S8^1o>7U9tWugPcsepnw7`4Jq6<XQ>C>WJR
zbgf5E0NSjOhvKfxhd)$T3+Lb9d7;-K4p;p&dP@2}s6+?)&G*iFuc+=hLcWzR*-i=s
zDh{>~T28F41z*VZ8HI*6{=ei0hP%71Q5Q+z1KFVlqazr5eK8JtFI~0^o>C<)%LmkY
z`z0ZraOrOgF)4IZXe*Vca>BHnWjEVn_d|ej-@1pK{+`BZ_gcs<nVx7hTm}<kk|U9R
zgzl)|2V+is7Vmp{O{j+L>@gdwKHe9nR}J94b?D8Zy)A8moFnaLY{#h$(1^dGPn43O
z8N_jUh&Vba+F*YG|HJ^{%*x!sbALSk3zFg^7MMcMJj5ZvriQU*hFNNS)v$pF($LnM
zr%X~C);u^rY&1*0tat?eHCi1x8k^Je^cXV-!V*TMZ|y3RZQyg)4Q%XAON-h};%$f5
zMaUe??q$B4_;bafynePIjVmfO2v)S7*M_e{SRJhQsY(sM_5=2@LLZ{RnMJqwncYj<
zx$pc^hJ$A@hXGcNBDt<Fl6~>qrT=e>44c^JhyrH&bqN`-F17X50}bUa4yeX-Fu^l3
z$)4F+GL^^9lETx5S*TK(HB=Q0G&B?xR5gs1RaK1*^1Gd>s2vc$mBXFm*wv7-!Z46d
z#&BfL-90Y1_1Bew1>#bjN9HsMtMqN%u<@I)FXx{c4}wt_kzdXu#?>{BrMNyD>!II?
z!&_778P<u<3&NA>wxIf=Ebh|*=O#PVUtNS&g5}&;SI<jPmT!Upaq24@056zqlE$CL
zCQt##X!vy;H_rTN9N&HgKf=(XK1nO+5XXJrb&Hlr*C{@c&myNaLjgYV;Z)}z*|`N3
ziK=MgE3m?}DsW-<M}({&qpAE%Kg~mhFS-m*Js8y6cM>AEUy!u7(O(ETaP6M*A?=>{
z=V?CkQxfj<jOlFYQN*85EXz>wJpoY|Be6K+Q|zsg=(TO%yX$ZQ{m&;5ADhWPLfXD+
zd60gc1ZxGUaT-4zz<2T3DpA|N8NhG>RjoLm2xviVnAK3<iw;k4mMhDFL#9PQWV4H4
ztBeGq?J%D>I~TZA23fL&zH;IL9B{G23j3x$W^59^+2PF~4%mVs=2AzfakG!7bgg~f
zw?Aq-*I5+k8&Gq`p>^l;nh*x}tIEZurR5P`YvSwBRYM%yj^QU?XW+KjaKYa&i+ll)
zPtkvi<CyGN-qU+8y(37%nn6m8TKd)0QkD@yPgLmAtYXP9aEk&BA`2HOY|Mfd1zgO4
zx`$R;SOXY*p~U>Lf~=7KA<cX@Ma?o+;^yxJM2(g#hQYt$da-!smO<9>$F<k-Cp+?B
zv%BEq@2(EQJ5C~WrmDI#3LtWRLsH5oO9(5X<n+lbLT}D_<|<g_3uUZ4oc!aD<u9m^
zw96C2>w%&}HB<a|l7o#+R9x#60|Rc4lwF0!gm3rP7s%8l+>hO0*2k~@Vw-H@>PdmO
zZtv|-Gv2U#Xh$U4aoG?FKfgu5(4Sx`&&$p~W)9fW4ypcQ4p!!2{a+`@T3C_elcEpE
z*sbJaMJV(B$7Q3JRXM9JP1S&%hhtVl3hx~l7Zy8a1$f%{xE%3NRw<mw<bJ(Sr;V|a
zJU;4GRiJb|ZMgNgL&?sV$7HGh1J@f??#iUJ<EMuoE47?5D_)>u(D=q3h^i!8O0>ng
z-Ep-QwNE|$hw1df9nHkMKB7bjI!;pchDd0Lk*obyu!Y<lW^X$rcq-?eHyN4};K>x+
zLKF{ZiX<UXqqL?cOFcztq@+PhXLF){Iy5+fnKICQoN}VNGlp-n^haL%_S)N(9B1L3
z_uEm>Vzb}o!^<B*X7muZ7_R9bQfs;YpXwoS?`v<T`{Oo$_8Oe?MyWlHKj6k(gNKgH
zsWId(vaj{N*G-?C)BD2TG4E_~7eqy}Aj071{+fBHo1c}<HVcDctE@F*c=jY!H~Y}r
zqXMrU_gc~~Vm_pQZG4}X#Jqe<N`1+#Q(6M>s?E_U63PHh39yDxh)?3Kh}W!N9Nwj(
zCC&+{8k7&MEeB>ozyY=aqMrGe2YeR8dHbN$ERXkUR-aAzqo*=5k4{GZ=KM6U92=O7
z#+tJc2aJQVJh~REN=Z4TzY~??)A&q<HOzc06g9lp^I@wZB;wL)Ccg;ns>zZ6i>CWO
z@?8F}HC<*#rvF3J)l>6PMbW^|70iecZg7r_U*b;I>q3tcJPN6)9$STgJk$XtD5*V2
z1vL(ak}S+eMI`fpl>}=6O+kdZ6}&(%xT^Ho+*IRIuROYG9q!yxY16B6YkQf!If?Iq
zqO?zTx#8k?oXWX<{qDW(LYAg3Aw`~;K5m}jQ74-*X!Gw8a?sSM7sTpW%%6JT`)GMX
zChMg|Is=Wc&Bvt2+lmzUj=%4t6LO)HM&{0`UjDY~eV&c(b)(WMr5vKH5EU*vL!*yT
zqQ<CWKGuoRZYdRPP^{2J#gkj*6#FaIz$~6vJvq&S3V3LSPUV+L#BJgiZx~P%^<SnY
zhV>Q`Zxt_@Qo?jb)oE&@QC2~2Ev7_nvz6URLB@JysZQ1jmO&S>D^V5FmZo3>*M_XN
zQl)K4(iR(0fm*hvPLwWPRlzBbT_(3?NfowMsbICHK*JZ5qNHL==cY`Qh9(^XiUgVo
zay4|%m+2Jqrhx=yn;&q^lBK3e`3~(ACdwocva*yX?DP@w=U*#PI<aX1=zZkwwYYmU
z6(}OA1s9J*TQWdDfJaG;D8ENc>p3we<7LTP^r@tcj7&kl_uS^4<-oTRESOa8Fh}oJ
z&N^vjaW==;+aB%ofbULUqpMM#w>>p&&JPV*1HnNX5yS=iHZ&;QtUjWvBgojEu!C2a
z=9TRZJ-W~EeU!G2EKY73Y2f_=f$-SDT`}UaZn7VsNjN%U#~2G*eYFe|Y~a61T?a-3
zP362}RC*IFtvFB592`qHTmC)WHU?j|L?}1pGHpF=es-+?t-H1qhZ9lE--FqMGIG=B
z#6A-nDI%2Njd*fq)qC#=-hpG0ah8MC=G}GXg-bE-VUyUgZ*jS4Fy0)_nr+&*hVXr5
z4Jc)pQ`rT_LcP1oqs)%ZYOuWid+fQB{(Tj3^-wYpZ?7w@dvS@ahdgNSz2NJxYjPJ-
zF4o+wGo(>(?pa<rt?E@S*@DhIl~lXe<9Gv=ISX`(pw8da(T+Cj-oonUv>(pXS6_03
zhI5x4_7--lFZ5^T`X}8lc-G&5rhEZr0s|NI5~|AQ?Yml=wv+-pBQ)M(o?Bve!ghQ=
zaGa9%ED%nikKi4J2lR8cJsWo2aoVwa83cgTSrL1Ca;MxZ3Gdx%(VbXN?&kS)X`@ja
zu-#`8-d94^mMEGKaZxsdI@m~J7AEF!OUp4xn(-8L!XDCTWx;YK(a45yFN=%Jh0qsK
z#`lAk8KjT@^@58LeL8V()Om0*5+pDH<k?a0&m^)O&O3-A6s#T(9(6g4aI&T^U>>RS
zvI^|}3JlOPC{b_I!s&3RmyptVL7Ztx#%o-Py%u%x@q{T8#V0Mf=7%~(fikut@Ow6v
z!_m=mo}7omlCUws;P^?3S5(BD6nrvfcvIb8p?Uqzi4`9@YBoyCHYmBz`^EHe>{r2_
zxZHj1ivy9Kj&gf|FJfVsvs^ymip|N9w>VRQDCimH?Ksvo21ADs$bpCh;6g&<s~l9!
zT|0cvumCX%L4F>M7iGnZ_b03`)7&zzm1~)%<<tnP7;Az|)32P}I7|g7Ks+=4<^aQh
z^j2H4KcbJHf6cpOQAgtNGPiUvYwQTg|3=x{p6uvBSTEyhQ-g>YxW2x!<!~kp8i&~C
zt>{8MVzx|KTR6n7T@%!O7MAtBPIn!tiOp4F`>I{fiF}81>{Pw@UxVQ{*cV#CEiV1a
zU&foL7@7x7Ob3!yH65Cwn?{hsnI>JYg2Vfsj^e{@d{Pm97M-(UH*fkqE++$7E8>37
z#O4MKc$;8HxA>U)H~L=ZOLh*#_un#1m@zcIL%w2RiSi8#aZ*wPgW&!c@<WRTA>b4C
z(^sU9*-J^vZaY8j?$Bt|A_O$vyRoBQf<7e$(!XV4`uV8xL=j62RR2bY#w6*Ifao`H
zPhPC}zj+Jz@<4EJo2bvYp!mkNezx2<An+L(qV1$_Y1}`?7y{jmfWG?5A`-_#ZSbQ~
z4o0H~C=jMZs2tCPgQGV%49tvdwKA2N2_9f127^!0eNe+eZ2DGA#ThE_@&N`~lKnl=
z7FZ#}U+I7Fl-R+CvvAW~&TwP#{1FH+V1?H$fVqhS<&42IiNY1`WF_opx<|Eq;?LGZ
zAV5&#;?n9g>%Y)>?HbYU#9E1wo6p|SAv{6?tty?ZA|9uv;!>)v011`|Us!!wm{q5S
zPw`@VX-^uE;rsOBRTJ?fwDxs0m|WYsUdj*v*s6(Pv8n0q-I-M(9^MtUa-2$QGS(6I
zAG>jZC75C1I&UNiXG5G!DghWBRV{TDBpbOMrB(!HDaU=<aA>8Js|~-9BL1?hnps(A
zPoS;~IDmmE{o#7w{<rJ!%oUG^IKy^cgd9S_J9naOCkAS?+s{02f2H3Ig8*QJpTmel
zzHDILK|SRHrG_3O8asCvW{&h~G}U!@e3!?T;fiNrXFBjndkM=XSwKvWR7|A7$rH|a
zWJY-XT_RtjIjS!k5-jrtZb6_4oEtqaPOvOQUT!z<xML5aci3f7GAIw1YA0P$o3o2j
zN5k;1uUigW?^xhhtDnU)_apx|{p(Hi6$xjkxyD+Nq|<eES|le`4MzHM5!v&~Gb@i;
z=L#Zo3A-sZEEIaFcL)UDO82eGlByps>G3I3ZXH<{3&k4>5kthx!7s?%t<25;@?!YE
z`-zN|>3=%y^%MuJhZtddK51O9Fh`^$e3*k0lV<AejjW6`#?+NXOmN1yObz<h;rOly
zgK~G1pG0N+kHsPKTiO~2{^YPv9$8W_AZcH<5WT~vq#4yHr-e9%rcoU5j|2(|_+NPZ
zyy!+7_#hv2o(OF>Z|l#h`Tq&q$??g>8?>)^Zvc!1f^g)3jY9tU))$W-n5*|vYaJ<z
z@be1O&%$R<Z<LZ`;qn*)k_(JM6~j*7i%(WRI1d$Nkc|&D(*mllgbo(}zO0FAP1ZPo
z;<>^we^I_h@enJb#`b$lNq(K$eTqG%q+bIkKyX<VQ;QN<yK{2>|5lBi@&5-qXJTS!
z`=3?xQ#MdkHAC&gP~_Mc*R0qvA5`#JNJKZKg()MVNEKwoOwJ^}Yy7-+zIVPo+3gyv
zoqPxg8ltDK?h35GqPL|N{gQ%%ma}1Iy&jlc4)^_6&`@gSYk5;*S9+tos`EJWbvpgZ
zbN2c<`wlNvT#QI6#jo{cK2a40rXxcM(UzA~nMapG`Inun+?Omzk=+ssEOBy_)J(<s
z^!q$-lT<9Pk%IsJQ)SAnDwjN>_+bpi^R59Dy;dc0y;gW~$5l94G-249I95}ESICw`
zfVeJ^gX(WxDI>!o?3let`6e~X$WoS^&b&Rg4wtHNld={i2L*)%m`iM(W4B&@%?k2I
z9_h1w3TWpfxe-fagE=i>1$I#|NVp=9d4nL6wOr~Z8g;2$1{%|*E}fLGzPr1hU(dqi
zW=yu>410w-Y^&skdM=YR+#)*3CK)kwBrp3y<$oD{M*4+Z>@fG5<t1WDu}E~pRe}vr
z7Lg*XHpaH*)tgnX=y9ot435+WRm_#DP!!S-a=<e(5h#iiH$CIRERnX-!sKyiE9|1f
zTFbI57L6;$Ek8sQN~|=*P#ls_dDJn}&EqU7de3Slo<aIv!E(h~mjO3glKtw^j4YbE
z3v#kzE6hug?8Bl&k8^^QxfWM6E!)zoeLu~l#-qlv)ezEG5Fb7Q__Z)X2~?;-P=EJV
z=EdBT^h`M#sP7-8hKaSL>B*d|COwzryxwV;eR;GRev^!hhF47-akFzz4Q1xNdQA#l
z+HJ~hEDvpM{V91dGxZ+gT0ixAUx?(&x>k;yWf)kU(~vbjnFjvVPie0KUjC(l)xJ`$
zciP~x#aP;uH?d?+Zj;=ru2)oGRmPb5|GABo7oVM@rInTc%maA(!4EQW@uGDx*2OpV
znb))G(BPU~w4?J_V)K9(;p~4{0Wvx{7}@Y)L}0(%9ZJaM8d&vGqR)=`9*^e{CZhD^
zXA$wt-t5BBV9^x2JrBLtsP3vL=2`aPV|>sH5YP{(W!-&xJ_i2j>B;vST80$j!@SXh
zG6Zn?nA>_AVwW0L+NoaIJo0?RL+pIGq-ShANMd0|<x6rt&Gs@|Ynb?#^-RITbK;gq
z;RA;AxEY2p9I4!Y(>^L8W)t|DzqI{2=~<7aG4J7Wac_U+cRTNjy2W?zuL*FTJBRSY
z`~I=U49UKgIcSSNxxu&U+uDPL!067S+QPZUu=N>`3e2xflz&`0GdQrow4&q`@~?9s
z-3g%<qLoR9$9|k0iGG!M-s14ESxtG#5zts^>~<oTD`@0))kJ^O{-zyV!qDYB`@;YI
z6XP0gDA5gcI}?5BOZTC(0O-;<sh&ID0pG!U7tAsVu<Ay9!D63blQ;JVZU|yvXaj#P
z96B;5gTS0r7_PMobb9UDUBBx&xb-_DM*J7)DNg8n(X3Gv{VvjTv!5aEQAXF_rpme&
zIIuzI3X`u~PVr+LK-LZG3)iKWY)+sQ7i<ev8#lI3Oj`#t#a2)5V%I2O@F|(+ZWqt)
z=6W5wypXu-U-R-;Am2~*rTP~pcpu;Lf9Y_{|35k$3)BC3PvofEc%rNU@HZr3Lp$lT
z`Q~I>6UpVW<bV&5@f|>L8WGE(4Eqm^vv^u*ry9+q(oFwbFRiVL6|g6gXt$E+CfHli
zfO-!NZAD-U;1cTPTuhco<r+7x|H`#y**4-O8nyYDE*5*aZxO7-KgOK$SnS^V?ETmC
z?&`&&8S&#K(D7+El)C*!f_lir%%sh<!i4qS&fWb0%sAUA?>4lLAo{1hW}4#J$%)72
z5n_irA$<mELwpn8S2AbmS?{k#nzQ4tO(t6&O+|-3mcus^BD<L<m;w+-M!xzyx9Qz`
zcfFcj)^a~0kmm<zH0t(BHZj^^-O>s*dVT(UYWBswQHnZd++n(6d=dEArqV5?>ZOZy
zg)bAPDZSF-YSbyr)>FBKAzK~}MfjVopMP59N|UUnQdE`vI+Te@pwW~8(iA=!m8v#W
zCp!Wusx_60=V;ddjXAeyNu(-q$I4a;Mw`Ye1ITeSrL0UhwZr7YbE(4Ol5D0_y|utf
zG*qBdRkaKf!z6BLR;c+(7Alpx(Z%M=qL#UP<SW%IpHPKqH?<u(B>dC>{Lxh^`HUsC
zqAWDaiyyXZ24hcV(5Z2YQ`pa1+0IyM1@l%ZX5yjI{3Q$2=*s15RH_dS^BT(9fNoix
zf|P&gy;)`_Ss&#Bm7TIhO4t~yl^EuJv32v8rjwIudzN?U6~V&G1hOJ%%35ob#e4pd
zM%}lz1JL!?>-N=<ThZ5Jlg_5y)VWb*Q&ytl??^8k>m=*ewsfuVQ)9V<t%f=aS9X>^
z@U{rr4Fo)aXeJO$N~@Awnq=|ETrR7}+tac1-i8ZqKSV!V1Ka~u(QMHIBRuyE>6~f4
zt<Ar=-(GDRIA<)sEOS{uj2tdBC@64Hz#77HZh60-%e%9B_4unc0p6iH55xHORF3h7
zy7#x7F*_7LnEKbh={9&pp=@vGyU0i4gdHvK1WjPbhAwkoFBdI<jdXB-u)4uVI)?H4
z?rGi}OX+%x(shh7*vON?+Xj@jTeR==Uu@aUZgS=)%mBfDT?GA@n6>}5B&U2u-1g=$
zAe#?frg=OR{zyny@7u?}6SbWe6^^bN)#~~u546yX&3i)6CD*=e(d~OsDs@Zcfb30l
z0=U~BJbuJC_oOJg-xN+DEZD)h|B%qa8=yym720IE#D7A(x6*J7v5Ch~Q>5aYgJ)!N
zAVeg3Tt{f6+ACFTP5bTcQD#jBYqJ=@vm0?;LI<_?j&YZ^0+p|NF9RKvYs2hi^okar
zn|6d!Icoy%6aPM&rXuD<f|PxezzoJZ-2#oDmop4S(RVJ3&-381m&!nCBM>yC(7e1Z
z6T`9WRhuWyA2?p|$?6GJ;XAy!f8ru$XSR-G560sa^s!{^<g<_8Il7y_yeWr#tYz=o
zE-7B{Wl#SH%HS)U9CW}eK1m2IpN#k*?wi=gf)>g+@pC$h^92*E1AviAyM2sjI?iBV
zoOVFu6+dJ75)V@03witJD9Az$sC8-k*~vjCW*DNU1mGG8$k5I)5<vfBK;%llO){CL
zE^?*jut?Q9DkyRFQU_2KPcK>YSUE5}5t@-FGA}(!0`O#W*06&~p^O|7BKxwe$onr9
zKo58SEDgBEwvPEa^$TS2^hOP}R_jnyK#1fwZk3Ua#z0v>JE{+#*2}CDNbWBUdfnh>
zqAY=qy`uRb6;%D^>yO?SFec30hY3vH)4a0QIdzrP*16DT3VDJA!hqz>e+iKu5{Vym
z+2qu7Oh0w!$<1R(zWMkO86^CZZlUlM3H)7pkD8(3_;~`Fa00S3x;>Fz4BtBrEv`QY
zA3}g420qBZ=;U+a*kvP40H-BdLDd+CM@i>V&M68J$dWerTXi=ut3|&y!zYYFehh6-
z6%sJ=6bzIM@KNgOruT#q&xrvvd}8ydugM#0G7j_ijyBCU&8UQJ!|?RH@22Vxj`Phb
zKov58e*`zY5N_b(IuMAM(jg!3{S!kYQ5#p@Ea`P_Gf>{e99Cj6{Ty+IuTXxnQ&S`s
zOABR6++bWw(7XwdlQhMjrW9LsY)NF0O_j*{!d46!sIb~8sjgkY=l;n^(gDZ0KT;CY
z-G(^wJF+Mpr6^_CQFRg?TsdYJW}&aHL}(Kq`F3=_+cDrw63t3>@6u(%PM!g6uc6zf
z3;M&T5022}5OKk!ZxUH(daK|rpaRm~QDY+s%mCLUBfg8@zP-l-^F4pbZ6RzWO~(SD
zK;eDqGng`)X`>N4m6XcaZXFTo#C~fE4uY3R2F1mXF1sG!X#%~#!<hFRv3)kX$JC7x
zNdI}m;8_0*Bs1J*H;UsPtN}tf&tI*zr&>{6yYU9L;P?%<td|Lp1m?kcu<=mEkhFpS
z<FC3UKo*qzb8x5mbse-ZQL$u^t3c#(YNoD9Z+???l+z8E`oa0MQR*Xna(1NUEss_Y
zqdCB|O}nMp36Ojdc_ZpIu6(k)C(Dr2&FyvkFTGWEl`h;&PIAewa>Aeh4-Mzt*Pa&(
z2eJex58rNC>VcV*`lWzM|B0N&ha}xEd<N}bW|UbtoHJp3`KZ1q7L!QOau-zLn830V
z^TW^juvh!vIm$(d5uU&)y7F}`pZc^eh}=<*T%M22H|TE0uR4dr?6@7G3oo*JUF&*|
zv>VZ$geV+qQpfHK$w>7b;x^o-LoeG*8NE4Q*T^w>(@^37-u1oRYiMNnw%VCPE>P1Z
z!U_5)KSo}E{BaATanu#Jw@ATi5<F?pLQxmRjamp8)+P7!*+&01eGLPL+LkLeKA{vG
zSofM4Ero89`s=C_Y3h!h=l^^Q<?2)r&7_uBHcm+Nep5zebXPHpNSrX~izqA_2A7{F
zmse-97M8rMah_#%k+M(^K_?2H{WlF>xMsRE1*ht&j#=2yA?SyxnlXCYE^KTHxjtu)
z3Z;K;p|`Zj3|+^sR!VfKjFP)93Xs;sWS#B_W5|EM-E0_?){3pG+cbk*o5M(u&+q<j
ze*Qn5on=gA&9<&_cN(|VxVtnCjk~+c!liMC#@$^Sr?JM}-52ie?(T4V-{dCW&OSN!
z<ji07W2P#ZRjG{ejCYP#-?&fo)ewNp@19e%O0C{~anid(tCo0e5icwQxWTS9p~Gxv
z`&+kU&ytS0XX^yS5q+im?%aG=nR1>LSWWjU;}b{8z6OZS%Y&BBL@4<|<`sPP_(Y1<
z>t(ZT=D@GYX+t}>jC_U_FRfNFC7ua816g_vuA%_RI`O9G3e$;946iMRLCtXZS1pMO
z5RfNTdVO<n^|;x;t<v?p9JikRDY0T}5fg7$y_obvEvRCqNTTD<yHgTWt@$Q0rx=vV
zhmojMSJ4=NF%Uv)+E*)CTj^%*Tc$$pc|9hn`^<(V2A7c7y9x!O*MvKzU)G{iqD%gV
zlsUdA)t2OJO$PELp%+>+-`mG=M;Eq12o7v<apn6PA>B`fYaQ4sKE}$V@XBm~2Tb@s
ziW`3IOG}F8>JT4M#yqRyZ}1OoPKkQ)9}oUEehDcfpp}t*HbKGRfH#sLlA`5M(TSNx
z+OcVeUA$E|P|x#5WFih#qR9_%#`!`{w;VWhFVblKAJ{oL0$yVD-uv`X1Sa_2exJsl
zVSdz(HKdg&Y9jDwmHghgIKHaIH5S4suQME6+Etn1P7i75v{ytDvG!ut<wAH$s9To|
z;u^>VwWp74j}Z?J1xM6wvu3q&=bZk}jBRAh3GSz!^@(9Fe<2o)8cg6kMBzBzNm94r
zBQMRvklte0COj#QDPyn>jOm%WY2{-Ag)}Y*zi#Hwel~dw*PK?Ayc-=)_aTn!Z9;JQ
zAZ$(;Ax|od?5Bjdp3^UA|D|8Tdv$S4y`mlGqJm>Gb$fMZ#u8~u3#JSlwsUqW#9t#9
zY}RO@*DjDAC39C7vA%f(69LlFst8)Ink(wWY{0bo4vZW8hqdjRxiH`$@lclOF@AXb
zA=dcn=Ko`LjmJXRJWjw$HmnNrmB@A4>(95<$&}P(COf;X8r^R>Z;DVbZq_={t7m!q
zF(v#5Cqc8!@zRrPrm1N00hL|nRk+Z#sPgw&(&N9#Y#cl)7Uqx&;k9}H!0W8B9?0Se
zQivaz=@XwQkX7c+5;MEJh9A8i<VFiuP(YV@FP@CPi>}hQPNe&l<1qyAlr*hs=Xhit
z)d+Y|P7}f2u-_#=@)0hJNxJ=V*H$W+Ak}=k_z6rKW+P|ifPb*~4$#jac)q?&Wxz9d
zS>P{*JX*XkXp91WocMtc;>WuE7n#XF9H{=sH7-sz?th{exfnf36(iiCO*ayAr;EN=
zaWPw7uAVvWVp6k`yhu`EndmT5i}6H)2DyV8wsD}e$!O$I{TyX$L240~0~|trq=0=u
zdo&{`l9BfGfqRv1hEjU{ZR_*R!}pD4#sY0{X>NIX#<iuccXdPUe1zoWO%+7RYNEXl
zryOHE@LT!JYCm@{1a0)oVGD5NR!1p&$Wf7>S{Og-9#RC8SfOHdxAa>41*V{918kD#
z9cbesBGM6AaqW)3l!AtX{NiKOxIP5i92YwotYhQWPqOzUys(VB_1uZ6rWOEb_Q~^g
zBVSq7JunUikQlZ#`!afFo*F4g^zyB4BRZU2m_0If#4B!sO^Pi)NTT8(lLa-%>-uL*
zVL5Yf)q&x~p)V_uS&8#t^-2a-=4oh#PVVSuDGrX0ex+qJZ=-lS?7-(d@}WZ?*_$F|
z2uaAClFCgGI%TtfM*Jd}O_eA*H%UGa<ddYD!>u60q%;qwmgqUdE+J=M@<7&L(>RXt
zewXxZ_vtW%m;2cXtzvJCpIHy=oV$9?2cP0alr{ZFoC9k-@!Pq#gf;v=9KT0kcD`8s
z-UVDbVE3h8)p12Yrqk~Otf)$u1J$ZFkIOtIhN%w`PhVPddeo{2Bjiu@XyJ@#f-(6;
zZh7@!p`<pNB)wg+wX+;fEV{5{ViKfdcz9wOdA##zwn~o?-WscKW~r)q`_+7Fwf*wP
zz?l!jx1^lAe3wbVv!#z7q{~;(RZxWE0)+|lu$8-{Sh;Sti|xo<Bio<XDh5zPLplpx
z!*i3X?!Ip|%Yi@EKpWh4%Q04ne$FC4#~++ZK34oiih>clsX3WJh$X%&98x^eKmWy%
z=KNnI+w5%rMiWg;4ULQpO-=W1k?(IFkY-#TkYm`{!NQmJ-VvYgw-0R=NLY~sP4WDO
zS>EH!KfX?oK2AmM3!)~{Lqhf$2%|j*l8E|ZfW=!01vo^YLDG?t!TQ<W7UuUSf%f_d
zAt6opAtA{Q-vUPSL3dFY(9x!H&>}K2PDIdTkdQLaLu+%SxvhNAoBat<b8Gz=ag@PZ
zZ+oD*1ZimG%&3F}2s)qln;XZD{x#!{w|r+_IilbjPy$kL?%d8?e(l^B=%%-J4Ct=@
zHC5;^k~=_>ldqkyrIb~CfW5D@l@jrkhb$5jv`{gliH*6TnW^b?)>A>?<>A_;S|;A*
z`_sgm%DY$cQC7?P?V*ON#>nULu)5l^p1wenR}A0gWclZJ>^Fq$N%#Fjhy#)YKZD2*
z*XOUxtZfx-1v-BlDOa@RX$@)RSE3J}Z&{pL|03tA@tOcxCQ`|#aoDcxUB>(AQc$WX
z9A(e&m$%vP6s8O2`*KUlW-PC7u8*-acvyAlI=RW{IC1<{q3q;5ATImLD43+~8nfg~
z_Hl=YmWPIVu`QZijMsU%|4e&)6+s7^=gv#}FOI=KSp5I^W@G1I=KAN=R-xhX*K(p!
z5Z#eay9i<2_UH#nqdZal#zLd(m20(C!-R1|1ONaC4EjnA#VTZ`2`>fr)gKDZL<k}P
zvpzz-NZNMZLg)O>Zm8<s<!NPO!!^hCwe!(cDDu3+=Q2m)Y2tBzFRR1v^snv2`vSIN
ze0-7+pxNa;BDK*jtjm7>WIjQ6!~h4R^HpwQL>Bq;j_boUqD!QEliS~%%$+ge-4rM~
z>Zg$YLoHCGOZR1-v^_B&DziAW>TY~b+DCxF+b2NxgOw%&0|CnUyKCM~&od%6y)Nm9
z$`@0!vxGtZX2Twl+k^O169<}zX7j_L3R(H_lwrg=w%r6Lx50|6E>GVz{P3(bP*f(}
zNVJU9YI0U0F_lzoq{1pjljZIVGQn<&y=I%``k>drC-~icl{GnZTcXq7Qofst6ZE6X
zZRx-q+Z={fx*>E!h&I&{wPb!mx{XnfXhcLIrBY&P>D>4inRT)8!UIL8Qm<0jk}hRb
zWyI3Z(&AF{k<3XYnNr}K<B5kA^uKfg6_lrciWZbkOQTi!8p1u}S7dGT^z$2)A7yi)
zOs7=>jsu|lC71zERq)T4ozcvqgJNb9zBIaYZ?NlZ4hQ}Fm?9u!O6Ftx#2DEP_UGA5
zkV?PGM*7Ru9Gy~j393C~>odu0{p{26`E*@DSLCvn3<QGsGw^5{(c%I6E=>1|#XffH
zF~1^zLoe58_ZZWn@#@?8MX<hOK~&<@>&I~kEaEp~f+PWM9XQVxsaK2`tjXtD1L7i%
z^lEDjrskGJXTEHPsA7&bar-&PU-jx40wnCR@e1qRjo)1Y$9=vGM0C9-zWNof^G}U!
zQ`dk0qHi@p-Ce>_YvAn&PKzpb70DHzTiL#1GyW)e&6CUh^Wp=yLi{v7w{s8(%cNp^
zcSyCJ>cA&3*xTBQ)p5F_98+};^?UuI>Y$QgK!H@IV*4<BE9vB{QUw~8^VbBlxPGOw
zLvCZ{$vn=Y8y`w6M0O2aRS&^6RFS%Mkh1u`G6!;QsuCa1p!oI(Yk%G`L{hMsD8N~h
zJ!_%f%S&H6=8u4vpfxO=sup=>;$91nh*%A{X;h{73TxPJzb8xr=<UZ^C0VGKll&63
z>Ex6Sm0B{g+@mF&wbe24ym6e5-GD9YPVxZ1oLCg9@}z;y=@a%|Yt(Hly&`@uIHlt!
zUnP6Wq?#w%iU~Z5^|8E53kAVv76fr$!fo?7i}HYcA%VR*4)5}64R8-~FIMXD-ih+_
z9gJbf0<FBNBnajNu(>+HsSPlw5yarNso3v#aC#0+7&kqd=;fUF?77C|=UWsC&pd$P
z?NW<GxJXNO%wKh?2nD0!?uTJp65lUz-Y}F%T8ET@F5lH`Dke1gQv#+Hf6rLuelGwa
zUEJA0!Z7v5ZvFa(F7Ycf8khYMq4j<qCb~R~E>L-DjP=K!t{5kCY?05E+uaxAY2_~*
zYninXkRD<bxM^$hKdSYwG|M58!f}AXpU`hi-ZjIY@dIWRFgi9u5LORX>=mHD){(I8
z&93+odTQ`0mO$bbOn}>z_sXo4F{Of|)SW{Tg=o_(mS+LRoosqXVI=a;`E8Ge(S3`$
zRvVWX^=dW&Ia~BhL@b_hb(l^0E_~k+X@ba6m2Qw^Z!>=G!hr=4j_N26ot>HVNg#+3
z!z_lZfI3b>XghNd5F~s*m42&Ur3($3$r`(iXSNraBs<B!U4C<PLjS?lFZB&8Hhxr>
zO09}v)*5-=vm&3SfImC#1uQ$(>B^PwJQ}w1@NYdLw`e-81~!Ws#uBXp?b7n=GFj3!
zZSUgY8*IK(##N_7Mkh%Dyh6Kwv2Sa(DL+znrd$EFK7}ZzL}^>+98*(BDgrE*$yvif
zwIrq9I>VpB#Tyuv9F!E1Gvv9~LDv!D&Bx+v-J~fC)-D~3JOu*|RV=L}Sipi9S0F)_
zS6tX*#A8%1{AILyG=MF<@VcV<#!=KffypFdVDuWOGsr33^%Z~Gon83_ST7y+d-~`h
zXF4gTg#uyv<}f>LU_&fN+-zIn?zJbVV$EV?leO%Mi7C&XQS6GLM0AgsFDg4w1~kMp
zzcWIgja*t=*xZga_5+#T6uTo|Tty4jS)`(fK;g@(TEiCf?~qE{P<0f-md%Wl&<syr
z)Pf?AbQLx9ra^8}GTvL6$qQxO7}7v@(ll^e+onw;O>8o_69Ahkq1ag5^s3?qrqu0y
zz_bkNSwZq{vqE&L0Glo_SK4lY+lXuo-`aWT%j|Gk>6faswfi=a<$HQ25m;r3U8AUN
z1b~=C7P{xCxDaFWE3Uv`K0*S*<TIFf%yesK23c62fIOVIn_uaU1<qSAu7#F`@ctO3
z@ZW-ZaZ<RlfjxE=O$)4rx@Ee*LkQ(z5vJ&HC3_HE_T|Oc4xvM5l}u_pGs+36Fa_Wf
z{f)bXd+mKy;<gMTA5pXsfVpGwf{dK0A_Mmj)&cmT1<PLQQT-+(dR*Cdcc5qre#&{B
zPg{>2Dn_`c!y0!G=C#h^`Pg_~lXyjZBx_Wz8(b;q8O~QQgQixk(X1AO+1j~HZeTRp
z(U>Ti0R<&X{l)`xKHT<x`(lzk*;87R+d_JL<*Y-CGeJgny!+AkqO;(1(xLC_=4<L$
zrKJ=%ipZ2>_pv3?!Y?qY<M1O!^z0{UvjxiI$=CQRGUwUj&encWS0p$L40^XL3FR3X
zq!O9CoNIHOy7aipQChyRY&4xSb=YrdN(TqCCu`PLB)@UCW1W`)ur6nR;_q>!ny?Zo
z$7{y^kje^%QPHpFdsmRa3rfV}JTw$nm{dvln8N{m@OqlM$ZoK>Qfh&@TxlRju@oo;
zfIm|}PW;xYpKmY6&&fUY<LG@JYSWvHxerFa)eVg%BHaM7Z}^)r+}I1RDs4_Eg~Wl3
zN5KUt{ybIh5HvyuA=UZt10tU|0~Vw?Pi@R3BuppZJjjy-CcF=y{^zUWitPRaA#nnC
z#e{q?VODjT_$>e*aQ&*|$Bs=EADA~#DMR4o&N2B1E?=bRX3|W&V6zX}iyz6m$uNWG
z1><<%nO*B+M-vJv<m{|+f<lJeHQXj!!0%W%m@c7BZ}gs=L)5Sba~Kj6^8n8l*Gz%2
z3nraOKEcZ8#Utb=e(vlvw@c2j`JdDIbt{Amv?b=P2znGPwQp<v;3Ju5yg#n^HHshO
ztf#o$Vcuy!oZFSLXCH*_!83=Z?H<n|2N#qqce+hk12W<{lj`f6Uc8o=neY1Hcmy$g
z1fcTLE}uhjA{C*!9Axin3d%HoDg%}O63bFX^0J(;;BP2(PV!Y=nTX$Wii_0(f{ha?
zU!~{)0tPS?>N3ei5vB#D^`!s>?WEh@V&?Ar<sT<HlPrVLzF6b#xQMCMEKZ50ffDgs
znC#m-ac(BGkD1Q-)8=`jicvng{2qqN1d+*12S6O~g3=$J-%6m%+akkTlxH6hFnXj8
z&Vx4Q&nP8nsFjFB6+Y4nVJr!Kuw-dx++}MVt5zfi!6hOKDoDop0~P@IY5EdMUt!_H
zpUmX2F-^FV@YNJSHpmyA`=^0NOmd6T$G;{E;(6xKBRo<yHo=sEGf97B#y60ol(p9T
zg<aP5i@RPS9ZoGTeFKJYyljRx<mx!Lq_lW4+aqlu+s5=aJuSNzp=1Nw+Jhh~q;xFx
zgH-y(8?;az4+i}8sy>*bDBbE<9Hibv@*j3y^4s$vEFJWex;Lk%um<~1;ee^%nSx@x
zv35U+ENKgR3+eSUP@)=#7X}8}5e2*`oVy8hATm$aD!C?>ZH8iD{|F!^sj6p;TiLcC
ze33OWvAm5tS6$8hW6qlir-P_owbUu5ty|a<CI99j^FmgGyyd`~@mLVNV>nREBWY;-
zrfBttoCtWYco1*zg?8ErqyN)2F@U~3rVT6#KXDY0$g~a~Wn`^}3hP?Ebb>Z+|Gxh4
zlyd#vmAhLhU_uK`gln9t58{0gA!!|#S+L)tVU@PQcST9`B_W4Jsc7rBs>cPx&zoO9
zBeLO}_J#>bQ<Mgz%l*82TazuQdQ#*o?6va<A6-+|1^y&u9vugkKB2RKsao7dsP7qP
zES;A;LUr$y@J_$nOR3($Gc$0Cp~Gs;6wA^c*Iuc#e(uw_7%#LZ1R>CDzm=*OS~oQ{
zS2kJs8d2}Wma5t&1JT|IKqxq{#2OMViFH6QvA%K;f#vIBwNzZ0q=E)Y;Uab~<8UfX
zt+bOWf$&(ARRw0*+Pr_6=VcfwZc~xykpJ%))jXYuwgD4MlgjzPjvk-FYsXWnb@R7Z
z&sY@j*}Vlbhq$yTD!N2u1XiiBw4h2*58Wa)aa~Fp8f#pLy%<q`jyjTHw0&>lH4<o?
zME?c7rnuA;E_<Aj>VaJICt1!4DZe+8m~S{#syw^hlJJUR51gl=^RE@Omt;{p5uf}?
zj1BLYLepYVWdMJMg_;QK7fIFUf#ousyfaTl^(MA0i)+G1%(N5;GS~wI4D573(oU3v
zOYZC7P@m`v^NLOBkvuO<P1p<kdgM-Xwf%?AYDwyKKiiYCaYpL&VgBtTbPmP)4@evB
zs*S|?csP&`w0k2>R4^Ho7vVUEPeUL$>le;+azfnP-a_3N-hRscqe3Uh9-@A;4HQCK
zlCujE2-LH33tCoO4jyXUiq4Q+uOluq16<+BSkWs4b{VLgaXZN$cFsi_B2;s#ZZJN2
z8@@k;iSVQ|lRZ0HkVex%@&)Ck1s~ltK^w2vf1;pA=q0Uhcuk2MbDr}(W4Beg3x&gJ
zc~#<-vWzF7Ir>GiyUIp)p-Fb<>o3$VX6!mq*Ol<dH4U1w;D}F-yrEH|wO^mn>q7`P
z-Soq@8Wsn%;C?&)`koS(lqLF-4MGRhF>Bi1+vjRuC$nq}y-|nR)y+r0NEg(~3!~`1
zv?JK^bZU5uka)!U5<kK<5lVUnE<r>fybbn><ZUWo(gw~^YgAGMrj4Gt7_8n9S`iT}
z*~n(Dl~PU~Q((OiJ>k^|XZV}*Bg_7-EQkzbJp2%`NN5WsEZ_r$-wB<(eW6c?mZk;c
zj2%DaUB?F`lr`e97s|`-ha}YF6=fRo)*WdMGOt4NXY=bd+;Cb7o`p8Xv~v38yh2&Z
zm>3pJBYqWuTPKNV>4lwEQ_Au6`~6*s)?Dh5d1<uV85@iHl2+MaQ}23oOCQ_w1m?BN
z#CU<b-4&IcRbu9~ajgqaVVNJH1TSm;Atc4^^<>0bPon}gQxq87nHGL5n^V3di+|d?
z=%y>K&>66SM`&9zeREV5YJ{uj{;OGKb&^zJ(&!E?2$zX)l^aIHo<mcP10l{I9mCaV
zP?J`NM(!))bwX#?ufT&7`OvNhVD$p=-7m~ld@IjkFx}!(I$)7ulA47<Z@6&*AFKk|
z;p{x~-malSLsx6wyRCz5Y9H)6XsG;8Yt|^eVUG>X2{SL9UUD3wql%VCWuu~oDPZZn
z)mV=5%+O4ak!ScKn9=_ABc?*)j^T>(F53pLbFgr$kFMl%i~u8tgcwS7;Is*74wk65
z_PxDFrEYOrdQ|G{tjuxIqeI^>I`vD?kfBOhL#r}1QANqhyPCPY{bi)N%2zpkz&N5=
zOYP6}8bU;6@$x1}8#|p}pCCm5oNRR-AXC*I=n-rLt52%l)hr?w0#M|YbCGjdFw3vJ
zvQeR?S#IqxAM<D=LTPCY?77ny;pGO0pU5Bnn2x_}L&r_*3u5w`93tG-C==VfYKlH)
zd&m$#hVIYJ!8+Wb!F#%Z^<P$YD2Wfe$mqOK&^x~Xu(MF9@FZTY#uVe|CYmP?T~r9J
zOiQ#z4XLXS3b$x!=*J02G$3Q7!%mo@)<L4#G-?5zQuu45WKHoy^EM1nE5Kwyk_31p
z^&<xyr?9TsX4B+PxC88d;_?{{ZwS2FXO*>6XM4l$BS}*8DwKUmnc|e8cKCmqf<A3h
zw2OU4lM=Br*QGnvwY=w5e&38Yy`d&l^lknp@8SC2s0i6O+5d@q{vY;B`hkdy9DX<R
zaD6{MImX34g)A+{G%iICF_QisXY}-S>`5f%IF#=e6*@(5yNl@!^f}*0JJ#KOrG_Tz
zy8|142_;m>5ohR(V{B+*`UP#g((Zzuew;~)VFDyGDK$?=!^u9w#?0(6!Bj#&DmzK7
z#LUXfyf6=)kb)qUj3DocpdUW6{JcM~oQ@FgL4%<0+3AVk`@ApIBMTk<#ur>T7uBD|
z9{k2095)9SJY~3;YY6=a_?-G_D97)-*!Sf7EY=dvEo9D81{v-OLn$tfy?JT<)agh6
z+F7o9<aJ^4TGtD|8T;8xv(i%Ss4vAOL7*e0p{8aIA9@vJmqVlPv%x=bZkgH2o$^?z
z0}xEs)69Dm+RTH8^`v~1J<kU9EHa;m-qv3>&b}1aj5yQ;?cM&6-`PGn*cRw<@%P$2
z3+D$g8~J{U5@)+0dcIQKgHF>ue~haVZ;&90frVp+Q2yusefx(-O5D=M8R*C)Ze!#O
z6a|{tn*y0+fp+H3764Y(f8%p{ifwj(wX<vYScJ2A)n|EnP>{ipki;)Szr26aPsjWW
z!yxy#+FVqQLL!n3kRTp0nMXeKmkl_f`o>`#upEbiyN>^*Hz@L}I#+JAw1j$l?Mz+S
zYtn~|b`XNpx^2Y$>HW;2eXfJADpRI}MU3~iSRGjl*bE}L9<Z`aA5B@Kz}Zk&%udrY
zUJIlU>~DH)HGtidcOnL5h>4ZPsB)4+>S7kpQf5i*IQR)2G@ngY^7krN=HFWDriS?*
zo4nkcA{YEqpwXG^(K-Cy&ZZ-#h#N<dC+~PSfx3uy@DXa8z5o19EdS^<^)CxuCIw?F
z6=z!}c>pKJUo%}tCuaaVH`{+>tINvC{co&$UQ5SmwHd{C)1aqG?l;we9BJu<yt$)o
zBBgDH%qn{(?0}h#9B?FgPyb<&L@4R6vaC*iHPq>Zl9UGH=?WaGC(SDeofz$cg%k=M
zpIG1;imDgQK#G(@X_QJC<D88YZyMiIfJGLqG|V5JChQtHo|r`(9X;-+6Dc=5ITAhw
zwHV(KUhGed33jS%iRPbp+2p#8Qhd;0LLJ5Mk9eS4c#WqoLDrSwP(NXQ^f*JNeUn9n
zD*1jy3Hbhf7WR(0pSwHsYbEkE(h?M4J37dydIVC%haEks2Vx|RvJ)0tvL5>kLgx)E
z3Y%ETFbL*M@;ex<KXh!5aS=EUZW%;3JRCZs#mtm4Og~yqpge=HKg><oC7p1+gg0`1
zH<&UqAuV_!cUa)DD4nSmhDg%2Przw_P&`;61O~I1OkDV>53~=i26JejE~HQ2mY_ss
zH^~+nWB{KKoGd|{1QK7v<cNXabOAff75G}<F4GbE(kVVhZVGbTB|c!Bqr0GTn{2BV
zT0v+xgV0~-b^y^cyQoh_XKm&-kjxX<g>&bQA9LR6b=ER;y&j3<$6h^`@eAHI4>^eA
zK+O>AAV;h^JR)R3247vu8mwyk*{Axifu=6U3`{pgK^OYC2>`#9j!$9!hB9mA%{9_C
zQh5)1Go>hjp@u<bM8{Dl<+qK;YFu>nw*45!Ov*|Mg^G@+PD)C_l+L!IZ~Jplriutx
z`rnmFnrgW(JUqdUOFb#HFWGk?agTk5q6NW#^SqVXtkIfnc0?!!u#mu){pU535@>P2
zY4?FxOlb=_zC>j{X`_y}5M$pleM|z2$M6gdJSK+Oqf&hEGAe7t4Qb@Xp;!{-sxywd
z-c0JTnl$Q9+dGZ4<-%_Ab#$+%b+bO|CQ*K>wvL^f&p68$toaZFTNt}we`dURwlJc$
zIubL7mFZ!{U8SUbXkrwLQ_E(X%VH;?+CH4L!pJ%<G*NeI=PU5#J?6$fQkg^xN!H9s
zump-@$d)ClK5BxAg8YI_Yi)WB3dimZ_L4eX&MhzGCw(Sw*p`4xx8xD<CuB41Y34@L
zl%l#Hgb&}OIKI9jhkL5!<FdC<p|cNZ(cE$SZ-xIhG8)<Ro6L_v=_uRft24L`(x2XD
zM=Lh6ij?9R<=uIMkt-8Uw3{L4VdoC!7x<bw%g#V~l&qu~M5LFWC&TId+|gPV=Gcml
zZUAqH44(g0s96h#Q{ixV>Oiq6$-c@LL?1@Hx`M4z0TGcv+O_W(pSm&@h@6_avH#ts
zQTOU&|082x*t})2Yt5!vmn&tl?iRX;XsW6}Nq=AV&k?cKaa)UB*;raX#XCYT5r16)
z^_ooA=1z5Q01aAl^E)U+EW`F!h{gliGd30`D>$;28Obm`p)VsBf0j|f^OTtm3B!bO
zAlOH`Z4<8REcjVLMc`q8LmxM$uJHRYzsZ}z;K!P)=f;FhEpPvbTCZS4MB7YEowMn&
zi{0h~qU5rnx(A%`c+@O*flEpLK4OA2<AV`q|1s_)&%ji2*G+uJqAfU9agd053M|9I
z*q^`6z*(p*@m?26nh^Zf@L@*K`y>Nxf|Kb3Y1O@e1k$FVd3-aP+}Q5!ZCje#810DN
z)P%7DL4SG!w8&7iYAW&MUEm>3ELFpM`KZ&U!BqDMno*%F=pU+qxrT#En<8pp65vT-
zRl@EZ-7M~?`(6HyeTQWsZS1zUlPZTA6Y&^`;UNT8bk?n@3hI-Z-h_IsvN#d9F?Si_
zj|jyT@+S$pGuqRUKJYj!<S?(1%{|5b*TJ#BRI|eAuFq#)Cx8ebQIc1nm3F<lmyh6|
zm-i(e^+``%2pv3~_i<vMF1(eTw^B{xuJhZp`g`m<w$6=SZT;{~RUd`Y32ZHp>Pok4
zk*>O?8T>N54UUH0hDm&+(qWI!hDmFLh*IUyk`sfvwc~Px+Aa1-L4+^;Ml@cx`!j*V
zk^l0~L%G9>*Dx!eXQ(e-w3naTCWVB<^8JsOhvTH}f*SP{^}DGqq|FL9Rp%a!%Q0Q$
zr-1h4*|BFIe<H%|I}vtTO783OF>dVI*F@$(2*lH++K~nY)I=kmUVQDgwgvsPRppiO
z?c%Y%UGqg8l8L@=C=yDZtxoaseabkx+@V_RfdTMLg<~_`{=jGA(S&6>f@mseCI8&E
zXS7IOhA;X$V-GRRS;Qje7|3pkENdRP9rR~+{4B)$5q%Ewsx(fhh)qN&$KdLc0ryx*
z&^MLd9$yBqA52{gpmrH;Ys5%MUx3-zuFfQF;vqsuNOYhj0aXUBZ?eUPgmd1QnTphZ
zYoVgI-#}D%B$-9*a`X(HMfGW>3p21K9>OmgGPg&(<L4ThCUh(g8QsI|jts9r;(W(;
zGhw&Xi)kNEA=G1Z{@=xSQJs1A$J`x1Gsmzxev=&W;VsjX{7x_W+EFaRlb;(Ka~vdo
z#}<8@M9E&n_7ZHyUJU(fw(5@HUEXhsnM7O(eLzuSV{&&1+Zi8)t(}`xOjEw{-fGCt
z+E~ijc;ptphy3Zq(9^@))Azcp!j&mng06?03xyIcgC|XsEX{kCKvs9S(*h#VmHdkb
za2ivx3rnt+NX`d+1_cob=I}x9FV}Qm^l+Vzm*Hak1;=RPZbbyl^cX#Lmp<`T=m{|8
z8YyTsXtViz+PfdfG%q&)QKi7W+ETsiL1;!te9QTOTg4wTUNjVRbf1RcNqEqMy%|sV
znI9v+Tei>Kb<TW+UY5K4McX4S>cvgq3St5zD98f6b>ZeE;2){A|G0GUx8eDaJ59Y7
z9)ew8R}e!i0de3?L}xMAoj_oD5red-+N#mZGyN1?9tYzZeSoT4Re7=a)X1wF*J~B+
zeK9oH(fZ8?6D*p>`(fxAG62<$&o^{O7;GdLOURSrCo@c?w!qP9yWGO-xNp{7`5lix
zq>1VFf!23T_pe9pUo9?77*kAheEIxb-fosXo)kQ>u@i|Th7p#nRBD$4PJnhmh7`8|
z|E{G{U$J^K<lh{SixzSalvjx5l#>w+9w@uZ<b|#&64jyKqWg~DsSBSDhco7VUpWzg
z1H9yA>a3=xu@$qKs8pA{#T2yqd1{}s$mBq>kOu9cN=~0M$3hL(w};@qk^O7zuKe*!
zUoBgDj95f7B%mxM?l5Cm7uT`A$+KC|nB9e;OfO{1-s5`adZNN*_IZxpkH+58<@PUe
zF8Xlo6vc!*nI!bM6~51lv0z<mcGJf$Gv7C9SR)T3L0ydHTu-*zop{B*p(>4(=vjI>
z?E;-(BdJ{cg)oA`Co4$Z9pJaL$~^=8O0*DRrojfVJMk4d`^n0->cvVBNM>l(eav=>
z^Q;zZmCj8s*Eyc99lV{-&JGQHJg>8*MqO=BG~CJ3&LRy1WjLy0aWs?FuIPobnG2=@
z9ihsk_*Aj$US)|B1DAH`%=+pyyLuiR%gq2SgP1#~)^v%s!(5~k;$YWbJ<(%ZW4Ci*
zuF`_7ot>@R&x^GS!=Jxu)!pRBZYVP)s9hBI+Iz0>ceC&$dseyy!-$FL`}?O3njoxn
z$Xz^4I=%9P26iYUldcTxipF~XGtT|?4`tH-jdL}Xfo4p~_V&&ImcIfIWhN;*Gkd_l
zl7_!SQ5`-Ob`w*!zYjCVHz12CJGU7pD;GBxr<n;Gvx(8aeir-xzoEyZV(AG)_{PDc
zp~LcbYF54fE{taR_RrLjVv<`PLh(!RwwDkTG!V_97amOvItwj}CM-D_8ZozlH9iD7
z4Y$-LS<+@X4AFsPIA_)SS;67<24usBqPf;+h?%ktHmVqGQX)(wzYS5z-a|cGeb@lO
zh4AQupq}jcfcKLFc<BT4wcL2+@Jl}c$%z?p7A+VKBm{S01do9hic|=IkE{l>T>9y<
zq+WV*c`)~KGg|)ZiuUN({-z=|YLdu>q&?z9t=;wh<+P!=Nues?ueR`k<LRn3P^6?A
zJ*yipO9YZ}8$aX4_ij}un#b%@xtR#zKR3q7*~rn^-4SSp@YgeliyeW2LQFv%;r{?z
CVJJ@k

literal 0
HcmV?d00001

diff --git a/exercises/Exercise7/Exercises_7.ipynb b/exercises/Exercise7/Exercises_7.ipynb
new file mode 100644
index 0000000..5640fc5
--- /dev/null
+++ b/exercises/Exercise7/Exercises_7.ipynb
@@ -0,0 +1,362 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Exercise 7\n",
+    "Maximum Likelihood method"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from __future__ import print_function\n",
+    "import numpy as np\n",
+    "%matplotlib inline\n",
+    "import matplotlib.pyplot as plt\n",
+    "from scipy.optimize import curve_fit, minimize, fsolve\n",
+    "from scipy.stats import norm, chi2, lognorm"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 151,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "measurements = np.array([97.8621, 114.105, 87.7593, 93.2134, 86.6624, 87.4629, 79.7712, \\\n",
+    "91.5024, 87.7737, 89.6926, 133.506, 91.4124, 94.4401, 97.3968, \\\n",
+    "108.424, 103.197, 88.2166, 142.217, 89.0393, 102.438, 95.7987, \\\n",
+    "94.5177, 96.8171, 90.903, 132.463, 92.3394, 84.1451, 87.3447, \\\n",
+    "92.2861, 84.4213, 124.017, 90.4941, 95.7992, 92.3484, 95.9813, \\\n",
+    "88.0641, 101.002, 97.7268, 137.379, 96.213, 140.795, 99.9332, \\\n",
+    "130.087, 108.839, 90.0145, 100.313, 87.5952, 92.995, 114.457, \\\n",
+    "90.7526, 112.181, 117.857, 95.2804, 115.922, 117.043, 104.317, \\\n",
+    "126.728, 87.8592, 89.9614, 100.377, 107.38, 88.8426, 93.3224, \\\n",
+    "138.947, 102.288, 123.431, 114.334, 88.5134, 124.7, 87.7316, 84.7141, \\\n",
+    "91.1646, 87.891, 121.257, 92.9314])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 1-D Maximum likelihood fit"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We have a set of measurements which are distributed according to the sum of two Gaussians (e.g. this can be signal and background).\n",
+    "\n",
+    "$\\rho = \\frac{1}{3}\\frac{1}{\\sqrt{2\\pi \\sigma^2}} e^{-\\frac{1}{2}\\left(\\frac{x-p}{\\sigma}\\right)^2} + \\frac{2}{3}\\frac{1}{\\sqrt{2\\pi \\sigma_b^2}} e^{-\\frac{1}{2}\\left(\\frac{x-p_b}{\\sigma_b}\\right)^2}$  \n",
+    "\n",
+    "where for one of the two peaks the parameters are known already\n",
+    "\n",
+    "$p_b = 91.0$  \n",
+    "$\\sigma_b = 5.0$  \n",
+    "  "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 228,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def likelihood_point(x, position, width):\n",
+    "    return ..."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "First, we assume the width of the peak we want to fit is already known: $\\sigma = 15.0$.\n",
+    "Perform a 1-D Maximum Likelihood fit for the position of the peak $p$.\n",
+    "\n",
+    "Complete the functions below which return the likelihood and negative log likelihood (NLL)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 347,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def likelihood_1d(params):\n",
+    "    # hint: for products use np.prod\n",
+    "    return ...\n",
+    "\n",
+    "def nll_1d(params):\n",
+    "    return -np.log(likelihood_1d(params))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Minimize the NLL and give the best-fit result, including asymetric errors and plot the NLL."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# find numeric minimum of NLL\n",
+    "# hint: use e.g. minimize from scipy.optimize\n",
+    "...\n",
+    "\n",
+    "# hint: to compute the errors (solve roots of equation) use fsolve from scipy.optimize\n",
+    "print(\"position:\", ...)\n",
+    "print(\"negative error:\", ...)\n",
+    "print(\"positive error:\", ...)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "What happens if you try to maximize the likelihood directly?"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 2-D Likelihood fit"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now we perform the 2-D Maximum Likelihood fit, fitting for both $\\sigma$ and $p$ at the same time."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 350,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def likelihood(params):\n",
+    "    return ...\n",
+    "\n",
+    "def nll(params):\n",
+    "    return -np.log(likelihood(params))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Minimize the NLL and find the best-fit result."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "solution = ...\n",
+    "print(\"position:\", ..., \"width:\", ...)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Create a 2D contour plot of the 1, 2 and 3 $\\sigma$ contours of the NLL and plot the best-fit solution."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#\n",
+    "p1 = plt.contour(...)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Compute numerically the error matrix of the NLL for the 2-D fit."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from scipy.misc import derivative\n",
+    "\n",
+    "# compute the error matrix\n",
+    "# hint: * you can use \"derivative\" from scipy.misc to compute numeric derivatives\n",
+    "#       * for the mixed partial terms, the use of lambda functions might be practical to convert the \n",
+    "#         function depending on more than one variable to a function depending on one variable only\n",
+    "\n",
+    "A = np.linalg.inv([\n",
+    "    [\n",
+    "        derivative(...),\n",
+    "        derivative(...)\n",
+    "    ],\n",
+    "    [\n",
+    "        derivative(...),\n",
+    "        derivative(...)\n",
+    "    ]\n",
+    "])\n",
+    "print(A, \"\\nsigma(position):\", np.sqrt(A[0,0]), \"sigma(width):\", np.sqrt(A[1,1]))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Binned ML fit"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "With the same data as above, we now perform a binned ML fit and compare with the unbinned fit.\n",
+    "First, create a histogram of the data using np.histogram."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "nBins = 10\n",
+    "histoMax = 170\n",
+    "histoMin = 70\n",
+    "binWidth = (histoMax - histoMin)/nBins\n",
+    "h0 = np.histogram(...)\n",
+    "print(h0[0])\n",
+    "print(h0[1])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Compute the binned NLL:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 375,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def nll_binned(params):\n",
+    "    # params is a list of [position, sigma]\n",
+    "    #...\n",
+    "    return #..."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Minimize the binned NLL:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 376,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "     fun: -138.93433719876123\n",
+      "     jac: array([-1.90734863e-06,  1.90734863e-06])\n",
+      " message: 'Optimization terminated successfully.'\n",
+      "    nfev: 60\n",
+      "     nit: 6\n",
+      "    njev: 15\n",
+      "  status: 0\n",
+      " success: True\n",
+      "       x: array([116.43876363,  15.33581135])\n"
+     ]
+    }
+   ],
+   "source": [
+    "solution_binned=...\n",
+    "print(solution_binned)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Make a contour plot of the 1,2, and 3 $\\sigma$ contours for the binned NLL and overlay it with the unbinned contours."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# show the two contour plots superimposed\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Repeat the same for 50 bins:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# show the two contour plots superimposed for 50 bins"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 2",
+   "language": "python",
+   "name": "python2"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 2
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython2",
+   "version": "2.7.15"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/exercises/Exercise7/Exercises_7.pdf b/exercises/Exercise7/Exercises_7.pdf
new file mode 100644
index 0000000000000000000000000000000000000000..26a8974b7a59f3a825f0a9da0d14d6148a8301af
GIT binary patch
literal 36618
zcma%iV~i$1l<l-_+qP}nwr#toZQJIwZQC}#w%t9=H=DfV?PfPG`=cs1sj6G4+)7T)
zIaOqeqT+N+^lZ>%M|an6&`d0ZjD!xx*3i7X4B}R{u4c{*;<iSvW};>$4yI-dvS#)c
zu9k!>j2w*o{Ln70&Spk-(4Lz$x;hTq?P&hDb@MX6nX-($`asvyTf3ss&x^Le&xW`e
zO?6sBr(?_|oO7PmUK5r>aVMbIh}<zqWVpi#5avASliEja!KfHNMWDqF40N}zqx-rU
zzCTf@B7{>Q^}7QoBL*ZX^=-i<79$S7z8ta%1T@NNir78ebXqtrE`~k=Jeh)G5SOFu
zxQenksmNlJ-%J0hUONam%Yf}haD1m57v%;K?0qa)32_?ha<jFs1H%tzE(zAQf|^{7
zwwVNj+p`b6VPe@zGDw0Mi*gMFq~%|JJ?`%P)P8GTUzC@1yh6UHy@HPMf!iQuINjgo
zj}0vqON`D!^rKpl??46!=-PoDXYUnth$5?!KzT?Q6YR<ZHh~B}h@1p^d2mNh3)$%t
z$hlWv9Ianq-VH-PHWJMAplM;N@y<ItygYC66jo+tN+F?CTw0#L*K;~uQe8}tN)z^p
z(fRW(L?GJXJ@j^PgV1y=n23><4rv^fZOcOWVp{O>UdI?PvZIYf(W2SXuF2NPr^5H+
zSkxlof3!je@}ubmuoJLNF+H?@JkMRh&eu2;TN%0lwdd~7c0s{{DH&812wm@rdRLe^
zM73V@W01{qC?aDFFK5OS3l>=nE{IWN#95<Naa}iE!HJ5HENK*&V4a&??{8piW3{_$
zKh^Z?dBYJ>=+*LYlBfW#+OE^3@{5zgo89#@2KJBleZ-dl{^psRrQ@55eR9nb+EG|n
zFOycijTQ_Y<n7T9g#r$)W}8)rPQkbNoEHaQ=pLUcD~Eo)M@t6Xq-;15<;0_HS)WC4
zW6^{)Fi!CoO5_ZR#|~W1_<SG2wz^4l=IdT=P?|<&b`<f1B5L(dkwN1a8mw`!N@F@$
z9kNbh3dL|_Lr&`l&05~vNrtth#u_jdC3Zz?)WxJE(7_?(4^R}zz7EW2&ZdSL1JGzt
z+^|`_Yhzs4roVrhg{3iH>}X-T&04$DO^}TFSvX5V?m=nAr}<uuX>pkkZOyS~rLqD;
zUAQdvjx|H_=3rMLy@`_pM=T+kI<AQ8Gv{OnxWp`ln*n8WW=QB5v2}F}qswE8VF&%?
z2p}t@@>)~{eKbJ88_{+m^)F2@DO%K2Wy-*HFH{sTNrjD2iI`_=mAXnBM}orFEP+i{
zNZ?Em<%A7F%rMXs<|@D#2c#onqllRxP$Uh+AYfQyQ0Eq~aaxL!T-ixxL5bW%1+axo
z!iN`4u()Bh9-e}xe}^>^IT2_34<Vsb#LUrRn+&nMa4nY*=OT5*j&L=}$-~_bQ2Q%X
zsVxSWoVl7Kf8*0u8e>i5fJ9rk98sV;m95Ql!Neub{bsKSPg$^rD<q-qz?|VU?l0%x
zgeqEw7Ka$b!o(9!bd*nIi<u2FGj)v^(N}QP2YzuG(LkpJh~z8c8145bl{Skkrl)$<
z2ewU{gU7;~gq1goTKD$vEg)z9oo02l&@DN#qI7!fpa=2DoPo8L9`!<QxKFw~p>FH2
z%N;8%(oRAfh7Vyu<e3Mj87VZ;6}h6-6gP(0Ea(;Gq~eRK<c3J2QVW*>ZNrgQR&hhe
zHK~GkT5mQz%Tmn+iB?!?m}0K|`!5C?%cwHR#>_ozQmKloKayTqd=a&-U~V|F!Nh~K
zFmx@5H4I`bv%$;-n;Jh8%x>_aE|tl`%mtYmKAWe9m93MdE^gY##KPMYEh-7*DLuzk
zEMvvX3K0Rb8O~z;w<x1bq-p&%A*NPp2`Nq2O54e_<cbfR<j>mj-fu^6<#5F-Df86?
zZkV*h<=^a*v6xj|Y$db5&4}GhF@9wXlS^FCa*8YeJJmR<_#f#duKG7tyjnOhbG*W)
zeoQDldbkD$1&x)Rf2l*lmT4m}c{L(a!aGR{K@MO;;G2<z3v+c>6fFjrk+}zfGsXW@
zb-5U3LgLXU2ChOrstW%rF*0`}6l$g`TA_>KeF(n}&h$oBCl5?q!wr0c;TA@nw<x;N
zgsO*&tX;!STg=N!p}d7F<>yxne2bL~aZ}mNY_OT3YY12rmDC%w_`iLaxwVP2i_|Ve
zZh1Mu#m6}N!JeZ2<x6R}iA#gn4lh3{WRg1_U@kdV%r=TxC~12V$%<<*m0rR<r1P{j
z@Bk5d`!^kRR`3E5x;6gd30wJJ&Lr+(;el!_Es=|V*HbxM^Gf4{mhE2_rX=q9lLr``
zM{RBx^z<@7%YkIg8!#n86w$E+x{@Rlu}a@B;>`LODuF>@aUv>(kx_;~W7-a57`6yW
zcHc2UQiBEHkU=bCnSvQ~*swou<BSL?2RK8DIEA1KDe#8A<!NA3Z}N27M3zQc%Gb=@
zDlnx2x(2zo;hC)~hV2@H;-!9k(P4B<rXL)A*=Cy;zyVjYt)03ivR!MfQ-MI0uUkL*
zSdC6yHg_kLpGR@yWlsWjD}RGe27cb>G_L&%zERXEULREV_@0Bliv$7;+IA{?{cfs0
zzAtw40QT*52=U)}wGDl)OMK5FZT`2jtmdzaZ^8Lfr>!+pB9}kiyQ*gk0S?w}@EZ~5
z1`ZwyZkKI8+o=!^F<X;At<QkM?Z1GBd7X>a?vFRf`lB*0{%r$;!d+|P-IAdCy@uZz
zcYvJtj~G2R@`I`!<X>`Kz=BI09jl+vSfL;B9!N}O`w>~+O+f=7{tSdh!le(u1Mo)B
zT(W{&HT#AJzbY9ImNND3^AFdIAl6EJky(e<czUr&kgZX**H6EnT9i5jaibkoKW5k~
zv~baanZs&Eb~LVld0cUgPV$}bgMlzpvB42t6~_|Pd9nNRz09<W3)p%e-p|`5m;4wz
z(R;4rHFO*U-F1O16H=4-yNUJ<h3GydQ=@{qVSXq4iVPT5fy*k2B&og5#Ybj@XNa*7
zXeTSMlI$%sif9d6z<*O0+(#1+G-`fk=g*CVWvRpm8}N@A35)DTz95L88~53MHqW^$
zfsQjQ!~K3KJbz?0fYep(%KmeEvD-gjYr@BfoYxhYU+Gl0Ih2WP`#L{AQW+&!x3C&}
zSlK!S%Brd0(xa`3)Ri5vWrg+KIkC^o2u@6I_}cbm6*;Tu6mDDQqQ3$07!%SRgpCL~
z%yG^umA9lN0(@jp&SInpu*8a`x&CnZ!Y&M+&@sBsgi=h+tD!h`B!LCiirs>}tY8r3
zjSzU=JQ7zjY+)x&U15(3h+Vm$6%3i}5F1pN8uY)ow(ry{;%{dx@fz<Q_Dy}C3`}Oc
z_uFpECWwXGn7>0ggslLIf|l<Q{#HU>m5cUV3=st{$=X^;&3H0AM|K+k*g-*2iJrm%
z5e{92AM8IdGy2#<*g@3_izse^(Pa_A!h%XOj=0%|y91$@C{po3BhmSr(%S8<Zqe5G
z{4u0$#1v@`6e60<U|9;UZFW10_<DotS0d0h$g8T9X3h>T+auq>eWxB#<G;JKA@<pq
zCgkK)KOTFzI&9!g{iv#z{|aSR{l1^?eqvMXc*L0eV35&Ah|PE~OMy(A-9;reCcPe|
z3i*(}bf<FM{HiF*uzxq2U_=aLH1A0l%nnYsrmsjP3W=;}x4|%i$^?zB3nKGYy>u^z
zlALS~*l#xG(54ix{C=@-R@{H6ITl&NvhJ$GM}8#`?<chFc%f8fV<Y1jE8{1%1E=Wy
z3%jE0!2gPH6fdW<1$Xe3&+zQ`>gxCGe%(8TRPEz?VdYiTdAfAq!y*4svtv=U@M@FV
z!3LiV*;nQE79bje@KJC8#+YQR(Wcdqf1zU2Hli%T!N7iJ*+8XZd9=-RD}sjGe*Cbe
z7XwqbF~7REQte5FpckM52=G(5r0+yWkPlUYlV>Ej-=z;lAuq@|6`^X-<Q?2WY_Pc2
z!6hO!1>bhS5%at!2XIFD6x(wZW(TO1Lv;16=!{8iJkq;@-Oq5mB6bv?eD4^$+`^AN
zF_DGjk<^i*Se%7|3SPt5VO!iZc?lK3l^vHE61YN_7IA&HY!G-PB+i24e_Oy`&nMqz
zVFYmU3I>YwWyM9#!WKXMeT;4J6Htve(+I$}x4}2?!f*V0ywg?KK|pN7$^)I^IG`wO
zR@xFL(`xW@7vQnX@eXZfZ~A|Uz`y7}r~?z{|As>_adI&KXAaS#trMTaj{1*7V3IL3
zEyP{IoFG$GEk6~BOO>e8JQJa)UBR*hLudSI%D%3vYk<p`s_*wqg)!gv#U;q^>UnP!
zrWyY!NDcmL(7rixh!5QPe);O1fu|v`SL6Q~`v(ye|7rf>rk}uWlINo4=EYz&fRqC#
zs^&R8yUv3^y>Ax|(NGs&M|IelzRy8OG4AQLueU}&ygI3;*cg%8ex&|zb#b5qq+UeZ
z@ME7baMY~OJTC0?S~yU5TR3VkAefSusr3iqx+}vERFuvTr3<V<uiXD+?C(oiCUL*2
zyi`q?zd=LT9iqcJFls08TZ?YGN(QRK6p<c*COL%F=*y{I;#8Ll{<u(5(8GkXlm;&%
zhTsJxeq#Zrg%kp!=Cc4(&xyO@9i$5sr=6)IUxgiJ0^z>lDb{?Zwv9?)g`KbsTM21Y
zStZTkrs)FubwcOtGQt=bd<jD#0rrFW;FC1mykt@{Q-6od?XPHUI#$NtK+#X6$PIwj
zYjx~M2M18{Ha|SGp;MrGGZ;Xd9n>S0>>%f7JPv!Aqh8MP{^MA@^FD<S7iSp4PWnx7
z_qr|7aA^0OeL@rbgNy3+0|BiA9(mVUH$rmp;AH+`!T4ZqHypE2f1_@%UI2Bh=EA8&
z^3u~?a7QGPkMpNf>e|XUIPj!zosH5yaU0yH_TSC=oYuRlQ%=M|Du+X7z1fCtBM2`#
zjS)f?bl~MuZWe9Wid?^6ae@G;vnWGelc*kFj53A5(okRT(!|v(r5o7@v(Zy!QoIzc
zd1@0(2)$lGdhm<{7#LMXubl=?J`8K6kbwxbDP|+hW6!Qxt)~Kf(-^h;ZsqFztqlg@
zG{?3D{r6yT4zCUh?!Vqi_+ap_cE_YkF$%X{9%q`1kryRfZh+*!S$B{{-zdZmU(9(;
zRvC5gHXV>zRe#j;siuaMj~r%vi{faCui`<`b8p<8T&tVZ;jZ6ZC&B{PhdHZitizd>
zfw&=_pVddUu&Crqzc9T65t~In+>})|>rAj${qeY=;<gVY=ZvK^%q9`5j<Iql5OYaD
zLgd@F5w~YU>NGUwlGuWkRAG$_(#bJ_!rToEY8ip`U%PIIaV#lgGX+7cHi4V1pkIF)
z!5Nw3nZ6vxYgK>1#|vI?K<!cjKwV{D*>;kv`VtGPV23~zGd0VV8dPJ<riX^X(K9Er
zuc?{XRR2cu*{@+h3+JKnn87Yz4D>1VR`tmHXMs!6I}8SQ^EWhAO*c}o89rct?1e*5
zlnJN4ekiI(b`g8OSE@zcqhbQZEB~?PxVq25n|gr+elrgy46*6{D+cU$^=pdT)i8|=
zwS0_O;Aa}&tmSV7rfl$4e`EWQ`&XvIar5l`IEO=vlhRseQ7Czosd^5oj;ckpW0~#N
z@l~W5_Te^2g7-yeBWGG2C!O69F#s*dmjVyi2}o!~<I`R%_rs~h&<K~dvR*3Fi`d6L
zT!VrHm4EMW1*-P;{=!nDb;^}eZX^G-U2U=!1k*Ch$qIO`)u5!hN(jfg8e8noM^$l!
zi`s}2P%|A#Rgn;x(o=VwXWSoOS78+DLB8tP@#u;OaK4tNswGUznhQr4?`m$n@(%8%
zChNJ&KWT}@*a~Mg^!JPC9>sZPGuOcvrher<9d6}I$F$t(xpAeJ)VBTBCcRzS28COv
zPu>u!cEK59Xsu#-BDq>FPAWiY5^1P<+?AD8GGnZ|?k(o;!gg&+FA~0S@*aL$auO_S
z?kq7?oA$*OBxcuOk#yR8>6Amvb-qQQt4;(%eoo9K?wHBni?WJ5F+II^7}a<uH<I2w
z$<TwvmH|q&5~ZH4lSX+!QLc^0w_$()s_3$$wELeP*bV;<Hj-8jHM2_SDLffy_t{RK
zysLRB(1a=mCkh)v5izm{-hJ-5<&;+MT295Tx*mjAl+@pT8}?bsVjpBu(4nC18MAWA
zuFS>RE!=mP=MxBGye3Yj8S)EkGX!|&J5O)h#)e)Wm9pWXzHFR~J?0A&DP`vqgVXg&
zVAD$GC&iu2VvNz+p@7VwYLHrakILQshkDWSy7TRxx|iQFJgOE@pOoGgnznuy!dZS-
z7+P;%LARB9f;%%89OEJP49p&R(5m5DCmCJht=&kfIMrd~?(OBNud4++R+`T7yzq}Z
z%J7%l%HeQ;6tj^S_g?w#Q|x=f*PXO{f<IuVPN$GM7AWHI#1)Su%4^eJmN?Ebg>QpE
zksJ@i+N(hV0_Qmno+}YYD|b{74MFMgI?ZuctcD#ET!#-Ff5eEMMZxbma#6BdwgGoO
z1@P#l{^}wACHjz!oPF2bx-uo)$jE>1zjig&PfayOpSpqh^Psu95vojz_ybHd0*cVp
z9uQ5%9_fOWiAooU<fy9*C`y$ZXnQ94C4)urMNnK+tr464sIN-1X%_i=-!;wm#Rt8_
za3fc)`XPP;qsp|);4|;1g^z(&y5w#r>9x#+&~yp}yb-v%Xkq?$RAy%WU!gJw3+sPI
z<q@r&_-t{+fLnv#o8iV2yH^~J%J@lDmD*%A+PNtZAF7Ewz7p0Z5O;cueZ5W7#tdO^
zjO0}h1DjW$?!6Z7SAGu}5(fy6!0-W;9$hc`20jeCknu{$3lUyxG78kljq}-=b*e{t
zdoy1e$=R2C-T8HV(nsHW27RpjC$D&#)P%f<aDUQ$Xc2yO(^KOm1wz18+47`>CLp6f
zfMa^)g(_y61zV?@-(UDs;>0YaQG4GFwA&|?YybxHHC&r|oWYck4FEW^bdTW}9#VPy
zqh{|Jw4VdSlv2&V{6!@zpgYAlaa2`^n=x@zQJQ$6Higg`UH!ssnKFx7U;RO))c}{O
zOhuLWTIGNs?c6+IzCx9MgF>my)QArSOE}ByUj*IUhaogvjbNi-E$EzJ-h;sKTICu6
zFs01UD<M&dE!@miYL`;WDBf=8+<IHoFW?3p{<gWh%4%auMf(9m?Ru-SQ?tZ$Y>827
zj9a`jlc#^-NfpP*&j$n8_|~zbpxZ;g>XeND+46Mazu97g-~F~&(GqyY)8zKUN+9Gq
zKj|Cm4D%=0O^~mOv(Ci4`{FRZ-Bn7=Iw#PRtDWEhs#}&kr6us(qkpD2U6&?goxFrh
zhC^*X6cPGPliVw=RP=*TW*}HVA|X-2K%{*bQ7&5d<RDyts2Y!L{MvurI#fQC$u|a@
zNDIzI#5%?oaO3E-Ed{!LmOs_{V$NKWmD=52A3P%$l^!Ffu2sWy>pxUn_waS=Lxb(G
z6RcgD6>RQKc{8Fkw&ugXctbHQKkV}B3N|;ZLg@!on4`hH%DyW{;b1SVgJprD$`F$n
z1Ggk8|F~tDP3)MCqCM~RjGP83$AMjs;GE>aQsyAdHk|b4Q=XR}gCL3(8t{`QXZ{){
z*56vu(;aP(g8BHY_mb^T2L*g5@1)x0IX1{W9FrSt<-WZcZm`%4<^6*NN6VwJOh1n<
zEeC+_Ah5syhXR_($x2>>p7}zKJO<8h=)?@Y#2@mRXy>S?b}-Rv3MVT;Gt&%a1A!8;
zj*dA1oBuZLy%wG+I%S*IbrfsYm{}{C5BJz@!&ZN5v9^0>loBr}Fu=0cfQ450F>D|n
znH9W2#@%IQsKYiGuxPeVI#TNL3*+v%w8vz4bCJnA%oTYVg;38d)#LuO5O@_YBigGW
zFec1SUdu5q0=pKE*Hu)uDO${xQ37>Ah)+g%{7@(m0331kTIQ`!TNu1yV(gwUP^hDO
zrTkHy2*Y#f!UkQ>-0s?PpF1_%agDWngU-X?-E84jez}O3G!YXz(=Z5vH)&+n$w)k}
z_spi}hvzaupW(jQQ5wBk+_iDa6mDxkMvn<1xagUeJ1`XLq;z>Om|4X$93jH-Da=4g
zVN?*Nd#0xx;m6&_3e^$Vtbp9e;g)_Av=L@Ful`_E<tRC(6bdpvs}w4tQGHmEnJe@$
zlOmNCWEnYePGL#nVdlI%=jZ5sw71vWgFWB#o6)iKYNvZcDOH3F`1$!ONxKr^W2;Bs
zfTJQ{*RlU5$qXWE%z9$ue05G}QoXt_m;SSvNzAb4La^_ya3+2&)JKNg@KhS#2Kv=y
z1_O{ABh+(*9X*F&i54=pWpAC&#IK%^;bg|`|CqB@Ozp#0dvnim9mAP35TVYb#Uu%d
zWxa&0pbX^{>glC?^I!{VeoxU#@<eZ+*qS*kWl9|tatUpQSvy}<a{099fEcp)iC#3)
zp>&oMDJVb?MlY1lqk-o{uh*b;9b<tfF}0B!SfA!m&)~3g`8_CxSJ#J$(Q?6X<WgZl
zE>Veg5R<P$a*4h`riZ)L@r_r^UtT4LsWheM9%=>dZ9d<ZAPh<i4KmYy_V#>Z;%oCd
z=7k@i_4b&FWdR`^rcKIHRc;Ony0HA&TAPN~m;NWYDB?D`x-*MCH)CJ|+mn$epjju2
zr>c<J{>?-ui!eU+3^T$kscwmuUM@zX!_~tF=9JE(dIe+A%a|`uz=_l}`Xt_VgW0QD
zdtqIhis!=ZrNpdf1iQHB7=Rx}<&$aTUhA{2mg3YwQ>)1D-4Cr-s?9~W>DDV#6)Rk{
z#_gC-r)(#0?u`rd68_%aR!S^fk-${gyZRC+qh^Pos)25_^+^9+o|PEN=??R?C8uuh
z&gH*e%T87+yThWuqYUH6er%pKCdL;D<7b4R7E1_wKwe=3z|iv*6O~xBuOGvRXx~1P
z7{f0MDy#%cd(H};F-fXBWOyTL`Lp)eUm+|6!9gCK6}B@6>GW;!moPSGF3UqVEU~IE
z9a02VP3iXMvFqm-_dA-@|A2*T{~Ii1VPgN!P*|fpVIRzh*z-ywegOvG-rCS`&ZMl}
z(k8y0gu56*tK5$o@$m5syxc5lR|=W1m*Mqj9*jU3_$(r*_+`9~zB(E)bm$fM3}lYQ
z%R3^-DD5OoxOHie*x(7hVUN|FVl+$zlXk%E6Y9k-RDew8#B5wc;Nt-+ep7Hv*_P!8
z-Me!tR6&sLL>h@Z(!()vSHM+OEpp9t+poq|uVz+F-@;PWWSt%7Qu*+%s#-PSv6uXz
zrBWw{b*QO*eX$MCI~+E*`d}vC${Hz=IDh*dDH;@oG=<s&tzJlWssHx|i9*s6Q>peV
z^V#4?t=CG#FH|oZ){<Q;{H4l0(8QxRe{vLOD?eS#Qi}Myix&T~Z0c~NXQgo+XNw+0
zwrkpIlZ|U^!O&6`5zYHE5>Zl`Lu&PL_l6(na&+_SzZZa&@&6S-#m(5&%h8NM^<T#%
z^8axi*%_Jti|bgb38kvC*2#~Q?4pfES6<F{E~vc4ty;ITy18kc3xSFRp$e`P_A8M{
zFeCgIk>GfKNCKk>EFq$~q7qiK<;tctUe$`W4~?~*jZM#Wjo(gTc+2h<gtSfmZr5(7
z1KH)DqpO~ugO9$Sy`NoR<10;JLc|CS+s*xh6Sg56fcjiuL`0xr$KaO(4q776=l)il
z1=N@e@Yo{&D*O^Ne0YKgf2wE3?Nj}2i}~OYl<8y_<x_<(F#ueCP~nj5(-Ex$agZq_
z5D^+0EFZELeLJ7U<^^Y|KI)vsd|J0GLOw1vJZwbjbUI(a06T;B%TKgZw=5-GT&jGZ
z0ICsE%(aA;+CI%SS(ZE&CRs}42#NW;@i}kT9>2O|N!`m+N@JqPv;hc7dG4(r(ko$E
z51%wzd9qAK1hru?B2i$GP)xO+SH3nvc{Yh&NgAYN(o!n{mm!G^Y@+Dz@61I@E!z^^
z%Hh6J=TcTyc0y`-blO-I$zvrUr2Bzx?@xYFBs7ZHIJNzIN!}naFX|Mh$pTF;*Bn%v
zC*5A>;nDW`Ku-NS<+$+DC`4WsO(fJgpT{vx-9TPxs@P<*k$Z1ovqi%WW^Q6atjm$2
zB9r4G@?n}|rNRK~e5%;gQ1D@JiSd*nXD`+7Cx!RGHK2tBq=_LLw4tW&d4_nENn&C1
zFRb7nT53;8ZfT6lu9Q=mWrIDW5W_=E<1E9IjEzB#Ty&(V(a!jLQg&pWNIcPBX!J3q
zki<!zF`hXYf}AKQkra>^LF9R)n1@tBWW&QO!%ZP6o{^rJ8N#w;Wk>-e=16tOE)i>y
zZ4o2G%Enj?IhsO$NY#<5p*W1;4&?uk@J2j`P&E8r7quntj-b<AV;{`EBl3;(;>8(5
z<N!q;kpC^MNWIP@&PeV#jaIHFZxA0#;3J1g{;4V&wJ4UKj7@#h;1!?ix9za~<TN(+
zA)CQ{Fd5>IyOa}<cgBAIO}bJtKr!3?)|FGpG2cqDlp!^ZZhlv}UVbBR{qjV|E_p>;
z>u>(eA~zcsV4YoL7f04=kf?H{mHvY*$TWU_473^8ivw$jOp7Is79yf4?Ry2z!ET*C
zNw8h<%h@c1F|S*Z@Q7Q|an1VZ?^cT~sZ`zD0;D1W>P{=fRp;DeCAo98lKp(vK{KWs
zA(9pUq}({lE+LYZxxm5ZFZG$ckVRqio{1XM<rBPnokc!2V=;=3K0yHaK?(v)-Oyn4
z<*$7SI`m#YRnG{3Oc=Yw=&-2x$`~s<`!dI7@ilZLOQ+XG84CBPol)9O+<Ek0?gQmj
z3rCPz41?`2r?2O*T?Gfyl_rK+=xy3;{3h)dT=d_~_R@PfFDrBd2E8%|$Ch7m<*)6I
zCxc_LH8bG?pM_vbfuloeD<c^$jRe4Z0ry=cm@i8l*q;|CTdSxH;cbTct3@GZOfJU0
z={;d!SE4AHmTu0oX{<9v>ufmYDHe@-T|eJbyGt+C#`?Z*WI)yu2eL~Yy2%OEsA6^O
z$I!VhcEGGqTug}zQRCy_VkXf&g&k5x@d=zf&5_r^u3ZIAztj*o3nvHAU|%nQ?TOQA
zfNmNtsIK|1`Nz-*fI`iE!}5g1BpXinU%N@UV*VQKxylbsnAP>YOYbX4Mho-Q1<XwW
zh6Fe_PJ)ObESI<>1XO5+r=@!I5xfyGu$QGL6q%uPSXg?Rv%>)Uk>&@2DS}wbg4uCJ
zJTh+A^QAa(&*moTP*2f#2Fd4TY!AnYO+zEy&^l2wP9Ie;?>9D^s#;UScfzYCM0lK8
zx&PX@Ott~sh+<qRI*v6bvqx8)PI~w%A;U{}e)euS4Jt8){*mRH*?#gFAa{^&j6G`7
zn_9trITz%9=C$WU%FwAFS$;gW+%utbeuLeV|1`)x;M^tr`@Dc8-3nbRKD{~D{u1JT
zOrouw;l<T}ej-e^M>@aEf~7UX<wgCPoh>k<*QLi{>MEU2eJ;s1ygy2^t5IAn+m)6(
zy5^-m1l^lJjz47w%Amh(=QiKwvWgqDXFlDNu$g`p+v1+S=q$hYt)dcwzrdd1Z=j2_
z9@w{3Z}0N0U@^z+zN(8*4Tvj=wV3_1G4iTK*~d{GPMA9zvWe=8p=#&rT}6af(g(I*
zOs1Kmv~TF=uPQfpI=959DVZQvzEU)*L{U)7(u03m>d-KK<Oz3Jkn~~5xO>>apcp0Y
z7xB@-nRuTrA}4;|L5Ss;0M_sk-kK45wybS08ZG9db3Nvt4sv&=yuMi&_96JM!=P<l
z_1nU|-%SB|?qTlXbm7<6c9a6J%R4I7oIx(yU12Da-Thlx`h8zA>Y4go4XoY1;p=&=
z27ZXM10$oaAN;(l&iM2yl5Z~<IpZrB!fKbo6-vUs_Z}GBkDg2_CCH1>uSR_{>$lb{
zFFyG%zmRcz1JGk7Y!=dOQ6`YQj5^zlsNT*DsKzJFVp;D0(2j%7&z5D0o&eI~r_=Be
zNwO&|{fEIb#~2|t!86&@%c6TV5>7r_ZJz9!f9;q6Co3}pP)r*|1J<Q`T>X%c4_S%6
zBgs1kF^C|zlmx^KzD|nMKpdkwJ3fzKw%a?R$-4%tz&A8-sAz*8%;jlb-UBh7%euC<
z9@+Cd0nqvEFS<9QQ|<v%?Hu8^;Uvo&y*JVMFMsLXLSq{SY=FaR8VZXL7TRinx8zja
zrq1n&gLAmwt>d3R9l!WQfxW|oVfMZ=O*$wnWRLR>#%UaOJ?w)0$X25i<Qq4(f#m~}
zx9uQO<%&{+RWj`@<*J|>!_waa1)eX`+-`dTZuj2`U*&zSZnI*j(&8z<<a|}ZgkDGa
z?`kW(N~$~$#o`{C=+D4Joq~eL&*A-^3jl;m8FrU6R+hDA;P<7kpKH>Dlw8{`v11p@
zmI)%J9gS3#ojGzlxhmkP^V;gquL=&n`fUjEif!mQJ`-*bA(d^zicfG)>x!==)*A&L
zhYbTz5TI;*+;F|@{T0z`U^TM3pvLwzuc+`*9L^HNM=|JoA5@f@jejzi38>B}1$gFq
zn{BD4L)(zmAzX@9E-64>X-C8g*4n8khn~508}!c#av*4$!-4IB0X)Uox(RnaV;2Ti
zuPE%~aiXZ^+H{%?1M0fnRkg<;<^tZxH?9882a6*j5rGC&M*|&2P`VY>;Hn8R<ugyK
z*UQ(#pHDEqXT0T23y3GaHt}nu;9y2Xlt@J@0V+*YooKNhpM%TUo3Rr6DsRGWgq7kw
zJUdz2r#fA*WxyQj-(lHn-ucf|If$(yAw*?}E+AIf#0a2U^XDKHe+5ZfJqBN0?PvCB
zWC`ksaL^;dyCpRf%I~LO@aR#J=diJ#-5G!5y%+=1h@sqzz;!q}GV8GCuntTQ8V>!b
zWmEFA@@uE2HY%ee=SnT}L1z}Xx$4o9;FLm9t;w##$o)8U!@z6Q>>|zN@&?HhU*OXQ
zhCoIMDkYU})N{3~XQ$99ENGhT5q~-NYzq?MY}nQ=8U&&^^0tZfP>f~3I4x(nBg<#L
zOT+)gBz8QA2-QQZa2XpHGuaY#zFSjZF${=@*BFH8$&DtS$C&;Z%OY=5`vH0yxX(Gc
z3a^P=z6Gs}d!9fT5?5;GhlV*W!*)Xafp)Tgaj&^$9G((jY`*-s*Oe1+J>wrIs2$ph
z?LcoQcgLrd<YlBhiM)jTTD3}WN;#MT+oidp6By-cv8C~bpsWq?NzWb}kv5dx%2~q4
zP$wpaY=g%*5XSPhX9z`4nE%6piouHB<mFa(Y(oS*tds!a;$^P4wtS3HDMXbg9K<X@
zqL7@~%)bK%^L(I-7#);|K|HV7tiOm5?)+k&BU=>>vcZ_h7z>_{J-~`B#yQLEIAeOw
z^n&Gtq*oO?R`?2D>YsVQkQcwmL7W;(GKW3W9!xVBv1VwU34fU&Xfej=*&3rb<y&7+
z`UXL*n{rUIbivA<%Sj=foDsZXrffF5;s_u_Jq+{u+LBJCi*}&*49@Y!P46w=fhb<^
zG!L14Fv_ub*)MqH{J;x<w19x0I4vu}2LSDUifIR;N)D-an;cPx#Wm`t0?v5e%&;RQ
zc8$y7zY>!@uLpJinY3|8Y(M>42B9nM8vB)fcUu?m`f^CV^zLuT&G;>_eq_y&(`cco
zp=hZe($^=>&5H-qX&h;*+FOo%9VK{fX14LgCE+*XbAVl&WPP}7xF7NRR;7`hctu=M
zZsKt8Uinbq`O9fzy~tVM&=kPktv$ivP9E=-5wt$<bsc<IvdDd$!`;pIk|TGnrO@p)
zo)m@ZGYKDp7%cxtvko@QrXz!7&Ql)O*pOc>DbOM;Q`(`TyMlekY?wse&oMl9fX4v@
zF5SckPn9%AkxohU;0re3FK*`4<<+XY4IQ=SL!XQ3v1Qy5KedE80>}xkwaP|(JLdy?
zi&mCr_D%j%?(4-O94^^HAhzmz4s5Ha*))w>YB}~}6mtR(gsl<#x8?MOuv-Ikc<q{I
z%$B^aX>`3+xfkV7Y-MN_H9ZoZd3^d>dh_aw$7X!L)WuDd$u_A*C$-uzU+8@@KsY2f
z@nP1L53>Ry$BDpA{0Gv)Uq)pM{B#{OxWT%tU{JG`11p+NE)D#NLxM-gl8(NLsW}Fe
z28Jff3iYu|q^p%wBcB?a&eBag4o}*mqYW46zPvf@iruN5(nv4rN4<MV;c&J|1y6rd
zSq};|y0EOhVt`TBb5^YS_ph%maN3z#yX7=<^w6k<_QJ)+*aDgt3<4yy7R{Hd8!;I?
zd|L0IkJe{46L%hpeIEOpND|=h30$m2@8mbJQ7D7&3<|!BMQ0Q+f1$48Y6Iqu$B?J#
zcqXB#H&9lg+#AGOk9wiXxu$+2CT*D_Dj*52NjC~mv&qBOkS3dPZ#gX&H-`qSHl4kJ
zUPQo^)dcOU?v=PeM)ZKr`ene(3bdMdQ{<pT21RCtpobQtqL5K81lhcmjl-s%#=ZYv
z3$k`yya~@aqBl)P_g~i?$07Pyu%^ptcvM}D7^V?uV^Y;zEXvHN(u3T3&E{fe@Lhc-
z0kRUSN{F|?-Y#w<@#*U#bk}PKD$q*Nb$$C65d0HNOxYX__$Gzj>)KW=95>Y$z}EX`
z)KnbqUxQvc>CMY`HS)%N!RHvu0mnz4NV!G@W6NsGNX-;~ND+OFR2IEAqVR}l_T8d<
zUOqC<u-5$B8@t+zIzwlwAf;e6!Vh1Dt<L3!zrBx^*OElqAKON=_%wBW0wg@8>tsIY
zTpa^zou}Q~nX+FfX^jx!CUZ1bF1I#f6NzMu@Vl4x-8n1hsx+RN)(cwuRReM8)7<C$
z(8d7ug~|p|*$>X!mnMtZPFd=g_`H|gVdLSBMb%;YxG**kO{z}N0{M^v1^OO+zekA%
z8dwZuUIzWux*EBARKYed-OUb_j&{P(+n{mn^BJoSTN*i^<q_+E>cmG5O4MWden>;h
zBIx^t0HMf!9lem!_Xjo;X!d_>Ag@%xs82txwILXSd_pyXZGX~SkTeQVxass(!;&r<
zgM_U?q|I7SXxs|hhawkzG7H)Y%Kr4e&kuz0TZVe4vUQrLb4|8q3Fyb8s(o+%zWG$3
z$K{o{1?48_MG?faL=HH(dm^F7bwn9-V<mZ}*lQ^aVoy(-YlSVi1Nvfn$B-x;%B;Mc
zq&X%(4P^~I{MPOUX)T4I<_MMe8wf#rzTwX0^BW>NHLZFuRLe;2S|<N;Y*E+S-5XL=
zvB{x}1baph&?_;U?i>Hd@%SXFm0AXeRMDKOvW|)s3l<D)q*GY;0%34rM=y4#hVA2k
za=qDXV+FPC8`P!OVyT-A=o=sTy-)q))u3l{!%uvX=V*lRptqgPfmV}8OPJ@RHsH1q
zwDYp-MMF*dDhwtN;BEIt9R4+bTn$u$UobTtyp#zjN@T5i!(;nNlsoS(6xthikdB=%
z;F&w^{voIcP290S2=Kf~c<rjyrO5jzR1#?XooW)oa0dzT-~w|HgXxuc)-Xd67MWNe
zC8>WjTEr8Ow1kEC{%AjOOzxb;Cb&kV5FVq1VJowq)k}6dSDp9P7vlEUQZpJK{?h%s
zs{HDp8h>m7R)<{u)HxvV<&R&;ElH6@yuOmx1KiVfBAnN<hei@R5sgLX7UdtZR75qj
z-58!)iuL)I=|)r>V}D~G9&G6?M{aqtwc6(`B;1DOWH;M@akJg9ivYW!S*6!10{7d2
zEBEi%PrkB9_VfVQ0?BU~oSL}wf39V*<72xGEAU=T%T}TOD-*$bU?2F)a>J}$v~Gu5
zfF8TG!Usv?*??IF0)DZtCBu-50O0ifi-{xQb1W=g-wkf)%hdUzNX_<bqdzPa5g7W@
z<5>FzT&n9E>0JOSIv1z#&n}b9)cj2`w)T%gOqbZA+E11ONr(plneSyEMRyax(6}0q
zt1dK!=lT|hV(m1JWj~pc-hRFf(`G>kJ|k@6FIHDzQU23!P<3tG%O{ZP6GzfU=i}44
zrN@dDd;9T5BRk@wtAjFm;=ysX#Q{?Bf&L|OpRa@y1aUwB^n*0+K$xAR!@4sM`5c-$
zBI%mBW3czZl2e$lRNZ!0h6`M%RcZBz(c~fZ-XTC(Kr-(;oB7*WMM_J}#A5A+M9|M;
z@><OP>Z0vLl3k>LtsO=KW`gl;ICj{m(c0#RtX$oIdHK@InRW9GrC;>=PAtEVX%Et3
z#FEL&`X_OyR!%WjA4^^>I%@t+*2P(fIf8_U_un^wWSkEcsU`$bKOy6Cz>IqOtb%YN
z(=D@KIZV%ZzqFmHyJEm3zZ}AiRDSQ{1RoS@wN0`ulUtXxL0y&smR&IZg=E*4h{pS1
z$LruHsgt{MfO6m1diMynHT>}E&Oya-_NioloA_?>e1a3}v#++t>J6wb{lh~V-Plx`
zy`2E@IdSenOUzP`4~YCLE<Nm;7`+u---R}ek8J2kZ}FchYIZ(44yfUQ%GM!d)!%uf
z(Q!_5r;x8nj{WOKT**p<)Jw!_NLr(ke#oNAG~=O0gP_|+Um5oYtBJ;A7S`l_KSLxm
zJK3be<ur$fs8M%MhL9j){~-5CT8;&`NRi<0ov*L5t`eqEY)#~LwtJAkfa}orLs+K^
zK?7o`%*wmc)3ghx5%pVPILhe8ec~v&qbp|`le@E@e<MAbGLcr_t{~NT?Wc6E7+kxU
z+={4$D0p|jxu_YXse=<h5hGirmTqXrT`I9dIbgkibK<-`QGSrXpa-F^ovx2~FGU48
znu?KC%~MoWQ%x5kEUB!Kov&a_2}@^5$G+tN@V$=~JJW!@#Rx&M<ZG{z<%>t*hr-sl
zN!$&gT~IWwT)sDFlM%*7dN_BdBq6l<c>UKb(hX+h=g1z$N9<KGs<PQs!7O{-{TJVu
zAHx+=W;I<)8uk_h8vz<>o<!3|nM<h}+o-qxsxNJ{2by88)A}a2o)<W2G!csciN^7a
z)W+4@)$rY4+(`Jwb31GucM$m~<{G-v0j8NeGnc~B3Ybi+oFxa0EE(u3L0$WNhQXcp
zMzILw#XKvvOtS~}IB8Qkrj@_ydUD=QotUwwX@}Yky2a|proTDK-pj^yjVUQx)Hi7^
zr_-ih*9|(n-hGL3wNmUS8Z9ZkodjmTiE+E8jcslE(EF^2O=7X$VP(uiJ=^8DC<G<!
zneTmAY?2Y`#9-EQs=A@StGb7ktM{fp-Y0JpfjmQBSF#Mk=b+Jyj7j&Yud1U`)g7F2
zB;K+I{FxB}`#Q?hl&O*(o*wFEH};76!NGVw4uyDejP5@iT{ssp?)oP|0(Uz7Q-8)9
zC^bYnF<Jj!bpP4Ab=<-`bjRr2Jh;@HWlf}cAaP^fe-dX8$fhT;AX_1&<}*;VW77j?
z@>WWzry--)p6!_mZJ4ZhN~y^Ujil{)>uo@M6<YUOt_am0!gtAUuh+n&lGZ0I!8j#@
zur&X5Gb}b6**dVoo_Gn6Cu8_|eH+^(Ai!L+XUdcv4Y~(FKtoTBDF}{I{6N6WlDPg4
z!v24HW$?ci_F33i|BKU-s{x~`qS46@l<cA^s}pB6XJr@R=DJ*DG9q86T?dU`hmMLi
z0wE@hPl}k75TYUIC6e?*%1d0)Jt%_&1{D+qt&+d8+F*9o+|s(V5O;mi(vz#dbq<fe
z*5d)OckQ~Wr)P2eC+Di^_2+Bv=i1>13DIN}5%_l?+#YJ1liF4E<=W`TxEptevQ0!5
zn*BW0S1^q498#|o@ZO_PhywOL4Ahex?ojwcx&7&mKBaFSA#fb;ai4?FU_iP+<n6Gm
zuTK?C1|m2jurOr>@~4!eh~s7HhKqh)oXE82{m49IFw<Cx+^VK2!^}TzKP`o%i-TCF
zjM*mnxzXxPrcBy`v8?hbZKKhRI(528X<}JBR~o&6+k7cXZOUvZy-u4!r*^Sg);X7!
zoJ6WzZM>)%I(7F?7Lf{$Ec4C|MCM?DhBcjb@Gh@T>oRTf)U<f2a5P0_BN}ya0HLb6
zYUX0IIm<Ww(m#)#ykoaWd6E`9Q@w89jPmQ?{?<BpmzX6B){_@5e!tWh);wCvXc3!^
z8JEdi>qsvKJ$Cyr-e&dCC{rkQPZb*#>)RGTM(aqsN6TEvOy|=nNtL;D)pmKp#Zk9A
zWuXKwG{I=~BAGxOAvIHV!Z}&FoH@Ss(&E_2zfqzw4JWoUXQ3l(p$&g{O?#R;*ZXA%
z`?IrZpJ>CcW4y@JzlZl5MF^4{6tM}E0jyg%1Z4_T37D%8avoB7ECaZyL3R<6=^hkG
zc79ki=%3!85@|I|Yux&ycgRMZ^dDM%QG}&2T0>R@(JE5Iu(vTtL!{>}L-nu2v_P~F
zC~n}h!1w-gV20QQsB|cW=$9m#33_WfH~T-%SE)`_PCbBYA(dcPArA6kIZjG_T71Nh
zgqiB+0)k!pJ6#c$Q-73CH00t`OL76U$a4~~UUP%05>3>^MCA(VXoxff(GW;)z1Wqm
zZMWDPb<42##o3&9>l2(sZKbp6VAXomD&=%>79CVFt~=^f$+A;Zw2~ub^9SqF1P2PD
z)<2?l3XLq*N$?DU#u)ozl$|XFV80Npin~ijGp}gt!j@>QC4QT_B|G+6I@rD75c!fr
zT~0;0xzBuUAg3qP(%kh!jdi^&L~>hX;>b7P__dKf*%LF7snOpq;@{lf+}tXh%R?g-
zUsA$@L59t9NomFPuo|c&+0jr>xnQtZwpBD1x0d&oSA-#*`hZemeu)!nR3AU!%l#NR
zC<OF%Y9JB6Y<z}*6^jnf&sfIZ1`SVe{RU|p(gL<x&RrorYjRP0fy4>({N2^hc6fx$
z(_y|IS;v0ntmyhk&v}CBYS_O(pUTn&&#m=D1f`LC+rE(Ti5-UFa3E#&#LlQ@x>lm{
zoq=CH=CL1c_{0vcvtge+&tw@X<Ebj@lel8;5Ui&-Ck)dz)zQ|SbfAE-zC(#i*ZU-K
zn}0z3v9ta9m!gy@!B8(dV`$EzyLI$+I~U|y;&+d$=j8*UW|3~UB~o94LI1wyDhvNy
z09f%O<sI4+Dc^R`iKIJe`*asN{_Ht59w>BZVPu5hTv-3awEu{{il4cfFqxP^*P@5x
zFM7oQt<P1MMNP${I;n;Grp(}?GNaqyFoHcUk|-bE!ix`xv6MbmZkgp(6C1#^e4h*R
zCuGs=qhaBef?YH{Ba7WNTqbuyvpn<dhMgN4oXlE-3awDj#3+9NCmpRq$Ze@KwC1`r
zTzs~_eSeJ+UnGqCH9T0>d~nuNil{10=69=Ts1cT=;|>vT0j<<8Ed-u3IR3_2tG0Li
z1$M&~3!D&o{?R<K<rjq<|2gZ<JsZsLhjI*G*)wn^#fl|XaLyZRzPMoNd88J(fpk6|
zYF7}>(XNr(KB~VZ*inr-xX#l+YB}}r5T)XR3G=tt1z`=yO>@hR^XPyB&j*iwdWRSF
z4J^s<qA|{TGkL3p2VMQU#bxj2>x1%LQfKN0|E#n(hJSKN`CIF|vqVLdKzw<AwNmA9
zF9SCT0xV}m?C&nl(n%E{tG+hQcQd)sbyxklIe|6N^9V%1od6pQKZN{a)MQ@i){7J!
zS#IzjVJFFPZZ=`nbKsiy6bu?7@b8E3^SJ~6gye`NaKP4eu{Xl`!5y1xU~pZ(d$`}a
zPdl<`ugEveR8Gs*h)EY>_^6fH)P2^aDQLKh^m3f)Up+Zy#4F20ci3pOdTfN%ha?|A
zkVR`RsQ9eWMCfD;!97*=s4S%@8buOz9$lHq4O4L}iJ)<0F!C1dT7W5CvDRQO2VDiW
ziqn*6-wDJjKcj>rb@mNReiz&A8_8@1UZE?J>1}%E`VOR^^FeuWIn_<IFGHU}qZUik
z+IQr<f={zwL%Wz-6GEo!h?R%-8i%(4Sp!}sQ({=gXkV;mXYwP)Aud4YT$58LqSmvS
z+tDY7a1W&fizb}%5~xEG+S^HqsaRN}_>GhE=oz{;@|4)U4qh;l4)R8bQQF@hpYGzG
z@e$oma|;sh1^Naoa91hZxu}n-gA3=Z=;Flre%&oR=U&&TaE*l(EQR-xc8dpVycx0L
zA?$2{59NaQ^5)zUTS$UN`o)?R80-p2CFyD~D}-W?%G;5s(z{24r`R_1D<u7#QDfVO
zxFIv>Z-==9V;m`sr=6s&zpL9rV)@j4((?CNJsAEm{Y$7rui4HTH#4+h)jn1gv<BUj
zN}d8Rw9ZD{dR{V!k3tmissGBo`(WdfyOyq~S9>?<lO?{yYf7N6o~>r%upbd+#jCEk
z_djX_zC1nfXTkh%awOo-%aZ_{Q$C$^B{iN+8WZ8Yv(s<N_vv~F_W-&Bc33sU#k67p
zoLjQo@SNQB*Eh%Tjy>D-eK3j~>iIe3sUph1HyepkBT0E=w8L~)@K$_&`(eP7cAbPD
zGx|Du2O;|_ENlmT5673#%_}Ma%6f%`lCE6c+_4oaH|)ZN4eK{tI;;#ST4yjX&7Q80
zX%*@*vQKdLnk*%i-5MHg#uk$FVEI19{mjLfEBY0UchSpDPV6OjKp!djuP%65#kfIt
z@y52Y<C(<RHtn#?ybUe>bVi*qbU`qL!3r9rO)2+aO9-xJ!`v+Zd`^hpU-R~d-h3AU
zES(b?TuFpfwNc~q7*;ZCDUB0HQaaJaMTo|l`m*L7a4n<MDD{;E49wajhnX$Z0Bz6A
z!*X9nh}hItVLth*4;$GHWFoxta+ggn3lxoLyvBuv-#x^Q*lT|_0@O>l1$57~^?mh|
z5bNUf(imeRZsETV8lNDaOx?)hRxg;fSwr+WN$w^VpeEvF!n_hyLC(oWw>?3cgewi;
z3OVl;^o3YTECzI38mf;$lMOKP^VCazvBR@_WW*i0r?hUytDIpiXkk`xH)S6t^N<}E
zDKk2I=~9oi_u5_ZKv)#Y9puRG*ckZ+UHWU=8~f3YHM0_~L?|OCChF%XclLrNGaK5p
zbVV^zd9cQ_At@E}iLTA*K&FV}U)^$hSBf+VMs1;UU(lv$@ug|4*>{uixA9FvYKsk~
z7VSR8J@rVrWfQ?omv(Gnh_jqQNAkdDW9-~mJ-=!U<O61-2JZX?t=b#B=}21Ub?F&Q
z>=aKZofP8>j^N^phB0q}O$mU`P!gJN-BhXndZh!(hhJqGv^PlJc#t8AQk3ZZ=@Z5}
z-pZ8}9pU~W@K?-wzqEAPqSKwDn{m6?IP-pro0@Ll%+C7x*i>({L0ovStF8cb^`!@B
zlLA7~n--ewVJ=m6$ogOX;`h@Cq&Dj$4!h){9278X+(Imx=V5OB&%)UENGBK+CC1{>
z+Y8a+IO*SDY+cK^r*6^OtA7mlI^b7`srn%2s2Jp1E^lW4;_ImK)Vhh_)IP_z=ARuN
zu>OgDtGjy$7KyVyYx@yJk<9YdC71ghqg-tOm(~>3D!EO#=ku9BTa#79mNL4z9*#Zx
zz?;DMTG#j<Fz#NMviEpt`avY6;0?MZbqTdsf)d2f3N^7%BDfr0Iu#xodT;c3)i*7o
zve3=YE7~Vf_$UA<4?l89?B45%`JrQAAEt{}{K5JHmpkKSi(#8M^t+=%?v82`zVqPh
z11jlhgK)N08oBC2{-JjS@}3P_DR3p{o~qi1jb=}N3b?wx<tDWYAN|}pX~ix>mV%~f
z<!J-53B2-ETU@1^mRr1?F1|zf;>ZdG23hzis7B^MronMl!?`2KIvrg5VFy)z8vilv
z$(z8Xv8)dY`{xwuP{f9(bgDXRFczbx-X+hO!=dI6`<dOf9Ow|;^=^J7S@@F_Zzt*?
zd{p+{yr?Ylb7oynR%s0xj_3#sxrZG4A9t6uQ&ra^<|o_w>VFwRQolo-f0Hm!qveqt
zlvLY{&9`EUC)P~uWn4!A8i|s%<!U8S%@Z(aLO$i4Yc-xbTm_mnZY!Q~a-XCB4`=5T
zq*=GD>n?ZMHo9!Ps>}Mywr$(CZQHhO+qRAK?~4<$R_qlgPOPhWpEF~O98bRa=8$)q
ztT$B8FeBs$v&cKC7ZJq=o8@MX-rTYPEHi&dRz9+~_2w!ARE74y&yl_5TnsXANMQym
z8L5?OhTE+L#k$uG%Z1EEeP*V!`PRz3SM7+F%1f^3NJpHu|9+f$Kg4Z4wS#ot^t5>o
z$qVeFzDnyWTQKb_SI_U3J$!>P))$V=+8H{vEUF4|2WMI+qwc@?Upy>I+R;zJ@!Wkg
zksZd@Q)W};CM?2|4oiQo4U6L|hOtilhpjxC<@c#)Mu}#yV#GLUYn{)3z@u<+Lb5UV
zPOGacORH=l!1Bl_EhxACBF9?n{{Y4CL8$zP=OXiep#x)~XJh|YVP23bgsS37Gp`_r
zgOJb>JGjF1Ub<>^5Qc`V0WAU*6sd21u5Q6g9OGkjZ$3=d3!H>(7^+4s%v?&ONF*u4
zk%T@Hazdv?w^L_^=g!7JZfuIn133Fb7L&*G`i0KV`-=J~1~IV+X@Z*N&Tgyo!24SU
zE{=e|pteTi?AQbuk!_6p%bNWen25U^X_p}ZWN^GG^QA<ijKFu6!6(wds6g>N6%)6b
z%a?xmb$htBv1=OBFe7FM6Fs+a-BP}=<PH>Ml=A!AYp@2bcBAQ7f6!xlzr9UkQC3pX
zSGE@rp&SJ8C-n>HO-o~oeQmuXOjthr7xhUnsK)iv{TA}673RLx*?!ZOeZ_-1naRYH
zv_^CH)u&6vkJoz1OElqlE|1@%qcKyO+N9<haKln$l_l{(uce+QhRX0(Z^9Z=fF2BH
za$0XHeF6rPF(Xq@+IT7xH&c`x{X*Ia2DCBw1NQ?kF+EdSN}5|FYD1{nuqu-ceMK4@
zCiZZ&TZGy`rO`6OMH=j+$Z+=Bpk*;DPFYe*nhOTmaGLtYxMcxa3lkRx+i;eeS?@n#
zY(FGhLsE;pEVYz<Ue&Gso7!h2$Tjenn5#fxczO7(lENdFhtA}X^37(`{JI5FgUm6b
zH7zk6Mv6I|-DKZx0UaO+z?WWUE*rVLJyc6jAWNW7Q>X?gNqd>{&U9wwOqWPyu+liL
zdkujPsVpeXbUkVU<1r4*+%-xe6<LILE_e$==KAjza?jZBo5dlgps#^X<yMcC9fz`9
ze_x;)FN3f<{9=N*Jpxj5`QiVud7MG$DwGWG`1sh`>fEkV^ICk9+KHQJ0aN6WUmR2t
zg@hmgB*lrTd+S)((Q{CN%(E*gSys)02C=*qt-c#}344oIV?nh&4D?Wdchy|3?tL{^
zS<P&i%sgviqKw8W7Tzibk&u?JOFu3<CZ?=_UZLL3)?S;Dd#$_g3t3sHsachl=ms~d
z3z{7$gDDmtiKfI+=v?<>`ik5>Afnv8*;GhCKjg(zjh8}0lXirGQK0!saX+cQL_<5b
zWM%kT$gQA;8NXI9Bj6vPg6$4!?Yw4hIZqV-{IVT?ELB9@msnU#GX)qX8Dg#u;Mfqw
zqoWP|DKX~9Nl`~w+Kld)1C9V=>Mdj33~PYBlWP!Y_$fW<zRmqr<9tMz_Y2Aa%HaUV
zd6*PWYC3Y-mz=hmvAF$WCBl?H%e0*^!L#hmT1O0J6u>%%?&;N$CpDO1smA6QA=8p=
z9_j*t!t<8~NPE1O|0cS&ns_QJ%RlY#%P<=6<F{CZqzr-1XZJs25v<q*NfFhNWyn~$
zwe`6;J+AfB`L&b~xuHyw^J2nSYicxjMF`*rMqr%N{U@P74C#W|WY2?lD)%^8xpOnG
z-0RR65~zmNJPc}G>v#ZL+UaFptWKwc8$&oJ4dOJjS!#)3Oh>X2IN=kZP*gPWB}-Ih
z2uMmXnMtun_3QM)@$jkWNq+=swFEheMFE?>FyhVIapC(_5Cn`xm?Wa(hNP)k!t>|J
z#nc>pfg)Db2@?swu5dIr8s^tGe@Y<&3TnQi@wSkb82p3R8{ShsCs^KtS;uE9eG#c`
z)w<0NnzHupx^{qXedK*&|F}X4*AB$>c9xc;km|+rv<Nzq2o8<B`~nj}Hne3hrJu?H
zNb(6BDw<0e3g%>2ur<~LvY<xrc*v|!hs*?o<eq7XZw6P5<>jeQ%N3I5A@~(9iM@XN
zvR9UmgJ;&ZuCTkfv9`$xfE@g~pn(^U2z2NzSOheXqQSF16K)s?)WHw1xKEKx=jLcV
zz!g26fgbj;J2hXsVD6*`v}gw?=QMH^((K*G-{~8B-Bf|^UeJq-6mbvJ=Ps4xr$F6$
zqOTbqxy)-M;v)>?`GvU#DJLZSZ50>)OD)mhd&n#cxE%7JH~Md<9;p6SiZF!X^*Iok
za~4#-p<GxG*2U+|;x1#<Wj9)bnHb2N_}xSXA0l!eI`_h}i0x^h*}X|eEch}X%^!Y}
zrhC)hH%R6LI$DbJ>q{CAY5v~x)80$zG&K=~wPoaoUQO+#vF)pP+NhZ5iS;}06HTUp
z3iw_D!T!^geZR0^ccG%*f-)?4hXQii&EFQ}e_1OLbQp$W{I1@`I;d*_{(1iD`mn_)
zk6*-mOoE%+^h+;IK5jpc=FN$CAot24O9+J!7a{Ht1^$5@_kKqL3^^^NSUI{&)`om}
z$)z6_Z<3bof>&pM9X>!~TY@#ji=EYrI16}$x#Q4o%?(I)>n>E}6QZ*7h02C`98!j|
zZ^AZN0V_8QCR;ue=kdd|y58PYSJPUdFRlha&q+HJRg<!%T=NN#!d~Ya&bOP^#c98S
zxl?X6_vmZ<s9lWlwhnG-c&A8_6xa0B$^R-*;Npcnc+Fsq%h(3Tw%&G;w53evYyBzE
zjT%9bhRb#a_8FbtGyHk`X3E*_I=rS-jx7jx!p^D!+a7Z_=uvif3$Vf0T)$-RKSTvt
z6}(&2U)W+{dmBNSx`2`*3tk5sv~6Yy3hU}g4WD&m*YT=nOOsOLap5iWm60}7>Q>kK
zwWO)wsFlh{NlKn;3@cqAz?POh3{dqF-K$?3b3bZ^hxl=8_%YaHZwyiqw&PxD>QM!=
zV9(Ou8)jIFkNl?AI1Hc5DT2|m*(|WfAWoxd?;_MDh*@>Dp$rLew12f}zC=h8tnV$|
z&>>6uF-HD6GvSdX+$V0%HxZ&_`I#5<iHL97PY!7vl9jlT<#!Vi&*$W!1K#(Qia)28
zj~+Y$6`&s!2uDw2*0e!{-;2#oO3coNfz*_RR)p@UX8|Pc%Iy-feotVeG(^#1e+bQy
znEPYhsWi6>r?6Dmb@;2+{siG;UC1-b2jF2n*fe33IIY~16^UsU{i^zSN@`lclFkeP
zx=ude(I<};W95bMckCx_h8qc~uajuEiI@(vOokUds1S4+TB%j3W79q0-a~!tEu;21
zsQ`!Ysmm5wxBA(T1nV~-fugo~R0_!&3Dq99Ff7gAbjk<D0k;FzD|9tnm2j8c>Jp2t
zwd1Uy;Y8F8K7eGp{vI9W+~Q^asFg7AF7t8uG5LBW@;bmS@@zLD0sSrC*;#?dDeSEl
z5}$5s{L<g|npg`vI6yQ<_J^G(b;3A7hOV!^(*Kb3&mhRj<S+%H*x?4qMV=%^bP9x!
zK<;RTlRC-fLInIzp7%p7j@wza#o!;lJo3&>$t>kq^<-u6BUw2`#W<B)RJ25`UHjkx
z)h#q)^<lD@YkKv`_oh_|#M>p*h8LoZP!PLNm_H+99#46vz8To+cRYGpb-*~ffca&T
zliJatTF}I&FcajNKE6FK4Br?DnIxSv>JEsOJg%boAgT>W*4@#l{g1q7pf83*#zMy-
z8aEU7upIhN;ESB}bGr2?N<ptITObpdk&baE?~6ojp$&#1ry_>B)nQ%@i{$qrlu8D=
zp(2XbM&y+H9q~V$S94&uQ4Dj|#yn#yWzlBUYlKYoYmXLOb2J?UMGTq7=8Qsr?S+ij
zrR$jn{QBX~FV`C*s^g1<A5z!Y9P{8{%mefpLcUyEr&D_ho~`^D)r-z^3u$X^iAWFe
z5A0VxV<Jd6$tq;mfP)?nNM&7oq#lYIl_2Vyj>jev4JE?)7n|k0G;a@$H0g~)Ef-h(
zci6^2pyT18TkV+n<6Am_!{L$*(PHV$KvL!6)p3C|6*Z+Af0w<Dxt+Zp-CWQtjtMis
z_AV(YuW&P=%Xl=rypU&G&VxwPL=txFzNC!#+r40`d4Z8tIO3w#y+%Y+Y8~YFWnek1
z{~9uPOHy1a!`OSo6H_iZw!<LrX?v2qAHP=9MOVb%AW;<1u#<*IIXYZCQa(()IR79!
z0a*z(v?5LG1UYu_oNtG}^g(sk&At8S{@h>&%^u?g*3<x*La%@CK~>ZvdJ%Q-Y?Fx$
zsm^7;<ndGt?5^>!Rpu3q2?)SqULN&c)CU4%hXX-nce|7ELEh{mUZ;x{^yvC=HZZrd
zZ}pAxAH5!sU7>!w=E$B0!x{Jf1aeb9Kdx!g=~#dH#@tK(96y4_htY1<pggVIoEx<^
zyX%e3JB(r!)}Swhu8Yk~LQBfdT{nZ?yl=7g^eBcsdKLJR6GL?Hv^X%!*;Lfo^1d*-
zC1BMUjgSmIduf=(85-bIiUEF$yS_YL^;f=VyqFD_i4CX7)>G`^DFl9&fgp=e`+4qC
z>hL43e}n!_ckgC6&i3Gq{Jz(<-0U3f^62+!&`;A`t`?Gw+FiZdk;PNFquFqDJUf8B
zVxxKC;5^p`qQch}zQBC!S=#th#?`24Qd~yVYy*JI8?$g=eYSv`se5Xg4Y(}M8Fgf|
z`^I5ZJM;)$qic}!Lo4YqLtEaJ0xL73vet6@0hcXPM(fF+aL1Y5Rg~zTTF3tphk!lH
z-#@1e)p|OvF6wXQxJL|ee0zh&Oy!XVWDG01u)^i8wSLlbO>KW6a0UhpeRN!4Rjh+D
zA$g%%(RKNt{@Agcmb-lzB`u3bU^nWez?<*1P0HvM0*?vIm7Z@>lK5#n)AB{L>FG;)
zS-1eT*hWp+BRZ>msFbs750eZP3-KeoDw9VbbMRV)S3y*UkXWx7h^n`i@Zh~l>{`*}
zk-c9&F9-JXz}5h@Bm;s##ipVH9K|YkU(>|JQP*I9#pY<l>BOfvk#xehLbUMWA-xT;
z&c@B-Ehy_OC+cJ?Agre<18X`NsC9_3A_xX0l$2a7^OVs2{&adOLMEHm@w7Q%1;P|S
z$!6_1nXKdR7Vu5qudZ%CF4u1b+eE+`sE>1hm{VBqkX=V#5GW{Y-w(69q}zz;A+-Dj
z&zBS~IPw$lb(eiWd4kj|5W?|Qe;Q`7mID!!*&&NqFH9i!@nmPdRHe>dFR>`<d~4$H
z_^~EEVL|dTm(PKGbG9a7934@@fJu;$y`KMK^0*YQBV;TA8{Qlm92*-N8XFrN+T0ur
zt|g{~rA{y41U?OuH#B3>)Y)TrSQfh+WxlHajOk?iek*{c-%B;gwn!BlE<{gqJbZL~
z_?I>-cB8+7cz{^nAB^`1pYJc-v^zSW2J*8J=WOpsPi1B$EU|`aZmVqva|`y*Q>lp`
z^&6LU!B)m*tB0n7E{n3EyJ~$VHU03&yWo1vnfZ=w=LUOH6V^e)#>u5g0DD|#`Aw7v
z4x`($B=?25r$~qZ<5w9LcaScL^MV;$K8B0W%^Hf>mtEeL1)ucd*S(kgn;6+Xv2JiD
zT&xx~Xy0~V?3Z1Lp!p}^FaLdbA=}RrHC#CaSWREv;HTSaG4Mm)xB}|dnwK1F;(d3H
z9kxY}_Le%LVQqpt`z3|)&F_Ky9#ux?Nkq7X<{Q4a)D`EyN8Nl-d%ZfgsWv}8s+B1Z
zlIoVrdX*Iz3RQTB#QKITDN=tv;h|4It#1benbDU~DJyX-Ev;~^eO|7fF4*|GjeA1p
z70dcmoFLilpOMuY7w^%0s<}T4o-3hh_=>eRe7K*rpXn$OZFrR<M5^G;4M!Xu;_@6*
zTTj*YNwo~08oVP@5Z1d}hQgL13)C8=V<~oK1VQeI*_RY{M&o|9*l8`7&xt$NF>CD|
zHtPpfeMQ;eZO(c-e;j6D2bnPR2dOhJ_N#mnQ7EUEmP&fgsIS6h<ZH1ygdBb1^02`U
zkA8Dts&YiXqaMhQ$gU*!+<zs7u#1m!$l@-+XbNg6T`bgkR`y{<<sF78px)Bum%_`|
z&^7#?*3khMZA-46#t)>+B_gMzCL?7lMrPMyBR4-H{$b$Y&p5ASo^h9vl^anBs<}nn
zTN+`2-~W*3lk~O9Y-#x+(j#L}p}a9(#G!@-nMNln7<9W=m396-B%7qLyl~<`SP=!T
zXSafGA|m~W2oOI1(1&_dW>#6`@2c@}HjIqD+D*iP+sv@-RvScV#A930*ujPsG)%13
zKT~b$kYQYV!++bv`uu#2&8_(XIf(GtRK4?|Eb-ax#GiQSYG!2Eg=Js-qkE8(l!emp
z&;(%(*)^*%zc75q<p6x12i`j8-CM~i&p0=lZac!WsYh-kQ4?7{5m+bIR3{{2jf}a?
zYK^VpO%^$(1rcF?ua%~QyEqW3ZW=f**m&`_oo}G%3Fqy9!dy@3_~R`kQlu6h4T3;}
zz=7@Mi)9w5UKg|VcN){A0YU>w`p>h7PpsOx<cA$9$(*+*J|djRocAW`4Cc$3$(XHd
zm`snh3_$ab;M<;?-H7fm!zfkt9P`dh*{DUXR_l6Gh}5VIO+;B9k1PD4(h-~H5Mcz|
z)@hMRy&F|KBD!M-{|;*bwvVU;_R^adbz1O5>`>eA$ZGg<i6L%KF_60O#@f&8P-{?g
zQM<RxVM48(9>;J#WSV4gtkLj?YWLfkLuPJ>EL*!*R6@w8;X!O@i9Mg*V0bB&d$j4b
zxJB7;Czwkt?29e#^={6O%$-}r>mSz=(GsxvFwz3nBHpTHsD)v8nuCC)v3;p~5<W~u
zAmd-;Ix@2JaAd1PWK^^nAWn8|qZ`bkE979`uBK9stJwn|?}IseB<hDL`xWsY4Y|?2
zWETtibW;Jb+CxTfOu-&;;2hc`-S3?09u+1y(Z7=TyBB0!I);lo&?b`-E(+-wDY=mK
zjbB;#s|Wzb*ife=N@Hn%k|b=B_Rr@(<(26f>>(MlWKGS1i;-DK*TY21q3j*$oV2|C
z%FJB6tiAQ5t)$0>1pgE#6b769fOOd^r~U_2$ogNPLPiEAhJQtcT1x+;EJ5?yd$^Yk
zPm+{Xx$sJ>Gth%X;gd4bZ1(2A{rP(<iaS6DIS7hG5av%nAaTt^fEpX`3rg|{5=t~0
z$+@r`ol`fDWnBE{TuOz@xpFy;^MzwY%|hE>xnxY;q$*+4<(l^+vnwa`{w`05Xf6_f
ztwLs{#$vPe{)E+Jtabf4dW(vsl>Skpb6+F(c|qjej1J%RMvzm&HAW`W_+xgL1(3_n
zJrgLr1~`n|schMWA$(H%CB}#ek(nIPgoZ|HHp(YhEm9tZYo7U!jt1@R2}C9rV-!&l
z6<QdUsbO`|N>U`1EfiW1EsKh=nLhT%NSK9+&;Z0$MinvT0l|(I@6KlW0+-34W}C@T
zVHrpQ#NPf-!j!xEM5Tu3D~XdT;$@zV+aid<fP!TCiBi->3CiH`B4s(dMJ#ikCckwt
zt9(`k$VFw!IEj2``HoUuItigSS%*L*VIm#5?9&+SJ8f2*I+tLV4jKO$njHci=qnkQ
zP_Lmk{YlG_Euh~sXnQ2w8`wMDgqV|!REBF3M&>Q;FH#yZ@Gz^XKZV+K`$=X<j-8#)
zVl0?GNGuJV*SZF9`i>%Aco7oi;ZsJgpFMI+72)PEB?t@pR_4Yaqx~C1yb-VB;0kR5
zV%4)jWyOh!3&rkeI(Zi|4gr?EgeR0_cy{2~IltCAn{@VN9Yjh+f{x{x0*v-&kZ$7J
zI!<uhRMzw@1JD+=2C|G~Q`&&EMTylPNb5i8)6xU`F=TNZSgs*fBY3OvnwfOfES}rK
zc?TU^aGYulrvxDx2?ULSK*|$8j7m`$G?>;s)R-Sr;BYjDev{wYWbSZ+SZJCOtp`Dq
z@?-h@s`%sTe&{0K3Q>e+e2&p6c1d0#8N|RG%3KP-yoXB&a^j}Rb;`6x@TbZ;{c^7^
z<Fm+Z#AR5$=;O0Rz{c&gy$WVwD5neAO?{moii5Y&jMM<$deL$O-*(({tF9E?_0SG8
z6OZwy3IJDBl*^~0o1a)FGB*2c+BlKs8D3E9{ahX_VnuJvQ+rMxwnp>h`(|=rRO|Me
z)aKWIdxnL%&CK>u-zCWsyjHGV3JRoS?_GhaygqvV%b~;1XR!<Bi6m8FiRp9}N~oQu
zC)5wjl=clc?b#zgx2bPo)E$tUs@n$|jGT+Q(!`fc-VH*U?ch1eT3rfTA?t2;e_Iz7
z<Z^aZzwZbVmPVxW*_Ohi<j{;S@v$)}LmMi1Z5Y;oxz5wbm_tCT@#Z&1_?c}xRARiE
zqqC*=_B!0AV1=4ViWPJmNP-^l;U74a*S;bm{l#(T`%tG4r~+_(xOjbtudu;q5uw{+
z!M{$UV=bjRymwAW`bvipnjh{WbrT7R)q}RW+Qsqm;2_2hgQ$hKGDDxYobE-+@qQl<
zDtA^@a}`+lr*V+cl#$hWD0qn-szBd03dXzV@*=(&GhplK70(JB^o|N8^CfEN>&jCi
z*LvM}!igq?G`b?j*;J*brd~x^p|r8Iw7w1&bl)j;N-%OXWdh~Ik_i-FHgocqNE-%J
z#*30zL3`Y0P8sNp$+vF0cPEOc^UW3}@;=Hd-dZpTM?c`z5bQa^7b{V_a(Z(m`^yii
zOzyc)lNh9%+Je<qWU!QbmI-?Tj0Gn2DEMB=twi<<&dTZlXX~TlkXoAvyg=s>=7|k&
zC+Am~NTv;AVga>?HJjS*Ny5o9Ap0ZVrL^YkYfRxCUxuYs#;FtL>89E+W%DTs8s?T|
zCS6&V<*TMJ&otFn-oXhRd@w_O;(Q)~__9zW)i9*Y4=jgs)K_4X2j%x0;wKvHI`y?%
zc@Pk#E%Vn?)b?JSx)l=1c$YrB+O@MS`PS~=-MN(0c<79+z>Xl8emn%7eQY#=Uy$v5
z@E!&?A1@5P5fy*rH1rSln>RZ>KJJe!R`|AGSz!n$$-Jk+X57+KU2Oz7`~=B*Cx8AK
z&+eC^0okTslQ2KRdo_pmgoftCl>vmOkiMc>-CYigT0a7VP@R2Ge%I%?gM#*2Azx0g
z5+b-Qrd^Bp32M5|xc~<WY|G4RGIt_e3uHhyTN#`W@t>;^QNSi+E}=r9HJYpfqHH5!
z24_W@av=x}?4F=`I>XGcK=V4#zk?~5K=d7156To+ElNZOMl!KXc1xr^S0P#NDA-l~
z@e(kbC(GNG(>O-zE#jhp<TXqtaDOLzMV~tzYf9TJLyK^YHm$B=1E&r{bqL6Mn~m+s
zr~N^N?11<d+MbGtZy^7K`0DD<V>CfrpCU(H`v<&lSlExiv81e&AtdO}pRL-~`6+Pe
z!q(>^ZxNTzg|qq<msa;gyBoVp$IfQZPD1h4!p_8I3enUvCrHBs!nh>v5s_npW$M}C
zL&CP=?QXCo`*{m8099oIcbcvDxOFCDm;U-cQqOaQYzreJWYb;`ldKS+={1m48JECd
zo>3#rFM@3vUqw!4w?&X#VvMjNK24pfxF+=*gx0R#+OKv&xP_T;m=l;GUZp>-GE5-x
z6;6O+k(h{eFmzag^@|FILpClD2z-Cu?ZKbVG>X|qkI?KimRY$%UeJ|~>|C8kL7DUZ
zE7vOK<oJ=0hzEm+2jjB^r8e+ErRCH)2$)gp8NM=Q%=@JdTo2mb5L1+y^2(vS&-FAK
zx{WZ?)jyr2oo|^B@jDS#3Cw4?={N`nOsm`Tk(6==l?&>$@bO%Z?|lJhm)3T8DGu*2
z=4O7%l}HH<gVhtlqOp3)<`rY$Zt2z)$CjhxpeQT%5f*p*>*MNJ(syg+cP|ysIsTs|
zoF%Qr(W{gkFD2!5_o+QUQw2CGG=_kYwuA4Q+o``lARp9z-~XXb`4{)g|8E5>2P?<F
zmMLEHHd5F|INcVQ|B$0xR{yZ0EwriB)p!m~5#wW&7DP6~yPIS)mqwc!8;7ort*)SC
zOEe6*-5|#A)%d6%gKFu6c|Txl1ZM1vPzF*gR^%_<FMX*=+-(T2nsD!jiG+!dPw$zB
z51to;v`m~}qqPwDp1b#N!5VJ45f*V#b{x-oa0lS~v_;;o&^aw<5jrC$-MJQ3trxm4
zvzF5#4mu3{&%4p}GT0c#$fDxe!4uZBRu?8=WAvcRej?+7AU#Z~go#6@m<Id?=*#%X
z#N0w6-bt&MBtLUdH^@a{*J_;0%F@!&ed(8ncpL=;{J~wH@Y9ZPR7L#0S$Lv+e9B)C
zgzTyxl<dESOKpL#%1R=92Sy=;QT?y}Mjlbdl+@MDO+_HK6yD<DHzDc%@^DqNJs_ub
z2>7Dh^`#WV&qty^4`39DSA{by4U8+FxA9f%rOGtrG=L<SBNO_JU0-<dCDA9KLOL2q
z%#(MD57&975F6%~=hu1yLg4pTo6_4&5K5}f1E%1o`iiGR3U2ht8o)tP^3_Ah;C(`P
zjiZFtjC_};Q2V=vNJNSB_Om(R2ka(&2Q52wo?a$P!^gNHJ0-H0!_TgUroX05q48A1
z*H4K8Z2dsw>xQ&@lc3sz5UfYTSWpY|LAx>SOwVy_(@d|jD)B7Rr>^v1;@($v&>|lt
z?0!^E{1x%s;gD-{`K-84N+BM%H_cGL5!w%;6l{>&^!g#Ga<erz-ZNV+iR03=-ds#9
z8ijSQKejx%p`r;*!qeL6>RhO5CE*y+8kU&Jp733tDCOL<lY#ecb~@sB2FW<b5{E&0
zaHDnF#4$fsmwg=<!8)?loVfX}EE$?O$dx1=Bn#z;>G9rms74ersMQSI)x}YuV!V2z
z+~lHM#W%w-vb%K3twti5z_g%4GiBa($hPri!1mHA9iLFrr59La3*y6xq@u1^bS-7&
zU}b5sc{|vW#izl&#%F(RFC*H{Bv?G!61LP0mI3}`HGKr%)gd_srx)?kq&iUMn!2hD
zHT5kk(FnWJ2w*D)PdMm8hr~T{e(2Or)(cT?dZ1GN%HBRt)Y!3vMvTKDwojQwy;KfM
zv2_eDiOF`2#0X+7u!XB`H6DFDeOi+#F-cySNQfBNiIo}E6)op^lN&K&lr|;*LU%VK
zIx!_0u9<Z^c}O;itj8VRE{T?O&{<%(FKO(&SXmwkg}s0kf{r}IGGmlkW{t5Rte;CV
z0bDom&6><y)Stv0R~^Q{y*zGQQH>>j{iV5^)q$>U{|~-5EdPz~4Fdz?zj^Xy#al?>
zD4~p)pfzuy`9p27V;jA^_kjcJzsV98D?`H82y~S4%gMT!+`tV?Ps1>QKqrmVLm$}@
zTe8(Q^1Ha9sJf|!10#lFSsV~ot>+3K5Cf!`i^JaY?1rn0r~T2s&cAH!Z{BtsyiYbg
zY^hn%2JALvU5U8J_}Ho*)9f39^r%_WsoV?s>*?k0%#MJh+z?da4;Y3@<)j#(5G_8<
z?xj0P?b+?$p364Y)(v46n77cI_IczacPi8`36};b!%~$4_EORKqI3p)dTor1aoAJy
z{pA<t=N;I)<#Nl+Aq|a<jDReNUp0V4bHx;>S8J@#2?y?^M<p<K)qeXa!un+y6O3|L
zSCg|RL&)DpC;4-a3iS2=a>z=`6PFcAh(*<-taSj^ZHD(fRK=#5!=W<vwM10)9TkjM
z4$uXGd@GHGb0udtlT&>0MFaYystUR8I|!8iCyi4BrRMz#hYCo0S8=P1(wvfsOoE7F
zwi8q_|4r=LIZD1sINV-XJqgG}#b%woo_2jo3s65>`I}^wK$t`Mw`JofQ?f`>Me#nE
zdqp5;oN*bhJ0}8x5uzmwO3S^7j!_BUZA~=|_YAZ3kiACB^}U&RU?h*h3H<j?O)1i=
zi77u~s5C6#dnHWIwGuS|BZj5aATHoY|Bvd>v#KdQU$$?J#VTl!V-019VC(0JNaW^w
zht>`jc})>F$5rsO`_+1NnBQ{(>Eiam_1Wsr2-e&}5T{(qbhGk9wjEFxu4Z=I+nEvv
z<``)EshTCO`HjsT3n3y$m*xXx_pq9$7@5gWehxP=kBxxn1w1FTg}t~a#k>;afh4J8
zw@teb;p@}jI<}<4!%g6TSV}I&qPu+ZZ5{yA8<E(Dc;s{_!RQtYJqRZ0vLC`+_9l_;
z(<&6>ECY)jzqPLR742QDMPfz~16q!Rbj5j5Z7*^#&vPq*N(p=TWv=K9zvhA{U$Yed
zjk*yho12ZBiQUg}FLy#ufKEvz8CsL+$h!_;<RaWp*yfQ0MM05*oOBpfh~nl_)7>MP
z@px3|QcC4ZOX&7;GJ)Uow%1wtcf~13j4E-?f82b6s%oiPeVm(c1hI>_1}^Wf)-1Lu
z>oT!wj@EG7?;Z<h$9K)%l^7ThZ<z*2w+^i>t3PoWLK}C%Ak|VeS+_2c?}FWGIeY2$
zVtk}GAAs&3uEGqQ$_mCjvQaV3H3iz`IYVI>q|c^-IrA-*LyZhD7373%+%N4I%`F<-
z=<DU|>g;F28`~3tbis(dv?YKWJmM(N!)oglFk@uiTHM<0JJcSeyma0fs-dX>4Tu$?
z%xsq}<>o8Z-TV7_QrR7*A%;kRw$8#Dul}h>#m>UTUvQ*2#k+#%lh9}ut9jFlnNKjL
za$zTJ8OgDOL9q>shrEWrhN*%E%W;eR41dvjGG(}0$@{iet{h~LA**Bee~&5UX$^NV
z_=VgI-n6y8RJ3yBv`fDXjZAcXd;|N+pd|nAs)psind@O;|F?5XI9f+cK@VfN$HpXM
zfwiPTPA)quLpOiAfXLV|E@E7Oh9<09{ozo=D#7hi${}qo^?o1mPgB@c>&7Fgh%AEE
z;|uBN#%X`&?{~q!DmQ64=vMiT4*@5BCnJJkb7JI0%@tQETr)4q9$f7gLjrS$Cg;XP
zE<1(zB=3afDp-!$3on>dkc2)H(4W?Hw-#!CArJJKP6)(B$$4?&a+Z&w`T=Z=^f<83
z5QcqMHAv?g>gg7N;BuCT(!qtMvp3MtxV#($L7w)!n@H|N-?=34(ANe)YIyAGDkbFM
zBj{L<Lit}K73QFS$RPr)&1A#3livdu;%ccK-v#1pzt$Q6HYCjSB$9G@#Pejr1A(&B
zfC_PoZ+!*|Lgjws1V34Ql9f?Ob)k)?FnKRNg%maq=Jylakc&${WW`i0HT|R0)-ZG+
z3!sf`eeCtR&pBWzKl8MI98SD(&Sx}rQB_j)BJE~jsvahv)6;nAKZe^lYA2;C8mfP+
zI}=j)1eYLlZt|{>(%WaZAMJ3c!webs{2^1N;D4NZjG}r;bjhr16)Gb+uA}=%q*Rd>
z#xT9%y1m?EP-|vueQ_!^BphO5s1F{fyJt(S$L-x|qS@_+G9?0vhX}6qsanmY|EkLo
zpq;EyYS4s_@XO7|+UxPA9Uv?)i<P`~Lctr(B^24%H)&2s=wwE8Bv<JiXD#JR{*1Y2
zaed3JEW<yXR@XR^7kH%5bMqxIG+aOn%SbFg8{(-~S)?xwr(k5TSTI;$enDh;{9OEp
z-287ugv?AV|5|Yy80zZj=^7dyKDap8J2@;mKf^{dGye)(J^X@xd)hm(T*haF;WNba
z8D;p2HTfo-CVH8TIO2m(pauaM(D{q-7Jx70jr1$doZsIr907!im>9yx>Y*@yFmdv5
z5C;UrfCmJG_~g@nJb&^r5(zBI5CA49Dd~U<MhpTX2{y91NR->o4Yo5FAGx?Wh!jg2
zwEKBDxfm}22AdHX9}i6B^>u%5-!-_Y-}RaA$OXU+x}S`P3CdmEU(ByxL;-90>_h_V
z8QfF^3&npVNObVF{%a;~9_Mf4EnzN)H|r(^0|UliLTg}YqHAnuIG6ca5O8y{d83qp
zdGqx;{i*Qfk#w5bw)JqL;;b_Evo@-%w5Fxa+u{+;y)#qsBY^e^l|AEnbOL;gAMc|R
z@$LLZxW?E~)lr~PU-8c;Pi;gkzZ!A$YS;9_B8!Br&SQGgEP+fqmBniP@Fvbj^AD+#
z%xU&KPeq5#eqkD4zBh-MRQlT1&ejA&lbd;$rh|*5h64+P&fR{_GxVCbq>Mqzfj&dd
z%m7DNNJU792h++wGSmh)SKri^4?)CV!BTl<{~-<jzf2t&ng1OK%l1hO{$uKRLUr6q
zG+ziI_Z#d?{Y-CV$XPDNjIjzRBaoTr@y2St9s(ZInDmBcJEO8PrfCkSKBA?*?^hS=
z_<^Ab9U>=ABOx_Jk<n_2;t@H{*m#1i2?LQ~&Q3~;gNIJGb`OSEYcr|`XwVn98G1<U
zjf0nEGR3ZH#y0Q?D8Yv+YRN7-dnftEp4MiUN04>~5Zp?=c!sg_HM%G>5IIFS$q1{=
zJY{BP>}e2YtgQs}u96Z0^ZAZWH$%Jj_iq{Y2r-F=68)>SWj?a)=ujwz3!3uGE*+l2
zfI`Q)P*&HDv<BMx`412iY@5RW5H+^{0;{ty{+j?-DF$GQEQb<w@@IiP%B*rIf0V>+
zs|jOXyeSz4k(n8sq{vjL!$GPzfj<E<p^UJUrVM24krgr>R6{7<zx4qE1hrkkc5gb1
z3e4qvIO>&@8;tB~eCX?%$HZY;7-oX;H{r|EP>|%qox<MgW>oI1i}C$u4G!OJehZ7P
zJH|qURL<0(^8@>Z9tN{(qs+5d&Sb6dutnc7jvmf$*eslimh598rEywG$@QVePM-=h
zjwN(|TTX)C3VMH3Hd}68^?@M-^Dz0ckpdCAW8)Sb1t#}XON$C4OV#u-b(E0I#Hsf0
z94G)2Q_|9CP(i+_V-P4xynK5jL5<1%Dutbe+sX0l`GzdqbjBo}w{p9>K}k~9!U>8v
z3i0vjP2&Z6gSm|5^v;2*S?Q=PZ&^1{POc*<tVDA%&JTz?4>8JY=|zBDXPl;C;{^z(
z0`tYgl$GtF&?rWdY=U1x5DtEku~tZ=4oz5#x#LZast?uvKo>7xgi>CJlskoF%$%@}
zXr^W<&SJ6KUdf(L(5&hf8!lL!FFN0BR6mnDIR*Ufa`)M^@kt&rDcUDo?;s$`epFS0
zV-QE~ZRXrET~R){to^gDDZsM0-$JDkym@l&YxI#&immd<>f1D>0u6Tu4>tQBVux&l
z4T96aX~&yE%>gAt&v`s2+k4l7DHG|0H3>A7)qn4wR{6jVwmAMnj&b}KImXP$#PqKx
zbd`#kqM}N3_mHeM84|&gmSHP83tZUTs(B=)R6A}!7zl89I03<f?5M2pUs(RAS_|6n
zbd2z_Ula&JU@Q{(LXu3sohN6rq$h8YbjaM?)TquJrkYkfO85hw-Q8|x0-C1Poz640
zcD6q@JbXd~0|OCo;7(pTnujkn@H-xxtTsS{{P`+X(G=3ta}w}g&;J}F`nx3#aH9>%
z_QBu^cy+U|U-cY=ZY4JywD7(PqrYSPwQUZ9P~HdXMAG5GP}S+35FA4kA`ck=j8h@j
zXm;xlox}PQ+1W`eJDWX@qf#l;XaujE<Sz`)U8IQG001+{p%VA@`3lhR!ZupnR&I~Z
ze!p#wh3F7bL`sYdOO8!MpcxAen2-vP_gSyPBev@;MEj>($&!DdPdo5ipu?f%9~dgz
z9ylcZvx$<CsX|RcPr_D0uLiyMf5k~0had{kBMMBC7$Y`>wGDy@vEOm+BkE(`G2ap0
zA=PEC@>`OyB4&iq3{nZf)FrJ3-&_+oBh?Mk5=DoSA%$)Y;t|EIN?s1IqC@Oq1R@A$
zCgD(w;3wiQ;yO~WQ?KH1Bi2(Fk{y=JI_06aRkQ+_lxNsQx+T1s6<Db(mG>^)8pw`s
zj~{2e^sCt{0jW2BUGSereb2X#p%BC(ada?W<U%FFI3#~R5eMN#sF{WrG+>l#W3Amh
zfRf<C1)J_+SkrVWeAQ>;fB6$DQej+?qK7S#XPGo!!04uPdi_RGQ#S+8u-`djr3aM`
zaSJ?MgKRVfu0pi4rZ3h#CSM=9m;vWNo#BZ=3+f(gdJ*K?F3ktqG~i3q$^~&yOemBS
zO7fL2EL)opxkLXsW}C4;;)u=Kf_HugX3N$>!J0`jk+Ei;-P%l_pFAQUo4_&xC~~Q$
zM?O1{hyM)NT>6YRz5L~y3eLmO?p@p$_to17YQeIrEguO;bAsfyRn};HAIL_h1yP4F
z?SpNE9U_XZ*fs`cGn4axE2%A6b#+ek&zesC&cZdbxjqH2UpTCrb&+CVsw=yiWW>`<
z?9TA2dv)zE5+D#>lBa-WOc?&tfp+1Amks8;t?tZ)JpwLnZoVihE@3sFtmg<dR3JwQ
z0|uQ2i?OU}EJ<%5JcN<juV|(HjfT(TTDM^B9KbVPZbeyyBCA5CRKKV3=0&b)GON*t
zpj2ZIUB)eYY*7JfiSYw(GjV~M8$_NbOH%mPe0f#TXQ$7E*g1JLDTyFPK9x$H9Qw4+
zOc?Rz)?tkUi=q@L|M00^WE?!pIHCqR=;7x|$s-C=3gPI^RsEFSIT>M&JTt(4u2VN~
z`gWL69SRPm=m=WNO)wF1gXt91LtZEdO3A7XDzGrWyU_|X$mgC372MCmjhqqV>kF$!
zYcnCvMa-7ILh_R5m}^}oNa+o;A}65LM<k^NoGB%hg`1h}d12`i$r(&z7%$Vuc<jX#
zNIeQlmbFQEz(A#*B%@hZW9}m-m&TNI{S)6n)IbG%vh#^e=xvV4dpatk6#b+KR@^mb
zSW8FjNFbcD=@VsrTC~^6t#9SeFU+ZC`yc`^qdqE2Y<!43G-kslwN3H1UftTI4jf`Z
zHs8$KOKB}`pDSd?KbDN6wCoobTd6o#N7wb0<DZWy)mPteBS(5Dk*^8S`4}j8h_<YV
z!XnX37O8EW+xyG@lb4y3{mc40x;Zwn?rB}@nBbK24mKpG^tVj%d?97dcaOt(%-AYM
zDukuvK#R&Nhl+!@YJbp!80KfGG*!r)^T~Gyv8oGy+k<KC_6qk0ZzTs8+x`i;i$=ep
zcWAS=^+iR$NxahNG5H>c)+{^*Y*|3yPfd4fA*hdR^_4P%K&=AHoh`6pmslPHHa^oX
zPP>u&plH*FPy)|eQ>-gsj5x>aLhJ1``rwv{Lq}TQL`eexAyrR1>b^{#p=*efhc`!H
z*YcMFwCWxff63KZtYV_}lKo&3gD!`ChVL#Gn<!=DfG|+P)3}{h645mT20GkK52`@B
z)J*!F=#7|-u{hi_CwM<*!16B3J)ZSj@ofEzLSPi$=A0tK1{1LbVjd!$t~l6d)!Xv<
z6==hd_lT-3m=<)G4t`u>6Kov8*rl5lIVs5zN2!`v+(aFsj`-^bxu3<8wcTl-dE8;7
zvl`c=?wcFg_NW_iTR79brXO_wtTmbP#$?@5OJ0k~X+P!8&dgp)f=nM8nnV4D8dAmU
z3YTE8QYsC3xw*TkF0dz1NY}6j?&@7hdo-p`NZ_SC8Hc)QWg6C+@Gc$&K^)CTdpD>d
zL<-6uK?GPWKd4?yu$nsLA6ETE`KNc7#=e|Zm(YIkWN<+KlA&Atz*$q**%n_8TqIF|
z8s-3IiQw!>^9f?t{d4Bf4vEF$Tqdto<%?8bDtvr)=ZNEF@Dm109TTOCuV)6O&vVxG
zHnJd^2l2j_X}x;J3fRp|cxgrT6ZCxkfnrGSBiT7Qeppb$J~pnMD9*=y`a!q~`634%
z$RCcg|70_`#UaQSEU_r|+JtP?<Xe4B;hjLejYUTmU2fQ0{Sk8}eD3};<>7kR`x|Ea
z=F4GpdV^i(8%C-3>gRRFWZ3u8qJllL!?Z2u?4iJx&*aeJaciY-Qt6o8hJ54&=ZSr^
zdGVjUjp^J<b%#_7OV4O7=pS{VS#pnu-kbS<s}xS7jJtp|4Q0Zr@?~9H>D^Rs;hb=#
zJuNSsa~h*mMXPglUUj6<mpH4-ChRv)>0Q#XVt~vZm(5IPO&1A>B{L}3IX8H`6n|m3
z_r+Ad6IVwMxE}g2&Qu=c>5^K9KglIjq6Y|-Bc8w(sY%dZnAGN3GE35QH$Hf+Lx4ng
zI|E^luZ%uu8*<1(D#_-pVAj^x?#G)^&{EP&_&h;l6wea1zIGWTHWui(K4j|&vdP9t
z$_EmkAe=a$I9^q9(Pn)oaUz~h+_m3{Eiik8^AjtiNIIh*B>_>?G;x}m{_5%qiR@I{
zvBSFC1%C6E|6!#Bnh&QcB%6)s5iUcd#kRvPPWMh6jUv-QPu-Zr4KN+MFy-0@UbCU4
z$|Iym^Wx8z6iHW1GN(vP`0!jS>EZG7Ua>DptoA|mGd$S78;!wO;~^-*avPfdXSWQ&
zA0$a=ShN$lijtR_&u7VN!pIT^^T*w}z@U!Rv^J=M(vb6C2~&sWKf0}TV56iSPGGPs
z>FJzT>a0;U$!19eL@w|qJ$DBf=}VHrA|><{u31Ob_4)Pt^Y$icDI*(;$h`yP^i|OG
zC6d<y6yQyo#7dCD8F0X5>n5iKcS0+eS)mh`9dyi>f(u$l7S@kfd}fq?fyzq(E8!wO
zIQ;M(bzh{xy#o|_lbpdC#qbe{SpPhAsuX~j1v{FSc;-F3pP(}Tj4ZUvmywKeYo-=h
zQ}bcMpUD!G$C`7E!%~uiG>K^>O;_)M@LK`o)T9U}!2VF2f-_C~O<!{Fd|!bnK6Auc
ziP&MnwiVG?6c%NGl%V`fP^j5o*M}Fb%OfPv>3|xPlubFeY%o-e1l$zFD$*ohBOB{M
zl{Fe;!@^~(%<EfWiy+>Dzc~-jI#Ytp52dBW?@b0HjRp@0*jXEOGsBsWjyXCR@K~NP
zwr+ll5L~rGfx+H$0;=?pH#}5CH}ew9#=hMjLauUNwC`{(_*~FN(L4aOuw!MH)Z*n5
zmC3q!iHiwL_7BpbCG8G}?DBirWun^KGYkekh9(USt8Ua?o|X$HtphVd<_3sOU@orH
zP^^bXgY!%U<cHXUZ@Zok)d8*X5nrcg;#{x?r|6e$h_JWaQtl?i(qn{vxvh_y_1fTN
zvedD7v@;3U!Q~y&XIIO#4jBgk_~0@s<%Qg;A?v+6x;+y>0|-r)ye!aN+$2xH^QpN9
zlL#RcB3}WS95XHk&yT@L7I-j(7QMpMur#C}JFR;phn3^Lh(V<N#3d59wo?3fB%{q=
z6C!J7Le*>G258`isOuo0kGbhDOJJV^p3rB&SY(ydvyBu_N^Bo)PT3&wjo>5d&AG^q
z`{tDMUxCU?v489%M~j9oi2wvJ%+pl^o9cYC^z&ebUJ)Yv0Z`^F@idhwHE~kiVb^0-
z=<s1L9n((Hk8s&ToENHAl4%k&D*Oy;^O|exhYdMjEM+zCb5!5DB9UnKyA#3y@nv?6
zVzBnS?fEN<OFMXfSm*?8(<Jhwb;?pwhxVmrw3>@oQ~4i-%l=B#GL*z+{AZ}He32`b
zHN>SM^HE<YGwo>h3T}W#sF4CUiW>}g2sRcOzC-+FI*;UBffH}!Q~h=4O<~Iib7m&a
zq{^2mm$wa0`fp?N>t$g`({faOJ$VUpsB3ir2Xt8DS!^wv0`TuCv$@Xb3qg1p(g}nm
zYP;|<0a>)AWy5-z`x``3d<Px94*}$ulFq0MPN<7_7O0Bhn-ySrr80GrH9w<7#f#>h
zx3L%5kHk&JcwC|im>dNt^CXBhzB{ENwab1!;Ix9<=h;~UT+T4#{eQ&gIEQbO)sIJJ
z)H3X2$F%gEk=MhLS6Zo4Xay3{l8Opd<)f)8X}=&wn+ntn6!9N9q92(<IVB+UesgwC
z!_D}9abJfj=n~IX9F*HVF1j89hC{3$z!L>I^zDj5`=ca{Dp|=L<twF!&MtdDUBhn3
z!Mvv9`tV0bvy5BDfTj*F(m=vwuJle&q=3!ADuxyppXYohU<()WVH#iC)r~kX*RtA#
zIw9gmmr~%pL$$f1s$-;{?|;h;wiwU{lhPin&ELUi7u>+8gB=uNi@*c=WY3k{0bLk@
zuW!kTQ}O2#d&~2UQUouQ5L{-aO^z8nuqFIEw)uBF-F0Q%@DnN}n`+`o{_3g`Ih$ji
zP2@RV^mwCIw#{+D#Aq=^We;B7f-!A~?qk?BXQ*#ZZTx=9D#uIe7nV-4zli`!PI`c_
ze5s&150s<VDeqX#SG_gJbMnVlu8__TZI$Leu34WvtIO)~??Y0o&*SVte7HzEGq=m{
zPHKXO@r_$m`$c?~mIM2J4G$RSl8vk9U6*8J9LfSA<$}^dnPYxo78uNGO-_0QnIgg9
z($>b&6pINk`)#!31Xi^!ZTj^n>6xSkEnWgIz>r(3`$f5h62uy;=h}6EJhdLb@e;d6
z=DYG_wJ#$DqT(gjKVIlb=Ya{$+BtL$I!llw`<)R2;ACJM@ZPEUg0pO2D#SV_&Eu{+
z4Dd2{Oy_WXN<&!UZ|>fNTx1biwMe6b>j_iLkIV3n-g*~}AGtxDvCCQQkH4_BJwvj=
zL&mN+PjGv+hV?4T^YhdzN@=Gxskh+A4i{AJhfDQto3NhxhcZ?yhutNtmD{*!O(6(~
zzi?e&R|?HrM&R(<h4RvDL4=#V!vc7sx3Ny>vrJ)UsvXg{5Q&N|kipytz{{Ej)pn~F
zC!VHg;6`>x5zt&qNa-6%+p1t31S*oDsUnne`>KSDk=YX418H$KlR9!Hky$9yPpA3C
zIW9p&IXicOOU8*`Mjv%m&>`WBjgO8{j8D)}F1Iq!AW#jH+VV5ZffB~dZ7xr%UG^Se
z%az47j4Du0z`(SG;bG_WlwhSIA*&j&9W~9B#X5DTr6w}f=uMh^mR~OsQ)q8Z7FkpQ
z4#%3am;m?Bxm=6J$+U?^-p3sd>8rK>O*dE>{tLRn#QJZok^hUI6K`+fpq-xPp6{Nf
zW+vE}XJI7(bW`Hgz+-7&v3jqB6R(2NXCd4V@L<V&dp&fYlRrxXloP$Zw@L^?-un=7
zH=z85EU~(d==!<_hA0SA)z;V4)KhfgG}DukGvZ5B6s*kiO!V}2({!cO<5Dx^a{sJU
zp<iACi%*6UPlA$mhtdukTYEbiT1$fpbEAOLcJFqF@_sww?~?+H`s5BOT#OvdWDfdd
z4vJm;9W<-ElB)~$Lhv)|s|(<9Tp4)veiLpB<KQ=8C<h611}7B}LEE{pc<uI~{^+jI
zJoUIX_-N>d+==;VrC4vPwbvGB6UEjLS5Z<jfeg6~w9cW>_S)tdx-!dX=SY63)*#?Z
z(o)TP;or%Fgm5Q)k-EyB>|3F~3VCR}X<m3QsT;Ga3p{+VmEPYwKi=c*bMo^zxD4YV
zpx5*M5yH!Mf_DEPdz!pRbGMyR#M{P)7XD=n9t`)t{~z1Gv=2ngEFF#P=|n8`9F2sG
z3~UUI=%kFSO&m=L7#Z3Al}2gFc32P4!)!jG;w<FVUgl|mf&_tp;Jx!_dHPb%Mf-*#
zk+|LNtjI^g;EMT+;*A+B!Jha@`Ja=qv*`P;#lm51VWIQ~Mi45q<wi+}DtFe;H<W)Q
zeoLwcLWyr#g+E<9&9B(x+IcI|Wr&(ad$Px9NSQ*U<NogZRleh;Dy0{&5aNv7ZFtFL
z3KNXRuGLXTaB%*GOCt|Fz1|#Ifqz0?!r)#;FQy&~Ijw=<wZlmARpU%wthQySoA0*6
z#jzuJ%`-bWKA%0lh}GZSa!MC|Z!h@j8Rx>=5dH-;Ms9idzvIO4FMR!fcJ<K7=$k7z
zTG2@pu(18Jwa4DUk${<r{{OMRhmn!>-(t9=rlGJgisZec!(aoQ)bFqq>1a-+gkh^|
z@O$D<Y>_L%v+jjZawrLzrT5nv-W$;vqa1C?c_zCE)BK~+Kl5bmV48>`uKca~LSjVB
zn#^&$Wbw#FsEE9G?A*UYAtXtNv!BE02cdP-2(SVRMH_*E0FkmnSo;pC6#E5e;=myG
zflSB9)qCdbh`I>_P^!~zy1jUN)69GF{!F{kz+LqBiKMw1AGJ`{ka*<z+z5~Vr*QKM
zY68*1uymvZX$le`AVG={T0-v%yR;xI1PlZThF+zFV1Wc_Qbj<QzKKYaCL)MIN<;)C
zl;~0d2(pwQMT!zi;JWwe-r1RZ-~NX)|I0sf<~uXzze5yYu2ObR0nzM7_iA~LdknnF
zE9W3b3CLW$=yfxj32CO%Xmn$_Kq?-`<=iIC3<ujJTwTX^bX~aqCr>N15DzZ*40q%@
z^-J2U=y0OvDaBH5VYB}6V*fU7Ze4pF-2{G*Bq6%Fwq6RjkWGSHhl_2FTfR*iKrs{H
zh!GFA<ww8RK3%1ithwDM8<nHTg-VdVzCi$lbA*DrqjeqUUP#5x)k|^CJ(fza{PtN;
z(7<c<uN4Uaug<5-?gs5IdZmp`fg`Gi9wQIk^_qAVE4)w+8(y8qbzcUbVn+HNQ3S0K
zp=zGY=okdd)wQ<t`q4)ZW&ow=@CcRfH~(P?YZ#2sGd8@9&>N3=TOa;p{k<nc$r5kK
z$e6jyl=88XnB?Gmhy#8;iRp{}F3A}t#2&R}*SM-Z>}(tlMmRI}$;)cJT^*K1=k!at
zLW5$LRaDTgDSLqa048u~Nn6BPHUwx-i?Wj;p&xBfYHSY_J8T$oYdpfIwTr}J70au&
z6FA}N$u?{%DUfQBq8h69q2zE`%Qf0>^z7WXS^bR0E2QbC5tLUXzoJayv<S_y+p!lk
zu#IN)X?%EecO)?iP_y^fK&po-inscHP7UwR22#=x4F`L!U!1nV_+$z{`_X>Qrm{1~
zhVStb)<d0Cp)M08wukIZT2oZN{JL#JX~X3$qIQQ%@&234cR0zWCmC3$WLSZS*ptc_
zU=nM09}pJw9xj@i)Smbu`AXrs07dnd?VHQJd3N0v1(Vj>Vp%4HKRoSbw{G8PE<vW(
zx<R_)Usr_ZDW-UPV*w{%i4>JSBWivh0g-V+o!ER`QG$iTCS~WpTnQB<ewkfg(2+*Q
zmC&&}@>wDi1%r2=k=lZBSYhxY^%1ZBB|wjS2JN&AXQ7-^=PC!+=$(j5<eOOtCE_y{
zvMfJnFxx3mQ%k_62*0{Appa**hN%69Iu-pV)-T;xiPX{=2Vhyy`tQ3dZTWNOD~al1
zjS-A_M*&AUYWQD=V>7)b7TUK7S3JGT<i7!olAf<yF5%9=9e8}gl>!mJ-(~p;28&X^
z19VNVEkt}t45mSfTg_V5ozJ^jwCbcR<gymur}$#}3nQhLl)$R$d1g3b24%HH|M)?e
zi=U@z?R0Hc&2Vc_RXt1c<jn%ooYA>cz4?-#xIRo*aKoINVh%Qb5?Km-Ff1WWC)Gvp
z7y>?Tnvb=Yh1*Ebb`00X>NptnUr)+Rb5@NnOU%m05!%Yql>)7BNW0c$i^!w8*tmd@
z$lkphyKu}Xs|M1ADn?Od`HJVH9m0qannPP1P0!j#yrk_Y8NB1DS90eFGGI~^5G;DT
zqbG@_(ds}SUG(z1INneX38hl>0DV^treFW!_eV2V$v36+(sDegeXO_cw!3MH7Aq>4
z)c+!()xdmjM>xj7%75+YOe~yPTxzD4ZFShM@bcd1U=MXx)N9ln=x~!*LN$SD-2$Ra
z%-Ovza-FruZOQ<M6Bd$}3~x;X$&qU#eW@niLE`j2A`r83TDn4ef_K-@xyaE!ju>Uv
zId!Qb03EOeuISZiE-<m^<Tfxt7Se|s$3hYnM+y>>_3T$F^~u6!L6$-*a>hiCgJb|h
z*_gDvOPC$OX}xqevC+1*JTSA4vNKmM8Bxw$u|7|ewD-3u^S6q;Hlok6>m2br5Ms4|
zg(V4&y2_kSZEbE~+O9RPukD#mPnUXrs{28UC3nAB{b15aMwhuv>>auOc$E{fdy9P*
zG(n{DjMa`pU@Hxg9+j;MEt^%AQ$1Xk(J=+nMhLyhWCvY`8;PRTQ_dosxh#cj*Bx=g
z);>S$zA%&^98ObM4BqT<Sc-TYlCu!4n9}gRd%h4DUYQA$ei2SG0!*7N`zvfyqBEsj
zmX~NoYHLh6tH2T0y~{%j8fAAcc#@Vwl!Hu%D33Qd)-Rgv8^&?r4syErv(<{Xcq9tW
zp2gZ)qwE&r$8+~-=?rpGd+37&sJt~}GqZRHdvO!O&A&ro2l@{MvS@fp^x^O7NDgan
zr-g<eVw_n5fq2qad~+%R3P)C@U89a0h_Aav`Sj>Ko+HOSzZsUv{Px55gt__&$AlUS
zrZZ4!1KeMps(uMh+dB}zK&$o$rLA_(VfdHc;%$N)cQfSJL=$2=5)K3#FBIB4zpw1S
z^b9oqrZV(B^zcE~rd#eYdB#*v=A)3O4w<du<bq&YUj7_3PKx+0%rfu7lU!Wvs=133
z`F{1dzl{vYs4Q^t2AyiawhFZ+ql3(#UJHAB{og0)dkp{wh#tM`(hBO`qRKo+%exH9
zlUo&Cyt~tzP<FJDYhvxyx5%$O&PKz7Br>~CbmB~TkSp4C$h#?dW2QV_XM$(wku0o9
zdyS`&UP$)%{`lH;9=n&EMZ6#o^mvrlwyrwb3`H8C5;+qFKu@foRe@4*lC`zF25BI$
z+Sb5!Gbda(yX)r?3i=jmQrwC6Wz6layyP-E9;9|)-YU~XBG^?j!mGTSG_5{5Y9lS`
zW$jh2@-*|^YYB<w*FC9}>P$C<A8(wrY{*QdrwMnqZ$^46xA-eYt7*72uAJOPy!*;3
zI~UA2$=zRLTgB@9I^`^Vig3Ry-eZ^WsTDf@H9utL45~da#G?gAUz=!;30jZEqLIyt
z^zOnCiH6l;_aja3d_SvC{GYezd*jxWu$aT8_$vB1IceR<qOL=JS^QpWaQ!s5MI%%h
zK<cyR_ttv8Uy|U~cH$s8S3Z9>kSPRuMIQbqJL&#MdF|iq<mQC&g*b(UMS=dl9-JV*
zhWdtq{{FH0DK8_9ZunqeK6+aEPz{(aMqgVCrlE`0$7o?-nou8a9bcHq|DW)LI0wXG
z_;ht42&CpuYc0?Jg=QN6MP}e(V+V_?XOidl)!B)>@>Qwjpm;vQecq43T@s?#_%k%$
zNvPV2a@6rhNXKm4<8oWLQ?k@=`0(McS--12!dHYy-ONem`|S^ay&fJ=Y%ZlPhRzF^
z-JZH<3l6&@c4N+FGHH{2!fAfIkN25{82U_#_%CLBu<L?<%AIzfxVkk2U$2Q@&mU*b
z1i>Fd8VDPq<R8#GTPmwf43{w)mYKaQD=Q@ShS^}*F?%cjyXKGcJ;EQ$+4@EZ&MQ6o
uyX8U`hS*ItMwSyNWswW#+5eeFWR&;KsMwnrUp{S^roJ8@7<>h8#rH4chm>Xj

literal 0
HcmV?d00001

diff --git a/exercises/Solution2/.ipynb_checkpoints/Solution_2_v2-checkpoint.ipynb b/exercises/Solution2/.ipynb_checkpoints/Solution_2_v2-checkpoint.ipynb
new file mode 100644
index 0000000..7cd0952
--- /dev/null
+++ b/exercises/Solution2/.ipynb_checkpoints/Solution_2_v2-checkpoint.ipynb
@@ -0,0 +1,616 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Exercise 2: Solutions\n",
+    "General hint: You can always ask for help from within python if you forgot how a certain function works or what the correct ordering of input parameters is. Executing \"some_function?\" spawns the docstring of the function and \"some_function??\" the source code."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# scipy.stats.kurtosis?"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Probability density function (pdf)\n",
+    "We will look at a few common distributions and investigate their basic properties."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from __future__ import print_function\n",
+    "import numpy as np\n",
+    "import scipy.stats\n",
+    "%matplotlib inline\n",
+    "import matplotlib.pyplot as plt"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "For your convenience, we define a few pdfs and functions to draw samples from them. Have a look at https://docs.scipy.org/doc/scipy/reference/stats.html for more details. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def gaussian_pdf(x, mu, sigma):\n",
+    "    \"\"\"Gaussian distribution with mean mu and standard deviation sigma\"\"\"\n",
+    "    return scipy.stats.norm.pdf(x, loc=mu, scale=sigma)\n",
+    "\n",
+    "def gaussian_sample(number, mu, sigma):\n",
+    "    \"\"\"Draw samples from a Gaussian distribution\n",
+    "    \n",
+    "    mu: mean\n",
+    "    sigma: standard deviation:\n",
+    "    number: number of samples to be drawn\n",
+    "    \"\"\"\n",
+    "    return scipy.stats.norm.rvs(loc=mu, scale=sigma, size=number)\n",
+    "\n",
+    "def lognormal_pdf(x, mu, sigma):\n",
+    "    return scipy.stats.lognorm.pdf(x, loc=0, scale=1, s=sigma)\n",
+    "\n",
+    "def lognormal_sample(number, mu, sigma):\n",
+    "    return scipy.stats.lognorm.rvs(size=number, loc=0, s=sigma, scale=1)\n",
+    "    \n",
+    "def binomial_pmf(x, n, p):\n",
+    "    return scipy.stats.binom.pmf(x, n, p)\n",
+    "\n",
+    "def binomial_sample(number, n, p):\n",
+    "    return scipy.stats.binom.rvs(n, p, size=number)\n",
+    "\n",
+    "def poisson_pmf(k, mu):\n",
+    "    return scipy.stats.poisson.pmf(k, mu)\n",
+    "\n",
+    "def poisson_sample(number, mu):\n",
+    "    return scipy.stats.poisson.rvs(mu, size=number)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**1a) Generate arrays from the lognormal and poisson pdfs and draw an array of samples from each distribution.**"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Generate arrays for parent pdf and samples\n",
+    "sample_size = 1000\n",
+    "x_float = np.linspace(0, 10, 1000)\n",
+    "x_int = np.arange(0, 30)\n",
+    "mu = 4.0\n",
+    "p = 0.5\n",
+    "sigma = 1\n",
+    "\n",
+    "# Gaussian\n",
+    "g_parent = gaussian_pdf(x_float, mu, sigma)\n",
+    "g_sample = gaussian_sample(sample_size, mu, sigma)\n",
+    "\n",
+    "# Lognormal\n",
+    "logn_parent = lognormal_pdf(x_float, mu, sigma)\n",
+    "logn_sample = lognormal_sample(sample_size, mu, sigma)\n",
+    "\n",
+    "# Binomial\n",
+    "bin_pdf = binomial_pmf(x_int, n=int(mu/p), p=p)\n",
+    "bin_sample = binomial_sample(sample_size, n=int(mu/p), p=p)\n",
+    "\n",
+    "# Poisson\n",
+    "pois_parent = poisson_pmf(x_int, mu=mu)\n",
+    "pois_sample = poisson_sample(sample_size, mu=mu)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**1b) Display your results in axes 1 and 3 in the figure below.**"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x154d1f655f8>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Plot the generated arrays, comparing the parent distribution and a sample\n",
+    "f, ax = plt.subplots(2, 2, figsize=(10, 6))\n",
+    "ax = ax.flatten()\n",
+    "n_bins = 16\n",
+    "\n",
+    "ax[0].set_title(r'Gauss')\n",
+    "ax[0].plot(x_float, g_parent, 'k', label='pdf')\n",
+    "ax[0].hist(g_sample, n_bins, normed=True, rwidth=0.9, label='sample', range=(0, 8))\n",
+    "ax[0].set_xlim(0, 8)\n",
+    "ax[0].set_xlabel(r'$x$')\n",
+    "ax[0].legend()\n",
+    "\n",
+    "ax[1].set_title(r'Lognormal')\n",
+    "ax[1].plot(x_float, logn_parent, 'k', label='pdf')\n",
+    "ax[1].hist(logn_sample, n_bins, normed=True, rwidth=0.9, label='sample', range=(0, 8))\n",
+    "ax[1].legend()\n",
+    "ax[1].set_xlabel(r'$x$')\n",
+    "ax[1].set_xlim(0, 8)\n",
+    "\n",
+    "ax[2].set_title('Binomial')\n",
+    "ax[2].plot(x_int, bin_pdf, 'k+', label='pmf', ms=8)\n",
+    "ax[2].hist(bin_sample, n_bins, normed=True, rwidth=0.9, label='sample', range=(0, 15), align='mid')\n",
+    "ax[2].set_xlim(0, 15)\n",
+    "ax[2].set_xlabel(r'$k$')\n",
+    "ax[2].legend()\n",
+    "\n",
+    "ax[3].set_title('Poisson')\n",
+    "ax[3].plot(x_int, pois_parent, 'k+', label='pmf', ms=8)\n",
+    "ax[3].hist(pois_sample, n_bins, normed=True, rwidth=0.9, label='sample', range=(0, 15), align='mid')\n",
+    "ax[3].set_xlim(0, 15)\n",
+    "ax[3].set_xlabel(r'$k$')\n",
+    "ax[3].legend()\n",
+    "\n",
+    "f.tight_layout()\n",
+    "\n",
+    "# Note: Depending on you matplotlib version, the keyword for normalization is \"density\" or \"normed\"!"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Mean, variance and their estimators\n",
+    "**2a) To familiarize yourself with the properties of the distributions, write a function that calculates the first five moments of a sample as well as the mode and median values. Compare your results with the expected values.**  \n",
+    "Hints: If you like, you can try your own implementations and test them against scipy.stats.  You can find functions in numpy and scipy implementing all tasks. The 0th moment is just the total probability, following the convention in the lecture notes. Sometimes the value 3 is subtracted from kurtosis to shift a normal distribution to zero kurtosis."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def moments(sample):\n",
+    "    \"\"\"Calculate the first 4 moments of a sample\"\"\"\n",
+    "    m0 = scipy.stats.moment(sample, 0)\n",
+    "    m1 = np.mean(sample)\n",
+    "    m2 = scipy.stats.moment(sample, 2)\n",
+    "    m3 = scipy.stats.skew(sample)\n",
+    "    m4 = scipy.stats.kurtosis(sample)\n",
+    "    return np.array([m0, m1, m2, m3, m4])\n",
+    "\n",
+    "def mode(sample):\n",
+    "    return scipy.stats.mode(sample)[0][0]\n",
+    "\n",
+    "def mode_sample(sample):\n",
+    "    h = np.histogram(sample, bins=15)\n",
+    "    return (h[1][np.argmax(h[0])]+h[1][np.argmax(h[0])+1])/2.0 if np.argmax(h[0]) < len(h[0])-1 else h[1][np.argmax(h[0])]\n",
+    "\n",
+    "def median(sample):\n",
+    "    return np.median(sample)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Sample: \t mass, mean, variance, skewness, kurtosis\n",
+      "Gaussian: \t [ 1.    4.03  1.04 -0.1  -0.05]\n",
+      "Lognormal: \t [ 1.    1.68  4.46  4.05 24.39]\n",
+      "Binomial: \t [ 1.    4.    1.89 -0.06 -0.  ]\n",
+      "Poisson: \t [1.   3.98 4.2  0.55 0.42]\n"
+     ]
+    }
+   ],
+   "source": [
+    "print('Sample: \\t mass, mean, variance, skewness, kurtosis')\n",
+    "print('Gaussian: \\t', moments(g_sample).round(2))\n",
+    "print('Lognormal: \\t', moments(logn_sample).round(2))\n",
+    "print('Binomial: \\t', moments(bin_sample).round(2))\n",
+    "print('Poisson: \\t', moments(pois_sample).round(2))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "3.8596744792409305 4.05525317035419\n",
+      "0.7756969873870149 1.0424239898265104\n",
+      "4 4.0\n",
+      "4 4.0\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(mode_sample(g_sample), median(g_sample))\n",
+    "print(mode_sample(logn_sample), median(logn_sample))\n",
+    "print(mode(bin_sample), median(bin_sample))\n",
+    "print(mode(pois_sample), median(pois_sample))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "What did you expect, knowing the parent distributions? Hint: scipy can also help you here, see for example \"scipy.stats.norm.stats\". You can check wikipedia to quickly recap some analytical results if neccessary.  \n",
+    "https://en.wikipedia.org/wiki/Normal_distribution  \n",
+    "https://en.wikipedia.org/wiki/Log-normal_distribution  \n",
+    "https://en.wikipedia.org/wiki/Binomial_distribution  \n",
+    "https://en.wikipedia.org/wiki/Poisson_distribution  \n",
+    "  "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "4.0 1.0 0.0 0.0\n",
+      "1.6487212707001282 4.670774270471604 6.184877138632554 110.9363921763115\n",
+      "4.0 2.0 0.0 -0.25\n",
+      "4.0 4.0 0.5 0.25\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(*scipy.stats.norm.stats(mu, sigma, moments='mvsk'))\n",
+    "print(*scipy.stats.lognorm.stats(loc=0, s=sigma, scale=1, moments='mvsk'))\n",
+    "print(*scipy.stats.binom.stats(n=mu/p, p=p, moments='mvsk'))\n",
+    "print(*scipy.stats.poisson.stats(mu=mu, moments='mvsk'))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Estimation\n",
+    "Obviously, there is some discrepancy between the expected or \"true\" values from the parent distribution and the calculated sample moments. We would like to work on the inverse problem of guessing the first two moments given only a sample and knowing that the sample was drawn from a normal distribution (but not knowing its \"true\" parameters).  \n",
+    "**2b) Remember how to estimate the mean and variance from a sample.**   \n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The estimation of the mean coincides with the sample mean. The estimation for the variance is $n/(n-1)$ the sample variance."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**2c) How to quantify the uncertainty of the estimation of the mean?**    \n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Given $$\\bar{x}=\\frac{1}{N} \\sum_{i=1}^{N}x_i$$ one can recover its uncertainty with the Gaussian error propagation formula: \n",
+    "$$\\sigma_{\\bar{x}} = \\frac{\\sigma}{\\sqrt{N}}$$ with the sample variance estimation $$\\sigma=\\frac{1}{N-1}\\sum (x_i - \\bar{x})^2$$"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "mean estimator: 3.952626606381409\n",
+      "uncertainty estimator: 0.030539967040206582\n"
+     ]
+    }
+   ],
+   "source": [
+    "N = np.size(g_sample)\n",
+    "mean = np.mean(g_sample)\n",
+    "uncertainty_mean = np.sqrt(1/(N-1) * np.sum((g_sample-mean)**2)) * 1/np.sqrt(N)\n",
+    "print('mean estimator:', mean)\n",
+    "print('uncertainty estimator:', uncertainty_mean)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**2d) Given that it can be very cheap to repeatedly sample a distribution with a computer, try to come up with an alternative approach to estimate the uncertainty of the mean. We will come back to this at the end of the course.**"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "4.001435884220608\n",
+      "0.033109197212323395\n"
+     ]
+    }
+   ],
+   "source": [
+    "# We just repeat sampling the distribution and calculate the standard deviations of the averages:\n",
+    "reps = 1000\n",
+    "averages = np.zeros(reps)\n",
+    "for i in range(reps):\n",
+    "    g_sample = gaussian_sample(sample_size, mu, sigma)\n",
+    "    averages[i] = np.mean(g_sample)\n",
+    "    \n",
+    "print(np.mean(averages))\n",
+    "print(np.std(averages, ddof=1))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Multidimensional pdf: covariance and correlation"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Imagine your're an astronomer and are measuring a specific parameter called the \"Clumping factor\". You're interested whether the clumping factor varies with temperature and how. You have 8 measurements with the following values:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "clumping = [0.5, 0.4, 0.3, 0.2, 0.4, 0.3, 0.3, 0.2]\n",
+    "temperature = [2700, 4600, 5120, 5550, 3600, 3990, 4190, 3900] # [K]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**3a) Write a function in python that computes the Covariance and compare the result to a python numpy or scipy function.**  "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "naive implementation: -53.28124999999977\n",
+      "[[ 9.37500000e-03 -5.32812500e+01]\n",
+      " [-5.32812500e+01  6.96598438e+05]]\n",
+      "numpy implementation: -53.28125\n"
+     ]
+    }
+   ],
+   "source": [
+    "def cov(x,y):\n",
+    "    x_mean = np.mean(x)\n",
+    "    y_mean = np.mean(y)\n",
+    "    xy = np.multiply(x,y)\n",
+    "    xy_mean = np.mean(xy)\n",
+    "    return xy_mean - x_mean*y_mean\n",
+    "\n",
+    "print('naive implementation:', cov(clumping, temperature))\n",
+    "# Covariance matrix\n",
+    "print(np.cov(clumping, temperature, bias=True))\n",
+    "# Off-diagonal entry\n",
+    "print('numpy implementation:', np.cov(clumping, temperature, bias=True)[0, 1])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**3b) Calculate the correlation coefficient.**  "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "-0.6593219263134944\n",
+      "[[ 1.         -0.65932193]\n",
+      " [-0.65932193  1.        ]]\n"
+     ]
+    }
+   ],
+   "source": [
+    "def corr(x, y):\n",
+    "    return cov(x, y) / (np.var(x)*np.var(y))**(1/2)\n",
+    "\n",
+    "print(corr(clumping, temperature))\n",
+    "print(np.corrcoef(clumping, temperature))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**3c) Interpret your results of covariance and correlation coefficient.**  "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The covariance tells us that the clumping factor increases with lower temperatures. This picture is confirmed by the correlation coefficient. It is negative, meaning there is an anti-correlation between the clumping factor and the temperature."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**3d) If the two variables are uncorrelated, does this also mean they are independent of each other?**  "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "No, the covariance only tells us about linear correlations. Consider for example $y=x^2$ on $[-1, 1]$."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[[1.0000000e+00 1.8069255e-17]\n",
+      " [1.8069255e-17 1.0000000e+00]]\n"
+     ]
+    }
+   ],
+   "source": [
+    "x = np.linspace(-1, 1, 11)\n",
+    "y = x**2\n",
+    "print(np.corrcoef(x, y))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Bonus\n",
+    "### 3D Plots"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Try playing with three dimensional graphs to visualize properties of pdfs with two variables. For example, try visualizing marginal and conditional distributions as was done in lecture 2.\n",
+    "<img src=\"MultivariateNormal.png\" style=\"height:250px\">"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### nbextensions\n",
+    "There are some useful extensions to jupyter notebooks, check https://github.com/ipython-contrib/jupyter_contrib_nbextensions if you are interested. There are features like a table of contents to navigate around in notebooks, line numbering for all code cells and options to collapse certain cells to to keep a better overview.\n",
+    "\n",
+    "conda install -c conda-forge jupyter_contrib_nbextensions\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "hide_input": false,
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.3"
+  },
+  "toc": {
+   "base_numbering": 1,
+   "nav_menu": {},
+   "number_sections": true,
+   "sideBar": true,
+   "skip_h1_title": false,
+   "title_cell": "Table of Contents",
+   "title_sidebar": "Contents",
+   "toc_cell": false,
+   "toc_position": {
+    "height": "690px",
+    "left": "0px",
+    "right": "1388px",
+    "top": "110px",
+    "width": "212px"
+   },
+   "toc_section_display": "block",
+   "toc_window_display": true
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/exercises/Solution2/MultivariateNormal.png b/exercises/Solution2/MultivariateNormal.png
new file mode 100644
index 0000000000000000000000000000000000000000..e57714acf424b2972992c62616b188e0806359cf
GIT binary patch
literal 162413
zcmZ_0by$?&_XR4_4Fl3Ogru}|Gt?lB(kVzOjg)jNF|;6pAf-rmO9%sq2oj=nhcwc0
z4<FI*@80LRfA}cl`<^-H?0xoLd+l|I(o|Q%$DzWxapMNQiZV?5#tl^XjT<-purR<U
zxI@ABz+WhC+DdXaWj^|S0zY6oD;v1oxIqrN`h#MVH3VL{LFulb@2=zY$lcSz)%u2-
z-9uL=H}DgK#-m4e53K_>?z-H#afwiY$?AIj`1KmQF2?$>D=}9S(yz_0^l40QO=s=2
zKt*?Hn=%2)Z~dGZUpon@n;v>EjZ>RLX9;RW$D!4$nIBnpv3~fg(u2k(tMX9PcWQlV
z%xzZ0ck9q^%0~aNa<pNLazOg>jB@mYLFJ?V^t%rK{bM7GELE6=QM78Y>VF^h8)_O6
zFu}`J6DqPqTr6X1ri=g2J7mKLv<EfBdEc4g%PXi8>=+?!=DZYm7$ML?P4QDbz8unT
zFuO+lhyOmV10S>U<ygUb{yYaBKSO1LD<*+={=6!WL!rf=WA)!CSD$Eqj7%VA`8z-x
z;-+F!G#lmL$HPf^|6haU1`@L{!LP=O)S{90&xn~2YX^hK-T3<*L?Pd+9a5kD9?`3?
z9b`P-8;~xRznU-d-#C1cd8(<smluc9-Y2UCi>YS*q8pc&7pG35?kkA1-(zM=9{dz|
z;duY%)yW{_G{xn2dXP-aq=?*Vz4ZTa$zxCfb1y}>%y)^lU#%tlqdBZ6GT~}LmP8Az
zw30r<-m%(y6lz&gCicUH(9+h61{x9Pk7A#}6E{QuEkge6Y;S<y_u#QX<I`d$ovVdm
z;r;*ZfL3Hj1Yuvw9V4r6`D(HQe2ltVwbTBIz`S+R_~2D%F|c!5VDcF9q;scRt*Hf>
zK5#Mjl^LWquPoctOTLG>vLP6|gG%&i9GPIMxi|mqTG;mnkD{idxfi<=ZUdr2r1xZ!
zZmXp;-!ZC5ij%(h{@=>}@8dFqv1N5;0q6H)X+$Bz1K|`KKWhiDrM3&Mc0fJi-};CU
zM^aCJz4Ug!vH_08-#uas1n#M`^f`@5C;y&HChe8^VdVd2k0zsRGFD=e_n_FY!g;p6
zut7+|nDpKG`Qh{;*vN_qYhDTn<nJy)Z!!qm&UHrZF1>ORqujP_ZwtX(>>!kOfA9N(
z?9Tu9VY$dr8Wv^aR{!%{$H|6WVD6tGV8^hr|82!h8Gp^9xB6VJ6Xj?no_kCE6;?fv
z@p`uc$$KGf2*f`>g5Ic?#Ja1AYqvA^X%q^!pXlNL`M>v1&rc6i?ML4C48PNvUz;MJ
zm*QF<ElzZ5Js(};rNj%b3;VlCNVF#CQ!fNkbT@dr8W<A^T#{Y-d|$_SQowa-vcV$}
zm_noit<Li#u%)a)6YM12KX2m?lJq@50@E(M_MD0mms`MHxPwn4qM7NtDQeK_-+Vew
zD1FA@)ciXonp#->U~|#|wZojZQ{vxV-<FYNwyIhmE9K#Q@Ig7!SMgmOLVS(u%7f4G
zW+Q%7J2bX@FMk+;j|Wz`_iu%iXrV#vAKaF|Bx^{Y>-wK=)P`>0A_u$a1GqrQNdSu%
zzo~GP9X1P=s{g;GG9U-Cc{%Hq6|p3a4F%=)y5i`!bT6^g1i=x~|932q42m&SEQC@=
zZ=xyq+9S33-$afTYBAXj=7be>Umdq{tiR?$$a0yr7*F|bNg7ny#^ZJfc*$VRf%#gu
zhhkSajB&3MfoUiHZ`z^4!=IhS5V#uLsg73zVf-Cv1{Dj!;Gwqjy$WkETRtKq5M>TM
z3TKm+r6~lHFkh8!RWBf53^+F<`v2YlXECwFq_Kg9Ia1SJhAtyrPSR&zdF2_+8vE+h
zf<61jRI_;hv`Mp1wZ!SK%H7SztzP<oGYiyy;O#7bEf7M;;9*jrPV2o<J@$-hpEPY_
zDDQ>flOMV*r<;wT8=Sk<M9zUX^7NFVGD<M;sgSBX%}Lzvu0oV@+uzW6bVCjemz+BZ
zpIRt`>q9yubWs+uEk&H4S{SIT?M~)2INK5N?aTycgxj=9f3(DeGvNGpD!w4tn&f|5
za}|4d|HW5C8N(J9A+jrmy1&xmD!}Rdbvb4(a4cCsRH$=bA62Vys$CQ`lu_e|bYLvP
zqQVPDc3e+NS^v#&_>l}aX&FARp<-pOi|~R>VYj7wS@jlIr^Wtn9>9LVq!cUChS7<>
zZE3YHu&sE0G#^)zshx9E>jl04k*xp4@$$XIS3QZWee#5o%)qrY^eFy|`hQIUb(H1P
zX|J`-=6H;w;<NLKe6fc$%bmk93a6dIsM?;Tv}yPG*}+ezv=c4~zlUAXF8p(3{x<)|
zmQcr_0^1)|c3<SnflDKvVbYPVp;S9fOJzqvAgfP)RK-s<`(oym$}2p+3bbZ_r|$9s
zL`Y@e26+}8;p&szqzwCxKWm<l`H**hZLGQxBv(vSIs)R+69hG0IO4ClB8`4cH5*Bt
ztT0jXS%#kN_VJ8DU2a6KhGLWCKKWU*d%T=w6alvUe=j)91eNiY9HCUq5=~#8Zv}AY
z4gpt``@4RS4Jko3oCrIFLLCz}L4Zj5XI9xj@8YZA+w#|bom@ygSv$ws_AqXGc8E-;
z`KRZ%D!g_q#~VGJ+NE$wG2-_W&$$z-<0we30v0+hS?ImhujahW7}p*f{_<0%q;H0x
zCkRc}7iY)B_2F6mCn}jfYwUItb>&4YNrn0~kJAKgzsfXttlt)Y`YUqf<;qA=>TrpP
z(W8OPCyAZryjB#~@mv1$O~Nl_79FrL*m6%K8sZ(DgjX47)pHu}9LtVy{Gqo>gx<@q
zJ!uT0NDnrCvI190+MI0c1BuV!|1(Z-r@!2EVG&9156M*%)ZfntXhaygV`){t+w2@p
z2Q*5}cz<P$zgcfNT={(AC2;8-l<SiWeIJAp%Pbf`b%i>Jxc?eg3&gXDdbh=m+UXiz
zj;qrVdh73`hi_^Oy^#&cbzSV`cbpJeCz8((;+W!TJ!1sscZ2$Xfo5?3_i!8};Tv$}
z><^~RVjQLM85AXnDah(1%RKrTr)h4UrtM(&U*EL=N_&_H0K(D#0wJ2_)GNM+;m^)?
zy46v5w1QF}4dtp_Wi4@+7q;k`xyzZp>Q@=w-uVhL@%)`SG5V|q>5ETwu?lgYM~jWF
zjzf~q@3s2&%BbfoswqgOIEguXxNLgPt<b5R&n=f1z6D>wdBFSUBrsAlzu(DK4r5;y
z8EB=Ri7b`SCmaIc&;ww_R@1F09AtOnRtqNytGc^sjXuWiQWTA%9WRB%U$<k#ksDnb
zE#};6J~aJ&d+ed^D~mfWgO~wlI}xAL@2Zk<>Cddmfz;6XH#HDxot><gRy-c(KVB`!
zf;5;HLB#?;$^#F0ow^6IN)-Vg1>~>K09z>EoK19U+6l+a112i)h-^cA{QKPi{y>l#
za@#>&)A$(jbM4ph2#X!7fax%Du6*ZZcL-k$7W&~!WOm5Z@;$Rofwnj5r1suBVww0D
z9nyctk$e7oeXK7K1vP`}$Hn>S{N}~ejUOym4p=+o)5qS*!h!gC<*d=P;-lD%ot%Zu
zFx#nxo<ud%3G+y|zovkG$`EzS1%Nwe{ekh)GsL#WT)_FsZ<2=Cp`kq0JTT-?z1wnO
z$=f7@H<5bHJ|5m6e$A&70cZT{o}CP$7l|5D@yC11-HX^~Eq<p~06OiC+hno0?_S2z
ziZ6WAkoM?&IoGy>GfY`<FKuq21G`lKcsTCgTm_;9*p`3LJMCt{niol7g!}%A)>VcC
zhyeZX>Aq(%-xV`d?Vz_h_%;H!Qh)+N$u^rE-JOjRkj|gkM)YlO{CY=`BAV3>(=E*s
zd!Gfk13Ju=tr#}>EZWh$4<|iMrhb4R@j(YKoZ_#E5$p_K%j~}09<Q)|QIwr%`^KNB
zV%hg_(}OM)hE7L%GV?JA72Bxr*4E&1Rsi?mly9lATmM~f%uqcMVE&d*)v2#52Rl^M
zuX2&vnDquA8F;!55_Q2ofl!kR1}jM!0^xzAhW<gR=^ek5{do1)VxpyxV_y&gh5q8t
zTx<!&tK99>a@@7nm3y-tztMe_QSFsb@>a_!x5cT&@$TXf036;2<2JP3_jH}#1S-Wm
z!UQ|`dBEoHmI@Qu&bVjrGOG7Af4spw99vciT!Z=XC5@Onl9(B%1mcrY7zq1@35qWj
zB+<P9bGI(@$a+zSYYcqU1(1U=)}TXa^AcpF`Qz8Oh+iPJGhboWSbmBj6lS}%;deuo
zHX3?mqW~`m|Fw^xEEaKf0#=)9W^UY|u=9+}y9dSTO!vV0p3q2meO+|EUu<|k|6ZCU
zttBr^^zU1EDAmGpJDSJvO*?)H2yN(hmpU2!+k2h3y2NP$X3uuQ&bB)UN3}5I_Xn*1
zuF#wp4R^sIhF;qA3STk7Fy!&BlRIK&rt=iQRF|RA(}Rsl0&JoM`zMnPBO~#Q5`0>1
z&EGI112lEm<IDX{pRRt*4jC@e(@5bmm^(aO5uR%F-g~4M*9+D}_9sGyqhVr<gL9Uv
zl6YJF<^&N1?_&?O6rm?SL_XERKJ_MZE=I9tI?JJpdv4xa;R|{{sb}WL0)Ui@j7L(T
zbeJt1fOKns!iDPb!m<9%Fpv$Ok{@PR^Vkk<O!*Pi!@rQSARU+tj=<0%s~bP6-NOBI
zNb;g@=InP-J1K*>{d%|oWKyo{EyE^|+v0spY=y-<5fSR)$|u67v49Y0&tp&xon~=8
zX9&ezL2VZyzQc<~a1vwLyw8N=4Qx<x9X*(DAby7vv7{b;;nMtl0`@k~V||n#pr3WC
z5NPL&bFlZl`7QALg4>rrr-^8b!86=Y2>@|IDuZA)t;iYxDfs<P-2Zz)=5d%j_HLH{
z<+&#~uh7oxZ|M{j@m9<~YQv;Y8hopSd4~(9Jq&?T&))cSqas#_A|bFFQ#PX6_YmVg
z3SwK|6`R=fs?PMNY9q3|j!$zmM0ud#?^!Qx<=$H!kj}f24@yue<S{jfY)$(^s$X7E
zb2&5YfUgkI;Eo^#Q?B~AM7j94R@*nAFBbb!``W{BU)L$*SPo=L#zvOvmosK7+_^DF
z2daZALB|PwP_>d(4OG*xgg~4AP96noOSYT<bp)?)`(_w_ZX5Mod*-4C#qjU`fkeZl
zW@ZgfH>fl>-gu$m1P&(~0(id&p~U0E`{SPSG=b@y%|5>q-su$FKC|<$lYM#1qtNGY
z^Uid@rC<9&2Y{}Npt^s0?Ch0#1%O7cPk$upGf?+ySKHrToo)?~sYdXlwjcSQtg?E#
zW+E*K_EG83)}}(mFJEj;)jWBH>j_GmX~rLfk)dBP_rt=x6fr*gb>0Wg$*dGhQx38c
z|8?moPjln$hXJY6&+#eFl*-7Ed6joYwI!#H@<&03h|UW!k2R5FPkGCbuLJpNuX-y7
zq}Fl90ABw57ZoCj7_0y`jRzT_=ht{;u{`4q<eCz_AG3zkaqhG8-(#at>{tNskpcq}
zOdx{@^!SHBv99-$f*%dW#1`1obGO$id^?1wwki{4*7<B26n``Ab9l2Ta~y2bv#a>a
z6EoYvME6`fKi~znA-u1h!14w~CxsbBOTIn1$&B~*W7}*mA&Mr7kCc4RJh!GcnsEDH
z38#K~LH?5hpM;@cSkwMzO)+jnOuk2GIq8R-Zy54T*QbsMmBAw5>}Y3qM9(bEhM~92
zbIbG?FdWtZwLp7tfuqGl(@7eFPypdIXs-<Xs070B<f%Ni1Y7hVKu`RSgBb^L76yF}
zez5~a!E+90Fc=_gBq-<>Upck7<bp_;i+}9^c;-`0-r7YE?yQ1D+CEkoB}8Nzw*1Dl
z*=HA+4C8zcN}tSeKLNn+#*AE<z~z~%fK{=WeVd1<T#3#b1}w7BA`p*)J>KHii0d7l
zUpCCsawPX&_uZV;#2w>Nb7lJL_(X9deDiCz{OgKwfCJtxHDE1fv$z)CLRVnD_mTl{
zGM-&7WhlpTCeE7^^|FL%bM@%!l-gdYd}7wUq^ZKzky0PbTBqJIe|gE*vCx5!A^jJM
zTVaI;L6xEfMNDtW|3Z;NMUm$lc-{#zf5*FPyTVYmNfGP8vs9e+_lLJBdCdbQK%m~;
z>8AfF|CvE#{Bk}{+St~(4!1g?a_IeiPVZmk-8meoT}1vfTDkhXblMl7vc6tL;YV;7
znXj0rrkyTozQk!+I*h4Y5KHf;aGTf@@`ZflEO2N56*uEAs6(Vi6Ue|<oJs?qt+Fw1
z7V(SK(c)M4vZN-(I?ai$O`sb%5yYD5)&6=j@dh343>uY&zG7YxKHVFc2cV)YNFdwX
z7S4nY#F05VJ&-_+!=n=;AlfGDy4i%DTclUIxw`=l<{uga$`QzF0cw&GmWC?V4=BMw
z!4wr6l+b8Wtk;shHG5p>@9oL$ouwzp4^!#lg9Y~*5uq?v6XiU>lYHaObpe=yPasw_
zH%-(n|M67Q`0biDhHLm80@Zm_xQ>ixS}d-ea_x26L4%25_03<@4O9j4&jS#jp~v{3
zi7mVRDj&CMfoi!NWC6A7qtSk2?vjsrtk{VB{_a%xXH-{E#E9E{eG~Kngt(H;nA2=2
z-3S8Op?(X-&(TEfUtWu|J_DSAfLA!NZFzeMhWxK<{7HmCWcs@umq!_}Ida-bg4!Rk
zVCUT5fBRWthpK%^zuU+1aciP}uj;bf_I7S8nFG{rf-smi6eC}0n>hTX(3A8;>RQYE
z9ZV-FqnrUx`Suv!Ya0~Ge%GFE@ypXGP_F<OzpjooI#1MXNo1tTtG+<<##Zmj;M=S7
z_n-(#R2j$+-wM;3t#%kQjG%yAbFJaLH?(D^ZmLXC(CpkuI5_>i&YH}rm%^@-|6Tq7
z1xAI{=P+K8hubPhVM!cH+;Z^4VfU*%VNXGpzdfy`GS*+DOL=p_36z5+7jdt7Pcfi&
z^WAZ&Aa-@$+KXl-f+IzEy^nNcUsCzWO>ZB1-N2Lxz3+cC8*y9HLOHko@n2>TF9=n1
zHHw5)to?Bg`sEF11n}1%@$<CePxV1b%!hNmXlF8=)L-gev{1Y7P83trg|Z%bRly40
zhh2EyV9dp2JyVy5Su|WHjDwQ*e%3mrg3?j-s$A_!VBrgpj&^{l61i>PG)ZXoRZwp1
z&`Ph^af7#iijwa~ytzJkXeeyyS`PUSyJPvCrX<!8XgBFlU8}EXwedQahjNqRu7`ja
zN-1qQfv518aZfgSrUs#6<{P=OPoWsl2s>twh_$Vgt?Pr3*91y@e!C$w7G)ZY48&H8
zIMk%^xx_`C^4?NG?W2Aew&3n@jUt65`Sn3%2t4w8{=_x-GkYV{rR>R1p}Q1~@7s>*
zsN$wsCeD4`oEhM8k_RuDq8F?wCHzPTMd1Lz<rxQ@<GT(>dLj$52<}^+MO8Vq-uohb
zS(cGK8d>`E(=A576X%!a58H?;-2k6^MY$sk5W_9jePjkrUgbq;Nmw6m<eCx`EPD+@
zZiN~$#t~@AP;vn<r{hj$L0DSB6u>XVMRT`?&-dUS`~CNY8E)!iEcX=h?xh9v<)um$
zSlOmt++%h!jmPC7Wl?9Wca2&=LS2{A=qhvz5y-s(9mclyZM<^KKNbvu$}>2j7;KZ9
zJ<}rNHnyd;6hWt<(~Q(R<UF2x!850Leuuc+b}-d-y;vdnXklzr*~s_F+r<gD!ycx%
zNIXhLe)B-(t*K0Bak<@#y{rHURdSOBsHzK5n5{DJwMU+zVgK-o#;R_|y5~M~hk-uN
zu-e|mL=Rm~d6Rf50XEQ#jfN$rX<G|e+FgKezGp;PfC3%n*jQYikeOP&@%5?x7t!U&
zgzOGDVzl?yxLNPnF|PU-LKw|{e4mMDl(FaQ?hf}hbVi<5t_rt;-v!X8*O7gj<SLVc
zuM-^%Us%(K!ojm>|Il)_t9n)3&K*c$)2>f8)&gc&2^4Y<N!m1KWEp9aGbAG)F?uI4
zgoRRrx{`P4){WWPzLaxl?bOL@NN(2ah)Cx~s=KhRn4c^I($n`<bXB|;;(ZtQ2D<wI
z`r9|4PnGnI?cH5ea{rQncu)-cBCe5ZE*rl&DJiPDWG>iw^BURu&w;i`DhO45Rd(M#
ze0+2hW&99;%^pxi7Guow12UuGO&KpHC*Z1|elD82W5zvHGC83A=A$l)3NEuzP3yvJ
zgu<hKM(May8s=(yNygu6Bc2H>Ah4wr8`bvqCgWN!ecm|{|CvF)nq~tlY0_p6@Q+V5
zg|G|?spAb2UVA{6OkLnt$VDg7T+$gx&3r?o%OAyW7|e|q+%{|*@ITv88n3cTq^6F-
zX=2?+nNkRlDEUmiG!TwYoiWCpDf7}1#NCB}%Tu$@qgs}QrKTJ4CsLPZrOXba;><Q*
zMzvec9MhyG*NMeS2K-8kvZ^gV;S`*xnA};IJ3a7Ib8Kz?fJgIPNyy3cP9m$**MQ4K
z32j84X|qIi3SPKv_;p&srf{TaUdYWQzYTLec^fCSrXEd5gm6;UM^q}FzX42Wy^lcD
za}X}yYfT#a?61tG?lN`SBN5Ocz-iSb4%?``1SzR+@+a}zAKy<z7R$R~+>Lm$;Tp`o
z2M)RLWThnL^V_F~Sr@0EBT{+AkO6PL6xkA2h;&;(vManGyT9vRFyT4P<a)g5wqF@*
zFP<$;?tNeoPaj~l@ta1u>_V<W?Wlw3&UM^k4ochreJYW>lKq8fzF^+y2txWzJSq9P
zo2AO7rp?CsODDhAL4Ax+?tZZgD3?BUY5s#W;Yf+9$K%>p>>Mh4uZ%72&=lQb!)k(n
zjh*+!ad*;i?#1IB<~i9$016T%;C^{BK{vq$(}L>U<eg^rb7N;&U!?}btz|c!`p&nA
zh-Cl~v#i=fwcwrktW`FnL?-{Hhu;8C=GbHwY}RCuE064$224ZydvBLDRcV(5nm*IJ
zn;}#(6WtfXvAy3==#-l<LXEuFF<BVvJewRyU!4Q~>D#&A68>I<cT&8+a3}_sjYO<P
zhXgFDLAl;7poF;~TH@UOp{P6ClN|SHze7<%=(;V4`X(rr?MdWZFYi>_kEGCurn9U3
zn28)Ru!)*@`U=?@rHtT7xUFWdlFDr|QO8S>gK`~;nP;@x<rm_VdS-h1+fWZXNi^z6
z7WB~-&+A;~jlWaK(UKiJRJQu{bNSs1w4nME`*;j|n3(&KaY<`Sbd~L({?je>EH*y?
z-oI3Ot>&e|!*D$NShJqxpgnH=oOSt5;@~?Pi>i1s8*OTP*ky{I*}0LBlT`NB^wXiY
zR;E7kRezh1pg4d7Zla`bgpn2cK-Q5eVGDnlF*mS;m_?p(KGh6nKXwp0vDnAD`$e45
zZP}mua6(6s>aP7Sfe-vFT5DsaSuN68N!%uepo72`LEzmKKJCw`-FjgL&mmKef^7AE
z&3<S-R?<JZ6JGd-eUxtxWVT4^!y*^RYxo@UmA$<msTQ>;_koB>Zn=&Sh84}Gfcf%m
zDC|U=I{LuD=Jo84hs8@&KS(^*pIh}XYUf4*(9XEG<i~xB!z*&gsWnd{iKuaPys`k~
zso%S6MWqd8qYt|;)YF}YtxrteKk$3-MWXV}gx5m@N(nE-ub;JB3tJR;gMV6-;h`v>
zw9LF_(J2LO5uZL%<7p8ncNsY~FOs4|%gfQ4xKS|)+}H8f)wyIvV<2?#eLRQ5Qa|dn
z7W#OkQ;i(inPR95wzL{Rm-lr<ny(T{Tv=n%_sZorf(}9|Uqs%%s42+ReaD)?(3^6n
z{C5Wa&Yut!9@@6M*p~-biH2k8`J`TcR0;M4ehakt&rXq!4E%xGvw(CugHU0iE$C=+
z+JI`Lq~$?Qu%Q^}A$aU@QRI+C%B`3#%ZZcon9}I@1!3knjFmVxXG>e6b6F?ej!I%@
z(ysY{9Lmeg0(-Ko-QaExTm3|CmIO*21JE9L`^_KJPw``>;@m9oLiBXMh=ja+*GZ2&
z54G37_I@Ok)<<!?d59(bC#*v-D9S0SfXkcY<JW_t7!IQHVvO*VGn6TO1*6~x8`Z(q
zwDby&UttaCY)p-y=A>xmSiUnHq38v5fxD(XCb6TE35W7fCPmS`=M<mc-!Gc*A;JCR
zjvXQ2`sjyX{O+Q{7fHQbONDr~R}z=fZjZX6hCs_g{nUgp%i{;~mD6)lu1MXZhuT#u
zcR@>A>7Bn&Csy{Yt#pS7o$&4czh&D;nO-t^^WBA>2YXByWzMs5<T#ysI5nZd;Y?u>
zX1*JD0hU*7KiIil{T@4uO3d9>&Ue(3IOBIT6^nLuuyQO7qvs?Gza^Tf+`#3)%bofp
zb`r0ZNr3Is?N?rt?=3|^m?hvI$&t4btoyq1PJD@2uqr+K@oV(Hn{Qt?Oyn^>M%~WD
zR&OS`drUvS2<T<q`A2CavdY8D3eqwu*lns-@kE=ir9Qrb#C~Nv$(UeC?@_TK_&JhL
zsMYb--4mqz5Df1oDN~teH=*#Jbv9YYqt@XjnFe>|WDehjrae=HTo_%vMaLtY)wl1|
zn2^K+qVZ$K@<)Ft2qNK+A|1*dffW=7Ur`4>18cL#N$f`ADnv8$=6G!yCrX}n-k55v
zdGfQNriYs85f;`qetutrN2aEDq;}9vMtXm*wJo)*O=&H;-?7hR<QBM_w&mEhazgiN
za?D>s&Q{h-<CKdH*IATeKe_hZ5eJ2jghgP`WH)vJH&*=fyUDWhRRhHTW#MoU6zZd^
z0^xg^h1S+=B&Kr(=-244elLIfIZc2yXDoQ|-VwEhXo^4z#Yf(52U#YK&qN}T=0=r@
zxi%W{2`maFb#l4Zs_{ubD1*F`nmm$D4mLPbja{QAT)Jr;)uvhl0^V<hkV;-QMdZHr
z&R2dV^v*YF=;i_z-z`!#>=!~5!GCNLUe~tN|0)mUJ9QQZru3R{M6ix=G`+H2?p_sd
z0+3M5hN5Yz3Uo?uGcxYVyr{{z)&5!2?sPVSK8GVB-a*#3gZe!dH2cl3yi}jH+mhRX
z5h8if3Alp$v0`wZ=KX}Tb+gN%fdE!Dp395#Zg3Er;Ym+bY92@AnM%S%or&fz{fJez
z0ne$j*qdxk+VcQI9`Ogv5eYK{odLSWt*0aVL67CPWzug6&)=dV1KyS>AT{dt?lVMF
zbXCC!PUojqQI}wX3kc?{x3evDNaZ*>Nn?F;L3ZeuEa_>JZxQsdnvAacKnVZ^oG(<x
z%gufSqEg&x{07Ee|FQ#kVaC#3;)JohKBJR!+Uh$g!RQLDN<e&Yx%I;35KtOo?7R9!
z_12M&*Z7t{5Hu(n6IJozT5pP;2zJsU0>a<O4+leE&=$RU`W2+84B<Atm-p*RF*}a?
zcv|tqy}OE3l+6KdG8PTCZR?C8Sxk2*i3bj-g+cJlY|8IY&wGLR3c%N&KaDd!+?*_E
z<za$h6GyyqZ0L!Jsselt0Z`3gxE_puT8I|7T}LD#VZ7SZZ;plgCkR<$WDw1>w_{<~
zbh^EnN!q_+CZ+sN^wsYLqb{}r)=U{xVUzEk<^-q9f=uix>X3G4h33QhJA=x6iQmII
z7KGi=qhA~CzDXs{eR`p?IbnnJ7`v}MR0VTgnqbv4J${8p{S?N=mtyFzsVE7z?n_Bl
z(JfO$rOVkbwCa0WoRAw8%Vy%8hfHE`YjipwU?dPhmbnsy%Us)1fB_a_WrA0Ji}L|&
z-w<od>#9{zsF$l^#4M{!qNrRb|Eg)4BqsX_!ZW(<3P#O~Xa!~88<L8^8g%wTocZLm
zb=<-~I-1v5oW5+4<<M(U`B597^qg$+P3Hm=yK!Cfvz<>8SzEYCY**5sVRe^m&sE|)
zzf=F@5|dXw1~!w0WBi|p^BhWukAGFpObXA(|4wiJ3PYj5yZA#CKp%aKVwmMpOBZzc
zgoz)IUQfSZcJGx?|Lcqh2!dF76-c_YlPQHL@KzrV<+{L=N>an|#d`x$7(FLkl)0PT
z3njK?ti8{F6NqK$<@&6|$BV&9>A4N73MGyg5;x8J^dfhGgo%SzTqyAcPm5OT!{;sC
z^IATeqH&MCdB0@q3m`b1h7043fNCn>I$Q}bWT0D%N4xFH;l-n83P#68(D|@UhHiYe
z1T1<igRsNsXHtCpa6C{}bR%0Y?(1^5HZ>=)xft&*OLNCbx5(dCr(solCB@eNN}9Vb
zmB$mF($gHg@EYAv>0Q9(MS{4eBfEat8<LutH=K7tpT2arlcaxP)W|96aO%}uZu{>i
zt|RvuO&Xk)6OK=pG$T4_u#5`B{PA}F_P?R>GFo`VsJm5w$f6qBV$8))vtSM+G2m@8
zN8+_y_#{CxZ@P9-##C>lJ|2f<`cvgKHUk-yJYc?WYnPfz>RDQV<`b(tS?kFfC%Z{Q
z)if+>x&ZkYuo-z*V3pyMp5xb8+SUeCgzr|f7TKM2Mkd9`MsB%n`(Z2spt$1y!%KwQ
z8X~T=StYj#p5Gs2;Crlm>J6BS&&2r;gC0bF{^+TYxL*Z52%152z^Cgq?>o~O!`%7=
zm~%fLZN{L|mh^X_1tkfH7P#U?615`{rNnog%9W#?x1bFm=sgpJxxXwdVnG1EGt;aj
zN`&4RT{sm(DR8^rLC$u5r~6e8t>*-rz@vMEOIkA>MefV58FdOEnKttyM3jF51`MC~
z0g*$YX>-FB1FmI~h~X+@i-QqQxtWGJK!t5{A~<TVBMTv<5t&h+CaEtWJN&XYFWMz1
z$&QAp?|?;bO@~QBv+uq+VL&DHm<=$uFVcN4)`}$I!4KH9PGoV&ByKyks^M1zByD>6
zSK16bMT-h9A9;V@jge)41Wb;&Y;E#R?F3^YtA_esdJ_!0XE#ndlToDH{W$dsT!?Ed
zY&Lp7$TkajOhB#uhZvL}4(!JX%eBe!8)YX^s4NfLkw=b$!a2mziiJ_cgI?4%SHQ3p
z0K1nB2PTbAd$wmgObQ{|vr#y^nBM*p-L|_qN9{QZFD48H{LhZwaO&~j4hQT3;F>EQ
ze=mdDZypzwc6!HSIW%7Q#8)vsdWZX<$;)L-39Cl8P*ax9_g8tKBz(96j!VQMx7C|;
zoLX{8r>#(DGV(riJ-3Tw)+-y$RA7l0(<7TKpRbebPZZ3-K@gBW&uNb2q$_>jka)O0
zHjo<;a-;oCp{EXL%@m3S9by{QIwk|EUj;DvOx#BFDu{xvy^cf(zDLp!s;R{&m8(tg
z#;pS%ww!+X2Ip*WtQMgR3eO`yXKtK&vKOkELNfEBiC9XN?BHVP@nA2t?|Sc&Zq%Mv
zxGoBLpY3#ja*;9Bn9&FSjBbES@LX<+X=rwE2}KY_cU{`x1?ng@sZew&$yNJy->CD%
z>;Fs@66!Fk(D`xKI#Pj?q4x^LCLp~akXvkdJ7-?8JDS6Z86uk@((@>xWBspvjeC<s
z-@{g&*zdzZLwY!Xd=7AL^u4W@=T3SFD1#}Dn+=@iaYOyW(_DZ?^YB}F5$z+lMF(u%
z*NA8ZdY)bqjVeSCYC(_N!1*0?+~-ML>_gQ>toL|HXGDFMoj(*yhkyOszDE-A`WLF~
z0u@gmK&Bt$@mx?NrRDIVH@phR6|J&&Z`cja`PMI`W4mrt<K`L(_uk39I8B4X3#XtL
zfoqfZ^-Nem2hQX2;8xBr7Wn01z~!=p-W3^c?B`+n&g>G!ZtBdTCv{*RMgDtR4rrPD
zVF2XyWeahqDC`Y7<=Si8gpunE`#{OmyL$Wa-Uq%i<ZQX7f_zoT*J+?InP&eO-w0V}
zu>K&Ad6()-8Y8)>zIvvA!wX1K$Xp+BhC$9k@moL_k+ZGV|L{(Iwph%<3XfF8qufm~
zeriC+t~ta1q(7PY&QOhBZ}OhkM*=#$fXi9YY0!ltSyf)Z3Wi-*X8N*qRAKT;adfo%
zX!i16vyvXm1|?>#QvK5`Rbk(}U5OtRBvV-M!ca8%uT)Pand*v7c|`b2N>#}5#OZH^
z#F=UF3F3#zL$yv1&sQ%5OQkF6@_@ieHCrw$*X~<B_lN#5pR5eFa%ns|31xU{OPinG
zTSeM^AL=y^4TLJUDEG+w^E==2Un877S3zSh7f{dt=|(H8lxhBin$TO33|>(Q?>?~*
zFu)4~Ed+rswNrdMU5<>1KQgxrJ@jS*jGakt?2E|53`m?dQ29`cx-Q62ZQ-iNYJ&pc
z9OAYOVR22;BxycB*yVSb6ESGLpthT8sy24{q~CmSCxLSe<~<)XVzH&fqqR2G+{o$w
z<bPitIsr{po%7_1n6fx}n*?La4kOAZRA1qvVBb{9`b4r##W45rW$e@HF3n)y?v=@A
zU(boL9E&eZ?EO@TbZ2x6!@|P&ERr4?)|C7>X|no7viJ;9D11Jd&7=VKu2`ycn<x)(
z)cM{q?}%AoS7OC?m%h3Wicd`6-#@)y>h~U!*E^KgSLuWQi@v*kG|sDo%}D0E7vsRr
zUlX#CrPT4Xi`jgkVZ~PKe;E>wl`agza|+*PruR}>;rlcaN08_hW4?293$sj(Tq4dd
zW<m&QC=JWZ+w3k0qk<W>#gW}gI7BSkAGjNkOp!#6IcT$#!j8u2V=oifpiA4O0q3d!
z(3u=w4(r+vg*uqA+Pwb#+}P9PE+BMD;EKYEBQKRh{dkD4le{ibYfR@FIGXb(3o*I#
zpp(!8aG~8C8a6=_CL$Mr^S-r_B0wA=SD_ys9J1P+bqO%Yk&fj(9UU_ZpdA?#^LQ^3
zwXpzuXEX4Xgk_EGd|CR^Wr6k15hEO^fHF@57|9(*c8vpS9=CF8t=)-}GE`8E@sSsL
z{GCfF7G~P=+LvKC!O-&d0g>4st_X{x1*GFEWXP$l{L-W;n3ofW1oE7C%R;dwkWWEN
zZTJJ08gx0@hK}&A`%RR?=BFEmdOElo@;Si>IC2Qo=5wZn=yuO`ALygzjQEDTq1qf4
zYB7f4zR2+9e$uqi@B6yx$kH}y2yoD|2Z7X1O<Lguv}>c-X3*YMViu8F|6;3wSesv=
z?yD7QQ_tKYb&DRzHh2b%#*ZpL1jYU?kWq=0N5D)`=ZOErR9SQ!t>FO*Y5%65H26EX
z>gud&=?i%8fZ|HTx(LW&#Nrr5RX{7+^`og?3De;e=M}CQDS9znB5C^kR-<~4q7hK6
ze<fnk-4fvq**5k+UOYKJY{;o>ISAR0ge-F}7>s;ag+4nwUU@scVnaSA?a;H;s?;LG
z^*+m3_%O{DQ(*^)`GML#{LRfRoPr*TL9$X>3FqKuAiGJ`q`8w#E3pi^6mTG`n#AVM
zjdF(Jag~gjE9>$qr4><pjmPPqEJw9mz&2?7#jjB^RFD-5f^}&ks6_L<5w|{w!M%4E
z;z2fr119mAkN*%8{6;~uX>bw;OTt~wC*cc7Ekm)V&vZ|Pcj+XXtV7d(wvY#08xzY5
z=es#EFK=}%g6C^VDYl)QM2^;5Mefb4vQ}G4+)#sA6-MEhT#u4O1hW7cUKHMv{PLIA
zlhB*qoY{u-O<l*UZX`Sr%8R{pIl3QSB6C$pAJg)9PI<qsa-4jCt+)~iF||f&ML}2=
z0LA`Pj=qJ!>kJAvALSYx*jqAGOU)3yWHD(F9KGx6IUI|6pnL#tPc-OO<KsEFE)9ry
z!{0^7SD)BM;>X!A6!gF2!WaWGa;|#UMc44iTM~j4In;&pQ$XsRARXYJCiKKmxh2Gy
zVr?j!BZ9e+Ji{!TeRu8!Ba7;DW0x?bjz-k8Lt5zL`NjNKa_d^~5+Bd?;@U@spWARF
zr;_zD;YnhijvqpKe4-#zFnf%g*hr*`Jx)~k%5~b2VX%0uGjt_0sA`L~MZrGXmwj(Z
zz+B0M+vM_=W~xf1#^LANjXEKf@qJILpF$eXR3J|;4qoA%&Y;$-#zX*B(Ss7*5KhoY
z9|HD@1YL!$2?2C+lKUSS?|M0WFsEU4HKk)+-bcB~8dY+JZs#=@#hMEmAy;~Ppnu&f
z16Lc=d4oEN0JXg)GQkjV?lHW0TNZ((Q>2YPx8yyL#6G`xvfiPTRQOiVqCHe_tHUfu
z&?Xisc^H}DY5I$Z<>a|sbr{YqSDe7Nnxnj(q<^5%&6~K=uNe4NqKu^@rEEbTwdwa4
z@rr2&XCg<8AQ2zki;Fc*QQg)SnwCS;w2P0ZL&iTNeq0RSW`9LR#yUK6>@dzR`eEzR
z`p|Lpc&$h~PZ57LFCdmuKv7KccM>VLc4rUIyxns&gzQpgk1D@)K|r8{SFPhDO_Z9I
zw=;njxC^s{w_uA+7(=Ql2SafN2*1F~HyC>q#r5WZ$UGjqX;Ym*r%}yg25MpMWQ&fA
z2XZd$-e&HU3DW_GO_({l?{$wNwI8jq<uSwEnY5P)rB<MDQP(roOdpPhD9C)^*M%!2
zP&~izfzt9?02+>GKKg5X=%9zyx-%5pHG$i4>et|$-|3U%lBZ87pB;?vrW<l3SD6%U
zh(5uTaA7K_mUdlAih`$@e#5W+8Hl2-ho4sBciPLN5;fzN!kui?mzwHhA9+TaeKSRQ
zW=<tHGJhoY1e4-JDMvO7Ia56D7deQi<kncJ*#vn;42BoBA_!J_#`Uh+%|7@n>(y14
z;_&&dZ!DghsE*!R+$>XUt38R`VKHp+8Y(Navq2XVN<J`b2DY(!H(*BjUw;)<D8HoL
zm1|ZpT5Y-sMAfqi??M*xQYB8R$xYcvl9`kBIP;Ll%c@fn-psWZrz6Jo3j?a$#Pw&c
zai*XxoH=*<#8=wRcP_&%H?rmM(Rpi|y}OfHxm@hL<j~fU>x4-CVi$Fu|F`^IozUmr
zbF!TjjK2`f)>Hw@Hc|I7G><yIGvNuuCst$2GSlEWC4@c7$Co3a<U#z34rk}zV)Ig6
zp@o{Xp6wQW&OEONW>RonTw^n+ILlS-D|KCdO>K2?RxG9-#}<^vVcnbLf=QRn44)E%
z3r{$Q;8E~?B(^bQK?P)<NQJn&NHW{TPa9Ydk=P$FlWR;m5w57JR?l^O$tr<NU#d#7
zj~sa)MS90)NWMz(r|<Eu%H?QrUJ4Zpda_KjcJ+IjXNM;7K(o@>P2V>SOg9L^5(OR0
zlPvr7zSF8F_a7>z3+{CG7w+sFzxM1^BRAv1|52Uz1j?~My;2!Q%CHs`FMr<$Xo%^N
z^K!XiXqUy5`$U&O7OxFt74-lSad#3vo~YA{8niMAeq{SI$7<DDbU9=9Dkb#*dF3LI
zb0rx$oyS#;daxIF#C(1~MV$XrZ>;tOcZ0HH!|HACy?$Z8@O2EiBrNE#s<cWHQcKK>
z_aF)qq26*}EN}G(6gcC2mtZFo@mL$y?XdaId@PMOAF5_-);KDUK)wX3;cxP*j&*H`
zzWWP_jO(L4WkA24ONBe>dys^x!ff^SU_Qd{1c^}PN%jWGa<*~nz{*5=AE6wE1G>Yn
zchj;Q8rfCJ?A~NG;@@KLWDEvE+NUD&)<6dBFKHABDL<sp$Y7wx!?+9#^}ph*9H~QD
znF7sa@AalM-yfSVoftS`TV!_}7df1mURZjSl?YGnRVg;Sn;Tam9I80w-0^#;{9(5s
z?puryHsatk9y6({+J27%8uywCn0kR71OpbhaIf3I-K7FPNwRHa9<!E-k?cyE{FOh|
zX9v4^<$P5<K2LS0J0wYE#cAYyGH#^`+l=hVh3`Q8qn$ZMJ+tD<ZJN0I!K6A<O^m`*
zZ2YddN<G}~0>Z%+7M9Xh)6A_ARsZQ)jRX62Y4^ej_w`~|watlL<F|?xHanIBHi^qs
zR#64oxm1)n%<wN_eey|FcfW3pKV5!JmqTV%QKes+UE|M#ks&>hqNCX!q1pbEgpnt;
zjpzl#K0;KM!qR^BCG)Y<@LkKwFNYnJ8QrEi?tOP^>q0nlXbr^l=_C{$#j(jAo2Di&
z@_TO500<q{XVU;~dc9`lEan~j3}GQq_W^RNF%!?%O+ebg-T75d<Pifb%9u0v%Ybx1
zdQHMm%@aSrOYW%AlhFwQ%veD)g*xbmEAa#1ft0&qX&pc%_a})u<FG|Xh!MWhwcnk7
z94<eCED(aVk7yZID!SW{K?m6CF;|CFC8yrXhQPYwE|n}fu(z7_VvS2NI*OWo8x!T#
z4YO<q<r*aRqTRQC>PZ<?DHG)5)ql<EK|mSdB?~n_7xuUeBw#8(YaWkFj1s+iPi5S8
zb5_Q9-K!b1DBhaaiUtuSJmn?d;JLL)0Fgt91R?J-5#047OB3BwRZ2(~`kgCN!KLM9
zI|m!sswk0n)kKP4p6!`jXd2dQ4RS1fWEptHI+Wkfs-3G0WjCm(ffT%k<Zyox+#dQC
zfKVJA8~*6_@dcrQVY|E_?AD)@rXC_}Dpq`1z(V?cA=BS*AsE-Hn2MU7b&>Q*?<>97
z)!SPVvQpb$8n+rk9q<Z3*~__Aua++C#NE1bdyQ<aswMT?l10bDp6lX5zT3cWA{HLm
z)2#r>%)A?DXaE2#tQF^y8QL7%P@*!Dd(9(IE+o-a)R`GVBti9H04`eWy=3O!esd+l
zh8w3hbi~U9vEs~A_IQ|O(no5apLjVt4o;tIpB!GYT9Pa6cM!gaRT<clb8LdKdu~ps
zH;C)f<t^;aEU==h(n*K{294|TSc9ZFYYH^{O;sXZDB$h)0QLE5j^L1X=l<rql((dQ
z|6w|WSK<_94~2V_Dd`Z83>G`i&-ckR&1bI1qN-%8K3_z&Pnz~}e}$3veyw@3NYtHX
zN2JswNR)SB-*YcRWN?HmdntDM_x+0_c`15HL&t^-A3MaISC9MR<zb~A>`Tc!JaS(k
zJC}$o-I9=HwG~W@L<;6%OSl!~x;bh{HMow+&3p@zUZ~&T{*Huv1>BhuSJAMNA#FX~
z6dz-ygbpb0jJ?0#7lu5>aro_%MCUa_W>7Yx>OGII5oAF1gCD`{WJuKcVy?<GPP;A=
z;$TvS^W}E8=k!M6Pe-GI!^-<nL(?TK+EuZrNNxuw#bjBiW{r&!!GAy>g4fwWg4w^+
zt>~zX8TvC@=@m~Fmlo4JNV2)(A%wm2CXMh2J+oxWq6aEIvpYmALjDqPAXqLJz{>QR
z!?s~KdNGj775{3pNrx)w4(Cs*yAXC=q?`M*rL;`f-#(H#V@y7hJ#9;8;Ko=(Mg{2O
zBE8|;Z1>WTVOk;hcuFS}s9V0fpCP1PobOl_UKhg-KA@w5%Q|ijf)&#(c)#ca_KEU(
zlby64o~hCde0l3WY7m<LnktzZJ7)pEIpi!i3{Xd~i~YBg5U>8bjFmRsTl#^_#A{PU
zdORjmdgBXN0{^)-GX)k2qiOoL-tnELMk{}h{YXytUn^^j<xGd)9?b3b<&B+g^{G!b
z5zt9;1RN7{#3!(wAGTiNXR!6ZgbF8fvf>Zl{83{Wh6MahB4?QA>aR-4Il=`vOBl*U
zi)SgW@0E02CzAtoi>>-n`dN$;l`{>9aw06rvmdl77S`wp29lUTD$<OiXljr^RGyK;
zYHaI>cH+AHg%-|BrCRS=i#**LaE@;quk=j`1-<u#7b)SduF79<sj9Df%yhlw#U*a|
z%Mfy0G8)~S{kKyC1oitvt=8M-n_A3teV#5WH&$V`Hb8|VB-;K*S&WU5<cWnUb5nw8
zmTwB5rh~U625M#aN?1v(_n~)&M@m!9oq(X5LmE9w44!nPIl1AzYJlLvH>^&068cLk
zl8k5Q6L}T~m&>5SuIKWX3XPtjnevBumN8V^F221<?3<_TLf!+Che#?3>ol|}6$pYJ
z^jugeop>qQQAcNsjf7Xlv^92@nib%w7uwHBcQVo#I^Sg|C-CshGQ(*)&A(P&T{2O6
zPR_F_5z~giJhW^JR)8lV>(^LpvYPeS#K#(Il_j1#?Peg|9uC|->?bn)17komk+)y&
ztX`vK2Dg{c$-QtB>gJfw-I;i_KD=x?SbF6u5~#oPhA3}9+B0epe{C%FiS+rxG1Llx
z&u_X%Lbtfs&JAl*(g!42i%tCvgjm8tvKw^s;J0iLM8a7HWkCV^gGwbrea23sbwEQd
zD&#nki?)Q$!vD#=H+AG7=G8@SP_80=+z%7i<`m~Eia^$jyLn%`e(#I8iG{rxG?bDF
zp2eock$booSu;J+JocU?;kjl}8-o461w_i1#S<GUK%C3&yU#VmLg*^)7)d~8RA!$h
z2oxxfyyq5Zn4W3VZn{UQN50^E1MUU|<!Rp=U(e%88I)rwRkHEYu_n!*O!clRO6QaK
zswo+|BSy7Pi*-wM*==D_<yZIpY(n$yHw_U1@liLyc=^K@G*XWTSRf3mbaOHm1`^wC
zU7yn`7eJR#d&n)ETW8Gf)mBOoXw%(RgXrn89S_k*(d5xd`iKxy?;ft8nh|4x6@5!$
zOCCD4d-Ei<ukXFyz`SkT?XTUTtZvG;I9QassM_fklx{ctuwwf!gF*?~Fb-GV#x<xd
z+afr3%4Z>u?gA;9E=&GHDz5~r+C3lJ3Y%1tWX`@FgG!q572#)fq^%b|-Wwx&lU*9q
z5YH?+N_-A2?Suv(8{E3dtu%Zj0>I&m<*d{$pGo>P52ryPlb^ME#?Wr!9OYhv6ZNgq
zIaETVi9Le3Ld>YF&CQ(4ybQ)g^ZPrWFxjP)%RT?`a<!9vZ><J*JErxq2#u-`-vYvw
z7qC>(+aJ=JAOuNrd}Dwg;FgbQ$y>-tvg&bd=GGIKOt@cRwbqU?dmH|(K*RI7U!-W+
zQeIdzao+05_~p>)Z06HO(*E0OBFxbxE*}ji>)mYhI>jlxvs+FG*-rp3zWU58>+2r(
zpX>ib5i%nL$96)st@C`1cS+4dR)<xXJsk;Edb6fKxjy#VxT}Oyd$ALNM<bzsaX&vg
z@%VN(=J-!=z2~-C2r614+ekNH0h5m0UQ!A8P5Y>3QVA-*U%iyfx3Ytk7*~`Ui^kEA
z^6TF*viyYf{J}9=vVoVGEW6D{DZzl|0j`CXXga~H%7KjZtr(dN9`)@lf5CgEYrhjn
zIUgwXCi}vH42-_3@B8K`U6;LL9%3H-^Ap`(v8TT%_&o;CPYK_HXveKLpgQ`YD9=_)
z>!E@6ofax~Z|5luFGnB0Wa6Cttjja`Bm~SR?S)VTj>69@pfXjm5-QMg;1QS^9~!&T
zXCRUKjQ>?)@-)FG5_PkDGamv?#NNE&e5G3SK%HW?jtb&ib?!VLQ?avAyZFv=a^WKV
z?xN0JQU-}dr$)6YZOdT2m~=jHCCX*i#ty0vi%Mtq!oi;3hIgg3&J)z<n9JJj4D0NM
zCDd?ViDm6atd5h|8P89mNZDrM+qEG_uOThZM~eDP4ksSc<uL?*O$TO&gLZBRWN3w)
z*ERlFTmBa!;TYYXMf&~x;o2E_->1}x4_)&{+BLr(q)DEZmazxj0lzoFe$5zab7wH|
z%g%cXW&t6&TPZl?8{c;VaV7iG_IQ*MY8+We*Y~Gmk|ewU6KoAB542-=xI04^ryWZZ
z!{RhG$t|p!`p}^EOJ%MS?sl2$8--~ag26Dp(IbNPEdr%){oUAqfEk3kFC|LQ;i<Od
zW|>g?7#(2uSy;?Ke?U9eN+nIuBABF{O|K+lkIe=Cc~um$zWPMHjS(JNt~vW!q8!`-
zzq(hH2kw+@E}Rtcv^c-o9lGma^?9Ox7qyELBZf+JbBeq1-h)ZM+KGbPwN`K@pEHEn
znrpK7D0mYvYu*a2yP%1E?8=NR&`2c_bBvSPjeMqEh!7m*`)O)aEP$iDDPTIB-889y
z;P7<I)}Qr~pm3lV^BNlxm*7QvIN|*sW#&e=%S2_tmNzB)f1o}zlnva<j9{A#2za<U
z`lNP7rge=&<7hHRK}ZO%?Yd!R8KMUC#u^-5=Xo2=oTW+6Z*d?4HtUtRzg>3de>@6d
z+wshGL=~;a#n5Wk%+}=Uv<3Gk08aL}>M&XXX3(xRUbD<5>sI|7&hdk-4WTEOC0g<+
z=(C+!W$Mb*hp?drC?3P|b{~)R9#DZRop4PLj@~O?=-#X|A(M2@y2tUus6=f3Fp)8m
zx&Cdikd6AY(U&T^0Iyp2TCm_0?ThjZEjM*zo45PL(o1tp`_;mi7g<^5%iGnGT88<0
zQPjaTfXP{c>d|L@W{QzRRd#ea!^K7q4Vrywi>N<&6(mh;&k9LhuQ!|mEt}@?vy30%
zr_A)FpMZuxBgga}`Pjk_VymzHUdz_0JwlV#o;E#<O5AlaHJ)qCeJNb=PANR8iuM!}
zKz`MyWI2CZ11pC#vO$wK5`s<xg;UUi4rL0s-)h96SMtm3o|x5Y7`at3<YT%|93guf
zj^Y%WRlM(<*mfa>9G<mU_oIQ7ja!_(oa2?Mb~p^2Dn2=mCI(H;Ei^xyWTmL9bR5~Z
z6IkPba*6e>O0G5wp+P&YEHY&zB<9IB*H**2P0#Db*BRcA?gC`uy?eGtN9T96ElbI7
z75OTaGy@~P@NMX_B!KGLW#+O4fy+f1ut$+Yl><U4)efGj9pRbfv3t+H^HS6I5DWW0
z4gHfwF!+=_RTAX+raJCmHURQh&xID%TW@!0s~qEbO)L$2QIeqh=QJJr74Dy>&p8)>
z7+4{}+s^a>Q_yxi-@`7EG^5>6M%ko79oP!(Cu(*Iqc<`r@9r0+^eOD&0*NnuvrkWS
zd)T6z-xeKzmR@nME;)o+BT^0ALoktr$z!2Z4z`#cew80m=yvd<Eq#(aEd8yi{@L+H
zEo;D&jsdYLDWcWW29f#r34A<d-8T0^)mK6}^Qlc7>qGioRd$R(=n*3IGh*R6=W~?;
z^>0=?mRn6{eItC9rrhc$7VsQ27tsX8J9VIVef~C~)f?#j;XqAtbq)4+TG3TN9k&6B
zkYSye-*ldhXUtuqzuT1JYIfpjei4(>(u+91=`#~T`*XjR4@)TV>X%&TbZ8E*DB~6l
z*FDma?`KaTsfpYA4qdk&P0DO&ywLmU0Z5*{(c(qlitNPgT1b#1Tp7x?z$Ba0M3>jv
zZma|fYwu05-KBm`pkeZ0t&R?&M=kRrd<U&G?HTcDo|Yf4@V<E>uxc<D3TbzrFec3o
zitYA$LDnOzlaCxe-AYZ}AHP2|Y8EpoX`e**Z8^1Rd56dMGA8CjYfe{*h(Z;Y!3DO4
z&#|=zHzp0|l<vh_6q;OTFW!KQXPyc4Ttre)BnIBGpk)wuBH_>%aN(B7WhBHj;=THt
zfteLf&ej&*nHy&QZpx*s^G6yPzkCu%=v=tun-BPxD1Yi1&qn3Pl9frzKOkSVdZ9Z-
zhf@r$!;ZA9K`8Hkq4@9jxqy>~Jx}ok853@F;kz}}tUmdCB1vEOyXYG<n_9#0Y4BSm
z?j)7Xs8GeT#yta>q{6)eL-ghUL(_T3Q~m$%zo_hW>^+ZjkXiQTSchzpO-hIm*_6HK
zG0G?h*@VoD>=}`fy?6HBzo+--`}^PR#&OPhJs;2Oab5STZ|+8Qhp1am0;xRZDTyRY
z%VGR`Dgq@?qK8^ugY9g%h4QUaY&%<sSoH6XYr;R{(v0u=@!#)LVs1b1zsP5N(Z$E7
z7}xqz?cNJ1xfYJ!XICfAHE*rYke$ZM@z7-&A5$WbGiY_KzsBfRsO2Lly`!#?jdnV2
zxM}P<8uMz|!*2hvkJK94!Rfv@{PS44<BfUBA1t^4yO@(A=M{&E4)8LpIpYT04<vL|
ziI)p<k2XrAyVTgOKcINlaNg@ObCSv`Y8nF(jJ^$Des(?f=U9m~HE?(ts=eAD<tjbF
zeYEMdzo#rb(yQJx5`HQ2MFV6`*SC4K1*20EVg*s^Psn*4|2KiN;UDMUJ*k3Z2uXS>
zCjiB85SrXInQY2=G4chND%CH1>J>S{AO&uLd@>YR_*8R3$A>Mu{@g2fRN9$5)-pqe
ziA^_8ti20qU28e@mO8X^tf)(ZTY@B$-v0Uek+4<OirD`gSgvYTBV4?`z7I*Md&Jua
z{{iq7F(r+LVtG!Jh{>#;*XQ6V*u?r~C8}bd)Rt7dJTQ(xxN*+kuZ>~5fzskmjMGPI
zN5_CTamzMr>p<9fiuuf<58+FDTDmSEcaLQ35$8<4X;K)=It<e$g^m&DydE#F#bm^e
z<t0fn_dxCb4C%>oIrw!GUu93bnKsGo9n?6z4A(2=5pY6V_bJgFieKEf!@PPPD*3;$
zUe4Sq_Vob!%4XZZoOJZ&*QSdY>7_p^3E3OP)kuCQ*2!`ohfX5=YP_mA_f(lvSp{WG
zpR>PgLJ!5q3U4~z9OIo{_|(jV)U6AFYb~d<R0AmkiF&%8Qg`tIE883FG%T;Z2kDIw
z1WtRw^V|k=%#rH`RDlqFAtHmmB+LhzAi|vZ{&AOq4`)jjRRGm+0xSZ!!c{pZ9Q#F`
zKhsn#m%mJWmZRExSJ3!oxa^(3&OAQb3;DoZI)1D81SpN-SHd#2I4B`e9=Bqs26{}Z
zB}?%yxt%;;!}UXI<;Gk#q-ye44HR~hKOE$b6mHF$`5(@q%Ou)&5jZWh#SG9<dEZ={
zKOGaDKhjSQx2hGuMmHfwKL=T0ZAL<kRh01k6}h&#r1dmQa(5epcKZMJ3TT41N@3gK
zE~xo~M~#wNPRiXYU*cGEKE<a|qR1k(pU)p0^e6bxi-!{v&a*1A3dTU@-^8-252&QP
z3{Z*b1Jdv5!<IAU@9GRgA@g^&ScLHvI3m+R7<~;!wJZiTysK>!b#+-+N`QkeQ8!E}
zs6e0B>AsuuDusUJy_1$NvKcq35B4khE|njwT#Kf&r(v?jZeEFhsLbW}#}0yikd!}2
z{H@d`XwC&RQ8e3iO7x(@go9Sr@hF(-MpF3Oe&1X6EDL&CN--ZPv-QH-<+E4Gtum~W
z>nV54r27Ha)>395g<l?ogQ&_`3d8U-u)OO`8zM=7=Vjg4=WpPy_WsXgCL%i`458*O
zt%bJ+W8Oh<>tMK`&5*(BXQeN9NU%cVlTVf4L);x5!#MhQvGUUUqb=No<g7k`PZk=_
zJR$Q+4`8O!S1sSfMrI*88e2n_`JobHX>X&|4j3}cJoDjpDhEDb$1kS>g2nU#{8;MC
zSVRo}bhqP4zk!SLPGdqiIQUI$6^e|@S`OrKvm19?I4ZNVU2F#SUJH~z)=~M%#oP2x
z0yeBzyIr?AB^c)?+ho<1U`afGrgaDQW+vh4#tovfQBa&Ib#NEZ#Riy(?VBs^NvjrA
zKD5r@uw4p2+~V?&!u=?Kz!K@5e=VO(%%QWQi*}i`ukI>;u}+$^WJwqscX-F<!`c^T
zn!F8q(ZnTZQ`Zqab(i4EmSmEQXE6{2&VFg{ziyku-|~a=TH)_xP=DWc<+6I8miBeb
zc33k4ZwA^bROnmgHxI<~Y0MIPASIOp5KRgWULai1@Aw&q?~I2)kjZ0f^_UYx)Xn#~
z2zZb-gZFTrmUg~2H1}1?rMR3T>P|3`fZ=`IOZg;ZqH`jxR*zB9ju+ZUxVGf=Go|?c
zo=<vF6_ZmcOO99c;j`-D?D_e5jIkHrI3+Fd`N+c=JT6QoNh*(LMy;K0imQ9Po_BOf
z(PL%ys4C$!Dd6!~l=>b~bboTqP4l~U%4K-q7ofJTo9u#CC9t>yjSD9~SV~;(31W3G
zyk6gfbDRDH=4gIW-Z<W70uWBtl8Hqo^K6ZZhpH$7w|3Gl&smSr?Esu9>Aovn+1jNu
z;V>F!X8u{0AVy4ozND85?>~u?_+!|r5UXJkx0-TPmED+nhTZ~+a`Y3nGBX?#BPhn$
z`d7&hCc0taz1PqfmZ)ouD0mPMCa!wj=JnJV`YCkL*$)P67?>nfug*Ol@^594J}d9y
zZ&+C$DNq3yr<@>~C-REXC|}G6%A!z}KqiQ7mFJPIQoImbq2vo?*C8(&Q?Z9)hmlUR
zbK_QOo|QimGwF1+>%12bANigKzb0^6<4#5_nRP~g?ro<O9#VBTo3wD_I6H`lri}X}
zC@Dv5QGy^|jG3(H$R&5Cz!u>R5KeQ;uuehoY7GB->LbMvGxTr#nl$GSw_olRlQuD3
zczfg7N@~fR>lX&7#Ln&ZmM!$Ru35MpxECStVK5}|<tu8evYN4*N5|@kpEQ2{FSyA(
z#l%Aw@R7wkuAn$pi;3ZPz78xokM<El#fWy=yHp>unIuPv@rY}dwfOfB*1KyQSn_?$
zO9&iu6c<T6twXHjgoaL~;Gd_gUhh86<i)*}HaD!)iZ`xPqyDHU7s2df-p*~jrd7fE
zE?gqpih)gZl`c+vhs-CBg-qf&6G4G2GCIj|S^KR<&}X4tGG&oW{P_~nn{?F0lWN^9
z{jKDYHH>pUCr$pPOx@U+Sp?txJ+?gw29Ll>_d=KTeQ6)l>aIxYW>%y-g$RD#$|pNx
zRx&E|y|^$Tek^AcK&EYB-~1bA$i%~z(mqX7LYQ-=k0$>+D7q*!_(JcD*lGjey9=`G
zP`CdbXLp!rV+zXpSBUN6GudpiqUBFb)B<NXGRI35TNX{~l;`nyQ89V+fuRA66J_}%
zIpwze@rU7gcjs}_8o%m4Vt)#Z9zjj(71LLJkIFYT3Uj(YS|hmwdB0yNG*#NL`mMbj
z|1OWkQ4qM7VDsm-K!nnJOnI`L>OYYh9S>YxhRY=lzr6nk)~;uy6moLJ%x+en6FjcR
ztuqXmape@vP3a}iLwt|e$E&vV1)s)<$J}Q&HAM%uMzmBqO4^>S?MiP6>`}?!@@11g
zo5Cdibh(tEAx8eQ{VuXLH&9&#Huru8cL1Y&xFkIU?z%?ojSh6`YrOYtKX~SaZr_I^
zNO{WbY*~{l>Gcj)JeHBK=&L<i!~%ray%q%s**ztDVyCY%WXP=hbl3nLI207n^kl{s
z<3E$1{JozYKXHBm+xk>k0(0Qs8m(gv6<fmslsG;8pSsGfdJhBF^+rKKv<pBo{2(2@
z4LLUx*hbN*!Pq&Th7LWb?5aE3;=20mrT#QO?vYkhw#M)`u@tpCi~5588;K7&f=2F~
zTWuTDnz2OlHJyVLb0nnikL}8wN`aBSy0PPjz9A`Zu`qWAUPIk%FSi(I2#*_Cd!-Y{
z-zr32%W8cL<DyQke*y)}FU&|a8bqjp$-~vTAhgWglR=HCB>)yg*H|poLv)HaJn1O1
zkB`mW%G_v$LpW26G`?OPg~?41$uS|i@g$gu>75fbyLw;ILr43+$38>1WhPRl`3b&0
zCCc<(PV9=t+>fmFJH9Xo`>9N*d;mOI(+pMb_epU6N4*O^?YKO1u5ovw{wltz^P;DV
zWdrLhY#7L?sL=6Fz7PMtsHK=nX1-!Ydf$Yz@nb17oU|xL%jy=teBz<CV}sQ}FMGq5
z&*UA)Ch|>V^2bEo+Q8G;DNDz&9q>#_fK65<aw{j?T{v?sve%D4bUOYgAb0UkHd5EV
zp`;F}E^$+z3s*;Bg#13;W6>-0#3o&?iVs<^_$F+(5=Z)3u$Q#E&S?>se(cv$5T$9P
zN*3SHPg*6LP<xJeyF81&$86Fnx%KYJ-#+_JY=o-hg+em!g+}W$(6Izqw^8D#b00<<
zD|TrZcEPpe>gqiXR(M)0MheI${5DN+nHJ;ud*T%)a++iR=V->hD-zVK(-v%b|2Zpm
zQG<qb;$jePH*eirhoN6?vuFk1rfK~(6L2X_cZ!U?n@PSNM6E!pRiK&aPa0wVnnhlH
zegfk@ZdU_vVznwo#&V0l3+?^c!o*^1)la9f>(`&AUU!aVUkK7z$MIzEU&t*WF$$sl
z>Ow_kymm*={*6kB7ZuHiMpvYtN6zRf?lEqaTCQerQJOk_SmHG{nDN;XjB!^)olwUl
z&gaOthp})ry;ZPP$!{T62(JaH3VPh3Ch3eypK{+T<oGv>tZsbkJMNAtpJS<rsP`g?
z=;qw_9h#;S3YT1i7xQt+wdCCIyz%G}Lk?TAw4q|38}8q`Swe)RePs}P<$iMu=EB`M
z$JOe`)>`na*&(DC6Uht(@Gs?Lo?kAKMqC6mbW);H-8#Un{y!os^Br33{r<GyZ^B%J
zY!*r!7z*Wtx0{CRE|6~hL=H8wLSI>NGU#l&140U-<9UIKDtS5=Py&Y+xDG2U7*15#
zZ_45N1gpvN(F(=~)H-(~wtBCVP1tF_zcFc~?5z5T&l`^Qdc@=AIQ31>*aXbZZWLuI
zwoUWOtn#V<nQpq+zFMj2eo}Oh>mXAnIa61{AoN#|K&I$CRCVTog&bBJYEK{<?C*PW
zzy&GU<Xza|_LV=#1qO6kz!qe}nKlBtbFD?fAenA-+27P1y98eSW~w%2BWhJG@l@h>
zz#ahbmu<qAy9O8g_0}l(eDMa@5#j}u80<-H{YX2vX64X*RYsd|wjWk9E>wd5@jv-2
znE3sZu={Cz_sO0w;JEj|H9KEYSL)SkLE0u|r!=cxmz%l$5}I&;n;5dgyLTz6<FIo5
zlxNt#|GE$#MBFYJ?}A9S(v&qYrA<)NQaww<PpC=#8dxlK(g37WT40so2A2uw%xk}e
z^6wgGQPe}-zoi52^PdC-?3R%>rNOKWS^D2Kp4xh8PacRmcM18ZD^Wa*ZHmPm^^Mm1
zXikSALhQ)5!6z2=kep*7j->i~Z{*#0&3bnnQLsq|NK-GX9@I&WUg4AYN2^-;qxj8j
zOFpy?=)m020P+^hy`^syP$E(_6_mjaNZ_#osUi{ZQqKB1f8lFwOx0G19|FH+aGICH
zJ20=_Y|vcQlEHU>7`qG_d+c|wm)rbt-cuf-@+UxZboE-PSv{TRH~h~k!hBEW3P=9q
zQl~qqoy@)W1j&X-tsi{82T{s7$P7gIqXE<v_+~sA&TA@Aqoxi>?$)phgzrtQGvecx
zk~CkS!{8$&CX>_xfrIAQzfOeTsCFKaKvg%v4oYDn7et)UvPl_U{iNC}SWGVuE!2X)
z%DEg)XuZGGCFnvDNx@(ABqeesvMoQw7&p0CQnK%@j1d|wSXSW-%hG)85LC}L++J?2
z!E@}Wi$wi}U7gdv%OJ8UCe^!)=6fUo?+UyqymLCJw73L7P{SVMnkqe;`+uLuz->HL
zK%yoW5`_uYi6QD?qxiyFb%4rgqE4&@VLSbkf@$g=1~J`yCMIJ5D{VM7ggLi}Ef;3X
z0fA0OLvmRHsaN<k!Fl<bHB0fy@11LfCa)x8SG^at`#swGYgq)YCf84WZ+v3#?&7b?
z-d}vblimKDo+aZ3fY1{2M%9EVxu5=@JC_(Bi2rS1lX%7%+rtZf`unt#Gi(;W(h}7K
za>?M6Be7|3*gee)Sn+Rhv(#$<7&5+LBT@|$LN6V`D{cNLI{erYvVL_M(Q0eueK4dd
zz+tUEo-~y%!)2g}xEbg+&c(zQ4*m@|I?o4M%Y7UE=`5PN$1n_mRacHhd?&>J5M4z$
zalX<6oy2*9FC)R0^!``pM@$R%9;}4mpA`02IlB2Owm%vEcrXb(4_<X6CCE#ceQW@`
zB4}cex|FXyTM-a&S+f&s^VPHzkjWi}Lt%D_Z~`rX$LcC$(wpivZ&5>}Z|6MW>QDMv
zO&NONC@y^-HcP^8U`Ubz9N5^+2%n2<$D5m@vDuBGS%sYwt?rK%WhEZ=Ir1T}9KV}J
z3v!ywQiGeg(P*U@ypWczCh3!@x7Um{?NtBYcgS$3-YO0CRbLv8K?%bU)ncXKu(NK^
zD}rADh^-JnO1dJQ*_oD?VcM+7%k-$Uz?aG-rddm{phKdepNBCgtAlDNKC73cU)Nyo
z=q2FAl^WM?!H&26MRK}stN3`{u`(6I*wJE4|G`4LDZOwZim5EKYWM<>qO<W-BkP<{
zG>45hm-AT9sy!>cCu=Jr8%{0r;{*#IOYcZL^E_KFmmO~r_PpuJ7w(UfFW9Vj7TbL;
zT6Ra|-Et4`EWZ^h(=C5loYVRx)2dfU&w2U$Y1GP=dL+}}V|!w+=EBH5^}KRvDoM{2
zNJ1fC!4TRSFb)YWzK4W1Ev3F0RsQkr=&-_8)c>1w{zhqXN?-y!2TIg$2x6b|)#_!V
zTGF8bIhtf?PgGD$)<8-{(Thu%Ugqn>D95q*tzB)$zwQ5+6<ASc>}79JdvpC`%23@e
zFl&_3QhJJZ=0aw4!{F}Xy;6vfY&~wxe`9bL6JMz4lQ1PW&p!WzekmV`M1GD96=j28
zpUVBV3~jPHE?mLZoOCFEJ&q0x!MBl+Ke9rr6x@a)`HrRY3wVQ{vQR+C-Q1qm)-S!X
zZTK7$C+D@VLV~SR^*ZBPeSe{Q+RdwcK#P9Z_fluIHwEZL3jsvPXIK4r2g?aya_IRn
zAslxM=MUlkr2d2O`qyRfzbHBm+R7%zQcYPT7fzGXPDKbLC+B=eQtZLZ*oCL&#he9H
zjsB<+#w9u%cc<qK!c<EmOj{H4!M}Lz9I3cR1Fv)C=LzX?t2v)}%66`o`qMMwd5ah@
z{o4jkd8Y%4tSLs|=ESa@54J<ORE_vbEZLH{Z3dsuNqrPQ(E<4qr+n?4a3lHm$?(iJ
zYMzlF-$Nm@vac9CR6t$<T#Jv!en=_5eS!ngDll#M6koYBeWQcb$7N&oeT~`UY?&H~
z2vMDFEQM!!woU7Z*!q+SJNjU+&b?53%oiQn82Z8ph#G(g@Zh~xdyBBknkJYj<onpI
zJL$3Sw~1K0Wov(Rs8V*?nk`qDaM{|xY3L19!tuYSMFSwTy$YUybvQlyJb&)>%djDJ
z!Qf9qHY{VazCXN!3*XntDrc?_EPYK?KjInW)K6l3K$PS*<7f7Xv#g+Csr=Km!yfG$
zlpL77c8R27mHRU=ZM&FWT#iIim&aJjbjud!142WA@5IUM=9SCHyK2L4#f5qamA3BM
zuh)oP-~|l-^a8ZS{M-Ro&pZCidqP*k9f=PJUgpnFDD%{RizoarG2yw%_ZRLGk<NA2
z(GnDq2$w9p5;QHhmR_)BpO4%-9#0dkNbd~3{0$PJwfpznIqUr&+Az~0!j5hc`0BMW
zcAUmF-+F6V%s99M{ozs6i&_oOafW)Os0r%dZB^ff>eKgOqI$u<{Ut!UEVs^Q9}CyN
zo$0@aht-|ee9C^$^DM2}5o?<D0a^BP>DxaBD#RD&0g_<#h1|!YC>qK!6Tb$v6h7uJ
zt7&O)pD&7%!ScE4=`O7%n=N)-e~>$8ovd1QWv`!|sS%*0QEb<FK6_Y-A;&QXEBPN>
zPRo0(?2;aqul}oeu@Xdy6ufft@|mqZH!;#Lj<*Kh5|D=NO>GTPRkS8DHNlrzI>r1L
z(0S-qy<TI`WS<(T-C9&+sC}b@adH(+pWzg!cVESJVwki=W$z0x;woQX?%S1FmOuO4
zJOGfUB>0L~J{a!rHmD{qdwKT*r2a_zES0v4yWe{?_HW9=Ac^NnWn*}p>Fj_gK7!|5
zTJX{g<+U@-W9H+kM#UM+DQ<?OiRpxMFbmiYTW~ymoy%i(k)s?($}=KR`w}0v-=`Pp
zjg5D&O*|y)1DwQDA0#Z5++#77;s|4g0OoP(<YFHV-h}!2>_$pYskB*{p^p>bdGB<h
zTWnbQ2ls-;pGa<B^G?a_zA5O%Kbe%Fh?Y?H|2<aj?83G35`Mi$Oe6N;PNlEiwUh{t
zJTxDzXNO`^uK*&%wOy0xn^eHN-7NXR(~1B@VyBWsaGBuI$zqhp#GGbqBi9;=ik=FX
z1Y7a1PtrLPa-Sp!4M_8v6`O9A`QlV)J-2>0Rx-2@uev1fLt*#*$?vMuPj(0zAu^kB
z?FXELazZst$LnQ&50cHse*S$<$75KdnV~%-X55`rO2eIR5#TVaA$1TrWm56gIQPU!
z@%Z1qOvTH6Ns4-!5bYmTT~{X&5kC8#9bhGEwWj&-aoimZ#e{*62BusXwM0YG%D+Ik
zV@qt?EP>X+-Jtx17t*20Co6iIbf|LEDQpmP<qM|s+3{qCfB9<qDzyU;R{=D4y{XqX
zKuuUMib=^m865;C!8HIzVWlNFV{@Gw;K}}|ByytN7t)!O_g6vrUJlBmL=Jv$je8{I
z$tvVv2P*un=F+WcH(5R2dMOn|6F9{$MB;5;YN<wCo{^Ev-!m%tU#R54O>?m7{WI#i
z&3l$<!E{tLGX{8qztidT<CjK#1+gc-+tZSv5&tAJp=t43=EBFAd&($m*|Y+-FoJKD
zzWpt1^B8@r3Z74v0wuF&9~nn|m2V@~a)*VrG5k3$s!~OT)_)D2;m_#KR?(dzd-jd#
zSKp1vg!ST)Xnm=oZtDXARIl+;{K}ui`A%h>0NW!J$B<4`8y0Vq@wG<jys14ASn|Fp
zfF5BvJ*Sh0m7;Mi{6*GNW;0K&!yI-iRtJ&;?ne5Y_la9u)*9atrJ&0DE<?skL_d-9
z_Dgbn4$VX{?<+5*{iTnb(?1wpGGC+}!^VH(;M9Sja06tMr9C_he&g2uQpXY`HlstC
zI`L)Q_q$<P!Ek74Rv>eqXR^lFlE8}|5geOiJgH<sJAgst@ut2P0iTDSUtjEKTodY`
z$dKoP`aE%_4ODis%I)FRH4q#{R103q?u_RqH)V$Cqu`c=DA3{HOe+kNt&!JywO5J#
z^1+g$<2i$>ppM9uT>qmfS4uQ}8rdk|V}d;sH9akX(wM=sr8tnbbEr{|=F0Er^quBs
zzW)INjn_;d1*z50+Ep^jfOfNyY<E`VYnQ2k!}UMu&3zmv$M%y`=lp(W&Nf4t0TS74
zFVUt44p-y+`zfO(<dwE`&(4iL|Djz?hb;ro(Fw|hflIfGHTCJ2_aSp~N#7WrMJNa6
zuFv|72^kXPi*xD4qiwyH{|xlRJ<63hXu&JL*|iwBU*54hlYLV2_2ydV;!2#J(jJcj
z6@H%1I?Q5{s7v)OpvnZ?i8`77*1qj!Zm5z3KJjMjj5yNswDe7RX}7_R5pC6|UxX$<
zdXDd8`*MhFr9bK>s5QVmOId0&cO>=P01G3sE4^SMMvffS+2SRkCYsGe;1S`?HgA?5
zz9apBDA7IKz{+0c;rmup>rC0l4VuM|H24>f`cGAPYRqF?wmfB4hr)DK-*|wVq*~wn
zD=0EB`{dvFpP(5JJ-IWr*YCkPTp60)S`@*giwJuZR%I_~gt3tJngoy1mo<@V7Hwzl
z9vUw!k@AjBR^GYcXN2<SN5Kt)R5RBW_z1fjDlMcVx=pUzvTY%FzJ1|hNR{`jcxZ9Q
z&D^~F)Izx(*WjCI{@358Y837GB!BTeGG4#aauSPf7IXgwzxYk<6m6}a4S899`nJnK
zF%&W&=@AW~6~Avy;);C_e7n|E?>)Ig%LEG=Db#lx8!?W`;LlR*ZbU6U2YIhf7t5b_
zo&g4_nqGi?mQ*QecROPj*1{P8;t$oIbsra2`A%Gq)mFHy{ZTR<kt*I>%Zk6*C|v%S
zY?kCRqp8XE_s}h^CeSp*v`9IELO|o9etq0pmHR4Mm|2lS0ro1A(FAXGbG1>k0HrqK
zl<2*=!WU-UO@(Xq?>QaQrCgqa2&zi%eg{{_qp<O7Pn`sSs(fcV+W?afcKOpesZDTv
zV^iJ4YyJ7^N5t#4A7s3$++Oel`esVA?D&J~H%&+Gk|JCpi;+1<1}^}YQ6If|GW{y@
zJ{s|gX>$aRw|=e*e@2&=DYh$zG(3d!i<@zfKb%>hbH90*J)FX{%KJ=s!jA#{-QH*|
z3~~^7-C7c^#~+}1q5-07bonbkrD!Y=93R3UFp1l2GA#Y!_lIIsmJda0x<w`X9`dpC
z5j)h(KRBW9)yAuf){X>PrAnzB_ClqU%mYdSN+SlrGYT&K@F|a6Yhj7ahcWCASqQty
z`arB8&30apH7Px+2{?xw{t4*EVp(S#?##}{`3iZWa)(%l_NY8V?kd2k8FGLZ{D&rm
z-0K?lk|TGT{g;uSwkBY+6c6D|+SwFuAe=$@ag_F{l?1<!zf?sp_x7GB8?L`)%pW+z
z^_htoE8_UU-+{gQo@%+zW5U||YjJdy+xM$(k=kod*kxI%i_No~;6);L)<n(x9B5Xq
zdLqk<UgX$nMnXV$`TC<r$5{fUyqH~=QzwT2y`p-kl^Hpv!#Ln@k|MIH%wbMk$V8I~
zLh9R^ACB56W6QoCrdX%=!a>M9PReJ(hVka(#!e?hYe5{?Q-r0KsVt@3J2iovXASYJ
zg~{)@bdNM%b<lf{l$seu9fCmUc_8+^(TS-1c5QYM;^8#_h5l|oIZ9p;qX@ntGUzrI
zg(AKPVntGzDU}2~o>(7WrG;BP=fz)N#fWl?G)q+Z?{bmZca!!!_n=RIVpM~|0^zuZ
zj=S^jZR@Z)JjsG@FQf8u=Ee!M;G2JxqU~q5TO^5K&y2A!up2;`3oKLji?!4j2lreE
zKQGvIhD@lq(`dN^FxG50+Y7T$CA3Q?y_?AM`_IFV{7Gdc)cQUil0|*dY;{fdK!lCl
z^O@aamY2}pZtL8|=dDvFO!uj}EE#ZVs;d4zowIe{e)H{WQj^x`j|+#h(+Ruf6*|%E
zitFp0^VbS01Kqo-r3i^^mDz*Bs+`NP_Uz#J-ovM@FV1%7we_2RRMOZKGWm)<Jzt+l
zDSf524*HAghIWD@=l_-k*dCVZvN|`!=df-&X`QQ8<5+|X<WMP=al+JV=rghbcln1P
zRPdWbrf`CA>FNxiKc)~~6=u8bj5;5_@+OAbO5{|p1c$>(K15VUL*HLR74|>bH4Z`-
zD6SeOklAt?D0oNsbOi{sul1})L1L?ywm!cyv5SI3nY3CG+A!Mul?>v9Gl%fhTh8;!
z#sP}m12ik8GgGXsGvi@&F!P^Ly#VP}7fwmFV1xS|yMTF@mMTIuBP1UyBO>1(enTdf
zE^Rh?ef<OfdJgjp8HH6ZNp<fU0;RB1Cc%2}aPIYSEU+K@?;iLC#>_qQKfMIq7dE~x
zB_KJt<eW9<RCN@hM`9AC*9&hdeSaLSF;jAtc|5r|qN4Z}N|NEJW)NL>?Tudgo-6ey
zXQJ`<aY0ojM?E6-d(?=K*`!&%M-~;40TTHxXrbmYr+YM=$IxPpX4E(>7`^t_c1E?I
z7pK#hz5w#BbH3{l8nW_zUlso&mTEqsO=nC)%#+;Ih>WGOoWB_h57H_#O<Ee{P@MV{
zQ0c53gEDOpc-L6}lvroPO(DO-ByGbCPKW%63?fsgt)*ATm@x8_gVtnr)xYuQmW08F
z;8}Aw>=yW+p4q|ijaMI*{RPkn`oFOWv&P1fP)J6~QaFcF&8v`gFN*W?WZgH0jma~f
zE(*3LAmI0DWgkx~VU(I?u*!!HJQ40DoW8%B-@X?`vrX?Bz~kLYupdv;5lfZ9TXLnH
zgUnGy@e%Vyi>{+xw<bpHyG+rD=%HK6d_DUmLx-X#77jQ#_&zZq>F7{TTLKW(P4D4H
zYvVe&<h(qX!T6%j9N(qV7|!;kb1`b9vi};^E-|bs>KhbXtMR>Nw(s1fnis7*HXID$
z@B=y5qJaz(-_ccHW9dyq7^IxlOzK&P@J0`pLWa+%4k=Fcy{8G)P`pIrdhc4);EpJ(
zDg}pOdG@7ICuEc11naCfPf3!PxUuU~%Uw0LhLK3?4pQc!Yve2z=4*L`<i0`oi0n9H
z7DfKQByLH8#k8{Z(Ph+}rDRMPb7+sm%%ijnY_9liVVj{>97a{yg@2A$KbJj0g=)|H
z<OM>~<$R+{iI+7=pg)Imo$sl?;?)vcO*3-2DlR_g57icFHVkiQH(V`!MKxnkU+!)V
z4s(b9-lbbw$U;c-5?5J@Lj3(2^JagCG6@QqOUwu(P?eJYp*u`M@eU$P=d4`?(lU65
z!f>AxJAJ<s+W)mBusttwIq+Yff7$!7`xubHG`ubx(@61cM~ab7+qId(2hP-$-+8n!
z&(n-+jYX(B81W7hx;Nk!k<LWx)H$a7SS?B-eDC2TZ)0)=i8bUUAkavfP)K&%(Y+5b
zG9ARsQSA}r3Dq;+e~N{%4m}PB2jGQ@qfI*v`i4!1@oIXDZzwl{j6YZJy_vW?6Hrod
z!ZXR5DK*k3w7I#Iv`eZlwtJFZSQ5WJAR%v&>{9yTgD>F&%6n9@xcGwGc8%9lg~Q~w
zjwuK!HlLOcYS};C*<dmCddFdocX`5Vm>zKJd_u{~<;3=MOk!mIJdkU%3V6V<&^FxO
zAEPL%JxY5VF+8f2M1a~2qxj1H?xO(1P}>#YMH}9jXp6ao+M@$E=*K!dlVKVR9A6L8
zFv-m@;2?u}r<Cn^XQ;MSa|Yn_n(4QOU-dD@&MHw|KB~)5vbBGVM*U=fQc#$2x;*^y
z@MkEWITLWM7}W68%=sro&_!_C8h|PoFI^8yzQ7dkG#bY6kbm=hcVE!x&i+NPmU!j{
zwimhxuYxsdB~Km!h%Xqq>eqwlN;=676zPs)5=|IJKKo|=q4$x0O|HUm{Fv501d4AP
zs?GGgBXlSqKq#^U*8c!6T&*;J(#8h+gqrjXp0v=Ag{`hhrk(DA^=H=tw^QE?U1#rS
z`P%!0qj`)n6W%}H-`ze_<m`)%wQ^R#*_x(o_Lrk#>9cR)AmIqR$ArJw8S&k273ZTI
zzm&&gXuJHGsP@3{O;nY(ND(ZsV$tHs#{@iH=BfTdNqKpSDZ85}T4BSIhN|rC%yjP(
z-ZD896E87C_}hx7yU!X4+2o&~MD+P%w3nJw6wea6(t`35&hOU$i_|-|GpqH@{4Y_S
ze;u6+x9B=mMg3(m$OdHEWk`coi)EPtF$+x?B!OZec0>giKW#U*iDFO;DZ#tc#1Y>d
zchFAR%UoIHtCQuH?9lNhk6aIpz_H$S3SWjIi3vKOsY1aIY}IUnp5`c``m)IA(1F!4
zjfcoz6;g9S*Ro@=Cg=3mhdatXz6vO^%B`tq$0U~di|7ie139Q_k7gT<_8lT-#OGgQ
zcHj3d8!S+97>kjW*zsg4yxapptwbfzc;shqK{>+L8rdil&vmKp_!j{>oxSl2Y)XV_
zT}`Tc3zIK)=UO2haM`AF;vD_sen2DPvN0@Jw66r1^inaiXz_uRWVZG4W#_W`ySR)#
zWYUN*F(hc|a&%o~k|=6z?05&p>VoX02*}saP&Gq@iU(>f-Z37Y?zLS!@y7LgjC_;d
zU?*J%v2)TIR(T}HsNpE3-<T0v>eOd6N4YraHV<}|HuZrR`hZW7s^byT{NX!k7`YWf
zkcTELp82_XLfb3rgy=vdf$FkZs!+EDbjdH_x8pBCG({Ec9SleL$D?;RtSwB(!82tx
z8Xy-Jho`$4YxgTpDQuv9ccp@qUx|YX5!Toiu@5ehKa3wlR?7&<V?WK0ho;v*q~-e@
zkzAAQJL-5fHC#fkK`}NUM#VMPNEOlCmAoGuL?1ySDq7i)<F<3=d^8f1A1AIkDQlm%
z6m=_Rq8DEl9Ij&yvPBB*W0`iweEz*Bp$mjq7^MkHpHsI+Z@cN{E>yTR`}H_0?4XoM
z<)M+tcD)d5?iMzq#bu{}7K>7t<uILYkX+vf{=O^c*=u)H9*vCP9pz(c>%PRd{k19+
zRmC}W?qC2vCbAO}q~1%TwIsUzkL8?;gZt^i__L`(K-<UdhR*&zBc$L9)qcDTr?w<j
zb*-TO(y?0zZiBCf`}0~`<17T+3EsqqHZVXtDx@t5IaXzX3HX`pR`hQ+-;VB=p1IEz
zQR)iLw_YbpRfd%0zvp<N+TFU~#v~=^gtHIzctFL?WvQ$lMa)xA78v<5_@R?nuY1mb
z54miV;=jLT#&xcC1y=b<T$)}T)U=gae(SI8ZxYc87hUh~?r>87h3wf$zWY}b-4LgZ
zMv#DgMhie~6T$iOv&PQis-E{7Sa-KjXOtJIO8|eGgvgF{x*8t%Omy&Vl6wr~*_XfE
z-2q~+C3CE{$#vz8H2}epCyQ_AWxw6XU2^S~#~_Skjx@Hm9eWw-d#iJyHg>=_)u68-
z=!kF1U`e|GT9#*-q7}iO=Cl(^@Xe9*oHFu{j#R(^i%X~Tr<RzeXokcnS|Mdh!R^wf
zLv4;CTHjLRS}R6NLR%SjC3qMF&Bk^m4^c)2!PzY7bIidwE;5V=bCUV|DCAFqOAI^T
zvAYAE+zWQL=-T%rh5&itqH&gm7xLobcn7TU#gsGUDwoT98<WyRI|~NQkdx2}T-7qm
z>Mk%54)0zmjIBS1<SX}(!lF5v-HlegoP_mfJ?+xBvr-RfPF`K;l)s$jeDdj^ieNS8
zOekc3j_y72b{Yk^EcLyuZ*l*}Q@aE?ack2YVnL>HYbq?{=MGQ&YE-_M^97;ym)c+Z
zcp)1$cgk21^aJtaB}B)@2Z8$*odWFgiE_95G3|n;MP9(^4UB8Sh`hT&pLe-_CBB!F
z*DH$})uekICCk|Np+Aft))}aBpA{!|K&C8+bFxfVMA-D3rpoc+lTXT75*|zB)Bi5)
zQoUw<_9@2)Ij}=`(MuZiIj93B?wFy|y~Szv^qB{*5bL^uV4SWBf&25K$qMIcout`h
ztVgrj+?OjbsD%gpfqnApfm<3r{dvG}euYWY42SI@;pnK@B%T<>#zy@b8ex^C6Ca_5
zY@PLt3IJ;}i1WqPRko+=Ewgr|)BBdvlMTs~^|a58IdDIO^OwSMSc@QeN;dszqUtyV
zf|8@BhZ4w)N@JH|IZxl*<K|T!{dIH%VQsu^@NTo+>iyQy=ZqE)WA~e9=nAw!UKN6Z
zmob%n)0|{FQfQYR5vO>k?kXZ^`DOnNK4T<1jo%54wn8|W6HJXNC4ZUO9N!%AK$Ype
zA(PsdWK&NL_{MAR2egWyl_>4Y2WE*=ov?n+t(|T8VC70N*QHjp!ZL4ojJb!V+GFxo
zF%+V^jSd|F-%As%-}OAIuQUqIiO;eMW$gO|1^Z6);&^IalabLI`$RU?B+B7eGyk$X
znAMD0sNw*FeRinviWo#7?2ZgQUy08rarSuX71baMp4u1K4c=IF#rg_ZwuS1n3{nq-
z{*HAc1q0{iI-sbN@fiQzYW+*!INBE(D(bj1<>t&E0uW*6-)#(@4qxtuXV(GxJ)B^|
z7?-kZ{YAaTM*%3@BJ^*Zgja`P0q|!;!tY1H`8D}#u$#W9E8FhUB&Ge8@%1KwLWKCG
zKqH7HC_;!lkg>>Sdqd1dvGUd>XknHrHJ|(UB^|2+;0E%vaaa6wWFcg1|7Z@CeN=m<
zrd~=)E+H;tA`vvNj15!U*jxFvy!}b=g+Js=!?_!PZDf%aA>i7G?xki{4OBgBH*a}Q
zs)Owcc~vb_0!#WJ8&REFh7e(1Og~CE;>{S5D61_C8+4==xL58Xx4&`)*cLQs#oZf~
z{<MKE!Ldtt4w9ZiL!~&Y-0Or<?2SC7v&w{i#Y?|lXfi&kVt{9DKd=0yBr`B))J2E(
zj|7~H7iOC^CA&z%x!WU>9XBBtxEYX9s*blsq|i6?!1)In$&w078z+3s_%`8XF9GbN
zC=8pO34jvVOA|UpaXpa3EBla~!V-uIjPo}!;+vv;M)wQr9c<uXu2|VkFSE{cQ$+bI
zqOfOWt|HLjnsb4H$A?FkYubo!xu3xI)IhnL;7eTTl6_z^6QyaryAxQ6bGee*tjI}g
zv+`WhWs#kTI(0TFC9D+AvuTF6swz=gis0@zXxgonAQP9^>7{9bAU|EK@GKTa`)Mj$
zH@qV_9{!9z2jE!hiAsexi85XZ%>&v+*N6jkdbotU1jh4NV$JjD3CBS6w{3xbONDWs
zik}Ypsip*P2g_=8_Ln^L2<A5?CTxth1Nmu%roP(!lmU4E?*sm3h2eSo6x^B>7e-A7
z1omf@4QQ`Cr3H!FxpBAI;z9n&UJ@1-GI!H|y%I{tFC+cTL|y)y(25BDT@>l%MV{B?
zg1EI5uLtM_Q9%{UJWDn-FS_5YgU5ti0^)IZdj*bvgTpRqq%h$a76A>J&N?u7Lt^1t
z^4Z{y=&(lvh`csdI47rIl{JO#L5GkeCRy>`gfRC6r-R-K3XR5y31ndFndQ}GTA-2;
z4uvE;X{scineA+hZ2O8oM3<=}k@>Cphvz!2<|oT>`0PUKZW7tF=Cu|5@#HhH;$7MT
zqPr;U5G9V-He*&~E=vkSj@>Zs%Hxl}BLGB8tW;=o)SnE4aSwNNlUY8vCXFC679<jN
z8GH`4l0#5%1cxZ`qu0Z)Vo=-{pU8NxrMr{t-^E+ho0h+9&|hWnsD86H!>_l}Z}N4k
z25(W`8eaJ`V6pQC^(%F3qp`M|W8y=Wl4R4s1u9i+@JYYD^D-p&wOa?Z*()zyBN#Yp
zMa#X<cJli^EJlks&fOb_#rs!wi+4W%2!{@6Sw>^&9Q8*m-M40bfnu7UNI3~St*Elt
zbZ@Su7_52G>*-HIMM`PHHk1_td;CaO4DX+`sW?>ozD^cSkxJlPP!>Dbz+Q2#;P|~H
z`|qFb1nI}{Cjp~8)LPeacY%I)wCk2Y_V~@rRJU&~{`}lkjrdh@X6ugSuohiEql#fk
zv00O^!ye4^hws%n4-Edo`w_EiTE~3OL^*W7{970Xg%|b?=rC*5&VG9kO|PvN^>eb~
zMZyaer4LHODW^U&662*EZF{<*Zh4Z(+jQoY3|z%9h^&DR?Q;H-8Xt}LysHMP0Oli=
z@Wmzm1X^b2BB*%3@IJfgy34#2U75CPYbGbTM~H7~Rjpris?rf{QnYBB8xQU3tyV`~
zf;#8ecqra_N`tyXec8t<a-{A%<kmz}p{$qg-%%>D$JdQ72^5AIli8nK=U)7_G&#72
z^xPfIXFZo193bJ)QCx~wmFRFg{s%i0CxmlcAp50$G){TF@k!6hANyx%7Q|FGAe*ua
z=Vx3Dg3B7DjDfC%Adt(FKnA`oD=A|b`bHfe(p7=5qXd+Fv|Y{Wvvk)a<@px$6*qGN
z4$Os4JV@{n#eH2%YP!+4=52rH1|ox3sBaa)rTeKifB<G9i4iFzx1?OR^f=zH!aPUl
z8y|wt*&r`E8*+?-w3z|m`E>u6y%SV&pqeewH+N@AV@kzm`<u;h86IH+(I>%cZ7&5c
z>k&435G})WuH<^d;!lz1hdSfQNNi&;87bn-sIL$OGO#j9=G)wU`V9~`0zWTvc|9l^
z@kQh<G0+FiSTL6&9*~|>NsgAA=%Tm%Qe0g1bRy&Ts9N_hml&_FCI<8VzY6s8!A1L5
z6D|$$Us9J_@CITZ5ZH3roGNaNh%<u*Q(G!Zf*9?ijm{j~5duKx#LV^vwwPO2%z*HP
zu^E$cAEpGFI0u~hB(?Dr{<61@m|YAxP-2iPRSo+01Ro2ed@22=^8n<6SYG1KV_rzj
zbTEj;o&5n@|D5+9%39iPn}dx|$}H`%Mf#-~pcr`yhGWSOiwSik@{_=grP9r3vbrAC
z+TU1wYq@je=)@TSYqe8$x}s{n6sKcmuZid$kbO60yf)wZa%<D}SE{N96gjI^yce%(
z6&?RY4D1>Q3Z3e?$OmMB&>Prkq!b1c#Zey|Tv07bsNIw~NsBE@^@aJbH~bG{loW}?
z3Yx{#{l)FXX08yepZyq;fYppct7lzVuIe=r7%A;so5b0m$iAWDZzxXJ%B2A_`F=6G
zoCAS1?X0F9EoJe8zS%<`4LdFXV!716d6Rv8b*jQD?)5GPE)xb}(JKh;`*=_tz3%4P
zXvPalRMF7`J|$K0rAPPCZI@w6Xkc#;ASH~^_S~N{{bu|$Z<k1g<x}b^MZp)c(QL;F
zj^ID5R2KnmMbGivw?zC#Vu9?*r?ro<FP9#0TkOsXDb+B1fCUqJ*hBT|TFv_bd^T=~
ze4E#uDEIEe-%Cu{$&bi6nRq@V;}KB?{vu|0J@n*Ec6#p3uTOeOWrcz^EaEPYt_-6N
zmu*iO*)5pv%dKl!z73`$*+wi#JimCwITlNIdue$Y3h7|Og4Odj_{JzX4n*2?e3gb}
z2kNp$x89|)Bnk^@Mar4R;_*u8_e+VVqIk`WI-;kc_-7v<o&50NbK8DJ%{QkC)$~f6
zPZQ)qtc6x(ou9CtS8z`>>OutNC)uykNK4Y4IxK4Uk^7O+KO~?9igZ%mx{5K|FaW;_
zqPgVF{n6O=2q^j3dhE%uPMdc1!3pqUV~%sl8$cr5wH;fTAa%42q?YjOpxe~X9(7PA
zeMcvno}F~+F%6!X>F{>kTQjKcsX;xflR)de)`a(;K1<g=4Q4V25QX8UCf(mp4m_tv
zD9S62HWIfd<+xYgqvNG5asih@j2s|ZZ|f&FG0*u)!XS1;nG#gt*Kl5E+wkUYkrgt*
z!Jkt~X?Z%i(ST$3R&XL@qej%k_->q@?tzOTs5ofqev$kn9s=e2>88B5fCu%ve&f=A
z1uZs;u#1uM)bu<47++YO_zF6JC0;VH`h??YQO)7n9-8J^=O1IUg*Z4=zky5h%ljWs
z0~P)AJ(B@M|F0nut=lA3JB%YN*g{ykLBC1qrN#F1+MhmTD7$IwHeai)c83VuG(S3p
z?<@Gjoe10$|2kgdCL+}F-~S@KPc0GgAvQ?HQTf6x!uOh}w2_k*k362DE_FoY#Q048
za}z%OrxHZmh!&C_Sjymr1X@T9w&18}-_}2L1FrtwcLP#ZqY+Kg%s&qWt-_@6LQav7
z7k+29^86#-naGq!2xd3>xdX5HwdY3^uixkR%p}{taX`lE^m+m{p2aayZ|gsqy(w9&
zOuoMKwLbN^*ute;P-fK84yX*Sjhi`#lp?x~l-B+6c(7IIb0SG|TF)h=?WBpd&#GCG
z<Xqu>sY-`PIrW%tA1YJ%4BE(J-D2(uSa#l~gveNFr<s*dv06~XZ%ZFmEV*Cq6vKVE
ziJ=|m_LJES@o6Or;a!T;f15^xG=%#bUZ=edT$k`G%tDEwr?c7){O|1DNK_!$<ks(!
zbIax6#HlY~rka1?U!i{E4wIbuoXAB9_<^Xn(PBHUyPueUrRtA)yiF*2mLTD>_YD%T
zEZ}`gi!>|{MbBodkgirfFaE*+xyM=@Tj9=*G$zV80VM4omC<}Kvsp@0^mp~9+^pkP
zyNP$I+7pGnI_C=!zax1&-WOYEc&i{{*)=lKw%>-A3*KfAa@MeNU8q)vmHE+R&&*S@
zl0xJ0Q{TX4wz5mEE_x5DXL*|9w4Ou;Is}$aH_bC3(Sh^knQkC5Qcr|-`5|TJY*WJ+
zdS{Fhn_7@c0Amy@(vyX}%HgW{DssXHFe*c$4?4tzU#es&5-e~3;HvqY+XhN%jr%bV
zKKSRN2)|gh13Yn#)h0u=*%@bFI^2@5$is#ioLN=A8XQ|8A>XfD!Y$|D-)&|zb01`S
zP-OG9hdT8(pv1v$j^OZozZd?nLf`eF?m+6!sfzG{{Mmjt!*ut%d?&}p^y~V(aw;eh
z(GIGX)A5qFDF0til{om&Z3j#)_&an%&5zHi(2qfHn!Y#zD;J60M2cA-cYWo2SF`_a
z7F(BrdfRUaYy?QjnKWzfNbC~lh`UBtxxnTkPr!m^Z8kYuEUEg<&G>Tk-fhFBat5IP
z0p?`9@EcR54DVlq2qt9TW*8YS5_2Ake0%8S@Y^@9z<yE%ojn;Tg(=rdw01eUF?wPi
zs#q4o@4F`4YajjORsbO*<wK*)=kC30!={vIe56E3{YCtl#??iPFU1dRs8-8?tU87w
z$<R4jxhikze?qM%3j8YTQ0<QbFO2=S0OxUPfbVjsP=h??XG+U+Rd;KVjaJznrsBXM
zmekZ&OmF;a6-?u*9YvQy1@81|Wv}fKdxQ=xJeI~Q#|iroxzv-`J91<(y{qsLB6~zw
z%I_0P(V~iK9NekZyU2{L+Jc1*k?V&>(zwbcai`{PdeUBPp9DZA!j-~Sk&J<y7_G#H
z|9c&w572FbOr;UFs!R_`m@#Dv^`h>+`}Sml;rF`)NKQKmJCc|o6rxBFj1v@_4@=6>
zP9@11z6e~E<>thODALVqgmH?@wcnPp{FV8;0Gg2r&rX<oF!-PQ5Li8IXRQ}yi)HP2
zyp-Nbyu#HX4dofan893Ovswa1gC~D}pOo<KsiCk{0{d=}?>44JB4I;rvp!iTP7%lQ
zTRNdbiTdGcAD!i%mcBIc)_p)oNA{ggrnfSZVNMp-L@M4t8vwtx{#arR%Al|@E&sH2
zsqI(|#tOOn9>Qq1ZEB@ns}JYbEfVVga>{bpII-?!zX8#Uvd^%Y%tHX0h@_1)sApsw
zJ2<mx4jq#IQi^TY{M;EBl6rx#vuJy%nSd*ZM)1-^q}(5{0q6+}=BU7P{$Q%!bq__-
z`-e(k-$I`$GxLcb*{J1t|Bns?8i*V+GFwQ5GcRf|aG@!P8G7taKTXaTX9X&)Q%oD$
zs%}qBnt=YsAqC1Xd}?R8u`ko-m-;=R2ldtcq5%93GGuyWyYlkq{VP-O>gt?tumA1$
z&>!Fy2=j-4e7|cI8qEA0R3A|h?*|N@a>(RXf_9V3Gf-wbK2?yY`{!5H<+NL`K)Cto
z2ghM8+@<e>;Ds?sgk5t1qi>qmc5T1Bl57v+gpC@F6{uRK{!L0NCEl2IWd<m#f4t3{
z(JtN4WRW*-JvUV)#=z#&_}%!6ZJ#W~sQq;hBAAw8{V6Q~A{n_nDdjFUdXrrKLK1|8
zvy<0Z8n^119-BwOZ121|0}nb@XM#R1xrVYup)=}C%iBY#PwksBDO8BCp3zv9<?~|;
z#GF>^tFE<tVxBiGK74Xdp)lz{_!|4VfZce*;m0?owb}(jcI?%g>~#;xNAicw_vi9i
z!M3R_C5<S9Xl%eIXEiPY#>bG%ZM3oyEA1MKwrG4l%;c4>Ec*N(f6>8`z3bZV=3irn
zD~8VKd$R9eWmh-0y#<jb@j&iy3hoU;C3I%=&{XYg7pN{-$evB+o&7p{p|LmqXy7mw
zYPRYWiIuS@JnOwE_mtp6sZZp|Err2VT6=fG2{b<b?|o70WA=RC+!oyXpuxwhBbbpb
zV4(Sqc)LL${AiuEF2U<v$??{U3K~v5qssWnL}Mzga36GAz5v^Y-*g=^XqESiQD0|2
z%EuL`Gp>0n)fNLzc|WoG<8Nc;WUS?JraYF;XoSH!r~ozY`dE6!nGpurNUV=>O`$`S
zOzNuyIYK~@rUU}mL2uUIUPV>ZBp||qLu}Ty_s;6qNE@+882vhl=D*uS1t9KT@Gaif
z(OQrc;QUtt5RL=>7zXn-3%ab!;K%QNH?O$Q2W6NPs*sKsDZ1(j_N6Ozj}aze%wsp;
zA?iS*dNW>j4bFv(r4)sF&fXo%-Nviq$){NC^fD;Tx`0UHr#%uOclP>xih1{Vi2AR-
zsb>f6LUL-O;jaHq^P;yfhw6^<5&3fW39+>Oc<rRF`Wp9xK9AURcUoP+@E_4FJPe+@
zr3dAtya$Kf-RuD3@bB?&J*21z0!PkF@UR9jf}iKAXY@_<@{q2N>j-U@=v8X5#wc;%
zhC=+TSvyZZWSi5aqy&)U$gB{(#nP~S7)WPHCon5YUhOJuFXMeH<98_oa;HfKqz%J)
z(atB#&BJk{-^t8|ISm2W827asFa5ILZF!9(rSJ;cF-Qi%*K>M<2^uf%5s9E5U3<2W
zT!0^|eQYztHm?VLPX50Sz(XaDs-NsJci(>+2kOVA7im~xv0<3;+bCtwg9Iv+eXE`s
z=?UUG0D@8S<UA2L>GDebwOYeiQpGa_BfGxGVk2yT*95kTbCr~L(Dp;!Tp5Z{Hpk#G
zI`x*Nhv`vA!x;z}XoKEfdVY(mgT&MXY%m;gadQKMJh$0(aY>U~54pZETzLBM+qJ0D
z1rXp`NOHV($YrPa3{)xSKfyh}3KHVa%R7trJKsJ^$k7T%(F$|{u{tdB69W;xEI6Kn
zXoc&BFmD2%Mu+OUOXs-kD8IMbB6u6G;@EEqw6R%0ZL9lx5yz^;{<`_uMiy}L>EnL<
z^K=D%U&1GduKf{TBvWkc#vd~JMPjP$$Tx(95Utu+|AwW)FZ&(FMc_l-5+RCsyGC!v
zbRsLA=A`)4ex%Z;$o})BXE+6UgM(FDfKn3vASU6r`gdtfnFIg#$jga_AngV1Mh|CC
zH8dT2kbca^bNcoakr?3+3NH3|u4FjvURNNO(558?Hv(rdSrf9a+BIMquqyTEAgzs5
zt7VQQGO$j-`@xyelD%x?9p+@Rg<CD8&eeK>ges2sl>}ZITc^14M5p5psC~oZP<yNy
zNHHMV7H|aURCWE>O}j04q)!>9jFfOZT#sLG$H|kL{XeGOf*}g1TiaF|siC_WN*d{g
zA!g{1kS=LNLP9!+u0cdfN<@&55RjGzK@b7y4(U$i+dj`Z=l%Wx1H;~Xt#z;Kx_MFj
z&&tQV8r;-C2rNT#zej^#RhSqJJ~sOJ(`PR^v=8kfM(zHmA2n|ssokr!18z9<7DV#m
zr;=(V!94|h(efzqSSy7*@#i**c_<FkM|0tQ!f)**=c1((2hx?f4Nu?C7mOk_zPRsN
zoxI}JRYoykzF&Yk(?j|Hv)VHsxReJdeO^MRgtl29Hs>5z*zatY#`7!mW0(AU1?=75
z+L8C?ASnf6N;R@p$fUVu;%kzKgU6I92SBuW+}v{I@mW%R0r)+n0p9HA^XG3@;?-i(
zIa9@kF8rL$bNT0+W%$gShf^O`PQR1eDcyS^aWTg|Be&`kBK7-RGUT&(%K$RZgWoKD
z`LsUuYlPZ4k?n#@-!j2D_W$ol#E$xgziq0h1>pH9M`mQAW+FmnR*hoWuevA(JcDs8
z<Jb#PTgOY%Aq%ROX~hW+WkA=+zG0>2C(oqQ-R;P+>t~T>B>nLW3XZ%BEZt_{gEnvU
ztRpUAQ9d3D$IQn;dx43!Kb3#>^V|}O$nZ{dEq9rr6>^<^*?bVVGj$TfSu<J7N24oA
zpWuXK`}1`=k^FyOZDcZU@3BVN;8F7JMiPi?emgwrI7?)BXg^l_^sGOelqs*m0$xW_
zB7ZEn63a;})Oq6xdRtoGfTK%q(_WpQ2;AMiZy{=q#4)PFA~={kM4!G#{(dK|6X)=c
zUHN4>ns!cRoWIS0fmIpxfO3T2Yf~%rSsU@*g{|j^gkOds*p}uS|G4Eekl$XtPdzns
zGU_|!&?qx6k{^`fNSY_t^k$$BL+;|SDz|P*ONb48`akdg{~n9kY-m_)zilpUA$HS6
z-DR_njr^tIob${Cd4)*jI%4RvE%QPW=<jeV4{KNJ3StYT#Y@^=D}in)!%DMlY4zb6
zkcDc@5mD6F4=5m27``tQv8Db5Nv6xKv*Zchr8zn`sDA6cYdgK=cs9$nycvgiAJYIu
z&|_u6qu{~u<B`YjUo2|3EpUIs{Ilslh34s3q@H(YcF|hw;)r|_35QF6Sh*l`rrFA`
zER^(NttD!j$E^9(j$!&0=g`0I!lC?9N}&C!X4R!Dm_(*o3+_Lub6ig7fT6GE1$XPH
zk8-MeSBox%<pF$s*F=i7X7{uzby_`^kH*FW><3&?ZfPJf{9}4{MUx>X;@@^M1dzwB
z)-zr5Uph<c?pOJr=*N%#t{Ny*ZdV6>D=@!e;dABD`Ofjr)?V;B@B-{9%-3G?a$!$F
zNOxZUlc<g*J2D|$LmT5IbhW)H1KueRrnwht#h+Ewu2t_N+F$YW&rZDev!535e;eR)
z1;fR>6pUNzZ1q9?%S1ce7QE<rmzOtV1U}9+$pJAI{7E!L=_ZxI#Ot`yYsop`D}O+!
zCv@b2vKW^h$^I}J9)P_s?=jv?!4{Ez)=$sCP8g`SZ<+v|B<ja-!E-@BAuf`%kVd?Z
zd?%*OqKxAEvddTqE6(8?C<QJJ2crt{_iUfD$@ne4f4m6`_kpAmO*%;<%=ib9?{a9V
z**>q{lIIKlr}Bajz;JC}e`654#<DE=Lhv^XLN@QwDm9Mp=614{ighu^8ija;Qa|3z
z1bl!mmQkEF_f_z8yEqj(od9rc#EQLwO~yLLj|K4;ZTlxE2ZxH`=_?=F2p5#{ltD%{
zXFRJ@OaK9R475{5RJF0jDWa`sQF`cWGAs#77Nc&T=P8U}TyK;P4mpYUaVFd+|4Pgk
zfi|^8YhL9HQHd7!%co>WneU~4ZZ5QuG%jN}yZ5W;vBS>(=NKsj$A2`V1xCV}A2#m$
zZae)Grq=JDLgj;SJ=3`#r=gqF4${r1&Y1ZX%;`1Pp_;bY2g*bEfyr4zMaZl$nbUeg
z$BtYZ_fA`V^j-oKPymLu#b7a{M+s=F?}0l+i{(mQ=y{Sc_UIdjk;VtVTafzSRjK|Q
ztwn7<dQ(q4o|bj9(h4aF$HZ&aX$7|d+{qj}-+zNTKjvS$<$YDp%|E`WXB`t(+ch_M
zl%{$FY?u0@3_s31eAarFFz2n$)tYx9-1_3sEQ=`iwQPs%FtaeWM%ZppCkFOHg6gh#
z2vPID<()WO7`m=aDn}&RfRUPhOT>2}QQL%@wE}yd7gTLjan!ga67RBa{>G7m7+|1~
zh&n}C9&5}aeOBwa(`_Mhu^Y*H7pXL1phUd583Xr=`SAU(ubxnzO4PFp{l<Jd4e}S(
zlrE9sO@zc7)Y+75-iI_vr#?KyT)iSLt3bJz!*#|t37z}^hG|d0S52sCCGKHP8XH@$
zQAl^(|I0%G0PYRGWe_<5EY{R{9JaPwtL$y+3By&GjKaG>v<HuhC+j_p)eGpTv~C~V
zTA<nMa9{fM>8?6o8Esejp<#u`3p(qX;7a{}SA?=0cs1WHb)(WLuciD+gu0`Q_ki8m
z9w`u91uMy<LErP?a4yW=T5a~%R%d_E5&h(o9h_RwrhDKZUNl(`6eWdKDS?{?FTubW
z7fz<u*z3|?*65$8h`j;97CJoxb&TysvtXUEDHBQ7(JP_PmpnL>_4Z8A&AgYDKo0hc
zBQ~}~5<%4i>3A?Gj#F~}(+FJ3#v{LIJ{Xb@WKSuf9*`?8SywHL3hR9^;G&igs)NNk
zbX#=(B~P{?8omH|`^F|xb%`hcz3;#WdN+zIxLhZ8wM_vZB%pbe0+!mPsuY-}Kje$a
zi*?>@ZTM@p1&&t-2bsL<{W3+i{?Zu6ECCuoK0Agdobba~l3q-(CXYl(AWo@E-NI5j
z?};%`&J3KviWEAi$_&cVh7{sUZ$CJqyWkZ#Q2nLquR)u{+)x#WL@=!c60SwGCRW$i
zO=cQ1BPxvkMTdHx4(u_{4I5c1qwAva_+HmII{?Kka0#ys)*FG19hJ(acPNcFWa7Fj
z5{0*Fptfic=EniwZiP3if-$k1qsBz*3nYa*qW{j3YY+{+3`Ic}I#uZVpAz}o=&PKI
zmdv?1hZfU~9b8&0PBr;HE2|tmOvK|$(bdczl!nTxrpu^TV%vnP#%j{Q%2fxli&n~!
zHl8rRHUN#%6AA@XqnO>uSKJML7}Xg^*G(bsFa~QB-NOWKQy1`Olwu%{BUjZ=%u1+C
z%))ee;7^*u+)KZ>H>ur9aUh<ML07W<qcnbo%l#W78ABCM7p?dsiZ55%$f>HqVeNOh
z`t}UHxUM2I*>l3(%v0D~oadVs#>re?t&oKO_Xr4-mlrVn^;$8*HRSR;vjffrjexvl
z`(gM5pP`1u<h;YP27q6P&N;uKVh7f?MCZat-Ee2(;s_6t7QyX-1aMHY^i|sQ!~u)8
z66NoS504*G>M0^k#KV{ey_RkS{YVr<XCP7?v#}Ktaf$6kwDioN*f5I`0RHxwKf}K{
z{UO|i#X06cNtesHtoue<!lN+KKsrxEt>~wD{Qq>AKxZbhM)N&&ym<iMsqwuJPb00Y
z{jjBOV>MrO&r5i1A`)lnOTIrgf?ow*+1X829Pc!34nBtph8ruzZBpU=Yof;Ldq@jM
z680eIp=&y2PD;kV-s=|8=`J5omx<?_ScM+DN=Og@N;EclWRq3*<tp(b7*+-Lg2)u(
zflqY@GmcmG6){|!(yPe#V8J~pILE+<k79?@pIjq|`3;gpx4%E!g%TPoX_v&gOaPNX
zZ9JQ2u(Trd2pPte;u)dZ63bI&a*6wQ$qI&nOP1;{8$D@dw+-zM6I*CocQ(Y8HF8tL
z=F-F8U_ibU5MdKR2EjXt?o&K#bM~|7fsRlQ-kjq$V{ko>K=J_4q?yz~O{KyQl@hQi
z!|C#zpnu-h16CBr8vW)oCawA_p67p(9R!HUcx=h3))quE=lVVF>vc+oeTO|Y*b~B=
zB>%1R{QWY)Y&fSvj`Qy~2&5s9-4*6>5<gC%02}_g+VD)D4@MYKoS3AMWkQs%Nh#iB
z3i_R})NuSEPx+rql@}KKqZOq1nlXn`&TS&se3!vyY4Sjly>K%?o$@HnH|G_SmoZTw
z^?2eXtn93Jt=Isu*i5<T1O1<*z=xyUWE|I=eAche_GYJ$dSvJkIX3kbO@BxJ_+eWt
zOYJv)B}T`Q+kuAjkd<rV0XlI7?{4Nqw{4~1X5hyxfN;2XpS5`y3wp};dZ{|JNxoer
zt;2!LZ>qmDLR=M5fi~Oi(s%Xc-T$@rB8ij7Mg}Z+$iCa@+Mu&6$i61&bFGZzsX4lS
z01ognI4M_Q@ru3;^*c>cpa&lv(XV}nOjdkX14G&9GUP0l;P6CKEDMZ%*y?rS0)sfY
z?Oc=t2553oa&kY}GC%G5vY~irqL05qHgU*K9MDJSK3S2)ib#p7gMb{PpDTak5?a&m
z59K+&14eB|;E9UH6L3y8kg3gPDoUiwZ7gsMi<Jod)0u<p@xl8N8$oo=t2o7a-?dME
zitN9QJ@yTV_;lU$()+QaS|Xb?;%eR6?d7)fdTWO7nC~^q4Lw_F)>fGj`tJat80n;g
zE+W#6rW>~|iRk}SxY6sN?3G6!6wiFRk^KzII|NIq1wCza?w{(M&LljtS~k_|4F%x3
ziZ3z2DnJdwftPUo#H%oo^Lb^XUya)42#%EBKc#VrH*GwRZk&(60u}b!Wk3(y=KlYl
z{+NVmKevT#-Z0a4qQ#EzKB@DdCCe+B@Q@#X@0{<gH;<RtD<(4(x_(tcRW_*rZV%77
z!6KzPLy0_z*H=x{A&ZSP<U{JX3)w?VJO8JT^t**IXmeai(IR1g-5FhUA4N!L=-Ne^
z2!OmD=klOPag^FWMkbm12j2mO2SWg6ulxHQY+LebCB7q;<c5gtm07JZekR^|W6ulQ
z{Nxg!`Ty;zbabM9dl$;)EzpXc<Cs>|6Pr@?{UiGI$s__<`Q_3kOWqwcTLa#>H+HFP
zu(~oCN+WVmpSFn+;bR#LS2$w2il1^0>AGnGsYQ<Pdx=M*9qbBxj_~q$N5#Ky4ugO^
ziO|(D8A##ad{l8=0b+m`k<6`698*cWtoCvgVG1-z!@|1YR)Ek8={IqZDVa6W{WI?y
z$;+Qo%ii^pi)psb=|{@TzF*1Ae````AnN}&K4|ce7h9T?3F!9$n|`D%m|Q>+!v<fO
zq%6z@fcAcDDxh3f*J8hgtbaL0IyM5mHieMb)8W_NB}1#kIPzAm#LY3j-yr!+#eN|6
zTv@QP%`b2&2&74m$eRhsDl*@{0w|gZWk@k*dC{5z5R0r|&HLV^NF4;~0R#zuy)(g!
zIKQysp)sS#Hv|mQ(*pr952j(e@KcE<myK8=hW~rTtzaZ$$S0h^d<#mkH+2J+F`F#J
z>{L<5Ys5_#7TT$CkCIkx!Z`gwU`f}DWxmR?y&9kxL7M(C2y=kkvRvAGM5VU0`|CMM
zJBa_(#C6`G4!&dymf@OUaM+wo>Di;nry-p=R_04<GLte%yHOST+k3GP=mxA2IsR{D
z5=@g%o=alejUiASZ9&+@+WBE=tg1eslRcnSCvs0~zn5k;;fMn9CF;4x;u!X>@CN*1
zUi)90*=GE|-)7KeW@b%To(CPr_bDIjS&J+WpTe?Z_Conov|CQo$fgMV>0o7^_Nmmu
zTivh!+Z|%nDvXs<t)wK${g(+4kY+;UO8LHMB0t4FGkCv=a$K@I&vM9n-uu`Y-brW2
z#6N;tURKh>&onS0SOl$lxN1)86dU+yq}A(vM{}Hm2*Bk;asx5-jCllDj&Nj0gxm#b
zfJ#dhiWoYTX-EA0AggT3`rw+PihB%e7k5m2ze%N+PE=hW!w=nDcp~^kOwKM*u*K*9
ztL!wY|7C(&k{t!KBuZ>j`@RsnEwknv$-T=q|2x-YPX;S{{4jaLSrSPui2YljST!k)
z^))Q^<rARokOn-VeJ>lh45u+zdddtu?8Mdz7Jsb>jlR6+mFM5vaw9?WaYo?2qA(zl
zQpQC+;d<%VXm9|mfaU2QeEr(}Ln=V)27BdQ(DnO_9CJ1o!&G+7>6dOv_819bd9>C@
z=@_+pUj|(Ii2av{{@>Rq63d8}89$6OFX{oC+lsk7%d4)4Wi%C>dOA+3Om36!x$AF9
zK5u5cp34+2)rjHT-rx>KJ}{<-F3P)8etiov44@ApaKVAwwY|>X&ARncRH9djWy6Yk
z9BU!uw+FMgd>#$QUKpKgH=c1(S#7s$m}68X=lV$Q>_B|!PmazHMcuyUL1#acUV~>t
ziT>1^RbV0?YT9(q5FVW?U1a^_VQuA)7`wMi*%Aknhar|g&Y}#M*@(-FU~<*6R()5d
zs^9;6=)_7CH-s{Jrd3b$;C3&K@ElsD+$e16e~3M9hTP46DyiTzGv2WK#x2E!NTew+
z$=!D_1s{WHIC-=`TdYd@umhcsIQK7dBS5VHZIzIjpqHsw2HGN2S7)56B!)q>OSx7>
ze637H#jpf32w|fmuEAa_lh6r!qyPaMW9!%K6F#V|i>_v(x`{ubU!^UhoxDj)<a|C-
zGjHZE>MaT@@wFP5U7Aat1!<ecPe-tTNY(|bYLa&uIw^-%Lcd6LJF83b0~?;$_y6~9
zl#!QytDecX4@=X}`T^E5)M{)PO1{iBDf>cGiDVCVa7Gjl0wYLg{;?*0IppuHl#@0=
z9>QYN;R(ePNRqozNd?QB>}nUJ+>)+!g8T>EDGfW1H>1}*G=3uzn~i4zX2(l4JR2Tc
z-x&jB_SVDkD#tHc)*I?iMk)Po-0rS(&#>(c&T|beh6KVKzP;&hQ(~mO{SW?FFbu|q
za5ZpM%V~k>`w{1n_2Vt`l(N~SNe}uc!Z`VclJq{Ha7vA+%7fW5d(m{cO&cnNTl_?+
zux_&~R$KvEygn5N+67tm76rq4YK{cC4Hf4+8Rr<{H)_^vFnrAf-)d13*$Ca7S7=-@
z)Cr{Bax59r47^sYOlH|RCEynh|L3vuFCHPn#^ZWC4i@>xPpn{4y1!>Xr>~8et1X_K
z4<IPNMqf=G>{<eScq-76DnI)C>*m&kXz!47>8$rZgKkw85~wJjr|!QM5QCAz1p$=`
z>r=<%u6h4We`K2bCz4P_tBMI-7Haa9aEC+(Zvpy(I4cM=LFR!*nv2!4ofG=k<V|G~
zsopT=wtM%F^O9@@Xm7E*(|jW9QHS0CsAJ@#sNUM2y2nf33tADVW-i@kt;LhJ*-5@8
znBA`N-F0?w6z8!_EY)9XB7s7({dlnBds0U}{^DapUwu7%?1|GCxGAyQRb8$<j7t(p
zG8MWe07ou!+Tc3L=SHP-uCx~A(b=Skx^buRX5W7NGC<?zg~)tGFYJKjJvyd^Y9u7-
z*7b3oZ?10vBb04JMIsdEk$>&eZ=P71EYbL52Wp?kjl#%CMA$|yt%8^q!vkXy(28h;
z665SxCUZ?{Y@%2>|LLJmzC%^3r4cFA$BsV~ACZ<sU|#_3<tSijloWe!U-%FaH|+|g
zin_sKpI+Nk)_A_}Sw?r^`;(k0Xweyzb`Vd00qXzDGX_>f0f&lz-Ta4-5e=F31Y_rA
z4OLn1WoPu<@||ca_k7&u>tx>YhCC?rZXhl>3`=*C#`)KkHw}>!?uh8_jcBG^q(8&s
zvnx=>Q$-Q}7M~pBzu?k+QyJN1*{!%m52iB?sZedC18Tt!A3R}XoTn;tC;q{-gk!PN
zzU%USy}aTMJtFN7=(5J{Em)W8WiX~q{nNYkO=AHYD{y@<BR9c^X@%zqjnGa<ftBu|
zy`v2v!2ENfZV2o!?67;y7#YC4DRbr@j>_I7oGWoriysF0@9=8dNfZ(i(57KiGTMe6
z2Io-*A@8ZZ-{57?oLo?gg$NSXtm@#YtaUR|={_Kk7|ehrH9NkvoQ->=mfF5Y>9(5I
zPf@%r6y#x&#$YuE29If<-HEM@wt0JTar5AtBb7_tvh=lyb?Zijj|MIM!S;zDHtH=%
zWp|16CQz?B+gS&)(M-bxZBi)-mCfWNG}gckG(PxTuv?4@dl>GFf))n)JyFv_L9_<c
z=%F~HC4-#C2mOi1k8dU3#F9jx0a$#|jW!oF<N$9|H0psr%h1^l)M9Y@xlYHzBabC{
z)jXv$dN7yud?~G5$m)|X?Nz8KdNbge$cdTBWL~fbcYAilr24R4bLzaNA$3Ofa7=bW
z6gp5|$CEMotH4d8(yS=}MDPNivGB}*2FSGdv_{h_t01{}NoK>GtsaD@tcr9zT{s$t
zwsq_?2W<aK>#=#&^Gr@=?X7(1AS(5Fe#riX14p>Dc!4YxAyDIaXL|(L@)9f4Hiq;n
z3T!^{geb-<Oaae&q>)m*A;fcFrV&I?Q$Ah<^4TWTJoOJL4fnzJ{2CB*G@R3PvGCHI
zvyq?Q0`LPXeE(@&L3Q2q6>1qn0{&;3Uj(Uuj4Cvpy|wBbG~VwQo_3(mc?C)z=uC@o
zby{f1n&nFRm^nTzhHNc3!?^tXRrl(HPzaoPKeq$toua)LZbr^u)Jz*5$sSY`-xlA_
z3_8D5o|S?#l=v@<j;2B5=lnNw_w6PYjfaetTjC@<niY@g0obeKV8tO7tH8?j)r6j;
z$yx9S-I%$7w-FgV%2WQJwB9{%YSBAg<MpF~gHbJ%Bw~0Lc!<Af%B23>VZrPtUas{k
zFD&q@TlP+Q`5zHcvFZx7wYX59;J&TB8zH-KCICb1pPP9m8h<|I6mg%M<_8pP(=}QD
zDQmmiATA;mDcwSg*^i%Jjms<rajXOY8J0<H++0XI4&g!F_-yrK9G4=f4TG7d#~rPh
zJ1Po~G8#H6g>(rcE@6U{*l@L|6xFr9+gqF&ArfN#qBW}yw<uMfNjh$Q|DpRN*0_ZP
zsx^<qd#WFs@tvr}5;)6ztgXMaBH(O07J)|twhibmlWIG%L2Hswa#Hc|6Ve*807~Rs
zbtWe<LPhPtpg)<-+BHwY4=ZtcD*gBGtlz3C&SZ;3G$C5WrA=e8c4I+jc__okVtZ0E
z&Ww^vubV#QDlJn>b9h<q?kZV5M;ot39tC2*s06yJw!@=l`yB?T-rG>Mz9g1-${&_x
z?TiPsFQLpg>>8xCUY$ri2+r`&8Fr3!){Gx7+^M>5M@G^)MtPHE&ZhwdJn%d37D$2+
zEs(1JO~}}c8SC0mXt{c>_2lz2N_(xmT|*lZZ&sOV_uRTk&EsZJwc~EP!now5W@1UQ
zg<-^<n4;#D{H^JVs3Rt0*IPd!0krA&jKP=IrHvV(w8_FWetCXhd3Og=X69_&*erQT
zRwE+HfrRD3O}Q`c12JQx_v9+@P&GAm{a0vcdSDGP(*?zADr%HHY*~mgR<dsvV8=#*
z9QT>Z@Vt2-PgWTmG}^>8n`cpZDFoL^VGVc-#O8wLY&lo*R6HwsH-9)87`4ImLpSbO
z*-*~%T>tvs=jE=1i!fR@2?PaS1_q!41e@eMb>5$2`SwQQ+XTAvQr-S{g-S4i3Pr+s
z`Xe&=Ef;bjg}=KuR2_8!PKdHDXzTb}Vpjth^1s#>qt-Ps1VYN>qK+Xa5;Gg5t3BCY
zY}7OaRB%bVX4@qc$Dc;J5CHG!x_7mEBrSx+Q<oW95#g7~nfLo)6+Z~YygzRAE_?tA
znhfs82q5zQQyfg}G&ik;@?3-XuahsHO%R8%wBx~l8&f1LXBAQOc@+}bQipBWQ0smQ
zul;X7XpU`V^>K%v7}BBdy~&SS+qGe?8Hv1a3UK=!Q8zSp_8J3^SBc4^9HTs~;k9N&
z9G`M3J?R74qqPeW`br7?3Qmh{t*kkq@6FjQ0qB~Pp7wSF5U%RV>Ncj^t^iWFe0cvi
zJM(iN0kY8oUkCuR<JRc?;r_g5?s1<O{YZ|2`5R2{{oXbq#D*MpVAUtsa7*w~?7zbk
zULPPp-scf<o=EAzfZp8KO7Spa4S@Kn2ueIwe}$a$&3EWy`jP>W7U3b<YHgU@B!33O
zO~d~MI+MvK7{nnXSZQwxM(>E7JMU`CG9Mag@ilL;_(~3QxfM<wblzhQ6r^cgrqW)s
zTjJH|zLs3R$FawDX=_GHtw~?;u!3=yahtw3a_7mR=aKIl-^7!E*I@7^{ro-RlS8eA
zCsCUs$~KDAzY4o%Tq_rLyiPihZB%S3W<XP;53KT^EQ$ZZG4xe>G%xXc?~Cddx3~oI
zzsUKi8<!*PXXpmy5^{lYHU4Wac0PJ}?&?dkTs6qVF&^pgT#{aUIrV$^>d8i|^f8h{
zjkeJH{1*$@&Brm2CWU$?ikoO^$Hq&lCCgsFpS@(punfK`8S?K4MH<w!dW7pdPAD@T
zma}2)3$<y1HtRipFTW`2GCBOM#F#)Xaa}Oaq?+U9R3jWd_-^3w0TaKP!lu6HKXDhO
z;#N7G;^|O?FNj?{=R2SGWif9?n)zga=QYdTOpQC+dCpS?KT~I`#iC$h(Xpxf6Gr&#
z)1PZrk&afD=O=AoMJknZed63nkGB|GN%M6#Qg0%{&iFxmT2U9Sv$R!=I~{5Jm5M9~
zwuhYWGNgfw6N}GpBpZ3^jeg>gA3n&qFys(WDM5Z#!iJq9T>s%ze$IWe$%{tGzx@vh
z;q^I`5c19H?`W>nR8+6qOdZ#99-|BEOF<X9H7t1)P|t5lBXBf`KSWXH7S^x%tRx}`
zz~RR9at#<HR}F*qEq@2M{Y-_nYJA3|;N^A><=7AGEk|ApS-)z-5roAa`nVPv>!m#^
zBr_}Y4i?Hf%&#Z-X((Y32?qlP8Djx8j9zqd{sP0rN>h$A=c!j5o|~`sb|H1xjK#yP
zbl;;O10Gt)*3pL+GKg(r5VEpq+Kz`wKkq$3T+)EzosQ&Q9@M%qLESPspE*c?-I!M5
zr{<_jADxO!f=-pAi7K;i<SXm9wsjmd;<xa&-urpP1=|y*U6A0t`YF23P<*B6K$$Pb
zIIXrsp=8O_!iMeT;Vpk&5Sg#O_|35S3vEmo!OM~_ljtGb>n}a!TEtGzb`{H!=Mnj9
z>NT@>vj0K6hB7PoD`j%#7P&PxEOse?zfl@9+VtPO&q5zuO|ssvzO2gR+-CQMCH4{j
zC{<q!Lti(AKH^-f{?+qP7tzgv3cE*bf=}N?w=y8#AteeXUYi8WIpdH3rUIaLk>aRj
z<YgYtNjkYTahc=@7=BD*T+Zb$ui&;Jvjae#7Rl-Spf9*0+nadP@1IfGa?HZ@<kXqV
zX#ep*?v3wf%vTRLM;>US#}rQ>5-#5OODiGSZd`cEZ%TlLr;|gS(y6@2E$}^VI!H~4
zi!a0K@stZ;@N3xu+HXe>$k!T^b|Pjd9B<UbQttz!iP&!}@2S>{K_h4Rw@FH`XD-Dn
zin?*RmPI2oD~xNp(#G@`%~pg$A_O}xStwa$&;?S%`l7DF9GGiM``FCIT&KUsqQQpX
zb-sbMlqU27L~7?TbigcV;r{c(;}XDoc+N~jsB1%Xxm8rt{FyA>rIM@IuB<Y6#B@ew
zd$IkM*n&6<=KzP8_F?7QCpcrt_VtbO8%lHww6N`6HrhLOH-6_G>hSZNh7gW~-?=PR
z4Nu@#2-<Ywcb0VoKh9Gc@De$%VnDy%;wPgh`m+(tXelgG6+gBIXet;vd4?5R(r~Fd
z86V&t`3me+B1V?HCL@yS+;d*Z!;e>xk6Zy9<zFLvJqJ(;G=EQi+*vs!6~u^dvmO@a
zAQ|{NBpG^yuCVd8J4F(w$7$ky>cfVtUy`td8Zal9WED5;7HQN{9DmJvMCX}toOkp#
zrMycL9`<DN_*A+I%wAQJ8Cbu@5P1Rbwo){HrjR{1D6}b4cYiUyB7r_tZx0U1Q$&f&
zD^sUjrQop0wuKV(nIXNY8m5wL_s)GS*omRtGb0m|tXVRQag(tJa87>@<ri)jQ2+Ea
z(s%S(Pz@UxS49U^uTJzmKP6s)72W5|PBPXB_ReaLKdd3g7kEtUyfe7R>s5)IDo0EG
zYHakHJnb{r@ZroFykK`&Xt7_#ix<K!x(h?4u-7Fu$>+1#jF~CTF!4upyP+5aBS{IU
zqIL<^1ffqH7lyNh*VEMbo?|-STpZ+AHXH9kO2#-YLcz;ZK6dl~G;&M>=;=>S-hk*@
zVzF)C(b$a@3r343oj1fjEzehQqJv?HS`Xj<+Z&O;7ILud3bw)tD8|}WN<-|7ic@)-
z2aHSkFG=LKn_IjdguO+Jhyxx^Ic3O_@xpEV?qB^X1dDB)1;c$HfH*TBmR|BkW6j!8
z$FQ$;o54QG5V!@i^?qQ;jeWVF`n8#hVM6xOsgJO%G|20q^`hCM+m8t$ZRE^XE%-^0
zh*5Sfh%cU}i3Gy+ge7_bLa2PaUpp$Bof2KmFTh|}Wx)${b?S8dUffoRPu+^4g}+&0
zsrBepZ`yYZr9To(#P9qp&y<qacot;$M_x#h0#w$g2O*1VEVv!}^C9q)4eZksiostF
zQ>dBxoxwDyWq&Fw`{@eiDC3#FGbM4DlHiKk9(96h75!(&F_w@jIsYwlYPTxKom|9A
ziGg0ZRJ+B|iSrN1i+j0tHwOq71f<w(W<>omyf%(!#{GM*T$`H0>#YgunKq4*ppR~E
z8Ug|OP8Bu1^qkz5%!`0Z9z=!`;1aVMyxy)<0e4jdwCp18P0H_p7ly9%J5N0%?bwTu
zli6vmICidR&FT7d`?&(jk1?K`0V)cYRs-7SqSO6(DRV*Wzxm1%7GK?Uf^GShOoXiC
zLqbWYV9h`($>$4d^O^oR8#jChak|AG-{bi)x?^00H8Ofzp0RU0>EYcB&YTjl;4Kbm
zEkBgbQ2dL+?u9M}c|HoJw1fZzm~d;1Nbb0REoF8-F_(oFCwIBpTM?qy=#f^k=hL?P
z5(=+a?&rl$C7p<ous{T{m8;mYgDJF<yn`wEAJ4K8pzq!ckthx`-|I5am^F4&MfV;f
z2aizM!pO0&U1~VWEctKlD!wRy7`Mn^M*6uVVWx|eZWV)jLwdWCt7NJ2(V8w=WD*&8
z>AO{O<`N^mZW{-n5k}^F6voxKawo>3<3E&6PJyb~wV<&^NFbQUcugB3_g;ovE@NNh
zo(kH{L$W(Op5DG?QPuniS~dOizKeUmN0?`bzrkXgb=30FM>^kWk(XIou6-Ips^v2S
zI9uaH`IO#3M;`)Xwe)+SfV?}u%j(KR-9!=DUy>5G8qwa#r*3!0>3BK0Y-=4iDl3l>
zj)f=#hqA(kwwVnMR=LInb{E>hy^qCm`Y!18<`CCH*d98*9KK|ivJkDdCXyz-<HkQw
zG<GylTSa>cgt`RAbq|>XS@OtT0?)4EG9J!*(Mj7OrFEL?yd_RFexYQ(+?u})-P#YU
z_ddQ@P2^mL+s%=XfKEK&K*qz&+2JOJ5LD&#+Q5{e&`*OS66_q*Dm+rH+pEC5<=!wh
zhGt7a|MESrv;&y3>uiIY;A{xnxz{Jn!Vnhrt6IyqSbfW+5+70NZ}(}iHONYPT$K1?
z`C(uN{#wNO_tu#_D#<aMbu-sU%Ppq)_=h&F-H&(Q_!y&GImg1xJ7=iWb#eYepgrp|
z8-RLXntiWHf!WQaj(+NVF_H~2fHVGOrh%PR?PZ<j__>^0rV1cu+>h^-Q{jAI&m;YB
zwpGl;`h1-#lBzHV|Hz+*uWr`;%eNkv(#(mh$+b8P-7e#3&T79tQ@7C5YS@7LnWRtg
zFz;tIdnn(Z3^up7Rv2vXzJwH<tYJukSYhOSbcK;j{(N-}_aE;Pg~xAmDe1%^;m8C(
zo()HjZ&cTh@ECC!wubz+j5f$)tkgndgemd(NfzM@8a%Xl6?kNukvvP#_?~)Z;R5><
zrCuUt9a>RW5A3wNKL;VXefRlgVO$i_2#acZJ@o<oCmtW|h71iELR#MOSjt*j1lGjV
zT9WKli$JV?2)--S{W-2(czR&>A}IBqM3)KtN;4+S;OrASzAj6?+AMk*q<9{VQI^-B
z&H^P$VphxH#r<XQ<GewY!M~d;==mTi4wBY^*$E6k4XP~6ohK`{(=M1a=Pn4u=2Dg+
z1P{e&We@3o`Q-swQCVg8W<fN;dQ=SKD|-KvfEQumm@-XpE(j<f;I>*4S)_ql;$Pzn
z@2^oV5!h;xhCAaBZmE-zQ7;ZtE7^qO;7x*x+8_EA8wqeUufD5Jj$5r+3Uc(DFY7$W
zUT(YD_j-A6Cz&6FE#3&L78`K!>ptO;@I+0?Z0cU^e}PuMBf}oAQ9*vi+?60?)*Zl~
z*vH4dWkZN@)E?kr_LJkZ@4Vf6-C#fC*)5~1XIA#nORr^FyOUil3yP?IK_RI|f;Ue4
ztYulN-NZ_OkeLNsRc~3@Z>43nZM8CWE~$l|CK!A$81OGfN<7tF62=`0ef0dvzXhJp
z)mQ-!K!OO@Hv5_@2cx$Z@6XF1cSxSpnSanb+V2$e-64OL&?PL2k!qD@g+ze36#ip@
z@);~vKo_5?SCH1fkjs~tJN#%I>{f|w5Om&m;i-?(F=zR;nDW|+@#&)7d_p}$QxOX{
zq<e^Pd5DLJ6=XKk$hZyED%-A|!4%AkE#&X5kpHn={s92bcTXBLeFFPH4F5`;&+s4b
z>b;46?MhclZ^Ft-$vLMlYIblQm!7I{hV5<Duik##UgOYhh7OF#z5S?&1zWggYrQ*A
z<6ex4V6xXtyUc#A7;5eN3Gzif{46L<0T4CA^JG3~<aigGZ6T{1zRO;36}dCR>N$8>
ziAlb0%2y&ywD(}4KhT^jA%n1Q1@f!<Mam3xo^TBxX<BhwF;|b@N_WjCTEFDq-;=u6
zML=oB1b{9q>Fl~}Yjqbr<*$_r(L9l8KTGq2oqf{6aR}qyY32S-0K9w`q_TiJ$F{7z
z<8y)E*Z~>+(+{uDb~XnE$vAbG-bo{18-SXoSNkr8(|*WMi$<)na%am>u$TaR8#csU
zEkV{wtvwz|4Q|oEE6?_YRSsS5LO0ViBb=9aarYX((!+fWlcC$>cF7KfD5Y6nE*K5F
zV|n@Bid9IyT?(f{U_avr<C;Qks2GyCyVzs7D`~n|S%XRNJ%rdcfn$L3H3rz3@j^%S
za6ka4<aw7cC_o*gy#`Y#m=OJn0JFhe89TnAnZ0H`?>+eQJ@XK5%R38<JT{vLr_{*g
z?o|+Say{&DsU{(1+`kpxCoLfD^$ei>(h0$>7EQy3y|yZh%B5k!ZgJ6nr}3P@=sfUv
zOpnzTv<RfVRMvVZWxyM$)?MGUAbN&RZbnHH?S{eMY4>GnoSqere{5R&d1O9#Bo|Aw
zR{fDvF}+%Y2I={a+Rr#Un&zA`{O12-X%;WR>B%v_%UHms2AqBX=k4RJWy$ddLX58-
z0#IlV&X^Su<9Zi1UwMtI-{MKdQWSRkc<)Sp<E`HA>9uiP4Prf-u*`Jh3Ewn+e6!ks
z$gtZ!-Dc62+l%GsC@TKau{{4}of0~ovbz?W2|AxtauBGfb%&ShTw@I$FP@l*^)1A?
zYeP%inN2=l;0~9+PEsuzW#z^e5*@^%C!$S49obS_MbbdNkMm#>Yw|)ZD*h-Jhck6Y
zgEPdrNe0Y@vG5^HTZKJo52xG^=gwPax16}f&m<mKALt6LF2^vwh(Y~DqfL*{+(2h2
z7*2AlqTf7lz+@t_;3?ZL%j8)Wr_L}iEmQw=9;nAW^BgW>(ArM)te^G(#-*y4WBpZ!
zq!?`1dqx{`8XE^fDL?5bpdeN_6oO%#y2)6h`gP#buy)0(vDE476N%oPrW-u&@-i(@
z{A4hYHs@FLuimGZY8GfAC^s59kqKx_WNDgt{D=gqsL>k51?Wo8=A@>y9I<)l&WvAM
zMt;=kpwP`w3;ixB-HG!V_L~NO3~y3pLs0LV%T#4OA+9ivQ4B`43I=NnFT^9X>w4sx
znyW<A;Ana$76#`%ouUMrtpdy_Sge;)xMto<_Zk%UW39@z<~MatwLiI5sHJZhTn2$l
za!-*L-5*R5CJ3bymQ_MA9n_s~csj1%*Cp4PlU`stq?3I56uA@4Mhe5V#-yM~(6K}(
z{Yqkk);WML7`6rpiTgsTEJN}46oW<YCP4=Rg+axwJLI+N?1C((Z%;<`KI*bI0U?#i
z@6<zLF7r3Jvw!52A4#m+Mj$NwWkAsCOzaY0RQSMn*^63kzzcQRruJCdVUGAIN!jzh
zSiw=8&geSDrv%HG#koJc?b2Ddo6ai}6UGuMKL=tlI7{ngeH_hnT)Gl-{4Q@1e6?O3
zgQcXw1g|&=qpItS_I;-178K2?U@;vdkO$UeKwo6yc>~rB1~E(7nJH&%8fCWFD9{L2
zO&z@FY4Qr;;5Fs@u_<A+dBT{s)sJkA`qP(}6p9pMVavgZ<b;;TpEfse%}l;iNHSz|
zqB+H9E9I)h8)4_xU0I{i#UVQv|JK(?I3YfauiG`<aT%IpBJ=vR2A*%U{!Vj!!}XV)
zARGK{A~&i_e*aElo=r8LfXlz%@KFGeLC)<aH1#ot%&qqe1{cyNJQH=qf2BiSZl7e^
zqL_wuc1WX$O_GqM3X<O1V$XzXmnQP+1r0E@srw?`N!wI4>7){l86#-~DGs+H%dJFi
zSZ%}3($EDbuZE)~qaGlK<w4HOYh(ENU^Cryicw#)cERC<^!hRr!t^uWm|#d%a3ARm
z1z79RRxj1k^pVZplx4M=?;y0g(+(D=<-<Ur^wyaE<Tqs2fu)3}dOP`K!M>*Ag`;5q
z+&&&G{HqLK5WnbK$tJb~yraXA+oQgH?}W80N1mq?ZqpuzZw(yB6cGU1%G00Lqm%}A
za=Rp*+W6lf$ERy-&4cMzF-w$>Q*?9-OP~_8yjCC&ColauR)&qjZ-H!%PSn*{O-a#2
zDXSFBz$yYkAf8zQ*45F8?0MK#)($qRux?nbT{$QC)Jia5kHUO%zE*4HN=jr$JCZvq
zeo~cpM4G`r%03oHj?dJ$4+LEVeOMIK_S~4`u6IsE#gZ=S9n+egeZpq|KYNc2o_+tk
z@{FX@7ujJL*A3`=g}fuwV-j3jW<%t}EpzwqCdFy0tmX<Up%wy9G#wm^UcAammr&H%
za#(x#mHq*-=<Y98xiW<x1$s~?lt_6+Pf>BC>TTnqBMV}UUbiJBfUX3Qkp@uZjkj!U
zM@qbMH26A}C4hqLK2lxdfH6$mm4~x@RN_vJE8q*Dsc$f7!n1KYjMvHX(}O~gn4{F5
zJzj*xp}byu5k#^cu|J1JCI{DC%&OUzf2kiT_Pw#{uUYL|>-#7-7#@XZx1;@6UHV5d
z)s{5TvAv>#{FO$Y$YN(L(W#W*h+jG-zeONI&?z_xyBb6soDg2B=!bL)3Dx@II^5>X
zbth(@Hfky%@U%C%5W+$rRmw-80KMjx$^V${6{Y1ZaDhm?^;$AExNxcn7fC-FZqBfH
z?j4*dLMS!8Xfps1L`o=kwK%by^file$Z!C>EYMQc4jatO7E}}aA_6av#H_ar2Fomn
zKrB2wTESj^YYZoHXZzG%aW%KGQH?G7wFG@lGJT<1Cu?!<gN1qOs#9w-n_hg0z)_h`
z%+D-Lf;O@|Atr~xf2u!>2e~{VhH9;HSr~&wZ!rsq)>1J_Q|O5jJS`$I?<!m{MOSYm
zXBiTef^yX3Wt~WdP-}%N(LbR`wkmR<Kl_jhuvs#y%sh~1TrSKiqU*#h7V2r2tuAyb
zeu@_-mLWsa%eEyi>Su(4CoT>0JD1xD^BYR5#cDQco~9ZRKj@Lfd4!!J?L~-&o`|yI
z-vK*UL)%$kl+2Zn*JP^^sW*r8$@@E`QDELL7A?414|uJfotTu3bQFYH<BA}_j0Hu|
zpGgIP7Qh|*-F0UF!zzJxl9zinmDTRno0!7Q*m`qCszeSURy!qP?o`5ScLn$MHhpMy
zAEc>r`KSJ3Oef>9fOE^6l5HPq1pd96K{&bOvvDEkmYfeR1f9@voq(7tlR<Y7NJ{aT
z;ve8qHZe!GmS9sj7&PjsGbBcb*J=n%9O0-VO*5(mZKZ{l{L(6$5vR+s)rZ|wx;FRi
zSZ_eekmQBF4Iyc`T8K*e*ia|t_?CfHGP|}>Md+M-oF|FiuyF5Ehv`CzI_2l+r&v_q
zE1pMkSHZo@_{@)AX_~SJHJ_%gTjo4@?YY-nzYjsP2{;_pC%R*zMLs{2mc6=6Pl==r
zP@=g?iY4X1YGM48`TTWc=hnd+8Xk|26{`ORcriv<nE7FeVL_p8DS7u0;9okL;AH*p
zjrz84N@-n>TPcxriu-B6T*O&ib5tY+b}a?8+Xbv3x=UB7k>nm@_QrtjfN$aukFH)$
zmVqp1asyr(O^BSFHBwJc)hxZ(Oz`pSf?e^pn46^|ZC-Ks7%N=0RgvV1E9<oM;-F5d
z-e=-GykV*?=&hR}q9*}tMUJ@5H6C@!+s3wHIy?kTwAv(+;l)Ks1XtKjIspa?mWzYp
zCF!xsCw>*t2pM}$W>S0wB*@ALjq(IXN=2s0``6!Y*AC}IDnlC?yIurbQ2ZyP4+JMg
z-hFpaDG~dEFix98_Np3UO+Z)thDBK=;gr*Rj1T#6D6*0D_4K|So58>}HLbJiTuu+?
z=bX-t`_wFJI7&Zu=-0LLyb#GU5?U3BPKoJjOTNadezK|W^V4<deohfxKPj*BQ3&`b
zS7R|rXUw|z_xfXnZ1t1ij1aXyD_QX+))~>qy(iE5Qx~-t@dz2EdA<bCLqqn7=37=x
zPKKFf!c-84IY$PynNd<~X(9{*&vuE5H-+&k^D#ntd4d!zzo*S&4%~3i!JNLmiE1WV
zVB5^Udu!(Nlc*Bk`s$d4)AWXVk34hf3c?QOR$=l%jiWbYwXaGvffpGK<y&b#Ew`xp
z`J#_5X#{divFHhE*FSD9h|&-b&NMz<J<}?ecbh{>a8^zjn$McfJblNE*9f28WkY);
z^=r|ggp~uyv909BZ}D=k-d>1hL*7j244d1q;<fW9)<Y6f3@YSf!pP9Wn{M$Ue@2|x
zuZOhKe${wCSG{r2vkvT2ZJ)aV=E4UCzGvjmR`z%|nIl5kK(*zFEtT<wRB}>_b1cK4
zi|X)F_vf2ysnl244s=$o9B<w*dqqifVI>i}s?k+HB}Kq-Yy+@Gk&miH327enr*b7=
zfN!#U4o>G#e9v`&hNbWJ&H_{G{*S4z*8wm~qV4jSwqVlH&XxVWFZLS~S)ZOB(aiJo
zZ2FY=EH2#PIpwP^H}_K&Pjj5%33KnG7^qgMR_Hj34NUQ|b%BebX3l47ubFZZ75pa{
zh0S=HBexLJt_HbM7!LcBD?`_#XgQ`ahCD^4u^6R=)Bk><R3B97=7r_V#rsp#aF!O{
zj7?Vkjpp+Q-?-YkjPK_O4V`du-%N+Mt8G)~k4Yqcf80x2+m{Rqoka`UqB5)wNSKig
zy3Tp`0dtl?Hn8y#=<2T%3snEXWegnv;^?o$Z@^xWy(7!gN54O3Q@P;;i}PQ6pM73B
zIsw@h-M?p5A-S)FmH*;KOIfSbyb_=j3FWMb+j%`(wddlNzuoRz%F?DV&PI!kFdUFc
z14(fRo1)otBNJgP?3J3a%Ett7<>E}=ev?Y4Vv32BP!{(J;3%9(vvy<SzQ->Uq8GxR
zbC&gD+oiB=VQlIKUR5w)ap16W7S?u|(0e^tP$jH*$^D^<@>E38^6S}IYMaXT!}^y?
z&C%QOazW-dFRxebG7*O-<|e8}l~1-=q*xvch^BSOE~UH$O1b?d(K(pYa#xS%X71<h
zx$=TPcSYV?OSt3xS^8_=#M}ttMJ`zoa3>L&G-;)WKdM*79B+D6XVZFbp0fEDF%TN%
zmh|`j0Z<_5%-^DqrfKDFR(h4hyU#WBPX7McD;{A!-4A0k^tY(~%~^w7dBr|LU-U|`
zy4&YdDoJf@Sez+cG6c(H@Ea9N&*&9?)$_qc6PSC`Iz<@}YsM?tD_sP$6KG@HvD2~f
zuJkxOC*aXwTT_}A4_m^>#f%Ft08%$Tqqc{wqawxkO2^iaDLPeH8I`;4B6kX=L2iC@
z?eo>kb)T2(S#k4=f2}cRfZu(BI`R=hHDw#df}q1;((0OP{b%6|e8CfipcWz|#1>au
z`9=C7gKju%{^g6Ici*z@ruNN8;=5=1Y2a-ZKb;?&TMRwR>gQf8ulg9=5<E^B(pFOC
za^@4;#-|-%p1SMf53$(X-bsM8k4zZJ20j0VdHPt*67wu$>}8{dWea8UPm&<FDWiZt
zO_sN$g%W{F)h!DrK26Klv}Rdhun4i;*ERWin7lc#GW#JK=Zo9-k>8Y1BT}QREJLU9
zpSS)vV&(a|QuHdR5dLFW^t7k%UQ(B(Wc$M#{)Dn^Vu{|(!wa94)!(E$a~H@7aK@qs
zesnYXG&)!0me#O8Q;B#liGbsp``4Slo;YTQ`dzI%r{j}A`9<Ak*1Aar+!=v)QZ?z4
z6iT1wAH)^D)akzx%Q08DEP}d{QV}FN12vFuG;f?ORZSot!Po43QpwCb-M*D*BRnD1
z6|pH86;pw4>{GHBd_NNKuTiW;KNiE<?9%u%FrwkX3}V~Z7}DWn2asP3I(Z?^3{b7%
zD;Y%zlII#8R-Lh2XuKP`h#ZHcK`v)|t|{G(jymmk@c2H{{$kv8t*iFE*@hw2BH6Sm
zhd|yChGFA<{OD_|pvOOqG=qL3Rx$4{`69M<@Lyg9<`(gH-8%0K8Ce=m8PNwVw_dA{
z%RwYQ9U!HD>{Cm~ZC)KaR~fmDv!TOqe=h2flqkmoHiJnm&GiLiiIYB&E)a|Zfg=!^
z*;&>)5c8$5$iqltU={gcEU}!qxpto|%iR=&NNTlaFN;||t+8eFHjHBM_otI38a{+8
z(}vga{<>J!8F0aL_)}Gc;woGdZToXGAK4C?{%c79;f{w>;3e?Pvg3;qu{JV6OoaiH
zz8{j0&b)%Usr<6<$sxAz0#M$X?`&&u*G{vmxz{59?KC^cuYQb+)J@>PEMc}O_w9}d
zBsf|ITtY*SXFp^YDrmdD8ksTQ>2}q!-CHlWB7r`@4S*gTSsgb)N>HD67rN(RF=q-k
zGA;)v3r9PI;DM5Czp<NjyjdUz4@hlF-$2|%@7ecmQL}LAt825>v)vX{Rh@~zEg4Hs
z-c(j8TsLc3hC%KtuDDMbL4zJo8ePc^spt9ouO)Cf^q<XEW@4ZDgoIYbU4Qt%->*a-
zboA^v_BVU$r5uh*P>_NU_l@7r??&iB3VMx6gHA1t!EGGt^@kxAX(yp=XieBSBzasR
zH^(YL#9iW#!9w#7j%ylU$D9`N4dw0WmLi@v8~GSQE8O~}ni>301DV_Jh}+}PQRbZG
zHw-G9<jtc6G{6>(K<zgc<vemVHlM7V=Vn!n`#Vb-&?owG=ObB3eLas^hv+gj2^3Rz
zBa~cf(kLmI+l3FtWlEBIWl3Vq41Jg1@TJb2PBCWC9%Qc+25@Ewz*tEQb&uJAhk>e~
z?({du(jGG^sdJOSXY~MIUX9>x^N+Ojy@lR(xV%akM+!uuYZvF2l5<XB0hiK27pCG3
z$ffN59=|MaXUyp6S3E_-W$31DVcVYbQqJ>J6x~77t2g9r(u8|4ofU>Fqad+|vJSnt
z)&-GXpPHgf2Sp9<mu>nBJY^L$!e8!;gFY;_{uX^~*0*W5$uG5@(iS{-RTTJT!}RKC
ziqU>EHaG3I-qi9-ngtH&Qo-ftHi60b;uG$7{NQZ=y~7f;uD+RJWO*BV;gr<YrX9e0
z>sIh+cT&gwLqIxyw(xM-Pqzn$^;g|bP1=2113#}y{qpupcU?QXP%)`>NesN0_5E~p
z_|)X04`l0xxJ~Xh14i#kk7qnQA}TKZqbhEKa$O~B1~w1;ExD?`2K8Ff+Wky=y#Gb)
zuJza5%kPv22j%>VmwXf8;Dax2PElUzwdLts2a(HI7%<GAyyv-oo_gDSVe~6&Fn+UL
z`!DYNT#I|{qi5gEX2hQH&wh4Io|RB~a4)9WA<zGvS>RERRv14o@V+v?_L+72JiwYf
zfdUq2mi9jlme&_@KsWcY;Yq`MmT+plR>j$1xp?0K%24y0RRnsFG#sXTH1GGyxhzpB
zS1}}|tH?lzE2q-$HS70Jjt|q<>YJyLdaq;kD(-2c7x+^1hGv@x)RY-14*Nb~`1~H5
z`AL2dyIxgZzcQu2;r{L~WfQ`m^7bbqxq9`_ZJP*)lK3wF0`6n1azY}`D06H-7_4X1
zjz|u{XOwygwj&}Q;BcI=1Z&&L%!v2pF`oBFe&*<J!9B5?S}@mgd|Tg^H)-@TsQ$ar
zi5>snc|FUIr#<aMTMxhM2qH(bue-iH0{yDMmg}8(xy|1@WV@6m;`Rj^0bXglbB)5b
zV!<%GziU_HrCyA`>KxNIRlg+lPv5t-pLY4W_W5V~K*dpQameStpZsGFbg~mq#0JZA
zEi(93g8-hNtUWjT#bC!iy1mAo;PFLe$5+c#`a2!VKd0;E{M#-#<O~l(@_%Gp+wcew
zz9~I+F07h5UC)rY^Xkh#KKh=#exjQ{G-j>}wYYy2b_k5CBg7@Ao{d5qy4NA+`{*NS
zg)@woX>8c{l@&0^Ne4-f%3ce|>5>V^-C=3G##GSF$3?xDB}Rt}T6LY`Bk#i$!g2T{
zum?=!t*n2U-JR@ZMQHx;-B=i?^(t?W6Mk9x%r{E^ar>eKup&OmJIUF6^|{49#`vpN
z^7{GBx=58oMMcxb-cQdj`_1S>2aJE^iKV?Z9zKw+Jf6IChYuQa6yqk<ioSUH-t(^G
z*T8Ikox7PSVP4s3lmh9({fL+en{rd!dt)33bgr)v+8bfDbLRL$?h!v^9lI@FW_;?U
zL1!CaCaMbh{{Rj_@xCo|UH$2@tFJDvivQDywON$7XxJF;(7^inJYzwW?6VQYjYUa+
zi-1V`C)(l1KK8LHfWb^kTmu}aD2#3|XLoTe5Ego$efGKEX6AmH+G$bbiy|Kv`NU!J
z35!oCC(b*uoHqZoa`=+NE9+1O9vCVp!Sa#)&O)(yWUg_~8YXWUnXB6Ik?)-oBQG2#
z-!BAq{CCOxC1ue)i|+3l*kxKUZ$bI-_kUbo{*srMPww`~N-=QJ1s9dQcHgUf{KFqF
zXP$Ou`OY`KQ+9moj^(7|J0Cjz=VSg{-u#9)mqWjHXqi8EWV`DxIOl@$o_D;b?D2^`
z%1zhbG~zyY7F4G;^mlucj*cu;`7@S0qdfeohnHD@npM`laNX+v*-y`otgl%A^zky0
zJIR8e6v)zFBcHhV#7dTqpLcvYZSiUMu{KEpfc*KmKbO<bJiW}DKfMFTP<o00DNvN-
zMTSZvB}%p$y!+knjy3e8veQm)Da$SQ*jR4A8rPN8vTONuBF9Wh+TFf$-a#<JWTeD#
zf;x1IBl|>Y;tA422P0RAY;pP!VFAyH>t}K88`s7&ohZ>nh<Z1fL}{r>4ylZ`tag?z
zyX-(P2bQD3eGg33uiSq7jzozo+}qyvw(6X%>uTpLjRjFcK=<2kzq0PS>jr;&VA*lU
z9V^QcR|aA1m%4*~he-mb2!JBGpU7q1CN6jSPLPY4)@D;VEslJ5WFYj;mV8g-b0a$w
z=!uaxA0j^~GGg@X!OVSb<;eB~tu&ZdZsZl0tXO8<73<4wv&wSwmaF#H*n4zN<g<p!
zr$@dxvNA5bXJNVXwmZxGyXTiO3ESfqEnZZvx%QgMZOWN_)`oM>JGWeP@kQmFbI+;v
z*j#bt6=nA6vxig^+<4QC<+Q(_RxbO;Wg}iwZolL9a{3vkm-8<;f839!K$aYad&c53
z%AUX6vpjP9N0xVg{oUn^g=gH`PiIGVuGGgObJtrj^2#ka<wOKNmPPxYk`TWr@=qe)
z6`8zBvrnE~-tzjllzsQux6HY%xA5}7DWw|g_Eb5-0|7EW4yQ<RA8(eC5T*CL=RL7@
zof_-dTPs|*^2(c+)mJ~Fa^P5Hl~O+XQAeW8ocWI;6uz)FiEcbyQlBVIEJ@lZt{;xz
zETtpc{2|K>B1$ILK2aKjWKsCaS4!Dzvr^VvbHI{x!U^}eg*n>!Je`OV!a^&=Wr1}m
z(^wEC{YrbcN~|JJdCF5Nt=~)z`A$>LDrCRbMEZ31)A%I&Qn*-gjoZDEKNH!Zrbv=K
zDI<xXCP;v@ewxpfKO1?sxx1Ckf4zBGVeb{n%l`hdDv-lS(kUmMQuf+&ukwfA|Dh~e
zIC*n1050kyH<~#!330}hc-bkJl|%PBwEXR;zqMNCklHUr{#4}8G%~p$CvT37NVUn*
zR*^p)`HPXyk9^YwH<fRF{#)gkA089vzPIA?!l3ZlYp=a3Z-nWT$kj-R5{uF)r~IwF
z>s{|I8*Q{+{Qqrb&pl5lZ+T0J{|^Lp`1;o=w)%79R8HLxrNL-*yD)}~)BdE5C*ttg
z$tFp{fB&>ber_5J`}dVWs}U>HX9lnR2k*V0cE6K;AKfQPeWEm?WM2}uwL=dr<&lpZ
zu=~H^4PvF<dy6pK&MXo*L%<4A=KlWozqdSUVWnkrhD)8qjD#q0=U^?e$<lK@PssZ1
zx8J^sMl{pO0c#LTl6DwN!l<t-=JwhxEn`UpPa0x!TtDAyw28*~@3a42uD!1NsrI(W
zH2bVDXGA`8Fe5_tj?n0@*2t;pPq(-#v;LettE_a^O675L9#^)A{ItkG>@y<!KWo5$
zL>85ebx1%5mVkQ(v)|`LW*NA3&aLIEU-@cz<*Qy<e*N2D-`_2+%VbP%yWO@iog6x*
zZ7s1p*o!Fek3MZn#)7$uB7rP($)(gG&od<g<dS(y%02(Or!2g?^S&s3KBpy<339>$
z_14Jb=gEVaWy!f$f=iIc2S&E1>F@LZUS{7uyPPrSjB?iEvnsiE{?i4K)9TbInZFJ>
z<dAA!??rJoS&2-WOU^#~>}o!7Tnfb@qV%nAeXA#;^q0T<rE-IQ$xB`wGR<9mUKPmU
zagQrynPp0O%wz5<-}uIvA<KU(7hd?cGI#C_BK*^3taV3}9x;g3;reakT5*iH!)9?E
zH0WWr>e5b>o*&ne!G`2=_lBWP+7;De@H;=Rk)^%kdO)8jP2L_Qnz3GdTc=E-ZT<3>
zOWAbOfqhBb?M^?v%n!gZH^4Y@WKR(5#VMzpQn}Wd=-Y@=r!*3x^v6H`F^cm4%9p<M
zrHXt>z>WoqyXDfJ$PsxWR}%^F_~j$Rtb%thC83vqlO1;0q1xj%ngdmv=`Y9qrM&t7
zy}5jC|F4xhZ@DjU=Snv}@`Ax^F9{dA!y>;hGB>9BJ|84$IIEC7CCJhW@?|TO<#%4b
zJo^Iy(k?!toD%uOMJJY%=AKkeoF6}zoK#MZ{Flgx*}Z^u@c@u6O|Q$q)HhymV>#!z
zbIP69-Z|nnK~n6U1138Wc=Am7;p#=Ujem2|d*A!sDuQZTGG1u*9aG`R23873NE~+9
zVPiTs1I*^3$vC4-yQ=4S&JDRAPE^8^2J_z{|6<86%2xBXDzokhiCjFZB2D(O31`kN
zk1Rhk^3x*=&;F;##zKyR91}^zwcjNdUs8_y^KljNnCgkA#J>CPTb&o_yfVKF{=n7K
zu_%a=1D}VQi;^h${+r+Y=ITtzfBa)0@W@$b4V<TBDPCroOM@)EuRP-!Pb{x~^-F_T
z9XF%BD8@UD1jltrls2qk^{WTNd2cof&ai%5`^5E^+H==fYKanNImiYV#dTR+FX-XB
z50C3NaV?JPs<<}k6Q!R`TGTr(P_(Gr&!$y!Zq<%E4$RE<C4Kk1mG{K=0x<6$goeh;
zo*+aA=n!ennOfsblmvtjoz0m^owUbGb0SB`3YqR@57KCp#anO;@;p`c42(93WVFsY
z>r`jCjpg_}_l$GP?mO*X-v65Smv8O+t#Zqy1Aa0<=CP4QBjZl@mB^yN?Hw6G>cwSF
zPz9Uxca5A;=|n8s>6)F&2fp}$@`KO+pv=2!U<3UP7u-;e`1%p$Z@>LpDIIhPxBm0i
z@{=QeQjYlH5#^5C@0gVSum?e(IIpDcqB~r&?~&q!(xy{XDwbAcfA`&YujVV)XFl_p
z@~dC{YRuLK;NEk;@|CYlN`x+!Ox6o+IMJ5%n{T_hy!uD4E^B>jt+LHE+f<?9JIO9m
z?_gmE5r1Vc?;rWl$o9TX$HZF#5&!(>KVPj;8F81FgE`Yt750sGN^KS;^U~R8pIQAK
zS9Itsvb4-HSI4rvZ(RRfmRs(>V-5Xy`QsmdTjtE^M=KqVMDdY0ZY-nTMOrhi!{fTS
zMr@Fry^yFLF#qy#{a0MSXl%i8Zqv$3Gf`SSu7!h@b<*xKJ%0biam^Y0{+4~BG&w}c
zVm~d<QL>r;?6XUG^{dMr%Pm*VU1pi`cOd!bqsyz__O|l)O*btM-*Cg2u$QSepIR8k
zk|^n~PKh<c_iXZ=iCQ#nhi`bp8>)lwx;^}EEE2za?aqp~w74oAcieH+d0QX+;0Ny$
zWMs5y!95Gg-!A=IdELifSN>z$|0v)2(|5|%k!jJ9n>;V*L4%q0eYwbOpwi~~jUxk|
z&cgANo0Ef4PmSyVSZ5qL=f=-l7u{L`=DTmYI|?#To)eEfvAkisH<T~${pE7k?d<?s
zAcLKD-l@F&6)&&cg&dIG&76sVAek)8qg7mRi*tzi)?05~MQyydJr^vv*Tpxj$oFaQ
z_&bv2yuD6=NXV@3x(A7HspC*(HrKF#ar5oQTAfy_ZMNB_vLa;JSiGDUI9`e!@3{Kv
ztE)rYfoo*27os^9>6)vrDIfU22g=*u^7eA_StnO}ogN?AIZcm?yy0CNmgO&6zRbET
zq<Y@0YM;}CYS|cIJr$bV{w90#$o6ze#)>vK_FP~7@|UaVt6%-<SF6z0k9o{vDgxap
zbs|b<o_WUL^F=f*G4In|E{N-<xDcij%WA8=IPQOCjFAhMig~%&uH0me{fU<8Oy-Rd
zo};v47g*gsrPsyv+Xl(uj<?-#u5*$8Gp>W<y0rG)YvTH5Tu<wjJ4`pCv`SoO4qB8x
zQg@&0^81^_b<1F*J*`iaCWj~~12>P~{N^{+qC9Pqh<0y$V<{VKP-ce=y*aLDZM0E&
zRS>=}#$@kY16HNSKmPI6#D4P0Cs)ztIuRuxC9isF5+w^fqBPve=QBBxqu>7ax7A`g
zl{Ta6+v!GJfooyWMP(dIE7?_%1z%YIy!FehBWIP(uiCu4J~CG%dy9s1qpSa)%M!P|
z-$(w<U_Nrmk=34$g-aUlT^;!Al~-L^e)+3kmQzkWrQ9>`p8Jf+OhCN3>cb!Ya0T?a
zq;+GJ(1x_=T#iP2xHE;zrkieBxhdIilu1NJ`#a1v<DqcsMEphD(jKe>h*~F^&+l7f
z5Ipl2U?*|uWa)a}``$NhhhGEcENWb}l+_%Z^4u)rP~&#Ed)w-r_ue(<u5$YEr<e1N
zIj=1K=i+ih<ljX;GO`fm2VHzndD1RVDziQpQhj}A`4^)6`H`Ky_3g;^%uTnXZO9JX
zA)NR_ANtTLD(g_^FLfeHXAB;}A<+RirGtY<p7}m&*0pgxCrH_|f{-tLX9k*s4m#+d
z3SV&Dos2~JVx55Fx>cz48ue~4@PgO_e`^q)Z^ZS~4)=etaeo=~M6=&%wWU7l-JmIr
zui@OmpUe07{YS@j>tMq@wNI4#!5q3y07^{OW#yHtnfT1rRx9t03H!ybc}@AqZoAzJ
z7-&de`qGzH3jxjiP(%qK(g#N#dE|XG!gZo_^wCFGt}xGd#xo{uLhPrJCBVQwH5%=@
zC17b!z~C<;e<$+y2J=rN3$wgl<X&9qHi`VE$lT}N6IpbzH%9($<T_YfJZEt^?da3W
zuYdUKa@|$`UbM|`r=NOy`N82o7`6|%h|!Gduk+43?>>J6*ml}!r^<z^o4shXyduci
zyxwVlP`An4f>Z$gb!(}22-spiZ;Prq(!^a#WHlDFw)55~CHCBN&uR}_+u!Y-5oOc9
z$xaq0L{fbaE`)qM5+cc3!8Nwt2l`oi>E}A}%%sd*YJc*RpH#qJ`P9dL&rV5w%;omr
zAk)6rO`3DXoO1YnhnF1>+_Aj%=C_tNM9wq3I%V#t4$u8jWZ}!FTjKS=-A+IX>)b~_
z`q9eOlU1{sx*<xu;T_==k1Ok~mqh8Jvi$P@Rd(3nW%0iAr(GKjm&`%tB6HFSjcB`5
zIgK<tc%-Kco{xG~jr{z5@a$AkuvZ<lK>e;kkd7ZbPxT=)Dm=J-NrS;0&W-DlJ%0BC
zaoru)$@KsbrB9UlsYAMJ{`_)TERg5NgnsQ>Yn27U>#VtExof-a%4H{=RL+Y95irPh
z1jZ00o8gBeN)~CGQ)yM}0El+RL2@s9*~==84N$^L#a#hNnTf7-v{yhGR~oK7(;;#F
z`TEztURffHwWY1!1U$Gl^3{Vmf49d%^e^YiY&e)Vip;gH)1jSfM&7WNeV0q!K9MiE
z>ymQ*73Y_WuD_^Uz3}SFGSxi9^R^prD|>%@@A9Tszp0#h%BlB}n}ZKIxNQB>tt+rQ
zR05C@596-s=4#$VSb({-8c=Q+k8Q+hEM4!?g^q=!9t^?nlq%knR@5w~`DCQ$eyJnn
zj6gJX0;b-|z~zqXow4Y>-enF<YYj`fGnrU%Zs`!OS)I&t-0#LK2uik(d86KL$_Zo|
zm-civ6CjV60`bTmxltd)u~VWTGna3?@y3+}E5U2l^Qx<_Ds%6-KWUtQ=lt@IGyhS}
zKkxi<-Q4S{@pDAvr$*i&va#^6$bM$1dwZE!n2afk0}&yOJrN?OLn24U4Kit66g<Rw
zdCocaxBPTNlo;;}F%j^d_Ov8Q=LJ!EPI=`kUl>I9+_JPCfu{{wW$g{3*qAP9wC5;A
zdRAPAHTEwZ71#F$BVgxe_`&Dndcur6M`>`MQiccrTwE)3`MoE_b@AZ$wy*mrmOfGH
zrw-|an5f?p3+8{VzIypW$j&XRt~%g;ckHqE9xPi=CVu-nSd?fbhqEZrTGFP~C%d#L
zS;#FEG_N+-Kl;&+u7LMU4AuaYA&j5?^ru_heWqeEwpb{>^PTThAYf?<w=6Q(FC)J*
zGK-JBK|4hDbF;|3SdCVX{O-s<h|KzQcx3KHtWeiSzVfUq%YM7=SAP7}AD0EUwujIL
z*njtj-<9wG;QRN91_mH=UDF3`)`NNiYU}+qR6?qL`qQ6QKKgC``2van*3L;oDkdr+
zG4_A5Y>ae&S-R8+@=$+<IwK9c?6OO>Cr|=-y-Pb>bkRlCXWHMHOn>^*pQdDh6Ea~w
z*3WgoU9Tr4m&k0uJh?%rhO&Gikmho(kow(xB_W+i7t+`!2ll}2^Z9+sw-5SunRnBG
z0_gV0T=R^9!w2&ZBRjwA5s~X-N62w!ei?)IKiyhxopS9x+?aD1xVCF^!|#SD-P@K|
zl(N-UNtDhl%PjMZvcU#h1qnN+OsB2TOLH2F17`=VN8ISXJGc#}Zb3RPuCK?nRsX=*
zS+gD%*D-^>iXRy6@Tf?SiR;+GhTOmHp1}@W>QkR6b(d}nGIHAN+2yk_LH}2fqi2Nd
z>=D<^E3H(@fd>wpr__>YU|;;=7k5RJB<<hq1DYaQwk@{Uq5?e#!mD5X>ME?NP^uFd
z)orRInrCeRq)n$ti8O!uce-1XY#={Aa_+65^#;)Hb5>EOJSVObXO)$Iw{m&fO;0P^
zMt<2~7W(^Lk)0z`PxW(&&X|2hdEdL<R}TLA!R4O2@43&|L4xe#15~rEn}oE}=R@_^
zaQ7O6z}s4DtyP_C(@bQPqGQ7iH>`a4n~B>TYX$2|+xbZ>G5&VRC70a$ncpc@hH@cf
z*${+702ueNY;fPzKHVfr2+`rVr6D@?xhCCxX#cgj#9HHEYwv8jD|VAuqkjMU-&bUW
zf{1%DK-`NmNEwyKciZMh0Gul!LYu^GxN%vR16SL<+KSt>IX~}576Jd9qtr?J?7Z{N
ztuB~|t-pPC_s^CauDhY*7?5s^j11YQ^z1vIT~`0&>SflSVr<<uFwPzs`F|tZx5p)K
zei@k=1(DR3h%$NVHQ7I-54A%##|sQK=8`B0iex<<iYV~{{n*D!dHBQ2U_ggY#<fRT
zb=9lNd)`x>;!&oN@nxw@V?mT^xc=>NEvS(szkeG)m~4(R6)Z}t4F+|1)!<$w=C(rz
ztxIj!o`a;S47%)neeh7)d+%9krIq&i$VWbMr%m(xkeB9z_ND?+GBI**Fo{l8H>y)(
zQG5NH-c;VO_S)rlAv>qVb=k7ZmiYmE3U`eT?ba(%YD++p@2#@RDrJKWHmFWvw=mfB
zj4({bStOI4Xn6re3wJN)LXkDr25wR_vE4?J05pebR?*@HGh!s@LPqPdcgP`{ZNx~V
zI<9nFg}B?TbKN>+josEL&wu^%%aOB>ESE;Ua4<V$^UBCWfjWJ6{sre(AoRwYZma;i
zdBd1`=R4n70r!dC1I7X@!s25e``F6z&`c~bK%G#?h)OfDR5;(rJwj)zQ+?e55MTMq
zS60CwSR&My1&hLj#eytz$7@Dg1X7KWbo+CTk_en_B!=b49E`N(Isn{q9ab+y5RlL0
z9>j*VEs2-)#68}b?tjJ{!h)cf=Z88-7a0@%)LtU{`M4odhztj?wTF8UDg?xQ>$`1x
zgAh*b#v0ugAxzt|{PY^RQonAu-F8*b4sD~JqWW>0PI9cA&Gyk|%C2pErk~7h8BnBC
zTC`wMx%}qK%SCrwR8F|$gtGlRwl8bGYt8b&iyv6lip(<0T~7o^hbB7{j5{6+6$@}L
zbFF<=qSFoMuG*ILJB1dnh|vb>NTOsMP>OH|wZ67JmwTP=|J>(FS%3ZcWrY=xrEB7v
zUt$@L@0QYK6zhKKB8>#cjgdv^>4Svmf`&E8bzxlJiR*>E276nYf;lWZ7#i?<gYI{8
zTN-Lq7c==-pO8M4(o`TyK!!*+w4Ni;SWQGaHbB@Dw%DR<&)qIV>1?t|Df{kQipWjl
zNR$8|p{`e7ef0`N01*8&vIH2>jss%T;ef^|(PojIWAwhr9~#V}%ysKZXMfUXBHM6Y
zor`q)SIQTD`i1iALw;T6UNQ2+k68sM4S*-)0s(NyGEg^`Va&TpNCp6ov~+qm_cbIc
z-LUEjIQL!qT973X8|y^gv!C#UCscC)LPddxpnUhc->nEEa$p~kXLNFJ17cW*5G<CB
zW<n|iWZ=%5#0R0uAPeT`kwhg0oR9~EVeh^7zK>O`nUI8o4?eiEt|<=}H<pay)+OtP
z^+S93og1B~qivQmL`5A%-D^fH1ol|nxDGm}u9x*tTiMe_F7xosV~;(ya<?3cARzTf
z8|z#z=Q>?^*_GvhJr5|q{N^vqoD1iaKYjO4<%@@Zu^e#Y0o55~Z;y;D8H2)%zap|A
zDEmgfB62UXtxb}=_L>k*a!Gzh>n5E<iPeqOkz#~vdz(cmlW9K0uWYtiJqlha&w3VF
z=+EzCm5}_zaot*zM?;h%J#Ns@F}raf?KcL4yPaJ7{GzxHiEHzjN|Zi2*5s&uIka<W
zIXJQ{O_g($SnmLYv5eL<mg(34Bb&bPg=OoIiK|vzaeySn;#P(M4kIN>7EJf>{bx1e
zZf8;OS^sqLktZODtA_JB0N1IM01}`{xsc;YofjgX7x}M|zY+O~ky(Zw5V@0k-Gd`P
zE%GxWi^TQx$bSD~Wak$x9L%)Y3l}Ubi{>tJy6lMOtpg7{u+k_4)d&ERVK2;h&DThR
zeJO}go29|riHrbq#(jcb_j5f0{3+e)<2OSj>+^JTd(CTJQ$@(DE9g9L%PqGoE3B|W
z6<h)_WNEf{hP#(&dOio#J6Tu(8S@9Qn^83p3GZg5@{YH^{q2<nCxgi#YwE#L<GC)R
z%y%*>nZF}A2wU^r>V&i?o3gWr4|Pzs_aTkOg7!=cm3j^Zvh@phKhe`tqunWZh39&Q
zXSca!B4+vkVe7@}TrYd`T|+%be<1kgc=9t;Qa62%44yvw^s?jIb}V~*Vvlmm^|zEI
z^MahsomUntUQk(Nxx{k6t6O%BUBMvsi+o+_UYVdy>d~e&_F2kA8<Ez=+L~;P7GGEo
z9gV^5oRY*`&WeYKl0j|R7BB=^dfn^pOO!U>yu^ILV`KWSYvfD)^}$3di84`mCyrz7
z>n4qaC^=+yFuL8J8+iTy;@T#zl?LzIeDHkLi)!~EPyZX&BW9ZG-O~r-s()#Wt}m>(
z;)(~n|NZZuL#t%qEzSFOrUFqi2^-`V@hQ3ACMNp7oOxz>RuG)m#$dZ<oplBdYUsiu
z(F;-1_P~q<Mf4h4Gwv?h21o!HX38EifW^WLppB)A98e0h(ar-LQz-#Sz>)*|Cc110
zjHq=1g{$9BjQpy|TSVqow?gFR0k2%`R*6ho&86-mk?n;zBeKX_vm^UyFU7q>Y5tNj
z_s#%J3r8o%WWsnbzJN#IfL2)>0%i&9n?N{0n?=2sC~)Kv3FCf5^WSM4DiezjH@pm$
zZO;u?FNC3~<N)ZAB9>y-2jw<5aFwgyYaM##GoM)<G>aHfE+Jq4{onsxHrs5oDzX~4
zDBn{*&oO@L&oGBj{IL#YpauJMyqm>`g%#kW4IauW#mmBXkQWJPN3iOyZr<yCEL5!F
z4i;rm5`c!KC;LJl4Hf0ib6CI-2?QoTOY)X;gK}vjmbP^J(MRs-#hqK3Sla+SZJB$L
zl+&}i^)69HZP?9xtuGM(?Ks@?^||?Bzy0>B<ZJfnvj_UC#ZB*&$U=)7pUv?r2*mD@
zZz_W>jq%!fcLuq<Xa4>6LJgOYZt|mClosQe)D<~ed+oI=S$fce9#qMf_OoBtI-7w-
z>gnd27l#bZ8$5(|fGDlDS_#rsVq9`B)MwLjsjQOmAaICrX}+0^lw{4EJRIS#jkm6e
zQlz!x`tP`Y(;!HH9^5|junymQQCvTW>xSBQ99;YVJafhba~SI@I&S&sz-y-Ou<1Va
zgE@4OV)4B)fX_EK+_3yO2H72m(%_;!T<V1=u^iEob00C`Fk|n(|NhhK5Z*CL2qQNo
z^&iSrr`Lptd+wo!9@={5%VZ`%42YyD9mz)cktW3Gw~_aZyjNr!z+W4=&1%G=B>2Lo
zB7b5qI}eFF&n0E}emd=Mr<Kp|`}uOr(Z`INOEa(!xN%==f@F(1FefrwFJu((1^Sm?
ze)+Q7Zo757&lm<+xOb*?#aQQ#BJe_!oM{8DTAn?W`;ceQqO&TeGRbGGKQy@JjokBM
zyl|Hz=k@5B<`FJu#*}hOECI-Qx0X21bFc4Y)Hej>k&k?2wf`z333{%wvCL=>7Ak*p
zC}w^SK|<~j&rS+5?PcE*OIaRH3xuWnY!Wli|Ms`PU6IJ#-^E>#9E|q7IUtm!Pd&Ol
z<B5AEVoV0dGTb>LnEf>z0lX|Smsw*}6g>L`>toserM)BHy!hsF<LNh+AAI2l<xfZb
zsVrQyu;=y>rB1oV>#*QVETHy%aaFXII;3{%t+%d-E&PHkr4pc?uDRyski{PbK>qc8
z$WprCg$S0br*ZhMjJl~0dU5NY?9>fW8ayxc`EdpOn)S~b**Ry>Vzg-&Bx{{PVpKm@
z>AJXnG`N3h**;O~ohUt^A0E7)3fCEv?#?@xMGtyVnY+v~<({?HDobAUqJcviy4dL3
zMwIwix49Ss67F+o9PKXP3>3`7KH)xLF&yhXS!t;PDyLd7hYU0UG>!*}SP=R0$Y(}A
zXE2{1`KZV*j=XB*)grGEnYHNQkvEUL*<ju@vQ6Q~M!qxh?X^5-;hb{yKd!FGhwI8K
zuDn-n5mEq(s}{GbS8V%=a^OJ+-Vd0&pB2K`XdX66YmEQ^`@@X~mWOS(-L~2r(gdl1
zHw6R>3?kuq%9i`p$OLOH(o%Qf(Ko>P3t#xc>ihntJgg6OtBvm~D+^b;>8U4xr*GR_
z_LPxTf%_ZR%lbZ*44B}|w1+?Z;g$A0_ZN9C7ft`K6C&f~wXc0`HGb-QbUce})dMPM
z4|OrubmP+N9`Ev<1nNi;i_Zr>@PT{FChD9vMo80*H04{lwL9`*d?QnUzkM*C)f|`F
zhD@11fnel_i;sCS_q5utg)sVD9ksJ(Dgz=mT%E|M`Ip=&uf9u}@H`eAXD77<;;73g
z+0i!0IO3>&W9bT|&9lFT3pBAPGApq_EUdg4s$T#WR;IKp?Z0^c^0lMBR-U%~)5_jo
z-n+6c^*V1Uk8+LoHiewOjS1sdBuQ&3xglGf*2}A}z9{7EWdV+#H%OLFtIk%MHS6xO
z+;RhCX&Uj1`=zv^PedP#ak;x8O6$gTX2Xgkc*Ap$s2)Ag9yRECcSh}5r^U5$pD6WC
zl=|l=^^;w;rM&Z<r36X3SN-&|mkDn);MhD0qD1Rsa;xuI00`Ve6FOdpE>4$}QOvj_
zbrVu{3NHi|@B>IkySFUm#no*zu0?}cLX2$iwr}X!gZa6UpA?xZU0vHOlHH+^?NK^z
zF#kF7S&_Nt-S0hjEGpkP;2Y)H&wh4w9emKi_sXyh`2fDn^ME7}$vV)SYt)Mt-n}dX
z+ABdbs{$a*B9WGpP6^opXpxR4==MzD8j$B&<vpTtAwd!nz#3z}Q`6#v1Rzj;ayt{U
z-#(V+27G<vxt`G$0k16q(-}58HR4Gu0DbOtE3UX=wI^#h3$yWJ+#rCxf~E|8oqL4*
zKM&OHl$4RBgB4Vt`<^!9Cf2sENI%nla~pJ?5KGPW+izbH3t^v|k@fUFnRw1~o>R3E
z8A4=oFQ0n=ME7yKGIuC1LPZYREDX)L2C3raEXp4X6w4i}v$`v<`jOlELEz0qCOm`1
zLzKHGKJkf_t7clRGZNl}t+Bdn@xV{}0_{0GA+qzy>>o7F9r(P_l8wqEuYP1%{g~Cu
z9_Q{c<@dlUpMEkHSYnandiZ$l+Z^{-Uwv5+r5D5!zCv7^mt~gODhSff@$=I7|Dy7!
zM=gxCciJv9(>8TUls2r98pry4Jg&7yxn*a|#zD1f^@&oSD0NJIqBKOZA@MPfsR+^y
zas4K)KLwEKB{ESPQKAJyhWwwK-9P#<N|);KWl;yZh68LsCjWB4xJ@R2K`t(USX<Px
zW;!YIL6HxP?669QxN@CqbEy-A!QY)><e*sJxubM{$Oy3fy~Dp(0a_Q}nXYGC&V&aZ
zN*kZaJzU!o^m3;HQbn?}F9G?uWej&;8;8J_KFWG%e|vd`>Mulyg-4{ly4x6w1hS``
zE`)3-lHqUW2>W5}V?xr9fTNB&sv?+39V?uW=Rmmr?`8h!H39N0JK6vMMK<(bH!fAk
zuQQ~EvjBSs(xI*_0o(vZ>|+H)o)9PRHFn%*zZVOcJurwVP^`_k)wx%BN3(7rh5Ct0
zqVgbotZ_(gFV7{b4}9PQE0U!T(~UZ<b>vz3+YTfik95W5SCpUs^ylT&Q%)^Q7XDiX
zS($P^cztAt)aE>}bz~OiHf6|ENr)}0@kc-U(fb7CF_w^Qd;ZL$bvIY8kSv!#@dF+(
zt33C)o0SiK@bj@QUsxXTh~JgfR{LJypc~6D=Sug}7^g0Y(t`(G=Jt#0@naa@V2!w5
z7uOdZG1MnYeWKJcO$DMv`@_wF>&bB1;t5MMRL+ce$U~}KpC_-setGdKtCY9A_r2xX
z9)MaKQR2ekcR&HC>~^mN*A8t&Lpj~g0Lo3%fMDBMCQD0Vp|Ix&2pn#svvvR&otFdr
zH4_Vn^F!YJmN$3Y!z8+#(7%E)IEYdtx(7vmNMvqx>qSmiI_Ilw9GQjb$B}L97I@)?
z$W4+2=-qJr4OM@&ZPZPzzB0PhB{kEC1fX)q5T`pLRkZc)^V2-Q0tFlkEsU7F<Rveu
zEDB>8@I{%mB`Zx@Q@E=ksu@Hj-PF9-XF#wp*NB_I4H-dBLM{X?5ZnMDuFq#IJ)Wx_
z5UO+~1ng{32cor`djwoS8tlDrzxmO4NtY`ZYl``x{{Bv5&AXL7-Qd*8GnJvPjZYUt
z?ann0DHml-eZ5B+@-W|~e6SK_unRKc+3KnMh>yNCZz+%8&5hc>lC5Y5_14$khZK5_
z{hZo?TO2~g@`i9AmhKZ8+I_tQ=4fjlGUvJ4$nSaPQ!^>6IvEE|!VbK%rm~W0tGv<@
zjQCQL4d)6t(xhzqM!Wm@fB*M?m6h&8yL{+=9tzy?%oSX3-xHY&-iDFQ5$1zmN4~3!
zA0Z!YJ&i^ERFAT#omo;@Cn-Pb3WWip^xBFjJ@A2RmalwepJ=0)E3dez?7sVF%DU@r
zUDjLgu=2k5-B_Ky)IVac_f*Gm-5wC#B~f}nT$_wzK)4m-TBc8w`b4Q?nhHe8q$jK^
zA9vd0Nl$>0xS_E~7oosop+0ucJ<Aif*rIIxrZ<(Vdhj_11}x^>1h}FgN;aR;WYf~p
z)^;0I0HG3D%Esm?86B{vCLVCJ(jExJ(i-xd_ETRQTe*RLsIf>!SP<II+PU*zca}dL
z{ipJ~-~6uJ^3VH|bwo!{h3z9hFLLt$SY&CR$ifEG8vio#Pb24^qv6V?zi7*Wk+Dw5
zip~ElZ|N=uTm$R0`~W)bF?YJL+`r|S$SDg>9c%;bEL-(GHpU<KKl_wK+*)n5)hf$V
z9gra;+^&$Mx=b6df)4=rTuKoFZg$+f)E%hJc>uZcx#x6pvolX?8{_BK!vvN|S2yGh
z;gkSr8{dW0Zd!}9xBCEY(dulNxA#ZbbCwp?y(aKhSKlM&$RdlN`J7cIgHCu>T32ZN
zSp=F9x{wutAy!^_<tl`~;2ErGxh}C*vDA6Ca<H(GiP5ZE%46KI4pLIEQgw2D2FwMZ
z(Cz{?)Dse?-qx@-B8i0Qhi(Hx^qK%wWzwhC6@g)dW7l3oDIgv4j2N*pAxY+nts?&{
zaxVwdPIf|+%(3QAW3@g{m`_=35v6t4eNeQ`k8*UB%P&8n?6Jou%flY_in7i+UyZeT
zZkgcos+WpHAp*zQJJ@YdkFhv#cDh`BqBM=uR3S<hZNTB2$+mrSX+n~o`OFeC>cHU(
zv3MVU=9%RgFMC;eP5l3w9>U{T_z@+VXIfE&>B&!ia@Xe!)RW1dMM31MrGY4MOR*<m
zX(meJK7J$rCGyWBI~#?SsJ_qWRfGAZkv|vt`gzxvi%z_#{Qf7uFE?M&{aH;(l=Rnl
z5GDIN0Av;kK$1m`Ty(3M>^7O-C@&BbL;!hV8R8mP2gks-d69cp21Kww3E6nT3tmuO
z`qGzHV;RtALGwIyXmgA6ckki)gB+L#kRSxDEtGM$_3#Y)tGK3d`(m+Za~Cw$%<cLP
zkeAezYZy?Ik?MeHKwJMN$VB*9NRWMGa-)qlsw_OiS%I|$z>XNCYhj%zadkwV(&bTk
zyaO?1krRZ06_zFbV;}oiHCN~_miQz}EO7{@HKUhx3us2*l#wDsc@R^53P?Mz3b}LX
z-@3&PQQ}srEt^EC*QCx#h{tL^NBqex@~r$Uu*PjSA<~f|mgOf$PIug2L>^1alJ?Vw
zLn)3B9##o`G*-v6cvtqX@!quRoqhJD<tb0uDgOS(vf66<l@EXT&>;R3<03g0DG$7+
zI2g+)vArZ?J8|4l5j-1x-6u+YqBQl>RB^qdO{GB|PyhR_5+LDlg=Lp5i#OY>+!hPY
zoS3+eKmGKw)eB!(UiaG9_C%B{;((Py@o2he<hfsrM5{<+!v~w@a%phAOZTI0&s><1
zDe%o5*WOXCyZ*Xz{ru}Iw>a)`*GK+m<Ud3fu}*}#_3l}(EO+g4W!W2+EstLC=<1-@
zD<fYO`5%#QiG26%cbBj2^|kV*ZQoQ*`BV4N1k>e6e~ksG13r!SlxJ>s&ItmpktD8X
zKzv&;0ALH)WxeTj93TK(-0XQ_k)d^e+S8s^x$4vdADD-f&%6WxC}Yl1EC%V82kasf
z+-X^t()#54T(iul_Fb_e1N`cXkN|FgY&|MneLl>yp*jU)Fn)|1z!+J2$U`1dp7D%l
zRC|Oxvp$cfo1U_BjbSZgq4T@X%q#i>5n@q6;FKL1V$t%`-w~5dH{G-%l}R4Rg6E~1
zT`D}Xf4x_Ikwvaj86ZOcsecA)Q63gN?Z_IUow&DYTZD@Hvh_*3BA2`Fx@#3Ifoopf
z^-f=TXVW6&bN%OYAPyOGNN>(P+5jnHEflFvyCTWTg9N)z-CUlpzTLVEBPr?5SC?0H
zCo>2kLYeY1lBE9RMPFz;&r;XyzfSw)j0Y~Y*I#>mSuk%wwSS4V8A)0;vU8Yz71_MQ
zRc}EVoi#Vzi&=L%g>BacESi)i+|x(nR+&lrvrMkC$|}{_ODxy*M8EKgS6p0HUU{D&
zOPj_$Pblww?}w_koI7{w2Uar1L}ui6I9dWf(xl&Vj_h^|MV%-`TCLMrNE37>>xSbQ
z{jN`xrg7>Ab7-V=Y>X`c0RQw!L_t*F{HBx#Ke!6uaPh+)R=)WD_m?AMVg6Hqx0k%=
zMV0S<FX!)AxDcgv)>)?lOU_fF?OU4n<@D3or8$?*DTnNFNO}9dZ!hn-_8moj`(U;i
z`vsBjb*{T8u20V@oB!|T<r_DAqcs@AJ#+6VNB{BY^3B7(SuVch;z_BKIfnZZ%LKp#
z=yQ8wbwNUaW<ZHzA&EylQ9iKDu&nkP*a2V!zF8<d2Y^QqSXDaBeFzay?R{<cARP5G
zbJAkMU5!TE9AK|cDo4z@fH88G)|`4$XVw=WnmgN22@qzfdRWX=2n-8?17Y)gp=MG}
zN*i;2p0|=#5(I&3FU_=e5nkQ>NCZ-;pR|KM0{FFsI&()vu#~xt_%)OE5j?@Zr?jji
zK>C~&A?HSYi>v^+KwzGuByp8f7C@S%-7^sqBul$D112Co$hW^EDCz^K*Y~&}4eIXM
zWYFIccVtOBjCB7j`B5g;vijPg&g6s!-a8P{q4x0&l@I~)q&<33WM~U*yvOc)luIue
zh&Je4HRQ<LvRq`5G2a&Xqmj=oqw`5qXN1)F>m;N4k~=EbO4g^bz$E(FdySQiH{Q5v
zr+OlC!H3H>+gun+|Ne39S=L_rgXNv?+%;r$>@DbHo$|afN`v}1nd)hfC_P}1s5~#O
zqXxrwKRK=yCV(8RI2Zx%khp#x*T#LK)F(>!OMRkLO92kbCqG#htiF0VJBZS*aXoD1
zmCNq??_d5Li}AKEe|ZJ&uI|F31dsta7C+j{=RWtjm9_^!7_Wmir&^+obC_n^*(m+g
zo>+>Gk9<sIKM%X$u(HJmw<ybeY?-p`Rm<KlS{<#gzzo|(-u|-f%htPWUB33oua(=c
zxxJ%Yj#aqwktC!d_vg^0KmF-XFUu{rT;<Z$O;kJyz~=%7XtxomW>QyH1hNRIb`$;@
zutIEDH4p<Jo00@sLUMrVI;hRi&yw~Pz+?58M`*>HZnmr&_8GD+D;My{ie~<GFKY!7
zAfQ2=NO>-oK;$IdmW(kLYWsOuUIc|$cinZX@}~6)=+w4cytwX)%qG-$f<^Z@8;X@C
z_xq@$vyrqjcN%5o?sjft^M*F10Md^v1)W0qYj4EV{HI(%E4Mm?S6d=S>Q|RZZiLEf
z&rq8SCSv5uxrG%=8$9Y!kE%d+Js~}apn4#M05}3O)OzP{h%OgPZP3XLRR1AZosKe4
zUu~zZO?NZzAkRY0BZI>Q=BOw4a%<F1E<~ACu9Hh!_CdOfR`X2|LT!c+)=4?b(q|*r
z=bE)6Ct33Mk))h|hFcHFL5|U}O0bUcYu^@k()+hhDMuYu$_rkwpscdW9Tj0(Z@o=I
zUOqo9){dOlrxS)7BuXm`l9pH1!hoL=*LULD-GQ=ktvZI}XthBiwohE&Yn-X{xIR(p
z6Q$P4W5yFDi!)7MEHY)|jmw>DtWow0FtJgPq*==^U-pUx?1U3fEUy?0U)v2)(vHB;
z%U<@f>g*Sr8v(De-g8k;wAnPe7Vd7(*%{9S5Zi0O&0(gW3)Lzkkwu^SS7g>7!5IEG
z@)nUFANh%qA2H_<Wrg!s7_b)2omFXgR~yU@jdbr%7XPGNa_1%GjBCy)=bdw2x#i+p
zY+jo5L<SKK@|Xaaiybg;&H-L&)LA3UKft`j9{B+{I~{@wAZF-l^OyhAqIUvco`V!{
zx63^`LzNl$wm*uNABglGdnSzMXFcm#RakI<8|ec~fc*Nr>33uP!4H0LS!tz}s{M(+
zZ*00(n*gc^m}l5u^pvMOrFt%RLqy3t5u7?1%foV6gwnmnT!hSc*At%bgo<zqGQma8
zo+Y3e0TO(I`x@6gBn+_vthKc^1m=-8MACTGM=Vq7tbbUlfN^EepDbBi?N~B^bwokA
zjdOjWo^^##o=-(t#DeEO7DN9>2y<^$Mo-jdzJr*lqc+oi<U)V9tyA8sPTGQdWj&>v
zB0})k$3C`lw`2jUTZud~Bh{rWkp%>TRj`wMad|^bS=`$8oT;m4sAJuNRqt0;0DbQ7
z+D=`DTZ^^3_M>$0U8LS~I?c7()WOB%$2iRTk<C1?_1-c?{(9tk=x|xo%nwDrZqapR
z?(I?5TW&4$<`2LBscn7KCll#v@0}t#Zoc{Em36Nz>7L;2<)trOSe9RY|FYIv4=D#8
z__;D~Ug@XtStIJMck2&^06(R1xa{$9{V=Yt#kEIVPl@ZHajhNKvO^G}<>Ojs@VU>3
zYwx%YjO#}&@Aze0pAa&<Pn7yZ>3*qCl*)jE@<%>0aIkEEm3x9P{pNrJ%B$b_#<Kn+
zA6aSojz8|W^6~)v-4dmAzi~E4{Q$-BOthr5x<J&tDKN7GPom(^v`&TcOto}*<S$0v
zCGy83?-cn7k!N+ttVi2K-aGQ=Bh&bbT6f1ica)QUdQ!Fb=!#3Pm@@SPY`KEi2b3;y
z6b3f-1NT5Xu<bLhI6xjS3)pk#^7r9f>wrowdjNeWYlb<F3?do8<Z$yLD+vY13M;Ho
zS&=gG9MVCa5g)E}NCqp-o_p?jKTC_cAWQ1Xnt%+b6Y`P!t^iZ+Z3vRZ+8!g%9cs^>
zF=b9k_Zq<3Jj3mZs~$Hhe+L8sX@tx^9?xHY{q-x7;`ub|fW7(v*{mAs!FnRf9kNBy
z!Q$X&x(x#L+}8v%;4YcL80vj4NQ83KU8|JSyrgX2rTp&U>Zk9M5F)^c9IH?6O+heN
z58bOB)ivD)>q&beEb2*i+7ip1F!S1j8&=&SNfybGGG5)oK2$P@lxow_o{5L3Y8OOr
zIBO}Hwl~c|yls2=+GLwGiFItaAR_i6ogVpPkwwd_&qJ??e8s=6D8KpEZ_4NQ{d~FL
z!rooI^r^AX?Saf=kzgCj0CUoCXMO$t_Y2A^Ua@z1-~-o>xnQrNnEfSe>{DAXhe%t*
zwO3rfitDBZp>drW*O77kEUs_Gb!dm{+k@_O?mfNbIp+=D^FQ_{^@BO|iPDgh=VTKl
z3nHLl@|x(qJLd%W`NJnaS^m84y5+hUJWF2q!m{L&OUk^33(L=b@r&~GEw`-52MzNp
zdLT;KwifS+vhL8(+VjyCzIQT`ex=3K4pYIxH5C%e(OHrI78&7jcFFRQ9R#~VWI%+~
z=rNI>8Tsjh`Kgg#5n1pCZZD$UITX@<f_v_|ryTg@1Irs<_r`MEpO5SKZomuB22zLH
zAPpef=h4f4ym~@#SP)n^jGI&zP#_>QfIO>A5{z`C0-WsWqxI)T)ON5gjlZ%1-dtPl
zVF8|L#?v(oh^)IH0q}YD4N%F&t!+Pzaf7V<=YRfZwU{DG^)k?aBO0C~Y8t|TZ~$Nk
zl@QuVbaMQ&VzJ6R;t`Li$WoFVBnI&JIrll`Mkw0Gif1Y_&A;EZXIdy&ZoEgkcsAmx
zjrAQDI&*>z?4o_8OPaFUH^YMES=`^8>89<S1*DwFk+Nvh>jsZRus$F*EHYbcvBkZi
zs1YU>Fnu5jA6K$wz#9Fl{m342E6O3FrY!2F9ITAmoP4D9MVZ%JbIod>C$~Wt(kroI
znVYR0>PsGcAAxO4%1@!>o_a6`iCkz8giL>`k8<Ujgv@!5detpMWLmxS!DwBKl>re)
z_=X#M+%iP~6eI-68!qWfGHFikHbBDFk&z{H(0@c;HL`i>`!{{R?D5Gx%Fa9NTuwjj
z^q%WUM)d)i(^uE^aO6g}iB(SjvD{`*6#e7(HurabBTutu|24+X<>iMz{6&!Nca;@Z
z_`fQ?6C>O-4>milNt-5)>vr$xP*Lv&gDY$|xMzvQ=f)PY)9ZC^T;GrDGjTmTuGRZd
z@A^b(*fbT0Qbxit>9=VL$0J$JE)F2|-d%Pnn>$Q42GLP*Eqv{3A<F6=i~qBp`ONC<
zkUCMy1<b@?ft)JV66MN`*fTyT#}rD!?s8>8Y=rTCN#v(Qepuwx(k>I3#mG-Cci)P9
zM&!Q_=2Iiv8*opHPo2KL=;DjYNhhDw@!TCL0U`_OaFGVN!O^%QI|xO$L>FiT*txiH
z1xw967qcv@i;U80H_PEZ1cX(m2}m;`2!OcfvktHV(B$W)dC|~BFGIS3AMaq@;i6>Y
zf9}6wwE%97U-Mv&8K8~UeUnW#sR)DT<~^BMzPK<chj!KOz`ME%)>0pT<_H8zo%~Ge
z3BYQ;L!=O(`t!{#NLxOKbttWH%ES7@@>aKCWXNy(FAx^xKt@?Q+^fCnE@{3Cu<Bo=
z(*B_&nLs#V?HP!uw#<MDz@&TJtG&0{YO9Jgc`qvxu%uqO--hhefw?v}ry@$+4#@$+
z$>k3z)key%?!c~lS>3qRS*y4W>K|l=Rf}7p{YRdo9}pZsE!_m0q(IxMgF4p%v-Tr%
zF7-g9)LnTbMBD!nlQsh1?f4+m^}fwE(ze>%`#Q;fJ!wZ{!B`rL$Z*O=2CK3yTvV1U
zT2dCyUs$eMbXA!h`L~fp*jy#@mh-kOd!DmrnSJW)a@(!lM=kYi^)v>^kp8n*ak#U<
zMv}Cl{;Ef#9Iic$m3O@3^#S&g|69tV9(6&?VK)haTb}l`fs;cFQ_Ve<PP9l8Ox`DJ
zKd?T9<II`e9)Hqpc<@LMA0$nm8+5@tBCbEib><MH=r3{oVbE&yzk{xI$k96e@Zf!-
z)Ki)YM9BbS4z~fi?HrKtNGD!!LHWo>KU&`Spa+$=#-RFeT&Ki>zvw&PF=JNuh>Vn*
zl-r0B&54c1eb=&n8Y;0MeJ%1{kpYdSOWm^~bHB4udgsW8M836*y=4{n1#Cr}8*Z;0
z0Bv3X{DJar-A4gXRssM8um^61SI)f%z$|ORXd*KK&nySPzYCy6Rseo=Lk@szV4d}s
zb%I4@z4g|s<~U_QngkO7?){x>7q=vU6KU~%))Hg@@HamJ_37S+6s@tw8rA!Ob8`i2
z1PwXxrySixfNM(`y$xu~P50(S1Xmfkr`bcq@>93km`7QAw6A)nOQUC`D`4|{6NCb}
z0{*puw(uTqQTAuq&mr&w7eV`xSa<3{H<U#rPjyD(+6XxE%l#6$^t)$hd-JOD)A*}v
z(~VENBI<xwo@WK{BF5Spz~*|!YRSd18AXko9YRGmdQBObQ9GopD!c!8;}Y5_`JO&O
zSW|f*Aw}FG^&OHp8HxNDA7ooZ%-MgRU4H!iAD62x_;)$~FY@yvvn+7~{!L`Z4s?^q
zq`s?L`>d<vhir^>GG~!#_30)OAS`6>*1r4h5p#!wzb_~oZ~V&u+~*2{TU{@BK?xZs
zF&-!{rp_9p?4m~6cQ;-K8V{Gu@rmQQo!>`tj*@72aXmP$#}4j2+BL4-8rM#7Z5G#~
z<65(Sj#8f}je3AhHc_I<GLcyb$3ipKDcwL@8Q|&o01LZ(;uB??2S2#{JZ8UZS6;c?
zwB?p%F57x-^Ym~;iSNF}+QK>2gB9f2C8;SM3s*RnK$9{cHd7+`-LG9EUvup><*YN$
zDtF#+rw^)hTjUEPpBWkX;pe|bWNvoIk^c+W@SMm$iVSSr9vMM$z~hoKHm-{L8aUSX
zWYqs@`iF~n2dwK?E^SCpw;Dp=R1gHf72pG$>th3HVg;h*Rjyt`E^{$b7FH+k61Ewc
z1Ev9Z@1yNU5?F=+dHZfyAp~=vtvAQ9Isn_kYIEyCdK^dz&?A!w58$4MUvkGXPo~=~
zuxB5Wy(tJ8*E$i}l!412@CWFd)0Dw8I&EB6W@Jp=5Li}W@5`uolJNy(BWeKrlb-aX
zYCb|TSVq*x9P09%Jc9^eNq0W|z#2ut<8#2+_gU8*f-N$q!&aSV#Y!Z=1Q$r|c!B^R
zINS_@eDdL$=0eZQzRr0?U6n~+XcNy)VuU<%EkhLC?_S^6UU|k8IW<=)8#3ze<k8>M
z0g)9|aG7P6srG>RuC>fFwWYEko2-7#2zcIsR5j0|v-WroH$?J-7@Fg9P0|PY)3fXA
zq_Lz--aA~>I?psV$cpyK<a<b+v4ac`my`qH*MI6h7VEY)7qo+Q&-kjWvrl+p`N82o
zxc44b@xvpp6M3b`TSvaA3@A6}+&QO`b;^?Zdf<0NAK4aAgd!!|RG+C!eU2a#KGXjx
zBO^)P(biu>t&zsxEw}tL#>OqNu79aK=s}wW5kD~IkLqZ}vcd|bthH7Nf?i^bmV(nx
z1?LSio#%eBTu(+y97m42RUG#Zob3~(X_011Fb9+7Vc+^zd2xW7EuQzh^7aijD8C4j
zbSE{@^PfKu7qmfkXqONrpoe>mP`_L}k|<daXq+v0Q$75(ex-%y-e@5ni+%<ufKZ#C
zXX?37OBOFFM|}T?vcns9C`bR|Xrb&XFmY&PAmSO39~`-EL3&W+Peiug=;X+v-i>LQ
zV!_Y;rKJXL5sJ3O%i`wVPQ;0fll{6&pXYZ1JiYZ_lVSh<e{FOK1Egzow}SL&5F`~g
zN~DpH5D=*$NXJHpga`sM5D=t8L>%R$rKG!b2uS+feZN2V_dmFPxE|+uzK+*%JP)ZJ
zp%<gum)zP!zD*_5r^12CR-^swar%@HuQXbTci}RGrU@q<nvS{Lb!)QRbc<S5q>E}I
zhbh_3+P0l{S=FRN6#hN4#9sVcBt<IGQ_9_GjZl49)VaT>1$ST1h3ed)I4|#df-z}}
z5OVml;Sn|8F-#bx?nL?5t;#1=teA)Mg+!$k@0WKY=ULt!5t`cYR}<x3wAApXZt{Aq
z6TsjubS=q{m$|+8a!u&;+9zT>NWWak-IG6Q_*qedjk<q8*dSM)nc5)#qTU_~K`a1@
z5ZoVZ*t9E(W~rBMoh}kM<Wo2peLu_i+DtHtJuLg!>DXytBHMWs_crH6gcHcKNU=-`
zt!Q~Ga6h(Ae^Bc+@^WG~%c)w;60i;&Mr|D*Q^-Jrk2?dm-&seJIwq9x)nQe|?hUT{
zu7*@<Gl|gu(TjC`c*wJxrPb}X_!*Iw+F|A#Ftf@_b#6MsxM^?x^XTC8KGV-OS(;Y8
z9!TiaUGH1A@Sv+~ke#3wg+k=DDi3#Prk?uqedtLjDFwouunYBn?=sMI&?eCTF2or>
z&Eu^df%RH2S6rXQ{a0<+M!AB`&cBxDWOMhg{QCqa06+4lGp$Ia&l9fnhr}(wU#RbJ
zHA6UOpRif7Nn7tRcW&u0!gd9Fzuz}JzpZh@DzpBM*4I)>l%Mz3`ej<VsE%CGWm3kF
zUeLAv%oS~W&J&4qOU4yKxGvD2KT{!xkxU^%(EstHuN`+r-y?I?UU!nc@c0E<Jm+Ge
zK!FnXZg?pFow#x)@zuS<j~s3R9v9$t<v~dR1Ua`~nwkq7u*#&l<cxt0fcta|4%9Nx
zj2`*A^|L{$rE0x^@jH017vJ3>sqQeB{det1p2VK&VBkR9_!t`^PMb+vjcK^6<J)0k
z!{#~v<3G-~(D04o^N9+}Vm_g+uOo?5KTk6YdUaS|HtWWs1oyoo)7yY4!W8+jhkEu?
zI|P?Z>_(eSh#hOcU(J7e*|<}bVmkYIeNPlApK%CAc-;Q2?Eiwl0YfESGSd1epDN%Q
zN>(%geqGb@d!5yEr(6Q9tZkqi@Xt^Ye1dp~`q(sOUG1M?u1ofUSM*pocJ^Utm8bPa
zW0U+h)9u09d5j+{LMTW1cr*g+K5@#1mbuG~v<IjI>!y7wmsHP<3Mz?0j0N&#*#hE0
z4Y@eeCocD6m%-A`#pt|_>DkYv6{)5ClwP0Ct7?+n@Gzc!QN{ee-Zz&!6ODq!42bw&
zak}<$4QG3USOvSM7Bg3oLGR<)d3P^%JUZO1_9wji;M!vyFMCX4>HV)SNqCwgB?;zo
zV~y{@;$$4Y+K?mmA&Vke2gKcm#`B$W!gjy>mWuU7)zE);>bE4ujG%YbRq<GS@}}zh
zFkxUK4+NE(bI$N5V}CAw>t**-vQzGS(^;#eT8g<90|rGdy61upn(vT+uZ*ReB8Z4d
zZT540HhU7^cXXVq@noON0e((WO3yCve#pSSl*q0WqTAmPq1q51lor2N(P<#Aub_SH
z=Ikb%qBlmXPp?Vq0J1qJ#~ORfolVvPGNzw3KjXFe4g;9z8o(2v1sWsWuFJ(-X_`G5
z&`-LXl4jmhqSpR+QbOvv)t`s!)-(+2kS*yP;$u7r@?<h3SCIAzLMrr-CyTsS9KOgR
z^8i`P4a+xd$;F}kSKoPF3iN6I^}4VHjQeDWl!e<nm2aKS#szBG`1<h_91zWO|NHyZ
zib<ebto>}_2*S0Nl$6^;GJgrL1N5dIA0FG0p&wJN($gKWI(}In?Zg7<U-I9oPFUPm
zyU3g{dFa8%47<xfD^8R@NCW5NDT*@UO?hDl_2^o?Ag6ZxJQ%|vT(LxRq6(GJr$nYn
z^C<L(b!DiaZ!9q-j5Dnf!KdFl_idge@YrA?c3`@ksYB^GyJ_-*4^Q+aPnFSR*)ogA
z9V*dxXPOjeUSoL1*SMA+a9_<d4%P?Nf2^OKR5+`^Y*Ve>6gf>!URl5Txy#$ErbXHz
z_PHx#KxHBe!;1vlt#wIKlJ$+{zkt(ZnP<-zTx6r%B+E{sp!t}G<i%>zU&Ye?Dd5mM
zw^`qx-@mvOeV3dw0<lJ`r<*AsKdc#XHa|4fG;M1WuD1XQ@$ch}$FgoNh;Ajn+*=%o
z&sAD__uOPyZ!?=<@z{5nwp(Ni_=1q(wzbMeY~WdngTDlNHbr_A27Z%JK6qxa9;=|G
z`dx4Q+kXXj3-uA=(x-4_dmM@HnLB_AdH+l&A+IJAYW$!B#}noOB4Jx5^gh&CcYugz
zo6wKPE~+kw`9b0Hp0gNE3mzVo3!MmywP_tvwjH;WOK8IgPCfZ@6U(PM(v?FTUuOwD
z@%Hfs^dFZ+>3bV`-pH&I_2nk2jS5&Aa@!#3AtTokgnRADfQk2Amg%#YDxT9nxbZ{#
z<E*V5=q^r^2GE%G1d0L$PB&0QmXx8%D6$@l8RVTQMs1u)$^5BD4Z^CW6m-^8U3WHE
zayhbPg=8@$40Z)2QTH9}0P<|elsGKy_}*Gi@~_w*exBOWnGcj_<IK{!VkX=JfTCk9
zo4>XHF6QO_P&KR+Vj$wuJsoGdonyRT?!Mq?c)nE;z!sqnQ!)22Zr&tm1vjE^;;_+{
z$E|9^CCo*i{cC-&pwjZ>4HbS9?z$gq*PK_Y7d_7f`J<1&1%u9sznoJlK0+*;$9eI1
zop1Z4h1%*so3vu>+^VJo19PgWl{7XWAbztDkZ)PW(5pipYitb_ou(J!_KtyiLnE)2
z)_%Ek1cbxiC<x+Prr*w2-*1vibqe*I<pk+|L$h{2sry8l6}JA@X_-$dP~rQ>O!P8F
z%Ybd*gS>9ZanzNwzPt5fVMXkuwsu<Qt%mQ*wn_V~^SAN0*YiV%jH}XD9oycLP%%A_
z4ZQr_zyC$Xo+jS!PeRML)ZJ!3Z-TLLh=}`{@1L^{7WS*A_MHD6TZCxGElU7N;$8O0
zO8G4u+r{eOa1}T$aY$9-%O0v|Xb!xTHCiGs>F4dj4~kjM*1O&>FBjP+f>!<{BTQ}{
zuerp;?l%vtP|kz@RG6SAzk=%ZbeUEzDzP--;m&LPdTA+_W7zrKYqk)2=Hk?R8%vl&
zmZ#-`80aw)#6${Y+AvM?|1-%m*`=V5&IR)H8Gq5tPvgQK8GzSNl!qXARDgER9h_qS
zMbtAUYFLJ4Q8kNWdQW;5@JshQTpoG9)-Imaym9+&f#rqyM8Xi0boG31P^ga&{j?yg
zN#;hMTOH5`hFjVrnLY4c(3@r&l5l;9%qb74xW!GO!K_f*u;NJy0}X!^6<L(h1=)Z_
zwvxky{hCtK$xdn}s8jr@Ex74M>5E^_m=wgOgov3~m>!AR*<anV?ED&b;3cT7eWnsU
zX*x&#VWh?+651M{=qCl5#sulZrwqB@MPE6P3o$EOvg23UcXqh6)5Gf4S}!{dh%X;r
zR9194=u4LBrZ#pe>PCvtj#&uF#GAKm3m``Stpz~MXL>;G=V@O|{)vd`Bn52?Y_sF;
zbGw)c;=W79cLebfjp%!01=EPuUQUvqSuycvxsd!Glv|(BW9Nw}oU+|t=Hi!gspwLN
zJbD{nVme5kbawi?uD5J%=`iA=-T$J7$UoR`O@goB->WOj;?w?Wj*P5>*3F0O?Tc6D
zA(Pc=O#aU-he6#5^^NxsE{ut2&LR9zq~b>VXH6Poxb>r6)WEab-39HlH{Cd=-u>Ux
zO8T!*u`uEmvPOl<$teB#55t>12b=6Z@S^U$hV>8C7;~Rs|5tMCZN8hOXCLSa#FBKL
zd8pmW$}Y&}0EX27eiCp=;bG7Udv)K9*zB7>a^2G{O)xNVJuB_&XU+~t<pn`Bi3?l;
zKa0Nlw~KBqvu*e7xmxSreIkBzn5SO5uH_4IbE+Kwajb}Xla>-3J`gB8$7lc=j8XyU
z6iE(-Da2``sjQE=PRIVZ#YivBx&@l<a`vd28tl??5H*uWhjVv*E~IC{xx0nHd760-
z;3v}M9M9A=wL%ZM@<Rbc9zq{@2AliIPMkK!>^t-Hc;Lp4xV%2w5b2C7iR1pSaP6pF
z-We-b0#1u#<*fwV1_+wEQAVoalrZMHqgU_PP5<RCy+#x>Jd3`r!m#)(^$R(L(@VT+
z48L8&1eKpl9xNH~by%<^9`+<s7m?p1F)vEK-`q<ncHg_^2DY?~F98?&^JwrcA*V^F
z7>-G6%nC(1@KcJo<b4my<c}{^@_qOxShlxt@It##%LjcQUs@y?+L!#$U-Ja=)Ok?#
zr^(scf;f#D^L(_UHam~;d1cq(Xh26sby=4mI{N9q2i;HQCb^yGetg$e<8#{i!Bm*(
z70P^eqPowc59Y=CYulr}8a6xib5A@DhK2)DAWJH5DC%`DbslJEk9p52|B-kxKk%-6
zeqKm~Z8mk}m+IAfFNXTGxcPU{*R4w(RD+z&^Jdr2uDFMoPQA?N%H#B|b=E>$wpB!(
zey`|g=)bRZ-f0-xAo!KK1>*lGx)gZ3euba=BLYS$G6Sb(R5prF<1j_6SCJRQ+1`G)
zyEEPo_&-qKf&?G2G0*rA1dHQ@u||E1C}G5&JCD2mS8>OrN*j=Q%ZweX!{!!1<My?;
z3)ya$liCNcnfmrq|ES8dwy>VqBSPlK=G$I+l5!dNaF3?(bxYy@B@r+J&yl2LeGOck
zH_wF7Pb7(a?5KR~O=&K69<5sx|D6bzzfRKw&m(=7x-ycINHQm12DL}PJOp9vbU-FZ
z5{kRR(0tH)1}C2uWrEb|iL+x2Bwv3(geISEyvB$#U=yLlYlPzazQ2)0b{UV7(a$V7
z9b^VlgIPa#FUi$Nl2i9clnUTM0MW8OrDwkEW2+_AH_H?|FOW0UZ#v;@fJe+{dv?p5
zwsuZ1X@~ohHQb`db(A$zedDZ1HJ(qis79)_v7Skoj~A=ikMG$Z4uDTV=%!R*Tp>=1
ze%$nAwtP~OmdxI{wK0PQfnyOHw(HBKv3qA-o6qEXo`l1!Id^tK2k)g~DMChL)!ddf
z646TGJ+djU)<R5+zNn_M%QfFR2*5;<7d>x4`c>c8JKSWmI@Ai+>)-vv!S5WO#LTSa
z{DOpZ;BJX-Kg#`Z!nJ75b`>QZlfqu9<PqBNu49&Q#zlg|fH&uyVRXXM;KhKPqL5Ib
zZL7se>sy*Y)F16~@wAhUy%$4+r!?pGA<GpH;ZsUaY96aKN*z5w7uMXepjREPCopR4
z*_`%~xeQ&VmJhE8<j_49;)_iNOPW<c#LbV(8=8iGt1>h&i{9+MkwdUG5<Dw91f5gF
z#HU$0Rp*xcK~U74yU-@lTO;bLN}-bvh8}<}nT~ybq8p5k>lU5z{(wTTYQHRslM&rU
z(QB8y2mFx<UQf`U9dZ(0J?bpxJR?CDM*ca$YOYGM4d?lEEpOmkabZ4C(HVprO&HS>
zSNy8^#I{PI55q4K2XL${C*ZPFH_nsWbN(M5jK|e}h<iprxY5+HdO8vcsyNa16<L`j
zDBKI=QMzws#}Er+`I~^>J;!<=HYyUhsZTNwdOF3*7JPKCO}M@v_|?-8ln%L<O-9!R
zrv!Af$&P=^?P91A*KL>v*E({b4QP^eS6?UHp}SGSa`xMp-vH4hu@aN(E43D@cTr49
zbP#Hwlx-I@6G|~g#Ptg~2>z@@cB}GDaKoRvDSri`j!6prw=u6tbf+3$25GQmQblR3
zL4?=Kt8@hAXPh(M6~ccKQ$l{e@+^`S;~vuygv)kVbvE*?RD=zR^bk9mF8*_gq!-Js
zo5AJ+5Fo8fuM#Dn4c+gh->^H-d)KIU5|w0WV+YScxvbr!(5gfUAB4<4P(HIok@<vW
zH97vOySu*}eDykQqWBV}&cx|r>gKHb59)Gq_)ND*AUft<&N<K9^E$<fjPOLT3k>Y9
z0u+<q#Mi#ZQ;Ra4x+0^E&TVEgwN#JUsfh9K)W<o^xa%*`&BnN~A`6e}5xjeM<ig+g
z4ga_<a_$AqmkpnXO0;eEG7e2Kd)Y-q4j&GwOa0p{%L(C4uSInH$a4(Nw(7$7p_&$)
zjJibn&u9(fEa>}H5EjXNl%S&#XUBN^wf~?_v^}iL%0+XdKN)23*jF$t@99xG{ECCm
zFVX|6yE5qEs(r(TO2nRoz2WYenO6DJfiY6K`fG249|ytOKgAoa^u+WcV9p1D?b_F&
zYe#&=oHIq`$d{*~u`hcEPUgSRvw*A`Pg=j0O%of{v!D@@_hD~t{6a!`qp1UxFsxPe
z%|uAOso1+A_BXthd~cM1Kiv9rD;p-9?ph0od*tqFQ{)*8ku6chhmul(zkA{6f90|H
zXB$P5vFXHX2_i?IqQNoY%ycjnB*@3CYusR{Xw_O11|c@v8up7PYlb=4%$*hL#Ji9I
z#E%i-UMqUCmnj!{*W>a<DKPLljYmxe|GM55oGOz6@tec@8}FBOBRfd^Q%)^m<WtFe
znEQa5mAVf>{`_GP1K};VU1)5L*kr2Ygjcoro)x<Ex6?6)uU}zh%uCZ+LU~}~;RY>@
z%9`bA3l$tE(|-%sakEJ17Ok^EF|VO5Fi?1cjSq01cn)slnK;q$$Vonr&Gna=C~^i8
zK0Zt^^5F9}+V~TF7SBjoWl2VyBrZb5>cnX-@4qdy%aynGc<YeW+Lk+1Hyy4$bVT=@
zpyIn2@z&hS<K4x0V7#%=^rU&`t*6JexWe;{>MAJAS>vGxGd}idJ}rJv{Im{VzHZdc
zy1KwMrC8<p^@YxQqG7EtSz&-_$jZKnxR`cr<(S=v!Mkyo`yXc`s{My?sd;9eU%i{J
z<ebkU2Pws%8|v|jzjhiv(||Fi(7exIgC!Jb1|klu3TOyN)&KoSiNXnZM(FFr$8r3M
zzo5uZk3go}uJv=_r~WqH(e*E>zAstJ7V;e>->UboREAECoG}@OY9fOYlR3&`Cua1%
zQ7A(3B<nBe)Y$<yRsg2we@6->cFsyg5Bqw9qGivu101kjbjw!V#(81W@qr_w7C8`j
zNOF0p?F#h1wGpi=Uamn<Bkc~%G2BSLW^os?M9`GVg&-}1w-3rHsD!P=*`@v=q?L7$
z(O?GtBXi^ku=6p-5(nSvVa4io42SFURA)!XaJm0O)Aq1Q*EXjp5si?)$Af>7)Az;<
z&paU_FR%eZ*LAN6@;qlaPL7xx_7U*h|CI0gkJdA_f$HKF@o)e_2ST-9G16GQS=Z+m
zG9J;2GinVx2-)d5&vsh|%(si;kiHZaf`LvUA84TZvs#PF$}}3#$h90DL7etj1SVjf
zyE8+XUqOz)f1PuF1a;r}K_M2vM`3vm;u(evReL#mO~g`ttJR0f)D#}F>cvBQ<GtM7
zvSs!Sjq^q95|0`1MFE_Oma)qx>J7Q|9J7bz``89G*X<SaN{L-ie5NWcTa$W{PXgYb
z@!TEP_;F?&O-9SN8*giEQK3cIhUj7?<eGBQRCbF{GjH6o$PP|>@SnS{eRXTNgBQa+
z|15uUY2W|J74QQ4EK<s|7U9?>GKwqz5i~>IrnpMLcKr8sV@P6FI{NX{v3cPxB6J^=
z?i`!=MbXsa%!e=-FZd5N7y>Vpc);06Fx9zl+&_sF3J1X5y=Z))+@f@!_$JU;6;^B3
zyq(H^D7;zZ@1uCUVllKw@?ppWy~rJsg+1X_6~HI){u9N&!X6E6y^sZ7G!D%A@H-8w
zkxT(gD9b{@LK_o>+SeG0enn~d`SvJzmx;y3J#lJrTHv(|82(ln0vTM%@Oe%TA#e02
z(-H=Je4dCKV53_5k5C!&1t8mvjZlNay#bA<VYT~kYrqXAx3Hh=a(erA<sh&YO6F_Q
z1+v8f!$)2mp>>%upCyRC0IF7EC0wI0qsFENUmdN#DB2iUXk`YHRPrG9z|c@)X_qzr
zX59^kfFV2$6CZJADd~XUM!H*W+62Y+qo|eGIBkk=sEfqRg#e_N<$LO{H(n9_)4!)X
za74q8zMVKHAnQy^Lscg4Xx5-$#9h}l6SuA?ZZVi@%iBFWdSy#S$7l&Xiu$<P7U#Pi
z;O)dskMGZa6Atf3b=-C`p87cWt|seVxN3!u4q<F7tS&yRlJnR3IkC{*RDK9$v^~tU
zP@bQEp}nA2wPbgq&Xao%J$*7ct?BvV#OXJm)lCLWGdXXFocw%TNxk?sQTS^d92M}Y
z9*Sl!u1fR!Qe$F=`5?)k&S6kw$c5fffdz}3lYaVc?Ct#qGrP!At0!`6V_0goy*AS{
z4Kug?ujSQy|5ge7Ca)FcTLF#Cjc=mt^IaNe10BV~b&P=qjho$?t`V&Gzk>v(vohID
zMT!1{ZpXcBLgp)LrDJ_oaZpa}zv-@fNw*%H2qT8`FR0p#PxwbKYny)p2L8ET;;)j7
zm8#B<=s>8VKNA(%3u>3k`1fpPD@9u4`32c-)?k$hRayp4Q4lG)K+5fs+Y94+POetg
z+Y%>>r7FiK6_$PODcD`zpK^E+bRkS#p<!PyX;ZRx{+?3#{Is+qE0^Ht{D)Ma*fs~k
zQs8=cP`7E!n(5MlXE|)Os?5U@dv{)m1Gk~p!G~`Q+;kwSlC*uqcwmY6#~_(6xEPQS
zizRo^*W=pDhm9RdmN2mBt;qc)Mm@&7)iIhu21U`s%XR`a%HYiKl9o2mLi(mZVimj{
zjN9n_Dly7M$eD%aosgjRn;fGcA~f!}Etj34hS!(zKzdJU%=bDKs2zF2mLEA?b$oZ;
zLo^+Kzgs*c{nJWTXdJ3VCGsxI<80c)q3#d0P`&GZ^Ur}PS>_19K88_v<G@vlj#c9s
zN}T_t3yF*-1g?S-3Di>wdus=wkWAcp`HJ^5EFgAsYLgrpp%bs7T=Ni1pI%Ws{9d0q
z#KF&8!DUoo{sx^Z@z|p5ySHk??tyEC)FEX4+8^gD9wLK}vQ=4U?-rsUYZcz_xd=S;
zc$sF6hBx+sAse-u2<Ji_x;zUqZ%surZoQinYi0$ZG!50ste1UZ9(Dri&Xa7LtX)px
z7SHM`d2F1gSblNvH&{*EF5do@Ilys0_~m4U^ZhwzmcP&Ol1{JY-?@eF@qOLEay*<-
zzsdbRe8F=_f#zEGmRD7#{<n<w=_|<wt&f~<q-P9l+#kx`HoHh+mV9Mlth9|~5qmE|
zz*&D}VXm;M>tifslzknZqo!g;NFc3X|0^MQpb0F3(tB^bSKo~>WqD%PdcFYnPp#8Y
z<$yl(es|;<LL58Y=XiYqy=$u$l%x|X*!Cku2W&bvSrOs-?diR*#m~b+`YO20Tsi56
z&R<1+xq6KbP7CvHJ^ZeIG5lf3iuUhg#dBJ{;Q41GZ9R4=>y90K_eVFtu1MPoyl`~1
zX;uZpFYu*`VQT?d&Ryh77&XNU&?n;8@c<;b61czinTr)R9_(eSrv!N6NLfHG$mCGg
zUcl%BvTpW{iC&>CxuQAAT62>N=~I|35RUsLZVAB;2qaP!y73qmrOgvHop6ArVImIL
zLDVsk?e2aLCC`@Tbzymo3n+eqW~XL;kn+!_Dj|dPcmAK;QgjKa?G}pts0QyLRaW-1
zzia44X(Y(u%H70F^_{(aJn2$mV{SJc%T)?FfR*HzIy9M9!0A{X1(e;YQQCNPc%&-n
zNFj}WbTud&2lHjs#_sI%ZAC={@U*LG9kiVCJUx$nK=w#e32l08SPk8cYbd@|xt0#W
z@vmWG&RKW}B;2=)bdZZ4_y(M_K=N%obJ7hRTK{>bFAM|sgNg5lt2A4T{hN)w;762d
zJD6$rwN$+$YaKf3$shRPu+4BzsF~4=fHZY!upCn%@Lg?L$rv`~*$#>4Vqy%6f<nhl
z%-iPb`$d~ISD^!{6=6H$#iHGp-489b6D}5f(7SXR6_jSY<C(XnY7?G%*fuyvK!y)V
zKF-S3QaKt|2Z1uzbEMXnX{BVytmU#LlVPdLcUG(C<b*E%-;5`^DeQ82RVyU7?mSZ`
z<gpMamcAxEBj?DZbem}~ZNJHCMullvb!D>)<l{~eoHFxRFt(K|Nn4C;7!GHC%4#2u
zVNekFk?c9umseS2Kmep>`!so>`XSg9T9N9XU5`Wltmx$k>0UowQ$chUMFgxp5uFxT
zY99~Hq97;g08z|30hE6=#p$Bad-qDUfi~ns<8x~iiUenDNG)+2_3H4K@r4sG-FTvp
zWb|&vKbJ@LJ&^qDH$BIOUScCo{*Vk+G{{6TOtE^u;0HhIsZ-U)<{h>!+VjJT7==Fn
zn|mrg+BMfqB>e#c6|H^x#ysxh8J2grbi<aL?FlTkfPIJrNt+wW1qjC@{ePUkN&OjD
zS6?P7RZ22E7yMaO8kaLD7dOG~dtz%zC_LU-S<vT(QTz=U`oN7XV&BIsAgW>}TyKNd
zTK|OPoEgtS-@$5At_}idt2M2`O*#G)CCFr+2PYA&+{<{@6{HvRk-jKmTu2-?8~;U5
zbxiZ+GvG+vo*a~;?v2hS*Zy+aLXs|1Mzmxp6o8cJ$&V*TD4+xaH62lAy9sKLc+K#A
zghixta&?hRZ@KA(=o_WCO|eGM<}-2cd-i|$s>u<PxS!PrJ29rby+zfM-WOS64<FVo
zxd$BSr^|U~ed12$c8-2k$=#d1X*uaK`qy|``S2USjd`%+oSZL~Rfys7KNxBqFVa|z
zYr>4b>YZIz7I!atG^~Kb2c+S7n`RMn(~O8gBgM*8@4H!h7A`e5(|_WW&g|(GWvkSO
z_0IpL&XEoC2J2iuUenXtXq(r~%(R)yXWzY2{YkMrB!|NrwImo|#De?7LM$di?PVlZ
ztFY$27r6m<Q3{DsAQm~w+OQ|;o>u#FBIn!BDpd3cyCnW;PMiOG<SUg`!7?*HIl?Sv
zn&f{{GxRj7{QV@bKdaZ@<UOp3qo}Ko0BR9Kk~UE#U;B7R)Qe7nWof1MD@FwXRJOf(
zD!>2yBJUvt@$67YCkVb*&G+vXUIzVM)atg-?;W9gVh+)Fo?0)_`Q{s1lEZFFlUdV|
zGqF2#jvTwRtReb^Rz^i2TDqxP0G?Bgh&<|`%^z4Qw<EC@5kNjgvfrr{@-eZkbpK)p
zyjNQLdtcHS+ymyny5Vb7)0q@BTAGqOGjU*##3^Sb8K$WUBh{rpUs2_PHt1BeH{UYF
zML6Y7l5Ty>A!gCM31=8<dt{R4297(JWVRhfu?AYXUTC0T`%!mLQtJ<VvYAT?NGMr}
zACCw`oQvCWXQ7h{HUa$LvRx?%EICVZHr{nY&SN$>vhzqCbqEZ_3w?j0&H#@7^VrjS
zkR)}NON<RzQQWxe|0#=(LW4e2r#*wwIsHrA7oAXF4Q*lgQ@9#LnMpBHs~5r$pni73
zbr3$wLs54)#gJY@I~iA9dVO0E!L(n_a7RNKr58wOs>kJ5P=m#vsmVU170ht6^CPox
z%{kFwI%YV7Q8(r+4v4Uh;-(<z6ZqplTB;#kYL7@r@>_3OZh~}_>*>u|COU7x7vOb!
zd}E98ehaE|cgz(Iaksy?k>_;{l@*7npSG{7@pzYCLDGypCBaS@7VcT3YzZL-$y^@S
zRFEWs;C_Fb1~gtF{`%N&+nt!qpPS=MotN(}yS5L#fXfHmNXx5AY(#w3HPj;T<*2iO
z%!8kQ`=jnyd^#FBDt@TRM6%1e5HNB&JE`)kVpPp5hGO(ByiRh0=oNL=|Fp2|PaJ>E
zzrM)X<X@nhmRf!O5XjpHUwU!F_;7b#-*~(J;k$LC(o0i&Hj6e+IILY2@F^SMrQFKr
zKV%Ui^ICe<Pg%L0ZTbPCC~-}OUZkJlsb})1%m0`b7Ut|GblZZSqepFku0~pVFcIJn
zoSi162K8d;>%7pU-`iAcI*QygK@1lL{@i+UpahsQ7}z6vgyaP%lPAC4f~?yEg-Hb`
zWuM55qB<}P&_U5RH6RX$G#j&mT~R9fq2x{l+dznTN#^iq!!+N_OaVx`J%@y~l4MK{
zGzioK!1Y_sU2T(4VDO|28rR<_o6*j3^lfOl*OCl_C9j4+Q}{vHojx$JHlO~j^@zvJ
zIE_U3?TMMoDEN;z8BC(yF<ySvAL8O~-`Fx1OKPZ2!X;}QiG_@j&9@0Z*<;ll`-46q
ztE98CMjSq+1;^icti1p3+hAtYP1L<9SqBO;y*;=}BOSfV_4wxO4c76GYED^>$2?R!
z(Iu{1ev5B7eo5l2jEV2ffQ+3ACu#2lJS@85YTKag@#N}|w_Dt3N8@Ma4Wy15O8?3T
z<k;b1(RY^>GRfa1ZaQ00gr%Ipv>Tq+;ze!bwrv^?Ld11XbUmzGoHSMwEA9JHF&qs(
z=@wU3WT*YFK28x_I8qywve7E<gjy$Zu&g0l0#85S&r`9{=apP*Q5+%`Tn=-Fvg%dr
z{&W2HS^`IuEBz@_HND4u*8d)&N8a!15&k!*wfgR9UL%b{h6_~tU4!`gPXXryv*J-t
ze#5}nL!8{mTSG#iN6jps$-|mXfQnxIaTL3wpACoH@V?)bBy6x6Y}-+geR+8_R4y~_
z+q{|zpz4T$yvCAFaq4kZ&}}ApHhZL_3WEY~zOO?#|5T9=z3+=>&#p}mB7^n1B=K43
zm&Y5HZp%UbmrCfXYdls+r<VJsjR}k$y6=`s(KCMsWZbG8$Qv{GHo51HXC{^V<MBUW
z>JX%7TR%_;ymk9a13fG<UIO2FvpjMtA_OJBte389bLPAx?s2b(iYOs-Xs~)w{^{_i
zle$4$V7(*Y*?2)-s#d9|rri(necjyp=B!%@sKXNZmQl|?Lx!)?S1r>}p~SR`CZoxH
z3pIlvqNmUba{7H`s+dFa8cb@)HN+ey()&X5qLzA)Vj@&wH^mM7xu@fKy45jh5W)v=
z?E98$#=Qn+A^W}`_gih9tgK0<n!(eg?LBCo_4PM5XA%#PyUrZZiN{1ZcL+X`U$a<*
zK>~B4yEPF_G9))4fm<HyZ|py3(^!sun<lpeEMW>;vU$R;Q>6pQ#$5;dHKQQgVwUpS
z%&mS>>9Mc2pY*)qK=JuR0g+RM1<b+9-ld!fvH|Tje-8!OCDomhCs=SDrnsrf|5>f9
zhXQk}!-zTV++KWLQ|Y*(eq{zr4%SOp>dd@N9!dYzRkLLvt;;#Z{7DScz;xQ07Aw2}
zLU&y9YV>UcPA(_e;wlQ;hRwhD!^7$p$isfFr|euzt?*<avegDYKnymy&RV=k5j)oI
zf*=mR3oeLOX1;VuQ!#UVx|`}_|Gj_Xjl95UIr<?-Yl`st^RibP@BaJmRo!WMnp^>5
z?l_*Zfm_-g&o*42TlLS&D{NH$<Xc^5`<}#M^zwiE?MB|a;s%Eir=6SpBPqMTT7GgQ
z{qiaPtF73@)b!Qa=XcS-CXow#KA}Mv#LaXtP*nL3a;A!Y$b9x29T5Hd4R0h5o|dl|
z5~8t9Z4C%GRCj^$brSm7e98XC25^JiWt{^m;thKkeTO?m?6iQ;7zXJ*7n4FYm=a*+
zz+FO;ji#7FKF8Z$+<{vG>dw)Oq{vdVHXQI|5|Dg;k`eDt3gf%4uT8HEu+IEaoBGfe
z7-a+m!dOdcim;^3Li;a>>tLW9u(q(eDP|a(v`9zEAA1~o42l}773YtSKT8TEu3w74
zt|vFa6ww-V!@v_CZ*lp-bV-Mzs19FsA}rcz$-Wiji_;=SkwJf+PW;DtQ~SLG>2I0P
zm<lBmPg=tssgV8F;GIBR@ZRlOElR#aD$FN{(YW=M@Bs>CfH2t)k|_rnjcXJ%CY@0a
zmiYWTiFYoOfTir!3t==Pli;xn+F4PjY>&rKELo=K@m4GA+k#0V#5nT^WPI&z=s9~1
zEK$rU@p4NKr}L`QhLukAs;YZ8?(DqqLMW*um^E_awq$sh;R)IS|Kf)kaU-(fv6tVd
zToBz*riQm^@LZYg<2koSosFBR0*TbTZC!>w$xc)J;U(cpB>pV-G_ftj&nwh4Z4pez
zA>%7kBCHqv&{iQK@&!15oNs{Vfp+HkcGp1A-d1dGoDaoi(?jgfud8i!{sT$d?a3Kk
zes>Ay72(h#OrAH)5=lz^o2ejZ!cE9jvf5O?_5Sq7*X)N}kzojx+lFdYBA=zwnn`g-
zavev{I+H9<)4pETU#`b=&*PMi(y)4Z*NC-gZF?DiRmyQEzDOB-O?f+#^43$<kNJYR
zI;l+EIln=}pbX#%SRZ!d`(5rxXK<w+6JA_w_OS!XCZn4u=%+ddsQjL`<laktZa;?`
z1`apCkLd(U+H1IwOuZ{Y(n-R=3vqgwN-6j+AEP*>{>`43ro8%v-*^K|tjUo9Fz))n
zD^_HYJPeDJmy7^zH{=$af`Xg{YN$@C>YoAI;$efte|ZY9Bt1CM==dLfX&j(BEJL%T
zy4)7A=`Jf4+OYlvrr^osA1}?zj7{w!2UKMTIVt_{w(3!#q1JF=)J<X?MYt&H_9>s6
z_;YM@D6!kGOX`0qna_$&^k9S)6SuIu3183bEEik&NaA`ZIc@sOJChW#-wz0$Ihc$m
zd&6th$+<-|m{>n@Yn9*QT&eiYBE;0c9p~W7A(?@dP&|%2tt>vN;Fk^>CSff_<Q;zq
z=2@BT|B#iWnbfiE8K|w)Sl`W4J==FE9xzKc!+&_5fT_D=^Ay*@Ix}4I9rioTjgmT}
z2n%ksOq5iM^Yf4Orol(INe2Xm7+PJMw6B{O!Bsa9%YPm;v(N4(c)6>uzES(S&Lxh|
zzu3Tw%Q>4|G;+4giEPvQ_l-TaXGvPS^Kuj-U>OjoKm9c?{h;fjc)cWAjbYWTf`xYT
z!7stS>IU&I=4Q_%X?KdU+x~a%5PUUE_tj3BmM`45M`(J#5dUpeMXucjQP!p))@lJp
znv709!Smq#2E!0@Aj;|MOq16;!KA8}Vei@*t+zni$rJMF#426eBTq!z_ZkgbPj;e~
zJtFB8+D)`0G}j0go3c-io#tz|`JS-N=&srfVoO24H_7A?&l;NzRb$!3ZZ%_R2^*JI
z?n>fb;9)39Bb6sNGHbLPDU<I6L+L=UdR()bo1ubW17(`V5bpiwT)tG=qjf|p;&S9P
z{;w~u4}Q)oXvFUAn%Vcze|M^aHNd4Z$wV~OP>dIb!|X*AJq^8;8PDA&<X727!LY2T
zCSs9+&$^u^tSyPHR(WHk{nh}I=sU9g6spa7@5Y^Djy~8wV2K|5a@bw2Z2SnD8tigY
zPTgaJ?#{ZIE?^Z-%=^oB|H~$~uZ|${IW%AFb?~}eYv;C93>(Fi8b%o-cs?tKP@j+X
z+V#XOIlh$wy!HoH%8fA%YrP+$B9AA#^re`UaC*@3yKilGhGaD@TRwO^DWmyhp|CW5
zF@P$bpMuXdO6Bo~{*2Rpr=IsaOgnj<id9CP=tIcIY#f~y&NNlUkP@WL))e~osf<AJ
zS_%hG`FTb%kMyj@#XBl0<~&m$XQSe6L-j0m+Q?&t#p!SH9HltTm&6r})8Rk$bSEZN
z40Ka+&Wn_<hO=m^Ccmwbg$#Z``*-QAHFmqXAnExZF77Dc_{R?faI+FPMf`>7C#FVP
zUj2iepAgm`L<Z(L$zE?IGkK4NBealX?>PFqK8{Mx`eSHpxJAtFx@4Z*?{<Zz)*Ej}
zU=9UEEBn5CXkdrc9c#PpY$Ue7gREf9sTT&@{>*REKm_f+eR6oW)3AG|b}m5Ve}V`h
z+~eqoUJOrD9emlU$Y_xGMUv8rsc$6`4kU0op8Sxhb>b8Z{WI$FNWAOo*Oluxfcrl<
zC5^`ZZN%TbDu4#qNl-F<!cT#6C<IP+E^M9~SUn63Jezv7DBAsZRBEuphK%XX1?pO;
zLD(>mD*dxlaB@eaa}<Y<D4#k_VfL%H+|NKLkO{P9+VrSDTNPycaxc1WIlQvu=80O_
zm(mcmyMl|jaQ9o6tZXd8>%8O>kAqj0Ylv)PkxG4OO<zvY&?%NxNCI%}G2!t_m5iL|
zu4>Z9Mb{9zgD~cv^6e8zNrN>si4lQ-VK1~~V~6aS|4hVBq&xdLbT@P^O#RK62wycA
zR$BzaG}OC;KIq4W;xAwdNnoh+Z`V$TsKF2Uz{Bvbga?zaOaQGI7mC)D$_F`L*&Lw-
z_m@hS_4GWx`3l)y;s)h8*H>*FsQ2a3+K~ccE+G6q7C)1OTdWFE##csyDmKcGZ34A9
z8naYIdgGc~|JDoF$Ar9hs}^Ei{TbT`Rl>ko1Mm6MS}C|Vy!<1_x7^x7#G4ipk&lPw
z3@W`E;u*_ao;niewzOC!)zEm%flM;pF>x6^F?iKd=dwh%$jA4y*piK@oI3J7C*7?8
z2ht2v<0*<mT|A*5TdNJE*k{uoDp;yPpv(TLDE_(GNp4k$G=&MX&rEl(A-X?`$RK?Q
ze4pxhO!WCjuFTioD(p6x2Z8hz5-rsYua4;<rA4Sahf_KFoA3%Wy}<ta6m0bgx54)U
z`}Cj@MQ8p`(*0rxrZ=HO<Hgs9P+Cbk{EPNDOb+kCJ>M0PJk0L)$$ZU&y8pMjB&Zt5
zBvr@y8J-^O_Q@C!12T8ik+gc>;;8ueY<<YassAtTft3U<^!jKB=h%R|S5HMmK58jh
zL^FEce}}*!@`nlIeDdBU3^7B=`jY>OS8uJ51WO4w4|kRi+pDzCx;0v@@f*;_cOcd_
zwIkftKJY5vj;QR^U*KN?yK7sb$d~4MZO97kta#QkPndg%@|8L3Fj5jqu%=V?3M4a(
z4ANi=9e^>`^@k_j=4tvHkMg^L<fWPS+%tSFp+^}O`?r3~_lxE=^3JigY|AxJ(Ae&=
z4L*nSAla@Ge820CXI@mP%`e!_qmj<)h)4jZvi3j`kv-<WD%33Dkp6LwOXQHu5uuIr
zSxd}{p@KF`?rW^pUJp`VmbddjlTl#XSx5-b)0$uY00eQlVJ6S7m)A1W3OG!W1peXh
z-V7DD#VBG)Jd;$FZ5QAgEz=%SrwxO`AA&^{Jd-wgNoR|{XmwtWsD^x_w-Vleg9~50
z0t&l8J35qqjJ?YxAlgjA;l1U?|NINa-|K_@U%+_j2DgN&i9>jYimIs34bR}0nL0C>
z3<e<4sNhhC`3#?&ljpaIB>B&dgZpBrIEG5pV812|&P0GcH;W+IFXhy)|AaY-@x9}@
za)!@Ux64m`#1^z_hcro?$rRy3G5na4pH)|oXII0F^zp0b2{(7iykq}BO$QT)q1*8_
zty#UF{-NJImu4&`IzE5Vggb3THINGam5-goU8EqclBmVH+t3oShS?%K$#%~D&yE-r
zOLIrn&Oe1OJl+vVBwF3-_|(~6q#>V7<m7;{kP6KlCin${NZzGzQgZM=@0gZ3k{5_;
zE#qPh5asXhC$#+W9F?ma<&|T)uK`5jBUY!tB`h{AGHhwDeB=4+;(^B%Ta~MY!UDrH
znVt(paWns|2$c8_@?4)5c%}=K%z2DD@+5&D$n`0B*QI)%=89Ys9t9#YzaQdi+ytK*
zGY|ASqMMeCU8a?-O(jKvOMMy)W1)}z+DmjKGlIps4Ey4hWm$zzkRbiYThpdO-r3`P
zfO>tSb~!jnO#0Rsq#47p#<|p89evd5hZT^*QKX~j`pC8jm**eqt7Kb3)RulnnH+I2
z0E0C~a_N&N6EN3=d|T_+eh#GjmelD+R7TNIXPPB@6(kd>Be<~eM+A~>$=rtxM)N_@
zhGz7V&J6B42XIq3qY7<Ha~g@?(Z8ontD4y{(Z@gum=q7SfmzKos5i1UtKpW^azcZU
z!BceEj7!S21(QV)CWhp^F%VD>phW+9(kL8}hxA=E3)+vh{VR#@O_3^o(4EPN)Q6@*
zpi*bS&fhkjwL(MUHlYkXYE;ONCl-+uOC`xYtr6fhI%R+a;S}Y>c+{>m@#d=s^ZIG%
zhgY-t$JCWVp0iAOdc62UafzaW(A=<WZ_Ga26Ge?5C@rU*&p0OjiDr~V)|uh!n9|kp
zrQ9HOJXqh8Is0axWkJ2{WA^=n3Osl&{^cRhUE8e=99Nx}y0>-^mbuiS0~PF?ep^eC
zAYK-^akP-NF7t*2@835Ks0kV^Udv`o4&!-&Z4aCbnE2Wr6@atg4v=qnOv^-Hr8)t^
zV#LtG;KtkLR?T706nIH;X>R8v?p4~ye_V?wOBrS-aw?Qeye!3Tti<WcGb>nR+dX74
z$&{Pb3N3IF;zdgxp}TSu=~lK9{A_g9=NhKrt6fZjk|E0b&L;0p#G{X{Ua?gFBapCJ
z0oT%udcB`1c^{qlXa9d6Bf&n@DdvT%M{Qz$CA7#V=dDQVS^wLyB0Bvuo^05d^)T^r
z1}l}IQjIRAJ5xq)X15x8vqM$qyS(6TH-P8Bt=cEYS5R*X>7j75kxa=~`ljO&^uI_Y
z5jXcdL2F3M&|8pb>d#(WuV3nU0rr4w0u}0Ox;_yuzl6H6_2eh8WXtZc2GW@l<rk`9
z#E`(cFXZmBS>1A`@-vl$(Zlb3&)tOcNCeUY4x!w?;`xEcFn$!J%G6<#LR@*`OF;v`
zu~#$p8PreQ<hm$|CAOW|21^u$Xq*_?B!44cdB~{IpLK81pLIfkVge0E>T~;sJmo%>
z^8R|y+@}5q57S0a%$HEO7d&y|)27FIRnDj?wyXh4PR>5W0V_DIlpDI)`!e2L{UG9c
z!%jQ}rp7zrMdYHn2a#do#P5Y*Oa@bbQQkKt3!UY~z0rJ(5;%0_bi6@)ex<q~gUD%t
zG$ea;KL^NUjqFjdL*5JhjIOC~na0O4PtTO`vmzm}NcP!Ja1B#<GvpS2+wBN2UVX6W
z;Y2+9Lj$?Hn#NJhF8hG&&w&PNvWY9}Ltckgfip2|)l4;=KQ*vT-v8T(ryi4CV_v46
z>bN(0Wx2New)rO({uqzV!k3V4DMaFS)n3C;&m!5Fs*G!d837Sne6d$mu=795kBC<0
z-hJXWFfx-6^DLbu;T0`jaDUSffqeV^PMOca7}X190V?s7vX{Z4?NcdMj*M-0@NvBA
z{0*=hV%o+p_}dmDb+7%_+V7rHNkYIkQzm+EvnG(l4>9D3XEQG)$g^RcJ}nQX!-iUn
zt*G<;m*@;iNc4i>)yaX`g8Kk$s%Z*ij3qDGE|k<B3I>8|eLNOGSa_ofch&FC<i;%m
zM3u^t5lArNgZJDHmiB>Tcrd_qs?m?M3~h=}jv-5quJ2juYHEvaXcO%t1!Z5WLWD;L
zQ3;JF&Oh9k$ZK8otsZ}9&8~k@c99cG#{*?#!D0Q$>HOoV{fV?Fk$zKx+4TJUxb@$P
z)hWfcg)J<+vGJhvo?{`3>`pj<xi*)Y4T4Smn~WAK+9Ya0wx4Kgj_nW6j|il^jC0Wq
z8B2T#X^0n*!sk6&XhLsJ%?U|7UZco$3Hhx&#81DA7+mIz&z0BqZGFb$86wYQ$iA|9
zN{vK^Ry0O;>3PBP8>3G6AD$P!EvBw-DNhnWoJLxThP$TSJhPx}AocZh3fEE-rpF3;
zzWFFCi1fp~-JY5{PiW9!&u}}Q<hn_EycQ9zsg@#u&&qCB3NbSdprqdozAEE7kDcdL
zo|PG>{T<bV_CB1%4Z>-kI$`S5d1^49Li(bA4_U`=Rd@UU&6CYM2$`R2Y1(|{(D8Tp
zU%<BjDbLiQfDu~f_ulHx6^(ZqR7<*(J6xPWPg|Ba8CvsHa>UKv`daMClcP^$tqQ7r
z+P2@yTBN^SF63O{UOnw{PGJA3r@kYcM1R%HzvgT_yxDxRR-|}#y!&6hmek`eL?G!a
zPh2L9X_C5*zm^0<x=HrzY@R>>*ZbQ0ck(c@d$=GfZUXe+@^@=M@-6b-#$0J;DUtuw
zI^28GO2tPtSm(2bkjN9*o<@iA7|_=d>5n?{QE3S*k<@Ludcpt5iR0lyFsknx^4S+O
zfK(uX4|5mkMe(<y-n9loIolr$ySb$aK!ozYKE*w<D|u~PO$yrzASN~d-2sgd``f+;
zB9S-!H0a$g>2j1_Pt%+@LA8*6*_<s}nqk~qT8(hq7B&#-CsiZS3^|3jx`YybH9X!<
zZBaQ&s@5NKlbmP;3B4sDc-(m_o(@J^GWL;5_;s?u_ZBnI36>%Kfe>RncQs?(NW*II
zKGCpeKxiz4J%$$aN7;d@kFQN%r>G{=Y`>AGI?M28GxcQQ)a_b3@^o1qQ3T<xkaalB
zy9h6GS*MI^f!5EC6tQM!KK(Q(yS5&RQJx|?Yzo%C;O!fHX`0@|pL&imf_95PD5~;_
zcTG1w)M+?6ynZ_ZnQGJ&QO=WQ8epsD1#7#aY1b`rmy&0E)oV%;DIp>XhKXG|rA;@^
z8ou#p#)p%Bfv)P`&|I-2>`h<oT1b}fd|Mbj#oi0YG^GUMoD!V2Cr-s5Ve3D>PrHE&
zUNjA8Z{V|Y7Te_Im!*yTsEcA=ALr`i_`7d^6hoyk=8BmZ%zU&Emt;Pe82M~Xf>&J_
zFOM#CnGU*1RGRIEXhNiSj9)Hy9tTYpIj0#t4Yi*b%Ov*2j+<?v-A1fAreEN{$4MrJ
zywm<i`)c6U{?yg~i`ohn3MVW@6G}z6tDlP~@0q%)pSHRz#;Ib$c$tPyzi3{Hm_<9T
z6eTb*S{ngGrj-|1Devw1Bb-DWN0`3i=~{qczx^F`yd;{NrPVl9KKXJ3dzdo6LAH3W
z)QrrwC4yQH2GRk*OKiqAWKTii7^IVTRjGkaE=USU3Qg~!8Z;UImcD#Qp9G=TxAEq_
zG18<26*y4<{lJi5Z&*p3@}w4h4t{t6O!tU@2}GJ#@QTdn3%h`{q$~4ytGPSn1}9-<
z$Rw6~s<QooRJkNY1oBtKIV$*YTF*AWBkgidE*by@><r$cA}1>)B=vE_bu)#MOV%Mr
z4))I9+u_LJm*mD=jqmKT3K@yet7Du02r|7nSC4&nKorsCku9e>BAd7k=AY3b@Z+TG
zc`I>p3CpP7Ldy}4(YeVS&vR@x9yCQR+mK`Z%ra%(Dl0U0$7;%=-TC57Eqx6^&SfRb
zluk{S3Ug=PPoswFVTpMU>|>I5^8ixY;dGTrscf++qn^bsxfp{?HEkpI?NDMUAHF`d
ziYmS2Uni9r%=K98dz$tPnSRg}_v>-va~#Vr#oq0Zos8OQTdiHm%E5B(6xyrOaEMNz
z(>*Q67FOP1ahJjSCMRR8PF+u^k2M|P&xTC~jUpg1N*)@bXd&%X$Edvz6UUBzGA2Ky
zT&b(Xa=s^z<!C6xQa$r^TU+3DgB8;1ad`lofwHNY6<nD>m8|XnXm`fQ2%BCROrTG$
z;9oGmVvO1DK`lD1THi<HLIj;XSUMLo{{yZTx;xH$kJ1b*-%96ue0I`o7_#RybdM0)
z{zeV+du()Bobqyg;P(Ze)2DyM@TJ-o{GhB<mt0mfW3p9)gN?03eo{{F6(WgA>Rlt|
z;g2IKmpSGqHZ<1<pVn7GB_%g?ev06g(7XHz{r=Nfd76K6_#4ghrjly^R{RNxxSTIZ
zZBMvMY}&zlp9dIIymh~_?NHyO@O}MT_fBkk^PE383?uH$gHs3lA`RGkJ*Ghs7Y6w6
z2CKPZsE;ID3shU+DL^JEVX^hr`iucgsAu5DKq%rxSpQv+G|{`pDWGO(Qy)}KGDM93
zBI?XlU&Wj^6c7vCUHcqvTL)3Gb~)t}4q>|4v(vdlvFERVkAw{@MrlBcLs^5xoKvzi
zm3>vmBzw53`%Q_X14(^JR_Is*#|3wyk)yRl#mHuSLvMXP(5D~WR>=)bxb!0<>d4YN
zgQvpfFaNNLSy)#C&j)D_mt?mW3`+104%r_*qUQ8poC+j3fd-KjYI@ob9R~)#HT+xJ
z5$<zjmMCrCy_IFwLGocf(Rl52hw%J^#96XW)rXt-*3c%NA@oO$$^nepB=@<sdPZ|{
zSH&TO$EEopxv=iXy_x!oSh3=ZM#i(Ti23!#59ixXYyTflZ{gPT`~L5*D5=q%Bc)rU
zMt3WrG@~S>J4Q%Ij&2YMksjTsh@%8aX_QhBke2@K{rY^5-+!=UySF{B>wcW)<2i%(
zuu@m251U`mU783yZ=f#qvz~OOKte^B&q&<_*|O|p7U;vHDM$X~ZG!rVdf5h|plRw6
z?IAM9xfG)AB124)_MGZ~_4Cs**&}8LiLaq+kKRo@UHl%1(a5@cIW|v46@J0UfmI=O
zj{D?hele?HG+dL#`x15qUx<^zoF8D$Q}Ro45^w4;m}!l4kyzRnrY6lgL29Xexz!>0
z{P(i#FMGTUlcue+c4VEFX!WgAygJI+wZ2-v=IvwGwj45|U22sbDiBGhUi|+_qzt9Z
z4*tPSW<IzCdglv|f{t%D061~iaQ~1c{?*kaz;*5M&0p^!@x|Ql`n}u~dsTNk##=>8
zz0Hh&v%n+Vf|n{m;~(cU>tIV6Pq;Pj?yE65SrO{$kMAzN<S?!E*;kAgg<SmmV+$Tc
zO4Yykq}uiN8;+d|@+Av+mKpl#CkQhuQ463jQZ!B(1TNP~7nKnM4dFRqj02?bc>YGg
z%}#zezQN|Ij{HV_3;>II+!=3ty|Ta8IIz~7EV@=mBN$BNe06QU8oe3FOvnVN@c{6w
zKQO2fvcW^Li@_QH#u{vezuA5TM~KVbBAV~9m977FiszH?O#?nde9cKa$~yJ0o8$qP
z;SX6xAyM#Y;>I>}w3+3Mz%c3G2?-q~q`8WykySC(=g+m5jO8)c%t~*H9?$Bsuvsia
zhuJ|@ZdXhTijUK{ZDwvU7HVDgOjG{yuJZFcclAVP0O~6-0cL#qpV4mYq81$(r)Q06
zdenpvrcV3Isjn937VaRm?Hk{c5FX}J!T{~-XBcVG!QBK;v^V=X((hmcg#l`ppm$;R
z1W@(h*_;Zz4>tQ)Ym5Ht0`5IjR}1IF>^)s6SXi7I^~=|ivw!!UU{TaNq8un_Lwn(r
ziqADHY_4x47252%MQ3~#bCa8q#C!d|qXA9s)JPOD-?rGzd<y!YbTt<8N%bv7vTq4y
z`<DZIzrgYM);8IAz&6!MR5<hRAVcj4^!%SG=eO2_H`3Sg*Vz^y)@oop+}P;L1KAU>
zc}rD&P5$Zs>K1h4#DA!0K6%sLClNdJdiGtPa6Ptqx9y8;We-2#qc&LQh6nk*hb-3W
zpz1e@TJV{fUQyVKg&nc$Cmb$oVdnyCJ--XKUZDiqE~eg&Zd`c*YWwBh|6%Ct1hDv=
z!YTPoph!Nc(}{iPKQMk9K3GBn(74qdggYvnan*Ry_`*|4H2ZPUmB$z`3t!`fzYO-V
zju!7A9l|EMY%nvAv$=WsNVyXi^lTa3LC(wlcVfSqAg1Gmm;29iFb|u4I9lslzK^t)
zsDltCA-;gH0ijKZ;2xE0ObI`T;5~|@G_`=%?$i576|Z|k!HK_2$b;*(`)~=NCTcd#
zT2{L3p1ZN08DeKgX<$;fm!&czl#4abj~6IFENMd^JdfpC<G*tccD);mXSKt($xv3o
z6KSO3Bc0m{|8hKN_bKY565uq08~IV5Ri(j7P=x?rePoxK)14F!8(NuZ*#9|KB@~HO
zcx$kVR_zy{K(mIuGa0R#GHkKC1x}A0TBINbEUs@`5OXD81W3P4w%6(}rd6o)qm*gN
zR4bD-safkC4!GC+%yBk{lP1VGC+ecZBKe@=qLuvs=@`c9>D1j9*iS2s06_&b!aGJH
zcug&T*oKKm{Ob-GMvHC(3p(+>T!&hB?wugL>91=ghG-MNd!9T%C+IMo=$4h=@Ry9d
zx#dK_R=oFJ#RD2WVJ{EZWbVFB#SD6J&ushQXtoc_ms_ibt8UK1WZFK2S4cb9T}*RT
z&oF2BrA)>&ydl({Y0s={>k{40huMYR^44EcVK-ssedn#@t_*exB3rhqqH$>r9&aC>
z|98|9#cfpxj(%h}?ze5VNzCz>es=B)!-8=WEXQfb$LezLLw8R~gqrPbds(^mB6MR{
z0@Kq8R-;ty<x$t4dgf)ilOK!Exg6bm-f9Gs@HK=S?LCBEjx+R`w>?8YHJA2IFPC6f
zIPHkbCbn+14Z4V1wbYY7%k-YD_+}!t>e~=9?0!%GXanDQ-M9qHrm4DvMszj@cmbeE
z&xIx$x7x+5Ir<KzIsQez(k!QVR>t!Mjz%$(EMkL((a0TOAwHQAI}lO}UXDNtCz2-u
zy#ay#XasOaBl5g53@^pyZPt8<^KT&E9M`i@K@$wYJcppg08?7kpS~Q_0TghE^F0kI
zJCOC<_#oo1Olj&Spao;87Ncwun)(#l0qH}Tde{JhyCN!P@ohFAeBaiKBVr$^<sI+e
z>69tX_&|~d7@VZl$q_J7Mh~(Njl}f0#K{IE6l7gA#sZg@SD@{_ieThEo-%(X6UspH
zY_luAoZW6wamgjRMFn`?7gs)2>mxQgA@_+H26;0sF(D?4m6x@T`{~oQ3Vr<eRA}iH
z)yVxBK@I6>krOLqnSc2GeU&RyRGbEytMZzY&d`5?4K_qGrI?Kcf&2L)ia)7HV#rEM
zu3b-6AL3wd&V}}0%RVF}d@G{lh$6?v?01Ds7)#i2_|^KN2e~w{1VwA6%of5$sV%fV
zSquOn-#(<IAL6}aWyoZ>d%5~eAHS+3z27jC)s~V`x;R<HPmG227E^wN?PTtJQngoe
z`BBDvM7BoW6y|guR^8l(5zFlvWvu!`saR_<@;dN+w7+nUv+0}~wYa-{0L0MYp8E1L
zN);CN1QDWkPZe|oDe-;h-Xc?>)gD(Q$n@_+u%}W|g=yB={JoBa&wGQPA_7kp+^<ur
z%f35Gu70o_mfnJy?@RuD^8fG{3bo8a7IMM|eqV$(7K5;Fm0{Db@ygB=)swLyuJ3v2
zNY3>M+}kVL=#3(zBQn>~>keUd7`0;&`0>@&(%QZM!tLS;pYG-{YR|Ucn&onmk3>M>
z-vQ}}{NZ9%kNtH(=6>5kedrmVwY#4P7+}WkcD~>8t6%`=nQyl~&}-qgD=1^11)%c~
zYZ>X3ij<_M19&90|M6M>szmx|j)v}l8-r`KVUC}Hu_z`GewK?R?KGA7W#886&*u2y
zAAv;4ApPI~iyu(67(zHgz`3gvSiI^UEjNlkO6Ced9&}?O17P|iKMvvVHhoH|6mjOv
z2mmj#2})T!RqmjSK4hJcQs8EA`QwT5;r56MO$fmMz+U4egW)lg`#T~!UR?#&>>+T}
zTRAc?WMqwuA%-%|#L<MWK4y8tyTo8xsUlRoM*K49uZ!F|&H&LeEJIhxer+@r_minh
zl`pZ1q^vPrtHSZ@cKACFe0SW3&e^(afV<_o%sS@bRM(PD4NDZ^?RwD2gFu+fBB>z<
z%c5QhUrwLuB$Ru?Nd$qTNIlh?oW*-h-89+ONBZ4oc7tRm9P*|Zqu0xfk(83#f5*Ih
zktXQZMEW3-$_&&g=I`B5fUb!PKhilgA=$*4G!fhcqFPB7N0onrLSe^OB{%aF3%2gM
zrmzXc?aT}#&<`b}mOLjqQc5QNXbT=6ePc1U@R8Sz0a_RmbC=?y7)pA)?N%aY#cZ?W
zQ)Mo}285ZkT7F9MO)h5p54VbZs}70ga8MCBCl#|8%<EIPEOvxf>`E1kW04mcvPzKt
zwN@Wkw%L1k#-Cx_c>3Q+Vqe71IzTnfSBi+LXEAz|E?%#~i(Kf*{gs-G<u!q^;S_Jr
zuXH7O6<)vkBY>TO!|7Si4ghI{MeU-4_WWwp;KZT*Y2I>engnZUsB9(`LYABr;s*S@
zLFHd)LL;nByUUQYqb?sd{Cn&Bt1sCvh5q^bkr;I?$yxQ?967YUHQ_6fIq_j*2{2{*
z6mea%mTl^BJ#w5WP;5jN-PQ&Q1Q3;097M%FK{2_HgEH9syNNf5(|}U^QY&sl=N_zt
z@!%wvFmqNUb-hL{1(HoPV~44l;;*~K{L)BS$+xLLIKYp`aK#9)k;_zPu-7b_1sARp
zz_CBLg4;L--5GH_FyK}p(^S%sWigzxjtBo8?HYLhAs?g?lJa=ga?p9EQ_Lk`FlC6*
zxvI!13@!^$oj7J57`rF)n-lO;>X<>PM*Ats09JHqWHv{{7R#!dGFdgRI`t?4&V#xP
zd@S(756s9HC%ieLXD8j^&0peX6_+x8v=k%OU+Ed1WfHReCE0lJ=p}Q<gCrPTt)V*}
z`US-aldDs<@d}0r*${^G^zoMqW?~<tkJ3SbO^EBW=v0)#koFL|JVmbK?yI42es3+V
zWKM|U8sK7apgAjn2sRYYjE+od6lbf_sAyvc!#n&{sAuA8*<aM!S<q*dsB*&cf1B20
zn;=5<{5z9_auRE?FZv0FmfbXmO!N<3e1`n^SyXLvWm7pF14bn!LSYugDM}hU&WaaL
z$?h163V9$f$1(9#|2PVtE7HhupY6&f7OmzCg=qmt80`%h3KFLlH|0+fJ0Ds;K>6)E
z+>|S+_8YX(eo8--n*T$zXGz-Ru2X}T?ouQr<%&nGYcB<UbDE!$+7pEEx!7Q5p)t=7
znO}atyg7TlD~Q^Fbktns8|Ms_sw;DQ%PMTz{IHQRh#@gcx6_P2vbOvBP$Zjov(jYv
zznH*~cpi@ck{b9-7x3zufcTrUq+AM(;WNgJ1E%Xg#(MEPeGdWC_8_<$1?*g?sl$?m
zaPqRrW_hMUAXiQBQ%OqPc7$jKPL9bM=rIbfWE+U8h5YX%hY))LCpTp}5QWs25-6!e
zH!muqIi>4gSUITtFHH1^SbK=$Lco34X#d@1gZECM%;@aAmE~92y|EGOt*Peo=U!((
zT;$)!sHr~8yMADTA07Aokjc^sl-h>I*_0H}R?r4=bS{Z};#W0X5osz9(C-Gvl0$Lc
zO&**yNM)0Wm5=D7I(9uA`Pnq;MLs0sYl0+Eb<0l#DIXzsnJm5|VF;eRI(Yz$mALSM
zCh?6=5h#;$fMDHeOZ1SH2;kku2^~{<%fC2NvAReWZ)`oVBBw#9O#n~y%=_eg7HKm9
zvgu`cBk~m`<^FLbQxSZl+s($e`8sy3ITNNySVNdth6caqw+kZWD@KEj2I`!+8`bn$
zD(+NG$hiD)?^@ga4IK{~@I(w8M{qAVY!y!dzNHy<qlHhomGIfnIXkQlE%2?sZiraI
zT*+zwOL+*9xsfCS8n6Bf%}mFk#Ndjz)%Y@Li?VveCv`?K?5x{=-v3I0^lX*|ljaqh
z%bLEbt~X3<qg#x6AXq+>zvJTM$8L;Iv1g>#dLlSTtf-WqsU0>A!f)AQaX88gNs{p{
zB|<2bfUaxbVac%Tf<E!N8Pz2=?gQ0Dp9ZbiLX!Xootnx_Cn*#!WF`=077G>kRujau
zoPE2WElGU%Ga1R?iZPPt$I+O-pwrY_X(_sg5xR%q&r*1)aJ0&h>w2USDVz~3WbtT1
z&Fq_gzm|G3Rg6!|mvv0ftx<HTT41<MM1$KS(8MYyfxax*vt$+xnLd+x+7abu*y7wh
zth4Ry3Sxh}l0qj^mCoyMNL{i(?X)ByKW;;7tU1O8&gL4@?+JMbA^r2e{Wg6wqO6rQ
z52Nc#EwRW;2qA9lPo8miwTL(3HweH1D9BQy+CPJC>pSGHVz@JJ!QxipphW0^^S9D!
z2F=sb@U93DhjRpQ`RLgr=ktsEsk^NX1r}54ah9~23|K$s@k1=Ew2$p~A4R=P4BeoU
z$}S1NtlDb$a@PtX0T2Mh3f({oaANnM6)iP;LIWTKiH9r(L_2&A&kN>Ryy|#+yF>4o
zG<M)^%1;jC5P5~`4cP<)i~#wk{(_6h6NS@bG$yrRvO^NOedwtxr$>$hYdggs=%bUq
zo2|`2U~F*Mz@PB5nwRynJGFSaHAJ3u#eizG=iZUvCgjIPR0ZiNL0XF2wG0{|4?^;K
zxb3R0LOCa%hSPg5UUH}{#z{o^5wK~N>f4j{cnj6!5VNW}CPmm#22OxgTYc2f%6h|z
z7vQ^XEoxbWG2KncmH?^Dc&{8jH{ii{7zg6>MeKvuNRGUS)|bU0F~VMKr-HXt%gS=f
zMPx>(vOjBVcvr1-PuPSU*f-#Ue7_AQ)b(U@92%zOKDzHI`v4oq$m0#1Bg#Loa%zD@
zU4#hB8rCeYinc2EI}s98fU|c&ed^gz63B>ID{fOZTRgx2;_5Yqf{?a`;}uEXe0{B$
zM$-Mx2lVEUKH6M=Q#Ax7v*yq#m1AaX<|SK_$ju%T>I{ykN>d)nWJpl#;&DDDy%bS{
zicUOf+Ya%;`aUx$Y<Dwn#&c&&@OjKR6Sv9cU|QI!aXN;BGYh|$1?{l~8ObY!qh_r<
z>Zij`VV)5RGRM;}rzcZuIg!r60e5>}U-UG`qcZdaV=1zDiZRTba3Lpi-B;z-qVZZn
zP6!9I?4YSVGp*;RW-xnK#g$toiRN3q|DGM*>H#7Nmdf;}T|VOA0?D2UXGqPHM7$*v
zD-D*ANu@Ic=~O>t#HArzVq$QAa-O8Roh%}%AAYkj+Hd#dYl3JhBz<Sdaz2J7US_1c
zgujgXb+xYzzjyiJ7vpY6GOn7FopGU)oH3z@)SDqpjhbFmiyI=lLP)hws%+)vU%<`{
zI`G5Kkub35qSIm6<F43u>c!_I2I6nWQFI~M&RIaX&8itN6JS|#DVW@FQNfjuI|XuR
zs&cQOnoIpuv=Mm&5b1O3F|uQIYPlZbOBVFZl0O@P%aA~U9_JP14CIaZb8#Gu%lT%R
zE^P&Z@%XGtAH0F5p!V9V&L2!(#^Hd(r@R-HfaUWf9`z{s(g(lPJD~LUJLy$*J*Euo
zD10!Wx)3SiF2jx~B5ZUb0}Pq5j@fqh<C=rg#0Dh^V|IACJ#ij6<-X{jZ#~E7+$%W8
z>Dn6vAK4Xw#mNBfuZWuLSmO?krgsFfh2Q)#2h(5W)qB!#CtQ~(vf>IrF;`<3X6Urm
zq$|@kaOS~FI;zRF7CCc~37AHF+zuPwB4j}YlEwC^=2~7Aue4aSOh{}n5I1@9<;c62
z7VZbHTW+t0at#hbWBY9OwGtVoUUdNO@uBQb+PAktGBkJ;(xVXbYhR3FCs~y=a-Gsu
z>t8Qlwj}f!91zv*Ghe(A8%%kXstRdhr5P#OKzH8JjSc~#bWFy+Y?{g?=_4@T#oL_&
ze1P*$5yKZVaXR1yd1Zqnq2q=LXszr^&9YndP~<aoLztO;++&W}+^7C)66Z50s<fNn
z`u@EBG%*K>wlr6?%gRfxi*!=^UTKB$EZNb$m2K@3zvedYbnLGd9g4S?B&Ek?Gfr)r
zQ@EQ5+Q^xh^JK56P4Qq`-<$Gzm}<(O0@nkvg5BXRWv*dYpL?tJ1m;^p>;E0)m%Nn&
z=%XR3CpD}LIX#_kWi6v`&}Hdo9=(PCaYbrH0609k0sX3Kd}7{7+>OgVzs!o%o`K4+
zEDKM|vj5tWS18;o0WS+q<nY1f*P$4e6M@=P=&aurRA=&+BiTJgj6Y_#aSGBesZ%pm
zduT2OejC~%42XDR;d3ETAz(lb07G29=>2FTqk!Z8(xjnE^v!^HTb6287`8Zls0<-A
zC{|E0CM;Y?wVi4o=w^WfADMZO5g^_l+^^}-RXniO$l-hzgv>YtlChH3LgAb>RB+Pr
z%jN4{a4C58SckQgUIQ`!Fxwi&dGKiD7~rk(z{V#0uM~a2As*T%RjC+9Igu3qJ)fwG
zD}nR3@(}%JzTPaaZ|RW|1B&27F`u-c2c>F&+qIoqDh%r=wNyRF6M~_z?Zdw4N7XRC
ze9`=g8Cadv_M_X%LVh!>@uoQiI8QgUw910CSbEXWCKid**-Yi?^~f^Ba-rQQeD~7O
zOhb5R`ui4UHW<HRtrsG?f)K6>S&UCO$Lk=gC5pk41912w@5I6`^~)AEP(}TsTpfHd
zF^f`BsgF$0PuEDZ7$cefD)YI5=!oTsEN>X#`=0S_PQ<iAe8I#l@5-au)}X$OkTcSc
z_C2hcnH=~Hj~y&gPqw(PfA=NKbfzZ-Gj9RuCMPt=X_Q|BJ$$aT!}iIHA_UWV1VnuU
zK@qM6LGYk4!XaUYm;e{k4e&y_g~&Dr<yBf|MmtSD`hKH#F~M)-pKx1U0VT~@*vC~@
z*$hGzX@|DyJ7fFygA}J_l#G2d^8=<2(Ks{nS=s$hm}fGs3@UR;yNRa@hb@50L=|uu
zkz@b=l>>7RETRJ9lVn$(R&LuiZX!jA(@H$>=IGToqm*n9c)k-k6`JANG1E5kcXn(>
zx-GdGk2HPx7!3x#R!^_Z$t2vqk|cC}{vWMG?<e~E836EB7WPKqTQP`|?n8Bs_`|Y6
zpd{RU!Dr&(pMX~T#_(fv{_jm_+TYVJ?C3{T3MQ8I6CbCKqqUW<TsPa*Jg@_q^l`oE
z;j?pj=fmUaPvOrzdV>&%{JHe!T&zIKg>s_5n+sg-1mCw`WZ(&DEpH%A07)VAy8Uu&
zLo|B{M?fN<>N3=U?^VZVBD&+y(j|Eom9OmH+v^_B*4l-@lXm;Vlb=Vd^oM-ZY`|({
zg4CI^47it-epTjr0m?Q5KAqe_#pnWsjuTdS@sklpeTV_>jDxR0y+Q3WfR&s9`~Y}N
zlueP%;s-1xlk_{0-&c1f90U$pBxI5u0e(fE%Tf?H&t?BUemma51RRpbL(De!apRk7
z)a19}yhs)}K~#NmUJ|r<t}s1U=^(H|YR(4;3rl7M?ctLk_j_9|N2Z8+i8b~pSeYYr
zl+5~D<OWia4#IewgN#Y*9EOu$cPG%F?SA%Z#<XbS`u-&;efFHAS|w%q(C4iD-H%`s
zg^cPT6z~4))2%nHo71$V@KQF(;;HebaKUuHFBhjW>#057UK`Y!T&KNF!|D0dl785D
zVrOy>o5xU2AJ(cV=paI!=4isab?oM#g!UH7nmEln(gG^!q4c)2gQhBn+WJJ#$ME{9
z{$S$TjQT8KVe=PBgno*&WC~~Xhx>|CW|qlp8?NsRW-<65U6-AFHMAJ7H#&OfhO?U7
zllHU+^ORJBmTF;rJ9L?py%596)!GBZBPu=jgpieBln*ma-KiMr+GUp;$`Kfvlu9mY
zp%i=XZW}yXgImg&)59jKD#HcIj2ik!s}=tLLOwIYo6eEV9aAs-rfdqHbXtG!7dKRK
zNjRakvn}fVfV(w>-Mnh!lBOk7RjNd42kjc;sck|E6Ux6*e_F1UGfdb{8S>x(K^|>#
zP5s`Me6;ARkI3e*?c2-dAUkko?{qH=I>_64`|{p-;q12C1nIyr+`$T7T)-)B()vy*
zpSir(-X%F7h^2j*0Jwi!`=kBf3sBa4ClhY?yXkQ0L1GQt4<hwRLK`%f>_SK?xO#Yx
z(3jT3ilH<@GCuI+&{T}?<N*u`AP-O>Bqx@d0%ZnqO{sp;-57rW`VO=$jx%7Tz<;pc
z`Pe(8S+T?q)FC?0s<fQ}IY#ml%Qo)P-KlMmJV|$P0E~>YAPa&t41oLPjO3=$r6?0h
z><6|=c*EN)3i>&~_cwL&a>3V9Sz{)F6FJ0UpgM!pEeF{+N*nq+yu%Z;@$zO<j*pkO
z6*%gBtl_k;n1T{Tpw3rMFqxgykuS(OCma-ynK`0c%Y*dOEhmioZ@L3I`k8q?HfMfb
zGqdk#<@B26k~b#xQ`V9bBkdRu==gHDXviYDgm$6&d5|5_-oiF8mhMnOaq%*nESq_b
zY0KfaZ)%&8O<Lfb6h}^>ivs$Em70o~1Ny5*aoYqYWH)w(O>QLd7j4No4%;F^#!?*h
zM|L{lRs0w&_zG9Vm~=uZMYN^U={?1zS}=w)NBN9R2C=}nFNa_Za(Fe~8I{B^+NZzG
zL)-(_ti>M9@HC3o1of3g%Rk1A7b$)`J@*|KD;`tUl~U@T{?1o2+7tP`#XMa0lSSFB
zx7?V-)T2F0aZG3|KP+-&RfiB}O}AiWRPQgHqabqp2E~2#r@VsmH;kEL4kt`@H9?VB
zr(8YBMR;fz;G6h{Wg~cCgwi<dx5UU}I!-6Q&ON*RgZ~DoI{2F3A(u6>dioA_AWf}9
zzhs5C7#~j#?_ToQ62>DO!lyV7d4uAr)dG~70dX0vxq1F#rDM7b>*6RAh7i5nyw>|C
zixJ{a$+gJ?y040{_~hR#3`mN<%g^xg`Y56S8THFwfWn0{qX?n!>Dtj-2oA3)s86{}
zvWkC*jMn(|+{4qQPNdK6PAzbXO1Oly<==|F3DfZhOM*afER-hSa}HAO(&0q_@&2TA
z1E85>&IS)akvPA|C>H~^BuJdsT!v93hsyQHC)u3+;3N&u1?z+@5afHCf~`IR)CGW|
zgW5=IsGvzV9eA{D9Xo7I8YT~2W^`A2X>zBnDCLnYgpUxwv0K5B3QIG_F+V?TYS0u1
zBmd)p_F#6-k>N$q;QQ1dK;8*+wrKW44~<I;xUhhe#XuVQp(h(Pzu0b)+sqmteq2D+
z7FYjt9hGaX(5A}u{HNBcYuhTm(L(H29xJpA$$*l%mmiWn--CGDe{Ei6rA4TRpl4VU
zt^f(>;xv(qjuL+M>(wvhZv{Hl97_qP8IQ+)zS}n+OcLSv{z&*B&7In2lOAOl9y1zJ
zo67IG$&g@2EXsu;Flq=LVhhsX)9G5GSB{4|;J=DKdSmIM5Z|=_5?@}Gpz}-~f&b$@
z-}74VHqpkMnc=ww!x*u&HE&#hhIY*Dkk{(dmmb27tuIriD}O$fOR{tr{ZzL%9NHUz
z*x{<9T(CF@Kxs9+mbFYszY}&MZT*_$q!etP=hMHc!bL(NvSP|neL%EXy2xYXLKZEe
z`~Rk&`1B8-){37`VsDNO8XqOe82<Edf_(mXK22-bJ#^-zGAuHwwhTSc+sHyH<qIRd
z*h$@64W5NM=L&R4C0*2tT2f<I$8Iz$f;2N@O33GKFZox1;1CxiF`#0<hdTyhpv3Xm
zr3ndLboAhSOHi@K%L^Xw`Yd=MHA_*3L+KbJ@$8T9hfy$JCqDwgM?hfgX#beBS^RD#
z&Nq<eGd}B!;!-j^$gO~7vGbI9q**)QIW_JaZ@xd|RThv}DbVblnLrxzNy1@O0Xh56
zupknWd6vbngAk-(sh*<kA?`;6e|dJ1EAwRTTuP59y>&6gj6a($CF?SHZw^}idd@1O
z+Wr7XG|q+Z*0!G_(dK{~93y5Bi6n#vb%nX$IUs(Jf6aZK>;FE7IOuOEHPnQ!9Ps0;
zHW(rY0sqB~65F-DRYG@cRZlx7kf#rPd{<w~6JO~E?<DitAH3Q)wAehY3Q40rQ@KhT
zmUZHdw&h`vwsAR;<>0O3L8KHEOR^FkXoGgG%7^s@Gcj!eTg}6)3uEgKG?dh+XQUQ0
zLYTZ`IM~=mG=`uy-dQ0mr+ns4OjA3X))B6F`>uL`t8tdh2hx3M<wV&(<Pf>(057f@
z3;wJUqGS$p{c+%C_G}w7b%E+^@uR~xv{TuX4=7sI=szufWW-b$N{0|^!{R?hsDJIe
zQD8R?ybSH)I^mxH`=F$gqw1!uz8`7TCT^mPqHui6D2gnJKIb+ZXlF|<HxA00zX!Vo
z#iYgwcKnB!%(#p|IX6FDs?u1QdE9*eGQvq3q|pgen{xNEq?j*kofp(j<G7o&EZ!5&
zPUto&mJvf8xVLPH!>LtS_r?s1w=4UB@WeZWIsyIBM~b}el^XUcPM2{^YCQkrsU28=
zb~?v1i&6?Q55nY;u%U49djIr?&Xy5C(fGc*{4KRw3HV~A*ulYzLRGtjuAt@Gq8Ff#
z#B>-jhb6$lDj4!8z(57{vm)mSm!`cCCg+geXbZj)FXCU9MBjmdD9E)aK#Ne$;58J#
z9L{V$=v+evS`2^?0~~Mw4?Cp|1p%*+t^V6nL;D0!+){HlWJIaCH1KJDpbtrnE;*?Q
z%qBY|<HBo}oc5#7o)b~tXa|1<PSiO|F5W>wNEr`nivYQ4doW;Ge!f1OU8wl;<8q6Z
z+mZE!D@z8nJg1?N8Up{_d4wNMKG5qr4d9ubK~$WEO&Lhe5v><SLB1o7)CF%=PBK4V
zxAKWjQKO&dol(rU5@mn$oq%a;sK`NI1-w&!c4y|%Qmriw#r<DcvnS1lgMiF50)p@L
zH9i>mcvLg21Ugl9w+k;$(+C^-8C`po@CU_oR&)5tuG3bLb7Q0`HDmCq{#-i<2Y=Wx
z?7nDcS7?AL*NI>(%v^(;4k6-TZYLn=$g7w{s-iA;N?1dYZfB*InZ%IOG*VV(F_L;i
zmtpZocETd17PTNd*;*{Bktnix=<ZtQ<!U92mpf`+2614m87gvO=pRuMT-xT}>j}<l
zAJS^da4ko>uz0@+ZDJ-qO9=}2vK;tZVdRymg_&M)g!cj$T26C$pCl<Xgl-AE-kjXJ
zq7vxx3(tP-MtLr+LWVm~UjqGt$vN@+W9(FkRuzoZN%{bEHF}n7*Tw6*F5)-X^ZBiv
z+uHw?aC1cmP9=~h8}nBdn$z_Eq}oa|D_^65{mOhI<N0FCLrRHwc`6N{G+@_4&6)VJ
zOL^td0v=<Ma6&wX&Yuw!iX(3Lb^PgDjJj%H<tg<lWf1VxIc=$o3P=IKPy3!=f%f;5
z1CK58ryyyjY(M<L%{3bXxNO8a(%OLY&5fNGfa3X@-nKej^GX=1I)Z^rx<C#E?0&BD
zPw{@<2<Gw7!@=W7o~f&5K~^!OEFkGLPYU3I;DXrzU&a^odkIbX*D4_;rXUDVI)-K)
z<M42z<(t|SM0os@o?OWp$Q)Q~?z;R%E92S&-~rLGjC29W!)6wCB7f!Y<U8E}vVVij
z6#DTKj-&<_ZgSYBBdrk}nuf7_>QU*L&O#=V2ocgqKbFZ9@t|0=R6>g6x9yRJyWI=~
z*#|iE^lg~*NRI8~9!=WU^pD9Xix%_jmbX`|;%6tBP)#gQkJ^F>z>D#cp0fbtR7Tda
zOrNs?FnD&c_~@x$GA3Lx-5!^AM=C1ASphzYV|`;6P2b<yr|0&Vg`aAkocLZ&o8Ze{
z+GRmzrjrfnpyKxNTc6g_Xc58!dcVov_l?9QF(hhqU9o%Z>c^sFv*kwkHnTauJh~wA
zhHqo#lNhQ?;S@4P`!4rlc;%@y`}+pf3b*I_!?4RK?sGP})FB~|%~&g{ak~&cl$ltV
z%*N9@Z@C=0Qx+o};HNcj;jMdlT3;e4bdT+Wn3Hm|l#J(JT#<rZl>Ob6%;O#x+a6D7
ztVd}0za~5ws12KbswusQ2(npEvVB^7`{(*ujtVA`w>w@<A-n7Ef00|mDgtgT;G;w3
zOo$!+v2Sov{>wq7i_f$oFTId%{FPNhQQd>X4k8Of55z0D0TlJG@Gy)*$;o}@q9b&J
zp!cLzFvBwQ8|_UgRlW4n0nE$zG2)lycYD956_%MRek{tDe3(tHr1w%H2k;IsH-hFO
zUtl}K%C*Y8AaiC={(4Cba2a(FlE|xcLHz(=mFQWpP3u`!_^6#Q;OVoM*pS7TN~OyY
zit!A%jI_1MIydV?>Mh!fzJj`dRq!+*Gbpab=W`s?-1ayf6g;57Q@1r!wKKS93$bff
z05BO}?HP3%^a6CU2sFUcBcngGZ#wn>$klPwx@=3SkZKNxDbTS3&G3Rx7~fnKvu)t~
z+xVI_SQiB6n|yye^G%MflYfOxX8+ZTZnN*Fyfh*MYs<B67{<x!<n%$`F0(E(=FYg*
zmBbE|&_U`MoI+k5f)PNYY`>o6sMwa?p5nCk5P2x}nWs)=iAF!`NF(HE3h(f^(+&Bx
zB<m|}NqAOuKIR-b1Y~~|`0(vvFioeSo@lAg9IbnCcF%U+lT4M`H<|`vaiK0wXxvG9
z<68HH_A7-tqme|60sb#S6ETEw0ZJh(dvm~PD6R>~KYKy0ImG8mNRA50;^9FCZkqH?
zMI&2dEeZ%N;>qaV(7xF`ity1Q=k}2qZ4APQ@qF+2;I$?pY4)Kf2Pc$2fn3&>$f(0v
z8$xl^(2XZKt(&v_<ox<?xI?p?6koFkt!L+T1I9F`W;C!oflF&#!f}`*a8Rd}qj)l)
z&)9B^G=_L5DhI(E`;rXRHy#9Tdy$eSxY+mKf9@&8k{Of}nJQV^_xp4$+Ro#063Yp2
zyY~fMn)lcn>QRc2Z+|Wi%VQ<O7St9y+rHkLCJls33sJw8{?lk`!FI|(`M3hFPZgu2
zC$`9k68A!DI4j(W6jM~cdX`ybWie2x)%D0*u50Br>aw|nImj^YQGkZMN<`kfC4K{Z
zmDub`xk|bTA6Nb<ICa_sB6d-Cf_Tw6H-B~7B;9&_Y65B;`Vc{R0F?b_(be3&OWlcB
zIbM^7wjlY=!c}nygB0CGy4-r?oaF+LcP)U;zg|`d*SSgnj|U}zZBbR`=#T*Vp3Ad6
zM3&=Q*UI)`74R`Z?V`X*@vCs}NQfQKWNZ7`(BsH>mrszz0g<1tc$$O(Rfxq9;1iPv
z^#OF6B&F1Bx-8D>0NIu8sV)%4aUlQ7ir$7Q4e`NVwOTz{TS|mR2}gM8ODvq#dgL32
z6+v4T@$jgqIGAa(Q+}jQp@oW&yz$WTOisH<kOL{fiKP~t1Ag~8uQuE>5s1V!$)fwp
zsqi|szvi(`kx>I_F-gG>Yy97_kQC(HsT`kGhgY}TKJz|xkF~lWayJ(0?$3aQ6>(@}
zs>I~;|AwI}@As+7GAARhFt^239#_yKZCBPPh;Ox6)YsRqot<4T*q<C6h5Mam)Rq$#
zg-@nXveGf7Gt#~t0@+118W`jej?ua33Y(y`vjt+lJkG<DYBp;^IF-xp@#SE@tv|;E
zhIn2*@ZOU5A!HxTkJf48c?kRY)12RJ2sGMo^v+`Fn;9;*=oeO(UE36(Tb4Ty-&Is6
z;Yd(Ui7C@Y<H*g^5qi|VT=GKI-o>2d=%O$tY?WpUeU~S;opKJD5z*OpI610=JtCdT
zFHfdW)`^2~EjT_LxxjWeuSdo46L>Hxcdj4$cB*i<cdmGS+0QDAn(ZE6Tm)MGCpJwM
zlt?h*PLpReL<>*O<sl1(2U`itC=dUc1Kz8GVcnTn(xnhRYqAwB@_3bOp4lr*zFw@F
zGW_l&QrWRe^QZR*brv%Qk1`LgM1sjT?&jrJ^i}7+idbj@rU`n~BO)r*lc0B%s7*rb
zy+p@FEo+k)JO42b0O;!Ec|I9Q8&dLE;58sl*;5WL4d@&Cg&Afu`bhcN?rY-p7(NE}
z4^bb(2)<V=RJ!;pjN*Czwpu4i^Jasb&j5}F_)CWc)ED+NnVb{kC>HE_O{h=s|Kb)-
z_rbi1Ns$jJgz*c(b;$g-lN87el8=w5k#LMnuVoi|i!!>K>2%5}J5YXvI|xh0FF2Nb
zi>Ws58ero@*7}RD9<Li8&N;&USL_Cfb){Qilgnx<X{Pi(fNWJ_O^2sH({XdzgU1hn
z_lwV$Hapdd1k2UBJ0h^uey;_-np(y*>6uS?8i5L6U8}6TCmw8&O606yq6?YxfvnpM
z|2~zc7AQy7q8Voe%vP+f^pJQeG6~szpnO?|zp7)yf<aj0Y-@OFTI0R&QYvHmQ}2q1
zG;)=-JF$Bw2jx><$=Z-jqD%DQm-}Q@4%Pn6Rqib%XR_dOOo!S4WW!K0+gLC%^MMPe
zsJ@v`@0UOX9s3u$EdEr$<j`(>Oh}c;i{)0%lju8&ze=~QIGpOmG!-6KjqWU42u{>N
zAJgR}s;CI-?vF@W9$n?|;gEjP?=dl-r!^e<(uy-@$h6_Qbi5zaB(k`#SZmDdMXQuu
zc$1!8ky|_}vubE(aT_xK`R3AR)KLAll0G%wL+bzVy%}E8!q$PL?E&-Xf}z}{NSx2*
z)>6sgp%*Ko89$j65)<6KdUg(ADLmXb&?^I-tA=>gQE!JwD$Y)^Ks$e;{SO|w^yF&c
zZ)_SZPkYIk)-$$ITu@=A-Rt6s-UO54wumkcIN(=%TI!&}btPskek`JB=*SP{q;6$2
z6#E3^g|%OuiD~bKA%J$@L=?d)SA0}pc=D^SaxF&#&_u$4@`*H9SfZKvOj?~snIiB}
z@vHc2xrxV$!7VOfia3?d|A1--Dz&^CP~wJ*=GTq16>5xcfKic}2YTn-fY%xX>pRkz
z!kv5uq>7+3UBb6eq$dw)vOJ)8fI4qjBUpS9U;o+5OyWmQ(u!(nI58YjC)dmRNQ2Hy
zUdtNtU*KhU75X|PcHNOoiF{Vk5*WDER`QyVt>#tG>!$>^+Jq#HHT-__k4l=gW&~L8
z8hR`~rom3w42U^kaRUz;Yl_kkT08@Vt|cX%_XLWDTqQMNz9({`7QIBPsRkr|YKB$X
z46NykJkiXoa@#(~^-Q}EBDNpQ)y%&=vbQGNmcN8fT%c6QR?LFU<O>eJ`XoYMTriNX
z=7f~BfYDKmQgu2DbD2K)t%M6txe#ni502C{TEh;u*jW&%SuC2$m1)V#acXU|kz?s>
zXh^B!btCjLsir!l)?p~JslbOO4{vPmy8G1O_V4TyyGDFAS%jIwHIwuS(FFLom9$LH
zzcmBFj222eFHEBQLg~W)6lqtrDW5I1CJ_*r6O`Ec>go5#5{pQcV*3W-&+E?7ik?kN
zg6NZUX2#19R1VR0{d`>R8uVfrEWMK6I<Sb<I_+Aq<7_|vP)D2X`mnuH+q;tduEZly
zGuJHNu8SYMbikM_(*I1MTRye<zd!F50_SG4MxMOdrLi_HRl@RD<gC~k2BZT#Y-yk-
zo!EF`JJrWY{hD-;<D7-<sGkUl&4zb!QSV@Jtq#kiSZln}Zuiz%t=bo%-xrIFtAeIt
zsdbRunlZ;j#Or5mwj>j(Zk^l<&dZLy1@9B6qUV@9e$7f8rC^i><>imPQ5hV`=Q_mj
zSpIG#UcD8(1ucnH!CuGXiDFkkheFSER0i#eLfm<X{}`ru>qqzRtO`T4o|{o-0DyX%
zFw)>t{urXP(aF{2Xn?wn;DzkJ--opZNNQISK<&6oX7cAGL@duY1UOTZ{^kHg)JuU6
zlAL}jY`J(W9}_JR$7QuL3@atLVYdc6o-9l5VW<X#ww4@U{|<-ej54bx_6q4X){YH4
z3gC)iuVc0-gYumaae_alQ{&Qy*+iOPGe6090RPU&tsiZE+9RGh{`PgEcrS!hK8l4p
zmf#uahLDamJN{BGrNf-j8O4}6OF^QpDQy~vHe*$-tdnN6Vl<kPT&9Ch<y8!Wpb0J6
z4suYF5B$fIYrcd!7k!SU_*;&|N?m67yn`6JziTa<4geCgqwMdOhbp2co$-?CTXgY)
zonD-5Py7#UZsBfFDup^Rm+|2fROYCD4B^qwY~>+2)8{ZZbN$;7?1-@f<z!S+Pwv?h
zy$iNHO>y(H4vaaBmvC5aLHsL*gWRch%Uo@!-OJ7WRL;T9T+&~TZwJ>trZyd{$n33W
zC?8=@tgOPzIYxos(WJ)~T|Og6JH|3H6jgW-sF86bv)uWjm%~r#9Q9(Nwce0=Hp^yl
zU?)>mxwE5)?$R6;^g#CEu@LMczh^#B?40{?*|Hgjdxg+h``!N2g%@>5wy_nM*Gj>|
z1n$R#Mmma5pUA2Z*m~R4#RrMFsZX{2--b||%ZJ{M>v{e|WCa12ON`4yo$mmQLCACG
zHQ7WiDg)8W{yn)v-U1x6%TzN!gw(r-9h~(qhx`Kh?E(nr8V~y_;*-Q)g~omn{YL%m
z{A8A8e3dnko#w&7w=!8va#LR9TzG-i&BvE1e=^ULVbhNu5MbR(O&ak8<NUgXpA%Fy
zx*a#$f<YiNF6YXnb1JW=O87`G_?Kp6Ps>$x|KZ{uirO4)^GV_F&LIFMoK^prE|{UT
z5=Y$v*`M^!p^)>)Ur6*-p_c;CCXGr3oWL3~Bs95sxSR`cJ!~KFCRFmz1e2*CjcO7o
zjWPjCLw;QYyfn$8lQ9T(-sRyIHf`WvBH5|kZYfv_FmyRDlg6MuZsbriCZC#Y3p55i
zY$Lvcb2ld9dxjXRJIuz85CXt`7MoDM{IGiMv$R=d%UYWQQx8F>D*|)069lThIOena
z$5JD*PqO5SL8&?%LtbXlzRrphAKO>^d^5N=?E}%8Rr^=`7Mp|fpo3AmMD4X*{r%Fl
zrCQrv7k5Q1vnD8KHl|i#3KJFOMjNd`A(|k&%ODCm&D((zc{`4E2|1`*4(Ikw9tv)b
z-J4VGz4?4%lwBxe{-U)TjUqMSE>FlB@xhRN&J4GI(^N@kzsry9G;sl=vv<PTWQLNC
z4)cmXk6`Fy1{!7QKK-_f!>6~l*jr5Bfqh&p7;bJ+xRg8>GcTEyyLib;yv^uyXx3Gp
zNBRRaLQl<jI7#j2n&OwjIl3IZ+*Z-d-p?(nJ*9?Hpt<}gdTX(B<?@GIs^|7XNTei-
zcIm!N@UuZ`(@e<UjsNCm5zQ8nPQ#pSg70EYK<%3<ftzJ+ot96Xx<C&kdz~~5c(Ioo
zl556EJ*TU*m^x}aWY^SnvM?bS@HxtO|JPf(619-@VBMBqweE}l;%^iqf6h4T#X;Zl
zGsWX+<K_Lm_h@yvPYQKLJX71@bZ5D2!SHMP926S6xEOzuNr%J$sVo|i)*O+4+EfL~
zg>A;C58OS-<iT1qBFdZ$#&ijSt)pjGupSM#+8`(tDXJ13OUL>|ftse|TQ&&BFH;Mw
zg_0+708-N$M`(Qn=Q3o$>bIFIuNdEpA>(B)70{95K`b^-5eDgxY24*mcUG6}qjCZ1
z@HfGo`UPF2N1#fe^8n2o00&TK%ZS?#m)7O4IUQ25U~5*u@+=gWGQ_D%ZR>NEz#hgS
zNn%hA!?s6c<haacF8}sS9f9=o+jt$P@vg?(|Fy=>rG>zF#4h%-K{QB_NNzghB#EmZ
z#>r1xoB(wuwP9JPkK=K=HS|AwS$s6p1CgGjzG~A97VU@%uf+@YEV(v+vYTD$A$ZX|
z)L4m#n7I&bYDqJk6Bmu9oLj3~!SIp4DM@1&OhB-{9qEi4V+i2qI1g$PXA8?z8hXGd
z?rp1E=85eN&ZrGZ@lww4Sbi4KQPA|L$>zm#(aNwiAyG~0PITv5u8~66>oiuSD=h^W
zLv3@dUM3Ab3tFjI&eWQ9Ad`JtDnutsJB$Yk^+wwkZkcOgEzw@C4!=(<Ojt$;s4FXF
zP(g9n=M_;-MC{dt)tM3ZYiP`1GuCcj8FF&nX_cg+=ya#_(A;%*PxotFz3*8x#jX{}
zK~-SIApHG7bBe0GjA%N-BSL3W4pwkNll`iq<N#~r!(k#{-KOso=VY!~XZ=(fXoX2{
z{DJM5Ob9{`2KKkm3k58HvxKJq-*MIMym+;p=s#uWL5|z!Tq*$R&5tH_qyKCj)6y@C
z|DD3l>r$0JB>)F*4*!H8{UkB3lf^|luae>Xt+n5QowT;-&!B3|D7~&ojc)tzuNdp(
zn+}YHs98FrXhHZM{JoPmg990zCOR#IBx>NJ(q4Y~XdqxF_>8JO?c3_K0fV+Xn@%$9
zaE=+kfCL7DjR0x4a^c*3n5>dwa3&xIPWBUI!^=HsSIg#v<iJrcZYmEW|K&7gB|b60
z__6H3V%n_Vx&1_z0rK%ARP8N^8z^gl3rgGp<2#EEUMVo)Het-Bnj<frDvZLv6s!RO
z%;*2X9>7^>)|VAxFn|esO|{;Wur+&ZR$!u(NE0$<u0h^dM~YOpq4(6&j*}+&*8F4T
z8rzw3I4-BPit}sF)cxawn0OZ-NXtHa{_3+`d5IYM+0uggiUoddo9eOI4{z(N<9CVv
zlV8tzbu@6&lM7qSH<uHa#rKLMl<B?ilL9JQUx&gpGi|ZDT(*y$q<nR9`0qqrlsQ0e
zl(Iwd*pzfIu(IS|Y$Av+x#H(go{M1IL`Lr)%-*_fnV)2ab-xNBgeMy8P-95kAxI)D
zJ(||=3G36VtWlPzA7cC3%ox@v<vbLbEsC2XiF4Y_!BEleR7CVOC0~w+Mqe_G=!;r5
z&21W2;1)jU%}Q<Em3oNe%8)4gL!~cevGF{q{s>K_c(b}0zKI3pWhjG+XG>(HSvTtC
z##-~4=1J!^Yqqqzu|nhXki729s?H-T8)ij@xz}zJ)yg0DmG`U85duo9!#rDOI-2+t
z8;92SU5$S_cY1QcFJE^eQjgo(dnM!*T5FvC0}+#hK=lBFjQ-Cb*=M(3&$vF`Zio=*
z?%j9a3d1%$%A<c*ZY5gZzUX|BkK;zGTL%20^>Jj9?_cp(0ycmLpu|JMmV{0v{f}n8
zMD9KJvE0_$F^Cd~Y#Dzm7enI-JO;G-CW19&*M6>PM|de}=LpYb%dS(=whXWt!NIz&
z#UXaJz|8Y51Fgw{s!`^3MSD``S;b6IpdaGJJ7vUd!V&{q2q>|9iL@Tn4swnbqVv5o
zi74fZte1pOL93H)vacn{Ex_v<gzXHQKzxW>r%E*oT>baYd-2~zO3$$(8x7dRgY@es
zJf2AFbxBf(ie6JG8gh&%{v>wG^Uk6(R8M|}sRL`}?K6fFoT*b6P}2+!>@4q^X#+w`
z$m)Fs>&@=u3puep5b}c7Z@mJ*f6YIbk?y06q)wXeX1Op)0kfL=#ltZO)>^v1tU_~|
zVPACX=-wAf=;Q$-+=pL1IGQ1?r5liw#)`T~e4fj7C@wizcxrOMG-AUmbdF>`Cg-vZ
z#@Ft^VAx;YuWLy{S`dda3rw%!ZjvnBbACpEL%LkKBsh`P-<+D7_pp@p8}2s1KH+I*
zC|dv`_9s$@UGz$XK!FpK$XQbq45Ju{6{p1;B8jT^7)Rpa<9D7AWow1KkZm__?qoPt
zS)cpTtb9GYvM(y$-y{nVJggsTcS@PNf?q0`+x|gI7JN$T{5IB^;$x%fja%G#Yl}+d
z{W6v-;Z?r(&JUSHOy%q9qRU+p`XuT8o_AE}WnYMP(ib`V+JK$1{}XL(539J7LsBfN
zRq4;*q{t!8-vU5!XX*A(tPjo<v}wr*a6cl#k0XAEHRjF*X*VU>$^qe5jDbMPE-xy|
zhv#V+3laXZC@Ifg8R28gtu;Y^K)=&eTZ)Oi`9ImX^T%~zNS-=5(`qXV6X&-sg^>+J
z3H)r22NV(k^WtzKXD}rh2P@UON`2Zc(G=jOk&;zNL!B!^9T%w%q!mr4(nWg}&|7Cy
z)hlU00C@PpLQ0_A>h@dQK4mr#Eia56-b~86Cq?fL&|NWWfohiHrKyCvNmhN+lafQ%
z_ie6Pk%TA!e)xVK7W2ucX3P%@i6U>|!z&wqn9268HO?Q`TMT4XtoZ41Tt^^_uwFqb
z5ij@pA?OAW=3rBCBp=}v_E#FcCBM#Sa6wdWt${jnrI6uM>zXItnc8Ne8+@}gKvYX{
z{&KeN>F;M4qbf_3i+jD>B1^)gI(`*42kj#eh4(8%LaXtr+e`<O+UC~m`#Ua)QDYm*
z?(s%mJ-CNZQ_KpZ)ivYSwcPNsg$>Q>C7_%HW!k{4p5qP=ZkInIK}Vjl;v%ezTFKI*
zpjR?K@eSy^=~_TRW4CV*G6Eh~B#JY!l;jAhdAah`tau_<7Dz&<v+?1^*J^=KQ-&9&
z*%akp0!69EBQUk1K4KCqn~ct1MI$O?8V(nBeg&8uJjpb`XL~})yK8^==3)-~=%3}K
z&)2_J-rC<5ctM3h!!kE=(St3V`!+rFpN3k45`Ws1<Fyu3T&|!@Q{BP?JUc!)=s)CM
zVG&b#Zn)l9)~>o(q{$1uF#wI3KZv&U)4IBip|~U8*eep5V`1?5K)vN^Ez|hy3pM{Q
zk3GfzV+OTYHxfidSjey)o|~<}JDr|+y|=>iFkK6Uj+bUona))K_Z@CvJM~`IxerED
z1p8F!h?-6xJR{NUVn0^7s^1gZ!o{b)@cb^)2(}Q1kp@xc2-RC@;O+C<*;#|uHMPe-
z6Xn?eJ8W2~U#Mz1H@KYdtwZ7s)Ygu|%JImK!ys&RIu;>NPS)HxM$|Fmz`_MK?M{X{
zC&>@Yccy<an7qwg>ZjmYBb(Pl;f%*<?%p%qoS^}@M0%*<fMa{{gwKoH(k9*EOa|x0
zvZ0wcgpX7KX_N=LEw$o@{~t@|;ZF7cKK^r#gJU0i#yPf-O|p-@N8y+mN4AWtkYi+z
zWABlXaL6o&%+RsRsyJpw2q6i{&-eZL{Ryw@y0827yw~GFz#MliJSf>k5m~{r2uNtz
zMHiI1EZ`eL=Z4l*#Lf&VH?l2t+iNEskg@2e-DQ@$cU9K_je0}m*?!{z!*;Gz|GhvI
zxji0?F;`b;dIlWE>P@_g#*3Lv*Q#Kk5e<>JW;n%ZZ<gphtBbdS>RZ;;D6i%;nAmXA
z?_UPYpS8RVl`$Snl!Fi}ts9xw*&$pU+hSXo5$WGgUgbSY@kxU8Tv>in$z8}28-u?k
zTyO!`ndd`Id_mM9v!LKpt&|Kflhrro1a*_YTx#l*h4Y*mA5JiQu{HZTr1N*Y2Rt@{
zoxg-QQJIwz(zjTbI9kYbJn5Y)Li4>ECGa`8D4E4D1->d{F1W}R*(Y(_{>8h`Pkd0M
zBQN>fzv5pf{Ef%I&AvR8Q{zpQIw)|bEf%Vc%w0!IW_#A+9BN8}?r3uyb^DJ?sHBH}
z-z_bj`Hv0gJ>xC$3G{?Xc)_4|W^#h}ZJU9r7=XMASg1e7O9mzKya>E2ys(+E7naEh
zJs-*FW%)|7Qk##4IT$I;h^Cnn!L^>C85X?DNzWbSL4VEae{lNK`~<ZS=z&%+Nt&Cj
z6=VTmXqJ~-0Z%E%bztHC>4giaa+EwE`6Y|ri7k^*<e1WZ0CV&j#;qLsr)tPji-`@E
z!NdjCK0rI3J;M9vG9D5&0a2T3g6^-19DA;~<Q;FwXCRI!3*OOf5#<uXQD-+)Ks8(V
ziwP5_>S7md#*@u4efF37mQubN5mPixuV{We-+k$|I|7P&(k$}z67g;7-bbxBP=$RO
z-Zz>cuP$WyFF1vbb!mrtD9J#wxc}bl{HXfYugDqz6H2X@=ix=ZLA@OJx9i=kjDuO-
zqB_gWuNT!TOU6cRz;f>Z6*<+*7^J3>`r<B;#>fd0ox%v7;D|=MnvnC#O|fUXEu^v^
zj-F)_H`Wfn^4n4>O*J_$VN4l^xn<K1!^X(`H%k&&5RZx%EL}KSFzok3ORXnYhTFSu
z7_8rC=_}UGFQOzX3y^!W4<q3Y7vqNzj?YToYCs1udg7GJ9GmmkJC5mM{#E-oy%6_t
z8xhB)+bz3^W{5Jk#TTOv2RBOM$C@`FQAX~fm8<a3jTNnfC*v`lg~F>bDbTwXBevCI
z<Mkp@-Gc9jd0av}WHR^c$0{GwI!A<!=s39<ztAYau8iD0XAX~VajyFRI5&ahjHrq3
zsFxXA=;Uc%S2Dw)Y*$NUQyA5JpEtfrHOw7>v`(OF;8`zMi7)yS$rL(+ch|M%J9gzU
zeooc1`{MD4XE&$xM1pettKJ#(!b7kd(T%GIP2L7lU_u?p=|Ib)ynIcGWRIKF`ks5*
z(~T)&GWi#JNGZ5KD;|gg#3(>NKcpt`-wM5XC6fIRZgG2BM$vZ?apzv!h^j!UWIdfK
z7W=G5iS(|N6FY{~fSZ}g@4#F7w`xx(T)s~VKx#r1%X*X+w}K#j0%{bw*ApKiR5=Bl
zy|7X~{kS@6n*MWmX{>AMFxZ0gP#c}y?#*Rp!=PZnH1(K{Vd{in1V)tjN3sfAE_s`j
zv}FaIru=0eQ1g7f)Dgw{?E+xZpxrmtw7Bb~JZlsP8X0)r*Hk4#r1M=R;saYQ>Gtgy
zS2z0o>YiuZ0<7gP?z6)s2t(ugkQ`M8)Z#XTQ#DV6wty!X53zFW=gqPT+W4SoY=vfN
zr53FJXZCQ4mmN>CM=AO8SpwG(EpDBATVY}R1P{O+V;?B8*d!SBFuTu`;R~*`vx(5m
zh;8}Z4|>%r*;XqsEmirSj|srNo34ZhCw&T>=F5cP>6WJ+y@jY`6xel@gzS@~{SBkE
z|B>X&SuTlV>RjGUmyy}V;qE{_Mp{<#GGdD6U6<79^m*2AMkV7@@2%pGQ@8?bm-Wfs
zC&dARZ>wvGMmXJ^UNE(({fwjH33#g@>mRY=E@e9UDp+fdV+@rlYgv#dl%o}Z`hesO
z)Knai7xsNk?i`_Ssw#W$xl<W+A}Hwxd)^lD9(;)4cG3G^cXvU69=^}b3}I7GuG9?d
z)Qlraa*uL9e|id+6#{+g^NipcHPBm0E55_J)Ah`E!`{f1>oxcbsQjYl841Hj^-F^;
z%D(*78BqvEH5Opg_f?r0J-P06e0Iy)uFwNL9xxJ7YB`@mxB1N4r97#Nru@B3vKE6c
z=|2F<f==UulfEz&!wmjq?x&9R$v#k)=?K8SobeS32u_Ij%*i)JEQsT;ExUf3QP@@t
z2uCX>KR*_tDc6fxdGZwjfle50v--)HF|ERvwmN#gimfX!2=}?a-G<LqeheMfnG^-B
zwlh1K3HWjYK;_th3#5F2F`a%=i}hbxXa5)gV1J_fW{|e}X!gxT_0mXuuN;v&jCvt+
zx|Z{_aUbD@*VLl*>YCQmFzQ%tCif7l(vQFpC=a6CI`Y~_SG?|OHyxk%(A?A+(Zm_S
zV1WFmJHt8W#&x9oFk?KGKJ`~!W{Jz+Ke~x#h4|Wn`MqZdp3kA&?0rntah<}XUrlAN
zRC|<W*s9XUHyP+AP?TY>gF7Bda$Xs|#0!rJAD4kaxSz^QF|vF}5_2F#Dud?KyYy=u
zroa*%Ht6^2N^?Tvd|hclfu?Jpzn~~Qo`Qo|Ew{j5n{|;uwl5I)&(7|=T@3~AOHT+7
zzj3A?|K8;;Sy1gp>ItwvmL8j_*VJ9HX2%ac8G;T!%RE#hea&t;v-Urfs96S-nC(_f
z^gNZ$w?;5{D{iH>v?KAOufH1G$RmCF_Mm((W;EGD7`H;fRYb<;6fZS9vEzE4<k+@y
zk^EnhE&d=A9n%uQ9pMO)zaPiB=}^qbN=7XoC=FOp#tBg1YBl={H+4nb_wlD08z7xf
zh3`EC@u>2vZVQxyIY0ze7Vhl=!UhJw7BVk3O1eP`2QdNV%4R}{2KvOrDgHjncNv_#
z^ry%9^J2u6qsG?=8*axyLmy+-Zt4sGI!BBrr+GlID2y&7;324UW}jwFeNyr^Ods-=
z(&en=Ntx5oaoUT~p^9jHZI*0Bl%~!CHJ^7T$-?D3OPn3nT_<bG3JrZw7>w3lfhX_w
zcV@XbpQ=}lkgdneHr<9lhqQNI-VuH<^Zeu0@4i>9PkD8Zo_Wu<VFEjE$W#E{g&tAq
zkhn<89+CJURT5m#)7~?<6yC0Lt=XwL#dqvMnc^5(qNH+1g&7-61Y2-1P1sq#SM~UY
zs%***FV%_ii%oE2cewRcl9RUN2^GrJ>6AAT>rgtC<SME*h<D};RoiPUCR`n3+*&r3
z5_*&jY*%*^!$gNjs>#+r+;b+hhM3%BdtO>o?rYmk-T)F!^5iHSxLGntNJKLFMKF)v
zdtgw2VMK{4A;t4VZ)tNh0?;~z`?ndO-7=Y9`HMAlK5{~)UiTj|vtvO?$Ul>p*5E8r
zF$aO({;Oo3-BKw!^<)s*l-ca*wNUdxlTbXxreG1c^M5<v)^&6Z5KMo>0Lb8*mxR=s
zabU1CdJduaUT&WLv6f3~pHa}DB7M<4&}rokeJ(ef<_o9mha04tOly-B7t#0%n7n?i
zkEvO(w@Lbw8b!dg+nCe?vxnvJTWvE620zf$;@Uiht?}7^j5=5U+@X@lQ_6jyYuCZ+
z=UTyl|JXBdlo{zj4<Vg|@~+bSc~cHh9j2gQ6wbb0e4HJ{`;N<D^Ci(wZfh(ufez;G
zS8-0l6s0?uw3wx&H`?DY5{wQ)jRaV|o*o4N-dc%;4_jk+DOa~LHPqc`Orm$e*E*d3
zi8{4hCM4lJ-nKUf4PcNeiIJ6P_|%G!0NS33Jl6RY-hY?mdo7Xb52c>GOY7qT!{maq
zW|k(^*)s-Q-qy@Yhc_HV!2YTU;v+)6U<HaTt=$k;{(U1S39qmJ=Q5#gf)P3M@I3Pf
z-Y?up^Lj<pH{rB-j@P4;(wJ@CwP3m&1Uzfm=}w3VItgF#UUc0+NCF<u8=;K5HrBfF
zg~7E&$HJB*K_6uo?Bu2XkY;*tGzGn+s6DJz8Az*wF(pvz-?p?+$zVDD-gPHe=}_Vs
zhIVs$ih-OpFzcPL@NxC6k^JUIUKpt)l9uW_It8cNq4+|SkA(ePe;4k}MM+@#CUiu~
zD4Xc$7p14#flbvDhtyAxN5|-#mDL`kD&@&e$IJH?eNI_1x}&|t;F%VN^qEsHo9}a+
zd$eOMTNp@$lsRnimx-B`&MK!Q)qM73W0OaW-fGtD()h9H+uv$NDoaIz0x3MBQqC*J
zo(~&So&V<>%N<400`IwPyD=6xD?FDkAtTEtL5Y6M>Nd@q&y&5YZk8_H%zGu@BB)cZ
zS!zBedv5>m;Lr>dJH6nUte6MZdC^ZycfeW`fa{omBsd(J)M%)uH~=&NOrhSHw=M4X
zF?1Mtw@!&LwfzD}{UrShIBNR_VELIO4opQ0_SGmJ=zpHO82<ef<OM)1M8b@&SuI6a
zcGbg30X*yw`Tcw4G`8IIOycE5fgkSvl0#E}g{3cn*;TRGroC`<pkz#TldK@k+3j5J
zh0uRHSg{5A4z1`m%>xQ2Vn;?(xsBv7SPOAN(Czr%%~Hx-vX;T8d6$V8?GtL>um5Ky
zf7#`<<Uc$xu$ju25(u_>w1Z%X@}Rwp^I-R;;B8fH+HJSmZi|O;0Rk6s8U&?QN)aQS
zfKGYP_5~T)t9aX*?g@Otu%xB3#@Lsw$$72284R(v95v|h-2QuCFH+5uZ9P6*oMo?Y
z#0l0El~OI<z_e%D3WbV}Cd?;J_Ja}j;bo`dL?R(Ck6tUX-YMaZ4$m})|HxoO@i(9n
zJUeAWl_mQ(wej8?Nd(tf`Aa@1SaO(quj@kXYpOzDni-(q{4iT{s!qTKv78Ou;S1|Y
zcPOOzMT2wpmL$nwi&(eY{cP~N@(a)K#qg*n2hWR%Gw#G;@Yrt+q@m!Oj>RkOjp8x6
z^dGHzqtjep-;_N?UOWF~oyum))*Dv6;&|U|@ZZo+-^EF^T%vNMvZ1}nS}>sX@yXAg
zL><-V5jt(Z&kTO5;h4-<nP9AY-&MN#*<AX@t1N=m*>&&1ju&p&jqa8BvXt-u+%>!o
z3n#oj&i*4p(?k)T@<y?Q4p2F%%Iz@s(p=NQN(<o8C~bRKxEfnT2_=2<$?1|hx-v3?
zwBwB%QX~a<sy2-w>dUcEoO%J==nw(u4#xzCDz6S!ldkD`w`~LS0WerPlG|`N(-r0*
z{RdCiH!=XY*@tAEg%Od#q=(@Eviz;JmOwd)RjyC81(XtqKTwT9@T*^;);UnlKD(vq
z!huxka;;e?doFkib%Z}_Du(6anwJAcs(yb?D~%GF6}1;(#6DUesns0Z9VE?|4l1oo
zVHF2<5I>>iIz1`|FOOC|hO&WUelG`PK)_8tO6*A@wdrLd5?Co*Z@i327|%L^bJ2EV
zwm%q$Wn~v(f;33N<oYB**l?XUhfO+g*Nw3&9e|m3qgx;_|B*(INKI!ZmG|D#GXXIX
z-o9RCRUfE+%V_k_)150kj1C}Z3Z3Plsgp|f^2i?XpredPOxdu~*jPHWwV<Wl7#Ld6
zjGM^Ofe2%j$CP|-U_OecTatOs*=8}vh?NW?CV^borYUoaL$PnbiG30X3{}&}6@m#W
zWxqIJ`Y@nhN~phu$NZKJg39ww+zqbIA#});ol}Q;@q<lmzVn>KAD$aY!J@7&!=*D&
zITX#<DFN?9{hL);3W7=5)I<97g`0ep2*>iF+$xmRB_YsI*2&v<?MGPDw;H}~EEej~
zYB}~t-?Q(i`|p`tYpe<rcl}f@aT8)G0vCXL6wjY03Ok&5B^?><WG3)dD||rl!GMlH
zg4V9ec%ihwF=vKl>iDM7&$xK)tq*q|ge(^5CbY_~f-)(n`h1RwJ$5&NbcA%@^s3_0
zsbdGzqaRP&)%<6B3($~=SYJRA`a!_DCH1Po%}1tomByE2{9++}e2ZkO$d~A#QIH-{
zEZ__AceHe_;AU1fMLyAn`x%a&FZ2OTqYeQXrKu79#+n@%s@k}XE2Nz}=Dt?t8i2%t
z+Dhwf0e+dc`wE!2erk|pkiyDNM_nOo_4*{*4nsG`D@mfI2X9G9OhMA_^I0DOU48eW
zt4o)QG>GNDJt9eYO3IycSsTu^re-@1-#t{=S=b82$DCOYS_uYlLCeRj9n?O*6tVJl
zW#3_Tafq5O#YTH?yfjkGe&N#%S6D8|Wn$vl*MC&FeRE219C3P|G&Wii_~NtAzo~XA
zQ<9HLDrd;eD6B}5PHxc8uFgzti$M`*xwS>w2U8~^E1Ey?0-i5>j+Lt{?4XRfZItg1
z4%<c({L<JhDk-gByxWcLKmmx#(?0+U42D3N8&a`IB|Jm0LLgozWmHgHv$Hd_ta&E_
zf9qVrNYV14hs`%~zp*|N+a)~qXeYo&1J}#D%^o1bZETM*ZyDEf8P7NFpfJO97_*LB
z^;NmD%nSz<vD1NGhA%HseK*(s_Kta=ieov3x3(*^HcOu&uk*FWmeaOZ6|b(a=CXwU
z%BBwtetRKvz@uU$DKN8r_DeqHA%~>lBO%a4#yqR%rUpm+^nd*!o=jr!cV7qo&vdZp
zWc7oq`~>h%zlJr0^jDNiXH7j%q|$LKHwEM6N=61h>6krhs0iH|%RF_TJ8wJ>2=d$b
zWAt9YR5=y6A_`|DWEX-=k@WJbiIs*CV^sm!N~}!O6y)cVRLkgsU?TTE$Id<EAGD0M
zdtY)_#&f7RiAa<(6UiNFJTuN*7C_}ba$>A#c`H@yR8x6jk{hJxzTp1dN=ee63FA7W
zWKtMK;kGHoR?yq51xy)+j@ejSqXVoX*@AeXQ7=UQ52+lukwp9sG<u*`j<mi93|f(J
z{n7<o<$)%!9#?QM+}VPnS=SzvQr^pGjJ7?;Q&+s-r#)9iB8Re@&y5a0-O=G4qeM@U
zr?EV_EkHq@`AP&Aud@QI#+rt5fhIhnz_Mj)QdJD_kqCV}sU-HVs+E@g1LIAgS$NI;
zHdUXr9i#1M`>%WB@0Hbsx;r1jNKX&p+Nfe~_*t|N`<c9dG{jFxDlMh;B|C5Ek*&kj
z-L;lD=$@E`E$?`K#Ya8Q>^CSOb*2+64|AxtJ(PbhS^j;#x?n0<5*WpLB(){U8nbMg
zerI?Q{q7OVu>oN@rASfr9(_INT%4)F{UUBIutUD`b1aO^)`?K>`F3O>sVR$Sl;dhn
zHvONlWaa}J0%n`HTlUyA<1m3>Oq4-6(RJa>3oO&h8o!>ubK)UYv8%ybN`oR?3}zbs
z78q4{`CsxG=A{oV-E@c=`)<;<#x!^IyPxEp2t(i2gXG(l2C~bZ@kjnD>|@dj$GI6+
znX-xDDAd3G;JmJ|I;qrpVLODIqI(@JslzZRT`&a|;AYJLcuKEmwuUQxsIr|bJHDO3
zD9m22Zj@bXcz^f`Q2-RA5P-$FHHpKGm>7~-9Ui||*bnt6s;2xArRpa?dU#1KPE*zV
zw5bpV=@T?m!6Z<^DH86}{}Oz?4SKTBK<(rH4QM+U@4^UoDDZuKkH`V-6nA7aW?Z^Y
z<i-QSeSP>9@EFD+WXgGf3<cDcT}(-3sLay>=C4@)^cnlO1b)VKnv?i#%&&!tkZ+N%
zvROG70K<W9*W1hBKA4eCsqob4n(V79WDkbUBJs_Y!oRd)-<|tLgCpJUiJwXnXz6k+
zvK7PhR08BRGBI&ayzl$u(ro@R0Bb4KWnJk>w?<P7Dg-m}G9Lw$DLV~0SP>avs@o`U
zaE}m&l}9g}mX0`7td37pU29w>!)V7|RQ!N@k1q4J>2&m{8sPzQ#+x5|m#d%cCja@)
z?4M_$=N&Jd$tu~0aN=mTuU4p2<_{Q-z6a~fW<m?Ct5>F~hLRA+O1OpdxX$M(zD-%@
z9*K6f36Q)CMz~PJu1j@|{10!jO3bGOoHRE(rpPX>zawo{r@T?VHU+tNq6*Z<mA06_
zb<5Yd5qVaG)`}Xu^?y~Q<V9fUGTb8F$kFE~HY#%YH;kXurTdh|Hxm92kGIiSmTd{h
z1pnB1QvSn0KPwUbE}i4)wMGf6CTFVgiQFcm3r#gk0m_s;Ro_kQe+@@1{^WFB0yjCs
zM||MDN+*6^3OUi#kyqZ$#U4Ac_zLb%ipTEPo*VY`6lLAQjcq>xq6q+f&`(uQiZ3aH
zXRJWgfhZZ?Sp~O@cE784^pbgDP{8O5x=f<MuV5cz>L#C~%pVa%Za_6XfT~&V8~1!#
zh(*rDxy0^BJCW%d!P|1ls!D_HlAx-<w=`$-A5xz`U~xdppPd2xRrw^J<KeW@6RJYd
zanP@kKn9{C-4=fp#%Rn2h}BfCk9w!Yn5RLSm5kczr@Ze=n)VE0%LsI^1q;gL;p~)T
zop$9(DZ%-DL~7u3{<&weWjxN$KyQ@kJqm{eMX6c)$FCRh$jqd_w}j?u7WyxX&OF%5
zzu1>h{zU7*Qx|kX+xajhE0!yYmd`aMX!w3}GJ(#xMSIdsxWPCYV4thG8l_@PpmgO|
zI7SD!r%e)qAG^jOxx+UYbRP`20l2kA>{XSxr$?%xv`3UvbzS?RJ+ke_X=6e-IsQ!E
zTK04C-mquZ3%?l0C~>ALclx%%{qrT!UQfl&_%{~+RA-C-8z~GsRXdQ`Oar%>nat8O
z&ZQk_N=^t}Jh=S27&|M()l`#m0qHjkHuuIBFUQ*!B*%eX;^|<ukFDIIKD^^q$#B@O
zoG9`vmO%K>!Z(@=pkgOEL3=KbU%&aOl$2Loxtqwhqn|-r*~VQkqxn`rzJ-TtE4TJ_
z8~a$*<6nWDCPp==TP6o@d=GhaH>U4Oye`sC6&S7W%=80YR{xir9`UPwsc?yE5}ziw
z4sqqS0s*2p3pZ14P)zju>p~g9sef1T)I3iB@CnpwvOWM)R1TpPP%OR^K+ozq#s*Tm
zoZ@Q?E$0#^&IUQCyOWC|0N$N;pQd^A`#8!;(B!$8qWQ&9fHd&DPjEesWyXz2npJl!
zdgHP!IH5`c@!mMbSQ)?{#)GHuZ8QSh>Vsf~h!D{oH-^A(qqP(6)iXnsiH;?&x)R{G
z427cs#P~c3a^GFKFuCc|=sB@N7e@!jH8a7dFuL9Dg}b_xs^fZ_cJFUB7B;%EHd8jW
zhX~Vv@?$@=wpIQHq$p%mURY`LWVzg6x)%$QJ3QtNHKUJz@W-ZNo8HW>yy$MH8hCjv
zgvOAd@WFVd3Pas0Z(E3Q=4}lvu1wkh7~MYWa%IUx>h6SDt(}z@(xIdIYm9j?d}G;7
z**$<=1KgpO=?rcVB`=dTQQqHC!qZ8<Cy&F!^h^8A26D3gW>ERBhM^-5XzJ-B4zb!-
z8sdfVEIcm~q?wKV<MK1zTl8?NtP7dXb-P01{BE=q<K(d@R<Y6?S{yX+EaVPdl7ru8
z9-?8gum12We4RfiiGlt-E<u=LD6V<v-ac;OjwsEtw~xbDcbgTc!ihcSf`a`>K@e8M
zc3+wKW-hh2&3pBDBV&A1UtjXAKbH%xY;@1prX`J~etz=PL0JV_oY_xE_OQ@1HD=^^
z@V}N&VYARwP}9zw(}$(FGG$d4(;qTl01Z5Hl*zjq+U@M#6<7KIMboVx8N3wx2N@*=
zgXZ6^GF}%4xZsWlgoQ2h?74xTp2_T`aKIsb6%?;F8mE(2dbha|qceX?r+{6*#7I#c
zD@}iAr}S@%Om$Sw^LHR?v><}$E)N@%fVGW_D{lQ_hQDhW2n7}c?)6bXKrc%*gWQ{e
zKC8xI)2rvjT7@os5x*M%TYbV5YWA#gW%DI_bgSP58hQ-(@l@W*6{sWUou7oZdq98b
z(?E1;EX;fXU8acfz_z>ujFpl$@z5*lf4=9#_D`&X2sD19S*;mDgldkREU!^pyKEwI
z(GOK0mOwKJXYZq*`-bG<V%vE{!T+dJwx6*b28oFqy+M0unAkk}O3SjrlL)m`QSJX*
z)jMIlAW5@qSn-_Fcg0zhs3+%Z7t}bGF+}T5c1F8)#PK`cKwkO;X)B`D_U_+60!FHr
z=vajF0s>Uc--C}w2wbwi_(aWG&fO5ZKq2Jg8SMQ+g_iRGg-YcxC3h2!s4c<dXIp~l
zbxtA!d22*;HWbh7;Db8(PhG?rTe4jpT-Y<UB^zmGKVQ>AoC_pDA^LLC8!KHIdK#Ln
zr(9+udUgA$qK9Lzo~l;)xivHm!;XeU_HxI<v!fiQ>6U~F*i-HY@`o+NZ+-}I6g4Eq
zgz`-#)z=h9W)BgjWv^eoZQd^Zg!PQxYhvh@Y|*Lq)C@AB%ly@G1Jd7jaUv2iY>(YI
z_JM~Px-2{}o)-_uYA)!Hda8>m{d=FTiPwflP#It9C;2I?<$qk1+A<PmI%^p=^+Fvs
z$A#ba(MrgV00V2a<cBJ$5l*HHLjE4`v*hse(7e2<{lB=DOV^-YW|kC9kQR`-@W{R`
zjOngw0de`kFeDog$sb8k5&Hm%#9P+@xbRWEM12Xw@|6W74(QupSPVC1p5y~Dhj_PU
zrmrz;=c)?6!!A_f*fHf}ZK={I%0i~}cW4vvr-5AF#%D%!prO;r9?nLF<@a-+l+cYD
zS@yvrpw*~fv#~ZNwCeM8u$Ac1>V(aNAs!k|jT?|F{&QU0E+DM?9pU3~(`TO;N>n-U
zvKD#T#?{g{9sMdqj;`E`Kn(P(;*U`R?Da)h%c3oF<u`zC0$hD>%DztO(BDM#vN}oI
z<I<Jc6posRJvGwIKHnqI-<;-9L7&d!uX(Xx)EDo4H!+5m_+L$07m)`tPHVOFZ?>Lq
z>~K}hUuOtv(2uw&@QkjUl8)V4ADGSoOZL-Q$-GHvPr<q_YxU6Ho%(nHup~jfdWfQC
zm1fFR{G7-(P&tKsl#<gnSx6fGSt0wwT}x#PK!CEPQeT>X)bm_TCRWeRNx1p2lu3R7
zkv^}!8;K5(O{vT)u*HY{diYMmjg0B(cob^sE_>RSre_7;-gRY#aaB{8jmmV1TD!j8
z`Ga57WU6lCY>VnDfhi8eO#MQYB3e#YbU+ZpN8Bd_l?H7dG7C*0qSoO@(S0FsYE&)e
zKcv`2ZEgq0CnnHCRfdn^*;=nl-Aug*B$$Bay_#lyYRr||#<upPPOx-#o?VF5*S*N!
zw=eg*9^%%9*@u5bsDjHu2~X%JOM;lA)<(6vk{w*2kd`Rk3?U!)fkKe(9bSOR%TM0=
z=(a=9XC`_La39gKTPP6clPK^190D{=fbK<I0BuYA>DF*Mi9^&5TCY0P0NH4Hha^lJ
zE*Drg3N~@^g{*RXUCZ3G8z#x)w)S;k#)I8*J_OUYJbEH~V^b4M*2jrf{dQ`8;AXkM
z{A3gK`-X}Mcj=P@0SQDMA^~_CPJ}|Hewbj<Gefx^LKtd_0i*j2s>Vd_@_>K8kC?)d
zTh5l`MBEZxpxJj-nx0+lqM?EjjU~k)R52L@mDxAfLb{lz6b>1mzA1L~XpbpDKsSYc
zff^%5YEw<Qu+anGDo@{hKbP0AC!Ub8PZ?jL0f~BPGTK5J>`uRrUrz{!v2ECwU9#iA
zjhT==(w6><oQ(5d&qHs$aH8U?7qQ%}{)<XTE?a5uZKmkRypMWrQ`;QB8b&h2)-t$0
zlq6+~*u~g<107X95T^;bgL7S)i+h%b;mjpIvx;AsXPBC;+2||&XpoR8(a^oHCGKDI
z&z5;Y3j1+O$vM--5`=Egl22vKo=YO|hl0Ofl^ML9aNP0PR;+4+xxDFpa*e+hp^t1_
zdtkV=ytDgHC9`Ea)NSJ<$6EA@1=|k-&G!HG53jPxkd^yykw+&rp~QGEy>>w=7e^4n
zeOk)@>vXo8KdsiPV~g#{KbCX%jFP-x*?5)Yar2lrrwg8q4brvFtf|@$d1&~5`0R?9
z0LzI8+)Sfcjp1mLCf!^#UwI4(%OALydd^Z0-F~A8A?U}Ak^l%}_9gxPu_wdk4)nl#
z6b6eG0l4chdoqEvTMyP{KiEMMGyhVBY`huDjQS23&>@$c0dL)z+Omdlgps1h?Z$&M
z_%5xoX#m7c=(i~j;8;*epOk*|cJos}dtZm7VDb%X6Z$j3*Cku7j7O!f<?zoP-yil4
zdC73Z#x2_^4K4K+fcuosye2Es)mE3hRWZ;%3JRm~wqR^@UDlTU7a0}|^szBNp!zE~
zf0|@()A%X@qLVBg9V-YUhmcPT!R$32bSfiuVxA;G656J9@*o_Y*;?zcCA)7$z5T3r
zt*}e&>sRTnN|(jmp9?+HiVv?12eVewuzr<1wyh5OtNq*w4;bKOe7a>;6BwNVcQ|8X
zqRwv7ANU}xqi@-SPTU?&&L{e2Pdy>lGglfBrrXkYF_fw8=C_=4Dbct^|01inufuOg
znCazuq3Z;kdeUHJgmL;V4@0AQ=4~XYa<bQ6){0!_y@+r3TsidMc2~~(ek#u{uqy>L
zd!u)`<u4qSX`P(C8^^iCfV|0z<x{vj>!|L$fJJ?k(=r$Q<>}{BtaGp2v-ynf?G#n&
z9|6}|fA#y3U?Nk$AO>c&fFwbl&VxLY?;=gE#lYXT&79{V-0B%l1`Q9x1_NHX{pjXe
zQ*TL)xH?Bod^+H<##%l{%amGerUXo8OR_<v{gIPg5hziITIq+sQ~t$Fv9q6j%2$5Z
zu{h$eP0&hHHftBEi{st5k<q=?Njx7ND*LFLab;fv1OIh%MtSe7K{u(ZqTC_lv}^4b
zB`Y=9l)QrBJCM^ydev5RlxRY|m%X3rrrdcGinRs(Gi#rEF!6pqCTO#+wz>b-YjaMp
zZ4jgy55D`8Of&YuU=d6UT@l4dA!kw+8BO^@+l%x}iiUGOr9~@(1~E+B)^XM?^K34-
zQ>8HcY+y4E{Y_wB0hr*s>qy*aM|hoNa|>RF$`?kx{O6>`?Pe`@K=GnZ;1=vpZuHo5
zlTQwpa^5oVSy#xw&`>Jhi>h3oHp!iUW@TI?lbw%ng1eIQLtQ;IL(`9hE7-5*LfG4h
zT{hY@K8UJ8GGjF=xpGI0_4m`T9XUhyky(u|a{>rAzYOjlbbVyU6Six}x<QmoeN^mS
zCW;WLb?f1w!lM$bE973qmY1-f9-kvvAEZ_$Q;F=r*lN_wepz3@S0GsXxLqfZ!M3d_
zj83jf@FgrCKD1!!Sn~Jqz!=VYsyO~Tw9`ruvxfLm*o}ehB@PXCGGuN1`)kA15EyVL
z4f6HLGo1ZMxm&7qXG2TX-My50Wg8-VLGv_MuT;E_^R0xWFvrx&*e0VNeo4U3QfUyl
znfA^`Sc7g=<y%ncRt2(lqE#N6N2<d(;7v#^kcx^POg=T_LiQ1CA|>Z(H>_gzn5sda
ze_dAO|9mXS#OJ!qL({vYdH%O9QTO&i5Ptfpxk8JFfJH;aOS@B!K<Y2Sbe^+PWkE!G
zE%3|2^{I1HJy8>GlaFoIU$diOdTQAqrN?Q1p|A&Z%49B_?|4jSU(fYxrkLzUDJ?Me
zQz$;EsV^iw=&Bn|{U%_}UbjdxA@L$9?iTH%eHKbceyne_i4aR73#|d}%dFj9eey5&
zQtA%{kiWIcA-qH0@xx!$Z^WHYP1V!ecNFboZzJT#h$u1u;jytnMM1q)1N?szaDFt)
zVphPlWb}6d;AnSF2VEJD!DXcGO3D*j66EL)My8_9cC<+<g!ZKp@6htc1^Q8p0-G;_
zhwecFSsrgL6yyY_5K&nEb{P>L2J7?xKu7PSh~b&%qj6f%zxyVhEO+_6Ssy|x#D}S}
zD)M0D#{I20(Cmi@A5w>NrGf!35Nwd%A*yt!2^Neemy<xe4gd=UieWh>2p>~!A$I4m
z$og~jVWchFo(24N0*oRq)Nw=umO=&pyb{r4q<FQwAhV_X!5Y;de@!SmJYr?4&3rJ(
z(!u1E=*!(RpY{c|VR;PQc-}tB!QDL%vy|B5sMQP!pYMPzR@<ld-msq6G@4~|I!(3)
z{~h1Xm580WaJFmK{`LMZt$};vl0Y)q@y<dZNbT!d=|sshQ_4RI2m$<Z1WzpfK3wi!
znjlPlQfKdd2Qu%UAsJePCvUi7@TI8+lw(5oVPh-4ZOYnmJp=mVB4D)ht^I^~EpB5u
z^=;tr?b(NUf`_N!o)(rEzA?7*-m-ryHb>@<tKQbM^|X4pTI_$RyK3?v|8Vbh4R+zS
zc~_l>@=R)bM)48hUIYBHs%TYyPciTxPx7;;kG={|US%{o%QO5h&k%u4X*6kgXsp@n
z*^9(?Rz1x{@xB8@(?q2J$%V#X&R1A10rP!*bM<W0yGxTf*@lI0?rwvr`egmd^ZDYl
zZi%dYj)k9NByOv16NKHem3Rx4AbJRr0jjr*jh?Zhg`Nj@M8sU)(Mnv40efSgfy5;(
zl0M$(S?m@(KJ%&4IpI{#RlMau_t#S)xnLvSB($41JI&x=K0}`H3b65ECLJ`~E^qOn
z?mZI-BfPV8@CC2Ls)q$6|9Zw~%x(BIUO(gL_|`#Ulb&GEYffyex<+~zbFlEUA7IM?
z#kIn)l0E<AV{T}e%3JYR;7D^k*-Gq_tDl@3O-OS#yF;XxExQ%s&Q+POkL`HWQC;M4
zOc{UgtgC+n=#JVK0$dNvdEpaIP>|g2jHsrU<80h%ogkl|zp<FTz31#H0{L{nXytz>
z!kT)TPHq~cVmqTZO?=|6MC(85Ixgq*t;fm}`3&r2<0&GVz+2IOoJioV!*c%4JsmlA
z>bBaRPY<h#JqoTWN4;*#LmpyO#bOC(&BYy@$8p7-G~b-P^8-BRZHt^O<YN;Yl8Sj&
z=93Qn<L<}JN!eN7U*Ap1ibw%u<J4JhJi}JmtiKz!qgyLcp(2`vR_3&_o?U~?xdDC4
z+jW*L`uB+R`WGI#CO3;Six{L@M8qyC@f`AXckNyDtW7e#RN2B`DP9fEFt%PytsoQL
z)D;F?P?!g3rH6&uKAoyO)Kv*tC*6tr?1PukUm|zKwa?_SN7nS?4p<RB6*^VYO)Dj*
z*`W>g8_Ef0&}vncJ~jvEynAfYfrJvp2R9ZC{Q6E^qV~nJIsVsd71HK&vu0-Z6S@AH
zz~01@l;q~4<%&;*ZIg$<@^1W1FjK;d!3AgsG60ehy+phhup*?^fYolx2=7#KbDzWw
z)5i5(b})Z3YrQu@8K*-VqW=yEePH#(wkWRfryEt5<-EXWStdM@u+1$zd#WU`j4@6%
zNt|JfT&<{FN1X32Gbs8CjAq#C`q%q*D|%!vR0hFq$;TFSs5Xlva~tVi38TFm_VFy>
z1;XH0It3%QYVt!7R&Xe9@+B=I4w>6r@kf>EB1@-F#*Qdd-3KEvpj0uR-%gc?2-1gp
z26$qZXw*7?RbnQh-a&a!cEV0;-rmqOuu?s(vsDOxWLF{X&*_6-a}g~DfOS(RT*|HW
zSQfZ2_?OxJvA=)1SjsyY@qfRoM8A)Y8?>o+>iD}%p(~baG0@TarixKp83|}m8Du?g
z=u+W!$)@}b&`;aD?7a*y#y88X_Fy$VOPyjgKUvFI#v(nzM@<hK9-k$B#BqTeKGCWA
zIf`CVizPv(h)?VZ^?H3zTiHBTyKITb>OY6>iU~Rm647aBS4t06rmGYNtvH`O!7ImC
z<#9dB`eKM(kTtJo#!T#|?Y6(~X#4kXuO^T4F_qj--}mUDoN-j-UAZ57!_nIfDgXOJ
zp;vW$H?NI;s;9VdlUe=kA#;tDSqL~_Tyn75B}=G^m+p`35H)I9YOrWOvuscd@3n9D
ztp9C22T=gExO<V(!}P_SB`OoEH}@up(U|uwYp#)7?C0>G^z&GHAMt~Ba2vM@C!5aQ
z2{vyD;FK$*2%_G+<r}#6X3(3c*=Y^d#^a=u={a+IrjtIQyG(|7rzK7QlldVJhWmL>
zOd#5!_Mvn*9vam&%TO7*?XRugFtwiEVF(?#c_^SW3FuY5O5^<5;01M5+GX8Tt87Q(
z<+hKuj;1mO9+t2=&iFp!%gq@8&hqn-0!Y3d(4vae()*##bO#0MuNsky!%{V>effQB
zXkq*Cl)IKMH~x}!9WPUFJ9UVAOgo4x#u-Qb5m67!YjS!nN%HKmSE@=sT9fz&uynF{
zF=4y2D|Ql{z>p_-Eua%Xr0x*j+tbX1>ycPB+6r4Da@ufFVi8*zp)!4VaUb>hDrZ42
z_E|GAp80rBA?1a{sU22vD{*s3NjAC0NkTm+MM%<}GsF5&?j>GNchN9(?NEAZ3k-KT
z3Ory_LFAqb-zjqmPYG<6WTS{3{nzpg3Pp{tKpzh3B0%5z>0FkYAf`+(t&7Uu;rVS^
zsAm~yAT$cx{Az(+hXElZ3`1z#3^zALnpbd}3%wVcVRjTF6)@jh<Rk}SsbsVl6uGa~
z|H)40hg=S}onI8yP{}Qcmpr|$-N~%;e%PLF(ZK74@!e)Ow^@xNp04l|EgXI<qpWmh
za)vYoJ#GwhRn{aQ$~c+%*B*=w+!&DpJMM}aJ;u^%2ql=6lPNwy0wIpJrivQ4*^nk$
zCMCR*30Zio8C!_8QuKvtQfUymTgsownJwzK^`yaRjCB^D&7J~QN|$Hf-E$z@ZyS96
zAcX-6iB<%5sGJxmOxVTu-P{F9;tfBsC2y<!gz{D;23BLZnuTL)*DD<Na}3<)Iid<a
z4(-RUKBvt2g~tjdK#t=FpE?xq9BF}#5$$EMUIQ-~#$q4)7=dL#uVoGdJZbtfhuQp`
z<{TKyRevtUI)TIuyZ06ZHcK<45U$Ogot!R2FOcCG@dCiYzJ^|%ThTFXaLe_EzeSoU
z!uN%^_1v`Pk)Oe2VSd-#;4ou2I@U$)JtXJU-C(@rFmUQQ7RK=V?p}U;$GLc^PL`kK
zBm_pm1YCUI&C2Tb#3ew5&xU<0_VoSjX`84;v!=PiAQIdmLi*Q|46Q0S=Vz*_wKJG5
zATDIK^$#4VBeQN$;FzW_K^Q(fy_I@oqQhvGgn|LLM*r~0Lj>vKcp3Pf2d0eYwY%8C
z?l*dK!7_BxWa1rKH}aVyn%+Wb_Ud_Q{JPELC`MYe^p4}6zEnAJk7TXPOoe`G<`nA2
zFz;bB=boyWV0SW|mY&^iEV_7<ny>Dhs@g0m@BD-3n*kYm@B%C2EVlTXaL#sPQ4p8}
z&5K)z?x{U&;7g*|8}E5zNkYey$guO-t@BQz*wc%Gnwgx>_60$mp*7Oi6el&Tb-DEW
z?@T9#D85Y_Oig5`Jw1K(Bw(&FY`62t`*yji%YS|kpPp9Fi#}2oalSthza4$@_^wpb
zn?;uIy{<IRGuy5vxh_8}w;sOxQ2Jtabxw8XhvMOgW;gK#!+#-1O>;ZCAfBI!zkeX6
zrJ#L)DF0E;qrsS&_i30#@AxWGWnk)~CmnVw$n1G+Vfm_7G94k}^O9A-cwn{4X<8g8
znm>xx@%t2qjXq&@u=Zoc3|p%RBO-nf>B%X`$sv747=ze=0|#uJa{^+c!_onPOadSt
z{}{@g*$=U!TFh`7p_|C^Dywv_iPE0@YcmaumZP|JykqZnK|CLp9-)fXxQ#3y)NkU2
z@y{zEoHz+IV`S67izHS0gwv&9S|@#_TNV}T{6m^kD;Of5r^>EBkW9Ni7T^d_-8p~;
z8NoA4=SzkcAwI~Horz)TZ>qUkI%~3rWjF3A|0-zOi?VOK8Yb68siDjrzQX!=3{vN-
zddO!xmDl>e%+4gs^^lVtM+~<#!|Iri*ObRu{jprbpZ>J6;grp6lg=+n&V>qgcYXX$
z4`nQ{$Qxo-U2`-{c9`a7c`d_ikJYro0HOLWTxIW7Z`f=3;5*2?XWXi>l}{yXHKu~|
z=_6Vip)6GugL^Rjt^2)Dw_drkp#=*`u(ce%xqVY1MW(*`O!ofj7r935t_jAuki4+U
zyS~>Kj#oB3%iH!)wkpcC(_2H;Gk+I)e;X+cN%Vhu*7R6W0BWdVrcz=9DcFM_5y`*D
zTB4>qXog)Li+8;62($ElTPJGS?#~}t;UTPo^Dq2|-<h#IyYMLVU5stax`l@%;AH0V
zWtwdI>SM#iT@oWwc|+}GW28Pw;Y%G-+k5r6t1J-EYM5TswO8B^i#4EOmLas<N-OSZ
zYo_n}pH1r|>I%9i1}FC{zOD^}qzf<fVJtqMX*Ap$>gE#Z-cp~+103PvP%qMoRLPnI
zv}L5-?|132Z?2J-iFfJBu{;N^|Lh3#?r*H9AsE6o=GNJ!z%lw2{rK+3htUt4d7%r~
z#Z2j&q7kFLYUP}iyTIXBe**aIdM)cyU1}<X+SB}g1FiOQ)3J(!(hk__Qm;KupgXqo
zXl`oqKjMDX^7JdIW;s74oyb)KgSn{M1h#eg@T?~$ISzRPIHs|gfAO@1D3@%Yr#WCH
z*6~nnJ%v401+jX~XGP?69|9W7^K&V1&_!5S0WtShw7+OU{<71;{ya_SicDmHoMx&+
zgCfMZEi#=MJV~N?IPolrQ!l(EwtC(pFBD!ByVR9NP{WTRVjo5K9=hf3Q(KA1L?eB|
z1)_g&_>(llnpK(n$3{20!Y8*g;AfK;TP{mT6>y{lUoPV}=Tz>x9_AfHELJF2Eg+q@
z^$~4IN)&T++)t#!eE!~U7cNK%Jau|Hce$jZk7425`@~rux|KF+J{OWtzmkgXC{?=u
zws?oaOw@?nlphQC8eUQMc;p>qk9-Sx7j~z%D8t!vN?Dp@c;>IZhvRFcb9LGLGt)v?
zG6PU2Qfa?qsv{T#=WbL{_9P%b{*c-0Q4G*ay6Vx%YA*lf+mQL(hZoi=82zAxdS~1G
z5;do6-%evO5h_x-v}N9Pv?BM?PIiT_p=We=r*7<0o1r+lI{$H3*J~TGg!cB`AA9`y
z3WKD&PfC$f^IfNetmD~$k8EOAR#p}zRFi4_kIHV5{a4n#^0ub27!aVUAT^IvgZy-G
zQG$s>Q#z8L*qGv$sZC$^V_rSda6hQC%ZTG=fogG#+Vcgzy%er}LaP5K-00zDR%RZi
z51|o)wRxq8?ASQBks7eeq=gavGrYUO6f2J7f5G7hAD@X0`-zUHn~+y~ebhM=ky+0Q
z;^wa|y~j#>^3&Dv7}~l_wszCC5#6JCLDdh(w$6qiWtp5#BhO5FWnG9`c<+F5JjI<%
za1OHLRx49v1WKM~gZ>{bvAHnccz~w@p}KkS;F_bq7bVC0vo)7d?zP$H`0BVVE_sA1
z(w6)MT!-hm?L{$j0@Hx`E}&VQK7s6YLak?BEO6^Xs9L~irrY?QOf6LJ%sUO46RsVJ
zwa_VV{}Glf^Qm)vVLB_hEKF|dfH+Yb1-XD^_=$bJS=u-+Zianid3Ysj+17s=suefZ
z(~_3qbTStGEnH*pifc^^6ER<4Lzf4ToC3K~6{*ZKqgFbb=dFzPER;|bN5A9_ye9p5
zKOZjDOORf+9El*_{eZ^~J(YdjGp4klf_<x_1p2T$n(6ZHst0n`==zXF|8-~i2hdBE
zg!;V;c8Zp;<Hsi`k(3;XR+V2R(-m?dJ;;ZXiE9RklQ)O27G9WNKAJ1;Dyy?zqPmEz
z<DV%BxYuoxF+_G&<0<w|R_V9SwTN@Ja>A=4u2c;M9x~UCP!~1#U*PW#vR$jM`b8Jz
zb!b<;gr`dW0Y-RLzQ!<6H88aP_jhxkyJ0BDp=GlT^)OYeyk1W+bRi`E4o|q<49TX!
z(NPyL=FbX&SyjZYh8Ko&2gd4SvN|v!8D!NCadfeI>Efb;?wzPC8MM~o`(DoCRB#h=
z-t%_OA>+qgNA+@UG+TYCYV!WVyl|$O<RjWY062Gc*!Pd`@(gUirqQLOY-;)Qiz@_r
z=EO|e)M`K|+?PkA+M(`}AK9hds1f^!iG6@?Ef1|NsD()aBpWnC;YMHs8nqQm4^`S@
zY&wD=!cw;~m9j+;nu+VisW6Ei2{sy{?`YJ{(s9r3N!3%WWjW~m<P%}elZ%!YjtM<h
zZghbV!+V-vW`2?6t?B54>*G_89?hwfTOp7<9gT^WB0h3_rDV0Y5#Y0WrzIE*Hzd%L
zHIv_H?C*|tgh)|0M#S0qUxx?x2sUxP`S4~gA+|{+H*(=>H9%k=OJ|;TQ+#Jjaimq6
za$jh5?AB|B+geE_eyNEPdbe(~xj4JriJ)m{4wDH&qgZJ6Dmv~ak8z_eMh1{cyWD+z
z?UKT#t<9^Y+hG_X&*s0&#mFh0s&<cXw@$k$wijU4VymiOsTPE>Fr3<jPt{y_zVnIT
z%josK&`0~$@A<-9BG+0=spcWVf-v)ckL^+d8%kX3gF@!E8mvn=x?g)xSbCS#d0Vv?
zO|K@`EEElQ^!k-W%}1T+zlv%ta~fpbGhp!=FLrHoEjS^RdNKZ&PX0_i!Xwxm{RIma
zIerr$gBei&j&C{9@k@oQG`bbf`4gz5pHY}vS9)Zb8*X)7Tee49?zc;~l^cV|)RyWA
z^k%R>*_c^meU)-ASSNsRvv~H0+baiAOz^}CFQ9Ts5Ee@%lbt*wNr^BHx-E<s@2i^T
z<IDAm2fM;K&yc-f!vzl-_GqNv+L42Ln&K>mV?f-o^k>b~P@P5-FSrE>=h6IAd36KR
zidQQrR}m3ijwiqz|Jk(+HiP^$)fC<FR>3KxplwbJ%-O{|JA}u%{u%<{MjSa@!NkEj
z-`=-lU}+ppA<!mTSq-vJu<=%-sGhKN(!0XomO*Qc5IOCGW>koJIXRiI7^(p>f@c``
zFp*l%TpxYbiV8`W7rgVzi4oZdxmbVhh9rx*NE>Y8&8I;CgYRB_BCSf%M5tK3Er#1(
zA+=I!_vSodtODTl)u}=!<#EyD{(D19lO62Zcj8ZbaQt%4&YR0v%adpp*xUK%r%`|8
z-;YOm!pA42Hil1WmA>`o7;$w}!yAW_o~OD{%Rh>cjGKo#Iql~~w_ZGY_Je2-!#H_L
zNWZobz^8}tVa_?f`82>EK<ZDu9<o@XjJZ;8WIx9|J_OYvFRQF<dYxKaAg3>o`xP$t
z(l;&-F)^PQ=33kGAz2uHmlPWrOML{If?i?<-XpEzmmhj)?sT1fIT?OGWmbtF>!y^^
zjqy_ziP%<ckTJH{H#WcYm=mmJNWFS8m;FdJ)2LyA$Nc^WA3;7g$Oi`V-xF@AAME6c
zy;cXs^*k>9o}~E#dn?vw@7k`5sj}TPYh{tyosSw&c9a!28A%)gE2pD&`=<nP-(mk|
z*y%39QNbxr#Fu>Ya^%0VocsNHLB~8$zy!MGCLcEKbHmduhtu;VbWzhhL$EmJV>BTS
z5pY0qs2bCr#A7_wzO-6~*21Y(Da4Y9??<fC-ozE{8#f$T;?VPWafG<$GTE0{d)|Sw
z0`{|L09lU!fnM5*Cn;#^57fFl-o(|O_J+rx$u~=9L2Gp2A8U2?zF|Nnyf*|45*R=W
zvNE3`LNuZ8m`5CBe)@|h-okK2H^jG1!3|?Sa=&Z(o-N`Ab{puv+|-NvJJRBsf-~D8
za+=ZMDC`b@IeVyv=QVN*r>Xe%nD}7kDz`VlMg0MmigzXyox(!T<Tap7QPtBjpA#6%
z!1C~0(k#44q#UB5WM4a8*{lH=nt}e-f0y0UBI_Sezw%bXkxG3Y`Bd+b5|}Y@9pOq1
z1@ICVI7zg{HHh6LL)(@&r;A@&=I7|OU{61AMTWoO?0mD=!j0TZ&{d+~TG!O=OA<^Q
zIm^YS;xi4B=NSg}T^fA9(n?uovtzY&q&?Z{F%nX;kZPqZ5ybCbAG&w6bvf2bv7$G9
zu3t-6R-}QR4!3>_B}4n0OTR>A_9^O^tts!bpLq8`vgaj-h^xUBo~g*!ljUmwM9nq!
z{?Gz}>m9P4s9yH|9F*6hYRcR@zU~U=!Teyqpe~RdOB>)E!SiN`d`gR)Xq#IYtK>!V
zyUNz<MjCxHAY;Kz<k$(jX+hGd83)?q#ekhTnMzP;CH(pCJT{g0iT{RwNUQUh9s{_r
zGKs#%crA`Xb1kuedG-S5|HsmK$Fudm|G$U|T4L8ujH0$udvA);BC+?bS+!RLu|w^x
zr7dF9CiX5hOGT+sqo`Rkc7NWV@9+O}&L8L8kNdu^*Y$k8j)X{D(}e#r(GYmkPaWJu
zUG&u-sDvc4O)a^NI+gPd{SbFgnZ~xk1SB}K`A1=dp)`Z;eP1i+4yhBuO@#yhaL*7Z
z<8E@!Vw>7Ea=3a4Y(EKvcvA|ntf-<&4(|x85&?otD6hF+Om@&5>QE{7u&X$Y()<9%
zBec{a%^7W>?1^=wmC=~ZxX$=uzLTKiMD9rLc*m|HUtz6}AtHBi)!Q7b9i~e<ZE4$_
zh5|%2j#puKOC3gknQZ))g+}!epG#K3#{(gD18-?79VKi@{Za*9oQe}B(Q<wO1n+qc
za@eVUbcTQn7>3EoNgCi8)H&4<yaurG;f`jpDhi5BO2jkr5GuHXCS$ZXXtgL3%SE5;
zUF_^zU_dYM2~CjuHLf*D`S*<bcT;WlFMx?5Wuiht(OOTazaApE^2sAMCV3*;F^L?}
z{l0N!4X?0XqEg88=9sz$r53L&ZDF;+0w{k?YTdS?lyG~Te<fxh0n%{SrDTD81tzB5
zqCmJALiOyjCq(C-E!Mz(%}XG6jJO7k%nDLMC)EeNE|!N;?fB!LB2R<roKes0Jz4W~
zkL+IN)V>Y+0IDB2=`Ls-nQbJhe~1m(uH~}kQ*xquy!1!DeL1sI@8jHo@z2k0jmFiO
zxv&bipC-E<;w?UsV@#`a=!t#k9m7Y61q=UtIn^j6csBbq`!Vk7_+(@!vV-J*OUgBn
zH~3e`Cbfc`oGVm5(7byg@<~&fl(2-C8F6w=z)xd6y0Z?n4u#jA5Q2COaZ8vD{P5dl
z@=zod85oXk^<+E@$+P1^pp4ms0AN6=Lm(#_O9J<xY6ChUo|Z*16TwyfRw#yASSzBr
zm#T07Iai|_NvEnpv=7mE##PpM2ttwbaUok{$Ilc9#gc>SUtP#{(=0FF0%aZ=KR|R`
z$?prg#=^8mXrhuFk#}_XQ<ET;0J9zrF?H0m*R{77!q+5?Bd5^=EF%B`_+~+loSvd+
zmJAV39g_MJiTkgp^{LeMO!mn6z`$*C^_cBCB<3!`Tp0CQ8iNUm30N_W5O1TH(W0aE
zpt}>Q0}>0%GGZ!KDH~yApjA^VCLLY>-p<Jl6yz9y$7Wy@5rhP{^{!LRE!0-(N7<pt
ze*DIJz%9xGTN5N+ygu)olzRg^8jP1H^*V)LDj$xdVpY$si|$yaOXLtS*}>&jTyB6%
z7s}F+NbQ^`sa(zNpC_Zt{Ata734DnxDh{Cu%pb7_5%$Qz#tCe8`s}w8w7UZdX@gT(
z?Iqyf=S52ftR!xrD}0h&CrX9Bz;PXe!OVWRS$GhDM9n3xkNIPcb3^=6Y(PlMpARG+
zI@c|Jy=i7gbFUhqmAT&)HQWE&?%e-e9Qvv6iJ>@nMT?U=O5S7XK7tpkiJ%)o^S8`@
z2)`WbSc#~qn)_<}rdz)!_FoN?sjLZiMVDg-^GnP|dd|REibO&57~6Jfe#4v*;79gY
z#$l-Mn3;L6rA-Ij|D;Nrj9=YxEb+w4uT^q5P)#aMa+I$qJ>aVR<-ZXF4ius50hM#@
z#@qK1oQNom!q>(9@1aCceyH?aA{F*w!eO;*rhX=+UwoqS09v@@%UAI<Dq!Fjz;U|T
z+sNPX6G>8a&}n7AFUcBh6v`<RBYAd~R3gM5v$^gfDWb5o8Qm6pz>WUGkKZ{ta%hlL
zV4-Ix63Sh-N;nl@F?~7ZuNC1<SP^QdKR!~|ULVD<|3WOEkrU4MSr8HBtvC2ek1(R5
z{#~tr)hR`Q1l+scjVcGL4q-#yi^@D@?Dl@ClI5?*D``EDT9@3k`R3gI_uqZHKg#pa
zKoCcnp7pMwA9P$lu8FRSft)lwW-X(${vpQ8ug7vcAQ+;=eHw4${`7chyog%T+qfxf
zwEhRg-nEU);>+!5?82d%F=EdS;q5SGrnE9&)m&<@4wooT`mB)ecWpPTl;na+POWE1
zH8z_3X^oR5v{-4mI%X9gT`w)_A8VO0@Ub~f9jC_Z80<fOGT0gNCN142_D&Y`v69R@
zNg_GiG3k1Ysb1glQFCnC&`Qq_W{gv?@Z`u!ubIa4y+#W**xNkrVjr*m<nfDBHnnUc
z8*qFiFjwoXGu%f!0#|V@ck8F;a0;tyy8rM~G`DMW<&cdjT5H%ukK;CGp7yyt!K(c(
zlj)_n$P*h^!9~)s$2v42HRhoIbi|pi-tp*H0`a8xi}!heb-WY4jdwwhSTyX7KW4oj
zA^L=YCj(qwkZM{9@Scbsh;U_yQ<ErV;A+gYDaV|{iz+RPg!WG^T}+Y%Uj}HmKK;ez
zLM=wJZIFBg^x(NiFPX0$=USw=3T^U0n84W?O45bs84tT4Ytuy5RBS5j$xqxty2O%{
zYu)>#cJOWak2))NRUibZsBz<X6E=bmCy$H5N?n@C(YhuN9in~{l{RXSL%1dyRyJjo
z(;V-^-f3qqq=0r8Nq++L&1~QwMV0Q6Ui9ac|5Nxvs2Q@~5}u06fO0Cr{v>40KiCp3
zWwM=)FnQEB5Y8b$Q!<LZRILQo;F^87&bCwCCwf`~!oWKm#z5f8K;}KHvN$H=oDJ?G
zuPzP09qiqteLa-TJ$J#M<bplfjwzRIs`<hmcK|KoH~7@85}DuTaB+Q?XEfdSw~U}s
zbO^2H&U?u<2{oJ1N;*OiSB6p&%(1N+ZL)>YN2#SBQw6D-UH!y!v~e#;uGrtRoN4(?
zQSE>9+e<)O!TiE;hpN=>rK7)cI_Qc0SF%6hI&CRueD0=8peNg(Q8z{}W;W7-5u=P&
z4QH8ivZv;=0fQAG5~pE~KhW6(HV4izO$r67W90)!KW3A?=f-gb2SPfC20V#h_?+o!
zdsqwt8!PX*3kHUzv0uLm$822S&1Xz4!;D+D@~S4M#yb|uFQO{rB`$QQO{(*rz8*8(
z8`U-4?U59EYH+hX^y(WULwSV7zf=NeepaEJ=V8pr&hP5|2B74)HRuVJu4_x1al^tv
z`%&}n9fG58IyJA?M`2z4ZP1dq^9$t+IIsT`mL`A*r5oI->bs;jJ6n_ZFu<IUMoiMg
z!L=;v#e{EubW5ik1OuF$k7iQN|D!y1Qe)-)N%-v=WTTEJkj4p1MU}+*5%Ia3?HJ*y
z@<8yFby<Fi(TZTgrTLJ@Z?-K;yVdA2@9cN#JZcR|paItxq|cG}O4~uA*N{4xB9M3(
zBq{D*ndIoR%oh{1EhNt-f)MGOHn>)Hm?zgIBPX%N^c9^cz(2S5^;bIV`5K6?iZ3rF
z{-Ek3?X;){9sn%YGD?Qk6PYXb|5_s1AVsa&_(mttcUUE0E&IhfT6qA#sGNW*r^c`S
z12Wk%r-7#IcU-ovEj{wwX?7fA{8+c)<y0`c>%&~5;&}f|k-gSF*7}>Q;}t7~R8ZX`
z@Gk)wv|G7WRXr~;0_#uJ1@lWV*7-Ld*TJC}p4x{A@C*{i#2`3CfLeTk+wB+eN_2@i
z=Sp3$eiZABq$x1jNAn)6?e_9`!$bLtsGJFEPBFVbsGjts9z4hU8e0mTPy9HAh|f1y
zg(8maQd0>hO??iz=2<$IU1p*}H%@x30fZ_08j&LsRUC=jiIA8mAF%;|1j3*b=s7y?
zoZK8_UtYoQ5FGr3*4H*{PIByHkzJZjl&PrhMa(ma7PfQ!u*!cOJCEH<53a{(?6;r#
z2xWCL)A+??+{cY>kNw<#>W*&ExHxc|&b=IOeXdSE<!AgZbgtZO%0!R8mC`hsV~f*R
zHN6Hs$M4maE~L16*fb|rRZcX(K0#N}I!Z&m4_lUdiEq)5j=A2x$E#inli95@IeNn9
zP4O%z_w&lB!pJtqf204gyZ6DPTm*fp?e(#vQ1~xhKb=9P4B%IaEqwqa+C0yc?*<5X
zm_<V$>(or+qL5u{)}Hd^%6L_1l08Gf1n~P?Y<NTEAlZumRkyop@A_guIlx2#I$TYN
z*E+fW-==Uh^PD2(>$zhUC6EvoxhSX2A-}LICJL<ocQxH<fi8Wo14apDf|_0}T!(a?
zvBy&Y->x@Nz{N}RMJ()$puwm(m8Vp|ELIg6Q~slx@tDXn1z3yR$24M34<y(wMyh@b
z^wu)FIqGMwJ5sme!>EvOSV^uXG%>UMMx_XK|5~%)s-|ztC(U0G;6>kfu`!bAzQu6A
zsUu6tdDU^gT0UeR%AwZoh~q4|lv5}4wX>okvwybuWpd*m@|kv+rNz{|@X8t<cOTMf
zRoBEdGr_YRTUFd~uD`ahl%`Tsi1duV&8fYprBraQt_Rb(8=q4Y+s;1nEI>Rm5jk4%
zYsZ#l_F!<CH;pw=cU<E(M<S3Tkz5%_@L5>r+b@|=9`eF*+8!JYTD^~g>XS95lxe((
z7EF7eJ4m|`57u8PY}?`*JDVd)_MhI7T+$=xl7W&CN8+=amZQ6qXBKkSi#+#^3mH4#
z>|wT2j`5WhAx+BD?`zGZ4LU6(I+q&VHcSOqlQU{M_b%ocE<Ze{3mhG@b%#%5%`YtC
z<a;d(rf033iSZt;)8BW;m^Z4=bT;g?Z^R0Kw=I2z{W3&1L=W{AcG~6K>{9MPEI<Yz
zqqKo-%oq0#^``w2LuJ%dQ-qZpo>ZC}v2-CQRBRhb-pH`7Rt+rd5g9Y{8v2IKO)PuA
zQ?kMMozK2eul)X$15y8C;#EDh1kKjhYpOb^I8+fu(wES+`CCTIsF8&3CFW#xdA$gu
ze$I9+F$U1>S4t!6$vJT>gs9-MZGzXLZM37^*ybDex!Y!j$g7G&UPWjTCaUqJyY8Xx
z&v}WOgkLaIqrJ44IC`j+X$<(fjMUAC#Ejg&2mXMlBia5&J9FC+-x3Eooaz^Y)srFp
z{dGl~L~l8r2CwP1DYHdX6cV37R65?6GdFaRMUPMefm=5<sRQ4yFRWY}v0e2^bq_Nx
zbb+h{Gt#Pe9{#xX`!J}@s1}r0K`WW8=dVz#tEIKvW24Y#Tc8>&XmW=mVPFEEIpVX|
z*hlt&25(}Q%xj(CNIs-qQcdE}B_paYpwzGaMyxw@lgRel52&nw{9vW%X}AdkK02cw
z*J6F`98=G>`<Nb;zSb?r>2ykd_|zu`0FA~a`ETDqRnklxp0QBJWIdiYD4TClQJ~`W
z+qldx-}sD6`r!WkO*Qmg$Im751CfsP>0I+IGdjHewa9zN&S-@*0pcim&mCPeA8)Jd
zSMxUm>8@94BA3L5+>s19`wbs}?@q~v!7<U>EzV0b82t5;ADU}!^N&Ew(Vpy-(Y9bF
z{i65st25TP56zXQrIp$Vy}{zn(;bEq0PTgcjVhDc_!oQK3ZCl9vuB1ijEzp7KckiZ
zG&a^vZ_C`@kTM&`J&Z~9LlBPU%r&Z3Z%9zrtrueVOec$d6{hK;dSm{>Qe>!~q>*qy
zkX<VL8s=Xu-__od-o!%8Y@uv(2&$-l!jM+JR{bf}t7Oy3FyV#lfles}y@Lzdc53dU
zJ!Q=wjHK*tgmQY#4^S=qWTlm%Jl%sWswkiw#Zk+)C1+^X2_m+@dxP6EuF9yQ{6&OK
zcE(XM{lgkni#eB8-5p}zzQV*3-Dj#PpgTQJl;4zC34ofA2qC;B{E`X~&wa%#3JTn3
z60kswQE$Z1*1tNVfPcE-)CFh~mXHWXneQZaFlzQlZO6prx5u+g+Htrb+{+?Mv~BPK
z*Bm>yM<C!OJh=Wlm=m2jP~i9u8VOCu?o-X_P7)v@e@8VFGOn1x@6msj+5j-9xyQ!<
zqx{(ShUCGJfnMiON->_(`&2aKrqsMYrVv+lf5eJTpj-e@{A+f{_B<ko?sjmjR)Ivn
zn0kqi2KW)$d%fg#6<eW|E=DYqiIYLLJ30h*3Rh@ZEq4Co95AB4;DNI<cNN)NCkvOE
zWCs595akAF8fFFb9&>cgVkfTsaWJ7M{YNh}3qw#gBGEo0C6f;u172&z-+(>P&(i&V
zNLnyglLN+Ko0nATGwK;TanzE_A5LtSz)=6+NdIml-f!ALcQ+d`T=;J;kcQ#~p5KHG
z<|Krb>;l&Q0bG3}ZRS5a#@XJNfd?-s`W9ymX}Wxu65+iVS!-T`;6^7Xzk0nBr9=@i
zH|Hj+@M2mPHZ1ozO;$1eeR%lR@p86e!9e>9s^giZHzu`n<;HJz^_PFV2B@sImD+68
za+mh4J{j=y=SQ=Kd~1wWnU#(WmHKDKn8BZ+H5O~MkG66NmHJ=f_obUaYg?;P)8<V(
z65K-<(}jCVKC1&pQWrVw`T_$Vd^Y>K_pPrnRX%@AVP&W3^v3-|>ioa7&q!*;3k_OR
z$$HK(Y|H(-z`M%Sh9Akra16R63@R!acyDEQw88g#Y%c~$m5YAGveboxG)O<Onb|2N
zn4~^a59C2jD|7M|5!pWS7GC5}7RxT>c=3}6AO#?#;t=7~w>U~59#I{f<3!yKS{eA3
zHOf2vL2_xqqTB-Hr?(m)>Y)QC84_GYV4Hc8K-UxMBhMjl>AB=w+<*if?ppt;PvgV)
z>m@~62@r=<qe(m0+c0L@3f+Xy2Z2T<5x{p#^&H6l1IC%}raj?&p=Jr6%yfYz;jP6+
zL~)?~aA4<3I$5q-x(>wn$W0C!hf;(~5I);0b@-wR+6I?2iapCZv)V9a1*q6>g`=}o
zy3*KhGbSFXsJ8P8x-)Y84o79C7r<@SuJ@u!QXb?m<F!9(HlP&tAxT+3SdTQ6X8svP
zW*hf032x8sLj|a0kCt2eql1c@UwVO+2S49#a(4{X6sR7%M^AqVe<U<DueB>U)0jd<
zdF0iR)b6*$GRxynxXiXg?57`O%aRtvMlxz)zU$SOBQcp*)ysB*rJlDcG0@69KVrpE
zPDYE@xB1XDzt_E;g`t9ruM_K-$RqYg3H7@I=nWACzK48&l4ou>|2C5Gk+r%{hZ{>s
zpc<kV-b`<Q^!cND8OB>3HZ!L;KK?pdRPAdr%$iYw9DSXxRAFrIsX>m*xmK6n4t=}1
zB0Yir=*z#**f@4H5Y+S4!;7I@uVPj5|4!9aC404rcMF0QVe+9^&k4+@miLG{Wuomm
z4ymji9rzJxNA!uQ7YXPNOR)G5cgmk{m+X47FWa8yIi0vpVj|l9JRi4Q%X&2|t6=cM
zZ&UWKCLBaF2^8O}7aGiAR*U>)?8X|DiJw4lK3Oy+TWpDKt{eyGdRil-;J)Vqdg26h
zFb)J&haAWyoms*emy8FS;$)2wQgx?iLff$q=_-`lNZ${{OH?=*aUN5bS$>h<B<H_h
z+6FZT)&Xr<5l{E=tlU^4T{4b=gx-}NjxNWclQi;#J4e4eNQHomVw%6cbsD)Nr^&z7
z72j5B6IcXj&Q&G3>Jr@=2dYeDX40jxP3W>h1S9S;u4KijQ6bWZ>EEtUZ^#R1cT7E?
zo1w7pPInZ1U=8~j8h6gGJbv*<7JKJHz@2uODXO?!)4EUpPc~M<bRCWHqdH8ZQzMOU
zq}U0gms87Wo;?H0027zY_kp6Hh3W4*+dv8gJQTbN>!I}7LS=&iVETb^Jo4Xi%AbpY
zF9De?e7`LY3xR^VbOEf_@23S}v{Yc6$g0Dxc?9sDkweEwq5PQM-Jyd*Zs5<k<%^-o
z-|~ZXH<|4YVdsU-J66ZpCtt&u%<bW{oTC0!wc}WZo_cw>mR}5~3$`P>{<CmGbN7Q;
z@?__l{nL=u%^0bm8ca=iaIQgwpQZgu-eB^zhrlkwKHkfH`i6JLG5%OOOYLZGq~*nF
zYrTzs=LfejH7?hVN}uZdHn-x6yp{ULdIfx=XNinQ77HTFvC0g)p3Tnf^<os){*;aH
z3*=8#<V`;1uW^QqFskw;dOSl*nesC1rkQ+dnk66GN}p}w8Y!KybFFRtMDfmo`afmE
zxx06uHxg_l9>o^<^y{ut#uiwMx(GX)k%77RUSV8E`j|rbNVDX%P3Kk5@Sm398r^)W
zI@gO$8un2&*Qm+`)FO<hzcd~J<~-?1>?NLiq?VM`$(FAq;-n72NDfo{4!<SD{pWmD
zxCo8d_JCO@Ut0_#w5R~C&7_u1qi9jK!(<7!ElMgv{!y9koZCcX6SuD!-`B;r2LXBl
z@D~D<XkfpQIufiD5>c@f4XV32&XT4MQraP{5fDdM_3(Bh^7GG?Gcdo-C5JWQhdKU2
ze}-Gn54NEr^7P?#<9|}eAvQo0`$w;OxQSKkDInTok?A8*nVO2c267gpf7U;7X}i$t
zu7#jD5Fd(OAsmSy4MJD>zfwc7rdd)UIOh@3?VL;7WShc6X}@ljFNLXe@jnYYF6G|3
zSWaa44*3=%c8zK)FjNg%U{PdKvkOadYYzLWgYa3QKeSfZVI-~7&e7IUH9bN0A)$S>
zv>(tES9)S2$f>*D(icygn0n&A3vRcIK|_ig*<e(ppR|%hRZP$Zmla%hA;%d{(@q||
zbE=B~xvNe5XzT-jT%VMu^PT2B!_G7)B>BfT+8rqW-FCgagku|pM=Qc*h=8S|VtWva
z4DJ}zxq`jL<ob*)yO9GpKP*%1%z63=nu(a&s0;!3Gy3XfQm$M1ddzR5tWj7|b|LU4
z`Oit5?9ur*nyn)*cIbEpb@hXg$CFA3l%J>D3avbU0pD)=bR9<Z`O{3Z=rPH0)PEHk
zocDwX@r8Q3YoQD%E{<j!+11GS+P!Y8J*>a^h!Do}UWoe#s1BHq_$dQvd*{Y>F{S60
zZ>(1^IsE5$IBN~^X}oR4?N6bI_G}G#6i1c70bWF-NVUMX2OJNmFI<OVvcu7NPN~d#
z+SuJ+JaWN7bu>)X&OI$8geub>tjxkSisE>LNR1)DgDb|SWFLoW|5cPE+Fh{yAbrgb
zeQyA+6!!zdDgN^!2?6m!BI}!kVA@ISQS%#6%B$oOgU!<rmA3Ib^N`8QYFh;INnxU?
zWu)ZY^~4xN-L3*GR(I}|a6}`|u|YZ^&QwLHw6QG1iBwq&igw84Zw?1uTy%#KeN(!7
zoaPVb*1Y#Le#!1cSqA;4g|42KQR2h=WfE)YJ1&G$Iw%}2&R^ss)SPv-D9@-!_@p}~
zF@HN_PF;(&q2~g@GKWW|xI@@$Yy~&7_tilKq`1g~Ok`9u@|o>b8k67PTZiz1HysQW
z>hO_#2_$k`QMaVNs`mlp0)S3egM-G^$NWe5d{<eV_@;P{R@qQI+l9aT<T>NfCVQ+=
z;5LqouzrU;$)n%0R1w<<u~O6{uDJdwtG9s%4Ou5MekDMYfk?U^n928-`B<epBrEAL
zf1#-Z&EI{MeRZ<5@MgkKU%tE)av!xi#yZWOw|)76DRwRj=J^v`>Gt8pZe8VdnHxKr
z$+s-2XQt}C7XFrrl3ti^KD^VAAfBkXQ`V;y9n&~-?rAOayuhC`O+Tc}M)-MxRO;@}
z6=T~@U)THp4UdsV?0>=UvG2LxpOi@m^a$K{lr_sB0}fLJ!?^`H3DUeR=PPB(Mc5#3
zF`-nUTX{+nN_(|=D~<mwZ>i*$8<q0cS5tpVTiL(vLAg0IMJs5uRX*h~&Y2UMumYaK
zA6JLkD+TafLB)os>VVD^>rogUj3+6SXEW<~HU`+@)nf$FUo=OiH0we+kW)D{@U%FM
znbwu?-;Dqhl?UD9(L`x=JiwQBKEP!_L?3E8zRn;=;t2vuC|6owe%Hv9nweIT1V>dy
zgta>Wx%cXg_I~}5uL|djVs^ESYKSH6c4Rr3l@Cn)kvE{T-;tV_b$}1Q%pWN7PHl@P
zeVjG$y|?%>^U}v!Y!UKk-F!m!?VuS~u8MY}o|5<dwKu4}j*+N+h=sIfXsnVUI-htk
zkqZ)pR=C&;p`tO7ELP?1yP^%yT-~}AtLWmqUs8~c2uSZK-+EiZAc8;|cLSg75l@ph
zTeehv)&6U=SO^ygSnW*DQxmOKw7<d;$92i3Hg+9+E)@2-rlVJGRuh+WlwOV~ujt!{
zCgLa)U2=t2ZEiw~>yKe&L&439&}E>*Ubh9bm3z0@FG+nU@Uq?COyK!C3#lmkNqvB#
zmYC59nS*7b!Qo<h$Imv-zw1N8Mal^}SMacni!nxFLjQ_wheC=qIsY(wr(Lk*81-uG
zi#bh%5$+e^cK0Hv^SASY+1k>@vVv9M&1h2E-o`C8tetASJ63sJ<u<=_^Q@V%%4F2x
zb)z5Kjn_0idalAa1mh+a`x>6OV6(hahsVlt)GYwF3mOKjY@=+o)yIazy%?(U<aJ7y
zlORNf=**4gFq;2X9jH894lNfd4<<H~B(vlmrg{6l61IOqo@dm71xOQ;E4yQi-hGmB
z@SAIL>u+{wtz7UKW_A5M`dasLx6!)g)~4Q&*_HEM0*TNi<R<(k^$(#&$O8HBO=s;N
zKn_<Dte5vKkat3Zri^ru?eJzF&*q8vIq*Q>_Qd1jr(GU*-X#i&j?LfBlaCYtb?K_y
zr8J^6>|s1y2|;nG+^u?ZVlq?5DGZ>5Kk5G2sryi}UaDU(b>gerz?N<Mld7CO+b=T9
z;aew;dDTHZJck>>XgYtv_IjmPq?>@NU^73;XE^2|elDNCA$a{~{w5hd)kLBqO8|HJ
zM{rC8Lr<hb{k>b>CJ94v$QR8tOixatywIXdJI~*p+uxT12H`3W8clQwDOALh`6T6V
zfbHhbIejY{?-Vck69!ab?=cGd=m_WzuvbiysC_8%AJ0YdW@8wYXn`yM>eb!FZZ?Pu
zB(Gd3y?yl(%AO{>D!8uPX*qte;uZ(2KV~-Ao(k~JSIs~(Upq<*WZn9Xu}%NB;^{4J
zVdvD9!-QYYnQ|kJdM?~XFD*!KUKeu~4#xdv5LIWn&gbX~M;DLN-nr2G^X-9n?LTI8
zr?SiyKJJ60)itGbXp39O3q8>v>@V~zv9L;$EpS+y+fUP7Ol6_Dgt{Y<*Q1XK<qerM
zv`JiO#mmGtr6A?p`4pVQwRcS&*KIfPTf30gXP?dUKW5?kY02)xXP|q)=utaKmqJni
z+uKpVr`K>II7sO^%&2I}g4#(&e&LU<@#XXB4$J0$6?2qTpYVbqA5J2$;d9RWVUx*{
z^EnIYz}RF;BEsj5!D{|*zY84ERo$r~EH!zLUPgvJf=g8g8}zl8oe4=;eho7pX1kl5
zWk^V1n_*m9!!00@;zDym1bdq-95n)XPRRN>$%y1bsCCe^A;3D%Y)@B!4e+vFzs8b5
z!?hP{<<qUo1Zrprc}KzmxA!3J<BnI;R<?p~k^Y*WfVyE=>7&?ORn109Mb*i$FrU=p
z_CXX$RVTBT&+`)uC!wM{YpInYkjjFAip+0U9l9YD2ZD%p5FH!_^$qTWwAW($2N}!Q
z2f914Y*}dM3onl}?bYQLB*&r6VrXrU5hrQIgu(V?V8*=!0frh!RxnwAOri*vW6;o+
zCMZDOM`y2kgg<?>XZ2zXw825ny8Z4_P3PpN{8m0zP}B?9if`Fa@u2bysd4x`<%}B=
z(?k}D(iiuytnb&nTrjk%D`>E|Sd;;Qw{xqC@O~duQKN6<VRlcynyG7`<jX#N$3F@Q
zyagNT4+?xvPaS-2?-$$7d^}s^_If=7>(aL*bY(vGYC&(xwNqBG{cJMvkC(A*{eRr}
z`^xD=PrqiGH%%7PpDO0U{^$`C4~Izho;zG;$xlsTm;Ti4qfIxmuOD^CUr#+3{cRF+
zG!{BHW2Npo^}}?LA;Xw+S?9rjJIZi$Rx{>#kk9Dfsr{=_)i+h~ag`*uW0~%)FsmCq
zq}n5weAlBndE``YaipFW#KpH%nF0!ef;BkOl=&mo<GngY^rv3z_8MPakXsvkSqcda
z-dv(|44~WYDag)ygj4m3ss!k$H2Zj1FtF87xT(<F(lQFyC=<nye7fX2-~c?Le@1BA
zh?lu)PY5ZBYY#tX$u1vgfgt?d2?-vB&9HN6|Lbc4Ig$RjiA7)(Ot}FhjIQ+j-+=h2
z67U@bo|G6R{hyk5YH6gFy7f}~qlOvzbzu3YLd#iqzRDYTi)Mqa?fQSknk?#WW&onP
ztPz&>h&rEzRTG$nV45(z0UwkrOwYMyL`|Ceiwu@Nlrqfbj`VO{i9)gOApjxT><*@O
zLCS8>agfxt_c~}!*Vbs2zvjz6l?^_nB$JDr?GTuyuub_{YK{8*C^CQ&S+MU6v7=Mg
z*U62x<Ng5SKC44v74)!2AObP=nm=sNW`|6_bx7jgxAr_5yp6|vcD7%ie-d4`Qkb$3
znzAMrSK%M$A>&MHiL3Uv(c=Kn(%_2RGs;+QyaZ7EYBM>0+5+10ATl%0EZrmLg}Ki4
z;WOAcuFU7J)lqa^o*({ss<ZDydease(r+X+0qdT;p3+DrvAO5T?^arzSwPlVl7LWf
z;MSiHlcNDk1ve*yos-<P*6CT5a~qYXw<bz?6%=jKHFUbeyg9Kd7VJ|`-bNZiAnhId
z{Y{N{bh}N2*$1L{>6fz`_RSl|U)WwfG_OT}J<V=>tGE$T)qRwxk~mq#>-`n>TYb^z
ziP42r?@)Axc&~O+G%vBCTTmd9lYlM53ZbA#O2)PhuwZhpK+3;z)oK6GCI@~gZe}Jd
z%Fr~Hd>cMM-2K*pVwHWoXRU4%5#7mSVc9Hfn*2-T<#bK887FWA!Ni1LWXox~0xa5f
zyWH)eSLuK0R!7MkC&}?E9P@i3wBAPiVfG#P`%WM3M_KHYL?$dp!&?*P#TH9=kdf{J
zE9Gd5jrN1`YT+2=v;rfZ50W4k(%%-+p(!Es1mCV1dk2&PMHztieU)tu2XcDg+(UQ{
zt{O+m#wL*XbvufE#wOE-&o1cD`@1k<<M?^fO`sG9wE(%J#jc+-AibZJVo0D~M<ErQ
z$KbA>^t649ilF#pG$14ue<8FMBb(|T0;L%Nv3q)Q50{+R*MP`EnrN%I>jF1r8==p2
z7KHmiFkcPnpFsTHmk<<P-`#GdSj?5S`Cd416G2jpk2ui7@ue%5fYI4&Y}~m7DsZs*
z)Cp9<giVKF`z>^FmotldHQ7d5HjJ{nZ=RV_cmpkAN=q7U_927(W=wG6LGF<qo#VN$
z219`CZSv0iN5BN-D#B)qojePFnn_2=tIZ2c^6_C{xtCNZ{q3I<4t$j8kabAFjU7?~
ze2S}ltu+0lW-i^BpOi@_AL}M-vRhJV32iolKKaWowTgb3wcOFBS-@EyZtj&U_mg-G
zNXF>Q0*jn?X#ezhUePiAfmTC);|T2enk<=2k;-rR3@z^I8*j*e6;2k(bN-ooOwPh{
zriw2?X4*X@_jya>27Vaq^9eIO4PR~Qv04e1E0c}U3Ix%F0@a$nh=oZ%1xoI|SAI*y
zi9*(v=I&|_o@q|wD)N-p4NQ_FI+QkKfY)e7MAM>$Cz@z=#&kfI&~)e#jv2+mg?zs#
zLFx=dgeZjqcLdlvKGSY`03g{SdM<ddu)na&5os~t%i>-y9x?_9RYUpr^v3)(vAA~G
zNn;4nz)5?)8eOrSm+a2$=?P#W5q-iofCLz+{wg{7%#iq3$EIG<^?)?BKfw`Aieg()
zux|J`J0&2Nkng)t`R2(r&t|X)htPa6mk5aY_aZ5to2_4!nonsylqdU1m~*D+t;33(
zisJ<)z#h?saaNJ);bcVAzwTWBI^5zgYgf<L93RSl_?0d-HI#H-(t7bj>Hs<RIH-(D
zS}hjEcceV15es$Wiuo()Hww42(jg?7TTV|-jUk7nfx7UGY7m+MO!%jB7Wn49W})M3
z8<&FWdZW$e0&ZO3&_m~f>rO6pk$(d}LOB+xVIcjH(xAVr+#!&+37fFaaGux~onM!B
z{X)76paC8Ek?u8^dlE(D>)jnx<1t7~@l86aVK3HqeLJAhujeed2t3Svpm{!a!@F8k
zJ-1Z#@RLh+rJm)jId^x#ST&`<hgw{~s=2-L)M47;VrQM}ewe&!LDjW%RUyB{Cv@-Y
z6w6Of5{rKP3BPkE*ic(d`9+8Xqf#!xM%?w}9+~Yp`v!L7#pCm_e<#`Sc!@%^<{yay
zE7kw{{bo6r1o;>81>3@eye~i8Uq<mO9G@^$HxVIX%D>l@`goqab&Do2Qv1bO^35Yb
z9$Zqd1xok{d0eEr0J#OQx0MX=S1QwG<o|-EX<yKe2+-~I9iNuCdfFhLB$7a!#n<QY
z5Wc28z;pV$+VsG^J(Ao_{%es@c@A)x7=s8`&g<{0?-WH}<38_mKAU4lX2yLCq{%{Y
z{LKoB{!SNAp%JJ!!%rAOMNK+0n9`BuVU4icuHk}#{CcjD+j%6Gr0-9q6Ye_{8Fxtl
z$ICH7Rna8i(k1!a$MDB?u|K|D-3mE+T8R=?7X0nm+`S6k>@~`P>TMuqCT-71AfpO=
z4V&d~FDBBAXYc_za63i<2JpadErI|hGBv&e577}?6#V88LVy#o9=0IfQU!pGJ(HU>
zwcvscCeftFv~70ceL9c+3ipSd44;9u?b1+k&Wz+TJ%i2lRt8_2$z7Xos4U&T1X4Ym
zXLxI@r3v9LFTQzL^bvNSefv((>mrU!G;GW~OgLT?ExWy7_S(`t-Lbmp50$j~*94J`
z{jn3H=M^dGf@6!7JSKc=V~nP_5BuI>muO+%?>|G56PNI>!_6*nnb{}kVi@V+3b}l^
zr0ztb@ta%FQIw-E^8w!@9B0jIi=A)_2}h7;ZQe-0LW-F6y2Hn>`tIL5O6pub|0sM5
zmY~>MdK~K*vl2V6YWR)xgnn1q8PTZT$>b>KEMK1Dw-|Km4otvOu0sD~G{2vNK?$oq
zs9X5><HGVCNJ+LCKjP6Iw3?y^NRagF_nn>Xy62o~FS+S970o4-NAd4NmQLAJ<h!#Y
zrI_l?0cNBmNwUk@qa^V9OYkxE*yW^E@Ez)4(n_?l0EMdj8R@G*{fL2P3QAtJceFAO
zE$YHI?T6N5+RhXcjc2U~@8iXUsE`0#OGV(RmeIQqI_SFr9XkT1V0vXU>$36Y#FJ<n
z!6`;lHTuLAqq~SuPhYe!dh90UPHxCdR!&#2x4cUF<;r{e4wo$X4ANF$63OE#GU}L&
z9P@og?nfqkSu76P1sMvQjcoI1Ic~OuDt58Hxnb%)UqXO9fQrwyTV)JWTYr`z5RALG
zVk6*ip!yfOiY`@YLVXt-u%UpuLBDKi>y5A{-m`PjPo@)d6X4|~Bjs^<pjwkJkNwQZ
zd9AUY#M&bdI-|fzxWD~85>AXOl%Dv|LSCrgGhXk6^7fWLa`<=r+cOSmR0iWY@u;XV
z7br$k$WPZi?iff=DMeN23bk{6S%9t9b{>qizqX$ae=gyijkveUlqaM0)8yrNrDbl-
z&e_JaQY6Og=+xZ)^-oViLffSVmio21Xj;`#GS&c@o!f_uJ@x$5oBW^f*Fp&3duT?e
z-}-@MpHioRq(;c@S<ZWy6fisto0BX7j6h`Hyde9K+y36ox{8TFb>gvPTWh6BgCf;i
z;0POfGyz6^2n%=V44GsKk!l1`-MyTYqh2BX4KU4mpi=*jMupU1zUD&JZ1hbW+YFlu
zM-)B5>*8pL9^m;k*AD(TXtkFxQRz%Fea&hi#K%!Y5)g#=lJos?C!Dkb$7agE2Nf?&
zCj+gJW|ZSnZt5H|9|++4T2_@cH;<)Fm`qs9w3c*}1=spFNDl=(Gr+*xT${g%VYbp@
zi#7W(U1xuoZd;DaW6fe%FLWNba^p01l317Ymp*`Edr}?(MBY1GRUqx}S5LmvJI8Sl
zd97)?EW@41vJ!L5$iJP65{?24=Ld<c-y--uK)njexL32ogPu|D849<<)UMxmAp$2F
zTfFY1+P-Yw%#9CssqoGPVT&O;VpNwsE&MehHUep|Jt4HM_^ki^POON*>C?vzF@b)1
zYt*wN@E3rR6}@MB9}YacBapI^Drz$US#xISlrU0#t?~oOFEK7r3XN{MC!-DSY*-(&
z2>;<%uuCuO#S6{BbDpAN+|AA0pFUNCr`$<Mi9wjZ_N^+Mhc5hQEHT9B^l?tkIRBm7
z5_-<i*#2d3hg<FYF=RQb5R@e2ZU*_u`PRc9!rY{3=&UiAvr_~uMIq1`zClG9{qfy@
z65b?Onb$i9^hBd2OM^d3y9lL?Jg|X)sLFKuFY51fzb`9gqd@9WpIJ23&znA}n(Lg(
zpF7cH4;Qx#>d%~|D`nI^j^y*V4nA}jQWql5!%J??^d?1`4l*u}$*eKWCe_hB$aWIq
zMws0$5lhQDuBM;}?Q%;^ALgUyk&3&V_>v+E5b3Ym5E*PlqbC@2vhOFTrlXkG-$iu{
zdt5ESr0+mX0Pf;=7S53`z-=*<q<B_%+<eNr67?hNjKe`~{RzHra==V&LIY044S@R-
zb=wLm+qD(<CxX$u72~wq33#eXDki_4yhtcGfDLnFvJP8H6LBvodrN=RYZN;I>bq_q
zsW?!EUyT*`(C~YT;37$|3iYwr_cwmSt8Q^+(#0UYEH!!t+`TYWG4ArT_C~9&raJ^r
z#`cD;&h(e8CtuCjK6T(%TSJLn+fq6B@C4SwagDNB!FGgwiTou&pS-w!)mbH(UO!zn
z;Zc~l79XaKhx#(y7ED<%T(~rIf^XD78|v_(%F~B0X{iC4+oqjq-0)Y{HI;)oCpJY}
zi+5|b|DE3FfQp?$-n2h@R9SuUD~$5}uu!|eg5QSx`M(tuMGcegH?;Ak+Vt0;W~FKU
zk4$)(KV96FK^*c_l)F~z<UyVVwP~Ryus8O*mHSpZ!eu2H4oVMpIh~(1hAj$;Jj_rj
zqFFJHWBwn*NDO=`M(-o<_JuZARlR~l?N<r$L)t_9KEH_*ml6_^qa7&1f)hk+)>R6c
zNHCf8d|{~iq&oegexIBT19%~%Ex$lX(CIMG^hd^GF!SKxZ`BfIX<nDGNgT@DtsgW&
zWxL&4UsoRYF+?Nf`e$^rBm-ZM1f($EZfI`(h=|W+r`4gJ)8ayCRTt>>;@KHny+SxW
zs0WM6AnX*N5|Bo?5<VsvlBl-a{)mw`B*Yltlgi=~Z+)t_P`dU?Pq3{A%n}7EtN)Ix
zm%O3~Qbt-%1^Api@(D+HIz+_`oHag#`1+sSYcT$NCeCb+dwHOTEW&RUmjRJdD+4db
zLLqzNU$ok!dDUaEx!(I#G$9TMy8eQb-*%OXxJCy^!v=(C;JpJoZ7*U17j^b0pnjrG
zq<&?SME5!8*K26@=w2LmBAwn+;;@Z&dkTVRIaT8R`EE5>G{^nOa6C}T!ubOi%X)0g
zO+n-MA48WchvkCG<k~ri@y}Yf&T1xpe1S3lnoB}uwPmxliC$rusnLcL?@MRsYKCMx
zwj_mqN7l0hSF(Ze^DNn`DJ0t{kv_b~8}mJm=KnsOi=CWS`y8aATdPaifLYiCPM?EP
zB_Gao5|36mznF@pdL73ZX>!WL^fRo|M7&u{qh8>;v(oZk-^riTd@qC4`6I31>&$@D
zH&0rD3-Z<Xd#K^b8Mi0|0px+C+hiP>AIH0#g9`E<G?f;UF1hLBEJ%s5dMGS^i;^_Y
zlq*`7YCG}Vne~9JcICZcLf%#<ev^SP+2z~!%wMlW?l$e$*zbX8dR}Uy2?wHy5zXEH
zq-Th|!vOpKxnEJKZFPB|`9%H#d}}x)UST6|@twhXqI!&aZ`kbAfVv`ZO-nd2Sz7&x
zLkj0r;|Rup@UHc1fLcT{(PTU#3&2~WK7qH&;w2=FDo!VW+48NL4E?U6AZ*4SNOKKt
zFf}h;Aj|lCez9G%iaPPBSS5YlpRvBieO3`qDhy)4j9k~(Y>LpYkI6CNHLA)_>_?J{
zBQCA%`-9^|nD}e>=SLjI8VrU5@`e49Co=q37yC3Ax19*hXCLo<;Bxs$bFc5YBIvts
z?X4wP_3CJ`<}|O~yY||rznMNsIb!jr_2w7WfW<UV>~hxeV4vos-?&0HX&Q7ezw$Iy
zThBN5sOr<J=>pY3)MG7`yC;i%hB`mG-ZmLevwh6F_c~n$;I?sI1|OlNwr4f)h-fgv
z&1|}KTk$!wrmMxP67-Q-w2{eR0fS0k+%VLk$KKcf9rlC^gm8#$iip<HBP)bAPM~pG
zE6SYu<)QZnA^8?J|7`kQa%1!3boD8*ZGks8Qa7o8&c7^akDxS}f|)9!%4mO2-ZC|j
zZ2B+ltyfitwLc1aIsl2??+|qCg)c8hg<{56=G43$jo-2g$JkP_dXhK!gux!b2-1kR
z<8VcZOaUaKQV25e=9yI%by=hYX&OJ5{yz+IXzNS*a?!~bXkK*3ulD&Z9oBampe?G$
zVVNnGu~I!L`#h27R*c&^VN~?o2O)#xgf9ffxRa$J#ICmA{xYgjEQ_osd*%#aF;Y5?
zRkQ>>KcQ7XA0;Q~qif40?(>g4Rv`B0E3I21w>JBL$K<?5^Bw}aPAX++VA!nzI!6J*
zldk=Mzvk8aKD1$k6R*3W78dq{|Ki)55bLmF@IgBNcRDfP{b57xOv=HWX2k;f($bZv
zOLi^UeT?7S%Z-J}H<TSg`iqv$#l|m}D+QlaXfFwlX^nitJbam3IpiJp^yJIrLN)mB
z;ngPJ@~{j1ZP!the~8rf;c)=Vjxo+Tfm{eJ@ZLi4@=+P{1FQe}>oz;|iX*6b`%6H+
zSP3cw(2=vc{P6m>M8UH}htbM!zdH2Puyz)~GY|K5ezLW_3_hp`P}q?J!kC#7y{OGd
zQ^n7?-+(^z_B0g(j0hD!R~OwXsazg3b)?@Eq8&15=b~AQ4+B)t`@#gLh>#xQY*7_K
zvA)7;6vu5#BRIPjSyIIgIJLui*9w^90Ctw*$LcOmoPfj)g31ZwoGg2)_w;Xb98~Gv
zMk$?3y3=H5aoBL$3_gO~Iaj`uMAuz19}?ElmMuZT$5}@+kgUpRIwMgG)QyMT0&#aV
z__r@2d9_erg%A2Kwn7}i-uo9(-gWnUIbV4t&(w5gx8{cXk~<73(?XLl?;#=bqY;fu
zoQBBs@YcIkRMa~G?3X{j`2I_*GG_%o)@v^ACw|kM>a^6kU>`BBKUy4KX9VfT8RXV>
z3^acB_d)~FIN@xC8P?=qCp<g~x#9TyrE<MbOI*8QGm9|DJKCrQz-q7ywM3mjwPCYy
z<tOFov0Jx?2+bzrn22-PW9-hi`f#h1*P+zzpN`UWMfpD$k4;l^GCnww_@AMqgju;&
z_b=7+(gE7iq~=Hwxq=%0cf`$^m>#sTgs6^%^`d#R&$#K^VWQQ^*wmLfo<Zwb;s@3=
zQ>&D6MOGod^%!K<A|uCd1Cj!#UemmWF}rkj6BSfZ6R;U7@w#l}V}&>j5V(GNa{+Tv
zi_~8UIZvE8w(qm00L*c58r`TnW0{jI4W5C;m+ET3c>$sh<}@WXXkc4gEtJ#Zx#Dpg
zY9$0Xl1}$jw4ZkNB<Tb}|3)M=DG)_-)y|-NDEkk;;C4ql{n_5a2J~@~DE`TT@SxK|
zf-CxJL7o~gLP|X#QDq3N9A|hCvH#l+n$ChTaL+AA7f~#1B;8L;7xjr)e_@S{exfU*
z^mj&4t@eBQ7Wu`h8M5?XY9pO`{v=6J%+6!pZ%E9Ut(uarvEL*<T7~Q`VHsN*kBe{l
zn;n%1*lo6B0)hMbWSKQiN&9_Qf|JMfqV)2=c39-e?4>19y^2Pf1Kxf<j)~IsWrMz%
z^@XEgW|;Jjv;8z%?VP-ED?Zb0?_Zei+)=mul&RH)j)Ex&g4m2UzBHPCU!C_@Gaf!}
z`f|F`(!4pY>eObMtzu5MLSpR2(egc!57nEop0zS0=v)(hf6FmGdsnY}nO)GOv1+4&
zS?u0D?=WZW&rVJ5X;_N?2HO*(-cmFk#C22QQ*tyMKj{D&_En!ErX6xQCfcgpjQ$Em
z938r5T34r2ES>y4KJINNX}gV9i3Oi>JPSmAYoJlPBs7&OOYnZwFA`Pg7#-sbu1R4V
zm~U|T&HM%c>XUzpeU^|2@%9_C<D*qo-F#>fJaJ*A64KapN$8-tgSrzTfr2`q1Wk}J
z^k%%~lE5A`xFglLVV<+0$FB5@Z(H?%c|)U@?AIc8q=NW`aMIj8fe^I!d^3=&JqkQN
z*#$RD*T=BexUZLZLezn2*4pGKDo=m0p<GUF;yl<zsG@1VSLiD9tbcWF8Da9TB3{Gj
zGJq0BQo+urZREMOl1*4$8*O5WUCn@}h{(g!rtEzUmbCIB0UcF>ezG2Is>m0OGU3PX
z*hdMMyu0PThC0h{zrnIraFjNT?OI9f`3k5cboe&9H?f3$b{or^s(i@T%g)nWHEApu
zqR`;|GY;3MWRXnd73WN-i>WV@Vy@)58-of|tnbZG7@PC1cfbF?ozcY)v<Hj_WOPH}
zE`N2%Tzfdl5xwj|)WQ?bliw66@dhZ`FV7TGHdku@?dnl;$?bI8uV88rpS}O4d?;K7
zb}>YusBIv3tX90*vjt~?FwP?r%%KrYaTdRTO8^g8EAT@NY<?=qAOeuNBJ?G*QdF>M
z8$wbuew)MN!0pZ14w!!Hk1t&8{W|^pkfMw+H~lUw&%<4bUg8?nKZd8ZOO~UZjEo!{
zM1Xv0AWD(-Du*ur5}C7_mf`QP7Z8iltvtphv0GG6ClFNJr}H}uCr6NX2P$AUuxQY8
z&!}>IUdJ^&u_{00v%Vvh!wF~TjQvZC;d#F(Whcx=ZnV+br2i;na(=3#@djp`<f`qp
zdmF3JH>G=za69#YuVT@1JWMCSF#YvCC+qAM{To4zVGKiq?6U_3LWRja`8Rc-ui;;U
z9X;nC8@YC*DY%bs6x_dY61d9=HKQpv=q+rR4f0W+*00V(`=iy=hl~oDq#?bb#^#^g
zma67_YX!1DwRlz?f8o5z(U-X@do|@KSw3SGDmWw0q{|?CWZSme?IhGq3qmL0{6Xd2
zy|XJ8K1GniXl|V`;E`izpb64lK;mD|XwCoMJV%vj)PKCoWYZChNvBKhk2(!%`SX65
z=_P$))U9KglKe}G?4HzCE}ZGWV){F;;h^YtWO0>MK(cPt{AZn(-OQi9hMI&%USL3j
z3OJc~kX#unIYZ~yy|SVn$hXfuF&yX=n$Y#Nzb^#1kxY4OzmCOVPH4%EEsn@U)kT2i
z{!tH<IZHSs<f*ue@zN@-L1nYAY7yqRM`iQ!zCR&n7o9#f(udMv{$YeUrhLa+?H9ko
zp*|pCcXDN0OJ6<2nA9;hOWh&N8m;Fjgf=YeX$G<3Ez-f8pOXyd|3XQ0|J|CdG{s*h
z3uP$8L4BhR3Xyc_Q1k=foBdK^A!0<b^$^phnH9Htu@4_CHz{VEGlZJs!8?u8>5b6$
zit#F;xPVi&A6JE~h1=N9B8yG3dh~5Bh!s8&-H6gLF(e^>4@D0>%okvA<St?6^sDU@
z&>I-qfL8L^_30%y^HZ5(zVI|VSH6$^W@Nl7!(-0+S!Qjsvub^5sU}bD9^P1Qpfy4P
zKi>3(Af)WvIbU&9@+j1`b{DTRu6P>C<PP3D6&&_%Y`Ve4pRA+VM?F3e_fL9+CHa5d
zEj{hE^2w5-BvEloYCRfYsY{X>N0~Zj`8k#8J_Fp9oB6NvAf97HJo7gN9!AqCU@WMf
zU~gX!X~wWntH*2Yi=}_pODtEno$~X&xGXf?Rb_GITyf>V{nXbFZ1d{fwsNkT+~AEN
zI^Qnmwrd@$qM#+|&7(v`CRRJh2ui1pq{j0{eaKci+r?;KE(F6&*>zck8shSZ6%cd)
zp8uYQjRZd!EW&zzdID0(;$repLy)uclR}g<o?J_$4A2FweEq`oE!#K)%sZqpl;m`e
z^q0jvaBcoR%;W2lW$NEC*0jtdcfQh22f`gvtu!UKo`}XAF+60o_|=?1-33JN)0xm(
zNyq64>|e66wM5F@1S8?$`G!Z5aFMv3*4lx-2gxF-0!X{jgx@31-whs}w36`&h<sMy
zgd~Manw7vL9b?r#0{<UVU%^mS+eE8`lynJ5w{$lgx<Ns@yE`SNyGuHxIdmObx<gVL
zLAtx)ZuI@`y?+4C-uroG)~vN=#!s6?qoSV`MQJ%Nq4a7>+9sjY;k(fnSHT<Fv7H~K
zQQ6ZJM*0jMNHd3u5POk@-_M7!4U0Z42Yd5~+k{GET?Um=rkqnxP^X1~)z2C<Ek7;&
ze=IOz*^kj9rnWD|K^ukq#r!rXs6?pd3kRKo&hTB@_4y=}4}gnh8pWY2CsJR#*cgL+
z`U;2O<538XXC1MB;K!afKg*EvL(xP&FP%(lc&z4AU4b$>dsUQC3v?e&FXwc}jRJlw
zxjm<s<Y?eSQh!!pq9KIS^Y%rc&8M{Fp5{t3oQp>&y0gz}KS+erkmA<eQfg_lRdIgP
zd(_`u)1_X?Pc=<*2~X;S?m&}BvkV;hM#8it4Qc$sbX}`N9U3cFYx6!5jx=Fn;vH9K
z&2Jz0!^=ArpOE(?%(M7(Q9QW50j;FME8`wwG;XL%hd_Tk%WRPnE_@hS6-OV@E!Y?%
z`)<1Ei~~LR4swffZ*n$Btw{WQ(|>+LCUX@>@8n|B1k63p)FM-BeVPra4yJr9%<GBa
z;f)$!><(_$h@u(9j_rwmjA4cG69?~IhfSm^8rML+;2SO2iY15?{KQweYT0Ke7Yfbj
zxVOmKGfR3-*Rm>PnYptEwS7-7bJd+;%$Qb>KDZVyRUP|!ttMX$OcQI+pNY*MF55b1
zlC!iUm;Oj-Q`vrDnMGcStLv3Z8R%nGm(gKlz_N$5l_ApOBe}+hy#=_<dW&gHMnW!+
zywD;jeup_!F@=1;@z6K^i#$nUgWlVMmde6&6kn~Yf(9sIh7g0<(RUCE%H6AK9Ye1_
zLKpVzW7&6q7tboQG(?dBaSB`PmCEaJ2ro9O$VXy!N}0Bj`??uxx<$E6@t9>Nz~-*c
zO6+h^Y5#!F2(;6@_rsCCIQi2hF>q8FG$i^?<8rT(Sq!aUM$#i3q_%D_#DPX3!s=*D
zuK{;akCnq9Qaw(>5B>lzb~>K2<4-yHZ6~2#6hZ~o($hXqD<^<zHby=ASq}^zrvTTY
zRdZtJiJVlu!In?9&dXn>dWRvhhkyrpn?|0@(j`b1?ySCjm#;9ve?F2hm~d0ptd-ZK
zPkT2YDH#78-Q$4l?Au-j$tMM6-!E;~?3dk3GA}1wrxFW!E9gN)9k|#PuC(?1IsAk4
zHMIn0aBB48-d+DN<k;=ZI{tZm-?Fc+*Lzm<EX7Est*CXR>mB3rF%%C&KWqP(4b?<|
zy(RkzaknpMV&Jm4GL0uyz3oEA<F+YfilDOHd#MM8l!IwDZIx77DrUraO3^aD`PcE%
zuFON6(<|va?-|<{cehK>pnlGCp8*XbSRt_5z9R8)rG)u9kt{U!(YAv-9Vq%V9I_Hg
z#v*9mFP>R({+-*L<AzrC{y^Ii-ZPNvk41is0x}Lg446Ou9k63BRD3PC(Pkz#@3~x4
zAY!JdtU-6iwRizt#dCdzdQzoGCkf@(?N{f+q`7waH3N>^PP0xjeN3e$B4iF^N8hil
zp}jxnI`3*RziOnQeI=_x)4xFQqw#0$oOi1RxfeU7oHv;|jq`Tjbu~*cn*|v&yn`>1
zC^l9!f+fqj7ZedV8y1HR!vsD0p?Z;wGIzhd&GLY<f|td}>$IHvX8TLimC!*7EIP|Y
z2HPvs+pB~D^9br$ilJ9C@q!n+i=5DKGZBmxVe|p3%p$sp_Q0TvFGO1C`R8$r$SbdT
z+g11nDjc(I{n#t)II7opq}uOa9eLDWl0>InRU3x~hGOE1LK*t5Qz9hv!3~P7dv>3X
z3uKS+?~XVRRH90|>0MvJkf45+PaZBWYk17R+Al3JqUbBuC?~Ig@VzJ^^T$$?Miy(*
zj2=*%U%ibpg87E^b1r{B<bXCXhKJ~_O0u!O7!oa3!$>y%ZKDO{D53?!8!VL=(;0EB
z4T;`-|4l7-t!iY#evJ7Vr0UU3ykHu*TT%MhYKb8bs*z_v1#;-T{W#OESx>&gO{R0b
z<g&X(ZL2Wec4PYR+TVTynafm-0g84QyR#Z*1$A~g2s>!vxEWXn(7<AcGX=C=q^<v6
zltVj0^%~=^X$ZwzQ&O*Mg%4t(@0DnZbchj6jy3l8Bqfx3HHY3s0%&P+@cN{&J706S
zV{>ebBl?nhqg@o9EsSxs?K{~=*{6)>m|<idF#Sb;NvfueHhNCN_2l@V_NnP{{?ZTd
zLN*Q+mli@e0!`4)Vml23s}{Ee>bjnSxl?^@y`*qO%xu5L8pi<YW(_CV=g@j}V!Vsk
zu9Y0cty~Cg^2Kpb{;fq6r2UpA&Ek<EAB-1AKgZD!P*M=^s#YH_gcqT~hXMm+dM}D1
zqwlNB#&pfdEqMQ@)?$CTL1BuC%X{H4V|g-4rNvZ9)Kxt!HgAxcBkbM5E^Qg7*^FuS
zv}wtLp^A}?&!69tlN@T=2}kYcM;Gf>-RZfQgV-VDV^JdAK32WW;^eOZ%b8^vKW-o5
zY`xP|RxG;XQPcL=(7SQD4YrBM8oz4czoS1vF*G7V!m%G2U1)9E;e>uGA>$vpYkyxj
zosdlKp+`$g25T-;#%M)jbt0+QbiXt~yxsBLX%CrHO}9;ISarQWm=yb1MEl;{J5z#0
z!q5YLI?yS#6aI{{xLr3XGj`YpS{ToIiv}hHjama*iCp*ViLRv>=_`6}C8OG$szfv&
z@5*+ZYdR}o^f`$++8$YJT!Rwo*VR4d8fFD{l(teG5wV*san2Nlkk1%>>ed&p@hl{0
z+E$mU-pu%cL(SGXCHEDF>88l#?W&im>ZI&NesPTdkol@lO5Z1Kjn>%@tO6PMRGkkI
z!oig$Qa6r+zn98>xL7gtwtgQH&r*r9;s2g&fHK$L^TgM)x*a7J6{gfjs2f2{;~=@Z
z#HueUM)3YfxFG9Dy#HiGc07O7Zh^n;*#Ekqgi0X^=>KF1QF8UowHyUY)XW`zs;4V+
zJ<X?5*xf4)<7?hqDw_<Zv-$Q^_nfVSza=++XZIe>-Z%rcS}Q!bhNiZPvtrm5Dkx2z
zeh6_eHX{ZrV(h&P@wkGj9^yCENJ^v!xW5n)N?w?GBe?f46eL#l$8NL>#7yyON>lcd
zUiy_^4i{IkYVfyw<<f>PGd`c{D^<!F?SFoB+PL+c$-2E}+l8F=7j*l2x;GyZH*UJW
z|6^J#R?;Kj@S9u;b1jM^A~X<SKWR^R-;;pILG3ZjOrPL{ThS=5MJI&)mr^`M-sOKu
zmdoua9;h(!MvH|*gb76|x_=kx8GqSV+POY02QSVkbkm7y%BnnPy-z-zI1(_+n!=Mz
z)>Et=2>(b=z11)escpS&i41K+2ndugT3TEp^=0j@J_tkJ=(5n>pNG+yL&FkL8x+|o
zdTt5%>AY!xz@{45@w@6BKhNo)^Ok{GVF&T~^~I1;r7>CUcz%Q33`#M6*Lh}XTcahw
zO3_Oad_JUB(Q348QH|2lRfMb`tL2Y!F4GgbjX3OXrbtk2O*Z}TH;-;Eutg^uFWq@-
z#c_QQ7tA);RQ{}OO=}<%XXiP6J2-jNn%k3W{KsQY4!MpB0Op?%VOE0U!9PS5UjJnB
zIgPNRv?qC=3{_aC52KR|JqwT53lC$+&rknGdD^i!Clixen1-<UV;7~}Iwt1S<}-xf
z#`#&oqEF3iM0icxJzRF>Oyz#D%vZKi(ADa0&ggk)_4<#pqUnA&D15($=p7eK0pycI
zI%@+ZUB>Et2MZN=n13Mcr`|VWimy4dBc*G{FkXyWTN&aKLPL-yVkfEnF#4a-H5Z}S
zWH%|-)!~qSQ!#O&re0F0ikP?PN5hXdZ8xM);=ZL51gMPiL0Aw;m@f61`kEu;xRgx$
zF`n%a5leyg5$#)T^z}%dnjY-9*}3|qmQ{iTQ;K}7{Q4D*h*ogh9WQf5fqwaVnG0ll
zz1>1Ps@hZk5a`07(-R0rAhl>E7P^FPaxAaee#i<!swPwro`EzMrrZ<Bn92?+QK=pt
zl|F73cH{@C>2Q}n`IZ&2J-e7{c32Wu)+>G<RjhEK?CqeSr<b1kY*Sh~s#A|)mM@^m
z!PwA-?aRUW?6b4_1vTb#7wmrVJpQ5aFX*vA+r8Q^rm#g{Qyu#7rs!)&;x~v6fwiu`
zM_jJ%1;nS-+Ru_6bOXcrR@C{qpxem*f`&C4prv)hBrgw7)E~llSy;X0Dv314U>7Hj
zBfa{t#Z_CNU-vkL&X4m(glewFZ@oF9LbgNw8rPy0np3u4(A%1~*0M~kd|HjP*r8YS
zn~xR;tQ8zI6uuRVT4Owa4mDZ>!noJ+ed(hzoIy|$6ar}|Dh|<JO+P7XUt|8}5i0)$
z0`!u6&#XH65`rv?x_@GQZ+Qh1F3YunPE8IZX5}l57PKKWxLf4NGvJcU59VxvI5(AK
zz_8%g2QK_0_B)LZuKjFRorbnPd<n3mVH$-X=-tex-=!u@w*<&eMrUDJUwfKyhRM~b
zVK^%iMC+Y7qtTJMk(NK|*kIc(=UIZq&ILI2JXTYWWwRk`m=-HV+w$2Z3lKs4Ili8q
zi<fzJ+n*1AU)UqU^g&(WQN}~Wvfhnl_N5t(j?o@}cXwnw8?m2#qlxq{7}-I;h8JFU
zRD+7W|Bfw|5BW%{9*WT^U)UNK8n=VMeiCS5i|UOqHEKU!kWvuk+w8!*HDNS~q}XA>
zf<E;l)2yIWnn4?eqjSSk<#4scEQLi(IAbJ?I`f9pfbdg1mZ%{NMKKc=Qa`9}B@`om
zn3n}j_A?uM-+0wKjDvI&9P5LQ155fSsaI~SM`@S$yNVs^1@JpcF5^Pa58lo0WQf|r
zCavU4<l)*#_d_SMtM$59VFz9qilO}<ouq}*%Uevn^92gUB@$)Qu37t5R#$H&@?&_m
zzFDV=Rt>ccHkgd0Y3#-FJLS?i>)lV)_t=uw^({Y%B;J*U>d*7kTS}@P?nndXdK<O+
zM#G4eV%<u4kITt+_4>_Hi7%g+u$I$jijxeYoBV3rYor>G4#@XI_V>(<Z>?2+@m*y(
z1z#3V`#W7-nLQ`B***_b?17`ZYRmuCpgOP8Y-|<l&b*uQ9n8q2z93H%26<vT_QLwP
z>Y&xWBf@uD6_~sv1BSC9xCNA)UmQ5uHH-5<{+fbHVVyS1E%B+cgi|@BQ@J;vQBTRo
zOwm+7#AXz`{v4%$vyhLv>elaVk#NZ+O;FJUtYp7xS-B&EV!`;`BJttJ;!@J_jvc7J
z{`ccnJGB)FT}YF-y1M_JrrT2V2%N!J*b-wnaQAAgc7=&cp2*sXC%&e%=+l136J1O3
zB}IOU!)AD2eeY!~{U5nVoiHK%F@YfdDntx-^!}QoA$88?$(xqzd1@&Y0Tc`739~tx
zdBlBB$Wfs>uuJxjf`4*5e2fpC6ArH`3!s5)HdhK>TY4O{TV@J)_@>?5^?XiyT;C=x
zr3#>+`-zpXU{+>|ip|yBN2Rs=Je%e%!BVX<L!n=yP;}w7H(wBYBF$nTr^(~zU;iVo
zYFbIw1b5EW&zqu8;w)96B4~PXKAhfhssJl7N?8eSHm4Iqa-UtuGs~yBTcV77i|=p5
zC<>SW?Nge57pf-deqEtjHy$tG9ty8*GiQtn3sZ+#CUK)xWT|}y@4&jnAXHNFGmzcy
z#|6@}mjUnfjnkA<OoLA!9ZU+g2xn6km}$UIJuAldVPk&Nikn1-F;^$YDO1kLOn3<1
z1S+cu!%=#l_UKJDnLC+8?_tnv%05BafWDQ&<r~`WCg|j^hfqghbt6*1erR-h4eKU~
z<eR#m!8`zMfP7D8iK^hmOn6^pG__llnU(eBF0<n6wVsdfg24viq)bS8;;g%C)MvXg
zm2wsq89jOWobeJPVE(pleRpY}zq3_yz1|}^xaOCmfN_(RN@5Mty6}A|`Ha%Getis>
z_~^&7`6m&}X{7I<6S+$}oCe!L%U?Dxb5dw0U`Z~>7=VQxTml(6ZPd;)Wsy_PQwNx;
z>cl+`_VVklGh@A1y6Mx#TO!F=6<CeH&dX9N(Jbfnezn69t}(goc4u4O`-0jj*h;-v
zq7qHIfXlCyt@V@2ZxLl>q{E^qUX_L2ysMTB_!)Yaq5k2BdbZRm>0);2ld-y!(5$Lm
zy*U~s6zl#c2hs131x<}xX-zY?f=7&xQ3#X1`%4~Xw3|MER?w^O+8hcnYJUjCsFdgQ
z{FILMr+0{Pu7r<(!Kx6o--_p%rnjJ%Ihy(g8TTN=+4wp2trdADTkp$m(#76zWue-t
zJIAs%M}>2d$|1P)(Cc(;7s<*CAhpJgciYBWxss@6o?6OgF-h;TOXt~S)oe<nZB9$e
zRqAC+sph-skjpz5T?ZU!KQBq?Ya!1jxGXAM1`pj;S4w<9kSNRT33p6N+SA$M4UdiI
zHfJV!>vskEi^dsWy-xVL^^q8%9QU=FDHC2h{&J}olNzz6w;-VTqWL2P2kyBJgd0C(
zxu1nyTqr*~42mWnRpNJkIvqw(5sfw8S=Y^flkg2us6)GczSLD}Cbj@D=JO)dc{H@2
z+#u~16zO^FHya^1N#1aojS@Wsl8exk!E?pM|3<#0N~}S=<&TKDzNCD(<tqz6c0x|G
zWYJmojwQEs(w@Fc=Y9W4xL95OK3yPuGmJpfD7h&sXB@-9>)3O37>DBzbf<;03WHGz
zsta8z%Y}e;IfdXShzc_GTuwoXg_47e1L#p&<R`&&D{FE)G|N64f@J+0y)So37t<Sx
z1x30Qx+)1g(dh(vU3sEBCrzc^?Usg-f=|=^QxIR(>FuYz%RS%FSg><}`?(|)#~<^v
zIcmj?ETWV`Pp@X@CE5PXd=xi%OSjLXS?fo<Sn6s8fx^q{20mbUt8SiOdm=uAa=Sio
z@hWW0tu%Ot2UASGx5E}$U$&$@kK5d`S<?K*{(XWUvBI8=^(>?Aim1*|;a@#)kSad2
zKHSLhL2Roc4{P=rEgp0?rbM5Q=DlUXlTRueK1J_LzvE;Q#N#tP`Pj@5-ESN1qVheF
zX1cG1?cPdib*#X1QO%-BzuFU_nxt?D`C-}xNmC3(iJv0yicZfSCldt|&B9tG-4y*S
z<_pNDO`}V$5IPl+ig6h;^gBVtxl^bn8T&pRrv&<@MdU^_6q1Xx-+F$<cTRMF+b5)W
zf86NXe7v#}wnh-s5jV#3C!-GVq!dQkt#I2YS*5E0%HAiKi%mgETK%YFoq7Q)d7XTl
ztR*V>>-*iwi$(ct#-`u#>}ez?GBO5Uy$N-^pG3A|%{(iFr1hZ4q3@SdDP(Eqj9oT^
zYYTO9>i<p-Az?PI613M6iTA)1G@okX3DZ!r60sqvcw2W=K8;(U!dj5TO-TD4o!>UF
zO@mN9Ot(0#?za+-Z>8nAs{Dryp3*|9srxdC;db4bs}|D^_Bl<b0`W#&cEabi)7`UD
zozFlW#rbP`qZ<|`*<Qk(iP0iZPF2`}%Y`)Sl}xdyb*u~{ZC#~zZ@0rddm-X7nfd^n
z8eb0qsYOF_EE9{o;3BbAzUMaCK=UL$Rp_O#-jc6x#da3hX^v3FG2W0t|I7JuU)6N2
zW436?r;Ay));kZ6qtxw5WkvoZ-}cizg59vvBo|ldavjzBDPmp$hQ?*b-43<q;|o7J
zq1e4B7b&@wM3vesT<`)EaycZIOsZW{mG}Plt@P;g^W#p(lr7(J@~oywx$~yUcLE{f
ziYCtRbZc<=On4O>x{jxU=F?B80@Zo8@2T~5NoyIoXf?XVMTyJtYJoh}*Pc$VQJiu9
z79h^d@NbAbV5-Rm$moo|40sNpwf6>w{sg7LbWawyG*(~DP#4RTkHED}bCXSkuL3(H
z_2}R_W3`A<TK8W#)*hooIX(9CY|k?%iyA8`+2gj&wq02!AKFn(4vkCrJnGL;z+}zi
zO<?W1A?F94=ctqwsmit!%_lc<8SB@=9ZX7J|4ih`7(_q3S|2FwDF9Q)>w86FrZlu0
z`n0CVM$l`*u+Cl1OUQ;Ff1q`Pm6sJ}iY^jzcny;!&pA)gkA!R{pZiIeL%gy>{pWFt
zuJ8FOQ%BY_(7?Uq6yn*Ga-|s{RCMY5c_tRG<jAbFUZa%rFk-3@^=Tu(Vrgau(R)2J
znM_>I4ZMJ?VuapSm>ag(QeoNFx2~dmbyAL6q(!}ymgQmXTs|0zE}O-%Br}w{GG>?h
zX8Bl(f@~LI38yKqCMQx%&Z?|iqo-b>P#(67c93RFlaxG@t&)Fym~HvExuEJ;o#b!Q
z59tz3{<!Pc-zG`LYzr;*O8&Q0(cTd%{00Ube7l*nzRu0Q-ZO8Z*jRT6K{CBnHv<KU
ztad!m#d4vtKk6OE+~N%wjq5WB78FzxlC|ST8+)BTkDs1DIqUK|R+3MNHnu5Od7P}G
z76zd5a3A{qc=EoRtIhanT1ymF$RsEmcU;iMkpI~cP8uDtv%wUc@DytaRp=kB{jea+
zU9pi0mn}_A?C8k5UtBwx;KrZI_;_Bho9L||t+;yYd?z{6r#8y_3wWpdr5d6ws`7_q
z$=U7N+k&bbF5TdnQR;fV&r$bFnY&}$+I;T~Hx5#jLcr-?DLf=RsJ?u0<!r`}ng4P!
z!p6fA;dutme|09urP!@;M{?r&&uRl2sOg2%qn`lRj-{CbG4sJGoVHLZK~eAvW8XYt
zi5xna18Lx|rtK^~xS`1v^x*yv?y06@&|#_$5H`H(SMbrkHGoU+#c$hWX~=7uIX#_`
zT49->Xz9Nuw3L6lSfj$%Vo9%7_khdqA$<2VyQ;`m$?e2@aVF{%kMR18ser5Yt)P8B
zv_(X=b(~Mr5z~}F<MPygTe29EwtVp7kmL@i2Xf6$WeNEcFI!`UW%!vL64(&W|N09n
z7k$Aax-OW-dm=@3ls@=C#`GgSYnpo_<}o)5L+kG@i;~e~nGi*8{_^JGQj4E6xbl*^
zF2)NrmX??raKuNs^14>H&z167R4tx0>_?>z7DFM0BUU!E!F`hyID>8cTJ=Q$K}WbE
zBQl9vh3Ht6Z@ojC>6ydvX_c)!=OclIJC5VE35TJyrq^k&Q)EFcg2nrNG5j$e7=^dR
z`O;b2wIi&uX%>a4ll_8M)$E&z%BD!3`aOL@|B&c!06H28ezZuxQ6Hu#+=9h8`x)cI
z-l80(f}CQyMnoB#O&QXHo9*R5Q@#bKi#rlhYVbK>cM|CRtv|OqTnB2cu}9k_f{hig
z%ZVP|FmKH|%ZSf5E4bV8#O-_C7E7=jlaVna-zuwZXW&;()~A>X%a~et<sr@@9#19J
zn7X57@6cVOVzZkU;rEl)EF27jio?+pmU1iqpc&QD1p9<7zSx0!Y8hBd7*y-$v$F)e
z*J@Dn2dU+<w$F{`eCgYs*RP9qi8BRp4GFm>uCy<C?X}YkH)$s;$Dds8v}X3Kj{Qrk
zJ;+ujolr{@8<&%f_@+DeILpLxyw?tLzio{1&1pMr(#P%9*H5O2YidOCPkrI7YLLA@
z{zB`zhnamN_T>4W+LIFil4S}=9x?FW#byB`<<_+<+I4ulI(_$BuUwU}uY}M1JawcO
zE5rr<?O{G>U$`20-f6tJaYy+*{Z5d)#DX$EOQ}A{R+px2KgX?rDf6+QZUL#x#31dm
zM9l!=r|w;6i6Qx(wS<tR<o4RH9$=yrD)6o5q@X{Y<!tkIl~-gw?jL8_yYc@1iaAM`
zvV5u<?k2Y3roxwF33v6=N3T9xEy|@>F>fl6y-bCoPbzNqvg~SOWdTV@!HTd}bzv8m
zuYiw<Py?4Y-xY8@j^K4^9%k%fRY5S)X4dfs@CN<Kcmh4lm5SZy)Qg&mJc-EOEA7-$
zAagK0+jlle{jRz{YB8g`$I-HmOOk*c`!r55=meRavnqfMM*Um6^TLOsyYcz$`>zu)
zu%hT5!GJl>N4fN(&&@d!BV`}Di_&eI!JEo!iXwiwn%}f>!>}4TzCj37V+)csKO~gL
zF{TX@?tiO|mPTT#wO=ruvzc60T*)M=#DDG8{?d-cq;goRk`M*vdVSwfM)jqqsI(8`
zH`~)=m?mb*dNZ}^p`OYHqU$zu-D26*aVZBCj{8-rnN#WIc9yrTI+&FlHs8f?=HmNG
zWlP!CHlyTmys3`m?8P(6qwj8o+R+e&G=GeyW<0T1UW_Rbr0=qytjBw-wQQV2en#+e
zsc5q0*xgO@uzhB-ymBdx$jUs1vtf={K8F8N%42U;v0yISmzu{L(0?X-?y0uw)<xyz
z72<xtep{PGKGHIJ&C%_STs5M!a)G-QcwZ0xPJg`&Gmcgro)t4d=9u4*-6H_+?JENl
ziNjwh@V$vp89%(y`bsV(Ke%4nkV4uSsypJZkW2`Js(^!sI+|5?;S)pSeHx)R;u58a
zeiYXM$;)8(f_bgU=CdG5NSyq(t5Y9F7>5Ucz?8z7;$%lBrZ7UGm1fzYmylN8F(Kts
zWIC2eH<YveOb>4qL2$7Bs7n2@noX);+D-B1-OqOAdYjT<>pF6@K0Q^S^5D2y9cNlS
z+hjkwaj@9iFH#t8ZI?S@BIC9FmR@&OrWU1DIDKyR*(RN!QCiuS-9*zVg#$3yq3Znb
zyoi&WB5FB~LxKzW0VuS=Jk(99sA8wx@&50MXL^TL<WJsyOtH)j!WpWTvb5Ck*ezg4
zJp$Y};E=77#vYNHIL7K842!FIGh#FYlnYOyf7oRrc<lAQckeifO!o7`_7nnj#zc*`
zaPmdamGYf^1>B--iw#OcQjj^bSW#q5$hy8^<d8sATG=<})ynqMWZ9JC_Nz0iDGSN$
zWTMu~&IdjOvgndDdJ`e=0%HA^o1u<6Szi>$(@iMtT3&_2q9CrtXrZ`iRZk_#q`3c+
zdz(ia@9s89x;mQ%idxA6qWr>{%7!X+VU$Tleo)6@-{Rzz(4ydB<G#=CspJZUjMT$V
zN#e|i-zd&eLT5YVUn;{GfL<=5?Y3I5keLtp&~`Ird7aOP(;wK0>C0ph^zo`%HuV`E
zZAtqUNoERm*P#!j-WZ(X2J7haW`FNmGwf0?1eZhxiM|RHjcNgBAA$yHUh_vPEGbO;
zAhe?%q)F<uYCgW&c~6|7`rNs<n>WP-cuF3<JPtbhC%Y8|GN{^FShQ@2z#NQr@uKu9
z^@^%d>2pQ*=Do<3jltYrRhH1C(1rz&NAJQY{Nah`&rw#|iW`R7QR&$x)?P>FDpRW~
zeG6{i5-3aiWN1431LV`bctb3u90)Bx2so@_?uPja#p!xp%AdthICSzHRCT`rQ&Pt|
z9Ha05TJ<pSn+<RwaiX@0Q)gj}El)dj$R~}CD9d~ssb$+_vO-|ktZw<xp1CYC>O>pc
zeo!-PiYs8u%zUu%{$(majur{*heqfM4oO0n>EhXbyblmu`(EV@5oCk~5R0f$X0r>s
z@JX9vSJEzIBSyAy*++}_v2JRLioh<Ok$clW(ZhWOvjX9__63DKcU_FDl+XfkX&+%!
zl~6@DfqzfwXAFIIt4oLTW1*M5(g35e(x=&)dR>Lp79T9BZzQc`B_8XBm3yFo_C(v+
z^Ceu=kn;Vge|<SS3KkseJYI;)tOhk5+QM$roZ2%ZWX(Tgz|X4Ly|ACilb-Vj^FP`c
zSJ(?Vg%5j9fLm*RGiT5sNjlCL6>)1XfMh8Eg_ovCv`A$?=FPIpji=M9@{)6v5y@~O
zy(qhTw~k#2^-i{D%7=8$&wFlze9=N%Hvpv|U=`V9)y5ww?+;B|&Q-F&Ro38M&cNbd
z-h)uC&cPzm?J}Bmq_^NgAw3!V6#ZM4sWs8IX+7C_7cZqSgj#TE-&4=)sn>aGldB+N
zyqR0z?)dkhYOmuDd@Ka4X52Ay2ZnW3io<!2{c^tG_ZPUWXwd>!zv{NqtO_fAF3Wbc
zR_|05x~5j|tPWYnH~M1~v!5=CoEA<xA^LCbB>r&^uRFsEzufH>jccsRu#CGNMzEK#
zz8(zc0tC~1d9LL;Jz7VL`8YZae~Q|7U|GaGaoJtr-cb|wSe;`OLGJSAz0zF(APyO;
z^^vE()$=uSE{9t;h6K_^ejv}Fx5pqR`Pdk(-*Os6FvX#$n_%nyJNROp>xt*jev;?h
zL(3DF_r@J38vpf<lf}c;#uTrRI<=0c#j%5+qR@r&f_M7zlqOw85|uWOf!J(>hlTcM
z(XV@NKPGeAWvmKh>TIRK>Sm<MWiO3L@XDIe{g}7a&WH%FWo-Hrf7=AA1`J~svpA1&
zZ69-8Qw^ZW1O7&9omZ%z8R4~A%yF@Q?Zc#Sh=A&G9Z#Ct9fWu2;4{&3b7<YL{8|G>
zUH^Du-6j~i)~)jZ?svr1;oedLhmBjV!c(m%Ma;2pu}Vnz^6=HvneIvoypw8C_#(@v
z{e;;;8GJ{rM!$}G?;2J$h{(5!*+8^;4H@TUi>$Mku*RVn6SZGir6u_0A9>LeiL;=!
zCw$M-SDg>f_1XV9r{zh0qzxxstZKKns&nK+ACB@;#H!u>!d6u$6#}V~s2lSNcY}!$
z`vp&XFnK^~QRJ^%!kNiL>q<wzQsCF-LPl=ER%jqeD|+vk<NfhA@~=QB7MkkC)#G2c
z<x&*5{Tv<EbXQs?$=}&G*q@!Ds%cwrQPlmMsOxF5L+f2mn3`A7wzY7O%=UT0vSn;)
zoPW9KKK)wo`oSqx`*N;E`eQqQw^tVW+g_I>GsMq06}!YimA@Xu^SP?c-sB$5#h}DN
z5c6Mj<kQMnYT3>)+&drRv%6cEYfp~QC~KPwV=Nv!Cy(YP6FTmplAf3@ALNyQ{u@3Q
zVBjYWBN&pEzmBo=zrW>Jw5~0<oYi7dJ3c~Kk8i(UJ~l0j2r(>r<1R}+#-t~#aY->f
z7)x8J!@esO8**nqb3S30W(JFmeh}9)%C(v?tLOVjpAlJ?e7|ykB%kth2y~w<yCDy2
zQc+aY&uUtnk8+MfNtTJ8T!qtBd~;p(z6Mk5AWyz=;&T)XB$>ejb!pUM<8G_+{8EA=
zX+qtccU`oZOSZMRJKB7x4pGyzD9FeUjgPc)rS^|M_zI0OaB4PWa&dKl`WE$XI;=y8
zeV-;)=!y!<gv#SW3UkHfpEI)g#(|{gL+RYEMwNQF`2vN<gq>gO{b8e?tcun#LS0}8
zk%#$JIdSw2i;*8i#Kw9acRU(F1CTjQa9O*)@Cl2credM;pZp+Vh%JX<<b<K9s57A4
z)3x9KvqIalte-?jKgM?iF&d*<z}>awfaqPz^N4EDRL9hIB-G~A_@q0juIoMSsl_3B
zCH{BZzTTkyfvimHNrAq5z82QXBo!0>V2|Hu`;!Kf2=*L<;-<_=@ur*jDR#LGLRY^R
zF#YJUR)*f|5*uVq)6+1mluJvrE2!m5w9dYC*f6%#A|Q5RNoBv60t^n$(#*A6iX16v
zBHX(JjcH70ko#SA;hAN`S#2M*9JdGPrYyOYQ(ATCB$}HnD?yzle)HHXnd0iW9eq!?
zFz9m0kE+1Cn}0w`!k8laX#a&6=1$w`AF4&BN2pWq*MN0)qeCOd;VLoM&TtyjmJFC!
z3<t5QKdK{RqZn)K72<y?M?3}9&9FBe7P=fG2hv-~P22;p1Wn^~0e(y70ogG}8^kp;
z?5H4POuA7VX}45n@`|v0<h#-9A)(p3&15Y`tqzS&=<m&|{}2wMdIr3ZzsBly`}L-X
zC+o&RJ+SSU8OVi<x0_WaCKe&zB6HWT7VXB5@IKU#$4vH~8Q9gYnRLad(UQ=(eBsK!
zuyK)6G7yB^MoP$ddn#oF!O#bG#X-^a&=zIH8{tw0Bh=w$V8bhMp=qn?5n*I$Eh#i*
z%PxqbtB#E-1W_ntt?E6j<4%PLJ^Tj$(ig}2uAa5q*Q+=l>1xkr*WHHmHx(=Y*w#Ln
zOjK4#6+BNJ|C1(-AvnUl5xGmJcRQq2X;GBPnSRnjT0mm}<%iWzDN8cP$s$+Ni4eG1
zI46Uz@n=8)eMnN~RgQN-QhrpjmqBr0q*n3i<;P=o$09rJDacw>A1`__Q#%R|TNZiH
zdRmEHCOFDYt%@x45%Zi&l~Hw!B$;k|TRje)LV6w1p+Q-mLq)-mBEqN<k~O&1-)sLu
zBxFAxm`%N98;M2Qna$pvlIPNSdVE7*JyEIY@8|b-ss{+JvpXPJz0_LMLi6PDdR2&;
zy}0AsY14MS*4pDoVUm1-B5~Z7x}CfT(+I6bgm9vEtI9T4P5VyRDbsYD<PE!Es6eL(
zhK7L(W^&pkH|PWDG=q{hzQGwLW@eI+by<^&_Yp2%-|4vj4$`<0d^qV2`7C2Vqi>K>
zH{fmWduhEvIl*9`72eMZm$e(-$0azf@r{35U{PWcak>1TC?cG5TIhGauDS7BqI>lQ
z<9^+4`Ol)RzV^*kZSg1|PVPzy-F+H!=lonVp;xsAKa|vK4^~Q%#TyMW0tl5s+c41?
z5gD=EtdO^!!imA{A%~;Y6)szmv9H7*Fv>*qok?A|=nxyrs>x)Qmep5jmn0Hscw&sJ
zM4@8E&FUsmvviiPmP)hXHdAfwJZ7@DeOzU{dsIdLCU(%AFhe}waVs0r4OBqK<Ds3|
zFoqkutsQx)nBNI0jhg04b*WC5f8lfYriWQ;`2MoGMb;v0Al~`d-QtjuI5#eI^$6>5
zq&s27557;@$HwjiW!4e1Kg?TJc9~+}>Q5jk(mw*KP$gM*K>Eyo-v-)_W$3;VbZC%x
z4V*2}QkH}aGiVG&@MbMq(Tu=68ji@F1kTV&Sxi+68=MJ0DGWzAUERwET@FiOQC8kC
zWLBNe2mIdi(5zlRkncY$=fup~0;oHNo}uaol*Nma>4<1AF;Yxealv5WlWK-4&a0bd
zS$9%_D|2JdeH{DK&-9Mn$Xp{DEt=S~g&daUuCmAtKUee&zb3+%_dlA*f4n1M6_r+#
z*PuYz{bdw0iX&G5G$9)~xI6_1@iNc;dkpw|E{;b_g_1#1P7sTvMUMa&A>#2eA|-6F
z+m|HULcFRDFC_cn4>g0$tvQ!EE<JlVC<X?dm_dCJtZ;`G!~}<e@DY+EO+OXQqQ9l<
z|9U%LQzGCs*XN;k6$7k|wC~HT#j!vrbr^pG3Jer-4rRfqTBWEfHzSFm{T;MO%q#07
zm%6+|#7i#}?zOK69@ze}Ip{HhcjhQ(W?_eg^v2z=wx;pe1BSEF_Ynm})0~DbL^Nm&
zFtRo9Oxxv7%8}+I6Egoj3;S1Kl-RTkU3sF_Cuwp<c_y`z_NCa0j;A}h3c%o^SLgDE
zTNO&0xV6g=%^U{lU@P4&%jm8-muOMyu+p`Dz+p78N4zWKi7LRswdQlS=TX!Ai~{{+
znpH(=0tZc^B|jMv?S#LTz+dKyWCGrn6q8DBKpIzF6%s-sRMR7@XzPNQP^v8Ee(*<5
z9tIyOOAVgOE~NhF?uMa_p4+ku&kR2)7upVzWnjr7=P-TkAAH*(gkoH52Pr?*e-IUv
zgK8i3=^&PFrH{ZagN>CI>er~oK17Q&cJDyUVEXy1>H{F_$TC9FqEMpkNRUj%t7Lec
z4bH=AtcPT}F3^_O41H)~oE!)6iM2E$j3(jV3<Bln12QeeRODyz-sW4CW*g^sa>a`i
zY|@g3lI=pI1IIQjISz*!l=^*yJYFppZKmi6nz17}Q$xOosr|7G=655=h|sdG7EiNj
zn6HT+j3`?MFiXNlEU-I7o#f-c<BPUKosXV^9fKXTIDqPV)9w5xP7<X_--^$lAPQ^G
z_kM9TkLWY9|8$dXme1WTkG{}EW|dIswlgCKiEZ^F$(3b-Jg4+VU(_((q479BKofS!
zvrV}E_hm0Ip?+D2y+DDwW!J@mBsE<gq*p-Zv~%pELLh0T)Gg|v3F{AgSJ3#Kz%cQy
zs~c4)V_EqH3!1@-Hu}woNq|^*xR#xj)h=~1whTfDliB90d21`kBEd4OkD1<A^Km3p
z6P*A1Z%}DJfsD<EtHQq?N<P^LM3jr9N-s)k*ELP?cWFk^Jd<@(3hB!e(*p>kIZl2L
zXb;%YQNO@h2E_J?1yb*1geP5t-r6Y@lgrg~yYNzU#BC~T6PZlL%QB`mELfI-wUso5
zn_ytuni$H6dtK(5^}isA%jP8glb+CaVAo@BX6b@y4jcDQfkSA9@>wD@|1C94F|`Qu
z!)c6fMy($#FXj^cjPJaD0CJ5f|HOO4k1ycCd9S1>xJ3nb4LCd_`R=E^xQeUKPuEi@
zd<U@%2e9k)(~*v@H8s_&Vb{i~F$>cF@6;jmqzwF`;G24Zq+6+GSqFyH4zH0NN{V_$
z!%yfHBvrm7?V0q7W>NT>Z|BvDc4oS*q{42NRWyIFO6P;!THr4urjVeUcz+e7?_H@-
z_l_I70<s={pgGX|tDG4HSI^5KdR{EP{pqr@D2j7|=J?qI0oLlr3VL^cT93`VXJ(4$
zLA9hH^<C*FT=+=X|DuIFiAy<q1HBkvY87^++@otD;1cMnO_S9=#nCX1(^fwnq|QEQ
zJ~fh72PhjSZGVSdEVP@RReKhU2=sUF(P)yXjv@|sN-d8xQZy8sM{CSc2cU*vRob{K
zoDJjhd(8}(>bE@7J#F3Mwck5W2wQ|vCQAX|-IMB*9|)=9`fmZ{2O>8YYYKn3tn?*}
z6&HI%`(wUn(<m?Wd^=vdNJI*172)zj%TJyFq=7|j7JNUy-#}DR(6VcDSur{+meLni
zjr4eaydL-7rTSL6`gGOEvrlajpN|aQ4)_!rYGCM{B43w4Kkhg7pIJ2r0pf=i{6={n
zn&l&(_$Sq;8+y6$TF0s16;lkI%Y88|@-4tr$O4fQGM4^<-$QhjBd{6Rq&EFv_t`pc
z>nOK`uS_ff`F}l&lP~_UWKGKwxpyH-WPXl=>JmyJpopW(e-HLUN|>q`IQi5JbrT{@
zS)qZbTub(%b1_IzG9b%d2u!twb}0llh{M|W#|MYQ`Z-e*5jq?q$~tn;ta+mr@4+Xr
z(EZb!(+ww#H}irsA2GFhkeSeSKL6EI;9(@*1$Gj;EwS|Tl>_BRQ(>sJ$MT1-2xqgt
zMvjA&DXk1UE!ZT#mfFI&ti(wLna`@Y#({)reH#C}MyoRq7?|K6d^x8lM9uO^N{wEL
zWCHoWyBWz0PvAe>^e)L5wLs}n99JhXAiHJou*aQ7stL&9fAaFnB@)%+)cIjjLii?r
zeZBPe%)&P{suo216W@Eb^F8Khh^GoQR*Xf;vVZV_Ry5OzURw!z+5g`=vE?~f@ra`A
zVa)V>y5%{9Kt<}^FU-{t$a0zVvB80@`}|1W!?XK~2<N>ziV+R-#iNg9L(nm9y{KKx
zdA=$NM79i;{z(42OTAFUE{TnQLg(MF-Z3vWx@cK_WK;`F2^)(zVN0%N2}C=h*FNp(
z8i*{CP;Q(tLdd0_z=Rl=O1eIr_Q#Y&ST>e_nzg|@d<o5UN6q_NWQ#>Wg#V@)tA3r4
zviVl6c6We0Ci3FJ?<X(xzYD{QspLrPG{Vw^mM4bDHo`3G6NVn|ZA-tQXg(MRfg|=}
zE<$~tZER1vTCl2o|DItn&02FAQDh!~o7qpd<G!XTy}yIkoaU0v$(1k~sUQLi9ZCvt
zsYICqZgV<r1wd+GdI5U3t@%G3h@wnHFjvb92@8~Xx|?46yf`Fi2V2SyMD#EI#j%Mv
z5IYv1j?eAp-}tkEj3V@KVy&|-1Ei?3KQ}q5uU?Da+1oNAF+%j)%?44Lv6nCrtl1YZ
z=-zVmw1XR0JW{`X@W6+6=A%-`_y$lsdIi{O+AJ+le006kQ3aF;!m1-~n@MU4LXW$O
z=K^0IpRPL;fxD?ANS&D;XHUu8wa@%FQ|Hu@90ggui7Hml0ZL1TmW65}_u?$)Nofs|
zwT17xWBQ1cC|u?eZu2H7){mON>8#aX6Kr9*WXz-B6z_@{X6o?zRjDXDR!U>z2|0FV
z%0R;4kaAj%N*3QHYUHKuKt~Nv({Wqo5V)8Sy={Fj8{ZR8PbKryR&3BanQdu!Nt!ix
z$^V9yF~IMK)ITXUXXD4RZTsz(Y{yM6(<E?BPEqCvZ3Vnm&@lo_RQ&5r63%{fTujPN
z@$Q2YYB$hvAfW;PWY<ZskP$+cJaXIn^_EL}s=i6E!H}>Bbd0tk8SAs=2gh1MM2{u2
z*;T)t<rzJ|Yh|*-6oKx@KJ!_*;H9ZImAyem3GWE#HDheMn4CXb-Fe!UyCy<Jy{`D?
zM#7Bo-&y6h@BijFMC;TRleNTZPJyfI{#&m2<=3k6eXF(qu}(P;EkYfeYEKM&nQ)bF
zu_GK-RV3!J%66453F2XSJ1>G>1Elts6h(KYyv&tqC&!$20-OH}hc1xixm1=Jr+3qZ
zd&d6;irnwtt&0xerts@nlJ=P7UUlDy;_>TxNkh>0TFBrJ+CA`Wz>)6PO0&Br<dE}#
zv%&&;WtXBVA~I3JK1TEI6~8xg2fDjZ?|8vs^w`Uwc=g}5kl(=v@m|0iF-_sXJA@FD
zDfHzg#hYsTK3?+Zo&y)rmXUJeEm&{8)HK7PZ>bWbPw^iHcgP{UJV}&kpcbAFxLI6%
za-CIQ0T@Syc~oF@=$7tf*hJO5Y5L|Wu#;uYnH)_V=GPLZQ>G}@oTgRz`)nMSG+xv|
zpIOvo{iB4oHpTbRYGv3QL^k%j%fJ&&rgS`PvIDidDN_BZ)r=Ut)L)_<TTCExd>skB
zK*KDPLu&xaFYcdB2h{(%Bvzt9NKE=HP?b`~Wk5z8Qx2&Wzz-%icj%Juf4Wp-ljufx
zZdUp}?iZe#XuF{B{E6ikTp`#|2t7>b9M#;}$|oG-WpuRyS4*f84M6c?NGk<nhGNX^
zJfRh6n5I&OD(Uz9@4<J(@_HEF&|=uP0X1gfZc(;idEKN;Q?gWJ0Ym~H!81_MfhtPh
z0i#2|wRt&yB1)eUJ)qeBLqv7`q)lMz{qF5{cJ|X*N=JTq;|lK!%pBY%lV!KEE~92a
z$ip)Kx5Xab`8{tO4YoZ8h8oG65V}z%S%xHFtKWmOsDlE`z(Yw0I|i%;^dWM10Kn~%
zAKTzix(xMjxFA9P$fS<C0O-?n7!h0J_d*G`6<PtP?0>guVo<CE<%mt&QdQ6UQrpcU
z-KJr1K983Wh9a3=Jg^`X1!gaYUH?ncXgM}!eI!L}uJ`paSN{$VE|zM8;sQe!3&7~B
z{NERWlh@-frlp|%?aNl`)Isfd*1Z>y{#@(c=)9eLDH*Kfmz}5Nk8k*dHF9_xTW#gt
ze<JH^v;zFeSwxwMu=KkAv72bHl;^TX2f&rE(uy!n$2wJ%jLt?oL;}-S%KytX(E%50
zhVJ=r$6+*I&>9^*PW(X0TooKIoY*Yc6xB<;K*-k-xYqIfc+B(WyM`A|r$!WEMOckD
z0R9qT;Du@Fm&-x%vq0$4b{j}-0K`$QsAVf^?riM%Lk0nNQW@3^8C+ucNSOb!1Iqnz
zhwt5inNWHFgn+IYcS31WeGI4t7}V@tV?(|i(fJtw;dHFR9V{C{?Bg!^re@zxF$^Dm
z^hITkR*btLok)dTuXs)=@Es<)u6)^kjc)ko?q(xqyxwi0DU?kX!^AJIj1F8S($M|@
zoP?1u`UOJmP>lh~jo7v-JoV@#*&6_nXD1o`e}T|`$)SJKFo=B=j1+amMO0;$;UF;v
z6ZDx~mSwqO&D04fgtyw!NzA`JUla*#QlKrz?!3lAqkKyzHNQ9C1{Dy}WTJ9{Kfq6}
zLLWVO;G}Fr{i_>Q;NoM0KV5O_)7^g23$2w2R2;jVaKt0~?U<|rHKC7da6(c*{Uyf>
z)ZjJPP0>oAIer<<&O9Rc$cZeZ7Y``f4*dj(3=W1ARf&<;#l86HBt&SWLgOVGF0K>s
zz77mmR^XwW!(aC)u3U0hpxsv?VOCm;zMt!O;y6YRvRXu_;UC)RXRraljvRqa-6!6|
zx+xE57F#v$ZlF0K1AK4?7#$MB^a=T)VtarP$wuvJVFom%{bxX%2!qq|nK)`(_0gak
zgq5#WW$H*kKn#mFZBY(rv-mZWK>AV2i$uH%s{H<-R`Er6)8w5G@N5CHhEw>)3yS?!
zIc^d$gD!p4=ilZa*cx{YH*CZQw5e-t<E{e~15*A=Hr49mo3Sj_14{!?-g4oknvkt$
z%YZT;W@sESy9Zub#(0x$buwJvpVt4<gWN>TB<Gn^%98LzrL}uqRXibDV)I(;xTX_r
zz;?Fp(?t-I7ioFh)x!K{u}YAbrjrBpOSd<bOLvqkV-p2K$HN&<G%eV=97*PFg|YUi
zM>xlU^G@UAuz>n&om5_p5LyroI`1g{3OvQmURmYXQ2B0QI%D=Kz~-qr^3RqOWi2=)
z*Mau`v1U2nNd+o$4quQa(yA`ZCa{K*7@mSVCoE~re>i-^J$$xdof{3MX?b9MPPwlv
z_v^%$Uj`usz^OkctVZ>lU1*`Fq0d|Y)Qp_?{>o1-Iq^*yxJHIbdRdjAR6Tjm>;@=w
zF)3)7HShn2B|4KLw(47(S$+_cDk+DS;raSH$g*w>hP!nkP}hAHL4qQauem2gn3I}w
zKD3{=uo;-8Da)J&cFnrX6nit#B?r=AeZc65gq7*So3%^UAGE-l5GQJpAA|-!U$V6(
z?DxR_uMxqI5xRy#&<Y@V<Z4GbwG_Hr=5#%yEb$%uQ4E-2vOtO;V@kugc3<{#I98LI
z^#|Z?5zp{yOSQCF=t<o_J|6oDY58jS6bu8{z<mi0>igeOk5GOfT>p<$=P4t;@R*(U
zK&Zrv`-2dr|C47QTzdv=^O}glKLhQHyy0N+2A5Cn#cW;I<VdrBxAu$7lu}X`fcC5Y
zk95nC6*f{-9*?KE5<a99*X%ud0RAO03xi;u7`)1}_*IiCxeSb|#x;ceO&9@>y<bw|
z5B0Lw(;un?dn+7H@uP0JP}ZYkYhUKtP}9ZED_{iJN^xVt*RlSKSAFl-UmG*ym$`oh
z$zW0|jQoMsh}V!gfxOy_n=oym8pggBqK(l)rq1?qR1okQAPot$T7&QMNvY6neZOzo
z^6HC-Em~>rL5xTj5JR%+fKz0u#)qIrVUL!8O+kzH&huZT63H{oa|gth%m}z(;-_DJ
zCekdj^Yzn==)}49mCnn{FgVfW4GSMI3rS?i;ZC_R!mQgaXLrwrne5W<ak|-k9&WTD
z9#t+7V2zCJqH%(}W7nIC$_l*g)n9(FF?44pDLOa-=<fgZmKdS5-vAtk?D3az&l+rU
zX4jleHLV>$kr}v5#;WJv%}=NaPN&oKWqT8T7(02}et%@ulGi@)6ELEs2Capn;_)Q@
zf$eL~gx`Ise3m5&%3P13VN+Cb*gf|n^d1Z~&2xh$0imC;WUn7>ta04?$Z7Y*LZl!~
z43G7{=Y{_h8n1>{F#GkU00EOK|FB_^5w|Jhh1Gaz1M{(Jh0UFcchcLx$ouAZ<S#%H
z9b<2k{T2qER4IS0`tBor!%{SM5^^MAtloI`%j5~IBq+cCavBSq8WU;>s@EEHldE(N
z2WUg_T_U#KO9*^P3ykWX%Pw=5w;KNaF9a1m7RszZ33g{*ABQ5#(tc%!AdWN>T0nOu
zf=2t<5FKA1ZxJy#A6oi?q>Za_c5gm2Lcp%7^9_&0ILE^1`a&24C}XCmg>DBQ-eY9H
zgP*?g{7VM^_$31M8nqZ0E%7`GrIG`X-uB<WpFV96KYzo~^Ew4Pi02$ZHli<jFcsM!
z-#Y@J9o2;EyMm*hC%~7~T8+J6k_{^;0qEDD#KYrm6h1H=E4=3Zl3MS5l7AoTeOPz=
zsO_wV*=RC_Cv`kW9g1zo;}!e$lGFBfEL4Xd$VTyHOfD9DBm>0t3&?v{R&OY64rC>Q
zxcwpome-E){v{e#ACKIoh=H_n(S^k1Jo=6#dVnA51OU(EIO0qojGhuaI{bUv&}$JC
zlT)AsWT;h9sn%vJ!<dok9B;-k)86+}DstxpN?!Wkh<N|^d?EU81Ai`q_~YNYRsZMP
zy~u}~exb4UN6QBo31S!>7Mys#P@#B{2jIm?E6^!S>=begYoakKdw0KcSycZ-<h7@q
zJ;V`be6a*T@hLq3(pg8b<9tdlyZixF@yhknuU<A_gT{;ULc&~l@>Nsc5B3at53r7U
zxGI-G3uElFzNqPLZTmX6-fW}n%H}5hXiMSn7-pF+s#yd_?%+4{3NZp#>S`@m=>7%D
zDLhpKN)eb(O{R|3Z=qt(mWW@N7=Xu8Ab#ADtX<4yUt^VURRZhs`K4DuwXb*KXEVVj
zf|f)D^a{TjhC8o^QTP3T9a{L8o!;@GHE#p-)x%eeYtl@1d#evH(o}iz*meMNl<fdS
z)jaOQcm7uBZe;!;?nr6sNA5~gWeDA)rCOvqbz-P}1wztH3xrE(*s`1P^M~`unI<p%
zn<fCo{(_zau3rRW)i3O0KP_$D`*#8#i{*JX6L05FkK8)=<b@tDc*;H5=9-b1TA4eJ
z0U6OAD74!QPoVJr(Xd8{{?I9Ws5CC(`AJWkOuni65z`2hfS8x6qq$JcsQ7>Cy7G7^
z!>*k%Ldu#o%P3OW%94<!)lLz{Rzf8El0syNh{VWF%94b!XU&#1YZ8$y`z|5bzw=mn
z-}jHNzxw&j%rnpZ-1j-xxvuM+;qg%73YR2jJ6u03P*``=KQSRt;#5mLNNjOqb#vrT
zg<S4yOsf<5ciXrxL{CS3SG{tBl%g`}d*H|AK1sdMqC<T5MpqC(rXX#i42p8o@4}k+
z6IYd1ybz0o4o%{_>8x7zXas+-)4f6aP}MM*$KHFN(&rMk-uZY#`xO;K;wtm~q32?z
z7PKuSc^fFA-F4Nd+Y|wyF-96;^mtp$a_wL1%V)ob6z>cd+XcuwUwt7Be+dXvrdBs|
zG$r}6u>0o9*jRjS?zrsf#m5eLZHl}j1NXc2tsnLdcxqwxS4WwuOzywHbjIX@$kqn`
z)gA@I6;qWBz>P7b^UrM(5w2|F*4ApLo*!?T8|s|4CR!nYn^!#ie3;Ihzk`*@ZA$CH
zkub}VjYc!+g^rw&V;n2;uW}$#ikGzrU&Tia_EMAWHatXlXv)8-Miy-XQq-0nRmSh%
z`;IyKyOIa6VSTWFJFv#ja{(-xiSd?Ak?S8Ne=?Pxf3qV|sXS}0bglHtI}}cJABo4{
zcW6;D;1V#9sxJ4y5{RkJpDybx9f+@ePs7}Cl~n)I$<r|^_x^;M9U@`NR{AoOwjen>
zaKVsx_q4n}f%Y41CVsCPh{YE;kX*RO7U6#<Oy@Z1JEd#oAKwc$5FHZW{0$3xvm+No
z|0{x{BCWIAfE)Y_F{-f97Qe=Ainwj9^|y>ga#6lSyODR&N<0+gWxaxmr?*o|ps;%~
z3OIHBwo8j^Ur3x(&&*q(j4VY3$-NXyEMFJz_BNavpx8{7**KZpm`=x4k%!pV7Rolb
z3`uoKA&jXJNQLbNtBBkv-y98y0px6+-|N(1qQu5*lKP{f_kHZW19?9-0uNKO*0X^7
zh7j{cgVi5<%yU`xLL}x$v<XvigsJ5KAHL85;{Kzja$Zb2W7?SAHHeoD;@U@Tu_&{(
zAY&utE4@;$Hi~7`m1H0-8!CPb!f5sDJZhh#T;VMW^8yuKXwvKvtGsVo6Hwz?XuqWy
z^Q<d6?vU_3LyMH+GlEWgPZo=>coZZUICXJDn!YD~n|L`L(KqU5buP&28LKIhN(06(
zU3z3>{_F`QPQg`Vn-enpTfwmo{8^(YeiE&~dDTt;6{BQ8gUhKF)VFnfog*8O_%W2(
z3X$QSU;})xJUX9>$R{UkyYSPM?04_`P6TzcRLq>!<}!;|@Exh`hIyf-q3H?w@*I!X
z=VJ<vwTcHdUeNtDIFCvHi%9UqUq^ON5{)G+XW@Vmr##1M>(gw8Fq_Ss7U{K)HEawv
zx`Z@klAlSUwXa}`P~2u$%NEXm_=t7EZQa}6^!5^etx?PT%)~nF-4%zfqdbM1&mAki
z<rM87;Y?dw*`)fR2+@r*e#(xaRz{BMgPP7~++4gpx|alX43)*N&zQKzko#!qo3<He
z7R~zFShP*)`Zc^z=NKxM-1;i*1?sC+``?uvBmPb(o{d7-1Ol~u0Pe~0nm24cgwHXB
zW7f8G3Gc)QJczS1`G})T4jEzFYc8@7$5PEl_}VG_#vHq8Qa-gay^<_|2^&Zq*XJ2K
zMPeou)6Hp}z*b>jd;i%K<3*#M>Ns0Y7JpP}UE33KIw8L><`+tR(reQpV`b6av;<#t
z9#RJ1Fe-TTbg{iOCwkIAv@$A(r=TNc>g=b3!X{U!@{6<8bZPK!zqgDeQH{3ET`J%>
z&((!kDi$0jj5*e@TEpm~?b){^DC1`cxM+eoGAlT)Yf&CdOzys<>T}*sk0z4Bn{~#+
zVD~H{%@1p%FG#3GbSP_IaKybA=0^-);_fOH9oF6v5lv@*@KH=BT)#znMsrKf^SL$q
z1Gn(^KUhTAjqtisQ;|$+eix?ir2PFjZKa?Qcy7?Q?yVR)^Oa(OP?t%_h}qWw;pT`b
zK9VAP{#_i#hOlrVKqZVk4K4`P`quM-7rTU*^^|4N3B1F_2n_>)T9|S5P7|HjS*?MN
zYCI03^s(`q-|N-2{w?X^F;R!kX_LCu6J5lE=UQJ*d*Ljt5%*#@0z;iDtv&hN+dAod
zt8eJKrbY1$bPQv$x@#0;T=>f?S*;f-lDrvjo@1QTvc6FBAyiA({F-?KvCS=xXQ=Rp
z+~g{#-kqjh+!^o6=Lloh=R$4AOn#6}B);c8`X^n~-AEcL28zmuDI7wztPaDvr$>a1
z$vtC5+L6fZz(&U)$d!?S%8x}yc3{N{(gJ;WS+!+mb@hdm7(QEjxGTMTBT{igLnSnn
zO#O1c#F>1#nUI7R(h~foe+SJGBg1?gqDS=o04<SYTgvE-uqmGTU|V+NsEON4OW*uY
z{P{h!#BJVtDT+EE84!W;ODCxC2!SC?s!Vd89C$9u@!=3P_h^i=M-k95+YHXx?z;(y
zR#wV?<R=z)XX3RN<>y&5?&oLUWYW*_w=&C$V1tpI!1}bR`_%ps$e9)^uK6;5Kt|VZ
z@T0%Rr()dSo<4N7PMvAi=#rNMp2AYBlX_~WmxL_fWL2{*<ArUC<eiF;a!|=_uv@9f
zK*66~{jRH^O*Ib4XXuD9{oFOC%PHL`RrsLFH>ftx_iXrE)-Da#ccBLHS6S};)d#bN
zGwW)OHdgg+wGIP)$Fx=^!TwC159oQBX|h<LGruC_YNu{-`vM+2I3}~@eEcCFYttW8
zYDtb`w|R0C^EwJwKCm12-(sd|)(dn_&N7BhJ|#t;(8(mL|EOaObWU!}{Xi!-5j^QZ
zbehrZV_?ynP7YURe~I@`ozQyOEsd92tW{A?N3zLDI#Y%!Ym&~3!zkwgMebfP?Yu8_
zeCJ{e=|?4aU#0PQ<AgQ04l9SgJW5S=kbMXD@Do`J@{!}dkc1b*Z6B{aoyT(hR8V~6
z5gM`cI`!@Ox5>w^*3tU?Wrg4={whTfqoL1uN33LJG`iTnQ2-bN)~&nGbFNuK8SKm|
zN$=YxP&|#d_+Ed4nGC)E#jZ}r=y^OgwHHYWUX|vr^+bm-O@%jV2L(-5b&dSEp|QxJ
zMM|vpZf2C@*Aiw5Mej`*0EIoQU1P2I?6%c0d9ye-r@#xKmL+9Zce4n&$4-{kAK=aU
z)S+h+8<IEffT3nOzf+i^49JF2;&|E>s2rNIsAi}+{y-)%u{bbQuJ6zk3hfD5%CC-+
z%fI$;jS}Uj-z-^Zj)r<6b`kjBB!0Z;L3+?OEOc!?E@ktk2gF|w7ocWh`S;PN!~ART
zAXiw&`&A7&uV2%UEuFsAlR^pFcg#4ZwfEZj6%eGYoWAM<{1JK+rjw(WX3m#(Yqc%h
z{@KQNDqMN%o{lV3_v=37V3P~@AH7(H7&boap@IW#ua&qrW9J_6gz(s%LoNT{_V2n@
zkw-b9>FTE!DN?i9^t*%K?>e=aTNrr|H@QO4zRN;%LtpqUaIW0y1wb1Ox3BibjGU%s
z64+JxiVkKUB!H%)6CttCD4gTb+bkRC1a)36P*G#k5B1X}#Ia?R0#DVG8^+(B%ZP|M
zB-V2p2pc<tkgKuu-l<*d3oUVBKu7DF$!ir@dseX{?Bj<_q)2n#c%VV_Q`RtX2R=}b
z)3&D3z5S+)H`2ZL%Sq`)4;?nx<dYtplblRGCH9NoCn68iaFbm9*75VAGcc4RMhSxj
z60s*ZNr5B3^%X(Eiy!CQ1o?}`<9C9L(+t_NmB5|%L(=2mZgah(WNveEUZ<(u2aoFO
z3*#30%!fvO+VRo?ak7t7y+?b#12IT+4oxHumg=`wnNXil1LUua)(Km59<1`~Qpuq6
zmIFY5fMGL-g(85E*}r=HPHc#F_Q<7=A>{+3W+`Yi-4f2Yyl2CyOCm!7!lU8s8`<Y4
zErcU%B4-L(txM)>3eVmUV>F>e`|Rd$oWMS#s$Od#qg5YoF2t>AdL<jYaSD>Q&i!&#
zoKh*u<o=zi8FcrNdu}tZ=bTk(+{1DKBqSW`5RR`EN*txvRu^=W8V<VD*88A;BXAkM
z_1B39Worgn2j0<%-mZLt#o+eb;h5Hn0hb#3r%JT=%MtORbB>v>?Jm*foI#2Jk#V6R
znyWt!(bCH=@#$1tOCzbdJV<%PCz`x3opLr5F?haefRPi&(!m2TG3YNi8xi32hg(}w
zAbTmiUvyR{EHOk?;$%7)a|%uIqO@daAC6t03MI+UXP82W)1x;AcwRj;a?+z6Zknu=
zN-bH{E6<HB|7;7)WTFM3@OZ6Doe#o!j~#A9H#zyW2-(nTA}ao+_l(@U-F>cev7N-c
z%90+g?+ORi>x(K69n!(4c|_}o9p^JtP?wxWD%MrROcBcQCv)lMpBy*q7Z32RR+!Ta
zI|+r%5p%bz+6p5-=e}J074Y&z?&SMa(+48=pMF;Qa9Vc)N{zw<Iu7|`s;yeuPbuQ1
zk5SAR8iagPIWESK{U%7V!w+hG-WedDCW@vA7Ax*ksrTz?PchW%UUAe3E20;=ksZ(s
zpk%^e&P6-Qx@f|_=F(JQX-2x8f&ig(b&|lADr<OdZ|BQW3*BkJlgy^x+PD`5Rq0nG
ztUR{YJ0)msK1uNPGPE1Nfw_!S*2l?fep=jG(^XA)uH4p|zp9@dbMTu)xzRWa;oIcw
zy);hs&YXxwkQSYvv)h&vITMP+6QaB2q2EIjG5EA0LF^VBWwlM6{!ZU^*dV<zD`)-_
z&2f}yBMkdko06bPxPg<7yTZ6F;N;R7?-TOOgmY%eRW6JBPevqtF-?(uSylW_itCSA
zTDEs2ugTrCS`H#Rs@Xefd{(3TBI)F~iDQ(l+rB$+hJMWaQL*&<ZM5}g^f+*9Eo;Sg
zYOV^=vo_Zq&;BPm;t0e?J-FUWeoC4`=!<5dvkcY-MpoFOs-u3j%)GW!t56`loch}X
z=H-OVjb~M};L|CjwvVKHg1LtxLE*A4?)U{Ut)Yfeg+G0c*iUybf(Q!^2#O@c3lWm^
zC3m)UU1`2#C{WULxjf6W{fE%Illq@^&*&Of;2n(tyeloV$UG(-s~TyZE2{VmDM%NR
zJ;ZNdRy#d|n)&A;a5ZqHv?m5zM{*mBq%o5nm<lU*C5J&}M&Vq614W`N-g*sx6>}?|
zJ$XG-wzWBdl<dni-WtTXa_mlX1_lg<)E$L`ez>2gd?3S;p*=xFbIg*VTTY_u(#ob*
zUAgwGaUv*d9RR+RyTQ_A?{wB!K!@1B`|hL17;3V`-Pu|=Swq_P_zQtZT0*$_pXnx4
zu`v>ExtzuE0Zw>PWzlap3QIMUlar~?s2RWAWQw^)tbLB~@IZ97lWnc!@490-7Tt#Y
zYzZSZVr@){GLKZd=Ppr6iKJ&%9@vhf1Sg&t+lwxXU*wE`!a{`YQp6bQcW+)DUju?u
z9@u47U56&o96ff<EhrnAsdWGWRA0{0jvVQx?->Lce=&yOimCG9jUA>wkaUVEbv&be
z+9+F>livlopC@}?t(BN;zU@K7{$$%Z#8P{<|IvaY#GGZxxAMJ5CJ0+|TbqKtpzv8q
zGOU(>dOH%}sP4Q>N|rRQL1hk|s0t3v;%2Hu(%q3H#IQQMhE6gx%FScROVkAXnTi(a
zd0K?|p$8i{E>K$SE<24KX;|#PMA8r;p(MxQ=IEq0O@+l%r$Wk*3~2(fgE!d(mJjE&
zE8W85BTkc3f8eh{o3Wh-nT{l0F!Jp4^=c`Fv<PFnZdp&A<KAc`b(?64@;P|Z@ynG+
zCdE7fb1yV=O*7Mni%w;-@F6?ididb-eHeqkh?!hNw*}12=0H`pBx15ZBrP@yw<KPo
z6z6IJB-o{52Z>h&@k=#3GQu6r`0Op(X<5vb<QRKJ0&!I~my_B0-}wm~Y0<L0JJ|H>
zEB!%MkpRkWPEm<J@5U+!-wdL`u%CbI3`2zFKZwE~2BJ!Q0+IK1p(bwwPBI(;qK-(O
zZG6~kcw7Ae1W1A+s6g(Z%O|wKL5@QpV=hoxCT&$wjvz07xNoTiH?~_N!kxKaeAEOa
zS=cNbgj^`H5=j`am~DX$5w;}$4tP1>x-%Y)@QOFYT0V$97<QD)JXBMw-r5OaP#q5Z
z0(FTm7V19npVf$1%deDXQ`5vtO@{6!**li6IUWK}*ap<kS}riwNUx7bZU6eXoXqFQ
zUx^K2tv|trucB*LO)u>HOS*%boLb@#t?;`P$g4+2N?D#x7QO@r4q^SlQas{PJ<lN2
zr|?`L>k~|-j!3t{$zob5cXI|B^-=a?92Bo7Ho#ERtQCTE!|{Xlp-b_1yN22bdOtU%
zTpOxP5{Q-@-oDkTjJRf~dmSOCe{fsJOGDoJ7&%_I$fF{0)bXb`<0zVO_2vt?i*Ey}
z0Wt+|yI>GKtn+%eb@5CaSfn_}+2KJo?1c@oMO5V`=myb9QJV|g{wS7JXO7)%@e8|*
zhR_iB&}ACtC6EaA;cT8OThdH^zjTw-a?eC`|GMca!b$R*`Wo5DzM{U8rJtq@nb!1!
zkV?d9P6ps`?wapnJ{0bz2br(T#e-z)gO;lr6m@IZ5JY>o#6hD-sQs62@N(tM)C>T4
zRw<s&RzmoTZ6BqwW4q#{jS81Z{X<M_599MY1p^Q+wB&ZfGI$^B%7<Rayy8EVQS$q<
zhcxd(uNRec#4OxVF2J)SL@e66e7+#7o~84>tqstFWo=0NOZx5p)sFl&_qCbuIlc2?
zl{sESgA)tSTK2=*@cvty08~)YHdPu%eVoLi1yW#GnsC*s1NcXh5h}jRlWSvTb8RjT
zQH&z$;JJ$ad#(xSGm6EtMHYy}Vjt6@_IUNHvb;&VMUv}z=uP?xZvLR5)F6p7x;>+C
z&DYVpc^jN?G(zJFJOX;GLPr&<5JFS=a^kmHo1PY1PZDySA)^z~antVVF1NtLxN?)j
zc{n(t04{w0--V6$&8ybo+q)*I6?jUaMd*b>Ht87+pl+N6B6vsgC9cb>!})X+-mM^T
z1aA`xvA&1K?kQ`+-G=qoHRM&|5wnL`?GYLGN2|3mo55RW+hjYoT{4s>7|g-%+3fAe
zZOcT_&~dGXrsAbSMraQcghtoBg&S9tkGHL+*B(wh(5Bh{px@2do+G;!aY0PLnQ~Q)
zZK8h>Isr7bnqb+wy}(1s9$SM9U?RA!-~^ekjD)KpK@=&!g%nusI+bPBfN3Gr9WZRN
zrnZ5of!orztO7`WnLCCKh!2Vw1!U}En`_?!G#a-l<zK#<??=Zodbo#A`5rx3Ok5_w
zWPHg5nsT4cNLF27v#;Y^Sw(Wprt|olr=yEwGh_>IGv}c6e`N|WU-=I_lz(7~%#ps}
zI|#Cx+fEUJDVW#R+D@gW;gKK|Rv@$Fz1>^5ZG?ft#^@Z%tQPP6rc%@-m(Cv<ubPN-
znB8*C6b-JS<+CzMH5t}Jbdd9io#HkORz!W;3%su_%6n%KM8gn`%nV^vj|J@JV3nZt
zo~Eh@ep+!X39*tQO#$1X_XD3_%0=q^>aO*!Zdc!EhjD=cu-|=6IcU3!ld^<u?7G1z
zfe@qjhv(`0y-#~zISHYe@!z8_hZ;9r02q{9a4Fq+Abk1Boe9bQ6;-E-V!$)HlkUMp
z<n&ni#L1hGiG|O6cOHxV3IyD$8(S412$<)jmYPxOa@-e=npLSgFGKMRg?t>E2?zp>
z*_P{aY@r$vHejxrK*00y#}N2Vv+nKFBGf%11ksBZvnoATHrKxCd<W@H5x&mipWCxR
zlwIYL4Tm?8*))Q)2_l~O^8#hJkA;mZ(R{XKc<Gk2KP*#RoQ4zWXI_3G(D>pTJwS1>
z>Nb8MWv>o{Ql!Np%P08@6rEsjg-V=ZFAf<H`O#L5DoZW3+vu>hA2_O?FLeEs;g#D`
zC+pcuxDsXmPFB^HZKyIx4PU7AA*MWsVS`90o?uXX>0tQZY_&&&bQ1$!RLx}}OS1@X
z4yGc^>9oCDl3?ef^xUK&t-irc<bkx6n1iHYLlIx4LP<1u(RcBLIxqJh2*Y6^GIgfQ
z;4K|3n}O!2E`t`=K$*?ImiOnooE-B~FL5ks>f<}P!}alxTLzC$T-m-#%)hI!z_0y2
z{`lR<f24pRG3Bs>6;J#P(m@9#`g0xDZ^deC6Et5`8^0mEz`Eq+N>@pudy=ux<SQP!
zh6Emd%cPpa{_xwt@a=p4eJ%qZACf|KfhTB#rn+da&8eXAeb+CZBKt9eHg9f$s6Xxg
z0qW3EG(BPwQ|&2_M_94ZWk*z%9w%;80jH17zv&x(#T?Fl<7I`)K)@tSlzVH>=7$v3
zhzqsxj6@k&yT1}sl`IszT(X8%`g<u7iW}ZowkJ4?UZfcFLkXQ|a<<ExLSu^6BY961
z&(|DD29kT0ZSG7y?0((V8nM#guVtiwb!`8IfpfmqE4_2>;S@p7hJrU{0Hr7bS8nM7
z+1&o8rl?<meM&+uXrLzA1g)0+$+uPqxUJ67zPn54mPc$;4wcWG8ty)AwR<7LvlPDa
zTTH;G=QvFTz!)Anp0I487e*fvC2u)(-T8`Wa0mh=>WqTZi!=SVh_uh@OP4>+HKk-h
zz>j;2%#bgfrCb(~2@Ze<D&z)gsp7UE1}?$ydO2RNJYS30hpfw6Ajcm~-Wt7Gw~@v+
z4}0t_)Z9|FdXbD72m#RPivKnTlyF_-fu>+IrFu!{Sa8D|OCO|rY!w9CSLAfr?Rt}o
z{{sR8(YKvblEM@bZMO|6|M=zL7<cLkmT82KU*cH376q*!Uul*2f}6pn8#YZb72e*b
z?p3U|++;wVwIG)G+JOHA7`SW#C8J>T^hX^Ljl<Rlacx|6{Pu#@tKE{_jX`0xoK!k!
zG{QL0(qJfx2{7cPU1vI`HFzMbI#!h-7kXR;Ltd{cJ)%--R2FV}>EEo#P#&8~&$(YN
zSVzE<l+E$ltOlpT3?;!~FtFfTXyLrg!l&-U7hq;`Z5~L5iDLTiy}7wp5v7_uQRLUm
zxF=jlcV~tN#OY3>$|vMD+zvr!OEl+!a~(N61`XXU3a3yuPYd-RY&!^%dOIQ5eXlwu
zbL7YJ7mT_=Ah(!U9O#)Wz<W#TIQaI`MlXKLa2Mry3^#)NxrfeM^9Gu^HCVzwK=<ge
z{fR1Xu$fON<pq~Z{%3z1P@w*>yG{r9b|=PC=JGVgI{wNY^D{&C$A5j&H5)mVqxYSU
z{w;I6J<H{NI($?Fu&2*XmoMB5<Ysuo{=Gx9^9PoH=wSO5clW;7_G@Kd6YT-l|L@Hy
z8-8uavI1ZUc3$!G3b&aI)S!9%*Igcq;_`1~@C?#tJgnxuIsZ(%1o`iwsVQ)<%&3G(
zV?S<6{`>RxD>^evrN1BIN-_3R-+t|Xzinh`i>TOy#M}HjYt=sZr*K8}GU1}3=l=nk
Cd2THL

literal 0
HcmV?d00001

diff --git a/exercises/Solution2/Solution_2_v2.ipynb b/exercises/Solution2/Solution_2_v2.ipynb
new file mode 100644
index 0000000..7139455
--- /dev/null
+++ b/exercises/Solution2/Solution_2_v2.ipynb
@@ -0,0 +1,616 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Exercise 2: Solutions\n",
+    "General hint: You can always ask for help from within python if you forgot how a certain function works or what the correct ordering of input parameters is. Executing \"some_function?\" spawns the docstring of the function and \"some_function??\" the source code."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# scipy.stats.kurtosis?"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Probability density function (pdf)\n",
+    "We will look at a few common distributions and investigate their basic properties."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from __future__ import print_function\n",
+    "import numpy as np\n",
+    "import scipy.stats\n",
+    "%matplotlib inline\n",
+    "import matplotlib.pyplot as plt"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "For your convenience, we define a few pdfs and functions to draw samples from them. Have a look at https://docs.scipy.org/doc/scipy/reference/stats.html for more details. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def gaussian_pdf(x, mu, sigma):\n",
+    "    \"\"\"Gaussian distribution with mean mu and standard deviation sigma\"\"\"\n",
+    "    return scipy.stats.norm.pdf(x, loc=mu, scale=sigma)\n",
+    "\n",
+    "def gaussian_sample(number, mu, sigma):\n",
+    "    \"\"\"Draw samples from a Gaussian distribution\n",
+    "    \n",
+    "    mu: mean\n",
+    "    sigma: standard deviation:\n",
+    "    number: number of samples to be drawn\n",
+    "    \"\"\"\n",
+    "    return scipy.stats.norm.rvs(loc=mu, scale=sigma, size=number)\n",
+    "\n",
+    "def lognormal_pdf(x, mu, sigma):\n",
+    "    return scipy.stats.lognorm.pdf(x, loc=0, scale=1, s=sigma)\n",
+    "\n",
+    "def lognormal_sample(number, mu, sigma):\n",
+    "    return scipy.stats.lognorm.rvs(size=number, loc=0, s=sigma, scale=1)\n",
+    "    \n",
+    "def binomial_pmf(x, n, p):\n",
+    "    return scipy.stats.binom.pmf(x, n, p)\n",
+    "\n",
+    "def binomial_sample(number, n, p):\n",
+    "    return scipy.stats.binom.rvs(n, p, size=number)\n",
+    "\n",
+    "def poisson_pmf(k, mu):\n",
+    "    return scipy.stats.poisson.pmf(k, mu)\n",
+    "\n",
+    "def poisson_sample(number, mu):\n",
+    "    return scipy.stats.poisson.rvs(mu, size=number)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**1a) Generate arrays from the lognormal and poisson pdfs and draw an array of samples from each distribution.**"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Generate arrays for parent pdf and samples\n",
+    "sample_size = 1000\n",
+    "x_float = np.linspace(0, 10, 1000)\n",
+    "x_int = np.arange(0, 30)\n",
+    "mu = 4.0\n",
+    "p = 0.5\n",
+    "sigma = 1\n",
+    "\n",
+    "# Gaussian\n",
+    "g_parent = gaussian_pdf(x_float, mu, sigma)\n",
+    "g_sample = gaussian_sample(sample_size, mu, sigma)\n",
+    "\n",
+    "# Lognormal\n",
+    "logn_parent = lognormal_pdf(x_float, mu, sigma)\n",
+    "logn_sample = lognormal_sample(sample_size, mu, sigma)\n",
+    "\n",
+    "# Binomial\n",
+    "bin_pdf = binomial_pmf(x_int, n=int(mu/p), p=p)\n",
+    "bin_sample = binomial_sample(sample_size, n=int(mu/p), p=p)\n",
+    "\n",
+    "# Poisson\n",
+    "pois_parent = poisson_pmf(x_int, mu=mu)\n",
+    "pois_sample = poisson_sample(sample_size, mu=mu)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**1b) Display your results in axes 1 and 3 in the figure below.**"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x154d1f655f8>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Plot the generated arrays, comparing the parent distribution and a sample\n",
+    "f, ax = plt.subplots(2, 2, figsize=(10, 6))\n",
+    "ax = ax.flatten()\n",
+    "n_bins = 16\n",
+    "\n",
+    "ax[0].set_title(r'Gauss')\n",
+    "ax[0].plot(x_float, g_parent, 'k', label='pdf')\n",
+    "ax[0].hist(g_sample, n_bins, normed=True, rwidth=0.9, label='sample', range=(0, 8))\n",
+    "ax[0].set_xlim(0, 8)\n",
+    "ax[0].set_xlabel(r'$x$')\n",
+    "ax[0].legend()\n",
+    "\n",
+    "ax[1].set_title(r'Lognormal')\n",
+    "ax[1].plot(x_float, logn_parent, 'k', label='pdf')\n",
+    "ax[1].hist(logn_sample, n_bins, normed=True, rwidth=0.9, label='sample', range=(0, 8))\n",
+    "ax[1].legend()\n",
+    "ax[1].set_xlabel(r'$x$')\n",
+    "ax[1].set_xlim(0, 8)\n",
+    "\n",
+    "ax[2].set_title('Binomial')\n",
+    "ax[2].plot(x_int, bin_pdf, 'k+', label='pmf', ms=8)\n",
+    "ax[2].hist(bin_sample, n_bins, normed=True, rwidth=0.9, label='sample', range=(0, 15), align='mid')\n",
+    "ax[2].set_xlim(0, 15)\n",
+    "ax[2].set_xlabel(r'$k$')\n",
+    "ax[2].legend()\n",
+    "\n",
+    "ax[3].set_title('Poisson')\n",
+    "ax[3].plot(x_int, pois_parent, 'k+', label='pmf', ms=8)\n",
+    "ax[3].hist(pois_sample, n_bins, normed=True, rwidth=0.9, label='sample', range=(0, 15), align='mid')\n",
+    "ax[3].set_xlim(0, 15)\n",
+    "ax[3].set_xlabel(r'$k$')\n",
+    "ax[3].legend()\n",
+    "\n",
+    "f.tight_layout()\n",
+    "\n",
+    "# Note: Depending on you matplotlib version, the keyword for normalization is \"density\" or \"normed\"!"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Mean, variance and their estimators\n",
+    "**2a) To familiarize yourself with the properties of the distributions, write a function that calculates the first five moments of a sample as well as the mode and median values. Compare your results with the expected values.**  \n",
+    "Hints: If you like, you can try your own implementations and test them against scipy.stats.  You can find functions in numpy and scipy implementing all tasks. The 0th moment is just the total probability, following the convention in the lecture notes. Sometimes the value 3 is subtracted from kurtosis to shift a normal distribution to zero kurtosis."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def moments(sample):\n",
+    "    \"\"\"Calculate the first 4 moments of a sample\"\"\"\n",
+    "    m0 = scipy.stats.moment(sample, 0)\n",
+    "    m1 = np.mean(sample)\n",
+    "    m2 = scipy.stats.moment(sample, 2)\n",
+    "    m3 = scipy.stats.skew(sample)\n",
+    "    m4 = scipy.stats.kurtosis(sample)\n",
+    "    return np.array([m0, m1, m2, m3, m4])\n",
+    "\n",
+    "def mode(sample):\n",
+    "    return scipy.stats.mode(sample)[0][0]\n",
+    "\n",
+    "def mode_sample(sample):\n",
+    "    h = np.histogram(sample, bins=15)\n",
+    "    return (h[1][np.argmax(h[0])]+h[1][np.argmax(h[0])+1])/2.0 if np.argmax(h[0]) < len(h[0])-1 else h[1][np.argmax(h[0])]\n",
+    "\n",
+    "def median(sample):\n",
+    "    return np.median(sample)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Sample: \t mass, mean, variance, skewness, kurtosis\n",
+      "Gaussian: \t [ 1.    4.03  1.04 -0.1  -0.05]\n",
+      "Lognormal: \t [ 1.    1.68  4.46  4.05 24.39]\n",
+      "Binomial: \t [ 1.    4.    1.89 -0.06 -0.  ]\n",
+      "Poisson: \t [1.   3.98 4.2  0.55 0.42]\n"
+     ]
+    }
+   ],
+   "source": [
+    "print('Sample: \\t mass, mean, variance, skewness, kurtosis')\n",
+    "print('Gaussian: \\t', moments(g_sample).round(2))\n",
+    "print('Lognormal: \\t', moments(logn_sample).round(2))\n",
+    "print('Binomial: \\t', moments(bin_sample).round(2))\n",
+    "print('Poisson: \\t', moments(pois_sample).round(2))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "3.8596744792409305 4.05525317035419\n",
+      "0.7756969873870149 1.0424239898265104\n",
+      "4 4.0\n",
+      "4 4.0\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(mode_sample(g_sample), median(g_sample))\n",
+    "print(mode_sample(logn_sample), median(logn_sample))\n",
+    "print(mode(bin_sample), median(bin_sample))\n",
+    "print(mode(pois_sample), median(pois_sample))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "What did you expect, knowing the parent distributions? Hint: scipy can also help you here, see for example \"scipy.stats.norm.stats\". You can check wikipedia to quickly recap some analytical results if neccessary.  \n",
+    "https://en.wikipedia.org/wiki/Normal_distribution  \n",
+    "https://en.wikipedia.org/wiki/Log-normal_distribution  \n",
+    "https://en.wikipedia.org/wiki/Binomial_distribution  \n",
+    "https://en.wikipedia.org/wiki/Poisson_distribution  \n",
+    "  "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "4.0 1.0 0.0 0.0\n",
+      "1.6487212707001282 4.670774270471604 6.184877138632554 110.9363921763115\n",
+      "4.0 2.0 0.0 -0.25\n",
+      "4.0 4.0 0.5 0.25\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(*scipy.stats.norm.stats(mu, sigma, moments='mvsk'))\n",
+    "print(*scipy.stats.lognorm.stats(loc=0, s=sigma, scale=1, moments='mvsk'))\n",
+    "print(*scipy.stats.binom.stats(n=mu/p, p=p, moments='mvsk'))\n",
+    "print(*scipy.stats.poisson.stats(mu=mu, moments='mvsk'))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Estimation\n",
+    "Obviously, there is some discrepancy between the expected or \"true\" values from the parent distribution and the calculated sample moments. We would like to work on the inverse problem of guessing the first two moments given only a sample and knowing that the sample was drawn from a normal distribution (but not knowing its \"true\" parameters).  \n",
+    "**2b) Remember how to estimate the mean and variance from a sample.**   \n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The estimation of the mean coincides with the sample mean. The estimation for the variance is $n/(n-1)$ the sample variance."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**2c) How to quantify the uncertainty of the estimation of the mean?**    \n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Given $$\\bar{x}=\\frac{1}{N} \\sum_{i=1}^{N}x_i$$ one can recover its uncertainty with the Gaussian error propagation formula: \n",
+    "$$\\sigma_{\\bar{x}} = \\frac{\\sigma}{\\sqrt{N}}$$ with the sample variance estimation $$\\sigma=\\frac{1}{N-1}\\sum (x_i - \\bar{x})^2$$"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "mean estimator: 3.952626606381409\n",
+      "uncertainty estimator: 0.030539967040206582\n"
+     ]
+    }
+   ],
+   "source": [
+    "N = np.size(g_sample)\n",
+    "mean = np.mean(g_sample)\n",
+    "uncertainty_mean = np.sqrt(1/(N-1) * np.sum((g_sample-mean)**2)) * 1/np.sqrt(N)\n",
+    "print('mean estimator:', mean)\n",
+    "print('uncertainty estimator:', uncertainty_mean)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**2d) Given that it can be very cheap to repeatedly sample a distribution with a computer, try to come up with an alternative approach to estimate the uncertainty of the mean. We will come back to this at the end of the course.**"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "4.001435884220608\n",
+      "0.033109197212323395\n"
+     ]
+    }
+   ],
+   "source": [
+    "# We just repeat sampling the distribution and calculate the standard deviations of the averages:\n",
+    "reps = 1000\n",
+    "averages = np.zeros(reps)\n",
+    "for i in range(reps):\n",
+    "    g_sample = gaussian_sample(sample_size, mu, sigma)\n",
+    "    averages[i] = np.mean(g_sample)\n",
+    "    \n",
+    "print(np.mean(averages))\n",
+    "print(np.std(averages, ddof=1))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Multidimensional pdf: covariance and correlation"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Imagine your're an astronomer and are measuring a specific parameter called the \"Clumping factor\". You're interested whether the clumping factor varies with temperature and how. You have 8 measurements with the following values:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "clumping = [0.5, 0.4, 0.3, 0.2, 0.4, 0.3, 0.3, 0.2]\n",
+    "temperature = [2700, 4600, 5120, 5550, 3600, 3990, 4190, 3900] # [K]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**3a) Write a function in python that computes the Covariance and compare the result to a python numpy or scipy function.**  "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "naive implementation: -53.28124999999977\n",
+      "[[ 9.37500000e-03 -5.32812500e+01]\n",
+      " [-5.32812500e+01  6.96598438e+05]]\n",
+      "numpy implementation: -53.28125\n"
+     ]
+    }
+   ],
+   "source": [
+    "def cov(x,y):\n",
+    "    x_mean = np.mean(x)\n",
+    "    y_mean = np.mean(y)\n",
+    "    xy = np.multiply(x,y)\n",
+    "    xy_mean = np.mean(xy)\n",
+    "    return xy_mean - x_mean*y_mean\n",
+    "\n",
+    "print('naive implementation:', cov(clumping, temperature))\n",
+    "# Covariance matrix\n",
+    "print(np.cov(clumping, temperature, bias=True))\n",
+    "# Off-diagonal entry\n",
+    "print('numpy implementation:', np.cov(clumping, temperature, bias=True)[0, 1])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**3b) Calculate the correlation coefficient.**  "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "-0.6593219263134944\n",
+      "[[ 1.         -0.65932193]\n",
+      " [-0.65932193  1.        ]]\n"
+     ]
+    }
+   ],
+   "source": [
+    "def corr(x, y):\n",
+    "    return cov(x, y) / (np.var(x)*np.var(y))**(1/2)\n",
+    "\n",
+    "print(corr(clumping, temperature))\n",
+    "print(np.corrcoef(clumping, temperature))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**3c) Interpret your results of covariance and correlation coefficient.**  "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The covariance tells us that the clumping factor increases with lower temperatures. This picture is confirmed by the correlation coefficient. It is negative, meaning there is an anti-correlation between the clumping factor and the temperature."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**3d) If the two variables are uncorrelated, does this also mean they are independent of each other?**  "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "No, the covariance only tells us about linear correlations. Consider for example $y=x^2$ on $[-1, 1]$."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[[1.0000000e+00 1.8069255e-17]\n",
+      " [1.8069255e-17 1.0000000e+00]]\n"
+     ]
+    }
+   ],
+   "source": [
+    "x = np.linspace(-1, 1, 11)\n",
+    "y = x**2\n",
+    "print(np.corrcoef(x, y))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Bonus\n",
+    "### 3D Plots"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Try playing with three dimensional graphs to visualize properties of pdfs with two variables. For example, try visualizing marginal and conditional distributions as was done in lecture 2.\n",
+    "<img src=\"MultivariateNormal.png\" style=\"height:250px\">"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### nbextensions\n",
+    "There are some useful extensions to jupyter notebooks, check https://github.com/ipython-contrib/jupyter_contrib_nbextensions if you are interested. There are features like a table of contents to navigate around in notebooks, line numbering for all code cells and options to collapse certain cells to to keep a better overview.\n",
+    "\n",
+    "conda install -c conda-forge jupyter_contrib_nbextensions\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "hide_input": false,
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.7"
+  },
+  "toc": {
+   "base_numbering": 1,
+   "nav_menu": {},
+   "number_sections": true,
+   "sideBar": true,
+   "skip_h1_title": false,
+   "title_cell": "Table of Contents",
+   "title_sidebar": "Contents",
+   "toc_cell": false,
+   "toc_position": {
+    "height": "690px",
+    "left": "0px",
+    "right": "1388px",
+    "top": "110px",
+    "width": "212px"
+   },
+   "toc_section_display": "block",
+   "toc_window_display": true
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/exercises/Solution2/Solution_2_v2.pdf b/exercises/Solution2/Solution_2_v2.pdf
new file mode 100644
index 0000000000000000000000000000000000000000..6687c306d0e769278d12015ee42b8ff5a4fb2e62
GIT binary patch
literal 73858
zcmb5VbzB`wm#B><1PJaf!QI^@B)Cg(cXx;28iIT9;O-W5<L>V6?%d{_Gw;mYdGGI=
zZ~xJ|tE;-YYSpT>p0#>!GC5IkIwpEHShB6N<2zU`B1R$`eREh|UIuY9OGhJn260P0
zM<Y=q0~<pl1}P(J6Gu}bCJq)BetuX7M|&eZD_GZsS~UxsMRv63HXS`jEF#E{qzWWt
z(3G(7hfT+W+TcN1^ziEW_{8qg*_+1g-ie6)N^!YAkFpTuSx?v3x3?P;HJ+;fz+t|u
zi3zTiH%naf+}4L55}rf6579kfuyz@XkVAVHPIEwtG2U~06MJcrj4!HzVNvBxG&ow{
zZb}+K@->wC;s>Fg5o(3R{8LJZeb?bOdR7@~>$L2~hTObQ!?a>7^i9kzOW+2586!0L
zPZ*-LZFFa?+55A=*)I#WzGtMtg5?g?Ef-6_H>cA$8!7GmLd8kZ+&JZQ3EeslIU(UU
zcwEeg!Td%2E^nDTW!!!|XT){~5+Ip??kbs%ceFX2jjV>;RFccwLm>->YVVoQeM@`_
z33{~57WQQX2(Ni@J{;ZTjRm=Ej}PS$ekd1PA~{A7Bf%`3q14(UKCmDp9DLBOf*1L0
z2S>W=iV3%j;-bm%vr1+1T-4EE-D&W!W-c`&ogf8cx+6)|4u4nktcgj0&D3ohmAsx5
zyxd~4PrU;og?aZdVb^2te0%JVS<#&-dx5N`!}sN-gfFD<h#`nQkQomyrjFsNHg=WM
zk?Xm}Du~~DrQLkvnQaR)XsPP)YS$@b{loV*qNBB{R}Oz&Wdy~1MrPN0W|7OPpviRC
zTdF1!Wq&lji(e<!CpbiW^>oOlx<B?K%nCCcocFc~ePl0_l@ICSKdJx8>y+}Tlt7+T
zfBe^@2;Bg=)rvrRC0}NNBE41zC9J0dzr2UAPw}91%a>{;Uh`2Kw{b^?XFg4?Y3F*4
zHb;FX|5D{MpZ-`g=aVGkWcJa4ixsHvjIu0n0tey>J8nGl?D6BZOeEvcSv9U}krKC+
zYg%J@;^pjivE)ta`=r_(F;Z?U6rQ|NKSuov*9}r*%7df%o|4bTEA{t^j3pEKF<IFc
z%HH|mZU_-Dg@Z;7p!qL^20YX(I4d{gF0b9`MUJ3F=2*)IG<b14_)OM`z&>VsZNgrC
zF7aS4=5S2`!HCcsmycm@R{uyDt{)2%(dHX*#strUY2nvj8d`V0Xpt`C=9y}Nk#}YS
zshfV?F)l?5L%}D<z)1<5YFynR3v{78^S+hcf84)T*V|wE%ok8uHrbZijER<dt)c5i
zTU3Ik-h^#e#vQB<W)_nNo#~Z<B@%I;2nz>yyd4<1AWx`*^^<?AW$vttxbZ;VKGkcX
zi^BF&hTCr5Hd6Vf<<E@*r!l3%JSB;oqiGkRvK3B4)r&G6JQvPSo0tod^$TMpsF9dM
zrf+TbS_-y&^fQR*i&@}#A_m^_W|}X?j*C~}3Ig>`mX;};<;>RJb+9*rMo)M}8Vp1u
zvgh9sri-0Fs5IgnQX8syEPaq?<CvYTiV)MVW>ossH+5U}s^O#~@_FwJe^yrQwwAvA
zL^Q+9(W@0~O!HekI&*NbAbauI8Q<^x3Xa;dis6=WOoE%UOE^Sat3emiochlZfs{l-
zgyw{VDddsiY@%Z@Tox?pA1m$KTBf%hycr|Omv_(2-85Js3SE%dU#YN9uX(@ycJcIb
z^B^~`;;>oqWR1I4awaSG*s_{DTR;B&^egkgkunG(#m;J_?uj^37AA~;h}C73dxJH=
zgI%TD0%2Q=sk6u0lzoZ$gyJal!+DJv1_@3_F8UQgtaez&z^6Z5wOJpg3$0Y+ocI%A
z?3UH(_pes#VrSVMpgezc8nRREeEiaiD-<RsqOe2xfW&RryQ^^V>npD41<cnfBm&x=
zkj|whQ1H(l5axJ0sse=%H+B?^*!{I^;o}EGG8+<H?blIiEFopl92ICEIX6w|$sN1<
z?2}xLgR4#ST0FD`odOP{qe026iwsdn)ZZKlYNNld23T!tb?JGaSBHxid>q#2je2C8
zQ>nXyleOFT>YmCt=0wzs`genHgDR#Ioj*>M`$;b9a&wH2j~%zk$jUg@og1N~M{<&l
zUngmP8kaVjAm?5@Aqll6iJ>3AcD~6{Pmf>oeCP5)@yh@O&r9DSYiq|{#3cHQwKc0|
z&_trl?Q6O$HBq(a7PM@(bn(!W<KPmQTbw4z>VlklW8yiU2`K~K@puiB%=FA)p>wt`
z6Uk#AFL-q%1F0_i1{(MwQ)7rP@Ko3&E0Y_~I?FY|L`~F2qz&R|H!q%IwE`0}Y`j|V
zLYH0d=1;`93eKNt*+?YVJ)AMeC8FU;508x8k#-Nl!y>~zhv0SJ|Jg5v7}6$j2+rac
zx62B^33y0$!P^POMAn3KKv^80#kCDW5QJp9v1WPN__9SoavzxKM;@Rzx4O03evcS^
zKiL9nWNrA5KLgI*JRlS6|Hc(EGcx`+SJ<kdZnMdb<b7JsI2Oz;n6oWy=I5ne(`-5(
zT-Uc~j!b!8DQq<#zhzz5-WKQTYRL8B%Lg$8#ju^h&m$elSr|SHXAp%9Ek8uPa(Xt~
znq?Xuo_9ZUl+=gcx`cBO-a+Ji62aD9vce#d{uC3wc!DvC;ZiI+=)RnBaIkZ^Tppfz
z8mAeoD@ujdLn*D0hG3Wqy0{w-B_Dcr<o#phh5ln8z>XbEir1AfWi1ZgtwwB7^zw%*
zD-$7s0(siG-`q(>DZ({>!iNrKWTgOR;sA1`u`HEWvkN1|m*%J)lB#j$_RBRAbq<0M
z<$~v~rkC@b1l0V5J*ET{<#!IDeA#I5MML}c!{djSxwN>YZUQ205$vMW-F?}-329${
zBDt+2Z9D6z@kjU2FTi7UU6yee=%e|c9mX1_Mfi((J0x7f`9xUA7MS@r90{o$4x4`U
zvYl2lw{^l{{{fL3T*ZAfsPE#lWW(DT9O(^4tiz9;uDo-ZwA%79JvXn{j^sy3__?5S
z1{NZih(7NYlVk+qmYxAG(&027zH$tf^bkd-K?x}#lA?<7aiEY#==F!fp?Bw8o)AP_
znWkaS$f8-hs%b3vQQ#5?E<fBnNfn6|la1nRz~<dNA;E&~-fc03+dy4Zw1Ic;oJTSe
zjBxytX+A1oZnLX-T#K`m@N*Hlll831l8Q=DS+sA-)GuL1l-aRWf^p)FlXm#+$-%%Y
z-k0c;9KVuD-awu&x=b2V`vEDVkP|#aX!?U8v#c9RU-vYH;E1W4yB|vLwq}+f{qnna
z(&P)IP$@mQ@DbNgr*z?Y`Ls-6_Z1t-J0E{i)Agzx2)dHnfQ1OHfF%!Ye)N+{;{a=%
zZy}BtoX7oT-%MxWlj2bBe@2{&9*29RV#AL_X)CD^mz(ZB9gxddtC>a6mWM(oP*&`u
zRb%0$Jmqv=I=(4gS{2|u<-PwJVYQn(mt0UBPoRdrA3mLr{e;h;>U2K60`V$=h=}`K
z$fv%Tb}{j3l6Xj3;gXp4TUj`W!MuCiUYXrWi$87QLJmCLynqcn4_8|m{2(D$($ERw
zQ8JITgCg8vnA{QGPyZ+oq-f=Kc0OtYs^wbe?!y{6TR<_H6{hZf)=u40PhXB#HsM&o
zGmnkIylyfdcCBTSbuK!v!ZBTs*I4}_=Z9WE-gO*!T5t#5D-{eD!qb@P7m7uqA0YP!
zFK{52@|Z01griwxymy45tCt*Xizjp0|G*Y}yJ+)D`JF94cnalVoFipDP4SKzZ`tMc
z)-yzBw(m2<z%$k`$#9Uw+;<2e37e*BIa{NNA3@Qj&K<fnLB^5|#|1Mz3f7hlCE0Xp
zRuo=!f}dn14y-yx&iv+}Ae~>p)_(eyzzv`~$y*g9l@HSqQ^K0&2sXN2qU*POV(km}
z{Z);pDw^T4@l2IqkSH6YROtKD4Wikw_YsUz5<-|q>{AxiOtSG&O~aHtvcGgjiiI|$
zTs#Vt)JKPS(R5jyd~V)9sk~gcAHm2o7?=*C!jbylh05ERr&RrhNQe#(7_50ezmi%)
z<vg3%l;E3m3XwhS8=l<X`gO%VHL8JnCZ7E3`b@Bs^y!j|hdL`rMx45fh}n}_a70>M
zaX=6Dej?3%VSFdcSg?=C=4Du!edVTOijS(1M<K(Mgr4ks1OMPcUR|Q*L*X56$hEER
zr{{?L`F=O?0J6A>3T*J?%ZL|Q8a+IU2qaUo(_x`6zA-QfhA4||9tA%O;~@j!3-RcG
zHz<y27JR>npyqa}eg27g-#Vt=EI8O(qBrs2NiY<!5|L%dPi#W$e}D%G(RNOxzdGz|
z4|?h)aP4-9+M28LToDwUYS{b{jQ}pX%}77$0P<HP_5FA+mH&a_h23IMiqggIs~a7R
zQf#vw1IjhKb^)>Pj!_FWKF(KZCe5#*OoCv0dJQKZ2`D+fuQzd+K$Ld0ayxXGxQFb9
zP(UDFbgwcr4VR0%M#ZOwPt_LfeuX3|JI#0N@4-VyAbkpuFcq;N;^^#`>rE$JZrU{D
zwY(dRcemY!L>a75hGOnA$Uh7PZ3z-cOhDUH?j|@cy<mopNgp~qzGhjxx_rFK9N1|%
z<UU{SsqZSB$#Gv!bGFK182IXgA#36AJ{4DMvH^E&{rj_<5Jw*}$)ciF8Np0**K8_0
zg8}z7uh@w#|1U+vo(k6&x1uWPj-b)e@gh~OTov>`wK2vnj9PcztxX*>j;E>>UN#pC
zpe}8(gsKJYzCX_Vya<hZhnN+uuT7C$E7;Nsxo}sJdJ4Q^E+`G-u!7fS_$mIVVfZdA
z0cyUO6Hh_*!~5&M(dLK7o`NbqZPjuRag^TU4-gsbkoAH?dMzngQ{YG6eEax;9ldoL
zEZMcSAp)z@4gbSjw+}{-BdFeM`TcmndpPvl&)Tu84Uw}1n7)x5E0tE|3Pk%WtQTVm
z$2@|St|)baL8hTTj(+;7TH|$x)Gk)Gw;18YQAHlgKUiZMQMR;elf4JbB)vky0)1*Q
zY1z2%Sm#mT<AU4g2-?u^d28jZ{CMMRTt3lElq^2hS-O0mBtpMLuezT`#a=4Z@ht2m
znKd|e-v}!<k;b?UHeEV2k;kaFNgD0u%5&fJOt@rujPZKWJiJESSZ}0YUo#hwnsjV%
zdp=+GX-33J{WoC1@xKBCfDHayjGVS;y~K_bboz)^g!_v<<#(<tk}?s85Tc+wn$>zU
zCnLL-8=WiS$w|K5!&RP2Hmk-!l{j8j<fio5D~^4T$UaN_US`PWp%+jNYvpV8-l_D}
z3HQT`39=oA3GL?iwN|ccNp9FmoKJ4mp8Nfa{rT0)LDzNpxa=%)xY6ZdxehnsZ7~6d
z)duSq9_y0xuo9uEg0Duj@khV;r6T`SB&FtVqPOGu$#2)=Pj9-rv^07)wSGIr0L`k}
zD3LIz)<=hXwbc@)?kVWCpFIpx{Taz@z+!9B_!**RI^MWczbh0~hoN;yOWz(igBV-d
zEDwi`YaNtx5-P!5-YGL#NIx4MeeZ6bjhbpk=Lou`8<&X8pc7Ev*xt2s`;1zztDcto
zfnCJkrP{&4X`<yzO~ql=6OSH?#0)M9HjQybEBJ4J56Cm&t9QSe7!c|>N~{sXhc~&)
zQ@d_%3@Z7i@>-XVIqvQ4t%|MVytTfSR_RDqKYTOEZ4xi!*q?ory$>-uOu&?zF<A7W
zPXIj@8lZ;WFfUYF$K;coD_FcbiyjY_XdJvSes79poKKRM(b!JD=>0mC{>ebQRFzEz
zwF#}rTuvnGHh}D2-5;mFdhnOUFBAsc#~$bS;F)yR7V}*Zvy<3;a~kNX)WO19^5YEq
z1wFfTW#eAS-orvB%I51d4xCGS_%Ip<CJt3=$e+%TMb?8797KBh`3gtxpp-grk<NrI
z1qGe@qU&W3^;ud?8zfroB9lJS2XX}Ti*?19++gXlXPY({E04ZoR%K30ejuMth{!I&
zdi;@_+^YgF2dxT!CaeT6r;M&>ZB}8NZ;7IbGwLN<Ex-y5UJ_bYmv8EOU0k3lYFA)3
z?H9ap650>5=-14|7D(Gr%6A|<b2B591&96H9}e4<7#92Y%surgy-iAJm294HB9DZ*
zi@pe(ytAbA(U>F$zjwATwQII7rhc|>k>)vQqhX{Av*k=UTjQ)O5zL|p`qceH#fw!v
zHQ^A31&Pd`8V)9d5SZPSU#Uhu=88Wb5`tOKamg#L;f<g~%h*JS8&zgNTQlqhqH1Mi
z%Ex4d4bfA|#lb5|x-w)8-AkXb6xx|3Dl`+gzs}((^4&8tee(z7fxH$M-@&^Q%r9KW
zk()hhM9u!v?*TokuO|t+NaIV}q6)?$RSs4aHlE(pWtk*n;@vQ*o}T{228~ott9sA3
zhQNlXwA#Z*Z2o<4h;wNnt|LC^u%_(WpL+?&$AlV>`y@Y^PHo=vV9CIvhC-iTU@c#g
z(+y0%cib{@Y%(D5HdadxNy2?>a=5c#T=&X7w4~2xNqU#!lmPCZaxF{84%^J(=4Ark
zT<sB?hy!+thePJ&yM2?5-DM*qh|$Ic&N8$EWjM;$jdHqG)pEGD*AG#3)(>Hv1bkzU
zq1O}`tB$4OXbai6yR%{4gSGiLX?u&QO{8QEHYNll+FJ!sZE6Vj9j24Lo8gh>EOe2<
zk~(E2$SDwWUaE2akj)DSK-3pIU&@yjYH|PN2-2k}Xo^)~?YV63gdG}Pb2Itsk~_$f
zafhOgs+HV&Aob_PPmOFE#DUJy8ZefjEDRzC$I?0OI^)bZ8^T1FEX`p(1y?5OvK=+*
zEAP7~n36^Z_wRm)c%9$5gK566r9dK#B^`{Vb|E?#7IH~J4O)pn&Eh=xNhy9uZDlPS
z943D9oV1O~C$NPmGlmzGdM>F0lOH{wi?PMNq!p_dA%e3B{sTXW;8y|>Vg#2KO$h6=
zH+io&iRZhh=B<|bHBbS^dv0oV4XHrk%O3Yn!m1|KJJOF^!Io2k6vUh$@+=N)fs*-8
zSLEBlH|t{-a5;B+8w^n&p5*S#h&O^yrk3S8gQJ$gwO~YeQ?r?#D!58!d1_6)`>a;E
zEC@7-!SqeY%MNCm)G&*Ww~`cC@~dU2O^tE|w6mvwOYI~c=BrJJmZ=}q-y=)PFA}V?
zNxPtCv6fRI9ZQS;>@A$yONAkVYYzo8P6AFyp#;V-Aboz(<ADj)Gli#FXwBjwu}5##
zWyDQg*(A;V971by_KjG>kDY<P{`rL_)z3r3!~Eo9YhfeDmG`yaaO-<g!nGAS4whS)
zBU)Ulak7tw>tr8?7=iEa!5sVKgJ+zv(|Xu`Nwh?8nY6k=$+Sc{6dhyfDNrJ(0?s|_
z6^kfzim|OoZn8=U1R`2+e)w1gTG`2A$0FcO2Y^}}0BUjIJ5=x|nmtvCfuDyFmR_O<
zYx%H$EHwhEc{BC9pTI2%#n=zIwV)ZW&H5?YczzK&o51ZlVjlheCnwGz2#%H;UTGn;
z2DfrY&d(C6c~3*Q@YfJc#)$mso@740G*-!5bIveE>!<U8mWMkFs>rV?!lgZ9QMFj#
zF)8c?@ruLd)qo^|gE^gIgjAQ96l`*pKoUAwrei>@v{wY0`>1_{BjcT)LGH4YG@m%E
zmT7bh0{IiRH5?lhNP=e&%r2tF$Gm`mG|+mJvEqC%1qPBwiKollQtlYOSbpAWfku@Z
z{>YZzp2~cYv?YIDU-tr2Ui4@8wZNC}jee%e>$ixdm^(LF%JOXmt4-M}lQu?Br~CEB
zls9mWt$T?_pAwL`cTlvLOQ~B7H5jD&Yl^UcGuRdcwf#v+xreZD-{L{L1v`hXO7^pH
zvnl@A_6V--N(adz#RNGYQh$A#|N5g|=4;XBR0RKF&gdtQzLQUj7DL++KYps9?E0so
zrh=MwnRDM!dZPJT8>+A5`UXXWPF{-3y-#gCI|53pFG&ImR8)1v2b<R4o~;>lM1p%N
z4Q&rM46g52J$)#h7azR$Tr*WydAD@<Pj3bfx|Xe1`9l}J<-cNpI<YZ>4{pPa+W62w
z44BtW7CmCuXYS`tr$guT`o%s|#mCIdy^i6RqvbE}t<O#f{wLiqGyW&tD7o1hF{sJv
zn;RK8GAKIfJHC~_Sm~J<F{qju0xtqMn3x$PjLb|-9f??(Ss6rZEN$!+ZS@R{7(|Vn
z%?yk_+v~Y82%9-N$Qjv-*jU-xSQ}Y8!g2z&Z?6;N^z5x1fENw_cLO5Mzk6~ZVtPAq
zva)vg>q&C<MuuhvjyCp0%>4ZS@7EKsY%Hv7{}sKC(>*kmj5>XFL|c3@8Cj7_?lsw1
z6zb%fekkOVRGC=>l3j@8bT67i>1lsqX?btHV?@LVq?`weLd2<x4b3qbtW-L${EV`R
z?8gy*r2FkiTo&6zb-{OW@r@ET|2ic&JfBZ=7)iTq69)U&M^KTNXc+E80@DN8Y1PtH
zy#=El-37_bq@BU#1x+l=SFdc4&Sx=Fm!+1ls8aFAOT9(&cC42Now83o)1gzZVplqo
z)f=t9RTlK3R-ttSW1;cekrs)0E?TgviY`5FTZ%tM_qnyXP7sV6mA-EG;EJZ*)>34*
zoHX*2*i8LSEE8y#jqvC>+}Ln(w50OeFFnW)^pz}VWuZ7?b@ntIZnkQ1AcqRpyuB}j
zlCEDlH%JXm)1GnheQZ4Qk<n(C$8FP7xCnRPwLr)SJBY`=uv;oAc%5f<?hS$Lk2XyV
z7=0pSdJ@=ucXTcw1j0aRDyc>a_Nd2mmwS~K9+vElLNqR=M$`8c-J0dB`n{PeSsubJ
z#`jLjQ;zfLE`{CG*=1dGXFYD$UAfyM0K~^G7}5~@KD^z^TxYqiyL9fXeK9#+0))YA
zse0_0MyOHQ*DN+jXAp^hn<T(_TkBYmOk8GGO+D^-h=2OUM=~VyGoSq`i!k1oiA>cy
zIQhxVx~nK*(I_0eS%ZkS&6BJRyHau1@}m?1OYKqPk?`HI%D@JUsSLv1VDeJ$M-6`u
z)Ak62&Pv087l!Ql)k$j_RN-n5H<T3G)M%|R5BRh*xn)ny*>k5Z&nNLjTanIdo;EeK
z8~N=K#p?Yz1#LGsdvW#55(*Ab?v3m!=E6%s!R>GwYJ&jl=Eml}H6mJuS!VnhLV|Va
zW~jRGoej>@@XyERWd~6H=QghKx%1oLwy=|AF5`AHkZKqq4_1Q}H2$}dB(oKx-Jc2>
zYAfrLyt8u11g8y#U%Ovwh)0^#c`_p}sh=5(V87JQoTop$swEL_I&(7XK$W#Tx3-tL
z&CXTME(C`ql%eP2@5*K@+9yulI>JjQ8s8gKRe*&V@0p-8#WpJwL7+)5O<|b4oMRJ!
zf&DQ)Gfm!(s}rkuhR$isifbmCd+{Ha0y)AvS_*Xdg+?ui)#_aHFuT*F;0DQvfSi$!
zT%B!Sc1JUqJmcNylB00@>~y&6!q?t!28F4LwY(JX$3IW(Cq>0m-4*_UVVbcCL&`W6
zNRa}isZzFX(hWK&>?Pf+?eC&DB6PMs^`x8T8<4?TdppZ@?2e|FJ*V6!MWNGhQg$%8
z>E)_Pf*eaQI)EnJ@3{XtK;<My?PKjFMZH3$<h_T5L`j2vPXgRisP6u2?AmrMI^A1{
z5uZk(`l{nKm%bWV3iNfFmM<iJ%~5u-I}*kki~A$~V*TcH+jk8@Y;TN?Ay629dD6Ih
zhQvZMbcbV!=<oz&pi4Qy!p7d7B22!4n0W#LWdyd1NUE<cw@SD%)Fcx=X;Kt0LqA{~
z!$6Dw92@If#oO5aI*do?0)zgwnO`t}*XH==bEE#FC)DC^gPa{?IBO;8QSs_pHpx@T
z_oDAv<mXBZzBr^-rSPYEWX&(6<lUz1%_ZQkF71lHI$bQA*3}vB^G*65-$RcVATKdG
zrmVg?eW`Y3Zks$KeXIOp+l-qh6v~HWnR<U`3=)P<dgM&8a@N)(8m&j_U>7Jy+I`6R
ztKU2xo!|TI*CRMki@CrfZwbv$7PkY9i!IpO82F6zmBps?yw~RQfhtQiBQf~fWy`!q
zeweyST~=V;kof-`{?U-X)I<sdBhCU;j4}Ov_?K|*WQ)`nD73ELl*fNxEhZ%ob0W}S
zr`8aVijjHS{l?tYfcL(m6lh?Iwzco@{w1g1aY@b*hr7-O^etgGcZ}(>a$=Om9zVe2
zKAh)mA%S*3%7WiCq!{QqJ!)S~IR&`x_?MvnT84kEz$=u0WHfqIs8a*?ISuP%B?+`-
zG&=v4^mqlQz+c5#o#>V0IEoZtVE%`vh$zc65W|ioS5bson?4_gKZ+D&8SrGPUd=Nd
z(WWFv$5W}fmz+gKcOd}k7f(HRr<Ua$C!=(qJ%5lg|EGDJ0yZD+(83tfhFj&kG5cA*
z0h=jMD9)Z7t4!ws&UOr((o`$_IPhtdS#Vt^E2&wlfkLq&i$9=9Z&Q#02?F!@OS}Fh
z<-g_z4^#o0Q3_-#&iIdjv4-o$hWzs#OCW}6O}~Q7|1P`(3g*gh;&TDfnhtoD_+P?e
zu;v+Tn;Q#F_U_NQe)~Pkb33dNWDE=xxU#3#L^riR5+mlgL@98CWa-s$Z4K8GKf#1g
zqh#AUX!h5Jg9LVqgLa1}JO*rnHQYE*r(SVhC%0>GY#nz)FT{dRqs^G|_g?sWL;Tmx
zsuEyK5RqqqZo1Q<_9hns-7G*ZJAT4%a$&+}A%xsTcYLCl8Twx7SOPoDizNv<nEGoi
z-!|D_8kK|H@waIH-}1qLVQ(8b{DGxJ;0-aXG8?f5%*;KV!_@qT4#^@R2U7aGj!4=D
zg#Fr7r%X$f)pt(;JMZLA{MW=WG=r~j3?H&B?TBu$Qc6p(J8ll{G-{DB;f4t(TIFUk
zX&sVa5q4MPIqQUNN#S0)2t9PAk(MYW$}bTzQMT%<1cZ@Fq$0{Mxl0ge30>d`VN^c!
zKBgN>AiVT^>m6^hZfQ04%AVINMX0lEbIOR=<RYMfNnoNqHFt_ni~gipyTY~^orLQ&
z8!YUYnyUL^%OEzDhUfCJ;|5*x_EfKB)hr0ep>LbsoN<od+(}diJJ?b=4P+!3ytvA@
z9UGe6#Jjk~xJxW{HmXG>KnSGI3uVl=(y*V(2vul7r)nl9iNv1JZJm!FNMYxsRa1By
zJO5BU=%l1Ew|+I^NapAW8uFNXA{tF=BjZs$5drR*mABF2qcNMoyP{`$^#N@zuZFSt
zl$4%Z<!qadv?{b(x8CdVAC6w}@s~>Vg2&31BrusTbcT5uAX(~kuum-QOa?A)M>s4q
zUB@*tbqK{m0Ar-xi{rZfHLuskh3J!@#teEo*GOyM^}m<5UK9?ix0HT+?&1z8L(#n#
z)@^cN>B49ys5`pUeA?RMcS#qvD)iIqURn-v^bnTyi!LcSV$Atxe*C)@7!O?(|6}Tg
zDEC7V4UuY*gYA9hAg2;q>sA}PrihrTe7HnoRW0V-Q#OG!x$b&9dZrza*N^k42p{(L
zEpK!xkP7i!nZHlojd#JlUMj>A!;mC1&(P~wKd%0cwL{lKE;sukua26eLp^@>{Uh@W
zSwM7MPotgK&g+8rk+ax{z>)>AvW}?c=L#=q$ba!47-d`u2eXci06HR!3OnSEjq6FM
zIWLjgy5|D<8Z`z|iS5a<^S%%*aK+UyphU9`6KKae@_xq{L2by>Pxn7N`sd6yzjwhO
z`ojA*mX)p!r+dg6S)zj5tzK1?AnHkHesh10x!=emKaQj`D2()`USm@46aJ!Fm5KNR
zyy%=#CCt~o<evgOM90H(2g#=)H^o8mHm;lEcW|FUS_sFDzl@}c#$L-7o4to$@snji
z3179{Z^JE~85mZGHkyL#CJ-E+=<kH@mZAcyS1s5t17|!6w8yuC7;L>Ah^uH-j-Kj7
zlCn0RZD_GtrmIa29>*4M=wx7L+bYg$E41Vx>)qlGd~WM}`J(z|NcZQ2`?YSZ30<j+
zRE_&U4^b3<gXlwn_o}-iC!hDu4;}V|$muQf*YV5Wqa1TpQk5R@gFRUkAMsDkzL9ym
zZCt`;vgmhKgRfV+hj_G~cnWFaT;;vEd^GT?TG|7;SA9Xx{2MM0c?bxR?`x6hp+`^4
zkT(9*v2BzombThA?}X>j@c&83Qw-dMu{sqCx`buus;BU=RwRc|y@rWC!(486KqB#R
ztZOsa<$zokUEG;=v|M)!Jd-@76y5sW@eq}69Mq6MT!e?Se7ef;x|_P^6RLu)Ez>BY
zJcRO5(bG=9V#+Q-kKg;e5iB-p7ar%idK6PeIjK&`UiA$mF}plVUde_}w!aA1c(=Zi
z2HiL8nmv0;fj%l8opsl>F)4r^Cp9~{4QVG+99y<lDAHraCn~q`!$ow&Zin!ybym-F
z0!<c7$d0^qeR^7a{g5j~=<_w<rpN5QqpfOd<%Ih%Y#W2Di-WWm&mKO;Mm4vbdU)S!
za9az4v@Hm!HJWB@+c)Mp%O?<XxV_gDFSm19hK<|Ds13}K@&8t2!O36mpfMEZkUMTL
zBX)x9moVQ>jNeek;O~R@f0%r==@dv*{MzuVS3dHY^@Hy6T$?vHJ@Frih`f<y6Bg8S
z|78^*wD{cnj)traWn`Vl+C{3-)VFKsx%YoV;@9KZ@m3OR?{MA0akITSZ+wBK-d7aB
zMbJF8WRFJanLq=sqC{eNcoc#9?x}H7c<J13Zr%N_KJvK!p~YhSJXGa2r?-pK*?&0>
z81|04`-u=}j-q^%OFL8m3oEYf75?U|^S{cr&(Wy_Qf;Y=!a(;O9H)Z<*sErD$kvnJ
zAk%}Qj%O;7Ab0p>KmaBF1yQ$aE&fsQlxh#2g^!4Ax5pRuTbtzED!ICwAn8iIcyy+D
z&m~ciIr6`k*Z*{p`3C>EI=e|!%PL-*mFWX&9e__dp;Bo_X)ZkB#-%`EZ#4|~Zw&Kx
z(Y{=%{9E)+P!_-z&)n?56<#=QBN@IB<3lrcZQm>R-8S_>C`_U{{_@{i8MA_#`^>E0
zq^ljlC*GWWP!yM4wHSze@Un@u`QRs)4?{|6e&Y(X#4(f*sIC$0{`jJ0**RqA^s=o6
zoo$xKuJ||!?niGEm|jzb5lGW_&X_-cvzM=Y4I&^a{`dV(5K-;3J-9HyaF#7#`<^As
zkrOK?n!aO|MSr8%oBRIn`10TV`V$4R<;9A?WA0=n3Uq4A?I{2L+d`0^K3B_fzpYol
z$LkS;)yFrk(E$bBzU%|h!}>0I?dh|Fwh$-`4Y=FK;pR($ff($|G0-+v=6@xA>=fyn
z_v>QN+$!0|vCl%T@_s-FF>i=(LqM4eFhBdes^9yTQa>Q#aV#0QPqKpLDwh)iSt^<u
z43j^8Kg;&F;x<kd{$}xV{6a+5@>@4HYCeBVJCPWs&giXG>L3!g_0lD52YaAniC~AN
zq6DZ2Y3QvW^Ir)GSpNSAXZ{Kq{%^ejAP9hN4!#cBMNtqn(omJOW68S?+HygVInt0l
z2O!(C<~>%&Avfqtrll&~1gmTVWf<dmKqCwO0`@M#BgBor(8xc~?JoqEP4LfucTe_E
z`HSi;NOF8{x=fMUT@Nns#&+`6j-UCx<m)(*JR$<!N`2qry_2Lsx8&$)TWw;sq{Jxn
z-|7#BW_NK!?eWj*Ksml}{!S>i>)_9Y9jK|2kpaK>>%T+VE<)HHJ6diPKV1S)wgDT=
zUxn{Lp$hyh9BVWn0O|9qbh!nty|w!FS1aYeTm3zee=+g@cjVE5C*UMxYXHaYrjz0|
zQR)Zjk1K0SM}8_<E!$MfM%p0H&F)tIrOP{CWhP=xe1C3>PjPtoId3qqL5*@Z=_Jtp
z)$yLxMsM#KB<-CQ4l1XR-#xwwM_`81DOL&5y(ew<Syl59xZrtr=({Gqj_7j)?vEbO
z|2T17C;c9ZCS<jZfiBl;0=$D-uxZ#VstnhrTs@QW^j8!J*5Bx(<Fxy1gaC=jmLxJ8
z1xSm9hnDLO(`I~KM0$lq%wC-SdGYm`u}p15f>>J{ust2pvbO0+`|Jf=yu30seEb&?
zB{|~vb+H9p*FWk49V#RI{y{z_9&5pUahuAmi)?J`N_%mH`v&vMA8ZPa(>G1+KsPcK
z{nMee{2J+0CP{A)sGhRuGcUcfto#rDu?Wb;o>-~f3~t6YZ$(U9ZYdfVMlLn2k**3u
zad35zyM%B3;@Z<9F<F|qs~fLkeJl}}yuGVo<`OBOE1BaE@0-*-qQ(o;G%{bg)=No?
zAtgvnFbOq=T2wnLwXnY2?>x5eg?=v$`=el<V|RTNMRKPPYrl>z*L84)bv{KbCY5)%
z+VfSL)F9Vk<_EyASRIje+Us!{mBKSjn;?~1^Ljp|-o6noob$ejb!PE@5b)=;&rzT(
zmJ<DL(k%fcruOdRYs6YUc>}_IJr}+LgKVOgTJmHdC)&AHnq)+TpLhQ=wL(<s&jPpo
zJTVsT77`P~wJ<--ZG6ROu}NruE806%!_}QD0mqA=q{hXj6t5b(kw}`KEElyuiWo@B
zQt|7k{ycZKq^F*a@j0lE^r?Q+M&XLF7bjOQ!tP+lfd5Z?`z7}-Fj@@4A6+(1%?}ES
zCSxkD>u_XL9~=gXCY3>Wbs|3tyknrZP>y3A1_!{xhmt)cH?)s1Dx3x$xyP<Y5Clji
zmI?UFczBntWF$!crRTHeC(KoYf{lNZ!)5x<9Pa<f$trvnW>A#YbFd)#E7hyO@c$#r
z3;UMk{Ws~_|7|{(<=^LXVVQt@?f*NWOT@&<!S&yMWp}*%-cxb=1$6$&YQeF-#;*#_
z$R?K-+^Fvd@i+STtUWP1l1+#K)A9j1O3emeBJCiOU%#|}^%1HwLnV{IY(USm&^ey6
zzZQh{rqr1&7-x<?POIvCNnzz0<=Ra=bsKRQiLt2Fzy<^JC8IQj00R@O02UMq?w=DP
zHK61V4F>jhqRVHGzt9oWI0;4B(2L>ygC!k|zI~=TrOSV>(zj3v983u}4wet&ZqQxZ
zwX5eh)Ly1;O}s(Fx4Zs88kj|KUsSI{OwH|K&F0~I#Qf^O&{s&%=V)l!_QH+_#D)nV
z9rj*?OGo=<1M;(AG;|mY6twD^KUK#N`ZwNE+d3jEun}e0tHWk_7vs=YwERq+hSbSD
zvLKG>gCEo|7`6|Q=+7ydhU!#Zbz(72egWW|k}Zr5h!X3d!HSf`211Mxe=TClM{^z5
zZHAVZtY^rNFr3Q?LCJ^-_h!Z~?_on6vOo|%^9z(^cxa8u3QMHWnM3UF*Br4s_as}D
z(i8K%+N|_pEkg<5l4_;piq_|kuEBB@@k*}ji-J*im5d3%{w$rsv*XI4I0u4(4Gp6T
zhR&fwfKlQ%(dbeKRe1t(lmwmt2BsE4*Q<8-@W5_5_UC3Yf82D+ycTUGF4Rb$?r^I=
zw(e*!SCp|*s)QyU<{K2;`3;3kT7{RxXHn6)7q8=sd?{K~3w3sa!q1_)`pIgl)W}D$
z;9y`FB5%uvq1dEFoG-hgy%ee_EG&G)Y-Iq7itkHh2D($s`KQ+q1*q^&idc@zlsaeZ
z(ax%#ZjDXp>gsBnm6qg~7`^J-<Eg^QKsd~j2Mp@?B8^(}uXz*NMj<F2-3la{g`#op
zXT}|H=8y`cUxP9;b=1_721_9HBcr0DF(;LaRp@pY1Vbr(LMw*kiSxh7<&2ApiefRR
z18)Mg;zQPXoR98yQf*{1`Mr)8YFk=(eSBVnKH@r`EV-@y@pC$yi5G@zQ0N~kZ|Ugh
z&?iH$S3Tgg9BjYeNqv1j_362&Gab*)mrkwX_eas&MA4hn)$3yutIO9Yts4~LtoSuG
zHI<Z<L?)RK(nCW;^vi0oj)ayv{%efkXa*mTSb@vw3XAcO>1<iL1xMha(=2v=-;YOp
zTO1wN^}y@HSwwv9t3Q5_$BXrt?SyVy$SP8DBzU%AOQq*4;#~}DNBRZ^I-ch}-|DTe
zj~8!iXSEL}^1{bQ>To%%OSGC*x)J2hGksp}?=N@Xzk4@fT@mN?coV9`<ghcew`W?3
z7uI7r_Dg7YB>nRGI?oR5v*LlSyrDf)H_Wrn7j$}38XAj%II3_AI`f&5`QAueKvZg5
zlSQiK{9X@N=bNDR%Ml(XCMJ*Dljo<)(T|Vqx>S$1r<hq*k2lB3$;m*Y(D3k7cB_R7
zy)I4fPE2}DwL*ow`W1J{r814$13=2%*_;Nw@aLS8&}*h-*=W0!jA3vO_=FJ~j(T6d
ze39k9&y_$gZ+kdE6nK);EMi1TjlSF&=CNH9l#!7skV&tzUjB-!(Z4@g&~r-Y^CS;j
zxN&p5SXo(V&>L}nbu}e0ktGsrx7kal-7>fEeI5L3z_bS~tO?wttncN7DQm0yH8&uN
zg<6ZxkvK*#aEtXeJ{MbGkQ3e`;N6XXCJa4=#Hm&)({82KXan>Jc%b=G!+Dd_p{NS3
zfOkuMeLer;a=VXDcL=inyIRw6{lNsf_4W0#Wg`7c8U_YgDf^E1IXO9EgQ8+$Rtq(u
zM(^Taf~k>DhCZPsBqhz|e2xysWUyXtss*$l&#O_Rbfwwlbgkn%u=vpNU(vC#_uZvG
zsTfqU$?YVDqkE5Mi}gG!*F1p;eQ4_j#c0DrL)9zw15HP{oeuUm_^DM&8qBAuy@CtS
z5_)ag?(@YXaX#X*vugCNxNb(ySDTEcaVbuz%%}q@R3M5(t4m8qSE|$QE%5quS!cEA
zesg5@(66Ux5dNsB9gE55-FP_^60_OfvZqxrtb=m9#9veWVY*lqlT2g@DHR_Wd6{x?
z!i$`?-K-ws``VvrX=%&NE&`WBEP=O8GD(bjSy8{UXjy)Va>|4fsjV^8Xq1Nh2}ZyN
zltCo4u#+`#qI%;O_APrGe!Y|SUdTNGy4JyyBsn|zK>1ptslI-89Hs=at)hYgFgv9?
zSzz<!h(~Hv8xJ4NSND+P&@fOUBlw5^SZBbXRjV+YB$qFwW8V6_zF$hfE3&@t@jB?@
zFo|NO&7#%r{oEHry3uQr>2=eMDys>s`o;F(Mwx_%f<+i$j6zp|O-XlAz6pvR9U6K%
z>q1mF77VpbLlKIGeyIVJn@TYs9-mw3scsHs=bI4U-f{C`LAO=9>iNFQ7A(g?qbRUZ
zb($Ralb8%}*vxBO&o@*`HLmw3L5qvuEE^~&C?t!hyzkb7VbQ74I37xbzQ21@Y#0oi
z*6W03o@sbMDJYRk!tRz^J#I-zNqh7kGGCuSRiBYPRLWGd9vj~CU{c^eQvX^;N@?1f
zo>+dKloeoGI-V|`WoUm=IQx+9gm)I3+5lFyZ$XN=3)pL9WMp!TzDffS0v_jCs!iMe
zY&oGM0wr>k9LXHI|0>+`<){Ed_6cCMO6=H0R*jci4_%}<@4oD$>zG-oD&Il$1==JY
zgj_8xY0A^5vYP2{aOsPbD&&2QScSr)yd8srNySNh+T=idVPOBgIJa_G)@P_X<fdF0
zb%eA^gYMp%;`XS&REJ0{(4551!N$|sIg-w^J4LK)e1X=wV-jAhT5fD;$Z9^-*X<DZ
zb-Q|$pU3Cr3D|wAg$jY&@8e+np^q3I*VBVNR0}&b9%>IPT3zfE?}M(=ZEd=00={Ps
z7#<<TWN$psg*Ag+0LHE76h&6?9I!#7sT?+d6`#zP&WQp3<Zubm2L)G%O7_xGX2je`
za@WtXWq){rqd0APuvq1C(QgHc!nc^r{}{=z9U#y64b2673ow`JPwd=mDtdtF_d2Qs
z@xR<{7Ah9l$AJHFA8G+(HlWS52KvWVm3`08KCmp%{musS>durS^B1PJD=`gCcyFR^
z7~M2hxQ@>=8!Ia-VD!69!pIH)oqLZ+kQ)QuoC6Y`AmI1p^nP-U9&7TZ^H|)$N6+9#
zcW?2$8FT^ESrAlPd?OyWE-UtMT)#ThGSu-)=sRG4Z~G}Sd2i+nh9G=(!JCnFj&UOM
zCw|vky19QiZL)eZS}Pu>kvMEc=F>$Zi|*tyCfX$5GNxJ_c1JFEN9#M3kFHgD={;3f
z<>iCAq{}rM^^N7u&d-tOH^Ld()T#_azvk&rza#66BJ<ZJ)+&(A)NZuj#<giVk1r@K
z_oBeq04$1hI=8Z=J~OFiXeb;};>`p|U0ogc*AL)r>XJtZne+qH(&}th`3+%2zJt5d
z>P9p8en2IMdjEbjh3&AoG1%(qWMff(qdOEZN8b0_iNK<d9#K-Vv0d*MWMZrqHa0c_
z{;&bqLx4$|n4Bc^x*A8PQ3*Nru5=r(G8+8;9x=?M4Dfd&X<R0w8Tjm$bIqr1-|pN8
zR#sMmURx|?>6w~YSXjEcx>U=xts+qq)W(4Ad3kYRKFxfK!m`}t1o!dd^VOv62aI2$
zbM>N(u8P@!8)@=+ec9XF`}0Sz+0)l-GGDn+Vf%Vk2UCY&D9RsT0V<81p=1`oQ=*cs
z0kF#F{nXGv2OGRj(nml*0N{}8eqQ3k?P<H?zQXw|o5f5w2sG$8tB7HD+!+Y>^6~<B
z29~GAh6YY~73b3xI@qrOgGfn5e6R0{r%}Dao*LS1Zfq127eAS+6j?<8BkR<DTLUpb
zxUuBY=5rNU>jB_(6jf)Br|k?F&;AtofnzmhlQ!+o&VYy3|4vFoq`TVY1sF1P#FOP_
zRiC@OvnfT{{Y<JlE^5`Xqy2rVHeZ{i2I9xV>o@=^4DaecM-%Y*ytLdY0{(rbOv~eV
zf$hD``)7(0_3He425c^SLucok-Atbm1*oq9_>xe~!nqQ;Q<91Fp;kPP+Vy64XX|j7
z^l4lUJAilZwjcEz2ex&_oIa9+$-~t?AZOHXxFp`88c68q@i8%3K2dFrXt04<%iYIm
zX?W^P6cY*i&fZR^E6br}(e9T!(&zV*PRzqQ!>QrS)Y%6OcAIKH1VbxSLw|vU?SKCK
zx#D%ZOp)Q54Xo<T^Cx|wOcs-<z8)Ht65Teer8cj{H(M(umBFhXhj=ale7=4i0Qwqs
z1O7lt<#IGPe&*Wje2hTIPYaPJRUdsbtAqcXO;>NdT%=Z+tx(ANN|yaK3XkgokngOH
zFb<#^d7@(m#9V-I!lI)y`8?`K({kjtQp>cO!+u>ZIgH-l-*c%-YV5mK0Gb@;POOl4
zC!^Koc|WV;vz+@Sc5G~Hru19t<%R#OB7MVpnM(0&i8_nt-PzEPl$~l#<J^henXY_L
zP7En2X@PWVo%i!YGK)!7)pr!VL=>Tl9|?f10Q{QnA75}|djA>n&S5~(3w2h-s}!B^
zDDDSXC*q=_A#5#cs5hsp<?Zrk1APzg%e7n09r?EXM=`Na?#?$yCJ;!};>>*mwXYAR
z#Zo;l`YC|DwRW}g*X$~2L^W1?UZ1zNwp>=+Kz95J0I9wEh{Njh;u$I41ux9yL2KAV
zY?er`#qE5AWd$<_-61S|veMcNY|izrU=Fj1?kxVKkj28nLXs=S=z;!uhDPfjF)_%5
zggSM)Oon|(@n37Lm$?((|Dk_VW@124BLf3rQBlq{h%<oF^!4=_4JL4j6^NOQ67{3S
zcck;Ube~p1!vO>i4*AY<sUZe#*$;hyUa=bsuCM_>*MWE%z=_WTM8LTQ@_0P1pDouR
zL`7vW8NvH(EnZ1QTbKL&`*$tl*H%hw{V=@a2b|lJWz=>W<s!owakVTO=G@&WpEF&9
z+VxY{Su540VoNu=cM|Xa8XX)4ZC`)C+hJ)v-sL8n>6q?Zh2HDSZ99c@%BPtx!B)M1
zQC)6vo9qet9`choe=v*xXItR3xf6E5?-^3q(?3%V9@0>7z9@QmZ|fJ((9i%tD<d=W
z#1rbL#g(i5Ne=6!2GenpV4QE}qJ>H+KG(2uENV(6YL%7@i8M=|ID8qN)a)F5^TUJD
zH&A4q2{0HxiP}mhCno{@*y$sz0ytJA2wuI#^*rDMs=vQ~lEujH28$eAC%D9h!JC)o
z$13|PHJCQ(CbG;sn+1mV&l)$zZD3Kjw6w}>x{@D0e2`3_#pZRj?F#yc|CI9<?*J@K
z@@*)EEgXAys!-Txwp)2h@~HA`hUziMphsiiM+(*M4H7LFE=`X-abR)KIBFkil-d47
z9)RY6<*D2BnF6*LA{L`AK&Jiu{X?7-dDB$80>~ZNyvyhh5ZjZv7Bk1;^N7|=ieTTU
zugj3U`hSIYs^{kB(rPwT$Y%1>Y1G(;A<{yO0lTKvetRGkm4f+EGa}0xOBo)Sl!S!j
zsvdL@C!hwyAIVMOS<imG>*bd&K6WQ~UpiIkSwUH8h}L+nr^J@T$6l@96Lxp!TCi6(
zh8q5LwoFS5%l)`^mY0_|={tfN+kn(oC<$C(9}-}zDewB8A8$oOL<XDZw=heJ=6Sbw
zy}<EFx9vyfA>$I)ahI+-YGC0KA^^a+QWl9;(vt-2Fc9c$cr*b@D05Z$dLHew1jGRV
zxfmcUw3+YCDq4ubdU;V+4OaJ+#$4R0ZMkT$?x&=>GF<Ms8Pn^sV91;st9+d61-!V;
zY8zc!LHeP=?cR8f+vN_S=b4bUHUZ1{exFmA$5`41(LhL^f1u0qjeQ>V^^*1uoGwe=
z3c7-!)|h~rrU1o}@K6*GQUX1NpPGT50s>`|On+$Pcr4Z3XUTqcn>DkzqL4}2>kCgC
z_cI6@&{g7-iXQkXEj`@VS0Pa$=lZ+l&_&{TTW$=MfjTZ@i1bgTe_&qGJ4=Jpl~$9H
zwEMfw=yaxIH;%ono8_il!a6hr4BInH`K@Jz=>Yo-=gAP+*s{Xin_QiTj)HZAA)-I)
z?zFTDY`VCBgaB6^5fS0oi^!nba9@voXL}nKk&3A_jl;}ACg$T@!kh`*UnH)`A>cGH
z&j$tf2l+cRo_Yr#+`Ju!_30e)*m?3hc@L?U?id%EJNDxYS67jVO1dKR$>heL`tt;E
zjhgF=HQzRFQ|*7AG(W25x`A74BGJyqIc``qHU}$aI>@bOU3lcmKD<VS{pf~|j|0qT
z$SOt)M!HS)$%e~-?CM0F@5eENb$@$BRE3da^lxU?E8sGG#NN?Y(huvB$Upr(T6=-t
zJVHM%{*=K(Y3E_!KiRns-&akZ-`*jR`+`10vR$wY9{nO+W}8zBerSy`0aZqEe}$lK
zUFRd}W0Hx49O0Sbh)hhyK^HSmjqiqCcwgH;x#<5oS?@NwWGLMmugJ<ESRqIiZtrY-
zv9DHsHZ!nxK+ni^e@@B4BS0-A+(9CCvTikB1`RMB`U-fz$xHMOGz<o1`qbs)<pj#}
zH$=zCe&lnBfZQ#`%7rvq<1!_GG2<fPZG-t&Di_5E_WcC!eKqR=5e+ONwLh8~R@xWm
z<$TnE9y6u~%&6O#;{ptH#Me(QLpp^};Sp$o1<^gPr8d<B%vRb1jmtHRf)&1v%-%fl
z_&#0b*I=sL&_zYY4td<P``tcVT4BJmyrTolfm@gxQcSMNsZ5+hpQK44q2Q9p_U}}!
z+dB9^ayLzghW^-Eb=R%iG4HKq8Pfmq_9P1^xMXV>6PeER2n&JnKCli5vcaa;)VqUg
zi)q=De8<W!mu7cKofLB2<U3WY{$fR)lhhrZ1>Wxi6MZfUk6D=kvGxCq@}W~L^89vX
z-})uCndX^bKlOaEzog+1NTMKOBvWA!w%mjKx7HV1Odj$KxI%ADO#oO{v|epXcYpB(
zw7`01U!;zw9YZ$ECU6V&)kwRU-AcQRokmNqwnxo}TLVG&9U$zE_Sa_{b?vgu%S^Ol
z&U=bM^CdRG`aHmW`J=pTl)HsVE=x%B?Bn87>$2<&$RMwvU~<7%d4@UV4|HyR{#&@W
za=kzUDWc`d{7m{#kTy@1@7NoGJ>&>dI<w8~{Ctk}V!i>E*&?-U#!)(dzB5~7vrxWj
z12~<bB&H$eKO1|Mw$K(cC2n_TS((LYz#s;G926~-CeUl8Bq#SvxtuA<Z!GJ74{QSg
zE8jZ0uije7oV)XrOp?<Vn@H^!3_#LvnVZGBQj_U-^a7PHrSF0Cf~=(l^D2IVO2H7r
z>$5QN%uyv0GN@d6708~P1MFks4UPdSLn^2TND@H3;S4^Hi!Y6XvL!{vmq57K+ua>O
zz_$=D1F>@<HA)*P?%;Cl`fO#CXPe9hQro*{J;>&}nfHf=<96Kpvjjd=qfpRYFmAAZ
z)m?9?rZxv;vho-!Hy-zgIaYZ+ZjMft(~{=z(Pm?_RElY$wR2z3;Y(?%?W4tBUT@`E
zF9v9U5bC<T{bj%te2CTgXbzPd3QpDe>Fyke0@}`daZgSZ_C3QQB1)7BCjiV&ejG`H
zi?l|&0m&1q2$(+roPK?F20#?}C3FR?b0BAZQW6%x768<0bH~0haUC0A@Z4UW?t#Fj
z5}wo2ow9FCHS4vwKSx->5SF8HVsar<v3nB895@fBu)RL*3iMbR<LOdesc}{w&X)U}
z_h14EgzL-8*r|YMT#k{!LCFB>1n^*eG|X<TMw$JWFDgNuU4!h9rsOhdN%8TuhW#iz
z__{)G_xFTjh6CjG(lkZR`{|S6)!rLFd!M%6&doTUS?WD}2;4Tx8~i_vy>(bsU)wdh
zLAnI#7NkVF8$n9CyHn|sk`mZ-OP5M_r<8Of-QCjNai+iLdE-0pd(QR!!?l6E*Isk2
zIq!Si;~rzKc>UU&YEX%i^4PD8<;VaEYIkpMVE-9|9~r7ed|cc)ps55wIRl89#FdRG
za$zBMcBk1&GX~^RJh{p-Trz#G&y7C!>*OH+8>?k*!uUZaO0;XFO~XK0CgpSrMzLIG
zAdJPp!&(*AH!vLiIp6xy1>F6Eg@!Xka?f9WaGEHMqG`&Y7|0Y4BLmYUI2h?;?c14&
z8MI}|^|e|xVxHNuuCL}TySocQ3<4PP+})yP1FmVDR_Xvc3f=C!LOxW3EB-=;npX|7
z9bhJ9(|DBnBUZGULnJ?>XRkj^TApcA=}PFUrz~{LCmhXaft$kXtPL3d0^6~VHK24Y
z12ok)q1dIzgn>;kk2eJBlf7@?WT1NtOS6P1IsM&sM7X%0t`7B_<X<EC8m{`a+#NQm
z<jKn=v55SLzTH17T^=1`rULPa0nnPv1Gy#)=YNO7r1x_$oj*|?_4~avcXwvJ6(upL
z^DyVL63aF}c*`1(jougz=)%xq*`x~c-P2e1t5}N<q(q(Y8|1i|=amn4!zr9aptuHA
z2!XJ-fD;T|l(O|{f*Y3UX#l!zy}c3OLCcy>LlYD6ODL{6k0`E=rG*|j<yKJ&fTu6@
z>+7rXj>?jw>LkI>E{=|D?6BoZ<t*7jlnNg23d<t^-(9F$m}uagoO|tstbTLLwHI?c
z(3Y0XquuexU)&{E))mUq*mSqh;KWHZbboV}jO8om55K<ypy1wA2{ET-oOVerC))k1
zj2E+9Hxpbb?X#O^o6jA}Wul-XU^N?c1cn$oY!8!e!7G|42t<iH<Xzzt!vu~qhEM)j
z954q+53sngg0IypP#xY<A9+O4Jk_&*e_(;;aic4nzxlL(4O9Ko&}e|&E>JFl(T$sb
zC8Ro>@gjo+dtq;KO}~9s5kd@)`mT_!NkPn7Khf>RC611f(FRP<hue!HgI1vuqt4c$
zU)5nj%ER7!<fGrF=-A&K)(vs~3f-yb9q#pU%~|Yz{@S1Zu4X^o)3Q-BI*wveiwdFx
z7e@IeGDH<dMyt+NPgggsQz2B1<Tw3=U=f!zU&Z86Op7Cw7=10fy7dS}Y4(I2PJoYL
zbN?`Cxuk%{4Z#Vn+_rqg0IuP9vArk#1v3K^Ev*Fz0)RG)61*fRQKEX{dd6wcoOn4v
z@l7IXoaj<{_|@2j34^tdU$Ngp(=*sP;q>Fhl=EeIk4f7u0r=v16Qu>tnD;NHYC%CW
zkjNaie7T$D;;H{>p5DKRmh7Ruan+<cc2NcTXuBF}<S}*6+TA|<%<LW{hWqNhwnJRC
zZie{AH_27LxZnng#;t>@ngf&|p!%zpI9awtd2)HTW`dt<AatmghmLOC)1{_9^>yn(
zsJp$iQS?;Ux3RJvn|bNUSbglI44mDN000c7=76pMOG&HQgLC_sYbbAZ9T{<&mh_?h
zO%rE2US#5ZX!<Lnh^@o9`!l_*T$<i}*ZPkl`ZYT4lUCo{Ous6u4<A0_CVTk~?mz3f
z&UAzYRC$4ZvxliEm7G$$Mq+BCPwJQCIwsSl`{XHm4jTEaynn(tmA7Z=RopBPE{H%&
zeG)&KJ-;e8UV`xeky7x7$?ZoF{P41}cA?>^+ef4V1QRya&B@DnSM(Ug+{ZRkf+u$o
zA(AbMYIdSlJmyW&dpLw_)w6_b+&M_Wk;h7l&**RK$Wz>o$E#&}W*QLUj2C|(ljx3(
zGUMn!whVn9!P&wjZhXXgz{V&Z_~|;ErObnF{Fx?FirL}VT>o!0{=(#kJ>p_N2lapc
zc(hz-Ie<fN01&!5X)ZE}*3`qBKZ#Ly;sTp5(>vtb%FOH<f;84fhd7`IpI?O^?();3
zSVXQ}hJ2_R-;Bgz*s$$iS+d*6r4d-TBHv;*r14-NNx62x`7{NlAcy$t_R9Y0qZpz0
zl69u8L_dlZ;}I(CYFW~$!Z$Vz*cU6<sx3A>9&He49?HAG4AD1YOzL{uC!R!OuL+8#
zoCn7d5Lj)`+Y({{v}|Zp_9Ps^kx!7RWZU;H{%8b4O1bD?*m<OzDeQv}@q<=L-+N~*
z$o*M1$COjjwiOa-8S=<ZoBx__pEzY-Xc$wf=)pUer%We!<{C`3<Ws->Y76UaxxM=A
z3Af5`pY1Cta_(<$wX@Lf=h0*OaNkP`<-Jam(pUevFg^eyoK&c7@^tx5#d|LPc7Vbh
z8+=eYM3Sp-@lPooziTUID_*nm@L}$Fd$OOnOJGN&3dTO>AEkyR|8$vBgVy0RSR_38
zt*gV(1ZR!7FrRq}NA9c6BknPrVbRo0O-U#YRYFd*_t)yVrkGcfTU%RfRbW8uedKx~
zuHp?1ckp*yNAVuEc9$LV)qzfMVb(o-QBW*1#dwrO13vqeU&2o}3g&p=oQS|+l$lAs
zH?~vW&;Q|P<Qm11kETC$tl`yi>=7~xQ&=wrT^GDIN-nMhEd)%4w0E$JxC!frI&F1b
zUkikf2E6UVrtHrRPwqI!n@Mio^=|^&HnGln))Dl05}6ImOJT%u^izKQF#TAXn=ZVv
z?1BkC|6;^esy~V!yLFjh%$2fawfD5rd0N)>`s<*FyE_PXIr3TN=;Q}vd~5#75nP*<
zLea2bqR6znT5V=5J=z3~;3TYVVQ&BkxcmFA>d@c^CrPr(x#rGfq1v15<&FJ2jdj<1
znW7<+B^WqV#81a7ZRZ0$U3j&riR2|lB98}w^a9P~-gSYw_NMN$H*KPaXl<g$g^Tp%
zXFJsf7&Iihznh*R;bAm1<W9;bvvm#C`56>>R|lFk6nH+&3#SA849eP3qR2*+9TzB_
zK-!kaXD}<lHBWXnRq3By925dM)gG{<e-olFMvQLn1pP8h$!)_;fw(jKnWbiVv#<w4
zQ~Kt!fz4Bt#U4P~cOU7-dLN)UVZPMli3Y79K&eia87$XnxoUTS&mNL}NzdT*zQ5iY
z(v@En`5VXz{Aw14^aKqHD@8en5|7N6nmc9TOi6IpUjAh#SKFbCI<>MqTM|~9AqwkE
z@nuKefT}@2BlxaL{}J<zP3IFK4vI4duBWd0pxT6wFnwo7pT|B9pw+)cwlsS8Km4{(
z4^7*{QugnHypW87K2TFrH$Pm?gVriJkNwLMqpl+X=sd`!%z8hoJ#QSfJLU>9j~CZY
zJHPonJmh2A<)zxYwnj^LEa9TgJjjhVaG}2~FK@j9jQdjaXV48}(hWA!0v#N{klg_e
zm4gVq#E4p()QOBEHnBuF3Yy0*U&EH~*kyF|!Kw1l&Xb?^L?{df39tO0J~E(EJr5hU
z0p;-lo!DCWY0uzE`u+Q4Auqm?RUWb7#wr)j(*jM_FlF!CsrPy9??}~8{`hQ}WiLEf
zOJ8>I531K^03ump2a|zh4$zMRf`Wh@ms6RM0Bnze*TrNM0AdpbD%F7p{(3P{MU%Vz
zS@`bLR%OSRS5~6~?zAISj?K~?7FJd4M<Lc&K3{n;@Y(;3xO-}>rb|n7>OxDlM@AI2
zJIu|8QgC0qIKAB~bJ{7;Qx<m^I$TNQ;`}O^b9`?dlj!Q&F~KmTw#82sY-q#{5X4#|
zqkw=XfbG?RjPM8u!+=LWUhN>}vi{CljP(-bN;WQu-t<NinmwSmM#V(7TRzgu>)0^A
zsPNQw!}wAphks!r&7^Mzg=4(Q{ftS!sorT@UO_=YyQ4ef^I-#6p9(1)1%OS0fq~%&
z77zZkoR70}gjb3yu8wS@{u31}q}TpML6K=bPhnbD!ryQg?$DRJ^)v<EmeWzF?Ht8Y
z^zQ@y$qPX5$IH6bi~ZTJQ{{qCQ3^kCKp6v^RBJuUM%3vyo7vmAH4)lWj0WxR`D9tt
z%6Fc8b>zdz?|tXDKo#NB_=DsUz5r*24JWdaR3^}G=weKU+1=fpQMW#Nsyq?U!L5(C
zdzAVlLY_%e_Q4mF{EJ<~7G;Vz>O@2b<M~eQUp_g^Z|z0pf!tl*1X_fqfYAr&(rhr-
zs~L3KL094aZuu!AIr+WSjNT&ws>Up?@k>_?gsXId>+79rRK!DXz8gT14t}T?&`BH9
z$r>fV_)J>#Y7?&&OiMr-f<`y(iHkj4OG^s?DMyQqSKxx052wBNFP^BFZnUam!y#jG
zgBNPhB^$BLm+YEz&{@Whw+CPi^h}M7p0#D_PFQ{l32K%OP%rWV#gB@L3gF{Q6i4(-
zeZUECfZMwK`KTF?w;WbevTG$qy$>#g=+TJ>eu8)i09imI^>I8+D>OGcbGtwrm#_w1
zLv8n{mNJUkKN+m@#!q|dN-9JgAKqH=1cUkr5gtB*fYpzb@^WyYVYYIC1UfbV%~K*&
zy$K@sb_3lb6JujC&>#Y<k8OE1R(jOLXic-yFDjKMO_s7fw;S)qO@8cQp1gHetGO?;
z@zbN)4zI@crIADRG+O`O`Pl5xC@y&z+AdpeZz79p@si$ZS|S(Yr&eg<G^n*cpYO1m
z4<<>Hxr<SS13KWySzq*Lo2Yt=Nndu0NbXSBa)oB@5bB|zK2u&K58l!7R12dAHR)ad
zo2D8-aS?T5ypgJX9Lv>LPMQIb{0a1&K<@DdjUq0<1Oqnt_}D%HrXH<j)tc&05How}
z1Hr@vyxB`hKc39*Ouriuq1|2G8)ms<M739+qo89ll;b)zmXm*bKAZjVf5ixM)gNVL
zWo>Pl!eDg$`Q;3s5ppwAk!Wt~wPx8l$p>xbW6gKG$^PBhxc7urXA<M_O9zdrlGP>w
zPzKb{5XN%$($Ho0F!FtEK8Ll=k4Ma?9&6%iH4<ITp$`uBDUQ<EnmV=~jtf&)0Cgb<
z-w+vSL=IM2`hOq(o%7^Db#pyk9T>6s#}B&DA!CVscu1Z}CcwT?-~;f`0(OJTonKlU
zBExh0!<-bF@<W#-E@mMv8tx0DonT<e6ygZ>KeG~ixjuChbf@uIwIHu>DhZ)tF(s4#
zO^&EXn`kca@!|L}E1<_hBwMBUF3skiA`z-d*YHOTC?B}(1<y4#HQL)Q09w=7x%9Kx
z{8=5S5_cWFv<}X->%lc6*q<YK=zhuMN8F)C0g{?XR7BzFJ)7Fl7BZr6L(M64LuLLf
zsvRsf7O7@?mOs5T%jSZfI+acn?&`=2<QKm_x)Z)mDKocns{DXCKO+4}Bls`g2=&hm
zioAZS^<&Q8PPpk|R0c5QADo;tMOVaTaE%$YuDrvZ%wsGj195*wnS6jco#bos*Z&Tv
ze;SUs+cKc=qGi1sv~Vs=eL>DW0&2);&+-xMPg!Hfyo4I^)Fhht<c%z=E4RM2{^R3~
z9`2(;_JKp}DZhvuUmPtjljJDg6d<-xyK0QRl<&zC>G{<ok(2(!1ORij1feptIBpw$
z`dg1^=?25JPi@8_!wTx8zeW9>y4&%=DbP#}rSo&Mvc`172;#+lP0esVI!m70AC&&x
zbhL#n=FjsQ)EHIkc2s2AM~C<fUn|%L_JhCgMYBlbiVIy&lZ81qD0eliqjaG{UDIpz
zS8dqr(yUGlpFkJ#Z);8|OX4qK6G6;gPXLGdQA$Q;+}fR%_C!C&=*TVK3Y>9sAHl-j
z#ll3ub^6oe=|B%6I+0kaW{5=A`^kHS)Ko8v1GnX^=6h}X6NPngCAF+S!@qMgguFcZ
zVyT~Qe)}Xa8!Y9>Bx)FV)c`U;n%XO~9E+XVSGK%7`~3VItS^hDTWZr^s%jTB9KqxG
z&aOEgEAv;}a^ymuZr0ZO*a`w7sha-UC6Q+(@t?M*LHZjdqN%o?Rn88*TK0Kbj}q1g
zaUO_oKrzo_+!+iwpW2Tq^5V~n*#A)xpyq`Ne!hgeB0N%QeTKe9ghNJuF_Qni`AG0$
zLIw1rsdY3%xO_B_USSWtc=3YGtUvDkSDJ&F3KP(K1Mw*`B;?~{VSF$?$j8{PBWV1*
zuoxI#fnpfwuYl+WZysBzwcNN7hdaJ3sftqh{9xV{w2ZxOS(e)4DoMq@CMM?|pXXjk
zS7`ohxELS(zyZ>yCJ@6|l{H^>6Wcle88h1%`H@Ky1L#|O<L6`om<GI{!>d<g89(zO
zn0~ABkG(!1=NIbi41n<LuT)^x;u|_4TL9PV%xRG#PQGGQpXnWk4u6TZ_C?_UE(Rsd
z(M@XIsrB@J#?tIZ$DPLUpv!$A*L7envg9}cd<uwijjbtr;Q$)Sf_O&JV#j3Aa^Hn-
zfJP>egy5hC1Uq10pc#Dy<SNaEZGJ#7=HwklStTCh8YbY{WdIUc;jwACQ-hmoF|{`=
ze?;$8aDW5P!P*bR#)Ocb_}%i4r|*D<>?07zHS%+k!=d(Ec03nGBjTW=rZxi|o+#l*
z9`v<2?Lon`Y41!8&q0vl%)Nfn=Fe+3w3l>hR8)MxaDi%u_BN8?T7`YP55V$y^>7+;
zz8v}co3PK}LW78ih@i*$P9ZiKnLHapTl*@&ZChXj#Xy_}6f1<Wj94f+^rpWi0=$a8
zM*09ooIQKl6jVtmU9fGGZ{!X|%FCVGkqRihVdpUT*h{izu0tXsa9g=;=BOUEOSCz$
zn1Aj}6u&pXLc+UD$jA@^VHUs@*S#sZ;v8^&szc&-Ybf*g&H%Ohyyf9|IE7pERzXe8
zN~$XEjoQX$->GB6ec1&Z930x@sAbJx_yDlx6@aXumIkC?NNZ?GmL$1Xb+bH}EP{f(
zPp^^iYCi#`0%)xQ+Dsus!2NQ6cE2j_Z*&u&pB(iRyjuz^0PPFL?~95J#4@6Un_FA`
zr_4_hQBcO8th;;R#N6^54FGi<EVn)d591qnQe>>Gu8tO}6}cSD37+=8qBIS*P+kMr
zH?z377-(-KB_$CN5s&6-iU7y}BXU<Q)N?=2yw)4QeqN%`dj8J-h?{pB5cwf=IHBfz
ze;LpE&w!bD<)hi+=`JDhXtt_KAMfTTMR1!oTJK<DXJ?L$gooz{rZf2V6ToW$winNe
zv?usgD@T#%q=KA$$<vPila7bQ0Wl0HQ|gEdj}=GR%#iRLU7#~+AU7iZ37+TW<;7>#
z7x=V0@i9Gun7dT7LUef*3lDEfN-_ZLq!Ea)yuH1v$&m1JoPb~m^xlD}q!iG=^R+g6
z^L5NNetw?2u8L|__l8H3pwP`po5!BG;XMkBV%^9fL#2H*ws*BFW_R3>a;V#iI}i%F
zsyx&ij%@U1e(WAH5$-v>7uK?keC)G*v^0ZS9*V@re~It1b3Nf_QT=hg;YQ~NABu^O
z&kaa$5Y$4MLx9E%@ctb=K@s#gZ9}w*i*}9@153|c%fja`zx14xJ>!$`n2da>A4jQa
zuNoW=jelJ5y0w3aXJTfUe;xDSPyuVBZ!6!vxPMJdX}R$v&=l{o`c_|}|C?2So&9!}
zTzf|cK=FF-@;pF1DpWf=J~ouVWT4swa1pfa0Q>J?Zf^eA$B~uUsV!3Vi$krZ`$v|s
z^6vvVKq{C!RZD(Y^u2uN<mkb6Kh#8qs@ymp{bnH!w=k4kYthALxaWK$Er6f*`H|`N
zQ%h=U>Iq=@K`piYTcd9?o?eY@1MT6cpYO+yANu;b41tELned16cC8>fO(rn6z9@=R
z$Qu4(gLs5o&|>jNfo)d7JomFy(+Xfb56A0ec7K1yj`A$Mv2<~}&Rg$}_`C|m7Zv3(
ztNCLn5(RZ~Iy=P{&WPXpb3`^V*+GQeIw%)8@P2HA=A&cmx^gUs@ZxmZPiGEp_f#(a
zGv>X>pt@8$c(901K3o5m6q~qSSrO#^_VOfHbn|HIfZ3J1sLr1cq!lLi#>Z-H8<&NP
z2~<RIA+Yw|+S``{%F7!RzIP{bzHD8n+^&C7LyEHunC9u7SwI$+%IjnfWEeokcXzSZ
zgHe;|2h}}nLnuN8ybJDaON6KzH}flA?$=BJ2-)`g-!I>ApZZuVHdk9t7R1HjtWQc8
zD7)Q+?NfTcY3f4vS=94+&;uPh(aksBVO@PgLyX0;)7F%6m<;_gsi|Mxx2MmaNagO9
zN7I7py1H`<)dwP;w3wuQ9<&;@coZL(PyuOsn0&YNTllF7lx;u`YCT^ommLcFI+urw
zcR){8tek^VLS<@U@uun7Ag?f=uEjz0z@l@-_j+bGEQVc$g_mrS`X0)*+RBed=l9F*
zfBM$IJkxW~NKMx6teaa~1<U9oTYUSt&W}@8IE+Pf@`lI2pu~lD@lQH`a*h>46yLaB
znTp}GWrm+tvR57%^wpm@bzgjAv&)z^x9G*OEd<=P&hz8K@u@{-w_eN_Zy1V!(g8HZ
z+dy%GK_)=ACJyp9puu00*#W={a4%>sjR0{Y&{|6IYS!PtRiu4?X<%SrrigoD;<3)s
zI{yxz|CCMTj7|BBO=W3Em6Mg#Ve_!+R#J?kFS0QsF`4GDzuG-FGcu@fmsX^Ok1~Z|
z@p$ay@{;SG=`>I}N0|->E^N$fP<Z>pmjPTN_~sX7rXL!GLYfxRsN{zIbpSVKzSx-d
z>B`aN9{r&*2dxw|rBf{;%=I}_!vqE_(X6mHw;V_-{L=VcZGplNq(Ob4G^#YaN&H@{
z+u#Uf7Z<b)FTT7I5Lh6*N{TBH+dvF^=?5>}+SJgnhhTJXwwlcO#%KL_K(tY`Q#o%)
z`nLjca=~12cvG>njf;oAy2PXZ?4Mgnk2=M?>})Dp+OTDyAf8+v84x~cau=(mBjAKn
zAbtM)xx!ajVUZh+!^6W{Vq05`>B(*V+SnhhR_4AVA2`(00|7A;^qhPNEY#Y_M<6KR
zjwBcMzVc3EIJ)vL!4>+F5x?s;ZFQtnPzxZf`kbhCgJX`Y`s5}17}L=}XTr^VUBPhN
zYn9G15XWXaz@hkEWEW-{UG3D>6COW;g%8q(x~;7(keU^$7AO}$DsVS_7i-O!umCMU
zzl_2lC0nevticfNs9?89uxhMecdf)~!f@KNYG#p_lLMNndp^Xr1c#vq0Z)y4IYYw}
zTc%qZrqreuLW6x{=BAb^mDLS@D$}fON*`$@ecoM3J=%PIpffLVg^mY?NQRT$-*4C?
zx673(_HKmUFUH((FKjqM>wmPS`sf^9-Ywp5(-}Wb^~Vb0fd&R;Sjwt`1iUz07-))Z
zCrAWFCaJ;u?Ad{KF3o*BQoO`BDmuD+0HoLYKo6=OQB6rbKNGERcZ=9G3;s6b!(Fiy
zmyZF>lqWShbrE$DrFS4yDCX<Kin_}}O<F)>0kUgzvl9j>pY!g->jwvHc<zu+(Ze!q
zOiaUi3a;4Bgb3*#`*%J%t!5^XjuHuMaY(G60B)&x$yy;_Sxa<+BdoD4bha8qju7`p
zj=6bW#dHtB&Su~_Vc^DzB<2l!%*JMa=eN!yfs+8;hY0RQD6>I}C+I59@)^IQzAHl!
zqHMuN6;8TTtP7$8&8H`Rg(BQ1uHVj6gbxv$9t{1maDJx-2^v%k4~K7*_{86%qoJXJ
zDRl4emK_@T82#ad<0LjXIJkcJ_m~b6Ev2i|mEtsqu9NBS1tDYBDd+U$)P$tOlxSgV
zA-Q|`UKttLVX60^x(JgQU;3Gx7@s>2P0JnXV{;zm#s6&?k#rYL<4Ve1R+&cVwKXUN
z0Q4f=9+gV}sgLwKA0*kB*Yv_y_`QTb2V<=2KO3H|uiZ@#x3yD>wM$RM{28vWIUJBr
zxILdgb>F;JPtxe-u9?1*wlp3%Yq`vrd1NxsIVw&4rQHGSA!%z~9m3AOWT-ua1IWK%
zlqew@%DaX}#!p473USD9E9tO&JLA{vb+WWy9_}I<jRr&Lupr*c0p*vu?;Lt+=C|Z*
z8jMU6zq-$ypi#QmnNDXkKwG11T5o5zyk29)AxQ^>#Unu(m-SKRnF48QH#_Gx<qqb1
zUH-__ZKhg28ScrU&wCRpQqqC<PQSsbC2sn-UekNI7xg3F=H~EnPJ^-F(dM(wvsqV(
z`A4g$7a}3w#p)$ORx1C<w@UD~v)Lr#x!jbve~za`W>NxQ2b5uc)S_+Dhx@4NZA&ck
zI1HalswL2^dKl&Lm-fG|v>=1G04vHYP@&n!)LZtz&s}<LF|}Sz9NO8G=}r9W5b)9g
zjg0ur(t#JXX!|^vgWd09Y+79x>}Rr_TdWRdG!(#DC{m_1i_uwExy`zLqECuD7*jD_
zzlxu558CIcP`>_I<4~mhAER>Q6M%vE=CE`FFA)%oL4dU5-!G8xfQ!ozxPkutA})6I
zT*!sD6%!&22OLsEb0V)G-^F|lzns#$wX4f7q4>Ul@q=fmq7dzaybS@zNq9}j_LC1r
zkrJ4|h$+F%DW78d)r1IvB#1`++XMEWX0ZR)cmF+!|Nh<oX(XG8)a_w<n)R?l|22z>
zl;-HZvYY#Y0Y)Nf#+7iyhb-ZpuOrO#+bF1*5-CKH$q@aJ<4ar^)X1rG#71!eK{_TX
z_3)nIS`_<<&=)%%H4Aq(ZL;ZDX0Vtx4vAJg3hJEPpin*7C_lT6Qt#P7=fKG=@B{7j
z7oxT7or>?WaSO|aszkEZmA$f;_KM7qX*&F_k}c@;mEU5f1mGAcO@@UL+h8N}kG-SU
zMBjdVD!HjCu>S$GlC+t*qHR5Fsbepbb<CuZGEr0UMr^FBoym&F4Bp-{m!BR5AMbNK
z(G02}o+@Hh=G&O{+Tw>62#JkVr}JyNN|UiPWyLi^dmbzg1}R);3JBfjiSJm3_YdvH
z;QlZ`$^OG&_n%HXx@1NS?u&~HP^5lo&IQJ){7K7?EsdSD-&~o`20(T3almNt?}R!!
zl%{USYZGmH01CeSNl(y8jrDR3Q4l)P-%6an4u}1ZHvzvJ*y+mn?_~H@;3qC66-=6E
zzduv)?{^<E&|%36fukMShOy#ZlBS4Ft}HUr@$=sSp&=n5A+=By=;{L>QTcRr;_jjv
zy&6D}and=>LX6aYeR*Q_${{P1rf**d$F6Q~<Ky9NvEj>(S=OAKoEZNEgj?4=JRR01
zGZq}`0BS0cPN0Tf`g~BXlc({!&UooSS?-_wJUp6NqbyFZvA&CGW&@9muX35WtkNnz
zo<RQM4*Bj=C;TUokmz0<ECz_?_o?7U2?vn{PJz5m+VMR|J(?f9AucdAyJhu#r9GOd
z$O1_j;FvE5v{m+Y0mauCA(Y?t_g`R?Jm0?#Z`Q|<4tSOTtPX~_qobp>1w?N&N<EKP
zz5w)mRvRyo6%rggvP@RAE=DeNavXR{<?q-+5{IOd5F8IwFu(eMjiu%<_o`j<*B=8=
z@pOsKbiPv7YDbXI*$_8K`Nky~z<LGBi%#`k8Rq75pa;*%&JLyxLkvPE-crOi_Wm6J
zTxv%jOMowm7p>u&ipMkgt+lQ&;3_!<;)DB#heSpl8nObVtZq<h&j1s|Um*-YFMwD`
z=d?<2^8_*jeSQ5k1&maBf;?oY5+Ip|(UetHRdsfDUS-y(2uv*eT?Hx*kn}LQCBg|R
z+)j)g-dVc@FUzHJ;S!U3^JQdY0A<RTFJCZb7?pGj?UP4wjcxT)ueQTmxXQ{NCEf<D
zZtYJ?@l^l42yCt33LyTyf4^eYIuuF!omSqpF&jSED81^mgNpuW{n48K^{Yuw2&3DG
zWLqONihpvqerz?@n5nGqgH13zXP!0l<bb#b3tTW;(|;anRrg;GAy8I7?N|K<VrMr%
zI`rJ9L8F*CV~A&HVWoiku^ZN&=U+abkAT7ftP+py0uK=!2mnYbf_Q1_&*|77dJEV}
z6All*dEXYS9s)(q1$V|Is|O1;HFT;r%NL{L;Mx&*ugb;f2H)dWW=iNK2NRXLimH6C
zwss2O+b>P)U?ePgA7U>^9bU0g?co~%g+hTOI?@HQl1-f%?$0d~PnLEI{^6YDt}O^D
z2Lf2NZMeM2XK8-P;3ovT|C1`xoe&6fBGl)aNjxPPql9)+1Q5vg8AGMDj~C&?a|5fn
z(JAEx6=vQClMP<hm%RM0hXO$G-N8c(i9;$%`1e?VBa}fCQr-;Ai+Nx)VDbHb_%eDo
z9sh5A8KqiP=4R&Rfk8o_KAM}JPMkuZ!@~RLW>)MkG*n5)(^pj7r00i(ga9AeH(saQ
zdO^pHUT{~g_NEmUE{~6S0DaQ>cu@w(zHb8Vm7-9TOw1<W!(>~$h!X}b9e}OE3E9no
zyaaeA#-9GuZE>)^&JLWUK>YR43J*L5*1(Sj$m1UuX<E;7#sGl{f)?nD<^W-CI^U-`
z;4%S9xNivwmn&`0Dxg2ghyrD(!X$x>g25J)(02eQgZU~kt-_@m0SNsJVITdcJivtj
z=i&GSaBdI*Ik}NuNDwzBgCaesw)p`E5lPCQtMF|JbkY8n`;5<(P%|?#+1S|5f3rG-
zY_w0SOpROC7!R0(sRaa>A?23f6d{qD;E=fVs+h#s#`DIYYF~?qj*gCtiyKPieon}i
z-I++Nr!f1Eu|eN`Gal&mCJR((ft3cdnlc4O=gbJ|>;_hEecAs;8AS>8TR&ca(}6#%
zYYVDep!+|~3XTsKaXsOL;~<Ox+k$%0B+wrc0{+z8JhvVQs*YYTK%@gWrQzDzPeBw@
z5V*In8>?~my5NT8L$jzulAciMvFE0w6fItb|5onv<oysa%m+{2<Z)3rdJ%^z)Cw4g
zo+$D>D2O*|GJlUE29_dH485iNtb8g2LM1AK4Po+^DScM{`S191);(yGH>(>UZ{pzd
zg7qk-a7T=IB37hT>D4?vJwY)(pzEj*hsG*y{4=RxxS~rdcR<0O3KdA_|G5L<Q_ig5
zqBXjo<w|5ZR4CKsh9&Q1^PrMbw~vA`z|z6zC~E<C@ElB}f7P)PB}qhqq0&P9NQ8%5
zy)_>MGNO|7l^$g|9@po@Ss6f$She=j==NqTHa;rIuTpTFG#<^k1R*~H>gN#>*jQLU
zS_RdETo6e{q=6&-!~svJ-n^X7aXa`?<fPHCR<URjNYB5Qz#v-mKA4lewisq%=vYVV
zfThX|r|o5%KzjFOWcj;3<b~22OED$y3QKXoyS?O8U-*W^LKmDwdbH1Pb+_VPLI%-L
z*w`a+oJ@zk>uD@j{m0=@v*HZvb?Q5+>@{c~@F23*MJ#9oegq}RsjN-}37<peDWOMr
z!{}7V5Ncxryp@STlEAKHZkQQ5wO$hQppXAV>*`geAS+lrv2uu_OAeoRr$eBT_@7?q
z>GL&vIRK%4J<6hW&|ka7)Xlo10D$*B80f+E9G?L}ZUvfNZY>rU=hP0CQ)!6jCtLC@
z97QgYQe@2pKN^;j6(r_HIyZ6P1Mm6p_elQ&g1;tNyEBm_dg_S-D-+$q8*{q(?Bz@W
zY;4c*>q-fq3S|B?Prg3P83O~65B&=n{{2b$p2MDcr)WX&`zqhQkY?;?3IsxHY<ZkJ
z3WbDvE*$5rWq>UjvfmlW7e?cLLVt&?)FHxThy;OH7YB66p<YS!h8{)Jo%a1bqS1Sz
z2pTY3eLfds9ANN#kLRm?2DyVnJ^M^EVR->!!UwBgZG?`(50Q}m;&4eQ!7j!+2Y|U>
z8k1q#(^T8kF(8l^8rD%zr4N{{n|u=QMW3WrX~!!e5UJ<_vaq!^(U^uWEMUYuXEguX
ztl<9ys$F5Bp_xE|1A?uDvolA;fBjcuTOJ!im+F_d#^@H>L`lwk*jL`1pAYune~VUS
zK2@xR)&P*P?*q`2m4?(S7SeVA@*4AH5<XZo_AhUbsInFmH~eYHf@}hSE^l(Z9mM(%
zARIw>2>)xQa>4wasbC)kNg&%&NaGRVpAiSymIHRswD%$Rj#LgFRl*#`*9vw28Z81c
z;K)s@);mbH@-7=fm81HMhCmNS>Pu$lcSW8zI5#gL?^)?#*GhFI$zP%oiuyDjfBpFO
zmztJaIJ6~&Kn)!nNWN__MvJBr8nW;<2^0fnvwp1BWjG9!;e)UseZ8uJ{DKQhAJh3p
zHA4pjdD}0&Q^DfY(N&HZfKK{%&WN)}BL_B0MB%%qdIOva{$LtLc@#?wl3mO{tM%_@
z5dU=r!C32m=I7H9!GOQ}nu$qDjwAh7Q4+d+c&b8>^J<om;iNAJm3apK%9lX?j^ApE
z0nZIg486=-sfH0L&r=F$r?oS$nWipKyRtR?6}WPwb5y*6`&V(}yY4WgNAY6$ssGSI
zDioNI;ePS@K}XHT<%IPYz8+i*6+R)LrxJa((O*tcsof(t_)4LpGBzxVKf~M$K7ryu
zXdh{)#6lN${h$i&p>!y*p_%atZdTDsyG+Y~ohXkV2m#Odh2HUh|Nf#|^TVe>_mR$Z
z2qY0ax$0Sj?-)EPKIVu~eVj#kCoMU|(4>~2sbKWOzg;skjwsYia+1u{gLTS5072}u
ziI%8;<8kn-MIcF|xFU{7fklz#scu;zi?%wu-L->rCKygYlg7ZyDP+cZ1+jOh6eNHZ
zYwI|KG#mG55}<eBfH{5(G3v#<orivt9e{<r6v_2q2n|E+B7Grh1^aiC4&O?aZ4!?_
zVcrSebHmppu7U0K>PWt)xhToA?}~ZmBf^ZTX#n(R=o3?9xeqZ~^vy1R1Lu3N^Da;c
zui~h{7LwFAsImy|-AwS~3Y*rC0c3|l;$N?m+zW}p1{UQ?h0(0m*wGLV21s0b2s(bp
zH#Ab*Kw^k4fgm2FzrB<9d@zKF<K;@>P6foU!dgk!_T_uX7;tg_Z>!H2J(-U_Kn5Qc
zQcNoy<YLc?;-m}>|NA=wBOhOiM6_Z#9V?yvA5pN!NM}23@7fsD_l)ff{Ja6mf`nCM
zASC$l2MLdjFp&u^EiD=R?0>a7a@*NKjOJQ4v{%%MHDcsu*kkd5+^@?396AgQrhM4N
zCT6Delf>U^8z6-X@MxY~HZDUwD8?Wx1{MC?q}yXql5kT=4k5^QRoM$op57c(u!F#j
zM-@I1IhXz2rOk6~kywy!^RRTETP(pDTDTrA{J_c%8BO_HwLw%CNY6~f#l>+2UZonH
zcY}V`PI4Vj7R-$VqCX+g@{S=3!p1X5+G|bl{i9l}hBhQ<2=nWv2z1-rpcTG3m(7JZ
zKkkc7(VB@ypm&%=U;@HdHVy+1Sx7e`ga?&&2DcqUgNVGe1emozr05*+1_31QJHT!Z
z)vze9&7JkEI)c;uvIeG2d#83X>>eTf-QL>BCV&%ARxRHKE)j4qCJi0HK!~(ccFf@n
z1useU<h|ZRf(=OQDIk6H+e%xT3(xuov|v}~WW$-55BDW=e>3P7V!wRf&=N1GiPI$I
z(dJ}8q$i;F1fspQ2U)6l%vW9Nr~2_SapupASC?L{lGhNILF%T3Ird&<YSFCG-khJ2
zQcYm9lEoq3s`48H%|MHU)%kv?Oh9{DtkSiudxo_M@Nh_9E|RW317WLNth1|2i89Hv
zJ{;_vg0nP1cTd}4ASaNF3R7R$KDv}<mE_R=Dbc9l1q=^-)~)%qWtg%99(t8Ov@1kQ
z*}gQc{5?k*Nvg_OXfS83=lEDlW7qO@+H=EZZcf3yAs-vw)&RE#70@GpGbt?{VxRLu
zX`^wzn1itSbMi8`cF+r>M#Hw6_ki<&gscesEtQS`hQm=cW&75GN3KSBn1UZ-5EkXX
zsb@R8g#$+eA4NDh0bZAw8usldG47OphupX2_Jnz5mjW}1_0=5q8y9!iaY^zSYbDnJ
z*Sfnk$$#h_DjiCkT3zS;F)Ygu%pYAkq)XKKq)>U<8~M9n|3v`lmH`52+}`CQf5+XN
z)$nYEJfRJ|bT&Lm;oF(|*>sO>SU@$2fDwF5>{OyqCS#*{^7oo1|7aUF<CSbd_w1kl
zmV1RLN@d@ey3F~1xXPJoc}T-huF0tt_VWi@H~?ZGfOJ_^q_2DkvHWP*<a0@p3v<e)
zV87B@82LXRkKS6_wjGFKDsVUFpd9S})@Hv??vF@)9iiqnQRjbk^)C_%Y&vturRj2z
z5t+XBonJ%yQ^Z!I*LdBK(0hm3WiA6^iK9b?Fus4CZSxBX3pacSq#UOd?1u9n%+$YL
z{MR-^|7Jm?`z|#YVgI3`Kw=N$m-9Wfjw7&jLeT&P6WBxpW&#zwAOL~P8u|~D1y&3o
zwpQh^|HW$ojBl^Z15u<rwn#BW1jSf64v1g>jUoG=EEUzk6O2TDE%*PjvlQEZ?JV{G
z_k3WE|6{wR8Z9lS`F|dYII<CvB7tTbn|zt`Cw};xq6BOhp7(4Zt}7)-TG=JdV7W^K
z8Ha=Q`KL6^m%b0>J#}F@P1mkp@R7WaDJhSiGfs7_@koA<`s!9eNdje6v!dHTHrFj{
zG|}7*P*>ljJLmjmYX*@DcpnT;VgCFTgIEZr1<SG*!^m2X<;nY}d;1!<D|RhI5}LHH
zU6Tj_8aO=^DhgNAFc^J~P%P25qXLFyCS2uXsy*3+y$ON7(<QVyo0LiOBrY5E-rB;{
z&4Wq1Ei@b&y0%L+*k1vEUIjO4`NjQen|xt{qq++NPpoC?jzH`6erQ*wrYx0XIVo?u
zcrgWwqzHj(W^$6LWS+rhP_*Wc&964Esp?NIb#_&S)^Ut}@V`Gz&6#~P<hpY#adB?1
z<jkrWoW6^%MMV3>vB5Bkuu(Y)9;6WE*M=^dZ5u}wb>8t+kFJoCalV`31G<rNN{<9y
z<HF%ryc6Cbrz-k83o*#2Wg9LE--2^8N)wuIrY#Do_6?a-$U>@J@>A-H?x*3TpXT<R
zN{D3mo>I!^zI!j_>~S?8Ng1xld7ixYB%Lu}FTZ+l>{5K;sfs`wG6p$^Ro|R7L5cZJ
zK)yXS2Rk!4#cVF|rZ}%~dra1>U#&l0S#rGCQaE1?Ny>_~_M_3(hX0^6X9-!#r`pe>
z&Zxw`inhip$*l><$L2?TK~}G-<#CdKrKog%c^%<Cc%DE<chz;+CEI6P#H+we&hfh+
zu~7!9iLN~>V5yc#Rj+KTaTkLUpv^f^VKK}8G)o*tiA{s9T*D@ey`<ON`L==I9npP~
ze%;}_F1dJHZvWZt>`q4rbd5t(WAm%BDz(4%2wr>B;#)rwq>AZ!m&{`AAzm02AHNnw
z1XWue(Fv9w>pXJ_oo7))kEJC!TS;Y4-8%*Z-a?0awU(9Lj@4}6hPdXdYZ;QGDhpAQ
zQ!7%Ryc(DbV2(_P!Tx}gYUf?#tt5dL{;xC61Fpj>SYG71c?YQQ7`1&0T9?7h4D@0>
zi9c|l%ipnU_3}|w`EzB+DW<L`xIeMUM6_-czAiN9^^^A*VqIhn>9;!FP9w2QAytiC
z`4vy2aASiQVjT>-1j)Ph;C5zfpVtw$?CF;K#g+mXKZZXX|9#f2;-+rAE$%DZnF54V
z*=ZY2)2(&I%1M9v*<F>C=UK0QHQR2AL?H!Lu#kyLj?b*N*6v-qO)az5HSzj+J^e&^
zkE1N|Svi+QZUPp2^cgB*XNvJ}^q;T_Qz9>a=TmqfVHFxNlkkm@o-D~v2ERSpXwRIk
z`Jyv>hT<$PZ3|uRYz{#YNM&a+bmOpyRNl08EXPRp79)PaFt|drB3J=?J8BZaQo4(h
z;$($mTH6>)0G^ds-AYn{N|o%-ML(d+$n@3x6({dT@+;R-F(>=JBw448Ce^*(ciXW+
z9iIs$Pc%>KjnDelGb1Pz6vD!yEsAMK-91tSa)0NAls$CcR+v<oNUk`OMMS2*a``gD
z`ytDC+tML<CSqa(lV7rQuUKKtLVI6C&}v%TAteA7`ECt`nPM|oXdE}gAsGuNHu!Qi
z&`qLeGA8B|qk*m89&9rwGa9_Nm5#y~`Uh^qy7ly_#V``%7Zlzh63fsr@Bonv@|mW_
z`q=H1`wFpO3Y0WCD!*3(;Atp0Y>-_Fj8)hyipahYnyOQ^Ptkd`=%ZAjvQeA;1-)b@
z&VqZHE)G4_ouvy0&bVT0{2swXSXFgjin=G@aFCdgy<@m-@TH-D?CuU$kKeS5^T>6^
z7hd-yMjmW5;t-ahW^VPaG)_#RXjl*HAaZKNpyEf;VV5p&S<UVKNa(8Yn&iA=dPBR-
z@MBQ8*J=ENYM`t{V}8=}_sw5}<yv#$HM@B-)YJ7=Qw={07R5NW9Van3Y}l4rL`Sm^
zd_=TYBsaTb){vii4!sxXp%r@=lAPqkcaak8jIl$grm8z@ID&Zdzm0j;NZN4F^v04(
z5<_Dmhe!0guKX%eNY_&v7K+>uPNd=o%V7u8O0u;1+Xv3ja0zJVX)yQ8U7TBuC^x6#
zFnMP;?)l23+a@ZClzUfLs(Ux{)M>pgqzgTu7AM#|ZWouxLo~S_D`<PM_SNvMWxidy
z0JeE_kcsUQzi!i!1l-RkgtA@z_U%8u{Hg3)c{?PCPG7tv(d}9AM>o@myIPzyoT|R|
zpm`YIOD__pqzbHh+vd8ngU8BaZZ*1jbi$eDX?#MMP-!ba+T^Xv#~f@$M~p+p`f(h0
zfA&UPtepHij2ME(t$KY#0$$sLI!Y>YHeAy{PsPp8^JC7PJiEtdkr_6O|4UNj{J%+x
zY-}uC|NW^nCE8YYd-YfkO9rcU@nJRX+y#&@Sk!%69ULzNO&o4~)GnnL6tx>Q8>VMR
z{5Tgcsr4kEuWiwA^zjOiIGRgEa<|$XBeV%O#lU%wnEOn8dxz2bbmpWZAZ+JdKfiF_
z$xQ&o3O(LB{Mg}^Up}|*j2p$}8fD<uv+?on;?ORY{OGMznDF!t0xAmni<+1K6zUj^
zF;;ik_2TDTI9<VkKR*mCA&PqnkjCB}*dVgl$fIMoi$<zKUF7=*++Sduog4L;=czN9
z9{9+$-|`f`kX4kT`ZUsxOciV|7EZ(u)x6E0P%H~$iX3~JN~%K9Rxf&IRlDfW?#GNi
zsOXES=bXI}zeJCt%G2Ds6uJN1u7s3yJ_>K<Pn!AWw)R&B(=^`LE0H?Kax}Wmi!#fT
zw3NW`atd6I*K2Pd9at&~dNH)R5Q!e`YJS!&|FZCM+sfoe@O{mNXSlc4a{i9=*@tg;
ztW2RtgSC7#4(hn>$9Uh2mJh$)nw+FGS-fA2K`cvjO2+?KkrZF0s#>_W$B~Al=A55%
z0B6De(+ECamtn3vA-fFSO2lKS9Pjk^Ne-Rimj`Pt3B-wMYb{B}B=+^p(m!UVd8i<M
zl2&+}k)4^u#&>xg-|zi;^Be7jn3#XE0@4e(S_L2R;T!7D)mI||G&tfkx?0huexyc+
zO!F=cxbMlv#3d$>Z!3Rbj8-9k`sMTcTh+a1`11@|sSCto3z3M|x}*I`Fvw1&d)X;;
zqcgu@RLhqFD96YxLgB?LTmMWEVNyRdvv+ytlN8URU@P1EmHZxIx9N>NHxt9lws=z+
z?ITrnpX5<R$@l$G7*lV2ApWgs$i=hKce`zohO@WlxdgE<o_Sa?BHPMO&JOye8B2?y
z)t#c~v<C+iw?KzDXmlnLIcJkHhWY{@H8SwT5C7;Q4%_5Ex4WDOXevq}WX<o!uxV%!
zPk)R4DBDl=F;Z^Uu_e5}JIzFGn25`~#wrzgArF&S*PLQ-H~HmNZZG~4jw|N^#&~qq
zhYgysrqv{v)}&{M7<K%9SSTL|P8bIRvLNPCLrDBMnWcE6W#;*poV`RvuI3#{BG`3^
z>x+gwIE8`mbuZ^K$z)xr7_ybvH6u))Z%NedVVf*|r{B$`o--D_jc?!$To&k`%=+WN
zk|_H|kx&*dR^6g01It?I^i$h2+%~oi<4e?d##|wv%*w_xpRCGJa|uM9b28mwlQB1<
zJ&D=55ckJDq69<^QHT0{t)bX`ESJb*!D0D%lsUBfi^_K;jF!QT7CB@_jUV9+N=5w=
zPHM!6TjZ&Bv0Ube5*HYW+)TBNK318%cqKulq<PvqljIVFMh}mziV|7jz9SxK;9E}7
z5G<BWrS`%IkFy1littBFC=QHKr$j*VA5{6@wi_lk+`>jzGpHCu#dwjJ0biw85)<ne
z4(VdsOm6d0>92d4b7^iK;`U_}pHox04V_+beCA@@lrg@|SUbae_q!Mt3!)sieZH!k
z>#lz9-e?kv;PYkkCp0ti-QD@Q5!IE0dHLGAnAF0=4STjvobY;P$E`(P)`BoH#Nx23
z_BikgH-z_`^?MsvmM5|Yf)o?sp{ywFnc)MJ6DQp`KY)S=3;D4w!v1^Dq%kf(dacj<
z%d*@<K`%K4kFQAyY}}KrdSefJs~xIDwRhLq^Qwx!Mte9qwLGus#=SfVw3J?{>0+3{
zG;85xD;(puzfIV`!mM3)lJluwVK#H8x3#m4Ww5@1y2D?4W?9)jD#1@19K;j;IOfIZ
z{G+#pT!h@Q7qg4R50{cWjD<!rbnh8SjVC9yqq!((>da8x@S0S{`R1f<XiM{h&uyAm
zYGFlZcnH%F3Kyavv4gU9`7jNiY_*Q&V7A=mpy`<IH)~!S>#z{123hJ*?|0Oge9RX*
zCNV<fCRdJiw+LoYSIv27>P``r6@rVW^Fi;4@2=IGJTiwH+<ls*y#f;6zFRKeTIkC5
z8(5{pAAj$0%tAUtf;j%({gjz>h2hHh<ak|g>*Pba46O@GB7nJ+>Zu0|H1?Q8){%1!
zB&QE+_!NhS(O6fk_=*_jK{SK%BW?RBSBaBlded93n-v4<)tfb_JV7*JUM_oS0=rFT
zx}<a&Ysa0xI3C1k0w?3Nm`)Du+lU7zRdkK1h;}**G>y~HT&@H1J68~vT-S^Ilmp%f
z*Hh7JWs<eWLb+&}w2U)gze0K5u+7Te2jnKp&dr$i3GU3V8q*vgyU<|cM(nH$_2M<!
zvWTz0Wf*zgl3KaSC9ID(ZfKkzm5R*aX6OHHcl>FM*{;^0Jh>>E3jt%MxYdWl0JpyX
z<0?hQD21KD)pNW_n58AE$3ybiU%3?D6v=;3f4*~xNZk(hAJ>n2Q!75*=uT`Cm|E9$
zU++%r5Si+l<mPbZl+0nfq@{A^Tz8AX_EqLHGU12KrJ&RrrPgmd<~GTf@n$6!9rxI5
zG6rfRiU%!C+Gob=be-QctkSmBb~Ts;B6a?ZUC33%mrf%m(PWKpIFz&>=MV?^y?)OU
zBSjeyLMcZh`8L8-@i!G-Fd-&P2J)?pZ2b3tv!6s(Q!HJ<hOg<mJsG?v6kk2F<UhvO
z5Z9y~%gwV2*;-9K*Pnu4!JJ_>kEQCI*TWHSixN*JTfeMnNz5-ioqlN^ck?@_j-wR^
zF&(b$)&r3iHr<cyBlb1j$LQU`5U}TpxMO<gTq;=}&%95P?eq6H8C1!Cbj&K8+o*oV
zO1{$EjQOsf7ukQNj*uvp7lQUn$FrX5LQ?@(fJ9A?gP%56+_%?OtU&K4?A9wkp7D+%
zzPemgd&y-3j0(``3gauu8K`kW`lO8x-?}rQW^BK7Ht++FC%f#EYkJX`&+^$>(QD3*
z+N_*e9Y?0!=d|oqLGK(n7Efs{Vk$lxc3sq^<)Fp0ZH?N7`aG>ZSv)b4|4)p=&i;Rg
zad>(EJH`Qpf7E(CmiO;6mgu0FFwP+E0vH5TxL*0#AQ+eNUqa!C&uU5z)u(C&Vi(e9
z4Zf{We~taAQ~Q}Ui0&toI<9a+Xq6p1TgP$GAP%(Et#)bX(VPAA6?-yPI9poG>=5aF
z3x^$L>Y}yK?<25pcii1wS1dA{9XSg7_cPq?YFlmLJJp6+y_z?dHtYI_13kt^NQDoV
zjj4l+S$(cSHmlx)SH#>mcP$>F9GfMnhtA!y3Jueh1!s8=YXd{AC5vdthx;u>_+mpv
zi^lKW%_o$<Y~K|wZR&6@#mVU;*7ejDU)q{;2GzaYK}?t1%Hfcyii@kP)z81F@TG9&
z!*EYO3a!mgorKj2TY8Yaixec3veQ8Axwfi!<5DMnU@EMtU^}9IsKsH^FfiZ#Tj;v2
zw6}S2>Wlet(zIEN!Dqk5xzZ)f`k*rd7RC_X7&?=&S=wTEZuO`sk>;}(O&YF;f6zv?
zSm1J})EW#DQ!^dP6wK~cq~=~S1dN2ElWQ~Z;)-Z-DY|G(3fe3Z4bdl5EH0Iqt>Y(A
zqjpM3V^FYA-Lc|JbtYZH9l^pVVs>U5M~Yf)y;eVCWh8mOI4xp=MdUz8Wf(5`P6Tt<
zkBH4O_$}otkwIhGU{O{0P$m<;ZOH<*lzf?CtSte#=^8l=!v_mtkyg?(FP$Z?BkCQj
z&S@L{L!GCvp|m8*kV}Fu?_<7oPbJ#sMB#nx_AFq}u7+JhbNG?gk*{b{q4R>us9H?y
zgUofS(y~={&S?gCzMaZT6b2Ml#w>!zb@NM`KpuZ8&lIJ?m%ZIgX%y{$thuvfDSu=Z
z9PA5M8ITCjx)ABVhr?{ogus+UDlCTL^y6Rz+QSwTW-3#u!s)!hRdZRzd2Qy0E`r~A
z6YRiB=@3E%-}PPnw%NroMglF>-uFxJb5iFQumd=iiA!2Pv5-H&B+4WpP;!a?D(g0n
z!%9zy?TnKS|CM+wHc?hGcekO9cwbE`UQtTM2>0xzE03_0(esZEk=}z*_HPli#1Do^
zng6UTVcd90IVHg<&Xd;Ne738Q&O|lI_*kw<5s}_Cl#kCYF?j0*OQl6B9{$qu7z>Ni
zK+1BY%e;G)4Oz#Sa3e<g^e17T6~eD~5qiqL(#EISQk}f~a^mjk##u(@?fZ~|Xv0Bz
z-cD&AOurWxVTpsI6&V=(XZusDC=ND*DlbLzK2r{*zI`8p^#RFi$NiG+J4Uq4U_4QW
zy8pS^v1#*5+An?4Zpc=2Bs>B0Nx>Es2px}j=Fb(KZ0*B1?YmjLQaq-Nl3-*Zq}b6B
zf|&395Z|E~vafzvf5>8lc@;sm!;0B|Cl%*`5STm+vCj86c{|1CcFaqFmBk`D{w&cq
z1$(jz)il_UcP}#f&2J)mzONllkETLFuc85qV*LTBwcwO>W4MgG(a7rvcF<#+E{VYN
zni)bTa&zy5$!_|60u7tV?;EWJNisutUu%;v;m2ZPb{R;iLrl5XZawDFWAdhWS(Xfk
z6jtBO@lThoADU7TOXN&yWnI6Fc0OUVA$GHBs5~kaZ(SLX?p!--I=;pH3f#+!3G0bO
zVOXOG^f(eJ@)sHw`yqRdK3KjajhRw&a6wpTP{L8<)RWE;lc_9*0`e@A$34!&SGsqG
zR!fso*d$WlrH!~0UbVc@%@4j3r?L%w9)CK|v}V;ZS<*9wD*Hq8Hat`;w&k^j4Cl)K
z!`M3n38REdoBg$I+qP}nwr$(CZQHhO+qT_vVm5Op=ElUotbNrRS(RB&?!7&At)YoW
zJZ4}%)X;{ZN;2hDC3K&xQ~}Ln2ggaP<LD3Id-Gn=X=I;=+=M}pik}ud$slF`BElVV
z6C_aVuj9|oIJ9Nv?e8mQrNbi`@(zM|dBdwj$)kydUs#{t;ML0TrA8d2_GnU$xuZ*d
zc5bl)jN4Dq(z;=mS)+$trHxrbe2ub?ad2QAv3uiCXf^dspdJefvBo#G;e!#Q4{zm=
z<Q493Od@|VuE1WQT@6UPu)G(9K7G0fbtaM;#>_F1gZK~rneik}13Mj?%b-nsZ_%$K
ziNvibCYZ13qbLW@O;-B)HL!B{9`-dSCP)e}sR@sG^L56B;g;rL7=Vg#rClb1k5${D
z9H^gSiLs;RJZJny^Sh<YML}U!*{4@Y8};Jl#QaopAg|-ZS<vby6w}A4nU(-ZgHu){
zt24^5&x+nUdfs9{GT0zftEWnYaD)~rnRBkL!>)lqU$P!xN;@Cb8kFA=4#j^=kuhns
z+K#e78639SBgKjQ?95c-MSkypP_n6@g%R3C&HOk2?=VHScl|Rec3+Ub;W4GB6a4or
z-Pl`2df>O;w<!Tif}-Hg46CwCLaXeHidrhIGzX(~f<0^nu6gYYMq-Kiu>Wi3@nUR?
zK0b{IX-!MWau(^h+j2w34y(cmJ^5}6F?5<Trwwk^64Q^t2K{8!`@8kBRxxi%?M2IK
z``HSzzhP--RaYD9q?iYpfuE%Sl;>{4LTo~K^yDHg{bOt#sjyeSh>6_`I`M|)G3xMh
z(K>1y<oWYrZfh|;PF#wyG2Hx;c{0T@2eoj_#PAw32fgy{;<dF=B&L>uBKEkcU>z~C
z*vgCXVk3|ocUtHB@L9-SfIgJ>pt|&&s}DWhBo(8+8)_-h<bP2ohtL_cm`cLPu83uF
zKf5HAq;{4h3cN31P-7-6mllz+5Trl6TuZ-;E3C~-iTNyE6pZFnS1PUml5(o!?tnvQ
zN+r`vx8ir&M?8yKx++I0C=?IA0kdFh(B=eTM^roe`Z~mh6YC!6xXZIi)cHENP9#VQ
zr{Lwl%XlORf4}_+(K^5SSbC|li2qzPc6$yg?cO^npij6693=hH_U-6C7{g8l&n}2=
zoVY^V9uS#!8{th_G!de<RUDdQgEZ=0sJ#G5>GoeYkff_PHd<Xl@ePPW(h47aBz;Nz
zIgR|t))Jg0#tC=AA@Ubbh{8r{=#4NU^f2QfJ+#`KnV|c{vTq6i`rAx(s6HObYKb<g
zIF3$rIQ$fOt66eMo@t96B8#O5)rZ$~f{YsQES_h<qmK5El~xeMFrh}0*htrX0yNZO
zPrdJ{ZTZ?x4fsv#`^M9k=Lh*;1QhH4k$_@mrvIM;sz=i%ZmS*PcUR9KA5XS73gtnD
z?q47v%qAg?r$`h?q+t(vGb<Zaf2y{|Z0(;<7`7s=#$+vZ*Nl##<RLWPEOZ!?(Cjah
zQ4{#DQB&~H--2~Z&d;yy>qDIPsCqiZh?$$*z0Uxl`=qJc)t7&WA?%z2^7l)<MWjo^
z%1Lkeq&JhEJqk$I%Tn2gps+@LW$&3wLK;<!-lJnojEc&dXA&Dfi(mT}Q}mdrX)w8w
zE%&V8YzfZY73-gBa=)N4HM?q<XRo;Ny%LX^g#I4p=xG|*M{h%2RpaL6rk_?<T(V)+
z(@z2Iq}?r+<KEqKrMf7B-ocfQ?wQsOJ{4;9<BTYm?M#zU^Rc$~26ijg(6scZxKyxY
zVRP=IiW0{7;B)eUH^LM7`x-WRRZxboB>@F4atX#b|2CT|visX(wE;d;H=?b$dG)vb
z>qMOXrs%F`+)Y}c2P+K*vuD?m%PzJi1b|gC)U#KuE;MEkbU(W>5R=Dj9~RFfwYWkQ
z`4C4G5De-3^#c*CRj*ce>4UNj>q-=eHE!QoR?x0rqtN%k?4}-a<p$sHGV7I{dC1{3
z<svz<L!|0IN;PfJw_KS#f2XBud4}JkTR}m#jf-D?eoSYfdSoDo3fPUv%pd|-ldrYu
zL~hvGWD!DUnaQW8FLCrF9`d&&hI{hkh;w?Nm1ur~MES~EY(8mGf~8#8f+uifhi#+-
zXJm;*B<g0-{Bh&-`(svecjPeY6EpJnFY<nYe*x_Xkm{rqQgmzJYTu&Zh6_yf3Y+`<
z@KNug<cz!jEy(qau4$?#$bqXDoq(`RkN{OLJ_bGOjiGW!&%q<^_-^n&EyEHc=#3`K
z%Sh2@<Kt!AEO*%uOt~Wp8GeZeS%k^!?#ZCn;0;KR6zIE!IzXaV3*b#5mzhV4jLpB<
z0tkqnYmq}0jr85+?2*tThEW94BNQYLG!~W4o>6Regw!YqrwSRQGT)Xta6*yKsVX8G
z73lTPc+}3E(Iq@dBN!kP6qc1vh9CgdSjV3k$b4S$jl*z`dRqRpkbEllf9c?Y?A_Pg
zf-?|Ej3}cCr7eYfvxVMiMDFJ}ia}i#((APiNeNBvoypZ$c^Z8n-@3S1=YDmbvf0+%
zRBYVBMniuV%^>FA1smy>V1Rtz^*_&AZk^KvYTn{!JJL{oPuq!_k=Sj(aU&w$rNUj~
z&^NQVhre&tLi|q55M{&PGNuRBbusUdC`otY-Xz|9yW`#o!B$y$)LD{2;7zT5nEPQA
zZP*-Gxozx)R?84%<Hh>yo-u|%q|Q?ZuELHSe<v&$1m5_Y-#_if4<;wJ(&yOF-wjet
zi`txV-7F?|R<Jlt&|>}ZLy$lVm>K~R1_qlSBbC-ImtwnP*%H+d!y?4#lF$k|Lb}1b
zqK-w*<#or{j$b^pA)8xHp}(Ctc{QIim`b_?wQEMfyBH1|8))wqZDd-k+c-K^A_S8l
zg~DRzYnvGPSiv-k@rR^<Y$V-6&bvqCU5lt^Oumz6YozH|wuMDMOzvbj-1!m++cbr6
zm2~Ch3WnD#$IcLv&kTW4^BA4$wLwZ?{wSvM#Dcp@8Le4_&u5Dtjc%Y<XWM~UO4==M
zE(@)9#r}Cv1(Fp{?Gr<dGZ%z?jYSqcjSfX{ai?UVAABg;!QoAL&gUttn&_rSRz?s-
z-PjsMmA8`qT0Evd)EDk%&cz2N@vg&xBMT;&Xze-Qi96_XkV!os@^`U-rGzHHGtZX}
zRD0~6%II|K!q>oT;Z#$Cs4|b-f?qsB@<DVEeF6>Ab?X-uIs2T<Z}z!fdQw0B80Q^G
znLDq~DFvBUGv~<s*7BY=s5CqAY>$5S!_W^@y!_l;STxW++PROL?Di|x#HnTJrG`38
zl4jr$^ki%iaogRzf?K80{^D)#YCU;O-5_wbnU>bwwRVbh#H#OWkE+v_IRAZt(0156
zfMU2l0;X|a$@y*rrbTv*$7p_fJ6@;>7Opi#GPD%4V>L!rkSm^UvmT6!<^#xWC`}1v
z2{-EnbU}@8?hC%EX0RlNAjW6V(=vT05kHTs8RY!nVi*^QD;xiwix6}BkVEryuk{H-
zPw7K4fhSIH=e;nofhi*o8=zKErx1puT@K`MKTl}sJ<L)kcZt%gyCE%F&Zq9+%52Oe
zm-HTItr#S+eO^~*>dM&DJtuLnwygm|qWeh_NCBdf((-nc8cX-;O&0#t)tM;Rs-ZTK
zQxP2?oKzR{ktA^xU2j&d1}o3Am#m*0qm>0FuX`T-cY-Z!IbYbr8UNS;l_}{#3!T@7
z3%Mgd2{D`0KBN;D1-gAHOLdwY6ifNycG&0owF!_?^3^((Nq?;?$&!rNq<fy-#iQdV
zkA=#hOlZ<M+j^_y8Y#G3UxwYc*9OO`IyRG7S5G`A3cSROpDI+4Dh}x-R+66s5@~D1
ze5q=$(XUJJlgN<`F~tQvqmZ*6yNE|hokf*N3DHSvM<}HDKun>=^HoUMr}{<c379DS
zuss-tFNhIE5x9;Bw9k#%*j5Y#I6KOx9EOAdia4;yxS>a6p+Kp?v@O^?H9yWTsyeFL
zgLaVc9#l&$4k!+Hb3l1N;4O!F=Gy{met+Z<iFP;UX?VJ9A?IRttzXx<+x57aM^eiy
z>69|b{4y4sqZ~ufNS}IIpDX&d!SUEuqH2HGbFNap>|vp}LA67$T-2&j`M4!`b|!;s
zNb89yLpqX0L0?qs9!8E3{B-g&4bV-%<sPL%kK5n{f%UM6AFri;V9I;5q7%kmNzE>5
z%{CEa!xvMD>3-~#sAs|M_E3G6XQEbbgWr*L@`7z*Lwo8gWSV^{)Q|YX>_Xm+^&476
zF{}2g82Z><v1N`*218qt#$r||xi^RGm)Mhc312qDNh1PXLVA$vDZKw?b{QH)?D)nX
zqO}xiUu1{)OoFf}!Nkw1&U$(F8*8H9{n(6OfSi6>0Fwz5ev$jSK&PElY-86Qs|5Uw
z;k>+4_*BC{v$6`8J#IXf$sLtz8_w7xgXrd|SKTo-wl7?IUaV{xnZnqwuH7)Kol5nC
z#DIMjr?+X)PJ><=4G_O*^TOKs&uP5F)>P^1lSmP6nGG0&ws}dr?lX=T<IUiKnsa2^
zX1V0`)szb^CEs`7j+@DXUAbN3^(#b%f=uBR4DdiL4fEYusR7YhjCOJ09vCHTPdS5g
znB!gUOc|>&MkEkRDM)1^?*88PzR@_LkUswxHOKLPqUJc*82+c2t5KD(J7Puec~d(V
zZ$XezEKL1(%4n|CT-JI&!wT9gyp=s(NU0#Pt;e@*EG3cY(&)eSw%e8j`3qz3|4awV
zSokL|k9&hDfcgFO<*|7aL^q}HfCGj;0Eoh_e*3IotlA8ox&ryV5R5<qp&pWTk~d&C
zMFtR@egPeF@fS4)5xrzg+WWyAS=6uomaKs+lsS)a{Tm%P@v+y=ronq*h&kuZ+gHsa
zr7w6U%68+ma`vpyzduEaj9%)%DYQ~}p{4rB;$BC=9zifW5zXRhKD8zBG=}of7?e&L
z$KCDOW@G_5^IXGfjDE`gC%}5lK)lhtdT$j?j+mZc9jX<Eil&2zdgW_xRu=dCPXb@$
zL+eXF(9;d=blijS;Pa!XgspWo?id@(ZcqZ(Ve<?5V`@dV>&&)+JuKWbwdzK=NH_NT
zzfig3PXJf>_%jXm+_|m;m;rKEZDzmZhD*tD7fXAequq!FPVSToFnnc@7>$0*Abbbj
zFO(o`(H3k8NI|j~`<6QWi;O+R^NRE&07bfk4QrEI7%=)3Y@xCD8hEI1*&-{Uw4e>H
z;C_^K=r4ioFD|KYnh(PvK5S7TIsdm+`H$d!ohGGNefua}z9^9dwIjwU$hj<L5^{dc
zh6J;hkpDoAJeXJstWG#ne}|Up;dqwrc<73)(O$#rJXdm}?Vai~P}PXvwoO~AKx?;B
z;G1mL_D7TD8rOWe;?i~ucQul=))Q{1LNr8sYK{x!jAQ@ZMnla<3X$q+UIfA}8E~xh
zRzhHpeXEtms`71lvJA=caFyh9CDPWwwa3@G`5AkK@lg;VCfjPaau8IS690aa1~GXM
zM5l2=J2k78Z=H6sUeYiSo{C~NkgEoAewr8`mS-=PrBXh)^!iPPMApV1YcpBN#C2gp
zq+Ln(g$&dQ202`Jx5Fb?$>#5pVZ#~*R%?CoSjhZ%kaZ#1Vv&)RV8{Kn-p}XN^i+5;
za(E7woFjeG^C6T35{~f+&J9YPw8*^kWp_db0QtHDt7(zjSWEjs?nVT;l8&vI=gG;E
zuDO(p*afBY0i)57?MN=gt*UGbwzR|uCtN9M^)hj+Lv|(|ah!SRQL0-j_l#p%6j!9n
zv4&1pj-sk=^RdfK3sV`toBUOjlcf^HRb~rMp6C8;yP_LjQ%Gj^hqd3-hx?Gn71S<_
zR;{%{dPn!kPR+iXLBRJ(w37NdU9rX>6)US~T{V{}LQ0ycQG$aSnDrgk$;M^+>wGnm
zc=XPjbfyXY+>TOu+@s&$GmbzN_`?5p9<ct8+nth&p|gj*$^YG}$RPCpQ)|e|PS5;5
z=R=7Ggfg;N+bu%It+0qtSyFEKv@lZ9LOb!O@CXc>yl^35V0a^R6gALv{ZRc7y_+dI
z`m4?Qn~Z>o!a_NNL!5$<G+}1?FmdKgxAIflUAJ1yLVDSs-|yRQz!F$z<?Z9n-EOA}
z4WVcpsW4%JanY3|R~QVF4r>Re7YLZw2Ph@>-g+_k10pDz0}_O7Vt5vJ>b|w8gCdkH
zW2WA)+b_`@9u*B4QgFzL(>n*HOZWYAu7BPz)jNYp35x+oBs@ZDq4vU9p`GyC=}hj|
zt8=yC=yXPtD&?Rd5#SGya-+w8jKO4&vB_WTgqlPupdTTVrF%SuM|~0XaBgK5tyT@%
zv<kpu#=d_24(k(9Gd8fYmj4>1a<q-DP6{5u>-006irwt<<E9zr#c(T==i)yo2#p8e
zeiJ^(1$}^20wU#4tT!M_rlm@ulfHltMIO=dJksgurBF%Zz#)_=ANCsgOQ!h-sgz0H
zQYw%e`{ncY#q|VueDBJ@RVD@rk=?n0<Mb6~Axk7LkPy&I6AfZ!t5&(|6;Sp|5T7Z5
z9_pnJ!puG?y$M0=aAPXE0fkZ+Q@BM*&L2{zyn#{-26P+L=}=Kn5!ej{iy7&c|LAU-
zj1e6D>cIE(I~;{;{U`N)CvcxOsi>*hU5#rDjaP|&0w_zk*X>^Q7~cCtK;S0+3j~~!
zBfTSjzPSMtVDd$}+bmO8nFQyUznF^+S}dC;P#uU~6L%AnDv+H@f(szb!OtNcfm0cB
zGX%KD){~kadIS>a;_DKt3kr4%OA{LpD<7g5Vm@Fp<fIF9%XSM)6PzMGg?|ih4R;L_
z8gdwjRTHZsR7J21YYyhx<F6uIL5LXw9SB<!XGgq+%M6<uLLCTNlW0fStOL3kav_Kd
zBTWS7^e-w5!z6@^%O;anqP|4YM)YF5yp|hAnbA;+Nr|#)Nm8#IHXNcmP<=3dV4XZq
zFPcS|MU4)}ei&NMvrjqP9{ILRV)D6sk3iHS0g`^7IZd9)YE)JKMa6pUK70_2CS3R$
z96)K%efEwwNNr>#iciSVQRs3qEI1v`6W<(5B)x5PTN1TZv~Adp{8U=NGC5IA`{750
zH?96L1P>;RR0%cX`tV(k2xlZhTY?Arw~vRvVXx2cSjySwcaO6Sq$V7PU`zET0gK6a
z>c7ImU<oc(`LO~l1BtY)2^PIJSyyH_vjAVRc-Tt@aweota}CB#HFgUSHnu;s%K#}C
zuZg0ZG@j%5!X#z3F*#ZlI&}&!h`62=bBTMLBn@8G-v@F6ylVu7+_{r)$9*LhJP8gC
z2?<^eWo2SsW?>Ftq9{@=Gj?xMcaGhd#_DZ4)&7)rroYHZE?7&At5-X(_ck+!v<R}v
z?Kbm^ebh`(Y8?O_><RlMK2mJ^UxA=e%`NNMo%cBtmFDJfZh1v{i?;W8qEG{A>@){>
z;JnuK>d}_mJ2pR*ju`NUci5L!)ST>xxoIMRm{V)3=t1+5^&S2EF5ejqZb;d&xi8Z&
z*HNj1+zkdMn<!8XorT!xeUPHoL&ca4DjG4hk`~iNslV+`CmOewJx1-lWm|SrA`FB1
z5DMauhg<NbSu`I{4I8|UN=sEqH-XFWqe_dX6A~8-EG%!X&o5S+L#Jvw^f{Oeb!EgH
zel7d$Hgi+79x^ZB3^|9DTY#&;Xyi8@tU&XPrQ*$*Ne1|pT55)Qg?%xMi18O><Q1aH
z^D0(%jmf2sg3`Oa&h9UL@PBAI>AwAfSj79Sz2MaY#_(qxP?%aFGYVtPIRr@upfiv;
zbK;!yCsxU+NZySl?q_CS7jAGPa+xt>fz`oB7GQ3+An&LY-}vL+fb+GYD?%E`5Bdm^
zVy=PILS|qQBPgdIO>fs!t)73`nHy=qAkl(vgoijAf;zhxg5smexS}BQL!@fv*PTvG
z<7oN{p=yR{uXFXW^uei-3)6OtFR@Y$@NDkT#O4jNMnrv0$~v#+08Pxi>7a@mbgV({
zCgMp!;J@TI-H_j9vvj_ceA#@a{b-Xe5!SM+uKQN{z!RIrTDxf9UbjnTf=+O@&<c!^
z+RRL?E9qKT13>tEefF<*)NyXirQQPEEyjzQv~-f5nyW)<oftpPxo{zR;nzU%hTVDY
z@vZGD`2u_z;Ctrz=?I5R9;eDJ^m+d(IN^Eo?`S^!vomNy<u+=yEBgh;?D4@YD`T<5
zD$J6{a@0>vs%PL>;PDy)Z+;aIRHzs!e_n+w7Y`m@x3y;@MjMitKzRfzUu3l`k17^Z
zg(f~Hk5P{(HsrT@W{csG5Oozn-}BfjlQ)T&F2mpzj}Dx_O9jO4Ie&v9yxqV;4L|iY
zflqRgj<T>}Zrni8A52g4m(WYFFitb`u+-6qx?ZN4o0v9sk?qWBH>94QUcP2FtN;0d
zApr~cd^pz3-KSZtI}%Yuv#%1b-g_XalCIuRMpkZAT1=nM>BO5I7%6=N<K^D;#HCdl
zcU(B@UN_}>uVQ<T2`+=#;f$D^E?l)!Sdf9T{k``dt@AdGcYAKlNmM(vJFW50lYW5W
z^Fv`c58>)7kO<dyIx)dw16SbkA}+IRnXph9H#nzl<db}`dpgUIgX5CU>zwhu2*gR7
zmr^_*ub`%`v%Ras`7Z<dkMD0VQ<hh?|G0|g;>3$#)&u>lk~{iIcZQdk)BTPbGitSn
zZo@Q@tP!v|svmaSrS7cQxG_}2T@{iT$oy1s=CIqSH11K_2VcOnVQz5$?O2vbwgryq
zW_1RwO;6zBtWBrLjzG&f?IAWoDmFTCs)%PG#q;Eo(Kuo$@f}5`K49+W_32O`viI{w
ztmbQ<*u`ljBJG3WDNUv!patf!j!i)w_+&I&@!A0l?Gi^UOa!VmyW0jRE%TH1?a(A;
zk0F*uV8=e3lWX4VVX@zp3u_(upljA@7?Wx858h{ulyOh^L{H-zeFi^jHXyUe$E_Am
zlsG?)yxDlcj(dua?}_D?B)9u<mM6<~T}xfz^8tc2rtL(ey`25Jcu~CB^qr*?ZPT#Q
zhcYm7dmt&Z$z^D9gcj}Yhtd5~3ad(5xIkSvV*b4>h2Gii+2}4)3pbg?tUH{F!zxBs
z6q#-_j@ouf?ak>cqm=oH-#|#pf=-;5<GMo^lD9!%_A2}OXb2x(P%?RjL50NH+eDNh
zSwPF-11-(gTeS@>R!MIo*K5R-47k`9rtBQ+JmafJj;d5#sc7@eO4N;0-quaLiKX%T
zAIVSiDH|sfh7XtZ+`S?UKj84b{*xjQilD3IXOrXNfT|;3GH_q>_;B<+vZ~0UQX<`X
zm2*lvII185ZNOC`-yEoRFYUla<Zdh6t4nrR+=0DwT(`h3dU_x8o4$aIuMvqz9U186
z<?9gsOUu!JZ$9@<?^n=0EFN{dc$5wkx$cuXuaB5wzR}Fc%Aeudkzc;dJPGA<H4ANk
zFJgUvT~BPD_l6eO<pk<-8ZccVcsRIia>_qpBllTC(v}0Dc`OT4{DWPObo!|s1Y*RZ
zs^%X%dfwh8R9fOY1=+KSO;DIOkvICh-B>1fQ&Bf;vj2PuN5G2MJ}k&5PC2zJ+UF%8
z^jX5d^>a4EG+!a~W4;1rt8smEFy@~vqAdW)X^GcOOT3If;*jeo%wA+VMd<~Jt_RZB
z2`EO3G0)bMQ43OK82cl(i$H>u{n@#AxfHTe8l-Tt(<K&e6)s#ep88P?UkW;qEs3yR
zW;%7-p<yy3m6d?4oLj9?t!#}jP}nc^-!mW)%sMTVSSmCu64#kSM*nr=%cGT-#b;Pg
zP_60sAK6nkv9z!b#Y0e9>VQFz#Q=qXZNCh(XN&aP-mh|6x>*d>+Z^)WFUyRnJBo>3
zJ{|4OiVd7Qo&FigIO&Gm0-^gGVbEB}X(APFfiTis<>L6q6q@Ul_jj!U@BZK%PB-IZ
z-Vnjpee&-WU=2GhffXoy3A~Qq)(^#71X>|h9b}*T3?T-<WdFIL4neC$w{f#)Z|ONE
zx0&yx_Mwya#_LVW9T64N*ZAyGltEd+9|Un_N7)yk%WMh}Xd!Ake+Fik0<u=!&RD|$
z2N5GP>i6Pex$yMRa`B-gCt47bHzhSLCK<7ka_Se{cC#2J&^ZbKW5%#bf*tUW<`yF{
zxKPF$CF)}Sdu;+L_XSqHO~EtAal?x-!F`*4C@=%ddyXE$*JjH6ga!iD&bnbvNT;&R
zU#?l^gGK2Jjb0SB=xGqJY+w_<EObHEe7dXt#>3}hEV+?TichqjPZMC1Rc&d<Ldqw=
za`V}XewL@>)|WwpJe_6y^J<xeTfxjj)XI)wN5iWG|7ug&Z$!z&#K=R%j+<K55W=-R
ze`;7+VdskhYY{KD8HUW!!aFwG{mott<Wi1jpOC3qD79)yjGli!ht4h(b$R@D_Jw{w
zYDCVBGWwh#iW7Tt2=Q^I%Z%n)nCVnrCq%dBr^iQ0jec(@W^xHLJqwT9@*Ui=?U>(9
zHcaV*+L@6Z81sIY9{h;c9{(w@J0t*e-sFyGmA3-a282tz&tT}Av*)*Wo)iy=9f~T$
z`Yk03@5Y<(v9e&<z9)yc{H@<Fh3R7OhUSaPn+<#LHOJ&b@*qUfj6^M>KOUQck4hq#
zjS`shJ*aWl+x3C{gRY%y?Iy_#bS;9}+Y!1uj5$g}fW)&C347x%hbjLy^ZW;i>l<)E
z&vMdi#0gKfZ93!Rm`xWG2<+2d(Vtf{L%opsFn+veSJc2q`{7?!lF=R^Z}rnW)m(rM
zEL0qS{{$lD$<l+FgsJ*1r&#SF9ywi>P{{e&e;@TF+#+=T91qf#QDv|%F6%Y0FjA8X
zqGgXey&u8$j#jd-d%cIeio1>GC+5S_LDZqEf~SHU>EyKg=%QqUI+WOx!DDf@j^99w
zdg#!4Hy%5E&yDLK=mV%_7gp^jRwr4Yw^LdUXZ}QaDwg)BYSOj2Ps!qZdA!EzRX|lk
zP(V{lKLDTE7144SD%NhWfEM<(U5IC&XNBpTzyN%yTp!ih_ilH)!RK6eL7@>>_Ig<f
zV}qPYWE(uFoWnSlYJ;#uoxO0!f~!}t0|PmIbkU1G4Sao*b%<=gP_wAO+@)%^UHlf}
zl8EXs%Od63S=&mF$Fy^a2T^f);9<I8FHD)FWJ$h&0RvKMss!X2bfxb^1^HO@QUhn6
z8Ysjxg%3f{#e$PLD6P{C%$&bq>Hg|VIMp}hA(FeCzPL#ua99rGkXzdxl-ZwWrgx;x
zEV3y`9?$P0;2Y$dhacUxS{#&STY)QOwajc0nfUZWVm|#-cqo%kbDxZjc-EF_N%fV0
zb2eOf_I|gy%UtYoPpG|c7R~<w3hH<dwwcanJr$fNlleL=u;$~6$Q%GSE5kr6*d3hr
z_^i12k_<T-fA1^t6;Ve=6xF&*cPn2KCGiodkG2O>cYjy^t*ngbJlI92O7(Ai6c(z6
z_-~sa;ocZ_XRSTqU1ag$W{@Z7I^5+-=J<!>CcM^n!kD(c*eqw@Lnj)=3%x+ri(me_
zO6{^y+Fm|zHOV}44PpEB8Zghe!_o@AE4EsW&bH!`ZhWZc`K^9!j^53adP**G{rKP0
zLuh;P-czOwNHW%AG%_zf-(0XK*Q8ItBgFY8T?nm{n3IXl#TQ8)sv#8|3C^jonXp-y
z{EZsw`HO)a=}I$`ty`u|qOe1B^|%yl!tNWHK@cmokBbKA*3JgbZiXW;u~z|aUFGwc
z+&WMV&e{QDNcqW{Uh4zE+xq=IO_oO4g1n-<g6hJ0b6Yo9c>$4?$@h7T6l!~|c@664
z`nf<X2J(uZ&47$Z8OAx&>#^JCGAOd=kn1{jlR~d&*`B3))FIg~Jyee|6Sx{Blw_6W
zMzUhxdA_pWcBRJpj_@^Jn5-`mkAGYO3WA3$d&qnqxZG9S?K|WRUg1<nM>bGXhpGXj
zKfS!EaQWWmfGy-hQxHgf`@liIoR@juCv<uN=~#MDmqn`Ubav2@gS=iT)gW0nqPKmq
z`53o$d}VW-UaS3og~;}WQ+IMWFjtLA%akEuUiDz|b{xC>M)Bw87k0&7`C2;#)sobT
zLYSdC7(T!qEk!3bgtG@mqB`}uwURp^i7xw##q8)ysk`Yufr3lJX!?Bzf(5n0X>cO%
z#C?a5e4C?it`$8gP&}@7{uTS`my7s!yPmY~kj?^W6=j75^_A>oX`XN+Otl-5HGJ3p
z_Bl}qpH7ugS5>grcIf`B<!WldDtEI`EbCBdY;do{2fI*qgMVRIbZ22{iaK(-k_EEN
z0__i;4-{;=?QX|hTJo1;du>J8u2-vaZLkIT1UoFBZne=(hL@g=k?#~5zB9k?_rx0z
zTZla=SwVt30}!guHeK1KSHq>m#+42*O3K4<#ItYu7Q75J3vhuI9c|bMF{XtKeE3yz
zt$3O?Lvv_dlu?+)^465qb+as&FB}{oo7&w<w_!vnJTg5tD?9eU)0v0Ol#zSx*&KUq
z9s`_oa%2=gc(1uZYIUwJxv8&vZU*rux+|S+HfwSTi`(BZVRTXjZM|h(-P6nEefZcJ
zburt-jd+@VJi&@#=ZD<~7}E?7LClmtNGx+;UY{O5LA4U=OIi5>Ub^ERb7JKn@jYg6
zzV5gFTZ&k{P;)=?`Cj#ZY5Ku*;NV_RPEH5Sz#Z}D88l;J;Rq?XC|%P5B5i+!Whk8Y
zYbT{IGR6#E;k81u+1U~^8>lCl?JP%iU8CcDAucOJ^{0Fot2l!?Mh{B2m@D389_vHV
zT$)f<M&pLF@IF{QFUlr*3G_TBD$}>bDU<||cexLr%sL6#JPA!@mU9KMy^%}yF@=5)
z=&mM$Lxac#Fa|L*FE2AP2QLNfzSt6sWJYz>cQ6aHEOG(w0bovex+9!WV^vyko26m!
zTk*{ZD)*y!S=3hIB*Cd*E&IOT9IJgq9qhBw>3ZwGv!R8q*)MeoS8HC^&oHe{rX4tP
z_*CoiFLO5QFYqIkWD4Hh;p#s@-$|ScrdN5P^5OSVSd{H*>9h|^gGbLr0|0_pn+xDC
zn@oUy-M_PWNn%~CG))I<zdjp6UYxm!)p2w~%}$=;U9pDTZ%PKWV?`ai$8vW*%wwbg
zY?BQSQ>%>vW<6M|{&?i~eO9YYY~q`z3iE%HP}kc*)AJ5ZEYLUa@<LmEBG+}|o!@y&
zuL8Opywg>!emQEY+rQ_mwhme4FRYyITYxQ&0nmIln^f&zULUK{!}%NL%XsH5%nqYC
z2ZZiyPdguL?-JY{HYbG`uzZKCZrA-8bqpwXk2f>qGV8((c6+b|yLIPWAbuh@yxg#V
zK=*lp-(JQ})>fW$%mO~P8vhXWOuMeW{DPK{XAA1?ckjS{DIOr8(wg`ZpNqxd=LgIQ
z>_+$_8tU7-E2_#K;pO-8oYN=cTK(K|ouS;--e30%oxO*LkG9)=5q43n9Ok$_sE5o9
zhZ+~%%=*aCL&>c*V8HXJX-)rPiYkb`le@~i<)jI&aJy?v)dpw}tg+%#<3D4ex^}(V
zfNVM5(duaUk*q88xy#mtcaUbwBPQ?bflD+~YKOP-m(t9!F<q{o;}(PF6P6JoZ4zVQ
z1KSaJ68SST{U)8h<epzR1L|c=587FDnH&vnEyh{<H6S!)bFX=w=Swjv5vKynDIIt*
zv$D~$kf0Lb!6V}$*RT73MHjD)q?==I4|gq^Za>%YFdAu>+PvnKL08QM7rjA`UQ6E&
zR)0pe@b*#!r7UHlIl67#7PL;FrUs|y#c(dozvn}H?QiY<!<1LVFJ;dyVQ93dzMt+l
zGJ0o}s#>R$2464xd&@(Mi5||7Je4Sm?kxsJWO^a#nw)OfiEjvM=39!D3cG|*-8ig5
z?+DbIL3COM`mvmzKx6AKAH#=(r_tlzN3-B06(~G&r}-x3X6v_w26=_bE}Rt!s-(dc
zz{=1Cof+T-Ye>_8)kP0ZAJ2;6bnuov3OtFSYT&qAZhM6UjSB_;!|nzm`VashhdiW2
zm|W1<`wIdcCq3CnsOMm5BPifBk{z(SE;pccN?~5k`V0$}v+5oREHup?5YwWB=1Ln<
z-4;1Fg<l=3GR7Q_UaQ-1^HPpnvR+bvY$WNFvXmUMVjnIIB?@-ynkES*AwRl(FqDVQ
zkS`!Zy#;@Gm~;ktwkBi;#MLvrn|00VsBh=$WZF!VNB1MQ|7D&KIM{j!@SH<sMQyqG
z=ZcJ2yZA|jXtZB<CEA`|H^4mE2htt0kpbK8o80A-swTy?`5`5vW?ee(1cqLy3o-v<
za1c|Ck6w>nAD(xTB<g)=rT!dtk<Y3tt81;&GTaFzz2i}T4ST4RVk)(K1YO2CI?fe!
zSU+_XK3WR(;9C)-lC(qC&(j;)<g^k)7?Q+i=OkCS9+_Bc8f!Yw3+4)cZ4AFUJ81&R
zMjFO&)COejrW>}Fnv{c`FtpE7PtH(HQ-DcG90@Y;($r{&l1puJ6mWL1*XNWKY8bhz
zxQ4WHHsC7+jy}4!`_@RxshAb}6VqOove(JyDJd(gs;j1%b$X}$1nK2ZwH7BmA<6t#
zWg4l=8~((LH%8k5u*F$EV3#=oE|f?+k*rOLaWOv|F<+1+eVluoxgK<tK$E4At3v7R
zNXfi%{tDjP`)g(lpo`{RGhRDn*0Klyf%s4oV+ECrVvCrfG|$zG>lnYkpLHln(W2hb
z%~(R+gh+g*?{}74H!n-;fAibgSTH+JKHlsToL7V2ggaJ9jDkoKpPQ4J@_5!30V}&`
zAfx86)uAHI)WOLmOa-QErr*?Nk;J$vKXX|4#G!&RI)yKwG@{UTr2L-?rO4~A5=uJS
z!FeXy^!(@*s=W<p_@<BqYWdva0hGAyI|c7hT|?5tO|?qpL&{4ahun(?iKme8a}Q~n
zl(Wrye`Cn9rJ=0PuAS*s^ZMOi1_45?S^QmQ1h*FViKb_upnKEzDLCbVjZh%h0ecGW
zXG&#(VuRbt7G;y$Lhk+2H_E&8a6O+6oAh6!!*MhYS8|dTqboKe_ut+S0ZaD(Vsx?p
zKgRd}vC+lC#_|8+LDZ;2s3Way=7vv1V`+|Bm{rRt+Bj6Spth*itbtrbMz-e%j;F@+
zt0T1nGzuj${fB1+Fo56+q!!>;80b{h+OlM8X>G}RZEb10*}Dmj{qy@{GGFoB+WmU_
z$m2eiNY8%i+576*bBqPR;y^?K2c8aHq5Jgw;%%9k+hY?hfrL;S{p9W;R7IKP(?0e@
zc9KDD$Hj{37$(w2WLq0f*rvW6rn~5)M&%7PAgbjX?lX%$+Y=(qce5t-^pZXcfDqFM
zlqRnS-$m9&^ZvWO=6EV2LSoAGv`lrg*0u~}6f8*K?Xo`*v@oxHo<+Kl%+SO@AbJtT
zOEj)rZ#UP7Hf?o|k|tdtH&*OWBuVnLd)(=8J+i!bUcqXUP@6I|h9ID<urn-|#2|f9
z2%RuoZp2XZuntsq`HEtuW+m0GP#7I8H<L=MDj`sG$)=W$K%Gz=G<Nt7(pY5I5NuYg
z5zQ-+Y+)uOAQmNpO0zU9nM|iwF=V&H{n5k4DoPk7`R^99a1OCK$);MP5Jp0N#H?t%
z;C52Eh0YQQ4`ZiRBOF)K;P^aszg{oAo7FYPOfZSwqD&;UaPKN;vS?qzDT|UAd0kj@
zh?qPCkGL|K9`3bjCV^y(Kmc(-nc?ZKmz(1CD{=S4NlYu7!r9I0`mW}7L&aj5A<6rt
z3;nC9JWq5{=a}#*?$Pu8s)tT$j~5_=HjVjEy+vqE(K=}J5)PA2cgi-12SrdgYNXE0
z3W*gHGqm0CkPaqlh{OQtg`8xl)Ie5G#vo_S$qJMeF*BMwa%%Wg|FQnG4mpjb710x3
zGpK50Ro}ACISsbuZ!^qV;KOF*yN+s2)e5r}`(eaYpR-O&4UjbzWe@z?vyTxVsSocA
zH5;eSY1p@=-Iip_7bA`62_aKh=0IRez!;8Iq>F*WH0mThwa#Jm$1r<H>oAAQXVdL7
z$OISTQ9dJd-LMCgh(;<Z!_99~_8+itYv>F&f%~wy<e9^i<7u{7<d(MFi4y02Osx_d
zZzw}o&&&JY&H*QTk{4oU5?GUVOD#F2+Hnqg1`QQBF!UKH1Hw703R;@h&C05$JQU@V
zmJ(IeMV*6<LMsD~@IRSWYReB$PJC)Qlx}|DqlbzKjRcEe%sxw0#~Ai?&3Ll4uuBUL
z+1l~ITT*{kg7MGl|7e24F2P7@=tR*WBm2ZaSOr+R+C<h!EdL>Tq}qwGt*ORUE>QYv
zOQi)Db58)oVC>Gr&?=)rRP6&d4nA`Usp|K~%j4r+P4w)<#O&;h#ma|qd>=85xA%Vw
zz%4fq>j6dr-($Sgf=u)+J~B^Zbf7!|`*b$v`R9U&68Avp(fI`~%vQ>(<WyU5IR)}Z
z?iHFlP0X2A+IkpkJ;iQD7ta910BDIfyl~G}^Go}Bww;QvicXzQ?EI(p7k8l^&XCsz
zK}&n&+WY2YN_T_cTJkPD<+LQgO2tmCr^a<KewK0RzH~G9Ht}NNp$D0i1uA*@loHYk
zrzphRP8aJ1jcprTR9sJz7jH9Q|2_N5$rCtwh>NEcw#{v<Y*F38Cbf4td*k!y(-%)@
zwr{w^GB%3#16B>BgB3<-W!b|gC6X?AUBSDYtvgw>5c0IA)PR_ruK;Yq0BdN$!eW6!
zhmXUMW-yGvYGSxUfT_jQX&#JED`q;u?cd+SVBi;!moVq<+i*6+uKnWi>{E?bcPNM@
z^x9`gxQh#O7O0ll0i5I~{&TVJXy}-ib|PzfR&58Q_4#2r<J#wb%30gm25z7OU;G3q
zbA+-etNjdYY3X}_*Vnz<YSIKu4WZL%4Az>aDNLZbp-4Nz!@Z|=)&d}lN4wg`t|T5W
zrq$a{+sx$y0B1*&4Mxu<{x_E7Ih2V+nM5sM8u<M#DQCvVF6Q0-d_r+pgHP?`R2-Ql
z;}&U2%lVcEG+YISthELfQkzOHOfeM&g`J9Ngrh$ml?LWUqt$411sMTIK6P}(+=9^(
z0W^%0dys_Z*)Pc+>Acz>g1l^l6tFY#;P~U2yCd*QuNATWvcoq`E5Lcv-AvNmOeD>y
zBuT_Ro!#z>!j>NFbRa}%pW!nDU3QW}5O~zCu)UbFEzD;N`E)8Usc1?~>9C$@?Q)u^
zhA#kQq*GSZGi^{s+k&(`*6u}`4P-XN;g=8jemTCU5J{(byy>_EglG@TW_!<=Ev)M?
zmm=5FV?tp^bpt#-u^Y}0Azxep*mk{J7y}FmZ?&Rq#Yt%%#L%a026O^#UTJf6)9#qd
z>s#Illuc+`%RUA%k|p4N`U;*yD3{U>$^d8P@E62CAt=~|?9=1+51OZl>*CHIuN!y?
z^h6ep{U0xE&w+DnFw+Fg{oy=R@@D;HJSZbS^!iM%zHl<9%TGwDH+@=13kuj|;Pdl#
zxw2+ZpN+MlmOl<kJSg#AZyNLrC=%iz_qi*OqBz(@7;+3k%w*!oX`JSpO7jiTco2*W
zicWskyY~>me;CfBcDZs@gblEW0UkM7nC+i-VjSZJO{9ZP`o;lcGb{OLSl@Y+4)0yu
zjxu7+V_xF5-qX+P<bC+;3)bdSCCVo29LF42oXY53>;Uc!=(L|BvU8FSU{FQbYv#E!
zxNvJW85WYA4^~gvHPkI`etXneD2&)+W_wn$5dKHEd&iNkp5U+fE^azncIip=_jW62
z!r`;Ppv8TAM#?0Xe9UZmW<t3wJ=T7Pa`qP;&$Nzr(KDBOrgzHQo*ba8IcnQ|T$2ut
z&k<0ZK-_jTh`1~RLR@|NMtm9`G#j9^F5Uu6=@e@o|Dyh{%K-mho|dWs=lzU&W_<m#
z+LVZFcw&EL)&g(}Jk;Lqm49<Ef<qV^H}wff1~2fUIgBp#WCIZP^kFlNv_)OQDE5-R
z4cp0sp+KTQg)mw$RL>3MnZOrC00Y!)gW}%?(tQHCt3ZW9!lyoER}9!fQL4Sbo9j%Z
zO)0GcbO!C>Pt5e_(4Ig-td)&MkZu0*@Uyq{i1Pt$=vC{S{N1#dpeY)q?o+azV#Pt|
z_5W6%QIJR{-<Ha5Xu$tXYbp>Usy8N-;i#n6$w9>SXV6k)AF?WgyeVu0;{ZJH2eIlJ
z6AL~Zee(A3fDJQY$J~iWOv8yfWX~DI2m0Ed%-|gWPB7SWf9Xpaxk~5;od&eKxu?zX
z_t6PBn6aavg&p#um`e2mrQO(D^Zi5H&o3w+x4=o|kK6t?mmAoM3&##iGRiURh|=8U
zNp7FRf=l6;d5)XyBzB&BZUV5aj)V}}&aF6p8Gz<wg$mmZ{n;<r5eqvcfyWNJuo1v+
zeaAlo5W9rH)4q+YZcHC|1^qmfwXTWE6PjBHD{p1a2%o)&XH9?4h1DM4@&}A^7E<*>
z!5=Dqk37N8IFG-p1ATWDpMTbxoL&D($9ds}R`lUU-}#rAd$WF({M0H+xDo5Q@j}UX
z#!>8oQ(`;Q)rQmodGm6=)1&&+iN^8uFOH8%<I^Kt)*DhVJwz8knq{PVQetw^{>rXn
zDI2Xjbt8Qn?VDsMW~JmG4Au<e#W)<u7uHTu&*|l35M%;Fhio`$W|$pp8OP@>f6hBO
z-(q~Oqmq2W?DSS{oX`zTOFT8XY+FUuTRwILhqm%%_w?Cmy{#@MwAF3uGR!^1BZoV#
zZi%%gYC1FwXHx-SNM`2;2liXd9R3*rflLOWZ2q44IG}`gNxF$A{GJbU9cF21QeC)>
zH(%E961MrK%_3UB9J(5&ZJTmd=0ArTx^XLq^u9P^G!JY$RsbAw<EADHWiF3+w@a)z
zdH`7hvkq4wG?&WWf!*cKx|%_V&+xi6BI`ryd{9ZDZi4~>gpBIQlZTmaCU3u5m*Bep
zq8gTf!!JewQN;RQg7W2@1Uv=$a9L@RLNISN@y{IrqY|&_1;Fet<~G6(|NU~yBUAGl
zd>LXG@Br3?<JsN~6+l*3MbA8WlRe((NrNw`BKQUI76h#Q%FEgvj?la~VNtNzM|XHQ
zUZ^@e%I9m*vegiOi|n^LEh71XoIGcN`7N<hV5QFNO&0>1`cCuijSg9a|4eSx009g?
zxoVq5s{_5#R1tgRE&2$Zs7iCZX9Al<UUOdKqhOa@2DAQ5>_%j<(IvS?z@W?GtPy)!
zNN=%YcR=Pg`jY+tX=dp!r>7zD6h4jWH>73Cz;JRXset>3=Rzg&>?cW*Gjwv~&l~F;
z26lOO*H3R*5Vf3!KUob;UPWGI{PjMJKD#5`Kf_RP+M}md)MkP@#whQ2g>{fsxN4;;
zCV}G(Du_AT&Qi)gl4zj9L;}HYCquZ4z`@(bjKU`1wkFy6ZSP@|(vY*~fVoMJ)zgfh
zJ7a_tkSSL()afatK|8L`-%22s#*#GGGqx{7z(H`sZz{H^K`5Tw&ZCu6G`mXw4g7Hd
z>8p$Lk}C$sdBP1khCkE&460H;kWEEw))S}+##aN98X!5O!TVMy6Cnap5=taZW4mr=
zE}-Pd)RlK*Iwb%>PFicnc}oHNuRz0b=9SzNWj9le&79Jf!WMKIOW!Zp;$T}j^C=t-
z5^N5ExT~D)y|Eou86`zCf^J`(l0-Kb5hlDmITQ$yUjE9xs<bcqw8V8_EaA%k&j;wY
zX6lGD5Ce*5JcizoJbbdnoow`&{E6+13E|yAC#QfM>z%R9^iaRcR_XV(Q#<pSyB;qk
zYzV?911y1TYm2|hMNdU;=%6+Ab71$CY9I37PO6TNHa4tbBXR?l+rlr=)#g@}=Rb#&
z`%(+UMt#fqO_cG9^2&BwVsE`0t^2IDL>ydMW`{WknZTgrlkSe5V|iT;8QpE23}xhi
zNKflr&LKS!FPpech*N~^?W(7J<p+HGRBVfC9_h1WN+v`oLjWeDBNcJqT?TvIML5s@
zJeWh7p<mw?@R_ArvDv5J*8&DsP#kV6?Nh$zG^eqonub<(DM0-~ereh4Rj&Bgq>+z+
z4`8;J9S-~Cc3fq`OuzSZT6I!lwTFQhZmVhon|{bj7#ee5O_k_MYZ|}#i0_wS@lE!(
zCT012@2koDDEP11r**BvpnR$hp7gvA1UM<sgJdN@HE?dppYCfyGDIqVZ(VIoed-=o
zZd0W65Ntocf7t}QFxe$%TUW7*8L$2C=h^iA??E~=5MWE1k5?~Z;P2t*%;#;{mH6%9
zs+ID|aEI&8tdS)$d(6+-|H=e}(j7kSosKO&9<7$^;R$5ZuQ>UC{&2c|!sd3o%NW$|
zTfQZJtr3l*jao)Hv4S2ka3CO@KXd)JW|1}5E)by$08Hi`YE9v9e-C4Z<hjllo=+gz
zae3j!#01$M#nh6lh)sC$G}8kwS(2g>`C?#fV5s%tDz}B}PLzoGb#XV~^^&aTE~d3R
zVrFNjqy&Tbg<UT9cSZg&b0p^`p}-x6d*IoKm<(XW{$exYfxNm${udqZ1YgbvSJA8x
z0=y<MiEXqLq7ZaP$gaD)AsNrx+MMJXz@OY~9K;NS!p@JqgYq>n$@cE@Q~oPy)9REH
zA14^AVL^7blhNw4jOj~Dv)iLW+^@bdZ&5>aLn#<K%87zc+egw3(<|uKiZQPXY%H*!
z<YM4uWAxL)RW#_U)wP>EXp()%sshhD$O?b;>k0Q?a5uC2Dp=D(jv6KhEuOPbHhCX1
z;F|j?>--`CXFJ@Nly&o#@^&isK??`H{BG`vVw=B*yoKdk;5eYudHJS^87s3V6(e%z
z8#Gr#elavuZtbKm_Fo796v_=~ha*Ar;*$)1VC6sM^{4$O+qpXAeW`aR=`N`q=RD-1
z6KGe^g%yo`dgw+Of4HX^fLrn|x2@jw`W*F&3`%<iPlB-|sf4o)i!P>BQ&aPmX)`iS
z^^;<vg36`8+1U{$q9|LgFh*hxp|9Pz1>Ej?j_5r{6!Ci{uHCnP+ZT-$+tOylJ|AWG
zm!JYuJF$!>F9r$=)?SH=&76G%gl!hhh`I}5?`3B;mw~#0$j}B{Lopq*SY=!H@$W>P
zh_9PDU;VGziy!SRl|TpBH=q>{of$bu_XjA`|1g|L(ScZedhOA2J{#6bJ*4qxc1_B?
z*QAn>)vqeHqwK(VOb4RL?{vive{00B_M&d$6;W2U7jDbpMq%x^C=iz&BeZ|QwjN9(
zEsM*ImMtyo=Yad2INR;W=^Z<}<sOqwClQ0cXQv@CYqVqRv+gyu4=B(7NK;Pxm}O!k
zoBkcJ+w}+}83uQ6_ac5R-;f<8c|&t2mYZ}6znf3P2sd7YgC6j`KK8&WH<E_@pvN5)
zZ@he&CI6tT>2nZ5Dq+N$*3+l{ipM?(pU3ZWYo+2X^E8Z|;*4}>kK?12qGOBI&gdJ`
z*IoC1@h0~4G*P#Z{iaW?3=?ce<i>=KOlAI;Afk1DbN|u2pPb+Q^Dy6|tI*B=0{HEH
z%ZY%%7PRHGQSp$}`!=o<)so;Ih#dX&f{QrBx4c`4R?&sGV>)jo!ciaaULiN%-l)Ez
zA5fOT9!<8^;>v~(-xhc!1LZPQaTB`Yy0B*yx_>w@Sz6;f7vPgBI}bCI$Y;NAe`$UO
z<`<4LK)t}rJ!P^Hhkm<=s#PY*zeR_@&XP}}5Y9}+A|MbvI((N97C7OR+-yLEQ#*N#
z$CtB%?~R>5<>)ACPpW^*@vJE}WX0KCLX*Ut$deOOF5IiX09ncL!u0o5*$YO+RvDu<
zyTs8dv8idHA__vf4m-X-JJ0^gDw|x-9TaR#=7uB)qr_P~lF~^maw1RWpTsb!w@p1g
z0-ql7@uWRn*|m}bmN_+T0}R|RTk0c*$UX9|u%cFgx%NrsH?PV)Hzs9m!FOwbqCAM^
zGcd+;oe&6R?M5mZ5)r}ymao)*JE3|51-o;HuHvZ_U3L2-<8`3D`v2nWow_su(x%-m
z+qP}nwr$&Xmu=g&tuA%(DciQq-G}e2Zw~gF-;no8L`GaO%BNu4Nwp1;RpZVB?5cZ$
zIADtR6j{;K-_lrvB_Go3Dy3&7m0GzZ>v5oP=H9?1Xm4uj+1~d@F$uyMGof<wufon>
zKCu(hV^XUl7bD)>EltalLLEBywv`BU=M;MeNsv79mL?^GQic_E2$oN`kfB!S%+0#u
zx+riW6PpfL3*jstnE*oSH4W84^wc(t6^zs`ytD<gtI{JhTZsM*VAdL&IhfyI<icSu
zXVtGve91te@3bODpcEx6L(K;*-CRt*$=g&}V{a#<78GU_%%PoKn}2dXr#^eZGW!@a
z*-^2i12ZYx(^iKiVuV8;l=Q&=ym7`i>lxHDOgD})_3Mik2g%b12P(=tk#JfyakJ@^
zTBPJ3e7xILH0J!okT>@or9qCH-4s@7Y?w3z`S~uXWG305miO+`zS~e*Ur@JlYK?Ni
zoG{v#{fwPoFTNj2izhbo%H>(a7EIeRO~kq@=FYW*W9CKFLGRLa%)zyFpesWX^2r{m
z;v1H4+f&LkPhy_B*>sslk17<Z0+ZvDuCdB#g}R>C98bLmc@OkJ$>(u`OAP(ez|>o|
zt*>HayBo}@T>wJ6u)%e2oj05W(}MlGVsQq;%f-_BYxI}8Mh%U*vyCg6Wav>FGSA0F
zX%9r^(fW~h;Q6oyDmAQ7g@LVcCq>jfv?U&g)G8e_n|7S~s(j%mHr@XB=n?5iN!1D;
zKy*K1U`}$GuVUF^kUVz{7CsYzMqu_w6s!ujzc&+zG87|XGZ{}r1~(gz_|B4JBl!oH
z$J&q;SHOHg+m^lwO=~PJ^kBNw{9@H<mh~<GNR4gG@&8r5IT-&J)ti~=KVV3qnozo`
zW-I+e&L_|z6Hn`k4jz(nep}2mLu`U#Lb}46ibAT-W~Q29cp*l7gdcr3(UKX5Q92#4
z)@(^x>e{`=YvxaVPdp|Fe*HIC=!Ny$eQ$$ic|Q3cIVU*}0zW@q35??6G5?YscI(GI
zbK`Dp3tetegd;Q$C)_^X;b5MWPXP4uJPX&Kgj0RS*~l>N&ba~gBEO<nx`kJ;u*4AA
z5{=`4(Tn;Qiqm?T)1%Hi^q5A`Ki9C3vD%Cd-=8b$l+O~>e!YIewdhm%Kj)jBk6nMQ
z6-rXKcxfc!d%vO8BKqr7C1&|)#=I_$wQ4mgPUQ@$GouKr`SSSjf%Mg(8E7y7cIw$2
zwa(k7t?OPx$FbSpTsJ@0?xsEZg|Ls11>HCTa?A_HmBVpb)0~X*Q{Ltz&5>(^m&Th|
z2r~h^LzpbIEOAUmvREv~Sfe>u09iwFW^`D9tTCuL^8+o332o*{#z`gyW@6^Vl&LwB
zOJ;heM#f4;>6G(mSfhzLBTc4S3}0(wn6^`i`?8V@PDO1yriC>ibZyGksEv6e<0^)x
zwQu-$BBS_}(>S_R=wYBx#~@a)0i0Qw1Lix9JC>99_$c#%=>ZUX)>X27g8mUKrv&_{
z)4~0ipEdJj$%N@Z%hF?EX6hPrbDu>`{y1xTmQS_;!4A)$yYSO+Ckh&RGF=lL|8DN9
zN^+S3H<2u|a_9|3b^g~_Glr>3!`A0K8|!SrVOHS`%noXS&Rs^&;_x27mCt$R>b)IA
zlJY)L984{wLscciHR@j6q{MJZ!bM7&h=}1Y%3?7EDy9gX5As!ENqQqkt6vhvCH9>@
z)+RH@<`~B!cq_Qdh0>4mTVz2`rAL3s{@s=>728^cMLIo2qmI=q`a^JVBZp#xT=vr1
z_h@tY{aV@+hK@i0&<)deC+nc_!6i3icrL+qIh}3{@@FG0`Z?<~XQyXZZ>itdMdjB;
zjvfX8cnv_iZCY>hnd?iUOuJZ&C#~wJYQ-?3qD0AX^5~AnxH3XX`R}Y~%J@3u3!-}F
zZpiGsnlv*E^CLlg5Bpt>y|k61tBqdv^>uf9UYD|md%DEOi+ivaNHJeu(TBUbiRGlE
zi2*+1HiXlg>vE6mID&d>YWlkM8~Uu`+GWL)2Cd)BZtkW`nYVBBEugGH1n~v9`5#q1
zlngi%9iJ)ZyM#^Ie<cGUhaV4V@HPM&TidD#x+d~&eVe#D%h>x2o?5?Hx1MHCrqZ(6
z`G^LnlGUV~BdC;5N($K$QxaDOk<2}jxV-%RVdc<=9i=*}!q7KknQ05MBYk%i+flwi
zD<T*q0jlaX_6YQ1$4xby^`r<@9k!NhG)Yi2P1r0u;pmgVmM;$={h~-g%y>;fP%did
zItlRByQ)r!OwJLM()3Ejro}OgI0s8k;{{E!p#{an@~@P9oI^lCv?zo1y?E=DDs)&k
z<s8<&y;TmLtYQ{I{Ekaxsb-7K?>NY+Ta1fhN;llT{}Mndj?_A^c>MgkA`&X&s8+mR
z=L<k1&N;$mO?SHsb3k|_#PTCl^TzLL_VAoaK|D@fMEv%MtRLG>J2=><H)}<<7%oH%
zEDSW}TQjGxiy&5sR0aFS6GhbUU%o(a!$J?-`Q>FFt4b<<mjr2fmJuF4p)&*G0Sab<
zzcp-b3rXPY?20;PF2-YsR6_%~$+_Y-f420?;%0tH?*GF+RE=o=rJ1A~{7|?w^E%4>
z^_JrmB%WTMm@~J}gNiHUDK+rKu-82N`=7-efF#lMZE;VFT`(8_ipw4KEC!r;CMlan
z=OT)d6S>eD@37T#r@$=23K2V_8+=R+RW_Tt+zxF=gh)5DqSo*pgi1ol#ZR{FvbP)(
z@pu|@>#FEDF_FUD2;JozPp}7$4oDXd7ZfxENf~F#rnC92-swNh8avYIuWB9K;+sWZ
zAY+-LE>v5OAWA8k^!FZxKL6|bxv?1c=hPZ7ZgWc&Zoc4v;$qz7IwxFz91@k)6=jYT
zr6P^|Fd?63?Ss(a<Kf;*9j;dAz}1mp*rcD9k&*-4s4EDP6737`KYg$3qd!Q*6$@*w
zC!!HMa`z4QMfPP`v{ii+>{@6^A-FR$317?Sz+n&qvq{&D5RsBmyKZ!|f~do1+Q8b$
zcx9-gLxB40We>}Ng9~)@^%CI$LA}hUoTq&B7pN1^)8f8V8iTK|`?Ssl2iJ7^P3V!>
zV7asNy>nq!b$o?-SlGxb!|OL4Zal#M?uo=NCUNdmzksQ)v>aUz`5F`Yf%!Ak?jOM?
z3Yr~MnR#E8H0YCDCMMW5im4yiEjxlM<losD<~eN`@x(B>LgeB>xjS4ZM<|V+`r6#M
z{lgB|SC_~;YYZ-D^@|%ufbJ(SBZ7aoZX0E^kfo|rL4&BZCZBuGK)$LZ5z$++Cgd{b
z^G}(GUTV7Ktimc6y;kQQTqp4IG0pbm_&A`LzAYpzd<>NQy7}7|4SR2R3}H@jF`MmF
ztXN;Sq>*!{{T@a3%d|R8p!8vE8n~(c83n|N#x~Y@LnVsA-{l4fH|#zn2lZVlOp?At
zd1+NqZ*Kj{&j>yA#^n%eUmU(5amyqzAR>u?UxKCc>u?1xM<XJxp<btyOEF&!p;Y%K
zm4zU{-9NV0QXjLfyCe~JMZW|;{QMd=G=0+|d6I+(iqilxI>AShgz(Z4LX|CAa}Zf)
z|4`-`glX4!u)escs6uRNP(CNxJ!^6)RZGkDONi0R5d$oFSuEP^@ecqiXx?sYvW+dD
zJ@+&$db2mwBa({A$K=cyDM!Knhcx5h!}iaLJt&U-a<PUJv9|BmuFo8HJoL=P=9OsQ
zmi<;_o(o^OK&Ow>n7S90mqdfdswv(5%rZQm_H=2rfuG-;)fZtB#*;BcY&rVHP2J@|
zK;c{uy&*Hx;tWyPLakNCDg+EARBo^f8bud;0@S4WOql5H3@ig+W_&ypx{kkzTeC6>
z`mtU&GMO?ozP#HBEqx0~mz_t4Vf%itHsI;WmlQwvc-9$h%q9F8O>{t79-w*3IHAp5
zEU<vyY5Rzwdi;?3OU>yG%IlDl`)`7N)V=h5;Y1UC5vR6nNK4GBsB`>fDjE40nGon~
zWvKOMlIMgp3WE7;6HPsJU2#s|(<q;GwGuZ>;Sh|m*nLLqQ&G!$UHQ_^>b|>lwP~DE
zcz&L9fXTtvX|p_K>VZjHi%Wtt8)lslN9>4(cFf%M>^+6+UWe$ftGJ8k>bfO-0f^qs
z>8F>7LdidCtA>z^D3Lu@gzf@3wIw6V6$Z4yM<R2t(m#4{G^=EncODsJ@**5LfA2l|
zD)de$>I*Ynw%YI55^pi%h$nIU=B!WHGdeWbJatL9iblktCE2P%mklG3BB^kSOWG(y
z|L&L_L?&L$&<)V}qhRF(x(Ix|=y^ft!qpPWs;k=jk~rcis?bTi89I=LiAW2_)GJ(U
zGb8Lqtg+tnIuXkM5T#jC_e0*asjoO06`XUzwyF`+r!KDuZ4jc3S(9Q4dF;t{`Z=P+
z(4%9u)g&_i!kZjNViza)EzZ}^2+|mW#lp|bd2_~%Oh>*@YTOm#Z^gE#D6A10{Y<Kb
zu|Yy-hppwfM``2+KkOYF=W2e5D1|`xQUNhUg;ynU{xmX|2?r7do5p!IzrSGue+@CY
zR2*o78KD*z=Z{AwS}LD7UbG;n9jV|YPNLa{PQXjNa>Onvp$e7Dno*YMTR-wBl+}QQ
zzs66hoEQ@4y~&M;!wC#@-p`0#F6dfRDRF%~B#{!+cG;Fs*B4>c-?W3*yhBWYY#Qyd
zg4bH!Lb>9te*!}JM326k_0oMu<`Z`f&{6N~P(D_=TmvfaXx=LS5M>Q)>g)u97>Y`&
zmT;!SnP5{3t|%Gc!^@7|vaEx%OQTO2%1VZR5mD?i7)@uR*wGlY*<9YjNsoedoZj9z
zoJ)DP7dI0Z6E78T<VUPaLdoo-vM-$$v9}s5EDp>%wHY9KnG5#s>})+ASIv?miw&}#
zWYn1PVn#cO0Z-<HJ^OQGQwr1x0Uv$JNP9hWGdk~n&xf!#u5G2`Tv#tzE+7Xr^X5mL
z$zp!VnB&$Yx<ie$w$ma#j*S8LY(;Bnfl_-!;(BB!T%8oO3<7^hig(B;xZeKe!ff{<
zV~C-NqQ{UX<RWBFacE(#Zjox9b{hI|-~vQ*WU7XZ)sJWH;v4j(HkLmke>%;xG<wJ0
z3RKP#_iII0raGARqy1MQVjn}&flN>F=A(3@Jz&q{aQ4fLqAz87?rN6~4jaLkTm!)u
z7SZuDh(I6J<F`(U!>{l-k?)Pz`O(NgU3IX%7Q||tVfuKJ$Z*R|vYmiv-q7hwwYciE
zH(pM<?G*h!*YERM8Ta}~ZyFEQq-<^2vlW7DQehWF(&#xyLcLn)xad_Yn9V<E9DUqd
zG0MJu$9R${7VoMf^UHmGWkpBFs*WV=IDZB7$;@|pLeMD%K1D_x2?Z8!wt#V*@_Lu?
zPwt$$g4p>`MxyMybe|P)C}1@E)A)|MsSZh-PZ0HI2uM-zXWrE=13j{{bU6KcIfn7k
z9GZ^c-cP58I6(VMlCz)gQ*qwk`@5aKMq97t=)FC5C&n}!lSYGf>Nhy{;g|MU-9%U0
zrX!&nfU;TU44ggFYAb`}O;^4J$x#;8SpIr)?MO`pWW@nb$n)7EYV4y1sy3E#F!XzX
zP+HKylsUZ%UoY*7LJLz|z5EnekZghNO&~|;H~FsE?5APP3xz+D-lLr5@`p{HOW1gE
zKNr1WHe}};-}wKkHMSn?i#zYSH=q0WOD(h!eWN$XQ4gx$XxWKc=(fC%ZFgI$dZm;8
z3Kc`IL4k5kyv`mCwVTyAnSLObaY~$Dr8h9GXC*S}<`HV31v(~^5pp`r2nm|6Y$n7z
zR!y<mHE1oltN&xhr*<gS|L9M2-=;o`<z=p;qMIjiMKe%qoJ<Ix)MQMEwJ*cvFD)!=
zB~JIt^xueK;L8diufuJwa0r~IKfu@)ur-oZb(VGTcaySs1mt!hc0_gUqjPDAz=BzL
zywqm%$>(CcU2tK<69{<hZRP5SnnEIN+PMQVj9e}|AXCA@;ng@&Fb3py-{&y(2}&jq
zBn)qajSK`!MWm$u;F+xEx8dnf*8y?tL_or?{D8fiY>i2tR@+wcA3vIP^3)jz^tc^f
zzA2A@CssVe9aTyR*j(?@zA2hJd|3we39MUr#oCWGn92?Mh**-Tj2<pzJSJI;IzF8d
z)$X4pQ*~(<a@D98Lc*v}S`TGq+t_4}7;R=KZ_<w9WqY&l!Hek4G=%<2`$TD<eU`M|
zWD>_diB)1wqa}5Txl1QX$NQJU_90w$gWZ_M8A-jcxGZm<P3f_{ti?XtmHV!!@HTtK
z=XXmSCsC84abgWit|W&s8GXsaZ>|uaZ{fQXQu#vIsii5T2@P(Kpi<3C)FTA{lgByI
zmoXG2EpQt?u$sUa_e0yO29`MRhA`3uzMYn=U+MAc$c_*DrjVX^U`@5Nwc}P*UB^11
zMA|qXk(jdeJyg=4$=o$9L_5cS*&i6Wf*;h>8$epJ-9L!DbN^Fd6`{^x-6%w0!snup
z^OEt8#_Gi~F8U{cCmcTkuI~$;PV|G<Xqy{HXTz<Xz|ZZ=dq-jg_e2F;(fZcRj|leq
zs{_5ZrRNd=Js@B&#qjiL0>%2XVs~ToBaY-5tt5xp<}X9SRVCp0uaFWYohYT_oW4OM
z{ESk;DPS^B++t!yoI$CN9HlQa6#O1x&(uN=PvTRrw75c-*;iG!Ae)x6n_k=W!t-En
zg`*{Zo4;{W$Ty25r&>&e&UX;7B6cAgdj9HBJ7U+sK0gx4Lwj@sYqVg9dYPZNJz`Q|
zz+L7F`{Z}u;y@n|4v5GkLaPGbwXUAb7N5UQ1gBvO<Mzm*90-U|JjieE(nL?`?ZVvN
z!$xTJquCCh9|#`IRMJwi8=6jZFX51mmDA{6=~y@bkw^=nzfT*Q>=*UJ+sJ*i!nNJ&
z>G?d4<}p%9ytkJ&vsSuw6kxTfz@z?sKeaz&fAerQ%?bqrza_xeQH)?krM!bNE9f8&
zQ;2Ns`^o&LcsRyw20}Pc2mS>6Fe75w1b$}uC2Rq)4jH4Dy^0$_5}W5k<R-hFpJ$l?
z{><@46DbZ0Z4XKN1LW9GtM4QA-%CKI@GBw0Ex=q;d0=KaY!kJ+pt_+s^T5OWeMOMW
zaj45yAE}OIPV(I8gKyKmLS{h@$2{GsIMdoZGGvn+xyNpkR~&pSc3zJrTKT?WHQoKq
zPz%gWaZ$cI-odnX=rH-6DQ`(~!fA{nn6=7B$`US6GEs8vFQRn`eAVIBrMvSoXRw@I
z3=h^0nipH`UkLcw60(re$`@oNwGp!hox2(*4jnB84@79B`@|KSib$Fg0iH+0DWwKp
zXb2c;HaG-k8H8o$Oxu}dd1iW^%76VJQH3b*X%;K`gZ^4J)3bpR1Xtv)Wb~)rdEZXL
z8sBm9d{YlTTFg)e66hA#1ZENnv_plJq2!%cj~oHa_9a(^eK)wi$h@+&5?ZW8EaRZ}
zU3dCv4xZiwV{oAf$Q7S5xH}4?I5dC6PyIIw(AGc1EtiW!d6rmX#YpTaY_1FW>`(GP
z9Nx5qd=Gk(NS1)ouxzE(jAOg3@vF?>0&;8E(aRGk)*uMs29O2c#B-9|v4!Zj&zP`q
z4^dP8dS!?Gl`j2Dut9)^b?M~k(EcgG8Q645?5ov&V5JNZOmVhsj8O|BmhLF^j=Gmu
zq8_!<*j>i`K&YyT4<8Jf$ZPzSl=OMn9rn|xtU||boR*?Xv9(tf!y3`_SQ|>M#F1A!
z9coQ^FohE3N)zR%rR0u8lZDwyIW>BenfZ@XJ1T19UBb(u@-tpJGsDJmQ87L-Gg&i1
z+cT?g`5aT{KK}#!*ppZOUv$g=Q`h$Y->u8Y@gEG*T1_uc6}9z#tMR8MHOuLYb592)
zA#p@S%n_w<EcNK$z-DOL>+^n6XGyGfY3w!&99BtXc1h%R3v7Dx%9Jvxa*e{E62MV0
z#RuP&EOGJge*2x?8Al~RFb^?1FMt((fL~sY&wbv@i%<Jb<nJujNMuM*5Bs$?7Aw8^
z9=D4X8E7&D25-tnRyF|&zR&aa&n?s+8K3|KRDk%yCh2o=>zvh34!WCMtcXC*SFx5S
zf*6#td04(udosn-S<LMN)IDMVnhX>eu(yVO<YyvSG&-FY5mge#iK$sSId7BY+<rJd
zF<=a3{t#*sgS19Y4v_p;`ra8?8Rmt&Mz6tN=nr^g4DB)LR4OWY$-mB*V+(RgW|So6
zHVWH+B$9>W5Vq{Rt^3Vm1G-m^atGf{LMnR6X$t$1si-W1){u9&));O4)-Z4J1|n;m
zC*Ia<>E7Oq!U{_2tSnKva^s2(DkL|KRKYEYErsc_W95g+@QTny;&UTwswdJX9c!-E
zAZ@AIB6Ss;it<WPYq}@c);MhiyMnyZykd6c>`GE=nAX&Fal3MNMV@gim|{vGraaMJ
z(pgA(X#Qwq@!Xhq30B!IyCnDd58+P4g^g1V2cP{md2_mjzdZt6HupLz{yazbi_<DU
z>~j3M*?j`XcNsShJKQGsF&O5be&#B+y0#X&zMGjm%Ms=g!u^WdTm)doyB9YlpW2zf
zQvFb-Z*~pPm$;_c8<_1<bHQqm=B=yUzu|wxbahlzpavwn2B+IZP&hwj`TaE)4ffNd
zNy*;8@S>jcwrBj=!To5&7?53{N%6(@EkoXz5=;ALfSzI`<Ydc&-Rq@t1CvL{=`sq!
zfD#^2lGV#=GljrY=$p8w-3+5Iw_(<6u+6~bJIy>G7DGM8UQ13Jqb+B|9xG&Ti5#Mj
zUy5^!3T+jer5>$VC0?1YaD#}N6i1{^gLl@l2VtLI(L*&x(|*gFFJ2DZ?L+uKPn)!G
zLo_vq4~+K;Q|xj5A=S?1>zf^Zn$Mb_0xK||=&3?)Lpj%}nMtP9|9k?}^egZ{7)1;*
zQ7WObKMiyQGkHd#I$4(eIx5ndgTzCLHETh;q?evdXFrK;U~dr2)oP*>Q?FRco_HcO
zYk@1pE4DoL(STaW#Q>wsGeDgt5V!c#P_6m7440nG`KW&Q7ZY*=AmTK4TAwbOd*2t+
z)0@Pn<?jU;9QY<;2fs=CzAcm;;P4W)Pt};g7POdO`3nW*Y^d6L%pKRjXMNN1!f8M#
z^jMW$S_2xBIzc)j&Zt(-KgU!v?oz^<8U!2$9%N#2Q+#Hr#^4QlE{WVpy$Y>Z1serX
z59P=&ze;2zsIOM%JfXt{p?QJJ?r<P<E5guW*wHn_sJK)G7=RfOAMxxk!%t0r3hEx;
zW(rD+xN3b~H#fxYZ;FpO5RSAox5eCWN5#O{CpapP29Mf+VQzL|hS`V)fdB`AST%c{
z;<RR;@nCVh|Ed>&EK*eb{dl4@|7Q8E)~AlT>3$5<vpa5Jm+EDzr<ZHjOCJofI!J=i
zQ#^pnLpr%j=w}f)6VYk5-#g#r`qyY;b7RKOb$_W4-OU6`M@({dg7t$`8j6t{ZXDOr
zV};<n!&}_<%<x3UF=O!DCi_QZO%PEkn39Wf_kuzEVT)1>G~FGRjH7q^Ss#(#On<}0
zoIg6X#hBnw+IwK6b&m5mOF2V?CaSKU5HHsJ6HP2&CbbxU?zyD=+T^BOp}>?VODX`?
zrgam3lR^8=b$^I9u6Qg#HR8H($4<9DKDrY6KCWOlKzQ!c;uCs~@n1pF%>M7LSMu8S
zm|D=_KIuxOV5!=_P?t}<2(bE51S399%yC{O@W!*7470fmp>r;3ics8E=y|Lfc@R?v
zST}v~X+-F?!b~P2Ru`NJnbtx8)aRhwr{9yxHx`ig`ci_~3}O!s_Ya~37RBS9+|e#1
zl01gLamKVT2hR`=HpPm`Ea@jic{)<jh(8I-Zn1FzGsQ}<^{D<*#g-6xiD+S==n!*r
z1-sdZU7`cMF|fn|NEMpAZb7X=+$V)kHY-5Q)ISqE!wcQB3lUw1ntk<MWw0IDx@0X8
zBt48Ao^w`JuGoI9@+1zE=kmlow8JoxaW|tZGYp?$JaEO|aq1%K0-Ob4SvAiyN-31r
zH@|<qpq&5y+mA4@26hU%9-9<`*HVoE><?AZF>*k!zH!)#dp$-MAV~LTJF?xyKQFKT
z>=?hP8uh+ID9D`Kcze+8Pm|w@K|kO3I|V{jwivn%L6-D;Z9n2;NrT){R!)>C)3Vqp
zuLOq&ic1JPya2b8v;*mvGC6WmzC23Nz??5hnZV|FKhB<y`k_zmp)T*6c3?xAf7%L8
zcxwar{sM)qHwpU#xvhS#psr9cm2cpzY<_Q9Jq-&DypS)+7QDRUBMH(C(u=_u+V~M*
zN8CU!?wBS)5Y_VpZt-8>hJSM|q`biszC=a^ISN_h09^AAt)Eh8S#w0_TpGy^^Eiem
z7eX4^E~OzpjTP7UV5x>;)W_`ZlKqayz~`DlYh>_F{ZQz75~FwveS|e+Xstuo1-XGM
z{S$PVW=)1;KM649L)j*pSP@EDbBaGuElLp<YuASl>y?jvsBVk-5lZu#&slSYxM%9O
zis6bp{V3Uy0t!7r$KX*i%BZ#_FNncf%n6dP-Ibe=Oh4Kd1%66U&@^x{IYT2c4-!Si
ze1>+{o}=D?zc1)lyQkrwzz8aM*EHvTs|B|D)e>ZM4`C1<(6+L^T`E?o%~f{SwK=Og
zwCuyOmUTIC-+E61RoKSh+1=(&CFmXUXSYx*1MJT|{&}gF`+ukR8;|H-LZc^pY}&VD
zJ|bNnB#C^<q##(AaL@fs_DCyuavp04BQ`o&+)bBJRk)_GM0?ci1VNkTrD)>fh(rwe
z_VDo+tN-QA3)J0y_A;^+K}TU<eB>O&oY_8#3sDw<&`WepYc3aQzs=%0ut}$JGc2Z-
zU3e}?c<5(;+@`UOi|3}ec^d3uDqB|1SLd>`^2#FZyjh1A1xMdt>>!K;HjdqSTub^M
zs4K2>hc?Ld!Y-j4(iY91cD}Go3l&!kU&&XXEN}PK+?*+KTp{88Nd|@j<@w0R3Wdk?
z5&_<kW5F`!_CrQ*VcRyXOGArMCi()k(3QQIIhi|%b>4(PYJYdf1G0Jh&{O~6#%elZ
zS%IKpbq9?e?_W>?V#)OQmouJmy@Bs}h!bBqut}4|4qi57nKInHjpF2h)R1PKHq<_e
zp!&X#2pgdKxynVXs#e86sI8U0etmUc4158eVCZ|Az8uGztEVyqe+5$fq2CQ5<+I|u
z_}ZdLLo>1sN06fwJ`GTdH(n2Q48P5ZttHN2KNUl+UVk9kZlH@a_t9~LyJi8-x15Ak
zleYFpz>i{{wR^u`6oQ{l2F7yM_`7fcq^}3Jwn2S#wH7<4*Gim@H1A`HDlPZ=PxWiS
z-lw@7@v%TGt4c!VRP+e<!9uJol$c+zUsuhR-@pNqtVt-IIlTm`HmraPXS=?t*r^2+
zy?r%r6n}{@2O95NuLzCc!J1aht2Ay)^LJ@%8tBpw=;U~5eG7)6Jr8UjJ_&3IyyBDD
z`2Bc_sB^0I<9Y#8x3zA&?cf1TAM03L%vf+mrcydIysg|mA@)m5Z{i4gP!om_Lev+y
zpNn!)%B&sS@%Puen)F~{tCW-2lBM*7ntnJKCtz~|&W}wU-CtpP!MVt^s5nbr3dTTK
zk7|lWOe;v0*MwvX>bk1hX+KkAjwxa;yHgr<DlsTulvz(Uw7P?%({hP%d7?q_ii@Qv
zdDCZ`3Y93){7_3_`?e@+Dxl)2g}RNcl=ZNft>b(9f6a#WFHQFQEKj|WB6rb_YxP7f
z*60${bqjf8@;*CLuuGjT*%+xKV^~MyCnW_|c`o%JW;YKkL5c#gn$lAp8)&e0>SoOL
zZQrGXktZ^911~Z9d1L(N+<Q~#1F)l23-c5=3A~o%<f&uNqfX`@pnoe-o^Nngc-(lr
zf^VaF<UPe82K3yOKVR-J`=I3IRJTsNuhKWLt!MG<@Sod(l#OWV4F--@RMu)mEewOJ
z#lo9TEf=pb`0<35!8SNSQGFUKd3d>KDQP)bb#_nPTv(mqAP@;4yPq}#5{NGqKjG&B
z3IjCx41>d78@C|X_fOS4z4x9_YT=&ne>zQifehDsmO!hJWW`5@Uh|FR#3z!}1}s|)
z$@aI6iJHE}Ay@h>tSyvKdz@O2<jCoPrcdg>Aru~k^ksrx+)p!S@A^mI?4k1K2|1`I
zG|Z4c%$E7-uj6-HmxK{=cCL4h-P!X3hOGVG%=$-c!lJaDt5vP4QVA`jwP`jKGy$%$
z(hwfa;|<N8e*4$|ygX*-G^dXMj<Em)Czy_$=jU5FBExr0nC=Wt0#$5lDF>YZ;*(d6
z;0QN;5B=ZJk~Wl`dmc=fi{hpF)H<yYm|rtpLUE4PZCyyyH$6w5zGK60`EyV!{th*u
zbf*Uzdeq!cSBzbgBA4yAsB>UCqqruf_n@M?%bYZ{;LZ32Xxw;Ld2`DPS0}rmDAEt*
zP2Gep21ap%I{u_k^M!)yg*9i?^$Q|cn5JxMXRk_3zcN_G%pyZG!0||+WRwLmuC5ny
z4f=v6_<_rYs2L6j+g<;u9bMc#G9Uey%48?}s*RJX(wqkF;5A!qaicgQ!3M}t-!F>P
z9@5Q{Wal=QvA7-BKIa>_coIsqJ;ONw%(a0s!7g4F6(`o_O3)^weAPUi%=%4OY;#@+
zpYiF&ZG)9dDDOb0ESeVF3Y%E+D{5ZCxRBUR{meG>le=Y_Tmrb92RvMEuOQPJ6_FAt
zXX?Bn$)!m+Xy>P`rF53}5w|E35qkQNN7F~mzkVmy`>Qv%s=Q!;Kbt{~Y6J;FT)BUD
zxGu4oDZ!#|OUyQ8;*q2txR87Ny4)7<mE}f+S{yg!RUp446LjVpPLuWO>q&{$XcAG=
z^JLp92+^X*Cj5@PCTbPkKvG7|ZZ~*z89pMikBvbf*vNsSE%`z@ZHRehd!Q;)WeG$!
zrpf|iR1o7Us!xSd20|%b18@zemGfj7`jTR9Xiin;LQ?~eN?06%=y24&I{C&2rf5c8
zVvGzpg&QWfw;4BC&mqbRrz!UY3Fz<HI!@AXTtjy8nQyo6^Dpu7I30r|pMuNUn0tk4
zL-+5_ZJ;<6K8G(^gdSZPv(k&#h+j?RC+WwIN2k1<r5q}5;5<KgAqo;BYJe;?PF>m5
z6sHd#v9UM2vk-FE*;okxGukN=!d63l_*U^#IxbR6^#UqzSc-*HlZh6o<x8l@P#K_N
zeO=t?5IqO|kCaVp8^?tP%it09^_dC~agUFEkP@R}qoIWBge2=po6&3>NzI@!OWK{T
z8q(I#X9LRJ`5-)+HPa_b$*<9nH;Lnzf*x912Pk!Fduh#~1vJc7%y+^2+3$aO9v3_n
zJH8YryqF6VV}2;m(ZlVR*rfd`g~~kBB!$gl$GpD&awuAea%*SOXNrhDpOKPp;eYxx
z_OZeMzVR5j*7Nw`-*HJKW=&5BJK?)B7Basd!K^e%@f4^8H1*WuUu?_sOH_N+>dD=V
zYnC-kr>ZuAyE9A6n?@rJ@m-%@*v7l05e6V#pE|mIhh`Q^=)`hj#Q-6XM63d+FJJ_M
zE22YPiMX9T%A-lwh3)DqR@48QLypcoU$;rc3<Y3<ujfDvSeyWBeC!x4%wsI!?SP%I
za<mwpMff;x9hotL@NB+;jtjRvCpDY-R2HY+OqZh#NfW*M^*ox>@qeAeb9Z6bG|^wq
zu%ojhD}Oc5-}Mi%dBe~}BJKt%dyzO?(R)W$2_dL34K>)!VN>T@ORY?$FK6*ij|+PH
zR<3QlfoSEzdmSQb>EMDzqq>)_rQ1+pm6OdO<k4|V-#o!PE$YKj^x@wx{GRBx93r#d
zFZD4E`E@Pkj#rL>Vc3qF@~|cBOz2R9rZm;`DyZht&LVI4?K-(3o*A3#Q}E1RMDojh
zN-R#C^dNfRMe1E+wmO)4Uck6--{b;!Fw#gT?qdNbW<bdr+d}5S)kgij$tlUJbN~4L
zKD_t_!QrSOG7f4Ltn1Phxb+ofiCr!h9taeTX9+|!^mNdN?8m??HgM=Au^J8tnz`!j
zH=9rbSaX4C`@G7%txM!z_&vfk>s=C3hnN+(;vU%>^Zeh@wmMG#Sdk~!gI5@<_MG#a
zh>)i6XWfB9K>`I*5Thd}D?$+^B8w*?82fP#HflxvcOGK*EZ^S#=h5T8_37z{7n*MU
z;(s-7xt62`2Zrqh#^ZG70$G~qOI^t2w&MV3`||SK=(Ee;OaY8%%=9*DXb?+m>*xDZ
z5Geo==H^@7ZA>3j;4Ag$$H1ldI`VZzyA_U)tU{FEkudH^Zv|4dYYlg&+s(9DumnXN
z&kB%hM32^z10KZ^V)D7=r(G4)dVPBT?4*j^(wks;($VYl>&MPtZ^XpLPodGnt#|or
z(uJc1Y3k?~k!4DORZ82c>$G;{hx|=XgS?p<8-LuwWhRbruW!x+%^ffNtn(*;<wX)r
z70lK3<&E6^g$CpBtg;J(Dw{MOo9qmmxpKDaw!_Su`lWX2PAhCRk~#`1U{2$Z=*y}i
z6<P|*bKK0ca+#^5nC+auTBsQ9tR1B-&n=s|J}v&G?QIvzdX3xGX%9_u*K`EzxOe==
z`hWeUXDj|M5+UpVU~Ob(Vq*Tk<Z3-N7)2Eg?7l!p7p16F6UFkU&CB&xM&J<$9j&ot
z>WCYR-RebTeJMinj9*fys6a~2bwHKBQ_^`4;ebq|72TI|+uZ$Pgl$&sWNNx|-8Zyr
zyWIe82)S#?1;*J^IhjA(KY0&dJ3j$CiHyorB#DydW4798Xw7bWqjK4g++U+HVugd%
zkL#o>)jQD)jQi2ZPI=*wd?Q-(k$i^-5@A1V-qR+Skw3Cs%sT+bS`$F`@{#Ku|J9fz
z88SOPor{yC<>~y=yKA&iv9bjvdUzN;6nvRY2k~H$CR&kZy{zm)c8nEvsB(IVCP5RG
zXrq=x5dvG(uU`4Ygc@~g71VcBG1+^1F<aNeXG^igXSzn6Vh|DkP&ew5jvkQ=O!7Fd
zY$b0i%-Y3WtHN1xMx!ZcbF$_rt!Yul#3^j@C@p}kF*}P+M(q?kd6qxQ<fY*=<2Qax
zNFwt?rpVN<hfGp^=LM44;<*D004P94S;4AZqauA9rkOfZ6`cWRukAqIAml{q7M>S?
zveQR&qhO~*v%r=$EU))Mklo!?z~CGq9bPgQsYUVZ>>V{WqWe}crLUL>NnzeJE^(jq
z3aM4r@T1#5yZ8%&k*6>wUCUKnO**<YZH#6$OMP&=7-Pz}r(#&&p;^Jjuiccdid9Nf
zE(tf6npQwxdK5o&nyQ9VxtqXAJen$5!i%i44Vd@YWi=;h$n3Z|!ltIO`owQMX#<%l
z)^qX$B@|b(u7N9ZIV8koSsLHKhz0=l)pMgXh7E!5{Msu3KR=SC->FBTSpa|Z&+~|@
zKEWT^D}sKR8}R8SS6qsvUOdCR#|Q7WTD9xOYT95&=73AG;88#be<-dS`wrz^94`%_
z!`aY8_2sLCu+NR7u<F(H-G`f*$i<coLY5UQof6-Xk+N)$59hla_9ep^1^hF9mpfEH
z6*)ST%jttl8X4XoA7>HvUGV#kl8IvJ?_O!Lt9S$9Voh^d#d^(Bj~{V18}z6@WL-<K
z2X$!Ic1z!<`mj;F&(49CZ^us(T=t5DWk2Tc;wBl4aQ+S_CTlL&T#vauj{RYnTq}fu
z_#*J;d-_(zU(=_KN_ma94N{#zPBhE<-G1MfBD~2EoB6fE#(wTBSsU}38{@$QWBw-5
zBo4OZ59rA!eLheiy!{tDOO8o1KpIpXqmicsxX6=K)gK`sie%=^@h}-d3ZGhbBGO{a
z`w<d`h=~sa;^zJ+uLhhk-WS?goDFXw-=_8i`~MoQ)eFNZChw5BPPbEUwGX>iFAfa!
zdVQ~k0=K%i$h>y`Acu>p@==ka9N|q)vi7!8?EGFXYQ532cj}0?tC9fGY`ezKl=B6~
zNH`gff#@IlejO=v0NfjS2Ik<JRoCfae$rrzrZZ2bJG(YH<sTG^1g*sQMB$9Q9BCNv
z89^D`9TQ{rOPVjERjSr5V}IMsV@zm2&w1GwBh{JPte^7UpwRkKUJog^t+Q>Px%Nc1
zr1v@(mY|`Z<Scs-))jV^CI16`fLtDhqcC<aLh+-&Jpa|pA2RgyOJWB;`xdi)`9_c=
zxbqYy;k(ZH;KUC>rc4{(fRnSb0uN@d9iN?>TU@*o&Gi&OGpF84pMXs|Q+O8=F2fEg
zSD+kkvDnwKH{u7!JiGmvJpD@iC^porxk~iNl2Jv>HnWIo@PJ$_KGfAa4_toqXq60~
zk2lriycHAb)oLJ=zX;3I){$JArBKO)QBk{cePLM3G9TRbY7iYcIAUh}^jZ;gxT*}E
zOE@*tQg<PeK2W7wwthV`-3}b@0dzxnpKoVQG4oAVIh(ce!8{qvaF$QJrJ_7Wn#D(}
zo8W!q<ALlkVw0I;PUC9Y*ilzV3ghNV@m-l_w?;v5T$L!unQ(xy^2Zq%3AwaZ=X0+M
zvcX&Q_s#Kfs@kstMVjYN$)#1};{H|dMCy1V>pUE0$JnDM3NG*&j`jotLliXM(u?40
zJlX2T955@rZ@5ZSQreKX-t6;zTYrjg>w)%*Tn;pF+<mwv9^JuSi8&Z>Vm!e}OGrqz
z-W`xx4(oAF#p#=Y>(7F?dWNcg&xG&$quy<CAcEmNF<{-s(b~Sdf)51bxCZ)DBd18L
zz?q~+>G&Ik;VV%1FH-N(Q#eHW0LNV0WiC&rG?rpVxa43^D!N``7DO$*M0jxtLs0Jh
z%RCidPmC7&l&}Z=CkhKYyeK9Ptp$L6v89{McfQc$b<1_v1inE{cHrCjF=5JHl1@vJ
z#o}lD(RpzINKC}%ARV#h6IBh{4l8Kfyhwb5ftCH1gEC{i2%SyM-+#N&*UXK$s2Sp0
zTps|da!2_}vCz&10v0!9$xyCT9<GA!k!lu$U&~N4OH4m#&3Zq`++5H+#H<=C?*VTW
z9-T!cXN32I>HhM_n>srwKczyj8fh^JC=fSGTCxmUUfuH2iqMG$2i$U^K#b>Yn~|QP
zv~91aNOAyvR~saEhS!8rKi?ew!eLC}WoP&Q&K_%9ld$R&mtdcPY8qi~x55vhPJe%(
zN!n$>YfI)thyB@$)zwRBEd|@JqImhUfYg>O%x>uVgLx^`Ti$ifg{Wa&)4bfJ*Am)Q
z!N8<z5MJ0Zy|)rT@ED<f*B&nyXr?FU`ce}GN0JKEF@N#6xFx&O?Y8IKLyk`KcgUnu
zE3q@vMQ(f@mIXO#()LsjzNFnecg|{BtE>0<73P*7d(<U*(y5@sNdCZtXPgcrJ@vrs
zXyk}Chdg%f!!!L|R)l{6Zy{$P96JhZrM{%?wIm+!7fOh-RB4N9F9qOY{8jS<l6>ip
z`d=<3|4W?)3;TbxX_%NA8W|aynx@>|J3l?cFFoHqO#>!5SZCp+<rt==Xo1HwzY>gI
zi6&n~;?BZ(9}vLO1orwFKBs<`hNve825!|5Mg8|75^q2SOV|<&T``OeO-xY{r|aym
zX=$e!r08a*WB^i2G*s-Y^DK;vPBRP@v=g!bN+m{SM#kkOu%t9-sZ?lrZ)p9<@wK<3
zk+n?dNG~dAeeXVRX#ck(!9iKD*iYWjlEs+e9M;fJ*3g7S$k18Cl>$Ss7s8*}--dF0
zt}8>Y{%>NPkz9fnOjRI}?%?F&;^;d!Hm`jFv>$!dx~D$ZCLhgzpmySaI;hq=8=Uo}
zI3#d%q%_pjETF<~L+taZ^!>K^My{-~ySUO`>U0PNQuQ<oU;Z(Tp&-1;Uu3WHrUq9S
zufiW%ZrT>!%Nxg?8bb~r9Od`-&X4!_2i*dF4lX142pNt1e?;-~++e&vD4wP+GQAzA
zRq?k8V8ni@0m4B3%liE<lqhzV|A@=@DeuUjDxv0S1(;r#QcEFqZsP;Dx9QDMCI}15
z07u6%GD3IGZ0Ghe^SrRX$al?&NGUqgu^6RVw~i2p>QhgOhWJXG7LT1OoYuDEWrue)
zoAo~3J8=s1-Q*0FQy(2gRaJE}JU>0Uk0OlhxjCcXFYdA6W@*02;Mw=?UoW7xE4hgP
zwO7(UUcD*M?Osy}9MOHQc6+rJDJcP8x$)y~oSEp<iXKG})UD8<0g0(mr+%FWb=A}{
z*_IeJ<AdvAVu23hfrty*$jHb*=@jRNnA_TxaK0)ePqEk^0EHKWI@W+T0Vxp8`qg2$
zlEAFZrm%oG2h0Y$nKFT9spKp?YuZ{`Qd(=ur>u(0rYn<6-)9I-QxY8SQF4y|0*xWY
zlYwTfA(>!mB>qjz6N{D1Gysdq-r{Q}|EtY)L5h%y;X%sSNzjh?!b&Sq)#=YG*w$zY
zh;gvzidDjJ*$y1nY~;ux7HmSvr7Yr>6bV<q+5w^ttRw4`UoH?Ra)uI$c}ZPK5j%bD
zE~72h&mzfyICu*RxiOjk4^0wH;1^4VfaSLrWXa-Gin&zfMLqv9Z02}{1L!Uf(5L&K
zG$vFlDDu5HWW}Wc%h->*<0>5gQvNq(UsXz%{w@+Y*Tab`A)`osp|PpPMLzO(u%5Ym
ze(&#lpuHtEFSp4GL~Z2gW}2HFI(oY)2i}i_58Cahv$@Ldwe(^JQT;<v|NA@9=d;(~
z%uebX9&1g5Znr-$1dnm;dc>3YS)z^aa!eif-rU*w87OLOnK?dksL3p;+dZ0S*{yH;
z1*_=LX=XevR|u`U8R~pEw5ovPQ3|%&gxq;|)SH`kFmDtr*Z+5<Iscak7!xxG`~Q6>
z<t7E#qL^Wh-E_H8b;xe&I%AajLCpzfh^WR4i5d+UPJ*lX$H>LHuGsi&TwR~)wWqm#
zZPi|F7+fsFi?_SuG%$nPl8cK<&K5vHkzymUegj29K}BN!3~UNX-P%)1ega<NDQ-Ue
z^IzV00Ux}S%Gyx{lu;a)*4FD=E#`oaT5k|JoEX%NPw8UA0yL-MM_}UK2<gd5B&q5o
zS2ASvs$Q2%qt2>V`MZk`kQa{1bdrdPryS!q3p5$Jf5|0b_Vapbq*iDlBsT64|L|?`
zo4V?M_AzR#7Vqh-tZCYl24(Z;U&~8NOM&nZuR1_Fl6lN5yJlVsBJ;E>a?HYXt71ul
z4G9CHbEK<1Dm%*^CODd{c4PPrv8IyMf#BXc5OPQbnZ%M|6dE8XHeip%AcUHul2E9V
zx2=XK2(Z6TB!iL&65WFiQ47HxOIow{y}S@8@iS_{#R+2t4S_}wTc#^^k9<%jA%8bF
zHEq+;I8*XX!tffEasvGo{Yndy;DSaEE^zdcP6M(@;)|s<QmOK)fdY@(3lv-j3tbQb
zCK+*Q86<K!Qp`!!mK_{biFt$S1=o~n9<(lz@@J~Mr%^Zu`KekTaB4BRqw79>%20sf
zmCClxf870X6H6Nq5CO>wWM)IXdn5}gDCI$fHbUfyBD6^)H>H>lYaub4Fsw-6Y61V!
zN2B#B-ro+PIGNef$*itfXYjtUtKUXc_L+R26rROGEAmsa{mq9rBTf#2MZkZI-z*{!
zGoVOHy5UHQdnW;$ID3JBIkkoSm<@53aLJH1EMbKDjr}K?C5#j41#XB77hZy;aSD)x
zR6P;hPViaKr|2%xm4LC-IP8m?A5Uhn$Y)j`x;(b@+ywNj`}$9B`|ZaG8v-Aq=OA!8
zMm?-$Aa6Y#S%&K^{ijN$m*U|;<xUfN|IDaf1_C7bk70lb)9<%4Aoz7Q-%+kYK5)Qc
zK=(1;7(A}&WzZPJogicG@}R3NY37eoPY794BP{N+sUL}-HV*5P?PfI+M?`uRR#|dW
z+>;zY9s|8BcMV@J)YU05US0qWP|_ohx4ELUxuB$*d2cA8q9T$U3Dw9PaP;8N-{bXl
zuT?P)`({cYQ8Bi&q}^i3zfkb&gLV0b)bhbI6;y(mJ0r%tD6-lZNCpj=g|RyG9`}3+
z+YqF3M^0O_s?}deH=b<Nq?oaI{6ivl9pAELsok2oB;h2=^F{n%X`(9n&7jK2VHNEX
zx|^g>(r_xk)Dd-NPyxF6#6Ci%L3XH+qddlfkRM9HTmgM1Anc8NYD?XMU^sU+_4(Kr
zCGRaT=m6pJ@y$gePGMU36B74{A~zgmb(PLz0wet=`FBXCS*;ZZ`lqm7s>jbv?%WW_
z?D0$Iwhzptw?fVYUf%D2$>+<zbu%r7qLbfOsu<OXypfLcEWzVg7%=%!zVg<Sh2Jvi
z^{Jpy8`UI0m?0opUk_=`>jH%p$mxD0Z4opj=2I+xJVOfTVsxT1K15{vEO_kKR=ykI
zx(Xery;;2>aZp}H%}rn&!3A(I1uEPIzXu?4-MSlM4*o=n^75rbQ4soju5MQ*mvds?
z5zxXL|N9ljw1~4l*_+l`5p(mo)3Y^jveEfcuV{1}Q=oyvii`6^(G+u#Kc9#sIGSjY
zkzr=wqcv!1cr3R#PP)gIPDzixJNW@F@Ap9ZFB|Ir;!R^``j19MKV^Wzq9E!us=HQt
z7hyFN_JBnUP|77T@7V|@5-+7~kU#{+S=+)j>6SbTi<=e-D><|lOp;^@4ETPmXi?;7
z_HeYt4~WC)a6p0H#Ne6u-SN@nVegCIj*kPr^7;l^h~mrR;`CW}^NI93h_?tvDycis
zxl$3oT;#hFYk@e$M$xuAiGO?fwOq+Nni!uC>EK`;sDg)#DtPjVeSDHKT8U{YizS4g
zaQ9}tx|>i1k16aB=OBA%lEVcFBRe<u`k%%>Xy;2<jWodDDx?RxjLOeo?1LV1Q-Z3D
zvhrI&QkTYMJV`3<5kM=EfqHR96~rMPjhZz})?gb?Fez(dBHCQ5$YL8V(8l_(7`x06
zcpc$z#ALTUfxRVLNVkmD$TLSckPz8t_WQJyh%1i1C^9}+?G-cwCaTBP%p|4KDbZNL
zND9Bq6tVMvMRQ8E(hn5Sr<*tyq}8xi?rAh0{!ByVcHn~MVMHom1xt0DoVuOSl=6ye
z)4k?&JPW)a8Llp8o*8b>%e4`=$|l7Oq7*$?5Otk!J)x(!Rl$Eo?yfdMkN=1`|AGi7
zWY`N2Arljm)SG5o*{`d82OLj?FKH|I+jPI3YD&I1jJ>>kD7TOZU=$tQE=orP$@*XT
zA)@Dp4#n<XY&2X<pU9GZW$@Cb-_X(G0{~0;x_Z0>rq4>;EhKJMBzcj}m8YWIQ(zkk
zD--o&J|6RL^MBe)<6{{zKXqfU?{dS)%w*IN0d=F9gvr%*?DcjfLpMTWf^AnzWLL8=
zblo*>_T~X@_I<i5Ee03sd$(_MJHMJik^jrs{x5nd8|Qz>rE+n4k}5{HqdOivGh8ow
z=EcPAy&QurT=DRUkpzdsgxZ<nphzvp<FV@y534!TvR1NQcM)k>#ya}TdbnND^zS`T
z_v~Ga3|)U~x?l6J(5x1syWR#LK6m#9BMuNpR~_rh`|I0ZQUC(8Q|Zymgvn8ve_TVI
zfvMay8!J(}wYetN5QPJ#%&s#gKGju@$Nxn~(T9y{@bL1FyyCM@bbK2<sgdyEBZskS
zD5mutvZGsWd(v~_AR`?a5y`%PV<Hm+HKN)l4{9>fMo{fvLPi?*^j1=3%65SyB6-j<
zZK~Z6k2hcvgZzm|=pcbj?9)^wJpn-q#PxM2n8k=5mJlNn`fMa3LK1^?k>8B0E3zp~
zR+9|BtN?K%iiI{BF1$EJTOV`l(92|^9?N)a;mjE8->Ei==4()noQbVxVbQc76oZaM
z_0|_e$Qbza44qMu>dr`Iex_RYEz4ihH>#2<E3<2H$;8@jh>D+T@pF%~#yBn&8|%00
z(7_IXN=o(bL<o~nbOMsC%`dB9Po6f%bq$+2R_@Qa$p~i-Y`V^_Nnc`|?3Ln{v1rMB
zyA1M+^#Az;vNi~|23vHMv7zM+JYeoH`)r%7pnu)1cB7Mccw^$;Tm7ia#4H{+jjQ8I
z!E;~h^0u6h>m|r~EYxzt+`~#fERq>5NM5>4kW&BuI=jxGCKP22#DF42iWCo>AV^O_
z3tg&oDM}43bU{EO1nD4BMFpfIT?7OKq)C%5EkG!d5_$=Wv`|6`zH@%OdFQ@!=f0Wu
zYj<Y%*Y3{Fx4Sd@H5cG#-)4HLz)$lk#Vw5-W(PexaUtgFH#~2x;$N&-$KVgTUcA6r
zlm%DKeOy2lW4$+yC|i;q4gIXJKmTUOutFsMo67wE49!wd>AwTi{~nsx{Cp2it{sp0
zAJZmDNs+~UUn5;VJzC!MoaTnms5r5QeFKwTyI%65`A)_X)>Y_JL;-+qySq0|qqsFf
z8Oh#ws7Ct6-vEg4^HYU+5sC|YQis=iH~;{I5&*!zc@a5SIDDAM2z=>e094o0^Wy~a
z0|0u!{`pD1{4a{Y#h&EE$@w0}*SDjWF4l%8lXZc#*@?-?6e1y{gM%k+J@bxj7llX#
z1J3A!;bfZV{K=Kc!rDn@VB<wABe0`q-V7MaeaMsQ7y9_FyN<`3NbgWx4`Z&eKz$k-
zplYc&!qe5>#mQ+rhgcM~yE(u6Ae()cL_}Yhl7imibDD94O>=+qf!{OV9zK|X+bTB(
zB`GeBR{U0DxuAYGidx^K*x*hMvrD-2Kjoc)v{befS=UzlDGDt6Eeop{zWrKq`}rsr
zBwG`N9(GR=G|Z6pnqAv{6J{fD`+*Vu9Y(35#e1bVQ>8Ezc27TR2DgYC0@nw6wAuIt
z=vn(oQ`zmW<Q-q13Dq+~q^~-H^G3U2aWNG!F+otQ%g#t$ASyiL<XoMB>=RQ#<Uh**
z_O}M5<?e}eej=ge>40?Ba7K7LIZNm}KXygB@jxK5f6qJoe#_%-Nt*ein;a7bpSBC&
zR{+rf0N0snZg9Bhcv5&QBPeih5o?l2!+9@Kn`;0uMY|cHANfO2PTDbY<~1D~?iO>`
z^90_9viUD{wI8<DV(QAzQ!n+bo>S}KyyB039$~QF`M#lM64}~rNx^cjto7ZfvN*4H
zl9expSm--MO~m*!wL5JqxY4{|k%PC?@T~qIaf+Expl2HrE4Vj>O2N;{B=1?hzJ|8G
z5wZvYk*fVA-&^9G><a@I6<~|%J4$22gP3=NlefCs8}SnH2T#<A!EXYT>*7h|145o_
z|BMqD{6B{;v%mOSjT}8pkzNvpJhHNXlD0naL-I(&Wc~wvD+DV0FEO05vJRM&VGP}^
z{%T#4Qvdv1pj2?P$AA8I4T9V5mS7b(TU<l4NJ5py(+SeiIjxQ{NK>G8H3nA>Q6+;e
z4pZ4v=JUy|>a(#>Ful`Bs&<U09{RkZ@9y%!XP9z8%GLJHd%$%ys2DGcWa2f$Zd%<a
z+XPJDdugV~B6$9Du3z`9IzMY~0ia}QU3XP<oiid@li#s_yHlMJ!Nm`RvU%T}btqhQ
zi{aV?K^bpbe^w7uEwfi%dae<t&7)H==4$*5e^p;Mu^w;!$#x|SD2*wgMqLl&%<q$Q
z53h^`T(wNBvxN+SnXjmJ>1Cju!l@s9Wihj$x;y3qxUw7(l}Dj3w^sn3XWyeR%~8K2
z;1{7gs@Mk;2BRZDuY9>)=fizI(Vl=*JLx9Djw7lqz-x>t;dJ!x$w!wh!~H0jC`6bK
z5$zu%+||I3#yOUXs0OI<9Iwfz!|j^@BFk<*!|teSXpVha@tkfHSu@WSF@{mX6nTZx
z?YQj}wslT|PZAJFASVt<bVnhxjLCODwHwAzWmFYXAmXXzgoP<7iw=HF?K!0-p;xv&
zP_<!_imy6cx*Jc%y;{wgTQv8N&!#H!JZ_Yr4E&-W+_m82t$s_Xbn|1Eh5N@nk<WsT
zHe6w+u8MpajVBf^g_AYUt6IzDDihta_kzu;7|dt(%@U7pGPnU**glsHmKQ&LXhsmo
zH0KmsFkn%}MxIfQd&SCMG~$TMKy1U08fq1dll_bG{&V@wTER2<(ptfD`IG$8GkNU%
zL`W{;NZ)P~uQZ2kURA<kn^TGp%Y7~mCR|@gmRGX#FdML@3>+Dm^GTgOXU}j>ha8E=
zd~e#`JE9X=BfI-~JLSuQAc)(ZS9JXcyzew31t)B<6lE`Ss3Wa<AatgR4OJZ7NX0)X
zFI!g}xa~`y6BgGFN&%f`zg*qQ8O-iQrV4DOlE#DB7B9wk>Fp2~Gke%WMb4cl_pAyx
z79;4O<&`t?6wSb@l8X^Vy&Zm&DU*U9!qYuTD{g1>$L3Ocg<C={<H+IK7P-utrm=MN
zhFD1QAt$IVP|Gr1R6g<HQ5%vxhjScq=EU+_bKu!}IK0jYJdo`B#Hgou!maCTzn+Zl
z9}O=tGotU1&1Kk3Vb!O!ll6C}loc|IE$6hY%{M%#r#Fyiz+ce=UILHdIu38VMP>|2
z6B<63jc-4aijg0womiFjSPXvYTI9q9Jx$InNzt5k5UXA;yY;SOb&J~QqogjcB~!9A
zuW_RM%grrEnfT3bq`=+NMSAcgu1_!bVXBGgjjWOCE{KIx$%sEj4ck*OPQZMKm$POg
zD6@|y*sHpJHMHp<H*fHPmo}Nr#p#{8)kTYK?`-JJ(ta%mddl~{T`;sM)1HI(ILoUC
zP_)o~{Q~q<=#^eHQB(}gT+;`ycL;1H)by5%x;BF<KQBK+F~3T2lO;XV9DGyfW0qFZ
z-e##^(-^>(+n>I@sN>(iLl&_V(9Kmi-Vr`o%AstVxMH~rd2f90K5_K;x`XFFNhl%c
zTta}AYx7t5^)!|rKvosMOTUh<u+Z4jYQhz>_gzt-7BnYJw|{@nk&9cRRtFMocgh<s
zZY$+EV?`O+btIq%V-GTBF~C?49yQrZUg~ql`9*d)nw~~>Lum@--?Hd@UvjJ&G0%Ey
zn41T8qxK0}F8DaEL>It5U*$tLWR4g&#9Aa0SWwY9oeMREpQto80hJZ(Uc`gy0tNA<
zdvE%b67&UmqVx)TIQ22P?4xEU$XA*z<5z%D_|Rx*Y+s*o@+|&c!USl5=jz%s;_4l^
z$w4apcE9h3{Pkw#?Dvh2e|{OsWs0}-iT!Z6-~f9n{9BlNux9<a*k;-bQ!iM=gA;kz
zQ;sx0ol)Tqhq;U9Phpk>yrp=V4XnQCW$^>|KaSow_X-cVG7e)f5RBQ-c5{2ql(a;>
z?9Zq3I}MclkBYcl`<XhVlcv2^KDKxhOj)rbU|br(FvI{&gra9p*aO>1P3~14vZL9=
z`E7%ew!Mf;t9S|Ysi0uO;KqeTEAKJGi>If(XnMRD2MI^l4U^qdNgbm;Z5<9FfkS@N
zbN4hye5pbk9JV{68i>i`fcn_wey1GV$QG@;uTUo~;?Gw$%c@$eiLJsp?CqIJ7+*+R
z2{u;BM>ea!xncEHP>c5~TbrCv#kkukZaKv<3Z+{isfguJ<tzbuIlFNM%oN<+eW;wf
zG$UqlW$jqJ$ozXZmcYPCnfn>pKl)Pvldx>f@ick-=hiQfOE4;}_oM?_9Yd9ZXHj00
z57^3S<o_rTcTv)4f4AaiD}g7o=diHehlLh7J&RW#nx!3X_LdY}ciBh^4e_fDz-ZtM
z1Xraux~7LC3gTh52sF`f<GC4pa@nN0$cJVS`sDumySd&bjRSGNK3o_3k@{<cj$#7a
zbY?z!GtM==R4^vbj)RM{WWsDOcY^2-j&t#%11x5#8~fgyx)~!`nxOuNywIfF$6cW_
z-iXlIrLO5a$X1<YO4bI)u{fEiA2l?#!56`ysP;&S92@@oin0+3YV=LDwIfBVPx;h!
zK%8yZ*i-|)7Nwaa?Wq-G*@uPSBl`(OIJ;3J-NSAgB0L-RjeEoR`;O!XdDg=BZTcOY
z3}M1j#0i7TFG}B&I{eS>JM?n@cyNPeeJ=CRF)RO$mfK^k-hkbO*08XB60O%|j0{pF
z*VD&Afp2a`<Ep839O^^H8EIk}sJ5$fn0F{NxFh3b(;{XZtTQ3(x3`f*3#bDPAb&m7
zXyDALy5S`d>rrfj9sC2==dEq5@JMd4%^VBa>6~c^*xbz;h!4lg`5D0~cn+&r((AH6
zMAdUnURyErJ@d8Y+43+J9BqX3-BxKR9Bnjxky_uTOJ7lkiy^FzrcZ$?$Jg?_+{I+=
zB;nbwShZni3`+REX+kCQSS?CHZQ4=AZl7T?KB*g*5=A1h25FZ@R{QS9s%Mq27>`v5
z@b7&JQRLIrzr=uehzFE{m7i>7gA~+eJ9Du7uWcA=JA#5y$A3uWg;=?2_*`DmmFeS>
z7sDqr>fi5YNhY5gj%0fC`+8De%~YcrviqP*<G=%vwrKSD@rtD)XD>kn)~X{^N@~7W
zhti_6Q8DQ@LA`tJ9px#0uRDx9hSyQ#-yk)~{|(hFOq^XLOuW63JbzhCBy=9Tc=P<V
zPx@2UuvT<%M94|Xfe}z=89668DMzTZ3{=Vm0R}@IWF;jL2<89V11@3e{>+&gED4b?
zw+8b-c_8rr1O@-~^eBdIhC~^&_3fX;?eQ+_f$oXcXgLn>#fH$<H0*Q=+;^|1eyg@D
zZWAdEwJJ%-zjjHG={OzrLubNVTz>iYh`JCgdW>zlpU(Gq&A+vD)5=c0Jm%^Sv3z}`
znUNALZO13aI|RC1u-aN(oU6_AUrOC>U)EzTt%)c_B?+R0f`o`9!<@HD3q(wG;EW$G
zo$S1ZDyW7Y!Pb9+>HA9^j!scJ`&*`RvFsdO4x&qPhATAa<;d-lEft&8wg;{0PL-_z
zI>$#_@HA3JfM&d6pm_<t{-l<fx$Tk31v*HRx?-Wi<N$Zh__Ha(x+y9MB~W4&`kU<4
s9$Pl*1*^%<)HZ8QJn!;96X}O^c!ESdadx4Wg+gUv)B*yUMq1SW1?h)($^ZZW

literal 0
HcmV?d00001

diff --git a/exercises/Solution5/.ipynb_checkpoints/LeastSquaresFits-checkpoint.ipynb b/exercises/Solution5/.ipynb_checkpoints/LeastSquaresFits-checkpoint.ipynb
new file mode 100644
index 0000000..a15bd2f
--- /dev/null
+++ b/exercises/Solution5/.ipynb_checkpoints/LeastSquaresFits-checkpoint.ipynb
@@ -0,0 +1,693 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Solution Exercise 5\n",
+    "This week, we are working on least squares fits and parameter estimation"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from __future__ import print_function\n",
+    "import numpy as np\n",
+    "%matplotlib inline\n",
+    "import matplotlib.pyplot as plt\n",
+    "from scipy.optimize import curve_fit\n",
+    "from scipy.stats import norm, chi2, lognorm"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Fit a polynomial\n",
+    "We start by fitting a polynomial to a given data set, in particular, a parabola. Compare a linear fit and a cubic fit to our parabolic fit and check the goodness of fits with chi squared distributions. Explore how the different uncertainties affect the outcome and uncertainties of the fit. \n",
+    "\n",
+    "Hint: You can consider a plot similar to the lecture notes week 5 page 29.\n",
+    "\n",
+    "Extra: Do you see any way to decide wether the data is better described by the parabola or the cubic?\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Create some data distribuited as parabola with normally distributed errors.\n",
+    "def parabola(x, a, b, c):\n",
+    "    return a*x**2 + b*x + c\n",
+    "def error(x, sigma):\n",
+    "    return norm.rvs(0.0, sigma, x.size) \n",
+    "a = -0.1\n",
+    "b = 0\n",
+    "c = 1\n",
+    "sigma_y = 0.0015\n",
+    "\n",
+    "x = np.linspace(0, 1, 21)\n",
+    "y_true = parabola(x, a, b, c)\n",
+    "delta_y = error(x, sigma_y)\n",
+    "y = y_true + delta_y\n",
+    "y_error = sigma_y * np.ones(x.size)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def fit_polynomial(x, y, degree, weight):\n",
+    "    \"\"\"Fit polynomial of degree to data x, y with y weight = 1/sigma_y\n",
+    "    \n",
+    "    Return fit, covariance matrix, residuals and chi-squared and degrees of freedom.\n",
+    "    \"\"\"\n",
+    "    \n",
+    "    dof = x.shape[0] - degree\n",
+    "    fit, cov = np.polyfit(x, y, degree, w=weight, cov=True)\n",
+    "    residuals = np.sum((y - np.polyval(fit, x))**2 / y_error**2)\n",
+    "    chisq = residuals / (dof)\n",
+    "    return fit, cov, residuals, chisq, dof\n",
+    "    \n",
+    "fit, cov, res, chisq, dof = fit_polynomial(x, y, 2, 1/y_error) # Fit parabola\n",
+    "fit_1, cov_1, res_1, chisq_1, dof_1 = fit_polynomial(x, y, 1, 1/y_error) # Fit line\n",
+    "fit_3, cov_3, res_3, chisq_3, dof_3 = fit_polynomial(x, y, 3, 1/y_error) # Fit cubic\n",
+    "\n",
+    "def evaluate_chisq(chisq, dof):\n",
+    "    return chi2.sf(chisq, dof)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Reduced chi^2:\n",
+      "parabola 0.9459782161747974\n",
+      "line 32.15822425109619\n",
+      "cubic 0.9557527151822285\n"
+     ]
+    }
+   ],
+   "source": [
+    "print('Reduced chi^2:')\n",
+    "print('parabola', chisq)\n",
+    "print('line', chisq_1)\n",
+    "print('cubic', chisq_3)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Chi^2 distributions:\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "(0.9999999995308976, 0.04164125577461551, 0.9999999976681199)"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "print('Chi^2 distributions:')\n",
+    "evaluate_chisq(chisq, dof), evaluate_chisq(chisq_1, dof_1), evaluate_chisq(chisq_3, dof_3)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Error estimates:\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "(array([0.00424588, 0.00439779, 0.00094897]),\n",
+       " array([0.00664986, 0.00388699]),\n",
+       " array([0.0162819 , 0.0247968 , 0.01051785, 0.00118487]))"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "print('Error estimates:')\n",
+    "np.sqrt(np.diag(cov)), np.sqrt(np.diag(cov_1)), np.sqrt(np.diag(cov_3))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAsgAAAFgCAYAAACmDI9oAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAIABJREFUeJzs3XlYlFX7wPHvYWBE0VARdw1TMU1xAcURF4QWc8ncUiz3JTV9M98237IyLdsXyyyXXFrUXDJc2kRxyUGTVH4uSeaeZYqKuzMw5/cHgy/5sgzjwMBwf66LS2ae5znnfoBON4f7OUdprRFCCCGEEEJk8HJ3AEIIIYQQQhQlkiALIYQQQgiRhSTIQgghhBBCZCEJshBCCCGEEFlIgiyEEEIIIUQWkiALIYQQQgiRhSTIwqMopRoopXYppS4qpWxKqUnujkkIIUQGpVRtpdQlpZQhh+MvKaU+d1FfWilVzxVtiZLH290BCOFiTwMbtNbNsr6plIoEPtda13RLVEIIIdBaHwPKujsOIfIiM8jC09wO7HV3EEII4cmUUjLBJjyaJMjCYyil1gMdgQ/tf8L7Uik1VSnlB3wLVLe/f0kpVV0p1UoptUMpdUEpdUop9Y5770AIIYoupdQRpdQzSqkk4LK9XGK5Uuq0UuqwUupfWc7NdnxVSgXZSx+87a/rKKU22svifgQqZWkjUil1IpsY7s7Sh1kpdV4p9adS6kOllDGH2DsrpfbZ+/lDKfWk679CwpNIgiw8htY6CtgMjNValwUs9vcvA/cDJ7XWZe0fJ4H3gfe11rcBdYGv3BS6EEIUFzFAF6Ai8DWwG6gBRAPjlVL32c9zdHz9EkgkIzGeAgzKRyzpwBP2a032GMbkcO5c4FGtdTmgMbA+H/2IEkgSZFGSWYF6SqlKWutLWusEdwckhBBF3HSt9XEyksxArfXLWmuL1voQMBvoZz8vz/FVKVUbaAlM0lpf11pvAlY5GojWOlFrnaC1TtNaHwE+ATrkcLoVaKSUuk1rfU5r/Yuj/YiSSRJkUZINA4KBX5VSPyuluro7ICGEKOKO2/+9nYyytfOZH8B/gCr2446Mr9WBc/a/8mU66mggSqlgpdRqpdRfSqkLwKtkKdG4SS+gM3DUXtJhcrQfUTJJkb0oKfT/vKH1b0CMUsoL6AksU0oF3DRYCyGE+K/MsfQ4cFhrXT/bk3IYX2867U+gglLKL8u4WztLH5eBMpkn25eGC8xy/UxgJxCjtb6olBoP9M4hnp+B7kopH2AsGSUftRy5YVEyyQyyKClOAQFKKf/MN5RSjyilArXWNuC8/W2bW6ITQojiZTtw0f7QXmmllEEp1Vgp1RIcG1+11keBHcBkpZRRKdUW6JbllGTAVynVxZ7YPg+UynK8HHABuKSUuhMYnV2g9rYfVkr5a62t9mtkrBe5kgRZlAha61+BRcAh+58DqwOdgL1KqUtkPFDST2t91Z1xCiFEcaC1Tge6As2Aw8AZYA6QOQnh6PjaHwgHzgIvAguz9JFKxkN3c4A/yJhRzrqqxZP26y+SUf+8JJeQBwBH7KUYo4CH83G7ogRSWv/PX56FEEIIIYQosWQGWQghhBBCiCwkQRZCCCGEECILSZCFEEIIIYTIQhJkIYQQQgghsshzHWSl1KdkPKn6t9a6cTbHFRlPqHYGrgCDtda/KKVqkfE0ahUy1jScpbV+P7s+KlWqpIOCgvId/OnTpwkMDMz7xGLM0+/R0+8PPP8ePf3+wPl7TExMPKO1dtkXRynViYzx1gDM0Vq/dtPxbMdj+7Fsx3KlVEUynv4PAo4AD2mtz93ct7PjtDNKws9UbuT+5f5L8v1D4X4Nchyntda5fgDtgRbAnhyOdwa+BRTQGthmf78a0ML+eTky1jNslF0boaGh2hnOXlecePo9evr9ae359+jp96e18/cI7NB5jLGOfpCRFP8O3AEYgd03j6k5jcc6l7EceAN41v75s8Dr2fVfmN/nkvAzlRu5f7n/kq4wvwY5jdN5lljojL3Rz+ZySndgob2fBKC8Uqqa1vpPbZ+50FpfBPYDNfLqTwghRLZaAQe11oe01hZgMRnjb1bZjseQ61jeHVhg/3wB8GCBRC+EEMWIK2qQa/DfvdkhYxHvfyTCSqkgoDmwLbsGTp8+TVhY2I2PWbNmuSAsIYQofLNmzboxlgGVXNh0nmOtg+fcrIrW+k/753+RURb3P2ScFkJ4CkfG6TxrkG+VUqossBwYr7W+kN05gYGB7NixI99tjxw58hajK/o8/R49/f7A8+/R0+8P8nePI0eOvHG+UupMQcVUELTWWimV7e5Rzo7TzigJP1O5kfuX+y/pCvpr4Mg47dBOevYZ4NU6+4f0PgHitdaL7K8PAJFa6z/te6evBr7XWr+TU/thYWG6sAZeIUT2rFYrJ06c4Nq1a+4Opdjx9fWlZs2a+Pj4/ON9pVSi1jrMFX0opUzAS1rr++yvJwJoradlOSfH8dj+OoibxvKbxuxq9usb3Ny/jNNCeKaSMvbnd5x2xQxyLDBWKbWYjP3UU+0DrQLmAvtzS46FEEXDiRMnKFeuHEFBQWT85yscobUmJSWFEydOUKdOnYLs6megvlKqDvAH0A/of9M52Y7HebQbCwwCXrP/+41LoxZCFGklYex3ZpzOswZZKbUIMAMNlFInlFLDlFKjlFKj7KesBQ4BB4HZwBj7+xHAACBKKbXL/tE5n/ckhCgk165dIyAgwGMHyIKilCIgIKDAZ1+01mnAWOB7Mh56/kprvdfB8Tjbsdx+6DXgHqXUb8Dd9tdCiBKiJIz9zozTec4ga61j8jiugceyeX8LGUsNCSGKCU8eIAtSYX3dtNZryUiCs773cZbPsx2P7ceyHcu11ilAtAvDFEIUMyVh7M/vPcpOekIIIYQQQmQhCbIQwuO99NJLvPXWWw6ff+TIERo3/p9nkoUQQhSCvMbslStXsm/fvgKNQRJkIYRHSEtLc3cIQgghCoEkyEKIEuXIkSPceeedPPzwwzRs2JDevXtz5coVXn75ZVq2bEnjxo0ZOXJk5hbJREZGMn78eMLCwnj//fdZtWoV4eHhNG/enLvvvptTp07daHv37t2YTCbq16/P7NmzgYwnm5966ikaN25MkyZNWLJkSbYxtWvXjhYtWtCiRQu2bt1aOF8MIUog83Ez0zZPw3zc7O5QRC7MZjPTpk3DbHbd9+mVV14hODiYtm3bcuDAAQBmz55Ny5Ytadq0Kb169eLKlSts3bqV2NhYnnrqKZo1a8bvv/+e7Xm3qsA3Cikof1z4g6RTSdxf/353hyKExxk/Hnbtcm2bzZrBe+/lfd6BAweYO3cuERERDB06lI8++oixY8fywgsvADBgwABWr15Nt27dALBYLDc2sDh37hwJCQkopZgzZw5vvPEGb7/9NgBJSUkkJCRw+fJlmjdvTpcuXTCbzezatYvdu3dz5swZWrZsSfv27f8RT+XKlfnxxx/x9fXlt99+IyYmptA2zBCiJDEfNxO9MBpLugWjwUjcwDhMtUzuDkvcxGw2Ex0djcViwWg0EhcXh8l0a9+nxMREFi9ezK5du0hLS6NFixaEhobSs2dPRowYAcDzzz/P3LlzGTduHA888ABdu3ald+/eAJQvXz7b825FsZ1Bfn7D83Rd1JXp26bfmE0SQhR/tWrVIiIiAoBHHnmELVu2sGHDBsLDw2nSpAnr169n7969N87v27fvjc9PnDjBfffdR5MmTXjzzTf/cV737t0pXbo0lSpVomPHjmzfvp0tW7YQExODwWCgSpUqdOjQgZ9//vkf8VitVkaMGEGTJk3o06dPgf9ZTwhPkd/Z4Pgj8VjSLaTrdCzpFuKPxBdsgMIp8fHxWCwW0tPTsVgsxMfH33KbmzdvpkePHpQpU4bbbruNBx54AIA9e/bQrl07mjRpwhdffPGPMT0rR8/Lj2I7g/zh/R9y/tp5Hv/ucQ6cOcD797+Pt1exvR0hihRHZnoLys1L8SilGDNmDDt27KBWrVq89NJL/1jL0s/P78bn48aNY8KECTzwwAPEx8fz0ksv5dquI959912qVKnC7t27sdls+Pr6OnFXQpQszswGRwZFYjQYb1wTGRRZOMGKfImMjMRoNN6YQY6MjCywvgYPHszKlStp2rQp8+fPzzEZd/S8/Ci2M8h+Rj+WP7Scp9o8xUc7PqLbom5cuH7B3WEJIW7RsWPHbtS1ffnll7Rt2xaASpUqcenSJZYtW5bjtampqdSoUQOABQsW/OPYN998w7Vr10hJSSE+Pp6WLVvSrl07lixZQnp6OqdPn2bTpk20atXqf9qsVq0aXl5efPbZZ6Snp7vydoXwSM7MBptqmYgbGMeUjlPyVV4hdcuFy2QyERcXx5QpU1xSXgHQvn17Vq5cydWrV7l48SKrVq0C4OLFi1SrVg2r1coXX3xx4/xy5cpx8eLFG69zOu9WFOspVy/lxRv3vEH9ivUZs3YMEZ9GsDpmNbeXv93doQkhnNSgQQNmzJjB0KFDadSoEaNHj+bcuXM0btyYqlWr0rJlyxyvfemll+jTpw8VKlQgKiqKw4cP3zgWEhJCx44dOXPmDJMmTaJ69er06NEDs9lM06ZNUUrxxhtvULVqVY4cOXLjujFjxtCrVy8WLlxIp06d/jFjLYTInrOzwaZapnzVHUvdsnuYTCaXJMaZWrRoQd++fWnatCmVK1e+Mc5PmTKF8PBwAgMDCQ8Pv5EU9+vXjxEjRjB9+nSWLVuW43m3QhWF+t2wsDB9qw+9xB2Ko9dXvSjlXYrYfrGE1wx3UXRClAz79++nYcOGbo3hyJEjdO3alT179rg1Dmdk9/VTSiVqrcPcFJJLuWKcFiWL+biZ+CPxRAZFFljSOm3zNCZtmES6TsegDEzpOIWJ7SYWSF+eqiiM/YUlP+N0sS2xuFn0HdGYh5kpayxL5IJIvtr7lbtDEkIIIUosUy0TE9tNLNAZ3cyZaoMySN2ycKliXWJxs4aBDUkYlkCPJT3ou6wvB88eZGLbiSVij3EhPEFQUFCxnD0WQrhHZt1yQc9Ui5LHoxJkgEC/QNYNXMew2GE8t/45klOS+aTrJ5TyLuXu0IQQQgjhYvmtWxbCER6XIAP4evvyeY/PaRDQgBfjX+Tw+cOseGgFAWUC3B2aEEIIUSwVRk2xEEWFRybIkLHG6QsdXqBexXoM+WYIree2Zk3/NQQHBLs7NCGEEKJYkdUiREnjMQ/p5aR/k/6sH7ie89fO03pOa9mZRwghhMgn2eVOlDQenyADRNSOYNvwbVQtW5V7P7uXeTvnuTskIUQ2ypYtC8DJkyfp3bu3m6MRQmTytNUiZHMRkRePLbG42R0V7mDrsK30WdqHobFD+e3sb0yNmoqXKhG/IwhRrFSvXj3XHfOEEIXLk1aLkHIR4YgSkyADlPctz9r+axm7dizTtkzjt7O/seDBBZTxKePu0IQQWWTdMGT+/PnExsZy5coVfv/9d3r06MEbb7wBwA8//MCLL77I9evXqVu3LvPmzbsxCy2EcC1PWS0iu3IRT7gvV9m+fbtLdqLLVK5cOVq1apXneUOHDmX16tVUrly5SCz3WaISZAAfgw8fd/2YBpUa8OQPT3L0/FFiY2KpWraqu0MTosgY/914dv21y6VtNqvajPc6vefUtbt27WLnzp2UKlWKBg0aMG7cOEqXLs3UqVNZt24dfn5+vP7667zzzju88MILLo1bCOFZnN0Gu6S4ePEi5cuXd1l758+fd+i8wYMHM3bsWAYOHOiyvm9FiawvUEoxwTSBr/t+zd7TewmfE87/nfo/d4clhMhBdHQ0/v7++Pr60qhRI44ePUpCQgL79u0jIiKCZs2asWDBAo4ePeruUIUQRVxmuciUjlOkvKIIad++PRUrVvyf93fv3k379u1p1KgRXl5eGauUFcJESImbQc6q+53d2TxkM90WdSPi0wiW9F7C/fXvd3dYQridszO9BaVUqf9u9GMwGEhLS0NrzT333MOiRYvcGJkQojjylHIRT3ft2jX69u3LwoULadWqFZMmTeLatWtMnjy5wPsukTPIWbWo1oLtw7dTr2I9ui7qyoztM9wdkhDCAa1bt+ann37i4MGDAFy+fJnk5GQ3RyWEEMJV1q1bR4sWLW7UMIeEhHD27FmUUgXed4lPkAFq3FaDTUM20TW4K2O/Hcvj3z5Oui3d3WEJIXIRGBjI/PnziYmJISQkBJPJxK+//urusIQQQrjInj17aNKkyY3Xv/zyCy1atCiUvkt0iUVWZY1lWfHQCp7+8WneSXiH38/9zqJeiyhXqpy7QxOixLh06RIAQUFBN55iHjx4MIMHD75xzurVq298HhUVxc8//1yoMQohhCgcAQEBrF+/HoDk5GRWrFjB1q1bC6VvSZCzMHgZePu+t6kfUJ+xa8fSdl5bVsWsorZ/bXeHJoQQQghR4MqVK+fwyhOOtueImJgY4uPjOXPmDDVr1mTy5MnExMQQGxtL48aNqVSpEosWLSIgIMBlseVGEuRsjAobxR0V7qDP0j6Ezwkntl8sLWu0dHdYQgghhEuYj5s9YtMP4XqOrFlcEHJ64HrVqlWFHEkGqUHOwb1172Xr0K34evvSYX4HVuxf4e6QhBBCiFuWuZPcpA2TiF4YLdstC5ENSZBzcVflu9g2fBtNqzal11e9eH3L62it3R2WEEII4bTsdpITQvyTJMh5qOxXmfUD19P3rr48G/csw2OHY0m3uDssIYQQwimZO8kZlEF2khMAJWLyL7/3KDXIDijtU5ove31Jg4AGvLzpZQ6fP8zyh5ZToXQFd4cmhBBC5EvmTnJSg5w/nlq37evrS0pKCgEBAYWyvrA7aK1JSUnB19fX4WskQXaQl/JicsfJ1KtYj+GrhtN6bmvW9F9DvYr13B2aECVOfHw8b7311j+WfMvUuXNnvvzyS8qXL++GyIQoHmQnufzJrNu2pFswGowetUV1zZo1OXHiBKdPn3Z3KAXK19eXmjVrOny+JMj5NKDpAILKB9FjSQ/C54Szsu9K2t3ezt1hCSHs1q5d6+4QhBAeJru6bU9JkH18fKhTp467wyhypAbZCe1ub0fC8AQqlalE9MJoPtv9mbtDEsIjLFy4kJCQEJo2bcqAAQMYPHgwy5Ytu3G8bNmyNz6/cOECXbp0oUGDBowaNQqbzQZkbDJy5syZbNsTQghnSN12ySMzyE6qV7EeCcMS6PVVLwauHEhySjKTO07GS8nvHMIDjB8Pu3a5ts1mzeC993I8vHfvXqZOncrWrVupVKkSZ8+eZcKECTmev337dvbt28ftt99Op06dWLFiBb179861PSGEcIbUbZc8ks3dggqlK/DdI98xrPkwpm6eSv/l/blqverusIQoltavX0+fPn2oVKkSABUrVsz1/FatWnHHHXdgMBiIiYlhy5Ytt9SeEELkxlTLxMR2EyU5LiFkBvkWGQ1GZnebTXBAMM+se4ajqUdZ2XclVcpWcXdoQjgvl5newuTt7X2jdMJms2Gx/HeJxZuftvbUp6+FEEIUvhI3gxwZGUlkZKRL21RK8XTE0yx/aDm7/9pN+Jxw9v6916V9COHpoqKiWLp0KSkpKQCcPXuWoKAgEhMTAYiNjcVqtd44f/v27Rw+fBibzcaSJUto27Ztnu0JIYQQjihxCXJqairHjh3DbHb91po9G/Zk05BNWNIttPm0Dd8f/N7lfQjhqe666y6ee+45OnToQNOmTZkwYQIjRoxg48aNNG3aFLPZjJ+f343zW7ZsydixY2nYsCF16tShR48eebYnhBBCOEIVhd1TwsLC9I4dOwq8H7PZTNu2bbHZbJQuXZq4uDhMJtfXEh1PPU63Rd3Y8/cePrj/A0a3HO3yPoRwtf3799OwYUN3h1FsZff1U0olaq3D3BSSSxXWOC2EEIUpp3E6zxlkpdSnSqm/lVJ7cjiulFLTlVIHlVJJSqkWWY51UkodsB979tZu4dbFx8ffqGe0WCzEx8cXSD+1/GuxechmOtXrxJi1Y3jiuydIt6UXSF9CCCGEEMK1HCmxmA90yuX4/UB9+8dIYCaAUsoAzLAfbwTEKKUa3UqwtyoyMhIvr4xbNhqNLq9FzqpcqXJ80+8bxoeP571t7/Hgkge5ZLlUYP0JIYQQQgjXyHMVC631JqVUUC6ndAcW6oxajQSlVHmlVDUgCDiotT4EoJRabD93360GDfDCCy8wZ84c/Pz8KFu2LH5+fg591KpVi2vXrvHee+/RvHlzV4SSI4OXgXc7vUv9gPr869t/0W5eO1bFrKLmbY5vdShEYdJay2oQTigKpWpCCCFcxxXLvNUAjmd5fcL+Xnbvh2fXwOnTpwkL+2/5x8iRIxk5cmSunZ469QDe3r24fv0qV69e5dSpK9hsV0hLu0xa2iXS0i5hsVzEaj0H/AlcB64BIcAlYmJeAIZSo0ZF6tSpk+1HjRo1MBgMADdmm50pyxjTcgx3VLiDh5Y+RKvZrVgVs4rQ6qH5bkeIguTr60tKSgoBAQGSJOeD1pqUlBR8fX0BmDVrFrNmzco8XMltgQkhhHBakVgHOTAwkPw+/HFs7de0P1OF6hUD8LJcw8t6HWW9jrc14/NSXKMU1ylFOr6kZ/nXhhe+nKMlKXQi9VRZzp82kLLtOgnWU6zhIGc5Swp/c8XwN7Vv9+WOO4LYs2cPNpuNDz74gMGDB1OuXLl8xdupXie2DttK1y+70n5+e77o+QUP3vlgvtoQoiDVrFmTEydOcPr0aXeHUuz4+vpSs2bGX4ay/oKvlDrjzrhEyWA+bpYd3oRwMVckyH8AtbK8rml/zyeH911imF5A72t/wMnsj2ujEXx90cZS2HxKYTP6ZvzrXYp0m4LzB/C5kILvtdQc+7Cme3P2UEXWH9rPEM5iRfPvfz1O7L/epEydclRp35KWERGEh4dz11133ZhtvlnW2edtw7fRfXF3ei7pyZv3vMkE0wSZrRNFgo+PD3Xq1HF3GEKIfDAfNxO9MBpLugWjwUjcwDhJkoVwAVckyLHAWHuNcTiQqrX+Uyl1GqivlKpDRmLcD+jvgv4A+Kh8eaYBH77zTsZSbaVKZXz4+oLReCPpVOTxJGJaGpw7Bykp6DMpXD5+lnMHU7h4JIWrf5wl7VQKmw//hDX1D2yAQhPFcSYeBuvhZA4sWMceSrPcOw1rsD/l24ZT/957CTeZqF69+v90V6VsFTYM2sCglYN48scnOZBygBmdZ+Bj8HHVl0YI4aGUUp2A9wEDMEdr/dpNx5X9eGfgCjBYa/1LbtcqpV4CRgCZfzr4j9Z6bcHfjXCF+CPxWNItpOt0LOkW4o/ES4IshAvkmSArpRYBkUAlpdQJ4EUyZofRWn8MrCVjMD5IxoA8xH4sTSk1FviejAH5U621S7aXM5vNbNy/H5vNRvTgwbe2nrG3NwQGQmAgCihr/8jKZjbzSdu2YLPh41ua20bPYulRA3p3Ercd20Mr6176pR3JePxw324uz5rPXvzZ6FuKq/Uq4XvyGL+WKsXWrVtp06YNpX1Ks7j3YoI3BPPK5lc4dO4Qyx5aRnnf8rf0dRFCeK4sKwPdQ8YzHT8rpWK11lkffM66qlA4GasKhTtw7bta67cK6VaEC0UGRWI0GG/MIEcGRbo7JCE8giOrWMTkcVwDj+VwbC0ZCbRLZbeecUFs+JHJZDIREhJCamoqX3zxRZa+YtAa/vgDVm++yIkf9nFl225K/76H+pZ9RF37Pw7t2cUmwAJERUTw8l1N6DD6UZoPG8bUqKnUr1ifEatGYJprYk3/NdxR4Y4Cuw8hRLHWirxXBnLLqkLCfUy1TMQNjJMaZCFcrEg8pJdfmesZ22y2Al/POJO/vz/+/v7/k4grBTVrQs2YchATDoSjNRw9Cpt3wCdvP8fVhGlAxjJQ6Xv/j/CxYzkz9kn2V69G8P1RrOkzk367nyZ8Tjgr+64konZEgd+PEKLYcWRlIGdXFRqnlBoI7AD+rbU+d3Pnzqw2JAqHqZZJEmMh8sGR1YaKZYKc84xuwcnP8m5KQVBQxkeNGl1Z3/Y1bDaNwViaxLaLGZmQQpsrG7n35A9UnzsX5s7l59LliAux8tauDpwY+zF9TcNvaWk5IYRw0ExgChm/xU8B3gaG3nySM6sNCVHSyQojRZMjqw0VywQZcp7RLWqyS+ZtNti9ewifrk7nl88TCUrewt1X1/Hwtg2M2JaOdfEIfqn+b0KvGFhfugxms7nI36cQosDltGKQI+fkuKqQ1vpU5ptKqdnAateFLETJJSuMFG/FNkEuTrOqO3fu/MdrLy9o3hyaNzfApFakpLTihx8mMGjRWS6t+4n2NZ+jytn/Y+ZZsHCOqDYRrBg2lPtffx0CAtx0F0IIN/uZvFcGyveqQkqpalrrP+3X9wD2FPytCOH5ZIWR4i3XFdBE4QgIgJgYWBpbkdUXuxG5YDdTmtzNVQXpgBXNrrlzsQZW5ljLVli++gosFneHLYQoRFrrNCBzZaD9wFda671KqVFKqVH209YCh8hYVWg2MCa3a+3XvKGU+j+lVBLQEXiisO5JCE+WucKIQRlkhZFiSGU87OxeYWFhWmrb/slsNhPRLgJt02BQNCz3IsPOXeBhPqMqp7lYqgzXezxApQkTICwso/BZCFGkKKUStdZheZ9Z9Mk4LUT+SQ1y0ZfTOF1sSyw8nclkommTppy6dopr0dc4WfNdLtX4ir4fT8Bv604GXv+cBxevgMWLSalSHb9RI/AdPjxjSQ0hhBBCuJ2sMFJ8SYlFEebv709wlWB2Tt1Jbf/aTDncmUemr2XB31059MpcWlTbw3Bms/dUHXwnT8ZWqzZnw1ryyp13cl+7du4OXwghhBCiWJISi2LiwvUL9FvWj28PfsuTpid57e7XMHgZ2LlT88Ybp9i14m8esnzNQOZRl6OkePlQ7j/PYBw/Xh7sE8JNpMRCCCGKtpzGaZlBLiZuK3UbsTGxPNbyMd4yv0Wvr3px2XKZ5s0VixZVZffFEBotfoY+zTbSnOk8YatL4tSpXK9aneujRmXsXCKEEEIIIfIkCXIx4u3lzYedP2R6p+msSl5F+/nt+eNCxjKoRiP07evLjI9OkuTdHm4ZAAAgAElEQVT1BJ9xgAhKMSUtGq9P5pBe5w6u9u4DSUluvgshhBBCiKJNEuRiaFz4OGL7xZKckkz4nHB2/vnfdZbj4+Ox2dIBjZchjfiIpjQq9Qvv6fGkLV8LTZtyuUNHiI+HIlBeI4QQQghR1EiCXEx1Ce7CliFbUErRbl47Vh1YBUBkZCReXhnfVqPRyJtvPsDeCyGkvfYUTW77meeYyuVN/wcdO3KpSVNYsQLS029cm7m9tRBCCCFESSUJcjHWtGpTtg/fTsPAhnRf3J13ze/SunVrQkJCqFOnDnFxcZhMJoxGeOaZqhw614j68x7FVG0To5jJX3svQa9eXL69LsyejdFmc/ctCSGEEEK4nayDXMxVK1eNjYM3MuDrAUz4YQLJKcls37EdH4PP/5zr5QWDB1di8OBKrF1bnQeeaMtdyft55o9phI0cyeNePnziXxbzxo2YOnRww90IIYQQQrifzCB7gDI+ZVjaZynPRDzDx4kf0+XLLqReS831ms6dy7PvQGOe3dGZx1rOJ4y36WVLZ+25c0RHdsT8yisO1yhLaYYQQgghPIkkyB7CS3nx2t2vMfeBuWw4soE2n7bh8LnDeV4XGurHtu0hdHzyMtfRpAMWNPHPP8/l5i3BbC744IUQQgghihBJkD3M0OZD+eGRHzh58SThc8IxH3cswe3Z8268vBQANlWKrTzNhd3HoU0brj3YEw4dyvHa1NRUjh07hlmSaSGEEEJ4AEmQPVDHOh1JGJbAbaVuo+OCjizeszjPa0wm042H+376aQNjv3uCdlW+4SVeJP2b77DWv5P0JybAuXP/uM5sNpOUlMThw4eJjo6WJFkIIUSRZzabmTZtmvw/S+RIEmQP1aBSAxKGJ9CqRitilscwZeMU8tpW3N/fn9q1a2Mymbjvvqoc/Ks1lT6IoVnp71loG4B67z2u1gyC998HiwXIXHc5Y/ULi8VCfHx8Ad+ZEEJ4LvNxM9M2T3P4r38i/8xmM9HR0UyaNEkmdkSOJEH2YJXKVOLHAT8yIGQAL8S/wMCVA7medj3H8+Pj4/8nwR07tgG/XmzL7nGjaGn4ni1XWsH48Vy6vT4sX05khw7/WHdZHtYTQgjnmI+biV4YzaQNk4heGC1JcgGJj4/HYrGQnp4uEzsiR5Ige7hS3qVY8OACpnScwudJn3P3Z3dz5sqZfLVhMCimT2/J+pQOTO8yiftZytG/ykLv3jR57AkeqFfvH+suCyGEyL/4I/FY0i2k63Qs6Rbij8S7OySPFBkZidFoxGAwyMSOyJGsg1wCKKV4vv3z1K9Yn0ErB9F6TmtW91/NnZXuzFc7/v5GVq1uz2+/nSWmTwVa7D7Ay7sm8zV/822lapjq1y+gOxBCCM8XGRSJ0WDEkm7BaDASGRTp7pA8kslkIi4ujvj4eCIjI2ViR2RL5VWXWhjCwsL0jh073B1GiWA+bqb74u5YbVaWP7ScqDpRTrcVF3eYMQOSGfDnZp7mDaxlyuI39yPo2xeUcmHUQhRPSqlErXWYu+NwBRmnC4f5uJn4I/FEBkViqiWJmxAFLadxWkosShhTLRPbhm+jernq3Pf5fcz9Za7TbUVH1+HAyfuoOKMH4d7L2HOlPsTEcCGqM5w44cKohRCiZDDVMjGx3URJjoVwM0mQS6A6FeqwdehWoupEMXzVcJ758Rls2uZ0e2PGhLIx5R7+0+F5JvAa3vEbuXJHQ2wfzwKb8+0KIYQQQriDJMgllL+vP2v6r2FU6Cje2PoGfZb24Yr1itPt3XZbaeLiu9FhZRdal1mI2doKr9GPci60PRw86MLIhRBCCM8ky/wVHZIgl2DeXt581OUj3r3vXb7e/zXt57Xn5MWTt9Rm9+6N2XGuO5/0Hs8w3kXt2sP1Bo2xvvoGpKW5KHIhhBDCs8gyf0WLJMglnFKK8a3H802/b/j1zK+Ezwln91+7b6lNo9GHr5Z2Y/jWrrQPmMG3tk74PPcMZ4NDYfettS2EEEJ4Ilnmr2iRBFkA0K1BN7YM3QJA23ltWZO85pbbNJnqsevvGNaPHcZDfIj18F+kNQ/jyr+fg+s5b1gihCi5ZAtgUVJlLvNnUAZZ5q8IkGXexD+cvHiSbou6seuvXbx737uMazUO5YIl2w4cOEH/+9bxr6NxDOJzUirXo+KKBaiINi6IWoiiSZZ5y5/MLYAtFgtGo1E2HxIljizzV/hkmTfhkOrlqrNp8CYeaPAAj3/3OGPXjiXNduu1ww0a1GTH4UGcfPVhunh9yKW/Lei2bVlUtT5cu+aCyIUQxZ1sASxKOlnmr+iQBFn8Dz+jH8sfWs5TbZ7iox0f0W1RNy5cv3DL7SqlmDixE3P/6MsjTZ/nKbpx5NRB1tS6C5KTXRC5EKI4ky2AhRBFhSTIIlteyos37nmDWV1nse7QOiI+jeDo+aMuabtq1Uq8MbMx76rV/AdF7zOHWH9nCFdnf+aS9oUQxVPmFsBTpkyR8gohhFtJgixyNSJ0BN89/B3HU4/Tak4rtp3Y5pJ24+Pj0doGaK7hxRJdldIjB/Jnp0fg8mWX9CGEKH5MJhMTJ06U5FiIAiQPw+ZNEmSRp+g7ojEPM1PWWJbIBZF8tferW24zMjISL6+MH7/SpUvxd7cBTGEcVb7/kr9qNcW2K+mW+xBCCCHEP2U+DDtp0iSio6MlSc6BJMjCIQ0DG5IwLIHQaqH0XdaXVze/yq2sgGIymQgJCaFOnTrExcXxdewUWn33CF1LTUWfu4y1RUvOTfsIsukjMjJSahOFEEIIJ8jDsI6RBFk4LNAvkHUD19G/SX+eW/8cQ74ZgiXd4nR7/v7+1K5d+8afUu+7rxVf/DmGgSFPsEG3p8J/HuNo+INw/ryrbkEIIYQo0eRhWMd4uzsAUbz4evvyeY/PaRDQgBfjX+TI+SMsf2g5AWUC8t1Wdr+1VqhQnh92PcWUybXYMPkupv48g1M1muD/7XJ827cCIDU1ldTUVMxms9QpClHEJSQkcFmeKxCiSHn99dfZtWsXzZo148qVK8TFxbk7pFvi5+dH69atXdqmQwmyUqoT8D5gAOZorV+76XgF4FOgLnANGKq13mM/9gQwHNDA/wFDtNay8G0xppTihQ4vUK9iPYZ8MwTTXBOr+68mOCDYde2/FMMvD4TQtWM5PrnwGRU7RPDH+Kkc692WpKQkbDYb0dHR8qS7EEXc5cuXKV++vLvDEEJk0aZNG9q08ZyNus4XwF+a8yyxUEoZgBnA/UAjIEYp1eim0/4D7NJahwADyUimUUrVAP4FhGmtG5ORYPdzXfjCnfo36c/6ges5d+0cprkmNh7Z6NL2W7S4i6//nMjTd48ilvuo8d6zLOs+CJvNBiC1U0IIIYQoEI7UILcCDmqtD2mtLcBioPtN5zQC1gNorX8FgpRSVezHvIHSSilvoAxw0iWRiyIhonYE24Zvo4pfFe757B7m75rv0vbLlCnDVz8+y9lPRjBWjaVbyjF8AQVSOyWEB/JLSqLqvHn4JclKNkII93GkxKIGcDzL6xNA+E3n7AZ6ApuVUq2A24GaWutEpdRbwDHgKvCD1vqHWw9bFCV3VLiDrcO20mdpH4Z8M4TfUn5jStQUvJTrngEdMbI7v0eHMLZtKeb9tYTf+YPgrsMxubjmSAjhPn5JSQSPHo2yWtE+PiTPnMnlkBB3hyWEKIFclcG8BpRXSu0CxgE7gXR7bXJ3oA5QHfBTSj1y88WnT58mLCzsxsesWbNcFJYoLOV9y7O2/1pGthjJq1tepe+yvly1XnVpH3Xr1iH22DT+E3gnjbiHPks/4P8ihoDF+ZU0hHC1WbNm3RjLgErujqc4KZeYiLJaUTYbKi2NcomJ7g5JCFFCOTKD/AdQK8vrmvb3btBaXwCGACilFHAYOATcBxzWWp+2H1sBtAE+z3p9YGAgO3bscPIWRFHhY/Dh464f06BSA5784UmOpR7jm37fULVsVdf14ePDob9/5NM5X/PKiECeMy9gf80D1NsVi0/1QJf1I4SzRo4cyciRIwFQSp1xczjFysXQULSPD6Slob29uRga6u6QRCFLSkoiMTGR0NBQQuSvB8KNHEmQfwbqK6XqkJEY9wP6Zz1BKVUeuGKvUR4ObNJaX1BKHQNaK6XKkFFiEQ1IJuzBlFJMME2gboW69F/Rn/A54ayOWU2TKk1c2s/Q4T34uWkQQ9uVZsbpzzkVFIrfujVUaO/afoQQhedySAjJM2dSLjGRi6GhDpdX+CUl5fsakX8FnbwmJSUxevRorFYrPj4+zJw5U5Jk4TZ5llhordOAscD3wH7gK631XqXUKKXUKPtpDYE9SqkDZKx28bj92m3AMuAXMpZ48wKkfqIE6H5ndzYP2UyaLY2ITyP49rdvXd5Hy5bNee3YKzxSaxBeViveHdrw+ztfu7wfIUThuRwSwl9DhuQrOQ4ePZrqM2cSPHp0sX+4LykpiXnz5pFUxO4jM3mdOXMmo0ePLpD4EhMTsVqt2Gw20tLSSJQSG+FGDtUga63Xaq2DtdZ1tdav2N/7WGv9sf1zs/14A611T631uSzXvqi1vlNr3VhrPUBrfb1gbkUUNS2qtWD78O3Uq1iProu68uH2D13eR+XKlVl0cDrP3TuQ/dSlzr97saP35Gy3qBZCFH1JSX7Mm1eVpCQ/h853tm65KCaihZGEOqswktfQ0FB8fHwwGAx4e3sTKiU2wo1kJz1RoGrcVoNNQzbx8IqHGfftOJJTknnnvnfw9nLdj57RaOTT717jzZfrcPCl5fRf/hLbgvcQtnMhhrKlXdaPEKJgJSX5MXp0MFarwsdHM3NmMiEhue/CdzE0lHSDEaWtaC8fh+qWC+tP+UnnkkhMSSQ0IJSQCnm3n10SWlRKDDKT17S0tAJLXkNCQpg5c6bUIIsiQRJkUeDKGsuy4qEVPP3j07yT8A6/n/udxb0WU65UOZf1oZTi6RdH8V2r+kzq5s+Ug8vYW/V3av68Gv+G1V3WjxCi4CQmlsNqVdhsirS0jNd5JchmTDzKOiLYyE90YDABhJD7NYmJiVgtVmzahtVidSgRTUryIzGxHKGhF/OMCTKS40eXPEra4TS863jzSd9P8kySCyMJdVZhJa8hISGSGIsiQRJkUSgMXgbevu9tggOCeWztY0R8GsHq/qup7V/bpf10uj+a+geCGNLSmw/OreZi4zDOfLGSuv1aubQfIYTrhYZexMdHk5YG3t6a0NCLeV6TmFiOLekN2KQjMNg0TRJP5pnABl0MpJS2YQGM2kbQxdxXwHFmZnuNeQ3W+VZIB6vBypraawjpnHviV9RnUCV5FSWJ63ZyEMIBj4Y9yrcPf8vR1KO0mt2Kn//42eV91K1bl/ePzGJ004e4ZvOmekwHto3/zOX9CFHYlFKdlFIHlFIHlVLPZnNcKaWm248nKaVa5HWtUqqiUupHpdRv9n8rFNb93Cwk5DIzZyYzatRJh5JQ+G9SbTBoh5Pq4ORrfI8XU4Dv8SI4+Vqu5ycmlsNiScBmew2rNYHExLz/+pWS6A3pgAbS7a8dYgIm2v8VQriLJMii0N1T9x7Mw8yU9ilNh/kdWL5vucv7uO2221jwyxxmDx/Mz9xJ+PsDWWd6GluazeV9CVEYlFIGYAYZKwU1AmKUUo1uOu1+oL79YyQw04FrnwXitNb1gTj7a7cJCbnMkCF/OZQcZ56f36TaKyqEUErxJAZCKYVXVO6zov7+m1A6CsVzYIvC339Tnn0EePcHSgEKKGV/nbvMmeqZM6szenSwww8qrliRzNixy1ixItmh84VnKYoPnHoCKbEQbtEosBHbhm+j++Lu9F7am2nR03gm4hky9plxDS8vL16d/TKfmxoyZ/gshie8yerSW+l48gf8Asu4rB8hCkkr4KDW+hCAUmoxGTuV7styTndgodZaAwlKqfJKqWpAUC7Xdgci7dcvAOKBZwr6ZlwpJOSywwk1QLWewaxnLrb1SXhFhVCtZ3Cu5189sBzFdWyAF9e5emA5GbO8OevSJZjY2DjS0jbi7d2BLl0CIM/a6PzXYK9Ykcyrrw4DLCQkGIG59MzjfiD/NdXOXiMKlqwdXXBkBlm4TWW/yqwfuJ6+d/VlYtxEhsUOw5Lu+m2jHxkaQ7Ntb/KE6kjntK0cqN2RM8lnXd6PEAWsBnA8y+sT9vccOSe3a6torf+0f/4XUCW7zk+fPn1jC+2wsDBmzSreS9pX6xlMjQ9755kcQ8ZvD0bAYP830oH2Q0Ius+KpY6wMv8iKp44VWLnI+vVJgIWMeg6L/XXunJmpdvaa/CzZ5yy/pCSqzpuXrzWwi/I1+VGYa0d70kz1rFmzboxlQKXszpEZZOFWpX1K82WvLwkOCGbKpikcPn+Y5Q8tp2Lpii7tJ6xlGEPuOk3zfbWZfu0XzjRqy5VNP1C7TU2X9iNEcaa11kqpbBcRDwwMZMeOkrkRanCXLvwYG8vGtDQ6eHsT0KVLHnPBGYlR57dHo6xW9E4fkuvNzHPzk5CQyyz795L/zmyH5J28R0WFsD3BG40NhTdReZSLgHMz1fm9JinJj/mPphCRtoL53h0Y/EmAQ78k/Lki2eGZffjvRjHKakX7+JA8M++vc1G/Jj+7QhbWyieeNlM9cuRIRo4cCYBS6kx250iCLNzOS3nxcseXqV+xPsNXDcc018Sa/muoV7Gey/owm83s27cPm81GtDLwXfoRgtu2Ye+yH7ir550u60eIAvQHUCvL65r29xw5xyeXa08ppapprf+0l2P87dKoPcDlkBACPvmEQflIXLJuYIJ9AxNHkqP8JtUD6l2jk0GzKR3aGzSB9a7lmbyHhl6krWErbfVGtnh1IDQ0IM/7ye81Z9ck8621H0YsWKxGZq5ZDCE3/8Hjn/5ckUzUq8Myrkkwsp65eSbJ5RITwWJFaRva6tjX2dlr8vv9zO81ziTUISEhzPv3v0lav56QqCiCCyhpLcprdBcUKbEQRcaApgNYN2AdKVdSCJ8Tzuajm13Wdnx8PDab/QE9L5hUO5hS+iqVe7XDPH2by/oRogD9DNRXStVRShmBfkDsTefEAgPtq1m0BlLt5RO5XRsLDLJ/Pgj4pqBvpDjK7xbYF0ND0T4+aIMB7e3t0AYmzuwKWC4xkbY6jefQtCXdoWtMmInjbl7mBeK4GxNml1/TgXiMWPAmHR8sdCA+zz5s65P+cY3NgXKRnf4RXNOlsGLgms3ITv+IArkmc0OadGUg3cENafL7M+DM998vKYk+b7/J1O3b6PP2mwVWylESdzmUBFkUKe1ub0fC8AQqlalE9MJoPtvtmuXZIiMj8fLK+HE3Go289eUM3uvdl4uUosnj0Xz/1Hcu6UeIgqK1TgPGAt8D+4GvtNZ7lVKjlFKj7KetBQ4BB4HZwJjcrrVf8xpwj1LqN+Bu+2txiy6HhJA8cyYnR41yaCYQnEuqnU3EDekWDDodg83qcCKen2vKdmkCPj6kKwPKxyfjdR68okKwYMSKASvGPFcXAfgutT33qHW8wBTu9VrHd6ntC+QaMyaiWcckXiaadZgdWIbvckgIa/89j1Utn2Ltv+fl+TPgTBJ+acsasFgx2DTaYs147YD81kZnrtE9atSoYl9e4SgpsRBFTr2K9UgYlkCvr3oxcOVAklOSmdxxMl7K+d/nTCYTISEhpKam8sUXX2AymWjTpg1vPxnIPe8spONbD7Di6Fx6LBmACxfSEMKltNZryUiCs773cZbPNfCYo9fa308Bol0bqYCMBMnRGefM85NnzsxXDaoz12Qm1aSl5TsRd/SayyEhHPzko3zFld/VRSCj9GOOsTXb0kx4e2uGhea91J0z1zizIU1Skh+j3+6L1doPn52amfVyX4bQmV0hNwZBIwPodLAaMl5H5nEvzpRyQMnbKEYSZFEkVShdge8e+Y4xa8YwdfNUfjv7G/O6z6O0T2mn29y5c+c/XiulePLtF5lXM5DzEz7kwaWDWHTiNP22TMArSy4eGRkJZJRpCCFEQcpvUu3MNYWViDtzL9V6BoMDiXGmzHWw87P8nDPXOLvLo8WSgNYbsVo7kJh4e659OZOEnw3sRfTDq4g8ZiW+tg9Rgb3yjMuZGmzI/wOUxZ0kyKLIMhqMzO42m+CAYJ5Z9wxHU4+ysu9KqpTNdhUqpw15YgyralRmVd+X6W/+N4sa/E2PpGn4ls6YSk5NTSU1NRWz2YzJJLtbCSGKv8JIxAtLftfBduYaZ5Jqf/9NaJ2xRrXNZsTffy6Qc2LpTBKeuqc92zZsIKH2Rrw2dCC07O3Q8q9cr9npH0EF/Sk+WLDaa7Cr5dGPMw9QQvFeO1sSZFGkKaV4OuJp6lWsxyMrHiF8Tjhr+q/hrsp3ubSfbg/1ZmvVyiyI+heDDr7Oitp/E/XrbPYnbycpKSlj9YvoaOLi4iRJFkKIEii/SXVq6k8odR2tbXh5WUhN/YncEmRnZ7aNc1qT9ofJnlTnXS7yXWp7pql1tNcb2ezVgWaptzOE3JPqrA9Q6swHKPNIkDPXzrZaFT4+2uGdLotKUi0P6YlioWfDnmwasonr6ddp82kbvj/4vcv7aNO+PS13f8G7vqH0PDOPhNo9+Wb5uhurX1gsFimzEEII4ZDQ0FCMxoyVH3x8HFv5oTC2Wg8NvUiisTVvGp5lh09rh2aqnXmA8p9rZysSE8vleY2z260XBJlBFsVGWPUwtg/fTtdFXenyZRc+uP8DRrcc7dI+Gt11F+WSv2ZySB9ePB/LkfeO4aUM2HQ6RqPxRj2yEEIIkZvMlR8SExMJDQ0tsAfcCqNcxNkHKPO73rYzm9gUFEmQRbFSy78WW4ZsIWZ5DGPWjiE5JZm37n0Lg5fBdX3UqsW439cysVl3Xj6egC9BTKlcns9XfiDlFUIIIRxWVFd+cKZuO78PUJowM4AxKKxofDjIR1wm96+FM3XYBUVKLESxU65UOb7p9w2Phz/Oe9ve48ElD3LJcsmlfVSsWJFJv37PpKbR9OEk3/99jiv783qMQQjhDklJScybN4+kAtokQQiRf86st+1MyUhBkQRZFEsGLwPvdXqPD+//kLW/raXdvHacuHDCpX2UKVOGqTtW8/o9XQngNHcM68gPsw67tA8hxK1JSkpi9OjRzJw5k9GjR0uSXIR42i8unnY/Bc2ZTWwg/3XYBUVKLESx9lirx6hbsS4PLX2IVrNbsSpmFaHVXbcFpre3N5O/X8KrvYYw5usV1H80irVp6+k8po7L+hBCOC8xMRGr1YrNZiMtLY3ExMQi+SftkibzFxer1YqPj0+x333N0+6nMDizdjZkbGSS32sKgswgi2KvU71ObB22FR+DD+3nt2flrytd2r5Siv8sn8fHvXpSnhQaPdaRVR/ITLIQRUFoaCg+PhkrBXh7O7ZSgCh42f3iUpx52v0UlsshIfw1ZEi+kuOURx9lwUcfkfLoow5vhV0QJEEWHqFx5cZsG76NxpUb03NJT97e+jYZu+66hlKKZ5fOY1afXtzGWZr8qyNfvytJshDulrlSwKhRo2RWrwjxtF9cPO1+iqrkNWu4x2rlBa25x2olec0at8UiJRbCY1QtW5X4QfEMWjmIJ398kgMpB5jReQY+Bh+XtK+U4ukln/KWQTFs8TJaTIhkqXU9fZ6u65L2hfBEfn5+nD9/vkD7qF27NrVr1wYo8L6EY2rXrs2bb77Jrl27aNasGbVr1y6w782+fftu9NOoUaMC6aMw76ck+/76dSxAOmCxv+7pwNfZz8/16yUrV86yOSssLEzv2LHD3WEID2HTNl7Y8AKvbH6F6DrRLHtoGeV9y7usfa017zwynCFfLuUiFTC/Eke//9RzWfvCcyilErXWYe6OwxVknBZFkdlsJjo6GovFgtFolN1Oizmz2Ux0x47//X5u2FDg38+cxmkpsRAex0t5MTVqKvO7z2fT0U2Y5po4dO6Qy9pXSjHh8zksGNCXspzD9Fw0n7980GXtCyFEQTObzUybNg2z2ezuUG5JfHw8FouF9PR02e20CMrvz5nJZCJuwwamvPJKoSTHuZESC+GxBjUbRFD5IHp+1ZPwOeGs7LuSiNoRLmlbKcX4BbOY7gWPLFhC+xejWJC+nkGTZSZZCFG0edKsa2RkJEaj8ca9yG6nRYezP2cmk6lI/DzKDLLwaB2COpAwLIEKvhWIWhjFF0lfuKxtpRT/mjeLL4f2x49zdHw5irn/+c1l7QshREHwpFlXk8lEXFwcU6ZMKdaJvicq7j9nkiALj1c/oD4JwxMw1TTxyNePMDl+sstWuFBKMXbOTBYNexg/znHvtChmPilJshCi6MqcdTUYDB4x62oymZg4caIkx0VMcf85k4f0RIlhSbfw6OpHmb9rPv2b9GfuA3Px9fZ1WfszRo6h7+zPuEp5Vj6+nnHv1XdZ26J4kof0RFFlNpuJj48nMjJSEktRYIrDz1lO47QkyKJE0Vrz+k+vMzFuIm1qtWFl35UE+gW6rP2PRz9G748XcpXyLH8sjsc/CEYplzUvihlJkIUQomiTVSyEwL7hR9tnWdpnKb/8+Qvhc8LZf3q/y9ofNXMGK8YMpjTn6T0jirdHJ1MEfgcVQgghRD5IgixKpN6NerNx8EauWK9gmmti3aF1Lmt75IwP+HrsEEqRSr9Ponh3rNQkCyGEEMWJJMiixGpVoxXbhm+jtn9tOn3eidmJs13W9ogPphM7fji+pPLgR/fx8QsnXNa2EEIIIQqWJMiiRLu9/O1sGbqFe+vey8jVI3nqh6dIt6W7pO1h777LiuEPE8ifREy5n8/eT3FJu0IIITyTp2zg4gkkQRYl3m2lbiM2JpaxLcfylvkten3Vi8uWyy5pe8Ssmcx7oAvB/Erd8d1Y8Zlr2hVCFC7zcTPTNk/DfFwSF1EwMjfWmDRpEtHR0ZIku5kkyEIA3l7efND5A6Z3ms6q5FW0n9+ePy78ccvtKqUY+/VXzGjbgXC2URhyi7UAACAASURBVHZgD75fZXFBxEKIwmI+biZ6YTSTNkwiemG0JMmiQBT3jTU8jSTIQmQxLnwcq2JWkZySTPiccHb+ufOW2/Ty8mLc+m+Z3jiUe/mRCw8+wpaNrinjEEIUvPgj8VjSLaTrdCzpFuKPxLs7JOGBivvGGp5GEmQhbtK5fmd+GvoTXsqLdvPaserAKqfbioyMJDIyEh8fH0Zt38gHt99FH9tSfr17NL8kyvpvQhQHkUGRGA1GDMqA0WAkMijS3SEJDyTbZhctkiALkY2QKiFsG76NRoGN6L64O++a33Vqe+rU1FSOHTuG2WymdOnSDEr6//buPc6msv//+OszM4acySQxom7U3JJqoh3VZhIhonIMOU0qpbvDHUonlUNHnW5NUtwddECoHDJMVNupSCQlZ18KFXUrY2au3x+z85s002zs2YeZ9/Px2I/Za63rWvO+xp7VpzXXWutTJlQ7jQFZL7Kg2TC+/roIwotIUHkSPaT3Tmdki5Gk907Hk6jCRYqGHpsdOQIqkM2sjZmtN7MNZjY0n+1VzGy6ma02s2Vm1jDPtspm9o6ZfW1m68xM/+oSFWpUqEHGdRl0PrMzt827jRvfv5FD2YcC7u/z+Vi9ejWbNm06fMFFxYoV6bjWx+sVanHHwTG8cf5YtmwpwkGISFB4Ej0Mu2iYimOREqLQAtnMYoHngMuBJKC7mSUd0Ww4sMo51wjoDYzLs20cMMc5dwZwNhC8x5aJFLGypcry1jVvMbTZUMZ/Np72b7Rn3+/7AuqbkZFBTk4OwJ8uuEg46SSaf7GIGaVP4oFf7+LZ817k+++LagQiIiJytAI5g9wE2OCc2+icywSmAB2PaJMELABwzn0N1DGz6mZWCbgYeMm/LdM593PQ0ouEQIzFMOrSUUzsMJEFmxZw4cQL2fTTpkL7eb1eYmJyf8WOvOCidt26nLHsQxbEncjovYN4KPltfvrpz311gYaIiEh4BFIg1wS25Vne7l+X1xdAZwAzawKcCtQC6gK7gZfNbKWZTTCzckd+g927d5OcnHz4lZaWdgxDESlafc/py7xr57Hzl500ndC00Fs9eTweGjVqRN26dfO94KJBo0ZUWTCdFVaJx7Zfy3DPh/z6a+62vHOXJbqkpaUdPpYB1cKdR0REjp4VduGRmV0NtHHODfAv9wKaOucG52lTkdypFOcAXwJnAAOBOGAJ0Mw5t9TMxgH7nXMj8n6P5ORkt2LFiuCNSqQIrd+znnavt2P7/u28cuUrdGvYrcC2f5wF/rv7WX48cyaVO/biVHK487wP6f64o2XL5uTk5HDCCSfoauYoZmafOeeSw50jGHScFpHiqKDjdCBnkHcAiXmWa/nXHeac2++c6+uca0zuHOQEYCO5Z5u3O+eW+pu+A5x7DPlFIkaDag1YMmAJTWo2ofvU7oz8aGSBd7jIyMgo9GbvzTt0YMfEp9gDPPRZe+7q92a+c5dFREQkNAIpkJcD9cysrpnFA92AmXkb+O9UEe9fHAAs8hfNu4BtZtbAvy0F+CpI2UXCplrZanzY60N6NerFvRn30vvd3hzMOnjM+2vdty+rxt5LFr8xdOPrGLm/TrpZvIiIBJsenV64uMIaOOeyzGwwMBeIBSY659aa2SD/9vHAmcAkM3PAWqB/nl3cDLzmL6A3An2DPAaRsCgdV5pJV06i/on1GbFwBJt/3sz0rtOpVvbYpp12uvNOXtu9m3aPPs3rVOdfFc5j2tx/a3qFiIgEzR+PTs/MziQ+Nl739i5AoQUygHPuA+CDI9aNz/PeB9QvoO8qoFjMwRM5kplxz8X3UK9qPfq824cLJlzAez3e44xqZxzT/nqOHcuEH3bTc9JrnPrLLlYvbYzqYxERCZb8Hp2uAvmv9CQ9kSDo2rArC/ssZP/B/Xhe8rBg04Jj3lf/lyfycoqXpiyl6r968f6snCAmFRGRkkyPTg+MCmSRIPEkelg6YCmnVDiF1q+25qXPXzqm/ZgZA2e/T9o/zuQapvJ1p7v47LMghxURkRJJj04PjApkkSCqW6Uun/b7lJZ1WzJg1gDu+vAuctzRnwEuVaoU3Zd/whuVTuH27Md4zfsftm4tgsAiIlLi6NHphVOBLBJklcpU4v0e7zPovEGM/XQs17x9DQcOHTj6/VSuTLPPFzO/VBXG/nozDzb7gH2BPeVaREREjoMKZJEiEBcTx/PtnufJ1k8yfd10LnnlEnb+svOo91P7tNOoOm8aX1lZntjejX9duorMzCIILCIiIoepQBYpImbGrRfcyoxuM1i3ex1NJjThi11fHPV+zvV6+b+0J/iFLO5f0YG7eu2gkAdgioiIyHFQgSxSxK5ocAUf9/sY5xzNX27O+9+8f9T7aDNgAAtvu4Gq7OLatzow9r7/FUFSERERARXIIiHR+OTGLBu4jPon1qfDlA48vfTpAh9PXZCejz3G5Mtb0ZiVnDmyO69Nzi6itCIiIiWbCmSREDmlwiksum4RHRp0YMicIQz+YDBZOVkB9zczUmfOYPwZ/6QDs9h73W189FERBhYRESmhVCCLhFC5+HJM7TKVOy+8k+dXPM8Vb1zB/oP7A+4fFxdH72WfMrlqDW5xT/Ne63GsW1eEgSVimFlVM/vQzL71f61SQLs2ZrbezDaY2dDC+ptZHTP7zcxW+V/j89uviEhJogJZJMRiLIaxrcaS1j6N+Rvn02xiM7b8vCXg/hUqVKDl5z5mx1dm9MHbePTid/n++yIMLJFiKJDunKsHpPuX/8TMYoHngMuBJKC7mSUF0P8751xj/2tQUQ5CRCQaqEAWCZOB5w1kds/ZbNu3jSYTmrB0+9KA+9Y69VRqLHiPVVaWZ/b05LYWyzlwALxeL16vt+hCSzh1BCb5308CrsynTRNgg3Nuo3MuE5ji7xdofxERQQWySFhdetql+Pr7KFeqHN5JXt5e+3bAfRs3a8bel59lD45H13Xklk5bcU6/0sVYdefcHzfT3gVUz6dNTWBbnuXt/nWF9a/rn17xkZldlN833717N8nJyYdfaWlpxz4SEZEwSktLO3wsA6rl1yYutJFE5EhnJpzJ0gFLufLNK+nyThce+fERhjYfipkV2veyPn14dd06rhjzJLfMa88VJ3YktuJr+Hw+PB49QjTamNl84OR8Nt2dd8E558zsmO+GfUT/nUBt59xeMzsPeNfM/umc+9Pk+ISEBFasWHGs31JEJGKkpqaSmpoKgJntya+NTjeJRICEcgmk906nx1k9GL5gOH1n9CUzO7BH5l07ejSvdrycfaxh195H2LRpMykpKfh8viJOLcHmnLvUOdcwn9cM4HszqwHg//pDPrvYASTmWa7lX0dB/Z1zB51ze/3vPwO+A+oXxfhERKKFCmSRCFEmrgyvdnqV+y+5n0lfTOKy/17G3gN7A+o7aOpUxlavTjY5gOPgwUwyMjKKNK+E3Eygj/99H2BGPm2WA/XMrK6ZxQPd/P0K7G9mCf6L+zCz04B6wMYiGYGISJRQgSwSQcyM+7z38Vrn1/Bt9+F5ycO3e78ttF9sbCy3vvYqMUAsEJMTQ4MG3qKOK6E1GmhlZt8Cl/qXMbNTzOwDAOdcFjAYmAusA95yzq39u/7AxcBqM1sFvAMMcs79GKIxiYhEJDvap3kVheTkZKe5bSJ/9snWT7jyzSvJcTlM6zKNS+pcUmifRklJJK/bQB8cL56eQdrqZpQtG4Kwki8z+8w5lxzuHMGg47SIFEcFHad1BlkkQjWr3YylA5ZyUrmTaPXfVryy6pVC+6z+6iv6THuTWsQz9rtruKPHDiLg/4FFRESiigpkkQh2WpXT8PX3cfGpF9N3Rl/uTr+bHJfzt30u6dSJj2+/gYrsodeMq3ly9MEQpRURESkeVCCLRLjKZSozu+dsBp47kEc+foSu73Tlt0O//W2f3o8+yovNm+JhCZWG38jcOTqNLCIiEigVyCJRoFRsKV5o/wKPtXqMqV9NxTvJy65fdxXY3sy4fu5c0hJq0p+JzOv0HBs2hDCwiIhIFFOBLBIlzIzbL7ydaV2nseaHNTSd0JQvv/+ywPZly5blsiWLmBNXkdG//4sHUhbyyy8hDCwiIhKlVCCLRJkrz7iSxX0Xk5WTRbOJzZizYU6Bbeucdhpl3pnMd8Tz+Nau3Hb1FnL+fgqziIhIiacCWSQKnVvjXJYOWMrpVU+n3evteG7ZcwW29XbsiO/fgynNPgbN68zo+/46f9nr9eL1eoswsYiISPRQgSwSpWpVrMXivotpX789g2cPZsjsIWTnZOfb9rrRo0m72MM5rCTxoVRmvPvni/b27dvH1q1b9XhqERERVCCLRLXy8eWZ1mUat11wG08ve5oOUzrwy8G/TjQ2M26a/QHPVz+FXryKr+sTfPVV7jafz8fq1avZtGkTKSkpKpJFRKTEU4EsEuViY2J5vPXjjG83nrkb5tL85eZs3bf1L+3Kli1L+08XMbNURR7O/DejL53Hzz9DRkYGOf6JyZmZmWRkZIR4BCKh59vmY9TiUfi26X8IReSvVCCLFBPXJ1/P7J6z2fzzZppOaMryHcv/0qbOaadRYeqrfEUZntzZnduu/I6LLvISE5N7KIiPj9dcZCn2fNt8pExOYcTCEaRMTlGRLCJ/oQJZpBhpdXorfP19lIkrwyWvXMK0ddP+0qbFFVewdOgtxPArt37UiXnTG9GoUSPq1q1Leno6Ho8nDMlFQidjcwaZ2Zlku2wyszPJ2JwR7kgiEmFUIIsUM0kJSSwdsJSzTz6bq966ijEfj8G5P1+U1/+RR3ihRTP+yRoaPtGXg793oHbt2iqOpUTw1vESHxtPrMUSHxuPt4433JFEJMKoQBYphk4qdxILei+g6z+7MjR9KANmDiAzO/PwdjPjlvfe46kaNenC21y9oTRPPZURvsAiIeRJ9JDeO52RLUaS3jsdT6L+x1BE/iwu3AFEpGicUOoEXr/qdeqfWJ+Ri0ay6edNTO0ylSonVAFyL9q76pNFvNPgbO4/dA/9WzfiqW/aU6lSmIOLhIAn0aPCWEQKpDPIIsVYjMXwYIsHmXzlZD7Z9gkXvHQBG37ccHh7nbp1qTrtdb6gLI//0Ju7um7iiNkYIiIiJY4KZJESoNfZvZjfaz57D+yl6YSmLN6y+PC2lu3bs+zOG4nhAP3mduW5JzP/Zk8iIiLFnwpkkRLiolMvYsmAJVQrW42UySn894v/Ht42cPRonjkniSYshztuZ9myMAYVEREJMxXIIiXIP6r+gyX9l9C8dnN6v9ubexfei3OOmJgYbpg3jxfKVWWwe5aX273JTz+FO62IiEh4qEAWKWGqnFCFOdfOoV/jfoxcNJIe03rwe9bvVKtWjYaz3sFHecbsGciwq7/RfGQRESmRVCCLlEDxsfFM6DCB0SmjmbJmCi0nteSH//1AsxYtWHnXTRziEIMWdOHpMb+FO6qIiESAkvZ4dhXIIiWUmXFX87t455p3WLVrFU0nNGXtD2sZ9MgjPHnuP2nMF5Qbfgu+knEsFBGRApTEx7MHVCCbWRszW29mG8xsaD7bq5jZdDNbbWbLzKzhEdtjzWylmb0XrOAiEhxXJV3FR9d9xO9Zv3PhxAuZv2k+Q+bMZly5ExngJjCl3ST27g13ShERCZeS+Hj2QgtkM4sFngMuB5KA7maWdESz4cAq51wjoDcw7ojtQ4B1xx9XRIrC+TXPZ+mApZxa6VTavtaWqVumcu6st8mgPI/8dCP3dFpDTk64U4qISDiUxMezB3IGuQmwwTm30TmXCUwBOh7RJglYAOCc+xqoY2bVAcysFtAOmBC01CISdLUr1eaTfp/Q+h+tueH9G5ieOYtVd93IrzhuXtyFpx76NdwRRUQkDEri49kDKZBrAtvyLG/3r8vrC6AzgJk1AU4Favm3PQX8Gyjw/NPu3btJTk4+/EpLSwswvogEU4XSFZjRbQa3NLmFJ5c8SXqjrxib3IAz+Jrq9w/i48W6rUVh0tLSDh/LgGrhziMiEgyeRA/DLhpWIopjgLgg7Wc0MM7MVgFfAiuBbDNrD/zgnPvMzLwFdU5ISGDFihVBiiIixyMuJo5xl4+j3on1GDJnCJuvS6Lad5UY9tNr3HHFRTT49noSEsKdMnKlpqaSmpoKgJntCXMcERE5BoGcQd4BJOZZruVfd5hzbr9zrq9zrjG5c5ATgI1AM6CDmW0md2pGSzN7NRjBRaRoDW4ymPe6v8eW/Vt48vZY5pYuy0P7hnBfh881H1lERIq1QArk5UA9M6trZvFAN2Bm3gZmVtm/DWAAsMhfNA9zztVyztXx91vgnLs2iPlFpAhdXu9yPun3CSeUK0ffWzLZEwe3LenC4/fuC3c0ERGRIlNogeycywIGA3PJvRPFW865tWY2yMwG+ZudCawxs/Xk3u1iSFEFFpHQOqv6WSwdsJTEeufStddB6rCRug/3Y+ECzUcWEZHiKaD7IDvnPnDO1XfOne6ce9i/brxzbrz/vc+/vYFzrrNz7qd89pHhnGsf3PgiEgonlz+ZjOsyqNayA0NbOa5mGvM7Pcn334c7mYiISPDpSXoiEpATSp3A9J7T2dWtBzPrw32/3kH3pOe55JKW4Y4mIiISVCqQRSRgMRbDq/1e4+3O7dhR0fFy5hD2/nBRuGOJiIgElQpkETlqrzw0g3saN2DzgSzO/eEh7nh0fLgjiYiIBE2w7oMsIiVIbGwsPe54nEsz2uN+zMGG3kCpKt8zasB94Y4mIiJy3HQGWUSOyerVq8kCsgGXAxMn3M8DGQ/gnO5uISIi0U0FsogcE6/XS0xM7iGkFPD4l+UZPf9+rp1+Lb9n/R7ecCIiIsdBUyxE5Jh4PB4aNWrEzz//zNm/HeTa73eyb5KHwaVeZ/PPm3m367sklNMzqUVEJProDLKIHLOVK1eyadMmHpg7m3ExVbhpu4/r593F5zs/p+mEpqzbvS7cEUVERI6aCmQROW5nn3027pG7WEUVRn76EkN+ncqBQwfwvORh/sb54Y4nIiJyVFQgi0hQ3HLnnTx1fj3Ks5+WY8Yx4XwfiZUSafNqG1787MVwxxMREQmYCmQRCYqYmBhGTn2H4WXKc5mbx+prpjOv6ydcdvplpL6Xyp3z7iQ7JzvcMUVERAqlAllEgiYxMZGmE5/lXapx++5hvJS6kZndZzL4/ME85nuMq966iv9l/i/cMUVERP6WCmQRCapu3bsz56pL2EsZrpranblTM3mm7TM83eZpZn0zi4tfuZgd+3eEO6aIiEiBVCCLSNCNnjCB2xPK04D17O71L3btgpub3sys7rP4Zu83NJ3QlJU7V4Y7poiISL5UIItI0FWuXJlBb7/OY1ThuoNppLV9F+egbb22fNLvE2IshotevohZ62eFO6qIiMhfqEAWkSJxySWXsO+OfnxGAjet7M/LD+VOq2hUvRFLBywlKSGJjlM68qTvST2eOgBmVtXMPjSzb/1fqxTQro2ZrTezDWY2NM/6a8xsrZnlmFnyEX2G+duvN7PWRT0WEZFIpwJZRIrMfQ8/zIMNTqQM/6Pufb1Z+2UOADUq1CDjugw6n9mZ2+bdxo3v38ih7ENhThvxhgLpzrl6QLp/+U/MLBZ4DrgcSAK6m1mSf/MaoDOw6Ig+SUA34J9AG+B5/35EREosFcgiUmTi4+MZNW0qt8eVo4VbwLzLHuXgwdxtZUuV5a1r3mJos6GM/2w87d9oz77f94U3cGTrCEzyv58EXJlPmybABufcRudcJjDF3w/n3Drn3PoC9jvFOXfQObcJ2ODfj4hIiaUCWUSKVFJSEg2fuJ+3OZnBu+7h+X4rDm+LsRhGXTqKiR0msmDTAi6ceCGbftoUxrQRrbpzbqf//S6gej5tagLb8ixv96/7OwH12b17N8nJyYdfaWlpgScXEYkgaWlph49lQLX82sSFNpKIlEQ3DR5MlxkzuCD9c9q93oOF3T6nxRXlD2/ve05f6lSuQ+e3OtN0QlNmdJuBJ9ETxsThYWbzgZPz2XR33gXnnDOzkE7cTkhIYMWKFYU3FBGJcKmpqaSmpgJgZnvya6MzyCJS5MyMp//7X26sEMc/2MCubrewd++f27So24Il/ZdQsXRFWkxqwZQ1U8ITNoycc5c65xrm85oBfG9mNQD8X3/IZxc7gMQ8y7X86/7OsfQRESnWVCCLSEjUqFGDfpNeYBQn0f3Ay7zc9m2OvHlFg2oNWDJgCU1qNqH71O6M/Gik7nDx/80E+vjf9wFm5NNmOVDPzOqaWTy5F9/NDGC/3cystJnVBeoBy4KUWUQkKqlAFpGQ6dSpE1v7tmUJNei/LJUZz//1RGW1stX4sNeH9GrUi3sz7qX3u705mHUwDGkjzmiglZl9C1zqX8bMTjGzDwCcc1nAYGAusA54yzm31t+uk5ltBzzA+2Y2199nLfAW8BUwB7jJOZcd0pGJiEQYi4SzM8nJyU5z20RKhl9//ZWOSQ2Zte17Po3z0nDrB5xcw/7SzjnHw4sfZsTCETSv3ZzpXadTrWy+11JELDP7zDmXXHjLyBfJx2nfNh8ZmzPw1vGWyLnrInLsCjpO6wyyiIRU+fLl+fHEKvybilyaNYdpbSf8ZaoF5M5bvufie5hy1RSW71jOBRMu4Os9X4c+sEQ03zYfKZNTGLFwBCmTU/Bt84U7kogUAyqQRSTkKlWqxIxT4phPbXqtuo2Z4wq+tVvXhl1Z2Gch+w/ux/OShwWbFoQwqUS6jM0ZZGZnku2yyczOJGNzRrgjiUgxoAJZREJu3759xJYqxe3VD+FwVLvzOnb9X06B7T2JHpYNXMYpFU6h9autmbhyYgjTSiTz1vESHxtPrMUSHxuPt4433JFEpBhQgSwiIeXz+Vi9ejVbtmxh/c8/0p3SNMtaxPutn853qsUf6lSuw6f9PqVl3Zb0n9mfofOHkuMKLqqlZPAkekjvnc7IFiNJ752uOcgiEhQqkEUkpDIyMsjJyS1ss7Ky+MnTgJmcTo81w3j/8b+fY1ypTCXe7/E+g84bxJhPxnDN29dw4NCBUMSWCOZJ9DDsomEqjkUkaFQgi0hIeb1eYmJyDz3x8fE89NBDPJSYzQFKU2NoH3Ztz/rb/nExcTzf7nmebP0k09dN55JXLmHnLzv/to+IiMjRUIEsIiHl8Xho1KgRdevWJT09nZYtWzJ28svcQFnOy17G/FZj/naqBeTe4eLWC25lRrcZrNu9jqYTmvLFri9CMwARESn2VCCLSMitXLmSjRs34vHk/knc6/Vy0k2dmUIDunz9ALNHrQpoP1c0uIKP+31Mjsuh+cvNef+b94sytoiIlBAqkEUkIowePZrRtX5nLxU59d7e7Nwc2NPzGp/cmGUDl1H/xPp0mNKBZ5Y+U8RJRUSkuFOBLCIRoXz58jwxaSIDKc0/s7/k48seKHSqxR9OqXAKi65bRIcGHbhlzi0M/mAwWTl/P5dZRESkICqQRSRitGzZksRBHZhIQzp/O4a5Dy4JuG+5+HJM7TKVOy+8k+eWP8cVb1zB/oP7izCtiIgUVyqQRSSijB07lsdq7mcHJ3H6g33Y+V3gt3GLsRjGthpLWvs05m+cT7OJzdjy85YiTCsiIsWRCmQRiSgVKlRg3MsTuI446uV8w4pWwwKeavGHgecNZE7POWzbt42mE5qydPvSogkrIiLFkgpkEYk4rVq14h8DL+cZzuGKTU8z/+6FR72PlNNS8PX3US6+HN5JXt5e+3YRJBURkYL4tvkYtXgUvm2+cEc5aiqQRSQiPfroo4yrsZtvrTYNxvRl1zdHP5/4zIQzWdJ/CefVOI8u73Rh1OJRuKM9HS0iIkfNt81HyuQURiwcQcrklKgrkgMqkM2sjZmtN7MNZjY0n+1VzGy6ma02s2Vm1tC/PtHMFprZV2a21syGBHsAIlI8VapUiWcnvkhvl0PNnG2sbnXbUU+1AEgol8D83vPpcVYPhi8YTt8ZfcnMzgx+YBEROSxjcwaZ2Zlku2wyszPJ2JwR7khHpdAC2cxigeeAy4EkoLuZJR3RbDiwyjnXCOgNjPOvzwJud84lARcAN+XTV0QkX23atCGp32U8SjKXbX2JjDuP7UEgZeLK8GqnV3nA+wCTvpjEZf+9jL0H9gY5rYiI/MFbx0t8bDyxFkt8bDzeOt5wRzoqgZxBbgJscM5tdM5lAlOAjke0SQIWADjnvgbqmFl159xO59zn/vW/AOuAmkFLLyLF3uOPP84LNf6PNfYPzni8P62T2x3TfsyMey+5l9c6v4Zvuw/PSx6+3fttkNOKiAiAJ9FDeu90RrYYSXrvdDyJnnBHOiqBFMg1gW15lrfz1yL3C6AzgJk1AU4FauVtYGZ1gHMAXU4uIgGrXLkyz014gWtdJtXYy/VfHjymqRZ/6HFWDxb0XsBPv//EBS9dwEebPwpeWBEROcyT6GHYRcOirjiG4F2kNxqobGargJuBlUD2HxvNrDwwFbjVOfeXK212795NcnLy4VdaWlqQYolIcdC2bVsa92nBQM5kfWY6aT1HH9f+mtVuxtIBSzmp3Em0+m8rXln1SnCCAmlpaYePZUC1oO1YRERCxgq7otvMPMD9zrnW/uVhAM65UQW0N2AT0Mg5t9/MSgHvAXOdc0/k1yc5OdmtWLHi2EchIsXe3LlzadOmDTFAPDDjnTlcdlXr49rnz7//zNVvXU36pnSGNx/OyJYjibHg3dzHzD5zziUHbYdhpOO0iBRHBR2nA/kvwXKgnpnVNbN4oBsw84idV/ZvAxgALPIXxwa8BKwrqDgWEQnE559/DkAOcAiYOuie495n5TKVmd1zNgPPHcgjHz9C13e68tuh3457vyIiEt0KLZCdc1nAYGAuuRfZveWcW2tmg8xskL/ZmcAaM1tP7t0u/ridWzOgF9DSzFb5X22DPgoRKfa8Xi8xMbmHrBhiuG7PCr58Kv2491sqthQvtH+Bx1o9xtSvO2GxkwAAEAlJREFUpuKd5GXXr7uOe78iIhK9Cp1iEQr6052IBOKcc85h79697N/9K8sOVuWEuBxO2vUlpauWC8r+3/36XXpO60m1stV4r/t7nFX9rOPan6ZYiIhEtuOZYiEiEhFWrlzJ1q1beejRB+nvTiTx0CZWth8RtP1fecaVLO67mKycLJpNbMacDXOCtm8REYkeKpBFJOrccMMNZDaB8TFtaOJ7ii1vLgnavs+tcS5LByzl9Kqn0+71djy//Pmg7VtERKKDCmQRiTqxsbG88MILDHUr2U5Ncvr1J+e3g0Hbf62KtVjcdzHt6rXjpg9uYvq66UHbt4iIRD4VyCISlRo3bszA23sxiCTqHviKVV0fCer+y8eXZ3rX6Uy4YgIdGnQI6r5FRCSyqUAWkah1//33s7b2N7wRezlnzXqEvQtXB3X/sTGx9D+3P7ExsUHdr4iIRDYVyCIStcqVK8d//vMcN2d/w09U4eer+0NWVrhjiYhIlFOBLCJRrW3btqR0OY8h5uX0H1fw9Q3jwh1JRESinApkEYl6Tz31FB+UX8is2Fac+tIIDqzeEO5IIiISxVQgi0jUq1GjBmPGjuT67H0cdPHsbD8QIuAhSCIiEp1UIItIsZCamkodTyzD4jpz+rYMtt37YrgjiYhIlFKBLCLFQkxMDC+88AIv5swkI+Yiqoy6k+wt28MdS0REopAKZBEpNs466yzu/PdA+udUICb7EFva3QDO4fV68Xq94Y4nIiJRQgWyiBQrI0aMwNX9mgdL9eW0te+x57k3wx1JRESijApkESlWypYty/jx/+HRQ++xzM6n1O03k7X3R7Zu3YrP5wt3PBERiQIqkEWk2Lnsssvo1qM5A60hX2T+xPI1a9i0aRMpKSkqkkVEpFAqkEWkWHriiSfYUmEmD8U2IZvcW75lZmaSkZER3mAiIhLxVCCLSLFUvXp1HntsNAuy91AKIxaIj4/XxXoiIlIoFcgiUmz169cPT/PqnMY1PAi86WmPx+MJdywREYlwKpBFpNj6497IXzGbyvSh7YKpZC/7LNyxREQkwqlAFpFiLSkpiXvuGcIw9vMDJ7G3yyDIzg53LBERiWAqkEWk2Bs+fDhVTl3JsNI3cdKWFfw69vlwRxIRkQimAllEir0TTjiBceOeYtLB15hHK2Lvuxt27Ah3rKNiZlXN7EMz+9b/tUoB7dqY2Xoz22BmQ/Osv8bM1ppZjpkl51lfx8x+M7NV/tf4UIxHRCSSqUAWkRKhQ4cOtGlThyGlLoBDh/ix963hjnS0hgLpzrl6QLp/+U/MLBZ4DrgcSAK6m1mSf/MaoDOwKJ99f+eca+x/DSqS9CIiUUQFsoiUCGbG008/zXc8x5i426m64B2yZ30Q7lhHoyMwyf9+EnBlPm2aABuccxudc5nAFH8/nHPrnHPrQ5JURCTKqUAWkRKjXr163HHH9YzK+pZ1nMH/+t4EBw6EO1agqjvndvrf7wKq59OmJrAtz/J2/7rC1PVPr/jIzC46zpwiIlFPBbKIlCh33303CTU/5V9lbqXi3s38NnxkuCMdZmbzzWxNPq+Oeds55xz4Hw94/HYCtZ1zjYHbgNfNrOKRjXbv3k1ycvLhV1paWpC+vYhIaKWlpR0+lgHV8msTF9pIIiLhVa5cOZ588gm6dHmAV+hDr6cfgwE9oWHDcEfDOXdpQdvM7Hszq+Gc22lmNYAf8mm2A0jMs1zLv+7vvudB4KD//Wdm9h1QH1iRt11CQgIrVqzIZw8iItElNTWV1NRUAMxsT35tdAZZREqcq6++mpYtq3N3/Jn87CryS68bICcn3LEKMxPo43/fB5iRT5vlQD0zq2tm8UA3f78CmVmC/+I+zOw0oB6wMWipj5Jvm49Ri0fh2+YLVwQRERXIIlLymBnPPPMM32eP4e5SD1Bh1cfkvPRyuGMVZjTQysy+BS71L2Nmp5jZBwDOuSxgMDAXWAe85Zxb62/Xycy2Ax7gfTOb69/vxcBqM1sFvAMMcs79GMJxHebb5iNlcgojFo4gZXKKimQRCRsVyCJSIiUlJTFkSF9eOLSERVxE5r/+Dbt3hztWgZxze51zKc65es65S/8oYp1z/+eca5un3QfOufrOudOdcw/nWT/dOVfLOVfaOVfdOdfav36qc+6f/lu8neucmxX60eXK2JxBZnYm2S6bzOxMMjZnhCuKiJRwKpBFpMS67777qF49ndvLDiHmf79w8OY7wh2pRPPW8RIfG0+sxRIfG4+3jjfckUSkhFKBLCIlVsWKFXnssUdZceB+HuMOSr85GRYuDHesEsuT6CG9dzojW4wkvXc6nkRPuCOJSAmlAllESrSePXvSvHllHi1dg+84jd/73gAHD4Y7VonlSfQw7KJhKo5FJKxUIItIiWZmPPvss+zLvJfbSz9KmS3ryRkzNtyxREQkjFQgi0iJd/bZZ3PTTdcyM3Mmb9KFnJEPw7ffhjuWiIiEiQpkERHgwQcfpGrV9xlRfiAHsktzaOCN4IL1sDoREYkmKpBFRIAqVaowZsxovv31doa7hyn10Xx44w28Xi9erzfc8UREJIRUIIuI+PXt25cmTcow6YR4lpPMoZv/RflDh8IdS0REQkwFsoiIX0xMDM8++yy//nYXt5Z5gpgf93DR+m/YunUrPp+e6iYiUlKoQBYRyeP8889n4MBrWJL5CrdxNQ/s3cPmTZtISUlRkSwiUkIEVCCbWRszW29mG8xsaD7bq5jZdDNbbWbLzKxhoH1FRCLNI488QsWK7/JK6RP5HXBAZmYmGRkZYU4mIiKhUGiBbGaxwHPA5UAS0N3Mko5oNhxY5ZxrBPQGxh1FXxGRiFKtWjUefngk+w9+iOMEwIiPj9fFeiIiRcy3zceoxaPwbQvvX+wCOYPcBNjgnNvonMsEpgAdj2iTBCwAcM59DdQxs+oB9hURiTjXX389jRuXJy6uFKeeWpv09HQ8Hj3dTUSkqPi2+UiZnMKIhSNImZwS1iI5kAK5JrAtz/J2/7q8vgA6A5hZE+BUoFaAfdm9ezfJycmHX2lpaYGPQESkCMTGxvLss88SE/M7//nPfwIujtPS0g4fy4BqRRpSRKQYydicQWZ2Jtkum8zsTDI2Z4QtS1yQ9jMaGGdmq4AvgZVAdqCdExISWLFiRZCiiIgER7NmzdiyZQsnn3xywH1SU1NJTU0FwMz2FFU2EZHixlvHS3xsPJnZmcTHxuOt4w1blkAK5B1AYp7lWv51hznn9gN9AczMgE3ARuCEwvqKiESyoymORUTk2HkSPaT3TidjcwbeOl48ieGb1hZIgbwcqGdmdcktbrsBPfI2MLPKwAH/POMBwCLn3H4zK7SviIiIiAjkFsnhLIz/UGiB7JzLMrPBwFwgFpjonFtrZoP828cDZwKTzMwBa4H+f9e3aIYiIiIiInL8ApqD7Jz7APjgiHXj87z3AfUD7SsiIiIiEqn0JD0RERERkTxUIIuIiIiI5KECWUREREQkDxXIIiIiIiJ5qEAWEREREckjqgvkkvBI6uI+xuI+Pij+Yyzu44OSMcZIUtJ/3hq/xl/SRcLPQAVyhCvuYyzu44PiP8biPj4oGWOMJCX9563xa/wlXST8DKK6QBYRERERCTZzzoU7A2a2G9hyDF2rAXuCHCfSFPcxFvfxQfEfY3EfHxz7GE91ziUEO0w4HMdx+liUhM/U39H4Nf6SPH4I7c8g3+N0RBTIIiIiIiKRQlMsRERERETyUIEsIiIiIpKHCmQRERERkTyiokA2szZmtt7MNpjZ0Hy2m5k97d++2szODUfOYxXA+Hr6x/WlmX1qZmeHI+fxKGyMedqdb2ZZZnZ1KPMdr0DGZ2ZeM1tlZmvN7KNQZzxeAXxOK5nZLDP7wj/GvuHIeazMbKKZ/WBmawrYHtXHmWgQ6HGiODOzzf5j/SozWxHuPEUtv987M6tqZh+a2bf+r1XCmbEoFTD++81sh/8zsMrM2oYzY1Eys0QzW2hmX/n/uzHEvz78nwHnXES/gFjgO+A0IB74Akg6ok1bYDZgwAXA0nDnDvL4LgSq+N9fHk3jC3SMedotAD4Arg537iD/G1YGvgJq+5dPCnfuIhjjcGCM/30C8CMQH+7sRzHGi4FzgTUFbI/a40w0vAI9ThT3F7AZqBbuHCEc719+74CxwFD/+6F/HFeK46uA8d8P3BHubCEafw3gXP/7CsA3QFIkfAai4QxyE2CDc26jcy4TmAJ0PKJNR2Cyy7UEqGxmNUId9BgVOj7n3KfOuZ/8i0uAWiHOeLwC+TcEuBmYCvwQynBBEMj4egDTnHNbAZxzxXGMDqhgZgaUJ7dAzgptzGPnnFtEbuaCRPNxJhoEepyQYqSA37uOwCT/+0nAlSENFUIBHHeKNefcTufc5/73vwDrgJpEwGcgGgrkmsC2PMvb/euOtk2kOtrs/ck9ixVNCh2jmdUEOgH/CWGuYAnk37A+UMXMMszsMzPrHbJ0wRHIGJ8FzgT+D/gSGOKcywlNvJCI5uNMNNDPN5cD5vuPE6nhDhMm1Z1zO/3vdwHVwxkmTG72T+WaWJynmORlZnWAc4ClRMBnIBoKZPEzsxbkFsh3hTtLEXgKuKuYFVR5xQHnAe2A1sAIM6sf3khB1xpYBZwCNAaeNbOK4Y0kEnWaO+cakzud7iYzuzjcgcLJ5f6NvaQ9sOE/5E41agzsBB4Pb5yiZ2blyf0L8q3Ouf15t4XrMxANBfIOIDHPci3/uqNtE6kCym5mjYAJQEfn3N4QZQuWQMaYDEwxs83A1cDzZhYtf1YLZHzbgbnOuf855/YAi4BoutgykDH2JXcaiXPObQA2AWeEKF8oRPNxJhro5ws453b4v/4ATCd36klJ8/0f05f8X6NtStpxcc5975zL9p8wepFi/hkws1LkFsevOeem+VeH/TMQDQXycqCemdU1s3igGzDziDYzgd7+q8wvAPblOTUf6Qodn5nVBqYBvZxz34Qh4/EqdIzOubrOuTrOuTrAO8CNzrl3Qx/1mATyGZ0BNDezODMrCzQld65VtAhkjFuBFAAzqw40ADaGNGXRiubjTDQI5DNWrJlZOTOr8Md74DIg37uqFHMzgT7+933IPX6WGEdc29CJYvwZ8F+z8hKwzjn3RJ5NYf8MxIX6Gx4t51yWmQ0G5pJ7lfNE59xaMxvk3z6e3LsetAU2AAfIPZMVFQIc373AieSeVQXIcs4lhyvz0QpwjFErkPE559aZ2RxgNZADTHDORc1BL8B/w5HAK2b2Jbl3erjLf7Y8KpjZG4AXqGZm24H7gFIQ/ceZaFDQZyzMsUKtOjDdf5yPA153zs0Jb6SiVcDv3WjgLTPrD2wBuoQvYdEqYPxeM2tM7rSCzcD1YQtY9JoBvYAvzWyVf91wIuAzYP5baIiIiIiICNExxUJEREREJGRUIIuIiIiI5KECWUREREQkDxXIIiIiIiJ5qEAWEREREclDBbKIiIiISB4qkEVERERE8vh/SXzl48x3jBUAAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x15c0577f908>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "f, ax = plt.subplots(1, 2, figsize=(10, 5))\n",
+    "ax[0].set_title('fits')\n",
+    "ax[0].errorbar(x, y, yerr=y_error, fmt='k.')\n",
+    "ax[0].plot(x, y_true, 'k-')\n",
+    "ax[0].plot(x, np.polyval(fit, x), label='parabola', color='blue')\n",
+    "ax[0].plot(x, np.polyval(fit_1, x), label='line', color='green')\n",
+    "ax[0].plot(x, np.polyval(fit_3, x), label='cubic', color='red')\n",
+    "\n",
+    "ax[0].legend()\n",
+    "ax[1].plot(y_true - np.polyval(fit, x), '.', color='blue')\n",
+    "ax[1].plot(y_true - np.polyval(fit_1, x), '.', color='green')\n",
+    "ax[1].plot(y_true - np.polyval(fit_3, x), '.', color='red')\n",
+    "ax[1].plot(y_true - y, 'k.', label='data')\n",
+    "ax[1].set_title('residuals')\n",
+    "ax[1].fill_between(ax[1].get_xlim(), -sigma_y, sigma_y, color='grey', alpha=0.4, label=r'$1\\sigma$')\n",
+    "ax[1].legend()\n",
+    "f.tight_layout()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x15c059cd588>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Draw the plot form lecture notes week 5, page 29\n",
+    "plt.figure(figsize=(5, 5))\n",
+    "chisq_arr = np.linspace(0, 100, 1001)\n",
+    "plt.title('p-values')\n",
+    "plt.xlabel(r'$\\chi^2$')\n",
+    "for n in [1, 2, 3, 4, 6, 8, 10, 15, 20, 25, 30, 40, 50]:\n",
+    "    plt.loglog(chisq_arr, chi2.sf(chisq_arr, n))\n",
+    "plt.loglog(chisq_arr, chi2.sf(chisq_arr, dof), 'k-', lw=2, label='dof={0}'.format(dof))\n",
+    "plt.ylim(1e-3, 1.1)\n",
+    "plt.plot(chisq, chi2.sf(chisq, dof), 'o', color='blue', label='parabola')\n",
+    "plt.plot(chisq_1, chi2.sf(chisq_1, dof_1), '^', color='green', label='line')\n",
+    "plt.plot(chisq_3, chi2.sf(chisq_3, dof_3), 'x', color='red', label='cubic', ms=5)\n",
+    "plt.legend()\n",
+    "plt.tight_layout()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We observe that the residuals are not uniformly distributed around zero with the expected variance $\\sigma_y$ in the case of the line fit. This reflected in the $\\chi^2$-distribution. Only in 7% of the cases we would expect to draw data that give a worse fit. Note that overfitting with a cubic is not easily spotted in the residuals. However we do observe higher errors on the parameter estimates in the cubic case and we could try a different approach: let's fit only a subset of our data and then compare the fit results on the complement."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x15c057b7f60>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fit, cov, res, chisq, dof = fit_polynomial(x, y, 2, 1/y_error) # Fit parabola\n",
+    "fit_1, cov_1, res_1, chisq_1, dof_1 = fit_polynomial(x, y, 1, 1/y_error) # Fit line\n",
+    "fit_3, cov_3, res_3, chisq_3, dof_3 = fit_polynomial(x, y, 3, 1/y_error) # Fit cubic\n",
+    "\n",
+    "x_new = np.linspace(1, 2, 21)\n",
+    "y_new = parabola(x_new, a, b, c) + error(x_new, sigma_y)\n",
+    "\n",
+    "f, ax = plt.subplots(1, 2, figsize=(10, 5))\n",
+    "ax[0].set_title('extrapolated fits')\n",
+    "ax[0].plot(x, y, 'k.')\n",
+    "ax[0].errorbar(x_new, y_new, yerr=y_error, fmt='r.')\n",
+    "\n",
+    "ax[0].plot(x_new, np.polyval(fit, x_new), label='parabola', color='blue')\n",
+    "ax[0].plot(x_new, np.polyval(fit_1, x_new), label='line', color='green')\n",
+    "ax[0].plot(x_new, np.polyval(fit_3, x_new), label='cubic', color='red')\n",
+    "\n",
+    "ax[0].legend()\n",
+    "ax[0].set_xlim(0, 2)\n",
+    "ax[1].plot(y_new - np.polyval(fit, x_new), '.', color='blue')\n",
+    "ax[1].plot(y_new - np.polyval(fit_1, x_new), '.', color='green')\n",
+    "ax[1].plot(y_new - np.polyval(fit_3, x_new), '.', color='red')\n",
+    "ax[1].set_title('residuals')\n",
+    "ax[1].fill_between(ax[1].get_xlim(), -sigma_y, sigma_y, color='grey', alpha=0.4, label=r'$1\\sigma$')\n",
+    "ax[1].legend()\n",
+    "f.tight_layout()\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Here it becomes very obvious that the hypothesis of a parabola holds against a cubic. We cheated a bit by adding data points instead of working with the initial set, but this illustrates the point of this method. An overfitted model usually does not generalize well when presented with addtional data."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Fit a nonlinear function\n",
+    "Next, we consider a Gaussian as an example of a nonlinear function. We are measuring some feature which has a Gaussian distribution in $x$. This could be an inhomogeneous spectral line for $x=E$ the energy of emitted photons. We are interested in the resonance frequency and the linewidth, i. e. we want to estimate them form our observations."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def gaussian_parent(x, mu, sigma):\n",
+    "    return norm.pdf(x, mu, sigma)    \n",
+    "\n",
+    "def gaussian_sample(mu, sigma, sample_size):\n",
+    "    return norm.rvs(mu, sigma, sample_size)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x15c06c9afd0>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Create sample\n",
+    "## SAMPLE SIZE\n",
+    "sample_size = 200\n",
+    "##################\n",
+    "\n",
+    "# Prepare fake data\n",
+    "mu = 1540  # True values that we will try to estimate\n",
+    "sigma = 11 # using a least-squares fit\n",
+    "\n",
+    "x_arr = np.linspace(1500, 1600, 101)\n",
+    "bins = 12\n",
+    "sample = gaussian_sample(mu, sigma, sample_size)\n",
+    "hist = np.histogram(sample, bins=bins, range=(1500, 1580))\n",
+    "bin_width = np.diff(hist[1])[0]\n",
+    "normalization = bin_width * sample_size\n",
+    "x = hist[1][:-1]+bin_width/2\n",
+    "y_error_const = 0\n",
+    "y = hist[0]/normalization + gaussian_sample(0, y_error_const, bins)\n",
+    "y_errors = np.sqrt((np.sqrt(hist[0]) / normalization)**2 + y_error_const**2)\n",
+    "\n",
+    "# Plot our fake measurement results\n",
+    "plt.figure(figsize=(5, 4))\n",
+    "plt.xlabel(r'$E$ (meV)')\n",
+    "plt.ylabel('count rate')\n",
+    "plt.plot(x_arr, gaussian_parent(x_arr, mu, sigma), '-', color='black', label='parent')\n",
+    "plt.errorbar(x, y, yerr=y_errors, fmt='.', ms=7, capsize=3, label='sample')\n",
+    "plt.legend()\n",
+    "plt.tight_layout()\n",
+    "\n",
+    "# Save data\n",
+    "data = np.vstack((x, y, y_errors))\n",
+    "np.savetxt('data', data)\n",
+    "np.savetxt('sample', sample)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Load data from disk. Format (3,12): (x, y, y_error) x N \n",
+    "data = np.loadtxt('data')\n",
+    "x = data[0, :]\n",
+    "y = data[1, :]\n",
+    "y_error = data[2, :]\n",
+    "# The sample used to generate\n",
+    "sample = np.loadtxt('sample')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Function we want to fit to our data set\n",
+    "def model_function(x, *args):\n",
+    "    mu, sigma = args[0:2]\n",
+    "    return norm.pdf(x, mu, sigma)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Fit Results:\n",
+      "mu = 1539.0 +- 0.4\n",
+      "sigma = 10.3 +- 0.3\n",
+      "mu estimator 1538.4 +- 0.7\n",
+      "sigma estimator 10.4\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Perform the fit minimizing least squares\n",
+    "initial_guess = [1545, 9]\n",
+    "p_opt, p_cov = curve_fit(model_function, x, y, p0=initial_guess, sigma=None, absolute_sigma=False, check_finite=True)\n",
+    "p_err = np.sqrt(np.diag(p_cov))\n",
+    "# pcov(absolute_sigma=False) = pcov(absolute_sigma=True) * chisq(popt)/(M-N)\n",
+    "print('Fit Results:')\n",
+    "print('mu = {:1.1f} +- {:1.1f}'.format(p_opt[0], p_err[0]))\n",
+    "print('sigma = {:1.1f} +- {:1.1f}'.format(p_opt[1], p_err[1]))\n",
+    "\n",
+    "print('mu estimator {:1.1f} +- {:1.1f}'.format(np.mean(sample), np.std(sample, ddof=1)/np.sqrt(sample.size)))\n",
+    "print('sigma estimator {:1.1f}'.format(np.std(sample, ddof=1)))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Plot the result"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x15c06ec26a0>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "x_arr = np.linspace(1500, 1600, 101)\n",
+    "\n",
+    "plt.figure(figsize=(5, 4))\n",
+    "plt.xlabel(r'$E$ (meV)')\n",
+    "plt.ylabel('count rate')\n",
+    "plt.errorbar(x, y, yerr=y_errors, fmt='.', ms=7, capsize=3, label='sample')\n",
+    "plt.plot(x_arr, model_function(x_arr, *initial_guess), '-', color='grey', label='guess')\n",
+    "plt.plot(x_arr, model_function(x_arr, *p_opt), '-', color='black', label='fit')\n",
+    "plt.legend()\n",
+    "plt.tight_layout()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Biased estimator example"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Expectation value: 4.133148453066826\n",
+      "Sample mean: 4.144495254000121\n",
+      "Gauss fit: 3.946186931764501\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x15c06fc1b00>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "log_sample = lognorm.rvs(s=0.5, loc=3, scale=1, size=1000)\n",
+    "plt.figure(figsize=(5, 4))\n",
+    "h = plt.hist(log_sample, bins=32, rwidth=0.85, normed=True)\n",
+    "\n",
+    "\n",
+    "def model_function(x, *args):\n",
+    "    A, mu, sigma = args[0:3]\n",
+    "    return A * norm.pdf(x, mu, sigma)\n",
+    "\n",
+    "\n",
+    "x = h[1][:-1]+np.diff(h[1])[0]/2\n",
+    "y = h[0]\n",
+    "initial_guess = [0.95, 3.3, 0.3]\n",
+    "gauss_fit_large = curve_fit(model_function, x, y, p0=initial_guess)\n",
+    "#local = np.where(np.abs(x-np.mean(log_sample))<0.5)\n",
+    "#gauss_fit_local = curve_fit(model_function, x[local], y[local], p0=initial_guess)\n",
+    "\n",
+    "x_arr = np.linspace(2, 5, 51)\n",
+    "plt.plot(x_arr, model_function(x_arr, *gauss_fit_large[0]), '-', color='red', label='fit')\n",
+    "#plt.plot(x[local], model_function(x[local], *gauss_fit_large[0]), '-', color='orange', label='fit small')\n",
+    "plt.legend()\n",
+    "plt.tight_layout()\n",
+    "\n",
+    "print('Expectation value:', lognorm.mean(s=0.5, loc=3, scale=1))\n",
+    "print('Sample mean:', np.mean(log_sample))\n",
+    "print('Gauss fit:', gauss_fit_large[0][1])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Fitting a Gaussian and gives a biased estimate of mu"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Bonus: Errors in x and y\n",
+    "So far, we considered uncertainties only on y. Consider the following data set with errors in both x and y. Try to fit a line to the data below, taking into account both errors. Compare with fits neglecting the x errors or both.  \n",
+    "  \n",
+    "Hint: A detailed solution is already on the moodle. You may chose if you want to try to write your own solution, implement a known solution (see references in solution notebook) or just try it with the scipy package ODR (orthogonal distance regression). \n",
+    "https://docs.scipy.org/doc/scipy/reference/odr.html\n",
+    "  \n",
+    "The solution contains a python implementation of York's equation, comparison with ODR and MC tests.  \n",
+    "Week 3: \"Linear Regression errors x and y\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x15c06f95da0>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Test data\n",
+    "X = np.array([0.0, 0.9, 1.8, 2.6, 3.3, 4.4, 5.2, 6.1, 6.5, 7.4])\n",
+    "Y = np.array([5.9, 5.4, 4.4, 4.6, 3.5, 3.7, 2.8, 2.8, 2.4, 1.5])\n",
+    "wX = np.array([1000, 1000, 500, 800, 200, 80, 60, 20, 1.8, 1])\n",
+    "wY = np.array([1, 1.8, 4, 8, 20, 20, 70, 70, 100, 500])\n",
+    "sigma_x = 1.0/np.sqrt(wX)\n",
+    "sigma_y = 1.0/np.sqrt(wY)\n",
+    "\n",
+    "plt.figure(figsize=(4, 3))\n",
+    "plt.errorbar(X, Y, xerr=sigma_x, yerr=sigma_y, fmt='o', label='oberservations')\n",
+    "plt.legend()\n",
+    "plt.tight_layout()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "hide_input": false,
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.7"
+  },
+  "toc": {
+   "base_numbering": 1,
+   "nav_menu": {},
+   "number_sections": true,
+   "sideBar": true,
+   "skip_h1_title": false,
+   "title_cell": "Table of Contents",
+   "title_sidebar": "Contents",
+   "toc_cell": false,
+   "toc_position": {
+    "height": "calc(100% - 180px)",
+    "left": "10px",
+    "top": "150px",
+    "width": "225.438px"
+   },
+   "toc_section_display": true,
+   "toc_window_display": true
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/exercises/Solution5/.ipynb_checkpoints/Solutions_5-checkpoint.ipynb b/exercises/Solution5/.ipynb_checkpoints/Solutions_5-checkpoint.ipynb
new file mode 100644
index 0000000..4e1698e
--- /dev/null
+++ b/exercises/Solution5/.ipynb_checkpoints/Solutions_5-checkpoint.ipynb
@@ -0,0 +1,693 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Solution Exercise 5\n",
+    "This week, we are working on least squares fits and parameter estimation"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from __future__ import print_function\n",
+    "import numpy as np\n",
+    "%matplotlib inline\n",
+    "import matplotlib.pyplot as plt\n",
+    "from scipy.optimize import curve_fit\n",
+    "from scipy.stats import norm, chi2, lognorm"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Fit a polynomial\n",
+    "We start by fitting a polynomial to a given data set, in particular, a parabola. Compare a linear fit and a cubic fit to our parabolic fit and check the goodness of fits with chi squared distributions. Explore how the different uncertainties affect the outcome and uncertainties of the fit. \n",
+    "\n",
+    "Hint: You can consider a plot similar to the lecture notes week 5 page 29.\n",
+    "\n",
+    "Extra: Do you see any way to decide wether the data is better described by the parabola or the cubic?\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Create some data distribuited as parabola with normally distributed errors.\n",
+    "def parabola(x, a, b, c):\n",
+    "    return a*x**2 + b*x + c\n",
+    "def error(x, sigma):\n",
+    "    return norm.rvs(0.0, sigma, x.size) \n",
+    "a = -0.1\n",
+    "b = 0\n",
+    "c = 1\n",
+    "sigma_y = 0.0015\n",
+    "\n",
+    "x = np.linspace(0, 1, 21)\n",
+    "y_true = parabola(x, a, b, c)\n",
+    "delta_y = error(x, sigma_y)\n",
+    "y = y_true + delta_y\n",
+    "y_error = sigma_y * np.ones(x.size)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def fit_polynomial(x, y, degree, weight):\n",
+    "    \"\"\"Fit polynomial of degree to data x, y with y weight = 1/sigma_y\n",
+    "    \n",
+    "    Return fit, covariance matrix, residuals and chi-squared and degrees of freedom.\n",
+    "    \"\"\"\n",
+    "    \n",
+    "    dof = x.shape[0] - degree\n",
+    "    fit, cov = np.polyfit(x, y, degree, w=weight, cov=True)\n",
+    "    residuals = np.sum((y - np.polyval(fit, x))**2 / y_error**2)\n",
+    "    chisq = residuals / (dof)\n",
+    "    return fit, cov, residuals, chisq, dof\n",
+    "    \n",
+    "fit, cov, res, chisq, dof = fit_polynomial(x, y, 2, 1/y_error) # Fit parabola\n",
+    "fit_1, cov_1, res_1, chisq_1, dof_1 = fit_polynomial(x, y, 1, 1/y_error) # Fit line\n",
+    "fit_3, cov_3, res_3, chisq_3, dof_3 = fit_polynomial(x, y, 3, 1/y_error) # Fit cubic\n",
+    "\n",
+    "def evaluate_chisq(chisq, dof):\n",
+    "    return chi2.sf(chisq, dof)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Reduced chi^2:\n",
+      "parabola 0.9459782161747974\n",
+      "line 32.15822425109619\n",
+      "cubic 0.9557527151822285\n"
+     ]
+    }
+   ],
+   "source": [
+    "print('Reduced chi^2:')\n",
+    "print('parabola', chisq)\n",
+    "print('line', chisq_1)\n",
+    "print('cubic', chisq_3)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Chi^2 distributions:\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "(0.9999999995308976, 0.04164125577461551, 0.9999999976681199)"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "print('Chi^2 distributions:')\n",
+    "evaluate_chisq(chisq, dof), evaluate_chisq(chisq_1, dof_1), evaluate_chisq(chisq_3, dof_3)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Error estimates:\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "(array([0.00424588, 0.00439779, 0.00094897]),\n",
+       " array([0.00664986, 0.00388699]),\n",
+       " array([0.0162819 , 0.0247968 , 0.01051785, 0.00118487]))"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "print('Error estimates:')\n",
+    "np.sqrt(np.diag(cov)), np.sqrt(np.diag(cov_1)), np.sqrt(np.diag(cov_3))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x15c0577f908>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "f, ax = plt.subplots(1, 2, figsize=(10, 5))\n",
+    "ax[0].set_title('fits')\n",
+    "ax[0].errorbar(x, y, yerr=y_error, fmt='k.')\n",
+    "ax[0].plot(x, y_true, 'k-')\n",
+    "ax[0].plot(x, np.polyval(fit, x), label='parabola', color='blue')\n",
+    "ax[0].plot(x, np.polyval(fit_1, x), label='line', color='green')\n",
+    "ax[0].plot(x, np.polyval(fit_3, x), label='cubic', color='red')\n",
+    "\n",
+    "ax[0].legend()\n",
+    "ax[1].plot(y_true - np.polyval(fit, x), '.', color='blue')\n",
+    "ax[1].plot(y_true - np.polyval(fit_1, x), '.', color='green')\n",
+    "ax[1].plot(y_true - np.polyval(fit_3, x), '.', color='red')\n",
+    "ax[1].plot(y_true - y, 'k.', label='data')\n",
+    "ax[1].set_title('residuals')\n",
+    "ax[1].fill_between(ax[1].get_xlim(), -sigma_y, sigma_y, color='grey', alpha=0.4, label=r'$1\\sigma$')\n",
+    "ax[1].legend()\n",
+    "f.tight_layout()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x15c059cd588>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Draw the plot form lecture notes week 5, page 29\n",
+    "plt.figure(figsize=(5, 5))\n",
+    "chisq_arr = np.linspace(0, 100, 1001)\n",
+    "plt.title('p-values')\n",
+    "plt.xlabel(r'$\\chi^2$')\n",
+    "for n in [1, 2, 3, 4, 6, 8, 10, 15, 20, 25, 30, 40, 50]:\n",
+    "    plt.loglog(chisq_arr, chi2.sf(chisq_arr, n))\n",
+    "plt.loglog(chisq_arr, chi2.sf(chisq_arr, dof), 'k-', lw=2, label='dof={0}'.format(dof))\n",
+    "plt.ylim(1e-3, 1.1)\n",
+    "plt.plot(chisq, chi2.sf(chisq, dof), 'o', color='blue', label='parabola')\n",
+    "plt.plot(chisq_1, chi2.sf(chisq_1, dof_1), '^', color='green', label='line')\n",
+    "plt.plot(chisq_3, chi2.sf(chisq_3, dof_3), 'x', color='red', label='cubic', ms=5)\n",
+    "plt.legend()\n",
+    "plt.tight_layout()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We observe that the residuals are not uniformly distributed around zero with the expected variance $\\sigma_y$ in the case of the line fit. This reflected in the $\\chi^2$-distribution. Only in 7% of the cases we would expect to draw data that give a worse fit. Note that overfitting with a cubic is not easily spotted in the residuals. However we do observe higher errors on the parameter estimates in the cubic case and we could try a different approach: let's fit only a subset of our data and then compare the fit results on the complement."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x15c057b7f60>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fit, cov, res, chisq, dof = fit_polynomial(x, y, 2, 1/y_error) # Fit parabola\n",
+    "fit_1, cov_1, res_1, chisq_1, dof_1 = fit_polynomial(x, y, 1, 1/y_error) # Fit line\n",
+    "fit_3, cov_3, res_3, chisq_3, dof_3 = fit_polynomial(x, y, 3, 1/y_error) # Fit cubic\n",
+    "\n",
+    "x_new = np.linspace(1, 2, 21)\n",
+    "y_new = parabola(x_new, a, b, c) + error(x_new, sigma_y)\n",
+    "\n",
+    "f, ax = plt.subplots(1, 2, figsize=(10, 5))\n",
+    "ax[0].set_title('extrapolated fits')\n",
+    "ax[0].plot(x, y, 'k.')\n",
+    "ax[0].errorbar(x_new, y_new, yerr=y_error, fmt='r.')\n",
+    "\n",
+    "ax[0].plot(x_new, np.polyval(fit, x_new), label='parabola', color='blue')\n",
+    "ax[0].plot(x_new, np.polyval(fit_1, x_new), label='line', color='green')\n",
+    "ax[0].plot(x_new, np.polyval(fit_3, x_new), label='cubic', color='red')\n",
+    "\n",
+    "ax[0].legend()\n",
+    "ax[0].set_xlim(0, 2)\n",
+    "ax[1].plot(y_new - np.polyval(fit, x_new), '.', color='blue')\n",
+    "ax[1].plot(y_new - np.polyval(fit_1, x_new), '.', color='green')\n",
+    "ax[1].plot(y_new - np.polyval(fit_3, x_new), '.', color='red')\n",
+    "ax[1].set_title('residuals')\n",
+    "ax[1].fill_between(ax[1].get_xlim(), -sigma_y, sigma_y, color='grey', alpha=0.4, label=r'$1\\sigma$')\n",
+    "ax[1].legend()\n",
+    "f.tight_layout()\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Here it becomes very obvious that the hypothesis of a parabola holds against a cubic. We cheated a bit by adding data points instead of working with the initial set, but this illustrates the point of this method. An overfitted model usually does not generalize well when presented with addtional data."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Fit a nonlinear function\n",
+    "Next, we consider a Gaussian as an example of a nonlinear function. We are measuring some feature which has a Gaussian distribution in $x$. This could be an inhomogeneous spectral line for $x=E$ the energy of emitted photons. We are interested in the resonance frequency and the linewidth, i. e. we want to estimate them form our observations."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def gaussian_parent(x, mu, sigma):\n",
+    "    return norm.pdf(x, mu, sigma)    \n",
+    "\n",
+    "def gaussian_sample(mu, sigma, sample_size):\n",
+    "    return norm.rvs(mu, sigma, sample_size)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x15c06c9afd0>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Create sample\n",
+    "## SAMPLE SIZE\n",
+    "sample_size = 200\n",
+    "##################\n",
+    "\n",
+    "# Prepare fake data\n",
+    "mu = 1540  # True values that we will try to estimate\n",
+    "sigma = 11 # using a least-squares fit\n",
+    "\n",
+    "x_arr = np.linspace(1500, 1600, 101)\n",
+    "bins = 12\n",
+    "sample = gaussian_sample(mu, sigma, sample_size)\n",
+    "hist = np.histogram(sample, bins=bins, range=(1500, 1580))\n",
+    "bin_width = np.diff(hist[1])[0]\n",
+    "normalization = bin_width * sample_size\n",
+    "x = hist[1][:-1]+bin_width/2\n",
+    "y_error_const = 0\n",
+    "y = hist[0]/normalization + gaussian_sample(0, y_error_const, bins)\n",
+    "y_errors = np.sqrt((np.sqrt(hist[0]) / normalization)**2 + y_error_const**2)\n",
+    "\n",
+    "# Plot our fake measurement results\n",
+    "plt.figure(figsize=(5, 4))\n",
+    "plt.xlabel(r'$E$ (meV)')\n",
+    "plt.ylabel('count rate')\n",
+    "plt.plot(x_arr, gaussian_parent(x_arr, mu, sigma), '-', color='black', label='parent')\n",
+    "plt.errorbar(x, y, yerr=y_errors, fmt='.', ms=7, capsize=3, label='sample')\n",
+    "plt.legend()\n",
+    "plt.tight_layout()\n",
+    "\n",
+    "# Save data\n",
+    "data = np.vstack((x, y, y_errors))\n",
+    "np.savetxt('data', data)\n",
+    "np.savetxt('sample', sample)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Load data from disk. Format (3,12): (x, y, y_error) x N \n",
+    "data = np.loadtxt('data')\n",
+    "x = data[0, :]\n",
+    "y = data[1, :]\n",
+    "y_error = data[2, :]\n",
+    "# The sample used to generate\n",
+    "sample = np.loadtxt('sample')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Function we want to fit to our data set\n",
+    "def model_function(x, *args):\n",
+    "    mu, sigma = args[0:2]\n",
+    "    return norm.pdf(x, mu, sigma)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Fit Results:\n",
+      "mu = 1539.0 +- 0.4\n",
+      "sigma = 10.3 +- 0.3\n",
+      "mu estimator 1538.4 +- 0.7\n",
+      "sigma estimator 10.4\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Perform the fit minimizing least squares\n",
+    "initial_guess = [1545, 9]\n",
+    "p_opt, p_cov = curve_fit(model_function, x, y, p0=initial_guess, sigma=None, absolute_sigma=False, check_finite=True)\n",
+    "p_err = np.sqrt(np.diag(p_cov))\n",
+    "# pcov(absolute_sigma=False) = pcov(absolute_sigma=True) * chisq(popt)/(M-N)\n",
+    "print('Fit Results:')\n",
+    "print('mu = {:1.1f} +- {:1.1f}'.format(p_opt[0], p_err[0]))\n",
+    "print('sigma = {:1.1f} +- {:1.1f}'.format(p_opt[1], p_err[1]))\n",
+    "\n",
+    "print('mu estimator {:1.1f} +- {:1.1f}'.format(np.mean(sample), np.std(sample, ddof=1)/np.sqrt(sample.size)))\n",
+    "print('sigma estimator {:1.1f}'.format(np.std(sample, ddof=1)))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Plot the result"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x15c06ec26a0>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "x_arr = np.linspace(1500, 1600, 101)\n",
+    "\n",
+    "plt.figure(figsize=(5, 4))\n",
+    "plt.xlabel(r'$E$ (meV)')\n",
+    "plt.ylabel('count rate')\n",
+    "plt.errorbar(x, y, yerr=y_errors, fmt='.', ms=7, capsize=3, label='sample')\n",
+    "plt.plot(x_arr, model_function(x_arr, *initial_guess), '-', color='grey', label='guess')\n",
+    "plt.plot(x_arr, model_function(x_arr, *p_opt), '-', color='black', label='fit')\n",
+    "plt.legend()\n",
+    "plt.tight_layout()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Biased estimator example"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Expectation value: 4.133148453066826\n",
+      "Sample mean: 4.144495254000121\n",
+      "Gauss fit: 3.946186931764501\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x15c06fc1b00>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "log_sample = lognorm.rvs(s=0.5, loc=3, scale=1, size=1000)\n",
+    "plt.figure(figsize=(5, 4))\n",
+    "h = plt.hist(log_sample, bins=32, rwidth=0.85, normed=True)\n",
+    "\n",
+    "\n",
+    "def model_function(x, *args):\n",
+    "    A, mu, sigma = args[0:3]\n",
+    "    return A * norm.pdf(x, mu, sigma)\n",
+    "\n",
+    "\n",
+    "x = h[1][:-1]+np.diff(h[1])[0]/2\n",
+    "y = h[0]\n",
+    "initial_guess = [0.95, 3.3, 0.3]\n",
+    "gauss_fit_large = curve_fit(model_function, x, y, p0=initial_guess)\n",
+    "#local = np.where(np.abs(x-np.mean(log_sample))<0.5)\n",
+    "#gauss_fit_local = curve_fit(model_function, x[local], y[local], p0=initial_guess)\n",
+    "\n",
+    "x_arr = np.linspace(2, 5, 51)\n",
+    "plt.plot(x_arr, model_function(x_arr, *gauss_fit_large[0]), '-', color='red', label='fit')\n",
+    "#plt.plot(x[local], model_function(x[local], *gauss_fit_large[0]), '-', color='orange', label='fit small')\n",
+    "plt.legend()\n",
+    "plt.tight_layout()\n",
+    "\n",
+    "print('Expectation value:', lognorm.mean(s=0.5, loc=3, scale=1))\n",
+    "print('Sample mean:', np.mean(log_sample))\n",
+    "print('Gauss fit:', gauss_fit_large[0][1])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Fitting a Gaussian and gives a biased estimate of mu"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Bonus: Errors in x and y\n",
+    "So far, we considered uncertainties only on y. Consider the following data set with errors in both x and y. Try to fit a line to the data below, taking into account both errors. Compare with fits neglecting the x errors or both.  \n",
+    "  \n",
+    "Hint: A detailed solution is already on the moodle. You may chose if you want to try to write your own solution, implement a known solution (see references in solution notebook) or just try it with the scipy package ODR (orthogonal distance regression). \n",
+    "https://docs.scipy.org/doc/scipy/reference/odr.html\n",
+    "  \n",
+    "The solution contains a python implementation of York's equation, comparison with ODR and MC tests.  \n",
+    "Week 3: \"Linear Regression errors x and y\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x15c06f95da0>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Test data\n",
+    "X = np.array([0.0, 0.9, 1.8, 2.6, 3.3, 4.4, 5.2, 6.1, 6.5, 7.4])\n",
+    "Y = np.array([5.9, 5.4, 4.4, 4.6, 3.5, 3.7, 2.8, 2.8, 2.4, 1.5])\n",
+    "wX = np.array([1000, 1000, 500, 800, 200, 80, 60, 20, 1.8, 1])\n",
+    "wY = np.array([1, 1.8, 4, 8, 20, 20, 70, 70, 100, 500])\n",
+    "sigma_x = 1.0/np.sqrt(wX)\n",
+    "sigma_y = 1.0/np.sqrt(wY)\n",
+    "\n",
+    "plt.figure(figsize=(4, 3))\n",
+    "plt.errorbar(X, Y, xerr=sigma_x, yerr=sigma_y, fmt='o', label='oberservations')\n",
+    "plt.legend()\n",
+    "plt.tight_layout()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "hide_input": false,
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.3"
+  },
+  "toc": {
+   "base_numbering": 1,
+   "nav_menu": {},
+   "number_sections": true,
+   "sideBar": true,
+   "skip_h1_title": false,
+   "title_cell": "Table of Contents",
+   "title_sidebar": "Contents",
+   "toc_cell": false,
+   "toc_position": {
+    "height": "calc(100% - 180px)",
+    "left": "10px",
+    "top": "150px",
+    "width": "225.438px"
+   },
+   "toc_section_display": true,
+   "toc_window_display": true
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/exercises/Solution5/Solutions_5.ipynb b/exercises/Solution5/Solutions_5.ipynb
new file mode 100644
index 0000000..a15bd2f
--- /dev/null
+++ b/exercises/Solution5/Solutions_5.ipynb
@@ -0,0 +1,693 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Solution Exercise 5\n",
+    "This week, we are working on least squares fits and parameter estimation"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from __future__ import print_function\n",
+    "import numpy as np\n",
+    "%matplotlib inline\n",
+    "import matplotlib.pyplot as plt\n",
+    "from scipy.optimize import curve_fit\n",
+    "from scipy.stats import norm, chi2, lognorm"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Fit a polynomial\n",
+    "We start by fitting a polynomial to a given data set, in particular, a parabola. Compare a linear fit and a cubic fit to our parabolic fit and check the goodness of fits with chi squared distributions. Explore how the different uncertainties affect the outcome and uncertainties of the fit. \n",
+    "\n",
+    "Hint: You can consider a plot similar to the lecture notes week 5 page 29.\n",
+    "\n",
+    "Extra: Do you see any way to decide wether the data is better described by the parabola or the cubic?\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Create some data distribuited as parabola with normally distributed errors.\n",
+    "def parabola(x, a, b, c):\n",
+    "    return a*x**2 + b*x + c\n",
+    "def error(x, sigma):\n",
+    "    return norm.rvs(0.0, sigma, x.size) \n",
+    "a = -0.1\n",
+    "b = 0\n",
+    "c = 1\n",
+    "sigma_y = 0.0015\n",
+    "\n",
+    "x = np.linspace(0, 1, 21)\n",
+    "y_true = parabola(x, a, b, c)\n",
+    "delta_y = error(x, sigma_y)\n",
+    "y = y_true + delta_y\n",
+    "y_error = sigma_y * np.ones(x.size)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def fit_polynomial(x, y, degree, weight):\n",
+    "    \"\"\"Fit polynomial of degree to data x, y with y weight = 1/sigma_y\n",
+    "    \n",
+    "    Return fit, covariance matrix, residuals and chi-squared and degrees of freedom.\n",
+    "    \"\"\"\n",
+    "    \n",
+    "    dof = x.shape[0] - degree\n",
+    "    fit, cov = np.polyfit(x, y, degree, w=weight, cov=True)\n",
+    "    residuals = np.sum((y - np.polyval(fit, x))**2 / y_error**2)\n",
+    "    chisq = residuals / (dof)\n",
+    "    return fit, cov, residuals, chisq, dof\n",
+    "    \n",
+    "fit, cov, res, chisq, dof = fit_polynomial(x, y, 2, 1/y_error) # Fit parabola\n",
+    "fit_1, cov_1, res_1, chisq_1, dof_1 = fit_polynomial(x, y, 1, 1/y_error) # Fit line\n",
+    "fit_3, cov_3, res_3, chisq_3, dof_3 = fit_polynomial(x, y, 3, 1/y_error) # Fit cubic\n",
+    "\n",
+    "def evaluate_chisq(chisq, dof):\n",
+    "    return chi2.sf(chisq, dof)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Reduced chi^2:\n",
+      "parabola 0.9459782161747974\n",
+      "line 32.15822425109619\n",
+      "cubic 0.9557527151822285\n"
+     ]
+    }
+   ],
+   "source": [
+    "print('Reduced chi^2:')\n",
+    "print('parabola', chisq)\n",
+    "print('line', chisq_1)\n",
+    "print('cubic', chisq_3)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Chi^2 distributions:\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "(0.9999999995308976, 0.04164125577461551, 0.9999999976681199)"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "print('Chi^2 distributions:')\n",
+    "evaluate_chisq(chisq, dof), evaluate_chisq(chisq_1, dof_1), evaluate_chisq(chisq_3, dof_3)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Error estimates:\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "(array([0.00424588, 0.00439779, 0.00094897]),\n",
+       " array([0.00664986, 0.00388699]),\n",
+       " array([0.0162819 , 0.0247968 , 0.01051785, 0.00118487]))"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "print('Error estimates:')\n",
+    "np.sqrt(np.diag(cov)), np.sqrt(np.diag(cov_1)), np.sqrt(np.diag(cov_3))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x15c0577f908>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "f, ax = plt.subplots(1, 2, figsize=(10, 5))\n",
+    "ax[0].set_title('fits')\n",
+    "ax[0].errorbar(x, y, yerr=y_error, fmt='k.')\n",
+    "ax[0].plot(x, y_true, 'k-')\n",
+    "ax[0].plot(x, np.polyval(fit, x), label='parabola', color='blue')\n",
+    "ax[0].plot(x, np.polyval(fit_1, x), label='line', color='green')\n",
+    "ax[0].plot(x, np.polyval(fit_3, x), label='cubic', color='red')\n",
+    "\n",
+    "ax[0].legend()\n",
+    "ax[1].plot(y_true - np.polyval(fit, x), '.', color='blue')\n",
+    "ax[1].plot(y_true - np.polyval(fit_1, x), '.', color='green')\n",
+    "ax[1].plot(y_true - np.polyval(fit_3, x), '.', color='red')\n",
+    "ax[1].plot(y_true - y, 'k.', label='data')\n",
+    "ax[1].set_title('residuals')\n",
+    "ax[1].fill_between(ax[1].get_xlim(), -sigma_y, sigma_y, color='grey', alpha=0.4, label=r'$1\\sigma$')\n",
+    "ax[1].legend()\n",
+    "f.tight_layout()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x15c059cd588>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Draw the plot form lecture notes week 5, page 29\n",
+    "plt.figure(figsize=(5, 5))\n",
+    "chisq_arr = np.linspace(0, 100, 1001)\n",
+    "plt.title('p-values')\n",
+    "plt.xlabel(r'$\\chi^2$')\n",
+    "for n in [1, 2, 3, 4, 6, 8, 10, 15, 20, 25, 30, 40, 50]:\n",
+    "    plt.loglog(chisq_arr, chi2.sf(chisq_arr, n))\n",
+    "plt.loglog(chisq_arr, chi2.sf(chisq_arr, dof), 'k-', lw=2, label='dof={0}'.format(dof))\n",
+    "plt.ylim(1e-3, 1.1)\n",
+    "plt.plot(chisq, chi2.sf(chisq, dof), 'o', color='blue', label='parabola')\n",
+    "plt.plot(chisq_1, chi2.sf(chisq_1, dof_1), '^', color='green', label='line')\n",
+    "plt.plot(chisq_3, chi2.sf(chisq_3, dof_3), 'x', color='red', label='cubic', ms=5)\n",
+    "plt.legend()\n",
+    "plt.tight_layout()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We observe that the residuals are not uniformly distributed around zero with the expected variance $\\sigma_y$ in the case of the line fit. This reflected in the $\\chi^2$-distribution. Only in 7% of the cases we would expect to draw data that give a worse fit. Note that overfitting with a cubic is not easily spotted in the residuals. However we do observe higher errors on the parameter estimates in the cubic case and we could try a different approach: let's fit only a subset of our data and then compare the fit results on the complement."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x15c057b7f60>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fit, cov, res, chisq, dof = fit_polynomial(x, y, 2, 1/y_error) # Fit parabola\n",
+    "fit_1, cov_1, res_1, chisq_1, dof_1 = fit_polynomial(x, y, 1, 1/y_error) # Fit line\n",
+    "fit_3, cov_3, res_3, chisq_3, dof_3 = fit_polynomial(x, y, 3, 1/y_error) # Fit cubic\n",
+    "\n",
+    "x_new = np.linspace(1, 2, 21)\n",
+    "y_new = parabola(x_new, a, b, c) + error(x_new, sigma_y)\n",
+    "\n",
+    "f, ax = plt.subplots(1, 2, figsize=(10, 5))\n",
+    "ax[0].set_title('extrapolated fits')\n",
+    "ax[0].plot(x, y, 'k.')\n",
+    "ax[0].errorbar(x_new, y_new, yerr=y_error, fmt='r.')\n",
+    "\n",
+    "ax[0].plot(x_new, np.polyval(fit, x_new), label='parabola', color='blue')\n",
+    "ax[0].plot(x_new, np.polyval(fit_1, x_new), label='line', color='green')\n",
+    "ax[0].plot(x_new, np.polyval(fit_3, x_new), label='cubic', color='red')\n",
+    "\n",
+    "ax[0].legend()\n",
+    "ax[0].set_xlim(0, 2)\n",
+    "ax[1].plot(y_new - np.polyval(fit, x_new), '.', color='blue')\n",
+    "ax[1].plot(y_new - np.polyval(fit_1, x_new), '.', color='green')\n",
+    "ax[1].plot(y_new - np.polyval(fit_3, x_new), '.', color='red')\n",
+    "ax[1].set_title('residuals')\n",
+    "ax[1].fill_between(ax[1].get_xlim(), -sigma_y, sigma_y, color='grey', alpha=0.4, label=r'$1\\sigma$')\n",
+    "ax[1].legend()\n",
+    "f.tight_layout()\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Here it becomes very obvious that the hypothesis of a parabola holds against a cubic. We cheated a bit by adding data points instead of working with the initial set, but this illustrates the point of this method. An overfitted model usually does not generalize well when presented with addtional data."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Fit a nonlinear function\n",
+    "Next, we consider a Gaussian as an example of a nonlinear function. We are measuring some feature which has a Gaussian distribution in $x$. This could be an inhomogeneous spectral line for $x=E$ the energy of emitted photons. We are interested in the resonance frequency and the linewidth, i. e. we want to estimate them form our observations."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def gaussian_parent(x, mu, sigma):\n",
+    "    return norm.pdf(x, mu, sigma)    \n",
+    "\n",
+    "def gaussian_sample(mu, sigma, sample_size):\n",
+    "    return norm.rvs(mu, sigma, sample_size)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAWAAAAEYCAYAAABiECzgAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAIABJREFUeJzt3Xd8VGXa//HPlUZIaBIC0jSRIiCgsEFKpIkBpAUEpYiu4mMoskJccW37LOtaWH4uUmSBuKKAj4jIEghNaauCjSISSkA6AUR6DyGZ+/fHTLJjIDAhMzlTrvfrdV5kzpxz5nuT5OJwn/vcR4wxKKWUKnlBVgdQSqlApQVYKaUsogVYKaUsogVYKaUsogVYKaUsEmJ1gOKoVKmSiYmJKfJ+x44dIzo62v2BLKbt8i3+2i7w37bdbLs2bNhw3Bhz1Y4+XYBjYmJYv359kfeLi4u7qf28nbbLt/hru8B/23az7RKR/ddar10QSillES3ASillkYAswElJSVZH8Ahtl2/x13aB/7bN3e0SX74VOS4uzvhjP5NS3urKlStkZmaSlZVldRSvFB4eTo0aNQgNDf3NehHZYIyJK7i9T1+EU0qVrMzMTMqWLUtMTAwiYnUcr2KM4cSJE2RmZhIbG+vSPgHZBaGUujlZWVlERUVp8b0GESEqKqpI/zvQAqyUKhItvoUr6t+NFmCllLKIFmCllLqO06dP889//tMjx9aLcKrEvbN8JxNW/nzV+hEd6pCcUNeCRCpQ5OTkEBJStLKXV4CHDRvm9jx6BqxKXHJCXfaN6Urz2Io0j63IvjFd2TemqxZf5ZJ9+/ZRr149Hn30UerXr0+fPn24ePEir732Gs2aNaNhw4YkJSWRN8S2Xbt2jBw5kri4OCZMmMCxY8fo3bs3zZo1o1mzZqxduxaA0aNHM2jQINq1a8cdd9zBxIkTAXjxxRfZvXs399xzD6NGjXJrW/QMWCl1U0aOHMmmTZvcesx77rmH8ePH33C7HTt28P777xMfH8+gQYP45z//yfDhw/nf//1fAB577DEWLVpE9+7dAcjOzs6fw2HAgAEkJydz3333ceDAATp16sT27dsByMjIYPXq1Zw7d44777yToUOHMmbMGLZs2eL2toIWYKWUD6pZsybx8fEADBw4kIkTJxIbG8vYsWO5ePEiJ0+e5K677sovwH379s3fd8WKFWzbti3/9dmzZzl//jwAXbt2pVSpUpQqVYrKlStz9OhRj7ZDC7BS6qa4cqbqKQWHe4kIw4YNY/369dSsWZPRo0f/ZjxuZGRk/tc2m43vvvuO8PDwq45bqlSp/K+Dg4PJycnxQPr/0j5gpZTPOXDgAN9++y0AH3/8Mffddx8AlSpV4vz583z22WeF7tuxY0cmTZqU//pGXQtly5bl3Llzbkh9NS3ASimfc+eddzJ58mTq16/PqVOnGDp0KE8//TQNGzakU6dONGvWrNB9J06cyPr162ncuDENGjRg6tSp1/2sqKgo4uPjadiwodsvwulkPMoyfafZz2DmDG5pcRLlqu3bt1O/fn1LM+zbt49u3bqxZcsWS3MU5lp/R4VNxqNnwEopZREtwEopnxITE+O1Z79FpQVYKaUsogVYKaUsogVYKaUsojdiKKU8QiddujEtwEopj0hOqEtyQl2fHm7Yrl073n77beLirhpB5hbaBaGUUhbRAqyU8picXBsHTl5ky6EzjF2WQU6urdjHvHDhAl27duXuu++mYcOGzJkz57pTUSYnJxMXF0f9+vVZt24dDz30EHXq1OHVV18FCp/esqAvvviCli1b0rRpUx5++OH8CXyKQwuwUspjxi3fydGzWVzIzmX62r28s3xnsY+5bNkyqlWrxk8//cSWLVvo3Lkzw4cPZ926dWzZsoVLly6xaNGi/O3DwsJYv349Q4YMITExkcmTJ7NlyxY+/PBDTpw4Adintxw2bBjbt2+nXLlyVz0B4/jx47z++uusWLGCjRs3EhcXx7hx44rdFi3ASimP+Wb3CWyO2Q6yrthYu/tEsY/ZqFEjli9fzp/+9Ce+/vprypcvz+rVq2nevDmNGjVi1apVbN26NX/7Hj165O931113UbVqVUqVKsUdd9zBwYMHgaunt1yzZs1vPvO7775j27ZtxMfHc8899zBjxgz2799f7LboRTillMe0qhXFT5mnMQbCQ4OIrxVV7GPWrVuXjRs3smTJEl599VU6dOjA5MmTC52KMm+KyaCgoN9MNxkUFJQ/3eS1prd0ZowhISGB2bNnFzu/Mz0DVkp5zHMJdalaLpzIsGCeio91y/Czw4cPExERwcCBAxk1ahQbN24EXJuKsjCFTW+Zp0WLFqxdu5Zdu3YB9n7onTuL352iZ8BKKY8JCQ6iZsUIagKjOtdzyzHT09MZNWoUQUFBhIaGMmXKFFJTU2nYsCG33nrrdaeiLEze9JaDBg2iQYMGDB069DfvR0dH8+GHH9K/f38uX74MwOuvv07dusX7B0Wno1SW8eXxoYHqZqaj9Pbvs7untyzKdJR6BqyU8oiCd8LFvLgY0DvhnGkBVpbIGx969tIVxi7L4LmEuoQE6yUJf5J3J5y3s3J6Sy3AyhJ540NtBqav3Yvgvj5C5VnGmKtGCSi7onbp6imHsoQnxocqzwsPD+fEiRNFLjSBwBjDiRMnrvm05cLoGbCyhCfGhyrPq1GjBpmZmRw7dszqKF4pPDycGjVquLy9RwuwiHQGJgDBwL+MMWMKvC+O97sAF4EnjDEbnd4PBtYDh4wx3TyZVZWs5xLqkvrjIc5cusITrWJ8oq9QQWhoKLGxsVbH8BseK8CO4jkZSAAygXUistAYs81psweBOo6lOTDF8WeeEcB2oJyncipreGJ8qFK+xpN9wPcCu4wxe4wx2cAnQGKBbRKBmcbuO6CCiFQFEJEaQFfgXx7MqJRSlvFkAa4OHHR6nelY5+o244EXgELnrzt27BhxcXH5S0pKSvFTK6WUG6SkpOTXJqDStbbxyotwItIN+NUYs0FE2hW2XXR0NHonnFLKGyUlJZGUlASAiBy/1jaePAM+BNR0el3Dsc6VbeKBHiKyD3vXxf0i8pHnoiqlVMnzZAFeB9QRkVgRCQP6AQsLbLMQeFzsWgBnjDFHjDEvGWNqGGNiHPutMsYM9GBWpZQqcR7rgjDG5IjIcOBz7MPQphtjtorIEMf7U4El2Ieg7cI+DO1JT+VRSilv49E+YGPMEuxF1nndVKevDfDMDY7xH+A/HoinlFKW0luRlVLKIlqAlVLKIlqAlVLKIlqAlVLKIl55I4ZSrij4xIU8+sQF5Su0ACuflffEBW9/5phShdEuCKWUsogWYKWUsogWYKWUsogWYKWUsogWYOW1bDYb6enpfPnll1y8eNHqOEq5nRZg5XUyMjJ45JFHqFKlCo0bN6Zdu3bccssttGnThtmzZ1sdTym30WFoyqv83//9H4MHDyYsLIwePXrQvn17KlWqxJdffsmSJUsYMGAAK1euZOLEiURERFgdV6li0QKsvILNZuOZZ55h6tSptG7dmtmzZ1O9+n+fYNW1a1fefPNNRo8ezRtvvMH333/P4sWLue222yxMrVTxaAFWJa7gHWwxLy4G4HT6WZ5//nneeustQkKu/tEMCQnh9ddfp3Xr1jzyyCMkJiaydu3aEsutlLuJfUpe3xQXF2f0mXC+b968efTp04cnn3yS999/HxG54T5LliyhW7duPPLII9jaPYuI6J1wymuJyAZjTFzB9XoRTllq8+bN/P73v6dFixZMmTLFpeIL0KVLF9566y3mzJlDxuezPJxSKc/QAqwsc+XKFQYMGEC5cuX497//TalSpYq0/wsvvEC/fv1IXzCNk/szPJRSKc/RAqws8+6777J161amTp1K1apVi7y/iDB16lRKlanAj3PGYbPZPJBSKc/RAqws8csvv/CXv/yFBx98kO7du9/0ccqXL8/dDz3DiT1bmDlzphsTKuV5WoCVJV544QUuX77MhAkTXO73LcztzTsTdUdD/vSnP3H69Gk3JVTK87QAqxL3zTffMGvWLP74xz9Sp06dYh9PgoJo2u+PHDt2jL/+9a9uSKhUydACrErc6NGjqVKlCq+88orbjnnLbXcyaNAgpkyZwpEjR9x2XKU8SQuwKlHr1q1j+fLlPPfcc0RGRrr12C+99BJXrlxh3Lhxbj2uUp6iBViVqDfffJMKFSowdOhQtxwvJ9fGgZMX2XLoDPN+vkLffv2ZMmUKJ0+edMvxlfIkLcCqxGzdupXU1FSeffZZypYt65Zjjlu+k6Nns7iQncv0tXup0XkwFy5cYNKkSW45vlKepAVYlZgxY8YQGRnJs88+67ZjfrP7BDbH3fRZV2zsPBtEYmIiEyZM4Ny5c277HKU8QQuwKhH79+9n9uzZDBkyhKioKLcdt1WtKPJGsYWHBhFfK4qXX36ZU6dO8d5777ntc5TyBC3AqkSkpKRgjGHEiBFuPe5zCXWpWi6cyLBgnoqPJTmhLvfeey/x8fFMnTpV745TXk0LsPK47Oxs3n//fbp160bNmjXdeuyQ4CBqVoygYfXyjOpcj5Bg+4/00KFD+fnnn1m1apVbP08pd9ICrDwuNTWVo0ePMmTIkBL7zN69exMVFcXUqVNL7DOVKiotwMrjpk6dSkxMDB07diyxzwwPD+fJJ58kNTWVw4cPl9jnKlUUWoCVR2VkZLB69WoGDx5McHBwiX724MGDyc3N5f333y/Rz1XKVVqAlUelpKQQGhrKk08+WeKfXbt2bRISEkhJSSEnJ6fEP1+pG9ECrDwmOzubGTNm0KtXL6pUqWJJhsGDB5OZmckXX3xhyecrdT1agJXHLF26lJMnT/LEE09YlqF79+5UrFiRjz76yLIMShVGC7DymFmzZlG5cmUSEhIsyxAWFkbfvn1JTU3VO+OU1/FoARaRziKyQ0R2iciL13hfRGSi4/3NItLUsT5cRH4QkZ9EZKuI6CSvPubUqVOkpaXRv3//az5iviQNHDiQS5cuMW/ePEtzKFWQxwqwiAQDk4EHgQZAfxFpUGCzB4E6jiUJmOJYfxm43xhzN3AP0FlEWngqq3K/uXPnkp2dzWOPPWZ1FFq2bEmtWrW0G0J5HU+eAd8L7DLG7DHGZAOfAIkFtkkEZhq774AKIlLV8fq8Y5tQx2I8mFW52axZs6hXrx5Nmza1OgoiwsCBA1m1ahWZmZlWx1EqnycLcHXgoNPrTMc6l7YRkWAR2QT8Ciw3xnxf8AOOHTtGXFxc/pKSkuLWBqibs3fvXtasWcNjjz1W7Oe9ucvAgQMxxvDxxx9bHUUFiJSUlPzaBFS61jbWds5dhzEmF7hHRCoA80WkoTFmi/M20dHRrF+/3pqAqlB5Re7RRx+1OMl/1a5dm5YtW/LRRx/xwgsvWB1HBYCkpCSSkpIAEJHj19rGk2fAhwDnmVdqONYVaRtjzGlgNdDZAxmVB8yZM4f4+Hhuv/12q6P8xoABA0hPT2f79u1WR1EK8GwBXgfUEZFYEQkD+gELC2yzEHjcMRqiBXDGGHNERKIdZ76ISGkgAcjwYFblJhkZGaSnp/PII49YHeUqvXv3RkSYO3eu1VGUAjxYgI0xOcBw4HNgO/CpMWariAwRkbxpsZYAe4BdwHvAMMf6qsBqEdmMvZAvN8Ys8lRW5T55xa13794WJ7la1apVue+++7QAK6/h0T5gY8wS7EXWed1Up68N8Mw19tsMNPFkNuUZc+fOJT4+nurVC15v9Q4PP/wwzz77LBkZGdSrV8/qOCrA6Z1wym127NhBeno6Dz/8sNVRCqXdEMqb3LAAi0hdEVkpIlscrxuLyKuej6Z8jTd3P+SpVq0a8fHxWoCVV3DlDPg94CXgCuR3D/TzZCjlm+bOnUurVq2oUaOG1VGu6+GHHyY9PZ2MDL2uq6zlSgGOMMb8UGCdTq6qfmPHjh1s3ry5REc/vLN8JzEvLub7vSf5fu9JYl5cTMyLi3ln+c7r7qfdEMpbuHIR7riI1MJxK7CI9AGOeDSV8jnz588H4KGHHiqxz0xOqEtyQt0i71e9enVatWrF/Pnz+fOf/+yBZEq5xpUz4GeAaUA9ETkEjARK7umKyiekpqYSFxfn9qcee0rPnj358ccf2bdvn9VRVABz5QzYGGMeEJFIIMgYc05EYj0dTHmfd5bvZMLKn69aP+jeKnz//fe8/vrrFqS6OT179mTUqFEsWLCAESNGWB1HBShXzoDnARhjLhhj8ma0/sxzkZS3Sk6oy74xXWkeW5HmsRXZN6Yr+8Z0JfroOgB69eplcULX1a5dm4YNG5Kammp1FBXACj0DFpF6wF1AeRFx7tgrB4R7OpjyHampqdSpU4f69etbHaVIevbsyZtvvsnx48epVOmak1Up5VHXOwO+E+gGVAC6Oy1Ngac9H035gtOnT7Nq1Sp69uzpNVNPuqpnz57YbDYWLdK73JU1Cj0DNsYsABaISEtjzLclmEn5kCVLlpCTk+NT3Q95mjZtSs2aNZmy5gCjMxZf9f6IDnVuapSFUq5y5SLcjyLyDPbuiPyuB2PMII+lUj4jNTWVKlWq0Lx5c6ujFJmI0LNnT9577y2OHz/OoI82AzBncEuLk6lA4cpFuFnArUAn4Evsc/bq42UVly9fZunSpSQmJhIU5JvTivTs2ZOsrCy++OILq6OoAOTKb01tY8yfgQvGmBlAV8D3TneU261evZrz58+TmFjwUX++o3Xr1lSoUIGFCwtOVa2U57lSgK84/jwtIg2B8kBlz0VSviItLY2IiAjuv/9+q6PctNDQUB588EEWL16MzZZrdRwVYFwpwCkicgvwKvYnWGwD/u7RVMrrGWNIS0sjISGB8HDfHpXYvXt3jh07xsl926yOogLMdQuwiAQBZ40xp4wxXxlj7jDGVDbGTCuhfMpLnc78mYMHD9KjRw+roxRb586dCQkJ4fBPa6yOogLMdQuwMcYG6CNk1VUOb16LiNC1a1eroxTbLbfcQuvWrTmcrgVYlSxXuiBWiMjzIlJTRCrmLR5Pprzakc1raN68OVWqVLE6ilv06NGDs4f3cv5YwQd3K+U5rhTgvthnRPsK2OBY1nsylPJul04f4+T+7XTv3t3qKG6T15bDm/UsWJWcG96IYYzRmc/UbxxO/wbArwpwrVq1KHdrDIfT11odRQUQ3xw9ryx1JH0tERVvpWHDhlZHcatqd9/HsZ0/cubMGaujqAChBVgVyaVLlzi6fR3VGt/nc5Pv3EjVRvEYW67eFadKjBZgVSQrVq2ibMu+SFxfxi7LICfXZnUkt4mKvYuwyHI6O5oqMa48ln6lK+tUYHhnxS7KxvUgO7g009fuveEDMH1JUHAIt97VgiVLlpCbq3fFKc8rtACLSLhjuFklEbnFaQhaDFC9pAIq72GMYfe5YIJC7Xe+ZV2xsXb3CYtTuVe1RvEcP36cH34o+CBwpdzvemfAg7EPOavHf4efbQAWAO96PpryNunp6Zz9eR045kwIDw0ivlaUxanc69YGzQkODtZuCFUiCi3AxpgJjiFozztuQY51LHcbY7QAB6BFixZx+uuPiI4IIjIsmKfiY/1mwvKcXBsHTl5k52lDg/4vkbZ4idWRVABwZRzwJBFpBcQ4b2+MmenBXMoLpaWl0Szud8RWtZ/1jupcz+JE7jNu+U6Ons3CZiCk+r0c37+fAwcOcNttt1kdTfkxVy7CzQLeBu4DmjmWOA/nUl7m119/5fvvv6dbt25WR/GIb3afwGbsX+cQROmYu1m8+OrHFCnlTq4MQ4sD4o0xw4wxf3Asz3o6mPIuS5cuxRjjtwW4Va0o8oY1h4cGEX7mAGlpadaGUn7PlQK8BfsjiVQAW7RoEdWqVaNJkyZWR/GI5xLqUrVceH7fdvfYIFatWsWFCxesjqb8mCsFuBKwTUQ+F5GFeYungynvkZ2dzeeff07Xrl397u63PCHBQdSsGEHD6uUZ1bkePbp15fLly6xatcrqaMqPufJU5NGeDqG829dff825c+f8tvvhWtq0aUOZMmVYtGiRX006pLyLK6MgviyJIMp7LVq0iFKlStGhQwero5SYsLAwOnXqxKJFizDG+O2Zv7KWK6MgzonIWceSJSK5InK2JMIp77B48WLuv/9+IiMjrY5Sorp168bhw4fZtGmT1VGUn7phATbGlDXGlDPGlANKA72Bf3o8mfIKO3fu5Oeffw6o7oc8Xbp0QUT0rjjlMUWaDc3YpQKdXNleRDqLyA4R2SUiL17jfRGRiY73N4tIU8f6miKyWkS2ichWERlRlJzKffKKjz88+62oKleuTPPmzbUAK4+5YR+wiDzk9DII+7jgLBf2CwYmAwlAJrBORBYaY5yf/f0gUMexNAemOP7MAf5ojNkoImWBDSKyvMC+qgQsWrSIRo0acfvtt1sdxRLdunXj1Vdf5ejRo37z/DvlPVw5A+7utHQCzgGJLux3L7DLGLPHGJMNfHKN/RKBmY4z6++ACiJS1RhzxBizEcAYcw7Yjs7AVuJOnz7N119/HZDdD3ny2q53xSlPcGUUxJM3eezqwEGn15nYz25vtE114EjeCsf0l02A7wt+wLFjx4iL++9d0UlJSSQlJd1kXFXQsmXLyMnJCcjuhzyNGzemZs2apKWlMWjQIKvjKB+SkpJCSkpK3stK19rGlS6IGsAkIN6x6mtghDEm0x0hb/DZZYB5wEhjzFUjL6Kjo1m/Xh/Q7ClpaWlUqlSJFi1aWB3FMiJCt27dmDFjBllZWYSHh1sdSfkI5xNCETl+rW1c6YL4AFgIVHMsaY51N3IIqOn0uoZjnUvbiEgo9uL7f8aYf7vwecqNcnJyWLp0KV27diU4ONjqOJbq3r07Fy9eZPXq1VZHUX7GlQIcbYz5wBiT41g+BKJd2G8dUEdEYkUkDOiHvZA7Wwg87hgN0QI4Y4w5IvZR7+8D240x41xvjnKXtWvXcurUKb0LDGjfvj2RkZE6OY9yO1cK8AkRGSgiwY5lIHDD59AYY3KA4cDn2C+ifWqM2SoiQ0RkiGOzJcAeYBfwHjDMsT4eeAy4X0Q2OZYuRWuaKo60tDTCwsLo2LFj/rp3lu8k5sXFfL/3JN/vPUnMi4uJeXGxXz0X7lrCw8Pp2LEjaWlpGGOsjqP8iCtzQQzC3gf8DmCAbwCXLswZY5ZgL7LO66Y6fW2AZ66x3xpA7/200MKFC2nfvj1ly5bNX5ecUNdvnoBRVN27d2f+/Pls2rTJb2eEUyXPlTvh9htjehhjoo0xlY0xPY0xB0oinLLGjh07+Pnnn7X7wUneTHDaDaHcyZW5IGaISAWn17eIyHTPxlJWyisygTL+15Wulby74rQAK3dypQuisTHmdN4LY8wpEdH/g/mxtLQ07r777oC5+83VrpUePXrw8ssvc/jwYapVq1YCyZS/c+UiXJCI3JL3QkQq4lrhVj7o+PHjrFmzRrsfrqFHjx4Aehas3MaVAvwP4FsR+ZuI/A37Rbixno2lrLJo0SJsNhu9evWyOorXadCgAbVr1yY1NdXqKMpPuHIRbibwEHDUsTxkjJnl6WDKGgsWLKBmzZp6pf8aRITExERWrVrF2bM6JbYqPpemozTGbDPGvOtYdEYyP3Xx4kU+//xzEhMT9QkQhejZsyfZ2dksW7bM6ijKDxRpPmDl35YvX86lS5fo2bOn1VG8VsuWLYmOjtZuCOUWWoBVvgULFlChQgXatGljdRSvFRwcTPfu3VmyZAnZ2dlWx1E+TguwAiA3N5e0tDS6du1KaGio1XG8WmJiImfOnOHLL/V5tap4tAArAL755huOHz+u3Q8uSEhIICIiggULFlgdRfk4LcAKgPnz5+c/il1dX+nSpenUqROpqanYbDar4ygfpgVYYYzh3//+Nx07dvzN5DuqcL169eLQoUP88MMPVkdRPkzvaAsA7yzfyYSVP1+1fkSHOiQn1GXDhg3s37+f0aNHl3w4H9W9e3dCQ0OZN29eQD8xRBWP+PL8pnFxcUYfSeS6vtO+BWDO4Ja/Wf/SSy/x9ttvc/ToUSpWrGhFNJ/UpUsXMjIy2L17t46bVtclIhuMMXEF12sXRIAzxvDZZ5/Rvn17Lb5F1Lt3b/bu3cumTZusjqJ8lBbgAJeens6uXbvo06eP1VF8TmJiIsHBwXz22WdWR1E+SgtwgJs3bx5BQUE6/OwmVKpUibZt2zJv3jx9VJG6KVqAA9y8efNo3bo1lStXtjqKT+rTpw87duxg2zadIkUVnRbgALZjxw62bt1K7969rY7is3r16oWIaDeEuilagAPYnDlzEBEeeughq6P4rFtvvZXWrVszZ84c7YZQRaYFOEAZY5g9ezZt2rShevXqVsfxaf369WP79u1s2bLF6ijKx2gBDlDp6elkZGTQr18/q6P4vN69exMcHMwnn3xidRTlY7QAB6jZs2cTHBys/b9uULlyZTp06MAnn3yi3RCqSLQAByBjDJ988gkPPPAA0dHRVsfxC/369WPPnj3onZmqKLQAB4icXBsHTl5ky6EzJH+wmn37D9C/f3+rY/mNXr16ERoaqt0Qqki0AAeIcct3cvRsFheyc1m44zwV2z6uN1+4UYUKFXjwwQeZM2eOTlGpXKYFOEB8s/sENkf3pE2CqdK4DeXLl7c2lJ/p168fhw4dYs2aNVZHUT5CC3CAaFUrirwJu2xXsmgRW8HaQH6oR48eREZGMmvWLKujKB+hBThAPJdQl6rlwuHML2Snf87Ep/XJF+4WGRlJnz59+PTTT7l06ZLVcZQP0AIcIEKCg7g1wpA5fTg97wiiTGSE1ZH80uOPP87Zs2f1eXHKJVqAA0jmxv+Qm53F448/bnUUv9WuXTtq1qzJzJkzrY6ifIA+ESOAVKn3Oy6e/JWzRw/oExw84EaPflKBS5+IEeD279/Przs2cnuLB7X4ekhyQl32jelK/dLn2P/3bvwhOoN9Y7pq8VWF0gIcID766CMAYpp3tjiJ/yt36+1Exd7FjBkz9NZkdV1agAOAzWZj+vTpRNdtSmSlqlbHCQi3t+xCeno6GzZssDqK8mJagAPAqlWr2LNnD7Va97A6SsC4rVkCERERTJs2zeooyot5tAC8vINsAAAVFUlEQVSLSGcR2SEiu0TkxWu8LyIy0fH+ZhFp6vTedBH5VUR0ktVimjZtGlFRUVS/p53VUQJGWOky9O/fn9mzZ3P27Fmr4ygv5bECLCLBwGTgQaAB0F9EGhTY7EGgjmNJAqY4vfchoB2WxXT06FFSU1N54oknCA4NszpOQElKSuLChQt8/PHHVkdRXsqTZ8D3AruMMXuMMdnAJ0BigW0SgZnG7juggohUBTDGfAWc9GC+gPDBBx+Qk5PD008/bXWUgNOsWTPuvvtupk2bphfj1DV5sgBXBw46vc50rCvqNoU6duwYcXFx+UtKSspNh/VHNpuN9957j7Zt23LnnXdaHSfgiAiDBw9m06ZNOk9wAEpJScmvTUCla20TUrKR3Cs6Olp/sK9jxYoV7Nmzh3bD/x8xLy7OX5/3td4g4HkDBgzg+eefZ9q0aTRr1szqOKoEJSUlkZSUBICIHL/WNp4swIeAmk6vazjWFXUbdZMmTZpEdHQ0k4d2ITw83Oo4ASFv4vuzl64wdlkGzyXU5dFHH2XWrFn8/e9/JyoqyuqIyot4sgtiHVBHRGJFJAzoBywssM1C4HHHaIgWwBljzBEPZgoYO3fuZNGiRQwbNkyLbwlynvh++tq9vLN8JyNGjCArK0uHpKmreKwAG2NygOHA58B24FNjzFYRGSIiQxybLQH2ALuA94BhefuLyGzgW+BOEckUkac8ldUfTZw4kbCwMIYOHWp1lIDiPPF91hUba3ef4K677qJjx45MnjyZ7OxsawMqr+LRPmBjzBLsRdZ53VSnrw3wTCH76gPLbtKpU6f44IMPGDBgAFWqVLE6TkBpVSuKnzJPYwyEhwYRX8ve5TBy5Ei6dOnC3LlzefTRRy1OqbyF3gnnh/71r39x8eJFRo4caXWUgJM38X1kWDBPxcfmX+Ts1KkT9erVY/z48TokTeXTAuxncnJymDRpEu3bt+fuu++2Ok7ACQkOombFCBpWL8+ozvUICbb/igUFBTFixAjWr1/P2rVrLU6pvIUWYD/z8ccfc/DgQZKTk62Oogp4/PHHiYqKYsyYMVZHUV5CC7Afyc3N5Y033qBx48Z069bN6jiqgIiICJKTk1m8eDEbN260Oo7yAlqA/cjcuXPZuXMnr776qk667qWGDx9OhQoVeP31162OoryAFmA/YbPZeOONN6hfvz69e/e2Oo4qRPny5Xn22WeZP38+W7boRH+BTguwn1iwYAFbtmzhlVdeIShIv63ebMSIEZQpU4Y33njD6ijKYvqb6gdsNhuvvfYatWvXpm/fvlbHUTdQsWJFhg8fzpw5c9i2bZvVcZSFtAD7gdmzZ7Np0yb+8pe/EBLi0/MrBYw//vGPlC1blpdeesnqKMpCWoB9XFZWFq+88gpNmjRhwIABVsdRLqpUqRIvvfQSCxcu5KuvvrI6jrKI+PJdOXFxcSbQp6N8++23GTVqFCtWrKBDhw5Wxwlo7yzfyYSVP1+1vrBpPy9dukTdunWpVq0a3333nY5c8WMissEYE3fVei3A3utGv9AnT56kVq1atGjRgqVLl1qQUBXXhx9+yJNPPsmcOXN45JFHrI6jPEQLsA/rO+1bAOYMbvmb9SNHjmTixIn89NNPNGrUyIpoqphyc3Np0qQJ58+fZ+vWrZQuXdrqSMoDCivA2gfsozZu3MikSZMYPHiwFl8fFhwczIQJE9i7d68OSwtAWoB9UG5uLoMHD6ZSpUq89dZbVsdRxdS+fXsee+wxxo4dy/bt262Oo0qQFmAfNHXqVNavX88777xDhQoVrI6j3ODtt9+mTJkyDBkyRKerDCBagH3MoUOHePnll3nggQfo31/nrPcXlStXZuzYsXz11VdMnz7d6jiqhGgB9nJ5D3nccugMf1+6nd8/8SRXrlxhypQpOmzJzwwaNIg2bdqQnJzMnj17rI6jSoAWYC/n/JDH977cxfrLtzJ+/Hhq165tdTTlZkFBQcycORMRYeDAgeTk5FgdSXmYFmAv5/yQxxyCqNakHU8//bS1oZTH3H777UydOpVvv/1WR0UEAC3AXq5VrSjyOhpMzmX6tmuiXQ9+rn///gwcOJDXXntNb1P2c1qAvdzIDrXJ2fUNlw/voGvtCF7poc95CwTvvvsutWvXpk+fPuzfv9/qOMpDtAB7ub+O/guZ896kanYm/xzSOf8hj8q/lS9fnoULF5KdnU2PHj04f/681ZGUB+jchR5Q1ElZCvPxxx/z5ptvcsd9Pajdro87IyofcOeddzJnzhy6dOnC448/zty5cwkODrY6lnIjnQvCgwqbw8EVaWlpPPTQQ7Rq1YroR/5GcEjoTR1H+b7x48eTnJzMU089RUpKij7xxAcVNheEngF7oS+++II+ffrQpEkT0tLSeHr2VqsjKQuNHDmSkydP8re//Y3w8HAmTZqkF2L9hBZgL7N8+XJ69uxJ/fr1WbZsGeXKlbM6kvICf/3rX7l06RJvv/02ISEhjBs3Ts+E/YAWYA/Ju4Pt7KUrjF2WwXMJdW94AW3mzJk89dRT1K9fny+++IIZG44zYeW3+e/HvLgYKHpfsvJ9IsLYsWPJyclh/Pjx/PLLL3z44YeEh4dbHU0VgxZgD8m7g81mYPravQgwqnO9a25rjOGNN97gz3/+Mx06dGDevHmUL1+e5ITKWmgD0PUu4o4bN45q1arxwgsvcOTIEebPn0/FihUtSKncQQuwhzjfwZZ1xcba3ScYdY3tTpw4wZNPPklaWhoDBw7k/fffJywsrESzKu+SnFCX5IS6hV7EHTVqFDVr1uT3v/89TZo0Yfbs2bRq1cqKqKqYtBPJQ1rViiLvOkl4aBDxtaKu2uarr77innvuYdmyZYwfP56ZM2dq8VUu6devH2vWrCEkJIQ2bdrw1ltv6dwRPkgLsIc8l1CXquXCiQwL5qn42N90JZw4cYL/+Z//oW3btoSHh/Ptt98yYsQIvbKt8jnPgjd2WQY5ubartmnWrBkbN26kT58+vPzyy9x777388MMPFqRVN0sLsIeEBAdRs2IEDauXZ1TneoQEB5GVlcW7777LnXfeyYwZMxg1ahQ//vgjv/vd76yOq7yM8yx409fu5Z3lO6+5Xfny5Zk9ezaffvopR48epUWLFjz99NMcOHCghBOrm6EFuARcvHiRyZMnU7t2bf7whz/QqFEjfvzxR8aOHUuZMmWsjqe80LWuIRRGRHj44YfJyMggOTmZGTNmULt2bYYNG8a+fftKJrC6KVqAPejM4b38OOcdqlWrxvDhw4mNjWXlypWsWrWKhg0bWh1PeTFXriEUVLZsWf7xj3+wa9cuBg0axL/+9S/uuOMOunbtyqJFi7SP2AvprchuZLPZSE9P5y9zf2BTTrWr3h/RoTbJCXdakEz5mpxcG23GrubMpSs80SqGZBfGkReUmZlJSkoK7733Hr/88gtRUVH06tWL3r1706ZNGyIiIjyUXhVU2K3IHi3AItIZmAAEA/8yxowp8L443u8CXASeMMZsdGVfsL4AZ2VlsWnTJn744Qe++eYbVq1axbFjxxAR7rvvPh5++GH69u1L5cqVLcuofFdx5hJxduXKFZYsWcKnn37KwoULOX/+PGFhYbRq1Yq2bdvSrFkzmjVrpj+nHlTic0GISDAwGUgAMoF1IrLQGLPNabMHgTqOpTkwBWju4r4eZ4zhzJkzHDlyhMzMTPbu3cvevXvZsWMHW7duZffu3eTm5gJQrVo1OnfuzAMPPEBCQgJVq1YtyahKFSo0NJTExEQSExPJysriP//5DytXrmTFihW89tpr+U9hvvXWW2nQoAENGjTgjjvuIDY2lttuu42qVatSuXJlnYnNAzx5I8a9wC5jzB4AEfkESASci2giMNPYfwK+E5EKIlIViHFh3yI7d+4cn3zyCVlZWWRlZXHp0iUuXLjAhQsXOH/+PGfOnOH0mbMcjmpKVvnbuLh3E8dXfwjmv0OAQkJCqFWrFg0bNuSRRx6hadOm3HvvvVSvXr040ZTKV/BOuOLegp6Ta2Pc8p18s/sErWpF8VxCRzp37gzYfyc2btzIunXr2Lp1K9u2bWPGjBmcO3fuN8cICgoiqlI05eIHEFS1HuWyfqFu1k7KlS1DZGQkkZGRREREULp0aUqVKkWpUqUICwsjNDSU0NBQQkJCCAkJITg4OH8JCgrK/1NE8pe810D+uoJf570u6EZDOYs71LNMmTLExMQU6xjOPFmAqwMHnV5nYj/LvdE21V3ct8jOnDlDUlLSb9aVKlWKyMhIypQpQ/ny5bE16k529WZIUCjlom6nbds29K4TRvXq1YmNjaVatWp6JqA8Ku9OOHcZt3wn09fuJeuKjYxfzv7mtviyZcvStm1b2rZtm7+9MYZTp06xd+9eDh48yJEjRzh8+DBrzkWxPyyGnKBQjkdEc2LnCS4sn83Fixe5ePEivnw9yVWdO3dm6dKlbjueT9+KfOzYMeLi/tutkpSUdFWBdVa1alUOHjxI6dKlCQ8PJzw8/DfFtO+0b/l+78n817kSzPqLlQg+W5E5/XUuXuV7Cv5MZ12xMfk/u1m//1ShfcsiQsWKFalYsWL+GPW+075lr9NxCA7D1O9I+y79mDO4JcYYLl++zKVLl7h06RJXrlwhOzuby5cvk5OTk7/k5ubmLzabjdzcXIwx2Gw2jDH5XwP5rwt+nfe6oBv9A+COfyCqVKni8rYpKSmkpKTkvax0rW08dhFORFoCo40xnRyvXwIwxrzltM004D/GmNmO1zuAdti7IK67L3jmItzYZRn5ZwvhoUE8FR9b6CQ6SvkCd/1M6+/GzbNiQvZ1QB0RiQUOAf2AAQW2WQgMd/TxNgfOGGOOiMgxF/b1iOcS6iLA2t0niK8VpbORKZ/nrp9p/d1wP08PQ+sCjMc+lGy6MeYNERkCYIyZ6hiG9i7QGfswtCeNMesL27fg8a0ehqaUUq6wZBywp2kBVkr5gsIKsN6KrJRSFtECrJRSFgnIAuw0NMSvaLt8i7+2C/y3be5ulxZgP6Lt8i3+2i7w37ZpAVZKKT/h06MgHOOF99/ErpWA426O4w20Xb7FX9sF/tu2m23X7caY6IIrfboAK6WUL9MuCKWUsogWYKWUsogWYKWUsohfFGARmS4iv4rIFqd1o0XkkIhscixdnN57SUR2icgOEenktP53IpLueG+iFHf25mIqSrtEJEFENjjybxCR+5328ap2OTIV6XvmeP82ETkvIs87rfOqtt3Ez2JjEflWRLY62hHuWO+z7RKRUBGZ4ci/PW82Q8d7Xt8ux/o/iEiG4/sy1mm9e2tH3jybvrwAbYCmwBandaOB56+xbQPgJ6AUEAvsBoId7/0AtAAEWAo86EPtagJUc3zdEDjk9J5XtauobXN6/zNgrvM23ta2In7PQoDNwN2O11F+8rM4APjE8XUEsA+I8aF2tQdWAKUcrys7/nR77fCLM2BjzFfAyRtuaJeI/YfjsjFmL7ALuFfsj0IqZ4z5ztj/RmcCPT2T2DVFaZcx5kdjzGHHy61AaREp5Y3tgiJ/zxCRnsBe7G3LW+d1bStiuzoCm40xPzn2PWGMyfWDdhkgUkRCgNJANnDWh9o1FBhjjLns2OZXx3q31w6/KMDX8QcR2ez4b8YtjnXXewxS5jXWe6NrtctZb2Cj4wfIl9oF12ibiJQB/gT8tcC2vtS2a33P6gJGRD4XkY0i8oJjva+36zPgAnAEOAC8bYw5ie+0qy7QWkS+F5EvRaSZY73ba4c/F+ApwB3APdh/EP5hbRy3uW67ROQu4O/A4JKPVmyFtW008I4x5rxFuYqrsHaFAPcBjzr+7CUiHSxJeHMKa9e9QC5QDft/1f8oIndYkvDmhAAVsXcpjAI+9VRftU8/E+56jDFH874WkfeARY6Xh4CaTpvWcKw75Pi64Hqvcp12ISI1gPnA48aY3Y7VPtEuuG7bmgN9HBdDKgA2EckC5uEDbbtOuzKBr4wxxx3vLcHeH/kRvt2uAcAyY8wV4FcRWQvEAV/jA+3C/n35t6M74QcRsWG/A87ttcNvz4Ad/TJ5egF5VzkXAv0c/aOxQB3gB2PMEez9VC0c/9o9Diwo0dAuKKxdIlIBWAy8aIxZm7eBr7QLCm+bMaa1MSbGGBOD/Skpbxpj3vWVtl3nZ/FzoJGIRDj6S9sC2/ygXQeA+x3bRGI/k8zwlXYBqdgvxCEidYEw7Lcfu792WHkF0o1XMmdj/y/QFez/ej0FzALSsV9lXghUddr+FexXMHfgdLUS+7/SWxzvvYvjVm1faBfwKvZ+t01OS97VW69q1818z5z2G81vR0F4Vdtu4mdxIPYLi1uAsf7QLqAM9tEqW4FtwCgfa1cY9v+FbAE2Avc7be/W2qFzQSillEX8tgtCKaW8nRZgpZSyiBZgpZSyiBZgpZSyiBZgpZSyiBZgpZSyiBZgpZSyiBZg5RdEZLCIHHGam3aTiDQqsE1px+QqwW74vNXO88E61o0UkSkiEiYiXznublOqUFqAlb9oBLxqjLnHaUkvsM0g7Pf457rh82YD/Qqs6wfMNsZkAyuBvm74HOXHtAArf9EY++3X1/Mojnv0RSTG8cSDD0Vkp4j8n4g8ICJrReRnEbk3bycRGSgiPzjOqqc5zqA/A7qKSFje8bDP/vW1Y7dUx+cpVSgtwMpf3AV84NT9kOT8pqNQ3mGM2ee0ujb2KRTrOZYB2KeFfB542bFffexnsvHGmHuwT7P4qLHPb/sD8KDjWP2AT81/7+3fAuTNI6vUNWkflfJ5IlITOGaMaXydzSoBpwus25vXTSEiW4GVxhgjIulAjGObDsDvgHWOKWFLA3lPSMjrhljg+POpvAMb+5MtskWkrDHmXHHap/yXFmDlDxoB22+wzSUgvMC6y05f25xe2/jv74YAM4wxL3G1BcA7ItIUiDDGbCjwfikg6wa5VADTLgjlDxoDGdfbwBhzCggWx1OHi2Al9sngKwOISEURud1xzPPAamA69rPhfCISBRw39knJlbomLcDKHzQCHnPq//3R8Ry5gr7A3sfrMmPMNuxzLX8hIpuB5YDzROSzgbspUICxT+i9uCifpQKPzgesAoajqyDZGPNYCXzWv7E/nWSnpz9L+S49A1YBwxizEVjtjhsxrscx4iJVi6+6ET0DVkopi+gZsFJKWUQLsFJKWUQLsFJKWUQLsFJKWUQLsFJKWUQLsFJKWeT/A7UNbX33QxnXAAAAAElFTkSuQmCC\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x15c06c9afd0>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Create sample\n",
+    "## SAMPLE SIZE\n",
+    "sample_size = 200\n",
+    "##################\n",
+    "\n",
+    "# Prepare fake data\n",
+    "mu = 1540  # True values that we will try to estimate\n",
+    "sigma = 11 # using a least-squares fit\n",
+    "\n",
+    "x_arr = np.linspace(1500, 1600, 101)\n",
+    "bins = 12\n",
+    "sample = gaussian_sample(mu, sigma, sample_size)\n",
+    "hist = np.histogram(sample, bins=bins, range=(1500, 1580))\n",
+    "bin_width = np.diff(hist[1])[0]\n",
+    "normalization = bin_width * sample_size\n",
+    "x = hist[1][:-1]+bin_width/2\n",
+    "y_error_const = 0\n",
+    "y = hist[0]/normalization + gaussian_sample(0, y_error_const, bins)\n",
+    "y_errors = np.sqrt((np.sqrt(hist[0]) / normalization)**2 + y_error_const**2)\n",
+    "\n",
+    "# Plot our fake measurement results\n",
+    "plt.figure(figsize=(5, 4))\n",
+    "plt.xlabel(r'$E$ (meV)')\n",
+    "plt.ylabel('count rate')\n",
+    "plt.plot(x_arr, gaussian_parent(x_arr, mu, sigma), '-', color='black', label='parent')\n",
+    "plt.errorbar(x, y, yerr=y_errors, fmt='.', ms=7, capsize=3, label='sample')\n",
+    "plt.legend()\n",
+    "plt.tight_layout()\n",
+    "\n",
+    "# Save data\n",
+    "data = np.vstack((x, y, y_errors))\n",
+    "np.savetxt('data', data)\n",
+    "np.savetxt('sample', sample)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Load data from disk. Format (3,12): (x, y, y_error) x N \n",
+    "data = np.loadtxt('data')\n",
+    "x = data[0, :]\n",
+    "y = data[1, :]\n",
+    "y_error = data[2, :]\n",
+    "# The sample used to generate\n",
+    "sample = np.loadtxt('sample')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Function we want to fit to our data set\n",
+    "def model_function(x, *args):\n",
+    "    mu, sigma = args[0:2]\n",
+    "    return norm.pdf(x, mu, sigma)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Fit Results:\n",
+      "mu = 1539.0 +- 0.4\n",
+      "sigma = 10.3 +- 0.3\n",
+      "mu estimator 1538.4 +- 0.7\n",
+      "sigma estimator 10.4\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Perform the fit minimizing least squares\n",
+    "initial_guess = [1545, 9]\n",
+    "p_opt, p_cov = curve_fit(model_function, x, y, p0=initial_guess, sigma=None, absolute_sigma=False, check_finite=True)\n",
+    "p_err = np.sqrt(np.diag(p_cov))\n",
+    "# pcov(absolute_sigma=False) = pcov(absolute_sigma=True) * chisq(popt)/(M-N)\n",
+    "print('Fit Results:')\n",
+    "print('mu = {:1.1f} +- {:1.1f}'.format(p_opt[0], p_err[0]))\n",
+    "print('sigma = {:1.1f} +- {:1.1f}'.format(p_opt[1], p_err[1]))\n",
+    "\n",
+    "print('mu estimator {:1.1f} +- {:1.1f}'.format(np.mean(sample), np.std(sample, ddof=1)/np.sqrt(sample.size)))\n",
+    "print('sigma estimator {:1.1f}'.format(np.std(sample, ddof=1)))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Plot the result"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x15c06ec26a0>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "x_arr = np.linspace(1500, 1600, 101)\n",
+    "\n",
+    "plt.figure(figsize=(5, 4))\n",
+    "plt.xlabel(r'$E$ (meV)')\n",
+    "plt.ylabel('count rate')\n",
+    "plt.errorbar(x, y, yerr=y_errors, fmt='.', ms=7, capsize=3, label='sample')\n",
+    "plt.plot(x_arr, model_function(x_arr, *initial_guess), '-', color='grey', label='guess')\n",
+    "plt.plot(x_arr, model_function(x_arr, *p_opt), '-', color='black', label='fit')\n",
+    "plt.legend()\n",
+    "plt.tight_layout()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Biased estimator example"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Expectation value: 4.133148453066826\n",
+      "Sample mean: 4.144495254000121\n",
+      "Gauss fit: 3.946186931764501\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x15c06fc1b00>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "log_sample = lognorm.rvs(s=0.5, loc=3, scale=1, size=1000)\n",
+    "plt.figure(figsize=(5, 4))\n",
+    "h = plt.hist(log_sample, bins=32, rwidth=0.85, normed=True)\n",
+    "\n",
+    "\n",
+    "def model_function(x, *args):\n",
+    "    A, mu, sigma = args[0:3]\n",
+    "    return A * norm.pdf(x, mu, sigma)\n",
+    "\n",
+    "\n",
+    "x = h[1][:-1]+np.diff(h[1])[0]/2\n",
+    "y = h[0]\n",
+    "initial_guess = [0.95, 3.3, 0.3]\n",
+    "gauss_fit_large = curve_fit(model_function, x, y, p0=initial_guess)\n",
+    "#local = np.where(np.abs(x-np.mean(log_sample))<0.5)\n",
+    "#gauss_fit_local = curve_fit(model_function, x[local], y[local], p0=initial_guess)\n",
+    "\n",
+    "x_arr = np.linspace(2, 5, 51)\n",
+    "plt.plot(x_arr, model_function(x_arr, *gauss_fit_large[0]), '-', color='red', label='fit')\n",
+    "#plt.plot(x[local], model_function(x[local], *gauss_fit_large[0]), '-', color='orange', label='fit small')\n",
+    "plt.legend()\n",
+    "plt.tight_layout()\n",
+    "\n",
+    "print('Expectation value:', lognorm.mean(s=0.5, loc=3, scale=1))\n",
+    "print('Sample mean:', np.mean(log_sample))\n",
+    "print('Gauss fit:', gauss_fit_large[0][1])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Fitting a Gaussian and gives a biased estimate of mu"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Bonus: Errors in x and y\n",
+    "So far, we considered uncertainties only on y. Consider the following data set with errors in both x and y. Try to fit a line to the data below, taking into account both errors. Compare with fits neglecting the x errors or both.  \n",
+    "  \n",
+    "Hint: A detailed solution is already on the moodle. You may chose if you want to try to write your own solution, implement a known solution (see references in solution notebook) or just try it with the scipy package ODR (orthogonal distance regression). \n",
+    "https://docs.scipy.org/doc/scipy/reference/odr.html\n",
+    "  \n",
+    "The solution contains a python implementation of York's equation, comparison with ODR and MC tests.  \n",
+    "Week 3: \"Linear Regression errors x and y\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x15c06f95da0>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Test data\n",
+    "X = np.array([0.0, 0.9, 1.8, 2.6, 3.3, 4.4, 5.2, 6.1, 6.5, 7.4])\n",
+    "Y = np.array([5.9, 5.4, 4.4, 4.6, 3.5, 3.7, 2.8, 2.8, 2.4, 1.5])\n",
+    "wX = np.array([1000, 1000, 500, 800, 200, 80, 60, 20, 1.8, 1])\n",
+    "wY = np.array([1, 1.8, 4, 8, 20, 20, 70, 70, 100, 500])\n",
+    "sigma_x = 1.0/np.sqrt(wX)\n",
+    "sigma_y = 1.0/np.sqrt(wY)\n",
+    "\n",
+    "plt.figure(figsize=(4, 3))\n",
+    "plt.errorbar(X, Y, xerr=sigma_x, yerr=sigma_y, fmt='o', label='oberservations')\n",
+    "plt.legend()\n",
+    "plt.tight_layout()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "hide_input": false,
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.7"
+  },
+  "toc": {
+   "base_numbering": 1,
+   "nav_menu": {},
+   "number_sections": true,
+   "sideBar": true,
+   "skip_h1_title": false,
+   "title_cell": "Table of Contents",
+   "title_sidebar": "Contents",
+   "toc_cell": false,
+   "toc_position": {
+    "height": "calc(100% - 180px)",
+    "left": "10px",
+    "top": "150px",
+    "width": "225.438px"
+   },
+   "toc_section_display": true,
+   "toc_window_display": true
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/exercises/Solution5/Solutions_5.pdf b/exercises/Solution5/Solutions_5.pdf
new file mode 100644
index 0000000000000000000000000000000000000000..51de0b724b050c48bbcd6cbfa5332c49a9b0663c
GIT binary patch
literal 184354
zcmbTdbwC_nw<X#@fZ)M{TX1)GcXtc!5ZoO?0zrbiySqz(5Zv8^yG!FtCBJ*;``(@V
z=FJ=afTm9OsXBY_we~uzs>zkaB<PtL*x|{y&rj~)xrv#G9gMBv`S=(mtZZG(oERl+
zja<yc%uF0i%^2m(>@8d@iJ7^XxCI2@on4&FjO^e&mMV0#qnA0}`JGiUsbIv!C6iDq
z_d^N?7D`7a<(@!_e@8;pF0>=5-Ij83TQxK^h?Q)tn=`WI!2g=6=<LWPda^wnB3J!H
z5*IM!?-6TDDY&Y*=0l8*Ml6%e*<vU!^*-laSRBq(3{lPC^JU(Z`}Y%Zo$c9ldBN3J
zd#$(O@AJZ;?@1=dVg2&6Udd4Vf|90j%ocK9Y-B&8S>J-eyB#hfeA`}~Fx_!DMgs69
zWh4VG5=_5F%g95pxzBd=_r4}c>o`Kezv-dsL9Nk>CrNsPd*~OZ@%j1t)wo8o<S<c^
z>L%~X81~K{n_(KxvjVp{Uu6hM8Kd`&&+ktNcLB_p3F1RZpBMVf+IMp2vNTCof9b%M
zwL3vd*j~;vB57AfO0TH0S6|Bs)f09}2fn{akESPU<f2kHO^f8a^7!I2?;!Yca7tAe
z>tILzQ$tUFZqw`Cs0q~vBRo5GtYrib7MP05Z5EB+B#OUG5NretPx?ByukMxLzT&0#
z7B1^6hpP@an|vo08Zoik)lb<K8A_m}L_W1(4mbNPNG6XR7drCr@;33Tsjc^;i8MZL
zGyRWdT++qKW1;$09JI?+!%$cT#>%K)9+h&W<^``zYMK(|Hqly6gQvA3rU$vAmIHil
zLs`4psaM^o8_-mwlNBP6*}l3}3YFJ-V)+QVF_!ODJQ!9^vrX~vYc}9ksy`Jh2+lhu
zeVz22q#U^FUEUTCP$xg=Q}<hiUJwtr%|UXeKpuCcb935~eccLPJuumRu*a*nX)Ju7
zrAxDzWO(nB5Z*n^^6~9*t#Lg9#2k{|+X;!*cSGdH%1F0{akL=gy;TmElT)bVtK5}N
z4soSg=@N>RI7}OMM;#r)BUNq5mShr%Q5A|}1_r_W#_9;W9Szg4_Ju;n+ltayVj2F(
z%W*c+R{QH37S)m%pF=ui^O=MkroR04Z=)cC>?-leRD&WzPx}Egy5rGs1ogTHIeTg>
z@lJaA+mv!~sQJmb%s}GSJ3S2TZ-uPd+Pr7sCu51_=4(Nv9n=hg#X3gn!EH{Ib&IN)
z^$c?p)>5|-Y|Qyalldybk$s(PAql^G@{m5(ZVXxscW!*v!tP_)Q!l7YRxJm)o9*|j
z*;>EMyz39rBWoLDsSrzHkZTHJpt^aHK4pp&Hihe=CXBpl_z<pDNXy<83(W^dkcn`W
zU4G#~ZLED((9Upw68zOSKalE{l)CXLpSd|8``WfckJk1Lr9ilvD{}s;`sJFmJe=E$
z`i184Xhhrs2kloiUj4ZWNrE|RMRufbas%477Ov;r?iMQF`RnBkzUU~;^2`z>ojA=U
zMoQ3&vf5ZE|8oCDEm(%#lH)wF*&2vwW&edsFuA1v?&GOt&UW+m*O^`gCu(D>J<i)g
zed5c+)6?zd-#u*l)`^;5zd>PQc^!W0(Pu<=ZmZU_*0Ew~`{+gREQoBm!G|`;Q04#&
z+q{1RgUsif<1ZSY!e<BldA?!hc*+g-V;5)3t^S}%H<JFrTg~{3(8A*f*-(~_Rx%qV
zenR@n$`Kunc$rnlM1rL!Bwohh-13QqrD5k;gM4=FOZEQe8Z&D_F$g3bsYsbsyf;6d
zfAcLO(KT-ML=E;{S2~1Fec8K@Wg^jaYDvFBo|@GaG!?L(tz+apgr6{Qi^)CZBw3&S
zNL}nN9<=s_ft&LJNy*LuQTf1frnM!gxQgd4o1ukd-MAXXXC0%+myCpX?}CdD&4pb0
zahddT>^TGR1fS1P9Bjw(E@0~5qLo4U<0`yX^nH=c{IZAK7@Qu))W?rw5F^#!h&uTo
zh!2US>OeVIhGno89t-mkZ;6dVyz!Yu6!t&Jq8K0?`W%iN5_}Wm_hHZ?_kIj3p-(>S
zgM19jQK|i5|0V}<`N#Z6hyKE!6j~7bwse-O!=g&x23&*ZtOAWZqaU^|ZS5G}y0Ll-
zE3Jq!YeRcL@JgqOVnTgvQbi27y+|%^so{s3uyOKxUuqyGcY{nKSvH^kR#+{?m&u4f
z-V_m4rr)kT5)4{cK4M{efw28;BdkawVmp>itP>WZnUyZuNH<!hpMH+YE7~zHw7~Ev
z;u{NdZ-A00;=4HO2s<Gay?M@|G9ykXmY3f@NNj3*OJZjPqezPEtbBU^`>q5^_;Ue~
z3-V^`?;u;(wmaJ`i<#ms$-q^YK~~2IJ#-Z@>VwjL<}{FizRR4Bhbxw?Y11&_fZzKJ
z5z@~=+3qmhPLT*fg<8;Gs=Qq41CUxB3x~rR(@?J5qy;BN1)^eX1odwmS-0_8+SvK2
ze)=qujS6*c;0eW95aS;zM~HTk?$t8CH}ETqMk}H?Q1OfVy;70=bm~rg&_;9Y^(qf%
z>Rd%6-P7L>ycEM*6c8;|r`e?>@~yP}xzWNLb^r9SpWHAheK|rhvyv0Cab9ZojdtR~
ztIO~DQ$4;oyPsgW$Cu4Mgy(yitcsv<nL{04b*>>JOV{c(fik;%0sdHip>Cw-ImGQz
zE0usk(i4ri<n6uTP>jz;Z9{igZLbjD5tnS>&FoG8qyGZ0{&Z|+w*QmX&BDa>|7hI}
zy0Xqd>+S?=T26^>M~(hQVq$G!i*s3$Ey7tRofp-O?=7KU{E)Eld;={F(hHAA46-F8
zROxBbJN0|~(Hl_&Z9x2t&G=#Rbz<|@D3Db82F2FT=l<k8?n!~{x+sFTs;0|;Va<0J
z;al$eJ`cwh?YEfHq}bRY;`k+fI}b;fvO1?%CyUUOJ}rYoSG$L9VE(~6Q|~kmFp6}F
z#rW<pAARkOKnr8)vS}Mfw!@<MMZMysi|_d;Psi}|%(r#Bk?Cv6qg7ECH`n|A&FLMv
zDhc(F=5oW6(KODmS{r<V9ymfH2J00a8%sx2=T{r?rDY!}oo{5q;*v{Y^R7R>NX(Ll
z@Q0NKd=JOaATatKhOFfrbb(xcm&jLW7>sFD+IlJ_Mi~BSu5LrU8d>C;&U+&#IG*2x
z4ZpGmd+b;8p`+uoFQrG&4LKDl51F2nyzI{oI5EdwW6-j|XWl3YE!Tu|qnI1c0RGcc
z!+Il)VG8%^onh{gx4XNN#f4%ypJ}&7#N)pdXA>+$v$Z{P1jABCc-MN<9E=b-KG&gH
zGE^|lq5kp|H6%V}Sud*UB26d_*2!QWDWIBrlSi=Ic+Z#IumsKg^{vIrw*=JbUJ_e)
z6QrAfdP}iVh-%~4YMshN#_rOe<#6J}(_~xkKBOk6^%1@qUW@Qt2!6Np3HwLs+n3Ch
zDj`2_gA5#=(4Vlo1ovIn!>>|-JZ6NrJ}^1Cl8P+a#k)@=vg@89wWCy^Tc?cOdj=G@
zpm6Uh&Q<dohD7*r+IW(gIvmecbGfi-BTzGj<tj)e+g?AS=saq;-r@a)6v<%*l-)42
z<8)lj&q7WJ6@JUln07iSUv$++ncljMx0<C{e`9y$W@gk-WaCRUTGoZAmKP|e84a6`
zapruc%Cz-1(z%PEvHvo2ieovY!%g`?jv69aUHgr96L}+yQ2N|m>rnUds;EYR(S%ee
zOxLL0IWr-%IQ2mpc4eqN%hB7=sq<}oQ)lw{!#ESWJZMLX!5$q+4^i*uE)Ccflrb57
z!XPJqd%invTD($z3QwoHHbuTET+SSl7&2IUxRXlug5_~ql{Q}aBOCk`jZaeo%V85i
zxP=hDkEXk-1LbuZ=r+5k3%!et5886))B3JF57!rH^hV#rP%c^I5D$>dkvFa9UDu9d
z>cNW~1j1U2DO854LOSqLvb^~P0rf7pR^3;Us9>S9*NFDt%r1z55cV)=P*$vHI8p@S
z<^gF56GEpXB@STDMZ0~|Ri_L~Z}`HI+(~nx6-Ydz2U5WXDmd{1FRVtzq3hP=vd&I?
zsL*nu=-$+|Q(sGGNtefP6P&pf;cAVqZ@@ZkI|%UKB+3Pa^lG#TZUZvb=4(dvkgY@N
z<!~j@*!y0<b|yAW-+0(gIHDk#+>D#8ArX+fyaa<9f^l$VyhrNn?pu+_St;I3%+}uI
z@3C|SuwYty$x&U|og)iN>i0dGzr<F8o2=UWFE~Ako|u@M&wVb%6-L&0ByLgykh~M7
zX;?pVHHeSxPHSwhFhEy%=BFUL<jFEpx(Fud>%2d<rQlM#l7YUThOlrhisNeH%=n!{
z!8-B1@*IEu;U~O&#Nk*e1(!<N_f!tka#{!%4hzT>=Qi{&g!a`ve1|^HGB+l=w)GKp
z5wfH8y_Fb^qMkoddCTA5++m9R#6X(SR&n`SOjwdN?m>sy4Jk`uGDsZKWLqVvNlUMX
z+Vi%b3X6*{W<7ZdeoxPdlzD#agV;qtVW_pgW1SjLu2mq@i`O({n4*JDcKvtZH#yGG
zsPWv+?}R6b$^`mcinWz@7e_ls+?I_C=L%98ujQ`YeqNgO*K@>&AaisRuePD5297Q3
zp+p@qSaz^9)<u0}x7PiDy-Xy}m9{upZFiI(j3~W2T2owFm?6Sux03ZZMV3w6Lzeyf
z+)s!Ag#bVd&d-+(j1nz)6BZ1T&!L&|M{medex#d#BgLgiFn&4WvuPUBM7h-QahwPx
zpI4*37vMh;vLc&C4jD^u%N|LYM#gw9S#z}QDW|G~B`|j`z;|EU-7dpluFHirZ=f!+
z@9`|M{OnNTyvX;J4DEpDsBMfGnYd5Ypt3OCrP@1^?B>%m<Ijq^ZF}{KZVkK#igL>0
z!p#fo#fn;PJ29%GuCnn9*tzX1BRq9=a~Rqd{rc}iE&4~?^y*n?xMg4T>j@jtT*E{e
zR-}}S_0fz(rA_thc5=T;zb?U}5~@QX6RGNw*f+>s@_B00cO9yPzc<n=X>25wFrhX%
zhX{9Nh||QJF5Ppi6P_q!qEdK^MXOYbsxI@hMl{bvitfu)T{!N1&)3Z4x2|{868N%x
z-KtC)a_T%V>OTA#U*qxhFG%D%PkFg~zMT=<o2+re34F7WWs5(odEJgeg<9D1<$ji8
zis3wzWBP&lnAZQOul?hc{91|OU4&$Lq{xb=M+-{_RJ??bSJa(SaB5H5apk~^d~ie}
zy#QH>Nb-J{M6<);>Qt2B#FvvPZG2Ies+9<jQ|D6bo4mpbiAvf4oD1$d7w_2xxYG4y
zy0cu=E^E-oZp!785J>o|w^m6ppmM3<BNp1T=@N9bremanaW*o?u&JD5c?~Sqv-xzj
zXEg=LX18ah5gAT`!y41P1DKD~2QVR#Id}oHy7!#YYp(?X*w3^1i?BW;VW@Q|Usx@W
zzR9G8{VrWL?^D%*uAKbZ?68nRS~1!sbSpS17rt!R!Buh>pu)_20r$!7Q5))$JK7g{
z{dg7?8Q0-QL&0a*qeX+Z|3+?b{!4DCd44iu)KWCIHZyTyRCP6W`SZWDosoqZqo$Q9
zU>I;RvoK1TSy@=R5VNwgFp4_ZIyk9*GBPn^6f<+PGBJ~MGV){;v2t-%GIJ7ju>0g-
zZ)WcT&jpPBu^p6*oa~$dL*l=_ftc%`nVgB4|GaRuvv>Y`CM72`Q!5h}2Pa|{0fGN_
z8v~w=nUnM1ZH$vuZ?$+c$o>lp5vPvv?ELJh{F3Ct7V5fGY@MbA39wpPY=Y+-qdE;v
zX7u*@AVSxuWbW_+Leo&WdD+6xiC@JxQDubOQF#||zfq#}FC&uX*bE)(8TPq>xfr*0
za?Z|BT<hm?x_rmDh_5<Dn0~!C`NsUuujF_99IkfE?g9k%k3CThExS(w_ZyC!udyA=
zUHj8NDk>B{`QWv5qEnxaeDcvvyaK!6uV=Eigm3XSFn)e^aoqz)jImc>uJU%wKTm~C
zTW%()x4$|?+n*s<c)GWI`Q$pA1Z&`HxmtxVRKKM+j*IEYjk=qeB#`RXcd*c$?|joH
zJ5cD=ax^{v%+#;W@!El+bIU;d)gNE;xx3XrtVDIp#(PCSBkJnP0@v+C315&<YvEve
zb~rtXHt)&>1v74xC<^S~vWYMJwBup$*+@3|9cx>Ea~ltSnBV%YWKE8O<MGp3A+q<_
z!QFOH<gD0L^p^cqCXM1C1#pu7v&c0iGBKgd$<^77=2yyY5xT>2uG7wX|0@b{(ex<T
z5uAtde4kqOiHm9!eWJZpaQ}^+fBsiP{Qe!pk;C1VjbmIr!i9^Ft@}IC$7TrcATgmE
zR_;gSBE57Tuj<FG_8)`OJaH9)QN1Yc&BVV>xF6eWSIJZF1|5Io$PZUt+qw;`Hgs&=
zICckWqqez6*{E{wUTHf>?)b#<clAfrDfCnJn;ItH&BG4P3H6h1r36S~y>_;k-MYoz
z4cEN&-5V@4b9S0*YvmCfJBnRo?2_YEkki7b_Hfc0n_$mC_P^In+RFGGP#Xee`ky&>
z!%Jd5lylev<JQ%<i8$_+-`=vD&oeGq<(cmV`M)?T?*_QUO)bRnS;lpeE=gDkCJ^ex
z?Ks({8?z-Nk&yINglF<g=+Dv~zSd>IbG^)<gc<Pd+8s-GE?CP<k*h2j7Thc{^p`nv
zhSv_r^hdkppA=LI5}?Qtl9-2!&*Qbl^7_4tT@Ie|b4$15A-*NY;;}zF)0fwut=sp%
zRn5!W{7L(H@Xz}+twMhl7SwmitZz5(Xd*OTJS<d=_rFfpR3A|JdK+lIF+bC$`809_
zuT2o0Zp%g&Jz=U-1dsnX?-;k$t4QEgGP4Y<;-;ON+7hPVS<TDDv~AiMs*grqN6k)d
z<9B;353>!Y>+^xu`y^;ny5w!UV};JflU*j+`h+Pz`e#Fl)n)CZDXw44z06ULBcHyR
z9ID{<bD0ANqHv-d<@9B?-uEN+a}~0=Is#*x@BbMqD2Xt^XT|^bvCk&>TgiVW`qx<e
zKa+ej0mfAScPII`iRg?DzA0Pi&F{=6M>e2`i^p>luZrp84{LZ?$|nxjIEGJt<amSc
ze;6jpNSMbEWk!3_qR{%NbM19jZ~$BsHGxGM@)zH)55-&;;YKX3>f^cn%u${K=Ixa~
zn4=^WIB*js*|-IsW^$5bN{%%9*AyXVAJdhbp0t55{V(y$VPZfS(E3pqVn{%n^`;ce
zowJ=v&PTK!*mYlgRMiUA!8mu=;RZx*r<8Z;vD>*Lx**Pk0i)9@ul`W`nyV8Qze&&{
ziwI5DDl>Y=Pv7xb)K+Fo>vQW6W#FN&0#&}shxfmLa8J#+`l4A*$!DQI{ATv<D@%y~
zRWssB9r<q|W+(u{|2M=N@B3#8*#60tj4C#e8iuHU##B>V|1FPy$NuK=&qV(kld}Ty
z1WfWb$N%*t4*phWeH^Yh`i%XAm~f7_vB|?TeKmRo2@!UMbNL}v_-hwG$dWkD3?zY+
zsOz*)%`vEEaxoW63$GuZ3=0-9MD5U2=;ePhstfrN{5h8sOYh;y>~K>{qrYPGS*B-;
zo~T2q!R*NEurxzFdaLcBwTD^WGYeH2z3*<lT|pm+67X?bRb6j#zX1=?Ujed5@R~~4
z{jmwR2DZ*03p-qL<@4J=-F#Z<Dv8*VQ2D#yKZW_H7L%uDI*?pD4102!@oXqDyzOD^
zrZmNtsUl6Z4`~d0jQ>JAGcBT`@Pj^Sfg_!){!Uh^#-2Tf*>^ASXzjpI%nBh8cskbv
z|A%o7f_IN-H>YXpIyp5~)D>P>{848}{XK<wY~%gymDy}jXL|ZOvRV><3(CX3Fq4xU
zdK5TZP;&H)hV>VGIgf>wZQLVhc0jhwDrNt7VgL91{o8W-{$lVS`25e@{>|Ui%zuvk
z2R;G(UUX<Ld81<KOUw_~NQQEBH1Ts}wQ?nv)jOdk&ZNelC-jP&8$3LZmK8^sw8+p>
z%C4IYp;N->3rzJX=;O<3>a=+D^_at<!XnMj;UDQ<|B9Yb=Zzrjcb|2%aqfyezoURw
z^s%e-OKasLlKqd&?LvDof3W)C>PV+)wL?m`v_yL$`YWQnxpo`NLHVD&g_!-5H@-x_
zS1>cj;b!yq_&+5pr~hxL|5t7Q9Rql1>z5(tUv9ove9i=~H9H)2(W67h{mmvrV`{9+
zR}s&%$j3&3HclXy%B%n6Pvjm~8*GQarB&JbH*=wvnb2T6-vf8--EC!^8#*uib)0@~
zg7o#VF_-s^2Z!8lEA~s5Pg~30>Z+-GCMAZNT>j+lUL?Sj(yKF9h56Yye^299eWR1q
zE?EK8#|PJCd*nJXUD4^tQ}CLHy`bP(QEN+<5^g`s{D0!|Z#lO9xAaFX53D=+4^aQ|
ztJNP$1^D$Z|Nb-f2b+Zez~!vKD|;-MB1w}sPRGh5knoC;xeQU{v?tt2CGSeusW)iD
z5ST0cZGhD_PGver!PN}MQz{_xtWrC7v;D_GA^4hVZIPeCqLXvt6@^<`&wnDI{vTlE
z#W9Tj$vobF*;)BXPIb*<msZn%)@zvyz-uKiVG4XsMF*OoY5SIihrq9zkOKWZmW&S?
zNF()03iZH6Mwb2G_k=T-)ol5PaLYYj48A>hoVXR%T%|85=vB{VD1VT-+NYr!%b-lP
z2Y;a5<mHv)VQL~~sQ|Y58~$NZT2UwOFL-OyX-A)qpSm-*eE?F8{U_mlNPAkJcob;0
z80Y+26Q-;!Cb#a!xW*2CKe@RT_EyV>nPf(L>UV$gX*&7`v+p?5X@4Fx&wu_h;k$ar
zh0<R;WA_)eNb;Pm<CvtGx~O`trXk4$-mZs5|M$F{SXcj;4llCL(rOUJzr<bE;TxF(
zfU~@Q4>+3tms$zYR8~s&7ft>lT(H0?%g$HyU<F=3Ysn>pzd0H5p2X&m{laBQg(lL~
z<<1Vku4HR&*N=STLQ-tF3d=UH@^9NItD?o9jt7?@kF3?j185!A(BUo$YP^+sin&OY
zJ5mEC>zt}|rjbJ{gE3!{rCfs9OPKkp-S*#d>VFc=pC11IO+w63CI8NJ7mOYD6BNGU
z^`rM>;1>D*58!W97!MA^V%;BroF%sYCF5MBF}tNY`x}?At>ZNy#A2@hnc^|wL_lZA
zYBtHxvT;;2Z=}aC^cSq_C&x)z;jdr0I`eRKWn>&M*-oz>H(L!~MLV4J;duRN`4{}>
ztv%deaQyB)&~8ii<fVmKe<JO1xIQ2?8XU!fixZ}}67%hsM~xCKZ7f?SeeG2*R>FUm
zl96R*fmK&m6ak(|Ir?vv&|jqf&qa2nPo097-sTMNdvV_uJH-JxEvet@;P{W6R<7g&
z8il69NB;PqMi~L70J5fPwZjjOkVY<s&&IdiA+7IPx9{kqNzlXrriF|4f8P%f?DIdZ
z(E!jbHQx#nz)O}kRX<U1s{Tw^Ky%_%HTt2(`Y*GNUd`eCpRg*P%|Trvo<jp~(dF6<
zA=MFq!29DX2c@ohe?m1f?w8kR7#bPYe>O}jSDk@6CLD7LRJYH~N$Bj@hL2(4ycotp
zFAEhkQ4xAr9`?!MytZ`$<%6+Zs}GJid1K8C1iLmknB;1eo0|<~&+3Fn57YWxAqA~l
zaa-fz+HL9t>wl}h1p+1eC7x3WujoCJdGJ|3(!<=XORaDpL3lt%5^LjwU;6FGnL*gG
z;^tm$JG9_o9=(<wsIWQH7W_vo330gDsK>HbpTHx<BE24!or^(s*_kzeDJjRYTnox#
zf6z@)`<a#Nsl;AFn2*31^Xg<2&8d>yvz5@T#(K0jze`lB;Ks<9|J>P$f%}o97sJlJ
z2~G~hMuz+G!!&hLs*0x%6h3bg8GQwCm22_f9P__oGE{(QZ{hl!3)6kUu{RD!fJ-Av
z7i*y`fm^nZpm7Oj?J!io$D&^#5!D*4RQEpArfi<=XN?n_Y(vx^F<A0o7U<`H7Kh{k
zdU1Q&_6{B|Ywo2#!zfqX;;MK(H@_!~e)RjjPg&@?y4=)?%6;l_7}%kD%Q;f>h?Ynb
z?chHqAoxLJ2g>q(HpmXi6XfToKe@M66kXQR>lA6J`B-!!K$J9NTdDQ9h_!w;;YpFo
z7~5$j25^AtD`%Uafu`Q;7#JGZMl5B+Dme_{75}4x@BH)@u(wc3TSfbQJOwnOj#4-_
zUmSiB-aP*RD^fNwo_de%y{ZZ%Dyxgk>a4ie^sRYjvH$q9tc3nBweUB+TIyjoEETHa
z!GuE3p6iMg(h;SR)Ys3=*L(CmMT##TDA$u0H(P7rC9;Nvo5-T`p_Xz-4$sYz-%k|A
zk*+(wIiQq7^t<0{u`Ooba(;R~Xl!FJDtWmZ=s(7+Dt%0|{$wDXk$lte0Q=)#JNfq}
z$4Tw4pgSkr!vDrI;rf?n^1pZ>Dv~0Ms`5t8HpI+-tQ8f;{~xXj{2y24zp+RDPyG|t
z|K2}=XJ-H387RcS%_Mf<9sha|<aiZUOLcx7_B`%Q4;IF!h-^sL4N&(NmZM_CrY}%t
zE#b4R;GR}6A#sp*0DLoqBMgRGab7MazPhg1Px*jt{+L%;>X>&uG6Uc%%S>4(XSl8p
zoa^3Br`(8vLP8+mhXx9r{LkMv2*59)>i_xj-w$x-hoA*eA;RSCM{}{_fQA~5s2Zo=
z88G%`iXHMo20;(x<~o?ScR*z8@ggY+-O;=Ofrd27;n0OJ>a~P$c)!PbqbomR;}?Qv
zPWhA%=kv&`=pNLiO4=ff?Xp4z6ZKkV7*(sszdKsifJg<%<6DVCz#JeJ$(u9Se~t#o
zEVTUgMOHeBW|Ofqf7eAsH5XAg!l){9yWb-K+YO2F@B`Yx6$I^Bmt-SIe4i<NX^+3K
zN311O!T*Ki^&~P7%pE}5e$;mcJ2d-4$vXMnEVpP+A_}nDdtOuOhg+-$1W<s^{VgLq
z#Fs-a>UI@5Y=yyTi4<QEVTtT33MuF69xA2F)hINGnTMvXJ1XKqzwDq#_<VS6#jHCf
zAY=hUL0JDKe*c7$Ds5m|zHi)Hw|Lu1klZ`?@ZbOk7gy%ea%y_|=H_N6_tWL2lCny?
zS}Z#{I(orrKfSe{#l@A(7u+8;3%xNuXUdP60(^dael2(CGN=4BcK8vwd$ib(1TI}M
zF)_r05_EKQAN1;e4GxQnODj)ZeugShg)aF)Y?UiP@n1gO>6~J`wzmm}4DBZh9p-Yz
z6k^8Ah*uctg!G03i7Ta$IfV1<ODifCp-MONHG&S2i4okeV?y;v4=IlMLEH9c%2wSX
zBh@w!4-aS@?E{krq6UOCGU2h|hQiHJbckkJhfO50XZ+Ud(_#J5{fxu@LCGDGJ8vR}
zaA$C`g6=TNskynis|-3GUY@;EY;N}_w<hx>os?CSQPkDd8?2@ahlYk&F1bS2i!~IR
zyJ7JNyUO|f2HgeJTXkb%K6s;$m$<(nM`1ZXp&np-_%$~*#lpr`elz`U0&FEA0rTt~
z`RyAGGmhGgGAzhV;IbA>_2{CiyccVP0@(n^ucs8EM&grocz9S?RHQi<Rk*+);zK!V
z%!{Q=nF0B6kE{zg0rMjQT$SXv0G+F^5nXDnw#tXm1*bUGP!KUk8POk5Fv%Ps0f-a`
zSA$~kBCr_^+T1GcT&Jd{mKtorbRdLU+{lU)GQ952NrIq-DiYGtB1l0(4JAK6=7d;7
zszuk=*9-VQB&VgVw>THsEjN;okX$#DkdkgLH#w}fx-K`^U<Y?s*VP>@)NI`x&9APm
z%FD?m((C@(x^QuIMIq!{$QFib{hpuy0XgjIbhRyo)dENb{2R#DPp2!~UhDT)du=T(
zn9r_x5|Iy=I|%w~t**4sm>%%i=8lF=Cey|zh$md5T*VSCW&Jz9e*K!#f3pfD<p^H)
zd!*;$YW9167)@o*^UmS-zCB*+NINyqX|Qhae|_-=pN58o8MZiuo;r(&ikcW3hp&}Z
zRx(s|Z&pS7lfPy>#a$?RKnNqHvV^X*Q9iHox_!Xo3~#@=*y;}q44j*rGcYg!)(F13
z(rx}k(ve9KFfbs6jg8$Zw?5KClEcEvy0y7!Y5ex>TdfYSn^xDO{e;26!D|^@Y#!Ic
z?d@$bFuO~%&@@aWu70aaO-&8;lfDaz)4>$Ey80}oV&+6L=@~k{5WN&;6iHM&A7NTw
z)a@6EMk<HWPcAO`ys#W;1Ox>7YqsB$lRdTvP+O}8Mn_LpT3C^=pkR^nB`K~cyo5yD
zW#TC0om^dA-P{QIJeWFEBqfU$DlT#9b`3L$VlirSz@McP<363@%*b481s1}$GC6Gz
z05`<(aBwDno&)IupRG69Eo;1|gV!Z6=<pIxT5tCh1yh7tM1qgz6)OTdI-V<Zn?mgH
z=&;I!gJBvQ8=D-~IapcID^ud)M1v7l6DD+3RaBIGiWdvtwnEBddO|*ANhYDv4Uuzj
zB%Z=vUS95#>oYi1H@lrI1wcTBAfd1cO$<MR&!#8NH+sXyvjo9FQ7k-cIwTZ*thp(w
zAnNo__VK&ia*yVGu9yD$waO|R=8Kg>adYn16b8j~E@l#vz{1GB2yE=AwxXOI61{9m
zIMpJB0&j>`q8!@zR%qrfo8HezY<3^W$uI7G0Fd(4Db>lNvbjxQhLla3%~a?eA0IE+
zHdglqF=+Y(oBaC5?RuydhD>mKvMdQ^+G%Z&W0~<(4X!}tVRMma!dXUdY{PfEPku&a
zq$oBsX!q#eaXRS95e;i~KU2WIzP{dP?*58SDN~uAkT9Dsjo$hWA79U7=U}?X>doN5
zK=#=!gMRBZz#IFMdDmPN{QS>{vt@~iiQ;E##mZz9aV`F@eyxz<DC(|E&+CP6mCGi7
z2%Cg;b@->O2T$bJZZ5%leO-Fd<wvIE;aOT=Hw-@$rozL+!^P<IeY^$E^5x50FC_i!
z$FTJD^k7&N`1#4Hsa#F?B-u5ZDtfGY=CE$PRp$mr{;jVTlsab(Q7+N8mDBi))Wz&_
z^726PcXxJX3+2<`&?!vK%^PjzD~)<TvysN@d0uSxiGD^b(QT>>4i>exE<KlFWo7mD
zesn)uqt~j=cHH^}1%sF^9ZIOh^#0pyndY>7K|ulBd^(roX0~v!h=|C;^?~XFJfzY+
z>@*su_ynKwE+_GRI0=QW)Vy8X;Yv$gS{mM}@=TSXVRKUsPT$awjG<wc0sPg^YNK8s
zUtdm#HMwYKnKhdJZjpicl_v33tl%i5TEuEZA}ssa8F*j`1o83l$oO1JcU!fPuiSUE
zQ-=#xLN&$8w!v(Q<<v?ZkvTaV&yV1Ppqd&-C#Tx;A@wqioI53TA`fVJ)CAX7*j8h;
z@1D@%2BOh+;V+hUcKRNz2O*GCO{fFraQM1hP8}_&w(@Ns+e}cRHrOFR#KVim3ensN
zXpkW%M079+`pnyRZ)MDhv~8Z}9M>G7qL4rFZrol!Pjj#C{Z>Pbu?e+<0})f?sTn`Q
z5^G@;Rpk)vpzaWy@-vzn$Vdz^?CI0U(0Gi2K-TV!Lis|~#UiCLrLZvgOtb9z7;RKH
zqSgx&O!}_+e0CtvEL5+Vi05Fp9tk3hNzO@6FciwKj_@Pg1$J^M(Pt1yO||qBqNCm?
zPl%46_gPBbL7@}px4kBO#sWUC6YSY#$*KhqpzK;HE(z!WJs5<cr5`+Nl$|-|y#|lo
z(5py2aj=)-s;Y4Yoqo?cT)2V($8kH(Y)ny!Be8Wxb-FWlj?<63F<YYTJsp|t{<R}U
zIB|g~>%y6A_AAXZjvNrCF(a9IfsXa>=xE}iO2-yAES|?6Dw;ck%p-okbRP~KdXNem
z<QEih^YX5D`TMVT`fhA&7<TzRUyTdGBEGNoy0P{;`U7ivo%oE5)mpRR>}+BqGFYKe
za|E2Hh02O8OQ7AzhJR-W1zJ)#@}c3kq$EvKQ`6^%o3f33_m=d1*_49zY0sjP%!BKQ
z=`4Q_!c)REr8<(eS0(Dg^em^ditDRFciRO=6C&w7<~E>=SKHjx)zknOP|By~7Zx_!
zF4h(oQz0Qm0%cNR*k!;~!UMuk)h%MnD-n^Dlx%(UyE_*isQ@VB;_B+^{9Gs^R0>E2
zjfM4aD}7iPQWJc2H~_EpZ2mP{Vnciaf`*2M1CcZiyG%iU1uy~%kpRY6+1S=aekL|3
zd#;yDXUELMM3w7%jFO598h@rIK!rs9PbBZ&V$o?;yBw&5B4dd>`|Z1rUZoBjVIOtO
zNx~9658hF>du0cGaBwK*!c2HYy8C5Z<K*mo;N501o*9Nh^s5d;EDcms%o0HEt1Bx2
z5b5aYt@5)48MJHjYdILe{6xH*hd#r58f9J;=_tGoZ4F3_*fECsZEhU^sHck*4=$4!
z4OLW_Oc6k8UyF;2$1->k9!--|Qab!!JJBddl}e$ZtyPK!)yK=odL-^Lu4D(LwAa1)
zBL;hpV78+=J6`~9T3KDKtE-dlUMZrWpr8U~Y6V!o$>Fu@1<L(&C5I#rjk8jX*E)Z2
z4iOKR<7#T;#_{NN<mu`>yH|%s$KxPC?*8eXm6`=}F&VNajg*Wm6ag6-8E8Ke6`*XH
zgTupLKYvby0t8T9U7g11K&llV)Mr|8)G?+tzi?J=2+lr5Jw=6Cg7LTkgl_x9)D)V(
zQ`p^Oc+2eSDnAFuEs%WqG!Fe*Q@CelKE8Hf^<G|HK>P~1p}Ix#FR-?Nng`(ht_)Om
zZnUAF>e5Y%3CjL3!ax!H6H!cAnA|&5QcO&3@~kwpSL4=t@u+ZKUY%Th*|*Usy60R5
z8bp;s!_@YkSSwz1`N3|>caxXxm}!3@TYX*=?hPd0Gzb*>&#<m<<OA@GlD*xkx#ajQ
z95}~;kZ%zL5~<~DONNZi%QKWFELEZRE1ij-MMmN=EwyRAKAf6CDajEPJu02;qkRt&
za+Iiyf3EycT3U*OgVWmDn!pt+Hd_GypmYGGvGP%v8E1t2R#s=$W(R#^n!WlcWy4~h
z<(8T_*x1BmzEWS;W35C903-?l&xe~82+(Goc*R*GHFjJ!huzXO!0ZPH2Y}*DrP+tl
z*#AhvR@zG?O3(`-39ALImEpuYo9BsgtSZEi&hL#S4vG<|8t(m245(rW35kZ1V;T6%
z%UmPUG0L%n7<Bonz{)^2>)-USvveOmn4T;(Aj0&HjC|`e2514$ipd65B_-7OyhmI8
z(FzI*idg~;O-;a+@x~3Zv9ZZXNli>o(|WA~l%AIiiY=~)%O=$t2-mpL>GSMI;^7$B
zikAdv744B+4hFpnL}fpO;iULrH7(sAWokk_oy_lX{&BI^3}`I*)PMQ{pd090#sGms
zDB!cu?s)|W-H^~wI1|ai$4zR;n0FMy6{z7igF2cOr}y9YI(R)9+Zt^>_Rk%-*U#Yt
z4pGt3SxpDtr6TS-0SZt?zkh0~Ry+cW4r_RI6=0>KomNKBmmw@?uMy!=p$KtI<GKZJ
zz1!sd)RFMi8@{7^>LyhrHoV`ipw}fWg6Tq9YHC2Rv<fkLw!dZ|=+`SYAA?a~K~xEP
zjamzsF%PFMN&M4Z=KB*9`AyOq5E>{io&oa^YV5GEVL-SnOid?t0L0_r;sSV^+BrWz
zPfy3+;Ayg7m64Y(Q2rx7fRe{{HN-#%nWg}9nqvNTYQc}XuF!?jMQK0uoV}^GY3f*w
zLK2v3Q+H^`%|5KRTXtJ(_XJcQApd}10jzj9Uu6hrC7`wE{SjY9vPlenettb;X@C}Z
z=eiC^lAKIXz(-{(%S<T+g+fW!sHmvP$w|yXEUZZSP2Wv@<Tfq>r=`p3L^yql-_Q{1
z7A}E3M1YbGL0~hD336*L)2za3lhx7DDFH)+V!A#m-(fn`T<}Kt-q5x;>?f%6vsu~N
zmY)w>5k^ZU(W226n=g%4dKx&RxMq2{83Wm0GO*E>!!p1E_P0|dAp%%^xf1H`JbAEg
za^^3+=5>ph2Uf~bvf@YA)zhlwg|^?;7hWx2$J@s}w1H1ZNt#B43K73UM}^0u31_!e
z`=Cyz*UUsrEF>x_s;a6gT5dj?>ihcQyY(wFgU7Wro~Amv9LbmC{Soe~(`gc+FhA)E
zS?ZisICfYr>T7D1nLl-=J*fEfqw-f^>r78a7cy*u5Z}7A?%zg3jWjg&BJunT))aWP
zu|G{(SsA}3iUw0tQpUw_hM1yXX1rq+5ol{;{P43(I?hXioXu(~q|X>GjZ><-i87XN
z?5jRSWAU=_{zXorH=R`@lY?#4eAs48o@W3}CJlTM>XJTaTmUqnT|F^BuP7x&<=lX6
zp}5p;Gtr#M5C_gki5ndl)EF8xR3cedf7>ylO78jUKWKqAD>p?Rd_DM}dnL+4OD4d}
zuy@9~ec*j55d3tNa1$-;Jc~t-c(&d(zIWARyU1cb@}8c)f`v#lOT*XRYu!a)+qQ0e
z_xF$BXaD`^q%T1+Re!E0H)Q%PCX6p)nsVB}i%%v{A@c3e-N9Y2;s!}vKaA@RqLhAU
zY|xgSFP7dX$<TQEz}%l}-J5(q1eeFA4GpS#y_Oq{)JbM;`y!*tJ(@Y)G1|;T*5fX?
z92nc#nVHQt+UXj_NhpVo;#~+_JV#yQdAR&e&9o_WDm2b2duzSUCzM@XB67QT_2x~4
z3dV^JiC*Ph(U}`T24;w@ewV<j7z$>>lYePx$mq-Zt+*@O=qPzg$_FyhbB3+qVQ`8f
zt3ZL7&*PEr={nB5io>?)bKiJZ)sMjT^f(zO6;(o5$yG}09GMnwiY1=aif`UGwVR)C
zIqmga;sAu}5&C*wmrPDG%KN8Zc<8R%4bhpkYk+>9Ar1I@J4kuJXqS`)&TJbCC@M_=
z^={92r8Z@(zm~tu_^D-uHkxoJT7lVI?p~ruNrm*`;hM<zO)B&&{6))(KJXNwfT6Z}
z#aPH{e^%Wa=|NY6m8KC-COXbYz7N|*Q%S3jgzrvhOKH}14oW)xF2cfxfZbo4>rr80
zxtr-ZYTPhIm;U34_HG@zsN#O{;qM(tVhZ)Q{up(z@9J1C#mu^WGKt%|MP?rL)EbK|
zhbm_#sYCW9Mn*<zYip4S`R-ovT#BNvtGcAN8x*S;1fFoS@^ANiP1Z!i&bkEK{Lg5f
zPeQn~bQ$fI*W7xV71q{<@e^_8S#KlHciuRSAqr0C+9R|ZgTziCWb7Kt!&wJ}R{T(|
zJcCAHA`TT6q4QKHY?Zb1RNq=*Q{k%aua#S=PR-~?H0z=oi@Q6HXT{mTna;O*u5W5g
zWDx38o^_vT`1zeZ6$)Cf3~W<|v5LAO%BEUjesmo=iEjvGH(&#)&tEDZQ;GLD4`pis
zA}OGhVmF;d8xE_Wfk7>ZMUkz*n^m;;$zMK|tKY?Na1TMtw&CZg<gZ_5E{|RX^G6Yp
zP*Bnzf7`KFSk|RCfNc%$Ph7U{;*aPwKryKyd+`uz?-UBC8JYLu2Pd>i$aS%;nHJCQ
zKTr=kJD9FzKz;p8J@5a7#ZLNC>)>NBhPuuhS(M^$)>mAj`3Z{+M71Oq$S9c&qYAB1
zP$I^HL1*0#@f+A4hYrh89L#)uG5HRih=Y@e&8R@&d-uFojK?9r$L{g^7zGZIWFctP
z4)gSWuD8aV1VjNGMoO}r(k_na=A$>HT7MA=Fr%7We6}1LM{`@;*2WE~V9NqD<$crZ
z1zzwrYOLrRFeH@o^-<Tp@gT3;w{wqTOe$M%(aCFK8jE<n7AD>77D(%ub2VJ7;80a7
zYu$*@W0RV<VZB`SJ|(WvmJo(9{l>@hIzPvrxJZozmq$<H!o6-mnjIyoUqhk04bU*S
zbmP4`ifJeHAlQ+Mriis>u4Q9-4<<T$*VkWPPVb&wr;)PK@ST^r)wg!_qmQAUqeH%v
zNwVOw&AmS>JKPdYHKt$%f$j&ezF@q?VwYYRXY<gm22}CuOeZhL*}lW?Hj8NAy^5FB
zHNA^F@2t^S@+Wdx!Q`~FyQhQF&X-V|c_p?#PI15#<}GW-sjKh8S0fX${=!cx8e<0B
z?v)p-Z50|-ZiLvk<4SAN(Lb|2x9{&L$2}0Rs*B`=i4_03%#Me}GjQ%GSx$aKI;TsN
zXIc389@VOpk-cGfmmWxpnQS<<PTzhTczD0u!VM?78nmQ(c$bc(h^BH%u7W_>jXl~V
zm{I0(1r@$c_NR5+q~ty3S)+zTueqAFi$6!@U6*g?2S*ISas6{*eS-7fTWE>Li~E|o
zMWvir4q-r`w^+neAIhXRdV5q^RLUXCPdo3|J>Em3XK$mDeynveso<?wZmCmdBImrO
zJDAvrsNnBN$rj`TfdEgrBwa4O(%Zm-={<jtml$6Z@v9&N{Gh{+k5$@R55M!YPls?$
zj#e@y6Mb7Acx#$oS305K5Xd||dlG0JV=({;jT<O{l~5wui`?X6-$Lkk;-0_WYH(EO
zlA&S?8LwT3JC;1j^dVos;xr)Bp;E!Wx^*^N*f5oCUW+Yybnhy8z!R9`BZN5Hl(mc5
zNP;q@q<jY#J2}~oMi0GNqSGX%rN?_#iRc$z@5Y*=s@3H}zSNs}_=p-4O4bB-D;uXO
zy^V#X+;4v;dCjXV_wyOJXqj}@T-Ca<*v)d$)>UuYw)TBIobdnpRRWzdd3QCcbv)~P
zS_+Pt1yRRH7OQ(@at$wY6AcJ7Kv&<PDX)z)k|0=#=z`Rq?$)xrW?zz0k~_!7Isc{0
z%v|tUmIHMgwTCQd;r^=2<xr$qFCG;HdPn!$&>*9oA*%iEVcMn)M0+ahC~Wlm2T{t%
z69v40=nh57J@Wzi!Q6@WYvA@`T`aAInOTR&MNq^x#GmYi|5_Um7~`#sk|B8f_(%qh
z{-P7vy8-O00M)+Q?6`RLRK<^*ZXk!XlOd30@+7)er7CT1zLCnN_<@qLsIZWShlf-b
z6ws5)`va3!<0I#nk2zoDZsfLnHtEu|TEki&4KJ|Slkxz)78nR2|GvYU)90bFYz+$>
zWsNOzm-ilz)6d3Qio*^~Vc39)115{%+=I`h4y05bJ(aYAIj`}>;YBT7O&FyT*nb9y
z(&2=RrOgT}>Yq&&%XXr6BA8$owkyJYf&-j?>PCf{_}_aLJVrr-Rb8cSivqSg^RxU#
za`){{9lLWkZjFs{6mjMV9BGk`j35jeb*|4`mzN&4SiDPQI^9K^`Mh#opAwRiDEWS(
zWsai&p$Vn<3it#RtrQ3hj8cy=Zc`^8<ncDodW@Ml98P0*#__mC%$`#mZPppAc|ATo
zMkQ8O=#cMhV?;$lfZ#8bQ?X=K#WMq5_9o4sQLwJ7!wK~!)dEH7-XMhj`pC4qJi=LN
z!S^t;UkRa47AE<U4HiuSPtBQ2iY@VZUat8{t&RcTQC`z^ihXics6y>ucux+klpU=i
z%F7W!Z(EZ+3~zwsrG*JtvUqs1s?f+a@8~cxba^?U?1KFy9~>dMfz24fH~bGRNi~ig
zA>M%GrMH!Az`xmD8gVb{V&j~AyP~kN^7ESq1gK#zuO5eTiHwJXKCuuU+M$wa1mLt<
zxC~Akb6C_f^mVN=$1|u7sIxaRet_wa*I(s5zs&JN=HTaZ)vuqc&v~MTmxq`#@Y%#|
zQQZ8y#Ok0R==h%E%1QaEG-L^60aSrHhS5!gyV!O2O|dIl9R9tfu`C%4nm&X3rCfIZ
zJ^P-$ywX1~%4x1wZx}}uWL@)lI!r4I!za3A-z(b&SEx$T^UU?##;a=Hyt9uYckXGH
zbH=;8rY?mCDT#k-Wef_%S2Cutb6@HN{N=c4g{3!(?Jw7@dhTcQRr(_4UHZXA9&t$D
zyuo$y%R6+2@=ngmTA%<K#Xn9rklpO00j#%JERo$G<qmju02%Jz{@6?NgXX|arR8+Y
zoR*09bkliyQNn$h+mC3+vu-UdIWGSL1{NqA4U=~j8(O$J#+9LvVZNhsI`mX{bv!?v
z`fQauT?72$7=3X?DATx`-#L0>df~dn7n>2;1NpR<jUFr%Tpv>OSo@wX{ZyAC-JXV)
zhvWGD8Ty&iLf_4oqt9!26xs^L@q2tKfFbJ{F>KPM>FaItbjjy+{jh@r8#Rl7V6NpZ
zY6mg!iULWGuR_hkgKI$}av!z+skLf5OOh(^*s^@~?l8Hmvam94T=ACoC~26{-2qN@
zx)81;&ybO5<LAo5JyB-1H@Fumau9r0OfOU;oUQyWhE(^ri{TrD`@HVCw*<I6dCT8U
z;Ff`HlF!JC)*3xvt#Ug*ym{H-sO^2i`AL3qR%DUdKu%3vOi#tNK|Dc+g%B(RMXW`&
zpxX1X^0dXP>FTuDLV3=+0x-ZB`Xq2UW^_C9R$(Nz^?KevoobJun6KX+OWFzNpl~9<
z1OnfR?*|kAp(em#1wVbcVyrXJfzkTa6`o{_0Nk_)dU9-fvkrGvoSn0z70u}@5zA<(
zq^q=T%WN@*pAR1c-Ox<S929!&WV<nNvuj<;eiC#N#m}X&*0#L3$eeONUX_|UB*>8W
zG?)*Ajoe{37wFH3(*O-BUR2xI-*5id4e6!*SPLB#DrZx%&NIe4*8aU^8Lnw(J`v$f
zkP|_A%3p32mz2yx1UK;sPHCH^5MRkE>@lR!Hpac$4^@KTkwHpG*6y0dZxFQq@mJZ~
zpU*v+ulN+b0mg}4*VNTr{_#U-;e5HRX{VbLoqxoygRZ)S7reKp@jMuYLd6IYx+z=L
z|H>Q%)U~<_Khy7klFEbJ1#v5)DUq<xhowg{@53Wrw3;f%`n*}6lGxhX^G|A*mtX9s
z3?B80OC!lUsLMb}!+rDP#6_E%o1NJRr~}b}g$Qi7t4#W%0FyH^Dk`z0zc43fvcYDa
z1`bOc){<uFt)cC2iVgV!1Gt3HM0dc^1$+$8%-iiZPNZRysr#u?Eg5~M?s}u?Ck<}C
zD~9894^`B|28gGb>FFbTR{#}F8WC<|$YTOsp%`i)!XDc6s>Zii%bP<rR!#X*Qdt?w
zGgk)5L6Gkk8O4=1#VD5MRHal$##qL3rVp#dZuC~K^ce-7Mb<uNsv*-64-7ISC;*-&
zFFG_kAXzuUEXpZT1WBcuqdE%w)ggN_d+#Ol@$@9lix;>X+0QWQ*EKvJy|EX2A(9~A
zey^Hp^qaVNlr1(64+eTtHXbtjS5T-&E-uZ4y>fxhU-P)IBBx@{YTR@3>h3;RU24$X
zw#zU}PJi}1iFj%zizz6>mkaaG07>YVYUp4HT%`N)AD#fIOFoRH8ROFIM~l~qdwI2n
zhK7Ffw1t@I2y#(#9YYp8a-Va!zuBQaQx$m#E2b`!ZYoKnkL)olXR?c}XXqFW*wXd$
zzp3#(w(l{|8wmgC4gq~{(tw7J!_AE-khQ%#wZmv`4zTioOf?lEQtb6+{`JibIvSeN
z(NMvN%GyEuwA#4KL*D^otwB1WTWgNFC^ct6`O|ThtT&Xx%Wj!qG~QG3!IlQNCjsv$
zZ=k`zXhJiDky$V$4Iw_ks`MQd^`sf!<18a^6PcNLa%E)&u|(l}uy{!u-bO;`MG(W=
zDq+|NYY7B_1l9(#)x_T3&$`fB;;oX64ig546`uEq&8yuLWnoK=*HHd=sWxI6B{tK9
z;-7W4B2u)6q6l=peq~et;!rsurI2XKo*#H=Z_yJ12b}C$%Dg{{U;wM(ecg!p*W3f)
znR%jpHRa%k=ZUk_y#_tM${T|tXT>pfQ&r&hq|W_j)AvycR%fSp+qUPHmk&b-4RpHg
zH`jEm>I1f#h!2bS7#Q(-k<l{AFqX*KNtjO^Y#9@3-FsKgfb+~^5DC9zOj0UqUcwBy
zm<!`-KXacrPM_4vFcy}<d9%1}aX@)f2slUncL%$U3`^(?2Gz+NR2#db5nBjwOS##J
z_&zotD6CV`{8?Dhx#JVJ^_fdsjJhPE?ji~T!=F@is2&Cm+yJY0<U&2Z2L>UAO#YCq
z<<QerY~hRa9z}s^e)(C4!B^^?xMA};aHe64Ik4OA%jp%x?oV53YR2oCQWO0yyr$Y0
z0!P8dN=1v{b}WUOKrfJzwpX)&C>#|~LRUyDvEw&FX+c40{vq`!HEou??DO~?t~As&
z#I#6GG&JzE2)cZu`NBkXN|JeHwC*b)IwfHUCRHY5?Nxz$0K2&td-Rdm&6baX{1@39
zS@I5&)=l=`NQ0kb<Ys6rzkb}_-DQk`$I|P;k<2Y0Ou+l;hB(PiW+>)2a^-XfDbRs&
z#mp#;2PcoY6%{osKdR26vb57E3+%eTlf4YdLzzRLtB6&DJbR35=WYxva&0cJ7PBhA
zI+DoIY!e(YUN*jdPHcX*`-|dMvgX@GZdhl?nbyD~-QqCZ;)1IQ3D9inRMc?$waHJ8
z{NvZ9KT`X!>lJ<IY2Ll}o6<x*_YA3q_G#7o&JoMH0(@b{Ns4gLXeXVS%Vcg|t}yEE
zI}E5Ba)2wsk^Zppy^l}z4&m@y@D~R8R;dmEZo2j|%lzHuLsR+1zfK!39NQ{5qC`S-
zs7fd?OLwh4@9*(EyD-*03-vr1{SV&W0;sNTTNm88OK=Guf<u4+!QI`0y9Fn>LvZ)t
z?vmiH3GVLh?(Xy=Ip;t3_Pf>ZbyZjIs;z78HD%2?=8*3jE9U$OTu+<g@z^gZ>0C=m
zWbJGn(uanfy*4;_Ncd?yOo%+08uqE_0zUpB<c{o<-zBLZ54BVsbS1(>{)`N_#`UJF
z-BjF^#m!s*58}_>{Qb!ybax8f$dSZ_O_&mCLiyCBM@=4T#Jqf%vz^pXwFXyLuZc)U
z*^xSZ1!28s9Ny=eMAm>utpeX9cKIY3)yo+AgvN;4Yvq7n-}txRv_fcM)lg~Wt@#yV
zgcX-5luLD1dmYY>C2EfwF?tE8s)l5&z_|v`n1mNT$2yJbgH7dzd!{&N+yY7<5)Y5F
z|3-+~bX6|l7r#D3`jx6nP=SN@h*d^COSBo=k&e#fsETMbztn<YMM}sJ5KQb?9_1q8
zahD5+PVS82^ORze#<XvkbZF&9Hj1+Fd<N5G)p~CzQ&hsvp5zxOy0fh<uTbe=J5EmO
z<{&mH>bD)dHP8aL*40%32lw{VsF0KbRn?Qo`BdfyW!-vY$k6#8ts+l2kvJn0>2}#W
zxrVui_RD?7;=G@4#29Y;uDjdRhcpxL-9~;b6Yr&_i*sY(Qqv^FNyJk?_Ku)u^VU*v
z3OJ6-It$D~4d~lS&4yt=Cdx_$VuueZ3IBY*=Jb(~u@9<^IT9Ef`QHNV&qOh0pM%!~
z+rj$H3~QK_@B28JI5mqB;lNO7&DdFtY6N|elXjDDb*^NeqVPCAR;MRln*PcT^*33#
zltDpa^=i8)mA)xcVZgID4<Qva>Izk7CGif9oya>qrD<rWm5^wZk<k(I%DvKJ`xYHb
zR$r1jZWdUG!g`<Np+7(NQWKpRjQ9~5*|=^!4$^OgSOOVYUq<_T;gev@)R*E}Hu??@
zHj9jyqeQJn{mQPCa*Q)euFV%1%7L<gO51+q=&8b@seVMF?{4Q2v~^xVNvNRT+1Cb4
ziIPVNc!0+$_;l^B$EsMHW{<UN%a(}8U96*?%-4a3VJO$F4u*ohmf#j51QC-YwYL0v
z2#$}})86R_PitB<+E36{JdszV=6*SlygpgfLKXb!^GaKg)ywX1<D8bw7RSW&qGp1U
zZq1E}!tfTN0XiW=V9Unf8~te^rD7Y^<Fq#Qx10fxA^lj@;C=}msHafu>%}B@W-*hW
zW2W9A5?xmDewnY{OvGV^fr*9R@y5e#&RqvAl&(VL97G31n70JFe|%s{k=xw4vtkw&
zv>w6OxtNui!F@&8UPVazzn3uj?z-SHKc5=PZA*u+7mX(PJmam;MB6xi>;p0fK{arH
zX%SyGA1nC^>x7ZiOUw?Q{TOHQB9N;y6yG^spDA<>=_i^1hbzCIf6LhC58y21S4fwp
zYD;NmOQaMbGNcpmg9`)vO2j`cCcTsWws~jW+sGnlXA9+oo>6P2{F~wPrhUCaDrPp{
zm&Blp;0eVUJH)Sx8Pz%=bhCdH$ueBll$Pt!+Hl_`o;TmGV+j}rKSiEX=QB}YqP11N
z8xwld4#N%Z$?e&kxUt%f!V4Bk&f_IkkN5P&aO+IfhYO2WRPu6=Am&H@-efu@%?_g~
zqS4l)kvtJAvN2ctFv*X%?ZzR`5!>$Z(y}6x0_!WGxWWlE^>_(3Z^b9UE910nFJz}N
z?=Vu+urS7qpj$AmFkqSCpO`N?jpIgUMn<bU))63^Qy-1SzCfDdV7&9>!CPA;2jVgb
zWuBQ|D&%N??$?A|MBv+7MD<Wb#)ZWJ0lCC5TR%zUKtY{FH8nLD6vEsAAi}M|rq#A{
zkNQwksy6}wTsePn;V8O(&LY%&+?aAb!S*>da!u)WV>I#4Ic-~erAJ23BI3h2RG&m%
zFn%55WemtGpgE@$DA};U1#%9Y<TMV`QJ5s2YyFP+oOdJt-9~JhCLk9n6!siAs#A-e
zM*Z$i-@kVUWCV)^Ur?&PbU`*E%f<Y4uEt1qnHO#!J2zi5!iDd6xkclW((b-dm|yye
zS88eg<#xBDY&=jaqu=m(p_w1Pm79MEteDPgQcNoP^xJnALZoQtw^jWF$5Js_gYO0@
zPp)9tBZA7^qS4JW4>Nhiq!gpxc?bm=a%;b#&D^d~JLIN!ld_jK79Mg<zj};YD1uH#
z%3V(0DMKeqo{Y57;7H{>D&OOUh*2pl-eS}I8@fV<VSy>^V$8D@XNm{{=2jwabxj#O
z<ejhA>79k;5ubom?l2`lvRP-)iD0g8Z?-TmolCWM(YSw;Z0I5M?(j^)(4zj$WrdvF
z-9(t$2qFPq@+E5@^{@TkyeQO_ED2e@S7jDR#tt@Kjr%{o-2{elB~!%2#sXm=K>6Nd
z0xS;}fta<=Rh3pBaOV6r8I!mgY2W9>vA?5v`eO33^t~(X=cAJf<DFnT52-)5_IKbZ
zTmTOzYLr(84aI`dhU^2S)cb21qRdr$Xn5ebwr;_{0Gqn9$cmL!D+bw0j)mx!o^n`s
zb#Ih%?T(C#5M?C*<W0-W#%5CWU5z%rva&vwf%>bMb22;HoYqXaF$HDl01?`?Gab_+
ztS`L^mzNT1E4;it5O}k?TeRglJhbmM9gx*{Z=Xbbl|Nr?S@PZ1Hh3;dn5cb%wN;l%
z)t#R(WeCf$76gi#`W)V$M$kImniJjJQ}Qk+M(h(c9?)E$*JzuYw$p!)w#CP#m`z)Z
zXPE5RAJ$zOrBX9qmX+D9PGVhFfIxF2SjJ3q--Tib%g`buNcGsFLpoGQqGbPu;9`Hi
zxBe#tr(j(SfY1=0>&r8JH&D;-+j*08h*$(%_*-=yC8DYb>J5R0+_E464Z*{Tt*IF4
z{>f{}{jg~fnMqj2(;DNd2-qeAXrwAv_S58hbj?Q$$1=h${1FO(;)m{dILprvO=rfB
zq3D-LxmNf^sM3lQ7E7+rv-a_B@RZrB^<}~~(rEoasHiR{H@AATee4JVdOr$r<14aB
z9hru=J0&Y81U%`CycEBFwx4euT3^6}P`P*iOo4Jn$}-z`wSLwI3z?^{68lGh)!vZB
z&k3_-63WWEAZo#13t}fuP5aiR-#2rJ7^DwRh;Ewhe6XixT-=MpM(_OK=&F`C2mTve
zVyx7&$vMN>mHl-0mjm03IQK5o5LQ(r%n>0|A+pQLGxkF)eiR+wbl=|Vd4htCB-rip
zO=N}zzIkhpZxufXf1-hun31c}JKUWeD-e-w`}p*=d&APBj;bmyZvcxq@>pv?-<gPg
z%%yngEH#mYStGkyZVw2q^SIy2QKc|Z@?le(`_|z9oJwXf2SN`s6$YY>nrGSEz~TBD
z01wbRZxCyaU0LPg2lz*6R0$~?@h+GAbitGbq}7gkSIOQdNP9M2FH{YhPz0op%1=ep
zYe?~8)Tj5Ia@jB1#&<+7?&{fTWHrk*G&&xIkWONViuUx+V<Zfuv6y2;_Her20{BP!
zF4@mzojvbQCa0!+>9w+JjH>3yc7}g8egmB<!v=YHd`JWWNk<2DC;JyAseH)c*yEOg
z#-T+p;69=arZG~(Ts;ty8QHZUmQYW>GSv!!X_3Xq@bJ70cNZ5Ioa(yq4C&-|*$g_@
zFGp;^$af%Qb%FP@Hg0?Ya1Gyjpw<828jLJ;>9lGS(VE?uW5W=6%&K}xJ~0?yqE%~v
zCTDk0x#O@2n?Fl*@EKK9Rpp?F-~RgW#X65SB^6xRQz)bW@|~Xf7yKOp6_uJ&!Jv-c
zo4yKl8jonz4^cBVNHL~ulO#6B=zvoT38E{qI49@n>B)%tcy$h#P}F)q%-`;y_n7wM
zy4Y^U%#x3%Q43W0>->u7SSI^YyIa$)*JE#p;EiXFqg$cMH2Z<?qBE7<E{b~dX0B6(
zL=O3Ca$oDM!&lAC`Ysi$0>EW!kEEom@SR%YaIxF4@o4U(d00=<YA`pv8*oBhu_2D;
zXxy{=McQzs0LvkEmiAhErX{~I|5a!75aZl(l+Y2>2|0Rfc6*S)GB_QMdQN%Ex-BBR
ztlzGi*$A4tH}w<yW>Q7HT7pVh_o!FaIKsoTd7H-O1VktZ^LS&dI5(Uu5K=ygguCN3
z59uxWVVH`p0gz-|A6+m7yhY>F?~E#~+wMR>FgsmXye_(8p*HpMLWxAJ!;#(MRL~1R
zs6!&i>BdXb0w5ZsYNY$OyHyllBkl3emMYoZLn5jB_S;%+hW*Rrl>d~}9CAq3xoKQ3
zF)FKwHZajljqVwV)hAoVXZ5=a8>O)R@#Hn%Mg^e^1;TxoA{auV%15UpnkQ55R<DEI
zR<l?GfDG^3A4t1I+?`HfZ5^0MysA^z(Q021is5TEb9Tv{x@s^l(eetRirUA<zW(-s
zx<q?jy2Ytzil&k)bSV!O5n{pRb0!xvJ#GC>{*QKYvV;rMOVc5p=B|kKsJ{i3v#@gE
zdr*6z60k=l4G~-dE4qPQ5L&7-8DotwIwo2lP}XjSmTcRwk_N>YcUbF~PlV|`4tdY-
zzjMW5Y(C^r(#0qA44{v`a5aYc^s=ZCDj@y_H>+yx_ONzd(q&5g8{wgxNr_kZ{F%_}
zpg(@wGVnC=<!r-`Gx!q(=H^{a?`Xu3eihCoZwh@5u`QB8IGHM6IHS2P7T|yuX_Iav
zdt$X=6Z>@T^!f8N02;5?EKN?H-`z|BbER{)z3EgPx!XWd!q~jdDSY*1&#x?J0A>J2
zE|wue7z;hzS2Am+5JZ2~%Z+bmD<o+CY~NkUDf30t!}WVQT~jX;UB8+ENR90bR+_4~
zt{<`&LbkR{86!^i_85dnmu|nfC;Q#+3zS0}CodX#t6c0bFgIB=h!sHUv=Xu?VQ3q4
z33voi6ntV@YEQFK230>zPNWw1+-KPj+~O0z8yFav7H1;(w`5-)%$%g#UsX#yk!P4#
zvaDR(tQ1m$yi;L9+cSyZ(_qvpW0)`SHG4nKVEHzz_h7nd-E`-6+b=B8u`nx7CObO5
zLPjJjzLZfykmV3rX@$s-Yb7yyCUu<+4tlAGj8K6<Ny=H`y%MYj7}|;-HF66IG*ne#
z6MC@tmM{ZNC}aU?!rud}21paYbu?+Lha6~NY#jgnyTj?~@9Y(w<m4oeaP}nK1?!`d
zXvI$)=4h^FX4^O2nf03Vt$RP}m!RZeK!vSXp|*MOr0XT99v)AAFM&!~-iP6b4a<uh
z)DDNfN0E!X3i}-M^YhTq(BxGD1%%wTGDmI#N-DgBpO)y4`@cUf;}jJY;pF5ijNas&
zh6gKv_@rx9G4f&TXCdK|_LI8rI+3_IM!6@S#JgkfST90boI4ReO)O1awJ(R`X}8fz
z+B;t3sa3N<<I#aYhDfjeB!#vU${I!CB1;^Qfa|_%f`}HL5v!Trp+KY};atO9%t~Ed
z!A{E*f?Z$T+z2uwY|3LD8#Ks!v8bz~g?Ydtp7OCmHJy@0gjmBH2SC97e)4@fdO~?e
zbo=!s@}#vj_OTVhcrYmatcu{yIq2h;!YLwyNszZ@L5MonVpDyYfP`_VLK(IlIbYu{
z`-*WZ0B#2MIwz!EJa(>gm=>aU%_l)qB@oZzL~q>7?+WVr&IqDuKzuTo_{S~rNRB?y
zY+2CtHFiy0_vbx_k*Rz#Qf6VBPM65@_?ur$gx3$AIQFF=iM~UET$Gvu-9*d1lljqI
zn`PY%es;H@Tp~B4Q?9k(AO@T?FgIz`5DEyq;C!HAeB%!ZYFGrDUSC@VR&zJb6ieft
z!^H32-xTd8(|Ky{--PH;0aV;^o%P#kD=s?gBmO1=2JOr!5OKmEP;rfIrxPSXq4<39
zyh%U?t~HP6)zFk+1j=3Vg>tsr-mdCO16H1<)76f+<j)afqpYach$09oE>U73t=4+C
zWo<Jjw)Vkd#od*xuLV)Py3mR7kVMeqsYI{s**UUDkOT~fUxMHP)r3l`0Ptnr%iog?
zfCd08S<GTdRoc?Q(gAPuVi#O#mB^F-M9gcn6ZE{Lzi+#A(8x!;OE~!?)sHKFJl@l@
zb3O@o)w>Ygqp8Ah?}G;pgqJ~&B?cW4b?(-Hg<ntjb?I%JTFh^Gksn(4=ZYf{n^xvt
zH@+k;g(M&#SoNz7&zz-|W4pMd=wS0A7JvzFo!fPljp%;p4#-%(6Vy;ai)J+5|4u_m
z0!&o}0XV+tFXb$NCw@30xn+Ah*@P&afWW;P2%>&v&?hI4)L;`tl=gCIa}hbgx;l}u
zTEbKQ@qXQel$NzfuIRvkUeH?|nhuLNwoDS*&S7M#Da}cgJG64y{OPg7?(D$a)RcX3
zE#2VY$6$J6_x9w9SB~FQ@#IH2TE;j;+^$-=PiZ8t9aMtTv0Jet8*UkNHO1A%eWBKV
z?3V^`rx@riky))<OF>1~b#0#Hj!<&lcq4Cv5J2!yI{w~L4;5XJn%1QgBoqLOJnET5
za31+hm941xK`6h*N_bNFwtSlobSE676g0#|u#%{K;qATu5pI{2w5)7hc{wV5B%QYp
zM6b^h6e=wrea*Br0U)0Nyjm7T0KHe6yW`DV*ZgegK8x$+H_=GV&z84a*;S!dyr0v2
zHR-X@f2hDS8d2n3#ZxkCV?5Xj-IBG?Ce!`P!R-!GQ^dKHY(0BJ%ZI#czANe`<QC{Q
z5}x$%Y2(Z)VaWnZW3r!2rF8MShsd~~W5t+j)d&{AH0R{xMAH=)8u`d)Y8GYB-utJ}
zS$_lr%>g>#Ilr>_)h};|_g7L_I%=ucBy{#)3QHSH3jrQxz_s=G$CHyfdv?wgY<&Ds
zU3ta+LHKseC~A-ylKnEI%YBf#Vcjl(8(UP-lp)>`??#t5uT=d(1%on*%@*3)c_*c)
z$f;B|Kki$O<Qw;8jq={050Rue5(?ByP_62nio5LTY<IN%6nT9DVB#&lB>v2@1YBnv
z9M7YTHC;IcIl;OkQl1$+LRth}CGL6YpQzV<ilKpO(9~)9o>lQxdmcJxW(rsAyhc23
zu7lO-?$$cS^h8t8uJ?HZrHCTb`v0s5p)c}vP%|m!6N}v!)jQ3*F7Et$1>fh9acn&1
z{Wvz43MYKoehi8LXb)idNEGB0+gWw8uMD>ilrcB~)Bw62jiM5lA8J(vO_+h5rXy+2
zYh3DpSOp3T$r)3f*e=)9?#zz{W;y<LwB~K2Ye4q>jpMY|ZuRtj+3=(bY$2UT7{CU<
z7SC+ZQ?E1Cv(meHX|TrBy?R(_OVAY6(rQszoVt*3`0V-O5WKM^n<2;ghb1|7|1$dW
zkLUkFb|gXbceyaa6gr6)+I1tgl_Cu9jzO;VpR$Y`Z2ND4iB_pCSs*C0U}Z_u;G@;%
zxtQch{m%Tc>l+RBQ-L6KJ`Kbx^OCaEG%bIItBgJ6(?i-tio2C9A#d3%yus(vMazyo
zp<`AJ*6Yh6FHT$IzgUQYt?Yn5qU&Yc&^!PMDP`Vae_JC(E3)3R|5?@x9@_WpqM>R5
z|0AI=vdz*ouX~~w`4Boltpav2089$RgkI&|%kMvq@VW`p9l&UAgmLhSzRKSH3D|$b
zY-Is7Gk4)^c3Y9yyzaz66!X!C2b)X#`3ET_u~^VUOw%7ujlwG`=7?=rf3e}s&Nxpl
zuuU&LA2^PsAZk>P!gHTS6<RrO@YK9yaJ$X|kxXky0Lze$MeOnq%R;F3wfp{~RYtO<
zY%0p)Q;r#yvpMD2Y;#TZog>fw;ykhqOsnhS%>BK+uvI0#oB9*_E-uYnU{51C1C+3U
zB&76Xy|)BFvHwAaW2T+-yWO2@T~Ax~Jvkn5SG6_a;ouM}dv6{h9?jCShVs8u0iFkN
z07(;){nxu&S8G+3WAfnlFuEbRc7+JNk<i;49-fRy-UGv{JXik)CQS}G)j0J_<QFeD
zFIVvddDK^Rn%J!0ED)MNUAId2q@~3J`LgdZo~l_}OUq+4yo4gI25V<{Ws;ML1|iSq
z+$i)f&VX3eTcC9mcQEf-Qt&=-(b*-(Tz976<b};BC@WgNrCd;4$YY6@uxe3(v4-PS
z(tGEMIN)HiP*ZQ12Pk__)$Gjpsc?MaTuEk(7H@UcvRSD87Dx@SYE(gD#iAF4UwOU)
z$^-xsc!<45D7(~Zu$3%ejo)NV;yIe8!+-+;F$g~Wzkaj>tI?&6xSsX_1SoK@*uGSo
zvvvA#S5Kdx{4%1<%<}L4Oyl4w<q|i~O_#C=?PrxIfS>V_{-7l*Tc*_sM?a={HS9FJ
zieV3UJ6<O-3~MOCDqi{c*RALW(YHPX&v)mMH7!p|YP>s9sb`FWY-RTWXkdKdu|3fn
zfWZ99h!du^WRiwsyS}{O_7{x(!Sn7MDQ&^=oj9JI*Kc=2klaR*0HJ7v3R>F&ohChp
zq2Aa*=(3V?3+Skqm>0SR*D%v^)wQz*z#jDkzRhrdUVNW39oq&e<+yKkZdhGP4-k@B
za%Owa&Y8Zz|BdP9!W2q3e%wO?)~05Y%ZjguU`C*0#3w?@j|nC+sl2P6zncp{AQnCu
z(Flf(5>$RL#yTwHh?qVIz6hKz_=~5FV@=8@a3=3lH-#5dSk9Q=NHwzBZ&Z#@B7-Jj
z*$LRSi%6Z~KS=#Yk^MCQWf&D7GQFo)P@t7qOJe0)m2@GAFu?*R?t~rLX4Tf_7CE1k
z;)MN65d|Pe2^<H6C-A5OLFsm*Z+voQlYf!NzYZWaSD$_<&0kKuH_+PX+o323H_f>z
zxK17PozQYk<iRZ{(?NJyZqJOw5(O2~j;CUh-g;$cLpv%E&pgsTkuH&Cc)7Rg;T`yd
z+*khS3)MWytbzi03yFSNx29(y#i*6V*hZ9R1M4ly2=i)ObLhA$ui9Zyt79$C??%$9
z`|!1!$x;a##K#p)ht<{o?bJLXM#l>@`S}RHIueL(8!ygTD%Fy>7+<E$Y~j2!26;0D
znO9II(Mhyu4{Cun)dhzFDh%`ki;z;DsNIk9%3UB>tHqP%Gr`~?K*VSw%aEv|fcIlF
zDAqa<35AU$lt<Rdx@u{98q5gnb#_HvOSkJ1o^m+5XHMuiK=L>D;l5n*N&k`K)P^gS
z6BnGx`5r?@k0X_P=8oCKe<a+vL4|5ZLD@sJEON_^buzb}%rCS_fT9j80#`s9HURz?
z($&}&u+W4~dVXu8KQo?c?vJzF8rMi*`l+;m$?U8*maJ{ZQxrH1Pn87;LR+`_WNd5<
z#4dJE>D)F~tSf!kJ^KRz@dpq&^2)Lz?_a@!ptunLyRc`ZpfH|4d>yOv)vN6jP5sGx
zfC|IhF;YEk9S#%&%zba$gV8Ks-Mb%dxuq{dzxpx++x~6GXEFWl#H~?xSh!XgOP^?<
zatgrtO)+IVu*)1t@8m8-e65M^ZiQVc-)lFxp*Xn(5<509wRzVuM`b`8z-A8De<F*_
z2n6c#fda+JRv8cdxVGXJzRb@XfqVNvyU4v*7P<o9FaaCOW6gu#<8^F6{U(V`4|n{v
zxIzwcg%AF6%kixmtT%ISqtWyLSUN!c&Ek2*A%eE5n22?gTl1-0QhD}g>a6<!U?o5p
zqQa#DR<qN}qVT^?$1f^s@^~g_F22M#*W`4=O^zD%f3vgsf<b&)x^EG@Ln>=(T0Sk&
ze6>6sacc36cQ{iY?fmgg4X_*ldwRFcp=DZnkHi1B?qq*k%j}A>Mc74l9lSNQOL!p-
z7@rK_gUUpsy+5kU%Wo{+3>_nlglW6jV*ya^T@k?qv#zU?p^6mgNr|xuilHx$3KM)4
zgCx}I;`Mm1<4B1L#79VJ?UQ2yT8Ovx%UI^-?o^6tN{H}nEo!e0(}FqEP&iFIMihSa
z!@>vMR}3D2@g2Wj!u~OU{ONdrP4r^au(EKc(el=d`%84jBJLn2Z|R3I7F>MxT0lZY
zA4iKggq!Ku7dD0${A1_-qCw)rI;022Q?ADI`U2ne-hO76QzkG#htRQSSy<7z<*ya|
zFP|ErWAa28S)^CmEd$k;=E4wb#=pAg={R^4i$8zcCO*+`@JuFNSGJQC-s$R&Jy}`w
zz^9lq{j$>I7pqFP%E*fdJC035B8#@NmsY)_fW9RiiefS-W#J-l(B>O}ziwnUSs1Ny
zT8arlG5CO2U3(%su^xcoxtjdH+PRo(9jfhZZyF#or}=iIy(L(6e|cl4du)i2ooE=y
zTEC3kru+}4Z4CoA?7|1JV62Gt326istSUTEFGDq89<(~7u-$~3VV^N6+J%fPd^5pk
zy}z+j5f&$}_bLxA?eiHxcwJ@ooZ=G!kI#AJc6=3feEpg_&>fIB<ZP^T)5D`OGHxB9
zN!r)5@kmP#-84Fw#BlI#r=uL;%N^Rq5*CfrC3zW+MlX0R9pjB&MLKz9>N9<(ek=Ia
z^e`vE9v2GiRo1`(zJLOJ8sRs-@;CVb))I6LJ{p{L133&{gU`Kio1MAcz`sZV@Zo=4
zNUsy9*YC#<Af|9$4b2WtI-0_`ePr(0=KwJ80i%v(+HCQG@1(`)Ci_d}ui^jdI(pT`
zSRx0u-cwNUdObg$1>PJBb#dJ6CoQK)9*OA|8aPiJ<oibZI@RAC7S6eY5lxk-eLNC&
z6M9&9n2-eG1Am$f=xFYqo<ab{eP}Z=AU3|^WQ4t{^s*J>KgRG>xbk0P7!&HpnFV7(
z4*)-VP4=OtXJXQ0xZTz;YjMp~thTI@J^tl-x30`qQcEYesCvot0RZ>O@Nk$mja&~E
zumL70(Ai%vMCZ1zf)L=S0qi7zS6CwG^B&dVqFF<pTP2vID&`+>V&pgxpc#_4NDJVC
zy~>y89l$F-lFpq2=Zo=Cck;z$^^HEqWhD8|=S6fo25iNI1MQhRa8H1K2*h$T!~V#c
zJXLX)r*!`Pk2<?&@1+;LQwZ=Ko44Esgl0Mc@L~Kd9VNjZS@<<MIr+f%<{s^PtxKEE
zNsZ8>Tx@s^o(_TC)YRlv!bRI{+SS0;PPX@-v%ajYu8vwXA83zTt;Q;2$9D(osQrhv
zZe?x9et6FKKn4%{dW}wr0AiK)!sdofuIE5r&dQ@&w`rc#|7s9ppuQRxt2zJyE5!%A
z8<pI^Q$k>bJ!Y_F%lz2Qv-2)Njo~Nwk9D1{93U74$kCVmxhfNym5kab8Tz7>oNcZ6
zP5KDBw++jhNVNf4<x6KUJ@SGe%7DLWH*OLu3b4c*f2vg(E9P8MAVN$ir&}w&+vgfn
zZ*xd>N_8Q-0BqawrNAFU%s_lCh1$?eLYthi4$!ySx3W1avu6+hfr*rE3X-p`si`|&
z+Bl0nark|(o34nT3-x$|q{QKY^h6m+d17<Yz-UKg!nJ%<nKGmg@CD^8B$n}r!qch5
ziz)96iuSTj@dDm8iG!e~3uH4`wOgi9L$w*e4NSy1P~(eZ_P>F_#Lv>f$-p1iw5fE@
zAS$7O3@s&=nOfoBV4&KSd=SAX4XA|&!2$F^E0vs?8{D6ssyj;y?c(ijV|5Dx3;r-L
zgqyAFw&3itOzD|1Ab6SS{LP<oM4>Ts=wC`eQBe^Q!?oU8WKdk(F^tDIhqgxNYe#`S
z&YgyyBZqjiR}%A>08?*Hz%hSQxi~~g{>Kt2H}_I|yTEIj6KzP462Oa$46nUGYBlH&
zUFO!`su!#xZ!4?$=FlwLJV8>B{@`}(ILDp8fkQ?3>loab6qFeN0M9(Ag!144qCGDO
z30Ms@W#7_Li>%FuSk&t^b`f8WE4)dK^9O)c=5sJa;BHY40QM+wWgiJ1lfuqx__3fp
zs#(DKSfD}`oY>lF5Fmucssu)Rz%w<E7aIf_?))}U(KtLk8+@Sh{usUu86(flkN4+s
zH&i4~f&hON@X#0X4>F~YhvWw;q)>(!Md+7+dE>l#THjZ#{_K5wLBDnrKDM>w!zAgF
za8X*rJp<r;Y7lDO9`5J>_%Vx^W~gu<km2MC5(yJD{#Tg+!1F{Cya&cJiYa?3M`umI
zU43jV{Y36em-1_V%-9Oe!UB4W*iG*>(tJ)WNRI5o`FlR3CLliu;Mb1-skToyY8fBv
zI^*)qeqrxL@WniR#`dlO0ML%`a!0m<znU@Y$shE<M`8{QuPZjK+&w?aG=|$-O|k>@
zd83winZVTAmF0-yKP7$iD^!~tkCN%?A3{E^xw%}l8A;Zq<vS#6Uo>wU9$o^VEm<Cp
z;J>H!*`KY*IjNT45Xa9cLpf=nOK{~(wit@FqJYo@*Ob<hSALa_a+*LpJ$0jO6R#8>
zg0^rIV)K@fL;KE;52YF80cY-O2|PSJPDhKBxP%z-eNUXjSHv~?>k-i2)zQMt*TOr)
z0Ih)=I4`MLKc7F^lu~bZIR1#4c7*Pg1!4WF`MDwn<m<xca}CF1QLmZz($xN3>;2R{
zm!J#ccs-MM2o9&?50XF@QB&Q4gbD`iV}AZW`WE%+upyHZz~l`iA{;oBcj^jj-YJv&
zSM4>{E1um_I@YaN%nUmX6Og2+vzUB<E(*xe+zQ>_;NQlGB)@ZOa0R*79Z|xAzX~d1
zZU={}G)|`<eZkFLyV+cp3Rc|@K-L<Y22DVNe{CWjK(@tWZEj-7(L!UmxPE&0$1(6K
zj}+8_oC0<~PmO2?xMIAPsUmwq!^4FEZs%*AV?bhx8grHI%@)P?h*%^RtoT}={z&!Q
zovp2|z2!_O+{(D9oVO;(fxgLq21@KfgbrtVorJwWP~;{YZm8*d(7z6p0qpUM9ock2
zo@c>*6GQ@8ASKejx;>P}S!Z?8WT)g=`ogt#v3~?u`@O{EaUeeJ!lEL}`RWR<m$?q{
z5yH^zH<90R#bc8_fII>~nkt`{;e^kZpVy?<%cvRv`YR`pS}Ka7w6xUY9!KW$v4pMd
zvYS+Nw5YNf9%VdE$djrqf`D@f0I&jOs%yZqS}#9R%+*Sms<$MfySy<mdIZA`MG!*)
z)eyGAgGDg|*>ffeA~h=3gYoaOf_Fw7w7H1)_gU}>sL4MluTTF~pY;I3F9`|nV>085
zd-tui-P06nX<>kl`nD79x(X$88tjP-L#T1+<)u9W0>aw)EV%PEnBnhZaNyF__yq)X
z2Ou2;B5PGfh7s1d(4FD(&)1af>rfYy`G0^HOQVKo)7TgJm(mvo(VN_!sdH`_stZP(
z0F6R;xLR(vd^Jtq%yB4>UX50UnosMPM9pjit5T@z8|YWh&`@Do;TYhUYK+r}ky#9+
zxboJ_ChE|rwYcN7p5odfSHCH}fbcbZ_m7GeD~X^&?^u>yqAdT9+a`Cw84gV84WMQ9
zmH_c=sr17JPme{L@s9JEq~a&7;8sr--hdeP%vpBgE%i2uuKX?lM*cUg&Da+(Ibi_x
zN8E!&r#_j<fBEsMZ;&CQl0sUPzq6dV+MsHd7W!!<|F3rkgrNc?RssOu@7%ST<E2#C
z@dW5~M5Kk*K5hv8OaJ+(@@L=Q6A8l^YiSzB`!3=YgSEm<5p`%WDI`^!IAMUS0QGA9
z1a<0usI^*3?$Y=qZ4|zL(etQW^6w??usgc8lOY;#(H<F-<^HnL8%t3L_j+FM%XoS%
z^|Iw8sN`0i3ng233N*wpq(6(S2FO=bs$O&6w_|Dt)kdWgOd5!?@Xyj*+uJiWEyDZJ
z-6~CM`~ImoDfXY$U~J3;Fv{=m7Jg6_A0(ZuTsfzn1NShZ#}<@&6@VbIJb)42+A6s2
z{E#2>TasxxFYjNpB|Zl<wp^#N=*LjxCJl?y%yue}gcdHSc;b9(Ad-k}>1OE2jWz2;
zkT4Q(4-c&AZbE|!-%E)rKv7wS2r$a$;2BdvdMf~_2%fG}UV08C!LvnHLjSH{zQ0br
zkm7W|vam7%NT|+5iDynal6k<lyjcHrgTrxBNkPGCd65{PZ46<8_=>;!tiTUhQR@u|
z{RdOd^&6xd*rji|)HwC~w;q6=URUfo1~0_%CM~Rm^UOoRit$>>Dl5x~it?tw%$!%%
z&;T^vX1$Jpbrl-K``df8%xQF)Q-Fq~EPF~LT}}xEu{g<q2%#~+w(%!TT?Lf<`1}y+
zw2-neU3oKmRBbgJhFzaiyiG)ac&;{#HT9vXtamu2t5FYrLU3%Cc>7Voeho1A<)qft
zX@$D{73?6|fv+IlO~7;TN4o#9`Tza#Ronv=Az|5mY%Fi)Qi{VM_n%inf6c-+_kxZ}
zdrB1)7!pIOx_Y*8My5JB4#-Ln$TnOa2|`1!&>A#Hkz%l@cLGN2<c0z*G6+-zWQQvg
zcs(Z|IsdpS00twLV)5@4I<xgF{iPbSX6&G5E*Vls`iJbgehcw|NA%Ofgu72&ld+W7
zw4IgL;|fu{S+g{X$J$Xf+h^E3t7f<8iA?7+{T3@HOhg5T&E=iEN^?c!i7@Xu#|oN9
zcx1Gr%9ctrd3AiiZJ+EXeR>#lMg;n}r(SBDVAx^X`7rp<JDx@k+x`6cR1}`;PAs)P
zRWl#Kk}u;u($orEN20}hPkdrVFCky*Q6#FlL=+gB%joXFRF*C_sA0tR5*Qw7j*?zx
zxV8I9KHp6Q`JsT%Rq+@lB=2i@^4;5^wIToO!$wznJdH?!M9r=vsse1UX7omD_>Wi!
zS3ljV*CZ9jp3Lf|liG0W3KmVCs4T<wWmHm+6`f>%W7+;*bGtT8P15QzE!4Tg;LJ_(
z82OYQa#e9QeDqlK1NPauIHRv>bXj@ub{wW>_aJ&ic`mmF9~XazcyePN1K;HKARklB
z74egqm|%Wo3r(@x(&aw1_ict{{z*OL3nn<|F^m}t<lPMp8dU{>@BmOmI20%oz*l|5
z0{t3azEH=e`5*F3{vX}NlZlP>zuo?#MpMIjK?KWlMTbNeViKCvv9uYZS90t)`(e7>
zTHb85mKGt4P9+B4E%f;&qBm(xR9^2>D%m^fJ-?lewbY~&O=Dyeuff}}Bpvs7xQmn_
zD<TtK?V;9}$kea#7(Z64N*<EN#fLBE9#2&o6SbB+nkjX7wLKf}R}Obda3$kjPU>D9
zJr_%5GV>~ZJGq7jVMk;nHpSmpMobnI$&M&xSMTeRuS^r0z>Nrt?l;UIVq=$`Fzl!o
zhSnWCT^rrCPEp5hqgcHZVjet(PtbK0oQB;%-!~a-i3kqR+sA*&;~dS{iLGcd+vzIr
zr<kNt8p#bEP`4UZ_N*V#6w6`ivqz^MqeV%xBF5xi0@3I;ep7O4<mQ%D4jQls5z*{x
zl}On?8mUy*Tr9i!TsG5X<dMfvd~U2Jt*nqMd}2!U<$cKqtRfR^KJ=fAdV}%()tn+*
zwas%%<l*NEx!fD!s+I~N%Qou1OIF20j>Soa@6n>ux}=x}M)XLN2A1bri?riEO3=h#
zzyAe$K}nD1BBU6BXq2qJ{GLoOi9*UG%Mey!%n();40zu(#L17Q5LANeJxxZR$RfjX
zCd4GzHf6p6C5Ip*M8Bnf)2Pn-<|j+vRrxptR-0~1gT)ZKs4tayJFDx(?Ut-Hq3klK
zWu87kR!OrLj~CyVOg4*n+BS<|2Ljs+o_dl?Tn$gcpBPix-y4(9pIBJNUk}fPTIhQe
z{8mySYPzVjKS&yQdOX-xwdTPis976FtantK*y8b2=L%bs$*Nzn7L&<%FVUX_+0)xw
z2Z!kkNM;gmC&?Wc^8^n*X1t>wL~LLr_%Br`)2R#;GzoJ`^#3ps#^p#cq75sCdB@`!
z#YgQL1ze0MzTz`%=?1gPG8;_1Uulu2nB86jIeK9>s->z}tnXS;0}O06dq=~YLa4Xz
z1R%JA8b?1|?nlemh#i2z!05ljgjQ()X;Q1`gQqjki6&?6&hpqRL!W4ONNs9S1i9qf
zM}r8IZS!MO`{YW67=7@(5U`Xst-Hkb(7p-YqHV##GTW%?9)P;@bdHHhq(ymeY4?fU
zPoF6EwG5vs2&yQP4kPKr5sxWhzZE4aVwy0=L|VK_DW_dS_>~pVAIeiu{VgBgg5!SE
zIIIRXUlujAKo*3MC5xJqCksN(mW|5E94Xf`-9zum(;>~ulXb>o_dss(qV+)D;}*&w
z7M08ZNy%hfikR)8m+w*I()%H@C^{Od%cjydQ<Uu5`l^=OEJIc}FGH52E<-Y^FGH>p
zFGDue?dH%$K!L^WTZEqNk#Z{BGQVM|p->LM;N?#UyE1_ghtjNg7E}2COW6$jQ_FWh
zp7s`+8}hTH2s!))4)}g{D>Q|gCTSu5G>+UkuEbP6+VoTqtzRmiMcty#$;^XULw(RY
zsJb1$U8USr)_hdArp2Ep+WlstLr8-6C|@CZ?>4N}qbbm-5Yp{Fzw>NW%ZzF;7CxR)
zcJQcL^kSS_AvH#vAN6?Tt45}Ae5Zc%(UUTn?ECfecQxy>sr$3?sx!~Zg;BMcN-|Qg
z=>F0-P6ty4E>Ffwsy7Ao&I?D;4HO>4JpQXT!LwaNil?me^eaJ1Si(;0;d3-`^Y@lC
z29^q+M^LtuQVOLOKAj%0!U+vF49U=DH`sL!VBYm5gob2S54PReD!vc+T>KECk*uc2
z#h=g~^uCf6&cLL$Wkcu}mCcYoKcpGjD01)=#$|+YkYc=pG@Jffo~HfKdb9Jzy*(k9
zd6q!)r=Tu3TM)w8HZiMy;Irp>$hITt4h(Yy&#cv={zfXlMVmJFVOhz!<wfO9KVDp|
zhDfhaa>`B!ZsqV~mSwl05fmHzTdQB@7qT*DR$Y8%sN+u)IEjOULh0~hi|=a0RIQkj
z(F<2z&U=k{Esfiz+kP}&p)L%>b_^hPlZ2TeXFr}#S{&btdyqP#hVMWXxvw5KpB32L
z6cF7XsPC!^7JPU<te!65NB~{0gx}nnYIvHKUZV_buglq+mL9V$h#nZm*F7ZsY*{m{
z(m76+M342fDy?=99ogz%ejoBiW{4{OK<KN+#axdZBr*90E_m+o49!J;V9cdi-Lseb
z99$-ESdg>UJcNFt4m9<enW}ewPh9JGH<b76!=75;5HYxe%^o;%XWj_%L0rQEip@j$
z^3zu>D+<w-RPl%T_mj3qss<L_-#rAQLTB~fm|~krWxs1k>m^qCnc+CHb;P9q>`Vrt
z#ZRis8oYELnu0|QuX@7)n-0K>vjfFNUF|w{8A;)bXgn#mCC%Tvof~;6%av!a9y4U7
z{p9Xn3K@&5UQ`e;ERMk%z%TeVJ95E0ux});JZ4MFRrm~A$xhv&6-D>k#xfP_h+tz2
zYMH*<fOPZfdJmG0ySV!*Xi?noPF2t*=qUo8^JrZm&nx}#&7yPL3Y0wj?$gU^5DEqk
zr?tl%{4{@Yc*TL_jk5za<z(flXzK~vg7+Pg&(XJ}&duyA&I{wW^F*>{YbjD7i>FIV
z@{mCVP_IWAvN)=x)ZBELXjBSe`<*iQ7kxjhD|q6uUv*ocWW>DzlCkzCC_Y6Jt8YRG
zZT%oRB+9LcFJEELP^nOE7r}y|dEEDZgh%y8i6CL)I!@w5uiX+@;IUkeiNRu(SMlfg
zEO;FkG8(^gAHWU9JH}8Bq?t`LQ1U-2&X&tL5)rJ_HQ-hJRWxZMT8}CnDWAh5IQ6}%
zrqIRh*D#T4NRXkl&x#bjWv_bVaRY6=2P0|;E6s)5*fjWJ>0*bvRJv?BefuSPV738w
z3BQ%oipnwEKHkwCapl|Jkal25Mmz!F6?S1}2MFz#7l0_@WGQw^dD!*Mb#UV53EFqh
z-huv=e~%o^EB&ePmTI{ojgQi#DWRBQOb3c@a+hwhwhl{9nD0jRgn*1q;i(5ett7|R
zw!2u7A>JnyB8p!P8pIFJDDfhG6S055E<k7heqC^<b7-Bd$M{>)y44fAg*^i+{;0#b
zj|iQwp#xf?LFc39=BHNfIli7Vb_CPs%eN@{)>nPWMH08U*r<25JnscB8pwl0HiMgA
z%(!r48JnK);x4V`0A|>G8bh(0BsSb2mVzAfHqo^xj~KjsH4R#D_LHceFPS#o&%0Pa
zmy}9n{XlMl_Ovq|U6yhuS@!UoVvWHEh0uJwa$a7(%X~+o9H5l)q*<06$oXEj<kWI`
zC9W|~I^SiPBMO$?qfJ>z$DokQ122itXC)jv4!NBtk}H{ph!qU-4kExR{MgGLAj+ug
zyrT8C>#Wosr!YlUQodBW(K*Fd>%uwRiNNvpL*;SH4_;6k`q!X?cKk`V%tJrhk4tXx
zlc<Ry2Lxwnzr}crge=ClO%^)eno;)jaSIB^wD8usLkm(l5R%<3B3JCSn(t1O^Woop
zMBI`w)=u`@fI`^&`Z`#>XEAXb9rotzgq!LS3BS%_qBhp_Hs7MmmQB9p&AmbG@qJ6z
z1Mz{qX9B-y%lX&)!_nam=}n&CukAu#5051*Og==LR5K?QUP*XM-!_Qov>|*{I>5UR
za9xzHjnT_p7VECNug@nCBB&rMPTKEx#@>#>Y3C9yj{2;6(!qCFpY@x-l|o4Yipb2K
zRL9()kldxmPHcISRB;30g6kxm>F!GB1%%Dt6#u^}#Y{~9NxuHSO|AZ#PyPR&8`izn
z^nZH8I!4w{tp7FL`q7e?RGbm$*O{nJh&08Z%MT`%gz)5z=dT@%wfbyxQ)eC3jw!Ym
z`e3HSIX_v{Li7ewOiX=+;T<$x{CADfp~#>p-Zbt9;RH`hYczI*$0DU>O5t@9$OmRR
z?_98QwXN`;ZguW>JRQ!tjo9@#3ZT9L9^#TOQ>3V;T{pjvN8kqxSQ>nDM1f320&&CC
z&Xyb5G@R6rtz;EDUD)ZQ(XG;M*Wt+Ws;Ca^Cy>Vcch?1olj*d0+^*+~WHT5e1H8FU
z*P;qfc9vkRl94~ZXgw@f&3F3^sBeJ-h3ET)_j_a?kTrN5@cN%bv$qXRy-(89=>#5I
zOAo5=nn*f*E|+YSI%`<D8KzaXeu~{KRte`~@G`p%N6>+_uIyUFpV>IpurnObY9ufs
zI6mjc-I-Vp=FKt(?~uCbaC0fy<@fGzM$MA6TGAEQ8$Rt6<WN6X>SD#6)WjW#CbQi|
z9*}*M>}qb^8tN|NJ%32Q_pDkQEzQ0a%+J`~O~K~zG|ScO>!j_y>S*t1n0e~K+UZ8|
zpFOoQJQCb_YCUtyC=9Nbdlz=nv;3vcwZGMH_mbln<@eq8M;~v7R-t!wZ(s$AD7J^a
zc%5#B@~Pbgk9TgV2<>y-90YJAF`I_QRorx0T5UaI@6$N4>X8Uly8G|1J&fJA?%^%d
zWZqeM$TyR5wKzP?tm4~Z*VJ9G;M)0-XO&x&`#+T7T{sQ0WnB3d-vl1r7H{IO$sF4z
zah`oY(fB!IkFAm)Pl-(P5bAc|PWw>z^70I{=N9coXPcqb@AcCG4Dji8J(OW>;Qrg4
z<m;!;uP?1Xypr1fl=ySB^^hU4?N2e_(Cg1Ykq3i6fBo~HHQ?tzfBRd;y1w(^U{J>$
zPRIR>-2JQz`(N(~r@+Vd*KdE`S8Q&yn%U?5%0B9dHmXqgRVY;~bQ5s|m2s`)dKSn~
zTKV>T&${Jl<Qxbsn|$cllojc@(QYhEJ2Yq7hLsd(eR+WT?CZhVs<307$#4KubAoc)
ze;sZ^;nXmgb}O+Pzw+{P+PpE8Mo|(Y@cR?g1KVr8E9EYp9SL_pkAXIK0-gSM(|-^p
z{=3JOJbz039hlc*&p!VYyIud=V@|U5tJlH%FN1W82K?jCQ7EFaGG?zoegD&EpyGe}
z{yHeHBLcj4EIBEwD{XysH@f2(Z{Z*b%L`dzeI9*Go4f5lm)>{U_giOm*2E1tfoQI3
z)>l!+Zyi5Isq^Hyr_~T7yIC1+`mbZ*Bc<ZT{_nAHcpZzI|2JbXV%_=p=R&={sAVvE
z{p;cxc?2+A{By~4+=HK>{{8izUO9<$9-#cs*PD%N<LaN;_OChe&oKRa+N_0I*FVs^
zDB9_xv{t$k>}jW=b}MvQ;d_0lwW9-m`Da}OtUq|~(Vj>)=p1*b%s=UcjGXW!2b3wT
z{2IxaX+xVk3qD!xE=N@fD2*T#j`SS6T-1|VL0LS0@QUm~>3$At#k(cf6q)G^T61x_
zCU21b>P6hw`sR3(KA`~}@lN@GWvT0X5qU@lJhZ=CD4S@C5}xbvaf-C^&4zzuEj3;7
zxijpGRmjp3e^COi4Y;Jv(YGnvmj!hCmrRQlxSWxdB!YI-SSP0#2If^rnuCR#q%C;J
zFwf7eCw^m2$~ApzE#j?%FEXopKNs3Wy1EMNSG>n8YXiEY0$~?DXv^^q7mRc^X5rum
zpf!q>P;u~ERImMIahB5d%oq~ZO8U<=TF?h?$COu=irjo~c^mheI2~j^Oo*y@I29bj
z9q)_<2faSpv*lJ8GMnhCZxD8^5YC>!lk*#nTT<dZ;_F#DZxFsTVpVrPeG!Mh0*=%U
zN9geI@wXHcNfV0j*XVmLeGc$(CD<c!CV-k!Z+kp)B|tR8)JU0K_;3!6D)joe{B_9&
zyK2JyKWx0r|Jiu|AMSecXMg@bxa-M(w&wru?t1dyHsSx>T~9uKVExGaU$@~y4;c5i
zU31Ti$J!??d&iS*b?-Fd-8!Jy%B5Lw#Kd5!qE2@P5@cL?h@+w)KP=%G_=a=N)^AX&
z)lRvcT-moIy!?QiZCv0?TZDUXZE2f0T0C4{9%df7XgwK}d-UWb4*xhVM#kjV43!%#
zf(M8Czj=L1z%`oeec%I!GzCijeVxY)h^|}JC`15XZeU7rJlvf*aep%TRp5heQ}(EP
zdf<&Bql$0(&X@Se5hnV>vp=jB%I~gHdyS}iJ|@_4D@C%yzK$q=-FU`1J18&IXK{T<
zpcx*qLZXFOZggWIx}A6TX&_<2*Tya7TaZeXAoE3)7^9+}V~>k@6Cc$YAn7TIP?~J!
z7L$KlO)<}njj1ll#bLYj&0V4=fV)HY<ZOVrYl;;CDib3Mhy3#zhx6LeK4Rd9>V+;V
z?Znug4f6NzJ)a83zHDCH`FR2O4T-$*15Yq8lV<v@jyO06%G;+8bw16^n`sS6PTTGN
z2w*Ym91uc@sazas+Un}}R~eDK&$mDf6z*kkV4$gT^QzLDSv%oeuiuXY<Iyuw1I%){
znN35zbz%&xTHTwtjh5~S-NmHWWE*-ZX*CS<aDSdx&+%!-8twf89FW1{MYGl2C6U+K
zPx=}oIu1qg{bbhaO~+yg3J=I+*y6V0;$_(PapTC2Do%&sd8-2I@nUt(q;1P7z}?tt
zI4vnPmI*{GVALB9Cd4=NhIB3&CLTkFgUDIiN0Q{$AX{I@6J|XIGVeXL7#!Cjg>z&k
za&~|4MZ!Xf1eev&Tbo0&aw~#c*w%|Tsu%X)pL+6Ow86K|h2;di%)NG7*Fz8xo{%US
zz2ZJNK)J^Vz?Bm6zX=P;R>C-D#9K5tg$n04D&Lc$kL}YdpS^t^2yN3O%KKSEPQr@(
zjfEHlh2bZV6%m1#=dKJZOo=1FFYm|ESYSX&kfZtah7wW-EDtQ~o3+yW%t}f`!XKY*
zCBGJF(uWUZeTB6D=10X(S(l(=U5U7!{0^FgCFnyLp&5zuPaQ?#FMeHkoOn%Uf#N%(
zD)|8~k-iP61QFj{POmg=Zk;!LtS<<%MkznSg;QsZ=Y9+b_&v$ECvPtls^scweOEIk
z-xyF5jNK_4ElZ_@x$oSl;VGVlH~+o%>!ekBYz!)Gu7c@1vFpHsboTFUGoiYk7*`5~
z<(!xDs0@6~o{=Lb5Ik)&JH8AeY>_KFFgzJB`f(_P=dK=}iy&HXbDyndo=4PY>*}C{
zbr{&g5)bDYFrAGwI#7<+eUC2DHxHcFtlMoEN~oOswG0e*-`P*s`6neK_up0!%6=o9
zN?w4KxgGi$D1m?G{@g<3^)2kmkcaw5%yv&9ET*pm1mZCx?{|)C<V8if%>iqOwhW@?
zo9Cb|($@3m54hW+$DWgcM9@5u*y(TD;B>|lD#}iX+`_X@UBM0|-yj}8-D2}S!g}uM
zUdp&U894QBbeaD?IFmmuoK7I%eth!=vct^=<D~x$#x&bJTSwXMZ(dj$9PW}ArUh@0
zk4XHxl>W#MUohm}@IEuIZZ#7UJ)^a?j%AGPTsf~!MW)Yg_`fV#-9mdpaNiQ$l0fp}
zdM>PTey*Cb_?QxIV_mb5I!x%^<l`h~5BKQz*2vb=)!vM?KjfO!J#@z0)-9Yc^7qDp
zQ{xXSA}CM(44q(oYCJ4|g(AX?<!!=>hQ_PfH%<7{5aOcEsoX3gnivdg?FToXya(VH
zSy!1Ln!pktyc@7_=6}nPxzB={e<l~DPi}pd{?=zuQ%~y}*gCK{Wo6a&$>xl20Zdgd
z|DB!Ic%xCpzz4{tG_dsp<Og*R)M&Xd^B;P`*@nVfRN(vl7@&OE5B}azH~NoVTf9&S
zFNUZ5U)DA*IV=fNmJI3|L~l-O7M{04%a$MSWkVPAZIhu>MSLf89g`U9{Ah0LqDbPH
z@|fZgtrX%hTH`0)DfrRk+uRW*ub5pW4QW?(nS67r8nHM_me%(DT|#sg5#}s%x%|d=
z#foT!d;CLESn+4YoJpIEr=@A4HtNLvTX2c6hE+;mWJiS=M#2Z!!8Et68(Yp6dd@{I
z<dOv|$2lo_cWcK5Y5I~eQ$ojIPV_IEABBKW2c|`q4|(vqSPfULH*^N#EIk2M=+p;&
zf-#L#%h_xoi^V1Wy_+-yglC;K;)6f@cUq3mSm*^3Qqd&_-a{vhpIXR}M_^l>P-eM&
z9Y2Jqp&ow0qo+$2ja-|s=K{j^FMBQ8WnKD=IEX-=>!+OQnVB8?bv|OOP~i4X6XWBP
zW;K0=X6EMRmX?-gX4Et^O*UE8t*u!#!=Snm-XF?Ej`W(nS`AD*%m>=vZ3$v=SsX*5
zwbD6Cayq|o8<ZciscU(*Mer|V#y={MZ|m;{O(~CAa!qjAXM3KW!ZBaP8JPy0i(XW&
z8@=^Ga9H3UA(wlhDqKdo=7I2I)P1(*n>VxZ2c1;f6C8bNEHRxalX=<ySzPDIe8lDw
z(ritt)AS@_*N#uXi9IXotrhuy0gFL&zVB_L`>+3mn`HnD*5fanCL;ENj>8Mq=zesg
z@Kg+emciEmj(V~e1v@&35k{BMIEoi=KlTE48iTgM0sBC$zINC?d?koLN}%SbqqtET
zfhH;{O2gGb@~P&knHclRntc$FhK4)?17n*T?tA;(Jw4VhPI?9hivV~|u*^9?zsI+=
zk)832rKjA<Par#YqPe-~S8*)ZwrLCuZa_#6b^RW<*D~TX>m9}cyP?lE(r@i+Ha4E`
z|B{vPXfiEV5zAL43go(I*!}~*zQ=7G7|{204Ysumv^DoO)z_arIf^xORbsLxEi-OP
zfn-|rm!mv9KF^Td-Dh=mnw@PX^RZ6z(M}2#p(hBLsbWTo2xU_VFA{PT{;276Uovw@
zaUe!OENE$IDK0KPd-kkQD1<^aCnqN>E9>hI8jZ%IM~@yncyQ02Jv}|W@4Ywj!3P)S
z&#$}hzL&Femc+*Vgv(8p(W!8oCE~*JCXe^iLBqB_{d3Jde{bvikxu?SjdXS*zWwi#
zl)66CN9VgfEa})&-g94p_UB8Id5rNFOl1PybbMRz9H`p|PCO5`KLX0OfYq-7wq)!V
zfxq>`X%9b?`p}V%BY(f}_eT#t`grE!Pi8;)jT_i{9RT7we`m<(*<yT~xYfFW!}t|~
zd<7p@{%Pq01GV@IND;>=MLrN{fk1=r`I@g@>u?+i@m){^6)*&F3v)TJT|kT&unj=G
zfT+=9?SX_7Ze;^ykc?b*Dsa?Lqs9s1;2v%<{1}EtL(^!2EA%;?5GlUj*5+z%ezfVZ
zv*|TwQ%hHc4L|nzAxJcsSy{~NY-UalBP|u7)5HDDR8$~i!A+BV`AwI@Gu&kv=r9j<
zm<J#;uEJeEC>4Q7p-U6%GQ^N#jQzM!Vu(~~xW(s99s8`!WbCf5>8h^mtf@S`?R~iM
zr1JRuX-S1Ml?h2y1siUxY#IJ793EG@$zG>-)C|K7svTyEh&@$Fj~Cz)BK)&}AgWO-
zDk`$u?R9l^kTM)Pbm-WzV^gL~nK^SNhlBU<5K~r<+Q0w(?;}T!y!qyvJ9g|ic+h<0
zh;`NKoew^EFgA91Lc%W@bj4V;JPf^8DTZ5_$@Wrv-=AB0xAYFbl&mcj;I$<yc)wYm
z`sCcWe;sRj?@Zf~+WwdC$tjEz6F;k|D?!pSaQ+Q&=(phY+2Gy}fGYdSrm;Y`V7hAh
zpNs$edd2I#*52o)KX(h|#?6aj5c({`SCx>EFn8|U<)e19zoo~8!-T0D7&`FH*g5)Q
zbblpwP}$>vLl_NYsX!D9+s8G^F32??QgoU-JIoy|hL%P{W1FcB0t)_&QIKd}bh;!x
zO`HbzNune=8nUVTeLhcbud}Ji+0^W)tFxDtxjQ>Yubj$AO<`r{F!QFc3ky*W=PIt#
zZgKTC8G9NGUG@6mZY$o9B#hW>X+pkSs^y012B0cwzEIL!dak{)%<FcG)SC3U3)5!J
zWAOz6_rcw5v6T<nF82GZ9#lx@&QP-p6cj2tsq2KC;@r7&<>loVhE1P7ea4I#RB9k4
zX>M-*%U}NT_S<j6lS-xBXPyxjOoe#-KWf!|!C&OK-t@l>UF~+)Poou2N6XPG3RH9%
zp59j0Hf;O#vXl))G2t$&rwnX+5I9U={U;zQFZdaFt>U%+l>KLI;@V$N`}J+eBleEk
z8*jW3(28%~Sd1H#%5se^6W}mh28%DTqaFVfzW;TsOOgcSsX&&3;|1LKQQ(??V7JfR
zVd`iyG{LQQxVCDjYS@8y;1~r<6eZ@$a`WVQx$;~gLpTBS4Glq<fSawX%u!k8hl(A;
zSh=~JSu@!)XVDWAuI^ccVgwS3wu+(7TD{Nf7sy#jQ{^esRct{xp5nCG8qS@nI(De1
zp%$f5Q)kV~yK}Wj6%}~bJwAK&u;pxztIdRPsN7jmoI(}UAd|Ynp`jtjBdV*b#bPn+
za-yT7gUdj=@#K?Fo;h;{zJB0=l!qR6D3y1nB>x%10s++G@&3G_YkU95GAaMxDLMvv
z`C`lt_tV?T57+iTwIJcC1&QIV2|hOOEeEz?@Yo?BOAbE%(uJ4au6p~Kyl0^LxJ~&&
zP?!__-S2*PbI!eS<HkrFsyF$5;Chm)0MbOD$p;AwK>FPv?@=)0X)x_ckh%(JrU9W2
zc<iA2BB<R9DnAD2{)2A~2S9fz4k%FOK-q~>Cwpd;f{GX-C@~5Y1xsU>!YAggguAB5
zrl`285y!}}&SU#p_q|j7&Y|{0&4y;5-zQ_qs6=N$SS)&MEPMKN!CiNWAA1zOz(gWv
zW20%$Uc=U{h8;WH9UTalizyV7LJB$)lj(Sg1&WM0QBate_4e8`edR~HhB_@QE{!W@
z5Y~oKsq&b(jCqTo(C~TO)hCWz{A}-VN4r!P!{qWV-*ton1qeG&L67Bo2JP13K1eGt
z0RxjVBfm%%i<Op^rqO7i?tp~C=kw`wx`2->6p9TSHZYmY6DLlTmk)HbtCCY{%%)>f
z=|T)+Us;ldAWI~CE{eX<HB@4>-YMh5cfPz87`iM|*<-N3eYy>ji>a}p#V>;ol9z$9
z4?yi6khu<jw8q<1<<!QJ#%=Z6W<|}CvZS{mKs@){a{++x?FNW&s-2Dlhav;S%>$YD
zfWjxi)F(jVVxZ0i92xHEXgvw)_JXp_;PlI&{s8Xma2Nrd3+TM7;1}@8=Fm8?g4k)w
zX)EJbK9csx4D}2xPiu0Sj&~m4)4b>1>UT@~N+HXDup*!nyFw@^N>!`b(+h?7-YZzU
zmL4DHx7$qzKR0aq$hd2lN3X{uVoWTa+-;ztP+ue$DiaIjD26~zQFWpl(h3L@d|Al$
z%;NE5a|<%&-GO4%+LOmFezwPI&@18+sSL)r%7qMeuACOlcQzWVXM5aTW?C#CVN62M
ziA18@++4HSTwGk--rk;=m>3{85Cnl5WZt}akU!VfwpZ6k3kpmI<9>;F4wWhl_zCj(
z>}V$Kjjq88vwek3@U_-JD3k>$@=m=CVnmFDovh%6!w795D1HqLHGs6cK+r7Sg{ngM
zYo6>nxh7!^il8JwT(;+*f4-`!3cg56O8O?odjOSzyC)z(B;EmX9sos8fhik+t_W8g
zmH`|is(0Yz;^KRtvl#bxP*A{;gR9D`gO84at$-5a&e%I4xqvJ~NEg-{>i4(o-%_{b
zaL3_c`!Ju*7c)b)c9g?mq^9zgE)_nwo*ozL85lI~-aWEqi{<Ds3Wm{QW8r>Mm&9OG
zH7TNuxltSey`x%R^+k6}`4EdogX$v`OwbriZF*Mv+=U*Ov+~G6xG^|fS#)ebc}&D$
z7bp-G)mGXM*#!KPDrZmPUZH4max$OKhXNL|iG+j%NErgkXtmlkYu0@F>8EwIO;uH*
z!s%Xvai2`K1jD!iB~v*}Ii0$(Yp~Pdf}G;Y*|+B;OG{ex@1JX*n<SI*7$Gl+ug19p
z#0X(D-oXUiMvM`|kNKeX1IWX&W!blt58}laU%Ytn;w=M+O`A5si}<FZ1ED;Dd_j@`
zV&{zF#ZN%;QXosiJ=;)^RPO{AH-X~U@qjsG7Az6ow_KG$u!!PD!Jg-yq<dD!uU2tX
z-IngX&3hqJg-Bs_Ta|1jWFH{|0W=x|wx1O%g!kM-RVW>m6^4(u8@6uq8jZB*Xq3yH
zWQYloMf1|<sQFT6cY^`4iJm5dM8kzh5$YolCSr5)6Q|ASYi=w%xUZwSLX(!kV2`_C
zC_3m`9y3$wYBgF;_jn9WMxt=S7{`^SR4SpwfX{b%c{$_}P=Ev!5DJA5BlhjvS6kcA
z(yW|0)1e<ZB$q8i(ZG_KToybt)EYu6mdm>gRTzTIOO}0dvFk|Pz`8sQ6%7sj>O7oa
zmTv*M4+jek9Zy#?T(i4rHx!jDD(g0RoMFRUzI^#NKw_L}8hZHck_+u4T_bjv$H!+;
zp$Pm2;0445t`bB|0jVp%v?sunM}ayA_f52&9`#(j3M#jQ;U+u-kEgy0pT(HLZYNWk
zxhj6u+N8B=j=Ig*wySB^=9<lAgJrO5f|3MJb_{iaEEY2>OK{(Pto(fM@Q`80C;CnA
zyV}~J62XMR$$eLZ0zn~tmI~rRV`+cI(N3$;sfgoap;Mut=t!G2k1LVZd~vk=&}TfU
zOsWlZE26NuOpyb>-+HFU*<@fOiSTsHh)p06K>AQtR#s6_0Xao5kKl5-ci(;Y#~*(T
zF~VZW%*p98880XlD=9%YZ7QE#X|})9Jv2+mRWUAOAB%>iDtT|6YBf9DbCYGEI}Ze?
zazXK{cp_%vqTu5x;*`y`n*jwt+$SZ*<N#v8>}VabmUS9Ft?JuxvFp|2&AZEbM)AVt
z{089#orA|al9%Dmi?r216o=>P*6srpTXC@HtH6;03Koohb=AYBlp)E0UC{E_<xp#!
z={>WfaR=PYE_0k9E=<3KXH`W-LFpk_wF)+L<DNYun>M-H+UW7|lZ6S`!^@+2sWVg_
zm#_L{Pu;m*p@Ib!N0?K~W8#vh&+4wLx%laB>xe!&D+d{`!(p9_cmXD7TZ?;bmj)n@
zKsmI?FNwq9#K*@&9s$K{Mn(pWMhnJ>1q&8z-n_Y@yiP7(6s0NgdTnCytl&$XC*tlL
zFdQ5*-mer>ui!FT5zA<IZ#s($4Fx~6(BMdcbr6)kgC|OiW>1ZyGYlaF_kAt<)+DY8
zNSq@9VggeMn9H+NkIjsIaGGv<oH$;_@%sGboyJcqAzpMtyx4WAyR?0zQ*VR(g2$jT
zsJAy>KqUg74^2Mq)}H<&JpB%(fu^IN{6lc|-*_m*<HFM}sEn(t9->3OBL9Kp2Qs7?
zL$;w$8b8@myQSaO4}~r~6i&JDc$^tUg0*X4V=(T<VZz<r&B)9^+3ZPO0Avliba8Zs
zq_b9Eex%E+cc>GESm>A~6c=f;=leXKi=XZ3sIH97$%7gs(Ay(tF_J|PAZ+LR>2U&-
zN1qf%#Kgpul$5l!wLy$P(ZI(;E|=%z<ZRip<?Pw&g^O1De7iX8WHviK@H$Z>mBZNB
zHRSO5=8DF7C8y|wA7AJyZPVYArwJE!fkNZ_n>b8BQW<1%{5VJ=An$`e9|;hX0EjE;
zT(pE;5F^I%Vn)pRX}Y3#NrH^y^MUd%<0!xAe(gl_Cl|X*+eSL{HmBRiXWrg;fnY!s
zgNH&Ot%#iqXlz`mR(yzaih&wH<KQ9Ht8!{06a*^UrLjxlX7}3nH0^;8U!$QhiW4Pc
z$-;e*#ll0R>(&CF&#-;F;lmH%1u)XnC+osw^XVzmRes>FI?>%!+OLWiz;{AZ4@8%d
zEmB8S9Qv&8%!%0Cd?qK*6@qeU%rvpBV$fRLM~~%Wf=OTmkH?cpBxlc_9T^!(Nl6KA
z!;ob-oz9adPnMJntyqy_Fz!<-?+SKCXqof@mm3O=J0v_g{jw^A99G7qzjwA>Enudp
zL!&T_g}a|FybChc;!da_5>YW!akA^={VDf{5r*F)fVjpe296hEh!>(eNAcpJ!f1#W
zV`FJ5x{aSz^?h;)#|x-RAhfsZZ4S5h_QeZ`0#IMXF96ei01_7AnVR+c@$kom_wWvf
zrwS&ek?L@|V2)<a-HCT&2zIdb;Fh{A<%8u3!UPF3T!jde3E2dH=@M6ahhfVW)BgRm
zxLBH2J4q)DB`R5@Nf9-b4pbcPVhiX}O*jvzM5Bw&%B?<r7;X?R*aBe?c;PzAUNvkj
z>BY4McM@7dDwRU+P+VLL358lsF!W~DtXYQ-AFipX@p?0|vwLjzcA0ExFqBLca6afA
zuC&<iQHZWQnxx<!Zx}pQHvs!BG>kJ<o(4)b;)8~gmlAlCsNC(1+Y^O}P!-<tNAkZd
ziot};b__2vRgcbydAKloMuH?k&Vj#GCB%!$zMVK;v~0i7bsplyFpd{u4xNVGW|Q>7
zXAZ&v#E5B607WLS4&l-RK2mL`aY7-A1IX1}Fl<@Vl+*4>x(BL^gY5@5*KF>xbft<?
zxwLRC78MBi%a*ZnbFHUNjl8$X-P6m;%R?BG(>@#_Be6iy-)5>f+G#htw5g&n!3d5>
z9GhEEd-_D>kpodF>0EJO?hpbgc#6ngHekCrz(^INtVuX`H5yHKcXw%NDSYHOLAmD;
zJ|IRwR$Eq9wP@i32=XbHo5p4bj#8r|2pdJ;?H*3!Fca8}E1eWj3vW8xF5@w>qC$_*
zfh5t`4Jx*QyvKsW85*u;PxGEGbJvRa6}S9J{KiBvt_3ghqeaUgUKGV_m>xYNUYa21
zA{0thj|uV%h!=01Y&qLJJY;dQ>6nB=yS?(^q56;}fs8dE?-6_)YRfTj@m+j?pvM6u
zi8xuEywOlvjx1+Q;u;_D?QGl$*@VqwOOvF98S8<6N~001T?4${k*!;eJ9p7yW9UR#
z%wdd5M-vO=UbnB}XxC7eB_>OPhK>lbc>KhI=?%rFD-Ius&dlKmMM3NaV<d@erG1X-
zVOF*@@_`%h_l=E>EiW%`XlTgE$q9Bwh{a;~=8+>u%E|^;t&TMsPpH-RQ7D1hDxJgJ
zKV&?nH*HW!kSm@Uorrb1Y3NAp03@^FNCaPvBR*ROGzG!J(Big?#XG|Us!1Qj^*5+2
zv|`pMUML?aig{#uOi{cTGKji9^RBX<4~jdsp6_VvH@iH3IhS#J$q?A*=nBEq@8N`E
zq!mx5fRC4D07#RsE|UVnL7}Q}McfL#Q@^c#+s>w)GL|es5N?bH1;g0W3pulAThE+<
zRKjaAv8GO?pp&>ukTqyhMJz5A@`$dw5r`4g&;d(2n;oBDSa<qF^@$^Kc~h@U%Kq{h
zWm9Q7p5=6ptINX5l182%L8H-RGTFIv=irx(i6IzjHEr6oUAuPG*Vk*J7Dh!~LJ^ig
zkQda9DRLV2Ue9nWi=N71UI{=l9({9hN34{cq70o|$&rKRW1#&s$p3C|iY@#tAaKL|
zZIU$gElstC5CHkb%mnF!({%UdYZK*Mx7UBPZUEAXjbAi>(Kys^a=<b&F8y|)6%+&?
z9+G_@NL>wF7I5i(Jm*T^3dHeO=bnJ#V!n3%9L=0d1DD_vSus=r$pw$b3r7TGGX7n6
z`7D-^_cvQkoM2C#iV6giI;kw0CywG&p6G5Z8;r}9g~JFIJ0>T;`o!V7QzsIp7QsIQ
z3Sa^T%BNXQ_4u7$NGKw|29ZcKGBN^%Msjj8q0mT7OoZb1(xry`@4Lfj+M`mf3-(sV
zvFM);8qXLk539$M2r+AaWiOPp_Z4WvJvk^1T-pSpXW~xiAfM3}U0*aB8rLSSB?02L
z7>mJSU}<VTWES6@89O^k3K>M}kac%?&xa+Q@1JY0?KRomUMZK(qTL>Y_8}Z3F9X>R
z;CU0}Tk!<m;U?Vm#S&dbWm1-ORs1ReL$Ir97i1GqgQQE+!%VUYsRU=%EJjL-Y1i(N
zZQB@0Nwi7I4WWXqjOSNHfDw>5=rXd)4}IERekpa<Tr?<*LQ`>kHjm{@4^_pXN++SY
z#xg8BIy#`x2yVtuhMYftzPh@aMoUjkYX^RxSUfXWtq7^;rk>$M79)8)iGbo_Yf0yf
z1c_P@I-Nlj3(mbZD)g5GAGEryyPJ348GC2=u=IaFK-_L)F%SgzGZn^3ApEbNrb|=t
z;IjkS#Li1S8^36Q+Op4RmvHD%nB4knW$^Hkrc)llIcnL5I82y(a1FxXP5N6?kW6VN
zd>mVhEg#l>c)suaH08AL&P#f1EN|IT%jwhlci*GXY0TW*N%1V6n5m452qV}6p;Q}l
z;nQ6seZ7fOi-ONWj}tiR^tOuw%xqcYW4fqRD&!6&B_)teC=`m|0uVS}d+oLI@|yb}
zSZ%THS1H#8jP-<;z+&v`H&k2g52(hCe@T#WKPYb3TV2aCl%W{}6%5sby8U?2DrmMC
z2~+Yx?FVX(I!l&y3m-9ZEJiqkd?8uEyCYozX~n`+xk|w3HrRKU^=vxZ{&{u3)#XtO
zSRBT!<`gs*h?@&?9>hJD7dPRv7<rs{N-sv6Y)udmmc%U4a5bMae)4hS$M8k?NE^!K
z@>i^IcXW(wdf(OA!JR#OQZ9)vG2%Er3^XAh9~BysaVXUom3HZ~JqUtErKShFHt4Z@
z>$zT6hl!Odj~u6DGFf9|V_jWcUS3|Xw^AyVHZ(MpmzOgbsp;t*e!n+3w-OnhsQ80k
z{d_S`c|}eH6-A&v-F2yFeW5O#GYezjDnpfnyPN}V4xM+X{m_7IU}gNuTljGOmq#(K
zoA$>&PmF>X@wz<C{Zn-iDH;bX+b?vzb-J~<Mep_bG$Ph5c52c&cvv*^9(;!7^Kar|
zQI;4VBsNL!3H+u>qNK&qi%a`U->-ciKA;7P0&3_%A`~i>yJ!(W5r`4?@(RxU1qh8c
zDU6V4IORvX2HMT>Q)EbJN24k!#n9J#@xb1w<aEAF9_+A0d35XP9)yL_wY<o`tVALy
zE-r@XKsdEbN=nMBufAGYS@+QTyG`bOQR@3qlo?DS-tHc<c)d%d<9snPE@RWVb|s&g
ztqJX;5okf_TfptWGp~cJ*V=oa^}zip_lFNKkv@pq%h+@*M=OK^WMwua5Y!9pBOhPr
zdizvsRgV!vC=e+y^wwonFa`Ld7CR4gp2K0H`4~`7!6&Miq(R-gB7TL(=h;%X<%_N_
ziqu76JeRPUvGen(N|k=|X6wn5+yx5|)+BrZ0y&E<pqC%%H0vEPS&~rwKYU-8mD5;!
zrtZv%)LHYW^uR<6s*>&QGTYAgv!KvmMoQ}h0zrR&e?>*blqpk!r>KJ@0_xw&%1RC=
zEjhUb!?*%LU<NcDLHeBTkNZX*REgPF&=4t?eyFCurq={}l+e-+#b=19{|rq3AqbiV
zf`Ku1H10?drNj&4Nr3p~nUF=B7At<ZP&YqCM#In(4TB$*cD;9|t#!!CXHwMy<}HhT
z@S{M^L-=&NRoijLgv*Kt+tJA>4wQmUQ%%c|W<a^IvvFs-BwZbL)^A2?3L`Cb<ioAz
zBS(0P79x`wyD>VqC8#*sh0#%ET*&?w#i;7!)Jq5U4z{-@7tN*wO}j#i;aSi1xqB_F
z99iTyAEE#x5eS0B#>NtI?AKm<t+uv#{X_Fi<`bHzhk{3WY8bS)x`$*mYCeD5w|0Ac
z@R3-Yu8@THSe0)9(M9+?5djK_i3dC34>3<Wj|7Nss^NnXd5qaf(npJ;^J7GAum3=G
z-<Fb&gVlXdpvKAA)LTL*VDnIBf}Hhuj`sOC@ImGB^x$yXgr?<b=WFL5>pHf%W;3J}
z>EiS-4UZlj&CJam*}Bzy=n!||LX<NJj8G*A^u4yqV_otX9$&@^<FKT&*q1)r%i;?a
zaS4I_F#|&wsP#-QCSlT)k)EBy;S3HAh9VIPh2o1ZzG!G@NQj@SRF<)rF<fp+phJ;{
z9T_%Nm~9WM#|4`qoqY3Ti-1L&9v3<Y%~RvU^Jq{ZE)4b@R}EE_4VFEa^5Crvw37gF
z9SrJcotS+`n&Oe77@de!-euf+zH`&r_F;<){t2IwTjRe>UWP~7A&EWrA7C5#a@go(
zOu!H<k6qq4()fPu`xdvQP*oV_VWp|nth_t}<Po2L&ReoLa;<?Yh;(sV#c=JJ-nd*T
zgB{ZAp@>WDuCJ{=emG_3oZ!p|s+!}d*W0Uy*#$~U#2NIEM2zK45t0Z#pTBL}w)Xbn
zwQJ+7wtBVdp5QWWzkh3=ezAlnyTVn;qG83Y`b(XLhYEEeZyNkLx=KOE8GP=-pfY-g
z{<F5vmd7o>wZYC{fIt9gSM3L&q3iNB`+q!R`}YgyB*~!YnET@KN8TwpRzKkL`ES7m
zMb7|FoCXDtgOb<5YcoM_`D82L&^Z5?_Yc^Qy<hYG&rkl`?RAGK%$hPq^X8lGfq{<4
zANQKgk)MT1N0&Z<&&Brno7JOB@`Z$wMUVdo`2C;1{z~xN>lB0{SRUsyxy&azBR{oF
zCQD08JAeNC*n(XFwtV?=ole)>+}zNRYBH5sEcL-<R>|;X$Uf>D33z`=hO%YQ+R$$a
zclyM|_(aEp1l3Me;tP(P?>m3HJ>=w^l-EIes07TV8OnR|HLxc+Qa7-rxMO!&w|{gA
zmYc0RU^kVp2xzC{bKVti1S}yQTAK`Og<q#gRYXVWn`<|h50*pSK?}`9s7fUxEmgni
zef!0Wyk$!xA0$p^QF#((#nCPZ5So;bNnZ>$hsI)+9oWa0DT3Gg!US~pkllK|Z&Kb$
zNE0AHK)DeW6&1t{5tqw#`0!!3TQI%ILZKjH@$A4}0Y$1T_Cv#_??p+k%&JuJnQxwI
z5wU5+OYD64VIl4+k|YK%oFSwOKdQ%9Z(pcecuO8*G7sX08;6d;hhxL^=ma?z_9P#c
zbZ$G}0hvUKl6SL5kqV<h*1b4`z^7NP$0z2V%+ys7BXZ@rYL5DY+7F6*ix)*N3KM>#
z>2x%$PXFF}&W1+b9g8Rt4?h*D*jA&n@>rK5md6jB8L5bkZ>zl2aQ0N@{5vpeAXL#b
zT<h6hui3>)k2C|F$Kyec(9+UUP*4yo2NDtzUVi!IrlyvM9$s#>9@j)|K!Q@6P(az%
zr=Kq5XqlI93!9FeZ62=ZGCouo9U346csi`hijT?*y5}|e8pt3XPJQ^6xFJI2L68#T
zdV2#fWJQ*0#}m^(`d%S~hQEGRSM;}|uN-MKIovn(I@l8Mz;-bIx1eSZczqVW?$ab~
zW&Fy&&-puiYQJ~rdnT7DT%UaBGWjomX*qJF?@xc4)G21HO%N(rhu^Kea&6%tDS^y?
z<oh<0@yy3tf{$W+I;TiwFCTRGStCDhVPT=cV5qCBBh(<vmM!yoy(hn5csz!|!GpnN
z=8CyYgtBkY81Vk0G)3cprBiPUckb9ZpzRd!dV-JS%5sP7Lp|1>+sordHt8E=1u>%c
zADg=K2h(%4!WR!U6#ez+-#@Q6*jzX9w?)9rpMnkhf!77zS_m#}o@@p4wDVq?^HQz8
z_VLdjH#&{s=)i*yh#q^?@Zr{xty?GIjG&>5zLV~>x=(%75T=+sHa>IS9hVO59qMck
zKFXaHMWLa_&)XwEZ*p?7Kp;4G?i}IKM;>_u?ze3_PNymR`gapxFN)3+^FA9i_{Mui
z<|g6uSsbq)2zTz7+4%f1J(q%yWJ&Q<>+*qe5+J@smZKHEy&-?!k7i7d6aVvQ6NHJE
z4>#Cco||_Cx<c^1llb}_yPw1tnDzN4_31g9IWNz7xnZQ?`-i@7b%*PvWk3BXd-klp
zKmN&DQWAOnk|<8z(&)z0f!2zlFvW`QT~A{&kG=bPu(uLn;fwb<YKC2%kvd8d1j)<G
z>*(kh92^Wjx^(GM$RlcMYWn)K^!ih7_egM=Wl}-E%TsC`cSB+%Y^{iOynb*3fPmI=
zJore0FoB6N%Lhq-`1V=4n*aLxyn|28nw2Ph`EcXx=Z}9_(&6#?Zqn)5V(`Gn_>zLB
zUj`qo$2IU|Y_?|hi?d&ZJmRTiPr1CIwE&8$UwDD0)pq~*C$6rp$m`F&Lnl_VPi(1o
z+C$<di^nfo_h3g=Me8Mk<y|>5R0s<*9i9Y<$j;7&FmdtXMFL(!r9SxJgYd=Y2S@yV
z&%gkI%2+7o(I}Mt<5dQ}xK5Jvbn}qI?G5)^$TL9uDFV5KU}@qs5+J_4mZadm`rwou
zPZVl|%>VtQa_K)$9<A%YNsA|=RIu#t;EuoGV+!9{3QWC|`t)4w+~=l0cdqx`&rkl`
z>kIkxqg<}$KmYdod|f~Mk&h^jNc<@bnY%v4W^|tYq$y0X%mqvMa>c2woBTl}2%%$~
zBDJG_Byvv#hr>xqN-8fe_jm|U>Hhog2e@PZ7@Z#7-@lhY*I?)@5%<85(I2QXWDd8l
zq*Wj8+}f#lLIjZ;0;-I9eZ9luAOYgrYg2Th9ZyVu^?_WM$M@*FrT4#4T-{^587HPt
zK;aL-eOtj$BY0yTzL@A_Y-Rk)XY!so)PCqMC4UJ6YP9HR)&Kp?Sywmkx4%VRxjdSe
zHD6PGvZt@b7#c^gqO}hUwzt-wJxS2u3zZZaVm>h$=8~Ihv)StF>j@9V#>V#b^>ucn
zju?uaP6D^Wd@--x;cBsu!@+5>Vj6}_pfWVm@JWCMstEzY<M-4I*N_15tv1L-mt-gp
z{dD$oE7I!w&C6do_1xZS^UVo!BrOJy9Rz+K&LfCiWnt{$w1*!_dt_VvHhfj6(8irJ
zNA$=eBU?T+9sE4<%Jc7xVe@Gxw>9`f=a?lI&6a4RPk*%88`SM1EGoM|X)7P_j6~Y~
z#>K_)`TPqPE)f1eYuDmaJRLhW0)T&jD3&o#gioSzba<TOgGocD#)wWg4Nbrxpmh>>
zYBHsna9=r8`E3LUqtW>I<B#v%yO#urFSRa;JUl)6*w5zNSD=0OOxyeyPwXx4xfzN>
zkpUk43?CHm?sCw6W>O!0YRXd!v<sd;|NQaJ<6(-)o_>mvnc4fRUq$YWpfRw*yW$7h
z&9!Gk${z}X%(!>Gp|7|4#9<;&gc=sHoa~PD(MOQ%?Ch477L$n}h`VmxI=J7n=P*<m
zLqkV`3+R~i1Qz|MKJc@UL_p9VwuDw#d=22LKnFn#Ma&e-n6f&3-OYa*VZ$UG332lP
z0;2WCjT@IQUrs{9xZA>_{%Li_&L0%Xc=R8Ac<G_HOInAlH~g%kSn%ipz*T_v?*dIn
zCefczkU!7-bBZ|S*)N`jYB^*}rqR?dz5x9Go}WGA_j)6&Jh4Eoh~=N#)f~oK8Jk<6
zj87`r^|3FgT1-G^WlOE+`+T-YxmZR9p7C8>O~CeZa&jQW=<n}uYl|BhIp^`1iAV(R
zjKN|X?`(zu0r%nD5K!xB3h*W130aaj`IZ0#Y?!clk`m)KwQP-G_meYzw>-71!*IvT
zUu^uM*?Xf$bMV#Rk<aj1a6h~k)O{NH=a?Atm3gmlsobZJKOH84il)&he*0T{dAa_b
zcOq9_v+hl_Tim7lTf>woSiR0T&|iP%3qlg%cl)dt1|mG8L?Tftm6es1geO|F2G4gq
zbK2tfyN8FrAap}Geg4xU<FF$$iqE8?=URrts*GvC*oiNh5o9T%lon%)-D4+x5TuPW
z6rpS=iaGML*|QU+fBv*??Q3UR1}!)ClpHD8uoqVxA3r=P9wB4NUYPSjuch}_r+yVi
z6Y=g`#$C8@@E`wlHZ?_9vsBAXE>u<??>6g0x)S1Y^TkoxlHJ>b!x=Ocho<9M&-M5t
zhF3@;dU|?>hldF}Rk;8B^AZdb4h|8BnyCUd6QP_K4-il}=0yw7h6jkK(KRx<FAxHR
z&+ls-X(R!HG@`c0V{BYM<>h;_n+7aP{&DiHQ>{04U?@`v9^3&WNnq;(_;OwmTaGN}
zr+Gg;)_Lrm>UYBQ_KM&A7U6Jue*V9HPo%kF1$V~yf&cuz)-axkf>mpWI@_Bs5{!N1
z6e>N#4rk*?gr`eOOM`nt5;1MsG?7Tu+}vz2XAch__xS>q3mrl7`RvmpfjC|mCvNDs
z425w+$kXuIYkSKGB?jEz5|u#$#5coWyIPf_KJ?SsMe&k9?5nx=jdMK)`;Enj^*iyk
zLbu!lx-LZixd&4oT%=p{kBk2ZGe{W|ixt29jiah+_|3N>td=WcWXz7LJKL-84e5(X
zo<55wm6z=Nh~V3#OArp#ayHUL4?dr-)9D~U1XCdtMVBtc*VH_Jp5pPC3`T++ts()p
z*6J7<pBg){8-mIJvJ}E7l8UY3(Rj7`S`r{g3ukg3<E@AD{%=iYRksmRiO;M1ZmiaL
zXg8kZxA`v6UmZD+{l4h;TCVn)6VD9ULk9MEix+bjEgE|DRaa-E6E{p*ti$MN@t&5D
zPc4e(-MOl_slKNnu&1S92s>ZlsMmY+k;XeAiGUJgXo%n|+<E7n01tN7Q7D*RPr$H>
zg!p1SXAR>>M7oMk$B>KdVOfka8y`UzJkJG%k}OKD8zvb9X%V!*b$OcoKc1-(F&}^b
z!f$t1h0Ec+M&qf#LwoQAW<OX9Opy*KWK-EM&3Vb{w*KbyZ^Aeu<iGp{1x5RQ|Az>J
zxJ)iBdx5s)(%@i6$l(=fv***8tV;(7qUP+W_~IAV3;hv(i?JyP2nToGamO7PhMhir
z7JyW}o*+Rhi_2o6$m#I_LBo&?HUDBqSb$Jv1G^q~RR&*=D5=rV7$#$t1PIbDTa1Lg
z<B92y&4_vLZ2Ri}oN2ppK~SL%AF|;vfe+RJXQTt4;)U@~PkH)e_sNeNJ`PE!<#Op$
zKe3)XX^uE`Dr;U8je%Y`&=#ryM2C!d3meXzGV23-eoVli#|dl~`Xh8zLY1LXsR#i=
zAP~%)In(R)R#yqk=4zKKP&H5y%2eD95qP_Db;7D{lhYHrk*jj>J(0>t5F|Lg&TdOL
z2@s@3WONMu^_{72Z^-L4+L!<9)X}>B8+9f{23Yql7^ndsJq)~&W)s|(d|#om@P*PB
z+KeIFv+(}=>8UCGfBv)A5>aS|&Z1__iE6$y7{(KkeaCX2&sTBiGXfHkC-)kh&X!1X
z*pri!ySuvyGo~Pk7<EG!0Pv5DoFQ^Ua2jpSe&@K`m7@{3yuRw5(DP`>M*WmT^K`@t
z@VT>^4b3D#kTwaMmn`4=!x>r;>(O^hUq0M$qoNz}3&8T1LGux?|EC}_MSxI{KNS6e
zico)X>K7g#akPShVanhA&NDDD{QB#Wl8E^ljEY=55OR#BK(2_JQc!;QfX5X$#*~>R
zrqEGa#3};<1l$vj%UG~r0o<QBaS=tigl>rGqi%?EMoR#Lfcr}whLESk{gkPM3?hz?
zuX5IG{FVU(nQDFQrV+8A+_*b8>gB_Y-`P}pqlRzfJ_Kg|3}4-^<c$bFA!o~fUHI$D
zp~^Qa-wf$l$js!gTBU#Yy$JKKnOs`h40Xf#e&e7$G>N#=ZZbBVKO2ZgRFst^wO0-K
z91(?ArBbO#BqCH9IXOA-P)A3H#gaa(KN;*s$>6Y<2<3ut+(c4x9z!i)PT+=6W`h2z
z;I}z6j+`ZLGBn-HM~h6g{x2~G6@6iC<{wt3ebF#@_p4_nnBeTHZSJo?;u3K1fARUz
zBD2NOi<d+%*;ui$#Sk+3Abt92fMEmAzYt-a*$cH41Y8O|Ta+$6i!YN`9wit|#mbZW
z-9CHeP$Vb9V+YB}t*xyNM_}v&L6G_L=ff9Om2{`G*Jf)Cc0*)wS?7&`QYc3&xD-B+
z0ab=|5LgI?e#Q&p!`#LjVPvZHe~-bZd3|B@dyf|M86B(tb9#bq>MEHf0`7Vp2xD+(
zgdx)K<g*3OGBM_JXP@)=iRZ$h%4Op3e#d<9pslntk_tIOdP<?P_H1vcNh>J`l0J7)
zM^%NPFEH_ers1L@hOJ^S!i!H#O!WDD5F<jVjN)QExHDoP7-W^lXIEM5_VI%<a<sx8
zqodyxI@&5v#rH(xzT*UOEykAHA&@}=#5dK7;w7K_pomS!?%jB9U&RfW>6F0-_in)#
z5a0G7a7UD>C1FaQ$$RGFz{MSnJ3^HaKk^8sRQCVv{~`=|X3f|5JpSs?V>Hv|%%@P0
zs^f=(VU(3Abv7HkmWbv7jf#q*(P&LXS>a!~A&OB{YA_IFh2`_vZoj|6JdVv}M&V1~
zgq!R`h9_|nCZ0fmxJ?2XBtU$FEnddi`GcbDD8Uc6Tzcnp>-9f@G!fjj5zh!d_{#`C
zu_}I5z9Rpn(w7Ep#9cSSWJ-Vdq@%LZawMYBA3_CNn<lC{(H&}u9j-_clQX62_~GF3
zoy;sL@Bw>OL@EQru-MpG2oS-@1>cW}i5VFg>Fd)O3?;z@a(Vc){Q)W?MaiR3KxJ3x
zKn6z&*iz76LkJMy_6TH<0P)Q>5u3K@v4T6&<j?J``P+fI>pz`D@=`GU$N19q)jK1U
zKff*fE$sdNTJqPB-l&x;Y0=Sx|M*9QBf&H0X`sYt3Oz<MW8NLs5q)b}plqQkSeT4y
zD<6vRyT--E8I4B5l*&1C=D>Yz4d!(AIUIpbRw<1d!=zm>jbjiTIu;{gSB0N0OqNP0
zF=F|nkxnBC5TsqZ6riI2d0@)@Q?;)iZ+>=r#r2;Hcm8icR6f}AW1x?Sr096M?`D4Y
z^Y+h6dWomEreK)#$3Jp5Hkm$+XqdS+MI=<P%8zu0g1^|@e6~PXb&ObLxY~?fV?+~V
z#>dCw&9a%GS<aj}6Yeiv!f6pC5y1tf@Ys++1YBW;nqS==79iwlV4w~J`5L%1F4Svb
z@=8d6AZ-$cB9uR^O8?3HgpV(D{bXC2`}$%8N(Fbng->&`{Sn}aNY8Ib+o0yC|5Eao
zP-zj|#f$05$%FrV#qXXtQcFQ7nRBBCJIsB}#ElI>(d438t(Qt2L0MS1%J2hQWU&mP
zP$(9Q3EdE50CDbI8H(~pLbxG@+@21{xcif?;`bOG;bxbD48mgvBW;8@+hOh?0fMya
zW3ab;dST+PmZj{k=zU`Ih3h<OL#P8Q|BYw$9REv1qy}UBEbnJchNe*Y*AxVi{^ZB*
z&Q8N85h<6XX-YZ^t2iDqfg)wbT(8H|P#hSPrAk>;B|E}k=NO4-Yin}{!4^~>3WZ{D
zaL`~#AY>3(T-*&&ZXV~&O;hvWzA8*Ur97mc5&}fKshtD}(ypg{XJ+glR;3@V9r*61
z((8;7Da%35dVFP#j&qR|U!Yx3peXp)rGFV5#My+LIdhoV*+Z|s>UT}dN~m-+X{w^7
zY|tJeBq5856RM(WPaO|F#7q;rI!qD9G9VCmJf5zgR4$0WV<e)!j$yaAJDq_!nkgIx
zl|l&!WTdI^Kt@#<l_AA5I0<JM*K)Obhu-WmlK?^5^|bZVbkDEJJlQz(gAXpcJl9og
zEdC2V;p)!sMKY8Nwy6Ip_#cbg@~^Ugg(@KV!4saoKI7hfk(@krhRWyh*PrVPRa?r8
z*<CeNrlG*-2_sbuRfeNhACbR9tA$k&W<z}ChQOzaG8zNNP|y)1h0O>62tJdl6SKla
zI%zDx*Meb!`J<wF(Qw~k>L3AvwCikZ^E7{2oqn`#;HhnwuBXqE&IPMq10(I=;4dO6
zo+wOQo49s+!}d^AnLTqRJtbxMjW;3*XNc7tMJ%uSWXOrXQ;O!meckC3f$T%cMg??x
z&2U8Gh`~@Ql~84b0*K<`Dhh>WG7${2%H*;t%=UoGOIPv34cUSwNdQ9xOOtAOTDZUE
zDuV=w|4w^gs_xf!rhZ!4_w4qH>p84THysrH7$2t5d^D2c-_QC!4W+$u>6MTq;_>gg
z+S<&A4n;^J4E?s=CgKiRpin9klIns~20RN?8IHygpEDxO6&)Q7C59_V_+(^c@OZqo
zwsxB>+DHTtSv;n};|=grCMmcbdRw^FSS5-0Qbs}ZYihU}5+F#sf%e#pm}eFxesZb%
zSG%jOXMV(-U*L0L?|T}!CLRVNVM-oKedu7@!RjI6iK@7Z7SVLNp^Y0Oxx)A<avB4z
zITbQ(G`VQ@U`Lz2r;9*kc>EDgR2dT!<Ma7~-4G~>78Vu)JPyh;nJRrj0h&xAKP3bR
z_{|#nE#Vl1+Xl>mUy6Y-lx$_YnFI*ZZkYXWUc!@e<F*uc{9#}1b;AfM11$ds9+`_Q
zqOl=ugODzK>B38d7eGN#@#Eih*3?>0O*|@$N=Fk56ipWgoFP`eh|k9xaAR@ctPS*N
zK81-zs4_GfO(-|S)T#KQSoL)-pU-Z!2I_)jHtwe^AMb`plH&_-HT0Ro0R*(6MnaWA
z`XETVvG&ZOq(_RP-#*>?ucOy*_;B<Ld`S4&e}mqMm!js-IFDyOUffrFw&yHi0sivk
zm_jl9#+#9xIi*PHb^AhPRq`ZKMSNmo$(bM+qA-(1jyioLb4)Q943$bn=!O^rh{{TQ
zU|6uq!2Oh*KtH8c#9~s>x^MuI7z!YCTwR-~?ItbnOajEW(_m-#n>$nQ%2B^~sA1=&
zuIpCs!rudy80`5G;VRVuwk~NMWXu09`!_L(pwUDRK42>;anwybQ>#?V6)ITu#UZEg
zO(>Y&-_m3n3`7t{lF(=KxVs~o&nh}Py1TnO*bR}NpAYvJFIG}0LX(lm4H4w0L=Z}%
zoKxQ)W<-V@(0QT!lxCON=rn#S0D_z_<`!baU+>N=j1@n-y<&nDpCUJ=5G?&aoIw=7
zI$>EFO8ZX6cPfS|&h`?M2*H{)6ed%@>HSE)OVTt&Z<En9L_EMWVd`{TWt0S-h@Kz>
z6yT`WM+gu;pRY4$8g8jn8XFsHv)P6QwMG*Gd&=N2jUI2;`03{o<=px{OPH?|Dv%My
zje`4|Gp3WAFy>orGz@ud{gh}i`|(YsO#{~{_a=1}KB>&HKNBt?7htR6SIJrO*UDcb
zBoU}Uz+bh}w0Ey(cw+oYEWjtN4n-n_3YAnF-B=PhHyp~Q(xSMIhKT%>8Vw#n=p>q_
ze@taGH#2OuCbv6~7o>2Qa9?W)^ivL5oZ%9#p~@I05P(q}93Z+ZT{qzgAt#Lawi};G
zeS1Sb4MR7)bAHh5ybd+Ou5Za-@qtE%e;Wyjcr4?wO9Piet+FFpzuxb5k8ImEVO3la
zL!Bsq01--@#}`cRuCKKk0<nOeD0Fq2z1E3Ge=ryfxm->N5CsMJs(IDbI5e0ofuWgX
zHe(bZ9034<BU%_Ygg73Ux`WftR2<b!V-Tbdf;4<e%eOb=4O?8_xn7Cb!e~4#qVnTN
z@~`hoxJ$~EzE<%X;Zb^QEPLk6kq<xgMUZ)&I8|XBu=lr`LL8kA{QlsHDj7*a;0Mko
zLxd!vtE<Zwl%6ogAj-<{`QXgvK!D(4sFp#ivDyg$qJN@5Mi7bM(zrYtugB6u0t9I{
z$ugq^|GY1!qRa4$Pb#mEdwj;zfTsqZJqsq9AB~REA5DK0GKkVX;`!!84?W}^95Np~
zIN>?tb7d%oG@K`%v|6mu2^7la(&9iC6UC)4vB*`1)9D->3{2e4$;qKosWmlqUavG5
zAVyUNBY;6jIdl=57LGxP@Yx>-N7Ji0>YF1mNPr;iOIw&Ke|iC)K=t04w(H^EVB-;u
z-g0nh^MqyBB(4!N#Gyz8yJ$L9tBpiu(3qGuP1JI6fG})Hp-^IT@;j?5-Oh0hm4Z+h
z2?9rhJ`!XM<%WRjBP%Nl*3j7r0fJyMs8lwy+3s*&K|SF0#LGBM1D0?^4B7|*Ktyq)
zy3M4-AnoSb59h`&NRj_xe{Jz~32LO@4YY;e$nSw;f`A+YV?3PpaB**O%`idJq9BNH
z-CBF;1!wcbGbzPSky(wd-VjUU#N_6C+^&wQiogp<6#2|<ckjfjlL>`FHk(c8hRDs$
zh5NQvs?FBy_1c0M1k>vWjn;A46enf3gjo`e&c)}R3&9|u#0WJBFbNQ(-I!k;q5S=x
zES-q;<d%!Q#%o?WoI(MM{|aow;ERdpvxg*tO=Z7V{T|^_{_53$N;Q1+@r3n6XG);T
zXbzDv6_t`sr88PD1v*3_gMfQyvmt`-M1drN5Fnt+fcu69$O!yqOW;WDlu<urO&~yU
z^i~(#LIY9^;j}R-j>_e84cmuFfFSLr8jnGJef<=>%lpLUi{X}#jL=lsAnRUGvT?$Y
zCzr;(E8(t#Z3p|T1aV4CEaonlZ``%Z=bRwULSx`6qdA1iz^GJhMs~}^(f~Dq^68j_
z>1>WD^#}rlUaz+V%~CZ65H&TpYam!<#4za$gi<p;(g^_q?pp>e;oJ}*7zDVbfeaEL
zzP*+p=f1EutE|)T$9=Wep}M)h0FVhsFhJwMln3GdgW3;>yV7;*yhfw>^NE-F4X-j{
zbMwr@gTtMHgPrJcd}piC?~T+A5j<)uBO?PrklNY?pHC3%r^FCSB8yRHaRdWIb2xyA
z!6TiXKv=w)sSqSUkQQi5GL+w$5%d1J_5)RY*IJiA3-Z4M%02*t^%JlSj%tx^(e{Sz
z78gN-VNIP%)oKmfKDtJg5tEaLciNXqg8)L{cY0mj6K9Jll}acyy1N5=Zx)M{oSf|S
zdi(p8Ap#l9fJmoW$fRLN%b+!!!~lMLu8p93g);~<$skC(MfU9C<P0_ce|A)K8?M<I
zG4mNfW#it;iR|IDhb?Xk;rcpbu1Z^Jsk5c!T2uyCB#}nPwp<K!L(pUKY2YGR_?t?l
z%4D*ho}N%{h_*I{)e3n;V7H&dW(>GIBks$iR8WKxBW6#)AOJxigMl$5Oi7QmhXe@H
zZjHg7?Unm-JYL_^Tg$F>`YE;;&zq{-2RhG(FPJ7t%ai9pl|eY|HSVg!FvIp85vq*-
zHsXw_*qr>HhFXVpT(d+uG)&Hl#7~L${Ax7>h~Tj37(g^O+Wmfy)f$-Kl)$3HeUoh*
zaKy<VK!j%y#HkRFM36oR(r%?iOW1#0onF$Wf92YTgirecV9UUf=O!$;K6QPswf98l
ziQq$+Sj?F{+q8G@gj3OJ)5HiuX{{ihv|5*$?f3b*Y64f#X2c0xZ6<#N6LzXpD!bi2
zGBQF45VbXUyR}#XB~{{RAR{Q!$?4GBLi&T*5<ue+ra~xKihgT92@s^+N?V;1byu$X
zm7|R%t@>-Kh|U2se+t@8g0|D)3(k(3tz;{=)^8;|%3ra<GdO5HJ3$6FgH2T?2wFp|
zouy1j#_06+%AkoG;`n~I&(&#;1R&r(SY>2qXTyC}RfFG8wO9iIB8ov{Ae07MAV7G1
z{*K`g({NEJIEf%)5DK=U&)RpZW`!UDg0wH~4=dB7gsh)^TxNB8uBoCa8*mbF;&0(g
zp$NJ*aqYR@bFD^#R>7G&2jTIIdv;HFwwO%GNUzl}K+y0oDm5x4y(1VPV)%d(Q8t83
zCPPs)7$8I<kxr+B<YH)0VYLK~)J7?kSSG#E>I?t~fCn-{s|-;zF@s<$9A3w*niYZs
z2-3bHxpeG>wOL&w_P=~~ZBb7u6BPXfw4Ru71lQ_>)fj?(R8J7FKxj1Hk|pLthrN~w
zvL2%|C2-#sN@b*H>3h1&!{d%3EGkvbcD9=){3;lRDHMv}K*rZ9qoadmu@X$>kicd%
zj%N@tQZ@)5=@dreQH-GBVGtnTzR%W20t9Kd*a~7qj}^siKG$)waqyZd%6k;g9X|fo
z@TDY7$wJ-2-A%h~Zi4=wzjB4&Zl7?%Yrd2zQn5n<L?+&hgH;ACn&)aaN0JQzRYq@b
zZ}70rjEoGt=61i++2?i#cJB!+MyJDNzryLup<_}my?r<|-to1->jc)pU>^hy5LOZ(
zNV^T}nLCo=r0i$6SC|~`tFMK|!q)_EIR-jQ!WX<Z>0Yzje4y<>@F8YS4ppOxNM&S6
zdYX(5qKO_P8Xb!-AW#|6Jip89?z2V$5H6Q%IA|uD^z?L`5j5j8B-l@xz@|g&X|az}
z89EWGeK_<~-~v1^3m7|z83YLsq}>k2prS9X%kDQhe!sWonkveF44<@eB2Pq)EGI#j
zu(fV$uoI3#q3~C%u$?P*_w`NWhLHMw{`M+@0Sgoa(WYlX1`$v{WDs!gYMWTttJR~)
z&Os_;3?S<3@f^qC8LlCN06~EaotPaCAo!yIVF;YYTFew<6pRE2(ry>a)(D=M6Sw_B
z*EI<Z8XFIJHXR0iRbfg&6j+zIu2x?QH-aX!Vma{pP5bwUKb1_!W$|dCRE9Pq%VsnL
z<XmF{2Fj;L;)ak&BvdLDs*FGgNJxP1x3zV+T&&<gMl6#Kn^r&|LnmhSnH(Xf>IuOh
z5DFq=$w(grX}6L6XkJ2$g#F8%Rd&}kjN^hNg2n-7{u#d1(%7YRl)kf(V3}{4PRB?~
zGaZ;<3K<GQ(WZ$yYxRU{i0Lx2N8J$R!EOj=n`y%EA%Y+>nXJD*FcSfyLvk{{^zJ~v
zJXm5dP(;h51q3o6K)`*cJ_Lzi@By6<3<Tpx$0D61K#+DD35`FkPVdm${&}?V>T6*N
z@sMZb_F&q4rSWLI`P%vWTlYJ?1S2?kOBOpSD_va^j8@X7ik&vk08vUMPa+d2lpR$B
zZU~>r<sF%LdQOEx(bq?ii7=)zy1H3VVgw(BDx)coLEwRmw(xEUBLP6j*>VyfNV~mk
zdYokKl&FngBwd9=l-<)M7FZBuX%Nw+Te>Bdl9KL{RJtW3gk^!HL%O>|x*O^4kZuV<
zLQ43a_xJq+Pu@Fo&dj-U>SnJ~D?1XcRW_I<J^@FNXMyeZG3nL{tNTk9P%znC^VyKN
zdBMv}BY{fA*QaN!#!Rt##rJp~3r>CRAtjv)yqVz3=Xt;kQ;2<qL-fG08>UT+Cu#eS
z(@SI2ms5GTG<HxaqA(clBBsMo9oK)!ZrZP!SEeif@!^}oZ!M0ek!Umu+wLEK{n-uS
z9|LkFerWqn-(Uv}Bg4!rERx02P@v>5OO*zWieEiPg;*<>eYR?&gqNCV99Yr$DS#nP
z>ts8uROraUk8cS?1Sx3}WjyX4M0LtzsZFLlam#)G;7WF@H~BiWOFczmfroWu8!DgB
zh-$1mJ9z5bX3k89_{t$d|K8nf)$On2{3TY8(K;^!<T}{z=+8+x+%<Jtg&CSK?Nh-D
zxIHGHG>3~xiz{%>3?&FR?b4EAgBILP`0i3Z6obXKf!=9+PRG``M6YY+`27a8>kzAz
z_>_ZAss5{wQRvyTCK6ii?`IXh$3GVCehnw89IR4!e**~r3xkvj_`zAv{uQ9ug>Y0B
z;&{D4rs0owh8F3VQQPWLbpDz9qvIiP@(8=ehofvo1?*=DPzf@5J+zvR=P42;ojM+s
z&oS-{q1(m`H<?-M*nb}1az5gpsyCd8CCKUx<m}-C31_?^424rjySeeEjh&vHG%nSY
zH++=z_h&I@*f5smA}Fg_A&-LjPf1{EKyV?x^BuhM%l05h&Kdk{g0mR5EU>Uq_3(4J
z&hqK))mF}(c;#@@uzsQ6YdgJXHh=vi_;SLE1xwK^yVv&GRV+ja&aBktY-F_y54%2X
zqDmHSPwVM^!b6ql!U6cvNN`h)OKR}5hpst$(>`nlO^WVVWe{w7dRjq2;fr?=ixxCc
zb-Dx>F)b{n!=gVbraHHOeOJsx;I(gi-kw9m#0^H-(v}wPMV0Pa#oYscg9mnZcek~*
zRWC$GzgT+cAV@6VsK_ex{%yC?e!J|(_heIw{NO*)tXso{D<NS(T6-+;$xlN_Glh8!
zmxe$Q^YL1snA=L<UM>Spqc)Ta9Jc7$CoPRw));vA#ao)?%JLwz=iXhXNQlrMu!nIL
ze2I%mD`6?&e{h`5z$k!q$PgAKkB<VbNZvia^~SxfM#{=z5@cT;NaA*5+Mx>^Fs=#O
z2uxtX204JKM~6DHxxE4-na0UXW>bExMRzO|G;sfi-|%s>Nmpj25{woMUPMD@5R^Tv
z)kN%PTJ)=oNjy`&mP=8PDcWZR{qK}$mKthYy;R*zj?FJW(W9dWyyxZPBiR`F<oWZ|
zv{7Wz1M&>DXAj2&f`V^>aFY67fMO1M7i4N1$6X+GokMwf-*YWE&simJCT*7AcT>eH
z_U%qQ{9D$lT6Iy|C(DLAo6<oql$ZZ#nbyGCA%=MAireG!qhgcp8&A7W;)|xnUdY0!
zNg_hFcTr&ic3><>2~At`{I4H2%3aj4EMY_29t+x*<DKR{V}?3MbZ8XI=fn)m%-9>Q
z6PtXCcI-ZW4*rx|#cMkk_pw!XimSVFMe*MQe{73M2jGGs0#PtTult`vMs`929(oq7
zA@4_%e_G?Q6B~iY_Y!l@O{j3HxG$gk0_^CFdi=qw;ThRm+D+2&Z7_uoMQy7eB4$Ol
zApi3m#yFQ^_|})mH&xbfH|AH-0~XwY|KiPu;>X5r?&mioFvCe9hw&~4yts!iutooU
zLTZAsHC(pI`>sI9vM93`k+Opp*QcwB{0)w|)Zkq*MU9Pb{~njF%gS>%oP7i@f;@VO
zQwaD16?X~CB5p4NCO!!{(>%!M#vxWCF<(#Zqynd#3cFEfCQVQR$z7$CZWg5y2womW
zL`4Ay_l=B1pJRQj$537rARV-7r<ulfG>`XRBKhcNlKCRp8=D+-)XxoG<irjVCwwe4
z#KuAWV_IVapV)T<%u;Uu>{^)L2QNo}hK{8VS>L5aG?cx-e)Ap}-z;&s(naag&myew
zfxP`pZh)Yb@jEuxG%)4}w>096QH>FM_4#iTtnN0pQcxS<&QVT@1!3WrV5(FeK_qc}
z8^XlGLVx9r+q<>By&0YZ<BBKmL*ZIeDIw+V<pO8o=-vf762y};B!zNP{%aw(OGfx9
z*r`9qnEr9b-k8N9Z+tiL?MZn4X2h~YN1mo(4gZBW){vqUiQbFa%WwTE%5IsuFKayw
zJ%XUBmgFA4&xKwN-l+C8?IkfG?km5vzYQi^?9VmlWWhs|R^J>QnNwE-MB~3w7gYdK
zV^sOHzLq9C^f^JNcYmBO+DKG$VpUyn-UKgkcd2G<$E3^oJ8GxI#;y@+4_ursyuJq9
zr<eTtEH(zag98CDk>}4%C`TzluPhB3=nZNt^fVMjRrn^fJW7B6Y!UtGeX6RZ?$O%%
z`FgsgOlwtZ)yU}6g5P!RgsUeL0zCjl-7;PHv(3kT4W{f!-1?bNrJw^GVf@URsJFMm
z@fRV`cQ>DnRmv2xZGVJSzi}kHhH}m`RsK3#ZEZZl;)se`=|i>uek<BFQ%P4<QC(&K
zp{#bUZMbQhYR;_sY+8}u!{c^Qbyn1C-eQeThQ~fIt#CY3)*k7=!Nyg3wY6iDNf-v+
zai1$nPX_J&5p<CaUOs#Y!|oxOxJXRv@Z~ceRHhIG%M_Kj;00i#<q7$muS(HjcU-md
zB)NPF(_W@fh3UTDl#iYDzFWWOnSCbo%2&t3s4>f}^+IK?io0;4h`zSunkubv+~_4K
zhEZsryH^>Bd_>YO7O=_up{DQfd9`{y3yGpth)dUy(WHm42)c5RYI!NHV~ERG4k(8O
zIp=6m{t9OD&BZ*s_xFRiz+Z89yI*~M^kaH!7hE!uukTC7lG<ATuEN>giUi!n(v7q@
zw$uN;<k4Dk_+Y-Es=8x2Gvk(<)~JvR7Lu%j#%Ed?Wlit8c)fkais8Hock!N5wY(Hs
znsSkupWXZ3S4<#%PDm_WPHd1PepHYgKl02@-GSWay31tp$HTvFMM8oc)XA?XZI4nW
z?SoZOhInNW>(P&E<-<)<VFM$KCxx0aA!z<6g=6DUW5Z^dVnz2uhsbafIn$aB6>J%V
z8mt)P{P>W}){ABZy0weeSeg+U2wx^dH)h8db_+uJL4i$k0G<Z5yU+(G$cHN8Mig#-
z=KSh@)HU<TtSYkL&F<rZk0kapW6x#vdBPo$FYC@$e`1`M>+ko{P+>b+U)T0FsT>Lm
zM$8o;nW~E`gelVk+~7Eq+*f|3Q?i)co5B!qU1MXCLV??|4oi;c#;cGTaeF0msk$#h
z!8B}&xNub;1$0=~KHV{{;|?LZIqfEqz-7?S<3{|<_IO~z{cES-E?e=s+c=M_jxrjp
z%q;0p|L2%gA|9lf;+hM=$;~kdpryLuWd56YrCD|*l3#c6sxUCzB>iPx7&3+8Lp86N
zGGv#Y5>6Lt)zvYxWK9QFJ#sVqMriKaH8z#i{CVTn^_-S=Oob~)==+hM)13g+QBUo+
z{VJskLa7XQg1s?*%~MKYB_o_<l{iM)%Yp5aw!hsNy;*a<LqWBgN<vbfMRO$_<zpl+
zu&6W56l64EZVO<uw(<++)_5kwXUf49Q(oro;ZFseFcUULF+VBC@Q{Dkzd7WkkN*kA
zU^s2<YiAY7{ff#I$;|L<U`xaUIrGHq_JngRzx5f&t~qkM^I0AZUbLyBHVH+ol5A(`
zEsX`%=@eH-&xAsp!>$NZO=Q8quobQOv>0nb24?W+6s8WODZILS!|ZV46}iu*&(`j<
z(8Aw8zmOa(;;r4J7$r<yRL1FkamJxX(=}7|`}!y(LfLa@iEjT!18#NlI-4Am9?4IZ
z4V;%Om^r-s(yk5SQVTavRM?g!;L?^PM%cd5sg9DAPEcwwm3yrTIaEkZx=FaP-Fk!B
z|7SaI0Y=bml#5)vBH;9WIjWb5{>6TvUTogOiCFYYbT-r9_X4~4R5gz7?sk7v@IOT~
zpvmx5{0jubAiP6$EicZQbC8^g<TlG+ETpNQ6H)dIMN%l>dS=wvSlq<Z{(!kp7el+k
zEDM~-(9Lf*ElMU<`8{tW|ENp%MR{-PV-2v&fTXw^Kd)n!;lgAa{hA!xWx@tni&R#$
zDFPJKlv1fG7sS4MIqbmMj@G(Tl%uyaw2$_gBO@a^I>h4W7#Ti-a`d^~g{h7;gUI+a
z3qBUGiDcLi_k6c}#sdESOOIIzLY@?K|MxHA=ke3uW2c$Fm(ua_8jcTSG3<Y%rQn0-
z&E~ELv80)4j=?(jEa)uAR6NtTJsZkJx_HGB<_v;CSy2~QOwDJIkD_8YQ`l1Zo1h;x
zb}B0P7Ln3DM!ZLE3R;|cdS7-bV+ezai3)j{kl$%>tIFQ4ydVPg?}j0te9hN^y_w|V
z(_e|t>i!S8E8NNTg?5%;B0@ayr*dPRV1s?Fiqs9Qa#3`~E9EpoP}{iiWCnJhbb_AB
zXM3cRu3kZZtq<9?+g(It#4E<-L8}WFI@H1nVnh)hB)@uw!hOEuvk(w{%|$m=91mP;
zXA6)>_TL!k_%FZq{km}IZ}0T&<Z`X>`ITs(Fc7WiH6mpNh9jTfy(f__t_|O7qCh}}
z-})D?5BlRsQo}ue#zQ}T)XRe(@Jmx$h1p#R7xIuAX@zME(u60HQ-^SKu<9*m<DhwP
z@v}U<+52j=aM5>OQZ4<>;4KB?qPu|L75N7H!fn7Qdfw7hcuF`E?5C3`<i}R5j(u5#
z#2dBoqRG#4ahe8C8^;(_d9g2L8U=4lBr|#jw*xSO)L=li>^Yi^H!M+7j5`&P6(W68
z>+DOLx9a#FQi>eRGw9I^yW(CWQ3p0mKsEeETmWB{?%Z+WgH^}y4H$y6G3v-}kE~qT
znA5cs{Ci9-Sa~zOdB@-Py#%k?%fUi7@lwn(#2~pc51P#xK+~fDQ&MJnP@n#NXdy@Q
z%OpltHbfFKuLgD$8BD`%4$<nz@%sN8`Tq|2T2pk?85J?$@;>VAuV%Fr$oU=zJ6Us)
zxuR2eEL1Ma^>kH3(C!_C-RM<nUg1Q_3dF1+E`8RLG}*l|zI2eJx)RTH7ZwrKZwoTL
zWEP^sncDP_#4ugrJ+FfUmO>P|mUIe>fnB;GH<tmZ#&NIx&dt7YND$gRnLC|AnLgQ<
zMwnNdA9A;huP&^s3c1QE>AoS4qglE0;UTLn1m);rl8w`XP^)^BfE%G6S!OP<-vk5%
zynq2dd2@BKT-udToZLahr5X$h*fd*z<iCsr4M;MSQ=2r9V4}6L{@;$}jq=jY;?c?1
zToBg8<A*dBsmkI0NP{puRR4CQ)zM#ihCjKR?{Gf5aX$xEVKO69(g*{!YAD|!r^T?+
zikAf}$$iL{avNXUraP6-D2z5d+eX1kiN=B~#l_(w!U$+3CqE+2YAF#nBKG@iSJT}3
z*{Do9Q2DVZPC~ySglco2Q5EiFx32u9UB(-Ja53b<_i?daCdOi=Yopv4rivP6Qqlr4
zT^~da`0h@F{*)+V<uy^K-u_jEbsfbg;B9I}ZK4Rx2K!G)6~=Kf3;^M~9R2t=zJ2uF
z<M!l_y<o~-7W0+Yft%vwD)pI9Z((b*UNed_9P(d=-mAvjK0Wp*ekKOBr79=c9IX>m
zHZz6h2J?XnSG5}Zz(Miuc?o!2JiUp-nDC8w<^BBrqT@3WdlKnL=pWa9AU$jB&SKJm
zfA0M}gMzoduFrmTc=PqZ=i5DodlyS!<DunG@^&sG`7DKW?nTJLGa{mgKExc=s^2)#
z)<<?bu-ZnXSb?AcqtjST`TB1G7I6KH5D$CO$hSdSxA|Piw-*>w`i!h|`m|soj2x#K
zoSXQjvC!%lo==rgr1;Ndq+LCLu7Hr*a7&W2)FmbwghQ#_zw(LFQPmE-BD7%d>-S%t
z(6IZlcp!s-?&LD<-?Z=}65z?nOhYa~`=762SVi&V8j+t0-qxcJ<EbHIY7-OmQL_je
zg}AiQU%vM}zFRAr=nFQ#WjK|EC~%nHFspKtO;EqHGVn}uJ`xb&lz2z;Bk4o6*YP##
zhc|j}UhwOIM1+}xQz)CW8NrrG2u`E*V1FF6SZo$pL?m-rqvkv|5u;2ng?<^--&Z@#
znJI+ig>c3Njv0xs0uZrBBBG5KM$~tDIU;yH8i5rV5hL`EGL2<Ba*<L`ES#9H()KS&
z^vh~yoaH+ke#9oiI4)Pe<vs!jjd$`8fdk*a^)Y@2w<&V9vm<@v+$H=q>n@y(?W@i5
z;`^9_mr{*-Syn?oD4=>2>Dm1Eci7`reW*`+U+nWKQRw8ewTFbceaF)_y;6mKSKo~L
z>rft^o}KvJiJ5JB`5s<8g2R(6$~nE#{BUaD;{rZzv;dUyyk|jX{H(M%XtO@U%+jzM
zyIS=%{yh1^k3V1jFvSR{?l`YL^2TYq99c*^^yp#PF8v!AJ^>HA1?&K6MMNX^gXCv}
zolaJ;s`%5mtrBrOyx8oJk1lW0ROlfDYDv&pSxmwpe?YVzvaqRlEv4dTF8lO|PtunU
zgDrk6SdOq)E>1Q{s80P~x*LgJT@5%<TUU7=&A$Mkh@|Ne!6)yF(sYZ;-%XD}C1f!J
zJN)*!4}Q9qi*Gs0x%XsiYfM{+H6dp&C}Cx|)Y|0=?&3n4ICS!b>c!^M3kz6PPgEl>
zBO||_EL9yI_?utErIXNCV4+qeY6Z7^7&!w~x-9>f?f#&5O!;26tj+K=?>!$s=y=}L
z<IXR=AITF$VeyF(7WaLasoh2q@=q*>tpK;hBonGi*@Mwnn*3cP6u(A_lN<;kLD07w
zNaB!@yBu?TYZ*jp!t^_8P55^Q3#6qgEJ$UMqMshKzhz*xHSbl_<N0RwN8<=$@;h3m
zYYz!-6`VEVc5Gw(_pi9MDIN}rIX?-7SJdxm^vh0d1-Ul=tFX7C&8yhc7@t39X2;$_
zQRR!$OsJ#DElW(8zKcJ()+@Q?+%1oV@)yd72CC~2|9tt6(Ocx}MZa6k<S6k+T}w@F
zlfDPT^kT;4{UdMdJ9-VuT(`^Oxjf8eL*92Ouf7o+QXwOMf#Fe1N80ISSw#p?z^usQ
z8}UUv3-#vMB+m*Ya-JA5Ulidf=$SF1%|-&^43`v?9CA^w93Hv<+XG{cn8PgR_LP6T
zjXh^L<#D3S&fwST|4g;e&VT=+hN9bfPJ4QnrQ@#jvy6mv%UfBD9Bqn{6zh&QP1P7S
z84ST3RQQHhhwiNJ(zhT+)auI*6-X;fiqy#ja<=!utH*YgKiObiXoH@Yx0YyF1)SQH
z2FvXW*BD=~e|ZvV(;uOwt)Y&;#b!H2!_H+QU61H2sin4BZuW4Tnh!=^dTtX5<*AhH
zFqh5UEFwFGo#wP4-}_L4W%~~wtR*=9k%b}2825UOh+%cExN!+#p3TkWTA^yRU@2PK
zE{qL6BYAUtH1pMMsrwcVU=D?BS~W{!n-0anTi?;kZwn1Z&#L-)LFL>m`{SX^BaXy}
zS7B}G{B+q$z6!!ygxmD@dBX2SG8}zQBR9k`yU{oHSG+^SAMxipOFAA?7V7yy(}#8!
zLVM-1$AW17W|25F2dYTxxocZfc$Y4$&Er!@T0v?Vckrp^M_~g^3e<NdwpK^1JXQ_M
zpY5qAX{JxKkS4hHI5#vE=_8SM@fDS&P{l(9BMj4Hw^B8v^z^5=1efnfxYd<Qtu_uE
z>Mw#8X4OkGh*1siT7){eUK;6mJlsVdN^X}Zc#vQIL{S#EzPkN)avwcLB{hERcKlPG
zQ|bI_fqlWXHT<I-@Eu2!j}OD64`tgXE5cN)9i=9DPyXJIQBuS}w=!KQ#zJL;J{Ho*
zrT)TZL3(B;mFI`G{t)EBWCce=YFiR}9SSfOrV$aiI>JX033%Nj59f={t4GO}R4WY$
zQB;jSIT{v<jf@}X2dxfI<&`3=BMFP466le*Bg%{@u3R;Snfxr^9R0}i++e8S;PX&;
zkY)f*j|2q3AB(C@g#v<;TYi^=(nNok9vpVRoMs;~{%trd01sPe^q;Qo5;2yxS?cgj
zZ-+<kM~+K2wKl4=?R---GhT|_G<{)hp_@QnTWeOptuZG+JNJHlxmlf{DlVbs*o_4g
zpq$|}tsJ$bPEro#<OgL3cY1tSV5J=jKbOq=8)}F8_=**N_C522eI=3~;X3hZ@VVs?
zV3jNSDu<>3Z=|E3Rfid;{~ZBkZY+~@jw&#tLKcIIPp@1X1AsffuV27Mt;RP30T>G|
zQ!dgsJD?OkyG78TC6SX;V!i&kB15<_mA4)F4QKJ;jngItG_|wdc)@RV=w=A6I!V;$
z;K@!U@-Yt@KW8N;g=qb<!6z2A2zg^#X|bNocj8tG;u0!h+i=lq9H`5vAV3uMSzXO(
zK+Gu4yz=l&)6XLT2;QDMw%=s0c=YvkM*L1NI>Gm0|D&&$GBYZtzZW^Q3i4`OGBIZ6
zWj)*$A=1UYGF<sn!A`diZ@xmsa>P#<w#pM_gg6#i!6sG~#UL&&F2<2L7a0Ijvb`rz
zg=L!#>ruk}Icuq%LIbLEoYwh_c$WTL?3+cd<vfu}IBScDc)WAxp>ws^Lv{x*YWb|j
zL5Z_!MwW|snZT-!?}Dv~d$!N(>gqIEe7KplF2(3&AogrLMpHq|Qv9aFUT_I}B$Vu@
zKLh<*xHJ7Wl}`O_#%R@xEJ$|?Y<+Cw=WiwP!)&UR-j(R_oD!^s)77oZI!+N=Jxe!l
z^KBuGEN(6?<GFN$?z*`b6kH<k4Z-nU7Ya&>)z#Iol30@%LMZ|bHlDKIHw`SN!(jzj
zrlruABfnuA?d0T+j2i~K-X87`r?Ymkj)D->vWOeK7~ZU}t&R4-OKwWybf;G!-o%tW
z`qFi7-iVs`_u0P}*^SyI3(_MwPO#|P*}Byv(KE)~gzXg|hjm!YToM(rTon@vb3O69
zg$lUE&>A1@_#c)J=$rrDjpuZYiv7=Yw?-`<QD19(nM{K7`xQPjEuf2&qd{uWNcd8x
zCQ31=_3g0ty~SEF)PGCk%@PCz<DDJ^gV1E*2Zw5g6BckjS?Xn>kFx8?dJV`MMT{J|
zKMI)Mfb|O2{k<fW-@x`j;fr=DPY43DqBLT)q7_>Ls)gBI6Lqp;?=$Q)iw=flaYCeZ
z1UUJ*_0!GT43nTHCu}1%YOp@DE?qMTxWD5+jIoj}RaoC264(1gXO!_zzhM4z!wUaJ
zH2N62^8ll%PZRy>kt)c=OJz%bgkJ5;z+Et0s%oqw_-AEbfpSJ1>)!7n+;_LM<A?z0
zONVI2pK+4{+)$WYN|>GZEE64AJ}%~bo&`2Kh7FI2$;Xj|oR!v7>)V<tjFeD(w6@iP
zl`9jY0h!%Kn<69E(4NL!)QJ)jX0oNlO1b5EET|qP;&D=nBl$!4&D`{EsLI}$!*3Xg
zw7pA2JlhJ9^q7MjrDDXA9(|!&$${U}b8S40KdFRLp^~}|OWcYEWjJP^g){!6Aox0;
zJp~%Sq!x4EIiP_sg0mWEka4=g8MDp-uKUkD*)s&Kr65bXp#r0E&prM!Fb-IbooBf<
zHCVE0+ay9Yfe>IHmtdnof_TGPAWd!7%rHG)*8Jfa@Zi4!ph)qxncHDG7aA?QgXho1
z50;ChS;m~+AMu@q^%WdMJ!g6OCQH!NTFR|d%(A-}N(Ymh7`0Nw^(5JTPbAIzf?nRm
zfu9<zr3oW%Ft58kCXpUlY5m`|OSjNlbI<Pj&Oeq$lq@Jk?}Rtetgboq1^d6;`B|Ok
zHmwfR<>TJ-uux*tqM&;hYdoI`B@aILd@Gk6S7<#*FlWQ}ii`kbvw&GZ#-`D{&T~;1
zCpQ`8&U{QS>@$zUe*qHOB30Qg8ZZzoU}%Z^^Lc5_9QjInh4+UnZ>lA(&Bf+#oldaf
zwBL9a!7tOp`T4meGmOqs)2C&cZ6YJ${^46KA8M9GXb#&?51OP<M9L3M#ZSNnF0?^Y
zr4M(Qen4dWFQ0_|Jw5ic6&9J^*i+Uu`~GT#CGR9vlRhXtK4%d!XLCE7rfvDWoSTry
zY9sY?%iC^M8-+&pj8H>TSUxpOb2JxnSFRbJ6uw{4O5XLBHR!zpa7ppyCDbaFOuO>o
zZ1PF(Z;>h`i;z2B5P&iD8?Kp2;K^*{=nJaSbc~-tu#gO(z@nHd3e%OL-f~_}PEhZO
z|L>9^;k#)+t9HTV<H5=RCn$>?_jmR1*cj?h%Lr@?+~Jf<OYff9KEW308Q60hViG1c
z%bl8*SInv@P^7?(<x9^XiEw8uVyMGV3>2fc2r*P<l%G}p#Kxe5i<29v(O_=Xso2;1
zBaIFic4h(~lf13U3@S#(TJzBUFBIsF%sRC!==0BX-c|b1USqn55EHoL<nC#tE<&a%
z6{=N7+1hy~Ugr{Z_9^fGG}Ma&{+tvsLbZYcB%cRe^c+uw{$YaM2z?D6P_vD|1NI5i
z{_wwsZWh`a63^udJFpytc8NgRQN-gE!oW~oxDk@)07t=Q&2n)uq?LW?kJ3{>*mqec
ziI)&|s?>E`&DA1{nZzDWGR0PNh`?7S|70KqX98pFpil!}h7z<&y)b9=C|icxl$wL|
z2T`Y`3pBFUJTqQL9!Vq2LkN>t40uJX6aUl;ew)F_gR6oiw>1cP<uRzPae99LMGxoP
zoz<+7g|zfK&emO}GqBEWY1OD>Yh=ac`}0`QfnS#?4d`EA)c;4RNdPQ0a2~3Z7g`d4
zK1`D$V)-z7edZnqjb-Vr_7ze66Q9U0&GbuIIR~_f7ep~0a2J0i3{>RMsF8AXJ<f*0
z8SDu=3NnoWd_B{Aa7Z1FB(qn!9ax8)T=^&n2d#WKSotj{=JrYV>k_U0(3Ic~;C#of
z@Ev^UkEx+lH?;+h*zQ%$zkmGvAUfowp**2m90EFIQpY0{#LG>LVJe<Jy?96m&Mi}%
zqODH0f`S9##g+x-*fbXUW^5dQ9D$46_F22>-|OP4sgIws-=(;U{%Jd8G)-`(@spe$
zzym7Pi@ZRH)Z8s~?%^xvhQLkq)91o0#M2205zdeQdJLIKsljA2Uzn$*VLP6R7-~Rr
z)uu+WWf@h9W4$5|tlm4zJUJQYpGmxkkDxIFa=(->&<8Mr;8+>j3_NEhCEME@#m?>3
zsHMc^m;R)~|B8r3QJj#fWGu66U*lt~-VBqp@QLJGu&T(RK^c^W51-~^5wy~u5FH)8
zyS1Ud`|5Glz%`Pf?|s;G6hIFkXJLTazN`|yFl^wMBBuL&OeO}=tfE&@SXfj?A1nz8
zt!WnyKK54%Ldqr>F_zFpGJ)rUlV=djRJgmFRwDRlk6T|p_5o!0yt;mMg~I9av7$eC
zvV<S8R2cEf5#NwgjyAB2Kza!dWeUE0m(GCGGdY-z_7du!T&Dquq^C6oE!;-xYinz@
z%}{7zbuLOu;%OQ{j4!CjpoVIpRNbqIMuedBu}D(`%cspA3PuY<?oa%nOtqjqz~}%f
z*s5FV>{f<he>{^jGyjw{+Swudh+hPO-J~~rttlx$GLlh{Z=Z<~B(Odr$)M<=yAu8i
zRoL0x3HhsWZdMEu5Mr$eAs(M}Wd`u<^RkFArq{w`jtJ#=7V}FC4N5d^irzz0Ix>_p
zTvm)6LTP?&LX3?1q3`M9(>7KXf(4jhnzXSOiV;FTuo5s}jcqHs42(lE^791JYl+a&
z|K%rs!Rx;vZO>b#)D{2=+ZV5QgK0?B+=5fexWx#8L;42_(!EldW;cU1zoo(`DPU5x
zfzO4GMZqDx-JB66@Z=0N2i{1@zPd<U4ucSAM27NwG|;%g0|pm4u20|19ySLn?;f?6
ze3VM&74c|b5)qtYzF9f80|UrIkCt}v)%yu0+dzEH!JOm6awLB}Au$0!L>wJM8VAgn
z<x6QIBj)vqX$r$7ol0_{ay1k%gbm0v7h03R#sH4Mi}G-=RV2Py7<jRmM*ae2?K!6z
zVbY)PnPMN-(?SIZFi^`WDFU>~tmg;8b?<^)<QpwnHEk{ESg*A(bD`OVR`&Km(%+O6
zEZgB$^<a(EiG~JMh}d`t@B)$1D-F_Cae|9-98%TP4h=Qb?sdl!0R5FNmJ3f%k)O)%
z9J9=Y3fr6v9m*r1`O+D(n352al32BhY*0Wj^L`$rP^_RAA3V*=8uZ`w@UsG&{h&XK
z@c{hyz$S-FDPAku=36O9#@Y{5K^k5Ky#D3Hh!=^gYsLiP;;~ePeD)az2J3|7#zMKR
z1=P(Dda0Vq0}><%FYT~=e_E5&hG6<&!vrpviQOeoa+!Zt-Q&de;YrcEQm1k|<UKf7
zW-0qKi?f0XWQC=-mwUos39=-uH~BhpJG?HAI3ZJS*#Fy`V5jM=0)B3F>*s0^VwgnK
z<QGWnZC$;+_OJv*OV#KQ?>_`HkHAB%!joY!Xkd!-9z{SKK#egVFK%bTdTQxvm+SNd
zIEq%xJOI5!ZVXnYXIe~a6=-`Cp>{RuLv+@0{}nm(+kW}6ADxhsQVQ!V7XjPx{5p^I
zV0reJ*R@@F02v#0OBMPyM;aSVKi|e{PoB!Z%=?rSMM|&P7Aucy<;GHP;(;dNEZRa6
zBuMuRYQY1av>$M)uKSil=x%L$@lT)CUdjZ(jYLiMOn-)dSmaDg<P&OBoY(ea<Sj!S
z8P;aqnNlj>Om9~MZ5x9OkSS>pr<*VdMl6d?g?Du!jwCzRR&^NNu8f+FgiJi46j>o|
zv)%}iuu-?x;{S*bfTR<(jpOh?$jv@YJIn=pF3kqZC4Q^@QP5}$r)#b-7%rMf79$)}
zT_gRpECC6KDpqACn^w%wVc9`Z*BaJ!7uHJz&|o2snftuOnW4dZn3WLIu_+P8+IZmP
zUk9Ibd;rZQz-{jX-z0w7Q$p90-8YutF693YQiN#(^C%Kr_sbbNx>#uWY*iul!zO+e
zc(4$g8QY_iLtZUNZLLB+e19JxA;sD=sX{={F+Xe`M14N!$8Kn5VhG!?`qkfk#`9_a
z_>;igFec-{qGc%*z|y=hT5GU_mE8DlpY_EMZvel}mj>kQ^ML2O8S9IO<+f5#5HNg9
zFluBMr@q_ORW20uQVq!M*p%N0v-Ow(2v^DWk~}PeQIU*N$hmC)3+L?%h+6mWY^pSP
z8ZM|B!+7M=Xjz0wnj+y&y5H`-J`J5r5g!P4D<nbqnUX>-6b9*g$Q*APyl|F=q5N}?
zh#6T9bPA3Db;m@cIh(_~n&5U|Py6EGgAQeiSWe%cb;A-q3JzAaW~jPu!ht8lQ4yEr
zIWtT1m0A#x%p`2wj|KQ(yDrV@CFErh7IDQ;ddyF%kXH?Ka`eVEnh^SLvi;o38NEO@
zD4e2l!IS<u!8?7dxvg%#=gfRlC>*<><a6$sY*lAg0G(AXlBdl>50zBC%7yR)<ZJoI
z1y>$?GMKs7fgUH8NwZurqS16f00=_$bTq;Gz|u(W47b^|#_0CDkPZH&L8H~5$e!T(
z>HX`WO;bTK`%C5orardiL*U3VtIP$7QRrrYTq2YZJ|FmfsmfB_%zyci9N24>#VoWv
z;eR&6@pwf5u!GaHVde1LF8GHKVbfl4Fbml`CG-UlOoAXblW*ygU7ICnb~=rp#BESU
zty1niGcsr>-YOKe)A5<&z!gIQJxuUn_*S3Zt+y=tVzZd9p9JymA2pVg*a{MFsve(s
z8!8(1b5wP-u&a<~i02D@5m!34)2aJ17VYFTUXK_J8X1xO=Q&3Ky9{Drn~PBTUC1=o
z`$CJpe1w(hLDM8TjOo7KM;3lovzEXB{^Ire`Jb(=fvbBha`HhDdMTrY*J5rR0oZOC
z_wh%41dwAXv0D1N-R98!jS<K5xr4=AUT&686T%G+eAU#;hD9IJE90fd<!wd5KSYu<
zPS+Y-jbR`gZ($9R!ZG`fM62ap7wQ3@#zxkCbb2BZ7}H3NMBHfH;bars-w6}O97zb;
z9>aEg4Pz7Q3|DMFz0+}=^`pR))oAk47+J{jv1I@TEr}d=RN9MVQ<?l>TJ+-hlTwjW
z(JA~?rprA4%SOx5)Y}_7m^n*mTR;n9AEuI$$=q;tpEz$UC>dk%(Oo+aYNLkKq@wJJ
zhi*9+tEQ_;3%;aM1a(jDhbt!LPr!JHW555`!%!nsgwsi^US6|;Etb-1P_*&LlS0$@
z0bTO5{q5Ing(Ig&8*vAV6%Yh!40Pkl?aw6*Ov2p+3#&pTgAbm}ZiNlcXZn!O$J59=
z5OB%qPt#YoYRFu*V5@I7I%a7DBA6irZNZovkm2w5(rCe+K{PbdL2H=s$uM4xNRxc@
zyaQcnHqw<w$E>^aBHONT)Lsj(M(<vS1~FJMe_z=;RvZdo`vAaAPtk90U2PXC6n~5)
z=JaePjjdnPD#y`9@FT>h3+cgdx&ss)9hlPl!_>S034hBapN?V#^Hs(;Y4DpN7g*d+
z;(A?6T^8^=lzi1*89a@>8^b|oJ<xgk3+>*(Np6fDf>^no!B|>PRFg&Y$~j7b`j^75
z1$@8$SMKOmwB+<Al>Q6-U1pT|rWjf$;36fJGVm&C+L{F-Q4a=i5vRNGKl_v3cW&vD
z7*y(|QE=6Lg(=?lCWuJ_7e9ZZg1nE63Z%Y{kC?z=|1At5I={k|4QI-Gg^12zM2`53
z1CGX#DmUCjpf_BciGVy3SKQghMlf-G_E$YU5+h4p1!9w6N(c62&k{Xa>s+rJ=2U}J
zocvHq<w(*Eam|FH8Q!{0XF@mlvL>*2dF!Mh$p64LVcX)-&fZsCvim*F`O3Z+OG}#I
zjy~NfPI?DdvvM^`VDOJ{z^jcY-6e>5b2U(TgGg7zI`_YQLt^#W(wyFHL_2MZ<Kv#2
zSV1~jDcnNCjLGrvOO|>)HMcbukmOTR;gfE{y~BPLVe~Lf%WSe|#{I2qI9r6G;Iie-
z=?)EsZvotU|M)ie{7nN-W6A`Mh26x?htlp&K7@xMlLgHbAZK6rKf9|OwKZiM(B@C*
zP>4C+$vtWp(G53iE|Iz0kRblFWMqV1Ub4rN4;!>6S8~?Vb^`tObAQ}j=bCgFY&3~H
zyb<x!uz?LHZd5hc!{O8WI0=HyvnmAKGdFmTxQ~Vm!Ux@zs+~iu|9)Eg+Yz#ang=6r
zzLAuS{}pL>+JZ?pPI2;&omz&&storAZ4VRc_M(>*l*4wTRG!~iT~P@AScikglrbLW
z8)~|IIGS(W7s$zijCmI-nGY1ii=o7DIa-Gi7iMr1=X+m;7G^0_16=a5z%+sShs)jS
zZK4$0FM(I~$f1R<LJuoP2iJOxCKMYfyBDMX9`Q-(gMqYP;P5_Xc=NnQS?7y;D3p{K
zVM*J&>!L<%xqPTpP%0O?nJXtC!{BKYn$tvx(J{CG9SWu*5w>Q0Kgp_Z_8(tn-wF=k
zoUP)Rjns5M)#7KCfO8^0<f#k+hN~s<$-K@KtA0^tgQJE7UXZT_-uw+aFjZqq7DOc6
z-vj8pYr^OV(b%|R8g=RHsoa280`uich137qNcz%~V-C@G#xE=1LB2yBDbT*{@%P=%
zdclmBRmSif0HWhXL<xstf9*@jWTl9gY1M`S$n17W@tFn#p`~6W((HLPb=@oJMV>0?
zsNal#EOeH|JkX6x^V{?+xVwvUtL5?R$ow!&*YU_hXRL^qxYIz=z%%9bP{e;_pZet*
z3~aP>s02GsY(`qeu%b4Y#0gol*AxHD+Sam^&(X@F4`WP~IqihJRNfjX$(987e>TJ0
z+Vlk!0F*u$uxT_2cDJ{$_Nb))GOY;n5EaA0<5-nsjyWqU@=0Z8n;NOWuB|x9?Xn#(
zn1PLdlqynysKTriu@j(X)pcw-k&(C+aDRGCw(?OD&^D;fb(h~wKPA4YuS+*?as4&C
zY<IUM@N6!A_L6I7&-g3H`|17Z{W=nnR%?w_<)TN~g4o#1nY;Ms%p*E7OND(v-BQFe
zYP-pVlPOG{KRl|^-t+(g9e!pf87tj2ME49(J8a8==R-un5v&BLWUck9I$u4NR@^02
zO>pSJ!4{Yg==&PN!d4wKLa&1+S-`3o{8$!?($sPC=E-rSWzan4ylkW#35G3Pw}cQK
z0BJ4d+<?^7bBZ4iwcg`MOBMsQn)$nY8B~)}GU7+RQvYlqO1*N?=|7=qoS+bX94>JZ
z8ee80YlLmwA~K<Xw?)G+xGA}A*F6IGbNo^8E(Utp+9p4!Cg~1fYbE?)q1r$V(tqyS
zJqGj@D~2YXvxr$*F7_2&f|P_aczZpi*zf8-+gHYarj>eckS0rAqZdU+2mrGxY~Lat
zEyxRw|6-t`rSTTWMM*ycegBs_>Uit%5DvPdB}gl<nLe*KHVkj@&k1MrzIOxSYs>rf
zVN?nbgo8_Ih@2Q?uL>!zfPW{)c3z@EON%Ez!lxDxncefcH4}vc9q#0t_%!t4Q1bm5
zMYk^LJVY;0S(Hu(;^OLJ<rNg1N+((#i3QNYnZvp_Ty8_p&6kA_%<M$Lu5jIECJ=y?
z_#C7V(NT05_wOlAM3j7o-91`E&ePv#%ngF#D9MX)4esJoJmu#-gna=g8Wv`oX&eP@
z7FTB10(EA+Nme8zkOi4-AS6~~*J3(LfMSLX-c3;bPv<dTf=BS?Y<ft3g%?;=u@$ly
zFbHBxqtH!^zSa7>OE(M7X8aEu<KG-iV)TXqp#`VtC;Pt$rNy!YSdbQR`#}hPnr`3-
zTVWm68HQn)eF`)79u8WzUde7MMIg}l$XJF9bJpK!$<9s}y-k{@IvK;m4bzxW?=UHa
z`sd6X+NyzRC=G@=R8f@`F8C?QL|;WS1C?$Wy-K;bQ-csa;gYFmsGsabWUHr6$aP^R
zhEqUmz8l%<XIPrIr}2?75`;=NVd!k_HGx&hVa$02^|vk}B9u0ig7{SqE6&wes6g~F
zt!*{XbV3oS4YFks`D=ly;9MxL41}^fu*+UKLa~k-v*(?xdYqkzjl`J73p=o`Syvf9
z9@qNe+fUPjoF{HydQEl9EX=up?WdE*f=-B_lP{PioC5w~9No-e8&uPax{?vhLVwUg
zerB^an4CpXOG8WTltyH}s!idWV<_{M$GjC&Gi%yJsyIWr0<YWM13h)`ooqxj5X1AW
z`iXG%FLBU}Wengb^+WNBBN248v2rK&>V!(f2nPAXN@k>49!?KTh&-+vcwSbrU!%!+
zc@6xCUm7on5m^_F!Upvq*d*@`_;E{SCgi+c&^0t}vwuFqodxkhG_VRN)mKyN8ziXH
zXC%fD((1Buksig~#zKD7a})ko#MghPGr6?49Tidd3s~u0qm=z=0$2jJd5|#V!SDTd
z<=~#!&?Ex%=>@{61af0i$Y+I!erz-raECrS+odyA1P%76BChUx2pv-%UIxi~1Au<$
z9{D@Q-G7mKC;N*)`d&FE3*%NO505MT6{)3~6vX%8Kv#>~nZnn!NH=!aE2Bpz7c$hu
zHu1Hco4<cxnF*W#jVX^;fGn|v=T}HYxG)9t+fzB!SkDj!(xLA25cY+QN93$mcD6vl
zfY<D<%dLWEsL0Ex%E~vvre`*tJ1pBk7xd`m;!=lH^LV<%o`xg_&<WLbu<H`fUhBE~
ztK(&yoT~l+MgaN=*|wd_nq@+%#C#ua?1DbC1k*gIU{s#<vL<ZDCEYrin_jN{*+m^X
zzo|mVx*WuzKZEQKWh4|T(xqNeRpr>5b@}Ydh9=g()A8$9apPY}pQQ3nTUvVmgfFvU
zqs_Woun~d`?xoJiI8q_fy42ZrZL@2?7g&(Xk*@l1NZ~X7xB>DYRcxAXIi9tg1syZi
z?Nq%H&}=x8#QdnEPhvS;RRqzy$-4Q{=|-ey{Ss$NffZq|mh~+sYVkh45_0yLneqH^
zYX2y3;aN(-2Kq|TPqXF0vwA^W!)$zw`5N`+<jPo762yD$Ve&#lD7ZM|bEnLAZL<S#
z&scscHPTWIqHRA8G%Ib=i1dVXlu07VZg>5228~u9^YZW(f&2q)>uwSj%>XDl2o@wc
zOf3uuWP|h4#YkDeqOe)v_&ZKZt(e_hxn@ApMMD}6$dD5VkAU|JRz4!64SigE)t@IS
zbv&J&X?5lJbnR$c(5Gwp_N)v2%<($u>uu8DNs&s{<KR0nG_te2Xfa{k@JaIXi>#-Y
zYyT=5*S_o~=**%Idu2Y2eiC!t86=3Kdpdvpbl$My&}-{iP+cW(`6MP{xFbWRxUvM<
z_rpTOO<&Dsudi2WL57c;AH^QGkcZ}7Ea+;Gh~CZ1+uC3z5HSHi$XQKA;sO*~4wh6%
zl!;4!Vt=S}2}^u~vuQR>-~DiG_O7cK`nFsW;-bY6y2}BsynNGN<#$!){k#3|)xw8H
zGb|*Pi2K79S!dJLd_nDwR<5Y?R$q4@*3%Zpqhz9Go4?M_;DcuhE?0}5Em81d1(Q?u
zojusohMc=}ToNhu!h$1FkOVP8-}0cr{D$P&Oh+tgxPlrn0bGtD97U7*9b1J29Gq)y
zOZr&Y9JUC^g-N7IG{YsG=`*kKDxn>ZS5;3NSmOR)(@sxMZ7ZAi0PohUf5)G~f6(Lu
zlb52OJP!+!9yeb<2ILltKWyTwi2tjb3pD&Zr!~l4q@2;R9VIf``2`B`S2yciN|_=b
zLAI}ksoXCASL+}h;m`*NH~Qy9P6&CiTtdl%W~1X;g2nwkPqZil8BYF6<a`go{w-~7
z9GylgI=64EoR5#4^%8GP1LmE1hZbCp&SkDNnvC!z*_0+ld?NHgStcR>8GNEU83M{e
zj{`I5D?*0<68=$Du#<o{J>2~qW)=Q&c`)I3%_D~sGx+y(^%?e-%G0IF({0w%ZC3#L
zhkw6xfR*z^T7$k5{$qMO(ZB)rWnukpzH#k#+~dV-uZ2pZXz|CJe}C7-#l)U>K5H|B
z>pJ6){J^dE`qfte|A+F1Fi@rBngb9*Nkd9igoJ-BHsIZwIWD4pP`$N~S#7N^hQ8_W
zb5pzi_DWmGjMZG|z@AH~Sq^e$x#!Znk)~0iS@EsG7_4!PFk%3qstvJ3l^6D@c93OZ
z0nND7?u!ZWuM?twyE<_mZqQ%fNaU!>70tv@ZG06!4^{aIIK1^&IiAIeUzMn|U(Flb
zU!NSEoV1_!Q|4fHKl9`(jaZv>TuYAIWs%o!D~ZYH-dWxN0v2jjZ^|b6Di$dTqGs}q
zF)tzFYe%fwG=X%!)(&fdUH`5Q>021E!BYxr)oE#I3r&Z^3kpK?7o)n<lE@+7hbIa9
z;PAy{`u=4hNRu-LZ054D^Pl~dxf}#=>-M~=M7zGie7Z=ezNmgsO<Ncb7JLN9SO&3?
zCJ7vc3(_vjVy>*9*O!&39NMQuOG00PR9{fkvcUZHX{D3|G8(kOH1rg`@$YK;@iO34
zFsqG^KPgk7+RuS4d&DSyJJfks)(NNYCsKKPo-*OXK#nV!f)cv%PW&#Gh5pTZm_!cP
zYwX1R^;=I!+z<ZVR!F~siAQveV02{B5#&FbeW(Wc^{y1xo;J`;FxhH$765TnsyW$e
zU_Mro@XvP3+$s>isMn1@Q8z2~DEC&lxKXfyZx&0K|Ksk5LuVodtXQNYfzaHD2=O9b
zeWjwz!uDkKeiQ$zC$Lt|@Lf;p7kUi*&H`T-GwgPCbxGli&Tec!{7LG3=ppNXg#S2g
zIjUT3^>~_idYAyl&U>$2yo#d}5fPzY*eGZfKEQ8nfBu57FXZ~OfuGun@~*HqAw9CI
zYr8kv);nZ4k(~$bf4re3oN+bKFUPp8)vS;_C-VIlqQ1gwS@;Oh#7owvCRuP)%$8m-
z?EYdwGTL~GVE2&6>tpydW1g@B##{4`e*>^y72iYu&Stk4R8{>RP2utUQOUhAy}C*z
z=&*ih*RHViN6;0`@XAjbMvUmOb8c8&6;pxu`lc)x^F&hK=mV13CeiYllyF-{jT(v4
zVVQtG-wVZ&TFKmL<{-C$zdp(8^x!Z$pXb7>`S7OE@^~#(oZowWx+;rvB!Z&fSkLF*
z`PIIGtbsmG%J!Q`3^gC1E&Kz#8=1z(C|>8021(?duQ|~rA#F)OZ3=p8{lT28lPU5<
znD{+TK2a-MC}KQtK+uB*VXfPy{15!sP`9AR8Hx&7itM!5V<pQ_QvCBP*qXeL_dR2Y
zh)f}rZib%G8)?d?uoh5*rw8Wg!QrtGt^7XlL{SA_o^1k=@cVa?o)NUva&W`MIz9jp
zUsXX{NU~~_6Q&yQ@&_bj{zkoUNBm3wPz3$ry^+ue1s9M`MkkgsgU1%hvr3^X*PtBx
z>9L}UnKpj;=3=PZH|C6p+Z}J<HV*TVBp}RS?wuB%4WITXJT+UEA8rf;FbL4$Iy_Jf
z)`&_al&+Yen0c*6<iPxKI!>_Dfw{_ze%Yb`3LC5`r!s2&PL;<dk0D=b1x8z)JZz)^
zo1`@K<DjAEX<J@`q9W%mn5p-asRdX;Im}$~oADVL{5Uj3?}!nE=i`j~W$194oSG!p
z&pq+_lxEcS73CH%LqY<v)LgJps53SwmgqaYtIHoWMUt$PWF~Y}TY={5?@ZBX2ZPIL
zsh9kj9=7=6eN(Nrjqda&)mgRKVo46t+|*xGho=E()Fe$0BijpPybu=LjPhCmD%NxF
zV+si{V%f244)edx#6rqjyUa1Fe|c^MV0BzoI4*r18yp;rq7pfCtxv_tjlx5Hb~UIC
z=>9MMGJmZ)9*o1AnqiHIqNr^fE<T8%O_BghE*U2%+F-+LxtJqC-JD8-FN4?Lq$R{w
za}Aygp^~8wQBMf8<jjnCR`t>Cs`#OGJXoUenW*63+%0s!6K{d9D-z437M}kxxL+i=
z(x7v;d;4$P-rgM`_kn}<|HKMnx#B36W?DW4K)>ur!W0+|oyd(`OhaHWrVn|*u^Qbp
z&Vtm2c|ycijx<fEG)+N%1p(0JIJlex!ej1X@OYO^R6+g%w}{W(Ot$XIj+ijcth4TW
zXONF$=f${4Rr6jd;DBNe1m^#eWzI{_tx!gVg6T+swVSG^yQ;IuEdXz<w0r-}kqSBO
ze0t2u$-#Q<6SMCncL9VtmD%m-sMq3ty?6qB+vgm<hl*dXKYhhKeC>C(vi?ZA_V>SQ
z{C8p7&eZ|&N9QYybB?D!9A^d_KiUYLYGxDpf1ihb;qR+{9Vm&`l%(w!QWVHwodH*+
zq{uU801>#1;vUUYAGtZdY3zvY9>&OF83x(s_E9~@@`)wK-nu-TS&BgLa`&+LR;o@V
zl4rXk96bVY;8&F~^3CqpXf`3aA3>N8<ZILANI}=V(axv)&ULq$_gQzdMIHC6zx^Ho
zwDn7;6UQ-x^>(52vHrzfaD)>8wX!476{*C0$RE}Ivw{D5k+%NO+IcVHxeeHbqdFqK
z?umL||NOjQ#L<2^U2Q%9wUrodx?Z-e*RaqxFu;HH7W{#9e>@}5El-T0A4iKq<z?gQ
zeDi_E^?J>vKI7pl#|bO<AvcpRv&#F$w!dG|!ZgEZJ`n0LR@74~<wCqNp9_xSPywAN
zFi?1v#!Mt{i-N*=Gf1Dw!Z|s(aA~Olt{zGcBPEa1s{&0O8{_7++U*G}z&tzzEU<36
z2gI(czupyn{RJqaSvz&v7{19DGtXvOs9`86DRo5`6%|oivMFbG+@H2)@cs0=KdEZ_
z6IRN{6b8TB0w70K$K9_voevd$1yFE|Kh(EtB2(0xlw+HO4td(8^5Io0gu~9}N;i=l
z2i8_V4@Me)0FFf_O2a-e8V(fdF+19~0SbYh3_!D(94%=viY-}B4G@|yC?CWIXmDt{
zRISJSDuh9(ld%w+<rXI5f9X%tnbTK|Wax4C2TWfSYbX1UHwUurf9`KkidaNqz4A11
z9<B2>Eu19lI1A$NI}d+-(}k7kwU_ck^u-|UqED6M*ZY+izU-zLONw9Mwg{bgsD!;i
z5k1UvB|(36wCF9bms}ium}V{(XoSUEcq|tH_Omass9>Hw>^x-Swu-nWl!bNbklVL`
z`4#BAKXl4u^xQQxr9e5av2#urhR>T78IZv9Z6AHWdNU?Lb;H$GpcfGt*`15xcQ%vt
zc)6G6sQn}vGyB^2QcWXr276uICQXdY(dW2&0GdEYh&}k?dtuXVqQNV#9Rm3{v+70H
z3DF`dI*i|IQ&XrxyU~7^wDNIh+{Qw<vTq#{g-y++Bv?XddL|E#FRoxzVp_@igao3Z
zZ58mz^+omJkJ6b7MA8L%@WDMfX&A`Xbof&8|5$tLu)3ONPZW3A5Zocb-5mlU5S-xd
z?(S|OxVt21aM$4O7Tn$4ox8|;zHiQ)nYnk)^IZPed$D$}?q1c^zpAdT>cYq-^&fxH
z9cDS2RW~6DH0RVb#x?*_cB}$Aq5z<J0cmoM-^95>;S>OE3yX`>%d2od%o|QyAN@Pl
z2wj#cxngmYK@|-leVaP>gDTk*d;GUckhms*#vmu%ZVoLl)!%uJAeZS0s13ku_Ho_K
zaA{h$28fM<A#NG=AR}XRco$De1U*eNz9(=Uj-(3sKkgn2=GPZoZP`TJI=iXGQb_S;
z+#8aOm!+?MYQp)5R(0%*<lx^AVetJYD3HpjXf-3xG}bhoWa+5czri0)cspds(rS|J
zd6W3n^}G#r=EpO{FGMWC*55FJEaB`!uL@n07G>q9TjEBiYda%ptF8twXLB~5oA|D{
z<y6j&4?Z`84Ifmf@w2L8!RM*hPNx!a&UjJ@TcDS!evr6tONmMp6b7xwT>6;Cp1~0A
zs1=Gxk;56Cr6<)gGD8Wixn&xuWr(L>p~bPtE$+265JI*N({;rIk##Z|8t~A50rEJ9
z=)Ano<U)9nYMigwUJ|9TyZ+iU7L)rmzgXztgo*QqIneSIr3W2TcY|^{egwtiv{P7F
zNrPx(>Q?%pD(_YzD@oxYv}L6Xk0g+MPx^gNho4)$y^`$(5cGsW6nYX0{y^;96bFkU
zbc{?=k&;mCMF?O6?cJbnRbOcO>i-M5PWyWeyD|vq2qaom+)o)P^>=}^P|bv*S?K$Y
zELvLZ(9GsVB%x~BQ_aP!>0RsRWErFRlQW2m%;t}^Y#bu(6`PClLdg+<s*N2pZ{{%a
z9j`(0#Q|nHk>Qr_2NFK3mUhnP1T>TDKNc59azM2*CIV^`t2i^k)Z)Y%rC+M{sa1`k
zz-`vK$~1R|j8bMnfpH(WV#jGhe20ub1Y*ZK5fVghnEC@zC4N7PS!AS5$Q#W~K6Q_R
zsR;v7MUmURHA=x+e=Rg*ja%}89o}c@F(kSXtfvabhJLc<Hs6#)n>u1Z>BP9-AWu<>
z!xL~U|2h=wixsr>OJZ-%6lWX3g%}w%#VZTZIr1dIW!E=S1(tQ6Eg0qD1pB#nG!W*9
zg3zmezEj3kht{j##HR^CI1nov_b!c)0v$Lv6&4l(3tMc)0^~xHwdv8|kg3s%kVE}b
zeu18ZsfTzemHK$8LFlP<2Uu=@CUlY9^dW?V3x8O<?|KqnKzy+Y*@f|~VR|i_izZDv
zhY}w`_Lr{wzM&3pnK0a=Wl#Jvvqm>)F<{zoX4?Axy(GgQZ10Gh^7zQsWe;8$UgGRs
z+^-R#m=mz1Sxy!{Ql~#I`ain?H?-D3CZj~AVCp}{ysc?+AX+*7l52Q49gh=>Z@nm9
zK)i|9ZrQ2k!u#;>@a9vmQ(m`=tu9o-Cm<IFc398)`ntzS<1QevD4vc^y>EZF-KVt`
zD$)c51fcR?Mbq#w%|M%mHWzix6gUO}9{U@6@DwerD-*-=i~h&T@(G<=fd)g-6T!d|
zT>=!ONQR+g8nk8VpJz#-APlT;(MA3HchyQ^ztS@QGT;W@NSU{loA^sQC%Q-p&hIJ=
zsCZOVLXR(HgJ<sCWcp}*k%S&wF=8_5T;S!Ko15pZPaUwtPk=~$T>x@cF4mu(zP_BE
z7iqXC^`<G{&i58r&O_+$jV=i203o?y+~l=?sHLQm6$?fE;b@>@Wa&$-sK|0PFCS~|
z*;rV6P30kqk8$!5{GG9THWKnket7yG{g`r9+P4?!CR;g=bh&$$SZ@N*Z(?G?k;m&`
zirPt#YwVJ9$re)E{t%Iu2S|-)cBP-qgi{&q9Ybn+(H299%il_*T2??)82xD;Bn}-A
zIMkDd7RY&oBfwKY$OptMH|NI)xgWtnn9EbC8>vxXqr6j=miU97^Fc*2_9(ZX2kNIQ
z$W(7zU-QtTTDkg?MDe~|a!>)a{A{zF;7;Oq!Lz+LOynlkpri!G=R2Z{GMAP^!PiT{
za|w+$zz(XHpeGIpsCZTqLD*)=%YFCeP+9Iwd=+?q61cizNgU4iA<>XkpO_WSp>}Ui
zt7Ry=xE&EX`!3!&$(#ofRI0Wx42KtHKh~P-4Te)qQv&W%xd<Ry-<=xr-fd9eG+)Rx
zE%THcC?ad=Hx;qsFEV{MpWMUpwwEAmZ7v^e>fIkG-3r<>gdFwmT&XpPnqua4{wu+I
zH`Gqgs?LSA@Wn_S!a}|aiXlB3@!djHCGX)D*Kd{eB#hdyntm!^=hu+KIT=f#oRdiC
zi~lxEh0sjS-&CKm{(e5$Em`C#2QKJsx6!lxj3sx(|8&-MyXxbUm)$Uq3M2;_9|vhW
z9m27Fwl=$MW&iW>eU6VAmu^x)7#QH3f#<(RM<8Iy*Y5&Ehts(1S%yRdle|hvJCPQB
z;A_M?5gskHK*w4dwC<-Cf4~XO8DL7A2$HJy<!o{|IeePDlcBjgqVB^iw`Ue&q2&+N
z4MvA??7y-euFHK&y>0slU78;bie2+cbu_0>c<*LP_DPo!JTnG&g6_DFt>}8{elI%d
z4VImlSDF&o9K+kUN7n@-=pP(SVxh9rLVt!D;KnBn2|~{J`~@QVy(&lZ4-#{ziSbCA
zEc@w25Q8XgIaG1@HaT2+Znx^{9M&~C;!}wdI(U`nkg-8Qb>Cu+3TP(+X-248h9_%=
zzX1TLsY&DGd#1e42|v|Q7mzd7@nFg01}ift9&2m#BmkbjL=e`|A7`gPPnv)8i>e&O
z6!~`H{4i4^ouD_le{|y%W#U90EZ+-ks|9Eh!7BuJ#*)NeHuj)*1(Oh<z>3GJLw8h_
zjP1sKXq1co>95-C)!Gdjuc^fqy4#UNYJvTk_~uo@lvq*w$&Hua=o2W0G&)Tfidzv>
zC_yu1f-MoA>W{-o83Iv6;+P?GjiE!Eg|3XC{58Z;T?}L$nhQTZ?oH1MU8$-MI9Roh
zCnKTc^KS7Om6MQMaRl<j*;fvdXfe<bU{Q>~k+pBj|AGZ)Q=>LwA&1Xx*1dk#0z&gu
z0jrO_XJqn>ytuFEt%cDMhREnI62Mzh7pExPIZNw1bDPm&II_?$B^VpDd^#gH@p$%B
zvRuHWN1RHn(D=r}jKlGumj?>hu`RJwJf#B=K@-u)cFfvT7dr|XA1hF!k*bU=GD+At
zpcbdC)b$%6CEkD1FlqpdcxL&+u4>$4oTVgHN?&C`?9hIRI21zmsgM{JMI;rlmFccK
zDJH-M_AM!A>ut-abip6j>C*Rr8dRiYYurmV`h}T<&DZiF7}KL0cfy$`p=9dHQAtFt
zS7S(Shg}I~gh{5n{09(nc|19z3>7m}9pW!HHan>-dv;5cj80s_az&9BR`wQST{-D`
zF+d6V0KbQ0kwLP9k2}Mf@%|G~{Bxjy)I9tL@#K`xn1OgfUr06Tgt?cUk$N*p2Dhtj
z=<?kB`{n(Ug#b&Hxr--l#F*xMJjwX8y37K+MTQY0^URI&sg3tLO%M8pXXowBf-IQw
zR)p58ynLdM{1a&;Awas*)NHC>*i-AwL{VZ^(k9jOzX7|@)BR=rs`sNy+oM+OhsTG9
zNCIw%*T99xmpw7T&O_UgY}&mmdw?Hy_bL7QAycQ(yTcSD=a=rKSKn<~suW}*a)}5{
zg@3mdY6IfG5Z`P=gp=z<86eotfwPB7HeYi<_sx23Sai&62%U#=5AWxzK+rPp@P${C
z{1_Ff$P!z+RTrVa=hr_GL%d$=4EBW)vxRf6dR_uD#q+!ed)-`?B2EV|O-JZl4$_tb
zN5mA>*WZrvo<8+3dRKCJAT%;+2U_6TT3H^SEwHn6rhQa`*#An-R_jZoGiJ~g)q$DE
z0e5JaCvRHd0GzE**A1pbf(^ya7GxeB^sFBC=G3N$Dh!f?j!7~UDq~^KKA(o`6c)M*
z=WSKd|FpZn!2HA6Hy1#YLBTse8gjf|1zCQlRj=DZdzt4roJyb97e7C6Du%aZ^rvqq
z>`pr2>`F%InWO3`!M;%7KZ&cdPxjOM$5<i$te{X0vd|_7_w{11n7RElC*gNiEBp*y
zd9^u(A;Lh3D<$E?8{F`NB>vWBs6-K(ynASA+%3-o#3nsdHCf`X;=TSf-HUqKt2)$B
z)TOePcOD<qj)IB@4`F5qVjFRER5i*QM}Ln4F>Owop-#u_>!b{1QaR9&q3E)30O_uK
zmiEXL@HmYw1TcZ1j$1pW1`D#3X~GIJ<zG5b*L>1CfF;A*MujHf9rRFOYinzcSX5^e
zvCos85C}^$JPeyrc+eFP`%{~3(x+i6YYUWcy6+mCgM3*7K_W>ih1BDs!Hxd5B#X>g
zRq}{>5~vCaAYlarvN&o<`LBsdMDscdp!uMZsb+sFP99bmnh@II0N3m3yg>+Wu-8SY
zgi5c+8%Ce|Ay^awU(9Y`dD?m&VYB3QyZqbvcrvrq3ovoME$vmL2LV;6@1PY>j+ov?
z9*FF$uoPrZXmJqxW|R<vGqbX``vlJLV@Tq2P7rX4n-WMuSt6P`SW_O9S~B(I9NwJa
zKW?0dg&|T{r$gIiSE}yczX#u07WOkM9uV+_o(B?Sa8_dw@}J^}pt>o|Ri94RyS`1M
zPW`F3`F&iso9ThlIMm-CvhT$_!$N?>4mwY-+IJN8Z}6oVC$fo>5faJKr-M0^jWt5^
zms57l->|Sd!0Z-YNX>(btfFCF^8hw(0Pg}5WFT{jW-wHY_6N8uZ6RB>o(aPlb6!y;
z2sy?I>mvXX*%XcDoOrqdNH~#c+;RuUkN3kx*Ok(R`rdm$m@AtP6gb{LefX>;qbz}m
z)|hVHHuLlaZVOJQm;r`qKGH8P9tQ_YNi@xbMQc!lAdY*r{|m}{C}xFpG(#atQ0Eke
z`XYWdfU8ZA{hHMI)c$f-A(qPM>;id7fPqm9!=K}pgzGYe8Q7rKO*Z%?EsmR>HZqR}
zwV~kNv{lsXnho(s?S04qbq*|exEi%rviy8xjD`NE0#PBl&!hwx`*M+I(}3fBcq8mO
zlLDwFE$RMqtHv!<C0CqD*XAGq9x~7qWuR^~T7Nj?DL4ov&`10rQwvBaJw*_fTA?m;
zhIvIdll7ivzl8Xg<VjftwXfwQN20>XmV25jG@DlM0hjZbFCWSR6Ci!W?$SC}vCG#n
zKf8=?>-Mv<V)czxGOQg6V~7-pi7UzO?4mBeS51r*3EVf^Y>7~#&g&ir1+p~NkqtB(
z=8)8s-vpIl_*wRyO(=?<3T+MA?0yNUFAbZRo@QreMhojH3!G&z{8pwOi*2y|F%}5i
z1;Uws-N(E8n>y4^K(%}U6(Iw?Zl|N&Mu8e?Ect+dQ5r}>HP*SeQ1K_8<b(KzoA<dP
zmXZ+0MfkcS5+F)LVM*0#;KCYfEs{uT^h7+I+L@$)y;g5E1G;kL^CPvx`t&mj+l#MO
z&c0-Y&MuFJGGd^-1tdpEAufFb?%Zy<D^H3|WLmeUdxKU7J}V*658w=g4<Yx@>d<)e
zk-*lP6zGa5aQqi+<sgE-T`eQSb<?3v%Q*|{(CXvLt-$lX>}C?Lw{fbMab1lYF+WcX
z-5DvT1#lP<5*0;FpSYttuM<KxYbvj61<eqYI2l5A{JR=8>R&*#XtG2Rl+a9Py!{G;
zlo}O=Kf5}(BnA0Lez;&B9XbpCkSdbP?BPjwPG2o9yqru}yOi=23O>N`@ttvn-Zv@H
zzW9D_orTxb%rl33CVsUembR50ixL5&=H;Ej_Ax1ivEk?zS+j|LRW@oR=&zVi%vfTr
zJ)ZAB@v6|s6{E4dEpegFAHu|>+b%bPM|=9xRX~kJzs^b43P5r4%aP}Mn{7f_DU?2l
zB!qNSaM#S(CcSi<7Qr<xvr;yi%`N6&7trb?2B}A{H?FvACOhbvDfCu1P+u3T^0KrS
zw~r+f<HAmx5Wj|qZ)qeWWu~csE}?{)5)wSux47}4f0BpX*ElIp!Q?6W_lLwbSNKm6
zuD0DTD^QFf?P?K777Fa>f3FNL$L(C9mfQBNvt{yL`T^^AE1|Dj;sxYd)t(TuIa9#F
z!LfM9CH~#dPw#s^CR7;)6H^(194g=0M=gfCMLgXIu<{1O_Vxz*k84hmetwmf<h_f%
zhQU)a2{OmX-;~SeOkf1*lU`GQ&h}^7I<cf4J}6l~V056Cv8ntzVRguBQB=N4`8hkq
zX#Ak_WIev75SWE~-KNS#x5;{W0}9-K%=Cgfl=!q<04K<LmtuC4(ipg5vAjicaB|&#
zijH%aS9Qdg=42qRFYT8FdKxZyVGVdg5}ZRJAzh)6EHdC$t;x=DrDqH=9<xe?NVhDS
zHxPWy3S^xFN+q@vLBYVlP{1J}Apzl9IDhp`rZ@dg_f2JNZwC}MKi~i#NJxVm-(Fep
zUUEn=93Zf~y&`a$+Weo=K*+D39u-x1Tv$cqT!oM0Q$bGyR3sBU=h-FTego+MMMUER
z4Gx5#{;j>j^WT+j9>=5pt>P#YQ3^|CaFq}{4k+}7VE?ssz<cX|?Is7$i;tWmY$Ae@
zwV{Kfy^)^P+m(&JIRXm@2@}cN1s@-ysF|gskv*fRrJkdah>?Mfp%J5$k+q4VDG4(N
zJClF_!hbyKx=^cTWwXcudTG@m{ee@YTidD9g0YvRcR1rcH%{6v!1npaoITlaqNaG{
z=F0=2TdtH^CM`A5WG{xBi|5JR()pIF4*vn#-B0cAHRAIfi&G}b91{2z+$1`Zuh}z4
zp%2G=qjHmIPob#Hdg{4eduh!t75OWSdAIQ1-mT|ls|r!EB&6`y)`>*O0f7PjBxG;~
zJIizek3Kr&Q)6Yue1_g`IwsR`-fP~M$sEqP1%WzR6a8Zco|V!n>_xd;-%x>=k+r)1
zB%@mG<B4{%@-&*ZRgQTGO1a`Ctety};CH6RNt{t)ye4C5mSt0IOP*K8GpK`*)ab{(
z3hLWt%wsj}*N^>98fPTp`f%y!B@rSULT6;z(1s*N5Ig?SV(O+gfjkpo5a7ldDa{S3
z%)a@|RdNrPH5@tiZZ$Q7h^$TIf$ZR1<Uu}{$+J`;LV@g&+6UrGAI)hgoAtw?*s82Y
zG`eByJ4whoH$&e`1ZLPvK#}cyB&B7u7^7iCjEH;m!8#zp#6YBeKqJl0slta0KMU`%
zYkxPxotJ^W`Lm6-z8D(i+;E!fA`N{POR0%9r$&4vH9sfSqdzMgx4MS5cwx9^oqX~&
z$Fj;``GUH*Mk)>)6(-C$4PDYszqMs-muqSlk~Vb?&q{rl)(}=`>_e5g0!Q$2Wh6T4
zyP=haG)`ZJz%S3*CoEtAvApjasLtobyXSm{iHS;emG!a}*~=23y;Doked3h&c`W1v
zf~C_uGdR>DihYOutWlty;432?gpre`siwIRig{U?`>GYX@)z()G1RssgRBdyaBmp!
z(q%aDMKVT-SG7l=$X|eje6zobqaeTKQKTAFtO%1FZA^Mv(-M$yUcm%GUnOT;*lL$?
zTK&}TOh=0Awk`|aiLNWo7bV5is2xY3Wi7Tu_J($k)3I^nrtrIZc_<}RMpbodOf&Ws
z`w*Q%5OxB~M4xgdsKM)n+&fiKoS2Rl^^++`>zvUBl?k&|p*;{uAJ64F(d|-nJ;0G6
zYxvioIWMfzVKFY_n%ohVpXqC{DOVKbd*OFzB6J5QPbZJQxbTn&F0;%Vzoi=&u(x?+
zRq9_Z@xxDFMigDsgZ!eie<ME%@QtbwuQ+1ZI$cL55ar<5u*0u8AFH<zt)<2L;W=O3
zxX8r~9^4$Py^rHTv(#=F!-_hx1x42EZqpjhJvMjv(OBMY(-6v-iBoG@!}^ay7A`W6
zKQNVtEDM0wp6*RK7{of=5(^(izu%0syZuhDBYc!>u+q3V4*rdp(#tjOE1YmebH-=p
zl_Jtu3n#EjXrp9M*$R0Gve?4q1!iJ?;#?Cafs8o}cfXk=%1Lx8Z|)AEyu-JtC!<{F
z8{*Fg@0VV&3p1?laO8R2u}NwTj{S3;OCo`k#4caud7Y!gBTBbymAmlijOI%CS25?U
z574@yY;<PVz)~ru0_VQca>Qxicb;cDjzF)935hSE!p47dj~lJl@L@@h#v$V}hE?>$
z3-;oqpoEPe+v_u$UGNPK6*jL9(Lo40=HaE<3#t2~#Ae3U1s68T+xJ4ohGGQj<b6@a
z<2A3dY3k`_$>U(py1hxYrS0su@I&lUH<`_X5m5}Ztq_yZcRKi3dil-Cy}rKESkBOC
zz!<IKok2^DHh&w}8a!e)6&|6jm{qlTJ~oO57=gzp1XxUpdIEk|7vL1gd~fmf3g~ub
z-dtOen$zB^cq>F-11q`pFOg__yyhu_Q}^|jEX&aQeGkhxO}y@Z#$eKzZ#Daqt&h-Q
zyU6tL!241Od?8{>li2H3to#?<H!%KO-CrukA8a80bnQZH;b13`(hGM9EWR;1cmaJN
zw4a^xjjw|qP%5t8DH}*rD!}#~2K#y`33)KADy2us?m7i~-L(1(rDj>0x8t$DdN*I<
z=eF#%OKx(IG{0NdnW1o|(@q=uMF`=thNyOYARGL~mq?1AIXCv`JiWcq<6jkA2KO1~
zD3~ja!m4|El5q^Y?5$qkX$Cu4DR^PsrSMKqTReUdjgG}OB>WaT)@?e&ZO&+tVPJ`L
zNWi{~y;6`dBc~f>)y601gXd<3cxcVNCsUu-H^Evy2Qy>O--y5Up(ApguE^|a`wH$l
z$UYJ>zyqp?A>QHG<<P`0>p@0<C8&LZxB2YqRc4fnT?MkarkA-j6^{0G|C<l^5TvNs
z|3+?c{|~vP=w@rgs4A;(Ze-xdsNkgU`1ZfJm7a+aql%d!Aig-6Ss1?<nVFb6lCZL~
zFn+SJw6RyP)iW?+6ftr(GcXdf*K=bOHgj~4GqV3=V`Xb&ZDj3;zy<vJCIsd5?5!LC
z@%Ud0kZ}D|lLHC!+l7;rwZq?*<m`<M%?uoE>`7Pz1pd!L6@iVNo9#af)#D5gMa9{c
zK2FBcY)lZaYZ!Catk*Lqv%R4H>6(I6#AZ_Td*PHh+0ep)dWT4d7){3li2b{28iv0}
zNYF2<5bAUDcGK^iTcQS&Z23J(ysX-47@tL<h-=pzN0D}sq|$M@o2$+*p98q@Z|AFb
z9R%i&QWCfYN#RHXLBjv>(Va4<^`7q~&FW}1{u`+WrHM|6Iuh;k_b2_2aTm!P6cdn!
z8~ljavcHw0+NNr8&spP$E9Z%k@_&fB)-k#+Sw}&pUb&}VMma%5Ehe`E`-3{c&*tu3
zl8fChZG7Y_4ORDXKZ+wrdCMc4c7Lw4HRM=}rE2w&n|U)@qO|~dPCVpa3SHAv+3sFv
zv7{Xxk6W03x_rG*s<aQXT1J80$hfqcCC=oaI3MmG+E}^dJ$ae^Ddu;?zoW7fMf@1M
z$s*<Ewluw3CO6|d!P|DO_xVe<!8#KoLq;t3`N64*rV3x=D$B*Bq(=J;%}d{nQ8<;B
zyX~IIB^ifQkqccFZyT}B%Ap`*dN_mHMPkQQ@5ZlNB3kLT(>AL|6#g9DjK{Xu<TT#=
z!HMfUUaXhmwaaNcH&*F#fgMd6=S-~vx(|`p4QH3T?zmCH^WXZgO=c6h89&_*mEVTF
z$mywQxAN>{c%}BOBJl`p_*B)=5UG$i6Z1Sk&8d?c+vI|Z`{>c5ERQeGzU~_96}&?1
z3R>5a<4e7JrQtyK+_<`U-gu!4Xez&R1l%l&b-J_)dZzp*8h7P;8bTR8(tYTk>B7C-
z(=&^;gR8pQitLUCf}g$GjlSA`x>Vi2-Qr(W;S;E?JVPH}ij^DJT}5gmW^_-HUNu{}
zIw{e#0M5Z~QaNoAWJ!L|!V*loZdckAXI`(?Mwo%7Mao`{C6t)$n06ArgTCTD`=%4N
zGTVA|K8}p4uG1e>g&GwnF*U@*@vQx)y2shMR}cw(qcJA6Z&RD?2KnjkcWB?LI_Gos
zlzm0*;M7y)Xu!xSPD`EK6PH=)9}zqEN7~uCU`n2u&D}1-!*j3sL3h4`so{%!+4@~7
zt!MmRWg5yh<qVqMO!Mt?s_ILxm6}P@m6W?PSGz)I+`v~<XJi6H1U&DBR-)IL#Jkb}
zJ+igQPkG-2-0EHixc{V|Uiv4N;=tD_!*kVnEM9;2ij?+okXM|@9c)+)|9x_Gw=~#H
z-E+FY&+rA$i|c$@XV|s>W+ukTIqG$@JGYJA^_r49vMdE{XcRe0;I7?*b$I?#!)>YF
zdxM&}{^=;A@AQc`*~_g&JGeVw_qCYIG3421{+rmW)cmXShHp{hX_*^E*;UR4^5$&@
zFa)gi4JY5Tq9D>1cQF|~%Uyb!q68$pKJf|Ozx>E9<m@CQ8gnB(irR@5*}g=0#pe88
zZk0-VQ`VIKl(N`xCHlgtGq#`S_`EQ-8>G@&C7mna24oUKm^O%MWurQQy@XX1%tAKI
zBI48up47U%t}XGBoR9mwEzM94Cys%bg*aDZX}5oMgqB5+xqfzcoa`HFxWZRY?};RJ
zmJH&f0HEMYUpI_DN5N0QlHYXf0lJIgjp*ThT8U8+6+H%g6t3ZY{5)8YM4RVU0a!D@
ze^629I8pFKo6M$q5q+*wXE;$F0!K?~yV(h1ZNqo8h_WFK!@lWAa~qYG6$PAIynK;5
zM8t8p?`#iNs{4o@A?h+VE1M)581X8s1uZ{Sc5Rlt56UK+iFR&NrOynoDsU|+ML!9H
zDd8R7+z8LvkP9WXuDts?%)&Q=9oBIuD66CbfBPA7#+7cuolh+95CwncSch#+Wz7y%
zJ&ITpi@4fKVy!SGq0ieDJ&L!-;07}T_BRtpv%6RHUBv2518rDj*j*`jBR({dOK9H$
z&3db8d();~v7c36j#CM`;Qd85xCLJ#h`!`MUX$n@U$4P<CtlQf@)|Sn+8qXbF#rCs
zLNBb(6-gZ^@t0EV!Sw%>=r!;M?pw}nF#mm=FPI&;|F`fP!On4J2vIazsNGFZ2xp02
zHIon1@ZP9BpliYTd3EXRC{Sk%!`J}X@?4c?ZlOhLb--08{lyumqcXgr)<g2OO7<ci
zRbPN(n%&5#OIe2whVR5Wsx6cIe(YVW0HHQ_gP|L)rCJpEvdY}1Nz-BMGij+FB2XK{
z+=BGPr!r01hZBF@*N3&(E6Bgi7T<o!N;ToV4`eU;0)=H8<;ZvPPjCP0tC<2s`AY{#
z{JMYf!W$d>*H;CI^bZNtRX6{G1aF-5BZ!^omY_!OH}VctvHe!FijDfDTcxpJ3DC8g
zG;>z@PYQv~<=VT?eYz2S_CHHQN<+C=v`ufPx(xUd(T;mS>n6x(3;Z&Fu%-!p1`#YW
zDsj}=mk(%uNzXZ__vL*`$g;RRLeco7MTA;dX%*s1FhdBjSX$;k6KGX%N_!+0ykW|B
z0-CdS!pvstt~fT0;?(K?f~-Ma<8dvj_<bwc&2@>i0;n%sIjzF#HF1O<@>WD0-Td2j
zH}B~*t#;d@v9^3rAK_tTby+74-^o+`cyqx?w^xwulu|>S7c|ij+&`_+6G=h?C#44M
zl;T^SH+=htE&dDbegL!U|C3GrMdSZzzWp75|2i4Z8))^4gD_x@LXnm(QkiBs+3^70
zjO~~~pf16UX8EPR=Tuz@Q|C%YXnFk7S1k52EXQ&J^AAToPTcx<2)~N0qUAT`msrjn
zg!TCLEgzq!pO!S_7*6J`Cj>|)CUqoUt4?|a$(Qf9Ohp~F+0V=e0`Cc>L7RIf;j|F_
zF0Slh7B(rD*cZ49qfuJSWN8wp(_8k73{CZ;7WDsL9OdbegS=?PM5&sP^9DK^bpZQa
z{8&~@i@{6HCXlx2kFuTzX4_Ce9a$aCM!##{&!1dPM)&|;pe}@wc8=j~T+B@e*G8%$
zxh~ZbC7~#JmyJ~OS|D3(+d1IRl=L0Z`bP9=eRsYL3ZJS_Vu;eRQr)cNGlf0*Uh`oJ
zW#~J0Q04mcp$peyMX!PQL}$}G-QUwna%sRcN_o37mH+o;$k%^gj;8{@Q}a8|C^eu)
zEuw{iNB#EDSuZIDwYU0TtBSrcBtwxj0GWyv8na5a9Kgu&LvvUHlT8S~mwzyK4yXb^
z9so!FLW*t|*1r(x4XON)HU8nI@IIhpf5GT4HVYwI{OkT3aM%3LKZf;b!E4axR@k?~
zrE~q7(OjJ}eH&94jpAb6v7;^APT&{|;D1<+->;SY(BE``9wegI6-WE;c{odzGW~@g
ztf=klyNT|3VWe^b&8lXkx+Z<*HM`qW+Rz&;F7Jey3h26$KKqUtz?m96KPKsOg^!o5
z_yaibvW6AYO$hpWOMIl+p(48APMBv6egEC5vRG@{<%~ZJue?p=GSg^EIvG0}#{LNK
zg#FL|%oED5f;PZ7YMRpO7n4PXyAa@o0z@PK?7=K@r}jzRwfWb{oq{q@;)AK=HyXj&
zoq58#;3hsId4omJIai5OuYUVxd3S5)HJrtaf!nxsYjg}=-Qc#3NWz!M6j(rBARf2#
z$0P<W=y9KCze$_t$l24nlqAItJfM~T^BAZi%voOxO;G^YO!0Q5`Njky00adzys^x8
zfTdvV*!31~-q`h-i1u3OEcsgX2AGA!fdJk}!Y`HzgcQST@Fl`KA3l(b0u?nB{C8;x
zu-Jb{rT-q>SOj6{*TYONy&t}oju8DjptZsXq{Q7BxSle}5$wPV>;n>w?`6NjNdiCW
z9ANYX<h^>B<#|C?o#cbmsw%x=MnJW2sqb5iV10}ibTxO~UW?r}sxabDbkvDiFlm{y
z(=C5iF+<kS4SVAu1Zt@zZ=dq0os_4;0Le+zcC0A!fYNCt)y6&1(V71Xr19Wcs91(^
z@SpZ(V(|%~=lwGcV1;C$vx1eVxoPM#3E76oYZUQ|utgUzZo(R98kly5o(=}9H|Q$=
z8AA9t;y>%y*XotNf9rnfza-D7`X|D+{oB3>8yaoSdc6^My3JOyUw3O^^lxbKZ?^GX
zOHz3>9O3|^9$C%5C5)`c>lkNu>htK^efj?~QU4Dd^mnAst<nb3T$i+n0QdobW*SY9
z?}Q=>kYc89%p~=%+nYIpL@sfC0Myh_SUPR#Q@zoBv<bmwojFV8OG~-!j1t#SD@2r0
z;IXpGdb#;KKvrNL%{XDE1J&5c{sS=MfI{<+3iKD9Y;FO*4DY+xm0)_qrMSr3hSrV5
zrl46zO~!p#{8gP*a&8;5)2G&=U6+kNQFz@xL;tKjm(-_DQyz?Zb<|utuWSFL-KJ)3
zsI*<lcD_N-6^rVTK%6V<@~kfHZdm*G0CRq%MBYQqnlo<Z(zM%@!j@)Psly#F<B@G~
zFPT#Qq)#S%TFDyUH+z1YmmTm5((gB`Rs&kIOp$w*&SR$8)5Tr#FwX3(jLpj7qEDqj
zG}im!UcB;v9s&Mhqd%V*J&Y;G&e-Ozf<3<}XjASS05zL4mcMgwx)iA1xyThLpjfcu
zzH+ymdUf;rR~w~zVWR~u@>;(x?`zAy84^-sU{=B$AChvO8p-bMcJZ%IHgP+p9C%dt
zca^&v+E6)jZ9D0gS+AJn-(2uiDhIfwrP#((Uh~&FYerh1&7E&(OzTeDwR%ToDApf(
zdLJLU_uz{4{KN>jacW7st=ip(4+|<Rqo!RP(yg?~EcJZYBFGB3=I70y`J-C|^m;~u
zpON(s-wj7)Vt`vlN;O~MX<_ZA{a*Mk?rQcodW(vK3)ip9=LIG1lyX>JqPv&%^P8tF
ze8>Jcf3?Mw-Hul898v*?5}PPu>2!KN<Qe0rmJz9Apx5-UmprT{Zj<HNeMBZpFhHuz
zlBQAOzsT6odWi`Txvlgu^mix`*Y@4?$ct~yL$N7G<aJ{?)q=*FT}9mqZS!bJPtmcP
zKK<{%UE5c&(^)s`)v7BujcAg#lbe2gT9}pjM|TgMxf-XvXn`h%Lp#zT){OOTztsIy
zY^C7MNz>k^tM6;JWfT=q$k!}0JU#L_Jf#i1`39bAhMI$${HlUQ-}X3m6ys|mzvHy#
zw2Sv&bS~Jnq*PiSEB8L0vhi3-_}r(Z!`tFko-Rh0J%OY20RoZ39oKSxvbUI4;EX+4
z701M<CliJ?9JySZc*{axo%57c!NV;pc?M`MeOvs!myGmY3qjM^jjNtDc_j90qOyb1
z>V2il;;>}VRr*QpgU>@&*1zp50Jrxar~5#vG!@nUuSP9arvG8o`d^Gx@?ye_3etKG
z79=chwl8_c|AyHM;mz#zzp-Ea-x|1B|Mvzi1ZMXCHwzaDGbb|}U<3Of`$oshu$l@g
ztFUK2=x`+JwIS-5LFUs<u#t*ABq&Sjlqe-LEzsCB42lFuwK4}HrB=0dN*1Mf_<aTh
zT*MZ{wE{g~7wfrrO9EqVH7~xH?Lf!c+FU$((;*L#H{LGAtO8Ep4j*_jBEf(^OQBG*
zw{Iv!;NxS(zc2su4K({tXkf(X-vgg(jz<P4Rux_iwHw~!easa@a^Q6ZcQ*GR@CWOF
zfNGhY_k_7<O=t|j0E2tytfUewNy^}O;G2UE*09h4MS#kVrN0nytyAxI2!lk??=`S>
zNwNCv^%84^{ubAPRd;<vy47K2KuTGQBnS*+z%OL1<HIRh`__bS_yJcb5f9xM*pFTa
z1Xp|A3J-i8lJRY-Gc&Oc8L>H?^K|gyQ=4lqR|XO=F5nRSey?|Z;8Uu;$T!3eCSX|3
zOmU3oC6X0#9lEs$9VF*xJ|+a3V1b@=ENUaV7X$&j6F<PdVIyWi!(qv_yN5w|RxjKP
zQoXuV-(sq<VZef>ga(!v27D+-PS>G-3;ILP-R1JNRW2`GkNOiTl(eR0+_(k2qe%Y^
zhH)%3@Pngs%V_CI`vbd+Yo%TSC5h_VR{PaxqDxe5^R2q|XC>cD7>93BOQ@5t=8X>+
zn3!Orc5*DUS1h-ta=wG84zgG;1n@_&5uBfuRbpJ)SnicFRteeLFp-oCDs4YW_~m?D
zA!iq$748J1>I{e-$Wd62`f$%*3=aNTmLT44!Osd9LrZ9OEa1ym7aaKP43H!RzI0iC
zRRUjNX?ESm`8{FS<!aUW0oZi(^dqygvyFmiVc&b`aseNo(CpDFsT7u%mlu7Vwq&2(
zWFL&y>8_CX{?L#8@$ne2Nm{zPKLP}&&a*;#y7jSQRZr#(3Onn_6+RqdU!D?NMs?P^
z<8<VKfTk*N|FRWGse(!&KYSD05;*@y=JmU(6dgr_@mGTv=1bvBAF-NBs#s@+JDN`Y
zi<p?0^R<qEBt{)PzSSb7;?uU*m5wm8Q`GCLs}jY+*x$c-{h?d&nO><p?O+kXI`j*(
zMZTj0U6m2M@|7v@c)DZOSn>4*D{%p<Ag-wdOS-w(mLw)7R>2t$p?|L-zuAQQm(}ro
zHuc59!9a9x1nGbPyD)osy0^2lODqtVmM&E&Kp8}M`*F=CB_)Ll3Jk<v?Gm)59m&22
zxwOVo`ZC=tdqLp-F=yh}v^n6zBS%$%*Zw^(KR-A)n8SMMZZc1@)?!vhMkX>WjMsLp
zUB~D78gN&$Txy6p7O)gE9skj>|EHj<v$II0yw!AqtW^1MbF<oFw#<Heu#&UU{hDS`
zfr#Io&FkR`?Y(G4OE~}C!(sXS>SzjUfliyZo1pdCT8GceJ*7vp@d(}xnkqCHNr-Mo
zme7=LoB9YAol;<6V8y2bO$L=h#eOWgEehFuS`~zsU1YrR!31NS7PreCAhJTE#+2D|
zuEObXCISfcI$3Hwa!Uk5A>x~>wNSZ9K1O*!dCC&-a=$rJt0T5rs4)XF;+>zL9}NKg
ztfW-aG@Yx^1A?LEi5#~FF%vmdr%Wev#pUGWPD~Q$HE#AMa)3P5^){=<%z37;>`X3$
zdc~LT!1}Qxf~U;oX$Hzxn~VdX7c1#&NXPg=IEt2Mmd<6$#q)Y>w6tN*hEWay&_&Gc
z)c5!Im5LoiNO)|f*9X(!;NW!3&93Jg196l9foEXDmejw&q7qf~3BEqDF7nzxrtht^
z{8ouvTU)!_9XmYaFl13{wBJ^*xAt1^4069d*e@9F@7L`J>Or|VDAlNkilr&Zw8vO`
z>i*#MR5kFl+Hk&*K%0lp=Bi#iV>1_Q^(7hl5TQ7YezV3bf`#S7$w?C?ErXi2yp7E%
zKtXJ^h#)1GlchqnYUA^b9_oTI#^BGejhbcUmAX--$_6C1zAb9MY$}db=sftGkLG~c
zlgP0%mX)<w3AGq;jY=Vv3_?SjDAnMs8>xnuY7Gb)laP`!G}}CwE}oyC2M9<}AU?RB
z>|bj;__hD?U7TtWl~Pgswpp3hWvgqY$YyWbZ1HVDkXgQTahV`gxsK0JXT0axy$EUb
z`qN^au2q9CL^^HSZ?|es(O@=Sjo6ez;JTCdRMp(@{%D?caaMH>l-722b=9oq$FJLl
zX9$frjRQwOVh%*C8!XZ+N(ese<s{N;WO6&?1CZeJbl$hp;^A?7V#Rb05SUiEq@T!g
zwUz&NfU)g~g@z{7%c2U|dw(*|l5@VnZZn$nQzoCA{l(S*Cav-o493lF0*z9vMBdTZ
zydlM|Sk|dHAHPO!ULG_I4BoJ(Uz>1Ug!>Yi!zxGp%&FBC^DSnlSCsh}%)U(epsCln
zenO|`p9)z5*5KKsM61hhr7IDyPnR9W-&>SMUGQ(`mVR?LP`QpK%(0TCS=FJ>votmC
z&ut2~rUhNTgLT@sipIQ}?<7G^F6pIGT>}Hf<FT8UX#I8u*tphw8jsa@_)mfC;Z&hQ
ztLHt1bSjQ4pU=yafY(DjgI1Gt8V3-JpYt*Run;sXtf2c96%S97a1a6zFKD>v^ZanF
z#-iHl$?Z`*HKhpTzwC=Vo-S6gx3@PnHMOP>nY({@SSYIOk1MzK%9zj@jTFbdxYqHK
zx~54@r>of-*lxOQ9H`pvleDHNI`J)+$M^PGJX^l=*<U#LJyQ2IeC+r6Q*W)CsH3fv
zURI~^qH>n$`ctlN2bs-u-b>}S0&zfzZtQ@0I0O{Rl%el<Lu2FdQX}i_>}T!^nRbrZ
zrM_hc91-MALfg-gOD0dx&*eI8qeRU>+&&No_D8VMa-Kzn);uwfPGy~DvQ5vfBcjSN
z*C;BB-(z;mkllxShgv-8pej$E-ox`&ZC<#0@F=m0SYuD-MG{XjC%CWLB%jeTOF)a|
zeZbtUi)ir4m0A5@LKbE|fom&%Rxq^X%kuJ2wT;3}o<>gZCx^?icWH5$wBEBmrrU#U
zDVi!zCf*A*_4chhLmH;c?Yql&!wDlM;je}qmQNi!jrK+-e$$AALEQ9}-oFc{^A})7
z_#z4lRVz0D|Ij<nK717e3=-Sq;(?C~V77dS1qAo}g6m!G4%PZSRgu<41@Gk>pEA?}
zhB+b`mBXD+^V0{4kcqWRL9h|g`l*$ZH|UcGx%O&5LNQ3O1fw&8#T^7HQX4t9{#0a*
z{jR)CVGc+OmM)Y}@v4soAs`JucT->`#_tlsl9DQ`pp|EH`DC)N&d>7QUPcL~CVOaP
zo*FxqSqc~c-g%9YP%bn^C(sVkRU<}-h*@6&z7qCM+NHHkE*bdv37Lwf(Fwk+_b&d^
zt3}7CtflDBXs_a`7wCR%`9ezvlT3B`p{ZDgusPYAVB?htelP31opTfOEbYOj9l;o{
zKNU7V(oZ=gSJ^y`vswmMC~3nU&AeM*ZmpGve0RgoH9j{FX135Iy`H|KR}5LSm0ob}
zy_4$oA^iScHN=0QU9XA)5}6$=^n*t7Q!UsoWgWlm8ISN~+VF!J&)6w$ME=QEy3LM)
zeUZgY+I+B?@2=;0q-@<2!j{6#(>us9%#$_TO;+4jr>$fZ8cj0QWJ&y9c3f!FU*KT6
z0e>7bS6QE0Q>>C<EL~}AE|c{aLsT(zKGhAB@y*5{7l(o9F22UatOnzOfqBaO!Oyc$
zH!7CRqovPCKD*Ccza2@BVr$<dn5iqc;0ULVE_eD+LrY;@R^xs#AqNT8Uc?}fDn3v8
z@LT@$?$;bz5BTaZ!X(v~k=T&DDB4g5)s7irt`@RFhAOa}H2Zln8k#{aB~!N4xjEIF
zcb}nxex5qq0N%X4UV46W(_SSvvG$zhRar-s$gL?k*(ILbhwnZ)vfcjaoETUmsEeM1
z?de5F35F<}*4<~O5T$a7Y|u+co{zE}qnVQrLcj!)*M`~Kwkmx74(n1xWj9wyHl}W~
zLND6m0!}<^h`3$?Xh06UHZLb?&=qv-?6s0f4724rt94e3FVBxU&8~KU2n86KL8mpn
z#U0R&0MVRRP@qz(4v6K_2MH9=NMZJS4Xn8{3dzjxLb>&P5dJ}Dqn|G9#%Q?w2EI}1
z+~=)r=5tc5>Kak4+6|X!Hrn4`7@?v@0q7=`%+xV7WHFKb83@<9yt&b?vs4on_V<=^
zYA@9PnH@fjTWz%s>*L?ViVmV9Xt><j%vAFAgolTxR;&8pAy=$i`rGAHz~knqHv<2`
zZhC$us2u?%lqK?1FVt2Kx}$Tu8hXq_xdZ;J+=hw|6&3XvGDRveC8hAs{z9!qy~nL}
z3AC-_M>|hcg@y`$U2x?;y~P-jq8V}@N76WnIjxrf_iedS^;$r7UlwF}7iqWfghCU<
z(-V01qc3E{`0A2MnNGn6tsfr#2130mDk`q790ze9F*+bb+^HP*Na%cZ*Snc~c~#H)
zh<)TTfA+5o{q)%fw8sZnKwW9HU56M%=+2;V<;Yy)&d2S9^$(&QIt4oc0~>YmH>&Oh
zPWb<>3x1E+P6*acEn8tZ;|hVyheqdn!RpHkhCx5#6~UuadZj}1bQWPW6!2pNPxgl&
zr9ZUl7yJf(%#WspWFNRi(()39wu)>qtAZk`gQQU$pI8x6^+{A4jO#w;f`L$+ic<4J
z=?EWXhlqe#$CdMgI#^E6Q31@BRKh1ulOjoc&d2@+2bydry59pzC44kg7ko|so0wA)
zgBDWe(Ny7|YLhWJt~$YYHwwQxm6$u28&JqzqLcq%e4?lp@Tbl4+d&hsgiR6-g?Xq4
z8$u!E2^6l-0h+cA#T+Q7hRO1fvQ+}c30^*E;DtV#BV7rQNSd1IB4mXs<(X0xhk%}X
zy3#U#2`mO?U9zqhZQ7hpmhQh1d&8liXxCe3DAHWt-pY!LQ!C_4t+Xvy8o(m_v3sPp
zCPMpMSK(f><q!pLZTq<b#2fdl@q@FJjKhsZi~6o&f9eN#DI0(IJa9}*z3D_YtI23Z
z{Mpt(Tu2DqrrBtgpwDJ+1dUQrU~ursV!bReZ}=;g#Xw9U1ZPx(To25Ds*>Ks>FMqq
z5W*FP?L=)a4~G;5W(zg@X*R6`M1es;83JBBoT_K{8WoZceIxG~lq&tuq(F4tZ82$k
zs}L<_n3V}AcpS?kBlNtyY5cl^^9M76)G=S|Df=IBLg}7&m-w>FE89oj!x2X_Hqhqw
zSAAAgR9rEK_gV<~{=LHqMENIQIt>u<eBMvV{N5K^-`m<=0b0#(K0n@?7#i*sa04m=
za7<5sb}wa?=VX<Uq=Qdbr{fY)Xqs62hb2Dwe!Q?|6B(Y2(Iwo37jlPppIRq_2fp};
zW@IY^Ar14@CiT|K>J)M8mUFiARYt(+EP!P8HZ}cKUtjN?3Me7r#DdzZt^gNg?z}iJ
z)>%=>iWwt{ATUW`Hn`<7b##*OSLh&bKs-)2jAF%fqG+(-Ifbr76EwB~kfQwR8-z62
zh~Rv&B_1FMENj^B>%rs<Z7<;jl^KHe>{VS8Y{lnJG%f`3oU%WHfrY}1F*Isw|HQMw
z6GM1rg;D!JPfyRpB=_oSh!XGNU1!L40EI6(=w}bsPrSsZf2yl@!4vW#9&ktj1d32o
zQ&S!KTMrn2`|^1aF^$RX>zG>76v=8%6B^0ty#LtwOzk%<`Y#pz{Cv2abB-AXH38K@
zc8(u`vS(LUo=<m`QBhHUZ0apn4J8K?v1tk0TQyrF3N*Zn$@}-*#jKM*7TJDq{AKhG
zP$1pU)`Zm6Q^Rtp>Y7#e!EOY2e!Jq!aw6_>!eFAqxOHkmB6hZrqWINebF8175P?9x
z_F!Q6L>XGm0k)FtSiQGNsYA$L&?8NL9z%n1A@$NgZsxGAa%9RZU_ffQhw*BV$b(^6
z6}77x>GEi(@^kUpC?GlRBZJ9(gfU3?`H@6WV@xnjp9>8Nv4A&d(p2BTfPHU}sQu;2
zAYtGG%aB2W*@=a|Y)@4d6?1>}HJ6o(Mgzu%Wg*1v4+ySs?-G_FD>~aos9{TfnZ;!+
z{~(19{$F5`V_1pR{vF%sP>6S8b+)}ZYz#qMn9uybSW=LH=irRWw_F;b+S$-hrXukW
zDZ|<tU@<@HdXPsAk%Jj41i_sW2vyj2TKatfC#iY+geD!8;j^QL0!%v5-EO;Z=5(6S
z!NSmBAY8i@Zhzf%nnH;BpAcXnEE*xlArXL;relpm5d#j)*CNx1XN~CHlxhPMQV;@(
zzkxt6E2KvO!nY*@;4yStd>Y>~1!$hVK;a;mVrgaqrzm*;AVed?xEP2C9`HagLPz1B
z^3YjDaL@b{4+BSPP7%868yfHNoFL%*TLWagBHo_$?3MRcLW^wbgX-w)sqcM?puhNI
zBUqV}SzgZ=iqc*;E<}kzmUvMXYTM6G0f)uV%Dz+nb@ynqTJZiP?#z81Ei?qr$ovNw
zxMdnWi2BFJw8)C4-ybV%&r6woB6v({l1Y;W>UEYg9jOZ~uFGOV`+v{~p?`-WhX<1b
z_S=|dtI+5w*i%iX%*sCRF1FfGI!UWf8Ba7J9?od17B7`qK8YG&F}lwl95XnG=|O;j
z`9nwB6;&){SC_cV-w)oist?cES&8{m=(V?kvqp|2Pgzc$iFQf4CyIbWfRU(zJw=#=
z$uPQ<$3%!#G1=|i{B)Imn#xhYPFH2ho(K^ip;Tf}!|eg6tqa~hpb{oiD6){}j(u~w
z(G>=7@=iHJqvz$iaG6pb4Of*E6trK3q`tD}$m39cpi{-i4>aH#f?1L_+KY*I>h7Xq
z`Q_-qp*W6#!prk{6L?WN?TX=!PFRU)_DWCPrMlTQ+#3t)@Q;~q#=U|OEa*eJV*7zX
zyW`EBy3F7Elle-!%1*Bh9>`gE3aPU2<-{l3Cqe!}%_&`6hG4`v@=zeU_U-1KbM-(n
zec?aw9yUi2v)xfI=2z)9yWz+Lyk+|hRVzkc1sE_1FUUDfaGtEx)zx*^Udu`%hSvuR
zAwGrrf<A2byHqf>qL@E553r(6neZG7(F;O`H4H6v9Ry#O|9Rb7j-%72<7>2?Z%afj
z<4iV+toK?K)#E`PszsqF58g@wP9>C(KnP7`%7udyweIFz=~#9pqPx(BWf#y^&A>^2
zuVjgYa1t&4_2Ryg@8NZH8AtuG-uTs8pee<b!|rkVIE}&P;7YlmFtkv@hupj~qt;kB
zHo&I{uoGIMC+dAc0|^vPw=4^bF#Rg7;A0M1KHl1rPQlfS_ZBHOin@H<WgncH8#n4|
z8Mwuw!q&Xo7f##ah={{GZK|VvPYY>un0ig%#tL--CHp}7S>NBlmJkYRs5JT;j6mG_
z!!HNZk>`Hn*MaZCN_c~#6lSe9w`bQk8H?^GH7@zb&2_BnmYqMwU`G^3(&rpE`g`9`
zG$wYbHgK6=Ug@5x`aI}e3{qD?zrIcEQCzw^VrW~hDQ-o0)UkyocFDAu(zwJHuWRvd
z6|<<8l(J(G%?U}rXG415uh?9@%+|L>&UVWRa`5jM4g*X|$)2W|r-+GB+oA^4PurW2
z<d#+WL<82KS<#LgT%1WoZMf<!X?{JXq+vr`n&M@7P;J~6FYhCiCy9OZoQrO@dyp~T
zCMl?+f3APsIDMpZs^0P*Ge(+*L&yyh1Li~m+7AXXZi2L4f|b)`FH30DHpsH$MTyM4
zyodKss*0+oRlg0gdW-Xe#l<D<@NW+HoQX0Q7kXs1L8me&>ixJ=0W+mW%rI;%3&+p9
z>R`~4m<_a;p`laewIVB5x@DCK=iO9lyULYhBhBa>jXQeFEss7kD&uXhT6ecswEOkh
z&!LYBCzLg^4UbROQL^q+nhFljLD!5d+CSRCFM8I>0F;Bv>+RIkWJ<vYQD{a)q0+aF
z8%y*T-tY1@!Ez*hL;a!G_2ui_yPR1*?X>J<mY?&1W7eKn<==@tOf9eKCk!C^!fkk3
zHP7v-oWTTaRcPSYRKZl0o4HE4)ohFo+5e1QPg*UA3=E*xHl5A;FEZp=KObBjYWzMe
z^e8}QgAbFX*Y-BP|IrjchJjW)G$T3yshZzLi)lp%7$Up-x&rc@pQBFFMI?AKl#9cn
zl8%-wiaC?Md=OIdpKH^IvMJ^JZNHdcrBP<NuUcx5OI9WQ5x-_1ydoWdLZAi_@fp#=
z%Zq;ZLZb=cn|YI_J1B9(Kr6xQCfRGJj9Gdl?FLe#uGCA5g%i`oAD#grJ7_@n;)b>W
zBO(|a)|!-wD8hFM@@M6ihYenvBaKBD^W2kjgMp3DhKG1TEIot6@TsiV8HL2pk2$W=
zo_E;m52LlB(2+FTY6ypbwYdW#ol1iW<DC;5f7SM%?#UeeB-?rYW%bwr%a^a2)|p*+
zDEJGyd+Wp~)vg^?pRIS*!Sf(1Xutq-YrFWD4>f$+9DNR5v8q{W;+6BVSrRTMB#lb~
zS>*e7%3?RCgN!Nw2`v~)p4XmWTUJgm3xSZu|A)4>3ag{-(grt9kl>Ku7TkloySux)
z6PzFk?h@SH-Q7KSaDqF*9j3_pb${J+^*_@GGbga6s`m3#ExDH$f1C%_?hnN-(V$+Z
zUh~sPV%sb8jz_6BlPAunBdV?a<TeCxgiZW385ak2)W82*Jff%VvBmEN|NbyrmX_f>
zrZN-|2u7&!oBpFUqaA*N1}aB<!_O(B#S^ZdmwPuaKSL{3w3l2D0)k<vxyqgnZZp@O
zuU1p6tSVZo-ltoEy@}+{!J#=V=R%KDgNrFnEzu2E+aVCVbxmz|dAo78a!t<`XL8<c
zUITd8drA9jdv|GiFLzK@kX=7^JIHR7X&yszKsY`5$W=+fu##E4JjCrcI;STU%94@z
z`HqqMoHH_q^M$UNYxN$P&Aw#1Xr0@$u^48m2xVLhiYOry^`MIaxNvfCSWLnO>+GM*
zr+?0d>=Fl8gJT<QX*pcOsZ<=t#H2`LF%z_^d>esE5qH;}d1|f2&i5)@RbQrV#vAsv
z;k)#?0ZapEiQk#X7DXnb^NWjTP3{qs((3o?2N?nas0pFqxE@OTTD*4UCvB?h&GRsO
zeCD0C-tCUp>TUaag?yX~;A-E_g}Y>b4uBuK3FZ|XTi^J)r8mf>77BbU!9vPB@b}-J
zryZOOlVi+W6sSFu?P$$kPAoevBho{7r;Y(wc+-WxJpG~I3wZ=`q-5;S3j%LU;V76#
zN>5)AMbpepD!NEl5<;x={Afg*L|dUpAIouJfB9j_e!p|@>G4ljZ@rh5NO8^+>N6Q`
ziv8X&UKySJxHZZg6<jg6HGGm_mk$grRI##ldypcf0tF^D1xc>%OcR<}KG7$w#@&h#
zW^;Mc%TF=loXSUCFFMaVmOYDs5m;;$QW^`jrjz0#q>(^~47kn5*5{V!E){wcJ2YCe
zUcPfB=^uNWZ!pPa<$J%0u)AH4F08K^aV<2@HzToXul!lO&7`ENwntgaHAQl+fO^yX
zf`c_8hI%psC0`Y7$ShoDTUgXGr-~G&CRBn@F$|XnCf=vCUti4k)rPd+Uis8srN*Vo
z;4j&mJGeWPv4H%%1aRQ(tsB_0UCGOFOw3}gOumH+3hr-;5RH8pMMg`JY2x{Pv&QQ=
z>YkMP<%{7?--yCs4*tsvvK68>?!%P7mp}v*d!i!c`NgzFy#^_gXER6h7vqoyn}dA?
zI0W>bmEpV^-FlB|-{TM1l_7dQrW}tPS*L!mBErDquDa=BQG$Ay0*8c0u1dp9N3GYq
zh`gBW5A_(t*1JW#nKug~lYwMT^$BhF@jT;huDv)Op;c?J?rdpaO+6vTh&d^35Zlw-
zQ2ZH%xs62^#IIl+Rz%@$)xVcHgEL+_>s#WZF!o)&f!$5k+)4BLx`e>_VdEwKQEG{%
z`Q_;Pr%v`Y>+G_tiI#^crz%MktT&$~QwYMdIha2bY<eS`_caDarAl+R()B26$7}~r
zIuCW%z1Ql|b&;w9pX`H!)~yX{EW)#6(JR&Kr@}<;`=fWN#O$vjpnrB0Go25Zn88m^
z*sm3`^csnr&ROy31`{RUQwO7=g(@OD9>%lm4iie*9`CF>g*(r`59B)00($PNPu-gU
z&ei7NbPe~R*RbfjY?6*uuxHTep!wCDAWjPG+H7%iY2V7*>Q<)Z$JIQHUHjv3tJ!I0
zcL^kjka-P~s7O0mmUbZ?5uQYHLBbg|KUOxgpv5JKd(vfeZgt7~D3(=fK25Kd*W`Kc
z^}fwLe-~w(#G_+%MkaLiZ^SB8Ie}n~(}R*or$Cl$E~XC96K7?O>zKew6*eRKiNeh4
zQ|eDf8UkzIdEee3QeZF?FXkB_eoy#A@pTRkpzPP^+Xl{bS+k4#m83Blgf94n&LA__
z7S&YRsM0#dhXZtpR95FrNR>*uC->V#&nPQSTjz~^dOM$wl_8l=<J<0s?#B!7WXP4h
z%8BP`K~3nCnaT!FR)F$Gri`Y_B6NIcjHV0a;L&3_dwfB;R%t1tF<jh?G*1y7$*XZ&
z_qo;UeaUy%o2jsD1q9q(<pt6!_tPQ0TD1olhy~NsiiAcO(XYo8J}8Gr!RuXUc$rGW
zYjv`if3rte9C<NN2Tv<@DLH<7wRpK+_^RXLI=4HT>ZZN?CgT7@0FZb3m0%z>BuuXl
zdY>4J>cpj_;JxMyE{AX_+^PtP{qcG8G|sO_qWCzS&0r5ILzGKMEAqbp+*it3ky4qK
z>C6a>I-bLGb16H~;u1t*tUBdKVvEX2Z_LB}H4W;*x3+J!IF<M@97YljVAc_8ag46P
ziydZt5Te%1{zjmkI#a&u$^L>~e-mMTyX2%f$JU?(AK9&VBGB&a%(ru7gepvy&f{s*
z0c8SX8S(m&q=6ZPEMLW7E}QKe4vndwdZH+))#&JuGZ<Tt!l5^{+N!;-F_{tK?6A#r
zzwr=I8Q$XP{AcQ$9dq~#>k|Ex-LaaH9bZR;3nEB%Ai~Tha7WFl(r*Km>DTe7x@@Mm
zsN|=5ou7w_#~s)+EeeIQ&fBmU^mNmo3_Zn`*_La3*PkDB07K_;XB<b7)wy(vRuN^f
z#!>2+3$#IM_6?JY6u~E9n?V+1K#gDC4vbYH2NI97&t?2$9qHAp>gld1<<V4tZU_3K
z;~brqr_=4Y8~J+rs^tv;h5Q?!Se9px<Bl?oP0bARA^bis07)e9PR(Ev;Kzaap<5Yv
zac8XQZQRZ?Fj{Sc?PAZ5GZ{SFr*t<W)Ou}R(y2d^Y0YdqTX@c`4PPf8)Hqa49508@
z*t`hRt-gK$Z1CA)%luB%+)%(EgS}VJKksxW<$O%-JgxZAp;6Ct4otm7+i$-$AADay
z^zF`lw0)5EBUj0J5k%wrKC8__ug@kO9xnhECudDnKd6^fe<jROMv2AE8J`|8aSRUt
ze07BSmMkV_Qo-E$OQ=b+XA2=q4R>yU&AVa}dwn#lvGi#uuLhsRv&whRmveFx>Tz(+
zVD^9jKrHedU=85N-5Dt*rp3fkgD*2RgRq#em>|J?<=7-Fi+UhrQT}x#Keg!4zo_}M
zZ*IO+vvsu>2ZvhfU;##|&-7#5tXXmCM8%S``^<zD*D_?C!8viAW|6Xgb+AB}XGy0b
zUEGls%`YQ#Sbr!#`5ou$@i{Gu1ff)FS<|0w?&wwC!y68rO4_*hcR$CHCmMgg<UgSa
zq3u!bd4D<YeKQDOiY}K;)=bY0_1nJ!pzRk0f3RmsrXWUzAkG^?h%ttTa!ZS~*Abm=
z_q=+s4_W^`H&Q4^NfkPZNg~R=<Ct_o!&2T46w{J>KdctMO9}i9^KFuo*UBsI+$bfS
zQmG7~0UlZZ%}WYskdMu2Hz&DnYuOp?c`0aGR=n<lxnfR>o2zc=t}!MT0@u)pWn6!j
zXYICF&Yk9*jN}I27xNj18cMok9{wVIgPd9Rd*}I06SM;?)41{_qc$n35BE-R@VS+p
zyWh|qbWua{0FZohCb|?MEVBs!A2!{UU-hC3;v&YSBjrDvF@s@GB}J-0$hfbWERUJ>
z(+3o_QQTXjldEQ;inpiQT;T%|={}2A_wx@`%in=6_?m78t4r`!x9cke>O|4~VE`^`
zm8j+lfK`M#m2~kiH+xT<r#kAaVH^KxbpABcSHkTM=hRYsFw*%bF$|r*d2<I7lD>&;
zXYFC)$XDu>_PDmHq_mXG9QBuxH6G!<_id(Iehxp7fDkC)i&P?A;1oBgqfF+b8G|Df
zXt5CK^DyiZn*GDGdg^O#C&BG=(sSn({SJXU=`_IbhcU&snd^hB;NYunPN$Ly62?u`
zb0OKM=)(JRJVmwwBcUOo7BfYr<A>?*33kE79&elv(c52I;L~x%^HwdLjb0V;3jhvK
z%g`Bqxh`L21311((8!vGu*@6FEuR?RCN==zE9RUa?svZKq}W+9M_78_e+v4hxHqAF
zOrbh97fM<sxN)l6n)HoE$J_S$dlK)8w>KM?Ywee*gK}8_YC<Le#!A|gHH-)-XZJ*g
zM)r%l!a~JWkuAf?#7D?_U*8eusTjf1_!880q8C8#JDhBuayQSoxGgLlS(58WKTvPc
zThqRwDE;(If+Sj>hY*ijGMgl~g8-!cE--AaMC~s>S7S3ik*tTbeLkNMJM&Q<YyVXg
zkXuq##-zvdy1?h#4-Elw4U!_-KuBT9rPVUu;K%06V70|1H=rb8O{7xY<^;JJoHE^3
zwHQ1fUGX{G3b&E@bcaAm$<ZTL65wYC7ikobvEsbBRT!+gKo6%}nl*uz3k4EkSTnuf
znSFUxiZ;sdd@jjj#N%HiZ_fClUgC-*q&pIye&-``b<VjTaw%0WzS%8)8jOtbE}EA|
z=q|u`)4Si_uY6wzUG8)`xEEf)gDn$_o2!ooRwnBG8)0&#-^jg-=72-y_#jb(b6WQ5
zt~uv0g2$7B{l~*~?$%(jyHk7=;7B{;-f26aEjVjxFq(Z*aD6}z&>e7jwLXh|v^>2`
zEQA3u;)N<eZg9_9O1L)Qn6$>Zo_)E7w{Unkqy{K8iO%_wER>x7A1Bt*qtZnxQZ)Hn
zN=)FkN>rL+v)Aq92nvb#o>t;}0<z-xqxH?bOa(#kuOP6EK6^^Yw4qXb4iia}y{%9V
zrzVAAV&W2qYs~I$?e_i<H03t-5s4oM-91+p78XD=h=)NEr>rkvp{k1SA>r*Ktg|;b
zuT$sJ=yY-bI`#2}duJh-=`+Ie`*=OG<IaDjYH>MsM_^e)duXBPl&tXf`dUUpLO`MQ
zJwiF1S2N1`m+$>3m}%F`kqtImDb_bfV#n0Ool4}It%(ES*&%4nF!%Q*0pPRD;*Q(Y
zD%)-z_M6IIv*$z*ZD_hI5-7S=<cv)fCo`@#p&S}BWHV)t763sB2g6^*P{#>8EBp3q
zOtvpWbES1<-$ffq6I)zzJG`5}oF5cY;KB@PJ!e(+GJN4Zi!%mhg*U+hOLA>O2bZld
z5?kU_oG~*BKHZlJNg@Z7BqB8`{)EfjY7W%Mplo<$)VBA)Md`*SLtV>f7$9nZ<nHDf
zr?dDY^+VQBkeaHQQcJw^;(=zdXfL8hfxLY^mf57E3pR+b4e$yP=_>LT`%9}R?4B@9
z9`=Dm#8H9Hyza)aG(1`_O%<_TUi(Lt%@eeou6cFL@kk&)5`Pg}4+gboeJ0DS3UEmG
zhur8Q<tVZN=lD{OeoCg#AtkCi-&9`@$Sc-1D?8-{L7?|=m|ld^eiSt#_bLj#Y}B%8
zS&Ow!85V14t|vK8d*(yPBNL+Uz(BtO5wYzw+y8vkz2ql^Mg(F%=J$?t%2&q{SEsEj
z^fLtt*N1iHF-P$ML3Ecu(1Aw%L*pVdO#ALvdr~sTXOs3@{JG+|8T-#nu{fF5=Z17z
zf!|xjiPZO*kU(7>-8~l$FgC0L&fMJQllYdi!5-I}ws4fH(t=0xQ@*cKz2D<H`JkY(
zgFzs;UTYlqfJC&13Rf9d6QdPVv~oKBp%Ufa*lOCH6E1(oZAwl7_Z$5}TCJH}i#N^=
z1W5ivq9x5(KU7>Zm;tyjDRcbpI8ZX!Ojpxn1)eW8(;YBd_K^*|L{zA<Gm;n_MDYQL
z3eZt;ri5Xkk^Srs<LFc?ABmueO&QT4nX**ieX|om_lsBgbx!07)Psol7f<dJO0-(j
ziL`mDPQ3})(C_*GFyFFN7aA_gp+f<*MR>O|opy-+C|Y9L7px{=xWS*omDFIHZ!#*+
z3j&wJAQ~o`S{JR&9Qq3u*lFIZbSfM(Z)*7FPO(~p-qO9j$-*jz(9oPoJ~1LNSWHnh
zF+i&=8Az0$+0W2wah0@qjG(tTe%f;n31qb^D3G!Eqi@^g0zxJYuZRQyO<iwnY+IW+
zS=`XhDp~?BueMRxy6>cW2+wOjmU0QY%ZcRctuF-?2y8m40v`>x*T;}T8OZP>!c9ca
zQ&f;fc+_Z#GrYBJar!PA;W77XGlI?h)yLy1_~?d<2QMFraO_`}csDq|I1rf+eU#8n
zN`=Ql4Z})hZTF~GAd!^ag~s~Gfyx#FdUK?2BaMDIS(@q#aXwipE%dl8fP!!Of`aw^
z0_;uf4oyxF=HqcSC1OamSWfQ^IyOeg%Zh1wKTd~#blw1k!oJB$LV}Tm+<;mZt<#_;
z+8;thMA{9};!U=+*ep3<5KZSIptpnkItMySc@FZ!YieieS7?i+s|~Mql9Y2G-QvVG
zvH!V{A6vfS=1#IiP1528%v}7dXN{o<oz~L@{K6qI2}BU&UvGYtAdy1x%MbjS+@=z>
zw9Pa0sl0a!4)eot%oeC{@BUfOV$N($QH&PG-lS7*>8ni_D1|qDzOpa^M?w4_lKI)n
zhDabQl{9_6<({Ea6NqEu{q%*?1x^H#5bgsc25|EzR4eih=b9OXfGol5c<F&_pb(zF
z-D9fVlY$uZyATe+{|%m^udQi^>K7Z{{o(euOQh-tT+SJ?Jf72GSQMW?M7@MX0DbmM
zR}s0fY>(0(oH@)Tr?6No(6xBoXNEw4AYlTsI5TiUL0vTxar;9+%q2^;yht`(ZPN8X
z5YTaD8bFwLAqNcvDZvL@!l^Q@2=$HH31Jp+hAvCw)!E>*-(b_8R1B-%m6Y0<?n)mn
zL_NCiBTK&HA6f(t$a=#OQq7K6JJK@KzCwAIx-Qq_;-uP>nAb;6joE8$A&TH{&v>Ks
zUaB4@|24PNZp32kz8`D*RF=o%r(I`u{F}=Vbf-X-uQ)=g;nDEqF0`{gsa6c=3du&h
z8PBuo*{_b^Z)waM0@=}Nyw68>KW>=j{)~tEuE3+>N>i);+{<)giw1$3SXVH>qob@9
z1hV%PL#!&BjbMVl@3~uC7)wfI1M2HhiOW@30Sx3H^bR$I^tPIA@}1_|o|7I68>3%b
zGG{`^L+H^g7e(;X^XnS*elOBN>siycG%Py#dP@olki85boO{@f_dgB>w@pH@e@YII
zjRSh3{f<va*b6f3Zz?5Hq+B4`Bk;}20tloB5)ukz^zu??ipYkbp}oSV2Me1O#aDpC
zw*P!PMMCZute?d+2n7!xP9MCV$$Zc47x$6-2hKMd&Xl8tM}npL++XUayePdt!9YDS
z7$1tHk-G&aH#mcb$6^u_G4CW56;a{v(m=}tY_@q!Cgwj0J?<|uD`iqey&vppxuftg
zcE@U0<vaNddBBmOsn9g57{oEiDpT(s9tzr+9Jj^MIoV+d2$otBT2E0hOwc(2f1_r7
zk*}|$48}&<KWBb=JF}LB&8pA@m3Q*Um!*jfvg^QQziSoW(KUbf`}*wMH)@G<O9ubV
z{^oCKU%#$g$ND~paq}ps0{8gqh_#euGc@s8<-iF!Lcy>%#tBciOW7ASr?jOzyO8;v
zNTM9Ia%{KTp?oRrE8vcq!6PV}QUjtpycQ}m38Wpmr%@j{{V8wMqd1{>&R0#7VYOxI
zcyVR_a6EP9&u<E9kUiA?G)|U%yktm{S)y=aMcnJCj>X-fKh%mh@zwweHuD=Q1F09v
zM@!~wA%DJ*QOShtk0XEeQXvE8IptjJG!I}#;b+&q@iVB*48C!{XsA83jyZ*WYJZN&
zWWRQDKS|Ggqs!NI?%kVM0GTpd4@C$xSYe$kn_X$@6PD{piADZ=x(CPupmC6{>3EK4
zg!z^E!?jxO!Z)P7hkM<+;P<%v0szqg3)wrJST%Tf;)wWYWLXr=VpZf|RPp%_fe*jN
zKi{jhveIe~=^!q)FYmL+$t|@eSxqdKVzqzyTJx6ys%#IHE7n8Lw+?;_<t{uuH>-Yl
zePQz$|A#w7$Z}7Xi`Oz!NV=<AK8uok`u5E+g1gZ+e>@d<AaBc`SMe?5NSX5ZS-xMk
z;d^sg&Sr;SZ!>zG;<5wxPoi`ecKwn}(r-}>&hnc^2+Llz>vq!wN}+a7Accj37F7Ln
zWuY%4)Pzp~9kVGL7GxcDw6`3V9~w?`AL~nL<YLTzT3Jc&eeo_e{M-AT6c5hTs;+yy
zs-G8EAaIE{wYNl5u1%&ERLWrZ5zvzmJ6D&q3Z;Cn63y1-IaBzD%M0cyn6v=W0EAQ^
zWN5hah~dwu(6q{f>JvZxVx>7672|_nzM3~5x_fZ7;lQf28iGR)8x;ZJ>;^BcnNn;)
zCbw(Llg=0*o9<X#Bj5*ORD7V`VQQA{d5NnXq(F9(Hv-e2#2brfu>Re$mBv1$Jc)U1
zozd=ff_;4?mPD`A>`0e^A5I?wg55Xnmdl1y`}?C=E#QD{Iy74>G&RNSMu&y(amC<q
zU%$|#TMO76RScXS@m33ztcmol+qag%68}cS`mO#sBbdSH9ugdYj0+tYOJ2DvFdC3Q
znpbYAn;N}7oO-y{rk-%WZ0Z*78!$fcCxv-cHmJZT%=jlz%1R3QJrr>_@JO?y4*Hee
z%C$hGs)@azI8%4KmTS;!K8*UXi?cvO95S<t1+h^B>}~@V5y$ZwGYrcKOIT1yM&`U5
z#`%RC5xeg?MWs6DvLEW%$a`G)^pY$S@=c^x(E;M7+OQWn!m)1Yv-0pNYphzeVVxNg
z6trUmZ6v{7ig#%8`E^X&q`D6VNEk6m5QN*;8*n3R&ila9=lvTOj&W|zZ9FADzHilI
zgbC2pZ*;E8C37Nhz%();yAZ2sV;BksVZJS{*%JRAPtf5XO3zc4Z0BZ@09dw=I=-M#
zqdE3)p|CsP;KdvL!y91mbB7BO&VO()m&QrAJa_o)*6Zm$RhSv8I$D1@HpHL&&s9b+
z?QB9ySjDTIDZOvglPyZr3T0B8L_gG;W_SI=)8gVAEi1UZX*D%t(o>ZL3%RP=xOnV-
zoByy=-$Ig=q{9u(!cIZ{)l?M`;zz;T)9gMFKMl<)b!KB{N}F3U&aIKrPX~Hi379I&
z4gRI(xSX6KPq&N9&n!WO<O{V>qnHK;E3`mHIR)V0<9&0I;xb)H1H<Q$JV4x4?Z(d@
zBNGMHM#I+Vm6{1w)(*P}Y{4Hv!GZq~FVZ&<Tmu=XbdW&<<qn!n(eLrMsmR6-mv4P}
zw3-eXh(S40|A`kXl4;@AY#3fG-yygo=uhNH)OoVWq=}y_C1c+y+wDkJOdA3?@Iwq7
z2Ha~l<^UJ8pP*u4NxjC>Ke=nI8w^K5cu{_G7*OB1p&43XS{xQQ)&FF$MtJI{beO@j
zPGt!=Ur`Jj2R+HdZ{b1k6Hz`VI3}m%!`;O3xc2vAvx58O%w>Uzh)xghq;R3S%o&IV
z#2vIhvzBWOMpSfsVO-Jz!6x_oC*L&by98b98=M9XY+tfJf>Hc{%En&3U_DJ`a&Fy5
zO&D=*_`nqSO9~!SULF~kqs|N>GJ{_TNB1zrvBTEh`nmpKfQ)ys_QCMC9atCBWbMj^
zv2A?9^Yqw;vB%N8LcQe*zlnv#+kGbk@_{fn{eeWJA0P^LTYIL-vZN*F^d|?E{LJRz
zP^>*{EoQ5MIbB?CaYbi)7?6Je1eza+0sy<u9G?rc80Q{S(GwLto*!0Zi1mEOuWu0l
z%FErFek-XQ4>~nm9!UaUYljhq+#)%G6rR+HbfN0MB>02HgV{~(AfEE-IQc$=6jf3(
zClsT(q9c<}Ec>4W$l|63BD80uskicz0O(fga)zHI{Il)d8|dju6OCw!WM{Lka?KXX
z-gtm`y)#;B!3ctWvpDoI;Sn&X>}3Emj#ie<2)f=v2ry4>*K>dVC2eVH1%K7bf>O2S
z%)}w%IwKeiYEi(mI+{em_E#6pc--XC=`>BWo~_oh>jc5od2{Ksip~`N$LVq6`vFK4
zT&XxOv!Sx-<`>4chtH1+wtx3saQ~<@EiW@pZzwltQ6}BuOvz#`F7$mm00y}Df9e1f
zU;~U|c;j7&v_|H#TL(l3mSNxVk&sSrqOY87wXe%);&J^b6h*`x#es19abcVZJRoth
z6-z%YF51SQ6wG9VDrhA)dAQgH*PxL%In06a-#xmOXr|_mPglRz9Ww%=<X5Jmgsv`W
zoXO}mV`048sd_3ecN+Bj?Nb#VC+Hv;!mH<HJ<6nE6V#i`G#*hv1wW2{Ob$0R^jR)!
zIWGRe@KuZ;6(E(J5$!g9wRI5qdgn8GiM@)nJ;LKKkjn}3%LQ@;<L%h`0nFf6FmLzx
z<%H#7pQOc?K*t**m{?1=KGd;B`6~dz-){{nrE{*77t-shr`EYU88U8fMKyQS{zb1{
zKU%rm61Glt0N2sr>}NAYdE^KjF}^3i8~}gAgBY?^sIj?!sFr1EHI~Ux#Z675Z|{mi
zK+B6lhra(y2!T7J!!vPT>Nl@0b7fNZd!SO-kaqqd1Ht=?I&%~}4#CHp^B*m0^w^C4
zNAZ`-b_&japA}j7M<D=e8wEVwlSdV!p*2U=LL(InM*ZvSZ!0V2Cz`h6(LX-mD|vd(
zoUUFSRWf_ApYlTevoaC*(1Sb&Efmi)H2f`i0N>~!-fzN7N(xSa`H@Wc%0q&PbEn35
zSD2`^VmbI_6I8UxH4|krq`xE0#8YY${ARJtdIEJ-0IG+O^%W<LMk=xVc@ml>s$E-h
zxj~`YP+M(XlXe8@ZI^^mY2CxlOd18k_~nWPg_n16a2QRka`d`=tA!wayF|Jmanjh8
z!4}_!Cnlfao<5Mi6h}au#O$G!?nt{5`d4a;0R(*Cxm*~x+k+YtbZp6Ttz2}lF4;UU
z#h~uW=BNmWKsuOEjIL9_BrRDqhLVD3WW4%A)Y&O1oo6`*{8A>b2nx7gZa~mnR+cY5
zBX|~U|Dcj7nM~${ou!@KQhk`uOY#vSgeRTf2lIdB{t>6_`LAOb)~pG><_RplMVSx~
zX8G`wA(r9%WZHlNlhgs1!QmXiFih^3ED1E*;~AjUSO%Bd&>##HEa9JjM;cU`BxxYZ
zmb1<a9C{d<!Z(kOQZL{Xq5N#d(<Bn893X;?P0k+!K7KAzDGiDuC_G*an1(=R`Y)Ns
zVI~dm_%sL=u^S_>{4sxS3Yyqkypmb%c5h*rKA!yQUJc+hN0vn59|F9T&C+ynRj=E}
z)#|3pBl*YyEig{>JGx-i3_PG;66+mxws_+yBHj=32h+f+B{4KRGjNpgK#<bE3+01K
zfp|+IRi7hXAkD1$hgqI|p@3mB^nc<)8{*cJTJ0&Vb0h@q%7H=I1K|AVi^L@=@xh8v
z8Wq(HScEj4kf2DlPTmN7AXE|lLBQqc;xU!Q%6v~29S`&i(_;<N9+i<1T~XSpLagi*
zp_6MyGEq1i%x~NPnF&OG{vb2n&8I2lGdTg5*jnt4APWuBhXKkR?J+IuQ|WpmYQ#&&
zxeG+y|KbdE|F^LBNa9Ex>Fbr+rx6HHC?s7Fh&>D-vph?eE3lmcW0BV8?V~V3us33-
zkZzEt5WUKqbD`u+VYQ~H`N+6GJ<zn}8<0dJjRe~C2g)la3QT3N*&=zLqAPT$H#w@K
zk)$I*e%xSh@L3SRehE|}C@6xseavpmpgveoVIXjh?Edvavw$?YbQT0mkyaxyJaQuW
zhVMND2`L1u%$qM1gvDfuzJOw8OzN`FkB{&AL^w(UnT#Viv;qqvD_n?*4tIeg#Qp2?
z1fm!AvZtr<Kg6@MwI^3;bbmz+r$9gd6ug(jz$}0Pp#bRupu)Lq2$w@)cq~QD6u(`#
zTnun_Ho#tIuf6+HqV(NrwAsg7!8CYfknD_$#r-?%UqH37Z^xrk`)wd{hv%@E_QQhc
zHm^}l4tPBlNEQM>b!jVY?knsCGX>dy12G`Dlj{YGRehOWJHtsbOL=UNtb~&unkQA_
zGkVY1h9a!r41pu_^L*X0Ol-KgyDC~j*{5$xF!l}qjo^Q)^*yo!IKNiuugurxf<DQI
zbU{0Z2?Ivs0QG)tUiXUfPCEw&8pVpA+}zx(9X9rLxB(A@NMhu3p-I%6hM9<$yZ}-g
z79GSoHUclU_=>(bIM5?YVSJK||8skbHK79(K%bs(4O$O?gM(M~f6aAsc+1IOSWvGT
z6yfCq6zK$=8M{k}=Cjbp9W@f5fX%+$J8Wq*i5@C}iegVxgMc8DS-|B3ctvg4Bc<|h
zs=q8pTv-7aboR;C+48eQho0f;RUbHFsZ`N#!QWL4!aMhXiKNm7B2!-3tHJugYqZ4I
z)KG9@e*$~c+w$gMU@Osuz)+jHQVN7OUF;{A%9ARYR8Xw+&RYlEk0&bv%FoK6*zi<4
zg**8@f$O*axBPRUL{_(|lU5-|7|2#1Z|4L`Is;&kfTF#dhwGzX)_>JR8KY6tl#pxq
zoHF`rO_j1jiE^?3^9hhwPSf_V=6MFxfAVs3<KiIrjr)tNiB3QZL4NEsHZTMsqc^AV
z;uctNQj@}(80Z4kotBoCYi1QdsfIyMpjZ_C^B&);fmrkY{(cZVR!$bbFyPO8-$bo4
zLaq{B{5xv+Nq?~E1de}0^JvylQjFuz?ZwWh&+|infB$NegVD|LqEex(4&luUf7kWD
z6~2xb1~4H*DXDiw3OWG|lMEUp5Ccj52_OHL&OMjwv3jm(#FY&bP$PM}J&cwn@U`i~
z)ssyp64e(R_$?23r|Icwpu6;Sl-O<;95jWX1jpfn+ROc}Ogyw4P<m6T)mZUzo-dW8
z4wwUeP3&cRb~wyyb=b?^-rh=zidBkWki=~3TWP6$zysbIjH-nJa`cygLe}KZX7Xf&
zbnlPuTQ|CUn#RdMegO#-3OyI&c!{s`J*5T)2A1^?Q@LV5in?Z%KG>tn>&vqXFq=rJ
zVC;0YRbztxcfg0*APA3_22>iFZRo8eKyr0;(>Mz&h|}<V;C9;R0z+r_Q+Wfib!qyY
zl8!&ThVMSc10~9S@HNUt21Ilp#9#seCnBgzod`t83(g(aJ&f^YI0eTR7VZISg}N4*
zF#R@;n#x#)2MWgk2N5e3E9L_X1Jqc)qdl$q*t-M<&=n}g!OaZ`7M7m_1RF;}7lb|>
zBBH1mJL5}?i5B**LSy!a?^s*(-a1P6Ca|5ix@<#66q-M)e)nQkZ>NJmLogu_nyR^C
zvY>9%ewkSIZ=m3P2IB7a1>ie<Y9QbN{0B(-KMK%g$4v2zcWiVL8dtvwlu|!S?UG^M
zk;Az&9?Zeku|5g+-mt%?RR8W>6pb~5Z#C2H85$kp@v)!%5<0a#$OiS&ZUJ%NuFJ6S
zK55Blo|f(R#6lmG4_aK133pHHI3M#Z$#MG+cfPRjfee?i2HQB3?Oxz2hvZAHDi^Qp
zo@L`JZuVUrlsWXK=XANfHx8|I{=fG0gsr;oVD%R`k~mGZ*my;k5A7`+L2j*>8Xarv
zK4BWlTy4*)`2k5hopx3W{mxyxha28#dSe^XAA8=hx`@#QSw&#v@HsD4df}l#muJ{5
zdNS%5*l_x&EP2}GP{Ue3-IZWI67{HxfjlQ`XPe!)<5Ft^td8;8k~sC>j>P=k-GgtX
z<P0OQQ5zKOO)xmGUdK4(vqSF^BUtCA6D{DPq;T>lvhu>Qqn<Ka#iF-gxWv9gndnBP
zc%4jSwM#^}v<q1O*@au};EI+m0}fqmP>Io&AhRu^)g=<k3+$seak9lX)6#zDKP6*l
zbOxRMbw?%JoryN}KU9^10@R9rzOOe-v++FJ(s!W^=2T(d6tcNCfL^d0I|1FaBF*%W
zFa0^i{!K&nQOB%)&M<}BbkXJG<#o1f^N;tssqfVltMlL-Xmj^%XSdO|gq>T9KkThr
zLe~#I8I>UePrmBo6=1CVH!}E35&xAg{(i7A{X&o>Pk8!2wE1BAcbkv@bI*0A&!1WU
zYwwJDEvcxjCX|jnwf!o7WBsxnp;?kHTvTJXS`NrxSzmDn0=`+#G$3nSXkIwG`FMvC
zizH~`R~pwc)*8hadQ+)lbW$m5p1DPNK)%S+oBm1qlA3$lNPae$*(@dz9UJc<$3>4W
zAc$sH+#-C)W7B!Jc$Cz?Q0VKy$Ky^4djbrDG5N`TR+%N@<tI!N3DdKt5<(iR!5#hJ
zaW=EX04u`Y1^1#J5D3X^08y-@z(W0c-5gJ!i7C}B*vY+GiXI`}wZ3TLugV+>?!qBo
zO10Ku(m%4k6ERgf=TL91)k9xXPjjjguf+IeY427tmT>nE63c7dkARH#tk_*lOc#GL
z&%WVXj^)Jk$}Mtesfp~0-~WK3F>Orf%u(moRv6GoEh(C~Z6VJarT~xwg|U|~c%(LZ
z4Kwm@hN}5~5l%(zDg}_Vy&OgCRIDDYX0wU-M;bM#VF3s7z@Z-gXk$7e;^Ob`Pz=Ti
zg-Nf80@>xLrGnLIg@Y%rASkLyK3qww)eS8UjZ~P&3?6!bbhh(zSq8hkX*E_9pF$Nk
zIuc={e>hAT)rsd2Zcd3ci7SD<!eVJKiT!-6jn5|;?==@Dj{6c5(X)DCXlCZtHq;GG
zoG}_EerTX8tp526ii9^T{lv`31+gmpCGji2uJEMuco^&w3qA4vxJkO5HcGBRlU9me
zDTYi)NM9PV(V*_BxAEd=dz3Y0Xx60TQ7Gnt_L(}Fh#}7>)HpLEhVN*S;llhP%t@#*
zv%T@bRi^U9u~gf-iOUJ~s7N{sGmO%y!OfzAiby!`48|EyV@pgfpES1z6MWqoHk_F&
z-_2)reLXAub6|_tBTNs193a~#6x5L<tfteXkQh^))o9A=s$?(<5-$7>AM3%cjXWQ2
zFQC{m_C@L4laJmx%CY0O%IIGtpCXG@b~OSAlwqJ)ha+kkN>_O#{JVwLj`k^<tC{h~
zN$i!qkSoHXv^4c7dBsb@kVtoP?lug77lB75{OFsxPezRmuuH237;<;PngvZ15@Iyd
zd#a}%N}?gKs6BhrpD>;5ebxiUIlIpKwVB8U(uI=MC&0`m>Owz_f7P(X|H0Fjn)vn4
zr8ezYWP$C{hHU!*!GV{}tJHwOSs)u1bWc|%-kBU<kHE8MEdKJ&SJlS3l!Ky|S-4@1
zo4Ntl!3^|^4E@?gv=E+&3|Q;;p2UoW$07D+)#R~G-5Hl<)3xNRqvJ*_V#gsFducp&
ziyJ66ZTXk9H^Pp#i)diL_y8pPXcM`>ljGS@%xI_8_|S8>Q|ie&zIhbcqPWlIlZCRR
zH|#FNRUES+eY3-|acn_-VqCXP;~BI1&SGE-ai4p5M>*GZYjRxCT$vFE@l%jp5fq=z
z*KWQHnZmeHaw(rN?H@M@8Ac7_fv2LmiCZUO^<QeT(#t!Q<5x>$-tT_=Hc(9@j+K~Q
zrD4jLX@i}TCzRaIV}ZB5@4m!<64lO|{lfr-fU9ZZcJTOqoV3m0sQSw69S+Y)?GZSx
zL954}QJC{_W389dwTlwx`!PyS7&>WHQO5x}4F^tRx6b6SpnKv!4Vk7nWV6aGuo$9p
zKg?6S+;WiG$uPN-nWsGcMFW3-X)$9%fk-xy{u=481IrjWKu|>TXCF_jTs6`!TM!RL
zUrL9siP2qnvzm*XW;(d0y0pboAhlN~A30`G#UI7JO^|`#sRV+A>37ia=_!V+48tJL
zFGvic^uz7Qz7#KC^Ng_L@)l-Q4gLc@BQx%e;s<6zldr*Zd&i{6ltFu?k9XL%E`m}d
zbMUL+$EVVu5TXn7z90AykGX!N^5v0?zE9UfZJlasb|;zhADq7kqvTON1`$aUO`$v?
zx$FM?l@#_VsyUPGQ0>mDxj3?BKEm$esi3mty0NXLZZ&%ym+5m0hq$-H7cP~bMNT9d
zCs#kE*-o%#JnU7h$D6D(cT83hpc~gmA;V1KVPE;e;vc5bcTG{BUG1HsH6T2HeVUn;
zmf;`IYyUFSx#*gj<jG^2%3}Jfcx1F-enAo<*~Uf4qQXb7<kGi%MQRP#*^SGHHlEF0
zbsze8h%mW2@Zm^m264=a7D44Q-*zI)V%4bVV@x-=1&coz$^xgb!FMnou|?bx+7ChS
zhp+X_y<L3Ng6g?8An-v;YIyHU#a`ahFEh`}lGs@_md;o->h2SvPri!lUt&bE&)RT}
ziz{V01uZpF$Qf)xsxS1n$tp+Y@eP)i7AvM$ag)U?qT%ZM*C{o?WCZIO5Pum;yd!#<
zEWd=bNPov?qEb>RSzBv+O1C4*Qy}7lwdpqu3O=}8@Eum$LCFwis1D-%7Sz%hI8F}9
z^NYd82$9own9e2LEZ^3ozS&~wf`uysu9#~%I#P2^8Lha$gKJKC<svUkE5dXkS-dMo
z!<(b=Ykr@ly7jL#&I&i=c`RzEUfuWiyIFL_zUB##a>gwYW?fG<?_&~$kg2HaZ!ae_
z5A+wh^yA>)iOt*|u>^z?-n0Z(h4=jQwais8uq~0Qo<q`>T1{9S#49n%_m`T1LMWDV
zg!rwu4|YRsK-hNrswtq+V=c2*EVV@_x$kLH97>4~Hd;yqnMROIlv(L-|AXl>iBAiw
zNsvKAPP5E?)~s~1nzlFz9&E|$kE4V^i_OgUDiC1M1^z>)t4O^bw*fRX4nnQ<C@S9A
zJ-j~={C1RW!&hW)pM7F>28v=5mUU$ph^tC>t@Y(hPLjNqad{6g;;G??is|R*Kb>sK
zKLg3ws=7)Cdh!f>;AY8d0o*Ap>gnSuE{vl!nhO_Cv`UO>xnnE4qpba@qooHr4gw=5
zVR3p=-rLV6UHH9t^fSxCG@`?|gKqt>_^;RWxhXX&F(15_M`hRlXma>ZH8{FKy^n24
z=5E*dVo=nsF)<m<^wqV{D^+S>M(e(Ecdq-~9|=qqp83q;vYy~X;Cdw!2ZAR7%{e!l
z6%&(A1+@AsqRBmPIuaw}_90{kC=9(lVP25dQ5WW}^9+RP1s7VZDsmA``GDyaT;stu
ze%jLSRgsPPg_OT%dt!sZ-xi!idKX5d@V;?Llj5l(2wM8{5J9+7`$6y~yEtkDRm*wj
z^TF#oVbPxd0cElLuTT~P!~YFs{l{b*1AVB(x{why1*&MI<!D4a)-0_6Z*^Qq$sG0`
zW3Gg4)`LodPqEX(ju@evO9hFB>$zJzgRNUJx?Y+aP_e$}g2?wsw6+W5JaBiLm3qsg
zhV^#`hUYg}EnO7B3gK8-w-@Cz50QgmVXdRNx2ao7vcINw&gc5=!Y;!gzyoKKcqqd=
zpB+ZPv1`@_E7y{vaz)BlB`w9i<N|H#)^O@M0(aOq!f*9ED^>Qy_^>P#^AB63=cC*Y
z&nyC=a77L+9vpxHcU!$@dk+uKe0z^bIGTNaQxyE#%=(g(M@)}PlsHv4g;Uk7JVPX8
zjvxGQ^e4ZOWWq{15C-pmMGbuVsNC_5VitP!b?c0g7!GptrxY*xfsvv)TeIBDoSK^i
z(2nK<S}~ZF{sNcUW{-IC2fy)MT0yW#aU?XS-LGuni~9RZIavL&IOk5WXVZ#0YDg0K
z6&?F-oyP7J>(m+7{i^K+CJE(7!&c)UQSM-5l{gBmax^teV%r=|81~d}>O;T89<y}H
zuDZY>=b4X3tZNmMLOh0N^2%gK<+u*cQW74^QuK{9#k{7uw+}ceLl;@0{mkf(rZiE4
zZSd2#NJIT$#k`v~OlUb%A)2|D<D-UcTDTom$q@n`YGA_d83yo?BW|qSH%kH$4#2jo
z<wAanz7yP!#&Z)fu4f>GA+gSeA@?rcaUgLn8JPb=cCVDs6OlK*7~*2yPZQttODQYb
zBKn)Ryfm5g6!KTj9vAa+Ozd$(TSMXcN6TcD8tPG+MQv)8L?*_JqS%^uljK!&R)=`K
zN}uDa#Abw4RckF!@iJt}7|B3}PKFWH@BL~+SZu;ky{)X(L~3*`FD+Up9CmFxWIm%A
zh;wD^PnOv3mLe1+5sdE`AVSVafDvL0n<h-8E1^$iM`9Y~QX~dlWgIoZd<nf(u7W>}
z56#v|+oCe5j-2KZA;uAb<j{{a`fO9I?aI7k#n-0mJA7L5{hosp)e&4|erl-fYt~Bm
zft4yB@`UMw<f$?zl%w0?^K(sdU(&=wpLJ1`jIH$yS9Gf;J{PrM)lUOdkSrBu7V$_-
zRTtScj5h5Y>#||`_Toa`NJHu(>AWgzayva^2@`C5>d7K42J&@%&<=y@Z_vnz8{#2p
zSt{+Sv6rK6l4M3OjfVq`wGEk3Gd3=A>fSloS3zNJqOXF@rK4`A;|Wz!E{El{OfJS`
z4OgPO+!gb~U`sfu!zdPRkdFA|F#O(J3)7H6y^OL&<?m3w>X(RJd!=LcH#Bvu+O9~=
zM0)sfcd9HXzLTE{G+@GwR)<>K(_n-E$%K65A5$6w1)EEbfgDdikJiBxE-k<Ngb;rj
z3){K~6}>Jk!NYG`HRe<yEt9|-Q{Eu?-O<BkF*X0e?RqoM=AAubkw(2V$NEu|wj!gc
zj%l3___k?$nM@A2yy{L#h!}sxld`f@^Ko)^C~|MiViB4G4+Qk#SSb8S3dZIVdN~d5
zp;Ny1ut-$&p*!p!8e*YV#Zz~<aD-D%g^BNLj~+L0+KIVTIS(R;-??KKiwx!D!iL0i
zzYOIip?;Mh_-N0w>_`(nS2IpuvSF#fHZlm-42$IbzNPAttQ;~@!%k^isAH~Gep#ow
zbFWc0&QUGz$T^o!DQ}0aa^zjT(ijA0X~(6J?)d7vK>O}MIJn?dGir`JlV?sVB@M+~
zZfN~w<9bsf$wx<gRtZAfM)}?=^sF=|v~nqFP=v<m%oW0v>*TCuMJLDa?yrZNu#DVu
zU$cHGWvt|J<Vi>3LaDzln9n?pf5AY=4m0Sh?t70!@CEdXkKG7qlp^@?@G0T2f!?I)
z4oA4yU5Me+^ggmEH_C_$uS$|rj9Vr#>BhNn(qUeP4i6Rgu>xKV+jNAG%g@H;^bxq@
zB6fQQ1G*CuqYp;yXk6l)w*wrA5MyLcYZ5#oZLP}Rp|3d0blB$$?N~kS4%-DQZs3vz
z$uGTvm`ZA}egaLZ>Tsw)53^Bjb@g{Y=1KZlk!QF98<)L~yvzfL@+1}fJa^6V5Q1K@
z_z>d2_}JVXA1len7ma;>Xp5f8QH{%?eMr9#=>a?IxC;r1RC>7&A(`v=+MN>u>!Z9E
z-*&7C1LuK_xH1s<YyM}(Wl1=x0W(iEc(rM_dmK8`kCgX6++|kftXvMDxev!`R)Uxc
z>I>SReRRfc)5A-)cNvB0B2!;D`fj#QY>rIht;O0&@_4+Z&mcG@+*B*xZ%gCTtu8mW
z&_03-y*HyE%&8jszN_D(@5Mw!7UiVce-Z=R8BoS0j~uh)oVCve!$Dt)Tv8kQnmiz0
zHuLt6f<W=I+zyPL($2#itx#+EQ-Gt5B`{YvP1CrElsP3$(@#C4VtRbFhD*kx<LS4W
z0+Cg&xU?#Iu*%|LcFRQE-bsa~hXn}}sYHn2FVUwTUak)rpkCtWjN%C}0f|AO2xPmS
zY*C8c-|{4Zsg6Y`Bi?k>8u1G_GIE^OJhyD;BNZA0LA)zFTjr)b7#|aEW3C_Ozfhx7
z=Zh)JB{Wt*_uZ_{`~CX<w4mVPbgHj>9YaDYFZ;+}o^H_2@jU5OGm%$LnLfsed^y3{
zbu-~1XXhJIwTk^g#mCqRG;$po<ksC9Gw=*Cs*XsFE+}f2kSBO~&)4~gG}{!r@;_h$
z_J3o8|JOFEZ@p9hH9d%lo#Fq_=|OM5^uL`R#Kg$@-+;@>im&qAHQwMk+XabyAHhAz
zkKJ(@5<-|zV$C=UR#GH|@9F1G@YM&(KI(T6)cLGY+rN<fm-^7ZBKV4Ye}8TSi@+en
zKgHx&L00!eB0h1S5#x-G5KgW9469@Pv-RWx!R_Poiszcose4*w=HtDtw;syhp+P!E
zf&3PlqMM~=?>#od=sO_w-bNe$>(9H6yG^M-|M~X3KWXECKIEjZqhmO+k}M?>bJ%Au
zlbzI1W0{#iZ`_MEYVx^`tVy?zeq@q*QEHn~4#ung@>=#Txo;FYM#gtC(f2M5H?I1H
z68ISLN{8aN?x^qu>aE0Z$r29^^x*GOx(Mk04!3*S^KP}yYa-64nsOo84;`BKns}Kr
z%4io~he$@B+H_M)J-!{O%{(WsXVNxS=+XGri(m5)D1_19V%@!M>kbvz*5B3t^A`B5
z8`$OB2H(E@KOCZPxLoVI*0A}$l;`Nr+}J{y@0y`}>2=2sDzEwrma|N(k4<N6Fn{Jo
z7TSE*E;gccb^9N(s3M?uI*5hV!#P7MeU1XkcrpVkRXa?dFG=Xrs~?xc-0v(K_>LC&
z_$YfFyDG5MeVaSGZeM5JHuXeXWiDRvIymnWD}QYhiLL&b8*VhUO#hDSI;cjz?r+jc
z4kJ3z3%oqH_$-?VYtsk3^pr)ww!g_hyF1+WG<SSdQu;I3zp&`C1&8t-qvGyx^U>Vz
zRg2in?RZeF@Vfg^=XCw*sy@QG9z(;g5-4+y3Ti`sFhBr)bKbp;ZMeme6L_gASE_!Y
zas9>1<wECf;CcBtb2+e*w*vy)t=jkJ<wN;(pPJlyUt;>6aL3ycBJEtmy*dtdN*VsS
zkCw+;Ooc~adMDmCFp2l-!9v-eJ6i4r`Z!rMh89wl&gU$=gCz}~=dLepzI98IqUoi3
z)%tvCk_n#zHu0Dp7IQ5iw!S(=em*zVIM)7vgW?M;Bz^8&J(B)*urdEk14t#!bm~&;
zJX+YCIw(D#bMh{_+~<6|rL0bMjI<xudGC1Xn7W)2Z|7n(UfXiFvb$j*4$n{8?OKL*
zzKxHoiLo6-jmyvusFgnVMm(>6gKMljSBVo9x}EwFpRD-t@}S)7WOQM#>gDG51Fa8_
zRFB}^nxS{q1oZ>S<M)m`*SZd!Bj3+QnT<ntOo`7LnO;)UwV&8GmpVJIk28I<E}p4t
z$OzWg9wVjBSx=*00w3W!e4A*!KO0UARq_4w%}%l#EI+?~H1cA(;n{T3{FbyH1w4#S
z?OH9L4qkY@cHVVuQz;9cOuJ)(KcC)i>Km>8CtLdei=6-Sv;T6)E$h|EA|Jih9$V}0
z()3DT_fL5&XnKg=cKexZ+rLY*&k&zi?@DBI!Sv?8pB26xuu*#cQfBtrX8ah=UT+KE
zv7-0J<p1J}{}uQDTR=SP-lbf-xgnm<4J?#(J}x2a*?5gVup#OQdDWk@9PzO_BcAs+
zcIHiWJ^blx@fm+;1;z@`nfsc~udgy1`wl!q{l^b4zk2c69rI#OO{a!D`X-RYMc@px
zh{6&zDscM1vHu>PG?D4(6KfE`8jiPdy=N5dS8+nKp{t-!QJynmYHsBvk~lGDvWV+V
zD8^D&)#&70q;d+lXePC^SYD=9vG<aCyGl08u=8Oo&$z`|U&G-(#63L*Ma2dCH9=zz
zL2zuzsR^-XI83bYj<M?Zo~RXf(Gx9%b0lo_vze@>!@^1Ki}4~;4BYj^o6MYJFPsy-
zcUYg8<2j_NCW-r13e>%Rvui7;+}8Ld?`g*2s>tqQp_+OqYhmDC{EBufT+eQ@R9n(b
zz+Jy)CGIHkesH#&v#QC@B048>x1aL@E+DY6`JEd>#f7?-J_BbVWxLq1<`-qI=;^Pb
zh7nGyiLM=sk<#~j54ypn3ccrez8m|PTXuL@Mb{@~s1DvozoS&{ToXU3oJq=WEo)#j
zIv?K0nsq8_us98)^-}F2s%g8_(RdtAoKr5{yW-DQAgLi4Ns_E7+^IEaY3!(f^e{QW
zp(2M*xuteL?XjXN)vXwbEe}cvErrvgiHj<vpSAV2=cXKO_t)0T+*M}EHWH8<R?}vw
zd%DM+K?^NuNl1o13CkTB{+7UPU;RhUuCz>Z2bZykY@erJB`kejmZo`G1ZuE+=3J?Z
z_jKT!2>DJjRFV;VpHr%iK)iy(T<!TQx_gs`gi6#fr?-kMnZfzMQ-Wpb@r(}5v~Oh_
zl~w6uf0H5x?myE9qF-V}-W3W1qqeK5(^gGyG&Kxk#E;t<J<ar>OOhnYm#5upW~mmR
z>h(Ouf|bk5)NiL;vpkYRcVM{WZoV^rfGahf&Ew;vs?+Srv3XDP(b0NLM5v#AD~jz7
zY;M$R%2`BL0<HRfn~u?I`&?i=M*KC{?~#?)=OCeEtZlrxRID##$Lwh@K-bi|+G#ID
zskrt-D7m=l)Xh1^>F7Ljn2xL9{?kTpOu<p>@I8%kd+2&xluHIe>1SszjwV{Wr~~93
z^!1m#=T0v-X=XevTzY}I(lUHh-|ORbG&=2Dm*Mn9ZZG@X<2E+8^r7@Kc9OYP>&!Xg
z0T?YAGp(9i-P;7cKBT6)I!W-gwzD7NS+_Id*DP^K-`5ZG)8+=x51cTK>sFtjQnRF;
z|IP^<8o$s>r5up{AAr?=8O!+p(82!m8~*xVbg=(7^n!bXUjLtTu>Wr$>;J8TJqG}@
z{%a8KB>kN_+QQNx5xmF^D*qxnWaE6k<_f>OFgg~02~_s{wQfv9X0WPNIlRZJ<Xq@1
zyk#jyyP{+<Mb0eLGMpwxxtcbpG?;ikj4-PvFxqQyjD<}`92U99_~Mm^n>%iJY<tXe
z;(qcm!{$naI0hdCVupez1Dc%?0%>(XGBG3&fc^>sIg{dQ<`0aFj0_J;B#?}bjFgp^
zA6RST$zv%FF?`nT@X{#Iiqi!bm?dIkQ%fLOU0G4)uF2(xb0G>s_?+9(!7GRW0xbd8
z<fKNv+#MH!ixDKPX;&Kh%1;<1p6!T@jV&oDd3bo3@-#m)b2(qG<;<F!gARPm$<A)y
ztPKqZ3BnF23Meivj*E-)!pXQHMhzJm9UU&`;Ntqs0OSiBA+LWO++vi#yXwQk+%EXP
z*!#<%D&x0n7=}%4debRvN<zA%*|dZLA|XghcPb#Y0qJh(Py|E~=@3v_QUL=5ln_Kw
z8WE7^;JU8ozVA0byx-oLzcb7@L+tbX#gS{RgSJfrp5EM~$bYvI4NX)eK8~EGgTt!w
z+0lfw6@GHo=*mdu{rbmVy1(Q1zEiEv(d5|TAg~d=U#s{~>>*pY_=q9Z#)y;qKNa+w
zGkaru(UNPR*BllW=GA?AkbTK?A!K)~udfgEHy=NK{Nerk_KuF<pWBSAt;46Q4830N
zo7}l`$Ju#3iAe%fs-cw`>iY|nN);3o$}1{{2SE)q<BXI+_||iqS1%k!KrvkH+V=kb
zDfmrz+D4IwOO`<5nt7brt|QP;?nO#M(slHW<@(Ip=mWLl6ZX)(x1i`g^zU#n{0!v5
z|NZ-CUof#|)FANs_3QHTa!o*et=N2S^_dopJvli6(MW}R2eqbkR#a3}1=1evVW+_s
z)MQwk2w@pHIqCp33&=Guc7=8{H2CT1>0P~gRYQYdo1YJhBqlDSPy*luyk+$!?9SRm
zNoI094R6=Qfq{W<0Qqs%rh@}wY`AmcCI7A{#r`;oQVBhdFeImzzoL1Gk%h&GMYU|J
zWid=bQZlOxkxu9uHRec)k7#;G@{$o}5<x9ksC@QfY#pRVx$jKUh@`6CieeA@qw?=d
z{q4blei*(0H8mq6BWll{D4Yo5y^R?+5McHDHX5{?e3_Gzvsfwg98dzf(qwE3X8&xc
zqe6i63!0L=#gVzWrXbOJBBsEW+9$Ze37`%wE*_j&^4MNk?=KN6%W`7xh)zy+Co(*i
zZgKPS>W1rbQVk#M|M*Z%zLbRS!@+4}fFmTw#>>k)e48DCixWWvUl<&mJd$8B9Op}9
z#mq!loYJS0-magk3@fC)7Jh#F7K6gW!J$OpFmpN6F4R?3Kl1>(koC%3X0PX`ibGJm
zeD&%T4-Zdra`Gk9XM%iuX(c}tLDnAdLm6B&G&CUPnwOi)Ku!I^b6)PKdP!4LGviW;
zjE@~iw(sxn3kwT_Vz`u;7#4$5>e5^+7;?*!m-hSSF#o#`Qx;1fIy4?TIXcdM`s845
zpSD?3BVcW9eblx9)7jhF+Uo1;fgud@0Gb;cxdW601a$O0YyN=XtDfZnsKBeMTq}}u
zaCbjBS}XCIZ_jQQgq=<H{dKL);N<;WJE84((GHK0!b_0Vgvkc~Rx<TLRdeP0_Xqj;
zFs(`+jgFAZ_*vi7#4`uto8enuu=nrZAHCrdk2yJ*^SbP{@UUxX_oz3vr8N{Efg8c>
zefR5W$HVK~?<7`UDBKDZQhW*0oZJ{0&~{ItyA%=<0{`Jq{!oxVHfC4PJ=H!eE%nsG
zv8L7Df}5JkiJPCldgk!HrVh?@59eZWvt!Hv;m<a<@u%xwU<!kjxH#v;8INub>d~Z;
z+y@IbSV`L681j+f;iKhL<toRN*RNFrQ8@lMtkNTI-woBTKBc4$3JOwZj??N{^<7?G
zo_hB#Yji(H>-+<|o0|6F1hX(Rn_FAg@fLIF+@b?>Vm=@vmXYe|>6!2?IF*{~<t~^9
zaiji6sqBiudjN_u7YP|+!I_QQyQ1^hLF68%Xa@CUk@UeR-b|V1^*hsbEkADfAn|Xz
z+@4TK)8wWW`@LVdQkkzvs82V*IVC|E)yS--*1NOgYh06gry8$~|3N_gSCw^?$Ik~u
z)U2$mU@pNL=kbQDyqDxaiQksU|15HvZLi#W<AI6b&gO%I<KCLtn47<@1zvKp_~P3(
zK0e+a^jmPhChA0M($GM0H*|9>_s_m2DbKnnHti20;WO^AY1SQ#3eIHyAlcLFYOfWB
zsTp?r(6KnqUU%Zl?qdvoMU`LUa&vRrSnbEbN#+P*9Qp7jh4?DZcT6j8#I<Nfotogx
zP<m=p1Ykow45NrRjE1YXaG&AgY@umg)iaN?3lTUrmH%^z&W-Z9W053v^ou-u_2sS}
z0*UW@Nit-R8x!~6_w0Y{HVy(eFV28(bY$dsHP;$Yb2j9q_<MHi^f1u?gq7vM8f5b7
z@AY|QvCH{8tE;O!JM$&sp;2;U-T(IMwzj-ENnqN+!oq@yiAi5P956F8vyU(|0_=vT
zy*>UVTx7KH04-P@TQC=oA3Fhl>*Y(urtqb{cou2*xReyD?yyt9<cW)mx8K<$@xw*p
z_ojzi*gIJ5!{zDpS=OXj6M1k=>6t}K3R274+uO#*=GrxiS|T)xOzW>lI`}Mr<`pTz
zygI$4^7|nm0j;HwsGWIn?16-qq$}?GV_e$xej6E3kS~z+`}W+ncWHS!+w_>2<|68h
zQ&v{??D6SymI#b!3CzTJ{#;Z-q5;}0+f5&=+z$T0LAzEUE%bY9JYgxsj1q%p95=NN
z4FFZ(=xBTk3k%~EUSUGnKT<S_i<vDX_f;8D@FOE7;s0PX#s(Ph<KyEeVL5GWM-g|G
z1iSAkiiuH4<D-xRs!R9({{8#$<Hww4nL8{SgFhAyGxe3KYipyKI8YJTLixL%fXM?x
z@C$Hlx)U=_^@LX~Zkd=g!xRlOY6LFh6@Pz!5ad=q{@L)p=wI*$E7+#^)YS03b*dy{
zEKbrAC?@mj7vBN~dwspn6Oo<ok4vN_CEe%S=?<bXIGXjgy@aHsiE(jpCWV*&%a*qN
zdoHklM{TIlU`ZXsg?k>NkUc0*7zC1&(`@q!VPrIAQrNX`|8;qzo#A9aQC5Q@?5l`X
zXIx@plrcOrhx=?}ygI!)+WhX{Hu+W6ZC$D#%+dE}YI}Nm;61_myn2p8mhRIsF!1v5
zjFl*t3@$hQBKak(P-8lsHnQXWRmi&Q1kMfk?idda6z0dLCN~fhOh`&{->0KLI%t%V
zkd(HW*!ND^*_5>(LV<EP>;YLVyv=6J#9H|7*yYTNSAG5c-S;(Q+;b`(8Cu`l<q3N^
zEd7awipuBsV6(OsIQ$6Q#=OSH#=%tf%s;9xbBn|sNQlkiXmCl@&=K@%8_zQ{r#{x-
zp1F4u%mc_C)9mvL3EfYJ=eaaIF`?}DO)oG|reMtExPRDzg@r}?1R)Q<X!64CgRpR}
zF5CX5kB_OTDfopQ8jzT%jKSatMNO>zo@)`|<E!#p8{gcc>5J#%=a=<a!mlOAf|-L;
za!EkIaV&o(O!@2Y-(D^*L4dC`V}S=v0#7^l@nafa>1o*Oo*pzeHz*yGR|o`<?AN*q
zz2uI+!tLs-yq|r-wxYrk^morCI5=2cJ$f|wz}4yR-glUR0+(ZRqXwrk2!Qrh+53sy
z?GdXpbJswjv}7Xu?DTN%I5vio{nye`Bp)7|x?AZ+NG6@t4jrzr+Pyx8m-i0YKxOXU
z8ERN*NgXCoG(UXU_v#gHPAsO;RMv>r4tT-f_zdUH%~+PvjT>1-MK2-v8T(_Wj(%Tz
z(+_69zy*5!C&@OKu^k!RSjVw!WM*Np_U#*I?~T}Lz&yS@T$mc2XlrlJz(lDtp^z8P
z&7QKOTrC4$eT?~)Yu668w$|T2Ef@$B6BHB_5%~?FtEiG{k_jP2LRZF~-Oyd92$Qsu
z68#NVclX+h;r?&3_(|ynuV`!EY;vW05wTtMDk^Q8pz2Ls=RaLt!kyBYG;^I9v#bBA
zo<7}-qvEP6A>2ZnS5qZyDATIEM5rv0-n(~?{Oni9?=Ia!<Wl$EuX&a>2skFwBO?*i
z)@kiEM<o}PNVjpns;@T^y|wSf{rPBlo8W#?(S_|>R}d!8Zp9}-9OF`2jcM)ZxH}!e
zv($z<yRXhPgq){Ct|N+;6JaO~4CZ)p_`<qa(BC}Ar>rXSB%ORm>j3=*6Zcjx3oESM
z?z2A0v2#{*cfs(tLq*8F3}xe##+DY|f27)k<z$wWVx<(4a1zGG#-0j2F>NV=@Z#*>
z@oyM~qIPFJI1X!8fmil9?0a{U@;9qiZ|w9$%2mefh~8LAu2g@?z6i-l+&7z)pMr|r
zd1EPg&%hrKpOu!Drd-W?^27}S)p(JqoWnhwO&X-6;-qpHM8H}wUPn%Wr!O8xko1B#
zsI9F{6Oo%G#C6tiaBwg&p|5`6pXQZ8R%Q^mw{EzSND+j}`2I0Y#CZtfOnih2Oirhb
zA|!{)nKaSGUR1*IQu*EE*o2oHn8Z2s^Du73<;3wKdCCiNSrqXcJkc^#9e2IB4F?MG
zKI~aFW#B|Jmf}^_)hUOa1lri36E$*^xDbY>rm=?(>*W{&VLVo%zuw<QA3*p_gDut1
zP*7A95)@2fm5u!T){qhr%CyI!s5n0~<h-~3c2Ao+cjSU{Scs-(jKRk^vUfsQSPzn=
zH&ocI{e3A($%n?TVeSZIU;@A9)!D=-Q>a}q-YS;_bUl>=x6pEO`JTkPySvNYGLO#2
zn>Q9HKRu*j!+(g4$+8;Zc&+HOq~6Z+bN_?&;ohF$*oY?E_XId4akw+<W^9%tY~VnT
zUv_s0;p5{M#wP?@9iTovRP#VZC{3H>7}G9tzEx7p)Z(e#V=hD%ZKJ=4OkV05lIN|C
zmFxo?dTpYSixq`02^;=;+#gPn!J!wruvsnzsvWpoZ%D%HYHCy+RnQTR%ZanIgDt|J
z_Ssd|Qk>?(*f?#uVs5CSBPwOHvmN9V)E9-gFG^TX@;!9o3>x=hW-PFEeEbX<J=#kW
zRVkMyqJL_;LEqzacQ(`Lp)=j56cKIrYq^(WFUa4it*25Gcz{QrR3^`vzCJD?J+iU7
zKKwtiYokZZ`r7tfKMjM36{`}fpB$APPI4M#38BXs#etPk9J#AsUsrfs@8}fE8gt}&
zW7A56CAjTOQ0<lNMA;hLZNX(f;d{4yO;s>b^Xq9s{)YKmma>vcCsxbuFh%i{dIU#0
zani$wc;CN&kD6b&(;WI;MsU00`-pGL{~f3M{ESuL7w!r_3j<%Depin|Y!XL>=-+P1
z*{xP@TbTJIslWDLu4!4VbZ}!u63xYh7c}TiiLk=EAQN^XGmPLwT^631k}dir+!>yG
zBjdH*ki&P=d42uv3sh9tpSNES;X1#=v2}8~><xk^kNwiU^erURZsQ%?qI3#(g81h>
zc<i*$(L?k@Jn}#qW^hYqdsF(rcBlw%9kgv=6-s99f6Dz>hGyBWg(|spbCe2)GLpCu
zg!{qO?0H3`!4!;<zCQe-BC^Bt?dRwU+-&*)(R$;hPmpCm*ufQ_`x}WjN`*YCy>IWF
z2P_k~o#2SvEZ@X`$jr}&u*3LoOL0VVFe(t~kAx%v;xI4*WzrYJUPlu@{D|U3AYPea
z(+o*g#mlOMjKZqu2(7ExtkcdUZ(*;~xx2f^P+ZXQv|W9ZJ)~nvEOtZTdbZds^`o8$
zPaecuQ2qCuZ+CI|tDRME*Pj4hbjpwFYAL7dkA76V;XgVaqCi{Ih8rd(=d%7k{)*V+
ztLlGFiu`OJ$f7R)_F?RG6O(V>zG-?)&{Pz(WjuJmk3u#|c9pI#c6V1)+zt-@TPW*i
zFy=fiEapKjHqFPBkvMW|YIauc@?{1l&5i8r>=`gXjfeDC=xKQE;PeLs1o+>hV@y-o
z5zVRFucZLmlh5@=+9i2b<*>q5N_T0CCtiJvi`|u#U>_ugj0j^GgLRI%Zjdpp(BW=O
z3PSh?oH>Uc83!*fuiLk8FOdVogzeKC4A!8YORaME`i@~6K88Q5j{%k+h9x~x3JZE6
zda&o=I_L6fM7sM*3ESQ+X-z`vn`s^BmWJDd6iJn~rI@M|J)Yc<th$r5C`Vb}6=cvs
zet!OS_IP^d`>cEk;^*@(II_uTk<%*_Wu>KG4mRgtoZM(@5#;{Vi&z;d6ta>y+CQ&U
zX9#DS&U*bNvwEozA8}+ANA^mk$y4+DqYmbA92fY}G1GY%GDcBM2cdxgWhBspazw1H
z%*|b$oMKOA<gx<%mI#=bzkz#z8C_%+PWO?~eG-VI9W<Jp`5TyZ)iO~&zBuj6`vWXq
zt?liVj~?BRjpa&wu&lzjk%zN62*YPCT)4pWzF4bR4079>H*e0&%naE%Hml%}8yFew
zPU$O;70KrYS<S)7j28D#u%S$>tVv0Z*I4i;xgq?7Di%*bd{B#Y$RBUWY^WX3xg4m{
z{cSjw_aP78^-Lafgb<ls{!Y@w8dn8;uU}GHy7KX3{X&`akyY^>LOeXYCp9%;N2?4g
zQuv6*JP4r_gLb0R!ROZ1)upFXZD*u?Kp`jXU`^GZ(oamYz%&w?wvNU|$ufhupD~yS
zdNg%>7>ol0Po#6vSu6uQT`VQLRc9cT>@y8-8$>dPq7ybUzrh=(#w{%^t*<*fE_QWy
zE9Z_trDkR$`vn?R`m*$!VD$f#s&o%B<$Aw*S8)uJ4ZMDBH<~B5;&|K6PE?hFgU~>#
z3+f~6oiAU)L<*+x2AD>|FCfqkWAb(`AaHMRLI49pIP&BIYJ@+#gDWc-yBHZ45{tpf
zMhjm~?6>nKdMpWMgi9-hyCuo+1mfiyOki;E?8lGmL#Nc%)o@n6jTH<9Ln=pj-57#%
z6;8kTw$;*7G;o5><EKf5;o;$j&7HgB^6h>jmhJu=Y&2~?=iezMyRMdTu9?*Nb8|Cb
zV*qpn?!e%lA0O%}$S~*#)gAb^yQ}T-wuyz8gRWrgAH+ZgglH6oqc0S_ul|@B%?Gvt
zOi>&L{RYZ3_qPeEIc^Um)i*c4e>KM&HU(vg?Q%yX{vXo+w%<P}pOhaIx0!}0OG@6j
zyosWv?OXMwa=@T_tUm_7*zyUOCk<Opk6g>)a#|ny!xsec-9RdPNnv5qr$rQUOL=^r
zpCMt{vrMVOFI>dW&#y>|AYPnJDI~9`DET@r7U!&g{=U}OZCdGRa=+$D2P5#QP!RjQ
z*v<Y3fx!JY^51ofVB_$e-LTnzhd%A@B;^#4DV{>41ZUAu6pi}A!z%qGF}=!uLMyYQ
zK`$xU|MU5DwW?>&22``ppF!u@$>*Uut^mABMk&`Uuuyh(oO&vYTFiQ-wh1Dvm+S8^
zDuuMB%wf$VQt9Z!jr1p6ZpJJ;A^2mU`bA~^xg0IS+2b!dEw;<`-<aP<-Yd9x@O`iL
z&#v~FQ;~ji83hVyxz>ZlTj`!MP|k~dU8{S!c((u&mEPxW$mnNWv?PZ=Ki!`9ORLWo
zs^ut~+aiV{3KW=P_#ykg3f=~m+)@G0hQy_vBJAkh(<1r1uW2RK6_c~xQ8hKz@zJ;#
zS!>_f$T!vAz)2l!2zR4RD3T;B(o<Da%go4VcK>AO=tvkPIu?~?h%4DGH4G&@D9k`6
z(*KRQhgp2I;PP*%r~xS}nxB!yF0PO7Ea4r+ynD(lOn<?k{~Rx#rHJ!RKy8tTj1i+C
zC}}J%(PzK_0U=avG9Hs)nM~+O?n64qChKcoXZ3O~Oexil_jhzmY;5?}D<a4o)CFx&
z<PEw>^vLL@CMhlkDp@Fo!b@p|?h#0FUZGTrT0xSM9{*WRji<rIuBV|=gBeF!C~deO
zU4*ogYqo{UF0!nQqm~0}uJpLQnd72Fc)Hib;le#HNS+}HmrWgv^2wvO<8vvP{^UuT
z0Oi{XtyJIyGH;;qnnF?x9=7m4j-yQ<8oELqN5yLg7C@BZs(~8UBfTbG_WgdzCk$^W
zZ<EcncwH71g;W{ilBx0{<eFua=-Ra-80{kqMKlPDwvIv=V-4A&J2m-ynEc<Q214!y
z)hg|X`8GeNENQ0CmVsx&)!hBB&ePl=CKEvjN5B`uBpU9|5uuDrDmXkLr~4nIq@*5x
z?xo!^7+GUvdA=4bW0c$k9{<OD$03xqCl`saHcALAT3C2|Zth-B1nzT9nqQX+R-Ij@
zKP-gATYJ4YeIfJ03>*opEcmtOl=zWfSrr1eV1En^4Mp#7fT@ZaT!x8L#FbF)cmOqK
zsKy@tUJQrO=y}WrBNG#q_Sn!+tM4jFEiTqZ5FQZhG>lH{S;@-Td8@^K)6A^aqJ`r8
zw`s}@u6&M#c}MGCo?M<wfeJ??0qMD4yBwj0&Km~Lq5bvW&xQ#zL9<pVbxqCuygXF;
zeK@#e{_O9cS^n7AV5X)<xHVGf>grNELw$I$Gl=Fr{NaR$4<9};t%FU<?|pS6_kBa)
zwjI<S^p#I(&p$V|y<Lv_CK;=YsGuO(duLLxmg*NE)Zh70*ZrCqg^bynF}0>+U<ila
zxO1q%crGt#QI5kcz8ZA6ebh<1;*Jm%1qFp}j^L}FHz^1u?@Oe2e}10}=YO-5eL+A#
z;C-F-@3pnH<z>7}{IPjYJ~fHT1#Il4hM$}>Kw&+gye!I$uUbHIcW6A<O^qk^?DvJ6
zkkLJ@sY!^B$Hq_hAOG&|hKXnnBRNOM$22b^R8V%#P>a45pO{EWN%{Lj9eZt=@JIJM
zU`!1SsoE7dQ098KZ*!#+Ub^%ondS0p=8gWjxq?4beIgwJKj>i}CUvN(seS$Yx=(g=
zNl8h~)HdB0IzRRG*~1)I=cmyaXLd?<rCTtvFHh_?+-%$AgPwtbm_XF|5GshmY{-$(
z^cbPm;8WX~wL!13La7e8GD=EpyDw%_p&k|(6r`b{G5_)&1FZh!1=m+IP~DmDK1sJ&
zbbvWd&z?OK)s@e=L4Qe1Ed9#X-uEd21TGl@TPl@7U`h}y*nzFsdLu|a2_s5jlq*cy
ztkA+ksN97ke?x|YdO9|VIqr+91kBH(rnK9fZDI^3hhHTPy+sgS_(Vh~Z{2n8CQeS_
zG^;fzp~ru#yjEm>>lT?C2JQSNe|?6hn#KKy=dAFzmQX8+WE32-bS|wwAz>Iy4}6ks
z+8vD#vF4ZZjmu7Ifk^E6@p;*C9L@(rBf}U;eK0r~nVGyWSq_{Oj5C!kATPk6X9+YK
z1hUeKi<O{~BO)U5<4eDD@H!vV3|nBws;a8Cwzgo>rtL35RWft$r9NWnQwmm@v?2V0
z-uo(YX;7wwxpg~m^=|@iR@3Zt)Oz5xVg=ctofWtW_KuGE4<5WiUl`Aqa&vWagR1=K
zSMO%ec@kv=qAbkbO0}-4UrbEQ2l`R!7vgB@fA3^|oLZ|sy*46#!@Uj1H8wgBzKy`z
zz{11O+%+hRCz@8Np{J8Zb=RSqoFg02S_dARccC^lRo~UMrmZa~tB!o7RL%K?;e$UE
z&=Z5HeW8%%mZ`-;q287c89kd8M0}dj9cE!=bv_^uCa6M<Ez|o$PY(|L4-Bo8fXK}k
zeQ)3TKzxixM4_jn<9qjR?9*w4o%D9z6(8&96`nonFtAQ73&EEpBr*e^bN5BaO+Ij$
zs&sI2@{?f|5vhY?Yu81Huc|oI9bN3p`tP8nJDF8BqpuMX^3<dx_0)@3(?%<jhevtm
z-dU5gDg1eped(M{4QCeSt_oXqBK<h}oEwPyM9S`l%=|SlH9dq3T1!jI)YzD6Fkq&}
z_~ONj&#6NV798z8J+G;PDs$zz4TWL^goBnacoXtH+sjCG3n+Je>bNVieT`iyM8<Wx
zs<pMXEA;rapubvBL3(kk+w%O~#r@q~E&+XUAIKj1m~OTEL&uY*mI&)iZv05&?nLFM
zP~XTvaxTrJD|&i+2M3yZD*FFzziH$1oh32`rl<W!a)h|0KR>s{^C708riP$}or+2&
z8qZ513L}~NUzK!=BH!(ro|M4KU^PecG-bt?cSCMzrT)5~p<)4*Ouge%TVs!2!Ml$g
z<qNdERUX>*C(yx^wb_^VS|IRD9fW=)r&wHWZZ5>PKdXtcX<;9AD1)<ukVe-0a%Onb
zGc(Y>++XTXgbMI^rQ*^hu6Pb%yzMJ$s;XdD4A70GL;sq_uFydv1B`743~PqmB^2s?
zi=l@Xw)x4($Ue`{Yk}D`BlGQkiLa_jFr>vZ)1^Vw!t8sMT2LW)uL{iY6+IP|SFpKo
zT&F&Myh8E3p@Hk!H!w3Hp`jig9)=YDxWc(Cd@%~VVQ^TX$yp*7kbopIMs)ap--*cs
zD!IJ8Jk5tFTC#FxIVIz)iUB!(>obS0jF2ZQUmSxDeli6irsJ!hJ`LsgcC@>uP9^Z9
zMmH#GW3=Nvt!1e1Hw(qL;m18!M!zpDy@M<kMkT925s68>Z()IrPLm~_P5aY3m0u8T
zzR$eB_A9HSV~hSMg-0J*BLin*6$;=hM?TOD4?R9eSIjUn6QrMbGds$a<S;i^`)i^&
z;J#z!#R0o3f$yMy@SzXo6n!a%E9R8MS8m_>jBdKwKOpTH!%nKcaq){WJ3G5~8-(VY
zZc%7eQLhDsVXX{B?v^;>(Xp}nrzb}TQTsP<Nm#X{bG@wG6Gf|HiV6FydoplGG1-@c
z1tP0@iwn!4O7gE{CgYzWIJ0y+?$l?nT-3R{;GFyLAXyj-8@MFxP5r!8KdE+vbi}$)
zuZF5ZV^=GlmJKplVua!UN}RdE$$U<(e52Zyrsb+W;*g)P=Bp`fX4(JLCS~%|b!27o
zYt;=*Wgsf~<MwXR=cUIxmm<<)8y@9>F)5IHvOaS&*d5QXq<(>>GQA;AZW<=4C%8B}
zf7v}+srW*dT!$jLzoIOg=N{LWMy?aX7n<(AKHN0OPhDz)G=k%n&Hns<;<W%z&u2Tg
z<2YqxWQKn8exlEOps&bD)>aE`!kWu=xC2w|OJ(bV<y_Od-{CepMfh-2-ah)G;ejaQ
z7S8YtPV2MW^eIV%;^vQUpZO2HzuGqQ80c<KZmT|uX=B`ZmhRu7+d%YW+fl}yp6X8{
zrQ%SyAUEmsbp`)UgX+O3gBzO#wP$7*$Z#&~{NGRtRb7MWOHs6(_pdx;HbP=C0D{P)
z;vmE+U0t85(Z|&l#hI)hLPjddT>u#ivHyj?D8*fGWsBYkaG-r^@nOaGLiTINsoD?!
z)W(g)DmG%@a#Uo0G;xwJzj%MlBsr(jz>Pl1@Wr%)Jp6DDt$+YVQtzROz2sBDYVHd8
zPCjXQzxGG`_HR4m_>}cDr5+vnyOTIv=X|hGQ%_stJaF-YOhqcwBg%^F8WXfS1?d$0
zS!T7H*&1#N$RJal#Oqf|h|r29!vcRtC5MCmIyEv2#c1kWUCC^2-Fwd4d;R0Vo_Avv
z{#0{_<O9`q#Y5T$hHo?8>}lItyvKZKqODO>H2<Q)ck{%eE4@ukT>GEa>P*>fqmTBS
z8g*&JM&EC1!k;W>{oRvfvd8FbN#Zg;R=xi9BF%S~e^m4CB;UViRTW>c_h<3A^5oWg
zqgna6ceHlGggBT2H9(g@yZXGL`2Pf4!Euoh#Z7xWY?~`?-4r-9_C!1S>OcH}Wk@M=
z${xO}d7?CR=Q=;+i&FQP#COV^Z-}d_Bw|?b4KCt}8wK0H#y4J38-B7tWdgs@+)l{J
zpEZco;l}9SXR`AzxI~V!!yzwI7yqIJ_=M5gts3P`o*$J0R0Qh;3dqu@Sj{Iaq_j_K
z$Qo-IF>NZBqScc73EA*f$x1s638!({@Nsa?zW`hCe`0OU-ynba3^zSY)8_vb?<6Vu
zzk%fcpQ-#}G7_@?CyJ=a+`?;4nl@~)4)r{?-?BW;)DU+^+?`3zz2E%LQwoGoj%7uc
zM#fxY@4sWd^hQdd%awfe+U~>OWKRNx7iAw(D{AoRtWYLg7Yxr__o2I>blatHdz~ba
z`<jePm&BrE-IoKMWPH35&ez_BariB2dzW4AsIKk*po<TiSNB@gmynLXx%cbv)Il~i
zj7jUmU2awFLLR1s0g0m-y0YYdg3S7h)}2<V&loEierp{rE0-@0@N$cK3VVn}cPkc8
z=6SoOc6W;{XIA0L<l>0A88T09EuftYMAXD8M&BRbjw-f1X&ZjL;hO)Utvx@xAp4)Y
zVQA9K>U`y`)u#61TWPw{ycH~d$060vy7CTV%LOm5+CTIxnVBnC&h{62uJy)jG~PMA
zxhKh9?=XDx9c2q|=tad8{O0}n;@ET!+59=XA5TS)S!u0pjnn;WhNbF#2x1FQ{tG6`
zA2Jj1^0S5*9$K4Y-t!XDwD?QRS}y6&8~9CoEPsnny1f(0a9q<;MoPV$Rfr8k<P5np
zF%>6I(s8=zF}Vrb?^W*w9t7O!@GKsFu4`E#^vE~ZHBstYu(HCJh=GUtJ-BVU8h)1#
z-<W0SS}fyPgfiJ*!akY^^0L6K$223D4cRSl+Zf#Yv#M#u{zi^nFEV3Ft@oCm#l4iq
z;@BkZQ1z5b9$ltRBc9gq^nLmEQ*X&(I~B%S?`6vS?NvK7cdF~|8kdb&y)^v2YiF4i
zWZC+GFF{)m-(&4vd+2=42>JB-iphm`15eBY)y59dT=Dxfbl(hwZ#-D)?C3La5YP~~
zWaYh5s(PR1S>&n47VoTP^o>R2s(-yJLD8|X&Q#v*&n?#X?cxf5n|}S1b}BIaq~XZ3
zC9IaApsD@1cIwEl>)ED%u~3TA$DePOm0AQgC`<_S=9*thBEM@#v-XRp(3a6R&}^(+
z(~u`Ax|3P$Y%t?0A(TTY(eQqk-??43>Y>L-XdC)!2WiOayW8Iv$lk23cDhQF)_ihx
zGbBu0a(&!FK4aJQ=2VwD!Km;}^&<_Vq{5riS=@XdFb5(j_v~<)EobXqzQuk{zMSx$
zPV2|(6c6#NHln`eEY{;VdR0#6fGmxdmn;!M)=?BM+}d_oKR?C^^(o1diSu|Y(eU<>
z7RQhNF|x0DJmn&n)x^{%{3KY}GYZPA#>T=KR%^Gjmn^65Y(5BVG7~XLtV`U#H7}Cb
z`Oex{zoS!7;BX<)_7j8XDr0rC)yhch(X*<WS3hy34s9v<)+ShQIE&zUWPE-=8sz*A
zTb|oH6!mddTx%yn{BUu>&Wl}o&iwJ)gRefj!z_Y1*{SJ*-{OyRhERA}_daKrBoHw@
z&#WM+HDqJ=^PsUFinh5ReQHBPY(aobYoNx^OqX8QVfw|q_P)imTyLm}(_*e*Y}WRP
z2Ep}BW!jlbqi5C9BbJs5Waa|8_2b7VU5Dy*=3m0rCKPU~+JDEIl*0Jfl;8AZr5r5S
z`=-}~p@(#WG~KT*`>s77qOJEl$@h~P4;YhcjG6n^*>}A4VYHuHOJa>xB397AedueQ
z(mfz?u@#}Ro)qg0U&ZUm3stoi{ie?F<|@NcNO|5pafDD>xGu-#V;8#%3+^ndTGe55
z7qfzzy^mk_T)a5eYPzwx-CG`<QRVRcLDDl`hH%T5*!j6n+%ogYagOP(C0#-cKmK-V
zj71Ui$?ZM2Xa9|HDs7<lQzbB7d@%6K(r_~;Ab@5)Fr3{@L}>X&(nba6$V0B!u@RRN
zM{auJ?Hzs;`8T6diAH_w#>}|=%y)ifPg`%o(^tdH^~#LV5-0bf>?DsxrZX=t`-%2W
zn5y&?Z2Dc<I@q@HYablGxp+_!nAGr0o0X4kguJ502;=!)X|JftWd5G1T1Ks#PV6o!
zGG%AWszuGi^b4gH;Z@##%P&JF^$kh)p6u<f<W;V6ksW)J(USLz893RbU~7!K+0q<G
zO(?EW=QxgfGLEBS9KAE5iM*!BuC~9GE_CEG9mZ*DpCpsdAXXR4yE_(l>tL$2Kp*R@
zNqDuR;U}rK{rn@c3Z{3gL!2V{xaF^$3D?Q4N?@8?91?1*hLCua^ReE^E;3*ARN5TR
zwkXF@ac+8UDC*8Jsm-(GFAYk+hNbv7^ZV|*+0)*A{dz0;?Bd@ZrqeG!Cw|rP3GM8w
zD+d%ONpAnSUGc8JsPJx&Ch?R%#Y#=EYw35qiSParyGkyZwDULrZBBl)dB=2tUH-Ws
zm#WkveQ$CngO<zXre_yyCiMd54aOHwlPaFET=+Z@7`*cLpw#7Kc$0U=9^q>T)|ztS
zSAt#iS5H|CaI3d!!psHDRx6e|ZYVybmwa!VxX^VvLlau!u|@iR`!j+iIkdT3(=o89
zCvuL|51Vew@>h&#N`*CKUAr3D+a2p^?B@U7TjEZZwTW*FV(1RVJKVg=dXx^I)birL
zE#;cI#{K^{PFLc8JKg`VxBkQ4`VV{SKkTjlu(!?$UjJcl{fE8vANJON*jxWUvbQ89
z|94>Z|AoCJc^-fL{{wqVOhQ6J?EfshoYS|AOlJqx=uJKm$3=1$IAaA8N#jW4?B<`$
zc*=S9-}xJ0emea(z)*g6cGje0^v0v7&F_Vsf7sCBIvAC;MpOw340Vy(F4DX{%$Fat
zM?mvbuuXemu{U!qqp+ax-Y?m+-LBt?iB?`-BBUy)hsqz#@R5<F7&I~xnz2X{{75t(
z3K>~NBV6GN0HWoU6?Gm54-cV|2`ArSJBk2~SaXvkCMG7|RH+9%s8R}muid!*_vpw_
zuUZ(onQ3Z5moya>75&9f_^%k@j{$+}>({S&YNH&EzEyrh8rU9IJBnc{lKZQxt3^dc
zr(w*rwEn*r6fZJVl(YUffom&JL5LQK&m@hx0PLco!ot4;Tavj83syj5$(8o}EF%Pe
z-Toua+^gAH^MYwCPmm2R!=oWVRV)TIlb`8nMf<<rf`X90DN`+XM@fGuH*{?u)gn~(
z%S;^J@-;#zqI*Vb>Whr;uD>AaTf^Po3jhS&ISPS3jkWb6^y9*JhIxU&1(2*QXySdI
znreXV%*x6Nv=GlR^H7O5Ha3RVE}#SG<JAF!LxDto1Z~~m;P&F;g&1-cKqQ>6X|ERa
zFaiRkHm9IN!9-90{<ju%(oQy87NHRVpoq|r5DfZn<q_~+00GOvgH*S%`O+D*J6<R&
z0V>1iT$09$moHBNRYgZf2gs+i*7|xXAte4=>gz^Exx$vNe74(5{il^M3vcP1Omn(#
zD&y(u`etI{@bHjG7gy5Z(+h`lEcu-~!h(V!fWV^{zJ=Vuo<Cbc`M>Zhh2g@cg7gjC
z6{2x>gYVrt@63ua*=Iu6fk#d*c)mTrqQw)sh(aIHK0ZF@Z5Dtv0Q`usm^>r_i{2o*
z6$2}SrEhF*E>;dN=DGRz9EnJQ%*2IiYRDhakEGs(xw$}kGr4{p#3c{k>54$p{yRXQ
zVQowKp&|dV#f$wUksd$NnfQyKAT;pi=2)0Jp<x6qQosrTM2wD#3ilrn!fuqdwtgHO
zB+?^RDq*Li%PuSgC=~l{U?&xqW}%D^u-WWR|L#Z6*8^%2&d^&n3>9>`rxq5@8>9eZ
z2b4V^IH=jN8e1P{of?$W(2A=f)b`!>&&i13=WXfXu)>x3;(#e2lE8Abou4VqEoj$^
ziJCG#RL`1(fAjOntEB~{1lT=&FR$MKLk7l~u8t017XfMeSnmOJS0Z_$pr<Ui2ZSu8
zz^$IqQ9&7*))y~!pi>C5W}v|y;O*VBqk_fwGfyH6Vxe!1Pe|x1!y5Sb*|T2&J_FL=
z93Z2}$;s>E4A7_xkIKtAIXFQ1nOH?d<(-rUoG6o8GoO<`+i*~Ou_nT$<yPmsa(Ebk
z>vtc0E0DkYD=8rXnx8-i19hy|XgPWLS9+pSQkI>8^@SwB<cqwTo|m`&^XF*cnzwl`
z9BDdsc9H!r7&N_*c{BnmnwXW9_1~BZ7AB_sQ~!Y^Cg`lg2{7<^`<7AN3P5ZCKp`Nd
z2i@y))E^MR&O34<)?J-mi%OGj3=9mkNcB_yfPhDjA4mD&4ybk$_f#Uu$&=&aS_6KV
zU@#HSvL{%)I)TgJG5c6QU-Emygwl`03V@6C4Gdi5;h~}tD|OEmyosRigN2*9H!MSe
zIHG*;;6W)B^n8UxZ^QDhtoXx*7Z<Y$iajL~Lar>v^)JEtCNoKBevi^KNIQqk@bV7M
z%~?E7xMFx#*AM%4QgBX-lafj=pu~sUD=jSzo8oKH&lMF{UF`xB17N+7rPXRtEiYd9
z-Zkxd{uKWH`V}~>K0adf^QdhAf}BI}z+edT@nIP73?;x6z~$6e+@~$LT0McY2D|kY
zHgJWdg=3my`c4skjy>^%&!2BKqEVV@nVER+zI^|#-E{k_Z%N=%{K-j3;oyBSq&gSX
zew^-)xrR&m6nzV^w|&nLa+jQ(*1`sr_z?K2+|*z*Z4E0h=={M~x<OK7MIQ5tgVz%&
zEZ=Y)iZHXV7++{yTVI#4>qB15dj&?WTs)i>*_RPZR;23Vip5li!{IJ7pRsKK7(s*;
zkYvl#@mtMk{P@X&eqtDYyxJF;9GRpdT;q@VS<CjxY1y|S^dY<U;vms?wZZoWhgG8q
zl@wpwad<N<Wr@%1fDs`Xrn=!^km~8mW8O(4aL;?D?00wEY-369oksBkR*x>lQM{Dl
z(?p<zOjRqcJ6ypL;``_Qb=t>di->6J@tuG?U^xW`%SGKvvLaq#r<%o*yyk;8tJXw!
z+P*l3>pAz^enwwF9-N+>#4URh2gb%^0nI2ThESmQiFPL$P+EY-U_fFiti4p`>leF)
z`5Ef{>xy1;n2Vd6MU$&B;LG&&CxHBL45%)!Q33w`0IOqF45s~i<2mx8esKuEi?0L$
zbqkm&P7V%FtzHI0A05Eypef$f)dd3NksUZCu(Z0}N6?=J)A4I#!`H`07U0#b(V^jr
zst7LpNKy8l()AB1&~uEkW8k3Wh}n)LP+7!xXc$apAzG|hFWx}*sX6$F{4jH+%0jdT
z2o$lgu*gF{AHbP8_O}<nrhs{UwGA+f@e*Zy_q8W=b(Bw+cPEss+x=Yt*`J+F*LWmk
z(cGhj=35Lq_5cR~{-x{h#uJaZW-4n7Q&S=mYIb%nJG*mO(*=dVv_Iyu4~u*j<*SMx
zGrkKc?I^P%AtePCL1_*7if(DX^ySMytA%yjpv*q*9~t=p-DR(?ql+Jr0B6?L)&@Ak
z{mVcw_4D<OlLT}T$MwhuHUKHMvB7y`Wn!ZF(WjKcdkg5tmC#o2KA3HC1Eab1_MvuW
z7ce(6GN_x{7`d;$Ty&zpZiKL?ZpU$JuBt-1&fz%Zi(-1>p4ZpE{q&m5yS~e40Xg&F
zr0fMJH1J=kw0eJiE&sa{PzXB{;s0oo2%HZ9<B|UB7kL6E&%F=VN02&x#k}M*mxib&
z^yr=b(l7NSYCcP+sl1p^NQUou_0ApXJ#HJB=iG@?)X%=-biaT1j@liye@*U&yoK8Q
z<`~)4Jy{mK!MB0YhBt4zxVw{`o`GA9Kb2DP0QTD>i+h<y=H~f1IRhE)6H>Pa$i5!~
z*j@MP)zCj%98}**#q93On@QCzhHI1IH(rPV(AXak@mJZz&BO+-4fB?ek&r~1%kWK!
zy^3q~Tcg->y!9+)obn=@fP4*GD0=A48-xo^V#MI`1%Pu12)vn^@>>~72RinRYwXHl
z3XB(kK$s`vvvjGhlQXmgIADkKcPA7NS@-Y-hcEWrsNAKDPsEDSuget&eS46Xm-o0h
z*cE!%fXc;XIGnCFI{P!I3~)yR>CK*S#3)KM9?<s?K0WVo5w(uovsPACw$t+B2Sjb(
zD|g}-Ui1tMQNkq(K`$2f00(x?xfKZs^lB`4*6g^Y3dndiDynVp)l^>Bz%&DwvKW43
zbtLyyEzm;n{cqtN(TS?{RO5@NT_#Yy!~JM#d{CdW(UF^nBwCt_{_r7NSVP<10-$qB
z!v4B^N-dpxAb(dlkm>mF_hm^*g|L%9n6M!r)4hFL4=hD)Zgrv&rbbM%FHU)3QIVp$
zbxLMa<AamFsQ}8~=V(ITjh+mJFVNm!giAQ*)lKdxu#F!UVH*du)|3>Ar-Ir~$sQQC
z@in3wO)u>1HcYTFtt#Fg%a;mO7UJh;l5ix8?F9zj2OuWEYZPW?`u)@--_+KM_A@j$
z*YPqZ%ETqrMWcYu<u=tIYe3Y>_o#!t4CBlT!7M<OoOa1VN){Jd%v)MoAd(3V3aaN`
z-c7k6FAbs0=3I-Gx_Sg_t<v8=*qbYkGXvJC%u)$sj&6@I04K=G%~gwcDcRzmU&0|!
zmB&h#<mbyd%y($gpLCT__x|pQME~1AJY0lG2a88!1yvH5c<}C6gDv%P@SZaYxp_&i
zRkFykfZ;aE9GI;k;ALO&G0IETF5-W&j_||Xy!m$=ka2*6{u+SpCQUH9d6T$1r3Hro
zVJi%vb$sGLI7f6xqc$oLT4?#I;h`bqEnuE-KbI%uMA_B1mX{;EaKYsnsB^w0Vh{PU
zyxaf;NsR-7z1{0)sycyzt;xycd2Eg0`5$+32a?eE1PG>!!2aIf`r3PUAxJCz$iT{q
z;`9V~hN}yg#l#@m)CFOH(BaTvWpc;3r%#`n#Be|q<AGzYl!sGXTnwNt<E$9x9(6B~
z!6$WHuAZJ@A_sk#PBPVqEh|Vxl9s{Nf<b{8hWnEWCI0L2@n?%fUrH(JULKfhIQqJI
z$i5_tuzfc_Z)s_ne29P-WnB!}1(y66#0lDZNiSZ!=)y(v7hLvwKXseoQSoCB6&ps?
zf*v2#EKJxNgEMdjpe`mxM!tL{{Ll^qK5i-`5m9V<@l?V);mLvy4@XBwTU%VsH<<Tr
z>IUevS8ILoG?O)#J!bo^D}-M0jJwe-LnM4{>~D8o9-|$<y0MSHzo3A?yKNqUxBZwr
z0?8{|A_zP@aI=_<+Vb+()6=wngEAx!tgtbT@3%bmd=b)D0JH7qC$T_Fi;U*K^)gsW
zL`1)RDT)(RDzYiMo%~v_O_e01q&%yweV9&T7jf|f_1`~weiv*Q(xQ29X2_aDeE5Q^
zmQGCA#t{76-NjHXE?$V9R=j0oim72$NC4L4G+r3=t8EQ7WN3`&HG#q3`;~W68XU*|
z0&d);HVS70?0PCaKI}hj&jp+xUp_gN@aPl1gdB#g(Mn+P2qX`pCVUTnSq58aLXh(j
z9q0yox$g(_HRR<x0IL587kQ<!u1<ed9<baL%{U<v$tMx%Ah-eC>#g^&ovxKlO>}~B
zpn{=#@1i#0IKm8d1A)R1QBszXffO+&S0zx4F$ROhOiuu83NTh6JpuEo&?pEU4T&*V
zfciKCfvwkK*HhOVfGAzn(=$MyKLZUDlZ%&^zWdmfU)In6g%D~Z>U9I|XiQ;09w+7q
z5<FP}Y+#L}w!VI<Sta>z3W#^E<rfqH=pF1QQ^u#8*Ri6aq6Y1u7va||?oQNCm$Q2k
ztE#H52QihZ+cY@;;QYXcigWb-^;y=NwTHnVKLd&3yYNr2MOP}s4+3#V$BX}Uu*a5x
zj03Y#uFFa2@X@}Xo;HX#AcVk*S@q}jmmx5PJ6E@NB;r=Rw*n75gC<1c%S%f+f;Y>j
zy8)X|cp0038QQ$=;ov|=ON)Oep(cpy^8-+0=)4M_9t@;WPdB#;@wJRRW0k#)6;#KK
zdnwY3Y~OGPw<towuU$*=xA4wRLjJM*JaB;kVQ7K#`r}8iI(gtSbxOA{MdQ2`<;0-P
z!M~ZCZ(S?HpnJ*pMkk%u#tI($A4;mCtMVvMmq4xK>sJ+h^uqyC$XUrQK{_mS=~Bg#
zHS7V9WDE-<%ZB1wLylh5=qe<!%3jykrzC)TXNcE*_D^228-Sz&SGCniswrRmGDSzL
z+TOZVsN=L9r~sczi-a^l5jPHCX&^OWxGnM}08QTqTl2KG7FL?^u{stL!y-!JxzMTY
zGj_$fyl(|Lt30^sTN1oc4WcJia#1Y!AcIl?S+5rp3BGs;2&we^DA!2jBk9AOkX8c+
z2MMUa${Lsyf313$Y`o_E3%o#wP3<=9%TQA}&E-rs$XX^3fGd(!8BNhyR`zOe&?%yu
zk(v38W5d_G+2bUc{qNuB^nQ)NC~Wu91b*<Jj!bjgy+JRZyMcI?bPezMu83v3Wca@j
zfk#@QakR2IIXMs^8B1Lw?urMhtU0RPQ{~EBNVl=cU#f_Uy*+I~yB8S=&?wr|L=`F|
zT2Rn^qF52q2c5^!Z-SVBchq_{<{4>U&nZ<Y&`l;LQe`M|Bkop-faZqH<^)iX$}wg)
zZyGY`J)%u}2r2#G;NT_8lk3;e@1_8+jMaG&NybY>oyUoK(Hv8F_0`&LnfO<7BjyO0
zuhi1qT$#~YQ&Y2;-01@HCFXeD&oVHmR8tNXK2(y%EYo4r&h!B-UMP9+xif9`!K)1w
zh@S(Dc78sgI6{@4B9?}knFLYx?j_z^-leR--~O}x=xxzQ?AjL$_pZ3$(RY~%n(*U@
znITjHnE;&+Zv$cp(+&<0%E<h-P-DU;Reh$+!K@r~oSRM7NmN6K2I%gq{?c`8vUUtA
z$l$x+5Y7tz@1|eos8A_Y!{b47@g847(r~DT+!BU2Co_r8h+quC4FZ1>tAj`{E~Xf#
z>Te}3H>SrIcmLdRm$EY^X!o0;9%Vey+mJM7D!{RCZf*(;laLM?xjrCx>-7a^M0XPx
znPYFjmEETvol!2Kdf~aX($NadByvMeqmTbpVVU?I&07Uh#3QUM;_JqS9LPLu;Kr#i
zaq^6S2G8F9K5=z9vMLHTcYwd?D%ya7@xB3{-nDC$GD-;=Kd1)Ebc3$RSJW}g^s=+|
zuy=Ch4)pbLqtO^1tc`8q_2XM)H%0K+wZ*Y4mpy@<Z@_?bPJ=@upuyJ?-QLSiC$fA_
zhISd1Vibp38kljw{RduZdee^yut^EWJ+y=3&oqJEBv;mTY{ZAYKJ{!tBt1OIOh0DC
zAQ|PTe6o4=jdo&uW({L*-Ow!p?+@NjjT;3?3q<e&K}>y}USw*{*@@V9J^09mv+onj
z6$0#?ZMTpAosw1;N~u%52{&aHW*}oqGTNxZ*sNo*)87_<(H&ONM%a*VFuv)s(gf86
zTiddR28jR>E_rzttxs+iWC&Y?)Zsf>h0u*BvRDjAV*ty7ya@CBA8W3PCAz)69ZGmx
zp03RV97QR~{|Nj7_t5jR{RMxy;;}!o)Cgc?7!eT>Dh^=j|5KGa-Gi!Jm%duu396!d
zJH}Ow4Dlh=K0!_)c>0Fnm!_W%3E$)IKLk2htgx=ZWiZZQ3Ho6w63@+0N%Do%&$Zqq
zKDSD!Ey?V!T+z`9{@gQQ<gul8Ap(u0pm_G=$;kM4Wggo!t|VePYnBTYJsc4eHLI<s
zx5AWfmcpbz-_1yfMQhrF5<wMhOwV@KtfPv}=mTQJtGgwm-wcWh^ws%bVPhptQ&Li(
zR3Jc}%Pi%3!^w&FS49O6;(T#+baerpePzDehJ2M#M;4w?*t!eylqw69Agg}0p||!!
z+^Z@vd6cPmD7G-#DH{1zy<&wR#vOWA))2TefM5skxuLd~o7;$EgGx7;uut^(I(Q8y
zq)LmQ!&v+G!2Z^9u&D75{`}N%vqtX?$g?m$o+czMw`RaIzWoO)u7Bl9AS;EvQRsrC
z0+kNJKo~MLR2(4O6WeorptcH+K@Qc0&6U+vl_(-p>qj`zP*^k2;&bRl0mK~2u(VUR
z3$N=6A{!a<V9Y8hCr5osJBBPZoP%7ARnC7MNPaf;eB?iJepTFdafv@<5JK9QBEG`#
zv5gHKF-8Az5F6@UwnmK>%4YU{MX9w_**G~3IyP*pK#@V*pwCWBQenNky5mQ$M9g)1
zsCh#wl4Mj4t_WyfIRIe9qW&KW9~nl3J|bS(ok?(gkn(d?u^i}<^<LLz%+S6$qo2ub
zoTV?1I`hAcH~%WWU9GV)qOH=)5+jxsN&q#I8O2UrOLPz2>!3wuTw%suXRJ4{?fm^u
zmJe^#KkV3~O)w(GpVKC=%Nrv?!;Q7JK6@KQc5mW4EkWdv!1%Ey16~>N-tajuZT!fG
z)W7~><80a>g}-6zy$kget6QY_jDmm-C&HHIS;_ro`TauE9Mx(=Ml%G2d>0oEd^@+Q
zdp8_4jAFelBWQ+S?5VX=@W?i-;V*s{8WMP-jJn6}JF=P<pwPP5;z$~O<DxhBqggyu
zpP8exSBl9%is1VLQOXP>;O?099wXHfY^ME<x_M_cYT-qL-|T4H??8b>rucf4C0VE6
z1`j{~@YEDdt<T0|Oo_XPhhp5lAfv~PQ)y!oS(@5a(H!j^9YAndaK74vo^&<ff_NFM
zrO3;@3>_z|slLzLxC-xNa(IV&>ELR*mbN`Cf$09rHk9K{R2*~LolSEsEh4Ij(*F)t
z8_Z8gbGUWvKHhj|92CCKZ6Q!_xP25NoSW#QliY%~zd+u2G3H!IM!G=J?Y&_3C{a#6
zXO^ILmOa^`WZ2ojwym<S?3)kG*e7^GYFG>@qKqE}1$KIRdI4hms<w7PWY6ojMNaNI
zq47FHt^ol}W`YBAPt)n~XGu58A3a(HRtpeGjHGaI|8;_$n{8*79(+Gfgde%o5D%>J
zUWfNHGu5<l8sHa0e~al1Y<WX<C;iI9cg!enpx{*L|7GQLS3D69Z@Xn?w!XAfE-Chv
z-1x?gQjBvm0d_(@cCj1c!P2e~w#n(~>G$smLtg~@d9HocT`5VxKw{YI5Q!n1*m*F7
zCXFt<o;DI)ZsxdHW+L+^AprwrrK-lK7sb?dHW9Y2m+5~ZhQc9nde>(_gYH!yr}FR?
z<Ewlqu4z$2D^qHb0=*kZZhp?S=s#*Gec&U*3Q_OAWAVpS5ou`?D8=&&mJ_>XfK(PO
zNst2DLa?K_v%Xj`Pg=P85U%G(4Me?UrVcJr6%9F`3fFY;*_SUilQId%+p;n;7^xm@
zru++0jO`1Ri~pu3elN0MqID;mj-lFnrS`4*X%=K)kTi45*W}7YH)3<YZf-t>m7u}4
zL(!D5pqx2AP1FE#O&+zstG<iFYr9iZQvpXN{ey!OaJ@%XuPYPWFy^MNR>V=k{2U}g
z)H{sOKZ@5%Vzl-2OtD2aacw3YHwR0RWnF(raRr!q5GXr4SLrxq=jJvrqRO(w@BaD>
zZIoZXJO$##u2okH6DIBj;cMiU)p3@}rFnmU#}jC5j4s83V-$0u3mQ5D@?D)}2Vo_O
z4m&h9=HcchAtv^Uq|_FnvaJ&HxmWjl_X>4{&f7W+CTe@@PhCGP6hw~|I7(#h%rcSJ
z)y1|BUyB2+zJ@%BTMpn?-KANc-7m+Wa=-lgl|qN<#gCNpO}_QlYjGgew=@nG2f|Ab
z0~?YGm_%e8rNfnJYHFh4y=FgtAN-x(191X;yWa~hb!Z@H>fJBLI2U7VblDtX8rqK^
zKVD%?zYE%_K8%XOt9VAxnFNx1-DPDe)yowyAKuL^(H1#IgS-h5|40Ox4vwlZ7M-8d
z?ih5*H<ErX7}fDl;dF0`winfUQuwWn@kKGnUD~3MV9t%T)N*ojEpFZ<-_>OF-3y3}
zR{eUn<8m*ZAr(?I95f7gP)P3penQoQJetcxjMB4nbI_GA4h_S7Z3$G1_4T2~k+0i<
zARL#Hn))~+Z%FwT(gcsZ0d2q>bFMAtfA{Va0o0e3s28FJ=s3|nNW>Bo6QOuCWCWQ5
zGyt%eFOAo(zChC|=3!!nx+gz;pd=x={#~&AgF`K*=LZmLvn!*oAv?($(ULNRSTZSy
zWkE^shEx_#@|&KXh>10ctpw0B#Iyk^y-KeNn+1AYz{VH2>Z>))8ik38h3S!>KYxaP
zLRBy)RfJ6?3$hJrmWipU3?%peD?MDtn-}a<^}GJP7SzdL1g4k=ezc&s1~kopFJ+XU
z!AP8dfTq^oZ)*$#Ov%L09_Re#EGB`Yf}*B={nZ=VGFg=?+kpoM2YqxLe5j#t98%Vw
z-s|qh`?RIqJSHBue?EFpP(Xx_A8}%+O{QaNX!v$&D#{qroFhtT5ZJ^{Au*V6Hzy}2
z8=Ib$Cs9A%jE>emdsfMbD$Ry&f*B7~3eI*!T5mnm>G+9jR}Z=EFfP)CF;ZjGmUn>v
z0Wm|O&BwH&2FzA)919Zv<5!#HCws=JY>FGdemVZr@z7T~;?9!=#vJr7c=TN^;^NzF
zOjny38=(EE?8aYjRJeGvwgt&`MsB{8lGp|5M1%b&pzCHfnqu&84&%@O^fc%f9zo*%
z$ZaEX;Z?xb|BJe}jH;{Y(zS6m5MU$0-5~^bf?M#Q!7W%I!QF#9fk1GFAVGq=1lQnh
zAq01K_=>0B)BX1Ndh|Gd&mT5>ZL3vlRn@FH?|IEGawqoBk5~ACxk?fg?OSi&pDJ+&
zQ||2U+Ah{-bFBjd8|(=w*fjyV;V(PzBs$vJ!3gSsDJcf=(;I=_Pn3R$u(j|6SaNFE
zZzTz2AgF<56H`;pAlfJufmdXu0z6^0b#)TH-?0)w2k`@k>1XkLM&R)I*B1SAFUW)i
zI}(r+mt+7VuEq?3DPw=}LjL_Aa&}+@6AMd^tSGn;?;bnA;t7T(7edF-kODOvRP_@@
zS)v?44sZg2UEg<P)IcTpbej70?Ck8~UxtA=VN!Tp43mc}pX3Pqk5YfbRRm{=ZgGLw
zs1aoJh!qfebahj|AyAk`g@U}VuV3*_Oud9xW@iaHp$yP1!&kL+fTdZu6DE0B90oVG
zw4}&9Bu^IXCoh2pE;izyw9L#*z;ug(=>kY4zy{*l^#I%kkk!N(ObNgjY|lQyWj8r-
zp_Cc6DRs7ej`ka^uL#y-asBC!*!5ZNB))5ckc;Z+s;R`(SGNik6wke~93D3t)DUVZ
zE5lfHL-%NB90c`PDIbBSgI3ubxPm@>&{J0rtU*K+-{f;+EdSO})!V+f=&P+qt2{LU
z^5=d}b$3hQl#?<;sHt;72te^YLC4%&!;c?O83}OeBu{}J$(qfb&Whp!iK-`vK^7LY
z68MM%gM!le9i^G^z7!yQvi$fFWK&{k65v9XsM@<zrfz3Z00bOvRg3mgk_K*sgeYdi
z-~j*?K?)vl04rrgvc*CiQN%utQq~4HMxd6VSflbFHhkWjTVe%y@#5KUB<|7t!My->
z`uPUI0GE+Dg6xR$;u&h7&Dip^j`PJAEHmFd$mwqf^%q`SDsvxdgQXds(##|zB*ZA<
zd+^)eFflS_SDFg@!(|Ie%E&}WiHGz%TBe~173bzscj9N~{W0^)bpjE>d;_F<_5A#N
zEsIICG6wFFyPMr|;7yzUl~J5A3u4O}8dC4f@h7xE!~`JC;w2o?Wmgcd@@!_nUCvK}
zL7+&quL_(WOkizC!0Sr#qxJZZfX>@NIpaTV^6?`n2ssS0w!K=cQ?`K>)&?8E-blg#
zV~7*GQz)3mfEOexnIopBVKkA5kZ_D3Wmq)B_rcxrTF!M5gw@N6ic|#J&_F+7gSem!
z8*E7$U9m@w7W1!>kr2nZ4<G9dLHT0f_Pztyy1p;quKsClU_draf(zL%0G=Lz(GyT6
znqUCVKd`6(3Qakwl(D&aEePm?T>g^~+}he&u&rZ&&O-w}m!4HB|M*J;e;j+Jm`)^*
zX27d=h{oFd<AwIRMk@I$H<;Rh@87>q$0Nme0_QuRVQDj?#mOP2xB)x{SaiWw>cjUG
zE3v*J7#pjszBYh~%C|uD6XoOQ|D;{UonHZbzaWX70q4Y&jEsyb780Uv6%aA@;?o_q
zxS*3AHb(VPWaP6WR?9RHcA^Koo1Qibm<S`l*8yvNP8mj=DM(O;)eao*3@)By=`m8#
z)!p6PlpzU`AB|Rh05p+y8Mli$i|sJ9W~*dTs6o$+p|8IE?a6GBarn3$9|<pSU32rR
z3at`SSRHe0N-=l@|B+1q&9&Cv4v6)-yRv61rgqd3d8A5ln|Qyu20xcAMgzz0@b|R_
z@1dzMFg9WeoD0&Zu^{4vRh5;%(BZO45JDFRj*lqJ-=am7^mKIJKqNl%@otl&e>sgQ
zeF0c{AK!s!rq0%4<s31O%?-`LL;9b2_=?NA;KoZq{_d%%=c@j4V5P5(5W`NL9PY(a
zed6l^n~i99!m7eoY;>K>iplP9Z=FQ`r9V@p&`_=|5j=c={Tj$T_XWHV+_e>}(c@3P
zL3&m6E*9T%@c;H#!~Y-!TiKaX-QKlh%e0IyU%<`^g(X-M@7Gca3(w(kZn9D~pT_YC
zKQKK$00XMHxMQQZ2v5>5ih><b8KSQM&;;=DfF9=c+9L2R0ENKORE>r-yk3bdzzm$t
zzG^XuTX&>WNCH8%ATW!m<VqVyOaY&VN}v|=15ld)hM{64(KlmZza;QQR@T=a(4Fge
zd9kN)lQb+oOIiRyLl#U`RaHWnMhLKxv4Il}@Dsr10lQ@)djw$<To>!o>jC4K3Z9C>
zmM_=$p82%hG>l@j!0Bh-3nV<He?UOLs;<ru2uY$G5#hwl%<_S#V>&TTepHnu@fJ|^
zKj(pp$RKAExw}g)2riq}FTf70E<lWsu>1<{M`2io+Ai8apMRUjxz$lOn0s;affa*8
z2YlJX&%-3OYCvxArxB6I#nDkaAjskR1sowk%i|!#1=bP#0V|N&{9DXV`m-k?Fyny1
z@$><%4B)1C;&+1J5D5KqDU}x%!r)8b4<W;W!qXu1;-4sHLcu2}n3|Nd*x-n4jgK1G
z;|P+_fz`pg>=v*LBv0GFleVIPGDH%@N&zRZBW1uG=oc74@_0i7;R^!PK%|MWv11T3
z`vqbUAiR90G}7OH49cRPA(m*EfHO3r_5$)Enod)Yd%QXU22{yA258DhVX*ON2HKA^
zr8e(d9<BLt?NR}_kWP01gM13e;o}!DM1eW*`}bV{D2TVgip~;coAJ8Z%8l_42>4m_
z&0b(kLV%CYa`(sz8|Z6z0nh`$xIPnLWu<~uxn8{M$EPZO<P|UAc8-pW%yacnQHd3$
z(5n5AJdHmId|Kn)?0H{Se(s@SVj@#hF_%2@{{~(U5DfvRe74AofM`<xf)}=;vJ++)
z`76z<_ok+2T{y~?-h?*WwkMvhbrVmx2q3Ni=HsOxd=Q`^-P`~n2k>Bwd5Q;SMX-}3
zG+lKDsr73AeDa&A%mCKIvkmx3TMt9D+Ri5svvS7J`c9!8Fr@>q!1##*4qyg3Fo^*~
z(ZkgmW=RO>CWN#1;5X8FZOJ~KQqs~Q7C!)QvU39g7)+ag{!{{Ll~lx^7y@=Ut}}q)
zqoHQS!iDs}2204x4}$#U?eyi@SvL^S*doh@-Ev?*acc%N4lZ8a%f49Z@Npo->3Bhe
z#Q-Ie0MTUit+xj5K`vJm+}zI%;6c55HlRm&+QEQ;{U?=aTR4EQ04fw9kgBPzHL=eY
zWlIO%P!Ot5N=-eSs|s1>_1lh3PUe3<PP4C@_7w>Xv=9tZe-6s94S+AnEhwM|1k4CD
zME|f*kFNal{ki9g$R>v}84ujT!ViEZSq0p=q~6eo?l;>NMks&;_>%-a_t{XO500ox
zK!wZchT@Z@k<|B}dXSZGxHdvxe{XIgKTlbHJ2gEmE_Hc)Tv1)!<)3hJ+4i{f>lfH9
zI4><N-O~a21rGrh3JQvrCh!ff#lU^i;#e-1pB$_tep2#Gc=Dhe$N~=Kr%z&bc5GjZ
znJE$h5*#>uLGNprN-BU5Ac&p=ikX%k19a%gpdjd@#&c0}uFMkp0caHEz&sI<04hRe
z4Spah0?Y2h{q6IQ1zf;8fP?b|#Lku$RbUbQ8=fYfjj+b=r!wk`ch{x(mPj0uIra5?
z00gI>nj)1h5rP2-3=ROeao<DKeu9dE0)|@e)D}4Npx3kdSNFdf`&@G#T7QoEZET!@
z(GNVTK*2$RfgDk72tZ6qX?({(0fJ7<b7oDakizvIK-aUX2uev%oxE9sbF`n!!6W<L
z?-~-z9U`}$#5Oevwqyc$wvwXa`SCHB_I`%H7PqrI3L*EY2RE0m?<0u#4tf_C6aYwA
zB>?FFuKIn$W=7{y-5Uxd9Fu|3o!l7m0vVfc0zKE@rUDCk=GElQm@?AXE$IJWPd^15
zzCgOwt5X;cBpgf4?m(TAZo~?}A_Lw7o2TFjIQLZG`lj;l4q^!w;P<{cd%PQYeBwm>
z$w5DBg?;uJxZl9M55nsy%PBLBNVAB5b)E{;Uh%VlgL6xl1W>R9CV*ZDsAb)M#6P_u
z9#w2P>Occ@W55?Ssd|rup{lA{Sq1il8Gu1;$*dY4Nx)78cs$e7F2I!amWa!u0HIpM
zntLGaT9}{zQ4h`zeU3fWP9ctik`mT~4Fnw#A8+Tn+UWIJi$g>T(#MWmw^YfxGXn<F
zfwpXY9R#!jgUYc%Y%Dw`hL?i_j+Gh!{|~^{bYe5Z6Jw8K6e7hbh7~DR_>7%Sb#aht
zE|BaB*hpaR+B`VGkVZIrXFH~IT2x<u1)QHsN=i?p8#^NO95PgGwB^$K^~YwfZ%1#N
zt+%!qiPc@~?6fLOq)bi!f?512#>Yc^D^g8?6zQapm!r4^P^cm|Thtv@Fo!UcpO!J#
zH#Z*viwX20;6jhj(HbK`EXBHcSwrquc<Jz_(c2DgEFHctQqu#`;Ex|afN%j|Ly78E
z#Xk`s<h`Rpci(GG9R)HPyB97XcJZ`AeF6%bt@{BGvGE0jH*f`kK;m_e3Hh5>9p>ss
z(V+nJ@d^-*eQy_5KoHPOBwfInDH{vSdGWUpF?<O#U}VdfX>vJ3W)S)Mon^q{Vm)2r
z4wjxL4F!POjdE&tn;ZiTM!8-CTn3`LW%~TS2?K?R^e|})YqPss<zbzn&jTSYE1zHG
z3wu9l01%Url6t~}0`p9#SwaA{YZM<gWZ%+k?`mZ%Bs@GE;GRTl9G#sN<>gO-A$YkC
zM$T_cut<ws|NRny_fmYs<oD}$()4ACAD=Cd2(jWuRss}t3t;g8z(?GH`Of;f8BiSo
zL57s1<WQyv`2huzH@Hn;j~Ia-5si(oJ-~Z2-L<{J&qKN(7bgtF>0E$Ut}yX;o~U<s
zB`NM-6X^v*Zzg*B&C<GM0e~dE!nOPHeuB<qBekmP6qw{*K)(s@bTH+BvH~N&eqo?N
z5&h|Y*N_1fmq6OZg^T4l;9Ij#vJtHSN7a|~bo2vRHMK8b3}1EDk9JaE|2FY1Xs)Qc
z6k(@6qG)}%u^#mYi>E(n1&DFZ7<hgJiwvM>@o;l{uLj|RAS6m1ybTM%6FBWhWo3tn
z<fp7Iurg*oyeQ56Ep6@6Q8a+eEL3aQ_vcX9Gjn-74#Wol%|l698TfaI0UZ%YsetY)
zB;kXd3N9xc{U5N=13p(^aI6B)?TY7N3y^z(!bbLBh&?HH&<=oR0f7AbhK8IR9Z!K`
z_q><ip_2w$xtzWiFUVgWbM;!~&uU(6EssVAzz;Lg2o3~Lm8{&LobePNxFcVP1MQ))
zF(m<oU3=W#gJk5LRDtX3;t#&;)Qm<HX(GPDA|j80f!x;9Kj!v8qyZ2Pc_}E2fN1ha
zMOwNCEPbYymH-#(R<8}9DpSV^T_Ktje^X%Z<j40k<X$m|S{Q&CAIuaKu#u0zj0BAN
zucoPZg#ySJBw7H+9TI|^aKVfh0Xzxqs)gUbdjewaziL7>F9Eu;)hf_VjAg$O9Nqgl
z215;3(qM@Km~a7}$aMrC1eBGP!LVdWL_7Zpw&#;S!S!9PNPxiz4GKd16-#J}pIh!c
zdP%)qKogb#x6jGMRNT}=oMKiN2&PdW#0I{c+UQ>&gy>;=b=oSD(jBua3}A){#^pzX
znt)Odb?Mgd;Up0NBPFER+1Q}+e$n|da9%H)0owe^+%3p9`0x;D@V<=B&!dLruOKa#
z$HZVdiGdBwPXkLgy^dBv52ln1%-T*|lq}pg=nt=(E~ci&LtdusjW><0JmkupWv<Qw
zm=Ac7F!XJ^B;2AJHQ4duDf1j%<?_9pP}qr#dbZknN@Mqkp!fXh<)7zP+`4coE$0uC
zI;#o7%;Zj;I3CQvy{8{02H?}B07<<PQ(|7ud2DZf`fT29V{a^qx)D2D0_b*w7CzY6
zU<kpJ$lEH{BIMT%94Titii*IOkG6=0;oLtH!0J?J6ZBApwYev)ScCssIPvDgmE=~d
zVydP)fj=Vb6v{J&BlK)m*j}8H4j`d?gR^BDib9IVIg@e|Wxh)|?Yv!(m&c`lFSdq?
zKDip*6*KMj$xo0*-|MBG_zKskzKpaq_6>SD#b(y!kq1rwo)vn5u7i|^9{h6P@1mcM
z53b8@+Q(nHO(9W<J3?ddFyl6`tvCT%I-S6AH50KDwx*PQ)t8Vn#PPjvIQ#EdzQsdq
zyvp*oUU$8s*;f*dkF;t9$OlQAsCfCW;X)x=@phuK*><Q}z2LF0tD<Cz*}3q!`?SQW
zeZZ-F28Fmk5(GhOFZ?TgLJiW}Qk|(wZ?G<dJzGpZ-p_sBzZJYl(Qmo-QOCrnc$FUy
zci5HQpZguG4(&QV?v~7D=x7%KeWYQ8+bDv6*X&nxp}zCYfWhfEu2;*GaAH{z+(Ht#
zV<uY~n55<E0c_gxzZj_0MPm`p6kmnncdq{s`<-im9{858hwjs}W{)kmd?FGwah0mW
zy8AUeBuI_~kh1@iMgg`>knoHFBMkPCq<*m>N>qdmsAFh%C<~H5MR9I#p#>_R1EJVt
zL>k4`s)P)J*l4#X6v(T@pg@Z*77R;QhAh@v7MbI`d!zWRjD%^J(|Ri;O^s1{r$5Oq
zS9U<t!nRruBY_89!?BPZt>-hdGyk*Q>7o<~spp}ek|#rnhYRCq8pf>3!{scLzNbj=
zbbg1pbMQ~ad&{)&#}9@%dF=jJ|7^m3Q7RP4Bt2&;S(cP6F=t{bj<JqcK=}II7y(*N
zIb*&Uzo`;(-;i)}&hXG@-H|(Dp-|djv4Pck<rI_gckWi&7|0pqWk^#-J<~Bhj(ycW
z$|~#wpcd{B2uh+m=?|`EZOXtiL!@Tr4h3$k8f3kFtIGunc|-2aD=(G*E~;<fQlHvZ
z=LnYd5SJDbXC+EVi)^S*HuSrDbgoFf7^cM8gX|=C5ev0*m`>`O#|o!waEI{W!;3)`
zT|k-%P|^Qap8;9We;ts-W5I(RPxJ}VAMnmQ`J5_3k9Bd2!$*dU2sg&|nrPps#-c}w
zNeZ}a#cDbJ{*@RjG{r?khkFNwBqQ`GZpS*}6!szaYaVWC0u@t=717y%=mGWT*Dw(s
z&)B~WCrE><6^w%2FM1055+W%`{OWw!pb-Q$7_tn2$lS6xS4>bV%TmKkva=YLiC0{L
z52<1vyugB#(`0~D46g&yY{1k1$hrB?#2fHS|77Cy9UVV<O&N3k56R{KS7pQ5xwzT>
zJ+Y`oN8WaU588TIKAJ7Mzm&8t;Z-Qg(p?*I@Lm_evj|PX8TYN$oCB?U?AZ(}Z8$cq
zKs4DIRoKhkwACT6&KT<GW^%HVtUEEX-V=O}g_g^MU!JF*+#voiNPSJ7pI6EUl(~)$
z4%(Q7ySg2uD%!3{)(i6bR#@7W@3o^S{cfy=9`b@0zuG!|VnBgWpJ^+d6hwF+{P}8m
zzq5=PA&*s7L3TcweIUY(9Pj<~5c6M2A$K45jaH10JGX+*Nj@OTzz~JXp~DemtusOV
zyA5TTsDl{wa960EV>Kuz1_t^hL|aCDxf16NlCRcaAd$t4!esHuh62*gG;A!aO(vnj
zA{keHY;A%My9?95#zEmk6`D1fCWn|*wm~A5-@Ly0sXe__WvEufKDU*muyy+)Bn3n&
ze;7Y+6~nXYe)?=1LLG*A&AuqT+imJd0KaaAsR%*VqVcF`PPRe_K6x>Z#%>}J*`74J
zrNAq>QH+S;Z|O&h>zY43C})}P^`+jJ1s>Fzv1Fcz#p8hG+i+kS5cLb8!@S(Rs#V22
zK8Ww{iam6{<4j2tnM98K5xoOzE$9Ejn3|LwUv|+d7e4ZLp>)&p)HH$DP(Sw>YTqp?
z4*wsMVF8f0Vej^+S+GL}DVYh<`GaTQVf#d2<FIV=A{6=*m2BV9Hz`;K@08e%87-CM
z5_b|klepaNZ)|dTH$8Q`bN`~oq746b>yW7Rn#Im0Wb^!UAXV+|M<vQ+#13~Q6mn4-
z>CJe`3oh=0+3Pe8b-_;k&G>B3)C>&g?vx;@zeG3k273O>pPP8jDg|;PV}h5V5tYot
ztwD1h$8Nddf9UVY6j?9kjR@41FlbFBHr}fdCao|ekzagy7fe{u(&qeyGYJs}WpQes
z;j}_h@%gXB_6*B9Kj(Y9{sT@P6is|+uwFXPtrwFx0usmjqX<aUWVq#9YTr2#s<|E=
zBWF~b&b73-C_687`i3nl8u+w$>ob1(@ApQ<5v|QH-Xx^oCTt8+8CFz*M;t4OFCn+@
z{#aVaBL2)<-*B{I<!z~{9<xzWAlYLkp!6HvO7F%Mq<deXX@QhNG=9r4_bLrzfl3K;
z*!2ra3!T2>xFPcAL^^df7U3=_61vnS#(~rG6LSMv$-ggE67n~2qbsW^p=y@{ivF5h
z4r3c=TqXlkezp@d!{!sAMYG&ddazl+(h5sW%pMM=0?%FMF)6XMAGO{e`X4Z!mHT5Q
ztotQsMYH9E&If9gddt+_whG`7qQo`x{%~wHz7d_2s+mZIM0&Rq5G$Ss&pyhW_vGak
zE_1njA&a=iEti*6t9cRLI}}~<BAk8}V;!18zlf2~61F+oJn#4{KYA;7DX|RZGvtF`
zEA8q9xeQ5+q$2s6s8yW;ah^M^ca?iRa&b|3IgGj*e`+K7pN`md*s|}Tv1}TW%en#R
zgSREEFC7O@B!7j0gazKNI7@uegPD(xQgu{IL~>ZahsZw`QfhWJ9d^^9WR9ziCB3Mr
z7P4pRS1?aQP)<=ASgwEg=EW^sHu?J9Z_JoC&$9gLiB!q5MWx;Bq&HJzJXdao&V~EQ
zmkgvSvT$cw4Cy37*vl6NO&i3a9o<cQL%E()nmk0)Q@y>*`&++xIH}FDr0bFu*}DvE
zI8)bptCJNQXqG;D)GfV>AwQR`E^_HRemAGTmF)1k>V9uJsR$=>D=OJxuI<_*cI4p1
z;90+0se%U1H_hZ=YMtJ1dCSG)`=-2%F8eupGtb$Rb&yHQxi^Qu1~|pqc5e48QV@P#
zoWL-Ywe7|%M8gt_SS{gHb;noVpSKapn>2`|UB$4AyPVEhhSSJDXjUA$8{2>N^3P2_
zT&e5OJ4BtHpPYy~Q%T6E)kAj$DAnJxW3aiRSr>A0iNYI48nSeRHbo({RA*PpZFk76
zSrzpE!+ig56$}3tf)_6*$G^|_H99MR;05(rF{I4GtZLGsWsHjpU(bf0hx1(cTE_=b
z*R_AvCqlRI@n+?aCT@J}<U_dX7@dmA%BJAS!I~>Q;v*6l#Ze3rd%z=&)(3x)gMiD-
zT)#LrT1NDPOn5PgpHgh!A85vZ`&_fRCkrn>5_&aazV1scZ)?+P`}!SCi=)!+fG7<n
zGC&@W9kb#0*Qr*%8y_Dz2|`)4m(BYUL?L6zo2<K?_QH%V-wPya=kTwawiVdeuN#-i
zyu9CW9q`R)XDw$j?_7~pEKpX3hM*`iHwj1!Gq#l{${ap3Lb*m!p{vB9MZvKb{1uu<
z8L)TQ#YnfH-_W1fYA-DwIJJJx_tT*L67`KC5u42?+3~rk+mm>~u0%=WA(KG|;g9=^
z!YglJ?&2k<DF*MZ#koXjQPEfitk)wy^E8<myb{W+jac)jYazvcu4e}ANK!Ufxjw*+
zT2peeC0+DOFtayJHe26e%k(LXGVaEit;S`^c@uhb9wSi0L+@>`RnC)KxAv;2EFc+|
z^7(jz3L}l0K677*!`;t?n68UYd{>_x62A8B1@PuWbK+1xNnzm1n7+m_PZqi;efDL<
z9=k{KqeC*ea=tv7TG<K4oX^=ZJl#u*M1<%+(TC4B-J6U~P*;Okwi%wQP*vTZG?Z=K
zG*?7OeVI`xB|{iqeYI-;@j~GB*HNxWgxym4&vOy3D74}l9MZWM9bJ8drWaSg^<Rfq
zU=!Q&oY~o&G+WrkV@dtcbP7O4puXQwrBo-+=(B0M+}wSQg%Rvn1noSXwQ2APCUPf~
zp7(a``cYd$eYzdc_BVg2k0d%qlPM;as8m|o@-XDZp*osq;7(R^s>=(LBVMzMchCyT
zEa9%;A!!PpKxq#sF^Af%?s1Eb<{J$?l=*WPZ#|3diJf@z=7sXw*@nR9S-%{Li>yp9
z*|a8%tK}L6`LNZ7m-{9SpA)-BjAbe5IVF63mwPp)8dyWY;=_d)%Iu8IILungtYcQt
zxw`G{?ZqUDvDv9@fW1k=@iMAHJ8i($eSPq*L}*-fOXKk_+xH;MHFH2s(dXcxwxzVT
z0u@@5NQoo&s`KssvzsM1YL+;|#e>0Ale#|C=bPy^!<Va<iOoZsQyLfNFedg$Z&P0_
z{K+2j63BeO$P6Mjkl0wxpk?pMU>JEb;N;cZ5QJGXoI59bJ0sL^+a}fG?!X4s<C==Q
zVtg@}{Wl8saIx2H_Jf}lM-FahkkI^#4{uBALcDAJx{ePWoSOWNwJ}|KTsr3na~smI
zWCh}o(Fb+O-r(;`H)&6S03>GrO9L{tgM=`Fal(ksB>9<1c5F!MegvtygT9h^=sOMP
z)09Jx0d-R1D+$s@H>6+1>yj=OGRbvBVH9&3=#+NI*Ld9uiW(K9T&R}L6z;OG&+F##
zcUgN#-~arU(<I&I(y)ZJ;E;?nuHT!?TU%X`FR)yG<NcF<fa?pDV7hmNe#I%B3yT(S
z=b<#JCXI;g7t_h5JZ$vs5ciBXiu*!!jIk;6BXwcQ;d|BwS2kaaDv1wXFVJLFHKu>@
zS)j?uORZ~{rgq2JsgjVxSfHWVj~gd2Nw9}en)-W!o~D^C{NjVu3_r_W;!1`i&-<&B
zdy4^<r3p%g6UUj~=wUvcdsLxzpV(~M(;Qu~q9Ym+Nc86u<}_Wk(to?93q*_74+Qg>
z!<ELV52x^ItLPc<)$B%NT#WCJBGRSB(0M@h$j1VP!o3n~6Hlcx*v-H3Z$wWM_;g6c
zUtHoOlg@lkEVl94Q|fJ4xE~%Agn<hvuv)BUomp7>=!%`AENB|ughn20eZvySkIkL>
zlPLJ%tZ@Xtx1{w}hyJmygm0a^$9UJ=+>1NO`W~@z;uQkv!)Ia*g|z)diD`B$*UmSm
zNRP<Es^89$I!GRv5vo_@qA1k+ypgArx`Wn{T$Wzm709dbt{{cX4wbN3O}?{nrtE`H
z>t5z;t7RwT9IB-iE5smmS+FruO1YUZIsg1@but96Vq2>^lwIbV)U$hKA&JR9=uF<s
z;#_t8<-zu5M}hlJCm2a(v=(SY7xn0&`z*r-EvM*C!S2)L7L7GubIo+o_+2V%`V5(l
z%T>{7wzQJX4`}t#RtJV5rr$7^D>meY--io#tk<$uKi1HRn#VjSCG``EJ0=^ZAQM#J
zM2YgSa%`!bcQRX?gcG}Klrw}ZMHuDQhla+ZeBs8ZK+1ZFWv?jN6YrSOK}&)g$l>v`
zy8lJlNik1kN4$JWA6HJrUw<yKnV+h+>~D%UhB%NkVhb0@irgO!!Adx8F(>{1UkSN6
z{)_(b|0q_Aor~+gfj0LSjTI)%NZOA#`6yzcdj3@XHj{E+!dNksJMn(yyVXy9MIUsE
zoy0XGnE1pJv<R`9=A)KxgpzRLV1L~DOkC!N!t_Ql@28H7k`)oHzduA=y~)?sa-`~*
zgmvfEd#%UE(}?^*`0j9683O8`QhkgPXNzr=I6gOjsR{DT5|2O#w&Pz_)}J0x0HcTY
zUv=1@9>E(feOFU|vk9K|KA^KioGrOR42N4cep!_1dsXO%>i<m6CapSH{XXL=`%A7u
z?Jr7&<1f63-3lzP^P4i+*I2tBb%P)`4mNMb84p5GHftkyo9I~9u-+=Lwi+~&E3MB&
zRB7WMP3`V3G&Jx2kxvY|+Vc1{G@WGQ#od-Uhc?vRklNK-CEuZVZMN9v)!P0~TmM_L
z|7q?2Y5RZHY5yNi<loNJwbEPYwn5VCli}sWmDL6Jg=0{fd_9p1JUF+WhxExgTL+P?
zOYRGq=U2m+4kSIU9StwnZt!#xk-6wx{_N)8Qof+j_0rLgE;lfICo<FAk??>&UAy}I
z`hKV?Q-1F?nLVD}<H<GL6Y=Oje)g$7|6}9+Iry)x{_iLEe?Ee;(*M-Lu-TrP=l{`I
zl&W1D_SQ!4sCz+^g*P9b|MRa3mdw}ZYr8@#CzmO5M^m2{I1cXZx@pYM+I-*)TSm$&
zGOd|23|X5q32z&Y)=v>z16f<Iuz{0a9&iJ|zyI;L3jTe1;px!-%^M!Y3`ZWSoRhz`
z4zH|!pPQNWz4&{{`*wZyXln0v<bjj&Tb<PJr{g2s(-#elS4w<!>xN#5q*^z`tp-ix
zc<N<4fA7WW$}1k3PxgWxn=`k4MNaob9EA_@IyJqny*~X%TmHRS|E+ERwEXGM|9&Qi
zrnep!j>#k!4{vpjr}nJ(HY*F}K4M%rZiB8pB)Qw+9K(7<YxMwKs-kg?p2Jh+z9;W3
zEPgk?Pna}T>cZ^k>&>kmi6HAG`)+L!Gmrh9$lE`fO&=erM4dM0M$DKLwv;zKlKyUx
zWbo_!{XC*r<k?<FrMoKX=s07QV0)*wa&P?Ii@A$X=*BA7Y43O>D0E(pLgX+ieoVgK
zb=`5hJXx4=^fxitGSTFTUX#-=T(^aC$`)B4qhRNhlyZ&jL$~)W6TVh419=UD8(%S`
zxL&c_?Ky=xvD|FscuuAIjGjKrWOgcwRQx#WWgu{7_ueL0dgZrmuKLRKThreD5ULat
z59Ph`*k3K5f2iz{dA&s|&u3|TyLxQte%7gQttDkJ>O#BnqV9m4IZo*%vsKJ{*|uB<
zBYW%Y4t0+M(P4q>+2$|uNLHy6PDjF5&Ik7SKR*vt^UMudd0I86*h;=ldQqyxWA-sz
z@NjFX)Gp(3<g|RS;G1vkq?&;W9r<FC$L7gz9`t4(4U6TsBuf)*$~<v(K5hfmYpmbX
z6CHIumP_a&+?Zadtp2d5D|wT2bNYB6++8Fmvb->Zp^!GCe$zB=%ji@{pS6+B6eL@-
z$vaxWI$BewBk(qJJQk;3VzoWj0)tUuF8m#`T#CqN%;2Ta675A!`<49jMCq=wiSY)+
zeDlnAo<Te-g>tK&G;f9LMz<f|ohSya%uE&PGKnnd%66{gJsa5EAtxFrTt80-aoy0Z
z!v~n=6@QlhVJ-SM#N+=$BKl{^`hS5$#KXbO_3!(~{d9LblBsk*-`v|Drv2l6(^_Ap
zL(Jyrv}(<~x<BOmE%A|nl0q~I&eM50rgh@sB5~cah58S6A89G)a+*=Hn`N1#;GR=p
zbcwFQ)Pf4t?H!O0+QKy2an3PHyFYpaJemrEsCFx(lUp}#2e`j-$+)&%ZtSicw#=04
zhvz_G+MOidAi($mwzU`xj6cA2!@`(jKR;7eP*%S5XmNDUAK7$lTIwO^Trg|;;Ht9>
zBJi@EDL?T8twKQdZ$oosUY==m`7@ReiTjmH@9i7(@(5r=$q`^?b)Y~%^K?}Ig)J|t
z*bn_$>%T4`!NDML2?-fFpk@g~C^a-SS$;=>z(0t5jg5`rJx35F$eE`E$riikO8Zz`
z*itG20s@oi{lmlgm~ib(g%uJ28_?kLx!o_Qk$48(6pG-7VyM)#hvX0AN0<%X@}7jx
z?)Rq1qdU-`bYhACLP{%`?Lp?d9S~zc;9&lS(Py}UBITg%XNDSLi}t;w5KwgbvV~)t
zwML{q-2@i?s)x4~(Dti9Bovv<EvvEdM&9sd9EjsV>hmy=&VX{p3?d@rVlcCxLdfDy
zPJ-H6>b1yxy>>D)fz}A51%n!LcR-S)>AM8rJ^HRo`O?tKMaL019&vhF7^oPGR@C;d
zN6i2TT>B~wE$#Wm1r-D)DHtev9xHmu7691@K#UlXMC;Xbb-xQg$itk6fC^0+{Eoq(
z{r=DVAumzhmY3|kk02z=VQ7#3Y!V1{HJo|T_Mj>4n|Lu;5#?}K&#W(3;g`dpS#My?
zQny6o4>3M|+?wkBa(^GQta>A|a5(X;>(vP)9}e_A5Ql$T!Fg+lDZWHV1#!IE#5A~8
zAHIFG-<`>v_bRyA4WK1FnaCW-5UMmo3&QzjYGwxNU0!(!zbLf2^FT+y!v1F9Ncm}y
zrLrld&G+tY?<tQ+&31G}+rbKJ2!`zYqBK5A6<p6NY*JF8TPmfm{<zdMUaOP4W7M51
z_XWsiucj1~zQQ9{ORn*@WqZMh1v(#grYG38q*rCr^53Vlt$5er;`SZ-$e_UbL*6ZW
zvKBP0A2s?W-MMn7)NhjUd#DQtFwlrXR|hypuBdY#)$6@e3e?3SBl}i6aj|Fp=R5}K
zFs7795|X(Bt#ACIY9a*%s78cbnSJGLKm6fRww<RjD8GBStaN2HL*$93pwl+mX^ysM
z)iMn;i1<<Z5MNzQV&HkI&<)7YFUs3kmX_k5&kA?Hfg9?k$a1t;*NuzMlZJA(iVt%H
z2lsKbUoiHNB%4ArN8YNc<~-^VQIw!M$=u6048DBWnk6)>FpDVB$u}ePycm6GM<sa%
zWfCjQ?vj+LUpfFc<q`ANddoil(o2Wa;c)?7_x}C5!3kySmW0^YY9+SxW1Q>BYNOR$
zf+|yT<3Ytlrs_wR{l;IrE(h_;2K?SB*zleO1XZqFqv-7Xo|b$Q_&9BO^;$lU1-lUO
z_5C`tP9#+H5```jPrNfUb$TM>JBvS|pgthqHe=&MKAu6jskxOu%nw@x_9-j^W4x~h
z+xolTz4N!e@ETs`7wb+@z=`>JQ$vziX`|*Ved#NwN4BSmDRy@9Dq<br5wOUvyYo?f
zP1W7aSM?CHT_s>BLhX!p^TT5_aewi^n^1Lk$9)DfjaVR}y+i<SwPv~&_a-rejLe`S
zFC|12YAhC8&f?*e_sVhj)pD*m;rjacjLbHyza8JMZjC2ix^@O{%;nx}w&J%}?s4Pr
z2&;C}HB)C@bUZW`Lu*3a-ScZ9<f<Ni6lpA>(-AsXY~@Ay$;ojU(cl;PTA(sA;a)Ws
z948x>JX3VUJeEsP7)Zy@zb-u8n75lRA<s3S1<Hd*Y-SKhM@5P4cNOv%Us+#6ecIAQ
zlnLSd?U;Axt?pt~rrv9x`q1CUgI{3{olZg9=9h>aJN2<piRF2RDh31I7oH;xwcx;W
z;;cy`(5}Py=NO@P`Zm1K^TAX6s|R#qC;|nfH|O#V3!DEa;@Bed$B*$eT2LF#7Om-8
zeRcAYUt-XS71X=h_F_jXsQto6Tso*2U)0q8xuWd$#EOh{G*%$nXIMx_Hm;Liv7Bzr
z(PcqFL@#xLl1dzCR<Jzb@Zkroc#3Lfvr=_U)ug9mn+~2GpWB$c?fn^;;;Jly^8H}N
z_*mQCkOx4;&MxHCaMn>E9oqhu<!{MZnGK*!ri9>A_+n07^scnb`X9oxbIb`rcWL`;
zIbr4_XhzE_(fPw5tTN+D5w9s2R#WJ)AaQ_R#>ixK{=o~u3iNvQ85Vp)C*i(<=TEFa
zA~7yrd1cHXR7eLSLY#jViuhIm0{FPtS$7vNeLgaMH&j{xv`8MR0s2>W$A|9Zq&h$U
zsiQXSE<T)Bmi+bt^3M_j#c*)mpbpJz2?@m?2$Xu2h>#9hb&Vp-nxYIN)SO10GJ||e
zSh3m6_F-nLL_}q%RXDo<6F#eIwGpY7gH4%&4%@-X!^L=8gq{L=F@-vd-e+!WhL05f
zKN?sYQ)oCHDd>c+{9#0)F7$O(vZ!bQ*J1D9$Hn<S-9ohfqa)WpJJ!dA!5r@YLs0g?
z=Tg-5Gh(&{Ap{N@xZfG_tNq08ry{$P*yPOMZwlIHkdB_eF5FSxnE@;isn`@EIRa>4
zI^x(I9}`Re2<FKq$J13rVU&KWrdFx@=Z`p5Xge-541)0rZZ3P0T3(N=;uMl#82Ig3
z0;&G}4~_L7%w7wpeR}o-yGpUfMNw;8wBe#vx$4!rn`*h9!OkJ(A2S^sdi%#bUZ39W
z7>@TzM#qp2=Y0VN9ADB$ZIr>L?scRdK30~~z(1WDho%?KmNRkiS|U$V*_pbVK*z!o
z&Ciz6TOBdWN5nE(<R4Dq?1G%#Si+g*d3lnL4P1Vm;mif!&AOhC{ota;B^REqv0wFH
zbfYfCiCi`Eqd+ssVWbo2*mp@xNZ_yMs%lD2{W2axl^aIUP8gZlr>};mrGLfH!#o$#
zsTNtbF+cgnnfjue4%}qc^762t9d^Ubrd2N(zVk0|`d_K7Mvr>?c*~g+`NLR4+2#G%
zw%@+>d!LD#c_MdmS)kvbm)J<<lsYO*BIb8a9jYw}&FBxp6L#XZ7D8mqfTD`u9}U!X
z9VX6d5iw|S`THdcfDu74i50lBGOr^?3=JIa+qS_-`zygWW53tg4{qOC*6uBvW7=;B
zj-~VOsUQg8;*>E;tG(_7!ka)`Sp`X`AH@ftBFGQE(qVdQ#7MLu2kZX>ze2)C+19g=
z8iK9?eR|7YcX5VhX$*y#R9~I?o2;b(Tc>yqWCUa4o?tdrA<C6FBC)Ic@!8kO@cuv0
zZ)hOYR1j}|y+%sVfY7~zjlqgA{o{V=Jc5qb^b`=!d;<NK=T=un?HX3!HD#T+z_2r}
zf^>x5iPLhZHXA%OQka%oAZ}_xGt*?!zfvIxyn*^v%RYN<Cz2Gjk;2M~Mz1^t5y6;K
z)48tiDhRs=l2affAjs%03gw}oXX|nA?}LaF^ns6o0bi#V6>23X$7|{7?mkcLdV_>u
zj8@r{O8nJ4Qd*LT2|@+tX&x-e2zv<vCKQyo<bUo?SgHPPjG;IVfPz9mFvb)R1a^iq
z5*k<#w*tV|!oyjd+^D~^Z7P+9Mn!S(y*>tK&hv@WJ^h$5IL8tVtP_x38EE2HA{K}-
z1w1s66k+UZk_Ro_uhA%u=U`~*Bd8?Cy+4y)veB*#d>U)(MMWsvOV!_G43~+X#v)fh
z!1%0In^O=<sx;JyNd@=kd4rn@Za&Y{7H((LVS+b$<)yguSSKqZi{ko|nKutZ1&P4Y
zs?T4(5)N(e&ILt#2R$3Mz+GX6f^h9zL<bQ+B%DfIKo*5+K9^KgEr5;CRu&tvKFeo-
zfd*<Is=P7kGMQGhRC2+TlEi*$0-J`$yDvoO7Bp0q#_y$-jA{hInF~ov=haR#i-k_3
zul8?S<&eRMfr254KnS*{`Su%x$~g#6>_;;<1>{A}h#CjC|Lcp(0)qwx4e)(rH%rzE
zxROTUyi4GoDj^c<>gpRv*pPzWyr%!>2xDmTt9|oiI(tU5?!x*|FiU7@KsVO$nH~z0
zzUFZ~U8rdZ6@@eeb|vot)PJ7lmMY%dDw3>C7Ac;yC1XPaX;0CxUPwX@9u6cQVV=W5
z;KUT9$@}^aC<j5~8H<Z)sKlEMs??r=>*|>FQHpUUa2G#;QO&zIQ-kCc+EoW)|F5sJ
zT3^&6)ir})BBY>Y*5x)C_iyRZOwO$)E^~N!DyW5%A#luzCJeA0)gLC|>w+oZw1epr
zi=al_Vnej`cFd%t;}h?mi!q<N31T5=6=D8q{MlXo4V1>DrKP<Y$%p-rW>}acgd)Ca
zQoMOu5Yyl8M?*7eMOhCFCsVP=1+jZ$6QVyZO`x0^$vAAbNiwd)ZF#YrO>-;~`cJbv
zN#XN&@*2MnCVI#>Rnhx-ZxFGtC>t8^!YF!NP{jP6-%!wb@1F-!>3NVlO-|IzeAnOo
zm=5`K=bpl>m5*ssOl|J_37#5a?Ic=w)=PJVt+wbf4R7RufOVktKJxTX8|*s}j8VLV
z8@%RJ{R^eQm12%J4AoMCmX@Z}f;**u%X&)Z8>V;<GRGMyyeO1T1{xcSN4Q23LGji7
zfovY!`CkxM&*hw|gAih$Rv9fBsOFB}n+9y0g3{I)a3Co%$5Jg0742o+yW*;+1@X)K
z50lfy`c+8auJdC|6D$zId#-KMt8P(KCvQQofdJ|FAf^Q@3s>0{A<#Gsi;;mi+CRry
zHoa$A(o0W=%n`CPzQ1(aF7|O|d}_Y~g7Hr<oiNGm7Cg;g3h#_WcXmr>CHOPUD8O9=
zLaEd`oZt&fzDWlM7czjeSjVUO8ch)#(tCgc@uP^`SmZDD95*>FDgT#c(gmRs)cvzo
znria&1?JMwufweBabIdsh@M6-3d6uG4dg}c0ofNuSg|BOpX9Fz)R1epJkAe?$-qS5
zIvZX3gB8dWdSLV3I=2x<j6hy0=%U`IWeSn<ObyPTO$rJY<yd2Qu{3@OXIOtWJt&wU
zj|HEV>Cno`zWa#*<Ie`x+lhkpVB(V2%;L7m^r!QBmS3UL^fIbjkiuy){gre0r)Q$j
zo(+7e3n;KpoSdFaDmbwriqK<HBnTmxLb(0=`aYjIy*SKuU3szd&cpX@a$oM_`ChqU
zlj?22LXiN4KSZc@EBo1K#^-UuOubnHnS9U~1gSA|yjiL?=tqh*FOLf%@~19-c78rS
zV?ityn&!A!VpDGMG|N%W7KU)NU!SG@{SKepvae1`5jyI4Q{94)Ee+kF)<&X$<WhwW
zDab0;s32quk1GQU4LUHGUq35->&b5;I~Zpcw158eDd^gzwFE&ewj{+MsAOkVBqR#W
z9$^g*F<X=N+&@o_Y!)|6*K3hOg!6~cl6z$d&A;?7)XKo;6d39N-6F2w-ryp5c;^M)
zyS@I2RM<TZ6#)V355%MsDeQ;#`^kLsi=M8SAQ2b{6-{LHKnVGK8N9fZy_CB7-=|q6
zJFihE)%0T>1|%Jod+^O^ZY)+Y7F5#mO`8!F51yoNo4STd6!@6{q8u5iIm@{78vj0G
zK?FZ2+k!uP8A^oU3!E|{C;g_aDw9hz)G3I`zD`&~)mp3J>%p%{1RBuglRR1FiqFpi
z+5D4CVQ1nKen0Qkvn<K*`8X?4#7RP!|I(rJH9Y~jSfp<Id&*A9l$5-DvmA;u?xj5^
zp5<y;12-`qM$NW#WIkmmTKnc{LVWy|NFve?C)noOvWt4>?@_yNY8F_1R=3Z&X)Q*x
zjA8$oTI$XD3>%)dWE>0e!-9veovOXno<+UXoecT&?Gyrs*K-x-wUa1d66^BAyAfQm
zV$!`i3I3j7^qmJ=tCGCzfHB6NYGZ%LJ+@cO^h$ki+pG?AL&IW$w%Xv8jCF7p=Q{-U
z?ZNVg9aOe{Tozr|NDcMAcE1k`=C@Xht@pvx#YwA_L|g>;VhYZI5^y15q)?MnvVj;x
ztlKs6a@*1eBQvazM3JEs9dH`Z?d`Zki(7aU1To^&K48co#Ja9bwd!Cb3I9IkqG{sg
zPR|#E_lICFEvag62i{SMHWU;RfawE5M6VQyyovEUuYh5<k~Oz3fCn8yMbLa-TN|iu
z^fea#s=Z3un+7ZfSTxb(Gv8jn)ZdBBEz-(3jEP(xa$*#Piv1pV{t>dKmgz7As~;6E
z+)!!hdCxe{MG-1>I@4xV_r>lrMfJOZR(pMG#esxDE^AR}#4lDVYO@&@deVsn{%)+C
z57ePB2o+k<q+jB&W#t^k*}dZ%8d>RlES5t4d3#p5GVz*|rVr((sr=<ZownQq>J)D9
z{YQyymNWJON>C=>;Rg1$Dr0wB!I#9)E^&SzJzW~}O(l9%aoy%j3OHyN6`zZOO`|L}
zPP(gU0^#znD^p-LnPgX2zt&FPTi)*rP}9Tm9en8e)#zY9-q8VOw6*obn%<@Zhn?el
z!)0@2+uO$42re5@=;`K>aex2V0qyGK9pmFS75gQZRZ=2e=8VE4fyGa-?)T2u^D`t~
zXkpP^@s%Ytk9^-LzAb~v!VVTIu#ecb?{`+fDO&mrT5YNA_t<%zd0vW8_YM61ez4oe
z!0*~AeTR(ZN3k)mRX8niy{&A$+a;JXZ}wuOz-%yrb-Wc;Y$(}vId)pJ;q;ds-}DS>
zUB9latOdOy3AAg`ZHF-|#?=eCD~UqW_Dd|5KSYbhm@}N6k6}dkMdoeVG%^;FSG7qE
zE?(<x=A-w@_4*eaC=xS_n8HSWVzpUG+ev~Qzd@q>#lh0e(xHbSv2|i;+MwXKXm1l7
zNGh(xFso)+ysiyOZsSswMb~rtk?q`S2T^F%_&ig2E0f_vWo@|n)$ilx<fiS@f@tZ;
z=+RNo-XYErOCcA}i$L9d;m0st&RYRtZCxb^WCV8jVj`al&V3~Q-RU6ReX^Ic^Uo8{
zg4AqBM~#gG#pu`3SYJEW+#Q7&Yc;*i&B>f~DZhFB^f}USDv_I5;1RMAA<lSy*QR^;
zEdidf=UMahw_4%-x;9dSr7tiL6Q#mc32A8p7iGB5CVDX<kJ`jbi*1If#^`84-PEBJ
zX?=a6Uc0(+3AC6(lHBkVq1M;ytohA)WS&E@-`|4gQlnF}E03)%yTi8!xzVuDE(BaD
zGOstfsH^R;$mK#Pet@l3?nA3A@-jx4sk>X94aPsIy-4k3*mj__Zs4+2W*?3j+XCH@
zhs%<aPEGZ1U7MM?`fKB3U_m#HP3A5AXll`axRTLsO)EyE@V`phvAQ0zsJ^vny-0C+
zX8?Af@9tJHD_zUKs*&(hE!3$Mf$3M&vFg=HD9;DrLV9(X=GHRF0LF0PYCruty$ERa
zy5PzfOTFFAz8y>nEo3H8TRJ>P;r3Eic{l=;IR@!BzYc}c({)%oPe-gT6Nt&aHXTlD
z`ZF>>u3ZAeY_DVU{wNE2HsQ$dp6&nQsAex8Z2#tcTphr;GHA_}?=x)mmU79aOK*=2
zfDuV&oekKzXq=rL&I<9Ez3z8aRjF+1ypAL0%~CLhXf}WKXG@&cbf(o36Ws{E>Trp=
z?)ae}p!!TmAZZ$7O}U7fCe-bG%J<`kc<);-w8F<bpZ#T?`}^@3fB~IvtL53=y1Ly}
zQ#Cy`FYPtHv?=ZV^d95F*x(Btvv4OB1Abe`0qRYb?@8p!M}1TrMD~XP?F>|v5?GiX
zHd%;D_A(62Y$7Ba1_o~ynO(k>?E#k(UUXIp`la<^r`*GrMhXckT(7|Yh-^(41l-al
z?c5Wlc$*2QlU0dp3kk3Z*5>40dB#}1wlj-4JP!&+M|cS_?7JT<a&9DMl5&SM0x}x-
zYWjm4Pne0~Q?RPEvda{U*0s%<=Cq)6UyGzt${A%oPBZ?RTYt4pV_`$GT8P^jkHp1}
z$}O$@&5QqK!a&dnV{acR+c!7rjsXnKP!YxJuL7;`9cQJfQ-e4=`_h34q&+fL=m%Fx
zS`kQ?=}8XF*gl*GipdB@qU+LH*;?o<b9SkJ%wla^WNgOHG;wzZoY^t4VVJ>Y=wCj3
z;3|e90PFG}hyLmMBW!%@%<%sZUHP{<5C4nijgOb--(wb~I=V5b(zrf{<)S+h6_cbR
zL93OjWGU8aHN|mdlVyJjO+HS`&mbY&@g9^PCPx^vMZ2`;D&i4rczJDdY!bh2W+gjj
zyMwak93guSmd->SLuj*IrQSu_^>wwZksdG9H>+|+g&Dr;VeDZ4<`CYBLu<jdd04mT
zsqaDldviUC*H`|PzluN3pQS}l8c7s*_#Ot-&e0}q?~|1c#V`1~bI;djm8Vwq8_RcJ
zy*x9jP^I6!TPZnEY#y8tm~gFX?5}(OrRe*K0cqXs+-9m_GcRvA#yLa`WmA3hXFH8)
zD3Uq{B}0-vR%FFvCG*Ak`Cj;Hm#><JS$<}UwX~sfDCu8Lr8Gn*IcUCu|D#IBB-`2H
zgcTn9ql|sRm9qawg6qM`!cz^+;n#oVeZ>zBBg{0p-y3i>oDqFX=-$XO8W1PN<P`rA
z<Hw|Y_f`hi{k!|<@5^;_HQ^$H&MT&ms?M4gQ7OmqyqH!D-)WH7+4VMW_+4p-pi2I4
z11ZU9#$P}X&`h2~@MuWWp^lH{c=SHbP*<up#;Hiep6W48E7LFd$pUJQCf$0^7>Z4{
zSm5!^QLsL}gSOFm$76(fLMhQRSQ6J;M~~tvNe<G+hYS)H^o#vVXNzE}@2n;?ScG0y
zp3~{>$UWC1uQYhsXAvl1XV+HZ6a<Gxvl`uP8+d4u<@3^tCHg+ok0|0^1Br1ZlnA2z
zXJ*}e<uBFTAExpt_9cdc?VYU1XdD_w`^uMtQ*`jQvQVjqQixu|TgSR>v0-iD_?nE2
zmG5g}ivEoXl;QqoQ-sr0^CVE>Slx^_LZbHtO!$M!X>h9fzXl4#(@DHPJqs3PS~=jE
z<H4E3c2#qBV@5ii>co3fN~Fv0%c?2AXYoVMikp`AS@|#A??}|24C57D-hPdVNUq=D
z&^Ect^j<2&S|@(C?jfoXN)RiCCvncPvaQ|Zbe&Xoesx$bQapwT!;87z$UGj^STnFo
zhuE{z>R?)W=NJ4IFM?1&;x7BW%lWxiD^nu3sxochRbf1gHM?+Prz4_Spt(U=)(V2K
zhh=T57-8{vYizzk7?!;~qL8dprjCBtSFxftK|cu(@zkHyoN*uVql^C*_mWq;Uzla?
z{8eMRN!Rjo_vV^Fhx{@<>iQz7e65N2a3RMkzIA=x5PHu@mT4AaMTApIax)H>a$G+8
zQMH9fWMEcC;KItL9Jb>;YYP6gT|91fFoFHyTGK_f&WmhgdewHqEesX$Q-gR)^gCst
z_dD8en3QhKX>KmO@F?(*?yob;1vC7A6G2p2w8>;sXJj@OyVem*25#$D&@M)oIUBTo
zE7Wd(kwIsyeFG~a_L^qWu#^yWzAaA6)tx8j$HEqDi`&DsiXR-=*>L!hIvo293diW^
zDGj#2xXREsZ5@qm<1vj+EhYp9oUuYoRKd#4xz7}29>;5@WR$^FrUkFC(a2P|N}{%3
z{lSTWglBGw+K0YYAhpk;A)+JVX~BDC#Mn#Y(oGG)ic)Ge3DrLGOFuShQP_n2Zu{o_
z4~my=G)FtJP?=W*=ACkDp~52pDzEg+CfwVJp<&9+{&3a5v1mFqc+!ze=EQ1(LUoTq
z^`;miz8L3A@zh|or;wIZj{Nxkg&4V~p*YRvyQ639@)3eDhk@-gPGpaFSy5xeokQu%
z1X+v4BXu6{v;;;|$zqHmm-$w)GTlsPW^`KYe`ZgBV=5kNfimBDk%+3c>C?f>V>^m5
zGF;NOdPiGlJ)*fhx3NmLX-Agl=3FtF|NQf<m-@#PF+GV+3;SrOVw4N`@{Aecd#W3*
zdvyBg?HGnX*`-@uTD%FrtQt3}|BQW_d##kWn_tzaXHS)0fuZJAShICjD(&=jg4kQq
z={WjZEJDBsrNfV<4o*gk!HD@o`fLJATkR$<@X`cKMwz6MrArCgnfPDsZ8Ph%u&o)5
zjp&%!iTcM1zp<16gvKNNEY{d*T~7RWlii46ntGAv@AK_d_Cm#8yhMVJ*reC{?r1UH
zFW0VKlTzv6Sy9q8Drv`|^A_|{he=kH3LfaCYJ#k;4UJ%QY7TvEH@3)7jcu2dzkxDC
zXBSJNRqokw<)SPEqVVJ!?<O||>6zze7~!LIWIz0Wl)VF#CC``dTUA|Mwr$(CZL`aE
zb-By7ZQHhO+h&(t_0I2q@4P$nX6CMWYvoFu%##_B5xFxWWAD#5f2kKT3dkx7Lpp#v
zLWH^=Ov_j}@6Qh>p&tTZRy*NfEU>N@0ei-+d=Pj=&2nyyj#jyvSHK%7C=2xqSZ$PN
zR<c<&EoVFW<j-$L3vx&(cQ2j0k=M!`%bKhTV}Ema#17_bV>nw*|6H9g4A{1_x%3<M
z<}vOp!*sk!FVXChKBVfWMefRYz^4?mgGz)GkDRe8>K3gG;w0!TObdF*wlbcvUO9W%
z$uCF~YaCE@b;vrMsWF108m_*5e!pyOuyo9<yi;k18fAT_=yu97)#*pRK>b>${W!zV
z7G4sTZT&T<H`Ul-U*ocm(y&?a2*SAV==72Ev#_TiYzdDfQb5vZ%W|XWWv}BKI0y()
z;lFq@v;Y6_!DHfJpqDVQFf(^1U}E5)|4)7r;*JLH^g<TSP6{TD!ge<HcD5$A&QKim
zA|^(5#wH2|jy6s_Jpbtcg8zLcCj!QQPWju$)`{SsD=9dd7+V-Q+c^>dJmKm8+hzk4
zC&z#Ebl8%XP*KDgxh*J2;CzLsM~N_o@Q-(9G%yfCNHSN55c0q<Zx}<8j;E=m!JrA9
z#fvbmB_kS_ffi9x=kzD$BGd4vqahBExXTh<Mkxx|Vvd+5rOCXpb4CAl_u{A-%JB>&
z2t*M28gTo-{lx%<uJ532%3sw@ANVRmNr#n!&j>^EctF%)&7CpHbGN57=KzboHuU0h
z#jO)~8uR|?eiCPMDb6=M(e;+mI!af(a*U|sRn?JsVc+)?UFW}>eLIIWj(FB<+t0Og
zWV#rgG61d=D%TL=PSQb=>`LBxPi8;&z^tJ&R%KEk+G;*5-AFB0h*Erzuj`t%oNcHK
zOsAD1(osim1%W>OyLHzen(n`z?fcNx40p?E+Q;eA1Vp0ie61cGjjmuHwMyKWh2m<g
zr)~sB#C(>zeIU_cp)2pEmfe0J>#)!rV<9@P48Qfa!TtAH(D|4fn&qqgfxbq%gA^Tc
zS-)v8F(T?xMg__37cu+9d?_)p78${#C&g4E)L#L=OLVgUNC3ym-hZ1|z>e)e7FvjM
zu84?b(q0z4&CHX2(QNU&sSs_4xps`ris~F=Jjoj~1fqJfXHGi4rvhetcm9B7(({SG
z{lO7Ut>!w*oG^Y#Cz|^I*Xu-lw^g=wO)BdJ1Fl0>Og!0DfsKxTe|p;_remDaTKuA@
zLy{sYZ?jZMuaqNhZ}VyLL~PLs%E5<Yl#{H>#@W#ww(z^vzIx!lE8#SKx|?@1UK-5B
z$2lkMr<5LBlSG2RSf9j2rhrd>$E2143DyPlky5gGyqhswMLKTVM`x2i^8<#bTW`fS
zb}g1^|8~UiUL&?}h-!sl#)^#E^49!8eVtne2dUIU;@2gE(&Xe2V|dsp%LS+7P&<+s
zzOKdW{fSEn0Kz}7h?~^I6^X*^<Pd3yG|_>)>5e@9+r4=#_|s5Mx0pL}alea0j1f)U
z(Q3^+&T?%(1akns{Ce_;C;f~;DdKq_V@RZ9Uqs5F-Eu8^QFwnh7>AcmuuB_k{i#cV
z_TB}9Qhdb?QO-)fu1uhbs?6~7u<EIz-P=s7EUzD+(tgE|PW-<rs6^YP&hR4hg&CX?
z;3<PhV}FI?Sdqp5FDw_w|FK;E$r34v3(+gf8aP=Iu>ND<{s$gg|HrmL{bSqyzg7%j
zr2eN?jQPK|Vo;0#2F!nN#t4`hn3(^4@8>GZ%38((zt^)!6x~8RpLSjTcMqGJGAvPI
zD>7CYysbl7skEU{7-*ci_5KfsHFiPbIZ@-JL2y0-i9yRH*58oQP5$TQt&J+~-#2_0
zg{GEtX48>Qub=gC%w{v0?r)PH$KO8LoG0HR-miD00GA))a5^9X5+D%}zzi@Eht1t)
z0&shcjVw7KON)!GI&=x&pDgjyi-MY(X&cThcyWXI^YcnMAtt7#Z%(T*L1Oc1T%nPX
z59~zlc;obPK)@v~>1^3zz`oOCV7Pnc0BqIq_qzp2*6RQn_NkgQOFe)o2_T#2<q=w6
zO#<ToOG_Et&Cbn@b#!z9S_1%3jdz^Z<ON{Wc(Ax@MD4ZP8Ou-+0A$wE!WwgDkf2l3
z)~3!%%g+AV@tMVs+q~mSnJ@{HBncs|gNX1K=VD-FRP-0Wuiaa#_#m*lhX>9=$hM7y
zn%jtd-ynUBdG7uAD;MNd?}r~vKmedmumI$^0NkI?UbaO5APgP-<m6=H2|xx0(E6hB
zc(H<4NSmBkNRO6|l76Rrzj0UmjkFGVUE=@qX8h7eG!Fn_EV?*4I=az>L!3RZr|#tI
zf3?2Woi$jiuPjY8Tgza^BF#zyBte=1Vp?k&wZHljDU}ZovQtuAAJ3NnIz_k~_PMed
zYyb=&fL`JO0OSD>4FCcgfE)urvCj4Qd`F<uPp7k10AP{;igF|XU%k`)3E(niq^AQ|
zXHEcO34pysqucI0moHl1<^A~v5Hs0sb23|Rv;h1V_j%_CP@^$lsonx;aR3M<ALmOI
z05veccjAd=o=*Tq$?*%o+?|M{5@KUv0Z6v}Q|x49Vpu!?5}U<X7&7^Gj}JY#Jb-Wl
zz|P&x16VgHM#dt{cH5l*9S9<%7d-T_9;Apw(eSt7;+&Z6m(Q8;frzhz6L*n#fG?YV
zx0j|Euy8O8CZE>}0H^YNy_J!aM4BTS1_J=;=>wGY0Gd*|bSnVwHvsghr1JgcwjllG
zYN2EXpfUis5hU@W!tT`=zmLXI5hdlAOeZncD7C_202C{Lgcd+>PJsxQ(@`WF10CJ<
za<vWsS2+VfRRENsEEWqT0P`Hj>uQ}f$+G+(u8Z<RtNVTND#b~s>rM7$qS0X>fF3dp
z7=jV&cDv1QZ`V31DrXD@^`6_zzqz`N7D0XGvfF5`vX*WIs2TZ-z#t0X{iDtTKwbdw
z8c8w5px|r7`madQU#y($>NcZ&PREmJoKD3&x_H@qf8GF$V=8=40GtDG695WUtZsjQ
z)k!?TU@!>qT{GPQVBK*Tq&1{|<^U8D(l|ivP9)$T>Ss+>ez-M2MX?J|AEgQbptrEb
z`Z)mC2LK+Dh5%?6<>9aa03W0bM1JrplW`7l#?DjECC;JG{5VY#_i1`-$r7aav(hP>
zW<QfW`vHk1$<5+%cbv@?Jl_rk5MltdUUP9N%Mx4~0JBP%1t7_krt}X_i71JO5kOD_
zfH89?I7raSU?pitiBXvWq%t~1xpa~}IZ-(>>|XW?C#HR)f~JsqT2Bxz094CGQZg)(
zWxLChoHCgQKon6~M?pu2$7YkMCwtAU;6>sE)YK~%FwA@jxmExPX6zGyODBT50^}|_
z@tf2^SneV_@%R{z%UMDPsX%2Bz(`HdaDKYf45O+i2gt4gs%Z8V%F!ZcQP4cqOpj>%
zzzjfCD~C^aSSwxzDXv$4vC}^?f}|ipg9Pm-*8+q6u7Knu$)i#N>|GIEjv{94D3IhG
zDdvu}AsPM~Kws>2aH6F<(w;=%5Apr^QhbjjLA*$XevkK`_opPks7$q|P%Xo<4eIfQ
zA!JPDG8#sD`hSRTOtSzwY+|GA6A2laRQoDGv<N^mE1OG<&B>vUiW>mHXaV${MvX<B
zF&MxjOHDWSluWA)0yo6vO>=j$Ou?E7%OvmntF*NAbgm#I2N}Lo8TfbcxK^0NE}x!1
zfk7mwuf#4Y-5r!RJ5jt%SVTkx0P<9{OB-*SIQ`&_VMMY4phs(|sutN6`VsWR7cwJ`
z<pD(X%ArAg8Igd9fC-R*0MoygL}`fQn}Agg5Ig?S_NAr+Lu8;&L`WZ?2l2w=J%|*b
zhY}ZCF$=C&ohYFs+>g}<PJk(k#!dyElvJot4CS{CAib$j#87fU=nXv8j}j7BdcG*@
zc#M)=7hX%|?F8SQ9Rn+hjRrWscxzKt4e<iWA(jnkg8N8ZBiuN_`v#C<KgcduO^?Mo
z#3gT?QE)^2qK<n&^jZ@H5_wxG+WD@G%r(Vg5Ci#NFPeXQ+W?k@3VGL~tsg5pnn7m9
z8SF3!k};yTuEx7a1WLX3;$Q^v!2f<;IXxB{6Sa)zC=Qm2rcBQ=jEm_j5!^P2iz#3U
z?kfQVm_k8+k$&h#`QPmTafu2M0x59=F^iHU?Xt#5^QjjA)snuc7tAK-$NI3Qbk>L&
z*ezN%e{fyOI9H41O#(_Jm%nL&i3=B6DfON^;};0Ob$y>x8y^q^0g(ZmuK^+?5CSAn
z91y}-vVt*uw|L}hhIu@s)eMm*qP|@)I04ZswCEF19E>lKr>_K<Kpd|C$&cJoJU}jx
zZI!_a0ffF1)rW6Snj+y&Yvu(YEJnDx(O5|K@Jqh}VkBf+z<+uJu>nUj?16<E1Ie@s
zmO2a?|ESOcBEmfW*M3uA_`Wuy|NXummOxBA03jd(P8A~g!>R@>arNQ9MA4gesu|TU
zePu^pBL*xdc4CP#l_QW2o=e`eP<+ONj@3Zn(naB?$~MWj1_Tr=%k~!<2?%lQSV0ss
zt`{g`GrWBliAb>b!|MS|R2N}P^YF_>Qnm&&G+cBluM`@vr%++>XB>eW#ks#kbOtKK
z&?c5@;?xgT?0y5qQ#FbLKzQ{Sr2@l0C!<J}u>=Q;-6ZUO({_r?(k3n=CgKD+UJjM`
zHR&Y)`ZEC$62>8{jTKNtfTo8M_lAIyxXAxoMeL#YsH!X#q#*=+L>#G+A%XlvNrVu5
zfr;8g^N#iEuzqqN(^n{pyhZY4M?8gzR{oROLVM(q=So~oJm0Uy>4`UqU*uBb6;bU&
z1HH<ca6T*IM09IQL<Qk^+@0P`4KC4QKM}4B{um`(LQ=$=9~n(hU+~BmUH6CE_p)~A
ztnHffMBsT|Lw=W8qsH|@7P_IBN<!8$M4(yLL(RBlk8`reAjIKB`2}g>=p6)w9-70@
zOz!{1q7My}rUYA-cd_;(0p#pob^TwtVn8vyBJf&$DOV#x3Y;Or)?bnm0pT@YDSwN3
zuF(1%$xOjTj3ba)egEfxQ$cZ!+Go`&5fTXGr|$BkP*4M*sku*&^XrB7H{*?|bwZ@T
zCDIPPC8>W^d_b-LW<;t`1j0az(})5gpor`K_)!PZo63k(2O;o{F*Ze%{2TaheWkQa
zE*d0&_HUxw>t}8-z$_a9wMYa6!O=88;hQTWgbnn)HxBmi68*2XVJ+dJHITHDlAcXD
z7gZ=|a^q<VzwI(Ro2XZnH_mO76e5w|=bYkct7v4o_a@Psr5ckM$b~`w`3NBO_+qFO
zYzB`;L`WL&$ZGs#M1GG9fK3sQigx4j^N~UqpkuT%PZJ`UpHTn<7bZ3@2ObK#i$nB#
z(KOeX#5aQ@4uun#py|iI5&{$ztFt!i#nzu`!~jiOU>YXXl6*Q7%f>{tIK_I8f<I9j
zRLYP0@&^TZo{#kkT;)P`jiEs_nPKw-QUarW@yv+CFs?4EjKQaoMdyWrhRI#dE)V~W
zh9#VN^9iu|O0H^EG8Rxm8n~h+);x(Zc6ifHO5>OT&1j>%#G!0|2}DVPNVzyx>NM{G
zmWc+Hs$&J-i}5dm@Nb6UpN(A7FK~d18{vP^efb|@09cqf{(YaIDM?zsUjSj`#yh+X
zGA!~v^$%_*2L4<|F?bY)awCCco>IA=-Ayx!v2Z|mYV*dQDIcGX25xBN-QJIZK8SB7
zH@rRz&=GVb4&f1;kW%>Y0-~Q;^-`RndU#rP7IWT_)q6Z3!HsXW^Z|x^<_ZgFQ4D@q
z)s4N-0trckB%*$*@h6(w5%;zi*QHyELQU5f-;?ZD;UiTd2F48YW6xfj?Y+iaIFb7$
znm<LVUh#bcld+Y*fn#Uk+E8H??y<`G*VjUn(wTa)!t(URf;;7?wsK7=tA1F-mlnYl
z!n-EJm5t>s=2<oHDpCiTxKRK85oc=N20LkaQ|Lf1>G}xI_@w_uSo}S~KR8tE;~~i>
zdl%D+VR~?uUA#LeCh+dpFsg}<*^j(rqwa!Bq$|#PR`iTU{4>w+sfE#Tib@u2?4_t<
zA0>Yq-E$}rEsd#XNT-1+IACR;GU{Z_v(EpKNdGw@;k1#TLdc0kds}%TnMs%0<vd#-
zmz;n`xVXqc6~FJJ*SOFMfwvpWf@5NLm>-oB;JsBF5mgW+XoW64`5OOpyo($?lri(4
z3!LNMMY}5hHgtBkH=+M0>XlLW|I#UBXJr2m@ufxXkSfaRE#Fb8n;U9#@!K8?RFX?v
zDi^cmRmww{2#DY+2#Uc#acKn1!2Kxo2?)n&Kuj}KR0+yU>s%{k%gy;q&C7dMJ?kq@
z3lrKmSrLYhHJ@rL@AMk3eY_`^d#>J|IbAcT2*M#qAbtY%-k)G4tP{l?H5-SAhzPrT
zRS+(2KDzd~;QSmhe=>fFL$5LpKrDMeP&fS|6>f9a*~roKp+k)(gn+*L{ectD;qT)S
zJt%L;;~hEEL=-3t0ZfQu4B3tHLEy&Xv>v&ZP#Zo5K<4RU;UM7_|9yl`nabpLt)67o
zdNLMimZ?^G0UIOR<tI7>?j!WO%l&In|LE98vnfw^uLwG7BE7=Vw#r$ZD64gLN~28)
zA0OflI-2R?0Y=ej3<pK;9;?Ksd5nxh2JQ9{GfB-cs*P9;0jDIFK&v*+V?2(~mI6W_
zFYZ1j{Nh(ukKnG6D^xb<Gl3$NW|u0i(7LIOhDW0e>i+Zc;dtrbnXgZ9uRgZ+7Y|;j
zcr8WAFO$}JY8gZ%x&f@L<SI97qt+a8jvDo{-hujG_&f{Tt5)PUQww$W@S?FwXml}9
zCy#F9CfiUZ629Ur%MqeMfs|k}t7R&nuU|!f%4uY-FA$G$3Dn_6j}%`g_}7^AJOP?3
zBJh>4c&U#nJw!T`@PE^uR`5P{Tz<ljft?XK#ol^BdVJvuLePXjY?G3~rCGLxYRJ%_
za%ogz*N52?HY4K2;{XeSA_)eD=7y?=s)y|bk}~>ZNOsC;AR6w&`jc&wh#|rd5^fW1
zlNk?c81mfGG5V!S$Pkes-yko9o(;n63mc*_X1t`PimxI_hZYSo8FJS7Ye=plxdigo
zh1(OTAx95l^@nLlR}wEHV?&r364!;TMpoIAHX}e0#cK@TJ3}}qEASp}5jG3Yhj{TF
zju3tu5!SiQ5Do8l<XHu8qqReSlRBc~60gLAkLgq9sMF}JJ)Kw7e7$Pj)IJWF!Smau
zu^Ntt4#ri8Z+r9hS*9=PRU8<i-@dYwbvpQM3+ex9@Lv2-e4K<Ji)o|fyju=#3E$Cs
zc}gj`63@ZApH8*CcwOd~Dlo2@h&LABLDZ=KUS<hQT^%asj}=|jtoS|`&Kup|;;X~^
zw0_DZc0_`q`U~EZnq^>hm70r7<yu?7Gl~k_pLG}{L%1U5A3!D=SEo7v8oz(kg8^je
zi_~)I&vmtesq&+3EO*wbelHQUi4ZaEPat+SU8f{beeHGYZ`#D7%W#TO!&&i7D6On6
zo0DO;5)?^$?lyMjA-Qo%eH6W@pcB;}z4TZ#UKhFc_)pwgnYaKII}8<MEq#4$Wqoud
zbebk(pOY@;9?o42ubftdy5q42K1E5}#F|F^=gg0(fIDve#F;P2@_U|r7m|%(>GM&M
zF13`xm{FCB1cKch!v=SRjOi3Z=J*lQMFO^wN~>I+BEo#8h&mP@Xw}l{@(dqe$=Va7
z`nEu^TY2clsm{#ocEeXs{LH!@r|15m_m-=f>3GZjGhx|`lj$ezn@+~s=jE#8f*h%}
zsLcT%wuW!qYoaMQMsTPukx$_-ZUyr&tf>iEfALGWx0j=>6D`{^D9oKelBwg?#aF2g
z+8h*e#^lrGF>SO{ik3J%5AsIxhmcTdN&P<A);QO?51FZ`_3H`t0?Sl~1s?WM@{slN
z<UXQURr%AjHOq~|&n<Ekz3Ao5dXB-@1@!4fcJ?RluUtq)-{3yhrJ-4;JMiqB=yy)4
z#5SIe<tmTrF+Mv?FBqqIG*KGZEX;P3GjNSw7Z!gwMe!2bNNG(J(;gQoMWO}8udAC5
z`6b<izx$u`kP6gyCD_wNm_a&W6obpDWQ{V+kh2oTg5bfR;9$#OE9^^4Y;2$_KY#6i
z3@4|$&RU6lF8b^sWus`;r;n;Nw5lrXgiyWFC_sW`XLS2w_Z3O{c@`N8RajMA)a}yy
z3MqFB+2!k?Sbv9N6GuH2;9sUq7TH6+hAiydf1p~#+Pz14YWSJ@rWwhqQPj#?^NipX
z-;0Fu<t>~EfXCBxX;EIqkQpc&<~Nvr0tIBwvdH9lQ3Ohv&nfLcf!I(xn}dfo_QfW`
zhmc;v8+Xp>)zR1o_XMgNgfrH;R%_<v_#j|Qm!B%=jbhOh!rs?*!7ku%S}{=(z=zi^
z^86tcFSc27tXPQSbu`8MSpG|?Q-sj=mx#Ps52`w@nvQYPyjV|VRn;-S>J3N2_Rl$w
zI20RR1ScVT<i6m!fSI<!6DPIv3HM6JzW#HAg>ymarH1!JS@Hk@r;~9(Kvcp2lLZsW
zxk@_+ZbB_1_itB=wmLMD{SI~ywrRJOIhS;2YBx0SWlCyQRYh^(_*AG7oE@4J;yLj6
zjXms_vik)mTHGS=Tx<`b=U26mq@a`5L#2KFp4a?5Xug7Lny<))T0O{XMeSw<d0(*x
z`2!rYp!D~_$5PC-IX03?$w=2J4PKxs+W228F1*olk>P<cU$kKA!iI#Ii8`eG0Z=ws
z!R-?5MdE^)(et5+5>z0KLOv6#aleewLIB_O^<MXcjJaf;zK9NEywUIMo%46-Ay(Dx
z0HQ1Jc35*~BaNxcV#EoGV4N8DM5A*lMVOcw&aPF=*1k4e8Iz4HVke~=!3)<oQz%nx
z%2W$my4s7-i=PS9oc;|OCNq$Q)4NJ&`VEiVVwn0()xACD8HIGVH6Hw#0TGWmPhQ@Q
zXFNK2vG&{SnWG-ifX>4^jI}&-yAx7UDyS@fzP~owCCxp}dWmy*kJ;g|P5f=cvQu(z
zR9<V_PG^Mq!*RgQthq>a>sxdaG-!F|Ceo6YjiKYu`U}rc>lmd?>ua9RCBhEL1GWus
zCbJ8Zhh!8j=81c0YVCH{M<7;-Uc2rlByzlo0w-0Wx+%O!C$`}>BJ6`s@dkKE5Bx0A
zfmx~t+ywzgk@g%HJQ`s2EzdMbi%0{i>?F`Ipma=?0E%~wY4kNtT>?R}xRp)cCdU!r
z60J$+2-SfhT6M+o*Qu2u*IAH<*r&*F$xTec6!G=}GRu!BQHjuj_!o983)r2P%M+0h
z%G)OV9`ki@@-poSxz2$uHmf-eD-BKRp04PAfX~*O%3oV``(i%F+6;EHfw7Kd`?l^2
zDRMovFXXG)(;jp<B{NShdcEtl`a!JaAomSV7!4z#ww!{T^q<Arr4ODF?{O^k<`-pL
z>yNv`gd!jw5kk->pTmrs*ve#%b`FP2F?gCSz1bi2`}Fb*W1Apyz6s%5Y*c9?)RV|(
za^}(H4^;Q`_CH-M--efRCSAK<PFDs>x*M8hgi*r90OCn*noz><A$<4MdAAN)PW=(9
z`3-8PxFQbzpQHQLFYDee{JJ^T=VLWCG}oYGMZ(t=g}bO%H^w*S*w`RY5Hw0`BC05i
zbw_1E3LsNKMLT-kufHGhIOP8H>~6YU*zA^UV@QSB<*UDO>&MGX9r3`&deL)l8I!Wr
zD^JNa_E30uX3AS`irI1uRGsEzBA{S)F$?b(Anw~Ywnb<v^*0p8#)$dHh~Wq12qwXj
zB2mz+>pRq5;{<St=;COe$CZIJfg%|x_cLvR<*$K7xz%@feVlm&5wbAXE#7ELezULy
ziy1mYXgY)hCYz>=798)rgC_ZCr1GB@EV);7sfo~mK(>T=A~O&F<Rn0>7h!|<dAv)e
zvmyBn&b$#mJzb`))Cy|aH-gx_ojK|<rLntR+zRvn%nnSdvEy4|g1$~6?NoQ{TQbH|
z$y?Y51$k4~>=TL`opEZ9q`2BkMKK_jyeFgQ!TZCWO2Dr7^O&Aqr`-(*@h^A~z{as;
z-Z#ksqlz3@DC;(EG;P#r+w#>4j)Xm^5VWZ;6fu??<Q4LV1`<3@uNq5jMh4gQEU*r%
z4G6Zv?36x<s+Lb2OyqkutI!68^}?!7HaqDZw37uR_p3q7hL{@c6Uhqi^4TGCHNmr9
zLk~W|^^nU|u=I#O|HX<o@GIO&3;cOTX?xcFgCd5vqGn~u5ApmLJDV+JydgPN-ESjo
z_gQi5U6JLv&Vj@n3Su^5y^QR3T~ve|u`pd?rR2h?Kv{$;`0;!NX3Y}2+28%BrAvur
zPW>1K3AKWSZ4oAa|N8EsQz;}iOI9qGE+ml7jrC!1{{;$?%PWZuv!0||()B&Cr>T<c
zqF#yx+e`dI4M`rw`wFh}2+R=?+z0n}k?Yr?P^}Ho&(;gFv$`H}u;?JOuB;%CS@wtT
z7*^OdllGZh@aQ5ovL18UTwCtZ#z?DRe710;3;$XL+k9ZV{d<HGQoAK<OkcM(vp2OZ
z10MDxhDHni2@H<JW~_O~-S)xxOw6_!n!T`zw=ZLZ;x!?!`To4h>->UD(S_s9R{@9D
zP4Z1pIvFTrEz@kr&RP&ez1G@x2z02q(=8M&0igo9oD|KRkwSH!*u1`_N)iYq1GJe+
zY|16fHcJI5ve?{72?=@0NG)W$kf3%)wNSzBQA|X03N2z@7;q(5^32e3IB?G6k0an;
zJ$$~FV82X8c91Vty;&l?mzhHZXFT*DB8Lcc*pHntJz!oWH1gNN+V(-Dwv;{_6db5W
zA#4Hx4c$T{_kKCu!e}Atud(rO@WYZ~mL78`rTC^dq}7NCt|W7MG69=mfzn+8#-7(y
zRzeD2Ug>Y-rT}0pgo(feGuX9O?9m}2YKOlVo=p<DRRdxith{j9m!D~}?(h^oivAI5
z1+sINq-BDXab+Cdk>!J$l$%z&(0f1P-cO*CC@pd)`oJ`u>r$@6a3=|WxZU@aW7s=d
zq8GE?6mHa|?0+^fwoKH<u=~V;5rFO2qTa*mID8`pQ=7z0j?f<n+`Z7cocAtN2CUG@
zC<DsMv+`J<qbA9a6>iAw{Kjj0Yq(-8sD`wXn{qj?_-jKbfue(>f~2C=SYA-q1yQR{
zi%InHr>Y5UDsKu)KsC&j;eE$omN^Vv)5^tSt>8C4iXEQH<}1>W9trm9&a*I$vA<jf
z?cA}I6G4QbXHM%MUsumd-w;N(+rksv2l98<dj5T<{3nZ6)o+77Ifjl|9TfJYjKLs#
zK64QEpKS&950L3GLb34V!Z2_h81PuMZ%GY@ot!IYo79^CIvezyM&pmFuQS9qjGRbj
zu+65;PmZ5?{rdKIa11??Er%?|%|MT*gUOr=DV0YQ7Eq;vMbfgr?o@*xKvWeg33=Ip
zIqiFmNe^-ot$!zT$p`t}upRE<0XJ}>zdMG5_wOKN6j2=!DbhPtmXh_fo!fhE333Q-
z>3b()ThP))p*q+Z-k@?Q?gx?_Y~8q_9j6S1Uz0+HRYmgr!fTSRt$@$EZuqs*pvr<X
zW<AEu)icIx_ak~?4Cj`QT?(o?sQZzS7Ff3qUS$PI8`-MgiU`Wz{yNpdh=1CHk7hGr
zInFEk7X2)r5Y4Qh3X@xTD<&jMA*<J*6;$&xdOJy|rJS>MxMr;&7F%mlGuhfM*h*!m
zETN_9S-{iLM#o9UJCl&Y3fE{X9h{)~G`#P~r;k_X>m*ft4lIZcr&;;)KF4XsWwz@r
z#h8?*Nm<>(ym;1G5%;j-_xR*bnF5K)Ud&ItgWIBYT!ctr_xKhz2<EVTJ_J&~2HUh~
zBxdU0!@oW3YExLknQEYi5}Z5koE&Pkzc5SK(#+C9j~Q6AG_5J-F=5>~VL6*l|1>9!
zQB6!#Ewb~HnIUbbmw`e0Y`0k6EjZU9&H9Q}?4^FW1;3|0uDUBFBy1AhqKj5WJM08P
zPsDAyn-X1AQqCR=XM<ARJ6b*d0G`&gQ0f(a4#ShKSXi`zLUQ^;dfN2m-~T&z$|(g0
z<%sR*lIliGT0<PHYx;BX!;i2n33(UU><uXK#)trZ2N~OG3i_0wUT-1Z#ciRR6p1As
zdQ^&OP`EM>`yHoJE}*u#hh$)$#7k?@asLU`cy9Cc>PNX7AEiFd?q!z;&okmcl(6U*
zIZsD^6Ou2?C4;_!Qw=`KU-`FRJ;OnR^hnWmdpEVl;+5&Pgfn)7aHs38&g(dNZs(Yp
zs8Fw!W`UMhz<PBpdS%ixetM8vqWO?`;vC~1+-{`f1MF|iPsXxR21cT;dTh3g-od<l
z!I%m8n6}QVT<obqoISN0pY6Xf6S3-}P_V+ba|3vA54Q!}Uz=`HggVNa@;cjJ7V>NE
z>XN{b;p+UE{Tu$6uRNoj@-~oHO|PLf5W5Tdle}PPCTC$#Ts@N-G_jGGxJyzswAk30
zYtJLbUizh9hVSdsF@mRS+4Y)0DYCI|*SY*yIh~z_y}%sFQ%F=-Q&Lm!DQWys+D%Mt
zvN_8NePXS(sWO)G92cVbR-1d={F56lt2htPUfwuvnxNl7Zti_6WxYGv@Jb#CL3w`a
zDm$gU7_e{RscFp*lcahIdr*JhFJCbnqaFNk_<IOH|D&~o4tm06kT5WtfG2OG{L!<q
zM_y-q`q$OzV~4-f2LWhenL#;x3)2P2pX6SgRDG+JizTGRDy|=7rQ<B7`(y!#+=a3u
z?{gLObEvz+EfCKO9O@=kvLE;}dX0|>^t&W<SNQBGm(1}hdwsUmz_tQ^PVJGqf<eXQ
zzX`TrJyom5!(Tv9NsLyL;>2U6q3bQsYF+f_%R5!?>>piXR|A~>*xRW%N$CzlLdhcN
z`hEsOzqS+U(xE7X>Ubq#TVDOxP|Y51v*lJB;IOA~sZP4ta--X&Y83XA6W6ry7Bw(M
zYeE~E&tOW_(%0b#InRum&C=A=P<Hs`V*}43992$J=qT<I+n3z4@OE*oxcTMNCi@$!
zGuGPYR_HDWd%(<A*}-O_iIwOvg})_t60M@RhV6;)n{Vs)?Tt00m880BA-cWM!XGRz
z2YJdhu#5xVq>Il0ojyb~SDWtm&fYl5aB1Z|95fxY4P^d_C`6~*v0FX_fvMI5U+faf
z(<aZ+yZPjRiK;fueA#h-ElQN*!xa@%%*VIMNgf+CKK<u`xVz}L>ErP(dr+D5{zIs6
zZKq7WohI+-u8@ubl@I;qYk;<^)eR5r)wOGJoV|SO=jG3eVX2|ZO4g8SiM)nlBRdO8
zxvVb`p9K3qyUFRVo98b0O3z`?4O0ncM)@NKDj%S%&m&2_?tiMPvui-l^VB@EW89xh
zN?+r$%slz|U3-s^t*UWnJ~wkcby!gh0_p!kc-}cR>^6dOCY&b0j(~?E#vvhgjYbQ)
zeiN0Da{XNyYFnZ;W~`FeiOSUAteW1$siX8ZI@f<*<<8kyHVN2Bf8TGmJ-Vrb*6$n<
zqpF@Vc46KN-{?Qbs88J{?~dT}DyzSdOigU0u@o0_bJGFk<28C5YX{+UB64yax(=w)
zH7VKSBK=yL>I{Q}f`o)Chb^-zEwd<vuY&A<J&^FHMD#RyES0}hc|;1u_hc~oOSKVZ
z_kGPn*9=D3Og1E@GK_h%0$m|N3YzrBzVE&Fd$@(Qj$s%u_?mXzyzY;N^N2jATCv&e
z4o>4>xLQR|FtMR<`^;vIFH*Np+VVI<f;hoHM!p!vqWM_?|MRZLP#BXt+ZMgc+7ssn
z8>Ux%zv{O_0q?;5$btJ;CeM1edmL-a-K$B!Q)4%YMviW<#R-Q*huW9N0kwDa#zI^6
zVb8KR<JLO-Gt^vNgc{SA8LySPFFfdhk9wt{ZM@+{NyeAD&aV?Z@!)8{!#)HTfR+A@
zSl2^7ROBPSG2(E<fT2G1%~?&e_8B*I`C%%!pu6a~0>WAcm7c+1z*GIT-NyWL&KwKt
z4%`F2i}w}Shrc`E>+D0smPOoFkISxp>yHi&`~{~Iw(>3E%Div<t1!y@lM(*;hu#Ol
zO^xnFh}oVG%$IbmzBHQ%dy%sc@1k+iA>kaVsh#Yzoqc*g;{z|>`=4HRN;cbSSf?D6
z7;h*)4xp(c8Vd@hWAx<Z7S}|4(DRc7mxI>4X8f5%Tc8=H8@SBGX!umOOx$hS-jRJe
zo%?7`X(x--+J{73Oe^<+Y@LU{IbL+8JR^#%kov|~bX&MdGp%6O)$7?USvOsu@du0e
zoXizGuB&_T0ocF)z+qYF0vDwPaV`>fmqe%s2M<!J?omp4!Ekwp1Q!sF$nB&&30VnQ
zAv8;J2|(<d9tr?ilz%0kG^aeUa%I;(l64>8<YB%<@7j(s^lC!Z6Utxnu3BvYq27{y
zsRU7=!%9y^Pe+1D61<Fv1y;2fc@qy!GRx>7tWA3_S}%PS(K8k5Bko)Y9GGk5LCQRF
z;WUsJL8n|7TzkJNKt-LGQz)B1M*G7{pCkvS=0<R>YCX3#z4W&Ud)v<c<x+ALYMB6N
zpiZ)5KR@kv;51ne$WU6BsW^)+lHr8$;;*Es;Q4j@G(&%7>a-p!UJjUtn@7@>=OA@k
zHM@;%%5Q^nbZGcywSwtnr0zI@D4>Fkh>?x}33IJ1Qx6ZH!#2KFw3R8hXNP=;vEeL1
zzQZ)p$)zF~Lnw{#7qH}Po0Z4#i<r<y;@f%Pn7Lmgj-&sxTjc&sjN~#54p657e;@G-
zzz01UNghclWa$TndW@x?))(l%tMsj}9q-+87<r9uLw-VAQp<Vw_am^W9F9Vg=M4#H
zO~v<M45eM8SCRo~j?$G{iL+wLZ>nom4PUOBQ|>bs7Ykz(kD9O#m4%dqcyJ4mgph(r
z_L=Mo=1N^8k|n%`zjtbocJA^hj?RqK?xFZ-5Iq^Ew;iLHW+*-mF@SoIN5MleNjhHg
z<?G3-6$L*91lp>&peLn0v?}+c8yecw!7@Ae&Kxa|P^vlA$0+a?j17(Wz@eZYt6V(Y
zVUZcY#=V&3Ho=iyFr70&5m%r~eNAm4x<ugRV;Bj3RmY=+S>R($Z6Uja)l)l`J5tH!
zsCvhx{l=U6g*A<usD@?frs-5iv#sje77T1v(Ig6qK<hUc8OV|ZO>PwLN+$j`b0pE2
z6J{sLNV>A0OR<=0Xt^f&Tat{r4(4~RHP%U*yXM+!S`h(dc?Na=*8IF^C{lRVJ{2-F
zDlHD`7=|!4(|BXjt@|w!OQg2{{YpJb6BR2Y2_s>P$lB}Mq<?*Ud;IxjCN+E-e=BD@
zOdba#J0CeEc@WYD&agvH#4{Zu{Dy}{zz$2SK`^MUkep$iajp<Ixd6D`9JedEn&@-2
z5OnI|{Da|_w2rr2k}dnqg{Qm=*e>t(N`0i|W(DfIWUMcBwRTzwjZ<78_nXh*6&X0>
z_D5!_p%kgodLnsJVN$>$&ZXVPC-3_38>6QWO-&Ws;w=+8&yXE3eFNByXdKo)X>5F+
zoV>@~SaDKPqgEL8D(xjxyPr-rVrkNk5@rU>j3&r3>QXSpzSqfcrUTU2T!;KnT*f^@
zsB7fu7)7w55o^csaN|L?7#Ph~(822h4_K8mSCeOn`t~5d{L`O8b+R6f;^QcrggsL}
zYDYb^#M<|U9$QFlRfaBk;TF`R%s5oj?cEn%GwKfRFzF>98+Cn;!e0X;7KvRKH~c!{
z>66W5agP;fuQy*awdz{>SDsa@BjzkvOd+rr6O&X}Y&e+PFZ$EI%9H=a_~O5dvHKs7
zFEX>U{)h3!EOlsS<fG<ng>+1W@DkzaG)0+w2!AyZ0bbwH0s&Dd6bksf#bS3Bi-kg)
zdG^)h?)bF?i}l8{j<bklAq!=#T!9JUUElozzG@=zwili5YL5q;@7>z#u5T2S#fppS
zhWfjjw2zq2D?1<08-6CkOAR5igu09t-h1bl^AI+hRiq-u3|6iL*1J6uwFbF#9fZ~l
z2${H_*J%QviAEshxxI&VhN*6EBU$`(Fx?>p!#M8=EZ6BIQR&>b5<H<Hv*8LvZZL$z
z{w7EDD<=38-JZ_Nmm{XXkeTs4C}=j*<Fibq7}l3!rgQl9adWD=IScfvThfqd&d**5
zh3%S)NiJxgG|!_vy4GzMD`3eQ^l-cB&ev3}h!>fOmTi^9Pify+qBL=n@a)(umg2~&
zwhb3%RIn>8EbpVxo0cu-(k{s=hS;RL7K@ilZ74meAnoSOEFLN6MN+v$bCRv=*ZwxF
z8!M9+TF|(3$=S4&%c50r>XN68^k7$_b#ReRg`Xtc!thazTQ+PoS`?#nU};Bcb>D%B
zbtoF8T&~NwY1y#uCUI4;>E0cmaCQtE*Ogtp(R3J->(wl$SyP(IvNcD<NJ%nB2}!i1
z(LlCThLkM;deJOdns(T{>V`+jKIa>q%Y}uh4dTyQ;_9E)JH{<%Q^A@ohJSBFf6ABn
z#7A%`(ViWp@FNCV@6+th)j@}v3_@w5)WyvrtP>PW^03iTNQ)sPgev%xks(9v&kSx2
zsu*(J0^L$D!q@1@RT8KnAr4aQV;aKNC2NR_AYR;lnHwV4rMV@qij|UBL=hGv*zUJi
zn#1rG8%$aWGN@r!Lh(`S6Mkpv$bJRPE3OPLMdmPgU*I;}xVeMy?u`zLlQqkAqV>x{
zr60nM-H$0ywCm?FVz8v^9rP0V-1Pe9$yY!@a^QF;(4!0y2HT2V{6I&c+y2Sp#-WZ#
zqP(&a_iJo?iF*Jy(yy~ue;3;?|DgTFv~)O!?ev(Uy$vkdQ5ffld<DWoT@BZ{?$ic}
zu0!uGx6WNP5~E(1<hPZJpq8eZDi!J4!_f=K`!{B|-k|B$72HqUpk#<X@xw;^BaTcP
zg&&VJjel9Iqm|f+yJK%2a!t%{sp;Q${39+~oVHPJRKx_Wr-NIq9rzAhS-OwUu&nt(
zE~!JZBNY0Ow)RCM27~rNz&6G?k^g?GQ*g!jXA8K_6VsYE=QF9*F46ELZt3!XqNGQV
z5rb?M`72}oSdVUei@eid7uD}t++l`5FMUS;zTz`sF~46XPao2hiRrW$?D+;!pvPqq
ztBH<^QNTRXl%|&QI+CqM`*jxQ8r(vw*~sdVHV#pozKG^|Uw!;QcuqEBzW$z9iWgPz
zDQn&h!pSyfAZ}XT*QRCYn#w=EV&qcr6jLuf_r(PyKm|_5CO8YNd1ar-`*~{;kL-Vv
z&p)<@`(?uJkz0y|oJkaoj82bEZijGVq|aYTQR9-N`qt^$NqaqxjyU97Q8comdv(vI
zwxqbPs+50(bN(V1>@9E*Fgqzw1F}W0(&unpo<RHT_^1HAMDwE4be*gOPh3rreKo$j
z0&T6B$DJt;hRhum2s5pAXROS(u(hWA2ekBA&emhUX2#ZAstKtXk3~;v+B7|zN}gK&
z?&O*p52q+itnXA0rSavrLp~K;JT&_++QYmxbF}nN?mH3ssLv|n8s2?3g9!L8SEM62
z?2o)tIWKdLbi(;lK@V80wx`1W;i^MN19XjF;NOMN$``IM$xuTKDP4H7{$<7|)BT0k
z!*bnc>jSPrxGcih$+vk4I^g8L3hxzW$tC<AW8DW=vuAC~bxyx#s=J!rpOhZ<nv>V{
zP7C;SokR+g_I3Bc!|6ExKqwB($Yx+Q9Ox~{jh;AxQvF?DWy_1U>sRFY(6ig0cb~y5
zu82P(1Xg)#F~@Ibh-`Iw!(KfbE4{_a;~>V_)%RVncVo}F(d>U1x0?GigbGF&u>x!r
zVoaB=ecWi6Lcui;;n2x3Ec#h(qz?^LC4d{dAiBf#)`sl1aF<XU!%yDI8;L#zxjc3S
z|6{~KF1l!A>SywN6aqY#cSVE{U;V;TZwt_4cqjb%ebC~aS(<m-55(P{dN2M<;}Pr}
zPC4{*Kpj%qp_<dq2JShI57=I(n+%q>W_ynQKV;<vP*-aR)!BW3^nwONy|!t49)zA?
zdmc80PT=TZnHUG9pqD#1lwX4M4M-;UG8v1&zzayvj?9T%u7LD5yY;1_J8)F^F^Mk`
zGEM#>Ml62@eh%8ztw7yKt+MY7c0Z!Iu!MgeDlR@WWH0BwpN-6kI&1Fzz9X==fn!&X
zD9U@eQ)ao%7&Ty25ruYlR6N$1I1&yH<i3otufZAGA|5l?v2y@-9BaQ5%llDE9b$Mw
zvuGQpf(im&W($X-FpiG0%N6V>2j0z3*b%zNsc1f%L7@gLEp>3u-kAi5Q*B{vgb?u#
zzE<xW(WaYRB_8icEs~Xd-vwp8>%W~CS7u1vGNZ_uff?O1dA=R*B3+tXu+c78116<Q
zty36Wx~WTu%h8V08ps?wV;FF;JUc%fNksfBlk%!DCteWrFkqszhWBi1&JQK{oXdSG
z;e4kmI)>$Hz<~QiM5afj#kiy}ljXXfo<vialBD7=vT7Q?G4p%hbgu8_<oyM!G!W|i
z;CD$eH7i1nU9Fv*p<2AIDD8ldsF^5uZB73dsKM8!wHS<S)KcF8$`n0DsHgZE2&{=J
z-Z?XiU|f>rs1z6#WyMu4RB-wqI~G_qqew+gyWE<c(TAWnhq4^pR%lr2ar*WS>KK$I
zhKJGUx5#?X*?^|eW~+#_v<A*I%~bI)C;Ay-<)ne#8lL5Cuf#Q7ll3G$LtvNV^@-50
zhu&GyC4vVtsHgDX1qLsMz;uUxJvt)tkHL8TCKLWbr^UYST9x^=JUm<V8!DaMsj>v}
zoLa5*Afbjs2uuq`kHv{4z!{*)Ta=u8jFVu0#=+bokxITstXbsNpdMH2ZfclFrTx#6
zkg^tLI?~<f-E!kD28%3AezFKQEE*u|nyQC^%QZBr!*JGwMlp)e$KVPMw(yc~E8qoh
zPhNABt^LA7gDrTU9tFX2*0~*sM^vn45wcX2vW-mAENZ9>U;v1)<_It+nf2-auIo7B
zNKi*lNbl5!zC>GHPIKXCX=q(?j}-EW%L(ecV{S@Joh_nBLKlYWNs!;i+`DN0a<WHT
zZ>+{#C82(7={?a8%6CXb;HQGYQ4z?Q)lfVrvwCRU$I{l{Ow!fw^AbHyL~V5uLRy7>
zkd$6w*k}@6`%8i<UTkW1On$7yc301>VNFihUO2iN>AL?GRRd1Q;Zx{%UY&GjmcEE$
zq)X`~7c?-zMa}7j4$$&!Pk6|te&jijIN0|~V$ud7eHRE{Lf2?S#L)UNufIUqzn~0_
z9KnvdCjrZ)mDO*HQ+6SS#Fm7To;9qP5hp<iy0FE!iK?IUVEqy1ktp*7<BacKw9qOL
z<}e>;R`*miy+v#AKI`C_I;(|5cXcg&C36*8vjH~ti+cKx_Z}9QE|KsoI2`|5UCWXy
zDlf(xp@)JoL#I#vp+LN$sE3kYm5OpdS8gEne(8on`0|FLVI(_HeG4$ms03Fn7Ao~V
zQUXi5?Lwa44{$IrB>2G!F&nUM0FEz2$Y0@(0<kCQl0NLENA^P^_=CeCaoc`}q6ON{
z>nymr7iH)sx5!A0)5bh#VDo3#0*ZQk#RFN%)(_s#6RyXI#C@X9l17Fxi|)D;Pn<Uw
z89QO@;%D%Y8X*TOY&g+LvzM1VgK7}4%pRy8N@O_7)X0Qg9O6*EpOwuvq-c9nD=lOD
zcV%Q&4vhzFYsPQhYOvqpdY}A>^$L{Nug{5hY?AVAup4EW2Y!?zCZFa|^SP9QrT7)1
z|M<aisAoQY@vy*7y&oz+a6o1Fh-T)EZ&bV8_d4DJGY+<s5^erPYsZ(p<l^<O`ILBg
zQ{1#V9VeGJ1P=BN*lNsIc!Oe!E}v0}Jy_KmJa+19ISDyZuP+5g&*kUVphTVWk}NtL
zB0ik0_7KOup5u+G$)cmDcI}wZ!@T~3&X;$3_U&<gr&0t*^D1_+ZZr8#VuNslwK2c7
z#Q?NeRB(Zpiq&D6CiO!Smhul^M8gAqObpmN!iXVMhFzI>15tDIOHppo7wFU$GHn|5
zgo-kRnPy#VUj7FAl&{n}WVlmHTO0L2EZo2TM7P7YNk}c5@2l-<61{LyRE(1E1^$5n
z3QFiZ$_^M0CEto6#SToZ({rsDT&1>L{oZ359CLE+BN&H4i4k%PJBcw?Bju1D)4=(T
z=ytmyEg-1GS2Ryuyj!_11MIt?`!y=EnyCfvN%0eWU7q@VPa-*1vnjhzGtKFzI%Q^j
z$}}}p1`{+CPFwHrWhK5+-qtq}FMY(_{;m^ACQ>_fh^s2&I*rrzGK~Rr&QHumE=N@z
zdoK4n8jVV_lb1()s%Jq@(IWNswL`CZjd&(f#TU}(TPB%<@!rH9YN@aAj7ii?4ZTu-
zuv2Fx=S#Z`7&?-lN7VDkjC)qzk}uZrw(Dr<J?_F{GbvIko=%$6H3vw7a{OJl(nor$
z2W1uSuQ~YD7!OD&0WeSwk)_j(Cg`bIm1oM1H@Pj{?R)#;Fj?PGtrV$xcXV<U$KKOF
zJc}BcpwncB8=Mp{?os1!E~==W?<@HuQ6O*oXw_~0tW~8wRysV->j=k@`#IdBYW&!m
zZMB+ln*UU>Zl@7mnOi4V#7wO@B^DjN<vHwE@mosZo#v|MYLT1C=Ug#cWnX8<kR|)2
zOgLb*lb}Ct9P<0HDY+5kovYLS4HPS;8N5;Ex45owRqe>sLtFz69K*dLotIKeL-swI
z5Fhl=w3peK^V}4VA5AYe6%MMkK|%pigYH39<w04j%ubVN_ak4MqgSl!`nL=QI}4s{
zyV0of9^HMiJDH3?!ltV2OkkKq>PL@Zj+gC!(FbPxKQW>I<32DeJLi9pS_{$u*FaHU
z$u63V4lR`tLr<8*h$5{EG!MsuM!=@9M8HNH6r!(x*uKb<M%4IqCQ5?QVJNAhvq)kk
zD`S{Wk`4%lBx^hN`Rbs7c)kDg>YlvT#q4^``JDOOd6HT2YjP4JNGQ)y_p)}=<g77i
zdRov=kbZsIe(ehN_$H5y$5YfhJ}yUTR6b)gzl<i0cU|DY2lO+|=!<25B3^8P`ktQ{
zc12zsv$|$(Vqu{Q(<#V*Yabtr*Q&VcO1cL8Dop9!%{?SK$Ha=;a~Q@Wi`!d#(JdtH
zAdAQ4?Wbtq%WLE_#3t*uMe+UQZG}J4scg=zasbH4&Fa<F-VG<;s8i>~b=1L1mF|AM
z%Lh}+(#3M+AKT;aC6`~FR1&zmd-6@#7X3UGsg_;7b!K{I$*CMn6`1Hl9;O9`4Qb#f
z?C}Qsm`=ea!Hj5=_QUa*eHx6!X^NBP!|8iz<QRUK1Cb^wk+96s?(v9ekdqCQ#->or
zN$%NoCZ^2vj5SP7Y3`F=roaucnuDv1a4OS=rjQMZn!_|^IGE(7qz99g`Hm|Lb_{fr
zTEj`2Bb8=s4A^P1k=PAs4h#k;k(l>5_hCe0x6P1dN)~rTXB-Ovrydz-nVZxp1}kR$
zU)#fc1D2X9!Bjqn#rE5qCj&acx(&4rtxTEtc!rdkdboAwgXn5jm5!CuTA!SEX>k$a
z+Ze2_D%a+=jnAj9RrL38oljwD@|TFiP(S<MjWS0vvA9hxA(L{)RX%?uuw9=y-0*NB
zZd4tb9kzfPz6v@TfRL1|AwYBQ{AFu-!phM1@OH3`G3P$2XsT;>`zh;O+G<GUxlaV#
z?vEEQDT{Nh)NGa+U9p*(7@rMvt5ZtWDXFc$OxYh&%^oTWxl&dc7!2g2FcR94G)Uq-
zL{u9RE7ZU1<r6(f)q;9m8pW%es$Y+O-g=B6D^jmMtcG~3qZ6t_bW#=x{9BuvMI{_O
z$z1i%F?8#2oSP1DYhtAj@-%=Z7UChrq4Q#1354BZqZv19kbAfFQn2xh*o?IVoc!UJ
zDZJdJqI2dA=QsB!`_R_i&%NpD`;0A@9)p(Ms-}{5IK56<!$aXbDm7%nU;#^G4-ktF
zt1v2JN@`rCw$bra%R0R9@;m9evlx@|kN8axiv4N6H{I5wwJwW<6D-g7Y1R^XSC<_m
z$(l}e`xqE0iBM}km)7#%JKg2JxJ1qTOq*N6H*C1vYFjF5YF#6`jY6JXWtXjTzTt}>
zrA$oc4OQkp%)$8aL?B20=xS;u!lQ0(Kcw#p)Mu4S)Ik`(grRe@WjVEWHRH7o`=@us
zqjs0E5AD=y1L1T(Qr|3G*sW*51IuNh#SsZwTUZK`O%sMg-7#_Wp~w@(k=q!RH4r3A
zQ%0Jb_g$6t8<xu~b`;;CJ)jCCC}buTEYn!Q(}@>rtEx5DK$N$6nXggCE7qK_9d|<0
zvJK5%UqE+{5rH0a9D^Yr*Hcw<Kr8u0gvNv?5!AET=u=TGmqIj<5wh~Z5?M15{EZDQ
zur>Z^#x0a9%k<LRBFw#|BK_2%8O4P^lWv74%h*<n-kSbL71i17aRmj#VM-;sxZ~n)
zFld}iZ<sZv^Z&!yJ9UW?Z4H)b+qP}n#!1_@ZQHhO+nFbA+qPY&AG*g^UH75J{R^=o
zcC5WHr@L1WJFYAVax&yonHzYrBu9XZF>-%b5ir+;NOm}KvdL4KF(`*-fR=qDC7(5d
zbJvdJE+*DqTbJlI9h#7$oT6>@V`_SEbn#TVbNHSibs_7gpD(uTFyDRStI_=Gx}f6f
zdLJ$C2FmRdL=Iq#Z`bnAPj1b?Cp=qoON46UDZWy4IaJWnTC@AZxt_F21>-sZrM9xD
zbBsE)C~C;(-6DT#Wh9joz>A}51eZshoP%bZouWO+fheGw>|n!iaJnqvVK}nJGc#rs
znfVKGtT4HQMTI~q7h)7BboLxj*@5x*kQRF#uXt&=YJO6UvpH%(zjNn8rD_2!h@ps<
z#t;_JpXlJ`;HTj2I%|pOsmO7-*+2q0ofKU{)uH`CBk&IKN9z<u!ae*s092%tCRErt
zD$JFsC5^mm(H1_1S+j|sun;j5)};@3VdHp-L=9i%*Nj_ReBR-Ec)C1(2T;C~ImZVZ
zLs6QOS)Y+6PQ{{{MV2Uq)L#0I@`K^=RyY5y&;6BkBpL^=q4^Y$B$dje<Ywpa!!PXo
zKS$7ae3MSsq9dQAmY7pLfwo+iMm<$tug`@czGb=a$-?u3{+C*vxLUJ)>)wDnC$^m3
z;>p8~<0mD(unw$Z0ml}k86Gw^s|6VCukv0tNdyEdUh?UB^<0zGlcdCG1o4k`Wk~Y%
z-s3~ele_9It|c|i$Y`lxc{CIhRKZxae2rqbSZJ5*m!ItRB(dLLKfpmT3G?IfO4z$h
z3Q^gCSuv)%{(TMC6<$8W1Z*G+O^3u1B=1ToIPe-MM!h2yHbiI$qZzm?_YZu?*t)6N
z2^~f(91iBl;hCbWKi0Nuepzt;j;Wh-#;ndIe4XGtv>yQ)5j%VNZN$+6=u#^DIw4Dq
z-6doG2xaOb0_Q$;(05QT>C6}W+D?fW->rf3mXRvGltBHFoqRVas28+(+SXvz&@nKQ
z+swyxB$lCh_&y<}d4iQ<@M%x`+L@27=Mj8^QE}|2FUfWIRmi_HH>6|-U~33HHsYLo
zkJH;;_5lAn7YpZlSRxLDDu^+#teGoxb!~ZNcevtW@iruo(>QpMUuxxV`^?SBsj(jG
z^T#gpkghJGXNeb1mj&6E9nK#?@-wt;=rvM<$5C7(jNhQ0hYIh)MIPg2)|w_L#Q>+k
z1__B_Oh|)p&H^q?;aI6_Nhy5B@Ce9saNgW3nG0wQ0Nf5>TxXqDm5xs5A3pjE`>If6
zJym#@XCZmE!3#EJwqDm`vSVF)KyI%o+{PRY-Y!2ph}v@29~9|_b(<k|>cFmBkMO34
z=FPo+w_Xl#cu3mW+qfdV+XL@!?@g!jKF+6uC$N7wJ*3qy+FfJ+c1=0()UE1te?5Og
zQ>#4_df39d{_(nK_?bDl{IXlhR0EYEF8w$kc7DLIBzxJRkrFc7uReL(Ct<Q2Sh9$R
z(dFDvc?Sgz$=|;yvJ6~CoCow(iMofB+9$4Hvpooe2VHfcb2_Q<yIo0Jo!v!<6LK;)
z0U2}pbVC&%G@R*IKjZkT*|^Sc5#^lqoit%T)Y6p<S7c&UFob*{@(>^MSnRxJn?J<A
z-smb7X56U}Ru@3^YmDrD-Xuqnc1NhSvo4;}sps;*j2KC#{f%48ma?b`x|XJ!s1Tou
z4%Q$b_LsHtD9C~H2LXaHzB?=0y;x;LkfPXxw6uhDet{?-k>z04T7?(X9mHMIr7!c4
z+Im~WsSW~5<XM6Ad&&|ER<Q|y9ZF=RH|jRy_3@mL*HsBX^uW7-7X)d<xYJ%$|HA1}
z$Q8is^WpZ5hwt_Hwr7FV-OTE}yZdY_U3HI3K#RCwNv%IzF+MjrH!}rL<KA-jq=pqc
zfEbAJ^6&^%qR#=wQSZsuWQ{WPd``#gy9aOVBJQ&7?4AGkYDmbAUnx{PTv`$~bYJ}#
zF8rJWpk?_W=*NtaTSV&ihQ<%0X{AX_*~)4-YofIo3IBecfsG9V=nI?DpqVccTHk2N
z`7|_vrlC6`fvmz^Z%F*U2`{hkK=#<3Qx-$r;AxNhI_|(5T%5xx-k&X>o^Rww{5ZET
z?jh9NwEw{x7jKag?xG<9N_s@OfEB|~lqow#MahJ+N=keU#PFomgxCHNjW9^besrvF
zD}igzu{@NaV?G2j=67skKRy@Z@^HJeX)tJG{Xye_#DRp`kCcr=yAwcM)9Rv~A)KKy
z>@nrpWo{)c=fD{%x>&P)&Uv*Agm{c_NL4`{V-$&#!(7l9Zv@fw@0<QEPX#{H>GPV1
z+5=nh9`XM4a0W-%Eur{0;x(T0XiAH$yNI?XjXyJuoOW6}8;3XA6j%$}zVjgF8`4ey
zo_pWZaPLiPW0yTPS0c4`M<cC1cvbrtUgOW7xL8kkpP1ZV)mmSID|Q0r2qI=~f9dgu
zXt+S+Q;~KUucS*PO`#Tt5}e=7uAF5qjwzppGrD7+Mq`Em7M@5YcuDdh@SX~>U2bdE
z=D{C=*xx2LoWcH}l|aB{-%S6Lbl`rSTEEwP5z~G8z|B^6>yg#@SFp*~9sX8Rr#{;1
zSzVWfyrrVzU7wWKvIw(W0JZK|lB-PzbNTteN)HvPxW;W<`$X<Vmy6DDGzG?@Iez+T
zmKxLk8mb~K3%Y1I$hG-~T5D{Vs*3F|H4=5-!TUu7<r~F&)M<-6v3}{T&}*xcD8aqd
zzNYaa%0mfPl~%H@33v|SGYJtBb4X2#b7ghi?q3Cex)Uh-zuWDr)EXt;G*vDuD(#6f
z!zuXtQa{3+^1VjEha^QsEy+=6i8pW02~mIAHjSIkaWONGW=>78QTuv?p>0AtkoruK
zCi&0ue#w@95=xETNSpm-k(GrV$SaSima{g-(wl6B%ntl#u;<oue;P&2eoC`9;0F{^
zd4g2TV+&4Nf&{Y7VPd(;lhBgJp&c4MU`On$Hr~tu5?yFC6B!JJSxyXS>U^$r`5x7{
zuC!=Cgi1c!cd}=_CC*qmS*rbJ>b}kKkWf2--<$M2X_ustB@DMRJUGZDt(5e*%LfFG
z7EYX~&e_PRmnOzuf8c6SX<|-oooxIfIK*GaM5yFsM?|Y{XIB<~)590RcL`%hCywe+
zrG5vCfr+?Y`l!8Z(e&-aBuxq5quDgiNhvcKHWY@%#3kbs^ODm-^S)bTRN|Z2J5ygE
zP%|sX4i3!WpJK!A#-NC&6(NH*<bJJ)GWd+I{$fS^aZQGY{I?PC=P{9xbuqM?YnQvz
zcPIb~JKGhtjMHU!CGUA<O+8^1#s)y~TN*pQ)M<I&C5N?U!|2JoSbLoe<BGyl;E$jb
zUf9pX<s~2StZg0$9N<Q}X9G}XloOYMot&Jkt;Y<Xu;Ryn#_tG{MxvycZJ-5CUi(wy
z;;bS06H1Qtp?jC+uhKerfK@46;rcatfTPG;Ek*3^DQeHO0x0J#$6YFjj-K;Dv3G1`
zP@A)Pt<oHbvcxw+)DKW>_VW<)f$19B@!@Pi(!2~D@RJ@LuAlN>F#!&frA)B<`my<=
zZ<b1wUB4)vn%PoX2hGO*&mGFluul5iF7M02A?ZTFi7>7aS>DhmtXL2fAt!?32*&v2
z6s!a^{EiX4b~W9;m*)yo<%6V+HUf=_``Xwd8>=4GMMozhYhu@`aiyk+WA>yutP)^;
z^AFhfUO_i2>){OPXK4pAe0uaYOhXR2Ei&}RUxn-`Lmw?yW!tjg!gRKrJES~uF`wtJ
z+XD}dg{7^*`>Jk&n|Sx68fyl1JF;G?J6x-_X^nRG=jWZ<TAd!(604dj18)2CAnCW@
znao|{TdXy>8b)R%;vu$LX}H2I8#k^KXt>$>4a+kT*AaN*f0!B@C``)xoZ*YIaf<wl
zBQ@9q!)${1e|!kjSMJq4ny2!pyldibJlxn)b)AaeAL)(PNjvB?-JtYUG+Q4@ZGO@9
zHbUX89}!P7cuA?}pln7=uQ^Kz42>M)+ivh0L$QBPBC~VCbUso<N9Ys!R(nu14Tw&o
zO*?w2>&&5p@69muCnoBPLX0k1MM%eZlZz2?tFi6Fz8e%-=|ZOmwH-6+ngY{q^-Wd8
zu{%&QS`sb@;EqlWo!s90g5uMr9tNo+%}-0TYMR{l@?HDrdQ8}C={!dEUKFo|9;hu?
zHo#qCJSK!ig5vZIG9u!5y#*jXPb*h1G0lac8M^(G-R?>1i-y}=);7X+C3X4qc9L>S
zDNPe7yR^1ilxQ-dV(;Ll*C1zX+=<AqWpA*-$ZVId(WECFhA^;e`-M{JDi#m9pwj*K
zCo^=yupLsHK67Z&CuH^$xZbU#p1u^My7cKVw&6-1J8lL&ZHO)JuLPXhH~7<72YjqO
zo0e@or;mo?80l498(c1@8eX#|31U~YuO-t|<~+MJljqAy+{)QwQBdycc-CixAJ7hK
zVTugKfxOd#uow(wmmH==W*aH;k!AsBH5&y54Gl#(TXjcAYpcf9*tC6QmPfnC^?OVE
zx-#8a+TLv9sXZ_4cghK_uw`~ekc$Ahv@YS|^ga7wY7Ur1{iR~|Cc+<_Ki?fbgTCI-
zVM*xGAuf8H2KTeZ2g`ni0;4b@q%p&S*s%p8DGbKmulI;R+i}!3J~M~*Lt(dxuk$H`
z1Jc9JCa?tD(lGwG_kvx9@E|Jhf%qW9A;At`<Qg@I2%y>cpB(f_cqMA?ee5@wukvXL
z_8Ez{7rfpN_BtNTNfHOi3BYt+-))k=OePA`d^GoCztrjO-@fH*?`}dD@8~r5w7>9~
z(aoNMaj;<6*y&zRQ(?`s3|X8h(PUq3i}N6|z29*EXfmRMGG^RfUZ<Pzy}h2#A2HN&
zt;v8(T;2l+5I&vwi-+#KbsuJ6d-$!!DYrjf5G=oHEpGL{iHPo(1KD%*->P~;D&p++
z{_R+b2V{NISY6>~+XuO+xG{9eb<<>uy<|nb9(c0Fulpipt+B+uw!%;`s~>TKrsGB+
z=xrTN*EzL5F29*Shnr*QT#<1;!OTzQm0`0UTAX5dbW0>S8N{~1sEW1J9l-y7RK#qu
zU;{sp=NarEAIHK%V<_THUroEteqqGJ+5bu#!1gVyDYC$}9R)*FITcs2MWAmf5So2~
zCMK2rUnVAZXshjg*IM^V6!3qcXCh`|Y%l43o`F;B#I#i_yH;?C&PVm2b1q2-p!-C3
z<1VG%Iq+7Ta&voI575-LDtnW3Qxvhb^sf2rXk4QTn98b~CNH&BnOz2}zw>f07SDXJ
zBAsuW$_rKtS`<Jv?RKy3%o#V=Nhwj<L+o1x2aU6tj}**jbY2|Lxn4^Ks-5DD8y9z8
zJ3TIwih2qe;a_3?U3P;c-Tha7LOt+PZ#^A!19!d6cj*nc`7u*bniii3*++)#+l!kr
z4%?32EDE67f4Z$pAk%1_BR#jA(b+R5G9H?~ub8aol%{DE<2OYD+NQI@iU$mWoL46b
z6L}~eD*$QNl!bAVTNf`5u~n@2-;KTf&732pw!UQXV<wV<R}jLHA`)Yu&Z|bNO6XjX
zIgN6sO<6|sBsg$fIGdKR6U!HohM1JxkQj*rCr#P+Fdyj-1w|-Cq=4(JD#E3U+4))M
zUl7)C0=o~WbcTAeAG$T^!f4m#BtwtOl10@7F`b2UO?<COuT)xI+z`R6*bA^7MBAL6
ztVlcOPfYSd*2WXb?@WqI+TRIVyDb2!hrlY|$i{si3iGD0!7tnv+~o90QiW+W<136&
zJl4I*C)s&=<A$Qiib1G+0*JlFB_8twRs#5#B@(J7ifvIfyB4!cn{0<=jo)>&BeUKd
zey92Ckn`MUo6D8`gw0fPs%2|(BPuFGf3K?9(8%NYACd@=`@sah|EX?Ou>xv!H5%4&
zP*84@+`B@{sapQxZzJQPUODKSr9Bbjh^zQg_7d3nUM=w*Qg%MMr;iHG8SEeIaZcL)
zsT!_YE2$YD2acHzU<gMU2VP+(9Z}n;^!5&)5ATq2OmPkoH#QS$j*+KTj(U)2ZAGxm
z4{Va6NztPp0WEnisWvw_!7}Eo{<{D~6Lv}SGz71Tc7pK(W%&S2OUvEd@h-}_%t>n3
zmZlyXZ<Y6^7b;7M_aBPh{{w{he_ixuVq<0hpJWjeRWD^FalGCD`&-e98to!S%@fnr
zl?y1AnhiAG74%+I{P5Nks{IrILO~!T1Qfu5tqz{zz&{XDKqNfC;i5}58`oAoIM6nS
zu3Cm&m#v$%%Nr{>UEE&{N&Z4zk~7)N9ZsjOK7G5lpQgsi+H&K@jhG3WtD?19ymp7B
zvS2+1MkkMzbEsfCfv59P3rWdO$udwc3I25~vV0F*KWF@yzb)^eO35_FRG1Zc_^;9I
zd3VuK;|TUd@rjeh&N7xO&<0e?fhnO++0n)^Y)bEeQmp(e$<g~0?KsDg49A`>-j#%#
z>6z8am&nPNLu?_G#IWl}v&P3bLAsb}<tmqn$)P02FT-jn^NLli{|r>tn>anBmbPnl
z^|A7hwPS-6Db|QBnUTe<N?sH#dQ2rrW^X5NN66uEN=g+17m^nm7o3)aEzz2SHN`~b
zD#p)Pmb@(y-Ax%ALN+CZfB&f%;4WM&(NTr3O9Oo<FiFcy6}c{uNXJ<kXMND#uAVSo
zwvRT6)Fzyc+rD(~sq;_gbDnU?k~*gG48y*OzwKoH$U4fw3F9geK&2MoQmmnGcNUyU
zH&`v7kB{Hue$+D4=H%#01xQc0j?cgseHiFb;`Y=DWSJG5uYphI4G6je?|d=m#(Kwp
z@j(e;3%JJsJ!;kOMxtuP;~yyArQGuMI<TUE2T{(2;I+>kSz~gYy`;3dQg<$jBCC_6
zEWnzEp&1Vrehs~kOS_^TWy%INpp@2kDuvPZ1LdTY;a>fX7L6V!AhEy+3=ucoFhtMk
z2Itlc?-%|`geTl10BJr1x#NELB^Rw1@$zlN-}<|2L+%b$3^ampNW+H*FNU#EOrD@A
zNwOKf!NDi&mrn~fzixvd>rE3W_T#KAsyl1Z(1GYZ!~!*aA5r4r0PvY4Udrp7+WzXC
zw`TktoU}x@&JU(|m0zb!1WU!k?O%2-9(*CD%mSBI2*=?>y`VqG#U)N}o_Hk$tjAwC
zgPZrwg6G#)T&Vaap==W<YTut<i;Vi+ovb$Nj&^5+Xk#nVm4IL1W(?F*;e1dq2*MZW
z0aA}YPkf<%qKMIK3?wfZ^F@|8@Mt$)tbA2>7KQ!@*ei582JPTb7NUu#Q(z?@<@1$1
zRJ%;)JcH`)n=YM3!wXfWI|b;OuBWR!L&W+h`Lyc(a-$)hO19~!DZqPGj<#!R;N^ka
z4mWF(Y&S3|eSd_@5PS?pR9pKF>O25n33f%(R!#X`-GSYR+sZ^9x4Nq&fXq^I)Szwh
z;LA!Ac=bXejt>9%yAfHc)(8UnQFQ60rr&}M7uKb?!;nURE8q#5S@|IdX=vc>8<`*q
zE!Ko2=v3LjwfZ=dz_USt^$Fls0^6%WoqHvDP3tIt_P+Ivo;eyOpVvp|=o5T1>B}&z
z-RpeQgPpcQ1D5NarX3Ju%QBnenfIwECDvT1M2!ZF%wtZ+l8Q5>YUh8I*s^MyW8>>z
z`hfEpOEAiQ($Y`RRZ|dj>f8O^^?=dMg9P5^kN^2&F<hXL!@nFf@BHFbP7sBiNbe?4
zNvI)<>=Yn2Q8We-Jv6pxk;8iKPGsCLyM<7_*Wt+XyxH*!hOR#=>4iKgnu-~V_9WyQ
zZfhUD&7&KALfu}gcBre-Lih1;cU^_A>(%$$vf7>e#V^d1CbPgpk^~AZ4c*kZ>)t~F
zTpMDwJM$hbn&YfQd?h~B+2sxfm(GpNAAsNY3zHd}%k`?m+=no&x;Ny!oe~A?7Ycmm
zs5%X_Qq-@z3Y-R0L)O};$W_<=@kc}MEm3DSXzdg;X{Y-B;sATAs?cdcLd)}5O<qe=
zQmZtAvm5G}h*RhG{Es0qPwMNO8l5VafanjTFt>3HZ7Q5E%U`d*^avw_o&i~(5*xDC
zVcl>-^Uw3m1_CeaRz07)V?AqTfgAE(L-*NGz!jSFsq@L1nF$5bHWTJ{c9xcY6jse?
zXVfKL%8Tv~L!Nu}_ksE2#Fji3gI~-a1u~HRDz*J?VK=Zl*avhHu-lPvN+n<f$W9Z{
zUqk+YWN2qfj=1M`=lOW0_!CnG9rRJyvW9TRF1lxwu%sfeJ<+4eiG>!#U4Eiknj&Un
zKT@!9Xv|w7^jx1_Sh3e}Kx_S1$TFl1Da&gio_FVGom=xRdd|;odEQRWDlbJUF1ac-
z$OS)(xQ8q4c3mC16`s+}Xy@m7ZckxB6c~9HEDc+l1r=}ZK*o~tSQ4aKAzZniYY|>8
zMX{$j_=2<Z+R{B`xt@OnXrapbI_6pJYsrkj`^PP!*){OE%;|JaMfaA|t{SmZypV1X
zna~~4KMnfgIn#$0Z>EHM|Gv)P4hN)Fp+L9QXf~N{?ya`*ecf$5!?X2?cuSuO$#>3a
z&`0dEhbD#1ZyDI0)X@;q@skD}=b}}Iuwv1B_n`ao&bDxhgIotg@rkc`c^&$|dnN{?
zILk}P-`nv5*=PUr^+m7=B59mRJ>?5HZJ7_ig9!B=)tU2fFyLPWYCXE%7+Yb=o!bzR
zPSZ}#XSML0-eE9f4E7)%mH}2`tqwJXmv&g(T}JtdD|1eYV$^U>Ppe92sSSzifT`_`
z(J5aXv`RRbcLMSCGgnilG|@7@;>p$psHQ4uZd&UB+OC(?uc+rznx>f{7hNOUsQ!Qf
zF6y^hMLa}7Nk=U`-^V}Uy`p!sSQUhZ74#YVrS0lnaY48>jc+{JCyuC9F^9pir{b&%
zvdJ-Ey(^58T;$|qtqdXKo;ssX(UQnQZ~nRtXe~C02+ejP%6sdRXBKdP+FXDPq{9ZE
ztwOyDy3*osj1<wJ4DxU#+W#eYE}BD;XPX6~)-&jqH6@Sy#yMSZ0z<aj!QuP7PPchU
zWYHt0#XdZFD7LJ#xB<tC`-qAq_lHDvJ;2E9syFut@Qu-L16F>$({^7Pl+zZP#oX{h
zb{@(#%88*tS2E$DWO3J7lGApEnrZKa1GK23uD``wrG;F$9g_(t<rRHesgBhRw>r(&
z>_a{jr}6kXBbyT;CiPX&thZAUR8?>-q+0|TiM()pLz^t&e#u$5l=*z>%4t9!;B^NC
zRawgKSP0E%jc_Hxwoft|RNL^<y(b^#$|NJ6I|PQ|3c6|)QwPKjlrsz00PqvF1qwrY
z-`>gU$24yOgkE5Lv$1A39VDJZLD?ly4-i?TRVhT(T%(NAE+2Ws{%2hlRnyb}?3YhY
zq7Dxdr2=EaJ&&<)VOwq$#*S)bJQ(Q$9gf|#uC*jp{&$T;aOxQJ2Bgn{bh#7I^mY89
z3sCMbw{yrGHQ<}s4E8E9@lyXH0(LpTPIED1+NI@#Usu`z`6c7&h~|1W0T)c==o<7+
z6+OyPH6ZA;QF|PPqBU-cP*<uU6{!v|WrQtlNZ*s@C~z{pkwSdw|AR(vrr()A#yq|A
zCJDEHWOKCXm4iP2XAMlgc?Ha61ui_M=<S^nlyzZ}_3St^5(`Tv(T*MD?O#J75HLw6
zX)9?~gIZ*^tg+z0T%cb+gF-c7ecb4v!GElH)Z!Po1mX?&|IAuB8UBk=4K_y3e-Ldb
zCuc_!0~;v!jT(0kbsUoyTh?GH6Jg+1HpQyHxr}6^7)CfQ+Kn0MK6-=#YiXv}r52r8
zrRSh>x3Y`Wb5{td0&EgfhD2l-P4Of184jye9T(C3e#|p!8T5}^pR73&+1xX?dDst2
zk8{d7o_$U`ue_(t{#eYRNDv@#G+GT#=QA}<tJONxGl={>`a6|_!PQU2dwITN{8IOE
zkZ)r{?rhnoqxE@paPnXL$U`^fcX!vjze5=J9HNj*NkM8SfQ}`-o(%3j0orR(ips$P
z0=MHd@wC}}`U8a}2ih9Go|c*hSA(F?xf$$n+IexZ$s@gN{H`uSe@fLg<1_HG;+yzg
zpTAljjaDN{j)tDrI1jSdhL4(qtCQ6oq^?qra2vGP>35qsU5`tjwb-qHroa3EeQsA>
z4!*8}X@IOASO&7|$;bGldFkC1@2FZ>yir@otbto7t<&`BQRPV)GT;@IIaoun#AC|H
z6q3p%E&wkCEtyb7MU~(c;gyui#Vf`rQ!~Y;^G*LWm9H-$FY#FNJ)=CcJj1kvd!~8@
zcqV+{K0`fYwIpkb(Uz$yP*<#0s4Q7p!nUMpir18}DPUK~E}2?Fx1?-{*_62|a98Lo
z*<O?|ND1iXP^(iYK!gr94sHyE4*5dlq3dG!ddN(q-x3ca>Mbmu^K|-MZ8g})U@UUl
zo^w35+l_URiO9#xXS3h06u(K{ijRtwieup;@G1QCzrtIRL6dnEnTqSg?tIQAhe-uL
z2IKSdJntjN@qGRzoVO|rNIy!iE5q?t{#rAN>j~>5nvX+r7NQa;kznx`*@t)x5=z>>
z0^mMWVl`=!%wN~5kQ#L;l!8?b3;;yH*T?QZX!OJ^7M2u>nluNqI=lJKmt>_#boipb
zu?2CuyZP%6!s`Y*-5H7|4VrhqV*f+1L3$+#jM^2#7iKD&U82J~C(K82IQ&%5x8AJ}
zq9tQfp*Mjh5TdNti#!%?`g@_5TcmOxpSl7}cVpuTor~HKqFNH;tuWNGcpO6cC^&-h
zp3-8Cyk<pPyGSASG*svP19^QBiU%iNs4}QuzD^84yeet?DcQI*wErco6|$$e30vCT
z4}A-i32XjgW=^{i4zgoehUy)ygZ?*aZ0sJ*J2V6~SA`cl6M44Q4!m>(tKP{?`2J9$
z+~tdhFk4MqjyuoK_%Z}Xzt}P)5V%C;Rz-Jr?L*7`A3=oF9d{>sNNRG@g}@9&PSl5=
zE4DSC*_s3@?M@o9TJmecekb4ok|9RLChIpAg4exw#N;u6deoqt)|gd6rKG$+NiC{#
z-neM}2*63EWrf^;xqxU=fowFPuxwXJ#i}2G5h+4>FkrCM&9YC2eozq`N*UErVZ_{;
z^tl>WX3rtNT)Gg3N3jk7S0K7l^V}?F&ala?rC_}9_c^p`3rsZ}5(Ma3n*@(u5GRvH
z0Mp!(L&y#aJn6FO^@Pk+vO6P8gF3>DEwXJ)+KFXj!jM)1L)OLM5FqnwUjE2ib`^vo
zR1aYWU-UF6n9}He+H0hCe{Nc#frt=;A6Yc4$gU1L%NTb&<8$Qm&!%xrSSQ>|eS#Hf
zn^j5R;TQRS8~IKy?Gv>nX*CShg!2Y>t6wd03N<Vkg^{tYBOksp_w{o3@e5YMwnd8|
zEV2gMFn1_OcyGW?57vy{9y;`VaZDb~pwk_YcJQmLm(xt%ymK>zNfC={iEcQ$AsD%w
zUY6G*aWhhkF!c|UyZxaM8&c>SB>Img`CgkF<ffzGI9)+Pyc<>x8MZ<5$Q+5Ov|!bI
zx$4YzA-Woxnx%2Ru`-(n^<@C@i4rgK4G(RmblQnfXglf_PUGAT+SvioWbIukA)p<R
zcV}cO#HBP2Q!B=jW{ymwx7wiXUw;<01Rsl}iF8{&M)!hub6PAvBwi+vGsTm>yuR3>
zyTLO1E4UEh`N0)`_^SXumX|(kIAA9e1`A4XXg#U^tJ0WvhY;+Q(d!94L5cF#zi{zh
zJ$n#K&TK-gIQ~JAs?UV)gvW^bb3tiyOSVAP5EWqxAMtxbmuGMFDSMyc^hmyUqofZL
zWqKK1T8cI1rds_6&F|RsJ}R$d3`0H@_gZ>>h>O}xHGe~<vf1wJ!_H?f*otTR;ji^R
zEFJoifB~Y`l~04f07mO9r*_KL(>^yZyZQuyp02tmQbR63yQI7IaClu&=|Co5XU4*D
zg=XO(`MN^5AS1<;slJRKKbhBI!dJ;q@Wn4hAk6Kkom(OV$M?ZBfuPF&`r@)jLX1!g
z&X(IWqOCxiHZ^Tr;&FUoGOPk^BALo-Jz&ehO-@g=@h&g(5q*~$>M2(8P@lW4w)x1b
z^Mi&EBVx-8Ki(GV3Kez?l}E*&A@ym7<M|yAN4*)~1dZw2THVU9WZ;Gl%%paL@YsH2
zlM8@?DqOX0R$kBjWudTiYAOc(L@!_{H|q<b58df_M`u|N$PMN*Em3JdJ4P?#Rk=?%
z9mDT!f9~}fp<|<fY=)AJg_<w~kBr4TL4QmN&UuGjr?fby<b-|K@!8h#xte_Y3fEsd
z>;KN&C*n`O13UO*q6a-tS3a+452{Hhv|HFRZ99mlB@<PwDQGQfrEqy8a!8UOQ=<}q
zB$bU7hURi$oFIM;!pzX(OV0?{{Jk-@3bK1oy8ng$UY?(LO!E_<N`_482A<*_=88`s
zB{v`s<+TvR)F({cBxZbbI0xU{WhZhX?1y_$hNNstRKWAssOr5m91rs-QI`PF1I%^H
z0x?B^?hK|evQkJ0-T~Wv9p6F^yTS$!#lGwZD*$aVWl@?C-Z_O)3Ky_Of0%ZU*zFw>
zn1ct>2GtNT#x$RLv@*Qw;+MK$diCp%0sY6c&TIAjTa-HIS+hx`BdT(x)MD4mrz>z!
z+TMsef9z0#nkgaJbVmrDN0sU^&B25K9BQI6k7+Coa{6h~?-QgaW`j9rB+iwj_E11`
zBUH1D9QRr{WQXhv8<{yKRH60Zv;7YkzuKE97FHN}14j_uG%?v6L{72nwxY;UuuK81
zIh2?Afryg=)W$e1HR1s|Lx|b|hAUkRk%#E88w-@%9=K9ag@~vyMXpGjdx)tOx1q$Y
z+l5t=IwQtX=2M~yk+yhWOeE3Vkh4jMF61%c@~U9W^2*j1dcwSEW1`uHqkwd7MYY1U
z@!il9B?^83OmnS7jzlPc!yQYDf>8r2C?6^&Eg83Zdj8)v?nB`)2rdm`UdQO<d850A
zW_<0vJ1{4Irodm&FPmO<j5ih(5;7DLOBM-f3W)`uj&%91&09;ZT)rqKqPe0Yiq1df
z`V-ZpxYpR|);Dcaa#~H}7WZqJe`yZYmQflw6)F_34eyGcv<AXteot8VS5K&a<OdOH
z-sUJ$f1wNpjBbG!(U~v|zqYCs80Xdyy|;Q4EbHcs&jpYnqP-+l(&A7GiJtmL5Qp#~
z1JL6_ZQ*-peV@L+NHP`9Y(D}U?Bu(^o!E7RyhT%aaqAFD?2=8!6Emo=YS9WJsgim%
z0I|bO%}r(`@;ecFBE`LC#t1<?6s8IwyEz6a$^?P`kjFak&^BH%PHaSQwqbwcniEN(
zu;K$MJxY#B9NxP%5*h#U$=vC?5O;K>g>S}z41jSTEzJ?j3Tni5CE+TH)}6L|=)zV9
zr+-3S!;=y?u>kVNsP+bn9ll>d5c}-Mu4o(6@B%T)brMR{0vCUOh=BY0vTHI2!xY20
zum@_+BI(M!0VvVXeanl84$e2CEsV`)LOc<~;C*vvzPZ^}$h14ILo=^iJb@aAzeW5Q
zt>)@%&3eZkl)<dR*0tk(v-kdf=4V-`gWk)_29SwskbswVFI?>BuP7gK9`abiy=*X7
z;HH$qd&%$mQ%`|kg6HYqXZ|Jwv+VznfSmtQ`iy6#h>LYKetUK>#XfO>K0aufOk4jG
zif-$Hw4-{r+h0GZ$Tv!hhx-NnoC$aXx!L`A+%QqKBh7ry*%E5+exA7QUYRO(JGDX2
zFrnLO|2XgAE+e5GrDzt?Ewo;{g*YNiBZS~2VLD79F&&ssHXm!Y$l>nx?GTa0a$1ab
zbK=PSw%#ecrywc%Y4~AQ8>x1GTokgauIQK!SUzN0Vic-4&E;{x#?Q-+3J~cQD{>pd
z;+%`YX|>EB*)nYI>^yRw%fUpx<B59zG8cgS);aNrp$lP}Hx~DyVlELwPlk717HpVW
z-RYnaDpC|+-$<WTy?#nc490zd+i^_B6<ej&i=EEGZCHAEXpTc6+LpZMbPt|8$BSVt
z5`_Fg^Lynls3^F#?jxpGOG^o0DJ_p}re0taMzd%|Y(bn2*Jg6|;;LBm_4VP%Yk91t
zo-UqG+*~A{V=h{|r|Jx6s38KU|2#ESOo>gF!M0AeMXTw&lqgWBAwjy>B@>ynwPRh6
zR$X`&5>(qJ_s3-My~$f7zGHs%X^n*Q2fxCVA@SYM^!LM!5}L&8b+Ds%ayT3PJ2NI~
z88-I`!X`XgIAk6=_h&g7PbO6=sfM0zn}(EIQKcvbI4t5zTL>><1X~+3-Ls=BI3-ZZ
zmSUDC-GGver?529cWj!s=N^h3Vl0j`M^(k>K!XD`MjYp+lWvyN{vWWh6!L!Ul%a{Q
z_rMZB)cfE3*4gv$NCP1OMWDXN6%`c;tX>pX@UPe8aoU=*grYGDjwpS&(4uE~$Ta->
zKN)4v6Vg`&8FF!cbOvYhJ(tB7qOt$p8Y&rKDf&N(8MoB#xiI3trQcP!77{!C+nkSk
zgXDWzj@^geIX!K1v&np+Ds~AIOhX(oxL%N-SP;LT%~NgKw`V%h5V@KFAhI@H%rgiU
zd~d7Q`4aW;qPwDWBLgFX3g%0R1<W&lJPw<y*SG4`aAw9=T<XZ;x24}?LzdLI&k;sH
zZp}4cF3eeI6Yi5=;np&k0t%ogTrt$4zRaLgK*eb>BivO{y&$)$&o95LR=r>VC*QGt
zD|aSNKbBlcIla8KO!j&=fDd1fkJAL{cI(Uw4YsVC_n{be@PKn$nsH)ji+hA!-e59#
zNU&b9T|Zei@}KZKd(YL!D;R}Znq8d)1K4C6V_kfeU+yQeFK|k{tj}TF#{0mTaz+hn
zGG_OTSRgaWSQ2{jT0QRwAN<&#f%O~i6C8UOZINFDAI8eAG;^!0@9+^DtA-DFKqHHq
z1*iPg%fG2=>RJnH3n*u%LH%>k!e<u>OUniru|M=b>uJFh*}FJ~K2s2vCJL2vlT7gB
z8_*D!V+Rm8QS0mIY+Fa2`K#O8B%_E#JyegPGz7$En91qUrbQXbMY_6qd0Ohp&!gyb
zvxOS=KeIQ?(a_(*9M2j!4aqUN$n=;TF(7$G^_OMdSxN1A704mIIJH=@t+9>d=uc8Q
z4(F{~Q$+g8DRAuIton{F4D)T3S@Hyg-P2oJi%qH9P!aE>kws@CdPPeBF-125=97^L
zAIZK<DeG7^(>~@ilFe}Q{ZDI=P#kN>VfYFr4D+fr+GYNRrfO;%5x230VVAd3=ihaO
z@s8~-93R5E9{3U=x+;x?l+4)a3xB+L!Chws#q%HK3rnGUhd|=^Po{c<w9IS=51Cld
zEWkHmRLhn(pFVy4c-OVHFOc9So{v>RNjVgYm9VG;Caf>zy)MTDS$dOVVq;G85L;2a
zq8y_hprd$2Z30#{VhQkch5x*K{;M;}`(oq1i7udUW%M??dqADvil)NReF)!aRpq1Z
zY|giCA3FBBjTmxzwN4+g3rVV^4CK?OQ45TsERAE!Y0R+0OLV?AI@DA9U03#SLq$a?
z<L5i|*-HLqu4&qLS;L0S6Dg`4qhqZNiGT~#RuRQEz9@f;^ScqRxS)3fm>>(HZ4KqX
zbEW7Oq)4RQ0*iG`ZcPWEMx((OzU%b4^ZJ@Ued{eYxalrWj=Pt~X!!EJPQ~?@N5&1;
z9aAzH+2A_EAoblM1(t287%pJ80JzK@ncu-K<yf^tl%?+vw}QCB7atuLUw(~@wF)2^
zOG~4Ko)|WvMGBC!CX?=u5HZ=Qrk<kJOboDK@`!bsVq@6kI?3)P93MOBW6?Ge!7)MU
ztx!X$f!r>9g>@x6U_MH{D5E82_j%*d(5{G`@>ZRo!-oA@QKEK{R0RKO22GC#Y_40@
zxv{(yQMgBW(7)etvEd~eQ&N_EQ?i1_gLxongz`u?WYGK}{wHW?n%psc`3U{$Z;vL$
zf>T!ckeYVf^0?Is>+-B$>6BaXnS1g$9qBK~T`cpja7!_>M@aJ@OCvKkF)m18@Zwqk
zx80U?C~pygC#GfeMz%!#X|}vW<pGQvQ(|QIvwtru=9Lvvnxk<gwU7@4b)TB#hT7!`
z8ubnW>El;xv%qgC_#%8-lE}_9c!GCyguX!a?KoXv`jhx==-|75&XI(KM4(swmwHCo
zI;hbrjHv_1rd8~99U#DBYSlr%oMeosNx6c;B^lTJ=4=Z`yf2~^o0>amRsg2ZS4ciz
zoo<c&*Jcv~RIJmPqiz&iIB3Qci+3ik-aTAe)6iQ=jWPqax#Z!faOEl$cbIfcKeYYm
zN#E&&t6<fo-h_RJO4Aj;P^UmtpEd7=ns$?VU$X1qxLD86zZXeYXXP2I;Xt<DrRYIG
z%nFCW>G7V%hXEF+qGoeNVOxuN4ot?SM|Ua!jq16C+M!B9kBez<TWwQyVc)hj#}@PK
zid%H0RXF8YR5M2k_?>vL?%FZuqodporeHN}&;v7*GML_M`NqN8wREijxAOX0=Lb#?
zpJRM)84|Vx6*ZJef%*KJ%gm?h^=}3*ulzwq1vHX6MfHqTO0(2==;Z|P#-pV~D3YjY
zCrt~-e7x}JAZ<K0L)JFmU7UA=UZ!fV51nUqWt6_ps(bCFy45En2OE=$Hm=ZRvGGnf
z+DzK+qA1|mGFFDMytfcfgLXts3@~;&&CaVr%-PDBnl=5rj`@T{!xW=S2_B|K2el*h
zq|zO7;U~=%{L^JSx8YdA+pC1QP=XD=*?z{p-oJO)ra(@!Gk3}#DrnZ_HAPK9$y~LW
zVfs&4;+7g=ZS`819;yYj(*2a0zvODOtNq+Qhc`@&lS>fr)>KDwYw>%8It8YI6Sn#m
zTHdda61G9pcA*Ml#KrPP;!(vzO?{e%BbiJ5b*SxYKrEJplL0<X=!vTQzmwf1mctOj
z8s(qtmpeKaC$@LQt$=%QCvxbVDMNb#>2N`F$uve$u+&Zi=_aSOr1_EndOJ6u7eD<B
zmMw1?)@5ouc?VbCb_}^m9Dvn-$hOAjtA15F6(_ETXf3ajiaIjm>?N7A@p^a>y{GFI
z_~`)h>8C$DO<c057du$c`C4ZIBkPyW*{v@kH^5*N-0USxUpc**%%=s0BtMAPB7J_G
zv<ezoGH63%esxeVsAn6koD;4zv*?GKNZQ7QJ)?)0DlVHAB&WF1n>-fzyjbIHfVRIn
zM9CKpZVmWlBQ>iq6D|v%D`R#gokc?hvp;Y^eaX^ZnX`%jy!eC3Mb0CjR6@EQoMoia
zd2MX!EB}nb3xUg<HcZPcr`kMNdhXHnaBo`%imx-|qqn2(&SL;mM6uh!&6J8EV<e`S
zwxbGuBQrc(Bo?k|^6>E;=-+)DqHX0*CC%zzLB7KB%%PJg^p_V~gCqitOuh8Yl>I3H
z(+epY2^>W`YISWi`MraRRyzGo%SYx2&#I-A4IlRki4L2*z$v|a=QG}CJSAWQHT!BR
zgnt~D&7MQ5O`x&t9334>-VL<IP$;@0MjztE6L?{SLg6)bUbQ0mhpLpp+jmMr-_fn(
zGPb-au(}AxyJ2M>A3_Fhs<>r`IgRbj_mHNMWzgrT&4_DAr`Ln=ceis3S1q5N#hRXB
zldaT7Cb%WA7%qO3Rzvn-MtPkn9OKV|ssY2qDcuHlP)sltwi@=m5HbKV&+-fzx?{*t
z7wmtkKK*%)HyBZNZ^=a7=p9V7J6iLGIhFm%9&S<8-d5pkNjAHXqW+}oq%7sqPS3W_
zL#uF^vavcu^MP9qNOBmi6p`FIP~HZl5+zlovlMqpM|4{+BBhJfB}ZC8$qt<!@Jy`%
zXgnJMRE5m`l1bECX~}7sS;@(XnSWR~2j*l3lh#DnMj)*kxU{~Go(<`nx_P!_wh3&M
zVmgW|D$g?pbPd&^y2ipZZByduSo|hY9*1n71NU?e1M{!DV8A-Yw^_q#2X%5e?@jZD
z%)fP}MZ0_sNV|7)zo00Lzvcg-5Aok<S(w@WH|4OAvA%(UzOivK1nm9e6U5TX{qywf
zBs<F-tduPMv?LACc*b|U!5iV^n{e!T7|$a-Xe$4HAN|+V@A44UWPksi3WA950eHeK
zh(IxGyuLHKp}vtZ3c_@)?F|jhG`%G4%#`%3<T5oS8_NPS1B1g1eL2m9%q)c>0}}(o
z$}(tTDwJdjl$<A&Zp8Td`|-$n22_LxC6umbuP2o6`?0{lKcmtYPe}1n^l&yy$QMgU
z{1QaSoc?OQKIkjK@0`EBEU)wG(3|hOXh#I6fEi;YaD*!unV1;b?yc2ZuOH24Z<Y3$
z*NxF<(;(z-+;2POMn}D)t|YrSww9!tii#Oz*j=z~9;L3&4)4gdMOG(g>T9hQ0e^~)
zdcmu}ZUH2?C)umab?(%_D#LZyWAkn6;zwD-xI;tm(WAZG!T!a`KHq>#fY;$w1TO)D
zf$y&fUakwY=O_8|)MbXJ{j@UP4nDLffb(c5#(#+4e}lbZ=VbezZ6u#RS`sLV7<^4u
z7M+@6(pvkj`9QQ<B}7b8kTn!cY*d{J!&g`9>%>jWhuKXRp~<#_6O3ILRXR&k6M=zr
zsoTMY1Q9jig%35Wx`l0+S@|>8ZEd=}PH(@rzrByYr|1HzXb2z%C!;ppR&H0?C@8O1
zEkrSNI|8Dh(sGp(nQ+xa@bzDTtg9x1ZNHBt_;_i@lbTj~-)vrmzJCw_YA=Mqsmr+9
z4Y9=Qql36vaAjNp0gVGF^?U7Q-AQ<o3e)vD$<U-p>%oXMf(F`YQU^(okB>36k;+$|
zf&-2zLVgMFISRnwMkt|%+OfpIiWNat?RMZ+iBT~N2d#v<A=@FPB}gIA#3`56Qd3yj
z*uacg@#bJUWDZoJBIlSi)PR~JQbG2xfJ7ynNet)$GD(V+sw+wq#KmBc2X7IODX<GK
zIFw)wRsI|p3}1Wj6jq>IgpNKk=I;LyjG3!R3~+&B1qQQP8Gum5X_Y7_t_W@u%tzaw
zswW#U3Sa#lS46A=`LiT~n}-l+Amc15W3NY7g3a+5%rQ{;HQR%NC4m-GA1EQVngO*6
zWmE=xfI`&=PIS?k_;<+?b4AZj2J#OPpc!_46WrY@*Vj5607x1L1b-}qP5-rbJ+w2_
z4$x)Pc6{qjS%}(++wJ+$GkDr3U4w?(`f(<-YeSBW7y8;yK5xsXfV|b}KD4-Zk?BwR
zIcw82C;L@>-R=zj2G<nW`twZIko=f;*oRQThe-CcrGCWXv@oskG=38C+SZNN<I5s9
za<|>(OG~b$B&odWvZ``z?V8<86?UB=7BMY2GAHQDW2$1+zpdk&AP=(Z*3p_`wSP!Q
z?J?!nsB>H?@h1v{&-Ar!`d07BT+k`HsRroVqGrEf*jN5;H#hgAkrZ@v{MTMnH+6Y0
zH?0fJPXuiJ9_#$etB9p{qp?pgr>nZZ+%Ls0Ejo>Jx-HIauD{3HrYGBMPWU?)lCaqr
z#+;xLx>W%g3BBVlsB9BtrLgHPG@E&saw;CB*-uBV?hhYMiyq%SW1T#?&Xt=<cCIsT
zkPRND?f>C>`)^k485!6Z|K|snlV~M_qk=MKhK5#PTOUZmJWtn#Z!IOzEEbdg1~5?s
zWztgUAICH`a|1Uu1H;|a-<quBn&vZ=<8!x>D!wih@X+W_nE)WmqKIrLYi(HV+c-Zw
zOg{?G+H{_|gjp%t=V|BYc>A5_<$Jp2r9i@rEU1W5=Uvo#{QPh*+P1@r2qT<sQF~K?
zOf+DAU{x`eu!L-W5f3Pd=~6Kmjgo5d<#LlNnC46RJ+j@u?f6XyGVj7$sd0liy#THf
zg`+Ba;)++eN=Q*C4nCiCMt_!LR5HusrwodUik_zI*gYwzWkbZFs;UaMp}l7Zic<!%
zF%d|7Q5a>&u_M=DNXa3pDA26fFT{*eZq2}&$DsinyOM1U!iplz2?3U#Z3814$dy^F
z9zX?zHUpUci7nt*oq=W%iz+49@y|Yahay>(R*si=z*+!umpmi~9oPxh3{VXhh)T(f
zz$wr~a>!!k{=7BVK(vROUAd_u3TtuzqDX+tLJyQ;&ZX<aFFr38j-{5bf@zPoyf~F0
zSCa{zDKS?KAW|v5#9@QNun07ZK$lV#dVu?+3ndp|TJ!mYw3lBUJT#>+sH=Ug@^T6M
zTba4%G1}smw)yDUUnUx0NK^9ZclYZnn$piN3w+_f!pic#D-0s&q9OqwNgy6?S`0eA
zge4T_9VgY<xRs0~Ma9mZ{2p^-tm|8~Q1PXle`Gv8L~6m!LvEmyHBgWi2)my~Xgj{4
z&^;gV*WMaqH<D&@@_}X8ZJ)Jni~Eblm6Q5odG2}GQ*D^8OCQ2BpP&R5=_Ey_9$%9H
zK8tAmeb<{JtbKMctt1$})83tK4Y8j8wJaJ`$M)C4FCV#!jDEb!5$QlmT!KH770m1y
z)y1%3S%RsTqcLdg^vQc=J=C{q^%`bxdmvJP*g8nA!;^vg1py~1>^R|SOZl&paEH_7
z@T$jqp6*8nfNR(%7}uaJCCU4k5AwjHJ*aPYeY1=7_yE!}x^6mSosc&coje`=m3N>=
zS+%S9tw#9kVDJ1-vD-uwn!5dWtj_j&CnJ9o$UUU{4*D%_l)+)!w(w3l`T+?^&9l7L
z&L~*x^LyG>-+dL?8e|Ti-EJ1WVbXULKIvE~K`&6*CRr5HGls^lrnH+q)&Mf}7_S-w
z>*3R2C=rR$wB*w3`%N3>kF!m{Q5Y~o{C215Cd&^XYL4Tbbcrr!cI6NcakV&(eW-&L
zmQ$ZymAT!3jVx_h&KztH^Fy^aAhz001dZs*KC1Vk-+gogeC}i4s;(=PbbBrRXkk&1
z@I1!wSg6+qbO8uB$!>--LXrfpLqZN~?Et==U)h<~LY<-PdF4qYRl3dLX&u^N`%CvX
zBTXB3#)SkGK1owFI1`fm4)akp{@eb3N;?;r5m*qC&lNq|(#!IHIC=h?IcH{OhW~l+
z{J)vnkqew#49x4C>kBLs3jky~+3(Hg@<&s|a~R7IJmx5VqYVF1x?gzH-+u`f4?0MK
z{F~qrFd#w&6bVL5Forq?CZ|*t7c5NlGYm|0_9HY^6w@NJ6befsD=G{v?X(L@Gz<(4
zDvpy<6jKuNBZ@b&G}9j^WNH6woRGb*qp77tDNxv~=)^=IYM<|i2gjb_EyJF#LT7GS
z+>nQ<M3|8LrGury#w8Tcwy$m^(7xd<WzhPp=l)_RUt2*7Ny~&lJ6|bFMZ7u1=vIN#
zaYhD4rpET~v%OEw*RG?JThA;^_}?Xye@x}?*0XAl?Z;EKf13fn9@DB?^E$eGbzZ4o
zacX@-B4$FymPfzc##I4-r;<;pb>4<NW?s83kkK2z<uYe_BQJqo2Pt`Y&`%!kCL9{l
zXHWOPeiT3e=(U~o{?F(zGW<6KJ<M$XF+3=4QI60M@XP;SBK=GwIW;Z=Cb|Cq%cIdc
z!gQx3e?8KFmVSHh9yjXnR5ifBLU#iR9>Dp-zlZ>cY63utnh=1oiHrQrT~(CC4J>>m
zEgejCJ>@EOB^wG7asvV)oO}gM6iq$FA_5Zv0vc-R;+vHvDwXXQl<uTtW@hN=XJ(Wo
zrlb|`79N+C?k4HMp-idFk4`B`N=!*kiYbrED1(c|D1yPdz(A?n!QaEcK&&nbJb*@@
zC+R0-rvN)66>%k+ng5?4aro&Cy?z_5zv>)E^AGxIBj)_=t@pI8(8ktsH8YZwc3rVE
zi<lXW4ZQ)2%^YWyK2mjky-nvZn+ebWKgB!C+F~y;g96tZYR1n+?%kBTy);NVG=v;K
z-OnB#TAm*A3H%qbSPfo+xm{+P2N1m6A>MCf&w#DoT+SqScRv~wK!8o0V7vbmy#Fg3
zDHGHGwlkok&}BQs0JHUsinCZye^sCZ0vrMijQ1gs<Lyr~AL}2EMB;I`yZR>@23I0b
z9B<rc8TK?lCh&rsgViu_Jsu8Y8w+JHIEqk}JwHZDT(!G#p{epS`A1qa7)o;6Ci406
zd12Kq-@#XzK1<v@)|(^le-(D7(NHjK7#7)=ESYS>REVs@2&u6SjWq_@_nB;CEMv=B
zB1<GPBKsPOLfQ99j5LM_nL(DZjwMaDe7(QE^M2<&@AvQd@%*}fUFUwDbKmyHp3J#G
zmL9rla+Hm+YuxCA5WY^2bq!CB1b~`j+iTGG)nlNX1^w{U+lMt`%Tkr#J5`D|?9y3>
z?YX1KO5(?LA&M2YGafD_;baYUvhL<}!q6bGU~o*Rx3h_&kh~M9`z!KsD6}E@_|$-u
z@4`O_1^(~X+F#|J3TAHJ2rOE`6r`&9r^Yid2n)KRsQxd7o=QrPziId>#~v|#1JF-i
zM*Nu8D({ZM?2TDgH@{<t->^|+V0|C=frU`}*v5z?akxT@5y#^^auSWuCM`wwtI#Ir
zChWo2d&<N2T}jGGTq<eO(m58!!uTghTu=pLgOeiK76ehw3l~&$e>ywpPS4kS0|n$G
zs-YzK482kOb`0<#1lO)CU#`1Dsa1mQT|lO9?0C?UKRT>iWWgBpG)|Nq&$SDbRHv|l
z7CxGDMl3We85ai-&ga~ZUJ}X7(0u@7CzrWcz>V1mOgPu-u_@*997v+RYv)~Vh@sKW
zJGvbq+JNkZ^t-$q5RX|E_9Eg%W9F+OW!;A9>MpweTlRCgjBLqskuFbnK>XdY83l}@
z4#l~iOk=$_UC!<@$;r1fI8k~G1=8ZrPbF&kvl`Ih6-#Hqwh*COn_pJ4r<w8$utQ<7
zJR<CuKAw>^pi5^Ah0ELv28DuAKwL6l&|T#|sGg}uI6d1&hAx<`mAma?Hlqjd{+tvj
z15vcxe$j`;k^r5Zy(lrmO3;4QbpjQrfSYn&_<+(7<lr%!ysLm@zB9<BWxjlT*s~1*
z7V7UW&W6RP3BVrh&u&56Q-2PkntZwHNL?r8C^vah`J@E}hS}^lLd#kzsK^h=zmP*a
zL>}*ojXV`ULp?=-&3y?b_!X?#oLci#NpX7p(1e%$l1vwwyL*)=5A5#j<*NSfW!}T^
zj{L4Li^aKpJealxIs|N0%KmAPo`Aj4n&P*bG5_>q+QwHim2)&h^rXqn2-6V!EPPLH
zdoa82AZq$g%KB;wM-(fybUiQ*Gv(HWNi=D9uDYm-cJ<dqPxa0wkQ!lFVbmzV>2Wfi
zSd)SwOVhl(-acl`nDhwT!<Tccx?2pQ+XP1Ip_1ZcePuD_5@^MX=>(blK<ibsNAbQ*
zAHN3<ZO(a~a4dya9;7jfw<;&^!W+R4g8=mpusavw)bIZF>R)@luFPUqq=4&j%siZx
z<{1dyOAOAjPh=a<0?mkdi=jNv^e25YntnWQ6`kDB%aF`eOX`(w%aQng%`_9o3pY&I
zhTvDawL2rZvX48kOpEp<i<{e!1J!&4u6FJ1{*2mCi4T*`cGS~K1mJoC?M&u#q*GnJ
zR2Duru%YCd32G9v-iOUKRR&yIe;hxcayVk)H!khG{S%Tu#KLvBC1BDY0055?-}~h0
zrDj$I5wld*vH?pv?UL_&bV~Aqd66r=SJRa81d~Tb9z9@L^(%#)_LI+AXdaS}+G~fD
zn`n9!`^^72ihYxA;g%uu7tXJ}hy+j~Cy}>mcdvR*ZiLvkM$Z#Nl4ZV_ISf>mSUZ}h
zl$iA|m$0^OOso8i!gsEmohT`6UYeP|{?cc6onrOc<hfdrerR2XZNtw~m`SQrg!@5~
z@Qk!ygq`Z|$&odsb*1o|i@W#f&l~3KXg{O2@AD2twFrs8CIXtDL7j+i$_IP#ExjFH
zdeni`&vR0(E+<L58X?TWqh8oP(cW!MSMSW7tvz266IJ;Ox$hqfb~HUJS0p(fx{9LL
zPb6DdZeF%6elvCiJl7TYdky8ayr%nP{cMg)!MAj&+T<H)#5GTU9L)K)`r^YE#tW9&
z#KzUlqZ#WDP9v8+*W<(0;27yR`!QEs=B$sV_UmN{|I61Fs>28Ysd5HU-g4g8#*#aJ
zm)UxczXy--voAIqm?2&!ZY1vTRK^vB**;;Hcv@LfXxQD2qdgbLS$1TLvJT^?YnR&R
zzQn{ahzg2Z2|c@)cS)BCR`tVjh%%pha4H$qih)F}Y4NL8&I_7}Rb%0_vsP{X1_-dj
z>fC1ojR!iFmq(jNf7L*Pu|cxCHZq<y0M9Z~EiF!gLdaq}a;UcR_h$I^(l<-UOLSnw
z6Ioh@UKzUe#HsE$9cPI65|h$pC|0+JJIQXyHE+B=Cu0$r`X(CMp;XPd*BaB_7ejI{
z)7bHAfj&8e4c%JWvGWL4C@@G1^;`y8hf@VwY3$U-o|BWd*<@nu(c~zS|5L=y@m5IT
zqVQ++CZJ`s^-A95g`So5&w28cG=aSSiG4?X=i9g{UW5G(4NFA5+Q2!-;<%m%9(hjv
z1L$pj(5G*(Jw&wz5EwHP6B-g2En+G;7n~POx(cWE^$M_k+=fIK?cCV9!+0yaAuo3Q
zz@PhlQ>dlG#~#xw@_49`mFEaK<F0_n1;+mG`3oVuV5r)s(nHO39p*lGSjvzi<y-k~
zKTfNIlNFXA-BwlO<}YA&q1_9=n&L+gbU|~=VJ2Q7<U*##XW`qY+ci65u6;cmYixUF
zT6=4*inK-Nb?VW?Y)%nhL3(vc!DC;e0P~8dI^(zkKcf9PnC6|Or2I=rV1Zw+@CH>h
zkAoZqy=s_gx%D!W5^{yLvS!^F6XMpB)@l^8Rhp8&2c$quNA21A0hKI;uQ;)9ll)9L
zjz6|sqUDFv&t#9xW%muPFN-9!h2}@8sJpg_*?xhWj#iX9Ct$ZT5pfO#@d==grhg}+
z=}KpWU(7Ewb$T+eWALkWPfHUi&|U`<ghWOxt2;4MmhBa!=&m#sEStp66#t;7Oi$|U
zOKAP@IU2MQF24<b7{RmoOLC-PB)c!J>*Popa8$Q@_R#vkhEG=$x3NCwBO9-1WFcm7
z&S3c*HLdTY!oW4U$&bo!km^(v7VniK85T6Uk49)KISLO4xCC=Wq(tlbmf4UEd1pv8
z-7!<1ahl1ih(fN;m~UPclJ)I|8?Z{ga_?#UWbl>)fzgcn`ui0FM_<1>7=yEn`3v?k
zvF?zHS8lA_#}c)>XUM~6NE^U>md<2~;BD?VVe}nN7kHZ~C#Q)drT6E~6sY$JW9<ft
zHbXTp{N5nB^ili1?-nkfE<U-VuDxY#K}jpe=F-<T$GX>NdTh_%(d=wVsxVhdt2a__
zPGVN&oBvg3n`?kbUR|9u{IHF^*4CAy_vmxhOOD&%fT>|+ZK2=H-@^4Y|A~`S{s)w7
zERZM#3k(Je`U{~f6ySa+4CpWK{m-apf6eWRiaHqO=H}{lMNJWmRB}~ALBL=)cUL!6
zcNHawJM{l=I4U5#?jYH~;42EZ?7<*qkdouSfS9uK-z<EU5p{*rH_tN8M${q|`yQfw
z-38sr6R(9obc#!j@v@q8$6G^q<0*PIJVJj$8+1KyJeT|(r+-ApN*c&t!Vk32B&dr?
ziATSy6K~~u@yV5&Pnj{VC<5aXo$uQgeP+cYu^jW^ubCP}cqu33CtGiW6k6&U=>xY`
z4oZX9zGHcxv)g6r$lS%zPE6j(zbItldL&m#RP^6Ln+UyR8jjo?+fUf_`ckn1p>OYO
zh4KVo23|B(Js2n2KJWT?z+uPCnJ^Pg`5?GA1<4%OHIR1{a}JWX`U*)|vJm@JaVl5$
zc+6<mlU1%_zcC#^0Lhi4LiB1pInhOOgsg?SH#(sqa!uManrxG<S(9w2*5^!B>V1-v
z?dCla+HNy}6l1e_7b<JN84r!NEvSsb#>x}UU)6EP@*H5%l^<44=a(pBxek6_++k+<
fqWllo9E5cZ#D)bTQEXr}Wi>?zn}h_+OrPyP{0j{E

literal 0
HcmV?d00001

diff --git a/exercises/Solution5/data b/exercises/Solution5/data
new file mode 100644
index 0000000..2f9e946
--- /dev/null
+++ b/exercises/Solution5/data
@@ -0,0 +1,3 @@
+1.503333333333333485e+03 1.510000000000000000e+03 1.516666666666666515e+03 1.523333333333333485e+03 1.530000000000000000e+03 1.536666666666666515e+03 1.543333333333333485e+03 1.550000000000000000e+03 1.556666666666666515e+03 1.563333333333333485e+03 1.570000000000000000e+03 1.576666666666666515e+03
+7.499999999999914504e-04 7.499999999999914504e-04 2.249999999999974243e-03 1.124999999999987295e-02 2.249999999999974590e-02 3.674999999999958161e-02 3.224999999999963313e-02 2.024999999999976819e-02 1.574999999999981970e-02 7.499999999999914721e-03 0.000000000000000000e+00 0.000000000000000000e+00
+7.499999999999914504e-04 7.499999999999914504e-04 1.299038105676643190e-03 2.904737509655529702e-03 4.107919181288699137e-03 5.249999999999940478e-03 4.918078893226444405e-03 3.897114317029929786e-03 3.436931771216840664e-03 2.371708245126257549e-03 0.000000000000000000e+00 0.000000000000000000e+00
diff --git a/exercises/Solution5/sample b/exercises/Solution5/sample
new file mode 100644
index 0000000..391dbdd
--- /dev/null
+++ b/exercises/Solution5/sample
@@ -0,0 +1,200 @@
+1.529870873031946076e+03
+1.533296951267377153e+03
+1.541745745425848781e+03
+1.538050016717445487e+03
+1.531865032820430770e+03
+1.536116733929038674e+03
+1.541835516650040518e+03
+1.528663915804065255e+03
+1.556493680342280641e+03
+1.544858003463946716e+03
+1.563981311086416554e+03
+1.517786003285926654e+03
+1.539421446924835891e+03
+1.546569003219708975e+03
+1.530293311105024713e+03
+1.535341062267174038e+03
+1.528633867031483305e+03
+1.550027656082381782e+03
+1.542119709410979112e+03
+1.563317578184181002e+03
+1.535424894465434591e+03
+1.529016519771415233e+03
+1.542578375596524666e+03
+1.525176109789066004e+03
+1.525888025713215711e+03
+1.547526040919889738e+03
+1.554597173143397640e+03
+1.532986758233224464e+03
+1.549490423726573908e+03
+1.554381314655129245e+03
+1.533749199139399025e+03
+1.538692201330529315e+03
+1.526212081780602830e+03
+1.510966601378296673e+03
+1.506655439306679455e+03
+1.528347221437730013e+03
+1.549659820104275013e+03
+1.537903517987974965e+03
+1.539576339311538959e+03
+1.545561143809418809e+03
+1.536060067205941778e+03
+1.539079943094946429e+03
+1.534329650390569896e+03
+1.526389397502558950e+03
+1.547278557687202465e+03
+1.547261821718864439e+03
+1.540854857618832739e+03
+1.552198078482798337e+03
+1.537595389384989858e+03
+1.535829245465445638e+03
+1.553933558808945008e+03
+1.545263291132967424e+03
+1.525262548008899785e+03
+1.561446177626339932e+03
+1.527275235112886548e+03
+1.534505794152838916e+03
+1.553818080320350418e+03
+1.552786135048861070e+03
+1.521181342877550605e+03
+1.533128916407230236e+03
+1.559447830527483802e+03
+1.535880821169179399e+03
+1.544369273778867182e+03
+1.543844112592955071e+03
+1.544798485413858998e+03
+1.542801575338362909e+03
+1.544564475876326924e+03
+1.533363202877219692e+03
+1.537017339304906272e+03
+1.540082690835609128e+03
+1.546746596680323591e+03
+1.535981445994849764e+03
+1.532652064680736657e+03
+1.532488770341622740e+03
+1.547363078562538021e+03
+1.551072088809281922e+03
+1.552506089057507324e+03
+1.530052907249433247e+03
+1.551155830699945454e+03
+1.551240935499189845e+03
+1.529748434919387819e+03
+1.534420029003830450e+03
+1.544436875384179075e+03
+1.518797161812581408e+03
+1.530604465134051907e+03
+1.564935383735057712e+03
+1.531621539958778385e+03
+1.543508803771411067e+03
+1.547213451982075185e+03
+1.556595013835684313e+03
+1.521089329195507389e+03
+1.558296818121613569e+03
+1.533818004406123237e+03
+1.521157890175715920e+03
+1.534353959596810910e+03
+1.546662138935911116e+03
+1.529386740867591925e+03
+1.529722283547238476e+03
+1.536441157203280682e+03
+1.533257844284850762e+03
+1.532260557242805589e+03
+1.527754699569532022e+03
+1.554814434063591989e+03
+1.546469003345774581e+03
+1.544317917017430091e+03
+1.549100102215363677e+03
+1.558607953665875584e+03
+1.545874293860520993e+03
+1.536133307153057331e+03
+1.528893991553999967e+03
+1.516125854511396938e+03
+1.542938390615595381e+03
+1.535305853633108654e+03
+1.530049826280729349e+03
+1.557103320105787816e+03
+1.534852787831978731e+03
+1.542008020967928815e+03
+1.543419232796401275e+03
+1.554849174080596867e+03
+1.540590424055221320e+03
+1.539602574035891621e+03
+1.565065724074901027e+03
+1.562082569376450010e+03
+1.548806654709629811e+03
+1.540225786626847139e+03
+1.538658933992134280e+03
+1.532963911108989578e+03
+1.536544240961102105e+03
+1.549785313872546567e+03
+1.528769627507651421e+03
+1.553710244082619511e+03
+1.546421621297795809e+03
+1.545523551170422934e+03
+1.552965643828807742e+03
+1.528668339268469254e+03
+1.520307660002598823e+03
+1.553446661904086341e+03
+1.549795791684632832e+03
+1.535093009417762232e+03
+1.550664049291777701e+03
+1.539458535874844983e+03
+1.563461398257525161e+03
+1.533433040907057148e+03
+1.555823819479552640e+03
+1.544338561193084615e+03
+1.533776424634749446e+03
+1.545624387443126125e+03
+1.549012557242275989e+03
+1.536343627685704178e+03
+1.536845362217866295e+03
+1.544325486004079721e+03
+1.521895149574257630e+03
+1.541981954453400931e+03
+1.561155551470342971e+03
+1.532489702192498271e+03
+1.534430701665578908e+03
+1.523057337055363405e+03
+1.539037640394216169e+03
+1.535963371294240687e+03
+1.541732550931476226e+03
+1.535919776740505540e+03
+1.536884796959576761e+03
+1.561398938114921066e+03
+1.537952332038284794e+03
+1.544438201554240322e+03
+1.524312178114626249e+03
+1.555312758040954577e+03
+1.542225934932082737e+03
+1.553485980450914212e+03
+1.550860595357351031e+03
+1.536615361701991787e+03
+1.525319490348989802e+03
+1.542911476349673649e+03
+1.551558322259055331e+03
+1.535003624335907261e+03
+1.548958043379415130e+03
+1.558410392743886405e+03
+1.545868962322799689e+03
+1.539308405570162222e+03
+1.556760869510119619e+03
+1.532280190948667041e+03
+1.535055790738423184e+03
+1.541938014712620770e+03
+1.542880072815748463e+03
+1.543471536247729773e+03
+1.557877314618248420e+03
+1.562502796042364707e+03
+1.524013998614134607e+03
+1.550008172021271776e+03
+1.547759878579495762e+03
+1.545377711047633056e+03
+1.524851118249196588e+03
+1.536107281343830209e+03
+1.539928525673537251e+03
+1.543854005856039976e+03
+1.530490780085420283e+03
+1.536347380266474829e+03
+1.545438346857276201e+03
+1.557946331680182539e+03
+1.536614232988644289e+03
diff --git a/exercises/Solutions1/.ipynb_checkpoints/Solutions_1-checkpoint.ipynb b/exercises/Solutions1/.ipynb_checkpoints/Solutions_1-checkpoint.ipynb
new file mode 100644
index 0000000..2e4a4a3
--- /dev/null
+++ b/exercises/Solutions1/.ipynb_checkpoints/Solutions_1-checkpoint.ipynb
@@ -0,0 +1,756 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Solutions 1"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# 1. Basic plotting with matplotlib"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "In our first example, we plot a simple curve with matplotlib."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## a) Generating set of data"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "First we need to create an array of our x values for the curve to plot."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Import basic libraries:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from __future__ import print_function\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "%matplotlib inline"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[2.         2.33333333 2.66666667 3.        ]\n",
+      "True\n",
+      "True\n"
+     ]
+    }
+   ],
+   "source": [
+    "def equallySpacedNumbers(start, end, number):\n",
+    "    return np.linspace(start, end, number)\n",
+    "    \n",
+    "# look at the function output by printing:\n",
+    "print(equallySpacedNumbers(2.0, 3.0, 4))\n",
+    "\n",
+    "print(all(equallySpacedNumbers(2.0,10.0,9) \n",
+    "          == [2.,3.,4.,5.,6.,7.,8.,9.,10.]))\n",
+    "print(all(abs(equallySpacedNumbers(-1.2,0.2,6) \n",
+    "              - [-1.2,-0.92,-0.64,-0.36,-0.08,0.2]) < 1e-6))\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "There is also **np.arange**, which has step size parameter instead of number of entries, be aware of rounding errors having unwanted influencing on array length, see examples below\n",
+    "(therefore: in most cases better use np.linspace)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1.  1.1]\n",
+      "[1.  1.1]\n",
+      "[1.  1.1 1.2 1.3]\n",
+      "[1.  1.1 1.2 1.3]\n",
+      "[1.  1.1 1.2 1.3 1.4]\n",
+      "[1.  1.1 1.2 1.3 1.4 1.5 1.6]\n",
+      "[1.  1.1 1.2 1.3 1.4 1.5 1.6]\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(np.arange(1, 1.1, 0.1))\n",
+    "print(np.arange(1, 1.2, 0.1))\n",
+    "print(np.arange(1, 1.3, 0.1))\n",
+    "print(np.arange(1, 1.4, 0.1))\n",
+    "print(np.arange(1, 1.5, 0.1))\n",
+    "print(np.arange(1, 1.6, 0.1))\n",
+    "print(np.arange(1, 1.7, 0.1))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### b) Simple plots"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "As example, we now want to make a plot of the fall time vs. the height of which an apple is dropped. For both x and y we need one-dimensional numpy arrays of the same length.\n",
+    "  \n",
+    "\n",
+    "You find some help on basic plot functionalities here:  \n",
+    "https://matplotlib.org/api/_as_gen/matplotlib.pyplot.plot.html\n",
+    "  \n",
+    "For more special plots, first have a look in the gallery:  \n",
+    "https://matplotlib.org/gallery/index.html  \n",
+    "which already includes many common types of plots."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "def Falltime(x, g):\n",
+    "    return np.sqrt(2*x/g)\n",
+    "\n",
+    "# create a dataset\n",
+    "true_g = 9.8\n",
+    "data_x = equallySpacedNumbers(0.0,2.0,50)\n",
+    "data_y = Falltime(data_x, true_g)\n",
+    "\n",
+    "# the simplest way to plot\n",
+    "plt.plot(data_x, data_y,color='blue',label='theory')\n",
+    "\n",
+    "# always label the axes (use r'$...$' for latex style)\n",
+    "plt.xlabel(r'height $h$ [m]')\n",
+    "plt.ylabel(r'time $t$ [s]')\n",
+    "\n",
+    "# make the plot appear\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### c) Import measurements from text file"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "shape: (10, 4) \n",
+      "\n",
+      "data:\n",
+      " [[0.49805377 0.3304071  0.01       0.05      ]\n",
+      " [0.67623611 0.28373072 0.01       0.05      ]\n",
+      " [0.80522924 0.44070176 0.01       0.05      ]\n",
+      " [0.97044345 0.49827658 0.01       0.05      ]\n",
+      " [1.12945511 0.45374148 0.01       0.05      ]\n",
+      " [1.28508361 0.52819172 0.01       0.05      ]\n",
+      " [1.43542144 0.64219285 0.01       0.05      ]\n",
+      " [1.59138769 0.60636401 0.01       0.05      ]\n",
+      " [1.72742522 0.59992293 0.01       0.05      ]\n",
+      " [1.89783378 0.55806461 0.01       0.05      ]] \n",
+      "\n",
+      "first column: [0.49805377 0.67623611 0.80522924 0.97044345 1.12945511 1.28508361\n",
+      " 1.43542144 1.59138769 1.72742522 1.89783378] \n",
+      "\n",
+      "last row, first two columns: [1.89783378 0.55806461]\n"
+     ]
+    }
+   ],
+   "source": [
+    "# load data from textfile\n",
+    "# format: height time height_error time_error\n",
+    "measurements = np.loadtxt('measurement.txt')\n",
+    "\n",
+    "# look at it\n",
+    "print(\"shape:\", measurements.shape, \"\\n\")\n",
+    "print(\"data:\\n\", measurements, \"\\n\")\n",
+    "print(\"first column:\", measurements[:, 0], \"\\n\")\n",
+    "print(\"last row, first two columns:\", measurements[-1,0:2])\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We have seen that **np.loadtxt** conveniently loads text files into numpy arrays. There is also a **np.savetxt** function to do the opposite, see solution for part **f)**."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### d) Plot with error bars"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now we want to plot the measurement data (from the text file) with error bars together with the prediction from theory. In many cases there is a non-negligible uncertainty also on the theoretical prediction. One way of visualizing this is to plot an error band, which in practice can be done by shading the area between two curves.  \n",
+    "In this example, use $\\sigma_g = 0.4 \\frac{\\text{m}}{\\text{s}^2}$ as the uncertainty of $g$.  \n",
+    "  \n",
+    "There are examples of plots with error bars in the gallery linked above. For more detailed options look at the reference here:  \n",
+    "https://matplotlib.org/api/_as_gen/matplotlib.pyplot.errorbar.html"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# create an additional dataset for the uncertainty band\n",
+    "data_y_m = Falltime(data_x, true_g - 0.4)\n",
+    "data_y_p = Falltime(data_x, true_g + 0.4)\n",
+    "\n",
+    "# plot uncertainty band of theory prediction\n",
+    "plt.fill_between(data_x, data_y_m, data_y_p, facecolor='#ddddff', color='#ddddff')\n",
+    "\n",
+    "# plot mean value on top\n",
+    "plt.plot(data_x, data_y, color='blue', label='theory')\n",
+    "\n",
+    "# always label the axes (the r'$...$' make the axes have a latex style)\n",
+    "plt.xlabel(r'height $h$ [m]')\n",
+    "plt.ylabel(r'time $t$ [s]')\n",
+    "\n",
+    "# plot measurement with errors\n",
+    "plt.errorbar(\n",
+    "    measurements[:,0], measurements[:,1], \n",
+    "    xerr=measurements[:,2], yerr=measurements[:,3], \n",
+    "    marker='o', color='black', label='measurement', linestyle='none'\n",
+    ")\n",
+    "\n",
+    "# legend\n",
+    "plt.legend(loc='upper left',fontsize='15', numpoints=1)\n",
+    "\n",
+    "# optional: set axis limits\n",
+    "plt.xlim([0,2.0])\n",
+    "plt.ylim([0,0.8])\n",
+    "\n",
+    "# optional: grid lines\n",
+    "plt.grid(True)\n",
+    "\n",
+    "# save the figure to a pdf file\n",
+    "plt.savefig('exercise-1-plot.pdf')\n",
+    "\n",
+    "# make the plot appear\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### e) Histograms"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "A qualitative way to check compatibility of the measurement points with theory is to make a histogram of the pulls (pulls are defined below in the code). Create the histogram of pulls and overlay the expected pull distribution, which is Gaussian.  \n",
+    "  \n",
+    "Instead of putting the formula for the Gaussian yourself, you can use `scipy.stats.norm.pdf`, see here:\n",
+    "https://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.norm.html\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import scipy.stats\n",
+    "\n",
+    "heights = measurements[:, 0]\n",
+    "times = measurements[:, 1]\n",
+    "time_errors = measurements[:, 3]\n",
+    "predictions = Falltime(heights, true_g)\n",
+    "\n",
+    "# compute pulls\n",
+    "pulls = (times - predictions)/time_errors\n",
+    "\n",
+    "# histogram of pulls\n",
+    "plt.hist(pulls, 10, density=1, \n",
+    "         histtype='stepfilled', facecolor='#99bbff', alpha=0.75)\n",
+    "\n",
+    "# unit gaussian\n",
+    "x = np.linspace(-3.0, 3.0, 50)\n",
+    "plt.plot(x, scipy.stats.norm.pdf(x, 0.0, 1.0), '--', color='r', linewidth=3.0)\n",
+    "\n",
+    "# always label the axes, also for histograms\n",
+    "plt.xlabel(r'pull')\n",
+    "plt.ylabel(r'count (normalized)')\n",
+    "\n",
+    "# annotation\n",
+    "plt.annotate('unit gaussian', xy=(-0.8, 0.3), \n",
+    "             arrowprops=dict(arrowstyle='->'), xytext=(-2, 0.5))\n",
+    "    \n",
+    "# save the figure to a pdf file\n",
+    "plt.savefig('exercise-1-histogram.pdf')\n",
+    "\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### f) (optional) Creating a text file of toy measurements"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# create toy experiments instead of real measurements here\n",
+    "n_toys = 1000\n",
+    "toy_true_height = equallySpacedNumbers(0.5, 1.9, n_toys)\n",
+    "toy_true_time   = Falltime(toy_true_height, true_g)\n",
+    "\n",
+    "# uncertainty on measurements\n",
+    "height_uncertainty = 0.01\n",
+    "time_uncertainty   = 0.05\n",
+    "\n",
+    "# toy with uncertainties, sample from normal distribution\n",
+    "toy_height = toy_true_height + np.random.normal(0, height_uncertainty, n_toys)\n",
+    "toy_time   = toy_true_time   + np.random.normal(0, time_uncertainty,   n_toys)\n",
+    "\n",
+    "# error bars for plotting\n",
+    "toy_height_errors = np.full(n_toys, height_uncertainty)\n",
+    "toy_time_errors   = np.full(n_toys, time_uncertainty)\n",
+    "\n",
+    "# save to text file\n",
+    "np.savetxt('measurement_%dtoys.txt'%n_toys, \n",
+    "           np.transpose([toy_height, toy_time, \n",
+    "                         toy_height_errors, toy_time_errors]))\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# 2. Error propagation with Python"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We consider a LC circuit with resonance frequency $\\omega_0 = \\frac{1}{\\sqrt{LC}}$.  \n",
+    "$C = 150 \\pm 8 \\,\\text{pF}$  \n",
+    "$L = 1 \\pm 0.1 \\,\\text{mH}$  \n",
+    "  \n",
+    "What is the resonance frequency and its uncertainty? \n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## a) Calculation by hand"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The mean value is computed to:   \n",
+    "  \n",
+    "$\\omega_0 = \\frac{1}{\\sqrt{LC}} = 2.58 \\cdot 10^6 \\,\\frac{1}{\\text{s}}$  \n",
+    "  \n",
+    "Since the uncertainties for both quantities come from independent electronic components, they can safely be assumed as uncorrelated and one can compute the uncertainty of $\\omega_0$ to  \n",
+    "$\\sigma_{\\omega_0} = \\sqrt{\\left(\\frac{\\partial \\omega_0}{\\partial C} \\sigma_C\\right)^2 + \\left(\\frac{\\partial \\omega_0}{\\partial L} \\sigma_L\\right)^2 } = 1.46 \\cdot 10^5\\,\\frac{1}{\\text{s}}$"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## b) Installation of 'uncertainties' package"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "There are packages, which make handling of uncertainties very easy, e.g. the package simply called \"uncertainties\". It is not included in standard packages of Anaconda and therefore has to be installed with:  \n",
+    "`conda install -c conda-forge uncertainties`  \n",
+    "This can take several minutes, since anaconda has to resolve a lot of dependencies.  \n",
+    "(If you are annoyed by the slowness of anaconda, look at \"pip\" which is a conceptually different way of installing Python modules)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## c) Use of 'uncertainites' package"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Look at the example on the official website on how to use the library:  \n",
+    "https://pythonhosted.org/uncertainties/  \n",
+    "  \n",
+    "Define $L$ and $C$ as `ufloat`s and compute the resonance frequency and print the result.  \n",
+    "How can one obtain the central value and the uncertainty separately from the `ufloat` object?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(2.58+/-0.15)e+06\n",
+      "nominal: 2581988.8974716114\n",
+      "standard deviation: 146312.704190058\n"
+     ]
+    }
+   ],
+   "source": [
+    "from uncertainties import ufloat\n",
+    "from uncertainties.umath import *\n",
+    "\n",
+    "C = ufloat(150e-12, 8e-12)\n",
+    "L = ufloat(1e-3, 0.1e-3)\n",
+    "\n",
+    "omega0 = 1/sqrt(L*C)\n",
+    "print(omega0)\n",
+    "print(\"nominal:\",omega0.n)\n",
+    "print(\"standard deviation:\", omega0.s)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Note that the uncertainties package treats correlations correctly (if you tell it about them)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "correlated: 0.0\n",
+      "uncorrelated: 1.131370849898476e-11\n"
+     ]
+    }
+   ],
+   "source": [
+    "C2 = ufloat(150e-12, 8e-12)\n",
+    "C3 = C2\n",
+    "print(\"correlated:\", (C3-C2).s)\n",
+    "print(\"uncorrelated:\",(C3-C).s)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "There's lots of things you can do to plots. Have a look here for more inspiration: https://matplotlib.org/gallery.html"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## d) (optional) write your own uncertainty package"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We can also try to write our own class for propagating uncertainties. Look at the myufloat class below and add the missing pieces marked with **TODO:**. Then test your **myufloat** class with the LC circuit example from above. It should lead to the same result (up to floating point rounding errors)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "class myufloat:\n",
+    "    def __init__(self, n, s=0.0):\n",
+    "        self.n = float(n)\n",
+    "        self.s = float(s)\n",
+    "    \n",
+    "    def __add__(self, operand):\n",
+    "        n = self.n + operand.n\n",
+    "        s = np.sqrt(self.s * self.s + operand.s * operand.s)\n",
+    "        return myufloat(n, s)\n",
+    "\n",
+    "    def __sub__(self, operand):\n",
+    "        n = self.n - operand.n\n",
+    "        s = np.sqrt(self.s * self.s + operand.s * operand.s)\n",
+    "        return myufloat(n, s)\n",
+    "    \n",
+    "    def __mul__(self, operand):\n",
+    "        n = self.n * operand.n\n",
+    "        r1 = self.s / self.n\n",
+    "        r2 = operand.s / operand.n\n",
+    "        s = np.abs(n) * np.sqrt(r1*r1 + r2*r2)\n",
+    "        return myufloat(n, s)\n",
+    "    \n",
+    "    def __div__(self, operand):\n",
+    "        n = self.n / operand.n\n",
+    "        r1 = self.s / self.n\n",
+    "        r2 = operand.s / operand.n\n",
+    "        s = np.abs(n) * np.sqrt(r1*r1 + r2*r2)\n",
+    "        return myufloat(n, s)\n",
+    "    \n",
+    "    # for Python3\n",
+    "    def __truediv__(self, operand):\n",
+    "        return self.__div__(operand)\n",
+    "\n",
+    "    def sqrt(self):\n",
+    "        return myufloat(np.sqrt(self.n), np.abs(0.5/np.sqrt(self.n)*self.s))\n",
+    "    \n",
+    "    def __str__(self):\n",
+    "        return \"%1.2e ± %1.2e\"%(self.n, self.s)\n",
+    "    \n",
+    "    def __repr__(self):\n",
+    "        return \"%1.2e ± %1.2e\"%(self.n, self.s)\n",
+    "    "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "2.58e+06 ± 1.46e+05\n"
+     ]
+    }
+   ],
+   "source": [
+    "C = myufloat(150e-12, 8e-12)\n",
+    "L = myufloat(1e-3, 0.1e-3)\n",
+    "\n",
+    "print(myufloat(1.0)/np.sqrt(C*L))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "So the results agree for this case!\n",
+    "Lets check some other cases:  \n",
+    "create two values with uncertainties:  \n",
+    "  \n",
+    "$a = 1.0 \\pm 0.1$  \n",
+    "$b = 2.0 \\pm 0.05$  \n",
+    "  \n",
+    "and compute the result including uncertainty both with the uncertainties package (ufloat) and your own implementation (using myufloat) of:  \n",
+    "  \n",
+    "$c = \\frac{a+b}{a-b}$  \n",
+    "  \n",
+    "are they the same? If not, why?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "-3.0+/-0.4\n",
+      "-3.00e+00 ± 3.54e-01\n"
+     ]
+    }
+   ],
+   "source": [
+    "a1 = ufloat(1.0, 0.1)\n",
+    "b1 = ufloat(2.0, 0.05)\n",
+    "\n",
+    "a2 = myufloat(1.0, 0.1)\n",
+    "b2 = myufloat(2.0, 0.05)\n",
+    "\n",
+    "c1 = (a1+b1)/(a1-b1)\n",
+    "c2 = (a2+b2)/(a2-b2)\n",
+    "\n",
+    "print(c1)\n",
+    "print(c2)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We neglected correlations in our **myufloat** class that e.g. the **a** in the numerator and the **a** in the denominator are the same and therefore 100% correlated! The uncertainties package takes this into account."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/exercises/Solutions1/Solutions_1.ipynb b/exercises/Solutions1/Solutions_1.ipynb
new file mode 100644
index 0000000..54eb5dd
--- /dev/null
+++ b/exercises/Solutions1/Solutions_1.ipynb
@@ -0,0 +1,756 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Solutions 1"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# 1. Basic plotting with matplotlib"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "In our first example, we plot a simple curve with matplotlib."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## a) Generating set of data"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "First we need to create an array of our x values for the curve to plot."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Import basic libraries:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from __future__ import print_function\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "%matplotlib inline"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[2.         2.33333333 2.66666667 3.        ]\n",
+      "True\n",
+      "True\n"
+     ]
+    }
+   ],
+   "source": [
+    "def equallySpacedNumbers(start, end, number):\n",
+    "    return np.linspace(start, end, number)\n",
+    "    \n",
+    "# look at the function output by printing:\n",
+    "print(equallySpacedNumbers(2.0, 3.0, 4))\n",
+    "\n",
+    "print(all(equallySpacedNumbers(2.0,10.0,9) \n",
+    "          == [2.,3.,4.,5.,6.,7.,8.,9.,10.]))\n",
+    "print(all(abs(equallySpacedNumbers(-1.2,0.2,6) \n",
+    "              - [-1.2,-0.92,-0.64,-0.36,-0.08,0.2]) < 1e-6))\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "There is also **np.arange**, which has step size parameter instead of number of entries, be aware of rounding errors having unwanted influencing on array length, see examples below\n",
+    "(therefore: in most cases better use np.linspace)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1.  1.1]\n",
+      "[1.  1.1]\n",
+      "[1.  1.1 1.2 1.3]\n",
+      "[1.  1.1 1.2 1.3]\n",
+      "[1.  1.1 1.2 1.3 1.4]\n",
+      "[1.  1.1 1.2 1.3 1.4 1.5 1.6]\n",
+      "[1.  1.1 1.2 1.3 1.4 1.5 1.6]\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(np.arange(1, 1.1, 0.1))\n",
+    "print(np.arange(1, 1.2, 0.1))\n",
+    "print(np.arange(1, 1.3, 0.1))\n",
+    "print(np.arange(1, 1.4, 0.1))\n",
+    "print(np.arange(1, 1.5, 0.1))\n",
+    "print(np.arange(1, 1.6, 0.1))\n",
+    "print(np.arange(1, 1.7, 0.1))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### b) Simple plots"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "As example, we now want to make a plot of the fall time vs. the height of which an apple is dropped. For both x and y we need one-dimensional numpy arrays of the same length.\n",
+    "  \n",
+    "\n",
+    "You find some help on basic plot functionalities here:  \n",
+    "https://matplotlib.org/api/_as_gen/matplotlib.pyplot.plot.html\n",
+    "  \n",
+    "For more special plots, first have a look in the gallery:  \n",
+    "https://matplotlib.org/gallery/index.html  \n",
+    "which already includes many common types of plots."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "def Falltime(x, g):\n",
+    "    return np.sqrt(2*x/g)\n",
+    "\n",
+    "# create a dataset\n",
+    "true_g = 9.8\n",
+    "data_x = equallySpacedNumbers(0.0,2.0,50)\n",
+    "data_y = Falltime(data_x, true_g)\n",
+    "\n",
+    "# the simplest way to plot\n",
+    "plt.plot(data_x, data_y,color='blue',label='theory')\n",
+    "\n",
+    "# always label the axes (use r'$...$' for latex style)\n",
+    "plt.xlabel(r'height $h$ [m]')\n",
+    "plt.ylabel(r'time $t$ [s]')\n",
+    "\n",
+    "# make the plot appear\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### c) Import measurements from text file"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "shape: (10, 4) \n",
+      "\n",
+      "data:\n",
+      " [[0.49805377 0.3304071  0.01       0.05      ]\n",
+      " [0.67623611 0.28373072 0.01       0.05      ]\n",
+      " [0.80522924 0.44070176 0.01       0.05      ]\n",
+      " [0.97044345 0.49827658 0.01       0.05      ]\n",
+      " [1.12945511 0.45374148 0.01       0.05      ]\n",
+      " [1.28508361 0.52819172 0.01       0.05      ]\n",
+      " [1.43542144 0.64219285 0.01       0.05      ]\n",
+      " [1.59138769 0.60636401 0.01       0.05      ]\n",
+      " [1.72742522 0.59992293 0.01       0.05      ]\n",
+      " [1.89783378 0.55806461 0.01       0.05      ]] \n",
+      "\n",
+      "first column: [0.49805377 0.67623611 0.80522924 0.97044345 1.12945511 1.28508361\n",
+      " 1.43542144 1.59138769 1.72742522 1.89783378] \n",
+      "\n",
+      "last row, first two columns: [1.89783378 0.55806461]\n"
+     ]
+    }
+   ],
+   "source": [
+    "# load data from textfile\n",
+    "# format: height time height_error time_error\n",
+    "measurements = np.loadtxt('measurement.txt')\n",
+    "\n",
+    "# look at it\n",
+    "print(\"shape:\", measurements.shape, \"\\n\")\n",
+    "print(\"data:\\n\", measurements, \"\\n\")\n",
+    "print(\"first column:\", measurements[:, 0], \"\\n\")\n",
+    "print(\"last row, first two columns:\", measurements[-1,0:2])\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We have seen that **np.loadtxt** conveniently loads text files into numpy arrays. There is also a **np.savetxt** function to do the opposite, see solution for part **f)**."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### d) Plot with error bars"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now we want to plot the measurement data (from the text file) with error bars together with the prediction from theory. In many cases there is a non-negligible uncertainty also on the theoretical prediction. One way of visualizing this is to plot an error band, which in practice can be done by shading the area between two curves.  \n",
+    "In this example, use $\\sigma_g = 0.4 \\frac{\\text{m}}{\\text{s}^2}$ as the uncertainty of $g$.  \n",
+    "  \n",
+    "There are examples of plots with error bars in the gallery linked above. For more detailed options look at the reference here:  \n",
+    "https://matplotlib.org/api/_as_gen/matplotlib.pyplot.errorbar.html"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# create an additional dataset for the uncertainty band\n",
+    "data_y_m = Falltime(data_x, true_g - 0.4)\n",
+    "data_y_p = Falltime(data_x, true_g + 0.4)\n",
+    "\n",
+    "# plot uncertainty band of theory prediction\n",
+    "plt.fill_between(data_x, data_y_m, data_y_p, facecolor='#ddddff', color='#ddddff')\n",
+    "\n",
+    "# plot mean value on top\n",
+    "plt.plot(data_x, data_y, color='blue', label='theory')\n",
+    "\n",
+    "# always label the axes (the r'$...$' make the axes have a latex style)\n",
+    "plt.xlabel(r'height $h$ [m]')\n",
+    "plt.ylabel(r'time $t$ [s]')\n",
+    "\n",
+    "# plot measurement with errors\n",
+    "plt.errorbar(\n",
+    "    measurements[:,0], measurements[:,1], \n",
+    "    xerr=measurements[:,2], yerr=measurements[:,3], \n",
+    "    marker='o', color='black', label='measurement', linestyle='none'\n",
+    ")\n",
+    "\n",
+    "# legend\n",
+    "plt.legend(loc='upper left',fontsize='15', numpoints=1)\n",
+    "\n",
+    "# optional: set axis limits\n",
+    "plt.xlim([0,2.0])\n",
+    "plt.ylim([0,0.8])\n",
+    "\n",
+    "# optional: grid lines\n",
+    "plt.grid(True)\n",
+    "\n",
+    "# save the figure to a pdf file\n",
+    "plt.savefig('exercise-1-plot.pdf')\n",
+    "\n",
+    "# make the plot appear\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### e) Histograms"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "A qualitative way to check compatibility of the measurement points with theory is to make a histogram of the pulls (pulls are defined below in the code). Create the histogram of pulls and overlay the expected pull distribution, which is Gaussian.  \n",
+    "  \n",
+    "Instead of putting the formula for the Gaussian yourself, you can use `scipy.stats.norm.pdf`, see here:\n",
+    "https://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.norm.html\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import scipy.stats\n",
+    "\n",
+    "heights = measurements[:, 0]\n",
+    "times = measurements[:, 1]\n",
+    "time_errors = measurements[:, 3]\n",
+    "predictions = Falltime(heights, true_g)\n",
+    "\n",
+    "# compute pulls\n",
+    "pulls = (times - predictions)/time_errors\n",
+    "\n",
+    "# histogram of pulls\n",
+    "plt.hist(pulls, 10, density=1, \n",
+    "         histtype='stepfilled', facecolor='#99bbff', alpha=0.75)\n",
+    "\n",
+    "# unit gaussian\n",
+    "x = np.linspace(-3.0, 3.0, 50)\n",
+    "plt.plot(x, scipy.stats.norm.pdf(x, 0.0, 1.0), '--', color='r', linewidth=3.0)\n",
+    "\n",
+    "# always label the axes, also for histograms\n",
+    "plt.xlabel(r'pull')\n",
+    "plt.ylabel(r'count (normalized)')\n",
+    "\n",
+    "# annotation\n",
+    "plt.annotate('unit gaussian', xy=(-0.8, 0.3), \n",
+    "             arrowprops=dict(arrowstyle='->'), xytext=(-2, 0.5))\n",
+    "    \n",
+    "# save the figure to a pdf file\n",
+    "plt.savefig('exercise-1-histogram.pdf')\n",
+    "\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### f) (optional) Creating a text file of toy measurements"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# create toy experiments instead of real measurements here\n",
+    "n_toys = 1000\n",
+    "toy_true_height = equallySpacedNumbers(0.5, 1.9, n_toys)\n",
+    "toy_true_time   = Falltime(toy_true_height, true_g)\n",
+    "\n",
+    "# uncertainty on measurements\n",
+    "height_uncertainty = 0.01\n",
+    "time_uncertainty   = 0.05\n",
+    "\n",
+    "# toy with uncertainties, sample from normal distribution\n",
+    "toy_height = toy_true_height + np.random.normal(0, height_uncertainty, n_toys)\n",
+    "toy_time   = toy_true_time   + np.random.normal(0, time_uncertainty,   n_toys)\n",
+    "\n",
+    "# error bars for plotting\n",
+    "toy_height_errors = np.full(n_toys, height_uncertainty)\n",
+    "toy_time_errors   = np.full(n_toys, time_uncertainty)\n",
+    "\n",
+    "# save to text file\n",
+    "np.savetxt('measurement_%dtoys.txt'%n_toys, \n",
+    "           np.transpose([toy_height, toy_time, \n",
+    "                         toy_height_errors, toy_time_errors]))\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# 2. Error propagation with Python"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We consider a LC circuit with resonance frequency $\\omega_0 = \\frac{1}{\\sqrt{LC}}$.  \n",
+    "$C = 150 \\pm 8 \\,\\text{pF}$  \n",
+    "$L = 1 \\pm 0.1 \\,\\text{mH}$  \n",
+    "  \n",
+    "What is the resonance frequency and its uncertainty? \n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## a) Calculation by hand"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The mean value is computed to:   \n",
+    "  \n",
+    "$\\omega_0 = \\frac{1}{\\sqrt{LC}} = 2.58 \\cdot 10^6 \\,\\frac{1}{\\text{s}}$  \n",
+    "  \n",
+    "Since the uncertainties for both quantities come from independent electronic components, they can safely be assumed as uncorrelated and one can compute the uncertainty of $\\omega_0$ to  \n",
+    "$\\sigma_{\\omega_0} = \\sqrt{\\left(\\frac{\\partial \\omega_0}{\\partial C} \\sigma_C\\right)^2 + \\left(\\frac{\\partial \\omega_0}{\\partial L} \\sigma_L\\right)^2 } = 1.46 \\cdot 10^5\\,\\frac{1}{\\text{s}}$"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## b) Installation of 'uncertainties' package"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "There are packages, which make handling of uncertainties very easy, e.g. the package simply called \"uncertainties\". It is not included in standard packages of Anaconda and therefore has to be installed with:  \n",
+    "`conda install -c conda-forge uncertainties`  \n",
+    "This can take several minutes, since anaconda has to resolve a lot of dependencies.  \n",
+    "(If you are annoyed by the slowness of anaconda, look at \"pip\" which is a conceptually different way of installing Python modules)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## c) Use of 'uncertainites' package"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Look at the example on the official website on how to use the library:  \n",
+    "https://pythonhosted.org/uncertainties/  \n",
+    "  \n",
+    "Define $L$ and $C$ as `ufloat`s and compute the resonance frequency and print the result.  \n",
+    "How can one obtain the central value and the uncertainty separately from the `ufloat` object?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(2.58+/-0.15)e+06\n",
+      "nominal: 2581988.8974716114\n",
+      "standard deviation: 146312.704190058\n"
+     ]
+    }
+   ],
+   "source": [
+    "from uncertainties import ufloat\n",
+    "from uncertainties.umath import *\n",
+    "\n",
+    "C = ufloat(150e-12, 8e-12)\n",
+    "L = ufloat(1e-3, 0.1e-3)\n",
+    "\n",
+    "omega0 = 1/sqrt(L*C)\n",
+    "print(omega0)\n",
+    "print(\"nominal:\",omega0.n)\n",
+    "print(\"standard deviation:\", omega0.s)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Note that the uncertainties package treats correlations correctly (if you tell it about them)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "correlated: 0.0\n",
+      "uncorrelated: 1.131370849898476e-11\n"
+     ]
+    }
+   ],
+   "source": [
+    "C2 = ufloat(150e-12, 8e-12)\n",
+    "C3 = C2\n",
+    "print(\"correlated:\", (C3-C2).s)\n",
+    "print(\"uncorrelated:\",(C3-C).s)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "There's lots of things you can do to plots. Have a look here for more inspiration: https://matplotlib.org/gallery.html"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## d) (optional) write your own uncertainty package"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We can also try to write our own class for propagating uncertainties. Look at the myufloat class below and add the missing pieces marked with **TODO:**. Then test your **myufloat** class with the LC circuit example from above. It should lead to the same result (up to floating point rounding errors)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "class myufloat:\n",
+    "    def __init__(self, n, s=0.0):\n",
+    "        self.n = float(n)\n",
+    "        self.s = float(s)\n",
+    "    \n",
+    "    def __add__(self, operand):\n",
+    "        n = self.n + operand.n\n",
+    "        s = np.sqrt(self.s * self.s + operand.s * operand.s)\n",
+    "        return myufloat(n, s)\n",
+    "\n",
+    "    def __sub__(self, operand):\n",
+    "        n = self.n - operand.n\n",
+    "        s = np.sqrt(self.s * self.s + operand.s * operand.s)\n",
+    "        return myufloat(n, s)\n",
+    "    \n",
+    "    def __mul__(self, operand):\n",
+    "        n = self.n * operand.n\n",
+    "        r1 = self.s / self.n\n",
+    "        r2 = operand.s / operand.n\n",
+    "        s = np.abs(n) * np.sqrt(r1*r1 + r2*r2)\n",
+    "        return myufloat(n, s)\n",
+    "    \n",
+    "    def __div__(self, operand):\n",
+    "        n = self.n / operand.n\n",
+    "        r1 = self.s / self.n\n",
+    "        r2 = operand.s / operand.n\n",
+    "        s = np.abs(n) * np.sqrt(r1*r1 + r2*r2)\n",
+    "        return myufloat(n, s)\n",
+    "    \n",
+    "    # for Python3\n",
+    "    def __truediv__(self, operand):\n",
+    "        return self.__div__(operand)\n",
+    "\n",
+    "    def sqrt(self):\n",
+    "        return myufloat(np.sqrt(self.n), np.abs(0.5/np.sqrt(self.n)*self.s))\n",
+    "    \n",
+    "    def __str__(self):\n",
+    "        return \"%1.2e ± %1.2e\"%(self.n, self.s)\n",
+    "    \n",
+    "    def __repr__(self):\n",
+    "        return \"%1.2e ± %1.2e\"%(self.n, self.s)\n",
+    "    "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "2.58e+06 ± 1.46e+05\n"
+     ]
+    }
+   ],
+   "source": [
+    "C = myufloat(150e-12, 8e-12)\n",
+    "L = myufloat(1e-3, 0.1e-3)\n",
+    "\n",
+    "print(myufloat(1.0)/np.sqrt(C*L))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "So the results agree for this case!\n",
+    "Lets check some other cases:  \n",
+    "create two values with uncertainties:  \n",
+    "  \n",
+    "$a = 1.0 \\pm 0.1$  \n",
+    "$b = 2.0 \\pm 0.05$  \n",
+    "  \n",
+    "and compute the result including uncertainty both with the uncertainties package (ufloat) and your own implementation (using myufloat) of:  \n",
+    "  \n",
+    "$c = \\frac{a+b}{a-b}$  \n",
+    "  \n",
+    "are they the same? If not, why?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "-3.0+/-0.4\n",
+      "-3.00e+00 ± 3.54e-01\n"
+     ]
+    }
+   ],
+   "source": [
+    "a1 = ufloat(1.0, 0.1)\n",
+    "b1 = ufloat(2.0, 0.05)\n",
+    "\n",
+    "a2 = myufloat(1.0, 0.1)\n",
+    "b2 = myufloat(2.0, 0.05)\n",
+    "\n",
+    "c1 = (a1+b1)/(a1-b1)\n",
+    "c2 = (a2+b2)/(a2-b2)\n",
+    "\n",
+    "print(c1)\n",
+    "print(c2)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We neglected correlations in our **myufloat** class that e.g. the **a** in the numerator and the **a** in the denominator are the same and therefore 100% correlated! The uncertainties package takes this into account."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.7"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/exercises/Solutions1/Solutions_1.pdf b/exercises/Solutions1/Solutions_1.pdf
new file mode 100644
index 0000000000000000000000000000000000000000..d41c0144dca4bff6906d04dccc7a73a53cc6ed4e
GIT binary patch
literal 148963
zcmcG!1ytP4wl9cVAP@-d4#5I6?(S~Eg9RFQcSvw|cL?qf9D+ME?(Xgm)5-bXeY5Vl
zv*x@tYkIBzOV#e$Ra<_0SM{b;6cuM+W@JO6oS5#NM`Gh3`AlMCXpY3oi^L>rVr}YZ
zM#9X({+Z<O2Z>1>Xz6HTPr@W_Y2augYGPz#Y=XqkkL2KJZ(?AD<ht1VMaE`}9j*PW
z5>4@Av|sR!NPd2{hh{duB`TkzmxI4=!@-xI48`b}HjhU#<^uzl+?q?$`1n<3_L;<{
zKc?5$Njtkn;hzKciVlMG`TK!_=%R>G0SBM5{j)z7`JoXoj|3dBB!AZ*mMgugF*_aH
zwtnO}M~k<UXHcL}908GRGE0l@eo-x$ajrO&66JaiwBLbC&L;`j^6d%kg))wgCryM=
zO3$Wna37=~!`adk7}yX0{9Y6wf=DGOL&zW$7yN<1J65h8;?Z2iJOdl`+$QOWExbW_
zsX&hZtnD{VH*PhS<^vownc!;@RPxYllVccjIX;TdG)Gf8`w&-?N}C~$lq;aA!2cq*
zoOouKDu<|m2xx@S9ZWq&70N4oiRA!AX04iIzVNg}V=B_*zo%^e5Zeu-gcoo={!r_(
znk8IJ8SZ$H{*#&lC1E;Jm^<EBcep5L(R97NYfH&mYscA9t(mRum58u%7-}j99nFu0
zN_OOPj}f-5euJ7OkH_B2Dn5mU=3YrtLps&vDE38m10qDXfQ=>kxfcalWhV?oza23|
zI>p8@rqGY@kh!_DR#HaH52xxUQMj6y8?yZ-AH!_bl9r5GcY~1KUc&d}em^~_l`d(m
ztm&8V!F%2{WgM4`y|dcJ4nst=N`;!F&E*nD-0F_t&D}?q2%E*JF3)6iU_~@>sNOyF
z%)rsvv)Oo1*Shz*`I*_gc_olXA{$HeDS{#A?cfVM>wCCVlJo-K?s4p7FU64{X7k*}
z`R(uS46NkDVM*JbkNP{$%H@m^@HZ}W9BD<B2|6?wxWfWk9t0fl`^iS(TjIl7`hKM)
zU!2Jelm}Vn19l#-!{u>ufxHMuS}8*+1f>N0Ch{RMxQLTEC*KtCk5*~<IpyL#0-B}(
z$$s5}$nT-0TQ&g*lUah3h4gPwV#?jJF1`zX<w1+<)<*^$N)65Cgfq{GznVcn`NONN
zt@DB3-DHc1{0%c+syItnLlWP0xs8}cMxlwNJGcWnp#35eNq^r5M;Y6+D3o?R@jQ{)
z7s;f5huci*;^-7nZ^yXLK}@rQsxkVR#pwIaVX7zq@8V*s`tdUlYJw7fwbC&~cz^NU
z)_gDLtucWIcO;*PQTIdi+vzh4+KI#t>hM1P$ADVVk+iLwp6S@v_>Q_v(Y>{K<ip^|
zMK<18eFED!)-c7`-0;{9XNkP9i55Y(7-dW0ev<R|p)tX0B6JvMIhaK@8I&%2y<E$2
z-ff+ajE^CqJr*wXae@F5e`0{lw_QM_VOI!OA}(uAUo+~mH!b?P)H>D(J%fRr>mI&`
z2S)&KrQ`*eRqFj(6}F?zK`)ZbD$$}oQB=c6O0<i@yHiN}#Hh_AtQdbWLUWuPhBT;!
zg(^|si))y2p0E3)5ZL0>{v{x^-tue3WQl(<)m#695^j@rL5_s^$S33&o!`X!sdjfz
ztm!J>8weSStWNU>GaTjlZQhLzKn%+Z{{9_VVHrjVK#b#5WgD46Q7qfpy3*1<HqL1o
z%hDVS(+<}x4ee$1^XjgaX+JP6Jf_sZM0K}{khQ{4hK#U?XBTAGNVA83cRK;{&axDk
z;IE82@jP`8Ujp4xW7k|{^fZAMIWm^|?ilFm?n)$oT*#&rcOV`u&dJ{QUGWdvOrgF(
zVT5p<A(>bk{{wsAr$3O%&h<ZGCl||qik&B5>|EkRYv0pAn1@KfW|x!BC4zLHhh^_Q
zH9%d`!2+U9U|Au~l;-MazqNY~HhkR4DPi{~K`>Mj8MEC@+Za)t_<}!i?+AIP@Csk0
z6sp1Yd^Xja#escg0gsR14*@suOA|NK#Pu`8#D-0doyBX0+&3Jz-IMLp-7JZoa}($D
z>*tk(%(+7d8q;1>;*cGy(Ctn9mTyXZnlpyGo9%+gwZck%<gTg2v?XFnNWK*a%CVMm
z-?K>#6*Wd2AsXx8{i<1bG5F)VP=q%1!`vZTaiGTg`j8hOc>FGJOQG;nwqk@k-3iUQ
zIZ*3f-e0@<I_qIh+>dUCuR%ZZ3bvdHX2F}CO$*}J7M50h?w?hXTqXOQLTa{@`_L43
zv^cnR0UN=pMMw1nT8+<ixR7;xw-;`hd5j_J0Feo6@~iHFE$Sq%FY}#?<+^4?tTiMp
zrVzsDcSm9uT!277Fh-G$F}BrEI)2MVR8t32rsWQmJ!PT70z~*>MCBFhtAh;e3~Q}t
zX<=g^F*R3-{f+FPMv1s+IVR61;dYP=d7GhFtZ3Y(>Qp;xWOP_ScS&&H(ND_Xtc^;o
z;n(ab-dG?>=5Wm{>TY{~PgW)*yh{d4n9-wF)t~g2pOo`5+@tAL1GM-W@Y=(b`C?2R
zvFyuPvG#DKuCJ*W0PwUj%B*e91(_fU=C2epd4b=KKE*!t2r?pAHkMz%#dWe=MQA<4
z3YRK{??eO#Q;Frpu?JPTx}7)Tm9sC@#5VV-$!d1f3FT-UX5RtP)YQ5vcCX@9g}<OP
z2{NY4Iw<#CdHg<|1Mp-rnlnRPQkQ_Xm9}p0i=+($<b{&#po@{`54v7sfgWU+x|P&;
zL%eS#;&!uL9;46^iB3@o_nPEvXS`#=%{=o{h>V2M${C`#BEOrJOj9g>!(GN3r?!tw
z)!AvPVb@_Wfb7qEFk3xFgHDiNervZwD>x0K9+X5Syk)anh;YT{TfA=`LF6lN=THOM
zGq5br3~NJ<!zG^8t*C>{Rla5D=EDWxL0wmoe_b0g-w#44g`Nvb_C7mp#jn|E9d6@4
ztfXHS46YBv68oO*LEz6i9J@Y7kJ>31@SGOfZ6YNlmT(qK_C230yaB%^^C$o97d7ks
z7k9I;s|)JrgiyFn8}ey}Ps{#KE1XW(>eov@B)ndlNM!I{Tcoh-j77*1{1I6;k|U!z
zK5X4HA6rm)ysPP~c%__Ydk|hP(8-MpL`qU(M)AngoW-h!j$29iDed1%S-;Au0i)>)
z-{TUanrBUB*a1&NlV3E<yP;;~dwp7cVaXqM9c65;MBzHm)$qOvQgC4F8Ptp_Kw5=@
z4Oh{I-6+ZKSQpA|a$|nKeL59LZ)UH@dji49$o(OmxA3Zls4@@5(>n_1{r&)G%cB!9
zb#bdvo=&Qv1|K^}$gSKfcrcPh4BgbU`=>kYO~E?>Y>=*r);C=!2*xK0O@6`lr+kv0
zGh-s_#4^!t`j0ZAlwz9=WUF7)sIMVb$rZ1jGN9=LeXQCSq&TAvr>`!^9xEn=cHKLj
z<P$Vf>53rkST+Uq57oKb@XLSg1#}8C2@rbUIoiG%px#HWysGnnqHeUW^B&7c+P>(P
z#CZ>&%9mUrVi4Ht`093WbQO;?)?5VdEA8df=qsJ$6z21qlK;;2r2AdW#8ltTOPZxU
z((Q?5i;P#hsFBlQ&FBq*KRloG@7T)0_V3ur$;$RGXeIf7&r44<<ZRLe(b}Fg%0MF3
zAK6)-x@r>Lix<TY%42LI)pB5{)g$bfl`gM%sstf;LaK9jW5e2bj$7SY-RR#{X~P|R
z!oa`25lYbkVPtW>A|zTA0&F3S=LYwW$%Jkh2wxVZp1BD#UM9ql8o$QKVUHkCiLCck
z9iXJS`IBQC5kq#iv_nN0>6@K1#>%DcSf}?)-tvdVxn|ht8pjlb;O|@*)gK}WmTg{m
zK02tP!R98f)VRC4$&1EYV?ge{D`AKyx34C8!i(VWfb>tK^?Z2rRMxEwTo=aH=z94$
z-10$!-VB4>AR?bFa*kTUB0-#^m*(6$m)D+|T7-FMi`hNu!Ek}b*7ZRcAzp`WiT0Od
zEyWOZ#jb8^Yuj+C)-Z*&gipU_;x~mkGkN1s#k4`9n<X(GKE|tGpJ-+UD6i!6<6H7C
z3RHd~<Jq*~LTp_SiPE~fCdZNH{ARv}ur2eJPM6O=&Qm|p7R9tB40IwFxZ`#}_^QD=
z3Jo;>m1vlrOa!@Vg8!+aensI{jBH2b2!MC$q)#JcC5VUfp8eR3|79y3OKnyA_6#Gt
zFQ~+j+Ww*JqOP=VF!88$?O8~-=!QC=w<!Naw=RUb-#GL7A(J~M(({M&sJ=1}VYZ;A
zcO{ZZfxIE1U45lu$xT}DM9s!g;l7Q)nQ4F_M^<a4y~i<Y)x+-3-7Seb>|x6^o`=e*
z8Lt}eZj>%wr})+W>FI@S$NAD3*9nQtH*8Z+93#w`_5n~(Nk)X`e)>&xgKn*t!&0=-
zVcS>sZQaegV1oF-7E!qP<%@k|$%c75*Atvw4S2i9R+2bX_#cP$x=3oAkW#ci16AZc
zIgYcAQt4-;b5o^_21lX@$r$OV_{kt5A!DtHDNnZ22(#jTm1La&<TFv(=wTnNL!!!(
zm@&-SlEsVre~6@l<fOl%PqfYsPC`@vi6{MmNf3hoc_!Bcg@!W0y2_G9HoJS{%Yf3C
zf}1IZPq7L)+(<Hkc*0sbM!*lby^!|xRq@l(+2+?sX5WfCzaGR`gs}kptg=a>{h)Y%
zBz_P_JT!I)o7Flj^~5ZFK|yO6pA`P6_db~kldZ;(froajD9<NdC{Xg>U6TF(?vko*
zwk9M@Ula_@O^h6om{gn$9sjmSSs9p`ATenGjlmm9RxVZ~CP@>ZshJ}Q3&&?9CSjnX
zgQAJOh>ex4jkSrjBMB!GlZcI_jlGJkff0B-Q4?pNk%_W|FnB~c0|yHdjz8N|<^S55
z0=NMGd3Lq(&hn!ZQKy~sJ6LJAFEr@mm|4NaP(<!H*MQkfq%QdAM-nnJgdnpXk+v&H
zqFfq&`BT;SbIzDjaZ#mE1f?o;3LG{hKWIqk$z0fw0x>N!7axueM`6y}$%E68F&~$X
zEHst+&n>J|Osuu6Zyg8_|NRs4iH7E$&lh>3M6L9rbli>q0c$j(BnAXXG!-J63L-xQ
z07)VTA&CKm5=@oE`16H0g9)gy&XCQdXJgAp^c}~;GakiD;Z$<Jt>Y$dYEo7bN`<)J
z+E>!7m+p&*J6)-lJXsn(-%wVkf=9I%h9_BwB#23%Ga13*I-p=s?vJ#;8m%!7A>jVF
zM=Y5NL7|+FNoRa*b^sC!i_c;rml&!svA?&?^<e=*q3Oq1cdRff?~SpT$J6UG-rQ6$
z_yZt=TO?=ac6J`ubR|+DdYrF+>Pa+jMH+tuPd&IiM@YRgvD)Z_ZYhKNKr>B^0(*QB
zyoMk=e3KKcl+RtS*BwR@Z8a~iPs$oe(@8PgfO_u8t2eP!h)URi8t=Ack4mo~<DmD-
zTAyT+n$I=U9)RYURL?%W-ctF9wi+}P`d*Z%yd-|UUgNhB97EbRy*>iUhlRAyN9R72
za=vp7)(OQbJLwK}l3ZLYu2u9})0-X*tQ2yinMbf?HPDisrz@A&Q=rC5ELu95idN;_
zu>^f@=2NHCltQ!0&^#&<el@jAkPM4iG}hoFn{D<;CWFUa>XSbTZw|SfueQ1Q_RZYR
zVJRh#c9^Ovg#tfqi*rFI9mgTdujjWLTM$f7QM(?oF&#sK^GTg#oBQC}D^(gt5lH*|
zZrAUe%<c*Tt?{cO|BEU-#lfOl*mQZ?vR9+$Ym+x}lKrn8bV=yh50fIOgT+PWF_)Lu
zRD-Aj)9|v&*gz=dxhR35GEMs~SgrhH%QndDvXM9KSV@fN6mxUq4PjcwS%tSWe(R^X
zj%(Imj>4(Fs*@AhMzPDDwUOo)FYB3Zg|cZ>JkI%hX<#XB2$McMS~Q*U)6!3qWjMx;
z!A9p|H%ehd!=*A(VqyYg78?=_y0_czS_vsb05Mb~scYVGy<r9UJ?-(1gOn_$kp!I4
znsj~u$R7xBPK6E3Mvmt`M|+obQ`^=3+bw4-j#klBoi1xWOi==iMOoIUE9~xdt6ac@
zPjN9+olHUR51_3zbPQ0T6m`nFWApoU!Mc^?%8B;8O&NpwN=U-C0)5_tQDcfq_X7a=
zdcOhhb^caXdyLV@a~AJ(1bQZd0Kwptw(G5WYgaUhYc<Z&Xqwv2x)D%Y=OdmEH?AFu
zLdeBYX1JH|KK?vj9@HY0^A#v5y^+s{gg(}$l#wx+`II4w2<F*dc(jx!zA*kP!>7IV
zJ~yP>I*tkyh^(YWInGbl=3x<Yd1dnK`mp(~!X}QYlejB8vp52W)N0Wz9u5UmE&Z!q
zE)}9fRedNNmwUJ223l2-B#wGGk)aF|1aqHPtpwlDwgKniAG>LovER_1i75hhXHOp4
zRQ{Drua+uB_)Yma=4G(2`#+>sP_4M;c_ll}sMQ!t6^18^f=Q88)e10dv+REGS%3mi
z?UUojfT8RQ1XmMXXmWQ{EB0Y8IZHc9!)Z45Y_y|O+s3zgk*VuR0q{FJNhg!mJT9~F
zvJKWI<;IGsWDUW9E{Qbu!9c`YG)inenyXzq8+4$(qp-_y2RsxcJMOam^-u3>p9K@G
zeCLzx+a;4NaqA5#xtOTbU$cP}Kg*_Zm*x32^Bf-!9>Wn8nqvy*O>OfLSF0m~XUnJT
zAnffOJC`gf6E5xHU`XUfb>OJw&c`;oS$x{BkBWk%bejiW?pUqSfuUMO=U1H8M7~G2
z$CEsb?~nk}Vq%wz7CM_$a;M~SW|K`z!5_GH%Wo{^KWnxm$}EEcv5Vv8=JL$vh!0}6
zVx_)xwYSZK!{R&GEbn*svTht2e+x$_XYpSimZrDFP=R0stQPa9n(M7fmGF4Ks8{YT
zNBkkB!kJch3k$24B;+3G390eo`DcW413rBVys)Qu6~|y-X!tL5e;KN&u{f1PDjS|O
zLBmOErtsT9f$@1g>Frem46(~PZ81;HNBOz_@1*}y%R$qi5Sj}F@_4seF7<(}Eg!KR
zXc=E1W3n#jkv`Z@oaFWN&%jPjkD`MMrWWyIoFaN|V%|>?^}>IQLPeK>)l@o)=vM8t
zaqXM&pK!2Vl;sWDv*mFD(dc^BmuYsZS^sU#;JMj!wyxk0@|cplyqPOQkEXf@UVUr2
zQD;zIYS5zorUC`@?`XdluISJkU%9+A9{RY@lB5O&f>{d`zC0*WRyK_K!eWINU>^YJ
z+M_Agvr^Dk{z5a(OU}kth6x@Fw*Zswn1@<{OYHSQ!HW}{MDCnf`mDpoiY)>JEE-Jb
z8ks-=!DQvtS*Bc{*{qH6`S5Q+`~Dfr>)ekIaF=3w_<02dr3^DUvv8i3n|VQ{L<0C=
z|8zadt*rGcnue4O^wB7E<Xfp&DsmL4M9u!9_m+3%9hU9mk7*f|`?-AD0yWk7Mi>|<
z78@cZVXXpMPkw!naXSkBc1bBfn$KfsnCa>OAH*Q{p$IR^RS>i4)$j)-yH!n4#__CN
zrcdTmeDp5R{5Cx@k815?k&;~EaDO?2XE+>JS$p`CP-_0*V31?_1g_bcUdBEHwZvlM
zoL@q+4k9p)3bAFD@WhZ4$Rh1j&-+|8CIoZ>{)Q<oGfwE70Mv&LwrI$t#+J3~y<VWw
z(o76XH!lBnl_rtPA2jXCe%=#skBhD6laa=8L#K_S(jP;UQnB}Zu71gv>hN}Fq67!?
zxQ+(Pn67{mKd4n#k*EyckJ3{YMNXUR3mo9}m3(UJ$;c>of#rQF8-sz*w@&R@fTqeH
zG*<uqZ7lxT^8}PHRpTR23Ud%gB{k%QkUyxK6Ww}~nAxJ4cG`Soyu_}K2-*vb%q#qO
zGoxXDP=Hs^?&ZWUP4WVw7+v`!l^U+BMrqW4*iE(4Jh<F6vK-+kqjDKj2t&w4HqKWR
z=d!mA_R`_XjvDD`b>QZ%pK6_Z{q<?NsjbKlTTMh@8r3PB0;$LC!1>1Q*Oi@}GSp})
z(01*kJ7IIam&c2VSJ#TP<Qd@MJD`4GD8YVK)4d8{nS8uD`Un9eljAQfiw7x3fVlPs
zB#5i|qcwrj-m{V_wRreWyQj~dgQE!tQ@EA*`5`S`FLyghw*Xs8%JA|)Sy`2yqt*OD
z2{tzSvo=t3{q;VnO?$CIHaJy+S9`wM2;zp<QBRCq8P$zn4`kf5l*^}0xjmVBkSoJn
zD~c-99|v>4ga>l>ahqtjej)a^m=Z6P+p^~X!|vr8s=z?~++i1p_?0A$qn7`VhTh<1
z-eK)uHGy#=jEThzLQ-XWZ3!f#l&j0?;>yL>J$1bmb=$e(^PC5cEMxCL+w!S~p1Ig|
zzwJPd&t0HgF;GDmKN5!rdo7|bfbGpAV{Ojyx16FZ{po~)z`_2nSuI>ZFg~qNYAd&S
zXd;RHKABd7tiTV8Ie9%-KROu_IR=@y?x)mEesk{IJ$NX;kp)0~J-2K{J<H==Qn~hm
zno#OySzNcxZs1r53cllfV;TL!k-pQkjxWHinVvC#<7i*0?K=yPb{1dp&#$Pkz{}@A
z#%CHj&(}3%g{jjO`$0-~beAP7W!=$JL{NM_fRd(d>iV?B-9ym3JwBn@$REHQpZ<1u
zxP97k^L5bOXPF4XcG}qgWT`-3-~_ZisB-})8TSE;DY2E8mL_^<cmWque*f7N9JMog
zbe&ToDIhvm@~A)`;Y-D!;mwl@Wn+H8Ig9vWaKDvnl7IgWs?MX={QVvwn(FPlFpEW^
zJfrPt6R!u9YeM0+%mH9#cIbS=9i784P~I1+^9CT+`aLbL5a;$drrqnt^`L1lIRx<>
zyrMz)ue7O!H%B*{%0==u_LLq3f~n!>k4K<}^7rJOy_6(~)Q++-3uS%cZs$U6zZ`cG
zE{B<pEqn!l(!WdPa{lm{$XP5v#BfZ<fRN(Jk!e?zQc+7l#>vKR({~TiMJACGpRX3F
zq_4MOBKGWjg^i{nw_K)KX*t?z#%Hf`fXp96pi|$^ZwBXh4OYjR5I~mS{;=HHE^n{g
zRs;|rV}H%s>+WB%=$V3$l5w<!3UiDrju=L&P~hL?MDtz-I|HX_3TI)#eY&YEVl4pY
zGMIp4X+fkS+QpSES`O5VnOv#3I+<C41N3MRFqF^Z3^DFoYC2V^*j67f>Gcv@oD&j6
zA-AC~@kGQrWn4eN9Z;*c)r~qu=?lP(DKwf(B617#f**(nh_%c*{*CfJ8gnWN3v)@F
zdQdDBa`JE*7k57o5;W>$$R+Go@Flwey}yuGkHapQ<xEUs6ADB=GvA{}e*?x=rNPVJ
z|DI2S9~!2lmSF7Ac~AT;2@>>K4)KywF3XGRWKlCLF#vu+AG~bQ)b=O&X6G4PhPx9Z
zQ}EJ1081(o`Mn%<ew}rgD*SCQw~QwhxkjH_tUsm#%Z*Y_?g}uP(#;PQO{D_5?!#X!
z5O_-=1m_Wj`M@o4Ff!0ptYcC60#afNholb{REmh^JF?oK!5Ydx82He1Z#Xy{Czm$Q
z24*jvnkqi!wMTgS?l1Yxk<+Jhp3|>I5MVh2OUh>!Iz4VSfgiyliZ8s_a{B5Qigb(c
zmlTr@JfnEp<M)4O^mHc^MWn1s;pFtUflMwZ8kv~UYH@&Lt%?aEe=r`vpp{=_uOKiQ
z3EsA;fK)YUGq?u}l31uY1yhlwr*vHoikBk^k756gw$v!&+_3C`+5+*XQw3PePj`1U
zehl@uQ{TH-ql4HZ`~(cA0if$5frH?+nHftTU%*0G80SKIoYelZ^b~d0KIzoec<luw
z$kEUzcXaj0DOH%1Fy_FeV{6sM7x1F*^SdMR9)+mjV8!Jo8u;qjz$=(4E2Sb5Fh=Kc
z+Vltu0gC^c8kunf3*{%O*Xa-IK2YaKn??whG6CDnObBK}7$ayHy!BMM?^39vTg@y4
zuJZC3Ki(TPa0#Xw&K%rh^~xvG0h>?QxgLz)r$B(V+eJZ+w+r^&D3-Q=1^V)IV|kil
zI=>EH7meJ`PAb(mO0eLSfbQ2ygit#Zr0jo%qheRB<4UTHPa+3bP+Sc@ZZp{wZD9W^
zw%t+p9N;9`KX1!TsZ@Os@P9_YV-<&w-1;U1=95r(H5NxIGC%#7OUG-8sRQe`s$xhG
zIL}ot-z?Sx(<Bvkyy@1|Y_AKkv;7^IZBA0@!xHWvmb5tW6}l$NoDHmhdm5}{mBA|9
z0@c~(6at5_{%O9y05#QS&Vo1NyZOa7`WYewV0P4UY;52VB%3iL4#q$spjNFo6QEpY
z@LzB4hqQ82e$bZ2e}$1FQs83K@L4dGfPrz-*r8%v1RF`}?v%wHpp63t`F#)~B2pHB
zwF_{vSYYZ<**=5?9x@N`-G?Ej@IvmhTo|@RYWu?v2IOceY|zK2&Ox_SD!JHJ-i#^;
za6qGx>wjMW<>%XNGMdS7x7@u81`<tbN3iz7K=J9r>);1Zr%=}_@Tr1ipDg`*UvLf#
zk&pN%5dS?~_}2l$rbj&l3(Sm32z?Bt{l}gm(?1WfREY!Vnq*)~@Jo#-2nKwFu_qBS
zwkKq};~<r||A?OVa!b)Fl!(KB9Yoh62f9_eYMDNUP^?&Hn*@;-q3iF-C+=^j>1rDG
zh_&1krLD}YG~ZC(qW4||PcSl0u0+s`Mhxw*c!d8lJ7X^x1cv^yHj08tSdX$g7U&5h
zUYb9fT*+W~IAdP)H+(}m8q&Xzda8E1B+}J)yj(M?6Z2@MRRPGIR)lhTTk1ZvF!gza
za{{u8&L&%GQRAlK@1qpLB9Ys?qcStKd-;D;%eCjzdJ4(EFW2Q_k+n~ae4B$=%L4-K
zy~w%UqT2HZS0Zq>L}LLD9>RO)o8d0~KrsEqotbKC>wNHgACJqcqe&0h%R&IVoVYGr
zlu>H3Xd+Hax1J7MRD+6}%k=hSy$Usa8)Lgrkc8YR|IXp(j#KNNd6|*u;dRh&H?-FK
zuY7&zD=S^Hu2J$8E=}m&#_e76GS`OWk55E=i^0r?pHf_|{@XFpBxkGL*n<Oxoq+Mi
zxruEpv0&=v{fU(8YZw3$H<pTe+)MPydk-~+3s-x5FiSkucSI_#hag~JFt2Ph(@M<q
zR}}ax#>@UA56`A$FfuiEe5zdBRXpUtPq2NZ(E&r}u|P1L4@z-Y7~J58BY@N}Q-H+N
zNj{<=NSTTeMV!#ZToS`KSkso{{jxpcf14b^l|X!Y7T3QY2L3&m`(Je<|9>6^O4u8?
z{nz;)3+v~9{sQ<9{sCC=vHj?R+kdeq2_!)yNlIqcN=2haGO(1x!wE%J>6=!ro9=^v
zC5sSFQQZFtsAmzR=62p+pC`tvll_2%6#p76y+u+l*bf!A9&^01gCJoq1*27#qh^*M
zUl8Eovao}G>LGQ!)4q59`mDsBW}2ye%hvd`vc2(17=bhp?aw8;XNM)kwAoiO@|Q1!
zKLjKUBoqP^3>pjq2?ClR8i_9i6vE#N59^DS0J7Bx-{}%$AbW7U;Oot)utQ!ptw@dE
z1f1Owe~en`NW_iIFTg<{%^JmJxt7CL!cEYT=?`ZHrzTDp#Gqu1!29n_N{t@-zgUf2
zCCoYid@8gxKImx8Bo!9KE^Qu$wl{TZHS(rVIaUwcN5)_4pIsX`GdWM&lG4~v9R<O2
zL;K&C|C%6)AcWArX8xyF6kYI((L;jnk7^hME414$-X;uZ2pMR%$8>1!q{r3IL$#Sn
zeJqKUD8Gq@$7K^rpj*kW^;7-t!mb3(0;>8i9Yjxy;f2>OXYhvg_Z)}`P+|Tv&N@qI
zw?TC>4?E~2bwcw~>#?tU?j+YjI=D@qhetLLIjFxg;h5YteJ2nM<N{FL$jAM^((CUP
z0I!?I#!%N7Hwbgyfhjn;3#9fgXHwzd$zsst>5N>nr;siTJR~@ad?%159cSRsv`BvF
z)<Xssrro4FYCg3XdhZtz{wAMVZEb->I6;%Eu@=&IK_EegP-<lUOf`cN=p0;E12y>Y
z32pGs7$xw1a9=6A?*x?HBt~QswM83Rle#s8zXw>Z6dyzx(FP$uXfet`3bXbs_iIz<
z!}zSj1nLEkGjcaJwpB+GKOS@dyfk~<h7m}<utEOOh7O@bZ=U_uHjUAPn%<ToDH>wH
z{mc)n!F!qb>Srag+BXDIC~)P3=<=ihqT8z;2|C<lG>9h4Jd`DoX&Acbov$QL&u>0c
zqO0+cOVX2XSe7RA0(PZ^7sDE|ik^~BX#DxQU{w$eb1=!cG=Ygl2T#SL<}gg?-+)<m
zwq#&-=i@|4X=%gI8P35XyqeVTJsRwzOmuY6!aO}Ty4sDF3&?@a2uw+Z+rF+1Xu<iu
zW)A~v4i89yM8W6T#As4~gn|_;a{2oG9rkJmMBuGUkNcB=pxJjb<hL+!l51i5tJkHi
z3r9<Muu%3}Gj>x42S-!YF#a(_uz&n9zfD+!h@DE_(}k>ef$Rt#^giOL^h>Z{FBw>v
z%k2gm)-UjSm0RGz7Wu*G0rRFfe22QLptz1QO)ZWeOy5DH*m<c2Ry^LY&kJGqJoJBP
zbpOlTr|_8)pur{vHt7~1J{le=S>I(Av@p47&li3)?muq~2z{4L0Nrk|NARPiCRNUU
zdCg~ibDx9%Kjc*0vWR!-I{{}9wIgsIzC2vCa}-czs(-igX)eP70&K%X!#3?g6w$DO
zjNo;xVFwNv(`U&{vAqZXfQiFqmdUnuw$Zb`?=q-u-WfoQK(zl~07LSMO4_W$2Rb-Z
z_^GVsB$d9IYmQa|_Usac<N{R`VNB9(ADPM~e>JOD9z)ggKhmbVwCj1h1W2oyBpJ|u
zXsWEgX~$7h!0Q|IDRH-Y^>El%=pSF=?Hy8ApAnAQ8yF?a(Cfy`J8Zc6aj~B;r=iMc
zi8D&fqt@0(WO?D$cm|E{>OPA3v00@bR(V>D6=b}?7=vnfSAy)Nm0G)!GMSlE!E&|!
zO|9~vcVmjhqS8n@pd>+;jZNg#@-u0T;caty#oduaDT~7#q)BDEp_G8;{GJ~QZ=1Ip
z_A;O`UhP}x`%?K#UCbZJ9ZJSq&&h3(%N2)|ZW~kRmh0n6bv30U#;pasZt7|{WS2LX
zZO+S;rzX31jdxyOq2<8~Uqp)0Pbw`PnXdTIv|aKh9<G;Q%EuIuU|wr$<y5BEWU8Xt
zRyL)~A!qCplB>2oSjxWS3?S0Rd$*~3w;ICt$~fs!(6*;~$8sbearo2;m#*gki|>!N
zrm7L5I$ad%DSpW#P*gGW2NW-D&q;_wXO3dli6=cYi*``EI?fnE)jv?%h|7GdQfu&N
zZZ@BmOqs!ZcnsCM(!+FO!gVVvBZg<+Pbvf@9-mk24H@Y<Dv#Z5fu;d!3l_dfg=tV(
zs5a9*-fPJk>FrE%E!aK#Y>sO~uLSeHQx+9pbKPpK6_hkEVUhgjwp0hr8B{fPf|pg8
z#v;SR-xoDYw@(}>-qy$u&y9F&Dyh-69QHMk90WV!+8Gx%A5Ohb@f`Ya0U0z&D&D~%
zyfR#ULVGa&NA$Z$>hRAleLvk_GS&?pO6E><3G&P{#=UG?2V52x+X?fe&52MMU32di
z-EIX;u{6{|j6^!Mb1hBOtR9057}>8@tk({4)aX1<AB$HiD=T7$_C_mJ+X{7War(;m
zQN#a+P&DC+2+?O4;cdyN3?rib*P3gNl@=zv553(HQ8k=C0nn2UMK9$+>f)6rFl3Qv
z4C9Y__>7nm5fQXq`U+)y7eyH}8yVyJ0B^jKg@)JBjs$0s?Tq>9*0yWgBV&?HWV-{I
zOX1!?ji4bW>=ZQ0zVqwZ!*ls3l=WA?c&lm>MlZX8>e0v+eMqt~v~sejGR=zygL@0v
z;IkLW?GP~KJH!10QkFZZcfX%!8@|WdKzw-f5Oo?2I1<+PBw0fh33S#%<CqJ1Hm-)9
zBzVLW-}eygA<V5}(@t-q>dEgjAb(jwOYuQTqIZ`y9;YBdha|m19<QeDN^^>Mt3SDO
zIa{72Xx4nQ%#?hxB|%S%iQWBlx~GgCEHv_dtLM|qU0>$ugWz3a+?CirNe6}e0~02l
zOWVJvY@DC}y`+MLnT6{=E?2`Fwm$mhlvRl~0g*77OiHU1549}km(t}pc|b0RjZ2<w
zwTML~Q$ay};pv6yCMjX>$jYG<i-kQ(mOIdWwdtl@*u|aQrC>VE_vY#4{9r^6B`zh-
ziltKtMJt6qC<qFl!1<QgCFj~LnK}pvHReu1N(_ughyhW^C%Yw`%Nu;8<ox*AIQ~cN
zg0b^U($`P)eY8n|-)IeWcATa*SH;w1zx-ND)VTK+kxDO66;*aO43Xs|5kL=68)GGU
zx{(EOMjHAZg@4ZM^;JRW`2HaiDHZDdaEwu~m}n0!OOGT$NOJ8wsfnmDy|hWJ8*=P$
zGPG{TDH{t(ezztKlaVf;&U$;~`iaPQhipi+j*Ckl-G#)D1YZ{UJ|{-gOZx+1;Hk*+
zpDU&MMEFuJ0Vo`YtC2NTlg&L%&UsorlxEl(aG_AgAFJbelvT|a_!~qV#Y8D^f&+9_
zciI>obQ^jhfyw14=+<o6RdF54L+@7BW%L>_q2ixT#;jDF%HN};$O<iN+=$O%B)Tiv
z2Pt66?PLe#C)WhVguml8u=`jDDd(x194w)iJkzdI=VlU5X=auZxCi1}is%!nUs97>
zJ$&a0d(#Y=zBhHNrndF9=uoKOiUuw$GOEX?z#rv;1~EA=h%CVWik>uH{3~+&XW9?(
z7~;8uA9>M@s|FZ&eCSHBBv9R2x?(CU#!qx+M$xc8{`5_-H3j;V6G7;G+X!%0P{XTZ
z5o_xjd5_qIzy=>su0Ben;9Sc0tXz+j<R{f!Gdy!^T*OLwAU8N@gpl{~GZgLdDxQ8h
z#jhr<quJzmvGcmFuN?BS9$2*^a7ho7Sh-c*n5|vyCH%5DMz%y~S2gFR@QrFakP`1^
zzIaHS=dpELoEBp850#`Ry6bKqdFpYZRf*^}A`$c&I3%KykH^%C_lJwE?Y>iUguN81
zV2Ux&95_z`kw~I*a*0rUFsT0>^3=LeO7_OUG{-q<O+OCdKMpgFlX)6$AeO*6#Rub>
zQECr-rtk|`+zvw-+oiN#oI(1c#VL+O)UUI3S#!xdhALvuhF>pA)pA;_sI_yf*@%ks
zhCC8*wbR?)K8J!U{8$?#mJ{K^TXIrye%fvWcnlhGCk0R*Fh_HuO2cw8QP3i@iHcJJ
z7Ca4dX5UN0A@j1ud~k1pk>^&_hYJvBd_5fy+U14f`(TWwM@G~W_;lRDrf4dlllAg6
z0%IFY3JDKFM(8Uhq222;^l)a)>q5s8VvQz<TkrC>hGdP9jimAS$$VK!K+|=H!|bUe
zNu-UTJp)2Pz(NQUKnn&p0XwBcb?G;{m6*n01eG+xe(r83D|iOtUEOG%nTHBXM@3yY
z@Fl~_g3iRgAUHEqemF`Kr)Ch7+C~{|wY8x@VZ_y7sqGV3{`iCN-QzadH^{AcT-?Ub
z5bENr)#_q7q@ei)GN!X@P)w%A9UcK!h{=7PL|u9FG6Ne;kuQomfj)!drcOQUWM^^x
z&k~+J{*#d04+ldbdW_3;bG<Xs7*>nUb2_?u)@l~2irWtUBjrppj2*3NqbFKPD?2M)
z;R8w>*$E-xA~nE9_QDKY^1xV{pL2f1uUkc?CS_78w&~I8QDc^2rPzuPUq<xxvUHoA
zG{iAzSiUA~3P7)B3H8NhMkpP<jSal6o3Cq?sv9*G5tN!Yjf{2)(llOC@v$$?L^k}a
z5~G-I!jIU>q)rZL@}IF^*m4t8T7hO&wMK2kA?-YiAbHJ7sN^uJH*9(WksDHXk#$)v
zd0NB*>Vt`OF_&jOyjr{oQE=rI;@?K`b#;!buph~eDE4?RT;9Sq-wvRAeBuzNyqWea
zabzIKSA7ZnKP*yWpq4Kf?gZj@O=S(0Y0Mv1nCa(;An)r^mR;ulx^aShrw}Pc<h8Lm
z2pM7ZYbHU<Y=61)gML?cQXFfb4r)U4lPSl|x3WvI<rzrF*@<Lgk2-;NNCW-i`CpH&
zb%7l-=DbO`e%?;WgV?ygIQ5V`8w`yM<Q{ouj%b3i`p8HTWxV^(a!MVVj`T1+lHxzb
z`KM25wAk3$T=1`2-MeGCO$NPri1!)G<_E9M4MmFcaA*+^lkK4yOxbW$U-vk@W1H)B
z*=}Z3i@04zcr4kU;=^|5C-ZA09an>`dq&}g%S;^~tw8shMv=!AF!xH)Y$+}B_DC=e
zWL}6n(>z$hJW5Bi=MM}i3M?253ziL_jbgi2Upc6>k8I3x9!oPVCAOQdgLm<}1{uh5
z0VdKz?#lUy%6~`b5vHy+(#)$xv3HB2?_&xKsIhbF+vo(@xBGSaoHkdSSOT_KC~-|~
z5M=tLeTD)lfCO!4LPYKT0RY`Fp1xaxmA%3gFPd>;9B9pSw3z&tC{~dfncB(~uR6`&
zVR*I1mAz@>mU8sHO(ku*2I^yvqTB==92(e_q>>5<HMYB;JB0jW)zF3BiHwQ7rX@EA
zuZbjl!x4+7ZD<{>LDaK;?5Z2{6EP+Wg?nR-wS#eME`2j@@t{<5fCr!Bn+@CEcdg}!
zGbmY1cHz=BZS08%uDq%22eY_{{i;i44ytRRhsUN*4T!uC*{AgY{fE^8j~DS3dGafx
zq~rXWH{a4p;mVt%ijDqRRLMzIRI&Pd9egPxN%aNzrVa1Lsd|mWq5Or519H-sZJQ9s
zQY7>I=SGEFp<$bAp+^(LXUrtv*jA$i$CY;WCyvG+R;=Q+HguX2s|}*N{igO+Q=Q{w
zyJ0#T-Um+4sYOH%J}7ZESGOuW5fUW8&7RTZTT)M5SozG8tc|-QQx4`AVxIhL*Qz97
zJld`g>cm^!P4~@$4+I*N6x9Eapm6?sg2Khc`XASPj;jBwp8g43Pv1QxC>l4-(@AtY
z`E5!2-C9kTaEw{lPl^&UwYj(U?jUq2won`ttash$Z0Qf+T6$Z_V?w^;6{{+bx9YXy
zRq<+x&qH?dtuI~V@wtHTwvT%V5+o(99vwE*w+3(C0ZM^wd;PZ8>akHS_=*zh3tW(0
zO&ccyoPBq5+3RdWqiVA>+DihKA*l4h0XfY3{qy|DBf?OhqtlEt@sD8vVw~fa>fQ@*
z%u-&Hbd1eVooU*1T<=?J@_x9;GP;&sp(Im?+QoOs7uH*30O9>FK7Mc}c^3k^fJ&~t
z)heBNDVAw`@%=z)vouw6(vP^mD*}^3ao)f{-^(d^+R=W&Cq+UIYkpKY<;PdS<?eNQ
z3Mv#kKv8@?O^C7}AlzH`mn-fnOAij^1_r^?(2rOdtmtf;K;dZ8jLIV~dM=D~vU`0h
zuMkD{;#bvP7z!D*!iQdzS*)zt?|CB5^`JX6Zn_^ysuIuAy-Hzm72$i)8eUE>-nDC<
ztD^2lT~UQNKPmNs5v76K2`As~DhgKh4X-wMWTtYAc%vu_h)`%lFDJxBJ_t>+li(O-
z;YveBRKIZ~NfQL+vc15g$CfwJ2*q=UuQduKe}CPpHv9GI;~Wp$2-%xz`bjvty5elv
zv$0r#nTMj+_Sen2o-XqgCy;+4raeIe#g*nTw~47o7Y}9m?~u|(x1T9EW9k9J1sd9r
zTFU!h)rU0|V*HUt8A8x_&`I|D3uByrYFOQBelb_?@ZUCAtR5QUs}WB>j&^zf;ZwQu
zV!=<@nh)^?@ee*)`NCXT3S)cQog>lO_Me)~iKeH!da&66+%N9TDCtC1m3(vwyFWVs
zp@eGd-`1n@_(QGB_EoQH8bmS_UfSuzY27AMLT@GgFF$L#d(BDpB0<|E#r0Kv&5P8z
zI5q1aOq&o0&LK?OJm`ME?0CIB7CLT7Kw<Ho!x^2wyfyu!_@GWP<E=GLAI;dow5jpn
z6Kkwu0lL#VaWd)L!>pJ*!m<g(h-T_j0Jf-9IL&pJ*m7(W%-l!?1;GxfpF)AVTg#GK
zZv4QnRdgt#?~~l-4`abDv&ZWrEwLw&Wk%4dqV;LG{1_7T=J<2`vZer(0TcnfV}p+@
zep|o-OX$`&NSL2u3sHN-)*7aLG8M>$xK|C>0nNrJs5Nz&6md@6+Tve2cQ6OTs+;^7
z$NW<-jLNq$y_SAGSk1JcI4mW|O2pnZa?BY}t?A1WGoI-8YTpWEr#)`>I=-27lfb!I
z@iRut`>^+Kh=H!Iyqcz1e=7b8gPEW+Gb9hi#<GR+fwrgpaPU6>#{Y3eD&RA?3iba}
zMJm^yUDW@sNaf<>V*ZaeP#K7>a(lD=Cc@!tfr^2fD$DFYQq9Agf4*<LJ7iciw~>J-
zj9*nIW>C`-fsZQPU&q1}smuRCBSrBedv{v)Cuu&VKZ!7f=9+uk-V)bXRqc4y_yX5n
zN9L8Q{Mf=R_u0bP6OToC+iHC<2^s<nl%L@Lc=ObeP6=t7V_5YE_n-Y9@|PLPTE~Qe
zVt!v)mKC6ZhLGQLF_bCrvsJIba^dYF6JS0)AW;8w(wVaow%BIfXx3PH?4?Td9b3T1
z-oD1i{BFrlq{YR~z*DcdC2$=R?)}Mn0<~KK<?4GqKaGj^dbkvUCWN|k$>R`u2J7JW
zgpZJwpKZ<HD?90AiI52vyXel5TK%2D4I`91JUk2xC(&ayrsJ6+s~Ak?cZufhg`Ort
z$mc^zuDvRc^VLRNTwLG3e{VMLaK9<R?mrbf(Nh}Lh^wU4Uyw^<_rqCF7(Wm7`R-=;
z#LoO2t)YBBbh_Gx-vd$U`9tHFbKgqSPh(YiR3ouX2@SgSl2Y&Cx#mV&ui95*9n%#S
zJYy2LzqfVFx&RHzFb!sAW{15owb$dtdW*GouRoIo&ls9fyAXpFsTHa+n-F}Vrw|Z$
zn=RK~#|(x94-v_@gsty6*}B~G%WjLJ5{pr1zRI9G6qD}ubcKhHPnV0IgTtjK_=D5o
zG_7v)bpR?q4mP&wd$QLvr=!`O##A<oKGV;0pk}9|`S~y2e4L!F-9J!FhT>B=Y%=Wy
z4Gauk9&dtBiNW&40Z-aQA(B{t2orOr#$?#aC2+E*)sVnxe_~@V%SW%()h+^$jmYa-
zY3IJ5$b+7q-g34~^JumlA7G}as5r6O=CPxlClU1xOD_4d!NcWFW$Pu9eveof7Hw;Z
zdb!qCUj#P&7c^qxajnO@GaKt^N=nL$i>KQY=t@DbN|IaM_XwTa9o-xc9n%y}>jb_O
zD;LNlF%Ak8$))urfq#wv%*)IilU3`KPq5tNum`SlOU*JiF-f4)IL(XVSI+K=S80a<
z_R<;~Nl8i~ARutsZ3aF&tatiZ`gRe+Fn7|O4=L&KT@H{BWm!sG>`2PUZ0+TVM<Ai1
zc34p%)7);|mq*}pTpGZm5~*f)of37}zdpMSmU^7E&FK_7Li9%x(nT@pw?Av>dx4b;
z7T2?ogqsPG&0b1plF{4i%d_YCde>}1?gHg@V<Na%Wt>T`^{3g}PLlq80=Yz_q+Y%G
z%#iTe{lykJ85xd_!|n*JN@2L+<Ue-OG*%izE>V+GHzob?N&5Z7a(lDw2GomB>x%GM
z3acp;H1u4h{#vn0k@w4^oA)}{sGg83b&e(U{ax9<cKSM>ECM$$pMF@%5%PP^KT@!G
z_fo~>wkc%s?_SWQZ4`sm!&-L#Y%x~>`&ZshUlz7v!RAfc{WByY!bIi(EYnY|hHDYD
zszve{xUAnOC@Ei_uf~bJt|xyg<!#4+Rr?+h@nJho;a;_}?H2E?vNsI7^y;vm*k`-@
zJ`ffb_OHn{{ibtbd_b;bVP(y7KP>*FR+7A@zp#xtJetM<7U5Erfrz-uXeyh>&0)R!
zjroJRw>N)B@A*b!D!UaPAz^uX4^gGPgF`?-Kz}&yR)SxE#>6Q#*tvWr!WOqSH=&@Q
zwnrt-k-$y}F3t%h$Hl@*w4~(Cs&p4+)Nl8k@yHc>n#_}!E7P26b#rK#@cg#k;&LW`
zn^~q+kF?lEMwz5hRS7UR?PsN-`3ZLNj<?sAab$v9%wsyH%aVER!1aVlu(!HP&hbF$
z;U;PrcM!K>ZT<01u>F+4ApYaf|4+9E%*whETrddzrAKPz_IZ5<JFa{tFCy5P|9F_~
z%+uZ39<aEb5j@}@I%BqP=R+K>I}@^SmC|IDxp{fR@wDCgGNPh^ZY$7@^xB%d1~-Q@
z;JKoppqxw<DX6QfYiVgUTCWTx(DmEA+%DN%4*2`~OT|&KS<Y8E9)Q4B%x+`b{njQC
zMFjS6OgeSX{hS~$rWCa%WM;mO`@F`jebR0?_PSfS>w+hyxti4@MR0Dp=ZbO!dkff~
z)DKyG?p9pDS~5BTvrc0%3B5r}1pssTVQ!S>k5DKC6sn!ZJ!3WS_A-4gKM7=2=;nbT
zDL`Wv;|F#|jq<%)^?-f6267zO?HRNiEW7++@4$5|m7026`aUl=U{E3>Cr>c~L$BvH
zzD=uAyQdqRn6U8gff#aVXlRXcElLFzqrUKisiI2V7IuFA*HMnO?M7u~<!x;xC8d+4
z#;Ayh9jTkOx7R<=hv$Y#L^Ou?5O_3KxrYrlUOWN<UiWJ=@9%!ybp{1kBI`EdUI>SI
zRde|JyQ68y%&(HHrepCDfzN>&i+h<tZYFl<3YR6>zV`Msta0*Lg&5R5c?R82FX<Y(
zqoS|PDepQAy?u{bk&uwMy`O(Q$GQFfv^$=~^cEf#7WX}JK?e~5;d7(-{Jnw7(4je&
zPP1b%IOH(3UB`NU->9<RkxnyG-J4NS;45fN%g7kri+OR$_q^W-VzZbX+0*B&B^~fv
z!iN&jApS0GL-KYFzff3{?C~5*d>QI<WbY8N2b;Ep{BzBRBL=CpW><|?ds9!Z`YG@E
z0W6gBhXkTxu$FfO7Cstie^2RLR!rSGFEI^}Ko5xW+4zoaK36dch|a1q>W{oce$IEh
z+66}-zEJ_m$n(%PfgRn3W#czOu@>If>0*^#k!a{f4jAtWex!s|!Ph~$9rI7WEALS&
zFE96pFoy=vd~3db6u37ZPwnk+Sx<=Tu&d~)j2FPAFWbax*>Mrg4d5#b!JuBGpf>We
zDoJF}W;dTU8%yWHKSgL!8sZwy<Qx7pFR>M{z$POrJ3T<>P#V61mbS=Yvueo?!)5Cs
zvcC5zPw`E%s`9y~eUrH2Xzt+IZBt*^LiR%E>(cX##sQO$L9$A*@<%)*JvVOeg6|r&
z-$o62jKMJkYt+tqrI`{XS>O9XY;v_+n<ETrV5H7!sR3-iVFDeNPrc(=uA7KEz9z$o
zn&;Apbk${=wLkPV+uZBFJ98<Yv?}Amy6!Yy&>ftbm`tXyvM;xhOPC!f<FSV?HWWmo
zb<w;w%HiX2$0yR~bO&|O;ZKR5Jq7e$9)fN5_+3wbzjhWW8(Tt@l(clL4-uy=2?a&Z
zx31fA?Z(R(FaYiD1;C-0FaN5(oIR@SsQ6tvoWvCL<A?XtN#mgiO`-;P;d=?NaW8=y
z#?wy$)*A_qcVii~1`>n<V*)YPUGURwJZe8laC&D|uXX{hpS|ANs%p?W?6=fr12mpJ
z2ck*UO4T!X-B_L+Ee~Z=SiYx-xL9Y+6srX33VYjKZ1x`G%vS2_XI_CJ<7BZujmzPi
zurO2yJ|-sEbr{L#pQ*Flz<I^<?EYjPIGktyro&S2AHZY%HUdr@lJUWyqXBif=vKgj
z<fON=yIXVn`trnTZ!#<<uz`uo@-_t2!~FQO$%B@bcXZ#zC+~0uHt)Xc^voCXdPbwp
zBFW?#sg;GZ66xJLa4e@{%`VZXTrH5n-XQ|ly74U50aY3-HGj+;FVrSDunfVW5{<0E
zG2NQal<?io8-bOp;^_5wP=wr!g!zdVj3adFWr};4!~U@EwCc@!wN9J%G8?Rxyf1pO
z<<F%}J|4FD%{PAyh?bM+80xr$)T?fBxhh;~@hC37zL{gOv^G?oDY@>!007ag7LAjX
zhTP`Amuayfw_f5@E;G)bA?xA#X*Akgd1ec|R)H13CCtoxeY-SWLkSC;@_W~_eKL=h
zF%kp{HJCn_B6|xx0W7d%Je+``eJ8Hf9)NyCQ#l{sY!|?&2cUFFH9EYv3ymtOHadur
zkR>hd6buc|=UB}a3lp8!$xu2L(ECM#<3xS6!w6X^eO&_!4l*89_Sf=_eNpl8@s>>X
z?GDs(S@Pq&BK77e%WdvAqLSvjvN3`MziGVFd8K?_X`Pp=iVMl$jyZBJ82*Nn;}DFL
zz(aAZmF5q%rV_O~wbr^dT!r&r4C8<UXzuHfDv7EE($g&*a><=pwK(Dz0lEXnmUefF
zJapmEbnh570i%$ALY;J;^U1>HCl()XM>DgSn7;9jXO>jA-cvn8vIu-?7PO%RjBup)
zg+WB<ZYuv5XKw*k)fe;&iz2C%gu<bblx`^lIDmwdheo=)Te{@np&RK2>F(~3?(Xj9
z+sD8E`+o0t-}~M7-skc0(X;nnYi8EWteIK!+q*YvcsQ;7YmixKZ*<Nl0t0Om(f#P$
zjM!+jq6qcM#mLZ{bp~{eEnUs-Yz6&H6pFDzGpDdzMat__UPzWStoGveWI0OH*qD_(
z=hW8fHw$-kgvi!x{iBtWTqVe`zR>ktt%HHSeky6bOzt{EtxhLK@@_YY6Ig9RP`~fx
zN&n4lf~M>Pb6{Zi(NVXlARVR&Wk`S7&`=!)G24Rk&gN|OOdn>&m6({AM&tYyEGpmq
zbh{vVY%gzjcNcJsYpbi9W_ryH$G`O2CnAb=Go|9cY8pKgn5q?`t?cL`a>Z{x*_+uq
z8KmPF9cjqb5TIZ(_jaDVy>-Ios%$9VXcPNXD6~miL{%~}GJ>)7W;X_Ooa885Td8ou
zxyG<F$myxx*Qgyf*%$fNT6lMuh)sCc7q+i)bv*q3J=Zd1JO*&BGiGJ}{h!_zDJ<{d
zdphjZwT!kmH8XT0zTTU4jy{TFJl)D#h9*fyprPU11V7was1$#ovSIrv5sfdZvNw%=
zn-B`w$5io8pEVaJ`~lI8Ec+TlM|1p&#g5g|(16T#do+@m!~XXh$s9oQa-yiEBr_Ep
ztGeRjID17`>(pxtZ-3YWJ3z#b9|_?)JzNtrC!EwpYPg4JPyFp|s@-N3p1n^MB_A9w
z;Bc~r(cyt4_hx?I90VVubcW8<*LE6g4y7>E{$LCLt*(Py1fR6Xa3Zb1J548=Bw6Gz
zLH2ir+2}}yHsyDmNNxFCO`)002s@<5$?{pwZi{&M7)bjTb#lfPN{+644kOW;a^=RT
z&CL*9g<LaLLf(-=x{N9p>y@PHTI7FXsI?$_`~H$r-%bM(egmum6_JAm26}QtB#BVw
zJm)h_{l1^|F2UkFkm}kj<f4T6;O81zQ`(EKy&$_sz#idRc6q65Be}OAv*1dpRu{)9
zMY8Y&GvIMcaj~hq9eb#z@~Dy4#!*Me^1*&}BBXG)9jWM-DXS&rNsp`dcj1t}Z_{Og
z9iari>45aV=YD>VIFcoqqUljyS8iY~ad(7F2ea7#aZfZlLC&?kAxaf5s}P)CKauyi
zcY=J`DAoah0HzX&a>rQPV`<SEsif||)D<L-xBFgcNmMh)_MBPFN7hd+;zcncnS(Ue
zHfB%&Vbhpzob55>A}{2)K_&}{?+za1K6t4ds>R8*?+1Ir(-FvG<j1gR8i{<RQGOZm
zlu)5eCUr>?&Gw+v%M}hY!n@Po`jOksg)wqTSL@h~EiFf8W8?1*5j46loxY5#zmhj)
zE>L=ZP$wV3)K0Ph0A&aX#J6yy*7+_Y_FSN%8FemmRvuVDv?_sEq^pQ{a#@`}!fTO~
z{Th$N+_-6?rd%vqrE<Wplg8oj$YUeiu)5ixZwGn{k8qD~CpqKLj=2_m(H&mfXJ(>3
z>=$=R66t>UxK-qVcX3%^Q5kRVe*We)H8r0CD#AaJv&1J_Ki0@_9jAEa*COc@w%b(p
zhey|WU!-<9@AvuMIu3DV>dMjq*1??#`V)(jkkr<zs<RO_*LC35+U?HX-7K6gdS^B%
zcq65LXc@_%_|&Z7GL-+O*n6;L<q6!wq19Py?{dipvatoRL`I1U-BDGw*@ndc4*FvL
z#om>IeW$KL-qXQ9C56XDv)Y<tg~a6Ol6QyR0m;1rY`YhB`)|TtRG7-1Tyl&@{*Cvo
zZFl>kcy0E#9Th7~tD`l7?mLCDt@S)}O7wCd%-754L^B~7bavNeW}%q>aN5Jz5~I>6
zs4V`zQJ%GGmsm-}$49u=FGWT`q|^ZD3${NvQm<Zit-<{#<xnh+sj=@blNZ%D-JklH
z>?SJU{P|rIqf@*^(<7K@29>>v#g8TSDh=_Mb;pI13?tBMU~8HZw>*xq56t=gonpsu
zLz+<V)-swY)6_?$oR<H#GgE4z!YTe>RUBrsHlHtj*hBWqTF1ixNJ<oDK3R^a@~yN}
zkZax8$FX?6wZ|=gyT%m@(MVbA6-o87o5HUS9UN}L%~TU#ij;Z>SHpTl8iMkf8>>p`
z16@hC+v3e$G6wtcBIdWJ{R!2-UE{yE<el0Un<*cx+8p+D)mayk)?=1Yb6439W>>^6
zuGZM^=x>UPr#jV)hzU?rukBTaii&EWJ$tj71F76UGe{QTbvcKYo3z&gaRA#v&HbV`
z?6xW5<XZ&F4cZaIB*b|VQj#U9uRomBK+Na!dm_cVJFezH(0PZENPut^y7K**{Ov5t
zoz+)Or$h)L@yEj+!`!(lce@}>yRDIOoz6lDU!S>JqY<j&6q$}pLXMx@3uT^kYg`N0
zXWjI~x-4t1Ns#kppfHtu5;W`V!6KyD7Cn%_Dv}^458or`gFbxNg#M<{md}ICbVT$C
zMIYHbIVVLds`GmDLCE<~q7{{xkZ0xtq$uc(6l$_Ja*k|Tv0|?24?8}SvDEk8!rZT1
zVoC-?9~IrkLYUBiRiDCjDwXLL=dt!#@57GWSnEZX^!3rLKYZNY7QomIrIzx92sY)(
z<vs?yK+;kqj!1NjHi%J~f^9HQfxKFIJ=S3J{$>FPg=?iGjp*E=(`P`i@>HX`D{rP!
zgilr(BQdeIibuA~cTPaL=H#eTM05C~|9(%5HHv)IreFs{kz*p9*&Yi1a-(osma(eh
zCWX&fc01I@7zZ;3X{^a<F)bqS65L@AuSR#!rUcV<k^qMlMY)j<eelK5M+#=QP7EUQ
zQ{~bG(*dtwX+Z*u%7)sk3#=vvj}=9Yq|IuwaQ>;rc+O7|v0>adgj~3s{@mt(%&AqS
zp`ZE>EQj5~fINKlF+enLKg0P=NohNYfZyPBdH1L2AX?`PiO2F|dBGLC3}0p=!+)kb
zXf$f9o>q^jy(E~cbP^%yjs^;L@sRIv1Btcvn?s7n{$m(KqUFYOKc4a@yN0a~WTb%^
zO_me+3~DDM`kx0@ZMyGcwlo~8YVgk0OsK5hHOOZPJJ#9l2qwE5qsg#-RVl0Du%o{_
zkAIyxd96+KMrdK5S4-0q!sR3qhu-oHBG}4MDeSMiN2lD)&KR;jNFa(EFK7Ma*yUh&
zy+2A|N+ubf$gE7^r6>><9~XXubja?do&nCgXSVyDvv%raKg~@kBv*J)z<?fVph4~#
zy1HU9XT2L<YmVRrEH%R_`!~pdh`;XR$Y<%L!Q4hT<;JgB@LtlEWa;;@ympEM55K?(
zOZ=1w0Em5_*Bw9CAa5i-kNkL!7cWY{6!SsmQ`F~$1`2HT%-7#R;xiS>+VqsIwxu#z
z*Qr&T@HkeeZsa8~+o0rRjiOI_j4?l$Ug4+vo$yE9;4(zP<DSUcRQPR*AD-5+MHruL
z{?PtR%tvj1B5)j#6J}6rpUORce-}mcqqM{{`r{wHKnw3=%q=i#B1ynWS$^QwMH$N`
zXTif4EozkBnV9JkfgE_kx3Jf^-xecbPOmMlx{u`_t}q2|f6@Jzujg~SJ5w(Zxr!+@
zcsgEYiwlpE1*8LR85-9VrJ-FZ+GxdBr`vf7<+)^I^%o<DOA%qIV+T))eyY<XO+NeO
z4Y}{gyLH{3lvOZc<<9o<$~KTGwi2aV&oSM%DZF5Xn?f{X`ruhE>G$is*S-*Li@4m{
zGQ;5_`@N@Qxv|f39TxDo5&EC!vKzGB2{O8qIzHV?Izr|=mPoYN<OXWh-AZpM1I)5`
zZG>xM<ucGDB6|*oy&E1hW|29#v)R~*rNPJmF+v)=29e4_$VJ}=8SL*$N`z01Ix%1$
z+cP8T)*KG%PgKerrzw88@5P;LZC*~N%w5*WEpUgppK6RIMMTtDujQ7TglEYtjMd*H
zH#eJpmepO&VAa8BYd8CGH>Fo}-X?6t(qM0RvFCm|y~E$sa2)f?vrS*l`7N<vAJSL~
zG6~D;3`~&N&iI@nalPA3;kx4SVfn{etrZBySU6o2q`d|xeIcj+e1~hQHDPh04_l&h
z(cv@}Ug;#jO9{iy4JHt&bf~{Mf>*@}oHEw%$#)rw3<rB(ytl3<t#+`0ZFh$z2^jT6
z)b}t62~a2~l@9H{(iom|KrN>nAX#gI-`tOPhBbK>|28s?n*d31t%u?yT<T56V>}@y
zHOoOpA=kYjbJv?5yCWHmZoXq@!{{}E-@lD+?18LXmr!w~gG1}Xu)A&t?g>5LOa_QP
zU)hiwqv&&EecMrxcst5lyP)y>l&oYfyRM)h9K67coh(yP{om*&_|tCRAgfdQ8jF#t
z10DJ!8K1mFPS#~N2B=gA`5)?NB}^N7LqlbQK5O-VsI@rkX??6wO;KqPY<a!Z%HdR!
z<L{yrW5`=$GNHb9oUgM5FopqyLO{9*)J1NcB&0I1DG}2yKg@YFq7*Q+k7VpPoKSyx
z4ipg=BPSf&QLa}or%Qa1F!867Tm#?Q)i8*?Mz71ZJ!*~fwNG9GipZ_ULE@1xa_ixn
zbD+>5yS^4kW4P8+OL(PTpGy~l2G9?8E^m+MxgqJcOq$XH&M4mW91#HSlgpHr0?(!|
zMGh`)_z}te8s3RNo$dNF5pcZ3#+(gi{RxyVf$8MMK(a%A1r0z|E3gyI_A-dmpC2^b
zMX8iwrRU0ei-u;p-2{m#n*Kn`Lz=#8Zg^Z&*ULZ+)DKcYtRCF1oIt1=_(F>N{-ykF
zRPdTWv3?5iJ(**MYy^o1P{k_SJfacjG>fB6BD)xNy?RY7prQ~ksjjE-3%C!6a>|-|
z^fA|F)!ye03S}WZN6fiYjb&{eU3fn(V_8`G1fc4MrHG#yFw=kllweIC=Q9fFWc-|@
z-HW3$)9DEPetHT;QNOta89bR60DW%pkaf;iUp2RA5D*Z)ELgp}yY^D(j}N9!N6Wdt
z{T(a846ltx3T<lLJU>^=k;9gXLy@CH-5$uG{RTfo0>l(a>K=gcoH0oADPjZ>4~t51
z4wD*|YK%HIa3XWpMrvEANe^&`hAx7ACK5c^7Nj2^qzsHyFenrnmEO~58>s-Fg13mD
z1!%sH+!ZGz=w!jR%009+9{Oq=&o!qft36>x4<GC|sV%lG{i9P=jkzjviA<rZ^c=72
zY+2jGpPRAmp*}vk++pw&E8u7%ukX6_iV;vM;mUjhP26(ao~R7+j{w?SK4E*wzt(;$
z_0ygGpA#cHjU1b9Oee+9WY)tAE;6H8XlSYTcSbwosc)XH$GG27H!%PwU<oUcK|-6Y
zcbh9K;R_xQUqcA1`8Ut^*f;vF6wmE9CL-teVGhEWd&hm7K^S|RV9cA`$<JdlLbAH)
zbXPV@Eva)H_`KV{fK#b+U{5IMb-3ZWyJir$&fYp-Fh~yMu&e$UORgVEZ=THGBfSk@
zxx`T=PC&lo^BPKG8w10t7J=>_Ydyoiv}_l@<pk777-<VQ>RHfm{xOboF9m!VSH5I&
z8t|@Eq2i|)Mc}O{&hNyK50BThjp;U<8Fmr{Z_)nTYaW5q!(jjDs?mP)Yi{;0T~s=w
zZ53npZTLN-fA6{PW8g{vinktjpQz{+3ME%Me;4aywlG7@<^0^9!s{~`3v0Db@;u+P
zv`qVkz9E#^WaR|o^Z98c^9N|Y=L5C@I{mTzxy<L^`s3wI^Scr1tlO_jlB<MJ;192z
z_d1->hk)qLM<bYJNL|U$a(mr+q${(AEsn_^;OBV2ZuA}jcrC)V{O?kl44|7b7nCde
zOyfNt{5V0g0q~z{iH$4M<szi-CWzntS;)hlz39;X+)1TeRY=>IrmL(iU8HKHy6vN8
z_yRUDTxc}(NfPpWj#0$MrYM$}N9gNkd=-xTM_*(d+oAKvYYJIVmOKsjsj^aPhTg=G
zhd)c-#4y3Uu-1Eed(x6`JoGJ!2`^nj#vwBeAK1Bo1=@1H;xfmL(JnO}B9kpr>LmfF
z0lZ8z1Eyv}9X}zvpO?1?af~mz)LL$W`2(^B;1~HEGs4^2OwATFF298e(vM8M`Z6_D
z6R#*5!~e(iM3QN+My&Uzq2U4XK^mRBu(_ghjej591b_reqE@Yu$)4dPdLmu?{QB&A
z9uW~xE8t2NfbNtn4r;6>|47SZ-kKx7Kc)8MACc`x#=Oi`k{vCGV+Y45qM~*`eR|L1
z^v9#Hu@#vwWG=NzjeX@Z#rmaPG-N@8IsT^XhNehDSm;{l{-<;zVt$g79|kG@Q+!=Y
zM}UfyIS<E-dD70*C%%=A<xZuBsIr;Qq>@Z?RWp3X0df1=djL2n8<$SUBnUcwfV3*`
z!kC`O$6qE2wT{2S`Cofk#6J`&NJn?I7A-T2s-&(_!_i(x%pd2SM#EQ@Mp};+&2OqX
zS0_FwXkv`+c=?M~UGGGYY!^QCB!o~a<3&+PNwy9cAur{l$ntf@CwrUs|IRB>S3fDU
zX|IuyA)6+Oi}NAbO`r0sZ@0JNlRfud1~w(3Yev;<6;2pNjUjd-zq0G{TlhlmL+Y$$
zXS*00aMya}>zyNFlp?f%hde*>IqGp}w0FuI(sG)L%~S;Leh#TM1r);{a5JrSLBTJ!
ze!6Dv%^XM|x34xg0WO*iw@6z@MAWcUA1KytzTEt*{1PrQV)!QKek&Wn=Dvt9T?$TT
z{d)Wre(e4#9D+_D_Zh3)R6HjqRRED1^Up~=s}tUfkDi{R6$OS335g+pN%^diksk2K
zCGSgW#@1L~4F5|Wz#x#;6jkEH&_V)!Ch`S~Yj*xa$1z0_lLoH%7scFw^v_vy?<7fp
zJs!E}bY0P$2?z$eGy(##7hC?)OkISACd{cra&)?tD)?P>+5+Ao0HkFkb%dM}O~$KY
z!_4#Q^^gC^7%;TD>vL6vy!*SYQ=<ZxC#+6?+6MRs^E5s1oKgTH_`Fqenf~(U<O`{;
z9!(E4v<#1vs5A#O;1Ac)5K2)L8OwXup6=$qL@*UizIgU5-Te-T1pam)tQ-dkG~XN$
zBrRGrR<n0NyS+Kl;mgQ)x0$#y)&*;Mk-!J}h8~U#)YcArLVlG@kaqHFm@P2zHyb3S
z^hy35rt+xgy1M^K*-6mz+)ODtezn;bqunnMi~d-EHG0v!ho+;~f=5vr4a#2wg+YSy
zUINFVu9baB^$aY(enE=9x3%fk>y70qcK>OBfOl6Al;0$|wa;swtW(PjsmCK8>G%U0
zv&$eCQpwkum&oT!!=op0R1o`DLTc~vt7k*+!(|YQVpRSvp6Cm74ZAIh+xHXb{@VY@
zW~(PJ4<f?eJfZQM+aDnu`Hwr9a4OgKwM0rw`#aY6fjsVo^r6phopAngzgcHI8jzZ5
zJBSTvA`lN9_K0XC@{lJqV+x%LPvcR;t=bz>cxZfX+MMTu_*XP(C))=eS<~fnzZJ3m
z8Aw7%V{J_~Ln0Mw=q~tI8&jMj!zdD(iyHn_n72iOx1SX@W-2lNWA+KCN=5q4#9soR
z_2=ck(NcR(yge+5^w%fSIF{SyplBR5{;RngV?W<36jLM$1pifVTaB@TPxa1$5lDaO
zqx^JteQ`oYE#3B4OKN<4?|hMqttkJJHk{#l)rItY^H}d+jy}D6x8wOl0P-0(92p*h
z#X`>GBn)@CWpfpF4jNkjng?zEn=0Ko)=rWlDCn;S9%BKWHsJ*w8eD@|gwx9%&;O1H
zlr=H~Gv2+SO1Gs$L0l%4`{1O9wR&A(4+Pm5H$A8xSkah(0vcXW6{!VRt{lVLY4$YU
zMM;(Z(}^<OO>-<l{vWMB%dN{pNyHcWCXTY@LMD|C^q82S={=)L47OT_1}D9iTDLJ=
z7tG;$RDI}tgYCgZrbum+_gvxJdgqYQZo9ORfE?ObdHDM3yTq~`hvA&6Q*!mr!myUr
z*q!}(*&lu&#tZjYm4VEQI7QMb_tLIfoyshQyN$6ueZP%~{Ep(CYt}lxb8VWs!ihPZ
z-f^-z>l^08o+h((Psi_&Br8b?v?ALQj<U(9Bv7pZKbxKZ<2vh=xG`<n+PPn+6h(ax
zwkwk%eGKZ%HBrSbiI8h`TNi^9Rt=|i($SZbN8jx>>@=z!cKy~O=LytTa_V}G>zQi^
zifW1czcxA6z91r*%TQ08bncwllX^Kg2_XrnHD=ASBdij=k!U=AN~m10;KOXs73R6J
zx4M`_tbX!JL1Bm6rjE@LZ%<{}$UG~f5~;9I{UF#pq1a_7)x~=EKLq=~YOMm{g#@}z
zSGx1m%K$mpyJK=hAT8Nxbor`D<oML{lD(+R)Tr2@?mWa=1&zp<_s@^KMPlXl;ZXv0
z?Qc!aYZSeJ2CO#O1&KtF^n$MeQDln6bt41ZKsT3$GU+<L#uOq3Fw#4<vZAsZZ+O?%
z5n55#kz;NB1BS+Xpq=YDgkLWEcSz?)GAxgZ&Qn7UZ&%s>bt~B4{xDG3!xMwv+Tj1W
z!|qSF+y4>F`~R}T?mxr4Ik^7YA0XJ9a|2-B#GOY6uQQhD-Z8Q2l#!ET;I~Xlpga@y
zGXiIGs2J;fdkhmzk)&mzPp9~}CESVe44Of2sKbhjYiJTFu_B!N1IieS__4$xniAxM
zEB84C6EOk~@~)m)BkP{W2w$xXyV$@inYz8yFD;r{5L}Jt!~O^4jik4Z6xT>_%TrM@
zAK$zGIdL-TJD))8bqg~@LZ9#Eqn$noXX@8`O<Hwk_S{L*-0(*E)t#9$UtG=38~K_q
z1=$7lqStI2doF#L&bN}9O10AZ2?3}@dIaZ2#ui!)$5}yT)GH~IE6{N(2WRRnbUa_S
zqkMGCc9JlcT)nj0uSvQjXNH(4B)J04&uXzS`45|(0DqA=F1|l(@?P{rCUJ!Sg^0<I
zLgM(t{etCMq|#_Q2lMdrp4NHrwz9mRzcFU%gM`c&h+n}a<}Q`OXhZwx&&8m8Ot+sN
z6XYj~ZWt4g9)Ui0+3MOkU;pbJ^)yWdi3-by<~y=~hIhpJXUc091b$GY^#7P&J)qMp
z;R}D25RK!kABpHg;TxW%1%LRA9uBT<UQ{b7)2+MyGc^fUJqRTF5Bnynp<f0wG!@GI
zry5n=zjjqYSwW=Iu|-MDxV-#$0Mo!dvvXm#)YzC~6Scs2J5w(jz`V&eLIMAYQGlF3
zV)!G0<|mk5on&VK=jekb)7Jm6={v(K2wK!S*IZgc!7SA*J50D3Q@9pUgo_~1O3v12
zrIqzW@leegn2AKc(J-isa?Fvoah~&9g!@Jy^2OnzyCsWDRmk_|R#XzwiV9zALQERR
zQ3utgj~Sgq=ID;u0?C7+(Nc|r%dY#lm_>cAJ4=N83Cps~@dRK#mqk`flC=y*on?MU
zo^~C_c}H=lN0|GAN1I7C@Mlk}mY=tG-c?ibk9?o?Mcx2b)UpOlNCG|HtoaoO^=IeD
zH#LfTN#q*f>rcrqSbzFK#6;amN~VaJ{%WG+=;h|sHaf*V9i{UoLF8rQmz9*;>uu+U
zF&j;%7no}RJDaNu;*;L)vc4pujfQ>kTP;Vdha`nwS$ex=`zK?Y=zDvlN&Ou<-vr!?
z(`0e($3He^lRz=?F1*hul1Q#ajy^BTzEGt}UcA_V9w}M>T8V9P<p<fgacYfi2lC6<
z?w&D`7z>nOjykyk|N9R9I$u>*#sP4D#B~yx#@|q7Bdei7LLz;1DYdt+UfXkM8RQ>&
z)gw#7th>BK71|N{Snc3#7v*SD0P@9#<Eib1RZeN?i5v}yG_PPq+{5A$Vrb0}sonAt
zN@!%mgJb=CMZF_WtB&op``va=|J7{u1O(s<CgZ~Q;?L@B(!!iPT0WM8S9i`BbDKT_
z>cvc-r!w$83$AKc*Qw6<2kV!XBd&O@JHhl9EH@%&)!Qj5{1KW5gm<YzM<$WshoATV
z)poc({jsROItJJqU|VntFy{a6K3q;y=l}a3^O_e@`_k~;-#E1EKCc@LSd~@1rh@K`
z{AqCRdB7$CV>Wc<(sIC+vz9e>hvA=fi8=yUUv!?>B)IDp;@l%6nZ;cGYsCMbdH*$W
zRxtr!R%JWL$<gP1z@{@7N|7<oNU-lxnU_=MDN{9ZF_9gGPlE|bP7l~Lm#l0WX-NjE
z;EJ4Oe|w`Y^uM10m>=Vi<=SCI!@N+u6E3~Ro3@i<+M<ixS-^bxI6m%IEw+3t27aHh
zj28P8Hq5MI!oBAvR8oI#Pvo?Pf9-JhKD6yn<>u!di`*7Bguf*E3Jp^cZq4*}uJ=KU
zDU+?c3&htm>pHJGdhjY9T!g+WT=(OJQu+LN7@R%b)%|F*_-Xt}t3~dKRokbK3$~KO
zx=cm_r~I6)Vp_^S4lUm}>Mk#3Cv7LIfBsExSVz}u4U4gQiao&suAZ(NCgbVf6P4}S
ztKo6uhs86g`o?-`531)CzvoNCC1qx-qLg|)m$=8OLG`*?6ARyKROe6&P{%3H^65%*
zc^MeWz}5_EzfJNb;wWu!R+|auDCvy*Z+a}|qvl1ooi+m$`y<{23Pl>3@a@e7BNCjw
z&hx}^mQU;*{^ZAx9~l(E6lTbox2`l6aXLhTijS7A6u|-B7I*9%X?^+XIA9h(IPQj|
zvXJiBpBEr$yfl38DMe#XC5Sc!AW22_ZjAOd`k<4hRHU}sVWxJV9Q&SHzM_;)Xv82c
zZ-SXDoAm<T&f3g2v?yTxs4z==<hhXM0jGcCMnxgrRu*__Mp|`H+8$A$L7$i9HuG(A
z*!S~6rD)0T;i;6zx|V6<;t_E>-lWb&M!PT}B#!|){MVG{b9W(u&0*b1%Hx#akcipL
zF0<n7>O;HYLf&J?r2f6#(yJa<`9y+~w1E%B1ZD$}Gu|jpV7scxS}RTQ4ci2V2|k^Q
zFT!|JQOLVGq*6O$v@_LBb>})yXu`{Kn)w#hDr_{uNS#e&6=rNz`I@S%x&YiWx5v~Q
z*Fkbs;-18>uQ&qYt>C5Qr_c~p=r1R^5|5VT+Y7p+Je(H*Q0OfsnI?la+75hs0(?`G
zIF3WkUpJ-ftX)YKN@!pIanQmIQ?s`rw3HdTpAN&aftUIY-c{--%fhPc%Pvb8KM_(o
zG!$(?GG38{dgw&J^6rwh6AmksLL*wDPY2%^57e!Xm8q%Di2ub@w=J{!iH$vH=;X(~
zw>>Xv%_}m*_Vza-eO>%;J+{&jxgI@Uu;*mTN8)xceChQNq9tc3$q@4VUPAYGx6lDI
zMuF>A(0N)Rt4l@0S?H8j=xI0~(fNSjG{^JM`=hFBC#Q_B%R3)eH$K^gnwlxO%3B#`
zIPo|(FgVpd3(ZRo=G9ntzU1V;?o1Qcy_3%yG*<a3d~tbxA(F(1QNY3Pmk<bhI`a(k
z?vqQDn4cfGF;OV!)41vW^=gSL4e-jMk;AG=wN?M#XQ6(Dn(?-7z@*1rrV{uLar><0
z=9^vbCMk~&Qpi6gWApOhg(95(@Qu5;JYBr1qdM%E?`&nb;IvH~^r-usZugQzf=$kE
zLVv1`t0V29REBSA=AiubSzpM7_d(T^=HHjzyia~WAeODy|1Y9Rj{h0p#md6U^WOzf
z%qjOmXSi_ZZWs`CWieTnf8geHrYa*kkxiAysH-4?;wzqZrBaN%s@2q#OBaYr6=#$`
zP9-vV7kJU{GO*V4mAGl~_p_Rf+Rmv~@P{Xe?~&MX`3oQWu!>O-Qn5Vwv6-QFXs0pe
z8q2J}B;j;%u@e0X5fO=5=<Mxg{_T|!zAFG-+Gw14F0EA>(<fAt`uQsA;VIqhp=-VD
z@qOWy+8E!xS-}KM-X|wYEalD!xBN>P#ol|@eS~Kp5+225jGuEmg!K4Me&K5`h%$!Y
zmgp&QKT|;odh|LLy{Xslle)~cTIfcH-pch?BP~s`ylaepnQC72nb+DIn8Yv}ok?zu
ziPWh1r9qXYg;lx0^5Q77y&$EjjYBnD<ACs=JL#<Lx+$`*LS;$VyFIm)?r)svZ0ufY
ztJJ<@n)PuXFCuu17OyiC6mg_LOuw%qkF!<Tb;l8}!XSn4**b~I0JV(k*50?y{+ccz
zZIxD<UZdbRb@7u2pAJ^6#r5uO#&f16hKfaW+08KgGc!sM4H<Q}Ft&TRo{UUhWF<@%
zN$h(eCNo3s^Y~ki_lbf(hW2BRAOFP4LM5v~IOl9pk7YprU{VpzX0Y~SzLNB*cTZ2A
zC%zLMgZgb|AO<$BM)*&Zrg=YZDwKP+WnSN+v98HW1-#Vetok%zYZwHXIZ%%Ebv;!?
z>ijitxf*XWQU@{ghX<l_xI+YcUFA~DH&glm^p6Mg!B|bG^$i!sO0}Gs*wdfSjXtQ{
zg!|f9wLZyJrS=$zmPqT@x0Vck1R@xu2-|`f9jLVj8QA}*kjL8%B(PUDo6T-NS4+ZD
zM*}C?+HqRUDK%9PNIkMmK&rLa<^Ndpz~nD$Y$!LapZUrH{cP9btEppb@-G%)EQGz)
z>;T70A!<`{`;3y#X2&mRb41&W496aq$7@yB-@f&?wRcO_cGndu|HSp8ZN2MHedD`M
zG5uRDqB7wsQCl$EuLePi;+s61EH7xot8L&ZwQ%C_SJQA0<tK%D2Lk)-rmv-h$T=w0
zwxw|F#u_AWyjflox|iTTE3UyGRAi*z^4`QE8GK<2EU0~W_2NzlW62jWJ6dzsUT<2Y
zm(V^>nKgn2V*e-M48^+&_dy4dgV^sXd7n7?93Rvp9@U|c(|wZ_vWnRCPYZxD7^}n-
zIh5bbeDl!>?o-EQhI9^|*6P$RAPIdho_@=G851FOC{RRDy-JQoBwawMYsks+{zOn5
z`za5lMj006W0urC2G)ao)3#Fa?m^qF$PNF_sSD)@LO=8~tfF8=2UH#RXJJ%IrUsbF
zm9mE#efN%vK6lZ|(=}T@gnD*Zs7NSUJ1Vm!;GH`H4Yf6=|H6~7{=YoQfA95UJiPyb
z{Nm#JzxWmbFV}xUesOd1aQt^2*WMhe8s6GGvLy^x0YB2DFr|9AP>nHqbXK-cJ=Y{I
zPLDe5%-*fG$S5Z=YMx;|3`UK%9`)%>P-*~**H6RhA5}lT^+0!bcn5F#5{)o+w0JlA
zu5aAdS2i4ZKz_XJnn@2Q{Oli5fNw+n|No&ln4FrrBYuyCm;T-x31=gqkV;Y%ixUw<
zj`A4VgzZJPi1HrG5)q1phyX_M`bKH#Lx#fh0Sgi2Ns0LWpX(rU>X1Rj6oNm$0l!=N
z0Jk9m4?l)tSyJT)i-<64HY5mESuTB0Qi}O1<N&l8E~3D1RL~QAxIF<N5n2z3VBn`$
z0#;U5^)46sr1A#YT&K+dVC74p8n5HwBG47?>*Mn)gu=kU;CdOrKvV(<7>|eh-4jy)
zPXK_5xGfi(F6LbZ=jR21{%t{CvuO@6xKpFv`R=e4bGH``pc3S9KHZ$LXp|xa@ml-b
zp8^1!ke94p_ZPr$r~V_{2Q!rxrlxNm7y&pL09!~oSjdt|4yTbR$=h38{N;G0eSN>u
z89F#Phzv&Ik)sEbdfc+QEgl>Q+3zYPrERN#fp@>(T<;WZc>|5szcHa}v;qQ&_n%UJ
z9wbLaeR&Ozh;C~Wz9wU555M2p-rnBZlOn~%!z*t2wE(aX78xl*I^?hqyg&F$UPhjo
zxj8_d-Suh$uT5QDT@yBi36wBG+-`73`MI7AE+L@=DFEylZfz8W8oI16wfgU_;4`ZH
z0u4jXZvZAtTUuN5Rt>wuUY9AaEiE-W+buRBOixc$Sy9e#EH`FURmB8><0Vg#l~q&(
z-L4}F?I#M=rzVB~Xi_5yi2Qgh`+bJOWtfBt7!?&&z~x+*^tX511}!~(kXeZB?qsnj
zG@BQXC$)%F(A5#h+-}TI8&5qWBk^tsr(?8uQv(A6l<J-B{4t1?VEYS=jR1xXK))T`
zUY+oRT`u<RtgKRKhdXWEmha<xg1jh&$3co)p}9P%omGj6#2G(-{W=8DR)w%M0BcmF
zT$<Su?|RsbTo(9&6p)4=<2V4r;nnNGy_S@e{O;@fv=`gVaIV@0oHpE-z#G06u-w|v
z`VHZU;o8|^^AnYT;RkmBr2)WKmUyil9J1^{Zdkau!V9Dza^sck8to8|`T2Q`SMhNb
zqO`bWhN;4%UBf`5@>FV$Da78|nr&vRPP;t_=uF4I=a!b1K8-9j7+hBcmx=DoR<qwR
z7t|?1_4{Imvt&rog5%?fd^Z7LKxR9k+X;Z7C{dSr1GeS#d-m$XoySI^0|*3a!cJ`&
zhY?;wzvBeSh>N4TSzaCM1qDvKy8$L6*(uAI$7R_@!^IW#MHPVb!7+Kdx>P)m!Hd}>
zAjOHu=YUE1pmbp8a13cKc}gH%V_W)&c>-`7=yn{CQrj;+gWDA%w5`7_@WqqIkDIXJ
z&x)OzPL=2_wfO#Hc2v$dI5;Xmv%Z1?+)RNxQVjsu!2SLGzt?By<^Yi2qrVqvSXqk!
z{2yB0#Gjkl`VW^{?{07Z$Jlc<cK5eef1iap7ySB#^$!)og8|r-u5ND4f2nW<&`2li
z{ZHVw?nya(0$}I>Bo9NT#-Crh-vaQu)1xDVzn4qhZq6&`ozedt3$QAq`Y)e9|4Wv^
zXtwMwqu$@aX92a;*`KZcONOs=|M+-R{S_yie-uz)JOKAQ17z~|va|jEECBMi@cQGa
zV4DEmL+)KY5}aW<QPEHmx7$mrKR%2HM*ui=zXRY+2>(ihmsffJjPQ?KSzqzl?-uKH
z{woRq61sCXp^5yLgYd90Z5^HP=xF4B#XMVO1>k(1{=L4kw6p|Z;r<;Ma9rai)zxqo
za>CP)^intS7R6$|lM3Af=oI*mKhlsQ<y>-_V06bTp+W;m0z`mp#;g^tSb1<5jEszM
z3MNQ|s2lGkq!VZ>FE4+jQ^A$0ANyf}o_9I6q_VQJQ+HM}hGhv@z!8-!vl+2gzf<`l
zZB}lYfq~Uc*@Z(2_yf(dQ=-@^;Kv3R`*Q#^Cv5z!dbM1+$gQ^Cng<;LFlD&4f({@o
zNrfVy={}$WiQD`~v-g(sX40^+_`$sHr6rr0O0`}W7BtNlT{hp7QlM+JBYe6MseqDv
zWp&kdbBG+<okh=oTn``rJIhRE^o^Wd*2R3I%y>RIHnv_$z`gdu*_exKYT2UL8i;d<
z=&p!n%)!u`k{qzTBmhK-w(y2hK5Kis2%?em+u=k4k+@#oc-W`m<FXI8^i!cHVET>h
zj+(vz1aPL9vvbYRnTjQg(SnA7?87}3b*4K=fP~xMBYF0m+0Rk*8|5i6>I+NVRLMAw
zt;J^V54Y(fa+%Zge3t9$qTB8?5*nxvpQ=5iHTxRlJ8r9I3W=m-tH_TT$D3evB@PGk
z0K_upw$qRU_zVlNu)TesLaK9y7oIE;27&6-@xkrlbMCZY_=ZR&$;QT(Ur<n5S}JwP
zUZzswT0M{o(R>{kc;;T(qlS3hmT^Jc()chZlGg-#8(iKqH@qljAItA_?D+&)^709-
zU>Wb}gqVPQ`KurF?S~<>(x8KuVB1c+Cp>aAVCRARN8plxtgMaGt>02N?B)y9W-~Yh
z1Wy<##*(R)i8LZkgV>qn0ygk0)1{%G^Sj^1#>R4SaVaY+XWp?M9i?Y?9aavn_-Put
zZ~q*&&8jQcW>*2zuZn%M$L7Hy8yFY>yrihR{O?onR#vMJ70q_M=8!}B@i=x6r?LTn
zf|k#c=3M7X;(4!9P62~Gw()U->v)O=Lo|R{G3!5F8uDmu6<O`#uA{YY&N8@LZiY$l
z(BJGWiZwXxl}`YB-1kG#r%!d89u<D7wlN4xc!Ll2Y!vL<<cuj4b#ByZz;0heDFDP6
zy}Qtu9Hi^Rtx^+Fnr)?Hy%!hIuClrRiO|NzUT?o&6BRnziY+?6ngr;Iva0HDkLS+O
zAN_5A6Fcm(v^$TBhXG4?brxuO@dVtn9x%E;QnDz9AN$sh-i-29N^by@%{ohXar=4~
zja)c~DNe0~NCo50M4(Sso}Df(u;`md=jTamdJ=cF->)w=-fWNfzMSk~!v{meo2o}|
zH>yogMvGpc!gPv}psPvw`EOK;^z`+|H{usO7Sh#4&(^yRrKyBPL8$UYo|IZ_K$s#U
zGx*j+_4ALM+V9Vnx|)Ei8X%a;Y$Z0Rn*tdFWNB|-cv5M^u{vWB8}AmA-`n*L+G9_F
zS7B&(twzWr#{x!tau+0KZ(k7-A|)y+>ej%^KWnQl>Sx_%Pf>0RcKDso<mTlbhya#Y
z&bE$4f=0jqK<2?*Eu<vRpzGFHC9}P*v7ykx_#@!Na2SMufGk}+-1uz7>#=;ae#aWh
z2{b}I9~Iw{s@-{S&Y^pAcy(UD(kEd4ZA-gRWE^0P_ou&QIO{fBo|@<PjL3<>zOPG0
zoL|Q!Gwp}|@x9`_Kp<dkZ9Q@kbn7}&&d>>a1i9zt0(%Nt(Rp_cMS6@EXokF?#KtvQ
z6l+^uT^({}6cDJNhPYnS8Ndn{oo<!vL5X}<Kif*^9JKZ@8y<s$mH<@{(FbtrF6X;B
z*CxZmt3~1iZLJ>{@z}o<+ncmEPHCo;6}Mk$0#<>xh|(S>XLEVD1nkHo$N=D${=zp5
zXL++rnZp>D9YHP$bR`<0drsJPPhkY5Jey5?4#Nhg-~JThk$YFB0z_g=1rs<gUc_^m
z<udZcHQLY^z<i%pyFf1O*q47O9}aSJtPd794wRqGFN(2(QtZ;x->8%s0{A?@TV+0+
zZw`OF@IAF1DS6#FTw|A-A8&<xbkMKaegF2VVEW2`^&2IeabsWy+8)azSTk(;hOj2B
zWn;JYW_vX7m0GwE_@1B?eRq1Z(T$OU07_q_Tf>P6ZV6spTpaqP)d~c?q0CENUQa>b
zPH14cwrKL&%R671g)F!I)hCwee&e!sMLBDg-jqw>2??eqCbd8)U_I#Pi_OiwH29pA
z7%T{j`##THOz~P0*lOvLC<U1F)&$C%VYGC#w6vU;T;p;ZoAjttM1`!}q48(#P1OpP
zR3^}El^(@T44w~d%K=QzOlcu-^B@D$j;ALzy_0KX81M0?l=N0mE&dWZj{Q(^etv!#
z(<ZJ>VKg04-CFLC412dk{o{k4z6eFh3a~ud><&!aapRTD?5|a~Ku;5-?rpA@{h10E
z#irT`Yf6C697WqrtYV_cxfbJZzJQqOeezH?YxN_*xWj<rD(Ay;%{>*?&ZwGUKB;42
zTCF_6`u%bu3$hVhYimclMG5?Us35vXc$6gEb+nQo5<&<=##Cq?R<vFklA7kw_BiV0
zd)y3D<6%3KG_|DC4+<8qGzWnhzK9vrBW`xObJ$_u1Pkr34$;VYyS*0$+)#So?qo`B
zlZr}QF3(#R`r^@4RENxmx6Hffw1AI@=^oG*Jy(V{T#3r%QTM%rJM~FTp`s80n^s(S
znpAu|Wso3p9>(|5cWG0>@{3k*<GcHF)m?1B7ybH1`M%VF*=R0~7w6(2dO8=C>($Lg
z<V=Qgp%-NtbX2LxY=-)_+#sVBW+7|^z0*v*is#k|eFXSh>|n|oVRx4|m2L*sm%joC
z6cE5?_o9|Qw1Je#v;`K9*cH4g;M_H_OZIU)_PatgtpPOH?R1YJwmUcQxELzPh{Kk_
zX2ZL!(>sm6h$v^{9{AN3iipm`#tZhxw3(}jidxLJ9>ZVSj~>gAxvY3(=`)Va!^Q}1
zZ%;RYK+_@_Wb+kk^!D`k%V7I=v0OOtD5=X9bi{3AI-j7c`z;NvH})IN>nJIcy7yE(
zIEjGnTW@p;QTfN8DqYBb82xRP&S#dcPSe=yyC_xy_xC1H?Rm{X<<SN=b&GZ2)Ct(%
z8A{4^xJUbCD!kyP({(waEfB3T+ZVTDcww#_5v9$@Ei~U^p53zU!H1Xd6v|S91pQvi
z?`ZSXKz1<ctO~aRNHsWBG-jZ?>(?uDoQq;_L1XKg<cng<pr_YukzB@oR<_6KYH#O7
z)KVw*4Y(oV;ra%Tle{RmEYoR#%>wX7PC{FF2J4rET>0)<dO7B)XOScMg>K3)l7Xv7
zAn`1v7kw0#x+AYgS178T6`4#Ju5p(32RVPlsU+6?K!pxDAVOE&jU)3746`r@iG|ko
z-Yp0Xzx^jtt-Tb*)%i&ZXo=39TjTwddE=0)r4Rcx5a;4tJPMs|N??qCaJ@0vscVAC
zURO55aCutRCUiSt7LKy!{!FF@yY)^$I_>u+YcOje{z6>zAUgL2DhRa|_M_E!u`33W
z{;<I{o=OpqpXJTg1S9{oQMun_-nYqo{wH(Tc56&hB{l!B0Q2l6Jt$nV=v*iG@(4<$
zn4fY02xv~duBN9I88mjHjY3eGw@M5O9E%JY2W*GlWjG0~{V5Nd`4Kv_&6wDevf2`t
z+XmG^yw&`U-nQP0as}i0H%e^YTn4o>Lp302i9Tt(O=0CW2#q^Dkk%ngAEuEW$|x%q
z`MChByA|}5BlYsZi!z*KQATv|D`sp&vkv_N#uLyho&t`viLdr29WqWtAn`gShMSCS
z4ImtK{026%a4#wE%6vN4Lr`D1Oog4stX0eO3uM&a=Y^^ECoCW1F_3xtTPLEDpP1Db
zkIwvD&XQ`pOGVwJhJGqXn*H9%#l^+aN8T`_4)G2re4<0}8hx<DLGUrS#DsWEUjYNI
zlm6^?pIAz$VdJtPpslT*LaV1uvUgD?nj}@5ZqGqW-`(->O`PYmyhQLwzW!XmMdo2Y
zC>Ccgh@DECz~5{<0c3QKlXHaLj||3nGyV*dC%5x>qs(D)U<}x*=_jxkyL_IQ2_YK1
z<(Yrz{3lhYL40uat_ZGj3Y{7Jsr16Mx{NPkZZ~d&5H_h~NmUNxw4^#kAjyz=#qTFT
z8|Eh?x&Uh`rTC3h*D}Xs-0=)BQ&<mg(ByLOohKJJ=38qb5Ne&;EJ3%GaP;C=Am&%-
zg*y}JPLlFQf|opO?O`lij8IB2ICc0Nr4|oP=H?S{xu$u+WFHAid%x-9$MQ`u+Fs?x
zOF8sU(o~q3@bS@j-`ZSTiOVwx5FkDQw`ekiO-{keIDo-NJ7inZt9QwN%vDE6SRiKx
znTYtU!=w{=U{P-nz%P~MiNNo{=71$g2@U^Jf6pT>e5*V~BV!GUjfG{?T2j5+$v)Px
zq*@0Ud_L!fx{)vLMOhFfnbc=FTE%*G1h(`kG##@7*6g+2z;wNf?)GTSfIlMG5Kuw*
zN)7{A^RrpZ6@8}a`dgxZa-7lGNXBm>i`gA7xT=TCq%p1sXOY3nHm8T=)i%f=EnO)X
z5ES4V*lo31pE}Nh%Ao1JvhNXqZ_+8LgaM@qt0XSKvstfhR?Jl9i@cVKOFWZ#1Rh!2
zoEOk{4I#c*D2HdQzE-yPlj`@9K0w4d9Lad5Z8k;^r8F_YA;p9?!K`_LM$?~zn&*@%
zZoW+qHY0&kts;_TY8*C%<M?A^BZ1T$M!|Ev=izV&!A3oYcD!rkuPI?xr|?k64|Q|`
zcuRi-eeCwIDiax^e#zX&7xw#IV;)1NY;P159V#%rlMQZ$6v<l{>FfnM!y~ZuGA)h2
z?QT~fhXDpae91a38O!Th8|HKTzUXrwtuGa~neUT$(`waLk5+)ty}L%~q*m>+v({w4
z@k|I<lfu#x0;W|S;|VA(K9Dw39l$sfN~uktY;0ojT<Ks;9DF}uF93X)*y?ka6KZxv
zA~)dQexSrN0O_1O1JCAAvWDPKQiCB9Q{tiGMLZM7Ubl;4`k>cThZ`jTM^t)H?WlY`
z7s2x9L>kk?XwZlbSS7d3dRG)9K$3U@<u;~bz#8rPogM9XM8|Nm_bjbynkdy<LV(?F
zYumT9l)xTaTbs!XO;;r1ks}0)h}aTr2yf3Pt^#qEC6x#?wJfx=PupP>aDPA&!djBq
zr_IO}#bMGBSY`<9WM`q-$EmRLx|noh-kRbb@8kxe>Mb6m>&V&2dJEDAWzD;7G9H|!
zRJ7x7lmf_(L$H|)e5uJhcEFAF@yYRkRTyLWq8n437K<>OVOu=8w^cm^-ze>Q@e<mT
zVnn0!2kP%WoHWCZH;DE0U;~L$m;$963wIw+fY>|iF+3vXC3YE-k~h|J0wR;Xo-e%|
zT?~*en?WEvlXgriEY*OIy1c^!I4Qu#WHISdW7^bI%oEWBW}AFDA4t3fm0}Gtz%!+-
z@A*p!oOnr8INUBqFuRk(J%i8rs8fI~(6=2}lS`*O1p_(NW)Hq-dddChF?EIU5-mA-
zkva`RE<4<6#`<8MvQNQIPHn@mJ&vlAW(cqXTTJVg^hZkxdgawyyuezEiW$+_OzG|q
zkPZ6o)xZlgwLXp-beF2CH9GvxW?CU`fxuI*2n(X#X!H$s-R=E8fY$@utIw9G-507J
zLPJl3;d-8eUtN84GvhrGoyI@}QhcfIb~^po9kcpozyNqYmj2<wqb6RWFZtj_4p1G+
zZn?P>jW{%rV^<F@BmzXo=H75EVKAJ)o1Tt1(Srn5G*?<UOikAczpP)okZAy_OI<Nm
z4in0&yM1b>NYVSq;4urO#-ZEM_i0yd>(|=?a3UGOP)h@v5aQki*~fr72@s6~X>R?(
z=Y3op_R>7f`)2^1!^=CpN)ho4`v@3N-7+m1^{zf!%Y5_2C%^+A!Q*X$P0g@6E>j>A
z1Xen^?rr^XWDu}#=b9a#0JT^uCTU}29-go{zvn#hYHMRZ0Mc_6M@E28>PZsKG<&9J
zc~e^I_B|)csa~mBQn17aB+R+7^l{yM_m*e+d=E~gRV#c?ux)f6uNQK<eF@C#hKfpF
zplX8!ILn7y<aR<Hwq?KPN8g9;GF+CPt~-YTPbo?p(E+~e)~-`@q>7cjin;y?2-NM?
zD(cYy(HT%5iHa2{F?!+QVFozs7%J*8*)&^^3Y7{f0%nPmN628b$zMw|kAvc;HqW_f
zXu=WzNnpPa64Exvn&B3OQlX-T2>^VvahU>I-~EYs>%4}`tgG4vm>Bk)s7l@Ii?OC_
zvw|58Tsqc;NXlxGUkXP9b@e+E(irnvo`YTq*&@H>hL%~#czI`deDkKvP*30@`&MS$
z9>`&(?VD4vD0T&EZ-)_a_XvDsxENJX1}!d%c~11WAN%k=WXn=oUoZu6;#nztZhDri
ztP<AXCN3W6X#ub1v5sfl7`+hSC|ho*apQB@L$OTffJ%JoPWgDN{ynyRju@l`+fNi8
zFIX8s=W$mnA(mLo9!T-fJl`dstJxw;4NjvB40r@qqUr(OMkPs<w4m;CK|Me3SGmy)
zMESjxs+D0F#69mvQ=eYl-%MjB!+qFeAR5-~#n(1dH=l4j5-Bku@Tg|Aq@sbE&kj9T
z`W0Njc(u#EeyT+I6j(j&kr_ei*R1Ra`T0G29cD9O%#Bn>M~h<8ptbdF!Oo3j8Y1qn
zH<ZaAp99=rFEW+AdL=0&*BiC=nNSb}5|2nrrO+4MY4ODy-2FLNclda!BBa_8h6=AK
zV((^7^75`OJm|-Xz5$bvtThl4F-Q#J@Qkc=zbJ9xj^&kyXb;9i_Mg(xQ4`YuMM*Ip
z=p-(F*c0Tg)$Y|P_PKDv=Qgi`*82m4(Wfe9x_&Q=4q)STOhn)AXzDIDS(`!ZjYfQQ
z4bacy;clG+tVm6tJ1hHDUla*bzeZdpam426ou#f}OG_?UfcjfIio(LaF&)GN9r*S~
z#dCa;$K$GIzcuXrMimLV4WbuUAq@PkOPIg_38RybQ4ZDvV;NrcX@&;Wf9Z>1)jiS2
zc|q9|D+uTn5CEJ{Ufnt9*Xgf{X`5@vQaQ&&JTG7tyo+#jg5^PC*TWGyp^reEwBg%H
z6QzW9D~6pI>u6Wf(&+{=Bl*gB708GYE_a`pVJRQG!KAG6ilxF0(=33!$t`7@va!c9
zAlFp0B7aH~?R#HltXNGjL|M2?uCLcFS(?Bf`$oOTwnHKiUW{G(q(}PFGmZRXu9<Gh
zC*R&yi7i(Hz>3nlWT#d)*t1%II?H}HHf65BEVbkhGH@g*WIelvGlvMw#I#m{WJY$j
zEu!B~kp~I)M((y~6gS482Z&%>$?i$uX)cS-lrfM9%arOR{uWHo3#Gh!o?c@m+^gi^
zpnoF_CQ|D7Q@-B*y%iDHV@pBI*P%dRFhJ{_hJ@mk&5Xl59>MxK0IzkuB}iOL@7Zoo
zhyP>fFKs1O`2YpIG!&3l`>*O>+CAukxRyS6twH4foD_zcfzY_jv%lfB(vR5{{HpAA
zZg0y~%(y}jH)~I?7)0U67I_5{w>+b&)lRj*%8&W}B`3<}?5Me0@2m;drV97>IZN3P
z)#6N#u<C%B_k%%!mk483Ulzs2_ssH%f;jbDITlgIei#R<$v^DN&Bw|LjhCbP7f|g8
zS<+h&Yi-K!oA`#xAUFd#(0eSz|LKo#Icz<r6MhtF3S5;0OGVmTo$|lKLIg?~>6xnh
z-+*&TAG)@Gk1i403BAa0R<oturUCw_8OMQ#;Me}^V^QtdTcL)4iKzeLco}}w`+wB-
z%>I9Oy!@Zqo>_TW{tr%+pGzv*z3e;^Vn$=t#L|rO`yd^SFioXmtlv8lYtbHD2ny?8
zf3v`)%~VSE`cqkyFMAZKah|>eZN;;v<j;j)S6!faA%56q(^Pq3vqit9u{vKU7A|Bp
zXhXA*JX7Dd7dv}0b(Or4uwoM`_fKOF@I8(z4<jn#Sah<kEJQ>s6cjQntaoH&-`>4@
z<OzIg2KZ{x<Hsn7K75a~Mgrp?W(PJ*F_O$)WL}Gkd{ue8ZGOL!1m2@**+Kz_=1CTW
zbrL97YBu9B05tV@N_lV=rTgsZ)3!V#`jC6(KQCx}?|zOrwbo_tT7~F^;&svOQXBWN
zK<E@gU-A+$7T?a=ZqK^hy2d%HB|+#JqV)KGe7)*_^RNFz@CXLt4N8F=YX~-302z;{
zq2k4pcCs$ATfWi?b7KS)HWVoKG%Pmvl_$1J8ul(_S<=(yAc6OeyxUI24forF*kmug
zv0{GhY<fPI=U4qcq|%!77578Oe7laHk4Of7NUf6(KH?MXmbislj3wO=_Vt19mVMTg
zWGEDfPYaaUT`#wVv4o@3K6Ij^Xrp8nON6ijf;s$I-7tWQ;brMCnnp7RV`-U-KhOSA
zO%_10`~R@^7C=>SUE46--AH#x9J;%^Q@TMqq`O19yGx`)5Gm<KK<Vxdk%s?x_xn9(
z-pBi$Uq{DbgtOPRS6plDwfD-f)?677Mw8&K9IleGL=#6x@Jg#xSq&szB*HeJukr(f
znm-xjW3NJ7PGAr{_BkB+2K+8{yOBK`KF(m$2<6IDVibC45v=YuOnwzNVkngm>t@6S
zz2Q`GJm9%2L=b6@WQxOxADY<&=7#}>w`jA3Xi(^^_LM|WsYQx$0CQ^-a>hyq(S@(m
zG6S5zr5e>e?Kk5Bf6V7fkAR6d;D@!ZlRS>ho1qndq{z#4m8yp`n8qC#7-%6Z$nhWj
z?h6MeN#=7o8&zCfoJ!FskevAhoSx(+^?W`zu=^tbh~04SS4pecpoM8M5dZ?&#V`qS
z8iB50=+{i0SLvpl2<uyLabMFYlyXI1<B7&vzE&z|Vv?Aa6$T&}Uj~{hGFJMA{0K%+
z^P>fR4kbru5{C>_P1BN6{3_;!aJ-ZUvp45;dI|c6fg>2WCDS*MAzf9MyR48RXcBYR
zK`qzWzCa1m37JVF$hP{uJo|QlA_8dddv0??NGFNh-h_12_-5yhdjcN?Q5hcH+p|L3
z`bd#Vp%c<V9ZVYQc-uBDV1c$5%S$Kfc==I^6u760Y@4F@$S?Br*ukM%w`t8Zx+Ci+
zOnr4?xv762&a{RC=h^b?&uq)}jr;~C-5VMTN-KyoqPF;CVW^GwQuL>KNDyK!t#$ER
zXzJ+^!WF{h;n+zlJfZ<nmyP?HD@x$Ml!yxzqyo2i-|_=%Vc>B_ZV`wF*>g)a@Tn|K
zi?5P6K}J|TrV6d*A(|CeHNm9L^F7ufoXvCLK&3`w;$qi({J5UFy*D)M=PDI;0zc8d
z>>TpO>{wY>Jvgms1GrDEyYi*BXfdJ@QBhGkz7a_rDj_j3F>goi1J?wX(ScSnTGa{;
z!S>odpldXffE3WM0|Wfh6j7kj2#$Y=7fS<mwDOJzxCm1Bl3WE??f^qHnpQ@8y$vT^
zJ?u_=XtXFdV45fp4C41`cz@5u``gLr!u>#Ba4PZd3m^?%!z(~0uET~RgDOK`7$b!v
z<9<6CL>jurF8W%^C?s{e9FrVq$s%aBD#sL!s5z|{uc6_xt3dqtzdz`o|N0-^te6P+
z!Mb^N9O1ztXd;d=lO5Tmx!ZJ&K*x~Llsz?kUkD3_@zY6M2$$I02apB=7x%uF?7Ka2
z!6!XuwF9|EQ|ZW$PX>%~OpUL3Y!coyYKJ9%I6N(RwSiZlZzKTluMkF~tBaewv#;S{
z#e=F#T(ro|N*(>^B8Wg%{8pwp{RhhAOPyEdLl@sF(^Z@VMBjD!^_3jU2pLx24#Dnd
zX);W`JQpyj)?SAQA(mxSoPL?H_g+=Ck^ZE!Mi{Z%3HX2UajT;zh4kENzTQjmw^cBO
zOSPI=YX3-hBeP|h3GD@^xQa2e1C`dYS#+A2|7u<9t`-qaGl%K)oqi+V*Ah5IL){(K
zkTo@d?+ESkon@|D{S%BW+t!P@wW2L%o~hv$p~OMcX{oIk1&gRE3mi|k#!Nb~L9YIf
zyQsHxX(#ag3{v{e8;f;Ki5R>FM&+u}Z}w8;d8<49_Q|+?tuqeM_?xSzAF+HNXZ2yr
z-tdcBw3=TFcu{ebDNuh3qrVt!a^YFf(rdivn-`iGovp-Mv|`Diew`9S+mKA-tSk1j
zMfZy*o8>1jw!kqk(K30*S)37+&O1T+LNF+nkbWt3F9^7LxU%FIotRKMA$e7BIvkHP
z+GtQ|MN>ALFI$x=u0`U-xN|LrzI!*`t$idNBuj2U7AE~I=4~ks357p$EQ4SOJpW(_
z^5*uIu2!V1ten3*OleuyE^d(%<Ef)lbgv-dZ9jiUM6ot0_Yz|!r-Y*wGvaqK42H(~
zH;n43gffa7{{Cz}byO0Eb2>%bgb-C!&fMAGEW^~TzN1a;?Y=zle|B@Zx=u@!jKL5?
zj<!Fc7^6@?E?{YAENWp>#7nq@E=nFhSydU@L1fxT%C-8mAJry1@_ElKsZ3u*;6uyI
zqwDQ16GLJ9=(3CNspVmgxAv~}N4pC*1GnRc$FUkx$_ENo8)ePwo4h=c7409}B~w9c
zJ)TFsz8Yh=iTm@56bu0|8Jm}<{@*WG3_|3xOHNP|QKhntDw)%Z#S!zJoFB&`Qwjo|
zeKZwa2uFn+E=nDv7BdQMKJx3g>9Av=^bCJ_i^O2QtLd493YA-nfhpeKFQc~Hv$HZx
zDM{2r@@~PX_eJl?y@^>8CT=A@%!JXgHgWt;^Xul@V|r#{&1KJ^iUcErg@@0aj3dig
z3Yps@{uA@IA3H{}a3HUdp4c2uBkqrGTf<CyeTF{Eo)8*i!od-9$iYrIjhD)ciYn-C
zQ{h331|dyhP<-~TI2sg-KgcR1nO4QL86|ya(MaXeM_|R$*e&X;mQ+D*fQ93x%g#A#
z$@NwNhh2O6Cf*VTS8(Lv8eE6klS4BfLNg(agr*>fKN$2fDe$F$SH*l;;Va~JM0y<0
zL$L~lZ&|GFm)8oPei$2a9R^s86dq@8*he>QVU>-D+3TS(=0*q$$)8to?t&rXi-{*`
z$^quow7L26kUc3lM+;J($S2yC_W^HMpj>iA`JW~A9y)($&@z~g1{%M6m{h~<4Q{tS
z=s)&N<9t8i!BuRTg+Zw4-ps`hIfp9acX-n<kNGim<iow+D_x}m4v|v*d#fNHMw)Dc
zV=7fKebslLWCi1eN0?pPj7%p>Nv`$lR=?%Gutme(_8dE&t_mo0&HHFTZUc^({%m@S
zBw3ZndLmcM{T-^{%Ad2ctE`rQo;DJLxAxHOxkWAey;uDNPYicxr(rH5YX8ff(`qWC
z3<qUPE24f=K%D2OprQMs%Ds<}iIg>*^uau`WVY8M<W%|8z*=GmfzuT2VZKRYR$G()
z%`02&YvpSnzjBv&A4TBE(a-ZLu29qpe7Z;L+XAJw(y!b%;vyrkr4AX>jm7D(8HFc$
z^NK=+E?ig*;Uq1EcsvGz?@BAU_Qh=OIr!Fig?;YqzP;DOg=LcMw`PtsTBv(AwZ9o%
zg)(ykI*)dIy%+5rKa!vW{8&etCAd~b6}Rm4OTb#}_g3Ee&h2P4yO>x_;|32zQ3i!;
z-!|fEU=~$S$-o7FX$DKe^M@8C(kP_pk5EQi@lX=Q>Rk)m>w|h}5h0_ECHDQsWZ&_!
zEbASr)~Eqbt#ofDC2wSiJgLD3?JL7izDh+@@=sA@l<MDy600dn>mEx~@ISTQE$gpP
z!a(6*iEXUZv!-H`S2@43r*2MWVUg73t?uVTs7B>01T$$YIjW$g^w8)%L!3WJ@j)Ip
zYB;O_fI&J#-o79pf$Lp-6F-4yZvw`E<UYthx0-06)C#UyK?(ol!_rC<z=9nZGPI`1
zww8p}cOCy2t9li2Gt(PLA4sS&x!9gPBhu3>io*8eDj5MLcl?Wyte*n==58b^iVH%O
zzZ3TW^b}eEWaP7UiQswESHrpY7nqk5+b&Jt5BVnYzY#rd21v!=(FqXKnSf2;LMZFr
z)8c4qix)S#FQ=!@Hy@Er<*d4feiRH6_0ZX)IG`?v;Fxu}Nl#t$DR<PY;aX%e47(pD
zDGPvBO*&ACyfGBUg$W$X|LWWx8?tf)zZX=+T_1%U2^p`HkGPrMIQ(U3O9uGG;?%Av
zv2F{@Ggi^y^j_7g^X(8Hh_6HHvLeM1bKjk#6wZS_{iyBUOU`1jqm%|S+eT<hK|dmk
z<({yDOH0azJxWTrl<bCBDIsKlKPUG|a45qHVvvjIeFJ7&Ao}!)PA^ey8yt)n=32mh
zkQmr-P~B|)B3IB>jg&iHn0Nkap$K{fe%(Wb@lpZBzj7RT%RJXDwwS*DaYI3Wz3<v3
z1|qWO$R{nVydVreJ+#q&`o8`9n&a*zpWWgyZ?T&fSXrABElgK^9x^G6x3@A}wg7KT
zZJqxjuAsYoewZ|#y26#h@!;fZ<w5*I3YhF7YEC`4B2tN%zHTsr<J^l{#;u(Cj|d#8
z3J~aF&=~mA8PGdJP5{$@Bf>P~#4l$ms;tHg@v@zkY@wEpZ`Qir@+=XQL75GK>znj6
z5>xkCDXsN@6>)Qf^pTtKmSN%qmf=NF;PFyk@Ks}ZxqsoTMVvKUus}*di6kA0Og*6G
zMWltNFo(HVuZRMl1W@ExBnhC4xjW}W6A;HAd(kUnSS3N{M`?;x;nfC`u5)r?^2>bp
zKz?6!MhJJ21x9UT%!z&wRgOo2iG@r;Qr07bj!dmDjIO$3DkR^JiREQn6xHYd$&{sT
zK9n#EE_$Gh=ZE75Pl02t@tJRrm2byhGlsuCoa{*i9H~MP8un5VzRt?+FjTS*3%r}!
z$jMf1pQu{7*g6<nA7$#3VL59$cw2?REsrFcpJSU_Agim&-6Uo`Rp7nT;oZUKI#E;E
zEH~nJPuMXeM4<k|@@X#5!oL*twyv3XrDe7{XdGAcoz8;)xn8Z$z$Ao^F82KJSk<(U
z0P>qt^T0fCk|nU=(9WVT$Z)}(v2lTI>yKG%7DAe&huQq`x1v-F8rv&7Cnbzuvz)oy
zxUvE@YA+BoM>`0PZiyUYFnPRZTXp!!*Bms$1L<G3$hFLMWf4I0X~|i_OmL=mf6{qe
zyUx?doE6?*v7HXIZ8K6js&1z<_Di|juAYf&g0oXxUn11SZx=eJNiS+CH$ZEbhd}WV
zftFvoC%=5o0GGkJ{;{!7mUjblBVF~qV`I7MM_JXRvKuFg?R09e`#D>IKp{4EqfYbj
z=L!@59r<fX7n)8-`S;;vGZ&_UAB^Y^?eV@B+>Z*MO}P<;>uq?n1@Gwe8@LMO2aY~7
ztAMNO{wG;t0sOO`@&{XD;pF<|i$AkEb`Fc2XivF@##?gRO{Ep?bX@?W{kAu>UyTQA
zQSHzH7<R8y#5I`DA5Pu3RO*Xq1CKrCqQG$6Hi%Ah`7UB{d0$QJ9ql4k<_WFnF|2ZT
zj}}uF7w~-48S{C)GaauCyRtDawTbDr|8dsGI5f2m6B$LDhYK?JFpVFVk9c!r871#Z
z(*M0`-I%AwdX{z*KLdv6sKFR6XH$%N1oNHP+ldi!Iwq$N%al_v;gL9>&%nva4Q7z#
z)IQl}R~1{lj78YFdbk{8Hah5Oj64jn`jR>{&m>{Dma(OQ%$n%)_O$c(;O(GOcH{>c
zPiR28>eq|6a+aksqTiY*^+*>=d#?nr>@dAM0CXji#oWmeLiB=qrxK7}X&iDF#XGZz
zlsF7`JzoiN@$aB;>f}vyIGHYpr+}k=aQM;f@Xk7QG>ea&Z};9QulW?tL}8?o+l`#k
zPnrzYp}!iVKbvo)cCG)iRc*F?Qi)m{eP?&jR$iStBHH}{U1Q9YJXz}N$oO~O^;uP?
zEMj;&hrOb2dX*WkxglCm!;dj+KGxC}vV>|2i@vnAtmV(epddqTlli7*Vr0eR&6ASL
zhGwcuneWF}rc)9$t5$ED1+^w3Q1b<4)Ra)1{AH#W604*Wl`~8^nRRGm4e~_Mvb3}c
zMAbBTW+OF%tuX$Z7F?utzeQet6HfvJJNYFUhquL&wN1mEAtyQuX9@Jnqn|pPnz5O+
zhir0B?qDKqC%C}tkG(^}!t;-Y05j9Q%ZB`w{_&S<gTX=@<FcL?>u1+_%>9Q9gpOsq
zH?`|}8>+C|?U0xH(_DgrWbmat>(i@3hWn>?Xw>SyDMEn6>2Rz*XhiP-^fk6_M4_Gd
zmXgNyj;U2gm+5lF>29vXDl~I}HG8eScBgdyGz^j-4$p%g)qzIZ?&a~+rdK;L7OESA
z*2i%h#L7Qn@9ZPu_&@l<E#<rS6Ecl+b@;=zl&z<F-oiPB?W=YG-X{oe&eqXGtEny=
zagR5iKFbB)_V_h7CCf+dXDwdW4Bli;?aup%*JjWFUX0YS%zjff_KvCPcS0#xHgTRa
zAizONZy^qnN1sd*Zy~!Ww`H6PPnMT|ETTlAwK~~`+Ye56PN3qEt5ID4L}+dMq9zuF
zDL7_qyi<j;fH(Z3DE@@T+`xmNu^ahC11X|q&_XB}GiAaq`O-3`MKtHg6(W3Os>IUT
zwlg?=t23)V{D`VUVa&;fl#X3%AU+H@yy)tA+4zE#i0V#8tfiRs=I2jL*;TG?$V|~<
zTB~e+*!Mx-yA@282T&aV+~|l(np$>&ugIx1Y~EI=EPoBgC=xeJKI@??IakNciqs5Q
zj<l#zqb{Hv!18+EJuGmd;$}c_4*S*fuCb$eJRZrd4w~#zh!O0rNuMC&+Yg=L0F*9=
z{k=qRCNTqIrgI%shKeVZ!57i6GK&XPA(q4*tLcVoH=+}R?|Ub<GhA-Ae3<aJNqq<1
z(D4sPA;vR?X^2GcGpOh67X*>B0U>H#{Ok!!wyr`a(1iEIZZYaJHfHZq(~D=aC=Fj{
z@?KG?a8OWE;drI`;i!UTFc-7-zj`x~FzU$p29v5lA9q!=uopgKFDa>=KGE4w<;dl;
zxDsM@s`EK}>mjf1>(tWrr7?wR_QBHeFJ*Tj_KP&Gu6cl!Yj};mSJDF7ZDsZ&)dH|x
z$!&BEV<gr!gp-mpeS>+IlRd=MHH(4(BWo&0^)x<#NvI0ABb>3o%E4~U21&ZloHcRM
zBO-xgIYIBi4QCCI!-?ScB?dSab1nAvQEFG;#QRb#a^%rAN!){f%ZQlc%dO<p#M={8
z$9usLn^4#nDw(RDqDcja3X!Vx33TcoX4Y%4!#pC*C-4>!TO{k4=18{}Y=3g(m=C*%
z1kT(|ep|f+!xL5Gsj*o&T!M)Rjm8^0jj$&Is7v4cd~(7YokIZ82!Y$%ykNVooWxx%
z)K+RZ!r5*q9_g$#Ge|Cn(e|cZVcDQa(J$!kFjwyLLzhQcnHIrRF-xr4IPkLeIR)J?
zq|J+w?^dhXz3#1$nW9R#gVo4dOiABG%c(5~m@@fONoXK~Or7Y459Ti7b2af6o;4wB
zqi@w-n4t-LLRl76(Id)j@y*f>WE1Dwd8fwF_DSJF++t;Mk*5BcY!44xNh?lWy46o}
zpm<uXjU`@mo65eDme}h-q~6sR1BPr1<^#6wosfNZz;;<yG;nTVwkHXQ@WujSvNAXD
z>6Bl-9;W-sAq#-zH&Ii|D<QkEHW$#UzA#eRv@jEHJ^bpGMg^&UNvf+b_0{UB<Lkf=
z`29?jFPNctfVKHY!A0Ro0rEW)@hC%iIP!EV+z*JSDt24DeS0G&>_S}lg-{=RORVZd
zXX@)r@dl}xsHu@W_iQk3<KFF$ZFtMHW?0cNRa^|i(ZcfRp$7$$5?E2bXR%Qd{8swd
zqEwO3p(9Y8=Hz?#x<?ueH#2KWm_XzO2Y^k#FCpSCPMi`4Ug6s`w^iHQ$V0R9vj^-6
zrVo>qr6yZr1&fO%y9#s<7csBxacSrPoLQwwRe6t&_#TmEWkVF@j^r}*BR+f8wD*Ta
zNtt1)j-=4J0d4Lv^EOzm`8(i<xg`wIe&KblEN)-d2R@}~G%A@>;de%{B&JHMNbU_}
z0oLY!e1{NOD89*NRg2a6wCUW~O5oT^p!XF<kJ!<(Ya2W*Ywcos$Ej=E-tqRsYdw_x
zbqNhT_?=_<=9CQiRrrqT5bPAAOg(wSm)Mo+E9sB=A`GX6cgC@L4kPnohH31gzU}6+
zh<IzNpOi&hssawCP$Ts8p|=hM9`;l8q6Z&Qk9bMu+>E{j>W_4rK(?Z6EkL&(R4I>N
z?9)>KmLHZHhijl(3FehDc4m3AOR26msh*lpkc{CfJN>I#=_U4^KFGYlPXDB)>rk55
zP`9(C0oT{9gDt$m&_ACTNiwG@e#gfwBP_;xn4I+{QasxI44kB~^noRd-i#8RXibxV
zdFPb@p%=_Tl7^`ZHn@^g@#6h`4fjLs`!(3Q5W4`RG3JWO;lV6-DgJJDx+(_k7_-<I
zGD57wj<e!RmjQ`nlg~l^0n2u{m^%R;2SXfWskJ<>NEkBa$n6W9mxkrh_-)iWOt-PW
zv`t$=Ow+HFAvC`%!TDNIQfnwCG#?-8{jHX7&Tnt7!1TJnRE1ZAK6BMV;NXMfJA9do
zF$Sg8Alnv~Cb=`4p3&v!)$9uthH)%HZ_3xZX6r~PoiP$x(aGqBMRz|?lqQ+N%Szz5
zOk~I!=(+XidCrIQ9M)TE@V_XEsX5QUkvJvauJQQ5efZe&HC9x|sQz=mfjg|FDvfN~
zwglUkDEeu9ac)C)h?hK1GB7J{5#hT;SzpO=!xw5$Rcv+htJ^+9z?a?8@qBK6M={n|
ztJW3q3jMYX?-80eX$2aTDTBPC&c*txrrf8c0IchweY)4G4F_Z8+(3~@NQS@yM_?BT
zGRVZ+Gj4!=&){#wX#HA7=VsONhU}H<7ODNxR=pf*c$@=wToGA32l&Msn5Sow==tL;
zT52RK2zZKU>`2p0I0<%2Eb>N%A^o$u%y2!?Nn2;Gxd|P@Ew_)aZ7ij~jc@I0&33%1
zyCT-n-R&F`>n6Q<d75gT#qaI<xsCo|1EFO;4`;S!=Q{eD(rg$5F(aQ<mauO0IFr-Q
z`tm5lPto1(gv)b<lYq-+gkhCyzuC5#_{R?ghWjZtg<B8cELEExJZxD+Z~0`27Za0S
z4=4O#utVI?XgP>QYsQI!NJ;F9h2#5}0hu8^voy^4wozW?<Xgn3twQoHxU6jfe4W(p
z@k}`o@g=UdB|>}7k)9Z=gOwW<O+47c(J1d}rdn4QSr?l|>{{RW4Q!3;OcG`v7t~is
zSu#Tqdh&Cq2WNT9HVn8PU*AJv=pxCfTL+u&XyPYYUU`)3)+`;gx(3^wH;zQN+kUZl
z<(VZ>P2}>RLnyhJe)Z1f4vPPAohp5}u9O#}h>#+=G!iD9(yEtTWQ_7$dzTgsu=kR@
zTvEm}JqsRB0=pE|%O>lwHT{v#)C#gE9-}l{Lfp86qBs@1;iEUHT(*zWWC+cTtv+H8
z`3vxY^;n<m$77r6cvcf9vWSf!Y8H}u#qqlSqR>-vK~1Kw^pzMG)anB{qB$^evuA+H
zXgA%h8vO+C7U$A>8-qLEMxOR&ShF69tE9(F*UzX93S`79yNX77s_?m8eCqLLydUmi
z(As34j&JX^{8oHn28n_S8#5YAUJUNX>F_uDoV{$s{E8+Cdu*dsvV-v=!!TfOc97WM
z>_SztgZl@|Q3<P-Cd7egEW#Fti6+xdG~|vJ{G7(jPz3mFF!rX$=Ua`SKCi31p2yBh
z#G;v}1uK7D=Z_OzZppSi?d516CPeerArHCzn0t@K<8kZZCK~V%DK@|H(LI^c^wqei
zJ`5;UPCy*F_dI~3vee>5sAzaDm?CmWyfnq#{L$8ZSu52>c_iERC@E=gOiR186oz-M
z4)hOOT!8s6vY~(>aXCKQ9`Z^>0~kKkK!s*B)<Pt{6y>$ovR;1AqnP$yaNzW`msd;^
znLhh|7PBjEczlbDU;8WTrra7zTN|_13a<8PC0i|s^3=F^`e?tX_YbVlE|$0*cV!_n
z)fQz}@AJpFn%rkbBA-W=&pLIw+7Nt~R^OdEZ?Q{!+s1g|yO1tb7&-CdG3T>-fSyWs
zQZ#4dL4;Y1LK!ha*Yz_R!(}~vbIGPLQrU@OZBo|${zJQW-kje=Yo_~&H9VxpI^mt?
zXxh#^BDuC}grNYx_Vaq+*?v*O{q<!?l+%0v{ZbvxCUdcHuF%Ulff}f_my9@1=)4c)
z#zjxin^;wvARG`Y>wg|Bu&{FcGM8XunQmxQ1}FpSl>wZ}02Xxshn6(U7X210J46&j
z-&ld_P^+F|uHM}`LYSo99kH+zldqCtXky_Pbtd03FiU7LCSTl8D>3L`Z6+mL=pD!-
z)El9I{0B%{ek0T%WK!)3us}DY=YTU*yeFe_qc!KNErcxyFpNt&LQv9xNc?%Y{t$)~
zfmzbp*45mZlv&c&*wtLz+|<F$96?YJ!Nt|t+}IAmW8p7p8r6SF1C;3xX&~SsphpW>
z|B?mgrzpn%t0-A*&x83W;I<$11X>|l!Aj$(!a=N<4Fs2C1^iK|_=OdR#tR_y2&0AO
z6{D37p|j#nK3LDlPQsO<HP)lOY-&`-s*;c0QE`SE!NSNH6Nf@KyNz1D-WfCHQ}Ld(
zjDyU3<$D4{7}|cdFI+IN^Zh}0;mA5U{^Qmpg**M}7B!n7(FC8f)FsP^z)s4<N@ij@
zxLydX5xhb-6WA^al7nz2xKh5#*Lvw@={HO`3E>%uvOOATuQm&`O`1+*r{fRPebq)k
zB++Rg3AS%`&^7HD4wE$O^L4K5&j`quRT>uLD7a(Yy=0fM5@8;vm3;;U&_ADE`19n8
z<&OaQ3jq240T7KXdOEe#pq_988AcL48pVgu{FG4bijvSmBXei=xI_wVU8v2NYN5CF
zK7B_Wo|hKhPFxDoYN9zIg~=Ncnq>AYUnt8+1PxOOMv-CIqS#?5I{Ld}tULLtSO#ez
zH`?+&rM&`6NdqXr*hk-5XVgOs;yoj+!E*$>)B~G|ei?8X-Ty+j_|4N^pPaAf2w}?;
z=_OV8*7-Y{MCPQb(IJbLqU0tlf{A9ica4KK?S-G6`&84+1xHv@lNqevwM=hnIa)bJ
zxtB($se=LulobR14_2%;*`y9=R-WWg{oRN;p%7lsri)P~y0m8Vw|@VO9fREZT>xto
z_?IuXY?Q28>-QXpZ0vv_CWI+rqB^}?CWNc!=jR*X9}?21@Q~+#-qzpsAJUKkr2eyk
z@IUN-n(TkKSdJkUp#Ecpti#iVShx3l46rxAFnbTd67&jAS0^GxSC@7na%hS_N~*Ro
zzUrOGO=*S@3G#p@2JrD#V;%a+dNtyTRlWrF=a%EZk|Lc*-Ao-gc6+AXX>%ntCh9`=
zlq!uxWPPd+5X1>mM_Kv1r#UyU9U|yp9FycW=$%vk1s;Wd`G;Ix5ZSy;x8Mp$Ol6>U
z2L#FE`P0OIL52VFN>J$i!VAh|eev(&5v2)e16qwTw)B{2l$ahJ^TE<OVAH>U*Px}N
zSJ?jIj^L;XcA<||80clv%CC=gL$HN9CsYpG^K7K;-_;=!8#pqX^fDe1ERdQ66fb4_
z=cwyHE2wP0Zgu}$@(x0ODgs@YUZ|c?2Y3Xc1=X%IkEio~IHc!)ccL(!FfjKpVRw*7
z&ms}%VR%oF(D-<^c!OXM$VeBkct<3FncyjZJP2KgM%mcFKZR<FV^9FK4<MogE8vfL
z+P|<5ITJs14NZ6oo(`vU2s&gGehG@BSCUK;@P{wSFMJ>AQRk6F*bTho;>Ou8iins6
z%Z7NLVLy2izLjvwvMhK7v2f$j6|3WAQDyj*F9stS@gRhjrd={ae&3bfK}E6w{g&2L
z_ipPwAQE1<i@0k@7VP@2`^=)21i8Y9snDvNP?F~sQLdH8>OBdk+KXhsoP(@mR1{yU
zjpOOTs>$&4tl2_*2e{TDg1A#4ZfCS%U1s1|WNM$(I(IjZAUKO-@F6H<xc<`$S-JmI
z(f@9Rc9V6oy~1cPYw&as(Y0~$p5O{S`<~#&>-^nVL7F%wC8;=3ZxWHW`p9uo1$%Oq
z5E<sGYR$4`AUL&-VEJA>JddwG<m(y5O2pnH@(()+e!dqKsL&ajCX9>T-#<}de3_!Z
zS*1SDGohiWzczAt7(ASsRyKHzy@Hj#T4@ss=GX&!K=j?mgscRn>kAAyIV?*;xcH<v
z_?Ns@FpEPk0Hdj?R<J_4;QV*A#M1=YJ-dkZC$yE&aVf!LQUq%@h)=>6aKZ2t^K_Oj
z#gO)E+f)F`*`9!$h#=$kK!I+g#u&S0AsnkpEtiHm(}3JVmONsc$L+2Ij)omQu^A`u
z>IB)s9p+1f>_)5NJlo-uxfKHWj~bRKZL0popVQqy0nQBqa2C#Ard{Uab@su+NMOno
zFqF(b5Klpb(%;H6;^7j8(dcq`)pIgqiC#3lJhS#e$cs+^Utb6-Xp?nU`f+<=+qp_a
zOjicGT>rzB@eJ`(+L9_`yT8e7zBk$Fl)w;O+m8HdOCGB0?6x#?Tc!cx@M>)ZyvYtc
zsj-3l$(XTPB|n6#aulPPI|m1*Q=oRXSkWvUdoC$Hhqi(i>?1wF_u8Xb6m^(Zv?2BV
z`0>IqI@zSruwhqiY<0Q=7i>|!hR0o#@q(!XEZutFQhlylr6nC~C1k-(<^dR2Z%{yi
zYJ6d1`(>aM##v;0m4TWm)J_f<;}hM1$mHkzV2`b>^_$Vc1^tW=_BhSb`_TOIy^u%P
zE66_PQD^-_pL=$KZ^PH~?zTMKhOzkuq+$=TzhMjUdKL7kPDj1IZt?Ocb(7mhW=>T%
zuu?Sl9&)HTRZ_368Q0!LUgfPmDPINvzyXEYw@?U?!Zf^c8x-Wxo*^0X;)3H)Y~6SA
z_{rpTBa-a!l%o+S)JFGl{K7S#aHy*~^YBxcOVX_|R%KgI(d$tc<?kl(_kAt5*9abM
zthS`WL{aJ+z93$d8(JSi@jC2h`hh#3IZ%T@jvW-UWc&5Vsxeuo_!$61uJZtGKb2qf
z1iGv43syQq3V8xJJT*)&R16ZS_94IS9W}pb`XE~017{<&&Qn;!_WeZk_W688s?B?1
z9sL<y=BugUXm#v&C0}VerBEm8ZF-k%G$LEg^yOVjeqv^#`EA#xuj*>t#*=wW_Ytm3
ziSmJFHXhG?6a|TI2lg%mwtixg>>fd&9xZGz;vxK4{Ki(Wy9jSh`MbB^n(ns(EB%kK
zAxdpfoc`x5E7a6-@(BsAQ}N(j6bA3+YIdnO&x{~ZSG(=R5OE<0dIMcx%vHw$mL7f(
zEqd(E6sw854q2ek14%o7Jyy8IvM7EAw$2JU8(U)yNuBAD)@eCOmun)3L?Kd^tBR`&
z%b<(vwo5G*-M}%ai4#B3)8*%<mt_bQ33aySEWdBm+;u%@)Ui=J%t{I##Z`Z8x8!zp
zM|P-LnKO>7YF8mQD|qm2_gug7YWkD&%HE3gBl)ppU_Go64QT`V;215HM?--9892_c
zhv^v$ALcE*{vd4O-g;{gEQ_&H5u)}UM#r;>Joj_>`Lz>{RlL+4_NTbLJLf?L){M{~
zX%Q(&#qKe3|1YA%x}K>dbDC-@pfCc#I$1e>-HB<8cm5VN&fs4`l@3vu83-qBKlQ%D
z8OM|-voa?O$_GY`<}~Q&WWmG)?*Q-6EBGZ=i9zm<cqmzh=g4fAV`&5LFzIDp^L}AB
zoHCI?eUay+DWm(P@Qz?vXuYj}BhvyKXE)JQ#c=X)WRU{ra768kw(XvznrHM?7#PMI
zaMb)KOg3o>dvd->g)LZyE>c=f9M-4Ccs59O@K_%Jr73UFiy1g;k~7hrM3Go{&t8On
zY5}~6a^lP`oaDTC3uA(?N%`hw{e0Yx&<8m{LeAvT1Kh(&3w%q5%2FHk<|no<m23~6
z^~b%N@mrAgX+yUT!8|lKv7aD_5kk*E0SbZ)v$FiU$?A^_DE0!-px0WQ(XBHHyIsJg
ztHv(WbaZhdv80t%s>%ZrA)pw4O!;7ukeNr(evWA@%)g}+dc890=;Y_yClKCc85k}{
z?B$32U1Tw*Q~-7T`_+ZweL@?}%1Qah>Fl=i+)lyH)$&E5rP>ZuAEYIrX%C|=EihI{
zMa_KdDQQZI7uHeGiN5o23mZ5k8E<8*-)$haCpeE145@_Mxk_N@#LSn6RVTn}yRQ0w
z74lR3kR4Fk8`FG%TyZxfGc`ha_%^8j4fnfmUm?Vg3l<ZI_i-1zEWUw43IwHQW&L%e
ze5XDk^Z?Mn*092R7zIU7q1k~JCqjDz`3B9^8I8uxRoV4@5c3YuIAe*5EuD8!MeV{I
z2B})}fR_eZf(66otf293@%lcX7{MBwz449VtC_Sxe8l=r_!;i|+)=P1jmI1bSN{Dp
zI81){2e?T?QC@L#932~1jy6}eulx4jX`j>ew69-Ne?1x+t!rVuj{#0SuRu}hP8_>7
zJ&&cQ*>&#qZmsrsSI=ZHa@FgS5?MYhP&ZHVu0NlUMXU4phgmv9YoT4xhm;^WY79GT
znx_vTYMybYdPd1TNV!C)PWPZN1HtK8|9tuW7wE47Fd&5dBza_5l+$N0tjzgc(9D(+
z&bg6T)Vex&z-q&!mG@E6)GgixjH)hLzrUm+*%o5M(aFQd*}E-UiqP!g^U7o!3JtP2
zs!qqI9xJj#=clW)(Z#UKR~4Gsh9=pUJF+VVt9jh326>1FIZG+3OD+wVg;tdOk(n8O
zU7DQ`36*cKTI;9*_@>CoC<*hJqr43$A$_$ax62=s{9|BgPN6#AKVSx**94~k4AMVT
zq*W*lE_u~b*~KAKgu>0eI-5uFT#^^!<0DKo5y5$gxBry<WQTjQyuX%^m*32g9-j{G
z<OoaLO;G6RXeUPWs0iC>c-V`Jyn2Qzh#k%Y3RF-z1^XZOYJLG!^NCJbC>AuBGaU#f
zd6%EedHL1cy%u9O&EExzoTZ(q3)*|QIyrO<ON?_ITfaWa+YXtQjyXGP83JC1ip41#
zg$c$E<8K#^GVc_4JiYoJ1I7g07&*W3_gj1@h`XM^wf)hO);kR;aF!)Sh*F#g4VFUP
z{A8K&xzI}KpmoPg2p9P5g%nXQOB5aUttLH;jI3h)bg$-+g@PvUJK9Rx9D3=+8iT4&
zuFjb5<J)}$N9Rh2=klUGJrN65YS^DRKdE%!>Q79h5Hv`ks4zvX28yZEdquS8j3}P<
zeS9^y<G(o@;C=%+{LW(p6o{bG50+mC@olVtJuoqo6dDOFvG#$QfHrbB^?NB7BK;vE
zS)GoYKED`@il|30Gp=6Bao?nA1dk$=mCb7`X2+K;<82z^S6c%QGmpee*Kuz#nqiJ>
z0eybWj}-4^RSmOWs}f`+(XZa_K6sNh-FcXc*^I4*U8N3lKsrHE;z%#j90}`f3Z+CA
z)<=kjYfA@-ElhivX<Aj&+QdPiq&U7M^qE<1?ak?xHv59&KpD5!h~KWWTqNOm3+zYB
zhRY`8yIr)zA!9EWWdsXNd*$vv{Yoa!71A(A(-9O*pppj8Ux&$j^3zXUg9Yju%<u8`
z8s|rcFx+ptHjDjxmbS4o1<^4m*rV&Yk?2&P@DVbL#5i&2A<64az20QVj;o%^r{evX
zlb1d{ZHXHprI+n4*AeevMv!N*Q*l-M>syP{cE7<gXlYqe&T=eEPcU-6{z3>Q>_U_2
zzr&kuJNFv(%6qvWp0OE>aWB1bgO6LW9uLt1Ri&;G)k`|F<Eqc-V=*Gb@p~m!K=~={
z+XKr0HDyC*X4(^Xo~u<ayzO&rXK&S@%sAIUsBc6vj??%K!6)xq431q5u9;qTyQSHw
zJqS_@4cxMlwT8m=(GG5Lc^U?UK0txxa(TS%y!;Zl0t#3VNd^n|uSXM!F<@Z;fCaOL
z1-nl69nKSXlAX%V4Pgf1?`UENVNfNtkIwss_N@#B9Le*UkVmI9?mH^ULAGocEEZ}y
z10H@-oaVQHA-}Q_q6>^UbW?EK*WO0kD25Z)$l4u-6tJ3Wk*2O;^}^PTtWD<qjc`qk
z6apLNDjY8)KVf?0u$}8#9&jk`&7wSZ=E*3Oq$YAl(MnWCAz|-pOpYkgC`c*DDPU%L
znKbH$D8R$sK)i`mNdbWc2gtPH*NLyf_}`^~>7(CBT`0#=prd215rW7|#8z2GMnx=!
z4RaDZ5_1ycV+baE%LW?mKCXvncGI@Yt0-Mo*V28_B<p>hO>hO8k{MpZTmz>?(@Wvf
z&pVPfws)_*{O>)wExg?&65YR-S1g-$QUol}!@irc<M*lC1*7o4q<nION<}Y0Bf1Bl
zt0zE6ecVMt5<@o*D~L%n;gQLFdIdc{uYAjDHj>}&YQQ5Col=wI%Ji75q4TlMjn(&~
zb{?S!(+@a0#=AbiXE=Is?%XP$`m;PocrcIUO@Swf1Zw{_P`H6g7})*|8U8KZ>OZd;
zHrmG#3Oa=*l`+?Ftyhl{hoMZzLsK(0vw|jwVq^ha!1-c5(0Yc%;FD*)hJYMVYWukE
z|HiGOV=YR{N3%h{;r&fTsla>*!^`F?pZQOdr3Dwc$BDzQb=2mU&JNd)Gls{GsYcqj
z!!zGW*)+$1!_%rJK&pS`5#2RC>2;Xe%Jxn-eL7n2P$fG!FUA*l@Tz-k!c5vv-YYyf
z6NJaLXxb~-^nZoN(j6is^&w6M8@Dhv8r-iwhode&Pa%GZ`_S==*ca2Ft&1bL$UEcu
z@F+*3C@>;nE#&1XRLw9aKTzO-$|U|pIYc40&b|kL7lLXoe1}Wi{fwC0D=X|;52bDs
zCTngYoP$6CS*{;R!q^TU*Og9I@2!X*Uovs=_;gyGsbq+&Ti$eM(v)efv9~fTGn{Bs
zkm4KD5m#SDwQ8dy`=qFUaVO?K-U&ODmiKbo`#s;gzO<Rw&@aG><{&mbVC9d%aJ>)U
z&tGth3*+3d9IgR6Fq6Q!xE*xy9QfB^)=0*uzpY=ygbB9a!-|Sdu)UZ>M}ngxEe=2&
zealx(m5FomLLjSeCP6Ss-L*Ws<RTx%^PM?`Kc>9~J1C$)WeEQw<uaeB17-+FFh*KL
z^qxZ$z06>Ct&Tx-U!0^}g@Yp3e^2UVR%7^ONeWEEX#1_#btAV4Y;Rlan#<B~XO!h=
zERi!@Sdj%<Zo(`mX2Y8O-<N4ExoHc<&l~wtxK(|8b?5Qy)9vN%F2VTKo40X>-=u}6
zJj@wtQbYobzz)K3s%~&p;LOZ<=#IQC-xvL;T%3#65N0<e=A&RZcVwkxt~}41L5N;k
zbdfPH+8z^p;fWul?xPFD^yoOt#Y?4rDU`}0JH4_qD%;G3DWRG3N<S{pM_w9rs%eJX
zcF@5RKk8;^_V=0CX*19e?Mm;Q=Y6h={zkO2i8}o{ItCP=pb`nduh*B%$A3G2gI<Hj
z3AFwJmw{+i|KUjk+`sGZx@TeCh3<ZP|BA3&bi7kN4tmZ;uxE{D>GGZ%T_MUWM-zKS
z#Mc+@R+$)*X5Y(n?CSE2xM`+)9T`}{hC)#8H(@?Mxe8yPS{Kminy^w1lGOso`Jixg
zum(@K@2H;#^B|_noj%0a`E1J1&sowk_D18)HRSObnvhjiDJz{#C()MKn#Vt8r1r0h
zZufaUH8}2cdXX5WG%*Ws!7iZ@QgMIgB5&2?1ceQ#gyCPLql~~B2qX)z1|o}Y{lXAz
z$ORlY@K-0)rUZ?$SvV+)yf7%4PIz)dAQO1{W}88NaYhpvO>^(PGuF6#{HXd`|3IPW
zIH$SEkMn{mI@b8Toy*hh+>lNDfdX!hx>8KZi`=W-d-s>K2iqdI28X3nclu#X5a(cB
z2mm$$7OoLE3vY2Lp+0zPB&Y}>DUrENkA8kkHo&F~&4=o;kRW(<qGdfrGN(TLc3$$=
zXctXI?!|~E7UqOwhoeoQcG5gpltndMd^cW6%$dHYBYK{MTN+Q`u<LsCppXKQ1+V}g
zaR2?+f3KhW{-s60lZA7j7VVuRiknj1K>l@(+laY2#V;FMyN%QB#15}LbjHDUwnF*1
z0-zA%EUhk~EN~U}j;4u0_N5-noIZ)zB)rNr3HD<0p1BQT+C2on^57wh_L3!#vxvS;
z24e;L5UEvE3C(DT_^P^8cU!CbD1H9<?ugW@>svW=Q=yZ?Lk*XzFJ_8*biOLn*>I>q
z%5X*fW^%Obb~Nx=Pc*dJkUA)=3)AmV+1np_xTNIFB0zxxiq-#%(yhc;-A}q206f}*
znBGDM=9?eALq5NQ*oC+MSsv1~u`Zz19<=Y;kR=)7m*ri2*`kd>qqAOmV`LGvTCK!o
zIrAk`k++_po>Ab<P0*XZfmGWTAL3jfnQ)PN6FNqfSGQlYcYO5-pLKl2bKX((F?PZG
z$Q=suEaGS#l9DfYIB=GSDGGno!HuL3%3ei+r@K5l80F&3fmH-s;C{b73Je8@gHIE7
za=qMGEd{e<ucPw5OXZqK9G+;*Vk%n3QF^q#GVwCrl^C2AwZ&PnkL0U48)SMh4VY&<
z`dQ%s!2=}gj6*dj<UkR9)?epf5)(heAuv-XF^_N+yM*h&wMvpk4;sQKQDq}-!W5gW
zFgF96dN2EhtH?ogH6Q`J?{Pw8ZN1^-=$=t)Gkjkhk?7F3VpjZ}sF2*ev_`!SZHvc6
zU8)Y_Z6fkeUeT4P`(jPXRzsSUxxhEB@pd@@Ef3(ua&PXKo<MVq*fV`u{j|`eTgx`X
z>E|U|%m$fQt@{~^`x)nsqCuEzt0Lqzn2*r{+(B$dx~}XV5K^NsqaDMzbCw4Z4xaKx
zFb`zPxDfI%6o^P-!w#f=aqqUVJuA0?#%E}vdkzXM5bT|m?bp*HePB%dt<c@TFW%M{
zH|Y&7O^#$!LZ2X;Fc<)(2KzH8N|?WU;lK}5v%%FJw>Bk~yqWz5jNfPX{?u&f$AJ$^
zS=5fJ=JYo7e3XIBQehK@rrF`<)-AFYQ7S<_&d96Cx}y`T>!<Um?SrSxV#8@ug;~ru
zuOWDbnZbil1j@BTRwB=fzo`s-J<K;f9}xW^@oY$ex%oCJYh*k&Xodmrn<6JoiAy-c
zQNgYGI80q2ySep2y|=VYJa0M6JE@ele5ss#t_S=A%N;mIe;2gQxz91ke6*5d7vCPB
z))c}~fr)p%?KKGKfYqPBA0GL$x#HhIH_oEiD~tp^Nm3HAWntfS0}(GypsD<}kpY7)
zKEF^~1)K^Rj<*qCv7eAiN;-qQ-YF45uRX!;_I2j*{5G^!`}TC^#7ld6zv}(BnQxKb
zzyCHi{qk6sp5(*4vZtvj{o<=9Ae|@xKCR#P_4HQR<x2WEcm2)>s%H;M89qwBWIFww
z27^TsLGaC%w@{ne>DoP-zQcl(G?5;{{s-Dx-)y&=ptxFH<aaj?Eglc1_6Sj~LDq`;
zN1B~9BbsdDV-{3-3}OgFP#aBXu0OV|ox~?eh}LFb2W25ZUv!TPe`8#pW+o4I098%n
z1jYBceqHN;)wJJ_OofWI=PXE!ccIyF;Rke}|0sU{^lnx(^IZbzFFOW<m}^Tyk9ZrV
zE>(TvW!~)M9jAs5ywsgcM2Xcs(41l1tDcQ(d)+#G_2YW_IM@KSt+lXvb`1ZU8(a4D
z<@CrW`>(h(y{80iJ7Qd3b7)?JNi=xs^Pd&nP^#!N38$2PB!xbr-P_g0Iy%yu^p^LG
zn?Yb4p1=J@esrWh<`+nS!}6-3nmg!Yy^STr_2y>7n~9a^@e!d7+WGb7c33@}`$ERd
zY)`3_)j9exf{z;?Ea{y`7v!|DDf8hm#>68{i_n96>kP=XnLi)jKcgk7;rAgcUmh1M
z9jx;E^6tq!LL%=5BZAUSP+0-{uSZqjsr2uLEi|mWpV|(ldIC)vnJoO#r~)ezNgCiL
zEZ;Q<7KFV!<&i8X+K-eFcfZ&ZQst9Kbbr#ac-b%uq#*m&Mf&fTLj}1msO{bpE?=DO
z?rVJgr2fk8b%ni;PUVxP(29^@vF@tAHN~tX$&2?SC9AaYG+-sj!i_{moa9_twSX*@
zVjoE!@`5EEV_|-@z<?SdrfzIU=MW8+`?LffXPjcZoVB-W%d8C!Z!VEj2gM(R`zuDs
z?|Y0Vy2_uOMK=-vbIbr7=}|Tc3crG0+AwEP!iP2>7XbJNC8{9648G;LrFUK)`Sbv`
zEN}e?3PzCR<kzbkZ)58afKPga#<9b>@1wUdE8C>f!3AM&q0P*oDa_6(&tw8CYa0~o
zoP&l^u2x7L+bbgiyID|0wCY-)Dp16(D3E;wJe^bOCf>hetKlPJPPEuKay^P}*f7Yp
zeSf{U$)MPl@p3oxM8FL-kr68*kcJ^RH&DOmxj$c2m7Mtu9I5KXC->mTApHU*StRMH
zcr^Ac1=(=$gJ*z{Ax~R*@m|k-4-;p7+VT6a*^a5VVc&}xbF3Bp-;G6^XAEg}B;?4)
zfkFiYJNY-1?QPsY8}G6a_jaUAs-WJk;r!X}5R`ImMf{uBD`%eX(Y(pbF<-g2NJr|g
zfXTEGn&tjN`BjY8GSUPEk|^}V(m$~RyJ3nJr(sPyM*rZ-nJ+4%+G5zfbWu4tXHNc9
z7};kK^A<4)!Y|Sux<+yrIi4_`pTLXxrdT*F`HT>oH?~c|+41<C<a8AWQxA(xpZnEZ
zpX87FW$UdmjAbinrG+mUJKvuhMcSozG{0Uzd$=hjf&v8u@nHF@f%3oeSb;b`6d>^s
zCsHA`OQNWy>0-GB!&ZUAU;+jMVc?M`c+iX3)FMZyVXTN+VjAt?L9fk>BkFla-t`DI
zMQ(g2n;WY=Q73AK4Kt)o$I_m}73b^(0|Y8NEuW7oO2yGv*~t5e?qNj&^NA_hdL3(2
zU;Lv(6fG2qdxA<;E5C6+VdKxl@=afsg>(t&mx~NKg>)S@Bav-?=nU-c<D-n1h&~l#
zdz!jboK*<W5j`z`^QHNs6PB~bp1v%!1o;t614qsr6e=JH1`E%xyLyQ@mY=9ViC{^8
z+k#(0tX0Sc!&dzVDiD#e{BO&);OXelcG}_BOw5-(K6!H<$4OQ37>~T^id_FxfZ10u
z%Dza}bt0WX(X_)&#gRrw*RnQp>qVNvSY|7|-Of_gvgQ1hk*9_9l$7`atu>82sZ9Di
z#y-rK9}-=pDblEvczhgz=urKcT{q9HYvp$<JxExmYXP{OGna)!!Y|}IY}@j&?+WN3
z(6;>vjJa%O*rZ<Gf!*5527-Wu3j}ds0p3gc)r9A-{;)>%$tGaiunK@p^Y@NJR`4ib
z$031e$=@x9@*lnn1Z?Qt<#r%YNt9IR-6?{PBM5gyi#8Sni7=#<MT<T@`#-C6bqWCs
zeTa`zR}hm@deymlfqGzoa|aOPyjZwE1aFpq(Ny}^@-=_19@r-Q_v(N4OA?5h{XeVk
z`ERRF5Pp1qMnd`_hB*8D9Cr}v|AzqW(xL&N@COkZSvY>(r~f5@#&7Ve{v`k><bMb-
z_7nd9RRCy6v<EPB|FvErG!<C#1CbDcbSp573!@FN_%&Ee)Bq^H^cyhBbabdeVM%~U
zSOxc6>IL;g!k97j11Ly}2qTZK?SmogL1-`!XE|n2(t@aZ{k-J;ueA**tvY~3_3!r9
z-=zI#d+SeO|D(MXsr5Usl@b{n>9=0W17LIOGnhQs`7<gU1X=eVOV{FH0hOEp8R360
zY!>ccH<Y)Ed_Q{*iRSGy3CUa_+{JJd=*c7<5rL)jI#oGD2|hA#=F}oM10yQC!4D`u
z7LY#QFHQj_1}7s}>*42FjxqU-Z6GaZ$3o|b>Kx0zCF{;y1{A(QY|E~Jo#}Iyg+EB-
zgwJ4XOEfwI1Kai|DryhPx=>kuNy6oGt?_vRmrG;OnTYKgjm14bJvg%!Jl*k3lE|kM
zmAY!Lm&`yWfnO#S#robfrD`DKGj<2~;I)w$sQm*`NMiYO4(Bf%lK!QwngFezzM@X~
zcVEGx3?_mpRx9qzl$J!3=Jz2e5**kCtDG#iI;~YuT+W^%w@TNg13u`k62Yksh}gbx
zRWUNEH3kdft;G28a~IW6DW4vtpp95;7GbCYp8mXD(A6pkiZXD4B2w)Cho)F|wMh<k
zO#r7VfL;A}N!Xzg2x###y0-rE8%PSk9_s*%|Hp5H>D>W4t-}ytervTpg1r$Me6D>q
zeujm6o_|hy9(ay>BoQO}EEFabBRu#J{2crk{*3xWa<i&DVATDml})iTID^_H5RB-r
z8rHwrrT=@I@)rIP!2#-nKUnY;>=D5h{vUTIqGCxvyqd`Ze9+-PZHx2&upj?_I6(+V
zG=4CXepKNge8t*e_;@IxbZ#)pt!{D1_*z&@()U-Sg(%_=km&wKP!#Dm;H)RG6&4$;
z>Dp{EP7IuE6&PShq?>s3!cb}NXtYRi!8~C>dqPlD<j-e3|Lq=`Rm7VNY7ClN!EFWq
zqm>+540tC8H6M`HQC1@=qhd{;)}a>A0HHi1LH44YCUs<5LL|4lqu{CQ85ZzcGkI<x
zpsPCWkA`xdB}N$#$^~?J|G8Hzzh2V%hYMi;%?C99!v|1^Macg@J|O*n@d33zO+U|z
z^!!wa9xR$A9>OsbqB;1F^^;D>s)1TRD8>Nz_qtZA3;?dbSslRo2jAfY`Y^VLzg<`z
z(1nEu;r~zYgUHhUC*64#dIHJ-iyz$khlxn=(k6jgHYlF(fAPHR$^aO07icw?P%N=(
zQ6>tu{PMt{1RiyIp0tsnBsn3y?!g$jRY=<LlGFWn%Bt#_!zHI!YgI;rC8vdGcI8HB
zBis9hMxVPTlm|#x&+@vTz{vC_j8+2>gGal<MSeK8dO1TogM;ZqaeV?M1}FmX=PlWP
zqxLW+n3&LNY`+VE2?>n`<t&2n*R3vUn4dQiRQ}g%T`)g@zhRyrA7CS>w@?QmBVle~
z*n58NhW|leZ|8G47(>4iY6w~(F&zFuK)lrk2^#AIxF{fS8l@Hy&mZt)B#bsHlpfd%
zyMro@8H0*P0{Bz-d(G%~po9lOAz1%~n{$f?mO+G(!id}=wy0h%-a?bYTZ?5V(o2v-
zV#p&!M6$957;S&yLUeHU4UP$ivphm0bKAR+vCP{#ecrtqx=lpTojgz6DO1;Kq&tUx
zryB;Hlj5H>V5!HEzN(^O>PKZdn)TYt+t<Ix$IV6L!eH80rVo<yHN+k`DLibvWS}`}
z7okx<zwj_2nW+UpXmx2p-!bPAsnlgL-ZL#^#vksk9FN-UUfMVN37k80nf?vLCDq_o
zIw8j35DtbW<iMr{8VAc{f$V`U5|m`ZExOe6iy)ne`v^>Gna8q?qEAzRW=!1wkF~c9
zszb}RMmO&6?jGC;1h+tN2o~Jk0>Ryay9al7cXtgQ+$FdM*l(xr>GS$_^WHt@eqR+u
zK@~q(b1xZl&N0Tc;mneVz;{zf$`ZfUl;%!wtZG49X42vM64psCRkiwBSYH7pe}}y)
zKE(>EE@49JNfMK{Q%Rn;0w@)8l@ite8CN)5JfSKw8p$g~56CFuvUow(&se*0Tk62G
zeH(Yz_U#~UkLvA;?bD!j&Bwfo&D~~;*~|r$WfT=b`vnEhVB?Zt_m`JP*Fra-UGM$7
zbH~wf1SA-ya$4=(0H=|#nd9i9CtTl9k!G`(>TDc7b~!w-NN_DR6MX~LFneJbJd6>U
zz7}*SY}h<gLIpf)b_`s0Nqd6<t@KbKllkku<{p_~r#G}8p~7?Q$^r%yX{?1h!wop-
zy;KdL#i0*rz;Cm2&V0Fwrq~tGa?4b}aJ1aa?W>*MPEkCACY6MIdo7@^z#}%0zVWZ3
z^!)#-hz?vZ=fTR>Mpa#bx4;dXmx1~{e|EtzE23BUHHoRBq8-9E=QALoEYozW5n8V@
z_fD>Jc4G<N6*m>TI!;VtwJVkfQ`s)x>>VaF2sFD_XgMq#CTn~5lAL8S?=&<fX~(Qw
zi1zPN+`NT;nj(*y#gRn>ClH_Nr#*j9m}`WVu_K8~=f{?d%1)+RO~F<znxh4%+Dhm}
zafgx=f9byL+C*^&{@NXhu0r6`#Aib=ks$sW=wmRS%xk<v2lc3^3=<(SA}^SX`Av;Y
zGzp^R=rFPaOpw>%H7N*)XY`*hBgg;ZW&A(B+X5edKa9VkpnND_*u6nWnMQWV7Z%z>
z3@YT_U-$_$P@}5=!U7XwSL_)GgNO*a@w!O6(EmtO-z0n2zqTn}!)X5-Wg?6L=(_&r
z&230QN-_o|P5FnCi~*Ox{?HvD{%@+p|4iHNNRWK`a2H8f5UIjPN&fiHxfD$WU;EEr
zgIa9=i=^}a#^*wUiwjCS_4MvhXpYDr75vbKaQOlVL#k*Ji-0JQdf;Fi2Ead%KzyKu
z?JLUx`NRC@Hsk%nK=v;@p~gf1;J6K3fH-c9#N967vx%Bc3fc6F!c)V->2uS<iV6x!
zwgueg_I4_b@cGr!v~WCaBxk?ey>fqu5d7F`qvrC|=B<K~x6+cmc*BhvAY^*bEF>uo
zg&%1T64ClX$Gzhdg5hzQ(h=ejN{l6rQm5X*(|;OpBpWqOQq$>ki<^<U=V*r`DX8XB
z?5MA1<K%xo8T3Q&*n7TWx#Y5<oED=z|H&-$@S_`Bo7CI<@HjWR{g@i+uo`>}+#r@f
zk_(93*PsROE5MEoM1uRPx7Oz<&@%uMdeDG`9;pI76Hr~O(y~Qw0!cT);Yu(=zUGO6
zL!x8dPB(1hH-j-X!oEBqp~pni-kO`<0tIVlkM6B(4AYhr&1W&vGL~EBXfjl+Y6O6;
z&E8Yux&0&53D`+imeblxAkmvdSpQ9Pg65mkhPsOkE*T7xS~<-feMpZ0#Ct?upAB>m
zCs@wb?4OX?SmwR0Qba)k86m*HUkL9nBMscpaijSIq5>d}3ELcuXb3y<S-vCJAq2J4
z^4i-`^T1OHW_cS*bpApyj}T~((g{NIRXljWL!@weq6;vMX14nxYbiDvNX+hboik1F
z3{8ypRrs|n_8Qpy8>IX{F+R8fhVq|h9Rqb8&<Dl(XJRZH{<|KP^Z%O|-vE8Hz{H*e
zeZro4^(MRIF*U#_mk4luC}9!bD!l*&QLljBWt8fPe_(#KB=w`$*7FMLWMlg~TLUD=
z5JC*pjW?j;=IsGBN0H#Yze|mh$AH7p!;3*_*?{>arsVEYpP)6Yl7DsGw$?Yf)~Lxm
zb$#wXyh&iX(U#{sWv)NFE{}83*S@?p9UM7UQ}eHrFOzW}tgGv7_ww=p%`+5OrLDE}
zG?@%Tv=m>4D6*!;F~Kt%lORn#^)VZ8AneVq`UcNHvxv&pQ4E7V66(#qWY6J2sGzag
zv$W`&%7SCOic>Is*k0@Sm^sWjOj4MyJu9?&ab}G51Coa5S$%I{z`<BqHID<PnP^AR
z3+#iUQU7bfdJU!ijT&A4<UbS{Fy{=&AVr4X9k?b#F^TfO+%MD3o^|iOMnzW}oVQWZ
zBQqHUk!*_md?|E0-Spv`%U2V7ueh3P9RQ;egZUQo?s?QC$~<T@RwZClWdzrRoeyMW
zfH?aQlE+t&@T6hLRiN!pSkaO8*1yxr%+jE2Y%8KUz$b-Sj`65ZlZ(E(2oDYX0@<!H
zsj&Gt#Y0m)O)#~Mqay0;<OZYt9R$wjtBF0wj@TrdByhjd9|%r1Cj;XM068}84G0+-
zU+34t^BUUx8}DF5RHqF%D`M~`*7O}J+o>yry5{(h89H=tN*kdZ<>0R%w5gd;pVXm2
z94OfoL-w`WJg{#bQ8#b0=DC(cpd-9E+tkf&Cv)iSGT&2IHqKH}vkI3xNUyqXb}-E(
zmTR4KedD^Aol#b>Gc|GWL$P+iS%PH)QtuuTl?Q?ar^(RdVyxQWAcvREP1sX~5*C$w
zb(E%3*+W+m^C6SAvHb^03GMa=cxh6GW0gAv$v9)ze!<RZ4x{NN4gWhgzg1S$z$<Ls
z3&bjTW%Spg^BQ#g8z(Je>K|3o-}>VKbAFJ>6W?EY?)^E~pTMz>v)T9K2$BPoZB~Au
z(G#~~b=9=-gP!S9%F;!zTOlKo`TKDU!D!O>gh8!a0Z0P$x{<WqAa}G@_6q@LN!VI~
z2xHJAwRHO7(eFTL?@R558L3zUwV2BEb*VF%DsaAofQWM-RrtHi`$T%SmB576xstUv
z2ikN&bf2r^3V8;uF<m1AAA;{l#&uoOlZ;<n`fFgeQ`Xv3-eEa*zn^*#$9N*|y*CgL
zhFQQ}K_c1L?^E^ye2Vt%d@We707kaIL6-gfVu4g*;y`+kTEZb<45|w*jkM?uW0)8`
zWf85~KiYK8+qn<4?|KsH)bKilr}Q=CFBh-&n*{~i+osfD1P<$fYm*sm!)|)j-(ODl
z_YPILa_?b2p={ZzV+*bw4gT5>yC46N;Jnq?a$M&bZ(mx34X76<Rfir!R!<o(8jG|G
z(^8mZnS+rvLl;4W`+yh|+%>>~b>bb9vUgeC)$}61g^?9L@PeWY7FR0PG-fVlLW`6t
z78ji^9;Q+&C2fd6MfWa=dCIFlRy)dpS=W=!R`Rt#y@oyihF;>&_XvdGK-lw-(uX1Y
zzhTd`{~3GY;^G3?Sy-xof?ZvtKp+@|s6YtFBLgBp?`aa~>-&Hba^L;p<NFL@2mujo
zLh4uayPN=l3KaB_@SzIf3>*jmb%(r#n4W{>e5n4wd7{crm#;1Q71+wg{nxEPK!!5^
zc(jP}afd%dqUr)JdujcGViLv4q0m(@8GdVNgd3N|wn(+9xXGYkq<F<K4c!|>q+~D5
zjeZGq;;j>AW++M1IH@e`5L{9TsNMT@Bw9#+@H7+P-!AFjiu8!+PnfGVt33S8ybJq2
z4yCGIX0EL08g-gV8ZitWUAs-p_kv!NPR3f`4$=m+haH}fraAEw2=jKy<v}?05chP}
z4Ywa{=-C`0l4|8dYaArgY}2yG3f&KrK81`BdpRn!*yfsZHvbTRgKQ9A%hdyLonsj;
zW2Nj<Yl46S>}UP#P6l=_wP>ZC$P&2Sz)>{eT|%{(wNkIV^?S+gtEH^b`Jm*9pwsXr
zOwf8WdgMQ2%*OH>mgf5FG43C(1~qjMp*Q&8G#+91;My#ku`}T7{kUQIcthfTiw`^A
zX_Dke*%@}vB-1v{jdu&Zdl6(kbb2Qm!CEQJB3&K60WQ&!NWb|dXw`2yJ5;#*9)6_a
z!nr+DXg3W?A`5<1vIs>F!Rg&b_HFCW-2)m?W|IslfnTHsp+%1ewW;Ma23TBHq_^G4
zr_c?+X~pd#@=LLssXLrR?1pnl^Q4G|*H--sQfB=dg7QC)6DXAv{eO>>A?a^?_piB}
z<Nuh;;U*J+j?_I#7wiKHEu;u|StO<&kff;m1(cWe5R0*}pup$+F3BMR^~E64eWd<b
zVH+yccm=q!f$F&bksJLT-EH|q`)|G_j9o6WRO1d>6HOOfo?;3)SVAfoTmf=OsPl;e
zBtv9hnDMct(Q)duDq1jT%pxAH<*?x4yHk#1<ZnECI!)#SQFxtH*V5*KeXJswi6k6v
zhB_RUX?~&C7=^)AeoS+z={*d=s4@ITL&#i&TWcxHvvqc?a)r_$?l;|kIbivHhsufX
zvroBz$9W5w;Ef{ETP(5G3%NUlCp053RkZ6N?#Eq(+8Sl8MQ4`9Ho<m-28#;k5y4#e
z2FrR@xm0a>w6lqMZnZ*s0UVB83gzwaR|tb_AZGl3M2eN|uhZrK+yhVs7!-d(e9)Ep
zZ+qY`+^I)EP>BL`QTqFC3Z@qYV<3k2CjGuQfe<WUeW05YE&%tBUdri~=_^VnHc;E-
zKi2}H_5Ib%?~k<rL_t=x7!ZGylpRB0#mN7xiT9=du8D(6;s4(?@vg3W{|gvVPaw&Q
zAR_SWcN6a%bl3j^l3oIV(6AaPn2EM}F!Y;(T`&GXnCK@!0L&r@fQ+6utLi060QCiE
ziV^?>eup3nhYS@m`onAqqa@_jPwy2F%F6lIYXJTKFk3=}nyN+znT@4mG_~qu{Wq^A
zP20!@$zikxL|nFsoKMK0DN-+))mMiO!l@1#Zz)O-4bCIEK4)0K?-L{<fA_VDw4nH?
zVk0v-c>J|wwR|Y6<iJcSb4nF5S{y(UhU}yDv!A<`RvCCr7zUs~jx);OB&T%84g1k`
zE9hp!zc3cVi}>tie`*&+LdD~W``V553an)P(|PHCrZYu=h&z6u+`coLG$n|F1!u|_
zDpZZ~>%Zuo8QeJ{d(dbZ5w~lS7nj?%<o71iD2c`k2rmMHQ{z}^Qsq-CFblcDTlzjk
z;-uo_ITIG<sc})OmOXg6r%R7>Bh2SHtXW1PtLz}2A(3K27%72~BSDihe<8QMFJCAB
zf&?iXfvz7o766?+{H6pEwqz#93pBfSWseh>Q%^8%yP(Q@eM9sZciUu-FE>=oD_t0-
zbaaOBgT3yJnGDIPx8AE8|1|*lH|XW%Q-9FQ*A0rC5Td%;h0-83&@#vsqlXki<%m!*
zoKA$GYOl;(F^RhkQLz^s5mkek63)+uTjC}=tw#&@;r99W2JI*2YsOu*rXsDfzo(CC
zSzbxcrq;MLKp7*HE30*0z8s$mFF8H*Xl7izM=3A<#>#T-bEOK%!%&<gXB1ngUg!<2
z<PWdwm9vHx4pe0BqOz&rOGYm8S`m{3*0tb0@|rua{YaL@%X5VlTvD1y=3nkP(85nv
z_DDL^4vCCBsQn>dcz4)&DF1AHG}BhV{Rk~l9QE<FP4o(iWMlj5ho*e8`aNjm8_0pi
z%ARFhpl2K*`Nu3(AIO64i?|R2f;1MRvREA_nC_cs9Nr<2F%m4VO?tbdp=HdTVT$hq
z@FsWjCYeb0qCekI(B$mCIgit|Qh6}9PP4*HGse^CY<TT3-HLO+ap4Z9X7(2W)9}Q7
zu~B1|#Q@7`7PP|yVwV_l$<W6xp&eX3F-lU5+&!cmusEL_iR`FP`&`J}LWxgOsXh(0
zAcfbZ)4mh0yhh0$c56jwMcsFolf?RJ)Ai=Fbs!YMQfc6UaWeICe!BdJ(m3h?;3Ie8
z%2(n87`@D>_1B{I3Jhia8y&m)308P;VGy%?&b-Yx4uglCEkv!S#ZUR4u5Ox>>Kbm4
zAfx+!3ajQot1)EQ6?M&>Zuxsu?t_RRqax;QLSE6gVUkX2HRcA6i^MEfMIU$G9Hm$(
z{^HG$PZBt?VkKMJt4kb#_LKum(UGwPXtuFITrs+uM*5Nrn_`jEGR3k$D{wmXH_i#G
z4%h55XgsmJ40mZ$?K0Znb&OyY5+~h*72s;Twq;&3=w|yHUDpqhAk!i})WCHEwB(*u
zJr|}dZ!hjvvwxTtZGh~w>NVl%;hnv0wp1PB3H3)Y$+pwpgp3cKx2FuoA77QqFglf+
zDvlyB=Dw43P1^lhW5-VQ>*LZKI<Zr8tep&FcE{{wR3#2v+Aqrp^oB>=`Y*9=rRs=<
z$tlmI?Cd*{fw>NOq7ob|;X-@{fD&(9BDiFV`VHA(KZv%H?obVw<Qt~uuSR|0aD#hk
z4ZFfWhg#saNvuTt+iDZt-;`()d>NgL)MJ%#uRLSevwgyLP#%@@0;|rprFmtafG7a3
zJi>py#`Y(m0Fk;t)Gm-==O4TPE*N<5<{;ER4f3E29baQLME^^Bx~ppv0C|Z-iT>Nd
zP!P}uB*{Y4gPR$r2n4a$aJ}_tkXig;Z&47RQ0{&EP#$0qPe@^W;r4()5Euo)4t!-G
zgwZ?%G}A}<3Lz#y?BXZX*B1N=*8Dqf@V_ls6~ra@gA4`!o4F@s{BMQQe<$w!cLvc7
z2`-RC?;hy?0EBpB^o$}wI(+Z%>xmluB$daXTn7{DjeA7G!t!ML1ndOiEL1|hB1!U7
zpdd`_6EMxUW(){|;eX_#miA7sy}_)nA;!O<llXJJ|0oI@%KU+|{#Qj|yb~ZuXTGig
zqVG||#TY+zbv^ljy?@cu^X}>bP@;4V2_ilT)dcx=f+f@$Mw<#z?fP=i53eiuOVp6u
z`%170KKV$nd3k}pflAAA^W3CT?xcHvIMQB`sNuZ!alZyKdH;H<_;~_U78OE-IS=uV
z&X1bn_cgtsZTYeR^Dk|eCo_`rD2FIJn^RKL3T_pc;3raG04iSNFM%V4J}SebTXTX<
zu>75lZB6dQ6Z_;W_1JjYg7h3I6DPiJF0?^cXyUX3bz9?{)Q0b^u{AT6wKc3W297ZV
zt#<(s_lgkw*~4qVWflMwfzbqu3;i<#X0g3x)W_2mEk2q3eA<VN`pb**X0<hSV<j(H
z^U9KOQ7+JEJG<Boa6zRN5B}F;@(K)P`x~WV>)$c?ZQc_ix`|DaeFs_aoz}T{110&N
zt*Ghx@!ap+iXLFy{leicI6v|ZB^{Dl(+|07iK!QJ)}5*{HmL&zaNp5*?2Nm?Kp=pO
zTcO-U6sMfO4)&TOxUmozz`F&EV#Z-DqTRM%*{VL)-gzTxs1~WyeJ_k+99nE2lIWM%
zr*UtMyBQ2Db5(nI?_@PB<)2B0UgJ<pUH|zI<5U0Fx7<7Bi&#pjam{0*%8D0Z1zF}9
zWh1j)CoX|fLp_T<gzrXb&3ax7&nu{v?Qi7SAaB6`lD&kniM3EgZMcKkq0f9}s{Su0
z0X|ad5s>O7G$fgueM|fjR-ZwsKgMxxz^@xb%bB=Ky4voiGgg^Wj8W#zjf1jw@aU_-
zoeAIjHT?sE$#Tw;9$^kgG_s{5Z@)0feQvareMGVHfV%Jm`xvX<3Q5akrN+?NH#U5>
zD7eYrjG<?(5A38Z;toj(K>-S10nnQ!Yt4Ds*T)%`f$Os3uPC@2rc3S-<s4VJHXlko
z_mv*=Mf159ZdLk2H2l#g4w5zbVm)+T%lM?-ECY9z=a^@)cRq{U16TN!7dQ{6mj16r
z?-g+SoA3Qsv$_Zn@Bk5R12@qS^zoxM(z`x{g!l@P{tM-HB~<c?mbe>aMaIYc89wYe
zY3ccE%6Q?S2)Xph7`n6)@1TcaD@9wukHN!{K-0#?lpign>qFZwZB+~nGNDTe4k1*0
zHOy2fWOE7mhtlO@+m*(b)jPH_H#LwWW>e_XRn_4Z;1n#szj6gNpbPZi|MB(p)ACfD
zjk1pG+6Br8hWYhtYkv)@{teubAI%DKl>`a4bA(cZC{DCVbx@<D;o%JFFk?<e0u$)%
z56Km0pd$wqdBCaqzuvHEQ92y&Vb#vO0KHzCT$y+`(@B&j*qOfFQ{>B!%p3NIAL_0#
zt2E|Cr6=U+uBJHs8d=vBG@ZHe8l^i7lBc7wo#P7E14CURR&a)-e+!n80cME=PQV0<
z^ri=oC<jiJBuYV)pOiShx5!P57z<U-`yR^g0eZkV#eHK0dT(!bB7Yp%^C@Y#G1iO0
zV;?Jrlr4wKFXt#<TsJ6Mdfwh_VaB)Oh4eMcAvV_6@F~aNFJx3f{<qrjzcb1P{tBaP
z4;mW!76Nq)rT<dM3Z?%B1_UQa^z@*&+(=(gdiQ;sfB;b<!PpM4f#0O%qTmfd1;jna
zKNxc-N}9K?Yz0vB<Ueo~+utzowT|rkeTf8n$H2%melZTq1kltWv1t60+!X3;1n6GA
zpMIqEf{el|A#}l#iqUHVIgy;bylK5UW>~*Z=)3HxWO{DMchPi;<xaKOzKNI@2&Cye
zpk0+t5b-_}6zp)uoJZmA$gjGXEVtDuTL02S;WGrMH^DMS@#YFG(p5x&Pht$KFqtJ!
zc&wMW2#ZPHM<DwgWem~1KLy-z5rfUicMI_24uKb92=756C)MdaNnLIWpXRZOii?!v
zc*&khscP6m6j@lwn;<%aWbz<72zHzonB^g-m01c;h>U%gS9lU^pgj1M!2R`f@567=
zA*i;iCrL!^Iy~a+<$H@x#i;*v4*L(y;Z(J#Vrwjj#k5Ndve-BZKNae?%XD(~NCK98
z-^wmXr%%OENvLZpoCJ?jw_q(a7Wn)Qxyfewtf*{SP#Vwkmuz9>74h?<u_)9dV>?!S
zr(r9V6dnK{IhrMk#V}IIAV~J$2j^xtlvz*#3{EPT(l+@RwKlU6X%}-bTEIyVL4e$*
z&igWk_bHVlUnnd@fwa?SJ31Li-obJ%;mce`xO!W14{G;<p{x=_?0f<xwJhiiO|)Q)
zC4mk1St$DQ<n90us|ze|0F;rd-)kH1HGKOgjr@PExg<;q{I0n`ZBT(+=IkK%Aes7Z
zD#?Xq^H`=wdVc%0OT2|3i~woK-ot};EcFd6^29yIW^ikjr9@&oBD;ngBFdY1L>@>`
z8<}fU@qD5imO5Js$XiH`o#1+E5@gPcJB_87p(7zjj*8DOi(jD6fBGp(+Lwq!8=!sQ
zI}68X$B1+e*@;b$s{S4oArQQI3W3N@b3+q<L&IX@BWYDUX#;IELOV;=<EA7QI<3Y-
zoI#iF=?XLI6n!pHI~d=+oz81Pc?AUjy#jOfSJ0yby%B@i!|@CDEpt{{IzCo_kX$!r
zkH#N1=c!O6eIJ)3hbi)8?N9h}VS4O$Idxfk$AEXUKfj2vA{E6c0R5rqPSN=dn0Lvd
zf-A>weO!G_#M?2)(+ADILjBH)Q=g9G&^eSfX)tZ8l4Ie?d`wPAd>R12u5tFe4ay>H
z{7tR%EYCh+PCFy0lwE{V++9qclKZyj0+XC|*?>}1FEhbnKmK!Y+j<5twvU>$)_a<S
zOSHg}>^ud^@0z|cBE!L1_<(e*ral;AMW<II^$K2Q<NO;=)Y1Rd+@t|HVB$MMOhS<o
zICH;A$TyCW^6gW@41uQwS&mTi{uZp}$)fRlz`!Z$4Oiku?R8!h@V2fFzi)8R9Np7y
zz`4}EBB&(5R9t&*<8Qz0*gevjNBYQ3>-c?b1k~R|`1q@pp}3iENR+Wu(gsCjhaBxt
z)<`9^>_~{Iw-owFl0*uIAS^(F9bz}06`U&;iA#aE$p_B{@C2@X<UKtNr(j^R&!Kem
zIY$HSAwN~hK3W$*aRVXZIfP8M(gzh<q|E}phtlFbWqQAwjz*>;QM3drjk9s$KQ6o|
zI!u}CdLbr`?KcMLDYZpW6)Fs`+vu@lo1yFf3V)alG|l5bZnVGSZkF)d-3)Xhr_jQ>
zIC%XW!F~i#;+G1A|Etv1t?nNJe*;C+5PP&49+t}FT_94hzr8GR;M^S@A}#*(#2^K<
z!!_;K<D^qC<p>LL;c$xqQx!Kkkkx(^{U_NW$%N@;8e4z9T!!<uFZA7Y3V47*qgB7d
z-E=A9^G&7;8rwlwtXZsdC0ZdR5oC0%j%mo$r*b;5S-s<<iA$Ijn`&U4KJW!038xT0
z=^py_T$|#xgZL`8{_8aU&!Fxoi0u`axl2C7@eaiH(z9NIE6}8agu?+Dtdi18fh4Zj
z`)2O(0)8?$YC#vcbm)G2SXWmauCrDX%vwy%o3&rlQWtQZl%@&3J63~+DLH-9HD8JG
z-e{rn&NaUL!RX!Q{d3s%tfGm$b9?aXD9w>`xHf=CT8No~EhaZntV)R&b5J&i05w*|
zj*hSjU)GJ3q|@g_zS>4XBj}S@3_NE@f5^MN99alF2VWRGK+GY`JK@cdh=x`>(@u<!
zjkV@8D?l6qUZT0uQQSG(Di-Wfa7~r%mktXr<WDY6^6SKSA+LzYe|xL`$0lO?8_g2y
zf7%`VHgh>4NOlDq?{5t=t^aKc*IoLi4`d66Y0bRypy?X#!_1<G!E$ncMr1}2P?>CO
z3KR_6+wq`c*BG&93{%$lHf%V~%x}xm_vJH>>zBRHqu<NN2r4RA_Db3lL1j>yig|7v
zCCB=hR!x(;Rhxjz(2(~CVi`zcChwb<qym@3K!ZxsO=3oT!#(7Q9(*c$GpZm2>^x=l
zVhDsTNFF6+PtX^)koC-q)z3Hu@@&R@$q)@Z7{EQvO>T=}AF@>jz!gRPZkq%=EiS`Q
zB;leA=AiOm(2Ou>0nKO;d{nk(I;l4SG!Us2`jNUmY3hARjJ6LjP1ga{uWUz<8_vH+
zm6ep0gPjAO`M=ya*hsn9I9UJr8|bh9`6DU!pG>s>`Av;*g5gO2w9vq0aUBckAM!4I
z&Hcl*PH!?^I9o67=j%1;90}WLEkCg+GhtXiQdq^DI5V`<IDXf)JI^(s?(whr_Ib~B
zQ{9C%)3e%2PagFf#0RWwskh1a=)QX}U{Lvhk>wH-QemI0EEWX?g^*<bUf4)y-)C!@
zJr>_={tYP*$)6=&k%i@Pxd2>37o$D~w#!o<2OxzDK#v0tm4Sl?4}gTC0#cJJDgm&n
za0ub2puvAqps}DBARFhUF~k4d2<cecKhp)^1P}mX<1zPtogynZ1!ixcvVdnHNxbdR
zu5=0T`2+}(f+Y;PeU^k}!jt4!{_tL?Yh{H1*7*iNu88cg>H)CjSw_<XT=`_T=_zzo
z#)9$beO%0(@#!N3P)Ifn>iUq)5T7DRbORQkad;qu&YPSs#Dw%|yKa60L_aY4R1hFu
z;Ak8(gOhAbZUTgaraF6`FJ7=<BkxZUgM^|hZ1e({2{!>%!L40@77I<GI+RotfP|1Y
zS~nB%kfnDO4(?CzhF0n)IU+;_<vAFiN`@!pOc+Qg7g2YBzy?#N1?D~0-ea;Vnu2j&
zDFdSKE$K@_COjdOzjkJ8Wmo1NA4T*U?ASVkI}f-8uoSVVFwKV*{O3=&S(S(0W-c_~
z${s)zLJW#3u&=)lz~TZJgV~}zw}hb3<zBQ!y&m=WBnlWnI)tw3WP-p3GzNe24q+wM
zW5xwUTn0ety<pt$f5n9a7urPS=tABDNTLGYKGx%z02dxxfV530T}tPgUZg%7mHWEh
z^9%6)<Y-JCzyhF6wNFPyNp&RhS!OH{ik^<l6M*YQh6d=2&Pyf*P?FPwqvGIzLq4%b
z6XU+n!~pH)#TF^RV!e0WJr%t7Pi`b3wY{kW2+k+f`GzbHdjQ%%wU&geBGR2ls7Jx$
z=f+W>{9b3yQ{{W$S@<0PCvC57ZEv&}62cRBmy74#ro;-GM>oW}n1x=Sd)pGalgg#<
zLBgBgDu59dIo%7}Ss`|I4-3LQ0&^ShSc-@Vk?fyjSiP4byvI`!2*GAW-MYMgFaf-x
zvo^f-7a*YRJ355<D~ulq{6JQ=f{iNTLeW#Y{?wH8(12V8J>9KiF5OBoFx;sdF%kWD
zoq>p`0P^SqOPsFHu3!Ln%CGky<%JCZV2#J_fKBjPNrrABuWWCyEpMN7#wQqXbTHVg
zt|{H;8vnOXP)`(eAv8zJXwO<a3wXfx7f-A%y>~3I7h{;{<U~KO`P)t<Gq$X4U%sO)
zp%9i9;H^F-<Egan%RBU_{9Hbnnk+KxpY!F$bQ~?wmV~c;gOgmo2i;SYNb^{3`m)az
zg*c$56<R2H__&5+OV@E=Oy1IKn^(2xz4_A?eJgw={D*5M#rh9rKkdrs$6%MJmdUWS
zukvT)g=#ko7X8H0=^w+dXmAI$0^{ZEFc)`a*{aa89#U-16;6Mxrt|Q<2qoOTeBFJa
z(An}ih1^6B@6S=#I?P-5NQj9Zv>a#+$3~QGl2kbx_^BU1Kl^jmyDvWB4d#a`>gCo?
z#iLrp1c6x@0=2y2hfdNrfUL9L#4h}Nu>3h|syw|BmZi5CI}x6r52JI=)?Bq3qe|lI
zvr%BrHYC<+*|>YE+*M<mkpauFD&qZGa}a_!(U(E+TDOT}{8&*qKgYA<TK0>T^~&o+
zoJb_3mG~VFKi>w09m#M+-h00J;K2C_%)=DH#yH({f=vF2p53)iW{khRE%P?IKWRej
z#jc4P>!YzphfHs&l68stOzXYt7St8;?0aCIPGpo5S}-Sluu}FHw6t@2)~Fhx43>Hm
z)-R@<Q!o3TG3Sh-hsxm}zoy>mZ$@R2=<3uML#cW8d$%1v&y^m@0LA907>n$WYfNP@
z5W?neI#R9}$xYkDQimgS6j96H+lX9)i)QXd)6I+<Kaed<eB(H6ayf_%KMbr`B7{=J
z%h0NNP!{6prr|sDdx?^Pm$xGW0i)D9!?$#TZ;RnQb%W;a9GDl~EU*>n3nJ0@NE>-!
zqfpIDrB4X=5mz<c>N!Q6rYle0TQ}g9&7x6zU<k?=odwjt)sUOA;LrS`kDmR!I4qZA
zq|R-|hw5pRUH2G&Jjmx!-1jc6lQC~vDN3q6xs~BQ_{}$@3!R>v%x?8T{C(1}xE#Xf
z1pMx~&FF&lPzf>DjI$rtJ*~7JGeIltLcVS!h5B%tZ{T}!<y&PO^t~J!=a+5Ye>(^_
zcE6bPx}(?JdB@F0ue7Oe@qG?{$kL%x`7Zw?E@M)%InIz;{?g}mC)J%mi9)M}X+Caj
zCB;yxIEk4chbv4qvrn~GFyW^<HQhT|cW--H>>su%Om>c{-}TfVPr3agAP+Ge8s4#1
ztX<-u*o-pNB9BVH<#BM4jV<v{y9=R2d)$3<RcS1Ob+B+t;n-B9VPsOBi&10q>q^1_
zM)v3iNqidi2Sa5rTVZpn$b?`mvt{htcea{mI;wAj%)ebo5_EJKj%1J_nF^PAc<x8h
z`BsUo{BolTft^opU#hvCiHHSqk(dhKyfaY<{^Zyggt7A8VZwc*?5#*8^aA7I26|_|
zb1jihd&FQpIY*xVWXP$huF`!wmrk|yc})IOZq{sfsn2y5$`)T{ll#N8<Z3?(gDPh;
z@r1y9L)!x$%i4>@=upR4jbZ84ZS?vRG_`jmq<A(zmCCHS7vLdhRckd$cSfsUJHpYT
z(@og>h%=ywYxy&al0&kq**bcKjIx!cIJ)pO*^}Pd<}pIk{>$SvM)K<EwsOW^$WBzT
zU{T4p>hz{0-vT6GQRTokX=^`QLqDTLx%cI1Yp%QVvd!q4=_iRhDA6Y0Ytn}s(o%U+
z*V|0(LW@nX;2${_8YY_a{m~8ai{xu$hFq8$;0%))Nwhku6<ws0B!MkY^XD_z`<8)b
z5(*@zv33+e9=gA}JshgWFH**3qxin86)B@FAiJ|Tt54;~!qqBi3qfaSTGODmc&Dq?
zlDj~oa^KrgnCu8Ae0f6G85qplAuyc1{v?4pV|zT`SnA3s9-DytrRry%^O_wFTs-!q
zcQ!a`e*{ayAS=Il`<)_By_MBwTK@deg!%5pi>RFJQ!{bnaBEN(I911+=&V4_xM_`^
zyc$#_SxvP~|9kK)({kKuLBq9a$}7T5dSfZC?Ma2TZnel|2m%MHniy<*CY#uyT3C{0
z7=skL{hziiH_W%ipZ4+(EJMB_Ew-G=-v8{rLbh@W<2&!;HtR4G)H}s2-@aTls!GCB
z!D^c>*=*OVrrMuUSY|I1RuCyfF_;dGGy0T}NUE6poKA=J)N!;%?Tw!FJ~?Y+^tr~<
z-PD*`1b;NTEwzr9WZ>RaP9t-OX_hqNY?ty}$N=SXMxA~+jQ*@>HXtAer=|b2n>vki
z8tuF+^jC~#7$S+61HYVcpX}p==|+)0>$h5Iv;|rJH~UPjcMdk1v1A4O<a*6A^H21p
zU3F)8OQ_cZX&k$2t<9snsq>)Gx1I@D>4DOlQ4+V2s&l!BaBkOUkua_L>lMj}XOx(6
z&5w_{Owo3kAB=Nn_1SR5YNdkHyD&Bqc#7aOV$;|s%~ATBjT6VUuDqOTD;~e_=TH>n
z%8Y#Lm<d;EwX{iVBaII$;XwU>dVsNB>jg`<U0(IwhhOs|M}KsWsNeXLqq<z5u|u|A
z^q!p#i8-|)N`&`O{kR0?<Javt=LZ7*{y_uf7{27JkVZnr;(0hkdecek{DmEk#$}x{
ztebK^j%pJeT7sU1!dCX^+uUd5>uM&%Z%R^|9_t*`+s_X@QNPqS_&r*f%%7|$Xu?=;
z6X0buQgmi@OAMG9nJhA-oZ`P}1t(0MKzQnQN67}ZPKJoFPG8ub)#xkAtgqdhdD(nQ
zF-x0-hw<!97p!cAtrT6o7VIj4=VslxD#1!Seg;2*X<fU~-OEaeAs_(O^jc%)tv+yC
z_oS2`#IPWYvt2fWNBLezcJ$FzA7G`doRHg!*(?tHD!M+s)1$~&DV)koW?kMiI8jnO
zjNz0oZ|`K<AvUICP}?bR;M2ev`j#Pp8ag0ow5+OnlE_#=5`#Z8p8>dwxJpe%QfCsZ
z-A*%gfSNq#7-Zuo#*UM13@NWAeq&=DhJ007Z%pjLEH8=^eEV3?pmQJ~;D^5Tlfc*@
zRR0^g`ljx{_nQ)&{?q&urE?_uD1-z^^~S0w9er;?cXv0RQw-eD?qx$NeELg*=;|&$
z5o+*h=jZpgjE%Q9Y1j~sFzcA}_TeA##C~bT(07xZAK0D1*s#WYG_V>tTN>Y4&Ncd3
zmSrb$wg$aIKq+{5_VD15$di~w+Ng{!B-=9)dtdT^%6Vkaf+f2!Z$Lj3;!Q@R?UMLQ
zM2A8+_=U=(+S?Rq;lf!b)8vt`6`@Vv_Qzd6#$yVBOQphGrZN6hxJI=dyOo{G%Y-{V
zcs74Z7>;?A{&VKHAZV09O7jY_@R$T<ue^vT-Fj-hRO6!KtzOl)JTWD+w8>uly9ROv
zi3R$&butK@SGUvUiwogO#YY@_qro_vjO*WZ^?se|_3OzpRv`)}G6;MSGqEy`-;}8J
zuZs}O&&sGg($F7YZq6ogv4F8`uR9Ny`o^l1@2sg<0ZGID2(M>7Mr4)RBv1C?`3<Qq
zxHyFR0(-=PW~%N12l@{MPwf~UX@jlAls3*SH@l%ukvfj}O#B2p_dp%a5`FRxO*Yrq
z3LRZL-k216ANj0k#{4xWo4Njqh&rCAku@Jha#IDrX-b`M^HMACmlq2xRT%_|T+;f6
z@p&;eyHpna$rn8ZCzMZsxwYe=2gRcB<{?hR=}DstUNLS9(;`q|n3g2?GwJi>O_L3^
z^Y4!|;vsh3ce1&)OeRmON>Q=C=2rabI-^bC`C@$fnLS2-v-deZ_hQiF4RqY?@XCjz
z65FyGiwgLS#m`ncy90-!GfJiIiI3}u@5<iGt=kXyIg5RB>y#s^dx5_z_-61#?gf*#
z;H5Z;JU-<+!G+wOSc#6pSfYF>FSstdl7<U=W21b1?ZFVtdZb#7ZQHzzzZlG9ZTKx+
zZ5`KCn@fDw3uV?hH*zd^GRRI5C;eRWVmhu_LtUor<b=tp;x?#hv1o1i+dJ=P3rcmO
z;OE=J!BCW&PqwK=T&0;KnrUIm7e+L4**(|HU}Xi5jwC&b?UGo7$cteL*3x?mB&dyF
zG;h8^;Pd^Y+%jSwSoXw5u3S~Hxae+$%Ow?11K{x-@ofS_vRcvXEk4JxmhB*1P*0xc
zj*^`bIDsp4;S&gYOx@FFuC@w)7mBw?ccX|Nu-?wFILJslW|%bdRh<xiPL;R7CL>2?
zQzj_CX2^`^(vo9rDC4t_H%nol5F-+GRQM3Xji(xR*=wGSQtaN&u-3OV)<vM(0uG-K
z7Zo6dx47fZ9a?T}eRMd&2tW)#cN<$&BcKRg!&_P<bLpYvrM#j$M5)Qz3^EYL2seSd
z1dF+78*SEv*fYLc7ra&lWFS|>)dCut2w4XvZ8M^JzOlEc{X8#*6Z*)OY(SH|$kAsz
z+-Xlf&IJc6AcsM&<487dZCAam#GZt!R`~SL>?N6pyeI?BPTA>uv)UqC@bX}Y{}yVj
z)z?5A+bT<+W67sooB#5rT1`>cSG}d{X<;J%r;f#^2>j$NC3{RA;3n+b!1&pp^!ENd
z(!-Y318FW}A9bSMahw~7a9Ry*b59*VC9~lz5T;!~aK5{8P0lTBVr6r^;jOYbmHg>X
zi0vVNZP;5X*?_Gdb_CrpCQ*#svx5JLT!xo~cTf)namb2cPbA&(i>?|I3lpP)0^b8O
zXV@eejuxlWHSB7!K`h-FXcS{eILxWl8*F{KrLE4bgVlQp9&c}3XZ)s%Qvk`GoqfDG
zk-6)M%&966wkXALaUgA#Sz7p*!(cTFYB$2h!{97shD&7z<Df)W!FF=>H2dX)d!J3b
z`)rrCS+Qirz(-hoim*F+tAOh7_a;Z&ZQzR&^Vbh|CT~KeposB^9|jUQ=X3LnHX3Zd
z)qgW2Z=aYm+r<re%y756Tf-koU)H~K&2TpG7Y5?^@;H=?Gi`|y)cPLAB+N%L#?usT
znR3hHa-K~wYB`GV-bwi!y0G7Hu^#ALO9w;s8`ezNIwkC|)SRdu6_pZX(@Cy-nGkRS
zX$cpXYD2$%*}i6p^tKM4MOO?z&;MRe8$0u<IX#+NPPQ19{=-ky+c-5*-z0jFG#(-r
z%4(?Q9!FC3k|$<T<Lsc~z;OU+-ZFxa!6-UW9zr9+Q=$`QEdY;a7=JV}zNo44yWp4G
zhl_S?`QZrmHvPLYG%UR0NuPmbo)Id>R_83cmB!L6yP*-PcY|e7pLYjw&D&CsDu}gb
zJRfgW&*hYZd~J=0muSK?iiZPik|aZk22n)i&`6$XhctpA74`=%Che#*PrusT@f~$|
zlzk{dI>jq6({U$s;uUX!Lg%$WWqBLsdI(lwh-$RbuUxiTVsRqqm(7-xO594hMSr*&
zJ}$W{y%RpZkm8Lb_V9jyci?8kR)o;hVOHHcs8%S%eC(oQK=NQfxz76$j;??&6~V4^
ziR^1;(l?Xu*X$9c{VuTyOov-bz0B-dP1>6IA1Fx`H}PmHX*)#+7cbt%5yTQOD_a;W
z%-Dv>D{2warIBFhRZ_RX+ISVN)T@*A4?bRA1hRezxcu}@LsI4@nwxNu9PYR!CYb1?
zt-nKlrf*SV7ZLHEE`Jebtd56o+|e8%4o1ZwE8)9GA_C;TDkrC+MW965LqFLxvZzV)
z)b!(obFqO$o{_go36=?Wn;WoXJf>IXG6ds;B#-S_nV9m7Le6@c8}muwa)Q>G*l;1-
zJGj}V@8#D8+l=*lcNVT>c=9?|$2g^_4G|?p@9qdNZib9u@X?BVXb~ZM_YF&r@+W>y
zz?=tVf8i?mpzP;R8+qZ6V-bGJS6g1DI@-n$``o%)pB^(AhVj|*D-F};vjrS$^}FI;
zZFE+ztbAonGxa-pI9m~Ny@_(qR=tK3zDO8u;vR8|7OK|xXZf;}pWdIkvvKI`MBm;F
zI14Jt4Syj>Ku716T~+q96v=>UAg-nFg4P>A&<DziUTgSb$(D|zo8Nt^*E!ev{1JPC
zC%f9C97}I_qU~Pvm)cyYiF)<1-XmIc$@+Zo#oIHE&j%P}E(mn|g}aZY1ZK+CLANb1
zSTmmJ^`4WMwbB9&XU&;fWIskV(Lbc0-+&RG<LmE7530MMNy11XrgQwLxhOf||It@K
zO`Ui8kQ(Oph^~d$9XyKD<bV+-Y&Ro~2L1M94tIQi#d&Sk_d<UnLm`2c{Q|#U!*mgt
z7(Yv8M|BDv(={d`OZ9Y_Ju|Z@<k>@hWoF9=SGN}3dCZk5@#JMwQ~%Aeg5em|j-$ec
z2^m6rywq>b8;$|r^SiC$X&~b5!7{DX;RR@bH8v|xDf(NJwcudN>^OWsA}BBMkC|js
zl-Vwh-F{uqnWf=!1LMDc&_MW6=2aoE_7!1;5s7>YTd}6AaLI5?aZ6#4z}B9xkiPuG
zJ70Winx4kmKzKSy)g}p9##Sh^siVFEAIu7g8tcxdHW<Nki$e7IR$7JtM)mFF9b}C9
z_uKE%yddLB6W%WbxZ%Jvl?n0bma)?ne9s>Xv_Y9PR}>M^RPCM_{hY^EWZoP6Whdp!
zZl9yF-n$i?VIEe2c)Xa{ezlbkw)8~{H8vkMcDM(J-Jl}Qz4uoY6m#vW;y&wpxEHva
zMhv-6$@59?bx*L@2KVLq?&$ucO0i6bzh2j`FlrBPKe<wI@BCE#1pRAc?}}6=aZ|?z
zeLUdqz4Q~$*SiyR*7wAfz6AG<2nC8iY+3Mm5jsSCF|FfT^S__9?)fMe+6)tY__#QD
z2KDW_Z5Y+SZ{^%SzLU<y+NHF3!1V?uoa5HlpSb_51CxLy8-IzPI!DVE6@^9oV}(v4
zbx12Q@#H;0X&;kO5I+G*0F0Xo@lP(3ekq^wBx?xW_q^*rCPOW5KG<xFOKJqp_<rsR
zfkP%g)He1W9_B=1U^K3*Og#>rh-uXq%8Wc!&*SX<<Ul0$?J~Ypun>FyBcsh7k6fAR
z>TM-LFzU!^vsTVyIs3i(HDZKZGQ6&;FQao{b=L#`drH-vB?vyk<SpwdgBLJSM-vrM
zZ5<9x0*@^=F+XGI=B@RFGd~tS<rwC}P~!$D>BSoDb{yYBL;IZ$&(E4w25$wHm)9-H
zQ8?z-UmDi?(8VKE8xoZYp<U^>QJY=8FJ<cCAdVzw7Mtp~hfiiquT4<|HZk(75Bon<
z=Rj>zTD^S#>Rdy@tPZ(S2$PH5C%#<ARDU;vgD1@Iz)wbh6p_=7k7=@f_sFj!U~uj=
zdUY!4*1gRw$}rQ&{gJ<^1Y_|g-h>YjDfNj%^5i*h-X_NedXlxw@DeI(?xC@_?m%8&
z-{|9=-e@WD7;$|`zh`OBhD$MjN|IZeh7KJ+6-cur7FDe>(KnC2Q|d7vfWw-}qZ2t_
zLLAT8^Pxg#UzhjBleW~F>BBSwyy5&swTGK*4Yy__4`R;TFCGK~18yq98~S0Tc=ZEJ
z2A@X(*M!{qE2Q!cOPUgwZ`<QAW9IS(E@o6k;US%bs&T2V?P0CV4rII1<wg!U0MN{U
zRC<-IJIwB7af9ReBfT9Y<d2ycow6xORJAMTE7!@mj5f@2>>rC{&EUJR!uzGZF`Rz=
zK_r_=o1h=@Vbxqy=OpPvj1Hn93u3@d_yK*zSej?WJLLt>XKNmcF38F{@kI2|B^etI
zxeAA9WHNG@!JoQ&U(mj^MAKjX@ad9haQ3(%LPN0lNk4v~2jY1Q=`>HQdj5K#`KnO&
zNMxO+)B8Q?L(k@uVAH)xFss6x>4<8<>gDF#M>Y%ByStTVOSRy@^Z}IUZ7t`XWzRsj
zlC|>TpE!Hrg4kQ$KNo(+$LSewUbf^Ds6U^)wV@<~`ifx$MycWx_WcNkv2nFVL9saA
zK^vmo-$|M3oo($W66cj_z8S0cw9SPij-_cF3G6ZWWwyHbyu6Jb2SdoE%Z_NUy6F1#
z7#sNPdB-k#<snm^&M)u7vs=Nix9r(uW^?)Kf+L<2_9G`wL9?qWB<BP$e4?{%o4b7x
zd#4_0L=xL58=r_;n8OUO#xLbfAlRBmF4aRRlP}-i*%NPiWXgsgHk~zEuHri|^ieEP
zMBQO@lnwc6W47;w#x!{W?BHBQrqd?WTB`BN2BnUzB{!;wh|aHmV8Wb#7^So*TD#$o
zFpbg>O3u))IT5glDrA4YwlC~;GZ}W4uow)Mus<&knSU9Z;euO|7A}P6orr6tudy<I
z3T$w)Ko<|>dNhc#dHj_@xa=L8EugqlPWj_R)s#Y`(Ic-Z{FlLzdqK8nGpuLjFuCu*
zxsFV>zMLzMO`;V>@e(q}wm<_hlc?bhGMjhaf#G6qny-DmAJg2#8h}hi(9lv+BNjHr
zs<G`SAnVhif9aPV3p$wUOmyX&^Px}uV5HranZa{U2031N#rMLEiNC-s3`nw8D{j5k
zvA$p!;RN|^FpUo^$*#;8jOUqdG3v}|D%lRwWQJ!I#iWWY!T4c{QAxhE`mTLD`}hJw
zt#!QnTCegyX#}8NB|Fc*_9|Ie+5g#~{MTM37w<nsW`Ctu38kWPy3Cag>mR~FTP>{Q
za1d*9U^5H`jZ0*rXL69}A)JRPE`q9n`yMqf0T~MNP(o8$SZRx3TVTI!AJneu;Iuql
zn*2QIIQZF`$ypRb*w{3l0?|*z0IFMryvC>G8>>1bDjFIFIvN_3y?shgHS8h@Zl5u-
zsNn`}-ibF)QI5XC8)9XDg9|S=TFR``KtPuPKwSy@4HW}9c*xg~p{LUjkx_691=}X}
zEE-6&+-MfW5E-o9&ETNc7X6Ebr)T(cz6&rtJUlvI-A$jGw}?nCe&k>RwDU-Nk)6|U
zUjecTa(1}$XC!oAc7If<qiE>slaP>*6}^>!4RluBzyNq`sB?GW2Ki>U>rZq|27!4N
zy*+U;6(MEnnnQ|wh)B!Qu6n1yf!GBB+k}A_+AaBoEJNQ6;LWR-laGM=r7OBK<Fof$
zSb(<)gil}>@ckXlqbWJh<wuseVYuC&VB(M6F%>?b{ucxyX^V)uZW#a|&Ifb~AZMZV
z;GZLamlPtpK<=au21hlf0DQGQmv_gd;aG(TL+j~66V$?dNr4tL{+w4!Kk|bpsLw!1
z@L1VDS6}YF`WGB<?c@{X2n^o&{sy=tU+a@q&(f&qA|l+~wV!$MvwgQH$YiF1<qatr
zIXO8M1#}lbSaM}t+WF*lffsR=uEe=K?z*so56lRJT{kxvf?sRbxpx)-agG35Z(64+
z%LmBubOj0S4`$kgfo&Ji+!K@KZ93S?lC*C6bUAIO+ocIPDZoN{d39IGt;OG-t|%3-
z-uc7(^y4CKj=PptAkbwHSW{e-<>3nuwnr0EP!WLy04Y;2U#Jc;%@MQD1&yC1%dlsl
zpoX3*umuY`8E#)h0c(O=8xSukG9-bfjEEc0+{4yllw#@UDCfYGqfU_*Z$x0qt~bSl
zHxA^52&q?!tM?L+gnf=E^8DAf4^24^HZwo~QwMqo=v?pfj9pGVhWB&#WnGQ&4JI?B
zaUR2#u3>;7shw_eEg9kQ&TSfFr+sZEy=Yeqizp`a%T+Bn?AFICU9Pse^q*UTM7aD^
z-YI%tcAu`^f6l`gc_{1?qoM?(#Yeh<*h~r+lm>w5wP9!IcXUc2!Xl6<s(Zlt*fPV~
z31<rna?`^>2k3v(xo_Uy)D3CEB|Y&y?|A}$u}_M_!H-B<VdmOEc=+`sv%-?hJO;<{
z&d(t>tVtPd+V1}SLrc_#(t5dY1yvZ0hacGXfkfptLlW^tg7Ipa_!)!XkxesSebz*4
zAxF0}ewCV*D_sjyf6<HNMyO}(MVFF*Sy^Vsw&7ap<*_q1n~AV;{%)7@q|jAWYhQi9
zSzU9DKzm*#?z=+XYxCK2&mWO#C!zQnY?@4Qb7X?up6KD+t+p7q4j~MKtd7Ys{*Vkq
zB5rYRD@hJ!w%*Z?OM{MFr<7g_Q*LeQUR|fW(=d{~FSGpxImy)2as?~lZ~ENl1Rz$y
z=u3;25p%(y9+Zg;JL)eVtR`t8iRIyRur#qgF#8h>l9g(Ww!06k`{C4*fT8g(yfwpC
z9ziHIKb<Cob~3)XwdYuLsBUFR{NBh!Ka2EhZ=A`6m|cFz!_5;JQBl3QJaC=^=Bem9
z{ioMD*%#ECrk_=<kIy2G(fu<0&f+E?NzOlf=0095mR@p79K^9-)XAYnNX9-@^@Tf=
zoT0(Vj=2Q8>nJAFg_ztA4Ova>SbV?O+eQ~dbbl(WApO1JWu=*{L@@xR-um9PDet9u
zyLE77#Z}hE;0PH{v5oieXxw{ujPe|JShcGpsK>r%#gXb-#3Inks%7{J-Ef_c_o~01
zLo-Q2rq$5;=fe-)*kP<?4rbO;`S>AD16}PXjFV60@W)DUQC4wFE;<?O%=Ajtxxf6_
z1ecjELKP3a&X-i^$@eQ82eKzqS)k4FN9@Izk+Flch6!!0l1skSwtl2h&`e(A(>btq
zv)`idnN`}}8YWq=TzDWSA>y%lTadUua!s!i`cm0`q%i$e!e3O!ZxruiA&H&w8+r8g
zD%+C^E}*mXhuxOY!_6y#7e~nQ+)Ou|MM1<jcF?5nt#rONeg`<9-_3@gNw1#dB4o?E
znA%>fMF}{DmZz{@DORNGG0Br0`HWY%+-NH_+ZQX!2B>A*e!>`H*cncq9^Wq%Adwz!
zVL!_(b{b1ma4f|%RAv=zr6JL98Z8+98ttg|fpy@7K?8Uhx|3^~>A~p`9-Se+kU7A{
z*fZKBL2ObTh3w-b4u?BZUi(eSQ+Ehu)3Eal-P|>5&OCuWGG0&ED>=BKl$`7)jWr@W
z*$vq}Aj^su3+4N&M&~7#^$1&b*tRuR$+9QpvN|D{i=_{ybdC}4fkd*ax25;&@_ESK
z`Az%}p3R=SkEFR3?+8&tgW=w-%Np=Nj860_NL+d{ct#Bm!w~wd51H2ZiNixk&ra+f
zcQ)Cos*yw(k2$qkPZ~*%C3?V7m6y2P(0{5<8y2?zNdGzF+iC~Rubi9Q#@3mRpv$cd
z{Y21g;i%wLNQ`()hgkD^PGK`5=KqJWcM1|EfU<1Mwr%5<ZQHhO+qQknwr$(CZM$l2
zzc)S65#13pkzeO4^Eb~rYwtVsdVINQWyn7RxeIERhEh~uU_me67#d<Tf!XTq)}}nI
zf;JZysl%{C-Rsqh*YHgjIlUX6I<A7N^;alvGRCRJ=3;W6;<p(o;tq0wZvIC!hl7Y&
zgR1@z-&2jBlwlZ))xZZ~86~+@&cP+l7`?8KEYS4kU8|7(BV{|P^JA7E^$l+_54sV{
z`w^C?EC`s)e%e&!CS&6fZ}-P-ZKCuc1~VNI4mJ=go49Uk9x=%hrDn-m$&=6dMj3uj
z0j}^$nf~Zh>b2@kG4p;S)Eu;2p5hNDiyv8Z6dPI;kzg|>JI|<7R4vCB+(m8&MqhXL
zdyUgAmrT}>t%mypL2)*_egqQItKL{jso3m|+0Qo0Rtt|GWH2vcyi~_p;U5jvl3op$
zoPJa~ZLylrx*%E5ySIw%#mYb5?m;>v?dw$F$9_ZY4oiM>m=nvY54L$(GpCPJW(ry;
zH7;lr&3r0Lj-UMJ&6$M<J&=S3@zp?jgt$^JWVBZgN!)F#%nZPF{1i|`7%mA&WL&&k
zA%RvwwG$1@U+~bzJ&ZdS2NJqR*Bmx;oDPe8H8_y!%z0on+<?fJo?Nh<i#ow--R1{3
z2Xk7#7ds`&HVil_1L<l9yvJjtj6X*Q(?(&XR%siKil$1zIxm_`h%wrs7X=&*RVNeP
zWK<mklMn14GLTq*EmIwDt^~*gYnb<hkU4H+Iv6IS#33XY!7Xt5kpm&l0S+0I8C9IJ
zD1mPax`$a43LiAWWeFtXwsK63@9II9uP~hyi9>b^b}rhD%@thRbq)bhvW7(~i~)2f
z5obvJy^XxyM5p4OMK=PGi*t6&dqqF57cHy|Uk@HRf8$;W2}z8XAhr|`#iCjSDX#|7
z+a&`-pq%C+IrI)fD3fX=*pv8YRM(~^X^kvDus*&<JjK1q8EWR~mCTzJUI+NgZudjb
zqhD5hQcoC^VX`}SV;UwYq+w@Yy9OKM`0HMQ#_`OMR|n<^Z7D}tD0QE9k%(`&?udbE
z67c$>;Y5+HQ?xNihHETlumroozcQvg&#&}&E{m_*bU;kIBebrfOaDmC9p3)i6vre&
zr;j|xhooRc%-3dSO~xEEKaIfX=I8xkL0}xq41#7BmqL%J3$1Mx73tvZG;y+$jR0nJ
zs{-a=o_6@UCpoY7SOW&38)$Cga2mK*lK5iwnx?X4cX<n%K3ya(YeP)mSCONaCt<p^
z$kBUjAjcOUS=-)rk9F{kkL^0hU6S@JCvhW{a}CyDo6H@50bFK<lfldS#X-NRu6<#Z
zl@pQ-RgyyY9my`>KJol!LLxjD!>E=?1z;%KNLVG*(qy`tW^&J~ntI#eq9QduK|bJk
zm0ZqWUN9$}b;l1F7XDRacUyEypvT>dj<_kO{|m$WGafa?aH$zZCEn3Et*61{_m+V{
zI3{O4%DLI<>Wc-f`a;!jqWdEMLt#vtW5q~zarPL$3)zQuVskNA!_dJ{`K&CR)aq1J
z-v~yBHhwSVv3B(S#;Bk*ZRLMeLb@d7RjR6VR+f>wF1K7_sf_Er^Mf=oS>cVTAj4D#
z>6)JSg0%5`u&a%6%D$r#zljEMe_MmI2b6y?Y1-%$(J7pxf1?oN%%Gs<PuPRc7N&|n
z3shrvBXO_NbK>izf~*Xjfz!hF6@-c3`*gQsQ8H#6At||uZ$*b7SUL7JED=^!uSlx^
zir;GBFeAwZ2UP_5aeE;KKYZRrG=-NuY4gFE90(DF^OzVPx4pU><0C-9_R6vQUe$Iy
z3TEIqzMQn41zZ1sOXrA3+q5V=w-?SJLuY(c$&5syyo^gU-OKHkWp!nHSimHC9&}U)
zgS~kIUN#`a?hp}!18isr3m(c?$avX$$iO%s%nb)kT7`0X1{p=H@g4SlsgvH{JrlNU
zElKicKaJ^1ou-7iU1Zp&3>xw0X!5U60h$&wD(_+f?j;yw>}k6c4j5g|eAjKWc{gN!
zOE#MC{$5z8ow6+2;`iLhnZl&Yn9LT+R`QS5S4B2&-K}+Q<taiAy2*gahi0#=7*&3B
z@-DlL@ita=uJ3|L^Qvs4!7(JXB?Z1w&tH5udQ(&z_b=Qfa6)D!zn&g1S3fU#@-B;X
z#YbK_krbeP8{%SK$?AI!8nv4P4@=P`7r<O)?AOgC>sDUllViscI#q6=q`6eKlImS|
znt4K@MvDP)uOnhZn{6CVN5vgwQu$%Ym$0AGyhJLWQ~+G@<*TEX?QQ4L478(F{SR31
z?FMbF1qt&U4LAcEuRaNw=GU*(n=9q*2dDew+dcBXgv{n{$B!~o#2cxx+qg&98V6Yx
z&#h4KJ4P=p3g=C+l7JGv#79L}6&2PFfH1N}f@p-TyH$4ef1>CK#!qp0h;$f2<^FPj
zf?K~#euOTxyBzcE=;YPp3(sl8Rl&;EChPK`UD!(b4BjrRp5LnN;m*9Q_XrO6KKVQ~
zmfaRa?g+sakqGO#VZVqLYSBXGw_hzt4|?3>EsdLts^vPoND(Xum6Jkbq$K7>*xa>_
znF(8via{h#jHMl*Ft#j7+qotbE?=mR4SJ=On#Hj}FPr7BFuZER!TJmfu@E<&1NG`Q
zu$L!iE$QRtcUy`K5|%vZ7<#`Nzuz^Mz>exCu#6M3H`Cb+N(lbSgPrCRP!;RcI(BG<
zAd>Aw`y5VucF(2Fi0?tE_IEhzcCd*6FX|$bO`1+o8c|D3;yPX3s7-*^8b&_f{5Y1H
z>=qEnwoa=L#}2E5M!L72d~Wc1Em0}82nybstcR>0PfKi6uSqe(Ur13@)`X#aA@n4n
zbFA{_4B{~#-q}0J#B2s~G(oqr8yag&N)&K>I)*fWSEi^f@S`oT&@kH$f5}+DNT>zN
zc3b}X;Bug@2uj|s>=^@AR{1H&rvYVPy{g|Ae8Y5S`y3Tf+c>J&qxpyhYIgx&R{q2Q
z;ijBr*hr{vW}Q&BG!$XQ4-BddaS_KAm05|*3W2?67VH?~sT|)zj85t>8XUA+t96oh
zhIi-b_==rVSKG~B+Pml?#=JWExHu<?<_N<>apCW<9G7p;bWo*{=AL=ra<rVGhKih3
z1S-*N0z~Xs#R=p{6x!tEJ`AFuAAWrISCD<lOtpvZt>~+&vn52B)3vK7xK`{8eTIuc
z?AQgV{4J_-sk1F4HE@IXfyAX0j8am!CeXV)T7zL$WokCQalO{hXjALbnCpCLNm;oC
zC22D41NaP6t`xh}gN$7ZgJ>2Ht4f6e%VjvXujV|iOk1vM{1NXhX51+>@#0wu2Jr^i
zTEnd>bnIL(vOy`n3~fZx?m)K=<b4d11n>-1#R?hxcC2DVKgh75LxH1LN1PiKp^^wD
zlbeOR(*?#gC83NrYYA0ztF+jO@bz^86bdWpJmpG((nPaVNI%1;q@H|te|0zqDUgZf
zYQ75nniyJC<Ej(pO$!hS_v;XHF>5|}1f?YhB1M+DQGS1nf*sF7*WVEbvdFBH9^*01
z0)2G+tN!_?NjPJBw$?!4Ws`u<CKcJM=JsWH$txEBc}Dc-gXRi}gf%O(=Vf2y6evv8
zfJ$W?DlQ7obZ-G6t2kucVs6}5#U(~XOQj3f<7|#%HG4<ab-Go{0W9;hXuBJJ?J90(
zXuMif?ddw0WZG)g{ZzXgqA|Mz@BpVbxbW|hf{PMNl6X=ay#cpL`R?0Y#e0^bB%~49
zx{|qrNg07nrenC0^%6SBL|~m_oEv!nh>ZOA!${GfIo|@+-b_)DIzixAR`F7PtQd5H
z4;G`^OpmfhVk4XR+Ji68-o6vka|5|n>j!sUrR$M_k*yV&TLdm+CEFr8H(VTDnz+}y
zj6;E&VyPRxM}T(VUv8~;(#-F`b-7ZTFs8U#eAa3`24o6C@2Ke$t<Xu!8{=M<aO<oi
z+U79KU>}2k^j@CFEAo6biWkjnE99m4nD?*hX+CahMxDnNShc|Q&{uV2Kx&cR-$2~M
zE$|d~z8ZO-#rI4$!&VF_+SL;j>@ADtB=~bs@L?i2^*!jqOi85(gFpC#a<?lO(ACAa
z*`ANVt{u<{k58F2O=Zik(ZpY}YZ%3fXSsSU8`ugyItw>H#S=F&8KG&x-J&1GgB1>U
zQTWN~`=3HbW={7!kDGZM_q`MSq+=ML_?A!tcqCtwf7sP9*<@1>16>8RifOxv!x%c{
znz1-P9g##xJb1IMp3%{o=W?_rKn?#)yqZ>*?dcBZ(=J9;bHxB_UFMcm&#Cp+m0e#?
zk`2kRH|jFZkmrCYCf6&ct%5j205y3(y2xwmx@B*EiD6#mr|F&+kx@r8a*j(QD%+HB
zhR93Jh~WR*QL};VuRIF(IIQw;4LW`QF~2X#VcTk_%Kt<vT|QDqML8SU7|J-RWtB5}
zsVAK;-J#0qy>EJJvEE&g+#KzCfw}9<rV<`wuR8Z6&ktJ7N1AW$gC4%Lb|zM0s7#UL
zx*{W~wSzKyhyAkmw>_2bJu3)r{&N}CeQ|eg6X&UF8#UW}5_KuRa+&7zmA22-F$V#5
zk;iqbSM5ywJr<(_WeI;@ti99V?sTuXfy9XQxEBHOaFXIrZ2~d88S>NV5VxZ=O1V@e
zZdd04aRF!VcKk!Iv)~{PfI|h{rir7NYbV=#IkJax*~PcywzmHF%Y`Y!_Gszs`xMAW
zEKU4pJhYVwQG)`m;q5-n)EM>$xU)u2$16Vy9;us(a2?p<p_(~$)OV4k?ZCI-Fbh^#
zr{?qYATqU}g*M^<G*iwiw>0jCyV>gIbtxpDiZLhM<_b)YcT7(9CQVWO^m1%dW>J{T
z6mc0fnL^6J8&W=sF%=zcIOn3-&6jV0rW@khK}~e>!K*>a)?ftS{_A~o5&w(%kd9_~
zRj)+%8tLOBqr1E}<*?+XC}Jt41d@7%QSoFsO3RImQoW5YhPu{L?}Sb>)<aE8PLfB!
z$BIsc&n!Y|p>yCUvmA9<kBtmd$^}0+>a}}ITS1!qZLG1^cg_<9$iSX;bq*JCPcMIT
z4%O=K@$@?6DvGD-zst29a_7!nSq*Pz=d9*6KEY~)N@tBL0MT#e@AZnE^uz&NUJq!t
zFp=(1jF89q)9us#TmvWU6U&XkfVj&G8p<Fr-HHOEP7F@z&g4$jaUBe~h@2xbK4zH2
zPK-&0wY)|?_d>dH=FShOqrElL<BjuxZFwk$NftxLcIJkXzS<H0Oqop{D4ANqai_wU
z#UFC+3{gX!SjQeVOT4;>>xPV{v4b0SMH^|yW&P%)KakX2vkdIJQ~Cl~v%$^u?;AZ8
zq!#jfS-YMq+9ueMW86fk{N3G_-HD6v37u4VvpRi3z;w~gl&fr(D;x7T7vDRxD@Ep1
zo%zgQ>xSy{eB`~xhU?t4ihuN`)NWbL7zB}4C~g#?6)Ih&(owLyY$S)w%p4u~<5`f-
zk)5gd@tm}JMYC)4QtrIg0ozeMy6#2PQ~W09ep=KA$+73M7_LUvGnUOD!$82q>wG%w
zoV?lVHB|L+;!GS>%$$Qmk&4&HR3vB}v0IwLQ_6ipkk@UUKJkUOp$6%UZ7m}}=HXS>
z?LngF6Ko?gWZvAoFJCl+joT1jqfbR{FlW%ZPL<@M6H2<=3{!I*t_IaQt|gH_17@7W
zU1#2BV!k#SC_6A(rE;>5-Z5b@P(t*p6&c0D)P<-F0t!jy%J?$PG2OaAws!7EK}2_;
zCu|mRYnbyRrqf_u`e8fSx~vt@HYG9ESqaf?I){^)4Zk<qJ!y`Gxdu%ySa_@;AK|(e
zKR}Gm#t}^j#=);Px%L8LosUtUG-z}z7WvOHmEv05qWs~s2ng3@O^!yzPM9JhF!RXC
z&qWbaR+N|?Os7*DrqFL>q^UUXZ=I%7l5Eaa<;v5=$Aoc1*?P&MM@<}>Vw+65Ty<E!
z7wEgqPu0!mWhEKMwH%U=+iryvz1t?l(7pIf2p3{ib8&SRERS>Pa`A_!{e5Xlud0eY
zw*}bAXdp=3!3mO|THpQG@~=2zrsoiQT^re?#o{fTH;8tP$LVWYl`%{Ek#|IQSzV^?
zI)0VnK2p!rdrR__nS?MeLzfFgTJ=GTN{4EAHH!rn%x1d3-vLcxId`=_3RCQ`kPuxe
zNl=P&Z)Vx`klD#@J(}!Q^V*fr55At}_;$Y*MTdwqWosz@wT(s;@vnWKjlqh8>SxtV
zlUM9#y7a#jERHEoe=f1hwyAWcwhUKOYwCGx9g&nx(n|PG-*L~c0%PLX1r2JJG!^j_
zv&cd>O4C5rLY+OjsiP6^R(DxoXNr^h0DJP?R<QVVfxf>?`U2wYx&vVc58tt~C)(JZ
zOi__k&bW-OOH=W6#>Cy-6J=+J9A#BGY1+}M&^==U?lum9`%0F2z_V3V<YcsN(aI4z
zpkH3c13V`L#vVxP|D?|{*w$?<610D=j>4l$wJ(;Et8OssD?jSesHUK%JqJB&bsO((
zEdw=+yr#Rx6=fCH2`T1>n65<QUZLKty7jA2l5}r|U^Vgl0Rqqn`<9ujk{te*#PTsL
zXoUC?A!}nj=s6dh02q+0cl?O}QlOq_9ijc`C_<#drgi)dBXBPuyt02ilg=iSY;^f`
zR0g8DEw%kp6Ek8rxe}4y{{{E?hx1eS(GF)#Exkq=j}#jpk51eOzX_sE509#7Zd`H$
zC5ue)MiCAc2LH7Y^(Or2z-g{5U;D$?{F|@FFF5Oe!gT*#)cg;nkeTg2!Q6i+g-q=K
zIsI2m_fIMO-}M&%517s!TsdiTfptN^GsTkMQ-<I3mVnk7p_~!{0y1$<pn~}vVgW?J
z6EU8MK%^XKp_p1c=??@%nZMqX`%U-h&#&fgYlG^;jag0xPvdDucGaC@z_jpkp1D~%
zD^>_9XawLfAXcd9zs~`HNJaq$A|3Pam_}45xw8BFcNOU*L?BP$;kSS8E|9~(A-x0{
zO^jK%5TLay0U(|PfP@x4F##M15Q#r;Ygf1_jhlaQ{GX+W5eaCUuRssOQ46-QxC_01
z5IKDP_)5$VKm-9WK0Y4)<qBTr(bt85K#tNk2YCtg*mn)#AP6)yhX4X|`5T7-6j`HQ
zRod0oR$E(}o&+umbA4PuLjiOrqOSv3LJu=F2@VGOMTMz<bOiXRl!?JX*SCcl@II*@
z8fxT|-K7tJ<1fswPXRMCj13y%0chU|JoN+vFp@*Rq-Pw|3qj=17X}Ov5%7b06Zcxn
z53yutMh*dTy?-A3AeN^MU=M->M!&*}F!*600w7CqMpuB06g2YA_0CQhFFV6NaJ!TZ
zuwh9CK&~C)cQMG{g^;5jjR6+$rBe7-OW$U+A}GKzhlfXxf+qAs%_kF#4im`b=6BCG
z?IM=%qY&U%y9a@S^7GFrY%OgAg>qQME-8Hv3kn+hjcM<z1E9?N+th>t0niEU|1O|C
z>}R8Ya|`)t^96fo2?6&c>;<&f#;D&9!4C5(fEVuY5CB-GyHmH<_m9^(iNFB?=0QN9
z?ZG$+<c<B};(`zE`Bgi7zY4wqS_iPd2LSZx)%x+Q@1uZ;6vqGje@0<jYI{gTOwQdu
zqtHZIP3#j8NQCqVpull~9{>U*5o~Ns95C<~6Z|UpEos#6?IM5A+<!~&{9xw%HreOL
zoPTfD9|GtHa}pv(=?@ISon8Jepd=t+8{6Ek#!auzj~~@9>aic{ho4!|b&T*&3-p)B
zo8Ofz9z@YEKA3F#j`|h~Mu9`4|A$=#>Q(JLORxaq4&JZL@*wAai2&?zpi&dXSqaA7
zi5@#pVa#2SKtBY{&@<igSBTbECL0M5C~zS|xE>qK3^c?S9k%Tl!A+YMNKn#=9Wo4?
z(C?NSG^l`(A8b}|GIQVf<m7lR&Z^axBoqR~fD9?h$*a~BfCB>xR+JOqo+S)e-@NWo
zFBUXNxEF!^PP*Qi9RLWz>yQgL?63^$4UN6oSE~q?nSiI!PZV4tK)4r&fS=nP`di!;
z!H%wO8-1@yu+;bM^Q{*k*kh2u*lEc;LWp?lX@ZSev!!)U!R6qP)Ff{GIBPe9qIypT
zc|Z*q$vP*p+cIwc@@gnvfZ1f>NNLboFmG<m+qOqGc1wDGR^0I7g^mgz0xiay>(d74
z2&713NAv`Dq)zd+9K(K|YZGbHv0P&&Z!g^<Y5V<^(!2<`>0!$jTpzH!qu^jlf2fT7
z<mFnwpZ?%*V>E74yHJw&sY%wkO^q3YXTUE&f_udd+U&!$-!dzM)!kqiTf0wp90@%1
zbEj2NW#O3y29T}8F>LWmBYnxyj%Pssp;8*>JcXRxi7Dkv_<<IQeX_<EgT~q`W3k>s
z=lrlZ@m0lrcdN|Kvw68iaaYZ-kZV{o&ZFHy@@l4%k&cKZ{(afw3je(<7}Kj2Ny7Vf
zu=HPmgOAvd+|Z=6g$5;T;IH=*_e3wm#FyNWnrsd-<5GW;tA&*KhHw1b3aOxkzd1to
zmG4f&&Hy~rbUf;Dec_Wf>t1J};cKg<-Qkj#mlkD*(<f{;K|1r`<~g%C(Z~klE7$8>
zJe!nsPermj^O{f=5=NI#W^>taff61PNrWZu9>IHQxSUX}D#xayZAmnw<<L_WW>c`N
zcZ@M()<?IMA+ihd^PPTwPTg7cNWWhq6x|yYJFF@0b_Uk^QDu_0NisW@E6H?r!~dCd
z-Ec#s;@8iQT(+H@saW^B13S{9%-1Bjb`_h_!MJ16bSIU1n!)UIY1^Gi@N#bnZ!U*z
z4t%?s)x0OHj4uYK!ebSKi>)}<t)EEiOnNcbuC0%yc5mmbzI=q-d2V=13Ad-QG@`1k
zq!P`R-e*S9&2`sKjOJXnWX~6#&~}_ZqJRxNDsAm(Cl<Zy=1sTng$FeqWd5<4elI<L
zk1U%FC~~z42t}ez!pV-+G&m(o>qqP(ERr#DsJkM(Vap5nW|iBFCLi-SVq1PXV^8}f
z*gnBORybYnwY~x4l>!g!ZPxL5^~YcFhpS_Xci$@Wxh{EkH`nVLJ0?poZPx^GjGUax
zpC~FaTIR?sE^P8y)Gzz?1Pzx33XUyZq&PI$c;az}uGsnq=<B9;*H5OAo{(fbTpzY4
z&N#y?T2=-J6T7QYizL1d@x9^Im=ac-{TR0)8S;kNTtImamu8%P6cu@}XmDbRme?DC
zlTqlLA>&0GKKn}N5C`t&t=dDmyjCxR<PxZ^5b}pHZf2KX2uoCQuc){SR?K$@C5fM@
zqru6L+MjAh_;S5}j1D){ls2TmuZmIxRZlnRGQM{zfY_+&1ns2G$!j&a$6;YKzdx2q
z)+8KtI$<Q@cN|DA#ngvI6`2EMmYWr+FDrBv%1$#|wQ)L&mdtUD!|Q}s`84k=rZ;6&
z&MW-fb@mNxvlr&POcKU=mKP>sxwcYvGZz{&MrB^Zy`@<e%%UKjWSDivHOk0TaD^W}
zGD028H@DBzk^b(F!LoES)$#a>_@v%yv-&_P+wrsY=>RxVVeY>YXE50kSg%^o0adBN
z!!O}e;*Zqrl)iCWD=n(lk@s9aa#}34<Q<RFm^A{{Is{zErrHWCV`U!@rSsjdEarzG
z<R()l@aBNoszoUZr5t|XrW`ZN<)4dV;#Xjr7K745Aa_*=WtV6gCsUdD5oF+5_2|+;
z+r<FYePksIi#1_Di?YhA0-BKV$=oS!lSeJ(Q+(uDV%;VGoI&)UV(^&CRi1zRs5?~C
z(^FfC@c5W}alU<4_Ox?HgZ$My9P@K1UdjJmKVnkhf+)4t#En!r`QryNSxWuEaLTqF
zlal`WhgzkS7NFeZHh~Dg&%7EuYASc@e%Az2VLX0^v@ZLUXou(f$I#f-=>hJ;r0pAK
z7P{x{xlo8pQ^VMEVFOwkTF>~%>i#GN)vMq7&*O8BJoyCi*Kx(HlhN~}w86FM7ATZN
z*7@nqi@Rz4k)>jo#=?bU6Lbh~SqROe%ECoeQO7*%&F5k1@ssPp&|m>!e*nXw4{*hM
z66OH>S|g&e+MfwRS>l|sfRN?T@i?Ub)MU})efQ6ru5>$8EpQKjuG9>ZLZXtp=4YzO
z_vVD3aM{ua>&`=&6iP&>uWlhTYd}WY#S~G_<5Gucxilfl`ty}d&vs)be;z>hxXGc%
z<SQ4(O^c|sCyPz1kb{p+%&cr5E8fPxjyEVjO%c*D9pyhcP*s`0OqKr3Y^gl*H|bvi
z7DYPCVK}D%xUi_UJ9h+YVTO7&(;d+U=fU}gN_S+lrRcN|e4D$Fa82Zv+v(dhp$}><
zwQda&EzAmC34O?Bm!NmP5-_njm&Q@KKC{yM49>kLFC12K`G6@`XO)NTAY*e1hZLCx
z|BjUYjPkd>=~VuF9u}5x_fOT5xIG4&p%JkDoOch>MLjyz#4mDT^u+Yqqo`Dq11@MF
z>DAripT@4lWG5R<Pk=?1<x}BbRAEA%gA!R2-Ms$oz}8^d=-OD!QQ1<4ubEwQtkpXs
z(_q|2PS3?^PD?*720W<D*1Pf)?iq^0&)*RQ^AOG>-@QuRHD=JTGpUNDx$~38PflgN
zb;`EYd((AP;5C~SsumSL=sa}-DG&AGVmeqR$C2h*TmJ+(?Wnsd<ERrIOpTjAg|)>!
z*N;rpnV2Pc_i&BL17bNjQoDx_31YPbUD2c|?V%YyPgWr`8F=2eR)qnK(tU^XlH_x4
z1AeErg%i=h29gBftaT|}6}fH%3Gc17%Y;s+KkLE8Ttce!2x+`6p?GdJ$y>=WQ^L8l
zX}p~lv;3Z%S1Dq6woBD8&01c;Yw5BbCE~geb*6)|zMmTWq9+aw!Flbk7lf75uX`v$
za+d(lsr~eRVwqvmiO++v&_D|0GN|(6`wKqC;Dxy&ziv|=cve&x1<Sq>e|-siL8BV_
z<NU}rD+_UH+w^qy`(&PRY<sSS9wU&NT$ud5u2#LSb_5izTaz_vP7@mbs(;&D@?`q&
z-@1S!_yuo#_|+pu_MC@;zjImr>O6PuvmLc5G9dy1J{OWJ$g(uoFaK<1Qos`HDr<`@
z9)`;XD5_v?)F$luE7FMdT55=XUMKzyQ^mVSTD>bibty({?u<_4Y$%$aD+bCWM+(TM
z0RX+G9wWc>OtnjbsI|?lK$LIbUs6C=<$PF!ME6Gz*&lo)Zl@6bmnY?Y!$k*%II#}b
z3knz-qg&kGcMsnafpLFlto)`!lQq=CKMvBq8aH=SN1%Z`!7kH}%Mx}(rv#9YWT<)*
zdQ+T45&k}gZy9@Oa#tZIpkD5z<nr;pzWH7z$D8b}@PsKLMb;Gh08Sw@OtHt8Zbo_}
z*X$`;8P@k;!m_%Z&s`=f9pTM&Qb?7j@K@*VRHE!2#4W3=*P@7G!6&fQZu{jqIV>B3
zDJFWCXiPpY-O=Rv#1xoI%S0QHKyBK=^01pV&0njru*W|w)*ku$BEWF$KlVUIt=d$%
zN)=gw^kM{uQu@ppduv^q6;f{1J#((<&A#YAfhNd)eOuRst(G{CfyQOC<b`UNrBj=D
z3$A9QbQsYz3fmj&d|M5x!pS;osW@XsDEPg0sb>ct19j$z-#irHbcbN~Y?PB@xNmJD
zTQ55H>UZbw!to2UZ`ND48Gk4b3GCVK$;h;Bq$te{?>0O$5jeu?qDnQ(#H+`wiV0dJ
zAt^Ou0_-#X)+(Wte`2!4#F?&J2oALjHBYXsfoX>NhhVBNZXK_%do$R&>S2UXQ%jMf
zvOhsu+iVOz-aS^EI|o_XY)gu+$%O^kG)EweFQ6iiRLi=^P9A!3gF9-5R7;QBN*=*q
zDFdWr#R2<7SmKlwlgLFi^L0wQuIzeO<utdaHdd;tO0;hIzP_LJBRoGBt3@5MVM`pa
z7e*zH@&@Gu?62-5sNhCOXFZ=t;n%hx*7My{Q8mlF-))TE6N_jWZO4I}9L^y0D&gkj
zzFQ{@Zx$N%gVGYK?ecmnjC}bv33;(!5*D@m;Ud*O*L=2OM}JJY6coBfH5@v??iu2#
zFKMU-s&I0GZvdtsoW8Eu7WMYTi+kYZR%to$JWPQ5WGv$hG%jfP80Rr4&+qhjQGdzi
zdQ^r=DEQT=Nnk5p6iU24Xn4`BPwesWu2K+kkb_Y?;E-wT%*@egB52&lbP-R|*e}gx
z#dInYUAY4>Jg6$7>|rGQ6psBt`Ed%T!h4=*cIrU~7KoH0?iPuY<zgSzc(pwDwVhRg
z;0u;l31g*1+j=~GT^pt_)vyj3_i*HJ-b>4Dn)cH<q+^(vw^-97n@}zpxTVGRJvLqC
z593U1QteSe$orS8fL>wj?D{L&$rPDLOnCRFu`$M<@%3-j+(}SufWa%H-#F8g<QC?R
z7yp?;)r}>(Nn**Z<Yb190)+^d7ULn@lSG~>)uFz_EAVa|N9r++CIg=6<T0$8s`ZV8
zKUZ8QSKRh=HkRlBFhp1To)_%26s%w+%<^D<A%0?<9G-`+SmqM91k-yms?=GR_6Ws|
zhja1$8Dyu&#x~rwgN9((BqhtnlI#*(_c&K#ZV28-M1VISRHRb2$B+SDSY-=DMr)xo
zELXc+?;SLQ&Q>!VOK%|O5LJWBa3_9bQbn?qdmY3HY|KEMmgUPrni|yXmmNga3j4zh
zxN6;evSV|G{YfP7JH3=I?dTosv*dmSWi9xprqpN4+Pw$5k<SZAQYrVYM-DYZoi9x8
zxXOBHg`^;e#T5y)I#XFo>00TXdk9RB%X+6qV@u>C3Ui)Z8dC&V7x$)yr2vdA`uj#x
z6>~O2{3iyS^+yie?|I}%cC@OTz<^~x^1-Bb*yVatNT7=n5bds0Bz7$pR!bt=ikRZg
z_2a%(1t#~hx@H>lfE8m~O!+GdxEFW{y}^d2nAL2b2Yj#yzFHc(I=8!W>G$#h%+$A{
z9|@IYTZ7F&2{4&ZEl?GTI3J-1|N2@3EEbCPmc)_nBIMdx1lcGtZm!RF)gJqB+r5NM
zw6a>y{(6!Lwho+`yyoGA)b>xG>2A2Ml2_KfH5wJ-13CAG$t|NZ%PAbKh6*>l#MdZ(
zGrd2$M{|m9sN3^vxfpWpR5r@l*=(TB<t--58Y}VzET{Eouw^`6Iuj_3Y~MKR7z#NP
z-bikkjzd?(r|Jrj%<h3*r}|IdA%?Z2s5GuDBfeuPO|t8-_yDf%nRD<<4=M3Gd$4Ke
z{+yXd=OeqYUq2Q7s=cFvq6ryg{?^^xO;#6b6cBLr?DNqlbB6E=HI#6|#j4V{p;YyG
z(oo-|CluAY_K64sKz$`0y>urCIhw20ERkZ$NcG{LTHPW^maa(XXDF5-s!!@=VaV!G
z4l4xbt;SO8(U*pDk7ZPX_pBLDsfF3Nnk^3A>}h;`Hi*I92nvAG`*HpHr8n{&PC%%S
zKWwLQBgpKiy6(`9cFWY>es-xj^T&(4&h|elhq~R8r`6$U2jv@E3;*f?_>W-?zl`=@
zKgg02_|b0q$0r<$v+L;6tB1QG_?~Dxk29cZT6<0xM(~FM@sC0b-9iP6%;BME)j}sO
zLn_Uq^psV4IZxvwDqH)4&G@qZ+&=EA<6lV-Gt9=V#&fRTJ8IMu+~g;CP9~vzQ{>Q<
zv6j~}#XjkNfM-WGYpi#PZ6<@V42AMuN&(qUtu2HvlL8#4%?mE2b>nm*cIqO3wW_Y(
zV9%D+jkwE44D-oU@e(jKU+d#d3S(1tY`+P6j^p>tFM?H+<Q97tBY0<j?ET;l#*fBZ
z0KRLeD`roOf0wCzY-+G^JR4~jKu&tGMRiQDm7S{`I!#yN0hPRE%eHxZJ_X^(JO%Ng
z^=R-stwc}AL={8v9zb`WC8QvCrvLG2-=DfDghPazzd%vV$B3tj*cY0>)oS%m8I}Jt
zYV5gz{XnI&7S=dEWQsOI9M4O|1_=wlc@UQ}!Z=}Yqq@5t1wFZWA68}cMx5h!LSZmb
zY<i{GMB&40)I?zAAKF2yZ(2rTQIJB}U|U!3JB~5>pj9e_c^uqqd;u4|5-!o0ci#}u
z?HYEEM;kX>s8RFclRxgoku;_bWAbd^p&x`VVx8pR=&_A{E#cRdGMrMp4D3r&SRL5I
zx^W0rlB&f~bu!~|omhhR%I;g_2KU8ruP(5?Lo(jRE%Wb9@gFMV9}sp+Qb0NAQl37_
zn|zPB(Gdn|vA1KjW)EGOjxg|ODMrkQx0JeURbe3~<x@R3_-i?3HeCY}jh7vw`siv0
z-g6SJ_0Ru~4Ggpd9losD3+2+|P0=TJR1~bZGPTeL9V_=5w$z)$SLsFQyd9#Dv=-Pe
zg>VpAT*8_~wme+AKIi5Xvm6j8o}Gl(bnJ8nBxNyk5Ve1R_?cW+7M*D#Vr(nE);0H3
z5mgp&hjcdX&flCKm_2-d{b>)21pZp_`WC?A8g=i{XiAP`JDf9KgV{ku*k0grCs6e!
z<QYlBj@Z?zv;C85b6s6%JyXryXPciaCvDE_tzPfBh0#WJriH6*7fBTmqqjOnehXfl
z<;$(FyKCn^4n4_@L%eBbGwPR++-VnDHw|c1p)p2_dnquvSchzSd4HylZ;Em1;A)o1
z^Y+H8ojD-FroxZgF7;nZm_uC$R1e3Zdwtd&im*v&pQWc^taYgtlT25}=6dG{5MHZ&
zuKM{E-+kw<aA%p4@c$lSfsKZUQBF(yX7m{2b4jw_Sh3NaAlxhL9L8!dN-E*SLRl)>
zlN8+)-@W&Hx%%{sY`MZS%2>DJ!wtqAu6P*tU!>M4OW>U2yZJxgXC=OdW){(V?7q0^
zyW7GY%T~Hy&~)AZv#D*-G!9=A^fq4YRW?4_!DLEHcuN@|Coj$2WBsElYH_=U7_G`C
zUAc~SWVyvZH_=LYnlp>!P&Ixd9b#vNVZD-uQ1PK2{BJK$rlWSl{GISI{@VRL(ypI|
zyu+8tJ|GMZs6g8)Ey5F!S$4$*^}MYFBcn!q*P?X88>*u8lPh1pQ~O@}Y#cUx=Q_Xb
zMd@%s{U8j$1dsf=m|;8?|HIo`v;s`D{BY*>d0td72Q(1kdfR-><*qaMmfOZ8B>UdY
z2M@;JNJ%ID1Hei`@7BuGoa^~=m$|2$YQ6`3Sd^-Hcz%n-Tw<aFyX0%f>Ly-0n*NlC
z_gT>yU0l_qldjV>HMyTt&VRy={}+U!Y02c`U2c0mlhBi=3HS0`<-}I}UNA+y8G7XP
zBEr~{tig*O&C2zoJS>gaTFzc+Yq9Cq+Tlh8^pzz-)b2r=R=r{=!+utqw&dxBkdK;D
zNJr{?bt(>fhJkEWBM#njI^Ox+wwv7F7;Oar2_KC+b9gSrtIP1|1JErR1L}Z?ZAMKl
zqp3wgSk)jE3Asr!bjsXQ0BpWaE8cf9PUHc(3e5@WaC!nbFFk|JVrwazOto5$D4MP(
zwsZ@H5$<iC3e1AnF^IdWX`f5alaGr_GPkL-x`|lmb?!t{b#Qz=E64_sg>orxC(%{A
z!@XwWeY35~M3Rd<T0JOXZp7(v#j~$X`(bvU43*Sr2682CD+^BwV1_D<TCR%<&L<B)
z`m2t|3aExCGb+vrqXTE6gVkp&?7O#ak<N@0%5GYtJP$G-DnDg*E$i*!Zb!L-XXVn>
zxwM*#OZ5Ho<GAi$y0T5u#gzm_28s_`Y!P|cv!8dyd-lOpbZq$XOKbHKylx<kOJpZi
zvYw3gAJjzyRHP&YqN<vWG6XkoCcBuP<*vKLSG5`5^U9Ir8lC=&iima9G4<JIt#DhG
zRlM&p1Mx+DMS4{3LQd_TiyK5myAfx3swOF}tX=PCcEAQX{aZ9HW+s95VbDc4xwAFA
z<j#*gD5)erF(L&=cR>(=l?NU5)h}I_F7E+P7><v;$VBUG2rP?_`c1PCO5kP7@L()z
zXwI=ET_CaysE4EqyN}jN3_qTfsmZdO84p}Y=1)TL5lU;{W=7xn%nWsry8P{{xpOqK
zGXa-HsW<FS`!I111M~tlWR=k#y}!U^nT48i>ne6#=G3t2dA(xaC(FWXz@!zMhWPz%
zs}jX2X`@Qjkb^BooCi`H1+g*Su`NT_6H>=B_r2>Cnrk-oR>M$}tder?#tU}EEs9%^
zfFR)*eOVXWG5g+Li+3*lhwaT~YM0PEvzH_*wD1uET7PIbSk{)iAyT21noi8HW1mUR
zG;moimHT$vDoOSa_3LmgSDklwFCVG@oVWss!p-PEa4Z%H74<#I*BDN=LamdeN#%>Z
zG4lxkTsh9vZvaoaqAjy{HBQR=bgZl)-j7TaG|ykaf-hWBqV#867_kheTmz;SX>_eN
zILnRopG%XY)C@t|nUbyR)thdWuu1$x))ES*NDr&(*PCDz9l=Fs*#}qN&>;j-`m~Oo
z;Vtbn$kM#ybHRSJ{ZGX5zen-^BbNUfZU2)|G7>QTtB(55>Aw<7W_qUooy`7!CzdYC
zkle{WtFmL6)xk8|!88m}EbFV*gkHf6PO}&zQ|XwK!Pd|5<kAo()R#%t8Vz)W>qMJM
ziN%6r3z87GbD!QmcDJwJH@kK>@3~Id|M5Kb77tb1pgzfi$jznums0eh<YW1>95D7H
z3EV5v7Zcd4lK_M)Ov{tHQc?mVh3Dx*x(k7TE&)VKsP`cWfa+TUz?1+gE&5ji+f$&(
zf^({WBq3sOf{C*6=L1Na+z!J90aDN;0=Wlb(B$AYh*?C6Ap@j8$Z<{NzQxf2kR+T-
z`%m?*n*egMMf#5_0HTDOhG8ItD<h#DiU1lL$2a&Bq75McSoy&mJn6#Dee1Oy+$x+x
zqR=3L82_j}{o+fvCQ*`9mGXfK1OB+9hyzjR66ibb;L!u#6F>$Oj=JAa8Vkrpk}p1b
zDo_Z+LXgnY%OeK8s*%LgBamc6ffrCBz#x{2Ll%rt0eKn&0LDiUAW6vqf)NTF<dl^H
z5{W+yC%$fuBm%-f2J9M@AOO(dMLdcHID2;dbM9dY5+R&>>9&RBOk@a@tP@CR5`YlI
z{hRX{{6`L6<{+T_N%qE{SjPYWWyA^~#QpT*@qXjt0{afe0YG62uI}9fpE%+p*g;Ze
zq~t_M3|^(~$c^sP^{z_KD-01xP~b>zPq&pqa-c)Dt%ADH!#`UIAczpfw&O^z3V*`z
zr3i!ApZumr*YLx*;_iy6+gpSCcF=(MTJL(}A@pcsKcG9ozi}U#6yf9Yqw?)V6+k~0
zqe-!ig7}kJQUjlW<{)soXM8%pcfuXixy4tVdQtfS!9spc8XpIUeD1trev@VpzuLvo
z`J?&Z1U{-E5aK?%9XkR)U4*Q&X=I}5#(sTwUlc^$NdTKmakNEH`QybQsJOnd=H;mP
z>7#jFQ81cXIduVq5#{pcga8WoNRxT-kUn>41N&zH5CZ9Zq`h8p$z0e02#Lf|K34MU
z%%AAMr9u7U>oIn7*M6&3GJ6P6xTp|8@_HwB`38Nn&zF<x1pJ|seU)BGHvN3Rd!PI$
z@@nNEfC5pM^8A6cWnkHXLx3$MCG&qHgg=tRpKR8DS?5B&PP=-y2{1s2)<=KH2xfJk
zWu<y$DAC7mu9%!LA?B48k^BdDZCbr~jHooDY)glm7<y9Id@qiiXN)M{6xOISc;!?j
zGDow5!R`BkHMc9qlb7b^u0Iqw>t`MBYx6}kT{iy$nh5l~Vg>nn3YKf|Tbaes8TjnU
zmTf8@ZiAX}A)b<*WJ(0(OSSMd>F)EjD9nJMa<8IYXt5rCcbV^iA6on{{kcSuP=WYl
zpSNhfdZyeOOavuheQ&$a7kn9XtzPygS}S&Vsk6dmV`!`o4D65^@v+npSrqQA-VZOG
z+{KU^C-Z)NX?ZwEYOuM~ApYD&E1$>u!NC5qmA^+4n;I6RZtgd(Ny&l^6fIN}8i}g-
z!oL95#IcD32IG-MRgr*q=7+^IG5Cphi3~Euy4>6Ri@k_1u7|J8x1}X!*+mj~NQB2+
z%fvu|e?qjQ-zIQ2Rx{M5sz5NwMVYhniY~-b-d5|Uike+ctA1%F`p*^MSl>5FE#!sF
z7!rtq4}|{41Aadzw0_mE+4Zb(kf=iM3FalXTuPcyUsrb4BwBQX(*2CCh4!3w)KY;r
zR|41+%8gOI*(paDJ3g@!wSCu-?>x1yV;L8;JXh~}q*OO4H0v+uTSSe;U}7O$Q&%sq
z?q2v4q-x5;c9OFkgK5J%yWtd;NcmTXx9cRqqgy<NqW1V2JvznH&ypU4PxUX1qjXZf
z&CFHLyTSZ<DuXwk>5S@+;%axlXKSr&mJ7C@xxpV~z19uUHGVbKO3Jle!05LgCb@2$
zK^_xs4?SKpM;W<ccarvbvz(5$1?hdiUrD3y>ku(?tf)OP?GU#Iq~x1dA3fQlsiQsz
z6wqi%oUZo@@vKDy_b8UaL?>d8btQ^htfG4N(-+S~XTu-I=dIx2n>>U#MlakQdD1wf
zxsUPfXU$aO-s@!qC&Tyf02M8Cn!dGat&?hA7jd=|&PH&u5qG8(?{&f-JA;+1c`ddx
zqo|(^eAgkc-BzBFa&{KRC4<u9YFrsZ43`xyT<)*#Pute%(X!5>*Ab?Jq@(hUa_h4w
z`<-(4l&>9}u-ceRrwNLTV-fCFk)26NbgQ7XPbpM0zahN%*Q}{o`cwO|Ielx<`8KC_
zsN&}7WMlLk^UTYK&PfkMRuUB%)6aD1(%cr_3Y!&fKV(VhC%Op82s=I0>TT)ebTJ{O
z9V`*0dsQexp_eN|B=XsrgC5;>R}DGtPgRX^&%LqGtEIlVL+)y6wLll}XMyWWD!l8^
zt)*a*W!cm#)SSAqL#ut5V99egYRYmjQQ1X5`~F((Uo>BCvXIW*K#8N+U1LXqwC#|b
z<bj755aSgSxw#*qT8}PcdD-HDhP|{iWy+_ILpiqrj!9j(9doO%cpIO(WcZEwGdy{s
znk_SqnaHbg!|GiKTR#1b0}gUwR|X1`vEIpj9nKDy2a@aIaTrLgO4Q_apqT`<zK+Vt
z)AF!mmFFZfbP`2yH4AOTS6fi>M{VgWS__7DmpGT2#~2q&UHO*zz+vYb_c`y!u303r
zj^08SzTE1>ahxKqm)l`4Of*(UCj0@5bgnDXo;A?!8>C?6p3(=Ozpr(%LTQ$?ClU#&
z+zHnSZ<Ug#L-n0sk$RIJ{jX$SOi&r4ZmUrrR9V|AGm(EwRf{nrtG7ZmC6rl0r|@3o
zv%OE9vIraMSnMaAA4Ng^wldzRee{TM+RZcKV)q?wZiZn<wWaARyk3i{zT0*h4!H<E
zFQ}T0sj6g{`}8A38U#lUGU6I~5+@s`uS<b+s%voT{iq+>TN!)IDMjyO<EWBy9Nl)b
zXzjZ1X#6`mFKKWnyo0g*Kn7ohdDbVEjcx|0M8v-CPiIOZw4UQ^S;ydLHn1SN+ex$I
zeRnm4+#$T=pT6KTeHp!(N+b^o#w6z3H?nF6)Dtf!67j~qn~Rq(KmMhxE^|eEu?*a$
zYD_+7#mU;Q(r8~{+q(BBU<Fs%I;UmT`B`c#I+0pAjn?H@E-jNWl@wA+8p%9mV$<uF
zkX(v_cG&QcN8pt%6R=RnX7)IFs5ZNVkfX#rSc)sT@x=o&YpeJ^uPO?T)`JcHgzso}
zrn)e+k}tj)z`n--aFkqO^*XzUyv+?pcV4sT?IE8|_5NjN*`+mxvdH08s>jWyuJVg9
zuxv?>qS8H$H*+zoWEAT@L5*>uQk($^kmQPgR=Zz5OV@OQxW@eHPDsgLr04jdlaORq
zwWjwbvY1iXgaMK{NUq#pu5mJ{HMq|a(43~&tRHb<Qf`$lX-i~Eb;?Yw1?Dg$AFZXo
zeF|mHWGI%R9g1Ut{WyNWX&LI70-vwW+8CC_SLK3s^Ngy%>v-TXF~;_Zitcjd&zrDp
z<bH@U4Y*4>`ywuVJRzjQR!X%P+Htg=)h^_+Lr^bq_9+~YR{r{73hZ80-_dCwa5noL
z?GSk$z(ZSbw~N}W;^Cx7t@ko?41oeJMI^hU(|TnAb?XpwGpn+d7m=<o*SXbI{bisc
zu78OZdA5Jx_<a{8*!9`}q*zD=pB2uylAn>~Af$`D)onech!p>)pVQBpuG^rN_l|l1
z@c!z4|FvB74w!umjr>f0Z(@1J-5__rK@Gv94r0?(Ih8vG$<pf=fMj*CO2S?q%+)AY
z>cSD>?1H8NB77GGu7>k7t&>8=c6KK2oW?}GXkVlYnN#JQ^bxA0$|}w*u)-@5S+)_j
zj!Vr+H2sTt02NBJu6_S&I;mH%K8Q^94_5%=g-pQ<u7e|bxP;MQl5w{W2qrv=xj~D)
zKS<rw>877YK0cb(Df+o}a4Oi()P5SCAo|qWcbbr&&<Q9gX1SOJR$mQfRC0;Wh9kX9
ze^e8zw``?F?6Q!^<MN4Om1)Fcxjx1vjl%ZfjNd@Wh;A7c*afZ|-iJxh+RsZi7ur2_
zOUuRA1@8<2w-tf7sE4R~^;a1XwwQY~9@Cfbg;w)q+*@1kiaKq(3Ql;Rcf#C_xui~`
zQjde%3N|pF-7Zfgo`-$bq~c@F%ecfM<w#w)GujSv8&_o%8`Ce}-wi!$XA{+&*p7jh
zyaaY~S!W$$BbV2%nb+V*X-{jJuqc~A&%;MhC#)VgrmF6MPhmO^sW`JpL0eW!4Ugfg
zBp#}J3E`0rSy;Th2Uq^gC73BkD5rQ)58;&zJvIL>ehTFhcijMc)K`vrDgb5=t5$?~
zgtA5UW{GUcQgY~R-Pc=m_eQ@O**OP1KctPZFQ!!;7sr+@qvL~IIW?|TkVZVf;+vKH
z>&{?PTz8p@YCXXfjk^yYu{!gnjLLM&nfpuri3o??3+?V9Q|G>@?4f<F*tQr!@^mt9
zq1@>sl5|;~{APa$yh+?v2#>{XL)4bWEG^eD$;Y8Ox51{L&72j|gWg(ADs5=D@HF9+
zB$T?p_<RlB0f}4*E}KmEMeEqX?8qv}L+l|XBRe%dk!W}DV4K<aw*JC?3ySlXPn-u#
zVv-C8?>8Mc-xzzm1dP^(9w<xFI-&qhtf#%Tls`8KY!*M1Y1z07p9Asat!;lD6<imt
zb3*rrzUPCDbEO>UnL`@l?dy4NpuG*Z<T^QBZ4}+C3%(Q(GrjG_k$u#ABlmn04nucl
zITC#013P+8b<E}xp>I5-n@hGi9xa!0z}{g`d(SLo#@p!;!(tL)2mZt6@+HDtqg>f7
z>Lo89*jkO)H8uEsQ=|y`d7u<ts_w|x75hrM(WP}6lUKcCeu=7%chZyN&L<qNH+}w(
zRFtc{z2s5DR=AcX&W0OM4B=atqL-=SmTdrhPw$KMXW&Og9oNd79oI;>Cqsr0m!jH^
zUR_b8km8Ko<e8U=)!n?p<aBL+w4gOtOIEA!l81SahXM~o08>g3^br0Eoe<k+dMYbC
zm@1Q%Ahvg;EAOgq?cq=KLPRZBp7+2P5w?n873|}S$L(hM*?Ll!oeJjlQS?=>j~34R
z%6#(LqiS8K(qAgtP{Z+vjwA{v?$4e(^MWK=2PcuqErY?4LQd5)G*{|WlcU8jz9r>h
z-zFLx0;|A(<>qL7#TK3qD$!Nh+^#Q#Zfd30yP1~eHIOrrg<3}h0*vjx7qZE2Amo-^
z*?wQ|j&j$BFw^{53l2&xqCLdT9ac+bwUvi1nsPSv>RX<^k?oWNq^iC{&>McOq4{Id
zbHgSjSL&U&_+$#$@Y5H(!<?#K!C>0RNGN)JvLDl#jU$axBe`72J6f<ACzM}gNPgky
z%~NO#iYfVjO48_(mnMDOoQIBMH@E)s-1Y)!KCc-659<Tw|6bVkZ+*bZ@LzI?7}%JY
z{!eqtf88H2ursjy|K&gb_x(Xtn~}4rP7^6kr=hC6ob_gl&1r-USNU2?c4W5lzmck?
z$8&eH)h=%HyR!GkVT9ewpr(Cg%PY<Sjxs-xu{tjz8&Q60W+fvvAvORyucD|%-`vLL
ze*r&0z`tl>VJAZ?WM^#x_-kf`BPA7f1R6S9*x8C0Is>@?>Od2K2+$b7!~|gE<m7}S
z1qj>OdpKH{nL7h0{$fy4Q`7vH<)09Mk;i{}{$@H^nArlz{=T>Zt?le>fVR$mA^xuu
zm4QHjvpEo8YGDlo2+J#IO3H}?D8%Jd0pdVgprfHRK*7bx+QJwhV_^)mbplcXOzj*2
z*8i~p7~9#JSp3tP6Ww1dK_`GAz{wtHZ1Fb@=xz+O|HnlGum?KYSU5TTeFs=L0n8i?
zZJqzlz}XI9VQXydV)74xzjjl*e+k(;+Wn2N`Rn@&reNpf>}2d{Vebt1TU9|s>_2!q
zn;Sa+Q`^bnuNz=z`Zv<V&e-Ljmj3nqh574sHngyH0yqQRo&Tw21O%8^IN4hpdi<^Z
z7tG$#;$IprP8POi|HXg?;0QD`bTqLBIywD?`HTI}bpESP!2i;{p}oDe$G_Qj|3>{!
z4i?T%Kx<PvI7X(wHI1GB);6=Sg`@vxt4P|K+5s3D{=;tKV*j5$SD@p+1ETn6&rtp)
zVQ6A!YwZCr0h+?m%h@^qEeW9bzf+m+|DDMH4<!D7BJlqcdH?^3``<PCUk>sA-}C(6
zp~YORt>p}Dfd3`|{_~Cj$N>JiWd65cLmLZgkN;Ene~(lL{s+hZ6N;p>;om_B+M4|p
zgMp6WKa3VmVixW|69o%rV{?G1q4nP>{cBgXH32$WTi62ss`>9A0kn(^4FAoiVs2q<
zW&6+AS^vWYv^Dwf)c*?fFL8PabwMo!QR@HwM*TNV;ji+}DjxQKsr`>mWbI7;=i(m}
zAt5_=fEO(zD-(d0nSt@|f&0sclbO}$|4{K?Ajbb%%Nja6TDSwW|F-=%^PlGb$Mat+
z-Tww7YHMs~^3PsTb~dy%`MbUTr{N#Bv5TYQUseA-@qe5C&-K3>9SC#>8pEwF*%@;O
zTc%~EJJ(|64o!$?_w_J>4~5v5HmDR8`2Vu&_Dc)f;jA=xuL+@RoZ|Xy^H@By|2UAM
zIvnZ2v?lLP0shIy^C70}EkM~M>tLro45Zg9{g!^N9_4klAhQjj#m1m^xI3Y!q*w9|
zg+JB7><Dx9MCniLTHudP@@S9fYn)mii_wYP2!dK$cy>;fc=Gf}J}^2wG}wIu*?WLX
zof8X7%58|{n|wx?<m_SKc;8_B3Hj>E8ea}8hv$yZF+shhBp-YjA#)0zd!p$d0A`q@
z&dX2ui|?cyx74rMvzKIQpJV0_&WXax^F#iNk*^?v^)`Zbe&6vYqR^v+KqF8kzs~r4
z=v+Q@;eiX?D4<Rfc(jrV!cCOqyxq1=h$9=9k}yp@n>5A{>mBJhIim@~vyKqp4>#W}
zx+fzpcCVj~_GjCDz+$T^aF~?_K#l9K0(ckdrTpv+2S14UDyMS_^{<?i=~Z=x3u#&h
zgRb=NuOp$9IVm~#<}M4_c4sil@DRMM_degQovPZXeLCBUq_gp-J{_RIQ1H2uh;WV(
z^V3kh{ZB*D8TY$x;}HCM?gUyHaEq*z&2NP4)7J@7gn2}%Ad5HrI6^5!*a~4RlI?02
znNOZ6UYYwM)su;p0Ws=>fT*UPG9b$MWGldgUeNKYXO;U_ngfwK(NwQ~7M~NQM>NLV
zUQEf^EQ*H4bJi5q-<_c@E0MLt6ZUM>I&rLEra3H@O{YwhTH!%n_zc}L@j8r+m!=?#
z8J6x2#JoDhP$WN3Q=I|oGZ+RxV>B;5G{g~RuN_JJeJFN&nBa%<MWK2%Z~ohJTC-JG
ziLq_;<Q6UiL>W6g|0Yx>=BvTecGQxeNcl0u1wm!Dh|gT1|2)Z0LZl=#Gnj8wxF2d&
zO~e%v_uLeZX(#T=;(2^)-j|MXlCM+l!CczfNb-fifYCKRjz~RB=eNtLT@Iv0v=ANI
zUAGx43hPxmPO*tSGQR+2wqgdUF2`_Uk?@3_zOQOMBPKpvg%b!-yF2K7;%aDNfmDez
ze6u`(0z<SgR6LtnMpD`?Ujd~h0UDA14Ka;D1-v3-D%7{fhhM(paL~J^-&T6_Iyz|K
ztVCp8TH_(mBTF=Jvd(sxsBO|N6mw)h$lFdszCTPopo=HeSW+ReL@)E;6XOZVBt(SP
zf5*0NlnaLsK-PtAT<?=cPSEbn)f0#H-Zz&~<A}{FVw1+#4%(PdE(qa!k1sx(zsl&5
zSVuu})4l|C`Y;T!{L<$N5NHr2ninC$%++G34m~gpUQ0OYIR2II+Ud9-pk}os-<B=R
zN;YTh&jTXu!t>k3>wq>BvA{Edc3fNx*$<oGxuqhJS^?Pt_Yz^x21GG$sY~;soKU)4
z8`mz`4W3Qr20N<)iTZ%?^P)nOZ>(}V(%D9KrBZQEo9NQ4H8~*keQ9{~eb;3NH+>13
zwL=jfedr<ob4Zw7O)~S<?Wcu$M=Y0{Po${+lXAuJs0{G_H-DFMeB*vTB=O9AJ{ZUA
zi-R#g(rNkc5~De<25?l>nBi)+gkw(@9G<zPSEWwCP<Yp7oAi=L>LU{4s}U!MYwZL+
zdhjaDNC~oL{da11hZ$K6&`DB_CZX!6kNiEbg|A15t}n38QG|{mDvGm=<Eo3VH!dQ}
zgFVHYcM?^Hz8fpkgcHH3By(=PLV}z~#8?P#GV@(iW{gIL*^zIwoX;^o7gQxTkbNWL
zqmyh&k<<wEIHksHt103}VY1s>&1w6@ecznc(HgZE0w=fg`@y)oXefP18-FuL9BPHW
zbgh$03^5?>v1{HxjyO%xa)1P1S@RIlNe`BsF7$~`yGB-mZ`h-OY9#W9spG4LogV<+
zyRk4kgOG!<zk#xRcYC-IYDh-R_JkDMmydj8epLHpy7>}iVltvLz8W?9BAm%6_{ET8
zj6B{+Bp)10iaFp|rYMsaX8Mq740?V=HO}js(E0*En-G{zx#EN5;kv*BR;Wa|SAbQ@
zr*v1)L_~!z$o<MM(r&(pLplPf-)+5#>JRnsp~?{0^nDG5o!f_%1Z?}S<31d7GZ$of
ziMRu5qd#_Y+AG&|Z19%)%HIlsEC#j<=VzzwzZse4QmLm|ZMOrBB_3}=6Y@grozXkw
zzPp<$gRS<P3XVw<339%vp}!p5_!vS8Yk5{)QECOdPv=;#48*doO<Zw49?n$0k$LDM
z(pvBVBj<G)g_*`(V6;S|N;3AP$>OJen6e2cqlsA#4M+KmH9Sg!oS)lgHaUzhE#iSR
zNRmbGxLw<Qh+y~)Q_+Kc54alN3FXlQC7p2Mu~#S7d8^5z6p#KR?QHjz9;d%qEPgGI
zux3r=5>HUrced%;=*(TNU7^Ws+3f!dr&h9s%-}<Squs@Tu0wB`{N`7*)$i;=wW#fN
z7ym9SLiV|%lpVI2A%y+%pc<(6>l}8=D&8V||Erd4-07Vi!a#76On6=&1zLw38!sz@
zq;aUo6c_T%u903=NS%@JSceqKS1i(^XfR8RPFur<b4qO2StK_Cwpf_f8pPP4So3`o
zs1vgqaN@+~A#TXx`^w5N_J-G=21?WOiznb`Y=NIzwe0>Zvtx}D2IB+~+Hz7(NbrH2
zG6=Um9h4?VU%wNiqt>waoK%wl9gTDas{m7^MP^3-wdRx}*+s&It4@f;Mv(n=AzZw6
zqBNRj+JkC7FG_Tof!afFu#^P`(<03SCaPvM+TY>N+&tAFnkgz_@PWkp`?(X0KCW-)
z`A|zeCCkmuyWs&J)MDASpLz5J1fXuY&~6U;aaVNB`dyofMC#NWM&akn6U~e`$x->8
zw`lWEKhO{uFJt~tELCTzx*9L?9t^s>(rOeKP=V>j=P`?Q_~fhGsVeNbrE{QARQ$q8
z>LU{Z>GK}QO`Qbp^!7{ZS=A}Xwv{s?RJyvbe>-nuPE6g{oyn#0P~mpKWl56$K}bYY
zjF=L2+1)n$i79Iv$kQL4`)TgK371ioT-#RAL$N56y2xxpGWu?YhtM*2OlmS7=b_fn
zSW5<R?}m5dye^%6x>d*-o~2ZD-z<`+ZD)Gf-yaNH8L>7vV??0DAerxVf>omwrJpJG
zVr?MNFa=;kzvBwOXYWOj8+9$YHgj2y<0Ec_8t&y5!4%nzcdi?}!!P(E=%FH45Xz%~
z5WHDSbPtCf&NR&7G&9sEelPAoK^e&6cxkq^whnRxsj+!qGX6mNMB50S*>74dAs{oi
z%4Q%OuS7o#e&pkcgws+asY+O6L>qPEwIzS{wGhmt{aTG-#JyYpMjrq^h}ZT20o|^7
z-xYMy3~xm3cT=gP|5>1Kkv`0Pji-O1yAtR4SYjVRcVuG15mqJCw<y@4Be!6KET<Jt
zL1B?{0-K<e%#F_+${3aGq$IIp5So9@wmQy|ZLh^~IP%5G9!OJ{bUm0?X2iaNNc!`O
zmDDCNh334;x%aw0Y$s7gas)s|EQ(+B0=+LX0#6r*{=|q8b1?x*CAO-2?6m``cC99G
z)r)O-aZRB{S;q1#AA)}VrU%$oN!&2m$a0c6mYlbf^ht3AQK1|7ks2~rs1l}Ax8lri
zF>Xf~Mr?)WMg|dLm-aa?O$}Mt!vg(<3z!C-ruz{`U~#aEo56C;FdU+zIi2H9^9H-E
zbb~Y>Vn8`6;j2~-;xXovA5D5^jz5EjC$=lwi);+4<<DB)jbHf8gd%4aZcJQg(mi(1
zbDg;%&@e?&-tF9rME*P=AD>zosI*ixBl=2G9eQc=28-|@&>@;5gC@|#VqGbYIF>nt
zWbOo&4+`=0;ikhDy!91mkribS(EfO{z5TmLYc4&?f?T%~F|n(D?2GS@s8s7BBQVcX
zbviCu?N9|8dVhgW@3*8QRR{P9?0zH9fwTEpUOUQI56`#8@`C3L2KVN1Y!=w3u%xOt
z{G;yqTSXW&zIRDw!J0;HR<8Uk&j4H)L|?jFj9GMg_VzDEViG=yr-u7K=lk4sEn;78
zTZ)*<+`n*Lz_Hn=pLH|jFCi63JxhAPYw_BM)Ny0<eAYP8>~&>NG-5w#y>@~TSih)J
zq-?Z0s-`U5bW4UQd1^!w|9*w<w+GqyK7dB%wW0H3WU)J{v;~YE1htNq!B<yY#A1St
zNft+Ts~5e!49*!AN%mrIMdIeHKX6y-$a@J&an}ARf_lYqf2|l*VN?7i)Q}q=c41Cf
zn>l0prSlE&lNe7zH*fEnoM}n}lzuLEkL~#T#ZrF1DwbvPVcKZFob%>WiFyqh`59ji
zoT}>6)X1`O#Jv;@tA%35?L%tbweWQq)h}*1UFo8dsMsX4vjrEZxyL4QwIT;?kXcFX
z^zm}b_IUA&E6SzJd87}{mJ6vB27e9W77hsMd@$W9*v#wrMb?Ohl&CfcvIO{U3Y0J_
zS4wDZ)kdSQPpre&krd69%DUX@bgc4Rj-87ZHWLk8#U#k7!yP`dYXf1HP(WxZ9^4%4
zqFigEe3tY9^aQ+?V5N}l)#=R1PD=tBJ1sq0;*}_(sk|Wr*IU3sh-|$YHz|}MntHFh
z`+JAW_T2(*8W7|8G3Ei$1V|J@k+!9Pxn<h05w+99A7MM|_ou0(y^(?~DeNa18t-0|
zQim4+ACiHzBmjHmb)=qJ(obd0N%?1DBu07TY9)H^u9P$RzKAL0Guv|BIA;a$dkvl!
zN|{qq#e;dWiemo_!L1)&$lTns6}1pzwiD@AQ~Ap7D+%Xa4Q^WI>UhKN>p<Y$ve{>@
zVid<E$h(dk2rp58{t5NZ4H_B+jhxuw(o5d-PFGUb`LNLQkKod8$&%9QzEiVjj=3Fo
zs}10x&A5j1D+pQn7J;X?w*lU_j?R&7lTL|vq(rg0#fh1(!up3h(60ho4t%`z5V2`y
z<}q=NVCyeL&;z-1A;_~(k-(C=d#R}-^7Pt1{P?OMn0Sj~F2YJ>#dZo2TZV0-Nbo{G
ztom+ApZj!YRusRTG<Ex6M1QeJ8j~=OD{3HJXfXCgtoHhqG$$yrP3D4{ZGY`Frw-A6
zz;9cm5XGa|oWt3jGu)9FC5H42oY~9rh%yC@gJQ}PE(;!7^_t|Yd^;#Iq*_JcZwnEa
zjIpSe&ti}<R5;4>UtQm09yq+GphPuQgc(euzPl4&>ul3{T+?Mh%3GQ7LeyDJ&F3R7
znrJ^yYF&KYo3{OQr2VDz;v@h)@ezEfZ0&pI&>R<vwoMPS%<X$-J*WUXeadawog(9Z
zX&qfycN4uljCspFRj=zve^)g#jIHuR?o|dM2<AmZzg0j3{v(@M_lH*q_eprDaGpa1
z6Nz_N!Q+Dl_(dQE`}QXH{0U2D+TK?NHspZT7NH0T;^-}Z(U({fVGN3z?|dL?D;Kf>
z=oC$=xf<>bz;eQ6_X|EcQJMTvfWURcX9J5(XI=4CgO-d#i}Kh3Ngw(5Ewpsbcf{2`
zUxB9v;~&an?sQ{Ch2R<Q%a?(L3arB%5`#oyJL;JD#WGvp$L8xlSVf}ny<3=r(a%&r
z`I&1gy~X?v&=rcJP|<l$f1T-eJ};``1QA_Ipa$go2zd*Oh6&%yL1pLj!diNrV&5>|
zzcatA!in~9$He<}z5i5~XjnS+ucI}IStKahQ5!yxsna|!5|Q16ZAftm!i12QAxKJ{
zW^;$d?DY+uYxC;(RR7NNP5Dlk=cM_jaC9w7W_>Qv?WPT(s*($T_y$id5wz-}ED3d3
zf~8Q~NUfk&!=J6zn)!*GYdg&43{g2et0HRB5}kiL80$QdU;u0ki9}%nyK#=f@nOsa
zF_J4+9A2CHBWAmM)ju9Vh^kG@54}mU?quW}8KdSKcAux<lmbtoR;MfB@RsTZhc+D@
zH?>Yb@8J~q9BNx_VR9U$g5<BhCfJ>v^Ds*9Q4g=HEOE_{kV^#j#^u8{A!?qJn6y5*
zb0A8v`r^O|RDHSUI_+ZlqqTe(fr2bNMkMu$a2Og=4cmS1o;U#x4-C>Tyt9M>=>e}v
z(}6k96B6Skfv~pt>u5Kz!~ta|LsWB6ZVX|B5T%W!ot;ct+zG5Sg+VFhh0>YF_#p0K
z>{R&Us~s2yd;=K%mXoehEfiXO@>BA+*R_(A*GwKh<I;h(QRw}qaZh$M1x=nFt2`me
zZ6<jjmX#~K1sz99XGwzsWRaezRBXJz_u&zhw3*>26>23b+IV-B(VkXB;SC0u<yw|9
z7?e|6wM-9&!rP1^{`a^RW1f0+$$jm~#y4MNUwqcc+zfu{@I>nLz>$S(1}eh=0_NSG
z#Qn{v3rXzdPjq_vPW?ot-m2kVR_TfOe!p@80a*2xUda<X06XcP9|uLS1GbIlYQZHV
z5dp>=iml->24~bGAjR^yE7ih@xCUo?KB6;5hxB|GcGrme(7b|u>y|%R6mopnD!!-=
zdl4BqOB5tH2(*MJIn7+v>Mjgf)WJP*5%a=O4r+fu)ADp(a5P?wgNY?T7QSHub$@x^
z%|haNp*wgFcZydNnyMYU=cBvhW){WR#CxlOWGQw4f!xpCC*W@E$juHafFn;2iJ?em
zz34bZ_IuLhT#Ddt^GIyvwaHU+EhZHOa<~ypcQcFRNhwh8^wo$E+M6l<-BY~UPzLrK
zw0nP)mf@+{OABB&o)g-6%x8Vy@4eS0nU2|Pp_#rYjui5ec<xs-^TrfuG}}6R?c;=7
zTZ@3Bnei^G2B@Am^g{5+56+!7Ay0;?lBR~|Xpq}giRqfcIl)hluy8u%!O23DiLxk~
z#ieqm(daNVsP-bCt?1qR9gx<6nOwPap**h!HwJC43iWtBtYa2<x$h|OPnXmkZPl3(
zZLq>fl<!T(vrA$ShR%u>@%bs`7*iJ5Q|*{xD!f4r=XEz1o~ltRD>l`p^B0NCb3xsc
zT>AkCyoD^o?X`zQ7-ELsH7s+_;wa3&nVcC--NSi$p34`CU4%e~v{gX66uvm1`+*+}
zSYXtGQx?Ns&8~A~`y{F$h9j`261dt;KP}6cL}Yi>uN{fJ)QFG$G&Y5{&ilBDTOS~H
z05{9xcta_p6ZkH)jb<z?d{AOKT;OZjw)7zzkRg5Ls`DjdISBQkI4NL|cAbj-8&k7S
zm6Iym89>S&^{o6@h8@Ck^Uh#fxNHQ#rd5B#-ugu>dF!`K<2wnKh>W`~M`x<AZe4aI
zLv#LiC?<E0<-V7~<>}Qqjx^$Y3iAp&8M>Z<%tmT6LnrW4Ug}U0`;6Zx{QJVz@h~sp
zptFs$I~a&|o&8~>gO|`mh+@K%y+pN?jw7c0*LLf}w1^`@(&nR1-zrXJ%QJ4S#&8rX
z`ZRr`LBA1Cxwr%ty@~bF!j7?{1obMKldaxmRF<eBbiv%HfUsJ>IAkOrqFz{%H~^oN
zX8o2)fHoIL8`ja4vbR9I4u=P<ETkF5S(>eEO(pug!sgRdmLxxCFNX^Y<&D}}J%K##
zdKiRsR~sinr33XkCmA*}`z7eM0FBJiX|Lanx?Ki8e;aaXtVzag$CE|C8u)<sG{Rks
zp8TB>f7f?1r0eqz+{FdXG4*&ZvwCzL^JmWpmXDCZNTS5F-YTeS#-HC0VXnC^C3kXh
zERcWBt}lak5{$#!Qjf7|f|T^xAxeZXNoL@FS9#*30EY#ADe0cl*CKTY+>Mf5M6NJ8
zNc9MOmwL!tQxVFtmoj<pd{=O8!O@fAP!Ic|pGF$pqULWhh9_kbj49=4=zU!$ZW%(J
zE@eZWg-+?W4ym$Zz;^VD-gn|3V(r=@`^%WXNTE}D#R`9Q)T>u+Y7Rc+c}LeN_8t1)
zn|J`M5l&)_6*7&b1m@11J02`Pc!Pb49<W<;t+JsVV)bbFF=p&$+nsGOiSP?!el&;V
z2j%>{fj(1C$A(QkL{#i^d+FSZ$kCj7j@+{uy<096ZI8&SQvaG**Qy7+R~PxssVg(K
zE$cq*1G}r(%#~CNxTMP()lv;{ir(ynmDd-$o12EH8C6~JhCxk2Ar$E?kJ>n%X$Y?p
zI3(s_6>$<0WL5%=>@MH<G5IH12!*)%O55~Z*0L2OQp{1~PGpE<mlIxOfU)SMN%ooP
zNNHbCl3P!V6zdXn`|VV<EacLCSx(Tad5vwxNg?%e<qh?<6#n-Cd-;=E`3dR<2@ab3
zA&)<2;j5m#z%sM}8Y9vAJO*@me17-pOGHr=#~D_UsA%?+&8Qm}%zNTHa>ML~!^`DC
zQaq3JPeB}0X+L=TOrzM5wMlu`&IhL~h^t$O6GoFBao#BVvm2~l%}^P8H?eA%-tWAj
z@}-rv0PdT73b;`C8?eCImPB^ZrBK_ghei4ON0KS&l4v>HH%YkhIV)BM7uhi5{Yi90
zWR80j-?Bzj{Ri#%!);zI*9{Y|6Ok1L*v}7klPH?l!Hlj6m^S4ZRKuj3qPp8yH@s$c
z`!G=kkLO1Yp6=sbg^1$U3d}8vZI3%e!DnDdUn<h{ByaHsMUlF^1mlF$>@Tfzb*6@^
zrH@zFlAO6+0oyJx#Dm+Ig9Iw;`aIZFDp_|HeJhhfacEvMO_;&5T{$hRFC#-Hcdua8
ztW<gu+>rj}Ml9z-29cv<fQR?0b4^Cx=O(uP>#>0oab2z6^4*E7_k2$Ja_nlBdT@OU
zdz&?LU{ZIQ&^OoS^=9;x(>hbA#_-AzB~<-pn=#aKqheHLYPrtNsP&6FnkDE9RuZu-
z>LXWFroir7kHW&-o`_%tBP7Za_@b>}{9j9RMBj$12c{bF!%Y_6>3&i=ibYTpQ9fgf
z4If^xK&U%?Arh68=S9YlCkJ+v>#0V?Afsp=fRG0#nM$9I4B<3)1~q-=M^+lAFz24A
zf1FX{qC3O&z`_BJrzw8GsaN83iLP0wKd!H2I$%9N4#%;jTr;@V;CM5R+?Clz4ZqP9
zo~Fr((bl%9Mv%B;`dfmr(*+B3lEGtIO42}-Z8ip$)qET@Z=chh)>|Zpfjf=KXjjL>
z#`z)+?Ab^ob3Ru&css)vx|fCD>Z9pA`m{w(x;cQ;|NKsaO=>I-Q!h*0g9PnAn;e^>
zRS{|OZaB?Ap_(DJ6jLQkJgC+94zYu!=|hly$wCXhGte|sfIl-_v9M^$$Bf0NlRoZ3
z*YKxNi_DY{Jw1-x?mtys+dM7ZpcnW`{*7kY9^jL*A&mY53?w%<**MwLt==MkYl48q
zKL|V898v5!gssb`%?@f|on|=png922F&**7DU@1+SciFBSL<9w`v)@{L$BNfT2rtC
zH>Ah61vEZ%F0@m(Y_LTZF)?<S4cd(rYv#saWpzR-lu+aY|2IrD6;y0t93X+K9+zv*
zk!Sudid|@mtF6oRxySf$3bY#dJm+Vqql=5WO#2jRu82nde6u>@?ckK5PU+Q5ap=_t
zYcKzc@)9dDEdIuOdJLU_Nw_m+E#0-Pxyajza@1hKxO=j%%up|jq`h_$EtF0`O0G(n
zEAA~3KJ9M7CdE;iwRp~%vSMQ}CXQ2lb&+){%R)erDayk4kiM;uA)##CTMna+ZGr~d
zL+e-NGP6}@da!J0MCr+9efd9QRIwCSYi!r^2~w^81}{wbh(&_kPi^|o>rUDz&#jy6
z8v%eL22l{&7H};DQ)*F&a%=D9zKkXh2%1+n7p@<d8ETb<QLqGC7L<~di#!u;UE@1z
z2k!@xD+6cP9Axn&wImGt?{X*x?MDPx^#T{AXcDyttM&^Em})&Rw(ICFCXmsA*I;sm
z$Dj!W0o}PHKE$>PqsC6lu;d<iiK_5mjU?nnTxnDHax#Z-S!zy1yl9Sh`ic5PwE#aZ
z%X2h*Nkz(St5O*vnv=sn(o`vG_L}v-GY*)e)SGTF<tvE_v$bhuuetSU68E=c2`!%y
zr*MKX+4&}M@j<s_2cPdj!}`2_^9uzqBmG`RQ3XRB;r0~kfEZPi0Xr$AiceOVyVQ85
z+T*ycfxUsbfO?GP2dNoMl7eTN&(=f?F$xBIlK@pzN9R!!U%e*0beh2}PcjF%#Qr)?
zm1}-B68d4sPQmqolswQHezy-*$`>WR;QomfR^u9b`pM<Ya>!57BN>sF)o%f|hqi}J
zDT}-#wYxMh^K<&H{~WBWp6Ft2L5j8mEWLm{89w8JXozbGZ>zBfhFZ(z^+mt4@(O=W
zOIOWN$Bhl7deTYzeXqR0x-HO}J^vS-BoPL#^_>*|cqx%zXUzMXco?xunxA;GKNlyD
z16A+6)YA9M*Rao?)X9TOIbD=bUkM?Bm7!(Ri}T#G8GO_bB{8qzUnLnXKc66n2@F$;
zI165L^P=V)narf^dAd%;NQuPZla;gHf9*ch@!Y(CxX<ESgP`kpGz6~l;Iwi*o~pO<
z(C1vy)e}Qk4GW<WiEE2xT*kG-tP}-B@s=XfqH~cAIh!2Ksrgu9BZp-0k?pRCk2igC
zc)nWLR?mw0C4g-iV)p6(;Zol5(>X{?o5nLu^NXZ>GH~%gLdPvex55Eyfuf^sMo>im
z2)<J0r+F_S0{p=b+Du_EFZB6{{;{-5-c?}>A3nexW(pTZmRpw!;}8hG7W91y6Mu5a
z@6CDG$=I24T=bl8EZlXvra8-|mbqz&Qfhh1VWThNBRdd>BT6@ljCEB^5<bNO{7@Xk
zu~n*i8UX7Gd{*`kB4a-{)`}JemwekiN38xn>k$Wv@}l%M<0%uUBqJJyYEY##=N8?#
zlHcLe{XARH@%XbLX1VG{!rejpW9IJ-e=fash7={)??+qiYqsj>zVO)H@5S|2Cul4m
zbCi+83J=0?grh(Fl8o)ntp04na-FQ@C+mAObXa9DFDWA%OW{Co8m;{n>q{d=5iQ*W
zuED9_o>tEb_!F<GN*3b2>P$58NM_1<D1-QVvwL5^C;F0~yD?9LdHO`hh;7NA;RL&b
z{!Hz!!@j*u4_}iJrCRE-UB&Z+px0LR6IV>CR2V}bc%oyP+}l>2OT+TUJonh!`?VXz
zs%5p$m3TS(m83OU_pD8lja)W44hHBOu`#<6kY(_^q2v4GYrrM0UA$(giJGi`c`$90
z(x^5YZ?y*O3;-37iYOM=JwrBg%u`qTs-+-n5@%Hw(ggN+mO&{WHC<z)N`cICmW+IN
z8@FKbskNutiETKdNl|D3P?N5swZgFiVSmGCswU~v=aSvrCu>@^*BsO9%zIuJ_LB(c
z9;ea!*?oit)}nbFDgqBwE(k^l!f}_@W}cj;S!T=GlvZC=GjY3DJLLS6atN^i=Mgo{
zj&@j6HHhS3g2ZiD6E5xE7^+JZl|26f<P6QkBL*L_()?Y|u2yM@qm6mIV`$nLOm1pr
z9Lv6xk{cNty@n(wNh99N=R+R#kSo>H&>Jjslx~0wKMbzCX~I1QxWPlK`6H%rCIrcd
zQu>R#<{fM|(saf<K|bLG)8PI3m&gK!ST$U?#QRA~QGjQ#rH+CK*TWwryU=hquN648
zX$>&3vcf#s#!zD!Uiq=`$O@X*6|*?I>_2L8e8dz}k^)ioa+PIys?P=cd=xBFo;f48
zlh}5<o-qw1jz+)$+x4<5bXrP*_s8}J)958xfO-EG09fh>nk6m`iB6fwqApcHBk+^M
zda_vK0TP6?NDEnV)JT6&ZOD9@aF|a>#2+mb-TW6fnYTk!O(fM|=@tQd-;akdq#?tU
zT@m~Smt!6+o*vmSyAyb^5?4c&x#craY;&5jWo@s=%x@RJxS&;c2u`UEgb#IH#}_}-
z#Ov!`B^uM-FZAahIuCL+FK@Z(y@J+urtnsFR)C#Cr>xzq<a#n4g+D3{oSD0jUbKWV
zCtPA&TZv4xv>$II#_O$$kGCA_P0#yk=Goi19b0v;d~w_e99T<ifcPXCZs{#q#lJE7
zUsJ{w?4c|r^ttg72q|+o$~jZ=&OMpc&ma^Nd-jhGOVe$nOxYA<wOR0;nJ*vl5AaY{
zvIDJ~!m?!;MXW=1QG6MVQ1D9}Xc^8I5WFsSG)#2NT}#A!bF(bm8R4J1XsuT^2gd~Y
zCE~A1wx5{iv)Pb`Z|68#F7wBv79h!OS<%*^`>+kiWW}@p^AB^rD!n-*O4BD_jc01+
zl@O*iE+btOV4NQY9;PG-b4`HqPrBNsSkrC#PYe;}$!+!lq~Ek3LZq4q5!YVS;*Doe
zdG8<R9l(aJLzEO-ZuQAR-A>}iNh&ZjejX<NP;J?+$;Fq)ASyA<W`w<4K4PU^QH8*l
z$E5>)dlkSqo+!~G9D|C&Kp9}fKF#7uuIdk5Y7@G9c^tI4+7G+JaXF|sqGE3;ezucy
z)N#VhQyx*bhovyaI6ZDwA093f@U~L3t!4PiD6AKDAB^#+>!&Rv0O60HszIEPd#Ag2
zR_H`zF@JR2fY9S#D<QqG=S)+IM)o*(AFk|?&zu8~@{8s)C)P!!KO80tcN29-)#!fI
zFCh1ue8(Tm2(V7%e_)IgY!&umjhlzo1VaZ9sqy)V$o7~Yvfm{|!=ykvK|xKII<`R+
zPUPf;G&GfYLQvFXv_gsbPnn+#XcuSW#JqDznWuC!nwv^xip=N*-h;@HuLC|`)+d>g
z%F4dmdEF><|G@bz(8u+#GYig{PZgx~Hob}iQkQnH<xbftQ2=CDT^@Z0vq<GylxABb
zS$1|_8NM7cGOyp@Cf06(e+&5{O{WRiCm^z#!xwuc(XjvhB$FeLcF4}5Nzay;5Da#u
zIcH=c{ZYjSTIiFX{$z~Jge^i&NeV&Um0$M#TZ~H>;@z@G#2X7^`6yd^ilvG}|D~hX
z@u6n(p)`W0$wI-1)g@_TYlm1nJXOjG^47GqP3twI;(0DaM$MJD*DinF%1AAbZ?a)e
zlzS@$iKF7Hnq!MvJIT6YK|W&>W&*Fyuvii0ySyBec32e}tx9n@Ow(;4Jx2<XfFG8y
z1gSY0$>Y<3&Ps?F2C)e8vlST;GI_0SiDfmfdt-JOo1T|f*bPHZuplX9^Lufnw!~aR
zXRmOr>blgr<*UAQGdO#N*}0Mvw7j&7jLob8z@{C9_se)S)<)v|6$sWP%6Xfn&;?B}
zE-JT2fxF(ih>;E5BRD5Fg99<!R?wRbah0H^@Gy%Nk+SDM?X$<%s$`B9j+B-2U4D&!
zX@gY~(DtodTO)n+Qy^OszmaHfnp<uZ0d~YB^a18q{Jdptj3asq+`y%)b!?>b%R9_!
zRLv+|UUSDG*G95*CP#Jr-VsN#P{jouM@|-d+-1@@O_2}ZszAt`j!pl%zi}v3!b%a6
z;3+D{!7QG;JgGfJUzhXv0{)jyYc%{uhit2uwr-tl52cyHO5q}GvY?<<yfPd6;!^Rg
zG1=VAg3Q>+WvIF-YLRIVTn2>JF)Qeo0Gsg564d7|RQzntiu@s6xe0j`cw17ujoxi}
z>i#P7u}YowtVuMi_&ox)=~9rLs``EKsuTAaY5Qz%&lWqj!2S-Bs2gt|g#XA=hl`8d
zN34#g!O>8DAojL~*CNX`r7s)C{ZVvt7meV4%<9+^tVEF{by}zceZUTfLM!Gu(?}w6
z(1=2hbnjEOhzG2R*uf`)CD*R%c8xDb=pZ7hE(gcvNVOHL=NHjybnfZtb(x1sR!R2#
zAIJ(SeP~_vl`lwB84Zviefg{87yb^zXW&(VV#m`I!R7u)6N@_RPB1Ul{Gg*}lkBki
zIbx$|qxksXXgp&h+rEXq5;a#Y0Sg&Ti6-B-rN@Q)clKY&uW5&&Gl=d~;pJ9j+90G4
zm<jP<xkss8s9Z%?V7SS+>=iOy#n`h<FpkhunM9O7G*HTN`Ed^>m_SA>HU}$qgp{jD
zkUUM&b4du})X{nmIYjD*3&@#h-1)jTW?{@>;m6Ac6vEY8F13ujhtFj6G(la*si?N7
z%`FclHle?NbFtF#=3uU_vRo4w!Wt#@HzVS5eE+OhZn+eLO;iMap>BU$NlzG6-IAFc
zW->VyBQho5<2(mv>0i(r*|<ubhicn_L0`{A7ZWUQdVc*?8Q;JMIp#&C27hN@)<8Zf
z12587Dxm<}u_pE2`u1C=xm_R==8V(OBnuo?zD)jS{d^g$a!%3ov}W9bU}lbZUCNJv
z>~>S9;ox19B@__^8!CHRSdsw8kwsh~9-p`9l9_h~CFByhIg?+vSvFMto^je>Y*`b)
zpD(5Q$yB3@`}Z*%a0tH6wgz6q8uk$lGvdj!JriBvbAezV8!Ca`87!=v{q*JLl0sO=
zPg+PTn~2iuqJ=Gi2hVY^Fy^J$96K1RemiG+QM~|<J{gmwH<;Ws-7@L8otzUWY}6M*
zIVf1S$^>TSaP-tHSB73m$w+J<_y+N&aV*kW3`Q=&HU1lRo=Max6rHykd--I+1i3%N
zm)t7>LD?#gJi{5RSC7IiguwzVirFba#x&aSA7MQ!&WdZ~+gL%EsUUE@DUg;rdRc9d
z@cGN%p{<Z~qtD`Gy_}7F&>5vabeeyfmzwhB(K|FVcsrxQ{^nhMz7@GMJPtRZYjGwb
zI6Yc*XJ@sN&b@*Ol-Z?~ZDY>k+OBcxP6cPVhLCsJ$~y~8;>&Ph=2WAChNaq|D#vX7
z1df}mnN6NAx*k4Q(T(41_x^Cu#&Mz2YeCaQDiH9hB#2}!_~M;)Q)~;Zz^h4CH4`~2
z9su>+?4R%SzQ+mg7!bWZV4hC%sJj<#sMcfX`-Es!VaH1@%~@(UGE{AFGGk4E(djE=
zm-HfHXUVuS9LPSFE2e2iF+#6S*gmn(!Z9Op<51V@C{|4gH+VW=?u3G;BC45siv0XV
zr%PxCqVJ%&y|lgPj`l!aukk%khn+qzhx{$nVIvpURsw;J!zLag#CpR)To3)dMn!3?
z3#?j@%*|*c<F|N%2b+AWK+jOVomZOJ1T3Np?c}4=&J%f%G6HNH{u*uXi7u#Ip#5$S
z);^oUo;)j`5LbfA2DqFRxO&fPrtO#qNmKF8wz7Jje1!94F~^}FM(IMm<kZ^03tCyS
z-I?)IX(nkW7pj~CdU{aWXKU^@O(ZmCEzfVlaKt;Vm~@>PC2tw;dqw~un?t+`U6O8E
zG;+qgOXt~LngiJN=n4y9&O|&_RxhE}I5Fd@ADla^fveeDC4H_0UYW4aO#1gRTFU{F
z+tcHDV|OAG#OmQ*w4#IIQxn{lkH9_Ui_Nzw!9TrclxHG$55zyG&RqmlUp(Ec3|m4{
z=Sr_+GP#3;9&2ao7!h2mLXaAVgkIT^`$pqbxf_iQLkZ++ddb6f0H=9_Z|;8(-#IKV
zc13-n>RZ+hyUVZUm9V+waVZ=c<w&i}9&k1#NyK%9#(fptOd?cSE!%5=Hejv+-+4r!
zz;6sY6EPo4lCAdZe*&)1X7PaMQqkJZu%YFqzMuZ&Wt@(P$qn%j=uUcN3BNJuPl&m@
z<pQ^}3c31X;Tl)QDdiTsaQ0q&W)MNp<=7G}+*woYENDt9Eiuy-%iNdGm~6f5@LAS;
zbO`!z6$PgZEOMUh`m!gKWa_H-Ce&HG(Eygb`TMzL2+yx)pSxhos+E?}VS|UiDj4Mn
zg0d*LR84kpZg`5jeZ5f5Hb}Qp%8Dx<<tV3}leOv-MT!pyGT9Fzy+WQ99goy-(w^r+
z#ls*?=Z&mamhmU|<#<4a;|(Maz9?=5p&B+)P%tFVdZs`1FjqTPIhn*4SlQZX!B}(g
zrLf5^x)@w1`wh(MJE64HDk-mr8G`i=hQB1AH(eexkIt2Ql+P7y_hRNR#|YT5!;?f#
z2-cP?b|fi&&bZBWSO(*k0@<2r0mn)$R@KiH%z^aqlho@sX4&IEjlhhEjNhZd+fIc9
z3mKt;SYy&MPEMB)p$d0Tu@|a%iK#*x-M<@Ty@Te%OKtXyJO^)_HYNte?uLDGsj;Q&
ze|=y0z((`O{rI{xyd&+s82AE-2q1n{xLeHcKt^V`Q-DQIF!IiuuIFO&i(B(Z;ea&m
z_H1=pg04o3O$FgTni@y+OQ-p!GYF$n&lDxNCegLxGhjNG-er|2KfUv3{R{jN(G8vG
zTu7hObSa(a;F#(~ZkzdN!hX_jPAj{}GXx*|wb<)_ToyhBIk8Ex$NL?gTkTn46;|cR
zcdDcmS6kAjCY3s=J*WOyFr`$5M~p3keuU4h=EQDpP+0PnXf5U{qay~|0F2x@mGrja
z05fXd&t;Zr3Yv2^L^a9aq9l7lhxXWuw9s<7{G~Ddv%4XJ0RIe$(Xwz^%D&);hBMA`
zCp+bFu*^B-XGzN4{ay9Cd9E5bQTlDVHA6kii=n1h*5%>32=z!hh;|g(#^NCg>9Z(y
z_&#1r?9wi8cmD8HB})syBx+2RiDT)~%bkvj^E1WQwmB;NUd2(3F){51H9b*X&D)dt
zGRdtcs`ltb>120W&}n>Y7%eCo?+4E|b&P>YC82V`v@-OGQySXOv!%-Le*7O%_)Mj3
zz86<M+~XQV5)Bf$`w*VO9aM<~pqL2f8H!?W*|+=R9giy?L))^L!7meIIhr5ZnRy4j
zOobv&$(@Of=G8b?T#p3$?MG;>58S1{u)h_QT&r71zltPj2_tks-27<2C9N>vQ=M3r
zE56Ph5=D>@oWG85ROldQyXViShmVlRS3S2D@=Xjz|MV>fVs}X@#$bQ3Udj|lXvD2u
zXJhxDADiRJc_}+^Wt?>)OAj|aWWI`5{jdo}sZf-AM!}uCw4}h7mF|o&H+iFU(ok6f
za_B!6fznql<%y8+!qq-MOOub#`;nDEoLbeWC4b=KMzIlEwvhB-5rEnSVy#X;e!T#G
zvDMv8j3XOD@PF?LGgOHOx&=7LK!9?mjyiP;@c!82wsIT0#gW|-iZbS4`or@~ed8Et
zi@<}S&dmC4zMw=ojw<?XATJ2kJHm3lA;#p;w9?Q!5Ut-gvY^m?o?s5*?m!4*EBS#m
zM<99_>yOmD{3oGf;;+r9O#{kb{qNJ7)$0{-cslLYe(pTD`b@v-d<u?PnrQOzaqy?&
zL?`*fw%U&NPJ(i$vf3Q)csIr5k|<>-RNR9TlnG3rk;y7?8S5~*_9O-9SkszUw7>0c
zc`U@rsj~A+X~$wsiFct(km=c}%pj_pQKwaj;1XhG5@O;*KZ3C&|9Ir~(lpDbkf<&M
ztLr%(nvOd-Mp*ntTOVs}NrR;;Hgup1M*8E1ovGh5{!yny{K7KU7YkZ1)dWdSkwMRO
zNs>HMk6H&I+e^4sb_@;6keJ+p)b}FI{djN9^T?}Sw<ld**Z}hGPc<Nd%twqXZw0Z9
zR<u!SWGNaXhkzPk%8y+qC6C3r1v09^T@{NRcj-<qxOTOFivz_!1e*<8k<sW?F9Jxb
z|C2?Ei$&C)GEz3Oj$?#}xT%o#Huyla`^wvWTOopuFE6OfZS<CXwHf4bj6IywFrk*}
z1`zUL-Kcw|05J&}Mr@leyM;50kGnDVUc(4hI98^UXlN#0Hk)_Le-|!qv=EAI_~Qqs
z@+1;G(`4$5j*$}botp-a^-tGnehu>{%<Z{E-kUYEAt>1*<6y#}sdmHMuFP$HzT=jW
z4^?VnGcN(AH96CeR*uJ9E&e74X%ZitS)y_a1}oP;sJ`(s>@>Cwic&LaswnE3w`u+j
zyI)x6t-i-3NiIrdJ15DM+DliksX-8nM30*K+Uai{tSzW}8M}f0n5N~M#cNfk5TMaL
zN8=ggqfBc7ltQWoml>J;mZQ1V^D7F;jc4^&$R^1Ss|N8E$4fepnrK{7pg0gRrmG-@
zHQV&l%i809HZXT&TpT>6+mrWZ5&_qRw_{ssx8E?u$NLtE>RxRMEgCAr?oA73MxX+Z
zNxpJg^-4l_nqqZICUS;+9H^hB`n<K>GFQUM5Aw+&Vwc0UGB-<z^xd@Wf75nSQ}fy`
zT$r{zV7rCef84tUM$P1$1!J*zc5NpoPoDSS7Zo*tC{B#^Eg8S5gMMqxc7CDy4%q{^
zulZHPmlbkzLY=ho-NavPjR|ZMCoUOf{-Lt7ta7}4Iy@RfZ*6yV&PV&j4C|hUk`3-y
zz*mBq9eVYgr9VG;3}Tf3MwjW&kos)jx7*_UwJNtX8%5bss)I?ap#-7$t{np!u!HSa
zk}-w$rDJP@F3g$xL1eJacLdTrB56Jld&TnJ&GL-PCQS(6Cwh6bWri9H1(ypt>Xpyx
z)D`}3aMtwSNI3WJ;`>B0Wrmzfk;B)!_B)1{&4n7~{xAiV&BEd$X&5Ml%V=U4_$IfY
zoeU9I!Dwe>t%5;<2v;S@;>*-oFBZQij$2v3pm_;G3C#IiMRE3t0FRevkebADPuU^H
zHY+d%F|~FhesMrzjHT|}B6Mo3spA=1W@3}lwLg-LxUpdfa^J9gjSRG>)DW3P$_ouH
zdnu>n%hRwMof2_5k-68fa=E~NHGt*EZM}Ub!mSFMt5f!WVW(;^=*3=c(!+-u;=c3p
zz&>uS5+oF<{o*Z^8$>-#&J$3&94+O-307$c`+>}fRKg2V?fs*VGLSE?;>+~Ug#oKQ
zLY~J^jn@|PvQ9-io&C7&$odC;W^z9-+#TuIAq7?s<Tzes0X3VWQ?IXwU8s+#L%L#P
zjbOd6>b!kU^CR(DwaA}&j$j8tyHGG{3(@V97V9Gl;b`zd39%Qv{mr!VBf%}YDP)G@
z+R=)~WrRJUY}_Zsws=j@3#Iflp06~4SX6Fdq(_otXmAZThN2(e&HbqJdKKUjIu!i0
zcRs_ZXH8r3$m~5CF1|1ItR3^bA>vFPYj5*qk;_|N_@9>#7=4};HI%Omb>^A$mj`~p
z`zU#Ynv<S|_vZyb0L%x;Y7-TY^+tmTB`<mztt*K3%-d|_IeVWk4#_%vl%n)1raZaz
z>I^FP%q|1Cf$Sq~L_z2gB$Qw;FMH&+l!m~Cku&;$AA8Ut-h-i2l9pR~fk%@Cn&y@@
z7Ms337jtsbfR#q7tyTvtR6eEJB4t4gy5`gVJ1e`*Z{`fpr!#Mr!rRscr)7eIGCkud
zc--QN$k<M@FVDH1XGawgVf>;4%^9JogmQO%1at75C$B~*+`ny)GU?Av`MXPv$5fva
z1WNlX$5~j<!Bq;0BHJzzBS3wBR~3-IJ8d4*v+CSh;XUTJ;HkXjJh|4++}0*0_P9(i
z+K~nuI4_}p;auu+Iy1I;;tRV_Iq3MZL@OwW=@zyKPG^66)8_!c1rb>^G(hY+cwi?j
zRwf(dh4->mDG3BmMm~EQlrNf{Pc&Lf8(%6i&DasNPAQHPKbw63qdu(D!=EHLrVN6i
z1x|-^y@}l;rlf<ymo(p)&nu)iv2D~L9M|5?UYoCH)bZo@TeUQ2$Yt1Up-x)<5awgz
zF#?&k!1@rn;)?!W&v#O>m|VLZnD9B+C}yCcBpuyV#0>3x-zk-iPrN4|(}p#X>`cdn
zgry4f*Uo#Y;E@IsXra3>(|8tR3>!iQkgzp&(I|02%j?Ad@IYW4W?hh8zao@DAc%UF
z$r*0rm4Kd^m#X+Cd7a2yq;5hTpf02CSFe?tPe5kNK!zXL%Fc5CO(z67pA?Do<s1Uk
zpyE(+!pfhDA4`7z^46s&9T38|+F<C!65?APgN`#>b%n@n4pvRFW!=S42_o^}qiIYX
zCh$D>YXl8POgE_9Z$$DNt{L2+y|*m8)^9rq-iWjtqJ{m1*VgHPZDwl!fCM8T(xhCq
zq~a}y#@kkf9b*%7sH#$P+e?~4C@2$UdptIZVpI9}wnZan$!Jbv-yL^SL1=pG$uSST
z$maQP#AyOyeRNmZmc_Ri7Aa#D1r5M9p+7gem6WtIkL+nZ<M#f~D-?YJwRbv}az|3u
zBYJe-GZ5_IwvMSvw};hrKV=ktC-W<g;J4uoI~Hsnl1oA7W_cq-MO_Cf4Ql3H{chkd
zNDrMXt{*D*yBS``7G&S9`Ab#ZTV6CaF5~4$!UtZx2Gt3rUmEiqJ%tDCG$-?@i{xoI
zn1LHX%-2yw7H%m3)w`j(E3y5at0u&?@H;ZM(xt(f_2aHL)QX44q2WFW;s6EIbRUXa
zgT-n(Vuv2QXTl+(-BTR(F}dhB-3V<J$d9}DF5kkIx}V~lN3amOkhC6AwE|6hJw&nu
zXPbJ<Ix_nxT#$*ZU*O6$O;wVIg<9oOh0jR)+m*v+BhheU2MW3-6NXR|+DhN`(;Xn-
zMHDQ-Rno4~!tGAK_Q=qrMc85JhI(@;0CfcUU)3fFeV<R@Pa?0P@t2qc$EUqAaLFbP
zDfL%dq}amcDu<P#umVMOA5Zdeyn5lis>|D#Ue`M_9=W6>(;t<e9`O$??mn9@*IIwS
z1!5=ZI*I_(C<eIhdczfszhku6MEGWodj0BcXi<mGj?|r72*{MVmPw2(u3zQ8(Z-_G
zr7y|oJ_z5%Hb_L-YgOZ&HxdNXKh+tcn;=*Ebt0>_<pV_ve34!+!0&?+TDH9QQ7K06
zWCJ(eXHNhq%;bfvJo$$!lK_6nGp|ARt^Ax)2D^5dbWu*x@|HhBwTf78ub=WTEoT<0
z_1>%6P55eBz?>eg=-%$QeTL}H{ii%+j$8vY@DXJflHC`%%k}JEB&61=+?T+sU?I#8
z)G#5_Gw`sSpAXkL@P<~85+M8Lb&Ij{`0rm|gW~DL84g>1s2!5|bcXa$KRx>nYm70(
zAR|Q3ryl$zIg4qf;G6N1w}XF&1o;LLcMFEjMC^|JELK}n5lS%KfEWud^?xQ6gJ2w;
z1jU<fIAut-Y$%qD?5ukWs6y?JL{B#+kez{f&-Z>ZIfbs$*%z-UV#{@(nL@l@RF=d&
z_149ui=cSq{hH_ls*OlC*g=Al0=H|iP<Ezcn2MLv^6}t!pZBkf7IrZkpBVd%#3KqG
zFC;}jYeg70R_vIhSU3&_|2Tg;g^Zo!k0?Nt<=b}mYumPM+k9=?wr$(CZQHhOYu-$D
zC-Y&F-KwOLI)6Z=&dt5Q<8yO;H~8fFk0J54Pi^TB6XAvh`YEI!>{pt#>OTQ7N#oN|
zXd?kugJ?UgDT5t2N-%I!Lvi!Cpu!go$OHmGOls@m6Cw1L^R)RKAVLz4@7|>W_w@)t
z9-v2=u~2ex{5R|7>lgG86#GL`i%CCAI*gw{gU*1}YuwrJ7~C<F2u)P`YiX|Guh-4x
zzhaUYCjX=Q3>zvCfo^|bBuqB=Dl$08Z4x+9zT}c^ej9`F%C$Su_LG$Miy9L_=^R~v
zVa2g~NaDoW2D=-lQxm?crUl}XQuKS5s$??@P5}Ki9X7aTl^T#ri|7I{DG5~P1i1Cl
zd=fehQUtp2Zeow8hgkat|3Gtp7?fSCY8j(v>&LtF0r28P#N2>g1*^U~t~fMZp`V{R
z`4WXEu<9|)Hd<k17?09Pai{4Y1$cuFXP+m3-W^VLz6nRKWz|w(Ti`(+x$@?_)l`S#
zdf8NIw*!wdJcXr_lv}*L4)r2$h|1)J{5lP)cA$!f8o)KW&0y^O-4t7wAgRE9zu@}w
z>UP){=Qw20K*HfiIXtE5jR6FJOmeajH=nk4$lC3kK055#R&oxE1|4C|l5iyK1^gnT
zHuUn1n+718Y&Dq1S+R`yc^?Rz#^)9Bp4_IYXR?6IDx-xLp)%XuDF%r#?+Mi~_iUs%
zSKy!(fX4ux*T*H((#1ktoLcTR5ZgJFGYMDn<ZHd_>%NG9`l)oN_f}{b=AY|jee2`(
z(}VzgSH$CvB12;6H*f&m7RtrJ8uO*#Y3{lAoE;1TXRLwXY)VGjYu$61f8*3P<PGY6
z_VLUYP1UyH%&mIc4v53+?{qPck2=3*aHH64GsGAcgP6=Jv39orrbF7AIyB1YAb{@|
zia2rn{Wg?*=S+l%5Q&z_H~dL#G>l06(l|PiJNkaUP5!z0R9M?`VSA;6Go3))@CTOe
z=B7BVAyF^PLecRMN&tp@JrenCB&|5sP~xn@wQLok;66Tgm0qVOYl<rro-VJ%ds2ev
zOOQrskk!>Q)v<8K!rd$v)wdf0v`)KGBb-^x!7{6=+2csXzaahAl$+PMB{>?7dOnog
zle=E&<gaEd#&*K*t`@bx%=n(y5E(`@AhelARyNIKkj)tOl0GA`x5l9crHs*{<4?1q
z=!(~GaM%yWsYNypYXUE*4RxHXY<7EYd`f1;^*6tJ)b*|_Pg0_a@Jot`ZtE50+yedw
zwyf4EE85DLHXm&s>0cbm^9Iynv9TM>X)U|5d51g1<(&S|&h<t`s<KF?+#B*;DEitk
z<~+nOp(N+3+10<)==qObFCM7jUSqDWb~22*ZEG8R0xVlNQ66<8Ol<7g=W+56I!weM
zwKCQb%;&_f;DOVjFUVse&*H?%3l*Zp5v!hrMdYn~td+)!la8@LL2>iJ%f!J}l)inO
zSrtVkL~r0~K@QUK_+o*9eyCkAh*TO?`4}wI^3|_ur>(7Y-*h<9ELD*ax%}j>>*P2G
z?z831>M7gwluczFr-#q?6wh(U{nJKp?)+}tctJD~r3f0k_>6n!JQ)O6P10OsM-qRo
z_)C2>13PG50v+svgBp<FWQ1;Pn0F|=`}<adZwTBCQ<szYX%3bO4qKox549m52Sn_5
zJ8J;%6s5i*>3Wa0djCxY9q;FHG9pZJx*BU<c&)6eu9BELs3NB|Zq%+KQ`>Jd2}w_-
z_&CepsoUo@v(qod?B+p{*ddPwo1{WmWfc#f6OGnWx{ZsO0AJ?yG022q)W4y5-MBXG
zIE}^H{V^4Ev~cWCS<@#{$Znt$JCRD8;KkYJ>sKb^ttMb;%m5_4#D)8C@ZZSx@h03!
zX#9z*2|^cBf#0ViGnw?6a8ugFI<KQpy+I58bCBy_C+M1Js1==-1-yr)Cqzk0yn>F=
z75pbTun3@2eCl5!v9?`@vj#M^>ezeS-u2BWSu4W0!8;|c6qwWNDk>qr6;*WRJ%dKh
zdSy1Ey{dvqA5&-l*N=(NtD{D%jA(vDZ?5$ypgVvW_85@}JnMB=w8}wjd2_T&D;eL4
zkOTV0$fZ9QusH%Zp+JbeX2#iL^C+G64yftS6bj#NGSVj-w-8|}2}`cvP_s&ya(O2^
z`~xd9u4bZE4ay^OICf2dc1R5NT7UBY0@%T)$G0`Gfa2zcqEmLaGsdTrGq6x{vWB9Q
z#b==Zht&}^cW`vVXQyZQzl2_VCKe_J{Qp}2Z>-L+=B5J{8<O{_+8&KdvGA6yN{zBe
zgRv)-EXw!}yZsk^fhdwIMT}s~fmyGQoFOd;g<_GBaVxPOuyo<PH|E)hF){KKBTbs&
zNZnYMz9PMZ1d};6Qlj#CdckO4(u$kW#HGKYbfKcZ|8T4!=bnfW*lqv9Ss%X@w+~FC
z^1~G87sv_7U-1(sboAjYCK&JbuogH*brXC^KeGT(Cu<gHRR$-dV8fLL2vk;nWhX--
zA02Enq)qx*JfYb)c(cPa<v8$d$!JOVX&BDbB1~sk58!$UF)RY$v+g)xx!yC0fX-OG
zNK(KJx1>U{`ZOe2K)5`DIbH<*GH`jy1GWPdr@nBC!fX7!`Mg934{(mT<Og}0fZ)(L
z#B1O*Ndf=@2o`+^A~VKVKr_VNJ-F>AWg7$@9~=-A{J98z{%8gM1Rwu0P;jcTybu^+
z_`Q8SXh^^&IqNZUIQS(cI|F>csF<R*AZNoO@Th2JLj0QhAe<2Y<%~GcFEG|P{xwbX
zDPVi}L-9QWB7aDKt0xzHhJPuDS$`9r!-tqkkswEa`TK;1!s3$lbH&g46T<UippB5t
zuKoxEKkabDGNAtp3X2{QC`1Nz7esghNXsS_A|FV`Cp!)<o|im;mb+6DbAug3S5(x0
zqxgrfAqx9G06ip&>aE#Pi|RF+JvlDJRa?yWn(6tpxw|UL*phdjIeLt4s>n|kYSEn<
zC34{@zwqGh*3%t%Ch4TKs7Q;FTeWNBxK4M{LG9%;t(`RIphAju*J!-R%KqbA-}8+W
z88c3imK9Z7LuQ;{(j&E~8jYCI)7?>H!qQ0@`959MMOpfsl|5TQrJCxyeC5SW;i_MT
z6=JIA210<zKCxbvQNL)=k|<rGuMjYtdEZq-Ycjkn3v0c2hYjoNtE!{q%^<L^r^?!f
z3ataN+tHz;=-Sw<RlO9~Em@&GTXA_%kmnj5BG&qB%+kHO#>w^~z<{wWjR{uTdexAY
zV{KlAD(b_Py*gZE4a?Kho*pxyp%Yd5+?M4#SQ9zl8yV>UE!0i_9qQgbWvYEUGfGX}
z6ZDaa(N?#4VKP!dr_>U6??QAacc&15!{ozW!-u?zMhEuweeFE{hIj`4m6x$%uBRy%
z1ni?dGv8m9r6e!Tay!vzU$6T;%nPZ^-u8z*Xcm}0EuSQCk)R>Pb>(<e*O!Rxq#6&5
zz2JP>=)oNs!)-vyhM;~2zIkn8T$2IDZe43k8bauyNy;eHFAy;#G-SXwGMl7%(=?*5
z!qep_=p1AEeZC4ayKmMDsD%z5!&i=}@U#onX(;K>T(&RvS=d&mUym>tOaZ#0-e!<M
z$t4mP1}iiSzhT+!OS<6Ps@~x>CF@^45Ud{$)otxX#bPl^=cScL<R2eZ>C@FEGpEV7
zen?-99}e8)(b`uG-^q)0nRbAg%<MZI!op&IjEiYQtIsyg5hs5x{g3<7Mc;l?VpZGP
zy1#X=gRq47UUi@^_&0sHxNOAUTd)mn+@3n<BM2@S37IA2>>)9v>Q$~Crn3R>ouSqc
zm2aT0$%Cx@C||N>wDJ1uCR_68&Bz&<2pU}Xw{ZNy+oMV_eP*hFQ5O^dQ65}=@IoiN
z%*>Gw5_4_H1P3;VK%G4*04~so3|7&TRK#ojurwW;f+YpGoQ=&w=f`tW&riIL!wg0<
z_;a{&HTUT8EyB9TaCPT@S@5r!@k|9ahHnf8j5#-YHuK^2sj^{g%$5REg*gnM6S-08
zDQ<vJabeZHA|!g5c~eZk3z@qP_s`Jx<1r^f+nc9?G6fGCS}arjUM+vdMfsGg#=BjF
z)=yLg+Gyd|=&Q$hJjRhtA~7;<M#b1eS!cC4qE!YnxFNWlEFH?ZA?JTU!fAX{4Ywu6
zIYN1FW`)GPui{#tXl{FAmkblZWjw^wp+=~H=13JXDa|u;KP3rS!XV2-L<XIeXk`Me
z0|y*jU%!S}s)G=4Iy+gSsjC&=(Z?RJS4*r@y>Y!B$}39-Pu35PFBWOt4=gsDJR5P+
zO02xCS9cr!x{f{U*QGGy{E|$-2F8`R0Q)Ag{R29S?6ta+8ZBnV>YJS-Fr)*|W80FX
zGp3JB@@v<n3h>dF8M7YO>>m=t-XV<JNkR}R2B-^XJkpkEkR_IlC^a$zhl7GBvqI*_
zjm|lxoG`fn-hob7We0MYU7K|#y1re4C?;0=+^ykycv$cNO~)qtmo8>$*tAM1V#HcO
z1a=PE?0y=7pijlSD`M~z3*VOGBGWFrHl4S6uO^pU7bsD77bdys0WN&AE`SIIV+gYm
zc|}S|KeNiPMzW>A75%|^%S<W@4rD9Qy*Kh*dcE-_eVf%#F3w}!BV#Ldrc^A(y~B&F
zWVAlaD0{6bGc}AT5h5@iEcN`@lD{f7n^YE0Wk{Im=RM6&A2_inlcIX{PJvr`+jnEF
zg{O_gSm_7;5sLrggOR(O`sg&3Tku(VUdCBysKj|I$097b6+Y&Y|BcLQt&Va!;X;G>
zIC^?Yf}_FTBFFh4ILbS7n0{mbKr%q9o(^t6a-I;3@Tpbz;93XeS|1@7l<bMGI>LiO
z)gso)+a&1Dt;#wmN!boCxs9QIbJOQg30yh8DbcLB$aBnGS^UhRMS83Hah!vf5lL1w
z@u&lqGOSksB6j(1r@3r;%;7~vrzohd7^xYE*)gKC#J<G-kFAbL<*sjD-&F=#*6$SQ
zEdy}M|61uS69h8faE8i)%aSz5X>ezF%%5H)tx!V2lr-O9FfR`!mrMbjq+lo!O~R7m
zbXkhFl%UR>UOc5B`k%s*x`H7|<9H~pb0-t&UXe|yw?J#8rW2^d>%0!2rEGO4P_y|c
zXFf^x1(Z|hb`B1*sAB6+;9h(OQKWkH+1ekVZC6)NcCbZf%0)`mXObr)Ctby+forMh
zRsbEy1<cjdGs5lHVyEn#)UwC5)Va=UVF|Q}mek;yOKUpl_5PQJIalz1SK1qEx=0Z9
zmQ;}f*Ud@7`Oa(O{7A3O$%47gOJjwg2CuR~FEe~pyEG?`M-{k5Y7XgTF6rgrgdP0~
z)+8f1rRG7~W&Il0<ljkct2m?e{+-vRnrYJ0o|@C%RUTiNp2)(GSV;;FOX8}yLCnx-
zkY6+Ac5Gs)9WY4R>Ape?c4n%vbA11qfd8{UR7|h=vyn9WWZHvoo$`vCKLnxRIb*Lk
z2I@WUUe}=0MPNPd*J!8(LqfHiz{h|<G>eVY)j={LAQc0)6byUsd?E3Y&UFmIj47e9
zpWIbWfhZzCvPI(OIQY|4!7_tUP8;G5KJ*F~T(ztP7DuHu>h8U<F2qdYM_*y)<Xrhe
z=KGVbpw|?~XA%eUsYp=gBYK)GprL|!o%B7|um3=Oe2G3I&J@gzo1Amy$g)c1D!x;{
zyw@m+S-2kb*-zeB#De8J@n8rn+ZV$3{_Dc`o?d3in~e}$1*4o?vkwF{x_a{ecNkU`
z%?@<{>2LQER2ieDc6M%~q!!K`IJn{^9XK?(Ysb*WPR^g&bA)iyj;0M8lL(`xCdX-L
zs|p(1Yd~iI6QutMb^nBce?qv3SFnu8SV0pKL*#;F1hpg;MI(82(L_O00_Q&=<DX#u
zPcWi`4oOMqfj4zh=a+B^1%3+G@?6VX+^FE9M3@E)L|E^9_I-*G&1<;c2ST`t;T6oA
zop!3YeJd&idE$R6r)iF~c|AngBtDoYNsOWJ@}(CH#m`@kg_Af-N5XNEzkpL*(ARuT
zpj;S@bP5pUZ!Tp@+E8NaeKsrRk+x_`YT)~O9%J!QnEjHDa8YzsCG@oPx7I8Up)Lk4
z1_Y-tcqAtOTtUCZ+`Sh^C1dk-Eq=tyQtbM8AV!oJ-GM71snX>t&JBsH<W_!M)x@6v
zWK0?An+5f*LVyNSFsz4;gtz3~*(pUL^s0jo-!l%Qdbt||wu^E;4uQ1{gw>l=!q@{&
zpwvTZRn(3gbw+JaV?MPcBPfWdK89BNEK+IpE`PD**!XGEfj1+%_R?adDm+u<X_l}1
z<L)qa7q`pvSpljm4F%27<U!>Tw_|;fxS$(K$H$|C>BEP&fa@P11$_89)4|JLg9)V!
z*`xjxMAG)sfDzZ5uRAU2>7j_W_6O1#{L652z-+xL#(e)fs`S>F^w_<dug46EiLh{?
ziXAI9V*LE=;NV}iiNG7Jtm$vBxq9vO{XTr}w;|pJuC3zA94>(l?EGDwL4IW<soDx?
zb~)Agv8J=mR+-KFs0_pYYGUSlgX9YVWw*!g{;BS+U~!@@8zy~2w-Fx{@Xltito$7y
z=e5KlmV(vNg#~wa`s1YoYt2j;V#=7Q#Q2Gb<(P5ww-7V$``gX){(Y<1urKA+7ws!1
z;%ep1c$T_?h(K#FCsw<FHD<>|=bvsvTj@@Dts_Y_wPO(hp%z@UL?$bkH!m-H6>3Wi
zyn^FQ-Jaj;v*lL27+;QpTCv%YWq8le<s+-^oF?bp#e!%~g;Q2k{qd2%#~qxl`dgC4
zOvs-k$hVKcKMB1DweMIzDZfLpXMt=#{%BuGv2SG=%1S;XQa&@Yqi?^+a>zb0-K@QP
zsa{jR(_~)}a_+;G=Hs<!kGz>r*ANH3TgvEy<t8JiT_?}SRn+d^#RqI(azDYp!?vk<
ztlta+zJPmrc0&J)b7cO<TK#YDC~a(G>SXp`Xx9Ic?7~RT%>Lh`*8hoSWn^GtVE_O0
zkX5ZJ%EnT4BoUpRjrOkt+uPgDhc`EH!JVG_H*laRH^H5qTqs?fr!Uu=hc3R)m$57(
z6JcW*Oa>-Ln2`a(63GH<Q~e<1`+Bz%eFI}t!18MI%8V_|EUb+Vafb5tbxdGiERBN-
z@?o67(K^-GSHn>{{@egEc^LdKW>9*@pmlbRj)MvS!Z_I7S=H3kI)F>Q3}$C%XTOsV
zau9t(mwY_`VA$#!Yry2cJ#E0&IM~;IFE#%OXJxAZ@&{0p!tdLfK!re1QC>(<jRTRF
zqAUX@_J83wHPrG;$PBMerr@8MKry=oDgsR7Vh6VRH3X!0u48NbC}l<)=~W+~1u*oB
zB$`{gxMj^NrI@?NL*wZ~JhL)AK6rS=(K`mJvtw;>{oVvt1NTerF0am9=L7lKn8x`b
zIXb(#V;i`sy`@V_2#l<ZEN@IMq}sDpC1D8v-8;81zxt4~L96Vw@e6$46=wohYW0=;
zSbar1sa;)Oo7e%}gL-oMCQF4x&@(wcFtfaRW_#787c;H&HE(NIU)TCV1NQJ^nj2VK
zpF%x5ctAU$d*R!DujK^%m3khDiD~)!d*kxPcH!$#=im&uk-;=jU-vU@e(}N98l237
zy~rmfvaz!T*f;Qn=fcD``uo%loc(K!@XTi#azY1b$OP`GMdybxhaDr)-~VhY3Gn=z
zJrVVLEBxb&fB%c@|C>AZ`75{eTQmR5IP~j#`ra!f)waGtiM|Q=yAAR-%LRrH{&t#B
z|GQfdeO+bM{Udk(%cF?=hDZDxOTSPv;;ThqWqYp%XCiv?ORw23uI>&rom8bQLj!m+
z(&GF1RI9VS19N6&X$=2WdD>eILSIME^vjl;QJh>J{mmC``U{UXw{z9&_)9h4JC$xE
zqI^`ueD?Fo^}Neo+`G|P;m+|>{K6+0?@Irf8|f=AER4+!$O}V_jST>%O54&)G&%va
z_cL<x>O1yEhcM||eH^WmUCj;H;X7$!^4|LPd;hg6`P<tcwwcM9_U)*ttP_1b{exTg
zYyLKBeQ|R2rMBv8`eQr)+y3)f2JD~1uhh0o0<N?pe;IWpz3OrG6)(ZC-S2~X7f(Nm
zCu2b(;@=GC4TurXwWT78=d2$!y~v%5^S5^v`xkp`4mak-a9#1a7_b%!@@vctEJB`*
zqLcj`E-)R&2j3*ea^ypsZo*pV%#Pj?`$m?s5<SVAA5qq|o?}p(ZxJM+9SzhVj*o-z
zJ+p*u#L%UXIWTV-EjL?<G?$xC0XF&@6XT{c7@NJXbTTruc#mb|MUIC^!v2}j!N-j8
zJIKq?RL+e-!sue9>TK+(=VM3Qr2g6xt_K;A&A+p_LX!@2b-IKwu3C?eUxZnUpXayL
zG*McnyPUA>G5D@c#POwpmgR9T_)Nff_1Sc@4p&>zitfN-hwV`_T=8r;Biv_^OYHF#
z_8`VK(sGa%b-Wv)B<EhSV!)9QcJU)A46;cw6=*9{BT8Y>f|DG#o<{@>mR|Pga!6zG
zup*kNPOtmTI&LBm)lkzJ-$n!TsZpXR86^bLGfWzn*tlFbZDrF!gWz_gmSsCroN-i8
zgvc{rQntg}Yo&?s#Pue+yJ^Qoyay$9`0;~LkOBS4NPdRI$a3YyB9$EI1}oOO@!<zj
zW;LFMg>%95#E&)2E{dw4IT1O<lpK^W%r=e)o7Zqat_V@S7as`kK=KCZ%}4X3hroWA
zuga))4u!>~&}6El5Ds_Q-C(l?OLvJetP_lfpe&x$tA4kp8NJT!F;=dH4+!HDK1wlO
za`Z(unv`n+F8+)G^X0=ju$T*x7Z0ZvrxDJ?ay%(En?<2_5&^TUa%;Upm&!4#Ri6UL
z>qE<h9VPj3ZYaZDY4~cNGwSJ&Y*BGZr1u2x8K+B1>3EBy9PneA$WP1}hLWNkl{Lgw
zfY8d{-clv`&y#5Q{bt}vC@Gb^CA8<hen<yWBQ8~~BcB234LAb@Uge6Le*aGbFB4%o
zcz=}(mKh)w+e4hFf0;Rm?iWT{=s$B-V5wFb-GBDW#y66m+h+y(<V^CmZt^;+Goz4r
zu6mY-D&EK7mOYA%f6z?@SiO{JI7WA5U72IulT6@uIg!8#fkC4ScV?#UA@3G5^u9b6
z*}b)5oA!8qA~T6c2`|tJ;*5&2wm0*j9pQ`equRP{2}#;7_|<(jH>>8#aND*am5*2P
zdXgdP09!S)`M!8Qf?eGFS2V-M#XNmft&cwvC3P;Z<IUth^tqHp{c9Z%ihFmzJxrQF
zDbd_%2y$!>ai{rTFGb^N_U;vcC!p(h1A(!vV67&I@S&>$UJr(3I5kO4F2l-vpxHWa
zVG4CExB$O9uv8)VuMQWITTp}R_tsCJD)XA2qAP3BOJZ$WUj!PS)=Tb5lN;oGB(%i&
zL_Ye`H*;46UzhNs9+?E7k@`s4AxD^k93#5bZ`KKoLX_PGWZIfjn&!I(I@3xL?v@=T
zvambFqSvX!c4G4-jpkq(j^KPesWE=9S6Z+CDw+wSk|7%qx4?5@dwc#Se<HwDO8?0V
z4RUG=o9#5A34vs40xUj8!ZH4B5WD6^Hn+=@$ll3BEtC>n>-NZ!9u-O&6+EvK;XM~*
zeU^CAB$X(Je#_%cnV`G)YTB|IHjSH}lU8>}hsF7LJ76fb0JD?nj^=uHD-+~lD%*0-
zDk?t)q19`bf+@frV>j+Owv0*aBc{vY@Sw9E^FI()4+o6^?VxrqR>RPBNGWyLUnzCf
zo4-|!$GPs-<|;8DsXJFn_LR?{uH&ZpGaj;6gnzykVi{=-x{SX8MiEq3VAqfauQFTk
z=XRqY?XfZ4;d`#Im`LGg(e+jmt9tBc6zVaU183<sf+K;`l#76HT4d5Sdv~dtK4`3V
zJj#qv33hdy*_zs-lMg1e$<DV*-(}TI`)2fX4jcNcvy7<GDVD1E^p|1>btMPN#AA?_
zx3uAW2Ee$#eKYqaNo3=-jX%yX^6M;a+Bb7#e5WU*hYqD?%r6kHLBTWh{td1`-Vn#d
zxI8cE9Cq_NU{mR?i~#WU5b{n)Z7yx?B_9$@P8F$CXWn^Wlzz#qcRawMg_bfCu&6aI
z+LMwHnBwb`ZIE(IoeMSK;Yx|f=0j~UVlkG{N~7}@Qwv8!i0E!wb@}NDOL!myDQ@Eo
ztY?i!Jvhkss|c4jdt$&*m={+V^T?A#_K(KM`x)vo7+s*dI4Q^a<J9^pDLO*3rEFWN
zoZ_$$St6)!1a?djZH;12=&OVbfi%z_|6LdTtF*6qxtFQHql=rDeLW#6l9?VEIe0s(
zN<K)%CAbXM>!9V<a9}rbAuW7xgYNg1Hv<eX$vih?C;K!*QT&lI0ZrqM{#dJmCi5}#
z416uqGlUDeNfwotvI5FAEwGNGMWvoQfxOLzZ41DdM_=`Y-68%&P9qQX_3s<9@x5eW
zI~zpbR~!A+6rio%kRhOeWeo@_hG(KwaG9BL8FG7s+5&VIhrtZpdch)1K>9NQxc^ck
zUFc%|kv9htT&mC5ra_i|qe`^Yq5w4<*7EHNy+|OE08`m(XVe@yBgCP{jLCz&Gd-kR
z)7dr2f}BJvm*ZieN~CG7#<(M4%C1|#4w=8dO2il8RG~r5T+CeqzQ0{0R>F9Lq$P^H
zJ?|6OEFlv%4cRfu=qqN4P=6t<a*(8&;Efy{ir1S@2|8w7X5&R%!9}d6Tv4JRCU1P0
zAq0g7x@dOJcBO%%F!IG^g0*jpw9%kIIcN@JC>wf%Sh)+vmfVO1yTdqa6{`iUQ-Z&v
zdadoz3C;f7$Qv&AtJHC-6CBRs0?1lZz9~*8(uj>${FE_j>u`IExxFuSD36%)bp=`5
z_c5wJWvir24?M5Z5&sfYElTK14{GCXK5@?2zRjN`OWj0}r;1nAxrZAsdAlw!3GADx
z)owBrZkZ1-3Jz%}$Y_=7AzO2XqgPB%*zbu}Kj?=cUI*iM567Sho!AZ<z62b#!xF;b
zurJ^ffXHDM$zV{Asx;TasO!iHPP`wCZ%SG*qY7ZtqVUKFS8|xStf%|@xP44zFT!1h
zX<Op3bONtox!KHR?Ij>2pg+LUke)mfoRj|cw&re3>>t3NMQXcwKA7B0Et<>b0lRvr
z`Bew<9xE6oW4Yxe`yo3-1Gad)oRO;L=m2a_DLok%*WzF2?%661>j&b&X6a(%to|R%
z2}T}46uomN!zPSYIphB_?Uk7;HKJi~Aa5$Bi<mW%1>(whj?rEXW98s4NaTyAdND9q
zZ9qu3WF*uz2=`kl+k%6Ayb%14)933#t)qI7qFQn%n1-;MQe_=c2syx|*$vlxz3GX~
z5lg#Xh$sDpHP9Vh&oH@AL((<1Azhs73!=ekw{Wr$^cj!!3$2@vcd7%k&_ejZ=%y2G
z3!bP!q5MHM%Hmfwc|DUgomkr=K|bl%kBwHBu*VH3X9XFpJivAeF!k<tN&R5TGd<_j
z9TAK8r0{zPO+BeSynPfhAVzf{Zhw$kRZHc@J_UYH<u2vWtgqL+J@l9^txqievR9bi
z<M7DkN=Jv`5lR{S5q6)1?G9z47%~GkcSL?2a}c%XN@e<)NHwH!j8-L9go~S`E7Vae
zOvay9blA$S_OIUs(Q7Q*Snv|ApJGk@;qqbNv;L-C$BiKKY6f@X$<W-zF#2}1WZGic
zUvcoC)cb+kVE&d|1i69-5#Q6g1Nu8Zk6Pl3UkMQ2)!Bd$NE@5gE~b`HrosG^0|w~G
z`Xl8^<uG;bh*BeUYAHM7KWn>dg56{J?7p7hfoRNnlCa$MZ~_pAVd`jfQ)VZAfqIHw
z+kb~Zlg5P<x0&A))+DB$K~eAer(Rb=Q!ZSxQ#@oH$mob2yAd$Cv*q!GD_ZRPZ*hC!
zD$k5QF&Qg2!{SIgMm8>2H{C3;8%W705%p%GAbxPkO=}|+Tcl9u&mm(d=bT>n^u{}^
zSAl|w_1IL;*oNd?h?TG~(DWH+n5fGd-%=56@mms_!qz7rlNwR#T+2rnAu#8Pqh6y<
z1=Z~hl4{iP^jlAYwel&-Vj_d0$+t@S=GO>l+q!XO$8^3r+YZlMNPe>#{I_T>q3`5i
zY<V&M6LhxP@68W>Hc^j0!)a8E3hv%t6Hfe$1f9vUGW6sx<U-;(gXeNSo)Gm`oYw_s
zUX6ra(<5BgN=JYX=wZe@XYVFAXv`h~B1`fQXpa+OaOW4%XDS-8G~Hof{>B^O526vN
zBT_p2fez&vc^)Y97;=DK#OpH|wefUx?dW;r!<N7vPd>wM!MO}eQzZJmhe>NQP>tJ%
zMrG>d=r&3mE|_T>DKm^6y<?os6nb{dx9uu)NKGCdyItPRlF7yrYjcGy^3a~nAt17W
z5J?dkvce}$v}Vs*n<y&HjH{}AKYTT&D4x4B);la;g};{u6Z{hmzlsrDP~qNm3as2!
zmMfecEMg+Ljdj$usSK?7!;zbs-%7MUQc20}A*L(O6jf|58I8EJi!-*@@$vTXvj6@V
zbjK5?j{;!V#!C9<nI?{4_Ccof=009qoVUqQ_2Y_3%Z5{ctf$~FMQwS?g~l^N?j7%F
zfahxCB*wu)M!D|O9(>FALvk+9w@r=H5u0Xtjfa+cnL%+gY@E?mEr<gthh^+Y7&zD>
zr0*j5ZlX1lIz3&=eD2Afv3Fy!6PD@<s`^E(W76y7!x>EkJ%haN-#K}=@H59P9o}#Z
zQ}nmYb>n&=QCckxP3_3n8rVoQ;0T8wFjgAW4oyX<q1rk}+xEmbhD=iZoDE}`jtY?e
zu52MY0}0lAd*ijY8PNla#YocgX1y@Ae>T_5hNu5JIOBdQ6wOZ`kAnEy=tg&A7ujw`
zelnfCHKDqh$ehfKXCOz#r|{rq>LJF=xXaNFk;_Lz(fI!Q?5{+m<Lf@Z%>kJPpinjH
z4P=uzK0KXVLIT&nW-^|0nqAWLFWY2x%m$%0im6KpB0T#iP}lZsgl;B5D!i>cGJ+RD
zV^#C-5~tso`HS5dob^}zhMvj?0lHi0WiBeq5DBgNB_gRF&potW{=jiy&=3h9yS|sP
z(TPn~h^NpG-t-%Cy4FU+R^`egFMG+C1RoJQevH{d714kqM-)CaTq`#IXhEQRG=IAG
zFT=}%eJK0;y0L{m7Y+<-dfDWYafbtp)2v6r50E_d>k?5yFER-VKJb_;AT-F%k~j87
zZgx!r$?0rWwnCik%JY>fczNV}6-tq_v&UBd4K(0JZe{)PNtDgS_|Ml`vH198*JqM#
zM_a__@8m(E)K^~$7-a!R?u*%&YCM)VD?xa-*Y8YsIg3e~OYdLch=IUXeJ~3ZiAW|h
zj~B7H+s@ni6)xbH0v&#ytb|~8#PT*B@-Q~=r5xu3lem5AeO34e^!XFk5SFOrR(UTI
zGjM{F=EV<V_>kP*ziHF(wYN>XHSsaXD{rx6NH}tdVe-@c<0%pAtV5jyGgAJ6rV?d@
zK((8@6L6*%%A?umr@|s)5<qTkN!v(_d*Z}^yN=`VmCnT=RbzfH5{w$T=}-p^Vnv%`
zGhxLx%qLS^3>xw1he^sytmU7evrBjI<}y);vM}8^8)==Q)t}4qqhpq1w$6Wc!5I%J
z*OpUn*aP>zP8WS55Q_@14+)qyl^Iz>JlqnQhOL|CZ#1&FZdsSJKv}c4^07ZOMszZG
z6_KlD$((z0nN_pIUT(Q&vs``C<T47aK?f@}HbGhHD}-EJ{%lU01HCXQJNS+l0|wBg
zeH+p2`wG680gfPj8iyBf2F{`cxle!N){@kH8UFikarze4a<VTw`@yNSoYdv5btSsd
z=1p07M_fr=mFC{Zj%ro;7IIOm48@BJp+QVM$=J{>yp7?E;x7305N2-~O@SXHyTYt{
zp;K_0r~z1(nu)eKAnXu2-%w~!hbh)iK_C<B$UFJG2kbM9dSi^c9u65%3VmHqHlq=G
zQ*3bXo9_5~*fhmjUH>MgiNAxgDSg6%@#|q@XrwVo@ctyE7U06IIOW6#^)x)j&xJ{C
z&@>sdaSVaJw%y4<8usULeje<F*;LG$fea8CsSzR|nn30;$#m2h`I48TUcJ^pxK@>{
zp2yAkXeMjgY=wHVKuefO-dRE$6~6wx<_YR6{#m_^R;v#YB>P#kD3USH4|De99Xz^B
z+XQFU`*p{Z?pdvG@(K;DF8DeVJOz+oXhZgL>MU1ASQ%YT0r?lW8*EQLTdr>n7$0~Y
z#OXs0>!L&8mDxdr=A1urinYg5*F2)Z{M<sWymHfrF}XCv1cxK>%#B+)j5!Nf*Qrz!
zkYQ)avD5+8@MX>F{Ri@z<yO-XjmLiEorLY7{uJRC6@r2!Y%4R5{95cG&1<ehWO&7`
z;}hKNB@DnjKiSTt#TdUWI=^58`Kp_ivs7d4ab9>NGpmP5C6v*y_v09)?eu=K+)_CN
zgR|I5Ef(VzU$V2;g5Kgn{990WQZ}NO8qtzi_py<9B8)86Dt}}1+AXW7AW2gvKIEOg
zdbCBMqc4uxG;l{up3psd=;E1S`o7;HC>t<M*X5S{jS3ov8bdJ`-51z`dGfaf%D{<C
zFx*+q^Hx=Se8uXg{%Ux?+bL)v6LpUFoTy%*vF>fF5bfEkVi`&D(VG|~-Iy+J3^B$<
z1Gvn9zU}AykEwNea2NZ@@gS^nRFtLFx@UR>nK>=jM;YacTU2#A{2EG>=l;SU-K5}Z
z`V~CaS94y@7uhMDxC1MSFeN^N@tr@g3Wby40n+oYd_oH~iE%w*2|KB?5P|%@KoT{U
zJE1*P(+H;IMNAn(2SKzk(tB>xK|zOoL|IRdW$5B>-E9*?Yt3&$QO10Hnzy8#&5Dsp
z@pVjCSEiETN39DsHc8(KWpYU{)0I9R9@pTmQ)xMMu}OK)^(1$y3G+kuO!{b(*QiYp
zQKczr!1Y5uE1WP<mOZSun1qoY50P!S?8dKoauYkANl)a*dBxGRxU;(wD7FvVJY>Qc
zIraD00xzy1$<iVv&zBhau8QN%HDp&x$tUf+<#?{2^;FVV4Lo}YNIp+Z3l}HE>XgX_
zkKTZ-mSQa6E<(n!l1q_ssuFeiGm0mY915{PKfvCxl0uB-%NoK(Wi_X6&TEvLIc5If
z!BiDdZg9iZ4o<z7PTPvCqrQw>;Ypz_VW;Q;Qa!YLX<oU=9-~N@Icq$08QlC|t%R=Z
zp&=Bg{iWGOTtZCYajWp29@^NtnHZRGdrl_Crdz5g%6Eq{Q!bZ!Ef*9P^2CAlnj;Ue
z`ar<;A4<F6_i*#;B}<*oP(A9;CT|gkYvo6wbuEjkvu4GU`rTv^^da1U3-l<;05M+^
z)Fn{C>H4)*7L}9<%A8t4AWY{*rBcpIig*?ZsSG0Z+HloytLMcqdwjnqgH-+ft4u4t
z-YMSrW5K(KCLrTzYS1#Cs@LMXI~X?KBO$&6P4@|Lb7aanM<H`7Wu}LtK+_Gfr2-n2
zpItS=dJ*8KnG+dD$d>kty1}GXBtS^j6BE@^y9H<$#N-5KCK#B&4vk^S>_l9h>TbOV
zGhvyCmX@_$mG-G1(spaSAV|ay()Vits(p_6o;eTITGX_Q--b@A<+n=4BZRtxWu@o1
zz}d#l2ED<+BCvGQ#m<ony~1ucMvoBVBDL+`t`&2`*(2szTlR#YtVHJB)%hPmvFq}R
zAif}$;=Fjt!IjsV7^3FO6IwWeo4E9W4la;UAW?gw)^5S%c%mOBM+)>5{+5}y42Gjr
z51towFjHp<vkKEw3Dm(W!6|ZHq~amYJznYvX_^v$!?#eI9-AXx)tStZTFUv8cMOC+
zu~We9L!xSQFZA*91%B#qC_Ozc{SVS(z_t-i4FWU%8Z<m}k`omlH)YSaTREWS6XE5(
zAJdXf!@$(rlgr@q&psyfnP@xXOH^6^?dsA9{u=QiXl)tWTe4!C2!RADptaS8-2FOT
z$FmfzK<Z6C@~j~DcS{b7_JSm63IXs40SZLy%&58Ci2XW<3ah{zsQ(Kw*y}j1+`qq1
zQVd)2Zr4iKAf4FbI+RSQ=-xk;oI03pm(D1ldoVid<k;cYEoi8*n`iho<A^+s<O<s}
z_B8W0X$653Q@}l=hre8X8E58@psE{>PxA9>2t!ijtwc2Mbg|BFN5NQgg@Z+#Q2I{d
zw0R0&iP`BQVSjdWCiVPAa*6tsI!w3qg!m^oi(bK!Xj{uUU#8-gEiq>Q=W@+7ht!sd
zZ}37c(OeHquQ<rCuZhE`IxY(>k$6B0kFYb3H~kbjq!pXnKU}XMgYyujkT}~IW{@w;
z{L|*_$E@Q^qi2N*PC;M+x>hufrA<(KI+qaMQHuXHL^+Y-w2uof<q5}f-Ehc*T64C&
z=|@Cd&v1^Vzca|cD<J>08tci*;alHf+@PV%Ux>HNoX9P7o<F{FGY%27F$_}9>|4n4
zBEr{f(=3wZG%_yQU01h$59LCG;XplP$f9RwED~lhj=V&bt-5&NF3h@hkT>Vl5B>*C
z3gwUdC0fm2=A}I@rq`i#y>^V2h$Id~ycaC$Z3bl~A$PPZG9^nL_Ovd&H4>APPQqr*
z>eRT3%b6s71(z)Yj__I0HT*=g-DH;4GvQESd{s<--FJU9w4RyrURNBlO2;$TC(YhL
zgUd;imddt$=wFS$>@Baap?nsymfbQ?)+^Edru<B>k$T`3WZHkP7g9mSAMpZxYXR=k
z@(jhmPD_Vr$8Og*Babgydbr242#JTH?XDKc0c@l$!KXP4DxjV3uf2gKy&5spJ@r@5
zSREb<=UYm{dtgND=!HC)z%Re9otE7^lYVM0;lDf+5B4=yCHax3ncpdtxVaw+9RiYu
z8Q0xex@qn^KGz8iJVyR%<Nsp--MC+HfvGRIh8^ARDyJ31Y|NBsBqq_oF(5F_tkSZ^
ziDw3&iCVFj(;6?{*&a<Cqds-_iYf)+Pl+oMh29fVT(|-(J9&D9czNjnF}C(TYP4%z
zDkG%owAeQ{I}1Y~W^t>@_crS&%@ABCX`pIzbSo(T1&iA1A#=Rpjk9$Gse%=0T=ml!
zes1KIO5t~G-ESUQC9hkPuZY8}7Jc54i*q<9b*IK0Gd~FDc!b7pv*>5(1*2y3U+WFl
z*MLGrQ!~;@2fMdu$u*7-r@E*ZXd<s?&hf&tv%$hj=TzZpc~uol$uJ^>?>%k_b@GFS
zs~a2(v0H@&N$p%VtFmL>XRTF`{^OpV{Gb;GYt!%t$q%)6_J`QFS*VK&76oOD#?211
zq_6RG(=(*&pb+2xjLgHF;s+9#?vzSk-xL%u6zcmT%7a_UpqktUu^u)o8z7VcJ5^wi
zy44IdGR^)?^Ry4Z6ezUcV}LIZr`0d!4bgp}Ss}$u18=u;uM>PF{Ho~pdLbRO^BdcT
z?b7KAI8N;TYk{2AdOBuAqb-sX=M5_uR>|hlx{Sf5m$20}TaRr+iaGnTAbTtP7k%ps
zYce<Bwh4b6mJiYcy3yhfpCIKLt1s(ix3hkm4n!=@HUT3TXZc~#_H#84@>AtNaFBRY
z^gHIJ<Kt?5Wl96sd<oy&)aw^-Ouk9x2|{d3)6&C<{M@epEaQ;N#eDAd3>ukJ#gt%%
zoc#6ejHD+N*0<0T&aYx>*Cwz3MtsgUvh9tcB_itI`^|Lgsz1PRFPz?ZKa9!Y5S<KT
zhp`5Xv!5H|j=v#*uE>U-QNto{$Q$0t?Q2W%pI9xVDme5EhK7q%;aobuwB6#wEK)D4
zkD(#s1i)NE1AQfe#hFO)(5l_STtAt-Z;(2pR9g_yqSQA$LRvPm1;Fg#JTIMC{@P<m
zptgHmr_V9$%2g3th6GuV`mSnc#(T{@nj!l5kvBv9j=fTs{Z!c<aj78X=-6&1(FZYm
zYCRcR0a5^1vfx$``Ox>nJx81*hq2Ddhf?U)s~6S<ST>NFWut8h@6)RL0kS7WpzD&5
zC>L=DpUuzOf4aL)WqbKXwDo|KGZhyat;FyU+CDKIBK?(oJK!gGvP%vLeK^4)l$Qzd
zLLAD0CLUHl4q!Gbq&x`dG=mqDNMT!`>RcS_a#e?%g3^MFp!!gntPzRV-coGM-|39w
z5R>!_d!laDNIDp~uu?c3tRcEDfs;l>nB#{{*nN(TdgTw}ac_+ZlvnxRbbL%HCSdi#
zqWmw@YU6NItmv{8G^`D9mm#JP1BfSi^6*2Jn?RA(ry8T>b>2p<Tx!iY%R~-Chkv11
zsQ_#kU(|`gXvq`2(xqf49;NE6JVkk}OHM!Zp_?xpg}7-<pVHc!q(WE~&T=5bgI0pV
zfpv?)yLqeg`F~>Oh_@}Zn3f4G!hdmDh7s@IEADj~qg8UDu_iBgGVJ4-=qK>C(^N{Q
zh<hGpDLkqPi^(2fumUXFZ4qC*^A#nO8N!DotR}u8a$7eta~#ce%M{YzketYCKHaJy
zs-1`%?eiaKPtkR0gtt#m>aTsa6?@=ION-i^s=N)a#8WP6DZP5xzSPrW9Pyh^zaG*t
zi_g>9SsVx2<egc=7qR79t>_5ES@#Ttp&W217&rAgzaq!MgKR8c(vH#Ki(#STcyue8
zsSN4&QtZXq&~3-wv~5+D0NV(HBZI%BaNMitZb&8BhPDjaP;=Cz%WtD1;NP{3^vf{)
zT5JeM!Iw-pWGiR`W_GUZmw-IdAB=#)826#iC-Z#pE*d<B$zj$S=0Z_XRNIO6p?UL?
z^g?0r{2`*J%!z|2xb>w;xb@Ug4t$tX*M|gzqF2u$bNOSoJLt9cz8%HFZzz-EENGn@
z&<QZ;R>i@qKSeRVaF<hhW)aueEJ`uAu`fRlKC*(q0tiuRmu3$-<aYFSilGJ(_ra6u
zLe6R4Hr&e3?mybLCyU;Z0j&S{481ZUIe^xkcU~3gb(a=>q3pPIO%M+tHmem*5xW!Q
zj@<pe#ivMFx7EqAa;gTva;}7JZYc%|%8xW6VPfiA)*yxlmpGYs_(QRFs8vH^3c?=r
zh=(@;`GqM9M33Vt5iJT#R%?h|_;IE7GB%F?l&5WBkH;EIEQ+VVwZK0g0yb~ll`sn^
z?!^hFxi1B$CjLFpmOgGSMJ`;26m3hL41PgQR>_q?lZ$Zk)wvk^A-NwL&7j1@b7hi6
zu^-63e0$T7E<5uq*aPp6FYLgE=<l0e{R%#-?A`UlXQq`yV=0K4^G`Jdli9&BpC%#A
z=V-SQpn`2(pirDj*PVeSZkz)=XiY#sVSE6dv_}%@jrI@NVQ*A<Qim)-2)fPSws~AX
zZEgY30`svp)2c>5-Mhnn;$iuACKP8!<kYZwvk@Rw;{dNlSiX$TL(%n!3iM%3C}zAT
zH;0cJU0o^FM}*IZsZ?OP0a*PYW;;sA*kbUO>IfTsn{ERn`i>HxwZ~Dwe+*nYbBQN$
z)5GZXP~R#PA1f4$OQ5jiAO@y*;CFTCoz++VBKLQF5c!#<-DZK?Qi71FyW>1RpYZtp
z%h^rorWC(<a=e5>5YM+a<+q1|Oa!{7#8Pf|-0p{$hilP`S#mmIwl9aSMDOgn`AVJ@
zmH7G>4I+fk$js&&mVnm~MywHe9DT~t74KHuCfRSZF-J-s1ho#hEdA3MP}$3^&;u2z
zNrY6okIZhcBSr`Wx(za^OrvfmJ#<R2m55O!xhk;u`$*4-YO^~xK6i%~_i4~tyW5VS
zgsyQ~fX_GaUr(XC%&)oBTtpszyjC9_{a&P@S3U6u*t(OSJ0)Yk)gi8NA9YCJ+CsTp
zNVpJU@;^dE&W79%OgjElYUjasbS=ceo!GxBU$)1b_J;86oJ5%G@e<gX7fGPiw?ce|
z8h@2%W`DJ`sgbsmTTxBrS>5S~wDtWo%or3s0ypvy7@mXA(6U{Us->Cs(3@1W^we23
zZQmzs4{i$@6r_LE@F^}_D2HW3d*^+I+gd{4<jktPa-hfV9u7*x1UR!Ga)es1(lm)k
z#fI?fkH|=&B$2V}Bu8A%zh=H%P7y3)`|28R<v{cVyvzE6BMQm76{qMrr&cxNBNli#
z*RvZ%(=&!d?mQ4*2uy!Vy~RkHVT1%p$8<aY4k{Om!cN5R8GELAZlfRBkSHHue-LuM
zrb1b2T9QHI=lSL?ppwq;G5Xjd%KbB?7Vm2vWiNB^!J09(h2}&5qcyU7@OksiiD1aq
zZ*T$G=!Ohi>dqVE*E_md8x}b9H^=rGvTO(wyiCJ*&e$Zn!J7E=7LMiES-n7bWMZ`F
zArTk?0~+e^ZyWFWS^($ejIVH_h<bWMg>*pmK*gmsQkY%7XRackF_IWH-Hb7TI(hXi
zkTg0SB9%Arn6UWJR!!l)hc9{{4Pg2uI*{(bAF=ki#?8;+bj>@#`3?wHX~BLLx1Er@
zF$ReGL2@T_u^j4^LrcQ4=@Pu0_;|jxrHy4tqo5X?^C3P)1uX#5r1Bgv7d7V~q*q2k
zJJ|u(=_PQ^<N7dL!PFHZ;9Z}e_h~*zATgvbRJxf>>Fp+RYU%8%d2$iqPB_}DK((@L
zR50*&h{eNF!BKf;JD)P<x-fZzP;3KLJ|LA6L%Df;*xpstJruo|s98w|6pw^+WSDi&
zT1=IQd~w&P24zSKr&?~b%=)(eZTjB0=|z*EtR!_u0OL&Z1kPW@WS^y7tBi5Vq@c=M
zui>A=7ovqgE+czr2f^7Fx40q*?j`n#{S&XanRUUhe%U1E$Kl8*hQsH|%w<}%9pmo}
zo<`6Iah}faCG8uTT9yy+h`C!><4|ZcUJe<3b=W^g*wmwE8`;YnwypA4+2g91(z{*X
z%OeogW9)hO$ch5^wE=ATVyMuii8bjT=~g*X%6Ae=pTVs<X839_O~O9Co|S4fnm56M
zrIN-NRhUh;STq*MM@Wn{jgx^`XhM?TS6aN%*~N072Cz&9jlL2I2r*Y;J=Px$Zs<&F
zMA?PtONuq-^DD-O{y-FKrpxd(2F{~z$N*$9w6y;2sUZ5p`SS;u$5{c2sOWc8F{<k1
zz}e_p(lVSm=^*QqvVrnZ&oA<b$isKDCLUce?p0m7rv$62p=}23ag|A~jLZO`)w{(O
z$^gWepJI4kJCwRH!}y5|K)CwW#tRI#66RFv+M(wxSm^b>q+fEtvFo&S7=^(e9*f+&
zk>X|xh!i?-2V6*n`s7j*e0t206TzzkqK08jZ{tst`OfgZE0i_ak#IXC^pB*M9)Pc$
z^HeiMgN$^64i<kzgbxVk&vB3ogH5?&(qTnOJ?$tARxcDsvx&ijXQ{o$)BpKT3^9=~
z8K|r-zf@s3TAGR$7?6_eix6D`Tw7n4KeDVS$JM_44o^erzN#s5kCciw9?w$EE<(v;
zkyMo~t+%&%>dXRQh~$0kr^P)`8H-idL<U>3N85s!#uY0t6~ZIl0YlhuJd1Oyd69kQ
zHv}YTkfQozVux1Ng(N)sWDKGcVsrG(CH0}cCUc*nq-h&A-|a_Ke(-oNovOZUpzUhl
zX__nzpUuhOB|3)&g>4Sx(!#Q#w;?W#v`aB0%|MibZGUA->Z(lCvR)b}(W+Ha<KtjK
z>1~aAN07<{C9H0;)IM$diadrNfolg95sixFzJkzZa-kN`bKTgSSwbfVDeu^s3EDp!
zp3~A7>Djly(_f0OBj>la7Xrh!hQmL_@t-~&6-Uw2it*q<vxpmMJuKK&e%H|9v0`6`
zB#=kA2+&!Hw>1*von@!%HM@27Fdrm#&J^w+?G7!lSu-<^1x83NIUEdTnifI9G(oxt
zvw1ZS5-3+|uUH|JfRx>O`)c^UN~~Bl9!2le;ZH6C`{xO3H0Wca26=0(vvoHiu8_iF
zaaeGM5}{!wu{1029D)weZAlR_btXekzB;8V=$5#r)_gFKUbGPK44a#4#!O1@6Soqf
zF~6CMw*TrF6&-O)L^ZX=qb1OO#=-vwW9J+rN))B}wr$(CZQHhu*S2l@wQbwBZQJhN
zH<_KyWHZTbQb|>%PTjh7|EQ$SIp6owbeFzK;QL{Q;wz&={)8MGmM9HyQwn~%(c&%0
zGb*-(WR&_KB#Y$RTjD8!#b&~>sFt_Ztm_(nh4CF7epWh<D@N<kP|M@YAyhDG;FA#H
z8>X|)`cYPID}y+X1C}|kumF0Q=UeaLR~zoOKg&K_8To|AUVkQ7zvJAea%hV@oE7vA
z=~AyrYA<hC`6EZfM5wj-r}S1+tOmSFSQ)iWir|{rI^Tb_!yr5?Qv5RQC>m?KQJL&d
zhK?5Of^_^74;Gi+^!91wtCl6MR^;ySStGv*>m`-X>pTxTDdxj<-eidSg5b{Gvw{Uk
zH^NfD%wPj)20Y~kYA3~s7HKdlM<LC7IsIOO5^xpp8EERcTq4pMQ_(GkM{8hwx^;Bp
zXUKibs+s+2mRA5{D_cCPheX@VsoNdu`uP{3cm7qjP=-gaq!aZ!z3=Uy?T@q8_bLO-
zF5QX>NW7XNTUNNYiD>`}bF}@Uaf1qZXEri@5XuMm_toPRkLjN{feEBQFh>|_;56c$
zuw4q_(bzV<7@*hp%7}uL9>_EMklW)1_&oO_=Gy#4Rt<t?AtLG|TUB`&Jeldu%A%?X
z5($U~b^Ckm-jBnD7-NLBE@Ye=NE6;!vMEMGgyUoFWHyo~4}rHCL8$ByE*uhjcvu2<
zn8AyA71M&kz}t_M&yFMqZv|z5Ykm08{>RgH5)1U<ps;h~NmSjG+G7U#(CnjyeXo_{
ztv_lnyp-arLp+C>43C?esmChKP4u67_h_v*t=sP|Xoq5PqakIz-YqAa`WGNPwX>IO
zL<Pm`dXdaswB^ZfiHwGl>fg_PZMm~T#iK7eJl;jAzu^TPATV_3kBD+rDv9owBL`V{
zm@CAF3AqySIEQbkZ{SzHtBa!vQKW*o$4)m99CeR2;t@*26+PpES`6_sUF$W~c+`n4
zV9f5vF@b?8=L3(T;K`pJdc7!9;5vTRpv0dHf4sL?@Z+~2SRu?$=>}2$4kG{`YIDC5
zxc$Sy9D2*M6czE5sJK;g*PxC%Tud;dkPPRm;wp7b;3nQjv5|G!Q)Ne=#dE929co*Z
zQt6YUP3Hw_Qgz$>bpx4BOo5^9^r__xQin<L1Yz%q`lsn0<GtE1)JcwNY~}Tj6st_z
zt{D9qc6v{ZBEQ32(SR0g8O|O?)MrtV!Q4zj<3c_vqKOVawU)Xn7;^k{hk-BHfx5hH
zIO>QY4nn$*ZzP2AC_7yxxa$Y%;Bf`Tmeo}B(C#?cUfe1t9}idahqVs|BRy+m_#N<$
zY#jLzw9uK74dYvrEM7Wn9u-qGXk6+VzS<(x))-8xS7|sX)i=dWrPP)pkH_N`gaY)V
zg!}1SynGf}crITDIgjB(BxfENfmb!MYA?3)inFMO%9!i7&2rwqN+XoKvYai*vf~FS
zsl@4VFv`#6hoL})Bu)Z6;&7>hU4kg^(~}C;78@esLG@h*Y}e`@Vco|Sla_Aw?=sf|
zqWe9~TvyIUg79lqg7{`y-AK1+L`?P{(_OpQk;5cmhxPqKFoetK!2dQe2V#1aq4OpE
zN-5xn%}vuJ5qggz%T#6nd$6xP9VNL>MPO2lPPrWE40)nmd-X2|z+1?~Gqp$t2-)>U
zm<Pp}3rhhxvUtQ(=uS{ow;Z`wI3r-*2a}c^n7DFU#6}kaQ!+Kcit$-fH}W1=_h<3P
z=d!C4ZFy(Th+};9yUNp~JIOXt6cPoj8{iAtLY#f7z6~!m_}<Bop|u!IXqB}s&XPzL
zMm``L>?aUVdm{wv^?<*p)*y1r8%+L@ESU~L!R=E0Xkd^HvJNO@V~K5wjC;f$;wAUB
zq3a7m+=J-FPPddGj~>coO@3Tk$Tt9DB5M9_R)Gy_csE306yAP(LoW<`O$->cNV{O(
zzu$FaAmeO|l&my&bscAmxXwB29$>L?REG^qS{?*;AETDCv=}p*4|uET_C%wJ*MySL
z9wM56>P*Q)blej60wvi6q})wVTsTzA48fLw3^>-_H39v`QOs%bUm?~#AnA?njH6#a
z54|*x>SAvbC5G=#r>ik$D%4jh+8ht>3@5)>KgSi$he$_~A`?6|VID8hyP!4HMY(8U
zGJaJO6?12)TnMp6#CfV!^0YT+rB!c0>p?&m=r=nx=T*cA;h;Mwc;DhRw*2`Z9&GzX
z*1kh-hdwaQY(a<^X2?|{K-5F9I6&o0jEP6Oi|U;1Zt!aIsy^Ue<60rd>fppIkg4Oy
z63qlV;TDvPIPTe#P1~c}4RWds3r0Fw{uuE>9(LD37)=QKomPWyXmt&cb&5o|=cP@R
z`{BcP=|{>B-|tJ7^Ip^NDTObn_8U6*HoE1*W(S|BJDk{xo$rWzm9xE1M#zhTKFQES
zJ{s-_pJblsY_t|h5@d2ATUXupW|+^8JFof<sc837n-Q#nFV%FMHR_Mk11dAm_P(*H
z8+-dHF4&d{gkt!4)jamDayyGQ_P!Djm;wtKcaskY<Bd9hLcg%GYct~4Bn#)*jMR=1
zf+~#A<4VIbZAViQ47n-+@<MxtHBbn;xzq|dgxVKAcSsE=JmMssX<8G8p>$)O)xlPq
z;3qMOkR7L4Q3XIMe|SBs7aDf@D|xAOtC-~qv>9nU*P4TZqE3^(ooO$+_dlnT8qCjX
zf%t}g)eO+(g*~9Bn~Et4Dcc#_KUXh1$1!K(*37PP6Rg*)EQ_ig_~Ms@0&2$SjYyRB
zg1ubvuQ8v6Q7mhQe!BM+X<us{Qk_zd5p5>nj9{bIGtEqT!p<Ivck52N{9sf}<^(O8
z<^13#9Dcfo!&l8PQORQ~X8ZVVmpgTJn4&yJCrICfnw3K~+c0i_1YobJMvRIOxmGJG
zw<IZK8`nj$p<-L7Qtt@#6~Z~(=f3VDf`qf=UWbCLB918~Qj5|_nSO2IQ9nu~s=d?K
zw`OQwi*)O-{^DWeV*L(WAFo!~XgdDBFYRn9h4Sn0xGZIjydyR`P}%=okGg$~b!FTU
z59LXm26}$#&&Ja6ZB$V#S&rGpN6p^fGeF18k4>;lQwiR<TCg|y+~$8+$8>_(;McxT
zPqMB~imO+ZVX%fS5HbF{1%Hj5iAN!%h66lU>q<XFFoxUm8qZCy)NKrtC-`XgnbrdZ
zbzRIrd4`IbSu;Xc;x66s!O^r1k1mP5<h9(^=T2y5WGNIv5_$B(Ikj+L{##P-jBlAp
zJBZ4D+|1TZJMSd%ixdRmTW@wf67E?3R-{B>b0FR9d>}k(LC-PY5UxHKsySv{YeqUn
z_{xhpJkoYM86t#+C^0x+*Ao19cK<JK*F8UpxLR+|DmdNueSfOlWc8tIuA<pW*=j`N
z#LfmSzKulQLrLD+u%q0PCq5EkYvT{aV7kigG9S%Lwx1;mDyo}-D<!2BZj641QJ_a0
zoM<%CQwO~-538O~H+7h|d?|+#jx2byUjo;jO+e%pLFB;!Wjxda`8Pw)f(m=Fe1^JZ
zTJS1h+9k+66K4<>hKgy&XF=A^Y8K?W+YKu5jR$627M{&Vn_lcW^yX*kDNTtgZj`n=
z+s6Aw-v+_zRWml|zBP}I9cmC)N^6GD$CdChb+P;4BB`77{D5}llMVJiv~FHDZ`Pq+
z3_Od7f;h`M((+ial26(lV9Z$x!>pw0`e`{VC+zXW!7+$;fD-%(=#h1sCXj%YsZ>@Q
zI1wIYhW40&Fbd^04rOgw9Mrz^5gZ%2T0d-*OjBoX-y$ul5gf%D_7(2dv%0G<bj-sz
zcfugGcFKTU2_f4ByRo~%gZoV!SJm{MxYSVkOKig#0-0NaRd5S|bPC2_uhWd{(r8Bq
z>GbTK%g#mfkFihYD$k9H^J(s3IS_%G8({)&={N<qCs6Yo7X){;_P=bU^i(r2$e-+E
z#vkAu+#en`@=76!xnUqHKi{WU`#Zj<Td~pEUq;Vn^(MTA2`x;w5XeCvQ02h{^fJ|M
zQ+gsm*=coob17k>SoPASBAI3Tnb?pqLZd6ZU>)M4+Jl_5s)sQnA-tf+{6i{V<zyyF
zwW~9;V@PTk6{K+UTvUEWciqEVv@po&%s&x;vQ1Obs`v{fY+<{8mknfkERUCI%8p`R
z=BUXN(6YA31(6paV?3OK{uuWoht}>I!CZR3tRqX~&<s19ozXrDrJy860y0aTTv5jN
z{kX5J6Ap2LP3Az)HIXs>sqdEISHxW}YNUktrw|7TRy)88#2Csl2P1Vo8pO0X=o!V#
z3;rXD`F_MsnoXh3U-|rlrzz{X$z&!<o*YT9kVdD`6W<p)Qp_A=5bxDu4Q`Y?uR6-(
zz`?wj)?IM!TUp9Djv0M^F$=dT0dcDcW?YC>mfDE3xCEYt_hnx(yL)0gU_2Mck+`hl
zE>r@cM_gy+P?~i<F^ax}lk4Zu4_?bZmuf0%hO#OXu#k51rbnru0sSUgUI?dEgfC^l
z7FtckiI7cQ<K!#Z$M1bIh}`c<)7?`BElOPE4f1N@uE=H}Kn?7)!2Q0i6(A<&ggNXP
zSQ?F;g4VcyDD5O~dVt~|g%G_giIQ>?SMQHE*kF;9n#3#|&9DaVXt0P*Mm&;{_>kRa
z<d#ZsLM}$#C!I7*IS&?K9INbUnkHS|6DP{N3;>up#2y)g2)cJYuqn8Hwz{O%8K5?!
zIX_~h@THn0Xh8wFA-*$1=Z^0(`PgW1^M7eCQiUl=7-T2d$0s2|yUz*Fl--54S!qM=
z9QrCsiM_-cGHv=+7|Wv;4r!5ybwu3?V6C7$Rim=h1bFfzT+f$$r&Den+B>hz7%Dag
zwP2TqJ*8NQ3DthwV>-vpu<K_Q51Z#v1=}`?>W1#v;42`HBc7pU19odCOT^Gj{`~~a
z;uk+oq`TlC{S))^L=un=+0iQe2Dk71;1nwXn<k$(JufHLD|)TzS&7+6MLz+q_u&tV
zscA_j>fKMn>t+N4<;a1=M(8(BM2Y{UuX>e%TjTS$2cbeJEr<Pd4}2N!eM_>of3i?s
zIkEC(#-2B&dcxA?*;_9J_-mL>tofYn`^-c&-`~RiK(<^k3yD&(R7e5_?a1-t@fWGX
z%v`FWUF<w<@yOYN%hXLK0)ekw@A?)zQynPmlfiRg3?pk^j#b8CBHbfe9Kwy8`4%;J
z4vSjuotAxuUG&DkbWsm!GqrjY%ox849NGxaDLb4ZlFEpKHtXCOp%(yP$6M0<LDK!&
zRQsX4xo3X<G#IrokAX$GSEelp7pe!SqV`-@V69lbY9*KU$)~Y@T`WYdyMaQ3Q_x8G
z=gUC=%SrkLQR`SE%tFFH;mnYUa?>(lW+;p2v1|{oPIlgXS({UxMu6X?<xu335pN+2
ze=5c+z^sqUZ&}s-N+S91x$GiUm_HCEuBn0T#AvLEZDa>MdK@HW55f#-mOm*5`Q0}K
zvxF&sFjF2l4RaH$I7I8F_o)NDjT`}WxGxWmX#v`NRa}53-H3<^f++i&Pa35t*~Cg?
z1$UuA^N_{6j97RFD{YKp1dHT&QQYX-xA852Rmq0#RV9b-o&Skt^ZwC*x=izmsK@&Y
zSJX|G?ui-on+rfck&Q$X^94H)T3c&FyQHddr%EKMQ~TAULw6FJRV)s0<HA5_?Rg2h
zVf^sf*SWQ0IhezD%|hfg3B`;39GGO%Nvvrz%?1%bKalQn+90`D)R3y>{ho^H&*tZ0
zzDh=hcoL-cnj<i);0-+DY&$<t;cIPSh?G6SzPWJ-I_agnmhvqZ8=*DXEO(n3o!T=B
zU54*jdXZ$aPQ#qLy&F&Z7)RNR%VDhw&T+7*QJJy*P0$~XnI9KKubP>Q%@$4~IDXUy
zDgnuJUdRZNxG^DoCJ>LWDS8oh62goCr60^J26wK1l!!;l)pyT8PLcMdwls8(KyQ6K
zG;^8dy=!&v6H&~1T>9o~EBowu5cx9E><LodJs*{8q}r1u&D#mEVAqO6jkFvw(^7Je
z*p|>N*|AbibmhGgr?XDI>wS+%5jdM5z^0EOo)+l{ZqG*i<nGz!Yfw7AV}#;4txy%u
zO#AAne&VEB<;BckKm-k6F)KP4e~VZIV5ZVd*=K6V8P&fLQme!GEJ18vg(QS?r-#~q
z{zjpRG?(mN?`F$i&ye`Q6q>CQ7%lbe$2LA67<Vq(V=@_HGb+!C2xN;TdhG3vvfs^#
ze{SWKFQkU)3xK&?jKN8DlgST7qVKfG-$O1Y3O9?V-{>;{(aoh&@dH<ewt{BDL{D$w
zB1ppmh<0_DZy8O-QyYU4uAba@^w4v|Ad#06GD*Q^>sReAsd9<-$lJV53re*wL!sL+
zNz@pp(^RLJ`n&Dp%BDHfO3UxwR(B6ogpn6KyWrT+Ny$mnRM;d^8a7(^HD9y|xXcia
zV6I?IzbM@d?W(?1${!D$W~V}CsU0^-9d#}h?-VEDg&1(nk3^cErm^rB-<dFFzR)KQ
zUL0WDwzVZ9qPWKy$&hDS(cf+1&`hNUB@9FNEX)p|@T)IS+4-KG1k#h{(M$>RHFxnC
zJld%1eUUW{1*oMHyr7&4zjl-FYDuuxC3<tY%?_2n$2t(~f|^^+;w^eOV_BJ><We}~
zT7)NLsNAL<VV-09a-q>(|242`kRjtdmsmf-GZ>K1Ri~JPnSdS9iK~1v%t@|cH2$lj
z;m_yzY&Z^~v(pqVq743Pg%;mtw1$*C;cyoEJur>Twws1@wliWD>m=xf%Pg-eQhrs{
zRh}IXyx`P#2{;QLl2u>8OAUW5K7}PB<e@D<_RKd<0UlKN&WXBwI9=W>abUPh;SnmI
z*B$Yl%x)Aov@y{T>|Lm9cQ4r~In?=A$v;NUuIUM7e%{gR5&DYIW|i{!2HRMGdz}3I
z76K~Qsg$P=zU%nGOYfOub@;IY)Qeo7N~&T3Z&%$7fm%8{B%~a<hlXpf=z$!prB1PW
za5{+G4{}tlrmdHmsk|O*@j|6vXg){^npg_6dR0tW{vA;@n&&SrnfR%;z%r)xI2@z`
z3J=K3!Ufq*+O_t9U`N+Dr4MNUd|-mPADuqU2{osWeNFXIr>U4ZT5Ju;nZa5s1^<lm
zEaw7na70NclvxJ~zZKV2E(C8^azY=_E=69J52;Ik&(8hUK4TbN%b%J`C-wp)pu&yr
zCN@dEoQ2q5dI6-~Yd$NZdsIFt{e9lOR}sI*75!SzzFC+c=H{q>MaCBCTa160>Z|Bw
z>MHuORd#zz@#Zf%N@JjqPwq{pBw=c)LG=-g*U6<iN+t~LJ`&yTdB7y4ZaK+$L993v
z`Urr{$g%VXo3LR`B;*-15ue(vQ<_B5T>`=kF~{UPN09b|gxl-{rG~n{?&n_rG+V_r
z3Rsu2E9;Vb1ie#=&>~=xi%a=`K=guk`l|u8nEV8xgs9UQpM-UOc~7<Tyx}r;X4T+<
z^R-i(F4ds|Lh3(mGnaW+J8*ZIXE;*JIAE^$ZdRn*t(+@zQ*EcRer^<PPs#)Cnw3k(
z`})_ncG)fF|Ea<m-ORh2MyVMwL5JM1jgF+dJV#=uW3P+1*xU_K`25@lY!PDg*fF!@
z<k19gV#G6$??4gVZRHq2O?FWnQ7HY)!!(3Sd!d}VIDqk)cHY5~VMA}*g0D5^&^~ak
z21?Tsn4w_Ncy<V-m{lN;nJj(58P3ATDd<CS<ZSJKtPeO5gdSxZ{paGmyLGop3wg@-
zcQ&TBNlpPS6OQ4(Rs6J9@g+DGAzO?j;>A@D9}RE|Qr0c<2WlxGGCuL9y~Hw{*E`J5
zt%t(5ZU=p8!{)Gzo{2F_f0;MEMRGgvS0MvVamDRWwleH0!=l#ttjS|;Z$AP(gTMlL
zx!mF`?N8wILuh$W*f198JBE`mIrH#~<6%!2FQkmo5t0n3>)?vlS&cfF%0^sj5mauO
z3yiwEW#dU{8QC=JL5>(>4S8LQRwEow-RF&*uj16rKIB=|=j^b+v)1%dvl{K-$>1Mb
zu`q~QZ^0cMfWR^U^le}fd@S^PIF|w4zys?pTq<di18l|9MF(C(#TT*qb1g-VTGYqY
zKcxSz+wq8Kr+2joDHLdrlxxI)u-7P+ymf_E{PmH1i=`*SmNX|4r^%q}StRkRAb=D4
znB~09Cho};K1ae9Z{c6G6P*C3xcqrpF;4?|=REWZ(5Bcy93G3$%r$+s`YX+Fu~Z!B
z7q6!LDJK?*%;M3+`!o~Ry6szmH>hpy1T8rS9w!0gyyHxBF|WSzV)r{C--#iYc!50_
zLpj+nuX{>pw-UtiNRl=&c6>|+h3PkA>`!n?(*tbyXfkU2w&T8Bq&+b1tlR?GT~W<S
zlPQ<Biz#(M@RyZIW5%<*AxI(C;A=im-F6fJf1}eSP1o<o*FE@3Xj-wyhI&su%449=
zXlX9Ncf$j}+YCfhtI6~IBCb8*FWz7XC<g+X<Z7K8pjVzT$iyZL{zi^Yc2iCKl)Jb%
zkW+77Aw2*u)K?Or>%>Yb7)&<RSh9_Bb`|e}y&qgZiIE%G0%=s5T{{evv{C2@VCJSu
zp^I)aK`@%b4H(ya6T)=<S5w+gnen2Q1xviQgjl6oJGUdYy4cQ`t(eW>5!p4R3)~6)
zCm?3<ue6F2^=*BNL9)|Pf+rPAwfuVlQhjg062d|2Z9_^mlV>0(ZuxJ0nue41bawu!
zI^!Zv2z$;QGzgRb%Ey+KT>S1f>KQJ!V*fGjs#u~76HCzwdQgZmnLpPKzVx<4Y*=nm
zfVVH2Re3{r9)v(hiy=uVAAn2lQhn5E`T6){b7r}+rHW>Un<OVugogS^bc*cZFyLCz
z1$Z9xIs-}upR40Z6g_cjf4Pts7unw0TtDcfy(W9qr?t|?^4@$6z`jBea>W98z~`rc
ztgSsXfg&R&M{$+9#H@rM^yovvw3C-Gzf6-G*U7wt5uaj|1PH9a<ohWf9WA{f9lf2#
zCR#dT#QE4#HRRw!*A2cn*$B`U8N(oDC6HRSnF8UTv^_YX9}-z7*+Y7DYo3``)bdA3
zMv|pH<#j`x0J9ys!G(5BCVruB!;`Y|7HE8n3Ou?BoF|ogbJQi2f?nESwI7*ZZvrfh
zcDVF~1hEGoX-9Hxt~0e=@?ks7C#oI>7m|VCF_TR&)z<SA)eANYM0BQ4aTd5ACcB92
zRQIpxO~}G@c^+rD%<H>|2B8zidLQ=czPO!WF*Lab#u;_Yc=nDApRjBHbHy`+WHHAk
z&oAK>Gh%m69XBe(PSrvXX0l|Mjk%fC@t!dra0+(8K8N|Hj0pz*$lgk-cQu9sf3^jw
z=wQ!1((f9?Zt@oZa?!MK^h2@R<D|af@ARF!x~r{4s?tc(-NsXSZYA51*(S`&CXwFP
zkQ*v{iQ2G9y#eNA9Ce<`{{FGk5kK0GN{3%I$I#YhB5Nj$tGSMSc2#+OB9Y%ImN4Sl
z1f^+W5^%b#v^vZ#LZITY;$!WBc<Jfzm0oQp?4^_d#Cgr#Adw!k<22Xr4bD^SSIH#(
zl?q(^AMf%Yyne;e@mx{kQj(QG?}~t8h6{JCDG2u=5LxIW@1ln(n2_5pL=B#r8K&=$
zLRCjqqdy$9gO*h~!&}b2i0*f@<5N!E4%J;}lRvJqA6ryZ2j?EG<tW$3L*I@!4P&|a
zu}E2fBBsrKz1axnYMJqLw19He=i29zxJyR~9KoHMj*abD+mV9wiI$MLny!p{M!&?C
z@_<IVI*&3`{8xcXKeSq?fChbclDjEi?R2_0-z74aGeXNf5KWuearN*=9xG`9uuNjV
z?RL!W@T8i{ZXrVkIyC608L$SBY-48$ZqwElHr^@JNGB#{Mr`p!PG;<v<Sp2m)&JCm
zH@|i@WPttEjgn&9f`nh0`hFjw6b(PDpw%2XxMoIA5b=-ks!0#zouMJ5jT{r6j5S+^
zm0pJw*nGI8r8^q|_fnG4JuiM~9bB@;5cxB;05an7ykLxociI0w@^5pRf09lUwDmV5
zysF7sNOIKO3WT!_a)U3hNtE<+Es3I0eM0S)76cA1PTv`Kk&N)Ba*A)mJ4mL-=8=w$
zL_JWw-O$&y$tR`Yt9$>-S%ARmwiCR2$A};^xLYQolkVQ}P;7v+-eMO5TgT_(RCM%R
z@BYMEf2aUx!+)U4@2@#~JU_#E2X?thqy-0R;~;@Yg>C3Pvb*8@M6OeJ)2rYKaOsv=
zCn@C}!CK7Ac?>IX7%qM3dS5d_3ucJ*zV*ZcAy<Rlq*pS97myq^u|wK#6oXsf8tMni
zElirVD~C%j68A5`^WP%*G&2WP(0jMcx`}{c`7{qlHsiBxy%QqgfGn5Jt9^`7DP@oz
zkN3z$Qs%F;4~i`8Iq(g3p$)^zl6b5N3g#4~pH4vlg_ISum@~hLAY1C$J{7CS%+Nt_
zu7NeM2eJi2H48#AGLB|*N!-G#&P!LYm2rZ+P=t_C>-4{k8dfoo+@hM7^haRWAjQBq
zmaD2$u=iFt0(4dSGv6&~O$I+B-4sZK5<MFXOsapNb{6_m1Z<~IMQx+IIQLYsAyyO}
zys{x0`0lprdebU(oT7J_{9#wy{5H2a#q=w$cdz0_9)6-JbURJIQuqi@mOd4#Y3^Tz
zS}O2Y%)#C1IxCnh?#>C-a-sgR08o;Ft>bVjG({MCI1WmnDJ%*OE<7VKUpHgV%#lP{
z%c^tJK9!r$d}y5Mp_Au6&*2P{YfNzDZl0XxpkxWN3L-}gv4&@sVY!=?q%)T$Vn6AC
z2KwUa=4>G9zr&VucVTLICh{3gP&khlpxjeclwy3~NXiD8KJP%4%#Ufkxp?24<Vuno
zC=<w61r^ehfN&)|qM@>1M+uJr%h=8n^at49gk~XrTZDN6VD~AB3ibC{Uu?=`%?vu>
zU=AnmmCX^6=%T$tvy?G-F}a*|Kb})RI9^w{g}iaX)2@d8Qm-$bz(Vv84!_KGj+q6K
zq@%yWa(T&RlH@;G|BFF&RM!^Fx(65rsruGye)*CL`1@(-UR%=G0$NZ=g4F8ItN~&I
zXkQqN+(AW^h&WXxt7c16n?Lj5SLsp<fp<7E)*mC=xnFRD>P9~;bI+@RdGlxso+NLD
zGif8b3-RO8>KoNfe!2*rUXok7TPqJFx*Qw9t1N0Qx+1|&&haTWpC{Cdo=>*A&@-6y
zM;HL2v`Of;rP7oGW7FP}*}KN*MOuDF(h+==%p&%?8oy~gf81;ma2`i$#R?|gcXgah
zD%`p$F|d2ts!lJ#=}}A`#_zKm=`e1yP>I(RN+7Ph031V026U$`I}85ynlrYV;bOpQ
zv{;fG|FN&Fn0eV!5!a=Oi@Q$tts)ngIWI|Bhr&iY!Sdwir!ya*h{>_A>qb1S_|rI%
zhJaAJT2}gHvB&(I9b^t=h<4s9BQEj57MEQHiHz0s;On&!-fa^0wyi%LC5EI54<EcX
z(p65?$+g&~QO@73zs4$#6!?H3pPj=#VU$39SwT!{+33BM7GFa7*Vs65M~WZopDh&!
zJ4}Ax;lI8>CP0}OAZ)_fb@`|!AL;TbE)6MMJ$H}R^G`S7O91==J`oFEQ|~44mJGv1
z#VS*!24j0*>??)2);CF6f{Wr;8e@=B3r#dKG{%RS2Apf0eU2%;5xj;SWTSfC_a?A-
zuk|aQ@bUM;e&)xK5Jv;vms2?Bq#Axd*I{#XhAM8<<Ed8dV-g2A+1kzu#<%sTOuW23
zB<fcQs$4o|gcT@I1udCGfNPP6Ysw=#u)+&!Y`p?<wBnV?`oF4ZgQN^U6}kwl6r~yJ
zchj3oz?rdm{T+)-KID_c3u&a`AR53qlc}jj`LKK6gKN;eT%c950VQEcQW!F;YK?x#
zT!9tif@3pXsT~Bl_TMJBmqMRfnoV(gW<t=}cCYj{oP9^AfHW!?_x-*#gtpaG-S=Gz
zIQS8KL$80gb(xH=!yglDH(H*&YX*<-K?}lMZvT?YA5iV(JU$w%<@AiQ$h+4Yf>e|*
z240;Nl6}OUh<Oe43Q<WtQ>0|+ep#LNq&c9q=9R=a)K=gYvz=+yRLDc8twjuWRM3M{
zjf@x}!*s*t1%W+pvsef_M3qiF0B+o!(7NCvN1UDm(@?)qqXw*g^z)G~C81y)=^}zX
zyl@XLm~s&>c|+B&r97e|krhlqn{P5~t?L-fMR|Un5e`Nl)gc>_w}2$-syu~85q6SE
z0ZN-)d0#&|q}IS74~T&Jo=iQE%~d?iS>Q+?<yny{!o~|Z%tfA&pBPP!tSMbfP^QL%
zpL9*D;eM5EqOhi5r2&qB0o~hA5dUkx+i}apc(%Ma`Mg0$g0Cpw&H1DdeE8Z>*GsfE
z+hkP+^J=;h0!PDnCrM@Zbdv!2$@jpmI$-@>d8j7gb!F3iuuC9@#;l^pSk*)IGJ<KZ
z=F4PC?607y>FYqsZH*he%inSrpdAwX_(AEv&&fbanj~8uvB#ZHq`89bE32gdX?TkV
z(zTl*MV8C$Yvt}hf$AI7RsTn7q$Dp)BY&klqXmvhrlOqlE!yCCWVZSfjL<LZhWtyk
zwxu)OUtR)q(tOQ1ro<90NolM;&K!4+b*V#l!Fcl3I!+-9{q5q&k8a0X6t@rcCsAB<
zGD?DOq?julG`l<4dGI0AY!-LXv%MZXG$<)WxLt&{JEgcxNE5uv9R54KT${FMM*g>O
z^D@LIZ-A(STJ&kt8eV`;*AEa6l!la`y7&*ks&e^JVj!ZcGtg|S%U82#wGhpAq1EIl
z5o-#=>ZEXFj~R$p&1=Q)gtdd(az2$1Rbq221Oiul83?HMoL{p$A=)<Fm%o56;6cc(
zH)Og(iS%x3G}K0)4Z3hJ_s2Cx8`_%!S@g+?PmZlpYu*qH)0GdR(hOxBW071mFPO$&
zJ}QzR{v9xI%x1_CI;%s2KT!s&KC*fP#ys2veS;qzdd8IJT7XNHRxJ%V==N+fFsebU
z55MW1wv-m^cQ>EY9NY0*^Q6Bm3Uo=O@Gg3Y&A4sp>_-vw1xao2x4zl@<y~lVz3t9I
zg%l(4IY-cFTNciH5@#7xq0%YQ?<R;}8abz+FOa}!^Q*OEbn(cO6DPIDXBWsfSm2a~
ze4ceKb@Y6-VU^u6{MM~2!MYIq71yYta7G_zaGR4C8Z$+U*cHnuzNGQ{wxKFZ{FkC%
z+!_9t^Wi9h?I9GjvErOKft#-vH}VJ;G7B9!t69UPULdDuqY9&Y+_!SH@SQTl#Z3@h
zg^#wir0Q0vg?^L%8kwSElExZr03}s3kbqDdvY04ILVL}m(4wv*$z`Xc9WCX0qF6)4
z>n7MubH>BoNR=6_giE-d6~lzBvsN8hj2Z)4K!KEfa#xHOaOems!SYX-BmguHZDV@y
zXjl4mC}Ocb8P9I}|773E_CMNpGW}yd{HK{G69Ee+6X$=O{df0HR(58#|6c+`wVDS{
z^7RL~^$X4Vz}hP-_xh@h@h*|E8AEgiBQu7i$#`y#f{2m{bOvI>q})q*1F5vg1k#6W
zckgT0>0|EWD(7k3={wKsb9c>cv-j<1F5(IJhSa4{e69j92@o|cJ#i^qU8^HV;GP2l
zNGMAh+i>6>z+cS>F%1oTSWrVliC{noXjO0yAwmFP3XJ@GfOvIf<w8WTGh#(Ti}Y4_
zGy<_BgtX-3kI9Eg&^^ota-ry<zAi)n!~<~zlvt5>VMGn0VLu!T=n$YfJ_Ubt7&y=~
zZNMB^+898ik<=FeV+x>1GB!Y%r{KXp0A~!0$UcE#Ks^Z-fS*wy05KwhI{j)xlW?65
z3;+m!LBW0oBMcPq(7(QdN&qM**YJY|;ky7BV0$rOgmpuh1_=NdKrryZUsV1UuR=d`
z5Fuhnyk8L*2{7S928;$QFd?7_jX<!`KV`_5zym)MF#(8%01HuPkb;50e$7wV#0v<D
zG!UVE0CfsDFY0vi6aYYj`Fr^YMs`G)P_Uo6Vgz8JcR!UtlmJ*4utLIn3NWD(!bd->
zIlUhsU&^lpC{TD0+*t3Mm-wuGbr=*|(xGB{0qMxmq$e+-gcc@#wy4U%=tu<guVJ`I
z;UCD*fq(trC9YO;5y{a20|^lC5CA|fM2W2<hLDtjhJKh+On=;ne&I=e@*uwW6JC3f
zUvII#7YTptXCHUVV}SM@q~rOY+z|Huxpf-Ay}6F}_5B{eJ%sfO|LFGZ^5PcwsaOI(
z!vzP;`Gx)FuZp@Oqb8^&DSiw>4Px{=C_o^Df_?&Y2{;^7`EFh#B2Zw#2@6DI{@{@d
zXz9t_*r(^f!(4^Ev3eKt6Hov>ygo!Qebec!jE@bEOsPER-!AX^3jF=i=tMw4x%-vG
zp^cCCqw%djJ12DXSFrRz<@A3t<s?0*C&uGp6@9PsOS)%I_|mtAMnHod1k^b;IXXH5
z9D2>0)u(RloyQ>RlNhVTHI4!$Bp8x?fghyDzzG*aZvOVUiIDwg(%HRTpn!uS^Y{k}
zawYtwJ1sA&+K~PVlf~iMB8mJmLXfw-k(x5LOsFMvH|jJ0W$LhqORU6K<GYH{#0l87
z!%5Ysx%HYzr->E2L)V2;Mxm?wZJ<jcDe}mwsOl5>V@r0lXf6lx@%4yq!U?W2Drkl6
zrkFgLXHYh6eu*3W05MQ-7_mxdR}rSQkF%N5W?JT%WQsM8jZKt^57!(0`oe2_al1o$
zzGr||Gu}y019-@n+B%3I8T%k$rxrL*5eK{+1ZEMh_n_+(*)37Suw~yfh}dcM<r_7y
z;OJG(_>x|%c%2q9c-loAJSwSLtd2Qts0uTPrs}VS2S7<e)O0z+m7@(BHfrYXZkX5*
z@iZrl({0pa)dE_)&m8Sccz*|1eQntgd-k(lT*EJgAwWK@)5nUCWsacyNXKpT4*bHR
zXG&2nF-G|Q2DNE)zT^P^Buc08z^kp!N6>CzUK>)3K!-EF-Y5OJ{ukPnMeoKSDR=9i
zj3N8huOuKQ7Van0t+eeZM7x#B3_KaPEmyIl0o@hs=JX?e{)z(cyS_tjhBEU+FA<%i
z?9FwZ)wmD=^{r2<1eR;0N$6I*NDFQZlBAlhaDanhiHl4V4WXiS!Oa#Bjx6D)7hcnf
zwjK}LAC8xct9nlPnj(okgu?r6kD1Iq^gnKDgS6D8&<C1lKqR>_Q-boD)V<bH9587l
zM0kB_yjpZJCnJQeuSZl@=k_g3is8W99ktK`C)_l=MP1wjQ3n@s7FVuK)T0C3gR61;
z5UhCls&V!i(jbhSN51ItP3YayN>MOM{Y*POTrM#kwv12VIc)kA(RRv)-ew`IHVMHW
zof!L;E+<imZYn!k9@fh>4QUz&D~7hU)TmYWkC><6A?cF*jhEecP4?+4GdnIFk?bdk
z(7i*X_|of>1364ijla{e&evpnk&nvr2ZpwxCHD0%|JppHmu)Vi=!{<rZnIVcU)=bg
z_8@nPL`QLpsad@I0eqRCAq&ASrGx1QiL?%i+3e}NNX}#)@eknDc-b-0I8St8_*!N;
zp@PVuj%6utnYg<aP)toTQdl^$lhtU|-mokb^zL0=D0OgQ4<CHrBk4^<3CP1Fqi;pS
zuW%1$r=ayFyUL*}7{S!1973>WjslyDKnW*_x^ElaG1lAlpDj=vGN-hT72fE|)qFN`
z)+Xt6i7Bz<4jgW~Nf&dus6XvK1^4|&n<oyN&RTPXj31Y(c25XgN|}Mu#&n;GfEB-S
z50ZVZOiV0IBUoYt_AE3ZZT97$MW^Inh=7?bTWmyO4IvsqNfKVWWS2=FZOsg$%4zdL
z;*i(_q$<R-dKTO($BHP6t%0kRiA@viTb^E2c5;XT$?<8p(LGnf-^FsaAwOL_)C$Jf
zRy(PaE=sw(bCi@U?bV^QQg~0!l*Z+fWzv6FpW(5Bu#HbA^uf~4qw$j=VNF>=Ka`Gp
zO|+yr|43@n<I8@p6FO4h6F9W%+?m#>%7Pa#p-c2e{B@@>a)KO6dM%Q2WWQ>LQ`2(3
z?l!1QLFnnYdyf%yb+kGGp8>LgC)Nt9y_?d(%&qcJF2*0Lof~>bEwaWHOG2_1(#+y9
z<;s6b1+;n7Sqa~}VJgqyK^9<E344^Ku5!je8OK6^=!R(b)q3>qjN47>r^ZxRF)PVC
zE`o$YR-mwe_2-8~yQSRCcz@G;Y*!o@Wv}UnZT#~#Q!p<<TB!bU5Yy@#OfXC#-<ew)
zCvXhGj~|;X4Of)B>te)uIFk39>V0U1|NUp_C?v|P<vC$y#Sor)np$s}ZYGS{wu$|q
zuh>KeZYKBVan=O&YGyZAFQ2twI=d@Dwc@1H2I}k#vutVGUG36bq~^XsgC5@AHY??~
ziDXGDutxFE_j_S<2f(;>VbcazOgSOkm-BVlQ3YF{xk{uGjiyb-{a51081hocP0ry>
zF8Q)n^c>Fc*)ST|bfSionK58V>*Z$NrA#>`mIFCHTF?Pr>s*-1XzWQ#^GYZ&TH~KQ
zD_r413#L1tBmX?JaRX;bo?_GY4%9<5^j1v|t>*l+lTXtSPht*+Us30Db+&Ys%i(|%
zZl91#RUX9SIC&PJCPd>N81{TaX@6SSM#t68cG%(pH}|4TdXpd#okvG58ZyUO)ND+l
zjBoC<)Gu%W8f2SmI2S$WDz-EU-JGl5BQl8nM?3YDQxD}3+Hy3^4_dFCWH|I%KN)1D
zmG_^?VAHbjxEQp<!|F%+X6(c%N)7vLy8)g>StRnnI{w&&jZzMQ3U5g`v*h}&B3M0}
zAe<<d<ru~{5ebNl&7^+OlX598W{zXkDXF&Mn8C2ySh$p33^*S}!a}Q1em!iqmWS-E
zF<R`VSCq5|{R=drH!IQST$CI=Lps9+S9hyomYmfyI6Nxb891eCRstNprINRSq(v=P
zJy9|k*`3@pe(2<|zqwbGrK|IKyO#Nf7y=cdvt~!Nl%J1O;a5H0E@-ZMj82S|pHE+7
zi&0Petsu+bWl4FMx~9FU%r{z15LGwrJZdj1<iMJkEMGP_W`3Ka8Dean(dylwmR|$m
zHWkxxee`Wbtq<;PYEtjRGg$1S*K1^Wf%kf~uCF#+{fwDf0>dvWN3kDTi^Sfp`V%9`
z6$OX9Z#br5%HMmZs|z{RF4Dt}S+9TJsxTspGg?{JD9*1^k}IQf*Okw4!rxQK6Q9CT
z(>6(|V;B<E<=eNIG)R~;F;g_=$wAK3uypaBtW0UpZPD~<<aW9R{{Bnn$i%9d=JAU1
zgexs_r$gFiV&XJwu<4!DpW4kWRGQyJqYpfD@Xh*2EvT^J?)@3SCo7+6#q)8!I>vtX
ziR7?<?LO_O;L00W)!6qq)x+o2c|YGnU{F$>AFdIF@?B%dVa+_jtYcpYrop9Jx?h&g
zlbz((=-bz~&{O}B;YvF8EzoXcxlK;doIMAO@U5~2-rXkHwuiiyLDU5_7eeXRA_r9r
zoh1C%m*|PFS4!tNpgQb%DSn|cf4v;o+7XRbzV$jvvEJYt{=y4C<s0vZOxKJw`$Q(R
zn!t_OI{a0>we(kjfLAt2TZA$*Gt+3T>#Dd`dzeC0cm3KeCY9K0B9k-%lx)`(=Kz7(
z7HBx)eub=Z19FhvTs5fF+)Yl=AoH?(W%DYG+)5#a7g;L6UU@W5n$y*k>*3WVLu=9R
zsuIN{eFujWC*66HYS)kCr`v@L{`h&^;ZR0kTR+%=)!L_!#ka9>FJXVKz-)L8CUP*S
z-R{@`Vb%OIo}eM{fa55wVfJdgTowSWj1Nn0Y^kDEJwDtP{L+BEHMrN;6E(SiYkE&&
zxE|}VDaAM6u??LI{r;tc&iKZ>tB7r}K}nmJ!Q-b$EZHcr$L^NE;b`qFqW!}of<c|-
z&x|b0DydW7kwB8Xgq<Yw1}*Pmh3Bb=y22ZxBeijwH!J(mrc)P$`213lPSHui_qMIu
zi>RwvX)!qSUb9WkrWLYJaB<a7V^OFx8dnshcTfT9s&R@MIMIFKj9KONwl^O6xW1jO
zUx#Xc`vn-QCLN7YE#!Ghl1tOTao4v~q4Gji&d5)W=(b0kbVuu{XD2aLsnLc?C_(5)
z;$8QMXB78kWHcl}r(j|QV!b+<x&*z2gq%7VwFI@ztlm3i68UP<wH)+}8tlM3;qH%x
zr|b@2Y1a-&W#+`lnka&v-YaKTd@h$d7-QV67gbdCe9fU%xJ|%)*&m6NFjprFNgAhr
zawvh`<(JQPm(oS+dutoi)!^>w=ZS0STcqTbBBt_+3yc^CwPUB#zt>^P7l)@Ejsb0J
z+}aqUar+F2si<(=40%>WQ5M3@w=4B-6)cnvw7`5`+r5Qs0fA_1Ml2Yt{MB=vfZ|wg
zSnl|=&%7$l&M8!P$7lGCE^0LOxTjbM8EPEO))87ak%US4CSlWN-9)WsEfGBvmiEL}
z)h7I3x#MyPz-5={NbydKf7Wr%FvOL~n=h-_;nY`QJ^)u97!IphLcRP<jim}y@C>r+
zzcBXilha(a1?i0ZZuY>;!}||7e1~p>mxgP+X4Qp9Y50jPXz7I%x9)ep2>r8-SDpO4
z3z)}wHzrb3D}DcLa}u#evJth+SXTM^#+N=7f@g{C!!N8A(|4Nm2)*ne-A>B1ws5?)
z=pIKe6iK9X=x^C<@fy^zbke?o&Aj}`#{Qzlnp=Vcl1g1%NhlBR-EIGH6ygdlQfxw4
zd%$w*ZtL>us^5sB4pet}z%GIc_rzo6zqS$AnxD}kCh=OWF!)W(e++emxV%>$E-L0$
z$IU)XgA?Hvn~mgROXs)Te$aWt;9);coZ(%jU+jt6)rgKq$l5X&otn1Twk7dx?_Sg5
zEPbkUo8om%8O3)Xm6=ss7ykUQ!~jlsO&-O($G!b2dQ$CN3HovOed0S+A<t4*4hW2D
z{)*Pw+KNpAzPv74iAvSaDBL~wR;O!*;+(B?5^ai!zdMN1s$V`+H>+)2Q8H!3&dmN8
zOJ85x&tSs#tYvsOC^%i6`m9I0{**`QqHjI2GpgYz4Tc5j+F&GU)``<i{|#jGTVF@}
zRyDQZ&-1z?)?^ABwwA_()2IJt;ptZ*I6-bT!&tc3o{(NnAIg|rE%eW#WHvvS59}?8
zF0PbLwMbUf;%Eg|8D@j5GQf-X9{9pp*W*(P|I@)D`_I_WGrwcK{BD45Tb=kHAuo_#
z$y{pR*}-UigcQ|>IRodrR-AQP`_>t&4czFlOwrPcw%Yv}a#VJIBdE|N7De_I2)$VH
zn+ZkrQi~leT3-ZXuqXD(=bt%4joB0iOSJsIf<HyWRY99M`sKPE!ua^jTrx{?h0-$e
z<x18K4|8{NphdM@U5T^aQpb5fKdqZ;rxb_Za|Dv4tAuP-Rt`(#@w*QCfpFQujs9Ju
z==PD6>Nwvf8kski<hrh(!}T{T=;^0+r!|nm@k5_Gb<!9%4g$jSS9?araEqtgy?70t
zo908%_)uga5c4ECfA9=A7yHsH6KL6Y9U2We{%al3B1b)MA$m9Y?8Pl(+9<Lje&+qf
zgekX5JGUf$lz3Kr-qf}Pz)Qd)NFV7)%h$`Nd+ztr$V4cWfj>)gERl-&!?OzXOhvoW
z%f@l~NsgA#{{8IDN4?vhRAAG5d%(!~1v6uyOVUhPoDJ?D?CCEzBV@f;x_K$Lb?_W+
zVI>lG46_oy@Zu23n3MRn`h=#E!K{U7qpdSL3{}<4V!Zj=rZU;$IwhEjJ#Ww&cMwRm
zx_-1zg}%wU7L0IE_@6h${wQJcSHSueUkMqyfUH-~RQAhxqR1lsG}W^pi>`rgEm_4K
zOlbP?L4$ABQb$u~R2AXoo2K;cy#DJ|@CZ0);hWIqfdqZ@u2(IIVzYTYn&6W|fiQ$S
zUBS3jmJS|I_YhnDp%J;HR*yAYwm4g*=ZDWSCf$bN&w4{R3-H&I@MAAE5}sPVRnctW
z5qweymfF!ftL@3yhu+>*0&>M&ZA~Mw8^r4+FW3BX?9)x-11VBp!@CyFQihvx(XKJ*
zphfXnYH6b*6Z{my)m+UBS_f#{nZ=poG`^`58E*LohqF1=nsm=jev8=tm5uZrU$XZ2
zL{B2$7)`IYh+)hXW#F(E)Nr`sg<Cp=CgZ#0slD2y!vp+0BhdFWad4$fn+p4mVp-Yd
zW>#yx#R<=|{s-o_upi0yR@C*GGxC(?12234tqdN$B7*_~-j|rS{rTJyWi1gI#dMrH
zYK%}wb1oc6rq%TG<urE3I39u3ca;X0?c+qB&;-`AzZG8;ceVlVW>!}U6sJyxXyml<
zuV!W8HXHyNk25EwG6JrvZ8)YM1s9&8eO+ELp5CG?Jo9g`;Nb1*uGS@(iPbMZOAS7<
z6))tjw0g19QS)Ie3O@!k`55|xq|8X;>62L3OJ-=Z?>`c+a?sv3p?~-l87a5}ZIE2$
z2!AK7@3<?uNu*w>FEQ8n)cmHz@~~VOw62eKy&VN+z~ja6+Q+`nG)7sT@xx3phnr-R
zaL6*ecJ;My4EoTLr6XPRvG$cC3;ifwzMVCjVmDA(t&C@VRFkeMNEAit*z@*65Rc~f
zMJp}{T1}|+QorlfHW9uGswxOv#&Ba-eRptF^cs2kDECR4S;U-}BpBn}(4DbXjEq;j
zD=_16M^Nv~at9X0q?#gDDvuE!{0(BAEovOQf8t+!+<EceS=Sab61W}znGu*EYV{@L
z-1i4hhQ;xEG~*>rvGt3GjZz5TsG=s4DKYQ45mW@3PCZI!zemO_Z@%29geBb1VtmNn
z5VjwQ`RdVRK<VUUSl^RAL#d`dr>Bk5R8sLYu6+91_x_CBt&ALlE-WZ*q->}IgvP$W
zH{P5_=5h}stK4u)P7R3r@@ICl@Nv9`Rd{97Mx%Sf*Pp3KZTc+L3$Yt}QLB`5cep+q
z0B2FQZOqD}uJdoa8Myp{<fhzEi<C>@lT7n7AB{Ln-=Ze@vbJKVJ$wDSc?+i=EG>jp
z7Q`1u0>RdJo9Q()-j=Xr@P4TkN2=5J@^%g&JuHjPU)U0cY$QB5W3N~tvLRoS50?m8
z+1{Szwl#0Xyzv{z_2~T%`E6GA|CZktbak<?ccP%ASFv=lG5xn!b~UmxHFo)TB<y5r
z=<@HHjG>CD27#NUiv@wjKM=B$lc^bjgQ2mtp}8p~y@HdyiL0@x6NQ6`*}pJaMmiP-
zIz~=PdTCP+H+v@&X9`NF{~XEE-cH2O#gu?Tgo}xRk%NJgiJg(1m6e5snt_R&fr0#A
zoUFac|0_hr$<V>U)P#Uu%+SW!6pCI!MO1@M%+<!m$k5Kt)QNyX#lq5=;NSM2^PwS7
zGj($Q_h<rUIz|>oR!$~1R$3-Hw*Q*vzxaReCS&=>l;A%mMM+@oVCZ7uY-&jG=YQYF
zM#oIYKncai_a7_yA3$g(7S{i^e*ZTbn)x5q>p#iROdS8GU_ijg!p`#lT^CT_wA<uB
z@L5&As~6nIz5k1?fD*pgHqO?{C7aYV7G$*8^2c;C;fUpW)lEVQij;Z>Od}t5cru$a
zn;B<82I2sT42Y;JTmcJ_{|Uks^QROK5;PWan=ttu5b{1tJo|0!wN2VPO~Dk=Nl18~
ziw{dAAr35?fn8~i(2%7sHypxviWqMY&2b3?olX!;MnNiAZwlfS^098vF=v)HY@dr?
z2_P6WeAYpCEP<pZO2;MSk_ZPL(k(<BWPhm;q5xqFfS+Q2OPJebiZ_jG>hO#NCIx$d
zxCE^*M|wFd4nLdoc8Bxs_*4rC!?f}th=$Cgfv$0D{=3mVf;lhrFsZQCzIJ34-7#}y
zk%SvFy3sV+dcj=}!4M5TY#sTXFTY0~OlEjoG%jJ!4ekmZxt&T<fEOI@;5k9&Bnk=D
z0at-;87GqgJa&>pmh7|Ag9=hkFV*QCb=YyQqyf=47$J(IH<{D}_|pLZ-v9)EA0l=i
zWM&VCZV##Fe-(1>;Y{#t94B&EBrAo`k~zx`CM1WNv!P86$xt>n6S6Wnue8S@F_Cl5
zg%p-UsOFfkJZe)$Z9T>6k%Wd)JafJ8f6see@1OU5-M`=OuiyRoUf1`!Zzf{;xZElv
z#VH?e@T=?0AY{9&s?5h%TGwwuuJ}?nHNd0j0$j1$Y*X^UnDF?0P)ZU%z%fYHzz1v~
zs}j5xBR^+$tHJYy^KxETf9oj6&9gWdLrJDxHyoBsuqL&j-4-y*L0$q6n}Y|&U2-gK
za|Q>tIqU9wbkk#FNYoxUp0fj5BVfy31q$`{w;hSkEvMj%8enBDZL?Y7o7l~b8`a$^
z3Hy^Q&b!o|-Jsq~agmxnCAiwD3vR6rz6ZwjhwCM9^Dpe;oR+GnsX_np9g$#<>6%6?
z{K<Rw{ymY7%H^n)YBLIv^4cvRUZEF!AlhTjk5=}re6P$6suA*bKE<oyY8|ZNC3~pa
z<j=HnGA#Y`=s2O8!|p7yd=*=T$zJPrlKm^LpzB2GW7@?HI+M0LGyC~jLxe@n14m^h
zLiSJxI41<RJ&mf5)o%!~h?>u2K~5eZm*o|pP=L4L0zf+Bk?}=u`ASM(M>g>qQ?T6E
zz+kjX-=Q60U$m&J0Tl0*ws*qbbapZH-dWOrk#^SJU+2|@ll*qx?fS0i8xA_W4UNk6
zs0T4m=lml_x}5t4W7jSVXaV=Fcp6p9BU9Lgpx0efTnk3G^n=yJ*yx{8CW&#1o5OA&
zE<hpD#9}GzL4R4#(gbbbX=Ibu)1skq;YNE#-=%S7{O5->z#KZrRB`2=^oMJX(R5+y
zFCy{i{o2Eb=3-UG-R8|J+${7|&0ccdj}#VbCO`4(?Z~b_1T9Q%QFyj&*|Qy|CY^uV
zk5AmD>v_4v%LOVt2zo2oYofX{EbF{xLf7=X%|7D_2dVr{EJroad2=_q6mjK!#&Fx%
zY&@pc=S+rvWHr!d;5s|CM{H>jqPJ+jIGQKB-rrE^J73<`IW%)fah_UXWasGzNTT5e
zKDt`v{jJm?{{aCZNoR)jd~c~hxcaAAKnBqyzoqi#Jw`5G3m)*MIlmJQ&)T`Clz1th
zm`B8C=fyDi3Pzbxvo>>-7etUQSd}b5h~q_fsPnO=sAR$#Os=(xb-5B%9Eus=anA6q
z#)x+IL-nD27(zVvM>wwAG;lXIiT0)O(@!J!(Iw<mS*RczF}i+?@ZD>avqNd@SFyrm
zaHQ{G>>~$D4E)`_X3Jl8%?G#%G45~46EahWQo@AhD+vw8D~^sRa7u1);Z5}zVSU$O
z@UgEIt*mFfzSRPbLUvYImjcHoP#&2MK}e*Q+@+Osl@`$TZ+$b`bU7H}SU7ST@qnUK
zypAtjVw)vt2#Rdk@T{TPXLccKi>&K7Oxq{2cLb(gaB}z9S&rsCy*Oi3rGe^WlQb=r
zBmyv@g<s>8d6ef&KRko&!dJ~Mot?cjZas<`sn1^+?kOEwBiTp@;}YjySpTsYmBUhx
zzGcN56e0-f#~LM;yMG41m?4<vMV=ZG92LeJd9N2q#ICIDr}eiH{ugo0{zVKQG!73i
zb~+!7qk#M%AQ-~jA7G3jTo!Q%A+naeL4HsW90V05(Gd}3QF6<aMsJ<KM~I>=gl|Pi
zke?;g%+wSLJ&uH%!0hbJ;c$q(oe9Dmia?q{Y>`l?xfSUDmx$i^kGBmv0{gFn4cY=3
zi7aBOu{}uyZY1tpL@;g(gwbS?G6)AyxL~%ghr7rG`^H-c&`aVXS3((fX{1y-kX|$7
z2F1z^UQs!xqEMDOKcMV#LPNPvu0{KLqpp7MRl`GmEB>!V;}i>HBOy-&F=NM8OQIK!
zX=<VlE8e<`HB_wK#U}@%|I+mL2^;%pYw6@Y!G*@=;I*i&ZLaV7Y2Sm53hK<Ga;@&Q
z1`%yf(|smR!+!v+VFj%q?IfVhwWf)5y=<TK5LVH|q`gxh>uZzrq#V$fQ9+`cu4_$v
zA+*`}#X11%H8<M8rH&yhAJWzQrpT~VRC{_rw{Xr))2SfcyOY!VT#IgdIp@6o*WZ^~
zY0f@HHKD*7ASaadPOUKR@5t9`?s+|@E>#>zJDE1Zzz5`&gr4+8<5}X>PltOs6PcLF
zJp_WT%H0;><}0Zvk0(cH#_Z;H2vrU<`FONbPDfA67Z;Gvv8S8R>o0)%iB&_Puxpt!
zl$G)7+$eq8QX`K7OL6ry?|wrp)XH%6GJm;!PS<gp!@lHmB#l+j9cG$5|D%<MvYeX^
zwNi=idQdk}?8S<dprz-BkbhiVH%+YOGyf$v@{!oh$J@L$dpL{bng=}hUa7wv*{JCG
zEM@;PljYC%GP>*3WFC@R7hE(v-`Wuwa<n5(hMiB1i(&q5<ckZIAgLxP4rSFqwz}kv
z5UNf|4<-V=v#e2k3}c%Bqclr&$4g*oz^tWOnQnjSck|*4JgP#;Z->KkaN5t_)&!QM
zd_yv0Pke(o&w#0LzJF}4JW6Lxv}^$@zd;gDGN#rpm?>0?cYNE+J|so~^vFH*-SzFO
zbUdWRG(d8C$|hr`9NgxUU@a=$wM$}KGm1-u%jmbGN^J3T-(b_iDsC;qzqCO$j)Tr3
z4x(Ki)$iytWMTXZ!w64^!U$~#c3!eOQwd8#m7Uq?u0#t->*GzncmMLnoupHuPPlX}
qwcfx=VO4v5*xDz$@6Nx!Z!|gR0+}LGy#SGnGdF_*z+gLfd%!>JL*dr|

literal 0
HcmV?d00001

diff --git a/exercises/Solutions1/exercise-1-histogram.pdf b/exercises/Solutions1/exercise-1-histogram.pdf
new file mode 100644
index 0000000000000000000000000000000000000000..67cf576067ebde6d6f92d0493d1214439ed093c8
GIT binary patch
literal 12270
zcmb_?2{=^W8?ZeFC2O{R7ujX@8Dq=7WZxCaHX{2N5z%6c5Lt>WAwoqVN!o1Lqtd?0
zQVEfgHs84;MLhq{^F80pbDneWx#zt5ec$(-`%0SY=xs(T;t-POo<V2p5oibn`Md5$
zsH#FpbC+G@AP56SEFq-z-T*R$)OMk|`1tRF)YTDWKX-Vd(xL^rp;Ub<stc72;pP|V
z`TJ2J?EJO43zbTy_(6F17J)RU_`6w=sgMJ}qoW7llS8Qx($E*+)cW0P|LzSTCkScf
z>EiC?w+kZ7FSj>y-A#6*LPQ$yzvQ4%U|@j(O+P<>Y7jsH)@uV&$bR6CG!H<Z;vXCU
z!E9<<K}a3)9xpetr9MCn?yM;;en9~)6tbV&-UZI*=L1AY6SBLPi<W;VfD0qTVKER!
zNeRYb4lkiDNX(KP<R46N10sZF^hboSR2Rt)CTD?OJue?1A_%GH17xT}cJp@!A~z=c
z?V@@@Xjn6r@WLP}h3w*s2+b+BH}V%Ia$TROe1#@O539C`9Yx<2db>HkZ)5gGj;DdX
z_FH*wbN23h>0g>rb2iQwTJJvAbqw3IH<Baf>cK`6gV$?c{`dOMM}f#nh8XNV|5&Eo
z=u%YV#CPPwgfqBbztlML<I*nMe>=dpOZx%DtaAE+{m;qBeS-=Xe4A{2^1hE(x?D2U
zi4e`Y@qLfzFV@b0sm!Z`_8;-v=Ayh_J{!jF$9}jb$ikg$c}%vlE30?YW{;t4P8Rc$
z6ISglJKBgcT?)Q-J((}>7+=!$=8Jn=wClT4|0C<P4)$3`xAOCuVd;0|Lc8?Foo$aO
zT*2)XyOA!#cgA0THs~7uc9+!QN9bR2YmT+LY88+5RYx>(%jfh@S^Nr`QoeR}|IrtD
zBdBYi@3@4GPBcvuBG<f=o^sQj7`~@?t@}<C6?tyvwLHSWws>z;hemu+W3BEBc2?aT
z+uaX^-*(MP5y773Jo9$_me|U#CmT;P8OFGFm*M$#w({cndR|k|IeBquJ7vugBl&Sc
zExSi6HFk1V4hAdT5RN3po#RbA(zNU6f#=c_6%&e)`+tO9+!tD1=cKM49kMfa<}0;n
zO(6@>qkFHyC|g9AwRpqDj+SGU;aAT8NUIHNOPmz+&F$6q-OMdHq|m+Zd8co2eszY^
zy^gNQK#$kPL`w;bv{;_fJzoPq?E{=e#%~NF9R-=q-b$YqK=O9tt1EM4PhDYf(c=!A
zzMYCKI4aL|+8C|t;B}o+av+5FM6=^HZarq6Z*0tmtEHq2TkLvx+qU0I)+bdBIiGNo
ztKI(X&YPb<pXGe^?3PV-`uK6yb+;muRI<hUTk&3I*+VCKshcWe`~>f`5fU7)WayN=
zmC64y$m4bTS#O%&tV1hyDxSL)FQN0%M3nF2b%s~0)6F>q=@S8YRt20&QAcj-zb%*N
ztEw!dS2E{$?)eCnXGQlYYSV*5VzGj?mZ!oI8-!2et4utlb9Pyu(s4CZkR`ha*$hUJ
zhMx~5U1rovlnvJ+B;rK&^`Y}7uKk#*`>|2|+V;rV+Pb<MKPSGuzc+dbl`m;_t45u?
zuhAeMsgruB(xJ%k;pFSC@4YuJAAk5?k7?6T6Dk*%_v1b#N+2aRVX(aIwa_KX?(z7o
z7iBDTzcZl>wkG9xm+ozSWEXmW)7cT*dxslzQVwAfM7dfE<cbH$wvfqlwiE5S4yT$*
zHKTPCo$rX`3l`gGGqyNyHu3K7(F-bUHq=&3#ZpQuW6jvs8V(1o-;nmo?t)tKz(Gwj
zH;r|fJ@}hKcvmD*9#eI&)>+tUtxb_XWn;<7lRLE;fAkfWA!GciV)c?9m8)FHsHlXn
z4wx?;ohY;R`hN4H3NDHV>vR$|;{=msxu6qG$+w(c=rzVS9z4Ut=o5fE=9rea@3w}<
zwzjc>is(D_KZZT8SJ6G?&%a$9u=8cFS5OqEyE+Eb@T|aG@AP5OCj+G{6JKKAIH7C^
zF<BbgiRRQh(|hlw^LY@MqHswmcBw3nF=HQ@XXr6s26Ed|xrObT3a{)4-5CB{1&a-&
zw##T`<tJcM+}A6r>~LOpt&D`p596$3+!nJ#F9`YOR4Qutx!S$bfX|bC8~V2#5r4?d
zE?rGHs$BG+O``h!UYy~Eo;6Qul-s2t_O{N*z>_6;r|bijS@VX=PTgd8=1K6(zAt+C
zp@&SN7aMDisf?_3UyU=9`fb+o(jwnL&CNnZ3{_^M<~4<kH+>26+pTNf&=+hw$ZlNd
zn0l?+<G>&jN=rLGB>CO57d3)44~|+zcfG#6UQ|UcZ>Fq!FxS!PVtCMPrw=azC#P=e
zhPH?bh(6K2a4THK7yYfb)9H0J2d?>rV4sSJDSx!h*+%9ZnE?+bqF4-l6>ndg+DT!F
z%GA&Y-^SYIpP$snauf$Vwh5~jGmgBdt$e&+w7u_r!R8mZl-T!g*!Ec;-fqTjuyy?A
zuPtoEPkq(&@8nNi7xdqYt=V||^2?^~mN!jJV=WYHVC)?Iuc{p<;mG>i$N!G8G~W*g
z2{evGTnM~qI1;WVa$r%oC1FGDn98vx6o+<r?jeD;#II?CpWLN0W`2#SW7=&G7{=4d
zAcM22N>m{)DYm)3*RNewhC&|Yb$8voq=V$?l)Y0R-~3E>wkj*BzDl{J9K25s)1^5_
zVolPrN92qIFSF-0U5X#cjA&j*#FefIPBLobeBST54bdGCJg0I^|8WRjRp6xc`}+b%
z1uXc6XT{74<KAr@*rzHZ<`|CB<L4f<>eEQBc050|L+i`h%R*I+n!620s-;yQ1&5g_
z`U!M%t#iL({ngas!&oo&`iHG-YTUK2?Z+cjvM;_9I;r#_azgJxM`DGiSY(@>^r)os
zGb<@wZg<mKd%q+7dE*5#B|))4g3$ZN39**Lu6AelbEx<$ARjBVrFXGo^v;;%DTTI0
zCS_0W%@_&&N$>M1NM!|Wz&Xci1dqX@mIxkiVdw`0-!RJe=^)*J;*HyyW@B@eqIcPx
zojr|my4MQ}uMdsCbw_is)!{*6_Sci`PLo$v`8Kj$J|u0yv?DhBF`J_QhT}h#y7qnC
zv7MAAWQKtR9#4PNX>&fV@%H8iq5Cc;y`;azM&C&`zzc6P-D%Wobe2oT>~Z87_N_JN
z-`^RlR;(HGHdA^$RwkWc2IV<aF&s~_Gv;>_oYomqY?;<9ua3!!o+{k8wraLijN7R&
z=b$vpnj#IA3nR7p@pdt%f`jvGlL~}nMa||2IJ}|wAdh6u<GXbZUw`F=|NOyNVr`Yb
zg18oEi2p0Y!C;6>#bu^a#VE!xN{UfT5lr-TzEhoLjhyk%cdz2){$(&Vt)nlsW4Mj=
z+~dhwe!;_nU-_*jE7iuDqTj34#jy?zXq|A`l!G6wJ9*|!%MGU??1NiZIj_5B3NY28
z(mrRjWOeSjWiKQhXj&!g_E5*i&1v)<?}PQmGK^xmV<^cs5kA?7s-i@avgu&_h--O^
z;*ANHh+8G-!bTB0X0u**^Gvk~sZ*i?(=YpzUK-+#>|ns8I=kd<8t&O<`nBI%|F*>u
zakTVl!9mRWyEzrEhyu12Wfg=RAM^3|K^@Q4=RBPpOjeN9YO_HM4!=ZJy5?7tw9y=+
zYRm$x&Oa5x^Fj<Vp59Q~suvI=wtdU7?c660-!$U5nC?HIucxWTenTK;0NZ>=AV#w%
z`m{#QXF;y4(AXz>esA{#^fWYK6G;pLE!zW1?qz*(d^DJ|Mw+X~H+?cutKQI>FVH$a
z(0H=E>Q2(bgT<G8s_&(K5KD?SGL)1$9%&^vS0n2n_x6b{&jo|QgJ~wX*9u#-kGXc8
zO1GFnT}qw~tV-1n?rndZL)x76c>K(m@xFETQdt&5vbiG|b?LHCj;0ag)uMfag~c}C
zbAG-^&72ay^t7P=ot>mbR#N|&lseV9#Qe!$jLE0A(KaAU-6Upl?*Bh64Y$<NOf0VY
z>Hz7z<aK;SH|o7NkKNMgi@$a|Yf2+jfRsHW$^WI*h~xDdhjrHnQ*E^OZ`^|ZfK^ay
zAH2V1_)5Dq?{&WNlPAmtTwnk6WNP52BE{u9*1SAQ<_PMUc~!)9+*u0ee9qrcN}#_1
z>)se&kldts(t1OP39mv0UpKaG%$8WCTn+hzO7aY}(&;cg7D(xKE=s(Zk<vohSc*8t
zyxsZXJ7J3>hvd^Auu%&iG}m{%Iy>@Z4^bOA^{TW!G=gK)=tAS$;&6q16<YswO&hC(
z7)o6GSi-a3V;!?)R&HvBvYKneWsSLfvi#TY?RP%?WapD{lv{3nn8lmF|0O@tQom-B
zq{)2~Vr86Y5H~Z|>7Ap7-JRQc_Ws&yH%%Gh`E?IX<98dO#_KSA-sZ9=(vRP?K{>KN
z887Vkg)ubFjlCCsnK7zrGBE0{;rB0rVRUDIhOs<fcUMH*egz8Q&FE@eFBYq`^!>0j
zqO_n`pN{Z4LfGM5Z$34c+P(XEIU_*%x#~0rD;q|QY*6xykm(*hC^A)S=YF_9C0{RD
zr<v=eY{AWnsVfbc&5mt;@mnVgM7JC-CStlPddn~CpCKI4!^=yIK-$q^wr{(}EuOkJ
zjqVY9r)<R^7oaew&0Lip+##FJ<g5Aek;Gene%1BQSgMC9)mM!DKHnI3V9$G~>~PU{
zjRsq0&<kf4y5=UfCv?rvp1k0fmHE%6KEbf@{FI}#ig|5sIkka!Kyb|ll5^9s^J^b^
z`qMS3ubVE^Yx%-=s#F2`)$`cn)(dpI!3F7I7E`rA<(AX{L#0**jfC?9y!Df!j|Pwe
z6^FAwvIQ%XO&S{Wo($a9N@Ja{3}pByF6`$ds(Z=DY=o_5i`+4+>*<ZneQc@skM*qQ
zJ<jsESLl0PykKQg^Kjnv=UW;{`J>+j`0Juxt)Loso4*>>5SD5Ni@9u$GQWVM=Z@u<
zLJWJbAY@}5HLjP%U(r$&FyLtJps(gQP$QXh=&*wpPBZ<GJy)@Zln0lg5~KKW7P|Ht
zzM&OpfsY?nL(4y2bVXV+xoi~$j1|^&G4y2yAjI=n=<*{tF0FtRKG;|dQVjkd-<@i7
zj1gkhnaMjOap1?GYF4%lWo`_2!kDn<c<ImRtr8{N3M0IGQ9s+Jy4`EvT@pE)o5YYH
z&f7I8H*J5gKUa33>ypl;&lfS2XYWL#1~^A5a;&TBELxSzmHCuPMmoNgkN>Ds=(sKs
zhhg@4Kw3)(7^-zi<SFX54T<}%lX51ltY~0QiqF@mwASk#{d{$%gzxVqH>^1nS-@sI
zWpXMwR^!dEY#4>@%VglyPZ}34H?W=dVI6GaN?faX)wE)N;OLJRbIMJ*BTqQ&l0zm{
zjJ>bEL899et{l)E=e_?)P2l_}hv-CmmK)_>g!YKzh0f=iC3k-OH#)P<Ji+7e3UXYW
z*J{n5vusAAfOFc6BVZvkk%WWL7%Ub(aQR<O3QI&Ft%6;tv~w#xe~RzINzTI2(c+_&
zHy9ks3Pr%%v<+d11cqwLO$CV{35b2u5ob;Jdvx?W5r`EiU2P(WCZLykqZA|VCWu3O
zCPz#Eqdv>NJZk;ci5us|OsWr@xv0SsBF$8GiJ7O58R=_O&#vyk*3%mLmf(ly9%KqW
zV0-$FXS!v?l@_lI>ZB(jNS^&XkEqAmu=FCqQ(Ve?FUAT7DJA1Qv-$y_cYfBtIG)qp
zeA9hsZM4T{+3|fRMJ=43yYIj2=J&vRKi`+~3pMA8hoYbMIAKpQjhBgx{bVi+QjT7M
z(^cni=zsPa=2zEiqgg>-!amruZ;W`Sv$*q@@1~s7S_Ccb^lany^f={?W9MYeMAeO4
z;~L_I{M-qWa=OU03z9Rvh{=ffVcwbN{h8I;xZ|FrNOr7Txv=OxBSLn798dkJXHD2|
zoW4{>+qjJv9iZOR&JSxL%I$R<IBsR}91-W(YQi#io-$+kY(h=MzwmH6^<%~C&~)dW
z%?+k%$vXAdW)r*vOOz2h0eZWq5~waMRc+d*`<d`+jNX^%Dqe)m5<T^)f(JhE#J|B6
zi)hb1sJlJN7?)r<y@GgFo3<$}4V=1(S2=H@SarfnoS7n}Jo05Ss`m>+Tb~7()rZ$9
zb7QPtxWvXW<#8y)T1~QK6*=SU^emHdKNMZ?+ZlXtILzwo=b1HfW_$Jos&#h?u0R*q
zf2`6aVF^nN)WqD#Pe+X7X;6aWE5l7<^gY`BHY2~DUTm@+lZ)N3-(cOlDw~a?R<6PE
zAEt^;j(1@8@|6ymJ)N`QVDOvkZPTo|oS5r6%>AIHag*v0PC{7vp~8PhRm_AvVp^M%
z-sCC@I_LA#;~g@(50ZS9i5m4OLYK>{U$~odh`-nBUX!MnbEfb3VNzbH^VSflODV#R
z>iq?bf!nVkuDBgZ^KTZ}R&dj$*EsOOPWlI>?N!^_Aj4R(o`|CGbVO2V$6k59#&u~0
z>p#c#pJK_l5>r?<bnJ)d69yD>ZDQ~{j;#mF>2AH0I}>q(ep8Z@)pBKGSDOW)|H&fN
z%&u@U&gbAtH^UShJ~^hmui7u%p=Ze|t}&TP*1wH#bY)0OCUGOZwtV#xI%v;=*Po6~
zaIt^ocZ^|Ol)_fKyv;i}QlDr!8GL=f_2Rs-d!cIOdX)fafuc3)*23F1hH0qi@NKIw
zY_jjSb=sYKfkC9Ir!iQqtDI@5Pug4dKXYrXYt*^?@F(Bd>HLwSEAY13{0SU1EHMcD
zQKPkBFQz?1ib0|*BclU0mRFR_yd=16XJcc3ioC@im6~W|?EK+`+iOW{v-9ekB#zb_
zlDNvuiA-jm#vE9myOJL}s4p>j4h=cB?+=+848A)@5GqXc*wNHy)q^<stMl2W-DJHi
zM$D-7WuB@kXXat%OR6*2@lwaP(Q&yQ7v7qp3!i!D2}RC5o&LoTjzPJtz};$7kbjuN
z5^6vSg2JKJ9Mg-{$V`pbN)Qt;plj|LI>a`7U>l2uP@&SA3aL1GPP*2&oKh<wUTwaC
z#xLD?=%!qO-J?#BjV2rS1=jZ$Z})}Fl?lvlt?{SZ99Jo6RsNr}c*(Pep3|tkYx7Mc
zZ519#)ustig!whMqECdHqR$pxF35_zKU!B>UKi|<anGx>qh<dmPdjXyS|k3w^hcAo
zU-MOK%HBGhs$@@((72?OYiN#+sB}G8l(T2Qc+Q4b>Vo=NvJ!!=euxh<f^;6&?dn?J
zFh8d!?p=Y))h0Mt@I_>CaNTT4O5TL#==_O@bfz1fn#|j1eE<6#)zF5Sx!k8yb&%4e
zBN?fX^3=k6;Gv$!sm70$XS-r144$?rANFq3b!^XU+@+&1h;CMGJ~Z+4tKi1jb6xG#
zYg&<yX9R6Ol(BVcwI0FNG|445$+cZ{9u+SS54AeoRJQ5Sn<I1TEkOc@_^(TN-bBuh
zoRtW@ep>g5n#a=`hjp1=`Wd{LQDa^`or6ZB3U;^O%tDV}@Tq9+wmVRW3TfS|n2hly
z>v+5DiaOoe@@4qZ(PAd2x2@#UN({4Ea*d2zv%7o>p<iw9#Li&2N-ExMmA-7j7xKQ{
zvHD6wt|7)@MmM3Gw`a65>vPBn%-L?0y_r14rn+AR!)uv1bKksw?41>(cbRlcUUDB{
zPZ(wDTK2Ye?PUfAeD~@RrbTVMMvpSTW+!Cdz2Euq<?$K8{d4r#fqnEVC}nZB^uN*}
z^is>C+5CYfUTsA>p>&21D*#?~aswRZVr}al?c`(vu42OaHTRUDdRbh5-lJcYqo{06
zt*@HmDw8PIbDyRNy)7U63Y&<(2>oZ>G8F!+53{sg_bw%_VT#Y(3uVz9v&~pk+ok73
zM_VT5vheF#lc;k(8P?~c_NcO~t35+L%Nkkc>+)V<lB3aPN8N4CEh$FlL$29e->+qb
zI=%ugt4&}2-8SMaNfpmEI7TB7PSAlp-8m!7Ws&1AW7Bscv}<e=vXl8Uyk^N_Hk;EF
zZ;ToG#tTo)v?Jnqj@d~*DdtEupH+>qzOk#b;rfG|^^j2pA}#IMZAtcD9LHZMij25<
zZ#gOvq88?ClNilS-^t5U<h@P#YEyR757iR-Ed%Q*750}!-1rPQ>jR(gO=y4G+a?5g
zoXAW-IN8SYM`})<(mx+DV{dH_^^J)b1|%k#rykgI@QDEjsha<4De~L4^b>5<u_NN+
zg*UkkyK-Eel)tT0rz(`*AiU|UOy2s<K;GN<Nl1~QSC@EN#c^GKqJM;1oyI4{j{Q5j
zAx*0XGG6<7%X8givIWU8AtKovxZ5IeT|BCBSnl-c2${&8MpE@Msaw`^7VO`-u_!Tz
z#h*T?FTXZ{QDE}1OQnc&%-xpk!3YgGo-pTca}U4n{q=)Ur1pUI3aVLc>V&~9-8XKw
zARS}WWbGW`3)2d^H*v8ub!P4zqJusvRvS{@9BAHZ#+51mVyexi=JiC@hFc-G3SM>u
z3-gn>_zdoDur?`mYS?&f%^ndChEJ#Q3Q>Zij9c6g#IV3n<JQMx!Gd_E@Q2=J9_AQ%
zfxz}=W~<;wZCOobC5h}~j(0fQuB?AR-f;S4Mr&fUgo&7?LBV^eGOT6_Q_)$Aq9l=)
z$akF*xh^&myE(o)8a;g&#M2{$Q{g|WN-Ea3%js-yVV7U?v!VN@uC>rvRW9y;8&9==
z=6o0=zPNj(x@G$7j-aXr6rcU%@Fn3ClS^NT$bqkFZ$@W-FqRfO@Glo2ZnZfcnz-~m
zHAyj9t0l%2HIq{)fGYUJc=VH}BRCS-^Ko|I(6o`5_L(L|QT+iu{n(zjp%>cGYa7@x
zQah49<tL7AN@jb{=X<NZMrFKP0WGb-BjN0w{_Lba@7LES*fkG*m}(N(pmW;$gg-t%
zI4v^r>OH6X#lbW8BoH}m$>tow0nCr?sjYp+-7}Fbb;DX#@T5}uI%|Q{s>f_GrNyqt
zt!z9K2W-_Qj_eqlrN8w><@*XeuQo*^{<9lNyv2C{#lbk2D=vYHH^FcXFn{0gg&4c7
zp6h;3#_1KGmsWJnlQu&$hw;4Vs3_i)3p;(^_FtDX3NMFtV}23#g*;>r5fLz5fuhwW
zRv1vAu-IGT%{w_k25@y3g1?lP;h=k-?X}=JZ{J=Om-Ta6_HF5`d&}B;WZxO7D;uw?
zRlIRgoc(;T<cE%IC{KQ(Agk(P-=hKN03oRz2l$E3Z@rR)@YL6bN)$7+4z@^s%-R<m
z{`R_=VCZf^${wcm+U?G4yX*N)>}yo>Q%vax<|3QMHxCvZ`_z#qJS&_t_EDwU@3r*q
zJ>q>7KFs}U-0cmlJh~fI_v)4e3{*b|>Bn*3_h2{8q^~gDlDL-6S$xN~10O1MNJe*)
zi}U>(ZqKaKptGu3E*h{S{JRO!atR{Sib6oe#cmhdU@K5)0V*&&T_|upNDzenT^*w9
z2Z|ryat~Ei1X9P#!-EVLV}Mc$2M8n=NKl;N28v8j5Rz7<;Y0QSwIj4kiq|erDug3|
zTQbGV-yH&O0~to9_(Mp4KTu6U4FOkFPYRigKze}k3<&ArwTBFmh!D~RB9S1Z8-#R+
zkYosr!a~Shpu<B*FVF!nJ`fTdBKQGF06Hkhz(Gi`9?(xkAcMh~1RyL7azgwrmHER0
zP&YWJpafB2Hi0aVumJvV2OD6D4mrq;;uS#kr$A_04GjPThD}_k6fauwi6ROGzX;&}
z=L(#O7`yz&1fMe@O<jBe*MEh*=u8Bz=m9*=7xv66#m>u}>It*}TQjZJWZ?tk;>=nz
z$PKP?Az^^Dv|R!W$TX1yzF>mkCz~-~I|&!D><R+fnx}dd{QgTSsQv-PMqX~3e!G0g
z5DFCDxP;OO1meH}SzA%bzP7MD|8lTMWJvozgaHLC@UifkIzR*z>i<VW{J+A%qLqLF
zqk*ZSASG~GO~4WmBs>Ztf-?dP5f5R(Av@ggfKK>1JRb|rk!U@Z2+#jt4YBYX3+BQN
zhOyA_AZ0_NU;!xMKm&kCN)V9<?!n822gr$V!-Kje5*`5>M8FduJlMM^fde=qc%z6)
zfB^7<(0cee7Bujcnh4gzFiA=z(9e&-J&hS~AP4s_5inD5BLKsO*Wv-=^Dw|%fR}cS
z#Q{Mm(HfpeqV*);&<S9ifW`m@=kekIUOYG*#FF53I0z4VB_J%gr!@>bk3bUw+{5^3
zN`nOmXaE95z&P<>4NWKu#KAqy(qK$D_yz@w2qqh54emjs=>tYL-w^oUI{O1^{sIs<
z4*~-?2Fvg;Re*AMIcx?C>RRZ5$`A{L&>-MI_y%m#$cOFV4}5qQ%oEL+03`VS4-}Xv
z6beCm_8S0+K!O$;YzXr<0NcURMw2zL3)m(WHJUxZHnGrXw({F1V0&2HV7h4c00wLu
z3k~L+fWiYE0$O1kSfP=WfKFh$05M^4Td;|R9u^AFJA&53*NYlW*YnTeIe!{#7qC|0
z-sz8XSiHY=foSy=mT*7-TPIrmo;w7@NxK5!0nS^&Q(E+(eRza#;DAE=iUtZCcR?T^
z(B^nSK<TtA9|-fiGSU|U20$D01C~91<qzrze_w%61>7rmjsTH{c8mwW|BZFDGpdD=
z`H%;sO9S^i?1A%f+AReFY}4QXU;G>6eDnn7Pn!V>G=Jl#g+&OcY<?U>O6+eggMoF;
zLkWX$aLfU?X+ahEVff~+hg}>f^njJlv-|h?;$%P*JzsyR31BP)%|8{SNH?GY7%);3
z^JkhO@JlPsM8QvJld%hv(JRk{VJ@O~k<R}TxoA)QcX083_-{Pj?Ca$h42opuh52{(
zLZG1)nED5Us0ipW@^XhW7+Q_0CH(zjK61_1+5TONTP#FvQ0z(d^7qq$%W7qGlrbm_
z3WdX9(Kr+agONvJq){koSp<?c52Ok*zAn@NAAhQkmn(!-L@8nwAWteaKpBbr`#{m3
zvI{H$DVKY&8z?wkye7ci19Ek7^9B)fe!c7>X@yeA9tcqKibH_t@%smY10GnG2lQtQ
zeska-<oA0FjYg4{jiHoalU+Iwr9=P*vUChMI$)9ij=^uozhl5zEFA-P1fbw0^H3-(
z@ZSH9VV2=Qp-CX%{WFh*!~aVkiiBA{rlf>g&Kn-J3?7;U0^C11Fj$bc{X0g$FP}$R
zK86J!ul`+&#V?aR3QJtR7Ka9b;UBy>ENb}}4mNi9_eZ}d9C5h}a3t8*EuDu)FUO0=
zfv>Rt&clO*{NFJWY8hVO>z9vVK`{DfEdjTjHv$2q`~S=%qCu_PvM~~PAK~9$eu>!S
z^nt`}8D1syGCKukPh1912@l7UB{<M%G)TJsj8Q2rUOr?BtuEit&%+;-ozJ^rOMidZ
ix6oX&l~)+p75(1xx=^UJ*A<P%qEQG*NnJBN#Qy<DMdN}1

literal 0
HcmV?d00001

diff --git a/exercises/Solutions1/exercise-1-plot.pdf b/exercises/Solutions1/exercise-1-plot.pdf
new file mode 100644
index 0000000000000000000000000000000000000000..ee1f9e4a9c725c39f7e2cdf3cdf82fea904ab60d
GIT binary patch
literal 16493
zcmeHvcUTlnvoC@uNpcWHTtRZ!yyToENCpLzwB)!fQ9%rVA~`8R5D5~L3<9DkNwT1T
zB8o4F<O~Xkz@1q{F`V~$zjL1F{&Uu+>6xAmRn;|J)iuACOIuM{2ri7G;JWt~l3z^$
zhrl4NmJSq>k`So2g)P<tf&fZ%Ay9o^H!K9IV1c)Aa<zp>OH*K7tig(+yBa9@;8pbS
z7I-WKwcSYB)ddehZeMF#;PF_T3j__`Qb4tFu2y<jJj4VTM^PE*AM1mMK-HXqG3CGI
z3g2=yh#3T`XJ=t;?_vwVY&SPPY3YEq!b3y|o&O*Q4g(5y7?5*uam9N8Ljdg+08+3n
zz@6B32P!yMPd5n2rh*;>s)+Tnx5DbG07C<J`Zx<04>t=O*2T(qhx6_Fz(7z<thK#`
zysHn;FE|(qiGUzPMZqz&!6x_}iRofJTs?7CfCxbueHS4p)m`!f$=RV-+1?2d5d^C2
z1jtYkYvpPUh+G5fVvDzfz`>c(1si(caaapy3ZIlmNt%(z8|i}QHUuWG(wM}uuKDk^
zFf17%@!>j3yWjC3v!MjUt2lb=QQFme1)X`b_Fns*Lxl90Zm}{QbyZfh$xItO@^#q@
z_fleWL)u@$|JnOF9+^j@?*}e_nAxPgJiqd2BmT56uI>F%nvkQ^B5BBFPR=t&byMVR
zlyf`_zN`lL#xaf_^u;!OaVouV>q{o>oroo+3+rwo8y;VLtJh4!mQ0rx&-OoFov1h2
zT#2|)do-f%?BUIhDVcw~j6KIMyg!Ptx-`O=Jv{~~htoSGB2hQ)4v+B*f81BzAXf20
zH~B=e<AQK*Jpa<c32dHkXKvZ;H&!fZ@e4iHo$0C!IYOP`Ymc?=cM9)gVbGsj=0r2e
z__ElHWkmLlGr?<AuS6(zubbo3Xpi^k^2}|#b#T$DLgFxH|9n`QK267`>{|M&uukRe
zv}tD(_wmdHwE1Cjk85>;St@cV2M>=B*BdA6Bs+{*-8}E+!AD;MKj~|uXwT|5lHoa&
zO5!1rOmsLckF}d61lGb?9O)8qOx*VT3FTN_-AmM05G3JeF8e{m<HFCkV*RfSzI76o
zKcyH;uZOn6z_Ke8`;V6jQ1#5!5g&8VK7RbkIPpGxapq;Sl!kVC%aS44@(H^hQqBUK
zS2Zt!swPjpww63>g=VujaK=HQHN~9wOU_6rD$EE|*=2<a3Te24(&0C(hiK)YtS&d_
z^b;EjT&h^*KlrF~pH#!%VA8^a+6$Or2&16@sdVnuYp1H6_-V|89x;A+uCC8%g$_(M
z&3W2G`fv}Y=)K^l9!niH!b0u*HuCRU?{;M+r=v$EoM8Ioul4FLwiz3Ky4537AZlAl
z!=9>hAuu9%&v`ypR9@qrU|V}1Gl`CIBIO&5Zz?30Of)d;f$#^6*m~l8xTiEx?vT5E
ztZ2}U>%mWHDTn9HIFd;y55x*66B9L04xso1Q)rIm8-BfD_<>==c*$dl*C^~vbVxe8
zU!uD6J?$(O)BD2=pV`_t=~05bCA>rIZJFbt=o3+TMGefbsW1uB{HG7BeXrt&Ln;Ns
zZOr@6vtJ;I^5GUvc*<^(e}u|rj!S*;v;s>cDkP(#Zz=pL$*}30(*q=t`lqU~b4LtG
z&Ny0(b-XD$GkwuxSsIe3!})<)Ftlp4MyQ3()VxeAu;v-Z#WFS6SY%hP&#hJT%<VHh
ziDczx4;{o9)tSD7a`l9xpB0_;8wuopS~6OxkF+VECiPO6V3@k2Na<$9+w8*;Yc(v5
zr+uBVSrm>UZxnEeHZ$jl$|7c~6OQ7VCoVZZ&?v>YJTg;56MN6xblh8q$ykwZFxd4D
z%$?6tyf*Sp(jA9!KIf~8dp=kr%wA)Ih?e!7+|v~61X+70r6*vedlM#w)zl0{dP+{(
z2j)X=P9tw8b5+{toYS~@`~_QVL6+Girl6$Q3ialZ=_ZQ%?rBeowbGkn{Oi2qXhvR_
z2+_PqDM7xZiQlVmJ)g@tpW5tEL62Ebo#3LW<AB3HYDQt^>EPC9_I}htpC~%n)IWW0
z9GaS7m=sCev{5=V9ejwvm}otLPEc2T@VxIzTAIt;W8aNN{JOhfT_0{?o@V`RF>lJ?
z8Oswxt<Yo+EgPo87Uim6hdb~~@rOM9mg?pFQ?DDHyY{M1?#1%xdcE!GJsViRC6_N#
z$+j<Pw{*g9Mr|yp`K7v7-4TNEZr-VgNi{JMnm=))R7P5D04Z!#*4ukaSVBbVp$Ga_
zW+Sw1=_RA|n1?2|#II(0XmOgIdBgHX8Bli!sN2ItgM<3Mu6?q|v=||ndMY(7dtNNf
zk>4@Mt)+gGw9oDKKcCt*2iemy=UHz!PU_w+Z(|SGL~DqlA2cGr^{n)fw#=p`XK|@;
z=+o=bfD2Abyg@6b1-T|=i<P$vs2o>~Myn>`lWUx5c+HmCg8lH-k3w#x>fW+R^||ml
z10ky2w%_6C`a9OShe2AW4s)1f27Qf<4&n&Q3u$#Yvh}%{Cm}`4iQRH5xm&J4)M0*z
z<mKXJs2{4Q@2Jb;7#Uh$?a5=@Y5wQF3MTh9-q{E_x1Mf4A(YJezMm%au5ICmV3Mai
z@$ScGu%EeT*Q)YKyO^H|8NBs?9`vCv&25T2^!b5`fUmWsUx8#nZflcpkDS34ao*(Y
z@JRdSXFG#P4UOLTXJ~_bX58hxMdG{|>;7kj)xw35hF(~!H>s7Tdauq&*&kUx08KA+
z>G+uGdcnWuu?oiQeSDjN|ED6=ef}TR$4w-CtqoocvKP+OWeS`daW^pH$IB?^*q)r=
zw{a|$2+H~RY2gifKi<<w*ys~hkKgKMEA)0@(Nb)B@I+x`pEuU(3oz14BgIkCro2V^
zFP}MPPWC!+CKt@Le$0F9mxO7`^ULcmGd-6Tl$_b`o1dH`gMyeAzF`+kW54&DUC@o4
zcUd}}`kd-j8H20-Wk-$a3WhplbgrIy#=zseQ7PlD=HafjT*FACfA?;^ms?$3xTlIp
zncZ1_HG98<YV?bb#S4*Sr+JIHR>;fSZ@xA38}=|b!7F~breN%~Nwp?j;oTI2<@Thj
z<X*ZuUDtRJCkDIM<HpCHV6-D^%cto?_usO7*5a-*r&n_NensEO&rIj@t}z-~x0&G5
zZc7<k_nKXO$#8zmfbLj8Ia`HwGHuiY)wsUHCwlEe)L-4UQ_=BSmL`?rIQzhbTuZeh
zQGbt=LzskLn&qup^WcWQD*Am+C&)c`160o5__{?d>r{FIb=!x+v6gsTald+cI1zu~
zfPk*{@fJE60Rx(kp@-)$)0s>f1h!M1nn!ja(|9=p7TIDCYQ<DsG5ne`9v{fb8F1+{
z>j54*R4|hak6(1v8lB7mROp@{rS8^CX&3K3%0T_Y6EmKK5lV7dF#5{Y^5*=4#gj<w
z*F%eaCMLs1U+>|~_|wBzyUjmP@#HiUOAOm)U4Nz3bM@#<2@}$~yKg--Ek455hA*<=
zIb3H3J~AEJD6Dk<VWp-_dUc9NikAo<n`*`9<?NH)2kj5Err(V&Ghn}0g;<v9+x(bq
zKs;XNUp1F+PLklLwJxn;=!I@Aa8=lQRZx4%*yW4l)kAu%>lNC)0{hc%nPuMAq7n@E
zf2Fp5cjIxx>pL9F^se_wYVGaq<J7Wly~}_+BpVf6k6^8%ogC<wqDVQaSA2MHas-=Q
zyy3m@s)>K#<sxq0M`RSUCE;r#2jtu1<ZIcGpBWZ33vyG-k6!17JY+AeQkG++O80JR
za%8;onMis43@`F<;=`G9ckh*IMA@yM5h%_}=qr>7d&OHbIMniJMdC9qgT2mXs>ao?
zmQ^Os>4pa8(Qw`?-aA1>9cYTXEX0;KF4QQ?g~u*ybr*PspKiG9aAd9cWrr1IXZ79W
zPSGS<-h_4fdMOI$)f=<19a0oCM)Z^S>!rOW(Na23XJ&PLObjzMJ|GA8c4cJ>ZP|NA
z)*<XBSQwhlG5TF^)bBUbfOsLAWJ{`=qik=ucl9^!S8P_hTt5-fFwxq0H@L#;)-+VV
zWdrh}^H`)Btpfi@M7(~-r45(4yUDc=C)&>TEi{mZLMAVTojPM5SCDv6Z&~?8P<KNw
zvn8%09z(}@^}>iO^Yg~;y<K}JLr2y6qJ(KUG<yQR1a&7qIi)xJ>aEfOzi1Y}pDEj{
zYQEEH&+^lhl626(NYg{%H((;%-Q!G8D9++~KAxZCa2<Bp!{B#nl!^3wj$yzVOsL^z
zO#a}Le6r3%?#9KckL=v~QbxL#4>8t|OQkbDvW><VuKB&?us56<$XIe1zZksS%dpfI
zlJ?5mNhB>KLSD1T^*|86Zf%4wq~w!;Xm`@r6=q45(BPL9=~tI${NB|}Sx(PA2nix?
zKTT7!fu3|6%a-do(>n37g0i$Gy6NL`!p61wMZq?<+O^enEm1XFQ?0tW<wr9&sHJns
z`q!Yz<Hxj(-aH=J?DAqn*1manWaja`FCY3uPDS=6$}yhl&hEdq73p1jes#`$YT#pk
zz~Gz1vK7Mbz3TJ*E0fkxBSH$|Pagyosd~pz2?`=7TjQ2H0)oOn%5vF?;!JA&v%QU)
z_xT;UqM5urf0OSrT<{YkpUK>@Qzi!VC%e8h-R(V*<#t}C)j2k%H^uL6yd(^f&V8+9
zE$NjIyjzy1z|wv}QR8^+f#E6k;_L6jX-}RV`4DlZHS#iT)U6AHu$R|i=vN&^pCVYw
zuf+GMESwm~E2>Guo08?zDRbwi_Z5aSn>8NNxIn)(a_9b0mawzLN3KDGI7esaHa{)A
zKRP=*7qICpWhMTV=8LI?54a!vwln#*GbHR-z^x4u0Y~p_7vbQB?Qixw2srYm&5o2d
zJca~LHnl;KN<>z)k@~2Lf9=9y3tQdkP&rj)MtS|Huh{r@-L=waF;Z>c$k&ihhtkgq
zBBW22lMd0p%sI)i*E)?3N$OFmcd1->R#HPv6rFNCP?5j(9n~AY<8e!ExwWfdgBb#@
zcTX&2QbRr{vltW~d21djbO&RVYHMOz6cjs+KEKKTp%rU(5i`(`DAONQJ>pAbjbM$u
z&ShZ1QsuHXGcClLf^&G+Y>gnR|BU8*r6i&8iB<GDt#OY&+nCM4%4JcG@nxwk<fcpt
zwU*=`M7Dc3{ja-05z(Ke6Qxd1a*RxICG}1uM5cS`czo~3<^ZbmX3%E_UZ<k0@q?Xh
zy-{Wh35Ew#S21&xsn_yzKZP7;Lab*~vtGAdWaK%{SjcT9`?k2ZFT)*CNA*yU+s3>g
zv`|qvN1J$3XVJkX;~$6YOOO2<3==;wvydqfa|_xEY^IUCoMJvg#e8!{q^(r)GsR21
zVEegXUm7LOfPEiNyO?9|Urq}?lC)A`$GbJcbB@`k{5j2FzI^&0SO?<*JBlH1;ejQ@
z4uFlhgN31|9<U!5I%(--@9qf(A?z%0VEDiT0{<2bP;vqG%3yQ=+|Mi8+t^^iy*se;
zHi5ulC<qkXK4TzIJh(~U4rF|9AuSE;x9xErc(7Lpi~<UdE5E(H7lOlpW<p3b92f}$
z1Ny=c_W58HMXZMv&fX30ii5xj(T?qwnihDRJs~t841<ANcHrOlD`0dDi*F+#&}ax$
z%fcDp=XV|{pn9H`ctXSlBp10u^|nlm?5**30Q1{{m0ht9kd7S@%6V9Uu?R7Q7#Ov2
zQ^nfb+Tnrcz-Ze~gb+wTBtWRa7Kn*p{+jRqnq)N~G-Pij=VI%G1;^2}@F5Ti(1`|a
z_3&6{L$G{X{=bM2YW$tRUHpIyg8Z03FfiEvM56dl`$57*At+!zQGgbRB1It>B!)r^
zU`s?420@6RAxJS8A)x^}!RKIoBnG~nBSpaa|CT6ro+E*}U;_KtNoXXXelSn~q9`B%
z9f^rTL_~mlAo7F;1{VPn8i+!Pp(%g_!Jsh^G#cmxgM^5nfV!dp0Wjbi%)#eKAOWkh
z2+$tvQ%qD0$hXVDoWKlHgaXV#B0#3VgaI@TY>NgM-|hpb3ye#+Mxp>gh!PT7M2wJ&
zi2(Eh<rp{uU~qd}6fiCt0SHD6Y=?rNfm{?27MK$f0<4E22m#E&@d=X#3J{<H5G(~a
zCK_l%5Xug5U{269a3mCX0|P|_k`1y3=0GCM3pm_%qCf*I5?X@t{c!;hxZMR}dvr8N
z6+rop8thEhP7X{N#SS3^s!$?upuzS$f_m^1sUS}TWdalmZ1uetkSG|8g7EBH2Vxj8
z;O*Q_pdxJR0H_B)CxWa2y#RG$S0d;Gs1rMhpex^W0@R1y38af~5A*@*#!dn`$G}8^
zIRt10b>I(43>br;7jRHgKhlm)?Bswh0Q8|K2swDYD-q^;`#D(Wdjj<WoK-M4`|e19
zOaIPd2dwYgfnZ>X2ZZ=7Q2NaeLF_CepmF#&qlyGf#qRj3RE$JSJ&a1>+^tJ2&0(vt
zBTLpi@hh7%(ufwrK(%W`yim`?l59zcJvaGQ_t21~#7FPfsU7W4$`zpuZG6wu1sdKe
zt(7E(SC@!4=GDV)nJnj|To2ZaO&a4@XMI4KT3>!`EWy8lMg*0!-!ojj?!dcVyHgY$
zZk}5bk5u}-(UL65`jb5@kt{k)qie@b-U*pFF??Q<_n7H9m@+f{h+emBM5*ch#WV7s
zsUI9FsgrY18!P3JeC_FXQrLy1gO<j+N`F;LXL_a!`FQ#SxfFfHknya)L{jm@p=8nN
zfH~#9*02J*V*$-ZJX2ieZ}qs9=&iLXj9sqxrp~7GW_tvCutFyLLxXikEsgRoP)WE7
zLi+`q<Ju_^%6BzWMSYqB!jqPL<Hvk9_BbthNc?Wn5xectzX=}D_1$tuR-m+D+C?zE
zE;I_G4=X_#!bFhTs_+qFii^form$i*SX(=N0|F(YU^$V*DvymehJ3(Pn(rHX(%v?7
zf`n+HjG6Nf<m~n^{*4@r$e+kT8Eun;f$4lB=MvoGsbG;4HH*fPE7*OO2U(LK#qEJj
z7rBP0qFBY;Ug9cW5OrQ=die)Zb}R3{k%B;>f1*W6`(d~OoN7vnl!eTEL-1Uxw`%-A
zt<(u+x1eLEWnxa#-@N)novJv_by!7NPKvUYC1@Dg@SG(`uJcN!Y|1BA+C-n=H_9&K
zUT&Q=^~f+WVwT3!ZrLvrKbyWDN!ias+vyy)5GG%(rqATApXRQykXQ0N{ME&*a;MUl
z(bLDmuc)hWao-5g<KHUdGvOb9qr_08I&v{q6E!3#qYz`+emhQQ1y&xh>|PSB;@Q>G
zpCTrd*gt!BM&mq<F?XWQN4{HQ2x*?&B~vZ`+0rB3BX_b4)>AeHqZ1Z6$_LVWCycms
z62p7%Mpa5~g{3WQl0@7-MflM4^Tq=4w|fZC$m~`uDP46JQaIVbbG}O1ta(jbYVP3d
zy9*}C?fQ8XP?8FG+zqN+E}6-gt4NK2PdKw-oyA8^4z{DmW`!}?tH!vq)xocE-AR_k
zC)rw{&1L$=>4xtz6c&Pb8EA-v{<%mx^q6gHfhFhZtD+!W{W)u5^AkNTvP}xztg5Hv
z=q%QwnZ`4<3!R4Mcp*kT_kz;#mRAoX5b-_EKXC14PXFzYEAcUTM_w$1WzL&CHM?e^
zZDKO8*T<%dwtp}>oDMnD&FX%N&QV5cB0~pJuER>BX?uR*9YS}h8|d~$^Sf3B+voB;
z=Eq0XZjQNe4y6vrQNowq3lmN{t=!}@jGiZzXx&5dXk#!Y{Q0?m1j9@RrZsrxyY26?
zG2DO5Zn<$;#z{Td^qD}25@SvcFIm7VIR^Lh#*Hi18H(Ozd-^yTdhNJqsp#uEXJCsy
zO}_nuOl70Xigp=_>^%{5QoJRNA5)l$?tZ=3QOuS~!+p-REHbXWJ?9EjkC$Ig#dE78
zJ>IG7U%u`UCQ4`h!|U+3+aBPR|7k)sbsjn^0<Xh+M$><YrW}1!DRs?0yDGGhMb$yl
zp?r6_m_IkEQw{Amp?N$KZJ=<0T?Rgl6qITi>5&<&YSCwW%#@dWQ=7$dXv1!A4Kp6f
zA<(-2eI%C3qjTk-4B8v!+$i&Vu4>#Yy){Vdx@+kX^>WGj2fZ~J1q+xukj*oOA|>Lb
z5GNllhT$e6#l8J3Q61(PVa4%LjX3rkiWt(<=C39W>s-Gi5Z6bJzth)H-TqJh*k>;h
z1?b{GIn_S?R8#6jb>ms*1kV@9A8lXOknmRHd^DfUKxacks+)K#I$m7L(*J?%RvK1d
zb-m^J(gr0HuPOf@<oCB95dm*!_xqq3E=T_aLPj!I!*Nnuv1<$O_hRd8eh>AdCtAeU
zn3XOqqaD;?vy})YM{T~FaW|eAz)UIM%-(6;M5t-p3VwO+0m<c(1^3G@)Yd<{`w``D
z`0aZ~^MZ}T_;(5c&*6u){<j4W3HTtpNmSBR$2G#p2F4goA;?kN4@)&#MiUzk;@!mG
zNiI{7kt3wAs@ZQb3D#Fe*cP*ltgrS)r71@!HqgH3OMg<ZSXGnIVA||*?ZiU*5t$oV
zB8ZNHuDk~-cQMzM(E^-f5QQs#hU4wCIs?}AQ(ng=#Pyg%+yu82NK4{8TlwPlI?KI(
z%{lJMEJ^ovU+E~Wv`WL}Q|+t?W$G(&lVa!nvg8RKgXa5)8tTd45H-AgGsw)xd(@yh
zRITp*qA8Drc12enzD6XFb^k#z^ZJ<k)UWJZiRz_kmhUJxerCFzBM8~-?6-M32yam>
z;u+niCFL&O811Ge+C(fHdViR)df~|HVJJ(%)uefHPjRefO<n4n;b-!(WOKUi#Pb}7
zUCfRsl{=jrBQKNTk3m{yvNv>-NB6{Z(lOrH_o?g9dgV3N!tjRC)W`2+>crBf)>)V<
zFaPt0soDL>|KFwt4s0iWnwqF8&%JOe1rLLZ^ge;&ldLi-r5krC?P~^QzxZ77xhh3o
zk27lw>OLSdSkaEEi9oy!|AcK?%_)}E>e_#hgTW|6?bA#sQh#Eo*Rmkvjww`}gT6iH
zL%uBMdGV5G3MK=I0pf)hrjN}rZ1MAtxmfHG&YP88(>=P-6WOmbnUo&MFW|`T@TQjR
zAxB(=zo`~BrTX0dvN~G<qji6hoH6mhKky2I8Frk;f8!O{=kI0}iFg2dB&a>~!OYwg
zqc-~}_<~JkALm}Hk{4!CrD|y1Q%V(B#+7pEs);;GF7A>sZI%tU4Xv6e3CE3nL@i}Z
zAOAqh-@2t>q+mBOO3~^uBoHvxbBQzX>xg7xk^ydO26a;d<8>ov<H`xnY<j^lMqk)Q
z^J0f}#Y8z<{;hE0P!7iS5&mW4i@mq_hTF>(%Rdz(aBnA$Tpm6!R*<4!QmNA<o+r*E
znmyL~C2#g?rC{r0&JYBtQ=b?$#_eN;MHoXyhoN`Kx?<Gb*xZa^uPCS0%dt(5TYH%*
zwJ__GTnG1G3P>l{Sk%1j87%u@l+O=G{&~Uu;gW38gBtQoC$f=d+AwOlhgt;}+^4<{
zZi(058hb-!6yd!fq2c)O0~Fp8S{0}?%h<Cd#d3d&>d0J6q809?zrvVlQQJGY?B`#P
zPOZ>rhuU2IgB<_X%Y*~_u%F}z#LVg;R0=C8@+$Kx`_8B0t53|;-an>U8hE!@cAq!T
z-rRChhHg@*vwAh9v<Z1<lg~KD1x-J)*E7&C^MhTSu76ddeLQ}_4&xy}d7t5k4Yglf
z2J3BFai+nUJ0rO4+0Hc;w@+t3sT9wqbTmA%{z!er<`duS`Q#%yX78*oys&cVbG*Rx
zIj^YfUe?De1D$5b+k0no*=9CKb3Meb{DITIbzl)dL~1vuQrZvc6oA;N6e;CM=lL0t
z=WSVSo6g5mGUYMy^l?cVEpZ{@tugobP9Bj~w+yKX`RHPe;o?_<#ujm{bWtq$UmImy
zdDojzs(`v-Cl)}7<j*^N<fS?$$&H_(`u5v;<d*}^coM@9_F@xASEl)^3JC5Q%|WWC
zi44~{s7`3^+q#ci(S187#pZhFY8-yPVC~~_+jF5BEvX2_>PKs#j_%pw6pC)j4vV39
zi^h^>h0NZ)XekoMa-xDkzcmp%6+CO;G{dzIs4O;xt-i`<QzRjwy32nM&)+)LK+I;h
zbd<s#9(V#HQ#_Y#zBho|CXF|~^ul4tiMMVitIt)6(<Af-ErLV#rcwz8>n-d{%rHk+
zD(i;dn$9S4IqP|G)K4$})5?DSlU`o#QXOrqf1>Me-Bpy>Pjn$MWx%2XL&5gPC<n_X
zL|>B+J;tI+)X@I%68UK0seQ7C?uhO$;0_TuK-4sTfcp;u`CGRN4g`ySl1Nk)s4t2h
z26E)|MP!r3&$_+0a#_|+l(`ZKg%on><sIeW$bS3EZW-oFou<iUDEOMYBK8pOFtgkV
z_)Q-zcz(u%^u&;!smh$ZN>7{km-cO~jTe^ejF7QXb?8Z+dCl?FG|95uag*DHlyUyD
z<)XLLwBi1RmKQTpye@F09Q;R`RV9&+)7{dAVtR#@$mX$8Wzz@JcY8#9|G?$nIzLDt
zq_LYzAziVE<8Z3B4T=DBqN&A&RCbM?^)0-b0V!#oQ=8-nu3nKVKrm`R$8q?Tvd!(f
z`NI76pgGlnX7Q_z%}S;%33axLf+O$-$%adF1FNj;Yxmk)O7}NG`&U>Er*p~M<eRP|
z%j)?f>iL_C&8Il>&iUwN*5@97{o(qSbfX8$CFaMRc2A&dWBHsuk296tNZAaOnb0KI
ztHd)VT%NJ-Y#ULZ5;S`DVGRN}+Y<5)MuB%=-c7#35eR3jqN9cF<;<qW&!ev+v-X;e
zH(@hHiPsYO>qt%{wL9H`Y&K6EyNjUBE|@sM^FW8md$Pr}w5sNo8p32nDYS#JbE+=!
zllM(Teuso_0z;OT(kkn@ioFMJeVFWbObk+fAof&%>paHG54ZRz=@d;%uBs~2%W4X(
zjAlml*9oghp-C@#+UDQiSYf@ewFfzTe$Vfdg8Ey3?^i>M#OQ%re`5OV>|lyf<phGE
zmA)F^M#|8(r?g$L{h}ZzWmE#2umYtO%-PZ@)7-{#pY<sS98&#)2)9Zf{Rdk9*1<sn
zL8RRZf(Cp~;58Vi)pNVpd9&6(6<^{oTYLWsk3f--th_Mv)C={19<G6``Vpg}D_q6$
z(vdSs?YfOC%%zOag$h-k(>)NP*sH@JwTj?j;^=waGcN99aYLG<kXDP2*=K(&(Z0f=
zvp#WJm!C)+Mhw!$MjxU*{xnJbx~6KJl-gH;kDoH*QTPCE0gttFxLihuj21E`h;h~H
z{h0!M?8h0TuBn#b9*G!U9gH%Hd1lMS%}V=@yI8aA$%nV`Wmgh!%9D;72lnDitG}>m
z7=I|bvcPY_+<RFw6`gy<rYyqBy547{PUBioa6P?tvVS~z-i^qYZ^=2cRW!Y3>Pk#7
zbH@}j){ug6PoMD~-xH!`b)4cwQ6Sw#u}4Lrk47oQn4et5-bxara<=1o=ob<6q${N~
z%cAL3>Z_&A{Yob1GX5|Nf9rbvG6{rLFtDCcL73=J9P#uYI{sTf3J!!%cTcZUWZQwg
z$EXzM^oQsdS*3GBdQ)F-7EZyE<m6Z7I3yNeWcQX9F<p)G-FNCmHZi^VR`G&ItNnBh
zX(pmVY8IKa?O{3%L8mm1C|GpfY;8_JJUBd5p?Ue9Q@sBD%U+WEXe#bv^T`5ooh>E>
z7pUqC&Qv}-AQPp2-}{ll;|ub7up58i<!^nNU$;qUU9p09vQ$(46lRb>ua1;4(%gXA
z_rY;zDHO^KLz5zy<L%e5#|(sGg==TjoUa{TTxp@W#t>t~^(KodT6;}0NWa#$t>$rG
z3LQi}o+36j<{1~|Ce@8WVYV?VN0~@YZz(@>gRm>~d)gQoG8|8dKdes*|0<ciM`oA~
zS77{r&5B9&K(+fDra6Tr-{wOQo0|!t6lR9km;>Y%ZmZn)UoqA<hIG%cskw!PYexrq
zU3{ZTC05G(FbDeORNPH+{LFQZ**j0@)!I`m&BVXZNaF=_YB3+$3L{Q@Q5A61c;lU+
zX5Y>cTW~|kRm9a_s#11Ixb?!B4v3syAFuuSuDn}Tyh*IsAaAxLD%3N!kah;i5F~xv
zvOjOYS#|De-e?)>1L+sevS);)>~r1Y(VbQiO2V?xZ&Ao*9`vFyX~bWapTW=k%hsz^
z-_5TiY!!j}f0&xTcD{h<`ffE7(h-Xxkt1sxWAc;tcsW<x7QM1HLD9PBa<BqKT*zI!
z=_GA}z~ExDLD|q;;=!lhPt)JGdLCvLqh(TkaZq0~$E=3^(S9#B8{(x*wBTjdDH0hg
z3K2hdAC0E|8BbR9-gB=UPugfB1X$c#8c6j#UpFV#pUe)UoH2cVpt*{!4|^~(Ild|E
z3a92VUDfnS?p&l?)ZUDIos4j{#(;@7&RZ4+oDNj$rs@N)JQz9;p(L2|CB?E-j8fWK
z8Yu<#Z`5==QPMw@FG)-9Ry&}ukup6ZGWepZv~hX$j7LcgjLCRmwES?CX8EcJba++j
z!_?YWlAJ6P=06DVZ{0KaFX3R#C{1elW3-o7Qtq(8(w9ggm+VY|5P;YG+VG`i^<xTm
z>q(BN3@fVycaHlMwZN%sC=uLe!k5y*rjAFDPck__tuB+8?GS|X2r_V*JI1|DR$*Kn
zx=AT_X?n4q<)C7w<4sp|nrCc4z{8hjJz1VBFF7evnj^HS4!e=Qekn!$mcDZ?iMv*x
zk2P5|jz*s)x}={xC@0JEhMs|4*s!70-1RdvYkQu4mRSD-&wuOs!G8&RNJYv~lfZ$<
zjBk((EuWXX@nEUJ$$_o2>Phd;bdiO@ndKE+a~!CXFCBPxJxe{OZ=Nso+6n6BM04t_
z%oj&&qx~#J558qDJKQ;3Jn@C@VT_@F*Qjzem)#|mX+8V53GNIHBxuq}zDd)J8rrI~
zT%*^hn0I9*F^;0y?;nljPHnw37(9FSy>QqcSo&M15&=N*cfSW{?Y0AeHGODH!JNZL
ze9=19@R8@1qjQ&p1>KgsadRA*Z*EH`--NofxCTvyaBVRM<$X`C>DDAjs=!=&Vo7oL
z>tS=ZL)>QqnMKUU?ZXeD@k5ugh2!NfHge4;p7%UA{`e%Tj{_^tYcHKbi#fSNHM6F1
znPgg&)}G<5fcjaXk@T3Q*3`pmhf`+eB}!d}cpSVqx^Ya1o>J7agJcX!?2^7p*>1z7
zecrt&`W_oft%N-VS~6kOMCKf4P6bXED2l1Sh{#HFt$DUWBTJ-L_Pc0+uZBOO3x9$z
z?1nBN2<Iv&px=QAl9Cj^Ko<ai6$%_fumX-#0AK3eusC~HYY4EqK>e^dR|wSA1;7sA
zy@4yd9S#dz+W^N6fWP)$z_kYeLV$W;y|6A6(CttEVsHr55@%uMh{Zc$Z2)8dfGYTQ
zk0W3M#6%!a3*bv7P!9`az++nwIRUi?ZU7L0GXx6s<q9Av06+yWjt7XRfO<mU0NYSs
z;KSVS$O-^d@B>)^e0%!|SphT?LIEKqxQG}E=md!b!5P260uZ1Z2^<yyi~oDbioZit
z5YC2xTD$Y<aJL)#|AeSOBDSgifh74a`TZ490i1f-jidk$ocusipa7NlPmmNld!+wG
zQV_nx|1Xl_|B9plH0^&DNde#nK#awIhNJ*MB7lYbZy+fU-%%7Gn&SU2k^=qjND5H^
z-2x(PcA+T1>m3BePX0fJq##(LouIe{gaDV&{calwwu0~_|3}%jy#QvCaI6grL4d9u
zuuN|o8p7vUpfbS_0RS38C4y}NuxNxETL|HM5KzYsfYfZ4*+T$xN9e^F0RL>4xj+CE
z2%&{505tk`1z0@*3q&aWW)cCs6yX*J0d$1Wg$IBy+AbrQKmcv??GCVtsBZ&)v#f+c
zK~skMHt4o7MG*$3AlO&n8$6+ZPtXn%u6!ZDhe*N|!4v~F8NB&nH-6fZ!XX650N_>`
z0RblR=c>DRd~)#Z1Br4#ogHiX{YWL$3NX&#7*ILHPBp;teuFdqsTy*p8cY~)8vaxf
zd;kC-e-I8*_e~Xk6z+1|e-!TaY&8M2)wUdeJSP<FXw{AtL<*xJLg1gB1e*MRJM@Tz
z?=sxVt~kI6+2&*C{3kHr_w$g>QF7E*AXG{#QcBQyHFYKI)C<STM2+E%2PE#Y8t+hD
zk0k?iHe=?svYxCSCs)@h9r1e^^`crM?BHl`3GcJpIzoM&o}xL=yWFhs*NYjmd-}$G
zGLk1SgRwGSicTF<mSLkVymF3Nf!mZ1zAxlGC3K*Ei!Sv{&y%Bf!UL>MD{TD1CHx^z
z-~_5C0LTQUOx@mk8<|Q_h3}Bm?c=sTBnX6qzfT5K0XXxCw|8|>1W(uUDvBdu2p9~7
zK*CWl1OhGqL-4?0JbV<}^#DvNud@Z-&B+z-WN!%w8zzhtgxKNnZsJhrj|al8I9s3z
z0Hw9|v;xkZ?r!5|Z3D3cokbVx?e=`Tw8RI8wV?p<aufh;?%NMQ9ioAE)CTgs3<T$b
zKZwh>GB_G82JrNA843fS27fIR0pFNk>Vas+-^u_K+^=O~FhGfaY75+8elJ4;u)tsI
zAz;7K0z+Z|bmh<W;3zS0$pL@gd4r)uFu&3QL!kjA=CAcYPxqHHv?vH)1AjmILt?~!
zYl{>EY{Sp>;7A032>rDT_Inuu13J^-@B0{V6cSt}e=38aMbW>NVL+(xFZD1;0Ok2>
z83?QVwM_K)wjxLn8V&xw(*hI0{5Iz>5fRbfWF-m*whO<s6-9ye<mdiG(E$AO*D_H6
zA^A(07$Cvl%24p%coRbdVToVcVgP{AuVrGOJq3T?c?0%RpiTUx3{d>v$^fE&D-#2a
z&d+V(aKJzKwG0h>-u$&p^mlzhz(H&MbANzJ{x&Y)sO^#g9%o_igvAm5TL3i|8&?Qn
saRN-5uB$7!rV(tRp1mIySQNm8!UJ!C!xNSXI2r{<QgCr8om8gyKhXX_3;+NC

literal 0
HcmV?d00001

diff --git a/exercises/Solutions1/measurement.txt b/exercises/Solutions1/measurement.txt
new file mode 100644
index 0000000..1038d5d
--- /dev/null
+++ b/exercises/Solutions1/measurement.txt
@@ -0,0 +1,10 @@
+4.980537739146572718e-01 3.304070957398243524e-01 1.000000000000000021e-02 5.000000000000000278e-02
+6.762361077018429478e-01 2.837307206165508577e-01 1.000000000000000021e-02 5.000000000000000278e-02
+8.052292433523977611e-01 4.407017550224799907e-01 1.000000000000000021e-02 5.000000000000000278e-02
+9.704434451011637597e-01 4.982765800216331642e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.129455114674715155e+00 4.537414756680630545e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.285083611438478268e+00 5.281917212757096802e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.435421444857490014e+00 6.421928523153139778e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.591387685845770950e+00 6.063640103412939464e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.727425218084549075e+00 5.999229259846586837e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.897833779891006545e+00 5.580646104775206506e-01 1.000000000000000021e-02 5.000000000000000278e-02
diff --git a/exercises/Solutions1/measurement_1000toys.txt b/exercises/Solutions1/measurement_1000toys.txt
new file mode 100644
index 0000000..8b087b8
--- /dev/null
+++ b/exercises/Solutions1/measurement_1000toys.txt
@@ -0,0 +1,1000 @@
+5.070608972888074328e-01 3.024064020512084450e-01 1.000000000000000021e-02 5.000000000000000278e-02
+5.024554211422320726e-01 3.021652841210368812e-01 1.000000000000000021e-02 5.000000000000000278e-02
+5.146468185412946816e-01 3.483181777646860988e-01 1.000000000000000021e-02 5.000000000000000278e-02
+4.897703132193391151e-01 3.003351973985336487e-01 1.000000000000000021e-02 5.000000000000000278e-02
+5.112141273250753182e-01 3.962200753048096269e-01 1.000000000000000021e-02 5.000000000000000278e-02
+5.277202772146797338e-01 3.678824381388534071e-01 1.000000000000000021e-02 5.000000000000000278e-02
+5.173917330734782993e-01 2.897834198106739589e-01 1.000000000000000021e-02 5.000000000000000278e-02
+5.052549696677017810e-01 2.456746654358198390e-01 1.000000000000000021e-02 5.000000000000000278e-02
+5.146309105128277217e-01 3.290909899901823255e-01 1.000000000000000021e-02 5.000000000000000278e-02
+5.200786212079983040e-01 2.775676192912227558e-01 1.000000000000000021e-02 5.000000000000000278e-02
+5.130277671816866336e-01 3.490042925735983848e-01 1.000000000000000021e-02 5.000000000000000278e-02
+5.218049455243295442e-01 2.735044728977774064e-01 1.000000000000000021e-02 5.000000000000000278e-02
+5.135007871831214210e-01 3.337268833021009296e-01 1.000000000000000021e-02 5.000000000000000278e-02
+5.175725107395344748e-01 2.495631121355812443e-01 1.000000000000000021e-02 5.000000000000000278e-02
+5.242063326021059178e-01 2.218208659028338692e-01 1.000000000000000021e-02 5.000000000000000278e-02
+5.182096363550174889e-01 3.209225147697468139e-01 1.000000000000000021e-02 5.000000000000000278e-02
+5.279416027880742268e-01 4.148932685299025480e-01 1.000000000000000021e-02 5.000000000000000278e-02
+5.148718294318147537e-01 3.001930700774648186e-01 1.000000000000000021e-02 5.000000000000000278e-02
+5.185658200444790422e-01 3.068263807740647109e-01 1.000000000000000021e-02 5.000000000000000278e-02
+5.356623985923774089e-01 3.307256896377007704e-01 1.000000000000000021e-02 5.000000000000000278e-02
+5.376198162168277506e-01 3.082813021113595120e-01 1.000000000000000021e-02 5.000000000000000278e-02
+5.193352571655041050e-01 3.165953505254833211e-01 1.000000000000000021e-02 5.000000000000000278e-02
+5.317535254858074545e-01 3.447488140117828115e-01 1.000000000000000021e-02 5.000000000000000278e-02
+5.350306616268238891e-01 3.370878960120571310e-01 1.000000000000000021e-02 5.000000000000000278e-02
+5.534882129667649808e-01 3.343209550735558033e-01 1.000000000000000021e-02 5.000000000000000278e-02
+5.307194125752333624e-01 3.492094040999452198e-01 1.000000000000000021e-02 5.000000000000000278e-02
+5.443860375603752910e-01 3.247959857494455704e-01 1.000000000000000021e-02 5.000000000000000278e-02
+5.290356662790374198e-01 3.528957216841325795e-01 1.000000000000000021e-02 5.000000000000000278e-02
+5.571640680416803937e-01 3.515927544674719574e-01 1.000000000000000021e-02 5.000000000000000278e-02
+5.415815065646411020e-01 3.451820336310689186e-01 1.000000000000000021e-02 5.000000000000000278e-02
+5.469434067210564576e-01 3.761002143251006014e-01 1.000000000000000021e-02 5.000000000000000278e-02
+5.354383426379991651e-01 2.233730637910124606e-01 1.000000000000000021e-02 5.000000000000000278e-02
+5.526195479010999057e-01 3.325399740268895066e-01 1.000000000000000021e-02 5.000000000000000278e-02
+5.489801193607908303e-01 2.779881850608343918e-01 1.000000000000000021e-02 5.000000000000000278e-02
+5.500851600584857337e-01 3.749493359129771886e-01 1.000000000000000021e-02 5.000000000000000278e-02
+5.515039630009576088e-01 3.563422719826037643e-01 1.000000000000000021e-02 5.000000000000000278e-02
+5.440539215385635785e-01 3.610934855952002143e-01 1.000000000000000021e-02 5.000000000000000278e-02
+5.633895634988524970e-01 2.323384529079435268e-01 1.000000000000000021e-02 5.000000000000000278e-02
+5.570519569764420531e-01 3.840910697960518982e-01 1.000000000000000021e-02 5.000000000000000278e-02
+5.591497354285636101e-01 3.499305844072504446e-01 1.000000000000000021e-02 5.000000000000000278e-02
+5.704548556013078198e-01 3.177341359939233056e-01 1.000000000000000021e-02 5.000000000000000278e-02
+5.452299544209572302e-01 2.962467407989805013e-01 1.000000000000000021e-02 5.000000000000000278e-02
+5.633819517426097434e-01 3.942859321397256256e-01 1.000000000000000021e-02 5.000000000000000278e-02
+5.593781603874983244e-01 3.796300087504722587e-01 1.000000000000000021e-02 5.000000000000000278e-02
+5.557095668593198257e-01 3.265112476324812385e-01 1.000000000000000021e-02 5.000000000000000278e-02
+5.581873820036300504e-01 4.025871943004837306e-01 1.000000000000000021e-02 5.000000000000000278e-02
+5.716082473023783583e-01 2.997232562668361022e-01 1.000000000000000021e-02 5.000000000000000278e-02
+5.623128916277191358e-01 3.250773187922597618e-01 1.000000000000000021e-02 5.000000000000000278e-02
+5.818767689901503948e-01 3.049260761489128724e-01 1.000000000000000021e-02 5.000000000000000278e-02
+5.659283648432382741e-01 4.011821364996768779e-01 1.000000000000000021e-02 5.000000000000000278e-02
+5.760512098239477519e-01 3.227015129907765401e-01 1.000000000000000021e-02 5.000000000000000278e-02
+5.620205653537188040e-01 3.839217378459949259e-01 1.000000000000000021e-02 5.000000000000000278e-02
+5.606070086651139261e-01 3.315055985053789733e-01 1.000000000000000021e-02 5.000000000000000278e-02
+5.655215453555637595e-01 2.433636163263351582e-01 1.000000000000000021e-02 5.000000000000000278e-02
+5.816100926049581066e-01 3.163810205847112367e-01 1.000000000000000021e-02 5.000000000000000278e-02
+5.913117118651334270e-01 3.492276076185820144e-01 1.000000000000000021e-02 5.000000000000000278e-02
+5.852741029102992432e-01 3.653789892476626000e-01 1.000000000000000021e-02 5.000000000000000278e-02
+5.814785068906287435e-01 3.831228620312552291e-01 1.000000000000000021e-02 5.000000000000000278e-02
+6.083403461755682029e-01 2.802462765244346232e-01 1.000000000000000021e-02 5.000000000000000278e-02
+5.957137870602713381e-01 3.585428355772288245e-01 1.000000000000000021e-02 5.000000000000000278e-02
+6.020043167587377786e-01 3.668925384693044189e-01 1.000000000000000021e-02 5.000000000000000278e-02
+5.770766813146525065e-01 3.458261419598001041e-01 1.000000000000000021e-02 5.000000000000000278e-02
+5.831741370340047803e-01 3.596689746953229960e-01 1.000000000000000021e-02 5.000000000000000278e-02
+5.822478002416777709e-01 3.321991611829274160e-01 1.000000000000000021e-02 5.000000000000000278e-02
+5.715474810482157331e-01 4.050228745331138080e-01 1.000000000000000021e-02 5.000000000000000278e-02
+5.925790405194391042e-01 3.079166671750003803e-01 1.000000000000000021e-02 5.000000000000000278e-02
+6.106171684233882013e-01 3.098511495984393460e-01 1.000000000000000021e-02 5.000000000000000278e-02
+5.922461687291219468e-01 3.500275461863625592e-01 1.000000000000000021e-02 5.000000000000000278e-02
+6.174491538391788659e-01 3.718699363006590097e-01 1.000000000000000021e-02 5.000000000000000278e-02
+6.115737359907557069e-01 3.288966723936410097e-01 1.000000000000000021e-02 5.000000000000000278e-02
+5.981174943029529123e-01 3.750813852704704132e-01 1.000000000000000021e-02 5.000000000000000278e-02
+6.026263603744241859e-01 3.675846234985968852e-01 1.000000000000000021e-02 5.000000000000000278e-02
+6.105022982288155209e-01 3.167289940248868563e-01 1.000000000000000021e-02 5.000000000000000278e-02
+6.151216351421019413e-01 4.607718303711479391e-01 1.000000000000000021e-02 5.000000000000000278e-02
+5.964641303822283414e-01 2.215783335889162375e-01 1.000000000000000021e-02 5.000000000000000278e-02
+5.882227321001924913e-01 2.782115200293563961e-01 1.000000000000000021e-02 5.000000000000000278e-02
+6.117436850383543012e-01 4.183467312936062221e-01 1.000000000000000021e-02 5.000000000000000278e-02
+6.127592092157096992e-01 4.174279517884761370e-01 1.000000000000000021e-02 5.000000000000000278e-02
+6.171602867780734414e-01 3.881583269581090345e-01 1.000000000000000021e-02 5.000000000000000278e-02
+6.156933833232649533e-01 3.746727179514120487e-01 1.000000000000000021e-02 5.000000000000000278e-02
+6.065151498035898925e-01 3.929200883630892815e-01 1.000000000000000021e-02 5.000000000000000278e-02
+6.301256744399899024e-01 3.283800023303200533e-01 1.000000000000000021e-02 5.000000000000000278e-02
+6.226162835429657205e-01 3.418201214092429496e-01 1.000000000000000021e-02 5.000000000000000278e-02
+6.201386292792104848e-01 3.566604850084775791e-01 1.000000000000000021e-02 5.000000000000000278e-02
+6.169409389061010218e-01 3.783594135746866094e-01 1.000000000000000021e-02 5.000000000000000278e-02
+6.402150855640913463e-01 3.229020075665217648e-01 1.000000000000000021e-02 5.000000000000000278e-02
+6.124726382136754799e-01 3.359165246688576856e-01 1.000000000000000021e-02 5.000000000000000278e-02
+6.295578231534935121e-01 3.560566046876079138e-01 1.000000000000000021e-02 5.000000000000000278e-02
+6.313800640505476958e-01 3.181148216388815508e-01 1.000000000000000021e-02 5.000000000000000278e-02
+6.172245832381815234e-01 3.551280640767622843e-01 1.000000000000000021e-02 5.000000000000000278e-02
+6.185542313684792015e-01 3.592234585873543273e-01 1.000000000000000021e-02 5.000000000000000278e-02
+6.091263318451216602e-01 3.843977979501782549e-01 1.000000000000000021e-02 5.000000000000000278e-02
+6.300926273622199369e-01 3.776387422970099439e-01 1.000000000000000021e-02 5.000000000000000278e-02
+6.113213495386417007e-01 3.458049849210783622e-01 1.000000000000000021e-02 5.000000000000000278e-02
+6.385785744523568841e-01 3.563364562437312810e-01 1.000000000000000021e-02 5.000000000000000278e-02
+6.397331397047208990e-01 3.100216743844523282e-01 1.000000000000000021e-02 5.000000000000000278e-02
+6.435457839753877529e-01 3.990198215705506035e-01 1.000000000000000021e-02 5.000000000000000278e-02
+6.288887289834925731e-01 4.370902005757104081e-01 1.000000000000000021e-02 5.000000000000000278e-02
+6.365841958129050404e-01 3.694797435230971483e-01 1.000000000000000021e-02 5.000000000000000278e-02
+6.418054423306102096e-01 3.012592497007736680e-01 1.000000000000000021e-02 5.000000000000000278e-02
+6.386899920845523493e-01 3.819850179798722767e-01 1.000000000000000021e-02 5.000000000000000278e-02
+6.469336619986744141e-01 2.827377530038750475e-01 1.000000000000000021e-02 5.000000000000000278e-02
+6.334475749619762341e-01 3.754061816923868755e-01 1.000000000000000021e-02 5.000000000000000278e-02
+6.321598650226153415e-01 3.449228737679611578e-01 1.000000000000000021e-02 5.000000000000000278e-02
+6.509067780967937589e-01 3.503771507430432641e-01 1.000000000000000021e-02 5.000000000000000278e-02
+6.350714163984872984e-01 3.197101588138007155e-01 1.000000000000000021e-02 5.000000000000000278e-02
+6.382461857280633533e-01 4.437673153825655303e-01 1.000000000000000021e-02 5.000000000000000278e-02
+6.449813934355191902e-01 4.408819402290548584e-01 1.000000000000000021e-02 5.000000000000000278e-02
+6.524860124857247978e-01 3.194627623005703576e-01 1.000000000000000021e-02 5.000000000000000278e-02
+6.577157654665457542e-01 3.832388131482945548e-01 1.000000000000000021e-02 5.000000000000000278e-02
+6.501416979556857711e-01 3.281407037733702903e-01 1.000000000000000021e-02 5.000000000000000278e-02
+6.540399735920835456e-01 3.058627790627297949e-01 1.000000000000000021e-02 5.000000000000000278e-02
+6.508517477411821517e-01 3.303471189295076882e-01 1.000000000000000021e-02 5.000000000000000278e-02
+6.707721177763477094e-01 3.271310522975454660e-01 1.000000000000000021e-02 5.000000000000000278e-02
+6.633197219743095507e-01 3.092498331996825778e-01 1.000000000000000021e-02 5.000000000000000278e-02
+6.422040749855527642e-01 3.476968138235264627e-01 1.000000000000000021e-02 5.000000000000000278e-02
+6.761204591213937354e-01 4.186211942433739619e-01 1.000000000000000021e-02 5.000000000000000278e-02
+6.715107988694226648e-01 2.998587645942395463e-01 1.000000000000000021e-02 5.000000000000000278e-02
+6.776098522174011096e-01 3.704494453472497173e-01 1.000000000000000021e-02 5.000000000000000278e-02
+6.744617128634884384e-01 3.256385577837517808e-01 1.000000000000000021e-02 5.000000000000000278e-02
+6.754646216422400817e-01 2.697280605346932814e-01 1.000000000000000021e-02 5.000000000000000278e-02
+6.809276992560853170e-01 4.388536968110946512e-01 1.000000000000000021e-02 5.000000000000000278e-02
+6.739074534299088759e-01 3.927722273366411332e-01 1.000000000000000021e-02 5.000000000000000278e-02
+6.790618213932693159e-01 4.661540853868908485e-01 1.000000000000000021e-02 5.000000000000000278e-02
+6.604829010920361121e-01 4.508062308722429745e-01 1.000000000000000021e-02 5.000000000000000278e-02
+6.695037905897057717e-01 3.025565098632151395e-01 1.000000000000000021e-02 5.000000000000000278e-02
+6.853533402677857156e-01 3.506704052249644543e-01 1.000000000000000021e-02 5.000000000000000278e-02
+6.691949471631699620e-01 3.251846467105514726e-01 1.000000000000000021e-02 5.000000000000000278e-02
+6.891615230232762679e-01 3.669029082435897315e-01 1.000000000000000021e-02 5.000000000000000278e-02
+6.683184903526452336e-01 3.108312761246229128e-01 1.000000000000000021e-02 5.000000000000000278e-02
+6.893023589268845175e-01 3.675944952026901635e-01 1.000000000000000021e-02 5.000000000000000278e-02
+6.910160045474087465e-01 3.963663853297839101e-01 1.000000000000000021e-02 5.000000000000000278e-02
+6.935137711489676171e-01 3.126171916158762554e-01 1.000000000000000021e-02 5.000000000000000278e-02
+6.633974434409514176e-01 3.774084778017995911e-01 1.000000000000000021e-02 5.000000000000000278e-02
+6.846318605552539349e-01 3.358325534098109411e-01 1.000000000000000021e-02 5.000000000000000278e-02
+6.979370236750825907e-01 4.745258875502971763e-01 1.000000000000000021e-02 5.000000000000000278e-02
+6.840788988468501364e-01 3.219887982742922761e-01 1.000000000000000021e-02 5.000000000000000278e-02
+6.907654314805129481e-01 2.799298489201751949e-01 1.000000000000000021e-02 5.000000000000000278e-02
+6.802415122351691545e-01 3.746887388594503099e-01 1.000000000000000021e-02 5.000000000000000278e-02
+7.072035051689796736e-01 4.570391447937616758e-01 1.000000000000000021e-02 5.000000000000000278e-02
+6.949189312851783429e-01 3.703991344331735713e-01 1.000000000000000021e-02 5.000000000000000278e-02
+6.957125534582759840e-01 3.692873658691474548e-01 1.000000000000000021e-02 5.000000000000000278e-02
+6.918040942206347133e-01 3.542467870673969843e-01 1.000000000000000021e-02 5.000000000000000278e-02
+7.074975957699396467e-01 3.858582907624741876e-01 1.000000000000000021e-02 5.000000000000000278e-02
+7.073306943128525592e-01 3.807698276181462615e-01 1.000000000000000021e-02 5.000000000000000278e-02
+7.080945104531328749e-01 4.302772382756781533e-01 1.000000000000000021e-02 5.000000000000000278e-02
+6.966439506113470959e-01 3.275231853230735624e-01 1.000000000000000021e-02 5.000000000000000278e-02
+7.169308611057054748e-01 3.843838322386374795e-01 1.000000000000000021e-02 5.000000000000000278e-02
+6.937672813169470931e-01 3.336643675247850105e-01 1.000000000000000021e-02 5.000000000000000278e-02
+7.129696051403836554e-01 3.024332599930479315e-01 1.000000000000000021e-02 5.000000000000000278e-02
+7.251903333111381356e-01 4.790034418349733425e-01 1.000000000000000021e-02 5.000000000000000278e-02
+7.234417807241702025e-01 4.374329063430760223e-01 1.000000000000000021e-02 5.000000000000000278e-02
+7.026342961198607240e-01 4.296218034022507570e-01 1.000000000000000021e-02 5.000000000000000278e-02
+7.204581506445537631e-01 4.768264012168756394e-01 1.000000000000000021e-02 5.000000000000000278e-02
+6.995107999940813892e-01 3.056400672914548755e-01 1.000000000000000021e-02 5.000000000000000278e-02
+7.204891297532413086e-01 3.908208778631384051e-01 1.000000000000000021e-02 5.000000000000000278e-02
+7.248849513792748889e-01 3.577504822366341375e-01 1.000000000000000021e-02 5.000000000000000278e-02
+7.317018640668583318e-01 4.189754899076328920e-01 1.000000000000000021e-02 5.000000000000000278e-02
+7.110543997606344480e-01 4.504522949113918817e-01 1.000000000000000021e-02 5.000000000000000278e-02
+7.376835220404003302e-01 4.154929063590779847e-01 1.000000000000000021e-02 5.000000000000000278e-02
+7.202584623861660873e-01 3.667205277851724454e-01 1.000000000000000021e-02 5.000000000000000278e-02
+7.151954943351997995e-01 3.127607856046231705e-01 1.000000000000000021e-02 5.000000000000000278e-02
+7.098351834326595000e-01 4.062567776411423193e-01 1.000000000000000021e-02 5.000000000000000278e-02
+7.271183593132408696e-01 2.896764090112305956e-01 1.000000000000000021e-02 5.000000000000000278e-02
+7.322004129883669110e-01 4.524139651039751908e-01 1.000000000000000021e-02 5.000000000000000278e-02
+7.251547498051729157e-01 3.443401670474434129e-01 1.000000000000000021e-02 5.000000000000000278e-02
+7.137270062376980251e-01 3.827957131407479507e-01 1.000000000000000021e-02 5.000000000000000278e-02
+7.506550881981380874e-01 4.346581993618443551e-01 1.000000000000000021e-02 5.000000000000000278e-02
+7.203354002592142757e-01 3.377187766041148120e-01 1.000000000000000021e-02 5.000000000000000278e-02
+7.131198557936053728e-01 4.364415930495361140e-01 1.000000000000000021e-02 5.000000000000000278e-02
+7.548205704691718365e-01 3.438023619377879037e-01 1.000000000000000021e-02 5.000000000000000278e-02
+7.336221953472833457e-01 4.458444144185423896e-01 1.000000000000000021e-02 5.000000000000000278e-02
+7.486511499373890155e-01 3.337876377607896794e-01 1.000000000000000021e-02 5.000000000000000278e-02
+7.578680451496401238e-01 3.716591648258270597e-01 1.000000000000000021e-02 5.000000000000000278e-02
+7.413391743351841479e-01 4.075644248445439155e-01 1.000000000000000021e-02 5.000000000000000278e-02
+7.344677162979212914e-01 4.433573611979529372e-01 1.000000000000000021e-02 5.000000000000000278e-02
+7.273918343466387881e-01 4.016106817890621739e-01 1.000000000000000021e-02 5.000000000000000278e-02
+7.610658625409360001e-01 4.265790747938559280e-01 1.000000000000000021e-02 5.000000000000000278e-02
+7.472943311757368479e-01 4.723869439363619360e-01 1.000000000000000021e-02 5.000000000000000278e-02
+7.446223749547828952e-01 3.367079469902013455e-01 1.000000000000000021e-02 5.000000000000000278e-02
+7.733710243320935929e-01 4.075660346996356487e-01 1.000000000000000021e-02 5.000000000000000278e-02
+7.540588971476318569e-01 3.891454313021228129e-01 1.000000000000000021e-02 5.000000000000000278e-02
+7.461999681088090641e-01 5.098908537549333708e-01 1.000000000000000021e-02 5.000000000000000278e-02
+7.415108857874628256e-01 3.972597573278329741e-01 1.000000000000000021e-02 5.000000000000000278e-02
+7.726051763745890311e-01 3.802858646522553343e-01 1.000000000000000021e-02 5.000000000000000278e-02
+7.591894374181230587e-01 3.981424031684708265e-01 1.000000000000000021e-02 5.000000000000000278e-02
+7.810122850864816835e-01 3.612288684654897386e-01 1.000000000000000021e-02 5.000000000000000278e-02
+7.622150057233713083e-01 4.240677390112669309e-01 1.000000000000000021e-02 5.000000000000000278e-02
+7.577670573262277331e-01 3.276681464959149692e-01 1.000000000000000021e-02 5.000000000000000278e-02
+7.692162366941341922e-01 4.524120604429378578e-01 1.000000000000000021e-02 5.000000000000000278e-02
+7.796181420161718556e-01 3.679183062358741263e-01 1.000000000000000021e-02 5.000000000000000278e-02
+7.891310153247905745e-01 4.684177677110344939e-01 1.000000000000000021e-02 5.000000000000000278e-02
+7.772344978525929093e-01 3.377563682288435287e-01 1.000000000000000021e-02 5.000000000000000278e-02
+7.738222521964502887e-01 4.014095471855363972e-01 1.000000000000000021e-02 5.000000000000000278e-02
+7.923540224561999024e-01 3.637963179264713798e-01 1.000000000000000021e-02 5.000000000000000278e-02
+7.732723116204460734e-01 4.083072623657723610e-01 1.000000000000000021e-02 5.000000000000000278e-02
+7.640539439548387213e-01 4.394301706750419756e-01 1.000000000000000021e-02 5.000000000000000278e-02
+7.724984776149210752e-01 4.319711891376115109e-01 1.000000000000000021e-02 5.000000000000000278e-02
+7.934128670984914589e-01 4.705210932766525911e-01 1.000000000000000021e-02 5.000000000000000278e-02
+7.917570873634108830e-01 4.868531597879799389e-01 1.000000000000000021e-02 5.000000000000000278e-02
+7.905034947465314765e-01 4.250090994630363883e-01 1.000000000000000021e-02 5.000000000000000278e-02
+7.792135178991118627e-01 4.126275787828020958e-01 1.000000000000000021e-02 5.000000000000000278e-02
+7.900342797926579452e-01 4.382960115617247077e-01 1.000000000000000021e-02 5.000000000000000278e-02
+7.827116543240431046e-01 4.637372417108351352e-01 1.000000000000000021e-02 5.000000000000000278e-02
+7.767587669060683764e-01 3.939495497151670467e-01 1.000000000000000021e-02 5.000000000000000278e-02
+7.833268480789523647e-01 4.113737840119418010e-01 1.000000000000000021e-02 5.000000000000000278e-02
+7.845321680448679169e-01 3.846913627101629651e-01 1.000000000000000021e-02 5.000000000000000278e-02
+7.773267284354640205e-01 4.315456829813401063e-01 1.000000000000000021e-02 5.000000000000000278e-02
+7.904514846702109798e-01 4.267047621828242154e-01 1.000000000000000021e-02 5.000000000000000278e-02
+8.048016124907878543e-01 4.877425174755010695e-01 1.000000000000000021e-02 5.000000000000000278e-02
+8.030522284170422687e-01 4.231802543573114361e-01 1.000000000000000021e-02 5.000000000000000278e-02
+7.918417827979636892e-01 2.915682844965841181e-01 1.000000000000000021e-02 5.000000000000000278e-02
+7.994458059773541514e-01 3.622298415500210256e-01 1.000000000000000021e-02 5.000000000000000278e-02
+7.994965340198262327e-01 3.843967281394262137e-01 1.000000000000000021e-02 5.000000000000000278e-02
+7.861836443978328370e-01 4.474628316232916458e-01 1.000000000000000021e-02 5.000000000000000278e-02
+7.995937387126630380e-01 4.158093396803158592e-01 1.000000000000000021e-02 5.000000000000000278e-02
+8.099509869437148124e-01 3.938093333538186247e-01 1.000000000000000021e-02 5.000000000000000278e-02
+7.967663394747096506e-01 3.865865534123223979e-01 1.000000000000000021e-02 5.000000000000000278e-02
+8.145866681541276133e-01 3.886983048434226595e-01 1.000000000000000021e-02 5.000000000000000278e-02
+8.119457745402104409e-01 4.177681929591669507e-01 1.000000000000000021e-02 5.000000000000000278e-02
+8.092389319538796366e-01 3.884583346706960150e-01 1.000000000000000021e-02 5.000000000000000278e-02
+8.133269857178130335e-01 4.677643590684170127e-01 1.000000000000000021e-02 5.000000000000000278e-02
+8.025751912380767461e-01 3.700550285460245115e-01 1.000000000000000021e-02 5.000000000000000278e-02
+8.040589687133691266e-01 3.631886388802113563e-01 1.000000000000000021e-02 5.000000000000000278e-02
+8.125531079042489502e-01 4.040746065760604799e-01 1.000000000000000021e-02 5.000000000000000278e-02
+8.189283284771464722e-01 4.030255683316606996e-01 1.000000000000000021e-02 5.000000000000000278e-02
+8.161282585022279212e-01 3.711290200735170020e-01 1.000000000000000021e-02 5.000000000000000278e-02
+8.097980659189142338e-01 4.676606979530106289e-01 1.000000000000000021e-02 5.000000000000000278e-02
+8.159615204902070928e-01 4.009844484000973930e-01 1.000000000000000021e-02 5.000000000000000278e-02
+8.267633936283519391e-01 4.851041464233857847e-01 1.000000000000000021e-02 5.000000000000000278e-02
+8.326547300968315524e-01 4.417588572948545900e-01 1.000000000000000021e-02 5.000000000000000278e-02
+8.368207099215796418e-01 4.407109400671179178e-01 1.000000000000000021e-02 5.000000000000000278e-02
+8.039923088373891469e-01 3.905317570483490819e-01 1.000000000000000021e-02 5.000000000000000278e-02
+8.077866097955339608e-01 4.400693333676263808e-01 1.000000000000000021e-02 5.000000000000000278e-02
+8.197541394511084212e-01 4.265316738328210344e-01 1.000000000000000021e-02 5.000000000000000278e-02
+8.292118306543494466e-01 3.482769423943659071e-01 1.000000000000000021e-02 5.000000000000000278e-02
+8.231064142389249438e-01 4.766989921950325337e-01 1.000000000000000021e-02 5.000000000000000278e-02
+8.390532472341055703e-01 3.873453050940095510e-01 1.000000000000000021e-02 5.000000000000000278e-02
+8.463509789503489422e-01 3.994244334732232060e-01 1.000000000000000021e-02 5.000000000000000278e-02
+8.295930037334541263e-01 3.432066306658433930e-01 1.000000000000000021e-02 5.000000000000000278e-02
+8.372379990093472557e-01 3.201080188926920256e-01 1.000000000000000021e-02 5.000000000000000278e-02
+8.485814579147301639e-01 3.817181714698132389e-01 1.000000000000000021e-02 5.000000000000000278e-02
+8.530664258211803075e-01 4.276272676007898954e-01 1.000000000000000021e-02 5.000000000000000278e-02
+8.372261412833879035e-01 4.303547641425516379e-01 1.000000000000000021e-02 5.000000000000000278e-02
+8.322296557631209124e-01 4.031296320411841272e-01 1.000000000000000021e-02 5.000000000000000278e-02
+8.581654601085882961e-01 4.690926495514244343e-01 1.000000000000000021e-02 5.000000000000000278e-02
+8.417674454075858570e-01 4.034173418854757887e-01 1.000000000000000021e-02 5.000000000000000278e-02
+8.261238923887080920e-01 4.290539001494967919e-01 1.000000000000000021e-02 5.000000000000000278e-02
+8.474613386092758605e-01 4.987341847909432246e-01 1.000000000000000021e-02 5.000000000000000278e-02
+8.473690778268317958e-01 3.636956122622782561e-01 1.000000000000000021e-02 5.000000000000000278e-02
+8.645355563067556037e-01 4.378998415165215041e-01 1.000000000000000021e-02 5.000000000000000278e-02
+8.398021648554802043e-01 4.042237933335898759e-01 1.000000000000000021e-02 5.000000000000000278e-02
+8.537427897179736824e-01 4.623187820678897331e-01 1.000000000000000021e-02 5.000000000000000278e-02
+8.571480625172833712e-01 4.404098197119210023e-01 1.000000000000000021e-02 5.000000000000000278e-02
+8.350258349618763232e-01 3.745222463378675148e-01 1.000000000000000021e-02 5.000000000000000278e-02
+8.578782293802988956e-01 4.373740191347575967e-01 1.000000000000000021e-02 5.000000000000000278e-02
+8.537570228529524075e-01 4.642074097115636566e-01 1.000000000000000021e-02 5.000000000000000278e-02
+8.708174715291085999e-01 4.332606223671188750e-01 1.000000000000000021e-02 5.000000000000000278e-02
+8.562964586713829318e-01 5.060214374693942085e-01 1.000000000000000021e-02 5.000000000000000278e-02
+8.806128863905964277e-01 4.228122614067658103e-01 1.000000000000000021e-02 5.000000000000000278e-02
+8.599686843027918304e-01 3.851789285155602371e-01 1.000000000000000021e-02 5.000000000000000278e-02
+8.535474173053297919e-01 4.354717931873741121e-01 1.000000000000000021e-02 5.000000000000000278e-02
+8.735038585187157034e-01 4.740287085480253682e-01 1.000000000000000021e-02 5.000000000000000278e-02
+8.584342440980748945e-01 4.794769563437948912e-01 1.000000000000000021e-02 5.000000000000000278e-02
+8.570838998785131890e-01 4.274775988704750951e-01 1.000000000000000021e-02 5.000000000000000278e-02
+8.774414568514010693e-01 4.336626453439739737e-01 1.000000000000000021e-02 5.000000000000000278e-02
+8.694559315070589101e-01 4.407113502569234287e-01 1.000000000000000021e-02 5.000000000000000278e-02
+8.689545264682099202e-01 3.843114329984029243e-01 1.000000000000000021e-02 5.000000000000000278e-02
+8.600874991466713748e-01 5.019586306743961668e-01 1.000000000000000021e-02 5.000000000000000278e-02
+8.790392840898424431e-01 5.042429178576819382e-01 1.000000000000000021e-02 5.000000000000000278e-02
+8.932048092916533566e-01 4.368058277258009547e-01 1.000000000000000021e-02 5.000000000000000278e-02
+8.865021396843075774e-01 2.739070374463205004e-01 1.000000000000000021e-02 5.000000000000000278e-02
+8.841933108001960306e-01 4.503683503147761780e-01 1.000000000000000021e-02 5.000000000000000278e-02
+8.999930942005827106e-01 4.676651530267195156e-01 1.000000000000000021e-02 5.000000000000000278e-02
+8.859941056054502218e-01 4.785401623493297785e-01 1.000000000000000021e-02 5.000000000000000278e-02
+8.760171259373087382e-01 4.969065857979497181e-01 1.000000000000000021e-02 5.000000000000000278e-02
+8.971730002799958026e-01 4.667100187607416961e-01 1.000000000000000021e-02 5.000000000000000278e-02
+8.848119818927673297e-01 4.416971947213396699e-01 1.000000000000000021e-02 5.000000000000000278e-02
+8.901362652340670811e-01 3.962171894469602162e-01 1.000000000000000021e-02 5.000000000000000278e-02
+8.987740302613900223e-01 4.206889410957546649e-01 1.000000000000000021e-02 5.000000000000000278e-02
+8.730565323254301235e-01 3.915371618623503247e-01 1.000000000000000021e-02 5.000000000000000278e-02
+8.865225288515570234e-01 4.111414179759269039e-01 1.000000000000000021e-02 5.000000000000000278e-02
+8.991096486245733210e-01 4.488478048926927144e-01 1.000000000000000021e-02 5.000000000000000278e-02
+8.713328577007465325e-01 3.686749778505306052e-01 1.000000000000000021e-02 5.000000000000000278e-02
+9.062988765916956124e-01 3.951189620105708400e-01 1.000000000000000021e-02 5.000000000000000278e-02
+8.870948605762134509e-01 5.189226167996823236e-01 1.000000000000000021e-02 5.000000000000000278e-02
+8.833883125511055034e-01 4.251574756667677035e-01 1.000000000000000021e-02 5.000000000000000278e-02
+9.118507331636798163e-01 3.551803907988336229e-01 1.000000000000000021e-02 5.000000000000000278e-02
+8.999642080766256935e-01 3.910944066602227154e-01 1.000000000000000021e-02 5.000000000000000278e-02
+8.903409662435411986e-01 4.243841784638837655e-01 1.000000000000000021e-02 5.000000000000000278e-02
+8.959401355869941463e-01 4.170798854620869078e-01 1.000000000000000021e-02 5.000000000000000278e-02
+8.973533263512692981e-01 4.220407377287970219e-01 1.000000000000000021e-02 5.000000000000000278e-02
+8.885744838207058072e-01 4.056623248674660620e-01 1.000000000000000021e-02 5.000000000000000278e-02
+9.097083570311109701e-01 5.116892026326462783e-01 1.000000000000000021e-02 5.000000000000000278e-02
+9.145886508302535356e-01 4.493157019706892630e-01 1.000000000000000021e-02 5.000000000000000278e-02
+9.008198757833529857e-01 4.301345351355645996e-01 1.000000000000000021e-02 5.000000000000000278e-02
+9.163869956639466574e-01 3.312410256953464693e-01 1.000000000000000021e-02 5.000000000000000278e-02
+9.172948963480690443e-01 4.410509963447606374e-01 1.000000000000000021e-02 5.000000000000000278e-02
+9.191884952097696404e-01 4.238534718593095496e-01 1.000000000000000021e-02 5.000000000000000278e-02
+9.277532304039869393e-01 4.089482338500201597e-01 1.000000000000000021e-02 5.000000000000000278e-02
+9.225344527549227402e-01 5.514245469356119367e-01 1.000000000000000021e-02 5.000000000000000278e-02
+9.374030310792198506e-01 4.982871759105358400e-01 1.000000000000000021e-02 5.000000000000000278e-02
+8.986873883681972819e-01 3.257311642980554311e-01 1.000000000000000021e-02 5.000000000000000278e-02
+9.195659538494391771e-01 5.004683106695579919e-01 1.000000000000000021e-02 5.000000000000000278e-02
+9.432315353541192993e-01 4.190556193208554436e-01 1.000000000000000021e-02 5.000000000000000278e-02
+9.487928474117568456e-01 4.001160842234339765e-01 1.000000000000000021e-02 5.000000000000000278e-02
+9.164525775360058413e-01 4.650941408566642288e-01 1.000000000000000021e-02 5.000000000000000278e-02
+9.208285984434361193e-01 3.661667442791302074e-01 1.000000000000000021e-02 5.000000000000000278e-02
+9.251115544828649728e-01 4.548089259430550535e-01 1.000000000000000021e-02 5.000000000000000278e-02
+9.275758474530250153e-01 5.128486841115509343e-01 1.000000000000000021e-02 5.000000000000000278e-02
+9.366369905681350971e-01 3.796070547589429078e-01 1.000000000000000021e-02 5.000000000000000278e-02
+9.456207295962987258e-01 4.998856726258943950e-01 1.000000000000000021e-02 5.000000000000000278e-02
+9.345795385264787924e-01 5.185024465422254369e-01 1.000000000000000021e-02 5.000000000000000278e-02
+9.326168132843797309e-01 4.556349187365501541e-01 1.000000000000000021e-02 5.000000000000000278e-02
+9.433428934319937342e-01 3.911754275083016430e-01 1.000000000000000021e-02 5.000000000000000278e-02
+9.523961643699346835e-01 4.107603998074627616e-01 1.000000000000000021e-02 5.000000000000000278e-02
+9.407232347279074203e-01 4.203181280432740086e-01 1.000000000000000021e-02 5.000000000000000278e-02
+9.484611392851700629e-01 4.297359515243864836e-01 1.000000000000000021e-02 5.000000000000000278e-02
+9.506038961473221027e-01 4.547989161883511100e-01 1.000000000000000021e-02 5.000000000000000278e-02
+9.485213388476273488e-01 5.192134060388348127e-01 1.000000000000000021e-02 5.000000000000000278e-02
+9.514272993765031661e-01 4.423975343767552748e-01 1.000000000000000021e-02 5.000000000000000278e-02
+9.484104881241508522e-01 5.180994951952422323e-01 1.000000000000000021e-02 5.000000000000000278e-02
+9.623342130957196483e-01 6.008963077779179152e-01 1.000000000000000021e-02 5.000000000000000278e-02
+9.307777713871678849e-01 4.199637747727307802e-01 1.000000000000000021e-02 5.000000000000000278e-02
+9.530008056389571713e-01 3.607068503880705435e-01 1.000000000000000021e-02 5.000000000000000278e-02
+9.573123372817460286e-01 4.699128586570268107e-01 1.000000000000000021e-02 5.000000000000000278e-02
+9.466469981548389923e-01 4.482979721974302278e-01 1.000000000000000021e-02 5.000000000000000278e-02
+9.466317860831469089e-01 4.408629401740034592e-01 1.000000000000000021e-02 5.000000000000000278e-02
+9.601860683009248998e-01 4.596042778052384370e-01 1.000000000000000021e-02 5.000000000000000278e-02
+9.839946224578202116e-01 4.304043225719447752e-01 1.000000000000000021e-02 5.000000000000000278e-02
+9.827967466537107510e-01 4.013147819576803887e-01 1.000000000000000021e-02 5.000000000000000278e-02
+9.554435188074706931e-01 5.020613473427647522e-01 1.000000000000000021e-02 5.000000000000000278e-02
+9.422430546369906512e-01 4.543545554048709079e-01 1.000000000000000021e-02 5.000000000000000278e-02
+9.691626180244877764e-01 4.099799675809863153e-01 1.000000000000000021e-02 5.000000000000000278e-02
+9.657302130364530113e-01 4.906561442938605633e-01 1.000000000000000021e-02 5.000000000000000278e-02
+9.758428388285583788e-01 3.432838749646667797e-01 1.000000000000000021e-02 5.000000000000000278e-02
+9.729791015620111727e-01 3.456095847115177033e-01 1.000000000000000021e-02 5.000000000000000278e-02
+9.770969554635006160e-01 4.670627982055561844e-01 1.000000000000000021e-02 5.000000000000000278e-02
+9.764701085384055457e-01 4.435156314586171100e-01 1.000000000000000021e-02 5.000000000000000278e-02
+9.865074662276852591e-01 5.403431728642609233e-01 1.000000000000000021e-02 5.000000000000000278e-02
+9.738147663120996222e-01 4.990682153222172346e-01 1.000000000000000021e-02 5.000000000000000278e-02
+9.857909833298961200e-01 4.305125981622993092e-01 1.000000000000000021e-02 5.000000000000000278e-02
+9.942221718690666954e-01 4.397967952743394027e-01 1.000000000000000021e-02 5.000000000000000278e-02
+9.740440654597513070e-01 4.951003962293232807e-01 1.000000000000000021e-02 5.000000000000000278e-02
+9.774805023830059891e-01 4.293782154340610169e-01 1.000000000000000021e-02 5.000000000000000278e-02
+9.774395475630969221e-01 5.140369447836020678e-01 1.000000000000000021e-02 5.000000000000000278e-02
+9.769208741992773115e-01 4.641019596274009174e-01 1.000000000000000021e-02 5.000000000000000278e-02
+9.879534248215895431e-01 3.991472649259572569e-01 1.000000000000000021e-02 5.000000000000000278e-02
+9.894966333468157016e-01 3.773187468407014911e-01 1.000000000000000021e-02 5.000000000000000278e-02
+9.766119651766570486e-01 4.737160727868355847e-01 1.000000000000000021e-02 5.000000000000000278e-02
+9.914011849075605731e-01 5.849219022964514680e-01 1.000000000000000021e-02 5.000000000000000278e-02
+9.940814113488887216e-01 5.322714391873211159e-01 1.000000000000000021e-02 5.000000000000000278e-02
+9.978496937361953645e-01 3.953619897005313244e-01 1.000000000000000021e-02 5.000000000000000278e-02
+9.983259963802354475e-01 4.439213918584726248e-01 1.000000000000000021e-02 5.000000000000000278e-02
+9.976113764273393247e-01 3.394591518790454621e-01 1.000000000000000021e-02 5.000000000000000278e-02
+9.977015085866962618e-01 4.646061847477814255e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.003752447139201420e+00 4.460997278069096517e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.017915817587683236e+00 4.057562615480080348e-01 1.000000000000000021e-02 5.000000000000000278e-02
+9.909977782312084926e-01 3.637417372970208618e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.009246062426148338e+00 4.697069190345526390e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.001953183762609667e+00 5.065912620526219490e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.011910898070643938e+00 5.148224906206306795e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.023701711841434214e+00 4.258205572698714469e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.008508118627344130e+00 3.982924621135485732e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.014127210570712423e+00 4.548200728542809435e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.006505545394314138e+00 5.424752534858099384e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.021159681666833352e+00 5.101448504932604155e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.007429164645764930e+00 5.357099245010410460e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.025792153240087767e+00 4.230712960612598161e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.015480974735811781e+00 3.829293387336230592e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.018058238317904873e+00 4.930861207417931991e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.017346569710103399e+00 4.297134854129064374e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.020534337785223178e+00 4.549209930972691884e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.022183512473785472e+00 5.313152831596070769e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.019064376547812012e+00 4.481316753260161656e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.031768430520933588e+00 4.756512304126700452e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.024545906039517895e+00 3.514834914322496062e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.020897524739618811e+00 4.478668302270371604e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.019266946914353600e+00 4.662562430152803472e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.024897394669427309e+00 5.367549291688319402e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.037615615375907741e+00 5.397041516669623951e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.016158577483349923e+00 4.436835561013426354e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.041924320455835051e+00 3.793717763374693797e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.042793330461388779e+00 4.429353604952322443e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.022271695269756364e+00 4.591267664534390835e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.047788514763608791e+00 4.628965446699748187e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.036936663610581233e+00 6.048757363030438094e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.035783101149661878e+00 4.950944519031420521e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.042093094663976816e+00 3.415968316876545341e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.045714784523923191e+00 5.190137760904240949e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.063417858508491021e+00 4.617445071711223092e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.053190485278323374e+00 4.484755718745528363e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.052189792512184141e+00 3.801433635063027339e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.058390858239320664e+00 4.604723153470801078e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.047801460061958068e+00 5.459564562480678784e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.045844432121264989e+00 4.545436008797334448e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.047061308907431210e+00 4.791132881420164802e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.030496085386635619e+00 5.035844196850418619e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.068623134287134624e+00 5.156882272005327561e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.065980863549277569e+00 4.873105984227578125e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.060414915802797786e+00 4.587979882448185998e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.057172077645023300e+00 4.489916387466365566e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.053299935335848092e+00 5.015017922765823144e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.043052851573002648e+00 4.232478261653873641e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.058018960527929542e+00 5.324934900726463471e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.080769031672960567e+00 5.003278712845128373e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.075210145141003215e+00 5.059600924801243016e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.083289628374849878e+00 3.929755233358329658e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.069180352464538686e+00 4.398659842398788267e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.065456030919460284e+00 4.614532547597692269e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.078326560956613944e+00 5.291061297069195035e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.053795557973045138e+00 4.506719066288771836e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.072608931304234625e+00 4.683509222356879720e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.089404481879944875e+00 3.855729357684279379e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.077942288082558431e+00 5.988703713363161540e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.070767737449038082e+00 3.622290351569159572e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.086853423856421141e+00 4.672507561082899774e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.082338627823365318e+00 4.931340632510298749e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.074429178503004900e+00 4.814698142253286961e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.098756724754226699e+00 4.963420506604801496e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.070757567361580342e+00 4.961285082793265300e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.081215240377159770e+00 5.142858417141038530e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.087533040436713394e+00 5.137505995272118042e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.086650158636951247e+00 4.828567808946283568e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.091780267557133310e+00 4.175484384529267734e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.095039590991996103e+00 4.814653199941095196e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.104355585232602799e+00 4.353651269125258794e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.094183646759028017e+00 4.441497007248097484e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.098735054811006862e+00 3.976818162782475019e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.111955980502097541e+00 4.558911419368917306e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.110377271142580780e+00 5.078878522296290665e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.087779023818080226e+00 4.402666356140856885e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.107185190121144247e+00 5.458591220887015760e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.091626495331006641e+00 4.311081147134752101e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.107252154784458931e+00 4.569834628440985203e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.098106852227764829e+00 5.102761076532882356e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.113245034283405932e+00 5.031787119754282944e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.098919448711248803e+00 5.274732536380539738e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.112142578963217554e+00 6.055691900874593525e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.121757762953613291e+00 5.404126123197021681e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.110076602194896012e+00 5.259699304739668957e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.133347701013354580e+00 4.190977072795491476e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.122268595209922282e+00 5.211330562708654801e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.130309361186334627e+00 4.763524250896060686e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.112881348788958835e+00 5.956885006656162940e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.135681998291918404e+00 4.793633859649227280e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.132110551620953087e+00 5.334120230608161428e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.108163750091404776e+00 5.527481478539273407e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.117850162168148254e+00 5.172127086294245890e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.120699540994053667e+00 4.493138018421434232e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.132539567439528438e+00 5.845855672366401246e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.140776728817386676e+00 4.001906341271521117e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.133403369113302750e+00 4.333961760596219959e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.133923325500669765e+00 4.724143844986462559e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.129716591969854633e+00 5.170135638327115757e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.139865926154259856e+00 5.133386278644302969e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.131886175947532491e+00 4.297781656903171821e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.148860349081070931e+00 5.197633048898907537e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.131288152010727188e+00 4.282421057834754352e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.154530691133272446e+00 5.069328152758246775e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.145406712686513506e+00 4.895668876400504077e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.138413314409535193e+00 5.102218782493448401e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.151516818523916630e+00 4.591489456300997252e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.143865171233981792e+00 4.635701883680092283e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.156263819316453390e+00 5.249830724283897077e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.166474308825270034e+00 5.108988242429376436e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.148719367067444486e+00 5.106820906125655313e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.151853933230789551e+00 5.186421411251831426e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.155887195649986321e+00 5.375405416779475143e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.159587699360253854e+00 5.097335769776375480e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.173133504930684357e+00 4.999365605761081355e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.162197201614210362e+00 5.019263195732920346e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.162580891815266471e+00 4.434168599494833218e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.162251264741076984e+00 5.066371955441235642e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.166716535978343128e+00 5.434226173240475077e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.169106671185764723e+00 4.965432571519173277e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.151638840774501293e+00 4.500778716694084913e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.156356175245692564e+00 6.508187016611153286e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.165239377967911549e+00 4.554667056616239496e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.149486195527495758e+00 5.502463127847734281e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.156405344689892756e+00 4.387773284976415056e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.162487984730592006e+00 4.837185343645494795e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.166396666226878320e+00 4.499124093540690650e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.180795837698294104e+00 5.139017824714453564e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.190072271582597097e+00 4.388996189404994475e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.185148764927528298e+00 5.062279857840151776e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.171180519838552314e+00 5.128908003920443770e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.187742116843077289e+00 4.634070149042565090e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.183788493005876186e+00 5.001397234824033200e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.183884858772088489e+00 5.009066197881755222e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.170475879235628458e+00 4.480068883118828449e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.196285579110331510e+00 5.250661060477527231e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.193413194714826542e+00 4.532065433260135667e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.189855167364935351e+00 4.944311191655296756e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.201805404495649210e+00 4.263591533362693919e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.195712551335104123e+00 5.368060661747949824e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.200703204220030074e+00 4.755172026022947973e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.191761885650656660e+00 4.971757435891855925e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.199740903697418926e+00 5.143591183871638606e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.212016887735779980e+00 5.144021268241857348e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.180650923382922324e+00 5.346051793301604960e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.206653346219115353e+00 4.053885747508852444e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.193116534661981865e+00 5.030282187448196218e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.198195260255027206e+00 5.244827646960242840e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.218395527007280155e+00 5.265917725132110982e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.197987845294630427e+00 4.672774304599782713e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.208808893975068299e+00 4.497077582348191216e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.212644297843262331e+00 4.367300090475000895e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.216980789282864128e+00 5.571731569459150712e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.213745329782672133e+00 5.713569415485968417e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.225728155501352834e+00 5.939767625198286716e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.214237857503516871e+00 4.476686383904340261e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.224516552401250014e+00 5.521350119354113684e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.218767403323090637e+00 5.190615871035697548e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.208725985778320577e+00 4.967300387077456802e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.231272127434162478e+00 4.615392186346877756e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.221574898781087937e+00 4.060329851705569704e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.225751749737219232e+00 5.467079340715337299e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.206516876702301122e+00 5.113894911868784865e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.214951108934914759e+00 5.260223890180131256e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.237263973642965142e+00 4.461182435125812251e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.238885523051506565e+00 4.735964456078223805e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.233933179959950222e+00 6.017767045880467736e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.233298050427343195e+00 4.516682237987821869e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.238091122645040620e+00 5.805510252046981456e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.235289137712422436e+00 4.252028751643354143e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.228542203748378636e+00 5.099008631655196355e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.252871786362364848e+00 5.825412410042347666e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.242509081350565925e+00 5.061822418418133740e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.231999502854351336e+00 4.657411114447391198e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.241652630346438579e+00 5.416467102557791513e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.252141164231694415e+00 4.969324297855469141e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.226529253284554644e+00 5.103090448253647660e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.238832900205217635e+00 5.102416374131791832e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.228049785847521225e+00 4.153758971608844663e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.248383475612246940e+00 5.268160680666835738e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.246116874486910220e+00 5.271924698204596416e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.261010446241969785e+00 6.199020284693423921e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.271153985508062689e+00 4.707524340717957889e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.257045078690215423e+00 5.241075097164791252e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.244315235224824345e+00 4.583644140292328362e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.248900327991398385e+00 5.021820503452414375e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.253341246002326992e+00 3.887609965154867897e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.262117000131550260e+00 4.292731968193769787e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.267677217341808937e+00 4.878093256772308983e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.276771330303668961e+00 5.134959656849291676e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.259191581102259372e+00 4.902274332789566258e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.251569364836793152e+00 4.916657508408648902e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.284644328140649661e+00 5.477509254514992820e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.269002709753100255e+00 4.971196808002399936e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.256566139959057971e+00 5.309402725728620265e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.267409302034499419e+00 5.865609866194158029e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.281266152797145175e+00 5.650910347657598365e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.275658273085048755e+00 4.581008794703114173e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.268417766244563705e+00 5.063257325825686328e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.261295088074056325e+00 5.410096486428164209e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.277435092788630211e+00 5.144407002369089099e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.255771077053677409e+00 5.323257199821913588e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.274912398488388998e+00 4.537575019975784718e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.299248876930802155e+00 4.717262193212332089e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.280473178158954228e+00 5.476047697644752521e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.290549672086999999e+00 5.364458967787459898e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.291916205068526891e+00 4.391601754982125128e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.292911605985727519e+00 4.693995407695493172e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.283433864707391070e+00 4.707760588442272409e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.291461439661065302e+00 5.501192121778808453e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.263644291314982571e+00 6.101761991719478750e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.283909527226061442e+00 5.121942700085513334e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.290433071772872475e+00 5.260660441294321421e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.301260981388350446e+00 4.345173772484268726e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.293084445358541723e+00 5.904774900576644781e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.307387650009363522e+00 5.393622532802805347e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.300899700585237628e+00 5.275691715504963186e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.298765527337877979e+00 4.823610850135411354e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.317624302364722944e+00 5.068392800749067506e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.325003905463422615e+00 5.736807521609317284e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.311365661548195893e+00 5.307280283260858988e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.323655828736025164e+00 4.966840578307115117e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.310221281532126136e+00 5.410835982318199511e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.313317165778768780e+00 4.857566178239019483e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.305608494928891528e+00 5.279443974323329014e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.314830696818504840e+00 5.151005572633005070e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.296695145957835082e+00 4.329811048051101507e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.318420593782454464e+00 4.360732794076840335e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.311601623929668614e+00 5.057664355630163699e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.325732181419113642e+00 4.143910973754460492e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.308250593209839829e+00 5.227763138036045643e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.307237957010694895e+00 4.951010008981921451e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.342248522506545028e+00 4.712259597102878561e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.327002097598580344e+00 4.799359467327037332e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.323301849696937094e+00 5.173162496569561064e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.319176025198599334e+00 4.740754039060053882e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.329367135932870259e+00 6.463874549515798984e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.316906357054271837e+00 5.684097717534667416e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.319176812169870594e+00 5.411996269344655497e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.354384502820447533e+00 5.997793730853235861e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.334472266411763419e+00 5.002660347017349185e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.337323015021833816e+00 5.642685595113827723e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.344841794043011518e+00 5.147909637497981583e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.345256393848623722e+00 5.418744281464338286e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.325737723355409736e+00 5.195492264791010673e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.342187175071367644e+00 5.275306911927434284e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.330308625814712986e+00 4.806017147336533180e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.352780114743733497e+00 4.511279964611535909e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.351276809568100035e+00 5.555439035757093746e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.351120756252651489e+00 5.929496656426784806e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.357959805797720909e+00 5.578742055588177262e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.373064929100263010e+00 5.329421551977464588e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.346272271274947974e+00 4.957615285656137849e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.352153738381573378e+00 4.191606023750757970e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.340417592023833704e+00 5.116474337631724545e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.371973510819171072e+00 5.546962384860033568e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.358803702470656960e+00 5.731437497645386348e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.344494387485616338e+00 4.898362042279096884e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.367039037437890014e+00 4.609493163802023674e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.352466655240663895e+00 5.981356358870811851e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.366608188577418170e+00 5.681049216008571268e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.370458850439565168e+00 5.076463521470594431e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.382875585529888873e+00 5.020273919864903789e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.374973552472164018e+00 5.420348811610603557e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.378876051285677695e+00 5.284753294262267698e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.364142078285284665e+00 4.290050910533428907e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.387403110764604541e+00 6.004576944488947410e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.353321624718564209e+00 5.016724908253240578e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.385760542427188824e+00 5.697087319270818240e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.373764782407235874e+00 3.697389939798511049e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.396179637499908921e+00 5.066044350434527610e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.386507843118326422e+00 4.195105441054113316e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.382302617278679424e+00 5.238277966026736454e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.376033173663792741e+00 5.326101991802707492e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.376599581538371808e+00 4.798241665210415952e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.386437356103490615e+00 5.291227695909861417e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.397111760635702638e+00 6.060073366879357160e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.385510436140259705e+00 4.550344910324533587e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.381296375920749719e+00 5.346900106651955031e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.396489988598038412e+00 5.599900615763723222e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.385066594394522399e+00 6.524869463511966394e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.399663711629641094e+00 5.510996390517066690e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.407807369117404006e+00 4.168790920080978069e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.382138771183360371e+00 4.996279665671912174e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.392836383030775282e+00 5.851757979892099337e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.387455994881200860e+00 5.689158025854166301e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.402899228151442079e+00 5.917173148646869274e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.381163895359734495e+00 4.745673299368366460e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.401370108698439587e+00 5.863490084682285719e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.407170079870654567e+00 4.425547849903622710e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.396782902961151596e+00 5.541692411518441830e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.410994605805233393e+00 5.392858751399260964e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.425993128258167264e+00 5.799013867348048468e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.421621678160754909e+00 5.779323039854774580e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.406672152626839978e+00 4.899973719629863167e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.425795939997400952e+00 5.363580309162417903e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.413594252713071375e+00 4.785308902534864961e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.420174975734245004e+00 5.178122482196759746e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.412774275705287019e+00 4.752943796085716821e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.415334203949448755e+00 6.276683427740761267e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.431536681701475189e+00 5.177671662952588738e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.414243363115259999e+00 4.433780049862920958e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.428959144921409852e+00 5.150902124442965357e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.408371824516686965e+00 5.293767727076178486e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.420008229503703312e+00 5.066636326039479776e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.415853673865342222e+00 4.749228100210866055e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.418059607837022451e+00 7.004230543383711538e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.433809904223959153e+00 5.669491596197925309e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.423071032223409915e+00 4.931766133867683810e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.421726314789624412e+00 5.243472005093983146e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.445021582544167016e+00 5.511533213262659325e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.408609741986967201e+00 4.688906568169934896e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.440324503572796955e+00 4.845282226491322008e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.453450897093306882e+00 5.949457865461658113e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.443005805943717412e+00 5.538935053009951259e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.442257499249331332e+00 5.856709799388607474e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.432056986995499992e+00 5.618597277318928551e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.444509995449338557e+00 5.852266333662231590e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.436987850809650302e+00 5.519976032634095198e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.444626301233060239e+00 5.562628796927210351e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.458212406431700137e+00 6.232191486993887697e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.462674682934414827e+00 4.904258254215122514e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.446516901129127675e+00 5.015581283692477355e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.436200710339636588e+00 5.583187313884588887e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.453894871257134680e+00 5.401949015242155649e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.472829649239408401e+00 5.141233812827276095e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.456493495674535232e+00 5.202106077278100260e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.447815030581711238e+00 6.357888348330212880e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.446976840985204626e+00 5.033397530076881843e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.449393126876493243e+00 5.378570366481031373e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.444908341006794661e+00 5.196915859467701182e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.469272827734610321e+00 5.958262521474656515e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.459415743876747662e+00 4.659625471355524629e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.474703945896828294e+00 5.439483630777031120e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.469957147164337785e+00 5.412374774909542996e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.459095433627501048e+00 4.877485064099045253e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.472503976853234420e+00 5.419413005321113141e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.481758824912173811e+00 5.969851218027967255e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.467870104222741112e+00 5.323003313844939521e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.468623491588113117e+00 6.187544136566570652e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.453615017002319876e+00 5.035389821305983338e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.478504842639703831e+00 6.826128918983024318e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.479432507743849268e+00 5.832252433439727435e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.477573516510128027e+00 6.559747046199497778e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.485239810218845546e+00 5.283162209779850294e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.479590929445157688e+00 5.604020830756368809e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.471020946859219025e+00 6.086806623213044665e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.489220071702568982e+00 6.140721850003023619e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.490403965676772913e+00 5.797516582650551475e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.491252861693679543e+00 5.945730334267277373e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.486108363969029789e+00 4.954586857534777344e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.490857922012642778e+00 6.657668016485092100e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.471382107121215421e+00 5.219567931138048289e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.487035016984119773e+00 4.963137989143761031e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.484511741064870272e+00 5.072420265606454315e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.499412405389229486e+00 5.552851482616797396e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.481593675582443037e+00 5.456214676312325373e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.499915325114021147e+00 6.109237362865553855e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.500855041457190175e+00 4.935857607906300459e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.506836659030778680e+00 5.930991977812686278e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.507825561661372937e+00 6.305782348154449490e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.512671374631940679e+00 5.633142765089360626e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.486906020422419816e+00 4.692538571432553485e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.521888513773423268e+00 4.866387421341178410e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.512738150356932820e+00 4.928549270479257727e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.519403408608230865e+00 5.266918875877597550e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.527059735951487918e+00 5.284654589333543928e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.511983154621050485e+00 5.615434761102903183e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.512725560773391154e+00 5.383794872819928079e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.505371546037229002e+00 5.276935348188565023e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.518670714303571811e+00 5.791116351645730909e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.534417806405725226e+00 5.344014004928963057e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.534040258854868943e+00 5.969463559139471798e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.515434576713869275e+00 4.640945908153806254e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.516283286625185633e+00 5.879264160715884646e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.514120349354220352e+00 4.892464983591849892e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.503513954907211625e+00 5.639963391595190778e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.528576605199697402e+00 6.498336227921028829e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.534231563926167441e+00 5.923202258648571084e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.512605662845341259e+00 6.037120445297998739e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.539114908196882903e+00 5.436113506633264425e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.531357077707388381e+00 5.490954837537710409e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.540244685061697982e+00 5.488861178813539965e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.549057818935651643e+00 5.869717733537941351e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.542627685443189511e+00 5.934266649219810397e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.535419161056313042e+00 5.370005311206841325e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.536971756426624314e+00 4.927915099132001986e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.543401023907899239e+00 6.225482228127964257e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.532027268409795306e+00 5.191549216138614531e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.549343536266282850e+00 5.572820299762802909e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.550428709986991560e+00 6.185886970567023857e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.544876977211516733e+00 5.700776419286791574e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.546635987249807220e+00 5.396561238194365062e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.544549979497032544e+00 5.319309088823871434e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.555980636085860347e+00 5.272581606415308597e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.538666816887753308e+00 5.708152800959329642e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.557947872000136735e+00 6.553219374291892052e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.549233323728811174e+00 6.149156306461929233e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.557862531396190908e+00 6.569624041011696836e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.557990392092764687e+00 5.518390436269022814e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.553091848872901082e+00 5.974528803994360038e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.554668404384125013e+00 4.620514163192269153e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.553967764736266410e+00 4.974482849659281336e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.585059430575535222e+00 5.078944709823957071e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.563514640016190338e+00 5.057145422208987329e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.558488179373384908e+00 5.378473627315915051e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.579195837387216361e+00 5.602841086106400414e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.575043772937509301e+00 5.902368713850320026e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.560797439351112592e+00 5.422712264025431050e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.563356288153196161e+00 5.445492190873493232e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.566047294512553201e+00 6.295679534719325066e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.561459948359499084e+00 5.827600710290806729e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.567160366126384652e+00 6.317684147116725546e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.574114859608741757e+00 5.820202036468367091e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.605422439198895379e+00 5.721727592050785960e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.592405001731970637e+00 5.893148369994404279e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.572577705741691423e+00 6.151282205881754006e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.585279128427618422e+00 5.354217425322036483e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.570487841601202472e+00 4.917130954989379354e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.594396264999914603e+00 5.009089687240987354e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.607662775126030841e+00 5.595543565623894988e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.580199050405755035e+00 5.558596206290402630e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.591300907695372358e+00 6.320318055714508887e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.585929738566909997e+00 5.437480379529249230e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.607470570888190009e+00 5.728316758104248230e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.594785830970498308e+00 5.494780720581523559e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.604715886606245423e+00 5.957433719359152002e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.593518700528627985e+00 5.338627431299121096e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.595444312865836434e+00 5.933167761468153278e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.592152733090458794e+00 6.077685523505966803e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.613562527948808834e+00 6.067168733423017324e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.598822295697445606e+00 5.647228299870792334e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.620407366611080757e+00 5.852371081546048348e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.606972212239519582e+00 6.037034960312415155e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.597677020620530408e+00 4.742601781129571203e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.609950151784434347e+00 5.491186228662209290e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.599903177844240076e+00 5.877633051756256943e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.613812036921651316e+00 5.342994850336784118e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.620435254877288234e+00 5.203866734464871469e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.609270342592268754e+00 6.207724721461731487e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.603357176173908050e+00 5.069189158382571003e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.623091571105169217e+00 5.889192865717012282e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.620186574786061318e+00 5.309695674966232737e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.612183750539466498e+00 5.720679431821042993e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.641241251512763011e+00 6.012901490516691094e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.610352596583580986e+00 6.903393884065031072e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.624901714562324129e+00 5.984220038752491311e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.613619700976479221e+00 5.952598099345235427e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.628988733955167412e+00 6.149908702665782823e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.630807600704223814e+00 4.584128820593823539e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.651157418924251941e+00 6.172779470793132717e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.624413413461501365e+00 5.546423893307694808e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.639560588206669056e+00 6.134315652931578988e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.623594830999849137e+00 6.642733088959110743e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.618675688608676833e+00 6.176147877153492782e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.633757730907255068e+00 6.067918993662146931e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.634774417546246417e+00 5.473430662313926165e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.645471156328544238e+00 6.221955462517599900e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.637593286118690150e+00 5.811736848092685648e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.632941415212228575e+00 5.409660374310569786e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.646651024871260027e+00 6.270517756104683205e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.650485040262608338e+00 5.673994417956916836e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.642132342804873968e+00 5.933143908444497017e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.642150281861655126e+00 6.116667435813597509e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.649489475155855311e+00 6.310934306504469848e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.651771972763851348e+00 5.728810027903321611e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.650169304091277267e+00 6.702326160792376752e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.645819702410578955e+00 6.043639239284052112e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.659184949337140669e+00 6.432748724034260679e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.655872147579457110e+00 4.904312825679687515e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.655881134556976386e+00 6.540391618338361468e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.666652922097772160e+00 6.384510570107106986e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.661128381375843732e+00 6.627933735707778329e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.667653601352988124e+00 5.107745201362514642e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.671843715892922244e+00 5.605659149099437855e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.655229743564212264e+00 5.508077976380263419e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.656104952347778214e+00 5.715340908847100776e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.659226618783373297e+00 6.440880101911701239e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.679874321763830558e+00 5.852384990189725134e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.681845007651597390e+00 6.503654880487714784e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.686175386755086603e+00 6.065275480715228706e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.681349810329310968e+00 5.825128037884342858e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.658511557790515845e+00 5.186339699474357134e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.669703739414968080e+00 5.535888088209240943e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.674982837837309058e+00 5.853547394941167603e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.674056415257790364e+00 6.255197215147766387e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.703976984118082516e+00 5.141125733935230091e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.683413204014295506e+00 6.067363056142018873e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.692802338074316904e+00 5.604699925865538335e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.712263918930504669e+00 6.320326483092429948e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.664710445101993397e+00 5.672779788585825544e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.698379834655682874e+00 5.506570181951253584e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.683472192277042589e+00 5.362580409217292399e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.671981652582156030e+00 5.513531094211959749e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.676130740058241697e+00 5.404771705879588550e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.696904563786798104e+00 6.156928689085370277e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.682708503809167144e+00 5.497063082467418260e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.691973141269971670e+00 5.847893092183409358e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.690501853914935682e+00 4.788607460262689353e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.684795630390147014e+00 4.939364212091015283e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.702427982820236885e+00 6.468541286596767304e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.711626408486330631e+00 5.579358087825861956e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.688729673988816016e+00 5.663871821174258914e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.697947566404552688e+00 6.306695713986337770e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.701429044416641556e+00 6.625802427301354935e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.700390602328074818e+00 5.501184350729457773e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.703331546709720090e+00 6.459301788218191342e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.690758722910292722e+00 5.683950643988323614e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.722969868438889440e+00 5.606398237334834223e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.701651756309750629e+00 5.337412258507815421e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.713676390109217351e+00 6.130415733345121465e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.721600295222754928e+00 6.098937268212415441e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.692339097590277897e+00 5.665641517965283036e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.704350784052603984e+00 5.899466367388015442e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.716597002139300221e+00 5.708787960486991775e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.707580777660683058e+00 6.943127612915110936e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.714135002555276399e+00 5.859410357209873244e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.724912864752048414e+00 6.253014500202167536e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.726755836258047738e+00 6.465682996887841538e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.723502761396746186e+00 6.425739130828399537e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.738179852211426901e+00 6.246185067744489672e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.722218507077188265e+00 6.320307624764588406e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.719317545444123629e+00 5.947366735150182437e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.734217768616626687e+00 5.879326590015075960e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.732604304432884934e+00 5.265909894405534919e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.724796719330479400e+00 5.834284214197652529e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.719063275890472919e+00 5.867132198599642745e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.748906333722318296e+00 5.643025389043261342e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.748388506828984168e+00 5.625143624633427430e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.747567524662012151e+00 6.392796182844893149e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.758209658746522353e+00 5.447445971550968213e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.753466100449644571e+00 6.213528144036131184e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.727697035047430996e+00 6.384096192397129554e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.739784789947825816e+00 5.152334599540708826e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.753468207045359772e+00 5.644917057766064561e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.738536807666735129e+00 6.681823409337758202e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.746011230817437587e+00 6.442119421687731995e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.755481207262474586e+00 6.368197998949765015e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.739493384162752898e+00 5.901912533764059887e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.771297276824850275e+00 7.045381641883703416e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.744758393362292548e+00 5.924889715044219241e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.756909394509908795e+00 6.028659029473040221e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.756472529426486018e+00 6.433714536231301961e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.759027852837744987e+00 6.554077681024259583e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.765592523681998927e+00 6.082293499519899616e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.761282911862464218e+00 5.749610914661227312e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.744347993937402030e+00 6.781176183960022641e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.770496308538546781e+00 6.111040306694985880e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.773765648839418230e+00 6.149177770596703230e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.750457444103572957e+00 6.442661095399001558e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.755204996346180213e+00 6.803984532023217291e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.776093905109982884e+00 6.058968515357103790e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.781009615829644233e+00 6.092728914673816165e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.775267399122904433e+00 5.728779670700425708e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.783773062364579554e+00 6.272538275400887375e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.778304781837745763e+00 6.221261046343112833e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.784745200978091306e+00 5.397385868445496726e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.781901875201354679e+00 5.869522690641039153e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.769335429263712234e+00 6.242323743606609243e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.783392917682670387e+00 5.752477628704800727e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.773434532867883773e+00 5.959695642737034005e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.790690633217543848e+00 6.710547986636323792e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.782921512782558615e+00 6.847525517001173956e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.790292326153309688e+00 6.571654675336763285e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.777917168786329993e+00 6.068899794420697935e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.807355687507388398e+00 6.277042397514541738e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.792159855357806864e+00 5.485509443515772521e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.795351583928896932e+00 6.329949645719282758e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.789568557272680493e+00 6.052982701821101319e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.813881062380678477e+00 6.627525228711509397e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.798461808445004362e+00 5.318081171966895226e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.792880423910089727e+00 6.379025949269367946e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.817389330118991397e+00 6.034424152316602008e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.806694086243136432e+00 6.189765828418514193e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.797559433656227501e+00 6.324882444318778996e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.799760034651048723e+00 6.065164587591655998e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.782149649494110255e+00 6.658525451665092687e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.800563037217880469e+00 6.332218748005441489e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.803892680970196283e+00 6.362512539270769318e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.807566044554460349e+00 6.415610891868853694e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.818037719576711453e+00 5.589172014370441532e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.803401270894729791e+00 6.586447224148209711e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.821323548270903592e+00 6.629900324810742429e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.809141584918297907e+00 6.449907470694830058e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.815979469045076122e+00 5.591172422506497375e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.829507904216594039e+00 5.823337575553371170e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.834678875758885885e+00 5.913232109466561859e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.816111161478354719e+00 6.614615678305723057e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.822379215077821746e+00 5.509341045904698753e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.835139938440348617e+00 5.594097766677659234e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.834608539786574122e+00 5.873323009347919399e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.831262777478611214e+00 6.581258931106568344e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.842830843039058308e+00 6.124546761860027999e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.825694874224014885e+00 5.376625179797842602e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.825036914769462504e+00 5.957308271968198010e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.835298603773418469e+00 5.777422784331673888e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.823736036913992997e+00 5.398578216995126855e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.820585458482084418e+00 5.860822166883299378e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.828347161207486193e+00 6.082790326333337161e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.851325597079048535e+00 6.647540252507342373e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.842251062967577679e+00 5.645104300159038413e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.852616237272616262e+00 5.388503446214246706e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.847521419974292423e+00 6.926093995685600468e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.852049641803320545e+00 5.374146994399452426e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.843195173725380887e+00 6.763102673198628789e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.854622471065789258e+00 6.439428663592247082e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.859622547364978340e+00 5.186858680058695770e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.835130575090023530e+00 6.034816352895440161e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.857871777198313090e+00 6.706839732535976317e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.832734426566375419e+00 6.653467338353120653e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.858463080069448603e+00 6.724727596348212266e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.872645456922821694e+00 6.193995853867595436e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.867128792319261876e+00 6.206758606023400349e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.863830980215145372e+00 5.340614992922211313e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.843579250142415749e+00 5.851759545377746008e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.858786027390380013e+00 5.994429770202975849e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.857383038656351903e+00 5.305096938892218672e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.846144355673470461e+00 6.776685514330880178e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.862091330485022400e+00 6.834174359202884741e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.872838398467280285e+00 5.173866986758336450e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.871036419798941974e+00 6.325811297861392291e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.871862297799852026e+00 6.118037716955757599e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.861603562753185948e+00 5.900565073707109143e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.868020918508436878e+00 6.779927551003184227e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.878205668862832356e+00 5.409489087589901546e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.858034725996858816e+00 5.182049168461219102e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.883222504051434543e+00 5.465653533465717473e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.871231298641290230e+00 4.792651682926548085e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.888601249964095619e+00 6.070486813560425077e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.882251171136668955e+00 6.742183457233686950e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.865713936629972869e+00 6.555530170127185086e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.889770840792449835e+00 6.663910394627130529e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.897811639833266728e+00 6.390059636850961011e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.893416152982587031e+00 5.481247938528562846e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.870020990711395825e+00 7.306272832047463472e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.895217359333800422e+00 6.333747197315706678e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.887124329323725425e+00 5.617580295575449467e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.893883154513609890e+00 6.178841004311662610e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.891386863304475474e+00 6.559345837945153024e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.880617437259086033e+00 6.424396841584190110e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.898590485962947660e+00 4.469407734075092620e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.876087460441707266e+00 5.783119040458608584e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.893799967059861089e+00 5.133425718517350411e-01 1.000000000000000021e-02 5.000000000000000278e-02
+1.912813174024332374e+00 5.967723115443738235e-01 1.000000000000000021e-02 5.000000000000000278e-02
diff --git a/exercises/Solutions4/.ipynb_checkpoints/Solutions_4-checkpoint.ipynb b/exercises/Solutions4/.ipynb_checkpoints/Solutions_4-checkpoint.ipynb
new file mode 100644
index 0000000..8cdd871
--- /dev/null
+++ b/exercises/Solutions4/.ipynb_checkpoints/Solutions_4-checkpoint.ipynb
@@ -0,0 +1,788 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Exercise 4 Solutions\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "from __future__ import print_function  # For Python < 3\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "import scipy.stats as stats\n",
+    "\n",
+    "%matplotlib inline \n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 1. Approximations to the binomial\n",
+    "\n",
+    "For np < 10, large n, the Poisson distribution is a good approximation for the binomial.\n",
+    "\n",
+    "* Show analytically that the binomial distribution converges to the Poisson distribution in the limit of large n. (Hint: $e = \\lim_{n\\to\\infty}(1+\\frac{1}{x})^x$)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "$P(x=k) = \\binom{n}{k}p^k (1-p)^{n-k}$\n",
+    "\n",
+    "$\\lambda = np$\n",
+    "\n",
+    "$\\lim_{n\\to\\infty} \\frac{n!}{(n-k)!k!} \\frac{\\lambda}{n}^k (1-\\frac{\\lambda}{n})^{n-k}$\n",
+    "\n",
+    "$\\lim_{n\\to\\infty} \\frac{n}{n} \\frac{n-1}{n} \\bigl ( ... \\bigr ) \\frac{n-k+1}{n} (1-\\frac{\\lambda}{n})^{n} (1-\\frac{\\lambda}{n})^{-k}$\n",
+    "\n",
+    "Remembering $e = \\lim_{n\\to\\infty}(1+\\frac{1}{x})^x$\n",
+    "\n",
+    "$\\frac{\\lambda}{k!}e^{-\\lambda}$\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "* Keeping $n p$ fixed, plot the binomial probability mass function for an increasing number of observations $n$, comparing in each case to the equivalent Poisson distribution ($\\lambda=n p$). For convenience, you should use the relevant functions in ```scipy.stat```."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAX0AAAEICAYAAACzliQjAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nEBd9M7MMcdE3M8uQ/w/eiXuGcmEQ6wAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x107ecaf60>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "n_trials = [5, 10, 100]\n",
+    "p0 = 0.8\n",
+    "n_p = n_trials[0] * p0\n",
+    "x = range(12)\n",
+    "\n",
+    "fh, ax = plt.subplots(1,3, sharey=True)\n",
+    "for idx, nt in enumerate(n_trials):\n",
+    "    p = n_p / nt\n",
+    "    ax[idx].bar(x, stats.binom.pmf(x, nt, p), width=1, alpha=1, label='Binomial')\n",
+    "    ax[idx].bar(x, stats.poisson.pmf(x, n_p), fill=False, width=1, alpha=1, label='Poisson')\n",
+    "    \n",
+    "    ax[idx].set_title('n={}'.format(nt))\n",
+    "\n",
+    "    if idx==2:\n",
+    "        plt.legend()\n",
+    "\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "For np > 10, n(1-p) > 10, the discrete binomial distribution can be reasonably approximated by the continuous normal distribution.\n",
+    "\n",
+    "* Choose a large n (> 30, with p close to 0.5). To start with, choose n=100 and p=0.45. Plot the binomial pmf, and, with equivalent parameters, the normal pdf \n",
+    "* Calculate the probability that X >= 55 for each. Don't forget to apply the continuity correction\n",
+    "* What happens to the relative difference as n increases?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Binomial (exact): 0.4911796759527426\n",
+      "Gaussian (approximate): 0.48595290935296537\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAX0AAAD8CAYAAACb4nSYAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x10d046d30>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "n_trials = 100\n",
+    "p0 = 0.45\n",
+    "mu = n_trials * p0\n",
+    "std = np.sqrt(stats.binom.stats(n_trials, p0, moments='v'))\n",
+    "\n",
+    "xd = np.arange(int(mu), int(1.5*mu))\n",
+    "x = np.linspace(xd[0], xd[-1], 200)\n",
+    "\n",
+    "x_ch = 55\n",
+    "sel_d = xd >= 55\n",
+    "sel_cont = x >= 55\n",
+    "p_bin = stats.binom.cdf(x_ch, n_trials, p0)/2\n",
+    "p_gauss = stats.norm.cdf(x_ch-0.5, mu, std)/2\n",
+    "\n",
+    "print('Binomial (exact):', p_bin)\n",
+    "print('Gaussian (approximate):', p_gauss)\n",
+    "\n",
+    "\n",
+    "plt.figure()\n",
+    "plt.bar(xd, stats.binom.pmf(xd, n_trials, p0), width=1)\n",
+    "plt.bar(xd[sel_d], stats.binom.pmf(xd[sel_d], n_trials, p0), width=1, color='g')\n",
+    "plt.plot(x, stats.norm.pdf(x, mu, std), 'r')\n",
+    "plt.show()\n",
+    "        "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 2. Random walk\n",
+    "\n",
+    "Consider a simple 1D random walk. A person starts at the position $x=0$. With equal probability $p=0.5$, they may take one step forwards or one step backwards, corresponding to a displacement of +1 and -1 respectively.\n",
+    "\n",
+    "* Show that for an N step walk, the expected absolute distance from the starting position is given by $\\sqrt{N}$.\n",
+    "\n",
+    "* Write a function to simulate such a random walk, parameterised by the number of steps. The output should be an array, with the displacement at each step index.\n",
+    "\n",
+    "* Plot a single walk."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXQAAAD8CAYAAABn919SAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nThAEYRFI0AmCICwCCTpBEIRF+P86OddqGMrfTgAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x107eca2e8>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "def random_walk(n_steps, p=0.5):\n",
+    "    return np.cumsum(2*(np.random.binomial(size=n_steps, n=1, p=0.5)-0.5))  # Bernoulli\n",
+    "\n",
+    "n_steps = 100\n",
+    "w = random_walk(n_steps)\n",
+    "\n",
+    "plt.figure()\n",
+    "plt.plot(range(n_steps), w)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "* Simulate ~1000 random walks of 500 steps.\n",
+    "\n",
+    "* Plot the average distance (rms) of these over the whole set with respect to step index (time). Does the average converge to the expected distance?\n",
+    "\n",
+    "* (Optional) sample and plot the running average to show how the convergence improves with number of walks."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYIAAAEKCAYAAAAfGVI8AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nSUVORK5CYII=\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x107eca438>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYQAAAEKCAYAAAASByJ7AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nYII=\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x110bd8cf8>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "### Bernoulli\n",
+    "n_steps = 500\n",
+    "n_walks = 1000\n",
+    "n = np.arange(n_steps) +1\n",
+    "\n",
+    "W = []  # Final distance\n",
+    "A = []  # Running average over whole set\n",
+    "T = 0\n",
+    "for idx in range(n_walks):\n",
+    "    w = np.abs(random_walk(n_steps))\n",
+    "    W.append(w[-1])\n",
+    "    T += w**2\n",
+    "    A.append(np.sqrt(T/(idx+1)))\n",
+    "    \n",
+    "plt.figure()\n",
+    "plt.plot(n, np.array(A).transpose()[:,[0,20,-1]])\n",
+    "plt.plot(n, np.sqrt(n))\n",
+    "plt.legend(['0', '20', '1000', r'$\\sqrt{N}$'])\n",
+    "plt.xlabel('Step #')\n",
+    "plt.ylabel('Distance (steps)')\n",
+    "\n",
+    "plt.figure()\n",
+    "plt.hist(np.array(W), normed=True)\n",
+    "plt.axvline(np.sqrt(n_steps), color='r')  # Expected distance\n",
+    "plt.xlabel('Dist D')\n",
+    "plt.show()\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "* Now consider the case $p \\ne 0.5$, where the \"person\" is more likely to step in one direction than another. Find again analytically the expectation and the variance for the (rms) distance travelled in terms of $N$ and $p$.\n",
+    "\n",
+    "Expectation: $N  \\left|1-2p \\right|$\n",
+    "\n",
+    "Variance: $N$\n",
+    "\n",
+    "* Modify the random_walk function to account for the unequal probability between the directions."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "* Run a series of random walks as before, and plot again the histogram of distances travelled. On top of this, plot the Gaussian PDF with the $\\mu$ and $\\sigma$ parameters as determined above."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYQAAAD8CAYAAAB3u9PLAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x11072d6d8>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Plot histogram\n",
+    "n_steps = 2000\n",
+    "n_trials = 5000\n",
+    "p = 0.4\n",
+    "\n",
+    "V = []\n",
+    "for n in range(n_trials):\n",
+    "    V.append(np.abs(np.sum(2*(np.random.binomial(size=n_steps, n=1, p=p)-0.5))))\n",
+    "    \n",
+    "plt.figure()\n",
+    "plt.hist(V, 40, normed=True)\n",
+    "plt.plot(range(500), stats.norm.pdf(range(500), loc=n_steps*np.abs(1-2*p), scale=np.sqrt(n_steps)))\n",
+    "plt.axvline(n_steps*(np.abs(1-2*p)))\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 3. Small sample sizes: t-distribution\n",
+    "### 3.1 Compare to normal distribution\n",
+    "\n",
+    "Student's t-distributions are interesting for cases where you have few samples and the population variance is unknown, but the underlying distribution of the means can be assumed normal. They are parameterised by the degrees of freedom (\"df\"), which is usually equal the number of samples minus one. As the number of degrees of freedom increases, the t-distribution converges to the normal distribution.\n",
+    "\n",
+    "* Plot the standard t-distribution for several increasing degrees of freedom and compare this to the normal PDF.\n",
+    "* Plot and compare the cumulative distribution functions\n",
+    "* Plot the variance of the t-distribution as a function of degrees of freedom. Compare to the standard normal variance (=1)\n",
+    "* (optional) make a Q-Q plot (see Wiki) and compare the distributions\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAl0AAAHVCAYAAADLiU4DAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAIABJREFUeJzs3Xl41OW5+P/3PVv2DQhbgCTsi6wGRFEWtaBtQa3aYxf1\nfH+21lM9dvVbra1tXU49tfrtsaebtZserVvV445VwAUBQRZZNBBIwg4JWcg2+/37YyZDAgEmkGSS\ncL+uay5mns/z+cw91xWSe57n/jyPqCrGGGOMMaZzORIdgDHGGGPMmcCSLmOMMcaYLmBJlzHGGGNM\nF7CkyxhjjDGmC1jSZYwxxhjTBSzpMsYYY4zpApZ0GWOMMcZ0AUu6jDHGGGO6gCVdxhhjjDFdwJXo\nAI7Wr18/LSgoSHQYxpgu9NFHH1Wqam6i4+gI9jvMmDNLe35/dbukq6CggDVr1iQ6DGNMFxKR8kTH\n0FHsd5gxZ5b2/P6y6UVjjDHGmC5gSZcxxhhjTBewpMsYY4wxpgvElXSJyCUiUiwiJSJy+wn6XSUi\nKiJFLdruiJ5XLCILOiJoY4wxxpie5qRJl4g4gd8AlwLjgS+JyPg2+mUAtwKrWrSNB64BJgCXAL+N\nXs8YY7qEiCSLyIciskFENovIz9rokyQiT0e/IK4SkYIWxzr8i+OL6/Yw6/4lFN7+KrPuX8KL6/Z0\nxGWNMd1cPCNdM4ASVd2hqn7gKeCyNvrdA/wC8LZouwx4SlV9qloKlESvZ4wxXcUHXKiqk4EpwCUi\nMvOoPjcA1ao6Evh/wH9C53xxfHHdHu54fiN7appQYE9NE3c8v9ESL2POAPEkXXnArhavd0fbYkRk\nKjBUVV9p77nR828UkTUisqaioiKuwE3v4PfDww/DOefAhAlQW5voiExvoxH10Zfu6EOP6nYZ8Lfo\n8+eAi0RE6IQvjg8sLqYpEGrV1hQI8cDi4tO5rDGmB4gn6ZI22mK/sETEQeSb4ffae26sQfURVS1S\n1aLc3F6xPqKJw8GDMHcufOtbEA4rl18RJjMz8uPx4YeJjc30LiLiFJH1wEHgn6q66qgusS+IqhoE\naoG+dMIXx701Te1qN8b0HvEkXbuBoS1eDwH2tnidAZwFLBORMmAm8FK0mP5k55ozVGMjLFwI69cr\njz/p590PvPzoJz68QS9vLw1wzjnw+OOJjtL0FqoaUtUpRH4HzRCRs47qcrwviB3+xXFwdkq72o0x\nvUc8SddqYJSIFIqIh0h9w0vNB1W1VlX7qWqBqhYAK4FFqrom2u+aaJFqITAKsDEMg8sFEyeG+dv/\n+Ln6asXj9JDkSsLtdFM0I8jceWFuuEHZuDHRkZreRFVrgGVE6rNain1BFBEXkAVU0QlfHG9bMIYU\nd+uysBS3k9sWjDmdyxpjeoCTJl3RofZbgMXAJ8AzqrpZRO4WkUUnOXcz8AywBXgDuFlVQyc6x5wZ\nXO4wv/6dj0WLwOP04A/5aQo0oapkpCbx2BM+srPhhhsgGEx0tKYnE5FcEcmOPk8BLgY+ParbS8D1\n0edXAUtUVemEL46XT83j51+YSF52CgLkZafw8y9M5PKpx8xaGmN6GYn8Xuk+ioqK1PYt672CQfjq\nV+HrN/mYdb6iqtT6aglrONYn2ZVMuiedp/4O11/r4Y9/hK99LYFBm04nIh+patHJe57StScRKZJ3\nEvmi+Yyq3i0idwNrVPUlEUkGHgemEhnhukZVd0TPvxP4/4Ag8G1Vff1E72e/w4w5s7Tn91e32/Da\n9G6PPQZPPw2XXwmhcIhaXy1uh5s+KX1wipOmYBN1vjqC4SBf/JcsnnwyhKqDtktrjDk5Vf2YSDJ1\ndPtdLZ57gauPc/59wH2dFqAx5oxhSZfpMsEg3H23UjRd+fyiIId9h/E4PfRN6Uvk7nxI96Tjdrg5\n1HSIhkA9L/xvKi6Hi8hd/sYYY0zPZXsvmi7z/PNQXi7c9oMAjYEGHOKgT0ofRIRgOIg/5EdVSXIl\nkZWUhS/kwx/y4/UHeeEFJWTVgMYYY3owG+kyXeahh5QRI5ULFzTi1yB9UvoQCocoO1xGdVM1ECmq\nH5QxiH6p/WgKNtEYaOSdN5L40heF55+HK65I8IcwxhhjTpElXaZLhEJwzZfD9O0XJKBekpxJOMRB\n8aFiAqEAA9IHkOxK5lDjIcpryvEFffRP609FYwXzFjQwZKiH3/1OuOIKq+0yxhjTM1nSZbqE0wk3\nfTNAY6ARb1BJ96SzvWo7wXCQ0X1Hk+ZJA6BvSl921u5kf/1+UtwpJLuS8Qa9XHt9kPvvc1NeDvn5\nCf4wxhhjzCmwmi7T6Zqa4I9/DFNbG6Yp0ESSM4nKxkoaA40UZBfEEi4AEWFY1jDSPemU15ST5EwC\n4ItfaQDgz39OyEcwxhhjTpslXabTvfIK3HijgxWrQogIboebAw0H6Jval+zk7GP6iwiFOYUAHGw4\nSJIzidzBjcy7MMz773evdeWMMcaYeNn0oul0Tz2lDBwERefV43K4qGyqxCEO8jKOvwK3x+lhQPoA\n9tXtIzMpExHh0cfqyOufhi0fYYwxpieykS7TqWpr4dVX4YorA+AI4RAH1U3V9E/rj9t54uRpYPpA\nnA4nNd4anOIkJdNLGFs3whhjTM9kSZfpVK+8Aj6f8Lkr6nGIg8O+wzjEQf+0/ic9t7lfjbcGESEQ\nCvDwfwkXXKB0s92rjDHGmJOypMt0qo8+UgYOUiZNa8LlcFHjraFvat/oKvMn1z+tPyJCvb8ep8OJ\nOAO8/76weXMnB26MMcZ0MEu6TKf6xS9DrFhzGIcDGv2NqCq5qblxn+9yuMhJzuGw7zBOcTJ/YT0i\nynPPdWLQxhhjTCewpMt0qrCGScn04hAH9f560j3ppLhT2nWN3LRcQuEQTcEm+vQLMOv8MM8+a/OL\nxhhjepa4ki4RuUREikWkRERub+P4TSKyUUTWi8j7IjI+2l4gIk3R9vUi8vuO/gCm+7rzTvjX6xz4\ngwGC4SC+kI++qX2P6RfWMMWVxazfv56qpqpjjqd70kl2JdMYaMTpcPLZRU1s2SJs394Vn8IYY4zp\nGCctrBERJ/Ab4DPAbmC1iLykqltadHtSVX8f7b8IeAi4JHpsu6pO6diwTXenCk8+qYwZp4gDmgJe\nROSYdbkqGyt5atNTVDZWApHi+XOHnMvFwy9G5MiWP31S+rC3bi+ZSZnMm9/IN3Yk44izLsycuURk\nKPAYMBAIA4+o6n8d1ec24CvRly5gHJCrqlUiUgbUASEgqKpFXRW7Mab3ieev1gygRFV3AIjIU8Bl\nQCzpUtXDLfqnATb3c4bbvBnKyoSbv9OEQxw0BhrJTMpsVUBf463hb+v/RljDfHHCF+mf1p8Pdn3A\n8l3LCWuYBSMXxPo2J13egJe8fOXBX/lIcTuwGXJzEkHge6q6VkQygI9E5J8tvzSq6gPAAwAishD4\njqq2HHKdp6qVXRq1MaZXiucvVh6wq8Xr3dG2VkTkZhHZDvwCuLXFoUIRWSci74jIBW29gYjcKCJr\nRGRNRUVFO8I33dXLL0f+nbugnmAoSDAcJCc5J3ZcVXnx0xfxh/xcP+V6xueOp19qPxaOXsj0wdNZ\nsXsF2w5ti/VPciWR5kmjMdgYSeJ8PlauCuP3d/UnMz2Jqu5T1bXR53XAJ7Tx+6uFLwF/74rYjDFn\nnniSLmmj7ZiRLFX9jaqOAH4A/CjavA8YpqpTge8CT4pIZhvnPqKqRapalJsb/51tpvt64w1l8pQQ\nAwaG8IaOnVpcu28tZTVlLBi5oNWaXSLCgpELyE3N5eWtL+MPHcmqspOzCYQCqCqvvq7MOtfF8uVd\n+rFMDyYiBcBUYNVxjqcSKYv4R4tmBd4UkY9E5MYTXNu+OBpjTiqepGs3MLTF6yHA3hP0fwq4HEBV\nfap6KPr8I2A7MPrUQjU9yfkXhLnm2uiolD8yteh0OAEIhoMsK1vG0MyhTB049ZhzXQ4Xnx/9eQ77\nDrNm75pYe1ZSFgD+sJ8Z5zbicimLF3fN5zE9m4ikE0mmvn1UOURLC4HlR00tzlLVacClwM0iMrut\nE+2LozEmHvEkXauBUSJSKCIe4BrgpZYdRGRUi5efA7ZF23OjhfiIyHBgFLCjIwI33dtdPw1y3dfq\n8If8hDTUamrxo70fUeev48LCC1sVy7eUn53PiJwRLN+5PDbaleJOIcmVRFOgiYxMmHlekDfesPJB\nc2Ii4iaScD2hqs+foOs1HDW1qKp7o/8eBF4gUuNqjDGn5KRJl6oGgVuAxUTqIZ5R1c0icnf0TkWA\nW0Rks4isJzKNeH20fTbwsYhsAJ4DbjrqW6TphUpLobEpRDAcjCVMWcmRUaqwhlmxewXDsoZRkF1w\nwuvMKZhDQ6CBDfs3xNqykrLwBX04xMGci7xs2CDs29dpH8X0cBLJ6v8EfKKqD52gXxYwB/jfFm1p\n0eJ7RCQNmA9s6tyIjTG9WVz33Kvqa8BrR7Xd1eL5t45z3j9oXR9hzgBXXqlk93Hz2PMOfCEfaZ60\n2F2LJVUl1Hhr+Mzwzxx3lKvZ0MyhDEofxOq9qykaXBSrCzvYcBBv0MsF81zwkwz++U+47rqu+GSm\nB5oFXAtsjH4pBPghMAygeakb4ArgTVVtaHHuAOCF6M+pi8jSOG90SdTGmF7JFjoyHerQIVi/Hv7v\nj3yoKv6gv9W2P6v3rCbDk8HYfmNPei0RYXredF4qfomdtTvJz84n3ZOO0+HEG/Qy5iw3L7/exLwL\nkmn7fg9zplPV94njh0NV/wr89ai2HcDkTgnMGHNGskWOTIdauhRUhXMvaMQX8iEiZCZFblit99dT\nUlXClIFTYkX1JzOx/0Q8Tg8bDkSmGEWErKQsmgJNuJwOzpvdRHJKuNM+jzHGGNNRLOkyHeqtt5SM\nDGXiNC++oA+Xw0WqOxWATQc3oSiTBkyK+3pup5vxuePZfHAzwXAQgMykTEQEf8jP9rIgP/4x7NnT\nKR/HGGOM6TCWdJkOtWQJnHd+AI/bgTfojSVIAB8f+JhB6YPITWvfLfUT+0/EF/Kx9dBWgNjIWSAU\noKoqzM//w8mSJR37OYwxxpiOZkmX6TCq8MgfQ9x62+HYXYvNCVJ1UzV76/YyccDEdl+3MKeQdE86\nmw9uBiKjX8muZHxBH2Mm+MnOUZYutaUjjDHGdG+WdJkOIwIzZwWZOK3pmKSreZQqngL6oznEwei+\noympKiEUDsWu6w/5cTqEc2f5eeedDvoQxhhjTCexpMt0mBdeUF5/I4yieINeUtwpuJ1uAIoPFZOb\nmkuflD6ndO0xfcfgC/koqykDWtd1zZzlZccOYdeuE1/DGGOMSSRLukyH+elP4b8eiqxC4g/5Y6Nc\n3qCXspoyxvQb0+Z53qCXP6z5A/e9ex+r96xus8/wnOG4HW6KDxUDkO5JR0TwhXxMn9VIRoaydWvH\nfyZjjDGmo1jSZTpETQ1s3AjTz/XhD/pxipMMTwYQWRA1rGHG9D026SqrKWPy7ydz06s38aOlP+Kc\nR8/hx0t+fEw/t9PN8JzhFFcWo6o4HU5S3an4gj7GnRWgfF89F15odV3GGGO6L0u6TIdYvjyyPtfZ\nMyPrc0FkNAqguLKYNHcaeZl5rc6p99dz2VOXcaD+AG985Q2qf1DNdZOv49737uXRtY8e8x5j+o2h\n1lfLgYYDwJG6LhElhI+w2npdxhhjui9LukyHePddxe1Wphb58Yf8pLpTcTqchMIhtlVtY3Tf0Tik\n9Y/bz5b9jI0HNvL0VU+zYOQCspOzeXTRo8wfMZ9/f/3f2VXbukhrdN/RCEJxZWSKMTMpE5fDhS/k\n483FDqZNc1BlO3saY4zppizpMh1iwwaYMi1ISqriD/vJSIpMLe6s3RnZsueoeq7S6lIe/vBhrp9y\nPQtGLoi1uxwuHvn8I6gqdy65s9U56Z508jLzYnVdae60WDG9yxPk4w3CypWd/EGNMcaYU2RJl+kQ\n//tykD8/dQh/0I9LXLF6ru3V23GIg+E5w1v1v/fde3GKk3vn3XvMtfKz8/nOzO/w+MeP82nlp62O\njeozin11+2gMNCIipLnT8If8TJrmw+lUli+3ui5jjDHdU1xJl4hcIiLFIlIiIre3cfwmEdkoIutF\n5H0RGd/i2B3R84pFZMHR55peQsJk5QRi+y0213PtqN7B0MyheJyeWNeKhgqe2PgE10++/pg6r2bf\nOfc7eJwefr3q163ah+cMR9HY0hEZSRkEw0FS05SzJoX4YEXnfDxjjDHmdJ006RIRJ/Ab4FJgPPCl\nlklV1JOqOlFVpwC/AB6KnjseuAaYAFwC/DZ6PdOL/O1vcOPXHfgDYXxBX6yeqynQxL66fceMcj3y\n0SP4Qj5uPefW416zf1p/vjzxy/xtw9+o8dbE2gdnDCbJmcSO6h1AZMrRKU58QR9nz/Dx4SoIBjvn\ncxpjjDGnI56RrhlAiaruUFU/8BRwWcsOqnq4xcs0oHmO5zLgKVX1qWopUBK9nulFXvxf5b13Hbhc\nEAgHYqNcpTWlKNoq6VJV/rTuT1xUeBHjcsed8Lq3TL+FhkADT296OtbmdDgpyC6IJV1p7jQgsi7Y\nBRc28rmFYWprO/oTmp5KRIaKyFIR+URENovIt9roM1dEaqMj9etF5K4Wx044ym+MMe0RT9KVB7S8\njWx3tK0VEblZRLYTGem6tZ3n3igia0RkTUVFRbyxm27iw1XE7lpsuT7XjuodJDmTGJwxONZ3+a7l\nlNaUcv3k60963WmDpjE+dzyPf/x4q/bhOcOpaqqiuqk6tl6XP+Rn3vxG/vKYl759O/bzmR4tCHxP\nVccBM4Gb2xipB3hPVadEH3dD3KP8xhgTt3iSLmmj7ZhqZVX9jaqOAH4A/Kid5z6iqkWqWpSbmxtH\nSKa72L0b9u4VphR5o2tmCWmeyOjTjuodFGQX4HQcmVF+fMPjpLpTuWLcFSe9tohw7aRrWb5reWxk\nC4iNnJXWlAKRKcZAKIAQuZOxqtrW6zIRqrpPVddGn9cBn9DGF7/jOOkovzHGtEc8SdduYGiL10OA\nvSfo/xRw+Smea3qY5iUaJk3z4g/7SXYl43K4qPHWUNVU1WpqMRgO8twnz3H52MtjU5An8+WJXwbg\nmc3PxNr6pfYjw5MRS8QykjJwiANfyMe3bk5i2tS2cn1zphORAmAqsKqNw+eKyAYReV1EJkTb4hqp\nN8aYeMWTdK0GRolIoYh4iBTGv9Syg4iMavHyc8C26POXgGtEJElECoFRwIenH7bpLvx+Zey4EOMn\nBvAH/a3uWgRaJV3vlr9LVVMVV467Mu7rD8saxrRB03h568uxNhFheM5wSqtLUdXYPoz+kJ8RY/yU\nlwt79nTQBzS9goikA/8Avn1UDSrAWiBfVScDvwZebD6tjUu1uSaJlUgYY+Jx0qRLVYPALcBiIkPz\nz6jqZhG5W0QWRbvdEi1SXQ98F7g+eu5m4BlgC/AGcLOqhjrhc5gE+Zdrwry7ugqcPoQjS0WUVpeS\n4cmgX2q/WN8XPnmBZFcyC0a0b+WQRaMXsWLXCioajvwxG54znIZAAwcaDuByuEh2JeMP+5k6vQmA\nFbZ0hIkSETeRhOsJVX3+6OOqelhV66PPXwPcItKPdozUW4mEMSYeca3TpaqvqepoVR2hqvdF2+5S\n1Zeiz7+lqhOiRajzoslW87n3Rc8bo6qvd87HMImgCqFwmEAoECmidzhJ96SjGllHqyC7ABGJ9lVe\nLH6RBSMWxGq+4rVwzEIU5bVtr8XaCrILACivKQcidV3+oJ/xE/2kpNgiqSZCIj+AfwI+UdWHjtNn\nYLQfIjKDyO/FQ8Qxym+MMe1hK9KbU7ZhAwwb4mL5e04CoQBup5skVxLV3mrq/HXkZ+fH+m48uJHd\nh3ezaMyiE1yxbVMHTiUvI4+Xth75e5eVnEV2cjbltZGkq7muSx1+pkwL2CKpptks4FrgwhZLQnw2\nuqDzTdE+VwGbRGQD8DBwjUa0OcqfiA9hjOkdXIkOwPRcq1bBgQPCwLwgvpCP3OTItErz6FN+1pGk\n642SNwC4ZOQl7X4fEWHh6IU8/vHjeINekl3JseuXVJXE6roc4sAf8vP1W+pIIgv78Taq+j5t12a1\n7PPfwH8f59hrwGttHTPGmPaykS5zylasVPr2CzNoqBcgVs9VXltOqju1VT3X4u2Lmdh/Yqs1u9pj\n0ZhFNAQaWFa2LNaWn51PQ6CBysZKPE4PHqcHf8jPhZfUc+VVVjpojDGme7Gky5yyD1fB1LP9+EJe\nnOKMJV1lNWXkZ+XH6rnq/fW8V/7eKY1yNZtXOI80dxovFx+5i7F5JK15ijHdk44v5ANgzUchNmw4\n5bczxhhjOpwlXeaU1NbCp5/C5CIfgXAAp8NJiiuFWm8tNd6aWKE7wLKyZQTCgXbftdhSsiuZi4Zf\nxOLti2NtfVL6kOHJaFVMLwiBUIAv/0sS99xrxfTGGGO6D0u6zCnx+eCWW0OcP68Bf8gfWyuredSp\nZRH9GyVvkOpO5fxh55/We15ceDHbq7dTVlMGRGq98rPzKa8tj9V1uRwuvEEvU8/282FbS2AaY4wx\nCWJJlzklubnKPfc3MHmaj2AoeKSeq6acZFcy/dP6x/ou3r6YeQXzSHIlndZ7XjT8IgDe3vF2rC0/\nK5/DvsNUe6tJdiXjdDgJaYjJRV527RL22v4HxhhjuglLuswp2botTH2TH2/Qi9vpblVEPyxrGA6J\n/Ghtr9pOSVXJaU0tNhvXbxyD0gfxdmmLpCs6olZeUx7Z99Gdhj/kZ0pRpLh/lY12GWOM6SYs6TLt\npgoXnO/ge7emxuq50jxp1PvrqWysbLVUxFs73gJg/oj5p/2+IsJFwy/irR1vEdbIpta5qbmkulNb\nFdMHw0HGneXD7VZWrLC6LmOMMd2DJV2m3crKoKJCOGuKF1/QR4orBYc4YgXtLYvol5YtZXDGYEb3\nHd0h731x4cVUNFaw6eAmIFrXlZXfqpje7XATdjbywuvV/OD2cIe8rzHGGHO6LOky7dY8ZTe1yEcg\nFGg1tehxehiYPhCIbP2zrGwZ8wrmxZaPOF1t1nVl51PtrabWW0uaJw2Hw0EwHGRyUSOZWZZ0GWOM\n6R4s6TLttmKFkpKiFI6pj00tQqSuamjmUJwOJwDFh4o50HCAuQVzO+y9h2QOYUzfMbxV+lasreV6\nXQ5xkOpOxRfysW8f3HOPUFLSYW9vjDHGnDJLuky7rfoQJk4JEMSLy+Ei3ZNOU6CJAw0HWi0VsbR0\nKUCHJl0AFxVexDtl7xAIBQAYkD6AZFdybIoxzZ1GIBygsRHu+ZmLJUs69O2NMcaYU2JJl2m3+/4j\nyLdvryEQDsS239lZuxNovd/isvJlDMkcwoicER36/hcWXkhDoIE1e9cA4BAHQzOHtiqmd4qTAUMa\n6NsvbMX0xhhjuoW4ki4RuUREikWkRERub+P4d0Vki4h8LCJvi0h+i2MhEVkffbzUkcGbxDjv/CDn\nzm4kEAocmVqsLcflcJGXmQccqeeaWzC3w+q5ms0pmANEivSb5WfnU9lYSYO/IbZIqj/siyyS+mGH\nvr0xxhhzSk6adImIE/gNcCkwHviSiIw/qts6oEhVJwHPAb9ocaxJVadEH4s6KG6TIKtWKS+/EsIf\nDBLWcKtFUfMy8nA5XAB8WvkpBxsOMjd/bofH0C+1H5MGTGq9+XWLui63002SK4lAOMDkIi+ffBLZ\ntsgYY4xJpHhGumYAJaq6Q1X9wFPAZS07qOpSVW2MvlwJDOnYME138etfK7d+MwVvsCm2KKov6GNf\n/b7W9VzRUah5hfM6JY65+XNZvms5/pAfgMEZg3E73K2WjvAFfUw+20tKCmzd2ilhGGOMMXGLJ+nK\nA3a1eL072nY8NwCvt3idLCJrRGSliFze1gkicmO0z5qKioo4QjKJsmqVMKXIT0iDuBwuUlwp7D68\nm7CGW9dzlS1jaOZQCrMLOyWOeYXzaAw08uGeyNyh0+FkSOaQWG1Z816QRefVUb6/lqIiq+s6E4nI\nUBFZKiKfiMhmEflWG32+Ei2N+FhEPhCRyS2OlYnIxmh5xJqujd4Y09vEk3S1VZDT5l8wEfkqUAQ8\n0KJ5mKoWAV8GfiUix1RVq+ojqlqkqkW5ublxhGQS4dAhKCkRJk/z4g/5SfOkxTa5doiDIZmRAc7O\nrOdqNjt/NoK0nmLMzmd//X68QW9skdQATYTFH1vB3pxxgsD3VHUcMBO4uY3yiFJgTrQ84h7gkaOO\nz4uWRxR1frjGmN4snqRrNzC0xeshwDHbCIvIxcCdwCJV9TW3q+re6L87gGXA1NOI1yRQc0H65LO9\nBLX1JteD0gfFNrTeUrGFisYK5hV0ztQiQJ+UPkweOLl1MX1WPoqyq3YXya5kXE4XwVCQp59ysmC+\noDbYdcZR1X2qujb6vA74hKNG6lX1A1Wtjr608ghjTKeJJ+laDYwSkUIR8QDXAK3uQhSRqcAfiCRc\nB1u054hIUvR5P2AWsKWjgjdd68MPFRFl9FmHcYkrts/hnro9req5mkefOnp9rqPNK5jHB7s+wBeM\n5PhDModEtiNqsXSEP+Snrk55+20HO3Z0ajimmxORAiJf+k60DfrR5REKvCkiH4nIjSe4tpVIGGNO\n6qRJl6oGgVuAxUS+JT6jqptF5G4Rab4b8QEgHXj2qKUhxgFrRGQDsBS4X1Ut6eqh7vhhmPfWVOJM\nbsTlcJHmTmPP4T0Ew8Fj1ucaljWs1R6MnWFuwVy8QS+r9kT+hrqdbvIy8loV04c0xKSzmwBYubJT\nwzHdmIikA/8Avq2qh4/TZx6RpOsHLZpnqeo0Indv3ywis9s610okjDHxcMXTSVVfA147qu2uFs8v\nPs55HwATTydA032II8ywEV721QVIT0rH6XDGRpWGZkVmoMMaZlnZMj476rOdVs/VbHb+bBziYGnp\nUmbnR/4WDssaxsrdK2N7QrocLoaMqCUtbQgrV8JXvtK5MZnuR0TcRBKuJ1T1+eP0mQQ8Clyqqoea\n21uURxwUkReI3M39budHbYzpjWxFehOXsjK4+ZvCtm0QCAdIcx/Zb7F/Wn9S3alApJ6rsrGyU+u5\nmmUnZzNg/pDnAAAgAElEQVR14NRjFkkNaYjdh3eT6k7F7XQTFj+Tp/lZeaJJJdMrSSTz/xPwiao+\ndJw+w4DngWtVdWuL9jQRyWh+DswHNnV+1MaY3sqSLhOX996DPz7ior4xgEMcpHvSCWuYXYd3HbNU\nBHR+PVezuQVzWbl7Jd6gF4iMdAnSavPrQDjAnM80Mnx42IrpzzyzgGuBC1vsjPFZEblJRG6K9rkL\n6Av89qilIQYA70fLIz4EXlXVN7r8Exhjeo24pheNWblSSUtX8oYfJhwtot9fvx9/yH/Moqj5Wfmd\nXs/VbF7BPB5c8SArdq1gXuE8kl3JDEgf0Kquq7Kxkq/dUs3A9CREUrskLtM9qOr7tL3sTcs+XwO+\n1kb7DmDysWcYY8ypsZEuE5eVq2DyVD9hiWxyneRKiiU2zSNdYQ3zTtk7XTbKBXD+sPMjdV1HLR2x\n+/BuQuFQbPNrX9AXefhsqMsYY0xiWNJlTqqpCT7eEFmfKxAOHFmfq7acPil9yEjKAGDTwU0cajrE\nhYUXdllsWclZnD3o7GMWSQ2EA+yr3xdbJLUp2MSlF6Vx3XWWdPV00VorZ6LjMMaY9rKky5zUrl0w\nOE85a1ojqkq6Jx1VpbymvFU915LSJQBdUkTf0ryCeazcvZLGQGT7z2FZw4BIkb/L4SLFnUIgHCB3\nQJAPP7S7F3saEXGIyJdF5FUROQh8CuyLbuvzgIiMSnSMxhgTD0u6zEmNGqWs3VLN+Z85hMsRqeeq\naKygKdjUqp5rSekSRvYZGVs+oqvMLZhLIBxgxa4VQKSOq19qv9hyFmmeNALBAJOmeSkrEw4c6NLw\nzOlbCowA7gAGqupQVe0PXEBkBfn7o1uQGWNMt2ZJlzmpsIbxh/z4wz7cTjep7tRYPVfzqFIwHOSd\n8ne6fJQLInVdTnG2qusaljWMnbU7CWs4tvn1WdPqAVhlS0f0NBer6j2q+rHqkU00VbVKVf+hqlcC\nTycwPmOMiYslXeakZl8g/PbhJILhIGnuI5tcZ3gyyEnOAWDdvnUc9h3u0nquZhlJGUzPm35MMb03\n6OVgw8HYIqnDx9fgcikrVlhdVw9z0jRZVQNdEYgxxpwOS7rMCR04AB984CAYDhMMB2P1XGU1ZRRk\nF8RWnW9OeLryzsWW5ubP5cM9H9LgbwCILVlRVlNGsiuZZFcy4vby/R/WMXtO+ARXMt2QFeIZY3oF\nW6fLnFDzVNyEqXW4xEWaJ43Kxkrq/fUU5hTG+i0pXcL43PEMTB+YkDjnFc7j/uX3s3zXcuaPmE9W\nchZ9UvpQWl3KzCEzSfekU++v59++W8PA9GTAbn7rQXJF5LvHO3i8leaNMaa7sZEuc0IrVypOp1I4\ntjpWRF9aUwpAYXYk6fKH/Ly3872E1HM1O2/oebgcLpaWHpliLMwupKymLFbXFdYwjT4fGz4OUVmZ\nsFBN+zmBdCDjOA9jjOkRbKTLnNCKlcr4iUGcSX5SPZm4HC5Kq0vJTs4mJyVSz7V6z2oaA40Jqedq\nlu5JZ0beDJaVL4u1FeYU8tG+j9hXt4/s5GzcDjfbSsIsmpXCI48oX/+6zVr1EPtU9e5EB2GMMacr\nrpEuEblERIpFpEREbm/j+HdFZIuIfCwib4tIfotj14vItujj+o4M3nS+oqIwn7uiLrYoaljDlNWU\nxUa5IDK1KAhz8uckMNLIel2r96ymzlcHHBmJ21G9I7b5df9htWTnhFm5MpGRmnay7NgY0yucNOmK\nrvz8G+BSYDzwJREZf1S3dUCRqk4CngN+ET23D/AT4BxgBvATEcnpuPBNZ/vxPY1ce9MBnOIkw5PB\n/vr9NAWbGJ4zPNZnadlSJg+cTN/UvgmMNFLEH9IQ7+98H4iszzUgbQClNaWICOmedEIaZGqRj1Uf\nJjRU0z6LTtZBRNK7IhBjjDkd8Yx0zQBKVHWHqvqBp4DLWnZQ1aWq2hh9uRIYEn2+APhndD2dauCf\nwCUdE7rpbFVVSoPXR1OwCbfTHannqo7UczXfHegNevlg1wcJredqdt7Q83A73K22BCrMKWRn7c7Y\nnZeBcIDJZzexZTMcPpy4WE27/FVEHhSR2SKS1twoIsNF5AYRWYz9XjHG9ADxJF15wK4Wr3dH247n\nBuD19pwrIjeKyBoRWVNRURFHSKYrfPe7yszJOfhCvtj0XGlNKbmpubH9FlfsWoEv5EtoPVezVHcq\nM4fMbLVeV2F2IcFwkN2Hd0fW6xIX4yYfRlVYsyaBwZq4qepFwNvAN4DNIlIrIoeA/wEGAter6nOJ\njNEYY+IRT9LVVj1Fm6tLRrfiKAIeaM+5qvqIqhapalFubm4cIZmusGKlMGqcLzZKFAqH2Fm7s9VS\nEW/teAunOLlg2AUJjPSIeQXz+GjfR1Q3VQORza8FobS6lDRPGm6Hm7HTqnniuVrOPtsWSe1BXgdu\nV9UCVc1S1b6qep6q3qeq+493kogMFZGlIvJJdK/Gb7XRR0Tk4WjN6sciMq3FsV5Vk/riuj3Mun8J\nhbe/yqz7l/Diuj2JDsmYM0o8SdduoOVmekOAvUd3EpGLgTuBRarqa8+5pvupqoKtxcJZ0xpw4CAj\nKYM9dXvwh/ytiugXb1/MzCEzyUrOSmC0R8wfMZ+whmObbye7khmcMZjSmlJcjsg6Y66UJuZ8pp70\nDFsktadQVQVePIVTg8D3VHUcMBO4uY2a1EuBUdHHjcDvoPfVpL64bg93PL+RPTVNKLCnpok7nt9o\niZcxXSiepGs1MEpECkXEA1wDvNSyg4hMBf5AJOE62OLQYmC+iOREf1nNj7aZbu7DaKH5uCnVreq5\nBInVc1U0VLB231rmj5ifuECPMiNvBplJmSzefuTHrDCnkN2Hd+MP+WMjdps3Kw89BGqDXT3JShGZ\n3p4TVHWfqq6NPq8DPuHYEofLgMc0YiWQLSKD6GU1qQ8sLqYpEGrV1hQI8cDi4gRFZMyZ56RJl6oG\ngVuIJEufAM+o6mYRuVtEmu8qeoDI4oXPish6EXkpem4VcA+RxG01cHe0zXRzH3ygOBzK8AlVpHnS\n8Dg9lNaUMjB9ICnuFCAytagoC0YsSHC0R7idbi4qvIjF2xej0YyqMLuQsIbZWbuTdE86DnHw/ntO\n/u9tTsrLExywaY95wAoR2R6dBtwoIh/He7KIFABTOXYvx+PVnsZdz9oT6lL31jS1q90Y0/HiWhxV\nVV8DXjuq7a4Wzy8+wbl/Bv58qgGaxPjs50O4M2pxp/hI9wwiEAqwq3YX5ww5J9Zn8fbF9EnpQ9Hg\nogRGeqwFIxbwwqcvUHyomLH9xjIsaxhOcbKjegdzC+bicrgYM7kKGMTKlVBQkOiITZwuPdUTo0tK\n/AP4tqoefd/q8WpP465nVdVHgEcAioqKuuX46eDsFPa0kWANzk5JQDTGnJlsGyDTpinTglz1rwdx\nOiLrc+2s3UlIQ7H1uVSVN7e/ycXDL8bp6F77GDZPd765/U0gMvo1NGsoO6p34HF6SPekkzeymuSU\nMCtWdMu/j6YNqlququVAE5Hkp/lxQiLiJpJwPaGqz7fR5Xi1p72qJvW2BWNIcbf+v5ridnLbgjEJ\nisiYM48lXeYY+/fD64sDVB/24nZE6rlKqkpwOVzkZ0U2G9h0cBP76vd1q6nFZoU5hYzqM6pVXdeI\nnBHsr99Pvb8+styFI8jEKX5bJLUHEZFFIrINKAXeAco4sjzN8c4R4E/AJyfYGPsl4LroXYwzgVpV\n3Ucvq0m9fGoeP//CRPKyUxAgLzuFn39hIpdPPdEKQMaYjmR7L5pjvPqq8rWvZfDEkjATxqWQ5Eqi\npKqE/Kx83E43QCyh6U5F9C0tGLGAP6//M76gjyRXEiP7jOTt0rfZXrWdYVnDcDlcTJhSz/NPJBEM\ngsv+J/QE9xC5A/EtVZ0qIvOAL53knFnAtcBGEVkfbfshMAxAVX9PpHTis0AJ0Aj8n+ixKhFprkmF\nXlCTevnUPEuyjEkgG+kyx1ixQsnKDjFg2GEykzKp8dZQ0VjByD4jY30Wb1/M+NzxDMkccoIrJc78\nEfNpDDTy3s73ABiYPpB0TzrbqraRkZSB2+nmqzeXU1xeYwlXzxFQ1UOAQ0QcqroUmHKiE1T1fVUV\nVZ2kqlOij9dU9ffRhIvoXYs3q+oIVZ2oqmtanP9nVR0Zffylcz+eMaa3s6TLHGPlSph0diMel4vM\npEy2V20HiCVdh32HeafsHT478rOJDPOELiy8kCRnEq9ufRUAEWFEzgi2V23HIZF1x5xpdajTG7vL\n0XR7NdGC+PeAJ0Tkv4isw2WMMT2CJV2mldpa2LJFGDflMG6Hm4ykDLZVbSMrKYt+qf2ASIF6IBxg\n4ZiFCY72+NI8aVxYeCEvb305llSN7DOSpmATe+v2kpmUSTAc5KEHkvjZ3ZZ0dWci8t8iMovIelqN\nwLeBN4DtQPf9ITTGmKNY0mVaWb5cURXGnX2INE8aTnFSWl3KqL6jiNQkw8tbXyYnOYfzhp6X4GhP\nbOHohWyv3s6nlZ8CMKLPCAShpKqEDE8GSc4k1q918Nhjba0MYLqRbcAvgc3Az4GzVPVvqvpwdLrR\nGGN6BEu6TCsXXRzm5SX7GD35EFnJWew6vAtfyBebWgyFQ7y27TU+O+qzuBzduxjq86M/D0SSRIhs\niJ2XmUdJVQnpnnQ8Tg9jptRSukPoputZGkBV/0tVzwXmAFXAX6J7Kf5YREYnODxjjImbJV2mNUeQ\n/PGVpKdG1ucqqSrBIY7Yfosrd6+ksrGShaO7/6zO0KyhTBk4JZZ0QWSKcc/hPfhCPjKTMhk5sRKA\nlSttirG7i67T9Z+qOhX4MvAFIrtkGGNMj2BJl4nxeuHWW2H9evA4PWQkZVBcWUxBdgFJriQgMmrk\ncri4ZGTP2IJu4eiFfLDrAw41RmahRvcdjaJsO7SNrOQsRp5Vi9OprFxlSVd3JyJuEVkoIk8QWZ9r\nK3BlgsMyxpi4WdJlYtasgUd+l0R5OWQmZVLrraWisYIxfY+sWP3y1peZnT+brOSsBEYav4WjFxLW\nMK9ti+xiNSh9EBmeDD6t/JQMTwZZ6W6mz6pDHOEER2qOR0Q+IyJ/JrJC/I1E1tUaoar/oqovJjY6\nY4yJnyVdJubddyOJx7hpkXqu4kPFAIzpF0m6SqpK2FKxhc+P+nzCYmyvswefzaD0QbxYHPnbLCKM\n6TeG7dXbSXIl4XF6+OXjG/n+HfUJjtScwA+BFcA4VV2oqk+oakOigzLGmPaypMvEvPseDB/dRL++\nQmZSJsWVxQxIG0B2cjYAz25+FoArx/ecGR2HOPjCuC/w+rbXafBH/k6P6TsGf8hPeU05fVL64A14\n8Qa9hEI2xdgdqeo8Vf1jT18N3hhjLOkyAIRCsOIDYeL0WpLdyTjEwc7anYztNzbW59ktz3JO3jkM\nyxqWwEjb7+rxV9MUbOLVbZGFUgtzCvE4PRQfKiYzKZOmJuG8qX345YOWdBljjOk8cSVdInKJiBSL\nSImI3N7G8dkislZEgiJy1VHHQiKyPvp4qaMCNx1rzx5ISQ0zdupB+qb0paSqBEVjU4vbq7azbv86\nrh5/dYIjbb/zh53PgLQBPLslMlLncrgY2WckxZWRpCsrw01IQ7z7jiVdxhhjOs9Jky4RcQK/AS4F\nxgNfEpHxR3XbCfwr8GQbl2hqsefZotOM13SSIUPDvLNxO3M+vz9Wz5XhyWBQ+iCAWMJy1firTnSZ\nbsnpcHLluCt5deurraYY6/x1VDVVkeZOY+L0at5fLoRCCQ7WGGNMrxXPSNcMoERVd6iqH3iKyHYc\nMapapqofA3YLWA8VCAWo8VaTlpRMqjuVbYe2Mbbf2Ngq9M9teY4ZeTPIz85PcKSn5qrxV9EUbOL1\nktcBGNV3FA5x8EnlJ/RL68fYsys4XOtg06YEB2qMMabXiifpygN2tXi9O9oWr2QRWSMiK0Xk8rY6\niMiN0T5rKmxp8C6nCued6+Tvf8kmMymTspoyAuEAE/pPAGBH9Q4+2vdRj5xabDY7fzb90/rHRuxS\n3akMzxnO5oObyfBkMG1m5O7Fd9617w3GGGM6RzxJV1sb07Wn+GWYqhYRWUH6VyIy4piLqT6iqkWq\nWpSbm9uOS5uOsGMHrF3jIqgB+qX1Y3PFZtI96bGC+ac3PQ30zKnFZs1TjK9sfYU6Xx0AE3InUO2t\npiHQQH4BXH3DHkaPDSQ2UNPhROTPInJQRNocxxSR21rUnW6K1qH2iR4rE5GN0WNrujZyY0xvE0/S\ntRsY2uL1EGBvvG+gqnuj/+4AlgFT2xGf6QLNoztTpteR7Epm26FtjM8dj0McqCqPffwYFwy7gILs\ngsQGepq+OumrNAYaef6T5wEY229sZIqx4hP6JPfhhts3MX2WrdfVC/0VOO4WCqr6QHPdKXAH8M5R\ny1PMix4v6uQ4jTG9XDxJ12pglIgUiogHuAaI6y5EEckRkaTo837ALGDLqQZrOsfSZWGycgKMGRdm\nX92+yNRibmRqcfXe1Xxa+SnXTb4uwVGevnOHnMvIPiP524a/AZDiTmFEzgg2V2ymT0ofQuEwazc2\nUFmZ4EBNh1LVd4lslB2PLwF/78RwjDFnsJMmXaoaBG4BFhPZXPYZVd0sIneLyCIAEZkuIruBq4E/\niMjm6OnjgDUisgFYCtyvqpZ0dSOqsHSJcNY5lQzM6M+Wii1keDJiU4uPbXiMZFdyj67naiYiXDfp\nOpaWLaW8phyACf0nUOOtocHfQM3+bObPHMbfn7JbGM9EIpJKZETsHy2aFXhTRD4SkRsTE5kxpreI\na50uVX1NVUer6ghVvS/adpeqvhR9vlpVh6hqmqr2VdUJ0fYPVHWiqk6O/vunzvso5lT4fHDBxXXM\nvuQA6Z50tlVFphZFBF/Qx983/Z3Lx17eY/ZaPJmvTvoqAP/z8f8AkSlGpzjZVrWNcaNSGJDXxFtv\nWzH9GWohsPyoqcVZqjqNyJI5N4vI7LZOtJuBjDHxsBXpz3BOd4BbfraZ+ZfVsOfwHoLhIGf1PwuA\n17a9RlVTFddN6vlTi80KcwqZnT+bxz5+DFUl2ZXMyD4j2XRwE/1S+zJpZgXvLHPael1npms4amqx\nRU3qQeAFIkvoHMNuBjLGxMOSrjPcpyVeapvqyE3NZVPFJvqm9GVI5hAA/rrhrwxMH8hnRnwmwVF2\nrOsnX8/WQ1tZsXsFAFMGTqHOX0eNt4Zp59ZSW+Ng/Xpbnf5MIiJZwBzgf1u0pYlIRvNzYD5gK7kZ\nY06ZJV1nsHAY5pyXysN3jcPtcFNWU8bkgZMREXbW7uSVra/wf6b8H1wOV6JD7VBXj7+aDE8Gv1/z\newBG9x1NqjuV4kPFzJrjA+DNt2yoq7cQkb8DK4AxIrJbRG4QkZtE5KYW3a4A3lTVhhZtA4D3ozWp\nHwKvquobXRe5Maa36V1/TU27fPyxUl3lZFJRPeW1exGEyQMmA/DIR4+gqnzj7G8kOMqOl5GUwXWT\nr+OPa//IQwseol9qP87qfxZr963lynET+Mkjq7jq0pFA30SHajqAqn4pjj5/JbK0RMu2HcDkzomq\nd3lx3R4eWFzM3pomBmencNuCMVw+tT1raBtzZrCRrjPYG/+MLAQ6Z26ILRVbKMwpJCs5C3/Iz6Nr\nH+Vzoz/XY7f9OZl/K/o3/CE/f1n3FyAyxRgMBznQcIBz5lThd+9PcITG9AwvrtvDHc9vZE9NEwrs\nqWnijuc38uK6PYkOzZhux5KuM9hbb4cZnF9P7iAv1d7q2CjXC5+8wIGGA3yz6JsJjrDzTOg/gTn5\nc/jdmt8R1jCD0gfRP60/JYdK0KZsHv5/yaxdH0x0mMZ0ew8sLqYp0Ho6vikQ4oHFxQmKyJjuy5Ku\nM5TPBx+852bqrCrKaspIciYxLnccAL9d81sKswtZMHJBgqPsXN+c/k1Ka0pZXLIYEWHKwCnsrtuN\ny+ni0QeG8/enLeky5mT21jS1q92YM5klXWeoMAHu/PV6rrz2IMWHipk8cDIep4d1+9bxbvm7/FvR\nv+GQ3v3jcfnYyxmYPpBfrfoVEJlidDlc1Dl2MmZyNa+/YXcwGnMyg7NT2tVuzJmsd/9VNcdV46/k\nrHN3k5O/m5CGKBoc2VbuP5f/JxmeDL5+9tcTHGHn8zg9fOucb/Hm9jdZt28dqe5UJuROYOuhrZwz\np5rN61PYv98WSjXmRG5bMIYUt7NVW4rbyW0LxiQoImO6L0u6zlC/fCjIrm3ZbK/eTkF2Af3T+rO9\najvPbnmWm4puIjs5O9Ehdombim4iw5PBLz74BQDT86bjC/kYe24ZAC+95ktgdMZ0f5dPzePnX5hI\nXnYKAuRlp/DzL0y0uxeNaYMtGXEGKi0L8tBPh3Ld96sp/Fwdl4y8BIAHVzyIy+Hi2zO/neAIu052\ncjY3Fd3Egyse5L4L76Mwu5DBGYM57NpA3wHnsXlbPWDTJMacyOVT8yzJMiYONtJ1BnrupXoAhkzb\nSIYng7H9xnKw4SB/Wf8Xrp10LYMzBic4wq717ZnfxilOHvzgQUSE6YOnUx88zK9ee4nLvr4RVavt\nMsYYc/os6ToDvfa60ndgA44BxRQNLsLpcPLA8gfwBX18/7zvJzq8Ljc4YzDXTb6OP637E7sP7+as\n/meR6k7loHcPdb46qptqEh2iMcaYXiCupEtELhGRYhEpEZHb2zg+W0TWikhQRK466tj1IrIt+ri+\nowI3p6bRG2TVe+mMmlFCijuZGXkz2Fu3l/9e/d98ddJXGdtvbKJDTIgfzf4RYQ1z77v34na6OSfv\nHCrra/jBtTO5625vosMzxhjTC5w06RIRJ/Ab4FJgPPAlERl/VLedwL8CTx51bh/gJ8A5wAzgJyKS\nc/phm1P13kcHCIdh0NQNTBs0jRR3Cve+ey/BcJCfzv1posNLmILsAr5x9jf407o/UVJVwvS86aQl\nJ9HoDfLmq6k2xWiMMea0xTPSNQMoUdUdquoHngIua9lBVctU9WPg6PvrFwD/VNUqVa0G/glc0gFx\nm1OUNqSU2198kLPO38m5Q85lR/UO/rj2j3x92tcZnjM80eEl1J2z78TtcPPTZT8l1Z3K2YPPZtiM\ndWzblMXa4opEh2eMMaaHiyfpygN2tXi9O9oWj9M513SwxkAje+v2Uh3ax9S8CWQlZ3HX0rtwOVz8\naPaPEh1ewg1MH8it59zKkxufZMP+DZw75Fwmz9kBwFPP2eraxhhjTk88SZe00RbvXEtc54rIjSKy\nRkTWVFTYiEJneWXpfv79sjnU7xzB+cPOZ/nO5Tyx8Qm+O/O7Z9wdi8fzg1k/oE9KH/799X8nMymT\n+efkkz1kP2+8koQ/5E90eMYYY3qweJKu3cDQFq+HAHvjvH5c56rqI6papKpFubm5cV7atEdYwzz7\njwAVZf2YM2lELLEYkjmEH17ww0SH123kpOTwHxf9B+/tfI+nNz/NnII5nPvF98mb8SHlNeWJDs8Y\nY0wPFk/StRoYJSKFIuIBrgFeivP6i4H5IpITLaCfH20zXexA/QGWLc5hyMQdLJxyHo+ufZR1+9fx\ny8/8kjRPWqLD61ZumHoD0wZN4/tvfh+Xw8U3b0xm0IUvsPngZiuo74FE5M8iclBENh3n+FwRqRWR\n9dHHXS2OnfDObdPxXly3h1n3L6Hw9leZdf8SXly3J9EhGdNhTpp0qWoQuIVIsvQJ8IyqbhaRu0Vk\nEYCITBeR3cDVwB9EZHP03CrgHiKJ22rg7mib6WKvrSihsrw/Cz7fRCAc4M4ldzI7fzZfnPDFRIfW\n7TgdTh6+5GH21O3hZ8t+xpyCOSQH8njm1QoqGysTHZ75/9m78/ioqvvx/69zZ81ksm+QQELYIoIg\nqxvWXdy11lr1U7W1Fq3a2n5qrVb70dZ+qn7qr9bW7at1a92pGyoIKO6VCoiy74SYsGXPZPa59/z+\nmCQGkpAASSbL+/l4zGOWu70vJDfvOed9zj1wT9P5AJ6PtdZHNj1+D10euS260esrKrj11VVU1AXR\nQEVdkFtfXSWJlxgwujRPl9Z6ntZ6rNZ6lNb6f5s++x+t9dym10u11sO01sla6yyt9fhW2z6ptR7d\n9HiqZ05D7E9juJF/vhyfhf7nVxZzw7wb8EV8PHzWwyjVXtmdOK7wOK6efDV/XvJn1lau5evXr+bl\n267g401fJDo0cYC01h8BB/Nlr9OR26J7/WnBBoJRc6/PglGTPy3YkKCIhOheMiP9IPBp2aeQ9xUX\n/HAL66ILmLN2DneccAfjc8d3vvEgdt/p95Gfks9Vc6/iJz/Mwoy4eOyFXTSGGxMdmuh+xyilvlJK\nzVdKNf9idHn0tQwG6h476tofJdzR50L0N5J0DXCBaIC3Nr/FpJk7efAvbq57+zqmDp3KzcfdnOjQ\n+rw0dxqPnfMYayvX8rF1N7lDQ6x69wje3fpuokMT3esLoEhrPQn4G/B60+ddHrktg4G6R356+zeX\n7+hzIfobSboGuPmb5rP282yOS72Ea966hvpwPU+d/xR2w57o0PqFM8ecyVVHXsX/fXYPJ567k11f\nTeLV5R/REG5IdGiim2itG7TWjU2v5wEOpVQ2hzZyWxyEX80qIclh2+uzJIeNX80qSVBEQnQvSboG\nsOpANfM3LGD5Iz/j9v/O4+1Nb/Pn0//MEXlHJDq0fuWBMx+gJLuE97xXY5k2tiwvYs6aOYkOS3QT\npdQQ1VTcqJSaQfy6WM2hjdwWB+GCyQXcfeERFKQnoYCC9CTuvvAILpgsc2qLgUGaOwYorTXPrXqO\nr9cNo353Bg3H/ZzvjPsO102/LtGh9Ttep5eXLnqJGY8fxTH/933OO+YIPir9gm8VfYsxWWMSHZ7o\nhFLqBeBEILtplPUdgANAa/0ocBHwE6VUDAgCl+j43CAxpVTzyG0b8KTWek0CTmFQuWBygSRZYsCS\npGuA+nLXl6zYuYK6z28AR5DCGV/w9/M+ltGKB2li3kT+duZfmf3WbCbU/Binw8mzXz3L7SfcjsPm\nSNrfZr0AACAASURBVHR4Yj+01pd2svxB4MEOls0D5vVEXEKIwUeSrgHIF/bx4uoXcZipLFs4Gtu4\nt3jrhy+Q7k5PdGj92tVTrmZZ+Qoeu+14jpriIfrt13hn8zucW3JuokMTQhCf5+tPCzawoy5IfnoS\nv5pVIq1mok+Rmq4BxtIWL65+kepANf/+TGOFkvn9L4cxIXdCokPr95RSPHjOA2Qyiv+8PgVlOli4\nZSGbqjclOjQhBj2ZWFX0B5J0DTCflH3Cl7u+ZH31etak/I3bXnuMWy8/JtFhDRgOm4P7bxsHDcN5\n/rVaakO1vLj6RXxhX6JDE2JQk4lVRX8gSdcAUlpXyvxN81m1exWfbv83P5vxM+467ydIGVf3uvQ7\nKeQOiaG+uIY31r/BpppN/GvtvzAts/ONhRA9QiZWFf2BJF0DRG2wlhdWvcCHpR/y8dcfM3bZPDY9\neD/tz+8oDoXDAT/+kY3ohlMxGkYwd8NcPi77mAVb5F7uQiSKTKwq+gNJugaAcCzM86ue57X1r/FZ\nxWecO+ISdn16OhnpSlq5esiPf6z41c0Wj3/7bwC8vPplXl//OksrliY4MiEGJ5lYVfQHMnqxnzMt\nkxdWv8DjXzzOV7u/4qJxFzFh20O8WW9w/fWJjm7gKiqCu/9o0BCezEtZL3HZK5fx7MpnMS2TdHe6\nzN8lRC9rHqV4MKMXZdSj6C2SdPVjlrZ4duWz/O6D31FaX8qVk67k10ffzuk3pTNzpubYY6WZq2cp\n3l/oodo3nWcueIafvP0Tnv7yaXwRH/edfh+FaYWJDlCIQeVgJlZtHvXYXITfPOqxeX9CdKcudS8q\npc5QSm1QSm1WSt3SznKXUuqlpuX/UUqNaPp8hFIqqJT6sunxaPeGP3hprXl02aP8cuEvKasv47ff\n+i03H3czi9/Oovxrg5tvloSrpykFf7rXzu9/k85hGUfwxPlPMDFvInPWzuHy1y7n6/qvEx2iEKIT\nMupR9KZOky6llA14CDgTOBy4VCl1+D6r/Qio1VqPBu4H7m21bIvW+simx7XdFPegZmmLmxfdzM/f\n+TlRM8oj5zzCBSUXkOJM4fxzHDz4kMXZZyc6ysHh179WlJUZLHo7nVEZo3jwrAe58LAL+Wj7Rxz/\n1PFS4yVEHyejHkVv6kpL1wxgs9Z6q9Y6ArwInL/POucDzzS9/hdwipL7zfSI2mAtp/zjFO777D7G\nZI7hlYtfYcqQKWR7snHb3eRmO7n+OgNDhkj0inPOgXHjNA/fn4JDuchwZ3DrzFv506l/YqdvJyc+\ncyJPr3g60WEKITogox5Fb+rKn+YCoHU/SXnTZ+2uo7WOAfVAVtOyYqXUCqXUh0qp49s7gFJqtlJq\nmVJqWWVl5QGdwGAyb9M8Rv11FB+WfsgVE6/gXxf/i/SkdDI9mShs/OQHGSxeJPcB7E2GAb/5jWL1\nKoNFb2aQ5k7DYXNw8siTWXT5ItJd6fxw7g/59ovfpjZYm+hwhRD7OJhRj6+vqOC4exZTfMvbHHfP\nYpn1XnRZV5Ku9lqsdBfX2QkUaq0nA/8NPK+USm2zotaPaa2naa2n5eTkdCGkwaXSX8mVr13J2c+f\njc2w8cwFz3D3qXcTiAZIc6XhsrlY+HoGr/zLTk2NNDD2tssugzPO0LjsDuyGnaykLAwM0txpLL9m\nOWePOZs3NrzB6L+N5pW1ryQ6XCFEKxdMLuDuC4+gID0JBRSkJ3H3hUd0WEQvtxsSh6IroxfLgeGt\n3g8DdnSwTrlSyg6kATVaaw2EAbTWy5VSW4CxwLJDDXwwiFkxHln6CLctvg1/1M/Mwpk8ds5j5Cbn\nUlpXitfpJdmZjI46uet3bo48UnPJJZJ09TbDgPnzFTHLIBzzUh+uJ8uTRVWgippgDf+6+F889PlD\n3Pfv+7hozkWcUnwK98+6nyPyjkh06EIIDmzU4/4K72W0o+hMV1q6lgJjlFLFSikncAkwd5915gJX\nNr2+CFistdZKqZymQnyUUiOBMcDW7gl94NJa8/r615n0yCR+9s7PyPHkcPvxt/PGJW+Qn5LP9vrt\neBweUl2p2JSN559KZXupwb33KqnlSiBt2nn6CSdmMBmbYSPbk00oFqK0rpSfH/1z5nx3DrNGzeLf\nX/+bSY9O4sdzf8xO385Ehz3gKaWeVErtUUqt7mD5fymlVjY9/q2UmtRqWalSalXT6Gv5sigOuPBe\nuiJFa53+iW6q0boBWACsA17WWq9RSv1eKXVe02pPAFlKqc3EuxGbp5X4FrBSKfUV8QL7a7XWNd19\nEgOF1po3N7zJ1Mem8u2Xvk1loJLvjf8ef571Z27/1u04DAdba7fitrnJcGcAEKjz8offOzjtNDj9\n9ASfwCC3ejX89Ho7f/xdMm6bG5thY4h3CP6In621Wzm28FgeP/dxbjv+No4adhRPffkUxQ8Uc+P8\nGylvKE90+APZ08AZ+1m+DThBaz0RuAt4bJ/lJzWNvp7WQ/GJfuRACu+lK1LsS8V7APuOadOm6WXL\nBtcXynAszIurX+T+Jffz1e6vGOodytHDjuaYYcdwXsl5lGSX0BBuYEvNFpw2J7nJuUStKCnOFGzK\nzovPuvnWtxSjRyf6TMTPfgYPPqhZ/GGEw6fUY1omGs1O305SXamMyhxFxIywcMtC3t36LksrlrJ0\nx1IMZfCDI3/AL47+BeNyxiX6NHqdUmp5TyY1TXMHvqW1ntDJehnAaq11QdP7UmCa1rqqq8cajNew\nwWTfyVQhXnjfXh3YcfcspqKdFrCC9CQ+veXkHo9V9I4DuX5J0pVA5Q3lPPHFEzyy7BF2+3czKmMU\nMwpmMCpjFFPzpzJr1CySHEnUBmvZVreNJHsSQ7xDCMaCeBwe7LhwO+PF26Jv8Plg/Hjwplj8+/MQ\nYe1Do1EoyhvK8Tq9jM4cjc2wsblmM29ueJPt9dvZULWBxdsWE7EinFJ8CjfMuIFzxp4zaP5v+1DS\ndRNwmNb66qb324Ba4gOD/p/Wet9WsObtZgOzAQoLC6du3769+4IXfU5XbxtUfMvbbUadQXzk2bZ7\nvplMUW5D1L9J0tWHhWNh3tz4Jk+seIKFWxZiaYsTR5zIlCFT8Dq95HnzmDVqFqMyRwGwq3EXFQ0V\neJ3eeFdV1I/b7qaxNonTTnbzv39QXHRRgk9K7OXtt+Pzd/361ii/vTOCL+LDUAYGBmUNZbhsLkZl\njsJtdxOOhfm47GM++/ozgrEge/x7WLB5AeW+cvJT8rlswmV8f+L3mTRkUucH7sf6QtKllDoJeBiY\nqbWubvosX2u9QymVCywCfqq1/mh/xxro1zDRdV1p6TqQljPRNx3I9WtwfI1OsFAsxMItC5mzdg5z\nN8ylIdzAsNRhXDftOoozimkIN5DsSObk4pOZPHQyhjKwtMX2uu3UBGvITMok25ONL+LDbXPjMjx8\n7wcOtpciXYp90Nlnwy23wFlnKwxlkOJMoSHcAAaMyhhFaV0p66vWU5xeTJo7jVNHnsrUoVN5b9t7\nrN6zmuumX4epTZaUL+Ev//kL9312H0fkHsH3J36fiw6/iJEZIxN9igOOUmoi8HfgzOaEC0BrvaPp\neY9S6jXik0XvN+kSotmvZpW0m1C1ngOsq6MhpTVsYJCkq4dUBapYtGURb296m7kb5uKL+MhwZ3Dh\nYRdy7PBjUSre3WRaJqeOPJXp+dNx2V0ABKIBttVuIxQLUZBagNfppSHcgMvmwuPw8Ie7bLy7yMbj\nj8ORRyb4REW77r4bwE7EtAgHIc2dRn2onpAOMTZrLNvqtrG5ZjN53jwKUgrISMrgosMv4phhx/Bx\n2cesr1rPscOP5YpJV7C9bjuvrX+NX7/7a3797q+ZkDuB88aex3kl5zG9YDqGkiGrh0IpVQi8Clyu\ntd7Y6vNkwNBa+5penw78PkFhin6oOSnaX7LUldGQclPugUO6F7tJKBZiacVS3t36Lu9seYelFUvR\naLKSsji/5HzOGnsWac40vtr9Fb6IjzRXGscVHsfkIZNx2OKzyGut2dW4i52NO7EbdorTi7G0hT/q\nJ8meRJI9ibfeUlz8HSdXXKF46qn4TZdF33XHHTB/vsW8hWGSk6E+XA9AujudSn8le/x7SHIkUZRW\nRLIzuWW73Y27+aTsE9ZUrsHSFqMzR5OXnMeqPat4a+NbfLT9I0xtkpucy8nFJ3NK8SmcXHxyv20F\n68nuRaXUC8CJQDawG7gDcABorR9VSv0d+A7QXIgV01pPa5rm5rWmz+zA81rr/+3seP31GiYSoytd\nkF3tppSWsMSQmq5eUBeq49OyT/mk7BM++foTPq/4nIgZwVAGRxUcxRmjz+CEohNw292sq1rH9rr4\n9XxU5iim509nTNaYvVoofGEfZfVlhGIhMpMyKUgtwBf2ETbDeJ1e3HY3pmVy150uFi00+PBD8HgS\ndfaiq954Ay68EE451WLOq2HcLoP6cHxUY5o7jagZZXv9dqJmlNzkXPJT8rEZ39ySpCHcwBc7v+CL\nnV+0dEOPzx3P8NThrNy9knmb57F422J2Ne4CYET6CL5V9C2OLjiao4cdzRF5R/SLYvyerunqTf3l\nGib6hq7UdHVWkN+VfUhS1nMk6epm9aF6vtz1Jct3Lm/5A7i+aj0ajd2wM3XoVGYWzuT4wuOZmDeR\n6mA166vWs7V2K5a2yPZkMyF3AhPzJpKZlLnXvoPRIBW+CupD9ThtTgrTCnHb3dSF6rC0RaorFbth\nJxyxSHY7sRk2AgFJuPqTJ56Aq6+G715s8uQzEZwOW0tCnWRPwuv0sqtxF3v8e7AZNoZ6h5KTnLNX\nUm5pi03Vm1i5eyUbqjcQs2KkudIoyS5hTOYYQrEQH27/kMXbFvPp15+yx78HAI/Dw7T8aRxdcDRT\nhk5hYt5ExmSN6XOJmCRdYjDrLCHqrKWrs+WdJWWSkB0aKaQ/SP6In/VV61lXtY61lWtZV7WO1XtW\ns7lmc8s6BSkFTBk6hUsmXMLxhcczeehkqgPVbK7ZzOaazazYtQKADHcGxw4/lgm5E8hLzkPt0w/Y\nGGlkd+Nu6kJ12AwbBakFZHuyaYw0Uh2sxm7YyXRnotG89x7c8BM38+crSkok4epvfvQjqK2FX/3K\nhmm6+OfzYVJdqYTNMA3hBiJmhJzkHLI92ZQ3lFPeUM5u/25yk3PJ8eRgM2wYyqAku4SS7BLCsTDr\nqtaxvmo9K3au4POKz3EYDkZljuKWmbdQlFaEP+LnPxX/YUn5EpZULOH+JfcTtaIAuGwuxueOZ2Le\nRCbmTuSIvCMYlz2O/JT8Nj+nQoie19ltiDoryO+sLmx/xfpAh/VizdtKMtZ9Bl1LV0O4gW2129ha\nu5VtdfHnLbVbWFe5ju3138ytYzfsjM4czfic8UwZOoUpQ6dwZN6ReJweyhvKKasvo6y+jF2Nu7C0\nhd2wU5RWxOjM0YzOHE22J7vNHzBLW9SF6qj0V9IYacRu2MlJziHHk0PEjE8tYGkLr9NLkj2JmBXj\nhedsXDvbwdixinnzoLCwx/5pRA976CFIS4PvXRolZsUwlIFCUR+uJ2pFcdlcpLpSCcVC7GrcRUO4\nAUMZZHuyyfZkk+RoO+N1zIpRWlfKhqoNbKze2FIz5nF4KEoroii9KF6o785gc+1mVu5eyardq1i5\nZyUrd69s6ZZs3mZM5hjGZI1hbObY+HPWWEakj2CId0iPFuxLS5cQ+7e/1qjOWrr21z2Zn57U7rbp\nSQ7CMUtax7pg0HYvBqNBdjbupKKhgh2+HVT44s9fN3wdT7Jqt1EdrN5rm1RXKqMyRjEuZxzjsuOP\nw3MOZ2TGSBojjS2F7Tt9O9nVuAt/1A/Ek7JhqcMoTCtseThtzjYxaa3xR/1UB6qpDdViWiYuu4vc\n5FyykrIIxUI0RhoxtYnL5iLFmYJG0+CzuPmXTp5+ysZJJ8Grr0J6+kH9s4g+6NlnLVLSo5w+y8Jh\ncxCOxVu9NLqlyzFmxdjVuIvaUC1aazwOD1meLDLcGS2DL1rTWlMXqqO0rpTt9dsprSulLlQHgEKR\nk5zDUO9Q8lPyGeIdQk5yDo2RRlbtXsXG6o1srN7IpppNbKrZxNbarcSsWMu+HYaD4WnDKUwrpCit\nqOVnviitiILUAoZ6h5LuTj/oljJJuoQ4eJ11H+4vKdvRdIuiripoSrDaO953phbw/vrKQZeIDZqk\n67eLf8vSHUtbkquaYNvbOibZkxiWOozijGJGpo+MP2eMpDi9mOKMYtw2NzWhGqoCVVQHqqkOVlMV\nqKImWNPyR8embC1/sIZ4h7T8kWld8NyaaZk0hBuoD9dTH6pvadXISMogKymLJEcSgWiAQDSApS0c\nhgOv04vdsLcc8647Xdxzt8Ett8Cdd4KzbT4n+inLgmOPhf/8B2Zfa/I/d0bIzjIwlEEwFsQf8aPR\nuGwukp3J2JSN2lAt1YFqAtEAAMnOZNJcaaS500iyJ3WY7DSEG9jp28kO3w52NsafGyONLcuTHckt\nXZs5nhyyPFmku9NJdiRT4atgY/VGttdtp6y+jO313zzv8O3A0tZex3Lb3QzxDiE/JZ+h3qHcfNzN\nzCiY0aV/E0m6hDg0+2t52l9S9qcFG9pNyDqyv9YxBXslcM3HgLbdlO191l8TtEGTdF36yqVsqdlC\nfko+BSkF8efUgpb3ucm52A07jZHGlgSoLlTX8ro+XE8oFmrZn6EMMtwZZHuyyfJkkePJaWkR2F/h\nccSMEIgG8IV9NEYaW/4w2gwbaa400t3peJ1eImaEYCxIxIwA8T9SHrsHm2EjZsVYs1oRjdg4eoad\nQECxYgXMnHkI/5iizwoG4dZb4W9/g7Q0zS2/ifGjH8fwJseTr7AZxh/xY2oThSLJEZ8yxLTM+M9v\nuB5/JN7qajNseJ3elofH4emwK1BrjS/iY3fjbioDlVQFqqj0V1IZqNzrdwEgxZlCujudjKQM0t3p\npLnSSHGlxEfT2tzUhesobyhnh29HPKnz7Yy3Cje1DD989sOcOOLELv17SNIlRM/qKCnrKCFzOwxq\nA9E2+znQ1rH2uikdhgIFUfObvXTUUgZ9PzkbNEnX1tqt1ARraIw04o/4489Rf8v7sBlus02SPYk0\nd1pLK0G6O52spCyyPdmku9M7bL2CeP1MOBYmFAsRioVaWquaW6cMZZDsTCbFmUKyMxmH4SBqRQnF\nQi3r2A07SfYkXDYXGk0kavHeuwYPP+hg0UKD446DTz45iH840S+tWgW//CUsWgT/+dxk4uQo0ajG\n4VDYVDwZD5thgtFgyz0cnTYnLrsLA4NANNDyM9+cNCmlWibSbU7W3HY3Tpuzwxax5m7wmmANtcFa\n6kJ11Ibiz3WhOupD9eh9LrMK1fLznuJKIdmRTLIzmWRHMh6Hh+KMYlJdqV36d5CkS4jEaS8hA7qt\ndayr9m0pay85cxgKr9tOXSDaYWJ20mE5vdrN2e1Jl1LqDOABwAb8XWt9zz7LXcA/gKlANfA9rXVp\n07JbgR8BJvAzrfWC/R3rQC5YT654krL6MiBeBJzsSMbr9JLsbHpueu91elsSreZZ3/eltSZmxYiY\nEaJWlKgZJWJGiJgRwmY80TKtb374lFItE5a67C4chgOnzYmpTaJWtCXJUqiWZQ6bo+UWPwAPP2jn\n3rvtVFYqhgyBn/4UrrkGsrK6dPpigNAaVqyAKVPi7y+/QrNls+aCC2OcfIrF+AlgKNXyc9nm58vm\nwGHEa7xCsVDLz2ww+k2rKsR/Zp02Jy6bC5fdFf+ZNBwt2ztsHd883bRMGiON+CLx1lxf2NfmtT/i\nxx/1t/x8XzrhUkqyS9rd374k6RKi7zmQ1rF9E6be0l5i1t46rRO11klZWpIDpdgriTvQBK1bky6l\nlA3YCJwGlANLgUu11mtbrXMdMFFrfa1S6hLg21rr7ymlDgdeIH6/snzgXWCs1trc9zjNDuSCVRus\nxW7Y8TjicyhY2sLSFqY2W15b2sK04u9jVoyYFcPUZsvrmBXDtMy9ioYhnoRpNDZlw2l34rQ5sRt2\nHEb8D5PdsLccq/U2hjKwGTYchgMDB3t22dhearBqleKrLw1WfWXwxpsm+UNtPP2UwYIFcOmlcOaZ\n4Go/HxSDzAMPxOf2WtU0ajs3V3PJZSb3/F+8qX/jBsWQoRbu5Gg8yTdbJWFNLVkK1ZJAhc3wNz/v\nZoyIFSFqRrG0hULt1fqllNrrZ9xm2LAp216vbUb8vaGMtg8MIlYEf8SP1+nt8EvOviTpEqJ/2Tch\nO+mwHF5ZXtHlbsq+qr2WtM6SsO6ep2sGsFlrvbVp5y8C5wNrW61zPnBn0+t/AQ+q+JX8fOBFrXUY\n2KaU2ty0v8+6ElxnyusrWLM+gmlZaB3PsrUFqelRMnIixGKwbaMHtMLSGq1BYZAzJEpenoUZtbN5\nrReFHXBjYMNQNopGWOTnQzTk5KsvnPF9awiHIRwymHhklMKiCHt2Opj7aiqRkI36WoO6Ohs11Ypf\n/ybKjBma116x81+XfjPKLCsLJk+GmiqDYflw1VXxhxCt3Xhj/FFWBu++Cx9+qMjLseO22zAti6On\nG4RCisxMTcEwTXa25qKLTa74QYRwNMZf73fgcJm43BZOl4XT7WLsOMXosQor6mDD8gwMAzQmWpmg\nLPLyQ6RnhwkELDZsVWgiYITQxL+4pOeE8KbEiIRs7N7hQhFP1IymhC07L4rHaxIKGlTvdpOWpjlq\n7MguJ11CiP6lvbnFphVldqmbsr3WqUS1lO0raumWJLEn7nHZlaSrAPi61fty4KiO1tFax5RS9UBW\n0+dL9tm22zpWwxGLS0+a0ubzH96wixtv201DrYMfn314m+U33V7LDb9soLzMzlXntQ3nD//XwOzr\nwqzf7OCSc9vO0/D/Ho8yZZzFlkqDO38TT6qSkzWZmfHEKhp0keRQzDwWHn0UiopgwgQoKJB7JYqu\nKyzcNzFXoG088wyUlkJpqeLrr6GmRhEOKtwOA1+d5vf/03Y6iTt+F+WoSWHKKjUXn53SZvnv7q7n\nR9c1UrXdxmWnDWmz/O4HKrno+/Ws2OTgyllFbZb/8ZEtnHJuNV+uSOZnl43nu1dVMP2vVpv1hBAD\n1/4mee1s9GJ7LWVd6Trsac2TyPZm0tVemrDvv0BH63RlW5RSs4HZAIUHMPvnhKGH8dQzUZSKf+NW\nCpQBhx2WxficbCJpMGeOiTKaljU9xo1LY3haGtmj4c23rL2WKQXjDksh25PCkeNg8eJvPne5ICkJ\nCgsduOxw7NFQXw9uNzidbU+1sDBeoyVEd7Hb4eKLW3+iWj0bDMmFQCA+OjIUij+CQcjOduB1ORg5\nHN57D0xTY1nx6StMEw4bl8LQlBSSD4OX51gtnzebPj2L4vRMUifDP5+NL4i3Lsd/nY85ZjhFecPJ\nO16T8kyUMWOzyUiSG14IITpOxrraUtb8WVqSA38k1qawvqcTs45m/D8YXanpOga4U2s9q+n9rQBa\n67tbrbOgaZ3PlFJ2YBeQA9zSet3W63V0PKmHEGLwkZouIURXdDTSsqPRi+0lageqeWb/jnR3TddS\nYIxSqhioAC4BLttnnbnAlcRrtS4CFmuttVJqLvC8UurPxAvpxwCfdyUwIYQQQojWutpq1lp7Rf/t\njV5sL0FrfY/L7tBp0tVUo3UDsID4lBFPaq3XKKV+DyzTWs8FngD+2VQoX0M8MaNpvZeJF93HgOv3\nN3JRCCG6m1LqSeAcYI/WekI7yxXxKXHOAgLAD7TWXzQtuxK4vWnVP2itn+mdqIUQ3aWzG4q31tP3\nlOzXk6MKIQaGnuxeVEp9C2gE/tFB0nUW8FPiSddRwANa66OUUpnAMmAa8VrU5cBUrXXt/o4n1zAh\nBpcDuX61f68QIYQYILTWHxFvge/I+cQTMq21XgKkK6WGArOARVrrmqZEaxFwRs9HLIQYqGR4kRBi\nsGtvWpyC/Xy+X1sr/Xzv/3XLVIRCiAFGWrqEEIPdIU15A/Fpb5RSy5RSy6LR/jP7thCid0lLlxBi\nsCsHhrd6PwzY0fT5ift8/kF7O9BaPwY8BvGarpeuOaYn4hRC9EEvX9v1dftcIb1SqhLYnug4mmQD\nVYkO4iBI3L1L4j50RVrrnJ7auVJqBPBWB4X0ZwM38E0h/V+11jOaCumXA823vfiCeCH9/urDDvQa\nlgbUd3Hd7tRTx+3O/R7Kvg522wPd7kDW70u/b31Jon4HDkRnMXb5+tXnWrp68sJ7oJRSy/rjhI0S\nd++SuPs2pdQLxFusspVS5cAdgANAa/0oMI94wrWZ+JQRP2xaVqOUuov4XIUAv+8s4WrarsvXMKXU\nY1rr2V0/m+7RU8ftzv0eyr4OdtsD3e5A1h8sv28HKlG/AweiO2Psc0mXEEJ0J631pZ0s18D1HSx7\nEniyJ+Jq8mYP7jsRx+3O/R7Kvg522wPdLlH/fwNJf/g37LYY+1z3Yl/SX7+ZSNy9S+IWQnRGft8E\nyOjFzjyW6AAOksTduyRuIURn5PdNSEuXEEIIIURvkJYuIYQQQoheIElXFymlblJKaaVUdqJj6Qql\n1J+UUuuVUiuVUq8ppdITHdP+KKXOUEptUEptVkrdkuh4ukIpNVwp9b5Sap1Sao1S6sZEx3QglFI2\npdQKpdRbiY5FCCEGA0m6ukApNRw4DShLdCwHYBEwQWs9EdgI3JrgeDqklLIBDwFnAocDlyqlDk9s\nVF0SA36ptR4HHA1c30/ibnYjsC7RQQghxGAhSVfX3A/cTAe3AOmLtNYLtdaxprdLiM+m3VfNADZr\nrbdqrSPAi8RvQtynaa13aq2/aHrtI57AdHpvvr5AKTUMOBv4e6JjEWKwUUqNVEo9oZT6V6JjEb1L\nkq5OKKXOAyq01l8lOpZDcBUwP9FB7MdB3Vi4L2ma8Xwy8J/ERtJlfyH+RcJKdCBCDARKqSeVUnuU\nUqv3+bxN6UTTF8wfJSZSkUgyOSqglHoXGNLOotuA3wCn925EXbO/uLXWbzStcxvxbrDnejO2TK0F\nvgAAIABJREFUA9TlGwv3RUopL/AK8HOtdUOi4+mMUuocYI/WerlS6sRExyPEAPE08CDwj+YPWpVO\nnEb8y+RSpdRcrfXahEQoEk6SLkBrfWp7nyuljgCKga+UUhDvovtCKTVDa72rF0NsV0dxN1NKXQmc\nA5yi+/bcIB3dcLjPU0o5iCdcz2mtX010PF10HHCeUuoswA2kKqWe1Vp/P8FxCdFvaa0/amrxbq2l\ndAJAKdVcOiFJ1yAl3Yv7obVepbXO1VqP0FqPIJ4cTOkLCVdnlFJnAL8GztNaBxIdTyeWAmOUUsVK\nKSdwCTA3wTF1SsUz8SeAdVrrPyc6nq7SWt+qtR7W9DN9CbBYEi4hekS7pRNKqSyl1KPAZKVUnx3k\nJLqftHQNXA8CLmBRUyvdEq31tYkNqX1a65hS6gZgAWADntRar0lwWF1xHHA5sEop9WXTZ7/RWs9L\nYExCiL6j3dIJrXU10Cevx6JnSdJ1AJpaBvoFrfXoRMdwIJoSlX6VrGitP6H9i2q/obX+APggwWEI\nMVD129IJ0TOke1EIIYToGf2ydEL0HEm6hBBCiEOklHoB+AwoUUqVK6V+1DRXYnPpxDrg5X5SOiF6\niNzwWgghhBCiF0hLlxBCCCFEL5CkSwghhBCiF0jSJYQQQgjRCyTpEkIIIYToBZJ0CSGEEEL0Akm6\nhBBCCCF6gSRdQgghxH4opUyl1JdKqTVKqa+UUv+tlDJaLZ+plPpcKbW+6TG71bI7lVIVTdt/qZS6\nJzFnIfoCuQ2QEEIIsX9BrfWRAEqpXOB5IA24Qyk1pOn9BVrrL5RS2cACpVSF1vrtpu3v11rfl5DI\nRZ8iLV1CCCFEF2mt9wCzgRuUUgq4Hnhaa/1F0/Iq4GbglsRFKfoqSbqEEEKIA6C13kr872cuMB5Y\nvs8qy5o+b/aLVt2Ls3opTNEHSfeiEEIIceBUq+f27qfX+jPpXhSAtHQJIYQQB0QpNRIwgT3AGmDa\nPqtMBdb2dlyi75OkSwghhOgipVQO8CjwoNZaAw8BP1BKNRfaZwH3Av+XuChFXyXdi0IIIcT+JSml\nvgQcQAz4J/BnAK31TqXU94HHlVIpxLsb/6K1fjNh0Yo+S8UTdSGEEEII0ZOke1EIIYQQohdI0iWE\nEEII0Qsk6RJCCCGE6AWSdAkhhBBC9AJJuoQQQgghekGfmzIiOztbjxgxItFhCCF60fLly6u01jmJ\njqM7yDVMiMHlQK5ffS7pGjFiBMuWLUt0GEKIXqSU2p7oGLqLXMOEGFwO5Pol3YtCCCGEEL1Aki4h\nhBBCiF4gSZcQQgghRC+QpEsIIYQQohccUtKllHpSKbVHKbW6g+VKKfVXpdRmpdRKpdSUQzmeEEJ0\nF7l+CSF626G2dD0NnLGf5WcCY5oes4FHDvF4QgjRXZ5Grl9CiF50SFNGaK0/UkqN2M8q5wP/0Fpr\nYIlSKl0pNVRrvfNQjivEQGJZUFMDVVXg80FjY/wxfbomLw82b9HMfQOiUYjG4s+xGPzwKoviYvj8\nc3jmGYW2QGuwtAat+dUtUYYNt/jkI4MXnrejiS/XOn7cO34fIifXYtECO6/+y4lGf7Ncwz3/n5+0\ndM3c1x3Me9MJaDTxnWjg3gdqcLk1r76UxAfvub85Ia2ZeXKAa6/ykuxM7u1/zi6T65cQoj1ldWW4\n7C7yvHndvu+enqerAPi61fvyps/2umgppWYT/yZJYWFhD4ckRO+yLNi2DVauhC1bYMsWzdZt8Itf\nWJx6mua9d+GMWW1/FZ+b08jpZ4ZZ8oWdm25K32uZzaaZeEwltowgK9Z7mDMnB8NoyqbQKAWzLi3D\n7/GzZG068+YPR6mm5QoUmjN/uIYhsQCfrhnK+x8UQ/NyQCnNv0uXkZYV5tNVxXzyaXGb+FbsWInb\nY7Js3RiWfDa81RJFcn4tDWGrTyddXdCl6xfINUyIgcK0TNZXrScjKaNHki6lte58rf3tIP5N8S2t\n9YR2lr0N3K21/qTp/XvAzVrr5R3tb9q0aVomFhT9WTAIn3wCubkwcaJm5SqLIyfZWpZnZmpGFFv8\n+rYAJ50eomKHxdzXXGRkmni8MZK9Gq8XRoyMkpYGobCJP2CBEUPZYmCYgEVMx7C0hWmZxHQMNCil\nAFCoNq+VUhjKwKZsGMqIvza+ed16mUJhGEbLa6VUm+f9LQNw293Yja59r1NKLddaT+ve/4kuHXcE\n3Xj9ArmGCdFfxawYdcE6Pt/xOaMyRlGSXdKl7Q7k+tXTLV3lQOuvwMOAHT18TCF6XV0dvPIKvPwy\nfPSRJhRSXPWjGA8+EqV4tOaBh2DchAiFxWGS06JAPBmKWoqhQ2385PoopjaJxCJErShRM0pMx6gK\nRLG0Bc5WB9PgsDnwGB7shh2HzYHDcGAzbNgNOzZlw2bYsKmm902vm5OhnqC1JmyGCcVChKIhQrEQ\nucm5XU66+ii5fgkxSETNKIFogNpQLb6wj6gV7ZHj9PQVcS5wg1LqReAooF7qIcRAozVMnarZulUx\nerTF1bMtTjw5yoxjQzSEI8SsGBddHk94HIYDh827V5ISjAYJxULx5KqJy+7CZXOR6krFaXPisrlw\n2V04DAd2w95jCZTWmqgVv/j4I34C0UD8dfSb14FoIJ5ctXqEY+Hmiq8Wl064tMvfFPsouX4JMQhE\nzAjhWJiGcAP+iJ/lO5YTs2JMyG3TAH7IDinpUkq9AJwIZCulyoE7AAeA1vpRYB5wFrAZCAA/PJTj\nCdFXfPABPPww/PNZE21EuedPiqFDNeMnhQibISziCZRSdlJcKRjKIBgN0hhpxBfxETXj36Jshg2P\nw0NOcg5J9iSSHEm47W4M1b1T6GmtCcVC+CI+fGEfDeEGfJGm57APX8SHP+LHH/UTs2Lt7sNQBsmO\nZJIcSSTZk0hzpZGXnIfb7m730RP1EN1Jrl9CDG5a628SrkgDlraIWBE8Dg8uu6tHjnmooxcv7WS5\nBq4/lGMI0Zds2AA33ggLFkB+gWbLtihFxSYnnxEiYkYImhqH4cDr8GIog4ZwAzt8O/BH/EC8WzDF\nmYLXGR/Zl2RP6rZWq6gZpTZUS22wltpQLXWhur1eR8xIm22S7EmkulJJcaWQm5yLx+HB4/CQ7Ej+\n5rUz/tplc/VoF2Vvk+uXEIOXpS0iZoSoGcUX8WFTNmrCNThtTtLcaSh65lrXrwsuhOgtkQjcey/8\n4Q8ajwf+eE+Mq68Jo5wh6sMRFKqllaox0sgO3w58YR8Ayc5k8lPySXOn4XF4DjmWcCxMZaCSSn/l\nXs91obq91nPanGS4M8hMymRkxkjSXGmkuFJIcaa0JFr9vOZKCCEOWMyKETWjhM0wgUgAm7K19ECM\nzhzNe/q9Hrs2yhVXiC6wLHjlVc1555vce1+EtOwgYTOMYRmkOFNw2pxUBarYXredmBXDbXeTn5JP\nZlLmITVTB6NBdvh27PWoD9e3LLcbdrI92QxPHc7kIZPJ8mSR4c4g3Z2Ox+EZUC1TQghxqKJmlJgV\nIxgLEowGcdldNIQbaAg3MCx1GKmuVPxRP16nt0eOL0mXEPvxzjtw9DEW7uQI732gsTnDBGNBIiZ4\nnV6cNie7G3dTHaxGa026O508b95B/cJqrakOVlNaV8r2uu2UN5RTG6ptWZ6ZlMnwtOFM904nx5ND\nTnIO6e70bq//EkKIgaa5fsu0TPxRP1ErSpI9iYZwA7XBWvJT8snz5lEdqMbSFunu9M53ehAk6RKi\nA3/7G9x4o+amm03u+L2JafcTjsVw2VwkO5OpClSxx78HgBxPDrnJuQfcquUL+9hUs4nNNZvZXrcd\nfzRe++V1eilMK2Rq/lTyU/IZ6h1KkiOp289RCCEGuub6rZgZwx/1Y2oTj8NDdaCaulAdQ1OGMjRl\nKACldaUAFKb1zCTHknQJsQ+t4dZb4zVc55xr8YubAzSEAwCku9MJRAOsr1qPaZlkebLIT8nHaXN2\nstfmfWsqfBVsrN7IxuqN7GrcBUCqK5XRmaMpSi+iKK2IzKTMftc1GIgGqGiooMJXwU7fTk4YcQL5\nKfmJDksIMYg112/FrBj+iB+NJsWZwg7fDhojjRSmFZKTnNOy/opdK9jVuIv1Vev3+ry7SNIlRCta\nw3//N/zlL3D17Bj/e189McI4DAceh4fyhnJ8YV9LS1RXW592N+5m9Z7VrNqzirpQHYYyGJ46nFNH\nnsrYrLHkeHL6bJKltaY2VEtZfVlLUlXeUL73a19Fm0L+Vy5+hQvHXZigqIUQg1nznIOmZcbnQ4wF\nsRt23DY32+u3EzEjjMwYSUZSRss222q3Ud5QzoLNC6gOVnN80fHdHpckXUK0sns3vPii5ifXx7jj\n7lpixPA4PJiWyaaaTQAUpReR7cnudF+BaIAvd33Jl7u+ZI9/D4YyGJkxkhNHnEhJVkmf6i70hX2U\n1pWyrW4b22q3ffO66b0v4ttrfYViiHcIw1KHMSZrDCeOOJGClAKGpQ4jPyWf/JR8itKLEnQ2QojB\nrLk70bKslvott90NwJbaLdgMGyVZJXvdGzZmxZi/eT4Z7gzcdrdMGSFEb8jKifLJkiiejAZMLJId\nydSGaqkOVON1einOKO60K7G8oZylFUtZU7mGmBVjeOpwzhpzFuNzxif0BtARM8KWmi1sqN7AhqoN\nbKzeyIbq+HNloHKvdZMdyRRnFDMifQQnFJ1AcXoxhWmFFKTGE6sh3iEy3YQQos9p7k60tEVjpBFT\nm6Q4U1qm8kl2JjMqYxQOm2Ov7d7Z/A57/Hv4ryP+i0eXP9pj8clVUwjgzTfhgw9N7rwrSnJmA1ZT\nwrWzcSf+iD9eaOkd2mEXoNaaLbVb+KTsE0rrSnHanEweMplp+dN6fWb2qBllY/VGVu5eycrdK1ld\nuZr1VevZVrsNU5st6w3xDqEkq4QLDruAURmjWpKs4vRisj3Zfba7Uwgh2tM8OjFqRfFH/CilSHel\ns7NxJ3WhOrI8WRSmFbYZ8b18x3KW7VjGzMKZjMkag2mZ2Axbj8QoSZcY9FauhMsu04wtgSpfA64k\niyR7EmX1ZcSsWJt+/9a01myq2cT7295nZ+NOUl2pnDH6DCYPmdxjt5ForTHSyPIdy1m6Yylf7f6K\nVbtXsa5qXcvs8w7DwWHZhzFl6BQunXApY7PGUpJVwtissaS503o8PiGE6Gmtp4MImfH7wToMBy6b\niy21W4haUYanDSc3ObfNtqv3rOatjW8xOnM0JxefDEAwFsRjP/SJrNsjSZcY1Orr4dvf1qSkwt+f\nr8KVZOG0OSmrL0MpRUl2SYezyO/w7WDhloWU1pWSmZTJeSXnMSlvUo99Q4qaUVbtWcXnFZ+3PNZV\nrWu5UXZBSgET8yZyxugzmJg3kSNyj6Aku6TLIyuFEKK/MS0zXr+lrZZ7xyY7kgnFQmyu3YzDcLSp\n32q2avcqXlv/GoVphXxv/PdaWsD8EX+33D2kPZJ0iUFLa7j2Ws327fD6O3XkDolhVw7KG8qxKRtj\ns8a221rVGGlk4ZaFrNy9Eo/Dw9ljzmbK0CndnmyFYiGWlC/hw9IP+XD7h3xW/hmhWAiAbE82Mwpm\n8N3Dv8v0gulMz5/eI8ObhRCir2qeCiJiRghE49P6pDpT2e3fTV2ojnR3OiPSR7R7bf7s689YsGUB\nI9JHcOmES1tqvILRILWh2h67nkrSJQatDRvg1Vfh5tsCHDnDj6Fs7Gjcgd2wU5JV0qbQUmvNV7u/\nYsHmBUTMCDMLZzKzcGbLqJhDZWmLZTuWMW/TPN4vfZ8l5UuImPH7Ok4aMolrpl7DMcOOYUbBDEak\nj5CaKyHEoNS6OzEYi9+SzWE4cNqcbKvbtt/uRNMyWbhlIf+p+A+H5xzOheMu3GtQ0OaazQCMzhzd\nI7FL0iUGrdFjY3y6NEj2sDoMZWOPf09LC9e+CVd9qJ65G+aypXYLhWmFnDv23G75JlQdqGbBlgXM\n3zyfdza/Q1WgCoViav5Ufjrjp5xQdAIzC2d2WFMmhBCDSXuzyzd3J26p3bLf7sTGSCNz1sxhe/12\njhl2DKeNOq1NUf3HZR8DMD1/eo/EL0mXGHS0hg8+tJhxbJi8ovhEpZX+SpRSjM0a26YGamP1Rl5b\n9xqmNjlrzFlMz59+SK1MO307eWXdK7y85mU+/fpTLG2R7cnmjNFncNboszh91OlkebIO9TSFEGJA\naZ4OImyGCUaDAHgdXvYE9lAfqt9vd2J5Qzkvr3mZYDTIheMuZGLexHaP8cq6VxiZMVJauoToLk8+\nCVdfbfDsK0FOPMWgNliLpS3GZu5dw2VaJou3LebTrz9liHcIF4+/mMykzIM6ZlWgipdWv8TLa1/m\n4+0fo9GMzxnP7cffzlljzmJa/rQeK8AXQoj+rHl2+ZgZIxALEDEjOAwHhjLYVrcNS1ttbufTzNIW\nn5Z9yvul75PqSuWqyVe13GdxXyt2rmDxtsXcc8o9PVa+IUmXGFR27oRf/lJzzHExZp4UpD7iI2yG\nGZkxcq/maH/Ez0trXqKsvoxp+dM4Y/QZBzwZqGmZLNq6iCdWPMEb698gakUZnzOeO064g++O/y6H\n5xze3acnhBADSuvuxMZoI5aOT+njC/uoDFSS5EiiOL243Tt8NIQbeG3da2yr28aE3AmcM/acDmtw\nLW1x/bzryUrKYvbU2T12PpJ0iUHlVzdrQiH437/sIWwFCUaDDE0ZulfNVFWgiudWPocv4uM7477D\nEXlHHNAxdjXu4pGlj/Dkl09S3lBOVlIW10+/nqsmX3XA+xJCiMGqdXdiIBpAoXDb3Oxs3EkwGiTP\nm0dBSkG7rVJr9qzh7U1vEzWjnF9yPkcOOXK/rVf3/fs+Piv/jGcueKZHa2gl6RKDxpIl8Nyziht+\n2UDRyDA1wQbS3Gnkp+S3rFNWX8bzq57Hpmz84MgfMCx1WJf3v2r3Ku5fcj/PrXqOqBll1uhZ3D/r\nfs4rOU/myhJCiAPQulg+YkZw2pxErSil9aXYDTtjssaQ6kpts50/4mfepnmsqVxDfko+F467sNN7\n5b654U1uefcWvnv4d7l84uU9dUqAJF1iENm122TCRM21P6+lNlSL0+6kOKO4ZfnW2q28sOoFUl2p\nfH/i97v8bef9be9z9yd3s2jrIjwODz+e8mNuPOpGxmSN6alTEUKIAal5OoioGcUX8aHRuGwuaoI1\n+CI+0txpjEgf0W65x9rKtby98W1CsRCnFJ/CcYXHtRmduK+3Nr7FRXMuYmr+VJ46/6ken4pHki4x\naJx2ZohJx1fSEKkHC4rTi1t+cTdVb+KlNS+RmZTJFZOuwOv0drq/T8s+5bfv/5b3S99niHcIfzz5\nj1wz7ZqDLrYXQojBrHl2+bAZxh/xYygDA4MKX8V+i+UD0QBvb3y7pXXrysOubHeOrn09u/JZrnrj\nKibmTWTh9xe2O81Ed5OkSwx4oRA8+1yM0y6oJ6LDhGIhClILSHGlAFBaV8pLa14ix5PD5ZMu7/T2\nD2sr13LTwpuYv3k+ucm53D/rfq6Zek27hZxCCCE6FzWjRM1oS3ei3bDjj/qpDdaS5EhiZMbIdovg\nW7dunVx8MscNP67TkeCmZXLb4tu499N7OaHoBF6/5HXS3ek9dWp7kaRLDHiPPqr5xS/sPJerGXlk\nLSmulJY6roqGCp5f9TwZ7oxOE666UB13fnAnD37+IF6nl3tOuYcbZtzQK9+OhBBiIGqvO9GmbFT6\nKwmb4Q6L5etD9czfPJ/1VesZ6h3KFZOuIM+b1+nxdvh2cOXrV/Lu1ne5duq1/PXMv7aZDLsnSdIl\nBjS/H+6+G479VoiSKbvQ2ClKL8JQBjXBGp5b9RzJjmSumHRFhwmX1pp/fPUPblp0E9WBamZPnc1d\nJ90l9zoUQohD0DwdRDAaJBANYCiDqBmlOljdYbG8pS0+r/icxdsWo7XmtJGncfSwo7s0z+Hr61/n\n6rlXE4wFefzcx7l6ytU9dWodkqRLDGgPPKDZs0fxwNO7CJkhCtMK8Tq9BKNBnl/1PFprLp90eUtX\n477KG8qZ/eZs5m+ez3HDj+PByx/kyCFH9vJZCCHEwBKzYkRiEXwRH1ErioFBfagef9RPujudovSi\nNsXyO307eXPjm+zw7WB05mjOHnN2lwY8VQWquGnhTTzz1TNMGTqF5y98npLskp46tf2SpEsMWA0N\n8Kc/wcmzAgwfX4HX6SU/JR/TMpmzdg61wVqumHRFu4XvWmue/vJpfr7g58SsGH89469cP+P6TkfC\nCCGE6Fjz7PLhWJiGcANKKUxtUhWoQqPbLZaPmBE+KP2AJeVL8Dg8XHT4RYzPGd/pSEOtNc+ufJZf\nLPgF9eF6bjv+Nv7nhP9J6BQ+knSJAau8XDO8KMZVvyjDpmwMSx2GzbCxcMtCttZu5YLDLqAovajN\ndo2RRq5961qeW/UcJxSdwBPnPcGozFEJOAMhhBg4mrsTA5EA/qgfm2GjMdKIL+zD4/BQnFHcplh+\nU/Um3t70NnWhOqYOncqpI0/t0qCl9VXr+en8n/Lu1nc5etjRPH7u40zIndBTp9Zlh5R0KaXOAB4A\nbMDftdb37LO8EHgGSG9a5xat9bxDOaYQXTVybJhX3itnR8NuMpKGkOXJYn3Vev799b+Znj+93W7C\nNXvWcNGci9hYvZG7TrqL3xz/G2ndGsDkGiZE7zAtk3As3NKdqNFU+auIWtF2i+V9YR/vbH6HNZVr\nyPHkcNXkqyhMK+z0ONWBan734e94eOnDJDuTeeish7h22rV95jp+0EmXUsoGPAScBpQDS5VSc7XW\na1utdjvwstb6EaXU4cA8YMQhxCtElyxdqkkb4qPWqMbtcDM8bTi1wVpeX/86+Sn5zBo9q802c9bM\n4Qdv/IAUZwqLLl/EycUnJyBy0VvkGiZE74iYEcKxMPWhepRShKIh6sP1OGyONsXyWmuW71zOu1vf\nJWbFujwNRNSM8siyR7jzgzupD9cze8psfnfS77o0X9f/z959x0dVpv0f/5zMZEomvZOEkBAIvUlH\nadKkKIIKFta1rO6uso997aio6Oqqu+vDuvjY9mdXbDRBqSICUqRDeq+kTJJJps/9+4MlC4KiBJgk\nXO/XKy8ymTMz1+jMPd85932ucy61ZE/XECBbKZULoGnaB8B04NgBSwFH/2uGAaUteDwhfhGPB66a\nBQnJFp5+s4mUiBRMehPv7X0PgKt6XnXcAk2lFC9sfoH7vr6PER1HsPiqxT95FnrRrsgYJsRZdLQd\nRKOrEZvLhqZpWO1WnF7nSRfLVzZWsjRjKUX1RaSGpzItfRpRQVGnfIwVWSu456t7yKjOYHzn8bw4\n8cVWe57bloSuRKDomMvFwNAfbfM48JWmaX8CLMD4k92Rpmm3ArcCJCefevehED/nww8VBfkaf3yk\ngCBjEAkhCWwq3ERxfTFX9rzyuKNdvD4vd6y8g4XbFjKr1yz+ffm/f/Is9KLdkTFMiLPkaHf5Omfd\nkcanPnfznq5O4Z2OOx+i2+tmY+FGNhVuwqg3cnn3y+kX1++UC+W3lWzjobUPsTp3NelR6Sy9ZilT\nu04966fyaYmWhK6TPSv1o8vXAG8ppV7QNG048Lamab2VUr7jbqTUq8CrAIMGDfrxfQjxiykFL72k\nSO3i4oJRpSQEp1Njr2Fd/jp6x/Y+biGlw+Pg6sVX80XGF9w34j6eHf9sq5n3F+eEjGFCnAVurxuH\nx4HVYQWONDJ1ep0nXSyfU5PD8qzl1Nhr6BfXj0ldJp3yrCB7K/Yyb/08Pj/0OdFB0fxt0t+4bfBt\n57TJ6elqSegqBjoeczmJE3e93wxcAqCU2qxpmgmIBipb8LhC/KTNm2HHjgDufrKIsKAQYi2xvPHD\nGwQFBjGl65Tm7RweBzM+nMHK7JW8PPll5g6Z68eqhZ/IGCbEGXR0OtHmstHoasSjPNQ56vApH/HB\n8SSEJDTvhbK5bKzKXsXeyr1EmaP4bb/fkhqR+rP3n1WdxWPrH+ODfR8QYgxh/pj53Dnszp/ss9ga\ntSR0bQO6apqWCpQAVwPX/mibQmAc8JamaT0AE3C4BY8pxM9as9ZLWDiMnl5AYkh3tpVuo6Kxgqt7\nX9387cnutjP9g+mszl3Na5e+xs0X3OznqoWfyBgmxBniUz6cHidWhxWPz4PNfSR4GfVGuoR3aQ5G\nSil2lu3k69yvcXvdjEkZw0XJF53QCPVYhXWFzN8wn7d2vYVRb+SBix7g3hH3nrTHYmt32qFLKeXR\nNG0usIojh1K/oZTar2nafGC7UmoJcA/wf5qm3cWR3fY3KKVk17s4a26/p47+Uw8RGxGMQWdgQ/4G\nukd3p3t0d+BIF+SrP7ma1bmref2y17lxwI1+rlj4i4xhQpwZHp+HJlcTVueRwGV1WFFKERUURXJY\ncnOgOnahfEp4CtPSpx23tuvHym3lLNi4gEU7FgEwd8hcHrzowV90jsXWqkV9uv7Tr2bFj/4275jf\nDwAXtuQxhPilbI1eqhxVGIIbSAjpyVc5X6FpGpO7TAaOfMO6ZektLMlYwv9O/l8JXELGMCFayOV1\n0eBswOay0eRqosHdQGBAIMnhyc2Byu11803BN2wq2oRJbzrlQvkaew3PbXqOl79/GafHyU0DbuLR\nUY/SMazjSbdvS6QjvWgXnE7o2kXj8hv0XH9bKFaHlayaLCalTSLMFAbAw2sf5q1db/HY6Me4fcjt\nfq5YCCHarqPTibX2Wlw+F1a7FbfPTagxlNTwVIx6IwDZNdksz1xOraOW/vH9mZg28ScXytc76/nb\nlr/xwuYXaHA2cG2fa3l8zON0iexyLp/aWSWhS7QLn32mKC8PIKlrDXGWOJZlLSMmKIahSUc6ALyz\n5x2e+fYZbrngFh4b/ZifqxVCiLbL6/PS6GrE6rTicDuoddSiD9CTEJLQvFje5rKxMnsl+yr3ER0U\nzQ39byAlPOWk92d321m4bSHPfvss1fZqZnSfwfyx81vFaXvONAldol1Y9KqP+CQ3F46Bm2WXAAAg\nAElEQVRpoqi+kRp7Ddf1uY4ALYAtxVv43ZLfMSZlDAunLGzVPVyEEKI1c3uP9NtqcDbQ4GrA7rET\nFBhESngKIcYQlFJsL93O6tzVuL1uxqaM5cLkC0+6UN7ldfHaztd46punKLOVMSltEk9d/BSDEgb5\n4ZmdGxK6RJuXlQXr1+n4zZ3ZRFkiWJ61nC6RXega1ZUKWwUzPpxBYmgii69a3Cb6uAghRGujlMLh\ncVBrr6XR3Ui1vRqdpiPGEkOnsE7oAnRU2CpYlrmsuaP81PSpJ10o7/F5eGfPOzyx4QnyrfmMTB7J\nh1d+yMhOI/3wzM4tCV2izVv0qhedLoBpsw6TU1uF0+NkYtpEvD4v1316HVaHlVVzVp3ydBJCCCFO\n5PV5aXI3Ud1UfWQPl7sBs95MclgyUUFRuL1u1uWu47ui7zDpTczoPoO+cX1PmFXwKR+LDyxm3rp5\nZFRnMChhEP+a+i8mpk08b2YgJHSJNu/639WjSyigU3IgX+fso398f2ItsczfMJ81eWt47dLX6BvX\n199lCiFEm3N0OrHOUUe1vRqf8hFpjmxeLJ9VncXyrOVYHVYGxA9gQtqEExbKK6VYnrWcR9Y+wu6K\n3fSO7c1nsz9jerfp503YOkpCl2jTfMoHYUWMu7SSvNoalFKMThnNxoKNPL7+ceb0ncNNA27yd5lC\nCNGmHJ1OrLHXUOeoo9ZRi0FnIDksmQ7BHWhyN7H0wNJTLpRfn7+eh9Y8xObizaRFpPHuzHeZ3Ws2\nugDduX9SrYCELtGmPfCgh8juHgaP1JFRlcHgxMHoA/Tc8MWRAeCfU/553n2TEkKIlvApHzaXjerG\naqrt1Ti8DsKMYaRGpGIJtLC7Yjerslfh8rp+sqP8tpJtPLz2Yb7O/Zqk0CRenfYqN/S/4bxfVyuh\nS7RZeXnw/F8MXPsnC7F9d6EL0DEyeSR//vrP5NXmseGGDW3qnFxCCOFvHp+HWnstNfYaKhsrCQwI\nJDEkkeSwZOqd9bxz8B1yanPoGNqRy7pdRowl5rjbHzh8gEfXPcqnBz8lOiiaFye+yB8H//G4k1yf\nzyR0iTbrrX97AR0XX15IQV0BIzqOYEvxFl7Z/gr3DL/nvDgSRgghzhSHx0F1UzVVTVVY7VaCjcGk\nhKcQYY5ga/FW1uatRdM0pnSdwuCEwcfNIuRb83l8/eO8vedtLIEWnhjzBHcOu5NQY6gfn1HrI6FL\ntElKwdvvQN+hVfjC8tHb9PSP78+IN0bQLaobT4590t8lCiFEm6CUosHVQFVjFRVNFXi9XmKDY+kc\n0Rmrw8rrO1+npKGErpFdmZY+rfksH3Dk/IhPf/M0i3YsQheg4+5hd3P/Rff/7DkVz2cSukSbtGWL\nIi9Hx+035VNUX8TghMH87/f/S25tLmuvX4s50OzvEoUQotXz+rzU2mupbKyksqkSs95Mx4iOxFni\n2Fi4kW8Lv8WkN3FFjyvoHdu7ee9Wrb2W5797nr9v/Tsur4ubB9zMo6MeJTE00c/PqHWT0CXapMpq\nF2k9nKQO24ntPw36/rLpL8zpO4exqWP9XZ4QQrR6To+TqqYqym3l1DvqCTeHkxaZRq29lkU7FlHV\nVEW/uH5M6jKpuQ2EzWXjH1v/wXObnqPeWc81fa7hiTFPtKvzI55NErpEm9T3wlKe/XgHO8vyuSD+\nAh5e+zAWg4W/Tvirv0sTQohWTSlFo6uR8sZySutL0TSNpLAkOgR3YEPBBraVbiPcFM6cvnOaw5Tb\n6+a1na/xxIYnqGis4NL0S3nq4qekB+KvJKFLtDlFxV5yGksos5Wh1+lpdDWyNm8tL09+mbjgOH+X\nJ4QQrZZP+ai111JmK6OioaJ5sbzVYeXVna/S4GxgWNIwLk69GIPOgFKKzw99zgNrHiCzOpORySP5\nbPZnDO843N9PpU2S0CXanNtu97JrXz/m/GsFaVGdeXrj03SP7s7vB/7e36UJIUSr5fa6qbRVUlhf\nSKOrkdjgWOKD49mQv4H9h/cTa4llVq9ZJIUmAbC5aDP3fX0fm4o20SO6B0uuXsK09GnS+7AFJHSJ\nNqW6GlZ+qWf0lQfRAiC3NpesmiyWXbPsvG+6J4QQJ6OUosndRKmtlEJrIQadgbTINOocdbz5w5u4\nvC7GpozlouSL0AXoyKzO5KE1D/HJwU+ID47n1WmvcuOAG09ogCp+PfkvKNqU99734HHrSRm1kWBD\nMM9vep7xncczpesUf5cmhBCtjk/5sDqsFFgLqGysJMIcQXxwPN8VfcehqkMkhSYxvdt0YiwxVDZW\nMn/DfBbtWIRJb2L+mPncPfxuLAaLv59GuyGhS7Qpb7/rJTGtgZCUXDKra7A6rTw/4XnZ3S2EED/i\n9rqpbKwktyYXh9dBUmgSdo+d9/e+j9vnZmLaRIYlDcPutvPUN0/xl01/we628/uBv2fe6HmyRvYs\nkNAl2oyiIti2xcjYm77FpDfy8f6PmdVrFv3j+/u7NCGEaFWa3E0U1RWRb83HpDORHJbMjtIdZNZk\n0jG0I9O7TyfCFMGbP7zJo+sepcxWxsweM1lw8QK6RXfzd/ntloQu0WZExdp5+r3vyHStIaM6A4fX\nweOjH/d3WUII0Woopahz1JFVk8XhpsPEBMXg8rr47NBneHweJqVNYmjSUDYWbOTOVXeyq3wXw5KG\n8fFVH3Nh8oX+Lr/dk9Al2oyyxlJU4haC6q18sGsFc/rOoUdMD3+XJYQQrYLH66HcVk5mdSYen4dY\nSywHDx8kpzaH5LBkpnebTp2zjlkfz+KTg5/QMbQj71/xPrN7zZYlGueIhC7RJuTkKO6aZyZkbBOH\nDdl4fB7mjZrn77KEEKJVsLvt5FvzybPmEaQPQhegY3Xuarw+L5d0uYQe0T149ttneXHLi+gD9Mwf\nM597R9wrp0w7xyR0iTbh3+86WfpeAleNcfJN4TfM6TuHtMg0f5clhBB+dXQ68WDVQWrsNYQYQsiu\nyaawvpBOYZ2Ylj6NpZlLufyDy6lorOD6ftez4OIFco5EP5HQJdqEDz+AhJ55VOq34/Q6eeCiB/xd\nkhBC+JXX56WsoYyDVQfx+Xx4fV42Fm4EYHKXyTg8Dia9M4kfyn9geNJwllyzhCGJQ/xc9flNQpdo\n9fbt85F50MTAm75he9l2rux5Jd2ju/u7LCGE8Bunx0lOTQ551jwUikJrIZVNlaSEp9Avrh8Lvl3A\n4gOLSQpN4r2Z73F176tl3VYrIKFLtHr/7z0nWoARen5Mo62RBy960N8lCSGEXyilqHfWs//wfqob\nq2lwN5BdnY1ep2dsylhWZq/kD8v+gC5AxxNjnuDeEfcSFBjk77LFf7QodGmadgnwd0AHvKaUevYk\n28wCHgcUsFspdW1LHlOcf5p8VjoNLyLDsYFJaZO4oMMF/i5JtBMyhom2xOvzUtpQyqGqQzS4Gsir\nzaPeWU9qRCpen5frPr2OkoYS5vSdwzPjnmk+h6JoPU47dGmapgMWAhOAYmCbpmlLlFIHjtmmK/Ag\ncKFSqlbTtNiWFizOLz6fj4GzV7I7/Q3yi2zcM/wef5ck2gkZw0Rb4vK4yKzOJN+aT2VTJfm1+Rj1\nRnrE9GDR9kWsL1jPgPgBfHTVR4zoOMLf5Yqf0JI9XUOAbKVULoCmaR8A04EDx2xzC7BQKVULoJSq\nbMHjifPQgfwa9lbs41DVIXpG92R85/H+Lkm0HzKGiTah3lHP7ordlDeUk1ObQ6O7kfjgePYf3s/j\nGx4nxBDCP6f8k1sH3oouQOfvcsXPaEnoSgSKjrlcDAz90TbpAJqmbeLI7vvHlVIrf3xHmqbdCtwK\nkJyc3IKSRHtz6aQgmqKmUTX5RZ4Z/4wsBBVnkoxholVTSlFSX8L+w/spsBaQbz2yd0vTNJ785kkq\nGyv53QW/Y8G4BUQHRfu7XPELtCR0nezTT53k/rsCY4AkYKOmab2VUtbjbqTUq8CrAIMGDfrxfYjz\n1P79PvKzgwjvs4ZIcyTX9bnO3yWJ9kXGMNFqub1uDlUdIrM6k4OHD2Jz2dDr9KzIXsH3Jd8zOGEw\nS69ZyuDEwf4uVfwKLQldxUDHYy4nAaUn2WaLUsoN5GmalsGRAWxbCx5XnCfe/rAJCMaa+gaPDr5d\nOieLM03GMNEq1dnr2F2xm0PVh8g4nIFepyezJpMvDn1BpDmS/7v0/7hpwE0EaAH+LlX8Si0JXduA\nrpqmpQIlwNXAj4/q+Ry4BnhL07Rojuyqz23BY4rzyMcf+zCl7sAVWsFtg2/zdzmi/ZExTLQqSimK\n64vZVbaLHeU7qG2qpcpexaqcVdQ6avnjoD/y5MVPEmmO9Hep4jSdduhSSnk0TZsLrOLIWoc3lFL7\nNU2bD2xXSi35z3UTNU07AHiB+5RS1WeicNG+ZWX5yD0UijbxPa7pczXxwfH+Lkm0MzKGidbE4/Ow\nv3I/O0p3sLtiN/WOeraUbuFQ1SGGJw1n4ZSFDOgwwN9lihZqUZ8updQKYMWP/jbvmN8VcPd/foT4\nxXTh5XS57QWyTe9z7/Dl/i5HtFMyhonWoN5Zz/aS7Wwq3ESeNY/smmy+LfqWGEsMb01/i9/0+41M\nJbYT0pFetEq7KrdQnvgqfSM6y7c7IUS7VVhXyMaCjWwq3ERBXQFbirdQ46jhj4P+yNMXP02EOcLf\nJYozSEKXaHUKCrzMe8KHLTmYu6bc5e9yhBDijPN4Peyu2M1XOV+xs2wnuyt2k1WTRf/4/qyYuoKh\nST/uXiLaAwldotV57V0r+z++EuOdTzC712x/lyOEEGdUg6OB1XmrWZWzip0lO9lVsQuD3sBLk15i\n7pC56APko7m9kv+zotV57yMnxO3mN2OHSZsIIUS7UlBbwCeHPmFV9iq2lWyj1lnLzO4z+fvkv8u5\nEs8DErpEq1Jc7CV3TzyM/hf3jbjP3+UIIcQZ4fV62VS0iXf2vsPa3LXkWHNIDkvm7ZlvMzV9qr/L\nE+eIhC7RqrzyTjmoRLpetIf06HR/lyOEEC1W76jng30f8MauN9hZuhOv8nL/iPuZN2YeQYFB/i5P\nnEMSukSrsmr3Loiv4JErZvq7FCGEaLFDhw/x3KbnWJK5hGp7NcOShvHapa/RK7aXv0sTfiChS7Qa\nHq+H8sG/J6hnHdf0qfF3OUIIcdo8Xg8f7v+QBRsXcKDqAMH6YF6/9HVuHHAjmnay036K84GELtFq\nLN67hJKGEub0nkOgLtDf5QghxGmpbKzkri/v4tNDn+LwOri659W8PPVlooOi/V2a8DMJXaLVuP36\nBPB+yON/GujvUoQQ4rR8sv8T7v7qbgrrC0kKTuLtmW8zJnWMv8sSrYSELtEq5JfUUXPgAqLGHiAt\nMs3f5QghxK/S4GjgN5/9hqWZSwG4Z+g9LJiwAIPO4OfKRGsioUu0Cn/6x1fgvYrrr7b4uxQhhPhV\n3t/zPnO/nEuNo4Ze0b34dPancvS1OCkJXaJVWL08DC2siL/ccIW/SxFCiF+kqrGKWR/NYl3hOsw6\nM89c/Az3X3S/LJQXP0lCl/C71Qe34jg0irSJqwnUdfR3OUII8bOUUvzv1v/lz6v/jMPrYHjScD6Y\n+QHJEcn+Lk20chK6hN/9ZdMzMDmOeXNv83cpQgjxsw4ePsjsj2ez9/BeokxRPDPuGe4Ydofs3RK/\niIQu4Vc2l411JctIn5jG9ZMW+bscIYQ4KbvbzqPrHuWFzS+gQ8eE1AksnLKQrtFd/V2aaEMkdAm/\nemH9K3i338xlN6f6uxQhhDipL7O+5OYvbqassYyU8BTmDp7LbYNuw2ww+7s00cZI6BJ+o5Ti5fcy\nYdn/MfCWSn+XI4QQxympL2Hul3P5/NDnBAcGMyN9Bn++8M8M7ThUphPFaZHQJfzmu6LvqN4xlsDg\nOq6cEuvvcoQQAgCPz8PC7xfy4JoHcXgc9I3ty7W9r+X6/tfTIaSDv8sTbZiELuE3C9a9CJlvMvLS\nw+j1Yf4uRwgh+L7ke25deiu7K3aTEJzA6E6juabPNYzvPB5zoEwnipaR0CX8orqpmhWr3OAK5dY5\nyt/lCCHOc1aHlYfWPMQr218hWB/MyOSRTEidwIweM+gZ05OAgAB/lyjaAQldwi9e3fEqlA3AGNzI\njCmyl0sI4R9KKd7b+x53r7qbyqZK+sb2ZUD8AMZ3Hs+EtAnEBcf5u0TRjkjoEuecT/l4eevLRF3i\n4q2/TcBguMjfJQkhzkMZVRnctuI21uatJTk0mcvTL6d3XG8mdJ7A4MTBMp0ozjgJXeKcW527mrLG\nMi5OuZiR3fr4uxwhxHnG4XHwzMZneObbZzDoDExMnUhyeDKDEgcxOnk06VHpMp0ozgoJXeKce3Hz\niwR8+TLWsHGE/VamFoUQ587yzOXcsfIOcmpzGJwwmN4xvYkNjuXCjhcyNHEoscFyJLU4eyR0iXMq\nuyabVZmr0e1/j5hx/q5GCHG+yKnJ4c5Vd7Iscxmdwjoxp+8cwgxhpEenMyRxCP1i+0mzU3HWSegS\n59Tft/wdCkfitUUyZ7bd3+UIIdq5JncTz377LM9teg6dpmNWz1kkhSQRqA/kgvgL6Bffj7TINPQB\n8nEozr4WTVprmnaJpmkZmqZla5r2wM9sd6WmaUrTtEEteTzRtlkdVl7/4XVCsm/CYHIzc7p8qxT+\nJWNY+6WU4vNDn9NzYU+e/OZJxqSMYe6QuSQEJ5Aclsy4lHGMThlNt+huErjEOXParzRN03TAQmAC\nUAxs0zRtiVLqwI+2CwH+B9jakkJF2/f6ztexuxwY901l9PgmgoJkPZfwHxnD2q/M6kzuWHkHK7NX\n0j2qOw9d9BAAek1P//j+dI7oTJ/YPgQZgvxcqTjftCTeDwGylVK5AJqmfQBMBw78aLsngeeAe1vw\nWKKN8/g8/G3r34gzptD98l3cduVwf5ckhIxh7Uyjq5GnNz7NC5tfwKgzcu/wewk3hWNz2ugY1pHk\nsGS6RnWV6UThNy151SUCRcdcLgaGHruBpmkDgI5KqWWapv3kgKVp2q3ArQDJycktKEm0Vp8f+pzi\n+mJGJY/ijhl1XN5DphaF38kY1k4opVh8YDF3f3U3xfXFXNP7GkYmj6TMVoaGRt8OfYk1x9Irthdx\nwXFysmrhNy1Z03WyV23z+Vw0TQsAXgLuOdUdKaVeVUoNUkoNiomJaUFJorV6cfOLhAdGEpQ3m/TQ\nvv4uRwiQMaxdOHj4IBPensCsxbOIMkfx7+n/pl9cP8psZSSHJtMnrg9dIrowrOMw4kPiJXAJv2rJ\nnq5ioOMxl5OA0mMuhwC9gfX/eZHHA0s0TbtMKbW9BY8r2pgtxVvYXLyZ3k1/YOVztzErzUWv3/q7\nKiFkDGvLauw1zN8wn4XbFhJsCOaFCS/QIaQDGdUZhBhC6B/XH3Ogmc4Rnekc0ZlAXaC/SxaiRaFr\nG9BV07RUoAS4Grj26JVKqTog+uhlTdPWA/fKYHX+WbBxAZZAC849MzAYvVw50+DvkoQAGcPaJI/P\nw6Lti5i3fh5Wh5VbLriFOX3m8F3xd2RWZ9I1sivhpnCC9EH0jO1JrCVW9m6JVuO0Q5dSyqNp2lxg\nFaAD3lBK7dc0bT6wXSm15EwVKdquPRV7WJq5lAsTRrNrywjGTXIQEmLxd1lCyBjWBn2V8xV3rbqL\nA4cPcHHqxSy4eAFF9UWszltNlDmKPrF98CgPccFx9IjugcUgY41oXVp0+IZSagWw4kd/m/cT245p\nyWOJtunZb5/FrDdjLrmERmswv53j8XdJQjSTMaxtyKjK4J6v7mF51nLSItL4fPbnpEelszxrOU2u\nJvrE9CHUFIpP+ege2Z2U8BSZThStkpzRU5w12TXZfLDvA4Z3HE713oEEWbxcNk0O0xZC/DK19lru\nWnkXvV/pzcbCjTw/4Xm237Idn/Lx4f4PMelNjOo0CovBglFnpH98f9Ii0yRwiVZLPgHFWbNg4wIC\nAwJJDUvlkrt2M+qx/pjNcmSXEOLneXweXt3xKvPWzaPWUcvvBvyOJy9+svmsFo3uRgZ2GEiEKYJ6\nZz0JIQmkRaYRYgzxd+lC/CwJXeKsyKzO5N+7/83kLpPRB+jpGdODgV0j/F2WEKKV+zrna+5adRf7\nD+9nbMpYXpr0El2jurIyeyV7KvYQExTDyOSR2Fw27B473aK7kRSahFFv9HfpQpyShC5xVjy+/nGM\nOiNdI7uy5fXZOOM6M/V/5eUmhDi5fZX7uH/1/azIWkFaRBqfzf6M6d2mc+DwARZ+vxCHx8GIpBFE\nB0VT2VhJiDGErpFdibZEE6DJShnRNsinoDjj9lbs5YN9HzC712wcDtj15UDSrvD5uywhRCtUUl/C\nvHXzeGv3W4QYQnhu/HP8z9D/we1z89H+jzhYdZCEkAQmd5xMvbOeysZKkkKTSA5LJsQYIu0gRJsi\noUuccfPWzyPYEEy3qG7s39ANR2Mgc66R0CWE+K86Rx3PbXqOl7a8hFd5uXPonTw08iEizZHsqdjD\nyuyVuH1uxqaMpUNwB0obSlEoukV3o0NwB0yBJn8/BSF+NQld4oz6puAbPj/0OX8c+Eea3E1kbxhO\ndKyH8ePlpSaEAJfXxaLti5j/zXyqmqq4ts+1PDX2KVIjUqlz1PHe3vfIqsmiY2hHxnUeR429hnxr\nPhHmCDqFdyLSFIleJ+OJaJvklSvOGJ/ycc9X95AQnED3mO7kltSxb1Myf7zdi15eaUKc15RSfHLw\nEx5c8yDZNdmMTRnL8xOeZ2DCQJRS7CjdwVc5X+FTPi5Ju4SksCQKrYU0uhtJCksiMSSRUGOoTCeK\nNk0+CsUZ897e99heup0XJrxAZWMlMYY0Lpt9mJtviPV3aUIIP9pYsJH7vr6PrSVb6R3bmxXXruCS\nLpegaRq19lqWZCwhz5pHangql3S5hFp7LdnV2egCdKRHpRNniZPpRNEuSOgSZ0STu4kH1zzIwA4D\niQ+Jp8peRY/OYdy40EtCqHwzFeJ8tK9yHw+vfZglGUtICEngjcve4Pp+16ML0OFTPrYUbWFt3loC\ntAAuTb+U1PBU8qx51DpqiTRFkhCSQHRQtEwninZDXsnijHhyw5MU1xfzzyn/ZEfpDoxNaZQcSCaq\na/SpbyyEaFeyqrN4bP1jfLDvA0KMITw19inuGn4XQYFBAJQ2lLI0YylltjLSo9KZ3GUyDa4GDlUd\nwul10jG0I3HBcYQaQ6UdhGhXJHSJFttfuZ+/bv4rN/a/Ea/y4lVeDq0YwaevdufKYo0OHfxdoRDi\nXCisK+TJDU/y5q43MeqN3H/h/dx34X1EmiOBI4vo1+atZWvxViwGC1f1vKp579bhxsOY9CY6h3cm\nJjimOaAJ0Z5I6BIt4lM+/rD8D4QaQ3l45MO8u/ddOlgS+b+lnRg91kuHDvISE6K9q7BVsGDjAv61\n418A3D74dh4c+SDxwfHN22RUZbAiawV1zjoGJQxifOfxNDgbOHD4APWueqLN0cRaYokKipJzJ4p2\nSz4RRYu88cMbfFv4La9f9joZ1Rl4fV6sWb2oKAniuQXSm0uI9qzGXsPzm57nH9//A6fHyY39b+TR\n0Y+SHJbcvE2Ds4Evs7/kwOEDxFpiubnnzXQI6UC+NZ9KWyVen5eOoR2JscTIdKJo9yR0idOWb83n\nrlV3MTZlLFf2vJJ/bP0HyWHJfPRKMkEWH1dcIYOnEO1Rg7OBl7a8xAubX6DB2cA1fa7h8dGP0zWq\na/M2Sim2l25nde5qvMrLuNRxjOg4gkZ3I/sr92N1WgkODCYmKIbooGjMgWZpByHaPQld4rT4lI+b\nvrgJDY03pr/BpsJNeHwe4oIS2LsllhkzFBaLv6sUQpxJdredf277J898+wzV9mou734588fMp09c\nn+O2q7BVsDRzKcX1xXSO6My09GmEm8IpqS+htKEUl9dFTFAMkaZIIs2RGPQGPz0jIc4tCV3itCz8\nfiHr8tfx2qWvEWGKYHvpdrpGdkWngy83FRJvSPd3iUKIM8TldfH6ztd5auNTlDaUMjFtIk+NfYrB\niYOP287tdbOhYAPfFX2HSW9iZo+Z9Intg8Pj4FDVIWrttegD9CSGJhJpjiTEEIIuQOenZyXEuSeh\nS/xqP5T9wH1f38fUrlO5acBNLMtcBkCnsE40uptIiYknzCTTBEK0dR6fh3f3vMvjGx4n35rPRckX\n8f4V7zOq06gTts2pyWFZ5jJqHbUMiB/AhLQJBAUGUdlYSVFdEQ6Pg1BDKOHmcKLMUTKdKM5LErrE\nr1LvrGfW4llEB0Xz5vQ3qXXU8kP5D/SK6cW+PTqe+tNoFn+oY9hQf1cqhDhdPuVj8YHFPLb+MQ5V\nHWJgh4G8MvUVJqVNOiEoNboaWZm9kr2Ve4kyR3FD/xtICU/B7XWTVZ1Fjb0GhSLWEkuoMZQIU4RM\nJ4rzloQu8Ysppbhl6S3k1eax/ob1xFhi+Hj/x+g0HSnhKbz5YRRV5QbSu8q3VyHaIqUUK7JW8Mi6\nR9hVvoueMT35ZNYnzOg+44SwpZRiV/kuvsr5CpfXxehOoxnZaST6AD11jjryrfk0uZuwBFoINYUS\nZgwj1Bgq04nivCahS/xiz3/3PB/t/4hnxz3LRckXUVhXyP7D+xnRcQSHrY2sX9KPmVf4iIyUQVWI\ntmZd3joeXvswm4s30zmiM2/PeJtrel9z0pBU1VTF0oylFNQV0CmsE9PSpxFjicGnfBTWFVJpq8Sj\nPESaIwkKDCLSHIk50CztIMR5T0KX+EWWZCzhgdUPcHXvq/nzhX9GKcWq7FWEGELoFNaJRa87aWzQ\n84ffK3+XKoT4FbYUb+GRtY+wJm8NiSGJLJq2iBv733jSBqUen4dvC79lY8FGDDoDl3W7jAHxA9A0\nDbvbTm5tLjaXDaPOSJQpiqDAIMJN4Rh0Blm/JQQSusQvsKdiD9d9eh0DEwbyxmVvoGkaeyr2UNJQ\nwrSu06iyV/H1R31I7+Zj5Ej5JitEW7C7fDePrHuEZZnLiAmK4aVJL/GHQX/ApENVOoYAACAASURB\nVDeddPt8az7LMpdR1VRFn9g+TOoyiWBDMHCkRURJQwken4cwUxgmvYlwYzjBxmD0AfIxI8RR8m4Q\nPyu3NpdJ70wizBjG57M/xxxoxulx8nXO1ySEJBAXHMe+iv388U4b8cHByJdZIVq3zOpM5q2bx4f7\nPyTcFM7TFz/N/wz9n+YA9WN2t52vcr7ih/IfiDBFMKfvHLpEdgGOtIjIt+ZT56hDF6AjJigGfYBe\nphOF+AkSusRPKreVM/Htibi8LjbeuJHE0EQA1uatxeaycWXPKympL8FiCOLaK4MIN8sAK0RrVVRX\nxBMbnuCtXW9h0pt4eOTD3DviXsJN4SfdXinF3sq9rMpehd1j56LkixjdaXTztKPVYaXAWoDL68IS\naMFisGDWmwkzhcl0ohA/QUKXOKnKxkomvD2Bcls5a65fQ8+YngCU1Jfwfcn3DE4cjC5AR1GZnfUf\n9aHrvSGEm/1ctBDiBIcbD/PMt8/wz23/RKGYO2QuD418iFhL7E/epsZew/LM5eTU5pAUmsT16dcT\nFxwHHGknUVxfzOHGw2hoRJmj0AXoCDOGYTFY5GTVQvwMCV3iBBW2Ci7+fxeTV5vHsmuXMTTpSNMt\nn/KxLHMZwYZgRnUaxYHKA3z1cQqvvRDN7TdBh3g/Fy6EaFbvrOfFzS/ywuYXaHI38dt+v+Wx0Y/R\nKbzTT97G6/OyuXgz6/PXo9N0TOk6hUEJg5qnCZvcTeTV5mF32zEHmps7yoebwjHpTdIOQohTaFHo\n0jTtEuDvgA54TSn17I+uvxv4HeABDgM3KaUKWvKY4uwqsBZwybuXUFhXyIrrVjAmZUzzdRsLNlJm\nK2NWr1lYHVZqbA0seWcA48f76NlTphZF29MexzCHx8Er215hwbcLqGqqYmaPmTw59snmvdU/pbi+\nmKUZS6lorKBHdA8md51MqDG0+fqji+WVUkSaI9EF6DDpTTKdKMSvcNqhS9M0HbAQmAAUA9s0TVui\nlDpwzGY/AIOUUk2apv0ReA6Y3ZKCxdmzs2wnU9+bit1tZ+V1KxnZaWTzdWUNZWwo2ECf2D50iezC\nrvJdbP4qkcryQO58TdpEiLanvY1hPuXjnT3v8MjaRyiqL2J85/EsuHjBCedH/DGHx8Ga3DVsL91O\niDGEa3pfQ7fobs3Xu71u8qx5NDgbMOqMhBpDUSiZThTiNLRkT9cQIFsplQugadoHwHSgecBSSq07\nZvstwJwWPJ44i1ZkrWDWx7OICopi9W9W0yu2V/N1Hp+HTw9+iiXQwpSuUyhrKKPBYWPxa33pmu5j\n8mTZyyXapHYzhm3I38DdX93NzrKdDOwwkDenv8m4zuN+9jZKKQ5WHeTLrC+xuWwMTRrK2JSxGPXG\n5m2OLpb3+rzNrSA0NJlOFOI0tSR0JQJFx1wuBn7ujHs3A1+e7ApN024FbgVITk5uQUni11JK8Y+t\n/+Cer+6hX3w/ll2zjA4hHY7bZlX2Kg43HWZO3yOfN+W2cnAH07kzzLhMI0Ayl2ib2vwYllWdxf2r\n7+ezQ5+RFJrE2zPe5to+156yVUOdo47lWcvJrM6kQ3AHrulzDQkhCc3X+5SPoroiqpqqCAwIJNoS\nDYAhwECoKRSDziDtIIQ4DS0JXSebwD/pPJOmaXOAQcDok12vlHoVeBVg0KBBMld1jtQ56rh5yc18\ncvATpnebzjsz3zmhV8++yn1sK93GiI4j6BLZheyabOxuO8mxiXy8GEx6Wcch2qw2O4bZXDae3PAk\nL215CaPeyFNjn+Ku4XcRFBj0s7fzKR9bi7eyLn8dSikmpU1iaNLQ4wLU0cXyDo+DUGMoQfogfPgI\nNYZiDjQTGBAo67eEOE0tCV3FQMdjLicBpT/eSNO08cDDwGillLMFjyfOoF3lu7jyoyvJt+bz/ITn\nuWf4PScMpFVNVSzJWEJyWDLjUsdR76ynqrGKw4VRBAZH0uUC40/cuxBtQpsbw5RSfH7oc+5YeQdF\n9UXc1P8mnh73NPHBpz50uKyhjCUZSyizlZEelc6UrlOO69GllKKisYLShlL0mp4YS0xzGIs0RWLU\nG6W7vBAt1JJ30Dagq6ZpqUAJcDVw7bEbaJo2AFgEXKKUqmzBY4kzxOPz8Nym53hiwxPEBMWw4YYN\nXJh84QnbOTwOPtj3AfoAPVf2vJIALYDCukLcXjf/eqYze3cGUVysYTD44UkIcWa0qTEs35rP7Stu\nZ0XWCvrE9uH9K94/6Xv3x9xeN+vy17G5aDMWg4Wrel5Fz5iex33Jcnld5FvzaXA2EGwIJtQYisfn\nwagzEmIMkelEIc6Q0w5dSimPpmlzgVUcOdz6DaXUfk3T5gPblVJLgOeBYODj/7zBC5VSl52BusVp\n2FOxhxu/uJGdZTu5qudVLJyykBhLzAnbeX1ePtr/EbX2Wn7T7zeEGkMpayij3lFPyaEE1q6yMH++\nksAl2rS2MoYppXht52vc/dXdALw48UX+NPRPv2ivU05NDssyl1HrqGVQwiDGdx5/wrkVa+21FNQV\noJQiJiiGQF0gXp+XUGMoJr1J2kEIcQa1aF+xUmoFsOJHf5t3zO/jW3L/4sywuWws2LiAv373VyLM\nESy+ajFX9LzipNsqpfgy+0tya3O5vPvlpISn4PK6KLeVowvQ8c/nEoiOVtx5pwzCou1r7WNYaUMp\nv1vyO77M/pKLUy/mzelvkhx26oX6Te4mVmWvYnfFbqLMUdzY/8YTmqJ6fV6K6ouobqrGHGgmwhSB\nT/kAiDBHYNAZpB2EEGeYTNC3Y0op3t37Lvevvp/ShlKu73c9L0x8geig6J+8zfr89Wwv3c5FyRfR\nP74/cKRhqt1tJ3dXRzauM/P884qQkHP1LIQ4P63OXc3Vi6+myd3Ey5Nf5rbBt51yik8pxb7KfXyZ\n/SUOj4NRnUYxqtOoE/aKNboaybPm4fQ4iQqKwqQz4fa5MQeaCTYEExgQKO0ghDgLJHS1Q0op1uSt\n4ZG1j7C1ZCuDEwbzyaxPGJY07Gdvt7loMxsKNjAgfgDjUo/0+DnceBirw0qwMZiinGDS0hS33y57\nuYQ4W5RSPLfpOR5a+xDdo7vz6axPj2tW+lOsDivLM5eTVZNFUmgSl6Zf2ny+xGPvu9xWTpmtjMCA\nQBJDE/EpHx6fR6YThTgHJHS1MxsLNvLoukfZULCBpNAk3rjsDX7b/7en/Ib8fcn3rMpZRa+YXlza\n7VI0TcPldVFcX4ymaYQaQvnT7YHcczuylkuIs8Tr8/L7Zb/n9R9eZ1avWbx+2esntHH5MaUU20u3\n83Xu1wBM7jKZwYmDT3jPu7wu8mrzsLlshJvCCTWG4vK6CNACZDpRiHNEQlc74FM+VmSt4Pnvnueb\ngm+ID47nH5f8g1sG3nLCotmT2VS4ia9zv6ZbVDdm9phJgBaAUop8az5ur5sQfRTbvg1jxlQT+gD5\nBizE2eD2urn202tZfGAxj4x8hPlj559yj1Odo44vMr4gtzaXtIg0Lu126XFtII46drF8QkgCAVoA\nLq8Lk95EUGAQBp1BphOFOAckdLVhNpeN9/a+x0tbXuJQ1SE6hnbkxYkv8vtBvz9lk0Q48g15bd5a\nNhZupHdsb2Z0n9E88JbZjhytGGoM5e1/RbJgXgRbt8KQIWf7WQlx/lFKceuyW1l8YDEvTnyRu4bf\ndcrtfyj/gVXZq1AopqVPY2CHgSeEtGM7y1sMFmKCYnB6nXiVlxBDCAa9QdpBCHEOSehqg3aW7eTV\nHa/y7t53sblsDIgfwLsz3+Wqnlf94ukBj8/DF4e+YG/lXgZ2GMjU9KnNA2+9s56yhjIMegO2Wgsv\n/zWcyVMUQ4bIXi4hzobnNj3HW7ve4vHRj58ycDW6Gvki4wsyqzNJCU9herfpRJgjTtiuyd1Ebm0u\nTo+TuOA4TDoTDo8DfYCeEEMIep1eussLcY5J6GojKmwVfHzgY97a9RY7ynZg1puZ3Xs2t15wK8OS\nhv2qgbPB2cDHBz6msK6Q8Z3Hc2HHC5tvf3Tdh07TEW4K5767o7E3wUsvysAsxNmws2wnj6x7hKt6\nXsW80fN+dtucmhw+O/QZDo+DyV0mMyRxyEnf++W2ckobSgkMCKRTWCc8yoPT68QcaMasNxOoC5Tu\n8kL4gbzrWrFaey2fHvyU9/e9z7r8dfiUj75xfXl58svM6TvnpGs3TiXfms/iA4txepxc1fMqesX2\nar7Op3xk12TjVV6izFFsXGvio/fMPPCAotupD54SQvxKSinuWHkHUeYoFk1b9JNfnrw+L2vy1vBd\n0XfEWmL5Td/fnHBkIhxZF5ZnzaPB2UCEOYIIUwR2jx0NTaYThWgFJHS1Mtk12SzLXMbSzKV8U/AN\nHp+HtIg0HrroIa7uffVxIenX8Pq8fFPwDRsLNxJhiuD6ftcTa4ltvl4pRV5tHna3naigqCOL6V0W\nho9QPPaY7OUS4mzYWLiRbwu/5ZWpr5x0ihCOTPd/uO9DShpKGJwwmIlpE0+6jMDqsFJgLcCnfCSF\nJqEP0GP32NFreoINwegCdNIOQgg/k9DlZ3a3nc3Fm1mRtYJlmcvIqM4AoFdML+4edjdX9brqpAtk\nf40KWwWfH/qcMlsZfeP6MrXrVIz6409WXVRfhNVhJdIciT5Aj0lnYtaVgVw3S0PGaCHOjtd/eJ0w\nYxjX97v+pNcXWAv4aP9HuH1uZvWaRc+Ynidso5SiuL6YysZKggKDSAhJwO6x4/QcmU406U3oA/TS\nDkKIVkBC1znm9DjZWrKVdXnrWJe/ji3FW3B6nRh0BsamjGXukLlM7TqV1IjUFj+Wx+fhu6Lv2JC/\nAZPexNW9r6Z7dPcTtiupL+Fw42HCTeEY9UY2rDFSkhvK3XfqJXAJcZYopfg652smd5180qONt5du\nZ0XWCiJMEdzQ+4aTnifV6XGSW5tLk7uJWEssYcYwbG4bGhqhptAjYUu6ywvRakjoOstKG0rZWryV\nrSX/+Sne2rzGYkCHAcwdMpexKWMZ1WkUIcYzc24dpRQZ1Rmsyl5FraOWXjG9mNJ1ChaD5aT1ldvK\niTBHYNabKStX3PH7SGJjYO5tGqZTt/kSQpyGkoYSymxlXNTxouP+rpRiXf46vin4hvSodGb2mHnS\nfntHe28BpEakopTC5rYRGBCIJdAi04lCtEISus6gGnsNeyr2sKN0B1tKtrC1eCtF9UUABAYE0j++\nP7cOvLU5ZP3UGo6WKG0oZXXuanJrc4m1xHJ9v+vpHNH5pNsW1xdTYasgwhyBJdCC0+Pmz7fFY2uA\ndWslcAlxNuVb8wHoEtml+W9KKZZmLmVn2c4TWrkc5VM+iuuLOdx4GIvBQlJoEo2uRrw+b/N04tHA\nJYRoXSR0nQaPz0NWdRa7K3azp2JP87/F9cXN26SEpzCi4wiGJQ1jaOJQBnQY8Iu6w5+u0oZS1uev\nJ7M6E7Pe/JOnAoEjA3tBXQHVTdVEB0VjDjTj8rj4xzMxrFmtZ9Ei6HV66/WFEL9QZWMlQPNRiEop\nlmUuY2fZTkZ1GsXYlLEn7KVyepzk1OZgd9uJC44jzBhGvbOeAC2AEGNI89otaQchROsk78yf0eBs\nIKM6g4yqDDKqM8isziSjOoODhw/i9DoB0Afo6RHdg9GdRtM3ri/94vrRP77/SQ/nPtOUUuTU5rCl\neAvZNdmY9WbGpY5jSOKQExbKH+XxecipycHmshEfHI9BZ8DldVGWH8pLfzVw661wyy1nvXQhzntu\nrxugeY/Umrw17CjbwahOo7g49eITtq9z1JFnzUNDo3NEZxSKBldD83RiQECAtIMQopU7r0OXUorK\nxkryrfnkWfPIq80jz5pHVk0WGVUZlNnKmrcN0AJICU+hW1Q3xqWOo19cP/rG9aV7dPefDDhni91t\nZ2/lXr4v+Z6qpipCDCGnDFtwpJN1bm0uHp+H5LBk4MjAH2wIpk8vA+vXw7BhyOJ5Ic4Bt+9I6AoM\nCGRvxV6+LfyWwQmDGZsy9oRtyxrKKG0oJSgwiI6hHWl0N+JTPsz6/04nSnd5IVq/dh26HB4HJfUl\nlDSUNP97NGDlW/PJt+bT5G467jYxQTF0iezCpC6T6BbVjW5R3UiPSqdLZJdzHq6O5fV5yarJYk/F\nHjKqMvAqL0mhSVzR4wp6xvQ85dFJx3ao7hzRGYfHceQ+94VQVWlk5vRARo6UAVuIc8Xj8wBHTtez\nPGs5ncI6cUmXS44LTl6fl3xrfnM7lxhLDA3OBjQ0gg3BMp0oRBvTpt+pBw8fJM+ad0KwOvp7tb36\nhNuEGcNIjUglPSqdSWmTSA1PJTUilZTwFFLCUwg2BPvhmZycx+ch35rPoapDHDh8gCZ3E5ZAC4MT\nB9Mvrh8dQjqc8j4cHgcF1gJsLhsR5gjiLfHUu+rxKR8FmWHMvDSIqCi4dIqGQdbdCnHOHJ1e3Fi4\nEaUUM3rMOO7Lk8vrIqs6C6fXSVJoEka9kXpnPXpNj8Xw36MTZTpRiLajTYeuG7+4ka0lWwHQ0Ii1\nxJIYmkinsE6MSBpBYmgiiSGJx/17OqfOOZdsLhs5NTlkVGeQXZONy+vCoDOQHpVOv7h+pEWm/aJB\n1qd8lNvKKbeVN0+NGnQGrE4rOk3HwZ3hzL7CjNkMK1dK4BLiXDu6pyvfms/0btOPG5ua3E1k12Tj\nUz46h3fGozw0uZsw6owEBQYRoAVIOwgh2qA2HbpemvQSAImhiXQI7tAmOy43uhopqCtoXk9W1VQF\nQIghhL5xfekW1Y3UiNRfPH2glOJw02HKGsrw+DxEmiOJD46n0d1Ig6sBg87A2hUh/PY3BpKSjgSu\nzifvKCGEOIumpU+j3FZOoC6QIYlDmv9e56gjtzYXfYCezhGdaXI34fP5CAoMwqg3Snd5IdqwNh26\nhncc7u8SfhW31025rfy4qdAaew1w5AimTmGdGBA/gM4RnYkPjv9V32J9ykeNvYZyWzlOj5MQYwgJ\nwQmgHTknG3Bk0NYZ2bFdT9++sGyZRsyJTa6FEOdAUmgSFoPluPWitfZa8qx5mPVmEkISaHQ1omlH\nusvrtCPTidJdXoi2q02HrtZKKUW9s57KxkoqGiuobKxs/vEpHwChxlASQxK5oMMFdArrREJIwmkN\npm6vm8rGSqqaqvD4PAQFBjUP4vXOejw+D4EBgdRXWcirCGDIID1PP6XD5YKgE888IoQ4R6wOK03u\nJlLCU4D/Bi5LoIVYSyyN7kZZvyVEOyOh6zQppWhyN1Fjrznhp6qpqrmPFxwJWLGWWLpGdm1eX9aS\nU/54fV6sDis19hrqnfUAhJvCiQuOQx+gp8HZQKO7EZ2mw6IP4Z1/G3n4gUCioiAjQ0OvB738nxfC\nr44e6BMdFE29s548ax5B+iCizFHYPXYMAQYsBous3xKiHZGP3p/g9rqpd9Y3/9Q56/77u6MOq8N6\nXLDS0AgzhRFpjqRvXF/iguOItcQSa4k9I53oXV4XdY466px1NDgb8CkfRr2RDiEdCDeG41XeI6cC\nUd4jYSvQwvffmXj0YT1bt+gYMwYWLZKwJURr4fT8d/zIrc3FoDMQYY7A6XVi0psICgyS9VtCtDPn\nzUewT/lweBw0uZtodDXS6G782X/tHvsJ9xEUGESYMYxwUzidwjsRaY5s/gk3hZ/RXjlOjxOby9b8\n4/A4ADDqjUQHRRNuCkcXoMPhcWB1HlmzFRgQiElvwqAz8O3GACaOM5KYqHjzTfjtb6XpqRCtiVd5\nUUpR0lCCSWciwhTRvETg6PtY1m8J0b606dCVb82nzlGHw+PA4XFg99ibf3d4HNjd/7187F6pY2lo\nmAPNWAItWAwW4ixxWCIsBBuCCTOGEWoMJcwURogh5Kx841RKNddud9uxe+w0uZuae/joAnQEG4KJ\nMkcRZAhCQ8PlddHgamiu36gzUlNp4rPFgeh0/P/27j5GivqO4/j7s0+3J3fKyYlSDwQVqQ9tCiW0\nlkapKBAlYKNWVKz4WG1FrbVPWq3VxGhNo200MVZRsT5V20ZiMLb1IZoqVtD6AFZzUixXrbQ+cBjL\n3e3ut3/M3HnAPczd3s3sHt9XsuE3O8PNZ2dnf/edmd/OccEFYvasDHfcASeeKGprhzy2c65MhVJw\nG4itHVtprG3EzBiVG0VtttbHbzk3QlV10fX4+sfZ2Lqxa7omXUM+k+96NNQ2bDOdz+S7iqvOfzvv\neTNczIyOUgcdxQ7ai+20FdtoK7TRVmyjvdhOe7EdMwNAEvlMnvpcPbl0jnwmTyaV6fr/H7d/jJmR\nVpqadA2vv5rnsZU5nng8xXPPpjATc+bAxRcFZ7WWLBm2l+WcK1OhWGBL2xYMI5/JU5er6yq4fPyW\ncyNTWUWXpHnAL4E0cJuZXbvd/BpgOfBF4H3gRDPbUM46uzv2s8d2FSr5TH7Yi6eSlShakUKpQLFU\npGhFiqVwulu7s0jqKHVQLBV3+Dkppcims8HlwFy+q51NZ7vW0TlQ3wxaP8ry7sY8b72ZY92rOa65\ntkAmI+68Pcuy21JMmwaXXy5OPhmmTBm2TeDciJNkH7Zvw77M3X8uDfkGRmVHsUtul64/fu2cG5kG\nXXRJSgM3A0cBLcALklaY2bpui50JfGhm+0taBFwHnFhO4O5qMjUUSgXaCm1sLWztKozMDMO6pkts\n+5xhlEqlbZcJi53OdslKFEtFSpQoFkuUzDALiiZkpFJQKkGhA0olo4SRIk06laYmJ3LZNFiajrYa\nSh1ZCu0ZCu1piu1ZmiaUGJU3Nr0nXns5R+vmFK0fpdm8OU3rhxnOW/o/9pmQ5r7ltfzwkho+/vjT\no9583rhwaYb99hNXXgHXXgONjUO1RZ3beSTdhxlGNpVldH409TXDM3zBOVdZyjnTNQNoNrP1AJLu\nBxYC3TushcCVYfsh4CZJss7raWVateFFFk4/FAw6f6KZWHhaMyctfYPWj7KcdcQcMHWbD6dc8CYn\nnLOeTS27cPbcw4N5pq7551/RzHFL3mHDm3UsmTtjh/VeecPbfH3RZl5dU8fi+Tvezv2mO95j/rFb\neeaJWk49buwO8x98uJXZRxV5ak2O008etc28ujrjm6fUsOtk8flDxBlnwKRJMHEiTJ4MU6ao6xuI\nTU2D3nTOuYT7sC1tWyiWioypHeMFl3M7iXKKrr2Bjd2mW4Av9baMmRUkbQbGAP/tvpCkc4BzACZM\nmBA5wIFjp3DSKW0gSCkYEyXgsK+NY+aERj5pFN86twMkpE+/vTf7yPHMHD+OzfXioovbwwx0jaOY\ne8RnmDZuL8anxY9/8un8znXMnbknBzTuyaiDxc+uLqDgNZBKBf/OmtFI065w6Bfg2utK1OYhVwP5\nPORrxFem19NQK46ZA88+C6NHw+67Q0MD5HKi82356szg4ZwbFon2YWPrxjI1NbWse/Y556qLBnvA\nJukEYK6ZnRVOnwrMMLOl3ZZZGy7TEk6/FS7zfm8/d/r06bZ69epBZXLOVSdJa8xseszr9D7MOVe2\ngfRf5Yw8bwHGd5tuAt7pbRlJGWA34IMy1umcc0PF+zDnXKzKKbpeACZLmiQpBywCVmy3zArgtLB9\nPPDEUI3ncs65Mnkf5pyL1aDHdIXjG84HHiP4uvUyM1sr6SpgtZmtAG4H7pbUTHB0uGgoQjvnXLm8\nD3POxa2s+3SZ2Upg5XbPXdGtvRU4oZx1OOfccPE+zDkXJ/87E84555xzMfCiyznnnHMuBl50Oeec\nc87FYND36Roukv4DvJ10jlAj290EsUp47nh57vLtY2Z7JB1iKAywD6uk92AgqjU3VG92zx2vgeSO\n3H9VXNFVSSStjvuGjUPBc8fLc7vBqtb3oFpzQ/Vm99zxGq7cfnnROeeccy4GXnQ555xzzsXAi66+\n3Zp0gEHy3PHy3G6wqvU9qNbcUL3ZPXe8hiW3j+lyzjnnnIuBn+lyzjnnnIuBF13OOeecczHwoisi\nSZdIMkmNSWeJQtL1kv4u6RVJf5A0OulMfZE0T9Ibkpol/SjpPFFIGi/pSUmvS1or6cKkMw2EpLSk\nlyQ9knSWka6//VtSjaQHwvnPS5oYf8odRch9saR1YT/zuKR9ksi5vaj9iaTjw369Im5pECW3pG+E\n23ytpHvjztibCPvKhLC/fCncX45OIud2mZZJ2iTptV7mS9Kvwtf0iqRpZa/UzPzRzwMYDzxGcMPD\nxqTzRMw8B8iE7euA65LO1EfWNPAWsC+QA14GDko6V4Tc44BpYbseeLMacnfLfzFwL/BI0llG8iPK\n/g18G7glbC8CHqiS3F8Ddgnb51VL7nC5euBpYBUwvRpyA5OBl4CGcHps0rkHkP1W4LywfRCwoQJy\nHwZMA17rZf7RwKOAgC8Dz5e7Tj/TFc0NwA+AqvnWgZn90cwK4eQqoCnJPP2YATSb2XozawfuBxYm\nnKlfZvaumb0YtrcArwN7J5sqGklNwDHAbUln2QlE2b8XAneF7YeA2ZIUY8ae9JvbzJ40s0/CyUrp\nZ6L2J1cDPwe2xhmuD1Fynw3cbGYfApjZppgz9iZKdgN2Ddu7Ae/EmK9HZvY08EEfiywElltgFTBa\n0rhy1ulFVz8kLQD+ZWYvJ52lDGcQVOuVam9gY7fpFqqkeOkUXg6aCjyfbJLIbiQ4kCglHWQnEGX/\n7lomPFjaDIyJJV3vBvq5PJPK6Gf6zS1pKjDezCrp0nqU7X0AcICkv0haJWlebOn6FiX7lcBiSS3A\nSmBpPNHKMuS/mzJlxRkhJP0Z2KuHWZcBlxJcqqs4feU2s4fDZS4DCsA9cWYboJ6O6KvmrKKkOuB3\nwEVm1pp0nv5Img9sMrM1kmYlnWcnEGX/rsTPQORMkhYD04HDhzVRNH3mlpQiuHqxJK5AEUXZ3hmC\nS4yzCM4qPiPpEDP7aJiz9SdK9pOAO83sF5IOBe4Os1fygd+Qfy696ALM7Mienpf0OWAS8HJ4pr8J\neFHSDDP7d4wRe9Rb7k6STgPmA7MtvEBdoVoIxs11aqICTj1HISlLUHDd8t8oCwAAAeNJREFUY2a/\nTzpPRDOBBeFA1jywq6TfmNnihHONVFH2785lWiRlCC6/9HXZIw6RPpeSjiQ4QD3czNpiytaX/nLX\nA4cAT4X9+l7ACkkLzGx1bCl3FHU/WWVmHcA/JL1BUIS9EE/EXkXJfiYwD8DMnpOUJ/ij0pVyibQn\nQ/+7KemBbNX0ADZQPQPp5wHrgD2SzhIhawZYT1Dgdg7CPDjpXBFyC1gO3Jh0ljJewyx8IP1wb+N+\n92/gO2w7kP63VZJ7KsEA6slJ5x1I7u2Wf4rKGEgfZXvPA+4K240El77GVEn2R4ElYftAguJFFZB9\nIr0PpD+GbQfS/7Xc9fmZrpHrJqAG+FN4NLfKzM5NNlLPzKwg6XyCb4imgWVmtjbhWFHMBE4FXpX0\nt/C5S81sZYKZXIXpbf+WdBWw2sxWALcTXG5pJjjDtSi5xIGIua8H6oAHw37mn2a2ILHQRM5dcSLm\nfgyYI2kdUAS+b2bvJ5c6EDH794BfS/ouwSW6JRZWNkmRdB/BgWdjONbsp0AWwMxuIRh7djTQDHwC\nnF72OhN+zc4555xzOwX/9qJzzjnnXAy86HLOOeeci4EXXc4555xzMfCiyznnnHMuBl50Oeecc87F\nwIsu55xzzrkYeNHlnHPOOReD/wME25jbgLkcHAAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x1a11a96b38>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "x = np.linspace(-5, 5, 200)\n",
+    "df_all = [1,2,5,10,30]\n",
+    "\n",
+    "fh, ax = plt.subplots(2,2, figsize=(10,8))\n",
+    "\n",
+    "# PDF\n",
+    "for df in df_all:\n",
+    "    c = 1/df\n",
+    "    ax[0,0].plot(x, stats.t.pdf(x, df), 'g', alpha=c)\n",
+    "    #plt.axhline(stats.t.pdf(0, df), color='g', alpha=c)\n",
+    "ax[0,0].plot(x, stats.norm.pdf(x), '--', color='b')\n",
+    "\n",
+    "# CDF\n",
+    "for df in [1,2,5,10,30]:\n",
+    "    c = 1/df\n",
+    "    ax[1,0].plot(x, stats.t.cdf(x, df), 'g', alpha=c)\n",
+    "ax[1,0].plot(x, stats.norm.cdf(x), '--', color='b')\n",
+    "\n",
+    "# Variance vs degrees of freedom\n",
+    "ax[0,1].semilogx(range(1,30), stats.t.var(range(1,30)), 'o')\n",
+    "ax[0,1].axhline(1) # Gaussian\n",
+    "ax[0,1].set_xlabel('DOF')\n",
+    "ax[0,1].set_ylabel('Var(T)')\n",
+    "\n",
+    "# Q-Q plot (optional)\n",
+    "for df in [1,2,5,10,30]:\n",
+    "    c = 1/df\n",
+    "    ax[1,1].plot(stats.norm.cdf(x), stats.t.cdf(x, df), 'g', alpha=c)\n",
+    "\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 3.2 Eggs\n",
+    "An egg producer claims to supply eggs with an average egg weight of 63 g. In a box of 12, the following weights were measured (all in g):\n",
+    "\n",
+    "    62.75, 56.98, 53.30, 62.65, 57.63, 57.23, 56.65, 64.89, 57.87, 60.42, 57.01, 63.65\n",
+    "    \n",
+    "* Calculate the sample mean and (adjusted) sample standard deviation.\n",
+    "\n",
+    "* What is the probability of obtaining this average weight or lighter, given the supplier's claim?\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Probability of this sample mean (59.25) against claimed mean (63.00): 15.58 %\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYsAAAEKCAYAAADjDHn2AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nQmCC\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x11061bf60>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "s = [62.75, 56.98, 53.30, 62.65, 57.63, 57.23, 56.65, 64.89, 57.87, 60.42, 57.01, 63.65]\n",
+    "n_samples = len(s)\n",
+    "dof = n_samples-1\n",
+    "mu_samp = np.mean(s)\n",
+    "sig_samp = np.std(s, ddof=1) #/np.sqrt(n_samples-1)\n",
+    "mu_claim = 63\n",
+    "\n",
+    "x = np.linspace(mu_claim-10, mu_claim+10, 500)\n",
+    "fill_sel = x <= mu_samp\n",
+    "\n",
+    "t_pdf = stats.t.pdf(x, dof, loc=mu_claim, scale=sig_samp)\n",
+    "p = stats.t.cdf(mu_samp, dof, loc=mu_claim, scale=sig_samp)\n",
+    "\n",
+    "print('Probability of this sample mean ({:.2f}) against claimed mean ({:.2f}): {:.2f} %'.format(\n",
+    "    mu_samp, mu_claim, p*100))\n",
+    "\n",
+    "plt.figure()\n",
+    "plt.plot(x, t_pdf)\n",
+    "plt.plot(x, stats.norm.pdf(x, mu_claim, sig_samp))\n",
+    "plt.fill_between(x[fill_sel], t_pdf[fill_sel])\n",
+    "plt.text(59, 0.01, '{:.1f} %'.format(100*p), color='white', horizontalalignment='right')\n",
+    "plt.axvline(mu_samp)\n",
+    "plt.xlabel('Egg weight (g) (W)')\n",
+    "plt.ylabel('P(W)')\n",
+    "\n",
+    "plt.show()\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "* Within what range would 95% of samples follow? And how would this compare with an equivalent normal distribution?\n",
+    "* Plot again the two distributions, marking the 95% intervals"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Normal: 57.19 to 68.81\n",
+      "T: 56.66 to 69.34\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.text.Text at 0x1a1193cc88>"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYsAAAEKCAYAAADjDHn2AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nwgAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x110cb36a0>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "t_ppf = lambda pc : stats.t.ppf(pc, dof, loc=mu_claim, scale=sig_samp)\n",
+    "norm_ppf = lambda pc : stats.norm.ppf(pc, loc=mu_claim, scale=sig_samp)\n",
+    "\n",
+    "def print_CI(dist, dfunc, cl=0.05, cu=0.95):\n",
+    "    print('{}: {:.2f} to {:.2f}'.format(dist, dfunc(cl), dfunc(cu)))\n",
+    "print_CI('Normal', norm_ppf)\n",
+    "print_CI('T', t_ppf)\n",
+    "\n",
+    "plt.figure()\n",
+    "plt.plot(x, t_pdf)\n",
+    "plt.axvline(t_ppf(0.05))\n",
+    "plt.axvline(t_ppf(0.95))\n",
+    "\n",
+    "plt.plot(x, stats.norm.pdf(x, mu_claim, sig_samp))\n",
+    "plt.axvline(norm_ppf(0.05), color='darkorange')\n",
+    "plt.axvline(norm_ppf(0.95), color='darkorange')\n",
+    "\n",
+    "plt.text(59, 0.01, '{:.1f} %'.format(100*p), color='white', horizontalalignment='right')\n",
+    "plt.xlabel('Egg weight (g) (W)')\n",
+    "plt.ylabel('P(W)')\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Bonus\n",
+    "A pair of independent, standard normal random variables can be generated by sampling a uniform distribution. One approach to this is the Box-Muller transform (see https://en.wikipedia.org/wiki/Box%E2%80%93Muller_transform).\n",
+    "\n",
+    "* Generate a long sequence of numbers drawn from U(0,1)\n",
+    "* Use the Box-Muller transform to convert these to normal random variables\n",
+    "* Plot the normal samples on a scatter plot - verify they are not correlated\n",
+    "* Plot the histograms, and superimpose the normal PDF\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYQAAAEKCAYAAAASByJ7AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nCAzDMAwAFggMwzBMAxYIDMMwDADg/wOZWY5I6imR+QAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x1106b9eb8>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXcAAAD8CAYAAACMwORRAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nlVIqA2lxV0qpDPT/AbNcQ8vwJSyaAAAAAElFTkSuQmCC\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x11049de80>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Box Muller method\n",
+    "u1 = np.random.rand(1000)\n",
+    "u2 = np.random.rand(1000)\n",
+    "x = np.sqrt(-2*np.log(u1))*np.cos(2*np.pi*u2)\n",
+    "y = np.sqrt(-2*np.log(u1))*np.sin(2*np.pi*u2)\n",
+    "\n",
+    "z = np.linspace(-4,4,500)\n",
+    "\n",
+    "plt.figure()\n",
+    "plt.scatter(x, y)\n",
+    "plt.xlabel('u1')\n",
+    "plt.ylabel('u2')\n",
+    "\n",
+    "plt.figure()\n",
+    "plt.hist(x, 50, normed=True, label='n1')\n",
+    "plt.hist(y, 50, normed=True, label='n2')\n",
+    "plt.plot(z, stats.norm.pdf(z), label='Normal PDF')\n",
+    "plt.legend()\n",
+    "\n",
+    "plt.show()\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Improbable events\n",
+    "In this example, we tabulate the amplitude deviation against the probability, odds (inverse probability), and equivalent timescale (once in 10 thousand years). Modify the code and try with different distributions - especially those which look similar to the normal distribution, but carry a fatter tail."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style>\n",
+       "    .dataframe thead tr:only-child th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: left;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>|X| ($\\sigma)$</th>\n",
+       "      <th>p</th>\n",
+       "      <th>1 in</th>\n",
+       "      <th>time equivalent</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1</td>\n",
+       "      <td>3.173105e-01</td>\n",
+       "      <td>3.151487e+00</td>\n",
+       "      <td>3 days</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>2</td>\n",
+       "      <td>4.550026e-02</td>\n",
+       "      <td>2.197789e+01</td>\n",
+       "      <td>3 weeks</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>3</td>\n",
+       "      <td>2.699796e-03</td>\n",
+       "      <td>3.703983e+02</td>\n",
+       "      <td>1.0 years</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>4</td>\n",
+       "      <td>6.334248e-05</td>\n",
+       "      <td>1.578719e+04</td>\n",
+       "      <td>43.3 years</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>5</td>\n",
+       "      <td>5.733031e-07</td>\n",
+       "      <td>1.744278e+06</td>\n",
+       "      <td>4.8 millenia</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>6</td>\n",
+       "      <td>1.973175e-09</td>\n",
+       "      <td>5.067973e+08</td>\n",
+       "      <td>1.4 million years</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>7</td>\n",
+       "      <td>2.559730e-12</td>\n",
+       "      <td>3.906662e+11</td>\n",
+       "      <td>1.1 billion years</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   |X| ($\\sigma)$             p          1 in    time equivalent\n",
+       "0               1  3.173105e-01  3.151487e+00             3 days\n",
+       "1               2  4.550026e-02  2.197789e+01            3 weeks\n",
+       "2               3  2.699796e-03  3.703983e+02          1.0 years\n",
+       "3               4  6.334248e-05  1.578719e+04         43.3 years\n",
+       "4               5  5.733031e-07  1.744278e+06       4.8 millenia\n",
+       "5               6  1.973175e-09  5.067973e+08  1.4 million years\n",
+       "6               7  2.559730e-12  3.906662e+11  1.1 billion years"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from IPython.display import display\n",
+    "import pandas as pd\n",
+    "\n",
+    "def format_days(d):\n",
+    "    if d < 365:\n",
+    "        if d > 90:\n",
+    "            return '{:1.0f} months'.format(d/30)\n",
+    "        elif d > 7:\n",
+    "            return '{:1.0f} weeks'.format(d/7)\n",
+    "        else:\n",
+    "            return '{:1.0f} days'.format(d)\n",
+    "    d /= 365\n",
+    "    \n",
+    "    if d > 1e9:\n",
+    "        return '{:1.1f} billion years'.format(d*1e-9)\n",
+    "    elif d > 1e6:\n",
+    "        return '{:1.1f} million years'.format(d*1e-6)\n",
+    "    elif d > 1e3:\n",
+    "        return '{:1.1f} millenia'.format(d*1e-3)\n",
+    "    else:\n",
+    "        return '{:1.1f} years'.format(d)\n",
+    "\n",
+    "\n",
+    "z = np.linspace(0, 10, 500)\n",
+    "\n",
+    "data = []\n",
+    "for n in range(1,8):\n",
+    "    p = 2*(1-stats.norm.cdf(n))\n",
+    "    data.append([n, p, 1/p, format_days(1/p)])\n",
+    "    \n",
+    "display(pd.DataFrame(data, columns=[r'|X| ($\\sigma)$', 'p', '1 in', 'time equivalent']))\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Code to generate \"egg\" distribution"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "55.30, 56.10, 52.49, 61.32, 50.20, 61.86, 61.05, 62.20, 59.52, 60.16, 56.32, 57.61\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Generate small dataset for T-dist question\n",
+    "# True parameters:\n",
+    "sig = 3\n",
+    "mu = 58\n",
+    "n_samples = 12\n",
+    "\n",
+    "s = stats.norm.rvs(size=n_samples, loc=mu, scale=sig)\n",
+    "print(('{:.2f}, '*n_samples).format(*s)[:-2])\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/exercises/Solutions4/Solutions_4.ipynb b/exercises/Solutions4/Solutions_4.ipynb
new file mode 100644
index 0000000..364e34d
--- /dev/null
+++ b/exercises/Solutions4/Solutions_4.ipynb
@@ -0,0 +1,788 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Exercise 4 Solutions\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "from __future__ import print_function  # For Python < 3\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "import scipy.stats as stats\n",
+    "\n",
+    "%matplotlib inline \n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 1. Approximations to the binomial\n",
+    "\n",
+    "For np < 10, large n, the Poisson distribution is a good approximation for the binomial.\n",
+    "\n",
+    "* Show analytically that the binomial distribution converges to the Poisson distribution in the limit of large n. (Hint: $e = \\lim_{n\\to\\infty}(1+\\frac{1}{x})^x$)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "$P(x=k) = \\binom{n}{k}p^k (1-p)^{n-k}$\n",
+    "\n",
+    "$\\lambda = np$\n",
+    "\n",
+    "$\\lim_{n\\to\\infty} \\frac{n!}{(n-k)!k!} \\frac{\\lambda}{n}^k (1-\\frac{\\lambda}{n})^{n-k}$\n",
+    "\n",
+    "$\\lim_{n\\to\\infty} \\frac{n}{n} \\frac{n-1}{n} \\bigl ( ... \\bigr ) \\frac{n-k+1}{n} (1-\\frac{\\lambda}{n})^{n} (1-\\frac{\\lambda}{n})^{-k}$\n",
+    "\n",
+    "Remembering $e = \\lim_{n\\to\\infty}(1+\\frac{1}{x})^x$\n",
+    "\n",
+    "$\\frac{\\lambda}{k!}e^{-\\lambda}$\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "* Keeping $n p$ fixed, plot the binomial probability mass function for an increasing number of observations $n$, comparing in each case to the equivalent Poisson distribution ($\\lambda=n p$). For convenience, you should use the relevant functions in ```scipy.stat```."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAX0AAAEICAYAAACzliQjAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nEBd9M7MMcdE3M8uQ/w/eiXuGcmEQ6wAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x107ecaf60>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "n_trials = [5, 10, 100]\n",
+    "p0 = 0.8\n",
+    "n_p = n_trials[0] * p0\n",
+    "x = range(12)\n",
+    "\n",
+    "fh, ax = plt.subplots(1,3, sharey=True)\n",
+    "for idx, nt in enumerate(n_trials):\n",
+    "    p = n_p / nt\n",
+    "    ax[idx].bar(x, stats.binom.pmf(x, nt, p), width=1, alpha=1, label='Binomial')\n",
+    "    ax[idx].bar(x, stats.poisson.pmf(x, n_p), fill=False, width=1, alpha=1, label='Poisson')\n",
+    "    \n",
+    "    ax[idx].set_title('n={}'.format(nt))\n",
+    "\n",
+    "    if idx==2:\n",
+    "        plt.legend()\n",
+    "\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "For np > 10, n(1-p) > 10, the discrete binomial distribution can be reasonably approximated by the continuous normal distribution.\n",
+    "\n",
+    "* Choose a large n (> 30, with p close to 0.5). To start with, choose n=100 and p=0.45. Plot the binomial pmf, and, with equivalent parameters, the normal pdf \n",
+    "* Calculate the probability that X >= 55 for each. Don't forget to apply the continuity correction\n",
+    "* What happens to the relative difference as n increases?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Binomial (exact): 0.4911796759527426\n",
+      "Gaussian (approximate): 0.48595290935296537\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAX0AAAD8CAYAAACb4nSYAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x10d046d30>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "n_trials = 100\n",
+    "p0 = 0.45\n",
+    "mu = n_trials * p0\n",
+    "std = np.sqrt(stats.binom.stats(n_trials, p0, moments='v'))\n",
+    "\n",
+    "xd = np.arange(int(mu), int(1.5*mu))\n",
+    "x = np.linspace(xd[0], xd[-1], 200)\n",
+    "\n",
+    "x_ch = 55\n",
+    "sel_d = xd >= 55\n",
+    "sel_cont = x >= 55\n",
+    "p_bin = stats.binom.cdf(x_ch, n_trials, p0)/2\n",
+    "p_gauss = stats.norm.cdf(x_ch-0.5, mu, std)/2\n",
+    "\n",
+    "print('Binomial (exact):', p_bin)\n",
+    "print('Gaussian (approximate):', p_gauss)\n",
+    "\n",
+    "\n",
+    "plt.figure()\n",
+    "plt.bar(xd, stats.binom.pmf(xd, n_trials, p0), width=1)\n",
+    "plt.bar(xd[sel_d], stats.binom.pmf(xd[sel_d], n_trials, p0), width=1, color='g')\n",
+    "plt.plot(x, stats.norm.pdf(x, mu, std), 'r')\n",
+    "plt.show()\n",
+    "        "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 2. Random walk\n",
+    "\n",
+    "Consider a simple 1D random walk. A person starts at the position $x=0$. With equal probability $p=0.5$, they may take one step forwards or one step backwards, corresponding to a displacement of +1 and -1 respectively.\n",
+    "\n",
+    "* Show that for an N step walk, the expected absolute distance from the starting position is given by $\\sqrt{N}$.\n",
+    "\n",
+    "* Write a function to simulate such a random walk, parameterised by the number of steps. The output should be an array, with the displacement at each step index.\n",
+    "\n",
+    "* Plot a single walk."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXQAAAD8CAYAAABn919SAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nThAEYRFI0AmCICwCCTpBEIRF+P86OddqGMrfTgAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x107eca2e8>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "def random_walk(n_steps, p=0.5):\n",
+    "    return np.cumsum(2*(np.random.binomial(size=n_steps, n=1, p=0.5)-0.5))  # Bernoulli\n",
+    "\n",
+    "n_steps = 100\n",
+    "w = random_walk(n_steps)\n",
+    "\n",
+    "plt.figure()\n",
+    "plt.plot(range(n_steps), w)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "* Simulate ~1000 random walks of 500 steps.\n",
+    "\n",
+    "* Plot the average distance (rms) of these over the whole set with respect to step index (time). Does the average converge to the expected distance?\n",
+    "\n",
+    "* (Optional) sample and plot the running average to show how the convergence improves with number of walks."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYIAAAEKCAYAAAAfGVI8AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nSUVORK5CYII=\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x107eca438>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYQAAAEKCAYAAAASByJ7AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nYII=\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x110bd8cf8>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "### Bernoulli\n",
+    "n_steps = 500\n",
+    "n_walks = 1000\n",
+    "n = np.arange(n_steps) +1\n",
+    "\n",
+    "W = []  # Final distance\n",
+    "A = []  # Running average over whole set\n",
+    "T = 0\n",
+    "for idx in range(n_walks):\n",
+    "    w = np.abs(random_walk(n_steps))\n",
+    "    W.append(w[-1])\n",
+    "    T += w**2\n",
+    "    A.append(np.sqrt(T/(idx+1)))\n",
+    "    \n",
+    "plt.figure()\n",
+    "plt.plot(n, np.array(A).transpose()[:,[0,20,-1]])\n",
+    "plt.plot(n, np.sqrt(n))\n",
+    "plt.legend(['0', '20', '1000', r'$\\sqrt{N}$'])\n",
+    "plt.xlabel('Step #')\n",
+    "plt.ylabel('Distance (steps)')\n",
+    "\n",
+    "plt.figure()\n",
+    "plt.hist(np.array(W), normed=True)\n",
+    "plt.axvline(np.sqrt(n_steps), color='r')  # Expected distance\n",
+    "plt.xlabel('Dist D')\n",
+    "plt.show()\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "* Now consider the case $p \\ne 0.5$, where the \"person\" is more likely to step in one direction than another. Find again analytically the expectation and the variance for the (rms) distance travelled in terms of $N$ and $p$.\n",
+    "\n",
+    "Expectation: $N  \\left|1-2p \\right|$\n",
+    "\n",
+    "Variance: $N$\n",
+    "\n",
+    "* Modify the random_walk function to account for the unequal probability between the directions."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "* Run a series of random walks as before, and plot again the histogram of distances travelled. On top of this, plot the Gaussian PDF with the $\\mu$ and $\\sigma$ parameters as determined above."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYQAAAD8CAYAAAB3u9PLAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x11072d6d8>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Plot histogram\n",
+    "n_steps = 2000\n",
+    "n_trials = 5000\n",
+    "p = 0.4\n",
+    "\n",
+    "V = []\n",
+    "for n in range(n_trials):\n",
+    "    V.append(np.abs(np.sum(2*(np.random.binomial(size=n_steps, n=1, p=p)-0.5))))\n",
+    "    \n",
+    "plt.figure()\n",
+    "plt.hist(V, 40, normed=True)\n",
+    "plt.plot(range(500), stats.norm.pdf(range(500), loc=n_steps*np.abs(1-2*p), scale=np.sqrt(n_steps)))\n",
+    "plt.axvline(n_steps*(np.abs(1-2*p)))\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 3. Small sample sizes: t-distribution\n",
+    "### 3.1 Compare to normal distribution\n",
+    "\n",
+    "Student's t-distributions are interesting for cases where you have few samples and the population variance is unknown, but the underlying distribution of the means can be assumed normal. They are parameterised by the degrees of freedom (\"df\"), which is usually equal the number of samples minus one. As the number of degrees of freedom increases, the t-distribution converges to the normal distribution.\n",
+    "\n",
+    "* Plot the standard t-distribution for several increasing degrees of freedom and compare this to the normal PDF.\n",
+    "* Plot and compare the cumulative distribution functions\n",
+    "* Plot the variance of the t-distribution as a function of degrees of freedom. Compare to the standard normal variance (=1)\n",
+    "* (optional) make a Q-Q plot (see Wiki) and compare the distributions\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAl0AAAHVCAYAAADLiU4DAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nwIsu55xzzrkYeNHlnHPOOReD/wME25jbgLkcHAAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x1a11a96b38>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "x = np.linspace(-5, 5, 200)\n",
+    "df_all = [1,2,5,10,30]\n",
+    "\n",
+    "fh, ax = plt.subplots(2,2, figsize=(10,8))\n",
+    "\n",
+    "# PDF\n",
+    "for df in df_all:\n",
+    "    c = 1/df\n",
+    "    ax[0,0].plot(x, stats.t.pdf(x, df), 'g', alpha=c)\n",
+    "    #plt.axhline(stats.t.pdf(0, df), color='g', alpha=c)\n",
+    "ax[0,0].plot(x, stats.norm.pdf(x), '--', color='b')\n",
+    "\n",
+    "# CDF\n",
+    "for df in [1,2,5,10,30]:\n",
+    "    c = 1/df\n",
+    "    ax[1,0].plot(x, stats.t.cdf(x, df), 'g', alpha=c)\n",
+    "ax[1,0].plot(x, stats.norm.cdf(x), '--', color='b')\n",
+    "\n",
+    "# Variance vs degrees of freedom\n",
+    "ax[0,1].semilogx(range(1,30), stats.t.var(range(1,30)), 'o')\n",
+    "ax[0,1].axhline(1) # Gaussian\n",
+    "ax[0,1].set_xlabel('DOF')\n",
+    "ax[0,1].set_ylabel('Var(T)')\n",
+    "\n",
+    "# Q-Q plot (optional)\n",
+    "for df in [1,2,5,10,30]:\n",
+    "    c = 1/df\n",
+    "    ax[1,1].plot(stats.norm.cdf(x), stats.t.cdf(x, df), 'g', alpha=c)\n",
+    "\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 3.2 Eggs\n",
+    "An egg producer claims to supply eggs with an average egg weight of 63 g. In a box of 12, the following weights were measured (all in g):\n",
+    "\n",
+    "    62.75, 56.98, 53.30, 62.65, 57.63, 57.23, 56.65, 64.89, 57.87, 60.42, 57.01, 63.65\n",
+    "    \n",
+    "* Calculate the sample mean and (adjusted) sample standard deviation.\n",
+    "\n",
+    "* What is the probability of obtaining this average weight or lighter, given the supplier's claim?\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Probability of this sample mean (59.25) against claimed mean (63.00): 15.58 %\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYsAAAEKCAYAAADjDHn2AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nQmCC\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x11061bf60>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "s = [62.75, 56.98, 53.30, 62.65, 57.63, 57.23, 56.65, 64.89, 57.87, 60.42, 57.01, 63.65]\n",
+    "n_samples = len(s)\n",
+    "dof = n_samples-1\n",
+    "mu_samp = np.mean(s)\n",
+    "sig_samp = np.std(s, ddof=1) #/np.sqrt(n_samples-1)\n",
+    "mu_claim = 63\n",
+    "\n",
+    "x = np.linspace(mu_claim-10, mu_claim+10, 500)\n",
+    "fill_sel = x <= mu_samp\n",
+    "\n",
+    "t_pdf = stats.t.pdf(x, dof, loc=mu_claim, scale=sig_samp)\n",
+    "p = stats.t.cdf(mu_samp, dof, loc=mu_claim, scale=sig_samp)\n",
+    "\n",
+    "print('Probability of this sample mean ({:.2f}) against claimed mean ({:.2f}): {:.2f} %'.format(\n",
+    "    mu_samp, mu_claim, p*100))\n",
+    "\n",
+    "plt.figure()\n",
+    "plt.plot(x, t_pdf)\n",
+    "plt.plot(x, stats.norm.pdf(x, mu_claim, sig_samp))\n",
+    "plt.fill_between(x[fill_sel], t_pdf[fill_sel])\n",
+    "plt.text(59, 0.01, '{:.1f} %'.format(100*p), color='white', horizontalalignment='right')\n",
+    "plt.axvline(mu_samp)\n",
+    "plt.xlabel('Egg weight (g) (W)')\n",
+    "plt.ylabel('P(W)')\n",
+    "\n",
+    "plt.show()\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "* Within what range would 95% of samples follow? And how would this compare with an equivalent normal distribution?\n",
+    "* Plot again the two distributions, marking the 95% intervals"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Normal: 57.19 to 68.81\n",
+      "T: 56.66 to 69.34\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.text.Text at 0x1a1193cc88>"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYsAAAEKCAYAAADjDHn2AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nwgAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x110cb36a0>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "t_ppf = lambda pc : stats.t.ppf(pc, dof, loc=mu_claim, scale=sig_samp)\n",
+    "norm_ppf = lambda pc : stats.norm.ppf(pc, loc=mu_claim, scale=sig_samp)\n",
+    "\n",
+    "def print_CI(dist, dfunc, cl=0.05, cu=0.95):\n",
+    "    print('{}: {:.2f} to {:.2f}'.format(dist, dfunc(cl), dfunc(cu)))\n",
+    "print_CI('Normal', norm_ppf)\n",
+    "print_CI('T', t_ppf)\n",
+    "\n",
+    "plt.figure()\n",
+    "plt.plot(x, t_pdf)\n",
+    "plt.axvline(t_ppf(0.05))\n",
+    "plt.axvline(t_ppf(0.95))\n",
+    "\n",
+    "plt.plot(x, stats.norm.pdf(x, mu_claim, sig_samp))\n",
+    "plt.axvline(norm_ppf(0.05), color='darkorange')\n",
+    "plt.axvline(norm_ppf(0.95), color='darkorange')\n",
+    "\n",
+    "plt.text(59, 0.01, '{:.1f} %'.format(100*p), color='white', horizontalalignment='right')\n",
+    "plt.xlabel('Egg weight (g) (W)')\n",
+    "plt.ylabel('P(W)')\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Bonus\n",
+    "A pair of independent, standard normal random variables can be generated by sampling a uniform distribution. One approach to this is the Box-Muller transform (see https://en.wikipedia.org/wiki/Box%E2%80%93Muller_transform).\n",
+    "\n",
+    "* Generate a long sequence of numbers drawn from U(0,1)\n",
+    "* Use the Box-Muller transform to convert these to normal random variables\n",
+    "* Plot the normal samples on a scatter plot - verify they are not correlated\n",
+    "* Plot the histograms, and superimpose the normal PDF\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYQAAAEKCAYAAAASByJ7AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nCAzDMAwAFggMwzBMAxYIDMMwDADg/wOZWY5I6imR+QAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x1106b9eb8>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXcAAAD8CAYAAACMwORRAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAIABJREFUeJzt3Xl8lOW5//HPNVtmyUYWhBI0lCKisohI1VqXuhEXlKpV\nrFttpYvU2lpbtKccauuvLj2u7akrntZibRWwbAG1YrVVFARBEBWEIBExgeyzZLb790cChhjIhEzy\nzHK9X6+8yDPzzMyXkLm4537uRYwxKKWUyiw2qwMopZRKPi3uSimVgbS4K6VUBtLirpRSGUiLu1JK\nZSAt7koplYG0uCulVAbS4q6UUhlIi7tSSmUgh1UvXFJSYsrLy616eaWUSktvvfXWLmNMaXfnWVbc\ny8vLWbVqlVUvr5RSaUlEtiVynnbLKKVUBtLirpRSGUiLu1JKZSDL+tyVUukhEolQXV1NKBSyOkpW\ncbvdlJWV4XQ6D+rxWtyVUgdUXV1NXl4e5eXliIjVcbKCMYbdu3dTXV3NsGHDDuo5tFtGKXVAoVCI\n4uJiLez9SEQoLi7u1aclLe5KqW5pYe9/vf2Za3FXSqkMpMVdKZXyRISbbrpp7/Hvfvc7Zs2a1a8Z\nrrnmGp599tkubx82bBjjxo1j/PjxvP766/vcPnbsWA4//HCuuuoqPv74472PKy8vZ/To0YwbN45x\n48bx2muvJTWvXlBVql35jMVd3l51x7n9nER1lpOTw7x587jlllsoKSnp8eOj0SgOR9+Vu7vvvpuL\nL76Y559/nu9+97usW7dun9uNMdx3332cdtpprF+/HpfLBcDy5csP6u+TCC3uSqmU53A4mDZtGvfe\ney+33377Pvdt27aNa6+9ltraWkpLS3niiSc49NBDueaaaygqKmLNmjWMHz+evLw8tm7dyieffMIH\nH3zAPffcw4oVK6isrGTIkCEsXLgQp9PJbbfdxsKFCwkGg5x44ok8/PDDCfd/n3zyyWzevPlzt4sI\nP/7xj5k/fz6VlZVccMEFSfm5HIgWd6VUwu58807eq3svqc95RNER/Hziz7s97/rrr2fMmDH87Gc/\n2+f26dOnc9VVV3H11Vcze/ZsbrjhBp577jkAPvjgA1588UXsdjuzZs3iww8/ZPny5bz77ruccMIJ\nzJ07l7vuuospU6awePFiLrzwQqZPn87MmTMBuPLKK1m0aBHnn39+Qn+XhQsXMnr06P3eP378eN57\n7729xf20007DbreTk5PDG2+8kdBrJEqLu8po++1qcV/exa1P9W0Y1Sv5+flcddVVPPDAA3g8nr23\nv/7668ybNw9oK8Ydi/8ll1yC3W7fe1xRUYHT6WT06NHEYjEmTZoEwOjRo6mqqgLaukruuusuAoEA\ndXV1HHXUUd0W95tvvpnf/OY3lJaW8vjjj+/3PGPMPsfaLaOUSgmJtLD70o033sj48eP51re+td9z\nOnah+Hy+fe7LyckBwGaz4XQ6955rs9mIRqOEQiF+8IMfsGrVKoYOHcqsWbMSGmu+p2+9O2vWrOH0\n00/v9rxk0NEySqm0UVRUxDe+8Y19WscnnngiTz/9NABz5szhpJNOOujn31PIS0pKaGlp6XJ0zMEw\nxvDAAw/wySef7P200Ne0uCul0spNN93Erl279h4/8MADPPHEE4wZM4Ynn3yS+++//6Cfu7CwkOuu\nu47Ro0dz4YUXctxxx/Uq680337x3KOTKlStZvnz53pEyfU069wH1lwkTJhjdrEP1tT197l33sXfz\n2FBbH3y2D4XcuHEjo0aNsjpGVurqZy8ibxljJnT3WG25K6VUBtLirpRSGUiLu1JKZSAt7koplYG0\nuCulVAbS4q6UUhlIZ6gqpXpkf0s6HKzeDDV95plnmDVrFhs3buTNN99kwoRuRwhmDW25K6XS1tFH\nH828efM4+eSTrY6ScrTlrpRKeVVVVVRUVHDSSSfx2muvMWTIEP7xj3/o5KoD0Ja7UiotbNq0ieuv\nv54NGzZQWFjI3LlzrY6U0rS4K6XSwp6t7ACOPfbYvUv0qq4l1C0jIpOA+wE78Jgx5o79nHcx8Axw\nnDFGF45RaW3vejSzOtw4q9GKKIrPlusFsNvtBINBC9Okvm5b7iJiB/4AVABHAlNF5MguzssDbgCS\nu52IUkqpHkuk5T4R2GyM2QIgIk8DFwDvdjrv18BdwE+TmlAplVJSaZXM+fPn88Mf/pDa2lrOPfdc\nxo0bx7Jly6yOlRISKe5DgO0djquBL3c8QUSOAYYaYxaJiBZ3lXa2OB286vHwgctJwGYjPx7nS+EI\nXw0EKY9GrY6X9crLy1m/fv3e45/+9LMyM2XKFCsipbxEintX237vXQReRGzAvcA13T6RyDRgGsCh\nhx6aWEKl+tAqdw4PDihgtdsNQGk0Sn48ToPNzry8XO4qHsCXgyF+VNfA6HDY4rRKJS6R4l4NDO1w\nXAbs6HCcBxwNvNy+H+EgYIGITO58UdUY8wjwCLRt1tGL3Er1SkiEO4oGMDc/l4HRKD/dXc8kf4BD\nYrG95+xw2Fni8/GX/DwuHzKIKxqb+HEsjMvePzvpKNUbiQyFXAmMEJFhIuICLgMW7LnTGNNojCkx\nxpQbY8qBFcDnCrtSqaLWbuPaQQOZm5/LtxqaWFz9CVc3Ne9T2AG+EI3xncYmFlXvYGpjM38pyOfb\ny75NY6uOmFGpr9uWuzEmKiLTgWW0DYWcbYzZICK3AauMMQsO/AxKJdGsggTO2X/x3Wm3c83gQ6iz\n27jv01pOD3Q/nC7XGG6tq+fYUIhbbBu4Zuk1PHzmwwz0DuxJcqX6VUKTmIwxS4wxhxtjhhtjbm+/\nbWZXhd0Yc6q22lUq2m2zcd2ggTTabcz+pCahwt7R2YEgD53xEDtadvC9F79Hc7i5j5Iq1Xs6Q1Vl\nhVaB6weVstNh5w87azn6IC+OThw8kftOu4+tDVu5cfmNhGN6kVWlJl04TGUBw+3FRWzIyeH+T2sZ\n39raq2c74QsncNtXbuPWf9/KPW/dw4yJM5KUM00k0jXWo+c7+GsYN998MwsXLsTlcjF8+HCeeOIJ\nCgsLkxgufWnLXWWeWQV7v6rcl3NPyTTm5+Uyrb6Rr/WwK2Z/zh9+PleMuoI5G+fw4rYXk/KcqufO\nPPNM1q9fz7p16zj88MP57W9/a3WklKHFXWW07Q47d7SPVf9BQ3JHufzk2J9wdPHRzPzPTHb6dyb1\nudW+qqqqGDVqFNdddx1HHXUUZ511FsFgkLPOOguHo60D4vjjj6e6utripKlDi7vKWHHgv0qLsQO/\nqd2NPcnP77Q7ueuUu4iaKLe9fhvG6NSNvtTdkr+zZ8+moqLConSpR4u7ylhP5+Wy2u1mxu56BnUa\nw54sQ/OG8sNjfsirH7/Koi2L+uQ1VJsDLfl7++2343A4+OY3v2lRutSjxV1lpF02G78fUMgJwSCT\nW/x9+lqXH3E5Y0vHctfKu3SCUx/qvORvtH3Nnz/96U8sWrSIOXPm0D5LXqHFXWWoe4sKCdqEW3bX\nd7k4UjLZbXZ+efwvaQo38b9v/28fv5rqaOnSpdx5550sWLAAr9drdZyUokMhVcZZl+NiQV4u32lo\nZFikf1Z0HFk0kksOv4S/vf83Ljn8Er404Ev98rqWSKENS6ZPn05raytnnnkm0HZR9aGHHrI4VWoQ\nqy4CTZgwwaxapRNZVQ91M8baANcOGsgWl5PK7TvwJvv3+wCFrT5Uz7nzz2V0yWgePvPh5L6uhTZu\n3KgbUVukq5+9iLxljJnQ3WO1W0ZllP943KzyuPlufWPyCztQPmMx5TMWd3nfAPcApo2exms7XmPl\nzpVJf22lekKLu8oYceC+AYWURSJc0txiSYbLjriMUk8pv1/zex0aqSylxV1ljEqfl/dzXEyvb8Rp\nUQa3w820MdNYXbOa13a8ZlGK5NP/qPpfb3/mWtxVRogBDxUWMCIcpsIfsDTLRSMu4gu+L/Dgmgcz\noii63W52796dEX+XdGGMYffu3bjbdwg7GDpaRmWEf3o9VLmc3F2zy/IWi9Pu5Htjv8fM12bySvUr\nnDL0FIsT9U5ZWRnV1dXU1tZaHSWruN1uysrKDvrxWtxV2jPAY4UFlIcjnGlxq32P84afxx/X/pHZ\n62enfXF3Op0MGzbM6hiqh6xu5CjVa//2uNmY4+Laxqakrx9zsJw2J1cfdTWra1azpmaN1XFUFtLi\nrtLeY4X5DIpGOa+PlxnoqSlfmkJhTiGz35ltdRSVhbS4q7S2NsfFarebaxqbLBshsz9ep5fLj7ic\nl6tfZnP9ZqvjqCyjxV2ltTn5eeTG40xpTq1W+x5Tj5iKx+Hh/zb8n9VRVJbR4q7S1qd2Oy/4vExp\nbumT2ajJUOguZPLwyVRuraQuVGd1HJVFtLirtPW3/FxiwNSmZqujHNDUI6YSjoeZt2me1VFUFtHi\nrtJSq8CzebmcGggyNNo3G3Eky/DC4Xx50Jf52/t/Ixrvn1UqldLirtLSEp+PerudK1K81b7H1FFT\n2enfyb+2/8vqKCpLaHFXacfQdiF1RDjMcaFWq+Mk5JSyUxjsG8xT7z1ldRSVJXSGqkpd+1m7/e0c\nF+/nuJhVu7vPd1nqVlcZu1jz3WFzcOnIS7lv9X1sqt/EiAEj+iGcymbacldp59m8XHLjccsXCOup\nr4/4Oi6bi6ffe9rqKCoLaHFXaaXRJizzeTm3xZ+ywx/3Z4B7AJOGTWLx1sUEIun1H5NKP1rcVVpZ\n7PPRarNxkUWbcfTWRSMuwh/xs6xqmdVRVIbTPneVNgzwbH4uR7a2MiocsTpOwvbdls/g/WIp8176\nOVPmXLPviSm08bRKf9pyV2ljXY6LTS4XF6dpq72NEGk4jrfdOXzo1LaV6jta3FXamJuXiyce55yW\n9O6vjjaOx2EM8/JyrY6iMpg2HVRaaBFhqc/LOf4AvhS/kLpvN8znmVgupwWCLMz18aO6Blz9lEtl\nFy3uKi0s83kJ2mx8PQ26ZKrcl3d7zr+b3bzg8/KSz8ukNBvSqdKDdsuotLAgz0d5OMLo1rDVUZLi\nhGCIwdEo8/J8VkdRGUqLu0p52x12VrvdXNDit35GapLYgcnNfla43XxqT5XNAVUm0eKuUt6iXB9i\nTMpto9db57f4MSIszvVaHUVloISKu4hMEpH3RWSziMzo4v7vicg7IvK2iPxbRI5MflSVjQywINfH\nxFArg2LWL+1b5b68rU99VsF+175J1GHRKGNDrSzM9ZHal4hVOuq2uIuIHfgDUAEcCUztong/ZYwZ\nbYwZB9wF3JP0pCorrcnJodrpZHKGtdr3mNziZ7PLxXuuVNsBVqW7RFruE4HNxpgtxpgw8DRwQccT\njDFNHQ59oA0RlRwLcn144nHOyNARJWf7AziNYUGuXlhVyZVIcR8CbO9wXN1+2z5E5HoR+ZC2lvsN\nyYmnsllIhGW5Xs70B9JukbBEFcTjnBoIsiTXRySePksqqNSXSHHvaoDC595pxpg/GGOGAz8H/qvL\nJxKZJiKrRGRVbW1tz5KqrPOy10OLzZaxXTJ7nN/ip85u5/Udr1sdRWWQRCYxVQNDOxyXATsOcP7T\nwB+7usMY8wjwCMCECRMysymmDkpXszrPOczHoGg0bXZbOlgnBYIMiMVY8OECTi472eo4KkMk0nJf\nCYwQkWEi4gIuAxZ0PEFEOm4rcy6wKXkRVTYSezOvedyc3+LP+PG6TqCiJcDyj5bTFG7q9nylEtFt\ny90YExWR6cAy2uZezDbGbBCR24BVxpgFwHQROQOIAPXA1X0ZWmU+R8HbxEQybmz7/kxu8fNUQR7P\nP3gEFze3/511CWDVCwmtLWOMWQIs6XTbzA7f/yjJuVSWc+av5cjWVr4YiVodpV8cGQ5THo6wxOf7\nrLgr1QuZ/olXpSFx7sLuqU77pX17QoAKf4BV7hxdjkAlhRZ3lXKc+WuBtjHg2aTC37YcwTKfLkeg\nek+Lu0o5jvx1RAPlKbHcQH8aFokyqjXMEl1rRiWBFneVUmw5O7G7PyXaNNbqKJY4t8XPhpwctjl0\nqwXVO1rcVUpx5K/FGBvRptFWR7HE2f4AYgyV2npXvaTFXaUQgzN/LTH/cEwsO/cXHRSLMT7UyhKf\nD5OhSy6o/qHFXaUMm7sam6uOSJZ2yexxjj/AVpeT9+vftzqKSmNa3FXKcOa/jYnbiTYfZXUUS53l\nD+AwhiVbl3R/slL7ocVdpYh42ygZ/0iIe6wOY6nCeJwTgyEqt1YSN3Gr46g0pcVdpQS7dys2ZzPR\nxuzuktmjosXPTv9O3q552+ooKk3peCuVEhz56/DE47wRvQuvWy8kfi0QxB2Ps+TvX2f87vrP7tD1\nZlSCtOWuLBeJR3DkvcOpgWDGbsrRU15jODUQ5HmfF93CQx0MLe7Kcit2rMDmCGTVWjKJqPAHqLfb\necPjtjqKSkNa3JXlKrdWYmJuTgwGrY6SUk4KBMmLxanUtWbUQdDiriwVioZ4aftLRJqPxmV1mBTj\nAs4IBPinz0trV5tdKnUAWtyVpV79+FX8Eb+OktmPihY/fpuNVz3ZPTxU9ZwWd2Wpyq2VFLuLiQWG\nWx0lJR0XaqUoFmNJrs/qKCrNaHFXlmkJt/BK9SucXX42+qvYNQdwdkuAVzxuWkT7ZlTidJy76h+z\nCvY5LA89hSN/NZ4hrTy6tGA/D1IA5/j9/LUgj+VeD+dbHUalDW0uKcs4C9YSDxcSDx5qdZSUNqY1\nzOBolErtmlE9oMVdWcPux+7b1L4CpHY3HIgNmNQS4HWPm4ZQg9VxVJrQ4q4scXfRjYjEWdD6NFXu\ny6lyX251pJR2jt9PVIQXPnrB6igqTWhxV5ao9PkoD0cYGdbJ9YkYGY5QHo5QubXS6igqTWhxV/2u\nxm5nlTuHc/x+7ZBJkNDWel+1cxWf+j+1Oo5KA1rcVb9b5vNiRJjk17VkemKSP4DB8Py2562OotKA\nDoVU/W6pz8sRrWGGRaJWR0krwyJRRhWNonJrJVceeWXbjbO6GEaqywIrtOWu+tl2h5117hwq/H6r\no6SlimEVvLPrHbY3bbc6ikpxWtxVv1rqaxurXaHL+x6USeWTAFhatdTiJCrVaXFX/WpJrpdjQiEG\nx2JWR0lLg3MHc8zAY3TzbNUtLe6q32xyOtnscjFJW+29UjGsgs0Nm9lUv8nqKCqFaXFX/aYy14vN\nGM7SUTK9ctZhZ2ETm455VwekxV31CwNU+rxMDIUoicetjpPWij3FfHnQl9t2sLI6jEpZWtxVv9jg\nclHtdOo+qUlSMayC6pZq1rt0/yrVNS3uql8syfXiMIbTA1rck+H0w07HaXNSmav7q6qu6SQmlRTl\nMxZ/7raqO84FIBaPsczn5aRAkPy4diQkQ74rn0DjCJb5gtxU14Dd6kAq5WjLXfW51TWrqXE4OEcv\npCZVtHEsNQ4Hq905VkdRKUiLu+pzlVsr8cTjnBIIWh0lo0RbRuGJx1ni064Z9XkJFXcRmSQi74vI\nZhGZ0cX9PxGRd0VknYj8U0QOS35UlY4i8QgvbHuBUwNBvEa7ZHptVsHer6qcazg1EOQFnxddOFl1\n1m1xFxE78AegAjgSmCoiR3Y6bQ0wwRgzBngWuCvZQVV6WrFjBQ2tDVRol0yfOKclQKPdzuset9VR\nVIpJpOU+EdhsjNlijAkDTwMXdDzBGLPcGLPn3bsCKEtuTJWuKrdWkufK4yvaJdMnTgwGyYvFdX9V\n9TmJFPchQMcl6Krbb9ufbwM6dU5RfstzLNi0jN01I9HR2H3DBZwZCPCS10NIdOsT9ZlEintXvzFd\ndp6KyBXABODu/dw/TURWiciq2traxFOqtOTIfQ+xh4k2jrU6SkaraPETsNl4RbtmVAeJFPdqYGiH\n4zJgR+eTROQM4BfAZGNMa1dPZIx5xBgzwRgzobS09GDyqjTiyF9LPJpLLDDc6igZ7bhQK8XRmHbN\nqH0kUtxXAiNEZJiIuIDLgAUdTxCRY4CHaSvsNcmPqdKOLYAjdyPRpjHoiNu+ZQfO9gd4xeOhRbtm\nVLtuZ6gaY6IiMh1YRtvv0WxjzAYRuQ1YZYxZQFs3TC7wjLT9cn1kjJnch7lVinPmr0dsMSKN462O\nkhUq/H6eKsjjJZ+XGw4wW1hlj4SWHzDGLAGWdLptZofvz0hyLpXmHAWribWWEg8d6Nq7SpaxrWG+\nEIm2TWjaZXUalQr087JKOnHW4fBWEW0cT9fX41WyCTDJ72eFx43YdX9apcVd9QFn/hoAIk06SqY/\nneMPEBPBkfeO1VFUCtDirpLM4Ch4m6h/GCZSZHWYrHJ4OMKXwmGcBautjqJSgBZ3lVQ2dzX2nFqi\nTcdYHSXrCHB+ix+79yPEpfNIsp0Wd5VUzoI1mLiDSNNoq6NkpfNaAhgj2npXWtxVMsVw5K8l2jIK\n4h6rw2SlgbEYMf8InAVrAN2rNptpcVdJY8/dhM3hJ9KoXTJWijSOx+ZswO7dYnUUZSEt7ippnPmr\niUe9xFoOtzpKVos2H4WJ5WjXTJbT4q6SwxbCkfdu+3IDujWvpYyTSNMYHPnrQbpc5kllAS3uKimc\n+WsRW5RI4wSroygg2ngsYgu3FXiVlbS4q6RwFq4iFhqkyw2kiFjwMOLhYu2ayWJa3FWvbarfhN2z\nnUjDBHS5gVQhRBqPwe7dgjgarA6jLKDFXfXac5ufwxg70aZxVkdRHUQaxyNitPWepbS4q16JxCIs\n2rKIaPMoTCzX6jiqAxMpIur/Is7CtzCmy83TVAbTYQ2qV16pfoW6UB2RBl2+P1VUuS/f+/1Cv5db\nB5awcudKJg6eaGEq1d+05a56Zf7m+Qz0DCTmH2F1FNWFMwNB8mJxnt30rNVRVD/T4q4OWk2ghlc/\nfpXJX5pM2yZdKtW4jeH8Fj8vbnuR+lC91XFUP9JuGXXQFny4gLiJc+GXLuRedDx1qrqouYWnCvKY\n+MCdROq+us99uv1e5tLirg5K3MSZ+8Fcjj3kWA67ZwxV7s/uKw89ZV0w9TmHRyLEgkNxFq4kUncS\nOlw1O2i3jDoor+94neqWai4deanVUVQCIvUTsefUYPdsszqK6ifaclc9Uj5jMQDusj9j9/j4/iNh\nqnIsDqW6FWkaQ84hi3AWvkksWG51HNUPtOWuekwcDThyNxJpOA6Mtg/Sgskh0jQOR/47YAtanUb1\nAy3uqkeq3Jfz05JbsBHnpeCcfcZUq9QWaTgOsUV0xmqW0OKueiQCzM3z8ZVgiLJozOo4qgfioTJi\nwTKcA1YAOmM102lxVz3ystdDrcPBpU0tVkdRByFcdyL2nFrsvs1WR1F9TIu76pG/5+cyKBrlq0Ht\nt01H0eYxxKM+XANeszqK6mN6NUztHQHTWecJLlWNVazweJhe36DzUdOVcRBpmIir+GXEudvqNKoP\nactdJeyv7/0VhzFc1KxdMuksUn88ILgGvGF1FNWHtLirhDSFm5i/eT7ntPgpicWtjqN6wUQLiDYf\nhbNwJcGodq9lKi3uKiHzN80nGA1yRVOz1VFUEsxpeRGxB1l8/3CYVdD2pTKKFnfVrWg8ypyNc5hw\nyARGhSNWx1FJML61lcNbwzyVn6eDIjOUFnfVrZc+eolP/J9wxZFXWB1FJYkA32xqZpPLxRtuXT8i\nE2lxV9168t0nKcst49SyU62OopLoXL+f4miMJwryrY6i+oAOhVT7VT5jMTb3dnzD3ia083yG37p0\nn6V990eXJEgPOQauaGrm/qJC3nc5Gdn5hK764Wc19kc0lQTaclcH5Cp+GRNzE2mcYHUU1QcuaW7G\nG49r6z0DaXFX+2VzfYozfwPh+hMhrv2ymaggbri4uYWlPi87WnZYHUclkRZ3tV+u4n9h4k4idV+x\nOorqQ1c2NiO0XVtRmSOh4i4ik0TkfRHZLCIzurj/ZBFZLSJREbk4+TFVfxNnHe6Ct7iyeTdbnddR\n5b5c+9Iz1KBYjIqWAHM3zaUh1GB1HJUk3RZ3EbEDfwAqgCOBqSJyZKfTPgKuAXTzzAzhKnoVAa5u\n1ElL2eDaxiaC0SB/fvfPVkdRSZLIaJmJwGZjzBYAEXkauAB4d88Jxpiq9vt0XnoGEHszzsKVnN/i\nZ1BM12zPBl+KRDjrsLN46r2nuPqoqynI6XrGaudF5jovLqdSRyLdMkOA7R2Oq9tvUxnKVfwqSIxv\nNTZZHUX1o++N/R7+iF9b7xkikeIuXdx2UDOWRWSaiKwSkVW1tbUH8xSqj4mjCeeA14g2jmNYJGp1\nHNWPRgwYwZmHncmcjXNobNXx7OkukeJeDQztcFwGHNSYKWPMI8aYCcaYCaWlpQfzFKqPuYqXg8Rp\n3XWG1VFUPyufsZjnlh+FP+LXkTMZIJHivhIYISLDRMQFXAYs6NtYygriqMc54E0iDRMwkWKr4ygL\nxFsHfdZ6t+lI6XTW7b+eMSYKTAeWARuBvxtjNojIbSIyGUBEjhORauAS4GER2dCXoVXfcJX+EwyE\nd33N6ijKQt8f+338ET+P6azVtJbQ2jLGmCXAkk63zezw/UraumtUmhLnLpwFq4nUH4+JFlodRyVZ\nT+YojBgwgsnDJzNn83Nc1tzMkKiOmEpH+rlLAZAzcBkYO+Fdp1kdRaWA6cdMx2YMvx+g/9GnKy3u\nCrtnK878dwjvPgUTy7M6jkoBg3yD+GZTM4tyfWx0Oa2Oow6CLvmb5eImTs4hi4hHCgjvPtnqOCqF\nfLuxibl5udxbVMgjO/czdFmXBU5Z2nLPcou2LMLu+ZjWmklgXFbHUSkkP274bkMTr3s8vOJJYCF/\nlVK0uGexQCTA/W/dTyxYRrRprNVxVAq6rKmZ8nCE3xYPoLWr6YwqZWlxz2KPr3+cmmANrZ+eh/4q\nqK44gVt311HtdDJbh0amFe1zz1JbGrYwe/1szvviefx1Yzmg2+Oprp0QauXsFj+PF+Qju3frBLc0\noc21LGSM4dcrfo3X4eWnE35qdRyVBm6ua8AGuA9ZZHUUlSAt7lnoHx/+g1WfruInx/6EYo+2wlT3\nDonF+EF9I468jTjy1lsdRyVAu2WyzK7gLv5n1f9wzMBjmDJiitVxVAras2Z7VacBMt9sauYu31Hk\nDHqOWGBU9UeoAAAM6klEQVQYJuazIJ1KlBb3bDGrAAP8amAJAY+HWZP+hE30g5v6THfXXJxAaMcl\neIf9npxDFhDaMbXrEzuPfddx75bQd3cWWZjr42WflxvqG/hi4RetjqPSULx1MOFdX8NZsFa7Z1Kc\nttwzVOft0Fb47NxRNIDxoRBXNOm+qOrghXediiP3XXIGzafuYxtFcd1dMxVpyz0rxJhRWkxU4Ne1\ndditjqPSnJ3QJ5cgthC/LC1GS3tq0uKeBVwlL/GWx80vd9dxaFS3zlO9F28dRGvNebzi9fBkvi42\nl4q0uGc4u3czrpKXuKC5hfNbAlbHURkkUn88Z/gD3FdUyLocXZco1Whxz2DiaMA95Gni4RJu3V1v\ndRyVcYRf7drNIdEYNw0sYZduy5dS9F8jU0kYz9A/IxIhVH0lXmOsTqQyUH7ccG9NLQ02Gz85pISI\n1YHUXlrcM5AxBvfgZ7HlfELw46nEwwOtjqQy2KhwhF/vqmON283tJUV0bkaUz1j8udFbqu9pcc9A\nD655EGfBOsI1k4j5j7A6jsoCk/wBrmtoZG5eLo/r6pEpQce5Z5g5G+fw6DuPEq6fSLhOd1ZS/Wd6\nfSPVDgf3FxVSHIsxpcVvdaSspi33DLJkyxLufPNOTj/0dFp3Xgjo7gqq/9iA22t3c0IwyK9KinjZ\n47E6UlbTlnsmmFXAUp+XW0uLGR9q5bnnT6Xb/7c7rP/ReYEopfYnkfVn7v10F98ZPJCfHFLCvZ/W\nQqgPA+kervulLfcMsNjn5eelxYxtbeV/P60Fo7vVK+v4jOGhnTUcHg5z4yGl2HPftTpSVtLinub+\n/v7f97bY/7izVoc8qpRQEDc8srOGI8JhPGV/wZG/2upIWUe7ZdKUMYYH1zzIo+88ysnBEHfX7NLC\nrlJKftzwyCc1TCw9Bc+Qv/PYO4P59tHfRkS0O6UfaMs9DQUiAX7+ys959J1HuWjERdz/qbbYVWrK\nM4bg9muJNI7j/tX3M/O1mYSifdkJr/bQ4p5mPmr6iCsqr2DZtmXcOP5G/vuE/9aPXyq1GQehHd/g\nu2O+y3Obn+OqyqvY7tC1SfuaFvc0YYxh4YcLuXTRpdQEavjj6X/k26PbP+IqlfJs/O5vwwhsv4Z3\na7dy6RcG80+vDpXsS9roSyVd9UMCdTYbt5UU8U+fl/EDx/P/vvr/GJI7ZL9P091wNaX60z6/j1HY\nvsPOTQNLufGQUs5p8XPL7noKdcOPpNOWewqLA/NzfUwpG8wrXg8/qatn9tmzD1jYlUp1Q6Mx5uzY\nyQ/qG3je5+XCIYNZ6vNi9LpRUmnLPUWtzXHx2+IBbMjJYWyolZm76jg8EqH81qWfO1cnIal04wS+\n39DE1wJBfllSzM0DS3hq6dX87LifcXTJ0VbHywha3FPMepeLhwYU8C+vh9JolN/W7OJcf0AXElAZ\naWQ4wl937GR+no8H3duYungqk8on8Z3R32Fk0Uir46U1Le79pXN/eocxvXETZ8WOFTx5SCn/9nrI\nj8W4vr6BKxub8XX6qKr96SrT2IGLm/1Mem8tswvzmbNlCUurlnKqP8A1jc2Mb239rHGjY+ETpsU9\nQb1dj7qrrpNdwV0s3rKYZz54hm1N2yjKcXFDXQNTm5rJ1f5HlWVyjeGG+kaubmziqfw85uTn8bLP\ny/BwmIub/ZzX4mdcp/dhV++rPe/VqjvO7Y/YKUuLez/bZbfxisfDkue/w8qdK4mbOONKx/H9r36f\nM/98OboTpcp2BXHD9xuauLqxmWU+L8/m5XJn8QD+p6gQj/9xos2jiTYfiYnlWh01pWlx72u2Vuzu\n7dxXUMB/PB7ea99I+NCWT/jO6O9wzrBzGF443OKQSqUerzFMafEzpcXP+y4nS3xeHvPW4R48DzNo\nPvFQGQ+0FjAxGGJcaxi3ftrdR0LFXUQmAffT1j32mDHmjk735wB/Bo4FdgOXGmOqkhs1de3pB2+y\nCVucTi6RH2BzV2P3fIQt51NEDH8y+YwLtfKjugZOCgYZGf4IWfdv4L+sDa9UGhgZjjAy3MiN9Wv4\nwOXkRa+X1z0hZhfk82hhAQ5jGBGOcFRrK07/G8RCQwhEAnidXqujW0a6G1sqInbgA+BMoBpYCUw1\nxrzb4ZwfAGOMMd8TkcuAKcaYSw/0vBMmTDCrVq3qbf5e2V8/eld9deUzFoOEEWcjNkfjPn9+xf0v\ntjqd7OowpdrE3MSChxILDiUWPJS18du0H12pJPOL8JY7h7fcObyb42KDK4dm+2fTdwZ6B3JY/mEc\nmncoh+UfxkDvQAZ6B1LiKWGgdyA+p6/Hr9lV3ejP/n0RecsYM6G78xJpuU8ENhtjtrQ/8dPABUDH\nRZovAGa1f/8s8HsREdMHsxKMMcRMrO0r3vZn3MSJxqP7fB83caImSiwe2/t9PB4nHA8TioYIRUM4\n8t9GbGGQCGKLtP8ZZuZ/3qAp3ERjayON4UaaWpvIHVnXdk4n8aiX1qhwUjDIsEiEL4ajfKvlTkyk\niI5zxHLdWtiVSjafMZwcDHFysG0xMgN8MfYgdvcOfnZ+EduatvFR00e89NFL1LfWf+7xHoeHIncR\n+a588lx5n/vyOry47C7cdvfeP+2+98E4MHEnGAdgY0vDFuw2Ozax4RAHNrFht9mxi/2zP9u/RASb\n2LBJ384hTaS4DwG2dziuBr68v3OMMVERaQSKgV3JCNnRExue4N637k3Kc3m6mOhp4k7+8/EA8nPy\nyXflU5ZbRn5RPs+8uRsT8xGPFGKi+cQjBZhoARgnczoNTzSRkqTkU0r1jAAmUkw0Usy0Mfu2ppvD\nzdQGaqkN1lITqGFXcBc1gRoaWhtoDjfTHG5mW9O2vd8HooEuX8N76Odvu+Af9/Uo5y+P/yXfGPmN\nHj2mpxIp7l3Nn+ncDE3kHERkGjCt/bBFRN5P4PW7UkIf/Mexx4Yent/hL9+e67wDnWOFPv159YLm\n6rlUzZZCudref3InkFK5PnMpl5ZcyqUHm+uwRE5KpLhXA0M7HJcBO/ZzTrWIOIACoK7zExljHgEe\nSSTYgYjIqkT6nPqb5uoZzdVzqZpNc/VMf+RKpNNnJTBCRIaJiAu4DFjQ6ZwFwNXt318MvNQX/e1K\nKaUS023Lvb0PfTqwjLahkLONMRtE5DZglTFmAfA48KSIbKatxX5ZX4ZWSil1YAmNczfGLAGWdLpt\nZofvQ8AlyY12QL3u2ukjmqtnNFfPpWo2zdUzfZ6r23HuSiml0o9u1qGUUhko7Yu7iPxURIyIpMTg\nchH5tYisE5G3ReR5EfmC1ZkARORuEXmvPdt8ESm0OhOAiFwiIhtEJC4ilo9qEJFJIvK+iGwWkRlW\n5wEQkdkiUiMi663O0pGIDBWR5SKysf3f8EdWZwIQEbeIvCkia9tz/crqTB2JiF1E1ojIor58nbQu\n7iIylLZlET6yOksHdxtjxhhjxgGLgJndPaCfvAAcbYwZQ9tyErdYnGeP9cDXgVesDtK+1MYfgArg\nSGCqiBxpbSoA/g+YZHWILkSBm4wxo4DjgetT5OfVCnzNGDMWGAdMEpHjLc7U0Y+AjX39Imld3IF7\ngZ/RxYQpqxhjmjoc+kiRbMaY540x0fbDFbTNV7CcMWajMeZgJ7Ml296lNowxYWDPUhuWMsa8Qhfz\nRqxmjPnEGLO6/ftm2gqW5Rv8mjYt7YfO9q+UeB+KSBlwLvBYX79W2hZ3EZkMfGyMWWt1ls5E5HYR\n2Q58k9RpuXd0LVBpdYgU1NVSG5YXq3QgIuXAMcAb1iZp09718TZQA7xgjEmJXMB9tDVI4339Qim9\nnruIvAgM6uKuXwC3Amf1b6I2B8pljPmHMeYXwC9E5BZgOvDfqZCr/Zxf0PZxek5/ZEo0V4pIaBkN\ntS8RyQXmAjd2+uRqGWNMDBjXfm1pvogcbYyx9JqFiJwH1Bhj3hKRU/v69VK6uBtjzujqdhEZDQwD\n1ooItHUxrBaRicaYnVbl6sJTwGL6qbh3l0tErqZt4Y3T+3MGcQ9+XlZLZKkN1YGIOGkr7HOMMfOs\nztOZMaZBRF6m7ZqF1RekvwJMFpFzADeQLyJ/McZc0RcvlpbdMsaYd4wxA40x5caYctrelOP7o7B3\nR0RGdDicDLxnVZaO2jdc+Tkw2RjT9XJ3KpGlNlQ7aWtZPQ5sNMbcY3WePUSkdM9oMBHxAGeQAu9D\nY8wtxpiy9pp1GW3LtPRJYYc0Le4p7g4RWS8i62jrNkqJ4WHA74E84IX2YZoPWR0IQESmiEg1cAKw\nWESWWZWl/YLznqU2NgJ/N8b0dJHQpBORvwKvAyNFpFpEvm11pnZfAa4Evtb+O/V2e6vUaoOB5e3v\nwZW09bn36bDDVKQzVJVSKgNpy10ppTKQFnellMpAWtyVUioDaXFXSqkMpMVdKaUykBZ3pZTKQFrc\nlVIqA2lxV0qpDPT/AbNcQ8vwJSyaAAAAAElFTkSuQmCC\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x11049de80>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Box Muller method\n",
+    "u1 = np.random.rand(1000)\n",
+    "u2 = np.random.rand(1000)\n",
+    "x = np.sqrt(-2*np.log(u1))*np.cos(2*np.pi*u2)\n",
+    "y = np.sqrt(-2*np.log(u1))*np.sin(2*np.pi*u2)\n",
+    "\n",
+    "z = np.linspace(-4,4,500)\n",
+    "\n",
+    "plt.figure()\n",
+    "plt.scatter(x, y)\n",
+    "plt.xlabel('u1')\n",
+    "plt.ylabel('u2')\n",
+    "\n",
+    "plt.figure()\n",
+    "plt.hist(x, 50, normed=True, label='n1')\n",
+    "plt.hist(y, 50, normed=True, label='n2')\n",
+    "plt.plot(z, stats.norm.pdf(z), label='Normal PDF')\n",
+    "plt.legend()\n",
+    "\n",
+    "plt.show()\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Improbable events\n",
+    "In this example, we tabulate the amplitude deviation against the probability, odds (inverse probability), and equivalent timescale (once in 10 thousand years). Modify the code and try with different distributions - especially those which look similar to the normal distribution, but carry a fatter tail."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style>\n",
+       "    .dataframe thead tr:only-child th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: left;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>|X| ($\\sigma)$</th>\n",
+       "      <th>p</th>\n",
+       "      <th>1 in</th>\n",
+       "      <th>time equivalent</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1</td>\n",
+       "      <td>3.173105e-01</td>\n",
+       "      <td>3.151487e+00</td>\n",
+       "      <td>3 days</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>2</td>\n",
+       "      <td>4.550026e-02</td>\n",
+       "      <td>2.197789e+01</td>\n",
+       "      <td>3 weeks</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>3</td>\n",
+       "      <td>2.699796e-03</td>\n",
+       "      <td>3.703983e+02</td>\n",
+       "      <td>1.0 years</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>4</td>\n",
+       "      <td>6.334248e-05</td>\n",
+       "      <td>1.578719e+04</td>\n",
+       "      <td>43.3 years</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>5</td>\n",
+       "      <td>5.733031e-07</td>\n",
+       "      <td>1.744278e+06</td>\n",
+       "      <td>4.8 millenia</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>6</td>\n",
+       "      <td>1.973175e-09</td>\n",
+       "      <td>5.067973e+08</td>\n",
+       "      <td>1.4 million years</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>7</td>\n",
+       "      <td>2.559730e-12</td>\n",
+       "      <td>3.906662e+11</td>\n",
+       "      <td>1.1 billion years</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   |X| ($\\sigma)$             p          1 in    time equivalent\n",
+       "0               1  3.173105e-01  3.151487e+00             3 days\n",
+       "1               2  4.550026e-02  2.197789e+01            3 weeks\n",
+       "2               3  2.699796e-03  3.703983e+02          1.0 years\n",
+       "3               4  6.334248e-05  1.578719e+04         43.3 years\n",
+       "4               5  5.733031e-07  1.744278e+06       4.8 millenia\n",
+       "5               6  1.973175e-09  5.067973e+08  1.4 million years\n",
+       "6               7  2.559730e-12  3.906662e+11  1.1 billion years"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from IPython.display import display\n",
+    "import pandas as pd\n",
+    "\n",
+    "def format_days(d):\n",
+    "    if d < 365:\n",
+    "        if d > 90:\n",
+    "            return '{:1.0f} months'.format(d/30)\n",
+    "        elif d > 7:\n",
+    "            return '{:1.0f} weeks'.format(d/7)\n",
+    "        else:\n",
+    "            return '{:1.0f} days'.format(d)\n",
+    "    d /= 365\n",
+    "    \n",
+    "    if d > 1e9:\n",
+    "        return '{:1.1f} billion years'.format(d*1e-9)\n",
+    "    elif d > 1e6:\n",
+    "        return '{:1.1f} million years'.format(d*1e-6)\n",
+    "    elif d > 1e3:\n",
+    "        return '{:1.1f} millenia'.format(d*1e-3)\n",
+    "    else:\n",
+    "        return '{:1.1f} years'.format(d)\n",
+    "\n",
+    "\n",
+    "z = np.linspace(0, 10, 500)\n",
+    "\n",
+    "data = []\n",
+    "for n in range(1,8):\n",
+    "    p = 2*(1-stats.norm.cdf(n))\n",
+    "    data.append([n, p, 1/p, format_days(1/p)])\n",
+    "    \n",
+    "display(pd.DataFrame(data, columns=[r'|X| ($\\sigma)$', 'p', '1 in', 'time equivalent']))\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Code to generate \"egg\" distribution"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "55.30, 56.10, 52.49, 61.32, 50.20, 61.86, 61.05, 62.20, 59.52, 60.16, 56.32, 57.61\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Generate small dataset for T-dist question\n",
+    "# True parameters:\n",
+    "sig = 3\n",
+    "mu = 58\n",
+    "n_samples = 12\n",
+    "\n",
+    "s = stats.norm.rvs(size=n_samples, loc=mu, scale=sig)\n",
+    "print(('{:.2f}, '*n_samples).format(*s)[:-2])\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.7"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/exercises/Solutions4/Solutions_4.pdf b/exercises/Solutions4/Solutions_4.pdf
new file mode 100644
index 0000000000000000000000000000000000000000..447f94551ceae29c14343d01ba42422bcecaab8a
GIT binary patch
literal 229332
zcmd42Wl&t*wl+#2KyU)V-QArIkl^mF!QI_SLV~-yySux)ySuwf!`I~9-?p>Qx%d3J
zb*s8&EtxXp88YWsO(rWSOvgme21~YmesT}X#74+SXsu@k`}s42u&ITEp&f&;g|364
zprO9CfgyvWp_Q?N2_Z8VCnqm2ti6Msp{^yY%i@T-hW%16n%7y`m)t3;1^=WLozDXT
z%&2*$fD+WBkxLUo*hogQuZh_f2Wxl7knQyE1Hnk^@cNiwPPy(!fypZ)yoRJ*=Y9r(
zo!*=prB!L8RcEV_!IXjl>kAK3!oI?=Nftz$k-`Rha|A|b97Rt84F$Jx&zC1<ug@0*
zS7CeJSHADDi1Nt{-cz0QN3(_=>kM`67S4}gD6V5M8NWdE2m&6{#(+nr!kl`V-1M9p
z$nQ*ijlb@~{a{K3KFc1*?zDoTFAvx6UE_Nt&y1VJD)R>m-aavm^sSV5e;oQC422`#
zCaORh?ZEI@lgRKsq6!J7+V@ANnUy^A<*;3df9%0!n6-L5`kLhze+bRH3JiLT4>&1y
zO$(N@JkRazUtK20B$x!*WL&khSC<J+oqKk66n%YR1f~2e8&<8H{OE`9^&qWCxVx9M
z)bPMmYO2w#;(hF0a61Ms?y#|BXo<L7Ob4IBR+p_g08d{>R(bGG*<)UxTV6w2=9Pts
z`FmIbG4<$1B9QT{W`J%_oC~soB)O)8OlvY7vx}dh>O2cJDcMB}=j1hqEmH_;i$-*n
zw53qux{QV8V@<Fr0(_5smUz`IqDBhII;XQ~^Ack80*jSUZHl^rM#PCOWM9;cLh4WA
z46Q#W*Ft27+D6zV8BK5(qG<X(m#10Ce*M^kXMwt<h%M-k4UTxRTP9I@GPh?*!>AX{
z-Rqr5&2%m+nLQjTa%?{vj2S&sSR828H*rfh{y{b*0*XpgNW^jM>$pcpc>=J0<&+(X
z49wQ^w>b}D()pRz2A^ikjFHxEQ7wzOI)eC@li`;37~G|vFR2dMYkp8Leo&?{XS=dq
zRsvNDDURAVRtl$gwaN<&b2~5PG-aAd+iB77^iE$JRxC=pAj0(#cS<69uG$ukL;HWA
z<U~w!bg*@#VJeBr>&P=uqV-XIBT}Nxaq+Hu053Cei&c3zypW~8nmL@{<67Q<ej0eM
zd7eK};l3P!>!o8=IQ9$mqIMpdJ}FUSqd<M9V~QRQVsx^!E_0qfqM>$dQ!D1^a=oUu
zAs{$MU0*GY$i<22iRPN#`Ul6ppkufq(`@@<C2c2)zE-5zc(~>GdI&S^ea2W;`xujv
zE@ZmdYF4-$r|;)ZY;m-&k*=01Y!t~~3p<s`{dMjW-V3k!LK5p`MYMl@_WIhu2_E+q
zmz|+8{zmtA^@w?Yc3KEAEA4&l2C|soM-JHgac)I(>!)a<QPQzigHPmG;18A^@xsYR
z76_JruRkhpC#c=ifV(`b%JF1|j83$eeohgtL44Qm(0l}p;CFG;L>YE@9{s$Ou~ZEV
zI`gwoU=+qO)jlx=Du2E7Aw7b9$BNKt9O3NQPP$%-SH$!~dM)4ZM6IRrz@!7I<y7M1
zyPbQq*HaZFxf+S@g5g?H--O^N38A8iS9>sNq<^H%hiZvf1#xZ%{}M(y+Ome4TYv9B
zFVK8@geo653MQ~`?nd|_-b2j1iR4U=v|>drZx1ie?;fyugCXnW<|rLrDqy@+W+$Xn
zbm^6IshpWrkd7oeBt%Rl*muortHf`K@xBA9gVd%!(bFGcyu8B;N=+?6*dbCFnpV+?
zHcs@b7<a_Sf-!%yaBN}HZqlePYrRyMJ&X~p6g8rciKJmL0DRRu=JgW(uh>(tx@bHB
zmGJ@N5E(;MxauyQX)r9mIZbEWGns?W{V?IPMQ^(4#9)6oIiygcFgiWtZPUG4V|xcX
z7FWFg{5d;vMnTB@XT3p1CW6TFY3Ma2cQ2IhU?>qT>OR+&qG2E7UZ#9<Kf$>+Ob7*@
z8XGhja)PpSu9PB|`=@tGQ$0f2F#NjDRm`cfzsT5vZx1%bzIV2t8SGR;uA6UDPYx)B
z`!07wjWj&8_2Yucgi{1$aj@7h7Wxo*rifvx!JDVyR>((TrNipuUHJ`zS49e|PF8@d
zzgH;hB6U`hpTKgf5VACV0W46VJ?A9OmB7Y@?_gRC+zH$XWhhTJ3g73;5G>GlSKG)}
zmO(-mY(lLG5i?TTdTW{-Lgn;TgX(69L^!-J<Xihrj(^A(N(aDXay57b3<B?MA`y(8
zKRSNj{VQh~%w}$ZssV<;VRiqP!UwPbOLZ31DfK$X+SvznsU4FQtbGH)NDF@$`)8^Z
zEO+%xDC)90at8qlt8aM}p6!O&0MW0%?+squE9hrcdP<9}p19?N2>B`%t{UBXT(vxb
zt|>+*LBHJ>?p?Kb`N~jX3eYr=vV3682wdWMQUeL_t{62iEV??eL^!keRMO_Q!oPon
zfS_@Ye9x1qDAFY?;8+1}<ImLJdjyY7DlGK09WRP4Cu0H^iaT>hPH!&qjX;6+VlkBF
zLUohGl^C&L<^bVqY5K1Tv+m#DBronV=kWvh5x?L#|8$kzdT~Yep`6O)eU#NqTj1eh
zw)dlfv*|6zF_CP}`mok{yRYP>R65v|rPDNe=l*JMk9^S9V5x8)?gwpH<8Uq1T1VW@
zVmNm2{I1>>@EiS<zkU7N3Sd!XS@R@lWv`j1@3f!3!!vjv(R=_FgW2o*3PY?x?Q(J5
z!>B;S{FwzT#Cfdq%K)?U-og3sN#vjrG$y)dBLm-FQ6~32K_=ZE1x^`C9SrFL{5RE~
zRPt*FwXm4FT{0FB<>VVYRS4gTK3X30)-9DwHnlkAIh>YZx|BTWZ94@ORp!U`B&;=i
zE-ybxtpHbcz0jOS&hCVa-Iq07l{`j^%CR)vh38!v%u#dv%*OMEjMnB-REgZ*Pc5a2
zcPm=Tux^<BOo#vdqk;p6nA+`8PoVnopyl@#h0sSeN0`v)vPFUX2tOIdShJ;G5n69d
z8*yJWVs;fF1A<(Blh9;hBfWkxB~H5F{!wy$8+$jik3XP&^24fhAO$_u6MQg#Wb!5j
z0|!J(R#e-&NgM2f@`$6yebsam1OxFdVk<r$6|piY1Yi4`AY*d+(nSrO7%Y5J^oXg%
zL55}gjw9Yk=^^Xo(f|<F^yB3BOd!pD;=~g2>>}R1jeAGwYuDw)kCAM(k(JVCxUEaw
z9=?Ua&)lzf=+B4vnalc^fN5!G#?n@OojgD|J0o324nUp{=R5vU<PHXYEuX>cAl<OL
z7kXi2C4s^G!2n_!L(I3rR2DHLL0IRbng^GW-MfJM3??+v%%wG*1$yUK@F%tfPCW@>
z&Gn#zTnyBZj4d)M4t6jhPiShP4O~O-xU;~%0)I*p|9O&r;}%1cdU*|c0}+kArl)rs
zuz2-+T>fGL>G$}y(<!t8C$@evh8!6hUbjF;+FswC8v$86w4QhH{g_Z2He6JXj~=+B
z!9@dcd56TGs%XC9(>Oh3U~Y<aK<GaP<#K8duU#wpHVP#i8aQ&rA}_^XbYQ_;n;D4T
z{?N{>5$rx|dPiN<RP(-%NU|kkQOInNfY)bBlHuGDhk=Oomr)n0F`c%cbIcjNQ4MMy
z##U}_X+`<rbWD5Y6Ka$K`fzi!|Ac6MQ|?!V#*@cfC0R$#D~_BxjH~kl30Kehq`+vV
zrnqIMogjC6_cgyCIOLkp(agOW5OR2Mvn#!3?r}2c3M}22o|a5(1_-9TL8kQp@*`CH
z7#*~t0=-6{s_SB6;KPsgmpx1h8M9U2+~Jd3wZ44s)Zx#Nh(Zw;p&Q<T3{I`!UZS&v
zT`<o)^YGcwGZo4!1onj=^T_cfwCNh`ycM~*8-yKnNE$+YXGrJ#t#H`c@^V$E)0r5v
zDp*qtevpZk#aU{%P^m;gM92jM?-M33SJjGfZ{DK2WxQMY6org?tF9`ElkF+DvyS=O
zVN^PRGm&Q4rE$v$LY|reIZ5y?@F8=s1JXlzx2C6Bh2I~SA-B4(6FBQ3w}X89g1kY|
zlN{{|S$pRW5cbR&BqBV)T48!_lJ;^X^7#63Z}<H(z6p4u?Fy3}fZ;ZI=&P3<w7oo)
z|Bum$6Kn5#q}L^a->`;O27gQ2L04~yJ`>kJGJZu@8$$+F89g&YeFp{wM?HtPdofF0
zV?zcNQv*=q&%(*XAZlo8Y~n!3%+AUnU~OS-r(mP2Z^$5M=wzyIC}OAU%D`{xU@vQE
zCtz)9V{K(<<p9eGdi%D0kkz%bv<Gc0{=EYs=U+3~6EeMBI9giS|5-`a&d|VA-@)3B
zkeQeFzu%64Sm0v(=k3VJinrvnBW~YCoyk7AS$KR!;E)iXpw|1$nccAGca4<<M*LM4
zK3`tM5xl1i41{tJP*W03_~2Cz?Mz=VO(a!JQg^6XiBMCh5`GXT%xcZur31pOaWu)!
z1YBBQ+X45Xb0-^dV=H$a4_|$CF@OL0^)6N50+qIF;#f(5SDF0K=z-OP;*rY<hy<jo
zQSNsc-4IgmUb&O(vR_?}8eM)88!i%xyl4zvqXH$W;&PjjZ=1NC)J9n{mU^`(yE#O#
z?e&7cUi1KHNNqKBfZZ8I)=vEw+m?^_c?2!j51Djp)2ldX#$0Zb5q!X4qwJu~=kXSl
z8#QQPcY%OkfW?bN2Cvs6wrh)i;geUp7POV>B#|)7q>wJst9GaOGwxx)!G<j{LA25S
z76qDU?}`4QN9j?4v{GY4&E7^h16gJHPp{2sqAnqJH2Uk0FUdb^?&NuGyqYVXAFn`t
zXwDDo$cP%syf(*`>NvMm2ieoo9y~{5py*GSWpDgSEQ<(GoV1*>dHLoe+FTO6GQAIy
zhI*y$dW!I-wgSC#B$Qr)?UbG?Ww*L44NJ5oB4a`>x!W?j{bN>;$h<~`@Vgk{yEOH)
z&TrQfzUg$crzoavo`qcsSxkvR5_p_<K>t(oY!rdrlz%f=oTwpFd}JB35O6!N)G;S!
zsl~kA-RiQ(2k(48?VVZkw74Js18}@vl)W1Fk)&!zX-nDZt9s&$tG&DM$9w7mnQrod
zDNpi1!R#<v$*SFBHIfF6xu0c`+|<48VX35EfHXRm^SDn~_ZsY&I-=6Cq6F(j-Ru#H
zca>8IqBRaBm`uKgB1|<>1L<kEJW87OOPp6-SD6A%z8Ahl(cGV#ZVKaAO46n`hnC+j
zMVB;BZxh4)8z=vE`-~oPW>%adU9LhwP>st?>n;!C%8_H1TY13$q9S&Lhx2MizKnHN
za5D`Hs@15lse&9e_kmB<jEcTMeC^~JJg1<y&zWsD`ATtkzV@9l3CC|0+ub4tfLBaP
zvNz`uVBI{s5yxCN=kmNce;wAnZiR=Y9kc@5EIPtVmYv>3zhsi<DtW3+LqGUG$@|}^
z1l`_wnypNJTJkW>O%zwKxXMW|qn4@|i8CrJV0AQ1Vh~pd3A3h_(puOJV$pG4xF|aA
zuFU#dAG5zk+D1U~-mm{e`tO>xX>o<ANQ*X7T-2vJJCFDG+y$qT)9o!7X*i7Pg%EFo
z;8}wE$c5xnmf)7dN*1ZWt78Fep@x&<Oew|2+bUveHr)LZN49NHm+~{zW|xKm@Dt;X
zPwT3{nf*%RtE2o%PJ#bTMGo<I3<hU<w{1eu0;Q9RU&j=BgNC2tUJv3@hEVA-)cyw&
z-)OHh@lepWv}hdm#_&i{RM4mE$KIJIP=jd|FD$4)=OYw~#VCc;A)A?2l`!Yf(SEl4
zKCU{|&cQaDTgP_JK)*e#qU~gcZB7eFEXeIuAAfP*DH8SK$dor*=}jaZj)+sal8*|a
z{a5(^yE@kL8pRcWmF_(y7_|?Rl8$k?w0KjzOy>)x;!HDHtV&g5i1&d7_&EjG9<>kh
z-FK8yGwW3&aY%7^x=9Qziq58m=qZ-1iV9je1*-(m+SEwKNeuLr?f-0X|GwmTl8I?x
zO715GsT?Y&#7RlE8jSf%A(k1%M7=yJDG!CWm#R+cq98o<NGlW;I7%oSv-Sv~|4$l6
z@=~CYs_|sFJl<y>%IK=3Km$qC+0@LVY`VTrScCm;eON`pu@0lw_)`rP5+bAy(~MT)
zBmJ!}?g6T-rhH5*&Y%8;oFL;ZMdZY3U}_dODSr62VEY2Z$W`OIQhL)l43EMVzBhAy
z=c=W%jJSgMo6?)^NT@fxa1%_c#+JGh$NnG4<h`fEZnM&tGNM?du!h#50O|Iqm7P0#
z9T5QXDY=LJv|G8ha2_#lXwV<S9#}biMz9?iu-n^cLpt>64LSp+A;KcC<E?*mFUd;`
zdlV^bsEAcFShrsI#@&B7a&<ie$WO>-_fYg4Lc9+Nwl*vNwI}3v{GsY+*$>doz#nI_
zs~@T2^3m}I(Tu#{l{!`zGdCdk2*Lj`@A$`x@)_!Ip`Y|!csdfDp^S3m;Yt0PkIDKw
zAvT+hiL%EQ{7W~X+2ZzuY&QTt&dW;~H0kK;fs0FfK@#j4>CDo_ym50Ym->NfYi0+u
z!>L=u;1~H8yfDw+qonXCtxJlNMl+;*vYeM!0wI0oLJSiIuo2>!Bo4yElv^*1#XVyy
z^Zj=%w(RE0<M8q=sfiqf*xdO`eUs#6t68&%`_EU+Ap|RK9*U3!@NX_3^kc1ifXiw!
z_5ZJl3NzzB5|#gwc*u+JGbl*u+M5&p$!Fvl{uikX>{}}HA99law@C}je@<FJXQ}^F
z)<Vd_&cX7pS&O^B;>{ZLx#J35lSQQokK$(NjR*$vF*Qug41t)iA@Jif>51OBfGWCy
zbA|8mZ!wI+v`M!9ybsZ!OoJX>%i_eyTTre(2eM65bb?NHY42&tHT7Y{+6uq|u-_d{
zm^(tk8cS^%-WzV-vmZ_1N@Efb5U>RMXr==G<rK^g`X3EpmH<p;2o?*ualjqNOjdb9
zA+<}i5?yWUasXVI7s_iQb}Z|m?dpVC4LT(Pl|?|@re0ZZp?Y3nZ^j}XyJd%EvYM3i
zOl>)9Z6VVFI$Z={;oTV*@>M5$`Gbs;lD;EB%Glt_HrB=RH@eGWk_Dxjw4IM-<#dWO
z@zqguae$nlQ%(n<wp^8YQWa`OL2zZEZGJ6~)@0&?`MUWbX4#&P7j!rPL@}sA^S}Q5
z*d2hXP!OFNW^y!JqC{|}Vt^Lv{flHIrM%|x@zEmkW5_NZdSG$qMOitJmeA~N+?UCQ
zRl@0^7vPig@%+V{jyKs$YA6ne)nv9<N=k|yG&ZfTw2F!fb&U8ew2t5J-%wCcohd2I
zhJhe5%Eu2R;~9xA(rS^M8J9-_7r7fCZak$MlkA{PKKD5j!WJ~KD*{`V2vW6<CG`73
zQ@9-u$H!&sFRrI##^RZ_K&!Rv#A;}J-powAj(B={n$H%yUJg)PwOzkFo$<ZgZ&^2A
z{9eY=#fh7D8P7%p9G921K2k5w*V}HbIIjUg=?|{+vMn1-%KEUb+#O)y$>M>ara$3Y
z-pw$*^!9jRYGnmjHaB;&#L#j*Y25~Nx;dOFYq?!qUtbU7d*L)5O%@^WWc6@7S*$_g
zxt4YLJ(|k%6d{5SBN6)fvOmeX)paUYYGbJ@4xK^!`D%=>Otay#a)^G2l})48Lao|l
zBF*~+OV4ViP>CYNVUqH7@z##tIQkXxkkp`~+H^{q_hB0n4t?4C709K7P%=Km(AI3Z
zRC|8L(Dsbt)N$H;HR`<P$wB)HntQHHhhTQ0&U$S)fu+uBWpQT4an19t<!TfFQl&<v
zf!OUB44?Pa?ud@p!wx7xa2eydo32|kEwZUjF9;jY2NuilJ{YVvyJn0Gyc`N)Xt8=d
z-`i|WE6ALjoe^MSe)wpAJt5*pX1_OzAZOQurh;qNPgG?z0xOX#o#M8i;pgP!^cyF`
zbnu0%$@wgm&-(?5?-@CD<>~H>0GOsV@<0o5PG~jCwU?~b<OBq%X8}$iG}}5D4{+Q5
z9Rf0~ipXSgZ2$K5_WE%{Se45?Wn4_lrUQ~_TH70Vlh_Pw+}s*es%A;BPWbFv*(Z`h
zX&0n&-*nara5-Q1g+Yaah7KxW@qE12ux?@NR&aK{TdXn1px0F0MM46OUi1+VSQTrx
zG?IH;_U&_Y`HMx1EpX%Eh(v7ZY;dPYOA)Mzy}Guvv}BY~Ra{o|;Wv3aT#h&R#xOK&
zN4`mYc&*aqVzWH0Mz1Fjj-i<`aG8pykG`rargftqQIPhUlS@LrK=F}stfp%cj>WqA
zp|AY-=I(SQskOr4U^4j^i(iiOX6x(A({)@doklD$=dd~~{xsn=6iaz`5Fhyq8*tR$
zG_pl-7A)(;8EhMo@U0pDqzMg?87`jl`1Dltw3?clnv6tmkX`oEcD?<(f;3l7UESSz
z7t)xHac?jdf%kL8^7mL&{=7r`R<c#BxCf8f<p%pS_tS<Y>oy+ml=@$x%in!D@0RV`
z9(I##^m_u)7@E)d>npx>{16I4uR+i`&^udgX#?Ia&8X<K7>}in<MKQmS7pF7Zv4gp
znLmHN7DyF!D^42yWl2_zrZzU`-Ka9~d>Ktm?jWZ)l_%qU)^Z0jy0E=8Z{BCv28ic0
zWHRfPo7rNus#>d+Mzfj1G2SQIYh@zlQNGvbDQN&*%culHi^D|^#+v(C3p5fw{054=
z(cWmvfit_GD$MI>631CVUMlQbrNKZXG``EXoxzxcv}VJhSdKLh7TSesQ-zY3Z+$Tt
z0XckWj=KrQ(y5=ht{DhCuP2hZ>_a&>{l~X7U3X##yzX_4Myf`+X#^)Hb0q34mu5ls
z(FsHG4Duv!81x=bCv{=mhxsc_&VJW08K|?#ApGu*Bu%ZMd5PZN1+$D&I{ApTLC{gy
zdr*{g6J>+UiXnU8Hv&#+!)_wrw4TI)LkA?B-VmHL*WCmaZP%Ruid4#btpghUY=$hk
zE<TMmh=GrthQP-gT@JIgR!@*o4)GFCt*v<vGxaUIY=vadJ#GduIGrrs+^u=*IL;^y
z#nQpO;_-QTI6FHx^0|RzG!-HP+y{9@9v&VgopK@%>jd*uug8NqZTFJ}<Fpla4^1w6
zn><Sw@NWGup4o*3ET@h)(St1ZsI-Y*Q09K}a%ZTk4FvOptwCK3+T(};JQKGX9^S}^
zhCrd-ldj9T1U_1KR}=fnSAgYWwG1DS)m_ZL@H!*_m16extS${wQ0gtdp=1k{N+=zy
zL<3|Qwm>{%Vv2fP`vNdejq0m*yd~JK`0Wvgj7!Uw&9IPiq!L+8`$F-!sth9&%Qr@6
z37s!Re0Fta6qmoifFguKuB4n_;9b@4-!;4RG_n**t=@U-fOKX!`yT_M4Id~gVJY~W
z15b<%p-j<m*>tltfp3qD`HG&UB3G%MI(#N}r90F-A>%NyV1wD6#mC_2QbZc@(|yf$
ze{pv~PTTUq{UaQ&^Hq;KN-H>9Qr3Gsbye{oerbc~#{+Blmj=|mACC~mjHRxudk}2r
z7Vu#IWLKFhh|(~~EDQ8f7P5bWErplu@KR1_`nFSMv99)qBtB=@(G!{$yQJ^NH`)Ev
z$;m)kpZup3J^OJu{61SwEKH>~g^A@>y{K9+>LcgDv?U62AMESD1>Zjr_^t6-e)n(9
zHX`U5%J4ETAv?%PWZlD#&M@=Cfo7XhHqZ_+cy|~NKpW^*kY>bkLI3U_l-y`8@X~pL
zG^VV3m;`{lYo3I&hg!jOL4PNtyg@F%^HBUeOYYf4kIA^`l{dfKnmjJXFaRQB<{FT*
zcWbyw?k-lb1$bk7XY2%(^$Wt|f0WN3A%U9c>5TW$vgeII(%0Vop%mn{CKKJY^RrUJ
z^S$yiT{B2PvQh>SyU{%UgJz05uqJIFbx-8PK93w;UC{syeY0jD<?;#pCpt-b|A@v0
z`e#Ws!_#g9#o18fGQRq~v|lf+x3w8<_OT&6hw);sz#n^IupA;0a7y<&L^pgqOLf*V
zE~n944Aql}`O2NMhYeRD&pW^{&BV6PwAaWg8%9)i9k3L+{WS>~>K2^@UkriL4_Ew#
z2(mxD{wY!WgOUH63jJLhUnE!v@De3o9OaTuGlw4W2&IA2T=q}QLpaQJW%7_^9}|M%
zW(#d-Fd7c1m*u4QvtI#viQTV}KVe$GmB7~7MI@DWo{$KOc_n_*{2==iSS%jGY1}E=
z@F-`1W;L-aHkRDKM#Ume4-U$yO5y<rC6NKCi4r;TKp>D^6_{TR+ehX+2p=*0spK)_
z6b^^>HC_HX3LI7P7fF8k)~V^t@W~MVdK|Dmc;H+wJ>V(ja_<%fI~@x62ARKU<ImNf
za1zd0S~lFaqf?J@{TIx>Jae@Qd_rNogaN(<9J9)_nC1r0FU9tGnGK#w@DD*T&qW{Z
zt}$)ZBw#RY514S}+M*6L?M~%8ev!rYr5z>AX@CoB7TP#8C@hh(pG!t4#rxa!wX+B*
z>sE55L3S6f73?$m=@Yl-Kc)ok-l8Q{L&~bPDZDK6szFSH@bX`P{$-`-^l`92v9PUe
z?Ken7v49B}jiPmpG|&5@RS<j^mFLO)j*Z{_T|Z!KdeXLXkYi`@)>)m@nrq8c##TB@
zuR4K@#$w>L2~!W$%g2{vJ9Rx<4qomFVrmxZ%A=92OE~%D&Xrl&f!c`aiNIpJ$L-_s
zf#p(#p{Pdlp9oMTsem3mN@L!}uR$MWL3)Z=9<a8#3}dRps>Xx?QL()(&DCectfs)|
z05&+1l8iin7^fGZc(w%~4Tf;C%Tv@(Xk$@wllr`u@jfl;{|0lAfT=gQG0}#+A-+p+
zfK)q_^2i+U^;eQsm$BCT7<eYR^B+ItvuL($YsiwVRDY1hxAl6op6XpFz_JrcwSrJk
zPCO_3A!S}0pmj=`7C}p0+-XN)wiu{>ijUqX><2ri5gn4WHTY$E8xgr?aTt!S!mecT
zxcitz#6y($Vh-(Ex74WEMkJw))|gdl$y?jUwL3;57S#zPq{9o{(oj2gvjc<#doE{<
z6$ytf91e=MOmBD<#f{7mvD<6^A5o-%C)zv3v;k%}%-TI$U}&MnzW{ob-$_>4Bv#94
zjDuTXi)<vl^q(kS5i<}K3uCYNdyp9mz4F!<ijEDaY&Vw&Xwwx1NJxC@|F}I-|6XtW
zmfr;B{$tj(K<&YO8Sg?6;tM-ZE?`n>O~(8Y5P~j8*|%jALG!}X;06JU_lM&8DSSY;
zRf_eFVQ1UTbcBf|^YX?>N~*thq&H~qv_;UAH}-4lj-kx^Gy2e7Q9v`sp$8q&h&-qc
zcxblfX#0V>ZYh=iK0^}mKx9rtEzKc79^f4+>ZD$N-vCjKeWn8SxAg0e(SVwl6YqDe
zifM%ZVI$4-kB#(y|BVk5>)ZbIAK&<tsA^a*v48SfD;w9?Au@8sP;194cfwoDb7U-k
zXN%lliwhS{)+aBI(e?5aDkx5b;2fLNjv*gCnisYw&g=p&>a!v6!@m;)x=8E%=(EdH
z0OWg;5H^_Iq%Xpvmz44Hq3@MDJUnrF`<5I$o0FDD+TOQ~Km@zoJzY7UU7p)OMXZ15
z82c%RrwaC=Lyd`FuKO2-GWm>WjIvtg2c3CA_!obm`3F}LItqRshk}npyagP2*>3tz
z-6WyJ9X$IUw-Cqb)}6m<A@(9-1mn$+9@4y_t9U2;Ly^0VBZW(=czQmWdX*edDT@jY
zqAq>%)6%5tY*v><kHUZ-M}MBvw78=Un=OL;iZTSnxS~m(xenxi&^y{X|3bL$+ik(F
z+@%%InO`mAK-K-}rhAo5PoWT*Q=O3vo_uY2-gstzJr4XMQ5m9_2(1Kuq({q#lAAoO
z+(6`6Gg>*ZkK`SS3W7vV?voZ&obo%h_p`R|H$?6kg^-oy*$(2wL(97b-zO8%e@PU=
zr21gq-)GHtW^-Qgp|!KGJ3p;D+~^yYG*vgW<?3Alr(|xlkZx<423jizLI=nA2I(*c
zQNro>@q{MM;@SW#wY;4}Mk2X*#ejF3xvmuwTg6Q}c#}H{G3X6gYCW}8)yTr1>4QFN
z8?7OTHm`AX^)UT9mzqU?Y+}*{WGv3qTkYz9u*EA4Y!<)D+br-k|K8Os4VG}wWWddm
zG7<Ns`j&^D2ts^o2u{2wjI2G5a$8(xv#XYJFflq@SCd07zGoyG|JcMz7$d|fqYL{4
z7SBtfs03><$tXrGpRyQsihovJa@=4L6J`%ps<YE4R>>oOn1oI8=Lhk#exDxW_xe*I
zS2dnv@SinR>-j1>iKo>|C_;F&0O(M^&YY8Qvb4WcwK#!uO5&$Y?r}<74r-ol2{c3K
za7EwB>DQwQoXjQR@5+6YkJQn}7ugg@E=jGdzfc#VMGz7=avpL?FfvtY$|@A=jcEYZ
znXZln$0P5$P5Gk2lU|s?nQg235(GU<-PkoTc^IxK$D=PdU%0q*qY#!rEIBLG^TvNr
zlNy`P3@8dJMDZn|J3uS6BYR=4g4VG9ZWFoG&z!oiIzIkBC3lP+b552{*CMK&4vqX_
zFtpFoCXswZ#S%qyP$!vqW!gv~JLz=c6FlrkN#~t?79UvRE)DC*7yj?;k5}FEub-`G
zlAi8YnubjA!3#?nQ#HMiuqa0dGj4?8vN5li`WT=V&5kl>#WG(%K_0=L;)l`Z($DWV
z2*Jzsc#d<=X)$q7$@xmjuknPWBtft@4QJ?|`G+_Xfe-0RRwF&yhxMTSw8)g{SJjtc
zv-nu86FFqYqpEh&sIg)7Yg$AD)>PbsNp%e#fek>0QPQ_r8YC-H?D4rB|Gl_9+p-xr
zdXTE7l;4MZ*W^b#DM*6h>=kM4A;GTHuc?&P69bu72RKnpcl8H?8>RCdle*=Tn8h(l
zg7ln|?I{dfjSEI@>tzm@?e8tV-3OGW>IAv8h$|g~U$A5op33kPEGfxgk@?FVS{8>M
zMI6ZU8|wYgPNLVU*nml2>1{IcMDZEHDa9;yyMn&EFW7tcK0`LV2Sznfg(ovaf^-|^
z3G!2U&-Shv`Zt^00c7-(gYYrEe4#~qB8(71rpNjKlWefjt`oVBx?~~sldYO18|>uV
zJuSF_@VrncC>8m;qDK|0L=&;{Di$uH)o8Oq0zjm0%?jF)A<yO?>71q3A}T@{SUPfO
zS`7dV&)#!W^W-1^UJLJ8X8%_*Peeh>`uiwcY!9tyPMhj6`tQuDjDx7eTEW%S_D8)y
zGr+vi5_bE{(J>+SUX9P@^J>SMFpJ*p1T(mZ!XTdx@Nvo;VWU3iKR9#Ne>n61#hE7|
zGaK{&Z_2kE%>VK=>EFw@Nc`LFA6PZhVJgKH2xCwkVk;7U^itE@5YLB4$McCJT+0@n
zl@<(Z8eI`!QHfXC#T)I0Q5jW0k+YkMcR{U4_FR4uqOJjyc2)76+P~PYVDQDQUfm!C
zFRebEuMZigo!u_p(Yz<}MdtqiYEUR`zl=v!2>(2*!)5XLdo2nY(`&bJ{Zf8!&b=<X
zdZc_=ftT7~i28hH9Xc)1Wp{rlgnw9!o>e*}0$9vc%(MdDit=2c{+sgSsJUmGGL9YZ
zVm!~y%OcasI!%W+m3?f#R1!sZz%WZD*NNSZZ}~676@pKn{6Do@F}zg6Q+OSE5J?+x
zUMTpD4g?h@EO*A7g;^SVYURw=TjSh|p9@G!ak{fUWh2{1!G<tM>M*nN9p_(&oX_C0
z<#`}C+x}|sQaKxpIO&b$P5KRK=@ynn&Y`xBdfq{?{!^wMIt|3vI|2`I-uw4&Rh+fg
z`_r5PmB3%3_>Q<9WcTu>gAY;J3@G)NGwTv3ka>6Feas+6C32{WkVnQ+z;qB=6%)-+
zO?SjNc+)F({Va`7e#Im^ecAx^m^iw=M?Y7>V_T6>AZ~Gh-}A7lZ2u3d!u(bCc^7)N
zs){;*L)<8=1E~YkI;{%#Kvb8pMg{<JnRD2`eT-X+XLv8KPe+mCeVtd-q4JY~ZPsWM
z4`_UIY4%lr1ZinBc=+?h?95&LWz4!nNKO7BMd_Kw6WYC5Xd-A>UU!8tlRNMKz&*jU
zPp`9eanI{C=^^Z2^KUv+*zM4C#TAm+uG4hKVViqOryi<mL$qRh6jaPE<k1=Kog@BP
zT>7rZt3E%K<`3yXR&^B9q!-M3LLyRky^BFcNx&3_g{y`E@P83Fi1oiT@dhqv*4FD_
zHl}<zjknpu>V8<Aaf7!Kdo*xgKG*Yccc}P+_<iB#toY!e1QJ8Z_LF5ITv~79rRCm|
zpP<3q(oqA5?;&eQG=BXhIj8C9)C#CKZr`e?Am#pjt-opgP3o=g`gApt?@a;ie}vgt
zx}=A#X9>dPoyYvddgHtGq7D$tv(+KC4IH(2;tF*HI%dT+Fc<T?7tJt>I!mGVjq%3C
z1#g{xde`*KkZW|P*iSoH7w;s#oHCzn=MPAs|EIM<hyzuL`C8pg&Gbz7Z#D(q77|w=
z@dm`zdxPDZor8+EUuWmdL6pQvD2%3k;vI=8gIddCW%HQJ-aSP!DK1!Zg2GRHz{|rE
z0)dqJ66eY(G;uIA5rp0yGCnw_1LyPqEbPD4EuX)<Y5JxJTZ;+F12ly`ori%Q@qOVr
z54O20)Vb1zJL_z_ir?9U>Jvs<FZR-?R>;|fp7=Q$K3fikf;urjgOu6(v?SX9BGx~w
z{aFObz~inrk<xYM<<aZSW<qLo)3ST<J*E*`Q9KH`_3XTko)O<{Okb+#N!6pe4))K@
zh~`oE+7z(7;0HKU_JJ}Be)(;;xSPRX3L7c?W19t_PP!$7;@lndfLGA07-20DTLb8i
z=fNhB-Md)nvGk(_+`}9Gf*8{9HHpf30hzzQ1Vz)Ey=b#wi1@7jjD=R^m|b9PY?E$^
z;Se`-q?IuC^uklrz%`SkO>9qGQ-Rrmz9)&6S<pb~xO9@OOV0*BP^B&|F*9}_H`B3n
zq9$^LZ6`xl!m+fQ&G7qZj!VS%Bo*32tmoY8+4wGj=OBWK@b&RFl(|nQw_7SDjb)TY
zddD?pQ*3K~&vp|P8OQS3$QW4;h!dCGhiliKeVB5y_xBYXqlcOS=HeBtp!tfa>?eaN
z^+x?-*^)fdP~1yjzdtlNybN;uW>v#G>)n3(+$GC#+TR%Zz^$^GSgE}x-`roQsd45%
zX76kB0^@)%$!&b}Y>dJ2I<@6Y@%VYYqtWFe`#?8^##YKz7}Q(hE@ln8`nGUj4`!pK
z)R%x!#)O9LMyuFfRG>&T-sl<_SCBB1{R_^Mrh#k6GezP8{Z-as{GwAjje_(TnMgzB
zdWn7iy4op4fLer;wyfrGzH~@H!{~OhE;31hce>%zpWp#%T6YOzm5+J;Lx}#T|5W@B
zg-hnQNdCVpT!Mo5|M!JUHV)Q**%_X!z_>`>z4Tq2Nh(WpN2*4Jlh=Pxudzp!cs2Q1
zJDV9apDAv8B1fkr7(J=3u2>|5HdAPpCQ7G7HfI;4KBy4bt2~V^Vem9oHo-b#gu>Eh
zV6<9hoa)+ZP`Y%*LqX8CmSFFtBVcA$2<9U!EDi=b8v>nsfsP$O7ocNZ&;{tU0(9Zi
zWi$KWy}Prclp~H7h&bQU;(oVl$KX4=%=8OX;c}lR^I2iRgdeT~`$YU_z~4uLT@u%w
zO|W>O#$FWNORj|{ctzjfQNt0AZKX)cMcypk7jHzqNT2Gg?CI!bE;o|-Vt?z~O1Y*S
z;1QYhHY=$Yt22%I{8C@vC_WImc|uoXK132G`V(rhxuL$koZ%f{Amu4nzE&;Dlrs7S
zi;u7EGZ3iMYt?_>H>fhoi$A$^CG8R5dtZ5>V&>5@V7w!K1EBz4W6iMAIDjhSAB74)
zE8kllFnk})<Tj}Ts+hfgeOXk<p>AEOm*{mCUu|6_JUu54TQdN<snts2yNUirj45Zl
zIR7>WewzSTNR_UAw4nJ}B=q*>Ep0^9OhKCC(RN9l;%W0sqhvDW#)f1oSkf?_JwK^u
zZ4QeqGmDU99R1-(9GZ_GnGO7hlPIc;C6;QB_0!9lb|cBvy2$h^w<DP$A9&ohdJnI!
z;&jBt1FOv`fKNjqILAiqM!f*FEQ&l;s(Cmk<`ggKA%!`_DL-KO=Vu_9u}ayi%>MaG
z>(^Xo8R6fRL*F|As}+vUDO#EQZq6rMJOd`r+fn|j?ycLM1U}0X8{WKZ+|7)}_TRb!
zlF&z=AL$xAle7@S0m>L>%B;@^{%Rxcj=c41>NSx=aSKOa4f>7+P+LnQZt;^MUe9t`
z%N~!c0DMAc2mjJpMRu!b`@L*67A}u?Po!{w`g`c1TFc02QaX*n2$Ewj&~lm5cngh6
z@KBDWd;4;sAfQcTx;ZX1t3^gBT|ZyKL4$B-B3G3gnCko9k)-Dr94-iaSwgbm7W0IA
zV-hBmck*D{q4Xix1BFVJ)@h9y*QZ*cP+yRY{T3IGPq!mWEwvLbMwZf8_Zdr9kROhW
zR8F_S`|)VhweqBPiuRz-G;i$HUUR~n(h0$?Xz`3*o)+TUg;Ck*%$)29W<baLc=8Cn
zx+fyf^1aS47*o_bToPZw>_q&Mtc0E%Q^WNb>F@OtHT1wC-xc>!dp>ouYuxq3(DF3{
z{gLJ*J$QDtBBXI~eNHc=Qy*`0UV5AaC`M*|9<4bJR)Kk#YL&h}At>Wrm3vv4STZbD
zj(2(LR7z<WT1cT0Z9Kpr!vS|vWKTt+ffJ@CesY@!R4r~5yhPs+Vw=4vEZ3vhf-hGX
zs?#;;mF0P#Wp4!W0m{3>8<g*0<xb<dx+)dhWJ?xn6zIUDGW6YaVF|$&?fQmWJ@uT8
zU8DO&9mm{M<GCjkYH9mmzfqx|E*YyBdn~UQ_P{o{DLr0iKqID`HtTz>@m5UP51W+)
zW3g=S#2I#gW3l>oL6)DoK;OaR7tccB@ThwMWAs2eK+iqc&J@yP6>swzt_6t*Lq{ip
zOCd#HyFUEwHGOV}^wBJ{*(aV?ALzbDorR%B<_wvc)QKgpd7R7Xs}=1y__du1W}<eN
zKK0GdLqfItcpjI1rF=cI2p~`xERe`_KSD{(SI+}pkP1O3!ra-|!TL)Gwi41Fj#I>M
zPZE=9wM@06tnj>Ec(=Q?|2%45${)Iz<c;DOb(U!sV0%+t(<uGc65?f(lWu>gOaT-K
zUnoZ^#!|9J@0w<nTU-LoK-7{esNV)S({Z!8rDkXUAz(vXZmfW=3p<&MmcZl7+eA?_
zHLun(IoQlr$?92esQlS+zNo)pcM#cpi`@f)juc`HQuMh^0*4GDem1sG0__w7YaRz)
zCysT0p%Tc<)hWAP&=a5>LY*(C8CZdN1&H;`$_dQWZnai1GeumfKG1k){;n;=%)dfO
zgvWf|nTeHuBq3#03`E<?2fyT6WfIBtXL|10DBRyBJ!>YhwPm)%_WN|6zLoOKBqUQ?
z#i((Bkw`F-RMgtbVwCTt@$Mb2s!xqwUoz^jUKWu5iilc#wj^q!YiEdotK8>#FEjzj
zKgDZgb8c-<tsD)5YA}o$5UN~a4Yu9R4CJ>zJm0hVji?k=3?hOmKp(Du%*D?u!*zEz
zSNg)?kTeKmsQvpiME{Zt^uzVE0tyOoSu6EisT@4WrQ^kunD-^AE2V0`uGIdrd=jqx
zFm%t0XARa1n6=CISYJ$6)^(qK(BzT%BUr4CF*2N#UMpbun{kt4d~RMfpKkz8oP_n_
z<zj;*J<$0(WfX&fNB2rkfEq3X9E83)t(7c(ZI}Dyicr6{tUsOhYSi&52{?C~avT;x
zHZKLTWDfP4B9|@swiTVfP!(rA&M}kNirLirH&i7`nt$AlOlIADGK>Z%bY|t!D(svT
zS#ky-lRzW>HO4CHbZ#YRAYblJhX&>Dt&?0HgDv42R-`Ydvfw<trFXvSiN@QaV$!+T
zgwE*#nNdN4!iBe9*9y=L0x;JH%W8@2O6D28eG2fL%tP03j>|?Fu6l)&!wHkw0nHD&
zlF6bx?Jg8`;}K6ca8lkB2!<naTfLJdN$PMn$7+G^>?quKxBb88uDhaO6)1ahjDXCI
zf?yw{>z5Z0thk$+z4>~;8m`hO^(l{V=|*~jFbe9Ky_%z`b6ORQrD6RH61+07G?qE7
z+GMX1`1fX14Cfggv2>f9rXjYaZwDfu7%5Kl7k(pF-fadkJC|Z#e!h+8oSK0cu6p4T
z9l)gLx`7)pz5j{Z&^UN~V7HGP_wmQYsAnc901=rCM>I?+7amckCBAft)NyKF=4wm?
z^JPq-<2p1$6tQ6D)*c^uULE>qnNvaa<C#>J>D@4xzek$Cac#hf%YS=>g7)0Z#OAuq
zu1o?tXV=;kk9}RA2u(q<Ds<g=KLbi2q;@$DoJLw)x?}Gef|0ioSsPBZJ*2DfM`TW{
zZS|1;bcyA=ybq8^BdPURkM1<{1*t(OGivA>y`@NtcLmrkTnH)+B?_uoUh1T&ceUwg
z({7-@jiZ}3^!04E*224A8>9R~Uz^^IBOCwY*^B1p<{$EyNw!@D%nF+?VRPPS0l6wq
z*|sc?>xk!jlE0{9J0Q=I81nz9Df|Jn$g&MhZsW4W0<n&_6x)~6FLCEK1<F#wA@V5{
zpahD$zFt%@8kdVW2txsWo&bb~k!#)|B=s+Sp(wb;vEFA33gXe}_%{9*(!7tqIwL0&
zv2~jBV|Af$%~B1$+GM%KRJDPRlSOgEiDYd+VW#yj4fbn_gQl$ShvJ!qJd=36KF9d*
zR2b~#CO;bIv>oj2iS%6M+8JW;YSt(cuuC4!WZ{2nj@sf%FQ!PHa&dQ8xcH0%P;PFn
z4@O30U1h4$G07dXA6$t1%rj%?`xAOdMMumJaT?+_8IV9Og_ZO%i9oDBO%m@VIm$Cr
z;z{)EtL0NRDHpJ1$m9$ffO{6d2J(5YFtnY~Y*XsKblQN21mMKbVFX*bt7e%6PAh^%
z{Z!fQ`N<Fuij_P<IAFZ<Tl{3Db;8n|POz2~6G&cBKX)MJX3Z9y?;6GDyz<^GY9M%h
zPXWHyRwGsx7BpmA6XwiSs<D=@vE^XEQxO7$2({_*se;HI3eTrhduE9%pXKl9Fr%eW
z>PY!+4|N!~rGHJc+x|NV92@>g_g6AN<V`nGa~gNfQqs{(`jn6$B-#}!_WVKs4D}HV
z-JblJH({;5=>D<rNhm6+00fVHo23~oI-z>+w&pwhH|1+_JIBtJ%$WINFv!FI;U?U9
zH6n?_a9b2)x6qB#`9xVFMP7#*wX$!odsxR{KZJjW*crupd#(P7iuh`8s6yZD!MPn>
zHCM+(8c#4oEQ!6aWWU;UGRse~Oq%Cg(N>DbHJstW&JR}e*iUqYMjF1n7H-W^_+$-x
z{s(#RHn7?ac7<8CF7@1f&z*475bVjIJR--!-N-PI=<(KcvMh~OtB~9B*YCnu4Iiyt
zX=#!yNs{#IxtpW8yiN4zf|U@l8}FNEYNve+I`?2$yx0p*?$vbJ3WGM;jbOX4fI}@q
zxvsn>{O4LMIFwGOK7i3JoRsxJAJz^yN1ka|ZJ(3$>+T7==iZ7P9sk#F5?d+7Qcv*S
zgJhADYr4}4J}8>Y!hF2dn>RsOZ5rZi-`uzGuAY9Ou>Km<PD_*^uq3E;2LASDTRXuK
z{Q8Y$3va$Ue;q^GJvQjkDAb*pL*`AJAa~#Q`K9tT#{Tuei|dcwIVqPmJ#RR9-W3-)
z?|%66D7R+cel%-%7@=w!n+K#Ggw2(}P<TA;he1~?wm>t`MC27-j;q$^*|e&JYaRlB
zFu?IMEl#~ATZW*)BjK$JA_zH^774H^7&-<qZ(7^g@H3nwRcldKE>6b;!EtDRVzRmb
z^v6XC=|5WS91x3TP${u?yAE%1E`SCBNiHXlR-3lTX@I8ePmdf4j2iUAU@R28KM;?r
zTa8R7bUo$bat_sd<4!2Be=r7xpe&Pu?3ddYK4bCq50$0e&yqH4{re47KhsiHnpJx5
z)B>;|lL6N!Rr)kXwU#!!=j8;v3o03lvp!It-v81|y&?2``UV02`@#|mkN$|y7?{+m
zGNyp^DR5aVEb=K=Onoj<6H1UW`Lw<s$%DG{dUItguaS&g7Wq@`a)UMX&VlDA_B;tC
z%voK7k~JC01<o{$y7UmTF`7Nr;S3!U-CDBrj^tdP->3HmIz5L&_^v;Gq?hkW*IHWI
zah(aYjdz+#R~i4He`aRdUD?W^2{`vehvf$4I(1=Ku_5A2+z2O(hgF6oYe0`e+#}+d
zLOE2`7_z6^oR)+G{c@NKP=@W~#Wt`{da^VWL-Xl9B>QTHb(%l`jT&$B)ID2~c0{Sx
zOlCek{q+0BK7h>^cMr3H&R><LQSexua~hQK7l^oWXRDW2W6Yk7_YY!%?{D55e6Dm%
zJtr2QHnn1SIpZP7=&;Gxgw-kq>9wO@fIUkZDW*`g?*4M%23<2sv&C{n9nMGO6&0Ys
zh**7o9$wZK8;C~6s)$ICi~lVZ(tMy=KI}|?YnGP4(qz8beXL(+W@gx1KL?+2^}Euu
za;3-EsRDdK0S#}!c~fU&J_sy5whU{=X|7##T(&)fAJWV*C_lXUk?E#E`xOdz(q-ll
zH)_o`(XSwH<R`ELE;*rac^e|55^U(n|K{qx{4&dhi8Gz9a(mcsm&l_64K@i(J>|*6
zQczGS!K87&lSc;i+lY6LDQ(GJv$Ks;1|4Ibz4hC-0l72!mdt6gUzUsgi#4C#ZzDQz
z>U2ufS}JhJI~)+R7+0FS#k`FZx+IRGWh^dvD1^vtQgLt=wN@QUb0qHYYULv&p6uBp
zE?0S8pdu(RQTM;PP!It5H^N22<%mgUsDCjrP3A}7a`B6ue*taRZ3aA3ZmYdA_>*Lz
z_2aL@R`4#1i`9Yl5q{>sd8pE69{1#~v0nu{nlD1RFfR#LoL}S_G!bKC*oq7vg>A)i
zN&C-S`j}rCCmE)hk}smsTMU(}gI9?{M*FK$NJpaz+b0|Jce?|<GD5dXtKbo1R0$H=
zz9Q0we?Ku)LRR{UZ{IiUl`;Jhi>`<U;9>Y+e0hKk!$Z#e%)Mq$L=ptbu3kxi{JM6T
z)z3{~lCTlld;!xXMQ(QQc7vUV&oBn_q1DK^2D)QW@CX65TtoU{1+kpgtyDP@3RpHR
z%*Ez;Y5m_JK>0r?`cLO)l(KB6QQG;Tv>yh3l4}9GJ5}Nlb-xa;wcHseD&#)}n^mbR
zHS1w>uRUHZ(MH0jjiZ-C>k}lvQPs+&NgBG23(zoJe|F_qO}lgRr_E=8p}+I$jv3iT
z{Du}6G!QPUHvUCR_a4OmJdwf=!vT`RMFP-wF=%-7G$*N1=xu}BBbDEN2CvXZ{S-P~
zRica3J>zWq`J;M9M{9R#SB*<Y6TJ-M;w~<qvcWB8ka6{0@c<#ycNA1Y%TS5yheT$h
zV98Wk;N!p$LlGu;1B5DrCp%Q22QJCeZNV>vz0nvh`{~D#jg86Hmil^GB0K1E>HBQY
zD>TZO-=b}=Vr_U6yb}|9fkV(pLzUci*~cwm7d055X;!gh5~k%>Kg2q<_4~9i??MO6
z(?1Ok1TI!a;w%r~wYx>87zjAtP&x}V;>V`)iA~oX5&>|AqGZJTLglM+ZBUs(lXO8M
zB`k^ZJimq;BMwgEP@#Oxz<>Ad@L1+(76qkK9_LL)ZLzVY9n3d1T6VX^#X1Z}vjgsD
z1uWyz(v#`m&2-HM)5)c#@pM67X}~=`^#%!VD$68xiyfg3lz4?$a?;JZ9!?8p9Wzk8
z-pCE%d*Qk&7AhOk3!j3;Mz3em^Rn`g`@1YhtSd|&bC`aj7(0uLa|-g%))J4A_mnc9
zi{*Blwl7haFUE8vaRwWz-|#y7Tphq9WTqMY^9FsORIAdv4HN+Ctbkmq%=yAUX9~r>
zrMta<pKt8#eL6--+o12&Hm=DF4=PG!;ZjrlW&!3z#^e8X+Voa3(+OW}%Yy=XH6Moc
zL2!r#G~MwgTOj8RDrMUl7AR6(W${Cns!zuO)Z%1Doq7ye_~1VkD`u%*E?ldskO1i}
z8p*zUFS|D@7ZfD((gB<Y6Ic#!r-MK9^b8%XW=STr>BP|%v<hCG-}hCq*!x4L8Ye6;
zOg|$*{QC8k9!()kRp%xzppw6EWhjfeE_)K8z(I>grJl&pO6RK4apY*ywruK9L@6?G
zU?hp_rRMbX4m{t*_NFHBAS_oznn5xOpdg*lt8g%u@DhI@ML{uxxp>V8jNN(qb-;BJ
z!G2WTa0S5f`|Pu_J+Y&AXq)aKomPL4n%^s)>YOe3!(|yQ%-5Y^+P)JntjSrN^jZ5k
zw7xIMJ@S*7;@F|kqz(g0Peb_#G-#u9B2=VYX{%2(f>%{X*>}p}L)S=xa<|tNN}gHZ
z&6m{Ot?EdbU89pPL_9n?io39YUcqG%3JE$9@yakI(5HoH$BhN;mKFT?z(6}#(7qO|
zn0bqY0qj$(D>~Sx_RmGgA%o1CiSZ{|*tM*k&wH*+?0Mr>(#o}*T$ITg21RoYOOMx#
zaY@q0`t}x{XWOZHPUS+wRrr+U?`T3+j@PG+{|_y10TtKtt%)`S5+t|<4NkDe-8Hxe
zZQR}6EjYm~xVu}U!QFzp1b26No!|e?z4xuTYv#^dZ+aDH6-TO0?b@}g>f7JhIjf|m
zS`5WeL<Ht<FXnF-2NR7yt8qC7Y;NWHWvg#U<mY&r5M4gtS|9l}qG<REEE1SpAUB9L
zUU;ka!*VFS8BBi_oV$s%x=v=F-d%WH8+xR*_%Od^x{@ng8yFefX@omsbaZEwuX|<v
zStPLhr{RartXn63rXy^uxC)k~{Y{&r&dOU>k)akq{OU1rYpQ@rcqa5ZX{FlxUvB?_
z=t6LdztAp(;0Vb?|9OLwr2y{y7XUl^fB3-vSA-@;uK!GEYS7TIT^2`$1TEUoqGrWA
z^Tw3$D>M(drT6#e7xfpCu9sCuH;$`3@y*&S(H4vC%Zs6(I?CAOU|A=ShDCuwg6lBL
z#(j;^1H-TBzQ_6OE(ot~U~cD*0SDikrl$jx#0OP&e0Oi_&>ZW~e#Llp#zjS4Jxi8f
z%UV1^^Iq~Zo|(RDoW{xy*hopB5UjY&&-@^gE&A@&V`+`hV80@Pc~Ur&y}U_@(of-t
zI`PukwLwV4UQ!09-nfPSJNK9rW4b5JM5xxD#n)qZyOm-no0OvCRU;t3vAS&m2)eFY
zd5Owq1rXbUzO1cHwOli_yMGjG6|JHpD#p)k6D`ac$YX6pz;rFvf*UEqtS5TgPr~+T
zvq!hm27B{r_VRSh3Rr^Bq$ZXmderu)9KutZf+RK+`Ci|<j-ektP6IjNiOG2HN>NEc
zl+CLxis-5fWCq?b<1LF+KwXN*8;~#&lKkAyC1W<dgtlC`2q(-wdI&FKkLshs&5vwC
z$VA*w<;0)ntOCgUtt_<>$vwykJN$gln%d}Y<v_=vib0<07<dpjs`w}px7iDezAha+
zX6!<9QypYkuJP^AIBdGp_mFqYA9qs{*xjDTI>52i_RYkdmj2UlG#mQYy()p0Bbiwu
z%|Jq>N0$zU2wv4`?u`gWpXj7MgWIPK{KrNG5x-@%NUk_%aVY&c3`<Up8?%r|J`t-L
zdPyeo-so*35*H^q0)wwtMYSR1Q;kUW^~=Twj<sRoZY_eYJ9`O|1Eoo@WT{khuq_ro
zvc-EUh?C8RK)5f8KipeyHqf+~(p?p3O`ChiPu@%YX>VX~iC^J2xgfRjXMV4gFaW3H
zEf=Kx1vT!r{~%QRD{f=Ow?C`)^n`|b?!M|fE+wN-m5PQYBjFtqOD)^OC+r;2A<>bk
zV>`;VH19clqm@bid<gyeIiPt%@7ka5+F8I(zf2^aYMwb211dmn%t*|mt{Khz({qCd
z<)t+`sVghz=Wp{rxz!EfR%XYD;97DQhgo}X2(JV#)D)0Zxc$C5s_FI3%rWhKTthGs
zJa3mUykgcaTGShfvF*=fwNkLe<P?(FJ$K7hWvGWOO_x}agZ&f&C$a$l>+7!#ePz1=
zPir(ZPWOw0vd}flBW1~YL{fT2%fge87rm3G95Rl>n6emjmAenW8x-e-X1VV670<^8
zACk=pX2+n$!+r;t!$uM=#L2Sl;vtCFsQG1azH4<@?fvozj@dnYgMNfeejU@k_p<fc
zTwtM*ry2Hl<e>l`pQ?&b5cZ;3ViGoDwqB`{D6fbvS4MrfObK+2gO^&|(OmWiFZ+)O
zOetS?IOt?*FH7)M2?)Z1%886Ndnv3R5=Z*h@$tMQ0w?ZEL4I8YdKB!+PEgPyN&!p_
zH<$}na_}T1fw;&b?9b@dY72*ZGI8hBn(J!BF{G8B72!oB`)LV%3zvB{`ZTD?dWxTa
zX~tW*95w6Yem-m{SFWI$-62dxek*o8*`kwgigH3TRxh;(7Hd~s>AUFzj>VHoHX$+p
z8S{{VN-jvgUH=I3sxSD^EPR?=^tJlrZJmfEy0)}!hG(#b5ZxOX;F2JOuX2AT6VBKb
zmKQ)Ez?<Y%^2>zfqm2ilHq}wS8`pRD=!^HGscFn(d8vN}8@!OXN!o(fct7#1^a$Bw
zIg!B{);gU?yNh+ps^}=-V^4X57z&n~b%rC{)EHLt7nN0T8)*VXWm8N}P1?-Z^A?=z
z+s<4#I6#FO4XAsqPOVanO$|w;6~TF$4vE{X&h?QT_E#U@@dX=4c!CqxHca$;*3HH=
zw$;6vY_ZS9OWPFN=;GeCZbl-NS{SgMUwFJ!2ZznRv`=8HoxVMfxY|Owqx~0V{QoEt
z1qbuLN22(@Ol5-9^xuv|!TLW@nIsZi34UEvIXdPgxrO*|S#ZDyH0M;8Y1OX?SrH|7
zv9}m$u~#97Nr0xVEk2&0TqL2k##IcF&>;CHjHft`)@&;6=PTPYcj-+Tp&K8PjA@aq
zdY$6!=stK6>WZwN`Hl*7c(vfnPhXvTxf79u{_B?+PKx@RwW<*TK76E0F^uNgO)-@F
zC;gL+#@J2ySNeO2wy%wHuTewgwB;>?HV905Ro;4Ia&gO0mlc6N`i)Ra=EUS=ptV&1
z!ghObmEFth#KsJrw^K5BE!k;@TX!tj{Y<#R)Z-qZ<oI;JF~Fs!VzQ<u-SN%i`2ik{
zGn8vy^gq@0&$|S1?LyLj%3i}<?RS6-;}7~XsYkI=zuxU+rd@k{JRbSxVl{PZAGKgp
z=wjUqDscZI8A|<bSK4S?$1IwDWUkGj`-lnKuyT;Mg`ug!{iWCu-LW!^k!q9)k_HGI
z|8;<O9EGHR{{L4N{;Fv9Ih2Qa`|*Qg@UA)E&+ak8hLkc^6eF3YjDud#PEA2E?+UIr
zBkPmHw^V#h2N4`#=ALKe<chff@ol>g9OS9Fw6V>l4UN%nL=*D6<H*ft`r~Y9f1C9`
zTK2bj`v0VVTKcaneJ}5jZS-+x09fj(#m2Y06m=J24CBKz8G+VJnyD&^={43%6j<0%
zT2HIW_K~Ga$IN+`8Y+I_baChowDXp;dKz0ho26Izn|C_PZj~+gza!jw;;Z`&b>v-(
zu^*b~Olp>|O4<*KqqAwc<?4<fA-%^HEz+36^|z&J+hZLu3oXfK;3Y>f`~mDyFeT~#
zPLvSAzEwHjP@|N3LnJ6Kd!jpiH_o^l{)0Zb)TbL%0Y0d8$Ai?u@T$31`eMXT&wRDF
z<jXq-QJ1UcmZ2XIb+LREzngq#pZo5=(|6`1yDqwI>k$wE-dozfINx~R?v%2wYbMc0
zM<9%1rJXglie=s~Ld2S}=(wwe0Q=;?)ktT0Wm<Lesr9kzETxQB&B+T*S9u&v`X8G4
z8r1)merI`^(I*3%IW%T^+-Z26|3Dv++EF}9>&UvBQiXaIa7dxm&ioalB9f?+r=o1(
zsxVTv5Z|+qJjs&b)VpA6A<I?Qz&>uBU{QdS>+!F8NhRWH$2%_58Dw#e@1+c#C6i^j
zx@xM`?8)xPd+>cn58GLa@cgNTKY`>!QPW(xoK<}ow6b5HDZ9Cg95TB9cNp5!aL+03
zS3j0J>|UJ`ADk+EOFS{Lm=}McZNRxZ#gk8+-8}-@GkTvIc!=6KX%ZW@oE&o~<;kAx
zh8T99T1>u@7qFk5)}MBE8(*2lp(?+Bk%m0D`++^wSs>+xVZ5AZe4iFCo<LiL=#(xF
zzaljx*KoO=Z)-0u8*@3Yb^38SlLIU6X=4iKuBCF+`#f6h2Bi_E@#e7*aYy{0k)bJ7
zATGfC)?qN-)+~QhL-E)~J>>iMexIN8?pBiUDZMTe@4-`YPf#_sWsdMa=&w%Yj%aS%
zN`~R92Gr?T7G3D}gr)z0*E<r_mrsbOwdl9SwJg~~ECV%^^4P8xsMg#U^!e(<E<3vh
z&i?h}+U`x>xTNIOwfLW3Yo6$qEHCi$+7lAd{HZ)V8*=futAEYU5;h^ensJsrEG*hT
zKfYt#R#to9|2*xm{P*GtF#nyM{yX%Xf1j8T{&H;p|Dosn=PBra7gvCZiIwsH9az8{
z-p+E~ok$g$yvAM^a7pilD2DEpBr2T*(pn>Ljb3#d%2(xe`AMlfQ7=nSlwq=DjxRqg
znPyI<%n^lOWM7D@)_5#OX>KqA`OY(ZTWmR=B49h7X#LMJ;1m&vZSdh~-P3O*A|hBF
z=;zsWhL7}t`wW_s^KR|T=goKgLEvR-Di#?45H$b92m=84fdCNh4&=`P>OW<+q5tg+
zB=gHWSn@)ifPjG4^OjB=8^BGZR;5!>-K*>FKw<*<ELM&26fk))d2|C80RWh>2?GBY
zg%XA)-b~8dp6U}oZp&tT?P*fwzq^@dL?*@oD4;JM$1?n~{=T)G!zum_Qgis`_^Uo=
zW+9F!(<h`DM8KEtV!n)T(A&R>QW#+bzjp8K(NP92&m!$W??7&jllJ?G>$)Pa_?2Pf
z`2(am#xY|=^st;&%LmRpF5O`j3ebkokkhc$jrdWNEWBVh5VzzjETr**flO!<lvhOa
zqbWURlVm5qv59jKok+p=BC95UW)!N1%$hl^-hl<d06&9aW0Mu3QQ<0T&dZ0`X;{d^
zJ%i_qE2#DePwVbK92wF;d)0DkZ2Gb49?6Ow<>(BV@BA$69jVZzjYE|e-fGh;Cq6s1
zwlHxXegke`3ch2CttM9^Pd-p=h3C@#78?=yH5x{*4IqXpZ6cv7p;fz>2a#BqZCeeY
zTs{j`jI`g;33DcXea%}#T2%7a6T&+9jawVmGzm){x>78I6;>{;u%91_BN_NyuQ~-h
zuV#Ip+^Q&CVPD6#9!;}+PCT<b_xjL0!LEd&@xFJ66K@ezK;PY(5`@nk`zYhlJW=2O
z2(BHu_?WuKXGSM_yUhGB;(p2zc%GvAocXNwxvgq?u%H`^<~-Kj4Q)e|&0M+0C6Qk@
za+DJ?ep8pEc1CdSdq$)}oZJUU9wyMnilz&3qHL{2d))3Z&5zRgadnV<_JCr5@xqrz
z)O4h^DM7034#SFy{Y?+M_p2aT_K~UNpr6DQaYt^FLA5LWH{RWTIr7ZmaCn^W?oHD3
zXpm4g^2AExMr?K{s?tC`Y(eUn08UPsgCJZ2MH@OqcpTlnQ8=x>&hb9i?y$=NJ38bz
zb{)J1XIv!07#IC*bm<`$RXoH9QTEti;bUJRCc!}0!RG0flw|a>W%Ap)83${!bQ#lD
znSv~LQ9h@Sv4~;GuJS0!JpCq28HD?Z?3yxOa>*y~led#QU;RQal^*UDPqv{S0ckKT
zOrzp214QS~Q*|8O7YVBtCILGbD6+3zca^&92oqR`$lq5b0w6JJJMOl8U%F_#g}EF^
zy3-taKE8K%m^`xgxw>(d-E+b?7{&jYqwDhoF4Ach$vwj%x^|KqhXr6l9Yh}}4g{J$
z?LYM8=JW~1Z1;y$MXC)(R^NUtd_RtIo5VwzIHa=-H-=cYYn0;3BXV)Q`XX`?z5Mg%
zrpoR<Jw~NQlQPBwSq$Q&QjClG^@qKCDOx$Ep)JH937d4neS3A;9cHrQ!A1KI_Fut(
z5ET@$Z)-~31GM>WY#A<S(~{SpV;#8TAsYXSNrERPAmxtpgLRU9QDWvV(GM~<3crLg
z6{0!!=ePxCq?s^RHMMejRZG%dJCqhd-=)cJf$>7q7~=W&AU+d$U5+Rn(hTj@Xo&I2
z?){To0^hQnx}xWfU9~K>)wzuaBF%VsZgn!=O~ihezG}hEqF}%jv1rz8JDPJ)s>tfZ
zYQqcLIuZ>Sgyo!t$5BGHDh!+%u2|idw>hm%51rhSF^&$^H9A{D?vt#jrVnK?L=Nn5
zviQ!Q&YuGsPU<k=LLD|?<5EVGHx3P6e9WkO*X|M_`9kw?8@m4SLK-1Jmc5SZe41eu
zW9Pjk^>}18e%PvJJN9(2(oL4cnS$GnbL+4-n<NpsSkqmuCIQEz`N1*Tlk@w)%%C;W
zL0Ew3_K^vXgKMiGg=MRvqK(^Qi#Fe`L6^Lf$eUam#hduNf#OTi(N8obJ_-!r@2Y9D
zrC{e}PtezHNhgs_a!3JgF&4xpj9EB1-tF4Gl-I(bg>H0&z6qn22H?lVUhQa6`PSH8
z6MZ>XCN+*GIxvW!Q9}koT9aNS+CRZ_)fCGmH^~u%hZs-_qE1qrQE3BQ3yoET0p$TM
zZKW^Q;s>plH(8t--NCYj2~RHZQ;a;P#o}PhiAyDE&*VfPJQ;Ce{6aH*iB_;b$PYU%
zVk1BTLD&jRIvJ#5qc9jhPE=t<Fz>R6K;hC2Z{F5p@i@X7%9*N^^X`fWXEi3#^<{Md
zdE)ujwcyVBOQy$$YsX!rZJqWXZ0XBI&;gvx?UiMK&Rrk~K#T*nGg!6yF3;z%TcYc&
zt_k0zbuD9ZHJ}82c*;C%@fiQx-H`=Eve_+oF|W_;Xxa1|AvSAmB`@^P0Ef#D9=jii
zebW<Fr}dUAs_dLwA_rB5mI2uyfZvCM;2i*muG`z4Q|g+JzQPjoX+83_i{v->*iiq8
zUF^0gS~`Cmkwgymo_tpta!~$M?-smg6<2>?t(6>pJX-{~JEO82&91!$m%9e|c@TEr
zD@JES8<nqeI0Q=|ei2zuiIzES;RofrE_MD5gm7>QLJD53^UtV6b5b=8@nM_pqod%|
zZ0wMD5totB9#VY&ILiP*;I9MeW*AQGzQy!E%l#+$JX}fJXGW?TrJQT}yrd4O-px+T
z1q6}L2xwn}%>p`{oOukI(oRo5I|xtV-fqqa5AR2@uMY7GT@3w}Jj~Mg00vY{-v%J&
zp7fIKt&AU|WtmOF7v$XLO4_1P)TF)UzR$sNq%wBwL#e7hvNaB5xV*jiz`Nxw{W$20
zI?_;Ycqhmj+fjo-4HQwdkwt}!CGX+BiLq#&$nWuIZ|*}fj=(kQmWFZ@vOX#BuvNfH
zu{Z|P0lA7pMA8BIo3lJWD|Mmff*LOq3+V_<!$;BJuU{!Jb^le;&PE;hh{V?U?k%*3
z*{G>Hfo7FAU*UA5YAkUIMyqIKyFW}>?7^(NU}QotF-8rFzt!Hn*Tp3J>#&U@5!JHE
zB66Qvn$99zJrf3u1C$C>Dx!h$B#XNaqnk;$KPJLK0y^uay+h4R`%4M~huTT$ZqEeD
z?X3&$NP%|J<sy<I=6M0pdrv}DmWFtKgVb!*fEL!nYGj^rim=+KRN@%K?4+QGV!YDd
zI7E9<84|3qxA6EG(jPb<Am&Q2*p0d17)#e@{&UvqLiBco0Oz}|1s_&$@BElkt&wb@
zowIJl_ewkGS{d;EQUW|Q7;q@r3?s=915=y?S{ctHTXjw52n;C!Ghd7sDE?e5*8K?M
z?VsJ$VpVy+Nqdx|q9~UfO7?TYruyk2#MY|H@nVH2xNXmgb|A_ivmwI_9BNx`_HX?`
z@oTIl<Gl#doxqFtVP9*@W!*e>yuHWFMfVc_P!tUYFqdwN7HlmxJXn@4m(!{1eNeeT
zItJ6gNQrQO;A%LuHQ-_#P}(&#M{0$Ze_R8w1!Q}i14EB;<l9eVZftQ~)V~tWmAN8I
z`~(p-sAY#$sgh?iPl?(@jgB_wfn0j@+2Y4@ktc%%Xk!pZVCWmtK*QLlqXXZ^_tYSz
zM_R?^+$JD$!WO?9O&Zi1d12tMl>8YZTw-S_nJAaYIlXS!l!hG<egk|A4EC&gm{%5I
z|EYf1^@luhM(VR0x|EIDUG|ugPXfq#2!>4*xPc}J4nkBrdSO76AJzwcu1-joq4u1@
zzjpj1w|L3I-&&&;vwFVE()t@)-&wafSOh2Ckw=bz!g<%pQUMrgBgidy@?$-_eGs-r
zYDbA{O`q+hEGsBZR9tTN^h;xPg9Yqx0Va4q+=cP=XXmB&PEpfOCL0i)v*%g$C8nnq
zvryD7$Rao;AvHa{_XGx$*XLc%q7pDP@#40mYu#<VFYD)L$S34XBWK<-JZx5F4bq~d
zLo%v|r|RjeZs#)Vw){Q^WOuE7*h%nlh1%!`qw~5Q94x2LGi0V%pPP&~BWNs}8jS)H
zsBj)}u~rzx#OkW6{g&ZTyR5=|9;VXH_(-E6wG?2WJh^FC7H;9;Ud&POk&f7;M1I%x
zJ?k@2jjKF028NcWTXRJmiK7F#C3Q-m6<^QCPRvN@lb~jG)L{V5f)>*GL^9W|JgsB3
zrA&!+_y$Jxjrd3d%A^!b(AMHyT3K0F*Uoo0E*T32*row^-+;Nou~JwkX>RXMBEm{5
z>L;bDVPA7w$#A4sm!eb+Pvn^WH9(yw&w$pqF>$^yoDw)CIWSBaE(a6F^<3S{5-|?B
z4OGEkpFW%H?Okz&p(uv$<Fh7dMomiOh`+^NooJJYI1m#&88Lqbdtl06Vs;l0GUWMG
zqwBH1-6zzSN*pBi>wp+XV8APTcCN<#&YprpcXpfEtgcym_4wY6rjDkW_;SQ3Q72G0
zek<M>Vnzk`0-d_>f08s%fIjV#Uk#HBmJZo$ucus5p=qQ3R2)xowz6p(=0=D8JJKf~
z^x~;4q?;Mft>UV-DF@3ec!uaI!Rk_&IAdQ-rM0<PCjT5Wsg;1#DdD!E)6-JEC`>G^
z%n2^s?2(S4^UAEP3Y6y%1f4r3y(0*V5Yn1YnO=%?XzWl*45W3Hv^K|qR3YPV;pmN}
zjk=83@j9(~pSb*?MRT`Kltw?XCoDgoR08c-4BcO^{1I3q6SHK&;0N-f%zbQGw1&E&
z&)|FPr@bqku-U)Fo@ZEH!qG-O_%KebISz@WzO<6NzI84H1{A?uVA=|ojt5E0f#JNL
zd~L1NvQ|;o?~iW?*I=1CFTO51G|IrtiUR2{FqM?$aJHdY5l3NbqP2tRds3#k&5lYt
znw!X(9hiE|W4ROQO{MXIuudxqq)Vk+A@5?+rM7)h&R*@@;_MVhC@_k1+90KsK;ykZ
ztIN^E3@#q-hui+m&HJ=_tIV*b=uUM<8aDBEQ%lNriu2NjxRo+py1T9=JDY+IA8qf>
z_Fi@T)|uk;B*V@iFk+sjP@PgD>CRo>`fNm?1=iuf%j;tCXXjIe<zjj1-8bGp%lBCV
z;FswJZ84%wi}77%ul?bB3>LCG`Rmzhpy`i?=^yD2eNA$hp~OF!3s4rKi?~&Z#&Kq+
zU#9owea=!XPng-E!S!&x)FV*9w9)bt@!1vmsq&((36c{>Z(1+0J|?uD0<@<rFOQsP
z==!(Uy}dO79!)!2X}U^$URcpV=a`83la^NBPrNcx0yG&rw8DNMZromK#m!nRr5eX(
zpaA@m8&#@o$owB}zM>SLa?%16v-5&rPfI0WOPfAGpB8f+nQF;x$RDKXGX1{bw_t`|
z;YhgCZ?(0cdcz^H9EV2M`<p2I5ty*xfxc~<Rv=f5^ig+|P2I(C&<GA(X=D7T)5BB=
zl*iVTV^z*NZ3<BBAYcJkAwQiy9{(mbqHcgr6I7?HH+e+FJ%1MHaA3EywP!LwcqiX1
z(O@!@Hf=1wD$nm#crifOiKTF}2r?#VXtvoV$+y&dU8B|bB4c{BRsCYU_lPwF;}23n
zFo@rA?zjYGJgL*vHDV}Q9%JLxYpn6{iORQ$6S+=_KJLo(9@%F$V$f@XIP4JIz$A{A
zWkkJFqg#*r`=R5M+76B<v;puA=CJ#6WODkRSI#tYt@lAK;e6VZ9$Rd2JcGuLt3^LR
zD^W@O+4`M~R(Z+_>&ttWKWR8bE><Oko41$r?2#PI&AWs7zKx?yIUA}Jff@L0#iosS
z1wpPQ(_)<-wDhb$K~6|BI;h+4=Lf%sfxVQtToHK3+41lpVon&@|7Qn`3tQDG_U-Wx
z6PPKhiF7%NoTJ%owNs5%oBx~Mj@3~TJnIJQag<ex9`<}M9lyX=oaNCJkyshjL9xDV
z^bM0$xx5VoaTJV}sOi-5$4}?#HmvtBm4=owR0Z1D_@6j3<mNXfYl6G46_dq}RqJ9K
zR#wbQ`q%~w#E6M<h*xN9WPTLYa?B)87mO&fYIoR<3aO~C|EXMXdZ-r@x8!k7s>9k5
z4<uCI+hSN>yW6!+Su(a#(OQ#WuY-GvwWw|(l+3Ri@VvU>T0Kw<DSedeO8ay&jqxMX
z^M=nqLN;0cr^6Kdh|%X%yy6E;nC6Hcc1jLB5}ZfRl+n<Uyk%o6qvAk~U)0Va_(6{E
z+Swz9ifZA>wglqvf1ByT%>+89Tdtdr?Mj_(b0<kbmfgP97YJB&&zHF?xJL9yCIOmw
z*j=tQb9Oy8fA_UxQO!bC-Qhta&npW4E7|iOLP`V~Dfy1IBJe@wg69IR-UGgNU!K~T
zr+1Wt+iDbrJ_?J7zN4_t(SP3EYN-$Yp7y@&VpaWmKMk)oHR|zzsrA}w7zj2aeeu;n
zWc~JKMi5x*Gx(~OH84Jo3@29TY`TgZD8%61tO!Vl7_6pdna+87-8e@}jz{^<$HO;|
z&1Q00$R2Q3z;~z9%lF%@;%o}gm2rP>gc7Fn{AE`6;VQYH043AoVqD<qaKc7P$rgd8
zW585=&DnjyfEn8;03j;s^^W$A|LNLN)|sq?ijF4PSN{_p9&}EqL}A@LyYN-;4>AvK
zwxO0b{~$z;@p&)aB>d}@yrQgY`vIFz=7M*-CY-55Qd%hom(S))AGJR|KDgc@gL@L3
z+>HChBknjC^?r*C8coXoSi(R1W$Uf?=<DSn1ey6OP7QxV71Hdd^^o=x>qXH(WW`JB
zNibGcMXG<X&BS*H{IP^s`I<Wt+fZ&3f)a1e)W$sqk5jU*r8$9wiG+-VmWPt6l7JwB
zXMuPAz79+UX$)9qe!Fjrt%_N^yMU4|(czhoWfHQq+P{1Z;^xK;zjF8kfeA3RV=GO)
z@UQ9gsWP<F{PnCD;y$6WB3o`2am|Y$cFXjV4vN)E?VsShKY&F-UM^`#=!{RLkCltN
zGq!XT*eA5Mpp-Cb3ax&Sp5IntxY+~wLDrYZW#+Y=U&SY#UP`EnT2=ZfLbxUM$J@PM
z*->wG9Fo5tD_saPglm;@XrHQ7J2d9X=mk1zFgGwXDx`Ne4o(wKD{#!Jc#fZ-mKcet
z0|f~cR<>GvcA$MqxK_@uHpOdJ^7;jyrs%n=ewS<E^Jpm9Y&UAW4~T!q*W^MrWwbjK
zLdpNeLF@F~3PfSi%AMeKdEH=i_ad7$Xjz#u$0@2eo338H>`2`dcwJ`0V@2><u&e0;
zEr)@&-M`vNnH3_9v<=*>?$GBlnl_~tO!ypZ4z0f)?Qn4P$PUNq2isr>tq93~k9(Tj
zXO=RKuCy&R$T-a<=6H<Q?k_;|OrF|^SDE#`?%sIF(Yb}`Xhbt;6uo+YJ!?n<x;hzc
zlQMM=FaF+`L}e-?QyovzH2d@!D}a(IUQEQ>d}i)gFN&gX|ENEG?oI}UgY5*`!JIUd
zCsas^f5?%Y<1jQb3A$iMJbB1&Jw=;tan+h#4hgcix9jlY4zs~W*r1NQgT<q9n$aH)
zMnW60QD`IM`@^3TLxDtzRI<R#XJT6#IaWa*<K(9S;$Zk&cq!=@t*=SrWn}~9-{X>3
z^bf~%G{9YF0a|+y43R?rCVVRU&I9z*i@KjvJ3EklEy+wLWjCLZf6tB=*<mQT?#7X>
zRY;_Fg|H;}d-VHBS}P;t5$dZ%>@*%52n>PovU)9Mbv56HS~0yC<S=E?9sDWCE&o1C
zT}}<@Tb>uzo@+55fqd18xH<)2MlZWcJw@6GeG}sEvx-?rWUnrWpuW@`f9F(~iOUjn
zs=3{9KBerOVp<KLX0OWTPURDPwc^ICZ+;K1R{+Cp)vavvy-Y)ZD;HeVZ)8>Kr+%-m
zI}_05aYXHzgszh)9v5@`w#*D|mV-7q1GPUoW1E%v;purgy|fbn1IdjwSDs_3HE2kL
zNE_t}gK^(kGlwCMKXYr-HhM4{=0P?uP6G{WpfMK6;(-;>in%}-I^b1O=;%^>`x`9S
z-oDp_3Af|5R+l_YC*q(x=7gd0eSV{;Q^D(fzYg&yk!~>$A9F!7^|k^0Eq6mQyp|FY
zttvg~-tP(Dn?{V9Sq*H3lK0OzbS_%8SFH+^lpTGElTG~910isWcVzWe;mP=cYV_@x
zc+Y6FAxGTb+3fQ%76#KyZv3{*-W7ccO<Wj%M@+>sb$e3*J!?K|^$au1Zg!51EM@5I
zoH0Ee#!P<S7IE?}b%3661<z^y?VZcRlIEp@K-%@^UYY^sV_nl#Is3B$j?YL@+lU(B
zWt}GScS&N@y*a2=P<TK*R&zw8gYxAP%h@y&h{eieNP-V=n9}Js&TfkMl)nNsyz4X{
zSR<iN*wZc%&@8fq9$m!VoFdbh{^_Ld$VbQFX7BJA+d8LkuevAP$_BYSy&{rxH=@{x
zUl*<2IF(p?(*df0u2Kj{kHoPn=K!1M*w#X5&RgW^<a9m7@Om%c^ORP9k;Qv+Smk~4
z&3xpH|EhnW^B&`X*;3aBnAaV`kbXinbUEiZAU0~!pUxg3xg-qr9IUSuftXS9871Q=
z3-6FH=9sMrJ3J85Ei=MBPaCNnb4~}FjxVZpIoXZk<6D~U7Y5x}lOGwUcc@In<-8(|
zYQNIr=X{{VZ)yBe4}q?9z~01-`OJ&+r?5yKeBK>QpT|M%br9T`qxrtMOv~sO#!f%D
zYi-Z>h))TAZq@j?OTbsA(SIKjnKP(TGu?ywbv49vt0bV5?`{7q{b~Qjri6a#4+|M<
zn~j>QWy|%pNc@k9$QF+lBkq~KJtE$`^ExOs5#?Vp$?_{9yEk8yJxO#QiAlqkXX!ia
zZ0QowM$nkFNVTWf-z?ZirV1G1^?F1lg5f1RNawA0W=pc6Vy#QFh2ps?Cx10YYPhlK
zvqoZnKZK~lM?<n0X-5N6k`+#SfraA0s)-r5rQnKh`lMeNBm<Pzrgp5u^55z)<YHpF
zwwWf$|K6@FI02>4yc5J-+oq4DyHY;|Pu9XL9=w;sdDn|@r{u=a$e5JVBif=Ne1b(>
zm2Jz~wE)=zV4iD1$q(;_o8yG5_Db0}`<=+R-(53?5!V1&r^d>cnRtA7N1DkO(D_`<
z&8W|&rQUiOuwWrxlj>D{sVa!8<O@pDYH{tk{CIV#Klc!1I_<e&bp)G|N_<%v!8ypL
z`hDp!z8brT4gs%T13yhO?|pF3!bq=Gjt1u%924Ut3R|>*qDZaJ+rkqmQsGD~{vh6&
z+;_Lngotvtwh*FmbYojRqrk|!Q&{`yFRWo2lJPdA#n_efks0*r7nl{qQp7pQR%cNE
z1f_#rYBfk2UBiMUKy-apVZa}kY%e7th^CK?!tmrEzy(RseGuAU0X0014_SGS=83y=
zD;{Ywz;!((@Lnu^fwH6<;SN3L{4^R0Nd9RKHBnur|3L8rZA!L?K9!(MYjehWXFrZ^
zHEmNrp1vYWf^>DE)QHnW9Ykne$qN0{y>tU&X!V$bSf|FP(GkK-FjGlp(d&+=UaJ$h
z*W3q6iW@3L+ToE=g22+il-1(RoUU<8?X8_Todk7Bx?>q$=bLf=dOL;&kQPe2$K5a5
z#V&&IqRxh9AN_^z#<qTL8T$#hze`V+Xk0XUHDdKOzF4YH^Vfsh#>RT~K3K>%V;=L8
z%RRg{SVPd;Rvr@@h)3oxoYx*{b1EwE>i1y}Q7;A;zd;u4r%Xcya$>8L!LQZo50<jI
zp#afQ_VnrhESAdum>YkrjjeT`_DcB<9Vl4irr4<NiZm=nqxFkn2LtFZy+hL{?H!)5
z#>R5WxYP8&h(=DoGO|KgK%oi2+464Z%a*^vq?0;aaCX+U4Mj#Rp<)lI7Y0)A9%cl}
z`#$uY`J85Cc4TT6h!S$phlRei)?$ShS(Jvv4H3~-I4a?*q6?AR6n(ee;>yKQ<E`X*
zQaqsCrNyXBjG7$%R15awJ*g$wNXb$1928?k0S<A-$Hx;gPEKgAhf%UXcG^{8py5=Y
z+Qp<@P0Zm`3Eue8lH4|9YAk)w)9)gp1p1bjj31*zjZx1_o<N^T9*7C(C4?mqaNw<k
zcyA8EUWp3$CsjVwso3bMtM8}Gzz@azn2ozB_oU6<pI5{HCxFV7%72dUSguRKB9}G6
z0Z7yNc7316sbK}GO~@WC*#DRlUQ3G^wrH{t-^5D$+TvuDRb3p3;)t$%@<ULZuu>ej
z8d3dHgCVD2M{@dDc58zpll&AelU(LseNrtBM6|2>gIqI704PRi2o@_ciVFpAdtdw9
zsp&L3I#PMK*K3O4Dgm7*&OG?WZ5gZ`LP}@|5yNhh=DF-|5CBdA4hL>@u$*!4eBUZC
zS~R_aLvbKT^PKfckLn_@LhZ@9*W*G3+wvPACZaGw+OL(<>L;$F1TFX*M*cpYdg^XP
zP2u}^HAa$mg1|H=-38fmUX$<NW*I_Ff~`_Zv<b7#%xXJ}9^;aGjg@djA^zoJ!XuCu
zLmDO|1XC$w_G8zslxlNJZ}@^EcNhQ%aRfAwbz)8Sow}$FRY*}!>77t)F~12NXHQbr
zkSnDgH$+YH&Yis@#-WduCN^W<FABybZM<(tn+1+KfxE6t)szBjnN#RvaTY;<?OYOt
zW(b;W!*(V8XX5&9fxfVh=8`xP4Q~bE%bZT}cPM8&SSUdt9(3ISDr~62P(|s>Fl2j?
z1s5{wWmD~16aGMLAZXBD_ik_(niwa_Px$ACz7(Df>J(@hj%s=l<LnkgHn}W3vUHnS
z|BS5`3>Xw+8LapgOGiYKD``?Y-_&g&&71geoz;H5)DG?4H9W_@2DMe|4D7&{RN?%d
za*DK;1jmF1aF{}t@Joq$l+I%>Ntndi723aXoOjK<unwgkb<&9aD8XiqV<H`-BoC=T
zYnXzQ@31;08ZkrW_Kvfh^57EVRx=G(ani}%WhG}mi&3DD8?XcI2V#BK@jF15XcbQ9
z0(j7^k<_Y<5LCNJc44D7?6hdZmbK^D0y2JY)F{en3@I0XFcpYn4>L6eS!#)LlYJMX
z4CIfKSYL|>r-xv*1v1ITAk}aXC-*KZ<{U(3gcul!I%UEaax{*j&$%PZ=zL5(x$#Eg
zp@^6u2Vuem1d2wHi?}yDdE;U#iDpQ*M#UnsXagy6n5H2hC%;SQfB|!LQiTFYV|wQy
z{KzRxBlIA~4pOjYcdo!7q_z_;doU4;bZu>?ZK@N*6_HPmuy*o~`_AelSZpV5076qR
zhfO(f7^?8|XkR-Ta_^hgZ7TDeG)cW{PMQbHB<W6-l^-}%^*QM;sLFxq9>Dxz^k@*0
z!wj-X3rG&lZ8aL=b#|j6ql?;cHyS^Qq-25YG-h&}4{A5CCKBmrTE3KziLv7Vz-I5k
z4K$e!+gnnmKXlp}d?PqgUBm1rkM1~qZZ!`a?8viS$igaOyfT0_`1S<oK!`M@iKAoE
zfFoxvn?A2g5w3k+12d3SoYnK%Fm{dKmMc#grE*)Ky)l`n|0gLe=rqAGgAV0(khjlT
zp$MyXl73{9@9E=O1AQzg^7*^@*Ge&Shg94@NFrZ0JPcgw)pa09paNvHv1nH^!ipqt
z@45WeSJEP#6Q;{<K5%eNj%8K32tsT4cn$H@ZYDrwhyydyz8V#d=*Sn?1<I0-;iwvB
zclJV;*Cuqw#{RsMh#wNks^)=$44GjumSPiHy~@(cdOQf7qK!D1-$fwpBaESeG+nC3
zDsr9;FdJ(o#!(+d#v>`o%ON}}K7Q#6Kpth6dvMva_q1+rfBw?l?fd;Iuw7t}@M%zM
zWo#e)Pc6mTXZ9u`d*i?nqJ!%5&-;Q7v9WONG2t#3H{IO<*h4#_)5amQ&xyM3&4M?$
z*S^<7AJS$DAWVp%Y%0kce{hYkdvFP9_Mbtqd=$lQr+(DpX{SXCh(Esu2eahT@gLt>
zKpFNVOpN2y>OvUgYHADl*a7f;vwQg%EH@|JZ|5xpoD3S-<aVOHX@>Qy&%Kn7p1EPy
zV;O@Xy90JZ&<emM5*)~G4ky{;mx&p~c2E@0IXzq!uUgI2)gvw=Ku9qV11CfiudP^i
zP}^sePUKu2mn_PZhkR81&e9PoCPKWr1xvDY!6}t{7sG<#9`jNR8W1BLk?k;Te5n2<
zQ%47-;zSe;0}5ihgmwx?A{9Dre`{-F-0Hjo3>K6Oi_I2}K=37P${yVpx@?hu6c6r@
zLz~hBY&(i3YoflX1FHHvP$z>9YB|(u=lvtF{_18Lqju&5mdh_6G0003nsqxp#FjuE
zZu0<<w+vwGVW-VsFaF-Lf2Q&L{A6DMY3N_Y>3;@7h63jM|H#QCLjfQwyI)SY072lG
z5>RK>)Ddzfmx*!Om_fA=3R1Pkzb?x9PoPX2LrghqAP9OT8>w*Q?^q0QNDY+z0AR&n
zTLy<uR(1{QGq<-8CgIgV115cTNUyYKNLA-V7W2|^Spa|&s$?>#u}QixCzHU4p^l@|
zwVdN>9?vNZ0Sg|?9~3N9y`oS?j!=T$!v(l$JJP<bxA&iUapVc(yE$+;)^4(Vzw9Iz
z0PYriJ#HUnGlsA4?p~LF_`dPjrV9eOri~$wW``Anw*v<0m|sX6<>ls%_g^nhUL;6g
zc1`YwgQTr;26tv#KS0n0gNXCH4MLodVej$wmj#nA<I&{+la@$F6aWorvco8;wT8NS
z@d3+0R5>ZZ>J=$?Tl0D4>#1vmgUCI8`5@kNC%R-TTy0tu#gDMR(wV`A5LlALHv~fM
zYZBxZBnLJBnY2(5gQE>`HyapGOV3Y^Q=D!??+aF#Xaf$IA?6~xrCP#1NuShs&_-ia
zdFT*(D1xqmuEH_(ICd)g2jy=L7t)zp$n3wtvP;*Vm`b6%|EUCy=8;=xb(Dk-8gLUM
z9gp3pKsRm7>IIqhlYN~cf>}mkFEDH~?t4D3s6#Lif*J-yhrk9@+(hzZtE4;Lm-EC6
z+b8y*{R0^;=boRy<=8P;z-*A1Ec8&+0n<~U>7zt`IouTLS%4Xvx0w&fHQj6TU>666
zQljtv88kEjuJipAT~y_kuJgmPW|ZmpNMQ*bz?}thz&^5}k`AaQABmfE&|jFFHoo4`
zg1{bOubI+hF1miSRFdj#EN3p2Hed5GmPxxWW|nL)mBj#)58Do=Ka%l9DKJYvF11M5
zlTR;N;_F+G#4_`o>Lt{CNvS-931PIL#?4mzGGok)`ZO7v3m2zP{fk2wLc7|Fm#&Pd
z2w4}A2oh#ObQChW>zqH))Y*f$ml`tCSF&SF|FK~d{KFGtkzAwV?=XZY@~D!=gjPX-
zeJ=;Jo%dwXvnS_>mYyw!KZQ(r6UP>-5fi$;6sB-O0F#{?R6`_$>=ZZ}NkNF45&#(}
z*;hjpG4c43oV8zDsye+U7Of%U3mbl?1m`T=!O&yWhW#*N8rXE38!MVdI3YTzR5J5%
zWOt+donS~j;!sq9phVj;E?c?H*R}^K+P(K4*4P*gp$<I;%t@mM!!#rA59v1djHyXP
zJ%oieXN07i(0~kipb}W<(`Is(x>H<Gnb6?Da4mT+G3M%^L(7Z-8$N^$a>=_xidJ8%
z6Em%?8KQ^=NJn;{1{(1&NR~@z<va8ox!C{plB?^xVdb9>8?j2PQ%JW~zx^fgP!lH$
z`mdl$bW!A?&pC)GEl__Y@c%;?i7UyjMiXE(HLc|LFHhknqy>n27Jpnj?>aWaI&2he
zP+z;qOwaHg-5x!9lTb#Zd^Ry`-JL81t-!1XXUuq^oD1kW-Ty47QyI$Xz7mj=;cVs6
z7W01q6bXTOx!LYf)_vrbwvL$u_>~ow=B!wspS4!r_t?9a0^{{PMu8bo<9$5AsY_f<
zSG#iLaf3TfD7lj1`PI5;06AFj43ZBx!zcsTe#(wtM81io47@%Yalh!U1sB%?0K~~s
z#L;jA)2^3djNZ??fRL%|WNI93pPtxao85jP2szQABp-A?1Ha52Gj5bT4>mBI({FVi
zHOCqX?k=u-$A{^%nef**(*dIwT4dg<US9q~LG$3FpxfCC8k0hIezyn>sV1Be9|^KF
zsTr><Hr}3!Uj{?mtbNL!v7&zwB3Otyr3y?jaOdk)%er!G*Y1bRjTE^>UQwU02@=VP
zA@oaNaPOvzIQoH>ry};*r;m?u1KWea5a$VUx-G=Yri%gtXxwkcyN1P!SHfSRe)gRl
z7B+{$h2}@ChFbvv^~jV_w@=+MV;1bsePypk+*<m1ELvzH1H4X^pQ-QxGtxjMvU$o>
zD3xW~Jbjmyhi*LEN={uLMS+WP*CY<-g(^6J1#*ND_Eb2J_&fnFyqA7c+W7Td0*`n)
zN!maqDL{c992!v2o+c&^bo^6L2mWX!37jG>LwpDDlj<S;MxypyuyvT&u~r%OY7;TE
zV9B&;^v;iRNGvjrl8Q=e`lf_rxvFz7U|&TS`R(#$nlg~c6c11@1vDtHudbfV4IR&Z
z8>0edxh+6wY=2na`Mn=3IB|!NQCa=*&|7MGE2Vz70RC=NpmG3!?6&`sotmAY;G_6F
zTc%cn{T=}D_B2fn`_9VUUNMzv_-pDU>4rcb4FHgZf-tH85cVhl523s|_cH+S1qV_~
zpv#=8vOM@Kt!3ClYbEz}+_HX|B4;VnvTXUcrO3t6N_L!Mc31(^*5I&Q*kAO`vTX7z
z7*RxPK1`{dt^UQ-%wVO@$0Axbzq}%+FN*nF*@6BUfCx=qNVXG^D_LBGYfSrY>9Cp~
zMfRiHP=~o<<Cov2F%i(YYCD8;*Fo94#i&j2gJMu7`t*mLne{1aG(&WnxON%@MfClO
zb}_Q0EvGpeX-<ye=;9(^05+1jlK>j4C(n?WOO7S&<m97q{?usYqo+=E$G2tc8pAP(
zXTxBKa?3(F{IB@85bcKo2qNCUA)7hq{1@Eo|6VA17Ix<U{N-kay7Z4_cGQkLHMo5-
zpbKt?Bo-V3jCsU$4Eq=~U$0PhgSl2Qh5L;<72kD}>wS!>(u(#7z8el+So$Uh3bN^^
zRWb7JbF$dn^N_r6MC~nMI?uwS0g3?_=&M$wq^Nx~Esa^C=&R)dz0y*BTYtuErzSY4
zJ{1eA)S&e|X}g0!EV)}I$?QqLFAB?fW3heqD|{>P9nWW&R^Pl~$^ZN^dFHr!&{K`l
z_%OHJveFcF0X?U&n%_q=x}t)3ue!V1QizeIDywSsvL~WpDyxd^Lsyoe`l*_Qym_Ew
z>=Y?dIkUG(cZG8wQJgGPQUL>Vmq3y_55~TdU?hiST-&I#X8)5pG&C&St^^C#j-oJX
zA<F05si*x6jzS_lW#u4>Pw@`q)h@r+U5Lf66iBIwn=mHdo(df))vCF(Hb<d)bP@ah
z&^W5@G0VLePGgtpNxE4|y_MIstro4$v}m7*%Y4%;&j|55ZL6^Jk|<;4h$^XFa!;eR
z^k8tqF~YL;v80ht{z?2<xJaDJ%gqp64vC}3RgdbYveCSjc;a(%p!Fw<9ecH|^fv>d
zu$|Wq?hlQ+D-mRW3gQ*yCnsYEWtcE?w=^d#$|t=>71i>wJr>?P+-}W@^HIOeAKr^@
z&F1Pv(B7XnG@ovdmaTuphQ2;GP?c5a1|i|pnDpX>1%YAL{M=z;FGA4D(LNN6ACj@Z
z4mw4JCjB}1?Ca%ceqsGim|%nZk{kp?mAh77TC$Q04a~=JqAC)T3?^4IROAN<Xh_S6
z`Oy1K!bmbHTI1qmff97wMQ&4}$=2Sq^aAJO&*8RFemaT&*7Ygaw<<wk8tqr=T9dbw
zCi$UAO1T7lpUFEB*uF>-4Og^wTxedl^)bRQ7%r0$C;4ZJSuZPLgvcIeCYeke(|YOH
z5xZ_bPGIaud%h&etP`75VqwbnPn#c+u)a#2Gp%286`tm~7nVG^5q^&lPyq%B;|c|h
zhX|*NO37I{{KPWOEO??Y+VUMOi1{?>cKDnuFJfR<C_zv8q@Um$9>GNYMJC7tdfKa`
z`XwITz~OtL9UL?JV(Elz4S}I3*Hb@@16|CV5#3EW=7_%#1qMZOM8s@ho@?pqO<jXw
z%u{}gC=XP<RN)fRO`yYK20@MuZilPhy+nQhLX6xmud}?Z#1e!rZ?}tFgTjhk7?@HU
z)nPz3?*KH(ZHcX`A2u>EJe;46&?8OFsH~m1I{p-Rz=shtL>gtp7Cw@_`CBTO-C!w)
z1|3!Y3VNJ}c72)a0An(jhn<+rz#mI_+ti+spe(R2Hdc%>XKoSHlS~XqG9^ICjP-FD
z@d(B<7K@CRkDw1~Mn+NiCpVgi^RaFdHnVLX7@Bgzw$o9pFWHIa!r(=LzvEX6b-IC&
z(N77VSD1>y=#`1t-Gt%kf`>w3e%gI(U(?Mz>n^us<?`k!oF2}I%->)>oFt}Cy&-X}
z-JLXAL%#*X_<3K=vn^?pseArrAHSt;aSud(oC1FPGRO{}@y_uV+T)BqOUBwyn$;E@
z=H|f~k&))%lT%j_LIb_>9yfQ}GpJ)dhLdL6J0Vr;ZY31Lw$@L~4eb&L-NW`xa$Zeq
z^y@}o4WQgAZ5vSb#M)a5ji73{J0NhUv5RU|>E$^DvBIusgZ1se97kf`B}jn<#JCme
zE3j~cS1j9AD|6T^cNNHn7ccp7YAY=8Dxu)H4-s`%xX)Vgo|bjv2+O5zn0Qq8zAHI%
zANFI^mhF)r$q348$LU&nOAzAtyLT>k{5|K+XfF~oOKEFbfWr}>?p-qaS;rF!_UC=P
z|H3Z+pGB2t|MyQZ+1dZ^MwMs(Z-0u(%)-p{pC^rzRd*GYJ;MGA*H5UJ6n=UXRHV86
z<YEB<=oOz7Fz5rQ8Y(xSYjT+kK*hx7eJPp>yp$@D-%Axo?Ynv@W_#(W0yN2){j`)S
z`eXC9x?M>1qUbVkd;Mm6!JKzDtm}I}^eY{GLOj=h+<BjMx2eJXkFdZ&&qjx40RyQ9
zYB1a4dm#X5IAXv5^Mhc>^=*SW^|%)Ab>1*)49UiB(!BVcrl`46x-uPftw&BqNr}Ih
z&4*SJV#!}*BW06{q0Cf&gjEvG3ub4q%rd}cS`U0sw@|aNRxT&nl;e|#k{FZa3>nm-
z4w6dgS0ET8xN9D#TGie?qh_3FIx#;MW*AgK0Mx%7vS(2R{UGlx1b_+vK*K=6z(K>I
z!k`kv5exqNh07F}V`1pb4rhxya~N`XIyk=KR+~GknALO)I0>{*Ev>JnJa-_N?WlLE
z6`iI8F;vy|7Ez}q#zSxt7+=oe@1kbi*R?hA9ag33QoKZZeWU%Ppo_fO3r&c8FHV)-
z!kq#I`;=~>ZeUD$i9R2`+6(sN4gg_{(qlE$IM71x`Ca93p=sdjW%t!w-Sj05{dKYC
z+mCKr%wydt=oqR6jttR=IE!n3{Sa9Apn^Jy`}Sk+kqz697QOCyfH*tu*YA~&Ok4Bt
zuF34MEg)&Q>yl}CYGFYN|DU%F&P!5?d%ti~M-553zQMagr=824x!677L779-T7KX?
ztFOX={ta{Kv3rMlS1CoD-61HqZpf#7cOxj6CV0P}dGz&}>m8K)fbay;jMoGGfbb;@
zd!((Kkl>sIUC`||BAf(z3eI}1l$npj&k(<QZ(@6*y<P`cA7uruO4DI6n0)wjw<*t_
zv`q#wVtevTU-u|`PI19F!QITkR}O?0GGf_m?}ZwXQ~v`<Ns}|HZ||X;VUc}W>J&n;
zUnqSVkfErdU8+J{X_}@_wG;&z$Sny_*?~>NTx9Xo=Z=s*g$Xr=<#yql{QXD?X$a+4
zF4xl*3t~tX!DH_ll?qHlB2PRZ&>7;K$pJZ;lpr6Pc{{cY>9^{%>r#`mLvF|`F1!_q
zn^~8k#!%WhDFmCfS1~|eMhAQ0>7d4t*^zV0-xuBrLBvrtrWUkpMhJabDYf7<{d^?X
z6JW0TZjYYmQPC?)oz1_4Xik`Qg^GR5qB@_IdYl>?DiMa=30REg%z}p<wQgd48RG;)
zM}K~TD4VwMMd9>|XetJz+pRJbuQ8xUE%e;|tGqfo=zAGGNX(&ce3{Y~)rrC6)Z^|z
zXbVC_wlH(mKtkNpU@~bm1<8$qor$JNRc<?RN?BspQPoa(?LTNSR2#0{TF>#8d6zJ?
z?<+bqF}qw3ci^4c<_zYPX~!YL-l$0Ujsvp|m(uYoVWJJa7N-CE4G(#c$PF<>?VXMj
zK2i0KVa&ow90ecS&hkI4;J8ABl8tD`ou-8q?bjR2*|LK!ZV(3jFunHEDXnuQ@yq#T
zI#~pt={Q7ReoefGia=-+xcXd(P4wN597=c9`)&|bp%cdei(@}{8{Q1P(6YQVnL$)A
z=~wwiF=DslPUanI40+6(R80+;!ML_LbWt1Lo1mb#f9v#5rjb-u|1_y-H!IUXz+Ufv
zWqXA8ZRb>;NQjkxRi$_X{B`Sp1=qHqOryqpIloSHdCIGUslyxO#nx1)N$?g-^J|}#
zeR@H>BPN#p<=9%i)*Nd8|G`%L4<qu{1PQoMUlxxVLzT_<G+bb4*!fpUvS0)Icw<BT
zSpCsNUWxmk<iA1$GuZXse{Cv218G0lrv!S=^n(sRwhJEA|HIl_2F3XWUxWDIF2P-b
zySuvucXxN!;BLVk0zrcXx8OdwTW}xTVdwXMYioC_wrbxGd%sQ1)H7H6IeohO9$%T#
z{ww=s_G(CLi~nnQH?U3y4voj^8}Y^}4adi81_K5nv5O2piP1%Og0V8B8~ld8C%?`=
z;eM1_JC1s?>~;-Fh*T-2VH)71SS!2!FKdffebMMpD<XH?yvpbNhW6*Ahr0CMs<w^$
z(Jgw;Y@UNC-RW*%x$>InO-2Q5pI93HXUB5$@~?;tZReTsw03cKEK{Uw+MhExp!Rb4
zuODiTXe-|q@E>}YPU&5*2BDJUBgQGIpsRMwqA8{2T3Zd8XJ+zZ>Fj&`UXKFs;BRN^
z_HuA(x_VX*IG;NiJL=qaBQ@66cjUOoeonqfHAy(rF>)htT9=Y$(X@i5u<5+9OD1i#
z=S;6H9qO_Vbd4eqt6Msl8B&wHFo=6V{9OB~yjRZCCl|Caj%F0;xVZ(p!1#n9m!PhQ
zdJPlr5>v~=)5$6)@Y^Ey#%|=|R)n4N-_fQD8aJ3`NtLNbMkv7jf|Xrc)AoUnTKLuI
zg7z0)<7$hyuDjRK_bMGrV!3}$b^=is;W!WCf1i+}#*#Ym?A(yH6+`z^%Xp94B`N@~
zpS7)gEjjn)91(MN%K`^Xk4yzmCx@D}!ahx}3v2^2*{%OV*~s-XaF#^|OpE>|1o;qb
z{|&x#K=f$;@tbm>+r{D+ji;<;nXZsouLP~VR6t^*-POHbb$moYZvO4a=AU(7smG+Z
z6%rj|U2vp?egvV?f8s9U1?OGFTHgeyt*!JHp@k<doAweYaz-0ZnixTU6yg{XNKO4%
z3^8dedL^-Rc?T#ZQ0h5;6nsVXyC+`1O~H9#5~DcJ+uhc)60oOV$C1Vc%Hk@W4b-AK
zjBy%4d96OIkL#>Qy!5i<u+k6Stpe;j*6WL67>ON4f$43YaG}YbnUZnmaxakEs|Jqt
zFUP>5UMB%@q+iJxSQ02u#ir&7NH0Z5+)~VuJh#Ey&v{2fLcwK$jJZxNpSZ!Uc|mR~
zu-cm;?zIJc)y5f|PR*_k=-o7fMDRrsjKtdqiHk=iv8CILhy=e^wBAlQ-evl=%o8XZ
zqQ5c{43iWEk@y(Mqm%uQ%*}qxWH2O}HR%6ErhwyrPo{w5e_N*D|3rmgeRb~tpX#7_
z|8My3|Ems~os*CK|4|2hZU_j#9(pV^m@rJ8F!U4W=eonqxic#Nz`%gSK%$5>l$3NR
zqKN&VD+w#2D=8uC#`2>T#gvCCdvv2-UJtf9a%yVtG+-VppLtAE5%%}@?`-05?z!E{
z<#FCnq`@Ksw@Q)UvN-7AMnD1n#3?BXZZ^>&At69?NN_7Ahk}9<mw*Ac?~(A}hJpe9
z?E(|;{gXp*@MmQu-5eYYj1tLmouT`0lTu+@csC%JZIVO6K<D&rYj4LWjSmSa+70`k
zr~UV^utOB(fv>U36%&(_8r6#ll`l^LK{`5LGAF20B}IYQHt=rAs&w(Ov2N#6cD$_F
zic(TizgK2wXC+hN;d8>&;O@!5as<PF#X{UZ2B3JD?-RgXRXrf?bk#I2WnleZJq}yz
zAK(RnG$@$_D%!c0iY21Jy5J71Ukbn?&D07%&`+ps(q-46-_}F>zIX&UGK{uC{cA1V
zTkRC9E|w&;1BRU6-b}EN3i_~q{raD#E=VNBI&2u=%lzbyx+082oU3SZWyS#z8~l`x
zZ;VWw$YcR4fp>t_k_Abw#%K#Snyk?<Y{R7CMg?fKOQ3aeRVPFrP;PxgS&kn#W%-oj
z*_U@pyJO^58|^;3B#^GqNUv-jt7~d%Kp>X@B3xXrlR0XNMVJKSXStmh?%V6gprYlb
z<Dx5ZpgT#MH^d-bxH&_JH{J2O;A|wpxv08R0!#K0g-yU{nO68?aDsX?yV%<m*>B1(
zO^7hfu)-<F`>W1<p{;gT&(h`UUi-33>sQB(O}UNS8Y#@*+(l97C@BXva<a3T^qR@k
zm4Ktkfy8ZbeZX=TY^nHf=}?g@Ji*k7tfVeN1|K@%idqDCv?6ZJca!*OdQ-S@Txqnf
zh6~|2%Zj@7M%}4tX{17a*aQSGBVeIyu===U6#@a)0=tVfY4kaO_tNAks*eNR1-9sj
z1D7C{lk|A8uL$6Pcjpx7+VJ+|Iw~9+hZm|mf`E*zl21>>HBR}bf^fw0e74IbdSJIk
zzt!pPcor^v7c9ekvC=?^;Se_bz4JAs5gpkF`W0KWH{zh@9t3UL9jX@fwiFw%V;HC@
zm6^*R;-?t2AAPgNWy0w;Fc4@x^FYFtx?LVeIB{Q)XNAYZnFs}5VCk3=Kwkoixc|y0
zF6O+uOn-N{$w->$k8dahb3e<+l1MU$`Ko$6k%By&=huS!<5VAAotLM_Q|K3ErGVU2
z;IjifSG|Fc9)Mgol?Ir?4`Jbx)l_h_a%L;$ypAOY7G0LM2a^`;dY@v0Ue5leP;Tl=
z?;*u54xQZG+50ZbFHqB%+#I}qc)V6PM+L3ZmZ5<UOxPhzEtDWXN*KSUo31GGj&CS{
zR&-=6aaAVpmAi~&b+)vl*`rbp!S4ay&pF0r5Z=j#uE%O*lQ<?KCexkeh|aAhXp6Q@
zML|uW1blWmIfx8Vv0-Hi-H@3{n~6~_F9BFRkc`|viCkQvqw&!KN))9_PY;!ziw7Dh
zW$yU+LI$qS=SQ5kn*L%8@O^xM*2Fb;>vD68#!Kg_|4=!ET6X}v3h_BO1{tWh>6XIx
zQpjgO`+kvr5gn{s5#UoVW)4x7N=1L%yInHQ;v|AWbq-~FBz7ly$M%_c0#sitG#-V<
z8%HHwIy&rdZ7+a^gU9QoC*n_M5@#mK*7CahVCIhNettOA(53I00)mQ-kzd67Fj%iP
zr(5$Uj1&LoA4H8P)FWtWU?G)%i{_?;3;-U{vMF9EH;;~3EUm-t=>HH}cUmtmZPj-l
zas_<Rr|Zh`=MsKr37>_)$s70;wFsBahh)dUKJ0L|vn*y0^tsA|{s=@++(xum6|INt
z`EeK2t0<0SKN2Vhwv0u5;JZ%x{)Idw36%4>!q5jYJUi1`R8LcGMqM1@m)%}c+#+f=
z`QnrLc*F`j5Bx$)`bj*rEb+}=yc!`*Pbt-g>LOrG>=j8+a7g4apU-_1_}E|nEqUfO
z<E*b9hm~N~_esX4(r<KhG>gMp*~y7HZpd#p0uOA!3=9ke&4=R`78WWiEC17V9~p^)
z7Zj6_9Crn}fuXPWpm6gxK-Y-ci81YH$8;;s_&U~j@baFjt-;U|=FzuH#3`WZY70&{
zx3_Y=WR=*ev%TF_*8nw64E9Fw_uki{Z($%|vOP>4JbU3_d)nBoA6ZJ=gEGvq<aS=W
zarR3!C$ptd2FzEhO-G|Clwga;AMgYMo6`X0-8hyWGAI?`jtM)Y9DxnjB*FvRYhc%i
zH;Dc@q5J$>vOnR5#+OpSJI&2jfv=!G!)}(M3XISn7m&P2XyN<F!3GRJY?e2Cg#A@8
zmccVZ{=Q!rfL3imJ(Zdzg0acr8NQhO2tyo_1U};L!^OI_rR4><6ANF<e5dmTyhoGC
z!S5yl3h=h(xsG8}QKGr8)o+9bOf<}#)%&6ldMRWy?r+5YE7@6kwZ2!izk-c2kb7e`
z)<kDU4(^iLww`vhC&WOl;RWmIf*fIa1w9PJPXu}v0^Zu{`zmLE@$1EpeHo-;hX;*?
z*c<k9oE|O+B_KP>HjXc^?RyGEI9_0FcHzL6{euHjv#`(G+uN8vEKbQC&HIH^Hf%?q
zs{)5l$kkBjjv7Xq*WVF;4Wur6?mm0EKWbUFc_JBt$b3CwNA5xBclTF<{C0OM#|<_=
z-Y+eagD^Hh$5CV~kc!WcOz5jXyt^RCczws8<cSbIuqEa`LYYaZYYAeUFb!1DU+&4?
zq2Mo0svhkVnNR2D=4|JG)*5y$t*__G@`{B=N~3=G!%}_3p(?aD-k%k}lm>zzey<k1
zzdW<(Q4z5bH71EyWCgz+08#;%2{1;hOhxNF61~^GcUZhF^b-ty{<B=<g$*Ez64G(P
z>g6zZ``Hm=+Hiwr59kd6nBgFV=^&KUB(kXqdd5oX4E;ZQ8hsUhN|Qd?ehLI;i`qXY
z7GU`EQQd$wx=lZorT93*!@}I$+=}R7J^^rWm<_ZxI<i6)oo|JVo8Pp6;_o9AvM3}%
z{x3mo2E2ZpBg=TkZbDZt+S<JxhCZDtvW20=(17o#Hec;xnn~!l_|s`&-frT0DPBSp
z2>XM7JW)=HrKcg3X9|6=mA|h1MsA+?VFk@y&%M>?Yb&8zoG%g_^!jkI?X!mxVD)b>
zX8n(&exn7#n~3ck6~zM#3e3x2@zvddMv*_2so^^^K}SDAn?`(-hzC$mhf{0js;4dU
zC+Mg2+&vSLX$`ns7Qg?%8sDUdmM1e67(u(wDVnxfLoypxcd;uUg3drg?X?!ZWSv#c
z_9<w&Yh*4Wug-GP5&L6JAUj_m;C;Dl9DK3S?hZapJ0lqk+Tu`t|6*vlowtZEFs{Mh
zVVuxb8h<HMe{L{ue1FBkV}UH46<QG}svduh`$pC^^eg@w&s?jNLaDFA*6>rpDx&+%
z0BR6%5XpN}lw%dgw#{!41<2k!(1;9<WjItJS5#IT{rQV}m^=x2>{w4|tqA%Ol1Jq{
z`Ksj_`jjvyVD(0=+q^FX@=u-*_;pP24s(xGDo!rqi`b}gvf3nCu@@Qj+70S@Txr;t
zXEB_6&1ZVL9nGNh9T$A@W2w`&{+wp<@|`u_*<Pz?5m?{+2!vi=7eQlU<ky~6jf1n`
z3`_de*8VIbw)tRy2Snd&ASAm&N9VL*2{o$c4L0iVCS3?oq{pG98}(jdoM|Xsrt{j)
zG_VbRU9e1k9}S=AewDFzJ>xOoCc<EYN=*^3u-TiBJUBePGiSv0x#Ueg3gN#Rs=GsB
z<AZlkGc+ri5fzArrx0_uAWf6YPbPbJu0QXClu*N%Sl7RM+S|e!ru|Nkh;1N}do3ld
z7FLh3;mXm%Ljb1Zf=pVU3k;f!X%xR`#Ze>0)n+2P;-m06$IY&Nb8uixkqxr~YgKl5
zTp6tf`?uKIIvLb01B<tkR6qoRPbP=6y;sg%et;o>6cXhY3%##84bPKw(-AS?ZxM|#
zQ(ZAR4Pe$fIzd`Aq?m>I2eBahs*L9?&D-&8XX};wZMRGkvk8ND*?D`wO%g-9c8S#N
zjCW9w-}(vpV^XNTbw5(YN+|J^>D2egs(vvrG)I@tkPrIz-Qh}j-)HG;bG;eC=N4u5
z=^B&mak*2#&8dz*-&gNvh9~HZwfengNC@{9>jN?tGuamwK0s8K2k0Puy>^2F*@#ar
z<{mL-qH5GZ1)i$6QH)ac7@`^Xqi7oC^vIZR7D<0-YvU*4!!sK;bc&6Nd*k2BGum)o
zvIWu3gAhPNbfxHEAo%Uk^*~W~V5DhAxYK#Nr?0Q?`*)b9S?&oyKqgpAg_PH&Z(zXu
zY^heruZRi#kK_8oNk!q`a4>qF$`{D=;0TK?>IpXP@K18MG71!foS0DH^@=~fj@ckF
z4DM-i{LF79H4K2MzdrQsT{#$XWg3z4m&qiox*{!eB8T*qD>{p}(sZ6;h<(YnF3@eZ
zna$&K2aU{#d~VQBjP!>C(!LsWc$}1H8ijq5YX;uU%7CwGiol$2vYv^-qD{?{gwYbM
zD-3)s@AR%tONEHq<FY|t2<(bPwz__{Zki5i2==RDY;l&|{#skn46<Wm^#F(1C3t@r
zZ<+}&jf<H8#3ht)!{JcEI?&$#8?zv^93s2&c26KRrkt`eYM06WXiCq*3$c&OF|atB
z!#XulCZtlLnn@^7@8;@qu8nv9<pd~fbE1BQu*cWaBb~;^OzAXVs}v&@Gh1?eKd_93
zNVtD&1pBD^qj0ub)r~X(*`q+&rn2CA=z!%Ccf0ms#yr4$>*BqRLwn8Yd31an5sl|6
z038?ihgAP5@=4HCexM?ary1r#pvCJ0J{9Jl-rlS8)snHaZ_x3ueYGpKyle-POM|sv
zQ`1%Jfy;hf+@+hyECt%sJWTRppOh~lB<{dd#2%jFVk;)o$t2$MzEk91<yJ_BK|SCF
zlNHU`$L1lKv43K{-XAM?f2|lL?e;%uvd62R)&7IqY6P~IS(R)Dsnz_V2yTRsg*H2v
zi^vRr$mf#NIuxf&a}S_T1@Q6lpVlWQCvlnd`Z!ix2CR!Nr2Wj@!_`W^3I!Q7`nUc&
z=QH6%?C?S{x-JHrA|LyFq*Xj4zUx@pWp~r%2(bdOONU3%q`m?%q3S;?B~&=&dmxZw
z0oO)N3;8-DDQL38^Q<7?W=tWIIYyFVRJBwl$*9A_R!$D#?EwsJs<mn<HXHHrja!`+
zyxu7VT>9uT-NnV<3WAL|Z85jh`Lt8~sa(+(5_z^ZzAz(yx-KMrKz*Ky)I4FRN8~W%
z;Gs<A@nKbQM?^%ZjyxZiCOI?(#1ryd_Ju;%>bE|CKu^y{g}eC1#@k@!(<1Qx1Nl7P
z9Q5?`lm&D99xKROe7@5@`)AVdttQOiB@%osU>_<~A?2~7jLn<NtBuir6xyJjAKw{C
z3?g1e*8N6_3Z+4RTnYI-G6&Y{e8tprn0^94w6Rog?Cd$!ge`mI@N$~eU&I?M)@Ywe
zBp;zXay3WoGlY0`y|*G>yj9to?0+4w^nA+)&KFB<XT1C!ZE9XRA}&;!(59p$6d5Z{
zHUmd)_@C#`X>7y6QU3Wx`Rv^)OdG|7`zp-!jnk8$*HHiM0(vZ5GgHfOyc=e<LPj-d
z*}q4a-_D~_yK;ggY5q4H7MNhL31YS5yeS=_>ip0$YJ=x8o%whV-wGVDUtz_a=_<M_
zzT4$l?%~UDmG8q~JZo|=JB!gG>{!PBWj%X}(uJnvV*MW#F$I4r;7t{Ll?ql}OP6mi
zF8-+Rp(qf{UN4DQ<>$ywtRU^xbg{SprzITK`+NN_{)N@k4Y-P1u!Bev6~a+Q*P(&p
zcgDEjwl6<=1kbuHJNTJXNO0{2f1C^V>w6dF<eUTxT=r$JKa~c0tZjT9`QMd88%2x1
z$#SL=NuhQHsjd{^ylcVu!0>kbq9V>Sh~b{J<st$J(swa_GV?w%z37vd$Cdy-1+70$
zAB>CcM-Mlc!2mT!*yYbaYQBKwD_WKY{o}($_zxy5r7ttMal5T@QgW{NuKJjNKr3BK
z|CV~7wpmSA-17?wiMjOlnXBF|t(U`c1%RZ38-ZkgMeh?%dOu|}sOgeM7N>Ul5rEDZ
zZ7&>azuFJb|C=8rjrXri5vM9+(;{5nmu<d2qNf-lY!bkBtPd?~V0Yk)qfK!Co-lc!
z7K)B$%s+FyN!}AjEYVDSlX046GKp#kgw7Cc^k{rQJX|w*k~P`dck9nw8<|7G-Vzj6
z=|Dv3;K&EbeZT)0BO5!_P37db_HT%bq~&u$G&D~G2U|%Z(+~9e^lBx{x~@S(G4WVP
zolHc8T-<*JN^;~5%d?Mz3o{{dk*0&Aj%i9rZ}=qw0p^Fw%@|FB0L6C<gN=|jgby*~
zPhg(LsNu>aVRid_G6x1JRkcN_dN4K^1yKulQ(Ps9?g%;6%*5lrP3OBx5GoA^SPE+d
ztC#<(;7~dGdb@RTe>qQFQ1@|bvAVMr1oT2)v(w3m_z{WoGbFG+yZ3?@R!}e^8X<}%
z@=+S_b!1B5=ddE#g_I4vpbv;lSh&Z2xlR%_C+sCHi*EIqma{)z_1zXMs~T=u5`(oW
zUS=bDx*BBt+R|k&7a`}DMpA%r^IiB-xHs{=1x%>?9P_mM9Z0|rZxi(qC*B`_F^c>8
zhmgHTx*jGFmkf*X!&~(Y{W*MaVHhN2RmzmrEg>qZKWFzn5@Npy4BRouZ5CLoG{7#5
zyw_W?vaHMAZTE=y{SiXYa-Gh*FI8YOwdut3`R&l@zv++sistMR;8$D^Lv$v$A*ee2
z;E*?}=1=zSd$J-g9U~7|3j^^qV@BL#P8@?*{Qbdj)U=744(_J?*zb|iEsGY#N($lU
zl+TaGr5fY*oAk%;k5Xwgjonk(;C<#v{WzZM>U@gaQ3q+*OLD{YcJ&X_;2nR62sLPg
z-?>>7c`cEfROSJo20gpo$0L`9LC`2ICl^y#NGiP5Y&#zYi<Fw0y4EU_d-3-ZfJOqQ
z^(by;(E(`T#dminsoUOk(|M3YiD1^Eu)+$_%$!4>1JmMq=y`~~n_lV1!*X|Tvvh3;
z<qBI+6yH&oJ^73jKk9{59Xunqj}@scm$<}(>)~WJoB427#+iOA*r@*r{tS{8B}kxQ
zVPRosPlcC}2=6JA?6AjM2=UO>%?p9}l!}v_|5Tyy&?oNQ9i3j@*Ynlg_BKyQBoJct
zFeywH4kZb#W0Ak6#J-1~wW6}c6PM7Gu(dtOG!A;c9w8kt&sQF4VkGVee0@lzR_OWs
zcx~PCV_;*$NW~ayTq!E*@I<N-0WX}QUad^MQK)mT<uU2&WkmV)$e(sdo_sYP3O7mG
zUgh1tF2SL}zpYtwhrIgN;$p!La)8QIKn#VbD(xIU7}mi-M!Y^=$;ime6Tv`EHGrXs
zxL>M#d2%Yh_oX6LDS2Pz>?kS`<MRd2e$WHDtjuu173CztKi^JcAwQE04N4RcJm}~(
zc;U13kFY22S$d|0@RuikvH{I+<;5Er3|!t-g_zsjfr_BHa<5*qrqwiBm8~Xg6$b~0
z&yV-(vG{mo5fKqt6-t>z!GI@LP9PXEiBpwcs#Vgzy__z?RzcN@5&pV9#mrnhy_4t>
z&pepnXNwH33D-IZ(k;S*cY~!qO@%Cm2JcEBb&;^1d}`ze%am(#SbT_$1S$kd9hf6-
zby!WEE`|36REvQ9=hutYZLmv7rin3Dsc@X|(l*(ak*S4S9qWhp9N$@a?xO&=7|-Ci
ziTb!d7@sxlZ9cVIT5j|%{vu{yt)YE7P@lB<Pm!iszLW~Z2Nny}?jI+Z+}xuFgJT;`
zf)`512ow@QpZ+jd`SR2+mYxXUmG}g`>GLg*RJ;fV3P5ehSs_G}_}&H-=D@<F?iyUV
zT)9*iKK(um4_{yhO-SwxLlHrO$T_}95?0~=R4JTR{EA`=4`A!)?394&aNUc{((_SL
zeeH-i5r2qKs?e4x?D|}CM01iOXVE)udV{_$lVem-@ez|jUkizS-GLeH2}LEt^7IE&
zpvA=m<lNjpo-qi_?ykY2ztM^dD3FGe?LNDAsE@&k-PsoZPD77EoO;}?{87Jz%uj<M
z;5SiQlIT<CPyKVue|{xhM75DA(qIA-NLTzntXzAxKjn_JUh6kq^La%-l6CJjjZZ%J
zP5qXtnbd4WI?vS;7RV?x+-;b@CIZXpWyRlX!?(q>{}9RRH7(hWIxQgUzKE(i=1uW;
zUJiHE{Eh0aXE-$4d&0Hl`q(nr-A8pdGUI{4db$)I6hrsp;%$Os>iTU~l5S4t8Wk)6
z^d>*%Mq*kftJEZ@qH{wlq_vP#rKa<UO}HlXHeP)RZWRO=tKH&A2Q7c;wVZ*MN%Yv`
z{9-9MW)>eu(RV-!+|K(Ho#?m3%X`kD_=<ywO+lo=vzBaFM;<x^qsi%^*h}@^`1%xS
zdUUW&+tz^MR&P4+R*+A7JX>lyhME4b&{VXiQFa2;41Kgj5kYrhz(=57?C7ty#U9pC
zOwNE>l2ODD4Bw$7z+1)G1Tv@dAM^nHor$+sAAp+~V>DPvXB>`IR~zj<>j!XAG8Jle
zgIWWkylc65$i}Fb#4x(c6U;z*v3^6=V@LGaOirgwTZum%Xm2{}hKVyrcsMxE&(B=A
zG+{VUNXT(YgV8Z}sS8YJ#^<hrfm*0Qzw3A0>Dzp+ICD8GAcQ_fdtL++kXeZHuV3id
zdf!E_*bN4&1;X0chy@J!rtIDl7Ybi}E^yhTpz$T<b{_|f;<K}LRaJq}RcLPLjNBNM
z$l>Cy76X_`h9lOpiXFI+ViU%gD%q2m@B~gV32-0~)o2_1a6P2KunYT}gh$YFz-g@q
z*$tZqx_G0<5&(tmxx9)%o=bz4Ig^l>SShDW4KrCo?`_?ARpj#vwf4~;Y=0(y4u^AW
z9_~&tJOVwV;7KSN=(VcfuX%39zAvFhGvTGAMdrHPv3l4~P#E~mr{R(G?7*rDP7F@D
z<D1*KRwsrJbAW1<Q>zz)pYLe3I}azSFYoo&e`L`{w~$fR?~a9dvpqt~!H~MkmDqJ@
zqh>FDMdY1{c1Na`zc4t3Z06_VxSdlzZj)w<dKW_dEt0REFj$!4v(;pX$~42KDAi&Y
z8;mug$QIbsr3p&!;r)dMh>`tCFNR(QOw#*Fa%^EdRU}$y74PyXDJW#2Ii)MFHW^-a
zWmfycL#7hX{(f|3&h~n~E{1EZ*NN3iVQ)bCA{^I0-uCq>;RIDKhou#UhWU@45xp`t
zwnGVM`MAnz?{_4U#CvU$RT`Jp$QLaZ#zx2Y^w~M$i48>Eyd;#62GI$5j#KSylP+E!
zCBmZlratr#FQFJ8v4HC5OR#@tp`|8e+0qG+>~Fvb9cq*iSsF;Dh@q1N*`Sfu^<8Hn
z{Ct;O>lU_+wpHP)CtB=Z5Edhb=B~W?8}T<`o1xyoG)7A9TW4LOEGr^M=acty<MR4N
z!1|&Zp9ZGOCu`2!DJueYc~QpYKiT8}>^pz_z)o|TWNS*|iX)C=k?OAS4I4@S@(!d>
zD&jz*8<Mq#C<d~Gb)fC2+-WgOlMtB=6igYfcaO`v-(muxd;^)Sbmn`03%I!8#L>EI
z@r((w0=1KEKZT3Uh7Y!=i|^}5hzjS;rojhf`XO`+wk!HzF?DnnJ9FrX{w$m+^)I@w
zQJhGXvHdNz>G_K&ASn}2=itExT|#5*QaZ+F#TX_qn<L_kr_yOl;{`s5bcladvwDvT
zHV3Mg=<NXPDta1{$>n5+L$m;~OGX^fx9|*f06it~?B33x$so`fc5^PrAos-5r0z2K
zQb{kFc_WN>cmNkF0mc4Y9zrE2bDhfo+<omjy+}QD8njo-*Mha(F(Kl@F}kT^a2YZf
zT%WOjMeO(1KzHwziuPU+WIba+=YnsLJ385UFg~uXQwd9E-dUB{IV2^kgywC=zSx3B
zX+2w?<UzQM{}+zhg96v3tk>=eOm;k{ApdzND*7z*a8PeN(_W}WXZ0!c17}%xk-Ty$
zXPO~Wsz}n<lOHd#_$w?w4)16Fvw0>hItq#}S~HQEFvq<NnDLe65RJP$E)6DR%|jG8
zmvls`(=7U4J0b#?)E&DrGJxl}F^QM*$EijdUu>R+3>^J=?0ud*Qwu8oa*jVUn{>J@
zPSnskaPDGCtezwf6_lGu^BY9}EqeO{t;`t0fc{gEDkr&aC!zBo6|$dgjsbmE^>?=+
zzfS?3FVH+LWrQH9=6g5+9tzt%**{OGzcA^p7AhaD+kV268LuHoDkFEkhZic)oj%uH
zr~j@azV_8;WRoHLVYlS@;@6gBJzycK-X%yANoHVnc@akc66Lr;Kg$)3p!?uKUn%}u
z@q5f=-NrV0GJwnRi_-Athu*>SZNYDu8+Eo7{sJzWkH*ZkchbzM+EPqG{&1qP0?3^c
zL)G!VE0v<}gy^ZrAV(gPh2l1`cnpR~A`86a(MT3rl(qB(_P=6Em;&B`|9X72JEL+a
z$XA5>8O#njkV+%HKxeAn)U3y%<7ys7bPyxL;>tv&Bn>gMAtiZJf;dr)Dev33><x>G
zwxi?dlpiNQLjx>>hFj*PKJ}Lmv<m*leS7;`#yr!zm@-jUy9t<|4p+zx>BRf;$)#5=
z1w9qu2_V4Fxn;NM58pf9UtyU-lD8!Y`_lN`MfJ9vyxPPDlW?;Va{W?qDSO^J*!g<o
zB*<HdMz~F&`>4%(sT0Y5)!6?jFj=Xb?VJzoJKNHw!DbjQyxEO(5fiLG9%X6G>-=$i
zC%WJ@5^pc6B#J0Q``dczA*D2a!vuj)Jk4ICAxq?iAP;rFh0Bqw6{_Ro+<HT(C;Mka
zpb&b*;}j>A93V4T5)0JBB(|JAddQ9+Y83v%5+pc<=biQa13rfX1!KnJTbLNV(9KvF
zK46c}$zhwX3CbuSWniV@j}{KAYF50B%|0!i17HpvPQ#tqSsaiQj8p<ptI!$6r6l9D
zDDT5!sQF4KMxb>4aKT5oER|k0{~{ItWWiC-e7k700fEXQ1=#HlWrfLm2crZV9yp~w
z85Ed#ZfH|<TgtHU1olZus!-k1@G+C*k5(S*i)FI<9WRAeeWcEyo5^|u+*W0zl(zK%
z^bLGuLqsnY(N$D;=i=V}D@;@hmmiVQhD%TI(RxWaoqjFMd<Saq{*MONPbUh(_I#mJ
zRqM?_aQvN461jl(m>Y}9C2Cx4%dr_v<#DyF96rm0?}u#(fQv$~BY@Fqd?y~yXp&C$
z*$VS)L|cz3uh&)mOwMyk?3cCJ2HxQw!gdxK#Y;9?!3$FKfSfsoMn4@TzglSsvxtc2
zz5GSb0F>~Pb(nMCq^^5-r|{Chh8NqmIKnA_<46%>b+awp?s85|HCbh&8r*1DV<bwc
zo?(}#{_amD{@NEEk&nuQ+^x8H%l3xTTG5CxI(*goBOv0_?=8!tRRhqR?|wB6nM-8k
zZ|fY}XtsF;Ea=)$2=4E1JN;etJ#*Ga1_`?kgn#|)=c*Fby3d*vQ$2nsBBCOmz^#cm
zy*sMP%-O+c0wZfMkue-Ai^V+8w6>~dG<lq`qvCq}V~4I5%{3pTy2Jbz-5V}R=&Zvq
z&=)r7nk-365ElhJe0_cGds07JRmk^0*b_ag+-=iUstaWt?)vb3PqQN*)E+Vg;G@Zz
zq*N&h8a2b<Upe6!6sxCJZ_Iax6KJ|kx6PgKA^~s4%d6FEN=FU8p2S!dc;jZtUZ}_c
z6M1jRA$`<q=%$5mP~B?5H-#-EpX<B16<w>tOpaU8!a}q?gu>|qcCt;%a{j>6-w#-_
z{uphkk?^O+(Cpd&uo8t{?gk!s6m9}nUyV+=y_Q~^-UYY@8;3tpq1GsmIKjyv#Xm#A
z0qaBi?aqr0ft&OioHt&7jZYpC(n;sa_19mPWVeW`Z*hJ1oE90;IV(1va2ZDTy;nzQ
z9}IkDD^yp@4V$quQYp2B9x<~<CN#~l86kZUoRTyrXG1UwY30O8pX>JTg(_RAXL?Uj
zpKaszJ0WX<b{lrh1VMv{VDPnuInXJkiIhMy%IqlaJCPML1rKjvpPnLm${(U$VG!`9
zIU|8*mCxRB7IKp<x4He4HMF<*5{~sLcBcH;Bj3`DVJTdLS@(|opw@{deMjkkh2B;W
zY4bo%q_G}7lk|BrBQ@`hH)H{&l*wHy^0`)OnXhB&=cp^r9_=3mv;rwNjulARNF4pO
zAOlI|Vkf?3HB*M3*+#plZhkQnaB`Y3Xnp8LBKT9GJvv@q@-^+OZkil`fBzp5bf{1b
z@!1UpP7v5Ea64_~3F0CZ{2)H&%jBdMyCATHX&Dh)Z|x=QFm-;!?8E1w@B@c1(u*_T
z68%xnBL2LvXZMY1nmj6q?m(|4wDhQ}!7B&D`iUgfNs`8m_cV5A{m(?wey8R-a1)%A
z@9O?HwZdV0BU%rdvhRL*tUts2l0YXAxZYr86<SNQgtajSCr|)WPpxAO%YTi~_6KJ}
z1jeX4v~KX{M)G4w6zN)Lb_m$FOhA?K3L@8Os%_aw=PrH)B?a&M`7BA0-(Z51L_G2g
zysUr%Ma>XW5Epr{MuJnnH`V*@jlsSUI38y0wIJ$pEacI%&X_t7Uvx;eig!j}t1Bm9
z^@#TAQk@&Z^5XlRRUcGOiVC%Gg9Ly3@C94EBjh=17Bzy8C-i6Y`P-NZ2B6yqj@bAU
z_U%^iU;T&?HIbz18K-0;O9#={SMNjR?AZf5*O#)Dt*Ghm_rzM>Gk*vaEckXN3DR*!
zxmD?kh^2VeLpZU@h-9>BK9F9RAs`+5=N!;iNe2}nu8zXg3X|ceE0Id$(UYr-GSUt7
zUmBkVQG6j3URMQ5Wu~h;ttW1imwi9aY#5HTowHPMH>6x=x-T5j4^?J?yXA+(K`z24
zOO(Li0$Lv{5#0tcRT<9xH<AafXVIFhxGx^)<#@~+{(F|ik~lnqCTrs&P?Aiim#eCo
z&vk^r>M2s3h`xDJ;`FkTPhmB$pnvO)8!|-svK8nQ;9+J;>nbf!{1yHpoj~(=JHv4(
z#NhhPP>=1o{WHJv8){hFJMBL*eguz5%WV=_35SCBazY{!9Up|Q45;Bi*bYHk;gekj
zy|kh_+fm;w^7wBU3y+u7$^_btp6k1pP5|LA+*dI(r~DGhVrX>^((C0t1uz97p*Ivi
z3tE6cO0q-<oC{*?FVJ03s_DSNt7Ha0DpzQXg(x*Gtl0iuI|GJU54gS2`n@d@Vve3T
zB(Ee^`~<XK<La&7?di^Iw+=qbF}FrGT)#-TC!=4XYFoc_WB%45Yo?73Kz7W9)Pj<O
zYYT?)$ouy_qzy~kBW-Z+Q$V0p`)NLqk`NMQ5zfXbHagmdw^qGm;V14_tfq3V3UVc<
z>F{M^*Ul85mRkM4?$FzWE#sNSq__#Q@XR$b?xt*eQ`UND0GS`;&a>?;eHIWB+mzi6
zzEG$gtxaY5;@yN%-1}vjYY*C}8ssm+4Gk!CEZ;SLg@01h8=_pLxmy@N_+K?NzZlcD
ziF|G)wg+9DU1V5fi&lu{a?PV)$ZPUyL_0YE3=1W}XtC*gda8Sj`m*S_L`mgT9}HEk
z*g#hES~Y3+Wy_hQSdsprj7ksJ{xhx-)9GynV&y&Vq!N3WKyi)eCzJRmslFmX_p^Lw
zV+v3;!-G_R@z*RYQ8uA;dF|Fr3qC#%-t@ivG~fD&ecw^;$ajxlpW5_o=1LNm;yZ*W
zv7}+BSfRo8bA-7K+-06Im&Rw{Jx61yWd5^8@>lJ{ld6Y_^K>KH269d*i-+;k8q0l`
zskE1%Y;OqIXPXG&o%1Vszjp3FSu`0{pTo)JkjK_}T;@)`K`)(T7uHZviilk4#<|M=
z-~$!Gt_W02Evp0?&k%uQ%~O=2{S-!_#5F9@BpOZ__+lJhVw^;dl{e+JTCb}-n~}3n
zac9$DH}&RHhzr^c_|Zf3yheRy{dq7|ef29UkI<3`X10(w1pd5(EIFC31ukI=2y7`n
zObcWqSI9|c3I~)EbZa^9yV9e9VqTrYmuYi1IM*Cie0r~!3RAWgV|qtZs@P*S>GMrp
z)*+U=<PN#+g0GcKfW?C~FjIsTS9qZIa~ET==t>9sU$4G5Psj+4Ffe{iF=|U>th>S1
z6Ez$e%%aDaI&y;Vi|ax94@b`(j`?_Rvo%wTdt}k%)hf1k(mpPC_hOD=-!(tp%a!QN
z@r^uUQ#`>O7<neT?(^b#N-9fX6Kw6~ZsTUE2%$_N>HU=LPfy(ADQ6((VDnJUjJp3X
z`LmEn_S<ilJ@0nM<uZ?*F9{pX{#m2-G+g0)@Zvw~Ix^~(L&C3$!g18^dXFKKt%?+_
z$PEzy7_36b{NcaM&T7QkqN2HBY;M1-nE@%PD^0<qN<ovz3)3fgGtei{ZsdgYb9%4M
z;gKX`N#|hOVk8$WUK;f3VV40%#H>SR2VwgyOg>_q;sV92aS|1UtJT5!t_b7gEG5s_
zE;AAn32WKG_i~6BlVrz6yZ3$JslBc??{tvCQg3N|B+&GbOw^InipLuub{lS3Jcl2D
zP}Xg#_@hDAel}%3C8(fpi_ywV?O(lpL119+-~>zCfXJt3At-(Vp@YA}^@A7*B`|Rb
z*js4-A!`#|WL8`P*kmPztXypjT`MO)L=L>os^V#tY^#URIb^rp_$n*-HhtdyIo`I}
zIefl$K|<_!gu5IJTdg17C7JAt!)s_h*^yCIB_%N%J~gt}3uOQae-fEECeH>%SX>g3
za7aU5IAPVU>AxykOK9nZ(tOBj6AfeVL%)4EPNh4m`6;5|(Id|mORrw%WcXmMcPOJU
zqy)kt0GT*R2NJI9a?(9R54sdtGab&BX0!+Og%M&FTJ7a!XZm=*rnhfO<or7$Cg(TD
zO2tZSz3@x{fX)wBRYSsPSc90xenQJBN!k+qxPwC)GtSAO!2#XdL+|c-tgd(Y+!-zH
zQL&J5AoA=d7y1?;?}cczG2hn-K|)@>iV6jcDML%TaE1DcTEXFr!&{@!qR(oJocO*a
zy3&X|U-kWdN0^>sxN5``<l&*<jkZjN`sdn@j-2a*UH_*q^uNr|wzikqMMQj#R@#j<
zXSzwWY%uHJ1?@h>0U9W%UC?vW#TLIPMWDU~i~6$_zT)ay2T#(sjwn;BtK-<p3PW+K
zv%VSw4-f*(_I?36>n!;{X8YIqj7i%^1l>Qxc5j3bP?};;q~{3*_OKdN8KE3(RyLZQ
z7d3&$C@h~r@1|n5X^-4MC@(s4>*oT=fZ_asur>5~p1X^N{cfJ7tNl@<*S*NC#TLUr
zf)dT*$YGgP%*BGI^Z7?4yP)dU&mK*(<-TyfcZyxog-8wlsns@>E+gHq3yt~?&ukud
zR5HCPYtYQjU{5=sM3tmaVLQAqvNSN8#R+0;L^X#keq)_%FE-;uxTr-Wpt4z>#_xl8
z=MqH^wH&9AICI{6#G3D8c1wS?*GSR4fG;)caUz1Vc54NqQ?<w7eI}uXw<>1u@@U|2
zI;GO^HCiSj_Py0@T8Pf>uU*edI1+69&tlm1dTTiikcr(R(0gRCPb~9Ss&g-Ri@+~E
zJL#;edm7>{{*oj`nWC#URY%o-^1Tr*SU@20@ehF<ZgD1=)b#Ic{k&(H`N4fg&g=wJ
z06EGvZ$-uXiqiI;`7HP_EsAUu7O=RwAS|fLH1`8a4MxWUpITf{%<|jNY~+vDClm3g
zmXXVfeQis{>E5_GZ`NTrMwbjN0xIFs*+H{*Hs9ZKmDev0X3}fWD;pSn0|!Yqyw#KT
zrKqnhcm9DE>uTG1e@+>Vm97wnAnYWX<se^q7vhVcEbDMHn7e{(D@!(FP@>|g>ZXa0
zm_!44HFF4DtXI3b+mqhD6SscYMTI9Dbq)r&oJ-t_RoI&BkBHigxzL~jSn{B#ZOPs#
zo*rSVIxE8WNGYe_C4MA44)mo$o=7;@m)P&iM$yYdUJUEK`rYZ3X?o`TZ8o;B3oLJe
zWMRz-F(}1DXj2Rh-2-?2C%K^`XX^!JPL*0@S)85Iq5WS|cr+;rG-e_4(mDKQxKiu{
z&9#pDAJX_zjwDouM}~X9H|8vCMFQC0R|Csi!^1>k4V#w*7N$MN|GANpzc%BebV)!M
zTLhM?Q0c8l*utNv5{~!C3&jQUx0REBjfN3id9z$FzJpNe*v%gz`NX{o__>k)Es<hG
zwe$K8A-hV^)dk`gb+-jR$}a*Z5sSkEH_ad;K$4AL-x>-wli=W50{r#$PKMnbe|XA-
z)hvC*gi?atT=HTM0g6kGqTb&3+Ap}R(Q20sM^WV?R;2Tm-p9>di;PU2P}Wn~T*sf2
z`9&8tMfATc&X}1=1%okDrf1QBBXt~*ap6SLJl-Y*Vb1+WjZX&LM2*9_|BwZOb_H9Y
zj~7fPvKlv0W<JsGvh<b7J6f=Op2D>xYCR+>(ZyP#r`)RJJ0}zgZK1UQjJV%)GhN#6
z7n)nhYc@q^!=Pw(8ZQ#nSCXULj;{`sm!-Qk;!1~zCX-NN;|-)%enaY9t_t?<nb&O=
zxDJn!2(t#h=&wnPJ>){_$>A*(Lw|Bd<K?Xivo-@>1~_3da-w@=rNRv09GSgMW5ki7
zD5+4OE_u2Vc7#P=9|Q$qv7b%)2zSnx)2-5>Zqh_mWo}Ux)m^U8qw@hP1`I*{b}31;
z^%Q?KRZZm&ejX9%TrvoF@eGK%bgNl;C2%Qiqt`Jzq>xt||7yy&!`40Ll9bR8vbh@;
z%qGxaDfk0dN#zu6RS3tWdr05D;p<#seb?$<Pl<X{!1-&cm2l-!@Q`vG((wfqJxRl}
zMY31Q3N;KqPD)_p+Zn3~PoEpQ-6*SZ_7fWK6Z4v?!>E7J1TrXCA2@bSXxZxLg87BB
zXux9qaz9J8jC=V@0!<vW&bo<zN^MlMjf7??x%{@+tP6eq<WH!f1v)w|hTKEv`M3;E
z792(Zk%cEPIa1znDxq;db-xgEc~X#~AYj(#=Rgr|wN{5K@1MY2F$oReRGc;a#z`|p
z{0DNo4=dDckl0US8ijJ|Aw)aB35!$DrGMi59Yl^N92d0Bfwv2!=$kH2bTB6$rnj(<
zXpr6*@MjSsUpf5}wnUdDq#H1EUhMKcg@tTtOjpfvxXV?=*t78wjYMa1@Z;~7HdN<q
zPAZ6bMn}{*NO7BFvX8>k_1g8cb*gNuddOE|v#I4{LwvI0i%#>cQbO4Y%zWxk{QAt)
z$d*NQt5ixSfA|sT9iw0RxW#$b68sC|K05~-3H#hI+RxpW1kS#fH5q%E7r#or`mc@G
z;YU0|7{^m&*h69HfewCt8w}*aI+lSQx`WaV`p3ZDyCB47vcE7wQX?gw=BzOBrC%Cu
zr=@U~bTDfs#rW5<dx)b`OSXv7Iq^Z<GM)Gik+v|&2H}JZTjBFTac8cwNb}qc6(Vuq
z;Y>AtA!(O?(Ae)#<2)Vyy7#p1Gi%MYXMmJ@tAX(CB@jIY;>aON13@4EG3qp7rj?yK
zZMjh3Wq3ym(g?IS+y*cv>9D3&NU--ewFaFg5(2Q?Y#n78cZlUD^E-A0KfGi+MSnVv
z)@ii~2orvk2MPBG+g65_e&0p8zO7obXcDjRxgV?sR{@B)54msP-xrT=ZE6K`^U_40
zrr<;Cx2!ylhg9$wH<I|1vxxA9PL>mlGsLxsoOWOTs-|gV>FaIoQr=Mun{M6wao%7W
zmY6gp;t&|<qRlCk<Z6QeLWM0AF6|A_sD#Jv@b7RR*G{|#^_=<ryl8E<Msd{EuM8mo
zbmc16sxYZ9X*d0a>MQ20mV~v9W;W6*Zd6a3zZTj(M$HgYlz4SI)bXD}mM&j1(TqwU
z&I%v5iN^T$uy|Lu#}CkNCNIb!oc8LgBMkdv{E8Cb5`r`02CdpfrCCPaV)Q;9qg{7o
zXq?McHq>DL92gad$9ZUFA55~2GFS^oD*{ca<v{?-NdnC19T!__q{a%4a1BrO$t6dE
zT+7%Y!OVNsL)=SR(}U2ZxmMWpyy!LM??OM1?FbtvRE8P2QiEES;xJ5CM~&%FC(F4=
zUtE@h`<({Yq*`NbjQOXq&ufKp+^>_=^;-R92c`Hm`#C>KXrv{On~*RPqQxW{QdB#p
zSQz%*O(IML467K^z&XlB==x}!3n0A4zx4LxxE4PutEh7DZ{n#dGB=pJx{Y4iK3P6}
zk_cOa_XC!)HOOm@)6IAGD?+BAA5lXf>(!79n7pUk!b-}oE&x!ZK-Wfv@j{dM(?NM9
zcIiL6`>wRLNB*%Je2lMk%sucI9~+1{-yi#HMZ{*>{}3a8h}}a1P6Xg;IDa+f)e{ti
z5*zhNai(jPLEN7<GO5|vYU1?}JDC~KYuwn`O02Y%8js62+kQpFLCT>*L8yd;QyDd5
zP10p-{z1qv`g_gQ5x5vuUcF^g84AtEuG#TGzLgHa$B;wcZFfC3#8=L@-7a*}>)HuU
zjb-Y2GHCo{c7Q8;p}r8SZZ-p7rA(YaMBgx^kqKdkzv3XrE8<(4&pK2k;mj&jY<P*<
z?AP~f;;>9BY1+qLetK+8sYPZuX*jBZ;ObqjAx@GOSyVFEaKw&uiviRlA%QM^6P%C5
z(a~w((@H|HIp+C0ILk2y;AUgSVLTd&l3M(YL4|4_w(Gb{S*FC3yjFsh0nP=7ZhVPN
zG(*9-zO^K<DVKir*RzquJN$-6?GUhvr@%$0%rd@RDlhTqz1*wtRZjQp-2ikd0<+l}
z71TYP5a-v^9-*E{SzaoImM+=yl){HL@$9+dZfC0GL;CxeKu>!Yadd&7dM6XYnGXcQ
zMYKI=PyLv|fiP8VN3~)zlK&Dq`$J}>a1V69uS*>eeRnHrKUKNsw;XxQqke`=pAX3c
z?!Dj=p%S8F2G3$D{Y2#yDvIe>buLc=voDr4H;{E^Xa$x^NvI^|w@l+RX}?^t2&z<d
zO6g@+?C^YJyF{+upKD%KucHXtQ!sW3mzrRp3??eKELTMf5`I>?h^orjebKH{8}Cbb
zoE`aN-uFhyym8%a@E0#sS0ZwV?w8r;$hWoM?p6vKic0ID!GLjmj*!u`5mfY!iNzO+
zh{&bLvioIPrnEafqTR6Etfk1Rqt?{)N0gHUX$D7l&UPTVegz_vl^1@jmPjBY5>TAU
zz1~xcBVmSapau8xOw50oerWB?KG1TQt=HJn*I(xC4ojbrTL3kEO_ZF}!cc}(+&{<m
zcz3MT$r;}!UJP64LE((rkonX>YXIBfqnTb=ZI(ivY*~k?zSYdSWm@NFsQzJv0<a{2
z1h@0AI#Z7d^lT(6B_Qre5EbbMV&b-`Fv(&QM$HdWy%fd($@jJ57RZkF3p<HF4{HNZ
zuyrt0Xe0)x6A2H1McpY*VS>MZ5igKzV)B%}o|Bw#H0PJ<6zIdYCT?wZ?us<zN@F#6
zGVt(zN%{hY?FuaFe^9X1IsQ4(eFJa?JW;sd3^6~Ns<F+p?Lm%obB`y9wemQ8<Yrqc
z1#w8Yv8bRrszgYOR2Zjk8d_vOVr24+2lb{WazuevYOW2%k9j$tupQR&x^mG-AsJN9
zO@)-j?cvuX1Du4;_hZH=$Q3&71L%wLeu27g4hVFVjqOt8x#;HXp6eEgpNO~%C+rA%
ziowpyjfI^vWUE3}3t&Dp7aviyPO*De1CffQYn%WP;UbuFO_f?%hOP*71-^xSYet(0
zpzGMy@G<r7U@9$l{PSvaUA0{xeJFc`JIJG_3G#P}n${b7^j~y<7|dWuyuwqIbRFKr
zCBMJ62lpLav42r(_MU1qMCll9t%wXMq0&!0xE41T((#{r-9jCkb_xVzz4ewKj&j2*
z73IDc>tX%1pgYGH@z-(GX3_lytsn~jy)xw5_F8*_zt>YMhzd;d6^aPyP}HO+euC~r
zhKm5}pU&`e)sZQCO2^YD7$@OfZc1U1E+G2B{vqh={>JNzYe?)S(PDv`F+4y^bHj3*
zUN=URhdSTfTYymefEkpKk3;#Asx_|PUMYD^!@vjhD@o;K@-xr#b~Led$0ipnli}$i
z5AFd(>@m{69{ApmcW7ZBsZLo&8>AbO3?{6zd%?2elH~p%K3BwEWpV8hmECDLeABU&
zEVsFx{s^JLIs)|+I#y!p<dh1O=pbJkd!NSj5t-~vQ~j%T+D@fYTt8k6sg_+ibiK{r
z3ND_##jHjcPL}dn14b}jsfWC}>sd7lUry$=luzC?3gHjG$>(Stau7ZK-h1wO*L&XO
zxFmWwc$CrDCp44AKs%j&;yu}8d)Fc!X{F$_@D7+~m%`Ez6bY<2R^Ct{?e*#3e;@QH
zTvY*=i}tm*?5qxW5F~tFFC9?l0aDM3Mu={mI}`zs7jrGew>_U1o5BOH%gWZb4YOjC
z*ERBnzejbsU`AdmD{(BESrML>yp_wtFo%AWzomB+7qusGkArGKpZr+D`h;mglm=EQ
z)bJ}}b3}l?v?@>m?*&!9^|U;(4HSo^Vk@VSh>k6{vH+(>FxNbt^?1=RJVHSoSLf}#
zwtIb0o8x8IyPfik6O-^0#89q@{TWC-9U4Bwewe!gC0~l3UZ_diWtByuBU<RWRk8La
zbo3yC#k9u%Pevpn)F}h!CB?g74a_L_HjpybgAsw$N09)NqmpPF@v}bDNe+Mxv=;Pr
zaBqN31W&NLicXZ9LYO=QEV>d7S5RquJM!YjKjk@eb4;cGItOnnTV7f|Ki67YehW@U
zCVa*1YE-zm+#`M8<Zo_GGJ<w<f-Jxr?SP9_NBn+KVJnhT>VGv?HgEFeX63<7*L$<F
zn2%EJCP-+z1Enxp-|*IfYzf=XuF8OXb9md%xZ@&?TR&!2$5f(iYsv(@p6_!?@Lw;#
zHhlj!XT39O`t^@*wvVQMS%5>-lEL%AtMvhfeu9O=AE)bBssDqww~mVJNfL#jX=s|p
z8+V$<-QC@#aks+V-QAtW-QC^Y-6@>L-Tl>XW_D+G-@fndp7YN42X*u2&7va1GBP4=
zM(-%LTr7)XGV?DD5lqI_g@!Ug-N<mVmc5(hMoXX8nvutu7F62PrDw?qq^|P)M|m{6
zQThZygGHLHZ3r)%q%BSuxfi#P0PXC)H09{aN@lD$<!rdnao0<^vShP6nWrGdRx)o{
zGRhW?2H62Y9W(h9c@hfigq}@-vyrsuAELR%^Pgh>*mOSAjTSHUL+U!f;*n)B6@@*_
zCv_wcS7j?jC^0Wr5V}!dFhi^*59w`Rl_)6IPv%9L7dm+gHq>yz-78UctWx-HRPJdJ
zzEs_{8^C%zXhmmoPZ(d|@|kj|!;83n<0a$je4t^<Eh%l9^DUViDTy@wv>tnk2>^Vb
zRUQ=+PJ)I94{jPwED`pDF(s=I`wGWQ$P5UV#^&G~S&K`REnp0gLt@g?2M^vSkzY)c
zN1K=q8ckp;O|Om@GtY-RI#urUAMqV5g+nDDa0!k3C8DNg>2kTO%<O-|z0f1@AaaC#
zeYK}N4`tTjw)9?=$pg5?;#q&d3@S-XhE{iAJdgn-&v*#8mMx&kRHCBWLW-mSaqgUy
zz|2_TaWhAPQ2I*Pj*#;)>d6}ij041nxIW91)_}aD-u-v=@uZ8I0;9C_sA+XhYIe>_
z3#|PigN2J=C)v-WWU{QF)hR;EOg9sTx^5uD0d__(wgS?hQYRd`m8P+#Ov|#E<y!Su
z%P-gQNmzi%+XxB{Gtp&x>wWdO^iP<l+xQ6mg>PNjZvd|gK3TlcjAet$3S{$!*=WLy
zb?&PbXZyWi9sIp!E||#o5qugimV#f&bT#oi`jo^r1UvF=M5pa>@8^|kjZW#PduD7>
zIWO7b57+splX@U0vxZW*li)1p_BLw8r3RT<Giz>}lGc$;VhfC<RYCeQou>Mwx1E>i
z0c!A6JL5ddaqW)>%xWqejaN~wZ2blcjx<+0Fj2)NL~L1ZkW2hB(|MtL*?MYyBzES9
z?cN<fn@79EYK&4iXOKTPip_qLj@RJ@<ePq;+^w2KCr*-_F(2^h$#>==`eGW|Bt-e(
za?rRj9~%itKGJ<w1A>Q>04C}n3%3~}^r&rjWWJ(dYGdWQJan(uqu=hX{fY8DQ1j^x
z$5r<k3iOI=H-=GN1MH<HS36vppHcTYDbDC=b_p0WuN$YN8<ExyEZp{_QQ1cyM-EHm
zY-igEI1i&YpMzBOWs~I5(SVbF-XUgFa&Y5dIGqWOCfB8z`q!VLs_dIMk~GRDDJ0mH
z942<1?4>{hMa>a&qE#}cppBW%vB9Q`x!GiOJ1ySt_th2|g2Gu+=luuOHh}wFi91zF
zZ(Z9ykV%D&i9BR`Gw$9yiPJqS2EZl_?64z-S)#jGN%+)uwhtR2@_mBeCpDD~<RLuu
zF*)!Qyod^YdOqY9`>qeZ^mO3$;ULRax>8HWt=dcfe&E`%-IJxlNdJZZ)#Z;o5$MfB
zt)Nqm2z>*CGB=H776<A!`{gMFvzBwiFaRK>q)G24;G}en+r1H=2}3+bpe^9$#b+~1
zK<9dZz>MG*WoYDW#yoYsLfT4?6Z*LCuy_TYb7NTMaQg^oA>W#YJ9~vIQuwV=!GJ3w
zhkuG<hO0Eq&|OBHXo|SAiLLf0VrhQ@hi!1ulL{v52v1MD@NT_3N%gUo_!yM$w|BkE
zZ<+#RjpG>s)Y+7bmc)=8T!gM0PwNLdm8GZ7qNyO(?3cc=n~1tF6R4b+Uuse_vFKpU
z9ydvvrVvvcL^6cXXZW2(_kYsPXz;3d3|c4iQ6JthIRV_(JTkO%(RA1y^A`@^j9vyl
zQdXtkd}Di=me&=%w4%5)A3#Bm?L+eS5-Ny7cGGH(&zKm>`(cgJ^t$6-N374JHkC0`
zfUHDfIbJ+qC$kym9dd6~UeruDR3+FIID6!Pf16+0Cj3_aonm`?R88Imf0)G0zec^*
zl3HN$g+YR<51X++C#V=XnOO*{mZ4Mg5W6<A4T(^PA%So%oio@Ipewy(e5+Z9Jem9h
zf|Tc$obJZi=vg+MN9i<uS_O;2*x7ANeyG^zeM^#FtJ;OV%$1ZJJD%T+7P%_7iR=Ko
zG$M$MrTj~tl-buW_WHWh9G<c_`4qrM)GtxTns;c~J07x1*B4-BNSaDgs|>*<V%-&R
zSfw59<qi4?3);4pyH;cxB5N}CMjq|2a&5}ao_R!6vIogVrc&!~gtX3-U3C~q$U_3O
zwGKcD$a;x1cmdnc>xbgvh3|EaELH;P_3ajA3w9!E0RPNkWLpD(Kbr&}(H2ELRK<pg
zvzqrAxI*>WtW6t>-%aW_oUGN)ZuC<wYgA6Yjj&U-j&^W4j2K7KLLS^@<<ZMwF*m#5
z(#?tRj7r&rl7Vv>D`sQa62L+@0qjy-1V2!woK3i4L6tT)!IiG16?dAcfqSWat&|sX
zVvp4c$9W%<xKimKRS&^QEC4F#u#!`ua3w>AEw{@*_UW-d*Bk|-8VKd^N2U(1+Gl%K
zo@!UzcGxUfH8OBz;hXnRO`gjd3olf8OK+vAzrkwiCd`1xNJREnxf)h}j~f&bWU*zf
z-Y~d$o2}*RDmfq^UJ7R~4QXIcDFo9<DK^nV|DJhn;g;Ye;-<NkYb125XvWa}0{s-#
z8~B@-7N2|!67{ns!$LR5h5}~MmW_qPwDtDhsD@dVa0R26avKC+0~JHX0zX_N!@&_O
z`!1&C8M0-FQz_j;oLzY*GgV$WO9SCaGd6y_*?PsJhTiKO&o0$-IND)@?o=;Jt2??B
z(arwxi>qDaB-vJts^Y}Pcnq7~Ofx%1Z1!_ToX#pw|BD_!`D7Zn1{tBG8K<?ddEFC(
z<F^_x$3_?-A;T)JW^^^;6e&stLlnvkB<33i=)pu~LKV~!Uq_rtQd@o9?5>HpV%>(h
zI$!1C#L`4{?o$C@0cWO?V@+zMJI{joPAR0Mz~*Y(84<yVJ+m0f95EBP!QZ+UW^;&W
zz()Sw(XT2c-|3ljmfVtUd(QWUZIULhA*zk-^9fafaw>*{zyMtJivotXdB>kN25N&p
z;D$rLG=2CDy})`}10+N}?N@zQ0O%FeJdkZ9-5Y5u)^CnC*Dpjt6`^@wD!+bkzMQ;n
zK&*XU*8UJ&_qRl!h3{aQ28X0$PVl0Bqbk=6MuDp0f&AdS&({Sx0R?usdz;3qP|hiR
z?mZH^igq`&mzWG?07LdBM8C1$Uj2Wlo<%E1)^Q@NIWO3H*jSCwwJI0gLjzHQ?<>yC
z#XqUMC%QVolaSW@IqU#!vt&BBQM^4avewKx^fnA;9J+9?95s;&8nj%jRYSzKJc#Lh
zg&*$2<Qtzfes`}J^e0)|bHR^|ka8F#mpF<P-*xL*pEem&I2tjdV+mj;VD_duUl_H`
zCCfP7iv5B2L7A3BBISMtlxP;pYC!9p2Gk4zen}PNnF}+abb0LUo=mPfKJSUm94701
zFHTa=$61D2?!cQ>qe1P|v|W#R>R6mSWT~uUe?dVZm&Hq4*|^2KnH&#t`1)PvL-*&e
zi*Gkq&5afT5YmX|caG^_$7>Jk<5>N0ukSKgqk$t3GN*gVq`&4?H#zeR(BMts8lc)}
z;lKPW9#JmoUO{HYjfmorSitJw?_IXa7L#v;2#e1r5Kd`?e9^|_d#YB&KNs&`J5zvS
zTly*s*=pqS(mf^_+-BH|w=?)9uQS^H^6bhmwea9R+{0Z;szhC<e43%ImWeU1yxn?#
zb*L)1x@W554_G%m`qiU%*W}W$pK7<mF=Kr2N8Nz6ohp?l<Vy;Nl{%N$>PlzgHbU-F
zh@GmHTX0+9PTJN9?qkp;e{r-AyCgA97QS-j;><xt3H{m_&WD^LK8;!EYzZCNvf;b-
zt%n*h=TiZrS(^0Ow?m0MsRB?L=|E-1*N}owFc5Pgjj39t^Id{XGCtE8i$i^s*l9<s
z1+{t2RHt&?2D^vHnkgwkpE{nZSGThy7dxj-JQxA>T*O-JD4x`$0GFj7XsaDVU56CY
z!<F~%By)Qgg(_u&Zhazpt3=8+MwRl4zBdZ`lc^SyzxjCw<oMT96Kp+&3%|T+D=+@w
znsRVuK-*uGa7dZPjp}#o7}hSeqj>ibS8|dZmRd+ScDSc~)NDuMQ*!;)yZj0LP5y;8
zFVY6{r*`5>rCn<04WoY&S(5r!Hx}d5`n_W0g{XOE2CO)S`kaI+l~NZgdRnhNIFu|q
zR(Cv4Y!@h#+~A9O1pf1mMn?W83$F!J2!t2H0jaZ~CzKM2i|$ir1?*Y{7!R&7OFd;4
z1XWJ{N|R4*lgWwV>EVNham_#{y`Z!-^R>_vhV_P0rZ<bPXo3x&C+G{%;Q0!vLSZ&W
zhw9Srk3CMKg_LNb(vkR16!+ArM<8evurl&Woib-1jYj)F)gNAM-~F<*Ldlms0<LYo
z)W4YPvqF+V>F$wk2wb`K?fK>voxB-<H=~5caiPo`l2ucRxV*Y57FSQlJdvZ`X@A{)
z4#S!aR}jo*(m2>bxjdHuH0o<f?jqX&KRYG5F(;oHC0-2DtF2KJG7gW~TCQ!(Jw<{Y
zN(=qmZwd&M?bql+WfXu(XSFVn$T+LT-V&O35neIS{0V#57)?cFMa^AsH2}B$$F=&M
zk{R8>+p)!Y)~TQ0=%kodJpfX*1P_;*yvnQ?&eoR2v27L3G6CdkynlvDMD4anSozbY
zS4t=cW1DJOwk>8RJA!^~ZVp}Jrk}w?33F*{1gW1ArCVjsz<pBPct*llTwKIH)qYFz
zL;1vI>-$xq2qv*ao3NdfrNe17;I}P{S$pm&ePvD#2k0oM?{F#p>s-wREh#<dcwC40
zm>9ERS_$O^j;&jhMhInq9Q%Cl$Q`U~MShsLH5W|D(Ke{*#j1Vk!mxbhy=^k;%wd0X
z^WW1N73!vf4awiD@al8I@;EB^=E5Mdw3IGPL*=JAbkvPB66ksZi=`u*l?QD8WV(Du
zYU|9A_@*p>kAXgm_=YwCWKEz{>Uuhm=5iYNxP%;n{*hInJd3^&ZS1ItTa658Jb)R;
zQ%*!fK%zE7<MRc2UA%fS#$#>bp(IrDcqA{5nA%WFey-%Acz+(|cIjCXz1W<JUxU#n
zE@Oq%jLit8zELiN|2?@7r{YTEph`%Iu%46m1-Ul=MCRw*W~MZarsLLRslRgssw@Ey
z71`U)w3bvAa>1lpwEId>?y7r>o$Ay$WQ20!u7$eG@eH;MHy@y~d^x+_7;D)A!+61O
zvR)-g=<Z&pseIiJFGBGIb3j6su1ujPy1MxCwNrz=p$QAGs-=#2$VftP%!%~0D+2<-
zqnYI4Lk4JifV6gETkkHT$H4ouP^n8?cPp*l6hZ?Y0jUC_qwpRYca7XdO7xaB*Awv-
z8%S^R^2#&Gg(6yuBQx&quip*PWVe{*5u=u?Z*|IAX@8mB2u=%UI&e^%y>SZ+O}N$H
zcOHs=0FE~b3c_pHs!vrParaTsVb+hg^*HAkglmF!>oDb1aLY6K?f5dh`9wudo5Rn?
z!=4qyC%bcLw4hg+ZGNo_imjspw{->12Y(^hOtMhA_k%w-++xdiu$2dUHr48JGaC0_
zNx5E-=@hy*Tq_qQ<qR5&$)%L&^VYk(Q7{muSoZwHqBr;=(O$ZkGXKr`!|?{OA-+KN
z37VFy_3=IUyS-iyfd;*IqJkVuc-U09IW;R{2QSPR-1sT(Ih$DSstFT~nHx`{<2HVk
z0(TkX67>@qA{4l#Y4S3^R%<Z5H1@Kc8$}GhnTQVWOhphD`I`fkO}!*^_-c?E*~xpG
zf&QKu@IIVuf@iX82-;{)y+j37PdQ7hEeR{>kq*CZjB#D%^6fOUL1m$uk&kBqGh#h#
zsa)-j&jV~sM6^v;wIGk$SVEQI`)9WsLQe;a?fKj<CT`_f3%fn5bI4=xOXG(;nx$t`
zcbG3{M_(Xe@DEHtUhl@RS3k5kL7j-nYZ!IP+?-F|;|Rb<2ian}KVy_rghR`OwdLKa
z4t2y7?KYpgwAhHCLB4hkdCNagy&5hE*7)^iX36SqLc5ZoQyi^De0wYbFVL?%xSDG)
z>&w|^s+4n3PeBwibjqk5KE`Vg3MFa~r-Y5t7GgSll>Dkv6H6+<dbH%t>UoOR{QGtA
zDAMYJ{WGMo^w<XSgDsMA;vVG0N(p9v=j*(dXmP}tUoJRkj89RF5S{K%<s?zux5$$I
z>M&J-3BHzOZua1e#7Z+`MpKN#c}}Wx;Mi4Mbul~*5`v%biW}G(M8pN(OJC8PZ|pd3
z21v}vnp+_qvLLzMEVQr_|EQG5vGn7-&5}jpUOaTqTOEd9K`N*E?LU{!$vssNr8nuA
z9VeFOQ<|l4_oVyP`UU)qx+O>Pj@Yp(y;Vq>G<}of&<K&8P<G*H%%{Tz*<!Q&5!K=9
zNsTv$Ig$adLP~3oiiWh5Y5fAtiyDmyH|&c$!#_&{_iH25f(G|5f{CqA(-B%Ha-kT|
zepfF#iN(sfSxit)h7JE-Ma;}MhDW*`c>KhX+tuIB8$epDTugZ`f7@O)k9vDNt|3Wr
z6t>?NLMb+UwngI5%2vXjLtP~yM4ls!ipnT9l>UKl&)*=8I)oAdjN;$$ct+jw7fLEO
zP(5y6XP?dwJoTAaJ08J4)1&)dDPcSo)NsshbZpXqQUDzG5LEZ4|BM1R(VBrajx`YL
zzevQFh3*@SuUliO0S^<lp?i#_JQtRiLFB?Rh|E<{T?U*!3!o*tA&^tRK0SjkW!^lp
z1SBj3vM9Qa60R-4-g*}r4D>{#%_BPyIdTN7mfE-ea&0-4nbolKQThc&k)u1-@A0F7
z04*3-UleuY7LHCK)L?9WY7E!|XIhn@G>maSXL={nPPR(1`K>Mnk4wgK6aszTsM*3`
zHb_^)X-*4a(%6)!p*%?tbYvPqJ|$w{%EIzCqG1D(Fr1$Fx%iA6y&!h2c>*eB1rwy(
zh(2>zK$H5wZqAHJ>9@_8@2iNvzUv90npb$BDTuDdxKjF>l?!)sKJAY%DM$Nr<RKWK
zgW29S5LWD3>wzD763>X5rHO~jD_dDJZwcKqxV6{`Tj~+ZH+JTz167nxTNTYBwXyvy
zr#la!xI)w}Q4d)Q6t06-<G&Rl=zi&2c?Dw@?D1cPYv}#WE;Q{Lb%hcnRna%%Rx=-F
z7LyUL{R#eP!Q_+U9=6@GYjtdhnn(%rknv6wp(Bk;^kR_>%K*hfL`!Ii8x47voq(bY
zXgj%yh#CaLN$O#nuqjw#j!ov!WFqLo`{NHhW+I7!!)3Fb_3?V;PJ?0o((%@kNj)Li
zL#&#xYiG9f=E&r5u{kBY1mCUum&1;o?E39tQ}3W!%ZAoZtG06f;p(*?Hu`u8>Rj<>
zw0laj@INE)wAVTb3A4V5s*vrkO%_GHa==F|0C{<kox3bd@f1h%VJRb6c=fO?*%BGz
zVh!OlTOO;6Z^HVNJ)yqz@6u>dTZWjQ4E2N?U8*!|y#}7kY*gDn;n-C#yYJNLuJv^J
zE7qqbV)^x=Kmx9wi#<DBxLCevOjlRff?M24s3JTz(pq*ovC*^M&lylu^!QSSv8$M;
ze3^Dqv&C4Xh))#IOm*smVMYRy6u|M9FdqV9*@Mby605i<#(r<ZRBZ2Q2zK(`=^lX2
zX(=>Er&_<3qGDXq|5l?5TD<U2#j}&!XH#mj+2EtUu5+<AY5~4-Q~ppUHyCYH&It4g
zQ}s>7J;i>qlLq+*-D;2}UOO~bwi?0Fhy<*il2Q&zM+?tB!dbsq(qM1CUzn#{k~rHL
zUb>kf@Mcydeclv1)~qF@+>AM^Kv9R7N43q1K1BSiQB*P4&|&Iiq2H*OL+{dB$GegC
z`J!dlj^iDsb71)zR3>GOM$bsafxiR_>(r(@4M=OJy9Nd-FWQy@OcBA14?29+10_@B
zK%*v^W!LjnJcTY-L;7p5WBfXL>E;}se{N%EP_;f2Z0G$wtzAM7YmKk!el7QX#!`Xl
zGj_KBvtJutfRlyR(SSp9jN}j8+&6A_;TQEeL}n4V@UOM4E_~P@ay34Mgn#__ipxN{
zUs1~#?m|zG+=fFkNAi^6k;>xR)~nurd}IOwdM;5uM&9#bRF8^6_^)q?PNXvu7~_Gv
zRsl+1rvM@W;$twsjMi;esMZ#3xg55sOTiPlDS&#V)(M|bge7mlh+0ysNT-(Oa5Tk=
zezJ;oGnN*le`u?*?9Nb*E?GI9*)a(Li~1SNp-w6ay3n;w)%QZMLagE$Ms*_}X!yge
zR^c-D@^`xf)gj_1tM4i8>gynrJE;<7qUGx1ra{s<L1nO?*zX5#kxPGM@E9eP2kTiM
zMp~HbZc(%_2J~ex(}hzpEuhhY7!Q^xtyE&KJPSjyZx4m05iIS)QJ9tqt{O<+*56Po
zouxmQn`xBiDq}79;Oug0+Rlrej!{^m;q4k8`gzDRi3#ip6iHd|yyWVD@ce9#))}!F
zWOg{wb2B#|kZ@Pn&>$>GhO9M)Q#l6kssC&!02z?lo)?Gt=t>7loVA-%=opYOB<jR|
z4NVx;P*pn5PQys#SCT>h-bP@wFf*zhE--3f)Lk4Rq)NrQT4gLLn55Vnb^YV3+zxtD
zL1jG~KJ>dK^Ks;~T%;UTyr?m@VXSs{ogFF)0zXYb4g+Bs6U<CI|0hSa((u~)tu3U)
zNrIB^=nZN_)t}LJnySxuFL5JD5-z7HN--d;1)))cH7S@o7OtT^h+oA^M0CiO6cafg
zHPK|2;Km3V4EbZ5$<QgvwHqd(KQ2WLo~T2P_IMuollnWOf`~&q_EPFIG@fwDwtXE7
z7=I_IL*Rw!RtUr*F1`B*nS{{0Y;f<CfS<BZXSflYfsV`7Ehkw;gh#ha(8-8{P0!$%
z5N1C){EAN2>@uN<Ya)m&Iayc_6Ty(K056NWU98`tZmmm&IACr0)RK{LUtFH(W7n@(
z;bnsd$+jHu0s~59j2rdy%OK&wt5oapXT1kwVLv<aXmH^#-|g@Gjh2!!^&1My>vO07
za2|4KucMMeVJ^kVAQvf<BGlPi_+^)OQRd{skD;%Fr5s$dLp_R_a<+g@LS9;sqB~4s
zs7eJ$<dbUJ<ZI$8Hit;`Myh!2HdRl6g<@i+Y3WP$vT)EMPe4B&G@j&avH5bFT>2fC
zKi+M*-L!aR0(f*a1ujyt0g6pUYm?^^G*nvEu#XI)YqILZQ-(OZn>!!yP%KpPF1mTU
z*q2ovsW5QNQ7H^gXsaicN+04NsAx1UN{bk8v_dE}h;>(la9ZecoODUto3O44+}3jf
z(i2{1IjV<GSD}SrCf+sJ@wiyZBOfD>b2z`y%CRN<wx_L1xK6x0y~mGuc`5+q@AFN3
zs*UDMPK+TA+?@Tcy>MyUO_HrSa0n05%&h@o*7?bNZ%u#1p(_S4R1YDV?3)wpEI$>_
zjX>5PeOb$`R{Zvb^UGIPJ!ula7(8*dgFxo_DJMI_nz;qzhdQt)ZK@~W+eN&Ec##UL
zwt#>jtPWpIegC(5gmByI=SsaFfrDG1tSW8Nr9+L7N6Swg9&*z$D#9v_aL5AeA|H_L
zu)6Tbrs!1Vu0xOKLNKo%s+3)=*@W2Iawid>r09%yj6X!(iVg335RYB5^|bL4Bo1F|
zew?IX#<6SAFj_;K*OK@aYV``a+VOPuB?q5x$J2!F)M-t%UpAfGf58?KA^Tt|+p&ex
zj;CFsTK1}t$hW>Ll%pQ#ZvsUWc8z{8DTe>PeMwvwy_(edxXPEQr0zb87MUAjO^UX&
z0VH%8b2);e#WwP5i)!Pa3Mdp~kCfe#Uw)E*Qkd~P@@8z~-DrTc(U*Nr9XF*(n+AXH
zN~5&zCSdk>Kp=6L#*6^v)|uzwCIiq*6ND_nnVK!TK|6y%Pv@YLcFyL$Ts=!PdJiS(
zxFD=x>yWYifTl=GP3Du{M7Hs?`A9q&UdXmS%Z!~{3U<)pAPMw0Ui8-bFVL}~(!0Ql
z(j{V`!Z@^dgCzZiN8K7y@+N&~$XNkmZ7HU)ros4X;Ys{>0umc@@w>PC!R(qc(tzy?
zi!DqAPMbt5U*uVRtehf+i8(Vd^>i%-z&9X35wRk`<<~t|e;8_30QJr{f<~&^U^x8j
zeZqOFW0FS9%^K49m+NKc^E=#^hj_sLws{6;^anSi{hx@$;i%oQl!KqqCtT9rM)jB+
zugdGmJQ+Fef~D5Q7RM9Cb@l_q`o)x@jKAFJeP^p@srQjp`DmkVQ)Upye^ToOz?x!W
z0Y-k$m+%}ASCzub>|VwYVtiyb)+Xi9KJC}B<&s42bJTdq{+>iERlHM~zLOyq4E{lz
zytx$_m6XMyWvwgFYE)7$T0%_`dlI*w=NPL=qz6FRWJf&IliOh$xeQX_ZqS@$>KVcr
zhdY9tzj#1j#IEh^%#6m@zDxEB_+?Yb!=^{>acoFHBB@Hu+W+p%RIlPN6CBME8>}34
zeXaQ3&_qs182pj%_^_lc+D=L;!&I^}%%2kB?si#RS7USxcQkxm&a0J@Hn{_+;4h#;
zxTkD{Rg%CI;!?3!tH%F1!QPo)>)J4w!BJx83CU6dFL;qllPs8qW49tzaF<scPS*Dv
z%(L}K0UtgeI=Za~Gg_50j(t65Z{=i{z@SAQFEg!gCP(i0shdstIu*Y7sgC!WCSNwe
zN6Lnfr~ToPugZ2sNhV7w>|8RcEhUGP@^+u#YF#LvA}Z$hwOqDrB^BHpvh=c;jQL1J
z3?ZG_XcFc4nE0YFFFThh>8%)o)abCid7yLl4;Cd-F8NS+a!pKX<j|aS<D)_g>6OZ7
zEJDrR;cNloR1zhed+q)Dj`3VxEg~c$UHQ_o;h021jl7xP2AHijMtAWlQ^&3|vzK$|
zoL%o%m*P#yf?k*-Z)pcnuG(#CAGZy42U%tBm-mn@zFXN#YZv#tZS=!4cQ<e5U9a!i
z_S5m0Gd%5`NXwk}=T5r=cdK_34O7<Xzl;$do;__xUR1sq3x+*VVwZek@P2i9Y2y7@
zxwsEFxZXcWQ$By|I2#-gk$!)D(>VCpaQ`vk)xL>3`#QPr_*7M4T$XEN#q%LmoAn4x
z(@@9=G+tP_7~OT^T{aE9_Au=r!5V3pXb4^PA)BhQw#LMx3xy?yTl{?H<Q@eOi!kZz
z=m4Co(XbrvBo@6jX=4=Py#9&o(OPdoz8XhFCel7uNz9bDEs^iqOM6?WID~$5I#FzM
zq=?KANUo5)**DT^-<x{Swi@uXc=}EHjWi`%J9Ig&gE0>8MkKe%w@oGOW?qdUAtx{c
z^^;HNuwf@-wLsVJZ}(ty2G-C=;67?@f?#07h+qW&yzHjz8$|(uTKeH&*{7qUt%y|U
z{$O<2b@XZcm|!TlhR|R!gkY)`qo<$>jP0P73C?h*>g(zAXzy@)(@yku7fOZh?kQFF
zepD*16$MRWe8l)Y&TMwvFZKvc%rgp32z`z(Ms1EnkgRPN@v*U)lv)MD(xcNXj_8m`
zzW`IJFW&%9D3Lh?kd<kA`f(aP9i~`c?FBJm;stYZtkksc>0OagUPW*iG-&NlB0poQ
zJFH`cvv1>)Ce-D9o7*O=){4o2UDLeEIIf0j(Ra<39KzJB$EHs|p-NcM+Z=KuQnM>o
z*>xTt%@k0pT!*<)mj7G_Az-cFrBRSk7>feLw)@f6SqkR)dK%-9B+6#!nvQ?4J~#HY
zPFjL<;o-Y599Ugo3n3U^5EvNdr+>TrMv_DU&nL9%a!f^4P1##Msy67x_G@Pz&Q(1`
zYBib8W%_A`r63`)UKl2BKi4nZKO=M%1U48R3)8C{mqQt)Cy{X(q=ep7{fXgo3-UCy
z%6+oHjBXg#DVh8;q0-(tMLS_Tq0dzx{dZgXr=LU&hS1%G7)MbNdG4EpT_pVwU+fmk
zk;8R-SU-cgfG7$2^MM8bi>Uv+bm!jiMoQJm{1^4ZSP19{to6-cxVUIVOf5h~|7b-l
z^c(;}00V170Id|j%Gkk#fS#F|<zGv5b*O7tud$$bZB`-1fZI0)?7t}xAW*NupY{1T
zK|Lcv`ll9G(u+D-7KziJc15?wa42Z1BrNxY>aB2Ob=vdhCwRz+`JILT3|r+**W6Sd
z!U)|1UcB!*#d2kl%4*jH=o(fNj=bi*9kp{Ilca2FPHLkU9rz8e>>n+>N?+S~@%bqH
zY(5eTB{DV_sy|KlyqnL6ijQ0Gjmyi`KZNcx7)ljXr9otAg>bjCQ?U2Wv<|-mnl+s2
z{GsRkBFK)cszNuQ)8^T^;;PpF<~>l(&1E9vk3?VV?{cLIq?7Xdx>M>;PKQ^CI|u!3
z!NfVM;ytq*moK6axBDlX5I28Jf3320xxT;;Bl|D<S{_-xiegFgg#Ht%FcL(9;$L7r
zv-}$A30p;m8`Wxtf1!^Y9jS^4+2((~@cCd_0b&suwYtS^Pc|wms46O46mekdLb73t
zX=yj02vGi(BnDfWuTmNNp^=EcaB9@DW<21?v(6K=N_hiwzM}X7Ru1+8R)TR4ZWHOb
z0Yn|BRh>s0?c7_lh+X<(?k<;Q|6$r;*15j_vi*lhl^{~hvE@neg}-_PFq?wz$3qe4
zm^CClSH)u$T`CEy@29MbzqGb36XZ9R7QCpSu)?YjFe_N?%X`a*P>EOw(+xcJMz5p@
z+Ej_4^DW-iTA9BayRFYuo9uWWPXZpNzm(i%(wTMT3C@kD!G=Uyb5uSF)PrjkH891D
z@tn@iuCt!3jhbacFmoTKWA+0qxvdIOZ3hq+3c8wwG-GncI~IIZ8&~!Qq&eEUa4bd4
zVo++ox%0FrJ^%h(4B<w+eb0D8?z-=>1-HbZ$HN{(9L0#+Y&!@JFAlANfN3s#PI@2!
zaeHz*vIr1o5)z2r$ra8AC)Q|y_hw-~0aF!Q1gB4S{2rD()k?==sSVx&JUHXQ&H5^Y
zB_uXAE(>L2%gB--*~ZNr6Dl8~w<w&R{u^aYU^9<B8%4exl)zrJVY-GCVi=N4X#*gQ
zdp?*j<5hMRM{3j)smb7K#=}CO5zJm5+TXD<N0*N(+PpJa2<eWuSsPcPlO<?WtFT-~
zGrn8qBu;KFF!W=8!U?cjaZ2-)HlF%H#VvT$yg;0=ww7bqq;^5!tj)uX4g6EGHylFq
z=o1N3dOBU|9>s|*Eg&vrI>qDw4OfB4N`eO0cdEN-|GF0&jMavtTprGmJxMo?bkXuQ
z8qKA+oRT@cykc&iw8OyW?h8`MaJ%zHv@vCzdNYeAya~b$c%+SA!*x%<Ym_>RNuXn<
zSR6|4jP8_cjy!guS5(=Ul=v@o@Pe=TE;srMk>={dBSj4!Lfo71iM+<o_|7d#QGunq
zqN+1Ah5_?h7FZJz0)!K$&6vPb<hiiUt2Z#p=ReTZ2xkq^d*?>|e2mYSwyC&nzxxTO
z-DrS=v+*z|&NqS{LZFJ7_X;-$s>{gaEB11!BA*UWuDtYUHAYys#^4DF+-?T0dcNs?
z+zfTa{x<ik&##f%2uJ00fFudTukz94oVJOi3*U%?Y;uhG(qu?*Up>mRSLVRd0MnZB
zJp{LcsLcwY`eXed%?+9}bV1Q$jH9p@+|BPtnEUOkSqsqF@ns5Wo#re=zZVGK-#O^S
z*&TK6<nz`G)G5=<ntW!JBD(7{Sj9OB<~!f=3XMVqA7o(?^d|xfd82(HhQ$tG|GNKm
zfJZG_cGcd25NmiK7(c=MUVNjd$O^J~W8)8NUh<xFdyk}ML`TY>JjC3ICTEEAf+tL<
zuxGGEWIe1l#~OwWDI48-g_XYKh1%gcjPOf31}}Q3{#4@THqS+&p*uIo^O+#1;Iyf3
z0F6E>2Wu4Asp?I&t{BCPZxqXr%9_9_+nF^&9v_UALlDp5){v~v64%k?wO+i=g(W}6
z6CHFN1n+i@LOJ9&@fu}{dkg4ngDGgRRvaWV>-G?COuS!P!@AJ@@RNa8VH8b?;=w9P
z1dT8CowEFH9*wlhPGA)lMHKi_1+#dG2!4rcqP<~~6tN`xMCG#Do3(ob9Ioa%4Y)5_
zK}i||3gr0>=RBd$_eCFIOC?4e|HaC}{?C;~(bWb(t17E+1~71-{pG0d@YlV#rJgZ>
zR>jm1w7@X3vHky6nUd|lUzw7gh5cWby|Z<1IVAwz&{fqdj4-Ycs0v1+<I;E;KwIxL
zSOJp*UZ&x@>4bv1k(Ee-f9S$zdU}NxG}APCnoq=WUz@Fb1JZ&xbaNxk)fx?I$?IFm
zP*4EqR>6`@?a8~IGW$F-eJ?j(=%(I|pRRVe9@05=@R0sdn(}Hi0_US)W$+;!hBPx(
z3qPLHlVLQ!v!PG#VBI9_gOn9_(|w3A_RAn`y8X$imoW~~mmj!y_arirjZ%}pX~qm&
z!~i1F-QIl}KZwlpxZu*$3vN;hZ7yQhjK-7mm%j+}{I=DM>Sr1F11C!npEoh7Vy9I_
zt4i>l-THv>#ZBrubPmq{w_z+o5jGTkk<cKD)Duaqt%;Hy^O7%~f2Mt`g^q%;$ApQ7
zFX+}s*2-ep{UG>9_#k!q&Cr@Co;_?8=H~elmO^3+Dg-wa!wuv|d--@Pbx)NH8~4Fn
ze*FapBdch#I3K9o`A945d=D4|EM52i{o^nLs?c-koFCQ!<M+K5C(s|`kdE%GEprdA
zpD)<gbn`Cy-+K1(@*m!M$AxP>#R=?VWgzA{PtYC+pw!Q$C!Q<YzO=CJW5WISezP0Q
zf5U2P<;Cll({u{)#c))JQz&n!FCIkI#=}=qEXzdsTB3%i6TUS5DX%#m>DW#E?78l?
z2Ox$To3s<`IhpG!Xs|zC$Av*tKYna+5IAuas#Ax|McaGJ;g0j4Gp7!^rO8bn6NbxU
zPXtfax_%5;OPKX)OE{?1b7^k(%`DY>>)6MtQi!)E>ho?NW2XNWrv5^vTZ^yh#Y_Kd
z=RWyuni&J#g{L=dLF4NH4D2lkeVxIUT~g;EZTKJp6?R<QpPJeMXFvb_k*vBE1{X)!
ziKE-*-IQ?U#sl=9A0X6sT-36<%089a!7>5UQtvvm>JJ_&#Qtm~{7-;t^EJMB*^-XG
z#z$H7Tr%5Qc)?@nF*;$;ov@9B;&$8AGN&@gp34@!aBl)X=`?3LoOwBC&;>qx<3<1a
z*F6YL7d#K;FO?4)^K}ne$6uft`agmlH(f>g{_hO*Z#>2>dck?pdBOz3i80U_G_|+F
z@`mz-?)rxe$IS*T5O(U$Iql_X8R^dc8qo1jXw-2Ltoo4gKbR>U;>Cuunv?aAV4}k2
z>UOn3_DFZx!`n3+eeBx%w7j+T4#%|7)`E4a(W}X8fUnG73>C4uv}cW)7RAD;Gcv#z
z@2hg*ZWO5}r;WVAR!`B=`WHcH=QR!6V`dutPWYlh&tH~ve-C@V^DI{}=Tq2I{>3yM
zZkzaBLsvG!4k@2e09Rh!j95?@x<26losSM62nRs!5MOwRxeaGq1pVy3?)pu|^}oxN
zCpt)mIAEpRH{aI%L2NM-%>6zPLn4`$dx`_->+|l>+)Lyze^5?PqhUPryx7ItD>o%)
zJn_5$HMBw)B-LUhS3YVY5cSFNvh>r5T)&c9BHurUJg=pBDrzaPm4lRm8k(l3e0Sk(
z?10`&{N&?$skfG(Um(L0^5@ZtpvngJ3-<ETxS6VOAzroOQg_47o2_=}yH2(6uAw4~
zgW-fmfiy064o=Hy1$-qWq?a$JZGg(sa#=Brt;&Rff&p{*_EY<^@H2?-ETv<b-tU#`
zEq)p1{?FWnrFI#yLCeBnt;Jf&P1aOdB^#{fDW@kR+d~m9b0opLJkiVa>hg~d3uV*^
zcWZYCO+oCfEN4XM=!Vppcf0_#CSS7yD7+A-um-^p*)9<!bS3wJ<TRBEm5a27^*>kS
zLA3r!a<PB-!&=SOj7J$hJoB{ODxDHE74&YD8cm;k=39;#Y^nLPS6e9U5wvP(3wnA9
z#Wvm-Mg^ru%)B`VdOOu5cVx5lU)}uQFN_k^b6}|5?J{h^_IEwze~x<)&^T&GmP++E
zeC|OTP(Eu|IvsgpSn2JAWRBv_;Rxa<uG82c69)nF-a>XcF`Bqo`EXm6TuQUIFCMk!
z3z(zUy1=x2AaCR-cM8x<W*P161h$iDyTXPto+q}EkY-l3kaaH=Vc<bcWsJ&vbDv*I
zv+Owv7S=+0*OJ_9%4%uLcbVvVKXxy(E%|CqHYT7ciMU?8dOZ0Q%qSqF1jui;%ecnS
zzaeQjSdy->fnMA>`339D(htWT+v&PnkVTw%FYkCZ>7Q}EjHk5n_{qB~GG%5!@8aV~
zqrkVd-2JOxPicjA3(Mi0t5+$-uG6;aEqd<jW@hS{i?>%opy1ytD0VG4;J`Ydv`?FM
zo#dPs#g&C?Zt^^{HQyC{l8H{Lze~;$ZkwBO<FVl-5J~=1zWZvz5OdfeSXf9RHGy=H
zP)aZQE%SmU2K$}C_a<+QFO?QMKJ_yC6!_NFa$j`9C%}6#_B3NO&9F)**5>GWbXE7~
z2j+Z;@1GX2!3n8MmQyN!Y-P=Xrx`=U&Q22G<J$)iX(ip;VqdYTCuCNsoz%RI)zcAB
zq*s0X93^Pa*#tl0{T}Q-mrVF~@B<L9?XS5#q-Zq$ciW1I?w_`mptXgy-7gzG0|2cM
zz{%7Apdczh`%7BS-kgB(FB43G_J3e~{lBaL`7d_X|Fr@nJtNb9FtmU@wre8TK6>(f
z$fuM+tgJqgRxOl}`6!75&T&u<&freqorVq%`B1`m5p<kw-FEz7-vm4|{k;SI0(5*{
zpCQCd5vr=HoWV5OoT0(!KOqwFMS%4~BNBnyl`Hf&oqgNWUAUb%<R0zrD$inn`oKw~
zMC5gHKX6VZKg1FUi9Z=hi}*86>Nft_`!iQ0TQoP4LUwm^aKYQ<Vf%AA1E1H+P~A5D
zrljv%{VTK9^GEh(r)lcNW*4Vy!TamYL)K;|JMyWFSGn3Ll#>hl;)UI1FZKHS#KBZn
z#!p@4h-=ok)+Qj`CkdQ`2&I_;b+?5io*5`QONKC#R2oV>wb@a_%E@uj?)`ZA)x4V8
z1g=juBZi*|M0_y7Ly&ChqTu-tZXeq>NWER%Yyx@=KZhWJp6LJEfNBFy_l%j+DyUCV
zfB(k6KC~IIqjejvQpN40^!$0V4fdTRubB2}z#vaF7j^M`6wUuLs1n{RrZJs{fxqlX
zFvR*qyD^GZn;I{gsyRa{R$-+mah_b4^c<561RbS_KWiKe?dbDSiKRvnIGfDGgsyVy
z#4K`)#%GjPUe1<)fv>x9K_u<Q30}<xaGGqGk&whU`JG_k-(*F=^#2uX_RoKt8^M3-
z22J;0yXbydmE_t0>TQ5ICIj^jXM;8+Cp}YOavYc$TG)*qcl8d^qCTd{ro_0eCyMnc
zN)^6=1`aGr#`wJL_T|12yM9^bmkL%?f^WOhb8K+k<rslEN1NwEx#y8Jcz0<HmM2VL
zLnVEay2`W2j?soBL_WC~!nj8tjHA*#+7h3s=rDyJit<jX#*@H)xk9plAR|sTrP>FP
zV!r74VDn?+{q^PTdnXq+Tl?>S=eK@n5bym*7X0@vQ2$?TpR)KD+~t{8Q~&5qxQh8@
zORh(P?)s6#Q@aRdSqKVg5d%h<)Cw}e8MCFuL`a&f2#JVD0g<1&+ao4%;@{=EZ2v(G
zJ<nXpy6ckCzY${+->KJl?z!Iqq4gish!_CSF$FtEQB^M+Cfu8x3Rqmuj$2yj`i}e^
z#O2-@!sUj!-5a;8*Zrd&q{08pSpUjL|4J4Dk?!+7uQQA0SGDGIxq2$~<`9EsKx;}L
zoK$8+ZbJSELlUh4(6-!njCM|f<9d<21$;4bW<@arIOw;;7wVWy5oSdSr~SYzy~_kv
zr=O7J+}dJVF8Zdp&{#1ChI6n-Vqdf9scXdW2^xq_QaC1|np9J?);o~^t*Xl);humQ
z$dv|F&VczJxn&O##A5%Dng0mwKUwW`Wrk39{OHQHVCwbycI%RXP-MH*cw0hf#>`|!
zBJwTi$OxX5?uo(DB2vT_ymYXbc5>yg-H`%VuCt+D=L4w^;;txy>W~R5-G?R5e=t=6
zYaGq*($z*sET7%iby)3kN7<T*5>%UC=ik1^4+2h)Sl~XPWk$^6?ThBbg?`mt{ddv+
zJD9P8VE<8||7++O_me1Kl6k)^;f5s%pKK&raoum*xOr$_#CL%>uovHq0ZSvV=BKQD
zPy+OC_Y~#0I3Dg+h;r1tx)P4I$c0MZ1p`IxvU<^A=Ps`|I}oht2$RuVTA>^7IP2vX
ziffHB5H{)YX*`rbqwPW9ngQ`AvJR>6^DmBS;`WS<w)K(NYaj(RqpA-z#?VOLUH%U)
z`gdI$4E8sD`!@zRcW{7y)LndmT=Qyc9QnH<{&y(=$p_26aJ1a$7`e1ax23Fz0cD=>
z34_yx1nVSKITT2`g$KmZgC_RK)z7agEdc7>hD;P0whSv2n6W<dZq9k<_x6Hji*Tn_
z6Xu)ZHfaSKEcR*KX=ut)Gox=aZ&OQMI4~^Ek8aHIGwC6kPLeN{(RUYtYb6=0rhyrF
z2M2{+s*<cM*I<F?-$;`y*$kGHj|Pv3qL^*A4VlmqdTR3#U=ZoHcVrlR$~vVWnv=%r
zPq4Nr-fhJH0MaSXxPb$rKvTDXlM9D3A{@0NO755PnFX(cm){YU+rg3)np5{}A!d(P
zPOT%`XU-_+Wq*(=>xPz`7oskgohRqH&yN@^FA*=EX+@q37S<gbc2k~;_940+iLm+`
zTiG@2yBMbm-9^4nngrgYYDZSK&fSWp^4_bWc#b&AoNQ}NSm&p1b_dE<h0oA@Py>%s
z3s#q!pMJ~P|5)>QT_5Z0H|gEhzWZE6Tv|PMYb}{!y(4V}3dQpW65%8kud-KsbfCQT
zIrVg&KPUx6yM^n;rwzSbm2;5<Y&I-(ZanLpR=mBZKY4RMAmxotiQWz|adj?;1dVdy
zrVaD#0R~8PEZ*pCY|u~6oKG)y=egY=S!__(8ULo%{-*V|DAYDm2ViL`S?}XcdWLSs
zZuA)Z6#TnvUCiWq5w6VVrA6o{#b?GchLV=YUW-$eo}@&orJ^P9HZPsc2}IWGExpa~
z>32l(7+JozybDAWtLKvO#nX&_K3Qz`ooI!SfA28P+H&T17Tn~LVcBAU``@(f{g$`)
z28!>0VMWpZ(~A24h<z~8)Bme&QlYwLwI+h-d7*-M^i4@MtwZP2UV#`+KHuC=x@%-e
zaN-IJ7UYtU1mNunr#qqWYy|a4AhOL!MhZ70r@hnS$V0$S46mQ^DDT^bHR7Fp(Wa5r
zi|euq<VZ=XB;y1`h71K*P@^L}-fi0>WeiVXU7J;L2gyGPF)%H}>>4CO_)y9WM?Z`D
z9q*Yvv1iCrkmyvti)rgbn#ctf>w88z?*k1-i^b3Z^81o)_SOs)))p94wWDs~Q?!oO
zj;h+a2Yz+6kv~4C6NiYa4vgpuNhid}_c}mwrPF=X)=X;z5cUWqbc?+Ti7;RSXuy~b
zj(~8F{EBA&<`s-njZP|w19g{4D%&mOZM`S2X<8p4@??EYrHyOFzXFWagyP4z$Y_t-
zskl1HkznNu^}sG$uDXxYV428`O_dWG{EJk>x<4x)0ko-h*Bz6Z^jDrmGiYlYAOw5J
zF9OqQKP>cG#%T|YYel+HTPB=bm8mR#>-hQuT{9<VfrY8ky-NV?1?}aWA@D}%V&|0#
zt*Q!E>-HPPt@_4n+@O6kCU!cJ)RJcM3z4Qsf|9g78~BLdIiE2_g2@*XeQ=n5!eo;?
zW6+-+ll?4CbMp*Y4zCIP>0<GScutoL=W8YyKuLT5fPuH{VpEz(m{*9Rkbh+sME@@C
zViO(`r7zOYrRw>Ky!c8KO!n(yH{|#Q$I!f_S1Ly8AS_lS`cg=&cC*M+p&r>JcsUv=
zoubO_lwC@dCfsx(ly0?u0y(SBMeSYFrCtLqTFsAi&7;g8peoXKI`i`V$^1*Boz<HU
zD;(_>o`lIa7MWEy95ifAcNq->d{xxXrcFkv3SUrAIet*6yQH!aQs72LyX(Ae<+=~o
z*S4)I>%j^iWf6+$o&G>As(VadQ!?xfXG3xkDqM3^8<K@18VTYubc67a*S@Hg_HGCg
z$-PVBoED3Cr}&gNzu&kY%SWyI`U92-`eP=jC(S}f$ra7?@U3i&u6gVv&EIfXW#t=z
zjqfan(Wy~6BCKCzhp4C>pJ;hOp4mFIl}xTB6nJfSLM&O`qh_mhTKi@Cap%;S#+p&M
zIrLV#Bu}SlaN9`?Qh6Jq&@VaoB6G6kCPXBD!{?S{!E*dh_^+9%9(<aDZzG+L$>?8x
z!*4Qg<@Neo&^d2nZ?zBMd~%U~ID5zH?s|BkCcsDd%A$I~Gr5TOOU!=f3{?l{s?{(W
z()7VS*mq)L0S9H2=owFY;qH|x@Avf+-Z^Fg+ynkWj?XRQOb^zut7m8MCmG{K#wT93
zVno~eMS2PudpSF{bLj&i&OT;BB!8ne<I|B&@xc7e_9%Hoo*rgFq^SP4qmfF-hz<`N
z!Ec>;_fL|+HkvIWy;pre!Avn&tweaZKu+!`?ane)907tHaiPyl0?_bPd1MJ9L2;cm
z?afUcqR;aQ{7YCkDNFaZRnXJuLGNWnoaW!`S-vW=Ti%!3-)3}tj4~(VB>jXE_{qtA
z;V-xL+i|}3Y!OYe+ubPx$t=d9Y2o_}A{2E5rc3NZ8L3{Z?&#g0B7J!ra{6wCGuFCW
zi=QHdL28>nJSpDfq-N+0ySUHs95Xs*Jv)l}R~+Rv)8cofK%p0SJL3Do0|8qpDjCt-
z>Z--e4KCVIIK?xnifiw)mJkeMu`>P)T>hyD?B+svCvqn`^c;GM$$@^~^m;GYxe?{<
zDDq)2diEW<pVNdPJ%z&PQ<LJ8J|P7h?5F9N`Ip(njddd{Y@Z_*vRMjc;hEF%<6tR@
zo;a8L-g`_54+&`M#2*9eLItCz$oYa!3OLt1KQG?3AjM2;H!J$5aROeK!z|YPqgE-p
zHZD%7xK9U%6EWBZHbV>ig49pmJ^%F4et<)0-L?J~ik#`6iu`}D*(G3LW}yAww$Gw=
zdakqrrVjRU06RfzOB-t|fRzIb8)$R=S43P+&(6{w6f6JF0|?mu{-5j#=>KXsT3Xrv
zEvO-92QV}>aIm%`VBq2TKS$VM=vnFM{&kfBg&L&h9dU-P5b!7@7=&$i>W9@>IGGQ=
zw$uWRAUUU$rG+J762`@PF}^NJ=o`pcQKLv=V#a*$y*aqWNCk{SksOX2&Lzgpd5uPw
zn4-COw!~vK$)3NwrQ%23dAvcqY;4@T>~DMCzj-w-sX+fD&_EdQJ0zI5$NTp;B#c`J
zEDH=>K0_#5$fO;QGvkk1@`)>HL9JhsNig&UhCZAV{=QfcV<khAxS5+|LZkh}H<@7w
z85Rv@P&x$*Eh;K{*@L?3PUWhE*8#<om2mImy+p{ClEQoF51~)@==5?1beuG5Kl@ES
z?z%!bNjt+^wwSB;c_|8wIXHQB+Et~t5uDxO{`8wc&6EhFhb+M6rgcF5x?%F)uY1I+
zMtjqN^^=`o^L|YEF3`@UH_~2o08{15mhLtTI-<%q*ZtSfOA=fQ`yHnsPs%#OMZF2k
z$<n<`UWFl_62(IySDxN~&jb2@@RagzVK`hIg`<XZXqTW>k+?^ZzD0qs(UtlBczqa~
z1?0>%dR>rk{t9IV``TKgwpz8YVthiPwchE5@K=}H^NJ((fT&Vs{d$>u=AD;}?8iO!
z<K3;j88gJgfyT}nOB>5I_hC<iiQ@W?t95^JjAHuBlcUWpa<{!scMI%7ZR!#o|G#Dl
z`bF$-YspHXO}lH+BszWnlZe278DoLQfH0Nywy9aDt44!hhMLXv5<TkK@q$>RlQSSV
z8_Z;58SGQfALm=0`)Dk-RS;aY&d8dZ>v{u?e1C96z@=z@B-!-G(+r=S7Y;pAV+7He
z;Oxj5M~Wu}_ZbT+>|DBl`|V5EONNZu!2cUybySb1IO*jK2|xsK(7=Eyh0O;pugVv+
z9AOkQ#N6L%RkrhE{=Henu5ii<>!KonP$4Y)vpzV$QmgxcFVeTYh&CXjK@8YYQePAe
zK4tQMp!$EyXO5wZs0&q_`F>c7v=k0Q2ZHPD2qBt_p?G^l1a=-{*!}r`@%9!_bv(=7
zC=e3d2^!qpA-GF$cXuZ^1SdEf_h1Pc9D)T8E*p0zxV!rs{&LSb_d8d<wcdMc?Sz@$
zy{o&Ve^u4fJv|S@3x93=wMS@q0-JQt|B@6ii9G&ChP7xW=lV~Ag@r3BHcBgcQ^$@5
zc>{o;+w|vufV@;(uL}UOoiH&5*cDAIzyJ47tp5FzEvO!$>51oi;y1KF<33}qsIT6>
z8MiOp;Ry?X2TZ`gg`i6)IH0C*rEvdieWBoyX??g6w(R4AbuZL4{c!|yHP&tZ^5js`
zT9v+2a;ucrov<DZi=q+0?L9I)A|@`*@LG;O^R&!xJgPD0k{io~>F0Co64+<<Vk>(a
z#ej9`H8dZS))_p!EClXR!P(lgqlgnSIKRbNpD}EiN6*6Wssb5;L(X*#cbO`eh3?S=
zNyZed?k0v?Em2m2qUh8>93PXo<rzFmyi5f6mc@EX1|OU5MiZhtHv^X}^G1e2Ei3QR
z{sMknoVG@tftqxF?mmoKFs=N&A7~LcAmr(6c*RBGh?<_Ag{yKae~E0q7UPotbjNFe
zNbO6yz`jPOiNGso4GW&?rbFW$PD^}xvHe)RTMlCb^OmFbSJTxY+w@2#Gv&qjbP^g`
zK21z_S%!L^pIOnPV#3+VvM$97%7?wT9;zda77g9A+>*igF2k;^)1!lnzwI1+N*`I4
zFua?XhCZ2RL}PkMD3^_~kVrf+dpqpOV_3}5O_LtmAj$cFHT3)08^3Q*yqIbvM=O?9
zf9pfwd@c2jc@xgFePuDXAf5N^V19J!)Yx4?U~>frJ0JF`#ezToF<)E0@zM;@jhn$m
zRsnx!!`z9cWwy*WFK2U{)<zxhkQ+-LO+CuJ=jPP_@5rzf94)9RKF_)RqGkFdcB~;h
zKUpA&+l00whViGH$+dV}z-blBg4jvH;<u2BXw*O5h1AzgCaGLb*?;4ZIsW00{|^WI
zkNIi#7r*^~IX?}!?0@5X|F_PW?cY1+|7m`jlbN0EU%c}YoQvvZ3(U#N^Dwy)94{Pn
zJc+3hoD@22pK`K*Qt3~PNGzXTbp@MRFn6s0_vNg6bgM>Hox;lb&cqde?feXuk{B8q
zYWTqiVk0COEGStKViHs-DUq$G=i}wH_p%mX^0ZCHm63k9!NE-6u|UG@*7ftsb4zCs
zDl`PD4LY#;5>)~M0+tLy2nSf|2ZDewf`;&ChJpw}gMjFOg}C^Ica8;m$IZ&mPf!89
zqv|SJa>h?)MchdG6F9;1wcs+@l~Eq~*m8^4sd9ikgO^SHna@<r_E)+<6Od8r8wj2k
zT5^>kn(Cg&SIq+qo0M%jclIGiZ~O^0B=nWmc$Y<gLI_=iSas0ca7xSH+Hb*EC>|k0
z2>C3gZ*d6iqg@l&apLR0fe4!`_i^u5RaEp(frPlL;VPRkLxT_+q0LoX>p3oswSf+;
zf%0z+i91I2y3VLs0S?!@m`D7jI<8TA-B@70sq7+SKu1;~<Y<7X3LmV@0ts;w{C4&a
zPG1-YIDKIp5W@@OFJfpk5yytO5FhKOJgYy!Q^ftVPA9uR>?vO0>MspdiGThvu{3i4
z(x?uB#`hZqZhB!--)ysm>`e+ParjTKji6EWE<<;Y!Mo!$63<7(;8)3I12fLiM5W2t
z-yE1_Uxg)~OYV-iqq~oyw7w_pA;MVSa&Oiiq-uG0-D{RywnG?%CR8I~Q9?*6^N#-v
zH!6L-u{<DzfBLpWg!}sS{z5}3dS(zaR9JIVTS0+!<1qnV)~e?vN5sA7cEWbo$;_I*
zj3Ogs2Q0DEnbzy1R!QfqybI6Pdi{c~LIT0M4@e4{g5m}wAP7>)2g)NKyhIU%Y`B_k
z!mMibtoxs4OO}oPPa-&wVqNw5kGIMc4BEnQp95{Lyn0zB9zN_-?XUpd6C21e(45KG
z`jA-9cjM+3cM;HRnP#?6Z7os(5bmJQR@?fZ=9lQ8ah>bB&2Z~Rce**vH0A(H=FjZa
z)p+`2r%fV@XrRZU>pKJA^L=tfr4AAxHu-*+Q_dpG+U6bFON3L#;{h3oA7Gh${vCdx
z$yx6aWt`rP>y32LHn6-vZD5QK4jBjV#Z8zO!-6}QyRd5~YcbM?m`U}zmBUz=6A-|=
zGflM+)wU47l0x?fu`DVC2^fuUC}<K8MjvC$yqdxmh)4Ez2}UNE%_<8*;!t-a0CFqu
z%a@GiTGB==zd^SAyzV`#-x;UjiLyF4dd-`SW^!=%b&hPFzruZGqp50T2*RbneBtBt
z5B02>CNGB#w*EV0g4<aiZ^4V7tY;DE6_M@h;oKyjf5AqV+n_zVo#20RcW|W<nl|1P
z4s*URJEqGO;*kG3d)DN5at~#Rm$Ey1Cz3TIeA?m*G9Z+FjJX*(`gB$MiZt@1%BJNz
zNCYi!@3gVsUFIq(B;-vq;#Vb$i0`$l2%hE!geC~t%QW=$+R~$$KE<F<L-hjim<q37
zzxKctsy1Q&(dDq|>Ej79mtGulPL`rz8hbUM1DS~zH}HLdB=w^R(`R6-XJfBmAxOMz
zvMna2CLG&T>N2eqIdlGgfI1PEh{9es{nj1?M>bl8pypB)vh|RNe0doMpIJLax+|2#
zhxItWKG5f`y4er|f;zsD6o!7Iw98jYZ{xT^)Xx}Cdz%_O4(Vde1aMr<=li+mT6zO*
zN9iu&vi!Lj|9XZ!*=gc<p!2LUnD5#Mobven760P;0PL?p^qqOzB5(X`ta6x#FWZ!r
z@|!GU^?)G}rZNy5_VOUy*AWe=uw=Wak<)ib0ai3hhLHd2kklR}`&~KA!PlE5J{Til
zwCp(I9IkTvXn7nu<Z&|S@f05Ay#lmNt-K>r;KB*QkYLn9(QJ9k4`POb?SHo$cBY4`
z7JO}F)3w9Hk|A#|i9!Z~5JAj6qm1*A%?rK{n6noMwys-;^#zDn8ky2CttE&<FB4eT
zx?VzYyc4GasyD_B3>W_Bsf-zG?2vL3IOw<Au>fjco3LBJu~DF-=*xUyx8*B{diarp
z0KqquJb>Tz8y@W>O#S+lw1wSQAjvN$t4{96*Lo#fNND09W!$$#CN*K1AB`VGtQ`vO
zKJVB9)C}dOOf*%3C_$}7rCTK{EXtxdkkBf*=3+Q8P^TuN)Oc~?h3>i4fXxecSoV6d
z)JTKE4}hl%>YcD9QGhFe%$(Qjq7r|QovLe?bc@4_$!!DbKIxX{g0Gab2u_LeR3+d+
zlR(X<Od9o8aWYVdh2k1@KD=lOz~VzyN^Gq`gH+57it!+~T@8%h5nLo;`@v!)j$p(}
zU;i|2CReasdE-UZoV=*=Df(AdsGp2kJcU0J#&B|7gX5g%VPD3ue+o@0WdL^$wnT1$
z@&oap5kR_GE`^qwr+S7Fi!Ogi3<rRf=1rNmZY)0nLd1_H=eNdhom|_|0O;nU%iT#5
z*E8D4;B+dd0`T15nq3d4^Ggnagh?SrH8E(h(uRn5576-GE33)G%wYUvhpeO4a==Ld
zpk9ES>xSm83W~f!kxx&eQ~MA^W)28oC1^}IZVdM^KxD5}Y+4{Lvt0)8Y>Fwo@<tqE
zgt@M25^PJK#e{GI=H$$%Fj8~rDUH}|y>oFgnAFzP01xw>4n%zibRI-Qk~*S@3-mrC
zH;;|4C?KR~qcG*PRCf13u`v+41CPD7BKue#cJ=zxwOlLy{Su;(myxPgY1R@8r?hxo
zHBZUnz_}8xYqnS`_UB%eub8h!Gt9lDFH(3vz5m7bPNd@<@WPaxF^4D&>N-9y3BmvY
zvBQIC$Qq?KlMYUT2?-zFfU-Q<lZ>+{+Lir3JZl)P*N5~lL0aQmneSq&Ey;)RcUB|?
zS)EZg2`H*q;SS^!6srV~ETtgi2t-NQm`LRg>bP13L$`!BOdt4MdoU^DsL%w@!tO!9
zkJ%`|virD0qpZk^7JkC<JMwWMoBe<z?inT|Ue+3@HeQIBAZ<`Wl)(8wg2hq3OEU5i
zj#Zq%R1)7mWLB?w2E%v>nchcLOO(j8f$5IG`&K`k1<!IR%@~iTyvkQ<5MOzq+97(u
z;w(Q#GXTjv|DmcTMRA^<p(bDUyJ$C`Gf`ehLP>xY$HMM_mUN~#h0<fyQH<5Oi@!x*
zV4C+lY!X^(l5<C&D|$?-GlS2dML{_TRl-o+Lb`9>;PHXgj(T&>Q~9kUt`W3h>wx8f
z%a1OrJZ>{S8&sRzkaZw7U{gxrCCr;lo41Oy*)^H@J(dV~{Q?&^!BhFD18L_XwA^&D
zwXGOIU-L`Bcf@8{BM8D~&;=WJH9sb`w5<~wFUAIWfstb<Jz#06$wOhiPkhLlmBcWG
zT%ZnEDL3r5Bk2istvn|={4}>+vJTk6!dt}JNgL~qzD?qh-+^gvwP-JeHXW7))ALgz
zuc9MWC*<)-K)<{XC@{H%c)<kxNvHH?sXel{0W+{<%w}fn<~&+8Kg;uFO;L<A*k-kW
zX2x9Wlpckn-o<Z$FUKIZ-W37AH{H9!aJT%Z@kORq$_ne%pl1#+2vb3Nxq!t6<~{P!
ztt5I~qe&RDT0~gQdXzD^U*HwVnO=!CMleG$Gcsf@vBa{*QLwPc6-(ei;>_evOghN}
zJ|^XHSb>gZ)nbqo^h@?&SY^<<iM3Jo72S;{#vk359mZ5y6q3%$IW<o{JMeZrZfy`S
zfH8tDC<f_TxKG|$H4lhQf1HjV-UCLUgiDzke^YK<ujSD|*dUi(5F3A>)nrm!ER9{=
z3JSt_qBP4@#7;JoTKOshfKG2#qqC^!vma#MZMOQvQ7f(6ZS9Ts%2pF#1{&d|b|dbJ
zy(U^Hy(M>|!O%*W-F=#j23WzA(4}F&;@&oP`Icy*lM0;SGA;=SLgWoEFR1na1LI=c
zaAsUmCG!%^2-+MsmuBVKaVcnw0#9{FGTnL})(CnFmQ3AIiCjGrlc2Ui)^wZ3h<xzJ
z#Y^oXPzHvCtb$Qjb^NSZqvmIzwXkn46f(sNy;OT8bLIIlrYkn_DKxV@WIg52xM+m6
zIop><=@0e?t;(<g_0U}rHe2G7P2;-Jku~aZ%%l3U6GvRGW*bTG^#LGAIPQU{BFJ@x
z2Mfo2t1t(fa`&+{3263jB*m>1Q%Tu++JU5?hJuv3(OAke(OqWFd`P{_mm00;1RA9u
zb!R3^b*&o*s4;Q80|=j%d7QM=ihSMnyl7VwN<rJ{CNO?^(Q?j7;?ml$i(?jN7Hm12
zjfkC`W{aK<=JkC-#ssn$5IALcS`$|MnfC-7MdpNVV>EC#TQz}boeNRS2~@!7PCvD9
zP%DFg^q#h7$h21xH<<l+ozXAwK(W&w*%(Kn)>w=`YSijp=B5jVUT3{;%<96~R!W$(
zhs%*~opLnVPYlH}40QmfFjZmvm`xF{0+9`H8o&UC5(ll6!eI)*QikOh?3ucKm@!ga
zvwe`=)lAE<JD7o|t85>&cKaED@F?(E0#A8JawHxkXa@uUqP(>w!ieSQCN7WUiEH15
zc-q6#-BaIumCP#qxg2-030X@&etoxq&f<tmm+|YMH#lyl9dK8jcrr*urB$g4)AtxO
zdtW$tqK)wU85_2=T=<PPoFvF+B8@__IYn>+;yS-;A--oEzj#*Fgv;J31%a9!@Qk1(
zkje7t*FtV6G_s`NZKTbR09?uZkRy{euJsBurW=vLX5gIX1dP++LH2>5I51q?Jn@^6
zq-emgxkz}f)P?y<iZv=_p)L#+em62*6vYGZWR_VugcJEyS>8f-A;>u8`&i}nOAjEU
zzV#p$`BG(D=TYSc#odkcR{-Ovk!8e5^R{*3@`wXK<2go&@qRm?5ByPMcwL5~rOant
zZeVWJ>dc=kC_wBGa2n=09=BwzWm*#xq$mlT(Y?D!0`v~qw0hpu%__8t5>y@<*YWkm
zYvT;@?N*-q*!*JVk?gb*0|IU#E+dx+ss-_Qmh9!5*DluB+~t7htC4eCq$XIh>iY3r
zW=n9a7K=v((8p+9<9K`NP@hp^RV+E3nmvFdmO9<$T9pKEkaL5CP18k_bbxU^uigL*
zEH0ItxNMVYV;=WZ#+etA#hZLnNpA-l-uQi)<Ki{BmI3-`4kmRyQSOT(l<4PXw?qWG
zGhxDxmumZUA_V#uj@go!S}-~93&~EiTGjz=`oR;OGSDQ?mwx>~pgV;3`qph?bCGhV
zroBw$<}9<wzTRPEJ6ZcG_<m-3mp{e7oMMO4WH~>7OLQa4oR#8we3OLcU$;nl$gq`w
z*s8KJk+(2O@PKUtw4jYwTUm{fH!s)xOKik*T>0|8Y6MUPo>O&=#%=2dP8A%ak*^yx
zF1GwYCuL07!L6cvEzuHR_O0l;$9DB5KS3Hne}((tV#QO!tf94mOxC{Jh27)9*?v{P
z;`UgY*|9$Hh3_Q=sjXVEgCJd{lqu>8YM=z^Dmz(yEap)L8`y6Q^wG&RGrozTx)#6Y
zY;}i$O*xk8F7)`ZD&Ix~wP}}+DXFIOeLdnd)T*PX3E8L>zv(&V3oL0#tz(vp2Y<F*
z^Tcte=>Y$RC0CC?t-ug&FgpA9aN-{WGupV%s#!MUh1qVoJj%QeagbTkrW%I#(KFr-
zKjv&bM1_wD?slMwyat0zYRa8Na*D3Uv$%GwF_3WVmzm*#6$KwiN9YjX;ZH&W^{vbE
zCz^mK+i4b{EuO3HG9V`Va;G<cY2b39^5rxvu9G@ZIN56@ZM&yE0hX9Ax>TvPY~5LQ
z367?Loiq~J=leBte4rS}VeKXgzYX8Ze#=$T|0HTmATO~QFV1*RUU6Ah+EKOoGh;S~
ztr;e@aUe>QBC2p=2Cs<4^+Y`OZr*N_@jUC@9R<$`*OzCRSL%T7H0}gGIPTq}N|?sm
zF(y3*U+6*;gM_l*5xATj@5Q$Tkd5LN@1m%vB;6TR19`}Io+s{FJ=K+f-|^1a-6+bD
zWI$A2(`)%zg=T$WCmaYmr(R2L+7vzhkk5vc=SAQFa;M_foYx8orYxQc9*n#LCTTVp
z<>TK_EVoR)gCKHpZWOx6TbN4VnjT3Ll+V8JFs!`;l4DN-hCLjHZALs+tjyCGoS`Q;
zj;ybH6@azHx&L<9lMEU1h2mfBP4uF6#60v=iLvX=K--C6g`oN5EI879e|avpKSm_r
zumer&kqGZ66ZMvz6(1nW@)mzrWOE@pJBR8)+Mvo8I{#3*()VVMZG?xAVkWKzrvNf2
z4Ruc#MYPvv*y0--RD+PHIvC-&9<v~JSbj2w8HylDJiVwKv?aJHBu+Hx$9tsO`rLI#
z1Ek-^W@D)Mn*w3Ou;t0|0*To4)wTrUa)8V}mduOPdQIhym!0l_>|01y7~)1X905?^
z|IKRmq=9Fh+q}60Qk)eZxAttmX3*IYc?*#99#}4P4&B|_R>BHCdfWRrLmkb@9}47a
z30LOxXHaunio;7gpiz#|(HR2wiPK9}y)fX*P&=lHDFm1O$e;x5>(Pa?vc1`>NP~pM
za3Cj4pI4rLe6uMiW^y)$%ReQ2TC-8_@&tSM=_QYZiAKKL_#iD?p!0;3IVO{E{I%a*
zc~$xn0u4~`F^uIlcsKev?@4qFx5a(on5JB?%ujlKs7qRXwD=1_%_GVldM}*JC~nIZ
z&;2~<u4G|)LWxG{%-J_<ShCSPy6v4#qVW^N#zQj~iIul!e1}}<o$RAo!<^cev7Z>X
zk6R?+K93Sn1`NQmhky<E{CXM2m#KLDT33_s%xXdyS>RM9zEX&cZY!)<(|ER@j=u3F
zyL**{CS+?(s(p<>>-8+xdwATpBJ2k^660^xQPTp#lGCGsg&Ot)qJNYbE=Rbpfc!S!
z6m4}6!>r5)B~1gG*}(`Lm?@X>n3g`voynT-u=pxNI&dyZ^uffUPXb5yfPKNLlg;Iz
zT08EkdN_M34ATXU78nCxs@5uEbY>o_&8ig@n$CYe_tp2C3fHGLD~jL<yTF*$=GR>N
zxl?!>QuIom1!@qil;y_l;Mkd~B(BqF;;_U_^ulh=^sN8^kT(~yi#u~V9NqNRkK%Ot
ze3|?l_M&fU<fU=gt#nL9=r`WhKjq;m!o8UQXxqYkI5ITNv{Us9LoQVw@w_`agux)x
z$vk<;O-4#G9-PP&{IYa<2h6>ZhJUr_WM9DpVxwSX(lkdy^bOMRSo<^J1lQh!OM;g4
zp%1MR(QcQ}qIw30do#7_fG@Bcm=V7eEJNDMs5#4j=<P;V_>l0Vpx+#-z_^ycs6x8z
zGzw<k<1Eg>V{ZeiK5tx|`n(OlrcLuf-9uGoL$XExyx$e3%~GpQo{7~j8C)g-a;q`c
zSMK$;h~o=(m!L6kqt2oU5zZjdRTkt&9Smy8OB?vkL^9q)W4B3V?Z@~oO3WfBZWZNa
z%9CI7d<lmF1tLfazRJmwLjG$N@H)=cnXeU)UsIBlz6zbD)3<7n`a^t$dG$f;4TR+<
zU@rD+lF^qs>Gy{Oy6C30RJS@oZxhTz$BR?zRj{Kje8y^5q@wCoQj+4Lkr~IfDrc5x
zD<G!y$Z8jD%Q(hu1mmx$OfT4F&zC+FMlL#X@lxXMJ?$>lafTcobkpG{3?J-D>qRly
zg&#T;w025iI~L6e3b(H7%GFL>|K1ZCn&>7XXJ8+%mZ^P64`*h@W{4TC(YkuGC)#r0
zb474&P1HgC&{m=Cb?cKLm42A_bvu8oZ<eZS7d*>3UXMI}KzGz%+*Hp~91r_=fm1i3
z=MwaUF4-2RuS6(qV{#J_I?h)%1-|<Zs`3074xEYjF2jbCWsZw|VOZ#GM$a>`Rdw7q
zmx8TN=ya8k5J`W|Z39sGZzjI^Z~W*-9O3`Xqlo|A26rw_j(?uuFVm89Uf@J+xz_mT
zL^BnR>WwaB?roV~kdU#~GeMXm8<I>Ni4mSC-XDFx-vl8A|8*)Tk6l_8y^E0$%K^K|
z(1j=twlhX7zC-Zc5Z9Ok*H}qCJt>Y==?9l;&`%-F5=-kZLDBYD*=vWN1aH011@6`<
zJAj2O&v$Gdi%XfK$%;WAz9QBZlg7r#!+%Kph$~fgW-S=`?8}V>LxTB%5i0ZDGY<T>
zca&dWHKZgI5r5E4Y?7<PL82ZDi~Sg{p~qLLE%nPFx3ZPLOxB6~HA>>r0kT%ityMUV
zdBBFP@n(UTqF7ZGu4te^1iYbQz?34bhw_3eoa+0CE4DZ+DHbg1`TTA@PJgJZ?_5u#
zu7!9Mq;NHGi=p9s!lX(hpbtac*4alTW3lYQAL0@9z9jQxrJjaYF;kC8!SP_e$%k@$
z%Q)gz^==q-;BAL&)Hm29xST|TFj7^tJanE6QdK%~3SPN5S8gJNFqViQ<d&1Y-j6sc
zVLwJ`yHl*Q?Icx_K7_$&L(}^028gjhC&0$pN`4-FN8wS~_7Q}(xw@BEKjo425#H+y
zvW0k-9m(e;+DS-mejTdMuwQ~6zHQS@Nf`?y!oNGi@9Ffh^aACcyJSY`qhu1(Cw#Lf
zWfqP<L5Nck%E?W_la?38l(yW_oO(p&oS!$bz&LjfhfIk@RrFl#g2;J|<z#96`$PT;
z;k0Mm&@d9K-SYbGQ9C^;xpKxE<7zhbA98ykxFOV_ROCZX_0(@TIBz@N^A0GM#>Ubr
zesW9w7DOi9;hMWmS5_TwWNKNBu3)==uh=Bp6WSH5D}~y`g5*M?P^_I4Q(XW70h|}h
z@>)ePVqrcm(BYRivA@X86lIK=A#nnyFw5=Ul7yJUzz&|T+k1Fd><g&#_K&kTuX1FS
zk5$9uzV7{cZ*IwIrrArxwfl)bO*vqlcFC3mwdww0rSub$9&GgW#B0pJriZJ_5pa(1
z9LO~T57f;<pW*3bLc*G6U1s8CGWn!$U$<LMXkyS<(r++<*I6B>-$IeeLS1E*jk~t~
zRM{8tIPw;jZ5+}X-zwvXQmiL!KMwnTkYD4z`KNOf*lD|Qc!IV>jlZ1L;HZf2Cu7M4
ziL*QSU~K7q^^}uU)yJ}XwIv4~dzAf}OcZ3O8_`Dagt2|26Tx;;6JbP!_^oEMivlyg
z`?~7<b);>*uM>v<_Akr2d|fv&PA%=p`E9GE-8$bC^{IBusjGH|H%@eZ)dN}bU$10o
zO*BVb5(2c&2lfnUTYXq(%v~%sP2J_Y(mrr|f7F{Eiga4XGe3>&J`0vZqUHj}j%XZ3
zaUfIktHKx_B-jidp(JxZ8Fm{vryuG9>+Gu~Mz(+SN4~N&&*(9WYi|yYKdo?$oU*dV
z%dA@%*VTGY(`Ubj&7P5H)fd@d!}<`IRux2~=H3jh8v?JjpB^*Xd`*;;TBaA%fz;-8
z>GOxOpjhpJTPTYX#i3b)8bgHId6rQO|6b(m9gea;9;;G3|Dc>+{8RIN2>qT$Chiyg
z;ceM#M04w^&d(OqlJ;|((TVnQQ4#*UqZ7A;&1nnN6CWo7!3t$9NJ3>PE~I0jTn<!U
zx_o5&$j5_FSiVsyrH|fx>qZd`z47rs@9cS#5PBf?vxWgI%HlqD-22lAWv!ohF)OHZ
zh`FQ))POxCTkQ(7$P6r2WhKxrNb*W)?0wVX<%HLmHC=2j@cNf?gDq$3ybI!tuS%}R
zuzzwXE%I7+bv}0Hsuh+&+&bkWLAA70H9AjMX{(s7ebno54$!8E`(3R>Y$N#cVt&b%
zz?Hs^wuJYQ4UKQOZ7a$HMLoD_>lC_|aPcR`J1;L*1gOuHvx#3ANt@|(m9A-0-KgZM
zuTZ)++#Bh{RMFj@9~vdcw6crtomM0?Jm8GuLk7z4EWBE0Z`PnKb7<gdFQvHOs;1<a
zz<!@Aw&NKYey?Ko%Uvbs#(IR23O3YhIxQFHqvAd|>iOfCYLciDIU;<lOn{}dk^%mq
ztMyU_!+pFR(xm2IssMemfh-y`x`hlAlqpq<UhXaD??@j!J32)C1jM8`!jKMhw8c2y
zXXliPs4F31OydZBc=7Yiu9@l3rXj9)@K?*+&uyfltv_|&NH`a7*qC_uQ+YXM+rU^S
zBh)!mx2I}=)qIlEe0dk5-q%wlh%WVE@UF|Q_ZcY=;dK}(l=Ccp!Q2FyR9<~9tEh{S
zsjZqwz#qxknzf{cPa>9e#SW$zU~OZwpwAtF(F!!}fSCw(ig<q{3;bkY>pZifJSJV`
z8yeYx2!a?!v`c7B@d&n9pS4=3-l5g%Zek`{N;MU_GIV~&#^oKbV?R%GxcmQfl^p-H
ztL(p756#8;FPq|Pb>tj?_0WK;MD6>=iE4M{)$rQeY65yE-<5d77$1@h`~z~%iGsuR
z*+(NUIV0aJt&@PPimvHXj6!wi?ONVV6Y6UeKe1KQ8*t>DPkS2ss?Pc4rt{tTC#44-
z1_AH!;NOGJOcA?s-;mZ@=_!@jI>E-Dd>qe?T#SSGZ}-;=CrWmSx{oJ5#tEs>yQM5R
zP7U6t#HkXHs!^=>OyzNvxMm-65A&GU%6y#RVgPAjw}$&$3TR)i3!|Cch*7?Eygq4s
z)Bf74U{$A9_et4=O1et9*qBuHYsK?*smk|-3T-2xZg9D&egvszx|Y37d9?+lq|==t
zx&wxM+>n)Kk!r`S(N7XcYrj+&TTOmFP^~eag-t5+kn7F<UQQ;&-lOL0F@g8MKL}S2
z^?={e!4*Luq*!8d=XvtV-c4&S&$EkGIH+{H+9i;>ns!gVWXV~(iR~xOg)zKllL)%f
zH~kdnJRF0Uauz8kcbGSkDst4tZBswuSH#RjC}%#B1SRi&dwh7%C+N!@a8Le_wr2n;
zIO=D7O;^Upc<-M#&H`+{jV_7OVjyxTse&oZGh5DUwx|FrNwco2tdB4%@V=cD!*kYv
z-HYZDK3rON0v8u9Y%X;^{Afs;O`h`Y!VrXKTI#p0)h)x{Y~Lt446?|NTg`RpInrBZ
zE?Im+iu_D|LQtXnD}np7o9Eb9mTfOw*?i~EjY*)$AP@=@5rnmitb0VivdP;n5VE31
zgD29*s#Q;<uhe9XYO=W;aQkVfyX}0X)7R@JcLKu1W?iZfb}BHgftuwM6Z|px5t}@!
zIkX&irXp_#qb#JquQ#Gt{@{ULTFz8&(je%T=yfVjlaKO{xDZzJ81gEYaS@UZ>eDKi
zzLBTUfiKAG%2G(jgruix=>E+pLaI!H7QlI0|G=pfq<hfNbsdhHx<W0&DO(8Zh&}g~
z&lDpJop4RUf}cdXKsxP5KO}7)^6k7zQEb+nVgiM&xj(#F>Y~~qJFSaHnb@&d2)i5Q
zqD#hs6v!n1h#-6|tP~+Y<DN!hyJ^r;fvpF1p4ATe@ZGEUcqCS08sm6KqfiN4FH!VC
z)}T$%S3)+_V5<V0I9QgBu-J!NanlY&CekeiN^BhdD%z;jhqTvgdya-^B-LE$>Roh-
zF|?#iw;5~V?UEswOG{$!zlG(co{D*8-JjWwe;^Nke=ni)Gg^)qo*4pcUrU0UGZRvI
zV2&S8(r5?k_1O)-s2KbJAvylhB)BF3@wfRT0~dMWQ3DB=&xDWaEm+U})67`yQfx}|
zvF~RUYkY@3xn)l>l%mKh2+1)o8A2YdOun2)?Xu(I(79$@;^$fR=awv{>GRtHPw-yB
z?WWw#)a9P<%{vWv0^@YD0j5|amj2+%g9oVou9F8Sqq*DH@g!#mL3Uf$?%=7l-Lt$-
zl=S)<0){*t4U|Z3(n2BnSpOkvc{YoaP*UmPL~OCFvE<uWJ4FWq3?1Vrm57X$0*<_r
z8XAN(iF)F3YV*wYlCUDAHD6Vm=IXl5dsqv*1mvjdI?p$|rFQTnj?<h~hM8(b@gMT<
zhrbvE(BLntUm>T{*6V;>MqKEMCyz{97MyjjA>|nJC>-uWK%c+PRw-SXDf4XAh;Pe5
zfJf5(5fiu|=>;92SY?-?E3bqfc5Kml!BU@HtgwGO(RR4~LWH}fSl!;s9-_ny4iixT
zp6nwgbwY81I-xXMJE2(r>F=z0>*G~TY3oSJsD-3>^RNkopi-Vak)qI1jGr$l{Ip)a
zhwNRIG}>?DN_%@Vae~q)<Yb|W5+Tjj<5#Sp<5z$GogKb@&b{*a1+3!rzuzxk%oJEj
z!z&R)UWwiloX&-K*H28Ux}{H->N-rdJ(Oz1^<b;7msLtw?_LsW?fDD6?{&b(d)7lq
zdRQUDr$7kPPT)fKKVOV~4?}>8MXDwi%FQX;{~d0K1#M|s!vb6R4GWt1gaI2G^ZFYD
zl|HI}o+(fdvx*qP$f1e&>qhxP*AXm)>eUwrd10Gx$VqMV5Pi(|P!Q96C17uf`nJee
zYhx`0L(iC<t2sg{vT`H{)obr)!&F?NBQmw_IU@fkBC{TtEh!}Z;P_vJ%t))s;=IDI
zut^BNLT<gjd^kwmBU`T~N8CKtutZx6_}~dss=0Gwzpa8$tAiB<Nbt7^9Ny<{&T3&*
zo=JvHLu}h;ZgSwfYK>?8K-O07tS(2j9rvXp1HRx9#pIz^ovf|T4~5T=uAyoFX7L&a
z+dmes{g123Ihi^BesK*uC-?uCtI1zl`roc5=ip?1|Ig{{VWyW0$o;9e4Q-%8r1&jW
z;(%juNl>*`Tq(F>O`VKFu%3dk46NNoT*pZzeyTI4id+Yd<J9Xi6K7C!{Lsy>RL@|&
z<^(HAFY*=%t~Rbb3G84V@5#zWdG4L<AwlNZ^2|Ik=sl8=mNEmBr151ROa5a?`5&tV
zRfiHMcNF|)U<~ay)T%cJV8s}@&O{hD{`?eMH6xy8(P3-Wh1l<zViG;o?Ob~q2J3!0
zIUC$_UhfJ!FDQG%s~^T*w}d)Le;7rosYb2QBI;^GHNs&xskgx5?!oDmi=dxrau<Qp
z?L9FaU$bMG>*=g8s07qZk$imMkz5v}?Aky^Tm2kT&DHpuW0G088UG!P*slx{n4BoM
z-sy0)rg%98*$iqP(v*p257Ti+qh%PIm8-P=Wlfffl?#skx9n<@^MafU<TDnMa!!Yf
zh$Ru5XM-M()wl%<&LQ|~P9G7ya_?$(e7Fs6wt_E>Bvl2ST54SPxJg*=zihxqm^qZD
zY8;m8uVmw-S<P`0G|ZQwSSK%kQGuOV!P*vhK$yrutQe)pDp@y<GDsSrFppu%sEcG0
zd<)(@zj^XI)Jvu9O1uI%2(99hJ^&2|>dKFk^jA-(R28&TEpJ15u4>HU$AKR!V-Xtu
z{P@3S{!7A8Sik-*)r1iJOYVk{hz+qN#iu`#lf`9e2C_Ytk2}O2>7ms7@?n_rYf4vN
zp>fXrPT51D({QTQ+}Fv#ahSg`O#J>>prC)%76JbXl2TO~9E5YKFSwx`S7J4hfW-PH
ztKJsnMpPk=zysUx?WwXfxGV8H_9y)_@Zcxr?aEs?X)tXDmJ0fnmi>%h3si&dOY3QD
zKaNuy^&1H4YrP=ou$^uR&gvmPo%ZZ|uI;tNZ-xi9D7k8vx{fS(o0LxomBVkI@!-^V
z|6uXHfFSv@Wc$Nmk0e^RTn4Odfc|Rh&E3Y^TAoD#6!o_QYE-TxYvW~Q>C-y)CTGuq
zFO}B31Y5DwxXJ4(Esxy(Fc0PBnsJ8X5(dJEyV@r;KZ65%|5LJJ<jO_@0Udht7Ru!C
zs*sN)+IDYiXL<pVEyDjT-&lnIB+Ng)_!p_u_JdpRLe)9@@`;#2_lUITh*Nx<-xd2=
z(&v+=C~ITIJE_@**X~k>=_>GTC_ajs(7bI(9RCr!^Hc}pyP;h1^sOqftHzRsfi4Mr
z^(#iVyr5hAx;YJNYLk|M0b$O2-Q30eHh`!>wZzZTESFY8%Sx+r?P2t0niIx%4=dYy
z@Q>E5osv?L;o=BpzgShpuaw8__fzMawF)LeQ`BG7yfof-=~4+a+xH<@P~1o5({C6Y
zr-rv!P9)fWekvhCbo+u|KEI%F{wjq;F#NNq?=`)@T6{`v2eGxtYUb&T*qD|hKjNQ8
z)bX|VH1GpSr+6!NX{-#De%;I{47rOq<HmSQie0d9$DwD=iOXuB-4<_RaznY!I}Xte
z1s*cX@a@19x#0GlzM~ki>h!X!>qbPnsm5+b?4!#_xB{EB#hTgsi6idh(CIwNPU!*)
z{aoh?(iA#<X)7(EAFJ`<8tu6~lF!?GrR3Uc;X9ld070OGHS}eV437Mz;IlPt8TBeM
z`0!*8{-=3{lw^;jN2nlO`J71xx7m3n@DE`bzKwC1Waavs`KRkvw3;qaMgk)m*GRfh
zT?}ARe3xjs1;n35P-5L){CrI6Pm=OfX&oH1ck%rwGc!B|kQYxUPinGLxn}dC$;PxR
zFz9@YraX1s4CWwkoOYr2H!6TS_JOXn5Qcr5l$u-vSVg*s%p2qQ(`EUD$qRyjp}pTO
z7IETMeFmkGZBevGI5YBZIDLQeoWs$KA3G&1#nfbIrE5A)iqXZ}1j=#I&f1J`1CT0}
z4fC_*`#q?&FLCNfaU0kNHWeQ+^c=Eo+Oh|++ms$v-q5+I&PW7fyO%Z&)qGcD4Bg8{
z+556Vs|j7=q)9l`R_&=u!*Cvk!hqv7e)R5b12AA2rUYdp6*>kg`2*?l&AzFKeqR54
zIII*0CPAJOWU_dW&NxVgp~KhAF5@<O2IbcbHY4aG!c{Hd#(`O`rUZ>2=+UmZH0L^a
zo_lIT2`LB?sibAV%GmGFcG*M{odFcNL90ct)Yg7#G52XMt+DO(PSBn3n$OC00s!o=
z^2;Q~+NZg?+8U4*T7!%K+q&;s@?eC+fmU8^8SrvABGMCi-%C$pW#~%T_$Qz~fl@U;
zNdinrF=K+*<}-6iv7M}f%@Fx86d&wRf5po9X<EI)ZjPTQLI7?YG~w{&pH*!VuUDKQ
zLv%lNeiCfn?LjI8uI&S+SoxZl@1nE-1=jz9*Ia7HFzm}0EaeF~(TCx(eFf+esbnO~
zz4g&}LG%wRXw`4yMltNWtnPSmZsivJU~TQI>3HPT{=gpP7Z?DfQ(<(WLD{hL_*oZN
zMoYs`?J_I`42h@|Ijb~rMm!mk6#HgdX)wLhn5MLN`P8HH!%#gD2)%aYqCeLrEyPM`
zRCAzK*I~2bl4;@HNoW?Z?6O<t1tKDPl#3^F42sptFAXY$7*m={^UX5rIPb0x-Pn`k
zQ}(;@E4Jrc7(^L|6)qEOZgwC$3T$v)Bm45Rb#=JP>KDgr5gRU&!S~jYp)a-gjqG5p
z9y%9!jMH#zV6g+s&H6484gUrOr+-0!ze_723Zha(FHxfMV`aGX0qbB-7GZq!;MU!e
zg+Y25_M(>xo+dPaWC}L{!&F(;j2LI#FxD0;rv-z*-G%ljTRUm(V~Hx4M=(?kq}cfv
zX*`c=O3N|YEE!9=_$@FkK&(ru5*Q}wi5k$R*kM}$2GX)I{{n;sLn(IB;8+=^20X0o
z(4P9b+aE)52n`qU;EaP`)3N}YZMuJf9{ux=C;;?oDob4>;}YQgBJK(<z?{Eg!Daug
z_bjGi+Mlv_opBo^$U1ai&3TUnNw+cnImM}V;m_D&;Xw_nAr^RTDUC+cVK}$;eVu{~
z(7`1S;69UW5jEv=3u`suAh)e=;2zP&CvE+KDf=`ZIj7f~UYl-<(?^g1N<LeSfwqNe
z&;SH|1sIF1^nKV#d#CqVgf@Y_f>oL#0|Wklz+?n~YNdbr{*W##;1Duox^LRr)0-VF
zzAWu73?&C~gbf+_4>MmRw)Fwjb&Am)v!k4?>B_AE6rE7gYtXbgU(>Y?0C{8ol#xvv
zJ@^g7X{?MzgZ{$vGFM=-M_)b_y^Yn}0LhG)5i#)9KJKso2;2U1P;uw!Ij8X)hT^Al
zanD~V!pQFdfLG1mAsa~wa`QVz=PavvcDr`?2AAJ2u;gr;saZ>h`v<E449(_JHn`bi
z>E4Iv0_su=_Ix3rLo`X>!a;S>myf`7zemL4<-J;?SBYMnZ$^0f?X`)*{3Wy`qgz+u
zXEVSfX5!(!BeI>rc=w_Oqa?j8TYJw>EaY8vw=_;9DFROUjPFUuErMpRfZuG4OO_Ly
z%Q?v%7hOOX+M_k3VwHsHx|bIrTX}1M5d<`Hb?$y9?q~Lm{#FP)_!PJRb^bIqWy9+?
z-Jgq$GPhFOVo_Zd6;xNhV{9niOiN^9D2HQ*Z)24v8TidGW2~I&ryV*~<0rK35gyOU
zRg(Lrx@+4yy63qKK_Rm@q$&Eim3sHp@`SD)eNW|w{ONW+0_poGifI9TYDmg<0t;PT
zUzi(tp0j}fdW>s~=w8b2`qAT;A7e}t6W8=7$2L{%(n|(;P>SC~R?qSMxq-&!gt1wA
z^te+?OWVh9sU+7c4lO?oXywwgY-3qYnlGB7;cdfo)dc&A@}aG7r|d^Zm6X>LOK|cN
z;wu&0EYI_Pe&3_lD4AOpEotwC-M{p$qim>i(52##Z}@4z=^Oe$Bz25-jxVOc+aO)R
zPRE-r<y*cfea`XB>ovU~IMAoY$s<09{Eq$_`~BvcZ@&itXn~Zs&TnTUc8@A$Q<`mm
zPRxnpy;H3M6Y7w|o26s|LCPGBfJLdO3FFL3{>c25%LjLaeU3%e#>P1_V3+h;9oJa!
zJEVymBgbprc+sTD`~0=IL@v3B^zsl%#z@_K3k>J=yV4AUiH$pprNuM-Z?sjqi%j>x
z>s_-Mu&UiiQ>LroMA#J~gLW8G>VV4)<A!nDqypdymL%w}O-#ko5yR6ovf9e@jSTkG
zSX2?ID@$li7J<P#ao$X0rjLSKcfPzQrtAEEi%QG&$*2Sy=w<Tk(G0h_s4d1%_zhvL
zPT^j5$&~r9t3H=z=QZz5P&C8rotB=vf3*3S#qST$y=w?Nnpl(UF0iWF&Q8S&Z*{tT
zc-&bm=<Sqm`@!qR>xAax7jfh5Smj}(^eToz=C#w`@BQ@jRjHo&*0%qOnd>a4AJVBI
z>aTDNh`^eG^r-R7$+8@Q?rlQ`kV<V=IRMujFW2Sp#xvq4Sf!H%;ug)ZV2;x9twssq
zbrtT00nafSb+vNf9`_(D-f8Z1t*^VLujYoWUu|9&Tm1)60#I`N2UR3t#wcM%%|F5Y
zzrg$dCNw+=eEV0l*p8t7ceKd#7r8%~7zs%sS$5upng9-%W%3+egrieyfS?=#vz3fQ
zVUP_Y#Ccd-EoAyuDkmTqo;CCIUc4oWxD}yq`%!tG!}wQe2p(ma`)9IX%f@=8$K_--
z94(BgBwdJv?aTC5N_AnC;IK2zNzWud7T%l)U5JvdnpCSBdEHxA<Wkc@asQ&T;>@Z2
zdkiwR;MrQoq4eE~<X_U_jQ&=?-!^tRyXuynmekQTOBWn(KB!(6!ungSUy8-bn8g0g
z(SH#V|HC2v9|C$&-k;L=uLLwxy=tbnrh*tr!_9`SF5)Jq3UpIB`NsCqY{W~>$&kWy
zM(lMeEt45TwV*}W1~n52_OA_38X0eU@ZKJ7^}++HO^$R~I6SkO{s4M*QA{X@;DKK@
zfzk`1euYSIbQutNDg2Ul(}sSb60Xo0#uBCQ$O_niO%9Z7W{PXLLX3Dw&QGTIf^YvQ
zik`vv|HBr3bQR(>7A2smi+p<F`4r$86UnU^^-r@Hk<kLn(tn7I4oND0(QiKeDK4``
zUx2cDX*g{yCG9U&Ppy=j(po>;D@Zpn*9M3JAMK0Q3?}dM4p3!$1{P$&u#DE)1g4u9
z9PnMh3zwQ7o-iSq?V7V-ovaX@0LS!4p8P-Nb^ke=4g~U>ztTc|!1eu^c6<CyuHpA<
zx{Xhq&80Hl5^~hdY)^QlK?h&HL#S*59L%kIwCBaHR-*qQ=Om3=^lTAma+lzwSbdDQ
zhStrA6asP|e)5lacYgV<{Gy$tmwu~ANV@rTMN^Z%7(%jD)pg;L^UYbV5ui{!K%pc4
zrkxb6S|#Au%t$ZI7Sy&;l!vxU_mu-i!L6?V-N62);V&};^0)uSSpQv^_g{|Ye^@m?
zzWy|Yz<&8*DlZ@YKUr|bA_=KCs*HE>z-wkt{Q+%rFXQOx)<YF2y1DKZh=Kt?Kj#^%
zAwS=FYgBd>Zu~8zw7HosTI!TPEtrLYmEp<zMKFamF|b5@pmP<HCP4d>e`x>OQ%L{j
zttQ+YhY}=Z?|wLON)Peh+VOu~DSguDEaxh<-lVO6(tx)#F3DzqZ+PT=u9jdz;Zf9u
zqlqD*!NSZDO!fzI&7Ai-8XL>u=ym^^B#(aa8da2cCPcg05(0!L+2Hjfa8#{+K4TJ=
zjZY-T`sPYa;pzR8{o3lq-lwTCB@FmuP#fil=}v*X#O_$9vGgz~(RaOqqGf~Ixp3h3
zpOO8aQA{Ja42y2TB{6yb@G%&n8?oWpcVBH!*=ioxI1LKe&R=m=H5BFQ-+Xu>%pZ*{
z3m-?wfOp_QGEU9gc$va~%+Vj`yB=PcT(`7a$a+`yP>a<dQW)~47p6(3d?Q8~s`lYK
zujLMPk>e)AfZ2bzeCijHtRiU&xUr*ZU;6);I6K$@FlF+rC&fT()>8?)cGb5Ct09?a
zmY6YQ(Fp}ysef!?6K`|h<N6XlOq#8`_dN$(5VEuh*Q9SaIk~oM6w3bOR{%D=Z6R`h
zrA=Y|q@%7elDahR(7d}Wap`3tHIUT!J4`CuXcE`>OU3QVT=nvkl0^BBF&bmH--Zo>
z$Fv$nGCli-i&%42kD^qeKV=xVHD+YI2gc%-h(5Hsl|7nNPi-pe^Io1-ZiPM`gOOwY
z#P_NUUEZzqjW1cNSc!)d2x8~y!u4joG2uIEUmZWi$^54%Ls~AfMes0-?0$YZ<viFy
zKUL|hHibZkY}_I%)@MB}<hU!zV7sq>bJ}VQ^B+-2$>iTrh}%LO_;%{DIQLaNQVKhZ
zlAse!E)rP}yTBz_cW$+bv)q&IaSENg;m`b3O7(TalNOi8yU194n+9pUtKg<Trk4&&
zp`5te8ueXWwC{D+KBXR5(ljKF5k?}4I~qu}o&M>0)Nq?g@=j5l_ruHx;X(o(C4~PG
z!AM<#pI_Ak1h4&@C%XUiVwC@8ANC(J-v5hz*#DU9hJTst{(og3_P<PP|4;j{*|^wP
z{$*nOAQQ$A(`>nCC2dasN7HUgY%D5q-nbW<BBWAPy-KCg^Ki6#C{^7=b-gHm_tCc;
zf6Ujr<SrtXByco&^-HGJJ{DW|4sT{sC8U<fpNaP0^YW%;`YfCaIN(QYdj=B21`<n=
zvA=<Qx%$dT3f#i(k^W!YT;Sv^Kd>4Ytn~FsA-b4WI_`{;jOc2@SCa?cR>ioYIsI<`
z48x)x+x|veDa+F&l>%)rhN0a}6<Qb-T3AmE_z!hb{N=_f`|1umowK9vAm7E0)_d})
z|D`YEZLU>XBjn9?#cm9OcH5@V!j1CPy3_K$2g${IF_O~ya~XQpmC!buF+<@xsqX@n
z{1MR^N23S0#@lW5CIJ#IQ0`bZ+y&+995c~jYbt`KNjUT$#b-MN-nIXdeA8smY}oRa
z)d+?y{l4bp<Rjv#evdsBk+)FmLp(csY>QX8#Ao60<F{4o+~xU>2gJWI8IY$Rv94BO
zwI!fkPocERF>bjT7?4Dpxr$235wPPgTwpzYSCg7ku)7Fq{RDB@-gFiiIJFmyAy2C_
zX{s@BXfk?03oBwT3~CedT6o5qa3<WIe^AGIzSW2>!EV@lQm2}0(qXp=xv3uMW2iTT
zz0?3xZ~QF3STm?B#w$%qdC-Dgg$)r5Y@BV}sy*oZsG+1p%ui=6PEHcwbph8K_*zU%
zhS=?WmJI%SDrcrvbE>p-4mvt>>|F^@UD8Y=9f|-a)lm`Ak43p6Cze-|%o}mSP(JN=
zW)r2e>g6_`tRc25`#Gsz_dfVxIYDu=T20+X0Uk<pdD%Wz6W0A}-Ces~LVF57*?zz7
zuGpQ;7^d0m*oeihkcJ%_SdB44xSwqZ`)Y6yJI}!%sJ+AO|9i=st>K)ox9bCAjHXes
zf7wZ#EIGo7M$6ro^*SB$Y+vbQ`u5R1zrEQ(jyfKZ+O)z0u`6g2F$OV3>^R5{i|jau
zHVT9aO8VaExxKEMS)HRHMNqdD%RESa*z?f!OGT@(XI97$%+Dolqw?J~Wi0P;EMWs1
z!+s8~2|o81Mr~~~e4Jj`qX2bD9+7FnB#FJo(=sVy;l`VO$U_}Ny*EZ=ffkM}_<i^N
zRTPmgad7aYiNgCqWDL2n-<ZOf?PT*>yg0B$QpIuBwxvkR+K0m&HXg~{%%U&G`|fA+
z6&?{m=TNGN+nIcnY^T$K*5lmZ*<fiz>3ULc$<UN^IJ#`!oY9jfzv~q6V5Jtv9I$CW
z|HJRE;F<kqR{CylcnF%+2Bg}V%x6q_H6{INg(%~vQf=Uof;kI^40a97%D)KoV&YMH
zKKtwq&{d_N0RzR=k+_3-BP$>IBv75IBL4fC^?61H+cXsxAF1~q*)>fu9jya(O5`SS
zf_mX)@<Ij^KNBN8IT{*{2hccUC4uX2!>U*m!HvDW_mQ1Y=2i2$NKm*%QJ7+J?9uBe
zGeMvA18ssZe)rXKr5WC@JRAQec|iu`_tO<r?n~l@$0owhT{S;DR9ACP#s+HSMgn4T
zT$CzRtcsznE)X~svfB@rqh8&iK?@Im1{e)>XkC5vS!zD$4H`pno$3T8L5{tdeqB%;
z^mnKMkeR+RQ*3{X09(inonyxvV8GvXLyGGW+}>I^ZB8MJRDTWo6uE4$%Ve&t(Ilis
zEXww#B;XrPLrN0adyQ9r2*a?-m%nQ6JGjCYLaDTVsuRnlYDm-J0n-KTA?LGe^CcyR
zCu@213!*i?_kZsahJv^znXf5U0}DMOxjOMPLoVH)F_4;b7XR0KWsu3W{azOw;tL>6
zb_aB{>6=ZUOi!@0EDRYwK4AJDW}_4rCG7sv3Zxy*S`kuua9=T^1~3y459a%<1TQVC
zh&Kq|<G*jLAqa|3E9~|<m3CX5Ub)I<bd~8xAr%1%IE<FG+vnyO%G#MeqbZtIn*!78
zNSq<;q^!82M$}i;3Aq}w^$!;HLp~zR1bKm5mU@K+%t+JbjHU-_QWz@v9)VmYMoiM=
zBhYN42Z<8qa-H`)>A*KCh;L6*8s55m3S(Vsl_GiUnbn$h<xg8TT$L12opwE;(b*iP
z$-HYqlrmBI`pOl%)kE&tNn`B83#W?n{Wc8Qe2bT%Or)@T#=K^Xd)bA~YtS^*Uyr=H
zpnkw?p@<Fhuk{#ml=3vfKdjY~m0xPy9rM?_k2LU~xXBM@6f|18YKoHH__Y}&L|WNu
zUmx&{dbZnWE7)D>8hdNM54<R4bgR6YKQOFb+z}uoB#b6mJ}lMQo|jLb_-&dgKMV+U
zvFc=fm*}w6$8o^eXt?KL_7<@_54#X$3<-CS4KrFwTsdFN%I(Xhal@QBn2%mg<{Je%
zqN;*wmwF3vAaPbsq}^>_a?)IZ7SA7eyf9Z2t{}Sx1*H{vt`PM-6pW0G1CQ0k@KM1u
zNTHpkquteFdP#807j7M0=zUvWEA-mVw%o(vOt|lTlx%i%z+OWQMZ}@1nogrcX)&o3
zuZu!1?h0(XTY9VExarZI&S1HOYEubX(l^}qusx1m?N`hdl^ee{1gAb*jy2DEUe?`x
z&W8ym!z+4I=Qb>0-mFEw#^un1dI*y<t1e+vD==>jljonEO%y^KsGGs49Hm=nz9)dB
z<MAoGjmdqvvCGopkba~QUkN3Nkaa$aEyuhD6bEZ3R>Sy7VT|ww!-EbRoKITRZKUKC
z=ZKN<TYyFCzKFUGQ>wI(RLXtt|Do$0<Lm0asL?i!lg74f+iHx)w$Zp@W81cECykTF
zwryK?=lS3Fe!B0c<RoYB-#TlrGv}CNjJcLU9S&)IOsj**i(5!b&BD+Gxd{x+xjX`A
z#HjH*kR0e`fiJqa1+nH^SxJnN44jh5@4^+P<#aYX;LJb`X6BMwCcqC#7D_7~Wyp&v
z$Q5zut2~})tGO?Y($l$pT=u)7>9G#q4BJvkGQLM*c6C|HZ_aKwT56dJt+&0$_opC)
zBz!(t5tMdpLZM{Y4A<}0;8)?dld9c$O-3_bZ30b`j>dsD{d<vUn;Fr*-jWhR@u=9r
ztQx^?Q<vp6%Pw3B&g{hEpeH!XA}d^6CO7N*<=oa>NNaz|Tmv(3M@E}`*EO+`+hHaS
zFu#R7Gu@Tt(jFqnX(+o|4StgqHj1w$S8-!{XoAP>`8syh-Robx7%3TkVEmaugL;QT
z5{p%jp&ZH=E3M(6Aqvc7=qHMEz-asr|GY)fYPvS0%1P7!lYvO97>sIn>ALqT^D+-a
ztl^`{K#XVA_A%rivg|mNZGb7tin_aw&yB4&F)1lV>;G;LvEfCGOvXuKuzXw(-2)C0
zrHlik2=+ut&-0E)LyR%T%xVg%?O);NG&^rk-}(1%0uU*IT=xz-5to2p3(u!QA1;FG
zFJn8Ege8Rxg2ZJXK%pHknF}&Pu_&G*0p7`M<F8$|(`4NXNA=%@USP?+zmPMaC=&}?
zDU!RK#a3S_&nda)$=RCNlJ?ZLpzSCF3kT%tFBW~ihJS*)=_<9lz>VgR9VlBTjXeWS
z3_gxz?YOg|OUq}5lKYUGkh+>i5aF-p)BPMaxp$}P$E%#MhW{I5@Acm9*n-xKmB27)
z9V7@8H2MMd!l^XJdzRnFZ?A(J2uV1W7qeiS;AAwv)YySRMqfH#>Bvkom9i&hQ-*Lv
zP|Wbn*M6^%Zh_j`A>FLnr3~jlgIOe2^YrnFD;%}Ig{HoW<4*>Wlbz2^*4~<Izn2`C
zGabC4>5%go+qhn)lcg|I6@pRGf=Ipc^Qy}Q%jCCc#Qrz5?o8gwl(3Q=EoOAEG6>CR
z_5wA`ZPabPitcs0es<NLEI+MU&1V)TYv66&T>C3g5toBWDWz5q{&azM0l)<ECz$U=
zdELAM$^;VjAhp0R#$sT!R(jkPT}B78IyC!nGpEk;aAff1cq28O4V*Q6H7s}H>3nT(
zOF*`_Mfc<;H;Gg!d^777ugf##ufFneaPoG<>&cS$2azF-&<-ck0M&RIG2tQ&sI2IZ
z76Z~a;t{-&No+n!kyiH#XPd^cg$QjawLR_be7*ytZq~>Ady|b+>vxeWkDV{H8l1MI
z6E`DXn)`g;+sV|VbHWf$3mI<3NPgjs<TEib<O7RHTjcp0e}%*QLmF1w3|+2>(1AfF
zAAI^Y!WzN@C$)2>9)(H&Evn21gAn%BP_sB6|NYa^@yxlOa7R!g=Ex`<b1;zv@!@tc
z3U=?M`~BwU#De3V-O;mYw{#sz_El6KZp;r$L<#)D%E(*a*FaM~)Z!`Q$KFRJ(<kXU
zP%bQ`uj_2*J-RPkivWoV_ug#?Uszv%xNl49qQ?2kx~5WH@mHQ<)s~}qf=e?)s6Ps)
z|6FG99Z0p4^o@jGv<=m`1${-GcNAqRF>UAhXK>^D^-vjMXhC4Gx=MA;*OQXimhN_^
zezYH}x<}rIJrQM0s+i^ATN2e=YX2kz)qfb)Q8i09+aF`Byn6yD^%9ulH-k2vYFzHc
z<6()Wn;kh16Ea#P#GpCC<bDrfJ~`JWIw27ckKLUV1?HW%ZOx9Jx4esw+ry#_ypQ|y
z`nD}v3d?*p#YxcuwI5gqBC?tKY*<E4PT^7T;4Kg=2=y5dHE-a8Zp!G~$x_^|_O7lR
zfvyW*vDs~6ElEn<_lYF1F!-uHd!XFM&%PFTI4{>(YZH=E7&mWRPDFXq9TJ&aNsd(x
z&uiXtwZQQd&zWcNt$2(H1t{kBeR;qU*z#WmU2)EAS*(2nFF+J@(?X~Jht+yxq@jTo
z=!#BBM^;eqQ!y3Qaf(&CP@zcxz5B8Szxna^{z102>VWQ&tju1bLK9EJ9p$5Nw=uM)
zywarTWwuya3{j;|30udiW{_VUj}T(o55or&MNq+2y_kgwgnij?$(m)(Hfa*%DI&i)
zdCL+f_FOK`1DB#sO%AJ^$-ZSYcCuB-m5+?atjl@TEtq*FGf8EsITq0swH4uqA4;(`
zKksOkr#X*Z7=jV<P2UX4mdYzkFh}D${;Z9|haCixb6A`?+a@&Wp{)hNFRC{q8(kki
zmaTNjHk0uHbF-G>Bq!^p7|h))WMfU&S6(eomE3>G@p6zwAt@;#0SVr28e%X4yZ8(L
zN44cQL9PRI&3he|iU_u{l*XAkA4mEH?TIrUc_|ER(ea7Np@jp#xo^GX>DBwQSeG#l
zxd&R8iZBM~`B_p`Ov+HUg6Kfz3n&5Mq0svJYIbb~gCi$5DF0xl;0O<Q;o3|2!R_Vc
zg;5P@kLXoH+hD1DFAwAX71A(q2d6ymN-zZWkO<V>Cx<{0n=(G|@_z6XUl4_xNyo%%
z^>vRs(ROIbPIKD&5{(XIMMzjbn+l;)Yh9j?r<*S6#{E88G_0ERuR}&I_wlb*6yMus
zI-*Q56S5R;)!X~Q(Mgmb;K)_cX$`50DVy*~F-USFWfRl|^d@Gc2FhWdVZ!6+2Ge=y
z*5VlRdM0aq?JXd#S(LO+{~VsV&qo<tcVrA~hk!O>PO&0O=aBmb<QE9Am;0DdXex$2
zb3<z{CjRdAZZ;tY9d!0R8{f0l<Q7~S{e!{rkJrTml_F)bSg3_xE{xMC-z#s&Um_H9
zyB)W`%7DF!dirYw6J&r7=G~J|GrQvJ(ou{`dU-dZEOG~^ZJlearZ;hR^LO*cT=~)j
zYwFo2H=<jiulrv3H_h!^KoR?(A8v=Yd{?D6VBGyLUh%}dy-3)Xt_^5lh9WxF{FVPg
zCTGTCS<QY*wK^wQN5Q~r(W+RkmDF+GPRI5vJE<Y)5)j0V$J1wGVz5a3S?0N7I63X!
zn#U%aIM~4jn(Pw&&S)6dV};x?{!;CEsUR+v+Zq#M#2hpVnnc-tm*`$bYLR0-&#LJs
z4>$j7$7%XNMK2G>m!LQ-I7q)P<^|t~T!wu~y^Wz?p27KCkUh<wow6_V2XH+4acy0$
z$==b;UzgYcJn?2MEtNw9lqsUJ8E80E4=SL#I40Uwe|)-{=rEF$uHjTacp4WKCdA<L
zF6goYS?oNDnJFMT0!|NlC9-PDT%&4_m=u{^Ilfcj)xy-G9H|R2pLaMYo>mP1`kBoT
z<+i2a7W8WTe2|$99<tNyWH@A6h#0Hb=$qIca<LS^wmo<JehO;dcrWyeJwSz?c!~W|
zOtnm&;HwY+4}@fXn}{2ZJM7{sM&LqEKr;60$XV7p%gm|SJ4IU@kIeL}Q3ZedHUjBA
zew99wK=q1Z5a50o5$7-f?55T4;l}06_*q>9yTXEj<(GzHr5H7ETHD9q@Z>UZ4e%bC
zXm&U1IoXxVS&`H6QCp}av>XEvNsvs?a!uG45S|Q2E!mE<Tw{CH1U}Q;6^g&f=q^i|
zpHnlo0%<sjJnG^h+0z-=N|Cti#p<-~SQ|zIqFY(#xCTUJe4%-Dc!;?P{r6823V1Ao
z(V62TdmcZv{3e4XvD&@>=+fVm4ycw@lc@?tWlvW7g*$|HJhmHmZ8@vLx+V-jRZ9`5
zt;#FP!+9QX64Ju1{`~iB2)%ys|B$ff2vHUZ`%ABZgN7jV02^Wbw4%3SuI;EkP@KXI
z?feHb;O&d@A0YarDkc~d_&Fqx?Y5YTcD)7uzjwjp`S88jBL7aH&;xBP>Li*!TwA;X
z)8uy$HZSV({0yM<WSl|(_iLN`=5~j)2CXxoAR|~@U+vi)e)lEt^SPh*v$6c~uA+K7
zc=zHS4;7b=O6Jfql@oU&VMt7lF^EuwaP5AueR{a0{kw8<cBmx(*X8(Q^;LdN7~_Fe
z<gg8r$-@d~nx*rH7o`{|aK{CDolA9_3O>fwx8R@o*h@`Ox=oo4+!;6%EvC0Y#liwL
zdLNE=N0Lf=^}*)aPtdvn7sY=RylDvD-O=+dS*R~dfR(3Iq-8k;6#1|^T@uT#ARJV>
z^f&h_uZb1cx{+%Bm~U)F73IK5w(Ho*Jk!k}uIOE0@ppN|_`vrBsN&1~`s*=fU79!S
z9tYekJq=2g=@!yCg~g%<AO#VO5Kgv5r{;<K*9Logdl~z<_s~o7S+GaAy9xe@Ue`U>
z>EV1FYSAp>?W3X<L>zB7{i*G2_^5azeG&YHnF@6Wjq1i2(4aO$h5<8T%t!UTk9<Jl
zVn%ii<%Ja?-#w>|Ixlt}5BHppZu8(Eh|sHMb~budi;A%yT2_`)0eU202IYIJsMm#7
zIvQsMbw(J75>cVNzr{1nz;&Q~*$ma_xeEe}_)-AtujtO)3L3FxFD}MVBH!Csy2kpz
z9ypU88`BT31vbXH<J}K6IvxpBl5%*j+GC@H77MrC+e+`4rd4i61_iT(Pk_g>H8GbF
z7h*&twriBJ;nh2uBBKxckU-R!&@cZAj22P5oYG1eC4l&{@uU*WJSPmHl*HMHS1QCG
zOiC)8)KF*uG7I{?lA0Mk4dnUxz5Bsh78^J$S7E`z!6`u4GqC4DC9O0<%<Dwu`BWVV
zH)3kqRO=_2gXw4^ov$|be>qBB2Z^O>k6^A)eEC>cq_sy$&Y=xXg_)@?7>%<3U+gnd
z^gtmqdN1qNxw2v#<YC#I`I_pshNJ9gu<$J5d4QsNCT@a-&s#Wbcw8rXzjJ=2F`CUC
z@#(Ta^!Q_c;`E|QrU>C_cW(54PhN$wSde6?ftgsc4Spe%AMK#SCOSQ~o4U)=EhVDT
zUt*h)4RCHc<{YRAig5%LBQRySh)N|YVL^1;Rwzv{o}Vb1_m+CdaK4Udx*|M2z99A+
zid5u592^kIqSK2lhgG+#!KnE)4gfi|MhiSRjTAOchNmbxF>BDpzU^m&N3QB99Ai1@
z8q-Zb3|<|SweOr1p^(#KUWi8y&SymUl_+Cv!-Pd<4Ay!G6LVQouo5VIXY1plUz?Y?
zUp8vEDwtwmUx`GudUf+06$m1n=37gWl5b>#T{F9{8z;bO3#8djWcs|mf3kdkjEpWN
zQSq7bGc9o(mDc5LOwYV&&~>V5g^;JTfw67!ePp~}+&Uc1<Q4j{fER<TYKL#iB&}A#
z|NKEI>7y_+QlDqNSmi^o2Gf}5)<}0w95U=2X-|tzhx!971POfYkISk;b)Nee5d)<M
zaD-dUuvG-`5fcpq!1}CZ(@u<g4bVSH^8AGwwV@j6g;7z6hrznwj=gn@60G|uRlKU2
z+0o$CsSe%J7;{BG#kBGBAX|D%3S^T9&GbYmQ!3jIRHvFWbIwI<!RStq6$RcThXV;a
zNPedN(Hov2B&%+ExEowDk7v!!lV8DW9EStnwQlah7slY~gvucon)jQ!9|GM1{`Fsj
zi~H&Rarp3J9FT1?)7KQQCPV?iJ5dFLBZc>4zSlG<YfoKp*{HJK!fHlXb{OM2V~>Bt
zxv|DB%VqG<!SoNqrrfF?)nuI9cnVzE!D19k!LP~+K36jtESh!T9mKxbx*{4rJDl8>
zO%7~~#Na2+wZ|mp=}M5A<Oiz?%{#4#3wM1Ft@GcDlC8m3OyE)|J%~LTE`zBls>BOf
z5VkvP@ztxp<P~G@R?jsRhe7&!A({tJ#+5v*$NGX&^&cd`s7lRprJS_%iT{fPvTs|5
zgMt99augiqn&V-_oOuO{GIZTb5G;GJ2#^EWZtpU6{!4?=q0qH1^k=&qlcG5e07^js
z$kKFw0*y63jJYu&uD(d!r=5~5qVN0sZ5k0_a#b)m$&<q_lJ1AFB!FaM0K^V>W8Ixv
zCgRu#BFX%C3V;~?ejiA&_7p_(p7^n1*R|zS`WNr{{zzd)cll;I)4|<Dd}4lxW!?V5
z!}Gm)@>lkx9rnX}7NI;Tv46MOF;PWhQm(oP!fluYHLx~ed&R%c!1&o*e}A%*cYUnR
zXt<zSo<k;9Sd)fK`?AqS`{AKP<w5@_3KWfvCTX-c7@FD<HU0qADw^LUF|ip$&VgB?
zO?U+o0@7BPVD4nEda&&i`^_Y%C5Q_hOyFa{KYU52`K_JqI2SGfmx?*Dn>X0}mn>bA
zUarTU<oh`HFIR~q0n1>5H)%k>xvpmp)_N?ih>IDINgN3IDW)&Ruf|esDPu~f=%0$k
z*dT8E<@HdiO_ymo+@eI|vc&YxlsrHViP_-elB*(eWGMT*9hn$fFxm%v4N-yQjBl-i
zn4B1WWFwP#!(I+dn9(AnwLNEbyB>DxJ0jn*N;NGb!xTW|=CL!-+GCB>wS_sy4Wxv9
z{r3kb@%pTG>M$~f%^N*m7!%!~M+G6bbG&Ml=@%n$t?rESk!R<x*#^KhAopP%C6Zex
z!eJV?D%>Z|w7J~O<u-c#tDy`u+B@`K3WVuNBV<Y-q~M|b3`b=4LP`?Zbra_nkm>2e
zD{UtEN4X)VZ5?aenZ5t30mUD?J^+Gd!Lx%}oOtdJi?70s(j(MJZef-bK=9(7X`#pm
zqwWX|wwN-CnddQuGKZdoIWc7fBlVNd-?Tq;`xy<uhF8i?-{y4Vmb(!_Zcnu?UbR+m
z#}yS6=m)#_-@;m$waYM=(hQ=_p^}1;LE&3-wgu!Q4{yb}Z?P4M%j(ux2CU~U3_Bq%
zLx##fJ-<!{%U{OyD&FdP6-mi}H3g#;VS`>9)M<8|Uwm9i3{$7oNqtY$t9Y&9`~?#y
z%<!E6Pn7(?d=q4lSKOMGGfpN#nsLe>JW9=grl!tWe;1~j)0TOWBSLxx#)XO7jcw4O
z2V{>ahDHq+c3fGcZC@#LH?bs%rFfR3t!ogqX6|OCD#6X(oB#jlZ<kVfFfVqXN+8o&
zjx}?G8vxV#Se|a9@4e%i_(;+**#e37Jv?=?Ma{UBOUoH!x76j_NO}l^z6713=8{bk
zU0F*PmWZ}_5t;mPL5En4{+J7QnDsoMf8}V+b{{3JLiC*Qk5WB{+Joj|5OFd%LX^;~
zET7dp3URyj(jQ_Y{`8_L?bEf985T^YTop<MDGVsJdI9BZEgJHOh+>|t1CfcN4DPS`
znV77|jtarLm*v2sLZ3rDGU7_jb-6IeboXsBK@17+lQ@wzx8i?sl$xu9Z+iu%5C%^W
z0e((&`rhE9VqbH%=X|^Nce$E2dE&~?SOSO>4pkQ$t3$Od;%Exn78eb>3#S=h@G8y@
zvNkgJ<cn$m<_j|dUxy-5S>&nQ<~|J}UnVAb^cMlXw&b<v>Jff5YWk--gdW6j^}f8n
zPMbWD`6vguT|b%XE%%9%nG;O|Qo`gD-(nv|$_eW>^K{xUbH>m*RjZ@)QyC^j?P{n(
zs7S=<;~XCKBIu^C=Er@eN<J3TIa(g0Kw*K$5%+c4_LXS*B|a(cRygvHzDiBsT#IY<
zEdfUzWW<Sj-yn5V5^MaO263rCJe@`HgZyO)e55DBy4Xo>)4r{BHG6TvF_n_CP^{;M
z7v+FPliFXpP=gs{f6s~2Q*^PcZP%ZloEHWE$0)se?)x>njYzu|RqX!!zAuHZ0x>Z2
zHs2P~{vdrk9C`!LXH_oT1@_QD$6BYE3<uO*M6nlMS29DEsK;>@cc6w?ay}*{5^sG2
zUMN^27|^Y8NqI9C%v%0jy3w2GcUu%QsTP11K;rw*!98H(9iuLfG=WOKj{CScqn@R*
z8mR!)xR#Zy^*H1QV)i`yhU()sOt?gTZ}3Sg5y+W@A@_3N+Z*I`hu%l7X-tfuSV1Z&
zQB2Y-j1?x%*D&xEH_f+5U)-dAt@)QG7GjIQ_?UN(eber8k(1o50keOsO|c4CzXw7^
z?a<9F=deEsE;#4o<DucW*$jsh051Q8uu2gty;6RR9f+Za8)xLV@w(VZP|O82*K#pg
zvvkn$^Tl@Lsw1kKRfGQbePux>h6>Z`iN9Llq8;bIj8s1ku$_VRkF!|OM1cm^cTZ4X
zp@fd0YV+fc$oypv<FM24xxJ493yR9*24DKc-Nlvs|5c31z)6GS|Gs6&a0%6ThY#<l
z^lilcq@MRj)B7u4Dq`V^r-pI?v{YBamV=nExA>hCn-1G8CHOq;F%_K~-Vw1L(@}v?
z#osmH51lMr7L7j1uOCjDUh^u-i2VHFf5##9N5p=jNPIGpUO~=cuvPP#02>RRfSXIH
zp@znq$B_p}jfE9e=hV{3i73gSt&fj=gFg(D4uHudSOGMtWPb}GA||HCr<)q(YAH6v
zG-X6!$vh#eQ2##s7|N#}n%qKa_?e-dfm;$0AF5UNO)&Z|<#<HON>CF->FaCdf#oPG
zA9R*l)*?_{I#}4h#(Oamk6;8&EC#hM{*Z_JyqkH3AH?v#hXVZ{B&<ti;~J{QmG)H+
z;H?~_29g&%!g;vb9ySLK*!FCzv940lRFF7l07)wBD9l22br>QRs<OGbUr9~Wm<=dp
zMAs19F7|+9oRSr5Y4Q|n0U3PTL;J*VZ+<_soU4W8_m7Qr9Sb|Ep{=MTeqTBvAGinS
z@@<SobtvNBcQ@sTUc``71|-gK@xq@|rZLm6Z6s0NzYugC>t?KK3PJI$o+)X%oUaC}
zQ5C+c)z&tu@UU#V>iHt(BlVQ5Km_vu*`@kcFDGYG^IY4AqL!<IW`r4*4JsFhNTLg3
zrIQCFk@U#D#DB_(sRkCMxVX|R`GR39Z;o6w>N_TOCDg@DTD!?3D_YUs0nP&G2Zc5Y
zhv;QP_BI*}E8)`1oVOum6v2?dLzp#f&EoMdk9PxA3Q9tIFm{@jY4P(o&9o6@9W#-!
zvN3K&uiZH*ojd=ubQC@)Et<nyKp;e&%Eh?AxNKm~GqIR8NHldh*UNOpF+>9LdRf>Q
zI2dFYd!sjPe6K7r*1*}&KW<H>${137I{xy+Vu0E3Rrf#3-3sH`BEw8=;d(!oi}c#I
zk>pi(s-0IXL!m|%J8`jw23;6oz(|4gy|q1rk<^xIIC(_giH6(O!v!zXp-cdHoAlIl
z{mx#0w_z9ZE^K5bG9m0O!Urg5>t7md`!@b>IefzAp?u1o|5T5-X&Op9Bl*1kH^38g
zZb3~L=jN!OpdiEGCY2`w<N@Y5bhD4zK1DqWJG)54cjC=QK<XDZ4$^7aMV)Aw18Sbv
zg!fGYK?1>^pu@oQVxAbR_*ABI9FJB*@}%To-uX{nhV3fm93q!3LF~cnKNeSNJd-pC
zK6GHh<<Hf!yG<3~)DT<paDUa@W{fBX5mGuX9aR=fQve|9CH9rly&PE(B~h$aT{DSu
zGCkpA@^<F7@7;JJCy-zfL61naQLc3$vMNGZjKz0G`}3#7QYaXT$jCV<QVPu=hbJOE
z**HDy^Vz0>vn2Nw*t@(h-|%JMJ}JsxkDv<fy7QlNCGu61{c?D=TESnOY93aw-&yV}
z=BOxr32=it^-vr9CJ<b>MPX;cj8~jx^Bns69gGK<REbM1BY8OG-8KQ9K@-{buD%Ty
zpNnsR8=Qvtbfo{<O#3#Z6nWj=HgN~eG^MnRk<VT00|E31TiYrTYAp)ImMl>w00AFz
zvZN={>Lmh;vyn^w%)9nDUn3U{OM!pnRhleToB0(>u1!C9Pg8uYwL(ubCkJ6Y@)BRa
z8cW(aE=P;oUF&gSqZ*^g6O}%raski*Q4|JGiq4xVBIWzDPjT3=Q+P%&5`N~yg6_`l
z=Ytmr37R-8ck;d8^#FzyXPMc?=HIhp-W%@^^pT!5R{v^&Rf7@g!ySc>g@9nNRI7aZ
z_l-lfmzc*`^X&~Z2sozv0LYWv3<iU56Pd}L0Xg}deZ!pM&r$w<K-`$su;@<wz0WIt
zlv^kw{dBr$D!=So5^)vDsq=ePb&J_TYcBE(HY{?=5VW=eni}Ak9~Y&80C3A9d!TVb
zBgP-FaaFp*XP+fsCC~9_zF!0s2U5B8SBop1v{#LQdN>_6qt8#tFl@$-RUR0B<>UnZ
ze5hc>b7-wm+2iw>w#@C;E<62qhGwBc8SAz;+!j>yD3TFbzd_dm8tR?tJU`<V6yjM+
zjl<ZbCEoh&B?JV2G6EanJh=*?iqV3RTsjsM{#$K;j7-1doZt4sJWhBcmQ+pJCyo5_
zeK7LsS;OR~hOoXirD$>A9}$jEX@+9xT()B+?m~-Jc_Y>JBq)gA!+PAZ6M7@&oftj|
zx0B1Ok6-0n)6pE3kPo9}O_uw5eqR2M&%gg42iNeoBe81S2KuGjvTuz_MK$DQfUh){
zg()JV!Q1gMZ^V%PA-!Q^d3(LyeanWQ^BM|}QJn0@p_Td<OB$SiaSqT-=jA@m!mE>l
z!vynN>ElQ0RRc`oH-B2qmd#-BE`qZ4QuNX7R3%FR?ZiUbyVo@QEq()wzS#%(&WS6n
zXsRLIqb$w15zTtr4mxg+*^rp9TLi%O70D0o(ha!A^gh{i5H{?kHmP);n#RBK1jhMS
zURQRP9ex+0=6U;lqetlc`bUQaPl2yvEoqdYAR}s4G-!;uQ978{cR*b#A*Wzbt#$bs
z5bmWow$<+Mr^;+gMk|M9VWs04Vu_8#ozc$Q+0S%RkMXZ+?G}3klsB<o)(L<Lgh<0E
zKWrQ64x4>T06xD6a{eFve{QlDQgHXXp{xGZU?9|DtD=$8U;I|5RoF;zGxdIg%HD&I
z@H|Xv*A<vTHRy-zLNv7)56>v&|MgQxXIvkJn77-|VS9I*e?Ib$Cx>tMr<(;-$8+pA
zop@T&MA)p@Gxghz#f?gqmZ#h@uSQBE{u^|&{v>dq-W&QUP&`{3TK5j1|9xNn&K7Qv
zFRnouE3DH_MJPgD1nEP2y5ssY-@W@cmZz|n&rQRgeiL75RO>1Sy9pi6lgo3LX#L^V
zFZhQld=M(QX-M{u*YJb9dWG)eO*e%?xxfC%oZsaW27|@iB(M}<5O2f2MO@7LT18LB
z(=ccPVM{rcil6g&m^RPmtM-vg7g&9IPO|sK8@s4No}V~7$H~TG^?fLJogEkFCz&iE
zzEmCa0L3!(b;-Qj9Coa|_5d|)=Kj!M{AI`XD{U9+)qGTxhnAKu&$M^GP<e5EB8IhC
zNB<wh2m*<&ybkBt#>6y>I}Sel_MPO4anH7&A<T0BfEH~+L&=DO3mn%2AgucRIxXrt
zOrf0cNPJof&7kH>e&}9|u1TA)(tg@(R<A3+X!oy)0P^Zp7_BPNdA2W-R>h;xDc8^L
zUnCTq%%l{ebcufH9GnN&<|8i`^r5q2>ltqBY6Gjwt5bw|2~3n`AN2gx)q(y5Lw~14
zn{F)c%)=@YU954%Q*&U)ysrnnqtbt}@HxH$=?Na*+tu`61^`~3@1iu=j8l@)=r=mG
z2vzdIivpuKV;!^{ING4|09NR0L6}67p>r}|4RULw;WY9WD|~us><TlRVphsLy6B>o
zM}6E}gy{PBOjXB&e9QOiSe@f>%%vFt%9r3Rd_B3y2Oh%q-?Q}|{3^H8`p#Zlq4cs+
z{lQ2%eOBhJqc=|Aax6I+-__Js6yD<F-^i}hWL~!IvdL?ilY$9;<0J;8aF<Wr<KypI
zb82ED+mK({PRrZ|cPX?-QDo?SqPw5Jzgqww@6(ab)rq718Vs{;v-{iHM&_fl&FJdY
zg3T6AtdQ`ld{$2K3<4-7L943*v6)@-7+8r}@nWYsda9kX*EDFgiuk`M4~IK;$q@U$
zuYdc8!#*4i`J>*i4*|c9)J3eP_wfsIf)-GA6h>6e%sT|81yo#8M#9^ode{+9BObz}
z@;qpkA`*_Ux;sAVxzkt_Ek$Q)Y6C{{%>Hi9#J5H($-a0?aV_p>FTkf}JLXx|r*?gM
zZ(1ZKo4_gb{@|e8(1MlMS+wOHwE1}1D2j-`8tgbn-&o*m{avho(kcsFv-jY`f!5*X
z(purcR?Vs&d!y~N!$;Lb1~#ji<oB>8W)&?~=izFU6q_e{z1jSIS?s<Z$YJZX_D>6Q
zWnu{C3chAUT4MWOt-+4;?xFY5e?|kE1~ct()III*ZkRJa<D9_zw5n-WE2ZrWuI0t!
z!WkyNH>viM)6?lAc*4&B^Xvn&vV?}WIEXoUj?YgmXqPEQukW0^a8N0i{_}nuyS9Lz
zUgo=`r^oirx4S_Lqtt{Ol~1r6=03$gl-fY5Ke9xd4DPlpqcb}U0;s2c<UnI*1mtn9
zX$_<4&hT3&az|ws@a&u#TjeSrMMU6@>b#C7r7k2|cL0~4Oq#ArX3Ar4PR3oMn>09Y
zey}fvL9hACrXO|;obOq2hhRjDiQ)ae@I}|_D7fdiF@KlC5jRl$e0x{z`hAq<9`;~U
zq1#iVc>^a}j@Ri1SjnVe*CO3dvEiD`@pGsV2lI%E!|)U`KA28M9EyZas2uN*@jC&8
zS&)|m7l>aOTimIAdzK7882Pa<1~Wi@O2ffJY5+ZMe46_n14FwSb;GipBsMDq>Qr;V
zso`}iv4q(B{t~i?#J7Jb4&(eiGxQbES8R2k=e18Ru+-g)L};FfZo*6p`G`&WlBu{<
zyN@4#0ha^mUOt>{haK3iq)rFIJ4Anbp{nc6vO=1>SV-^3zgWM1aTU5W9yZafX8W<i
zRhgTeeZw9B9(*lOy-D948#jc=0|6AE+Hs%xJzn~HpeqcZ4;(9>FZ+)fzir*(4n6q`
z<;x#|kZ*5c&tOfENgvt_`3124yq(f5d)B`m9;MEZ&)U*!)?Jrq4#1DwAuw+*B=yHV
z14pJF0;1$B>R&O#YXP1V74)xv^?>4}Rm3Mp&v1MI@Vf8@xZ~s8Y&yW4<Bs-zvu)Nm
z!#~W2+jyHf%Cfg`=;VIF9jQr{K!^fE|0)1nh}j|f;WWPhsB5QOP1N_Bf$1(f<VN!F
zspw(D8DW^>_cX11_JF-hTeOYsz<R*Z*OlgfL(N!BZ>*~A)NpW<ar^Q7I&?DfFgkDk
ziw?8pM)tU7um!%#1N;8N<<*-%^ZiO-_^UK+u&Hm@7g!TJ9t(EBin}n_^?rF7l!BTP
zwv8S!>g}S9>$%~H!9YUf+x`fmZ7cOYf3Skx)pZqSs4JjmZmf|$73@lY-T?U}QcF}@
zWZ8d*lj1yU?cGXHyW2XXiI?#_Whu&nTxOeG`fq|bXt8w{g++LtDy^2&#l(Y`+pSXG
z+T-!5Dxj`lumnR$8j7RHVzf}fP9us{wi=^i{%KvDkHw_T@>@QRQ-Hv}EaT^R?A~a4
z)3%-Zu>QAEiU0e}*3vU{V(OwJY<M)oeu$brEHtb^6Q0DwqkmBD^||jju8dk5Rafgt
zG?@&x7vd;tbEga(&rw_?roM6BK<di!*=Sbq5C#9^?kb3#_f!w}V#3{9e^sbK#Iv90
ziu=MoNjGx%6h70{mx?Oa`%N9YBFyWc)}<25d2YNXBaS~M+%rOgA56Rwj!4^z$_bt6
zt6xV90_^tagh^-W`?35*&W7%br=(sez~Pg{KD|huYf8>=G?qZ!?8iL1zIQCl8Az7K
z9c8B8&QjOuP%<aVXtJKo5N8=Y61>6Tv~XP>Dt;K$q0KgChI3Wp`?eCA_KxwdT2*7&
z;5wbe#bvT^N&{s=D8s|MXp<2mvkP5e4Zmui+4b$rENzo~SLMu@wge}~U^pSO*Y~Hp
zzzf@E&$Bar<B!`7>i4p^6Nc>Q_Z|SqtiFZipT(4Mu$gW?_J<`5tKjYmctTnvlpHs*
zHfg`l?ZJ+fYcwDCtJA0Anne60HqP&QEq;0DR<Nc8f<yqeEs4XywPK8#NSMbiA4?7^
z(^yU$yf)vh{bYY~lVK<S6K^2WwC<Mx_p4HWgbEycZY%w+g8i*hd%q$2MwVdO!A)3+
z^z@szZx{40)|kz`fO|k?T=S^vmYxBnn1rf8WSE;|f|puKa=WSv&#A^~r1=-|-mY1i
zlCo*pjl$#4v@%QDh7IkPwYHZHPp6BrFctb#OxBw_&MNr8qMt<Hgt1m0yn#<AaAY7Z
zGF}njCy27e?pvhZ=(IF^94A~ajE8wQB)o%XKtU##RfIqdI3QU>z3)R*^}lx5Gk0h9
z-wVZ}dOkCDZCa2Lvp-RJ0D_HtjjQWwuc7cj<zj<@>DG83JQ6xN{rkbKH+YF3L$d_L
zGi9n*=!EVq1s4+=1hxim@%eh>mwJ>g6?bI%WLt=EqRJ7Sq*kW8f3{WrLa%^O)4_Jo
zU|PsST-Xl~n#SSM<PLYCDc?&`KTve-p;CCUl~mnv_+{~$<!*cGBq6y1O+b`=$l9QU
z?1div;Sao<t*&cpatr}*_hU9q0p8l9xVaNU_|*5PZqAP)H1jXm;J%))CGg#)EvTu$
z;i$gJ95a6byv|jo?V0K^nEBAH_KOKS!h`g|ng|Ejs<velWn*y+Nc;s^eh$T?77_e!
z&nHIzP^2}`2{=<$z4`CPxksHAsDp|))FZ)k)l6voF2nC5-%tFz$UsS~INSCFhJ9S%
z+g|`#_9ON-Nqm48=>nZUIiYIq9^)t9#DTA&Lv>EW#vu_1+ulwuzHp9~erfQ$=mx_z
z_+lY=(RfHEAYt60d3F-&(#85M)Ns&3*Z6LWBGo)ba#w5C2=jdKJY9)f9Pw^<yePB%
z8$#xrrAF!D0m^rvkhZ|<eGBvxhuBHh&geKkECp~K2eZZgo8fsDI=7sN;dBSXIb398
zsx$a5!Y}w;-?D!V8<U|j<v^y>M_9;Ef4GW?@*aK(*l973nX!><{z0k$RtzG5*gf`_
zbMN~e=^d%v<lL7-hGCJ~XN@CLW}}I!Y&6;)f>Q(wR-o~K$rkTz`ewU}Ru%J1jG~H|
z1yS@)6kryGIsCZja624Tr|9ro<m~0`3rg%A&iARiI+)aOJ$8n$ZSkBO4D^V=4Vg<U
zkLdMRR955w+5=p>>W3k>8@R9PwW+xyd;K<-LvfgA$ecMCp%#EsmQNdi+<!?YHIoRG
zeR{f-D&xVaUqzW^rn2J$0($^QRMYAT;5;j636J?<-tXj%h>jMWi~&0ows}1=948k$
zLRUnfH;2Ru%9;a}d<_^`B@E-)B#CE6v6!e?DV!|d=Yie~K{EBsF^X|l!wu}J!miZK
ztS`sSW{<gqt%MYz`J$$S1eo}IU(AYf8s5lM>SK$;c!e5To)_A6y~m$0cM(Kn$!D}#
zPfMZnk+TITE9p%*(f;jCb>|fjQKnltC*RyXWoHxW5bB}j;3P3A2B_6+bDE4)jRxVz
z=99oqh3)jCYadOH+q1!ZMFlLU>c1@kD@`XMe-j%zk6SR82>F7~V+1@d{5s+-lEJ}z
zpJtA8?fz{lc_Yu)DxjW5@1s*gXwq{WGK*dmqzm;k^>`GpkIRn;oOe!Do080>bFO3M
zk+;_akXYI^TQ{7XD}KuaYj0Hx%{^0uNSu0%x&h6U1Z&dw4jN7d_0iw}#lrTS_VOHQ
zk+AGp@CC?R3iRQv1~1;b9_T6SrkMUPMp38OgAdB3=9rbsFqyJhpc7{k46XX#Pvt53
zuUj}dA@Y*(^5`vIyLWa*E>q{o%H#ylfBE>h&z8#nl>>Z3SNp*px^vfN)h&c6KeGjy
z6-07~W0=+p%yW~L&NqntTXb_P<>_T}1M5M_+$a(4Qv~obeZb{Tq3Dve($QtqmC@i=
zgMrQD{8YcEL|ph!q*iIJC{h(Ch&it#?*ME9`<Qw5j3=DUP(d59xTI+nThi3k($IPn
zeXHuPJRTp#51E2>_b9!y_L=<rXjJM$?5eoR_ipe+Tt@}lsTT?lAVLN>1#BV;Ui!{;
z!;by&Yj=B`OIoDk1hgleP09p=y*y+*PlxlgL8?gWp|wlRciRn|5AaH$X(k<s(O7G!
z`B%;k5(v`si+o1ou61<;Jib-^Oy6>1g4LjSt`3~DHt>{jY5CoXPow4k2rWT(B0vw$
zX3s(l@Gx+CTmyiSH4%%WGtUkp86Gg`0Ja}HgubdDsG}h3h;*d-B!G@i;4iP!JG@D@
zrKo~k7XL%XHPg!kQ-Okk(<zg>l7Lz@mQ?DH`y%?)B|)2dMriL_DU;sdj^|EyiE1hp
zVJ+at_mbo<uKJ@R8<q}Uo1_%?%^va!#tAn#J`Kr6gU^X`R*yhdkN_|PKGWPVH+Kq^
zOTfr}i;#wr^q~lpgpV{6eGZEvv1X}hG$viSj<G%>4c+@QCmOc~3JsBYVKnz6>86Ya
zPFCoVLIfA2VC#;!3W8P|PO_45;jj25m!cOaAD9ahp;eE>M+_j$7SjF%^zYC>Xo}SC
zyg%ZgbN<BgEW*E!X>Y(o)>;!*y{oKt#Hi@tL~F3s+LI6Oc54PP5)-`M6TZj8OaJbu
zz)R;fBlP$F<;mDiC5^g?`-b9$Ku-|;tMk`oEvGfYQS_I{c-*f(C^H5OZ-ASWNeKBX
zl%ei$XZUt_cx?CrvyO`$+#?+&G42*W2&jGh2Ss%(L5H4L4LjVG8n6<}mTO~yUJ{i%
zBxj9KQgas-sN-C65M$#`zK`;EtIqC*v6+L6QZ8jRRo8d&DIx~vkgp+4<ZE#D$tcnJ
z(G=M{fQ|cK$=H%|J<KXz`ZrQC4`@>%pOtz6e>p*zGHK;(UptLxF33<lid4V}a2W%5
z`yqCA({;UvR~?c#*b-xRh>csL#PLZHGO@nXnus6_5!iOxZ*Xa9R}`Sd(<ov?iODAu
zDgikv*e^-zH1tb}L@J=B&_9-e2n#wp-KV576U6x{`8RhGZUu^y^FTXgYkAT=jsV!*
zFk$eo%qLdeD%+~hSr_gWA8;FNZRD%DbOKn$ZnjVe%r7dnwD?P<I*lkJdzNyG>8pB;
z37KVp4ZEN`2Uha0qM>=nR0FT7_jp0Tvj>Cq`OIHsK=vXJl+)U71GRVqG8NFV^YsVv
zz>vVv(^Jia3|w4xQjSheTn~AGWzQC{oMpNq4}NoeFV9~uH3b6a5`#=~)G7qD0Y$ue
zW`I3FTlvYb%yQ#@l<m&QB_GWU!}B9lpw*eW*l&3DeDrU4jTJXUE;*=WXhAq5PzfvV
zQx(Vm(eil|eC=70R3bf0iRys>mBYXzoekY|!t}<RwG|-F=>gTWi@T&0rKrvnEX@|}
zMgMI5Sq#bt!Ga=!rY~mmF?x4>7H@ukO3Ut#h9zkDQwu!|?kv<Uv<a<|g7!wbo$~)p
zi<Dpg)j@8vljiU0YUh=s(yx?pnFV3qFK4zu&kH0Kq{ZwlF$IF)9MBYJE@)m2b`>@)
z)*TrhGpAwMJ?fM1qhmw8c>h*%5nvyYgF*3_y$#>sl|y4_R=C9O3o7Jug7w9`!mkU&
z;Y$I-5MeVyT`w#ty{iZXj-Y4GUsSr6=dx+#tZ6x3(L{Q3Bq)<nJfEE*?u*Hn`yUsr
zL2obiZB<c_<vBoy<dRHv02lP5s|37!h>t73stcTsWQZd1oS6}p1XYhlCqlxdB%CDL
zGDdbR=n=Gq)bHo^(NO8Fuy_Mf5+sDK_-vF(Re5d>5K082+DUn}pQ@m>&?^;Bnp?Jn
zjLjGFWU+#+&?*qZt!@An0GQYG$_eq#i8be>I4bf3Se*M*yiycM!Y?e!?h<ZS2Gk~|
z`+-F`MEKZ6xuuld_m$DXdXDg2(mR^8co_x+obQYCebVX2-D%b4fq$3Nk5p-h0&-U{
z{s8=Vb@}xMuEw76Wx<<pHexLVXDw(+X8C-f6rj&;Q;Fhtgma$!J*5crBMJ)R4aP}z
z*gym#t$S+gsUnj((tanNf8lVWH7FInZP!@&yUP9U%y_>Gm$GDu7GYBjo4mLLx!!yB
zGYBh5@hYSew?OMA*cI3nMf+bF=<hl5bv~-;w&{~>U#>KPRCkA0te~7y8mWrsLQWz;
zs~~IuDG&DdDVkwcvBC0`k(hZAw*Ribr|HkXRxA>%f3hkWD2j-_uFX+aQuJvd1=7+d
zW05e+lJ66lwdyA)hGoo9qBt}giCwowbGJ0w9$o`C^6NfYg53wpTw22Ba0z(6xz#By
zpqgJuRY70l%)8fpaC&5=xFxW%63iTyvuFL#<8?2Qtng8iHW>t5^kE_=yE!*ODJTLO
zIVdo@<1hW2xSM4(n;+I=-k)K3e-SNpq){YLgm(C7$*#NhvSJ)mc^725O7mD#@lq}6
zH*ohG0EWgD)Qa-4?P>vkUja!Bs73MA$xF1@h%#v73gQTdNB;nH0YSng0a1u?e?MPo
zxI0WYz99C-T;b&z&0F~s5Nv48>JW8Qb&t4HcwNvEuxaqw*6e%m7d^VIZG~2faQghL
zV`HfrtCD!!nuIY8UEI|yG8opIq_LmK>wInJ{@uXV04Xy;Pj_CH*6yu~@3&hf_)a4?
z>wu9ws3({O&i4-z1l3WtW@XSD+j%d{F2eCe&ouMc`B!MkvNh=I{V%$M%UfW3WD>Z^
zP!HB^pbb|Of{-~LM!(U<gP49&TyJJWzcU)>*RVg{cDRVpcTtp-=7N0F4%6E3WH%{W
zCnNN+T9=N*A*7^~Khi&M_nR2mL)Y0$XN<#QR0Kz)>f^A|wj6q+)0Y216j8<wIs7Ge
zag7EZ_<`4l6#Mm5AYVAV7mY;eP|)i6EZTL`wp**UkuA?%+$;f%5>-RR7UTD~Vj3|t
zrsz@Qm&VxVI^9!9kCjp=Td<&IMW(JH3L=@=S=ua{j@hAp0zMm=Gqf!fClQn%*i!IK
ztzX2;$X%kTgS4S^zJK=W!l3s3Sp0S7Q0YmrDFCv?VHJRm;{qR#U;}&n2-`0qg4B$n
zg&@hh*dx`PJpyK5xs7xSPeT@8!Gfro$Q%IXsDhU8JAV9MzPH98I0PftcV17fmMw7m
zqXH4C{?i$Dfo;hjoLyOo(1@V-Q*%`(7ZbYP#4pob9AbSa#v|YSI8vs!N>8fc(q_o=
zg@biT&yHREn-F#mW?)b1IcT%3qwy$&SY-yz^Fh2Je@HC;;*>1_DK(4Md%q=OAt3>v
z11noNnUVaGWOTs=d_w)k^D<}Z4-?Jnqj{+&IqM^C?ur<6GjZ!9uoZ>L$n;gdekW3L
z*%ft!?tyvg;xKn}TrU2$nIG2^6p(ViB67tMm+2=F5&qV|DV+a6-Frr5N@ZZ_7ZI`V
z?(Wl1Fv3f0rt3jYi&!kbEhZ6n_3x;aZoh}1Z%ZhdnF0T%q$$jMRc|<o;WhS<n`VOm
z^RT(I<Hn+A&5Da9ecs%AC&UvJglFFo8ak9nYcvQ)^;eDrnK4>#Afm8B<=;vgJk*}2
z=ac9{ZPR<djRVJqn>I~its*9Qj6G5c3tMJrNC8I>$4fMvQ3N@ST#f%Wh;N04ZrakQ
zaaMt=0eO7_d1Yzx{6wiDH(~eBcu7iC)Rf7H;T873Ir23sFxpClGiHx9HNqN=`T*&V
zHc}6H;jE$C&$8F(nQ@bpG4r#@&e^zOk?xM(`K70ijoYR7Hoj2YrmO2U#~Ag_?J6S)
zXnA{`GQEBHXy|N~a2)jIe>+9}DDAQ=IdiOhe17sy<-f{;$ECc|(n0NbHBWk{OlWUv
zk^;d+qinwXfjk6(9~}(IWKRMG)xEdMlW}=jsEihRIa;cmpZ!H=w#6a8F|k&iqPPR=
z`z!wkoy~L5hwD5e+my=BeqHyL)}t#^-Ez(L`kdD2maEvmg@d{tbrw@&PU7}`O=BTu
zZm$6kHzwm%oNgjdn-Rp7EqO+_nmA(6zL~N0ym$>MT~>vuyn?EkXRFvdHeBxrvoV6b
zeCm6DW{w61XhX?*1E0b#ZyGFzj^}7Z+3~CZuFSHB1#1JN)`s_h?~sd6`m9WzygzQk
zi`!d-$|+(K5*}R&*oU4&QB~ZCi6};X?4SB6_i0zK$nLV`hlEX+?U8`D0UL5yfLcW*
z`sw3ysIuZ!G()WuwZ3}hjux_zS|V(iDn!ZLKmA?rGS02wzlT$N3L3UmM%Pd1R)(U{
zkiB*TQSf1qkTA)kWvZNk1JB31&G|UK^-IsoiCrk`7P|&<Ye(}#Hu0m=Fk}2jh}x&w
z>Zgyw^2P?kx;EBlbbI-8L2p#3M(cYOII3mCw?`G|**Y9N29sZgkxNBr(Mfzmr@}F9
zz}|#q8nEnTEQd!oqE<bWs0WFKev}QP3Py)1j+sy&Mb8i9vBgb9ZhBoxu%8%+$5$O0
zb~By>eK*lIEU7lB|4c`&ipTuxj{L-1Wzf^^a9cMTJz6!($PJ~5C0tfE=Cg_x9c~H{
z`g-^3aJNoNu8L{NP3q&u7%m8diztg<Xielm1L}4*>p&3ly(c>xuOe9L5-5WH^I!ue
zkFh1H8K;jQgLVR++k0TnHuDxxQD6ywYRHI2)iaLAvrR5hgI@%}Q9s%S;~cLhbA&mP
zz6rX`g6Pb)Q;x<$PD|QgMf2WdGJk=;0KLdxNvKM>Ihp#6#fvVY-DO`?WvAf1cooNO
zzYjKn>FO8sa*6KrTvmgND)<axy=Rxs9st;_$7ci(c60j|kt<h^sI_MConLn&(>Wne
zs68L!xZmbJEy4tl*AUGJpBoQ+x{rZf9irzkHg{~6H7r`>&U4@|`h-<t?)|eb>vw%V
z_a@+@4tPPEP{`whPgKDl{=CZTSM?^LPX1nfBOh#NV%<6T?*=HrWy!3?WS`c>v66qt
z|8zlFp#<qD+{d`Md2)!tT7v_u<kp}HZQ;uy4I7}Yc7<T^Q$ku7l{Lr+T!<`NXJC%X
zE2{pIAx^-C>d4|jPS=;4sgw6|u^b!FAH}U;!rGHw`j75&<}Q|BL5m_@En<<VECZ6X
zm(ax<$$GiTv4YrlW*%RyYWpZa$gSVqgF0Ht8|X8*{;wGa*fOEz%VOOjA;U-#{h&PX
zw<BaIk4D7I1O1bClr+X?4rd0C&rMnSzE@&L0-q1iO+RM|=mz?~yqHF{)8Pj;k%^k@
zP5>HNf!#lVX~dLo&dOg@q*?iWO%zJ>*~>HZMRia`6!8w;3E0EJs9MVKMZU|Aa6U-y
z1Nf%|f1DiX+vhJI;IssvZ-<=LLP>LC$yLk!+>-{DOVr8|L`<qigokvVLGp8a|GoXc
zW~a~L{dWl<Tm$d@KQA18bhIx978cl<n2^5VVp`#FISw&Hco`;&F!0~k&<VluxLy(r
zKr<~~E*`D?-?ya!#E;Ka5o!PHb5$HRRs{S4%r=}1{6v6%`g0stBwdiG6V77I&~u7g
zEmUfvqOCBI#0?Gkt*ICh`8nNpSiGt5kOVEiUzf9jdj)Z17v_&hnl`#cMS<?O;ZkLk
z2|-^HoY%pdDBdH;?>o*H`CadleQiioh_MI!@8)+m2VQG5&s*G38_8bUgTJq*yJWCv
zyY9uW0-cbE(pQqRb-n6TsCK*`RXRP(Ml%k{#~3yof+Q&w?!AWeyhNf+P%uODiOl~{
zWj{F9I5gMU03KyA?CHq&y@NQzK?xna_Q$%X-5Bv^^`uM9rsx)j_jvg(tu=-hMa=>I
zsk|tQH3-Z=eAdp;DfP&7m2#ak@flyFKlag_>?kQHh2e3_-?4iIh_R4KUw7y`-bAuz
z+7Cn~8_yK{?fFroHZ<+zcMMnJP#fIke$HI*D!NG*>cMAXrN-dx%dY*?^X!N?LDuf0
z2_cZt=e&2kGtbakfOnkIR+W*G>P;3-HK>VRYYXY($7Bti;2I|-W$O85Ym0>0KbJP<
z7RE{t>V9urGM=VfoGyu4r5H0r2p1L?o;H9tTt!+RXOQl1BB++pUN_KFFkY_Z&Vg>V
z@g$zhu0h5vwXdV>bTl_tEvLjH%vfDOi7IS)-jb%{Ho^HI$dOeT)GNC@GaG6wwlxtz
zD-+?DF-)&G8z%c8C?sS|r2YZzJQ_Rnw|AKHJK8rC5Wu)drz7c~6LQN3{_W}A{krQT
zCWHHTSwgE}%}vGwt^(t0+C}VR%>5-rn&J)u$ym37px|i71E(6$sRgt-Z|UJi-<FNR
zMIHqAX-^|F601x?BhvcfGSZruf!_-r>p?VDz71s9sJnqX_75zV%a+aQ2UK)T<5xya
zg%Wszp}?2~soTzBTZ>VU@<^bsVBrQ4c%z}lrvA&~mFn-alRDon9J32g(W_`$%4uyn
zL{miqQATE_YPwg$L0fG7S&djCL1mmTPa4mFwCK}h$2X+700!TQXpgc>2f#GVg~**(
zIKZm=_%gCzf3+JW;?+BZz3s8XLz~XXPvi&6Z5c$PZd(?V?p}P05Klt<CVrnu*Xp1#
z!vyJoJB`?AOAee7%J<>BXFe!Z^#^8GoJ7OAJb_rtFchhzz#=tQ5DfeDyI30{)gL<?
z=}e*WOQq*wQTrA(I^^Na^hyf+Vk|8js)Xn;y}^bCOnnG@lwyM2ViRu=-69lffX4u5
zQIz?^^yNHfsVpo5Q(Kj9G-^n%!szBebV>(a*_7KfXdC4CU&k@}%0K&L)EMWXaWZ&b
z5jI=0ZbA>8PGX|a48{fMlBJ$&y}O?VsAlkoN+mTYeZza`m^B)?DnGRRcmyN3OhE-z
z?o>L~bAu}WACj&zs;aK*K6H0UhajEOoq~XLr*wmKgLFwscXta&OLup7H%RBVeZN1B
z!5H@*?>T$#wdR_0u5~&sznyp7aSEVRqRnAZF6tkm@gVAD`|rC_RuWg|le8Rh?MWK5
zY-5Vfq4E$P)g$-LELl?h5Pv`22s?XI$V4&FTW_~8IL!PLhu$s7zx5){ad4GW+OXo+
zBKaob9$Gp1068}&H@*`MaMKSG%p4g0fv5YSx>ri4oZyLsdGLzu5FuCiH_`JS%`y&0
z2j5ITKz@rvA>|7^Xd)Ok<)hYI0W^q=vVb+zeyGqLpV6Qp6VS}r9=H>_K&t$z?=xF@
zSuadc$V0%65LmjTqiaqR*n@He?*O0ctXM$``t6}-UrK+?X2?&}4XAH|{{BRw<deJ$
z2%edtI<h*F;|GvJG;{*axr=%&{2cxpiuBMHXccV}&8f9=NLInb0iCd6)=T1jk^9Ed
zPjjdI?`uR58-77g(bE`8h)F~i1n;{ml>etH42Q*<j^?ZSmF7PV#!BWn?VB!p^cj{f
z3+A)tLP1$sQ)rco38=tL%#WWV7Q3r4Aq2M{6S4DeAP~vXoI>(UvK4n`fargq!IXQ=
zCyoCa{q11;MCf&}mg48=VLTfc7lS}jGo^w+{XO`B{)NvFJ(AN%&PEROl@E~bKyyR#
z5>z6{qsRrNqvs>WMeM%YfM<J$&WoK<juj4>Xoh151E14r39kg#i6r;k<D+$s7m&00
zOF1PoJ%(nBxa|b*2DbM+((qA5Z$?j8vML2p>iJ27&<^k$`)<nusrur8Rc}LeO#xLB
z@XKH^z`zOkFVOGA<;}5!^i`DfA@4Lz8PW)NaD1@)1pTjxL_;X7zqnw@yVZ;9S~37T
z{ddGi+Lu7bsCZI8OsH^zI}FeQb}n)G_8Q%K39IRl^!GviIKAgXVXs0FeI+yOGWC7y
z?WwMWSnuV~wq}B+0$e^V?+Cs1T#P0dR<XhCsgER*d86j7s2iK@dqMxYz?#TTJpcEl
zeqA5O7w5x&(1$tc2OC5RWiF$pE?VMsQhfu6$B3PLsFNs@gU($R50730X=#pJ^BR^P
zBxOH=XW<`?cs0uP3?kXvW$xPN^z6`>ct=aejrY|a!1?FWIzM@pcTvi&>Q5x9m4(a0
zI7VGLza%BnfP@V55bQOeweL!B2iTh@qHqPkJ_7G4aO7^@iq4Q%qj6&BT^zIAV&qe!
zFUj#lVew)Iap3*Ec1Io+#@N(Ldgry%w-U*lf-2YHP7n&WxxK%Tv8ixyq}fH=E6t+K
zu}4rHrr2EEKp>an<C?wG0UW3?<(<TOoY%F$b?xDVVmn~uUhlrhvA8(92;6WTsMbIQ
zJ;T}<+YEkR9}7eV(3sP)9qiUbi7t*o?Mx<RM^TsI&km+UX=JL(%yf+~(NN?%Pa1wY
z5p+~O`%tX99{+6`E=2I)@y<|Cvf0o1c;J@!<Z7Qt`rM;Z5qp?m2sl;CgmiFbY7yf1
z-HR_znU+xYkSRa%u<$T)S5T`(`pLr7x&n82Ym_>%kghj@4w*5_rg98#u>%uP1Nv2R
zb`V+MhLyb%oc}dXSEKSps6w1@;*4~?Lg~A5Sg`MA)Y4G-gj75Zz8-c0$vOBDIY<bC
zKCC(PnzcVFme6j;Xj`hjMvrbE^vDC9MCCe_^My;bCw(a$G(nSvd46u&>l=YlJb7&X
zVw=s2kg_9u=r<DnZ<HAjTLNKhd+OXki%NWUJmIG`^kXa}2YBsB*?`Fqvdmylw%`P3
zMoHup^&zR84=4oqg$RGuAGrh`+;%U4{L}B&584wU>=@-DMDq`}PykBP&8>{mai^-f
zr6BcIMahK+1Ew(OTmga4m{dW_k@y;ShvX)1*L3IK2m{D<-Ks^u_amhvOZv6?Zhq*9
z^glpn^Cy@ekgGv9{=Sea@=pO@F`zG@g;8~h!}+Pp)yuKzMF<>knzEz5@L|JbpSA2=
z6I4gfM>fiRi}}jLSMwDYSYaY(c^$?DybQ=6?0<F&P0;4{0P+sG_dAZ2DMV+V9|cJn
zH|*!ztDK|&yW?;QQNcl)u?59yBgxIE>n$fYBsg!Jc0haTe0)v?#ym;5=(8<h8)y*^
zQiBh$2f2%zsav~Lm!&MKV09{F2}>fa)y%=ziS_i~_)-5<;hd_;t?Gk{$I5+gaIF6t
zoBagS=c1WEQb)2*Vk<8x(B(=f6j?2ij{WsAmDt-`8I73(U2^{F`<YuvH~pZ+oNLE=
zL9H4^9Ha6R1|2KQ<=IS2e@GOZMBs_vi$7>bqhH)#mSdEg_mAB`1x@$sG3%DgwCS)n
zC*D*~!A~fE=w9jWzBhuwO$&W){a{JFKgpImjzdJ)6+?)Fs7cG+TbQe1R-v8N21dH!
zRU%}O^8LZOX(Vt&Q9<C^vt5aADFNLaTFC_Ij~&Tyg`Z?$!fY0O!~QuX7ezTmmuce(
zD;hGufl<rS(A7|f$JqiVW((f`%z#|H5QRz-33le^-6-qz&d8LnNTJ`pbbyf@e(F$)
z)=MF-g?l5wpG%gY&w2f}%!gJ@Ggc8je%oGsy%yn5fw(}(X&BM=DUyIAER|3@m9e3C
zviQzY*07SQDX|9^$pu4PyarGFu<f^Q!`%z2ZF`#zcHSog&9QOs+mwtH;=W(!?r;08
ze-lL%l=Nv+=wsuFydAC;hH`rR#V_#r8dmvy&XKPO**jdnGcMzvMw8(NSePIOeS#ou
zVz+H-B@#W_Z^*z6N6OLq{*n9(vORK%D{V2m7qnn9GsTzgv(=s-_&eT`IBidHL5Sp1
z>fc9wvuKa5n-|hGV%VuGrx!MAI2qECS{aN3;Yf-3ukRH^_5A%<pqJs8PiwG^Iaa8i
z&+jHc0XEWgycA|l_9fnGH-=f%1zyA@iU#iJXY;R?6o;Xn=bpwDZovSdwil=Ez+&en
zbqBQ4j2rLyx0ij|;)nryWhGGt9rJ^!FUtZ-ig}5`*>J3>B^6?YiNvxz$@)Lh3}HX7
z4gUy&BW_Ffwx4g)rB;&vp7ou`XZ84)wbRJ|Bw%6*-Y_<KNv?o^*7{d5LzuJ@dDYiR
zjkUS8o4sbtvY!4-BB(*@?Fmz<ju^8inhu=3^c17v+6)}d<MVYw&>S=5b=xFTLfQUc
z)+DXp@`?{_Te$BPb8+h1sGpno!&0EM!f5$Vt4c}Tb-%XIS^J+B1s!-354j$qP-y)<
zP;|2EzqXWCf0Cn+l12O_t_7J8_I`dcVPA`RQ-)TaC&Qj#j1-rfVTSg)>2Sk!zP&}y
z?PY%RPkXE&9Jv3o*m&`ib;;Bgg_Fa)Qnk>EWVONqN-|0Q7_aZ5xw0r3M}4lIm(Fp+
zala{T7PDGO!#(5mXA+AU&tcmm#E>z1wpg8O{7}BRN9z65{wYJH{9^O+fb6sS(Zq5w
z<;O=_99o6JAJzi3IXsDZ)46|sJUGZsBoC7RTpu6?6>HTei<HVQp__T`qB%-ZMN@4s
zYke<^T_@{tz5#{;w8m2rVhxSyjn3-xx+Coag*9d_$qyDq@|~m;)$oRw@8FDtX&4k`
zkOTch3{>b#4G6oen60Wj#&t;4`2{n4v`SQOZ_buS^R&f|BeSH1I36z!Sdyv*x^G)c
zVzvI={G@Mot!^}8O>^)B7Fe*p+;qhRdzRg7lwnip7Fr$N-#O<W_NbX6v~iON_j{bR
zmd#E>Hc8vPl8B&1QaSp<tW|h%jQz+QjQw>eX+3Q52%A2khMUV`t=rxBv~@-}FW!Ay
zzs8ytBNAhhZN7k=Cj`()54Rw0;kW*>uUq6&wm;ce!ugQVMQD+gaRANIuT$#ZX)6=R
zvgnVD&D96idzv&`+PMv0?`{q9D#(M#q<%v2P_+KADMFX+4ptx!l<tt+P_Dv?r2TTT
zy;_v!`8$|5H(-b<O!vxW>vtQ!Co!q-N~*;R>^ZJ8hP$TE;~<N#p!1)APHGkSaC5J6
z4N4~77%$-tY<9|UiURZAU7YpxXOgP{a6HCOx$i2N-UpF|YARNp#*5~v4A7BTc{N0Q
ziL8Yc-Jcm!F{}I=@1m?%-62<#fB&+5-f!IRg?&3Y=cj!<Mg?dPb`m!H8e8JY`XaV!
zu@b**4=vBQ&pVb9H?KP{L<De%m%no1UoW!Y-ZY);6%$%5)fUQUW6SKvl%OpX!OGIG
zBZ7y?0b6X8kbAVq#QAM$G&ik=r$)iz`tERjvEnaMA3dqOfZ+BHa**2PdCptrq(R?u
zqs@%OPQm^BALQNLeBT$P(ivKTa(=RhfLhcPig=>|>`0VSXIgB$vG7coNqw$6+jaR@
z!5La?n@}>w@Fo0?dP^xR4iA(RGBIA7>4EZ;Ids+eQ$uA6+Az83VC;$UKD+9-4ZQ26
zvUqhSut4*0e_XzuefzrOC*0EU+i4%{DgF1jScF`d-nlzxof6rhO#FBmdan1E205be
zDTZzBAG72Cth|}5J-Bb+Xs<8w7Hy{o^Tv^b5b}<?R?$M$Fd<o-;t-iPh8@bFc)k}_
zX>b_4)*bQZ0{iVj_GIU6CyFyo5Ym}vquuMn14n5pl|UlHdHr)m)S;-Z_-ksHXh2Jm
z%j2^bV%S!nYhDa3UM-U`(yx%;1gJUss~-z24<(BPm$F*_B$-v|o}XRSUrHR4OGR`o
zxX`H&lk){OLmokgNkb0pGhS3K_kPHu+ji=+h*iSgBbpm8T1VpDDo`u*ak%}Ne3Cq7
zoAD-ay>3Ia)Oz~XRd#5o%L)hKd-m!Pf{xTdE$n=)WKh#6+u=}avMkElruL1oiz-*|
z*j(Cu=%71qx@<~avQlrwh_x^*^1o(EL$!lzUMZ|do1z#pwIMo#JUu+Zm_f&seI+`j
zlf~QVW%&@w+MF`g!7{bJs^`r)G$LNJr|eVGAm178dc=nt{)@%v1JA^v3hm%$%+K|;
z;h&9~wVRM|y0%ODb={a0eH5ML`O+O4x5uKsU2p9~$apeIC|VmUiYJMu5^Z~Q&4yc6
z=#|aT&>Q1Ghh6->aoujgN210aSnmD(dwnTWEn%*|1?@mbEVp>lqG4^}=+7vrT;`&I
zX&*KIulJ^0S`}AAr^TP-yJ5a+!)r0Jnlfx|bv~FzugGll=Ch$ul74?N^6yK|Oc#q?
zaPpou!xK6_TDuuYem_3u7~QHmL51N+6G#@$RxewcD5{7}D^8bDs)v{6py}?U<>c4c
zvApC7vPUrPj{upBLGE7{U7{0XK7uctXbUU%SReij?nj`w7h3!Y34z1S-s|lQ2yWGx
z>X-SufXulQ8ukztwAD2!lJ~{C&&gx8XkRcW+1C1EK|kc}@r3^_^no(|AZ}o}gFE(1
zWND$xMZ2DIUb!D6Y`A&q81^mo4z=)UXEs~#Z-TY48eK7}ziN{ey)_X=wMLNEP`T9N
zXt$&*lo0D_GZGW-+iwtS$DA9<4Pb~(4KhdByO@RA-i_0uvx#2|L4*nw#ZkkNR@)PO
z*DKm3s!>90ui{H#<IO$|?du-ad>q|0B)XXHW#9Ki1jv-aLyMsPl7A_m@BL85dki*V
zv4AjY-g7QNDkgmsgZsnoZohJLK<ewqqb#rhe=a8<nb961pU82sBQ_tMx5kLCLhq2u
z7x>|#ps+P*rURbgQpD|hIx)z?mtk#}Cw!s8a~GxG6F|7jNDOmhiWQ@arfSeuK`T0K
z@z&Q`nP5wlXn9;;PPU=l0gC<Ubd7Y<qsLK#HIL3{YclNA#}Y3MtEKd=^u*WLcj;mJ
z19tEHAfA5u?3Ng+%$8F5*ih;L2g7*6{E^ya1)JvLJ?C~Lm3EFI@{_lQtjow)4xiA$
z1;kP*f=H4$0co2ES}wdru9|*Jb+s&`kpjNYoKGLEpfKBc;tD_=7`20IdTlM9PQGW4
zI}aQWWn1bm4sUq(XGyDnbY`4mQy3^O5Sz^{!2pj*?3mOj!%$275lsy&UhY|ujsoR_
z%ce_o*scgS-pJnjyloL|*R~cbttSif;t$9RlT@*NaF+iyX)!6yhJ&$&@l4tc6(I>B
ziqEnyR;}f2!O+O=XSrboQ&aT_R<0|H5X+8yV=tvo>deU~vnzjlJ=RPfL-owurA(To
zO{g;H?Kfd`2b%xmI1&Ei*SGFrdOh;fg8C2Xy+C*_kJS5S%BkdHXUi~Y3RM{q0xAN8
zNZG&Dw=ChL#v$rgm6W)YY$YSXkMkgvb5YtZxHuB#r-75NKj)azlF-uhLTcYmxWiMd
z68MMI;$Gz_e$guw?qli4T(7+HWVLVRpHJfS&)%+_tE%>$(gT7;V-f>uPF0H0OK&V=
zMe`@E?vdMn*K-97ea}moO(~j|M@r~YD4Kw1hsyMhr%?-gOSvBZGSB5#E@#bWFL93J
z!|Aj4AOsxlncX!;!Ba}LX}<OE`!7}cXgwResejA$Osfjd>*{j->Xx05sKfNHBXaV{
zkY&Uw5toj{?1`2xY`)OhfWar<iP~oy7ohWn*4&<%DAAkKyJ;_*plMiNYJWX?q?4Ep
zReP@{QE3_2v;tbpxkjS0ZscrJZQFKX3^nU>AKMzvV?XcYcje<#n_b@?wRumtoQ(RB
zF~UVwx~Nd>xMS9f=co!Z!Ef~&UHtev-(%e0fe@<~yUO1@d#s-~SK_+1+O#*6ifQpV
z-fkvU(5g!}wR3_Dlv0BD_&vMaLp$y%4Md%XKc>O$zBas?Ox0q*Nj6^TM*rZMs?!Ct
zJuTRUAL6wN6v*Q(jAKp}rLaAt+KLjWgJk`9H&q^Kx1TuzpkU|0|21oMC$RFH&BvsY
z=68r>bU+^3?ZwHY&g(o#ioXVPqeRKk@U>&9%2g~>p}gP@uAA>(-Q@GJGqRl8a~9Lk
zp=($=E_Vss6Ix3-m1m?hR1!lH;AZ}dA?`Ej*QIal%V(3UBf!{tr}P`*v)u%#JMb#3
zT-BAb51{%IJY&s}kCp`?IAfHI$KxA@%VZY8of^*NDHJKdMRk|nt0p-xof0);5&d@>
zK*k7Rv`pJd_654+m%-w@mWAZG5X<wK_CI!S%`aADO@U%EMoEG+o)djJqc!&Z!7!*+
zQR6ybY2F}+zE{&7@YV54YM>OfCaG5iwg%!Psmmg!u!G{F4$WVKzpr81Qi3BiSAjNP
z`5@*4uVV(e>kl8IRfV@ykjC-!wY=IcnyWfo-v(Z`{reRM;!Q0f0ATUGzoM`BLT@9E
zCaq<n6=(&WAe-TlI|t$O?i%X0UL+<F9baa4icS+JKTJAfD%@a~@nUa+I|edyoWE+W
zBrD_39tg@*H8M=5`cB&|FZ?@ZF(Z3xFc4rS05cCyPoLc(me3}5;7W)_6<C>ZW}nRW
zBVkztcIj&fBIrn;%H|PqL<g{kO?KJYMicfCQ*t$^*6xh;6Iq5c&R=aeT5gFvryru}
zNth$FZ~K1=kp-;qfBoB!L8{#s&I?x8_hDC^SVZ9wd3?NV5F|E^5|+dMWg(`M+G>1f
zN-^}E8wJY*)vHoz_W~*MEw)B*1|SnRK5a&VQV%nXeZW^CP$sZsl14qgW$P*&gj`X&
zrrNt9_l*5Ze6I*r7*Hco!$Q@#l44AiDA^^*Bq)hxh_;-a5ttJ;@;Y!3z=<#{-JxLC
z=PRjceqf$HbVAjEBlU}xqFr)!RgcXdSnu<d>OkLiQtz2A`SPdfYBY+EkH&b&e+;Sp
z;I-G$Oc)$;2ETktF*#h5vFjB`dkE-Y0E7j#lZpAL5+#gs?9qFaCJ&uOgQ|7Z^q82<
z6gAHvT_vN8M18tupUY(F9951L0S<aMEzWJTT_5?@e92rd_agFymUyGHH{@C`l?I3d
zUnRYQTbqijGcWZ=r-QTKVe4{;DcFa^VTmH>G7<%<szdbTFxd3Ha|~Acg!<@0126_=
zt0sW}vds}{gLsHu)`PVqr?Kc+uqbB}-W=NcS+p6aO2}*9_OmTq5vq(~(zk>3^GDwh
zQxWutp06weGqp;Z7B!9UrbM{txy&jZz4V?dwzTJDtwO^hhl(aGTtH|5$2Fzg+Pq_e
z!+WkSP-Fx&e=@=ZH8W@`hSdd1=lK8b+3T?-Y#c3D69?<~C5P*PoEh4e`DZ+S%R*>;
zZr1rLgW_l27U#3=)eyOyfNv!70<XIT%mChbx?OW~o@wCSF){WC{=4k2{ev?hORmGB
zkL?YD>y%#8LzS~32Bf)~=sj28v8#2oGMUFh;1~RkH+r1I$0OxSr0h4if1r>Y_qhf*
zR^sWrootzf!Gu+uYGo1mIsiC?%i)FZj6q&n8tQ0P()LM+JT}bQz~z&3UkO6dH-~Q^
zppe1DTkZ5tZFwmQ8V9y>wsQ=n3f$cmJ0D3^4V;mz(i$TgheR+l3O?@89PEyZ`DD)d
zXthy+Q8KZ(oqp?eS}3z+W{^o^tI3TuS@M5GUIQi=;0-??pm@Y8#ZwQ_CnX?hK*qFL
z!OSbsE0c>5;c|~;8lgR@_f{?0o5B4Y(wX&~^=Crjc9I*e18l$O$!eA3O~OTM?Q~hG
z1l?rcE1~@m7jzA!<+7Z~E+AN?GcW@ca#WuapVK~7;cxI||I#)K`cwnLw;k4>ph&cC
zL7g=#ccgP|&?7ZPOG9Q$#Y$16Z=!@BqKq1iLPB_i9sY0aMJi+QX0Ug{9f0f@lF*;A
z#dkP+c;<CyP!Wuf><=CQLxi>(1tb2V$Ig<V71Sx@q+xLcBe6DIFBe>8K^b#_YJmtt
zQ|>%s9fH3GENno|C(WOV8H(gp)n?0O&PPTi{dFET@pdX82O_v5{2d!_(f1%#>gzT_
zb^Ut~j4P%qZb=)9L#Xp}!Eo~YZXCqNnWc7)C1??b|0?9P=@`FMNyCvlZE*9KxW!S8
z_<PxFY*Z7djxKZ?MGNvEYHr)*`4HmqqmpL)J2Y_#Cdq%&dn4l2Cq8z%C*J8qae|N~
zoTCWt&rzJmnl4SW5F|a2PF$bYENtdIUS@C7esU@g&ZxGjGafa=Q+tSm!YD-lt}GX=
z@yB6BbDS?soCG*w;O^M1Kq)OxmjwWayOW-&vuQi``t*<^*joqJ2b+J;n13dGvEVYX
z$*ND}ZJ`BW=r{L0-zH|fvRqrmR7v~ED;2>%9v}ugQA%1E`^H1S8o=d_F+U%u1TB-9
z_F~V1yXHn`9-d8O$@wUAdJyS(_&xIFeY=p;e0E(1PXQ>`UaS{QBa`|I0)UCC*>0!<
zav-^Wygn22UG1&q{(|l4Y`r82sr_~f@jLggUTK&2G#FquOUxUQLhJxC#X+?!-4j4#
z=HRt3h3}AIYoybaVwI@M;>;;iR8v$CIi~2ZboQE}<-EnomDO(bnu?Dpwzu955YF{B
z`@ueAGP}dG%I|o@nkD(On|OgWh1*9pkJ*E7rp%o_4yWoZNhrarr<6>Ff;?or2+lZv
zxzvlzH&+_SclMsgHxrQ1!@N3n|JoK^Xyf!}&tI<!jnH+>=25gnt+{$wlwHaZPr_+1
zF-)t>N9l4$<S+ozzVjvSNxGu8-uqbp{Ehd$M=(@BxxL5M8iuDL_PTAmxx=sh1_eU2
z98yjsl6^JmBmw_-9|xF<v*d-?rT)U`*7j1Ug_Fh$&?Lm0BK$HYNWq$?Eub)wct#}2
zxa-a)H4PfgjiEI!=$q(G3ezLB9KXD{5ROPg5X)O|SSC+^;;K6SjhfZ1?Ijw<WZ~Ya
z5Zef^!Xh7^L_cJrfPaxz*<RbMqb8Ww{iEJ{eyQekLfSMZ!nv)ok~H;SNi(^%CoCN<
z$o11^^)?P=cn@H+91$&l;anLlWP*$j^=uq%bT6NKu}ZY03lzqUGn+J>K6X)2c!)ZI
zl%6{9!|4*{lqXcP<}N#(tdZh;b(msr!x}F!Lp57Q>m5gUDx3+LQkl{;-IOrKblJ3g
ze&+Z8?$*WL1OU2V3;XfG5>nTzCOQX>6A6`c?ql0|@q-B{eeyUM$neCItjQJSavCpu
z24DCJTmu~p3UaT!=G=3X?(OgU%bv<%EyS_fXa}mU%VwUWJcfRegpNOqru;zmcA?YX
z_5JF#d?}|x4HiruZjwqHQmgIOV6i(w6VJ%O@G8(94@fI+PGlXN8@D6N%W!s*iq=D3
zmzqN2RaPzv<uWg(c0<Om4aLJ2b-}xz-#6#)lB<Q;;jPMz$d6#`8mpP<F*aI`*Swi5
zF#lZs(ic2kI4{uy*{GkXa`fmJ+ELWz=vfErpUeYF8xT*(!reLCKL>rD9Xg8c7J4ll
z=YmZ9FERk+JBX_*r(yM@0+N-7fW15p^q%y|qdK%95}kqOZ@+h#tc_L70(pZn@x2Ov
zYE!eLg5%n^(4-&l;KpvWHe}zqJwX6(*dP@jmz<vbS9AXycePF5B}=ZrE^LYI_qGll
zKK(!LDAHjgCj6Th3s3Dw)Nx_-UsUqRuIx7l4<mnnhub2kcEwhFdbQg4^Np7ae0Wq@
zqj=coK>Ygg@K<G44){sPlZQ?qNj=5g^_upZhSl<y^WR3T@Ns`-(SijO9Q|`+RXj`M
zqpR<`uXx|*gwY+v%CX;G5AIg)T-O=f%RU~oA3XM6tqt4T(}3$UI=pszca>Nl7RQ<+
zLbGxy(JKKUz@6e#E3G&SC@@e*1`zvCG5`#wEnWs1!Mp}gOH(Eq2rB%@On1}Zlu;|c
zXgoDuu_g_zayZS`-A;eYr2yBhaHM=NKBXwI`7lYt{_oi%?ABgAnC-vCK87e25s|X{
zEN|E}V#5OUM`|QxOY{J>@}3g;3%F?4WPBbH6T<-BevADoxU=Qu7Otg#)1oLHA}mWu
zU8^I#POP>g=7eKDNan`N3Uc!B3J^5lxo9;uylL?UMVSJd5J9wjgC3caQQ?pMY+CgE
z!H3gLJIC>{3N<yjWtcbCGqe-ZC_o=VuohKFsllwv5eF55%K>o5b33un_{@zPR9`vX
z%-rnj_hJQ7bs>5$HCKFzfHz#v1kIT_3bX|#GHmx9SNj|Po@0UBVsFLp`Fr0xbhm8H
zf1lo5?s4uB?%#E^M=*r8AjnkV$9HwpageBGoOaP&cg}Ko#hw-i_0eEVX@R{&5hU!r
zT-cHyxS>y<auh-4Y&d-UfGBc!zUmA3r&pI(b6oIY+C`@a$<<S|wU-V;@R%Y;7JvLA
zO~20;SKBf<<=`?rJGoe7;2oQf2KPk^8(^^P=d?BRr`t3UY~%b%8ce)V0lqu8BYUPu
zK8mkB`>TTIJ8KBba*E*h?rU|{m-5B3+m0THp#Hl%=sJ(n+qC6B&DRM^WytpMDGR1s
z@e{OT{AP8?M-+y*<&n#>`R9kMb^g-zqE=)B%X_AiqneYyNa6*)Z8l$Dop9uw1_JWv
zTZQtBM2}8!oO%XN75U7w=1*jBdG|5^>w1%L-`t)gnG%+}^CW%GU*Bs*!`Grw{2VkV
z`uCOlhp}<-&z`XAoQCKhBH*tYSl%JS#i229|H;<TBiTZqS}PIQ34QT?;&ULL<Oaj3
z@d0l9y}Drf6!n<ods{T!k|%<`V-us6!ud3E0*U~%_Wq0Sn>IwtfZ8S9B=EB-0$*Ch
z_(`lxL4UYCTO|2a8|>0n`*^M0=0NA;%E~<D1E^LwHx19gmf*Xq1Dvmf-~aa=p4x{;
z)2l@rin4aKbEEfk{;dXbMOm-cD@B@J<?u<NoVPYaji{?DEzS<7(1icH8=}GTG5#zP
zvClkT*YP6#$n4_EkILQm_cNr0b}e>!XpQ4=R_QvJvh%F^T~JTIjR=Bg-2O~|s4wn=
zmN8>G#SNncC9($Z5a|Vw2O%bxf>eBsh|#K>EjwLBfO3e7+^Lt1SBY`CjCLv0Tt-#r
zR~B}s>qRlCJaVOQm(}Io;guXi%TK7?|IKxYBw0gucu-Htjqq_XSAAd};wsHB;;UCX
zHQ2>4CdJ;u_J5IML<1RjL*GX{HC)%#m3Bt@Ca<&Yad6SPPErN~WZU239;%PoPywHa
zh&pp?ab5L^_K5oOS@@crPfpgF(j>XCB}VYx2;FjrQ>a0h&*FML9JsLLcz!*Z)SVz>
zq)Zb@@*)JKgB@o3qLVjPK$pghNi4^-R%smIQ^g7o?2e=VJ!xi~-~PUW^E_Yp1o*!Z
zkNqCLoY@8}V<w4!5V7LNRu0bGhHDi}-K%=B$oJ9F42{+QwOWX#Aqen$#7oIYLDTl|
z^!V+fpP(aKP{4vo6TYW@zWC#io12^Fm%oShj?*eEI8V~;A)rJj2pDy+C#m$Ksd3~5
zd7cIf(q&GGmc6kaP-rMx`R_@xgXDCI$ezehGXm6Vtq1J730dPTG2;+_R^sWomf?Ha
z-khYdo&kKMdzDbErZNtm>`qY(AC)WT&HA;(Z{}KwMHM!V8P=JEbAqpg=m0|JhCXb`
z1m!xnG^c3UP1Ht?F9p^U5lG&AgU-ky6o`w0wWwJnZ{8`(odty0TFvv7L#z_}ukMl0
zHq^TGt%-UGXLj3mqjjdriz28Zs6=qc%q;qpdK=a~Z$ix-{)Qj;89d)*kPusv>ZQ-r
z+r`KAi~ZCPJczRkMAF1tdYQ33TWpF!)l2iK03}Cq`a9f&*~$9%S%a=Sye>}1p=j8w
z`d%<XUY7_`c{Q&`9v8-HlYDF#cQ2o{*rg&8J~<60QCG0pYz74teUy7b^oow=Bl$}B
z-?Bq}YlXMAf_R(A`cPp)QaxUzsF-#R6UFF{k)x4y(if6LVohg?AET3Pe9*pX(C!$b
zLEluLOV91R#|vw?Zr8c<UVb#LGzQSZoi4a+j%`X;#EBR{FTpH1o(xi^Llc1%5M2I6
zL+1T-YenmN%3JpSd=5jR06_hj5o%R0AYu$}Cn6<?#&pkms}UB3##tB}j2S={3YTNH
zT<Cx!;*RV^;0MB^{8ZVLulL0<AP~XA4758Z|M}0xpKN^BaIW`Zf4@j2U))2&Hf(9j
z?%0><?@D)V$gTAg35h_yWr=9nclOE1^S*4h2^~;zI+_4M3mLI{y-&zuo;x9LeB=C*
zOCsixpU(Fh90D`8v3u}*BKbHOzAbEr_b=Pos#_-Lt~LYUH)OIKL*`GqXn#_DiVbe9
zk1~nsUz!FeB3^pyb&+LV(F4>EGX+vQb^Nvu%xFHFlkS9%HusW6zUJ4dVzeQdDuVW#
zO~_89fLRp5CYQXfzWUFi$EOIqH|(z|V6Sy}xh6PSs00;g^S-2h2c}_PrM&XRm_`Gc
zDclSZuBz$QUIE<YYHN~20sbU+)O^+{`l*y-9R^xX^kCr@;RVyFWOH^fUB%sz>U;<s
zKGG0xhWUYy+bs(`=1(`>q}U?x^>S_7w^6F<YJgdwZpdi-;1Kz{gV6X&_{#6c5wdH$
zr+!xZZ1|CmV&!+Li@Mu7gCzyp13vHrKT6t7Ft=7u1RYNewX3=)j^>F+NADjL-+NLp
z5(adByB?gZV)9PV%s{~31VwDOPS@9cP-OzC>HB=^_ES_^f_6KNeqn59F#UMK_y(Zc
z{F}`Ygm*k!?r#fU)4;>Mby|1rSM_3<g&6g(Gse8a6l}R)jLanjBuaXU+aB>BSK*65
z#LdCFpFFulc2;%^P^2Hr*l3NVzuSC&-;c2@2RvR#pw1N&2jOk6$FWpJrQx<FwLy2k
zt3MgfA_pvS9i<DxK^D@u$z4lJQC%A35Zt1Hl8BPE97BpY2>}au?cI!GG4>LR%?3MP
z193wJh(d|oNB6TJijl&;d@H&~6+6?uUg(}%M+Lu8n5~qpz5V&-JlbcU)y;}xe&n1j
z>x)tMtKnyRAkl=?L0@%&vX9F0v!zL?bdke;3&?#B$bzVS!U}kdA@Nu7=as%(j83Uz
z*#YWDDV4s@E0#{=gNyh!0d-_oR#)W0D~`Ts0Ymq>%XnGNPb0x5V*IC0P&`|aU4%gD
zlAU;Gl&V2q_EjlGa8B@6$1@MS*6t`bD4Xj*i1!6UyhX-?0f_VKDcg_d=Hq&?yk=(4
zw6TezDRtI1T}xSK&(O<`6nkE?+w|0xKQ9%uOqPH)57fhNNf!VKP|^*8{cl*m5M{F8
zVP9>syhXVIu3k{{w02hZpbqo``U<))Me|{7{=?x5gmYWf68RE9%~?xsv_BrLpsTRd
zI;b;gOLW(8e~0D=ojJ;nL<j$PCo%yy#=ozlXxmgrw9=DtZaZGn-=GZbJP8RQl;!K=
zldrx5Bu#BQ>mY*7iD(k*D4!XcYnPrbh{ff&I+N*}FV(C7so=D!-7h~9qTwS412GQB
zPEN48`seMst1Uz9weeTjvC=&S_NR-tF4lawkwF2hpp8OC%x*$nh&4kz#szITBRQ*h
z){1zMY49`Z`%mw<iP+H$YiQWRNo`-M&yJb_0e_*^t$w37J6BV2Xr5BXz$?(KpmXs4
z0w0s5C)Glil8G)6yUf{AL7cEK5@k48t{@Np-|jPh8a=gdvgZpq!)Ad&Cw^MKSElJ%
z?D3-Z_>TatMoM_uWp%awg8|CH%75z+V8e(veoBtAbV|K%y$6I*vdZ4T-1}wTI#n=m
zC80z#C<f>SSGpn??oK2@vLZ5IH{uSo#d&7wK3fCD`S7#|_!nS2`78KM2%3`Ge&S$h
zhoH-dmCc{|Hu_3Dhl8i=8h!RB^0A`GY`ht~1;@<qJ$ypft=#F;)2-R`0>JnFsgN<B
z1T*YLYa2`!4gYs0YeH4GY9#wWz`I3WhQjG=n*#6W>x?E*n)8}E9uSe<moq2tahM>y
z1{B1@<JEl3Ll95M3-^6L&3bY3)d%I_F_TOgCVAN1Gy>k);Uy%@{vV}3EG6pwSh&9t
z0lDQnZO8u>+>rGh^2a>0J$OeZ`8MnE8liNDWYJt<-D>a-CKFnL4$^Le9*6KpuqS1<
z6W5sANR3IIoK7Y&Dn8AqigQDt0!b-*uEf**_Goi4K)P^_#)_;6FndG<A>%vRtmVRZ
zHK*7jx^>2!ddqG!yAzM?$3QI`uNnj#;^EO+y(ndiSMBP6q6Z=2wQmn*=x~@eX3LJp
zrZcJEIA9*A>rxctm+5vBCM_mG?U|{TDFzuV`~yCP%1ykfPRD9usoHvhowN0qIRnuT
zzicKgHtK0PAN;?&K-CjEzvY1*-v2oM_kh0`&*emEka8wkkUcvrW=~GZU}|ci07wr_
zS30i9i>!=JT5UOZ0CZWt&IYzZ^9KE+)=x8*#4t-HXljENmFnX~3>MgIM6fGT=DtA+
zDu7qJGNy!>pshb`w*lYE>iHS0T}ZS<TrT;m7#&Um9$GyL{LI~fpFQi;4|j#fuNEs)
z%7;ZjF8^m4!G6W~spab&p`*x@{@<XXc1Qyd!F70(+;};mydbWp!YyF*YfLp9@j>Nl
z^%0!PTt5wNOZ|8p5Cj{y83<$wRCPz154s%fcX$QqWFFiyXTpYi2i{o}=BpPlM{E1A
z?2=KsuU|jxk!HLaV7E34e{v%~?gCo~vhb_Y0HqM2(AMDsvceF72}+PG1P2Ub&%eD;
zBcVXYNL=muU8_hgIdNmiHAn!MI+W_2oF)z^>@h;dGPzT2M!UlQv+%*5!7l%y|1kWb
zPd2VSWi*&u86iw?oMFWBE^heexLKyUInJs;^WD{niM1vUt%Q3n{{<3iWOt6Eh6?UZ
z#DYsWSSmrV@n1ck0@(FZ9cTvx;cvg=t}!&@ZxTSTI*5dAUF9^qM!3x}u;Wan7ssfU
zw=|!HXbZiA?_P;=#Jkft>2M#DUIICC`mpF%pr)<YZ(0atqNb>x=}y9+Ezw5EFtWv3
z>rRaFcWPvj+z8wt%{|0_9)5f-l8<89pXpJ~SmDLD3IvPN=wvHDo*6w%s?U8=@Az{y
z{V?zsmKKr)2{?hH^IUe#?Vl@S|E!<ARC#)zTAV(dSn#%;d2HSQ)rgjIi0n;dlWDVW
zPVpYsEl@NMxXbz;tjKx;ov`4H$N)uk(}yYG>f0tZF#MBx$kXhN4^Sy)YkbqoWxwss
zMD;|&>Wr-~G-8!h7!%T0=G*m!ycN(nb16nUs1mexqksG8cskn@v^p3c$CkZUCrlI0
zgqi9*s5@ZO)U;}ugL;9x!uL|n*Px{JfBJ2(_#NG!2!xNT2J%0daDq=5yBBs2zjti+
zbu(lrsrJ|S%rGVH-(}&g&*fBPMd|}@&?C(JmC{nHyT*I6y|XxvfL9^3SFCErubn|K
zIdVB%W-I)>w+D_A7<GT09uNN&HFe<#HXnXieH^0+2eeun9r1OXT;cMsQ{WDPO#Cil
zl1)Ql8qb`{kAl_1H4K7=5n6P;$#S1;6oLyp=h@6ry)XYmfY>(j6)7|%t)g`F3GyY{
z1R7E3Acf%^6MhyUnge`}Ub5ptrh(_<<F(DfSeA+)Pnx}2X`+k6G4>%2LaG_R@rhyH
z^zo1PtWwaR5XbmC+xrJ=*2B#$yEm(X060LJFS@!wZ727Idw0Z<lbAQly<Jp~mDU*Z
zvMz)!XY=tM{rwtMtRgOkqw^pLip+-X+=(6|k>?GrJ!@e$)ST@8xt}y0dAHe(1IKjs
z5p1X%FLY$XOhvwk6tu>DDg?;$E80$k*M}^*ob9j3`45MNVv&S}6CC{fM2vb>miHSw
zaFSU>#JJqn>jU1-A>V+&>{NEIrhZomgj}F+BSv<0civoJ_<!!jrR*Wn#dK(&y(|MB
zKEHIkFFUCZx_iGYPWCeqhFtS5KIzyJt$d2N!!{28(D~7&rbHJjpwmow(L)rCkbwY+
z9dlVOcCRR+P$|NulzH%F>M0K`atvqJ_T-PfK~j+ODvAFtQ2Bw+>e1s4r>rNTb|~CS
zn_#}L3JU5pI5|zG>n6I0<P8+zFkAkIIn#d*pCYk-cdkuQ#bMGcQ%%MFxBMMwE3yH2
zn-nIswT7Qr6U6KH7YGxkf-RuvBCr<8_9`RBBbOAg^${xcTFC9Yhwc*q=)W(y{3mk@
zAa-5>SQgU!)oH@Km>7U54&G7uW3RaLS|p^;PCEQ>tkg+?9kydBJOJ|7Ih#K%7q1`T
z>@^Fs1`}O+bULGEeEeW88{-{w_+gfpLq{?<?%Sb`6ndw~J8anK&h7@~o)u>dqH?{W
zZ`=K5o*{X>?cYIAF3L!D0j#46$}*fy$VB6rBCvVU?8YdNr?1$usi33lL?mqGsw6G^
zO;;^LrU?&yXr$C}++WQA?AiCzQ7BH|sl%UmlrDuPf!(@YPY>5+d}ws>x2>uCjRb*V
z?1(5j|49p`e-B`d*^6bK`^z*fiQSbXQkwXW&|9rPhi2%EWtIE{6X`M6YW8v5^nVtw
zGm}#o5vP*6c+tY=>dxM=vG3g<(f`^H8h*a~@$+ML<L%|yFS7*_v@+PI^8t>lzbX5G
zO0PCqltao;a}^ho`s&x1Ev?s^TLUfYx0W4}u|b?2${mX`W$lFFemZ33d-V#o@%JTT
zV2w<JwXQW<7Vuo!T^arzVdr{xKyrjF%}>0i-;zA$eHX|**B1#(f`$^748yv}x*h!k
zu5+35+ww{1A_|AIXpOJRD}0?YuxDPMAXGIY6v#3TW9$~!(P=j@{_L7L;K+^wnd(#F
z=2v8pn7VK-mBtDRYGmxNS3|#rar54qoBbXe7x?SZc>~bYKaArsk9u1gqZ%4d!4#k!
z(|dEnF_IDhjyvwIyVzDRuqy$X<<aPb)X&8aB?F=04g?xd0&%r3Pkhhmvyl6ON1KqC
zC?z8dsryhF^TF~)%bHI@0Z$tm@X^CtiGE8Wv(-<ltLGu@>k7u+zYg{u%KrkOBnA~C
z+6I6LQg2XoXVG7w6fte?kPhkysh=Qp2=d=-R(iJd!A7s%oK>2s2eS>Xoo+4T{bKIA
z1+^_kC5Cf^K>R6@L6iz(y{T3JZkHr?xDBWebAzBR&p}iptg>m1bL|Z~uDi*o_}FbK
z*s@U?*7Gc0EDmV{CD}lN;0&!o-;}EpDc7C1wAO6NVaW1Rx(}Yf!Z2G{?%8N7wqG*w
zR->8DK~+psH?npQc&)4r8mc<*{98S&q*9s}K*Y%|4xsVZuePv8m-ycuGh&7M1Gm~j
zTpd+C+ib>pyL-nTTDOq3xzjJ!+Nja6F9Y)Cz{xCLhS67=Bd)a!ha-B}CbU?t%hYHe
zaIk>!C4!wwLBT8|6($#6!ow%&0VTt+>Us^Z=dfKYlwYNi9W``M*+&gQk_Iq^E<@UE
z;_`r>HzDeOxP5rXc5pT8{JB-gT}aBvv*`9o@{AOVeXsXa6rd*Yqp+PT<6;q5IAl&W
z6vKo@aV5xvAeDi|+l-qIk>Ds|R!#BLWRx&MpQg9ra6NhgOo<dNSPMfB)#jx7`RWb>
z$i(b3Mc^sL&zCN9*x_ZPl`T;FOr<P>U4l&~XB+^~0s*kUbVs9!CD<43phgT4%$u&1
zqy7T&_N$C~QZeO4?{n%isF#|XTt8;od0mT=&jOup5t1(`eW4b=3_1VMsJ*~mhI~wW
z&FaBXexP)WH_-!m#x{GsBdU`480S^V!lEYs-SznNkh0Z;XyHI^O@}6#WlxwjC|ZU^
ziqT&|*WfzI3osiOaUB5Da0AK;C#|tkTM;cvEd7wc)jfRRxWat0+W7ZlAgdf9^J*vB
zh%)@0fS60tCxC>ST#!;WWO*kja7P<ING#^UPk4HG-7LeCREeYGxWO%pnxgZ2n)dQK
z))*M%MTMnEVPyS8%g8V&|9|ZfCn^9^$(_!t+R>dns&9u}?_I@<w)?o2Z{v$F^ZsHr
z@EN-JLm{Z8>%&TJSu3S%hZ`zRJ@WRP4lVB=tz>ayKfn$h0Yiik>T8YOG|gq*=-WnK
z0Klt&c?CABBLNN;CTy6=Ou}`PD^YX_y0D=FN1(y~uSQPe%2u66s>&MVh6+zsOMvzC
zKMlZh(qsAHCfk+?<NG<K?!@ir64L@<Cn?|sE-f(?3b6-9A$p<OIvdtNDcMMJdW|qb
z6>jSKr1|%7xB(2SPTNbidx#RM#dT^;r_pf*C$flMP9rdIj1|@ZM`(5^osG>h?a!gW
zt;m0yuh$fz@e(i)_B{5Wsss8HT?Qh>3h)Uu5%2qFx+Asf-}uMq=+QUg0{ox~wUbnF
zqBSd^Macxhj>g9d5!!8L*6`W~q&-DN?AQ-**k^#Q@_U6kzA0(f^&+H@fKX<sAD!{Q
zdiFmDe%xJxCa8W0Xw}l#IC#vg!75ep^Z{5AU28pTnQ%Mowf}g^y>Zr91d%MxS4is6
zv1^LV<VgbVJUEbyQ$Vc&Xf!EyxCgYY37(r*+mbq!4$SJ}@;IgLBGM)91uRFipUJY%
zUt&HKN-3#OFdm+*wb{A{f4!7mO8}WRwnB1_Fu{r_6P^&AuPcQtJUa-uvHvkDy?Y|S
za0Y}wzFyb3@Hk<3q#l@RtQGDVS|_yyhCE!sC)r)qdd?4guw7=M8GuvSdVf*kDheK|
ztQM><%n@pW+TRCj>XN|F2nj{;5r0s5v_37~r--xi3Os_7-TXe{2k=hNjNsHF!auFt
z+XB05(LDaLTx4!&AE<RkQ7iP)6caA9Paw>5E{Rtb+y;i1%E~<Ffzz0;$;BV~5(aC!
zHI71WtSwzH0abJ3m$CtV*}Ks4vK>`W{TB<)Xx+>?-zFIr^SPjRc4{zS{L0o*Lp|@)
zD^xI)FUW#a40OZD?Q0&)k}ey<G^J%n<em;Upi&FVsr&nme|uW|f7M+o3}yml1s!7r
z8_JqU`o=#!H$hJIiS&cA_HzB%?jn5;Yd&D*2T+flncnz>S0Ql3l?;?joj=eR0$PGt
zmv8P=-w5-t{D`4M>!`=sUqP!vIcewWx0hrVM?^9Y)-a_ZlerUt8<(jkC=FL>o1k(R
z`dj2z9WCQI0NI5pa)ojK4n9Dfk+mTsZqWFDzljqfTMT__FWjN?qhlb?MW$i~go6eJ
zBWvb=wg`t`vu((vK(DX40ey?wIO6O&od535)lQlO7yWJmKW`Pe1ZCzJavb9btOYxh
zCY&l-(wGgQ>>sa`gO!vHNtRIj!`yZ?Jdu6S;!(AyANeipyKK!F0##fb0Agu<F8eLR
z>}<fHx|t1%Z0cGtmtvg>DELx3DwMQr?Sw(-YnxQ_17C_3f1SQqKj%K@3d3&#J?1~s
z^<c!*i9_e?9<*T+|98j8ydd2c&*g!R8G<8Mv(zUjf2(`>S)Uh-s@il`hj$f{XyxMQ
ze<5?ZwhkF=0eS7I<=(!17xnol`@sJW9}aKP@q~&FA^!*Zrolh*;y&12=@J^csuG%j
zs}x(+S^k%y1~{f7x4;hCTG3`e7$@q+QFk%AT+E!UBvm2nt#kR#5(PC93)FrO#UIBX
zV5Tyuhklk8KqhvCZBwd6kT;IW{3hUux3Q8zCKm5X$VdM*jF;7r_hE<n9da;6g=BKB
zK)qasVnWVT2^o4|qjEW>@@Ey)mvNt>|J9o9p9=vb%sYU!7Ct%bu>ZE(A{xImTS0H#
zc%}2@oSA2Ji4abOv5XgRX^g*ku}Z-U5QWo3P`z!u^cC>Fzlx;7GaKbGdCBBsDoVvu
zyH@l^8rEp^G&%mmj(ZX4kf=yRrVhOD&^gayCAT%VlLU1au_6W_chjtKzk?Fd_4;ca
zwgh>e?KP%j_AvzybTIAC?%<Q^sSVK^phb@%*&`#=p#<{Z^qxwfo2U=aOH)!Q`~mSZ
zy-|)WQZ_eH_y)r>`0mxXIZeBQ=JEb3kN8L|IfNKlCY1ci!Q;M?8E{Wo8*pNKlKP?1
z<my#{7P@$h|KAX}x+^X04+3CY7bD4{GyyaHmPHc(fX*^seaO*&JEzP6hM|R<5#NXt
z)Ow9BbQ+mrAS2=_xK*^H72dhpDvODRDi=xJ-fF;OeC!4MeU76n+9Bu(6Axja8xGos
zyrgi0$Qmq)p=m5-0VTGgur#$=7;r0^)Uvs8wF}Q~pl5Zu?lyr=jQ_&naXMu@1PADJ
zAlPIEFhn3Doe`fA?xo&O_avmJvpMvCJL0mUvE=A9H9`;{!3*6XqrI-ZWecMS=>u-F
z0d_X8Z19#Wtx#zHpJD{+cQBBqMDym@q8IMIgudGWBkHO5fCm+!g2aPH2#f|+I?2lS
z39<>rlnP<OvZFayw=Y8)S5%{O0874CEP=*pIU(PRtof5hyo@T%5lWFZ1gJN<Iz-Tr
zn+ud25OPhSjIB5N`qU#!_r_67P!1A5Lw<%H47V`pu0^m%hJb+L37oeYaRNr(&7li*
z;Wx73z?q4Zee2QcIee#j5dWO&!=<)YTKjYw0zKY`O|@{<7{Sp|Oz|sNyj>@O<&w(&
zPW2xeB$vVT!o4tth%P&xuxGp$RlavNZ%l~59r4y;vpxiAgQ129<i5ozf~dJyXa78j
zAq+rPj2!J7reDzp$a`kKeL+lt^b{{$+7D%CL$O3J{!;lN2JYIVv6<lZgnZ4HRPyZo
zC@lO5GOSo^e&}Zd#AL|rJP4zbKDcn8C%gyXCFmL{A4ZSNt}k-QW;AQLFmN2&*asAs
z4s56y+wgDDL`)e-q620Z<nMPT-F-in26=S4ef)-Yff{E`Gy`)3`An{z$e1=3?D@S%
zF*otuc?jtEO8OX0oTEt7BlIeAw48<*&TUH+*4uRz%uX!~pVL9lA<dUa%njN2?w;U(
zTpD45%wp1yW38DF2H3FTg?&lisaR~fhl~CDT(>(){=5}+o`TjNa}pY&VrI&q3)JNc
zf3bRhNs7)a*c|Y48oNp_y#@$2p%KF%xt=Lg++UFeZZRJoIchtF30S^suvBr1r1Rp)
z5KJLED;9x00o(N-3`wqV9?ce%1hn&|@6TVnOa>|uW_R;^*V4##94j<J7aq!%0O~oz
z5{tL<wxM!vw7ERJas60=J-?_q`@K;mmaDmpD{V%=wXy0(YxG8Z&m1mM7YAvo#`|K=
z=6&kHm>~UVf@gCl=MaM0Q)`kRh85N!%<y$!dAFiDKe~f@h*`R$To*3#i`lk=BK*--
z1pmZ+P3Eu8{y+FKY{keEbOW=%+pRc-T<_ccb!86g?FIhaqwV!iNG4sPeVz;9D+XDB
z$21;%oS*E64NNT*MNe1*GAwMwl&<Wb2keLj+2$(9ZNY`A%Jr7va$g+(PB<n9U$IT)
z!8zHZXfou^%qaAT9`$F@@C(rVc?r4cr3hAe-1ki!WcPYRn4M3mGvul9VKguc*+sOl
zR`eodLa2no$l;hy?cXq9CIrt+0)OEb>O*-;yPj8Syr-*%TauP5`@1(>i-o5YEWytK
zBi5g?AOiia-=4zmUSkDK)m+@kJxx`rmNH=D?B7;)Q}PON_X{11J~@|r?&4@)efm-L
zxxG7seaF`{>A^>SF=lfpP-AXK5!LyQRFM2^S>N>m)q6=i%7Qw{D*1{e;9MTjLM&A<
z<Kp&1Yb(63?*P*0KBhujTaDb9zP;0?d~qs2^yx&Ch+GgfUK#Pc=Y+m^^7HfSJ>Sfs
z`eCgpV^I3bmcHP6RhZ>Fa-s1x5g&F(v-j!tMckb3uZ^xY1xu#>7Jj*we|}KUeQb34
z=HgHk)2{Wn@3UQy+sp&Iz~kWmnO@!Mv%DR`l%(7F>f~6Yyb}V|oNt*KD*`W6oRI$@
znKQ~hf<}HPRH)TFyp>f~Uobj6Ja2|^oyMJQJu|cgZt24k(_x3RxxDS0G?Y7dwc91m
zmL?fL|8uWdvlidk^QAi+ch4N-_@d1yMf+33TKf`0ncG><((KLcJZx*bUe8s*cvDmr
z+GX$l_@bSajMJfi5OmE5_Frb*IGKIUB?1n3L&4U~*SQ6C6p;}`r_a#t#}8Y>jf+-y
zk`h37#}rBZ(tf{EY3uBb`Cb{?LKbv8<sbg-HG4tNQ-o%vw*Wm@F@3{$VB*%lx1fW6
z{>{Duf8<L{75@!hZ*qj=gCq-m_|6nYHZEE_u>ZG80E2i9VeS7Ydk5%DgJn@PwylXK
zwr$(CZ6`CaJ+W<P;)!kBwr$*h_StXmv)?`UuJ_i;`o6F8H>#_;tE#K3tDyt<G17~I
zx1gH}{#!UBfYT@-w$X58&k^-M;xWDa4U+WFh)2Vbd0LOYQ!H#1&>4|u(TLXZ++{xy
zY1h#X)p*9|^iNj`Z+toKx)KKIh3H!n2HhQ)U;J_EzP8%oonRer$%cN;myS#rF@eWW
zu$%jOJ}y}{Fs<uc7Ba$K!+yH#g}NW!fp(Ar?qp&#>Bf@^=Ufjt!5{A-EFG=UmBSfw
znd(R&qZ~Bb?WONb7<{5(E>qGwM9^>S*Q%t$f%m^KP96Ootp?xN&19pdd_Ax2jr#4q
zF;5F%I$z`V?C>ih4Kqq;rp3j)9nc5U-Hbo=c213**w>tJDHoNV9>C;*t7)5o;+Gfp
z1$lE%EuqBu<B>~fi0rkN6#D|@R*%3%ThD)5|4}fWw<bN;3cqMEMN722`H6E-c=LmY
z%@*0qu`7O%Mu`5?6=!8=;?!)@COGiJ4(CDKD!xvsMkZA<(UxyY=vT<7WXD$Nj89Eh
zdxF?qampW5=QmfJF2hP_`(nG!PY7#t8$O}Gai_-rcY^*0b^jx=FMH<;B<t@EV`^vO
z?BZl<X#4le-pC4um6ecz@b3o?551VBjf<%hy_k)mi>ZjIvAu~Yy^N`yxr+rMBQpaB
zA0Ny=i+Ze9X{_0;i6MDksUsZ=Q_kaDeW0*Vcu7Xy(+2zVX^07EH;f15n<&<8bX_^5
z6s9D8Q_SB6(@AhPn>yE7nlTpn9>C8s3-=XwY=_M&mRm_Y>Rp<GLlIrP2!h%>#v>LX
zk*kBd0~TB-5YEJ8gx7*TlgJDnUncc_d#5DYRg+JDd9+k<e2a*o2$HOUqr36_X9N<1
zkf{3ISt<x5ThE2fIfhL9^PG{QoL%*&5XFTcHuzwW@TZbYapo$AE{#KvMEj;pX^i^a
zhHmRk&gCtMcJk+$+d<BOUB~3t-C4uxa8-8wMc@!gux|oSQHDueQ$V9mIxT*aPe<#M
z{J@7Za6*{~d=4)Br(>Jf$*8iMKWz2hh4AUPNE-ztWo7-DPtsAvQDyhHwAzQ5BK|b<
zhNyCmhn!?frOV8jll88&0m<wiGnLm<12(rk7q=&)KLVd(R3w1gA480C4@6BvB8ot)
z*R_UFK4eZ=M@!Yj0(YaMAQr1tzX#qP+q_ZPyWC~_8PsS9Wjx{i{x+CJn2UlnBcZKL
zxEk6U_1m5h3uUf7@|kwz%FVN`O1|zkh#PQ7uKLa^dPqCRP-*-FWXk>Xs(QWnZpDN^
z!A_CE5uxmZQ9h^6B%=z3!{!%BD(pMd50GwtL}-Yj+~dbt{w&deSu$$7gf*I}CamWG
z4a|szp@j>!5g1Hs{QaGK*4K(9*=g?n!LG3ib*$+0p<{MMaIMEg4879LcDbfXDRJ6e
zf>HMTjiTeJ=-;ZYM1%!hUp6Tj18auVe}c<(RHe$JDtgaeKLpIjZCCu$?89(##asvM
z`PyeIpF2clBnTH>98F<CXFh9a;C#I2q{fM38e0p__Y4tW5|obV&R<m!a4MLRKVq2F
zNfGXHv2wieF-vZ9(b&=JDkhQuktD#XIe9TrM;Hv>WXeG}{lbZs8KwsNl@z2dIK|-|
z5DGA&Ow5gXIlxD~WOSUdx<$0M6LTWN2lEZ@@ln}=!dY`4{-iqSpc+0ByM?)Pun%iw
zMuyiCO2&p;kAbFI+~+4985zN8m_D@|-}}u<IWhs##I?PV*%cciOof95sAXK3$TUl4
zUm{T71p2A)EG9)oP~deQt(48Ne&mGjqmW#gZbbLV1`p8wdC=8wr5{k|XUkg0URw-s
zO&N9PD##&_;FW1P@Lxo+7;{YK)S1NAqE^M^jFiSh$T|_r9VlkL8h5<snmKN&p$1PM
z&fD`FOf+tQ#l7-dycIZenF1ZM8elM5@MfBqicM{;?O-|~e?OR9@hJDlYM3vt2Hbx)
z&I4Sj5li3&;>zF$&e{!s_6<tUJYd-dqK%Mk%!cuDoCHg6L1nB5iSK>9wzdsd!4KEj
zBKpR^l_+!&Oig!fAZd*=ThseTR;+|xyDX65WA{!bA25U#Bne!j#+l@ls;dddUg|x1
zeP?8F7rTgvxeGBwA)9XSmOh3x1p)ioigi!gtU*Q~bd`?0k*rYL*zWQ%oNJ7`0BT#_
zB9XGaH4!FZ?U`Cj%5Tg8g%xDX0VFrCO;&HRc{aj}bbRdI+e(V15nV3ai~Gl3^m2l&
zl5`V<MkOba&EW3V)+pAb9k~34^V3hq%Cd`#a}DLSNR6d=<!ENKsMFvUlKn7g-7uSU
zgCAx)Tl)1pUs}a4$6SH#UB=u7fu7hK?>Msj(J>FjwD`vI%tNfHn+%VGTP&OpzVj*`
zTP%YGt7j)ESD%oL68=s91Bd1KH-}a6bTFmYkT<e2HFlv_b~SSOo0haSG&iMJw=@AT
zT4qjGdI?iYa|;(jCN?&DVS5{UCuIjiV^ew&Q#VUvQ*kFlPkJFs7iR@iCt-VA2YWkH
zI~N!Zz^{MVwSu9Otuug;|GzU3a{O0K&V-DAKU{6?od401f|IF<rLl{>6Co2H-~XBY
z!!WV4G5mM-f3X3tA@hO{d9BY{+)guKHnK^Q(VEd{iIPNHgPyT;MmmD(+;Lxv(PmmP
zFgbTx7s+O~cu?G{<QejSVl@y#Jp)VBgh`@Qq3B7S!t^wbH#f0j8Kc!a*SK|c=V3N^
zpRu>~wU-Gn_-~;i|J5%3`6nbEPU(@Qm>Q6uL^1}9mMb6t3<LJB7Z5lFAg$mBf|mRD
z(;x5w^&hWXq>x_c4#9b=!x`56oaW47d+e%p2pA#?xLfhf$yB%C;e6w6U{2Rb7X>p(
zDnTNk;DqtO0L!FPOPl=6V)!)i4COrmBA@}1sTCIEZE~Clpn{}^b~9QEu-w%19t94%
zc?B>WCbWgK)T%$uk^v`u(5Hx!M56?H@jD3>dB>Ti@+<K$3Q)gOzkM@A`i7D2Y0w=@
zCO>K;i6dmls4>2af%spL|5qpe6LvuAf9l(=Z2mNhAGTe}dIn(GHLBO(jZP7iTWSxK
zu{E{zqgN&8_vS@493@CF5Zc2B`bZsfK9jP24s_NeTyN-W@ZXGW=bjnW@a<j39&M!B
z4gn|u`JKQtOM!U~4Qc#y`}3t|K@bc*@(OV$+!S1v>@hIF6sNe|7(chKcpXPE*=F6a
zH!%f?`i<1)fXo^Se1o4J5E!q{1}b|RL7F}OL5F{W{hze@oB9`}{u>ePQ3l4~4d(5g
zhmJtA{qjvIHXlBRD-Un;zE7#*W<oG1K1OBIsk-J`AV;L)ho`wmzrrPfWfgASt4H15
z3_zC;P`v7y5eys#-)BHD=u4{4J=+V;Frn?qr?4;YviMDCG3353*BJ*v)F$Wt;#SR(
z<IZ|BigXT0KjlnF=GP1?$PZRiz?htrJH#$nnBs&tARB_<mwmlNqR_HpwBFn0IlfeQ
zd;x&Ck6X#pj<yt$oCoE|G$AeDr4g#l(?jXwnV9PK$amSK-3MJ>Uw@a2z-h^tYXQCl
z6W8ymM-Qq1CCj+DGQh@8OIwA9CZv2W%boRbpR@@Z@x>UN9sdS~2BKJaxuP#af`9(x
zOhXY=#1JocrD|$&JvRu;Aqd?SV0Gsvi(ZXhwOxAcdu8N8OSe|#N4&FArpS)zpO3Df
z^XeyZbS^jpLs56J!k>$jnSaT6(pa^q>;@)Ets&T9^q+`VAOz3|1V(|dCOBXYNbFGE
z#9ELD47>z5%3|uSJ{Kt{U`{^|@9Op$nF1_to5gRTFbWPRm;o&lqITQ4&6EZlryGn<
zFzX!^n_nL}yg&#DMh+C~E&`66!XKj^Kre;ue-G+^&(wd8h5yrp-Hk5r_ycUSZ*?=*
zy_bs=0Dx@qYl+)C;fJ3P6pS4xR$Mk9f=w=xAK-^#1HBsp{{q1O<v^Et>v*?)WFNp;
z{Gh!9yq=yb=QISNg?cQ3nE8-fAtN;bFwwL7(ZbRZI(5Oqtnd2&lU4j<ECPd5AOVKt
zzhD0iQ~&y*T@WpE3kc(VsZ~DVzYul)!69Cs$mjYGaP*P;c}%nY|AB^7nh(fTFA)U5
zjNyPc$TP=^=qtCwWbxYpdk8H8pmOEXiG|xNc&v#qQghNC=uJth?HLNAzGVRP-+b)Z
zw}hf=gs-#8BgsnTM&*I!<0CT-q15X`6+pIfJ?TR`!G*ILA*B7M!ec|-s=jna`I?FH
z@-3S}DYlYKA%qGx)*P*AwIl=CK~*=dM^n57b;=3pT8ea~vekQSG3sVkCDu55ol`2r
zPRFtejJv2YF8^Vt0k>?&->SUNg9|(`kY?WJKJAaVulYXpaa1b>jpu3~B^V8=jGFHJ
zDnp%3SrI7Buw@$GO!8lGxy`vK>PeDTEAU<hi>kH8+38$W(YIPspHkp-x&etK)~%Lw
z`5u$r!jl>q#jl)z)u$qLp32-CZ`bkPCU)0%A&GACjb>~m|HWz>)Ni6At9Efy%=BA_
z2Kqm!F*^ZZwcjmNu9vFVsa1g^kiAbkC~GI#90rb0seZ|w@;Bys>sRj}%s+RYH|e}E
zSSyAzDi%T4V;eNYCofel=sl#G>)5CgPPMSS&GOQb&8)<!^6VK^cpOz|GPcb<9QP*N
z7xjsl2Fg~cdcqtmZ4}LKE5oinHI_MiW9_65bxEl|BcPf8Ip!ZS-mKFh_hZ57FQ(#n
z_GZtepIXBD-EDlUcCj(FMStEctTRBi<a}M9LS#g%AZwC5E;t4lRfv$ruW>yt&~60q
z!Fa_UiFB)Zk26<e0-*3x9AKu)cZ6LJ{{ky2&sR^aAuH+l?0Xa!rd+RT?=g#iEYDQZ
zVw%pt`DrDntr~A$n^EmOQ?}w+_vm8ps&G_PK4zTEYbs6Ep#E(S00R0XGhGwaFMA!d
z96Vv`X8=KN_i@#ojb6Toy9J};Y!<lq)|bwln|)X1K2H?AlS-p{RECXKQM7sGPX%qB
z@s86?e14_**f<<WpvHSQPhbh^HNq(y-3C<M=>Hdsq+2bk8+GmSx_|55&7l8lT>zXq
ziI(3WHB0K$a+f_GNep3E8syumXkI0Q|8aA2_Iz-fLml_?Bvmic()kLxVVa82RD-s-
znbLDU&ypX7a6*@L24v<Hv!?B3QjvGLI+MYm;Ct2rfeL0b{Z2)1R_bQ4aukBPiMChT
z<!Ss5r6Q6n_Weq}XmUq0d~%Sx!Z=N|Y+^k)le6?)82ruNZ43mHz0@7$Y^+OL9s%uA
zmst@P$Y(MEwUP_w74^Eu&f&H;K{ZbFiR~%0)xsyp60W1Yr)YBJEsxQtf(ti?z^3y%
zR0cZvV3Pd9uR$`hI&h|^Jo^LJvmSi(-Ca8t$<u|0Q#{UZ@S{e0oHrw+Qo1v47XCY(
zF3wC8%mepqFQv8GS7OFTUb?9cfmE-hK99Z&jHI15&zrjqXC?}#_!%0*7{l4YGYfIV
z$-U<-@S^+u)0?_ZtJuE3JW012qk8e1Cg`A6zA)|cQ8GePw~CeSV-==cCOzOq@4-Xi
zmDtViXywY&m;0@4{7<zUS0XiF67Db>K0%~^n<t$1W;0iZ@nxIuYiGOezHWu1ZR8mG
zWJt}heHG+qWwo<m`0fENOck6NpPo(~V}0DEW}a*~Nx;qU2%|Ni#5a3V>3T?BNEq=k
zmaUmgnj?KuS`eb53YNK*dR4Al*rz$Yt>HKJ%J8a^fWfI3bG*zt(cP1gles8*)y?mK
zt-AAlJy5P&o0deokw2`Pz^FwNZF~KusN2*$Saj@rm2QvabDtF$Q|>)ct0$KDV>87B
zc(9RW<iuIVer|3jZgVi;3hvA*i*4nE!sDg!GiSm;@5*Vi$Yvz4>J&FRNB8ve?VzK_
zGj+MxJUr+5J=3aU<5m91{Fq|9onk{R#fs*WE!phC;Oda48e_><@x0^PUBo}t$cZUm
zphY?utN%e&=lr*-{=cc-O5#HF%Cd&e)`YBoHF_ob{~yXc%wJ{xf6&VRzpCxb|FhZ-
z!^rx7)7uFdnHV_%n*G1+HWyit8ffZELsM#Aahu%4qJVRvRJlf1w2Kx-SBzFDvYYMU
zgQ*%u;ot)Sv~I>mH57yaN@&QxHh)~z+IC_OpNU%#pVPICz3{%B*N-D`GapU9A7yeM
zWlMjD`WvjB1cfUdNDxVd0>BCWJ{0o)8XNyUg8$mA{`EkI5kwN{XCQTMZfIy2A0Myv
z<dReJA$_sSs|qR>AW5jLtyL3ik|-65yG6sniHKfuG{c7{-lC^<7E~sThmIEOqd*iy
z0_^_)>iIu37AKQx0aD0J1Vs@R`i?~*38}{l1fS?C*oO!A3WQrPgRDYPSRX`5i$y#s
ze9l`sZaw+OJV4R+m=*NABp`qb2M$nof03-?Q_q$9uopNDX)QMV;iUn<Z=bAN4FZ^8
zw4Snt>VmU>Ggw={HxLjWP02wQ5FFQn2LKN;X>hp14^R?bU{~eTS^~V{z|7k&HG|qx
zq}ZRX1kioHew7X$K0w(Q!3+dgztQoIfWQ5aQ_MpWL913imTdW=Wmsk~!{qRl3s(ev
z2FXt!GoQ7*r5WUpv|wR4hh>)Y*G604gkoRpUbAKxz*TXX$t+cXBIe;xy?&AkYeh)z
zGn^$-Sg6s@RMdGOq+(~t+t!uyG?R4w5HjT~E^3NTuw<9l-tfmrhvR~Gx!AemPQ-P+
z(&k`-;^PD@uC`G5+XV>p7Y9Q!laRop*zH$Q?^Ln!+24MJCBI)3)UgLwD%V(KzngMG
zq{#25EQy(?=z63bqNe6oP(7tpyp+%t7uxvW2{AbOOW$pelcb)5^Oh7?0y<b|j7Hj%
zHlh#*$vowM;o&4Gr1v7<Z1lc++n9LJd4J9~GSe+nkyaUiL-*w<vpMjq;oH-u=Nq{X
zZ|jhZxp8X@Esd8%_<W<NLNat<rvPYy5Rt?WN;!XJR6lL-oDGPAP7q+nUCrC-x8F`Q
zufBsT@!4^B2{W@@qxD#OUMC4PVfXPlSN%4h%(q$^gPoc&VjC>mb7v0ToILOJjrb90
zRA>PWq45n|p5;N{?1DAsmaOg%FV}w00da1vYbB2)-%8m&W$cX~q@^MC;K!Don7z(F
z{Wh4rVjGqMEr-f{T$BorWgIkv2gQPge#PdMhrwK0NgIB`D`hLeQe$SrNK%)t@)aGO
ze-2hHso22$`Ghmy`#i*{nCjAUqdQ+?Y>Es`oh$YT^b_aBOH>c#bDr6+gE31X><v+e
z#XJ(Qgc$z=0uf`+EKN=TLsPk*smo$836v0<RPvN+MWw5G$+Re#1NZ9FiY`2j&2Ryx
z0T-NMO*Dd76$2AYXzNF~eWm&f15CNS+XRSG-Z{2Q!G!rsi_;=E*bLHFJ9~hGp>3*H
z@Q@rdVI((}P`F)QF0dF~=N0YNc;*o>!fuH`o`m<&u^xFjWGYg~O*sz-ia;~G4VJR+
zlH}fe+Anr{4ok926o^SnV4Q1!lo9C0L(-L4`2QkR#tDdL6Xa3ZU^wJHzkKSa(CNX5
zhOlB7F4129)!+(UIciy-JZc%RcOkiIrqGKWt-MuGt`L!K#w}h;D-sKh;jaIz)#g!&
z5C_laN$^JywfvRnDgT}gIVa9}E5X~-nGDsy&wh-sKhT1ZzrO)kP9Zc1u#PrdCQPbl
z@|cR24;@Wo?d@^Nq#73$@hTyu5_iedseSd$LJ~U>rRa7YjtzNt9|0j~iKYmEa$@b*
z0f|6ZAJoUom+en<6lK>-WgR*+tfH;5oMe4(bDbd@XlkFx+3Jeg?7LGypE@9o=*xtE
zQN(|1v^|4y%m<NqxD`Imhw-bud%#6Y|2w0Nrf!_KiQZNO&gSV|O$!ZrsuNg5{7#Si
zG8v$p5@f9KA6h3WhMTAUfYF+25e}RRU-n5ko3-W{<rh<{sgx1Xw#%V_6@&6c-DS#h
z7jj}$DDskaLmd2>ZBMECF>NjEIH`9x0XdTK_rQwW#>pej3`u^TrSPAaM|3im##rfE
z`xiMZ%Ti;u%f5s=jO$vE#q<Jt0Owk73X4S2b+6fL@v0~Zu0M)MEr?!I5NkJ(_(S4m
z$?=y_{;dJe?)&_E=anvr?0Vr8)kQu1pbBx);Mq`AD#G@qWy164y=uwdXM+TQ*t>Gy
z6BXC`<{}XJBvTJfSXKqQlpjU22bS6Aez!#2T|7wiPD4Xf`c0$$!U?Jhf(dp}?UfS}
zgQApIm05U5(L1Z4d}o-cMs*+RQLyck$Y`^7{`AU%7VZLPMYS5nZ-&5?DvzrK?B53R
zp`xPN;D#6Tgu)bzZ{#hFXR#M3N0!skV3$%&ovvqTG4=2Is7x+q%-AhnrtdxV63lI{
z;7e5j1cqU7!~;QQR@QG@lJp-L{!{)pKlIHLvk|1y_@^88Km)lE0_D~itCm`}K$|vV
zR=DR-@g#y*A!D3#Gq9U9)YVI4Pwxg1oKDCNuth$g@wc27CfIXe=T+mjI7Y>U+^X8D
zM_~{vwg`sgomj51ZctfIHb1Tm773Oulp>H`^Kc$K6MSb+OV#tAj8wkzPDYm@ltTOv
z-Psj8cmfx*e<tH4=sxa={79ONxx%TlDlIB%nsr~=QX4%BSQGPbj~wL}XsttBh`(&O
zCq%rU8wkn?S(iw4<m$*=extAp%DS|G<PBdH<RlA>dMGA<e%)aAxeHrB^1-;Y4LI=c
zHZeYP6(xW<5mLeI+so;8VOW-3bz)8zEU-<pp!aV(u_<OOs6js2CB<+qn+X7SyuPJq
z!lyzbKI7Q{W+i2K#r%2>5NG^T5vu7f<zuG(?B#;!NoIUDVUhnvQF{yarK|Gb0+7sc
zni27Y`&b+kM<*QbF4U_k%8ok*%;6iFw&;K8d8$5kZwUtWd%~&|lp%n-3~}>?i0UG*
z*bHiWjPD5OXLs+YI=@-3jRv{LcY~-iTYocElCfs-^4rt#IDsE1TaGGK2&0LAfZ5ao
zO;H_iiRMMzgV3~$X_!m8)-_{1=9g=br!MEZp-CHEE1cOGnOctjTj2IsTwxk$sqqbe
zAZf{=i@=^7UD7tNvVq6ewIiDhgQ>w_8^*&Ky6$@|BS2cM5YJ%7X9v73-4r!A)6X6v
zFkIx7P|?11T1LsDhq`yO14H;qhTD9&oI(N6^BoFI<-^_;)j_Z2&ZiP2^5!7yQmNq$
zz;Bq}(lTM?WXlW!*3=W{;{zfAjEzZXKIhjRM2I&*vgQi|mwQ@9a7cV&5uXodZ~8>x
zl@}812Znz-<HA9#Wgh@?84iV?9<Km)gp4rfwjRBpl&sCtuXBnZBrfVRf)u%CU#D-X
zs<>ys)JJ&9*F*~d=R-s<xYy%)`JScv+Ei)=v#$zmMaGgRhSUzDu>I5N=-W(N^?Xl4
z&P=pWuuvf$AL6?YxGtps#4|wRrdd%<p3+yjwj~~<>U(G<5Wlhf23zmS+g(h}SndUY
zqr$C_QpV#HE9=`e;*+A>X;=s!Ni;w{4v^6Wb}&B(29bHq{fg0fK7zfV`~baGIup16
z>(J9V`~3rda0RzC=BkW-0kYq0b_t=`CqeP`T9?SfCZydWtTW~!3MN>B+@4Ozk;#)(
zA9hNHzI>CRjysF)!4<OPk&$Idi5OhnfE~QSwk#9~gpZ9C&{tOkv=pb@`)_~{$OzA5
z_iSneRt4WdLkCQzmpru+kk7J8b*`U7%12Q?*qhh@`E5mX{Nhzc{k@XXG?ira7MPS<
zJb%)IT7Nt6j@^CXB@0M~f4&;rq)?#{X~m<!S1s+&MI18f7u*69H+GG^TxD{0faAIt
zL*78WrLt=u2_eEIq=(scfN=#BhzNT{jZp5ssG#1#W;zsDU|!XmQqy<+D)7kOK%-(G
z^~F3C%L8Bp1p5~39N1Pn=Acx#6WUv5e#3Ow|MjS_WR5NBI9o~M!CZunHIsuHPyF<6
zcA<6M;aGp7Gx|_dv!un!yK}k(<xu$M1xE%I-ggrJBL-9z__9rkzw^#z*noVSU4gyo
zf-$@u5HWFnqp}fjC|Pnvmv=thrNC|ottXeB@Pe}t%;*iEK*h_}5wMoKUAK<ya59|(
z0G^dw5>r3OwGZgNQC65O_9_5yOp$z|Zxp7yF@2Ta<xwRet-_+X6uvTFT~^Bt)owh|
zpZ27BT;WN0{i7IozyZQnl9U%L{-ekBVapztS-N})t~vQ-<ZS3+zIj@SYdb^W{sV7+
zfzW{<8yqm03U6%?hb!)~`0j3{8|Ks>e*F>mkTsmPqz=^*HBaG{D-ZFBEVF_HhxQ<r
zK<h1gn7cMxbC|soV>i65D3-2;Cka@m2<rgF&$t>=Cpl2ZPv0ZCLr~#Oz=MC}kKw*1
zF?@bEZ8(A?ItG$ijx7n4c~OX#J191HQ^xk{DvZ>DnB5k*08K`j@jb~Shc?+}UKOl~
z#AA;X)Zei~kJCf>B@0l)b{@bfrUvE&Aqsf%W7w>)VG>Lf<K-rTm!6orT=Z1bR-D|W
zAv$1LryDv$Uaf8+y-~YOS)p34dts-buvJPCPnX1L#rsDizt6vQqle~@#4nxh^M&Z1
z9v`Genc88{D@W)hmIq#lKygaS=EX=rWa&7kyA-EiG&3<W_B0`*E|4O*!Qd5l(x?&F
z%o30-|4!4rd>N_yB!m2k+Ww9L8TAg5|CJBNWI>yaqbre@enDtoG&H)hg1sgt6Z8U9
z0;02o-<gt|*Pp#mL%oT+uNf5_IN`n#8;#G}Ke-McfY|4E&~37}Ld30zUlqeP=w+*k
zjSR_y07d`|8`}&r2hjT?Au_0Iv32KA?jX{s#u#ItNqci$6|!X)EW}NO0Wytrp#+Tu
zEmy4{>Mf5eZJ1gi4z${O7)V?+MpCaxcM-)3rt%M{j9tfwlqvy`7cbGZd!-FIx1yeM
zoRy@XLOcmn9c?xNkZvf+0<b&rhkA>4yt<`&6D0QW3s|uSB*~XdtKt%dV$y|I)WDpG
zSU!))tAvLLx|XA^b3cFhL~g>8Ic9Tcb@_Wkr<i&#)2)nHU@J73(J2`f+HCkQ_1p--
z;+a(bK4$YL_8FyQ;RG7z`t2KVK0$01I~6a6!Y+{v0H4YdT%?wO_VcHX0XGxaDBQ6?
zL1CsXV&r|80xwElnFI^AJN`R)v-<BqyVWR98Q+W~RulO`LQY{U;=R;bw3C-^KcVOg
z2T{7(qK(4=BILJnCXAx5|D8FXp=cvd50kqz%g7HrxP=f6>nx9CojG%4hGirdB-;R(
z;II?}`rH|+HyTdVM6OY}DJgMm*;{({(Z0Ie{6{T3NIdhtvpa>jST?{ix?j|lF(?N(
z^aOMBM!Mq981J;dRa9hfal!9b#V!)PucYpyZfW}lXaKLH6OspMj%PMl0Ic3#rIBT4
zp>U$bS`{rtRo$Iby3J^U@Id8A)4|bUka^p+JRH$zLcfw0XqzkDz_}D?8G6;0l(a|A
zEQxnoLvFc7G!Oxei%e!nE5uPdXaW#Fvnin3bR=~=9V}Qv>)sqSLU&j38?4|{#rKb2
ztl8hp;Q~NYhyi+2kPE!=x73o5Fhlmuy(Hr0b>B|J6q7U}lnD<ZRsn1anG`xUmoV8u
z^Wq@Kq`Zt{P>?`&7RH9m8z9~=M??*8>2eJvqe%x9ivTPG3YrUu3Zf_C68VHwdV{7_
zrEKewv7+mDEAiMEKe&#%uo_3<ZhJo<XV`E|ZgC%O%Q`iouC%6IdIEC#ejr6%TF#$z
z-iVRtIb6gDtwh{l0PTr)UIY!O#n0q#VhgAiM(VLF;+q7h5{WZ*&{M%bQiR^Uhku0r
zMShU1B?Lh2lY>Qo#4H_VJVNMay0#V7jpHrp8i@|8lsV(<R>>V?CI+<r%hM4Iin!0L
zCu{ip52&o_fhM5RdXF&hEC_L7>AWc(D%}Da+`rU^LIfu0l?&V;sae7!3+#0Gg`K`%
zFs+=BvpRIFB;jRPLn?6nmDUdXw|!wzg!yE2PP#UfWX6t~xR`RD^nGK^L%W_GSUC~o
z`5>=6J_T(+7(YN(5MWbH1FmC@e6Cn}4K6V1g+@qN+k@W-ujYYD)UQ=Rt4dbKtbd#L
z9|hT7P~)uOz)a*SF6m55MoBO9uDs5rTFa7M0GaDSa>07m`8%Ly1;4^wa+&;#9Exip
zJzcB1rCh&ldz*4k`Yk9V11m1g0G2ZnLlW>ot1v<f*CGZ~p006ASm}k|EAMNmN=rLb
zRzVwV9FX7uH5mftJ4A(_!WjLK5Vep&Zg5D!?^bsKrY%#}eKDa~)PFRmce_{0NvV3z
zEN%S*x1vqMB43gVDq2kY#4t@y#TzojuMQs}0MJz~fomdzQA}nQYtI1mOAHMQQ3{XM
zQ>`-!0vHK$2%3VQrRVGxsBy%ZnlrfBAMi)K_AaXPz6<9<BEu^CeP^&DS1Z_gJf0Zd
zL0S0eA{@>4t)p(Z)gsRoat`KSVgoaCF?pI?;(1!FXKb^HVij?|Y+C;BcOP6={46?Q
zP&vQ$x@+JUvor@No95Me0o^glFX+h*b%e|YM5>_8y&dEA1!AQ^BYLE$L4UD$qy|Wl
zR;>r?rUU(7kKI)vzl=qmX1YS4mStMW<lO00G~iy9*dwRscb7uy&+;wl8zhgO0XKw-
zzv1PG;VXYH6AF^)2QQ_HVo4*oDV`S*2Hqggy9=+sxr*I;3RcT;N`){i>As*==%#e$
z`aWu1PT0GUD3;+-qeDiffnV4G1%~7Tb^p!RXDwQgdQn9PQtFwa7CQ%#Y3+lM&T&#`
zY<LXm5Hzq$q&`eLFZY1%+Koa{InDYsreiCpzMi>BmOH!+G8j4>aJQ?6f{wXKLzu+#
z{J?pb{%j}33n&IY(oZmmYA~`*IKUK?6)pU9mgZVWKECp<4E5e8CUGDr&3I2-&yu4?
zF@U3Gnn%-chD^u1lYe!4Rr)C8MDh$S#}9_WeT8H#qHUd12}!?jwQ@7H0X2-r0Xw0u
zv%%z#7WYKqjD*y#O}p2HhR9t@km4rXvlg-8Q;|kIU!_p0s*Eis5z4**7oE7^^gd}&
zQlU^Rs3K-}_d6*&qRbegUGQmvK*1M!QnbKeq(mEyxR1kyzj=bqWMB7Eix@;g99yTE
z1TXnaHvOt7M`neBK$o&n)SGJ<7yFG%><D%VnY?=$Pn4QX$>ySR0FWquUa6W}mWU<1
zA<_&jPysa>h!wdg#v|C*B(F~VK~oLB$>Pwww395Ec|>mu$i$fh``$@`xIs$!{TS)R
zE!T<!2r-&S?tXsX{E?ZDAil|Qn<vSEo<a%&-kBwG7`;3x3^9(QJc97uCHNsR!QNw8
zEo)oMQ$r6<l3Ry)7dM?9j3DH9+cep(LaCZkjzT?2WXh1AwrdzSf_9NIgNl0U=F+o>
z#$fuUGP-^iXzWC8slOTWA0bw$nf?}ep&gap$nQyJ+2Tga9(%_$YF-+8(6UcNWR<&m
z`7qKgzR_&R%85&(%V;9Rg!ZcoqC?=Qmh<Dv5=RD=c^nfX7GG!M1B=;C^f3WvI0lrt
znj!$A>yo39Nd`>?A3;_;WIhd88@{>FRfK5i!cVnyXTEDLd@pYgx)W*$heC&;Hk5)?
zT*_m)h?-S$$>sWnDm#Gf9o-b#hgYz9egxTL2|SH$k&F=FI$DJ75A^Q7<W|2DXA;7x
z5Z|4?v$#L;LG=R$ja2CT8%vl4It()#X;vz(0@7RPI=Mg}ceIE>X*Gkn-$-n@nhv^g
zCbXCgA6i6WkWf?Pmt9Qyz!W9hv4A83K-Zkb9`8ag5g3$~@C<=CA+*hs1b!PMjahE{
zh3hGIW*y*?TPsTf@eTb2yxQ#RrY5bjdg6W!*eq~{j98IEg*e<j*KL6c*M7>OJ6|Mz
z(20HQuE%l-y<|$_F`vpUtu<Fx1nYn(USijH80INY9(g*|0#33Z1LemI(%qNY<KBpM
zz?U2gz@oC`ocr#!Kyi98au&&}5I>UNbLbR7rG8K%luut6nhP+ex1vx^5%NBhFoug8
z=P{C?qI)hnxsO$G(&_y9hkoM9`0l<qY<WTNCI~4NueohhoL}x1$BDd4U|`CvQc5SR
z#X#~#a1?tqy9|Iq%zr|IKmRSJmk2Kz2Ro_YZvznWKGkN{c;yblkW#m4n@h_A>PIwD
z!7XjSzWGB42FJU`gGxZ2GPn!D4;L_!5I|%#c}87)-WuW+Gd&yHqfsT=2qP7Os!q;6
zR^vx}CNyjt7TDSw?)DLYUfBsQS_8V7cpiTYYbn^ZAX5r{?jMh1!Lr(HY>TKCTW(ew
zaK-kI99#;WYZ0lfm>!oFa3}dCajOjC6~dX0^NBf@Jr;mPi6+S<zDe*^X|{&60Yc*a
zZ^yz3&7M-G5D1=47>?|7W2DK{@DSrDT!drmGC2Gg;ZF`Qklm4~KQ0|-0YnyXmi-#S
zj*kXwTiLS9@m}c>i&Y^7vi(QGtnoyZ)^tBNHwq6#E-TZ<i5$?f8|u7L7rqBGdb-&5
zK9Rc=a1o7KOaE!}y6NRK=0o6<zkV)3OW%P9IrETU9CaFhF!s7E&k`E*0>82=5bUo~
zAegU7=AqdKZRe7vkJ{3EgO2T%6FxDMBIpBX5@iP6Pa6;*d^m-)E^_k%FlzNm4?cO}
z;m}R+;3e@_Z6A%fZ=n9nIKrnJz+4wy34ri~>0X3igzbOeV(Mqe;&+7`=hYV)s5|g&
zZEbCEBewgZ%G>oloF$a=7)^ixOIFXJlQ<CP&4Q}#DzU(@sZcBF=f!(Q0=N8TF;n0v
z-z(!z=h9qm9AUF(tEdZh#XE1RCZry{2BNbWLgJ@9oH7}2uqB&l=lz0h-VMP$)vLoP
z5#?bNE~?u=9&-MD?wfa?@Ag6#NRH0{W?#N?Q1uQwo0@N;bvz=?^W@=Agd;roJicI~
zIvk&8TEP+PYbgH?4^ON`0D~+U<o+Zc<JTF>{!NxB&<8lM(`axx@;xugfq+6nATU=*
z5?e&8R_R@T=*9Rh7gtM|%#lTH>a?Re-5_cG6`{%>hEx>_Y+z~892F<j6LkiQY5ewv
z?kH7xUitDyDZ7AGA+Io`4%3`fs=)2qOw~AzD@{x_+m!Fq`c_S3^B03ZD!P;u;67>m
z4=lRc*GPV-Xrj9)7nZKP4xKih;^P(Q1_-J|v|qPNYOFpp7?(d~X3k-zEgEi|mx555
z#!J?~k_P?`M1T>JdXn&PU$s*k%rJQIm>A1Vd7R)j0i}!Pa@Oo`9bm#pmPboY(1UVx
z#TIU~Vu(*a^O-iO9+cc@<SIg8NXvrwIn4Eu8$#K?F~hYQRGRbKn}bte=xUR1!%hPX
z%P8W4ig|+5h>Rc!%I`B4(R5***@8w>^cS4=*}D8uivq#%4c9SH+z-+CXg))_!;la-
zh1R<)rn<y!0FfKhKM#mP3V`C~dSq{>Xle2;Bm`BXFBf*m3$FOJvd^j^B8Y!+$$^m<
z23?9tVw`&JaZLqAirYhDs<NWMa)!NrC|@x%6thN0z&cmRm2bc5HfCzd+v29{`Y<to
z2?vZZtF<xJmSKORsYLO_0$!z5?(o`mD_E89^F3Sr#m)jgD4?p8#kGr+m;3QFx&0QW
z0g_6k#F3t<O@BWYY?{q6#Q=iy_6ujh*d*;?e+u09)9nzaW{jKw&>F&tsH&j)6M|to
z>0TSnv``=+ykb_JI$_PH=4H)~D{;VjY_~YQZ|mpjrWglme>3+KK?sB(PC+RV|3NM-
zuk7W^C8Q!JKpB4~gfg0I3>z4D!y<f6Zw~4v6G$%_((swmtVm^-)BBUGq!12RhC%0a
zqct{{@L(Z#U7MAlwpt(+l42RVBT2RL_vNvg2bOaHw)jN$!N}3|TJN^G4?Xoe_cTAD
zSfFNcU{1$JAZdx5opx>>Qi#C<sySW&obFkIx>C!T%!1&~0Gs0Hgl@>x)h|Tj3+Rtz
z8l%ms@IP;BeHwroRT5Pk4|9J04sf4L&VEF9*90!jll{fU=!BbR?W@r9HkvSfR>K4^
zuI%*fKqqIzGt(ky4Y&V@V81QPMO+25q^9v)bs-?-dMZ5qJ)!=+S1A&Z*H8$lpM7c5
zgk<Q#^qUts!)Gu%q;0@Yyj8XaMs7Tg8g`0Jy`NkxL^U9Q6&Mb{IJe=G<doWx1OAS_
z?%LQbmO)4$n<L-jb856X;L~snPOfj3qUIjAhcQ`s%z{&uH#U8K`&yGu7ias1k0=2!
z?bIfDPR3=oMAg%!O%GYaTW+yJpgzty5hJR9^0mH_D;3hwJ1eRuezbkl#VRgnyfv?9
zBDw2TnJW6#VB2g=Hj^`9&Ub$p-9~>Li-|Mqv#)BnNFXD4;ac@w6Pz?sE>z9gq$?_M
zIv-$$LA25=SV$jD8+Uk<eJtkxeqlk`RV&wybiX{(_k#Qjp}-bYIi?h?^N;n1yNA|H
zJaZnC{+XQ2hXQSQN+p*vV!qR&-p?Xov>bygI^d#(OjaHu@OcUKAvcf~Z_`Yti}bs;
z?tYLo%(BTpHd;<uoAm6AjB$Dyo8jYn&Y$SGR_!ZYWkLsVlZY=I-mt_XJZ!>lL33*t
zB^wCz*P%t`eeL`g)Kl>jFi%EGr6KY6pU%`D%9E3mN><Zd`sO~KC#-%3>~j_5w4Rji
z-YkY8OK8ON!=b=)a(+`-&FZ+Z9<MaMiCyP5%Zk|OlzF0_4P1O!XN|=2Ox=yOQLZU^
z3F1xJj;45f2X330;sKo=f<(RIdgzBb5S&ShHA|NS1s4Q4{9pZJvsQFeDtG_Tqg~U}
zsnW;q6BER}=jk2$lAs8;nFlu(#=EBOLhz4b2c)5q(XB!lo?zya<)dC_ty>=|?sqje
zd+O9VQYkCX>R0&NB(nVl1m0b^SIHTI-+6*Bzd+B<b$z<7JYgMe7>Kp*@wOA@96l3f
z;?BGe@%6kIy$0iQ>tP{NEvWJlgWrYWQ@UP%dUWu3&i1+0wddSibZu7;{^3M+G+@Om
z#tTj~5A0ja`dW{JyD!E=NG$sOqg3G=aZP22OVw3-(P=~LbOFL|A7ANI+P&%{AlT@@
zu<-0T!(CvBpXXTZpR2PzRJY?RoAjS>DRER#sfBYsdL=0|oZqxGG^Ks;Wny_ISF?nU
zeFk5OQP8G2!ZaLT(OMQ9o~Wgh-}R{xsyV*QJ`Tcy+9RUB+a9}|Tv`bPTahpIGO>NU
zgwPX$fwjziZ`b@05B2dU(_UHtJUY+?1T-0>83z9&ck(D(bNgd!?Itvb46q%3@zMV~
zBOVQLOE~z|qMY-cmc4*3q%h%$48(wg_ng7nm&cGLwjTQc`a}kz1{4DK(%#IGs(FgX
z+CI1G!R#Ke?}RYv>ckYcm}#g|8vC8C<mUl_Vff4ZI&LA4cdtiXmgg?5AzMy;S*7h<
z`c>wpwFY8VFU(}A_veH3qNmo>skxF0ChcWA!5a%(*UoGC?YMr)L*0xj5&P4_LfCaf
z{F6Sd18c_I7D+{+*U5?kU5*3W1>35i{n`bVO>uSPnA9(2{|sx(qvDAS=d$Lwv(Y<4
z=2!?@9OE{JUui64Pq{*k=zy#Bx@6XzzNeFy=0GHCOgvkEKZ_vfT7tQ31^AuqUFpD&
zaeVNYPLapJV`h6K|D+)-TY2zJHMqh|>AH<|zz8h9zAD_&{D>02dfH;>9jt15+)Hh5
z?5k4OhZ;&gJ>~F*aT;~7g?^4Y<2>4}S`cQ_i+(;)yPsR;xxFl{Av4V?e5R?9HAw~p
zoxRy@JG1}u(`pbN<GP7CHsRJKn%@K2-FWRFeBabban0E`=^*uEf|V|~Fd;{hzO1tN
zCQ*cjOV?la=q9{OE=1nwZnWHUSp#ZW-k%dQj{`ggUY$DT{<Gn#X#^pUa-iVx_3ML6
zxz!0pF^}(@3*hd+W)!QhU}WfRQUYFdw6b={kb}1`yE?2(mzWO@3pkJN!x+>)P)7BB
zwj{%X220J^W`5t=;dWg!=f!rL*Zz@EUE{Q%5DzO$nSN;E!bX9<2Kxd@Ew6D8lxlO0
zEgkv!V^iQ1tbSJUb9eU){ixSjs)+0Xub_P~Y=ZLZv!)qfF+SxUki@f)+&t&q$Tea$
zpP==RRA_Rd<9K))y4k-<xLF1GKX`jTOuW@#xuggBpT@H11E#|}Lyui>=mEeqan;rF
zNtN(3j-V6E&im9eA~gC@g}mT=_7uQ~p+K~2#tN(<BfWQa@mlJDoVwWVmR(H$P<yJc
zJyBe)WCy^1Z<}&wG?w^D2lA>gp{&<OD*VLw_91*W>wH%2ZiLbIMD+s}xZxy|Ac$0+
z1RNcY$%%yZz(_*N{`*6Se%w=QW*v@Fc5ZcPWS}9lv&Kp3@B@<)AaHM+SL(y*-40&1
z!LK5WCPJkp*<5&7yMR5N>n&(=a%QT#;f(b_`i)GPHrqegye|p{9$JjA3Q7<u^euSl
zokxOr*m@oMr7-ASbuqD~?Qq-Nm3!4xXs0&rt5i-e2lIjLijQ87e-7ZgMr%qZsLFm%
zD@jq6;+Yhoe_h$1tuV|MZ~l%qpOGXb9gy>n2DSV3)e|2&658W}w7v->4$;@JSB!C4
z!hhv{Cd=lDnC<oa(xUge6?IDVTlhsG?~@>a@UnAa#qmnl%1~8!Ybou~j{of!LgIUG
z*2M&YxXbv>yLa-Q0PKXF)Oqei-y>fX`GUh;D!v;;c_CtwqRz*~!?0Jg#ZZG@P7Vwa
ze*)1az)6J$g14j8YHuM-tjo+kSE^IoG%4;XMR@kA#(9jcZ1al(UmNuW)B7+$kbr8P
zn;BY1Z6@TyT*yAqkk=&!oaEbsv=>rRefNOP0|Pa5W;!tXDS@pQPKx}iY+PE=gTJRY
zdkl(-y@{S)x5qEBW0|8*BtZ?=pW4>+opuc-9|wc23F>;<^ztM#E2gN35KZ^ig>2r=
z7dbu^3I3GiXAlhEC76#_d*aEjue$)Y?A^;g6ME7;pEI7esY`pI>=$y71p1Idz+lm3
zS=Le3L_n8P+J+5Vi(3iJCh0#+T(VBs_VpgezgOq4mpc`nvHTp~XqR_r_(uFNOxgF$
zt5S4_YrVZMfz^Vt-Zi7Dy2047)Z23i`E#%19mN|&`4FB(bJr}dfS4Y-ZQT{UJU`i;
zm#1%ypR%K+HI5B?&WW9oKFYW~3KJ{7TA5?!+mx+{TP+?8b@mdcawy>~Ow=+=huP|J
z8D8dI16|GgbV5JsRJ=UDJZWF|6l5Cfn*+f3)6jpAoC7b_fs?E_aWMB;B}=8y81b@n
z6!qh$!TVsAPa;3?St%h?SUZ)>vbbr^)(cEEec*HggB3Rxr}jzddDhAEfWujW|HwYL
z6D?0cj%M?e&V=~#NnKjj-Fg=+rHUo%lbs2h<<s=Mky7d@bI9fqJf*+{&tQS+xVBb9
zUB%C4-F0~<LC`4^kz~em*hqZAeov44?|1|V8$;c-R{}L9Y00yTk_fnBt2)rK{NEDm
zPJgz9p!|q0=!xnabaRzAQ^*T~4kf>i9Q$JC*;ACEOObz{L*69JQ$)7nGv(3gZI`uD
z2HG-&v|rTj1)0(-loluv&ha#0$JX-@+y<ul=Ks#~EY0AcqHI8mZ*=oQ==}BkUXh3G
z3swTypIOE&jaOnF6x`AfmRW&js!%|0<mp;|hn(SzHNG95Dr8s)bqau3H30&@OG7kg
z{JCJ61XvlodCb+0L7zknQwFZ7Punx{@I%qZ2;J)&uz~3;1tgIN$^rIqg*zjtIoXNp
zhFk34vfTG8QsQvRl#TK3B|>4*%d3#U;!^crcz*k-6m<uI0M0~C`)!G&9uqp`iBFjF
z;EN&>`MK$D|IB2+L^q5kr<yHNJcj7&3Kg^_I%fg<(er|xYY8rQm-mFHuy|SAueWU_
z);Swleko$MRW^o4qMy7fb-CfXZrcEvY8__O%crSOx(;AlvDtAo&jIbN`{z&is9#%s
zb2OG|b6CI$*i&#8qNG9b?7r`sn_)EFjKlzkp#24kIGkF%{Ay+1W5h4OZd$;E#xXA}
zO(>{zcGOk!AMN;>sNHf%#XmldFTU*(yx6HULxqU7(=Ltkcounp=rf}xP$lNNr|481
zWe&;i3@`qI1vg(y9VE|B`x<Nw_i^{raZ%IR_h|TnPpJ2sgE>zc+bXa&JVeNaoQ4L%
zS84Oq{20Ac0{!4&fI}7N$BfBT7e<VUiBtu45wMUM2{kx$Iqh(gWiVm%&kvW}w_Uup
zuCeq;lMOpcH3}v?JmX?(>})w-S3B>c3JbCL+^L_73HWAGQ>aJ{>Ol+g>wnmv18)Vt
zO$mTmqOP(roSI+1BgcOLoFfm(+W&yjh?W<OT5+iEiS`A+UGmtt3}Y(v5n1VxVU`WU
z(u#|pqD#e1ka$aPwT}M5QCQ5#UQWS3l8QYQl{T6MYXqZKFt{4XdQQCx>J1{i2<j=H
zE7)Q1p(l9}eD{_BM6OCGP4gQOV88|%1Qi@o12{tfw($+QoR#d`ZZoqjj}U6JpNdJ-
zrzx|D_gbKOXCwJ<{&e%{7)0mc1wbj6SMru&N-ClhDHqx&okPZ@l#x1>8ythZ#u!|R
zjil{UsPw^Hz*|SLm9Oi4sY+8fCq(cp_48u8R6+R<XkLav`5$Qaz88$%5<|`6v(V)7
zzi0^+VPcdByuwPrhKw^<G01v0)uKs3eOyGFl%D)Vsj^5$BFmZn&As<(;BawO(Q+1Z
zGt@<5qnM2(Qu#~$cL?W0t$i<_20qZ1S#PO+(8HP#uC?&|j2Nx;*r=@=ciMteDxDu8
z3aBzdB1`b2@$QPbQmhEOdJ0cuUS3d^EbTh_G+3C*SXPi{<d=>D85x=FSIw)6y3Lqb
znkNfXG{6<3QPqp@*lNMo!Od;33E+fl0kePs??rGXxpzJe6%a;1bf3t6dRLv27lJb{
z<pu12*GdFMY(-E`x?!p2r;vmMhMa>K4Dh1{_!9!0UH}L4{`F9b3R2UDhK23Rq$G0k
z<Xi=cld_n7IL}F>WxQgrLv9Y!KJTYBN(?0o+y7T-32qQcyg)!LuL^)eegx4ZX1FZn
z!PDn1aI{rX+BRbt83Edf5wt!G;NlQX6yPum=R1vh;u;i97Sg7v#ms^(CUdfp0%1E?
z+L+>sczck|6sN{$!rY5rxut8ri?%Rzxqap_g4bf=s}=G&w017zr=6ulQwmq_BW(9N
zes8pq-2W&;`&O_VAHAmZso!!`{!A}6qFO4(PD)h0K1g86!5qyI`@3iY`UVTvPNFbD
z=Vv8$uus&k&5&3Ik|j4Auc63BOn#e(wz$xb(Tq_MZJp7WhF=OkA}-o!i<jYF_{&oK
z!Ik(0rE7}sQujSbN-in!BvkWnKZLaH0%g@NpE1I}W+;c2laqoeFo=0KwByN<8;zCe
z#pW-T5uh;^KUq*f7>CugdtrC*3;q_wIg;%tDGuFmG2OAd3H&0iq-}Cy#~5X7DqzkJ
z99njX%(oj-Yocf}MswwM+c!ZKUZ#sMPMoHw(WGYK`DDv}z1TFGll`fZqd3HEA>tS{
zeOh^+&oP(`0yfiVEDcF|XpE-I&DKU6hi@z`PJZh4eze3;f+vU+4(>b(2pRDo4%~me
zuYXm;qssa<=xGX~ty!6}*w4`{n%&uHtt=pZV!m@Hm8~?8{Tz%2c=R9{wGjsM;a?ah
z{a$&m4C#Y*h$jLVJzm?()n>PFb>C(ASw6DVq>NY}V)>5U8t=KQB(O$*HTZof4D+ko
z2x~bht?K0f@v-pi2J$aEWHkl(*shPn!oUICxJ8um+r{>5v|Dww<Es|MeHwIWRv8!7
zTlxY;Ro-EHC=lZmE`+}m9t#uo#=h}cc}n-rpKs5`zLp`ra$MkdZdKj|BNG62%EP_x
zv(>16d8_DtsQHvci%zCjG0%wE+PaIQ2PonbgZpbpPhIh8tQ0&*wM2K?md!sww)NqB
z3au%FZJya{k3J9snoz<x2rtFZf&}eo;=x8U@A)B;N)F8i5%9NpK$Hw2z~k@K`PU5i
z$NU)<zwwRVuu}Zr4<xWL{QE${|0OyD3mf1Zz`wox2Q>em=nU7SVR#xD9r_?(f|13d
zcl&4%w@Ba+w2CN-5iSy3PFnX{-0fECY9_Rz1;0ou7t?1}(pJ)#3%0EVK;RHjC}4a!
zQtbous{@z1e)}1i)DrbPFl~bv76eirNEm$6q34qy&1f6A-5q-f-TrX(L%z!K{k&|S
zP=~yyjD{nP>1^((20{Z1Erb*6mk=+f=&Qqu2tbNXEe1$=7B~=sq5hUdtSEmRlHmKn
zZm|3iZlmqkmHnMtBdV}WnmW@Cr38`;L~!wNK`jM+enl#zrD}h+C-ys%D0bR*BYd(M
z1r4~xRBL^WC@7Lfhdpw=4YZv@J2%BK`LbRK3v?N1;ed?6YGFs1aIE!m$~h=lS1OsZ
zU;CqRT-2k{0_p#Yy0?t#>*?}66Wrb1-QC^Y32q4%T!KSzcXxMpg1fuByK4xVd-!+H
z)BVi!x@*mSHS-1*P`{$&oL#$iefOuBCAX+Q2SKljGV=}Aid^3Z2~nx&av9&z3=${N
zWJlthL=cG-R##KSOG#gb06!)WNpy&YD2GNc*}(mZ)gKeB==%;{pho~UL+(S3<SVCc
z5Xz@8R%}ldRB{-#Z&g<xXnZZQn!JiPsQ?)?En3(&`{Y14GE#=$fHUi7H|3US*1Th#
z(SPtbn7&d6-ROXiIZ;+e&DElBo5y4=V1_?8pvJAU9J3nPr@Zow6n0k77D?}&cQO?F
zFr=+1;JO46sj8QYe)Le+nNJe-F~+h$)WI~S_jLtfgct@~-J{r(6~@Vs?2`}K@e#i2
zr&zn#b}TEw$3f_A9KX`ysthyh{TRE%vj<d5zikwY-Jo-pF;1fuh`j__f2qW{N|;Lw
zI7;F2or6sC2rRndm^d_y2hI+jB?d9zDpC6cln6yC@szwUByETK@Tf(k+?Yf3SXF0+
zYDZU=mK<3aFsugS9E_CA)3&JPW#chd=WNM(>8URB3$|eI%xF||I#8Um@Hvz=**XWw
zs<X`_q_4~v#t@VJ?;-Tem^W@-D8cM^!GJCsrd012n$4LZ5y~Zp^1Be4q|7xx=*q>c
zzQS^B;X8!0*J?{FG{&x<>&ps?&Y-k|BW~gvmCO!b_SvI&f@iIXbMs3(ro8c0egnz3
zd!^NNyD!mm7N~~_2*npNJh@}_M^Kp*=87ZKOZjaY(VyXH=Eao6h#wze45EY`o>ylq
zz=K5J5rae{MBsGv3BtFX3hzQz_}Zi^VwG0UM3j1w$&>Ezv{?%Z(cz@OQS%9rwmbjc
zi`6(PM@1+i%bRCm{JOxwIZ;nG7W_D99lv(s#jC=_M1|5@Qd}Kt+YG+J^(~3G>8vGU
z5QILLY??F$aa*||3*pOC@$XTdvAwSP2Do^pP|Xtx8OWu;PGOui$@Ne28V*i`&~h;(
zNzu2ijSNGU1!)))ed1nc3nNYk9%jqRXU(e?ccnW?U;bRvht8D{&hj&EO}G6Rgsdk2
z9TU*3?C@L7Qj&shgLL4lAY`L+mqBIPT5IGflr<zvxv94*z2HiG=iRA^L0T04<?s|_
zldfWkONM8AXQ(k8!*2vAo%5IB&qikiyhK(TnkMd^k9V`o_mWhV^h<8-q$l~4L{#Rg
zy6zt9D71?!KRA7l1wW@^I{u}uae^r5C8BBNeacTQqb0%aZ2{c9E9ma&iMrx8gPor4
zv@f_Orv|pvYCd$eU^t&5i4(+o#Q7=HZT*&ftkkZ*V|WHg?pY>ph>j^u{5Fgpp<te(
zLf>%VPx>^b3`H!tI~Rx)k2U_e*f0UK9(v4@oWd=oD(?qtn<B<U6*6O5$7tMwgh^N4
zims`2ob^^uDR%a221A<OwaVR!KB6tYlJ#W$gkFll&(u>}lX&`Wi+Av%N(BGRT~x1*
z=&nHN0qdfKbay&riW&^px{z>|XxpnXF4?WOWu%Ui6cJq-!Z|GzP3c9El0M)BwJQHd
z?F4}d4MTc|e9B++;&T4j)usg!g)ypNRt(8c%I0ciawO<%ZraUWB9zxR#eY8$3$j1`
z59Xiszs&#ti)^$1Z=oaD+5Y?5bg==eCjSpQ!a3s>D{Lg@mo$YlrOB+cT7ucYz8imB
z1=mvk0M>XyDTJejW->M#Gd6%l$*n`!L$>S>pq=qYngb1;r%KfPmEicu-R5ZXo4;|P
z<$_`BN_&y}<^KE2i_dw--?lkhe3JjjNd4z)Kop4THIJ$(T$n~29PhuSd4J?~|99@f
ze<sBL+c%(uEqCAxL`Mkwc)sHoK}GJ^DF^LFd^8cI<AbQyc#b!kSS`see}~fyo8sXZ
zwVyx+IAkg=_^jo@+_5XSJRwhZ6WI{(+pv*KNAtsO#RW8Ph;noB2OiV!n|yJG&)RXu
zVYs43T~hKp)l*7pl|#zDsPk)b#H0Ai35SW5MaLONvZ-+n|G*<qi!iZKGQ91&rz8|*
zE2MG6h?W13;SoxlKTob=*0VLHDS9;;OxOW<1i+J^KR!C+wohB)n);y`jPcPKc^~n-
z^NN!G<+<<1#r6KzvTq_v<~ZF|EnB%~poHgmGd3q089D!a_*)w@+HVb5Q|wFvFaPOq
z%0JLFe9n~O6z4t408yjpv(=7dXI6ZXw*|R%@}V0vnh2fg0=yvv?@O}B<}uPkuEM;w
zF0EZS<p0Qr$oYSw1OD?jLP9rx<!?HVkm65igQGh?1$Lt;^Y6}GVc%m@KywwYd;Y?V
zezrdOW8AH()Bq*EMSGsV%$EILj;WZy%~~$|jh;$7z_>Sc)?Ft<BY8%DlS{Y}D{wvc
zRBkl&yp>*oF{`<t-2S{d$Gj9hAvHhW-(nSO!BK&6^E?o=EQ(Q3;dtPC?m$Ii{9cYn
zUqfb2(XXjIbj79J5`a`e1i6<nKQHT?YzkB}bJH`0(#Z=KhLQW{!rpWA7i8K*vd7IZ
zuaf;&>?(A6C%@D4W3CcW4H-0mBGCiftzw_GC5s5KA|f1}>LwMWrK!OKr_o+iCcpA|
z6kdT3{7n&&?C}DD@uiA;B%mTgLHzy1j)T^#f+b~kU&c25@@-BWm>C1%Mz`ixl%+5Y
zJGDRFcGPtc$rl<z$d9l0$2#F3xCjOxvAnw`09ApO+H_8+1f`(-65zC|aYDu8mxo@F
zqt6Vy5dWP)@-HL;fJ_)jm+v_lAe}3(126~xt|45q;c<Xl2?{_jI4jH9x!+=;`6=1C
z-;~8mJk_F^fBJ3#qrj-Y{lw3LhhDi0=6tI7^^fs4E`X!5eZR|Gy<LK2$vk!ffDmkM
z{x1l_zh?Pgu#o@TYx^%;$|hG>GH$m-C9>e1;Aa5t0>BsEe7e>+YaxM$dz<4Y@<~GK
zD=Zp_k{wR>Xo{w%<Zj77h>0KB6^O0+n%<u~Tt%{THU*hq;53wgX5)0jDMDNg7;EGV
zoCDyGG0?jZ-7DY!Bf3eBkiH=JQQtDiU+otyl8@%x?|NaG*v8ElV|+A7TMs0qc$`%U
zN(dOv_`-LwwWv`5Ljzun$@qs()Lv}GH9(&)BWF_^7{06r8;|%sj20`{x|9P?tToNq
zG;+z>lmU7IDxsNGY8l(w=&Pg-8TK;QGIyN6_LEM$YCyOj7&P^2^1G(I(+5prs9Q;u
z>yoBHn~P<$B{NM+4j_s*L0_0dIx7FbtGrsaOE*$8TNLYsEpe|d<+wWzQ=~z~>6J8B
zb^)&@4{#aEMk8%?%gV_4_Ua-?Sqy&pe_#Osk_3BnZ<H2cTUN<aQ0KfAhf?9wzWc;S
zg(($2NbAF$P1#XM&9F2EsD=fvxV*({)GTD^tL5haG@?wqmy<#L11*qp#fZ}a&<o0F
zWPu8^{YLsnSoKpy?)>#HUS_HBvX-tpc}!DTIsb5rhPP4bu%yogPZraUFt3*X8642K
ziInZA9t5Bf58o;O1&ydsvQn-BM;Lfh^Z9^AEcV?qCj3=oyAre5N_H&4XK!aruhbNr
z{G$6+O3kR>>KVHt4}IaRL6d(6U>+Pyu`)b#;t*oS5078Vd{7@gKolwK7w%Rw)X$?f
zae13Xi)9%s16!@KrJF!!9~-%S_<=gx)QTN(2^duV#=kwJ`Zz4OU#Qc%0!<9Oi}<IF
z;$)rQ-#o~tT9#>c)2k1IxVybPCDj(zta8GJ={jvmDXE%d)X09#4R^K{rp?6sM08aE
zvs$3!+bb*I#n#kApK1viWOO2bM@^^;(>Ll&%lO?Iy-EmMq?A1Co({cQUKeI@BBWUM
zsF2W92KFeFJosC@_-KE4b?%M*b7nF%V>EVC0yXuLzH`RM>`{HR!>7|;*~%EL^5A2N
zGi`MzLZ7C{R`KJieX>=&<Vgl=(&5!E8kEt-8BNQUUGi{B@{Z>TQ$d@1ykg}8{tQ}e
z@D~NDj$U{k#ju5l0DqSG;88Tje$)^C0gd4B5&D2el>K*T#08Mn(phwqwyi4tU|Rs!
z;jLudD37K8i0fQSt0t&P@>Eybku;sONoo-rXfS(?BWJ66+afyWO(t~OW|<e52k%_n
z<~pS=@TEG9%P^ila}Hk(w_fn_>{ag{XaqF<7~3P$<_7tMo{9vU^Yl?TX8Pl%z!#L&
zTq_NGr}hl`nAEiNAnOSHY{&8Jry{g!kPW-!^wwC1jWdi<((S8XGkJBFyKhhF44XKf
zFMA6nOdaz4!j%FsN1m*Q_|eyS3zw3S*kLxo5W^k{SQd@9`+sIFY-W%DxHbtL@|<U8
z*zhmsi|8&#QpobR>Zj8dn|#I|r==9^kQ<?0Ll~Re%C?koCm&4Nq##7+z|}Qh=Zg+U
zwN-H=JeYc~UZEUFidfc6aqzzn)63txTLD?QLu%!a=~qd~sL89TpjvI#QD`$pWUk)l
zNmd%pZ%TC&hJ4?ZR{Qz@b+@J#_HI;`I+>PJ6w+?qtbDp_-nt;Hg^KT!Nw1_&Tjh!*
zYvj}u!J+$=`nQdS{&M{w<}~FUe7h{G!sef>l8P6{aa&dD)k6g8<%JOw)g8{N&{0&h
z2^fT^Ruya7PM$lfbb*zbo8T3Yq+MlG#f9e0CRd&Tvy7WYH;2}9?FwBfak5xt1!W(Z
zhLkvAz4s?GVD75Xp+B1}C1v{9N-SaJ$IrP_3zk*8bbh;J?DCS}z~v3?eecn20;<e2
zbm__;9sAvsLbgGe;rJjL-QhB<|EoGRX)Lsk(KUX2swRkn*VFgJ+MS1QaV6m~)7v_-
z&oo^EM&G;g?-6V2Qd@=#jn=bW`l-a8X7TD@Pd5WY1d65;ty9uYrsh%O=*xO=i%2v(
z{REq8c@8umT={yJIu}QqB^Y)CG>^;f_J7c)e!va#ceaZ0{}VKVgYCbZ^#2Vsg5$%N
z|NjLvf|;9*``=vlOh{eTCCnj3jDU_u{W|fl{&3>Rn3o-o6-|xW))%+cjoNEGr&%Rj
z;o6U7{*iuuBp`Lg=aFDiw?2wGvRZx=#L!1CBSVUE1a4QE9$QzLj$6NuR9bWyfH)Ww
zxf<JNzmL@E|M>DxgzVqLWd9W_%g%*8GAb=A!vUBO@ws#1-$Q*O7rttHD`&~Qmw%1&
zLOyAcZCDEw%UE7sMlfxHDiuM#MMFg;RhVG1Hy{HpeF?Oo@8a6KJ3M=FH6p=x3H{L)
z6C#?c#55|3Bu4g6bn}BK@e$YrRDJ)QVG)1|fGtiGT6C%8{qw|1hJ%WhKB2|TfsvC=
zH$g&Mupt7Cbc$y>;16m+jq$`UgI!(zN%7g8VcLV_OEicyHyaiZCx;5VhqUuS{VEBf
z2Q&WGGn{^(g7zRyzFU*aLnxy!Vv0p(;4eG2y|}d91gDGfS(Si37>E_V7O)^zRR<N&
z5*5RXHvQZnGo*Q4ayI^1$NY54js;N}oM8^Nh49GHoA1B<7xhbY5Q3T1KdI=sWMF-T
z=-n0|TUXuA&`c>;D_KaSUYfUwxUian{(`2WyW&vGD=PCJU45cjmbWv3=i~X*s=#C_
zctX;V8?@lWSW0#NZ6sV%o%@?}BfJ$)Vjc~xMG?c=lF-PMOrPA|1XMy5=y)3}GVk^!
z1i;ylrWEeOi3|f8-JX++F*y;G%sH}C;JX!Xk0)fQ{LnKU0jqfXMIKMwBB-(HRY`|l
zi=v}8R7XwkZ(h!6bwOUXEd^@!)FT^#QL%jfOwXMo)AdhB)}0zbIT#R9Mm8X=6_Bnq
z79z1B!0a0SvEAkh{*zPDWOP=!KZjV4&32m7$&-KKj1QM!KA)5?JFrket9Yl#KmL9%
zw;t8TxCFU;&#DUUg_+_4sSx46XJR-mP=g%~@qubUJaFd5Ba{|MNyh@i5Di7Np~UdU
zer>pQ-h<=pQ-nw<8<uH$EVgurtLKj}ZN=V{@oapOxNQ)fF*$c$jIN7SpAO_++PPPQ
z%95l|T!|27{UB2;l~((qn&fg}Q)Gk8`N~^O29+~LNqj}|T{{7<>R{tZ+!ZTSd9`lv
z9A9wRkR{hwbfEd6c=5fo5KMI{R?yp9U^>;d5fNF_ZV|Q@GVP0<B@1^Mzd%e(@#*v6
zFJI;WzKI*A)|&qO_}hB~3je0rsn>bWC2ftlWq#zxxNsp6h@uSeiOu~QHB+orT;Q4J
zflr1_2F@dxuo{Ey?83^m_8ujI3X`mGOkOUaXrZ4We{f@(B+_Qzv#KPfVq!TmPz1|u
zXJEj|1lgJ5aN7dHZ;==U<WBfqMZZU!WB%NRAoc?xH4Tf6SS^M4%m=~S;Dv)!GmO?>
z*yR-n-Z*&au(P8~2XNdt&JG~lhaWjs!yZz<z+E~iv+JHJsLQQW+?jZa+b^f-B&+o;
z`JUXgq@R0Iqb@0E%HKo&vNA-=Ri>eV9cWz-;ceSZzXCR5Rsfyb&girGa__L-gM7v>
z-)I&YOT<Y#2TtXK9fcG)oSXDXiP7xrNJMFB2v8TSXV!v^z-fb;kDSy3{4|{5Q0jAv
z6r|x?X3ebd=&{mefl|cOq5kg$Waf}&JP)%mIym=SPwMPk;~;C$c7G*@p?ZD@0ljW&
zX(MnT>>19?F}^*!NGMl?UHqf-XU+V^!d9%s#k`1=Sluy7fTqfVh*d;gZYTPel4^v!
zFYq4xC1=GW@rDduG30-i=q=y6DpYWo)!F_wzT{;|D}$eQqNY8j{o~diRS##7Fz{KX
zr5$9|pc?}iX?KDOoR$`>1h{TnQ4NcWGRK=AtuTTS4Mo>oDq)pXy@Se4GuYp{Hl&G*
zQD3}4M>L7w(VBq#EWg*j3)^9w&ivo=s)b2l{6#9%U5Y2P(#6oGM<)1A&_32n9St7R
z;$M|9y<A$}$=&!qjv$0i@k6-bY-D@7z3w6lj-J%J6l&&FG0a16u0Sj38}|hTaW&QE
zpz<F-C5skC7*|kYT$}3aJNjg;RhTtAxq0A{=1Gdumx7md7<RXYG{lkaq_S?ndD63k
zd7{ZdSRZ|TG2#_j%JBICjMc!SPF<Xt2-9*@AB6}Zj5Zvn6W{Z6VI;EAQIBcKxG(Zr
z`O3ZeQ5HZOtH3s1p@%dsV`wuC8Q3VuV~uv`wjeNnB>#T6kZ6ROEAn;*-}obB^|i)-
z3RyXi37N#j_l=FDbItBRT=?Lua_<6|*$rndpe_7z>hCV(%*B4<Ar#b#E}eZEy;RZ3
z?T)4Q4-d800XPVdRo)13w@h|#{JoB$BU<MsyM{UQnVdQ_2S@flyt@KDop)AhxhK(@
z$&qSN&ne=ikAQ)z#;a|%#ly!3YDjA#a^NnFZ9Bz+Ib#UApN)BQ%F${jOlZoRFY7tn
z(sTEw7Vs&U(sq5P)O`cN2>Jwnmh<q33h_jEUNi1<LMQIB{L1^%SbNwKuH+gFT}O^d
z(J!3k6Y8q2j4w0&KYMU<j=A{|`Y}Wa%f6dzatSf42E58e-NLP8sHr(pv6=YgSH<Vn
z#o*yY%B--h{^-9lO0OcfCcym^l(i4T%wZxV1HD!m{H#Pj*dF><W%F7ibTf2jjkA(-
zkOPTXW{${`aar-e{9fOMSU5`hs_Mw)!l;m5usVAtH6<fB^<cXT`jmG2-PEd?;6k*q
znD>HzShOvisksY04GiwjVf6BPO2sYxo;&TB6x14X6_)uAZh^lqvQvycv(bw#6nyK7
zi-Jnc-HT_T0%)`T!%)OaY>%()5+crH6J(H}h4x;~f!9oh<j3EGwJ9*vG9yQPKc5MS
zVAxdlEs6pOufY7+c*DcEZ#OwTcFf161=$AcD3^-x6mYdSC)MZPE~bwps)0MOPtUKl
z9D)F+d5e64h}BsrH+l-}kA`0c%{}mW`NebjzrY0EUO<)CwFYJSbbuuX0*h*MSKo*`
zpbt;!wF&UR72d$H8nW$-6?0W6f13z=jj?t@qu@Tlj~texH}jvu84f2FRr|<S70tp6
zu5-G6<KQUX9i8(7*Qd~iQR(%Wr+phwg<DO+m7X@E;qH5?e)yVc{8643=X4nIw>tx#
zYS)9NzWJ~$!z~9!D5z#)qVDlGPuf~0j^?ls#ayyLUFwf4&;GFBm)t{ixYzg9JIxzz
zbb~*sva%uLd~v_BBmxwyJF~_cu@HEo>}*llItMAlfe!>Op(FjqO?hHJX?=I69nclE
ziBK}dYJemzqJ>D~AE#?e*Od#QQ#*<$e*~6%F}aY}Ot~?FYx(QF_4~NhMDGrBHRSXL
zwh`$mC2{w=NU|njL8jZF8tbp?VSwq*yAn!_chs$rSS;7JZM&<B&e|oSg^%ThhKOvn
ztPz(nnh2zH+ZXLvC2`6JtZglkB_f-e{}OPy(7E5}^Me0`xYOdJlFr0tD@@m>Ox4m4
z;*Ao!e~QMs<j_b#jvBj0#tl??loAxtTo#p)xYGpGR1sLk8fYm_`rl~5HVz;;Lg`=u
z1NklQ@pCy!dlw&GlCs8{5SR>!7e_!Z4>k?5ica+f+@JtbXQnPR2r!0XanCX#tKf%y
zH0bn*0R@P4v&W?Uj=DT25?&lNnnvhn_kHJG8zqweBz5PV3SCf&k9s`BUPoAJ#518i
zy}7Q-sbxeaxNIqwFFAnhz1yx+;eFsT#1J&tQ@>7+b;+!xLES(>j%w=&!zr9q;;O4n
z@L*@ki&ZOWicB7Ukk63!cDz|2M_<NFkW<wk<i{XWLMKv@Z4%0BZ?hmR_RSMIc>}I~
z7C~6q0aM~c1d=_xVaQV=(fHU?17`s1tfL2R&(oy{`J1N76dd^2(UDRj?9`-jI^bXA
ziRvO@zq@*S)3Ma&RoMf3qLj?+^4hTU+R>~Ri|U6L{~~(s;^4kPQ%Ru7pMRi=oqy6h
z(uObg&&3uvwERKx4pl8LfIDAa5afKTjNn=oFstTjkF7%hj4^nY!Ot+O<423>zVo`(
zAix6(J<#I8rEbHbCWMkUON9qTSA}{5AF~CpEh32ai9xoY>UT>L-Y=rF{1a+zF1e1K
zbq!r7<z>&X_SHFCW?!B6=DTX%fQ<MZiU$NVil0MaIl<iI?C`Xn6a9a5q2C%8l$Y;_
zDo5P$hR<Sb&;WNDirr`}<a^>b_8_~Q^$2ymKvQxfZ=qZzC{CL4hRfA(7CQrk733KF
zK)p{ugEbrogf*~Q=sS=Mbk!zEs`P6GO$2G-Rh?Hh9#y!1&y9SWBmH5_2rT9SiPzTM
zYG2o;i14Uz@!b$r(QmHa)J9PV;|nwwT;?phEd<E)0^`)qp+vxK%j7w$vFAN!?l2ky
zLaMcW{H%%4&?w_++1)BTAL=MN3VtjlbdqR8mX(7oTl{tBJ$Upe?PNVv?rn~D^pddZ
zyCl*OPz$eGZ#8R9%5}-Uj~Np}!z;lTO5WvR1o}InB)+;OQD3~CXE86nNny{89xKjv
z%!<9MAR^EF1Clx7w#D|iASss#8dLr0`W|jsG2Bz!EluZF_n6o|O{@>x9-h;}0U6*u
zf;WHbHtop=T_){U%H1qC(0j&1rkZ*AIR^3gEdaETf|9$aS|OUpgTkL<YmSSrk?pb(
zCj~Ju7_x>e9EtnaMw>@(s-@$)jDZ2+nuJh|CDCuFFHx=<<!H~hPie#${pf3RR!l46
zK1UnLz#RX0)Ik_o8cq$JysQ0wPZVhB__N&~nO0I#(hB!8vP)cr+EJ6$l|LJ|^fOv=
zmaA<{Vu7Ly>#~<1{oK@=($L2;y@!`GTjLTEE#hoyezH-|hZl?j+_f9B3g4=Lt+Kd;
zddYat5o^ZcUPNx%ic0`B!G)AmJ7dC~ktr;&(ts0+U$C~*!=)BpThPohuj|h<O^O$M
zYN}!*<Ob0~zd;zMP^@g`f%W@~Y{?k&(C-C)%ygX!R;UEHk>K=Y?t#HzOL~<HXe609
zJK+FUm}hYwe?P^bD93s7s@W{7h^H)xV#05P;geb?O>k9S!U}%Oc6|aQw*qJ+W&8X=
z6?S%7u3Rk5p?zB07VCW%Mimsi%QAOgzG_r1h{dQ3N;3f|@XaWVxMcff6%>%--{2b;
zSkw}Q<LT}>sysJbDXP#i4&x9XWag$nlI2~LQe`p9&v^I5blup?En=IY(*nBn4MtX^
zo|L{w<oD_3Fb`8<0p_gLH~7VO%!qqj5HIP(qR28o|0jjp7>Vs3;4^5>aZh`Iq=qSL
z27M&{gTba2x}nj5PMS^2SbjMcVjn6_c4vfN=|G%qsglh-Vej8={lFvqbc-(>`Jv}d
zM7E(J*|Q-AE`NJPE?h`=O(I(tpjPG^%E%2}6dL$JasV3;)JkhEzn_z}#B{|r0kMV!
zId5P;NPQtF@O@z^p$;%R`shwbj#MWCz{P^9XC#wX)UuR{rf!&ZBRih|^%g-gE0Zm=
z>>mWrtsmv)`SI^v;|sA#FK)=%jP(Ugc;H`zaOE5p<KqjiV2M#LbE%MLFZ1L9EBYRf
zDa(Mp-D+-N-W11X(w@(dRv^Eh5>Z+Ov4jf4()cQ@;a|}a3M^BvE;++=6KowbF8XMb
zcB~0WIr-A7CQZS_nKJxXsgRSfQiNsK+#kS+9m>RCF|NpXrkBw)u?4DTE!a8IJVK&b
z+^hOWx^gz92W*B?@w-t#ou2#hNv5?A6~N||g6p(&SO!24n06-HgQlbYDL{Fa3p6v!
zZT*MgaSf>f-;W1g9vhrho6eQtmL*{x_XxPJyu&J_Of2P9S0IL*aVXIrbrLf`eMxNe
z!h{evgRh-oQR;E)%{0z!mOeNeoU?$d!IC&k=A8eh^ZIu#Nkw-fe37TmMd12_=5Y#u
zXS#1CHA!ycpaoJr1~~rz^oM<9DTl9q=G(~5HJql&H^ALtlh$0Pa$vYQaPXS8CmI^#
z#`{1j^s0uO$q4^-=<pK$_9l`+xi0>k9`6|X7#I`!M=1${`X7V?)D<u_BhGpajC5<d
zqLanRIB5mnF-HU)LkXuhpmKwu#y0^_p6i*A^l;$l6D6j`9^VckX}5xotnV*QHdtNE
zSm~#LV^UzxhUlszJ7;uw8A7s)gKJbAN%8b)bCNtthS8&p;8Wb~7Y|fmE|9;%AaW|(
zve-KVds>nzHnx#q<`FG}Ea@#&Eif;$?z?cMSpSa)TOi7(j?IPNBF1EvlKU(d*s(XC
zp=`On(~Ma`JpDcHFh^CR+TR1(u4Tx6<Sx>qh6(yBt2K+MI_1EoKS%j-kp1U_c?f*K
zGtI|P+~&T<7yf!FWXW93UqU@Z2R8VCMfE2<N?Mr+4zhIhg!^D~cv5|(pBT4%2|O}(
zlvq16A7-5)Bb)+J+>_uqGE5`Ea*gpNXoI%y)#RZ;oZmFD&|T4G3t0ybdxn$+ShcK&
z9y@$Sn}#Jcto;&qu4;>Qwc!Q+#qkSPRzta3t-`rH!)e5wmc%L(oHp<3;e>84>gj|e
ze24N$6k{%ZGi|@THE@bf9n4YmYp2KdSL-!}ziHg5%Z;q2bC`Tq9d8{7wp8WSrqv8r
z)IP(!*>YKH_n3PX4M0Rd157~OO!=+zW!!s_IU<2^aq+Qfg##7e_ogf3=ZC}?d}xSE
z^Q6nrTkUW)4+h2bcV!Rl-#<+5v;GXEYp2O8!2TA)@JK1U2OiCK)7a>d@jgUG-z6;S
zZw@GMG-}OMYLGiRnif0EjWIL<L+z!7ZsL|-m7Py)QD1WSU89my%L<cTFR%S(q8zDm
zyM!!oO13?%dT7+XXCGg9Y+A~|a$am){<HfMh~d;q!wdguK-k$dH}DYf1Tv$H>4sa)
zg^yXrwHn^!KYoh!8#XPFP-*Mz<+@B<=hZ5`Yy1t8ojt?nZ*Kd@ESze3ik?PuK^HZ`
z@0(DgCT*TD6<z`j;VbBCGN{whX$p{s+|~0Hd?*dGssNW-0)38Gzwz1iz6Iy^o6?F@
z9+#Lxh_nC$O@zqS?y)~@vQybSiWhJDU|K}W-_g^=rv>IcI<gk)9yEeh@tiFkO{_rm
zZ%qg8z}_sRtIIH7Qj|(vDYr>cGw;>Dk$Xqw17?$t?~^=TL+G|=2}7iHb1`q`3f2xe
zGViajXTf&1S!F8#5%L-|k~8thw&}!A?YvJn*Kl%uj#!f&W{RAFxZt}L07bf@9D|x@
z!NE&W?4H}q>>s3>_GeN}6I9${P;n$J$3~vwX77Q7i4Z0@Of32upQx~*wp&=e$;gis
zLmL^aF7BS2K%#3?VFZ^?@~B6~;l@eTfGwN|4Q7u8IiCwWt{VM8&zmyOw6TjD`Dcza
zSf89rA1LvO&aG9`m}N}7p;9hbk<taUYWlB3DNfX1sK;?SqwNPN@^iZI(*z)*!ePvP
z8-|Z>4YnPorDC>0y<*cfBK1%rx!MKh#_N>rYF&PR<_-=~|GMgStuM(D&@Mn@kWBO9
zB|Yhgcfm(w?wik3RTwjLQ&l%I<${tsiCuv!%r$8X0xp9tB3%QCx!PCq;?`rr(Baov
zBfs_f`0W?^VIvBhZ1l3mvw&W*f||0OnH`$^T;I?JMF=b!seI4{`0+;;0gS}P>Yl%_
zKZv*)XCHAJpncF`%DF<}YJI!Q*5}ydB>Xs<33-$@{Db%${qiq6CAr7wUQ?NayB*1;
zr1G@{2;@?c?Uy5w8ru}stgmDPQ!j(UW>L)JE{%bO*`82P82|H!$tA-HxPl49PA3r(
zVNyE)SCNorHoxPGoFUi<sfxRImF%Dom4V0c8iw)1Ls%6<`e066i?GT6K}f25N^;u3
zwh0<#ITmte9fL>#QSu{~6xG5h(3SM}<jd5P{ya1m7@L5rbpKkhs42(e)kzs8<SYn0
z((xG&ozXTp)p@ef>);rD(-f~N0C@u0E=Qz^WAv<gsv%>~B8P-FlmZzd=}$_QcY>py
z>3m4V7V#1CF?X)~AKU|C5t%{I;c)0A9LZwY@yA|dDkEW4U+x9*zvWht9IDVF&3VAC
z1=xFml2_hXCv8|EimE&11cVL<P(-qoraPF7FtrmA!27{0+zm$sjZfc=&&0`<2I_z5
zGiXstHUU;#-So53q?ll(3WF$^1yJaX)nT=Ffi1V(WM+z3SYoj@HkSR%VF;mJaw-`S
zAi@8EETntqpz;po^DN74y*);jQXUB{_uPh^wXh&UCbUu4KkY#w)=9^5zzut*zp$kt
zm6r5?qJ-s(#3fI#l|>9TJIbNM|7a2`mBL|Xi)R+Inuf4j7;F^{RXf#)6x}Vemgat6
z?lkc^=LcV7is&AA&wrIx{Dm=Kr7NJ`AOFo%Fl@1zfHxk=3y#kHk<f2n%oh1_eh!|N
z?H6|V1yZjeIEIv7_^*>;_1{E9lDlluA0Hf!{!B=nWwg-jvRBO)ooy8ck{&Qgy`r)n
zd3PThD<U=Yf6659qV@Xnfcrl@zy~dx=kbey@I<z*Zh4waFfjy2{!%Vc+Px66wkeQ$
z*WlbgGfMixsvm%5RasiLc7{?Y_-IcQ1rWzVsl^#vpnH6+1ToX~5g<m*5t02`GJ)t=
zZJ`ZVa9?0d_6!j6-#rmxS4w@s3HCa%^&Ldw){RuJpsSp;gRtB4%b^2>pABS6)pQAU
zxk^vAwy0j_)<{3a37pz_3Vf5#4{BS*DjUt=7mKY9(pGq(H{^RD@VCpCxRCINnhdri
z7Hx4yzxvfx8b#ujhEcP=1Q$u1hgCj;V1I?Xd^{E@2u@yVjwCGUmPu{-uF-Cn(EJ^(
zzb}031^2BO@z~$lTRiOmbX<XXu;yov*EOoPXpXYJ*?l?d?fs>WmsiHr4QIyMmZtwY
zexQ91L6A9otb}kH^Y@yu8Cq}H>^ETs%|e?{jt|4A-gDZGOsb@++Sf!Z-K8ks)s}C+
zKZ>jn=ajKWax3>^wylz_7NcOfxl8gpRIErNr$!*MLGUGN@y%iLlkS3UJoOv>{0zY^
zQvEcLwc2uNgD33a23kilP2?AT%ahMuNqDzI<St33{}pY})`U?DcN#L1n8P)#G?Oj$
zo#w3RHqsdd_8p{M`fe@QGMB|RIAc|J1m*<feDFD4kj(ym{zbkI4wTvCLMk{j!yW~^
z_IejZTNFt^?R%`{-5tU^m<9IE00c`p>!YmaL)ZJEIXttdfw=aVhl0$o`o`IoG$44!
zS+qYaMhv_r(11UijGn=R&EA=-2`l=-vQ?~^GfsDM8Zjv83s4rQkn^86IZA_Oanr&j
zmM!wx`%$EMtd~<(`sGV@sw4a1p@Fx8oJZ|YTI{O5$3B(a9t2@0l=;%saz`S2m@O%B
ztEY^vL@7`7{fWKX2(zt{RjKE*1GCv`i#=zzJdY`6GYReoVgqEpP}0Iok7o*xeWaRD
zTl8Ex*<{hpYwW<zVX{gBAVNU%>)KdcABpRVk3nx4#cmPwQV}-G*6lsqRVPz!N`91J
zKY5HgT@XN=GngF`L#&)ptrDUcusB5o*F7i;r~P$K@j-lvxos_y;kfsaMvL!J6%_q`
zPZiOyfLhtbWG)ri<>w5<l>!n|_6$lLWz%0dMwn{*Wup7c&UeLH6F1{4zZDLAj7cu(
zyM4l0dAy=p&`Jxu#P@q4bGEYal1iENvf?3)ov*NJR6BkXJ9X6+p7OCQ*}rME^M{NL
z_hNHcd&+l>#l^9;LD6MGsOZNp#-ynnb!aOC%C{fPD~hd43#et(3YIwQW^hWBM1<Ys
zGf8MU4F*^3rcoLs6r&#s&{w?EzvgtdGlgGQe7ueua^3@HgK=^YH47A1AT&+jAY}~k
zI9Ruq^3_orVIm(S4a$CbhpQXZQ>DSA1>@t@d|@OMn7~{ci9$Pe($4U)QXvnZ5U$4+
z**MMSXp!`+I@}D-a2L)%42W9?@%*vlkc;{vI}n1|5OQ*-=HI6@Ii*cY!&b5j1>5gR
zF3i)<40|xE=-A5ip6>h3pC{rB8mRuz=07=^Z}7b)9YIbUfL8DT*<4))soG0|d&zIa
zC%~p`+x)AeMn;JC(mk)Fgr4!R1W<}2jRWmUozwzZ(I(S1WyX=U0B{c`qL=ht4+&ZF
z_gN$^KNW%CfLScX-s+BMG`}hDUN2D+qNy(caVpQFsh0L5<fY_kM*laq<3Lp17=&DA
z^L!Ut;fbS)4%p}+JNNyc05oX3<Quy+RMa|oBVs~iw+|~0n46$>2Q_FXjz}cS#9!9@
z0t{fQfC+D<;9&G1A(2DVoWOGUNoq7tK1N~Zh8oM=CV`JZCtY1>WzU#jjq0<T;z)H_
zuxJS(Wx#GdgcJS@g;a61GET(iP)y7^`LDK|ic@_RWMD%iyNq`kHfLWJ`GkulDLjS#
z>;=&CT7&!$$PJ6x3o4V~^noK95q)x?C~k}HWl(u&E~5j2wDpln32<0CZ5OCeaP|d!
zJr;*0*}<abYi|W<be-&(`>@;Hp`}G<h0Yz?GR9a3vO(j!So=lWnf__)^*Ah#PfDWc
zd%=Al4moK1Vhkdwl2NXf$?ViTKIs=+GXgiP_GLXSiPy2(+it-A@>F8xTG5O0lf?>B
z@q9n4Opuqb|6Qm#Ii}#=>(ny__hg7eHExj5nfp0Ibwx)-nfvHCzZK%fz|Gf8ncxxr
zYM~AzJ@h86RWlo-qAi7KuEbL127&2TfR-1(nt7j;y5M_XiWf^E^wI<a|3u{%J35W?
z?;FtOcxC)ID4J%cHYz*CW?A=3g`S#^q~bEzg9N*~_l5zKDTtq|)myQBG8|=~6=}7c
zNRk%FCP^0f^P4VyDO?FGEiGZ{rN2Mq+1-Q=ap0)DkaH-Hg&Y26k5LDe8VHBoffx8r
zd{ryjJLs?fawtHhk8|((s0In$>MHPn5+lN%T;OuWi<Rx*gtico%NrUP?r%)belTfH
zkS`b=WG^W+^tqZkpg0y0eh@!M)VuR0+ZGq=&q-$9L2=f3iWOrL$pCBM>3E3Botc|4
z_SPG{qV}y>B?-C6q{!du9#c4dC8(=dm4H&Sou5}FN7;*i@bz8F4R>y^dgJ5bkeYkI
z2tsUNE8S>Ct$F0>(JBW_^Cq8$e!(jCYg>$D|6nu>vih`kv)u^8NFHE7h26Was+1^5
ze^)O5x#O}JZHn)<rd5C4(&6@>Xwy~XUVjAOU*lGH*l{XzFIQ_nuNzOh`2XAz_RLlz
z5%JApjo1YB8Be9F*Z<KwI5kNw$Bl%~HKdhsTfkKOYO-V3BB<{nJUqvwQ<?9rc5=rm
zJb_1%4FQ1~>3@x%Ud!}H($(HVY*F#0c&!*sBYF)rmRdp`Nhwz#>{;S>7^^)HwgX}u
z=U7za_U<50%Qo6f+;TsO{CcdZU8{Xtaz3Z7`5p2kFR@3g;xll3;|4ef8JdX?sK3Sa
z-O~ORb`2ofIIY%x%3(2l29N10^hfRi-YMN=p?m$Ihz`tTcO>P_2LBn%to^W_)E&A-
zL^6hA<DS|S;5gd%BnotaX8RcGc1}xEXay_M9|{Ks{FMWw97d!GONj~-!>g9+_{Pj^
zHy)-;6P}}ZPHY)jIPjE3m*H`~FXM_)P%}B6PhhDpBki%8uQWB^E5-&UxP-|V^69Y0
zG%#qjd*#fhC9Z{$j9;MH_-MZq*u|G9@%!Ou%xNBF>xBV=0@V$H5&d7U*kF;MG;jI)
zBkmYD7(0PdGlu;NhEk&YS16IyoKfqmng%E3uv0-7P#gS>)%m$BGzls3DF%(_`+MWF
z+P#kY=hh+<Es({<v7&o(jD~i>P~nv9UtyUFh&*}O=A+=3ElLk+xu|~ZzLx;bPSS5q
z<X<6(=w+&GwcIbFb3SdEd32=qmYwC{wJ?1AkeHb9-g6CxcsP;)P%z*S@1IZ{>})~R
zj&$U?yr1H|xAf)yTqumbCFEJ7zF?IM=ZJ)&TMV$%P7MwnsKGS?*Gr9RXoVs8ydjD-
z`}so3@q~tghcJaDJklu5`wJ%b5DPw)nl%G#XT#Fk%ien8HPJ2tQ|=Y%Y#H=OCu6me
zD8R}i1b=&V=M5amxk#}R%xlId1VkH?fYQWJ5I^V*#z~*-s^}e>qkei7D!Miu?3TyQ
zlm-tE3RMwo`4<lkRpm43`xz{>kHE-Z`-sZYRwz2IfG4F3luUgdE6L-cGwCc-%Vxnl
zn3}nw1hZ}<AE{1=cR}H9h0S5q&*gZPk=Nj)iYXqDWM6lVnL^epE4%ram%fd)Y>osj
zh&Bh?w8fLWf?rPuTl)8wJ=1f>0a2M#DjEEg9j;*+|5r9*EQU%VNs{6W(0bqYbwX8a
z>!@qkM*W&?fma}5s46gnN~Ze?74HG!OG$NCM$5&dY+iizO*5WlIdy~MALZP4QD?8c
zPRJ9;zS$MLOCb6WoN>e+(BYhJ58}AuLA6dY0hj*Wwzg(%kTmf1N*17nQX!K3=*_&^
zQ3r`wsV+NfF{yhKqwM&rC#~y#TN|G{+iIlo!@ZX)FAU~^6J9<;6|uhg??YWhlP}b<
z20X`-Y7#})a3t}S;DGe>6>HrlYGhBa>#ZcyeIJF;AE+G?H#*92>MVm#!4R?G9PU0R
zMrH2Kzm8T?G7N0Uqu0>|dKEmLKB=<8a{~^{t~2P%ef~MHUV|q-0D6h<!CzKSY1)x1
zd0Lv0^M3eeV94<z7Im7_iZw9b|0GAYGogPX<2j&+*c6>>$ULtQY;RuNgtU<CWv%9$
zAl%exaVnFfMMIv;3>g(bOVU@F+qNob|E7E8T~y`+xqtGwGis)kgn&?QH0^FUYVs)2
z|G5YxVig{vZ|Bxw`3RmNyxS}iPt|+DFdT^TA_f&fO2qK6&r5u-Dpoe|B3CQ9ByKD@
zbp2|2z1NPT5wFSE`0Y&|I`H|0S0vHKOY{&GFrMRMhWmjXILMrIL4KXIuMeQuB!GTt
zJJOI<LrcQAv1@=&QKdLa@|&KQhGBIzS95l3SEDJXY2G7Upr!flx-0_^aw7V3otU2l
zdB&O+K~RVDUu+^zJp`k*VUB#3?nfOvy)gA-q*`%pYzZsK7i>Fga<Hku<BshYd@Q%l
zg;Mf2d4Chf=m0Z1-X}2~U+zt1+xGmbsyH?pG=pu2UsJa<Q-YIiXxRoDYdMA8y6nrI
zdGT3g=Ypu62AV?i1S;uY)D1ZU6DZy2&-+6D<ez=Id-rQ0YWa%_h5#A=`RmbAQ*vU#
zloMyhp`{(5t72kt9NI|kAL|oy_KVNTPK1ajZh64z*UeQ4FS8|UZ^cSh3~)wqIwhiw
zto3qkAD@4J(Ec7qe27#k|B(3A?y??(&AMibgdd6q{(17#8uaxsQNTOMi<ea9?U&CI
zn`|`SauOkKM(L|zqb7K9<Lh7NQ7xd;stX61bm$2k**{Aq0*bU9=7A%PP0#l**FwyA
z@N_^p<&9^}ThW~4r&h1)+j~^N#Lo|Dg^_|tT8?J<FnAeuuY@IMXCNW7kPnK~h#3_L
zM$c=f1EE3c%OKtF0-Q2?v2?T1bli{DgX~uMa0?h5Myfq&m!UR$l6O{a5DCCMl+_8`
zf3tVBl>TCAAq^j{P}qV~KJLIt_DUg=`o0mR4nM^xvMxOw^{EwYSrI&W;Oi#6ACa8f
zwXSqjw*WsVAT)dR1Vk(MsUQgTLJJWxg)Ql2DAf<27nM)GX=Wd6dFs1T3uMdq3b=>8
zO)PkgFH|-hSny@W)7`=(>S?=h@sZL22YGuTf6GQ?TvN*2A64!er<G!C4w-9liCH5W
zJa)6Ezd+IyF<pBc^Ty*biXjyQ3LY@UXW(Yvd@_!X^FNO5ieJg%FzIn;8~#3LG4E|!
z*8jba>T+&9%wuHWF+SY;SEfjN2-CoTlv_#VB9#Ze8dV$w$I*Sh6((t7Xq0=v2p!wO
zJ8NG_F2d|(Z}?b@1L!HOkV=-W`J6Y-G0%?i52#sWHJX%rN&|LrB%d%jnU*K?_183A
z*jbuSa*)1Z&^rt(XSB%GQ;-k9p>SX!hGbEqL{T!$m{g9)TV!aO+^dUs+`TF34?GUE
z{`lK$Z^F9#oKk|6FhhX=JSa}=Pbjm?k4Ocu!)5WHNB!kw=vf9tg{Ol!KiR+b66LBE
zb><w2^pgN<C?KT^#pF(y?XQo6CaG2m7!n(uJ98Kg57ga#2=PR|MDcB9=bH4@_a9Ml
zd`(ddCgO)tSFK|rxS^Q}NNy&6Ku9gRKK}HSm`0uoBGaPjJ{jQfHe_<V0|Wk~NaRY8
zP}I3Vt5pAK4B2pB{?KpC!mwBbX;aiyaq@0N(}aJ2ASM>(hQzacnRLHdY}i;Za$FUY
zAkb3azVE<c?e%p-^TPVoci6($lCdUT4uRfcZ4ZnG3>goO0_!)a!L_MVAysSzxC#;^
zOl$ipC}PoCLrm^sVGYkT9l`uW++4s&arYT4({Bkkkr*3)8Blp0y9{}SB6~eLF_;I1
zpv^O+j=B6vs|X?}k{s%>5=kgO&riMVD({mcvr9`uALIlfn1zv>BA2S9{Cv_Tct0XO
z)sjhbBw`_3&U`}wPCuf|pOAZi*d}FjRLK$kl&Is=P%#qNP8;Gv)@1{=P>BqneVq&j
zZbd`3Bn&(19~=xj__DC_d+{<O0Mt>cxV3+Upy`!Xyup}3qzTNkyG3%H^s*NBF+`cb
zkEj<GkZk>jW|k01YPa48OnCaEsuPyp6zgdc$c_mX*9+?ikMnT?9e)qBQ2tI%a7nn|
zR3I=5hbN7l5xmPOnjW6E{$BfYi;E+VAY4OH1F?ihe>VAVU+}=EqilU*wl0=8ukNbx
z+ZrS{oAPUD(F7-xnuGiAGF0BlOPe~4dEIPZw?%iKI@AItl?_J*@yEafpUbuxC3e(H
zFgVExY}Q8zE%cGqVvd$KFI`=uCS2xI^(W)`e;f|Gc}MwK4Pmrxf9GwwWO}uceAjEo
zCoLd7sQi}4%5X5$fTZXh;N!8sumk;0u-wBH*pfxswWfS6bH9lc=aU@AL^I#!A!26Z
zU&;CYhu`6EuFhmFEAE{V-JjKswRlQ_Mw9Q15}(j-+Gu*(d9pA>h(4{H%DMKeIig7f
zx@+o`H9pQiH|W@oukm+2D!MifAzTjfaStJ4Mf@3dwCa%bZZ?7RKwIX%ZNnEVs)r`B
zIvV1Ju}D}dLQl)~p=^7HysCX*Xk|0KzX4@?<&r$MV`uWtF!A^d@^`hiBOc|N`?eZ?
zZ5`8Ks<xwk&xQB>?R$!96j8{uANRq30sw&HzyG2DNKFMrQ3&x=Rdh#tIWwg<J7auL
zcjLszJN5Vr^PWCBZ>o(dum?y~1E75MV)(M77uCO6u?m(yIf3XR0isT1{^~OS`$r5+
zpEJ%%pJM;GFO_fkkF9zaZwinoK`dl6+gxb3kLYU|zcUghmneTe%166iA#!XLvE~|3
zBG$?J5`Rc7WD}N*v#dRmzkAdz+>A$0h7UlFKO9`#8w#NR6#>-I0ctPPP#=(vtG!MJ
zugxr`M($q?vVsr#gh&3xSIsA$M*V<P87iNkBi|q9t6o|8R?n4F-LO`=GX+m+B_W?u
zpkYFAh!D}@=3U#P#_!`66q|6{HQI-_&7GRVuIVQ<Uzg{#REG}WUxb8+yLpxc{*Lw4
zH_L*9soUo79aHd&^!-mp$cIq;pYi**L4=wme@FD{`0M>Yq|mbempcD{Lr~yg`Zt0?
zqqel|ngm+sqvo}W8pCe#3TFOEuC;bSa1%AyJwQ+(l?_v}43(p_@qd35U=W>{hLKk>
z8BS`v=J&bl@Cq6U4xj)dWSfEWkz(0OxPl(upE5{G4pzVIV{?0FRiyeXODA6>`+8dE
zN@>l~;l}FL*wK=pCcmlMp81~Eo{@!Q2v<<!n{d&x`iY&U<@qA9ErSfaMYSXV%vhc%
z$>8d7OaevhPN!np!^0tQ_2)OX>BqdtJd)7)h(t!k(>kyc_~eAbbQjK(y|KJ;zqs6U
zZ+&HN_1BsBHNFYkAqLb^h+dIBA;d39ek#Njo@@~1zNi9dprmjWP^l}7W>Qo2VcF>$
zw_4)LP3h6#Lv)a%>dmAXD6BcoEkzR>4}7vq={TDgd=nTZM)rNajvQzhoX3kaY>yZ2
z&R*#@zw5(Q-s4~qBrvO{##-pBRxT0J9rH$HnDAB%LCBbo#8Js{t~r$!c{2h)O67=f
zSu-*e-A_H->OPl>s)6t2E8D>&l=d*SfK-ay8;ND2OF9cV{pyAeCI<n7FW3!I#zzEI
z*7_V40M^`Fl5ll2UF9ULO+%lsL6=0?3p(bkmvLetFXNBGGu<Fq(fsrfUG3fv@hvA1
zuD~ol{lWV3_T<)vx0>~okC#)d{qHRkI^3cbWK40wr&uq~P6TwtI{nL6n|l;;$ii>?
z>@u(!w~IK|+3Ywl61CGcn3|#+^W4dCLWM`V(V%uKTk#q4QTxUk-4LbTG^J1i_J&lj
z<(~pE(rgJ-bZKa$5<3r{{Vz{<so!6>FxdnVTG5RlT8w-z%c*kol8^m6L7F1Kb+vNX
z$jG<Eg)g6{T!%#kjpqGg&zU8$vBKs2n20U0VyyUT$rqp4d{WHJQjE<ootvDq*C!al
zBo~U+=TmbO9arAZuN{cPO=2kOSlff1Rq$J~4!2+s>ZBlwbwOxh<S_45Gl@G7*^tUF
zcG3(a+xHHs-J0Q2&=h;xirsL+9g<nwlw8>GvcAZ>UEW>5AkKZuR=drN%;G=?Re|G7
z!Q?s&%*s7u`eQuyaC%vjB9Av|nhp9UGUzu_t3%Rf<Zk?875sGYK72lLi)MT@awIy4
zEi1)?RY#e$=`+5lQ_ytlKXRN2&UyJX`obP0KXNDDu^*l(m^)Ld`jyGLQOa}Fa^TH<
zoK&xI8M<tDu$tQvS0wpH_>ZssCP!L10jLrF%J2P2?6>G2SWsfH(<%*6y>c_xs1X~h
zxGvE@e&{43-w~36!E-K;cYZ;|md7U~#0xXo!YsQ=RnW~o3txQ0k4kH^a^do69#VZH
z(Rf)uhjtq!btaQzLb4{^DhTd!33h7J?$;KsznO3Ji^?qSHgw*gGqcCweqp8gsm;H<
z#9v!{?X2iEr9J(pl@e!diGSh7YeSV_V~Kx5@%T)S_W1gt+mPNMTDvnfhpTX;r}HxO
zApFI%-Gfk(EDERaVrPA5E;xU?en$B4M}VjG7v<xhz7McMgYph2lFS+2fco|KLIDJ+
zHtm}yxYN4ARBC+<ul;?GCq6AyI%|#sjO3*qB3uSZ9=0|1`~`PrN7n^yVL(QQROGvf
zcI|_8w74aBL?hMKP$^eN>1_eqG=BN69o}5w!dtI(PPC)V&l?Qt?9J-CF9Sieco8e4
z%Zl5xUq0{qgK2A@zFpS5yEISC(M>FtUzpb7>zM@%RR2L;k^gdT*WixQCM_a7m$ZR!
zw(!D-;u`9hhHu3&LVox=X+<}BItIU)FbPC92y30x)%a2C*(O$;%gRA~YYzXr!QzGA
zbE`p>hPs%ic0v-P<mJlOz<?#jzqeDVQ+&ks<ayuii)CcJ>UPK2(?3smi;Pcv3fzdp
zzeLJf7OchZox|TC=G*Y;Z*X-Kn+@$OKe*q=3FkB*fhg_p&nzEJto>Zj<gn$b`6kX@
zWa%s&!&{^v=Bq-FoFUOa<E}D%8x-PjaygM~qqC1EnezPU^|7seh(5eiEZPI(Z7g`b
z;dsm|xKgW@HMMd}n0dDwEt`gCmIpb;BTdEf$<t>1@v_Ua8*->L2<yAGG-w~l|Dx?J
zpz7+mbzvNeTXA=HcX!v~#T|+}6t|5#6e#XRH!j89-Ai$Ix4Zj(a?btFchC9m9ph(Y
ztdX_JO6E$k@+33!nNf^_3@|+Tj6E1DQ!7?K&OLmsji2<=AaA%`a25aMv6$jo&H7|T
zk@IHSg2wB0{+%3Ksq|k!(Vt_39ZhWD1q7J?8z|yt<@(R;s}5ZQ$5k#&zs+hE7R;pZ
zbTnpJRl1t+kG9cBOAAPc>IR5WRXja*!znvdht<Tia#uy$e0lxI$0kSH1liBDYBM{t
zAxdJm#Mv?`?>)mN>|So1ExwQ#a<XJl3O^j?E^(3Owb)v8{o!b~)PaOy-4%69^Rsc<
zU9@1?2kJc`?qJ`czBYZ$En$~6uTi`n0gT|k;=_8?jx}aHsdHe@>Wq1+a9h_ZRFv^q
zmrX_n<yUeYu6#2p3*H_0;;U34&|zo^<ZX~asDI~U=hxCn(6mlsY+FaM@W^(ss4}s~
zAP>UNYCLZLq-d!MA!_6AF6m#uWY3lAwXX4a`(t=Ch-RCdDp)vLf!Iv6b-giY)YYlt
ze(F0dQf14ytxN3Vybl|@r{>0_|7O*qj=7Rmvi<E_(DnC;I&wb8EDOD^_VzJ&=d|}M
z$9nUe#JY}`BenAujXq|>-e?}*1QwUT0GPAK<c4q6)e7-d>8he6$w@k6kpM{_An(@)
zJ}?UKVTPZp6Nj_77ZN_`5;&Fer@DgW^~!CPpUf=eEd$>e-LsA>%f|=m<j&#Q+gT0Q
zaq8}`LF}jN!&w*#R$~A^&R4MWV<jJankoobImZGKU{=wiL300orA=#glTP;X;4{a<
zwouHiXYPRVilnCGKy9=!&ZUC^7i_VyVmVWzJu5KbkZcON#;{OvcD0LBz=q?PVW|9N
z&zPkhKLhXVo6b$Vq)x``aU)wU7cSHmpd}rEDy^Uey_{S@w?2oRJz=tp+p<b0&b8_s
zvBt{X@w}^8E!s35A2Gb?7;X$?8(9)%E)=t8TaJ3teAFuKyz8#(7Xxd!b?CNwxV^?D
zXJvVQiDSO!1O7}whs)PojviR;aE|r*m~Gax_qtK4t(gbQb|5bv$F1X{$AnV-{OSBB
zzEjV3>F)Y8xb(wC0KH3>*IZWu1zH-{s<Lu)Jge;2l^PSD9}yH39FcZhR}B#o@#(Di
z3vS`;p(-*m6l*Ce#d1traYbXn^+gg2tjhrJl?y~xCkZhbJFCSfOP`{nMk@z?ew=Ip
zIzq9%909mJQZLjj`74t2>K!NbI+Wo+OqAhZ9D^iS(Ksq)?vhqvey^_~d>_!|B)Gx7
zmCjfoSeuOZ_hU1CKcKDJO%7)Z?V?t`X>gK5NX;|Q!DKn#Jy7jrCg?x%)an=u*yip>
z+)Aej+@tf4kdu+!|563F@usj_Yt<j0H*ghNS5C;jrAVxa=WT-)BYA6wNXGcyvrI37
zfgT+UrJsEJNQ*=OCtP_kJq)YgRJ61{8k8Ud@93b_w6KH7EYE2GD2iF#v#f}i2ko+Y
zSoS4e?0jcF`R;iAlZ=eKZCn^&cU}G)?ld(L*`BC(pvh}&Z)%TqOg(Eb`E5nO;&yS5
zwL(7Y9-}R9oFB%~>KbRiew@Fx$no00ovp!3x*E}bW8|>?Vrs90YIADOpkuAcOWEOU
zrM)8UnJdnT2ozy|0!5@(1Z2>uoP2IZm$)W1=2^cuT*K|<jgxbwam9T;ZZB;*B*ULR
zYVkjD`IUHC<$n+sk<#WW9@56M%0((W)@>l!#BfVaCI?qEV}YS$s}JyP7_hI%59pc(
z_(?#Jkt!7Rvb#9bAVRebYq!an@2GUW1}nXgS+uKspoq<OJ5yc1KJ)q#V4UMF<ZIeZ
zmq~zb*$?O5rBq$d`NM5^j(FwOdj6A-w<spafD3gvbo{k<J28OjQQIOm0$=dXjZ#=e
zXzW&rR+w{9yjf_?p8t;cjDj@MRaphf=Yr6o?^JLt!UuMv*H7-T><jstar5h)V)0?2
zD-w6utFT{E*|Q&1LcI#qZ1v(wBd@z#q-?*7Lxi8u=g*5Bx6f^_qf(Z#i5RrVC%zo4
z9Ov&nXXc;z1tze<L(1~{-8E0^_L;k4KP|ZjPmku#!sKX|^wPm4`5dwI%6)Lb?sTAe
z?Kv#Gz(Ak|;2*hrN*p$Qfu`1g-|vxRQEx^eg2zs1{m2C2&7|LVlJ^BI%XCYqaY}tF
zhvSRg7Mb&TXmm5eg+X&kNG_dz$n!#I1q^z#%NAMs8-XQ}@8MOM!%Q*J>$e%zg&MX&
z9)q@R`bE_tm4YSFQT*N9$2s|aq{wKdUsb<v#B>OOLw=h2Mi>!KAi6Wv>Br;xVpT1c
z?;+p<#{CJ7=_EG7EWj_~kHe*}>6m_3TLn)haw4tT#6eZ{n#EntJm1m__7jLgu2k0b
zDVyQ@Yn~e0@y&?dzapCy8SKhXP)4><le$`s1o+((7x63{{8!}TX8jN3`=9+7@$fLq
zm|I&~x&Hs|#|TuCinF<ywW+J4GYOlJ(Ek~h;aNGj|94XKdVtKF2f^TFTy}nn93%>+
z-IFwWBe@g?d4#hJf5NJk`1{Z}P%=VFmqRDrlsdL|O$^;WKKVPKzX!3Yf-xpxkC-dq
zR}JxJe7$&l8w6^urDPYnxfi9!4Oov&pUs2Er|q+ctG&^6!3W<L-`L6h0<{oSF*wpc
z7adWHM|g;gaCgMQ+1xO27^vX)I8hC4GtX#zNZH^TpgP)mxDQMGY4n0C_IjVl%+H0c
zt()3MY-s2s%1-V)_nI@cOU9MJi{_!%2MU-Vn7hNs`O|qrL?|RK?qw~5QZu^|0whR8
zmY-n%-UUuF_;&oVGfWqe0tp=*#^lLkvs<K@*{0(Br^o_H%P4DPV5z8UNr~HAh9HRv
z4BAEoCm>n^sV9?&(AbX7w&rypUYW2f2;7C^-!JozY^m}Ewqt>TmEEyr7Knw<`DV;8
zIG->2z6z2wfCNY7;K@bfJ`xK5f5=y>>h$CT=xywJ+lX$?cHh6s;MrAtDQD9R6=gv*
z8Ab1U(=Ss-!DHSxBUE|RLor504356{zEj?R<*n)W<Xz;oj5n-Yf<%W3?#51`oAj?T
zMes{V?_S6Dl{@tj<{S3p_2qFX<UZ$u>1KIoeqDKyX<*tlTC7)I#uEf626Heyec7r_
z<jwrv?IS1Ia&|T-;^~k2ScCy-9K7Et-z_rr*-C5qB;^(d4sR?*&Qi(}6Q;*6!Ry~>
zF|l5fCNj<r&>A{eV>3hTPqiI`D(wVMKRP#n!SQ+UAGOE6q;8pu<7)Er|Eeqe1Pk81
z8m<U<eK>=HNrb8P<yWtYH6~fZJ{|V@BYkz}gWXofD{MEHXDe)>z-f%mN}v@#$hXBK
z>^7NdaAjupHbBuF$v@&n@fP#{vTRT{j5EBC>9|G!m0X)tZiaDu0(!l={b&|c&WrZf
z=h1DP2prI|;`>Pc8iMu`B-tW1rGfhtG6j;_S=sMwUK|-b{k4NOBc6ZN6;mx<I+Zln
z#Mhq%w^L-@M?IfsN)gQ{VQ^Gh>M%J}aEIi?hTu*0lg+TUFUu^3t7H_<uZ>bQZTWq~
zK)&Kz2MD~0E&W{DtMjx<*vjeI+WV{A)b%zaM&%&4^C{|a;*>e%%g+_c0w7l}e!zD4
zoFA-x8PupR78UCeQFwRA&I+r_jye!pD{z@Du&!l_&g#jRl=ZJWc+j}kUu0R*+R`vD
zmpk?!HVl8;x2`y!qSv1_a8)m0s2z5h(}nEKrx5mX_O%f~bgn}#eW?nEaEM>qO`kh5
ze&YVNV6(ue$y&64!(foS)8u@4S+<wL2h%da8+`Ly1Yg3#TbEOQ(SWXVw`(NDkELMl
zsMBgXpJEm#RvBr&lcvW9DD|AwMx1+Nq>&<w@p1JjH56<>Qnt7kr}wh#eVycQJ>C8D
zU*G$5MT4?(r9<_d&Ajd!G9Wr}%tSzF{zlm&+s|a`u!-S0xoW!LmTJ<nFQ#?!P{W~%
zbw_=$+DgO0p{}Y`rA(~*>tBOlhzv)786}p~xwMvv;vRKZ(ey{%B%2^`n7(p~Mn^T`
z5L1$j2qSJKH~-*3D>*rTyvIkM4q}}Te+{3cA@N0z_$LnRO#cF@*!}~l{u^f=e*xOR
z<ILlqSO(9=#l<XU?dqap?kw(T|HaY4+`*NEhgrhh)X~gb#n{>21+*;u7YY87!i9vL
z85D7|cL2ryGit%Jg7lXEXV@ZPW#i-q0o#B0hCMYfh$EEFI1_T2@U%emv=h<A@%kN)
z5YK=i4pTGqv1Q0GU4m2xhx7rBOO>Km$`(H~hOz<w>^>E4nRJ<^o8F#jNb_e6*BH+g
zoewxvc&&ESmy>>W)5#3pj&t7Kvbi}uO@r6<l4mb79m}Vi!^!fJjdN~5J`ETcSScQ%
z00bDAh!iMJLjK=I2Ef-xs()eRKJ*1b!d_5;eZOpWJxnCuE%0U|qiXfMB-_9M1G$O7
z5mST&`+f$73l0Xx2FiL#Dv*m@QCOl19Sp2I89)KsVy_J<hmG!A7^uv03UWofnPypk
zPGRBAM9bHsb8Y{Z+;^ALy{8h(lHAAVNukS7UVtzdO(Xyc{KH1xyQWZLE;bgHZjZ!f
z!0zeo8<wA8ON&zOA>;!b7{$m%v8WMwyaGkK%vYgVECH<F2jP=1S8o{mhXGFjScu_L
z5yF^sxMEufYs*?ayaIgh9LyOqm7Gj0n#R1;QE3iFZTC79d+S1n((Idu<swtdv^|AN
zvt%q$DH3uk%l@F^{BQXohZ|7#WfI&B-i@}@AA^6&glfu0i^x-wi-;)n=yOm7j8`Ns
zJMcr7^vD%Tf|)@fKGL*lB+6Wun>Ai1t$3EBTcedS#%FO^$n2yPv?#Lczv)caEh?2?
zCLakEFir}4Svk1#AwoRm3iW=58ivEcr<iGXo<mZnDWo6{UF9Md4K;dRqvouQg*&Li
zi2DVEASt*MR~x6tUS|P{m1i{l5`q+w8W&wZVQ{c*l?ol4WO|Ht0~>-X<)oW#Cs0VH
ziT!ZK4rZny=nM$?A#u${R<;6tj|;|Do)d`|fB*B5!<s<7s*exMY)*>f=NH?ZSxg&k
zO%uYSra9sWb~;F;As?12Yf2}DvW#R;_7w{?4LC4dAf~<{4u5Y|g0gHUPIRf{oMT`n
z%$hhPViQ+PrO?8SFQs~^8%cqM2?Ju&+2Q`)Y^L&hVIw1^E(9*;x40vLOw*FwVC7?B
zw<NjZ2uxU9@RV-LC)~&6^n*i}Q9ZfSay+H=!bDnl#z4SVr@C^P46A6(Y4rd-3?!xm
zvnUy-cr+E<8AV~moEd+44%eq!dg994{sC6DP7*Z&Gbl4j8&-<-8+JM6-%aJDWfOS%
zOg4m|-iauu&L{5NdN{aS7RiD+ErpW%SfpxIo^sjWy>5zD2TEacG^}(AsphyJ+TW|Q
zLyLQFMV>7OU>>;FEX6=ImLn->MIZ@9-er(FSlgvFrL<E%W!}uNYwP~~au25tfje&i
zM(a(pZxW*9&CeC^zA`|y=~a%M>wj}n;n?N5RbJ4oYIt73^DfwvY$GkDOTIkNpi0jN
zM|rC<F$$M`=?*Bi4MbUZO4PS2PckF(dRETW6Y_Vb1t#}7hVz_(x;4LXDKzCykr<f*
zEg`&eatgs*I@O}2Wk)g5$l;9EWB)9I&x69##64F)*GN?(hxbzyvyNT?YoYmSg@M#s
znvF02^g+#0>V`ym+IuIs*R*01fC6bce1Jt&#TQjBHKGkuh~(5ABHDk)bP(;ivQL$o
zu+PZtw**xoU4Y_(XK-Lb?toc{2-}FF5xxhe0e8U;ndp@}fZb@meeL$^j*yU@ZPJ^G
zwf0GBDMiFnC&2+qe)~}rK;AqBOI8XG!<vh{#n0d0pWv~yc}vs3FT0sg&5JDB`@nRd
z&f2m=2`5A}fL^U71wkb!%%ljXRxirANi1h(<{+y6ghZjt-OS9C%x$!%2f|Q1R9xrC
z2s!x2<PP_QSt3H`U5oOY$>Y=bt+CqBM#jeQAaax*I~^Mx+Ihy=>*?9v0*RWJ;z_Dl
z60m5xyKWYqbG%zZMDhEP(TarTyf|?*!zLRy`mteW16g{+mlEL!O|yeuJ!+!I*^GiV
z4>wIJGGDWDdUyG}VdriTFF$XND%=(o)zL(TLq`#k-V2^8CFJOemJcl~m}Z7)V34fh
zbT$i4``m$Obs4A%6dBy$#0EMz?5`D1nLSQhu`v0YcL6BAzejRopRK~)mIs<a9bIB7
zI3vz0%Gvuyp<^UyB=!J`&e-{9+khKs5DD<D<B-%7;j0t$g9adnY;$Y&0xMbP?Zg?)
z=;ZaaiD<uQ)6Kpi(w2^IKKwQvNPPC)3U@^OMxUVki5z=SCPr?2U22O-vdx|%I}ngr
zMzcKj^N@)a8u$?tjocQ;ODw%2!YIef`tjyiTZRyW`|Twud~QEJv@ND5d%R3LlLFx@
z;E1i=E;HhtvaA7^DB#K;dRe4$c7{SGYW8~|60pH{8S%muO0tc0rh<=eHup)AmKKoI
z{hr9N0<VL4d7{AFkLkt|ORYMgbk7ltX20A~(Uus({}8iph=c{K6_w%?1NM&c;Jz#d
z#56Xi?A>K6=EoJT9ew_$UTttmI-(v&Oc7eqWzT)&Cboip0$`N8bdOS@I|dczUfLL+
z@T{n9=$)+$^DZB?tC;XR-u26>1iN?Qny5uvSe$}eXb<Q5vBE7?h~HCL_}+w3=MUXv
zZw%4|t4))$1&8)4koSZd(i0VX(t;&!12HAj8Mn#zM1VV4Ku?`Px}q*ev4d0ooLofZ
zJ?wG8C+XI?H0COS_Z`w0m7QGmj)^CC4YTiaGYd?0FH5ecb2>}$#Iw@$j*$m*U)ZAj
zW+j;$X%__F<$pH-D^{mNunRPH&sMv69VT7Rx7fQl(21$#q(7itzHIVc!krIM5hbso
z!+=MsmRo4o&3*QDTF!_dW?xX5BAPDSGTSjlr*o}Uv?RH2r%=)hq}(4RZMi_0H7(=c
zIvOZu4RShHh%+kD^D1<VITmON=y;rXzNx0WhX`;gF>G`8aETAa1O$_6(L2T=RBxGS
zw8+ww1qzgjI21D8quC`!x3k1-FK}_iIEnYUQWNtJF)vjKOibJWj@$Qx*&N17`-j*R
zFbXt`6Vxnaq?Tm|X=S=S<CqzU+^dOCowhi?0qB@rQX-IC(1s`lBY6kNv)29iJxk)B
zmA~!tPqK<C7}gXZ%J7W9vH!Skg#Gw@!zRRWyU%u)*obO95avKd3B4^iV6%H_?Z~fW
zmTUq}!2B*B3(;a4#rNcn6VQK8O@&??q3gn7jgEfbBc4k}Wi&)<@1x>dz=a6Pso<N8
zp1T{rE8p#$dPl63vaD>kdz?d6yPz9+n`xiKA=XcuQGH62UII71LCoF6(G%D6xoudl
zeaTCD(^#BSFMAEoKF{t<R|~|Ux#EUx(Pe;%3C<TfS&Et^_fFsjbvH|y;E#x+TESEM
z!7#wUB4B?qjQ7YD+0X^iVY|BFH8K)3dYD|NT@I0SJ8{t`mU4(?es4zSd<-^Xd{ySR
z?fp!qmxPiPDo>*#d+L-EENU|qbbRe>^%Wo|hCk+s>&C}<zdml;2s&x-35vX=lMC0*
zuYwTU&R2bFDBbUQLQptcab=z-jW|5Lbn(Ny)wu*j^mLDTTY|m}xN5AavXni`w)xzl
z#`Cc{%6QooLau?YT)XRK`G7>o+k%Ld*_F2z!x)*z#bxz}irKAM$I{xxNIBaIzP<7l
z7B+>KG3K|&u$4;15FT?!(=4%I7+g*j8ys69xvJT+?dNfijm0C!W@=tP|6>&HhdeFr
zMlJ3IXyXJ#H^A;Vbd+G0%eRLQm(XRVx{CgjJLpX>^5O6)Ht*zj3g5sBQa)jjD)kQw
z4K)(_u-U}nGDrL8EqfCPy<uYfv<gJ_fHB5*@rZPl@mb+_PQ~zo)A_mdu6q-wVZ^kr
z&o?n<rahNLp%c&lYhF)Ydy|Wtzu7$ZuK5UGUJ87rYiW6TX(`yJZccNBx_Ue*Z!XOw
zYoIW88d}b>WqVF72R%<gpA73&l0dP+d<!!xB%c)%g?3~J*}+{rdXOr9y7~J-gOtCo
zy@kz3-5B#6zo*JOK5%w2Tm`P2kpkAdKGLA!CmSihS!4Oc2D{_iQnqB>BMCCotoTpo
z;AXz@s<H~nZPgOy#Pjt^gVY~>&)I4#3HnbN<fT;4^n7`qMbIYPr=&W93e1Yt;&x9~
zxH_dgh2@JyO*r_m>AUZ@wT{AYNRHmrs542EXQe#wse__moJQ*|5P-r)2ifv0$KkI2
zDx~)NqHJnJ`_PENC{FPCr$(a_tg$^lU|tT)7{{v0*h*|;nWI5v?Kr_r)Pqr0oz6+1
zJ!110k^62#{~%G{y)4D4*c~|TJcOj4IC(S_n%*Db-n^4Caf~UVA^EQtIZ{z4oC`3e
z6y`nzizXIpnHb??lyNU@&J^*P$P$}oSg^}!!@@v8v6bU2`%$itoeZ0w!B;ucB@U4l
z5C~Qe=GGJ3h(=9*3x$TsSUo-^Y?+tgSMNvl22|kl1#ajc=lY)fVr^T(0R;5`!-I#u
zQ^l(y_zYXzq+Cd*KYIhb48L7TZO!Rr{;X>>a8<1`!}7V%hZ?}fLgD}Poqgs+?VE0l
z^9)X3vj%EpY3MG6Vl<juJ5{@zmu%bHQ+92lYy}NUqAKzf%s2iOma^f<lDKQR8N6NI
zwT+{M7IVw{oj&ePo|4QW!19p<0dW@I9pH99RtYCjV(&9wV%&zH%UnYLo(j6)?e1rO
zil_rAJ(hzMUPE;o>qVokdQG+uFYUzRrSf_VxCtT@p#^-i(y4L-nLg30iR_u*JfC<B
zPF<7<W*kUyLJ>xst%Tkf@UAhkzsRd5adE*S+@BT%#M<3{ksts~Ba{XGFF+kcAf=g6
zc$z1hePRhblzr&rrlU1k7HO=k$ROZ#LTk8Bnz)i;xeD&dw!kdel$YsjW5i0OW`2|H
zt}zWi*YDQe3->J%T5&>;zf8gvV~evKQY%+Rph@WmLQF(Qs<Zjv8S|{jw^GBJT89s;
z5msq^xrPiBULOeM2I%{Vx{bQzTRU`<v_x;TadBlMt^NHwchvXwV(H~Lzp63!3`9tu
zoyUci+7aqB5GmeZunJ{z3s!$7yPcbCZ+O{u6r$OYBQ{qTNtrm%W{YK2JCTc>U$soY
z#!s3M0ZFeqOcBaq;995mx9pSddV0i$OC}z!eo(q1QU~b;v;2^0a8aYmtk-U&8kn9S
zGq}?8@*(wv!bpIN<h}ZEl5yq~oq{jyxm$-dC}ZhlHU1Xu8-;sNy-8VahAwYL0df<g
zDj6XIJ#B<P-vHR~cym!C97?y@nIZvPlOFP*mQ^*D%9wmxDknV3D?TBW*%FM~_<6^8
zsJPZ8bwj~DW9glPB3v{1NmT@zwtQ01DdNq@w4@+-C<C{qH32l=Ug#6V?=MUbd>f!u
z#8B8#+?Gh~fim1fEDr+|GEoW5^>yb?KTia_&565+B@EYL6-_WJXI|mOz;smodeKiT
zn9j=yxQd?!boe;`mfqyST+-8>b(GDG5`=!e>me47SdZyz67C3i*2bEMYIGAJqcg54
z5$^JEu(s;5G@Y5Q5owB;yb&-ewX%=*M!PC$M^-P2t|9)Ie5}{ZT@{w7I$-7Zo=Y9Y
zs+t0w;j}kP)3X%_niDNs=+;l%C=6J#mU9?)gX{Lj#Sq*PRN&tqSt0~CB>Z~vzl_bn
z#-JCpE-C1O(M#w`6=Wd2xHbp6&z$mSNl6>VclTvY$WulFnw)8S{54cM<>j{lY*NRz
z!raK;C3+KVrO37!F02N|Z8dC7@K82zu_#O>NazV#A(MF(mrBKuT))dEZntu5om$Dm
z50WV>OC^DaUbEt~T=%)-vS&>6Wv$C=>ed#LT^XR>q<?G#PqyO0OJjbwSc~*thhp$h
zIASByTdyjcZhG_g(8*{^iV<q4nOr0ELI+{FxYE{2sa8lxh)QBkPy^M*iD7vyQz{hO
z2>dFT90!5DJFzLdm0?}Z>}-8?=WG3DyYzK#NQ7g6dDmRZ+A>jHXoezBegP#=?E~^i
zkA%yrr0N_5ZKK+n4efEYP|dlAwj-qplL<!aeHxSD>t4^xy-^E8)wxN=7VEM8Gln_h
zj4aJ^hlZ&E3z%8*-$Y+I5%MYaacEhmNh=GDihw@2rcZklyQb4>i_B)3QB2Z?&q}RR
zHW|Lz2}%9s?P4E%L&Au%Ad@!T2*Yi8ge(Govb@<@a)+k#a^;T>!&}{P8;-vQv8!w&
zq*xKHUN_M68ElA#-VTx`;U|OCr10DG>WaXLBN9jL@lt7K+$z^)SJRo*AU3iiH^wkM
zM{)X<buI$O=zNXu%-Bv`=cUd(AG<fmBhG?iS~;y^RtDQ_=%<{%Z`jQG<5p^{S8d9(
zM|-q7B;;wdB?QLv)Ac;lUogkWbe&`_$Z&)jBVAk0b!=|;BT`i9!M!mDD!IKU1}7Qa
zvOmNjt4G`6ltd+!6$zlt=gH{>mO;qMCo{w+q(sWe6S^Xj1O)07t3T#08fTFV(<H=%
zSrp(E*hL)>79UzN+wDYJ8u_hW(i}`YIeF~wspopTz1=9zVBHtTSaZ9_tG?0jx=jgw
zH8JPVE{HzdSRW58$OKMvq3Nxy*wg)nVbFcxq{_!QJ#vT7FOk4@Owi#&@qHIAe0^)7
zMt)RJp~jGXYIN&%2mG|qcmc!p$`kUt^3{^jdkU3Fhij~d0#KMFkM>DzDPFh8RN;m_
zw2<GZR=x)cwzm3~Dw4^@Cdrca_$w1@2xZ>Vv{waLd9Xx)KKU#aKDd5Xi0O@112c_W
zAul1e%orMTc{b8JTSYgkC$vZcq1P}C)HzyFm$)~t1-hhhmbnjmBy}0XKLpUKGe3R6
z>S{0w^EPZN+GM+|qabe`T=n2eIx@-Bj`exv6mtTIVAk4W<9<9!k)bes9<pkz$>v?K
zlLsmiyuOrZ%$9LjS1gLp4oL5!r4@dZbWV4h*CR2>SI5?<Tp(sP(oLAhL#auIOZjxT
z?p2&uTFe0yWOf7&d<YfxqV>=303u~eZG1#Rz{}*w=$&q-WPN^ErbNfEqS(!MuL1~Y
znaH{lC&`=nBCz63Q`URqWZEYzP&q`={CWX-!TG{}s}@<+unb&GUX@LgP`H`RmQ2WR
z&dSf7Qvb-I>jj_B;7K*dfrJrtKOV4$Uu2D+W*xqpy|{ywOA0<*YVDicUzQ+wTKBOC
zIBaF}2*>Lsk(u6_rOYesCU;?t5%UQkL1#ZLba8oz%?Oc<>eVo0!PlmNa%@3U7(UtZ
zd&u%`U?lHlpopQMQ)Sf=i0B{w5PY|Anh021OYrOCZ?wwCU`3mbLbW^y(A)BucGMk_
zF4USr(aadF|5l)HY8|lMkQYOtW{n^4I*8v$E?qF{Z+uxZg-c049}^j*+Ti^;a>@6;
z`nbHcP)?~sNoFSBlOF^Q%o+~*?GUG-D-ho#V7lTYDx*$g|9YPx1K~K%7?&sV$F&ln
zF1@`g+I^bd)4rG5(}B}*2aFM*Dkj;hJT=m;trGlcCyQFY9cHC>U4)4St>fh=G2feP
z!s19quJ`H4rn`F&a6NkHR}=6t>;ZC^Cg4@S{`^8THS=vv_=b1rciNqN46}4xn~I1P
zUdd-N-N!B$Goit{SJ#H5IKmw&F3~zJcq}pX&+r}^G5jy2vy9K-apl6={khAkm-^N~
z1snGevpLkAPLY0?lag4vFYi->Go<^Pog28Uxay@%WGlZwdbSoBDUKp@Ag`%L$-%1e
z%jwZT2c6XmZaEShx&ig1+^k{4xJ^sND(wVNcws9UiMoI+(GUG2%2kQ*J&GS*lq|BG
zZo~r8^aKrbcS7k^N4<RTw}$#^;Ro77eQHhl)oz0wnFm}#XkK**$0)$s681RI(Vxs)
zCtp#0pquw>D|vi!g_(Dmn)9_Kpw!RD^p=U)m05ewI8Xx%f&5gi@Y_w&O;avjkyc@n
z0}QX=yj3!}^V`*JmaU&@HkWGHnrJsN8>bC07KY9lwrV8Na@m<&&gC`;uoB9UGH*X`
zH*Zqc+bZyWn=`?djgr(KbGozp^5Nu>k|294ooH^SJBptT&&mHT>%Q|f<<$l=G@&f!
zqwqrk>Q*drq^uTgq~El@9Odb)Qb2rbKxN5vIb0~A*&NLS#rQ}Ne+7OP*`}Pm;egK)
zJcGYimv=)JAhN;p_s;?iFvWG6h#)IOqRJuBtTQ*O^4X5bi5rK_<+-}y14y(~WtrB`
zKbC&~K5TGISg`cHl5F!9mHJvOSAF*DivjNN)`{b$UbADaK;~p~GM1KJ!t$^~^G0=8
z6p{bu&FZ%=<cBS?5vyxa5=JC3_rVGKId9(@o1C{7avAtKTYtot%VU<MFz#mrFB^22
z5-NkS(J5a<Y97`%gZ6M*rj%nrWK0H^wsPvn1!9LAKGCS_=$wWiiMd3=E9+)Qx#pda
zW0vYVmi&*Dj@eqB71(A_-GlN)P0%NgrskI4<bSQNRrPRaSHavV>B@R3WWHp&j3n(A
zc>r=K&~EyLH>co9y*dwmPNwR2Ry56<VbXnwy0tq<CpQ_)6?bzURPYf~gmq(HKWeJ*
z;HKwLIX`mef>jNm->=Q%?73;^x4MjHHf%1p={&=(nvN=AY#DbWl&38CVwq?1RG;8W
zLCw|GWGg2^=$Dgyc*{rHRKAkxFpYAzryBs$rM#c@wQV=$%g8_ls@(zZOXjieY;q0r
zGQZn}WH5o43*h@^PUD!oWy5j;me6I7^A*-XUwc^WcD7vxN2ed~Q@fVWA{7c;@-aXv
zs)ir@jpj)QmKJqBSGB>7M>kTDUvHu_hCfILth6UeMkthcXY*-jxghYncAd2<<#U`q
zjxNUd{gia0&{<oNvDdxe_M?iNq^3ZRpe8Typ4XVBlAc$991onWHd1wquM(AryHmD|
zbzZ5XT&6}sj%5&(Bv_}M-6{(YV`um2ILa5Dj1M=LjYXBcZof+kGv;_~Df-;j8D!zP
z#~iVc1zH#tOu0=YCXf`bS<76lzr4IW2npTy`Cn(>KbqKLQW7i^HLzAMTs590-fzf=
zvEPAaCy6wP{KsEe4RT5b$E}jolGc4kglFz&luL9XJg4Gml+R|Lu|d*mlF*4wCJq{X
z`b0Bcne<HZ1)u!I9M#QNn#xp#32CoDwQ@W#PI-0Yxq4&$MtR(be)>|`)pv*8$181d
zF70BS{JKFSUQT{;&C0P6H*e>8MoP6ZiOB82bUHFcpKU~93oa$g4ZeKob0toN()scs
zkx{(8@6AG_v`lc8X{wTHkZp0=_YH*X{!hlKIbgXnR~2}v3K$Ou8uwk*?~K(2r#as|
zFMBlIWKL?^G2ej_6=_%i&v%e&+`M*s?f3;40RX5e@GVngtBsCUCY_T^jVvI}s(kc;
zQvFB^Ay`Dk?)UZ-oI4X%mi23g?fBdigk@d<@5*s+W^H1o*`nGqCqEy>DAi4d+ZlFm
zxIk)-1~Qx1s=%i+JC>_9f)6*DVI8s#KXUzFN7$JeZI6kME<hlC<s3NQ{mbBnWQxo$
z=IMJaMWdn?_dt)eB;2}--Z6)AANG8Dqzuu{t3U*^g(X$=mw8-b=xm49MW=9nG`+|M
zws0_}jY{`rlUMxvzFInasTk3W;Ga0X#SRoom$X+u<F=K*>qhEjQe=NS*_0G%$8*Rl
zVTA4%S<VU3yZ9De&(-=`-nh|4LSn?Gwwrm0gjue6_t}PZoZT+Y6%|;uJv|u^1y!4v
zL8GdKn}9q`*#%Of!nl=TlOTjqc-OBSkEXv&!zk<1M^!*`4iPnc<hsS2^`#SpA-}KZ
z5KF3y_d%U1#hOeNk2ru5%|AgW%eFMsy<04a3a%Pc`K4_=RI595%j|X)z8A^w?tTbT
zBAcOF&<~Ze8qcO!f-OS;F(R$iJA8mihroD>Eg4ou<fe0nRwQ~_{nJ;`TZnjFi=l+&
zZ9_)0Qa`fS30QsD<aOB0KIsn><RM1;Jn862>M^5zJ~R&S{5J(_?7FrP%WUc|@1ew<
z2^{ryOXnVKkIm&F=+XUH6ta2iu%@3hxwx~?;;YMO%u@FX^|gdBIC1i8qY7?{PMD5=
zGGY}`+8Hm46WH^8?hS%enFN^-+#D1UxpYLblrdoFz)PmZ5m?u<^QDT17B+29cu_S*
zagy?nN#PdZWcAm8R#${9k{=@MUu7WI(LQ82jGbm5zCI8Wxt^OM!eZxC^2MtflBBqs
zn0#U;UDLUhoiADYT@vrG=Jay*<%K~F5K%R6sC69#NrIaK-XzV;Wjj2ZQaru`+V&Zo
zU~1V#(0dDiO3F~3KJfLawNAr3Yw*P|P;ldaOywi{;B^1vY-U(GhO;X&VBM@cIs}K3
zuu8PRM^bjDDh+xR|Kt}eZpxr^qan!(#Xh9LZk=F5dw8JLSJBqFDmNG6+93)0!<tUs
z?=By;!x-^iLGW}2pi$=7lJVwCxc&W>^5FjI`gtw!?q`JUo5N)C>AsDuKvv=Q!d1_7
zl*#JmX6f9?NIXfw$S<5u4nA4)XAb$38p6Sa4RYeTh#+*jQhPh806rJjPtg-I{xghv
z(-;^nfe!c?R?g!{CcR&%qaSGLVsN5EN&{Mf?KGMRCyn4{8n`Cm)$c*e!_^+Pny8#E
zv%^NyjU&q+OnR#QFGkEwGjG00hcxiVZdUD<9gcA25?)z|N`ZC@FIuq2mY4wBp(osI
z5wr4(Pr;n-SltX)#!(GCj65`{<-TM!>%Uq4mU@=LZkwSg0pcQr#-R#csPPv`RxyK)
z!tKZSo@SrA(dg{eKI5J7AV4|P<S)OsVR3<|MXh`KL75>rHhCKe6C%~TP+^4?XV5-l
ztx4I=cPoCb5i~tVeciee07vgA3|l#()Nrm;S7R2VD2p_PxOzqe=8rffxAk56Lp{@7
z_CI@in~zOKMi1Mt6iks+WcgJEbgAVi%qu^pc9@ad?(fa=EifCPaW%)Jb$=$*e(kC`
ziTZiaV!z*%AYxJC<3>-IC4YP7`hC#t=rXwWhZHE!4?0C@u~g+GsKmT|YWHNS5~5iH
zJbS2t2H=3pg~sB#twwY06*hmq^3AdR7iBCI=4@XM>+Izz!&H9ki>WPrxCu+{OBz0>
zyfq2pvtQO9g`dZL90r_L^Spp3CzaFbL5=Z%5v>+`F-}@5-`d-;NwpZ{n{MHgh}ide
z*gI?BnTrBEsQ(;v67*!E-X~i0P7^fFKdF3>=Rk|n6qDRvDj~|e{?LfIXq{B(OOd?>
ziH)y5g_WpUdnzsvYEuw|^^g!m1NNQIk04P72f6KLDCLn~dN07^Hr3naiE&!p(A3ri
zwFQ!@e@&@R!pG7(R*B9z*I<3(vR=Ls*$L083A6`;8W-<+h?O8=gKs@T%KzCgQAw@O
zLP(4a4?nrm89(%h0ZIN&;a+APq~3u_5WM@e<AB4o)};K&7*Z=KtHwwBe!Xjtd`B93
zPI#vL_iQEkKqSs$GcF68Y{2gh1ih=-{Z#vxhtukll9b?ddNn2KZ%oA2kEODV>hY?o
zCn{@)05P{GGSciXZrg_YlDv?I$}37U-Q&|(Zd$jn339;D4WBb1zB7oDN$hvu?S2I9
zf{^`~6Y{N`#?d-8ibK#8(LPO_&S;le={7i13#mECpmf9@v;1{ZM*a5|?47LOF}b=y
z!Hp<}wMCtLl)`5<*}|<g&vVWO9@m~<>+I5+_8LJ@9m}Wg7RE~CffHAMlyg9Xe770<
zLp>8g(}^gvi$u-0Fz`gx<x_DxF`zdR7X}+P(wQaUU<0lR2T-oJ<#a=^UX#B-mzeUV
zrPy|wEqs3X-B{GHP3B|zjN%-~HIPRYVa}1uFGuM*9PLNtF8`uS1HmIDDI3=J%(QAo
zquo6*pgYoE_6hUkH4=jfDxd4)rkujuFX!6^9IbN*_}e4~F%2nBH>}|w&5kgnQ7v)l
z?RJn&_iB`JTFr2C1h+BR$0yO3bpZ4&H#q%C1jk<_WTc9&RCmQC+F&-!BT!s_XMz^Y
z<Bf%p@i_Jzulgp@7i3PvQ;O%Aq=giTSmJb~gykU(=;Ixzwp*EpZtk~X4!{IDY+T;h
z`gV)^Ez3x$R(wH9`&;LI{Cp)DPObtgD1ptn^y?b4>7m9b3e_)fS8lmemaO6pF+PbQ
zv@;{hNspk)eSdgS-A2a978qTEw?Pdoh(&7zY4qBp_X<b~N28^%t+&lhGdSkY$nDkW
z&Te2gdD)bSBMu9Zn7BOA-NZ<6=MjXEv5DnVP0jANA$-^Bo!m^>1LZmm8W?bf5mvk+
zBO0Ak(n6ka^=d!cn~o;MuA9#sT>o$grv^^ts-<XY)_`<TDYz81`w-4l<fhewpK4{^
zY(TnNdxf2(1)9boinlVuL7qAnS_=vKt_ThE9Ujc)#6H+aI18ul7a)G571L=-^i!2k
zaX!rjol04)KnJ0}anr&xXt!?9>2zAj0X@c$kXkZ3zGs*wn~y30SW`1qPMt$DaCPQh
zZDk5OjT!nR`ABrD-4S^K+Krtb$|S44T`8SMS&vZ8Jln{)DYMD<KfW9@cjqto(Vl5W
z7qx*W8*p%PUutS;n$H6FOA*wk`Ssa(S7xVEA5P$~7f5>2I49SS))iAkXUvz9>~<$j
zD69&Z${7``bI7(%TtVj-cxn=79WiG6CJTQlKvI&h@H=(M^?jU?vwH}=%w)&<VErMA
z?Q=Le&rS0}kRDRVcz<}~1C~i@V3S!ZXc*Vt)0c}~BghQ*(_sKYANI1)VDUVGCZx0c
zoH{F>pplbIaziFU2t|LF+$0|~JgLt<`J%si8KHi+8;1MAf$zzeV&meJ6$02(4j4^H
z51j0LYP9hJR$as@cqi6?)Hv@TrHI<k;fFed{i6`#j3XN*S5vDckn*3+8%Bb3@I*Or
z=?=6}rfvQ$^!cvEjXOaUVjj^(S5Z_vDN8KImd&Fyq4>LQrulfhrGMv%sWX;8P0q0S
z4!KEqE~r)Vme_<Yutyi`YXCN4Y+;-&rwjvAEPCzfmhrC_vV7u`z(optdK)et(_34R
zIgj+%W2ArpgWd{*1~<X$^Ba8{FRojU@z=ODrl<x%KZguu&MdQ?2-AFAoQloqZ`2)9
z!1_-i2VzmRF#O^)ojhIAT5&}cb!htEJz08HJdmH-sZddetj0JvC+&FLzQ3PvpO>kH
zHCWCjvxi;39ycLJZ<e>Ft12zqj?mV+`C<t)SFA>+M1~lOvm0q107kaU22oasWR562
z8AHSp3D>bN$*n)tLZne$H5*~T!I^RV$jsc=EuVm$AP-_#Gx!jLg@H8i4PCm|s+Fjz
z27^x0S+X^<-Dm@#7l99&EE*h1!fHER;^oC7b5`QF>=l9Ynj{F!vXNYU1+4T)3z4r9
zu2UD+f)3Z=l964&sr~uY_EY1I8u5kI&yIo~#}E+wYIAdO?y3S0MyKffpV=zcs1N6|
zJ-0Ga!4papzUQb`ZKbm(GB=ZoV_KvHT+Nk7sy09m3xU3mPE)l@iKEI&qTE-h2nmND
zC@y^m0Veqw3HXMmJjIcWpz}r)%A$eip8g=Sh_4U4<X#fRE7#xgfh_csC+CVG?^ji@
zkzqlqiI(A#fwCW!S8Oe$-RwYir@*(*Rz$G{pSjKZxS;C{*k{<j7lTLUedIznuv`bm
zbIiirN1W!Iz*J_j=a!atNX^xb-!Bjl1q_Rx`C{j*W_-7utMT;N_*|kGRe)QiBPt~_
z@hfzbP{?|<p~XV(xgjHePW_9I;!K#>;rA#;y2-PQv$6py(E^9j1lBQKpzI}VPAMmS
zI)d38)&)ec&6Y=i<Yd=dmp6PTilRR-ao|P;A<DU$mtGffx&uK@&C=&5`sJEGtEmL=
zk~&DMA$uLxQ9<2$3Gmf(XQnkc?OC^@jn{TED{c3wr9%p*e9oct{bgrg5Wn_Ge5oar
z{5>O=xk7Rx$k<SC=Uu_BXhMr&ehxjO2w*+7V(hi~87y)76Iq&4UVAZ+1#B6wV%W_P
zAqQ&(V-eP?gakjtV^f$}o82vPIp-i_sT{9#{uhMhOUU-Gvmyv*42puiPtg*ECRVJ)
z6uSi=T`Sk5hq8O<<c3umR`;xs!Im|*GW*Op@C`Iw4yg0|MC5XB##R<(;gf$>CieK-
z{fCuhVX5Ma6Y<*m7M+#@1PN{oEXJ_pe7&C}e9ZZRw-M$IYo>gkvai_N3+JE`j?HP<
z!JKke!--L{7A7ybj)K_$Pu&<q>e$xQ9H6*p3HEH_khX=LW(bZ3YRy5Ae|<Ea^JvNe
zYWt5Nj@6-Qz5gxFD?&PG9<ptj7NL^k^E-b0nTZ9F3QpKAp3$Q1f+b3Pxo*tvrrbm<
z^2hH66AZ`}WUK39bB)^7!xBi`d1T)YMTbXlrI)yUwPh`9=I4if9w89JCV09+cz)iX
z6|pFp)Oi~NQIBQYf^H^%yRXeHEPp|T+WvQ5ia~~KQ9WBnkbN4mluqwnjMjdfDeB+^
zw-S8Q3&VK(#0SR@!>b!3-p;lC;tj_D&451-S7K<K@mlF5+F@wQf(-PagJ2x&ZaK-%
zn_+8xI-S%=oc4Z3UXx8t+lxeI4@-G}^hnkSiU2*Tz`k1;Tu9Ztu#)vlv4YSF>iP}0
zwxgqu?3*|0bv%J}T7qvH-={ESqD(8vwaU233HR)}ry|6C)=Z0op-n-y^MKS5$?YY^
zc)-`h?`{~s>01&!>Tnb_f%&F#&qMhPaM<aoW+i*R55!!JPn0Ne<-E5$f8GuJ5kcA^
zIyk}O3a_sbs)1po2Q)CF?;h9tL}*<7zOzFk;#~Mqx`5>ds!R_FDkNuF4Urk+mqz1B
z*U5dAmQ|I+Ozj4Yn1ZbKY{_*IsTlNZUl>+y|4|mPdlb+}0)z_|%bpUVK(EMH5I`gL
zdM7E^i&jGZvfX6qtP?;#GbYu~n<pqsp~bG~8cGTKKoU?z!8X%6lwV@%_fr#pyDbA>
z6K}dRpSPgc6>~r2jslIAbS5;?pMnfp;Vk0-ULN_v+Fl?5slWBETa+^-E?O}HXF!m=
zf7a+5#!9Y))g6357b`sJ^v6_!U%@ZHL<;gL6~&)b=$SUXtHX&Ar0ZK;6^)+;qFuzO
z;nlxH;6(mlNy}t}UMC+a6_F8i@b@Q5R_)a6!70Z$K^#-P2Ph!;I4K=SQ2a(^MK>@W
z1{bP|p{9!m^sqF@N)1$VOj{cpgIB87X^OG?^OJqu$!W(ZM9gFA8B2(eSR)I2|M;{>
zJxrES+$J5bN)@BPhNjV2{>wV$BICy{^Y&SV8Z|HW*^EJ!vR`65#cZL(2euJWeFzNA
z2!bkfpzZ)rR;f}{4Y3+4Uc+v9y#qQxr^8MvC`ClJXk5+BeV2Y>&JuS&>03ID^(!w^
z;}>;5se7y!b07(Mjj|KwHgWwu$UbdAP_?32b^<qsJ8bk|BdDv=FcGxom-`(t%Si`K
z!OWq|JY=5u%n>$fg&o|*fCgIUpIw}@PZJf9sK%kf7Fg?fnl=LjavF3oD9FE)(#iTH
z2Q4wg&zZ=h-CBx@!~-%568jv6RSj{-g`CX-MeIN(dYJi%o-2eaSQ2ULPRO;;W>AHy
zvgLH2xr*T#<e)*8s$vgtE|I40#g?`LxW_?In)9eNxZvCH(%Z642x=<xBQ($4rrQ``
ziD+QWc*?Du&4s(fq~f9=t7&Wkf2k&f8DU)rHq0S7D+sHN^3#tl!ky&F-GsZph#9wt
zo!~_jA)U!%eNz(9%g6a&01nuN0S!=W1|SevR!HV|;yGdCMbGV>Jb0tKh)xwLrU#8g
zA0?C5Ylks84=1;A83Pp-*}${VR3g|+>PG=-A7N<8L7$kKY6zQ2679eQd~k%ijFgBM
z4O#C3l{)644boRLD1adRNDb!*W=#(mNkJW}VM1FGEqPH2mdFmr5L`1i#?dc!8^+76
z$2juO>~v5OYyH=-B2qvwGLOp_@OV{5uw1kp;dsnYkK~V5dib0VfQy&F*7yVpl9&|T
zpvIJffos?!d8q@M=ph4vfbUF+s{H2p<zH*?z|733vaO(CDThz5udh9kmNVdtIYAqu
zMN)swQ@<|8qe;$zfqzX@O|~WG)6Ee1gakGW=c|G6orDyOZA8Zdat8d*18}9#s9KO9
zjK;e}gM-7dCE-Q_1B*uk*`W`?{eL1~k>=a(m<?J(KG}nT^<ag`VcctaF7ohC#MX=d
zF>vqvr-6I?3c<`VI2fpQz`(wsFpjTOH&*HSO&G3}(eu&@`%rp|WWnm?hi@7V%QPaE
zUO=qKzMdk|Csns4u2$cj!~>4xq1<?U9)s(8igR3#;HmR`*HiSRfWeH@uZ>O3cs-qg
zsfaB$Zf5bSV{j4n$67bbqqv!|^^lq|y;r$id>bQ({aBVaX|P$!QnzzMBe<Er2w7Bj
zTgwHk8BiJ*v$X8Xl@NyWf!<DpzFza06+4^_25jZmYZtz<{p+5*g_YwW@ExNm0vCKW
zGoLC0B7VYvea8}k;`$pO;IG>GLk##U0+hgx11p8vPAmW!@PlmoK^+&99JWkW?IYDC
zWIO<r>JO3N>z|0ppRPspnG{057t&DkJF-HE6WpJFltO=I2R-?xvi_n6{Obs6_&e+r
zX*c=5AcOolvi0wT{r{P^$j$jr+9D_0AGU_0v-%ffQ*#i-!`<4{T-w>#>%Z_j;QxIB
z2<v|kKtLt^+q6Y?Zr1;e{;?KdmF!0F^Rl|_a3L-mnn+=4jW|+g_4CO$i7gBE^Td|S
zajtQrP{3+6z=_1>i#WbK;_{c*g!G1wGQi|z#7asFv8X0EXR#msh?cevF0q_+i&d)^
zUhcPpLMRUxw-X!Z!i~?{y}j$v;o<wd&2efHfBUPQ=7DlwI1hSdnmY?4bYC(KCv*?`
zNBHXwbjkkw_we@wOUBeo=O5f1JGQ6vlB(f_b<jf$P};Vy>b03G+c5o8Turo~PcoNs
zEAL7P%eh|4e~h1p8({e4c~q=4H;9!1xD5r6+L-QuIV?U}MN#~~qGv82*~pO)xk*V2
z_r(Y~qA?WbMqA^!u5YU@#(sl4woGhK4kX(1yvU0u$VB1UgOp1j-2Wc_2>kC$4HDcm
zY<;O_i~ilGa0mUwdubdAf5JG$XfM<N<#{d)sc6C88`)j(WG@U!(~$ExLuu)@1|Oe`
zO80zO;4%&g-<z4uiQB@Z@z|J~+m7KxqXN=liE~{951;Eud#zP<8k|2)z6Rsu@1Yl&
z|D@*61?nH+ue(2&?tg^868!y+2=ZhgET+85bkV2-qK(MirFY=!x}b<xvgX)0?&Z=)
zs#hHp%U!9-(-1$;`t##bO>G|8^JL_^-O`j-f{qhOTO2>GB1rrgfQ2J9cl?S2NemLc
zy>(Z)(UtMO(}#braY5DeC;U~le~W_Z?JKC>YAcyKnsMF#Dgd+Oo#&)=iJZ%>eG?wk
zoDoR)HOHnPiHvLNjf11d1V<bkmcAMsBNf&fn-158zaRKPxD%y){zr!YY`OojG5_8n
z1!0yz@A5%015$Dw^zNVrNG^JgLWugj*z552@M?D3blLI4X$e@9h0n$0U0wd8=(Wc7
zPcm1}o)aBN8|U7EeByUbj*`uh?gF=?v#wfbCR^-`cgzM?NX#nr>4$+aB>Y|L&flEO
zv`e=NxcFS?!b3O=Z@BK+jtZqapBa=T&p{_@mc2ad|E`+N*%$ZI`ys3Tto1&BI}*<U
zZ1i^0Qb}j+Tug85_3|sb8qC^ECgA(TtVo$78{vpNB<%v_b7iNR8*osokg(2MY3Q>?
ztym?%*{E%wR*V(Caf@)WcaJyQXjeE4@x0cuNeS#CA+#NUBxj#=;HM_48aZtU@9<KN
zUL6VUiItXPRQQP5$$*lbx<sD(q^K=tTMnFDnz<+vaD88%xt<woAIPNBTe@~>a?|@<
zz3l4p=!Ovfd{Tg7|2b}nzy{W5BxBC_@@V?)fPZz@rGaZW!1Ba?7$d;rRNu>m?`~C{
zajRaCl|=c}xf(aEMB*-DJddkJ_`z*l6~PpRu%ev?uv0}hdb#S<SmMW7zGpCcltPXD
zEnaxr!sx6tsxf+OY$55HQ3uLDKrg*?ohS7`ptU<k4;KMDBQ(35txYBK>TI&nnKW^k
zoGW~T^%*eGy|_!eSe5tOxwJ}$vf|RHXZG%@dJt7Do%uv-ERv0J?g5Y79M*aN>A_H(
zZz|y%Zx{4NQgP1PUU>Y<V+%{l#zDlhXVc8E(q9AH&HDlIM**XMMJ43;5A^siXrLx7
z#;mSr>;jq={JZCZ{}W%}*+BU6FPMV=4^+bcj3oaPmGFNt_Ri6@22H<UY#S%G?VQ-Q
zZQHhO+dR>UlM~yvZ9BR9{bpv(ch|i$>+V1H+Ux1v{d86Lue!Rcs*97I{eO-m%!Two
zbKM>(zr38jwvi^2fQpO=P+=l=iU99ZNL8k#49dZK<fJW_p|MApQNBe(qs@V-CtIbI
zf)+(Y=8bwp2tpzhlGtM3FmpT3H6aUQP5R^8n@=&r*VH6wcJh+{HvRHaUqU+pSiQmE
zVTb_hy(tio0ybdH9|r>5Lk(Eo;r`!eDiA<GAR*KqKiphgTr4c8L=c3>YYoQYB*joc
z70gkCHak6d;6MVC!bzme<Uj(e|92Y!7Gj}DKne^&glHHT9b0xGMHsXbW##28aG<Hc
zyGaV9!v>5dW@hYqE!g&>jXG*-WndIEG_csHK<Od-MoCFYgiI|8AVndiniz=GpaRD3
zXh9BVhJk<%2Em@20zs8iW$=35<Jxx>+WcJT|2T2}YHx23{5{y_cn}7U>$alnzO?yp
z5YL|_LmpMM?Rk=WQQy1WJ7U4wp;O1_``-0_UTwSHOjO0ai~|ReRhD|?zT?|!ztf|D
zjU>lhURTpnzS~j=C4FGQnnfm^)^*ztJ2o~Z`{t{NrdkEG*;L>A-BHA2y;A3U*)Z~a
zy`|kPo_4g>WWM9`F!k|rqh7r<{!QTbS{S(l59qx}84C4MaXcTl^>U56o@c4o$yhSQ
zNIZeQk`jQ@MuWbfm>9SrYf^IZ>-Cmu^?JRic239rK|p){Z%64IJ3h`6X>`?UwMsQ3
z_eT@hVVQis9gT$=C`0a(*&OwEFe%?(9|SyZHEFHy8#~{BUCxw_{I~r+u0}I}ppaSo
z@*LYf9io4_l#R-%y31J~&qAP4JKS%+KObh)Z!!UI3P$APbh})AdpP0$dRzd|5ZkCc
zmvYnlqbI4Cl5)PH@2^Tb+!qX)YGQu3Kg41_{fo=#a6FafrtJXX$i7@QyX^BjgUMK}
zMuW-c^;WDWqey(K!?n6of39&L9PvX{?2c_*5Phb_^+K%z4$4z#Fs{d8L`b9(94l5#
zR2k4y+gi1YohHu*GqTuw8qt>b7#y~7ef}awk6VMGzmy<*4{uIRPAR{~-2?>#`TG<_
zM$TN)($n3~=1YlEJ+IqOzVG7wDfP|(EUx;noSIwu58>dkoQVO9mh+@Te7ujd!mj*P
za-N)V)tcGGT}o)B7O_8=DYlAeTXrwW@_g;SAJ2bnH>lIf6Y1%`wKBr^RF#^5K_S3y
z!RC_S1u`UMWV$}CJF9I)cY1t#U-w~6Nf;RF^?UtffB5}06>Gu=_B5I;6k&TJ_l?lh
zU7s&Cdqsu1;eZ+jSY~)x+^=<h{yOEy6Y%Rb;C`>~l>MMKt$mJ?#8a**o0%QAtmtjj
zFccgrF!X*lCRsh5Er^dLeZtEG2ElD}uK_+8L)2!sD-(;A(U!aETdA(BbK6P2r?Z9A
zxje<E*(krr9IZtp6PSPj(D)BpQ-S{r&_L9n{{?6x<i~X2-Tg&8gE^P1rJ|pw75$Ur
zV|;Fx)02GvX~4DQ>A*l>8Fjjy^qP$&{y*Qh%s>G^3JMAU=I{D_y#r8EDVxo{0~jQk
zXV8#zV8WbVzr9@gL!coMelccGMPo9=jX8&m10l&ZHa2=aUsl@aI8U*Cui+ZS4>Ol#
zq1rCtl_-2!-iiqYTF8QZ4&*w`^v<*plf&=j4+{pCS*A~*>+6**>-fd?f&&Gld1PAU
zgUABkT~7<K_4oirSpN5uilpS&Y@u+fjMY>YTSGl_?CLMkfC9dMGmc^{iq&7DQ=C-e
zl#=Qmp}0A9GKwN5I20@_&OhH@-&f}OzRSH#PKTqf_sp16B{*-P0Ho=NG0nKCu~76i
zC!3~Tp((164hzjm!@XxpLa(%M`qQ<#Ja{3KnaI$UXvtd18Yp+;vHZpq>~531Zy5|3
z67#L1<kMD59N^J@^ZAaSt~m9H`<S%A+9JOKuD-J8tItv+F;d7Z<4(^CQp)8fin|ko
zmi=O5GbO0@95wQuH!@}Df}ltBMbx%^S--TW$lzzeeyM+(*aUW##X;SNNUf|TZ_#Qj
zq+*h`8+h^ejhz)`{axo;?Ua?}TW1o`)Hkf&P1~!X=NP;31>$P3dG7cCF)h7RO1!F&
z31KVU{SGn|DLhVjp4hBHBRj>fz1afoO5e(*3*FlDv)ECN{{sMvGaq{ee1u_FU{7xW
zwugj~kd{<plwWy8iFI<dt9R@5?~mg(3R(*CFUGM8VN=E~I3W1W4fP}xL<?5?T$;0^
zJJt(^`rga;GykmbeR+nS_JgU)?($(3CC|HFTd|dslRR&`xBDZjmJR^0U@>R`^8_GR
zFLHW2VKj^)^FH*2=Zn#owR7msp!mDKZi7uB`FS2S-KsCOH@2c?mnZ%Og6jJ|sisP?
znU15G8V|={1FAFRsLj=@x(0gA&)*A5Cf8?nb$Yxs|HxfrZL-Bm3#(jAe==Ik68?z$
zzCT*7y(b_;otyp}N`%_?S4y%7dKH(%_J#flFU-v3m_{>od_O+3;J#g$okV;%nNbIW
zq6Xa$Wq^CnrR8a<A-?kI?ZjAEo_(&Y&R>7s7)BpaC;TG<%EI4uKTabx@AC8YB5W$4
zqadZ|j5@i}sYCM1N8o$!RIb>&<T0b&wrnXFn}|N+O|S;%?dfiWpkAYan1O-8Y#3U|
z^q*)(s)d{4A*?BSV8@|llwakHG<*Hk*47PiSBpls*Ha8OOB$Va*}|F8af!kQERJ!#
zSodUs|Ekdx3JBM~4r8McMfJG%`_c6A3v_685kW#BHsqDTNYvl>GXS<lR>ADP?D!K<
zgBoFLGBHCRi{#%RN7L>8_z2BFzfs)l?|i>^1wQPv_q&?=jZRI)b}xtwgW(+ZYPof$
z`PTGTbv8d2Ojgod)S0LJBj;Venv*qWx20JRQy5`az*gW#5uB7`EQjr<>?Dy0Z);uX
zrdm&F?>sJ2#RV`hQ8ni}LhN@#|2WtcQcaX^^G7jHv^#YixsbEis6JsoNGkBtE=k18
zR2hQajlnkdBIwrI-t<J<#PMYoYTygYM5C#xDN<GkN^wYNZ5CcbWWh7$;()))Itbf?
zm!svMd?C0g-tB`s%j_~+%1sC34O|jxsI#-P(9lpaGO{-O^YeO?Ulnj|A>rtPJ{Ihz
zUZH`4?96^)Ed<Zoc`wH|`2?2K1B^0iY8Z9l(vaAw6;twB&k16`x2=kWomrFto+rlc
z7&n`$U9f%O;{k2E!c9!FXyM=%yn@PDo<kB%!@T}GK4AahZT@eZC%H5{^A;u|-io-n
z*lSiV-@SKZ^brLxz)U?j#D@2h#IZaP_SyX4-YQqM{JHt}QwUYSOqj+czOduik?0mq
ze>l}@w#-}f6`@L@mfAU?hy<wF<YT$`fu?QP6n=CXm3t?(O(vdRKnSSEewQ#v9Q;jQ
zUR`?%JAr9oZ|ASO?y8Ufrj+vMAG->~Ao1HTb-@M1kg?HEJ9F+G^+C)pk9To2NI(&e
zlWMm;Wc`a)D@Ba{!SExlwsS2ezFq`MB-!i>2YqEz$E;oKdxTi7wvt%b>n!FpLTQo8
zi%XaJ^YofHO`My=X+Tg=I&i}Paj+hPWUw%ENrB_{=H85z)Ys4+g90U>-#i*rPVnWp
z=ew@RjNhDrvZG_;nx00b$__;qfX<6)j5*>*b+A}8sj{W>@NAC3er5%+QD&_IJu%<K
z?dxG+KdF22=5`Bt^h(BR@|bQNQAsb$VrOz($HzN2)WCGXMaVr{Es8IlcM=bestCfQ
zS-Ur7>0|SP7cvvRYue0OlUYWPG*SzSSTm&3GBBMg3XEn5xEv6UE#=rj-5mC-+-SSo
z2Ja}7=YN}(Tes(ZShtJDIt7gfoSMRyJ1Z{c52Vwh|0%|OKc(RLz$@1tIdk%0WhXKQ
zQJi1~O+Q2L@a#f<-KukM*N)yt0Sz|-)U-Hij@lk;Y~^_!)p^Jzk3e?wgPq7LwnW2F
zfe<01dHh8+QJCY|)DGu<%Pd*9c%h)7C~N%0O!_t=Tz6c>%Jzb)flb4$IBt`fcX9Px
z@)pE5GjNm2&_4A&;8wIcaHUyyg5S6<nWA~=nW?3uq^?OwuQ){?Tb5ubCh{louGnY5
z*y_oUEtoiZEv8bBEwE=0C@OJIYQ7d3pf2Lxn3!n3{iSSNoy^F}kO{09kWI3F><H{q
z#=Jv>OD5eqbUC_cA4~eD;J|uU!tBSRT)(VxGdVgc;6sx9#9bR)M9i;y&$8L)KXS&N
zzqZNW%|A{br*d2bQj(CH8~Z8-)zYzwU^!A6Y2;BpWX=7RQwNrBzg9m{z^n-4IyYHS
zXDHZ#wv^E9o1CT!C)E6Cq%dF7hj4;e?)S&jma-?~o;ixFaw+M`e?y`Utf+WYw$qoC
zi&ko$j9<#Bb=E|Kr+h*8`{<MrR%Fsdre!^;YNsfcs>(3NyYAkD9<#`jEvdR|Tc$o?
z#?gJvGsU3BREBVQo~$fo8AEKH7ZLMkU21-ir?m+uhCN_B0FV<xM3o|?;^R2@ZF~;G
zn)AP&Ld_(^dW)KyL5ZP*<<JUVUfvxW%b)@V&&AL>%Hlc4;sUvJ$G;+&zhEv$#ZZZ~
z0xm*_q)9xDYHpcMs<$`kG_kP@#tniRlo&me>H6;O%2dBuuB@8Wt9vugCr+4#0??F+
zWK(ELhfc)0%h(Lkwx`h4uS(7!kkIjfh+wAcG%~Gb_sH`mtU(^t#vFfQ0&tjA;KVSM
zwT?k|Qc_!${4mX=_m0hKxtb&s=ukizGFUB-Ad}nNItj$I>7QVR{b)DL8?gp4SPQh6
zcq8`fC8>Ee$FjKCcdrHQ9D22+``&lw`o^docS8--E;uNLAOH;?c1|5GOml#+>QE<o
zYp&dYB0z7T3@0+_QpT}ejni?YvpmefW$l+5DRlr8L#gFzkbSK7(5p}8DDsz2>?W%j
z{uh%#?B=?!AG!Os+0xXq)uxZs${+Z1rh_fx0C2yw`LrcrxNVs0a1oq8-1UqCIG7Yg
z0HFGLri7Ax==?Xn;h|&sO7ijTz99%k1tJ79e5Ik#VZb)Cbx2RJ8fu?C+&MVS)xc@h
zf|3?V_%N%AgJla=bvSpOJr7YW+vr{={Py#Nd;pRA${~dZKpAF?W&9zVbls{BR(0pd
zh8CJSktG14e*_;`-J7kD*s-fu?BWL6H7B$-F;du>$&n!R<a1baWwegpicmi+Yx0Vd
zSm_vXdwHI=Drm$D2lq?`Dl~^2GxZ9KInsFcY0S%W4ysVA6*d8q&ML~%i>F!<|EXaM
zNLXzx^GbAM=I%t{6i}qJ?7*E#>g-`YnP2G?$4pfJyB#AGzo9^noLzJ3jN7wIQ?Vt$
zWTHYxkgTC8i8Z#}O6KTe6E^#bi3$ur1w^^(qn;W|Mtu5^9;7+^wtB}+^A&3pJ~gP2
znF)1g0-PJ?!J&^cvorNfhXLDvJs0WGb8p$$;)cOYu)(f<s;!sn#!-#~s3sAd<BTJV
z=Q(Xm%uA9BOcM>o=;DsR0;U49nx!j>jl2=@45`?Qc1WU1r<{NdxuAI6WlZ_Sl^cch
zT}y&Q68l+4^98jz_@F3YHz9Zwprt#?D7;UrTP2qTgBSX-WTgWEpjYJni93}R(P6lF
z9L|Cxpb(49aL^RVvOrx=C5g+`bgC7+9&iNfn`94PesSW0UFtE38)H?xux<!{;#IaO
zbbu0=4%}y8em8BDY6W!@vjG*cj4uV|)`+762WTA)F!f4POK@}R+F}DvPisU731})r
z6o*BYGZa<p8fb|;_hhOYW4yj@NhJE+DSD$b!AY}6-WsIAarQmU=iPIzhZ<oZRzwcH
z0rFF5IdcGeA4SoIt7&Mz0WR*~KY$EhkXp!YxHM@7>^Z0cJtL!B#tPU!$2_U04`x?X
zTP{oTE^eF-sv;0lffWUhVRue>YLG9tmIebk5ks>GA-Hp^xpK2fHF*(=bxlVhmisEV
z^Ai&TU&AWUp28*9wOaxXQACXY1nV2k+Rruy@3#^2VyKj9Nu8U?FL^gMH6iaHr4~8H
zlb+LHCWyF-EHx%e7RgIy6)?{gGC$#@iIGBt0FPJB6QIzp6zT^NWD(jO<VM4JK}wz@
z$4RJRiwat`n0)Lk+s=nAhhPr3<(K#bwO|U}iO&x{z{-ZX=3Jk>b4vz8-7~iw7QfS(
zkTpilHZPn=CT`jyMOk@ybfrTZnZ9%>fsuf0vI`+CCCIc(sS~Q<VAD$JlhCGG<KAjF
z(@H)>K&U|I7<DvDd)8`|Vd(rr+CC$ZOf@7B0!{@^52|6l!g!<Aln|k{ax7JE>%TCS
z;X#J^j|$dr3~MPHY0-&OSixb51Mo%NqEgZgvH(m$=>@U{dQBzhzjjcf-EhvFrkNqR
zf#U%f2g=wl5pmNYC8D(FWJ@OxcX()Y<WSIa010j;d=alit@npm*9xw0J=%!2`I_@T
z9hj0&kKrLxJaN{ERA3FVxFP;vl>jiX0Q~4!4I3fq?=_aHKeCC~A6Iqk7t|8SzW+3Z
zo%x%T3`Q+R+%_n;K~Gp|TdELP0ecoMmC634eSxv6fa+D}iqsw@iM#^N0s^4_%&#v5
zk>M7@WI<4A)R(!ANTz}}02(5{n`*E($;7bzjT}Wztl^zl0>=Y@l5D4;Pk+<_4Zyo~
zExcVhD~gsh>hmbZ6S52y3f7`9f|(u7j?z6HHVqj{RGHF1{DULyp#xJnxfR{^VylCr
zAA|uE$M~$Fv~pdAU<bI+0s}M`Fc!4j9Z-dUmkv|9Vc37#9z@WHsmSs`X93ep4B&mf
zQ1sS2s1xD<l%8PP57$gmX?GpR5n7OL{;a87w-KS_!j}z}kbB&3iAqLsz`CSTd^)kO
zkD<SCp5<om#=zODCRpZ8M7$tUhX$5kO=rAKp7J700c0XmfDc&xbxtX}$`5`+iAEz+
zdo5%cX?dd*2BZUHCTOxD^JjlU*B<4)wzdicCp4>=+HA4asxcnt`_{>bfa*c$097`s
z&~>=b5gqh7K#$_|Ei0RDiYsU!A?QIY76jmZ#jUZ%GuAmDl{V!Q5~iD&)2>vZ{$(!9
z6&lJ%g7?67_#P&V7=pN#r+4o-6=tPJ9?QLUE@gq%Ml%iuOr?J}H7l0VDplS#2qDCT
z9upt)StwaNVG92#@iAR2kKBi+S0Mv~nA_Gx;d1RK4<Tj+2;)x^y|y$KM?>`n5bHjS
zS~sIm@NQqwuo@KF?j^|!$uhV;0Cj^W;r!}j_qlE*(t#osr@}}j+=euODHrK(qqi|{
zEFI4IYC^F54potjEL+qKYrK7ok{C{G9A%$|uUoYVfw+1JCHim7*@u66)#^z5>Zb7_
z^~Sk20=i3PlSs;FyRhq0Z7sJOqzKu;%ad2#s|z+3g4IIUbU)p-d^z`MhR<OM6iSql
z5PIJ)`>R5$J9Q`lzCxz!{;W@a;pvQ{bSFdsEV7U{pY$CgBL+%TL9knf6d-fShVqUZ
z-8!SgTKyeepLh{nEAt#tnt@_vcnz9*Q!V(4%($0730GYDZVJaC^vKB-DQeSwX_sAL
z*^4jTa!LE91x)wrRTfXM4uI`N^@W5|1s#|q?{(cqtmNE>9U%Zv9867oQ%u7u{1qG~
z+=o@2TT=xtOc^Bde`&Tgp=_9qmKOX_P^8M!S#F!=P4b5LL|B6)rzLMb!Fba{;+Xu)
zUra34Vgf=ZrdCptTdjl<V&sm+C>ITCkAtvy2es_!r|*Y^$Jfy9>6L$}TS&{ca|s#6
z!v3-x+YaD$-3N%W3Js$NF;;99gob0rT`@10FfF||A3Lbkcp^zKfc6`kB(bBE+z?g6
z7eX<`zj)+edaPzLdSs^Y#?E->01kf!TRjVv*D^w6i(1Un0wkY>Wy3=dKsVqgc~V|>
zRS~n0;-J(s&);`1Z62R2>Jr}q3CZNoYQetfija+{y@jkg9Mb(?0K-5*{Hss}Tcsyt
zc2tECnjbdJMpL5yz1dLkzU1v{5-W;XGQ*0*KmoIbbjf~<`g2~nmM@_&06R0~2Z?!)
zt{c1%ibVd=BM9>qaSol+M6axFcCnKH?B6J3sj9X6W1{He3KTIW-t!Vs;XuSQ$$gx!
zbN$vf1dSNj#zcTZk)*EhXmjhMSqSl^OqUli*@QDH;fq2hR?u>uLCi;&DaSY;Xc4m8
zd|UH}?K8U9bpp=s`rv$~KqSJkq_QO#Nk-3!cM=@re*K!iLl9pzA`iXMQda(9l0>_v
zMR9=Bj@k7c-XX4#8ZlO4uwqhvvj*E`D-H`y5m0K>El%(gZJzD>_IXilL=lprwiUCP
zN0@qYGLP8^nAAuszV#o)hU!&kJ1iLDc>}2Zp~icmu*`V1D`|))X2XNFqu0-`hj3lK
z%Ln01u;b+z-a2=0JLg-1%or0*O;dGAAO=+3(Z<jFv~*YMt){gZVjScN=Oc_G1Vimw
z0ZPE?IMUkd*ZH;iJhmbuH@{DW-QIlvmRIwq3NBnF>?;C-xy+(yQT2WJnWIjw+sh<8
zF+xmaFJG$cxCr@W<C3MFKC9W@XPS0xgh)goGlF7;6m@V%IXPF$)^2(P4VnR)idBVS
z7%Ajql2UhBN4DDZn&oAQZWd?blqWcbidm!XWZAi+oQ5_%n?anl2xM$wUE2%Rcs;la
zT93R)wk@CT*O!)&5JT$+FmBmXEO}QcN97|Vo2B%Ge=*8`^gJ1`gY2v%(xkx&vZD+E
z^7Vo(bMeHKkJ1!U3pf>WCq;_-jY3m>YML1&Q4tcDhcZ_AyFc*USLI@GR-xk}$HfZn
zGK!fSS8;m?6h+9TQ0TsPN4ZU?Q{HS?E)c1aj1sA#why6)vVILUAafDtNuS=9nly7S
zGFDDhq27?xsDkQc>Ko6fNobp)#x5CVjN?pF_ci+{u%fbdskDT@6fz|Q(oFw85lcBQ
zKW+FXai9Z70&{zZrb|z_H(b(;MY0j#WGbsK+sK0{WFj${ulyQrUeV4uIN%||VG>5T
z@fkGIwVCw~LEexB`Z5iY00=t7XW!l0(<{0WUXO<1K^W^osVB7}uCWu`vt48)k4(k{
ze_sFL85Rf=xLdR>L8J^ADu|mLb*XW{Zu>hgr%kU~RAARsNV?9Ft=yZ5tTKJ$VQM`a
zC*}S@*Z_v{!q{YC6scVEM2f&Jm%Cl{zQ&m>gohDxCe+^8&<3qbW><6UtgM9e>DFUg
zwT>72VRwW%hqhN=#iw+Ir&1NBmOmmuz*MMDUA*VMGvg&nYSs=9F@yANM|3E+OV9S*
zoccfx3=CTtDbdt(x{izB@}r=4;>*jCij>@sky^<4Z=3sg>^i%?Z(IG7e#dvIlc{-4
zB?tqmMoy6Pco<dG;3nXJt980>Xb=xV(=8XDLBqSJ<)IdEfYmO_VFdW2gxs2(tQP;7
z`b2%Qmn_bXD{s3Sh7n4-o!Swf^UEM^U3RPccH%vWeZ}ct4NF{V)C5|~p*!@S&3E~%
zq8;2@y&ZO6<mA_|_my<bX1`6r9n2S<<@clcMqoM2Adpl`=9YPzAj<NJv$4pZJ(q4J
z@2`h2|G#ssl^^07Kcs%I#WkY)kJ#^-iz$+|N@1L@nB&xwcfaq@Rm#XH(<fWK?A(gh
z<P?)9Xz+ir=-)9z!B&WkrQT1Y`C71lZ`iDlH!4ri*d-s0QyT)bB^YnkbM<5t1Pj*R
zn|YR{N9r&_$I;acK4nA_1&uS?s2Ce`ax2nfk~5{`3_8D`->ma*vNqAF0SZsA)|X$<
znRqU{D4ISUJ>EDElLHW|oOu21TV_!ygdKwSeNXDkk;yUd=G9(YBwiu2ql_=CzC>g5
z(PWyucIjuy0*-GFG<q778jpH$Elw&!0dk7YJ3UXnv&W226Vr{y_j%9~$KAA}nm0py
zc-O5|@VE%sRPIf&TBGtgwXUbn(Mqq6@4OqcVzFPxrc?e@owz>MtWV0s@fpm8Mzw9>
zT!J}k&56rZx1OkP7}skaTiv{t`tD8jY&Sa^HL7*W{31jxyZ_a|M$}&_u`+Y1845cO
zqg{$(rWKkt5t*dVkRkSEEB%2p4D3giB$RClR_nKT5pRw;ztqcxfr&xZlyEco8TQQY
z>LvWTEFSIwM>Wa9a1(e-d%O7C0HC7eqeKL~H?AVHd#<hU4yV6QC|tIchjRWK#^+Z~
zFOME;SdEjhl`>Od9PPtgD^>}~#dN>Et<Z2q@y#<750lZfC9LHYvz$q({AqE$JAMOf
z9FNZJ++OmD-u}c9KYhBuK_cSyvvbzDDh(HyY!BUXt(q5k_RM|;?3E_%!Rn{`cZ{EE
z`JJ)K4(C|9a8)vNREpcnN~l~Q>pvH2or}qRNqR{QO4Qa*7ar_SU;`;t)ZT4>22s{V
z-gU6`yfKel<5}^R=m`fCPL9lihQ^Z*5!8x5e=Sz5IQ+8Ksru}9g*_=dSK7edFvVg{
zQ8(3olUydJssA<5vMUGeC^{p`Oy1kbT1V~*UeQkJf1T``%CB@(O)6bPs%Ed!GhTF`
z=nt?z*B`n@+Z~CUZptd@1))huRd_wgecY&Jub4e))aq4F885ig)_+z&Bvm{_lo+Wt
zg6%ogza|)m(UPjg<195LU?ItIYF^(s(eS6ffD7teXA3u(=Mm(2%)8yETrGAeUr_U6
z(*7-0wM1PLDPFfwz_t25RpDl<HM3m)k#ZJ?(j=3Pxv$c>8lfApj4G3m60HJ|l1+ZL
zqhIZplC8B?{~6M&N3P&z))xrg<koBwQuy4@_a%k&^PwH2Xsi;HS0~Rwj???yF7xb|
zWRNCWOqyXbCo(Fd7aL}-DKVT1PrQ6EDwIS?`g{Eg@xt(BT{?%1tS-sO+_AD+#k0kO
zc3kdAT#@)EOu44Jv4H*{sI}Ff=c7p6A7El|ckkl)q;{TDp?RcQ3M@m{gAZf-L<TY}
zzkeq+583<og7iYkzo(w*C0b&-woqEG%W33;j~p2LNm8X%KmweI4kVBxz^2Iasmi!F
z6$S(}7g2RaUiwZq2@6z!J%&jN3WSsw0}Zgw=P`wj=;YR4LkEt*d?`fU;xny=!yYvV
z`BV<hfTPB&ilwDP`^Glvh@K+i1h>2A5>Yk0o7B<wB;*>O#$z4mk^jAUcEDpAd=a)l
zz<f`1VZ#*=a*JcS{S!}^dY7*q!GPC15)9RABa{<99G4FL)xD*_akTr}8}|$guVewy
zK-~tZkvrOKZ2;*J2w`}DkC$4RLusj8#hD}kI5y{y(qn}ZjO>||T<6Ttz<xktEaQAS
z2|Wwub2c76(pEI$3XWB$R;w^2LyK(+m}|i!KaoYpU6eAFm9<LiLJsPvGL$xBFDcu?
zpR@3W0f?}z0Py|)SXzPpH`dqv$R9|Bw`B4EjSuAiS^hj0j{oju$Tq=**e?JTwzs8!
z7t5Uj@|)9+&8kQ{c_SuyxYqLh2XjX2ff*PP1FDT|bUo}Y=TCDO)P6)W{!;Gh8WS4z
z@u^?*jlF*^j2z4G@_8ehxn*$KU|KRE7a6QeTBe9=algNJ%_#$jA+-+J6Z{gPbl9|W
zS%iS^)(i75{HHMIp|%eoUsH;k{|z<&M=6+?Sy>qWJ2@@dn(+t2$bK{Rv%$d17pnF<
zBgD_b37if<ppuM;z+%w>G>&di7mX!rQ>4%S@@#81s?u3_dj0sqP8#~EnAJVk)a(?!
za;qWz1nf+|08t@Ezuy+N4!7cc`Y9b38^*RkKmCqpQnEMcZdk-f4Hq(<=|3#HH|+V3
z3Dk#NhS8^@W`>SEGW^;;a0ULbKutrPGzPs|g1+D;;t@;;`>K3W=-;RfY5N~HGT_gQ
zUS|0FXdR6dwe#pax2BDho3{1P@}<8uY(mU_D(l%Hj0D_b*{xrdPnvA6qL}2D$28Q;
zn>AfIc~;!ynI9cHH}L0-O;#a$GC&oXT!jX(<by<@u}*MIoXK!zqWAKp=0Biw7V+_?
zUF`oE?T-FD<K3{s*lXKd<i~#EN5|^)<rXP8A>JW4R%Xt{9&5^Pow(oQ65zvV`DfG#
zr=WP=hRESd6(6sD34z965e_nJ)-*0fPo~57>*Z*K;zsNSuCh_{tC;gLN;I=xZRT6Y
zn}p`)Uwsd6TJ*@fL(A5;5&_=0Yy0@i-m$H3gCCs3pSA0iw<^A2?yuqm^5Eom_h_G9
z|2o76nqrU9_>rRdJwl0b^vdhE30?Hbl!`I_9~{J&{PP41^Se`8<bAaGsI$wSz;?9V
zE~U6qEblirqKG5n_OF4D<iza``940Jm|BE-oN7uYAu<ERkE?f>U!+c0T%8D}w2nEY
z!9O1?ybH1ftiwRV_h504Ap1!QX&spT#;xP|AKbB);#e%uChgm{FC)%_G>F++MKNC;
zTw8f~x9|<m)@IFj$;BaDxkXomFAu`@9FF46+`(NiFYm%BS(LUjHQETVUgU&kM59&V
z3cQa|UV@yf$pXJU$hp70Gx-Vh5TVG?_O9U!^XN_%UUWf;*NXxa!Ky*`_W)rl#o|sB
zJ1E#co}ja5PvWA67#viq1Pr_z=qZjyB~pr^YIK{p!AO#B&o#<}FuOWsY=wVYjOESp
z58rwI5;2244;YLLpYjB9uN#>#5y?B{cH?m60Sxd(AZwT-<zGiJz;7@=4Y&GijfYaf
zGf7Ki%3&Ir)UVIrHaDc9g#)3OW<Y`^vJO8ldC2d#-u4sqGbj>$UY%Tg_+s~P={XYP
zdJi(K2K)0U)JoNo(Nv1iMX^$YRxr6QGZ+XD)l9^|z8o$AK~luZ<&hX;#2LivWuQ6o
z-p&cAU;)%AMoUp)#Lk3s-@Is4poQ=hMG}-yOU+ogv)tv>sTVUMIaWq(-9+ey@WoEZ
z5IykNhVc4P`{{F|5<qon_3vdgxZqI(6}G1phD@{^AOwOQFu6qt<Rzm{SS_N<tRi7U
z?<^=MDGtUUB!bX8Muv-rxF~oDEvm9L_L(gb18tho0rn|?A1z5G?(@w3ZGKB^c%a-$
zz(TgE&9aRo?HSEP&E^dbFJJg<HaN{Fgk9dHME$3Z_wAK~jHfTb<;x0*hO;y+$}7~#
zxVuOsJnESmb`GhLeSTQA<AW774tP~Ev}>K?hZtsre$iePnjMDl2eX+W#(hdAGv+q5
ze8w0sve!!R1QSbD=hGh;)=NwH(OW4bL6PdemlC&{N##llY)7X84r(QjAnGNIRD%?E
z;OG-MNrE2GQqh|*BuPmXTDhVNud9>7^1&2Bn7gEN(Imj7pUo@6WEj^SqO7F2V#Z;%
z%8XH&(WIZtC<%CD9&nY=mKo_q4G%XI(3Sx=IEZ%dSNfra3U(@>rQyaGF`j#%g(?OX
zxj;ts7lBenX&1p?2rEiBfkg>fkSL)EnTDPUDM~c*iwa6QXoqoAVPzJ`{o~j#PX>{f
zA1a$1se|dg&dC!c0=ZH`C2rXB{be2DQi-?&XMYfKj06HMgo39vA>jl>Km{J>hKIw&
zw3cA<6SL$e*3>GaSz>sYBRa|<rB!0M=n9q`!tzw*%pJz+JV>2a^jA`=E~43dpxfq9
zAk!9&SHYn-?Me3MDtkT)bp|Dgd;bykcY3-rM6|7&aHk|zZ1YW}L4#^L*X6w^(~MU=
zY*xYWA6-2d<M!6>S_`jwtk@Q&;<Ni$<FHbZHWqWr^<f0_hyvo&805{@C|n`kRh$HZ
zhaNNb&iWD{Ub+o2M>s-Kx1YirIZ|lItTA$P9#Ui!-bB6{VC(%Vj=h3~`V%A&8jfyv
zhR{@Yv5k{#dC_n?!Zf^uxT(v~J&o`q*0hU!NTzn=tgG7~c1N+*O;+oE4BLOdrVADN
z8*j6G9Ui(gvkesZ)Wq?Fdn}bdu|a;6+JPOHQv9*v;O&dT)19zfmkpCa(s+J792EY5
z&5Pg*k=hdf&Gbmf+0o_ofjP5~XnGuQiar|P-Iy$QB#!i+oRzx-MwNu~Fpe;+jjqL-
zm!RFbikfoD1LQne?fGiKHN+5m9WZ9&#wzTwW@%z6YLPo~Gfc2}G5^^-6jRS<jp5O=
z!*0lO$G4~TO1_&e2Ur7myZ?$-XL_kW{JL4VnoJJ7MN1uQTy9t`0Ymv<DH)9a@PVH&
z%E#N&r2AW4{#3>>Fo*!E^ynLoM=o*sZ6aLX`Za~m4c1Q3IT!P%7tb<T=#-u0aOe9;
z`^)Bdje~^Bl6{Pj?%|DdbxI5e%$JhC_TlX@JF$yEds_eDyG5>rB&v+*`LA<KZf4OQ
zyLYkxk`^le5%J-c1_G<X4$Z<^pn&T;EjuL7Y`>FwXEx`oWKpGDfo876k%lenSU_Cr
z`MKm+U>qL7QnJ!NE5&SW4<#6JxK!xopZPjB@^=XPU**&qFHUqF+^T}HnGo~gZRB0=
zYgh||Og~6qH6<AxyX$YfM0I9&GK%!?V%V;G^Hgo0^g^h*CeymC(KleTFfDfZA=koS
zCMpq=_6O)ORYqcSWJ4uvL+*t)O7$Ef31}@;j?No04!XX|QEZLUvAq92Iue!Y1dW{|
z2W*EmAaOKjH4usCJl4T@q8ZQ=rTi+DM0~Dt9Q8^gf>~8KX#`B^R+|-qy`g^lhx3AR
zAZz)CBfT_5O6M|D&VC28Xm|(6q0_(R0x`C~8jcq%a0Hu@P(`$<tlI?f!>i~~+p<!Q
z0t<qJmXcGmDLEMnT7HeED$D6CO4`&id$O=a*GnX5xmK!);~@l%z7jgkp+G6O!C2Y4
zO1|I3UL^SLk$utnVY@0-F{Ea_Dx`vdH)k;66I2x|P~GnH3>PV>(fE2kD9NH)YHtRC
z3v}a@ky&4$vnR*?<Nv<(XEg{H{9koA&i|H7?EgEBCI<)Se^=p3)urth1dw_jXlOYT
z3d~k>x7Nf<^#m09;8V1<i5A)vOE@iU!$Bi{rU&B(FPcf+4oUIr-Fv@|+mlbJk`i~N
z#W3DTiauE18DqEohb3Tc`zI{k|J_h*P~6YCxUnP^0FR*Fe4QS6rNvN47^&1$fADIO
zY3eK%*C)Syx{^&}cqY>M*+mU`vGrv8#q!Bc<bo?aetA)tJy`iKt?3g<l#hS))(oR2
znYk?d`$aDRHqWO)c6W3p6)21@)7!b3yxEw|!c&rDG|=|7IlFADfntCT{>PsBe1(T5
zvALKxHhn@WXGk=MLX_>oFjuvBVQkqe^|M*>em3l6eqqqvW+B@wJ8RuIXUI~CRMXEO
zUYLbF;1D>M7WiSvrxoGxTKj&3pYOt@8cP(?LOopSWxxd|LMNb7Ze@aa(N2a#xP=)b
z=C5r{Q)(ju?>IYY`$DP{L`p4+_Y+C{!iZ@SqN*GW>?gdyjGDX@<hUVFhy3vYj8vt7
zwtDD+K`RjywvjGg`D}=TKw(5Q3*6+;N?3qH&d4;wm20(Y8p3fPK3wLv2paHvoM@x|
z8y#lU&X@taT)_R;7714o*n6C27HM0B)t-|te+nkfRJfVFmPNZsXI$&N>=Z?Be0nfl
zsX~y@rL1HjRs0WQ66V8(;|1kT9@~?mUeua@6AInNcA7x^nvrTwf~;$<HADB2wLM(*
ztN1d1>F=P#umrd)+?ks$Xu}<Rj9L^+Yb`6`70{Wg^T>+^n7|($5WY`e;4jVx%%ndp
zeM1J9v(!pWHHV5#kCQ640Wq+OJGlv!Apu3G?42^UqJ}$g=m?{3uQ>`M3Qn-JJU(r@
z(g#I&@xR9sK#VI}9TPgS$#?>X#oF6{O%6&rKnM?xwNHzi3wje2<vAo2tN$v`<?OA_
z<y-3(1wrXJ_lHWUxFEykd#A6mODmT6&|=?^TAkc8)4UwXU7HBP!OrI5-2pXZnU-9f
zJn2?pk=r~S+hquc`zdrdv~>I1fRR&8T~%V%*Q)*lWI_G;{J+A9iRr(^DJZ)dxp+F5
zGW;jnjY;_bC$A7EBlG_hS61r<t)aZKejUfO)>*40z1gu~g%Yk+iyywcuGKP485IFa
z9RVtgWkPNk#weVW8X^G20)_;|LRkT~ukh>KW=X5MTuFO_`)}TQueCGAkMqqs;qtfF
zxA(RG4Qf7}3H;7W_r=$COHcPM@<2E$FTT7@ui1lGc<e$hhn=evB9z0+J**qR1#(xw
zz3xu=87~NL#CRm0P-BcyZQOilV6MF4Eno9ThaO8F$>&b{m5cUT_WFzohCg!bmFejZ
z9TAq;Efg?A^b6UI`1S%9i@855dUgi8?|aNqAJLqlA778uS`NVRe1f;gbVkdB<`9NI
zpKuNBQ#gc|kdssaza)ohgEW02rN=3=RX;%IuTOV!boT-E+M;UQbffA$p=XA7z$Dcs
z$-f!p<J}V~M5WR4v990&6X4+9QQqL-uFn*|KEbHW#!{HfP8nq4m&bwNL&dy%q&mYe
zS`mDV({hE~d{aM|O!*A&1f^>-Wr&H-AYRb^ZqA2S@0(r1Qd&}4tX9Z<4Bk}bKSGta
za1XGG<9|fU(cOaisC~Cmb96%q?9#x!5AyW-^7eFCXEsu<{RCk$mGDasFFi(0_57j|
zfL<(YFy+xvjyZOP+WHwrPvkFHObnd}UKs0v+KmkK5K{RMlDm;wyd@+A9{;WG2I|dR
zeNds_1Kni$*Y*9_s5&IuM9?!sVvnR3^!kY-bs-M`?1=7>FJZF&&P4uHEpCVO{ke+B
zW|K(*Mp%6mAz-3iVqJ1EgkmV{gV}?W`*i(jNXW&|Sy;j`WLQEnU9w%mF%+aBDGA7^
zk&=TZ`?^Ljclb=H8Dd@XlZ4-5lcZ+IO_5xKkb~iaI{PF>_;*Zq&`kLnLbN1ls5B9&
zA$Sc@8nUaX))6fspbc>jq|2ysgAhg}4UwxdoT%CnwS(;ap$=qj$WlhXM>Y)y-MivF
zka;;TuMFu2V8UGyJJ~MXd*JoQ5s&q;dyHP;`^ftc`(^vx=HoWyza{P}p%<0TXRWjK
z{a>C+ILu<OIQE}M9kOfLbM3=o_hUO=({8l4{$|_eM|6nW<9yNJv4mkQ@VY%|q&M_3
z>dB;Q(EHv0{o#q;Zs)a|Ya742iH}d>l)jcWm#iP{GVMlnljr1g{2H7{Hlm2n(fR$h
z-N}plOmM!A1lzfI4|y1_`{yfO4-4k)<~i!|)-Rt2&rpi7B7pxk78-HGU<T|;@e1rY
zg~t+;5Q>7q97cf!`qyHmUlyJfc>-GP=Ms<rdVxr+ccAJ=Y*-apE6(TRxs8JwTy|l?
zBwv`A8B`AmQKtQGqDly|N^g?;Myyw+#^U5A_fY#tcPVtp)`Z&3%4ek#56^ok-u8AB
zpxbI3`5t2nC;Mk@trR>g92_habS-6NT~%ZF1_)_e16}Tt)ddXv&w@-uvIKd^Ffd36
zHOQ4k?dl7w;9B5$r)P|A5xdnQ_0!-%)zcC^DhaY=3FnR$AqC0nP}dIP+&Kk0_P=Kr
zf?TSS>wicuAeB9PoBUjUZ<Z~9ej=xbXcH6nlN6JlnSid72k;ETwVVx>w$bqtQ{QW5
z%kJz5f;X$VI3XJBUG>7G>x3G&QxIIAYfH}j(SG}eJXzW7c7GcmAXwJPpi!kcBYE05
z_CM?#^OWA*<8jA{_op{)Eg1RHoB4)5g_%OQfJO6w`3V0qF8~^tZ=LV=9=|S>*V%Ay
zxQnCCzmt?uvX7S5F8H(at!ap240@B(GY~#9hvD_MHCs1JKvGBU3*Q6UI;Ac(qrO#l
z-Sw=ig3e-DHe&YHJiAndZorhFDtDH((A=?ou`xr*`>U>ZfIsN-Y~sWk=N!TRt^!{P
zC=Q52MO}ztoWRqE@61ny()p^`s&)J=jqMnH7nPC0(sA&9maA|h-CtnsPjBBVK7RA_
zZ6P{-L823?N>Kzp7I5ls325}}5OxO+pJW8dIJpg!^bSs&sjxHfNEz+39seEoWiR1L
z&t3mi$m{}sU2mo>N&(USgr#AZKv{0HfeLjd;0=@L_vhJ*(62@RZR8w`^*`sMWqgXL
z^<|i8Pxpq%dbq6CL|&++(!gG|Hd4vf$=33>*aCfL`U6aikP!8rQNz;3zGvbe<%KGT
z6h98jANcYGIPcAyegW_m?Isu577d|+-NXl?&y=<pLhmJV<)GYz)AJNGC0}!-t63SR
zHQTKJyeK$P7x*v_gPmq8N=4{A*U4wg@J4H<!b)@;R3^VAhH+P3YoT(k7>;E$jA$>n
zaR1(Z#}gYXg5!<LHsZ_A$2galVxJ(ms9BE(V59A7PQ5#cl-<l0xneuOyO`mBseN#@
z$q72mp_3-jLe?eL(KA{6QYM+aq2ylE@kEZe@ww)ehe>N=5Cvfk+83}2#>6pv7S;B6
z(O&T`qJUg;>5$WyPu}qOdq;_s&;NJGFfw@n#u7$ivC_qno7_;^ZM|n-$(Ri(`g-en
zo%us|WQnDeCqUe=Kvl7>DuNq7X)uK=%coH%PLx2(61er>%vHX=jSUSS7`LGMA#ncq
zAC%HDvT&5&?XG2iL34_(>3@g5CaD^jFFs!_vJ~uzpZxi=)*q&)2yYYc{D^{5BOl|w
zRD&Df#)f9B@@%BC{^C>-!6mt7=fFmK9K5i9aY&_5)>2hTQ=%<~7O|jcRR|&#3bcN9
zgSCMYGdYj6xOtOVjV3U`h4Thm$p5MPuWt^)zjxAD|0fPn^pf8~!u-*K=^<83Oc_K5
z>jA`IKD`7tlMI`uW!ivcwNyJPGjICzmuu&r<!NPk^_#{Lrrg{m@NnR#+ec~<!tZsq
z2ntsSda^rJo*q-QJ9ONKPgu9eTF2Tk$Uaer(&L9ZhPV}d5Pe03+cP@S6dOpsD%Wk$
zi3OmWyf9G=y)Svj8-)Da4UY6@`n>BK<aXP1kNZv8QHX*&v@HJ%U5Npvc)#ESLQ8kw
zph0l3s+>)f<d*fp6lRN^53D*qxx)BaviA#;XJrao?XR^iyp`PzrP&nCENz|b)%I3c
z1!1tRCwFZ{qxj5eMQ9P}XXBhErfJhPtdp;E&;<9_GjL9jy3ifw7E)qnpO*X}(|@Wj
zer8JC#u<U$WPu_;i%peUhHmj^GJh*UBML=Nx%dr&HysHphWR??{yvXCUobw+|IIGy
zbRhDP@fH~(4HF+dO62KNv%0?;QV3W`dWHN&2He_(NW7;g<#JHY@&x(M@_aff8Fd!d
z8B;zbKy~S!fL+N0W;~P+w`&HSQ<;Eg)t}q`;mjMwp|<Rx+Ksp7(R{sdx)<F=8M9-(
zUgu)HM+SsF%(K>!;C2|qxhH_1<CfOl;O^x!JdyQ~!(H{)jfcwz6e+Mzc2G3m7d(^2
z<KxThMnu>5ow6j!YQDD8>yacmIGqSDE%kF}1sclwQ@40)a9D-Y49X9$`k&;i7Sk<r
z*qnM^U#G{*RutT>{5}hbyWQ;iB*s^dSJ<7U<=@F{v7sY=b%isDU($uJooLrcy98_!
zWUB&BLOgYlQeGaHif(XQ^&5ldArj0!x8Z#ax;R-&0;#*C_#r>q(v8w{ao?q*;7wbR
z8wRlStNqXkLS4ZL{_+54dhdif$p)7rwH5Tgpm$3oJS$Rx=FPneI3r69ME?-MFet5x
z$6+#7=bL>Lz;N!PfBddG?WtdaQ5Roe8RXI~L3+ltg{bD-Z(F{f)uCMVsYaM6?M=-!
zZA8V~#PU(E7-F{ZPI56?!DCGlbbp#d3Pw*IVpp~4{#EuI;+q*DkNoqYUx21x=!Wls
zP-Uk?3H}TUHr-l|hO<F+BWzs#tqh(tM99R>CUd}EfF`}0Ju}>z!bfmp4mBl1`~20}
z=1=}hUKp7d#C8rD4q8Q>6466*x0pO*2}i>k797!_pTVs+avpV0-nZJ4R238nvglSu
z!m0pd%*@qX%#2!t5|9-w&|S*IN6P}Gm{b%MmEmH-rBvg~SrC8un=P`H73kub&7rWQ
zdv7tYOnO(^fwWOpkFNFo$S2KpRVUGyu2jT2vMy{qc$e}iSQxX2)B>YJHu^8=^~y<r
zQkQzS^Rg;?-!yPH5j=>LuLj1@HUYg?c*S_tKZ;-H@pAd{$93+)LY8m}fk3}a7fw(-
zyX+5v_1*|Rdl0?OOdh!9J4c5EV*VB}adztzfT9WEwjJJ~$}^nZb1SXnuIV@!=DBKq
zGc#iPpfs$*LaW#3L(TG_!2}Q#P%#QR;p#B7Z^oR^s}S7(d5pDy#{L(n`xQRA7ZR$L
zl+Yic9)J?#1&z@CN*>n*^lxt0a3DRsUXKe5`W6x#FA*$GgfB_u(u(z(eL%ERyA!uQ
zIh$j(Mp)>#SgYz*Av<}nDCj8!S@=kqLJ<UK&{u99Gdp$%Q&y`AWYKuC^nr9y=U;VN
z$QxkR%^MQ$lc%~1;{>3~e}o}sikltF5Kb3f&p<Ui|7tHrA&VaGdtT1yr-fiz{iaDZ
zG7`_z_E-e6BQYP@xM>(cXja2>wIND5Bb+X`2D#%z58!r9mK`4y$JHw$mj<m_=H046
z_7|uUtwqioAGr)@B9<Xd75@Y_pIf_L?b0aJ(o3?3GKo<L)++cS&h+(x!u2Wz_CMCH
zo47iOMR9VIY1kre2If!8%L~G4rt8*p!pQ~XJ-#1yTZXw5lq|C*?lw2jyd+ff0oijI
z8X(@zavK~9{jnF~|D()-&cNesV}R((wBF7Lv|aS0-&Hw9KbH%lTR>4ue`IP8;@TM~
zGtp+{)1aIpfjL+@>a7#?iN61sf_Hueid4^8YCn%R<f|MXWOhY0^$($EAi)lVV~E{1
zGZFHA@Ry@ze*T*;Yj%7**{+0|d7tB8{Z3jFkOC#9Z&+CYBUQ#c=Sj_hDUXE5e(*WE
z0~bNuhIn5nX~n|XR3@7ldGS4HEDjiuBa#HDGnd(xT<zi^DQKt%3^ZC+NX+73#nauh
zhmyJpEcPjQE)j6}z|(y3uEKgxNU~z86yZotFFVjBN4UWfP|f8bBr)CwqWCO>*fAs8
z+Fm+|F%uL=Y#D-ZI`B>Y#Nu8C4ZT32cAY;mwTZZx1xYjhDpBezXvhVzD_Zl1yVy5o
zLVoA`Z6l3>pxQ;W0L?Ob>K}B}0<B>AAAW=!=MlKZGH{4Ln5RG9O>)9!vGrOTzZtR^
zjg0(+vzzu%AA<!BA4>z1C2N-7+P=?^2f2j^%A=>?E0BvfZh2`#E0?@!9;#jTLA?a&
z&H|n|AqIo7wjh3^3k9*N%Gl6W343F=g?|EWNK`|vArkfr|J;~`vs4$L-NvTJYIMxZ
z4W6SP=`iB03FWOt$91V9SWLYP&U`u+Pw8ds6Rln@vufFIE?698rKJ;7-QQGsgt+Y+
zpNU-OzfwsnAtHRkyIg`K4(~|-rgq{!=0KW_LCCq&5wQDRU$W0JoP#g`JpDBUDlu%x
zfPtl8_`?K&-ZX*%XXC!#XYZ&Go(Ic}-%0t~Gu&&XAXERg5#5*tA++t{X0!2XfATQE
ze(+LGsU-O6iyw-%|FXZPsHVT@%X9e>#A0Nf5uD8voh+8Kp^ool@D_o+Rd^9$<RHm5
znI79_`G+H{6&~BpKh#h@3I6itiwVO7B$r*gW@;%5Xqy?4ANB(Y^La23%J_D-(jR^{
z{}R#MzaQQI$(gL`Gk8Ys%(+kzEi16B2<Z^Myx`1%LZyFZ7X%BWI}wgqBhhHnvh5YR
zQBZHx6~hbuW*;^YNG`;Cw<ABlU;eiJ8gK}lQxPuy3=hLf`>o1~2=N$Om@UJW8Ouo#
znM}`e%*j2ODi*-$TQPPya4E|$tK~59x^vTqjEI>lL~O~;XItI8y*{)~2p2?@qrBCw
zLkLJA(;^9;_N^Ya0nfzyrJAX^#c|<TB7=sPjgfj>zS*o@bH}fmpPpLE0qRKgr)-6+
zuaYODY_MZ-Ib|EaEap*4evv%*%D{OqXxMd$NzDCV>N0J!n9boZU0)FA-x%7X2<o0j
z$shqjCm|Y~sRhbodd7R&oWoc>7Onl^GZ9YQvBXKB>Jz=W&NKucSg*qDS$Vlb&tRGT
zGmx-N*zNcaIsrIm7mjSpFFkpAyL=AoH3$(&WkF_zvw4&Zom3(cc~vCq(NW0&1Sm#L
zs@LSK+?-S!HN!eLoXJ;@uofoB0k^b+xLzW5Y1J6wJ;So{0_xVneT(Q8+?*2pI^H-j
zNs*xjA=dxF**P@{0yN0BZQHhO+qP|c+BT<c+qP}nw(UFjX?G*OhmHLYRT&kPnJ2-J
zN8s%{F)T2_T{fepypta;^himM6T&`5@b<2+Lry*4ugF>g!A6;|r$^D3ZdvR{D)?a$
z{e+E+`&f5N$%+{Bh+h&e#Eg`Uk{hUq=pk-JoKvdONi+e_uI25e+sEFZUFQkVZf&_)
z_AR;PgR@n#hgdYhZ2iJ+r36bW2-_xF`+4zU=|$K?JF)v<D_*b8@ku@frKMbTgk|iq
z2q~<IjR#*~1*UGp`;*;wfOY<4evB-D@n<7lm3*Lok#;Q$?=N>5#SQ9ix*msuLCqj(
z3EDNtCA7zvBS&I5==JV;7`3~>LyTOoFGDx7!HItcFhFu#s-00FHcFXqmAwL454jFZ
zWdhBsZ0~I0RK>N~h4U<mFFg?a0&;f-?Xm|U@BTVt6<U*I)J@=ZM-^T2hZU0E&G$Bc
z9}L(lj-UAGRpY4>`GWQaOqyQfW@jI-0v;KwcO}yLEYk33#K6b5f#HSPQs09!8?710
zDwHsipMpo`EBA_TAMor7nLD=DJ;~?^57M?pXV0qO%+fYtSiu5=HgRE5xC(MPa=q}W
zg@xg(rdmHH3S?oBlpii0%x(M#fVJ19Z^%>wnnVif>9vT49rMF^F^&#gLQ9u0L&Jh{
z753Xyu&NIB*2~JMMR1nt=<|omAidax3QpmcW-hg#cW5vw05_|}r2Tk+`3!V~<*H>W
zVGaKbgu60ww;m9=qjU^)jdE2!HnsQBfE}M?S$+4B1=uW?_vyZ-<IS3>u9iI_IeQP?
z#r$-A#RypML<~-qvtQF*4;gYX-O)Zd4}ym#kqr$lFhx~<ev}*()K=N+u;N;TGm#v9
z;-MF!)&b50>)DPzK0rMmum$D0K}D?OL$P3iQM?YLil+}-2!-=C>2|9xHkBGSV#JF1
zS^zVZFE$GlqJ_Pal7v<YgI9J7dvG%ts*z<l%z-aEz{!|Tg`5OVkkJ5?V$A<#ZWG2^
z?T4Uh0%zUA26$3qkMmmC{<cEbQLRLlvpLnPnv7jv*$u#J6@j}OX?6MF39uOX!^4%k
zDI1fg<P}cbovN2V$q+DT)Rq#gycM@!V9S0dlSJM6#%w)SvGmo^Am{7`j<5^<*$vxR
zD#mP~vnIyQfDS!aFq6$rN}9J8Z(FoS!I1&?;1KTxII-AzoQKk+kzplG4b4yg#)a;+
zp6w8PoPDZ$7UI==I{5m@b7rzwlg3L4jXc!PYa;1pl-INy26EX%SWX?akhXHNGIvsB
zEYO9oVw#Hj<C)B<vg?AaQ%KN!^a2ur?GOV8yWJJxhyw>TMJh_VlJPxk>wm%9A{821
ztIuM8x$xoCCCe#_XL?@PRe}F@MdA?)hC9-dWj5R0(~hTc#V%T%TDx`JjpQwn?~XU+
zZ!aAicv*Zh(BMf{KF7VlzYdO`@nz~x@F@Ozj$a%mpl%|m(L%g}`3AF3ncX70I~dem
zVC2STEpNYpZSw$1D3E|w^5Fp9$T;VPnFfxgvWfp<19PF04UVReU^x~1<rO_f=!2#g
zH~2xMi>G%7q!9W%M2Kx4XS#uIkG|&nK$;~o{GAvY#F{~p%(OPm+6SwE&*cri>)&;F
z>YH=}TwOsXs_C5rRb;S+nlPFtO{}xfXMEjTq<YfqWcpx7pa+~~9SASZTBOBFPRSwa
zfr-PSGqa3(<|KdzI$pdw?-0ShR{fwb<-XYnCj$f*GPC@!LW>Su6NENrWJO&*LpwP;
zRlwG#;aMq`|J>hfRW`E@!NolJSUCZQTXh2jn<u4;q|`&sJyw_DMa%CYbU^2M*Z$?_
zFnq}30=@X~Knr6NTG&RNQ(xP5(0@!~RTDI>zhfVq9@FxyZ+{;0)7_C-u)9vQ5?W(3
zIVOzE+-2?apcCMMzJ_+2zjlOw1clST5NoSnOygqm_h3O)BPU4OsPo{ogOLa&7wHxj
zsBGOGHxEKC%$~=eO}*8<HYu-tpgOY4igJwoSS{Hc^Z8>+rjZRgcM~qqxY(BU8uqw)
z52LQ^QF(%*=?BE8+<38z^@ba_xZqc#`|&IWoA&y&DEoVc1?5V;=G|g{BSHz0+x<JC
zwkWT=qPn?l%w$>rWXEL(r;W?Fr|vr+o$NL8q4Be1o_C<1g~~g0W2PVZgw&_GCY0N7
z#oy|hl<w4>PkcJnrq|&uDk@BeFEI~o>lHf4?v6#vAF*eO@T&iL8t8QBU!J1YmXA>&
zI(#YoZ1@GH>y@0-b}ygb{?X=E;Acutk<`WL*v09jCA|7xd*P=`2l{uip$0>1B`Y=6
z72Lf&<u`JY71gTcsa?|{VY2l);7hZ+!0T>M9qTg(L^)AS<XTdJ>X7ITkytIaU^0=J
zocT5R`qJ)#f)m_Mses(hNBcZ4K7KFn0>Ro=Rn=D3LKR^p&KNR91Sk1=Q7=Jxn3@1D
z+TdkwyAb(x<oITf&=<l8GD3O?$;4ZOxQqamplq!dF7?cwkxw?_oe@of-;gV_@)65%
zrB}R0_p)V@+8`Zy@WumDb0)0eU)J}A5M@$N>|PYH{+;|inkxZqQ{U5&;8os)!jOEC
zc0ipr?oc=g&;eC15mm)<*7?ivm)cJb-+sMCBT((zbuFG>wpL;VJ<M=}6K=jH!b|jn
zb#?Xnk3RfEDy+Ggq_-n17H+2p>Y-nM;Rc*Xkil2ww$=%^63M?UCchI<I6DSrmn#`o
zS|^N#xjTErPcJ2vuUbp)G<7)PKg)N!r^&Eh3=ILgbA6S);PAVLgPtpKV#*~lBjQJp
z?xn(5xwxU<fV*M0GG0U5yVv=CJx_R8VPm$F{Mlx|2<W=R9W%fF<!>0QD+4HY$iwyD
zOVFFW=)6q7?N;A=qHo~+exMK7V;g55OKu9l6%CP73U8>3TFdX)9OC;pA9%OlJALGo
zY<53LCxnz(uQ))809kQ1777Ma)ATi#meki^OW%txiF0?$xl;*tV0ff@wi-!tFl`=z
zxa;GCn+JM27}31~g)UbbVi$PRU^DgRw`%R&AJiGW4lL;)<_#~Xw^0+OI>0TeS8TT@
zdhYKfL!^FJQ&rDv#&K~t7FW}M9FmG)P|Qy<%SyL813g47B(j8xWmNZU=kB#Rkl-#k
zUllwtSV&nw4Rectg9&<~a3ly^hkK9SFDgH%aK+cKpkK)jWuN<aPvwF}0O-Q^kse&E
zUkxQ`DWd2fN+*Ij9Yl=WT+CPyz=3|(!s}#TrtFKt{{%!p6Kk)<VcTf;JOzYC*NVJu
z?2%xtl>;mD#Eo3ZU-vyvuX*Y7r37{yMN&vx9OrbwN1dXFXb6BWZ<~Az5p{Uqhu3DR
zSdzMuyL!R8YD;-H@YcD%A~YL4;!v*X-K-)iW;@WlDCu}9S%T1&XXzeM-_@CFXLc0Z
z7QuF<JEXj}rT(Th7b{}7H}(I9f&kl3P26yeltm6694-|4uTif!X+S~mTQHq+VC9mn
zi3b9Qnk}{m>xdzumqAG|fm{>_5H$bQIie2l3!BtO?pfn1d(84AaT3SBliL1H1KGdt
zmojyz^Xo#t1MP3GFTNKocilq(?Ho(}q$la0sPg|?_t59#NZ!WIc%9dr*YR+G2?k<m
zylj_bF(R5Z6r!^@DQPp9BiVenX6D9eOS)=Z&|Ke^6TMQss@!jqSt4OA1=eO0E(<FO
z;gZ`&0zwKh$uDT%$3|8ykUhKYbYy~l4tI%3%?Yb6toYWbdpZGYw}fI9ZP{*^2K0Rk
z*%6{Sd_t8sTRm;R1o*}^Wj?_TEk(1XI{d5x-HG+d08akq-9$NKkNejL#u~#FZs$if
zk6~A(-1?YCW*Cj=-aKywnyebDl8nE>Dd7$OtR_$S4Pyl3(diY9HIf#5kK;*Tj&O>O
zSv75fwsd0F#7sA^F?}QI&+u_5m0iud2OXUXzzH^3SM?GaiN6J-#1ctSrA~abB&2-A
zQ*NAuM|_06OmmUSVbR!VvkWWuaz=0^CZ<i$I?GDBd}D(%b5uZEAD4bOb=h$gAt?gq
za5DMHCo2)vU&hcBOR1*g`x6gFoPZR=Z>@xkDk*88gS2?=fh!#!Gd;<K<}#+ur)A?4
zcoz9m#Be0%16j!u6{)5pikVNG8aqZ~{kV#I_GXhbLcvr6DH;^`tU0;|gI8QGZ+#=t
zr~PIJ!bG(!#j)>Vftqrm*0`wWZ*ywkzE7Ph(hMn`Ueu3BLU>dy^>kEHI-}yk-%&j~
zCxVeE`$)<n6`|q33q=J_N(4O+aBR1qcsE8q=_|QXmDI3mb~7Qf_u6CAnjxzN1eiln
zf)!+(#Fl&Zf^JVJIeYb_cYL^Y>)d15+z8pn8r=nSLnsg}fVI3e&e+0fE7GcOCavg4
zWwW8)*Y>hCK;nJ*B|*C|Q*(nSi_GYB8NA;k1E26c?e}{>1@=E=%@Ivc)h!UDwU5rb
z-}=OQ)}Gz3F=UDJ7Az*Q81tFwstiUXEYUx^wPGHV|H-On`!A&6|Jtf&VPRnT?@^d+
z4M-2<<CUB_GE4+0x9}MVGQ=h#0(59SkRzoR+=-Qjq=hEQj3&!Ow(}IFj?!`OL>9{o
z7yFKd5IRcRe1Rwe0tz}+l!VJbD81U9N<GEirF;DzN{F-WkKedio^0}S_EYX#&RegU
zcQT%-UHrp)kEIP-?3S7hF6+~lJB4&H;>5~k>|DK?<+1i*(H_b)d#c&jGmj~9yu-w|
zce2mM#&u?Xyf;V2Uqu*f=X=4M9M*)+NwR!yHsoy)+W-tdn32IZ9`IY)9oU`L*{Sr;
zE108$a?M7w+PMp+zsvPhK~L!+(CAWy%th{MMbxOCt~LcM5Iz#B7EK)>q6LPrE}cSz
ztYs)l#ae~&HET#6UfU<E3z*hyw@EdMBvgM*u`Dg5n#6F4x65J`NiI<3C6hNy%1>U<
zEu+~KYuL(~BR)76Og+nGDpo0tmc}F!@CxS~p&N_2M4bY)YQ=Mlt-4v*G*c`T#)`qJ
zRkAS(B@>))=6{nnM1oXOt3)p$EkJEiE0(dJ7{{%$1tWPvYjk|D3k6A;9ekgynrN1(
z<`bQ4WuVZfw^@T^vddDjP|m?-<eP-3)GuAJG{GLCS{6tv#R-efmLuw4yJl0!sEtg=
zonnOeJLprBz3wG%qqIsEEt$k@n+xT)pKIC9P_d4)yl}o4)#m;_nYT-@OL2C8J?LYM
zWHP{dWxPi364|V$sjhL^V9TH2bCc!d(l8LEzZn9qiO?Vr*Nvw^WCqF(mKetw3$Dr7
z;IZLr$5!{L8CBLTuW6aI<86l94zL+(*D2Ugx8QCDTn`P@IoP1LU~fi&ItpP2+4lXD
zg|K^;uG^8LIL-_7Yx)=Vht_*}h`*q%!KQD>9`kFZqbo7ocYR6Vk)!gswHg0<r{DhP
zfaqj&*&ZefYm&+Ev0ir)dsPDB{`3|m0b$_pJ@*C147v4yE{fZZx>U_tsMKI)28NNb
z_3tEAUT5yBz<Z+%II;V%{f|V-p~Vc^s_}L+%(xCji%<dG!`r&c*{4Ln4GE0`Z*3AK
zDibE#6+OQ8?)te6<YGvB%$eO*@&wU=B#PjzA9Rsb(Wnf4lG@M$KtGFUgc35dDjI5O
zlErsK&I#p!9Ei&!O94zStbuS?<QEhDj-K%&-2<|0A*|g<O^SDs2s({D2f;t<bbn#j
zCZUcq8w8vjJE$Q~y*;DxcK2_A_9w@xyT@sWgbc%D{A2_?^5<<VL5}kxfu3dBpA{0a
zP4uVgIrg2}o|@IW?SrJ3*!xr0B@Yx3EVs~-`{_u!PcqoEY=LD*0iLyfe^`+ai1a&5
zAUo4^MTqoA8u@1Vf%ierS^?yULNE7q6fKKn^?Ta*3kw|(_8joCU>M)RL9*noqd1dv
zk}txW4u_sAyLEy0h<^GPQcEJ-{nM_h9kJZt)=A57U+{V$?$l$;+-$<5^@tTA7gCec
zcw)7zdGo^e-NRmjY1W&K+eJW^jwPQ%JE}U81i$2R_L1>H2B<hv?p6juh%9y}0X`T0
zCZ)jYxP_%1%Aiizo$b$G9R#-`0RWJiC9P>>4y5dW#44NH=^!t#{qVfy0d!4vS%t}?
zCFO%$uGL>HBwrobUa?$E7ZQo5H@7Vv+PST6O6!MmKvq5L=EO3^b&jY;G_HmKt~=0W
z0iXVaQteUy{MIBTXvMO&y;s?XlE;@lmK!!%_@knnYB>w?fx;afAI%x+;D|sZZS+}y
zxUr-6C+dKBX0~ul`qU*GO?v+x)S{SBI=hN;YCdd{D8qH$ls*?J+R4b*lQ?j*FvIqZ
z?}<o2D?>}ot3qpWWG65Ddt#I}ZnF_5>H=0!Dj2!0XMNA*1}E-O7iil+=Efj_M1PHc
z8MT4;`~pqkD0u?UZx}m(X7wXEkkv!N#qrQDELNV&#DgOB^PT*memG}^M_GsJ=ea1w
z70P47<%q}66~qcot)7;FE(3ARLnJ)&3*Zykj5j+G?#)yPCP{RYSw`6`qSLkcEvs%W
z=td=vWm{2q3BvnolpWO&n&KPqrLjUr_(s6r_#Ju*_lc8)^T!#n81%7!W8a`s^dCF=
zzG*Xke5DQ|frRK>c&oTwZ0s_2DA^s`kQZ4uy<EPGxda@pF>pi~SHpOiy)~>mg2IsD
zZ`}N$`6IITv!JHEp?pSm>Spgg3(j$A5R`Dpu>x>frvyRb2TZTpwHT%X5v8}WAAlZ4
z;G^frO+RF!!5kPdv%!^)-jC_P5;yJ`8owO~#Zg@+VkvEY<j>>};tAfpRUmpk5D`O#
zFkEL`^=40YO)cUF+{X<n($9XP_kqHs^$uiVKC^ixw*VXT?i8?ZDX`cSQW8!kYiBg?
zh5&msMMn+CJAh`*stkX(3c{O5HJWm?Ci?<4g#c{Vx7L$q1QN1IlMXJ+Bv6e!L<qK6
zML{>QRrwTs=V>2y6vEo0huGGyA&=r^Yf>8z%K)4wh7P-S&&C4=$geo0A;9pwXhR55
zLW?LW#f(WTVrv03urn^%itH-L(I5Q^mE00;v;k$O5RN?)2fu^5tj7_PcD)JiQ_u(-
zlRRx(eT}L&7x!yCh=5Ho1L&Dol6V&40<}@66=fBWZTTs>;d}0LsC$e1+uupAtg&C#
z%nJN9QXVdfgph)RSuM{OZ?iFrViG^s=Q~Z#w$pSEd4i$~)rDq)t^N0M4*$f1J?DO#
zYq&F5K-(a1*A#;ih){H%b8{!Q)8P&Z=?RPC3x|&mkbQuD#=;EL_NZaw&CLM{-}nBA
z_1=;pe!kF!hU61uPxW(rK}{L6y@heB8BWr6K6;quB73%{?*3MbB00f4CXB^^zQ<-T
z)_mBy?}KeRB2Zp@^NxJ7y@dazyH5`T06zpi&qCir*JQ(F_7u5MUo6WRmbd^$WgeZT
zYzWzga+nO=C$Iu}A?zHD#R`rwEFG@|r#T4eYb0e+&<HbpMiFb^V0?L?CGdk#TT*(Q
zP@o$QZgc}}W|CmNuSkOPjaiN%>Z4!>bv5Y1C>8k^Cl<Y!oN^Hv055mPLj(xylE;1-
zE-69h@6OuU_-o&92juSpt_N`c(tv;wb2M%Q$}zWE5y?q8bSs)t{lMahX9ov*uoC|?
zN+82y%0S(R_g@Gur!@JrSn^kMe9?s9<{;sjm5ZH`!)sbtVIN%eepQ^05FIZW8t@2C
zs_PS8lQuseadB7tzFNH9hoWEx**Kk698gsK>Px|-5Ukxm{qE!w$ftsO*40-C93)P<
zb#AFR)TG3ryPk@r1C2a@4Im%2wosUVb62n@hgV(JkNYn72zMzt78TrbC+$m`lqDhp
zyv^@eBz%`RT_l(Q-Ii>^<0<nVy&A!xe{c7tb-KPS&`1UV3RcX2r#;pI3^SB3S2+Iy
zqQWCR{wtCp9<hbkVSAvNm-aHF$D*?TtJ9oluSzOI<Z4VOV7v5dO|}IfFU{$o>+1$?
z{J~uvKCJq;j08EO5S$6yMi3dTaHE0A2~%(1A=gdH2i392RnXQS@la<1A;&%WIKC%2
z4^KE)B~S$<3ED9QLEA2u(*`;v7Vzr|=q=?9=b@X*bs_(EJsD9F@(-gw6u6hx-#D`t
z6gCaHy*tBxPKG@W?SRI5+X5m%!Z}9pG>_0GpXD_#ye!}t#ud!5W6Ksdh?~!^=fgP;
zl(nzdV+SH{IJxK`-y!!Nv)tY>U&;;&TvXO%_y$2T<ZU#?9>C;*qdn6_5&=!T)ijk%
zwe;2GZYT0|ZH0KK1pyn`k-2_RFG(Yi>2Z?0!Bf8`;J+4RF`qC>_@Yl>5zm2`CmLMf
zdl@pR;y_f=lRT!ZO;L2fgL_KC`uK8Rb31xFprHx@NvO*QHbZ%5F4jwr)UFeCcW(jI
zsoI`165)um^46jZd9==h7tQ<q{rTVY(*CVAjnxemMV%P9ZV2aFoI|6ZX@y_VB=@Z0
z4_0Lu2~qz};_)cz4UIrBd>S@Q3y<6$72%rY#Hxl0nq7l^Fwv4e7tbEaH<x-*MJ{GA
z<UmbNtZ{8~8Ct|T+0!1U%G{P=Ng<GHxBS#=BP=^~|8iGOQA&~Ue6p7S&>XrMo=#Up
zuzJWDkM+v{ekV51>i$%p{GLpT$X!nmWRJ&uMbev&Pr{T)dO!qS-u<US!^Wo~QFJSy
z(ktN8eQ>ZnFb#_6@bIJw?!`~f%uG*D+l<Ayg_Jk^Yv1#BSFNExel+&<upfon^6xn)
zSV!<~;~{m-shVnuGDDGayw$XesmGeGf;d;Ut!;JK7U|L?u&U{Dtdv0WYaKjf5zTJi
zDyjXh^gBu`@?Pii^k<6j-@DO-H(wO6FAl?`OVk|20`%P<;tFgZWzpUQE+V6(y$djh
zq@FRvs>TuRs2hZ>`PF5w9?^P{DLO$m(f50_Y1>R~3kz09<QhcB@1S?}jyCg97sPwc
z4PS?FIzXQkeM_K7V|j^TM}p$HvGRTgx)Mj$@Za}KRKtAZ4lV7rL7DAEhm6yj;Pl<7
zi(kUcVWXD!xbc_B_%u)amc^08o^tX(-40Yq{pG%O=#r_~?DlxvaoLak-ftjTeviy=
z<{V!8VOKDcFM9LGGs#zpHvVkh(@@8oca9wS*Ha9ok}KCCiuQf*_Iz;_gm;(RgJ;5J
zShS$7HS=AS{uBA{ujtkXwI+&iRRDY@Vjba4Rv5~Z=)<|eL7J*3VlESB6@Lz+3)c)=
z(R<mJzVX^3kOg;tk2jvY9?yXZ^G)#E{UjWbrJUS_6wB0h!@3mxcM0IY@;>CU&C=LU
zlzY|lj?ZGy<o4sos}<D}fwPMD@CfT$x@!-^+aQ-r)X%VWi*O^IqTsa}0g6G-*CWrq
zy~gQ-w@h$k?-Ts`OY?K_zwA9^kAeXotYU~^vj3LS?7kO)R9&WYTYhmQ^W-Sof>7}g
zu^D!901c^*c*zbH=_vH^xzX7Y{IUp;dilI*L$*~t@f~Vv2v0~eR|;_NY_7YAZ9&Ht
zZ!YL)x3Ah9hLxEX{O$4-Ow!Lx$+VsUQ5g!T5QLoi)I0M|Mn!p;h3>c17C%==iHeq7
zHMx~)4PibfK;0$MK_8Y!)T26SG#xOgJwSHkwxFVPjl-L?qdP8&cBcG7a_qt>c5Jr4
z&=)N^o!k3DKuy7J6JWKd-xE3Ng_m1ecEYVxI#3v3|7O~E4iUM|DLbpPE!WkLG#t(z
zjqW`$jbS*OdZ8b#&Rf^i9GB&53<F=BDu@GB1+FP+5I$ROi=(M_PgdL9xH5@zNcn@F
z2%e-SMR--OGFJ>r5C^n=QZ)daxvpuVC#6@R$O%uD&@L;QjN?*T`1ib#NLbB;4}wWB
z9#)f0vx4?`B!j{w$9MGVzFLfKaH_yMkWiu>dpCrca9odI7d@#3O`T!5CiZXWK|^o9
zQ$y9Ds$Rc~Qzxy!=pW(^01bmd)B#^<R^3oDM$kwaP8UU*N%F?ycCJG{HJDuzcj%MG
z{XdrN<?WOziangY6txVUcX^cDC?yFljMqSJU0a$LXt{L{h%=NYG{KLIaOPpf{Rb1y
z7iGDA^#a)_-CJS;#o@SvQj1TR?O4#oKQe1vdt$iX3#B9pkF1@+^1)qU8?w6e*C5q8
zo%3r8R;s6g`!)~w3UfQxP9B&O+}dMCS5tSFA34#XO;l3Jtu)G-d(ouVGLRK9a0-db
zUB9QfQa}+nyLh{g1$6rlM5wqC9S3NkK3#2nA{p!AZ@fU-iH9a3pEO919DtwbZ1k**
z!o5G7RIy&D_+czdQCwPZHf)>MduiWB<C0HErVB^5+-cqD%acHY0vYBe5$2WR1bC;X
zz6X9Iakx0(<a`WgH1+76QQ3#1gAs12+nl~YO|n1w%_5)wVHt>#@xK+x|JOe;9Bj=0
z<0q!s^N)*)YR4@k;i(N6a0v|z+-Y8TB*mOR1QP@m1cfsO?4!&{{>dL1)$0nW>BqDQ
z5Wrx8G!v^5C_q>^2&lC++sf(|+HFtwdbsYJ%~GW68?W6<*Im!cYN%)NWlr<k+s)lq
z>>i@9v(418@o~&9?~}meT3Xy@7dORu`w=<cgijlx6Gfs&johij_VlpNhRgL{{*>2|
zZumz$^6)H2_ycz$zHl#gn3J8{VSD0nAR>11Qu^<KwfRH+N{UL9CA)GwwqB#hl#2&L
z(Z3V41sAw)iN6o9zFU3!J~^~$8|Gx3sTy<UmNO50_OF`aHUOlcAoHnX#1aMg^SKxT
zFeG>c4-tYx1_y!#{(Fe<h~7pJ_Y8^>Fr;{dc%*pb7J-C0B%#Rih{WNg5$56M5vSpd
zLmCI{Lm&sD2c$+wjOl4oGQ=gw%;D-0tKq8=nnN}Rutso<scGU}qEqB(2+$FtLq<lB
z^+^;&DTtF1s6$dlsP!2hnG|Fz2-XpQe8O=G;O|Xm%K18-!H?{&xx4LkcW=6W{?MO@
zjw2Q&W64|t&iWgFmppH7`?>v61HS=30rz0EodslpSH1f@y2D^`mD%1E^y9+Y(T!2g
z-QgXN<718DkM;7KY{oCb$9Z}T50)t?y)7&B&(Y(pKb)nA`+3_89ya2$FOojZPBER(
z54yp8zC-RJzvig$ykFlP%5P&CV&5+=4wT06GqdtjiYPBw*kH+`9_Klxi+<e~Db%D$
z08OAgQc{|Yp3st5Crg#HltnX}OI&l=B5s)^gQ^-r*qAJx#PpRt)_sP5GYI?x%&+<p
zAQS4dRKP4TERJDdWu}rH8v3yiK})0^7gcV%jV;@`YU<9JLQ;P*K};RBaD85m$dW}!
zS|tFq?EdZ%vHYbyKBR6xJ^U^cJ2|FD*olgCnsFy2iV3gLA(`1am9N2m1^ai0bo4#6
zZ`inL6<{xET<1X6Y?ruRQ`)XT>Xheh-TM(FCXiHFjU-WF#dWDZy2q?vHuHh5imZGp
zG)VTM7H^f@d0Kbey&<<yy0{s?jUe)9RlK$l6%7)XVQAlq6{+*Fmc&FUH^kU9g(24<
z{P0#8*TG|zAw$&3RH=qtK7MrhD)MG9W2>Ff0=S^)D789uz|qX2z1>KgN0|N4dEn#l
zwpMRmm%OU=xc7*n!jTFBdCjBdD&6NRyW*JRwlYW2(_xIdZIHA%b7;LHsu`%7oEm}n
z6_8TIEV<VWoXK?N`3oL)^EY<$slsMa>pmRYx`Y`uC4?Sp((>1hLjAXSP0DNB&~)HZ
z*yV1zmY&Y-EqC&nms`>2y5eN#Oc(|IwQf7#yV0x9y3bhLMQ{HlPGt1?s#@0!c`YFE
zB3jnWSLf@rIt5LJz{vwHf=*0kg}qU1UT|aA{gb0Vql#dvlxh;w0z#66TstWtA5Nif
zhaEzC7P2${z>JnLP^(zsZr&FExpEl$*Pxvkk9`aG0XgoyV)cm8gBSqx3lC5%7eDvC
z7#y~M5P=sO>hupS*s|tJorMh|sCtsTSDW@|?4_%#<VHvI04&xl4W!cOl*U#1vHYNH
z;Wxqer=c(#zpo81);_L7qXMdkFUIwCaJ}u^`zr114ciqI=?%d%FQ-4|vy_1G`@=GU
zY^FCnn^NMOfMWVqeE(O&Qs!9cPZC0SvJxhodoNXqr4rjjhDx7|ZR&N^Hl6aBi6;eE
zgFlNHc;4=z`*m#du-oW#af*@#6iL!B0PGwDh_7Z=?0~==R)Av~%P1|Sbs=Z7_7d3+
z?aSr&1^`IC0LfHeNwsf%E0qFn%>yuYGksU7p+3hMT&DrdDG^agvO=-yx%4pUfl@+s
zf)`Bq0b3nt9z2PvAtG&nREQ|l>HzEWrNEE8kV=x6C<hZ`tW0#zQ6v3dq+}g3*vbJI
ztXVk%Qb17>BnUA}7QmRo0YrV40;ot#H=qmwv1Gw=mGbzxcN>1fhrLl0VlMRNjrR}P
zWY*1Mslq7`Q3bT5Fj~bmgzz^mEw}8z(*E*`#K&`_pf<3SpzIh{FAMS^dTP0+z$R6d
zK4oH{^mnL4Iml#zD|K?qB`LD+9|+)}j`%4s4gB%U{4lP{Sn0fB|5=DbgWfB+kK+Q*
zrCfT3B<g)Lg$<o~vCgrgMpl5${+o#uq>t5>!|ZXH{$wRZ^$(%vi;04YVpZv=T;1=h
z4D(@Z>Q(9$?Y2X9V9FVQn~$R9^*uOpE+gFg*h`Biaf>TfmXX~v`FAW`Kzj@@0D43<
z5L1?08T;SkN^a&Ixu~)|5%!JMY3@jxSEu(YhIOPD)iJa_(H+oo(@rzpj6c|VVd&hO
z9d5<IxBajC;1BYx`XF7{+_R|J*|31OWr)I=h_6Js-Q(~|cXBwqPo+L@K&{)h&dwA*
zkT<!x8mL=WdhqOhr7iWfJf;KN<4AQ#)66<x?<+1RL~=1`?xAeN`@6XxK&sV>QlxsS
zk%Ej&M$f?vGGeMdSe`B;Vh4$RcQ6MV;qf)0W&vVk+j>2=iDI@&8!TeU^WmRRX%wk*
z+UOyDqBoVh$aXTOsr5z9OoI+#@Rvu8R1J?JA4|iF+2*slZaaW_P_W(~T-@=#m~#5c
zyxV0}iGrME;SpHM^CG~oL00Hb0~*H8H1QC{SuqGYz3pZ^z+v!S$J3^!s>jQws+Ir9
zTS-K)DE<>gltP8{;~qov16PGep%TR!W@SZ>td=T%pw&|7CSY#%Hz(iWH--Fn+#6hQ
zgK>TC#IbT2qbE^fYC|ODBNAN!Ey`x*+3;N5)%g3MaRcxnEu=UOv-&Mo`IeQA?eKPi
z9MXo+Z4wVX?|X{}=e5(68531umENSC(Qw(52P3u++jjU*no0#_0D(@X$^N8sg_-t0
z;bW5)2#5L6x4k}YhRmtCF+xvLT+A{m{xvy+&v$?sGv>}Gia?|%*11dp=jr5p5ZqJX
zgcdes_J$tfiA=3&IblwAiTD2WXsYj^Q<G<Sn5?8!XV*t?WvCOCY*Vy4jF!Vg$B8ad
z$w(9aSru>=N-o+NaTD{~JTG*^#isJ)o(a}9^c@NJK0B`#EFawXw4&UU<RwNrV&fV<
z*v62Q-D!!M5)XFg`70G6d0JL$<8`n}OM06^rFqm?a4khIUho2U=jDHDdpLZ~si)Z5
z7!?++T&%k1V0rP_Z$gqt)Rc)+)OfxhAGpilFPwpSJ={lLuK+xew~BV?z_w!H+zQ6e
zvliJEaL`YHF}$;ATg>d8Cr5Shw*&f|hlmXGW(@j<v~g3}SV9uVt!rCp3`XIMN9&E(
z0@d6&J&_Uhe-RbkOMl0JIw0t(x4SUgG$ZXx`P$stqJBVfGPL=@Ey^?}&E<+q`=_@f
z<q+~|>>9yyb$aB}+SB0n7t<D|J0qQ#$=@hMtW>RH)P`S7!={b(L$7r5N^F#1YIF)&
z*R~)rz<noTc0LfM`&C$$Zs89qKp8{SWg7ASc6|$3K&uM>Q#LhbY6?LG>?pCU={6?r
zglgn!*?+UHet*~_dwaipcJ$|Yp16OmDm1P4qwXSlXS%e(d5(4!cHt4n`*yqM??34J
zK7Wj!y5V%X{EVGvV~U%PI2OZ29Z0+jt*=QLJ+iZ>PQOt%XrJfc?6!D*_<T@%T1gpj
zGpe%CJWVGyozK=CZUB#Azw!vUhTN5Jx7XZn2A*vnPvmxclJp=>Z<ckB{Qq%FM8fD!
zvPQT&6EY3w=z3g^w4yoS1qi%05l!+oBQ}v)R>XprnGC0&GM9DFhHK|!o<Ka?U}(+E
z>)Yw0O~*^^qXqo=z-QT-T7g-yhDYa216|dsb)i`eR;HdG&FTK!a$V<N@LzN}!S;Z2
zqk*NY!LaeNI6&l(uH%8scD>jm=*;%K-rjw;Fcc2KW5v?l*)Am<CA17qSI_I#uUM#i
z8IC319T3A%Gb&ByOBF82YO($qK1z0GN27A3qD>OSJkj3v!iKi5?dm>wRQ1i)M?M&m
zKv=ZbIZO81v7*;$^V|Os1y8ao7T+HzQqB6|%62AY4y9+OUTy;<B{Yq8Gr({)2pRrc
zpGTh&?_rJkVy*=5ScYa`l6yKS>xo*zt|+lBQut}Yb0X*Tgf{f#_DhIlPD{<}X*B*M
zY}u2pMXsUV(9@u!L`Ij|)!eR7gKCuraS1J&1}E6N`fn%e=z6hLzT3TwVeiGu03)<s
z$Z?ieh2wqcXfaW)VvDL42fs`y05pmsAJVdu4ZWnbq*ZSVX%4hWkawhPCMGL)3}*DI
z?N^Q*{}$burcAYB2#LQW%3eZ`6dZ#N2IEH{Y5!V&tcwUsKeSoq;#8;#N=Y8$PS%q{
zg=7%|Q?GcaR%W5=J58xd4x)!p+1HPy<Y|?O%R3P-gLuS3*TnmzUuc@~yV@2`LC`MD
zGa<hs16OAPj?Nq$QVexPG6ty;<qO<1-4AeBm;BMD$a)!+EhkJ6)f&BtPWNuqj^u{!
zpFBQ^Z;igHnkGq<AWQhHJrSU;!(>*)>`ayqsx$QaHXS@fL+qK(uxE1s@Pq8ioO=eC
zSxXScDk3b3r}OWs-`8Vc36L5NAgu!K$cGKdTGyLt)j53=0UKa$Nm$@c6ndm5ss}S;
zw7EQQkb=Q2FzvX^2+H^oGvnX6U+LodVgu2R^HrwL$OX^@{o2(r&pCtbk@fy=sxGxk
zo}e&m=T$CIDA4M5+rgQDx3A{i-%)!|dRXqBrwEI~XNBSq#J2y0Jad1SaDdRpN+b!+
zDoe@=$QT1^o1r*%HAtp$CeXDl#{_DaZB2~m%R(M=0S+P?8}*;_Pn=U{`96_FjOd6a
zBE&%L3N@}l!f4AxnsVij)!G^S1QXAeDbR&{Ou*I6_T4*7fxR%Yi7z)oa3zUdE`sj5
z5&tX`>(t2mWa?H1BT2S5CmrNL)w(vkmNCO8GG<aoPR3WFlfNZNRd@@PulZ-E%}+q?
zirQdA%ppLvg4AFtpe~UV7@9+Wb0%+BBeRV25=E%!3z}0yo%%v($v?;u9vU^^tjZ4D
z%Fpd8x;mEYLiPJZRw-JD1eVc_IJhcih>_wPey_!Z>hW$(f9{W7*|nOxQ^QzdW3aT8
zV%@i-k(QzG3jNHwKR{3kp7^bU-9C1dO+}n1z6#4*0>9w366aGC$ui*_%1RU(pJ#nz
znDCEHKi%$KmTT2^5F%CEm&{)Qpu&3`!ZM|X8$yq1B|}*QsRnWiqFsA9L3IP5=_7ut
zE#&iq#r0p-^XqeFhO>cX?4z#zR8*Gr&?t;1i~F#Q<Rz#Cu@MX$=hrZWv&v;_A67*s
zwMi4s#a|_6a9&Zz<3gE(>ut6g+!i=b@>KxqJ#g?K*jrCpaj0^EFB^%~Oq*E~EfY5i
z!h!>Ch0b9z89jp38%37^FE_zmv?=IMmEFYCot+ekQ0>KuF8dM=<`5Q}RRdk}Y=y@(
zVRzIwd^H=~&wKlzspcMEzjJk}fRZYd4a3x;2pa{ST%Xex);5@<s2c%P6I5d+=$`!L
zG!fp#obP|RH2B0f`-FW~1xfPaq}(_N4^5Y;3$f}eAF?VB#gu*^;`IOtc0%?2z%$gq
zF4ew?8sn-RS3li+*WDKbSt;cCs@c({uu~sIg4iJvWvW)dxZ>ofsik+zN12hLT)A6p
za@Lh{)6!+s0gmdgM9EV6&HT=Mv1_;CgzOj0n%v!Zy^0vtrHv)q<-7#-fZHnCgPiRA
za9_?{&N29>f*6jy{P^skGZ0+{-5P%RKMl7m7tn(7HbJ)<O3jW5#--g0^?*i_@H+*2
zSkm=w+1?*chfHlCbS;!UbGNP6wVS(}sSGhw>pd)Pk8)qu@)KU34<M;BSzbxvBZE02
zIya~cq!xf!0zdDW@QQRP*wD8;*b3UJ3Nh~Lf{{m93GvjMy>(`MOvVf{*qxF4I9Im&
z6S$Wv;gzhabx=P*o~87K->J@uzu#Yjo3&*#0(XPxxnE;_H?q3U?zHwEi}gHRoK26o
z9uPV`Uo)H^yl*!tcRcDL<uAApZtI1JV6dfFENy1g`Q-xyJ6)X|T~LAL@>OP(U_B1D
z%jgAke+&oW8HSs0C9qmI*Z_%H-RCnvYvZgxg)65B>6jx-SUyQ^$V{{BLSN9#X|yK>
z=O3XLL1nisAYMng@5$v3VB2f8grFR~B782$z@|yC9jk{>_!t{OjCbRQH9nkVV>0Uj
z*_$bePPIOMjEfN5`I*x*u~>(Ka!|7I5zJ;>o!p+MM&H_R4(7CWGnSlX3zcSBdGycX
z+e{siEeKzQ#Rrz(N@ae9{;}K~R~=({9aRsNB@Qys7>p(K1^{3&-~%*j&=|Xz4*>WW
z)zRG98lU7|#+BSOvL)#Kg|03PgmSB*R)16?^gDbyd@_76vKw5YGh_Gn6Q3jML0L@#
zV4?{@rJ}>z4lvE-*&RFia#`2ScZNG#(7mFvhY{KMM^3=ky~ShNzW{iE3_H#J>Zm=&
zcTM5f+to&E9Go3u%oD9My*j-z&8fG%LGb)f)WoR*#fs-1Az3Ey>cy)S;~|W36Su=4
zZh?cqTjSc(bXkcM6%tLf#;R?kSvxUXFtnnlg;~j$-%Kr7@YgKCi5irw)i6LqFNCJ8
zmNz$+N2G*7tI~22QCs52^VnmV$xKenm`e_|w3ZSX1vOIhej(L7AX(}Ye^IsW&D}(E
z;Lsc86tGu}Nd@A^<H^@`tP>fblz@&$mm>boWTQ_mkd-C~|G_qOUE#i-8QU3H3pfi{
z3-IvD-`2f-Gz1;%SE!OSNM)u;5Ctz+Pd5QNy1ZguoM--H$AtE;_%MXJ1xey5zXyu6
zsnb}Gl{9)u<Psh|ur?v!Y9(vzc!-rbt_FVTAbk!Z6`-Y2-{#_Ye85#LSraS}h$%o@
zW5XbtMlZSZkD-2lJ}lJ3AxEcP?eb%SbK4!G(^b}$xf;0v`sGDDheBoX3Sxk82Siy9
zeOuHlo(1TJR30qVsAsu=W(F<~R@OjOpZxm?t)jVvQaUg4w{<&$5Jz2^<i^6pqdE&O
z`FAnzuloradndDHk^&$qU%T?3jA`D%7tX<xf{3ft5WIBZj6Qd8AwsZF)9H<|#4?=C
z{fSaV(mqZ|E1|TqUq-_BQiLKMhy|~pH$VJ-xqK8dT0Nk<mervrMjT;!^gKxu0I(`L
z9HV?zZk9mbDEyE9?)fFU2wjmzj!*%6M+wmpD>|?cZA7Mkula4xT=jcWvKXAl1cs`i
zichX}47j0txn;z9*t%9p;W(aI_LBM1^Q^{giEu(cCo=+mv#3pm{+w@tKvd-MnR27Y
zQJ?l7EN2Kjmu_U&Wv?x{Gk-wMxwM6=78YRy0&Jdr2;)*0SgTABbt=m0hJ#|96;BQL
zId3Rm?!yBM@-?v<?{$V+B06h}@L?<ri&487xH;`B`#0n=&BQ0{)-Ft5VQor}o5>+U
z^~}QQozO?KGm%v;#N?U&H2dz1%#be%Rb2K^Y~2Km>%t8wIEdJN?*MKJ@C5#8F%<6b
zej8FcN<a(HWugY@vxu~;Whx5Lzj<6@<{x$L@1g3cvuR-cSI@a_jC1DCg7i<OkDIRN
zN5kCWBhw=j(?&xR)$GFf`%hoyW&rq^Jjg6T8)<C;v4a|Ue4sV5<@yr01e!|B`<i2a
z{ii>1GlMKXXu9HUxNioM;0E&qHEQ!F2~aFe{{F>{w7nzc_%wbz?GxNS|GLuVf>dTm
z(6!OT(v>~*hkkJ-<`#2$Vunl4mPlr^kFf7+{+?4|OP%&QP;Q|wNL#GqZFZ-RSK|2t
zo6`JbyOpuSp+*g4rb;G5w$wxA2}L>b0_l~(F<4{fcO(qXHV@};_;t;FDvwT^pFc6+
zXeb|cCpSgwX$!{-Thm*7J2N7uFW_C3h$k}ncE*to^(O<vLvW!v-}p1fNbaQ(J{q$h
z&LHgT3y8{KX28X1RuTS1ngQrQsWq4`OranJLb_!yzI1?{Zh{|p1^Vl4dGLJJfc$E0
zPv2xwi<OOb)ToU5dpIs_WNyoD2bKjL`>@=kmaI~>j58&c+0VFBkIbt(0|1&8Y@&Oj
z4l~b3^FB$zvm)JG0Ub+d<g4Eyu}*KK?jI7fG?oNzzRNx%&+ZTq0_k1?x{&|9#TnWg
zdWI%>hK;g~4*b39jVHtP=Ic+>PnWRDoTuJWOo|IW$Lwe02lx}&uF!aKsFMaXb=QHx
zw5OsbqBnLO3YM-@_7AT}+>QGVs2vobo4})qCr!H=mS32ol3MDvsdKZ0cC!~j+xxp{
z6`Gs1yEIm2cZgT^vqsa<-W*0Wn2n=@O`{*UE|hdr@&cE$ZuUG7O%a+@pcDAsw=Aur
z)RhqjVh`Uc1LPTn#)aOl2DFMz<m{)WZM`sdQ_!Qiw*10w;;8E`*S^Rd6So$10s25`
zVu#!f<mfzaPZ*cu-IV1A3QyB|JB&s7a%M_xp87z{b?C0y?4A`l(ITiX%LOc*+s-R5
zku~Y?r(}VGqzIsFWd90!qBK#(o%FxCNvuEBhliO>sXW#0c7fV})cUfHsb-?6SmGrd
z0UOSiq>!7_qf~CIO?Hp=A=LX?`!Cl3-QT^|3pq{#&YU!28FoAD5{db5Ht3%KD>GV%
zLG^MoxSys>`=86E=FjgWPo(YAO7!`f87QlLObPNq!|?E!Z$OeUWn_AbOKH)@W4#GT
z#T5>+4*cF}e)AV9SiJFc5_#lNGY@ctMA%-4!`xI#l*I@BSm00COn8pujew}zB=&mZ
z-)r^V#{OE#NRxJq=96{s*Z(R9B=T1mm^>wcIHkC@Xa^RgX|%F>+3IUL8jEU+YB9g4
zSlO<0LC6~Ad8_z$pS!$T7%-to>*LP1Bt&U0WDE;pO@e^ad@s_@;X@x<>GF8yaEVtS
zFYi20UR0F<+j)ZMG^P6FrbNQDbWI$OySnh79Hc7~$2xq;Y20({COk)SB&a!9p~t2L
zCW&d2M;=OK462gfP&4*!1vf4Mkk6D{QDDw~UAMo)%vrlcju5`}5*1bRBh}}Md&~#h
z8^dl1Sr3Ey_dTbfq#VhPC)`W{t|BU^Wu~4H*fFDLw;x^9S9MNTfN%o^*)Y^8=HxhQ
ze;t+6O=caTe{ydW6;7Lab4Ba}ut5%#s#uplTg0X7us0}?Av_Z-G*9_6_KcsDO8oIl
zu#W8zznS3Vh$qDQI-UMrYe_!9efwF(=-qI>@>zNCqo*_5->(BMXciJ|f>ThQ-pS{P
z1QFaNSQy-0=o1k6$1VKY{#!KqqSPLfYTISTO$*}ZbiX9CCAH7Zc)MsaJ=8OcK4Mwz
zkbhcb*ikK>$C#=!kMfQ9$HjZ4jdg?A6=yB;ZvI8ORwuu0J+R>a<QM4-`D)d%G2Q*@
zHPE#)H(oqTbdmF7kJOu<_J8zl@y1NH70K;L=pX+UCpM#tw_W>}AKIYeqK<ef{#+f3
za>V+G@r}e~xFX9817o@!kS-4e_KztrfAhUqDb93XK;P97pf57(V)WzVMeX^Hc-g4s
z@Sp94rS%9}v_d^I?>Sb`+d`l$)r?G%cmg6s=c~O(+hm259>0Ph&Ks&JS`Ki9XNRF3
zzz;K6LmKFzKe0IggMgu*+r&SdBY1dmOo{?qc=MDQV-u8E@c%=JX3Npt*O;gYQF=|G
zKc4+7#DWdT0@ZjBW{D#4NOV@;{q@p7ym9yshc?)AixIA8%%20=3wG;!a-R1(=nS^X
z)wg+A(J)r;Yt<}XICZL`be%lsRJM$XLONglyjgc_5^*sPqvKe1RCjeK^`r#Q1F~cA
zsbSI-q5~R*9Qf$3USU}$GC@$rAVo6eVdoZt3?0m8!>c@(?jq6v8o4|{pa|z#gB>$m
z_JCt{?PwAsMT->D#CSrjCPeoVD-5su`?O|TwUSXJCs{3~DScUf;5twpReXz+pMWJ>
zR3Pc)>_$N|`tTG(KaQ5JOqFs@edc}W1>Np@B*SkvT)6AR0zj!@R~f#<V#`;~3j{wn
z_`^%<PoBbIO8aDPLUdr&*P}mGCAPg~?`?PXPX&6ja{Bu<il<5?7)sY-NKmvus3HtD
z#g@q+l8)H4oAGnN$^xvGz$(wiHP|ghlx|ilzQP%p39=tq^)?g_s~uN+CixUG)-ZO?
zb}=??SX=sp=l1Qpc05bpXKJ{gdpv8W!8gXhYz~d6^jQb_1fWo@7};YD-4bZ}eWPOr
zM*nn<i6F4YVJV~c6kuf%2#TQJ>e5_lD+16SYfH9uSoZj3yKA@AAmW9c$j#pGUC&+b
z*=DX@K|2E=G<<lg+2~U05fMH@)?r`MHknICsL=0$6;5$Fk$SCoODyDba;R)tNl*$c
zJDkB;lr67XNjIBzC>5&E*Gr@nHin!s7=RT>mP-#r55XmU{)tXBaIzco30>4CWLEpm
zQf$1?HGMXXZRm^Mo9crRk5GPtvkCRpzVk1wo^xmLDC;>~4IMyi&Npq#E^&S66$ttr
zgPTcA1VS~pZZ#)Y&3bioPWprUDo1b1W<<V0Q<b_mv+!)tnxZ1yICzFs3N;Cr)RKzJ
zuqnIcw^66aW>0bTT>O&N77(L}?KYnyqI8hLFxV(oZhjMwyVPo=Wsw(j6pdKBH4SaD
z4)|)Obb)BfYqB!h8)(1q+D09qLs}t+b<bLTlLOl^j_?H4b#Zb%t*!08A@l6I1;0AN
zrJP;XprAlFL@#NZFKEtGMnJLuRb9rVhi>$f(VWfu!*(IN2ma4S_M41cNcZU$=ZnuC
zI0YfDSqGDBY7rtts^Zddi~m9F2?Ar&<Bx{oQs_r~%G<VwHE^&bAXfCkR!&XYI7Z+}
z#>Fr(_XkQwGQWlqXLX_G{L4G}_sm8Q`b0;bh}+1U&8ul0!aAACu1z6G7j;`;#K57v
z*5)?YhyEtrV3oT`0sMj@QHGpttjK!E`J`VjbV?9_9fT>7Se%d5^GG5@s3eG0;3$RN
zC`xpsAQtUp@QNTSMKFDy1aNYV;&~9>@B1VSKd?U!nJusl?iKSBSCe{Cm4UiU%iEgi
z%&s_p^&kXqhNVzB`%I<QAfYhot57z-`wm24R*_-*T!I^^g$$SnK&hDxla}lhL(HPd
z7=Sw}FluM1s%%hCrAS4>`XH16nRHY3v}ymZCoLN{R-as6PP_Ia?8!;+Dt?Rwu#lKk
zjl)#v9a5+^RLiA2ZQN@tuxE<<|Mywbo`fi-D(HCMK7&wdP;r+6rFuC;R}&38XYHYb
zycRj%hXdq-sI;ViQz>2wS^R91y}@;0HE@OpKu1Ev^4ljMTm3VVI7LRsUbEf1b7}zx
z6m`0;cy-|qOn4BYi$vrwi3!;^Q$0;=UGZeH_58^c_+uqv@>z=@l;fu=!<i8un(nEB
z9|5BZ*RHhwh=Dw?_Dogcu?&hg5Rr?%&OUcD)#X|n_+9n^`knhZ0d#wqUz<5@xBYsb
z`2iBBmQ??TW)2g>|Dl<~#_~TQwaFfTl2DG@ePN}a$@jupY_XE58pBV}3j!l;sE7g)
z))CDFR{mdS-xQ^3vvgUmE?Zr;(Pi82va8FsZQHhO+qP|+Z@H#tX3hG~S@R9nxyy^Z
zif3o$es)Cc&|^*Ya^C@5U3u+vnB@3jJ$qp#3#5yHQVg4hBBRLY>rYNay{;=B9(%Kx
zPV=<&@L5l1D*8;8RlD~9ytYlskj)n<$_on({|+;;NUm1@Dp-QSs7ZTXtTcK3o}%Lw
zM#IPZoyi^lx)lHW8<Q>nCF}P?8y7;1&ifNr0lA`5*sZsH5&Y(eZot9o#la!t>jc!|
zXng$d7~Y3P6uF*FwH=ZqrDpE>MmrB9sR3e(M46=fWQ{Mr2vW@pbLy`ni2KF&ZaE$u
zMyJPxMN4z*v5z^aGBo04d5f~CMU`^@>W-9r(cb`hgHn$&g>znWZYMq#zjSdPVVPpg
z^Y=xBQn511MT5nIMU+KmbIvE`Cz>Z5mY8%o+FYGHbcKjg)v}>QWOK@<NDbMlTor|i
z(xt_p=T7&*PD)tHvQ;I;QMr<(NTj8O^@sC6nD?8I(vfarv>5MSN{tHa3%+>Ule4Aq
zktT%@a^-HqqT(WPxt@<(2LN<E0F0vkc>}=bZZ}}q2IpHBU_x$*D2q-;W{oU^5`?ez
z-dnO07j9;EQ%H-TfYALq+Z|$5WW~r`;+sRY+jftPLn(`DW&y|Y_VW|IZsCy899Vfl
zq3Ji>RH}m|dDV9DcK*h>y)Q0~IEu<iDxR%Nu*2NfAb;Tw$|Lv|;p5!*RkO=!vl2-P
zBT3<<#v6{e`6EG=gjfTIjBtPR>>TLWXV~YqFZn4vQ4B$qL}{8zEvtIco4HI<_qbX8
zTIAs?YYXUXxs45idjO)awSXv!D$~>-i`wy~rTa80{+fB{D%m3G^M*^KYf7Z=?wC{i
zYY+6UL5_CEvdaq^j-Ipi(f#V0l+Z`nqU}G2*@vRM!%4s`$l2Fv-fm`tn9StBM*DTB
zFi_f~IBU1XUzMLisAxdhci5dkavxHxYZw8niSwdMk)Sxcj)Y%(SZ+GJdE#BPozXM#
zCl{Gypr`yOEMk!(6ZQ0cg1g7E2k3VBVrc6SJmRrr#^Q{4?g*&R9)Pba8=Hd@U_J=G
zl<)cvWaR6G;>9au{{9PD+ty59$+kPjOd`ltKM`WP5Y58%?uIgjycToITre5w1V<u-
ztUx6a^iFo$uaaB%^@`P=w&LpUc3xtx-o$+)@D@-1SzpyZCE^FnW&#n+zpaw{G&u2P
z3f1>ID_Pgaf53?{v`6~3rZw7TgHKvBsr4P##~C1#Yzax8_5(uAuV|lYL6@AUI?@Qb
zHg>i^KS$|p9VhCidRvABu62i7iVE3<-|`$&3(2gcj4YHbNXd869S&N9HchR=bc$4b
zf0iAjGanRhY{o^4pQC3Je1Uwwz<eC0xSoe)?8|c}`7=wN#hVYm%8Wz>=@-iT<9A0N
z@ZqX^US7lzU5`LVAA1%~oi>q>e}yLoNfqSmpFr4}eF(phWD44p-3{LSI%(C2ODmvi
z{Z+$FZDW?m46>fv=^PbQuy+tQ@Tz<L{rSWc_P3A~!jbMvp96-Bp-sAUQJX@Cpyb=4
zPQ|c{>8f$DYcLCyYwFJ|Xu8~lfQc=Yywq~Cu`OKI_3fjv6B1TOXf$pFUJTwh?03p4
z%AFXYB)niQHDttisa199wt}^&LY2-mCwO@wrO>mfYA5Yfjx7h>yA<#Zg*3(2ZTffn
zt&$zDx2Do*=PhApM0H9JOVY3T2O5VK)+q;#ER|ZK5dOHj!c=XbwLerYj<x^}eh+wx
z<w-BQHP7ZaYw)f$Iw4s<M(a_Ajx!kC4K`j5moo=A<+H9zO`tk0OndRtGZk`@4QaHe
z2fF005OCK?D^EVq5T}sycULgflLS1R0Rypk(N3E3mKC-;hEXX1Q-^0)*IzPaKE}q#
z)SC0PLtrnZvX}s=aqISb5+6=1ASd(l@Z%~^m;xynR?@=2TolH*VUE+{YVv-~LRC3}
zg{%$fkl-q1=<#G?x(b_0?v#<%jeks-aZFG)eU?lmTlZJv2MyJb4XQU6?0bW&%$-Es
zav2L^EhTa=PC(6Un=tc$peVMo8{&!#%mbOfGm1dRkdad!?WR)Pu-=>>JY{?^JVZ|3
z#{3-49=M{G%-^EsW?wT`j>?ZKFg!Fy&sJZy1h=W~;x;<&;5=7JLuHOc)+3jjNm6+`
zb=bpIXSOvH?5bqWpXwdG1>tE>^S4Zm@<;)x)DWi?d>>efG?|H6Itl#!LO0LdZdkmc
zLG5{1ur1Na=a0~4%kg?GTXPZdC;BHSCk0|d*@%Oqay~*ZF}N3TbRKp`{cV*X_{c+Y
z!g9m1qVVB7dZD7gUZOAvR~8nP8m;zwAkB-mX08!<B!#Io-UQ}nm2x%#MYZl0L&t41
zmb+x^>_4%<m^wHJ@diGiyuUK4vA1LCq4f^YRvUiyK$`<mDLK`bPi2e)SDAj^LT8}A
z+<L9tP4;9<fhG8%=AwkvTUDy;GIB)y5oGmK{p+awhs~Aq)9TYTe)S6Q(i=0MUK$+d
z*S=QSy)n5@IX`xe-S4$}FN1$oezTn>qBJ413L{|}wNMHSXKw96hQ%F+&MG4Doqx-#
zMTtYF^<_vedW+_{dK<z)IGD6xA0<#7e)>>ps|$Xnb#46CH90l0O8gwGZ8zV}c80Lu
zpi<kr)f1TU{(@~!+=qFaL^I-Ww+ez6R6<8Djrgp;^Id98SCglLOTbc<xbc$&HySL$
zW)2d#9W-l<ZEQHfS|ql1EKNCRF>uC1RR~h-3aC4usg*Gm+C%g^-8mK4(?-MLnWO=G
zQ`@ikk4-7tY6OUXBq5cadGGIw*u0T56ju>~xW~xh2h)-vGbi=Sp9+3kK%)6r=cRdB
zmOWe;qr)>1T}ZlMM6nEmjbK&yv&dBf!_XdR5{k)`PP&_P176^sJ1rs5-xGN_=m#zG
zEVEQ?T>{z7h3e7^{pmn~S_1sIVeG%}YZJF5a)z-%B<y2nOch}Cq)5ir6#_>+v2@8C
zgf7D%_7RCeqbc2nma#43*3OxKEsP<oK*oRA>3p%pJRs33rWGEt-zQRzAXX2~px)Yn
z7|k`sz1Un4^>CWY2tWpY<aKr2y4KO3$+^Z;4BvQ0zaVf}?5&{BI;NlKxUFXzb(pTr
zpW{!1Hj27<#Dag!vTz?fP>0#3tNIJYJ+aV~TK(+D|64Ma{Q7;6QtG={n0$DcHdPC<
zY+bg_XmXCdX}CL_%fYkB^Zhr`?$gP|Mngspb#ukbfn0IjtYf6=bu;atpD}q*(O`ya
ztdlFDZFUTiH381Gt|R~+F@oyi+n)zEL}Hx|<VXkCRyMA>F$K%-NU*)8yXsX0u<ztr
zLOU0+Ue9YTyF4Gc<{mFdOra#Mnzc{@12KE{;>?IXyy0zXECmSlqWMa+3Dcy^i5*=U
zx8p=QHAR$TsL+<{{$W~&l|@l^AAMiXg<IdkEo>{n=`1Y<cXP-OQWu_ZbZ;Xuk*aek
zLxf(HKv#1MKqTy8+-Re0+DxRf4lhej1U1(Gb#B@;kxNERGo_(8nezh_B6x!TRhVc1
zI_5~35WFBS=D?`HNAAGRXYk<G6g|3BPRW_M1bg$zFv43<I!7lj6nkS0ewy<B2$aL;
z3(@;pQ?OGQpxrd>rgvAlMq`uLO-GE0<=|jwr^N5bnZlRB8^@K?8jCspnSWvMLh88g
z%rf{ho~0HgHf$UALOW@Si{$)j{k;7oX@=6+LbWuxpON{Z_=4FXu(b-&#b)%f1^m@(
zbau?yCL25_;$w|#u$lLrP6~g12k~sjd6>tHRNB0V#Xcku6Pg|gzGiUaJSBHs*)}>T
zBkRUwI}9frFB*SRYgFrl=Q0RX<r~5fjXx=`U!g#~(p$M(ST}EHRbH$TID1ltVz)h_
zwVDC=8+vAsOm|`valH1^2dM}`9;V7J)ipn{gvRXH#*r6o%%lkkyrKZ5q#)p~?-WS~
zS09T1$`MoWnnC@VXF19cNM5H0eYyYH@CphBdfp^Pn<H6SMCp<&v}=`LcoOZlVHW)w
z`Y`TR+*H+&E$_+NA*J%bd}b|Xm&eh`Vs8W%KtjuAs&t>oG4O25B@))Rn)Z@5N+=kO
zNM`*`;H9tYwN|-ifzhBb+uAfxsQK}wZ9IO^^I)Q|jW&ld7)W9X;mz)nRfhV=D#c_z
zc2|87;;0$FbFC-ae-N=AYw~EdPQ}*b3j#IcEhM=e$(PVg_6)`Yj7-8=q{A|>RantS
z+0=YKF$7PXr(d9%##oYz4Ps}*=`Kz)nV?YsCR=2ueh1<gyWbxyMO+t`2(f{SHhpDm
z9k5zA+C176`Xh6_;!>}Ea7~P}-|E$1pY0A5oTfcgB<p1GtCUUTXI7D7wm5Qp;LN@T
z+Z!<L0;Z9@{`V@u6m1(*lbOJ-_^m3|2ZjPM@Jb;!;YMWi<xz|o2O+P~WUP@?PAlu>
zX^$y-84g5t>f$eDjDU^PX;-2N!SPfDccc>%*6k0zD4DV-@pX{QB)MLI4rr%rX|HY;
zDyhrk7@(JH7GU?-xN-B0B5I-fwitQ4sv+)R3KPffK{qV8iv1i&uLtQDNxy#_#jlbe
z&qy_kZJE9tz3d3LHXdT^Al1`hJ5wpQsy&-?KAF$7N>tC?lZG;xcxM+3)_%3#0IAl>
z5<0STecR5n3*N~0TV-gw3OPxGelI6ZRE}ub5a~<9kQq5?Dc<XLykQtN*Uwd#4aTPG
znhrq7GsPpwI8?AXSQ284bn*!Q(XWaL3m!nI-^>&q89W#8*Al{Jy=}N1em2?YOz!&;
zO4P^j0kNgfafi9k)n{)b7>C*#kF2ufI@@Ian^TULSoZof=_Uw}dvGvl=F&R=kSaAz
zcxNs&FhV?;W##{=$vG}KigE|`OIeHJc?i^Szf=ZEo{`BcXmXpGTO6w8WXCeP$`_w*
z7b-=|Fc?8+VA-NybI2n+LSz$EqdE{9VbkT$@?Dy4`fq=4Y-@MA%a{-csBO6kCJj-j
zd-O274)X*r>f9B!Yp)nMD57im%r;4%X#DFTX6lcFjhZXn=^r+0FS}yL97&pF$X|9d
zLyCM|;H4ci@?0_42Bcqc2^irX&}!lkEJ~=M=WSxJn*~WY7VBg=J#jNKcnu@i_h5v9
z>qbyU0r^FCM;q-1u;~04*;pJ|uk7AjZd|W*t69EVgH+zaEl6I*T_7epN2);SLL>AV
z)o8w}kS3F7wd|wI1<ReR8wnTZI2y^`S!ID&!^>H;EYC~L8vQO1{csUs$Y+U#4ORo-
z`)%N!UGfTr2%(o%$0`;TGS{(KNmDkJ7FsY=^6rU1y#6lu=dt9dw8&-$<1SY3X|SJY
zA8$(dc<(9u2`;$OI7iHOq;akR!^P&ST}<jSoCDGa#tsF#+??+#m)@&6msWobx&;ig
z6`u;!os2uePOpE?W;)>pyC8g=R~3wj(`AAm7R9hLVe=@LGLupx6Jf-DUA>SyIS}8_
zbVvz744wo85`t?Znm?F8-Uf)-fun=0qs1fQ61olR)m99>$)wAiPZ_7FLnt)X^_+t}
zIADIzc;dN%VaX^BU4n&-7c(qLj9&n8m=%mjq+)=BUzhToM;OZ%%MHmwu76(5MsI7c
z^rWwWB&Fj{%_Z0&<NEXL>$yV8cLdJ%IW%gS#Upo3)p_3eob<TIIy(9K-Y7pbKfzU8
ze&nAc{j#x|^sapx-Z<#;@^}UbCzp{#r|FzYND5o}JHOH?NTNK2T{BL-_FU;xf=o~s
zHj{gq12Z(tD^71Bl~K4TGRPW8EJ~!v%=GVo;=+YM3G;~(524q^zC*voK5?<mg{`1q
z)+_aqRf*EZ1ZEcK9-4U0MCg>nLv1tBtQ*rAr(gY^I!STA`Wtq<m0nNwVOn-`5`b65
z#je)LuOpE*mYiAi7Kd(_;TTo-PS-x%d}v~AM<k?B-ddA?%{A>>*Nw(9*p#NIE`pd>
zMYzNokw0SglMbmu@x8fS>@4h)fiPvONa945msTgh3C_w~3YsY_4<q;p+8xcF@36u_
zA?#HRjR;34QWisysyRCU^*GvOEV{l+retjrk$v0{=<S`D7qCSLdiVHthpp+9Vcwu8
z^;+U^g6}#nN=`weN8)FPRe>9;5)9)kpZK+f82(zXow{mufWiG{4__16d9X&xv}@ML
z)P00Xd^t7fI^p!=Sy}22kTdxkk=I&-6*#6KlHaHB!I%9i(2@*X)0_5uca%KLRJ!pH
z+Y4>q-9A{HTP~|E%tY=J%N0b&b}{KqN=X$~F+!)CF5N5^SNeT|OGsaZlO|Mc-!23D
zV~zcle#wv#1k7)Gncy>D)8!lld_=(BZ}Qu7dSLKVRw;h5M4h8pO_p}2az>*xZHomC
zmQjk;O=UO#HrRK1L&$F@UM^rmm7+EY#W_oR|A=pML+4;Y8WK=DA4Z;34qPsjzoaG)
zZfljhHkdMiQ{qS#4`m2yFfB!gypa>xXRP5oAibn@#1}i_I6L!+!;i9??HQ5=de7sx
z!mam_P4`Ua$P~JIw%dW&!ugcp!Ibav*beukDEJou-plrh;7)T7$0zonx%sIUEy;c=
zXy5W<e13x4C*zj0mCo~m6;N3ko?I%UpbxsP_5rqq6muAMOufd)*m|$Dz-xss-H+fE
zs`@pe$;Fq}XYE%-`}8E9$_l4+?A*(3>Za1OZA4r)92{sP@emD0e8~q1O~VdNai(|?
zcITL$Y3z{YR#J4We(g}FZJ@}t^y9nj<-UiK{{!kdw$CSI^x@Qd4#N8C6vLDwPg{Ln
zI{dFICrX{A27W^Qgqh8H&VA8cYrfKD6a;dhm|}ZtFo}Rg({IX+23k!4*Tk&huEDv7
zn8`O5j~~t@koSRNEJO>DO_{q;2O6{4E_i`45O<{;JOXl|t{#p=9nM^NDDnF=+^}}&
z^t;6eXA?cIn-0xtRP$FAmKA{*xn{}ESrf;H??gu^&9i)9w3u0-iQ=Yc+~jFl$w68O
z2*UQfh0^AVlSsFnXgsm)nQfaMd7m%aeIQ{x9<%~1dngR&GJ_F69_j=09c(In*oYn3
zYo^XL;A#d`B$StGdA}nqpC_dztf6WScJ2;MvoxR<{4ye)f@X2>zDVvjXs3?&=8}NT
zz2{O42r=P3h)C{T%H!uXth5JUrLn3RDK%W*Q?~gK3ctbyL3#mpd~?c<m;q$_t9?SE
z2VvPbl6ZCnmu@NH-~~g1{S7n)V@&-QUSr$S2w%Fbkw-A?I=vH6)d=zG-?ppP$gJiD
zpt~*!y-rchY#HfQv`d?)buxO&t*C{A$|mO1h@@?HP4DGwrw1b3HMbS1qPJ#8PxtHP
zlKc3m<HFG`?14{of50WqwrjckyK^7L#CfeEC04N-$@k=3QPu!skDuISCQDE(3t;GF
z%JUyQbwV~P98UwZ8^IWfyX0f@p9j7a^US&DgwBocDwmsVz#T%@OAJrDiJC^q#sd%>
z1XTe5)wDm$DQ+kgFEO-8{jFG5f&#Nf)=kup*h5u{&h`_<pyj`wo@*Hvc@ue1&cdJh
z5ujmcJd8ju_Rw4p+^(QgvVMf%ni0LR9#f3ABcKY_xuy>$)Xw}d&inn_%2K{c^{>Fy
zcslylk_KAKwXpCc*RJMz1?{a*QcYH%W3}D+Tg7@Q{dLj^#Wj&qGyRZARg)p()P|3n
zzmTDjc%rqyIIOKt&7?B;XQTNF2=_i+1MEJ=`W>b&Hh$ypm@C63!6F3)IzKTChFC~*
zuxe3<fTFQli5`h&8SRbMB<Vic8UU3`A20is9hn2;CenpJonz;7@5SWS8`;P6$1=St
z0+dh43_>{L666u&o&n|yjSr7c!GTa~u&2NE13%BNJM%6f0Y`{LJB~VJ6~C@|C986U
z@4}<FBVX1FNas2R#4X(G7@TOFXuN0~#EyV%M6fI<FX0KF#YOfi&Jb&AHq}b+sw5%m
z(2eA$E0a97d6ctpjKThky=ZxIO6#8MNy(t!Lia;Ae<tR<Da6S98bo%?lS^4CcYL2t
zlZY*B7)SCtn0hX%W#z~(x^@*iedU6vZxa!O>vpx-s$`{Fuq&{2`BZ)lJ`Pl?nJiyC
zL5Ai9)b`oTP<zm9-SU21N?vQX*s!m>fcKYNVr9vJYZA7aOVJ{8E^>zHK#Q-Z;{Ndh
z83kwjqWQKW?`$#H(UXH!V9kMV3pFl_B9U4LoA2c9B46vdxI7lBNTph>jAvnUSt_B1
zf}z)%s~;7h2to{-rQdr!W-x_>M2KrC+<!I2lh@U$4*+jd)>HgTPV@&<uF9499~lj0
zkd2cLN@_eCU}jL9iyVkvE#_BLluKb5i7Dp@U`W(kdQoIV2_n`HCDfbpitUER${k?6
zfvokLA7*mUD0#PoNBc!(IG`3l$l7G;6EX;rXQ~d#rpbhUJ;q7EJ7rE}L%E1@<z}P`
zyl)=|7~LXQx^LGSs)1#SV=+DG?h@cnf=e8gDG?CBzZCc}<fiV36Kv*JvanFDP_GPJ
zn7ZxE&!pkGh&2*NOGd@*>$7F!7jd*{_C{i1dD(69^w}9~9t?YP96zcVAHk0~2YJ2m
zk$thTPRsn0dm1C-f0Cp5#qqD^X<iC5a*O<^)*T-dvm#m~cLbLazuGC*wqiqj;Gu~q
zkT%7o<?rOz*RE6N&5VoHm-+Y+l=Y<(BcRCT^x5qY)e$5Yg`+SY^#uc6<9u?1bTX|`
zZFIUjI{{rc8G!9k1??=73Hyz~)u6ZvwrI3RE<>f0B6b%lX^A~x8ECGfS}CZ7Fk7E2
zB-(2VE}^Ha_(I2<sJq*2OoV2r#OT32Ny&0m5_R)<_%WFKc&#B>q`yX+jG}C?`i4jp
zuF2b=U9X<q!Ch+)gH`gDN$ejXB=?bu+(&kX{wJxSAoPTocpVS}qXJ4TA~T6maIKMW
zhYVn8$S2olTGd)~CbT0H$mU0|dPW6AGwRh@M@*euFrToFf9M!%WBHjN>^aC}5n4lK
z4TgnbQ~gAo$eoYDY7JJi!vecTF~SD=ii8(SL`1~VejFS?4ADUf<}t8?iCB(UW(5e~
zkPZLg5BE1i9>@^Q|F(&`Sk>#DgN^YjModj@Yt}*P791~XeFlrs8+kj~21QlpWU(4F
zZ@*IsOb$EvrM7AMtsZ*)x6y1_%DhtBXA-MEV(sMvamNRM9~yHj-OD2pXJpju^segU
zX3ykW%h8tU-req{-{eJz1I_F72p;t|k<iVF*rXNymVc=7J@Y8`t@$LBYbgvT)w1m6
zL@VwM|K@)<SJQQd)Z?SitgtxW7M)=Ntvc+r6poA#FgdL8<n$KN<Le_Fb)JzF{XC|+
zv(x2sQ4t}qZ=J}&YwJbCbGhS9c)7TI|M5Ij0@v5Rv$~Y&YOiA9UdXbuqH&zjqH&Br
zW46AWnG9<T_=Fg#fn58i9OHj7sE>o4@!#=#A%~-knxk&2t~Ugd-w>r;jUPF*FIaAd
zQYSF4B41&>fQWBaHE_MPWoB#=+~(3wQ{)E@vZ0eb!JAuK146k7$vd0OT$P@>%&dfR
zLA=h_Yx?o0hdrQRj?45ZqiyVFJ9Fg5Wx6fIC_Dly+31GvR_o%cE5zCLh3?f24-(%C
z;vs*xNlQkQ)WFh#Avl7?+!BJq{<HPAWWd;V=zXear@7k2a($Sgc^Mie<EF0G4o2kI
z4thwFI&F$I*hmr+@`0ga(tA(`<yF2TMof@k&<3#V?*#%o+FJnr!dcspm!}8n(heAx
z6h;FPfc+v5T}M(0a)uMapC!Rp0)`iyy%)qb^+Q&2i#u*y;OzzOgCPa$dnOnjM|pbM
zvt7lFWp1g2IPX%D4D{B*mltNc9&ECnjRZo71?d7Q^3BpI{UeZk?hCRM>x<|G3hd`Q
zE|tuf>_^}xG6h-2-Sln*1uBwM#wW~^I8IFLIa&b?4N(ux{hkfsEzp?n2HRO5Xo~$~
z)cfaILYKym`42AMNf=}hOlN(m5?qiRU(~o5d2Pv}(tc5>pU8#1KIcySLBKjsVTe|@
z8j5*ZynW`N>S6nTo!!txA}tTcv44M`!(a-`-87?vs9^(tZHIEf^fOe-7$S8Ar&6*G
zqiXh88gQ<WJ)5`Lb&%ta)%0LiSB}#IluVEp4jvMAPPyp5a#Kz_dfq+-s?VS1s+DeF
z)5s@+a;%??TBGSGBlAB8IfR47ZJPOb*fG2=)IV8Q@&a~KLda34O3cX(LMMh(=Ar8O
z(RHNoGMyYcq=z>RqPrGN>DjE)BHVbABW4X6kEouucD~&!s63W-%MbS0?jzC;UH#b;
z?z*>sGP=6=cHYE57QeR@J+288ad00=U#u6G&n(ovoc*pBUasDJaW`V|x&BDX9kct5
zxiAXlv9w`c&UK+daD(dIjnx#cBA2_3w||KrNp3NEI6ApHsh-7C!<F(F?+3OrkLK0Z
z0o5camYMxFeC(`$gDp9^9LqFN78WG;#9{4dY?xkn_4>6av0}H6z_%p2(YoxhR_c|K
z^{K+wpknB0@~BMhB?b|V6xJ!2sAtE{?mzmw-vuA;iqrLlP+w(rDqOksxSe0cK84Qv
zZ(`$Z|LHY`NjDZ@^zWE2ny2G3heCeFLIw_|xbc`MlatG1`r5Y!xqOewb+Jiud=F)v
zuHQnB^?;{&sT-LhXcUJ*9lSBjk+AC-YgUXgqx$ZmTzq2E>iK-7goEnV0>{<g$<*4^
z6Gp>ixY12Y>vk=??l-zu_PyNybXEK(Mxno08JPa{uE<jGvcx{Z<t2AIkEh^|Y~qSq
zXUmf*(-7<^53P^-4RQu)b|Hw3GAH<H=PcXyg0sUB{cu)8V)A|2a6pzqKXT&9LME|s
zyT)o&#28F5D~L2}y}lY7n>x!2rqI-38@JkK>G<vf7%2d3p8$Ag9z1q(Ak<sn|0-Xr
zije2oydR6l#4~D>D&h5<;_Y93U6n~x@i7y8qKEG+_ruV_tjVLr3Rv1EALiE`;mAk7
zj;?Ipd$x+ULJ3GWyF_3&wT18T(Bg7R<(pN4Or;Rv@|R%EdFMUL+M242FQUztOc#!)
zG|kQAdZpUa)jT(*v$*Uufxg)S_#_w#C!)TkwHuL8ugnNHE#bas1a>V^{J12-do2*s
zu>p9iQK~n*wqQFltxz>d{ZqWyBI|eV^Rlg2`Y6|hUF=Vv2-YQA@bRQ(Y>ELl`nJ^5
zu0OcS@QBv00!zd#`*;Fdf($X8q7q=Q?=`k)_IlNSl5aVWpHQq<yvwdn?c>o`-P2MP
zNT!(u8(r$m5A2s0ypFZsFxAVbG@nkyQbjGuHq8rccnw*5@2XLs8QUhCnndujg1E#+
zhSWnS=_-#OklKjd{wmyX8u5a;Ce>1@2_Q<{XdlvSTfqO))z;*}qq2Rc@#;8-^ock6
zF|O`#0{7LU`kV5DPbgsJV5p4n>boP1ApHpvkx7tXDir(f@5~Zg?m$bS^H}#o0{b6k
z!j3~olmf}jGv>TBW;7y_ay48!ZRs%KUHj*t`bl4{s2W=au89l5<Lwjdr;@c{(_rgz
zInhQ3ioXdXqX5w@LSXy7LmX%ZcWT_oF~e<F7S@d}KDvV}{C4JDO5-tq-8P5y&fKVN
zP6o*TKn0*c5mJ5#IOo1~J|3-?tV<c`j-}kkv!-br*Bu$U>ujhzJ6J>3SkYgUsOmf1
z?NK=DBOYlO@bsu%h*BGcp>PRP`e?j~R>r^5KW<hn(FX4f8t)5&fnYi!$bT+YH*q!^
zSY$Rhu)^c!>dKiudFV*dkdP6=1ie()WqoZPvGeuB*vJIR^5X^M2?x)3k-6l@iie$P
z5ltaH6qrDe$8*ejIou8X1TWj_-Z}*JS?5SDoIUbg)dk8jjFl{eoBt$xY5%HMn5Gnk
zS8f~&4s2E!_NTUgx=6pvK)y38dlV~M>~&;DoO##w#dV_ZG4NOaGHkz13pG|G4>1Xv
zNzvs%xH(rD4H7-zm6yE0k!xXYrZ}n<15`AO@UniPZ>N7xz-9G|zr(Iw>|+PDjfDlp
zVa=l1e;K26t~xI67j<a&VF<QMuHm2`$*eFd%#<(8Qmbib@2yj?&K7scvohgaF$j?0
zMT_OA-kSmrvYgO^FroAC4NHnVg6SqRxtyRos&TY8d0+|{nB1NBK8Svf;hMA)&#8gc
zA__wetQ7;iY&Z)$MuumpRi|fk{80!7>p|B5v%P<OZ-TmRM-9fiB&*U7>(d8`%#95f
zWAEsXQ%YV9As7fz&Kdh0SdT(w+z3#FO_&w}6w5M|l6u^~-ZFf;@2)v!p#t=p$a<SZ
z(rG18{+f~2&XzquYjYgnWR87*Cd`toukTp{z=eE8-h;tV3Q+`!oMfnS(*(`&>f6tD
zOLt6zIQu<QP4gutQ0S$&W)}_Z!N9b68Rv~GK|^k!B7P<)Lg%Au+J)IaZ7AOl$2={~
zyKgfLnmzSoL_|7(bb`;n$!)vdG*OonBDdzf`|?~UBw=gNido5gsie4xQi;7<+T(~i
zwuR1CQK49M{50j$xODec5Jv!J&4hBPaKV2V5e!M}$<GV8()`>Yl4V{<nF&1`Vt$VM
zkQzvI+~F(2|AcaMAnrCGb6u=XWcygZvA(Vc2H%#x5dLL#Tkd#qyltWUHt|XiyBqWL
z$TH%7pvBFJmxhVyN9tNsTWhbl?hEZyfFQ>;4a5o_u{P8{)L_bfVt(iB1hNdyGeGz&
z6NV0e0is(_LSL=L!_ft_3Kl6muWdl-`0=nUor(Zz4B27vEi^q~x&vOtyYr4PgT>V7
zPmg@f@%SShuFFvpX9BnxOtJZrOpnie$Hx<4O&SB&zf6sjJx1kCq)k0BI?QOeLoz7G
zTeWWQsKZ;gJ5i;lPe~+ba0fuYhl<qjPY=_7;+w?6{%`M+NVK-Nq8`rhj*Cgg0_%xb
zK|VJZTc2>2m`HM5kZgURvbk_dm79G*^BDJqv_sll>ir0@q&dpQ+{PoBs63+fqZ`Vh
zm7|^xn3w$Q()Z*_1ncki*S?3|hl9L9Q<6jl&6Jvgl9rC?iwg_n2oVe4vW>D2uLSSJ
z(~(jggIPCOKEq1Fz>g}L`;M;B8Z;3+`d-`Wa7@akL9r6yx*^)|sqNEQaT;dKL~zwy
z6;pGIheepF{8ci?z#=t@-IyP|JvfQ|-ff81P@i-B;_+f&D`mMf2w9evN~l9u2nZbo
zb$ts>PyWd90fITqG(*->p#5v6!sv7_1M#)*Yh6+{%-pCcMX9?H@Zxnp2ooLVIOla*
z)g;G5lWqqM2@)~KSnlJKXCK}S+q_fDs5d@;em+DG$UOG~Q%T3rHAq&rhjsbF`DV%0
zp<JoGnj#eoG))#Qq4ELCY^A6@DH~&&;JD^``K{?NEkGLYoToufTt;%9eB2Ckfxj{-
z;oiRDt<x2p5v^<T$@-Ee$(Cd?Kq;=p?Nql^f~SIAPQon@QK>C$*`@kRNSTxIOw0_l
z$lcVvq5VXU3y_g=`??E5^UNN=__-v*A#2n78?ri$Nqx2vj3mnvZe<E<lnR<8o?Fx8
zf^-awLK2-(mWIRu895zd4OUlvFAk^O;ZdgBZ^Gj)!oX~&{$ZlCMZ^A_Zw+lE+h?-v
zVSu~&hB~I^eA_6JX0kec?P(~U+z1;|1z)2IJAG+51&b%W3AF|O3zYc&yY-*2$?;$M
zfT)?JqmezmsHL8xk+6}0jiC{}w2`%mqbUI+6YIZ{ByIUV^8p6fu6Jt3OH5)}P$Ni`
zQw0z#!>!}z*#ZOVG`+g01Dtz6#MbNpJo4!okA$51Qs&PBP{W*U$!K7AtHhD9X&oX*
zc2l8y9tzq4l_DCkJ#m$$ey>m@!gqH*{GIia@}ri_ZY_EzZ=j&hEPHjFe(#D56y0K9
z0|R%MNF?7&k1TfZp5E(H%MdvvE{`Cc3}EM#aPq*J>CgR8G9Xf;wBaK_Mxio02jLtH
z6M<f0a_@ONiRog`cJZnED>1j;ke<<ezN^e8TvYk#jh@`}c8$jZN_;`97PjZdm^y|s
z<rh#{l$G#5M2zXze?{#7+Xao4_22$TNn>oJk?k-?d>}(H2vj1%EO1TerBj3!lMC9$
zDONHUB|Y>so26_7$8vC0TLu!>q5NZVaiH@VDw~t(u`8-Dy38+(YRXesbX%HroLmT&
z^XRj08cx<u-nrlP#Y==RgVSu_c5piPc`xi^q5<sZ8NlCPYDY2kO9Uw5&3cSwaT|^H
z5z(u-%b!eUvV?(Iyeccn$L&93%EQ-{=PL2UqA-mc*Vk8AT_}Q~Y$ILP2j0%i!k-{M
z^0255^-3vkNLUVRAJ=fBbyNvP@fi$949V9H9G_j{y5K_+a31F($6qDbhTKha&Q)a&
zP5;bGHE^%}+SOaz4Z60DKDbPzH$-4qz~Jf-pPq0Z-Ea&w&Yo2~l<%F1XQRNE!3%D{
zXI1UR@SL27J%g8?i`0#p^_9owjKRy#O+RF54jr1~q49hC8AynGE^PJ){y9W;*s4w`
zxySe{l-xLuMQKTiny0i+Xu080f$-34Cql70nN;F*syLLZ++8t?iRMW#DF+KhTpK86
zA4>>bo2f<F{qgx)vUp3_5-;ihL!UWGty{O0)vK_`&()d|4MHoFe-I+9Db8g@s+%nv
z5Ocd;na%g1*qD~b*`RGkrx!1yvq%LoWHxq42t}zCX+4RA6e@;s{CvG=uA&Ezj5q@R
zt6t!cAPN1UReimzxS6`3d_!$n(rY|Wq-)Hs_hIm>O%(4%0V-UiztVF9pKG_9YD1%~
z>3Kq-i>igra)<!FN(wQ+L@;H7^IHq|z?B9KEsPoUeoflli78`B{J>+Sa{rR5vhTRh
z;MMx5UU_l=n&i$&k4NxFEh37sox^F8{aH}AJL85prsF5+0@9qJ8Y|XBaD82;5o6gB
z$yN3XLg@w?+&@A6U&O=z+vV_YYN57#-QUXraq9_<tvHcrO~|b8hrdEVNF%AN=@^-m
zi5Q}kU^3*lm(`l#Y3{-G<Mu&UK|ZI_vT*KBU)fLM#^H0{0PFxk(^{rvLk`n|eb%CK
z_cJIou`@oS82+HvYdOA|{g=Cpx|E5wNgV-$4#DXJvC)<`&cy}9x*E%iT#6q+JX7I9
zuqc<$>ntygm)-uue=H$8B1#vE@w$2pVpu_lGs)X5p9(XF`=o*d*5M`kFhlZbI0P!b
zKaZ#j%$8(=;l|XyhEI4k-vNUgh!OqqPe{s9gp+vl0t)Rzp>;2<X)fM+MF2?QB%6SL
z3fh07STVEzn@OU9;U7J{KZb^f4$jf`u+S^c&rVSc%s^qO7!#h9v3lA72Z##<^<aGa
zNWQ}~ZwiKQM3Xq9KYtEU!QyB>2;v2R#2H8ZQUQT~dvmbdicsH6=qSZ$B&Q}MWT+*^
zr6C(47#EoMaX>*0ctAmkPd@x5Yh(RI>x=#Kii+Bz{p0-niu?_%&3_x)>u3Ah=u3#2
zTkAu`CKMoM=47U6D3s;uCHz)U_)}D)tD{q$lB!vmqMK-4QdS_Jtsb9;n5dDdp_H7S
zznh$+n!lTpno$mP7l#*UKRrWBK`TM4a62U@K|2oGo{DtpJ?uOT4`D1SnjHDS$NAzt
zIQCJkMKCbWnse7N4StLt%-EA5((%sa!S${WXx)+cu-3V+JW~WjT;DXvc>!LhAG9sU
z6R)?gb)w6rwyx}1w(`E5Sjv0cy1YFczv-^Osu~YnE?w?+niVf*R%fn`PjfbTl<|h5
zgJC9jV!k+t)<3n&PCYJd?rxf+7P_icogEsR99bt^S^&<EE|>6p22DpTcSHe{R9o&_
z<}&3$>191=AeF@a8R_j@9b6pg<W91&8B1QLgO6$WJrCKh*K?m;sR1>fyx6nZua_OI
z&UQB0g9D(0o%UKZNw3_QJNV&1KoMZwO8*3$|3trFWd66q_rHLHdJ7eO>-@9??+gR=
zi@BB=JS^=a*7${R;87^~HH3ZQCUD2cNNvkUM?dK=ZcdTF;<7#n>Xm_n&3!{d1Ysv5
zBWdj0A>4<jU)#WoNXtZ^yI(2GMBJbWlfcW4MAG<?n6V&owa@vduwbPlBc*{ocleUM
zcGT$}dEUDh%=RF6JbV~KlU){798(;A=$<~M?=IaeX)Y(XReN~3{N?y)IQ<A@IH$a`
zKDj3PfWm7DoP;$lbd_nUi))}hI{5BkGrr7XPnXo;=MGPz4jhaJW)Hk+32rSO?#-LF
zXZs0o<D^2RM(j@vU?7KO4ZnYi=6^!E8Cn1F7^2!bA~?eSSb%qafc>A*RR8>6(Nw?A
z!<g{FE5_fak?r{&tquO{9Hg4)?PX9z6qY@Jj7tFHFN6qvAsFnP939O!*UvS%%*@f0
z*NE3B+|9^|*GMcuR8WuA&`E?szN3+t9FrDR>;-C6!!;F!07W4tRz%zZ#2D?kB+^r0
zAoJb|oskEaI=fhydzfke5#l%CH*qkLQe_f@^jL~)=dk~@oOpj@mC;~7+5He=f`5{^
z&ROOO5_0YH4_`a7?}y}U_p6NvsIfaon9078U0C%_0n|>|{gP`q*~=UpSQ;4k%(kF!
z(fXqzAt}-^=Ys^qa;44o55Z;oFAAjOW@|(*r*E$4Xhknez`;Q;YG&`?NWlJ!@$c&Y
z$S2vD+5eSLPNi!)Y&0W}?7UMlc$KkWNwtDrIx-$uTntVRIis}T@i8zOJSZV+hgmzk
zoz3_N#1T=B>s=m77u5YgPTBg@t5Xw1O12{-MNwfmOKps%9AiF_<}fZa)?ln4D#mrt
zVI)okBx6kw;s1I>@-Of+611PO91KKG=h(E+OC9J!3U~XNgMAE#60HgAR}n`e=!4;g
zzSp*rE^>NO5b8E1T*w_jf~5ZF&?*`(5-&lQ=ijevkoiM3<b*+h--tO5L`C?g>Q8(K
zsF3N*pRuU$)TD3|b4nbeC#Ds&VEyoBs62Inem@I3m>vfi&YJY(Wkd^0Ou`Rz_CU13
ztigdKHl+lvh*5Ez^d-%5g@b|#QVScOqz|@%n0E7kDp;s!P6bSkX4n6q<21Ik0{rU#
zP{jw&Jqjb<upj?uVvvP_LT<(AB>*Fn`^gwGI2*|rg)Yrt1$H@XX-Zv|rx%=%1oBWx
zVl2tu9a07oSOug493an+CXt+(%}*|3g(?QT{=?LQ_7V}=iUvot*RGP3{BO${%!fLp
zbZJZ^`OF_WS2&0HIiAVcSSSQ0=7rpYOd_;B{!|9w{p^J4_*aH3{u{JKzYx=OA$xF`
zfL#uF2RVm5Vjy1)B$q-EPnuH%T_}x<AF{}Cw(MDcJP<Nr?8?F_aXaxXV2Z@{@Gp;Y
zd{OWX>arNSgY6u+xcU;P;D0EfZNKw@uEIfrX`lncFhoFf6HTvZD!?h#P)W;z_a9CQ
z*d8oBlMho0*xo}59u5_GIn#&P#&qn~Xz9dMXL5X06>AkrewHcx6w>MZy3H|^hKngy
z`}tuZRbHx;xz3;=`A0pygyQ#D8DX_>b4f8Nw#DTIP^s}(!=%|KpQxc1Y~ZoN6$^CI
z84FGlMm0_~=;XqW#*;3iW&uR^@Jwji5*KQXPTI4W;s_qumeaq@uBs;h^$~%}=r6wb
z+6+EQAg}CYlzc7ixgUR77yR!)1M)K+l%3v4@{v=>=jiFPS0ciZGv+Cb6iv@sQV7)o
z)jMV=7v4(-*W-cOs%87yB9*G6UdW-_$&Tn{_gW*Bs;6Gaoxz4sm&WD*+8%Q&$ls~W
zv}~c!Ngo8|6_1QvI$Cf1p~#}`!uhSK^=n3T+BzT4TpWI^o}pD9&U|TFUM`f_PNPV{
z;+M%z?Y~PJv51s4m(^8k99D5s(4u&UUkMdruJ1@5DJmi@l_(vlD>lg_vRzbt$Gz)E
zF%l^tQ*y~q%y=c52V<``x2788&X}hcESvwh4c~7BdWoB?F-MUH%)$T6iSnMFZkXde
z&cUI|CU3}`*FXk9|831UT%NTbl|5K<Vm0y^N#N5s7=5zao=oTw9M{HHYKBx6>U*v~
z!*!1@Zm{HyBERI+K74o_;8CANF0}AZK1d3<c<LkNYHd?*3}(O;qDfM|5UOP|W!8&u
zb!U@UCHf>+{}{c$f>css{?g;rJv<66IB@-K#=bsrP3WfS{DAC~Ag!5GKuHD=`T#B`
z@yT=8U|p%5S{$*zs(Y@#0Qw|LBYj|(TLp|wO#u-40JsJ8OF9pOTb9l{%hStatDIOX
z0h8$uIG&Qn2aa(BjM+vBxwe`_HByI8Va0U6Rgpg>#pkTbgif1iFtUW_&Y9H>0S|Wd
zy&YDiJl{9k{(IK(Xl3;qG$oWBsKw|zDxXD{Yr5grU)44~nisk(x-?e`72YDxm_)X>
z2JS;eQb>v`sPY-GYRB10AmtlCM{mz9S)We2ukD)M*VMXvKjd9N62Uo@eU*l?t|80@
z$wY(|kp?>6H@ZJNc;=pyY&woMy6>xA?PJ^dRV_;$g#|IFJJ7;dDTy@0U#7pNpD%CP
z1=_pswOc&tJ)Qyk69VX1@L7aGQg$b=LZDEJj@rSA_C|>)n&(a|oD$Uwu59m)5>M!I
z+9W9f*Ha1XGsl`EcW(B5U$PIH&M+JgZufCpLkD~W*8CjTPoL~*HrgbM;RwVZ4FUN~
z`yaf|6;+@3q!@Ub#w2aY3Va9mkkdYxsP;NLqzzfWSagalL(II<DmnUw+1{MuI=g4c
z&tQjxv=XDC%G6&S+b~d{RS!YpyVzsM*Wiq@SuIIozGvq!aQPM1F3zEF(Sqgm&M@b1
zZN*gE*;dEiKE@F7O2uAFSc(YXk!f%%^w4p)6H6@-nQ-cjYbdx#k>NMMmV_NEgF~;l
zqw*rCmm~5P`1ATrqoboagx7bKDq{AIT7FT5mJqTmgJTH`!g*>D6{JXpcu;w8k`)>l
z%Mj(^>_-Z=*Axwt3C+mMLG~{~Zd5}7*Sx4%_n3mrW-|g}wJUujFO$%kde%wOjcb4~
zNwikys}PS&_ohmh(5+Nx+j&W-<u&Iu8CixNX|@d2@Auesl$wI1&n23SqR5|*CTAzY
zYF%;5d_vlrN_O5d8cNLjExKH5?%pO25guJDopxS2(l_YvyvCZ-?%_ySnQq87amo<x
znv(8L-Cjk%WazI%wb3^Gp-gC!s#Stqymmr=<a{$5h$8jAGVZe!%$oC1--LwL?q%f1
zVZY(U3wGoh^gJW@^gG|$ypJZE{Rk9FN4NSw+(7Y4kur9oMIq#@!Oq(989fWm4xM{{
z<m}N7%vlJB1+|IvXt~QAyY0se7ZfW9SYz*yHDTLRB{6TqrjKXXo{M-`I9M?0$?o<+
zkt4fvO2@UyF<xL7-;hH;aB~l_r2Oz~cK@VZQfJ|pwFgMWZ*LxpyC0dF{3`ZD@L<P9
z3%YN9)OQRVB*nd%v?hZ(C}`kL>lRx7#hE$CHkv$;+L7371#@_Nns?fjVGrblMuo5-
z;cs%3N0Kdn-8dJX+@5iohR#!D@lxmsv_oH`+s)&;f!diNQ@wH*w*BH;)<0tL5_vO7
zUU<GA0!XkD;nTdojc7ExZ;<iMm^>YuX?jiL<Wsy8idmbsj8g%ijks{tY+q&ZaB*?-
ziCNn$-;Hg*1XA4*-qh?RY;BvkLh>9w<cjsUQoq$egtMK!l658D^W=#j2%_-h;&$`#
zarnTsiDBoK)xcS4o)WHD*+;L;a)=zBhPYa+j7n^itkmb?b5Lp=238de$cUWoFZD$%
z?BsNXeUHiRe>i8wUQ30hc&bpxyS$-H4W#oCx(5F_uKV2gDjpMNO>1<#9xLnu-u0>*
z7G6xn>G?PU+SZ8f(Z;Nc9`<Bm-k6)EIa6CWV+tJMK;NSsEi*1nsgmER@|Kgl;=ehM
zMYLptd<#$wp`SrWM~WA&acGEHBTp))<^20W1G;fv!0uqaaf26pSmyGfYSAv()aOz3
zxRx!t1x<&bSHH%lBU<?QHn=#@JaOYMda$dB6bn^j!K~YpbD~&j`B3cpLpc`0C8|ty
zR6q2{PS$D~9Ho2of2g-gNl4tGlj`V3+KZfBMXYdjkRv)jYhsXn|B%L3)#Gu#m|lgW
zZYB=jN+e)knZ!yowKp%_#$LRS>;@^q18VATYyrGapLvXOwx0H*dOcB8YpbgG7V<;z
zuAAP*$?#V3FU_{?>?>G=nKt(4<>OKI-K`Bx1SCwkCGLIab4xZ6DN4O&Pi`uvU448w
zpG<X3nI$59H#18%393x^3o6&!6x_X^jl_&`r$r?U72$YEhb`s+)UzkY5IHHo@x|U9
z!Ty09v;2!{@;}(Inu3ury@HL6Bf)={4i)Gntc`65{`;oCOJPlJ1Lj|BdipH-931*a
zhK$UNdiusJe`hTi8R;=GF)*;{^ZvhgXwxg2xf?<MVqv0J)%-=kM8K&1e_`Lu|E}h;
z%y-yK{S_ncPF}q=j~<nt!kg)om7e;~v?uaP@lsThw#~{yndcDeSbj>QWe7`uLJt06
zNaI{3VGZb`DT7B&7l(8DTYNh~o#V+nbNf!Jixd|XMW8@Je#?&659102X^SXk3rlYM
zqh`-3Qz1?esovC6?WRqZG%ZU>awnCgr&E=?QlZ7t>%2Mb_0*ylBFPV983FQP&A$c-
zTN!<#$|sG%C*~zRK!)n#roQW#WXhjV*WoVIT%za+&1NbYUYAV|+NRuj$~7lss+_06
znQz&=`s3D#sk!v)vgPY0r!sG^twys}S?D4E=}gq&I-v&2a^?2)8*pL}68tLA;n{B8
zJ;TKJn7(uW$rzTkc3i;*?kYV1Ahl`Uho>15q#3Z^4Cbl>iLm8T(MLl!fV%G2;12Du
zL%1I3Qj3DDg#Z|^Eg$^09H5i7P*Z>EVK4i2xBfsGrM#5w!Cb7EEoXuAx&InD9HyE*
zW1gw;yjgKK-P$$NVX>XLz^-XA)unIq#ytJxHf`GZJ^0`jg~02P-50SMlX3^GGo*(z
xWS2RJ%^ual%sU<W0a~UaiTuBV&cRX7-qF?G$QYWDk)4&D0h*LlL{1d?zX1uPBiaA}

literal 0
HcmV?d00001

diff --git a/exercises/Solutions6/.ipynb_checkpoints/Exercise_6_Solutions-checkpoint.ipynb b/exercises/Solutions6/.ipynb_checkpoints/Exercise_6_Solutions-checkpoint.ipynb
new file mode 100644
index 0000000..caa6eb0
--- /dev/null
+++ b/exercises/Solutions6/.ipynb_checkpoints/Exercise_6_Solutions-checkpoint.ipynb
@@ -0,0 +1,1767 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Exercise 6: Arbitrary distributions, moving averages, and Monte-Carlo\n",
+    "## Solutions\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "import datetime\n",
+    "import scipy.stats as stats\n",
+    "import matplotlib.pyplot as plt"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 1. Sampling from an arbitrary distribution\n",
+    "As seen in exercise 4, you can use uniformly distributed random variables, which are in principle themselves simple to generate, to draw samples from the normal distribution via the Box-Muller transform. A more general approach is to sample according to the inverse of the cumulative distribution function (CDF).\n",
+    "\n",
+    "A simple example is to generate numbers from the exponential distribution.\n",
+    "\n",
+    "$$ f(t;\\lambda) = \\lambda e^{-\\lambda t} $$\n",
+    "\n",
+    "* Write the CDF $F(T,\\lambda)$ and find its inverse ($T=...$)\n",
+    "* Write a function to compute this, and compare your result to that from scipy (hint: sometimes called percent-point function or quantile function)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\Matt\\Anaconda3\\lib\\site-packages\\ipykernel\\__main__.py:5: RuntimeWarning: divide by zero encountered in log\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Quantile function\n",
+    "def exp_quantile(p, l):\n",
+    "    p[p<0] = 0\n",
+    "    p[p>=1] = 1\n",
+    "    return -np.log(1-p)/l  # scipy equivalent: stats.expon.ppf(p,0,1/l)\n",
+    "\n",
+    "p = np.linspace(0, 1, 100)\n",
+    "l = 0.2\n",
+    "plt.figure()\n",
+    "plt.plot(p, exp_quantile(p, l))\n",
+    "plt.plot(p, stats.expon.ppf(p,0,1/l))\n",
+    "plt.xlabel('x')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "* Now draw N samples from the uniform distribution $[0,1]$. For each sample, calculate $F^{-1}(u,\\lambda)$\n",
+    "* Plot a histogram and compare the distribution of points to the exponential pdf\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Actual: 0.2\n",
+      "Estimated:  0.19919715788379894\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "def logfit(x, y):\n",
+    "    good = y > 0\n",
+    "    ly = np.log(y[good])\n",
+    "    return np.polyfit(x[good], ly, 1, w=np.sqrt(y[good]))\n",
+    "\n",
+    "N = 1000\n",
+    "l = 0.2\n",
+    "x = np.random.rand(N)\n",
+    "y = exp_quantile(x, l)\n",
+    "\n",
+    "hist, bins = np.histogram(y, bins=10, normed=True)\n",
+    "bc = 0.5*(bins[:-1] + bins[1:])\n",
+    "\n",
+    "popt = logfit(bc, hist)\n",
+    "\n",
+    "print('Actual:', l)\n",
+    "print('Estimated: ', -popt[0])\n",
+    "\n",
+    "q = np.linspace(0, bc[-1], 200)\n",
+    "\n",
+    "\n",
+    "# Check the fit\n",
+    "valid = hist>0\n",
+    "plt.figure()\n",
+    "plt.scatter(bc[valid], np.log(hist[valid]), marker='o')\n",
+    "plt.plot(q, np.polyval(popt, q))\n",
+    "\n",
+    "# Plot histogram, fit, and calculated pdf\n",
+    "plt.figure()\n",
+    "plt.bar(bc, hist)\n",
+    "plt.plot(q, np.exp(np.polyval(popt, q)))\n",
+    "plt.plot(q, stats.expon.pdf(q, scale=1/l))\n",
+    "plt.show()\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# 2. Smoothing data\n",
+    "## 2.1 Moving average\n",
+    "The moving average, or rolling mean, is a simple technique which can be used to remove short term or periodic (e.g. seasonal) variations in time series data, for example. It can be viewed as a \"smoothing\", and can ease trend spotting, for instance. One has to be careful when interpreting and using the result; for instance, it is generally improper to fit on such data.\n",
+    "\n",
+    "The simplest moving average can be computed using a \"sliding window\" of length $N$, with all weights equal. For example, for a 3 point moving average, the window would be $\\frac{1}{3}[1,1,1]$.\n",
+    "\n",
+    "* Write a function to compute the $N$ point moving average of a data series"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def moving_average(y, length):\n",
+    "    return np.convolve(np.ones(length)/length, y, 'same')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The following line of code loads a dataset (into a ```pandas DataFrame```) containing monthly measurements of variation in the global surface temperature, stretching back as far as 1750. (More data like this can be found on http://berkeleyearth.org)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Year</th>\n",
+       "      <th>Month</th>\n",
+       "      <th>MDiff</th>\n",
+       "      <th>MUnc</th>\n",
+       "      <th>YDiff</th>\n",
+       "      <th>YUnc</th>\n",
+       "      <th>5YDiff</th>\n",
+       "      <th>5YUnc</th>\n",
+       "      <th>10YDiff</th>\n",
+       "      <th>10YUnc</th>\n",
+       "      <th>20YDiff</th>\n",
+       "      <th>20YUnc</th>\n",
+       "      <th>Date</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1750</td>\n",
+       "      <td>1</td>\n",
+       "      <td>-0.121</td>\n",
+       "      <td>4.187</td>\n",
+       "      <td>-0.687</td>\n",
+       "      <td>2.557</td>\n",
+       "      <td>-0.364</td>\n",
+       "      <td>0.897</td>\n",
+       "      <td>-0.160</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1750-01-15</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>1750</td>\n",
+       "      <td>2</td>\n",
+       "      <td>-1.278</td>\n",
+       "      <td>3.177</td>\n",
+       "      <td>-0.691</td>\n",
+       "      <td>1.733</td>\n",
+       "      <td>-0.381</td>\n",
+       "      <td>0.904</td>\n",
+       "      <td>-0.169</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1750-02-15</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>1750</td>\n",
+       "      <td>3</td>\n",
+       "      <td>0.112</td>\n",
+       "      <td>3.550</td>\n",
+       "      <td>-0.721</td>\n",
+       "      <td>1.568</td>\n",
+       "      <td>-0.401</td>\n",
+       "      <td>0.918</td>\n",
+       "      <td>-0.164</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1750-03-15</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>1750</td>\n",
+       "      <td>4</td>\n",
+       "      <td>0.026</td>\n",
+       "      <td>2.862</td>\n",
+       "      <td>-0.734</td>\n",
+       "      <td>1.609</td>\n",
+       "      <td>-0.452</td>\n",
+       "      <td>0.951</td>\n",
+       "      <td>-0.168</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1750-04-15</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>1750</td>\n",
+       "      <td>5</td>\n",
+       "      <td>-1.420</td>\n",
+       "      <td>2.611</td>\n",
+       "      <td>-1.043</td>\n",
+       "      <td>1.553</td>\n",
+       "      <td>-0.439</td>\n",
+       "      <td>1.022</td>\n",
+       "      <td>-0.167</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1750-05-15</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>1750</td>\n",
+       "      <td>6</td>\n",
+       "      <td>-1.029</td>\n",
+       "      <td>3.379</td>\n",
+       "      <td>-1.004</td>\n",
+       "      <td>1.271</td>\n",
+       "      <td>-0.414</td>\n",
+       "      <td>1.060</td>\n",
+       "      <td>-0.176</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1750-06-15</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>1750</td>\n",
+       "      <td>7</td>\n",
+       "      <td>-0.262</td>\n",
+       "      <td>2.722</td>\n",
+       "      <td>-1.049</td>\n",
+       "      <td>1.026</td>\n",
+       "      <td>-0.411</td>\n",
+       "      <td>1.023</td>\n",
+       "      <td>-0.183</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1750-07-15</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>1750</td>\n",
+       "      <td>8</td>\n",
+       "      <td>0.290</td>\n",
+       "      <td>3.219</td>\n",
+       "      <td>-1.137</td>\n",
+       "      <td>0.792</td>\n",
+       "      <td>-0.466</td>\n",
+       "      <td>0.933</td>\n",
+       "      <td>-0.210</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1750-08-15</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>1750</td>\n",
+       "      <td>9</td>\n",
+       "      <td>-0.851</td>\n",
+       "      <td>2.121</td>\n",
+       "      <td>-1.107</td>\n",
+       "      <td>0.775</td>\n",
+       "      <td>-0.375</td>\n",
+       "      <td>0.945</td>\n",
+       "      <td>-0.230</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1750-09-15</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>1750</td>\n",
+       "      <td>10</td>\n",
+       "      <td>-1.448</td>\n",
+       "      <td>3.078</td>\n",
+       "      <td>-1.167</td>\n",
+       "      <td>0.826</td>\n",
+       "      <td>-0.394</td>\n",
+       "      <td>1.023</td>\n",
+       "      <td>-0.211</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1750-10-15</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10</th>\n",
+       "      <td>1750</td>\n",
+       "      <td>11</td>\n",
+       "      <td>-3.518</td>\n",
+       "      <td>1.996</td>\n",
+       "      <td>-1.160</td>\n",
+       "      <td>1.283</td>\n",
+       "      <td>-0.423</td>\n",
+       "      <td>1.094</td>\n",
+       "      <td>-0.226</td>\n",
+       "      <td>0.879</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1750-11-15</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>11</th>\n",
+       "      <td>1750</td>\n",
+       "      <td>12</td>\n",
+       "      <td>-2.538</td>\n",
+       "      <td>4.091</td>\n",
+       "      <td>-1.210</td>\n",
+       "      <td>1.458</td>\n",
+       "      <td>-0.451</td>\n",
+       "      <td>1.143</td>\n",
+       "      <td>-0.250</td>\n",
+       "      <td>0.894</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1750-12-15</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>12</th>\n",
+       "      <td>1751</td>\n",
+       "      <td>1</td>\n",
+       "      <td>-0.659</td>\n",
+       "      <td>3.318</td>\n",
+       "      <td>-1.094</td>\n",
+       "      <td>1.533</td>\n",
+       "      <td>-0.464</td>\n",
+       "      <td>1.148</td>\n",
+       "      <td>-0.258</td>\n",
+       "      <td>0.844</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1751-01-15</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>13</th>\n",
+       "      <td>1751</td>\n",
+       "      <td>2</td>\n",
+       "      <td>-2.341</td>\n",
+       "      <td>4.503</td>\n",
+       "      <td>-1.047</td>\n",
+       "      <td>1.776</td>\n",
+       "      <td>-0.482</td>\n",
+       "      <td>1.131</td>\n",
+       "      <td>-0.231</td>\n",
+       "      <td>0.914</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1751-02-15</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>14</th>\n",
+       "      <td>1751</td>\n",
+       "      <td>3</td>\n",
+       "      <td>0.477</td>\n",
+       "      <td>2.778</td>\n",
+       "      <td>-1.068</td>\n",
+       "      <td>1.673</td>\n",
+       "      <td>-0.488</td>\n",
+       "      <td>1.200</td>\n",
+       "      <td>-0.201</td>\n",
+       "      <td>0.952</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1751-03-15</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>15</th>\n",
+       "      <td>1751</td>\n",
+       "      <td>4</td>\n",
+       "      <td>-0.690</td>\n",
+       "      <td>2.489</td>\n",
+       "      <td>-0.933</td>\n",
+       "      <td>1.504</td>\n",
+       "      <td>-0.492</td>\n",
+       "      <td>1.245</td>\n",
+       "      <td>-0.184</td>\n",
+       "      <td>1.004</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1751-04-15</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>16</th>\n",
+       "      <td>1751</td>\n",
+       "      <td>5</td>\n",
+       "      <td>-1.338</td>\n",
+       "      <td>3.435</td>\n",
+       "      <td>-0.771</td>\n",
+       "      <td>1.606</td>\n",
+       "      <td>-0.486</td>\n",
+       "      <td>1.336</td>\n",
+       "      <td>-0.184</td>\n",
+       "      <td>1.019</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1751-05-15</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>17</th>\n",
+       "      <td>1751</td>\n",
+       "      <td>6</td>\n",
+       "      <td>-1.637</td>\n",
+       "      <td>3.336</td>\n",
+       "      <td>-0.721</td>\n",
+       "      <td>1.085</td>\n",
+       "      <td>-0.539</td>\n",
+       "      <td>1.393</td>\n",
+       "      <td>-0.188</td>\n",
+       "      <td>1.075</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1751-06-15</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>18</th>\n",
+       "      <td>1751</td>\n",
+       "      <td>7</td>\n",
+       "      <td>1.130</td>\n",
+       "      <td>3.753</td>\n",
+       "      <td>-0.876</td>\n",
+       "      <td>1.400</td>\n",
+       "      <td>-0.527</td>\n",
+       "      <td>1.212</td>\n",
+       "      <td>-0.208</td>\n",
+       "      <td>1.084</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1751-07-15</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>19</th>\n",
+       "      <td>1751</td>\n",
+       "      <td>8</td>\n",
+       "      <td>0.858</td>\n",
+       "      <td>2.757</td>\n",
+       "      <td>-0.409</td>\n",
+       "      <td>1.841</td>\n",
+       "      <td>-0.538</td>\n",
+       "      <td>1.097</td>\n",
+       "      <td>-0.221</td>\n",
+       "      <td>1.106</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1751-08-15</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>20</th>\n",
+       "      <td>1751</td>\n",
+       "      <td>9</td>\n",
+       "      <td>-1.098</td>\n",
+       "      <td>2.928</td>\n",
+       "      <td>-0.382</td>\n",
+       "      <td>1.840</td>\n",
+       "      <td>-0.531</td>\n",
+       "      <td>1.123</td>\n",
+       "      <td>-0.225</td>\n",
+       "      <td>1.119</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1751-09-15</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>21</th>\n",
+       "      <td>1751</td>\n",
+       "      <td>10</td>\n",
+       "      <td>0.169</td>\n",
+       "      <td>4.986</td>\n",
+       "      <td>-0.429</td>\n",
+       "      <td>1.791</td>\n",
+       "      <td>-0.446</td>\n",
+       "      <td>1.151</td>\n",
+       "      <td>-0.219</td>\n",
+       "      <td>1.148</td>\n",
+       "      <td>-0.276</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1751-10-15</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>22</th>\n",
+       "      <td>1751</td>\n",
+       "      <td>11</td>\n",
+       "      <td>-1.577</td>\n",
+       "      <td>2.326</td>\n",
+       "      <td>-0.302</td>\n",
+       "      <td>1.688</td>\n",
+       "      <td>-0.437</td>\n",
+       "      <td>1.160</td>\n",
+       "      <td>-0.222</td>\n",
+       "      <td>1.178</td>\n",
+       "      <td>-0.286</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1751-11-15</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>23</th>\n",
+       "      <td>1751</td>\n",
+       "      <td>12</td>\n",
+       "      <td>-1.935</td>\n",
+       "      <td>3.412</td>\n",
+       "      <td>-0.129</td>\n",
+       "      <td>1.784</td>\n",
+       "      <td>-0.426</td>\n",
+       "      <td>1.293</td>\n",
+       "      <td>-0.258</td>\n",
+       "      <td>1.173</td>\n",
+       "      <td>-0.316</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1751-12-15</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>24</th>\n",
+       "      <td>1752</td>\n",
+       "      <td>1</td>\n",
+       "      <td>-2.523</td>\n",
+       "      <td>4.962</td>\n",
+       "      <td>-0.154</td>\n",
+       "      <td>1.757</td>\n",
+       "      <td>-0.431</td>\n",
+       "      <td>1.296</td>\n",
+       "      <td>-0.262</td>\n",
+       "      <td>1.160</td>\n",
+       "      <td>-0.299</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1752-01-15</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25</th>\n",
+       "      <td>1752</td>\n",
+       "      <td>2</td>\n",
+       "      <td>3.263</td>\n",
+       "      <td>4.891</td>\n",
+       "      <td>-0.311</td>\n",
+       "      <td>1.743</td>\n",
+       "      <td>-0.461</td>\n",
+       "      <td>1.061</td>\n",
+       "      <td>-0.216</td>\n",
+       "      <td>1.213</td>\n",
+       "      <td>-0.299</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1752-02-15</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>26</th>\n",
+       "      <td>1752</td>\n",
+       "      <td>3</td>\n",
+       "      <td>0.804</td>\n",
+       "      <td>3.040</td>\n",
+       "      <td>-0.166</td>\n",
+       "      <td>1.570</td>\n",
+       "      <td>-0.480</td>\n",
+       "      <td>1.053</td>\n",
+       "      <td>-0.192</td>\n",
+       "      <td>1.258</td>\n",
+       "      <td>-0.303</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1752-03-15</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>27</th>\n",
+       "      <td>1752</td>\n",
+       "      <td>4</td>\n",
+       "      <td>-1.259</td>\n",
+       "      <td>2.243</td>\n",
+       "      <td>-0.263</td>\n",
+       "      <td>1.645</td>\n",
+       "      <td>-0.447</td>\n",
+       "      <td>1.072</td>\n",
+       "      <td>-0.185</td>\n",
+       "      <td>1.364</td>\n",
+       "      <td>-0.295</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1752-04-15</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>28</th>\n",
+       "      <td>1752</td>\n",
+       "      <td>5</td>\n",
+       "      <td>0.196</td>\n",
+       "      <td>1.576</td>\n",
+       "      <td>-0.090</td>\n",
+       "      <td>1.758</td>\n",
+       "      <td>-0.449</td>\n",
+       "      <td>1.030</td>\n",
+       "      <td>-0.178</td>\n",
+       "      <td>1.431</td>\n",
+       "      <td>-0.293</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1752-05-15</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>29</th>\n",
+       "      <td>1752</td>\n",
+       "      <td>6</td>\n",
+       "      <td>0.434</td>\n",
+       "      <td>3.225</td>\n",
+       "      <td>0.040</td>\n",
+       "      <td>1.815</td>\n",
+       "      <td>-0.390</td>\n",
+       "      <td>1.072</td>\n",
+       "      <td>-0.179</td>\n",
+       "      <td>1.504</td>\n",
+       "      <td>-0.293</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1752-06-15</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3195</th>\n",
+       "      <td>2016</td>\n",
+       "      <td>4</td>\n",
+       "      <td>1.796</td>\n",
+       "      <td>0.111</td>\n",
+       "      <td>1.454</td>\n",
+       "      <td>0.042</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2016-04-15</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3196</th>\n",
+       "      <td>2016</td>\n",
+       "      <td>5</td>\n",
+       "      <td>1.260</td>\n",
+       "      <td>0.112</td>\n",
+       "      <td>1.433</td>\n",
+       "      <td>0.040</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2016-05-15</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3197</th>\n",
+       "      <td>2016</td>\n",
+       "      <td>6</td>\n",
+       "      <td>0.882</td>\n",
+       "      <td>0.078</td>\n",
+       "      <td>1.387</td>\n",
+       "      <td>0.034</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2016-06-15</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3198</th>\n",
+       "      <td>2016</td>\n",
+       "      <td>7</td>\n",
+       "      <td>0.935</td>\n",
+       "      <td>0.046</td>\n",
+       "      <td>1.385</td>\n",
+       "      <td>0.029</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2016-07-15</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3199</th>\n",
+       "      <td>2016</td>\n",
+       "      <td>8</td>\n",
+       "      <td>1.433</td>\n",
+       "      <td>0.102</td>\n",
+       "      <td>1.348</td>\n",
+       "      <td>0.028</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2016-08-15</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3200</th>\n",
+       "      <td>2016</td>\n",
+       "      <td>9</td>\n",
+       "      <td>1.058</td>\n",
+       "      <td>0.082</td>\n",
+       "      <td>1.321</td>\n",
+       "      <td>0.027</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2016-09-15</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3201</th>\n",
+       "      <td>2016</td>\n",
+       "      <td>10</td>\n",
+       "      <td>1.019</td>\n",
+       "      <td>0.062</td>\n",
+       "      <td>1.280</td>\n",
+       "      <td>0.031</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2016-10-15</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3202</th>\n",
+       "      <td>2016</td>\n",
+       "      <td>11</td>\n",
+       "      <td>1.079</td>\n",
+       "      <td>0.095</td>\n",
+       "      <td>1.278</td>\n",
+       "      <td>0.031</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2016-11-15</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3203</th>\n",
+       "      <td>2016</td>\n",
+       "      <td>12</td>\n",
+       "      <td>1.259</td>\n",
+       "      <td>0.077</td>\n",
+       "      <td>1.271</td>\n",
+       "      <td>0.035</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2016-12-15</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3204</th>\n",
+       "      <td>2017</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1.569</td>\n",
+       "      <td>0.082</td>\n",
+       "      <td>1.275</td>\n",
+       "      <td>0.038</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2017-01-15</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3205</th>\n",
+       "      <td>2017</td>\n",
+       "      <td>2</td>\n",
+       "      <td>1.746</td>\n",
+       "      <td>0.062</td>\n",
+       "      <td>1.244</td>\n",
+       "      <td>0.039</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2017-02-15</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3206</th>\n",
+       "      <td>2017</td>\n",
+       "      <td>3</td>\n",
+       "      <td>1.831</td>\n",
+       "      <td>0.052</td>\n",
+       "      <td>1.231</td>\n",
+       "      <td>0.037</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2017-03-15</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3207</th>\n",
+       "      <td>2017</td>\n",
+       "      <td>4</td>\n",
+       "      <td>1.301</td>\n",
+       "      <td>0.144</td>\n",
+       "      <td>1.253</td>\n",
+       "      <td>0.038</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2017-04-15</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3208</th>\n",
+       "      <td>2017</td>\n",
+       "      <td>5</td>\n",
+       "      <td>1.235</td>\n",
+       "      <td>0.132</td>\n",
+       "      <td>1.249</td>\n",
+       "      <td>0.036</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2017-05-15</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3209</th>\n",
+       "      <td>2017</td>\n",
+       "      <td>6</td>\n",
+       "      <td>0.803</td>\n",
+       "      <td>0.089</td>\n",
+       "      <td>1.268</td>\n",
+       "      <td>0.040</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2017-06-15</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3210</th>\n",
+       "      <td>2017</td>\n",
+       "      <td>7</td>\n",
+       "      <td>0.973</td>\n",
+       "      <td>0.079</td>\n",
+       "      <td>1.235</td>\n",
+       "      <td>0.038</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2017-07-15</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3211</th>\n",
+       "      <td>2017</td>\n",
+       "      <td>8</td>\n",
+       "      <td>1.066</td>\n",
+       "      <td>0.086</td>\n",
+       "      <td>1.180</td>\n",
+       "      <td>0.039</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2017-08-15</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3212</th>\n",
+       "      <td>2017</td>\n",
+       "      <td>9</td>\n",
+       "      <td>0.906</td>\n",
+       "      <td>0.093</td>\n",
+       "      <td>1.142</td>\n",
+       "      <td>0.042</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2017-09-15</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3213</th>\n",
+       "      <td>2017</td>\n",
+       "      <td>10</td>\n",
+       "      <td>1.275</td>\n",
+       "      <td>0.048</td>\n",
+       "      <td>1.145</td>\n",
+       "      <td>0.041</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2017-10-15</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3214</th>\n",
+       "      <td>2017</td>\n",
+       "      <td>11</td>\n",
+       "      <td>1.035</td>\n",
+       "      <td>0.080</td>\n",
+       "      <td>1.138</td>\n",
+       "      <td>0.040</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2017-11-15</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3215</th>\n",
+       "      <td>2017</td>\n",
+       "      <td>12</td>\n",
+       "      <td>1.487</td>\n",
+       "      <td>0.073</td>\n",
+       "      <td>1.161</td>\n",
+       "      <td>0.040</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2017-12-15</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3216</th>\n",
+       "      <td>2018</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1.171</td>\n",
+       "      <td>0.093</td>\n",
+       "      <td>1.172</td>\n",
+       "      <td>0.038</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2018-01-15</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3217</th>\n",
+       "      <td>2018</td>\n",
+       "      <td>2</td>\n",
+       "      <td>1.093</td>\n",
+       "      <td>0.102</td>\n",
+       "      <td>1.166</td>\n",
+       "      <td>0.035</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2018-02-15</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3218</th>\n",
+       "      <td>2018</td>\n",
+       "      <td>3</td>\n",
+       "      <td>1.366</td>\n",
+       "      <td>0.091</td>\n",
+       "      <td>1.158</td>\n",
+       "      <td>0.042</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2018-03-15</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3219</th>\n",
+       "      <td>2018</td>\n",
+       "      <td>4</td>\n",
+       "      <td>1.342</td>\n",
+       "      <td>0.112</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2018-04-15</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3220</th>\n",
+       "      <td>2018</td>\n",
+       "      <td>5</td>\n",
+       "      <td>1.147</td>\n",
+       "      <td>0.170</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2018-05-15</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3221</th>\n",
+       "      <td>2018</td>\n",
+       "      <td>6</td>\n",
+       "      <td>1.078</td>\n",
+       "      <td>0.122</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2018-06-15</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3222</th>\n",
+       "      <td>2018</td>\n",
+       "      <td>7</td>\n",
+       "      <td>1.112</td>\n",
+       "      <td>0.039</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2018-07-15</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3223</th>\n",
+       "      <td>2018</td>\n",
+       "      <td>8</td>\n",
+       "      <td>0.991</td>\n",
+       "      <td>0.107</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2018-08-15</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3224</th>\n",
+       "      <td>2018</td>\n",
+       "      <td>9</td>\n",
+       "      <td>0.804</td>\n",
+       "      <td>0.161</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2018-09-15</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>3225 rows × 13 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "      Year  Month  MDiff   MUnc  YDiff   YUnc  5YDiff  5YUnc  10YDiff  10YUnc  \\\n",
+       "0     1750      1 -0.121  4.187 -0.687  2.557  -0.364  0.897   -0.160     NaN   \n",
+       "1     1750      2 -1.278  3.177 -0.691  1.733  -0.381  0.904   -0.169     NaN   \n",
+       "2     1750      3  0.112  3.550 -0.721  1.568  -0.401  0.918   -0.164     NaN   \n",
+       "3     1750      4  0.026  2.862 -0.734  1.609  -0.452  0.951   -0.168     NaN   \n",
+       "4     1750      5 -1.420  2.611 -1.043  1.553  -0.439  1.022   -0.167     NaN   \n",
+       "5     1750      6 -1.029  3.379 -1.004  1.271  -0.414  1.060   -0.176     NaN   \n",
+       "6     1750      7 -0.262  2.722 -1.049  1.026  -0.411  1.023   -0.183     NaN   \n",
+       "7     1750      8  0.290  3.219 -1.137  0.792  -0.466  0.933   -0.210     NaN   \n",
+       "8     1750      9 -0.851  2.121 -1.107  0.775  -0.375  0.945   -0.230     NaN   \n",
+       "9     1750     10 -1.448  3.078 -1.167  0.826  -0.394  1.023   -0.211     NaN   \n",
+       "10    1750     11 -3.518  1.996 -1.160  1.283  -0.423  1.094   -0.226   0.879   \n",
+       "11    1750     12 -2.538  4.091 -1.210  1.458  -0.451  1.143   -0.250   0.894   \n",
+       "12    1751      1 -0.659  3.318 -1.094  1.533  -0.464  1.148   -0.258   0.844   \n",
+       "13    1751      2 -2.341  4.503 -1.047  1.776  -0.482  1.131   -0.231   0.914   \n",
+       "14    1751      3  0.477  2.778 -1.068  1.673  -0.488  1.200   -0.201   0.952   \n",
+       "15    1751      4 -0.690  2.489 -0.933  1.504  -0.492  1.245   -0.184   1.004   \n",
+       "16    1751      5 -1.338  3.435 -0.771  1.606  -0.486  1.336   -0.184   1.019   \n",
+       "17    1751      6 -1.637  3.336 -0.721  1.085  -0.539  1.393   -0.188   1.075   \n",
+       "18    1751      7  1.130  3.753 -0.876  1.400  -0.527  1.212   -0.208   1.084   \n",
+       "19    1751      8  0.858  2.757 -0.409  1.841  -0.538  1.097   -0.221   1.106   \n",
+       "20    1751      9 -1.098  2.928 -0.382  1.840  -0.531  1.123   -0.225   1.119   \n",
+       "21    1751     10  0.169  4.986 -0.429  1.791  -0.446  1.151   -0.219   1.148   \n",
+       "22    1751     11 -1.577  2.326 -0.302  1.688  -0.437  1.160   -0.222   1.178   \n",
+       "23    1751     12 -1.935  3.412 -0.129  1.784  -0.426  1.293   -0.258   1.173   \n",
+       "24    1752      1 -2.523  4.962 -0.154  1.757  -0.431  1.296   -0.262   1.160   \n",
+       "25    1752      2  3.263  4.891 -0.311  1.743  -0.461  1.061   -0.216   1.213   \n",
+       "26    1752      3  0.804  3.040 -0.166  1.570  -0.480  1.053   -0.192   1.258   \n",
+       "27    1752      4 -1.259  2.243 -0.263  1.645  -0.447  1.072   -0.185   1.364   \n",
+       "28    1752      5  0.196  1.576 -0.090  1.758  -0.449  1.030   -0.178   1.431   \n",
+       "29    1752      6  0.434  3.225  0.040  1.815  -0.390  1.072   -0.179   1.504   \n",
+       "...    ...    ...    ...    ...    ...    ...     ...    ...      ...     ...   \n",
+       "3195  2016      4  1.796  0.111  1.454  0.042     NaN    NaN      NaN     NaN   \n",
+       "3196  2016      5  1.260  0.112  1.433  0.040     NaN    NaN      NaN     NaN   \n",
+       "3197  2016      6  0.882  0.078  1.387  0.034     NaN    NaN      NaN     NaN   \n",
+       "3198  2016      7  0.935  0.046  1.385  0.029     NaN    NaN      NaN     NaN   \n",
+       "3199  2016      8  1.433  0.102  1.348  0.028     NaN    NaN      NaN     NaN   \n",
+       "3200  2016      9  1.058  0.082  1.321  0.027     NaN    NaN      NaN     NaN   \n",
+       "3201  2016     10  1.019  0.062  1.280  0.031     NaN    NaN      NaN     NaN   \n",
+       "3202  2016     11  1.079  0.095  1.278  0.031     NaN    NaN      NaN     NaN   \n",
+       "3203  2016     12  1.259  0.077  1.271  0.035     NaN    NaN      NaN     NaN   \n",
+       "3204  2017      1  1.569  0.082  1.275  0.038     NaN    NaN      NaN     NaN   \n",
+       "3205  2017      2  1.746  0.062  1.244  0.039     NaN    NaN      NaN     NaN   \n",
+       "3206  2017      3  1.831  0.052  1.231  0.037     NaN    NaN      NaN     NaN   \n",
+       "3207  2017      4  1.301  0.144  1.253  0.038     NaN    NaN      NaN     NaN   \n",
+       "3208  2017      5  1.235  0.132  1.249  0.036     NaN    NaN      NaN     NaN   \n",
+       "3209  2017      6  0.803  0.089  1.268  0.040     NaN    NaN      NaN     NaN   \n",
+       "3210  2017      7  0.973  0.079  1.235  0.038     NaN    NaN      NaN     NaN   \n",
+       "3211  2017      8  1.066  0.086  1.180  0.039     NaN    NaN      NaN     NaN   \n",
+       "3212  2017      9  0.906  0.093  1.142  0.042     NaN    NaN      NaN     NaN   \n",
+       "3213  2017     10  1.275  0.048  1.145  0.041     NaN    NaN      NaN     NaN   \n",
+       "3214  2017     11  1.035  0.080  1.138  0.040     NaN    NaN      NaN     NaN   \n",
+       "3215  2017     12  1.487  0.073  1.161  0.040     NaN    NaN      NaN     NaN   \n",
+       "3216  2018      1  1.171  0.093  1.172  0.038     NaN    NaN      NaN     NaN   \n",
+       "3217  2018      2  1.093  0.102  1.166  0.035     NaN    NaN      NaN     NaN   \n",
+       "3218  2018      3  1.366  0.091  1.158  0.042     NaN    NaN      NaN     NaN   \n",
+       "3219  2018      4  1.342  0.112    NaN    NaN     NaN    NaN      NaN     NaN   \n",
+       "3220  2018      5  1.147  0.170    NaN    NaN     NaN    NaN      NaN     NaN   \n",
+       "3221  2018      6  1.078  0.122    NaN    NaN     NaN    NaN      NaN     NaN   \n",
+       "3222  2018      7  1.112  0.039    NaN    NaN     NaN    NaN      NaN     NaN   \n",
+       "3223  2018      8  0.991  0.107    NaN    NaN     NaN    NaN      NaN     NaN   \n",
+       "3224  2018      9  0.804  0.161    NaN    NaN     NaN    NaN      NaN     NaN   \n",
+       "\n",
+       "      20YDiff  20YUnc       Date  \n",
+       "0         NaN     NaN 1750-01-15  \n",
+       "1         NaN     NaN 1750-02-15  \n",
+       "2         NaN     NaN 1750-03-15  \n",
+       "3         NaN     NaN 1750-04-15  \n",
+       "4         NaN     NaN 1750-05-15  \n",
+       "5         NaN     NaN 1750-06-15  \n",
+       "6         NaN     NaN 1750-07-15  \n",
+       "7         NaN     NaN 1750-08-15  \n",
+       "8         NaN     NaN 1750-09-15  \n",
+       "9         NaN     NaN 1750-10-15  \n",
+       "10        NaN     NaN 1750-11-15  \n",
+       "11        NaN     NaN 1750-12-15  \n",
+       "12        NaN     NaN 1751-01-15  \n",
+       "13        NaN     NaN 1751-02-15  \n",
+       "14        NaN     NaN 1751-03-15  \n",
+       "15        NaN     NaN 1751-04-15  \n",
+       "16        NaN     NaN 1751-05-15  \n",
+       "17        NaN     NaN 1751-06-15  \n",
+       "18        NaN     NaN 1751-07-15  \n",
+       "19        NaN     NaN 1751-08-15  \n",
+       "20        NaN     NaN 1751-09-15  \n",
+       "21     -0.276     NaN 1751-10-15  \n",
+       "22     -0.286     NaN 1751-11-15  \n",
+       "23     -0.316     NaN 1751-12-15  \n",
+       "24     -0.299     NaN 1752-01-15  \n",
+       "25     -0.299     NaN 1752-02-15  \n",
+       "26     -0.303     NaN 1752-03-15  \n",
+       "27     -0.295     NaN 1752-04-15  \n",
+       "28     -0.293     NaN 1752-05-15  \n",
+       "29     -0.293     NaN 1752-06-15  \n",
+       "...       ...     ...        ...  \n",
+       "3195      NaN     NaN 2016-04-15  \n",
+       "3196      NaN     NaN 2016-05-15  \n",
+       "3197      NaN     NaN 2016-06-15  \n",
+       "3198      NaN     NaN 2016-07-15  \n",
+       "3199      NaN     NaN 2016-08-15  \n",
+       "3200      NaN     NaN 2016-09-15  \n",
+       "3201      NaN     NaN 2016-10-15  \n",
+       "3202      NaN     NaN 2016-11-15  \n",
+       "3203      NaN     NaN 2016-12-15  \n",
+       "3204      NaN     NaN 2017-01-15  \n",
+       "3205      NaN     NaN 2017-02-15  \n",
+       "3206      NaN     NaN 2017-03-15  \n",
+       "3207      NaN     NaN 2017-04-15  \n",
+       "3208      NaN     NaN 2017-05-15  \n",
+       "3209      NaN     NaN 2017-06-15  \n",
+       "3210      NaN     NaN 2017-07-15  \n",
+       "3211      NaN     NaN 2017-08-15  \n",
+       "3212      NaN     NaN 2017-09-15  \n",
+       "3213      NaN     NaN 2017-10-15  \n",
+       "3214      NaN     NaN 2017-11-15  \n",
+       "3215      NaN     NaN 2017-12-15  \n",
+       "3216      NaN     NaN 2018-01-15  \n",
+       "3217      NaN     NaN 2018-02-15  \n",
+       "3218      NaN     NaN 2018-03-15  \n",
+       "3219      NaN     NaN 2018-04-15  \n",
+       "3220      NaN     NaN 2018-05-15  \n",
+       "3221      NaN     NaN 2018-06-15  \n",
+       "3222      NaN     NaN 2018-07-15  \n",
+       "3223      NaN     NaN 2018-08-15  \n",
+       "3224      NaN     NaN 2018-09-15  \n",
+       "\n",
+       "[3225 rows x 13 columns]"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df = pd.read_csv('Material/Complete_TAVG_complete.txt', skipinitialspace=True, delimiter=' ', comment='%')\n",
+    "df['Date'] = df.apply(lambda row: datetime.datetime(\n",
+    "                              int(row['Year']), int(row['Month']), 15), axis=1)\n",
+    "df"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "* Plot the data. To plot the monthly differences, for example, you can directly write ```df2['MDiff'].plot()```"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# For example...\n",
+    "df.query('Year>1980 & Year<2000').plot(x='Date', y='MDiff')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "* Apply your moving average filter to the monthly data ```MDiff```. Try (for example) 6 months, 5 years, 10 years. Plot these on top of cuts of the original data to compare."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "df['6MA'] = moving_average(df['MDiff'], 6)\n",
+    "df['5Y'] = moving_average(df['MDiff'], 60)\n",
+    "df['20Y'] = moving_average(df['MDiff'], 240)\n",
+    "\n",
+    "df.plot(x='Date', y=['6MA', '5Y', '20Y'])\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 2.2 Electronic response of RC circuit\n",
+    "\n",
+    "In general, the response of a linearly time invariant system is found to be the convolution of the its impulse response $h(t)$ and the input voltage. Consider a resistor and capacitor connected in series, driven by a time-varying voltage $u(t)$. The impulse response for such a circuit is:\n",
+    "\n",
+    "$$h_c(t) = \\frac{1}{RC} e^{-t/RC} u(t)$$\n",
+    "\n",
+    "* Write a function to calculate the impulse response as a function of time, the resistance, and the capacitance, and input. Take care to normalise the integral.\n",
+    "\n",
+    "* Now consider a noisy sinusoidal input voltage $u_N(t) = u(t) + \\epsilon(t)$, where $\\epsilon$ is a vector comprising samples draw from $N~(0,1)$. Plot the noisy signal and superimpose the clean signal.\n",
+    "\n",
+    "* Calculate the circuit response for your signal and compare the result to the noisy signal and the clean, original signal\n",
+    "\n",
+    "Play with the RC time constant and see the effect on the signal.\n",
+    "\n",
+    "\n",
+    "Note: this first order low pass filter is exactly equivalent to an exponential moving average. The \"memory\" of the output is effectively determined by the time constant.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Cutoff:  0.0005\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "def rc_impulse(t, R, C):\n",
+    "    RC = R*C\n",
+    "    return 1/RC * np.exp(-t/RC)\n",
+    "\n",
+    "def rc_response(t, u, R, C):\n",
+    "    return np.convolve(rc_impulse(t, R, C), u)[:len(t)]*dt\n",
+    "\n",
+    "t = np.linspace(0, 0.1, 5000)\n",
+    "dt = t[1]-t[0]\n",
+    "R = 5e3\n",
+    "C = 100e-9\n",
+    "tc = R*C\n",
+    "\n",
+    "fw = 200\n",
+    "u = np.sin(2*np.pi*fw*t) + np.cos(2*np.pi*0.1*fw*t)\n",
+    "un = u + np.random.randn(len(u))\n",
+    "\n",
+    "print('Cutoff: ', tc)\n",
+    "\n",
+    "plt.figure()\n",
+    "plt.plot(t, un)\n",
+    "plt.plot(t, rc_response(t, un, R, C))\n",
+    "plt.plot(t, u)\n",
+    "plt.xlabel('Time (s)')\n",
+    "\n",
+    "# Try different cutoffs (remove noise, fast ripple, then whole thing)\n",
+    "plt.figure()\n",
+    "plt.plot(t, un)\n",
+    "plt.plot(t, rc_response(t, un, R, C))\n",
+    "plt.plot(t, rc_response(t, un, 20*R, C))\n",
+    "plt.plot(t, rc_response(t, un, 200*R, C))\n",
+    "plt.xlabel('Time (s)')\n",
+    "plt.show()\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 3. Monte Carlo methods\n",
+    "### 3.1. Particle propagation\n",
+    "The elementary processes of particle absorption and scattering are random in their nature. Propagation of particles through a slab of material with multiple scattering events may be impossible to calculate analytically, but can easily be simulated with Monte Carlo methods.\n",
+    "\n",
+    "* Consider a beam of photons propagating through an absorbing medium with absorption coefficient $\\alpha=0.2$ per unit length. What is the probability of a photon being absorbed in a unit length slab of material?\n",
+    "\n",
+    "* Now take a piece of 1D material made up of 100 slices, each unit length. Starting at x=0, propagate a beam of 1000 photons through the material, slice-by-slice. At each interface, you should \"measure\" each photon to determine whether it has been transmitted or absorbed (hint: uniform distribution, $P(abs)$)\n",
+    "\n",
+    "* Plot the number of photons which are transmitted at the end of each slice, and compare that to the Beer-Lambert-Bouger law\n",
+    "\n",
+    "* Plot a histogram of the distance travelled before absorption for each photon (free paths).\n",
+    "\n",
+    "$I(x) = I_{0}e^{-\\alpha x }$ , where $\\alpha$ is absorption coefficient"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Generated absorption probability (mean) =  0.18105\n",
+      "Fraction of escaped particles =  0.0\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\Matt\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n",
+      "  warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYUAAAEWCAYAAACJ0YulAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMi4yLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvhp/UCwAAHNVJREFUeJzt3Xu4HFWd7vHvS8L9Dtl6DEE2QlAj48EhMjhyHBzRAUaJ46CSERVF8IaX4zUqYog4Dorg8YgIqARvhIuKwcQHGAEV5ZIACZIAEiFITCThbgDl9ps/1upF0enLDuxK985+P8/TT7qqVlf9qnan365V3asVEZiZmQFs0OsCzMysfzgUzMyscCiYmVnhUDAzs8KhYGZmhUPBzMwKh8J6QNIiSfv2uo51SdKgpJA0Nk//XNLbh2nd/0fSzZXppZL2G4515/Wt87+XkjMk3Svp6jZtjpN0l6Q/r8vahoOkmZKO63Ud6wOHQp9r9YIk6TBJlzemI+JFEXFZl/U85UV0fRMRB0TEmd3a5WOwa5d1/Toinj8cdbV6sRrK36sG+wCvBiZExF7NCyXtCHwUmBQR/2sd17ZWmp//NrwcCjYs1pewWV/2o4WdgKUR8WCH5XdHxMpWC9fj42JNHArrgerZhKS9JM2X9ICkOyWdmJv9Kv97n6TVkl4maQNJR0u6XdJKSd+VtHVlvW/Ly+6W9Nmm7UyXdJ6k70t6ADgsb/sKSfdJWiHp65I2qqwvJL1P0i2S/iLp85J2yY95QNI51fZN+zhG0gm5e+NW4F+bll8m6V35/q6Sfinp/tz+7Dy/cQwW5mPwZkn7Slom6ZO52+SMxrymEl4qaXHufjlD0iZ5nWu8a22cjUg6EngL8Im8vQta/L02lvRVScvz7auSNs7LGrV9NP99Vkh6R4fnwXhJsyXdI2mJpCPy/MOBbwEvy3Uc2/S4/YCLgfF5+czKmeXhkv4IXJLb7i3pt/lvvFCVbjBJW0v6dq7zT0rdUWPa1Np4/pydnwvXSvrfleXTJP0hL1ss6d/y/BcC36zsy32V1W4raU5+zFWSdsmPkaST8jG8X9L1knZvdxxHvYjwrY9vwFJgv6Z5hwGXt2oDXAG8Nd/fAtg73x8EAhhbedw7gSXA83LbHwPfy8smAatJ3Q4bAScAj1a2Mz1Pv5705mJTYE9gb2Bs3t6NwIcr2wtgNrAV8CLgb8Av8va3BhYDb29zHN4D3ATsCGwHXFrdH+Ay4F35/lnAZ3JdmwD7NNWwa2V6X+Ax4Hhg47wf+wLLmo7vDZVt/wY4rtXfonkbwMxG2zZ/rxnAlcCzgAHgt8Dnm2qbAWwIHAg8BGzb5hj9EvhG3uc9gFXAq9rV2fTY5n0ezPvxXWDzfFx2AO7OdWxA6o66GxjIjzkfODW3fxZwNfDuNtubTnr+HJz37WPAbcCGefkbgfF5O28GHgSe0+GYzwTuAfYiPf9+AMzKy/4FuAbYBhDwwsa6fFvz5jOFkeH8/M7svvzO6Bsd2j4K7CppXESsjogrO7R9C3BiRNwaEauBTwGHKHUVHAxcEBGXR8QjwDGkF4mqKyLi/Ih4IiIejohrIuLKiHgsIpaSXiD+qekxx0fEAxGxiPRCe1He/v3Az4GXtKn1TcBXI+KOiLgH+GKXY7ATMD4i/hoR3fqfnwA+FxF/i4iH27T5emXbXwCmdlnnUL0FmBERKyNiFXAs8NbK8kfz8kcjYi4pqNe43qF0TWAf4JN5nxeQzg7e2tx2LU2PiAfzcTkUmBsRc/Pf/GJgPnCgpGcDB5DeBDwYqRvqJOCQDuu+JiLOi4hHgRNJYbY3QEScGxHL83bOBm4hveB38uOIuDoiHiOFwh55/qPAlsALAEXEjRGx4mkci1HBoTAyvD4itmncgPd1aHs4sBtwk6R5kl7boe144PbK9O2kd1nPzsvuaCyIiIdI7wqr7qhOSNpN0s8k/Tl3Kf0nMK7pMXdW7j/cYnqLDrVWt3d7m3YAnyC9I7xa6ZM+7+zQFmBVRPy1S5vmbY/v0n6oWv0Nquu+O7/INTxE62M0HrgnIv7StK4dnmF91f3eCXhj0xuUfYDn5GUbAisqy04lnTF0XXdEPAEsy/vR6LpcUFnX7qz5XGpW/dRUOU4RcQnwdeBk4E5Jp0naqtuOj1YOhfVMRNwSEVNJ/xmPB86TtDlrvssHWE76z9zwXFJ3xZ3ACmBCY4GkTYHtmzfXNH0KqYtnYkRsBXya9OI8HFaQum+qtbYUEX+OiCMiYjzwbuAb6vyJo6EMFdy87eX5/oPAZo0Fkpo/udNt3a3+BsvbtO22nu0kbdm0rj89jXVVVeu/g9S9uE3ltnlE/Fde9jdgXGXZVhHxog7rLsdU0gak59tySTsBpwNHAdvnN0I38ORzaa2Hdo6Ir0XEnqRuy92Aj6/tOkYLh8J6RtKhkgbyO6/GRbjHSf3LT5D67xvOAv6vpJ0lbUF6Z392fmd6HvA6Sf+odPH3WLq/wG8JPACslvQC4L3DtmNwDvBBSRMkbQtMa9dQ0hslNQLtXtKLyON5+k6eegyG6v1529uRwu7sPH8h8CJJe+SLz9ObHtdte2cBR0sakDSO1E33/bUtLiLuIF2P+KKkTSS9mHTW+IO1XVcH3yc9J/5F6cL/Jvli+ITcHXMR8BVJWyl9iGEXSc3dh1V7SnpD7q78MClUriRdkwjSc5Z8cb16YfhOYILafCihmaSXSvoHSRuSQvyvPPl8sCYOhfXP/sAiSauB/wcckvuYHyL1hf8mn5LvDXwH+B7pk0m3kf6zfAAg9/l/AJhFepf+F2Al6T9uOx8D/iO3PZ0nXziHw+nAhaQX4WtJF8XbeSlwVT4Gs4EPRcRtedl04Mx8DN60Ftv/IelF79Z8Ow4gIn5PuhD836R+7+brF98GJuXtnd9ivceR+uWvB36X9+3pfglrKukC8XLgJ6TrJBc/zXWtIQfPFFIoriKdHXycJ19H3kb6UMJiUhifR+paauenpIvI95KufbwhXztZDHyF9KGJO4G/I13cb7gEWAT8WdJdQyh9K9Lz515Sl9rdpA9OWAuK8I/sWHf5TOI+UtfQbd3am3UiaTrpE1qH9roWeyqfKVhbkl4nabN8TeIE0jvZpb2tyszq5FCwTqaQuiKWAxNJXVE+tTRbj7n7yMzMCp8pmJlZMeIGuRo3blwMDg72ugwzsxHlmmuuuSsiBrq1qy0UJH0HeC2wMiLWGHxKkkgfmWyM53JYRFzbbb2Dg4PMnz9/uMs1M1uvSeo0CkBRZ/fRTNJn5ts5gHTxciJwJOnbsGZm1kO1hUJE/Io0amE7U4DvRnIlsI2kTl90MTOzmvXyQvMOPHWwrWU888G7zMzsGehlKLQaR6fl52MlHan0wzHzV61aVXNZZmajVy9DYRlPHXlyAm1Gh4yI0yJickRMHhjoevHczMyepl6Gwmzgbfmn8vYG7vcPX5iZ9VadH0k9i/QTf+OUfu/2c6Qf4SAivgnMJX0cdQnpI6ltf3vWzMzWjdpCIf/QS6flAby/ru2bmdna8zAXZmZWjKpQGJw2h8Fpc3pdhplZ3xpVoWBmZp05FMzMrHAomJlZ4VAwM7PCoWBmZoVDwczMCoeCmZkVDgUzMyscCmZmVjgUzMyscCiYmVnhUDAzs8KhYGZmhUPBzMwKh4KZmRUOBTMzKxwKZmZWOBTMzKxwKJiZWeFQMDOzwqFgZmaFQ8HMzAqHgpmZFQ4FMzMrHApmZlY4FMzMrHAomJlZ4VAwM7PCoWBmZoVDwczMCoeCmZkVDgUzMytqDQVJ+0u6WdISSdNaLH+upEslXSfpekkH1lmPmZl1VlsoSBoDnAwcAEwCpkqa1NTsaOCciHgJcAjwjbrqMTOz7uo8U9gLWBIRt0bEI8AsYEpTmwC2yve3BpbXWI+ZmXVRZyjsANxRmV6W51VNBw6VtAyYC3yg1YokHSlpvqT5q1atqqNWAAanzalt3WZmI0GdoaAW86JpeiowMyImAAcC35O0Rk0RcVpETI6IyQMDAzWUamZmUG8oLAN2rExPYM3uocOBcwAi4gpgE2BcjTWZmVkHdYbCPGCipJ0lbUS6kDy7qc0fgVcBSHohKRTq6x8yM7OOaguFiHgMOAq4ELiR9CmjRZJmSDooN/socISkhcBZwGER0dzFZGZm68jYOlceEXNJF5Cr846p3F8MvLzOGszMbOj8jWYzMyscCmZmVjgUzMyscCiYmVnhUDAzs8KhYGZmhUPBzMwKh4KZmRUOBTMzKxwKZmZWOBTMzKxwKJiZWeFQMDOzwqFgZmaFQ8HMzAqHgpmZFQ4FMzMrHApmZlY4FMzMrHAomJlZ4VAwM7PCoWBmZoVDwczMCoeCmZkVDgUzMyscCmtpcNocBqfN6XUZZma1cCiYmVnhUDAzs8KhYGZmhUPBzMwKh4KZmRUOBTMzKxwKZmZWOBTMzKyoNRQk7S/pZklLJE1r0+ZNkhZLWiTph3XWY2ZmnY2ta8WSxgAnA68GlgHzJM2OiMWVNhOBTwEvj4h7JT2rrnrMzKy7Os8U9gKWRMStEfEIMAuY0tTmCODkiLgXICJW1liPmZl1UWco7ADcUZleludV7QbsJuk3kq6UtH+rFUk6UtJ8SfNXrVpVU7lmZlZnKKjFvGiaHgtMBPYFpgLfkrTNGg+KOC0iJkfE5IGBgWEv1MzMkjpDYRmwY2V6ArC8RZufRsSjEXEbcDMpJMzMrAfqDIV5wERJO0vaCDgEmN3U5nzglQCSxpG6k26tsSYzM+ugtlCIiMeAo4ALgRuBcyJikaQZkg7KzS4E7pa0GLgU+HhE3F1XTWZm1lltH0kFiIi5wNymecdU7gfwkXwzM7Me8zeazcyscCiYmVnhUKiBf8fZzEYqh4KZmRUOBTMzKxwKZmZWOBTMzKxwKJiZWeFQMDOzwqFgZmaFQ8HMzIohhYKkoyv3N66vHDMz66WOoSDpE5JeBhxcmX1FvSWZmVmvdBsl9WbgjcDzJP2aNAT29pKeHxE3116dmZmtU926j+4FPg0sIf1k5tfy/GmSfltjXWZm1gPdzhT2Bz4H7AKcCCwEHoyId9RdmJmZrXsdzxQi4tMR8SpgKfB9UogMSLpc0gXroD4zM1uHhvrLaxdGxDxgnqT3RsQ++TeVzcxsPTKkj6RGxCcqk4fleXfVUZCZmfXOWn95LSIW1lGImZn1nr/RbGZmhUPBzMwKh4KZmRUOBTMzKxwKZmZWOBTMzKxwKJiZWeFQMDOzwqHQpwanzel1CWY2CjkUzMyscCj0gM8CzKxfORTMzKxwKJiZWVFrKEjaX9LNkpZImtah3cGSQtLkOusxM7POagsFSWOAk4EDgEnAVEmTWrTbEvggcFVdtZiZ2dDUeaawF7AkIm6NiEeAWcCUFu0+D3wJ+GuNtZiZ2RDUGQo7AHdUppfleYWklwA7RsTPaqzDzMyGqM5QUIt5URZKGwAnAR/tuiLpSEnzJc1ftWrVMJZoZmZVdYbCMmDHyvQEYHllektgd+AySUuBvYHZrS42R8RpETE5IiYPDAzUWLKZ2ehWZyjMAyZK2lnSRsAhwOzGwoi4PyLGRcRgRAwCVwIHRcT8GmsyM7MOaguFiHgMOAq4ELgROCciFkmaIemgurZrZmZP39g6Vx4Rc4G5TfOOadN23zprMTOz7vyNZjMzKxwKZmZWOBTMzKxwKJiZWeFQMDOzwqFgZmaFQ8HMzAqHgpmZFQ4FMzMrHArrucFpcxicNqfXZZjZCOFQMDOzwqFgZmaFQ2EEc9eQmQ03h4KZmRUOBTMzKxwKZmZWOBTMzKxwKJiZWeFQMDOzwqFgZmaFQ8HMzAqHgpmZFQ4FMzMrHApmZlY4FMzMrHAomJlZ4VAwM7PCoWBmZoVDwczMCoeCmZkVDoVRzr/cZmZVDgUzMyscCmZmVjgUzMysqDUUJO0v6WZJSyRNa7H8I5IWS7pe0i8k7VRnPWZm1lltoSBpDHAycAAwCZgqaVJTs+uAyRHxYuA84Et11WNmZt3VeaawF7AkIm6NiEeAWcCUaoOIuDQiHsqTVwITaqzHzMy6qDMUdgDuqEwvy/PaORz4easFko6UNF/S/FWrVg1jiTYU/tiq2ehRZyioxbxo2VA6FJgMfLnV8og4LSImR8TkgYGBYSzRzMyqxta47mXAjpXpCcDy5kaS9gM+A/xTRPytxnrMzKyLOs8U5gETJe0saSPgEGB2tYGklwCnAgdFxMoaazEzsyGoLRQi4jHgKOBC4EbgnIhYJGmGpINysy8DWwDnSlogaXab1VmfG5w2x9cezNYDdXYfERFzgblN846p3N+vzu2bmdna8TeazcyscCiYmVnhUDAzs8KhYGZmhUPBzMwKh4KZmRUOBTMzKxwKZmZWOBTMzKxwKJiZWeFQMDOzwqFgfcMD6pn1nkPB1gm/4JuNDA4FMzMrHApmZlY4FMzMrHAomJlZ4VAwM7PCoWBmZoVDwczMCoeCmZkVDgUzMyscCrbeGZw2x9+gNnuaHApmZlY4FMzMrHAomJlZ4VAwe5p83cLWRw4FG3X8Ym7WnkPBRhS/oJvVy6Fg1sJwhM9Q1uGQs37jUDAzs8KhYGZmhUPBzMwKh4KZmRW1hoKk/SXdLGmJpGktlm8s6ey8/CpJg3XWYzYS+WJ0fXxs11RbKEgaA5wMHABMAqZKmtTU7HDg3ojYFTgJOL6ueszMrLs6zxT2ApZExK0R8QgwC5jS1GYKcGa+fx7wKkmqsSaz9VK3d7z+eOzIti5H/lVE1LNi6WBg/4h4V55+K/APEXFUpc0Nuc2yPP2H3OaupnUdCRyZJ58P3Pw0yxoH3NW1Vf8YSfWOpFphZNU7kmqFkVXvSKoVnlm9O0XEQLdGY5/myoei1Tv+5gQaShsi4jTgtGdckDQ/IiY/0/WsKyOp3pFUK4ysekdSrTCy6h1JtcK6qbfO7qNlwI6V6QnA8nZtJI0FtgbuqbEmMzProM5QmAdMlLSzpI2AQ4DZTW1mA2/P9w8GLom6+rPMzKyr2rqPIuIxSUcBFwJjgO9ExCJJM4D5ETEb+DbwPUlLSGcIh9RVT/aMu6DWsZFU70iqFUZWvSOpVhhZ9Y6kWmEd1FvbhWYzMxt5/I1mMzMrHApmZlaMmlDoNuRGP5G0VNLvJC2QNL/X9TST9B1JK/P3TBrztpN0saRb8r/b9rLGhja1Tpf0p3x8F0g6sJc1VknaUdKlkm6UtEjSh/L8vju+HWrty+MraRNJV0tamOs9Ns/fOQ+zc0sedmejPq51pqTbKsd2j2Hf9mi4ppCH3Pg98GrSx2DnAVMjYnFPC2tD0lJgcvOX+PqFpFcAq4HvRsTued6XgHsi4r9y6G4bEZ/sZZ25rla1TgdWR8QJvaytFUnPAZ4TEddK2hK4Bng9cBh9dnw71Pom+vD45tESNo+I1ZI2BC4HPgR8BPhxRMyS9E1gYUSc0qe1vgf4WUScV9e2R8uZwlCG3LAhiohfseb3SapDlpxJenHouTa19q2IWBER1+b7fwFuBHagD49vh1r7UiSr8+SG+RbAP5OG2YH+Obbtaq3daAmFHYA7KtPL6OMnL+mPf5Gka/IQHyPBsyNiBaQXC+BZPa6nm6MkXZ+7l3reFdNKHjX4JcBV9PnxbaoV+vT4ShojaQGwErgY+ANwX0Q8lpv0zWtDc60R0Ti2X8jH9iRJGw/3dkdLKAxpOI0+8vKI+HvSCLPvz10gNnxOAXYB9gBWAF/pbTlrkrQF8CPgwxHxQK/r6aRFrX17fCPi8YjYgzTCwl7AC1s1W7dVtdZcq6TdgU8BLwBeCmwHDHsX4mgJhaEMudE3ImJ5/ncl8BPSk7ff3Zn7mBt9zSt7XE9bEXFn/g/3BHA6fXZ8cx/yj4AfRMSP8+y+PL6tau334wsQEfcBlwF7A9vkYXagD18bKrXun7vsIiL+BpxBDcd2tITCUIbc6AuSNs8X7ZC0OfAa4IbOj+oL1SFL3g78tIe1dNR4cc3+jT46vvkC47eBGyPixMqivju+7Wrt1+MraUDSNvn+psB+pOsgl5KG2YH+Obatar2p8sZApGsfw35sR8WnjwDyx+K+ypNDbnyhxyW1JOl5pLMDSMOQ/LDfapV0FrAvaRjfO4HPAecD5wDPBf4IvDEien6Bt02t+5K6NgJYCry70V/fa5L2AX4N/A54Is/+NKmvvq+Ob4dap9KHx1fSi0kXkseQ3hCfExEz8v+5WaTumOuAQ/M78Z7pUOslwACpS3wB8J7KBenh2fZoCQUzM+tutHQfmZnZEDgUzMyscCiYmVnhUDAzs8KhYGZmhUPBho2kxyujNy7IQx+MOpJeL2lSZXqGpP1q2M5lkibn+3Mbn2tv0/bDkjYb7hps/eOPpNqwkbQ6IrbosHxsZYyZOutYJ9vpsP2Z1DySZd7OZcDHIqLr8Or9PvKu9Q+fKVitJB0m6VxJFwAX5XkflzQvD+p1bKXtoXkM+QWSTs1Dnjevb6mk43O7qyXtmufPlHSipEuB45V+f+D8vI0r85eBGmP9f0/SJUrj5x+R528h6ReSrlX6LYsplW1+VtJNSr9jcJakj+X5R+T9WCjpR5I2k/SPwEHAl/N+7JJrOzg/5lWSrsvb+E5jQLO8X8dWtv+CFvu+qaRZeZ/OBjZtOi7j8jfi5+SabpD0ZkkfBMYDl+bjg6RTJM1XZaz+TnXk43NGnne9pH/P818j6Yrc/lylcZBsJIsI33wblhvwOOlblguAn+R5h5HGntouT7+G9OPjIr0p+RnwCtLAZBcAG+Z23wDe1mIbS4HP5PtvI70jB5iZ1zUmT/9/4HP5/j8DC/L96cBC0gvqONLoueNJ3x7fKrcZByzJNU7O+7MpsCVwC+ndOcD2lbqOAz5QqeXgyrKZpGEUNsnb2y3P/y5pELnGfjUe/z7gWy32/SOkb+MDvBh4jPTuv/H4ccC/A6dXHrN1dXllfuPvMYY0rs6LO9UBHA98tfL4bfP2fkUa9x/S4GzH9Pp56NszuzUGgTIbDg9HGtWx2cXx5JAMr8m36/L0FsBE0ovcnsC8NKwLm9J+0LezKv+eVJl/bkQ8nu/vQ3qBJCIukbS9pK3zsp9GxMPAw/md817AHOA/lUakfYI0fPKz83oa7clnPA27SzoO2Cbvx4Vt6m14PnBbRPw+T58JvJ80/ApAY/C7a4A3tHj8K4Cv5X26XtL1Ldr8DjhB0vGkwPx1m1repDQs+1jgOcAkoLG+VnXsRxozjLz9eyW9Nj/uN/lvthFwRZvt2QjhULB14cHKfQFfjIhTqw0kfQA4MyI+NYT1RZv7zdtp97jmC2kBvIU0psyeEfFo7oPfpM16GmYCr4+IhZIOI42p1EmndQE0xtt5nPb/NzteBIyI30vaEzgQ+KKkiyJixlOKkHYGPga8NL+4zyTta6c61GLbIgX+1E412cjiawq2rl0IvLPR9yxpB0nPAn4BHJzvN36TeKc263hz5d9270x/RXqhR9K+wF3x5O8STFH6DdztSS/k84CtgZU5EF4JNLZ9OfC63H4L4F8r29gSWKE0fPRbKvP/kpc1uwkYbFwHAd4K/LJN/d32aXfS2dVTSBoPPBQR3wdOAP6+RU1bkQL0fknPJv1uRzcXAUdVtrMtcCXw8sp1nc0k7bYW+2N9yGcKtk5FxEWSXghckbscVpNGpVws6WjSL85tADxK6lq5vcVqNpZ0FelNTbt3qdOBM3IXy0M8Oew0wNWk7qLnAp+PiOWSfgBcIGk+6RrCTbneeZJmk65D3A7MB+7P6/ksafTS20ndNo0X3VnA6fkCb2NIZiLir5LeAZyrNH7/POCb3Y5ZxSmVfVqQ96PZ35Eucj9BOobvzfNPA34uaUVEvFLSdcAi4FbgN0PY9nHAyZJuIJ1BHBsRP85nSGfpyV8AO5r0e+g2QvkjqTai6Bl+tFLSdNbyR+UlbRHpB9Q3I71bPzLybxObrW98pmDW3WlKX0bbhHTdw4Fg6y2fKZiZWeELzWZmVjgUzMyscCiYmVnhUDAzs8KhYGZmxf8AUnZaqOrwmZIAAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "4.577\n"
+     ]
+    }
+   ],
+   "source": [
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "N_slices = 100 # Slices of material\n",
+    "N_particles = 1000 # Number of particles to simulate\n",
+    "alpha = 0.2 # absorption coefficient\n",
+    "P_abs = 1 - np.exp(-alpha) # Absorption probability in a slice\n",
+    "\n",
+    "# Generate N_slices x N_particles matrix of uniformly distributed random numbers. \n",
+    "# Transform it into a matrix of absorption events, where True = absorption, False = no absorption, \n",
+    "# mean(ABs_events) = P_abs\n",
+    "Abs_events = np.random.uniform(0,1,(N_slices,N_particles)) < P_abs \n",
+    "Abs_c = np.cumprod(Abs_events == False, axis=0)  # Propagate the absorbed state (False propagates)\n",
+    "\n",
+    "free_path = np.sum(Abs_c, axis=0)  # Number of \"True\" (i.e. _not_ absorbed) until absorbed\n",
+    "N_transmitted = np.append([N_particles], np.sum(Abs_c, axis=1))\n",
+    "N_escaped_final = np.sum(free_path == N_slices)\n",
+    "\n",
+    "print('Generated absorption probability (mean) = ', np.mean(Abs_events))\n",
+    "print('Fraction of escaped particles = ',N_escaped_final/N_particles)\n",
+    "\n",
+    "x = np.linspace(0,N_slices);\n",
+    "plt.plot(x,N_particles*np.exp(-x*alpha), label = 'Beer-Lambert-Bouguer law') \n",
+    "plt.plot(N_transmitted, label = 'Simulation')\n",
+    "plt.legend()\n",
+    "plt.xlabel('Propagation distance (slice #)')\n",
+    "plt.ylabel('# of transmitted particles')\n",
+    "plt.title('Transmission of %i particles' %N_particles)\n",
+    "plt.show()\n",
+    "#plt.hist(free_path[free_path!=np.inf],30,normed='True')\n",
+    "ax = plt.figure()\n",
+    "plt.hist(free_path,int(N_particles/5),normed='True')\n",
+    "plt.xlabel('Free propagation distance')\n",
+    "plt.ylabel('#')\n",
+    "plt.title('Histogram distribution of free paths')\n",
+    "plt.show()\n",
+    "print(np.mean(free_path))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 3.2. Monte-Carlo integration: estimate $\\pi$\n",
+    "\n",
+    "In a so-called ’hit-and-miss’ approach, or ’simple sampling’, one can estimate the integral\n",
+    "of an arbitrary, well-behaved function over some interval by scattering many points over\n",
+    "some rectangular area A. The probability of a point landing below the curve is proportional\n",
+    "to the function’s integral.\n",
+    "A classic problem is to determine the value of π.\n",
+    "\n",
+    "* Uniformly distribute N points over a unit area. Plot these on top of a unit circle (or quarter circle)\n",
+    "* Calculate the proportion that are within the bounds of your shape for some number of samples N (for large N, it would be unwise to plot)\n",
+    "* Repeat the exercise for increasing N. For each run, you should compute and store the error $\\epsilon = \\bar{\\pi} - \\pi$\n",
+    "* Plot log-log the convergence of your estimate to the actual value (to machine precision) of $\\pi$, i.e. $\\epsilon$ vs the number of points $N$. Compare this to the expected rate of convergence $(1/\\sqrt N)$."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Helper functions...\n",
+    "def mc_integrate_1d(f, dist_x, dist_y, n_iter):\n",
+    "    # Hit and miss version\n",
+    "    #\n",
+    "    # f: function to be evaluated\n",
+    "    # dist_x, dist_y: distributions from which to draw (x,y)\n",
+    "    # Does not handle -ve y\n",
+    "    x = dist_x(n_iter)\n",
+    "    y = dist_y(n_iter)\n",
+    "    h = f(x)\n",
+    "    return np.cumsum(y < f(x)) / np.arange(1,n_iter+1)\n",
+    "\n",
+    "def mc_integrate_1d_2(f, dist_x, n_iter):\n",
+    "    # Sampling\n",
+    "    x = dist_x(n_iter)\n",
+    "    return np.cumsum(f(x))/np.arange(1,n_iter+1)\n",
+    "\n",
+    "def plot_convergence(est, sol):\n",
+    "    x = np.arange(1,len(est)+1)\n",
+    "    plt.figure()\n",
+    "    plt.loglog(x, np.abs(est-sol)/sol, 'b', x, 1/np.sqrt(x), 'r')\n",
+    "    plt.legend(('Result', '1/sqrt(N)'))\n",
+    "    plt.xlabel('N iterations')\n",
+    "    plt.ylabel('Fractional error')\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Pi estimate: 3.124\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\Matt\\Anaconda3\\lib\\site-packages\\ipykernel\\__main__.py:12: MatplotlibDeprecationWarning: axes.hold is deprecated.\n",
+      "    See the API Changes document (http://matplotlib.org/api/api_changes.html)\n",
+      "    for more details.\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "N_trials = int(1e3)\n",
+    "p = np.random.uniform(0,1,size=(N_trials, 2))\n",
+    "r = np.sqrt(np.sum(p**2, 1))\n",
+    "\n",
+    "print('Pi estimate:', 4 * np.sum(r<=1) / N_trials)\n",
+    "\n",
+    "sel = (r<=1, r>1)\n",
+    "\n",
+    "def plot_pi(p, r, sel):\n",
+    "    x = np.linspace(0,1,200)\n",
+    "    fh, ax = plt.subplots()\n",
+    "    ax.hold(True)\n",
+    "    ax.scatter(p[sel[0],0], p[sel[0],1], c='r', marker='x')\n",
+    "    ax.scatter(p[sel[1],0], p[sel[1],1], c='b', marker='x')\n",
+    "    ax.plot(x, np.sqrt(1-x**2), 'k', linewidth=2)\n",
+    "    ax.set_xlim([0, 1])\n",
+    "    ax.set_ylim([0, 1])\n",
+    "\n",
+    "if N_trials <= 1e4:\n",
+    "    plot_pi(p,r,sel)\n",
+    "\n",
+    "x = np.arange(1,N_trials+1)\n",
+    "c_est = 4*np.cumsum(sel[0])/x\n",
+    "c_err = np.abs(c_est-np.pi)/np.pi\n",
+    "\n",
+    "# Std: sqrt(1/N(N-1) sum{(x_i-pi)^2})\n",
+    "plot_convergence(c_est, np.pi)\n",
+    "plt.tight_layout()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "anaconda-cloud": {},
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/exercises/Solutions6/Exercise_6_Solutions.ipynb b/exercises/Solutions6/Exercise_6_Solutions.ipynb
new file mode 100644
index 0000000..8af7b81
--- /dev/null
+++ b/exercises/Solutions6/Exercise_6_Solutions.ipynb
@@ -0,0 +1,1767 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Exercise 6: Arbitrary distributions, moving averages, and Monte-Carlo\n",
+    "## Solutions\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "import datetime\n",
+    "import scipy.stats as stats\n",
+    "import matplotlib.pyplot as plt"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 1. Sampling from an arbitrary distribution\n",
+    "As seen in exercise 4, you can use uniformly distributed random variables, which are in principle themselves simple to generate, to draw samples from the normal distribution via the Box-Muller transform. A more general approach is to sample according to the inverse of the cumulative distribution function (CDF).\n",
+    "\n",
+    "A simple example is to generate numbers from the exponential distribution.\n",
+    "\n",
+    "$$ f(t;\\lambda) = \\lambda e^{-\\lambda t} $$\n",
+    "\n",
+    "* Write the CDF $F(T,\\lambda)$ and find its inverse ($T=...$)\n",
+    "* Write a function to compute this, and compare your result to that from scipy (hint: sometimes called percent-point function or quantile function)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\Matt\\Anaconda3\\lib\\site-packages\\ipykernel\\__main__.py:5: RuntimeWarning: divide by zero encountered in log\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Quantile function\n",
+    "def exp_quantile(p, l):\n",
+    "    p[p<0] = 0\n",
+    "    p[p>=1] = 1\n",
+    "    return -np.log(1-p)/l  # scipy equivalent: stats.expon.ppf(p,0,1/l)\n",
+    "\n",
+    "p = np.linspace(0, 1, 100)\n",
+    "l = 0.2\n",
+    "plt.figure()\n",
+    "plt.plot(p, exp_quantile(p, l))\n",
+    "plt.plot(p, stats.expon.ppf(p,0,1/l))\n",
+    "plt.xlabel('x')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "* Now draw N samples from the uniform distribution $[0,1]$. For each sample, calculate $F^{-1}(u,\\lambda)$\n",
+    "* Plot a histogram and compare the distribution of points to the exponential pdf\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Actual: 0.2\n",
+      "Estimated:  0.19919715788379894\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "def logfit(x, y):\n",
+    "    good = y > 0\n",
+    "    ly = np.log(y[good])\n",
+    "    return np.polyfit(x[good], ly, 1, w=np.sqrt(y[good]))\n",
+    "\n",
+    "N = 1000\n",
+    "l = 0.2\n",
+    "x = np.random.rand(N)\n",
+    "y = exp_quantile(x, l)\n",
+    "\n",
+    "hist, bins = np.histogram(y, bins=10, normed=True)\n",
+    "bc = 0.5*(bins[:-1] + bins[1:])\n",
+    "\n",
+    "popt = logfit(bc, hist)\n",
+    "\n",
+    "print('Actual:', l)\n",
+    "print('Estimated: ', -popt[0])\n",
+    "\n",
+    "q = np.linspace(0, bc[-1], 200)\n",
+    "\n",
+    "\n",
+    "# Check the fit\n",
+    "valid = hist>0\n",
+    "plt.figure()\n",
+    "plt.scatter(bc[valid], np.log(hist[valid]), marker='o')\n",
+    "plt.plot(q, np.polyval(popt, q))\n",
+    "\n",
+    "# Plot histogram, fit, and calculated pdf\n",
+    "plt.figure()\n",
+    "plt.bar(bc, hist)\n",
+    "plt.plot(q, np.exp(np.polyval(popt, q)))\n",
+    "plt.plot(q, stats.expon.pdf(q, scale=1/l))\n",
+    "plt.show()\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# 2. Smoothing data\n",
+    "## 2.1 Moving average\n",
+    "The moving average, or rolling mean, is a simple technique which can be used to remove short term or periodic (e.g. seasonal) variations in time series data, for example. It can be viewed as a \"smoothing\", and can ease trend spotting, for instance. One has to be careful when interpreting and using the result; for instance, it is generally improper to fit on such data.\n",
+    "\n",
+    "The simplest moving average can be computed using a \"sliding window\" of length $N$, with all weights equal. For example, for a 3 point moving average, the window would be $\\frac{1}{3}[1,1,1]$.\n",
+    "\n",
+    "* Write a function to compute the $N$ point moving average of a data series"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def moving_average(y, length):\n",
+    "    return np.convolve(np.ones(length)/length, y, 'same')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The following line of code loads a dataset (into a ```pandas DataFrame```) containing monthly measurements of variation in the global surface temperature, stretching back as far as 1750. (More data like this can be found on http://berkeleyearth.org)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Year</th>\n",
+       "      <th>Month</th>\n",
+       "      <th>MDiff</th>\n",
+       "      <th>MUnc</th>\n",
+       "      <th>YDiff</th>\n",
+       "      <th>YUnc</th>\n",
+       "      <th>5YDiff</th>\n",
+       "      <th>5YUnc</th>\n",
+       "      <th>10YDiff</th>\n",
+       "      <th>10YUnc</th>\n",
+       "      <th>20YDiff</th>\n",
+       "      <th>20YUnc</th>\n",
+       "      <th>Date</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1750</td>\n",
+       "      <td>1</td>\n",
+       "      <td>-0.121</td>\n",
+       "      <td>4.187</td>\n",
+       "      <td>-0.687</td>\n",
+       "      <td>2.557</td>\n",
+       "      <td>-0.364</td>\n",
+       "      <td>0.897</td>\n",
+       "      <td>-0.160</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1750-01-15</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>1750</td>\n",
+       "      <td>2</td>\n",
+       "      <td>-1.278</td>\n",
+       "      <td>3.177</td>\n",
+       "      <td>-0.691</td>\n",
+       "      <td>1.733</td>\n",
+       "      <td>-0.381</td>\n",
+       "      <td>0.904</td>\n",
+       "      <td>-0.169</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1750-02-15</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>1750</td>\n",
+       "      <td>3</td>\n",
+       "      <td>0.112</td>\n",
+       "      <td>3.550</td>\n",
+       "      <td>-0.721</td>\n",
+       "      <td>1.568</td>\n",
+       "      <td>-0.401</td>\n",
+       "      <td>0.918</td>\n",
+       "      <td>-0.164</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1750-03-15</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>1750</td>\n",
+       "      <td>4</td>\n",
+       "      <td>0.026</td>\n",
+       "      <td>2.862</td>\n",
+       "      <td>-0.734</td>\n",
+       "      <td>1.609</td>\n",
+       "      <td>-0.452</td>\n",
+       "      <td>0.951</td>\n",
+       "      <td>-0.168</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1750-04-15</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>1750</td>\n",
+       "      <td>5</td>\n",
+       "      <td>-1.420</td>\n",
+       "      <td>2.611</td>\n",
+       "      <td>-1.043</td>\n",
+       "      <td>1.553</td>\n",
+       "      <td>-0.439</td>\n",
+       "      <td>1.022</td>\n",
+       "      <td>-0.167</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1750-05-15</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>1750</td>\n",
+       "      <td>6</td>\n",
+       "      <td>-1.029</td>\n",
+       "      <td>3.379</td>\n",
+       "      <td>-1.004</td>\n",
+       "      <td>1.271</td>\n",
+       "      <td>-0.414</td>\n",
+       "      <td>1.060</td>\n",
+       "      <td>-0.176</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1750-06-15</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>1750</td>\n",
+       "      <td>7</td>\n",
+       "      <td>-0.262</td>\n",
+       "      <td>2.722</td>\n",
+       "      <td>-1.049</td>\n",
+       "      <td>1.026</td>\n",
+       "      <td>-0.411</td>\n",
+       "      <td>1.023</td>\n",
+       "      <td>-0.183</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1750-07-15</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>1750</td>\n",
+       "      <td>8</td>\n",
+       "      <td>0.290</td>\n",
+       "      <td>3.219</td>\n",
+       "      <td>-1.137</td>\n",
+       "      <td>0.792</td>\n",
+       "      <td>-0.466</td>\n",
+       "      <td>0.933</td>\n",
+       "      <td>-0.210</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1750-08-15</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>1750</td>\n",
+       "      <td>9</td>\n",
+       "      <td>-0.851</td>\n",
+       "      <td>2.121</td>\n",
+       "      <td>-1.107</td>\n",
+       "      <td>0.775</td>\n",
+       "      <td>-0.375</td>\n",
+       "      <td>0.945</td>\n",
+       "      <td>-0.230</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1750-09-15</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>1750</td>\n",
+       "      <td>10</td>\n",
+       "      <td>-1.448</td>\n",
+       "      <td>3.078</td>\n",
+       "      <td>-1.167</td>\n",
+       "      <td>0.826</td>\n",
+       "      <td>-0.394</td>\n",
+       "      <td>1.023</td>\n",
+       "      <td>-0.211</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1750-10-15</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10</th>\n",
+       "      <td>1750</td>\n",
+       "      <td>11</td>\n",
+       "      <td>-3.518</td>\n",
+       "      <td>1.996</td>\n",
+       "      <td>-1.160</td>\n",
+       "      <td>1.283</td>\n",
+       "      <td>-0.423</td>\n",
+       "      <td>1.094</td>\n",
+       "      <td>-0.226</td>\n",
+       "      <td>0.879</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1750-11-15</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>11</th>\n",
+       "      <td>1750</td>\n",
+       "      <td>12</td>\n",
+       "      <td>-2.538</td>\n",
+       "      <td>4.091</td>\n",
+       "      <td>-1.210</td>\n",
+       "      <td>1.458</td>\n",
+       "      <td>-0.451</td>\n",
+       "      <td>1.143</td>\n",
+       "      <td>-0.250</td>\n",
+       "      <td>0.894</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1750-12-15</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>12</th>\n",
+       "      <td>1751</td>\n",
+       "      <td>1</td>\n",
+       "      <td>-0.659</td>\n",
+       "      <td>3.318</td>\n",
+       "      <td>-1.094</td>\n",
+       "      <td>1.533</td>\n",
+       "      <td>-0.464</td>\n",
+       "      <td>1.148</td>\n",
+       "      <td>-0.258</td>\n",
+       "      <td>0.844</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1751-01-15</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>13</th>\n",
+       "      <td>1751</td>\n",
+       "      <td>2</td>\n",
+       "      <td>-2.341</td>\n",
+       "      <td>4.503</td>\n",
+       "      <td>-1.047</td>\n",
+       "      <td>1.776</td>\n",
+       "      <td>-0.482</td>\n",
+       "      <td>1.131</td>\n",
+       "      <td>-0.231</td>\n",
+       "      <td>0.914</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1751-02-15</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>14</th>\n",
+       "      <td>1751</td>\n",
+       "      <td>3</td>\n",
+       "      <td>0.477</td>\n",
+       "      <td>2.778</td>\n",
+       "      <td>-1.068</td>\n",
+       "      <td>1.673</td>\n",
+       "      <td>-0.488</td>\n",
+       "      <td>1.200</td>\n",
+       "      <td>-0.201</td>\n",
+       "      <td>0.952</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1751-03-15</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>15</th>\n",
+       "      <td>1751</td>\n",
+       "      <td>4</td>\n",
+       "      <td>-0.690</td>\n",
+       "      <td>2.489</td>\n",
+       "      <td>-0.933</td>\n",
+       "      <td>1.504</td>\n",
+       "      <td>-0.492</td>\n",
+       "      <td>1.245</td>\n",
+       "      <td>-0.184</td>\n",
+       "      <td>1.004</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1751-04-15</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>16</th>\n",
+       "      <td>1751</td>\n",
+       "      <td>5</td>\n",
+       "      <td>-1.338</td>\n",
+       "      <td>3.435</td>\n",
+       "      <td>-0.771</td>\n",
+       "      <td>1.606</td>\n",
+       "      <td>-0.486</td>\n",
+       "      <td>1.336</td>\n",
+       "      <td>-0.184</td>\n",
+       "      <td>1.019</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1751-05-15</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>17</th>\n",
+       "      <td>1751</td>\n",
+       "      <td>6</td>\n",
+       "      <td>-1.637</td>\n",
+       "      <td>3.336</td>\n",
+       "      <td>-0.721</td>\n",
+       "      <td>1.085</td>\n",
+       "      <td>-0.539</td>\n",
+       "      <td>1.393</td>\n",
+       "      <td>-0.188</td>\n",
+       "      <td>1.075</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1751-06-15</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>18</th>\n",
+       "      <td>1751</td>\n",
+       "      <td>7</td>\n",
+       "      <td>1.130</td>\n",
+       "      <td>3.753</td>\n",
+       "      <td>-0.876</td>\n",
+       "      <td>1.400</td>\n",
+       "      <td>-0.527</td>\n",
+       "      <td>1.212</td>\n",
+       "      <td>-0.208</td>\n",
+       "      <td>1.084</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1751-07-15</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>19</th>\n",
+       "      <td>1751</td>\n",
+       "      <td>8</td>\n",
+       "      <td>0.858</td>\n",
+       "      <td>2.757</td>\n",
+       "      <td>-0.409</td>\n",
+       "      <td>1.841</td>\n",
+       "      <td>-0.538</td>\n",
+       "      <td>1.097</td>\n",
+       "      <td>-0.221</td>\n",
+       "      <td>1.106</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1751-08-15</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>20</th>\n",
+       "      <td>1751</td>\n",
+       "      <td>9</td>\n",
+       "      <td>-1.098</td>\n",
+       "      <td>2.928</td>\n",
+       "      <td>-0.382</td>\n",
+       "      <td>1.840</td>\n",
+       "      <td>-0.531</td>\n",
+       "      <td>1.123</td>\n",
+       "      <td>-0.225</td>\n",
+       "      <td>1.119</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1751-09-15</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>21</th>\n",
+       "      <td>1751</td>\n",
+       "      <td>10</td>\n",
+       "      <td>0.169</td>\n",
+       "      <td>4.986</td>\n",
+       "      <td>-0.429</td>\n",
+       "      <td>1.791</td>\n",
+       "      <td>-0.446</td>\n",
+       "      <td>1.151</td>\n",
+       "      <td>-0.219</td>\n",
+       "      <td>1.148</td>\n",
+       "      <td>-0.276</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1751-10-15</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>22</th>\n",
+       "      <td>1751</td>\n",
+       "      <td>11</td>\n",
+       "      <td>-1.577</td>\n",
+       "      <td>2.326</td>\n",
+       "      <td>-0.302</td>\n",
+       "      <td>1.688</td>\n",
+       "      <td>-0.437</td>\n",
+       "      <td>1.160</td>\n",
+       "      <td>-0.222</td>\n",
+       "      <td>1.178</td>\n",
+       "      <td>-0.286</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1751-11-15</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>23</th>\n",
+       "      <td>1751</td>\n",
+       "      <td>12</td>\n",
+       "      <td>-1.935</td>\n",
+       "      <td>3.412</td>\n",
+       "      <td>-0.129</td>\n",
+       "      <td>1.784</td>\n",
+       "      <td>-0.426</td>\n",
+       "      <td>1.293</td>\n",
+       "      <td>-0.258</td>\n",
+       "      <td>1.173</td>\n",
+       "      <td>-0.316</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1751-12-15</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>24</th>\n",
+       "      <td>1752</td>\n",
+       "      <td>1</td>\n",
+       "      <td>-2.523</td>\n",
+       "      <td>4.962</td>\n",
+       "      <td>-0.154</td>\n",
+       "      <td>1.757</td>\n",
+       "      <td>-0.431</td>\n",
+       "      <td>1.296</td>\n",
+       "      <td>-0.262</td>\n",
+       "      <td>1.160</td>\n",
+       "      <td>-0.299</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1752-01-15</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25</th>\n",
+       "      <td>1752</td>\n",
+       "      <td>2</td>\n",
+       "      <td>3.263</td>\n",
+       "      <td>4.891</td>\n",
+       "      <td>-0.311</td>\n",
+       "      <td>1.743</td>\n",
+       "      <td>-0.461</td>\n",
+       "      <td>1.061</td>\n",
+       "      <td>-0.216</td>\n",
+       "      <td>1.213</td>\n",
+       "      <td>-0.299</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1752-02-15</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>26</th>\n",
+       "      <td>1752</td>\n",
+       "      <td>3</td>\n",
+       "      <td>0.804</td>\n",
+       "      <td>3.040</td>\n",
+       "      <td>-0.166</td>\n",
+       "      <td>1.570</td>\n",
+       "      <td>-0.480</td>\n",
+       "      <td>1.053</td>\n",
+       "      <td>-0.192</td>\n",
+       "      <td>1.258</td>\n",
+       "      <td>-0.303</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1752-03-15</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>27</th>\n",
+       "      <td>1752</td>\n",
+       "      <td>4</td>\n",
+       "      <td>-1.259</td>\n",
+       "      <td>2.243</td>\n",
+       "      <td>-0.263</td>\n",
+       "      <td>1.645</td>\n",
+       "      <td>-0.447</td>\n",
+       "      <td>1.072</td>\n",
+       "      <td>-0.185</td>\n",
+       "      <td>1.364</td>\n",
+       "      <td>-0.295</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1752-04-15</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>28</th>\n",
+       "      <td>1752</td>\n",
+       "      <td>5</td>\n",
+       "      <td>0.196</td>\n",
+       "      <td>1.576</td>\n",
+       "      <td>-0.090</td>\n",
+       "      <td>1.758</td>\n",
+       "      <td>-0.449</td>\n",
+       "      <td>1.030</td>\n",
+       "      <td>-0.178</td>\n",
+       "      <td>1.431</td>\n",
+       "      <td>-0.293</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1752-05-15</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>29</th>\n",
+       "      <td>1752</td>\n",
+       "      <td>6</td>\n",
+       "      <td>0.434</td>\n",
+       "      <td>3.225</td>\n",
+       "      <td>0.040</td>\n",
+       "      <td>1.815</td>\n",
+       "      <td>-0.390</td>\n",
+       "      <td>1.072</td>\n",
+       "      <td>-0.179</td>\n",
+       "      <td>1.504</td>\n",
+       "      <td>-0.293</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1752-06-15</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3195</th>\n",
+       "      <td>2016</td>\n",
+       "      <td>4</td>\n",
+       "      <td>1.796</td>\n",
+       "      <td>0.111</td>\n",
+       "      <td>1.454</td>\n",
+       "      <td>0.042</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2016-04-15</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3196</th>\n",
+       "      <td>2016</td>\n",
+       "      <td>5</td>\n",
+       "      <td>1.260</td>\n",
+       "      <td>0.112</td>\n",
+       "      <td>1.433</td>\n",
+       "      <td>0.040</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2016-05-15</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3197</th>\n",
+       "      <td>2016</td>\n",
+       "      <td>6</td>\n",
+       "      <td>0.882</td>\n",
+       "      <td>0.078</td>\n",
+       "      <td>1.387</td>\n",
+       "      <td>0.034</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2016-06-15</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3198</th>\n",
+       "      <td>2016</td>\n",
+       "      <td>7</td>\n",
+       "      <td>0.935</td>\n",
+       "      <td>0.046</td>\n",
+       "      <td>1.385</td>\n",
+       "      <td>0.029</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2016-07-15</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3199</th>\n",
+       "      <td>2016</td>\n",
+       "      <td>8</td>\n",
+       "      <td>1.433</td>\n",
+       "      <td>0.102</td>\n",
+       "      <td>1.348</td>\n",
+       "      <td>0.028</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2016-08-15</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3200</th>\n",
+       "      <td>2016</td>\n",
+       "      <td>9</td>\n",
+       "      <td>1.058</td>\n",
+       "      <td>0.082</td>\n",
+       "      <td>1.321</td>\n",
+       "      <td>0.027</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2016-09-15</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3201</th>\n",
+       "      <td>2016</td>\n",
+       "      <td>10</td>\n",
+       "      <td>1.019</td>\n",
+       "      <td>0.062</td>\n",
+       "      <td>1.280</td>\n",
+       "      <td>0.031</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2016-10-15</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3202</th>\n",
+       "      <td>2016</td>\n",
+       "      <td>11</td>\n",
+       "      <td>1.079</td>\n",
+       "      <td>0.095</td>\n",
+       "      <td>1.278</td>\n",
+       "      <td>0.031</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2016-11-15</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3203</th>\n",
+       "      <td>2016</td>\n",
+       "      <td>12</td>\n",
+       "      <td>1.259</td>\n",
+       "      <td>0.077</td>\n",
+       "      <td>1.271</td>\n",
+       "      <td>0.035</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2016-12-15</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3204</th>\n",
+       "      <td>2017</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1.569</td>\n",
+       "      <td>0.082</td>\n",
+       "      <td>1.275</td>\n",
+       "      <td>0.038</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2017-01-15</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3205</th>\n",
+       "      <td>2017</td>\n",
+       "      <td>2</td>\n",
+       "      <td>1.746</td>\n",
+       "      <td>0.062</td>\n",
+       "      <td>1.244</td>\n",
+       "      <td>0.039</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2017-02-15</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3206</th>\n",
+       "      <td>2017</td>\n",
+       "      <td>3</td>\n",
+       "      <td>1.831</td>\n",
+       "      <td>0.052</td>\n",
+       "      <td>1.231</td>\n",
+       "      <td>0.037</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2017-03-15</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3207</th>\n",
+       "      <td>2017</td>\n",
+       "      <td>4</td>\n",
+       "      <td>1.301</td>\n",
+       "      <td>0.144</td>\n",
+       "      <td>1.253</td>\n",
+       "      <td>0.038</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2017-04-15</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3208</th>\n",
+       "      <td>2017</td>\n",
+       "      <td>5</td>\n",
+       "      <td>1.235</td>\n",
+       "      <td>0.132</td>\n",
+       "      <td>1.249</td>\n",
+       "      <td>0.036</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2017-05-15</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3209</th>\n",
+       "      <td>2017</td>\n",
+       "      <td>6</td>\n",
+       "      <td>0.803</td>\n",
+       "      <td>0.089</td>\n",
+       "      <td>1.268</td>\n",
+       "      <td>0.040</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2017-06-15</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3210</th>\n",
+       "      <td>2017</td>\n",
+       "      <td>7</td>\n",
+       "      <td>0.973</td>\n",
+       "      <td>0.079</td>\n",
+       "      <td>1.235</td>\n",
+       "      <td>0.038</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2017-07-15</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3211</th>\n",
+       "      <td>2017</td>\n",
+       "      <td>8</td>\n",
+       "      <td>1.066</td>\n",
+       "      <td>0.086</td>\n",
+       "      <td>1.180</td>\n",
+       "      <td>0.039</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2017-08-15</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3212</th>\n",
+       "      <td>2017</td>\n",
+       "      <td>9</td>\n",
+       "      <td>0.906</td>\n",
+       "      <td>0.093</td>\n",
+       "      <td>1.142</td>\n",
+       "      <td>0.042</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2017-09-15</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3213</th>\n",
+       "      <td>2017</td>\n",
+       "      <td>10</td>\n",
+       "      <td>1.275</td>\n",
+       "      <td>0.048</td>\n",
+       "      <td>1.145</td>\n",
+       "      <td>0.041</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2017-10-15</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3214</th>\n",
+       "      <td>2017</td>\n",
+       "      <td>11</td>\n",
+       "      <td>1.035</td>\n",
+       "      <td>0.080</td>\n",
+       "      <td>1.138</td>\n",
+       "      <td>0.040</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2017-11-15</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3215</th>\n",
+       "      <td>2017</td>\n",
+       "      <td>12</td>\n",
+       "      <td>1.487</td>\n",
+       "      <td>0.073</td>\n",
+       "      <td>1.161</td>\n",
+       "      <td>0.040</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2017-12-15</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3216</th>\n",
+       "      <td>2018</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1.171</td>\n",
+       "      <td>0.093</td>\n",
+       "      <td>1.172</td>\n",
+       "      <td>0.038</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2018-01-15</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3217</th>\n",
+       "      <td>2018</td>\n",
+       "      <td>2</td>\n",
+       "      <td>1.093</td>\n",
+       "      <td>0.102</td>\n",
+       "      <td>1.166</td>\n",
+       "      <td>0.035</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2018-02-15</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3218</th>\n",
+       "      <td>2018</td>\n",
+       "      <td>3</td>\n",
+       "      <td>1.366</td>\n",
+       "      <td>0.091</td>\n",
+       "      <td>1.158</td>\n",
+       "      <td>0.042</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2018-03-15</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3219</th>\n",
+       "      <td>2018</td>\n",
+       "      <td>4</td>\n",
+       "      <td>1.342</td>\n",
+       "      <td>0.112</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2018-04-15</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3220</th>\n",
+       "      <td>2018</td>\n",
+       "      <td>5</td>\n",
+       "      <td>1.147</td>\n",
+       "      <td>0.170</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2018-05-15</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3221</th>\n",
+       "      <td>2018</td>\n",
+       "      <td>6</td>\n",
+       "      <td>1.078</td>\n",
+       "      <td>0.122</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2018-06-15</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3222</th>\n",
+       "      <td>2018</td>\n",
+       "      <td>7</td>\n",
+       "      <td>1.112</td>\n",
+       "      <td>0.039</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2018-07-15</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3223</th>\n",
+       "      <td>2018</td>\n",
+       "      <td>8</td>\n",
+       "      <td>0.991</td>\n",
+       "      <td>0.107</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2018-08-15</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3224</th>\n",
+       "      <td>2018</td>\n",
+       "      <td>9</td>\n",
+       "      <td>0.804</td>\n",
+       "      <td>0.161</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2018-09-15</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>3225 rows × 13 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "      Year  Month  MDiff   MUnc  YDiff   YUnc  5YDiff  5YUnc  10YDiff  10YUnc  \\\n",
+       "0     1750      1 -0.121  4.187 -0.687  2.557  -0.364  0.897   -0.160     NaN   \n",
+       "1     1750      2 -1.278  3.177 -0.691  1.733  -0.381  0.904   -0.169     NaN   \n",
+       "2     1750      3  0.112  3.550 -0.721  1.568  -0.401  0.918   -0.164     NaN   \n",
+       "3     1750      4  0.026  2.862 -0.734  1.609  -0.452  0.951   -0.168     NaN   \n",
+       "4     1750      5 -1.420  2.611 -1.043  1.553  -0.439  1.022   -0.167     NaN   \n",
+       "5     1750      6 -1.029  3.379 -1.004  1.271  -0.414  1.060   -0.176     NaN   \n",
+       "6     1750      7 -0.262  2.722 -1.049  1.026  -0.411  1.023   -0.183     NaN   \n",
+       "7     1750      8  0.290  3.219 -1.137  0.792  -0.466  0.933   -0.210     NaN   \n",
+       "8     1750      9 -0.851  2.121 -1.107  0.775  -0.375  0.945   -0.230     NaN   \n",
+       "9     1750     10 -1.448  3.078 -1.167  0.826  -0.394  1.023   -0.211     NaN   \n",
+       "10    1750     11 -3.518  1.996 -1.160  1.283  -0.423  1.094   -0.226   0.879   \n",
+       "11    1750     12 -2.538  4.091 -1.210  1.458  -0.451  1.143   -0.250   0.894   \n",
+       "12    1751      1 -0.659  3.318 -1.094  1.533  -0.464  1.148   -0.258   0.844   \n",
+       "13    1751      2 -2.341  4.503 -1.047  1.776  -0.482  1.131   -0.231   0.914   \n",
+       "14    1751      3  0.477  2.778 -1.068  1.673  -0.488  1.200   -0.201   0.952   \n",
+       "15    1751      4 -0.690  2.489 -0.933  1.504  -0.492  1.245   -0.184   1.004   \n",
+       "16    1751      5 -1.338  3.435 -0.771  1.606  -0.486  1.336   -0.184   1.019   \n",
+       "17    1751      6 -1.637  3.336 -0.721  1.085  -0.539  1.393   -0.188   1.075   \n",
+       "18    1751      7  1.130  3.753 -0.876  1.400  -0.527  1.212   -0.208   1.084   \n",
+       "19    1751      8  0.858  2.757 -0.409  1.841  -0.538  1.097   -0.221   1.106   \n",
+       "20    1751      9 -1.098  2.928 -0.382  1.840  -0.531  1.123   -0.225   1.119   \n",
+       "21    1751     10  0.169  4.986 -0.429  1.791  -0.446  1.151   -0.219   1.148   \n",
+       "22    1751     11 -1.577  2.326 -0.302  1.688  -0.437  1.160   -0.222   1.178   \n",
+       "23    1751     12 -1.935  3.412 -0.129  1.784  -0.426  1.293   -0.258   1.173   \n",
+       "24    1752      1 -2.523  4.962 -0.154  1.757  -0.431  1.296   -0.262   1.160   \n",
+       "25    1752      2  3.263  4.891 -0.311  1.743  -0.461  1.061   -0.216   1.213   \n",
+       "26    1752      3  0.804  3.040 -0.166  1.570  -0.480  1.053   -0.192   1.258   \n",
+       "27    1752      4 -1.259  2.243 -0.263  1.645  -0.447  1.072   -0.185   1.364   \n",
+       "28    1752      5  0.196  1.576 -0.090  1.758  -0.449  1.030   -0.178   1.431   \n",
+       "29    1752      6  0.434  3.225  0.040  1.815  -0.390  1.072   -0.179   1.504   \n",
+       "...    ...    ...    ...    ...    ...    ...     ...    ...      ...     ...   \n",
+       "3195  2016      4  1.796  0.111  1.454  0.042     NaN    NaN      NaN     NaN   \n",
+       "3196  2016      5  1.260  0.112  1.433  0.040     NaN    NaN      NaN     NaN   \n",
+       "3197  2016      6  0.882  0.078  1.387  0.034     NaN    NaN      NaN     NaN   \n",
+       "3198  2016      7  0.935  0.046  1.385  0.029     NaN    NaN      NaN     NaN   \n",
+       "3199  2016      8  1.433  0.102  1.348  0.028     NaN    NaN      NaN     NaN   \n",
+       "3200  2016      9  1.058  0.082  1.321  0.027     NaN    NaN      NaN     NaN   \n",
+       "3201  2016     10  1.019  0.062  1.280  0.031     NaN    NaN      NaN     NaN   \n",
+       "3202  2016     11  1.079  0.095  1.278  0.031     NaN    NaN      NaN     NaN   \n",
+       "3203  2016     12  1.259  0.077  1.271  0.035     NaN    NaN      NaN     NaN   \n",
+       "3204  2017      1  1.569  0.082  1.275  0.038     NaN    NaN      NaN     NaN   \n",
+       "3205  2017      2  1.746  0.062  1.244  0.039     NaN    NaN      NaN     NaN   \n",
+       "3206  2017      3  1.831  0.052  1.231  0.037     NaN    NaN      NaN     NaN   \n",
+       "3207  2017      4  1.301  0.144  1.253  0.038     NaN    NaN      NaN     NaN   \n",
+       "3208  2017      5  1.235  0.132  1.249  0.036     NaN    NaN      NaN     NaN   \n",
+       "3209  2017      6  0.803  0.089  1.268  0.040     NaN    NaN      NaN     NaN   \n",
+       "3210  2017      7  0.973  0.079  1.235  0.038     NaN    NaN      NaN     NaN   \n",
+       "3211  2017      8  1.066  0.086  1.180  0.039     NaN    NaN      NaN     NaN   \n",
+       "3212  2017      9  0.906  0.093  1.142  0.042     NaN    NaN      NaN     NaN   \n",
+       "3213  2017     10  1.275  0.048  1.145  0.041     NaN    NaN      NaN     NaN   \n",
+       "3214  2017     11  1.035  0.080  1.138  0.040     NaN    NaN      NaN     NaN   \n",
+       "3215  2017     12  1.487  0.073  1.161  0.040     NaN    NaN      NaN     NaN   \n",
+       "3216  2018      1  1.171  0.093  1.172  0.038     NaN    NaN      NaN     NaN   \n",
+       "3217  2018      2  1.093  0.102  1.166  0.035     NaN    NaN      NaN     NaN   \n",
+       "3218  2018      3  1.366  0.091  1.158  0.042     NaN    NaN      NaN     NaN   \n",
+       "3219  2018      4  1.342  0.112    NaN    NaN     NaN    NaN      NaN     NaN   \n",
+       "3220  2018      5  1.147  0.170    NaN    NaN     NaN    NaN      NaN     NaN   \n",
+       "3221  2018      6  1.078  0.122    NaN    NaN     NaN    NaN      NaN     NaN   \n",
+       "3222  2018      7  1.112  0.039    NaN    NaN     NaN    NaN      NaN     NaN   \n",
+       "3223  2018      8  0.991  0.107    NaN    NaN     NaN    NaN      NaN     NaN   \n",
+       "3224  2018      9  0.804  0.161    NaN    NaN     NaN    NaN      NaN     NaN   \n",
+       "\n",
+       "      20YDiff  20YUnc       Date  \n",
+       "0         NaN     NaN 1750-01-15  \n",
+       "1         NaN     NaN 1750-02-15  \n",
+       "2         NaN     NaN 1750-03-15  \n",
+       "3         NaN     NaN 1750-04-15  \n",
+       "4         NaN     NaN 1750-05-15  \n",
+       "5         NaN     NaN 1750-06-15  \n",
+       "6         NaN     NaN 1750-07-15  \n",
+       "7         NaN     NaN 1750-08-15  \n",
+       "8         NaN     NaN 1750-09-15  \n",
+       "9         NaN     NaN 1750-10-15  \n",
+       "10        NaN     NaN 1750-11-15  \n",
+       "11        NaN     NaN 1750-12-15  \n",
+       "12        NaN     NaN 1751-01-15  \n",
+       "13        NaN     NaN 1751-02-15  \n",
+       "14        NaN     NaN 1751-03-15  \n",
+       "15        NaN     NaN 1751-04-15  \n",
+       "16        NaN     NaN 1751-05-15  \n",
+       "17        NaN     NaN 1751-06-15  \n",
+       "18        NaN     NaN 1751-07-15  \n",
+       "19        NaN     NaN 1751-08-15  \n",
+       "20        NaN     NaN 1751-09-15  \n",
+       "21     -0.276     NaN 1751-10-15  \n",
+       "22     -0.286     NaN 1751-11-15  \n",
+       "23     -0.316     NaN 1751-12-15  \n",
+       "24     -0.299     NaN 1752-01-15  \n",
+       "25     -0.299     NaN 1752-02-15  \n",
+       "26     -0.303     NaN 1752-03-15  \n",
+       "27     -0.295     NaN 1752-04-15  \n",
+       "28     -0.293     NaN 1752-05-15  \n",
+       "29     -0.293     NaN 1752-06-15  \n",
+       "...       ...     ...        ...  \n",
+       "3195      NaN     NaN 2016-04-15  \n",
+       "3196      NaN     NaN 2016-05-15  \n",
+       "3197      NaN     NaN 2016-06-15  \n",
+       "3198      NaN     NaN 2016-07-15  \n",
+       "3199      NaN     NaN 2016-08-15  \n",
+       "3200      NaN     NaN 2016-09-15  \n",
+       "3201      NaN     NaN 2016-10-15  \n",
+       "3202      NaN     NaN 2016-11-15  \n",
+       "3203      NaN     NaN 2016-12-15  \n",
+       "3204      NaN     NaN 2017-01-15  \n",
+       "3205      NaN     NaN 2017-02-15  \n",
+       "3206      NaN     NaN 2017-03-15  \n",
+       "3207      NaN     NaN 2017-04-15  \n",
+       "3208      NaN     NaN 2017-05-15  \n",
+       "3209      NaN     NaN 2017-06-15  \n",
+       "3210      NaN     NaN 2017-07-15  \n",
+       "3211      NaN     NaN 2017-08-15  \n",
+       "3212      NaN     NaN 2017-09-15  \n",
+       "3213      NaN     NaN 2017-10-15  \n",
+       "3214      NaN     NaN 2017-11-15  \n",
+       "3215      NaN     NaN 2017-12-15  \n",
+       "3216      NaN     NaN 2018-01-15  \n",
+       "3217      NaN     NaN 2018-02-15  \n",
+       "3218      NaN     NaN 2018-03-15  \n",
+       "3219      NaN     NaN 2018-04-15  \n",
+       "3220      NaN     NaN 2018-05-15  \n",
+       "3221      NaN     NaN 2018-06-15  \n",
+       "3222      NaN     NaN 2018-07-15  \n",
+       "3223      NaN     NaN 2018-08-15  \n",
+       "3224      NaN     NaN 2018-09-15  \n",
+       "\n",
+       "[3225 rows x 13 columns]"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df = pd.read_csv('Material/Complete_TAVG_complete.txt', skipinitialspace=True, delimiter=' ', comment='%')\n",
+    "df['Date'] = df.apply(lambda row: datetime.datetime(\n",
+    "                              int(row['Year']), int(row['Month']), 15), axis=1)\n",
+    "df"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "* Plot the data. To plot the monthly differences, for example, you can directly write ```df2['MDiff'].plot()```"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# For example...\n",
+    "df.query('Year>1980 & Year<2000').plot(x='Date', y='MDiff')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "* Apply your moving average filter to the monthly data ```MDiff```. Try (for example) 6 months, 5 years, 10 years. Plot these on top of cuts of the original data to compare."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "df['6MA'] = moving_average(df['MDiff'], 6)\n",
+    "df['5Y'] = moving_average(df['MDiff'], 60)\n",
+    "df['20Y'] = moving_average(df['MDiff'], 240)\n",
+    "\n",
+    "df.plot(x='Date', y=['6MA', '5Y', '20Y'])\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 2.2 Electronic response of RC circuit\n",
+    "\n",
+    "In general, the response of a linearly time invariant system is found to be the convolution of the its impulse response $h(t)$ and the input voltage. Consider a resistor and capacitor connected in series, driven by a time-varying voltage $u(t)$. The impulse response for such a circuit is:\n",
+    "\n",
+    "$$h_c(t) = \\frac{1}{RC} e^{-t/RC} u(t)$$\n",
+    "\n",
+    "* Write a function to calculate the impulse response as a function of time, the resistance, and the capacitance, and input. Take care to normalise the integral.\n",
+    "\n",
+    "* Now consider a noisy sinusoidal input voltage $u_N(t) = u(t) + \\epsilon(t)$, where $\\epsilon$ is a vector comprising samples draw from $N~(0,1)$. Plot the noisy signal and superimpose the clean signal.\n",
+    "\n",
+    "* Calculate the circuit response for your signal and compare the result to the noisy signal and the clean, original signal\n",
+    "\n",
+    "Play with the RC time constant and see the effect on the signal.\n",
+    "\n",
+    "\n",
+    "Note: this first order low pass filter is exactly equivalent to an exponential moving average. The \"memory\" of the output is effectively determined by the time constant.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Cutoff:  0.0005\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "def rc_impulse(t, R, C):\n",
+    "    RC = R*C\n",
+    "    return 1/RC * np.exp(-t/RC)\n",
+    "\n",
+    "def rc_response(t, u, R, C):\n",
+    "    return np.convolve(rc_impulse(t, R, C), u)[:len(t)]*dt\n",
+    "\n",
+    "t = np.linspace(0, 0.1, 5000)\n",
+    "dt = t[1]-t[0]\n",
+    "R = 5e3\n",
+    "C = 100e-9\n",
+    "tc = R*C\n",
+    "\n",
+    "fw = 200\n",
+    "u = np.sin(2*np.pi*fw*t) + np.cos(2*np.pi*0.1*fw*t)\n",
+    "un = u + np.random.randn(len(u))\n",
+    "\n",
+    "print('Cutoff: ', tc)\n",
+    "\n",
+    "plt.figure()\n",
+    "plt.plot(t, un)\n",
+    "plt.plot(t, rc_response(t, un, R, C))\n",
+    "plt.plot(t, u)\n",
+    "plt.xlabel('Time (s)')\n",
+    "\n",
+    "# Try different cutoffs (remove noise, fast ripple, then whole thing)\n",
+    "plt.figure()\n",
+    "plt.plot(t, un)\n",
+    "plt.plot(t, rc_response(t, un, R, C))\n",
+    "plt.plot(t, rc_response(t, un, 20*R, C))\n",
+    "plt.plot(t, rc_response(t, un, 200*R, C))\n",
+    "plt.xlabel('Time (s)')\n",
+    "plt.show()\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 3. Monte Carlo methods\n",
+    "### 3.1. Particle propagation\n",
+    "The elementary processes of particle absorption and scattering are random in their nature. Propagation of particles through a slab of material with multiple scattering events may be impossible to calculate analytically, but can easily be simulated with Monte Carlo methods.\n",
+    "\n",
+    "* Consider a beam of photons propagating through an absorbing medium with absorption coefficient $\\alpha=0.2$ per unit length. What is the probability of a photon being absorbed in a unit length slab of material?\n",
+    "\n",
+    "* Now take a piece of 1D material made up of 100 slices, each unit length. Starting at x=0, propagate a beam of 1000 photons through the material, slice-by-slice. At each interface, you should \"measure\" each photon to determine whether it has been transmitted or absorbed (hint: uniform distribution, $P(abs)$)\n",
+    "\n",
+    "* Plot the number of photons which are transmitted at the end of each slice, and compare that to the Beer-Lambert-Bouger law\n",
+    "\n",
+    "* Plot a histogram of the distance travelled before absorption for each photon (free paths).\n",
+    "\n",
+    "$I(x) = I_{0}e^{-\\alpha x }$ , where $\\alpha$ is absorption coefficient"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Generated absorption probability (mean) =  0.18105\n",
+      "Fraction of escaped particles =  0.0\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\Matt\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n",
+      "  warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "4.577\n"
+     ]
+    }
+   ],
+   "source": [
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "N_slices = 100 # Slices of material\n",
+    "N_particles = 1000 # Number of particles to simulate\n",
+    "alpha = 0.2 # absorption coefficient\n",
+    "P_abs = 1 - np.exp(-alpha) # Absorption probability in a slice\n",
+    "\n",
+    "# Generate N_slices x N_particles matrix of uniformly distributed random numbers. \n",
+    "# Transform it into a matrix of absorption events, where True = absorption, False = no absorption, \n",
+    "# mean(ABs_events) = P_abs\n",
+    "Abs_events = np.random.uniform(0,1,(N_slices,N_particles)) < P_abs \n",
+    "Abs_c = np.cumprod(Abs_events == False, axis=0)  # Propagate the absorbed state (False propagates)\n",
+    "\n",
+    "free_path = np.sum(Abs_c, axis=0)  # Number of \"True\" (i.e. _not_ absorbed) until absorbed\n",
+    "N_transmitted = np.append([N_particles], np.sum(Abs_c, axis=1))\n",
+    "N_escaped_final = np.sum(free_path == N_slices)\n",
+    "\n",
+    "print('Generated absorption probability (mean) = ', np.mean(Abs_events))\n",
+    "print('Fraction of escaped particles = ',N_escaped_final/N_particles)\n",
+    "\n",
+    "x = np.linspace(0,N_slices);\n",
+    "plt.plot(x,N_particles*np.exp(-x*alpha), label = 'Beer-Lambert-Bouguer law') \n",
+    "plt.plot(N_transmitted, label = 'Simulation')\n",
+    "plt.legend()\n",
+    "plt.xlabel('Propagation distance (slice #)')\n",
+    "plt.ylabel('# of transmitted particles')\n",
+    "plt.title('Transmission of %i particles' %N_particles)\n",
+    "plt.show()\n",
+    "#plt.hist(free_path[free_path!=np.inf],30,normed='True')\n",
+    "ax = plt.figure()\n",
+    "plt.hist(free_path,int(N_particles/5),normed='True')\n",
+    "plt.xlabel('Free propagation distance')\n",
+    "plt.ylabel('#')\n",
+    "plt.title('Histogram distribution of free paths')\n",
+    "plt.show()\n",
+    "print(np.mean(free_path))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 3.2. Monte-Carlo integration: estimate $\\pi$\n",
+    "\n",
+    "In a so-called ’hit-and-miss’ approach, or ’simple sampling’, one can estimate the integral\n",
+    "of an arbitrary, well-behaved function over some interval by scattering many points over\n",
+    "some rectangular area A. The probability of a point landing below the curve is proportional\n",
+    "to the function’s integral.\n",
+    "A classic problem is to determine the value of π.\n",
+    "\n",
+    "* Uniformly distribute N points over a unit area. Plot these on top of a unit circle (or quarter circle)\n",
+    "* Calculate the proportion that are within the bounds of your shape for some number of samples N (for large N, it would be unwise to plot)\n",
+    "* Repeat the exercise for increasing N. For each run, you should compute and store the error $\\epsilon = \\bar{\\pi} - \\pi$\n",
+    "* Plot log-log the convergence of your estimate to the actual value (to machine precision) of $\\pi$, i.e. $\\epsilon$ vs the number of points $N$. Compare this to the expected rate of convergence $(1/\\sqrt N)$."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Helper functions...\n",
+    "def mc_integrate_1d(f, dist_x, dist_y, n_iter):\n",
+    "    # Hit and miss version\n",
+    "    #\n",
+    "    # f: function to be evaluated\n",
+    "    # dist_x, dist_y: distributions from which to draw (x,y)\n",
+    "    # Does not handle -ve y\n",
+    "    x = dist_x(n_iter)\n",
+    "    y = dist_y(n_iter)\n",
+    "    h = f(x)\n",
+    "    return np.cumsum(y < f(x)) / np.arange(1,n_iter+1)\n",
+    "\n",
+    "def mc_integrate_1d_2(f, dist_x, n_iter):\n",
+    "    # Sampling\n",
+    "    x = dist_x(n_iter)\n",
+    "    return np.cumsum(f(x))/np.arange(1,n_iter+1)\n",
+    "\n",
+    "def plot_convergence(est, sol):\n",
+    "    x = np.arange(1,len(est)+1)\n",
+    "    plt.figure()\n",
+    "    plt.loglog(x, np.abs(est-sol)/sol, 'b', x, 1/np.sqrt(x), 'r')\n",
+    "    plt.legend(('Result', '1/sqrt(N)'))\n",
+    "    plt.xlabel('N iterations')\n",
+    "    plt.ylabel('Fractional error')\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Pi estimate: 3.124\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\Matt\\Anaconda3\\lib\\site-packages\\ipykernel\\__main__.py:12: MatplotlibDeprecationWarning: axes.hold is deprecated.\n",
+      "    See the API Changes document (http://matplotlib.org/api/api_changes.html)\n",
+      "    for more details.\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXwAAAD8CAYAAAB0IB+mAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMi4yLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvhp/UCwAAIABJREFUeJztvXucXUWVL752EkIC8ggJ6WDCK15weEgHA2nwkeDARwEFZrwB4Q7MIDBcwHTHGUcHriSt6FXRqyDgZxhg9CrX9x0VRJBx8DGi4AWHAZQRP8APIYo8hsgQQqD77PX745zqXmedtapW1a59Oqdz1udzPt37nL1r12PVdz1q1aoCEaFPfepTn/o0/WnGVFegT33qU5/61B3qA36f+tSnPm0j1Af8PvWpT33aRqgP+H3qU5/6tI1QH/D71Kc+9WkboT7g96lPferTNkJBwC+K4rNFUTxVFMUvlN+LoiiuKIrioaIo7iuK4rX5q9mnPvWpT32qShYN/38DwLGe348DgP1an3MB4O+qV6tPfepTn/qUm4KAj4j/AgDPem45CQC+gE26EwB2LYpij1wV7FOf+tSnPuWhWRnKWAwAj5PrDa3vnuA3FkVxLjStAACA5RneHaQZM2bAzJkzJ/66z6xZszr+Tvw/cybMfPJJmPn00wALFwLsuSfA448DPPXU5PVWSq6ajnJWl3dBj3TJBP3udwCNRntdH38cYOZMgFe+Un8upk9j7k2pj/bMCy80P9J7+TMPPADw4ouT986d27yeOxdg112b766Tj/pUjX7+858/g4i7pzybA/AL4TsxXwMiXgMA1wAA7LvvvviBD3wAGo0GlGUp/200YGx8HF566SV46aWXYMuLL8IW9/+WLW1/N2/eDJs2bYJNmzbB888/D88//zxs2bIFyrKEsiyTGjZn5kxY+NRTMPDUUzAAAAMHHQQLTzgBBhYtgoGBgYnP4sWLYeedd4aikLrCQ4gA9Bl+nUCIADOI3fb731cusq3sv/orgE9/ehIM1q4FuOyyfO/IRR/4AMAf/jBZt7IEWL4c4N/+DeC005rfu7aE2qD1KX8HIsC73w1wxRWd90pluvdb6+N7ZmREfq/0zMgIwFVXTd774osAy5Y1++Yv/xLg8sub36fwkdQnf/VXTUHygQ+En0+lqXrvVFBRFL9JfhgRgx8A2AcAfqH89vcAcBq5fhAA9giVuXz5cvTS6Cji2rWIZdm8Lsvm9eio/zlCY2NjuHHjRnzsscfwgQcewJ/97Gf4ve99D7/2ta/h1VdfjR/96Efxve99L5599tn49re/HY866ig85JBDcM8998S5c+ciNAWX6fOKV7wC/+iP/giPOeYYPPPMM/Hiiy/Gq6++Gr/97W/jPffcg08//TSWri2Z2sfJFdFk9+aHviIHlWV7+aGy+e856+J7p+sH1353vWxZXP9ofdpodL5jZKT9vlD5KeMlPTMy0vluibV43fjHlbN+fRof+fpdez4Hf6S8t5cJAO5GA25LnxyA/1YAuAWamv4RAPD/LGV6Ab8bIxjitLLETYcdhg8D4E8B8JsA+Pd//Md4yRFH4LsOPxxXr16NK1euxP333x932GEHk1DYcccd8eCDD8YTTjgB1y5bhpcD4A0nnID333cfbrrggkrt61aXxQBBDTKtcl0bDbvAkvrQAasrSwL5kRF7/8cKUOkZWiftvfwZLvgAENesaf8thY9ieCQnf3RD2dlaqFbAB4AvQ9MfPwZN//zZAHAeAJzX+r0AgM8AwMMAcD8AHGZ5cVDDr2sEy7Kd09yHclpZIg4Pt88oej001FaPsixx48aNeP/99+MtN9+M1157LY6OjuLZZ5+Nb3nzm/Gggw7CnXfeOSgQdp87F4eGhvDP/uzP8JJLLsGvfOUreM899+CmTZtMTasTYGMFSg4BVFX74yBHtXIOzto7aJ+OjjaHfnCwqQVzNuHlhfo/l4Y/NOR/r6bhDw52fjc8PKnhp/KRRYjVoaCkCM+tiaz8XruGX8cnCPiuxTlHcHS03f51/w8NtXPa6CjiihXyjJBQgpZPuXX9+qa6tH49IiI++x//gXefeip+/eST8eOXXornn3cevgUA9wfA2QFhsGTJEjz66KPx/PPPx8svvxxvvvlmfPTRR9vdROhnGt9vjUb7b/xaal4dgOaIg44D6xjQ4e92w+mGffHi9uHU2uN+o9o81wGsAkSqX4z7Q3tGe6/0zIoV7QAv1T9V2MaMeU6drtc1/Ji5NT0BP/cI8tnBbXE+691s4DNieNg2GxuNSdt42bJ29XJoqFkOqUMDADcceij+6Ic/xOuuuw7f97734UknnYQHHHAAbrfddqog2GmnnfCII47Ac845By+//HK87bbb8Mknn2yvFxKGapRt1R0dRVy1arKKiJNVX7VKbqbvWrqfy+xQGU5OSl0Z61YYHGwf6vnzJ+W7K9MZbJorhJYruXD48FrZNMUiy/XMihXND28PM147+kC6pt9bhBjv06o6XR3WQsy7fdfWMmLq37uAr/VWXSOo2bac03z3adq99hx3lnLVkAsfofyxsTF86KGH8Dvf+Q5edtlleN555+Gb3vQmHBgYUAXB7rvvjm/aZx8cHhzEa/7+GjzttJ8jwBZcu+yHWK4fbZNpXC7x61Qqy0lw5c11AMXBiL9fAlZXNn8XJSo0NPku+eC5gcaBStLqaZ1iXWgpgJHjGdp2aX3C3R8rYEL3c2+qr/9jaCrWi6ZqDaI3AX+PPeycUbU3KXGVQutdvroXA/ocDSQVN4SERnrqqafw+9//Pl5xxRV47rnn4ute9zp1vWBGMQsBliHAWQhwFZ588k/w+ec3tYGsBK6uWbyZoW7mcoxfS1qxu5YWFV19rKwh+ey5fJeGyz3LrQrqCqGfFStk7XVrp1A/pupdEq9IxrPGHzGgz6csv7Y8J11b3jtVaxC9CfgLF4Z7K4e9xJ+XVD3OaZIqR907kt3ryg9p+PQdkiCoSmWJZVniY7/5DX7nppvwY697HZ4GgK8GwEISAjNm4AEHHICnnfbfEOBTCPATBHixbbKkyl6+ZOI+rvs4CNAuGh+XZbJPSEgTzSffJYHA3TOS4HHsQH/TPH2G4fJe100W901Vz2pIq3d953MlhsqlvDY66ufRXLoktSIp/7SW7KJo+mv4y5d3d5WF9qgLa+CLt45TnPa9YkU70LtriTM48vh8+KHA6VRynOx8EcRuLwHwfPgYAtyOAFcgwDtxwYJBnDVrVocQANgO5849DN/1rjX4hc9/Af/8z3+NAGUHwFoWKH2yzU0y/vv4uDyRfEAt6Qkh+a6tE3ChQL9btKizXitWpBmeUxm2aiVt/GKe5wJZWz6L0ZA5H1qtBcl1lWpZaNZj7FSOtRZ6F/CrcFIK+cIx6XcO8IeH252dQ0N+OzEQpTPBXTwqKPf6BHOClwC4Fi5rvmLZD7BslLh26A4EQDz/vM24//53I8A1OH/myfiauXMRoOgQAnPmzEeA4xDggwjwT3jYYf9pCkEMga4k+zQ5ybtSYh03BPS5gYFJGU/fuWpV5/oBLVPy8btYdXpPqmZfxzJVTgpZaFbygWMVsAyVK5WVq02IspKQuu61bUTpdFPDd6TZsFwY+AKsY8rno+/sz7rWJ5QZMLri5ibYt9pSDo/gWrgMR4duwVUrS1y24HFstO79w7nn4atedRvuu/N78CQAHBA2lhXFTAQ4HA899K/xG9/4Jp577tOipu0zaqgfV/Lhj4+3l0PBXmId7u7hC7c0tFOyBniZTpt3Qy8t2OaS0bmmQC4XERfUWmxBDDhL04nzRGx9NZedVBbnx9jprVkKVYUW/T9kMSP2KuBbfPjdIovdmbtOFsGQWq7C/WWj3aIpRybRpsFmdYNwb2O8ge9856MI8BUEeDcCrGgBfrsQ2G23A/G///fz8Itf/CI+/vjjiNgpR3mXaiGhK1fK3cFB3XctTUDN7eQb/uHhdjfTokVpYZiW4YoBUU659Qhq7FIN1nk1reVLgs0SJSWVQ8m3KE/L4iAqCe7QGozUt0NDTeuR9w/34Vt0TNoeqthIz/cm4IeidLpNKfZhLrLO1JD6ZnEscu7naMP8GuV4Q3E9PI9/8iffQ4B1CLAKAeZ0CIB9990XzznnHPzyl7+CTz75lDjpR4ZLs9yXlijoNfXMxcpqbQGQR+bw0NDcgJoYrFWbi0jSH2JTR2jClFpf1rGn4KiF3dJrvgu50egE6VAshqRUSNYOrz835t2H8qy7n1qjzrrlvO2oNwHfF4c/VaTZnbmsD6m91pkaE0PHA9mlXUuScHB5AxjCja78/kT8vnuULkU0P1vw5JNvx4985KN43HHHKaGhy/C1u5+CN3/nZrzggk3NLl72QxxZcUdQtvJukSZhVf8sHw6nhdKynJtJG1IrWV0msWVqrq6UOkvlxSafQ9RZl6+h+PQcbYo4ASxF6UjLZdrGOY1PpDUh91m8uDldpA2LoU39PCzZfRYsCE/d3gX8uilGoGh2J0WBKtaHD7C1mSqBs0Uo+FRgrTy+HZVy34IFTd9/6156u1ad8fFxvOuuu/CjH/0oHnPMMThz5vZt4L/ddtvh4h0H8Sg4Cn9y8ikIMB7UyH3dlBNALYZSDsq5gOiIC6h169IMaR/L0U9Mn0rX1ilqEWaSAavVW9BrRMHIlQr6jLMMeP+EghLcDmfJGrXwXB/wJYpxaPoAUHKFxJIFsEN+iJBQoPf5/vr6R3JK0hlBnJOx/uLNmzfj9/7pe3jRYYfh4QA4A7j2vxsC/DcEuB7/8i+f8k58rZtyukikiJ1chh5vT2jorSTFhTuZneLm8blR6hCAlDRjONRPlucky02LnfcJf190deg57Xnpw9vUB3xOVo2YUt1B0SH11ArmOZCBlscRc2xM5zxWpxgDirfhWQD8RwA85DXnI8CrGPgXuGhgCD/4wUvw7rvuwkYr9rEsEdeOlN5ukroop2skJ0v43qXJaY2kbR/u2oF+CkjHGpk5SJqKnS7Ezvdan4vdBuMT/r7YDk17HxmR93Vqm/y4ddoHfImsIMqf8V3nqJOERpaZlNKeEFlUOPepmlhHacPo0C24dqTEBx/8NV5++eX45je/GWcW7cniBgYG8MwDDsDj/8uHEeA/ce3QHaqRVLWLJDOeX+ci/i4paVyMgPHt/MyhJ8Qk2Usln7FNwc839r7nLGVp9eHC3yeE+Lu4oJF+o8KZRoTRujYhZO8nsQ/4AuXWiKvWRUOj2KQmPPNVyu4fqUxNJeSczsuxvk/Z4liOtLf9P887H28EwPPmz8c9od31MxNm4vH77IPXXnMN/v73T050U04NtJs7YKVFQQfSKfWXFplz6And1PClqeKibXwRUr7n6FgODbWvk2jj6xP+VEv3+fClTf3Dw52Cwt3n1lzcFHdRYu0ZXZcj9gGfUR0acY66+Hz4/BlKdEGWpldev35S1agSy0dVwnXrZFVx5cp0JBwdbZ991N72zLQSAH8BgB8HwDdAez6goijwDW94A37yk5/ERx55JHv2Qt91Tko14vj3Wm6gXCCtaa119I2kq3GLR3qvxaVnmW6OfDxl/Y1aIG66aoKC1k2zFKa/hh87+3KoI7lnfA404gHAUsrBWJcVnyFUJeTWREzgNH8P52p+LT0jWBm/f+IJvOaaa/C4447D2bNnt2n/g4ODODr6Abz33nvRHQyTA4xoGRwscoOd1Sj1eeM4y+faO+Ciijjg57Z8Qu4QbY2jLh3PBwWxv3GDngoDbq3Q/9unwXT24VdJ1ZjynHVErJQbLWSRHwb7mJUwmgiexg5S9SR2RsXMRule4ZnnnnsOv/zlL+M73vEOfMUrXtEG/vvvvz+uW7cOf/nLX0Z3LyWtK2J2msa8mzebDytlRyu4h3Z+WuumRZRIrJeqL3FdgL+Txr3T/s+h49VNoVRe2n3bjoZfdRRjuS60WyLGvuaCg7YnR84cPuusGjbzn6sau1sX4Jq4Lywhtt4hsLdsIGvRli1b8Dvf+Q6ec845uGDBgjbwP/jgg/FDH/oQ/vrXv/ZWL3U3Zy4tkg8R3aQjAYSkBefK0CHVLwT47l10XYK2LWbxmQKeJvN5f23N2UetUOZLqjt5PZ19+Bpn12FL+1QL3ztD2nNOlSNVw5fUx9Dp19pz0uyzCF+rhm/dQKbQ2NgY/tM//ROeddZZuOuuu7aB/2tf+1q89NJL8dFHH1Wrx+WfFrhEZSMvS+sC37Wm2UlHMErGVh3uFV4/7Z25Fp9pORKrh/YBpFgWqdZILIWmgG9X77YTpTM6Kget1sHZPnDzzWJNe44N+LXUT1MzraDP20TBnv71Pee2CvJ2amOSYqlp9Yrsu5deeglvuukmPOOMM3CnnXZqA/9Vq1bhP/zDP+Bzzz3XLLpRijHbUrIt95GOQZRkUkq6JAnw+LJKrNyvQj6rghuHKRu0fDqXtkmpalu7bRVoRq5kYNP7qFIxfX34ZWk7Vy4nSTZkiGMlQeFmnnTEYUxd+HVMtIu1jj4ul57jfg6Ly2srsLdffPFF/MY3voGnnHIKziEHv8yZMwdPPfVUvOnEE3HN4T/u0C00sKdd4ZNjKfLOkSajuw34oTaEMlda66SxaaMhH4wTs4TEr6uMSwqFNHyfvknvm76AX/dGIE6aCmNx1vKZ6bg0lUNDuXe4Kmh150iqEw8o9iEVj9iJaRcfr6onpPuuA8/+4bzz8B8AcNXixUi1/h1mzcNmCuh/VdmAX1u6IsajZXlmqlw6Ppmt6UqxAMrLcW11+W9i9IxQvVPGJYWswkXKhcQ3401vl07qwZGxgEBHwHcEYuhZTTjR3RYxMz2X6hFa+te0fW22xG7fzKnh5yiL9OmjAPhhAJy3/Z5t4A9wKAJ8Bg8+eKOakTGmKzRzPlC9Dhag2zBoeaED2azdol379AzNEIzNQSSxJp3+g4P2acnrpU2lmHGpQtpSHxWYWtvb10Om+6Kt5MNPFek+ssZN8fpx25an4nP1t3CoVG4u1YPPVski0XwSlEKnjPjaUlWA5S6LtGH9uga+4x3/Dw85ZA02k7m5DV5z8IwzzsAf/ehH2GiUba/QusKXedE6nBpAUC+n0yVidKGY9zn2t2rJTgByrZSzvdRunyGqWQxWd47W993S8GldHDkFoiw7T1Kl22E6nRzTVcOXRB7XSKVnqgBCiqsgHDjbGbtmobpUj7LsjL/PAd68jfRvzpmVoyyhjHJk7cSBLO9614v45S9/Bffc8xikWv9+++2Hl156KT7xu9+1FRE6fzeVJaWubDQ6l7Zo8q2Ubg0NK/dq8nulkFYqLELBYI54lI7Goint48/n0B1S4MLdxx0KNAx3ZGRSo+cWZG/78EM2JA9G5rFq2nMWtSsncQ6qEkpap+rBBZIUhsKTjHPSUisPDYWPo8olwKqU5ZnpLpkbbdqZg/8X3/jK0/CVr3zlBPDPLAr8rwccgGeccRsODpZtLCYBrxTN49N6NU3Q/a8tJldhkaqasDSVU0DV+ewtGn7VdlXxDlb1LEq6IReuUi6k3tXwLT0mqQeW7EdcLNaR21YivnnLfaynWlTZ/FW1jpI1ovUZn+l8BZP/zRmimkMYenivrZhy8uzfseFhvOnb38Y/edWrcCbR+g844AA86qgrEeC5ieqEdrz6tF7NYHTRsBoIVpWhrh5aeakyNma4OAjytMHSwffWd0sCh+cdtBrhOawDV44m0CQPa/O6V3341h7TwEV7ToqOqWLvxpBUPyuHcguGqnIx/n9rPbUjfCRVI2ZmSQIkNGYx9c4x01xZvmtP23579tn4gdFR3GOPPXDS5bMjApyHAPd7q+drgtRVWmSyT1anUFUNP1S2VVhQw55O39jD0x1psp3nAgzphLzOsUtZUnnalHFlSHXvXQ1f46KQ68XKmTzN74IFzTgnX7m+v/x/6Rqx2qKxzzrIKaikPpR8BTnUlaq2Mydf5BCvTy5SEOvll1/Gr371a7hkyVFIff077rgSAb6KAGOqDhMDrr6P03qrGIGxQihGxmqsFppW9H5JE49tHyUp3NGnE8YEq8VaCNKU59Yfpd724ae6XnwqgxTKGQIwy3b+GNCyao/0d8oBudS20Lu47cjfnUNdoUjGn6nSBkrWLa+p71LQmf50+um/wAsueBdutx1N5LYXAnwSN278g1is1tX8N196BxdgVbXJqVE61q5zz/tyBAW6OwvR8iVHQOhsW3rtDiihY2E1xFO9t70L+KmuFwtHSNvytM1aEgf4fNCSClRF/aDP+SJ8clIo/0/MTLP0X52uNN+MrPpeQ9m8K//wh//EJUs+gwD7TwD/7Nk74dq178ZHHnlkotjQVoiQZi+xZNUu9slki7yW7uFrEs6IdIYrbQM/2yfnUPJ6SX2sORek+ym8SBvzrJq+Lxpcot4F/BgxK/W8xUnq0/A5N2srYr5ytaN4LMHQnCOkJCkhP3oVrVlrf8pMq5jwrDLVqRYa1Fv+U3PoGnjjjd/GJUveNAH8M2bMwNWrV+OBB/60DRj40g2tvuTDHxxsdm0dxwzyNmnXnEKx+u4eyXNIvZbWE6mqEl/CsuiZ9F4tmojXnZehlW25D7GXAZ8G3fKe9JEv6Nf1VmiHrqbhSoDvs7VpdIoWiE2Jcj5dr3DvX7y4s3zNRsydnyZHnJnvb93k85FYn9euI2ak1I2nnfaveMghZ+AsksMH4Ah861u/gePjjTbA5+mBJWGgafi5KNZ7KRm8Pj2MTzWX5z630ayRxbkQ0gfd9NXYrq6lpt4E/IULmy2VtFqrGwFRB05famIp3sm6rY9rkdJx85obg9aVCwYeiybNIN5+ixsjVk2rYjFMJVXV8DMLz45ubDS/2LBhA1500UU4b948AvwHIsAX8F3velkcNhrCqQFPzmGyGtGubhT4QnULTbW6jFmtfZqepoXGOoFMPb1SnIMeUln9BLLaAR8AjgWABwHgIQC4UPh9LwD4AQDcAwD3AcDxoTKXcxUlRWWR8rK661CudwkgKFjH+PClXTASp/O6ckFhyU9PKQRyuS2ArZWswq+u50MkjMOmCy7Ay99yLAJM5u/Ze++98TOf+Qxu3rxZrCL9v4ohYyFfyKFVb5HqJgG+JU6gLmNW8sRKw893wbo2U3cbhwjpaGgXOFiFzWoFfACYCQAPA8BSAJgNAPcCwIHsnmsA4PzW/wcCwKOhcpcvXNg56lSLtua98XFmSCWQ3DOxUTrSQidAOyfR90lChnJ6ikYuzZa6QWxroxzuKD42lCfdPfRvbLlkHEoAXLvshwjwEgJ8FgFePQH8AwMDeOmll07k6Q9Vsy53juRptegtIb2Hr0kccojc7YEurNx231TzsUPogBIKG1KfpCbQdVQ34B8JALeS64sA4CJ2z98DwN+S+38aKnf58uUyUEkjwUniuhDY+sqImeCafSq5ddy+aL5/PjQ7rKMfmv2W33l5vu+tdUp9tipVfXesAhBTLhmHSbCfHI7h4XEE+DouXPjaCeDfddddcd26dfjss882i2mU7UDHrzO4QbjrgbN0oxHOfW/x4UvZPn0GfjcEndQXHKIsy46uTto6wdiYDn0WqhvwVwPAdeT6DAC4it2zBwDcDwAbAGAjNPf+SmWdCwB3A8Dde+20U7XR82nLdIJKyUpyqgzUoef+OruN+/KlmTIwIAfjpgQ4S23QLABNI161Kl1T7mUXksZPljBTC7+wcRhdX4pdtX59id/97ndx5cqVE8C/yy674AePOgr/cN55k881WpbC+lG1i1OHw7egSYPSNMB3S1BU25XqJLGmr44aK2vd7bsOkaYP+vQ7noJKgye+H3Rr0/BPFgD/SnbPXwPAe1r/HwkADwDADF+5y3OArcR12sTk3O+Amqc0TAEnrgVKo8lzxq5fjzh/fic3+XZd8OvQZiNNLQqtKKWMTVUhOtWWAbcYfSuL1lW3gKXlFnL57Y5uv/12POaYyWyd8wDww0ceic/94bm2+vJypCaFhoNfr1vXXl3XJY49Jd/0+Piku4a7P7je5dPYNUvFquHn9O7xvouJ0Vi/vhPcZ86cfIZuFYqBvq3BpfNLANiTXD8CAAt95U5E6aSMCB8V3vMSB1m14VTyadQcUCngDg7aNjvFxniF2uxL5J5qO6fa3bEzNEY4WO+V9hFYIrc0FPWtbEYqOD/60Y9w1apVE8A/HwA/BoDPn3++93krqEpRztJUcu4cbSGSRqxIB6+nGtgx99PfuGspZv+ijyVDyQH4lOfHY8yZ03TrcLawUt2AP6sF4PuSRduD2D23AMCZrf8PAIDfAUDhK7cjH34M2FrAy4Gt9EwskKXUyyd0+CzSbNXUGeIoJCR879X60dIHMc/Gts23LdEqJDVBwgPBNcAPrbrxNkgngkQqOGVZ4m3//M/4epjM17P77rvjJz7xCTGqh1ZF0z9oIjJXNap10mUo2kwOeHSpqixtYaOxQxO7L0A6jN6a8oCWw69905jXU0uNpT1rpW6EZR4PAL9uReu8v/XdJQBwYuv/AwHgJy1h8G8A8OZQmd4DUCxamVUtoc/6joKvShbg4rNvfFyfIZLakCKw+O8h99NUaPjW5+h9oWMoU4WkNB4U5KWQFKmsUJtiFY1WeSUA3gqAQwT4lyxZgp/97Gdx3PGTpylaOgbJWHZAzo808AGee69F5lumOZej/ForN3bDuoVijGb3e2jPAYcnSho81Q74dXxUwE9JUGax41LPxo2h0N5yPlPmz5e3HFJg4eXFziQ6SygHaouR3fTh83pa2ybNZn4QO69TrADi5bvZLAVX+9pnaVOIhL4sR0bwOwC4bO7cCeA/6KCD8NsnnIDl+vUdU0DqMgooXBeimr2716rThASClaokbKsrJVWoTlL7aR9qrLNqVfvSkC9lRm8Dvs8U1kBDUg1GR9s3LrlRHxpqn6SuV/l1LgrZgc5u5qs5g4P+1MRW8NJsSmpvS2sGDtC6FaWjbWW0AHPnmW/Nj3TITCzoSv0sBWCHhFqqsNFIsWgbAHj9W96Ce++99wTwr1y8GO+84w5vRIyWvkHqUgqUvlgH2mRrphGNfHsqQwYaFVS5Ad+9I3RN3zs83LlsR5dy6EJwCJ6aZfcq4EviMTRRfH5pbrdRc19jHpUjAAAgAElEQVTSzDjn+kaxKnERzoNxQ758C9D4fNDc/aG9p2pfWO10Sz2lme3b6ePbsWMBXUs/W4RajLUT09dSX7TK3QKAnwLA3ebMmQD+1atX44MP/lrsBu0gMq5zcMMppH9JyxWxkSiWPZWh5yWrxnroXBXS+rrRkPUvxzoSWy9aNBni2l72kt9hTwI+5R6ucqQAn8QhVEXRfPh1x49bNPxlyxDXrLELOk3V8m3xk2Z4Vc0zR59wwNb6n6uQvG3y7IhzT1kBndcrpZwU9yW/ZnNl47PP4oUXXohzWsA/a9YsPOSQdyHAMx3dwLswtBnKyi65loHclJambKgMTfeT3Eu5WF+qO9fUpfUTR7ytmrDtzROvKCeEoiOk/DIaN3FhwRf1+G8xZnoubnCfOXOafzn4a1pujODzAX7dbY3pE02wa1qwU5O4KuoCwC3WoMUJ7LuOaZ90zS1ZbWsqbzN9nu4CFubChg0b8Oyzz8YZM2YgAOD228/DT3/6Cnz55ZfbdAONNaqeiaMNrbXbNJ0l1krgQ1/FY2mhlFgSdx8/TGXZss60E81+6UWXjjTJrXutJW7ik4iWwcGBl5uikqSAAq83Dcblwi3EiZrgC+17j03QVhdp9becXNVo2OPucoF3LopduyjLziOi3DMOISQ1tlX+fffdh0cffTQ6N8+BBx6It956a9AYdq/WlnxCUyNVw3ckafaxkTYSqHZLt/O1RfqdH9SueS2bENLLgG8BcjrhJW7SQvToRLGUa1VJUjRHbRaMj+vvDXEgr3OMDz/mPbnJZ6GEooR8mnGuVbm6SGpPSH2W7nGfFSsm2+xxEJdlid/61rdw6dKlE8C/775vQ4AHO7qQe8ViDcKQ8WkFakmeDw83v6+im+QQRnW8zy3xcd10xx07n+1NwOc+fEvWy9CEkYDfcYm13BAXpHA0raeWdyeW+ywasm+VSCvTd52TfIvvobGYjjl7fOOvWb+SZetxD23ZsgUvvfRSfMUrmmfuFsV2uHz53+Lzz29q0+ZTlxmqPKM1lQevuQiXKhSj21V9TwxUSHEb0rO96cOnUTrSdnOfP5Nzky/UMKSipAJ4jJpAQ0alLfs8hi3GZpbqzOO4LNq85Hx0Y0Ofyzk7NAFjmZG8TXVbLDmFIW9fyIfvA3x6j5Enn3jiCXznO89CgAIBAHfaaS/8xje+icPDpfr62KZX6a7RUdmtwT1YKdRtDT92XZ7XTUrb1LtROq4VKT3DrzWQyB0tQd/pAyXJeuA+WPfxJeLSytUW8lK0XA42PAOotOWyLkoRpnVr+7kid+izmrYulau5dPj4R6qud9xxJ+6++6Ho3DwAx+OZZz4c1DUszaxKZSnHHuQC+1R3U+p7fdcxdWsOca+6dFJ6RqIQSFh73Pru0PssS/WWutKAZPpedwZbLsDX2sTTPKek9kutg2VGdmMGW95hFToUvN39PPZOaqO2aOsCyysEno+NjSPAlQiwCwIAzpkzBy+55BJ88cUXO+7tpidNYkeqG6VSjjbUJfSsdZs+gJ9C3Rbboff5QgFizXj3m7RtkYch5mgz1xBDwqkOCqWn4PXtho3ue0cM/znXHk0YPzLSHMuQU1wT7twCkBbrlb5ob9YTCHD6hLa/33774fe//33x3rqnGWd9zcVRpXzftY+6sWWH/s+vEbd1wEfs/iJeSkINzrmSGc85j6Yu5OXk3kClCSb+qRPsaV34dUgQ1F1H3zssQickwEMrkT6UolYAB35lDugA/gPcbbcDJ4D/nHPOwY0bN5qbmUJS03Ls2M1N3RR6PnafnoBvEXXa/do9lndZry33aNqxtN3OF7WilZML6CjnOjcOjx6SZnhdwMqvfTNNE3x1ZUL1AXpoLHxHSVVFDOn9gfI0lrv44pfwkksuwdmzZyMA4KJFi/Af//Efzc2MIR+w8S0ZCVmlkygkW+s0KDm7S46A6Qf4oQRgOUc9pK3nirn3nZLkAzTJ989TF+bgPKpScbDnO4Kp66pbi6NSeynYV83WxYnXQRM2fIaGxiL3SiSvY2SZPnB74IEH8PWvf/2Etv+nf/qnePbZv63FqNQ05ip6XOjd0rV1fb4Og9K6J296Ab5Pe5N23lbV7FP98RqnW+0+qd6aG0jSCi1ZrUJt59faLt2XX26vS8KpTab6WGY/n2l12P6WvEVDQ81PzIlW0hg7AV5nv1WgRqOBV1111UTsPsAuePTR12LDcIB6bPXr0JglsmwB8bFgXW4t/i5tT970AnzeeulDtf9Y7dvyLqnMmBGuksOFvoendabXqSkSeL9RLnZp+6T+pgnS65qZvr7Wflu/Xs76lZoa0Sp4qOJhOdHKZ7VVXYnswhrWY489hvvt99YJbf+4447DDRt+K74mVgerS2PW3qW5S3hqLR8L1iBbzbA3PQCf95YvAVju3g9xXApHpnC9pOG///3trhR+nlzMO3j4Hk9BETqhOZQUJKXd0vMxY+3qJ+X1rVPttCgKnOpciazL99FWZIn/5/98EefNm4cAgPPmzcMvfvFLWJJ3xcoeSZOtO0OGM9D4O6nXWGPzbkTpSH1B2b/3AT81bj2HfVWHhh9LEoBJ6ZPpGkCVd0gLnVRLlvzh69aF+6HqbPD1tc8OdyGqvL+q+vClWc9Bn95jybs7VSuRnCoIiN/+9rd43HHHTWj7q1evxqeffjpaB6PAq20xqIM0IaNNDUnW8/Jy1YuzvxTM19uAz7mCJ5Gmmpvmw4/VvqUeljg0Z3q9EJfQTVa5/bz0nZrNSE9oGB5uXyj1HS6Ty961ulL4M77Il1RXiYYIoWOifAvzvHzfdd2UQU0tyxKvvfbaCd/+woUL8YYbbojSj6YC8LWh4zpPHS6bUL00Hz5n/94GfN5aPnl8UTo5tO86onRi30H7ATEtkoNroL6/EuD7TmiguYDojOGZtqqOR8zBnvSdOTV8HwoNDHQqHg4puJDpFlLEUlXBzOiRRx7BVatWTWj7Z511Fj7//CazDtZNlw73aEpZ0y3ZueuibWunrc885tpdDo2Sv7vKdajsmHqmxGo7TnHcSg/cXrt28tSH8XF5drmD0yWQ5q4g6Zq3N8XiShlPek8uH35I7dTQyXrSxdZAORQlQo1GAy+//PKJU7Z23XV/BPh5lK6SwjL0ed+1+8412W125sPlrIqpNMAs7+59wE9lwK0hTW4st/naR++zxJVzwaelXubXLu0g3bXrm3GW+vO6pABJyvMV8sh46+FLR6z1lWVRe2uhqigr0H333Y/z5x+MAIAzZmyH/+t/fRJHRhpmmZ3CMrmyUXLDdmsjWqfeBvyqmnq3xLH0nhhXjXWC+RKmSfdRFwstn3K0lKKBpmq2HGnkq38o+icW9GOAiLZdc36mkFQPHzrl0pq7wc+ZNXxK/+N/bG6dodt08bzlLW/Bc875fRCA61r2kZ6hzQ4tuUyltu+Iw0xvA77UoqnQ1H0k1U8CNInbLBqo5Nah5NO4OchZP1xQ8DZQjVYq3wkl+ow7gYkDf44ondjxqcI/Uj2kgAHaVzlW+7oxD6qirPEVN9xwA86fPx/dgu4tt9zSfkOL6gzs4q8L3cubnnM4UgWHNDy9fQAKbZk0SlNNvgkSyuPCgVLaOZy6SiRxb8zHvVPiareL1EXnOP+4A3Qn6Jw9rAkEPqap/ZzT0tPuk1CB18O32S0nctUExBPUJQVrw4YN+KZ99pnQ9t/3vvfh2Msvd7yrqhYdMj7dlOTeUV/35hyO/EJtOWLPAf4ee2zdWj0ln2rAk5vxuOqy7IwicfFfEteFUkfwetFyQ8cnahEkXNDSeHyATp8/XfHSLIwUgKobiLTy3aK2+15KpeDq4UOnHMhVk6tFfJfvOtM7xoeH8SMAOLNonq61avFifCKzNWHxsjmvaGhDtLXslPpVERztU70XAX/hQr0XLBtYuk2SGmGJqHEuHe4e4GDCf6cca12Bcho49dkvW4a4cmV1Tuccr+0XqApUdQGRb+aFDk7X6lFHXX3qakwZueuVSq3+/BEALmpp+ot22AF/+IMf5CraO3SxbhzpHdJwpIJ+yvSYHho+PcSc9sJUBsJqpIEy14S5Ji1pvhzUXfnSb5oqIP1G/ebO8qDCU7I8Qm2W6s3rEVNvXr7vOjf5Zr8vgYpGdVgjOVTKrXE9rMUnTwDgUS3QnzlzJl566aVYVhz3OrNbhtglNmVX1Tq4dvamD3/hws4wNrp9v24/ppV8agQ9mJy2g2rvIcD3adO+dls5nbclts1afVItE2vdcxJ9Hx8rKhCl7yXKZafnLrOOelUlxidjAHjhYYeh8+ufdNJJuPHZZzufiXyFdp0qQyUDUDvpMzTNqsrx6RGlo2nG3cjKGEs+gApph5pLh+8UDvnCu+lSCHG6FJViXXvoNijR8leskHnujW+Uv/ft1M2hjXPqRvZXep/vOgd5xvqGE07AXXZpnqP7ql12wfvvu6/9mQzC38pqWtN92U6ox9Y3ZLnYnd7Xm4A/d27nBKM2U4r9UydJE8QymhKIU3eO5dQDmiGzG6Tt3HXXVaNS6gDL0PuknbiO59wBL84VZ83Fk2qnh+rqu7aW4atXNy0sz7sefughPHT33REA8BXbbYc3fOtb2YU/f71vPV4iWm3epdZUW7m7uzcBf4892nuQg8rWpOEj6hPRt1EqRsWgv2k7ZLvZD1z9kdShKuAUA5Z1gCBVNOiidg4Nvxvac0q9+O/dcvt4+uOFTZvw1P33RwDAAgD/JwCWmZPpVG16WerplK1wlZMlehPwXZSOpMl2kxk1olzi3DJuMnMRzReaqUZOn3XlaeKdxo35FoJ5/ejfuignx2qgJEVn1eXmoIoGYj4ffurBNL53UfJFsMUoGFuRUlU2GviRFuADAJ566qn4wgsv1POuyKZzo1vaStNth0RvAj6d5NSdszVE6VCXBh1hN5npDltu10nBvny3qY8jfOmRLa6WOvsjx5j4QImnF7buZra+T9oo52ZsLACm7r6OEZwp/oiYdB/dRCmNSB/dAICvaIH+8uXL8fHHH2+7jT9W5ZUxTfdtlqd5eLolO2sHfAA4FgAeBICHAOBC5Z5TAOABAPglAHwpVObyuXP15GBbi0nsgJtnk3RA4dOYqqTJldIjS2Gg0mKqz+VStT9yWV0clKSTn3wgHfveUK4fXxx+SKPmv/msl9hMX3QhXFpb0MbB4k/YGjR8gZd+cfrpuLQF+gMDA/jTn/60Nn0jpuluaLkOxvP3d8MhUSvgA8BMAHgYAJYCwGwAuBcADmT37AcA9wDAvNb1wlC5y0Pm/FSSzwWgqQWajzgF7LXNXBL4UXUjpysh1B/WWaJdS64Kn0+8ijbqyqDrLRSc+U5b+j3vU0tuIGn3day1oqmVO+zQyRsxef+rCPBYRcxyv4Dmz5x77kRKhu233x6PP/5rWfSNqk2XjEOeSbsbDom6Af9IALiVXF8EABexez4OAOfEvHh5lQncDfIBOOeQkICwtk9yb3ALSKtXjuRdMf1hsYMpYNOZ4HOY8ndU1UYla4LOSDqGlBqNtOOYYgW21BZu4fj4MNQfUtkpKnPsM7HWDLt++eWX8bzzzkMXr/+GN3wcAcokFqjSDHqPNoR1GNY+qhvwVwPAdeT6DAC4it3zrRbo/wQA7gSAY5WyzgWAuwHg7r1SJ7CVqriFfADOY9C5aV31IAxr1I+vXrn7NRZ06f3O8ghl0JTeUTULZW6VTpvh0vt8i+4Wwam933fWMScfusXMD2s/cqGqzREjT5ZliZdeeukE6AOcjwBjlXXEFGjg+stU6qt1A/7JAuBfye65CQC+CQDbAcC+ALABAHb1lbt84cL6nF65YsTdpHVH2zlfvkt+xqN0YlPy+erAr631ys2JqaCp2cBSrnrfO6q6qqpYCFobfM9SgU2f0wS2Vhfp3VyIWK2EHFafVHfpDF/nHktZCFfoK1/5Ks6cuX0L9N+KAM/X6iPXyGJwVtEzrbQ1uHSuBoAzyfVtAHC4r9yOA1ByOb1yMLoUDeOCbl3qYO2AEk1Dr9o+aoqvX99+4rI7s81ltcyt4acKUM39pPmstXdwDTZ2vSdVJUsBfFe/qtZKWXYGf69YIWv4mg+/qjtMKk8bS66MSEIvYZ1usugf45w5u2Ezv/5yBHiq66AfgpZuBRjWDfizAOCRlubuFm0PYvccCwCfb/2/AAAeB4D5vnI7jjjMSVZG911zke3Eu2XS5hbzkj/cCSBJC6zjEO3YNvncTz6rgF+nOl3p/yFXl2ZRxbp0eLtTrRX6DF8/oN9xt4nUB7n8D5qGz/vVl0U1kRcdC/zqVw/i0qVL0Z2b++53/yatLRVIY0fJmOdD6KjqdOxGWObxAPDrVrTO+1vfXQIAJ7b+LwDgU62wzPsB4NRQmR2HmOemEKNX0Vpzak0hCqkV4+PtdRkf72xPN+xMrc6WVS5LOa7toZw9WqoKLW7ON4NTFm15HWiZ0lhoQC2V4axLXq4UZRSzDTREPh6U5pik2VdUQNwjTzzxBA4ODiIA4OLFi/GXv/xldFlVSZtOUrfUETzXmxuv6gT8EChXdfvk0pqqtodu8pJMfA3QYjaCpVIo9j3Gjy65Rxyg09kjjaMvbs5io6eEZbp7fdeuj3xKh2Z5UJLAlO8vSO17X1214ATNd5+SU1ihjRs34hvf+EYEANxtt93wzjvvrFxmLuLQUEfw3PQE/FSt1ArmMZp6rIugDuKcxA85kTbmSNES3Qwc5gIn9X3SLIoZV59NbVEOeF1yUFWlQyuLfqqeMSy9J1R36aAdep2JNm/ejCeccAICAO6444546623Zis7lTTWyw0X0w/wq26ty7m9PNZFUAdpoLRuna7h+3K7hkAzR30pVTnBTAMz3+zh42o5FD6HxRYrHGKUDsu7JdWSl5trjLX9DTRKh7axBoVibGwM/+Iv/gIBAGfPno033HBD9ndYyScD+VBUHYLpBfi5NJ/Q5LNMtlgXQR0U6g/uw3ex3lyzsoBBDqo7705o9kjjKoUPSm6gKv2RK5IpFewl1TInymjvReyMTIs9Xa0CNRoNXLt2LQIAzpo1C7/+9a/X9q4QhdIr9TV8jXJqPqHyQ0Il1kVQB1lCAyRNTtOMfWBQxYWR000htT3kTpMEnS//jDWhea52S5ZPVT63CMU6LdA6xjy6CiX+7d/+LQIAzpgxA7/4xS/W/k69Lu3/h/ZoptD0A3zXW3VqKLFbv+vWlkLkAwttoknREhIYOAFWVyriqhOfCzCfO8q6uKjdH9NmbXZb3uNLGBfbX1PtdqxbQTNVocT169cjAGBRFPi5z32ua+/2UR0Jbqcf4HeLgSzabO66aO9M0axD4X+SZbJiRTsYUHComsaA/m+1IGIoZm2GUihhea6+1ywnnxbsczfFEK2zdgYD36CVcz5pY17FYkygD3/4w+hSMVx99dW1vstKmTJNTND0AvytwESsrS4aYGnZGlPD/yTXhrt26Rik2PJUF0PI7VJ1vYMLjBgBUpfFEetG0eoR2kWcS7FI3QZaRSmaorMtPvGJT0yA/nXXXRf1bF3yKScbTi/AR8y78McpdkRz1SWk5eUWcD6/v28jlNV9JQEZj0GjrhQtj05sO2IWBOtUHqQZHHI3hfq2Lr5P7QdLfbrN10b61Kc+NeHeuf76603P1Ak7rrwchub0AfwqmpyFqkRS+K6t5NPy6nBhafX2md+Welj85CMj+nFA1tUqDhLSaWKh8atbefAJTupGCfVt3ZZtrIoZU5+UoAJLfX3XBvrIRz6CbiH3a1/7WvB1U9n9MWw6PQC/G+I1dkTrsO98YGvRrHO8X+I8fp6w5mi0ujPc/dJhIDHjKtV32TK5vr4yfNcpJNWLR285wKN1XbasuX8i1Lc5BT+tcwyPxdQnVrnwUUYscAu5s2bNCsbp+5prZSHpvhD0xAaL9T7g1y1epfeEGNintfAyq76/Lg3f937J/LaGEkjt4MdADg217wamYB3bPg4aWv/V7Btuq0+IX6W+pSGiDvQlfqpD8KcKkyr1SXlnZiwoyxLf9773oduc9d3vfjdYZd5cq/zx3WfJomHtqt4H/NgWVyELA4eAMSWqIlRm3cLOUSg80BosLIEw9+E7IaAdBmIhiS8ki6Gu/tLIggKSq8ulN6aaf+5NYJxSQbRKfaoAd+Z+KMsSR0ZGEABwzpw5ePvtt5tfa81qbdUB+Pv4tUW2Tg/Aj2lxKsWaqJIrIRYYKdURpWNtNyUpMiRmgoVA2M0Sus2e/m49g1WyfX2HgHT7XGTNfqfEd0JTgVfFro+lWDdJDk2bR+lolpjWjz4soNe834W6lWWJZ599NgIAzps3D3/xi1+Ym6vlw5ECqnLIx76Gn4NSGJgzHfXF8pGP0fSl69DfWHLPuYnO0yanuhKkfpPiz/kkTx1XKSfQsmWIb3xje5ljY9116/jqKllP2ofe3411LN91qD1afaRytXQLdBFbe0coHwF9hmZlpftPhD4bGxvDk046CQEAlyxZgo899pi5udalqBSdNRaaeh/wc2gTFrKGmfE6Uc1sbKz9O20Brq76xZRDQXLBgvaoCZ5G2QfKvE1Uc5MAn/7mU5sCWlnHb65d739/58lP1FWS2zLU6sjrr1kjvK5WzVW67jaF6hMCbJeXn6Zq5mtEmvKgHfSiqd6G0N/NmzfjG97wBgQAPOCAA/CZZ54JNtd3Lr3m1onVbfpROnVpNz4NWtMmaVZKbfJWBZpcQo8/Nz4uH4vn/vIIEj4Jtc0z/IxWLf5cGlftIA/LWNOFYAn0nSVThXwaa0hZ4DOe++x5nesUUHVTSKBzLX3ZsuZRnDQvv2QBhk4Nkfo5ok+fffZZPPjggxEA8IgjjsBNmzaZmuhbisoxfa1G/vQAfKll3dbsOdBRMf7yy35NLYf/OHSgCa1r6No3IbiKosW3+85tc9cWPy3XhKueCiH1k7O2qhJ3Rbj6upQUoTpzm95ZgNIB96HTu3qBNLVWy9vEtXdXBrd6LDxutZoE2rBhA+61114IAPjWt74Vx8bG1HspS9DX8GmZQ2e1lDF9AL8OihG9GvO6yblunc5kUlm8HtZ6+sqO8av6AF9bj5DUC+0+CRxdf8W2NVbTlRZDc1pZfGF12bJJ0PeNOW+Xc2EgTvqbG40o3/NWTxpg+wC/ih8kRcMXrv/93/8d58+fjwCAa9as8TbRuhRVRWe1QlXvA37dmo2VqRyjcs2dHxnnPjvs0NT8uUMvVdRrzsJYu9HnWqDlctDUxkGb0FVsWKlMK0n95EJAq2rLPodtKK00b790dgI/a7bOg2hyEm8r5TXeL8PDk9/x/Rl0/qVEJnG3kcWH75mPt99+O86ePRsBAK+88kq16SmsngL+FqjqbcDvlmYTAhiqcdH7hoYQ16yRmZaCgTPTq3CHRcMPcQR/37p1k2DvFp1pnS1CUDPZUzV1X5mWceRauAPWwcH2MUhNmcFtdwlcpLbyxezh4Uke4uPSrc12OYhm33T/Dw+3u6VWrGi3gFaskMHeHbvo2puSYC0mSscwH6+//np0KRhuueWW4Cst1azi3glBVW8C/sKF3dNsLCCpaQru+vDDddDnYJUKhDE+/JDwou9zi84rVza/56Av+ep5OyQXhwNXX118Y+HKdH1Ny3T38me5K4k+H8r/Tv+nIEbrJeV/cVqrT4iHfP90PGmoYkzfTQVRS4UuuHKQX7Fiso/4Z2BgUkjQ+aUlwLPwEL3HKR/a8wbhevHFFyMA4M4779wRo68Va9HdYo3f6avhd0uziXGD8KgCx5hShsnQRK0ChLHuGoszkeeeoZFHDqikA66lBV3q4og5LtF9T8scGmoCAS/TCSeqddPoHvdxIB2qBxWCVMBzIaFZP4sWNespqWy+sdPSJocOZvH1n3ZdB0kuG+mjJctzAo6Ol+uzFEpVtQMrro1GA08++WQEANxnn33wqaeeSqsftlcrdngtEND7gF8346YudJZlONpA0yRTNPyYhBuxqoNWL+5fliaOpCFZ95xrbRsf7zT5qe+XhjNycNTiszWekuqmAXFIwGmapNa3EgBKgBgax7rDln0UAn3OQ3xMfdp3bD00t5jkEpQEOe93Qi+88AIefvjhCAD4ute9Drds2ZJeV4zX+RCnc5ROLBhWpZB2pIE09RFTQALo9EdSLaYKKPuuq058zoXWRUOJe3114ZNcEw7SR4uzd3Hc0hiFBKwGyNKMtKYEsPRtqM2WvEypPFXVIqDjGgJ8n2LEBWUV8s1Tqf+su6Za9Lvf/Q6XLFmCAIDnn39+9mpauiE0bL0J+Ll8+DnM3FBeE+cyKMtmfpjBwSbwjI5OMtSqVZPlxYBySv1T2xwCWh/Ya9wr1cW6XV5LO0AXmulHWjOIifTQAFlqf+ivpY/4BqKQG8dq71sQpKpiQF0hVpeO8+e7NnO3GW+P79pH0lnNPhyJTNF91113TUTufP7zn7fXCzt1vjqCsHoT8HNE6eTc6RBKDcyjUkIjaWHobprpFrdGCOxjzWgOwBJwSG4P7aNp+FKkB3dPSVZAaBxjXIG+Nuew/Nx7QuMVqo/VyqT1pwvPc+e21+GQQyZBnSpG2jjE9GuobhaFxWcReOiaa65Bl13znnvuCdcL29mwzm0WvQ34iGmmZg4x6gMzF6dO7+MLdBZNSwP+HAAQS9rCZagdVqFI2yi5T/j7Bgcnv6NRHNKHavyaD5/WV9OsXRu1GHm6XqK5ZCRfvgXEcgNdiO9SfQra89w6cecgx0TcVOF7acy55h56l3F3c1mWeNZZZyEA4NKlS/HZZ5/1dpe09JNrawin3gf8GEoFLY18jK0xpE/Tov9roX++nCB1gT2tX4qwtKTz5f3AAV/qU+f2sLgOFi1qaps+wPQBCt/ERoFKGivJDeXL82Kx6iz3SM+kgKSVT33v5YAqlWcBea09sXwv5bvylRETs89o8+bNeOihhyIA4Nve9jZsUN5RmlPl6AcrbY3ot8MAACAASURBVDuA75PYElPHlMsZW2MmH7NaQ/+4lle1/imUom362h7KoOkDSi48JB++S2JnBVWLGW+xtng7YiKTclLseGlWFuXh2LG2hJFWiYizkkX54O9KUXIQ8ZGREZy3/fYIAPihD31IbY/UXZLRkYO2HcBH1Hs2p4YvmYucaSRg4GCghf5JQNMtDZ+33XetPcP7xRebT699YY2S5ubbFJZSz9D9FjdU7N4Da1191yn3SXyp8anveTcmdH+CBrBWSyQH369a1bmJjQdP+NpleW/r/psBsADAoijwtre/XX1O2qDtuo/equkdvmpTmv6Az1vMe1aT2DGmpWT+h7R4Wobm39cskFQzfSpJEmIUQHifxWiUrnxEPSmb9flUQOFCgu+upYIrVTvlFKMR+6595UrjFuoPXi/npOZnKFjWb1KEgo+qlJGoCKwHQADAPQDw6XPPFcFeS+90yCGTXqT16yczgPi60ZHEHgB7P4nTFvB5i6WelYDFOpF85WvM5Jt8nKF8E20qN9PEkk9rdN+FNj/5yubXXA0K2cauL0P5/S1asfvQ3bUONFes8J/GFENW4ErlE65W0jpb8xZRsqqmIVDNwfcpQr2CIjAGgG9ogf7b3vo2LMkz1OChcQgAiHPmtE8RF9Q0MGDzRMnssRxx2gE+BXAKKNy0lFbdYzUAfp2S0Inex+06n88wVnObSuJaIwcQzTVmLROx+Tf2gBRumdG0Ee7a+nzIhZH7wPluaMTWHE05yAqqOfg+RlvnWCJdc2WOYclvAHAOzEEAwE9/+tNtxdLjmyWDyveJ1UOmn4YfMkct+TksE4n3bMw1J2kiSqF/3dDg6xQiXKDSMdEAUgMWqc+4v9kKcKEdlbGCR9sklaoM+CgmvDDWqqDtcCeC0evQoTExvBQSTrHO61C9eJ/41ogQm2NEgwecf2VoqInYPIKHRmmtXYtlo8Tj9/kgAgDOLLbDe/71HrV5XBbxalrllFxWL/vwJbPRBwJuQnChIE08TQOoy5Wi7TDldaqTcrZNmuy+Sa3lv/FNdm3ipiyMaqtm1j6XeFHin5wCNVeGVB9JUU+WM4BTeEl7xqnAufgyRbmiWMJ5jOdqohF2RJCUjRIPnn8CAgACHIwAWyab1bpH2+PnPjyF1Fan4QPAsQDwIAA8BAAXeu5b3ewIOCxU5vLly3Xm0FLUSgOimbmaVpRy6EIMVQEDCXBiysph/jvyTfaYcbNoxRKYxQKcPDPS4+JSLcSY8ZdcR/zaUpcQjY7KZzpwtxmvWyov5SyLlkHJuem4F8CSItuHLVIfs/o9/5/PI8B/aYH+hZNgv3Ytrl/5g7aANe0ojcHBrdSHDwAzAeBhAFgKALMB4F4AOFC4bycA+BcAuNMM+CHzj/aQWzxz9/BEZhrYa2VXmUB1UGpUBKcYcPCBVmiC+p7lgMv7ngrr8fHqoY+0fjl2voTarwmwFC02pOFXFeKShcznVKwA5wDK3xfq09g5p4Vgrlzpb6fGq5IlKH2EupUl4tqREgF+ggAzEGAGnnLyT7AcWYsNAFy77IcTQzg21lz3B2gaVOvXTxoiNMu3j0W6HqUDAEcCwK3k+iIAuEi473IAeBsA/NAM+BYtnE9cN7Aud4c2QCFztIqJXJVCGpA17tlXfqhtlv6JnaAx4zky0u5PdjNA8uFbtsP70hrHaqRWEOQAnLKgOzramVLCJeZzz6SGqbp7NcB36UNiFK+YOSbVJXbOWa0grXzL2oz2YePW1jUjJb5v+XJsavn74WHwAxxZ9kNsjJctgdDZzXQ4Yry8/PdaffgtN8115PoMALiK3XMoAPxj638V8AHgXAC4GwDu3muvveRBsoCfFvdt1T6qmshVKEabSmmfpW1WrTFmgobK5OfncguNhj8ODU2mUIjZDp+S1ji0r4K3kbc1JNhClokGxi71ttaWGF7VrIhQva07zi0Crsqc0xbkOdhLSgVVFmiCGykRn8SbVNBiO7tsefFFPAiaoZoDcPKEIGisGWkrzoE9raqFNParG/BPFgD/SnI9owXy+2AA8OnHq+FLEzeU09qqAVc1katQ6N1StEaMdsWiCrxtC03AlAnqM715WXyC0XJd1kUe4GyJqbe6GCzjEXpWGptYIenTvq3jGGpvWXYetehATbN4fcET7toq4HLMOV8kEy3PLbLSxVm+Suoc6NJi9sDAZFI4ikVEaSgb5UR//CsAzoLmTtyT97tSHMoUWPHpIXUDvtelAwC7AMAzAPBo67MFAH4XAn2TD9834HTRxv1uNXNzbfzwXfues2pTmobvm0C+nDVSXTTQip2gvme4NslnhM/60MIjcwnmFMGWS8NHnLRg6DMjIzb/uYWPaV198echxUuztHxuVV5XK19y8mn4rjxXPj83l2+Sc79Rd6LzuVCNnyscUj+0nn0/NLX8AwEQ4KVJmTReJumSoelXN+DPAoBHAGBfsmh7kOd+u4ZvCa10Ay6BobOVqDR2PWbpVd+1j6oKjJA2ZfHh+4DKqvVJQEMnkDY2PheFDzioO057r1aOBio5KFYrtwg2qxYrjYNz53DhSEM1Y4Syz43oEwhuzCj/cSDUdr1L6w5ubcKRZTHd58Pn5ww7/rIetENxxPWH9Lw055xiNTyMLwLgfi3QB/iQqAPERp/6pnftcfgAcDwA/LoVrfP+1neXAMCJwr02wHcnXoXAhC/GjY9PmmF8V2VqD3fLDcCf17Qp2m5flE4MUPE68LhlHtbm7rMK5VCdONhzH74E+toknWoNP1esucQ7Wmw4BbyUukvzKqS90xBcnwXqynE8tWZN+zrbunXVNiFqrsLFi+V5KCkV7h7ut+eL1s5q0OaV0OflIYP4drgAAQBnwkx84M/OmFi4pTIvlrRq9ObGKwtgcu2JMp2UPjc2mgUxTVtPAQn+nNT2UP6YkBZsbXMM4McIt1Cd+E5H996hId0NUXV8Q/2QKrw1JSHWcpT4b3h4Mp5PA1davg+ctDpqgkYS7pQ/6bu0WPhFi9pdJvQTs3uakjQ3JD6RQnp5hJ9PibDMK0EZGYVRPBCGEABwJQCOrxnGtSNlVLojbYh4NXoT8BcutIGV1PKhIcR3vUseOF5WFW09BLjaRPNR7sRRKVaGK8Pn0pHe5Rsra53cpKLP0e/4c277Ox0THtdWhbaGBHZSX2rnsGpuHT42ucJupfvo/XSOhEIepeMpcwhtzsMSD/KFXGkR2IIHWn+MjOAzTz+DC+fORQDAzy1d2lzc9VBK5G/zby8C/vLlOmByJuCDc/jhOlO5waM9mKKt+3aH+iaLBGC8TbFaoEQp+cCltkv9xutlFW6p4OnbdCa5G3ICvqun77rbpPGXcz9wHvSBW0j4WsZVAkdennvWt6kpNolMSj9JB8dT9xRfg7AKSpprh/axcyu13nn9F76AAIADO+6Izz33nKnq0hD5Iox7E/A1DZ8v9khaA3fn8M+iRXHmvwR8msnLJxNdY3DMtmJF80O1Mm23RQrY5NLwNbcJ/d0SLeKrf6guVuBKdQX0GtE20sVK+teixYc0+CoavpufFEjpd3w+Srupq45hiG8kLCnLppVBQ5d9wssRvUcK+ySL0WVZ4pFHHokAgH/zN39jboI2RIidzehNwA9NcLd6TjP7UfCRkkFJmr8F7H0MzTUUZ27TtLvu/TzWeWBgUgDR56nWkOpOsE7Y0LOurd1KBxzblirt7FXyAShvu0/Aahq8VWGQ+MTn4qBzlb53cHDSnRPKdZPSTyGBx9sYE7pM+9LHo4TuuusuLIoCt9tuO/zVr37lbULIyJKa0ZvpkV2UDu9wn0bvmGxkRHfrcD+hFewlxufmqaRlaotT0oc/XxVMrSa5RJYt55JtGSOUYsjXlirt7FWKdbtIz/uEhdX9Fhvr7z4umQy1fmkIZS4+Cgk8I0jnnm/nnHMOAgAee+yxWCr3pRtZ0+EAFNpKyQ9IB0rbEu1ANUYbTM3YycHSAvb8+ZAGl2KZxIChZLpqGiGfSCmgq02yvoYvU2rbrRp8KCqMlue7ds/SevI9Mr5UFSnviykrh7IQIzwQ8cknn8RddtkFAQBvvPFGb3HaEOmQOB0A39exAO1+OBpS6NvIY9WYJeDjz2oLTlp9Q4CvaXCWBWfrhI4hH0PniGRJCUmwLj72AsVqlFXHODRmVcaUv9uS8dNanmVHrrXuuZQFWo7kw1eCCC677DIEAHz1q1+NY2NjHb/7mkF/6+ze6QL4kruDMpADQ769WVkxr2Q28g1HIQ1dcuvQRE2ShaBtZrEsOOfY7Uv/94WzaYd5+6wRzbSWwMvnNtoawiarUq7opSpjTK+rCBNeJ74R0pfNMlQe5S3N12+te26lSMMcnruK0EsvvYRLly5FAMDrrrtOLFYaIlrV4eF2+GhOxYM247QAfER7mluueVKGkaJhUoj3vmQ50J2+zk/pFmsdWLrFXM7EWju5paK1I1ZrdCQBiXaOLA9Hcx96rwWYYt1XXGCktHNroBDw+PZ6WK5z1Muq/fraIm2EDJ2ZK5VnieYJ1Z3zZUyYdqi+kf32pS99CQEAFy9ejJs3b456le44mC4aviOq8ZVlnB/QOiFingsBmjvZwMX40pweNJZcAlNqt/nyoufSan2Tlm+84jlUJGFk0aToxKNl1HGIdgrVLVA0kOjWgrivXrROlnZrbeHpr635BKTyLPH6Wt2lYATap9Y2hq6N/dZoNHDZsmUIAPjxj388/G7Pa6Yv4LvWxuZxsVJqOgXtWnJzSFvVpecd2PMUrhT0JRdKFYrRUjStSxJgUnmSRUO1QAsw1AnI3XIZ8dkrue2sLocc/ZGi4VvaEluWVF4o8EKre5U+dZRjhzJ7zy0334wAgPPmzcONGzeau2Tb0fB5i3P44LpZrgVE6f++qKOUCWQhX35xWkcJ8DUfvqb1aOltLe2qE5Bz84JPqIcAyjrOFkDy1alqu6W2VAkv1pBNC7wI1b2K8AkpbhaBIoxPOTKCR+2zDwIAXnjhhcEq0GKlHIO9GYcfAnzX+iqaw1SUq4GeI2nC0gVn7SNZCakUiqrglhXfUMYjEyz9qeWGsW52ySmctXdU4YXUxChaKCytH/3fF7UU4yKqmjSQv1/apW3JHMafd4ug7sAbqSyL0AvNQ4n4wqz7WNerPP1z5zvegQCAc+fOxSeffFJ9PZUbrip0Ga13Uyv44vDptTR49DfpWQulMoWvvBBwSDsV3bWWxU8D2VTy5RfnZ5xy5qfX9EjCEChLVoJrf45+tZQhXVusE2v5vj7QgDiUtkJTDqR87SnuDIs1wMkn2GLLQpSDNGiyPE3RibWmYiwNX1JBqT5cKCvvP+GEExAA8KKLLvK+XjMu3Gt6H/BjNj/RwyHoPTFAmFvD9wE5N/Ukk3NoCHH+fBno+dmbUkbLWLLETUtmO00xS1Ma+5KfUc5N3VZfBZB9ybCqugB4HX3l8PJCAO37nbsAuWadi699bfVdx5JvDDiY8mtejyoWoaSYSMqN42tfvQR+veOOOxAAcOeddxZ9+dLrpene24AfMhMlTZP+FvIFx060WOZ1gEJPdeKCKGTGO41mwYKmhu1y78yZ0ykYtPrFTkKLD58zvqR6SNoy788qh19UATELb4USlMVQrGBKWSBcsSIsrFOF41SSVG+qJFEfh+bqQay+oUyztKkzfdGi5iEv1Hp1CpA0ZgS1//iP/xgBAD/84Q93vN41kT/Kq97bgE8HhU9qHp6pDYY2OUPaXQpTUAppZaHTiSiQ8yidZcsQL77YNnljmVzrb5+mGHOubIDpJ+6J7d9U60Brq+/7KrwQI5hCgpoDodUdFyscY9rpu04tU+IXN98HB9vn/uBgpxJBy4qpI382dDwivXbeBkkoKMrObf/8zwgAOH/uXNy0aVPbqzV4403sfcB3LZbAzWcq+YAwBBahDS9WijHlfXnF+WiPjdkmbywoci2X7xb2LTLGaI65NM0cUTo+3grxnLV8Oo7SdQrFaPg0c2sOy1WiXBFTfE77dnlr4coUcFPrY93lSz+hk7wCZZYjIzgEzfNvL/vUp8RumP6Ab9GOfBqyz53TLZ8mB3L6fs3Uc4JnaGhyd6770AyhMSBuaWdoN7N0+kJVDb9Kv1fRKqWZRNeActVxdNR2fGNMvTUA13z4dYWwuvZoQi2m32J2eY+O2g5USRFw0v3cagid4KUpNJpAa31ubC3eLl68GLds2dLWNdPfpWPRULlWKv31gX4OTVMjzf3BtXctnnd4uD3nDj8DwGUdDE3e2HZaUx1UsSDq0jStJE1iNw5OwFapY0hTrbphLiZKR7NOqva3JadUTH9pvKG5ZjQlj88rqyJCyXfmBa9rKCOuz5Jjc7MxPo6vec1rkOfY0XQTXmxvAz6iTTOhi6M8SkcSgxrD5AQdzhTr1uk5RXyajRY1s25d5/ukOqS20yokfOetSbS1JDzzAf7ixXncAdSaC4Fxahvo/1W02pR38/ItaQ+sZdI+klI1u/u4W4f69K318UUCas/7IuucssbDlo3WtzsK8eCDD8ayLKP0pN4G/BjNpCzb89JQrYMLByr93f+ezHbR5J53TCEtmPFJ75u8PBeJ0+x9VKWdViGhJaCS8uBIGpr0WzfJpzalasPc4vSBU+52d1uYhrTslLnEFQ1tn8LKlZ3Hh7r+HRlpP67QVx8fmoZclTwQY9269hPsaN4sSelU3vvSmjW4aNEiBAC87bbbENE+tL0L+LHMahGD9LuI3NXed/JrDoKOKS6+OC5fjG8yhSZSlXZa1YkYtWNr0eolsloyMST5d6UFRknYV6XcbhvL+ySBmeoGk1ygGo9piRSdZW+1dmLf6+NtB/I0fDbWshgdxUsuuQQBAE888cS2avJqc+pdwM/FMPx53z0xkyPkQ+XanXa4uvZux8D0Xksu/BzttAK05R0xgiGFqgCc1ZJJIc2vm0PR2FpI6r+U/RS8rJCmTd0jdJupu0ZMC0e2WBbS81V5ULh+8skncfbs2VgUBT788CPm4nsT8EMmWOg6pLFV1ep8zCmFi2kHOPt8+KFt8tbNIqnttDKx9A5J64mJ5LFS1Y00dAx9+yRiSbPO6nYldpOkOcC1aqsS5e7xuQglwKduSxrtFpMsjreFzzXL8zXR6aefjgCAhx12kZnFexvwOcNQH73U+m5q+L5yJO1OMxE5CNCJ475LAaM6tVffO3jUkQZ+OTT7qpaDZSd0lXpJbp1ubYDiPB8qP0VL9aXNcGWE+lFSDugzMYuiKZFPdVugFejHP74dAQABBnDNmpfUqtEq9jbgS7HL7vuQhib1Dv0uZFpbJoCk3WoAx01ELaeM+yxe3O7jd1ryqlUhPmkHlbVrO6+1toTay3/3WTm8PXVo+DmEmiXXUSyF9jKEsmBWoZSY/6qWEn0mBjhDz1jOj+aflDHLvcZUxcXT9liJBx10UAv0v9Y2lbTko70J+MuXyyFN0mDTgbKGcIZMa4v/LqTdSgu0Y2Odz2qMnBpit2pV+65YF7u/cqXOxKkM73uOC8O6NCif2ypFy8slkLRw1VAWzCokWYcxoYFVxyfGepYEhWVO+w4EqiI8M4F07h3HV1xxRQvwj+mAB65jNadCrwI+1UwkINQG2KqZa4wWaynwe2juG75ZKpR1UhMeVmCQ8qfwa6kvqkx4ble6/uE7PgcH850fKtWdz4TYd/gER5X6UcqdnE97p6QYhTb/5BJCUj9aEsFZ5nRdGn4uyiU8iev6P/7jWZw5c3sEKBDgMQSYjPx0Hwc5zaWOXgV814GcGars5uOkMZplAviYmB5eTrVsKlSk90tiOxaEtBOkfOGfVSc833HpDkVZsaJ9kvIt8lWI91MVf24dGr5G3QhRlawrSx9o90tCnf6l9/ms3pDbJtT3PsCv47jPFKrKS2U5oeSWwyO4dqREgJMRAPB/LlmCIyNlR9Opftn7gC9pwVVifa0DY9H4fNaExsjU1eLTTqvszAylN3bEgTpFw5Um8I47TnJio9FMFwvQXJeQzPtUyrG1P6dLw/cOSnVGf+TW8K0L275+1KzVFItHSiozPCxH6UwVVbEW2fiNwiieCOcgAOAfzZuH42MNMYXPpJerV8+0lQa/Su503qk+RssVScEHXorL5yGbbiI4l0gsCFk1/NCE9AGErz99HxpxlGtSck00ZaLVqXV3c9MZB/uqPnxpTcqXp8rXVk0JSYmX9/FqDM/6rlMpR+I91saXAHDh3LkIAPiOd/xsosm8C3ob8BH1+HS+eGrpQH6tMRrdsu3AN+XgC01rooeYuI9L1jUwMBm148sSqJF0RCG19xqNzjz83NdeJbxN5sD29/sEl5QzxUo5TGl+XRUUumE9cEqJ0nEL/VJEmE+YS22Qri2nqPnKoN/n8pHXIYRD0VkxvMjmz7uXLUMAwKGhtaJ+NplQt5cB3zWed0YMhSJJKPH871I+eAuFtCYOstQu45k0uQYbIjp5nfk7ONj8XoqTltYJYieABAq+SAqJ+X2gE/P+XMCaM9qiW+sD9J2cb6qAKBfmMRaUpITw6xiqOi6xvJIijKpih+CSuxOaefIXL16Mw8ONNpnSVR8+ABwLAA8CwEMAcKHw+18DwAMAcB8A3AYAe4fK7DgAJZVSgECboL5FT+nal4FP8rFygKwCCm67ORUc0kTzhbmmWDKcA7UPLz8HKHB/Pu3vWKGZW4Ckupq6RT6hFKvhSxTS8FPq67u2PG8Rwinuphjs0MoQsriWALjX7NkIAHjWWT9pqxbV42oFfACYCQAPA8BSAJgNAPcCwIHsnjcBwA6t/88HgK+Gyu3IllmFUjQs6wS1hJtR4lLfB/hVtvbTdvC2+07kSQU1DraOYYeGmu3gLiwpf0xsdJHUVuqqazQmhau29T6273z9ooHQVGj4KSTxPNdapb/WtlgDCbpFoTmeKvRzCHcpo8DICP71EUcgAOC73/1uEVoQ6wf8IwHgVnJ9EQBc5Ln/UAD4SajcpGyZPooZBOsETWUISduRPlU0IF/b+cTzparg5VivHcNyK4Kf88ldVqmgQEHejQE925QKmRiQqir4fUdCbk2g7+P5mCgdXqal/KmglDluFfq52inU5Y477kDn1mkoc6NuwF8NANeR6zMA4CrP/VcBwMXKb+cCwN0AcPdee+2Vb2LEDEKKfy/FenBx6iMjzUXc+fOb13Pndm6W4ousvKzYtvsyD2plpvhNqWtLWregC9KI6Rq+5FLyCdQYsLdELoV4pltZF1PJwvO07tLf0DrZ1iT0JH7h1/x+i9DvQjvLssQ999wTAQB/9rOfiffUDfgnC4B/pXLv6QBwJwBsHyrXmy0zrofiB6FqWlVLfbnJtn79JOhrGn4VfyIHQkvEjFZOCiM70ND6KeTD5zuELYJX+1jrq52GxQWV9n4+Vr76I07tmQE5s45K1zGBE1TA0O9yUsreAgsmdWEML7jgAgQAHFXK3CpcOgBwDAD8OwAstLx4eczkDFEV7VS7pt+nmnD8Ht+pVhRwYkCXt93tAHaCxBoTX9VUtTyvRek4a8/d6/ZI8DpzgVJFw48FfOn9Mby7NWjBIS0+9GxofKU5Jc3NlHDkFAolzYvdFFal/yLopptuwmbK5MPE3+sG/FkA8AgA7EsWbQ9i9xzaWtjdz/ribBq+ozo0BgmEfT5i37U0YXhKvNSdt9zs5gefx4SLpQBaDJhxc/qNb2zfkDY+3n7NQYHWj55t6oA7Nt2CdTNazL2WfsrN/1aqqunH8IfEB3y86BqQA+Gc/RHqc2t/0DUkel+ONThGL7zwAs6ZMwcBAJ944omO37sRlnk8APy6Bervb313CQCc2Pr/nwHgSQD4t9bnxlCZySdeWSgX+MdscInxcfpcL9bcOvz7qkm7qoJRCpDQfuAHxyxY0Cm4XJ3oojhNgZ2y9d4CYinWQJX35SDNjRKjwPDnU/hDek7b9e3A37eBLIVCgQIWxc21oUr8fQQdf/zxCAD4uc99ruO33s2lU4cpp6WsrbKZRstTKt1r9XFKC4/W7JmWiJHUSVnF3ZAiaLUFWO7Tl/qPClSfa8FXX0t/5QL8bmn4Gn+sWiXzWKgNVfmDCzm3G5x+Jx12kqNfpIg5l3aS1i9EWsBBTWN41VVXIQDg6tWrO37rXcBHzNtRubY9O4qZoJZ7Ja2cA35oUoUmX+rBG1O5oMjXNnzA67uOoVgQq+rSSQXN2Db73uPmQko7UvnD4orjn1xgL1nSLj+B2/Eew+d8btE+zsmbiPjwww8jAOBuu+3WEZ7Z24CfiyTGppI4dRNIjAkeey+fCNR9RO+RFi8l4SLtrK1bQ69K1GfvPpIPvw7qRrRWlfflBFmNP2IAtqrwkXZoSylI6lICJavGaqlIfeqslRqUJRqeee+997b9tu0AvkXj44uejqlSOj+3hi/dy7Uwa24dyUzmboa61khyEffh0/0JCxbE+0ZTBJb1mVzumJT35XCjOC1US8FYF49Irjjp/XVo+I5C74wBe0mZtGS9TeBNd8D5lVde2fb9tgH4lhQHZaknLfNFX0jXMRMuZXJWjZaQJi7d7FWW+dMUVyGtn12opvPZOyGwcmVc+XW6pKzjm9tCShUyPg1fOl2OK0S5gV+aU5JSVocPX3qnNTCCks9dHFp7S+TNa6+9FiU//vQH/NCE42e7WgE/NBAxA1XnXgCtL+hRi+5zyCGTycRc31jKjK1HDIX6pkrKZFpeqjZsoZy8EkOxbiRfX1CA4t87fzatd04+kOrlhA/1p+eM0pHe6UsmGCJfQIg2ThV488EHH0QAwIULF2JJ7pv+gM87jmstnKG5T1gKnapDa/PdmwNUqcbgS8EcE49e92JtN8CYv0cT8jk0bum6rjamavi+KB0OWFJ4K+WhnNahVK+Rkc6ImVyaPX0nFSiuvfTa1csiUPl1aJwSx7EsS1y0aBECAD7wwAMT328bgO86LiRFpY87d1UqL4dfNkQ5QJVrF+PjgYB9UwAAEi1JREFUTcDnWSpj2uLTgKwTwEI5TgiyEOcPa9K4XO+uujGLl1fVhy9d8++lhdzUA3Is9aF/KX/xa7rBSaq3j3whvDTlifs/dQ+Hq1donNwnZKkJY3bKKacgAODf/d3fTXy9bQC+RYpKky3EuLEmcyzl0P4k/2EoH721LT7NWJoAsZYK1bDoO3JvVtGEftW00FZy4MHfXUXAxCgKVSxIPgdyC+VQegXXd+47lx5k3bp2l22uvD9cK6/KIz5XDxUqtG95agnaR6R+nz72WAQAPPvssydeN/0BPzSIkgbpAy2p3Lo0T4vJZ207X58YGMiXNdICklWSu+UMlfW9J4evVio/dK3FlVddfORWFteEETsT9cVoqj7rOJdm7xsbCQxdkkF+JnTKnPGNvXZfaCcuJV/KhVBbnTtLcqW11jf+ZfVqBACkeDn9AZ92LJfQ9HSpGLdEDs07ts6pk8mnvWqMVGXLvDRRUvurWzsUNf9wSp/HgmmdgB/Sjum7q8SWa+kOcoO+D+h9n1hesc43fl9sqmuLNSFFRjmwD/T/HzZuRADA7bffHl9++WVE3FYA33WwBPyp2fe6FcoXmkwWc1za5Uc3aqX4Iy2aMdcuUyyV5rlsk5+6wkV5XWPird33oUU+DUzrcOlYtOOq1gzXMF14pAOpnD58zlf8O9+nqoJk1fC5MhISnpZ3aW3XnmdtXrp0KQIA3n///Yi4rQG+ps2nRmTw+2LMuZj60klKN2q4CecTOj4tmT8b2wdcgIZA0se8Wh9YzOacFGONUCWCLmC6yC4rmGpjXRUsrdpxSFBLRNtNFQYa1ptb+fG1wfdJcef4xp5+zxOi8Sg/i6VURUnSBF/rvre//e0IAHj99dcjYq8DvkW7dWRZAKlCls1d1rrSe2hdqUZFAUXTIH2MmfOIRG6RxJqnqSZvXRTKXuruoQLYtcXlW7GAaV3ttAhZXx3dWcMacPush5Ar1Hct3c/5l9bVWam07lV9+FbL3beZShrvWA3fwheaokDG4pIPfhABAN/znvcgYi8DfmwUAgWkOjUpaXBifHtSme7DNXNJy+Htoaf20HJzRbpQAJTe40AytAAljUG3NqRZy6FtpEDkdilLH83qoeXndA+GrC5NO162rLnxjl67dlldGj7XhbaOFmqjBKxOqFLFJ0eUDq2v79qRFPDBQZ/yC1+7CQF6SPFw90v7IFo7oG+88UYEADz66KMRsZcBP1YrsgJkKvncDyl11Z7hsfMh/74E7rm0Y1/MMn9PqoUVa8XlAk6pHnRMQmfj8jZK0RRVQlZDdZSEKt1sNzLSbMPChe11fc1r2q+Hh/W6+NwRjvi4s0iSjvGSyBdJ556jQrRKHH4sSS4VF/bs+MNd87Za+NXHF5oQdamsyxIfe+wxBACcP2cOluvX9zDgx2gYrqM032UuF4E2AWLriqhHjnBtUoqp19wqOSnFFSG5E3LVK6U+/HnfNX+H7+PcD3T7fwjscpFUR8cPZdkZpCCll+bPSqkSLBvifALI9YtkEWrtqoNvqpDU1wMDclsHBnTrl5cZWwdKTMEsGw3crXUC1mNnndXDgO8aa2UCx+ycmatGQ9DyfRMghWGlwZSsFDdxqkRcpFCK0I0VfHXWx1Gse1ADR5qfyFkBPISO1su6yB8LCj5eo5qwlv6Xf1asaNeopbMiNOEaEpIW4Vw336SQVGduUU2FIsbrBoBHACAA4O0//nEPA34ME3BNQ7rO1cESA/u0Ietk5nXWNLjQImFusgqyqhp47vqk1EviuUWL2rM1ciVCE/jWdZ0qG9Z884KCduhQEQ3c+bnKsUJS8nfzOdEtvkkhzQqX0qxzS7zuupM+f1sL8L/1rW/1MOBr0lUD0pz+XQmktfKpdscZ1npoCW2Dzw8uAf5UaPia5trNZGsx7bc8x4GGasfOH+4z03n5Fg05Fuxi71+/vhOIdtut/Xp4uJqFIvGktnDseJjzRN18k0K0LvSvll9IamuddSPjdWYL8K9rpkzuUcCnTGDdOBRrGksUWjmXzOeQJhDjA/ftJeC/1akJ+cDFp/nlGIPY+lhBPzQhYxapLfXSNFzteYsQiwXH0AEf1HqMBayynORxvnA9NNQeFaTxMC2Llz1VFMIAyXXcLQ1f4Lf3vPa1CAD4sde/vocB3zWONrBuH1kIVHxmusSwKRqpz5JIDX1LJQkAY3cb1lkfa/tjxiEFeHxjZgFRDsqhjWfWOkrt5haLppGHxtPxnwO/4eF27ZeGVlpDpWP7vg4h4cMAHo3ErRsX8cTxKjcxfvvoRz6CAIDvfd3rehzwHaWa8inkc2OkaJipmhMlKmg40NcNsrz8bqUzttbHAgpVfcSWd6b2k7SwOjjYmQc+lqR2OnDma0KxlihXPPjZC3Qh22o9xArzboXo8rHT8hctXiz3a13KGOlDd/rVO9/5zmkC+K6BscCZStq7YgVPDkFFtQju5tFcBL7rHNTNschBKQuj9NmYdRh6TwhEqcXk3GROM6YnPaUAGQcnCkQ8DlxqoxbdJrVFCyQIWbnSvLIInRxC3NJ/Go9Lwj1mjmamb37zmwgAeMIJJ0wTwJeYqi5zKQTSVrDzTQzO8D6K2dDUjcWvOq2tOoUV7RPpryNtJ2tstsmULfzS+KYAmc/1p2nXVsCnbeF1lgDd3RfanDYVypRGKWXXWR/6DuH6x81wTDzyyCOnAeBLpiifhLkopDnEujNSFp61+oQEXre0nrre4TsoIke9Ef3pIei1T3vN7QpCDC+sxvRtt7Rlrvho62uxO3FjLMc6LE1rf0jvSq2PhU88CsQDDzyAAID7779/H/CjKSX8MjQxOHDHAKUGOpKFk1PL0JiwDivCl6iqqiDR8uNI40nb4wPgupSMXO+rW1vW6sytCXp/6B2aMqWFhtapUXMeX7eufV3FuXC03Dkx9bGmX/Bgz1NPPokAgLvttts0AHzXYEnDzZmu2FeOD+ykgdfKTGEIre0+ja0qOIWY0Kq5Woj2S+6TryQLzRoqmUvjjqlfVYvC1wafZopojxTy1TnkquR1cu+QBLwr0/EAFRqpylcMuTLWr2+POKJHiNL9ManKoPU5D36MjY1hURQIANME8F2DaWNTM1T6ri1En+G+z1DcdgoYUwuHApDkY82h9aQybxWS6u0DnRxla++QNE2qwebui1xrBqE2UCvHso7gS6+dmuWU10lK6U3z7/tyFKXM/1jSBDDnidQ+0fpFG2sPfrS0+2kC+BqzxIBSblcEf6fkluDgHwvG9JlQyoicQF2nuayRdGpXTm1Nm7TUipA0TSmUUeMbXteUXDopUUGUKB+6v77c8VKkEL8O1Tl0za0AzdKiUUkU6DWQzWlpaqSBvuZSTamPRREMzMn99ttvmgC+D8gs5nmojCqgYtFaKPinvD/GvVIVLHjbYq2RVLKe2pVC2hiNj3cCG9c03fMWtx0fJwe8TkuOGYsqwEHbQNvomyOxGn6ItFh1uubGBXzIfdENPtRIUkZyRQlalCsDfrUidKYB4CNW38lo7dgU8mmPtPwqFoYGAFXWFELv65aGT9/FE5W5QzrqssI0QKRardPq6XXoPZKWXLdbTKpLDGDG7va1vJ+DE7eSQiAn1b+ucGyNqM8+d11iFNEAfrRi8KcJ4LsG0v9Tdnzm1lpjJ1dOE7QbVku3fPirVjWBcc2a5nuGh5vXr3xl9feG8uP4NM0YIa1ZEt0QmlJdLEqIr+65LV9XnoW/JDfKyMjkjl4tOi+3i4fXg7uYpHrE1iGWx5Tr1i7baQT4tIOscb28c+piap9fstvaXNV3lmV3c/ZwDbCODXbaRKkKTNJ7uJacU8GwttXiZpT6ILfikCJIucVHBaf7281Nh6Oj7VlvnRCQ0iZUWbj1XRvove997zQEfMoMlmPlpOc0M5Pea6XYGO86KKfVErtDMxf5LCXtvVUniVXTtArUKhp+Ls2UtyEUSOCozoAGre2+NtN5ZVEA6rZKHS9o192oQ4A+9rGPTUPARwxrZRpZFpJSGN09G9rFWQfl1PCnmGFVN4T07lwAZd30EhJAXHv2LQhH+majKeTCqkuA0udy8BHtD6sCkNvajaU66xAYn+uuu647gA8AxwLAgwDwEABcKPy+PQB8tfX7zwBgn1CZXsB3jU3RarmU5htGqgJcrkljfVdugJ6qSUPHgmpy0gaz3O32jZmlPxzAOqvOAezKleEonTrdKb7ruimXEIvlx5zWbirVUQdDf7ZOu6oX8AFgJgA8DABLAWA2ANwLAAeyey4AgKtb/58KAF8NlZus4cfS1qAVVKE6/JbdnjR0DKxuum6MW6zLh7v2nKbPy/S9pxd5UKNuuNy0+6eThm/sh9tvv70rgH8kANxKri8CgIvYPbcCwJGt/2cBwDMAUPjKNfnwc2q1U60VVKGc2txUTZqUtYNujFtsnpPUPut1HqyLrArNVLsj666Dgcd+9atfVQb8ApsgrVJRFKsB4FhEPKd1fQYADCHiGnLPL1r3bGhdP9y65xlW1rkAcG7r8mAA+IX0ziUAr5wJMPM3AI+77/YG2LMB0NgA8DtvhQXaG2DPBQAL3fUzAE/RsrcCWgBNIVk7ub5wfcCvu1GHAE30xdY2bssBlrv/fw7w85hnE9vSNb7oAVqwBGB2TlxIodzYxMnIY69GxJ1Syp9luKcQvuNSwnIPIOI1AHANAEBRFHcj4mGG90976vfFJPX7YpL6fTFJRVHc/Xi/LwCg2Repz84w3LMBAPYk10ugU5JN3FMUxSwA2AUAnk2tVJ/61Kc+9Sk/WQD/LgDYryiKfYuimA3NRdkb2T03AsBftP5fDQDfx5CvqE996lOf+tRVCrp0EHG8KIo10FyYnQkAn0XEXxZFcQk0Fw9uBIB/AIDri6J4CJqa/amGd19Tod7Tjfp9MUn9vpikfl9MUr8vJim5L4KLtn3qU5/61KfpQRaXTp/61Kc+9WkaUB/w+9SnPvVpG6HaAb8oimOLoniwKIqHiqK4UPh9+6Iovtr6/WdFUexTd52migx98ddFUTxQFMV9RVHcVhTF3lNRz25QqC/IfauLosCiKKZtSJ6lL4qiOKXFG78siuJL3a5jt8gwR/YqiuIHRVHc05onx09FPeumoig+WxTFU609TtLvRVEUV7T66b6iKF5rKjh1x5blAzWlZejFj7Ev3gQAO7T+P39b7ovWfTsBwL8AwJ0AcNhU13sK+WI/ALgHAOa1rhdOdb2nsC+uAYDzW/8fCACPTnW9a+qLlQDwWgD4hfL78QBwCzT3QB0BAD+zlFu3hr8CAB5CxEcQ8WUA+AoAnMTuOQkAPt/6//8CwNFFUUgbuXqdgn2BiD9AxM2tyzuhuedhOpKFLwAAPgQAHweALd2sXJfJ0hd/CQCfQcSNAACI+FSX69gtsvQFAsDOrf93gS7tsO02IeK/gH8v00kA8AVs0p0AsGtRFHuEyq0b8BdD+/bxDa3vxHsQcRwAngOA+TXXayrI0heUzoamBJ+OFOyLoigOBYA9EfGmblZsCsjCF/sDwP5FUfykKIo7i6I4tmu16y5Z+uIDAHB6URQbAOBmABjuTtW2OorFEwCwpVaoQtnSMkwDMrezKIrTAeAwAFhVa42mjrx9URTFDAC4DADO7FaFppAsfDELmm6do6Bp9f24KIqDEfEPNdet22Tpi9MA4H8j4ieLojgSmvt/DkbEsv7qbVWUhJt1a/j9tAyTZOkLKIriGAB4PwCciIgvdalu3aZQX+wEzeR6PyyK4lFo+ihvnKYLt9Y5cgMijiHi/wfNsyn261L9ukmWvjgbAL4GAICIdwDAHGgmmdvWyIQnnOoG/H5ahkkK9kXLjfH30AT76eqnBQj0BSI+h4gLEHEfRNwHmusZJyJictKorZgsc+Rb0FzQh6IoFkDTxfNIV2vZHbL0xWMAcDQAQFEUB0AT8J/uai23DroRAP68Fa1zBAA8h4hPhB6q1aWD9aVl6Dky9sUnAOAVAPD11rr1Y4h44pRVuiYy9sU2Qca+uBUA3lwUxQMA0ACA9yLif0xdreshY1+8BwCuLYrir6DpwjhzOiqIRVF8GZouvAWt9YpRANgOAAARr4bm+sXx0DxlcDMAvNNU7jTsqz71qU996pNA/Z22fepTn/q0jVAf8PvUpz71aRuhPuD3qU996tM2Qn3A71Of+tSnbYT6gN+nPvWpT9sI9QG/T33qU5+2EeoDfp/61Kc+bSP0/wMC14i4ozXoNAAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "N_trials = int(1e3)\n",
+    "p = np.random.uniform(0,1,size=(N_trials, 2))\n",
+    "r = np.sqrt(np.sum(p**2, 1))\n",
+    "\n",
+    "print('Pi estimate:', 4 * np.sum(r<=1) / N_trials)\n",
+    "\n",
+    "sel = (r<=1, r>1)\n",
+    "\n",
+    "def plot_pi(p, r, sel):\n",
+    "    x = np.linspace(0,1,200)\n",
+    "    fh, ax = plt.subplots()\n",
+    "    ax.hold(True)\n",
+    "    ax.scatter(p[sel[0],0], p[sel[0],1], c='r', marker='x')\n",
+    "    ax.scatter(p[sel[1],0], p[sel[1],1], c='b', marker='x')\n",
+    "    ax.plot(x, np.sqrt(1-x**2), 'k', linewidth=2)\n",
+    "    ax.set_xlim([0, 1])\n",
+    "    ax.set_ylim([0, 1])\n",
+    "\n",
+    "if N_trials <= 1e4:\n",
+    "    plot_pi(p,r,sel)\n",
+    "\n",
+    "x = np.arange(1,N_trials+1)\n",
+    "c_est = 4*np.cumsum(sel[0])/x\n",
+    "c_err = np.abs(c_est-np.pi)/np.pi\n",
+    "\n",
+    "# Std: sqrt(1/N(N-1) sum{(x_i-pi)^2})\n",
+    "plot_convergence(c_est, np.pi)\n",
+    "plt.tight_layout()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "anaconda-cloud": {},
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.7"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/exercises/Solutions6/Exercise_6_Solutions.pdf b/exercises/Solutions6/Exercise_6_Solutions.pdf
new file mode 100644
index 0000000000000000000000000000000000000000..f4f61e7025bd0ef03c1a4e4c9dcdc7920b4ca5d4
GIT binary patch
literal 270193
zcmcG$1ymhPwlIo>5G+V=4Fq?03mV+r<v?(U;Dn&Tf;$9vcXxMpcXzkH$v5-Q+_^LN
z-M7|zXVp5ZtE+eIUE6D)>O&$cBtpYL%K}TXd3tmU%fS4R{-c$?DJ(ZPorsCKJ;;_$
z#9YrFBm^?BG6c~{f-H^gzkXz-r)TEng|)M{1?gG9ew!~+*9cpvM)5c)K^pb{1Zn1n
zA(v?@4VxK@N;3*!fx(VwgY>oOyckSPp4d6^=>~7vuwR)q#*jJ0l8JzC*qS!;gB$D@
zuQfiDcd(w;rAyF!%Wq4T!@b@K!Gw7)Xn|pLs^S)|VUuy!HhB^}pOcs=3;8TG4)Jyq
zyyX29j8y?O+*?01jFFMAB~iL(Tly+v*rZRr6sD9v;qezz^bj_csYqtNz1^A~{SE#-
zEkm_qRafd;Fgr4~cj5FKgTi=n^a2OeHF}RG^&S|Q6;>zbnarUI7peA##-oJCVQZf;
zB8m?8tEgDSZP)uKmOa63-mI-A=NC~W$2oPo^f!LeUO)I@G7{QlOcWGze8Mi?wxb#d
z=^HS5<8Gx@@vGc{eZqfyGV@&E#uP$F3Pwpn@_)x27lnORV_UV&47-BJ4$n-kR3pf8
z?p-TXc3w&Myml3yy?&#Mr7xkf!dV|4hHgU~h?~CkP;Xj~ETrn=cyEX+b$*c-<?sH)
z9gClaDc9x~V8Z2(qX!|3L?dW>rQcEZ4n46pW{tZNJLYy*k*-pc+b7YaDu9q^52oUw
zOF4-6=jK%5cG^;o*7VkcIYUPr5xk8fi66dt!;mQC`a-jp<iIChA;F4Y_oe5~4km1#
zb$RU>6A}LGc6~6m$KJx5E~&7gwVN~NdH&Th4fVP8L@gTVhWMM@Jp#l@;RUH^HFGJ^
zWY!mr;x=x)J@xDvEb19p<Ob3fA=%X;A1b}+#BOEkOW>Bsa^Hhy6kY9NW3FAynI|(=
zzT&#3=|TB_{+j9Ux<E;TU9FbArW&_kwtb}{LR!zDlTz@bZZ2E-T32W_d(cU#f-WWG
z?Ik_2y<55KMfvwmtL@hIoXWn527wx$DABc@9=C_Do$@^Oc=O@;iH$yw@^Ozj_Y7}2
zI_&M2463av)+rPeQ0Pelz6<QZp*u#`@fwm;s%d5WeJJ;IU&%}z(kckHK*F3=mmAfD
zlDpk@fMJq^E7;S}XmRdWVDg}QF}MOf^`w!vrhF=F!!0Vu`ljIpwd_6dVG6r`uR=(5
z-uEtQx@s+&QXy}|*{nHtFSj7xCwkcuVX{eEpgE2-*YX@CVPu+Du_3Z#q2pOLQ|p=8
z=rZz7RIHXOO5@>8fz5~K%ZrAgej%>FwC-+^6`@{@Pg4!B#}#yYXM|QmN?fqT!=BqP
zE_A!A8W@;O6<)p!&3*+1j8qMpu7||DzYvfZ2H&A#lSdywwFjDHIx5r4g%9ZG(omid
z5faY|!kD)+kdtFe|HcUt&aU|+vf85Tl?owCB^zuL5RNLXTj7n>Pwd9zCxk+L2Q%(f
z5EjKz&i*SvZ_BKUB;pjKC)?2Cqv#c6Td0sO+=m}lGw%v|+EhicLg`69(~ToILK><%
zA$OJ91Yru6;xIxg^tTj)OHX92%-p_+3+Y83fHq1HR1*whkXAjnCC@Au_vMJ2*PO-h
zI*;NG^enmtVHXD-dARC5?BRPglm)EFrw-e!6jusP_svf)v06J1XM-c&6beXZGk<*3
zK8ftY)a}pT@gw<-z*k9;eMZi>sR61_-kA}SYM7!>R$BBCK6!PxAL3lb`y$q@HI5E`
zL>}VXDV1S<=q}z*M%gt(|0EyX*S(uia|AvQ{v>Q*8du4L(zBV>oQSR-WZvG>c&J@a
z;YDm-(8L4Fo<~?p_%+0-hGG)yU}+xRy4T~N9wM&njn>?UUo!rv5t#!G(%ZY=VW8h2
zSN>u>Crv{9rhd851DSH!y9buOc<B~X`f{lHfpJ>UwRVMSpZl?ZE!bs0q^49Q3}x4_
zt|)EpxIM;B_oph+=Po4J{H31dNP{9W93G}qLRLL@gKc$l<?c!|wy2uz{hOW&DAcPL
zGcy7L(ZB}0xBhOeWfY|^TsLdXqdsxIYd_Yl5<epUYMzxn>R1d!rVep(uVj-jHgc@6
zKc7sRU{`8@vtcQ5w&v3uGQ;21|8d28#oq@t=7l9+1KD(E)|PCf>0o+4d(h>ql@M@0
z)rNw)5L4V&FQ`DfJuCVhx~aMKxv7i0$@bcXCHwsMC<#n>l15f8zPTu~zb_a2IdY%=
zhn`v5vp~{NgN9C?rg^G!1?P7*$K@&*r@scXnOBBI(IW?H?xO`&7zswvtrw*777ix}
zJ;j40ofPjAIh_Pu!UFbPa!d)D2}@vGPVVN3H7Ry<6~7+^j%Z5QE+pUUT9Iykld<sV
z{bD{`>Q)Jto?QZ;q8=V=k!)E36~2-1f#BM^d?uCk-i2*oV@9wcGwu@E1r7ap@LMZ{
zjy{6(I%Y%pOjCSgO0jCAOV-b+UCydvodtaIMX7g#K7=q2LlYUZdEB!|uu|_NWGrI&
zaIpiRcfe{+&Rq9F#0KyoVLT!!{NpMLWP3dbUM-23=5XEHttE_5`6)RvZ3;Njdbb_@
zlcjbHjN!^JOui*KL4)f8T+xBFm1KTZzLvBY;yz-5yxUND0rE!j=_PEbP*Rdq8yPf~
zg_9$#oKCm77|hJf?K{tB5Wd7?ckf;Fa>1qoBKy+T^V})M8wyV)Dmi6^I-j%R;g>Im
zFz_sH-49=q8rB6)&!pPCZp;rqtRFkAV+4AF%+?Or*&5#N|I`lMG(^;hzAn3ObUuTR
zZg=b%Bp46keyZW{bibjK#>w`C1z8&YExZF)uL%JI`#&-RMQ3XeovMt!DagQ{PQgLn
z{<SY=p=S)DQ!z0F5(FkTM!GK`6XUP;9~oI$=>)CJt!x#n^$b9CLLf&I1CXe#o->_*
ziM^dH$X3wG!rICbWN8n}4vfC08M1n|7Ir`a@~;nkWdCa>yN?X77Y-Jdc7HI*+JX#C
z4D79JKQi+2{^zs>mVt?d<)71*qb1L;Q4YARXD7}R!4w>G<}-*5-T|BN{tLw>yGq@m
zB>LG4Q6AJ~Acr4}@d^-#cINSLD$sR#f*8zI<dJrFX62PS)rlx|t-c4E1rrNLmYXaQ
zi^UhebB;>BHSf5q)Z9FzZam+fu4!{~FLrcve0qcY=QHM+{_YK1Ise=3e6x{mT)RPr
z9$dTjKkd(D5`E8K?a$D^^eh|il0)JJuc{Po;+uR42dCl^C5=gI=xAY-RWge@G?(hO
z6k-RqnW~ok>lp1W#3XJ|2?b2z3K&x_ows_P5}Zx);zAU>hk6>q8fk{@Eakph&VFzc
z4yd`$r^=2oDDHktQ&>R^M98_3InhS%Ofn-Hgt2>VuSEVUrGF6(eE-wHUp?-+AsqC3
zn)W-llG)MVI-(2bweDQ!Z&W>X?RG(PA$PfPv5(u%G(L6W7aEY^Y?4$4I?mUc;dgul
zxI6iSQoLvRRhV|d+fvn+12`5HM9Ua%aduKZ9D1RzZ9C53&zE$Z0nq;G^56Ez>YNYf
zE|5!jdbr27%Zx{A;60Bxwqf{cG(DAVItv0BRP>km9;<fZ3RrjM?1pK?%y`#<9@#wi
z_2J$3H305knD7@r{?3yB+`AxVyV1sKj0vezzc>Wz;n)_ldR8EpR|%_=zgX}{b}T&1
zT%EJ4deKN)E5hA@9a8Ag662}fqu+)dI=K_dbd&A7`xngrqA35F{ce&#&J@B`z@TDc
zdFWP{V{wNVN5wp1w;gnq4r}MTEpb%PIiU;<V2a%m9kLsEGm>IwDQ0vLH7x3BqA~Wp
zuK(pH)Iu-<x9czq^0p7hYm0l)Xu=S6lS1sQU?|?pRCQPJdv=y_<X+fFz)vh-=y3~<
zi=7Uezns;Sin@GqH@MI4o^u!0?ptFiD84FgByUg;$RgPOX6O%>9DbtQTb$Bt{Ie`w
zZO6Jppk?bmC#_od_hzt%?NRro1Y#krNBS4r+qDTuy7`}mDnWhzr0m=g9XH3G#LPo<
z4fW55u1pC-oH;wbI0oBLAgF2e0uQ^R?J?w?8H!j&BHNC!a!ossN{~g*O!3v1_f~t0
zSUn}h2QEw{Mxo0#6<kKzGxNm53vTr_W^CCbbFQ6GX8e}y2?ygwb5m+1d~^9TnmD|p
z95cJz)D83Y%rp+48#3z}?%~GDhcgy58yJ?t30OvsB5}~yDS93YA_{tv$<Ov#XOtUW
zs2ew;vUF@+LefaBVnJVqA7zy$8ynp#hiTxJlTTu`+y{J1d*Nx%dyAHzhb+hB$4KuA
zxDNS(ilLuOM`-3l_F|2TX|?6aIg+lzpG5Db#_S1p=1bl0D)x+xuf(K1tF3OHXlg_^
z2ML@|Rag3=Z%Z&67@GR*tk)mZ+SY0N);5^j(_O<4O|k4OW@QZs3KyvwT)IQW!p}1}
z{S(diV%b7577%D3TJ7*Z3%!1#;%ptauv=(2p?&&UWg6>pVBcm*eSI(({s=EV@OW-I
zd_qrtw7T3N^&V64dT^bjyJR{Q%sb%qv+Fso6NKPN`){HJ$3LRQfAjwGq5^aZQhIh~
zAO8dudAk1>@dWlYp8T7@@V|{5O#eP|z%l@l;Xj9tk1TAA{}MW!Rg<p1x1Qbju8}Un
zcYt+Re>gVy*!fAstU^5uQ>tjXnm0v5<-owK!J_7<`r!Ef+vV)kQGKyxk$ZiSNWt~!
zw`^>wcZ5joQnm5meg?Hzae79}$*r_Qq<+6Pp|)>5p5w^ClkS>Y;@zH_P8u6OiANBF
zc`drdL4b8af!TobYl3;zf_<a~3xESd!T|1h)yVgHgfTEMs29b=Lvd?MNe#i~+zt?6
z7^{Wia<n%>;>(~4I|hLjvaLh>S4d#C)guvT7phd(FDo9wwP3#@%Njh)Uf?1Hn}B(f
zwxyni7wEJ6UI#4Jg*4L@`+(bp+0e$Bzeh{ItV8fiKidhCu6)Hh`Be+XPWa8kaGvh<
zX*C}Uds<mBO|afRy&6$5gK8u+8}N@gXJel|$7n5olF@=~GBZr>G$I6u0}I(?W^y5-
zZ5tDN#Tg?#f)F;*`z;mdtj1y6f6=$crNRI!KTpQO{m}9B2nj5k18NXKs@KJZ)&|^K
zV7Gl&(o25%ftVJ|hrFQ=1y%GFEu&Q^YBp{frSnNlE!gs~;Ut$8+M4Mr#R@^O)$jr0
zz~h_DW}IcVbEpykfT^B7KfBF$iK0fQ0`>_uAvx~5k_h7DXS5Ww)=$?#Z+A#qN_%G=
zwCboa4C|<wp-$<4s%7+%iC>00^K~2h7E(L-B!H#R^fGei1w=ntjx6U#-uU$*tU9-2
z7&f<Vz0d|ItHn%&)g|qbv0x*Nps&&r`+u6(V|>%o=P~kADF$>$Wt=~FKC)}inL^*H
zB`Fo^8>CD~t$po!t>&%<_ASN6AnanAj_C8Xt8&s6nY3C?VWZuf)+R~B)Y?{%tbO|7
zjazqZsV;cq_qo~Q#t`X2)s`h9Fm1hB6Zv;?`3kRG^OeD8yUpqMc#CHP-k8u~DWka?
z4>O<{#6?Eyb;EdF<^R(zoiYp%--NYj(FLod#utDwCd1Z)&WTY<;=naU>*P3Hx3Brn
zSD~R=9OJ$h*U5N=j!j@2A)bGrk(+xJA^ed;bNXiLFPT^H!h_rkR@Nq;R~_n@Whc8U
zP_PYrB}TFm>oLL|?I%mU=Q_kUimEI#Zf1j{<5WOgUng_J&`I(7abM;2qemv`mLNe9
z(EDDK>Yd&|o{U14#&s584RWKaX}qaE<e>I>o;N^0)S@lH$PzP{T4O?#kk)Hv&ombv
zZ3i_k76k*$7TfH;)j3hK{s%ybg6vD1c4{Z))Vv2fB>)%%0P@c025Vl(h>*~FIqjLU
zV-gKhjaMTBfQ&4(_|_D2Wz&d4nqVg=jIwhFk*FEsM6_Ur6}nZK$?U80_3y)kHNoh{
zl{|INyapU!$u^KW;j*gAN+o$^Ro2Vz4mKK2+He5((62P6foaZ&N&|QNP++m?4;!(`
zHTy+1fHB+7Pd+J!$;jI(BLnt=Gx3XXVf?G&iE51~_l9qau$<=o4`MO0m#a-W2y2XF
zf)fgitXUd1D>qGEG%4^!MctmnCw)y*%e#7>bVNLG?&Zzd-tD@K`&ugD)GIC&TT7}~
zh3jarTHlR_o2bC+FH6T)ZnAT{<`#8a*dcy9DAp9uMJ0$ZV_03Wb^4kyv6MNub39Mb
z_MCVlB&(dk?nifaqWKZW0T)+VBRyJ2?`Pu^AX>W|T5bw2Snv8viZ3=&o{DxSUcg$l
zW-Y1}pUgr&XF<(Vs|3RNn}Z{c*1k*$v@y#*c3gRW9Ug_^j^F4F$`BA*-l{2l5&VGB
zYJhO#v@?Ql8rHHr!UuS&Us5{_FKI}4VySlr2+sC^mXHoKauhkq81T#KyWj&HO1!U0
zQGhiFEkd-M1pMLZcU(tT&5erW=DfQFM?iMvd9E*P*rG2&eNP8>i9b7`POT$4kky}}
zJ}aUf-}b+K4=6)QcHa4r<igo=k}ImmGoZh2RIyKdSZGABB~4Za{+~6$!p3j=2+nCl
zxyDc4!NU>)0$nRzfaG=SB=1CBx-3E<fVTnX?iq9Ph}Scl!Ekr)h@}BA;le3PXkL9-
z;+>B$YuSh002I<c*Zs*jFv4a(;^dAJwU{0sSdw?&*;sH)D%;%-hWW}7ksuMR7unaR
ztB$pC`&O1Pt5u3RU@qn!Gd^mZC-*8_CzrLZVkXLUh+4LWq3w%pHc5S<<Jc7Iyzj^u
ztxZZT=R);O6(xrciR~GH$vb0S;nfw%u)K+i(sa)$2nneLJMP!3346S^)M-P$^7XV!
z-l*+_vVOr!S4kbL!L{gpQWh{RhI<cq0ZXx!G1<|_$a{XRyJLSXJ0IXVzG|q0Lx~$z
zVZN!@RHrsPB7lyg_hWP~tP9iQ#Sh_(gk2zL!0ofy9gdWkA9=1>Z&uZtoj7dSpkTBn
z1H>eRH7{>v(A4gzVGsa@x7h7H#dx+J`<MmQqTz{#iTmnh_-)xuQ=8a;8P%Uh#ISse
z`E&s^{v9RUWnGsdphp$)lDY+&w~_#w(bAWz@$H@i@0ysED<&r_CL3@~t(CL-P4}Av
z_sH><dku`bep~?0axZ__ik0KSn-wp*6BvXb`;73^GVgS9+NaMEnUOCp=NRCHd3i7s
zmls@0<BBf`B}SQs4Ku4F7Fz-eUJgurYy~ejI4DQ=!z0{@a*abr-6w*gU>KFURh8UL
zTr=uAI@hYt)mq23FgvBTYW)af;L`~fZS$v-$NCp-`9muXXLgS&9~Z)Rwtg@ImWBas
zy?n^3v7}J5YjjP}|E+c%ZpCH=fy2L+{bEhCI?2}X=5mQVS-R%{lKq55VrfEWQp!^{
z{(g0O;d!~JM!0PPruJO{sQD~!<iO~LV0^jdit1(eIMMF9CS685KgnbcVijU2(Re;(
zS&7l<R(6|>s`(ZYaW<W)+ZVE?wLTt~fQ8#musQ3@`WcbR(X)ez_2&|i?hgbk&Y_ms
zXg#*c7(O+RAs+k+txl-fFHJNo-|0Mgl^F?I&Y_mFxOhw^0^myZuz6i*?nZ7jpkC4v
zwFjtbdpoKDe}<E>zttm=NHFnW3SqSp8I!c-jPpg)>#AC}YVvu<T|>lE1w%93V@2Zl
zs$m#VDY=a9=Czl4hXH{N!YK=o6W^A|j4@y<2c|sdpC?m~rNfOvqj=QTs#}-=_lrTq
zfvZHvqdtB;ft(uGw&Xd!WpQ>ZMM~S6kcf-3=kk0Fd+|Mk5F=~(slX>!)oK<9`Gsf2
zvQrIft}moA*j!O}r!g8WC*=f3fd15!0Yp9SH-PSS;P1#AiruROoP1R*cx}L^eVQ}c
z9>=xNs;l<{3d&F1MDXypESP{86wh&Z-QkK)W&RWkHCB>+NCvVz$1(yIV)CR#^MP(j
zL$8PrU9<T4+%C(sXhs^!27FUbqZHryZmj(})gP|eAMQELeCEfp-64>em}ONw|K31E
zRGytb5UB3<Jloj{G#P}k0jKR9ocy-o>6tTld+vj&tnfS$QE+^E4h07cMQvJ7Hb3c)
zuZi|&o$bdZ&(%EDPI3*0exhx~-nT4fZ8@{fyHd@u>Ytq&u^Myt;~=DMg^I^yscw2~
z8ef|+>RioFI@z;)^n-pe0t{yQ#}uQ^LjxHbfu{&rd|2n<VExq@4FhGiZ!Or4)!wPN
z=G6w;xUTzeTJxO^4c3b_&rYaXdMW7T%ZH0Faf_Y?_lPhH*XGmn5hgNV<l~X(%}CQl
zhWA|pA>qCC8eg=w6M?@<0G##bdh7zy8(w+thrZg<J=y!2Aj?N1XKV5a0Zp%qnI%o0
z=jl%*Ob<C(zxy{`FD&mZeQ%xw00K8BZ4M*zoCv7mpV+74qcgeeEFT++l7KwV3vNvP
zs(7n|0ApB53Z~5;<z&9`U_m2eBMyBXB8jYTE%(Jb%yUa25hFnwS7U5B)vluh4S8%3
z@YlnUpk+7nlZ$@89aubf5yETP=^D7=h87~`q<DZ|&e#N4=B^iel_@Jxa2r~@^ZV(d
zLA*{i{d``3hWAZ47%!Jw&=ane1K_T{XLScP$2j#3E7FGX1B)@JncKf<+ElsFOv#ca
zxtkKz$$C5#gi30Z0R~x3C!HFff~$>X%INh`^(htbd!H8}x5~~2gQ>cOQM<PIBqBO5
z+n~hWiz;j-#t63brrzfYRI<HAMTmL)`0`Uj$>Uivw4g+@7gjUUq<8dr*$yVz^*r-C
z1!1PLv%zCD;wA`<f$0z(e5b>*g2nyO@2EHEjtM$RB3ij6X@3#=+-Xs}%?h$kOEbnX
zRJBF7V~-0P{vkyI=hommJu9uxRIX*{iigbgw!Bd{&BnTUzr1Er4cQIo&N^5mLr=Xd
zo3uwno$nh%<I$A4MvyuR&W{0j-<mDUmO3K(xIUYiTIwmoI=URs0kND7Dc(YufyO%L
z7qjuQ*bAfgmdF-7ykh?H1d<Ks#R7ECih_uN1Lw=5dm!-X&<;y0j>~KFA*+h+Yc#kD
zQo=^%I;#8ig$X#?X<OJNvLkewSYHELRfUL-G3^@9!u^^&!IN!R10#yx9FOuX>l(In
zb82mg{rKn@rruX3;-XLA$MSjlx}6xa2n#a5!cv?JG%@UyCptXprx}-MdR1xU#O3RZ
zVXh4P@~e!nl8`MUJTOyRu%ZbK*7E=q))Mg18dt6gqB=A0PX<s$(9shae>r<@gKAK%
z>qr3&E4ZthoBrWh$@<LT?&cXv4+?)&_m;R)Lj+kq2!N}^5Yx8S0g80UewtwQuzVQb
zA)F2pfi;CI*j;whnkHdG_Z4R3>x5#51{O5qXkU6CLjTlEZKe=UjsPczz@P%XrIV^;
z<3N$qgp9Ge=;$`cWTE7`lXy)U&np(=7Xg(v!Is6jH~e*}_|iKlIw=(}B)jpIDgE4M
zcioX@$%`?8F;23(8mQP^g_9}ErRbQrnC>^o7`j)3Op!<aH^ViJIruzr#0bRiygw^W
z^=M;6J=6`-sGyw{0Q)>u9{;7~DOC>n8n(ih4R{HIw69LI@}bZeS7yyovX1WZE*?a7
zLYi?9h0FX-1!>zfD=A^}#-`)GdzW3GD5}nCd^X)!Gt5QxWZx;gS@)j8X4!W&U3Rqh
zh@$sk|BNjU*ItS=h3pX?=U7+$n7|zE_le>ot!h!AoPpC2*YtJRdUt+RXfx*LI23QL
zA3^X5T#CY;S$)*&NBBq>h10jax=u$A)1hHe+53v3GG!m*vb;inH}>BiWL!99qG@Ut
zu1-M!Sh28n_qgtxiyrtFXOVYcN#v2XiC;on7saQPkU3N`(X^D2kb0@V*RCEC`*(~^
z85XXPT`G0UOp}QJDD$SLf^tkzP1D4PZ{n%?s=@mi+@{S`mF8jz0lH^__dO<iVZ6l`
z6w=5fuFvh+DXVUR?L}i8u{oPcsMdER0F{1#c{&|-+#Tnij?F8YX~fj7KI*q%&Y)?J
zh1Uv4d)ARw<i`Cd|47SDwcuTqQ#U_(A86%SWm-=t$0))zLmgoirlX#`Px=y7l;Az^
zVX_Zq5H$c!nUn)p@JS0Ygh0c!phL2!a{AhSEQ0o3hJX(*@d%QWozQgF{9_O!#{+&R
zlrakj@BEClZgl~r?(Ns~I$@IF0%>X))Fls-D;qAfzODU5i?f7a%xfop&C>F^xxM9V
z!4G&!um)O-_6iZqzglh`nsSd3sE&f^Li+2;IdZsz3evr5bwL-~@ypvCACjlmb+rZ+
zE4r6sX*iQ~2#|>o+7c1^lhY`+5*{TY)Ab|cr~yv2ax=}%xP2=>2>4p4lP@-{FV*c^
z+T2>WA4laKF@R!A_fyHNu96y99HR;Lc#Y!F<s_vaj=u3c%8{S>StqnW2#lUz>)`(G
z5jb|Ti=D8hzB;kLG{2@bX+#IZ><q7cql>l<##dAM_=OGM@T{<FZ1nWE-W6}tOJF4_
z<#)`L@)go^VFKWW4VSqmX4IL+w5SkR%@fQeG<W6UjH*7e6U&_>(w9=Su(8q!X+-?e
zDpn*VkEa7DFQvyi&P`^*y&$j%WT_#Wg=WNXvKy4=Pt`G%xP1RPnjl5Kuy2~g-;az5
z^pW;fF*2J_P-94Pg+Z^D!!4i3CQX!PI1i(R<zqJjRMw5C4&JX{C6Y6F3arKt_lyPr
z1S*rl##2w2^cBmje}W)sXpfppRQv2Wa-<kx<_cdG#kDCREt{rBRGd=&`yfRo5SR_e
z(&1$%zvz>YRnyWeF7sS*(m^PONI7~73)vkGYQpmAdGYMwYA1w}h?u#XEqjj+__}R9
zklgZ%F4^m3xYrKFN6g;gS1~7*-O?0f(%+I^TT$)?4#O+%bY-J=nN={=zQ^I$lqTfB
zCCyl#buh1P5ymuui$7LWzfa;~*9%&y`r`3ccwP6awpA1p35VQ#yU%2jBHxRmHXcA+
z{1hYK?2`GuMLDJhngO0(#nKt8d$4Y(+T(FBD)VaWB#!npRx(=VaNyWBmkNnVp*YyO
z?S6z|t!Hh6jnVHZhH-rMIV_$Pj1zj&pwKKfcYoj9H83fOP9*_Jqf$;X+M<uFyd04R
zBwhtwK)7sq?1;&9z&`+3qN_5InbyU*c+9qpY;>$J2R|SY;oN66%BDxmzJydJm^KJ2
z(;p%;pyDTLla9T*Z^KbXE#axv&^(xE9Nbi7;7&80w!yohl~&PIrYu-UpYrU<r;DmD
z3W1U=fkMH_!_Lbkl86x-7n3fVc^U;_S+7xv8l@m$N4Ra2lZ+QnU#aG|&{bCaYpxkH
z=rcupWqPL8<97x^^Ip0#vh;K&qA8tdtoTZ`tRXpHbNRDF@Dx-GET!htqsnC-aXoYA
zItE&EY~@@8y{;wXJdgg%Q=h}ysKwlXUGfz2R<n)r#+b3M%mt4!ZXN5c9RpmSDu+XT
zx{SL`?1(b-KRHVST)aPyh?LIJ*z%P3m=N?nMH&H$h0iOnP+-c|?~UD(fH-^Kf`-s)
zphEiw%IguRs{K_(Tz{=Bwu(f&T)reEU=RC(f3!So7%;mpm<mpUF`mU?w|%W1!u+Wo
zT4&%<4evs?_Ct9ssLoK0CGXuY=QKJTnFdj?;Z<8#!{yU?&S;Tp&hAQdx;|35`Ofi%
z={=bDHo~PkYGKYSezu#?OLf?!b)_AL-LRj-u78l|5_Am86yCWcuJe|`yEd)vVi7&Q
zSH`>^pV4y6ShQ(Z!9P7mPd?z2Bw(BW();kDdc#v^7MLi_MTPP0kgD52d)=~zaDe3|
zqW|{PHXPJy>0(sRCEbvCyz9I+uURHX_w~jFg4bczQZ6PMk*faid91(WgGzDQL#XEU
z%a7uf{;kUc*VOWz!-NZuX+1guDC?f^h7F$v^NA&=O|lX_5O-!5Rz2AC8)$p*KLuBy
zYzp@GQtC%0C<QOLHeV=!BWCND6Y}vN{_%`AoI}4b&TX9a<5pE0GCNqI=3f=t*V65u
z3H~bQek*tZuX@ng{O`qdM*4pg)Bp3KECyy4`hWg)vq;s#DnkV6d0D5eW}Vgi+P!UD
zslGaPpOR%iS^i2efJol}8t*OiMni2&Gi1ZBu5FayBF#_`)5T%q2=#EJATB&#ryT0D
zo^iFUN0SY0Nt_@(tTlg9BGltj!Lrcm;-J;iO19qZ2q`VA-mQvajNUD8kdZTMm?~y=
zpCG1X1mpGe*r7ya@ON^xljcrg^c=l!o-IEk4xjOhoD>+6zj@o<T(D9_Dm_{$8j4jH
zviWk!G?QMamJm4J-|1*>x}X_X;MT4ar5uD1PmD0@NFQ51+w(56%e+@W)k~$4Gy9Sy
zq^iGX4V7>k+Cwia{!@HoAdKqpF@-C&^0;$4`JGT1ge}D{86k`BzvRb~?0P?=rT=u`
zD88`z5C(TR9MoG_TmWsuHfEaKjnh;^PAXJ)2fO@jH72Yqd(Nnjh)($};arsPc_~dK
zcY$whBP^}}Ql#})cWz<Ecl=xXs~Upi;lBv?+1}jhuOm}vjr&9O2YF%kH`M$d?Sy-0
zQ*tgKfh}4gsv*WMrh7P?ZeJ~x!N~fpzx(2AE3*HWi+6(F2LTPH(%qq9n8l75p+ZTm
zXWu7j+c0a+zx$(+CVY8k(s?lT>yy}ODF$wh4CU{0g#PLgSQLF9Y;QQhkJsSX-jy=%
z1Kp;Od?HVvRd7PVg~9cDd@{PuoDyO`gb4%-pMt6I2*_>?5UKCYkS88=QR@_CaFQEH
z>GTjUA$GWs>Fn-s%9Pt?%z!LY5Hubehbu4{dBqW~+{SvfvnWK`?;b@PsAHbC0zUX=
zZdNf8Fll|U<YL{mmQ|-|Fh4}IB@iQR*mRn}V4%8-5fL!&EQ$@OZQOXshriXTbZ*@J
z{)5rnp6n?>f`B5wURL%3U92w5u~&j#aH_lmw|)d;-lf4ho#hhKKX)_vjpp`UBM<o5
zbxp*y%TY9kEEx1z57y_&&Xoqj_4{sf^cBlOBp>}=h<V|MtyZX>bcncXf8U=g(?t%S
zc1FE(9XGx%x#@W?2;k_hPSc@lRO4H$PHUm#Vy_yt6ZUte(42#Ik1Y#w8(b;;1TTuJ
zioJ|Je+g0T+^X&k4de{Eds8r}*HdpTSSSFBHBQdq>b3o%Vu2P}xp%Bz_%%u*5_y_o
z(_$=lc7q|^4TmNG9_j3cAG(uHX|eN3O;gz2<a~`?Meb`seWAKrVorJ`(QIdFj>UUp
zewps?v_&%pQ0UnucD<g@cm;StbJ@diq$Y0*REkEgYg?^OVD|bF)O2?gh9G=5af=f^
zqQV@`1drt88~td)S*ACyK1(|`NVaC3_xfUWA3UVon)z-fT47)#JYsUoMkjc*$@08V
zZZtvHk<2x?{Yfr2gU6_>?a=k5GDP=QYQyN%o6AfCZ}68Xn!hPEn<1iDHAfu>TpJRy
z?J4~9ea|{q8V>751Oi*<l4M)-FQ>bC9Jt9jrnrFi$%pE|P^)rP88%kRHpqs;A;Ws}
z7>t5%m*moV+c>{BTRDRC9CH_?%0gC@!#*qHeybdU{IYU?56U{}FfSZdlE9EB^6heV
z%dbUVf`Fn*hQ&cDszi90IHg!Nf;*6`O%5GVaL7$}ItK+ZO*m3df<uz6HSckuuPl9C
zBIaeD9f8c0?aU1AQb}~SXViE5x<v@d9^>zr#Ck(4L)9yVb2fCOrxl7;fpFtRNPyVf
zT+`v*mEEGqZ$B)_Qeb-f`$6l3pVXu($13=MsPC;Cq)Akn+7L#|JoD&*u08$B)b!pV
z^E%QzoAlgoK76AA{zGl5`Oy>OEmrZI48&bnoiO<l@n>;@n!D@lc!p=gXs2FsPxZo`
ziySByOHcJt0#;D@&;D;7tg)<67fMhri<GsNpIKMBZUv~Wnr1~VLdh$Wt-T-|UDB$M
z&yanmw;NoKHn@v;A(b!673sg;)!$aD49f65A}TXdWc(X{&-4#}|G)TYl==T2wP9iW
z|M#d3Gu{9Cs0}MU`#%SxqeWNADG+AsS!|Gao)7u9&AM-VOo#pt<^dQHicg2E4%&=k
zB;J9<DScfeZw%7E^%;o`A}3INoFs2t^-Zfw2dhwLv4Tquu#u&W`0z=3r!{Xn>LLO^
zweHd5MYhx5_;js?V25ob5A|;+Y_yWFAVigSeCyq!!E-Z0Irnojr2pRj)_akeV#?O!
zV=4i6P`eT%DJoFH-CuW#i_IPu(b9anXgtb=%Ok+meR2>^Q%63vg?1G3ITsb*R!_yj
za*Lj!Z{H_8fRt*qjZ)@Y+K8YG5=zb?o`iAuTay#T`}-wuPp+kG5@X)9gYGwN!SQlt
zOz^tS>E;PN%#RFN3}qSDDw}i|2g0is8N$3bHJR5p*05sd@0kp1=w|8*?*(%d?N=2i
zK~qk_=3F>xA)GQ|xgYlb*8Z&QA3cwXU@UEiT(|;PE9Y4P2a_xB$#$?w->Fo@&^!Sx
z$t6cv3LE(io&6+%i27+!g8KZoT9|g>W?J{rpPVo!oyCbJ<;ecDSL*+E3EU9HyJ`^O
zasDnwa{O6KwaJ-ghI9YM&*hs!SNZ)l8waA@&Xh&Yum1hPJij~5gY^<UKlC$CLYM*^
z)5-BNCfQh}m(dZ;s?xU(DG{=N&Hrc8|AOL)RG7|ksR<X5MIcp**`7{NeW8oT^yg#x
z_5B_=!D6g|g_TXz@nn^nWC3xiKxgiep1~?wm!cC1Iwj*2`ZU^nd=Bfa0Kp%1)1ava
z;|DFanA3@<|4}53;r$tHhz@6F?pg-0`{Vo4J1M-r^_BNNSJf>We?vv7m?%@Sk&|~>
zb*z5mi4U;!mk+--$IpVDC{X&a0?NJ*=ik~NE&E&VeyQX*c5k|V7YC5rjnynpJ@-3c
zVe5yD@d`$1v<1&!$|5f!jpZ=pYm)t8cpD8|I+>16)=(p6tCn~598K+a)B&b(<THN@
z;oC3Y-(aiP)=&)mA0=-P2D;8qSn>h5R(E##sArW<Td;iTHP`SB6_`P%m^RqGfNfu3
zzgpo7+4A1QTtxF9gqvn@$O}EzMFaUaB@V+BLn2zrJlhUcOl`sc6Sh|iax&z6DB-&<
zIOvCVu;@=4*vzpsnL{ekxYJg8!*2QK25tj9AmbX;@U2=3f7-}Y+e1boc}l~Kff)NE
z#i?jM^`n)c@(xq?XyfJ4{JpFBvP~$`=-QkM!V?R~`Epj%Scl-fm^!?@HT7T2)H4^e
zNeA_LNv$Gi9*fI6C-QQ5*$Y`CTbmU-1IEvpN{FLP5y_*CLn=jeN%$*d(p)i%jnl`p
zKh{)F`Y}2j1~|L3&0;vN%#R(JNga(Xx1ACl6vBm}66ly%TY0E;DkN5FH~T0*7uCBv
zuvGS{B)`jNhzgwy-J2{zvz*V=F~V~dZ#jI<kZXXhRBurnQjC&zADyn8r67Hxl~NGY
zR<P@)oep`DxANeVC|O=jQ*<Y2`ePw34+f%cBYUxGJ^Bs<nw0)6iE^x(&L>w>l3iQD
zW*n$1`o{UXN>&E7j<MAyku>`T=?(^vbeDWc?wxY`h|}>32p30ESDZ=J6>tNE2KS(T
zgPDFrYI#=?v+Q&_t-}-t(yWW{(ox~h-Of6DP8{3s4aA%@ZV{d0JUtFFErq)d8cGY@
zB^w6i$ERv(7tta-D%^ZOcb5*6DG!-A*sZYKRiDroll>?dFn-F7-`{h~+W8*ku09+r
zv3Hg-P-WnUU1fg=&&4-it?ePW_eUbmPs`RT&r^0zn?@clDVF)=-6kDX-x^X|wi?a@
zjZ`pT-4{r>q}VnqB<eg2J@+?$d$f?cC-B`aRu0UUedM#7YEKyU2Y%%cOcZ}quPHPz
z&o-LN(9lY{P;j&5X>rdIW>Z?<vga;W%PC$E{&Pf0X!`k$5k%9!IcLQ9Pw%Vz59f>+
zUnA=O>YUNvgDET{5KR9+I%o7xVe|j#oDnNK<9~Ux=4i=DRbdAo+FItVPZN>rKm@W7
z@-JjjwPWyUr6Tppsr)Y*DiS3Xu;1Wv9f0FT^=Lw@sunP-oze8dsL&nj_~vW)TXj)z
zrcg5PVchsUPkmY=BO7-%6IWcNmv2b0ad2G0zyhElrGRftF#b+3u#fs+V91cKJuo5o
zzxPJ*sTp`^V+UARSw$Wo?42>_wB33>g4+ng$J42)seN_#q7D1_&OQq4x81*m|G0;2
zcisIXmC3T%y!9~!n4RGZQcO|k$kmEt{pm8(AtHdROF5)EFljXB_Bm(`hlw?Xy0FNq
zRU3SlQ8oD){+&1G#_t`=VJ)m!9FKMN^jzw})Sd~Iu4Zt4G(oK5^uuhi_gpy~y!nNA
z6&!Y2`q~al5{LL;iVTY9<<qu|a)@I63$(T25eoS0J$4LJt<n&$aA+LY^)`(^z=sh+
zcu0@gAC?%#D)u=K$pC7iHHT{Z;CLVwrQRpE9N`hNM|&bR!2UjPic*(?C!Fpw-2$n$
zeJb?7utz6A8(vIg$qqYi5t)BitW+pNLGG@ZRX|zO+hZI21K*L5U!_2QSM%*cn=FmT
zch^<X?Xz~quu_GfP;DA#O<~KF$2w|tRJ*!24M@Ka6dCOOB$(9DnR<cOG?K4-rLY|6
zUOXU_9O(LfzV(HFor=>py4rTY%|zfjt`rVDv;3#?hFg-9(jZs%zWH-xS{mWjh?0y_
z@#YJYu!0zR0}?;wr)>-O^lc>P4L;_Tz1`g}cXnEv1^`mUsdXx6L@G0<td+Pmqjp22
z*;ue~R0h0W3>!1fqj5K3lNf(7m$fGr7-yUIa(cd$tZEcTGB0~k$%4W(zOTJeCe9u=
z3Mw131zA)ZwxZ_ZFSey~i=T}csRz9vKUN`6P;?D6L|WPt@HjCY=ie^&uE0eKZRtI8
zyF$XJz4?WzwZ^0)DCgKA<gYb>o2ob!vJ`PAC}62X<0lL8#ZFWV!dcynBR=1sc<V&6
zB|gE`YKfg|MqBex7Z7Ze+;-_qXcZUxKzt*Q^G+RTEIkKP0LCjI4PC8~82a4!LMlwN
z_6RNI+LacV?xQ|A30cRZqw2|Son^i)BltrFI&S^rsxyKkD;SZVxI4V!#&!d=vpO*-
z*4ZxyuSK57M5Pl9iD6vIuj}0<b6Dz$UTzxFw_EYn=E8C^%;`4(rl_oH`AgiH)wL$3
zpkr5Gxc5m7Q(mA%018!~F#rs4BFfh-rMcg!r=0ABQ^V~A%8~CCmk2g2G(?hM_uN(#
z_f;V3Ark6TOJ}(hn11KJC0*RdfIw-#AJi;A49aInx5?fO2s}9}&-b&Vg2{HtifH&&
z*r~TjMdBXw=3|#Je0uDxN^lA000Y$7go9~ZduAxOfs@y1qpyAQuw0fe<wqKJD9j&r
zsr(9z8#TVM?=hab+Ne$;-N-Jl#`lK;%g3ZdC>s|?kVljne&N{IUY_Wp!@<5P4S)rP
zfP`^26nL3NvU^}gC!WGtvx-?fKv)s%YZ0K)`5_($3b3XcnjOW(R;p;JK>}~RrYW<}
zs%YoXEn+2|Z_^!@+azund{Den0`}%R-Z?03&0nUd=5gF`cT9w8H3tz!f$5)+Uwzvl
z5+5&~BBHc4-`*dqX07iJ%JBItTHLKo@uXL%wSWoX6)H5i5M%0kV``zVl`plZPLmF(
z=iV+x@q;u*<c+K7yBPSYgmuH-R)CKLm<)Ia7y%A>tS<7|M|WDJKjQW**Fi<X)9fof
zhCNy|2v-~N)@at3V2>Hs$w7PVO_`pCziD3vjHUHF8XL=Vn!~*<OaNs%1y_QX-wr8x
z+IW5oN#hz(CHZCifH2B~+e{14-{0i=)=IvaS%Y)DRXXXpZqDY{xv7lX@cS%Q0rAv9
zfid8}fB*cRZEM6X(^D1Oz3;Oql~YUxijCZo<og7`e?@?N$0;rKne=gk*VX8<q%iMh
z#nA<%IUIxyjPiHNnwDl6?W;iM)MFm?QC|n5W@0G+mI9PZQ9cS(gl+MVIs#o9ADqXR
zs^fOKA1Pk=NBg5916|~Q_auF8N~ewfCXz{F9Gme6XuULE!mUVuyWR#3)N*^Z9t%yY
z#NB};4tV{n!%HRoojRKHGVbsP(|4drVSoT3c-fyrhHf0Q)C{8?k_u-BE5*_)c4?ao
z%72Xw#;))Zl<H?*`L6pfubDiIpc<h4ne1rbV|GJ)CiUhGi~PgPSrQD_Fsc%_Xa)bx
zcdMK6HhwLS7s;FqjFSk8i?J}1O`dwnq^@S=xT+7bU0^KlymgtdhIZT~QWfdiE;qaa
zz+sDIC~}KmT%Y|D?)_Hd4)-~Mg$7{$7FgKpNu8NQT!>3idPE9=48XjVwb*%@lKqvJ
zXNKAGH272X&nn#Fgn46Z4_k7g39UT&+Br!A_=dW^uEgt~y!*b2?I$asG7(NCKJ;N_
zO`_oFyn3%kCA1n(ucU!koZaHa`NiYDt;4mTf8clB??e$vrxex5h$!a`-~H$A{XjTw
z)?~`X7{`qf@1Ii{7Jl=4zw5y8NPffn4{2^1pmB{zNvJsvf0wZHXu6%LFlC0bF9|TA
z)EnhPA;8g!j-y*be^5^ud>Nq9Jk^-St7Nqw3YG8S6q0XJO!NWRU<Vz4H~W<?1cjE9
zGPp^--mx(Gdp)=M+f3xMXMzYT4TAHKm&4uNxaer#>n9`xgpih$2DF4I8P+p>f&mlA
z;4)Z}FZuyUCqKg-la4L!$afjW#sWb|hK2BD<B!*>*FS3)zv-z35R6DFAk#pO+hb<H
z5GOS!JT_xJdE#2>+|FF+;f%Ar*fhN_koCUYUHxJ<Nym(l?(uYaht3n*V^ktT0fcei
zy9@Ix#Zg8t%D3bb3Sy1}Q63Atdm3krMwi=zqnmE<X+E?XUHrypRa02H{aTT~8m>mt
zqYlqyJ+(az9GKxg>U%L#o*!Oq9Eo0yX`$0#w;T;tf{O&X7omc5j{4&UNGzbqflTE0
z68E5MHudHUSC#I`-g>07=M(+(wG9Xli&Lq@RfZ(2pm%}inT;M6$X|}KKMtt#jvKu>
znL_H{bEWMqi&vr<9{yDW1W{zjWVjsHEN#9|RX$M17yfZdRU8i*KckfZI0EUIuE+TW
zc?GfRsV57vHVFPserR{DLN@=aoA)55K&wWYQEEUZO@nPl;m$Vpnu;|I<AUpBL*!z`
zkGTtl2KdR^_m+!e7^QZcaImovlUnd1;&$)|RSyBPBKaUl4mcFqY8|XjMU0HelDC|R
z`6a;vcr8blS1l=NY5)O>L|js?**9Tvx}F=)L3%ihtjW}!fQ^d6<O(5mszSRXUZe~d
z*m-c<(0o>`US3(2CirVo{{r#`XWNw{g$EutIW8Bw?)pq_K>ZaN_!$UrEHD&8s$iTK
zCM+|z7vZpscg*v@8-W93KnCQP%Xh->W#=x~;e%)V!$4>x|6r8*BZZP=!JaDvLv)la
z)5a^)<6JD9wucl-{AA}5s>g*YyfW>heo<?AHNqb}AWwHVb4rB58Xq>%Pk#=3RMG-3
zkcHqB=TC0-Gd+V_#BejjcdDAC;-=A7{W*fT`$)NsXQpm|N0RqxK;L5v#4xGsDi&qE
z+&0itBry|QHYLCP8^qs;JW-Z4pCH&XHijHRCG2uQE;?efl<a)bGijExW&&8H=iyhc
ztAWqK$QQ@nSkO8c+^BKEbC24~(<S}!By#B+D&m6OXYeeguq1B3LJLh0wBi_4)<&sg
z3A*QDPk7G)Q6H0RE<ljO!(N#An3<9{PqTVR3P~Y;swN&z)UFP0mWxy4^qwicF(~S9
zJMkH67f0Q9b9<j2^9&BB#?i_+I)GP4aLjp)W$CccX+L|%6=3f2_WpGEE8PK%D`2IB
z1QhFge|CtVIL0^{nvN+2c9NY&-e3~ZF>DeVMrqb#*{7Kpr=kP|!dLUz0u^nU=Qq<b
zc>1r3m`dLP$tidfy$=fD7#*TG%#A-d6njga{pc6t8j)UhzNP^38ltzkD0X#*YL&as
zLG#%<G8jCk@18akpLr_b0mFg-;<I|u+ne-kkL~5c){a;L?vl?BU_@_uzbsUd9N=JM
zxFnGM>~AD`Q>FrhX(WbI@cCkzyd27!(Fr1tX8-T?kKTf@#z!NRp7J4n5JK?wokRo@
z9IO?0D(9@uh|V4I8Wb*}W~ZmiSTiTCzBzcDo;X|Z%pJmJ-*lvcS^>}f(N)q=Sa4fn
zf`^#&$GcPdtMX}eY!Y>Jnx9Sb*z3l|nQ2}kkV5d4bf5%-EE-0I_rtclZfnEyKHRBa
z+Gez4n3kjf`4PSKVtn&i(v}g|JP!-^(>);rowh3Www26v<|yM5`fwN6RizR;umP}4
z+O*!0n+lNHLnbSpng$}0A_G`d3{%FT!_rV~!MZJL618MM?WciwUQB^N%C}wu`>>4R
zAMhMyoZOVP_$(#16K*x((}X&~WX%Jjl}(X6BO=#|gA;kfy(ffk#z=R`n+m`zn?p+Y
zT3zMWOFc2o*v7S;OrosMhwUPh;A0RDiU##EK(bwp96Cs}qu{jJ*ka!wNA9vkX&296
zWgd8Hu8>|`nk8Y5WDMyqzP^R8WtQ`Dru`V1@b<u&`N%>Y{bqZG$XJ-!RF;>~6+!Yu
z5!+bqVj@K9b24WfiU~X}ah_5|95?!uX46CN8w(TRL}@f5ROLcU5L3T)&D0l5Krka*
z&3n+}cnl{M<6w7uMe=VYs@hy5^cc|H43EtUnr?IHPfw!(^N^Si-n8VZ#;KR?Y%C*~
zjN+2=Y&Ru4U8AmyO5d(M=FxgQiGn!)X6i6lg|~nppYsEwD4dQTjr7Ou;1V&2a79z1
zZ0l+0Q(fAEFnlo;_!d0NW?xkN?8@)GS^4eK>EN=iz_{W?IhU8^4`3v{VZ}&%+P`9j
zttGDm7hNBQQ8f-IZ>Hr$2JCx6QM}^5QrJ049@PL&Y`jP56C21U2YqtVVDNKIo<7(v
zg|Xj-y{4;4Vd%)xw|y6RN9SC=l9DuR^|wm!V4z7<!*;UEIi`%4pahqeq8yd3E#>Ly
z3^gPMFhy+hym*w(m(IDbPTK{9QSmc?o+Cr@Q;@uOy~14hRH3B`-yNm36N8J!o7YYm
z1#Eo9Ia;s<_RhLmwMFTcu3%*(hA2c6fqiXmryf)6`>LPu2Y?N8J;Vm-;5RCs!57F9
z>4TAja!OFRMvic-f<AEEG!l)!X166~gs)IWjN1qdGVu0nTa<6`>NpKfd{N-zUNvk$
zQem%#PBS*F5=hw7oW9M|8UMt=mK}b@)nSOlv?wY6<JLW7vY9W1$);xx2W@1G8laC7
zD|g@|b+2+cHCE?&(n;`snlETyM}Lk~>^u0IQ8c7C49jD>zlBFm!i@b%cgO<7J01^<
z9Drp5ta4zE=2*z#v`ikac@!H0($(i=d08!g!l+ze4J?z=#;pL4ocW$?o~|gP8a+hz
zzoG+W0FdGq$m4O=Ly=&~Dns~rVc3nc^;>vYmNzxkwr$^ils6!;`h43>p15=Ib)xJk
zw}jjN4`0f^qw^?u-yTF>+@#Px^PDVyNuhPlFbV&WEv(p!^98H#XWX`A+hWs4G*plp
zmFPlf>5LW%$zp=?<hhKhkS{&~vlE?{;;vO;N^NV#(UYo>@Opln=OKw+9_uejTvi2O
zk2}Q<Nu53(k9^TBzeOO^tP6~&2Dwqk3J0mvvU7~4lR0;$Q=TLRIBO*6dU{2f0-IWU
z;k=xPLT~DW)w=~J0+k{>vw{-Ru<P)`P?Tbl?@`&37k5j3e%(65%dk?zH=#(>XH^ON
z`klI~8<8%zj&$}jf~lX~_Dz$pJDr%7b?_4}&aRn+F$E}Kl;HO<cEx*X$AR=IgEkt_
zRJ?jZp3_ekb8ZT(9|)R~m*gOJ8Q*!Ip5%<wtQH3hsin&QBBzAA+L+tV`wbeEgZLes
zjbi*)`s8|IGPFAsuh)0?mz&aWuI7Z4tTRKgt$9?7N>aez?y+O;Mtb#blXBt4B?MM2
zp4kdgS{=7X#xVpV9@;;Y$dLihx2|z1iLmYoi0fregBRJm(hTkE2SB3HsD^4(B6E+|
zcVO=m&-RWlbY6D$H~Q6?;Q&`F9@@PR1IN9Mz#3F1zm7%w1tmI7Y91=b75o|8f9SEh
z#}=qeL;(M`0vYROQK=GAJ*K!_`6MIh>`VFf_YZS>C18_5b5_NvbQ){?ZQe@FN!=YG
zdN3B)p-1#6g}fx5|7?Ul@(Td}c9M|V7sr&VV*A@ay81jlqur!oWW`x^?op-O!-ONa
z!K9yx8Jp<sZ+A&oO8k_ga+k`XER8TXA}Cizt7f+*SkMAYG5wYjVp0(JZZ8?rk2qkR
zU#yA%9R<n}8@`}456Akrb67y{eJ>Tau7`ICp3B_jx`4En5eUibg)J+xz>X7$Xq71W
zm|mfu-sx-Vyk=Nb>$WI9p!)oz`upyO5i@IH7{Fu1r-+v)lHK5ebntN_pGwQGFTOXS
zyCm4nN>)lD7G}KRM|(}QK7>>SI{zQu-U2GlC)*dr-3hM2-CaX)cemi~F2OyxyK8WF
zcL)$9xI4k!$^H1xIdA6PGjq<o^<=GHbbn3Nu3dZowv_hDOoWotnDC3c90m60r93BQ
z2~0mur8u+Dr)!_rAFX#L{lhC$-b8^P6S>ylKe4+x*#d#4IAgT>z}6$Q<W=zki=_B#
z4Gd^c`{;Ph8~e-?FwTXmtHAMM@OIf9E5KTFhB0}bSF}8%>O8I1_>P#X2_&-*kY^e4
zx*Jz!ak({ydsHh1uFGqu=xtw*sa}na0n6}9HrD)6-9aC^d_*Te^K!LGZf8~b%gDNM
zoh=BBfE?s7cb#W!c)yJS&oarivX7PTbR>9X?I0Z%h^{|p^uMqO5G~Sre9Vc=0CFpN
z9|5;#A(Li+1K)F4ko5#u^{orKl)wW!V6x^}1g`6{HuKE#$H`2&!uCoh$c-yL_5dY=
zm~aJKH!p=2K@J`x9pd#)niVzRA^lhY-_c%jFX&U`42*s+qHwN5Qol0Sq#zs<$?1f7
zsqs7T2>>W?LV*zK3SFCM1(e$_uOs~-2^e~~KYc%U|HJqVlYPST7h5@@M_^wf>g>)=
z1llLFZJIIL^xYb#^qqpSW#$al1@iq)8w@Fjsqd7<!{Qj=KOZ|P8Z%|)gyXN8;B5;#
z2tOYgexLsBJHTI_ok~cgir^=r5NA}4H_VH@x%zX_Xgj)og8-esyvphk+2-J9fvXxn
zGe5xDr+}7phXgE7h?Wd|9^Vz-&llovK;*`H)EH|_4Gg)`ydAiC030{4tE3-}(13t_
zST(#qz4pGZ4@k(m2tRWV3BAv3@vCxg-)3g-+E`Xog&$oyFhG4m<Hr6zICa1HGJH$#
zt0&Lc(QTMG0h5jNn2mL8oVD%l4cI@L6fi+`RTq|VUR6!O`RC`J<}+imU|DjTx_=mG
ztT`}sH0`w3g*VT;;~A{(zMOZh#PJ>hiH}kd3^6y@xMS@KN;>GY*nl}SZDc(l_V`H+
z=>!LYGFS~So22joY4V9w_jChXQBePp9>~#=GgH{;WJqA8Clt}RUP3GuB^~_rjwY}T
z@-J_sBqlCuyHFC1M{(DBUE?@Vd^569{cD~xHv$x73<ynaG1=`nZmQn4yXV1@`By-C
z-Rb{*P8=cG5<C_kH=+)Yf+uqK2x{0k2AB&>YadnDOLk<5Z-gU%FJPNJuT`dhNy&qA
zCewY?E@WSP=nvEdk@#SO<aMgiE?loOvXbDM4U8f^pZ*BHd%%l4sGAuHq}{3g48m6X
z6<!^Cmh)^fzVc|gUv~n&eM&YUHwg&_KD-t8_Pkl-ihWaeCQ)?@x&kv=!is8Wr2A`>
zI_t?S?>08OgjYH=>9p}=HZAY_p&08u<q|fPHLH}J>xx#Itsmf#0zs4eYqC&YW7DAU
z?~{e9SywX4Um2ht!HDVqmRdm}pJ!=0Z_w)4Z?0BCEf3?r+h<keC@~nHOlM_diWq!1
zZ!&>fBbzb)RBAbKE8xK-2!s(I!9gcK?wmMEWj~3)xxz&J)}LY92LXoxX@d+Prr^Or
zs+&WI9@Wc8%^{|Pzdg{jpe=Y8yLq|2tK4xjFzQ$nS0&{z4a#XzQ~vXw4k&0&Mm8+C
zu<lg7=T&t~-0gI8F$Z5tLujBts0ylx(V6q6vX5SKR2O1aJkujH#+E&S;GtkqlVl2b
ztcd!pDICtPq_gt+-7#UHAT+>aq1DhLLE0%Wm7L&~rHxDOtt7gKA_dgBvFG?D0eAD@
zoqOb4cjhi$2zxG2{5#H}4GTnXt0rwcqzm_Rt3XkYf-WJj{4lq3RQ`6A#tMj(m;lJ)
zfFbw$as)7N0j9tHdIDyxK9!&@sGl?Fb%bg9fcnJ-9l4J(<v&{d>>jkeVyPAFkO3AX
z`~(2I1Iqe2lhm-GfxM%zZ0_-Tu%V+#R$fwc3Nxd5rXgG{<zlcwH=T3&?$W|N?Z&Qw
z*ShxfQur4pPJ99*|Di5Z`z3q2zFmp4G~OTaRmShCJ=3%C^qk9sdK9nKiumFcBF>3C
zdu!{evxUm8_!H3l6L>{`9!?V7M8=Wb;RH|G&j-?Fqa^-7e_laLY!VI)(fgRicx^+@
zgxJZ78=a{pyL>~N<wByWeN<fHwUV4mJc>Ghgm=37IV7Y&#XF~EjoI+9`c(T&^l6GS
z{5GlSYx(R2i?&T-6YB?hPN69u>va--fR6()auUO^1@m<^w^Duc<L}P4ub=zJ3m;Q0
z+C?1%{nb##wvbMDotP^<PZ|iyoU^l0W;Gk`SE=~VBjj3HB+UBn+-D(mdC)T_V+<_a
z$h+3yK@5ZJ?!o>;?Ekq$1SsJ@mWU1`-rzxu+7kZB78druZ(;e@TNb(4IR0^yNTbH6
z?Fti0=RGaV4GLZj4iZ)zT`ZXHOg=w*u$>}O1RZ0TwTEKc%U5c}__C2;dQE>Ug@FA>
zhy4kJVe@YwsMla*Wgh4ss(uc`w!QVCpj3oIu>t3FYQTbrmgw`QjXebyk%%ZZrcWVI
zf0R#6RX2{Wy}%^Lrb?;xx0Thzf_@sV;0q>CVlTffriBz`qS6BIFQKN2qqkDKbnM)K
zFxW+IQv#)6#_9->l5+x`#MQ<|91ghZHFDobiwqLW9A+GCfKJk|R|F4cbuHBprbI5N
z;w=bbTeUvRDi#tljPYO>_I1w{3Vy81$qaSUMFZtZmIA90@4!E|!Q^6oXb9`0Y?r}`
z)BTZrWD!kXNXY~P|418_GAljX-^)uX%&sBMt@dqC;Iu+?Q;h>@{rOv?L^wtgxYVRu
z4V!3K*s3ME+j`otrNqO__JPJk^7TPh8E-J=a3bblI8ODE0F|ZXz>gIr=ZZUn*y8EQ
zDH^4!;m|k?m-{|Msb+Ghbk5Yl(bcIz&5Ns0$a>WpuLxJ&C1x?3Z7xBV*~E(J9Lbt(
z+TqHzvN5PiBWb)Z(9hplU9*~w$UXLXeoE>$v<{sFr&q~AN5T5n7g?#%WTEP^g{&7M
z4Ulme*fgG{+ia8FLa&Tt$aT(p`}LxJmV8+#UE%yDtMzmFsb9|%tEOUEq1oe=>AAU_
zt~&maK_lurrdmb7?HofNF;dPBaa6?dgtMy;D%4q;8dB%Lnm^Fp;gj2ACig=2-o9Pt
zk@E9HlvMRXYGehQQmGW79PYX3sgI%f?T1rC-9e!TOEmMz(k=*YS=Zg(48oRfYx;ai
zY`dz*?mVWq?q{>aYi_P4?B@*ykUAKj-A-gQ5?t<a%J9v!SjzTs7KCRtkBGCgQnpL4
z{Ydq=4)$m2S=a+TNek_nzYj+U{SfLDFhaU^RtC8gdQD_;`nkyg?#Z2RMp`heP3dRV
zq)*n3T$$(j`HRp=+P-kA0Q4m<p6{&RHMAssl->Q2V^X8k^#_!y38P$gbJ))`gN178
zDJM-~lF{L4xz~a&kyK2m@uagg4I`m)CT$&)Z@aB;O<*_NLT;i3)!Zu|gn21&YH3Y4
z`^WTKy{?fPH;@qjiAnz7y1<Ex>;JD^Wk5^+?yfQx*8gKy*_xMv$^p*sW#~7o-zLtq
zl_)kaM)~xcB6%7aVjSoNa!?r!w*gVnLB$beiHZ5S%t?7D=Ck1x%VBb8y{06J3Py)Y
zc!P)(q+-P8hCG&%mFR;b6W9TUEzWMEXm<*CqGI}~ThA~3lS{+mciyHO{ALH7j3ggK
z760>fo4?Ej*<5Go&3t%F1K$q<4hN1vg}{`{)Z^C&JOujKFdaZvr?A=xqGy{|ML$;4
z=~7y7qoto3h1>Oiit%4a3l-q+Iy8!dj%Kf7Vg!yqH6{hmcN6IwdeowT^Fz%ln~vj-
z+Vf@CJ$8xi@74XX<mZz-iDCL_$YeK(UT3^4HM}#CGJ_UcJj%LIk;c0BRw#jRgZ(4N
zh7~5JUQnllCEwx~N`{b;L&(IJTb<`#lX0@d>tZYV^j7Nax*E(|fB*Y5Xh!@$d(<nT
zLhG6JB*k@QN`zW&d}pa936S8j3&qHGH=gYLjn031V`;emD2Hp(9<7kmt&}iDTPdGS
zGxdOgBmB!hP*wh{JM2a~yQO>{Ujj!8OTR9m=14n3LU|(au;_u3qTe4Z#iYLRS24E}
z$K3mK1j^x5SIfWDg$+Z1XF+AL=;`M?<R8G3d3ny}?NR_8a~^YV#CyoP`6KnhfVi%f
z_S;=O_Hw2ha}b)|iO)ce?6`C^mvm>3jY`vL@y@jLYSEq_=e-k?kqgN8(Oo{&5@73i
z8<x*97q_KhHa&h`Z2y{hpjUyQR-5UU@yW=iycDZRRy6vK;DAzwBGvotpQm-b9P8z*
zdjTy1v&u{trGzWizF^?=@BelZb=vP@CPz2+Hedq!8i~XB=bOJMvL3%BYfaE1a{N2X
z+&=sDm`7|(#vNju98ug)Bg5_2PW5tKLH@;7$_tSTG0jb$w%(#(PO?OF6xZYYxKP+%
z{6PQJpWYsJ`w928b`xXiC%irZJsv~^oNBM-$)}XBf8e|%j@TXqTO-h~Ck=CFbB_h|
zwz!2VPj-s_`aEAmezNnc#A;f?YRGmN9r${pC-xFo=V4yVfQWHop|}dCdT_-Ce;VUt
zXYhYu1K{tb*581!vFMnLUi@?YSGr3Z>_cXz-4mH20~?dP+w<WR4L!O6KxiLV>P9+r
znzSlIDY*#^atC{2>!tD2DW-JlO(zqd6Yw8NSoE4VfN!<g2TXrzWE!w)@Tc5^T1|iY
zi=2sa5nscZ`zrG|1w2%Gl`Nt!Bdcj;p<POibxt2wm29gxV2%Hca<N;W08qpd<Tb)i
z@i$2sU(LalZ?LDi-~b9Hz^SIB92R@E&O4PD$?!LnrQ7?OJCRGO?m)a(dQYyu^61la
zYJt#z>ulU1kbYgJB3F)D=DkZ=>(fpyIN@+M>b~X^7#+D#w!{&5p_O*$7&bzHCAQn$
z6nHO0R4I!0J;#L)m7odjZ%u6k4T!yZ`@3i7T!y>He8*|H{(|}5qb!{2Jl_l@tlRKZ
zR$nrf3isHB-XKhbmj9_^HBf-U{Cu;L<2CLsXDq*rxI=z`Sc$2NNs$8=uG*Fh^(XQ=
zID7|QNDhya%UT>0P|N}-#*R>pxQ4|R_qF56p1v-}&T1QSo+Jw@Br?TLS9&uJ1t{ia
z^E>_WNz~G5{Hb|y_}l+vKK{0V`FK(}T|}vh{wFA@b94~|?4i9REZ7gz%P&eV?fO2I
z2rYc=*dLrcX^6psYTn2l7tj-WHdA$79{!e@_10*vGre=po&AM~(FWjVq0~+3I{0e`
zn|?-{X~%peH!aJt^xfq)A>x0h@18GycrB(K#uL?AK3=&y#85m4fQ1m9AlwVO$8aGV
z=Jxl*mP<78^Tm=(24WvF0pd}T(o0Z>8}vW{CjgpN|E-weKRE00Gww3oeR)qmvg~N|
zPhn?y&YPBQ``OLupq6pz456;J#rf}&ZZ-KffO_si|10v^=@ws@e=<Y=hNU-8@nc!v
zU0{HJQ&mQV$;y+7y5MW0=c1_M+(h#D0`}UkRz|IKjN{GnxRgNPl8c^J$lHkGM1A#f
zt#28MJ8qFSYj**bYF;UZi$AwNu6rDxkFh5)wtkGLItiO1Z|hsLx}oVrDNFzL?aJ{H
zw?I3PX|M4*GK8S_>Y(k%?zAL8E2O)HXzxH>Apd0Ae6ZrK3i2$}1HlUW#@4$pd$j^x
zTSd1P(j#Ia6@`PvF~v}g(Xy>wJ2ZnC<r(w3WJ1+vK0Jb=$iH)awL_DAeA*?)aj|(_
z_wv4W;%=erP%DoGJ0&5TsacEv*wiZfuMYYCUTLyD_U}znB)Kh6g_>5E_SLV(R>Sw!
z1L`N`V(+VRY*kMs$;4g;_ilI5Qvc1<bLx8FkfIeN)!TOM`(Cq$WL$MWWHs+Z*v0j_
zSuMXEV(qxjU%1WOQ<-tM&Gn0&zV^GWc+>}lJy*3XO9wWq7Q1esJzga24-ChAH(SeY
z98nv?C>*w$<`irz7lMS&R-bZoE(;xf8Y(2LAUf`D=oD}=G`=UmkxmbdUgdqBuNGZD
z>l(73XPROR>CAdUZtQhxOdBo!6+zKW^SIVP<wjJ<?IIuU_^yi7b9Hs^x5tvA$5u^B
zeKb*Z&7|Rw<564vf(i3xLp8gxwFT@+xK-NqYT9(y^kMHNpN*>XtQ`FkbC!)2S6h#y
z3`IfiD}(Y1{^HbM>y0C?HKW{m+3iZ;?~dQKex}(C<Iz)PfTI*arJgDx|8K8SAH_f5
zp7BpI|0K=(_ikQedCvy_ogI<?S0ecT;*Q9FO!@xD9g*xDtp7MjJY9qJR#xqTzHqX0
ztmm+hZc90%Z)i}+4X!&U`Bo~wA+FQ2w{;d-a7fj0z<1mFgX_t`855gGK;)A#x`;@e
z=qFby`XYE`a|>x{&bITVv#*?$uBpZJ7LtxLH}~v1!tr+;2Rc6d2V||jVki(`AfNEQ
zQGkF1p@M+)!GNHMfq=k}f_%aT1tE?E0Wp98@nZt>^Zz9EN_la435$#@E)UYu>2xr8
zdliTLWI#rtXlY6F1lx1Uj1Xvn3(~>^(0Q*2&;cs`MF*hTL(mrn996&se?kB;5LqJ4
z$?|w{>flEM9<&Q%o(weuxdc8pgAzxYM<8EWbELwTXH46GoRu_B6|X%A#p4OC&+ma#
z5V&aywycD%MW704Ib`TAqZ}RkXM<zYkrps08ot6d>us0a$MoN7M|OuMnhY=j1QDv@
z6!nL>I0agaR!&pyGdgOGyQ)aqRYGmSu|-Hp)f=8Ut8vx$5;=0=7<zK?aBjCThk3K~
z32|{Pj~5^)pfK@m%x%i7=bo)F4*5WWrUeTMHv$tU8D-?;n)oBh0xYnV<8`%Sn82`g
z4CYQRN=nFnUj^XKPG$>Qf5~qNH-oRMX8#d!D}Dglf__&_nxfZNbd7lo&AS_V8%^wZ
zDq;ewrvQ$vLt2ncQoK@8+co_bH&93RMa58bI@IL$klU}9g~d_PKm23J%S?LllNu=x
zG9WV6NG?61(L`Okei=mIkv(!i7EIaCgcQ)BssOXdcl;8nNru5XRFJ-~{VCdyprpf&
zqDu=*EMT9or?bmMQ>%7W=bN!*4h4Y<6HM_L8Nw2f$x(pH68pn6=4;?tBaVOi0x0nG
zHVPL>&ZnB}2w}Gd%8DxCT;j#ib$jMP7(n<j<<pwW1YJ5BsFJRSA_9*zT#^Qs2xc@a
z%v|p+A(A?<rjL$O_2O~>jF4<>iR?40syc)IRrN_(TH<GF?F?9j0XT7F>D1g%$l>+(
zV?0{Cm7m{0Y&X@uaowpzmhSwB)wQj)!X}r5Fv3?()Kn7WMLOt*vfp5;6X>^Qw3zWw
zKW`*F#S_~$Wy{DhfF;dD#_L%tvG~lZZR;!svL=Bi!}n71y~+>~ujjSI!qwqeG|Ka-
zi;T-dMaWdlA?r2~1q8|k`<5E@NqM05SjMJ8|BTQ{+eS#$sjwRQ3Bc#vD}H54FxgBu
zNwjYNNS!rX1ieJNRw$tH!^Mnf{U?6#-w&Um<#Gdt!ZI%F0-Qt}4<$gF5exJpxYkn@
z=GCA09gbOxur6^pL^TtAFbp6z=V#ecXtx*FWav4}>}+*~jWQGqHh*S;*z(FcxqRGF
z9a>gUav(JO3h@bB5wQSvn<A|<Q7woG5UJuWp7e+8dR0gZ4UYH6*Go+nXK%EO44=m2
z14w5|K+M@cKlpzAXi+}iy&iN1P?$!KC$Sx>OJP<92P$A>k}<_o*YW*L=<#J~J&MY^
zk7c6lhjM^=*V2}QsEll<Z=Zfs@Oyp$ki{H1<kaB<q3qm*GA;%HBH}Z>-PcUwPVZt6
zR`z#5EE8|z@JQ?07uVqO!vPTB<&wg&m>i|9v@;j(wR$gMknn5hH<MDd6CPmQrEobU
zwiJ>KhD$I2^(2OL_lJ$DNnvbI1cf{5Z3*krlfy-)%J65n48HxDX?XI+N~n4z3ZVtC
zmeG-swy+)9?vxAo1CyWKlXGlC$3os7p3DGlS8dbh0rd<_n{}TFL>5P6gwy@UxA$dA
z3!YJvc$nedhGEO+t~-0|Fr<D$gko7qx;8kNOL<`hzud^S5{C77e5y)|C(PCC0m<4I
zSZL+Mk9bneNz=G6Fg-p!E!!1y49;pC*)a^a0`1X9TGMU28%eUmp*P4GB)j3sChG9T
zg-!*7v`Ay$Hij9=OYY7kSp$gQW8EM<clW_)!~F84RvuLxZ=8oG(`3n9N@+bDp4<h|
zMvQ)p2drg+&1N$^wU}8Y1h%N8dXxPx*($OQPRKAEyAzsPzKb5Xp}x8X^7|_K-*iue
zAH}BEqNJ$4v>1W}^{!}cWm8)@<Eu+BK>!Ee!z{oI_D7s*tRa~EB-Spt%R-DVP<JGT
zybh$AvS^Q5Dld@Xr<Xvmaky8QeRj<$<b}0<_5$sU8IYMge7Gaw!3H#>j7v02GE!2L
zcK8L*r7Tom*1k~*NhIJY<_NaX3l{58iow1j0D3<{5!70pG@L4k0YwpG!+-^S^xl*K
zB0=8>29!AZ5<Yov=x3l-L%+l%Hf8P7p()6MSq@3`+PsysCF<~5=GIO+#30?>i;w^;
zKO&QYxY#RTAvRh|N@f$Nks_XvMBKuTdN0&k2o%m7J-|(NOBw^U*O)v9Tjwstm_JmI
z%_4l;f{-V`5Ed=81T~Oz59i^zjR|GB#^GO{ohT-xVLthSBSbL$Rz6$o4l{D233uZl
z;!uu%n-ipwG1a3nnBD`iB^2wNYdUl7EW2Lc-8Bvs=ns5JJs@kBkiKorUrv-DXv6Pe
zU#bi%xJ@0oQ(8c3oXVs#2wEvRK-L)$7Y2d)f_+;MW^|y-za}l3jG{>LKsTSck)yn2
zd3+4=Wfo8{hQo=$YSTvMP0gEviL|VqiE?B&R3{SCMX|-%7PL=h5T}e8>)qa$VkkhM
z2Xum&RH1b9#wX&~fZ<TFpu^&HeaWY0Z*Tct(S^~81eBpXLuzfcAjx?BYRc_N0H{lT
zSEjEF%gfk1MKeSX8o)$9wZxB1oW7-o{NGoenOpBhHyzeKH}u;<h5<?((DwI`3DQi<
zG77*i0R*LrF|W-zd|5h6;r=pjRoHtPS(k)GYlLV(c`Bk)A01E8Vw`~V00=ah0Rs$~
zlR0e)Djt<c=RWV=DyxiICQ5k;zI{?(_#SZUhvAt*iR&@uv#!Ou{WzikjT;FrG`9nh
zoulos-zPIr_@Dp}o?>gmY1)`l%H49qf5kJ!L$Ym*HW?>Nb+NM1A3MY#_K<gFRMOTA
ztjD;pKjH_Vx1(PaXVg4Nx;>Yf^zorVs(4v`D~m4qB<2*+UHXK;+|$aP4agvV`6M%I
zT|s5_)k~akn#TI8lQ4<o;Q;jB@V#jg`;p=!Zck&01=3z7NpvStG~va{>kHFt5kJ;8
zRt)@L9te{}|A>VszSjre=8IR_@fPKrELkyZ(3U5*i7aZqv_;;1xM9aCwf$tyZ3Phf
zqd3K!<ox!ITvH=<t@I%}MPnO%D%mw}*Tv&++YW3XE!!id@)#ywH1~(UNs7_W!_3$O
zPi4C2P1$zLLF}jF;!ZJ4!Y52jb?G8B<8RD=_%FCT^?l{WaHD|Q@&mDNjhp_${PfX(
zFI(lcVedp0AMssqeBNm-yk~~dh5JbuD(pLcK(;lVaFg0MS0mwR+CNId2K62h!Yqs>
z=2+7URwgkxch(kxik8?RWc;|l(s3%f17g0v`(@mEkxiBHM(Bkcx`87iz=m&1Lzu$v
zr#J}9zEkmg6yctNz5{O00@hBWn6KjvBDoeQ^x+YSQn8*WnUE_{SxIL$;O?CP)-9Z<
zTFboMo8=uya|8e>{EGGA4EvBV;2Bdol~&BT{O^tcH;^1X8_8yzz+!CO9gg-LixFr<
zWHdPoeD_tnieXci@A!!;8woNKYs>;}Vrn2owDWrh8QYnIP_bs-Qv3cP25SuGComvs
zRP<!tNtR{D-zmTDo}v-<0^u621b$JbI}@UAd_il2D^|dic}vsL@|Kp+OHcs2llWt-
z2@XCL$Xwq@8-r0ZW&SA|+q_vGK`hbj%mb?tVR}l_R%L|fhFKCLK1LWgq~8_WL^4ez
zc&?ANpp1_{d7spTo3DU-+VB`E$eP8ntP8KuF>nb$5bi60rE4UkAv)c}rmYx^YoC$m
zG(ogLQjqVY1p-I2__7bd)z#JCnkE`jaFSo;W9$TnHeq@O`%CW)6vfdHH)p{;kWAtU
zGwj}Ki%8B_WI8IKKDnfuj(aMIuWB&5uW-dUStzHlTIBqhG`e4TCfOf^9w~IEgz%dc
z7wi1sa6(?<sfCSO$_DzQW%wW!$%#EhVI0P9dE`(nf~#$6RJ(+Z(&Bg{;p^>O?}*nB
zlInO7mpMo#4@*E!nahv4l=zzxBdr-h{)lV*cc&xek?tPSaZqbAA)iv7eq)XdXp=lM
z;K$p!g77-u+aa{zZvMMAZ^&MFVewx##!lOnu34miPZI*_)H)rAU)h+(rPWvQz8^ZX
zo>K!dkZE)QQG6nLBUjz-rx@J>>Ay;P?srW{nGm^qKR%x`avaP4t)1mhrb7{zL#(i}
zlme^1u>T3bZM^5M;bPfgr`q`UoKw6n4w49gIk8f~r_=EOG9gVo0T_Nzg{Mjl33R9|
z%1TXD18DjSm`*F&k)a!wF^xp$<J46aLSF9$0p!37A~5TD1ibF=+2C;9;=Y^AA|%;c
z_5m5MB|Pgq;V`s0s4a$E*fh}=?Om8QL90#mOON>K_x5{;AJ}JjrzJw;AYY6{gxD{=
ze?9ENG#!#3&F^WmM!K6I_6U&{_+UAl+=(>UK6+FUJ=WsM*5|}}yR|sqh(mk(czMww
zQG@WtM7HslxSVgzXkHj8G;Z=1hr$K<{rb+Xk3Y%f`N#tl36+_B(gi|Z(2H%3Y^BI$
zz$LeQmuutG>|Vw!4c>~q3h1Iox~sA-g*pE(s}ue{orW+YePg(|AFIujXJ2mx-j1=F
z-K+k@e1IL_Tm-8*7%8n@nj6<BE8)&M#sqF1Oe`4*YuAsrX5J@yegF{=hTmLeYIC4s
z+mz;5C#j`Adx75}=o0JvJx-(yIM1DT5^NJg%+W5RV#hpIXYzR_XWG@T-76gKFMDs5
z%TyaU%x~@os6F&Ckxz;PwenVCXVk?0tzIq_N88%*@;MJL)?dG&ievi`h>0~}IW!bi
z?0iA><!tS8oZi|nV}}yF4z^q~+EM&~x0x&^_S1J(?<47%*2kqnn#E?rVaUu*A|RBN
zD{4C4TT+<+*ue0mml3?V>>W2pdxt0syN7wk0IhrA@BTGtWIJvz3|TM$D<Y@;bW3?k
zBh97jf6RyD__4-*k^w-K3K3yKwpozWYAS!bm`@fCb)=pC>-zvPR%9D^Ln{v2w;IR(
zJoHD^zmYdcNQLGIWyZiW>GpM4%iHl7c@K#6akFWI`XbB=91g%`=4U-|(p<AJ0GN_f
zxwjvv;^{X?yp9{E?oX%6KK%`%*%NeYs3esu6u#GMQ5!b=+L08XfPX{d2=~~<^G!2I
z*^Xj#J$YEk<LeN_1G+0|k9Pgw6E*LYv7OcMnxXg118;T(M0v$+^nN}cKY09Fu{%o5
z-;^;`{83Zy;g{<2!iaFKe~7_7YsKE~vi&|By=jc10ggD*4=OEvwb@@r-vRk~7J7m<
z=jFJ{jzwyk1{ku_Oiab6(T;9MUFsYy#AEO_SA0D^{=rdzg9sjo0(cR>7SR)BL5n8-
zC(Xg<oMO{8-`i0^LAH-7C7=62#KhLfqN7CdE4!yQD^3>8lF3g4jbD^wq`Q4f_KQmX
zbl6x~GUFnmi0Kv--VcXy|JpMMJm!13>!ThC5R8B392~Q9t*JIX_4ZvdE1|_zKtn?j
z<2a-)X@i)}rA2q7HN=w2=4Xh>d=Xqdn8M`$GZ3nOCd1f^0t*FRM$oO^tlXMyIFdy(
zww!jLU^=Q!wlmJWQN3FA=QO8j<JtX*w7Z>^c~v~B2{dSWVs^MqPSH$r*!H^g+F*2a
z^e_Z3xO#CmPO@uvjkDFI+A)I>i@<lMAQZiL{dV<-d67&2ML;jM*jJiui3%E>dlr^}
zj5fKBZ1yhu4DQ?Wu9byf-Sy{->*AC2Ue?nU9<ZS1s@Nh1bL8T-ba*S$a%(s2T!-ba
z-gUd`)$z*ZrbHgTAzP1)+WUgu#-sJu!?KYxc@AG{A|q{U!$=nqONf<Iq|u1JAaz{c
z1W!nRN2=1~_0=7i(Vrs9KsgED_ZMQ81;@%qG(LX}l6h*p8g|p~xKaH&x-PG_cG=yZ
z%#gv(!aM*!0S-fIU*!ie8kiwr(e`_DrFz${jPb#>pVCPHr|qG4i8`UcB^3+24<?h=
z9HIG!Rw@3wz|*u->jw7t{mF~>m{Z$eeTl3#7mY&i76#`hY=2fn*<^v5IF$owxtG)R
z8%E3pB7w`>u!Zg{Ub4q^XiBkAgx-=BVCHDD@LN3HQ|yl?#;sK|BY9trvd4notX9og
znLgyZ+0^J1KM1yB{LzRbd&1{<Oqa(asK+JWVty;<2&DZX&~AEuQIGRo=8lSaZf>P6
zi2vNFiAgI30QBICCK{_aDr+cP;$_-OzH+yYX8UAf{hPa5pV!ssY8d3~5E)sphji?F
zVajQ!x`dqj)EB<8j*${OxyeO=P80T=%ln(_>GDGAp;Smx^aM)Lqd7<X_xY-Wi*E^Q
zZ^hPUc!eY@sjoIF`&ET)QDkmLZ-YIRFQrD_eHH??2j8yqU->SYfNaNqCtL_UmKck%
zUR_3~)+c&*)#juXnoLlD;<cBo=HL%zXENRzKHn0f>s3o?j|35W04$2wroD+sb1$EI
z$4IyVotFnohD-O^*y7Xa$*;;M5{tX_3q~@B$~VdMR7vLP{_Vg9cV;A@l7=S<@96Ni
z6#dSY*oJx7;9)IB0(fdZ{<<^hDsN6|N^-$Rd`uErmHuGAxa_!5X2t1kYosLzK>K7x
zSm<t{>Z=6F#}weyQ`RlsGV#7FMKrVRrwG(!+j~z!Q_7#+gZcV42}t3?eg(%wBETZe
zmR&C61F8{%4vs{Sory?E>#luitQ4Y}su-2Gp_TepJ(j-O7Fy00FZG!r7OwX=62)5=
zb-f}g@sLF2O;JENH2Bf$+3JJ4JUT#S{eu>Tcb!~^^GgzE!t&>@kAvxb6-{;DJ~pO^
zH{&a*oqkje+SNK^#Ojz0)32l})nuP5gx&85P;Eo0^eyeK_Vr6atLFpcomGRD|1JJF
zA8hCR;Sp1aSQcYHy|2wGC4KCAs<0@yF6lBP5~SatNspLZQ1H8IY!d7`dqtJv{!0D@
zC+$}pWTLVm`K`T!ErQJ>^xW&KD<wy4rH_$q241McwnVG5DS_>%yjtkQ(-4P!6k!D=
zM`k>eTe8Ljhf{M83CpT(P#pS<9CNxxxKg1FDnW-%op{ksh!jefj2p$8vHGOTP+}`&
zmzG-{GMULKR`fe8!B{0%iDjL;=^x>c?Sh9KFBQt>9H}XVBzJ{S#O5hu8wD<n4vcI)
zn>3FZp_|Q!b!s>DWnNw>7*0HM+?g7FUCXM<Htxx?@6o$pgoGS>&$<*BMa(WhVO#zh
zGA!znTw9!(<+MX@;mNne{7@-kn@odPF+--9UPz<&{ljuIUkOEK#E43y%*2R_QkoJ?
zg0u--nGCrym9b;dUcSN@Mlvn_u%zaPImc5At>)nGUJ>-#Arc^h$i<0z6*JPuc$KGn
z1|4aKc(U{^6Nrryh`%bM8bgYg8DmT>B+8R5=3_D03)E4+mVQ=9*Dl)UNvoA*&|r|t
zH<KjtdBlN~gpZ3jqSlLyY)MMPp`Mc9Y2qWrpwpK7Lcah@i?Mj8ts%GCq;J<9eA@C2
z-K=igCB$HmmX7qzthFx;Yux2BLgMNgBgDqH&^;!oVZ-VXal;4Rfb5i$<uvA`C8g0D
zy7qGMZu3L6+sojU@7)33+hcdM%`-vw3o6l?-bid?gW&8XR@3W-*)Wkq>L`q$FM;rz
z=4FkMy7lnniXA13oUMmmRlRA@HqqwAEp<_f%F$jK{5jvk{ZqeJ8{hYfq%$$SVh+6x
zYjYb*D+{(J4%{0X3gL|DpQba-=}|VOfi1tjsY4WGr`|+r%KzA&w|~@1iew%Q_a`;A
zcpg-z--2KrZ)WyJw_>Reko-)iU1nnC;Wu!_bSb7svp$HByybgDS`tWNs7n=Td#X-4
zJKiw)*6w8o;`;~mwZFahKGe1Y=itO5{nURZMp5<8-QjK{<q4`hikI^w;i=|8?TU-l
z3mhm&2E`snv<?qiNl9zcZJ^ipRw$642f&C@6d#s`7p#YV@)-pOWOHVg4Fp6zt+;iS
zYO*zl(P8<_(_^8O)b(_C&7~c_gkSvNWcoDcOEYxL+WiQyrHMq&J8RODdEMgK<?0Oo
zRff0QBZbZRijd%*ZS4(~=jjsxFsq?8y=mGiT)vRmWJP&C{TND~<O>t+Q@|qk(NGy$
zVHDoXXL*Rb`$wf&SF}$wKVm5Xznx<r^K21wd&k0*1-13?c?jf57h+)V;C1vo;So}F
z=A}Q)@E<iX>@{!q&&L~;PG~>5%Ms>VHxCaMO=2cJ(mONn#;3iU9R+!|xshs^eqS-z
zQ=W{R4vWY=7fHFv>>iHhCd6PK^Yc7GAELQNAC7_6o6eay^m2zbCa5cAI#GZhuW|dD
zj6HH62CZp!pPcUUD?JWc3}<Z<EP`yJ)tEv6DdAtMA^&w1W7G7FsC<hR<i9RSvHpAa
z7jkoP|KmbWqo$4BiWu6PPj>)}LLykjZI^>&@kr?Ut<tXFYF?#7C<#kIV?%tHLZOV3
z+SR$8tC?h6qUCs9V3&RCYTDQRuVP4N;Ft;zp^(g>z7|Xy5cmEPXrB{A3eWIjxbhU~
zG>Mhs#b{EW2)rJi`a__*6MOA?H|zqA@A<yO!<4z(*F(&dZNf<eWDJ(X$`?l};X1OU
z!KH`zT94Ifnhjcly_mQBjLKeJf1!OikR6TywVA68kWRYlfH`hqF8rebTt<5nSavhU
z<+^aL*+V61F~&za33D)W*{X>L(y!8TITtWUe~Zxpi<*p)T}p9L_QS~+K_ZgjC9UEv
z@u6svV`X(y&1nCaq<!OY;gu(&`f^SOtHter`NXpRLd4`;4t((?+*4OADgkcbOMnQA
ziy>R_vfpmFqxq?m)%OY=W2a}#(E6XT7^S*(Y!y*e<DU=eVY9t|supKd&{^_jV6<!)
z;|21vU$C&BY0>)s<csPfUxx(gwxq2-_><S0AjkLoIQj66rM?+#ng4X@2)(bPOncbf
z=+}6H0+t+OzG@nb5L5{oAx9}GUvIysHV0xX&)5fMt$>PBJl!7zBW$pLl(AR!k8!a|
z9k-%-w`hzzR`*(J^)vGKAfMK^&Ain0p`7KRQ^!BZEI%3>=}s{CDFhd<?@h?N!(hPb
zYsIR+HC|5P=Flqp8SOuEc6SOPkT2rmXyTlt;1sRSCPNAwXSuX<Ooj#iAZ823JzO6I
zPg!lZU)$8iBXC_C!t8JlTwLRo=Vatu(cSrhkfVJ`k0Y=?=h1YLL9%K#n7`WHE5C=9
z(@L#2u{><M8lx*oT`T>&=IvR<osukgH4wULSvsA)>XTcvDUFB73>iUK7w3}Y+FYrm
zQ}=A^gmPrax%RcY;QjnjMe*gwi(2|#e*0GLNeOjQ{ZEh&F0JEOEgigtRjGx{F8Sdq
zv57_gWPGyt#`F8*&wdhl#x>_ivPB@OKgkw*FfVeiYLcLR8;+hn{3%OBMWwbBbtf*9
z9r`@jmmM@khXfIhVLB~eLiZ6yUdav?%8|9`h4rpST&I^p(QI|4L?d3P5*yzLb&Cy(
zTSX@I6SrxmxUX!lQ6K1l-?ndpMu_S>_6>~P-d<Jeit*9uA^4=%Yi|o>W{N1B`}lAs
zR$?9xD3Kc((b~8_d>dNCRy6ery!AIOa>a}1>a{wpJlug&j<F9B6(aRMAk?G`ogUhQ
zh*Vt7;~&$!cilfHPH?^MG}|FF(RU?@OYrM7TW^gC@q|hA#eZxL*3A9_eHxXLm6fbR
zPPTs;i(&cd)SYtZz`x&X>{t=0`EWXB$$VvD!*LYum7Pl86czwp)K^<6Q(qpz$}|n(
zGDpeKMDQ_g%@<Sb{I)P1y9Ar+tLrWTwhp4jzR9J+KC<Z@8WV@VAeID8Op+#Makk4{
z>jxzDfDs8KO?^Sc@bbi(Amk&I9~eVK?h2u>iSi$#HviyP^vZpUycAmh@bVBr6tlgn
zvu`-UD^`7eU3EL8^TQ`dciIT~?b4zVt|+%bT$smhO>sF3E)C7Zq=r1~jskp=Z0?bd
z@jlfTC&)n!Tc>pwe#)cuLi}2Z(Q<X}p$4aw1F`Fe^?}B=;;BD^F6apac_}@}PtoPG
zxwan0o`bKyEM0;GO&H2&)I{7sMKlWP^ZPsf?;EWMPQOhnp9BRN2SpfAs+_;98o(_e
zD>BlaBEmnUKT_VYvLUH^c$7G;LU2g)bG`*FeaiTbH}wQ3G~eZAx9@`#RDO2z0?*<k
z4EB@3CHR|b;2<iwD~w|9Yz+C;r{A^__%}NqcSW?5Hk?tFv>e)9c`F#-3>G?U(@$59
zk9n29GPgdOI{&JGU2jg89f7=Mm-@ozW9!0?A}YTy-@mmp<|*@Q{wMe^Lk|lmmHerL
zwb_*68M7bJ=It#vS7f-yzat$tNlV04a5^9Os)<au;GGx}`VLm3?`cg|tERZ@-%1Tg
zCY*g<V`k*2q8`1kuYG-A`R?rsAD27SctTVxD5Hi4c;X@pMi6cJY4lfqGcIA)seo@D
zS59DJrR$50YOI|zoYCgR$9(WsKAk)SG07jA-W}9Q<y+~YQ`Ds)+TCY9cB?j?fr$)E
zx-!XrQo^S3DOML{zMoiCSZgA)v%YNOigQS<HG1Putb}+)q817_uLvj7gJ)ucDIohA
zVv!g*ymjbRU6zl)Dp9?oK_}SBwAQwqFhq8VuP<E-3_y$!I~T3kn{{735FW$pFan{(
z!8Y{x#9T-XV^-RoOxs*BPo{O(`Ivg6RliP(Kwm>YnZ?N2H9t1%!3wfo)~6DblPqMk
z2MZWQ*ZWr5s}_jsBt18Gf%z@8<jgS=Fy=Mk<GdogUL-!xtZ*hkJ0(YmDGeRgjlDS@
z_=f(7*he7#obULY$XcG4GUUVQanIeY1lDtuiRkeMHn4}*1cYr0{inPpyMWsMskP&+
zju%{*A(rb@6D88yMt}C&;TypTb*`CxQRmciK~|LhXzrce2XRa8{`~mwJ3Y7>H?0KB
z@-dAg`K+-L8YQGT4WD$Zin>j22V>D(cTXsB%MVx@W$#A<mv9U}?-Cr9*eDgMU}J`E
zpdb~U4s0Ojy0>bT8itLN;k+Tet!%s@6R*p_|C3bUAFj6i*XIJ5*|?bhw^SfaHfB`>
zCFJ}Cj>rs|cTU__#X-bdLIM_yK9p+oBOC-5ipYmI!Bd>}W0xA<Z>i?@U)=6x1gpMR
zHIK2rvR{gv#+1;U<`ggZ$~aF_>x=omVA`&~@_c<{`uY|nCu2r=aI__h*OhZ#Cyy*B
zDERfy{qunvBT-jIm(QXmEsMUbKrD3msZN8nTK9{guADMy#+_f}q;=inV!ggzXS)^n
zsJ#4}_w!rxBhT#7sCsv|$843;L<yx}=hsdI!N4su=FEl5V_6xdEt&RjWmt?19rx*z
zT1rMUrax%W*Vd%toVU}D>P8-~TAb7N+Mnqjoj0;Y-5=}bvdix-S@iXX_A;+M*C>|7
z#u2k7j<1NXIg^gFA8&4c&C)K^Q9546N+-WQ()~)dx%q+7;=Q-$qzcKfGr5--J1(x{
zvF46-EHjV-x!j$p$)jmou3kqYl>7}+lZ4nvxbDV*g#ee%TS%#`ze3`R#cz#Z{F*ul
zHPeAQaV{3bR!28uwBI_xtue|oOzKeL6t@CGBqORsy-A@u<ab!-wsXIk<qR0h5Ona-
z*@it2zVD&xn2cb6GKjWyeur6vpHPbZX7Jg6{03FWWyFMEWT0$Cn(Jgx>pA^5m`;(1
zH?=+N>~Q~jh{pPaNm_d=L#y>5NzZqjE0Jys4QlXY0SaGkN8J=w(VCJGEjF{7q+*Du
zyz9ByuIr#jaNOnnX%9EY4Zn(*jpW+fa~DRC&!}r5C34$b@xo7u@Mqq$KUcVygtiD0
z+3hntOI?CLA|vbeeOY2Q`H{j6mX5wmrku+*MDPLj1L3INsQnW+zUXF5<W;;B7?m&;
zd}(6zcAAR>=KjowfK17)mj#rrh=UEXkwoU))&%YnBS|^o28Aw_0*X?UH5HOYDbWH=
z85p6l4`2<&!lC9KVad64FiDb_N4*#-#b$Rn6aAI}7JfE}Z{?Wy`AHwOU~(;Yg9^EW
zT3KMOhw_T#OOwIb?u;6EM7~)oc%IE4%kdT=;E+urELU_*<H9$jBg^|Ej?zFlmdZ}x
z9(9G-prOV}dTVGf&H4R+db<JW<=yK1k`qGOlKaq9J7CThIH$$j=qSE}2`X;>S(vR<
zm+z-eMFDjojI=p>uu6Lz(WpNM47-5{Jim_B-Zp<)tEeA2thVYf32m@1&bJxq(OR&^
zm*?|U#=1Z>$Apg_We^cci^bSuv7fmNJ1IM<Y|vc->)6oSgwc{Rt)hc^z$&q^2bt6*
zu|;+68_X!C`3pYTrWgrX)2!Wgd%!J2Pv&`HJoiB4ks@jS*6LwU2#yAh$4uvs#-o|<
z=WW3S+sWcphTN!f2&wR-*ANx}5~9j?k+itgi_nXecBfPCTO|}lHj&9CRLfZN3{wj=
zryeWU+mb-%3VtK`P+J%&&bXaLbEk{h`sM5`M#~&8Ui8;Q^iX($gdB>cJ6i4SwCn-K
zrQq;R-n4fZ=2xkiivxB@h6xuqHlq^?KM$)0+(#PZv9RmMjvC$S)I&kNi6s+Wj`O0%
zroN37%GIilE><l};>fz{sd<_P$CoyP$z{m(Fea6&3n>!V8z%Lx#tc+i`c&t~_2ZY~
zd!9{N;VyjYvY(T98Qe0?$G<Wz_AsiEF8hp4FRy|v=b$a8sp5+>GNwYjF>qZGd`iU;
zhX(B?4}=9)Tu1{0tXMYd@vbGSx$Yl{Wlc8yeoF`YSFdNr#MyZ$gW^Q+yE6U+0dB{>
z{FtQn>__!6&IT#axtSP&$_WxXG(r0`gvkJREe{mkIJeB@0Ao`o!A>k8p!jUw<W;ur
z94fxYif(2F+LQ1K_Jf{@U?JL}-_yk7SS+IyzVd}WlmPyMr#1n~cj1m$#o!GnuHKA;
zik!Jlj@`h17@nSpv*4OJM<)TIEB|P;#Zmm-4FUfjZUV(0i4M&nyMDL3z6!qS`Y!EG
zFlO)^Kxdsv2XfY*GUf?--yzQjp&0y=aKQ2J924VY`ro2KXR2%rIx_+g4I=PY`XN>x
z9jnZ@dJM=59Y2NjTjrrb$HqSVAxX2Kv~?SjDRlisI78<18fip%lqv{?n24M%vIoEa
znBw8aWGBcK_=VM&EwtdWm!%HJQhj9q;`MkEb-T+MViEPrvM;YU8;*^0ZRsyqZ(LD$
z=IrG!{yVEN2$dUqub<-j&MuE?8&JMjt7>kW>pD}eS-kMoM<X+kXE-r<PXdcHabqcF
z8#oa9u2ECU$JM8J*W9D(vM)#mcXHG)=X9mUf>lbb1L`7rXqO`BlT1FKP%CIWTZwpN
zJeW+meHtLEljxZ4=6A>3?A?-dngbJje~{(0zb~3Un9wwG8T9R`NRcF2{;<uIlH9X$
zu#)+LDls6N_(}H|@49wuVSATM9Mb{OdqL?3O^%(np>$7rOugGu8{|qW3XDnv@m;VQ
zTIy!Bxzi$yYE#p!u;qBDNOhkWjkcwOa2rqX+0?#D6J~j)VxpBN21j*WqjEwoXH(nh
zdg4U;He0H{(h@WUM*t(TV9fCExr3+U>+=H#4kc_NV<j*5r)MLyUYG)`M?WT{V3la~
zW_K`zJo_(jOdv>mA8?c8u8UAyVd5x62tL~?9yX{tAG#xsxWPcmQaQtj%2K()kjqlJ
zz|^8yo&6leRaj>T$}Kh$H!R2oj+I7Pn(pk+4K*5O^hH$(ONd_)NXe<f5lC5f8k{I7
z!6PY$D$RjS5`4BCG&oZ*bjb-qB5yo|krx9`tfi7K)67%F(^-zM*~8H8sLKk_l7Fo)
z>&H;maySlN$;7gjO#GQ-v-qWbpA*j1e!R(ckVCTDU}>H)G@|GzVx?xA!>%T3ylH+p
zMN*BIa`2i7EaqHzv#)fO)9`iH(Ic;~!=`|0p^~ca@{Or8MCcJW1Yd3xfBaWe+TfPP
zzOUysa;*NU!auQqf7q`3uM;9JHsF`-;hdZuO$=<{+?N|ubnRxDK7^b<V#LL2(9jPZ
zEJoWv=;VTeg~KY?DXe_^F5N6?Q(u(T^y-z#Ws^YhZAWP?re*#zVZmq0VRci$&o3Pd
z0&OA#s@~+n0=wh24=h(i8L`)%&|c_6De+i)5K@SGL9Bl?bi&sCCy9V~9FcV|?oNCW
zY|>_We-aTm2N6+-e3+QA2N-Nr@8NjDip5?fLb|0fH0{Kye3+u2vdX$_g>%BGXPJfJ
zM^j+GzvMx~<J0Z;eun;bZ!#M8`F;i-IZ;~eq}LEB`R0oQw6sS}P+$?ihQq-O`0b}r
zTTKu`x;>!MxMEb#Q@zeF7(z|tHBDqf&2t*D!>rriMTV>Oh0x<gKt23lFw231yeWib
z&|`{`Fz>8<MouokjeGxB*lOUf3bDi>%W+(s)S79{r<SHlN-t&?EgiF?ZG%=VdAN~-
zJpRj1w*%k-sT15`T;lm@<S|Yc-$-i`l?X1K;%t8qa3gYZhTG`Kaw@YhC6$|V;hV!K
z&rUhJtB3RnIHy%{aDF>*Ts`#$VacRi0#Qk=FdcpJMj7<m;+wFRxMI^?r;o3D=KfmK
zvE<w+YRpZNW&>B+dSv9<E*U{hfd61VkzLLmJx0~DoXKpL?3&f+4-rQ-?ni{xeC@$2
z9+x`E5KRM4L8f2fz(r`iHX>h>7`A!8pv&5B%RQWK;Q%ROrRLYQ2X8cErQ(#-_@jn{
z?hK|Gy)0OM{nTXtsVYj~L-0uD{_pcjd%q@hcZ`>1itCg<-&v|x${_spi#>Gyg}%ke
zqKS5gpmTxXuGgt~m$hVrq<LWxZ>HV1kE7YcTY92h7${WTa3349*OY$6OG@`4jhe~A
zd)8S6__M@wTAc`vvOSx>xhQ1F*j3<ooO?O#mWXdW*34<dSTvH?*h*EYM3(?R<;?2f
zsS&lh%Yk+4&=i>yJx}7HnTfF<s$AKeZ$h0T5T!6E%g9)~r}lEbjPq;4^CXTI0SSSp
znflecLOAjxXz-umk3+_@Z|K|uTvlz}zCKx>Iz)_6pR~zTt*FJ@i>mmV&oMKMvc~I`
z71~{H)+SUhE2<H-Hwt5ZLA#9ChWk(a(vZqeqzM_dd8t<S1<i~l;{LDZ$j0*bfb>6K
zz0bw^_d}8#|DRsH547}eAChEa{@<?<Uhz=)V1^5I5hJZZrpTbKhBb(|jV6i!Q^hP5
zAO>MlKpvV~L<L4uvZc`Dml;R~;f%ufNg*u4FfvX_Js2B98MJZ@1tkmy?F089Mu<>o
zT2>iM*@$y{Ug%lnp2yqIUbFpI0iOo`*&<n(0OG&D%;Nk$#aS#s3x>AZ!1`PS61{9f
z3qJHB|NY|>gYVtdd|KUxP1koyGsB)KmOp)G1>G0WjKkAYy`yHoJudzrUtn?Y!&N~D
zioe)Jw4csKn#6Q;gh7sI?57m<))9yca=xHzlc|-INc2oG{BgkROr~OwKWZ19Yw!GU
z;G^T}Ph<=<3+8h{`ro|$6W;#DW<iMWW|eCZn^$iL@1|MW`-7LD*D;&bMg7++4-c~)
zf4k@e+zitTr+(jTw?G|4T4;bF(Qu`=pDPe<jXS%nPY(p%r<M0wTa_s$Z~pDeNe`}I
zF^(5Ib?`65;DQgRfLuWTo45ZWv;Pnu5&Mg)1x={O%myc!AlXa*rdf)9k*lQgQaTNF
zcM)T>&-^2>yqb7I7QT59;@g--scA-^v=O_ghIhmIu(ao<MYGp@MJm$VJ~jLZzC^TD
z<%%RHP<QV?>A>~0w5ML#_*G+HO)^Y~RTGB;`GwB+qCsADG&+V_Fy)Zy_o@=&|3k^<
z$iDfneE$`r<=n#Nm!tdEyYjs`{jsIW8C0<SCD4sSFQTjHNkbpZN9_^XuKaDfDtu0}
zIWR}{y&!Fh)MF(NjBWYQsM*{F^1525O>)dJjakQS-Ak|2W|L)<krR*Zc_2}W?j|(O
zLvM^9(F@Ff^Y(wD@;?FkhiYqqo4c|1G5kc)7t+ER0$|OH$Ah<_-#DhN(f+1>Qr~FS
z`XUPUfqVNLSAGQDW{GrKP_(0d(=7bYn8;E!YHQ$&uP0mOVw2n*j9a6V{Z$;r!HWg{
zYsD3%+lA|4kQ9+Q{L_u?YbV@G#nmw;HV2Q3)|30BAhDX<L|^M)`~wE_H~z(ueOCfE
zyvr8k{x}!EhB5YBLX{k{J{=`#NB=L@-a4qQFA5jMy+ul)Xo@=&Xlbz&_u^XIOOfIp
z+}(@2L(vx3;_gsNaDtX#fk1-X{O+AQbKlIH_ue1x{I!#uS?A2o+G~I7TWjs*@NtLG
zxkvK96*dsF(w}@P?~gwHKI%2z<r8_?ig6)Ftlh~+7^h;}`j-9WaPx}FtM-SJ*16p&
z?~=HHprv{JzZ(|F==vjH%U_&-S7_eWz!t6r*^FP#LC;DQ5GA{L=QLIB-BlZ{OyN&9
z2;8%k`P-90-Y=XFMiSy?{<S*LfNb=RxeS}&$V&93yX9+H$^cXr<WB*xt={ad{m4vx
zXMutI!!y?DsZ}+v^JBT0CE;6+Yl5%$KEF08Hv^_j&&`KdiY@1<vw>QBqT+#6Q$wEl
zSyZP!Y@P4=T&@U?*|<0UU1n6Uw<K13y<2GbEYU2{Ws~ID^CUi=-#XY5tNXfh?MeL6
z;dsM8vIZ)m%on_Kx=E^?NVDe0af~rEWAfLYVD$6Kp1n4v?^*W2RAbIvy+zUe<Eoma
z-zY)kxoXS`eDM$NniAQ@nxB%>H&~mV;g4VZu(0a;g;<SPi4Mu{g@Ze`+TC_By{lc9
z^Kw$Zg53M!Di5h+dJJxzme(GDCh|8N2-vs56^-Y;N~7i1gdE7cp}~N_zmb$zBJBAj
zK6kU#q6c8YW|1nK%I-|R#edjK80N$S^w$6EC|IwFPAhEUjtIVY)T;8Qb@qSn{^Z)?
zVfZ=qK0r?=%=W>hQ(p1YB4*TB|1Z~>fF@uuht6bd$9tYq<K&z9rTI=q&uLdCncWG-
z$IOF8afX&0rZWwqH(qmp``B7?e$#xxNwdl?{Ub35yQX+dC8S>Im^ivSo$Dd=(j(LK
z6;_f~m>aog_j4nf;7H!=1<e0s{hk?NW%|9huSK>AJ7(rQ^1PR};Wc7YG>+F4`KQD*
z`F?&TL^0%xiCQo$@>{+~LMgE9U;5Bj*^|$eIOMvD@2gGrmX*4LY`oV=tDoT!RO<}G
zR8_oAXy$9mO+;m>FBd+Kg=N1DRr}2D$Dy74O?>D?jd|?R9*$jr8)g&CgVTwuLcEAp
z8xdO(gUtW<aL-coZj<6eM<!>UM^qd4-TBEGh?4_pku0qsTd6K}7sh1~nFNpue@lqU
zFr0VTit)DgmTbxe%3U)@pT*R$etU9WuMg?aGo(0wU1%uv;5!l&(dUFeh2fGahBt+y
zO$+4s`=d<__zlALHUfON$n5fRwd@7#ds>&)V)u>eNJCq*J0{SCg7V>|M?DR5);oyU
zI*yGkbD;1~;_d+MSzrIH8_mbP(UjE1hbh<)KECad$;Uv`+OBU~D{LesRFsFlsbTkm
zF->}`7I3fDlz0x-ozcloLX5njpP-;-Ilc!pwwI(JKB{FLA&?DN=oGlR?3Fot-P3JQ
zpW9-36ds9x8ssR2K$m{s$4q}A&p25(Wp{qV3;(!ieERFG@7vQ9d?~+xqX=+*{33$)
zHPr@A7qm_%#rw{Cqewq6Q~U~@s5o7(Y2$~;bz;P|qMt4eZ_DH@6n-6td!LZs+Q>2s
zvd^;c+sAx0)R1huS1d3M8!p%t`UsGl@$L)U9Px$Q5s+^P0{XaL%$#;GcTdKz+?)V(
zXS~r?P_pB8PFte%Wh)*1#U}GF(CScfGW}ts1y0b;ORaThzhTTaXcn;<`BZE<zaJGW
z4%c}dC5pK}bpU}%4)|Z6{{0)MwG|6O(VA#jlBk2eNMy@<r9P$QATD2wF5a0+ge|$-
zC*zxf;}8(SoGl26c}{@}*Fuh~1NQ~b1GN6S-Ooqp_y`wmfc~S8z;2fQ7k{v(C(Q3t
zlveMNHZj%BZKq0@|JCDBw!q_p4Tiup5|CtmwfCazWi_{U`UNF@7uHLmyX&iOvY-uE
zmV8T!>^S3&^%=|pF+e}saMORKAM>Y8U45sb%u|DpjmIfmb4^~2A@qO49f9L3K$ym<
z;r|Vw{67fq|Az(@{}Xrqe=5iP-;fhkjw$s2Q-g~C6I}kkm17F<zZ3qyc6I{c=Njo`
zebZU@vp?%zIAYE#ZI-|bW)5Q~7%wgiiTWKiTDdC_7b*h$-k$}xAs@ERzoHjBZi|0C
zJs>GVLP@?zN`hgohKH4cwkm3W=I3#G-Q(Ber<T`YxNZolLFlgB1>8Yaj@&mp;9$4M
zgQusSGyj5Kb@tmfm)+qX`xIN!eepE!6M$gU=feTi(1{lG-%oRqs3G_R!g+T%zJwSx
zY;%Ea569EU1O6NSuUm<;^^uAHP}k`xM0zHEt*iBPt<^9Wl8p&NKU{5bqzU8)w@vw7
zAs>u<P$H+?{+HDM`G92w*6BMsD}k&M`9#*#u@0I}=MUh81tvwZ0y%Ko5(@$i_V(So
z>zkX;qLp1X;Zo+_-mjbjhGJg&Q@iZyejg{)SCf~Q2T3L-ez@9hIZ1AEvB&cJ5hzdq
z_g$S2lGk+=@7WURc67ShP>q}EdN52BzdT0MGszE%{tuyefn9L@^QLSlc|N;O3m<?3
zpB`E_1CW$G=i&MJ`Bha_>0Ejn?hBs|^8I-4C*$95ct749nEGD~C4PLoy<RcpQ=HdQ
z6UW}XUq1`*TDAVZ?teAqAK-S?^l8$H5PMUfabV6C%0m&`eSbM2C@7e$u-A_ZFlw^G
zf2g;wYv1qOeBkxJ7-dQ{@>tS2hyVR$-+P%}zxA}?bCCBQ>^va<5utI5$S`!GqNODY
zm-2UYyK1v`^|=)tN&hbYZ(f-Xv=xlI7265zIQeT|zx326OKA+dNDRDRHvAS||M4<8
z5$t!?-Ma3%CJqN<D!_t?{kLMc>N4~5afaRD>t0u5Q&YPcEN!XRVhHajr2k!OrzfDG
z@^(ed6zspyxt#TIhTMD#=u4j9>-9gF&I2I-j7Vr61==i>f0A%M{8*2A@(;t--}PPa
z?z8Z~;oS>L6Yt;|2PcmpAOU=AMjBSMp*wZQA5XcgmG^tO-Zb!Vmf<z<yRm21>dg7F
z4R}BH@~bvf>w>z)jJNJpWd)5>+xDD1fR3JC=suWE)U(#OGh@cs>AJpazkahqRTu-W
zj^<m}#iZcO+xtx(!xpgb$&bKmy}+RZ-d2R3N&Gly(tS18P)%*d7I;N9_5Q2}2}Qji
zD_QhQF5UYb%HAz-<XV@y$#p-dy10vI;r#IZcOFCC-N@YRY>Ou!27W-aWAIVn<IUQ)
z75}1My!TG@x+d5$lJOzHw12PBf*WCx0o^`VHo%W4-3O0M`2%EV0Uty9;@7~gfP;X7
zQ0avuhac_pwTw{iarLje2EC{yqa$EOwJVr)qdzM+K0yV~QnlWE{4vsVlP|c@>Ql8c
zH$tR~>d!2wmpl?b!1ewo(&WQUnQ3p=gYx$<mVTP?8VRZ-x*R3z;9Hf+MTF5GaKTT!
z$N|*l(VEiOLkt!(^#iM_GFL3L%ZMCMK_J{K!s%HwALmf_9UdJC`=XAV-!faD60UU|
zU7>$()NL>`n3<b@rDSalo_tA73}RN?fuOf9y+@D){_tu-s{24_3lsgHe$~qj*NnUE
z3i-jt?s9JpzLs>+x)o$={bwVpEjkm`#2UX<7PYCdp7B5+LCC_*ioSN@Rv+`tK=;jf
zcsH2+F!e&$6Ceksb1tCOGXd`>mo?v6v2MO5jvXHoWur-R7|3nT)XR4RI9&|{;gvSU
zhXOZtfT}0RNfazAA#VNJPCvhD6e?D`10VBI_PNNUd_N7MmbBUTHyMY!OoK>%d<7Mt
zY>19M@)IV;yJ2(kdJevW_GtlM*Iwe*y8)1yfS-JvHYG^ll?y!K($#a?zHT-ZC76>p
z$i0qX@O$=;13HLK^k$@AUi%aPD$M~3W#=RI$>WOgaj6$<z55MveKrKT1ghozbf&%<
zcZLi((z_N6O8}%Un(jmreOypq<U0eJ$c5oqT2M=`^98ZZNSXuS^_Os|{*v}-;fnU@
zUp;F7&DYO|;!WE}zdVlKt63_qk(WXr;M>FPYU0I-+4H9?5FHH<ekgofsGv=)8vPy!
zlTciCt5C^<h+JjrMSrSj`uNw^dF8^TCsP<+g$es_He8^BYjDI8((9w2a~R(~Vi(vh
z7wt0_H(tA<`g!k8G1fr;ExVWerD_S8({%gHeV`Y|*&Qe?l|pR)9SvbLdhh1@fw>C$
zcC~@rD~o#|eU0Pco0Y7QE~UvcxI9a+TLA(BJmS_fal?3cnI`}_ENN6w&mFV6v#8_a
zA}3Iq<eKQ7b}R0p9q#hB){&0ILA}_=lAJFh6HHJyIk9cN9E+HOpIe85fpO4|DhDR-
zJRJyxzrOjC1b069Hdwx*3k&LjI@XOoZ1^1MJ#&)F7lqzClg||?hw}l>zXt@LX?cMw
z3N=`+{y1O%wt(Zo(RncoUu0nA1SxEi9sot4OwmR@O91%TJ92pPfh{+?@8DS|??D53
z!x)I&t47@8MfVKIMt~Od0e<lYkz^Yeft6TDe3_TTe(P`x>UlWds7fp5`5HK1Oa{9^
zKBVnZ1#F%N+7@+vKrGV0?bv&XeTMZFU<!llNk|n|!4wh+;o-k$CL%5l?s8YlZI_zq
zlyH$17Pwt)=J!|Um@DTBIzCvEOJ^nE`FE{i50Fi$KuGmH^ENPLtDd+w@m&=M$%0mq
zfpdXF=-aZKKrq|VEB^W<RC}mP1$jCd+BTM3KjikDAz-awa_=TL*Za#%Pyn5VcZ`D6
zsinyV92xb7^-c73X>v|vyPD-4NGjzuLBNc6UM%?FU@bwc$Lou<8du-+metqIzC{kg
z9Hjt#kYvNGYxrF(FpU6ZC?|t28AI`QVoTJ-Q7U|n+zSbr1T~pxm|%A}v)Txp!@qga
zk34lej7j%J^jts2S9rYVI+Si#Q?GoOsHQ`xFSnwsp8PoBqpFpwqyMKBQq;^-!_i^*
zR^7#2F<{%=!Wm3aH9YjE-{rG;GqC=TrsBc|GJqwm=U;UEaBGl<mK3BllZ=vkK(XzH
z$2K(uqSBfz51>GbKA4&mBOvj=RZe;@NJR`mhAB+iGjj0d&dJzV+y2W<KlKv#!+OlJ
z($qf$qOaX=X&cLe4SHSEX$M}*gSz_k*If#_$ih6kduF^Dua*PXT(6c+HoIM-<>@K%
z`I!j@PyR7LhFk#+OU4&7rcL`HTAg)IW1bnivy@nC{vhwXsi1Odw806QWj;8%z?rY{
zL9`VP#bje66(2`E?KUttPW}?4?vv~wO%N3XB+Qng%<YiSC7V<p6BwUJSn;tE;kkX_
z(HKiN{1NI8DE?1@QW@?N!?9M{iCIZLO?oRtPmC)QcqbyU{yZSl!d(ieBPt?sw<5MP
za(-V%FP1l09Xx*YgA)c`6wFMrz^lr(|6B}EEX(XhCIW7^Xfj5mi@ltp|Iu1-?g}VM
zz88A)n$M*`jN5DrXd-OaF9Yu96$TZ(!VB6@X_xCGu9S)8XEZ>b)A-F8uHS7z14PHU
zvc|%}u|U7axK8H!%7KCC&0hBq%=n^HVg3ug-azY<4aqZb$_^Y~Hxx+%%eo*z^o4|#
zJ-_c49IpvOy0?Zsoq}loT1r+o*eEIYx5$*fuRA8e0E%v9jkjeXH38!%AL-ZOV+Fv1
zJ)+UkS&ob`A9gz0cH(p0+uw6X6?CAI<qc_fW^?utCZUpIwdqta8$GxApgC((LRCcZ
zmi%X{k+amvy#b3tjal4u%1NPDRKBmi=h)mJ_jjTSS>Tee<b!Sy;_7&8jH~x)Zvj_s
z903~c9s=#j`0se!Xof%2kSx%zqPJ})MpUp!;-L4RwU%`?p=@`ie4dgkj9&_nLImM}
z6@f<%O7o8N8I36n)O|kr;)9}IT`UUoX_`yxm^3C8ZUYss<6o;Ph9U7h+tm`jcHAWb
z581S4M^cdgfIt@ryx>cpR8&ISjEsPr6|)8nG_-mZ2=5;<d$_VY^T|toK6=o#pMXSj
zyuVJctiI%I7+0PEg<xEp70O9NUz{V+{l{6SUeCFmw8);-^S(YGQ|aJFbh$e?$ZzUD
z$$XWq_@jTAI*t6dMEtZLTK`}+5s+!iw*hM@)E|mxtMJZ~%>f^9^{%h4lYL%)3Fkkc
z=V_Z6j5E6FOJ^i#_G&`=5;B?G`{n>BijXr;E@&(*Q)+b?S%`%CKo4KiPz*TbO&Jyw
z*L1la!cp@Kf8&m6;93x&VN!eBq-)aVCSu)dXhI_<CT6^Hn5v$pcZd6QI=$QAYktX6
zW8^6>d|2rW*qs?aAY7a~pUr=)AdV|-hM;B~v}G&i8p53=OIe@m_(k$#uj>;uFu+Xh
z+R0KNE1aJyTS-Gs8Z^NFV?|gUCX%{BuAN4?Dm4CbCb{O*+k};@4&rfF0G6wK{>N8S
zYU_9RKN9IG?O~FPj*&<!dmSgL74x{Rwo-gl51}7wn=qQPjip-^923A!!gaR>D<rv4
z^f_>=mRTN5Xfuv2G#xx<b?t=te!qknTdV{E6zNrKNkd{`9PXT_bDhRT0lu<BrRQwt
zaS$I|doY4=WoWWg-$!jmKC1i@)Jn^w+`hMgJTpBl=<-`5edXU)gE9p+LSO4t>bfp3
zeOUj6A7YLxzqbGLkEKtPI=g!3Z4UEc=sV3{q+WTKN*SnT-r5!>*6oRx4e@1NAyT$t
zR$4y7pY=vYAg%8*Mi<{9%z6^(U}O7NlD`}52SC{a<!oW3E?Yv-0O+Wd0H#~MYk_vl
ze@_~|h?`Ue+DXD<S7D(Zgw6)tBF%05yBF`L6bZXm40hqk7)p~h|G0lg#=j*;bh$n3
zBz9Nn5#3g3QhBw{jJaeHC9)ppAU|lD9L83W!G(cBSC@@0zJgQcV?-c{_uIV4<^u%D
zgJ2U<SHq<DuM2Ga?4x<51-p~)UL|E2=AZ7Fe>{aX)@@&5xZiP{3*=3xyzg$kqc7)e
zX>63T>lxva^=`m#k;u0xn+MY0^dH9v^+pCPgrU2C&O*T>T2=O8vbH^*Pfkc&ds|xP
zf{>I@EjsAcX(T-0zPII0o6`G*BwzrSipjF4Nzl{#1OJ;WDSgG7ar>3VXgZK(gjCK(
zyKP%y3@}W4Kdv0m$Ij~wBhZ^UbNk|r(E12^?4e9RE;>jD$vo3H>mg?BJcgUMCH}e`
z7s|gZzWkNDS+V8zXNsadaV2d<+7Iz<&DU9evyus7orv7ifoi2srd4U*+<KG2us2jv
z+b@3)N)a`l0djvE7Ms|HWd=u=7G3`S&}rK>9rLO`SA3vQRE)AGw||TrM9N1;=XFAl
zrZjtY1q(J8`J1VM-lUxRJ*b0YsO?XhKL92|InGr-yaOZ$)wUBua}%bi0D#nkR@&g3
zdHkmJ5jKBSCF&}ZgUbfxcD3|-;OHK5L`wl(8#YE=lo&@>kwS2Q#3_FZ#(iL78+n!~
z#hLmo5eJknd@MT@webxDabdi<>O1znW!6Wzo!H@xp8vO#XcsR6G_<t79PFh2Pml?{
zTECg$p~J}eQQi<Bx{|_@(LwRbsxO%aWv5|@-uq~uH|eJ`U^+-ney+O7$0)ut?H`*U
zDY*i>HN~~(_rM@vsf~mItltPIf&Pc^G?AzWW44(S-l%RO0Pimr^usMzx(9ycRqN%4
zkG+>?&ct_{06DET1seFl<^?G=_!*NkDNodq-#2^bKPEF07Y99u088*!Pv7yU8t=&=
z7{~x^fTTuy%6EacV3XTWcjXsO@VO9!Qgsmm>^aXK;_092n4ElDNy~?+9{G;m7TnQc
ztD*_E`ZuGXe@IXgOS(8Co?b-WYQoChoF1-_|6Ssnzoz#~69pYtKFIfp^GCOTi2O;^
z(O*Bk>55jFjzbn+{$2YR-5Z;UcN?N8&K5t58^FqpgBG;EZ0LndjqJJDvWLA(j_NE}
zzh_OMLd(-3v=BI~OpC->ccJPWl5jDuWstc9>(Gt=etx^10And|=F2j7dd;2&7a7<K
zX4@IBnH;v?EJ4TgwGeCXs2S6$1L(V!?Aeslan?71*(UPuf9AqbYJ+PmD3S*h656ed
zu#=Mo+;UHv3!bC}VfK6tjsvnq!Ff`rYJ^{4Yn!lw*>DS&X(<ktPlyQV+1HAS>{wUY
znfJq8T?giMsob^4rOok1Obf7|SHdO^5=DNf$DLTXeWU4{GE_U^JkFMQP5o95kil02
z9^u@1>{Di&zB`D#c%+#-AsV0fdy?z<$|Z8@#q#N2*$t@z;}KSJIm^!6sHsf71mQco
zDG)+Ym$%k!m2#I^DDm9Ifg7KQ+*WWh;6<+*H3`<fOU`G8%%JDKd2VeZ*&KX(K(|*e
zxb6}?|KW_<7q6ZZ9Eul763Y}c;~~^%i^d5ldjp7X&y3kV2F}DHar5XNvX~{hdMZ3d
zzHfL!kEa<Cc#rxDXK;>UwQ^WmRruv!AMy<4y2M<?ud|uo#ZfNRudNI}M6pc)b;w23
z0&Y1vw=oJ;CB)Iy3nDnsF!t^(Z4Xn~Fk`{EbMgwAGGN2#D{o1yC@1<O?sy;4`YV}n
zEKpd2epKCF6At^nlQudOy@+-;a?FEQ!vu+2E-_cj(oEP+EXHvy!qj!V`I}v_)9dpP
zt(Ud_>uifNXJwZcy))<9h^5rjn3eXu((nszj12ir1<SlE%{loHYa0!AwQJ-b2r$#8
zsl0QnqP)Pnf9oBeQ#9IK^X6}rZJ@9r7`LkJUAbAMsH}Ql@;8jYR}Vmp?JH72n4(qE
zwL0vgY7P}Xo@9jffiix{Bjw0E$qDW!%bp%@iMhnb;kO%-AjuTFroY>Cj-p~1T!4%p
z%(pCgm!IdT?4uRZkbUA>gWA*M$HShvBq9~IK`wg3cue+a4|o?W<FPWekCqdUUV-1Z
zq$-%nAl73b#OP~S>a$LUc48{<k@oawA8UsaV%PnaFK^g!x^_JxvT##bIq`+siEFRY
zy0t@z7R**Yt1-Ye()5=3;le@$YgiMYxO6>2*8*=+(0Ov~9Wa&S!6f7vbB349wjhop
z^I3mlyh;Ust#0tcl$zK5xeJdgLq0}Z-}*C^QnPo6hXAnyS+5aLs8&tq4!b{zI~s^J
z)xhW+MHL;*vF&pGP;|W}&hI*CXNplv+S8;GDe?zm;$Hm#{Jlr*Fh^~P{so;0YJK8n
zU=ne6ln?qXm&eEZ!?xBg+Os1#L#U`-z#l1LJ99%0_3wuEGSqwraCD{18ajFV^UOC_
zXwOO*=4`UwR&!ip=YT8XE<*qVI-lCqHZ)8apK}D^1rc&4E2zJn@NkO%(oXCe^E3Ml
z(@Q`sZ~lzG@QWGC1kI)Moq*lpTn%?KjuYCJL_OT)7oT1HYsRgwX_=`M=bAzMKaOK|
zGolZhe|~zrGcW!z*{&7=s(4dYAo><DT7!9e&9Vu;e9He|wGWRzX`6{C`(Zp~&I3=5
zd4cxvxq)Gy*d{I%uq6IP|JIzcb95R2E!h`YGv@i(C1J14dE(J?(%wT8(4(bxZJzRA
z(V0^zLv4v;A=$s9c&!R3_>oDu3ul;l{{Cr~jkpoh^?_~;0P;X{A#k2W+3Nkq>fz)$
zENG>~&Oi2g8wkYaB{X%+D~X=yp(9LszC~YunW1FH9QRO5(SuNisx44|<Nf`;Y=`Xj
zaADz$4A~L$2}%KkFs0jy1!Kkn4xt80BrlTLt#{xWZHWkw%AQZW6?5NsWMGe{>-_8)
zrl{_y3E68uIr3X#b#0Z1mJfC=3Us!bD8}q(oA0Fva&f_(;)?|U)bv%uaCX=_ozh{;
z)emQV=xo(<FT&5%y)l}rj<;DoIcs6WmT4}Ur!9-YqpQdF37Qko9NQsfPV+QSm%8a^
z1-SlHsxUe6xKQR6p5Mam^1z&)sW*xSIuG(A-1Qjlt%w1$CEDu%Zc-kAnuQfS7|P`{
za?$!K_F*U5v#hU#hulSwJINP*tpJdTI8s1XD1yb20Nzh9?md0-;^XRLUL<@+IXaU0
z6;_a+N>Lx16!F_1$>n#0(VEauGptyEGr`qmVge^Kv>>4QBv}uqa<gL0ldDxh-K_sP
zrpe-oLuJ=<eh6GjQ@WT~=}_^i;rnk8EjwRXtyN9@;=5cm#H^xb9Xn*dkz>*}Ivgv9
zjpyw_`ouV6+Xrs^zIn`0En?`AJR860O2HXT2@M@D8Bz_GMnbPSj)`f(?ifDj6*7nk
zjk%18<UmyZ2;Q3+`t{>uMl`7x+M(=}x#*!_9c>|>w)5{7p@{Qi{<B=00!x8Gi{GF<
zPm!?aFi)8kZif)b4Ic+u0F?nwybnByO-jd};_5du>!)W-5@9Gd^ENvnfiv-b!4^Re
z@(6N$+x<^KFr%yQ+Kx^KQW`#|ib+7ZPvk02sr3wWoqo2<#q;fU7W()RDq_G7*BFTJ
z1C?HaBV8ym{@uikyg#EytphGbH-O)v)*i4Wjq;y|<UA50Z%DkO68h=o8*+-S{eKw#
z3#wUO&k*i42{^I-fVej;Lsqb((>M%O^L*YOBeTFa(;rqMJ;FEN?C^@Zh!%S)?aY7K
zcRAQW8S*Z?v@m=f!sY-#$7|7@qNhjj9SUpow{0s+KJhk@-BQWu?*VP@fYiOjAg0$n
zZ%(fUWys2nG8UzZn>F@<EpxaZp7R_%15j$WCz{c_;6gEHR~4Zj!K2_v5A?^8jEB}Y
z$=VeMT}@vV9o0>&AAra1V@K<6wMW}_;lWER_=9Jb%03!NoM&(bCZ?n@+=9!={^aKG
ztqkzHy}X&g?E}$wSV{9L$U1;Gb_3pO%rjEW8A-tk{!W6Dxx&Skz>AbCg#_9F65xk>
zJ|o4{Ma+642@1-Y%NzYo1s_uZP;+9pkoS6GtFY32KWR`r?zgH@&}gPtYuE+VDt2@?
z!ns~5ZP15s7wyU6d8~A`{AR*tWKVK8rq(FPYyedZlB%6hx_1Z?`C9zR_@e}r9o{%9
zgw@{qlPAXLmt;Hf{)fh9+v1=4M#I}wUes41>m1fQCgF)l#2H+rn#~9Xmao#@5`stn
z-3NYn-9Rut+;#4wXrqKC@q%LPGJ#!%^^Z2n)-?q>$4q;G6^=A;a_}R=rO?~RB&2Cv
zUhV!&z`@p~Z}N|)$M_PhM3&koBugPogmRNAw}DaD43`7SYfKF(6UF$TN&wJvt4RC-
zq}qXxI#CQ(SQ6Po8pa^pnaHiMoES^T^rg}ExPGtz9Z|x`huKRUCm0Y`9>6p?)BTVd
z1k~rmY>>Fd+^bp<rcdFNEU=n8W6E6d01D?DEs{C0XoTQgi|lr)2)tsT#I)H$69hFq
zM`Z?}3p*CfJH(3zdc!h!aP$9u6tf%#HBFP%o+3eJF>5aHo%nw8nT#2s$y7xOJW?no
zAzGoii@g<!wo`PiCZs&JTmRHe*GSMt?3fg^%{dt;37dOerJ0*6nVUpnrmk|~gb|OC
zhf$!q3%TvMpmc$Jcl;&_OohfpZt1;Y_%k==9gZw@W2dhQ!x9VmMX;hx!8`#{Xss=s
zJ_~Jb)e0#7Nd+&GP&CKxbz8n{(Ni5Cw|8vK6$V)`7n$#9RmFiI;rH0`#1Yw&fC2`1
zF!sAQinDBt@9Hk4W_ch$16^U!L>~?a_W2YQqM0AyE2nc=9sqdXJxu@)wlk~1VBq~%
z-jLS(-H{!8uh!@lmp8jUz4B)!KKk_aNhW+Mki=Wi8D@@CkFWS^Gg8n$bFOQyUoT+E
zi~fKD=ph;}jgS=gQ3_lKnu-`(h}^uaH)N&q;*KyrACGqR!GHBPa%i9l-!`B?<vD-C
zzC<5oP<22rfYK_8l&SLV%U<GkwOnHY#Fw^2d<1#>jEWFD7O%mYhw~TeK}vRO3Gpms
znlHo1i!2+qL(Dyx%CF|kQck|!F)A;8jPq`2hJrE^7vHKOE7oCDg`z1Z%G1k10e^zq
z#`}itu0dY*H*2*Iz@yyGab<~dvyto27o_FhtGM#s#Q^}Bf*~GK@Tr+uu-O2S;jh;L
zpS=XR1C(C;b1iTPmIW-GsGuSoH3z_dVi{PNu;lg`ZC^NkTQXQx6JI_2N6>2ev$+Z#
zxdJ@n8P&A!^TpJOrj20S7e42-()tx$HUdL+WGE!oAS@5Yjk?px@kKM*ddFj*h!ICm
z_lez)f$G^GnQfrP>EEB<dY@_oyL~YO;_VE8zVlL%z)a3xmG9!H$e?myt}5Q)<*!yt
z!Q~Mpu?BC(<I*sOoF(iW=Jo!u9tI)Js?)LI{erk}PRSuU)=*3)9LhAeJe~e|jpehF
z(u^qUC4i$5zA~MVTf4(64HFpdbD~eXj}e1mzZ*ZnB=K-47W!{Nj1l8pC60tFM+n>B
z&w^B`K9x2?j|t_6rw!Mul!W^KjpG~&rlJR|(5JT0Wgs2nXFlu$f$vmQXL|Y~EtYt@
zxn!WPl67yYlRmGdG0ugQli*2kPQA^^K!+=0AflYZKYp~mBX0Q@`F&}Dq6mx|Ng7zg
zbBqRhbH&bGUoK{ym+dgnnSUCJ#N8S=9tc9U1o@EM831q*nhkwgEQ~uT9Ka!e+po~^
zX3yiKp+`;Ekvf*;mNmYexL8dU(y?@nRIle$H4ue7bGaKD_<KH^ii~Ih&_BQo)#jW5
z$<Bb>rUnMSsMrrzTY<#S)JjHh&w_uutX5#UKOPB!&OVgTVC&%ucd%5pPw$U&^$E}&
zrxdc(QVfQ^NpIN0({0zSHjggX6FNsS&!14d54_pY+Vs1bhp%6zG4&GjY{Xb9+voTB
z^;0fV<0BSFMdIU0psAtAk$Z7jAH_pB{>~CnJMjih7h#JfBNR9AuKeZ5u+6l=>f?F-
zqwVvA1~6`_?B|k{g|Ucd<7E<xld+ujYXu*$67dg1Ra<4JNF6Bl@gX|w<kgV^6g|EX
zNGk(Wv`TGyOp&mb2W>5uifzPlSpZQQ|Kjbu@cX&Q4$EYu|LLQ6!NY1o(>RxcruSRo
zfCE8LHvNyN3C3ujPnCzEid=`-63J3_<siuQC|9VdpV>^T{ICI}%!Y=VJVVXqr-^NK
z*Oxtg-V#Z5BDq&}KTE#F{k&LmjcM8;Cr(4R_kM`+^frkMC?G@es*xHdpP?^U`JBX(
zqseLhl4ntu-_M3Paxg)#@nqX)+G2p`v77BT+^<7jG)*&6{*uCJ6moWHFrg~~Nu*X0
zY(+<<7<3s@5caOvt8pg&vPm()D3Yy%e?DFc_<)8HQ#!K>uB3wK{00#nlnI<_#`qb|
zfShrxuApKZ>CA0ETc9+b!DPH8U86#T#Kn>YAWW_7u^+Ag;r%k_g2LG~s!#*3xoqAf
zp|?qUOiMyXf0ff|G{FJsOtGBq)fFn(1$7Nw!7B#rEj<I5C1`<p#}{6+Mu}&1Awzw`
zU5s*#{^EkzTO~<<cN$o!hD?pN<2Kn^+jJQ1P3DmxXNi~}G6Q4-pgp$Y3YMSavHnVW
zTA6u!Y4P(H!K<S<`*UnM=!h+Q&V3O<TW~P?wkXJAGl|ye+p~dUr)VtW10b}7n1Z=g
zqRe_CL#M9G{Rg*sd^B_L5C;mjUwI4R!q@OEd)3HxB_ncll95*HJ3uP~0`v|y)r_?@
zb49dXw5$Qtc6=FJ#35moDTki^d4X(40M0;6xQk!9wcXoIk^y-S@Qs%(AX#v$`?tUV
z-J-!K2r$BD-1S)IsPEb|-8tdk0S0G83E-Wrwqea443$`pewZ~U|FsS8r?J=2V|Cb4
zwKVD5{ZiscZCWy3!%}UWL=Ivck{gzVqWF9%?2+7kr|s4q*PE;iq<X3r*CqBl7LhZp
z1r9C^lReTeb#6hzcIYKxsG`n?fx@mg7W(o2fH`Au<5h3Y`)_q5ph1gdvySL~?S$}{
zw2`r*QjAlEHFC1+0KgxsB!|Q_3e5NC$hJYyypoKt1TpRyR)i!86NIJpFr;kyvc4x7
zOPrbXIbPZcM*u*le48NoWHE{H{9Jtagm(6It+f%yCpz08Cb|oYNiLhxt~WJ7>&0K7
zfpHopX-K06n_#tRw=9D>DxTpk<~bY$NkJ4s9YvECtq89zkry8dPB{a#(?&u1aN%O2
zdx8bI?op6>YQFZ2|Ca}#B9%SbL_|e1uq?@sVjTlD8``Yg^g6OBJM8;qg^8uxz*kqA
zR+Jl8smRE!cX@#^VvnbDTcRs`zPr`WDM?GD=pk_AHP%SdWgG3){I4bz4wn&(&VjXf
zpQ=7S%J^lM0`#Rs<(kP%h=}d_4RUZXbTsJCa?e4QI|ZvqN4&x6kWxDtgazwMW+=TE
zj=S`4i#Xt3om0mdgyzg|6$>P=s4R64WEck#U2AZrE~JIxQM(lEH{flaIX%ZD>L2!V
z;7+zc(VXSYdlA$ouk}JzMJ`2bJE$Ord%&hm`9j~wDUacUkeM>e*g5`tDsL?`-k*Ar
zz<umpV!ggkPT6TXv(5k+Td_KHnz-C&Nuy}7aoBWPWXy>vq;Sc;hby-XbfRaf<rJ+E
zE-C(JGw&Utjs?oFIr%nyW&s%%%I;~7b#yT!GpYmB)-&8SdtH(kQ8+<GLAw4B=bGnM
z=a<*m@v01#nz2-Ct1#ZQ1#fOpR=!K$lZITSAsYnHpS_>4VcnVTI55`^Y~TGe;D<9Q
z)(MMOKv9@|^@Cko23xytA0{9I{AEzg>fh|}{*Yja5?}mH12CB|FvBXfl;`jDmpQg#
z&iaNgn`fyEBOi{63^ErpLC>YpEfe7Fo*Gx1-Ii0~VcCsAK-ShsE++@>Sxhqqc(NN%
zIT2TttQ@m9DlvMuO3$Qcep*aI%&?@NfWnyh%a+IEZe8jbVp`YT%fx3nC9u;M)1SsL
zQG0+>!^DQ+=Bs@FpD58(T8)&mgTa&9!LC`RLess6Jvwfu_*rUloc8JIoRRN(CdK~a
zu)rj<yH>!4Ar5H1iGaka-t8g>8GDEdZ7{FRs*R*Re(Q`tDzJbLP#l#r%{eZRw|LD)
z!>5Q3pZ9sr9%>tomo8{-dO%@m(++<gV+L5RuL}hMdRo9w0+EeA`I(cUxKb9!<eqJ;
zoU>iQZ!21h(_upMG@gSX=RbCieF^#`%VM6-%HnWvu2L!MmT^<|dh9ewownvG^<E`m
zSpI3)wreA<bhsrS2RTPra@I1y`;)OCOZ(s>Y1u~Y>Ml=MJo={3-JcJ~gEd0exq-(a
z&kvgFI>zl)Ynk_DnFPxI4Jfvl<HP4x9Au%P5WlJR#7<H#mSQ5p>#Og%F}Aa8Jd`RS
zeZlNp^*iGKXmaO8s}I{-7~$s5SjJ=#<Amk6&A5>lCz#S&HsqnMfLkO*dvQY(H!A+C
zxT&xc{9P5b53YaRk>V{C{T$q@Ao{dJ?0<i+^S$2m9}|Szj97uA`z>a*C%abYGeA}V
zMYNCgI5fGk<idp=ZZJ!(YZNSVGNdw{rZrnMx<N|(fY3TH?lDge0g{#_v6Aq|Z^{F!
z=`*A<!19uSa>1EH_wtY+N)Xbb>Qah27F|6T#evjR*c=mrVuCYKoPo4rEoOalZC4CT
ze`YK=5TpCdhI9988?lNO7Hg|(o`$R@Wvwikkel*lFn-bEiN6)|oUEp@QaiD*H7EH*
zXTuMTzdsKLIO~54;>tUxikU5b{TdhT!w}_Q^jE35a<j*Yq4ufw?kHicg?>=bTA#|q
zjdW0^qdK(AmJda>X&DrnxeBCpG?Wwx{DQ9r=!`O%C2>D5zfoh}ygMCP@joI?bM+h!
zZG&|zB5Vl|Sf6Iuz7&34f&ZMp7_6347}WHI;YX)!m53TFFw56>;lZo0K(l89gIzH>
z@adA>^WJxdJSMh>1NlEC?DA~B8uj6ch}cQK5H~leE-P(XoDftcTl!<HIsl4s<vXie
zF(?cmFl`S__BK+s>Y9GVWuPF@Ja~q(1k9FDrpD)=<5%w5rAxl&_K++W(Sx>jhf$o>
zHC0{~nu*U5)p}cmM#)k7cQx@ZF*V9BNUy5Gv7Q=E_9F6iJ8We_t<xxriDeC0ek{^X
znVuAYJ^V2idR%~LwDM&Z#jhm`a<Sn>GuAEccCk6ES;#%5^zZ)q<oR^NxN<K;=?Rr5
zewXZ&yhvoPCzAb1Fe^`##?DubzE)G-a{LN(KJ*%HQ0Q<b<73@u)uAuhQlu8|)1S`Q
zbvO6P6qFQ0?Jeyd{hN8&89=SkG#Yic+6-zHcWTWdZ&`l!vOQ5d1Ld2LVW5wL$e4AD
zodKXpu0Gd-9RlGWom8x}d2ZgsL1%9`US$27)g}lqb5WM5??QWY=2sB_%UeF7ngybc
z!o2eT`qxCjvam}ZT4LIv|K(+b#S4v+aYXVsJ0CcU{Wa%E^i(KxXw<ImP08|M{{ZYm
z{=yMAB4yG-#l1yFB_Ig#C0qFcc(^AA#e6~gNEr31tM*3Iq@;>bu6z1rQR1OxHQ|>g
z5s|MUKnvp{HHr94yoOMe+d@y3rD18JH0I96U+-^3b|ezHwDvKX+DuhBIh<!WW{zMi
z4ynH<^6`8gfA(&r+ouc!d6`1XSzp+f0^Cy~bRQ>NrwSPdnLRYWfL>n5ZusmoBIsmr
zn^F&k%LPB2>3IyniB+jYK>o*)SkkcI=!9Qd@;cCxdihZCQ|E6hmM?HX{nIwHxb)x-
z{2x2V#p7I>I3R%`%51%=Vk3L}*LvH)Dq1Ls###V?4$%Ovxb;+@9U3T}S7*)vWGZ+J
zOH->C<`U+?2DF^xrL0YCS$62zt4?Y)c5;z#4hPb1gN4IvZ#3gJDYaWm@+s0h8QGpz
z-9TkOrBqL%DhUAl@%wS2dEyjz-p-acN10?Uwv0Py6w8icX30qa!laEC*wsP$777ex
zGq7g1mzDkS)#wB&ccrde1r3RU^t{H<&;{!HALZ;n7!X?{{Rcb5FY24k4n1_!zE5*4
zQV}v(ul=k|PiI&T0Hh<yyQi1O2}yAy(0kM;Ufz+acg&vT>K@Y`i;~+*uvvrvc~p*>
zRCKaABK(NNfC*xgZ~x#m%JkZ#InyScnH{U2aCc2-M|H9i^v}s7zc#wEa4(WU!at3(
z%t?X(tZO8~cZn_ZR;HLS)TsYs<I88Trj7ZwhtR(P%kIx#pxnAo4O_s<=f%wXkW{T9
za<7*YpoI9TEgw?fasZpr;Cw};D<Ja(gN))&h8}NJ|JN1T@{x>p)acebmr0eIYz{{r
z%a&R}Fm3%gsuMHc0y*Z!+D+VzauRk8lZc~f6mh%h7Ad!wVarI7G&~kWiyn+7#60;e
zF55c~Tjx)9UZh1^;vm#8_3Qh+q|cK2as}l{j|46d@9z7POp!$)LP%M(&yv2+lEGkJ
z*Q+v2ZXrvJc47?^Nrscs-#FKJxvd>n)y)}whX3qh`+iDu=Wtvj(Gcp;)EUZ0w-ZUx
z^hAD!B_a4+Mt*}FfEyaxJ`H>~LxYRU#;dZ^+he(w^K{14DjpEz+>Lhq`>!P6H|V~E
z6P1?s{Lr=h5l``L{B+A0Xdz9t-`5{M!#D4`_c7VcufEr;OinZ{w(Y=xukG_>9~!6!
zSpg#cCnkCAr<;!%^Ho|x`yR+qHf=uH7325E)w?S4)vp7T+2l((Z~6Po;5QG2&GnWQ
z*}y%ur7|HMTb1-_<M<^AtAKZNL|z1#Y9H|d@ha-8z6Nf@-sl7e;cm~o(LvOnf)B*b
z{QXweW}D5fWuVub{EVTbuq-1JU~BLXCmJB%1v@Cn6b!Q1;ewx`P)R^WV~$y_AOu@9
zY1DbEJjp8gOd2T{t-|#oe$zH7P3{qKX39|UH5SZfgaev=VnkzSALrsmvEnX<1=LOz
zY-G%aV77`CU6%0)JP^>|>Fiy-VYxIw8NyOROb8hU<Jx=m*^gHv@=Cq9oSLG*FJ>ci
zmG^;rFL+diWHM8z<w8khDu+mKcj`?ak*5D(RgMB2|7FCQ|9RG~rXp;?YKm(Qb=M9f
zCp}dpgdi*UzSg~(+izVl$a<(x324$!k8mW6&RuP}<Q(q$3FFMe%*a<58z+qR3d@}h
zPJE07h<Bo3o9N2KnB`l@;*NClbX(%hZCP%V)ij7q$qjEDL_h?`XL`GP#N3Aar<Jrc
zL@ofpgHVXM9EYU4(gqP~DSax--8gHmWQmKM7z}y|?`L{_n<)b0$d7K#vcpJ3`yL`M
zc*32}@M+V)0Bg5O9`LHwzv!?i)a#jBXohsm__5(&o9ObN@Epo1$qf1<0hESe^Kc3m
zL`GI@*99qY1pp)p!tOz{6X79hXYeAlcpnLUPQ1ix-SRGW#Aw3Dow0y{@f$(E&l^2;
zIVFkxDQJ-EuTRXCXzGe@Ojt~8e`h(Ou#h6*)S^b4nh36;k@k;?*o#Nw5jHa%rfq87
zIX11~lYf0@CCSVU#tRnOHej~eV&Z*Z!i-sVZgE%nf@{wDn6{^;)89|9w5`_qE`j!)
zJGGY&SJb^~>QE?&%x1i-3HMQsP4?M~QU_(cSFQj-3N-&x4u>uc+a~O1l=gzh*`J0)
zd#dnO2b|@DshV6R@3&^%gFXZ`3_DFW`RM*311<jjhXF;uEqX{*96Bp`F5vliB%o_@
zSz*cixaUn)w_BM-d7>AwGUQ52h3kgh_bZVEyKinJ3Tb5xZ7%PO4*M2Hu4Q1L`>$O|
z_4DBzo7(dizh$&NEy<dw*m#1>@3tS+v{ZRG9nh%vFK^QIc=bEZqc+(rPvJ%5Ts55a
znYopmkquk+w<c^HY(!UvW^IY$kfFlF+Aa*Babtd?oiLl0R$af5kM;RvwMI`OU|mik
zM5@>r8Va|J0Ty;CJ*YcsC~gW1{Gfj+Gh!f9cak~OPmc9evDt+9#``(m%A7MjJf7{V
zA`O#dKL$AXt+^6|C`OQz*4IJ0-o(S^*(g*xG%nMQbE<OoKzd#nn&ad70bd{WOx$k!
zR^>Z=CZ9Z6>y~buL+MNTJ!$?di%CNA&gSuIkw;oxROwW!ReiHe`uD<?z)^-h4TyEJ
zBi<`p07<l{ZX(b#O|S3}&Ja&Rk}a=L{d|~@RVyncQ*YK}GFF$4D2DuDwZI>Gj`t4>
zPlZAiV072;`As@_^6UjODI}>I!`GTHu1q<dl9aoj9}`-_T+0cTx0xNc)1(?$GQyHk
zYq#ro2Peh0|BBy-3J3h%-o~o@d`psR19a6LPD7Fm<HCdd6s?;Gf3yk?$LI!g$V|<R
zc<TknV|cn|UbKn*$rp01Ykoig??N^dQ38zrsIVDX*UrLK()6-T#-%pro<7-5co%93
zW5O67*a$qkP(3QmzECZ{sV34k53-2al#gyR>_!x3JHFChTPb|fDe1Z)R;7ED+p`A?
z-`1oo4<C3b`t#L3vTwaV1;epe__Kj9!s<!{!V-VnM*PgE(l1luCax=Dis9RDqT5R_
z+ZQP4d+aS*EhV38lrsR0JUbry5L2df0ngwtY;Y?(w#bF3V}%TK%(p_tl*Wm`8($pb
z94B0^^5E~&gjoNBqkD!<|E;X<Vw@}zHSn>HNuo@vQ8IJ|yd?SPHAC1Fa^{EPD8@iP
z0{~`kOT%OPbFw+&+YV3}c4KB=gi9l8oc*w11`=ipO)p(2^`Qf-=!|>a{toxb@a{Mw
z*v>CPRJ=2+nvJl&S?!Ys|0q29<a%_o>vW*LIjgHCqqcRFe$qb8A%_%RWUaYn-7hiD
zr@b!t=b?&)Obiu_wY2C4x|s0{?v_Iw&vy>v(FbX%^KrUE5gOaT5<xVsh#p6O$|f#}
zlvJKotV)9t;w;y9+9n?1z`gh#;UlfFF5(y^VGRB4H=b?Z`WErA$3>rSjlkp?b@Zg@
z*A``?J0p#<wt>}`n#&Rj>?a<>)DEJF2?q5((!InOk^G2R2kFb#hVW<cP!+XFs|zCx
z4HFI&HQ6A+hoSUGZc=#~-v2h&`QrAa(mG{A-05NJQ*L9akBhyGUDE3e4HMum?G}``
zIP-bkx-~<`0Lnx;!(-f6n9rJG&{E3+0R~A@X&%!&7MidEVPw$eliLqn$<TdggR|1`
zhVCQd6aTL-2_?4Ul?{oXvG|IX+}62d_h+~%>7_Q-8xMeX>Rbx`a@8ZQMBr+r6s=)~
zi=!;@Z3AL2FoqvqUCT|gkkbffbJXM6WfJ;1ggLx>EAv|}&r-66{<tmtw`sWl<DmZh
zXRSl&xi&zGRZ>IMC1Vlm?as!jyqRk2NJvFh6S?(svfDV{CT3=lQIFxJwJ;=)+dOu6
z>6H0JcaE=r#~!I*y<jNtZ-JPfPfm%AA&}-7_JZ&!PAK}f!booG!lO$A&Ep*RY~E1?
z?eW67GfZhmx+|%vmmJ^U2Kn{vWGH--Cu(`-ZGSQUqD3$N*WfwjZc#DQy!>=wTP)Be
z0Z5@N#R^>i1QY$)L)@jB%j?Mqrpl|fMNbfNN~|90GvBmK(@PLPe@TdMrwj=Pyl1A2
z;*0D1%s->1XnQsFS1B9A;8>WjUN8s)_<@@<sJ*Dlz4@;xEq_Rc?Z~su4MjpmfYrg5
zglPr+4CjMGxFp|E3!h2I1WoW*!X|Da>(RsbnRqAe<A-cwR9TjQ?zr+U!?60CJ_lkp
z;^%0zOuLx`@H^MxEZYa^<D)?-DO9r{A#40-`q|-h!kssz^|`r!8h)nd?S0Q*bsPeP
z5a_rkBheZ#D4PSK<70uZgGrp0$>-z?d8G><iXEXPGH+2PB!wcG@f+gf$EJ23YOtqw
z8kvW#PydX*Yk}AXnWqcJZ|sHE1(})w>v#OrM9!Jllgia7+7=s>2C#qrJvZZ6TgwI;
zxf)@?k0JXF^ULd-V;MSJ!cP5KX$~E0Dwjlkj(P&3WNR}<YY2RflP?4~c#Vmabki^1
zp$qu%DJ?q;mD%S($mqpnVq2rr?5QDGsZo@Bfa)*t^E^lG2$uYp>Gnf0HBwNSN&hc>
zxV_iAaMCR!7&6I0rK+6kuby56Q^K?=Sc=0D=dl01E!(<5^q0dl%CB|~5z5~WkLy^y
zN8I0I-bk$iCo%t=OeVDKE0Xa*YhFoT^Ct^P_a;V=zrN2(-%YLI7K|3sZhu?TME;~W
zz!=qH{HdsB1}f~OQ^C`lNMg47f#+N(i*xK^;qCC-jYEZM!cnz?F$01MoETSWwF%>y
zo2=9K2nT7IzB#*g)1{%!KbNLzbeH@neJoy#x*Xq)<9t|ut*kKhZ)O_W{<QFo*Y64s
z9Q&Cmh&4#Jb^yW_=rjG|>59ju_qJo>=HT(d;+6*n?xeY@E?cOepOf#|e&-93_2dyc
zuWdwSemnfl_l=mXH)UwpNKqLy?lri}^bK__l}kWSPSUQWq0c?fcq{!^-N!D21sbWv
z%JI=1FzgTDM0<BQ;9P(P+?~?<*5C=n$oAj=QDNjO_ntSUoFo>gugpC$y%-hmAPsIs
zg>yQGKtZv_f&JCe1JNJ5{O9A43LTl0ob|~^Fnz{QzHnCnHzr!3LPDhwRl3|xZF}OU
zP*L<M4_>>(>*ubu!Nqqusz*k%RzqKs&bD58c1(bzXhGcV#BrpK7m{vy{4Le41tIxb
zU1u2;W~qyKi~~`uXh!^-@?`G@X)GOKwImYh<mWk!JmnMm+oNAFcG;xALUc1X>#x;5
z`{kuOLF~97kcIRJ%}dbezp$rZj_@{a?E4$an*(0q>$LDsc(W)(%5Gx&41qOaOG8FM
zHS&XDPg8oM^~Z_H2I&&HO_O@lk3BPVcD~vdAr8qM&jm}Tq7`CT%Ce+Kzx*6@1;?`x
zEL``f<-sG;u|U&@HVhkcs{bq)=<B-1<J;I#X&zO0DeZ~bCqySQ7a}L_B<$p`MWy5R
zisHqep(pGit=>&1FLdObJ>Jrs!;DEmGLix_aPzr?!_mpF7};sadzY0lFaMK9^k1#H
zGnG>&U{u~%+N03oL{Pt3Zt@3FOG)9#XYJ7>&bcXxiY|>wCO+1GWTRT?xFdn?YtA(c
zd}4E}6Kgj&YFP-7lG^9D$>iG_5`asJVFMV$*VtRo{o(q8GvM<e7qx3USVRH&&A9pE
zcuF}BI_NqeJNJu)mJmPh!i?v8v%cx!)vyko!z}e$A~`|$L#WoNt7k^zior|5+RO2r
z(<M^P8nNUtMHM9OPd8mpGud>PccvM%UN^QAob|Nq^;q|a;38Dk3{}3WWmJq%{Ja}L
ztj>DhBO|9xnUTw9qub>Ik-t1ude6>?F_n?4V$FqT`-;8(fAIE}(Q!3RnxJGcGgu53
zGjoZVnb~5pn3<W$VrH<I87u}%7Be$biKgDKd$zmh%<lA_vuE#*I!e{8$jH2zHzOmT
zc%pj1SHo?DW7BJl6WBKF+XpK6y}Yrs_m;=;9i#jKLp4>z-v>khhMf&~)C{7o6dQSz
z53mqAdaXB2(G6Sc5titGw0_xfuid~zf+z5myU456(%dD$g391!A|mv!@jpG&%&CxA
zzw?UQrelaM`QCu{LPJ*UfZ}?5I+p(TiX0Mz55$Do38JD*l3goe*0(e?&%RGt0t+j3
zZEvsF2{4h%5?i-m^&B#hu@*uyen9CM*eft$nu2eOm+%x{D3!P!1Iqyo*9khNzmbPg
zH6$b+mL6UkyV!(4%=>@{D?Seilr3^3J)>lbY;kGNS2#;biG;2*t=||`hvSW2+wsvj
zdUIc^ZparhZ|AQj`*1E-+#tCJ4$g2QCyiyFUqRsUn=(zHuXPZCw;r1*`rR#!{@^WZ
z4fE=*(tUq!B%g5ts;esKsAzud=6eB&cl9p{N;6&n<xfD)G!{HG0IIz{PJ2i{>^B%4
zsVRjuwUAlRE|yyyfV5;kU(F?|1dDpfJEz3S;OKx*xhph*x~<b38Tl(ao-97$H+u?k
zPOVE(d<>BC(gy&%`ZP}r&l3(x*t_LZeBbMo69+?Xzm?4BpWowMSk`{<=4{MyJ?{7H
zfIP@5R*jS_OnOpIJ@I44BHu70U66~=k5B)Kt@nb$!-#EHW`MpaO+qxYUr~^7*0yO2
zY~HBgMZ4FSD4mHh4pPS|S(th^Nh@hVaCjR0WL8y!Unaq(C4J<mTz_Tp;8t&6`{)Le
zkQu$`>gQgCizH0HZArfVwwoh~*g`)@Rds4m*;k{DLl3C79Lie1<#o=2IVRu!;zxY5
zERFcokAxJDSH2!DOnjG-e*Q!{J~7)aMYxH3G8BU=FT1|g?Xs)r4Sihpys(!q)Esdu
z(>QL^5dqjg7ZF=1iude^SIOM~`DV}b_5mV&H)k4(I#OyL1APiK6tfgl7ArS&H3q1o
z*;cKGV10B6GDuIJJHK(UP3O1J%X>kt^kq3hS&q3$*C1}cZ1INM`HgR-Z*7;ZhsUaQ
z>yjYG*TSP}%d%+ZiG}sPfdGkdgsRpIpl`Y8((Kh9x%lb#R(3}WOV${p%{@|euS4=g
zWiNf#q2R%i@#w?b49C1SP`IQDgyP7!tjGa{$}bY0BS0CGJ)ox3N?TGb)SQP<m?4m{
zp%PMYKdpQFaPzUmPT9Sze;n!;1R@Zuy7vl3@%?#hFs!!RH9Bjjdz(~oAfoYpVH5G@
zf%>1oLQrJov1xvA?PeF|4B6hP2)&TGa4R<wkQ&Xsf9NWi(%>kc^a*a37@{};AJrA@
zX-*7p3|ZYkD}<RE7|7B;-+i_M024cPg)YvLGOtHT2dt=F>WFtuOG;wYmrJOhhHNmQ
z)lgR$3-n7Nn#z^z!{d%xZA|$;pTDqA!)_zEE3Ea_3%=CYaQYp`PD~hF*UcEdjLzg-
zU9y9mVSLQo;#DMpoFUyu`$I9Lgb?@rv4NbGrAOx5;<GWH9>HNmHoaTTF5vbLu>d;0
zJ<#GVxK`}(;pn>YG;rhZZ^>kD36V_GAmiMYWhTQ{hd<gC1g?U-vV1jIiZ`}`?PT9X
zY-1U)%g2)|H-0g43lCH}NL5Z*%F=LWw2FPJGwgAhX(9Ir@3XP*a9DB`+zh)(HwQr&
zoMJ)oJpA64kO+;h8OTcXPb^9X6zdi(b@+F_*BTt*1=72QFZt3!`kRb84wANHnI#me
z%F@PY;@w*?e%z%5`H|$xB?CN(<KUVi64{f74@SPH1C-AX0p1OXMT&%L_?oW#cPI{v
zP0lX|aYgQeOW~^UErTDC`!TDKVe`bpXSVw=1!(Tk8922Uih6CQ?{$VzHgJzKAaC3&
z6qjI7F6G-0J%;1x+FE_ngCdt+fP0ck`^?Txh+aKQ#X>c+lEJs7uWMy}OZ6Y47+*Ci
z;10(Tl2vJ^ITus~Jt*~#n(0N&>^!Eu<zi{`k4Nyryq-9XMs?=dp_Ajme9LcTPz_Of
zlvHq(`SghS9EOAKF<K>BWH9M<ogx{46&3b&8YrNIEP|Nrh(7d_f*l1p2`{i0xS9Qs
zHMY~)pqM$hG0@<e{3G_aCE%jOK!7GW(f@f-7Ud$b=(R38BmakJ#ow69E+3ga7~-lf
zp^CuVf#+!PNrV)f%b6`N%d0_eV6W$tgp3;qty&)K8Jbf044|T7pl?HYiv=&j%adwi
zmV?bFVkgi_DGzhGx*wieLyoTn@$Z|ImUlU2Top~j`-(u5X!eap&a68UBMlv&oh(7S
z#1yFM>)WEm?3uDj6F!aY%adQ$D@hZ^#27PirM;Gi$oBjOotZMjKhpAaKzXFk;GB(-
z1Gi;JTxlV7ND}iEDP?7T^p)IGNP-&*t@MAy5Z#OMA{c`9m>Q`+BdUUUBSBz?Z8Wc0
zx(HA0e8qLqV^Lb6N~EjA36duku{<^@gIyY0U_Gg<bgbu#lqdo+s)3pTlu6u=O3LFR
zeubzV!02carmtn3(_gYRr3V`OOS`4dIw9+=U&o?!-ZfWNq-vH)KmZ5^P-zI3J8wOS
zFfm1d_Y-=zh?w0)r7ynXm^@Dyf`6cc^LOes(SV9{dgpvHHn^eq3&6GC_gVoIMkBe1
z>dUxQe@u5};J$gPpzitWAK)qa)73)JmPPF`%7aaD=586)h=nBrZS2-4=qwhpj4MgU
z+5B3p(bY;0M|W1`zQ3p@N;D9(UtM&Mc3(z$5{tl{{W|y%Q%mqnQyr6>z(#sd)|aOE
zOeZ5hIWJZ3G_J;DrA#(!4hqtmv!E6C`tYQ<$6%^~%ufR{)|*c2uv32w<9iPl3tohr
z2TaTb1`-B+Nu!437V?<BVv~iLYA~Cqyhl5i2w#*kP6T^8u~%ltly@J=NWY;`ZRiGt
z^$k}84W*J4ZReenQp%s)Mz-C5`5_HJ3LK?Q?k~raTX106MPvkh(3~ULwKZ)w9Ltr|
z456Ul%)w}<Li=v}3F+mBtJn0htsR6cfr9fq!=IIqKmbf2O+jl~1bc2mDh?tc5ya4`
z2_ET8P44WdlC!c%ljq%`wKH+v&#t9EL??L(HtoTFfd^Fw07@**?W-mHF-EsfCZO-a
zMl&0c-ZB9%dv<<=X!rE;Lnx!)v;L{8IU5#38%hmiic(b%5y)-Ha#+IizWfzM;Q0kX
zYXNoRj(1cb6cDvF$^v8$r<i;y06khq6s60-xzXff)6p_j@AqM^L^Mlm&d~Tcilh}i
z3sp-m?!c|Ct5DNdn6k+>FrcHMMJND-wNud0x&XoIqX2A~dUI#lV~wq4v~^ttp984A
z%ne~eVPoO>T_V<__;awnGR<Mz&ZAf4{sdsaQZu{F%pp>h`79^mmOUxYOSWX`G3hL_
zch52Y!(qF)cL1JTzk)hF?&9W(?QdUZ(Yh)g$OYca>Hb4#I#gIRrLdW|^H9EAz&Tr_
zXSbjDYipgr8AF-t$^qb19Gg89n>8mlpjfGnjkeB6*>g<t?ho(16=x+qjzt>^7=aK9
zFE@z7;p8VA?u+kz1dsP+a0Y_Q#rk3nNGT`6q#rlz1PEus_{R`QoYVOz8_8|Aw`5Oq
z5VLxTgb2MTv~{(msLa+{wfjbp^=Fe1OgSX2)*bEr`<*6(CROi-C!83wK9=6}Z3&Pk
zEXCp~RUmuW<Q-7-$I2~w#LP%*2|f52v?I5Sl>z&)00ME#BL|hBGmZM16FZjm)Zc-2
zw+sAfX9*zPs!{$7*7rK8D1?`ZQsp%%34^#31V(+kY-QL+P;<9P*UO|S$sv*}V0qG@
z#?QEz2)NX&kstj-Ut!M)pg>7zg)bZ1D0ay$IvYw>!qlEodlhpj-so_KCR1=25y2W7
z(^^J?1}w=$b5_o01{-y$nL_GUmYRi-q4I37QL|<5K|rOybso~X``@HPnF{0khW@qO
z?*;sZdJ}S+zO)0mrsRkw`$4CD;KWwQ(I9`-6@VpBR{syxQa`+!cQ)N!)}*o=2V}uf
z@>+3-`hmEq!O#_kk*Ptu8e{y0k`W8jH|iCF#K|{+5m%3mKKnOh76ulr`@qnq`7X|l
zN~oK<?xUF2pA*t+GmL@*o>jcvBM3in56rgCx|#%jlIzD)10kgq5wIdbggN()g`ce-
z@45i5P52d1fHRmt|KJKU(~T4m6kCqU<nKN9bLHF?ua52T)riNwSZ8C3^(nD?niiM<
z4d?eW5%C$y78V<h=+!bBdTmdb?Gob5;_kKuf79(WYFbMTefiMyT)OJ><NW0xJRvsu
zT|XCnp0Bi0-zZyplU{fGs7>7_=s5O|fX-Pk(D(Z#2JEshqrLOq(@jIa?cn>@HWZ%?
zyb=mwD6RcyodLo2%WSv#?EKyK5^AQ3kUZ1qlF*P>deU{O%&}iNZp*Ql5x5dttI`;k
zE#(W+M&&EtVtlWtrq$QENCR^-7W38Z(%^l@9R%MgQF`3L{tj9<%>kYYT%$qZf~Q&N
zZMVDYWnaJwkN*@t=EX=22<rd3OvGn|M4HD&a2u;pHy`trar+Gg=^HR+2*S&>Cf4%7
zaV%+{lnm1c*Xv{vrfS--M{F<Z)n=g!JcB?X{HZQ%N8?o)SIHAA_pdI45eO7x@G4_8
zs&RnqSG5p8-(cV~=7fnfxbUTWZS^L{hP=0eoW|)ZwYq_bvS<I2$34Lz`!>R)!862D
zvi#BVtb>$5WmFJC8%iPE1+H1gV3p)@dXqRbr7Fke`=Qxj#*)AcVc?SjT}j#jfSDq#
zNdg&(Y8Md*q6BGIJP2z8YfSh-90@vO1??fn)|MEmQ(jZ{U$=37{%zGFNxTn-$92q0
zLOuiIb<pp;=3B~bc2iD%ko*(ij^e1r?tG~#Mhv(80SbfIY`lnAzo(>L7DKIeni=Z)
z{zQ-j+QxG~Re<?YGs1!i<!cB*TFoAHTRipUY11%a34<c9dV#rYjC{_C5@g^&$TvTo
zno1Ce;<mKK67?Fb7VPPTapD^}!;@9)bC=7w&*fY=;;;=wvVa}hHsE>&@nqNlTNu@s
z%xRH=CmKC8Ly8wl3iQI8D>JzlhWm?`cz?`>@BP|wsn)V=Gkf*qe~h&1HKJ9BC~H9S
z1eXT%rA~kOvt?j}YYBQ?p!$M%G2Q_oU6}AaCu+!9tY31ZiyO`S9lr|ap`O5Jj?;Fp
z8P7=FC1Vr#(muZaR*j;6PP4pz_F&y50#={c14W*!%w7_+2h(t@+&x7PppuE+r#NG=
zQTSX7s^^%8My5u&w}l-c>=G;Fe&=zVu;>o>uBLpL2nPX@MAUY!2xdU0ziPo&cvKPg
zJSiRhq6@L;L2BwBpD)nkvY{enW20y)g{ZUVuHAOMpOaaNXZE{nTEk{$lo0n|U}$p}
zjf1|ME;2{Wb%rekW$M#wGBo3|bB5WkKkh3sUh|jxk$*7KGG(E|&9otaLtGAel@ipl
z*d~z&tq%?etYcmdep88~m>1TTei=nS*eRkQ^L?*mi;f7^70)l0ByXi%;TLm9*M7oz
z2YlOMRx!ONWvP?vA*slG4f7)|TZg&x^<a|k{)POUD8?)H28a_mj5-{s5us-#0Woo9
zN7b1q5d>crtkTv=DSca~QtX-rCsqxa)I5E1i~tnINBTH|oG6G;aS9e-e>Gi3Zt~(l
zG5!#8FFO|N?YEUmJ{0pj+0qQogT7zb_ETu4)-7^i#b+>&0E{a^rL&;zfxX@;a6|GD
ztygiIn2~QvxgTe7DHvGw%mE_Zq1^#k5BV~_0nmZI`?yg%($O-hI7fie$pfmT9OsV;
z1nQo~X4Z3O;>a6NXMvsp=~Drw>EL`Ch;jo2F4NfzsP0O5Eq4tH9sv;`QIb7nzAHbx
zShJHC1j$ICWBmLsFuUG%>oo2gHQe-<!QNAfq1Hh5GI@IJVX#<8C})=O2k}Du9EqmE
z$VkeGBO-xt+AA#Rv0SRMJPD)hu3zXccRP?}7Jzeu@a_%~)=zd&Z(u@to|(?XX5VRH
zS*aZvC7f?;MdJWij(PLw{LX;@83ZBaOOz5-1Cvk|ruvB`Cf*rSj?^QVCmMAFaALu|
z=~T@er#ubxL*i_iniG3;7Xu6aH>_(-mDPvCDz0m7o7%PmUfOn_0`$wle3Xx=MkEjc
z4y^BxklEw_!=Tr^)yqQR7PB4k&=SGsrVodZb9D_GCPC*&3LAEy$)xNq6$3X=tqCR#
z6RfJ+wbDY8q6p-X9U+m-qs8s;qmA~2v%atsBMW>5&8W(lz}Iz464hVE?H>ErL0fg{
zfqlM*UGFRW5_wAE$3-Xm6d)oZ5SPzf83P{VB)A9oZg72(qj6ZV5MX9*RChhRhn%MG
z?jK1DhwE1m1Vnrm5l%z;y2gW&<M`7B%-N&ZC~&FWz}yyC0i2O;(1vCK)UP)j{5ff<
znte*1Pz|7WYVa#0-9BK+u>Q1zs}6+XSic%<R~%la;x3zP7rk&Qh;IvaZ4K%C>0vft
z{UZ<l9pJ<vwp#YBuPPeER0Ki?OqDeCCsj0sR!Dfh=Lzc0WGbq+#RmZzy&m3*p(OKG
zsF{qX+fiDmLOo-gwFZ4Xgr6%5XXwRS#Wr;Uiclo;##5IFUVTN5`TA%G_Q6wD^A(jc
z-mCmazi#m3q!fWPv4}GTndn#K2^d%aKg;sJ<2D*a-HujtZ#%(bb7kmf*6@Ru7QKoZ
z>M7}`t{VDq*&|B>yY|#f_6B0aQx#J3EsU?d&RM?^CST4EaGED+fFO_-#CZ9S19kkh
zrY_gJTh4R#DG>h?u@H9S;0h6s0GL-<Ck21&Qvk@VG2sNdO?P9_1+*j{>?z@lq>DA%
zqKzGcaK)*R29;6xTwJp-E3sckZ>mNlIq@*_-bbUioK{5+V)%|gjC(6;2C1a5SZ3F)
znVOa>4Q8<%LlRUBAciqY9cKbjYtzH0f~McQ{{5T|o9kMl^q6a=P9V-crvtP8RO+*x
z!Eqf){%??PAlDKxy#2a)G=RRxvWx%T#(+0^^`*=O@aT@iV2gZLust%BcH2NRdhE`<
zt`*Q+6t)Xu@Vop!bN%V+FB2?0t@QZv%}DK!#Aa>ndGZ25tWiLph-bRmPRZ@@Xb?vr
z5i5x3>fadwTP0Wp1@neMEP~Vjg+#Eef~_jgLTxdA`(dr?I?Cq9Cr5^Ou7?)Wv#(|I
zU)l{sWcOw^%1R*GBI6o}jHa%iuJ-ZyT9dzS7PMH-KCD!PdoJa*j>K;RC2^4xqluNT
z>e^PH1W^ob_>~RR@#64eoC38TN&((0*8IrcQ<c*b9e%^=@A;m0QQdKXFo9k@wXXAr
zs!`L2hZ=ob3yHPx<0JXh%PrUS1^2(TQ*>_cK%nM15Or#dF4q}GsfKj68ly|MaN!zw
zndB^*d%kOLJ2JR2pqk}8J^Wf%r>S@Gr0W)8UKdmO9(5M;nt{}+dp`;ULM>JTVH@9I
zqt7}AUZ!dtJXYWV6Vp+ZBXp(t8YMRtSG_WI`7*CjPZ*Ilm0gnyX&>@xBXA70S7cd>
z{xC-+x*@%l@kVZ+=4Zy7OI)2lb?cUYOt-ZKAQ7@$I&nplI)y&B^@J~iys_bklT!IB
z<?4Gtj^~I|{jC$as_hR#-;kWMvxmgaJ=163EMn!Acg;OC+L77uFw5ygsp_xy&l$xi
z?^-i{o4R`bock@S#Kxb%hg<mZkMF6qhAV}YkMEj~u!}uClDG+40<&`o>PTDW?Io(Y
zC-*=s#qm?$kfqwIUApU3UU>@$-niPUzt3$>pQ*kfMXgG~lI4QE@u5cpyRVO2A>*}z
zS?{wC@#W{hhYxr>i?^du@`b#uQS+vGhFsA9j{3<x_JQ`&?H!#TZyq5X`v7b|*VxzY
z22`u&Izbeoe)1&?!IFYnK|!JdgVc0mk=E!L!;7<-V9D#>4|E?xUcLYP45(^gZtyEg
zpV`^t-SZk2G%n#e$bgo}Flu`x8hri*o5X>Jd5Q(l;(2%^jz|eT|5het(q&N4QS(-^
z*3buiZ`hki-0zC<5^?7`TsK2(+(X0k55Z-Y>A^q4_&<|j{u?i5%_HS4>GomN3jO6l
zf33<o$nB`{ba(&}`_Es0QE|U(w6Ap7vr97mGXtE#1FDTf&7>Qd%x3<@qB&xYL`UEP
zGK)uQBz_D+{lje6rjFxf``A~gK&B7`5-~DmHm=$N$vc_@ox_~{1e)-%bpO)mU+kOz
zEuv2C)lV47^dI^E8#-0?|KR8UzfYIJ!}?zs@SC;t71x+CeYfha*HkKS{ns*wATUf#
z(C0INL6#ii328ERs;hm+y?GdX6&qsjE>I~$Hv&98Pr2R~L0kdg^Mbfih5l$Bxj|e)
zSfNtVWaAbg2oY_Ckj!7crf(0#VJ0(Om13@c-v;-&3Y^eIWDa79VmU?vKTIGI$U%pM
z!p?uX#e9I<$q~N7d7iWRMS_kl?Hhge;?r<??A~CR+RZe%$o`5uBQeYopA_$s7^8BR
zACH>3e=@*7&c^n$x76I|=hW>I{3XR$a?->B!-Ezqytp2HkH_cxlm~ayAD;$zc%7*r
zLiwK(-EmPx;*p9)e(aO<iT7-o(^OWyrknHxoD=qr<Yaqk2<RA6&2>!7q1nR}&$RN`
z2kh9AxLAJ@ztU|+Dc^f19hj>zxn%yFur&{FXeY$|`8jj-GEe;Lat<Q;#*$x_^04+k
zez?AL#U+=XS4)DXx=^baHi+QuMca^3g0dx7GZC3Cz5vx+-u)*L6}{JOLw`I34Wlnx
zyIDtMIG69WoqXf=l0=MSdHwB$K^O-Uq^ks3#D(N9IsalE21>@te8BFKJel{N*Z0t%
zBq?H@bwgsL%VdqNlbgCu0o*<8nY#DU)83>}V2f)g?#XTulR#6|#y4hj9p?2`HrL%c
z2DEoDM+z-Y9DM4w5G5(7yI)HWNB7U@3dqQeI}Mpm!I!^wD3Umw<<5mg3re?K1vtc$
zS&NLzETNO?ixVvOou!ro5913ktMh0Yg58r<8GJ_1EultLEN;TC9?_UpT2T4ij!>EH
zYdKN^&UUwrV6d1m+`?V7d}6ORwuzXG(706P@<lPvaa(9J!ZGX7m7?xomjfw}SzJ&y
zC46L2+$O)=gA!uq6P+*TfYARawI}`fkr+KN#MtYuC~igg`7oVdCm-kP0XgjGMSp*&
zXWz3R506JZYbCAcLS@#%%D{S$uZ}6%W=uY;loR{ru1@Ivt`f>rs+xpCNf~ivbN%Iz
z#E&$bW0IVx+unLzh|W6g(`oOBxj%P$>@=KN0i#>AU_zzD)=`E{ML+aXNbAT}xfAEF
zH5?AZZayV;oU5~)$m|`%_FTXD*}mNWxaIoFJsTS`G*!*B5{sDW=7Y?oF}yj9+=@Wk
z6Kt~(>HDU2p`hO>yirgI(c-L-t$1sCrtsBb;~e+UyOpZb-DN7RZl%|6QGBfzXK}H`
zr6mEK)=a0W@=(y{1?9`*0PFu|f^+@H1phxL;NW8a|0@9psHXpR0uEMI&i|aP=VHTK
zO<n!V=#6iCx)SM}1SX}pHD)NPq?r%<&oriy$ps-%)h`&Z{Z9P@m{^#?TBqttY=#ku
zWafu#c{V;2)&Lb$!y*>i@&Ig}sKRycqof+gDdG+1=_u!7;K#@8@NMhk#?hX^TDNgd
z32F$c2rTKpK0a{}Ae%FF8Ov?*F#O7k;&@NXixiY3bMoVOg$Z33lqKKe%QkmaHSgwK
z7Mj)Fo%vqQBl|6smwdukntpJd>Qs3XT(_IPGtkSqh{({nmc5VRFE4Ruv~G3$`TbSG
z)ODlsN^pO_yxHq17|NXGg>IppqTs*CCv*+sJq6`(g#%Umui8N$|5n5R)!t2+2{GZL
zw50Q%+L>2a`;NT#3(IxkQ|q(~X+%ddXQ9JOp^mZyj!x+)E#x1C|4o*%BuI6TK#&Bg
z|B(9cMNsXaZ}ZYR{5$+SX-`)x&zK$=0N6vHDkU{utwSFsNg1IXrKjK@SCPv4g-T0m
z0`<M>-Of$hcFb-WTb&&{CRa(O<nYQ%kNez4Js>}9GI{H_|E0G7rG=n|{CkIj^eZp=
zNLhfu=;kGs)Hiiyx=YepXx=h@EQkYM>H5aoOx4|F<(A)Z;@S|ps!W`UVz;SfpT_h4
zzpbKa!J_x(+DQru)WD}#3)i0+5gmtolL-Nd<u<iF6i;jm94en%YeqMIqQs_moEdI5
z0^Eh}6c)I#i|?@l6FfZDHGX3|E!^mV|Lv;ZTdNHG4$?oX#~7gU$_(0TCq)(7OD5_*
zqWLa0sm5K+h{h>nLG73QW^0v0y_D)noyATXlF)(A*9gqpaT9e&(zjPDFp`NHA7724
ztSMjrb%FOVm4h8`Ye?d*63c2p;HX|3TiDRhQYcMFduA@?%k`0P8ZA(+-#xu?kcQEj
z%ObB<LSO2xJnOfn*<Xw$PypMp(PdHJu@63l%_FGl$6;SnpwqGb7IwlTnSN5ypdoTp
zcsrutG%;|d5cyW1o2CgUOXsr^SLCjKN#y5?UTTu(fp_~hoHbE-^k+%ud=gy3{iMM=
zt$_NM6nAKIdLz2Q8r$hz_yCKZM*Cvd$U_hU2=}U*UA5Kwy*!NrB&a;z-`Q1a09x7K
z(&#tJX^l(W@$bAAR@u$V5HFN(<Bjyms@;;BuysR7?ek}p-`5XgUqsA#jzkK7DVJxr
zOwYTGh28U90MA11i(RqJ6sa|s*0|WM*LiM4&d0F)+pFPUmJwjW;t4QM8m^8Z=I%v0
z)|yoC&6d{U%{P9d;<wiMXnigw1kG2|>K?E*9&h#EWl><y<)^D@w{6bPtmWD8cNS?$
zq3D11WQ`i{bTMp~7U($5KU^WaC~^bGPvH0Z85R*Hk|zsCS1kse)tLS5oH{3^H|LNa
zsouC4*<P{%xcb)<2b+;*W><<IEUyY%ZLJQ`?l?I_&eP`Y{34Uwt~_s(nZekR4bA#~
zY^)=#MOI6Kn;p~~E?aE~Fn_gi?4M4$YYe+n9GvHU_oj~yn&O(%??QN&I1VS`cJ`3I
zV%hzD*}4YaTHE?v7=A$<d9<n_v}nOwD?2Mv-k{`>-^j8ly37=K6?&BP$KxtdI$v$*
z^HyK=igfzhwJ}(ier}6hFI)KLaXd6g2*6`ZOCOPK(ydWw$8+W!{!s1}Ru<uR@Sf*5
zSWZ5%>i*7Ou({83K`0s~DrbLtGrr#C(yE}V-sTDCoF`DYRruFr`?;E<=MRKj%fz6U
z7}w-m{`bcX*P~76T)N^3I!er$+q&K*mb(CL3BJ3*`Sh@vkB^5wrN)cf-@TGiN<|i#
zQhdn*9A`gWJSX)~wuTN=6fH9fJ}Zrd>gzjSDh<=$+pE~(Dt{G(f2)gW^-G%w;*rGb
zVfEGjQ95VxOWQt6d--boLHCz*?ki=;Wg_lNnUl1Y%zVo2kaOM|?u&Hiv%>nJ-BV$b
zlS|9dPG|36@8BT3<e8h{EiG~<Rq+Jc=m0c@6u<FIOjmM|N*c5e`A9dw=z3Yu5V1mS
zukCqdbKCfDvSzfR$@Ce$|5=8=Rw02)|G`FQ^jYft3+M4cbNKhP_jtG+C5rv#I(HlH
zK8tDP$lHj!yR3Gcl9DieL1nbNnl2}uWBEubOacCe;ZdlkBEA(4eeRu~Ho;dQHqs75
zc~@fNcP^{frLlQ;aGQ3sgpG#kV3WDWn%fBkH~ZACD}F2gF0;;<0z=$d_s5S8@p)YD
ztK^0Ww4~8t=u+!HQhCy48uZuEa4L5aryZW-jNz5rwZ!}>>I%yMtNZQNFeIAFgNFHn
zvdgC0r2Ub-Dy)<uvkjKU+gQSpj#h16-9-n;koaR7v0~56YMRjAKcbV4jnSPNXJ(<I
zx>OrHn}B<ByYz=NySa3ojawR8Wvq?qu8$7m8|~zEC+&ldpx0(Y4gUMsN`cP~y;rjh
zwuxTEZbDzli>ph{<(<?N(%y3c(VT`TQ94@psR`Y7k@DQ-LyW3ZA<!GK=!M!e6?i=E
zCsJb2K0O)y&pN!XIizM}*lqnWQ-%_szC=<ee#skIYJ*I{|52!UE+odTg2O~)GtauF
zEOtP6L#vaTxH@4xhi;^%i6hwb9{(bIM$r4Sndq-e>0m)0*>^R~I?Fqw9YgtCB<qEx
zR}J{1YyIDr^@HEIhgSrc7s~yAxlqyKm3LMHvYG2V?4?R;lYf=L;-fzQZrmqgwaz1G
zj6M8osCF6a*044Ajn4NFzvH%FkWBR74Tj$KHyqSfV!8i~QO5lrM%n*U5{rLqxBpHO
zi~q}P`~NVB#eX)?{*Os4SlKxKtBIx$<AEuTzqwIGlfH=$7qZDWUyP$%S@<O=A_xgp
zT&SKLmevGH`5H!9DS=VG@*bHKj&NSot8?GU&i!*Gr*mp^bMs)H>HB8*(K0BP#PsEK
z*PY*%&mEG&ANH_-fB+>nl38*g(1jTcDp5m&gDz#nFJHbC;zB|~im||gt_TxU&^1f~
zx}t>t=e^tC68_G5zQ4VZk(1Bei3@q`JHFhS@!cg@N11^2>UFrl3HU;WND^_!k&D8C
zgBRHUoACdT{cpk}Oe({Mve9b-#T;-zHOoDSIurwaU40G@tBx<oKowT_DcNGq-mGc=
zdyxj0CgqXfpIc+W8LI!M&dP}MO#jk!oEln`AUI%~zWiFwroUQ-N>2{G0v9sGtImYm
z{Fh)n{y*)L3mt>HjsC+J7AuDMMMQuz#a3G`C<hG<&DDaMG)Y*f{0MVUIWk|s+28GC
z9t!kx$dK`86WbdEP``+%(~P{lK;0015$Wbiu@1NS;Lc;Oc1@`3{Gu%b3yTzLVlCgb
zKB2<2iq^{XxI@hJR8BHT5(g668m3YsY?I$-VGs%a6?AKJz@H~4#BYlM)Fa>H3b^6}
zl;|V|hnkNBR|&VSsY`bWg8$J@T)EH`eQu-#ixborP2Is^^Ju8qFbZ-k)da${AYSH|
z*9Wg^lj|sN(}W06CDi43Hk{MdCiUb0QadW>yR-h1Sl=i)cVWmb=sD8DXDIX*e3eOk
zd@6(w4p0(~BE_#QP%|b>PBOIaR<$7!+$5~~i>G8JB7~~SqK1^SvZ5|W;lm>7V5W2R
zw&!yTWQ!Ce=nSXD_!7kN8`_duSZx4@oC21G@OJ`x=wAm>AyiEk7o->5Q~BeK{^yux
zDq?O477vb}`k=m%9A5X)N=eb3?^BD(#5A|<dRy7dihC0y>$gM0CIfY|@(&D>a9`&J
zV`VvtZKgEC5dyjxDNrLzqrj;-5+Y2-g+cLnS+vkBh+l$mW7I?mn`&$GevXtWsf$?l
z{id5suQI_y9Tp-{5fUoLesibl{Ie@cb@`nf3pBWu-rG_#U;uR`m=UUoE7u}dv<{S!
zb1W|Rh2^**&<L-=l}Pqqz?t!8HfCxRf}?$#FlFuoW|f3jkkXg#)a2|zzJ{`lBY!~a
z?re@sLo@X-Ngc8XgeD6=j{}mQ$u*-avLF!CnNL+eIQ}bm0l3khmwnK{mQ;(Gmhy9c
zuJzR%ERtaP0#Sy}S$6D+I!MBfnt<8;L-M?B(E{?lsyzFX>X7vLJZj6SziH6`{fM|}
zr0Rq}!54LmgdJ9BkZOqlR<)3AARjkLeib!c!-Kp#g3se%hfn`nR~IX9I_<LjF-Z#l
zH-@BRH!Kpo*M#sfRj*)bAD%ofIn*a)UgU&Cp93HCc3p}sB!0~eqynUL#Hkfq{C+-%
zJ}LgVbb@xVI1)s`s#?rkkO}&v!q=K*%;F_ke=AAmee{r{EZlJ|J7d;VWtME!j>B{0
zA5eTp3e$_l`!-(Uuj&iiPC8*cF64KqTZ}b%tIdXYU<>@HmB@@btxBc|(i8U)(@}hX
zb7YRw(DI^Q-$enO>J52)0W{)ZQI3hSc!g1c#hk}ypOm!%4tKa~VM%`~Gi+$&uF$e{
zB37YA9&lyMX{|m_dLcC5MlV`5Sa6<d9Av){k?~Q@3d67C1|4awX*bKWG;gyHU+C~&
zf6yr-HgSUwkRvw#t+$l4jhv3q9%<*NU$|9|$=``dW`;BIqk^GO5;h`-pXsaQkZR7Y
z!2QTcKbGcW{|2&iYode`17X!ie|{3ZIRakd@1*I6BbHqAP1<6Zw}029s|;M9lYbw5
zb)i;bp&#ZSs&v)(&Jj=TX#X=JBBC-^(pCZLbA-dIs8i(h2fl=m$B^>mDNc}GFf@Le
zLPhEK%fGm7-IkIB$8HYPtTReA{dp@HI26v&r47styp4LAY{NhBs%6s|Ic`uH=yRg@
zL%dilW2?DCD#1FOuvp<JFXak86BsUDn2Bt=sXSj4orKtPy|h?R-ryb(EGa@9l1Wp5
zgZrm)#Hus9CeLYcHQ`31J3%5>s6<v##`Gj#@n=kUEal_BP2VRE{oc3~IKipzkGx{@
zf7NOgj?7BK;*)OXAUwvu$n`*bQ=E5|VaewB?)GEBp!49>#Cp|Tw~}ga9-&hLxUjlG
zYJ*&GN-Hr4r1tE=1vZ&zQ0HdRrvh+L)nvRpw_oa@U+SiQTNjKXC+VBx9;T=ZY*E_Z
z@R`XyX7H9|=xk$NqPHcOh+$5xytc2^75m+|@U1_}70WbjzO2<=#Yqb`8VN<DBe15R
zW)s&ZRW+ND%cx9xDDq@lapQ6_^({q6BP1Dem{0&>HKt$@CuRNQ?IL#ldfp#$wR-SI
z%YX1!;n9>5fu4-+%%_w6*@4<?mu4gkcHXpDMIUJ&ULAo{ABkErK50>e_K&Y4y?x+T
z0U!2%cCn(mVk|6!<5y90llZB^9}Dqos4!95^W3AZM|^%7o#(4SJPs=zMALMv{uGc&
z30Fb^Q}a1fLP04(?C{_+lzOps(PcPGw~8qX{^YI5EqOyuEHNG_d@QZiA<mcf+2FX9
zG`rra<9KgTB{wv5=V4Fjll+!D7qDvrxxu&fN#+5|?&M6uF7}B4r}l<$#%R<PcuI-z
zl+!c%EtY_Sr?HPJ`<Cyl@T^qYFVf};G_hwTnDqZ*pr^CP=R0>7zUx4GG8?u5arX^k
zZxBr(I8)Bc#SS|uydB<lmpht}by}KvphHJGQO83Qi=&#LFBQ3!Ny6lTQwLjw^4a6<
z9aPFZJB6K>0aUd!XIQblLCUoQs#wij<+`<9U914A+%@gWsPAf%<Z>()mj`bi=eafb
zP1nN_6&Z#Klmxvw`V4=QSKMvy%Gfv!fE^7hf{DLH>O-IJ2fVe1TY7SO8Co_*Kb;J|
z_~|%UQa_NH^d{EnFnzL0kQ(A{hg%J}Xp6q9!_OUlo<-EX>AfB1WM~$1mBi0^iAkWL
z5X-w?-9oe~Y)xd=Kfa~IIIL-~aKhAF$Ogy=vW!2v)Cb>WZV|Re|AZ8IFd1?$gZ6#T
zt8r-6mmc;Q`-<2F506v=5UWNW<@6}0426`AVLVMqXVJN&O@DhoHC+M_OcL@Js~asz
zUF9mH$$BwKYf$yNY*RptMUdxID#PYVCIU}82&tT&Y5{)4Z_eu}`IcGsc)I3lW-c`1
zi>nwyjqGRypaAF34IO?tn&cm}(ANmX8O$#mzl71o;A0q(PL6pla7#i&+7a+~TwmUW
z>v!JxS1|pz1lnHbH)U~hb=r1D!qV4z<T_Y+RIcaw5fSV&dT@IR#2}&*bxkT)3BUfZ
zHkqnPvam}fHAEC?SCW#q!-igU#B6*KY3P@Ftw8TZP`8kQlONyT9n${v_Iu+C&NA*Y
z#xQgbZJxVVOD=;BL3w@w&U~f#S{%)^Og^W!hA#TRXc!afeGn_JS%`B{cZ@=}A5Cf#
zZgO7h7#v_E&!W`$BTVJEdn#w2>2$!4cKBv{AMLwim3ufR%xmg-N{N%r-%kCl5=-mh
z5-bBh((M`8lF|aD7?O#=zKu>n#zA94If~2yA(e6+a&CwoUXRyTa(7ZQFoN#P;ff3i
z4+-JJr_Z)dB>K5?dHMLo$1i;9G9yS|RIk(4yhAGnN}Ds?NN)OkaR~o97#n%~fvz;6
zCnR@)@?lBsE^Lk(Q?XIKWiKBgUtsm)c5@gm;M4QXZoTlT$PLlYkGFYqGQ4ubJ3Xt;
zwgZx0gSCfPZKy;Rt$J8zRTNwn<mnrhoZVr^^xGtaCFRWWb^Ju4fIVyW>tx`WKZ-Wx
z*ohVv+I}c?F|O$M@<(>yShnRz9zssXqzWavp{%f}xKfDceT#I2f72LILA3LJ`39b%
zasVR$kfN3YJ5=G=loBwgjIE7Os2DOrlDklqy)=M?l%*H^QT0ysS=kKDH=2Tni$(q<
zD22X|9X^jfza>hu*Q5SBBFfFUTf|%5p2PF5W-2_n(r#+SE1PLVW_UJLgT>J*G~hN1
z#DOi)KHPzi63u)!uhH!94ja!2=6)QTQr9_<W{kXNxS&%MHHsI*2|j>FPOU+Jd4>oI
zN1}e}dA1d{>ZwIq-m9gznR7~xGL}RqU|i0gcK)IV0StMtst-$&H&%&@`nBeh_x7<c
z>!5J`B)9eh&VG43&`YqhP2y_$q&?T|c2{3vyvSNM&_I5A>6ng_ymzUgC`S-O8Gijb
zC@lS_*G2{8@yFf3f_rN6(pXPO%fi-^KVM2u=q#`&E00i7(j;vV&&lmPlCuI=Qd&Yr
zs;nuuFoBwgBg(OU{1ye{H-e-OOOU9-##E$UJr{@Jkb72&F4?g<<Tew5GD8^G)5%*~
zRz;kb!`3fdZ3W$dwx&YIsu@4$wxmCN9ZUlJV~@1FA~xcRaoVott3X+-4zWay>?AKn
zzVR~Ld>43j;PB_Vhw+9dO`sqJ4}#lc`cTv870s8=7b>%yg7Sxml)pkpdIcFQBt2(J
zq^aD&>~2I+j=ynCC5I!d!TLz5;<g$oP(OOIzW|sh4_DAI4}d1ztd;1)^-qe67_x9b
z6Z{PoTvyQn^xvIIhwjqsYEfCEUCJ&GxILKVEIc2DABHLqQnkb`&!piMT;#@>pwxeH
zp~1As{&;hZzuE^_<kGV9r=qPA`X_{YZy2c~8oJ!6BNpOH>ZS`A#w-qotlmaLw6@<N
z3>BtuXdp?fPKS;&Ebkc-)d4RC)hfOV6yV~%A55Z&7P|GvRaNNSzFUWs)97niYx%zQ
z>@~2<)|6yG#f6N<zwLlpD$KEg)XO90bYj;!-Azt*7}f^F8+(DuG#vjMmz3R3V+R-S
z-{Fk=q)XyI?!)2{T!z~*4iiLW)ucna-XjxK<n_0G)l>z(?MCD}EUHhIb1C&kFsBAB
ztVS~oJ&eb@fl;rzcJhU;lrja!9~=L8ar5I0Z<gcAuD9o{uSQn-B`#aoh6FU}SpC;r
z?VZT4=-?^Szu+>qU)QuA6$IFJvESEpy)m48%{`R;)R-sqsr4v*H<kBMoIXQw=R`6J
zW7_Wc)t@`w;e|8JTBzoVm7G%c^?lDuSn<ot>wR~&!wSt@kPT{E8@?sx6~A@OFVF&{
zaJA;KgCQsBC_GtN3HcE_1NJ_D-O``0VxY(nLa%A~B@*t)pc{uy7qpT_d)#Z3)@2PS
zmcVjYxEyv0Vqe7YIZ}Cl4^KdJ1D6<Jxd2-dTy>*(SoD(@^A)JZ&hJu7?|r;oIC59D
zi1C}VTlE!H;(AYKxGA<DstH!p42#!dg@1B<zbLWit1T6OW`#2uafO$iV1mj+$}u6R
zMs)LG`Qo?ZZn^$t`3sq|m;kmVZtkiF=gEVge3#zAqHFITE`yQfebYUSyfKwCgY&Q2
z@jfigy|-e7Uo4kRTN@-?VMG0UzPp`$ur<18DJk~ALz{@f<KVLBcKv<C%g-gr%?Im9
zCAn6)+fFRM7|YFzyWjYz=#4Jh<m0%j;CkhvJI&$!enWbFA>T14-!NB>OxjsnBKU`g
z;G@`q&Hoz3p3W@Eb5MuGP|mDF48~vN+JNnOY&99hk1y-EEfzY<w<7TT`lR4zn`0kS
z29FKDwjv@$mr09VPJ~Go1$$Z<7SsZdyX@q-V4XJK!`1gnDB99t7??>+Q&vwXMCWG-
z^Z1!~f?j=2^mGHQju#QuZ#35!(+tm*-=ByZC{!k!(gN0>Jly(9EW7&!+{q*+_Ekg%
zvap#Oe1EsjxI%8E>Yc{0P(Q3}oL!H-Pl79KQ{`QBzwFd{j8<5yd(zle!hY4&H0tqa
z*eu6A#L$9N&=uk12hp(mK>hX|T~w30`SQeo7%;EJkYCG{7tSQG&B^R%Tq-M^=g1QI
ztKnr&4`kc;Ie4GvAY$m&@D1;W*u`(@lDsyifAb@8#Cr22F{>imHbUX}#aN@XGhK~H
zm%5Ek<Q?kO$?a_Ch$lK`9v*4R8AA>e+L2h;ZbcZB*O$x}ydD0o<;x;qG~swyfmLr?
z7>}V8lVzs0@<>+!+HpUTbnY7$JU;l%eX$AU#1{_2aRHDrarEBjkWM|%fY1LqdUb$X
zwm9j@>RSyOiH@NXN^;5+o<V2xAHzU)NVq_Lyc@aw<rCx~{5Ye`H5FI<9xTIdxWB(%
zfG@B)DM;CjTAvkibf_#t5n)1GX95Xwbb2MysC$)!bZnDd@4K$4X*uVFx&G?Gm6<kF
z&=Q$bIb~3vJn`mnaA1l3?(ew$*tDrE@k{tvCC%hHI36^vSpdzW_;*;z+xzRmz!ykz
zMK4a{1_9|v);|Ryv2cfe9Gb1PLP;>sW@(B?rny4jPE(dY>{C9|wbO+~bBsN_Q)nK)
zq~B793F5$gl~H%C{?QD+y~1HpbwFDmTnP`)5%yt7*!xn?8fd^>*IC6xE;H>g>v+2(
zB4UtuASXhYkNPuCuO6q=Ezk1n@ar?qC=U?}5^3Yp&$OAL+tjE`S!R1EGQ*<QrX_l!
zlt42Aqr)F_aM!{=(;<olSVl}%cP^oy#S0(uGBuewWgnc;2brdt&+>@pbs3Ezqq9Uz
zDojv^xfh;KqBM)(KgF0~L$hL1DmgP$pPYnTt7R&GEXOaVqm_IwQ%r|A2$rxQ=si4-
z*<wUMq|kEq<EFjsiHg1*sa}R?wXpATBDo~R#Jl?NC3GPzB+67Vz@dovg%10@+#Qwx
zhXA@vHQcEEn|=1#;y$^*VaYFT;Q?LJ&<g-fBup`@T&rC8nD53u(^KTn8;?C{0lb^h
z%C>Ln5hetSIQlobrV+^;rg7TTI2O1*p_`IY&gv{eEqXFvdk<|dQocgVEy4ZG^{GG8
z`5D}UyZpR256?i<)}JDgb?6xi@vDnyC>D%fP~qj4#ujh=Yr4k$>6IpHv|L;lU*@R_
zT|a`ta2^=VHc3DGJNjJ{dEms?X-#X&?k8KRSz!m!rGTCtAQy|=ZQabbdcow!4sl5#
zT1Qo>E&NTDJ{kpkYV{GEU|mFrKZ^^`6Vr|AH#Fop&A!%gzoBcNukM4uA2);^!D-DX
z56MrND`V;{&ekt8?kA*Q@{KI6j)LMY9i1(M$upjw9#$`NPRS_W8@svssc=e6l1;QO
zQ8@ww3x-yhD0mM8{DztzZke7(ztK{;%+|J-W(&_fgS~4;y3|Y8YgOR>fM#U3&wASn
z=k4vBPXO)g&s;EU3XnuGTo8v7g5KNcxfu4PTI6I(0e73%Jj7)iU_RoIO&O^-Bfezw
z)fz(;-&(b<CNV|TLJjMq<j|RBLL6=LOn(eF8GED=iqh2NFX*gB3?QgZuo&y)_Yala
zjdOW7IL|ZxHH%PZSWQ)(t~dH;e>z@=V?DarWFJU;6*t2)m+l?Od;datn8ICXlFy6k
zLYg0%HF{P5nD>FeY2eCZ;tSo!L?1iP_U$vnmO5u{Rmp3&Gx>*w16d2X;}zWQy6ws1
zXC|C1iEQ-p4`(4CZF92_+34cRjK-3@HjWqCKjH!c;S1$_6BOvbDOure@(K;RSQe@^
zzCFy@*X&0mdD^QtyMEUb@Q`pKux~T!P^nJBpyM1DypmgwU9fL3jdMH=MrX@t-$eBJ
zkblgv@ksTTQw)l%UioIYs{E=7IJ_Bs*{`h3u)e(y8YwDYMLo>T2tS+o_NDN}$Ci&T
zjZg@~zxCAXm4dej!BP?VtkBPpaO>XbDR<7V5Li;b07gTZg=7<QIM&r`%6$druaWrW
z&l3rg12gi+Z|-KOTsdP8xSF@Iy$#GvmtOv%2x$vT1wnC|N7Ec4OSWM4cO$!tZ;=y2
zU4MT6m?pp{=P4<`s^xYTH`MEvczlo>l)KjH<?(?T`E7Klz0gb0?sa%8$hcY1q#Lww
z!Ksjdx2kS1YhUNJrA+ATmx&q9q)bWi+|FT#q~NV-INOe)EQgfI4A7cOZ^)BJL8hx^
z9|rRS&->64=2RkW;q%1BAwNFgj)&gI2_PL3pIy9(&!iQA<w_g>+ijkq`mn}@TNP;%
zqh5lm%HEip-N|tn`!T2NXWv3y+5!FQ&vRom)|15sBi74S+`iY_k&(e9!$N!>wdO-=
z_E#tx5{N%TY2W_+Cz$80J@QrnQ0DOuMCQFefLC*cw!}f~4Z>d}6VT5G3sAVeO2Jv~
z*$LWM&WyT{(?8ji3zT)q_Zp+oQJ#%n5W}iWRhgX4YkOo`^bEZ@F=%j>jbIR^Nbl+Q
zqc8mH>x@evi!sg~lXi#+BP~$t32oa+!6x;~rdGnLScgN`nJUfWaV4p@`-c<$7&lBG
ze#j+OMoVpsi7l>79bmH<bc|4DLq<kME6j4~k@>zGi&OT;&(>`G4DV!b+cQAfS?vaS
z{Ze85E!?YG?~m-PO6@l`-#A3NyB)Ln(yFVrakW3w9+{1eaJlw&p)2#gz1cpX_1!X|
zY<@L+2ZcpXe!32RSZ!r+psRnmY2bB*!ku-obL-qr_Q%%={7633CsYEy!klh#s6ZNU
zP~6XXSDFUn@bv{UPP@4G>ya05jE`p`&mQJEl*O7J1Mu#~8M*fH;3Bd<NMA9EmATff
zJk;(gbKb)b=|Y{sNv-Uy@OAQSw%ope={QW8RR)Kkg7>Avwi88KU-S2s7snXWoLhdQ
zz9q0w#dGQi@zB}a`GdHY;TQ*rR=dEa_O+#(k;dXde)TInTk+T!*JvA8stGznTq8>C
zi2e^=HCY9k-wxlITV_uR-i{;fz;ZUsDo?O}YHLi%lLi_y3CVs*+c^!ZNhIe^?<i10
zT=M8}IZ=t6{sTd`qa1_Rq&<5S;BQ04=hY(NVUBArLn)?hMvujjA~x!%vlB^~m2JHq
z=uxlM;Z|^Sz~j$i(%HL@M{egb@u@yJQy^fMqZbg8omBE_e-S&i{2;+}M*gzEY`*yQ
zL-U0KD6sU$u%4ideyNPSZ-DR@FGRD6&#Ti67@q<UU1+sFici#By2W}o@D=Ntr*H$7
zZ<Kx&4S`GLR)$V2KZxjKN?xv7ARl-77X3o!M~sSxUJp6nIr=yv+r{vHqL*lA&|ix?
zXqAqHySzucRu4Y;cB5jtdcf_<*Lpap!O<5&GWM?!93@&GSjl6eoijsh$;I=*m$gnm
z0d;D*Zm#`rSKAU#s6JBgo_m57l~kFOOO;66jLPB0(DA>9UbI37qKnNBajMy4mbSH(
zlkS|l?rE_$65Gm3%p8s_GHtmk_F+3E(2HerqtFVHhRn#Bk0dB)pzAI%x$4nFcFihA
zx>z_`8GgR1V3L138n0|~nF08sD96XQ4@91aJd3pu`CO8BHj@+e{rM8b(x^DkK>LPF
zh}`2V;<Up6ymE+9OxI<W`aWz68PZRC(3XRn_tY{<6ft9oU4A;ZyaZhH);=BayUfNJ
zOp96>@`_H2u~M~16mL(aZL9Dap?!@H=)XN0#sqkdeG3VO)FG4b&(@VMTKu3fd<%En
zCaR=zR_)luPU3qKXjt{U>MBa`ngh^C|IxsGL`u*<F4X?)FGntOA8ti_R2oxn3PhX!
zoIHOA5^ItSGYk4ZKJ7LuR<bM^WoLeG3O;Y{yB^0Hj*%*|mjK6b;AvL!jlL#`oS9FT
zaJ|}mdqk387wA(Jq8iF2DN0G}+y+`KloGi>@H9a>Ys&&|8f}Ml!<LYm*a`0rEaWDz
zoLf72T6HQoqrg+^B%o_n##v-Lg4gy!(+{=)ZfkR2-4$iW8+=;H(e+qr!j5JOF2)^d
zqh3uy3AN05gxuD|kl*w_+No*-Ycu6Xz!@U<8`bFQee=xI4t-=h-l_Ifb>v9BZlCW(
zguuVhti8QM{2<@puUEI`8{k%*`cBlIs{`^}34KKZ0*A8Ri;j@(E+Y!ms@Zf@<#dEk
zs;4vq6(;{2gRGsM&hFF##?~DCN`i)RHdwOR7=S(Hj2Xz|^GDoVa+v=&_E$Apmhf_Z
zKW|5ru_>b4Ecuv|C#%o6TLzn~HW~`Z+9K@3eSsQ<%SvCn;@ttScO?PJ3Ho`$thKsC
zlw^IggT{+HSwuS#0a7c$jsL;eI|oPlMgN}@+v->ov*S!Wv5g5Pwr$(CZQD*J*2MP2
zwzfat+O7Sqs<yiNukL#4>3;4x=iY<Yn_Gs4{QA1Z+UX1+pu;+r!t?j<@A$w7r`)Zy
zF73w&&HZaj6}D}n;|Qc5`A<Aqr=ISQsAI-`O#4p6So$`hn0IP`9O!p&<TmNhhvnL)
zJFgy(5<Z!QCy{lMyu<d;J5XgyEwSDrv~0RMyl837`DWaFO9OZK{rI|Qi+O|W(86=?
z^H)VPgK3m`OhS7(n@$t)__RNIBGJ~=qyIL(IEw2kzB#jBD7(<Ok!_CzQ1(Ym#K7d<
z89xJhIoR4**p(dLD&un~T(-#Kw)|ImiG8lx<c8$q>-=-pgXA}19a%<HSyhNo{Judj
zOo$A~JgID*2(+n+r-hrh)WloY(IVfbOk7I*bzl-zd<3`nN{yxYU8_#)i&>aIX1cz%
z;y2-=$&U@>UF_-S0woA5QJ6g<aB>8VqzeX=oys(8b*21G7{oEFJGNzAd5V_4L=<Xl
zyHMHG3pWaCz2bziuSX%dE!NXL`ZehgL(hXN46YB9v9JvqKJxbDgHb2M{a%`Dwn;hT
zqnYmz)bd-CV6v^ck{|RfU%Ev!z1n4|(pP4^y^O$!NmWL|zbqtu!f;9ZOVZ`5D!PKe
zsMuH=DZ`=E*zB(xjTgr!l-^BO`N={1?4P8=llW@}iz1v=J_GFu+~0zS%Cuz^MLJn}
z(r5>v7H+;ah@WQHHX1p!dg49?JoGapw9z-Ux}F>t=nMw}V1h#(knSPJR^E@Pl?VRX
z)dRAoa1z#iZ<$Qw1Q^H1XOUJ3<dKIRx8p8bg#V!fCoq3N?G^?ms-3@ybAhk9a6ddb
zO2&~x!tJ%_Hiu<@7{$`u4CBDqSR$#I<kx6YAAMxo*S@>JN$~nd_~SFPyUNh+yMCP+
zu(SgF<Ydl32GUDPCp+&9DkZKT5~nljr@e;XytIOU>|xzfthN_k^78-ILA-2kz2rGc
zMw2OZj*jW6vm+U0TH1`Aba+LPIVVZn0SCgWjNF#4^DXj2`gFQTEf*WE1t4Ab2~oHU
zLU|v50jFBnbz2><QMg~H_jg!-2(p{nu__2;!c=IiCFnh1PyYleIMdk$=A`(g#*4kl
zCJ-R^JJ7ne*hU-fiecvHOB#{EAc%<-9n}}rhoESa`wrMIw6-gaK^L2+(IXjpwEUdx
zE_|d~Qd={*xC}1vd*D(4!SU<Yf3>`e3x^|MP=rD`iQEE)pkeBn609*Ektkv?w^s2W
z@VFJMTUjE`k5fAm6%keD{o4@D&iW5w+CzVx;G%?Eg-|ilW5oa+?<7cb2NGwtu@L}N
zG_Btq`hSx&_vM6dlA9ofnW*4<e%rQ2qn6y-rO~>|KP^e?kzDg|)SLT-ld9ej-uW`<
zOBL#ClJ=aiM+ZUa{ks>IfLEwDZU9#6#%dN7gR9GN1suXsbM|Q0!h-0W#XL(PSQSqt
zL7E&THk%pZ(n%fmB4jL<ohAP5j89$m{O`(k_SZ$Na;FbV1N*GV5=B>=8ER_D6PnYL
z8ac;rzSpbGGpbs)V8$(Qnc)uZ04?+}Pt3?4XG<$CGD>&FzSiV#-!fZdtQKn4$F}IA
z<!4fCOlgkt17kF%ldcWKoB3H};-Q#{Fog9zh4o9ZM+%C{F6aVIZo=Itar@klLYyrF
zbw=~{M^g5`#n{+EvOW0Xv>Osxo_p>JEzzIIJs;z}aPHi(EFw-z?&8#HKmAZ(`+<)+
zNP}q^jghHomQ!XNm`oi(Y^q(w`tO&kZBebIK@Bvhmpd-$O$CbUi{yzGANTrr_y9O0
ziB2BUxxanGOS7zm0A<RR8wV3i`b;oOMna<w(L+Cv$MvQd5D%lu)Q$VER|c9K5xGr&
z@@&C`|Hk;YuG?gF8BHNi2+<}Ugq0~Hy|TVx#%%|UPKkBfUz!2wdqo5QF?uMI<>4<T
zk<Hq60aqp)HOxU#BIk(fmL6>8{*pRL^=eYup126K{`s$#-*h^mXi>@w7)&%p=~QwC
zGQ$temULjjw-rt^%MGd<MBFCVee8B5?%DQS&sL(}lg~1D5I)=Pf12ePFMvkeOs+LD
zKk0wX^Zs5|&EzL~=N4b|$=Ka&pp&}BFj}`%G4#Y7vFrgau~U`+uLJMDu?~YoAnd2K
z!z}(5(dq+5pb{Xf{*Rd>v{DNLb(X&b)fAtAe5IN-?WbWTj6Zx0X9|mU-E%H}YPHhE
zy1kyjTcc$2rj(2pjqXSG`)CSGuB|_nurQp4{1VMR%c|A-j@Sqq6&ca{$;CLE3fCUS
z(Bm~>cMCr;x`x!wRq=kqfvNOD&Z0@we31;tLGqN-+haSjKhc1Rd87e!3!;oRM}XG(
z!}EsEhPt0F<hc6qDGpA%%HNDcG)U9q08Yl|rI~IYW>LP9G^$Kszm5`?p<0_^vBlEr
zbgWk+6sOFDi>@)#I*ehG3**Q^m8O*!7~7}Z5TBkmuiTK|2spq0b-m5|*Vdl{EhTSQ
zN+t5uXzt|vJl3bMC{aS1?iq7i=Ypy#a&RObggfLugmX>&Yn~fh(!wbtlckJVMES#Z
zDnwV7NNdG!0zKG|m2@cEFXy@V!B*=Kd!l{$OIsNHhwkm;<E^GN%8&G%?M~5F#c#`s
zFQOD@auiWo-ePt|+4Q_w;2NY_Qy08>8hXquOxaqMh}y8@*hdl^t<qdM?LR-27b1zp
zjDbf*Vz#`QK1-<Y?G=Xs>;20G&Q2pAPzVSDSC~M=E_&flLnC{-Pl-AiDGih|Zh#Nk
zSfm5AfIlxj;m$N?lHRGdWh{>ZqqYjzwt^-{i*+Znyyd(Qrt*~8-dZ+`TSt(UYREer
zTUdrmut$(D;_S$hfXc2+T@(?Wr*IFKG_Z+=;kT|NeG@;&pH!y!Pk4Ua#Pj+EQC6Sk
zcP-*^j(}*V-ZQrHZ+xU*TC($%0NFnfk9X{j*cXSfSnz))=JOii^iDYka08@^KRhw;
zd)cCV1)<nSCw_|h2&7t#Uhc%A+MCZV<w$9`>`>A%+Hm}Ci<OsOY9?-C8nB|aKlB0#
zfXjq2<ytVui74W6d5LU=_@KMg?T1Deh&x~oCcoc#bAnE^J1ZCTjKmlO7U`C0Ld}4&
zzd_2j+Y?HTOvM`FJ*|8xzSE3t=3uMH$6FnhS#6A$6DOf1XW^4#7Tc~4ckDDwrxLo;
zR0S+dB*bGi5B*Yd1H&>vW-y&%V41Pr!ndc@BXf94rl^&8&;fAR=#EhtI3F1a-9Api
zhxf+2%wX`8*{x%wBm!iQEa4WXG*9!<I&!}#v$X+Cx4m|XGJgcdb6_z^Smo28MCAh@
zX0jm{XdKa6?M7gRL}*wuU2@KA<(>)s^T8^4NN22~so2)}otjg{*UMl7S=3Yc0EG-8
z5$1)V{Jo(v&dL=Ltd=kVgpd?3!sp=r-`*tu-D3r%1bdFFP4pUjkvS!WmOjNE{9AW&
zL;c;Ng3%gPUPd^m4tx*BCP{wx$N;uonpCtu(B|h4%}k4@@!a$>77KRn)9g)}yd>xz
z=td@(TzAWUJU;XmyV>g<)!NC6gu9`KYoRQHpEQMJN8X38B5YxsMm+WCpSGnQVHg6q
zSv4I@K*ew_6wCT)61(oHWNVt@!<`)0&Q9lu2(EiO^;kdBS1n?znI8UvxG+*Jd$|Z4
z54KltP1pWJFw8;OyO)RI8m#IvukDK3&b`_7*j7XiXOl}Qw2U-?DpAn3DHB!R$M_p9
zH0ISv>ELIU4Sccvtrb%&_%LErCa0RUMef(#?RApzqxmTnf&pvweiFW#MWw&{PL!^i
zeCpU+tFdq33>$7VTGZ1n-Jsi<E>e8j>+v8bi!4aJMWNYirh06AUaVJF`H#B0XF5Y6
zGMU_lwu#If#kuQ<?oi0OX&q}C_>GqkX>kgXP}-`@&c)q)MhO=#@PyArkS$m3<(gL^
zt5l6Gl|py;`ecN6Wa#!hxk1PbLNfJV)L<{)*z$Yb<Msq?$kemv3(9q6;Oz^#jM+IP
z6-#}*R91VcE7HPU@n=hy4%3&5q{<JhOyRd(<_($(Bv!RzI{`r!YabHlBTb7CGQC{O
zeJq?vbxCb>Sa<AvnPEF}YI*9Mw|J+Ww54wB7goH3<bZ*+ueDf;=`s6Ts{O=GGH+9)
zun^uJH>b?U-QT1+<M!DDZ84A<bXoZ-;WGA1A>WTj{X-J;p1+!XWaXFu-(<g|G|G>z
zZPHQL{kNW{KIGXr0R|WeoJFjZgHFQ=BfnNGiQZc%7_0iBj_%9$zf-M_m{-uT<YG8_
z3rpE+FC6$*91+t9e^H}lmhq`RqA=2gSbuAgdNRrFt8LJc`VFgJvXa2vy~vq5SJasn
zxu}}To@SgLtuSmfzePWMP>hwNsmTbrtx#Zwz!_8?g9qquNIW0I#dJ>_k3<t`Dl=V<
ziMVl0ML<qTE1gfiAx|`03Sft4_xKFgZ(N>}VKoygYVKn=`qQSTsrng>VWf4$&?a*J
z-E;H|M8@PafnkK#+&n(l7N6w3#1A?|PSV!Vcl!~K9D@7u1)DYDwy>%8mCmbt<cL06
z?sVH^d4ShSsoN>~k!SI^tA(brET;cPzPH$PGu49N<4%&Dtm`Jk?U)f%uf>@NVXQ>(
zL%UtDl=TWp8`$Fw%yd01m6W+Jl*V#U=okw=?}I}%OXAz)wsOnN<Ah<uGB*j5b!8IJ
z;wnhP>yP%5<h+R0D@>Ft%`5Blzk${aq*EzbGEg0;ube@hw0k(Z9Abzuu=|z6Vz(`X
zV%{pT5a7lVyMiv6J_E3tLcE5+R!IE=3;LhSH5udFb2q(6$0!UK<d{RfQ><AebEp~T
zjMlR>IV|;*`FLPyRsAdRg>WFdC-~D|)k0oJ4WGs<V%!-JKI5YM3im2Z89PRvF`uo5
zIuLKoUG-eb>y&r`hR;WVJzcvAW%)y~NxR~Q|0I?2X!uvKdLNU_zgdPmZCbg2ixY3Q
z)~kG9S*g^3H4C<rOmNR7!^HIwsX=7DEb-u&v)Nd1MPXJXo+XXOji^69UP@FkiMN@8
zi?si!w|cTmI)%MRHSoR(p8xU)X2&JQJ75@Fq?A`SqPMVoXCvth<S)(5&r{wn5X{P#
zW)jv9WryJG+0Y+=+$>$DrVJNii}#V4(;fUiMoFRtcSO-gVPn$}qNO^&p;_9fDDmAy
zHww`reKGOJI<a(%T=VWhC5bAb^OXg7iz~KC1Z<j`2IP5?+iDeOOfw3n+%Lw>zJurb
zsy>tk>5TpIT1nX$t$%=&HciC)gbdhlOO{=<QYI)Ey++<OqB{mmZFSrxg!jO89_5A1
zy2LY-j!vR6H+)(YL1G2UE}kZLB^x*yu7M6Q<Kpr}zv?237oWPP7Vx!fe87)464#Pd
zDC(>^afI%cymsV??MV$6t7&+R7r%VS8R*Tetf9Kc#~jbNiCD+_u*=;_K#mm<V9vve
znAZvatt)0OB-S8_iN@@;d`JGtGu=l?0<98(kEMY(Jq|pjmuR-1sBHv=ZoHq@8u=$K
z4Goh*HG<EX>Pe15Y8wOm)7uu#VTdQf%$GfnfE<w^j=n~w(rIMYWO=bvI7$nRG5pB%
z7C<&juJ}Xi7^`KWT2GsSk$-22DDsim+xEuywiOTU7vI*q?sm1SfLp`j%{rO>lAo(z
zFs!gG>99n_7>8m8h7?x=B6;1~{A#`{*iIJZ*_@LIa8Vt6>+N?={rKjcVb<*E(Z~?T
z@S{E&<O7aNXLVU2P66MIUcte&8ZM~T@hCI=bTkjmH2aw><oUx84LyVMhQ;Tkl4gqW
z)Pt2eX1$S#f+4G!(yzOP$x;DvQ)Q>tyU_O@)V&dBpeJBXZzF}v3;Rp4;86zc$g~K~
z)@7cu_tIg#q<tdI&b<*gfiK=;<VicI-M$dr-RgjJR%A?rWCZ{82yiePEu%pAv4OQg
z=CVXXWA+=YiC0)s9KLZx)3D{!Ick;~@8dlU{Zxo{QqQfY?3u1sLQ@I$$G7o^u<wI~
z3WG)*{un};=;ho=aRwtU@SD>e{%Im!PQMT~s-B&SW0wi!DZ`_ZdLs6nN=978-f-z$
zc2&$pC_kCCflqY?M5&90l=!KD7-Q49-|vrzxb8)tjm#)xzmuPAnVcbf1D1`ZNUAal
z^S2?r9=5Krz3CD#(_{H;A0H2Ki?GH&n@vq@Z5WfkaS9NI2y`H)ca#F!eq0gKVgkPM
z_>(p=iI<+;LM2{qR7#YK{jkVs>XY`Iw5~2#H$QsDrxj-b|3p*&_-vK{i1gXcWsTp#
z<Ilakg!OsSsaRKpGPbuOp+sknBnPjvgpEcls}Sc*qS&yTACji~?J!KerDOJy^mjI#
zx#QoJuFA3uK0v+Qd07=Hkv^TBdsF(l$Aov+qwv59x4VX4q9ym)aj@eeB4LM7vM4Q_
z`znZojBM5yk?zCiV!@5-R&Ac<lUa&~9JbT<bD8PZKY53?!Nn5Td_s}GV9xR^K4~f;
z$urR<yPbotj(~~+iIE2V-3*FOpLBv}=r<F>SwqJB%h9{%E3<)(8fSl?JcX23@!u4R
zlaQ?mzaJi^uGodxm+dB?ho5!(3+cZo8H8!JQavGcx5TSpwVo8?tU<HC-p+I@U>h&w
zd|IX2g<F&i-CKu=xDowBIm)WKZbWr_O6o}AE>e+sLun!KWt-w)i5$m0BCr?a=R$`c
z5?^N~P2^vv*ywN9?74*3#aDD;ycOqW8~LR&Y4%~jS%vu*I>QNpqvzWFs>WMj?X`F&
z3zQ_;BvaR6uonH(xrdHqeBD##R_Ln-2x)h*kJN}y-_%v^m{X>>3%i=6wD4B6h3{T0
zk^lDPbDMKx6USNe(EE7p0DV!If0)<50I*SMRovDF*Gz#?J11QLC9ENIZmtgWA`^C3
zW|*8p7KFKa?2@Ggh*t3w5LcdL?hmS;d=~yNQO^6vL$cyV^yY-*=>|adi3SIp$;A;t
z;Nw#izGSl!*rN>H<=Z>G{;pwgy}FQzoHgx<SjEYl^FGmc8+hv~f*G|j?Zi+6MhB@K
z*>6txE~C<eGAj0}-Xs6At4Lorz<>bgPwRn*8d_arb66e~Hc9348pIbz*V`XYW+obM
z%?WxmSX2z~tk|uZ9al#qj)<AObJNnKz~Krkb0o{@EOp4N)R!#q3{i1?!+BvJI<{h6
zi@kZAYlM}5fZgE`%6(y{3H6EI?mfQu$Vo;tw<EaWtg9%)9ut<^ompMvlbpMX>t$QQ
zMShi^sN`!A2djxvK|gj+VFD|Ed$xbqfR(E<faU7cEZ0v+WHDhii_y!4R=#Isur6E}
zrg2z^qT0+z3(<TtG-gfMg1dLkFH>5g+OAwF%f}l1g+dm(e5a04OjFd~ic?P4pF}*t
zA!2wUGn5L(fs2ToVPvqijW+@Z4;{f?<Q(_QDl+Lk8N`dBiCXn=b;KlEA~uxCG3^NY
zMI3UM1T~7e39IZf?Hoa)AxT5^gUFz#xQXECtjVc1=?>7}|NcEHU9>~2B#sqtCD~7P
zG3`uwDE$h<r!B@eTLeaO!<IVG1)~9+;|rzQcx0FY8p~0~`p8arYkwpC4j3D$(nxK7
zseK_$<3u86P)x&A<E~o2H+jT1^`UIzhXb#p;tTv)IIE6}&iD|<XHL?Ok<vDqhxnTn
zdD<a121@;g<|ena9Vn>$LqrQCaxqa^aveuZ>4evDD^foV%lMS_8Ms9&K+j5rYuD<6
z!TQ^fEh8NN*b>)&2BB_7V(EB5yisuR;qTA0m(Za?e$6Pcq$)&s#LT5mpjBv}&ys<I
z9*nib3>&6X!{=hYk#ZZ%0nX3kh3=#csYfj2A-y2Q^)Sj!p#_G44Q#v;IAlN43ia}T
z5iyi?uaRl90XhXFd4EEu=BP3p`gseC^rxTe{Qr16)3G1zqAchGz@manxEx*U&7lg&
zpa$0IZ&?=-%H==4=6b(}eHq}BW|w8T>AZ~l41*IC?r=@p?FEpTby_~#`sq>^M$nsA
zOyFL77u?@Ly2|d|AFW-zyo29FeeSD!`R>oP8krr;_7L6qH9uGQjQc0CeQl_X?3HNJ
zs~@>X&%fYDr1fl4SQUCM90r{|#APk*{J~gF&7q&OVnviGp3o-M_L)>*N{!92FuoWY
z7&4){Q;jO%RX<y!(fM?1?@VM$52G#_@dl!zSsei#4F_zM(CSr`F9uCz)Vyt&+Um(4
z^id^G?__r~xn!T@&v%1U4-9GFmgf!N_2x}yjg>WZO&go8m9HZ-f7Fd2xIw464HkxY
zj`JmIM`E>2LoKXjUmMhK6%wCyjumxJ(7WGRk1puPJwIx9%v?0fL5vi8Ytw3pD|iT0
z<&_?Y6*z!DbH=qHZf0!|Ofd5v7pxnFzg-}PAH<rN9&Ak>n70N=Qn=zImvl8X?1%}x
z3W4ErmE9Qj_Wm7(9z$0vfmsPGt@H@S8!HrraiUSiX<8}n`N*f-W$<2e1d}Wb__wDJ
zc;k!Gh+OehJMtA;A1)=p+%qHq=Sv^rD9O9XA7l%NFp=I(E7s1n^pjeZwyFjXf;ObS
z$)I3M%Z~uV4|gKLckxG2vROe(3{#AVy=BQm2)w~DiUQ+noO~lI8_GVE*}}GQLDz%S
z8iQ0a@8w@{b{w4GSO+KDXfhr#OesVk$cI<Z^n2>)ZQVHt&<N(0NhKd=KC)`V?n!fR
zV3=gkb7-thkYXWHXnr5mKyO}QFa~1Jae6-R+#C~oAo5DFv0^$k?<*0|1FKixF&=0(
zG#K7W(rrE<_8)hyR~DgBbj(GP=y$!p8h&v@fZC@_4*%2_FCQwBKS+y707hPt36_>e
zvF;y7j~+oaApWO2ZiI;X4)%YL`3EK6hBbzpRH3Mt$FaJSV8$h*aznDG(*>anM2?u@
z5(oBT<DTk@lq^|)2~uneY}*eo9Ye<0`Df4-rit@?BUY4A?U$dksAz7I6vCXn+!Cpm
zv{sDb%#prg))=L4<D|6EJeTcCytHj&%_LlY_wag)bsfbqA3nASbnMvuvDB98qMu6l
zGRr*tqmOB0v{)xCHID*T1pXfU9@*F=q8AnYuW0sItnc73B8BB1xw+J-{S>>~G9mfG
zW06hCx7^B4NxpiLJo6+CUdD++vl}Quu9O4Ac=e?5(6NrBQ2k0~rpeB*G4UvU*(Me2
zi#O~3>Ke_3f95=7`Xxb<RgLq^mmGw1p$s}nxtR~Ql1t!7A;kfvc3%H_Kai2Z<W&=p
z|4dghDRQ{h_u7bp6TWvH{5fuLzQQlrW3gLP5^x<pQtuG>SokGmfz`t>NKDcww>RP1
zzcND|<3}Abmi7AbsdbbhBUt&ZWf6RGGcmg4crqye2v_WcZNcP~!^Z1FTU=Ye08nPo
zXM6D-XG#CO%QMAbr$N?pjEHPYc_N5J!2(X3A<mT-D@VlMaQwr+ly|WvT?HGbu<ssP
zvmwF@JQaI3enNZlD6W^6mVs3vdCoM_V*2k+A1{Yn5<Qag^M`9oAH%IpY<_eOW>yQs
zw8&J<qW4C28&-^|w>XMpxOOtRX}~>xdG|J*6mJO`$5^31vV{t&ee{&m!w)sPhOw&~
znpM~G+2f+oZG9@2dfe*^(G$%|05!M27^|BYf30rQubv@!Z;U~rGl>lf^R8%{?9x#6
zkQ6wMbs1#Huf7pMH4j8<!WA|J=L_LoKV*RpVlmdiGH#y(pGcDAJ|3~1KbDcyBaF}9
zsZoB47L;%b5}h=5h2X5SDVGsbBcIHnJY_mJPJeYrd}gF#0|ge-=r$x*e-#FH8*^4Q
zLD-`(uB$}Bk+%i%CI$3MNO&*x0NNB2fa4S%#wEuPzV^a!`=I8wItkwWmY)qAY$JrM
z)d8<wk;lib*}W%T<1oM*K|MAOz?U>g+p^sqm)+aRF>0>T#^TZ2%|zFKBuu~NJ@Kuc
z^gE4HA65e{BaI@YHB-EDH+ymJ!2wj93`m!So6*vF(j$4}Z-~`oWLB5yhWKPaN{-u@
z-BM7ye6XHNYQTVdJG>seJvDd#0<^Ycibb-pr_A)55Bn10N=tc@FHOmPeE~)I%SXgG
znU5Q6*5gS{+>s@smoE}XpQ&-S43|8@euc?!+7;<6mgXFb&c6cp==F1z%b?tztoLK9
zMD{L0iW#cP%O%9^p(>)i%xmzTiow4lBv$$EO`74bb!^je)uc_#fQ>o6&7ME~x5uou
zR`_q6gvj_J@VfUc8_!%6cBIe+v8Oh3Qz)wQ@{gL<?=7;0WgyjgEd%aUWfK^2+bLE`
zFYgQGa*G1RP3#PbK9BpsNNT*9K^2y(h4DPTD&?-HssLrMAR1V^aQg&8-4n|YZs9Qm
z^Cn$_O+aI39>w-Lg7OzA+0Pjbzq<_>^`?B==3UJtr!cMlpnnh5v$UzNl!^Go$cDTf
z@b72k1~=r55bQMcaf4z0qscsZJr)w>>V5xWlX<lSB{Zi$@&NrNftJJqi6z{HK?z`u
zV_2h(Hj_4uTRPOP*3j`y$HP})uK`Vo?v5x!!iDXn3vr~{uHs*1R01Lor-afyaJcG&
zG-?ov35mAnt_OG{?5~{8uJzs$_u<<;$ZP_eA`SRRFfira9X(ibg?k(9ehK9hPR&x9
z{G4#M(OwyQj2=vu?Fo7>m>?>ezyr=OXU{n`P0+ozev!TXy5nl=58sqM0Y!~Xl3rMq
z$#K(lEe><-zUq<<C=**)ZDymW1GZ;g5f|*{YQ0k#tuwIxDmpe>Ct^mz#dgJ2hrv1j
z%Ya*nxcngM5;EG59rgQAE7}wiM_TN(qna=mjK6ONlMgui7{g5sVeDkE+F1xCj}b%k
zcN5>z91RQvWKRhWk4@;}Y4y3yKCZHQCPsfvW?882eY(&(KEQcWZA|rAGPty*V4RVu
zN{7|5G%kiWn$2f3we7sL<J!AdS&kv7531cSOQyQ6S8-rE864TUm#vG5B8J2A9S<~A
zbou=aX`)g;afJ3q7@3bIZ_ZphT_P;#(4aC5L^rW<N05j;0bpM$oBZ_9VmN>tRE=;J
zTLEI~T`~u+xK|8UhO~;PPR1<>ebNpma;UXGI>b9i-u}A#X*$isU+wxgI2dAA1@8Lg
za`q@U)|&2mcqhytF8_ebs4LiCLpa>LiB@KC+McZ(z&_(VpjtyQ5-trm8)Lg9ryYJ{
zM|s{p5y_6$YC&ekv46^tU#C}CoOFm#AgVsCnYgIQ@cI35x3X#28oR+r{k@AO;BVK}
z)tO{Mw{)|eKH)g6qfTzg=SL<?SN#cm*9VG`FKW{s4x$~*<IQ$3Cg1N&_MeMZ!1uA!
zs4C%Ma^FGEIS+xZa^Xs-fpC?A{-A(=_|wOPM;8rlPWWvjVYBr=9d{kwnrsTu7c7C9
zi6QGD9>2BO{<JWJIQoNM+k<f+UMu|Uo%Blqv%QaPX?Z~HC&5w%4JM`6m|);}fpotG
za&aI0%rBp5M;xNk@C{Ip5&q@S16lvdkCZYhO{rxZBdrj>VG=eSk>{$p@1Gvo1o{&E
zIp_hSFH}yUaFm>0d>>}qIwxvlP^Pt3J=n~53LJ|ShFhlkRz2}fAd{*U^aY=tQDP|B
zY6$mBnDuAm6{8#9*BesET+>!=v|!brNSscrez>*<*$b@06EaU%Ge>Fu1{2e)8Kxy7
z;HdE9E}6)6F{EclR>rn2NoURp`lG?j9}<*9szS{|lPKyDd(a41K4cT;^ZjUK&e$)C
z3Ih=&vN}f<nD|3oeN)e^uAu#^gFe%HVn^jlV;5D#^Sg_*4^YaMm@aPKXuGaw`_Xj%
z=G5CK)jii=1n9f|a0lnEwCG&TiNTtX%QrwmS&|0?mw}TDCZv!Z<3ss_bDdN=hq4fY
z89Xn>^bhU$={?!d(NY)Dw7Tuj_d=c^!N%b=*T{7fQ@d|@&}XCnU~|kS+%~``ISkWU
zEA#r`%MbSBt7jMpU`2>6MnJvFC4d6@X7~~4-V{~P<gfni@U@t=z2c8Peo(%u{Ruka
z7RPKI)UlOKrUmLEOi5+@Qd}`7c{)L*k#j#yYZrwegHGagi!6aNp0x>oW4JC{cZaJ;
zLC_vZJM4EUE-_W~FQCF*VbL3Az1Sw&h{{yC$=GGkAuma8AsKhj9(YlBN$Ye@Y*BuG
z)X(vHQ%uSxZk(JzQJw5r_141%FO~t98*mpU0rO8`Y83(sYr%k7pw{&UvhQa1PZ<gj
z4HS2Yno;)vHpn~;A4bX3V5&uX;71&H2PCtKSxpsKH^fN$-K&)SpEF=s9+mj|1Qc1v
zv8fjlqBvqD^V5wrRDLWZ$RsgJBr;D_-*DFKo|&7dtp8o$PduDo(F?*I7M13&aRiLu
zFF}3RV!M?8s>PMUlhWpu0CFq8O4s<$XxzQpInaQmV&@G};?ff>z6R>Z%zx?FKJtH&
zq-s&h&aUC`&+Vm8?{1|S^2Lcf-y!x(LYfe|O}A`l+-VF}nEUbU@TVyc+{_W@xvGTe
z#zmBqMxaqJ^)WF{CR6}2X)!!&ET7MXWUX<f(pKQBclm3O8%oQLM$ajYf&z6fI~s;!
z5GO#T;<<YIOqPyvTSkyd4vm88p(YR=u3l<j2&_83)@qW{9U!OOCJ&`sC|jP~#!ghN
zDl<db!^DgI%Y^p!&rdt1g*T3y7Ch+?goM>`Jnj<16;SKeHr`*EzY0G<eIp7SL>_N9
z<X88NjNd?b3ZN^A>~fGB8lIGaIw+ZapW?*R86}tAE0%<<w~l5^Jh#diLn1i*y(ELR
zt-Kl{kJ)}f>mY_AQmfq7TkJp}93Natxuk(Z4k%-xv*oyB4;GV4CG!h&MEY}ZC6C7i
z*LD}F=c|b3v^KnVOo$v1c-(3y-bH3NU&8SF+uMH)WhHUr^wu(UUn?6v0f*b9NoM0@
zEJ>%>ha+5VJ03o<(S|LmQ7bci+C@v189d-P?ceRwr;On;U@B}(D;&*L9R@aGR)?{x
zBU7XnA+GoQF?sHoBix>eSGO`$t2e~D@>#$;qdB(>yu7;Zd)13Qe;eNw94nI{wF$SG
zZylKyuJg}KtNb2+m3`&73$5h3ktGrzkc?6bNuV~GZ&3c=ulBqu;b5gwp<ZPeAeRFc
zsMcx24Svv)krl9Hj1w@A>xbFA(4Ra~Q5*+T^jlQ`He6mdu~;T8KH@6&cd~lo5gx%w
zF@lb}>rYNUSG}!xB}tqkb_%EqtjNZB&^a{RbL@ZQ0c@ybjMmxux{d2XL1l0+)`_*+
zzwPmyQ8DYx&2;pE)%nD8S@Pd>@Z5E!##2){lhbS4&zV;JRS*l(;OoLabe&J;iwk-Q
z7fB&oAbaF@kqLV?p0i+wS2&XF)@e=pud$VVx)p?DZ%HpOqK<zFaDsDk*LgEH6Hu`?
zfrOx7-+BP*?U1h-srWgWOm7&M+Ttt;r^CPFF*-RtY&};KY6sULS9iTN5FH1mSP0yt
zd65=DjWZ<&=HrVzesd=G*1T1+3PrV<t<hs(cEf-o&`8H$3Ncq$_Hw|4n5I-D{V6O=
zi#0%sV|7$VF;MlL^kO}{&2p2Zom-ii0b@92RhE%PztBqea&x7JQIF-ULEu>JMZ=U}
z8i!|Ao~;D~1HQ=v9mCs~1}NVYWal5F{>tW}FziNcTrJE?2(;byEGYgk^c#B|_Sb?x
zxD>_QZ>tTed|iYJurGjBMI13g=5&6eNw`L%Aiidxuy?J!kkE#ZT>d`%{Jb+PRb^F8
z04Fw``sogfAK%9q@fTCx80rrOAhR9RbgSM!0<zbOO29NNuXOWx;pQ=}2|>w^bQC(v
z`T$r=tt8S@#fsqF-5Z^Oeu2MRpiveJm_^9%M-&^uv?IJjJY_~e&JF2Ir~n2ma(I~3
zu5!r7najq<Hw?W(xb#04Rso>7mdos#ZJtDpCK$_20{QS_vR#Ii+**=rFyU$0(f1!G
zJ}r;!QCE097gDEgTYE5ZEPktd5@W5CZZZK5AmKAWmPA_sBQ8!Zm+(7if*?P45QWO$
z$aEBkJc20No9ZDfZI-WK4Pl#Q1%h(v%7ppF<7Zu#FV@}e<I`VsZj-R@EG$#$V;ADD
zaBE$9LY9|WZD^;54i=x^dLqn;ie4Xv8FQm;_9ezTMPKqv{AQV{p+~3OQGzthYj#xu
zE3|clcKFBj&Tx5z{P>$3E5g4p4tob`R5G=V(4|b18lShf6y@Oh@7iXK-tzAQ^eOxA
zxEl5R%2sv_1ta^!J)?y{{2DCIq{*omhd#VQv&?{E?L}Q-OQ-(yXh&nMep5PL#e0BF
zKjoWp!FPiY^w4j~WKo3pDX4!uBd*O}3@kK<%T~HfAvB?rJnIOB!;8j|w(6hn{=R>X
zSU5xEwtG3}8sz^|$Nxi`rE0c}Bdj6K5hKWLkW5Al53U5nQUcNzpKo|V2;lzmfkx#4
z8U5hB*}36F0{opz^u1%{2>#1~?SjhSQx$e@(G*J8JZNuwINO*jh5!ZzTH7we962QN
zqXZN!NEDXp(|d~5xfgh(7Lp&#`G)LU;M!+~NkNhrj$!AM441p%SfkXXS_xs^ScVHY
zgU8}*=s|v<-Eu$$Sp<B@RyYUnj18HHk!1qc&se~-K}Ujp>nGTh3}HaziiO+D_TMy1
z0<89E(7j<c)EYcv79K};N>?65ayROI(<Fw>CsmYbcO)@RP=Xk<bzYk5-sWE>{gIcI
zTy4pi??j~Iaxxl*Z=8ZG##^pCE`aWlZ56yIb<yEjDLowI)}9CLHyejoe2X^;Bj2Lg
zHrSMA4w`n9ncQ4&k9Tc9P<1>K1!Ka<&Mrb?dW?#?cYZT6vJ^_;)`}g#9{v-@bfoCt
zuO}fZyVwwE`hz=3%2-1qO@^}47gM<=!k<r|FL%MyDe@kz7~o^5&0F!X9C~?RiKvFD
ztjv&zfc!qi<yLMV@DB*GWqy;7Rb>w`HBHmG!{R?b%XGiJ7bum3$Vk~<Q`ibO4vvmO
z=7j|X1lG%Pl?F#BmilMkW~F2?;;RrT_=mf$HNIyJQ<;S~U!(i7@EcoJ$2KbyWCZfJ
zsp_?7QH$CGW8wXLZXO<-KiTsI;e@#9idwR4M8XFxKQgS^lM>>Tm0Ud4vb(|}>b1)T
z*+}t6Ek|)6uFH3!;0G6gH%&62o^QhQlQ@`z)G?hNJ}(@`6wtW3&P5LZ($um$)`+`!
zTE)q7LGt|@c<X96LKpV6{I0vjEu#-b==w_w$g}U4?osNq8U$l9)2IMJFsLj-xr@tY
z1Jp*h{_`?`VnjU`V|QLt&1Jnq*0+gm_W3sh1OBfbg@1%UgB27Mu5TxpMu&&NC~nVc
zdux6*noOiHq^GCTVzHwC@O(VK>Um@2;aQ)Fo!jAYIR}8=Q<<DvO=gP4KmHr4S~trj
zFw|&eo^!$gr+od;R{B66oDI@uE5K?kb^Ok1SCW(;9s`T@jU--wFl4jCnU0dO#8{d#
zQp3sVzDB2$*@Q#D`Fy2*cD8{XnG?i%?{vKq_<C=w*sQqQ@NI|STVhNDVPR-~&g^j|
zRG6Zf);+%`*-wnL{5P8$00oVTdY)HS_HQU0W1$3?sCnSfo{Ezwa`SSW`vv+neIqyf
zqn;0^6v-Kr8GCzs3#Ib4TCFvwnEQW25(E==)Qp07jv~<rjLU2>Awp-KGenD%9;`#K
zHN7Ek#&VwVPehYH-78UJV{_1R_nvn8A$)zm%9TsO;TGB*k8OL6Q$%gnnrIY&FE1~G
zjWQW*=3k#5*E`ufuHfAZRlit?iF4`HYn)F1p3Er#4<*no-^w<Mw`ZuBe_0V@TbwmV
z_=liNZO1BL0iwkOv1_{@*A^?)H#as?7_^|DVhMS$C6e!sXP!<LLTxGdo;DoyAiM%D
z#6^aaGDo0BClrA;4w7Z5qVVf2`5=AwVDPV++Du;Q>Ppa1cZ{l0vs%zAf)-xUDXigK
zx2J2}$NhplL<Cp?bOJ0YjdF<$>7>3|dwaWhEK%@p{e<uR$2huPiYGwl<LJox6yOqs
zB)C$<ms8pf<XvcUTGM40iR;0ed`m`iy5v;1jZ~^;TdllU%cq&E*P@Jsn%A7!JlW4f
zG)zoPA|ju+$BUz>Ok!?_BGpQ@)zwu%=fP?Mg)}r0-Vou-)3&dC4zK%E2K)8QD6#MR
z-*RQIXk}fbVh8c0Hi+@}p2Oj`dz(|7yWKc`S%wX4#wuk)y+}WQ1_;ahn2=`Vj%GKF
zTC-l-9Mhw3l!7Dcs>sQB77Vb-gP;ow*zXOwT&(GIx>)ZHG*p?hnoOF_7r|{y;jx+^
zJ{^Nd{T$Y7_Ywf9LmJ>2Gpcw~W~r(T3=i&I1&#wvev+y%>}3&Abe}+7y{#ItfGf8J
zEo`!IC^8|VPRGzkyU&M5KUjEpIDn^PXq($|sY-kphn%H!?&qk6{8%}G?5u%LR>n;}
zmq>O@p=ub61r_WA!o$46Ui0pF)9=KHat0kS5>5ut&e&A2-AkX3qSV29dD>%T(M$Xq
zt+3t%6tAY?`cb70y?{&qa4&EOcRrW$OqvIQ62l~HMiOVrzpC#A4>fB`BhRnbW0yl1
z2SHg8Kb0ufN`Q=4{vs`&6ng}Wbgr4T%Xr3ScUcB03D&+|YZL;@zPdc~itCK)21erV
zo_wQCjqLJ=m`l`#JIx<LI}n<XLyZeEcJ-klehTVgQBf-8D(K-Nw8?V0{2zbf$?cGh
z6crUmV+c`*`On=tCNtQRzBQ`YHsY@GL%!2JF$1yoMXH}NkADRC>KS^aUSGv-3DZLN
z!`Yv&0Mn46oA!|KIlgJ)vYE|>68k*QOEWD(_Nt^A{9B9G$I+HGEviyUR$g;GGAI<X
zge-+pfU8um&0*FbczrnY&uaI2X;218H6wzRLb_3Bt;nH!M*<1}K@s|2;Pm7zw*n{@
zkx05Gp=u$BxZwPzlWAax#h}NJMEJvLr_Z0q^)hL~lGZ}oqLi&gC5n(P{O!NZ^=a?~
zO-mZeMSc>u(w`qNwmXqDL{h%tJKwpyU!5c*B-GTFcKQQB4sNd1cAIm{%@AbMA8${!
z?U7P+78v%`n)lyqOhNP~mhhKV#$8_>Df?R-+@p)Nm6;16IXEws_huV3)Ny}nBqir>
zKKfPuettm15$!fRFa^Z^0u#@Th&9_sxU!P^_Y)To8i=bmpRHN_yE<=^+_wPMyrLqJ
zYPG>=->;8W*-VaR$4#kM1Cr&xlygpKT9QlV-2`m4ZacsFCpF4a$p4bEu(Ik$Ko`z&
z=??^fyel{P@T_wx7R~sKY>+{g>S$VtP<mu5sPMud!0Tth{gbf3et!fA1O{Zs$H&{-
z+sheIFOm+88fU2vH^CZUmz<!VTpU;+fb~lv6@!irz}S!lj?K$x?-_ak>fT|11>h%y
zIG+;mL1Kt`=MF(r83{}=s!^D%#vl$V0E8wivC!bnz}3MhdUo;okLTvMh@d-(z=wxq
zjRn}r$5aMB90|#v6WMdq5UX<ix7cA7efqQ9X4mkfN18;Nk@At_6B1b5Zx5oP5Ccd`
z<a7B!(Wk4c%QV-U19aCcEDoT+baHZHI1&XiLVJ7tjWBYtD7W2UoSlrgSo-1Tjxk5+
zdlzKE8W5;XJhMZk!(cc<F8fFM;^}lY4+;^l+sEqz1ProvonAi+_CzWZF({US+!FuW
zc_YYW;~|Dr+#wtt^rV5#<*(*SR4Ux4s1-3gJ9>Rgdcws@%~q={3=G!GH98>Y3Fg^H
zwS!rAvl~imv)S>m)@<qL>hpdxD!#1%+TcNbDI5x#acTJIzf^Z=fkFsEF7{pLRI4vy
zT~y8Q?zL{ur^!i~KAt%5hK7c|kjlz4bzSd8&~j_t_USZVC|Pt6fAB<S-d4D6(0o7x
z0~7KC#V*jZ^S+znBqG|I_~d)|tNi=-@7mr^k7iu6hfIUX%Pk$-d-zAC0{9?>ZN(bs
za(F~&h@I4M45}DV9C^nD0G_XRg-P`LOhJ6e^vkj(M3mUD2y@~~th}EfvZNjco2+~$
zN44=pN*`>1r&tU@yZ#__9T>lOG@h#Pa`hF6WC+$zn#J$!0TN}fUT?XW&4+k)1RV|$
zu;~E;KJOjQXP{Bi7MsloYB7~z5)h-&4i@w?TjWL@4*sB>kz-(BRNizvtSbcErHC~l
ziDAKl^>Zs;sZIuqLUd~%{1g2jlq4Dn6VpJUzlAPX{n7ZUA0h<^PzfPb%)-gpij1-R
zW11;1E3XL&<PS-B!C?^)q-ABNrl)IgtYD=gL1fH)ej&=l#o2^;XJjL4(*Fa7`9IuD
z5<%G)14iARsGqiIsHk?w#8h;2beEXLqQinHkXR7^(H>#t!2XA(3EDrQqMlNnCNllU
z+WZgF_`hQ||K|e&f7a1FwCR_+)A#s%gFF}|rU<Z!ok=295OnHR7a%jEOl2$#x@eIP
zR^hazgg1Lq)`9zfU)&2CuIs5;fd8+7NbdY5SLFghQ3!Y{g~B5V%zE_al6)vzH{g;)
zDu)S4V5nE0I4X7qKJ!@Ky{uFWIcr4ypbuYALouZedGNH?BvpQ*4dD>hQ(9sHI@XFS
zIG?uomJJ%fo?2EXi`e-H0WGRMG+3oKmy|BKS1QU{(#lAcqMGLi-FE~U@VtGOhEcn^
zI%q?xJCqQj$75h?F@7<YAr?%PFX5>YE-8JEy{O|{Amg(Qb14N{!wqOMhGg4xm5hnn
z3CiLKE1=K4(yr+*Z9w?DGHBcfDx)^5@m1vtRxc_kn%Hmj*w|qjR(duq=-w=vN5kuB
z{VcJGB&~b5tN|2I>{>dQdXgaJd%*to|A2rj$-<}rm@`~Qmc2x*{?4p8!bAU>`h_EW
zePq!NI9zJcq=WMfWU{b0_P>@zQJg4CiQLVrt?+=G{_$FpBCTYr5|vGg&a)}noSF1E
zRe;y+bQnRprnRIgX(bt=>BHorsl$LX7;~dhI9xaEo#r?LK;8N|rI`0I$FJpSQ3^P;
zS=I+yi1#LGC1{@26PDKfpcj)>M*+)RQ3N&%{J!j>9a$4FrJGC_Ak#n?u2Z$lE&{OY
z_!H2>O$jsL6mk$E#o!c*<%W3gGARJ%&RohNgvWYzYMXyywT$pjIR9Lh1X}E-bKOCp
zS+6+%j#_iaYq4qh&2%L6*Fm;I__0|zYK=ZQ5p&R-IP4#+#~@7zVM=W&9Id6WI4l99
z^Fx;ca2BE*XV}f?Pw^u)v}49U5*5UIK?NX@knkAX3jhm(4^<^<%^$OyLxiymAg3B7
zZl#FY^al<ce1QCaS^xhhI5wYDID!Z)AkdB(MfoG6DAZ2{F#sdqN48H`kQO?>2R*tv
zsQD2igT&+Kf80{Vl=>M$uWn^x3>s1pR@ROzDU%43kL<rItKgTCyupHOBgkS0=Pmpe
zYIND5hYk{yogpH;Q3^k-N5zxM79zt2+0Xx2*8f<}{|c!6ADjHY^Jar0cD{CGX?Oo$
zAi~`LCq(%FTe=0v!Oiu*iYGK{NZW3RA@)3}!*ZLipS$JE%lK)ZmWX=5qPzMN?d2&n
ztv%#<Sl!p{`|3^$Yf94b;E#%;pPPjo#1Y(*r|(D$f+PA$pejE{1~$R=K++$<N(&|N
z6_WkbkPrhWBBd~@d*lgu-m~rx+^*OWzCNRDd(MW2BoD@d<KWTLVI7Z8qF=ER2*E@X
zQL`+@q(6Z#+PRj^DYn*0{^7jsyhKpZFt(xI?AWWGdda!yymsR9V17rnA!_2P7(jja
zSkX$sOigsz$Wod3erPdO2fL0>t{z_ZJD!Vot0=7#4uCEmcg7-p`o3k|((z|QDz#Oz
zI<dhycglOgB-n5+dVO70r~SbzndkMq{o{jIrt-FByi-lRxbR;sC*}xOT?q5{wuYsD
z*hZ}5KXnK?Hp;B{DaE*AhYVv;Clv@3<BW|q+|aR512iyMg~=3T4CU<z8R2%R6LRu&
z68ij<?$OnxQOMDVM81!(in*S_St`2!(`<!|Z5rbju58Yu?SgE)=cwHL#RB}rX>E-1
zPthQQAVKBL+k{v9r;+jcCtl@R#w;wF9MzU}gQh7`5bPoC#?mVXolcW?k85xE8&I(>
z<hXbFaO=V?8YjD!tDxJkgJMQB4EP}MmJ=Aip-hqpLA$Z>yn$yhR<g-P+K0xoFX{N#
z>11jMB`zprjk17ZajKHyq5nqDFQ7{$PmC>mfGaFM0%$J;02|E63*#=h2QxIpB?!+M
zHnyZyefAi0R3R}(MFXB(lBb+bLC>PuW8dsB=3$Z-kfd>qz$#;7f*FIuwOOo;2ljfH
zWl>wWMJq26aL_oGIYYt6U(aueknjhAL$!&|@K3^qBpIwg*4C#=-;J;EKpA%J?81uv
zoZR>+c8n?6|Nb((hk&-)>0GtXd$j8e3zG=#O@!$6vf+<{VwlbAE<%Pn45S{?n`wW}
zP`Y7;h(-fp)s3D#vF_@YswEL5t&t_LFFRNs{vqjN9~bxM@m|~x&3VMJMjBQ~a>%oj
zH`-H0Hdq@o4}9}R6r*uS@(da*lG&tb33Jo@Nr3Xba&#<5hzmLt7CI7129>}@%}(+5
zs_&r2L3c?zkEyal!eP@;f`>$U)x@)`<6l&2IGGvB-zVOwuzek8m^G=$NALQ$mXOS;
zhWV-Pb#tdx_T(+f_P5K*7^7HyF5@_GBjmk>A@up5>hxV=(z|%VuY$U4sC+0+aCk_w
zSTlKN>&eHX7|eKsWnlxly!mM<LL#s?NvjSB10wfX<Gc#co?d}@$Ef43oarbhP}S{V
z4e9e3{_r=u3^LoJ+K(Bt9l<paQl%FhX)3S~g0UDtkW_Uw{<<(TcCf4x!R_Q9J)jfu
zShm(hm5XY9SmVIGinn1az#*uB>&UT{{~speZ&JmB$PI1mBsS5W(iv1B^7gUnnT&lm
z5&dgWka6Ktv8<RzC-$_J3yj%tgz%yf>onU;LQkM_UQ9X5Na+B(o!Sbn8M%yNu#^k`
zS`;^<_&=W@9)~rQDO4geZ&uNPw~DiM+AFZeGqkjoowT3UzTv5>hZlgrvG_h7SIK+B
z#Xhd3W&esL&kI}_(&=LWDmjNCF&WX|)^T|6g<LCVBlv8rrH>5@ANg~Ev*AM99@}b{
zixH3EKjefzG#bgQ0XK&7(H`o}t8{;C&ag6*XUhb|Mb1i`iIX1+h-vN8)^EWgh3BLG
z+%2KS)!`&~%g!$$20rdfm)a)s(rJxa{T?_M_VX}sbK_d%=+Q4Mv(ah6FVF&rPetv&
z!|$^-H5!t0d`xsvHxyJ{>B_+D_9Lk-uc4cqSv%hA7<t36MrG?v2`2h;Lq#>NpD3_O
z{O~R!O`N<&v9_>#7_F;%V$@o(dW>Oi8V^7oY|Z0+=Ne<;u=bsD7^fsAO>Mc#jq~+z
zR$2Jh`h;gtTch)MdJCs1WGjE5vWA`IO3O&nePOhEE3pRWK~^F6b%NiMKBVlECSE$Z
ztp7Ieel`9XaeeD?NDrmqoaZFcQ#r8<KNRXKb~txju&4A4lV>us`SH5`N;}JDpxjbf
zdaCysI6zM|@54W6uJH;(aHA6w@K{9+$cgDkr3mxvsJb}4{f^qRUh}&*-t2ZzDZISF
zfc9Z83w4muYQ#$(g#cU@h~q_jnI*nIFTN30%gg&K8um?sAY9nA3~WCzvK@_Bu;nT~
zryx8Fx0zoUVOTz}*1G~4bAN$~o1BF@=G=%SGHR64Vmu6`gewF%$u3q=`gOhFt4XNW
z^8bu;>@5Gk;v6f-|3#y0N>j(SQViAms#fq+_+sPi!UsbARJyQ5Y1`t3p{GI#pGpFt
z-6#2W)oJ3Y4_niSP*mZN=vO<vdp-$z#qG@aL27u%lN6m2BfE@F@S&d`zotGmc)|N@
zKyH6cLIfyA8KPSugW*t<Db{s(ec#h-^wl^|9K1a{THYy*2G`k<57BWfN0=BQNsLmE
z7$j0|f9sa1P>xq0(;6YoVP+v&VLY9gNX|2`=jdHNdirpADc*O|s#KNWM(3J`Pkhxr
zLf*40x`3>EDIX@?^3fIXTdyo3!z9ORAi{S*6*5L?pu0^a^OS7C5iT8F;I2fs1R^sS
zTes=(w*ji36iS+Unl)3n(xU3G9X26oo!1YS>rD*NQL^lgNkpW+xBcsXbPm*Vu0%kK
zdH7m1d<a+YR7L$>Z`J1GrkfKj<%dW73r*~M0pI^d**iyB(mZRUZB5%YrZwGrPusSQ
zY1_7K+tYSW+qP{^+qQ4NzjMy|j_$hm+bjRbsHzoNS-C4CA~WiFfFa%Cfe+|>MrPVs
zgf2AMBG4^2yjbl+;RODfbv>v&F^j}(@lp@W#5=!9$vNhY<xy(*bzve2BKd3Zu?^bh
zb0z1kbrn`p7|9_bPf$<`6{sLdd)X}ot2(~P-S-_;GlRkm<7c9yo&WO1&C=YF!>O|g
zOp;XBJ+fzy!GPK3kOyT+AuuQ(=|(Ic6c-4AS)%wN8EXro(b2QxmTK>t+~et{Mt%5*
zw^~9~6jDakwtZAwf^0U{#QIB)O;x_^Wd>3HZQN1pyy(ngc(100#FD##kiVAm(H}x@
zwTCwPKH37V5%IY1l>C;l?BbX!+9nEGtW>hdW@EBYpBUkai@AU#O_T~uZ%z|s_+Tc2
zPc7*H?FD(QRZxUuVaBQ|xWa{S#GR1LC4`cfwrK*VKWlad=*o1L9>Hws17)|{^iw{|
zI|1L*XxSQR$9&E-%KT|FbtgfxJS2n=hX^|{HdhoMEFj+YC{9a0YRgB-=&VO3t^Kov
zG@s<&nHj^81x=FWE;S;|h-*A@_mNOJ%=vFk+gEZ?fpsX!X;OZ|BMxDmyj#My>(S+P
zS|0b~>*#O$BZmxXl`Ps;@7GLOa~bl7i#7N?u6st~F#PxwZf^DAK>tM*3&DMb@l_2=
z#(jfMhUmG7w2;8YSWwt%GrP>j<54}f%t6cZ(C)m(6n3H#t!P<myy=pRz^qZGKis59
ze0m*n=h3712co-vn^Y=@xvFcTd@mb}uWa@@NVa@lK2;HY+ha%2cLOd~EA;nGt3cnM
zb8GG<bCFpdzGTzX8B8y^=Fo9$j!_TJv52}McgMdd*POY8O9A#NaK+(DrwA=~n6SFV
z6(v2KL2uv#_Wq`H?E_$HI&`inV5*Fs^f!JM3gSO@);**_9tJ^|>ciCw%0lXvb!dFw
zlej<P3=?kE9uHt_rxJ%(d!bucj{!3W8$W2&po@&^jaSDv)d!=<aQb@^FfdwaN-sse
zHQOY`S_Da0q4gVZ>p!b7xGH{BGCUk_$0ml+XiuD1iC-i&NTlIMU>W4Mi<S2W?rzrr
zeSzFrz*oEXiW6Ih`<GvaVegC+Il+tXkFD-zx&SH%2-bod7;Bi<C(8-+Lbr(1{2IZQ
zy%TkNNQ1I~8yO6c`!31($RRP2k3ne0A?7IVI)$P-SWohS1f<Hyj0q@Htza-`Ay7M2
zE@6G^)z;@<6gYWwH_jNo@2XW`G3~%vLT*g|cA3t2cV9R=i(6JCTUF*;CP4Wgfw11;
zHp$@XmJGOIZWl$+7T)I4=9Wl2X~_PDjQXtVslz9=W5Z1<B$)E-h^4A^=Sl$TK}RvS
zZxzhJH5gfBO?Av{(dp&ra61j6Ai8jz+kh!(m)9+ly^Y@E|1vW(|DT!p|B73Vg@xgN
z(4L6e>$x!qm^nJg8QBZkSlQZG8(BNTu`vi48Q2&a$?4f!IdF0Pj|+&|{!NnuG2owy
zla;jt@z4KET|x}x;bHiH4UgaeEX<7mIXpUFvyf78!tcJ$0Z+n@Wz8gwT>!TKRr;Y_
zj13lJ2p1d%aU1_LnD~g0Arv<a9uAnc7>`ntT14Wfs@9Eepy6vq$qZ+L$AK<{AgKGx
z9o++^{J>%FdF}73SC7Nh*$}%Y7iZ>Y1et))zkYYTRS5o*6@ySt_erf^JaYdccFT?C
z__y+J(x%hGghbVZh*rakIhMt|a{1bMm{wG)`F$iCE%!<Ig2`)OYg0Lc&W}m4Qu&x~
z){WJZ@H-l+?|Vm+1W-;Y;(;6||5pA*@AhA%e-8ck<}en{9}%skWdyV;Ut&oPD<!<f
z<W#*wMmC?BPs$cTFrq)ST%lzvTlbHbr7ljx7d!~ZHU@u2>faN3n3K8LD^>E`j~A+}
z*n)kvU4r(-EcdVSFT%S2D*bclKi`hKi^38`T48^FmC8oNdR?t6$>rZt<s9@~dU@24
zRs|=Y4Ipeba&W7{{qrW2Q<LrXgy;pMmexl5`mToEU!(a29`+YYR>`RSkvL`4|DyT7
zw)4L|<uoPk?S`Uo3Ku!m@(bnZx6TSdt!IM4sZZ(oq;ewe=F4rW%fD&b+%{JMe`Z!t
z&w+oUEVQm0I=}blXPaOToU~famH*TC4~t7p`yUYh*Ij>9SfW~<|6QfF!80&}<Fs&s
zb7|D}C3;1R_AAdPmGZhn+XO6abj`cn$}corB<e6YFKv`HFDmo0+lTOHDWm?kQT&f#
z{ST%<nJ=osf0cjz$7D)er0=h)4|Xl&Rm*wEUKR%VS-)!D-`Qh0Bkk|Mt+YaUUH3_E
z_WbO4$!8@p_(u8*sdIX;q3TX!DvXx`1Fc+MwTw`|&CRtFe5qCAUc1yF;|}tT*(<%g
z;AHFL;k-MYijBPQCNnCTNMYv&!lawAi}M8j(_}0GM}<{tUV8SQamM5ExOD1l_**8_
zu}}+C-K{q9{8PjXsS!hmXaoquH5odqwRW<aW?BDf)c(B5mOtgb`TzjMA&p=eUE~(Y
z&q(r6!DOIAgC#&i{oACw2g<tEzD>UxJhaiw!{PD;tsa4I{mvr4R`LA&Hv;^^l<Tyc
z9n@=d{SPiD*SLlcx!_AmbA1xW7K@6PLS&Or*mf5O>p_KE(u8|ok)GsQwO-9y{$Qj0
zZuMm%?*)Z<MivHk!MiFV%-z@ed%uQ3h_Ex!SZvUR%~+Kpbfog*5`Ds7SUpnS6PiMO
zf$<ivo0SoGtG?idbf00VDACTG!%`p1d&D~a$pFjFC%#?3%Oa0E^(4zX+qTwC{c_9J
z9Mg&yPn$kjyKh)6XI5;GP4p%RCsc?Qc%2ma(XPs-T=w%WA^L=x9ero<7*+Src()A*
zoWVaXT#Oen3_{ixmhM<emVBO+E*YI6CBy3pTc)*N8m%`=&7A$LYb?#hrk($09nMlP
zAI8pdM!K~OHSBbck<w-gfE{!I%)2tIJ|w@M-D~!yz1nRY!spG?l6%OC_p@S#Jr75f
zm21}SEYAQaFE;0T)n=Dtd!|bjvx=5yV@BbiH8s0_98VR?-*6!Yt7xuLJ6D5w22yQS
zX2KQh`{+7;c`K?#b1sXq`Z(@c)JQ_-_c9&9h41RE{Q7fhKFD9>Me<Fie4X&trO7WN
zpY;yw;YLWjR<llGPWGRjN-y@O0}oO4iKLkvI`7$quWhV@#@gYogH62$iqK)6zb%z-
zYNcf38_k>?It1;2VeX5etF+XYLR>^Gp9wbj_M+I%<1ijt(Sum(EKEofcD*CyCo|a!
z*vfaK5?dKd2rl+R9C6)dB(<BDx8oAW>dYWi2_>f{W7J+@+xdeZ-mi#VEfK<*p1md?
zmiGw+GDXT;pI-iv9lFcgsX%4zir9(`^~oBp{!}%uze8l>-vkNIT9{g_ha!_}2cljE
z3D~}Ho3$ifMYh-1OiM=fz1?4F%p@?e|C#%<Hga45a@_xmQDyl*M)iLfNd-{>21RK-
z2Mc26e{ApnB{$fY+5PVZ_RD_$|242b|91oX|H%!;0$>9CXA^s-1?`DHu&}k_E_^<p
zVWs}Vz7NXMouMdA4vZY)1`<_3fEZT%doCJkyc_~-AfGQE@f~5hG(fi+ba&H8GTBm6
z<#MTUlC{pGX?1O*oC71mqw5Ww?0n;VqW!(=)8iJ-7a0|2L`(z-<PtDWCMNr;L+JzK
z<01`-DgO~ffXE470y#L~KM7<wq5qtT_e=dc^B+Q-VIX1ABRw5mMOoQsh0T}73!|?c
zdO$ct@Hc(r&I;`gj=C3|k>O@65WYeA(SMrc_^%rNUEK(u)R*o!`2K%%|GV(7+hHLc
z9UUuwq>(79G#3e0JsL;#<lL{BI=Ot+g@){QyQTeE9v256F(Q^;I7j<C#|sothyEW5
z{$1OD>Kqw)+HJwVcXQaAj+#*8lz<d?YyR_}&Z|j@REUt)LE+$(BZC7^T0SM=&g!NZ
zK5pk;;B7Xx_9L>gw!933$Z2sf6XI$pSUXydFDTYj7hQ~!^}CWSwAH(uW&b35u!F@r
z(uw3V4u369;k>7yQOS&-?R#&3UuZxww39NcBD;`;@<)B4j!e8C8-9w-4fdGfjT~41
zeDsI>28?LaVUwU@(2n+I*=+dkm-2V1-#OmK-9Rxqpk@0GCk<m#EF3vMAXLU=)`|A?
z@ge&*27}?_?5eN)8>;8X4FLgj;R*$N^tecLD%b74b#zg5s(zQ~lurlrJJ+tS!k(;_
z-Fo7<eU#BD5C{y`2hg5y%uG!=JCc9I6TN$22oy%n!Vkzxs3cq2eD>_>40Ik;gs86H
ze>~{5-V)|ix3n<S1>_fkknMd(=}SgQIBRRXgT#Q0H#~^7Xjktt)o_{4;gOoI8w-^u
ze6Fu5B7ZGNqab^0F$f(ffG@W{z;^mf8R9ADz4=2l)KS4pY_v$j++rerZuE&0bR1@I
zF^y|{Vmw&p=>ELzaog3|<)x#;_`uvy2@dXA)FTa1{5nMCbpGB`<C$Pkc)GzY*~oii
z=B2=}vUX0y3E@pQeE-MV^z5$7a{#4p?_45(Bj|P>Iz{$}qX~;f3jOQLrItpUr|6I*
zFR%Xi=AN1!U}Wc8JfV+$i{Lwd%}KC}j#`6BJI(r7d4>wJGgA2k{{7|h^zrKhacD{k
zrcX1O2yRt{f2U4t!zVsWcvw9Ee_IRRW2FO@!Pi<54SuI$Wh21*<252U@_vp&@f$FC
zf0JVE^W%E*(`p&Z>y`B5)|c0l@iH0-b>qc`p`YAF35}_W*ZTwyBNb?kk^F^ZgBX-b
zJeMw_Q0JuMV=P*MiVWaJdAxNzdB@Qgxl+af&~MCVZ225#bl7pJ>j-394uSX#Zt+gh
z{mDWhaEO_5^~FwogCZAx_(@Kwg&NRpkw;P8pCoIs_A>%!E?>M@Hs6Hadv2K<*wgUd
zI?FKwDWLR~C=?6?6Pb}0P<c;U@aR&G84B*`u=~?;u@HrW4`}`Te5>y)WQc9{rb;@z
zz8e)eC+hO_&Z0*W{$Z=CQHS<$swS1!U(o2Jdz{=TACg+GqY*<`-0FP!(Y!kRa()uZ
zHX$C!_3^NOk25bOfy?3L!m#2!P-hVw_P4#M6w<Be#YCN<`P@hIVCz|)*i(X;kf1P-
zgxIcRy4RQ_6sKc3Et!-;?A7S*Vmzc%Y+G503*Y=)lF2GE#c)THI9=rtI)pZCg$Rig
z87eo2-|kI7stL*D57+C$MYAd;Pt5ZP)*^+GM;G@jCIjGZ+lVu+@)W5kRT0mn;m?ze
zzyWT}W4dOtC!VtqxY0_<+V!HXg9ER-;ouo7uEWpD40qefi?OY<_pUxFiedeom%P`P
zLAl`j)UsIDw_7!Jfh_HhP9*2{YMT2e6dWW2RPw#We*Hb^Uyeupu+LT3P=EqR{j-84
z1_)uJ;WBL64@PTsshz1@LcA^)@yeJjq_W+IIWE>cW0ps#9VoDuw;gq)FBD&$=bW6y
zPr4f4fs(O|({z-vY0_qKeyPaf_v$WBPggMIe8Dg5X+%KSaMq>u+{bl47J|>KtBLy-
zzB=q4+4s?=mr%UGUrT|rn#tBy8_V-1uZVr*v)=Iu4=3rAcMc|+vw9y(i!8#Lf;<E$
z9oTkLO!jGj!|z@j)0HhL{PNC>O8$R#2AGDxY`h(A+we3GR1OF@JC4@A0Y;4QSKeg&
zY*j;=7&FG~80EQs2NEXjk1)~Td5p24Ug8G~Gpubo53Om6o>~w6h)8o<i%-mt6vXOk
z)r*fe3*si%THoZb-FeT+dK?}##hP~^S6VbZMT;66?szCvfLYeMe_J*6X}zDIZ)<yl
zRm`wK=qD!HN3yZP+0u)C@0G`WS>SxkQTokr+J@;P$BBRWmhbatb$&jjn})LHWXhY}
z^{SP}k4`jUzQH5PZog@f)dweA_tq+Gpn!r?*JQ05IdkF8r}VwVpM;$o@A?ulm}LGo
zV5sZmwYF8Sb$ftlv8eu=eAj`bpd{vJLF29s(fRb}?FrV>saD!tN#UzSGwb#I?;s?)
zEe7$Pr+A2;G+Sy*cl0LFZ5*>xPx`6h4Ib_xuXyJ^$aA}B$enZ_wnM2ucMT@TV@T4g
zDQcV%y1dK+?$K>5qnowVm(-p5+Kz<-2W7T|Wj(D(QHW<IB&4afZ7p~@x=OjLwz%tQ
zx_Wo<QPy71nTvfqJx&*s1C7Io<w`n_J4RDEYjaCoWQ46rFCXqk2XpYb-Oet1Gfp#L
z8?;+mztd{56mkFJPTaKNYV{ih^yueNunB3VD@SeUT%G`rjY#PsVKce$W6_44p9Kg`
z?yQ^>KRWWZ%e87&9lbm26gh9u793dXs{ubcpQ9#Vy{)}^+ja3no?hSdH82=XB+e~w
zt!>VUK9O%f4pi2*hEj)(Dq+={im7^HJj{jPHq-Mmk7?0#s@&ZA<g~3dS4;;!b1#K(
zPlH2x9neDh(V;>f3j!rVS+l;rz<vP7LyK0ZU-Y9FET_M@nQg@U0v*4D!*LXgU@9M{
zScydd&b0g(WDu*f)=UrOMWM(18QblbPfT6R)7+jr_qeDmI*}2}+B$}T6&*~CWZ@Vw
zKVp}EvH1SFaBIQfdMDXNJ2xelY;D%@mbMUv{QB+l9^;GK$yHlx&hc{ge2d9>>U9^|
zv?UKgOY)6ca!=_x%1K&7H4%MQ1l4iz!TEJdVHXHXYF~S?H~rqz_`ybBRA%k^m=*Op
z_%lLWsLI0#oPpZsvxh&d%g|zMm^tgi4UEDKx0={G$P?6DX%{Bn-)^bXzk?<wLYTK}
zb%D7F$QoYi3yb?H)ES_i7uCKi8C`*kD0;jus^aYaT!&N<>S4EzE@Ti>;uj}v<zrxV
z%XmDP>YKXIwi_a^EpIVW#bzyW!o&oK!*Kn2*+T8;|NOC%nF9X;n?~|Gy_bJ6TkY2m
zuC8lETS~Qs@~Q<&2?Axc-&RW4Ks7ooaBndN1M~0RmcpWC@%&WoEu0^}oP~>R*TX~{
z2LJZ0!UjPjgOnEF_o|c>=Vwn76ZB9&SdPZsGro~TeYB;vFnU?fPK)pKzvi%|uup4J
z+fpo9vt+57zprV$<XbD;Eu7v);N~pt^~5;e#J4+2o*p&cdz%Xzw7QBiJ=<E~y%fE=
zgGcbLqag2@bXww9GJJbLJ7U~8oPLi$yZofnN`?hob8c-oU+}6#1_ep1$^(_9;G0xG
zr({oOK5;KK63@QLernQq9_AWxJ~|=+PoyWjWdE4HoxZNkbTOzg>M8kCPI+am2mT5S
zF5NiM*;?zYDRnu|-t3}U@uUhL2w0kB=prR*!b@HaW{eMaiR2fbo@}~<l=3|P@+@Q0
zRG7-SOqwsJsl>$1RvAIL(`rxFNGCsjs8nu?He@`CuC3a*In-IFqq>Q&UyIBX<j%3)
zX-BpG67#x|e5n*ogASf*!%-~5LV&KcYK<L`7~PV6HX|Y%mZI!Rl<P~;+h*V+<_lj~
z@Oj$$$|Oc>u~&51npQS3>>ooSq$OWYE;aD=`P)a3zwt-5Gsp||eM<^x2CZmupBx%#
z?VYe9AMU;aGedETOS<qrH9GiZbmwIgEuSMk#=g?FD4%%T4=8k7*As|>Yx|b2ECj?`
zAz<0rwtUXYt$aA>FOq0a+a9oFtlM+2=??Z6Jnn0z*EFH0KciC>Y+arB;b>!_z!4#q
z_CjQhX|gw#SfrV9+AqX@H$f@DQb|(OQh$xHN%DRq>6{-mH)S9Fi`j<c)|vEG31KvI
z=nU3mztP?2SEd}lzs-G&tiU<@vj4s>-0x3-TY3M*t_aknXi!vnIYh8vq2tQ>`XN?9
zr?2Y;f70UV(y|e3zcwe|BdI^^Krc38JEhbGN4Ui!%3gvSJ8NS`!i2UG74qO@`p<KN
zNGq%3Z~cC$aX+CRJS0RuE(Vl6;upI~JYn(9DZt6+Dz7le8W^bu^z*hQ%~260_&F>x
zmU%@Hl^9nvMaJUlM_&DF>S;<*Ytg4^dKTAJT5dP2c-`FZZFIs*NKTN#7g#%A)g41<
zR|!Zx?Nk|@W!?U|9>$9d2|o)}*hrh$$UpVgLAQ@(P4c1RVV?JEBzPpxUjPbf3M!29
zDhlw^B%YpSt><i9y%a}t7Up+tE@N~Q94^QJi1Flm-<!*c@*G$qwL--iI*;IlYR}2%
z7+#AGpCxb(FSdYFF|7uus@bpZQ)Woso%-13wNYXykLh2oFny29+9uF9vr_V-gJxw-
zmrDms0)(&XyQ^qab^kD4JhiGp81Ll~pRF(H8Ua6aoTK;994(&D!`aD{i!;~LOtf}a
zwWWuLIVK^>-d$^;%FJo+_B7k@BjGFXhZup^BUQ);#fNT!Q}7Q(O5c13mIOL&<f^|3
zDBn;DNH7yHD@d|Zc|Q}kJh9cTY2_R%PWzx=e#mTl@vN`1kZiH%GnqhKiP6fL9^Fwa
zi|VKD)l4}fJc>@MH^ke}(AtgFv5+DivOoM$=J&wk<In)5rrYB@fW=mCtUVi;ACet4
zDd6M#xExW|ST<D_bTO~8)AT_#sy|ZvMVEBuby9bc#|;6iu!Oy3XG;D}=wjDdc$A}p
z^)aK@7;EDoMM8v+Hz7E+o`~Gc=@flQfM&1ZD&HCu?aP73x^#k5eY;R9i?il70<W0y
zDgT20pUA(yj?c&sY4^#6Y`JOaIk*f5yVP!dpCv0Fwa+3W(!j%HE^JTCPqN{4ieF!g
zm0SQwNgE<Bm7tIZuxBMqQ7an~vp3nE<)nTN24hoxI*hht*l<ISs;ht-bFO-^v6;aW
z0ty!J>=|X_uW_t7dIw|e&uOj?H<5pN5z0T5XF^|GbXr6X>d(?@H&FUJ){i5*8VZFQ
z7Rtpp)xnFyADF7;2RKfOH0lmyw;vE;ZnA>brzxFm8`;GV8E<tut~@l7Si}%WZ>&X_
z4%lepn|Jcoc9ZdzEI;7I$#{aDTptpQ8AM0P2{KDRv_Rq_ed7FDfpr+rHKLDTa7Obp
z4G!Paz^AwbvS+OH#whrP)g-`ic`73=wb%AwPl|@et)40JJ;q6W?)Mf^=N%~}Y7g;I
zm=b~3v5LUK5RWuWq6^Br8p>N5rqY%+L8-3HzJ!iw<-<HHLTr0Ay+S@r6?JVs^1n@X
z(w?>k=W(=p3$}%bN-)#!H!w@T&L3CRfajSBA!|7|B3N2ChJB&3SYtmRUN>3%t+iBN
zsSp^?y6dvaCE|}HJ!mGT1M>=(TOXb_C6OWP3LE`;bNIw0HgOGq!sz46R7wPTE>GSv
zhpT{BjOh71Kef#qDhxb1#`m!_h#<$2<WR#5D{+l;6?`@<F%;n74k7mQC50!{`=tAJ
z?eP}W*+W7rOjx9<-dSvJ%%eWPQ;aUabj-y(LNBtjA+79_yv)@;insXXmTJIukyece
z%hCbWu|7`A<yS&Y5xS9I1OZJja7Py=FujLTfS3KCyJDec&*!;Segl~09}3-BIfQYO
z3Qu_;;*Ij?kmF(ntn>wS=0-kA%PzzUAhU6f1dH+C-%%6pgwRQVA+Hg8*WCM0J+t(c
zq8kl#r7822TusnI7i2H&!u_%<blS^3K(wB}Csvjxc$6Ku;K%`QQkX-nl%c5~8?;(a
zMpvoc;U^>`rXl%>`LMBhbE?j^u_++SgEfzgpAzSDZS8Nbly4GhPJCrO)OX>JEDc<b
z4bQ(sXn_}U9zvR1EX*eO+`K;SCbYA=^U8r1WBXdGUFE_xK?5q=2JG|Ahu8aUkt~=s
z*dpVD0Ccr77`8Ad^$3sDq5O*9V(c1-FyCqS2adW9nmff7Lk9`T>)mt&VkW{KeOTU;
zAEUaSw-a=RV79v&I^8@hy39Bg5L>BI<Fjjzq!M;k#b&a~yAKP|s3K4WeEbka?qdh%
z@F_wK0%`iiW;=EsuYL~h7BRNryf8mJm^x#;j~~{dV56PX2=j=!6J}uYwtabZU9Q%Z
z+1_9Y>gMMJQa|MVH68XHEDRjSMV@X(Pm1c7N4{e4$2}|rULWVErM>P1@1+9K%p_SN
z$tt(B4zd@#@8oMd;=Zj&-%7fjfnDIixDxNr9aaG$qrOzfpecU``|*sa1>)cUQcC*<
z0QIM^+E%gTZ{%)O76aRRi%exI{u}U-L4R69XW9<6-*;Z-2;bK0320Bx!(yC&uw-tN
z56FMCm!FXEAuOL932rx&12Ghp;cm-3&!js~XLy|f<DkFwnu)FeC=opX0Wqb}SqUQ+
z?Ye4n8OpW1Qc9&%Fm-CBM164qIw)#K(wIj9`+GlP{lzWqh9wf<eMZKsh81_vk!+(1
ziF%6KkY^ySyOq+q^@7g4Q<b^Y6m^mIAobXE$(ZE_+-O6{<fgavM}A0r1GKN!jDDbR
zqKP=keI<ha&5n`Ru>1~8k1-MKj!J0$yw@~BA%01<DbW&*q5<DHIfSOUqb}{xN9>Df
z=mUBSSE$kNg=7r2#4;J1_yjT0)5O@x)xkaMg3JlaZK_7B_KsW%^b14I!w9uf=N@3%
z(9i+`-8+lRSQCgou|7a6b5A$!m`tev`4$Ey-Ry<?dD^rrOzg>M9u+Jn=75FKvDjQG
zC{=mpD9$xg>y6R{Pk2~`#`{1}2mfO!rgEYruT;jv=L(V;XMJ9lHeK9(ZhG_nsBQOY
zvy=db9K&1Ro<(f4RN4Q@8k3Ts2{0w3ZeEcnH+&kbuH18&eXkR$tVlum2#8q~7#D)w
z^2FQbCn|x-rgyXkv6tU$7YxGod}jh3dXg>fECZt#_bM$S^ob#XcE1kcL>}YohieHD
zR4?DXe7&nnbt=xGP)XuALr!SZMyVU{Q@5N9-kM>cCnW=O`*L+jqD7!U22@Kl=OhgU
z*&`Cuupt~g2lQd^BSugOmL2&*4p?XuE{s<bgdy_y;gN5!6xi?z)Bc$KUI8C|%C07}
zbn=nxGJi?H&ngpo9rMVD_dty-@FM2%hwoFb*rrD*rEHNIVSPX1)8!t@bK1`BXat2)
z{plsU(yjXoN^Vr}&5kxIpWY-^XH6gyQ*my!pAXO#k{pxQD8!6$a-A?}Bm|;idLnzL
zC=Qt&-T9h8hOn(bzfrZs(3!CZrEa7wSn+endJaXZjES<xX4ODP0*qQU;4Fh<=#<!W
zi@NIZqvvDe$s~&*fo@Y20`M7!8QiNYVAj_Jp})B{A1-B76&fSpCG=ejLA?tzJrY$f
z4uw36B#3MLx7<lFA7BoBEs8hj?I6jve>>$)vq<!Zh&L9#F&sYnzQae2zSSOb$#&JG
zuzMKq0IwZ??4h{*&mjT=1jtlt82*N{DJa}^aN+)bh%VS_h=Q~#PxVRvE3DpyTrYHP
zE%@NrzHXMbR1IkR@b_4cUmUx}TTX0ry^Jm0CZ|UOvA$aJ6w=YAqf(>pH^%yN-w<Es
zLRhZ8R$S{J={ntN0>5#xXrQY2=@Bu9J`1sbe+$K!pIe71dB?U*^yQlcCyY}ezCz2r
zgcp{jLb&Dcs?>ty`<e<@zVm-y0Aoet^N2)pi}hfPK*%bqP${Cy<ruj)k>sFktD9)^
zwz;&ju_81^6l}LX>J#92;1}aAd^-eT*6xgoK#4oIlcamORUI9lc0s>!nMt)|p`lR0
z{3&0^nDN(;K);=QMK+$n&Zo8t(>_tGXBW&9YkWHh_?FGYoe~Mdrx8pc4nl?8XK|bo
znPwo#(uA#ttH*>xP(1Tvp%D?@?$p3zpWy48z@CGS4ZnG5$dCvwA5yDSVsW?HibA>p
z-TeLddgV0q;M86jC<xH%!TCYFh?(|x{kS_4RmwM8l7+M4eSH0H2|kp*CD(>d1)Bb8
zAi2Ts<!6k-V%6k7_PQ{dd>3*=23I#H9R%QgvphU@k=4vt1kTdu`-#zscbv~;(bB$G
zxQ#yrF60fZ;^-7nU)i<t2FlKq>O7JI^XI1mVOUnvJ+rTWBq1Rp8wV9FPllazGWF<h
zDCwFcH<rGGCL+o4H@Uyfi|+~admCE2q_&E(oA^z_P8mvfTuu$}iYnbPKOnsjeGv|7
z7zp5M&~z16l9NVGc{gav|4q<g%6$2I!`wEfj%m1>nSY_p0!9Wg%|jZkP9ec3?X*&K
zpnx~GbFWOEMwlYyNk+IFZD1IHB!ec66_9O*2p|^P-s9=5{6yu4*T}MlY7P5s6<ICO
zkbLX^sbJ7Yu2I?rlofh~p>%oI4weUTC1uFtQkWWxp6T3+lRZ_;n=s!07^1od_z~}f
z)pAcQiZUNZK_|4L?b3d)n&>$+#r=M}?}Q)h3!N%@hF?fXFY;7lZ>z?93rcZDQ3oew
zO@|CR$IBn?S{>IH5fIH}g%`@tE1PgtTM|dS=Z|rwca_AOzu7qPgN$Ip5t9Y!b8><o
zPX)}~fUhiRV7%FBq|(OP+r}Xx*%&j02Yx^cL!^L+HW(YAqwvLAPG@UR(3dOAEW(Ho
ztsom#aO4LwAx}C#A1lar-fRNUqA`Sc{Y%{cJ7}(AD{c?7++1KJt@!(6_||D3m0SVo
z0V%~MSb3gTf5F-QaO+2N(<ffYE?}QL$Guxm>Gu`|Zw9-DdiuGINB$HZ3(qs`&R|PS
zMW*Mj2ZXil(jV44iGxKHOh=RQyX{{eh$Lz=llr0bwLCR^<Y^z4al$+(L<e-?t;`-~
zgJ>fFW*Z`Q3D&R_;Z(Q<rPTqH>eA+4J!WK_x`n}_n$(#%Z``AsA9E`iu|6)6CN7-P
zL$+|ScaZ&6-z%NJkT+%;8-<X`*@@hgvI(lBw<GHKt}u?()W64xHOCaOaQs+eI6`Vm
z^F9BOVwkgSDr*3MnWO%#HlJTNM1(Jj+~-IZ{0s4&n{mwWJssyo&b|#pKa;c*$X0Tf
z4K=y|cAM|%zkYbaMT(rc<01xPxi7~0H%<`*HNZEwdS5Rb=d@07%G-6IL(&4xWo7d9
zP$CKn41^mjy7&(5hwyj5g7$#Xl`5%vWT`j)LeyVYB}O4-_Z9{)P{Seq=aee`#0M5e
z!G42mGY1z1SFnl6w7Tut{z7S^?k9RJ=BhXK%U4RXoiXU==D)!Tsh+Q>&omYejbLA+
zUJdThEQGM%1zt?JVg`MsaVoc^Kpm2C_6P%+1Jk8VJX}Htl*03JY=tbN_Ex2Tx&^YL
zHfqGtzMKUY?n3MLcRu$lULK%n(^FH{lPe&Cg6>d4$2+NnvJ3a3^yCjuwDxU{#s>Z7
z#hrx~-}y`2HQ)mgOb@+y9KU_d%e%b#kZVpNokg2ps-yMVEtl$MK8I5OcZn3o10Q_4
zFpyYKUC7Hw*ayty4hrh`oRAa@v*#0pN0(nx3Dsi!GSoHXHqQO<`-qP4ZfyHd#VA1`
z>HJYt0SgBICA?T}40(PW!`^o-QcGGT^nUS%xrDa7B+3E`8Dpv_LDL^`7chbhAfrsM
z;8lE#_7MGi4$BefRO0PZFaVl3e!3!jM|D>26lxq^<a`L)!VkBW?j;Ld02{N&9kD+*
z0#gQ96Zu>*#n%hn&Q)>e=7l~^36;kszuYQ=GC<XTi{4os(ii@LUFXV%82snyGQGfD
zS?23fYTK|i6UEbNJXvM{M`qz)#XZp8Qj`Og;9+QO3u37Eia0Or03=yHmAKkdvtJGM
zNSN3Jo0jS$%D<hj=kw?Y;$Lt)*NiBYjX7!ihY33rvT>Vx{BkXOZ2%bbzKMs|?W3Pm
ze+;jzd{SQY0~eTkT8l}nDXO4|!LN^aHwjWgRbN%EhXQvL3MyF5##vj$?4f%hIkb<M
z_{Xh-KL8!`=6*NJv)AB&!zod3w($r<K)N|8W&&0k1&R#&kgR+NAs!YtGBWTdGCg^)
z_scUdh}fvJJ))1(#C=UZvEaz*j56Et;h@6IrG=6kw~ZET<W3++i(yA23S?Vu?kx?e
zl%{;kuN#K_hj1+yj*ybP07;j4)R;9QkxaSGnlc8*0l3H(vfVfc0pF)QOe9jV06?ow
z7~UKjl8E^x-vcz8L=OSptOKU#+Y{NUKQj;IUufMlPu<#Ygl#|Fe&yz=1sHA=L=b+`
zxCa(4aNiR?Ccc91M_JJP2v~mJfT~F-zY_Ldw9w*4h@>Ge1wRlLkmBc*hPG9c2XxZ=
z(<D1a5{0r`XuGkX{Pvp#CnFIV{!<J%`f_fkApq+Br(e^7pe1&BQ3|HiX6HvdsyvGF
zn+TnVJ`}F*Hm=bN^4L`TO-KH{IA31W+(uQAS-;nqPDk~RL7f$W%smPAjFO7;8f8Tk
zuA|dGs%3(mk#f_+f4h^9-%v}9)5u9M+~i)Y$zKl1G@Harfyur3TdqRJL0oI&3Xn(z
z{o3(N0`T5kV(I|}gruS-`OEEdu4t7w%jioov}|>|&ey0x43Hq+QmWy}jZyd4_-SjK
ztH1(iN@*M~KyXLmL07~ZZu|qst;(P=0NEc^Tm>|GUQL`Rg4xR2a3C#tH6pnxWSFG}
z8~?iS7-|UVz7?T9=E%=~w{#Yl-!$9(Z3SyY|99$TA%<b;g<&?Z*6!|Bx3qkQaZpKd
zLM51n)cu^e$RK=}NMf<fLL*Sg;agUQAbipQSLV2UG^R;I(&Iuj)!@&`c`mj+Qu$~v
z&sQ@!XQcyAZ_s-)x`o7>=zJDJ?5bA2vICQA|4e}@K&f%dNy1`}1lkLD>mLMTEcn4L
z@<80saHLilTjUs+d$|#{osznl!-53(ZAq*=Rc@Mo(-uie77k${mB25)GvUgea+Bs?
z-|(zXzfo4WmeM&YVH!M2{aC?o2qkf!0tydrIxYG>{??twagYHq&R0N0yB;OwExd~>
zz<>UgUztm>h@i(B8Hf#VED7}H8CfqL(x3i_E<e*Yl(}7C3{M*foUAlM(>4sN|0X~t
z9HRe219?w*qq|*q!~HIvM&4bTM<nE2iz!0sb{jp~10lXjK9CV$6!quDT-tXp>)=kA
zO^fp~YLgGTl#=8iy&^58$4T)kBuNJg-;aOub<N%<`OYWSku_NbL9nf6gY+_?1o*u+
z(&LHGoEeEvYv4<qxlYdW9cK%l*kWe#ugR12^Y>dU!?iNy#X3Tnfhqt5Orw1TNyc5!
zvgI6*8VD<T9R67)o&1fAe4qMxn%GB(_`9tZfE36!p|vs870%088!r;fqP+RHopU4?
zRRj++zmQXggqbOErpO{@vsoV?OcP`sLYPoGuQa!JKOSG*)FP9nA1M$U??EJ;C0JR%
z{NT+8E0!b~W=)!DT^~~D5LSa40l-wYfC9#<RsS7aVige>xBl(`r^CVwlaAB+bF~E?
z2STa4Fk6k6HjAsooNoqo56Zz4bms(oSRNI26bw*yZ-8?T8=^rB72;aRXT#QVJU#L}
z2s36(qZ)yA^m>%6*O}*t>=rk_5P*!_5!RPz4i8cA0;AulVtnSm4`CP!da0YJ9=EIV
zwKwCUvqLBHZA&DF$I`OF$Xng<xwiB43PL3AE98-pqDMl6!h&$SQ<vR;D@!~I9~3cT
z_7I#>UugTCig<~PEuDDfo~{O;Ys+VEMt5L#jDA1L1CnTW_&q3J(LIH1L%#P^xTY;>
zrzc`Xrqb~@?`(Pez*{o%mLKJ!U`bTsMamDkrTId!3(Vl*D4Yx7?*6?>QV<`A7D#vp
z!4(Kv^@^L`vW_C`-0_r-{!O=zKqPe*_GXE;JbDFbr)x26=HwvS9o^ydZOAu__h1>l
zdhBgF`xyHEdSEPQtt3H00Xq+xkocoB1DBzIE7>(HIuJt!+&uQzp^$tdPi5#ptmB^)
z+w|}=@hWiYQFMM!Wge9#zSsd5HdrB>BWPF3N4&kc0kuJ)@Fb?NglatR@0S|29Sywq
z?aYfc44Nu56mG#9JStN&LyC<TsCjtrGWTraQ|dvc(!^nE<M-o|C_>rCX$<8dgn=y=
z^624PMdqt|*}=yex@vV7Uv5I|2|9`zY-^}kD1Ua#xK*wLRor>x;$`+tTs;x{)u5Wq
zcT{4`vqHcYXUbb|MY?%|pLV(*a%+T&J&cayneXK2C)=p@r}aP-L$!~0c#Df+GXH5_
z;!Qbq6@82gfr#}V`Ke#dK&ck<cd1<PbL>12tOj`fR*?%r%ImU|st_m<qxd(Q5g;W#
zSwp!dcle@zGyYOIA$-5YOYRu%F<;qMZ|o02dKmu?{kZuy2V8ad-|tz0WZxjpq=L``
z-8+r=v)pa1r6=bH<X@8W-<S^riw*Ff$eX`#i5;c7_TQa6+;>>=AV#cOnH`s-hgZ3G
zf>w}KLKW!87@hkF8M&b)8dr0=<=g~w3m5T;-j#iCz!Ye(DAw8ptt+}8umZFzaq2Kn
zg`!dMWs$*QeAT2_$7?eY|5j9jL?~O~n$E7U`KNw6+Q&RSG!xxK<v*GM)nYaaqKh*k
zIJP@yAd>@`go=_+H7C0up-dR7P31uyCp#52Cb)3-c?I=(A|7BCG8cmMF4g_o%n7|K
z%{I~=9GEaR(fO&!tI(+uptIc^rs3b5t%Bs(d*OampqQ9WazMvSu@YCNek8Y+!Sg5D
zhi(0Oz_7tB3vIcdVVb9LR+o{bf|6Al#mzuN8vCr8VtIgVVg{lSjW2VF^`}(nbR82P
zEhxx=$nlbBtT%@XcOpSzevtizjFq0#LG3RqzJ}JC!!U-OJ#K_+xy-*D#o_vY!g1#y
z^q|Ky6a()zW|mELgfu+m14c!h?xD8PPAP6koNM+4_7Go{JQ&f=R9?krp-Cg)hZLK!
zke`2d;T<e{eZC=}A6SYeWQ5uVOD35n{EV*10po)f*6M3FSD4`POCVuKmLUw#&yG2m
zdC`*YvPJ@^H-uX8%x}_wSI-{LM{I>?W^1otL;5F~YB59s1n%lIU@FJa<ttRBR}k*L
zsffZJPapTQkfoO#E(WDTzDm2Q$C@jcD@0<FF%c~0ow}4!Ls}%6X@ts4s<<oitU42U
zm>JP5=i9)kn2sz7@2N>cKHQYN;UXVH)oK;i_V@HO?u8DR7K2*Fk+k<b{ZgT?z4a@K
zn6W0ze(xG_p150u_K)$#91A2q(7%fz2F;hQULYDduLLjU{g#;W``wfXvO#d!aUC|6
zC#rVjH|PnwW0<>*Rxr%B)fA|7L1Gu)TUjzZ`jox7?_B1_ALNStXbiiG<RUqbEqJso
zm2>@n@z#kVT9+cr)y7H9G2j)O<MAh9@ZaGtq`VT`l>#Fyh)WU+LJAx#mFv=SE9>jT
z3SMDTa;1f`g-|!hpQ98E{-j^DKDU2X>rR(`KV^@`Up>xBNeMPxtjgPT&uA;0v5R}i
zVeP7+rs*$1de+(rYUl~T#fI`a!MNj85v7*YZ_q-*o*i)t1^CN{k<XdibLg>`@-NXw
zP9b%x&!=c+VKvP#`F{xd$CzSis>4IDmFsqD%uANvL34{892u|;n6XamgXYw<K#*2z
zP^B|(n^Q+!&dXQ%e-9#ag^xaydrKJe$KT()M|X%>XncPOa<VhfTdh~H!l9tJS11VR
ziFW%v`7j~<L2REGJNWVIiJmlg*w)zYHE&?PipthTog3zN4R%^!B@+e@3EXwojV)w*
zwv!cnIk#9gcAng?F?6R)9IA(CqR?E33eve@xm;g6eCArE*hCc88KMGMQk9oavEo%!
z%X2Am*+1mn%=unMV0v`Y)7>ROMYO(FMHRjF6ArGKK7a@;7KbyG`Ng_nj3Xq?$NS=|
z^Mm!yyT~rDY>p4$hPtpMXGxEh`^71@Gu#p+hH(U=M?Uab{<&hKhaC*I{>AvW7^OeA
z>V?AbU>v2g;88yJ+)(UOMOmsDpZjyDqsmbbh?=ru1hNG$N`Go}NMsH6odbhlj@b5m
zk7t!%AhMQCSu|{mCEP~PVH*l>9uH&iGgW%gdfBU*sAvebXaar(SM<80|Frg4Ej)51
z;R_!acXyP|Ybi$5i~$FrCgd(hqxNaDf)}SwP*^?9?iAIcph6)L`RRu;;>-aA6L4H<
z+E6}{P<Xsb39SdcPqE@^wT0PWkF!Fmjbwh%Lya&KEKBtUq^@^_Cb8*|kj<Mo7(sVW
zo92sR=yp2-5qNBuU)B?P1hmwQDseVuc1(t~stzPevH0%!P``P7%MVS+C-nCf)*vHP
zj%1u<53x<O6$=M+0?Ptnd}Xp0SjEyEeBr`S%qHlpsvqRwOszGo{=DVmdSgvT6B#1Z
z5t@Yo^5?+DT)0WYL`CK7r<QuvK|qxvK_*NnXf^+NeF*{0jHL;r%z02_Q}Gqsy<OWX
zxLL@HgRHGe{n{L&q6=XB2c)W!s_hywhVUOZrI+aYa$-EjZe2n5pSSHFp7HfvO61wG
zGBFTjMSgaxs`WeRBP#52&rBYdQv6p5dxUMls9|!byI8C-Qc_Qq_!Kt0XCtq|W;6H!
zi|XZS4NU7m_1SQ<mmpYNf1iFWo82o3H}Byz|LawSF8)d4-i}hSqBfyQ$k#ufwNdH(
zi2NAt3eoT%9rEX;OzO%XAQ`;T#FU}pA9MDhsfL_TplY!C+};6$NPWC%uOMsO>Ikf2
z$@O+%K+{5|P?Fve0^IXVQn)xeD8X?=jyBNM`FL~NDo{5qTf!YMm`F<BnPX;Ijz_s@
zuP=Bsi(&%?Y2Jx-f_sUtu&C}ZuB~1{5FwrQ=Q4tsjYXL2m8lKRj}V79QJ#TOi4W0*
z5)-Bdh!GdvEzwc9AzVKSDHtPXoQNe`yfM;Zdpus=lU*tklz0!SF+*@(cfp`j5$)NU
zH}DRjMk_V??$qdn`hpBy!!QIWGDD|`Fhb6$8VnG1SQX?G2uXZ2C)W_Cx>(V5%=3!$
zy_R(+W&dTFxuJu0lsTn4bO|7I*mO_kqD@Z~FSz)lf_~$-tBE)g2YUNUyRUSk%DcLB
ziK%49nK=%2wOnLPtVRnW`D$TijL`OsSy2&CeP8}yZ|F!OK`GAhHlvww7jGxWJ$ED8
zcn^dCjBjilalE|3k(+DccTBUne|xF-%t<}<wa8wg-tX!WDen_GhgjsM%215%LFEFG
zv6?fzV~9iJE};e5PLV^J%L#aO$F!n%OA#7{)zZzA%hn5mm2es~9AAM!V?jK>Iwr*Q
zn3~>1u8S$BVS?UErgH^RA_9KHY!Iek3D4oPt6c*$ooRfQzKutcZeC}5Hh(mAX_*$S
z*jGD3`(yX=hOCBFP^=DdS1)JZ{LL;SMM`ro9ESm*36I3H?;rmbbjxV$p^lm`vpd>I
zX4CQ?eGSdk(u-_u+|c^y%QIZZXj`Q5ZLTS&(o}h=eRxkslvdDgYa4QxDS(uAOi{!P
zZ=AC}CC#mLED;BQ2aZXB2a%rJBB#iK!*&!mf-SmNT`i`R7>p(P0B>gqWD*NB`*R`>
zavNY(f}??!d;!=~vN5IEih9AaK@1kXp!WK8a5Z&qJ<(brN@ze5t5?tlDFZ-ws;0n`
zdoJhf%YMU*A>q|(sWFwtKA12+l5cOwQO85EX+WU3XoAN{PK(0rvr>vniIyL=YDdOY
z1{n-SUcHJ{j6M->@B4yR$Z%!csY*c-fg5~rm&V>7sraa3#+Xl2e(a=mAf84yZ!4As
z(xEkWLx~Qo3~`GW=dkC>jK(4FI5t<dmNgGe{Bx~)@H4{g3Cg4qDhRjV@eEMR)}P_r
zN0>eGo2V>fgOY58bPb{K)g4d)6Xrnu#g$meKbEeWgxND(hfe$<I^GWB*n4RYIuq^F
z=p-1xb_!Icp42#$OBA&2wYDV~(jt(t{$=HdEfJKubX|%M6H)`Wh7%NeHeCMT6Njkm
z;z_e?;w0(9D8nr#3Zh0ac+yPyN(mlLMev$V0E$iLqsZf4TKg6Bz{eTBE;l2i`JRDn
zN9GL`k}nN{>;D{THOv!&a1qB63Bc~Y(+WBNX-D3`3<2<rgLZu-HDDDBa~t#lSvewh
zrHz=m;hgW&ABS|Vu``~q6_yk;_PwtlB|tf>Q02j@<PoOyHN{e4@)#MG97WnOS->mV
ztPqeY%o-~q_<>Ko>-roMbn3{Brqq6g9m|v{O~s7uyL(g`q-XVG`b}u#U~Y$4A(LY$
zk~zK!Di)~vM)*SQW);lww(~?S61pvpnkyBDR#z6|xfMp(+;g=w8tcAhe3xk`4sTC1
z-T@;N!zT_7ax}x5-xuNhOzf1}<FqrysIs$Nc2MVIfPtFv$-ZxzztNVu-e5;x-l)qw
zKN(RHvzt8sVBdM{A3A>oqHSuJ_EBP_1ZVFP3p$M&=g3eLAQB1|nUg0R#2{yDd!=ZU
zy>Km$Z3E&vwa<<lCmP)ah%{0W<=v!QtV8vBm$`h~3RB(jQ{OH1C5~l%NBDJX+;tJ(
zg$w$idDg#xIK=PS?MF0OvWV2O^~YaMNSD-<-<?I7+wbSIQ0PGY>{Em+*nwXoQ&k!|
zZ^GfpMF7imADoDu$4+70i9~C$e5|fLX?Sp_b+v^plIzl5mx=Ui#6jGEeuN{@Z-3$T
z^iG`A&Y0XbvDwCYIBXdT1V#IBVuB+I@tB_8-ClMvbjjKp>H3eeVNckv^bACvT)QTF
zc@B!FZeneGR-?Z2;!F>@Ab#d@bU?_6X1Ul(txxU!ptsLzq(Kozur$Tu?w_pa1sUia
z^a*_@rBL~pa1QL>Cg5ZeK+P=IHH<W_!lcwQ151w>7q-9(pPHwS)5LJZPF%kO_IbED
zqpScav{`n%{*M(n9WfJ|aA%v~pT<Weo<&}(XvO<nCTB?&<*7VXKCQ&~Lk!vZ4|A)%
zwR2>3-o2a`K?MpV;v!XLIqUoCQbB^1mh}f#W@jGXvgnben%7E82Hf+7zZR5rZx%^5
zsqUw0ZrW-f0q&7S`D8Q5W=nUMTjW}V;QDr?zhq$jKMme%UVP2U!aV)xPx7{Qn~7$+
z(8%#KO2PwCGpz;XwLvJ;%Up+vd*F$OeYi7ugR3f(sfaK`Jl-s=FBIX;J`%s-4G#27
zA;CkbtG>-zQMwS+V)(*UL*@*wU^BZF{36DH0ptJ0r#1xq!C5f3ky8Y7s#+k@K!8#v
z{+K+BYM@%4hdL;+m;1CJcgm7ni2`AuMsRLcl@L}?rBuD|xxx!rwkJdxm=gVtrXf!K
zM*=yeS&KF++Q9bI3@9Rn!28DZW2P;^yVmzeusMg8aS$fRZYaZwi<qm+trnZ({g~7~
z{V82LoFhjIR~!%F)0k7dNataJ*t}@5Vys4xC9y7UWb=z~HiYXb8J7UdXeH=@=~FVV
zAD91*|8M}@N3g5ZWCf{tD<Sy>19?^SnDF|w$V&*Fj=_IG*UA!ua)~BsF}5XGBCEm?
zwZS^iK(JHHSa2D71CSVH!>M?qdTkZ)dv9hV5da5Zq!ShvhRs(P=cUbLVk~AYk5c|i
z&GRYlDKsD_oZV7Pr`uM4B<Oppy8HIDeCd3QiWWgKPmZf-O6HkKe!_eio=Q|;cOjIf
zQPSZoJC%myb$xW>4&Kic3&)*+g(w0wIwRrBc8$ehl8;Y^;(=6D*Mt&Rqp9e}T*%_q
z#{ZlOA%`i!WPJVe<qM*2E+V^>RNZ?yHm)SC))@1bo-1rCP}6Xs2!|!&NBJQ`Vm{7{
zy{Fs>FPZa|z&_>Nd)uGHj-Qt6Jj(X)hXE!YUU&qVG2|)tZ{L&QN{eSU*c@V?NZzkQ
zs>+cGGH76J@OnpTNT8N3V@YwU_Se88poKiuHiZ<wEjWOjy5ugXU?b|7xlQ}2{XECE
zJZu%72mw}NWpxB8bu)OlH#t<WP@E9g{s=Garg9&a@6B4?w^S)|DXl-{lY>5!g472L
z>T}}<hp^f7EhnS9yv4QThqgMWnjD&8mJOjlDPkXHKZ7*_-i7Ew<pX!RFKGL-^Mm`O
z#ZmNUoiY<ci9_j+G8|P!O>*<*``~tS%HW{;j+@7oa;Fp25gGBBYMvgd;#KpgM$zlw
zO}6BVNTl7-5_)zBdIHvGS<=&mS@f+gdN4n-DH_~y3HuuGiJztom_o3olQeEcwI*3z
z^5VZA5{CS_;ok-qGQ@1zj2zKljm3*E2U4!TDj2)eNF8+#=cOm1#>}q|+u3q=3Zw;N
zmaD?$P&-(WXu~G&KsvHafcyA@T=8d&hH;trE5-^=*_d7E!atQpYu<VkrPNRTKkU6@
zkSD>{F4&y5ZBARi_Oxx=wr$(CjcMDOwr$(CxBq+JyLV$_V`C%s(?+}>Dl)4oEAwP#
zRp!acbDn2=pO?AWAwSfJ0L3HJg9V*c7Wbq+C2dDQ{fVTWgE5PM5iQuF^4aXmR--Pn
zeg9$q!l3Kmm^UN>?x2NOYn^ZNM)wfwA(wYDV%|B+;d%lNl}5>w$m?`qTRzj(N;*ql
zITmx3mgO1?>;?J~hcZP}wuUpB{mmbi=!lhZf_JqCOB$Tk1kL};ux!%Ba(b+9js+gV
zAu$uvBt@f+7G=RqSRf$;6D1Jae--`(*z%8SKJS&540N4D5w%WOix+6(<fv13fg26E
zs0Pki7xOttJYv7I9HLC`FvTDlr@N<7O^M?VP>4z%YgO9-Gs|m$gAnd{S`4h1DN}u%
z=)L!S047X~J$>{nCCJWK$SWh5Z{n14Zu?%y2($)?o!P1r0maY+Dl&Ij?i?EbNDwc0
z>bI&mzoL9B(S+2vzj#tdiqkU5@d8h$!&}sW!Rtd_01Gu*ho-N>vNQjNDy3I=EX<`w
z_FGjcbipPR;ZO{nh&(4RAIiYSuWj;wa1+j35u-}%#0~l7%P+`U0{KgKt!TnW?S|3%
z;2@(-7!aH6z7j50CUwj1XrH_j9J)-y_8S*G4<VTY=^nk3oQ0k8vNX)>67Gxw()Xah
zp@d6NT){+?9SDzzW*1np_E9j7USI<HV1hM804oxuM{vbVUWvUdVIdQ6U*odudw<Ca
zptezLJYze|5d=NJrhF{QgBljr&F@?vzOPuaWQouf^G&kPCe2`#a03IjIM^1Pfp3UW
z%CO^j5)e4z)sB^t{=^*WBFjrN7h^Y`A5Kdc|Itlhv$q1wu8XXc$`kL{y^T`>W$MMh
zEk>Nz+nw^^17`T$q+%4(Ga$E7)6D~N(p^LYLBmk^hVj)vPJn@upcr)#8VWb_nHk3A
z-{23;61^6K3>wzxQe?KPht@>nI`CgDZ6MFp$TU^raLlq{{>C@)H0S<;Hn3~=*iow~
zKpnp$RPUwbOh;cVY)f=M>$ww{N5T(Cn;+O*^Oi*t3Ib`0k@O||cmh2{TxY-nLUbQ?
zOEJlFpdA15Klzg5QFET4{pEKlB67ZJXF^#Y(1p4voe+w&uDX&2E$-jDH;8)aqOWjF
z8em}pboEXA>sE(;@7t;;b}L<>52FPQ%}9JF+<&}B!6ce^Aj;ndfUfR({;#kkWBg;*
z&^C%k1*6?sTO#3dRU!$Tuql(I{?%+68EUZ8JB5E7zS+2*LfFC%UupP{zN3*@0%<v7
z;g2&G`n~9>Xpx2aW=zRy9uWuNG$4*D*~l8io_3sWzdGEZ9PH#v@qpuV1jxIIE<=bg
z-WLBpq8isOJmz~;R86&i7)<EmY5Clhlc+xHA1VyWJ30li4s-r_tGp5rt??BH@-yjn
zU%i?BEH@8IdQ9ay5HFrkro6Qn0+BsG%9Vvrv|X_OwVTF?*$V~96uUTg!n;PgJZ%K_
z><oMnWK;SUOvNwBehP@MN@^qArTBnk&@9(fg`#Mex?c#WB{d2OZ;v{}XR3lRu`Gd0
zgN;P+iz057b!hbBF7gMg!?HJ`az1?ErIGA!>)6Iz`4=@QpD$r~4d)N8NnwyM!oP#Q
z_A9wtUpYNDtk5xrHMZ=ZtU8#Ga2<*8s%mDLK1ton!#pRywO*pLEsj`brr;uOPn6<U
zb4CL&-J!+!DW9d0ar{qH6dL7XEydRgmx%Lx)8H{^mtFb$3cUeE!_lypVaWJHrQ<;O
zB}XdeiOBu?{OJuOnV}%<7|Z(N{O9q&F)tvksrgMRyB?x8U6u<qGFcguv+?5H@V@;u
z-W@(9?-mN{(rlT(a&=W;@_3A>WOlB%dfU67a<$OBzV1g56W{?L#Se)^bp^=Y^IGxi
zB~o6}fSB@;_pA&(99>ma%~=J#9{P=t=90vcH{u4>H#qRbqC)U7_{l%QasE0T>7%sD
z=a@X(tLEMOCV9yH?c<n{!kV_?5+RI%>uFz-8b-!|p!|BIvUj(bsQJiF`TEfr<u7jD
zM;0wg7Xrh#{!>Q@(HCF>lu29#kL1mCF}(yAj@qDp`Q0{)5yNtW{Wb~({DR3DC%VFz
z%~r*Bc85T2Z`hb-Eh%3s7yx4|)N<PgUKnAcyb?NQ7(5_bgK>RwPc{V{{f=~1KykK{
zRG_N$_l=ge3@?#|-U+pG?lvk(WeSLQ3JIt-OOpszD_V%a?v>UqwXfsdgS2aw#h;nc
zB?(+-_qXBDkGr69s4Gcv8|<TNJ^(>H<=A3tW<NiXwI<6du6sWNGgBs`(1wx(v4k@f
zGfp3c7{puN_|)xeFIy7AtL--e)MM7l%Isq2wHJ}be8(V6lknCk``yJ8-i%JLk`nqp
zW6<vDa~b7lyy862VWPNjAniIz!RGGNSjLj`4ZFe+%0*MDRBU}%Wq>2zL@bmrh~jt}
z#qOK^LIRaNSa=VeL&^k(V)dJes%#ypbVAXI#06F!ku@Qb$N3PCSw_<y)ZcPq@&wDO
zzbW)c34f{2cN(7qN(F$pCbrCS#Ths0fGae}CpK{gZhI(D!UP3*sY%5NghSpn_-fY~
zWtf$|=wVoCL>9kToVraIW;azL7}snGoCZwd$^LYgysRmrkg=T3N)f)3D^_bW+aQE|
zaYF*gJSv>ig>$U<-3k&##7)AwWgPhx@x|YBRby@iLX8OoH&vUG!c2fuH@jt~Sf>-&
zYG>6g^z%```rWA;FK>9OWJyBO_(A!o1o$DSkNx;`^kjpA6%!4<NHqBf+|Qvun>Apy
z&RbE*=>+G9+AYzz+>b`=9G<p)?c}ftdc$9@ZVxDmK?>f$G4NPtQEQ7DTwF|Aat>hf
z*myF(3%gos@Bu~uYo`}K5#Bl*Hq-KB%Eo1ga#vb+EVBmk9sK3peP#T2MN{_FDKJ)R
zTzhS(LDeud%Xoq-<@1L{L1MFO3QD6ons4EDijQD(Fqdn^MWF+B`3VC;d8$zr^uSJh
z;V~QiJXV3;(dG%cV}hFC`KS*hL__%^|5eiYX*BOB3Rq$l`9fBz$W>v^hiX7Zd`<jE
zNp<#G@@14uqwN{d_G!|nUsh?ckXh{_ZUF806%f*lH~9!Ao=$>HZzRo8W33$C0x0b)
zn-$d<_$tooXN4+G+0tD`c=VoK%_K><e0e$%#HRIZq9~Y@&Yb%F*nI1kO$@duVq!u9
z0XtC**Ya&gFpDpbY0T}j!8B6DL5b0k=%?B-E1zcwLO*Tg!-q%0`Hs=p??k+K?J@gP
z6hEBuf4Dm>0#eZ@%kQ1w;1{|V#wQpnoYsb2U^W?%hC!|QMHf@?Xa|X^t1L6*t2U_z
zgm9CG{eQ#FQ|mZ+9m*+2gRcl3h+p*BOR|%X6W~tz<W_X{`BngQkOl{Ol#I&)!{Pvv
zei;f*4y--KH}6X;9fUuZfx%>SeyTadVnEU)xwDdgg%7Tt;My@2R3HMyBJ{Y2h!+`F
z!4^ZU$s!uPPxVOQ!&n8g5pjZrng{p{mnrhgGUI3QVC{;@Sl50liG=^yz_e}E7Bo|N
z-R>T+cA4hs6vu}#pr((6-?(M|O0UF~i_g5&Q`vGuzN3=nB9Lh7emnO#w<$|Hpg&Zv
ztI~y$KJAU4yvine!)qNwSL^!ZZtORnV;zt%`xcVVkiA+c#FP<Um1GKI(#%XV`^&5P
zFQQ%(4xyqXd@6kIqfNsYcW9o#tG@Sbc1f0vbcZP5GO3oOp?Pho(Ve`SfYio`mjaaG
zXL$q5<I?vrMpXT6cTp~E=DV-l-Hv1Q-MF%Bu5CEO3oqZ{qF^Q3TzNn#5o;{O@wQq>
z2G=n`nVPLAlmlMh2Xgp4gDF&5*u<g;kWc7VFbTnE>n&}cYgqh{LK!#^QzD!TT`W;y
zk&jY5W2(o5N6>DGH5%<3^AR~C9R&gkv+;CegG&ll8gcaf5QXXPyd7wT$+$MLW%nKm
z5YG{sG1QVKoH0^Sm)Tn&)0y-Sl-u~RmG&hRnPd<6vG=+aW7-Uz-g{D6U+g^B_Ma^8
z{hJx>=D#}tO}O8zI+;M#&={ozhg}v2+dw8Hi(;_v;!QL=$h7c}H%BkpjVMuqQ+QK|
z*HlTmN-@g%hedJDpLj-+OC~CCN{^@gB1O0%YCLU-##)Fr^aXw>0?`^@b0d|oM`WB=
zj+}1CXoRXsrCX*vY~$7nl9JYeK>n;aMvyP=ww!Ca^;Dy3Nqh+i7lq?}`7XI}$0;u5
z14xY$fe{EC`6Slrv>@2Fxq(h3h9ZY9fw-Mgw3F1Y>fhx9C8ydi`!a$E_Hx%mh2mK_
z2*Kn-j(s|%oW!=hR!S%vwxE6P4V>u~3E?UBJf~CJ2Sup}^aj`Y#*);q1gF^rRQ|>s
zc0K@xz>Gv<^$4g*l@vab!@kp{iT)ZoG%p&ja)(;FLC`BQgNQ4rKzPQS4?WK4I#hz_
zN10gTo^aX8k{p7+CJG+;{)}vmc6KIi?=gG@?uJLqc;aCps+LFMxj~4z?wA7}jHzM3
zk$v50_tPxFl<cJ-k%$F4{FUS~srXu+QcEU^lpSMIDtg$6I8D2uaoTX+QV9Uq2u@8B
zfuS)rN{T4u!8Oa78`Gh=2|X-&D&gHkG^TO^9^xD`6cw{Gr|5l*#_$sE#|Z8<N=9{)
zqp5V*omj@Hov)R<JUAncVD7RFt<C=ObHHo)#Xs$gxMg@y%U45MVRsAlU?)5$W3G?3
z=mn$-16ELs1y^Q0n>fNbo<$DZP_(%*`xm;)Cb*IcGJ_KBGb9QknrO=x-nbsL)29=Z
zX4T;x@S5$1Ksgv6j&%Bwtr_`J8?s@BSSf)(E|!lYt-dPfX`^(!T!3-Z0G;a})mB_l
zumCdCF|!#T7b|BC^w&2<)5`8cqk+*x@BF4n<$wT8h?<zB-UqOU@dqFSfG;|IbJyJT
zj<k~~Z1nlUN*UlH_&ptTMeNUgRau{l`;X~R(v(3XNEum3cNF6)npT~>4fm2#hc63*
zl&ETI{Ge1qK(lf58JvB6&l#xd69(O}5<i7JN@}3X;T=GT*P-2gwsJfs2X}w$T&AL&
zZVa0jao^z7xh31dOWK~X+Q)(GXaI}V_Uf2=c0c0jo9HNie53URt}ZQeVEkvBHPPQV
zZ~dURLr6;$*YB6E2#uUraU$`hSME$&OvONjAT$NK#nLs;(X5zDG*OAhT_&fw_#Zj_
zGxH^|9}@@E?DhEWuUE<XjZM_Uvzl(9m#)h<=B=ia5+)OTUB#rAgp{KdMUM!uVD$~T
z2co{j_}~VKsGJK;3y*`rtK(TlDGZ_WAhO3XuAJKnF(V^Deswne7<pRfzO_?xQ!ku0
zuR63%NaAB*F5<w<!<5*25f)G*=Td%Zgj{PVQG-)azc<Y1h}NK48{y-0pw{xT%&>qi
zVN>uVTxxASv-^2^g8^9hXyefF(|9N5JoJF{@4r;FtAF@^BYECZRNA@#q9o<3AGQ@>
z$k!BpwvLShd*u(v0{=Rj;fY5L2^~)y#bKR_cE`HFB0+|#Cf%IGf{qLc5EI{p`p6`I
zq6haUM^j^N%&J)KRXpzri?FP@VzkQ;f*3pbPR@I)F89ghJ$FE;k7DndBvPnRj~#`W
zpZEBV1QjvJSV}qb_8x-IXR$jmYS-G@!GXsLK?DG?hlhDlgpRZkGQ&I1$r&O{wn5F`
zM{&=K7wA32EC$%xpk#}RqBn&=V2-NT1VEa@fNt#Ox4)H<a9bD#d)B#jA5>Aou+yu=
zO7!$^{~2)Zz7B97Y7UA=6pZo>6Q@GIU!#wS^6pZ(9v4c02_80)%gksEHjF>Dtj`qB
zTWwUy5V)};EAQZKJ)QMeh(%%y>oZdPsKghxVLbtu@S!NR=c{V)Q)2vCSsSdt>j{&S
z4jxNI>j>KXhwq%w$U35KX`x9pilgrw)%(P^zsNnw%X;<5eLTJS5~?_dvKJACRHE;Q
zP%dH984_SR2pZq-Sr=$rk9zTEtyy%|&yYa6jhL{6N%lbM)`0~`^vaMz(NyavV=j5*
zPNcTB)*Vm3UD;xlOoJ}$;ZFIta_|f2cXRf@HE|LA#`9vSd#WmY6ug!9eFP25rZ4==
zorAtS0SSeK{TZAPZya?gdl?52hj{{-@Y`kGZ1%^9{SqxV3f9;r*BtbaG=m}$NG+j_
zgXr|}dvE!*Cq96zd?4A|2!7S6d@?357Dt~?;J<PX{C}3l`zb(0rK4-|P^Ilg=i-1N
z3bcBkPuQG$2cAc)eEb;?(mCMW#XV(lS`6v<ArpB3qRD0z42thM4FtCrmHdv$Wfh%K
z6ZE_5K|h=01#A=0+S9t(FFgY3?LIE<m#Wn%;&^PosgYlwsB2E~SZN}xIDxkr<z*=#
z8WpE^c1=M(e~mAUOYIX?^y?o@8pEz_Lelf$ce4kplotgTCY702-1xZYt$z`<Gob$Y
zB0Bo{5{KH&Va7~24YCtj|Gb6Fo7(+Fw)0R&RG8#D<kWg)jX4x|h`lCU*Dn`p)qni;
zmru57MzbKJWTk9B%b`1;0f&mMabX^1)E0#vpuSUMe(~aj`9@15D$Xf0r*T8TGB`t*
zNWVaZGhNu-%QvyOTt;+&-cgihG`RE(VD$>eiS7C1>|bHt`FKsvSp2+c8P1sFv&+Oe
z-tvfRi8jUndsUVffuhzAqXYuYcVS41E&4PjzNjOmBQ_`~UOy0VY8*gR3|5DIGV4Zx
z$KkI7N{0@jbDfRFkru87>qz$EjD}XuG7nFM;Ov=7J+y+1|7PhTt+<*l6(tE;zr=lQ
zlq43GdgmTs$Bc-EFP@i}L%B-%NYupb)8&XIA#_{!lvkpv{(A(^p=nto4vQ}+7At0|
zsjO)5<}QYV^NR$1KS_-|sAg<9o$e~X7*AplyI&F5ZVOMvk~U9m*FHl@u|kOhXdzsx
zM};6OCC6krM}cX;H4N?{+(Yv=?YlAIqF$@?c1Ge!_LtI4#Rr`bD+1gvdW`DuJ*K?{
z%qj$vs(l4Miu?mI{)@U(zw=p+e&7ApIlnLVwsNHeDNv@kazybNe);V}UZ#u)2XTwg
z9J48Hl77N4e!XR}c?~2JP?|2iFR-<|*XvJ?0;RRJI4kypb>v9OXq^VncUO~A2!pp{
zqUH8sMe5fQ<a$mIc2L)7^CH~hULb(QV*TP9KM}#KIlLIg>^Q7dC$(<gd;a`_GE6yk
z-shyz(oQ3Zv)+Cmv`qV~3~>|$^AJ?0D1+rMih2pD%qe@9An+)+I{pCQ><ZYy?F82!
z7s1J#W&?2!_AuNnGjzz(p-zDig^Q`u6w6WR$fWGx6n9&>iRZZ)g4a*+=Fp^i#2Aqw
z0|9w_+H-{(kHWMo33aRB<|sdr>)5ATBa2JaT3ZA`OBv=yuGj|R>AMbExwIVzZqIb@
z(#Cm{)RfXfcAuW0Gz?#tR_C(3Df_uQBxqeaCxYM{$L`OEG!0o2&{l*Nv#X^IBj*qi
zcnwaRbMhZ^eKr<jA(|4Y0F}THNLGK-9!_Ugd=4!+v?)_*XZfs%q(|a#&eE)de<;fp
z!uBRN+ve(U>q<Zj6X4R3@Dxm+ArccIB5roj!!5K1!pCsh+@(USaV-vsK4hqxZCM@|
zmn1<4VDf)!jSMZz{Jnk~;&4FByVNe^4~1+FRhzHv?s!07+pfU#M-K{}V;A2DhYjLN
zE!8R6LP*P^4Tw>h(o-j|%8Hlb<9kt*3;TSPhT<_z@{sJ#erJBKK9YM$5|waGyFdA(
z^?y=&t-E?XE|XM-8*2P`e^s&j`#mJc9jBW>r9G-eA^-rui0NZCpR(-d`gt0S;+4>$
zpJv&ELDTuS>5bM_kflmx*164g`A`G=EFx|<1D(5Sxn?v+{?~Zy4{W-g=l>w+xn`1@
zk~AviFSt7^uAZpdzm*XTPhmmey^T(s>ai{h73~U@w@RdH;d;A-@Xf3QkM-7@kP#(I
zArS)pek-)L9HFwrBg^TCjTt8x(EtN3NgUkJgzy)kd>g=s05%|3adhUtf6Z30bMipg
z*Dg@-jJzM_JELBkV;V+BoFHL&2{<v`bI~1}dT<e|;NgghH~Ba>!h~G*xMVqnZRAgu
zF<Wwh(Zb3Hj9(J!i^Yd|ko!ZA%?3T#Q;!qZCx(`C>`He&PUQQ17M+a!e2Doq7TuY`
zes?S1liOay<svlWM+#i&j6+3Ne{8F>%SS|gPU!nfNB&%Fk^IJG?9lF9a3Rn%W0d8^
z0xO73VGcW0AR6)mO(I5pD0jXYJgYNAihQpi_Gx6n&8vD4vQtQ>-xaS7nbO?(x0{*#
zm52`Zo$kgbkvLt$t8dV!SMt3vwgGt2`!U{ny_HWaMQsa(F$gNq5n1ALOt=){IyAkb
zB915&+R+c0vBoqJD;t(|xd`)Du%U~Tr86x<0ht}F1d%jyC-RL|rnled;{}hK`vuW5
zw9D>y9D95nD_s>TqZXoxIA&z;^hU0};*F<no&Ao_bjUj=&`3bMUbEP}Ho$JgrnKVn
zi}>s>9<MoAgF-N7=6SGyf1kMhAXv8*a*Oh&lesPX>evH#6QG7uOCeja5`CZt2E!S<
zJ&d#!8-<uUydXJb(1&fGBb%ec=+hdeuZ4)-q%wR7O>KPMlk{9gAUjI1EP19R$kn>j
zKXmgPsDO2f+v^|gr|^e=wyaKTPG4EkA|G&bp_al-^@S3XeHC|<@%NAZ?3>HhlvuKL
z(bI^OlD<&JKwV@Ss=6)&EY+lUYY6BM<jmFa{OF;N=E(d!Q2#AxtS+CB+~g%0Vd<%*
z)0mq78|bHl8cv%*<7$eeqvjoI1gjv0H%JH0z6ggQYPGp%iXVL`j$ITGb*|nGot8O!
z`0}>K#xySNaCeWTv&*W(5?nbq60VQ#66p0O`J}LgEY&`FqB*&i>R~-bDy-DQ+lxjj
z*2MOQH8a!UR%A3BO!!+tRo2?{g1e&pZgz#AWQAH?#ZRJvZV!r6h;Skl&vMsyas3#{
z;b1vLHR2jeNIl)zJxp=fxC=PiV6lHNoR&vdQg%fmC)XvE^WO)se$Uok*l-e%tEVgN
z)JK=|2NSh5lc_ZZ4o=UZvP`XE{Kj$Pn=D=$g0pmY#S=(gA{|gl4j>9iYF2dLF75^j
z`d527vTp5{47jRowY_7FK@;nJ3a#&*%F-<P9d^W?W`BP%vkwIsVr!B0H{&94;^RC#
zJM&L-nCTy}O2v}3HTSTF=9@M_bjTe?F)l~YbNe3ccpL)&@$bhtqi$lRAJX;gL=d8z
z|ExE{z9{u~*QbIa1hLbicSODqJI6Y62{gTbAbsgrvpp?7UBbjNeH_J-k28mI%!1>?
zt84Y|3l86l2TO>F3#VWp%n0ITIi!Oz*GD*P!1lMGSi=0lO5wE~#qz03Q;pc3e?=h{
zZh5H6#Q6q)?sLNix}7hjO{?n5PRgdUpa_*GBA{*add>E|=~hH;bZ~e9bf!uuA(lsU
zLIWFP=+P75qxhS@+Z8Qo4snW*X$o<rr^U#v_1pHrqX_5u%TU~u{mYN#x_jM-uKchT
z!bY`_m@b`Nw0Y0~3*f&#KSIypA@!0eJ{PBo8H1qeUzQw=bcHzC6TsMV01aBfq}C$~
ztM^pM4<QNAX0Y~4H-LF)dhU7)W+?O8gnyyzca9Si7W{ugzsK!A_9%c`y~GS2mc4QX
z@u=fmwJMukU+&u+>0+#~@iI{I+UM>^@jNj-6grcB^G<ZPmtJ@1<XUUitJbjJJyIK@
zZPUI^0-$^j2QqtFF{wZSBt)v&3{52q7g>^Dltm4v^cV4<sO}80-syoazDO3OXtGA0
zs9rKcP@kmX=T4(Q;-2J^Ao{6l#Uk#tJvYSB+V~>bs*w+^=rWjNpFOUR0BT!y`<ICP
zP~vGFmlcAk3~mE{0-Ug0Wl)Jctqvj4bCGQ*O}%9EgEo5tfW=JIFMy|d)`?qiSSGHH
z*9s{|f>4>`Fdj7)Ec3hBm}mlRAN%T@R~iQ}=X%<;2|^Agbv$oh3SG1-ZA2B++rMv{
z-G^;qob<a(|Fm2JRclTN#!CkQ>07*b*?@~|Xg`7E9vK0*B;K-y^VP$)5+(RDXY;&M
zl94J8Bx}_qK#km(BI&X!&6LIJ6eG~T(}0(9Mq3KDYrV2NE>n#qBKv<JAy}kze~7s^
zM4wdmiHOkXK^0OunWExpA`v2IeYrEWg&s`&w^TB!k%dX{(aK7Gb1Hp69q0{`TZOmB
zzC7a+l+KCz6E(eNzeGM%D+^0>e4i(l6fci&eG`TVboZVzrD}VA)D9UmVoN^sY9Wsv
zAMz#R(h#NlQ`L~`%0EDfie2>(f*=_wv$)nyh%ac2`J>d4-3^>OFl)dh=?RB|dOm8Z
zqpylP+x_fyFE={DM|lz)<0}C`G_UOery$Y?CK8_zaSwhI7~@|v=-c02?oYt%>FX2i
z5kKyQO(@dNM(|h0qO#r3A8i;Dxrj?~5+`ODcDp>0mvJjt?=mWV*at|{a9`9<jH+m)
zoBMa=1O$vO`0Jlg27ApkGLo|UY&9HIYZ~AgbWqJXtKA5)FkbxlG*7hQQBDE}6r;Gr
zWVzIi;=?=|C8Z<&A18{$hkCz9o8W+IXZt31gZl#zN{!nbvRO-GSH+(+!z87vapU(F
zAAa2hRvrIZM+bk>`Frl$y}pzT#Hgz>yti8mdKMK=-o*>}!g`g!ph?SPA~d1`ilZm<
zt>5aCMj5!gOLEhV@RItH;YAwW&QC-*Pch=0YaL8ilTs4?ICn)5d%{DXq|HR>a%cPp
zUw3?hUR+Q0@)0@~fLRg2?no<&f$W`Bc*@r&DdK_*5R7u=aOlgK7(Xqcm}4uC9+4fl
zn8-bNT?{`0x=$kYtS8(1G9#~KW+7Fclwaq`N$CzBpEo=ppoXt|$E5XTi=juMu{JsU
zb5K1x)%4?pzPKTZGRg}sPz#{GK|4nGp2ts~X8d9)7yMj+EmdK9osL9U;_UUkeMb@b
zEH4TzutWU!V;ID+Ns*k!kWwlMrE)d8t=w-@h=+3YxVv&=J4jt9Af;D8Y|Nc58VCKV
z<;$^ohhmD#+gNE6M(3f0<8}>xucJBdG&4h%MVO`Wvtx=&o>-kNJwD2I7xVg26SqHq
z0Xwl<jG#9GS$&c6skjn=c9NU<HqG#9&#(MtcmB-+Q%awuYygX_>Y+8tvtSr77)4s|
z<;#z3N<9{5OVjiMzDBAToOF;)2W(I`3<~_?861VNl((RS!8+51i0vWar|%ayFvvX0
z7IM(BIv!hv-(KReL6f|s^7|N<eF3Yo-j-@E?AW0JNB!u8ueX^#_SW<MvXm-8^CGhp
zj0~BCZlpNE8?uR@HZ8uC)-{(G<Fn=m&=f@<lb79T@5j$L)Zk6w%O?doX%sx+!=+jC
zT+g5~MayRsYIy5CJl*h_;N+4G<<uSJPDGyRipz7VRwadu0z&D)#^Oq{?^wA0#S`M|
zZlL@JniikHOB#tf27hPq7oCHOJK=jaO+d&EsqW6vOUxqJ_LL@6WWUc7V((R6c+ML<
zK>;~|jrBS~KZZF^;hgm9K=`TXZR<Y$47f;`IR6zp^`MC=x>*5-CS8v9M=rJJ8{(ty
z$_%cpf&Y0`MGVI~0W~sINzwA9WQ1uP>CT7{a$QKOi6v1^LXtx0tZLp!kXt*Yo#VNy
ziT3xI$mRCC27Q*|?M=GQwqakS!aFpqF#I9I%ar4hmWR<|<n{&8lWczC-3&82N+9w%
zvuR%dxOvYq6U}Ah+|?s$X$H9Mp{vfdzayyhFkrgSmzR>BaW;`Ro9TFojg9=}NcO|5
z?+1+7hk-0QEweD}Q&)->J-W>T^dsph#1ij8Q+B*GW~}QECNu%RmtYiISojgj=?mkM
zL6?fHF{$m1XQ))`57bzRE)N}jd%C+%ivbfwHGBHPdZKA^s~Qz}7?wYpm{G=^+(FL$
z(GP^uB`}(}lYsm6^6-i2eYyE*<6f9!i-(%M^n=;wNLV>OS_|T~ENPqF^~s*3mWWLO
z?X1mhx#WCqTGGDWRYz||H~7@CEvIU2`3bMF%4k_dxpIoOqt+Vl!y<JILlm;?mpT2Y
z&&4-7sWaCP;+?2%Q9@D%wx@B)jT~&vYt#o7vv7=T7=Vr%vkF~gR@k3a<RgyCQD{F1
zYf!xF@_`7JOAXG{X&sJNdjCurqdm9hgJGQ)3qrWiIgB%dXH|S?SSFUY(1l;Q+dk>#
z1bH3vZq<31_qI^*hb=g*Fe&MoS%j3Hu>yo>Y79r_0k&;$>OkN*hShds{6}$exunK1
z(4BW%E>MbAi!xntJV^RyX4Cl;D_hO&<M885BD|_K43s;P{PPb<B(cz3GF8_#KsQOo
z^X5LSnPJv7Q;9*V$(Cu(zxMU?3YBF^ePRVQHdEXAUi?>>|Dn4~skZHbGFx`ildLO)
zA=`~Od6w<n<Rm%DE=bSHzgZ=Zd9{S(aPVrZ;y?D3Tgv3Hw&8*ozxlu%WrVRc&UBpj
z7GMXWpa85!ss))}-?rCUhWlmJ?A?|%x^??4%Ptp{4dKf#NaQm0;g>7+0tO7>i)2K=
z0+OhR66O--VnU>U1IqHGkv;`p<dn)Ay$Y+4%at`X<TT6{iJOSUC?Lx;%9JC*-k<|S
z8r`kQb?lZnIU8I}j;A)#KR<)ymN+{+kGD9#J*T|0=GC-qiV?)!<E(gd-`NJLe2RX!
zFtSh}mcBoBLi8RWCW*Ki7tarCkK-Qmq1qe7vEwmCKkQ65I1&f1mkFAC;$AnpuDFL%
z7(>mp|7d8QrmE<idaQh&{c{LW5{jS}Q;fTQs{cqY6lhk$${vOyO_>mp{Mf0M@1ypm
z@8g`hraZ0fnrFM1E_qHISWtH-YvSSVDwrXm5-q8Q8l4U+J3cZpRq^|hYTDqfbJ9Tx
z@t%3salhdZ-_}xB@vxeXlNVT&()cvY0EglYh9GE0=HBj<g~?#M5?Yye^!C;KdDQ)A
z`s3!|*_I{KG7Z}-`@#~=h1`Y9vMi7_H890#CD%kMe&`BKOJCgd47DYP?7g6A2#|Y)
z8#{g?brf#f-Nojv=ujt{DgM#eqWE$3HObrVDRu8_ZA)J}8C}77_eY|8A&odWEsJye
z!Em`GCnc0;@jLIvfB*VI({ul9qujBPE;Ad2)!6am{?yDQu#gi%`)e@f?Jv<j=+*W{
z*FO(g6zh#Szh-JI$m)Vz_=TjCm^wG<U{u#R1;%?N3SrRjn}Mo|1Mk<R)xcKDKSRPg
z!R9s3hgu1|q$M?7OToHGTC36QjoXz!Sa`33D9`G$)JX8bf;-{2mY*aZnS;%BcaPbD
zpVeHD@Ujvw1xefA8zUX2yvUs|o-H+9cBl1G24*=)CbL#o(LA4XQTD0zdNvP@bRUld
z-+hIeN-H@)OK)2-%8@$0*Px_B?q`3;29C9=avV@i1q)tRa+GC1pXkY!l@8Y^4H>P|
z9i?ClOICdS6zMF3+zpMpJ4!0_wL5Qf+e!4OF_wGYrrwD~oYwrh^z$ZHIW^X_yocR7
z%YF}u9!wA`*jB~{$Og5P`fnVUdyleBd!6iHzz-V>AG7ivrmc39!)u8XdJ4I!s5tt9
z{U(zZ4eSSaNI4`D0eF5``pyD%Tt)VzAzDadKJR3O6$~}sG+I{HrV<^iZi3%go0i5T
zdOEzz3R|jN&Th9?zCxTF65@-aV_Zpbi-9usV4yjB>)Y7gD%ifOzdgmpUuK-Sw>@sB
zG3c~E9|^X(S?_Bc8^yMk!Z{m*=T4yL83<!c^t(QKjU1(g2{pmO>zH-4x#JnQSA6Uk
z^LvgO`n64`vwew4Jmp86;L6HNv^uFxxp^`WuB}r*aurmta}>{pU24pbe443=RdJVU
zq@GW=|58zZp8PAG=-FUJ=KQ>Z7%vbwLs#;Ks-qt&2)AE!id|AUm|#=Gr+od_gf8H@
zG6FSeRBJsqT+iZZcSWE5sJwR$n$g?&8GX7g(*9t}&B^;QNU{#m-E1hw#KfYUpjS==
z@YxygmtXd&X#b~C0zvXrrHgzw+9t{(zWij@X)aixeui*~r)@xi*zmpWBrO@D{QC`)
z%tW+l!mV5^s=e~mLaOF>N8HP&OsEPNLE4q)4>YTRrgj1$bIjzFkw#5Q^>*{eeiXMN
zXw{Eb>&m>t-Hp)Jyp&iJRT12j@S}3QZ`npZh0q#dR5zynD1$whfxD>p;(iM(6wBU6
zDqB^Zi&zt9JHrJvQ%W1=aVe`3{2q?pRfZZmvP}MKy7N@-?$+&SqsCk#EaiM^A82@6
zKs4qpE~-NGt<e2M;e0k64A>=qI6`t*u{!f+=?F#@FTOwwftv5rhI`-ig~sM<<nq)-
zZe0DYhS#?z4q(HorG?o>8^iMm!8dtfPb@07ygV#S)XJ=kj+dtcWkYcLGCI_jq7SG^
zyUQSz+t+Nb|66C{@j!BKyVKiDUMC#u+=j#F%(g3Gei<+0;#Ade7b;xdp>~q3Rymnz
z(=gOXwZG!Jb6bhFahmqHyY$~`+h0dT*~WYgo3o~@cY%0J+Z>}}6Xkyw?(w6kEl*eK
z9qdbuz3T=>XE5_!JN0Ksn=(gX8X5_;mYG>``*;xDeesyT$zd3b7f9M`10;Io_rz6F
zugoSF{2WeIEU1JDfG;GUNlk3Jd#_bK<IA@sQxzGT4S8L!gWQ1Ptply6b9hi*w!H3c
zM8GAR3K9#?BE+TQj!1AS#tGKD6o%L|*K&2lmNag7fm>qIB5!l#wNoUy*ncMz_MpSk
zR_nGD-4g8%Xb$?Zgo|e~RC=(FSF>d6HX<ARN-D0ed<e}Yz*AEaRX%V{y7(jHm>V%@
zpj6F1$#SR^@MhD5x=(Sk?>z7_r6_V2{~B94exyT|D9%wlBzukJ>wdJow~SK3&;N__
z%*SE6mHE5k$6ZCs=_U<KeM`RdlicTXHajpI&Q_XKql`n??frzcWJoI^&A{YZY#FE8
zXg6|6#piJzRH>CCNl(>-7%*tk!&Pti#~ZweT>_U72#C)B2q+v_4hkbd_D4Jcp)OEJ
zVy0LbPffqmzNg%59|Y=OKt>t>sb>&B*pw!%(U~RW2ZkThRG_V!V%qTCVri0g!CE!I
z&JXD+lR|()zo+@AnycfEElRk2!1=r8&mOb^A37;M5a}<V-gaIwG_k#d-RSSvZQ(QE
zILtT!*TxVDpk8^9|J;wm1p2SwKSKSS{SVpxXYiBaN7Db95Q0c2ipuNx>EJaowH)H7
z5a>+La71{qi4VS|@?O(=P``h*tQbtC^kFbS?DT;2w}3Gqzx$YkHd>}<6&mzH2VlYk
zRndtiGD+kAy}hY0fQXN_<YYF*dxSv*fkq-Ht6WcDfJPE9f7X6^a-!lJo@1lsilwzq
zK!lY6zYB6%pF}t1ts<oBjDH#fNDLN;5CDW|_OB8e7)S*c=mq(CZxB=1e?((_$rkTv
zX$qO%WeP{WXGPujxpt-5GfX$)vID>18lN>nd++Nr$iW(()f@-md0CCB8Txa`rS4PN
z#F^C>eNIs6YNeU}X+>UQlGyE@7=v2*qH>&T+nqvZ{md$%aZM2QlsqM(%FP;QGxE|y
z#f)yK6lGduQ02o#2~!fL^6Z9uMo=l_RHu!sY&$rCqjaS&J`KnTl>iTs#i3T&2K>{h
zoIYu`bkHRtEY#VWIqNSgzh3zdmSUt{T|>31X*x+6_kivo-3Ex=Xv8eU@c3PUw8|jY
z#lugTQ@H+J2Gob;;2oi5S{D;{$GFaw=$V^cK!ivop1}XrSO2Fjb0k&*HZo|8E$p3y
z6M27ATDgXB@I<YGNfGM1wG}k3H2`XZIwzqrUS%h{O_91oEu`@eZNsdMy$sW$fI$Y;
zD}yHkA#yXoIffL4>c#398YsRqFA?gE0FDI!^dFGKfWv`n3F$<L>Y1WbiJ@M85usQ0
zNq2+@TUKHMDHlQf<PY?p<A1zE{}n{|e}ihy>yP~3u;H-%FE*V2rkMM0in;%$nEP*v
zx&Nk^`)`W5|E8Gx|3Wdx{=X*G|0jyM|IEGrzo3|7W@r1qQp}~zAK-i^I6YUlU<~8A
zP8bH_YN`i>m9?S?fs;pg(%X&S)wIe9p$hY-1O&n{f*B#K+S%E8PERq9eoM|h&9R!#
zJ1=wI@jO46dx~dx?R)!Pi%>*>Ad8d63*{3jVS|K|CWsjT{&PNH@Dn8tCq*ZT7eW@N
z5&4NGs73um|BqXPVn0tIN5=mr)m^X=QM-+omzRi02y-{G_`=@lPG2B?0+_&{0io|7
zyB$B~xBpy);ztyh|4+UdA^$IP{!gJ?K_~}zpzfrU$;!zTjiiYoJ7Yz!>yA8YZs>w>
z>+g*zB7($FgJP&gR5dNV%h)7zaKZmmu>T=GX&vT&)r<vM{EPIzYQ<uBH*bDfhDh{S
zk^G-ZppwLQ#KjHq71$XA!Wy|Ud0+i`EMZ?%*BwYV);3Oa9S>V3*NuOR{ii6MhqtqD
zlPcaW&QbyF2oq`gN_K}62NoR9Rmg(|+K^xdxujbItl`wt)6+RERv6(^FLUi6g&f@C
zI}eHMQD|M-=-;2D7FeidZ5Z9V#HPdV7Z+CMcA3Ei%y;{EH!QA4YDlFs$8IVKy5E<)
z*%Du8X*A|RzwZ(U+0(*6r@+otVh)tI?+&YV@$ne)tg}WGU9xD;lW^0E6va_i%^vo1
zBqgv|Yw^BuX;(e5y0>PyvCb3~Lz)vqsI!VC_vJ8G4P3J6Lu=NjQ^I)<zpkJ7t4V1y
z_)vfAnxCJ}-NJl5dQD8~USzagIfY$yO4$C<kt3PZQ>671QApw&AvW;pvXGAsC#6J9
z&eEcleuJuN-@3L&E?*TND0W|;a+QeaY<5&2`L@B#$Lv|9B5IS*<el8kVKN&+uL)hm
zu9{PO6>V6~bw3FuJITRxk+GB*%Y;&;s$mPg+GASErmF<F^BhUpYiFxd+3g3Nc7MEk
znO9L}*)nSV-I~<#_1J30&6OFgU=Bj?LxAvbKb+lg=)a=`6)d{c$ZUr`K564*+;5r_
z;ye-3zs!O&{b?J`r<LB?u2DOFK=&PnnDVb}gHQ`;x1kDO+D+HCV!GBth`Z-Ql@KKp
z)>kGSnq}QTxJ6r5pKkN&2>PvH{SR>H0iSB7{|1u)RtM^^JtsP|pgVEp#L?^!^vZq9
z*I#WE;=B;zZG^j0j!U^;R^x5kvFOmUx%0zUwze0qQzCVWzK8Xg+rfXub~#?(KVc0M
z7N|11x07)o8T<=a!t#!c^sAUju8aoE-2&L|?aq!S46tu$!nH!GKXJ}eE~oDcM$d%J
z>y4d{aCp`!;plS_;W{qok=0-i-@#tMo_-^u(A)v-s2hN)b<Q<270>B;ESLq9&$Abx
zKOd{G#@JV>n`%Vj5kj&MXj(-=nh6L)Nj#!K+*Oxd+B4Uzodf8rxX5mis^7jBo3jeA
z3tt{D6|;s5Hrp;T$y0HU2J_{>t066%1R9zL!@$W*BhFc~D9jzu?<eX>>^QUOt88Nm
z;fl9|8Od#9FmGNq?nscAZ^G$P&P}U$xs7l85P`FrzR~z67j8ALAf&!Og>s+Sa4vQJ
zrj${Hn*i^rgIlsT)TNa)WY-ZXpj$RHZf(B2^7>>2yYCAQvKaIaO{joq0S{E4X(*Nj
zq`_^uK^*YkEC#h`p(~b`7OJ9udybyR@4W+f8y;$9>z~K+k!}6+b*;D|jJ2yqr_(fQ
z3b_x-<@c})o@H#Sdyq|njK{!Stvzx2_raU&#)B?Vg0*GOKd3lNTwlLB-+whCg&TC9
z6$G>Bqw3?SU?Ib}ctjxoyqms_w-<BXU+b}Qwp=2lgsuoQA!`FcKBwk&87V^sa%|}R
zqKm&Gp~yjXB62PeXwe75wnO39U$P!eAC64o@Rp_aH}W#n<*XkpAj<$dY`(X$h1_>P
zg9|>IX|0)qhIcDa0qj!1IZEVh?fScKtFd2L<t)&Wg`_FK;N-JMrp~te^LVdRa|--<
zPfmgl&!4o$41>);QCm2;#fn|si>4>!8JdXLk||tV)185!JBRvqb)x4?u6qVEBT#?#
zUhf%}I0cGR*!+4>LZyp&H%KBToNGJ^Vio;m=xpB?kNRwu!$WL1h&pxf1>Jt&9#32z
zW9)37B$*>wiw_jJN!jH^p^WAuqz+Jh0X$;Mt$jB|K%RMK(~mLel-|8%OKrGgq(?R(
zAyHflsu5Wlc`ou{`+j-v0)rQ)NJdo^uS#wapW^wH|G}^#t?eLoqC2mW+p46G2xnc5
zi4N+V)UupqwrUb%l1~Bd^U!vL0^LYny0;%~Dqw&3-n0I!>k}O&|DeBsacyqbiI0SI
zcp`8tdEVx7$RY~;w8FWfMcJH|Cj^m2T@J&i4)-O<QV9ENdr7^J7XukYary=P+o$=_
zE6nC)U)uB|1H5lo3tg@DW!rR&XVYux_BLHNhW#%igncrrs$<Q|OrCu1g;`2oo^kw0
z*w{AwvF`53t$_LsQZ9tN{fUEfS#H^DT^dyv_+4r&TQ(dg0N=%cCra-hl>H%gj;71P
zW+X-qt&M9g{j}R-&5lFRhQnu&?Q^>yq|k-YYYSPqT#c8yqvn&-mrYO9$^xcy;m8)X
zYF79A@MxlNfk!xB$;{JZQx*5r$n3|NO}V^5b+ei4RiLVA$Tv+Tj{;Dd*I_dhQeOg~
z_~{~lhsS;%o2nmV5E7gn5^l14W2_5<N^7Y&Eg`@!2A>s~xIp_$KZ8SiG-A%RgYL_-
zPceNe`*=(}m0;>+bEnsX{Uv%kCh}twceXVTJoCj8)=ds0XGbiXx|~;AYyD1rqJ4j`
zbKCP|<S+-t?!_T_B=_AX^?5CWooF%nR)(I27klKiGd=q1gZCyK(qPMukX@Ba@}bb)
zEm>C!YOmz{IHVb0O^rfApcpg~WSsUdo2D$<t=$iHMC1TofB-nG!|kFGsIXI#h)u0p
zhtxKqNG@Xi;6k_^yGG@~&p7@wh%8ItZ7|Wcn}?25b3)7Gs?3v>7t)%vbxdz7O2Tix
zKH_(i1jfq!j7UBQ!=MI!pWNFYlG^dCImr<B$5!lJye;*|9XPvoP%PRgJ>vszXuh0T
z(<;KZBvwL2^(m_6YW*w5LJmf{i=(}deF+-$ze^Z(KF_BOgK4n_!}3#Aa9K1*r-z==
zeaA)%hNw9{)54OtQtJ4uz@33B#V;YH|LyB&@mnebu))1Pp9O?tm*hbT0+1;`2Dt)B
zgmEl}5n#t4gA1I_J2wv>kL<e?y6`Vpz~VJY{#M%yuKRK-Ar>?tq0=1o{=POzLi<6#
zP0GLg2+iLMBP@6Q`eA-!{`4p#WXG8xsIxV!riWRWKd2i7V`zZ?O{eMv)>VUAK$cFz
zyR}6s0fcjnJ>V8xE0>gVfws<5g(R^^(5dwJqY31<)^CSl)OQ{SYUDGv{9|DnsYt<a
zT63?~@*9*RMq5Wel5>EnyZyWAR$}X_16dSgi5LyFYZIB?^wk*Yt8tK*MfDdgi*$Fk
z0<c4qV`_eN<Y+=&w<}~npYr(SqiR!Y7m8@jp$?MUSiAOeThbr`bGx6jQR1OilwJ#;
z`VOnrgL&I?*_R-N7~b=}T&~%(fd&tH*idkg06?;+`l_)0b3^~5@aW3o-k0;b9p6_S
z)2Xr&o_#}GpkIz<*IhAt`I@?DBw|YYY@KU{YK_2sb}V99$`Xv%@Hn--F%d*wLyB>o
z39sIv4a`U?G1fr!lY5F{CW!sfRWfwyv%GIP)|w~Em!#G%yOy)9duyB%7&wvdN#&s*
zHorRPwwRFCnDo3apJP)ca1A2^l)72lk_usrKl(3QIL&acY|D-<dC`88I;}~o(Q*^<
zoamImNYLayc=xz^rrDV+Jm(jweIeWAEVuabLIu~Rk$h8^L3y1r!!KsyY(Nu_GbS&l
z^R7BIkDfnHCi%kXau)JXr&*P=0Tvo>rK=+ncJ!mcQa6SX={5;mj#*!a9<~byB6V?f
zOG-;4J7sE#>GJ~|pFezMyIi}LNnDSWm@%ZLx(E;`OkVxDL^V(52c2(h^*UOCt@l4L
z{RyG%bU3IBnty2_TY!@ty_H5-aF(8AAfC$kSSQLQ_G`9S`R0hJa--SM3<2!u)P`;W
zoSd2m#sAT;ZjLx#K+z)5{zV}pNBlm_+XrX)71=Ztw!PNT^nMHRS^!4DOAwS>iAp6n
zf<4wG*h?=3AuuiPNOymtuMuexrK2Xb4+Z-5M>xCZ-FVwLS5*%P0SUgH6kF(PkDv{w
zSdNc1nRpcCS=e1enWs4J6|hY#>DV#<?)wAXI_rO&^5+n7G2^DW=Ces#ZTHS2l&!$i
zK^eusKebc^2-jF-j+n4Nh--=FNB=uwB##=rRt`Za+D@UIecx^o_kCRc{`ji;pb|8%
zdQQsFJPr;f8TCa+(fZo!_N$K@$52AiO`)}P7#yJf?0mj^bacIf=9ghjRdnP2araOp
zu5oEEgQwC&@M@XfZW8dxf61z=bqmMAj=~p3IwMX`@>}@pH+_p2>2l1|{?a?;Xza+A
zTg9r@oJ030`%lmhS1WlY8`phTNe}a&`qi_OvAn|cbMsp8I$9du@vjur2&@DYO|%Iy
zWB@IhTIVm|1Izm2*s+st7ugY?T<fmk*hni5^#t^aDTyT(5MBcm)SqEbcqtwdX>!{q
z-`<A;ePx1zm)gWAEya!fP9_TemR-i!Bp|`Fjus{y=L3lFnRa71Bl=0Z@<En`uKbZG
z8vQFm#Q)VJH%7-*Ad`>O>G3AY|B^B%*H@+aOntRRTWTB2?=MnH^fV3GNerpz)|>PR
zeo9YR>Fr92qERM^lIJj+Rnp&OcI>Xzzsh4EIlSwBT(^#bx2_3ACf4lS=5ituNg8g3
z%#vsRM`1xei>TVg=43oyWEb1!cex*W0C?b`^)+tPv<1)@NV9k_EBHF(Mg%rB6T`AI
zfiIBlN~KQ6Kx2`#HG=@j$ETzZgoEuibt5ZMBfKmk-Byi1+;Eik>I%A6kH}n0KO{$*
zvZ-L59xr+pIxInG(_hGDoxDlC(c5J!{fsi;#(}daxU&b8m6ickmRM-YKvuM6jlCx=
zd3RoVFF_!obmP0hXrgULUabrur`}_K+vD2vQZ-15qY0n>QTP)g+iQf$>RDoor1w0;
zTdSo|@z}i%i{f;tK5cq;6UP<rt8JrhAJx6#q|~43`4G)-E;jHppWuQqRAI+pK(5)h
zQ0RJzHCOkEk-p2MJ<d<(_%VYKXvbbbs8WRk6LfxhwGA8rH7tDh)3!i87vG&O_r&>b
z0dc{l5H<|m=mRcvlt44rK8hcY5{+<Pj)Y&KhXKByss}y=$SX92Rq=P2ayUtC&*E^6
z&Wp(2A*%5(t~99X&tCVoiBdtKAbM1KDn!k*I(o%TIi5!Ae%#*f>saKDR-}RIN9wjA
zHL)5wfi;+WW8oVS=n7w>vyUK;$leP=Vv&xp&%E9Du059!I5MaRIc<xjic?`%>-+UP
zsJ)|RY5vSnTXi29h*cE~dVq<80U}m>z_7=((ues|m+7$?vzZuaHtqLNk8VvFTh0Ey
zu0aa?UX49;PTf;aLGE+=oAC<iK4XbU<Gb;v;?VAw`}K699UcUu=;!P_+*r9Y2%H6^
z=57u+C!L)!O%X*<PHLo-#T*o4V0tz<J{=E~W^P40#@A0P-YbWB*pNDP$}Kcw{mdbF
z+gih)CVT(7@TFcXV^Fi`s-4r}d!nz11?N7==;o}VXKtY-D@m+4A!lJ6o>CS>!@X13
zLoz4&t_@U7Bcv_mIuCaTFpK;L2Z(u|4Gk|%V?W$>ZgUa-{F9~74aw*Vh?F38u*i9N
zjSG!fjq9mok0U_+^ZGvfFZf%_%j&kz0~p)~)5EB34{sXdG?+O^Gy#aV#9)j<lfN8c
zDaXN{N)&jFZ(PG`-2b^6)OOt0XxpnQjT49)YoPddeFiYw?IM4x-Q$L-&mV`+jcmI4
zI?hmnms+1XGA44Okz?GBgNpj%8|U8u()kCJcWKuHgEBU^))fN-O#Hgc3yXEk0lt@v
z{|#(FlfQ|Ucs<3YNiqxC>mD419^md4NO?@HLn!Kx7t|E<Y6`+To6V3sFRo%f<Qk6L
zD6csa|C}%1mm3@}rX!7$ASm~>d=IvDkz#f~^h|felMwEK+Wx!HItIQbBUMmQa*wUB
zX|>8X;A;(~rGFjJHSP|P?M2@0L~L__@)W8(5M{v8CP*rftj>BLJCTO1m++6H4v<b=
zhtaheFHk9zedGV3eDexmF%(Di0b5I&Y4~LLx|ZnY;k*Yi8r&|jUPntyMyOgmW<|p-
zu|V<hYUOkc5bjX?#?Sv5Fk_2*F)E>@Q(9|U$^?VE)YB3YNvk>KU9X<yMfVQGW#E(f
ziA`37G~Es*c&U(ztE3__Oh3P8o#^=TBl*|vM>?DoB_M`<bAaq<q?97>@J8(445Y}X
zhsv)qRB>XUKi+f}q6`SEpn4d{SPlDV+jft6r`yG4r8XKxgSr=F%g3TlhP=XRqXVPe
zg4&{ft3f0!T`X@l&W(kGqD1fF2!uuu^^`3Zs0KtFCfi2<qX&L53ZtV@zOvq&L#8;y
ztOrurrS5aGl`j7xgrSPif7brz+fLwu_B!tzBn05u<0Z|M+{1rw!*<GPzKpO}DWK>C
z%Nx+WJorobEc?b$CPoMJgE1cpmA)tY-Y3J;)wBK{#4(q$;da#q{4XCT$es3G^yG7_
zbaBD>3u8n+X_d!R63AJ8Qp|Wtw)#uOXWZ|(czLd39(?`5I*R@HFWKcvVKHm}qb-qa
z=!70LgsW9jksd58-^u6LS#m@>w5T(B*|Qpd9lC#o-Qw`1Y>ms7^AO)fg%|>xaN?FP
zcO7zW0@XO#YjRF(%hkeM0CICJV&;Yv>|yj-3=tfe$1Tq|-giW4_|_j0%yKYL$_3UN
zI++&POgV_2SJCr8Mn2!U3~khq8Nb)bYH{5QdcU_x=wBbm7DCR_4b2rJR{ka%3hZAX
zlN({#hl?#ni<n3XF#x4AixKoFIRqoC?L$gKN<Ccv8^tH=T4$lV4@<|l`fNViWY3k<
zAIYnpzUFv{F%0<CwGIpQlj1`$hLTCx21e+0c6`ISBy!0lp;P{&GF`q|rhJe0NDVM)
z5OW2kgY!7h^f_bqaWX&(G)tmhlr7^%j;%jl#AVZvN29`g8qU(0;<^B*-m_L-R%aGz
zY|g7R1~{|#%C?9<n2iWwX(cg)6(KF%7Q;{kFzM-xiH2oS0d#S6QjGUK%dT~T68kLK
z=vihPBg+rumDPo$e7txb!7DDx_=?&Sez}w!ck(W6n`pSN4oCs(ik@q|eHYQy!%}-q
zPP-v~v;%CKD^VRc5`Bzz=I#!Q@~OlICCmJ_hue#1BJ^0tQxS;c+%_VtNqv{Ax)(yx
zRf>iyPx<K(Hy{lTmBB589|Fe3|6U?VYT-W@N6+1H@KP|-f?OtN@T{$k+(Lk}rAgj}
zc%01GO!O|b+sGW^IT5M4B3KNB$2CC9{ic@EDiK@@JY}-=G|}NO6Y<vpolNkpuRrRD
z%l1&dWZt$MzhJZL;{NjQ9FL5^X-Dz;vmLFP9EAscx)YZ(bik&M93k7Cq3Bw_pUCHV
zxXu>xD(**!JL25bSY9A`jB4xQvaHSC#S-)=_J?qE8Fin`=5<O!sy(n&cNCK~ZT+;>
z$JZV&V#Cb5!iPZARZAn*V`VLv?M#c=s7S5_6LAKNTtfuCEEyKl7ddl9u$X92$GSNk
z)Rh6ugHRqD0UM+^2fwR^@j$WG#}ibB(8VWU4M=j4^*TM{;NUKx=Y8+ZER>eU`)##w
zzhUmq7s)qnowqylwY7jQa98VhK`+*}a;y2BS`hIYaW$Yr{`q6T@~L=;6V1+;r3ysG
zxM9pBMjpP7Kg9jg3S@lla++d5?IhVo_~tBStD807O6RZMg$c@kl{i#iW5g>2?4x?K
zb@QnbF&xA<9l41%4g$ys#I(XXgaB9yLgTVysz(~+G)K+UA|}1FYi~Q*gR!acaKk#2
zwZ*X*L|;WkaKBkW41=%zIB}&8VuLtL_C-$t^gP+Bh>8`uI9po)?vrcHb4sabEn<dB
z0V0kC1cH`BVLStz-l^lh*fnRv8SyIH?O!#h5|M!RW!3@>V>)RI2mdrFSPS(4)yd_X
zT1q2Fh<=FE>?8u3#q1@sTn;Xz;_0OfT%qDyc29W@)rx&H!+I}#ZI2cTi=ujWVdGBk
z3VW;}U^G~Q-{ZNdN?A|9Xm45;N=HuoMhy0Fqz0H>syDFFWG+L>(m%ngf)qBPcc9J@
z6vNUvlJ4Ny3+0j0tCX~FXfS%5Yq*IQwhgmLawRb&he~CG0+K?be{MWkvWbz9E|n{V
zmf!i(x<HV5Al(se!{{RTb@sxS5FL!g_;aHmGpeIp3sWRB0`ptN|D0G1gM&&aTHELL
zsQ-o-%S^HBQtpQ2I*^XY+x}Pe6!+sykEscP+R1nge*qMi2nBUP=twRLkTc3`A7M}h
zbp+Pj$=Mf--qZ>SZPl(Yvc*(8qZqanKnFz7rS}Tn00_$|ougn9AJnHWHIEnv?4=&(
z%Rdf<V|dC>2Yt#_8Ts)JpO}l^bvE5;I7aJ+b#~=kjaXt49%(iP-5Z4%>VYeZImmeB
z3Mr=w<d4YKIu>sGgofEHWtdKiFH4yPSwiZ_B+JX}?B9m{)5r@|WZqMwO{JM(E^MeO
zq**v!$W7@6?jnE@MW}jLQ9&=e$+8VCoW<PH2kPMzL~AZ6#-Tt7omj@PjaSmvN5Q#|
zM`})zZAj)}kCU06FWboE6TA*$5zp-et%n=!Vr`jq7_Z*(mEu$Qnu)k(QT{>F86L--
zozsanYYyU~8?v}2-g$yzK`pBg^)@+03A3ggUc*oluwlb(WUpU|Yv7x$L@!?-+;e4e
zuY?KJs`ORCS>P$3-d(8_q6cN`;p-_X-M(Myct?+|tx-lr3mJ4;GK;ynXRAnHlp&;x
zEe6uBj2Utw<{ETVM8&SK4&i<S6{TpP7n&HTZPnZN(rGF^pNfY<IKcEXpLA~zbrRTi
zx~G{i4X>AcYU$x%+H}YUU(XS$9T4_k&b8j=zAXPEGh9;P&y%fK|F;X31D!jIxgE11
zxGJD?HW1KOfA<J%NS`Xcq%Ao31Mn3GvQuOl?xtbocvEDiuQ?IWh6z$cpND2IS0QI}
zRQKhoCa8hf!k~_vBX*E`xMiF%wt3n}@AAEYF#umwO?1iIpjuL<HIxc0>#cD^F)YA-
zU;e!_<TIoomATGsOzS8$#DNKb-zSoQ)b>Nqa4s9|aM}7zU-;t6;o4bUXdP4WgzMuB
zT!R~m9=#LW=H(ddST9c2g_`lyT!$Xyw;549ol+R&=)__)h^aw!;;hjMN(bCh9NEqA
zv-Mi;!bX+FxoYDHg}e`X)?LyLKu@uA&3;o0v{7zow4}x)dg1vq(1Iw2Z%?j2E||pN
zp=Q|)m@PBIgOxxCJS}AW)`Z)>W&`CbDOVPwL2MCN3*{d<U$+HC_d+zmno-<Q3~&1H
zOH6YD7l@w56vB9w2@-Q1L`D>>VsJG}H59!xzX+^<Q?ObQQsNe?c<PAc{h*jN$72B<
zxm#IGHsf@<e6jQ5pc&sM`0f1AjEZIJk2GktK<|)~+=nm%1?6TSc#NsjJl9HJ990%6
zlYZBBcu(7QZeIF^ds0&-r6i#$rw!&f-BGH!meT6nOg2GG8{iGg#84j;r71rh@&gM$
zY_BdT<S*k02Ko+X;NxAmxqIdfTb95b#jubNQaiLLL>oLTPz<XmvY5rm^g!~U?gwPF
z-gKC3$Nh2%K?}oMu-!s3AsIA;a@IiU2g1b`!%eDY1*B1&vEa2|kMVxrANAhzgoS&H
zts@(do!zj-jqzo%9uD~H@>c>Kv%@LJiBKd~+k>0>WyS*3sEFUBZ8tL_>y6=QX$zvF
z0f)6+CKDKgnob_GOOx@XB6c@vOg9zYZn*UDlnQ-`O0Q`%Umm*?AXe^n+Dm3#pi)rv
zC3xlK3H67gD*#73&Up2&Lh&euc|9)Neb}YcmeXm%v?r%xz=vD>-wqtJ$SCaKdF8}p
zAZR9MmoYbHl{IL(JMSQ{<wY@UXbiAdENG#MlHEGxsB+|>dBiPdN_L4%bU&-sS*zl>
zHeAQ1mhsE}&w8f|iV<^f1JM&9wS)oDWs}@#=|1;@Z`3lyyYI;tu^(CsCvCAl`>mXe
zIMTino|66M_;L(Jms9G3@;5Mq!zf1xjlE5heYxA_Wk_dC+xAms!WPP^!>a});$UNZ
zO=Rac<*_>;JV6zug-Xku*yxB((^ScSkd{RK#i_>pNq9|P!q9$P4f0fOYr@b*mGqak
z-E2j|Is~hSs$7m-HZ!Uf{6?Gc6}<8RondBNY;?wFsKWnB?_!dbSn?9EYu}VJ*Pkx-
zrMZd=abpW1(32tLFibEmlwHQ$m{m4H@@2Ve8?qZ2LCehnWGR<Q8DjpVl!{h`EK^Aa
z!hpvl09uk%@Ks1VOSSHf=0af4HSBS1y_oufBK~hmS@Hm$(%WJfTDxYu{$?)gWT%0Z
z(uk}|5xZ+0TA6Mp5Hmpm!M-^$pmx3T)g+{P8_{zT{z@YZ16xHYkSlaWE^#YhwgMIT
zAd%N}z}iq;c={{&BeUcSxT*(R=jA&qf17YN$RZx8xlE}dfMK6%s)L~;vf4*BJ%XSn
zL++!5kv1y7syMJILiR&clqzX0d-@|F3Z(#PiNz}i<JJAS@IX(gQc2vH_avr0qfFeO
zjNh(^{YOtKZ;`d4?Lx*hQ}H#{x^7=0-vZsqH)G|R%rA`j_YwMz{tUag!*-l4yZ^g=
zzdbR?YavR+w@G*XhSdjW;rF~E|3yfv<!YubD2A&G<2<{Y=k6|3Nr_1HJ+f`DS|9aw
zSelJ32BMfsDuQoBKzAlZy#tc(fEDodP8sWrAXO>LH5uPvnq1NnUm-&mMd=Qu|Eq2H
zks@vjg6W;4D}pMMe}n&$$L=~2Q}%RnLVViS02R!Ed{VZCO@Zl&<}}VY%v^W6``a4>
zxx!z{*SQ~;)5*>Jj;~4BddB$W1e=!=LmBm_;Y+r{NC6X@+bJp1O)%1K*CkB*hvTY;
zZR!kShJ0L|{%yIj+;I6pmcKB+rMt1-WVrZ@KIb`O{=LS+JDw}kKbo6qxbOmAJ-}t@
z!#%@V69|1L!^NldInP5J)YE8vs<L`~Zf8U8RJ>{s1V=v;qi)a}9=)6fXJ6g=2U+Ct
zpihbex$IyHP#wsv`t~0$aZdp{#T5)gspfDEfe;~v#W0~vHB<zjyvc2_^)imX^+aFK
zKi!#$;!^?hRmj;OYD!^Yi-|>QD=Gf|jchgFt5I7gU`Ne#L|&;*`(7S>$Wl3|{_LB^
z%a7_WJiq5s{*2Oc5|36UZqj7T!_@RB>yY|o5s_rOd5W05grS{2=cSoB@y}IOS+seY
z^lt&mk7*qZJ-Es5A*DsTbA^<1&s8ry5L>I&o!IIymCnXL*73>q@!}F|BXoDxzJWgo
z=vojRxL^Jrp@w`Ml~;c;;tSH0&QWYNoe`*TA*3bATG#y@{m}>OD{yNZzw$_?c0wA|
zK&VEUOK<wI{QRRewFgZD9N+I}G8O8r35H9Lkfu~s>Jpib*StzSjs2R*S4^+8&h+(m
zPa>q_o9npx5d|or4z`XhdAhhRkY%7mXobbX$qT&+6O5o?(P)LUs9;TO|C4{kF<;Wb
zb=PBbtC>D+V%)E@iir+cr+jTZwq?KaiC>j(CsZD6QMQ<|*gncG$C7kRjv`j4YV`~J
z`z^U-R&4Z`h*f~Gi9n=dM3Bd{gS44*A$T#Bj4_wo1X$lbm7=Do8}$arZVZ<mh1$C&
z<7;jE8LQJzyq^$5S`zdZo;Kz^Na3+x<F`FnQ#Yt63sn_pGroYR-etAqswQv-C?IxT
zZ?*;~)}~@>{!qT@xTQCK^U`;HCVtQT7Mo@_MfRt=qBP!~6lxULZ<GyAcj7RJMIOB`
zFZS&6$cQY}fj!#$@?^3t8g<Xu>*cLh6Q(r%g=hPvI2A>`72Aa(R39d(7Xt)a;>6wh
z$WlHPAC2gFi%T5jWJ|#ttEJTRHX}8L%MTHTw(8X7@|Zm?4Ehb(&(F(OelMTxF58}7
zdf2NijO|v+4x^RyKNSf=gPzEF-iy0ia3M#2jOh<Ug9`y$7$Xl>aZU9sy<@!}2z!XQ
zQP&Q70e^yhGef?R&5u>$LF<dq^tKqxLhtYn_IgoS$+~i4m#Wn=RHi5KsCoqd1&jPC
zN~cYjLE^EO(2GM9sU|d^(6==e-A3W@d<tq^wV`eO`B&sohvZR56^LPKsdZ+>qYi$0
zj-s=l2I#4c`4iQtKWj2SGv!ZkK>8=K)lL>)?BvbFuHVVQCkt!4wDP%CcDJ`Sw2jvW
zGCtgXQjmV$E4bkPCVImC_BBB_FdAu|y;=D(y*Ef#O&%^I__~@HBy^o?&|^Ev9(Q4X
z1A99p&`=wJQYQYTh_i+5zXzOBPbu=8rF@vBY!nbMa*e<aAGCIAYX=lU*)c3`x*yxP
zfVk1~;55YMP)cLo1FF=es^m48`VwAq6={is;$$d#bh2!14T>lz++9N#lTXZLt>5p;
z6}IVk;XM!<0H)*uVILfvAhV{ecR9=vzyg_RPt!L?RkVGiJ(ABGn#6`vlg>cFJ>iSh
zh*^ce3129SX{3)@FwMme$ruHo6cnW)DI5WmCV6;^VKE9Lt>tbYve}Pg2NfLrcM)k$
zG~`btjCAnJarM31HR|cA4i*g80Lv&nv9jW6i?O}C?dlNO{wnnR5!GXKQ+HB>S6vPN
zUYF0%Ax?PBRwe^IOVm$>s=m7QVs)EuEfv>TDux46m5CO?byq6Go<naNrQVQiwQ7lK
zIE~LQKeGPWA$~mI1#yXUfk)iI)#=bT&ogY49$vhL*DiV3S;4U_dJ2;x#8a~0c3OI{
z&5s-)j=eoa{AS2sNOQcY_!h#LzAo~ST)Dg!MJq8>Z*2QT?K42268LrISsRtFTp;O;
z>ig{B+Fx65;ysu(Gru8U!eudfO1YF6;c{dNh-t`ZrR1jkd&z`NirC%qs3VN>f}A6*
zy)jx}OU6k>)!&xtet^(=u(o=h7uS}F9u2p=)C0M2n6iyLZx-uF^boN7o*nKvC@ttE
z?WRE+DCI8%4=Y%19eS5+I|@ja`wU||);|#=8y*M2l}Ud>D2mvtqb?<*0onk!?+KoI
z69z2CMEh135u@of61(HQz3T`=J7fNRaJRDuY_s;iQU7mN-O06N|EFa1!AndXcrUe@
z7<?6znGG?vqrQobPOx+V<VYzMeUVCH^!y}!?vtwI)wJ6pW*=c_vluJ-P?ct(6@$Ge
z0iDw!kK3iF&`{vU#vhy>NQ0$dk$jd1-&QQB?TKDEL$>BVBb0KbB}L8<kJbz7Db)|e
zb?z70NcMqBD>o84qQTZk&yF%-3trvFRCGJbvTIE3$5?D(I4|P;fpDh*)_WC+7~lo(
zXCj-1m_Mlo{ECu$aH<BFw20X~#ioRcaW+(8ma^+D<s&fdc~n-5Dk?`R)<zNJ38lh>
zAjOFr6!BZ|szK(`F+ne03N_-AT2qpaU-x`NX6sw@gzWc6@E1H>haH?+dj@=0)4~&~
zhj@>SU}};UP(1c5$Y0>=G}+>JtaM}@>-J_VInb;SHK@~mlxcD;m4ooA0TiL82sjbc
zdDXpU1i2uWN8svh?XsHE7K3mo@H>zjXs!YG!>B>WR7vYbXN)%HO?L1XdfhVs2kMr6
z5HFpoR}nUpaUFVqv#Ip%UcEdjp}{*#s!WA<>vJEs>f55qG6kl;Nw3!wUu(#_A36K%
z;=<xVX1y|TL$!J-PU4NsVxl?O&v2o5$c|%6TXaGjqQ#!WABV4-4md^P^=4S`Yxu*w
zd+bGg3j4+p?zMoa!vNP`xpJv0X)RvY1?s1cWi!F@#%S}*{-!UW+VvOF-*KH!?{Upe
ztYZq(L;{1OSHxv1QRrFzAP1T$c@CN4_(mvl3NoDp_w-jvLDYA_s|Q$0uYae!^7*Rj
zg~g>m7nSHQJY}uxrik02h~Fw6HqPUeEA)`pcB#V9Um)Tx6_l|#=zxi)droH4d=Fi!
z;?cL*=K?lnst4+`WU2sYt#u5n3+*Jj0ecFyBddn>*c!-EO~zN4D$kfd-du7sX-Q=G
z3;l3UTb|ZZRCkMIrujjD1P#W>{NOJx9|J7LqcAcoMq651we040H@`Rdy_M_MIWM>Y
zpMfvnn?cw~7j&((S|Tk5IcbG|3eu`1%zBxEB9Z&Z4TaWNgqbjv%uJEwApE~5i(Y5e
zN8kpGA_)Z*Bd-~Q>@mue2Du8p!at(;S_&YGRj*Ja*lp?<PY_D7x}yrrF08&;H^kIx
z*Bd)tpE-0EDsJ>E>dkQx$^fUwB_JvlDZfnd7v;XoEh&k`Zgk{0XMWO|c)Su$AoYU#
zvmg2KfQtdxQI>`2RYF2gU8mS>E+1+s8_g9<5Qm>(a(th~pXB~@S2v>-Yan<*zLIrb
zEVT}I@HiQ1g_9U%u2&Mcj3nd~0l)bh>Cp*X9*iF&P!xXIk;<>e)U7OKW69kZ2@fFP
zB*ND?P-Q^mQ}I=`sJ33AwxBW`{$cd16~(luNv~=s*4L4+)|C&pwzkR=HzKkclIkgD
z;ch)$`r_zLflT6H%*mVnjO@2hm_X12ZY+k4U}my^8+KVcq>#Y^q>IgEJYy_tkMNZ5
znr@x^H}-;k#v9@i5v*>n=_6F8H(q|2wL)+SY6<sRUb`LJxq-|c5IiyIr=rBNm7t&)
z$YM6CBe9s(S5a+VQQuO(e#5w&opGj`2#Zlc;E;E~#gvc5z<pgwMd%1aw59rU{1c7U
zMDq))tIPQ^h-ajw{(bPR-FB#fi**|K!KMJCG6Kak^ohvRU=@$)1klp;Q}DTMk1-eg
z9MFCWEBfc5=q+)rJIIp~pFCHOGncX=7=fU0Cy)qg!W4B1O^s4pqlkFzt4;Ht9qo%}
zjb|`*2o1F+T3-7}F#|9?iG^6qHOxAmY<Y=e<YQvVovXlD8C&B;P7>+W?c;@Ri3Z^a
zu1Z-Uk2=;P@}^y<26@&m5Q@~|CY?o39ywHFs+mKbI&t!Hw7EXYQm@60xo8|bIUAh1
zgv4nG{XG1Ckln*sRulXN4ik{S2^t#66@i$sU=I%~M<Z@Rb`b=|>-%HZ152OS=ad%a
z%H8-cH$O*3)uXg@G02oAnF79AFu9fHQy83F_<fl=S|>}jR#ovnc4y=gM1_A618RXM
zi@x5ipqCK!WmHr)5XnJb662nht=0j(i(WZJ_6Kc7AbYxBhn-&*%dnxR-&)5Ym|orH
z8w*uuxcmrdYRx-=yU6bF4+J%Vpy_QfJ2xF}voCU~2@XU~0igYD5Uj&Fu9Iubn4H<h
zXf04HDXRvU;X*<&vO=ZHlPAIh%GFVDOIuJmj$!mGvpANi>3$kdSe_{caig#UQ2YIp
z=>Ii2j@kKI9HU*Z7nKtzD{{B~jY2$LYc;8@W|h@~Qk0f5ek}j$1ZeXyc_%y)N3gGC
zb7P3QXG~MoX7kONc^57m>})MfyJ#3(SOlf0j3O|SgiMfUeQyV+7rpwuQdg{rty5@?
zGKEp5Vpp@#`H%gE)M$;A+y%a~F3e+O=AGv0E0phgO|9Z~$*HoXt2l*eu(6BWGSMz#
zf~))wqh5_@^H~g0sy);Dt5KusyB7Vu^LSZqL9DqJ4wsz76$C8+Fa}d-tmX)d0b|C2
z*bJa-?M6FXu_^`4aP@K({X>+^GHL>fa*euPky<a;z!|KUtE-g}aFauR4bcbO<oC)q
z-y-|xCd&sj<V3Iy>Q@kyy2<i>DH;P0AlSnYv>STychz)w%}nD@a1!DDu#gu1Wl}}+
zuumxhnTAhpixy4B)~~np{lhd6GP_9J5QiR*Kns<6rLry(o)~MYfv@lpm~-jJX&8~c
z;M~ghZ;~teDs(l<2za;jWB~*&%R8BR6i<RxOQ>t>6l%R(UA0_)b-{V~IQ7g!!9R$R
z*=)w4=8v~4pSR&DB=0O!lHG;wU@i!;s*}uPY~)a7b}=bJwK0Jygyb&&;r&<QTHK07
zo9kMEa*G+;WbTobxIkN6(a$>W(&<al<`XfH^_%OXE%j>VscB$H5^x#QK$5UtYBwER
zof(cS#(`_n)6+$sqU<(eN7>FYkoD@lLu5%wvc#mm^N&FG!Cm{;qx-&A%(Xc_O`m$c
z;6+8NuIQyV$t5E#AAO{luSW3%qAmw_QfME-U;Iw-QMAUe-#G!QEmEjcntridyWid`
z?^E=S|0%vcZ%%{o;fonJys>@eyh}?S>v8)>%NiyiS2Y1{n{4AevsD2xKP$K4>$?Nv
z|NdidqM@oyT``<UHExEln6P&f@*23)Y87zTX(4-s>$n4&pO3~oMy^pK=0YrbH1%&s
za0%`%xHIAMWj7~W@e0aPCsUS_y5XjSaDBj^Kq1$o$Wlz#mB<-`{tK@R`7eneLlsAA
z3Dr=;VGVB5GJj>SZ=b&Zf`+JzAZoQ%gVIVKqEc8!u)_L@(&p7YQ1C0QO!Y#LN@Zk~
zG7|nMya$Vt#&I-=g9uKhs8wj;AH1tqCJbuRrM)aZ?o=&#-xs?aqtswmGCkpW5=*ey
zNV)l8>$*>@cPIg#uSjTz7M(G<Zl^q8n?4Fb4fK9)_}6IcfJRR5?E~hlom9^!Rlna3
zcD-PlYXASJ+eVz+RP>`&pJ*zWOc74eA}GT*BXDDavHB{N`K8{`yZ4+dJa<WFF~Dya
zX++8?s~)jwVK)>1m@RS{MHWy0>kW5%r<^=PC4WlIJq+3?C>+ktXUN<tEJC(BDifF0
zIq@TT8Hl`KNqmI-UB)mwxZzW&vV0ITmH4$-7RyK@6&z~XX^1746WZ~nO^{hYRuW~=
zYv=`LQV|x6qDnwi3Mg9&=n}L!An9%3$e~}sXt)TRCb-z(U#U8VVe2#=rx1iiaOt|3
zFhwyAyG(X=w#ZXbZ+Y^mLb`#*%sa-~OoNJZF2^|Rwbz>GIGr1<E~s{GRl(-1v!Y->
z*{K_v`<C?qmt&4F{mER!1>mdP!}exQ0hO0Q(&4=7CF{HQ&AxX2vH8lBY)k3&6rs6V
zz4>3I5<a%hT6Gc4gv+HU!UlA|erD|KqQ6`QNI_cRI?&iT4t%2`==YJhG2U2B8;mS^
z)-ZtpstqVH19=vC8Nww4P;>%$u~aRT)ld}6C?}U^KGTHgW#AOfTLB{4gMz2QN|(b}
z4Uqr{l(7a=bTVFJRKu+U3gj}AT&6^<6aq|SqzTp3o3pCrvDM1RZ(kTAOHO`iOaAKr
zyyqOg)^DIPk>0dAoG18wq<Oa2E3j6~iLxydnIOG#O7{oBV;`sz@JZ3nZnq6zrV<f5
zTOWlU*hJb^=E#(ZzaP}Al7H8>{g}jKQB@H_C@~6<GLu!`&T48^Q`fahm3|hk%t>R9
zC!U(W!zsr48En;a@&-lwKJzR4@vQ$|95m{kKcNB$d3PkE2Ev(oilQ@1SCK;rfI`Kp
zA9|Vs$X-*Iw3t{!ei@wcRDC&nB;-EjAo4nNKW$55kQ&_Z7)qHjSqfE=V6CE;jn(X!
zdiYes<%bzg5qUW>$)K&rb$MnbFw0OVXBz%cfFnv{?EpRAq5oCwmz}SNaGpdl599x4
ztMn#jF$mJA-8?n+@?SZW6Bx;e*SNEoR!rq;HDXZ$%VgPxDk|a<mCU<>-J3MA{#tJG
z&PApvXBRYgFVVT63jP+d|G<a|*C+l?EmK4vpa|7b%=8Fg>?0}jwRjX>-3PDkM_Fl)
zz#Z<Ds1PI`tJ$r6dNzJ{EztZ#jPm^8tmMDeG*=Bwr&M&n{ut9oEUSV`Ez)kwSgCv)
zpmY`P<;ZXgjaE<ySm5g+)nfnw{q<imdW{uog5Sw!pFrA7M;_EOS{uMDg9ooVT2_Hq
z4#lhcl9mJ}j+ev$=@P-E?nOI^q9R0A_Oxy3<fsPE;ML~2X-FB|UA$3bE_9sC;C_rp
zYAg6tOpry^>u$kz@tLvo%#VIb+lT;ikxa@&OBN_T?r!>)o&6tn=UQv}8q6t**qsPw
zBI-Nb3Jz9asGdNJ73Yh!If}TQ=Hi=~6e=KO@7PObv8ks6uu~8#>2<NWQ7KNQMu2~y
zqV0g+n8q;{H}axtB7F0kdFap)V?Nz-j%&$+TKQ<%flRYg2#gNYA5%Tr4wTtKV;DUs
z6DiuHco|N*{a{j=xK0swiZCTx%7>G9%y;T{&MV|93JMD<jv%L0&I2(t_UV|MR+XRE
zn48yU*OZI@sBwBboTpNKh)`hq4O}7<Co{=@F??08aUio$m<DlWF^@Ctt}k)?sVs_l
z%c)iqwrX>4!-Z!p6@z2zTfJLWF~6u3zV@$-qpUh*!d7j&kCcgP0b)MD<CU}(lzdsf
z?0xz7o-0LQfFLO3Q|qmaugB(`vgO?(h*7R8)x=(iPCXHqsgB3hvG@wzu!4w$GEGc{
zI;u)V8x9s?H{fzvg<6*%nRqd(RZeuPq}b{_T~fI^8lDbNi#qMQ6BQ>+1>?=7H<Fel
zc0Eu46>16a${63mB^0uT1(r`G?jgI1?ACsaYUja!?;<Uj=44LmFc#FZQ|74h@dD$*
zhcwR4kB;Uup|Pgcro!>YyvfG=2R<yXm|s%%Q&Gw0f=l?>R|$OwRmy5*;$OUroV}l^
z_U_$NWxvDMS68{Fcd$j#S<5Rswq}YR+t=i-p>$B!yIy|4U3R;=r4a-wr<3Iy5X@mq
zAXKTpK!#|k9B8=sw6(4$X-YTOcYU~~c4BSay|wj=s-6dwqN$9n{-BuPvK29K2*Qva
zrf&@5a%%3fOkb;f-3qV>#8bOJ1wz1OF~@oj_sMk3DcoW-+|A}FolB4uX{B@X#><x8
zD${Q!df$p2z7-t)vf}2CzyxK&W<}f%Omo4I`-D3ae?i`$wj_e;5+K<ADGe#b5Q=S|
z0LQ74iikz(qd{F6aMhC`Ct{9tjWoGHrKk`*&F+U%0So+S!)OSuPX8WL=Y24E*3<=J
zv#2x*S@34n6S=3|3-tx9qtr5lbZHWFNHxXbR=0ga*-AY?&d!2I7Z+Zx>R#^0Gm0q>
z7d*2wQ3to~nj9#2T>aK;)ypan@r>n(PZjev5wx;jZ+6W{VDqGZ<ODk2V41N+`4YrF
zyZE3V>DJcHQI{7YSw~dC5vtw2V9QwZ2Mm8%BG;WTm5jkFhd4(5Y5d7os9)M3C98?S
zBH4PlpqNIW!_!6$Ztohi2)8|Vw|hwZIZ0(egwhTzsu%4nw3(=dYdA4Wyn$g^?S)+)
zXB>y`GKpubDC<I0n~Lr<72SfV^A&M_d;Z0(N)Lga)J&#@uES+8jmd0wubIf28-6DS
zx_{5Ir(X6DVV$i#8yq@HmAqP}$u$+;bzR1FAS{W^<aX@39|`{ZWLqG%<qHpa!7|0y
zALaI$`&RL(x%gJAzU`Yk-(7NStILsW-&+D;YsjlFlL>5V7k3Q8h8@&$sz$NOzIzxU
z+xM;n#PH1{ZQWxvWtOf&Xbr1~iczKfO7oZE8=<Pp<?LplfXnhB{G~rgr(`q?6UF^o
zSb1>QXO7V-B9vaF<vZiLdid`@jBo!{rl`+jmS=g7$stpYl1}ZG*}x}vFCY|CjH8jK
z%;F}Z53|FQT<|kq-5;;%sYuxDQp7~&TwX|v2o|i#Lh{&Z*p5z#7Z^v+IKJYcbVqQ?
zo#$FFqBI<HlnQ4<BuZqYI&Gmm`e4t9JJsgw;2PDm3M^Qc`k5Fx!u-)~=C|9Lf6g#1
z*=a8U=_-&I%e_lR<q@*&DKdQ(>6BA8&ipR-Be#HQianb7Zp(C+4Pq*oK;kh}(gK<8
zFkh)A5|LG}*XZJy%B?%kx!S69oUWT(at}&oGl9Rcj^+HgSsuGnoj(8Wl+;%e;%3Cg
zF!hY-dl)bK+khaZl#Os6&1Ki(Rac7~)iK?YuQ(Kg_<5G6^SC|UH<0~!kIADFUDgX5
zb@IOTUY8@;eyMEz0T(g?#RbM5e5?5#?vjExXezpmG^VLim&zjl<Jbu5Sou8B@|x!d
zbd)kM*{v{aubd|so!>>|z#ru%Ng(n{MZ!jH`&qRe=HJ*lWp$hx)0~4}Mky{W)<y2m
z*ar0@Q}MV(x4ik^JKlo>7ftT_<nZ<)=Ormh6~wi1fokPYaBOB^w;Mh{c5=74MFQh_
zJ8O2(QL23nrQ!XdJ)9<!qs+6PusqG0^XS1PA`H1x2vciy>N0iO_p*o+5W8rxvOOy0
zjd8srI=vmyVdm#4_}gvjG3{9)t8=aM?=G>9sC9$btm)7;gR)v>;$PZ!vs6icA(*j>
z(j8POmqbNsGe4&*uElSDI;Q<ity9pb!@VMJvy@*een>GC;Vl{Crkw>1#)Ls(P2V|)
zSE>_ONnA1jWh+qbZ2O>dzTInx6*1<L5hzc&qt-|ni;xdf9Uic}@`GYlnnT;7l!m-X
zn)L70t$(&u4zN@Vr>qfiSlKSMYHv(VF_!m$Jh6iXM=SD}P)o!em;l#SWC%xTZ#X`J
z=|bf|F_5FFQdT0E*;?NLukIJE-Lx^rtcjruIGh1sHi*&GCzlr8Pg$c_{OHv=IIL45
z{1a`c3+=*ihqo0>8>Tqtj!ub4^$tGNvw|3U55}Xt{zG)V8au&;M4Y2q!@pI;p@u4@
zq{evpVMXjeG^R+i@sUSClANr<YqCjm(h%kLB5k!M{X4v_i@E4FZj=p2IR&Vq1fp70
zS^AT_sNAAZ>Q4e7=gc_GC4V4Bqsl^c`a)Eu$Ll&9%ZIa-z3!F$FigE=ze@FPj8d7f
zc{>F<ny}o0N1mHBapyi7*nT#?_tU{^vy~pJZ8<E4cZGXpqK*S5^iN;nRzig>9p;XW
zxpW<R0Pakvxj0NbNsLxs>Xy!%PbFF?>@U8!47ijXy;=4#;`DX-QV8i!QEk|zX}I(l
z6i4!yy^2`6c3D0hyPQb7!c9pPROGQpi&jUsA2a4pwAOWV2iPB4?cr*e9#a**2yS?r
zT&D!MBaa+#%h01{Rr0U$sH23jwXyJSiuOQ*8MS|&$FvV454KSqdkS5bX0_f?{UM89
zk)Rl6o0{NI)Ke;MN2f?&h(wHqd@0CKt}yk?DVOJlK(&a9<%UF5P*DuqvL8vwp^&gt
zUL%h>glTeKvu<mP<F8b|SIL~xS9WPa^@eQiBt`rdS%(?M%aaL1dk2akDMYWRRmHD?
zQ^S}!kUWH1poxmAeizf;OX#!gsuS}Fs(f|Y4}g;CFFZ?{lQ|ZgR+DjSCSLz?`5W+-
z7RzR75d3yagQkCT&sITXF<NEN6yC6?-Rs9$z0#svMX8=2)#-y<y1D<?flH54f(Re-
z7vLOU4}pW3l9lmQDKDVnS-YK!F4Gz*x7Y=d!>_zl@jiza%FHF>^f@o!H9akr1G}26
zGfT^3&OB+j_}uqJ<s0$}wZ(J|6m{#L<gwcU$A|4GE|18|)vXsrc6?8hzHmeO@uBIh
zRVk}bRX&%^(H0gA(WEVei`ZN~)R6l)%U@_{hL#Pda^Z^KYFK$|y*}Gy4o@3@K+py;
zJtSM_hyYlwc-Nucx+=Dm3PmXQmd~TH{c*s_I8sjO)QH8^jYY{IFf<j8f1tMR8l$Pu
z&XUThC>_9675xmCA3+d8)8<>+(vKP#jH#a=6|2eoTo!Q}ukNG2_*DJr8M0HaFDocg
z1GPGBu_AsGqJZ$3+(7ntBf5<y<7+@+hRY9g`3s~jOkrK-9u$z@4Ma~nY6f1@Azu7g
z$uU%nN4Q1hDESe@*F7~VQYGL0nxG20fut)T?;$+->fXaAPLuxx8AlnDcH9}c#5ND(
zmFs$-*0?!MUz9C}uYJ&SE{aSvqE7@>+;WE$9tYPYeDjNn?mjV<Nzb2#PyQO2n^@iM
zSj|vX%5qiGI=asOh36plf&aY+qg@zvk(ab7bQM0xo~-p**UI*#Mf|%$cU&8N6tC`W
zt?x{jTIoQ^{Ak_bs7)w9jRkjGYkPBV5@M@<=$UzPTlz&W)BHR8wB~jQ3Plhl(iG6B
zFLGud)foc>TZyuYuSGiB<pWM77X~s6-Bo_D!NCbgwR;j{d&cnpFWI?4<}+)fE1J8<
z*tP!rORD5GbmN4?4WuP*9#(0_wAR`zbMdVX1*!%_WSOn6S+9D+RH{ta0{>|51WAYC
zTC01TN^SzA>yIuZ#!Qg0UM(sM$%6uN6n2PZl?(3At}=LUQ4*_FJUXOpcAFS6+@nQx
zAh0p%6rFfzBVq+38Kx!U@i#pj!Isx_=<w^5uX#4yxW{E2Bxy?|ug5BduVf^r%Psu?
zY()j8^sLfUFws;p7M0gUw4b9)T#H4VIuc!RYihE*^)I9)UVq^k2!#lJrw;Xz(<-XI
zzC>Cou9oSJt6MLLX#dfn_J4j66OGlkgILLhzW}-tAbpcJFddQ3k*U=n^3AbbxWgF4
zKy3&u1H3|NwA$};nT`t$UCPe$m07W~7_<9^+D7zr*1u?QdM%#;Zn|tSum=R#_&Am2
zU^Gyi81p8XipSx#J>cB+u2()=4B-574KyS`>wzkYu$c1iuRs5ysql8IK66k#m|CDS
z72V~|Ul>Ix^#gnY%Sorp=mhltm;h)9H&k*gCCdA_MXTjghPD$q-Be5JK;V~d<qzG^
zJ$VDCGA3&uW!EL=OMW}Cky2^|9L(JA+P9eBWh$Y7hwUYZxb(4np0kOE#<$-q<}jJK
z2wr&&WF>^5jUsM`ChdpWaWPhan2K*QT$(~zZGea^RaZ%*m`iW0KRaE2@d>z;pRcU?
zrJx8exQESJaP=T?s#vjZ(I|~PMDvmor5e?h_TR^7e2yjkdSV~HfA<6@&LEC)1|KEc
zmhuIduq8Xy&QqBL=pbyDehfRAxCaGa4KNnJe8xmwv&06uaO^=SUNz8EFu|BNc|qmb
zhm4oq*DS=vR%0#0lH&22zQ)UsO)Ia9H^zFhyEFAMS18|Q3n$%adCR9!N{h9v?H;L7
zUgdpytfu9#n6?Sp7>)ep>pS^m?)8JNN{{K39Lbqun|oCbti9}|;e|34;KA;S5*tTj
z<aF3OLbYdI%e;*#Mq;!zZU@nw{a6WXz|GNrgEXfY^C#7vnQplJ(5D5)`!2P|s|EwA
zL7A;*!YkMYMo=LqP3dqcPp+x!YQgIPzZ~Nx`?wTg<O(zhp@TBWFOSYT@a9N}$$dSs
ztM8@P8#uegCMVz=vHSc3{9d7X1a5)C-O!7`xu+s)J~vW|SfrK*4j_69g>g}gfyr5U
z2wUqW3k4HI^pvWsf(v!w+|;_e8o2ZV?i5LA0=^YI`)s@o;l44p%R?RuHN13vr?z~J
z3Ysg6(I{kxr(frTThw=KKO&3g;(7LP`_JW{uU6Uuu3pFa>!d<H!bXug-8mx+jn3I0
z$4M_$Hm2gXtQJQl`j6yaI~_r42z`fs=DJ!wYl4}Q`-JV*kb~JbzgUMoVBs(Qn>%yt
z>o;^B)+s^!kR}Thsl`wvKCoJUH}`BM=CDy+8p0iMAY;AzFUgk0Gn#J&&<V|>W8xgd
zT7TW@Y(rgF$D82R4?TUH%H-CdD`W~^opX~<@5{c2uUm-T?iYcIQUr(7L2)HV2)_8q
z1}X+|YlQnoc%>+{ZRIFr=(HQVcrX&9LA9lYsiNeQEyimoQAQlk%$b2=EkQ@+u`R41
z-DZB9EfeWZw_`ga5#wOgDamat`meh%;e?7&ycRSg$I!?&JnD|-#yh(+WWXAMn0Oc5
z_jDcg2K9&S;D=KlT#QI>)7Jw{pTBB-)RE$RTW=n)rd9Z0`%j2ehG^>V?wjTZk9!iQ
zgXJd6HjHP?<nH15cDSK*z)uIUe8jz}Q$3~TW{ou1O+jt0TkifTGEL@&S=ew*UTmU=
z7w6>AxdZ_Vv3p0l`Az2c103g%?(F&auyz702DE$@<C86B)#F}RJDcw-$6OzF6>$wO
zuHg|NjA`~p)JgqeRbnIHR|?pA-41e8Eazrg?XXuxJfM2qN#F?gDz-4E_p?WZv6Wem
zQJ&PCDxfO@PAbyuJ9->@`?}zmxSVW+L73dW8L@tx<;Z~?$V`e*rAuCOFB`bv-VVV8
z6ugyjBM_r|`dd4sa5Q#+_eWo|KJ5M_36Q?%S=7#zTvC~Ow&vHE9+cT4fd8X11tghR
zO46xbxjyU4MKOsw--P6UZ+pt+dul}cDMQ=c(~lO9XWnmU0>6#y<)VHIUEvPP+w`m;
zJjEH@>mIP}%^a=`+qwisA26*wD2BFfI9sXyWYQh)z_znkhD}*|2A}HFaLF)Kr;>}|
zFo&m&5`WnURS07s;<O4OscRpZ0bcnf>nD0gzQ_rcAa!+=+C8_s7n;)nEl{`y(NJ(I
z>ln*dD^9kA0wI{~<OF1P6I~9_9zFgr2ta3|U1s6%Gfbafhqw}A8=v5}2TdRdle;i(
zfh^|r8#;TsX*Vt#sq*9{(JE!Hj>%HWXl;Uhby!2bu#R&~o&^YAB%eyir{cMx7VOI7
z?PBv4!(v(?Jk^_~<F57h*nHwL=Xy({P`abA5}|)$a62cRxrq72+cCNy<0Nc7>y0j=
z;sPIYUfqXV@Wol{<Jkpo-4w#20eAFi%}U)(&kfo0+6eap9CN{jSqmr{tm~(a+s!r_
zsjs30t(z|Ol9d35+@zlIJl@1H8?lY0XUY~hAqAwWnBv#szhW9`uszzpRD8yo@S*<I
zF)8Bw2V;Z);b|(p5q6)LB&KH1VpMWWA<RlUBn8df{gL(VyUo)kSzh{-xZU%S#Z+tx
zXt4OQJ<sOZITV-bfW}P8G7zT%lxz&{N@A$X!9Not&yuMJop%MRWU&Ut)EKq%#cdyZ
zAUV_IKRbA3;6w4^b)5pdf@elAR@?H1{5vE?rbMaTomLK`*dvw~yy{bDRFfR;o>)-F
zTcDHeP`Y2`RAxAmL8EnU{=0{m-Vbb#G%kxN6<{%E8M_!{F<fSMkxIU^PnwLg^4C7?
zq|3{JwGnpTm12uY7S7py>!z+j{Gq*6=WS$fuZKYvlgx1{u@!=aq@NP8+G8;rR$DEl
z=B=s)*LRF_6__CyZC7SJ5pE6|+x)Xp5<w&-A7E+xI9-tjovCig-;PSmHNcYuy2EVq
z9<t^B0n-C7-o4)&0K3Spe1;g>XyR^*Ibtu<=cv~a0Spm{NeIQT#id4KhuU$>J(Syi
zbZMhr>Bb604%gy``0EW^-!-{aP?O`E6N~xf(E;vF1sxct@$aXQievoNl%2)Y1CQmY
zU9D@v0<DUQ<n3Of7YKEE&O+OUpSM@f>umZiC=bXR3V?(-O>%7a6qj!>aWd3hgWJf|
z?r|v~?@lMF3>(<N=zI}2+$ohW8ha~K5`<fZt~FA&jj1A54YY1m(~!#~?FMqlp{tn8
z5-6EG+{{(zc@1ARKOw%co9z0%b@Tw!=ROVcl85_W(}CYb_4R#yw!b|53S}{^qSfPi
zrH0S_z_p@AEbO{;C`#1=C<ZR~O6Py2A*DirRn?T1i(>jCwidBMwxQq1Yn&AXT4lH@
zJvtOmEren|xvN*;7e6#@)ZBY|xkN4bZEM^}_HAccoa3#`vja$Wq+^{aRwq40rtuo!
zmRpB%%h272ZDik+^Et&|VnXBE*#jwmRLlZQY=qFAn3xc!c)sX9VQhlDV|}2Q;~z|n
z^o~JP3akk?PRmKU4XLe>wtb*<dCE8JRh>O6N+nbb#gYFiVD66QvQBPyss-94+EQa%
zR#SM2PI)$7(33dr#y}t6C%DofDKhXOjnyh3Lz#bHpX2Y0=JK*grJtTDu1#w8?LCAJ
zV__hzCZPKlC_bha4?c;X`-6yRj%Uznzdma=cHsjBOaNXY<92kE(K~53(6)|o#iW0c
z1Ij4fD>Aw-%~#f&vQ1x!H%PIx^X1{`aSf6FJz7VF;l^w^EN1fH%ytRd&|QC-bM)s7
z`9cqhl1tiTq(_f6TO=t9v9+EUj?qSzQB1QT_jTz)!_5El46OqPETJaIWg?Wa;CHeR
zejEq`f3VWdct`)gN`ZNVD3$G&<MKW4@oc<bBGaZmo9UJV=wPcPAz*Slwle{#%|vOp
zz#Qw{<(w)aB1a?5Ig>~zHO%wXxZ^asYtoVFqmU~hE+!0VkKOpk?P>^NFmJ&7Nc4~j
z!K3ZPXju32hxmBjD=#KS0#2_e*8O>o@i++8FrJ0qbChhGVOo+)rMN7k_z2rN8_<@=
z3g8lD<J}e$8osmb#bMt~5^Ux-vP)8+D9c;0@&aNk_hXF!8ZBoM5gf7CY=z)GL>FH5
z2Yy*YX%ZOM;SJbc_<EgrmWO*_gUbOEoJ~6K#3>#lfI>&HT4l5*$n8)h7UKi$BE)*K
zHZm;-+t~fqt(lUYX9Zf6v1Z_36t-NxIJ;d2MZm&IeR)s$inw{4#VLfGHIjBpirith
z-tpQ^2e$Psg2vt48>N=>rr`?N=6NY2UOOTyNr;!t64r6VQ=2$Xxx(BeMj#w@S;m;p
z*nk7TIIZ@QS!|II-mDaO{B77j2q4EWSt%E&6c6{n7O@9Rxli`ZjXhG>08yPv-ePu5
zi()aM?ix1{x7Cz+RYqH6*xL8Ja{g~7E6>r$>0!o5Tq}^|#;!00Ldb#jE>30HH^z1m
zUq7X1>*&XZwEcc^UyUM|A-3*M2Z8kVmxgh_>6<~bNTI|?v2(|nJfV2M(CJY^mi1B&
zi-`b+e&`uT8fr&AYgBu*P=^>+DBiW(ZamFJo!#^8$ddnJ!c~sgqPE_&enpUCaJlF;
z6rfcGBp&0!bP@2vb)8TInYI43+vU;4){*zgHcyfLNiQ<H^&}%slUw0bHjVW+J5p(f
zqQ_{KBv9qk5jMU;@pdniZk#+rK&A3@+bHsyJtVqqy!Jx5eq7JgUFR#rXhS|3Gy+uA
zujvrqE_j6k0-+)1PnIY?c+B#Ay>JUamc_8T&u-<@z}}qXgIl}Zot-v&!x`SQveC0*
zZotvzF^<ovv~tRz+zP#i)7L+HjmRezU;ITAf^d+Q#bSa)Q-`<TdYZpE{QK64Y>zHT
zz0KbMrqarrtB<>|n0i}X5*vz<GYKpd5DLaeR}w?+z;>|w1=1NREs*<P`^+_!ddr#O
zx-cBu#Sirt370}mm#L(y&7XNbrV{8#3Sn6co2fVjr;8lk+rJI(@rTIvSE07rLrxu$
z_{eOBP6(fDjMXYUm)#Lr(j|B1SdmiX`B5Doz9#cZSCk$b+SWrK$RN`2PKoc_+?8)x
zC|uBW*H%&Pp&V?%*MVq*m%4)T<mGkg<+zRP8OY76<XEc!DpdN1S&$Gji3bK|gwS3f
zaPI`bDu6BlR7cp_W6`~j9siAESU@Bv`EwGdB2P8tWucOkZP`4JKOWr2@vlDV)7N$g
zu9n^WSPTYrfia!8zBnwTwiwjjUFd;9{A>C%>n*^!!_oilbma)F`S4oIR{7OPDSjn9
z!P*_JJS7O*H7Jz>z23*Iy&EGuGDag07SrUV|J^0X)61ynM5LNi1ipbDx;k^t9X;;s
z(*QZST_3xlxt@P?AJqwJ#63*kcpm1AIG<}=em}Wy_-xze#A3XbEDlOd<JD6Kdp_V7
zE1l-MlxRryO>(nz;y{*5Cb2zI@5268BNpCX)V$Q+^hHpHD@x4+vm<@1x8N0;Jkcob
z)leJqW3@^T=9mPe24{lqT*?N@F!avsG-m{aOk(mOAsr*zdV45zK5<hm@S^X?d^hVR
zMXG{0#aEW1<kLc&aa)wV19T=`(<u7Hnb@{%+qUgYY?~8jVkZ-OVosciZQIVo#(C!b
z&iZk`|J-}-TD|%~@4dVBF7E2;Di6aNre;YQPKSXcSYh8Vs}Ty8wOiu@A{v5uqQGo=
zL%=#sBs4N=9RY6g%<&w>VL5U&<X1!aDwBD_#XewCDxC_RlJmX$-m}(qW~}QWy6BTg
z15cDf-HCLnKg^_S!<#vB(FQ|=FPW_wubqktexKl!S**_sotoS<J)gIuR}@<*^+FhU
z;>tS%CS^u0)J|kaYx>?Y>}b3X0IM-;FNA>Kx8l|8Qzv11dZGsS{UF#0DNoOfw;&bA
z2Ikw@ygt=D>07+D2$b=P_&+#gN>ib1zJ?q;e}z4$8zF?s#<hh_z@|z?s+RAnXEhP+
z{4m5?a8gSvs+`45tk3~&#y94TgG_Le8rE|zLL9!qJq^A!81_gzPsInnL3DM|e<~Mg
zlZMN<?J!C~F9&&MP6IQR6lzuNCJ@_!8J08aKw!+Pkhk1fqd$qa{`%VC<`rLSwF0Y7
z!w_JV+Kd&IKiJW&?Q!SmxzX`)_2hp75+%@0IC&enZaad{7&%bQKUa|DXx<@MjoSik
zrK;>9;czc{^zH6izmr2yPz>$X?PGYN5ABqI^vCu@uCGVqR_Wu{)<LSxqNqb5;Rj`I
zF>P<}D_VfJT>uDTIw=x7d7>x&!Uwh1hTIl`=E|u^Kri+8G=frXR=2BGMvqA55^&9n
zTj|XLssb_Qtrm-Ea#p{0vTl*^9a5t*D&kX<)(m&mv@}fu(=Eo@&8y@Xj{c62t)nf{
zVP-ZYi>ucb*DN-wy|ni<U7YBhflj*6Yd|DE79}q)@A0Fz=wq^y4S(c^L~naYyLa9*
zAh!o2K<hZ$6>4Y12iUXurFA$uz$f4VnCBB$F*S4tbe4vB`fa4RpQI(mi9Vurk?-is
zN~U=KF~u*=6ky~V(yOqnDYFPnLP&RLR@IWeuGKhhN2S9mFrAR|voi=YMt!<0nc|;h
zF~b+6*z?39Wh4LEbO|gF=8a^q{FRo+C(FHz%W?rV5$)GIc7&~tfXYVEf}}0_a2dbe
ztR{i^{lqw#G$aR#{Ve(ttLJD<u}e_hQ2Xmw>0U&++46nDjT3^dZ|lZC{3{RiANOi(
zU=~(wQpT$o;ox85s<T<**JRd@Hh!RSe|FjCu8ZpHoZx*^^VKP_Z({LVL(50cos1*t
zn@$}uY3+*&jU(SsAR8d<U*R)ifS;1NJE_M9v-ETf9Ie>5@yF?(vB9m;zkv?eMQl-K
z!-J}+I`O+O<0~qBBxqW}u3OjGm4~(Ep9ZQpKc4_YBIQDA838LrW09FjW%T&HbALB6
zxgK(NU_wj_+0#w}chk6JOij-dm~D1Sd94Vq<v!++)dAS@>+`H`L%dXKh89|46>VVZ
z1DE}b$C|W!d1_gY4z~3`-J0C?xk5TLpC%5c{87I#7|8Hgc=zHmM@i-#X=a^)F3#$M
zTC587Mx6;wT(7OOCCK-stRlu>Eol;-O|ws2*DqaMRgVx=$;Q3#rq?O6du-YxJT*uT
zr&rS@T%H7=&`j;ckoiTI^T-$E7^oQi^ur1;(H~k>zth;!5@YutKYaTiV_wY+y$^nR
z`%br6R9j)6fzT8&#!gzx)M1j{fna+(X0gf8QBZk%I?nSTPW}-Rso>yS^ITglog{Xo
zraV=fFi-X2@Lut=GvXIbuBWZcw3GGMVYy2!1hx#land1s2FLX)Fpq;r^V;7;vK-oE
zC+EaDa?k;r<ty#tu1lvfd?3@CGxY(_EV!9HRv@~=N8UK8K3`;jc=@${lR&JX4ysgy
z{j*baPU73~C=K_+34doOPK7gd`Lj?%s@Oz%?biUqiX$I`Ig%auJ+)<*(z7<QcC=%U
zV8O|Q6GQyHkwqq;AvruLP49yY)oEji#H@*wz&6WgJdVD)FIE)(?P8vqfIs#;y9~AZ
z`EGh4BVSAI2kzE*H{<K%dE-|e`Q3)j5b<^c{5tPbqq5Hze1mPy4R`elPVsW>n{?Xb
zfh&owUt`tA_^6w0<zs9!Rkq!Jd4KFQuQ_FdWfz+$=?+SdZAxax5nrwrFJs@L%V$mv
zet)@ixjf!^l|kkGr3IU#39NYq_A3jFm@<=(i&X?0#cj6q>mgcR(H9ia+}J<(ZP9VY
zC{E?mfv`1r6y$B%tBILV(I?pjHlc>dCo3rs)SGgpi4MN@!_iA_cV6_M;8OEs+e?qC
z>&($xMh8Gk#=|N(7Rjtotf6%bnA=uK$sEn=>M`kM7aTikEie;JjE%G&1@mGO-9Sa4
zC-%E^!rD}_wC-aRNjH3t<Hg2o`@%?pPUHZ1Zo$0sHHW5Q1Kz+KJN~X<XX)eb00wU^
zi3v>Vy<Xc9+Bu{zB~RT!<r0U;KPaPLw}7vn0blS<Z7&YImx~t2)TD<LYXCgFRFCxf
z6*OP3j%ZpK%+Qr!BRgOwSQ>v&IZLwHaVYK0{f4Snglft%{~T9RKY9SVg>tsk;}C;u
z7=Im%irh<*MH^GIQw+0v^W9M-Y00t9?D*{?Uiw-HeO+f=a({9i`$cQkUwx77T4lJy
z*U|O+FAEl_h8=4R-OEhKEXMw5@B2erJ0T6(7z_U*l89D}0X@JTL|z*+EVJ(2HvjH}
zCjJ9=!D+MkmTTtf%%%k4VO?846F-$D1Wkmxu@8(c0t|UEh*NS<l1g_!Mv88b(rY0;
zfvMSNl$DgXmC(iElSAEm%p&_zyr&S6ia~_fpSs<au&j^ME-m=0j5C~*<LQdWofJ}M
zOA3pQv7f1}#bZGEK{_&J>y{i|{D+~iUAvg_dR6faCegsa6ErhUo=<_o$eM!^v1l&V
zrKlC6ptp$Zpiy_E{8*Uh^tJq%Ws~P8z>Ep=YfKJTUhHf4B2QYkr-R<oqr(Y;F~z<N
zv5+-Nv=u_*>2*--gRV}oLk>YG&;-NI%h=}+qpU035M;f~M7Sn?&wqvd3e5t2=%!T)
zj!=Qp4$sS^606IoZo<4H>$lop-?cAc`Szg{&ll|Q{UZBz2VOJNgBm=B!g3Lt!TbK?
z3Ab&9=ON}YlkDSbn7iEa+Z$5NiX2Mx7fcfni%LtkvH&Wr_mq63)Td~qXK=Y-gE~HT
z9sb8lg93_~=e-<1Us)BqT&zeoS}fWXlVFEc=p&LY`g8)^S%e7teC~Y1E0<P>x~zqI
z{!OrhYtu2($bsp7$yiTnWvcb#Q-teZtV7M;XCnGTh}YFj;t$aqe5)Nxo8&`(`uZxR
zY?9n$uhm$0et2G!ii)5((hGd>Ygg<XKKh2o3_i*lw*7IA_^M~ZJdsY4%yNy6Ytk5%
zB1h(ldTL*~*-4NBDBJ3zy{wI$f-h&)cKEbzd7_OwTNtH{#$Fe&Kj?k?iPTc#V{bOZ
zu(RWts3oTbqw5eXUll*j|Doibm0swz;H0DMWZ&VW7&KSasf&1)j#UV6cgDWisuVfE
zHlct(TM0ul&=HzwfScr|xYc;aB|o<bnbs|JXb_2$aVEWRAwsRp+!)fmY^HA6xQ8LQ
z16);E(;#5*Q5*_s$}EvoRAOUNU)n0AM9d|Q`-KLHnn_DwC?js%0M|Lh1pY!d8&5P*
zxl*X9gQt7M6DSF%tD?cHp;NTsL{=^#PMF6+VN-Gi!B@Hx@)c6krYInN!VRSOMJXZ{
z*;I*5<mzzoB-%2|D}rsb>B->eX*q-b>B!U6aRsJYZ4u+#Y(p6aX6hBe8aYGOYwJ`z
zqx7R@CRJWel=)7d%TjAoNo7UVK`Zk?E%v<u7+780i>`Q$3}c1#+pr4Z3zzlDjSj`z
zelpVQc9#?P_}}D-ub&qib=GLYSWs7b9lu533L-<Lz~zP5P7vU*(9r1MA_M}rVf%km
zB86_NKA~72k*1IS9*v)z9F_PoScWyZwl;COd2!L=(@n^cDc+R>_nDQEleIt{*Riqr
zR{P2Rc|U0lZRuxqIq~q-{I<p_HvN2>*>u#(&g$}5@#D;cpvI8r@%iDjqXgE(;V7uM
z?u+<1n@_#d^PJe`?-s|0yXbIEHcz~U!IUPk@y1v~0|A{m%`&rseOPb+2_zCT(g?VL
zfYXhb)nK{Dw8U#o^C`WNjo<ag5TgXMP)Z>%E=LHxdh*j>bj^1yE}vOB*qg7t#rt;j
zIkrE|DAok4d*F0SO1d@fQMI8(gQ4+*LtGB}vBq#7Is&U=cK|E`D{%K#SY9dNaq5rn
ztA8Fo60@3#`A&=k0F_**9|*xeCnV;7U<r%>L1G4;#Nhrp5t#r6hrO7UJN#E)ovu`Q
ziA>7+H*6H3CHsM|f7~0NZJ9yElq~CvJ9R;08oeRU<ua;3VttBH`_Q?(6&@Uz$HKZQ
zjwhV|#Fom~>wfU&2mWrb5EXSf`V-gDgT$pgQsr?2QAT6JrwJ;NLv@hL58u?@Zc_y2
zN)jWMI5s$8$!kulyP__a3=o4OG6~c;^P~aC8UO&J;ciW46@suln{(EA^!{+s2CRrW
zkom8GeHxgq6#Gc2j@s)XO1sCQm`^Eu_<s$vLjY#lop0?fQCdNMkw2-=Qg_Lo$Su5!
zAI5LL8(>#VM)(}|y7gi}P=Dw#Z~DVGzP0MaA&qfCEc_y!Umw!qzS@~=e<WnX46pC0
zK+F4dA@)RvUsulic}$$3eOcuWy?U27e>iHd*x#5}$aV}{V1;@<-2H9e*@`r=F5f&(
zwq>wc;gu`4SxBkuoa;HU;XLSUkMGdZ2GL4mr7#Xa!4@S=@ST$>deW0d!?cu9%{`kv
z*x5Whv%IN2T#TW;`y)S>M|`Zg9kVR{z~H}OZKW+U(BJBnOJaRJzPX0+x4&S;g|dG;
z8qBx4PY0DjDoCd}=^tfmOoJm_4%B=61Bu1=KL?ZyPEdc5MMnh9yADG~WN$5VaI+`}
zU&q+6l&Hb`sgg)A&(oPgC-DWLA%j8uCclU{|L7e{#(Nr!315_R*4xW(Qic<YB#1Eq
z01sp!iT!gj!g&LI6dIiTABrt;|7TqY2`gJyb7w{gTVq#qF>_M~Gjm2cb9)O{OCn|#
zRsjJR7guL<V>=kn)!1legx^AN;64U*<Y-Rkd*=<h;8N4#&f#T3uFA=uoZ!E~rtO;=
zLa;#%NG#2%_n1uvyVOlIBN=Id!DcvR68Ycl^#!&*SZKbZh6&Q$%c$Iyu2kToylWH5
z^%4(b$~etd9RFl@_SnB|Mqk;ujZ!sP?f{X1r{MT^_&HeqZ{TO)=4AV?6R6U)bJ%M|
z{@64$KKCnA>DP%wyLYxzIyYz!r(YFcg`Y+|6DL}ep;JqBOZAoh@C)CMC(vFMOO5ei
z7Iz|;fXl^3pf-P48_)H+iW7{+dOMrYSX&utUwKuuJ$$t%0nggt?|J|Ar&pb&T{plb
zeXM$W^%n2aVB%LA*XL@AygR$aZx?^*#{mV-7VU<Ws<X;J9-ry&?~}hl<I|p?$1=`_
z)_dAM7Wkx5dhW^V|8TdQbd6|21Z%$PrwEkDchRDszAAovA-L5aWZT@3kP#0`LSNya
z&2s%LsHuEUA>?jX*I3f7YIjHUJbU75l}Wp|H!LshZE~D-u5y^KY_zKPGZ5Gn`WB($
zgVnrt>i#`(f@18ktx_&4gn&UlCc~w2q5N=^?8N#qN~nDmt5T>j@i$GmD<X(47#;y<
zS?py^wCf7SdWp(O<Md<h&DW6k@PZLqJ2y|0Y{&YoUaux$&zX?ajLYGch3e{Ca%j|x
zZz^30e9$NsDHxTa2{>Uz<~kjLAXds9de2NlOWw;j)5jo{jq|Z|Rkyq1EooHxN4npd
z7Sk-j8x0Qf)<}$_*}5DvFr|aTtB<T&7_m?VQ;ibFNH$ZS%rzIEc|y1kNs?K6#KCz=
zbvF`Oi2^Dos)G_dgHUTIw7qqsn!l<Vr##V)wDvn8V>M4AMpWkz>4w(nvsAgS^raB;
z!cLi1;!&WRN-6DuyMPgd6crGDmoj77CdPs%Md92<Mrb{O70vx+Y$o9uPxmqyYTrL@
zN^`=A;c}#O7;BHN{k&{TBmzn_9KczA8s=GXe<3lz!8RQ}=9+=&bz8uTzO}=-AhLSW
zW|LuMFxEbrA0@>sQIVLiI*Gc4?lk68hKr<r7UnuH3`u{lG{OZ}-;I!$jbtCOVoh`q
z(Vw<~D#sI-P4RA60(^j3eq;NMKg#p!>{A{XVjS3|v)3x>#<F?qTFBrmmAsT_@CAI|
zI%F?}(`;-qIl}kIhSVi#w@eKxoNvo>iQhNb1(Wo%7)Ru|=ZoETC>B@#{Lmadib#$n
zawb;2F<eS!Un33c;aJ))>%eB>=xoGT3E_sk6j6i~!J^8aid$(LAd8*gv!7o*s21Da
z?P0ZHP+xdAig(P$n5Gm%N5kqJa(YRdy)sTNOX1C^MgqJPU)0>~XLUU28pb2~>Qx+W
zCCml9Y+9B$>0;Yq7_;HA?ivgVjRy(bU#loicxtlz5U?>>2G|EJ<_oA0*NLu~g&baQ
zgpMgVl<Bes;(KDj>{Y+;Ei2dU&igs~10cdiaH1=_ijV`%JNS1Oo=p7DC7e7Ze3c{D
zOen#AP^R1EDHAlyrc~xt?c)2>6CMoAQKY#rW({QukxZ|sdMM^MIByIt&?VA~Yd1-;
z&<W?cG_7guBQp~f%OgUKll&wESN&ujOJ07<EoUpq2nan^mKd2&6@YiF42(qo;I=16
zIo9yhsz6@_V0y&8x~@$YY8a0!H0tf3d)_?$q#A$e9uJ%!ghfe8wRJSy9MCcOM!m)W
zHEqC!sDm?5*|;(vP1_PTaoZ1{Uk}2q=^0di;6j)HmInKwp%)4&$@eG>&3Q<MW@IdL
zuCgX#@?#7Y?0#iXZl5Bd6w)zb7YTYNEYiJusYVI<+b{eiY$6AY_VpyWMAKj@kPh%Y
zX$mt{LA+c{8p5W^@QC$N;<t6oJ!CBKHPBnEpYXXH*ptqRBfo{EQ?vORA#f_iRS}iQ
zL+L~n(+esKkBfLfb;d3dj-plq{F^4;3n_R7HzqupTy(Cd0JK`$zBb~LvVqJiw$kJ^
zF_5I5%Q)}vM}^;2eo3lLiR!D*9pf}o;4!_mLQv2?`^|wt`-rk_{DF!VA3~5yUKrCl
z@HJ|Z!38D>9<n^V9flX1)~L!nXA^V+`}qot4Ge}9mxVZ~8%`_y32(H8*T7N4i$Hxz
zquD97A)#pP3(9FPfqnjqheJbLvdaE#c?DKvcq@S+|BsuOP4Pz02jx9cS|@R?P&14d
z?rn+bVn$vUrU&~y^IEfG#x%juzE}->{gy%RFT}O#GWho-Ikn%<75w?D&hV+-?k!B~
z?fFOEBw~LE6lo-QmLCs-iC0_a_{V>96#UU2>{L2D#*?p|=`^f$ExII!;=WNywZ2)x
zbIRVik7&;i&!{isIME6iB9OoY$v(GVmbIxtQgWeVn%PMu0LQ^sSn}|qUwRCGGcymE
zt8?0a@yvGfL5xqrmjy5$X@Q<9DhjTrOkkY8*`?th1rH|-xU0pWP<aHxaC9R7I{)>X
z#y19oqhQJvk_^zvg2auUG30j!-Nic9=N;BVvL%>()w!Ca%Dj@W7mL;qGyaGfA&g1`
zGA@XkU2$il@zuwzxsWAK+je20;e<~*xOV^i_Q?H5e_=Ezd|f!EGb$DDcx1cn8NK&~
z!7R)2;2=R^>c+@>hk$}|wOvfKJc43??Sz%d?9tiMi-^4|%WVu;v%La@(^Y9>*_!C6
zzQ-{HK5`F+dLv%l#H{m<?~~S+4eP#r%JJM}hC_+&HpktGJk=F8vm{gvbrIG1MBq02
zD<TaiaZ<)lu^mWr*7Xi0?xGhhb&~G01mX&@gvkpvghFRuXY-gdGHK<`mL_Am8CtZI
zc7iGh#5RFzC`)8d{aigp+st-~ayuvSxeu?G>vWHk+q9PZy~t^Ds1)8oQd<y|EB|R8
zx@6{-Or2eDW4I1{9NUsGVii=8iFpQaJLpck0<Nek1YIrRrD)iF;zLP%TnGC>`=U5s
z{(_Tl+jgU2c&ww!XlaRe+=LE<wjqk_nR;u^ai*U5!chB=m*ny7YjoLa&Q2KazF{_7
z7n@z}Dr+tB5*S;>)C#AA-?gUE*L=r_8v3wl$;6tVO^OHm1ja58BJb}UiOV=Gl3&#9
zP0AJXC#mnt_57AkJ@xg27dWg9T_&H{<MMtq9U}j-@`)tsmm-d+!PDqSx<BR>-Cs{(
zR5pL@T4#T>+sz`mQsVG(LO~75VBoWIilLOggIGn&RG!abwPvE>3_3Z~OZg3N+`bZ~
z@~g3;9tES!p=kQ)dp}_fiEKK>Y6i`!c;ILbxET{Qksa~fDc+B`OjnC#!NQIL=jO??
zs_L$Fi+h@?`ulfLgGAAVhDX$}q?lj8nk0wy<b(-XJ?1X`!aHG(+;9m-VhYISH{K<x
ztC2FCoEB&jYSU;pu3jI;k&C&%1Hq9O;T8l4ov*MU%Ak>pdFg5qMs446Z=@@SO1u{}
zGgPMq-N7t~IR5xVi|0?&P2re+RE3m(6A-VZgfd09!L@^J(X6`sVtX%r8rj0+7aNDc
z0ABwi4<Q2MnIgZBoX=I808yX95v^=0(Fjd4^7w#DMu>4j8T8#Mw;GNdOn1-7|I&}w
zh1#H=3ERj@@q1KoHTDvwjw%<Kx%!Oi)g#z3HnYB%%CE&<D28wfENyV(`1mP72`bvF
zN|J=(8pEgQd&CEaHeVu>J+RfAlsxzdi!pH$jeT?d;!4}!HZZy<NLmh3(mm}OdW4u=
zf_p=?%B89KHS1Gq8jEnm=X^<l@b{S+JTw%75?WkSOKF~Cw%^66KXNCzFp8Z~X=t7!
z`!jxAC*_@GO4oFpccqa`hMy}rS;!vz7|Mm;^M4$tt}8WFLon>v*({SvTX_;Y$(4H?
z7(<8g&xe>!Wkb{ThgyAsyvy@=19^vbEl>ve>4wh$uXPqI<@(EPF%eLfUvL?G0?kE8
z_OWsj5pm)=&r{Y4zV&@Z>W5pMN<SnvNH@~a_aLd-?o&qH@!V3q9#Ej5uYjn@)==UW
zWujOX9~tn&?8X!b*Oe?M@lk`~*ARTOzYC^;45HoLx5bZKZYX%(w+r+`7p-FyllHNd
zuX7m5MO&?by*oPecBtr1Qo<B;Q#>8I;Xq*S?fiO3Tk4{A*(CVQh)yCaUnA0#Zxbky
z-(L|wp$^NwQ~nP&KF5FB`2XJpVY#^&rOd4?EM19MIJp@At=65Sv#}SWh?T30lDV^}
zgPo&;y}7+B3>V|SHatqk&UP+*eE-&f=zovoLd5)cNH;rs7a~^Ros^u-&8$pa9h`|+
z1OyoWj~gf$Hg>lEwgI`=@ZwWDBZRv8!z4W$T8XQ?Lc`olC9PSACOuYV_C>k6s7T!2
z=S82yu6Z;TV^^i%AnZY<J~W}IT7%x6R!2<amX}q+W4N;=gK+Qd$^Rog!zJ6|$4Q6b
z5%(1L1Ro**GNjjt2@4J~<fM$r-I_yJ;}`KWqV5>w>G{iZK;@O3E8gRRYPf7pz^4X?
z)I3MeTdTY0pIA{lt@9(Nv#?Gz(jO^^Z~^;H$VLM<1d#-}U34GvZ)iDQnC+pE2`Wo2
zv5aN+cCUj_GJf1sqzm5k7B>3^dV&X<O*hg$@`iYdhHH&aoZrg3^tJjNrY&fv67f&j
zBgf9l+%9Rmcw4a`B~Rva>Pi>AJOjG#TS9QC72Lim%hY-&?B`ylNnEN_Mr!A~r-4Qx
z7mk8G1m#-y%}XlCP4a$6AW+-QaG6fMAE|J{)Con!!FjGHB(ias-w|94c{Ug*5r5b+
z-KvNicJT(?nX@D}$jC-)u;wjp784{r1&kY3Tv@sk=M&dE8~kyBr6DgE7yqNxbvchC
zz^c+VCw>E!4Fg}gnB$oEb&@A7y+vH^lcwqN81JCtE_d_mJBp;GWeJXeW(nW$2Empr
zX?T@W$}f?<@fg?cgSklvoY=43`R9I*VrxiEqsu;B>oi<Ai)T#$66;iq8b#1w7AmN%
z(Ri^Vsa|XB*xTth_Rl6MI+NkreF7ZN@a=h4<HGEU(C~i<EmHoVx#)3iUM6i4s^{pK
z&Myy=JBZp?=O}ZKS=hf{R2gE=n9awI2mMaBLsB`Cw}ks;o2$@hnNjMFVh5L>?<;u<
zW}D!C&sqz(keig=I4D&XYd}L;6X`Q2Fm8J0U6Q<=6plOn*!vZY2k#iTnF2Evk(TU1
z8qG~O7+2{e;6>R|+;*0U@c!rC(M?Bbenq$9!v!$kaw4nbB3JG<S}b%f^K3}bxrsBO
zJ&F+bO^DjVUG|{X==h{~D%_UMXFE&A5{obaN{h{k#OJ6E5uL>5*c~A0%-O=nv1kQ$
z{?Xd#sn2rf$H4!ieR#&R9wu*=Zp)&YFZyfd<TH~zjt55WuJ!z)KO~Z7g_fNtZ)<ax
zs!i?Z#mP#ynwKtfCR;>zMz(sutO!`1$37G$0Qn4THfr6JRBA9qGQ`DJ57y4h)^dMD
zL-eq#tgnT$C)6)WKALYR%=@iX_sV^gd@}sGIAF|lugeeN@jY45F6#0QXPgw|lybDo
za=cBa#Lw<3cY-}^)V!YLOb>s~K$lDwp2$65kv%ux!a{$4=GbM5-b$lYM?5JkV)@Bv
zB8+F4|BnrNxrTj{#rc?N+7bsn#}7`rbX_;OlJnUby#{x^!oh-!61qt*QpZ)drnvLl
zK^WMyp{+PR=7$6Nle?Z}g&Jd={GJZJm*_!`_RT5Khi1!iSJH(umh~S)S&@P|_q;xj
zh!IRQTX<jiaG1V^pW+wT%oiEE11p|5K+?QQCNp#h={fvFQq;RT86#;?9B}m(i_J7>
zvMU!Yl*ru^{qx=Ev79e;IBl{^*fN5_CCLZ5d(zmiyCU=e-jjaQ;JH%1FAoxuohe;`
z0OK;adHhVEO!&t>ZXTqykrEg?Q`rnR`-668QOG!P@vL@AGSZl}1;w~Ftp7~e3^5Lc
zI<qTu1+*UZdqZ()v2jGc+z$|1!)_FIo}ywE9_etDLDpwkQU6Xf#SRmuL$FtLYuDsK
zT<SoXlcXSQ#}A9JR>Gf^E2AGtS-8i01m{Gt$pDc)3tJD7LT_9G++~$~$o#ruKF8vi
zsUq>zuzXUpn{W`-Zcug}5z-&TuO@wUeP9H-no7UvEA(%SF0HS&;`sGCUPR&Nh5k8u
z{$mzFIA#Ckzye-}v;I46C9shHcBL#x7=FG7`sYl;;jPhqBhNh1H;E6#*%hyahO8RB
z(@`4d1jTq`9{*E<LPLAiD4gFjMhLg($ZB~@wmBkU5)(UWWBZ{Izc&b0FPjBd^THh2
zVEEj}X&Gj%s}rhjFSWF?^*R%3o~^d%PHzS1=8>WX(+@N`E9tb?eT&lqCa`O4*^hH2
zx~;C!E^+Ij7KgT?VG9)(Zbiq~XQeqDP?9*1aLRQC4nqecocTCH3yd2zodYL*<G!B#
zq(2NwN?>nR6ImM0@Df3^pUtXbUFYmL6#5~pBGMnL58JeES332(p*5ZjE)qdKU*R?d
z*X|Tn4;{q}2G4o`@l<_TKnQ-v5^se-z_~7n-rzk!M3kdH#(A(3k6miEbeFVjrW(h1
z{k<KFT`G?`{1q=22Z$hv=<Ftcu-Pw%Wx_DAXKnqW(LSwZGs-y6f&yVdlHvJ~)NFJQ
zMIn)ER`Wzd+IsF}<Y&4T1>&*p3re4bb+IM|kYCsyTAjY~Tf~aPt2C<Z5@r!774EU8
z_hkY<3)%P4OfIN}qLn1!Q7p%uIEIUR+K<iVi9}_|O)!Tfo#ri%ZU@a#=ZWG5+cHH7
z0W?Y>XgG-6KH3oMQu)1_N`;;5$2hwH=D*Ov!^O@=VzVj(TZr>O@c0!{6at~EjveOo
z3s6M`wy(2ky11!0V<;0&6-Yt3fP5&IDq!|wquryjjtA0nG*s3r?BCu4t{4$$JKEWJ
zI3u!zy*)zhhqT27$3K6OccAJPhAFYlh8u?LXfH5*^Dw_lp<<3);QRZ(BikTb`PI)t
z^a`Zc9FMkTi!$P$1ecmK8!obxTa_^uCTDy-hEyKOVZzN$3d6jLKO(Dcua}RR;D6bY
zKN%O}G^-U&2hvuPcku^VnAq?|!-^J0v<EclW^)zv6SPKLDcHv1h)iQa!|o7`SAQS^
z@mEmXX1#tFh=X6_<fjZ7vsxUDQ#3ujwUl-5&hTLpNasg`b8yjuAsOqwc-&sTB?e#E
z`FEWf-)&sWAawqIUEhK5juljui2c6Q%C>i2n~*h>2VAqTZ!-=Lbqr$gDweJeSR&NO
z<v`q7ZvXihE)42^B~60-xQx@IPf<j=&U-^*A9mRsUiyo_G8@P$KwedkLra3N>1iW1
zC18`%tN@Z)ho+EDAgWNJ-TDcf{d;c|X>Fpg>CK;5)7o9=pZk{OPcYVv4Pm{RLGv|T
zy&*XGMauOB$E-c1f0=X8(zblKka_abmmaWLN)j&d)BD=OXyPwCXja$Cc<ig%q|QV1
z^F*<Ob_?N6Xc!NUBl*nnw-Nk$&X#2YRpypM9m2rIdYno;ekKv-(M<(j<{G11g-z2y
z8uYdP-IWP%o&hp($~0+7?;4xE&7MTqoSuP(R`{MK@Bi(#A4XrakDu0Hg`)O(loX`1
zBgaX?X2+%xgH{$XtVI^%Z!dV$tspg-=xawXyt4r-$%(pRl(s=a*;`ydS0-iZj$KLw
zs}AfZo&EA@SZEl`Klc%u+1JT#iKN5fazI^qRV$k@#z|ru0C6;fb+#0fN|%`E;6MaY
ztdr<#ShLv;IB+Efn~+!L#vBktfRp(Ni`$Zf{)>QpIFFcx1+^_Y;7ka}zLP@|smQ?j
z7uH=%u)wkp7hHE)yi3AFhv9hAf5)MB_Sc&OvvzF^YwZ_@gs`*{RSp5iF_3*pQQp=!
z<Fbw~D87N~yM_EQ0OTfn_`S*v8|4r>oadJ<9wjX6#%_rRyD`yo3q08%IGp;gS=^j+
zPIM5?*#mAc&s5)WE`H`?eH|aY@{R)1AlK`fuwyzu0dNM&WukoB!y9BXTpmOO=XzHJ
z4BTvxp}2J{omZT7`_tRBK)J22R~Qhpe#B4A!d1vl&9;A#(RowKy%)16vZQqeRkb<D
zPeAY;hzae!qfz?}jUykWHo1Tp;F@Zio?<QiUOPmU(Xpq@xXgWnv}nYr4;yJ3-w{=1
zfWuz!b!$l~ZE0Aev@H*<s2iEy7Rqmth_0fVL6-$IZv$^27>(I>`kbbwVBc(imr^iQ
z%L4@*qD>+m5$$-SYp_zVUeCezHGnH0auXY<IsRSk{8cso_4q%komhoJV`LH>^az~&
zX{1ip53A}8Ak}^gOQ_-g84UP~U^!S#M>f6CZcNH=VNKlm%)e3N;BCv53dPGj22&1J
zF$*ENy<1az3tr#q!lN|<{zKuVH_v)5n5?fm;M12hy)l~eO78^ut)g*G<(ZDUkNgI<
z)6(i`y*Np)*RN*+?jGK@!jH~i-@bXw`+mqqAEsziQw}rX3w}{*QbWsOZxO4_Olzx;
z(%Xk9_sU(6<!NM*6~YaB{Er@tta;k_3JMWa>-0hkGnxhcWE!w)4gXS(vclFJ<x%;f
z(@PEDjmDmzs_~y}v6)jI!UL9{=1GPl3k{V=yp1C07><&?hq|P?M;mpuTtU_vJkkz!
z=NBB~X*G<{iHwDZxB53yLW~Ya|7gpjZk-MO7T253QSi!@vF^i|G$Zx~_RYq0@;tk~
z*Lw*6+oiJSx75b^J0s6_Gvrq?_2^G;`1)8EkxefcwFcM3iLCx2c+iEnG>Han(BT=C
z>zK?bhZ(?-x6`V!3y3bwzXWim5WBAKI2a|VO=7cJhoRv%V@Kg3tWq|Rf76qZoJ`XI
z<~6OUPY>Wh)*Sy*ypcjcICL_|^XU6-?;$>Z^ICTq;m~|xfk0>iO<ssa@!2LJ(VF;|
z76vcp{+C5lWAUZuU;DRS*Hbn$WrwNqkH?Fma}VL}>5M85zfw!KP4&f#w3j#s5<;tm
zrvjP!rpHq+TVKe+t|{SYhDv4fFHX;Ekv5{EM**FQFKe&~C25n8pT1yrvV+%*P0c6`
z%xkC-u$S-3n(eh=?e0pe;)Pa)8mcP(VHJk*w04H?`xNd|bvo<{Y<3w%`%WkJHgS}4
z^xW;YWct1>!teFbld5EDan1~t^udO#>OY|&&fwu4CNA%(U_G(c3kQos+rJG^%q;DB
zhgm86al;{oEm~h<qGmX?$RtjqZ`e*o!Od+kJg;W>O^L8eUxJD6K8L2MPVl!?L$aT!
zt%fMCyw~5;PK6Sfa`#t#`p<YAY(J8mo9T7DYDib~^RAo=gGHK#EQgGpFq~RIu2nBp
z(qq4a3%y+~`-GD%<A`IJt2itVK>|x}+xEg)OkxmbP#~JqaBM`Sw3R%92t7v#gqjaU
zMlL$CVWh9jk98>KzKjm~G8{D&ad{n<0I0HOPb+ZX#U0JPx}!&#+`>@D=kyk*I}OOS
zJz{c`_pKg*=`R`u$=ffOp83SIJCye<mmyl~9n-HB;}3I%v#CVnjiPXMNk#4*ow;=j
z1CEI_INg9?%r-Yf(%kf)2@k$;^pxFW7<E%nJTo}5C6Y{nPO}mQ9yff+o=W5edh@V_
zT8_5dc2X;@i<s<QFk{Jpq3SR>*psn1lc@NoGT75)PISqI!squ%x{Sy;#HOt{MqDG}
z9xT_sf!GzU(jyrS5O%3x*O$)^>)1~B?o1JoR<kHeuz0ci`bU7q2P`K+>3++BSz(Vt
z522d1D18IbAC~0`;rJFS<UcRYo^=XYn#Q@w598O|r5V{3=U_+wWPq(?(a&XbKAju2
zQ@~&ImOSqH)Um7m5e<qyP3CrF;}}`Ws8n8GR+-_`_J%kYP_08L<1>RugLDBV-?2S2
zudwI->V`Z1i_(sth}sh4`sW{qnFID7e{}K%s0w$jt|qUho+D*t#v|dnWHe5Qm92Jt
z&L3JYS?)NmbOiZR0@0jSwNty;uFVoRM5E8-v!(42eBb@udJ`(0tad39()N77;|h|U
zU-XZTwsa4a9@o2q3x(n9%c9|y;4kFizngus^L>T3qr5)X#|_gYFnr`Ex7KQQS6#cB
z2$(l~h1b>pp%aN<X!C}o;KllApxf1MGvDnnH}r(X^1oT}ZEL?<WC1nee{fN8{-=xT
z-&{^Ak|K<%^2RPUL>zxTSt^YGKb%-Ff1OzWPj?m28})zdu44V4-BmEm?Ei26Dk3g6
zHm3jj!2BZTi>KPc#^6*AIcvrUS&|&Z7vc;#BB4MsF=#1tSYN8u3aSEi9bFyFpE`tg
z7bd!dYV*7H;&$brqm(piry6uoY8opZAR%=1;3y3aW;q$J@06N@&n&VONz%@LG9Dj8
zcMcCXS&x3WO!5Bt9OpiQDWGBj0OC1m&_DnH6$s#08UUaO1^^&Z2LJ%&pa92ENO1re
zxNzRmG*pf2<J0A#n8-|sRWPRVA9SK3m|6q7)Xlqh{Phiw^AJ3D?`R>E#d0|QfSY!~
z>E6zK`FLXRz&D~FLw{;|4`N;ufuZ-IT9(LkYsWt+T4`G8i9K2KQqMg~4fm$aX>agQ
zO<?p12&txe8RuSOefLPaN4)*v$timg5me^$4GTRc+vy6JXfUf`uJ9DX(jpwzpzE4K
zFDxw;DMPS@VKMgGNOb3~1{~F)gWIm_2*qrIeOIASz6<%Zo!JEI75-dp@MtcsL`a-M
zVkB-AbN7}^r<PJjTPeQkGKC@d1|gF)hH;>a=4UvNz#bA$GmRKaDVB>OfqUB|62#*d
zj9cB6e8aG{q3W7%(KO{6<;GqDB2>ZEsL794iz=h(MD*oYz3<qsh*Ge3i(je5FU?_9
zx}@FBDyWFbQ;RYOy(qg7&$aTF(HfbJm$(Fbm-j&hjhY<Aoxpn!mAu|iQtaeCMu8VV
zax4v^hyE{QKm&kSsfGdca61$JjL*)tZCO)R1UODr>$eAkdpW1k%$DcydK6%QZ2#Cc
zWk&*l0?__J9uRMVQi0rod4g6B4vf^)R#sNR%3`?QodTq!qLm9G6@j2cIV>4+?(O(<
zTLm!6Xq6QeWe*Jv4X+9nZF<^OV@-=paO~E^f)F`TB4va07_#Ex;>V?T8iwkQ_b1Lf
zj!5br^e`azo0ux-FV?s@Z)z<VQ<sVLbCN9#Z}KBEV69&O+i?abl{a))Xb(^&M`BqM
z7`xO*)}updbgM2unMf%S?_tR1t=;_n1va7@uV-Z$^<8Jg`K2%VBT+T=-4=qtb^`DQ
z2L{+VIKuqj+T6}eOiX+m<dqvn%nG8Nd2-_TAC}pd^yU}`Ua$HP=g{9DTDsLM)WoOD
z(b3VrdCCO45p^~%q~!a7BG%Q_`8-}0$0`au@5cqa?Y-QewP%4^fl!Ndn{Dnm#;YY@
zJgF}?*(^~|QW6UIdY;ag`%$aEs}psSsBg;FJYYvIx~5UJ;*8D+1To&<-$zD9&OXd3
z3cgPTd^9vQxxL(-#PQugUud_vp1eKZuC1;zbX@)Je!p;-N0TP<zs=f;@8N$vZ$PDx
zgHArLtm%3grLCEc<mTpf=(rB+HF|oU3Q%~_($n+3*&kUl@Ua_7B-d@W%{Ma)cy~KV
ze*M#(s}{!6V7tOnx!`@iT-W}mtG%Egc(Qoj*~NwWTTxLFzt6+;*->tQLPq|1=hJR<
znx3=t)tUF@uOkdb13BE54fi#_$Bm;4kX)}rY-bm<VE6N7r?aXSoLGsrGWAN08AEDQ
zUXM$%G3oG`YqJCj`OHS|`_pg&?}#YNtL<J_bTyp)J^Nn=7^|O^hVQ2(S@tjn2s}CV
z!|}wfR&H)?uIOZ~Tc00~)St;{_(UF>2OHuGNyV*TI)1Uu5=>u?Xr}2>hDkpKJdbVt
zmP_NL;sTCO6S`irva(=dVSzcoWn^UqU(Z=rMPo096G+!u97<muR~xNxIc$kKwYq${
zBZD<6JI|`yH+^p>a^KG_jwW-)#>TREJ?yU@J1^VL?7QFHS3R$Kp)l!nT&MUSr`_e{
z;X9YY3H?3q7c^DtVS%}ZdEMzUct^jG^LpP|CJM@As3n4%Je;q<V$kJ<b=s|QczaMM
z8T#EXWU|{x6$OiFB~dDt$a49=ag8vXrJu=)2t`gSs0>D;QhJk(XBocjVir-G%@KHR
z2MJ-SYimmjlbZ*kU5rrXl0js%S|FTZeQC0qD?u7FKAz4GN5Fr1Ijb3p!ESKclNeQz
zmX==T86e@+>h(zZAiCCffYT^b%`nxWnK=(~07xx{fk(*opCT}{ZiB8$ON5x&e15#o
zPfE)JQ>I8uC-D6GI-Wjb{*p|@KDoN8BXfE)#+c@wC^B=Albf5HnJH7#Xt6(>u<s;Q
zp+>V1%eg7#W8d}s)q2x)@o*x`p>+rHM9i6a7V_eRWjGO3$eBU4M2?tHj6{SYp<f&Y
zK?MqBSD{o%02c?R$nj=K@Z)+kh4#Iq!|hB%B_o=#Yk8w^cJZO}kDuZy;{jj4GZCUV
zAsCrDnn%(R$w{(It6(urayZU6Ck?FZIUP7Vb<13t{u;_CqWR5XI;CuJP6GBHyqB$g
zx0F#;Unerz9X9#??2QOZnFtGr^Q8MK2@4CCno(={e7-+Q#^Q(@qkZF^Re%XNPEnIy
zjc>+B2>)ZzF?PU`HIbO^O-ij$N<dOi*(eOUe{oMGL@G>Xer>0O6B{<0)?EAy@W;*B
z>SJg>uW#A%Zy(cxpoU7MagMf_`cb^D(oa7S$X_m8eF$hirK7N5&*Z-<Zf#B1X&*4j
zpL!dJ#*{Ve%i(q@>FCJT8;L@vwG4Ss)6~r6^O}7Xi_&{=-?mYKW}yaygtK&TD6M5q
zwvDHn_L&*fb?9U_{vG=C^mKlH?y>15i4x)W`rrn9`GjRgSBgP#RH8IJxn6#foR&5^
zG_b#*ygbrm{BhGKE;*Pi=0itaUHq#&ixL`&O0Zd`rO!&exeAPXdro3;OpbtG=B~*q
ztN)v4lx`dWzsy=Pk$9oLL7D@%(ys(#vRH~4O5SInc<Xa8%))!Ecmag>T31&Nu+9sW
zcF{;fr_yPQ!21J5o1y=6G1`3pzyQ5|n`?grGEk6WU6dYHELa1tm|H&wq44=WIZ8+X
zUtWE?of3RGE}W-0@r1b}lmCK%ofHCv2xJ~9slQx#k>Bihl;fcSS(dZqIwx-?*AZ8$
zbps^y&+QOI-}VJc#UFBMq89wu{ix;m@lDvA_IPK<8fhf!+%AVo4^r?hE-u9l7(kAF
zy=YP8p!!<GH%$O@cMKQ}FfiCWo+-47Td*c~VRJc@qb4fufXC-ydvbeD1>+taAD2_%
zA2CjlOfuQI;>|P@C@(H6qq-};cc;r?eK>}7d(4qz%9@cu2Pq%C+UZk9@Pk~DMzLHa
z3ZkW__uKh~xv%=#o9jK1QaRgSI%^{@{sn08bo<`TNZuY!xNza^2lDMvCeO5aRX}!f
z#~PefPSz7+)glBE+Mf$WavxRbZdf`YYB`iM4B&qOSU8faf3@N1TgL&KOe{mm`v`ym
ziSa5BfC{-OxV#)aYk4M93nV})rK4Pva<I{;hv(`05Mh8XkVPRNAijP3w$^Ode$l*!
zfX{;_!Q<ZESwH*PSk?V@!1&{Wpqdp7w0CXwRDP8VI3z){?Ml7#!RSW2$C)b+N@jC&
z^EX&bi8p<zijTWeLFu(DP6vrP(TcVb_1x_F;BP{`Ey<~=K&>Y0a%~x6Kq;SjH_keR
zhK@ckFt7ns2sVp9rRC(vs-EBf1Oxy{qx<8&X0Am3ww)o0a>Ayj^WpAfj-H+#NS=Sr
zR~iUCw?VAr_`F&;w|rG~_!QB^WgHhO)Psrmp0-0cH$C@&N}9m`$#}Fc2n=cvDC=BL
zRL09AtjnDTbx>`r@fFe}VkPltqSO&<>luFw^3&(OZ+`(=Q>6+(6XtR{UzD-)PGvUw
zuY~<@y<F!;$)n8mbp|TeB8pu97ix&GhOZMhyh~GAK<1<*Cr{vu#piVg-baz|8az^`
zlzQk-*DJ%tN{a)bHaCx4W_df1(ShVzQd(;0dp(dF@ZlA2UJLkCAx~G?4NbjST}6{c
zvBr!aS^;YQ!C2g-YJFK1@%r6oRXMqFbWJ^B+~?<Kv-U<Edb?~cr-CuIqp93a;7ZIu
zdR3B2e%GSI*fQm?Tm4I(O*%P1vHQ|$I`kdL_)dF+(zj3}!85qR6$LsKT{*g{{*Wrg
zOmAKV5klU~<zOlW<g%%r4@n%-#OW4I7*i_rU1tTJd0gL32I+O1fzOg<Sf~3m-Y3m(
ze|{0wzR^d81P^baqULY{baH6qiZ6gJ)**pn6Ix}F)Dy{4c<uqw72ej?*55BSTfVz%
zV#SUweZRW4)`?gc;qZKOpitUSP+-y*20t4N1*je!yV6}(S<nml#oi-tYP!e&xUQ<J
zD=R@wNqLl_Y4Gdg<*c1=%tIdy5sj5Wt2Qq$&uF>Ucov4TA4mdLi1;VkRaQWp>~}~2
zrSsrLdFc<J@+P$sb-M$v#t3p+yRVpg!Mt6Bf1WGKeFJHt4XA~Nv;O2|hG~O_a{$R}
zYy~}B1oU99`xVwKE*jgz1NR%xb9vJ9#`dw!@m`qV@zR|xRO&!_H{~soAqHtTFjQBE
zXo?caDex{`MSMqt4=A24X#tkA?cd0QW?^BGk&&UHL8_J9D@{HM!cnGCR}hs^xj!u7
z2q%^Z!4)~QY+5}(w@n6%7z8Mp%P@bLP-k~5)Bnq=A9<JQx~jTPkWH|FBAKp!3b*m9
z^56er<3TegLAZiq3>O<fpxQ`!DB5GPngu@4DvzhZF61cYO4r$d)`S2Eg1_+?>WoUF
ziBHv6L}8bfs%Vxcy2^Xv5CN2l)Hj3CA$t|?G<r6tdwF8ntM75=834-20Pp?6b{>@B
zi06_yS%oU;Tx)kD02^S2Se-whK;oEM?BN8zDhxzJGsV+?LK^^>QbDHzR?AFA@_<}r
z%%<#EF*fn_{yD8-2xbZaptKAgi!F8OOdD5AGtpF2LmND?Zt%e|yWpfmp(`&l1$erv
zCzfAtT&Avj+UVt>dGGbcz7)xM^J_Z6pu-6@f(O#APyVt0qG$HHw5MWwx2oZXgANDe
z67*(YaGn{>U*0F$%hKf;3d%@lOs3cXg=z2>apOwqG?t}GFqu907nalrkTZaoCZLQ<
z%Aithr0px57F3{zX7SxY^;skJ!(vjI5ui1tLI^pxzQ0mBH&d-UiDkmOlot*V2w(zj
z#GNzIhPCOf9{@O>Ry=vmCRoZ?6#wjO7Q?lXU<d1|syuy0n*I>aIVONu)%vcS2e={t
zgmu;j<3-53%c`rA110xn@#va5_2qB5MYuGoM)kD3?;zWOn>so*TXlm&>b2l?1YnCG
z+pFqszi57}7mpUvq9?J%dSZbCvSm`Xp4p-rG`*|ZR8oFA4E}}UqJk?=A<M*?RuUhq
z2c&mU{agKNa<<iC=^#h8DgC`1VO%C8pjxJ+5qCo|)B;}15ClM=pHeVH^(MGGr;TlT
zZ;|eXWsL)9rTI1LSm5*Revu{y6e3Vk^T10ic9s%sP{$TQc|pZrFWeh4P*!tbDCS@v
zd=Hy>McWN;0S*veJt|rwP}zxCo9SBGpZumnR|^Fwt&T$EDWXx|iZ}u?Pfg`65o9Bl
z8#$f$mO)6qI)Dg}%xIl5Z8>aa7qgIU1O}tD0&v6v!06FQGf;f}^-2178&9q7D;E5c
zjQ2<`Q~}u5svt{HY3!7dl`co#(;17)F9F%ssNjkKxxm<?W-hKmQGp1oKY76w5|7hs
zp~US%Q-w6OL<e-ppc7Wpkj`91rlYJihH&G{RSEln_2>f1?$m|I?{wTk1Vrh1uraiu
z0%gl@LW5)oVNcmiJ3s+?P=Wl!t+2Oro7<hGY0d>r2wzhY)#Sy1ol7fEcUV&ou9ma<
z8-KbhZv<b%037Mm`CHU_MWv<;eQFrTQ}GN*C`5r^vjSowz)p*_{YAUCDOYWq2@dm3
zE!8Wq{nY<X5Sdua7=|PnxK-tMQ<o0AFd6+8yQaJXT%YsRv}ZpQYeFR4!s?mgGyMCX
zzaa170Yw!JQs1zS?|Utu+*Kvfv_qKB0FGz?$i#G}c`=GTDfW>NZMz}QZ+9YU+<<H;
zbqBEkWaO?|W?*`{!k_n9HLj$}dw?qv^&eOACy2q1OiT+pG!!SY(bviJ05ln?=pHJ+
zP3P1B&vEuZb%MVOWdRr-svpqC=yP}4UVfPxVP;{dh6YHfA2G#Kf1a>Q<dpCYmbg@Z
zg*T`32Uz0+{EJ5SN}oI#d|iGMY4m9wXac=6$q_U%)vC1!E}xP>f6caB1s_LL$WR(Z
z@hVRL^21GEnKQsMiN?MQgzS8=)pR#-an=L~y)R^-ukRPj7Oc3)lT+&$O+cV(`7T)2
zfa7=y!J6Kn4zAJ}fa<%^UPUW*l|uhn(*X~z3$un>a<O4x6qL}l%77|n3a8y6p2ozw
z=_2tzG)Lc-j0dIf0MP{w9tf*Bu;pqV8Dn-Uty$5zTZ^-R0MJXZt)s;K9)0rNfe8i;
zz@Ud-<EO5Tv8Tc8C$ll+NyP_nRKFo#<8QPM99@{Su5)GvjC!d1zgBP&_2$t>kC>PY
z$q9`a&GQ4I5CQK3LqT8%&J^&*>M`nQ4aR~(8(@I|r4p?wYKw1Z?(U2<1ONq4p~(Wl
zNV;gJI8vXU6@INRXn+CP6iT=|TH~led**Ww__lg?TwcwA7z{N*05rh-wBjK;WL>w|
zN9hr8wO!E~12UqH?`YoMGI?&yomTJyvMpTk^a{x|g4O4Gjn0#GPY2Wi(0~J&NQdSH
zPpzE3$xDfU;OL5xQ+)F6wgY{QSxDPGVgEZCI6>~Ec6vr_)xzbb2plwku(~2i=i0bl
z!aS1@r9m%PM_eW4RdgK$K((pvG!l=NL^G&VJtpCS4tSG757<z*PKg&}+JWU5#s}Oi
zV?zlwLIrLNJSyL8!Ys#;62q`g;L5Tvs@8mMD%CQ<um=SsUsRBMxSDUdz6>-as9iX@
z!hwrV@c{6o)Wzu3BTnuPT4D=XCI$*7s4ZvofvN)}Z_9{YC6z|%<p)1U7X9uLACa&F
z2liA2l(`8z79g2HeRFhngGnAG^M_^j5q@-36p8;tGHn#d<BxiaRC~9_pRigekbnUh
z^(oAtlVp)R^!QivJZkBM!#-?Hn=`G6B|0mlOE7?$LQPC1$`oqb;Jwr5OBvj)zh<}y
zF(kOq%>Ux;9iS^~vWDRsb<|<UwrzB5+wP=evtyf`j@_|sJL%ZAZS%YRJo66znR#da
zwZ8vf>#jQIo~l!4tFWtf?Ok5XzC$dYsf@1_lY9}Mbcj-WG}T6P&b0ywpinP%<GrB_
zsXAKk%DZ$c8%?ooW?hC?gW#X};1i~=obT%5o*-ThV>kyQUtWUjYSW2sl;UzyqeJc$
zmf-}*<^jYtm1xq@>Pj;LS1AF3AACU1b0Y1hw>nh6azemSivY<S7EjfG3kDL35os>o
zyc<A<y^RWxuUEFSsQ?m}Tg&P|1CoDHvCs44L5A*jEi5fA@MD0mA5`Nc?I9Il#MD4W
zc3j+)f&oy-RnqsUu9tMaHed`n`s4T|q%Q_)fdC*NeLMzy9G!?Q=Y)<7PL6LIfino^
z6!|6Q0Y-fT(QfhqF|;^ZlgM2fItX@f0L%|6My_@g>e}Q?hrM^%nlxOkVbRqPY*0WT
zgim{kjGgN%1m>Q9cAJKU@c?hnD!?aJJ6<e4fI=OQnNyaXVe%V4j~k6d{BWN#)ygLT
z#V-{RguvuIIZe?@huzW_0!c7HUn5mfBGtrG_lupK?A7&iFkhj`$$__qChr+EAa19)
zw=mhlelcVQhOlai0b}*gvE`RtbJbn0)0l^|IjjYDFenH=rXuNWKJ4VpV9~HxzUM^D
zaGKT0jxq3?ohrXnixnGM>~X2mTUuZM1bi<DAh#B!9A|Aq;w&F=O?l+`vJsgr0X`+M
zO;wt+al=ChRY5lLSOAJn6_I6qh*0XQsk2v>@ivkE;tjDzW`I0D-~ji7s1QJWCpSrx
zmKZBVI)D2ZxH|oN)Kj{iVy}(1#nlSG4F{2rExD>!m}GGF{nFXMzNfQT8L5K%^_P&X
z10ZV<5aWIYNjAb7WndMK(dPr~M-c%-pJMk~Tf3*fgl$t8F95O-)ksCZdc*bkq}E5b
zz8y!79O|3Hogn}=igyOp-KTi1e%7~vL~DQG$1E=n{;o{!k2ZwIi4v?yiV-AZ+&9i0
z^`iDeA>bJd@TL<l<{5WO5Gn-|eC_~ezV;4c4G%yeP~p+2U${Q#q^tYokn7QI)k-mN
z^&RN&)*&VyxAPUm#{;YrsyN6+5$_U))N`DXEwwCKO1MB_eqVdq>jdsLgI$F$DOd{}
z&;;p|A(jwCuFDhp+<UEFHrGxcCxQO)eTK8y^PFkVa)1`pXC<$gTy<Ej0HyZJ`d4B_
zcC3OU&5}Oi61#8!!8Bl^V&IUJk8~W*rA`uq$5ph?Th}O6w+486x&<7-DH(6W*HDWa
zltOXeR4^Y=RiVyvDMpH4<q$uT6oCx^cx<LR4=icUWBM7AG6G50YpNVn#W!pDdePfV
zdPjXe5DCivj(pTpmu;IdimH^4W8`1wt~-#z02ov&&eq5!0ZtS&&Psiwlm;5SLV)!(
zNvW_fpB*hzB{iA#gi`>;M&+u{h{fw`3uT4tK<L&g1>EmPUw3Bj>qC}m@cDwXf$QU>
zdLwf;^ksY)I@gznvD1H+>AM4NLJL0sjK5Yu0q_^^gnyOaM4E1#%o~;}DOSMx{Ya|&
zt9K=glSKmWw!J~b?>T)_Y%E{iiuMEF>~Ml5m*3Ry%GJQTo_hEYP>S0%+b%kjx#A#y
z7Yv(?8OAAsa53X;JU>mBv4Tf|0zOX0q8{yC|5{y*#G1*|e1$j!2GsYpf3<~aIPCYg
zJx|_ky?ZExUHz_hd`hv(jGnYMu>-5)BKm#FJ-;4P1Qe)%Bp5}Z=JR?PEZ1w4BY3x2
zR(vu<|E+w)!Jg|A7rblMNqd@a=TIYrs*l0|YsWzXXFC-k<lI;3Sy!`OyKg~##+5~v
zgaBr}Oe8PBRbXpOht-n4FF740O1U)Daw|~%#$D`4eDkVf!J?s@V&Qgu%f=XJl(_B}
z`~ehD6iX=5P`NqXkK=iHTU4w=ZeiSBT~}R&n)e$z<3{eXJ?3ejP%RU0wxgP$FeP@H
z1^eb^Xb7<1xxHl7neF5D`3qs_Wz%c!Cfic;qk7l<37;@}0sKajSx|10_V6v6PO7hU
z=EG9#C+aM^?l%SaYu97t2GKgHYB2_)mjHkoDS2%5V*EG^d&S_)OgZD&uQCny(c#oR
z58JGAOwZHb2#Aj5b)Vd7T8t@;naq*#IDK0tSIW@o_@X9}3I<jI;0I7pBiAgHWmd0B
zonU9^;xW5a$UoeB%eX!9ZJR`lW^j%rBpdNVg8=cB_}O1+*HF`)S&3T_wbZ<Uf9hR^
zK5AqA^cH_yfyX@Kb^TB((W5v+1+ohcMDjx~#zNmc%=$Ab_tW~rs+5<;%elrA+<4-O
z<8v%@5p>Y6Cd$Me5<M0U&aNyG1>hHsY?RH}9I0NR@}57Iyj|=o!JbLz+-KYkCEi83
zSu9glt3F~HPXU$uSXd_HkBo)zKp}q}_E9)NFIn*Q?yK-NSKY1Q3ujb?Z?l&<K|PNP
z^Vah|$*U8C@Li<sUtv+Q*7P_7OkdPKMD(hoZV3Z+2vqzobd+|ySy7u8UXC@^KB`s=
zEwuHzZL}qyzNeYtt})#P35|`=kCC30&f-#7JUr+pD<f;xF%S^4t(M}OvVD|}z%>Qx
zI>)r4I@t=TMiIUcU@^sB6O@PZh3e0t<y;PF&-+)dCmSu6_wPsZqqw6OuOs%yKf2=6
zCW_fuq3oPS)K6KI=MJPgCgbb+agb-%sGEPpV1AjY(u|ZXnKC;NTfHm}n=Gs~e?nrR
ziWjA25Q=49+mjwt>?4~Pi{fkYW;p``uN(4AYBtm&g)*0kct!_Nh37B!h}ogUN)6`C
zh<t2%F8KLd(P1UmSf!lPE-^2Pv?`CZDv~lQ`Z`LUToT23{Gt|@vrV2oOg0~01vj$L
z>!?gF{d8Z}&9sh%oOG_R`pK%3{p%d`_PKUvs4@h*Hd|`s`x8rElegtI7~Y0`^_&}8
z%C2K`Z-TQ)NsmMY?J1o}4}0ldZvGjIg@t1n(d+DCwxKqif@A2}IJ8@y-bzJwp>v)2
zGPYxsTV#!h(G<2Mz4XhS!|uzlOGm~!@B8|rA(#i#<04G0P`$pN$zX56^>(qJI&F4g
zA=K>Y8x|uQW3S<C1SYrFn2;tPng|riI+Q#(0Pr~dUJjGq2molb6w9vPZ%)z!0Ljf%
zgP#EWXTXg?pu_+G;=tWPpi&3u#BP#vU9Gb~7i9(jzNHeD_HvHHq!&b55-)ouTY>uk
z01&@-bbl8mAgUti4+jGdZNOb#pc)YZ|KJ+u<Cj4(X_kwzJLeKT;!vLTxuzTqE7oKz
zKHlUUJ16nXR#rRy{1fR;riq-DQ?c>apIM)&O(%cyHw09l#SHExlmO&U_yr!7=hF9I
zL9((a!P8Ib?T6FARIcK@G3Eo?sazq-CSR%+A@gVn->2?U8A;c$kw%zvC7sFi#+p=a
zCeR?h;$7<!s$9)dnnjPCE0EqnKh08##5ot(s%Si+ULw!gF1~FRxK$LnpTzq$E95rH
zg<dZpNnDTt?f0V;ttEF;P)PSQ1oChXe(=}3-WiqNx4U=sVgY<W`St#?rTX{#+lWk9
zD89&t6)JG81Eu#KFKz{dn(Y#|cZiP!hf>>>Zm}TiCENPFe7P`=*d5cn&((~=7dmmQ
z==7GX%4hn@;;09xKX$pX*y3*e1@#OIm7Z30(gt0O^e>Puyj(uGCDL8b(I#S17<MVa
z+xs@E^?dMDd7)5_!5BK%do2$^Hj2J44UvbxP7Rh9WpGJ}JAZW+&}=c81xNdqOunA>
z4IeWs&JnqnUy2|=aR$hxbeJNmZw;r`Z3-cqiZP$T$8}>|Zro?R0x2Ux+jZPB&+<RI
z3Id?ih4)s1IgT60$;jP16hiFuFtC<(pM%=vwQYIeh-Cn3s7@Kwicyq~PEb0+ptzK*
zhj{ho>$_sj+A$=(?YXjt-@M?~h=UFg6d?q|x|>N!yWCSg^RNoQ!>-ZzJDNctYn<~C
ze$ei!$eykXP~g?kg$Q{mpugdjlvWK%AS@1gzj=+{D&cRWLi|;ak`9l;1p!<t1L}Hz
z6V%@l=c5b);9tM+-dvd-`4@H`9DlR(__wEhfbJ3)IR4&xqD&na-<t!)YhCYq`lP9V
zxsKkJHq~V%P@5C4nJQI3?J;6u(kH-zN+wZR_U;(4L$*YQsa1sq-@bm-=Ecu6JP#K8
z{L#A?mh#2Wrv26{)s5X{gczfbgKW+5@DWFZ<f?oYF}m$=YnWnPdAnjddM#54fw;r{
z^?L0|v-HCRs4?MB_yI5j8+jr@LO9x@g2<#_GE7U<NKD^|N43g=S*MBx3qz%CXP8eD
zg#yR=>*L6J#a8ywZk8`u3p{GIj>d}-HJW5fB<XJW>B6YeZHykjpjuv=cE*gq?vEaq
z$ogY^57GSct1+P^KaLQMLj1!XCm|Sw*ye#E6NaLSrd2K&9YZdjjFFnQshKgqoYTcH
z)9P1T+8{Nhx9z<zqx>xiHV_DgxDJM8E=Abf6wnHtps>q4CfHXz_(8*_qQomoFFFXy
zuBdCM`qvyMn-o5@`k(I4ZNy$D=7S#X5VBVFKGu*~aV+c62qpz5p3w^xYoFuAs`h9?
z57qf^s8|?yoM%f0MIp+{`+%i??4qfa9j3yhg{AEBoqdwJzquwER!E`k&}H<9KMgC>
zmOi=#ab>7o&>z}7!jTS3!JOXGg$Gl({f10#X^e5kaWp)4<U`zDH@kJo`jf?ntRNA=
zGadS4k(xXunHwyosmTN-JnJl6(bwi?dLuE4KInl61mB({y`gVHM?MK^?lC!(-LelW
z^J7d5)1_Qxu+X9T5rjz}jT5Dqv!d$~;Wkp$3RL*`S!!{|#tZ2=>fmtLxnKJFL5AVm
z*z6qfoS+vwot4oTaPC*;^T;7nn2Y76_F3>J!UCO$YKdo*y%}d&V_Vu_LQrC70^Mh0
z+#FY6Feb{iK){~h?WL4%&|9=ig=c4dVa0igQd_b?b`8c&Ojto=&h<l_SWJ)kU04V(
z#w17RY@qMIoVj=N)IaCjM!(J}WI^|FrGGAY@KI!`{JO*-^fhmp-$j8;{!#F*gJmcv
zY1?=_z<KpFc>!Nv>0C8%;q3m~_pvkTn&%R}#OGc>d~iv57?M}_oUcFNVaCBb5j3l@
zR1R7M{wx4n8g(Plp$GApmC7W+Vif{?hMAXqDP^nFudJ*}X3{8bx1y|CrWJO`Qpu89
z#2BUOS7W3Oce1@HpOdjSJ+ns09?=kfs74d@6%XfTL`tYevqT9wPmJkJ)f*dT$&0TT
zm0qV%8%+IGzYgIZZQ8n>PJ+Ytd-@J|20M#4gy_DB_<jzkV=XBdR2X;+!wH2P4E1lP
z0}MPNGpu`~ddm~fhWnm)7ib5-HCI_Dp{r-B*cYv#Z0T6J?Bdv+dFc_CMZrpv#oQVa
z%4td6{V#7ir6kfzVN~lU#ZTmyg`(|62yuL?AH4W&>b}EV@+laZ)i=+Q$;|8Qn_0=q
zev=_;xiuQ#QqNI6vvTN&I$`Dy=u~V?zI+uxp)RvpYV;($792zURDR<#nDS66!u*x&
zt5CS$9yX&57>7lp0xn}s8$0LpNZ^QMUjre9sj*wx6Ew4eLw?_p+pltce8)J+T)f!H
zR6{%SPtVX`-OSrNnJk@r5udy@($;FZ#WO$Nm5Jq2#}WkKQB5T(1k{0MdOzWhbOzS^
zbMME___yBgKijZzaQ^>w5EJ;N|MnmzGdlz4-*?TYtM1yP_rQagE-v3QYb}Wqq=>Ah
zg(G$;VuSkP%tdk(rRYZG%8v`>DHFfW!HHQ;tyF7gn^tFvruSO@n6cAR9?C1B+jnVS
zR~oR|UY@gA*SmImb&$Q~%{;$!y?gX%U!UH776>4K^85X(aHf0AeSbv9Mk$`fmsof^
zX;_bDCJJ>oACAhaojn_p-;=0R^PER(g`6DSwW>Q`<h6Iee{>9f!weYElYqO<<m8uX
zK6=#=jUO?LZe<VW#LMt}uK@Qm_mo0fP^-o0>XUji79~6AxM_T60bl&_Gxb@;`_!dP
zj5PAqt|)j<Vf~qfN8F-^=3e=y#3)GzEH<R_`K`OKVkU$Rdx$gq=cB6-GXc_>Q-NNx
zx~H3TJI{uX#ZsxxN3T?erQF=a=s4@uN|_{+<!+Fz77JdnSejmxo{sj}C+b)~;o}bU
zs27^nIt&ik?#dj)u{6-IxR0gkjzR$0jO%8~r$}hckm58!Wz6kt^yyq>HC8KV6P<A|
zGFX-s9<fs4Jh~lKmhV0|bw_Si3VFVL<6~;^bDpJkqmKn_;kL%t@U%aq?;70p6pyza
zB-OTS*i6*Mu`{W(jlJc2)M=&O#cw{udhJgMc`R+8CCfV_U8L~U+gJFAEZk0GIn_*`
zh$H5TdX!s^5hzDgyqxV^kp+0%-K#j4@*hh~<<_G5&CCiI-aFEpU_HIX>b-AAnT_@+
z61Z0hY{H+5ro)ztl_5VR;xum06S_*VgjbGGnq|m!a6RK&FWa1Ik>ErBu<Tt{!5Jv4
zZ(e`bRah3Bh>vu3Vhp!EGBProo{6n@Va9yTZF2PaRM%mWN>y7MlQ@pgnw(4QopsVH
zA10qX?r|*q1x2Hv66%uGmpwI~xU0&0w(^w%I+SR6kawI&`<6#~I~xvMjB8HmGFRFd
zF2<BEm(|T$j`)U>!26S0cAQO=?tD2J`S>J_-zbA(`)*9vXFmKG60t%T@@1!2+MN7M
zE6bkg?*<zS-SNL@y6IsN6k5z*Q4!FPP@D2$;P}Xn8pcZ7%<rBp@iU`7DcGvH<Ok~U
zaSMxfR72_b^HM}FP>DS>x3uDu@&=pgC+yWg%^D{bzC$?WXPYfD$ej^ktKvs`VhOZG
zv5l7!D#CJofAkT5?B_=_$l{r7x>%il{U~m6ht`+U*=l?ik@1YqYgz+U?Y$kBUU4Xy
zA}ZJ+l`<TWCUM<vNpj;vC^@Y0X!|zveViz51y{r{x(aH<KE*tCoQR=q`7?6A&5>cp
zj?R&$QBue0-1c{UT*C`+P;O*Z7k6Wl!L{MoHN25|Hwau*DaUE?#taju{LPpAMoc_8
z0>rkAq^j&uqQ=~~2#GmWFAjp}mDe&UVOBaY=7Fye^ceJF9H}@)amrUXehn2)yLeJ)
zU2h|!z8d|34F&k_pqrZy#kmoAuXHKz%?9-bUgB~lcUfHt_>u^@^xmMpO!nw^+-LSL
zoB??I#<|6C!yU;a8VZwA@9vK&45u3_O+i6hE>{HuZhecwx9&3$48j_v?d(bm#z@YZ
z4IT9-00_5|#WbC>NxHs<v=vZp%M1GveI%MMHx9Czyun$`&QJcfkBbTm1L<c0{V{4j
z`6gAMgiCc=u`Z~zTJmj1!3OP{4Id7js}1X5YYJ@8XFrE#;os^$E>XC_`;PRinR6U_
zwkZwvMnU|99yJkrtm&;CYGL%ch~l_??ZI|RMJ%1WFPBKxxkPA*n`<^ve|W#kSkMbo
zBl|QRM>Mh?59&MKE2A>>bi)J!B7i9SJsVb&IZsFT<+|)sm`svbUSz$B^%sH{xmc^a
ztxPTD-WsU-_WC{SQQuBjP)w2<sL8{YgX%0OC=Vi%;Iw>}cN&6=P+QZ;j|K{C*E7>U
zKICjO;I?H$;g3|YzJH-WGuB0PhITv$FI0$|B1<intM=&}7w_!DLi7QE697P<_yM5*
z<`xl{4FZbzZ!RxFh?Z={y@7*x?$-0F8Xg!>9t4Fuk!`MHhbGxcX%!I&*Z^OIfX!~Z
z+JHGk5%d7~=x+$XGSYQ+<Z9QP!fpdxE*SpYPp$%$_~b$uM^ZZlD2yKA62zjQnbVV`
zso2U4r5I432@3Z#R=M^)LJtP5Cw*$q6bUZu!pvI)z8)8MmiqTsX-<g@R5EOTf%{(&
z`DaX_5Pf`rxc}p2FjLHQP1P4CU`pGB;KDa+LA8rvvijxr%kMvp(VR5&@Y6_gP+?Z6
z@H>(6NO)h{1FcZstik*0VSYKmKVg#HGax^tMP)OI#MM9GAb#VQw=B`$W)WgHnb7$S
z^-^n@pJISaPcb;nNTWK~zajfL)Udxq4jPcXG(!*Oh@tBcpqWtkQTF4nU(;^C5?qAn
z+uiXAeZb)|xWPkw5K_0YCL7W*jW`Th59FL*b)4P33gzGUL}-b$n{<=_>brj&;9}2+
zyZNsPL;&PIXh6MRR%1?Pz85zMERaDIX9opQ!87{+Uv%(3+S*z|r%geEi{C}o9UsLG
z`8ObcK1O-%ezJ}w1Gg;coOhf#G>34aO^plu-U;Tn73HPDfP3i_w&WqkhiF^%;Xer3
z-*^KGPVfgf|8((2{pqR&!FT@@n>_E+3F-GmD{zue)fF}$1Snqv%T$QT)>t6myoe~v
zRD;!r&J5Q++)wY;0ee=cMs~@}#c0I7>xZV&JB44;a4-FSbJa0=6g4gT%JDZx1dp^t
z96gc$UqSl2y?{gU^Z(OjXPlVDrX&lA@8OIE8u8(`09+g{>qh;kDg&j53VTjc3NTT&
zarqAF3;A1W_!d`kOZM`5txNPBAB%`q70)0o1x~4_Pf~SswaXV&H>Wr-yC+&YwBly{
zX4@d@Oz#4xk=%}LM1b{fTqh)Mqd?xE;r|xMrBeo42bzahtZ|I9)4D9QQ6eb6+BO#N
z>Mp|AV7Oatw7lP)i?~UHZ|~1ErJ{+PfCM}*vl^bQ7PfxNotg61IIWgbS+l}3=!5ty
zVrS3ZB4w5%-llXL8L&QbY;DAmMx7~j<=#|zb&bKTv9~nIK-5j`l-cP|E+h30+f(JO
zGrmeKlDJeevg6mwiHPyfs0E4@J3M8d5i?d8s_PI32^ItBV8d>vB01vih4o`=%|10a
zCr=@1FI#ypJ<HF#<x@AtxixTg|G*Y)rwQDhI>0E0x~~l#;&u?Sb&t8#;4BD|OV-|>
zqf<H>DAS2QY1b<sJ(1#xO0uQAMTe`9(b8T{fs(Vtc$@;-9NuoX7ii*dMk<=ctVzS4
zntOJb;}19|D~_F-pTB#ge%!p!(w6i*UsH{4!Wx`+B!O*6&Iz2J+zv(;qviaHJK(&i
zSbmBOq5SBO7DbVzI0xnz_(}7Vz~!(0;c0<KgWzpnF;1e=HtcG36QlygHX=>^vxh5G
zNB2dKt+_u)sdIE@h6kHo#ZGwCZ7iF&C?a`mf!1)8-RV``RaAZNrT7{obx7xXmhJ-<
z%2TnNXh((mF;>zTy{&ffJ0?1|*<rNyQhXwpo%#LgDb-M@Z`&tFdu?;h_>6;W$F&kr
zboM{z!dCQf=%FVh-gyu$t(lY;%17rfB%TIy2K(+O(~pcQ2giKUfvVHH`mgx96muPo
z5R>>`iey5jUPF&ceQM}kr~b~kH1DTbReV0$d&Tb$W?<7+En+_$+6zaNTg@%bsm5Wu
zc~{*7`pik4RJAgMsd?KKQgF{Ui!#o+C?4Nlqg6@Ht6<c4_I0Qic@;+A)E0+X8!+F$
zZ2TZ)ERmg2gLWf+u|GVh2r@l`CU6Z@%}sXP)jf{KQse7_KxM{7m34;oY8i1mvdr^0
zAT>}rVl(MyhN>#b=axo1^jbjFXr8iPCD`0Cz_lL<U*?+_Wx>H=*H*#7eOuCjY5(*q
z0$joV3bmy5+vNud;hlo>=3(746Q*mcO9sqt3T%WU$4pFUuCG*P4)lZ6f~<Q=Ch?xz
z)UT|dz6AKtIlMnFitfcNBKM}gOs$X_A=1FkSa*<dHKp70Qe|P2!N&0wJa`7rX|Z74
z+MnR?AA7;#qn4J%Z;^^MBAW@Q@+y%KTPIgw5xYP7(#ysyC<SHxh)qiCk`>d<D0XXy
zz6!G(pceil%r5adLPIc!G_ofztXq4MY|}tGTJvxqSVyFjA3QGio$%w05=_=ZTlzrD
zj0E_PI*kCq7HxLlR2;&0SWZ8UB8^5LfrW31QS!xx$4)fzcMHc4G4=}0;<|*+gXAd(
zC*VE>h^79eRL%2;WRVnehl4K_!LfyiiDsXY@!qmY#4Fd7P03TEf)?*@aXquu5)bZ`
zYYcm&B8L&y?ZZ)Q4D)k74|^z#i9+~gR<-<cHRe*JFR5~<Y2hfkNdGoXhyId^D7f@1
zpLo`C_~q`&m0zA<;<HP!Z4;MOsC~K|nSbS|>LO*Pz{KKQxJFXT?I9*w$#eSBBhogh
zV1WhcVbxlAxz5P}8&SRU(RN@Hra(PI<~g@o5W_bs!*rK~3b{$s@2&!;U1Z>8^pNbO
zD<+u50>cQPn+Vq@61z&?6jQ~f-a|?evRkN|*xSy_^I6e|-~zzk8^|O5yeyIHztFG$
z&Y9`|a*FH^HT=IhMfO(>{Qu%9vcK20|I;ZlCMH()e^j<lzo&9xe3&5JMvm>kL*JyE
znn;KVst?Z%cI>`ASsS*6fbtCle@Odf^irpecJY9!XT4PK3AW&C(+r3a16)D<Px6bd
znV==#4DTKPj6bZx*t^c-R_x$?lliq$0pB^(j^pxepaJSvj$yFuyR{MH(ySmpl8vsB
zmBwq6pPBV4pF3<$A{@bX-F5Cy)m@q8&26zq;eCxt5&OHR!WLYb(H^mU)zHo1jK}4;
zX8oJCInRJNIiwpcH!vb#7;K}Pn%sb~8T_$&gCht^IIY*BS+UYbHfuiIs-XjfBKtCg
zaXE+jX*~KQx5S4Qgo0ML50P&Mxd!xucR8bC6t580^(jznMkHRO{T*Q@$N8v%v)L&J
zk1;x2my6cgPx%6j@w~5_uunY6Q~N+e!p~pqt3v42OBz%~`^5r>3m?P^tKCRMck=Z<
zhdTV^!&e(`+!P*5l)N&%M%_z>V6-zFbzWpCg@q~I!7Rx16z0_Q&hwtilGj`U(+HDs
zy_w2sTI{oS#FWw7%h2N@R{Hkl{>?n+SN^ZMY;1H^eaELzp~FF?OMqu!x@Wx!x@2C%
zyG@xT@2Sbjk2j<zgmQHu<7mqz(=x5q5=;}Zp}iw?zV6Pqw;rS9H4am!QtF6#S!AIL
z#fW+MP8XVf3&0Wa2I=XS=gIr?b(@CNWRQY;pvCfF5{(d^%qdt3m#5tUl4ytbJ6p-d
z&DOJd#Jo@2V2oG|d3eSffr~!9D|n2^=;@+PpJSWuD0G%Fo)cKx^Q*NiAa(ycbES{H
zD~ppAdOB>}SdNYkGa?7w^E|+{cJB0b`*J)D%oUs<8u0%}c94M2{`<t|Gw2S||0Gd+
zv0tfVrX3rW>v26le}f_L_CyJgwN?8Fk~d&+N{$Poj8{QRl@%1NS24f8zI*p}c|R}b
zfHEV-oHuRCQ1||LxgE?2g0j?MOaU2(T_FQEq?iFQsDuwuH@<CsZH6IQOrn4NWQrjH
z3RNhN4>Cy1kN3|B5TQZh|D5F1G_`queoJ2bSLoU*CekRGrR2A))>~~U0g{h%ytcqy
zT9#v>DA;u^_+)rUV|@g->v}=W%_&@d6MjSSY7`G-COY@*?$wK(+z0u2o=Aw(=jB$R
zQpJ6;2H<*>lXd61V#N#BBOVWKsf!OsECB#PJ^BOCoKJRUR{zs-|JH`Dpg3Icoc6o8
zxbZ1AkmZPc+9Kwgse{ESvh49FQ+$TD>j>4DK#yj`dZ=|F6FtBwcyy2bBs{@qMskWu
zCP0-7qe+k|D?Wq9hynTYm*g*91O;S=3GDALg%JQrcm<W^CzViiLqE&biot(hdJGO&
zo)hbGBHnfZVZ*P0qql>VCyyq|`;i8!s9m3Wb0S{m-gWE7O70`&dw=kL=G>urWW9dw
z;s83BAl74Eh|lxEy0i4-a&!=%(AL0`kNNH72|aO9gb(uVUq$6_Li7914Wi*5p#$kJ
z&<$pN{KxFS6R|%s{vIQr&qUvkCH(#(hR$dIP69=abLbpVg!x}TyX99l>sIXbP5@iM
zc}3<Eh-<CHnRMj#*?kKqh-JHX87#3OdzQQ>izYZUah9nkiqR-g`Y56DixjY_SJwE_
zO#PlHSYzWv_yeDy{vh8!C%;8gA?@1cyBHMiKSKLIoB;BK?<VLkV8@8X87ZM$LH+Xk
zEZFGtc}x8e?eUWa<zE|OX9t}|{uGOhON-U=<bZ#{hD5`CbVrBRQ2?`(V9*l<RQg6a
z2B>88{E}RFENp!V78=jnUZ-E(Yg)s4%6@oaMciYG?7Q;|<t2CpWh`f+CmS`M_bjB(
z>~hv8$8JjQO|2@lXHCFX^{g0Ws9_IVrIja2?g$WT2|I8aYKs|~PS-y~HEn2DeKehm
zarXx9V6DNMLG4$9vQ6hfDw?(E?T57XPf^PFqZvtx(nDPbUsDv?Kz3V*IF{-?_#)V{
zZ28jaK1H$XE=!=b>ffB0%*~ri-+nkTcwDaqNo1c>HJ?QfpEPfh?9*(d4$9f<eq~T8
zR>N#iJ%6ug(ctR3JNXz-Re|XDY}Q#WncHgN`&m;X{=}%RG(J?N9w*XRVABf<OTGku
z&Dqx6ZQds$-L~~J4E$U9)f`n06;EZ)P@xB(vK~}0+~7inKW?Uui??^*uui{;^W*A>
zO1<6)-DRMQ)473Ynx%KgwTMq!<?CO^QeZm--4Dl^<@B;(euHHxDPUtQ;z>L3|IAe|
zc}sPUARG7c7#3keVz-0DO_dk~86|pD^nhedInWU)>v8v0sZ7VQclCf}8<Nf|&{7ip
zbkk3azV9x_&}8D`Bzco-+FQox71OiWJ8H@j<!(GXNiyIqRJIFRUP(zx<8fOK!UPxI
zrZ@WeJYW7b0QDA0469=I_1Dz{{lG_yu%cuA(ub;KRO%PySJXx0+FEu$=K2`1Qi&g5
zmhF_vg_KV;MFjjJqOMd~MD2`P`%@;h862^AtH1bt7tru1Y%HYGnCvcM4;v7B!rna+
z-I)wm6%8+sW^94ucs)%(L(0)=L@PTD=bfKQ57N2T4zszYSLh>`5_1TxKyWtHn3Bg=
z;5!UJIwI!x{JXIvW|glb<J0)Rn7}drZG+^0*&b$LVf*`OQ5pI#>lL<}wF<-ue^wun
z@Ni*Degw0nTFQwmiB%;W3>-KUQ6Dyos_l+*Vth_wPJ2zTu7wTa$Gdo#Gf=GFiyU90
z3v@^k+!ct=!xCv<DH~j*jY(uV(Lu&T#it1D$v-?E>7+w+)OH1L>>qC&(IS{iUvU(4
z-QtN8(_tWS!j`*a3JM@ovS(JjLwU7{THc{>OF@U{^%;YErBaMv$AC44azys<EgR2F
zV$Z6bl6{fDNvPzzF{fGHL(M)gps`>zr#?8cVnXjqB4A=v#e_~5`solixGfxk>_fZP
z^5GjZ9#u_<t!a*+O8na9-IjXPcXMY29rzxIjH2>mcIBWls;ETl?V!q2+$Z@tojhD>
z2-`&*y3dN+bTG37w$%e?{W)N1o2X|?ZJ(CaQ#<41ehBZVy3g7SSE;>j2PtCt5hPSy
z2&k#l&WbtNjOa7L-76_!@NJd1&GQHfX;}nBjJ&N48Yu=Xi@E3(B8_!Ht;`qOvFpI1
z^jyD`20JnqlW8hEGpG#3lIZ*PDL1;y#JoHmJaDMl%+{9<T_ROQNR8wXS2u4+KvZ#=
zwaltK`)nLMqAGcx%iy6AHP^G#38M3}ERn~p1*7+@Wc4m>6B{dXj`hqZb&BK#{iLxq
z%=Ia$k1yZk-=V;x6HPqhhRZ0I`ayE{Z~%ZB+TpigFEvsy3&$x4Evpt6mygPBu)&bZ
zDA12(_?3wA&a&HndOY-$8r))!@odi>iFJQIDYB2VQGkPpfr2M1!`TdmZm^A$9PF)f
z8bYDJm4yVU^eJj-4EN-ce{;@Z+dx6nS!CC-sjAN;t3~2=qdVTQh9&uD)8b3RaV!TJ
zFubqrYT8z0G0dR#G1X>RO6ZP6!lM-HR@pTY9$(ycm1V|+&2rHq9F*36=hK2Lk6@32
z6TF?xD`5b=D2I8zTgfvitz$vmP{0|hzE}Hzc8lFdkW%?!e`lz~a=GY6-dd$PA=_hQ
zXLGzE0s8A!|A+Sq1|Jtv89n46%s#PIvt#W+4z+IA!VXhuJY^W!2{yn`zO5No;~U5a
zWbJh~5;)xT+69cIRPb%f4jCDY3oi~XbbxlPQ!oVB!9+Yx|3U^)+eGQW&B44V_Rr5c
z;J*+$KWD{VpjbkdMqEfYU*1nIJ>>WqnPDL&7>^Z&YJWm!B*FyW2>6DTf2oZp-&>T~
zz;1_AaS7^Cp9YN5><Y@Ico~~CPN8k$YoJ>f4D~kJpBR(eV&zbw1H&H3W2BB?duVRP
z7!OCCTJJzt42<DN0=R2SPUhWVv=~IT+^#%aA)tXQbQ5={cj!~|8wl9@)u77|Yh-tX
zNJlG3!qGbarFk$q*0dT6PJ-k9<_Qy|dv$%8cLNd$uur!|hE!?|PC(ui(Mr<2zx~Xr
zf%!{m5AkF}x`$n!WnXnr#&>~{lETfUJnC(#>$$t#R)4-F%3bx;tX56(!xMqT_R79(
zV?JrCWuObRj*z%`tWvOTohBvaFDAUsXU)N{gewqx5s(PJ;uJq)`eVj1B{mgwAc-P<
znfdTly4K$)+b?Q=%5Z}9qPPLgu2;5U-8XkLP1cr19p<i-q|;LNgg_jjdEcBtpL?Fz
zK`1^_4$09UgG+8qXNNsrO<H%7Vn${T?1Ne&Nxh6$jJaV!AY>6pl_uyOKCW5FCJN;0
zf2%fn$x+~g_MTTUnOf_7gX2IHY>y2iN=u|U_!`QpVD@n~X=8s{_FQeDD>2o9Q^%zd
z<zkd9q4a2L&YR;<tJ!_%&Vmf(YQ$*N4_5kBie<6&)w*MZJ=%JDCE;SFc`@zv-M%A}
zMt?7Y_QiS)^+RRPG++<=BO!(5v|NYKh8&D7n0!H}%MNvQlftvwuyA+!p_MV~I9ZK3
zvs&DtmBpmim_S04W9&o=Gm2;+`O8I5N9**%^}p!r|IRD%zwhgr*jfJ}bV{A}vi0^L
zlIOS@)L@S}8*;Gcrii~2o}ZEbsQ>qqr%`)w&1|DDPM3-^QE3G48EDVhLJA3sCwL<f
z!E7E;VLG!avnnF+Uf3}L6mKyCf_>|@4C^1eeMGmyU+%BhQ`Zwqy7cfqWO|br@ptPZ
z%MNZBaEgEHB<97)^g@yOfDk^giO&~Fj0K0@*Penab}_n~c+LLx(xWhJ)tTPAmc|9!
z_4YO|sbg^1yuhntq<yI5@PflF)$8fz<|4Xl9WUTUu)k|a#Cph|LR#Uh<IJ9P##!xG
zc%o&C`u>&MOH1>&mbmOU6z5YRb*umdMeo7MEKuvWS)$Ai+6ue8$;0X{r~4}KA0+yW
z7FoHv`8}tlg;$ESdr68Ca_Wd8x94q5l33pf;0?7TKu9C^3?=lmNh5Q7gy&*G%_%>A
z8m+Ex(*$=a;>*xujwXSNm0y!gRtvU&uKE}N)v~Jq*5Y~E0w2&1m!IVihyaI{!K!>T
zsNu8Bf29r1rOE2XB|JpHHA=onpiv~w`Xr@5kq%xUwe#{)MG+T_cPmJ2UV@HAT2?nW
zWZ5!Q(6jahDqK!tX@dkmyjyTiGyMC?uqUZO8U&*S)^X<2WM_gS5qs_Ksffa`;EAV?
zt;;RecLtcL7>SNVdaZk9F>?trx*@H}4@GR(8dYUiD#<F35j4iNJ=T&TGi+DV!NXj!
z*T(Ae8IQvnKVO-lh-L^r;t5{TFYY<%SgNfO(;5^U@V^FVlGKF2LbYI7x(7gY8`=px
z^+ADie}K-d4n+=`Ht1dy2{v%IYkoLjfESnaEQ2Kty^%<;5TaDN1LZJL!>zeXakMpN
z$*7(0oz)2bggN?sdR6Pk;L{lf+_Le(M#&E){nT%lv;4QsYbrI5*3a&`<yWVl&nw%u
z)Po3eli{yD^U`j;8s^4aKO=>*N*S${dd;p1<%c2?)ZN7Z9Bjo6ia}8)AlST+Y|F8u
zg|%Hl2uO95G3w`yX1bWx8Nu~6_I3OMWSfvQQe*rL$#Sjm#<1T{OiooFPE0Q0XANR6
zZmw7!o6DcpzP0rQ1QZOST#tPyYJliY=sS{!ac^}#Vtwn@k*`7>B&k9}(LY|5eBnVg
zR+e1w=CdHKj~gN>4H?e3uS4ZtW{8dAR!^;0?47l4Y#@0O4~<BV%C1^jM2}5Nmx^Jy
z3@oItqlwH+@<?a<CURn4HsW%+y86A9(gCYp?YxQoK5)Zqg>C(ui)JUF{!|fSVY|@9
zIoAQ%h;(cbDN;pedQ3fuqbBBN3-8(HtAlLA%FrIgTIJ&UP%$r6qb40fkBxDYHV&1h
zi?&;}wyY`r@Ggm!R(TYKqFCsCjNlJ~%_=8xkOeOE$8*8n_MbW3p^be{%Kj%ADAr7n
zJ8zup`1;VRwbqI?sZ)BurS)9<yN9Q&SGa^poc`~r9trMmZ<-|q>$C^eg}a(5*DPDo
zlV7yy>-JxJB>2g{-@bt?*roIATimbqAg>a0JGd|NxS4?7w?z~cqzI9@`)9=2*f(jh
z+fyfktvv2zopshc^>H%ou4ujj+^@^LHtx#3Hr}d6_Q>l@EX*KXHO@Rl<cAu$h*z4E
zwSKX0V8s(F9jYd(owK8W6V-hklJ!~|;`W&NJTF@|WCS95@Z3Rv7TwqS_$;T#+b*Cp
zEx7VpN)>L|cCMFXKy?b3YN7sGZCwYaQ!Y^dMN30D6X{TXixC^tDYgdQ?_f(_2=P?>
z#{4JoA+_NtEE$b>3;ON|33%s|$y+m1h^5X;lCw8}y5{stgPpmWx}+T&Mkj84pS_wj
z&Iy@B90-`jLLH0C&k2S1wWmPU5D~*R!mjoQxwVe6@-VvuQS+tCkW?%=)}!^Md@J3h
zJhCj!EjU+cT?z~O(y9WfM4nN>P4{nKS_+W%+6%sqevu=V-n)fo6>Jlt{mR(hLO4<c
zd#GD3Jan!D%aNaSn5@!7V3dbO2W9uF6XQ%iIG5*FF`gM>;TXl8kIc1ba1y6D*t24>
zNSTP}+ajxubd>fbH4-8>gUPby(dQ{MkY;lKxfmGv@2yYmr{I01H83PLE_ma2ha?fU
zvsQ;G*jEo5!wMe<Oa=lgV0iJUijWdd-Ng>{pKdVXkNyrO^v(LM4kpUGct)gXyBYm#
z=j+XrWyrb5Q_Yj<FujM`13MUdK&L7eXV*@88ZtwOu;V}Bn{k=owz~m)`ufJYm<MU6
z!)d_JYMoz#0qfX?3{e`Lp5mbgo#NwO&;FG{W22Dhf~7j?z7~{c7Gry4xb~sw$TfAo
zTh!18EjdRu<w?(ym)WwX=Q^49irJz^2rFbe?6efF`}4>WLHcMZ+}2#5^LxsuPQI1n
zo=@%1zCys(3NySBF&Z<wSFSjHgitO%novv#3u)0(YHsBA@s&uj>)dkect3wO!ESB|
z<$NAaFUQPcq=jHRT~O(=XUH;shMcUCH{Nv7tMrU|<A~?}v_vOa{eU%p+sPd4f+g-M
z{m-ADUqGgXTnA}i_y}%c`pkxFKbImFbma~pgIMy77f*-WUEC^gTn)VO3b<ozob4pn
z(u1u0$Wf|95x^g(7cO*ZTP{v=@Xr&!{na@hmNbKRXi1i{T+$uvu-{Bvz4mDCe7Hpt
z>t0Nq6zmqWtd=+?9kGt`lAdhaOL1#ieWe!sc*|i)pF@w!_`R!kZZa_nc}cfFhra8_
zj4mJ1rkN`mbFeaX`ePK70?x%u^LqfpITA=?sEjZm%|aMJRT46R(q$oBsfCJ()LJ^o
zNu(a8Pb6re(kcwDe~XXgv*IocI5Rm%+DzCu7Xk1576rA=FSZ|Y0q1B$1Zw*n36=1~
zhy-G*Ml85PO+aFR$n@+3sGQaqANGgeqcJ1@fk`Zoc2da2XBQpUD>V&{Ilxx+!B%YF
zMCkiyQ=}VR%D1nxued;=e#Px=lKYj~4m$OeayPr}IQUvpL13a7rsda>s0a}43v;R(
z?LZ4JzCRW*LImC7z8~Q3z9ajogXa^uyxawJV0gs+h^9*_Sq3SBSU+B}Xs2VD-y`P2
z4%_-OG8JNFuuU1xvKJV`u93W~rn!0CvqYv68BMDMPM^H4?NJf?LbC|Y7H{T*rK&mD
ziqiX9qOGv+#i0Fx)8pm-{O)XSs6)p7Y@j}g)A>+F@zdb9{W6`<Qj&eNdVlfRq3Iud
z4*|BT9G{&4!wRdn7Q8vIaF02|6M87&;%SP@KluWh5MBvW1C{ubu4`_an<8_2zZ@Xg
z5$~_Y=$3|E|2QSPt!w4`u|srr^y8FDc*F!=P8i!?e9DJ;%VapEp&f+sp6%SX6nC#)
z`Ukch9sS6p^``^Bs8iYA1N1!(6~bxQ*~Peb#QUAAZagOspzLnTKlubS<}h8m-Nopo
zxN2~vHeDFq*|34c1slNX*Cf{g)L*f{3#@FA3H5Qcb_Bdsg~tiC%EgzIb3+#KBJM{_
zzP9krb$#YFXn@~44DHwCXswYkDPTnEmDsMU=un2eO;OWGCdxc7=y_&DqNewDTX=Ed
z`aBkPDS`c-|8Bvz{rtA!JYtGCeRFgi8<)<lwlA&RhX6}MW{xF@Mus;}ncpyQ>Xd=+
za9Jd=pHn}EgrtgEl4xE(w&omc4rA!1crL>&8IKn$OoCIaiDA~+@pj=&>8e=tFQ&nN
z7eMg8unA@Ot4$~?)BmTzIXD>pYnxC;R`!2bk2ziO7N2p!8MqvG+U=L5YSh*UdhZo4
z|E{h}7H0t;O&w6i6d!3BP<#3!){6s<YnTlIxfC!XJ@RV6cPADxo$`$krF1hHobw=P
z%Q)3Z=@`K-!nmQO=DB>MWZ$#niBoMHy|>S`{ruP;;r!XFV_=^aoG<`U5Q-4?$DSvz
z!^Nb$MJi3RfAGjjiYeUUvZQ5#CM=%fj}w?hcE^U$XAR<Sy`5_+o>Gm`?tbsIbZBOc
zBPOErBj)S6sFn2Ro^QGM6_3*A^+1cSJEqh7C94GHLs0k7J1dQRG4jMvH=%T=s}M-{
zqGMgw1;c2bE<ATXo;z^&6X?5zPbXCac<v{9B@b);yvB_B<IM%@hJQ<<d3b^^G=`hW
zZ`|j2ey-P?LBIa-@`7PU(n(oefBsSDc0X)M1S;-=R4uz3-?s^+iyFj8PZK4s*s4U+
z%*{b0y)Y_5tPgTuIb6QbPrmuw7C!WTLI0w&f3&;!ljn6rDaOc~fm}aRmq@s%E7@-C
zCBg~cKV#Yk7tNpBV_~>P@5uB0KBL=<uw2J{@kIB0KzEBXzpQ^j`!Rn1InIIlWjEbp
z&fY4Jr|a!K<Gb*QA!>U=_P##QnbeK@3Fr9FKW_huEBS(<T;*?stGrjcUC0?fx~Kle
z|A}Xx^EYMgfZ1pxysZU#JDe{rus{qdP7c={UB|b<@$ZmN<6r9v+GK8Un(l@&-b&V_
zbGF?sDDQ5hs@3jpS~B*m8qK`Ao1+EBi#k?`R#16>&;v8HVBu4r>Z1(kFL9V#3)VMk
zWmR-{Y7#7{|3dyhLCOF^4CKq7-1vhfRsWCyWY~fseky|)NsxgT{Bed?^ed72{m^F!
zyNw}dggfNtHYPcN-iCq2s>G(V__opP{F2g-?#ZvZ$ptZPU%c;Yd9G1N7K8UGX_^#R
zW=5M6&GFATsUgXFmoBvt?ygSaU15AH&qy<YKA<V}$Lmn<z8L@kdP8j~0B+J;tO6y)
zke<=IEs*E<H<0@;c<$vEe}MU?4E+sOV8P#X?6}%|)&yeXg>>_Lk?}T_^ZxR9SOF9g
zAVoUvr*}*=f-4hE-`6v|2o;Z+{v<R)?%C#i7vyh(BAg#0>=-_ub!gl(9&#?OpWNAH
z5KrqaPG>%)-=dE(?q(PU-QT!aVIp<xexaYv08;Z`Lh~O<3WRkUdR*yS4y)D41mbTV
zyTW94xYN7~?NJT_fn|H&c(6O{Vf8xMU8KH7In%%o0U0iY8dYLghka$3<e(9rn!%@k
z6IAp=Y~LI_H^EB36qA5%-`wcnj7u7B$%A-nOoq2j$Vsx0wxfcfC$$Sgr+~bZZ9O`t
zyheF)0KGdjoE~?pKjbx%6sGyP=HT-~K`^=CqA$Vl5q}qiyhiw2MtACZe$+k!uXlB$
z!u_+t7hNet!9fqPTQvWAWO^CT8C4?NSG%t*VtS#UgWs)xmLp@+molk3nifs8j+?;q
zVwqe05OE=DRewbjQdY=(Dl7{Kv5Bs6cZ^^7L$EW;k^gM3b#V)<lUUUa@e4F{2~hN^
ze5=t^M@smt35ADL)Yk`8c1z2-RS*=Ue>P_G;_rX!o@zQ&chQ7u6_TF{n}m--Ussn-
z;*r(bl0^rcbV%o%%5^hVO9uYzz{DPeW^5RRQE!W)QzU;2WA3CbC(QhGD;)c%+)W$>
zD+lvfYUW`j(DlJW;=SYvC%vrajPjS=&H#1pjN?fMl-mZafR=1Axlpg;N{p3Ft%Hml
zuW-WRLb>0RK#V-rr8J=BusmAk$}i}xlcG&sJ$~gn`Lj>_u3PFBPS{)stWj04rA@K>
zkH$EoLbSUA<Lz3I0P_zWW_^eM?=Ct3vAXb@-jH*L?w^N34u-$2{{L_08^1@)|H^#h
ze+Ek!Cg5QCf5!>%-w%ub$$W#Elb!V+qE?-FLMJKr%{sF(KoO;O`}o0%k|?O|3h_hq
z=@28M*o7k@X+sg1&X^Kqg(mF?;mCK9GGaww108gP450-e#fV^_AZ34~Dx?v`QU37e
z@N6ZUSzfHAbMK5G+n(u`mC3#8QlvG!IPQ+z;&QI~ywqM_acbdSqO!;i{^uWJu7A8*
zJco?^QRuJNCEn~B;7iY%d1kZSUg9Lk91|W#FKxH;o>qFx^LKTgv46hs4ktW7+kGO-
zrgX9R`>~Z?c#&?OHL7q>v2u}Oow<rfHqig5j8{JV!FzwCO2-4x8-1}5g39rHiA<{E
zfP35KrfF3R!TN@nXR7k^jgpFP#u>n=$Xvx~kkC$-?-)8`oJnrEo#&xoRU<=HF?(o8
zk$k2U6KC3_&8=(nn#tn5qYmgV?paIoO3v|N5$JFd8|dNH)%6kRLHB0->I&ca?1gU8
zIWGRRvnvYs<?$5gD)NWry9C>ly;`;F-A~ck;=oik)@*M{R<65+?(1=znMS)Do-&Ca
zWKKsjmqbxFbRH?6?{HzV<BPV(`P4288CQ)45ml7qmZ1G1OD1z>*W&1d5!AFA^6?~@
zZC@_U4;7sb^Sam%)YTJ5<`WyT^=w)dA~8R4@xE-GnP|vivxs-pIAf(>bb=Lq3fZc)
z!fsUt?$5qGcWt&B%edn<^L?j%V(Ku=Sm#!g4<|8KE@||_ahwq7c$%$f6^+9Pa}ahp
zTY&}*7_aDfa~n>e1^NW+FL1utP1H+>kN3=;A#eP)JG7x^%)Q|QmSW77EzFT+T_DQP
zkUW68a=?LCl_hl`xI}Ae!2T=pbMu;CKR~WP3;`I=QFbZ&2Cm&v9_RG-yoG*SHFisV
z0FqvI^p)nCg@~g1GQDovC?~O7g{5t|-gPvEjY;}bT~{<CeTK{1yKEcGwUx?5NbHd^
z{bK9g;@Rplx8p*i>7;1h!Fs;+Dx25yY5&z#WH5d^&}r&8-+OCqZCuFw^DkrG64f%z
z(b&_$+v8MPoh8fDCF1vo0po38FyjNl_hggyFi$^2$X|oszp3CD)}DC~;pgsggk;@4
z3G3O?>WK6GvXawE4+yWkyd-H{GMfoW(*WAueK-ulhPESl-jK@Fxc(V>x!3n&=?$NR
zOIkH|BMuVMRaZ2wnemITIj<}I7v95yE`JN4lgeC0v8k|pzJVw>S&mKc+ZBgzFl~MH
zR?8tbapih?%?3i0npfXmpKeJ+%f#zv(hFqa-Y91)jYo;mX*iA&J1i%&`zFQkWim%o
z*~wv0?-FKky_g+;w1<k>t@KiVz>Mzos%w)B@ZZ6;7M2e&-+=d^etcq4=<6$%!B%Oo
zHb1um>$8i921Wr*7md#0P9?5L7wrvntFzQNlFQ}*gDif!Rhchuui48FZGxi7m~pnL
z{~R_oTb*M3xnsPedt<_-!BV1%*)d2J^R>t03aen2q|b$j%P-@D;`Hl%gAp5xWs+vA
zy3RL39a*=fB@nQ=_{0i}b+6TCCyRk-y@HvJ_^0gBlklyqU+pnm{VXB8(VTlpJ@DKg
zjj+ID*a<uGfRo+PYFq)lXT*v^qqMbn7O473L<qYYJ*x^l7j1@Gc~LSM$w)?N!q^|%
zw?&iM1|z1-N&}ssrPM?t`P&KHr3IIk*FY)aa#lWX{nQGDgms2oDhoU?7|$m&64<sU
z#a;o`qI;I@Sce~M+>fVFiJ*B{dz4p)SQ?4-Dp?YmpqNN^I4kVU#Lc#dczKqnD;c`f
z6Exd#T)qu-d;CqaJ`%g+XjUnJzi51^?%DL_0AapVqs{FW65oSA6X*)lH|Az9)#fVF
zV!qrHIIXFdt_O<Vq=|unK_xh<RhO{&)!A}-VeH1QR95ONP~n@9xlTnXK@iVh*H)yu
z)=YT)IU!pYEmQ72R44i!t!kkWpJuZz^CR6R1Sq-Wh6aP+C{wcS?Ar+skaPweMARmS
zk*jj9A0tcYcZvTG06#&%zAa`w%@n@qc)@~z?(nZHBFS&uyxmO`aeh4Znn&F{F9J*6
zZA{fFoW5wUnB#~__8}Y76zi`(8acFi*eax6mgMPG#*AsQWJ#0Ble-AwwR7jrk3aqx
z1n7fgD=8`2xN+lxWaCFQY}l}6ty(SX)@^mefYqyyfBNZT)$9eMyU!f5Z8$xd=pj)Y
zdNS>Q+KQk8)E7<Oal8tKwo|7Jm4_N>3Pi9p-xDZ~v;@;JQ6EVrG)0LhAjXD>4(R+>
zUj_d_=V=1z8+4Vov=pXrw@mkHaK4Yq=|ga1pEiGfs`G4kqGRTOS$VsI*zvVI7m?U&
zzI5|^2`qWHF;yhd`SjEDFw0lWaYRkCp`1_pM|(*7m307zm6EmsTSvj2Nn#@2hijou
z&^{J-s8+4oPm=B2bI;{3zxd*d6`75`nMwcXw`=<c^ea)VXlcu$rN=4tV6R^7?zyMU
z{`=Pgt-<pl2w)rg@Fn=3!KKkn?lMsstM5q~Q1L9sawLV7hzJA|lg`s8i!dNwOxo~J
zC^E7UeI%NkReA9wxUo;8x2E~zm5A42>yHm_H;J8;3uqkZOSyb}*eh;GcjovItmrCL
za+O&skBzA!iB5UB{S`eKlVk8C`_Sf7V=g2}P$QWo(-vdV@@U^{B#c-gPc_JpM?Xof
zIW^hX09Lww{d?5zxo6qiZ@;V5>fJk)TAKX#x96W%lO|PZ(V|7$w(XWKZLib`N*%X&
zaSc4ylP}S)w0R`Cfe8y;HhI*jQA94&mlHM7oZ04bgkWOQdCjN{Va;@_^Hj{88~j8x
zh;9zuYH!*v{Y4~y-Y&mk-v<4o+wxpMxu<XD%n4rKC-8@E1-m89EGWJ<^)X}?7Fwl{
z3+w(~wlP&C(K+<cAQH<uZ=Q@P?CohjX;;Bo$o3sL)QOD}ed*94^(&DZI;Lqq@W4>>
zBilBlu|ZP_FwD_q*+kvChi=<;=zaH{`}*tZ?YHTs1Ju3a)JaMa{aMnW!Mj_xu3VLD
zO8ql=S-}AZw7L4~jwf{Jux?$)C!aiu21v%d@g|Wwkgc}7(1~EOWmnP*qB!l_r~8N{
z2qDF~$5<z}()ro5QzjuELI`V`TfK4PAj%HfDm}4!b$vGWj{P#Kh!lypYjTA72+(4;
z`3!fEQ%OS&y-!|92Sk!^0xO1DWfuAsioEpSk;<Hvl;_>XRKYf#y!-v6zAL{Wb|4!V
zJ%}?zXH7iS^yco}gD>e5Je`J_J!0{OnO6{$ndI)IwM3+AoVsyv+gYO*|9VL0;-cb?
z*I!rm=36DJSHF8u)ftCWfBC(#9c5*U9(<7g1-GeD<DjCV4n;*BiJG==PoK5rI?#5Y
zqzgC(L6+YW#nGrM*qtWJNV^8a8M;-YM(T(of}v$Pzie502&H;Tmq^BV(M9R-Upmcd
zHr@;N%NPz)sS*+$cPnmT!G~-_cbh`2=1S{@BnN>W@v~@BvaU_2Y2Xvga-b3KWgAn~
z;T0BK{m3Ja^d|<+dJV0hv(C!HdO78k)P3-ej!BoHd3f<fb@tinnrqbN%>h@v>#kJ5
z#>9yhg;?H)8r`9Pf7Pv9YK-YV8#V-Uf?=l9sPi<;P?|i6Bd(%esbn~P;J{J~It*^x
zaKq@OrzW;;IjhNvt?ztrP}S{U)jPTV_C{;AUvtCGokRLx_x#pB)hln<^}G37_uqe8
z+1E;~QEHY_|M={)KNlCj{L)MB7Z#S?e}99A9@?Keh&N#Qk6xG79|$J>BMA>?1zIt*
zXV4wGYSE&AK%j$d+604#JIJ{@^UUC6TGPZHi7L_OM;`@21vDgdOk)4N_o_a9(j6ee
z#Fszvh<fwQpfmJPnhmBn)aY6oT~D6@dPsdhAVgIAWjxi4&V1p8V9>C9FrKs=hzwEB
z9illp42l4vK(Egc0}wy6(gk@3Zt_Cv9?c76eZDizOW3GN`G}?%>k*AsB~6EWQ{r;$
z74(qzoNOaTjPR!<6)EYgUtdvvKTRgqc%uAOGakF`it>9+-Iu03&-H6Fcn@E2c5wC3
z!9>Cm!-kDc3`vX`0+)Q1q|#D%%$U_`e3c}3+coOGYx=E!{P6`utdx42qy(iFE?Kg$
zXU{+vI`F__cI@c#^wTGI>C&ZX)234XV<n3oPK$#X8W>%jIw_Hatr&>bVO^7L#;kLp
zNnR%%(q{ZIIzZpko#)O?X;`#x`OcAim0_U(6{MO$G6vPgi_id`d~!f%m2%xjGklnC
zJF6)~t|m-`*;u(}H$0swqtL>F=j0%$WJ~~ws~b0_B^_zgF=@0mhSWDYpJ`%jk+UKt
zozbKF_w5^BScpV}G@i&U7x7A(Oo%~9p<=;;fcU%lsU|$^=}&@K68)obNF)+xr}$a|
z?2I09*Il@N_q?}<-c)7$_<Dl}x12x!?5$h-Em$zLM~`mrzkfvX^o?a@pDkXzX!7LA
zqeqW^^2sL~G+5E4OIf2vjehsLTKr66vyB_=e(|LQ7#b}adXFAG(nErUMPI_i2#SMa
znk#xm>M1WW=C@{~42=imLOOZJj(`c%+fSJih?~St>8B4q2#9vnRe(?cqE`Gmue}zG
zD3874LWpUYf6zo041H#vRxZCdy+X&1fn?{nkEFa1aEK_p^it3bh&))H+Io~L3n5xK
zpQRWa)2-;mSFA{hGxV&huTH1YBfgPNpgzl9(G7VJNN%O>O`aTF#r=WU3lRpY0**oE
z=p0%$xSWn}x+yq;zUkL5SS0if{mJ{F(nvao9HRRJDoz*Ic%nO{Cz4PO$`xi>2;--`
z24U0TPibk?I3sN@+OPaV?T@cl=f1&htBkHYGm#jcG&CJhTztAxrzd~^u2-)`?b^+q
zJ9o~!dGpq-TlZM9fq@;@@aRAM{i>H==`v<atwC+)4C=67QBg;$7lvg-DdXUn=!G-}
zx$PQk$>zvSI+IO2q|HH0jI;_Ip#Yw#8_Gx-^VGJqnW1^hYM0RrACRy)jmv5Z2}`D>
zV;X!8(tx=VVi)d1ePd~b8e~bUjHCGnnlw=j8l?Ke_8s<=X%YS$Z_B%iuBG$YEa<k2
z|9FPv*j}or+PNv(^4^YQ^0E?<F!!n3)QL4nVnf%iX%T{sWg?KNlN@wV5O|lI-r)@{
z-u>m5iLS{TCWRqk+9o=3;vt8e_{uA{?b=noWXX~@-gx89H{YB&b8dP0(s#dDdhU78
z6%-USsddcqrS<>Vt8mOa|0JnaDc?+Ph!rx%1U3$tX=R;izMB;OiAh@hmRZ+MSSWy}
zv?2qxNU5Y5@X@xyoHbe9Ayx;xBRC|CG9qjW`(H+NqbJ-MP8<UXc>8TTIg^9Xvu7X^
zVH`l?Xm~NvT7-Xx4i;Aa<(H|);WKuK;E$085yO|@*3PXP7y}kn#<3ESFgKh#oP*aO
zKFu|#aYowH|NeK1g<*P$;_Oko6DlK;O_I~9Rjam7w%cyIt*oqU-oNI(b^CAMShQ&I
z4?k@9VHv3=Cq7T-zM<6FN}Y4<wP!v2a6_L=Zq2S?i;m2`)yvBx+i|P{lne#%ya^IM
zxQ!c^8q}<gFl}ZukB>V<#bmPPH`+tP9K13)7RQ<>Pk*v33BPMrR~mgUeO}C9=zGoM
zM(U(w8)2^L%4gGg<dOdU`{#NWAMIQk72tbf%R!`33G<g=ty$f0n!iEY6LQ^|Yfy6u
zkVZ~Vx%=*nF2N#_e4O;QSv?5UNV+-lCr6&vuV23rBZx{6yL!LX^&2;l^!%w(?<qCq
z-FN@@;qlYH{r0m1YSn7oxG~*~w!n}ZcNZ5Yw3pND8uVtcL}i^W>Bic&E!ed5nTs{g
zI`(d<e%G!*yn+a@VM9QD)~*f2$C)#|9AP}vcpX}R?EW+DGEvE>G!GJgr2eqcO5WLh
zCP{qLr>BR-G&9lhnP&!S8dHCo1<mJ$)oOK_(P<>?a8T{Qf$ID3;e$;#xbyJPNH(2H
zPjoyTr#?H<|8a7wDV1R%)YhHpYL2uX*?fmvk-Dr+EUl)=lbMToX{&p>R8y&+DYdcL
z-EA){dh*H3k2>lnyLRpR_u5bX^UWu-X3c!$(MKPC_~Dh0KmKUZ)(O+v4L)JX{Pj)c
z%bB}9tE*c|oFwhT3<<guZ#Lu&sY`S#8ZDSbi55s}zB!GbeU^%aV`KIeR|GPPzxNuS
z_ud=GcbEivF3~UQK%YK&V4#r5iH?|-W@?Tzp%B6B5=IKG)Su3Q`(LhBrP8)DX}Tt1
zhrgs_>JL%vZ@&#jlE$-f;~*-6!w5!<`1|nTH~wECI$5QO{MwI9B0__Td}`ATr)@GA
zQL!g{`XJXJU4oEaOstf2XuZ&{{w7ckP)#n+Qzj2O>x8WhF2Af}$Bw_e;))>~Kku|?
zQQd<M+E`Zh>GthQzx?vU&2Lpzf39CJqpVh~zSkW3!w<FKw{lQg$3(Hj8N*lQxFLW5
z;B9HsCOFROjY8-Zv-;A~;Hs$HfrfM0vXs}x>i)}-fztLy=#w^FVQ0dgMN9Iv*HqJ{
zK?s`FGi;^)bPjz_)KktW9D<O5=>8x#5OdynCrzGIG8E{flT^EQ!5!$@|NJNQ>owP4
z$4nSLy#N0pqLZUE5s$ITtu}IGn#)TuiuXj_y6X1Z1K$8~1e$zWn6y%8nY&%Pa%C|)
zzWj3a_19^6hrXe8v3z-`!`>8~h?`@1QlkY$&uP_?JG|d{=eF3o_2)BZ5?AV_)DcRR
z1-P!eKis-?>eQ*DMvbag?Txl=w;p_O;}1TlA0s+4UlONwL*A1#wDf*q@~#>4!m9=}
zE2@@+SAb-J&CkGNd10XI*QaP1O(_kO94_-rxdut|rQIGj&Q|JA@><QBI``x+pMPE<
zmID+0-FF$}Y0{*C68-5<!3lIj1mV)K&^dczRPOmNk#szBsH5`S*g1llZAfE1Rxyh9
z1VVRd_=z<TgP`exC`=sLs*q@1l94b(r>0I#Z4QV|bWH1lJ|W1u15O}1VS(Ue>Io4b
z(h#KmE4h~G??R<cRO)J_uKVn>qg%89QpdV=>t28T^`fGpty^Dc+jiqY2OU^gSeQs8
z{K<zo_R<j(!^7%hJyRhuFIp%zw4_KP7$T3Dc)=!;HBiVW4ghA6>C=N*lELLNfH+LO
z)lx~!A+=I}_m^znp>J5Y#|i~`!aiWAAh~i$7q1*~Iyw5JtiA-d7u*43>gknde~I#r
zDxp=(KvfJI7RC_GdI&Ol@TQvrp^0HWaUbe`mo8ojJkn_qcCszDpwYee_8UFAU%!4|
zfBp5VufF=i3opF#$}7u~ZN!M-M<0E3@7}#Hx#W^#jyc9Zuz?|Uw1nx{Nt;^7HJDVT
z>@hEh&B3T3#Y{yB7m}%!Xz(sO0z$Kf|KJWv<YW<hVn`#54+bj7cz!EY%KD{s14);x
z6)H%n!z;bZJW?{BKSq-S#?&E*OHzd|d1rXeu!bw6Leej^x8~R!8On%I4JE?ts2g&6
z-RmwZY_Bt9u}+;jmn7SF-+lMyn{Q5;GKC1x>eZ|7PPUdUTlP-2=FOYCnFvGbWKEck
zq4?4xI(4$Zq#{vQ)`Wm^p+aJ0a$mwSCzc+RHQ55-5@xo_vSq2*85yKeK{#IV_9bhq
z);##+af3?lGLNb&`(WpB8XT-)Fw<LaYkDJR&knSbcro*)(5$%=vl<f7L#rW?$A=rc
z930wcnEi|ON8+teHb@%5ai+3Sty(prEZw_zA3AjC$tRy&P*6Z>*C(HRGIQq4v17+h
zojP^fwr$CTuX>tI9PbT-E8Ueoxi8ED+<9za0EVpiQd_Yibz4|5F<o?mrU#L}%X(1k
zOc)?;OXm)WY_wkJ)i@`XDC}pSrH&m72lZ_r)X4RMfzo`l{?@TPLJm26#n?0mfWY=*
zcA3<(BG`0(JoO5x&6;Kun*wHqF?EX@@Ds*^W0Y0nG`ZP)x+aOhC9ee#TF>mzAqDEo
zj0SL|4tc;Tl)FO~&Gk{O+8N0<Zrr#TGiE&d?6V~Myqj#d-g;}-u3fLZ^2&=Yx`;%i
z(f93Mupq&&4jn|Ix1bTLcSHs|bZ#l{vO3E!sQ}Y(>p}4vX*yau$enj)<isL$5O3;O
zIAL%(Qshxad|+15Ebov*R#`bL#98$A<NpTrYTC367)zjS(G2I}7;wOtTFDa&;y`xe
z$@hV5k|)sqhlGXQ0Jd(8-nCjL9AVbnzW+WQtRAK|`a_wk@Q}rjR=s-l^O9}Hjvdpc
zO?&2<XI`2-d3Lf57%-qmj~-Wi`|bbi`b}|wPnPPwweW*PGBvT>-8#42jZ&SB3a=Jb
zt<$P#Axv4>pmQR@<v;;sM8U8H>kw*`%0J{#7)YQs#F_a=r1s+x9oj`&qi=E?EeDLL
zBk~{DnsB9&m<!7+DjeICB@s;^!Un}+QQ>G2$U8&}W2pewwIXtOS}C%L={0K9_yx5d
zJ+|F&!=&-!pKQ`(_J$2agPu`p|7vrU`jt{wD%DP)+Us0BtU#zgNT_vV&?IQ}L9!-o
z_%Jdn8MorMDvu-yc@^D<&L=^MOZpHiaTtsv<pt(Kal&L}DdYV9_kp8TK%i89Lo!Vo
z!1^SLnU+8jX*0NCOx-oyc=H1{h2$^C%sWhNR<Ss232Tq$lwK=*J=8SPOnA5))^rU4
z#Z%gjDyAnAbsu`@>izb+x~PZ*>j_GY+V9XU->oN#oBY>KscTxcylnL7dRoG?`#pRB
zz^xl&!Gb_g*6PW;{dOGNuy%|jZ~k765I2d$sGOyrC0k^nuT7iuf~Cc4H5f<D3(Oah
zrRQ2p6%xgP69W3OQkCD3tdfBQB_(MMt5>fe_Q*=!9auJUv|wqJK~8|T)9i0rv?kr0
zVFIWR#GnzFiLiu7rZ%4@gCb;{13eK@Vr+^;tQgAxh`@J8NXAByiFe_i$eDUdby4cu
z+qX16_~%ut?zeH{8l~1L_39U26s=vm`oaqj>fiscbIwV?K5o8;7H*w24%CdM+Oj2x
zaGEkD6<tEph-g~acRj<4tP5?{tP~)n%y{YARZq^K?Z<fryl~8-<{G*qM}X1-#5#ge
zHqyFND$kL#(twQ^CE7!hr+@r$Dj$WBc>ha+13~kp(of68^!P;D3|1IZYZ9?sv+uv3
zo-yhJo8uiz)iW)_ak(<^si+UYSGB^|LuSz;Bqe+%FK|%Xs01+{CC#J|r?lPnuUc2X
zH1*s80|pNr+UlKmwr<<DdinC@fB*a6pMLtEJ9d=)<j5my9Z;L;D!n?w^O-dG$AQX(
zOi=H>eS_$0x+m>q6k8-^9jw?h-N9UFB!JMCLuQLyeKIiy$ri|HrR|3;YUPh|-JNqz
zC?=@~k<V~KR7B(~uTVIIfgc8MFmdGR(}S0V3zT!&A_WIlJkbezOXSRIK(ZX#&0&w`
zW^mXkJR>nUy_;E&bhNtj8ew+Hk-328PM(}ntdQt}M#7g_^2%iH*kQ!q;kjifH|o{M
zk#PuOR7z@4h{Twp;zY-`7yhJZOhKzwEpNQ>%8y@YRl91{owYZA{PD+cyz$2U_ucp2
zbI<Oq|FcBmh-J$X5DmEaABK?bq=_)5;J{0RR_+Tgq~t|9XwoD-igZBK$6p4uE;Mi-
zD=9Yp+4K#zsDnS!IhZBjA8IY@I*ODR=r7GVl4dalv`#txgspO;MrBB>O8vdkxXi;W
z1z=B%$OP;bZFKoe(i*t|4J-FTB$!*+9%VDvg7!Kbf9PZSb3EO|r(#p2Sr}aN7=t*{
zh2MS0r6y9su2%fPzd%vc%0@O5>+pIDb`Lu{wH4Lwzjd9P81wYbfdi+XdFEp`-Z=H6
zk0z~JrB<Exv!DI!lv8dfD!Q_$sE*1kF$xfPmhIW3!YcxsO~Q<!K(H13CF-15jUx`c
z8*MdZwdrzAY$d9I;g}I^9QGiqi5EksnViCAII62l^Ad8Dy9AlsbF0c@(<v+rRgM6_
z%dblU9MV^{GigX}x+$&xa;|Y+az05rM~*E6+j1YQraV*$P|1{AxSZSuAh+sFub%`(
zPJKg54>d(O^Vft8;sdZ?QEDRiWGWUluQ5h~o3f|(q)!I|^wzCa#*8`o>8CH*vgMk-
zed~1Z{=w?i>(;IN_rIqpMauPhrH=Ug^Fris%V}52A~;BWhX#OFF0o%OrfxQCgr>h7
zVa4}w3`tgDkrTwJ&ydAnfjaQOKq+$zDb~EeB};-_V7wzIxN4G%3^*W}7%Hu&4sC_`
zMh!}>GnFYg&<#|p#a)2fYoKPxqG`||jg?5pb?SsBQktI*MHUi=wX&?)I2nrLF*z_N
zHu;m-|8x^TZ2K2UG9w|WlQDwlt)AhqLIM8zY-T49dGyg?iNx)9+%aX!3pd^LP-*Eb
zrN-00TD7_q6%8mV>Pf3nzH}Ils$3@42Ejps3j5e}OgVz1_&gJ!+}UBWhB})EWEHTi
z{nXNdXcK(>bz0s5GKwa-Sj#zRyEE&UXc9TzOY2N!3J&yFj>4o{lqQgotweoju+6g4
zG`gtI#DK)G@3BN476a>(*u|2uRL9_(k4JH|5~Gk-<;{I~kTps~sW@Qx+BjhJn0Cht
zbhW8$)v6V|{dTX?(q30wv9eB`agRRw#P;oTl%ju!5n&lVyl)~=*I(b!mWI5LKGADW
zn~pUeN2v(8##yt&7_eV59a%Nf(<*?+(I(K!H*3Z7tol^r#u;Q$*&^HJm@vg*bAmr9
z|B=+IYp(HCj>4R!ByRTW7hEVIW(1gChVKEcJUj)*Ru6;(oiKxX-BvYeJjiNwb}t_d
z@&wj&D|BVp0tbj2e*#udLqLdX=TbQraB-?^q;oAO4xpbOez@?w^UfVKXyAkiO~3wn
zjZ$lsdVTch2fzAiQ;QZY>esJtqCmh|Czpwav&g7-DCy^euj*75vm`)iYe&LCy!T$p
zs!cn9LpxCiCWiX!uzbtnd9-#kq-4!HPJLLjCS#zGSr)d6VFSR0Fssw+Sk=HvQ-X$-
zBj!gEZotP%h6x1PEjK!r**jc+jPn>YxOz<@hj=1+xGW&%b}(uS+)DHP)9*Cfuu{H-
zjI)-S$X!0oJ<v5gCqt%x4I5UwwDg8SgRVRM^jB}V;l9$+nMFnSj~zRvXV0D`B|}R}
zIw6Ju`xY6ptnYN}n2|_Bn+Ko}$SR13?B}mdhQ$ZNt;V`Cr~u0TVurZ{aE`g-T8OZ+
zB_(LmV)yO=N{X^o*Kk-Mv~`+$DX9u3I#_lzHetB+)&+{p77m2eG%T)5sFg2tcrWC@
zW7*Qel-0c1WFtr2ge{mfNvAv7l8GMIYqu_CU5^p-He=GANP(`_0+AuAt5Ff7N+R)d
zYL{QWWaY}yB_&T!oH$V_`q%NJk8atu>jI^!$OVPr0nuStfo025VSBObF?r6ROnRbY
ziAoZth?BI<pvC-sj!FuE%D8Do)mV;~&P*U2q0+RpcAKy4sN|XZ606{{!o+tpEtV|>
zVY^7%>X5KmY{B%>;$mzMm5EJbwQ<M}&^<xIKnHP5H{@P%2XOz{vjajYU7hqLTiD>y
z_3=q&m4H00OWNc|%-hVx9Fs+A3yY0#u2$`(pPJA%QRT@E8%93(;Dh7GkKeLo!7H!y
ztXAz9rEVKA;F7Up3*<l|>VR`>ePGUf-aBj#neAhBuxxEUREphbL=dsuw7&VQi?DvO
z79qJOh=s)2LKrdWSpb%%3#G_TFUTuhzB1j4mbMQ_P@aiSLJYue(bGic!0Zkyq`YZU
zip<J{KeyH(NeQ$Dj2Tjs%#=HonfGd9Q&~L_L^xP((u#&@Is6i~fb+=1BoT*J;t!LU
zM~0Fk#qr0jixB~0ed*!D6Uk9((xk~ffBfSe+qOOS(b^lQuT-i;sViT8`S-^hb4#B-
zeQMUMX%Z|g={y&x0xg4_aU4xfWR<GTCeu*0({d@e&RPU-1)?}~t(-3QveHwqCl)M7
z>7;Ty)~S<WkVYj>4Oc9WVstehii1Bj;zEBW+`T9)%*gp=?{b*dV6B8I?J}AOV3rc5
zJDWBR%>zMip#>grXd0V`lnu5(=8=a<B3Z#mDrg$>?sJTUTW3WJVXurS==sSQ`#<#1
zy0){F>ZH_DO8x2Mk3SeNV8C5>-8FRR(Ce<du1=jgQi+HpmSxqgq^$(r*Dy{L_E6f}
z+-A)R^`qyqF2W?^FyIzhQBQ71I4U*&a2OImg-bdt#U$cA@X$j8VF!&z+J{@pIcJJs
zHTP+5WAF&m#$k0c@Gz5Ryl7Eiu4awE0b5{#yZC4&j>5#6Y#mA3&Ld`A?z)~fXbh7l
z!x!A<Eip<o-$UBYDq7k#IaGZH5598j*eQjDBR6ggz{5t4964~{z#&711WvA9yN3)-
zq{OoOQpZZgvG4O56e&f4RziwK7=VsMn&k~oe<J@2)|2JS(~^bsJ>+h$4+A`wQ-R<u
zjgm44VR%_t#gsP3!iB1!Aa9oAyxDZZC=q{uSnb-D?dQ)e*&?q+_ejT+rULLyOwcFE
z!=)A`i^7A;Kwak6sFCX3JJqo;$UYyf#FT(c*&4m_l(zGZ8JCN$XQG904l)n5)w~r#
zx#;J5-Mb9#ntH#YqN-J@TvWI2g`GRUe$PGk%$YOi?z`_EGiJ=tcI^iL^r!U-9~wM3
zK@=x3bjawsB%=+FXWuuA2&3bjJEvSuCMUv*(4r}&wVP#qtDx@*<q?Uqm@$gzhDk|D
zFXNy=%|Cqb!TFIKtt~W8XGGF?jbG(4|4Bj+4m212@ICFR+i&k5wN{&~gsS#{$I9p)
zSrU`(42WN#KzLRap%Xs)ER7I6b7t@*X9v=YkjR7DC3MdfE7TWXr2L$~BcQ9ah!ri`
z)KQjC19UKBVNh+xCpso-C{4x5L`qqT4l-~9e{yt{HPKKql62bo*0b(AD^Vq(M3%)-
zM;+CtPoFc+IOCH~K3TPD)vSO2`{6~4PWt}!+aBvx<F~g_9};cb(p7Y3q@j{?`01yC
zZxe09hTtmtq`u2oF=AC;e;p(=Oqr4jAcCb9uO$<TwfsyibSNv7N2C-@Ws8m?a?_zh
zD(7BOQly?h44^Ytu1ptstXfrV-yWQX3WdBR9jj*V#pz@*>l7A73rNsJ3>n?Sj6*8H
z&_vns<WG$O&unrTIM+Csh&uDtR{>G0Q6m(xPvfC7VNq&%VYzzB$Ps}bBCE6=Ni_Fi
z^jWEr;{+Ku#4)y-Gtpt0WIGD`V7hllj!;5yfBC=@PdstPj2R^F+<E7nvuDr#-Te8Z
z>(%{@QUk8P{(#Y=4Rd5@Muj;%q-ac?njvT7Oe7gF#rg8f;3r_egJf_5B)^s;^32m-
zFJYBgiE{%#hneGOM7jU)c5y@sRz1BDt@LHff<Md4gC$QI7T1H9DKQnUUb(F~S!_Lr
z9vZ<YU`-1!eFKi27BzIuYJcJTvxgH#C5|nUwir)P->}Rk4f8Tg3-dIi<l<VA)r^!%
z*Io*vttQz5Oe$v0>dg$ZOv>(YKo-0K*%(!qE?s6)d+V(`Pd|O0Qnx8JYU0FOjy!U}
zfC22`3L7f<mi+utqf&Y{^#WEt9yYmYN#2105F;e<;pJk?YKzZ$4!$JOMgmNNX*#3|
z(;TM-jlg0-edbx^bO#U}l?*I`O~;KTlV|mcz8@jt<sj5Rh+?hE?J~~*@yc47OiK#@
zz#NOSEZ%7<7^BbRq7g_15E3xI_`G?+{be*hG$UCE)(87a3#W%-i2Im#_DpK_thSsL
zpo`THfHfh(owy!AU#RDvs+`Oek=0e8TS=RN%)gf1n>{<N=OQ&Eo3c!IX*Om|pW@=q
zN`0u*>Tka#3FwI@o~U2He(Tn)tL(8Mni&<G#?LU5vbHlQ4rm}MyUSWSu#W>FPA(QL
zt=uPtf)z0dJf}6Tx1;t%w)$W?YAz6MrDOH+td1O+8W6K_dMZP2YsWFvD_8E-aMAcr
z?R&@N7UV#;hAQ#kP&o+@UlJ$}HW!)9<qt@yO^daHF!e$1<Od%NRD7Nd+KQTuDJz_T
zsfjQ|2H%G>r~C_UdxB9&EVQsrYG6%>nFVl%d`BPJs_jC$4-99mTW1t#H>+`3yXT!7
zo)|p%C-dj`-Lj=aadAn_nyWW&e(}W@Crz4k*!1bm-+y17a;k<C%5MqcrZKY8(v*xt
zvrQ*pMVOn`YPFHjs}5Vd+y}S`_JQ?{CE{0%V(N}s+1jjP#)yxb?4C}h0i@l`C7Yjl
zMqPDP1rVJan~oglW|+J^cyvj^YuYr$iI5DretjAb8W6ZJh+#0IHY@rYuP>5u9(pLS
z>0mZ!0bYD@@I+~KHf*2zcH+eaY&(`6=m5Gk-wm6SF7MI93kJ@yl+2|KABICF!rj=t
zL1!Yr;Q8l$3yE8WFS^4y=cG)m4r1ylvSk~D#93!Gf9a+ErKQc!I_uq?J3svJ!>3oT
zp1EetF(-B|Ep5svi~JJkmq|MS8%uqs)kobA^H}g=u|lsp8f{wM2M}$;d;k(w*&o}|
zXQiWu+N=&77z`2EJ-zI;*QRvh&70Ng)xlPyx1+5F^H%-(>1~&+Syy=yJ~jQ{KVX+c
zTZodZm0}^Be}2j;$w>@I=8ET?F*y-ik?Eead^lF&-FJf%7}VKqA!eJTwYnOY_v3ZM
zI=yr!XElVmLunkf-OM-^lmXTnGGpEGM_D5V&@qSU!pEBGm{~apG-ZnyR~b6=ilU-k
zN<F01A8OQ?vtdKWPMt2g=%PRV@gAj`%a<?<A0tH*twV?O>I?H&unh`U68PF>MopMa
z-x>fLme?B!+cHx8gEp&k=cZ-`Pn2^-{1LPU+pclrplnKJWM<Pm*mUwE>8x6nBICe@
z)hs4pm|+qSW?NR^CD!w&KLrGqMPXtw-MU5gD>_KzvSle)j5E#%VinknNGo*Z%5+jQ
zb7LuPNQTIWNhnsO)z)Qf5|g(_ijHxdEDIBBC15p~h?Q9=dfaH+fzf-aat`6kVcu|y
zY*}YkBT29XjTlia8OVI?rcEROEn2i_`SRuC$B&ydY12zDoiKRt{&)$k@G(Pwp>^z-
z;(De+$q!^ycD4t(qx&QT3(*}P%VZd~rB*^ChM=#-Ie-My=Rf}$K(9-K7pzxu+x6)a
zP-nSciEdYpY7p06+n=G-k!=PGo(7Q;W-{2bfKn%4cwsOSbO{ZKwq0`ve=&`kG{8i0
zYSd6WcLqMvwQJKUD_D%I?DCeNZm_L~yCD~s6e9A1s^arHrcT7MBCOKVP9|D;*kNg^
zN)%_x6gzEo=zVVD5=I}bS3v@i1=7u#Wn^})Nl*P0Cf^!6cAWl~zx3(TXWO=IOO`D8
z{PTDA+wakfFW&$7<BvP{+^X~vvYr8emazj-t{Vm*w?Y{S&t6+!+&E;$dWnQy2g@Mp
zK+G{mCaZaFh^Bz<XhliTD-wm9G*RDtle+f%?}K<4I-mDzad9Q71~Fnpe>Q(>ehqEw
zT8nFFsy3ZZJAnwSb_Z|`ALv)l>Kn2rWo3Z{6aj=`!h+l_X5k9yjgpOMDC~j$VU6*4
znIzCGZ)&R*+n$yiLx^eE&u&kQr|{)3vO&OMbDmY+p#J~%w*lV=NrKY)!3XNiH`7u<
za)H(9)vJ5=?mc_<T)lerhaZ-__S&>blO{E9-u(LOf09|xAT=kEAl0JUqco#+d3j10
zbzG>8JzY)|CofD^yR(#KB=v;l80cSI+lPzA;pEC>(~<hWOa#_q!ZqcxQ>Tn7ooWwC
zqSS8X9jpN_F#_aFHTUM5gWwIB$KuE!)1pB%5K^tSE?Z1;L{;&To+V&`wU9W1H`p~|
z1@72u8Sq+Qk!;#PK@{O27HTA{U3_sWD_rf#0ZuN$R`u%5m@(s}mtGn=bZALQ$ysNe
z)vVdi*R2~hXwcF8#>CV#>xnzfK58~TF6rDkV{_0qVO2e?<88t#<dTr7l~%J_?dXZH
z6P$c<+U8ZORuHw!C+8<lCl8X&%P;q*6(G&@)|hb3WWc^#upVqtIzVwK$$GRqIM|J6
zqzOcHsI%<Ge(0gJWk-e!WtA)toeZxDP$3~Z0Qg@PknpjX<|QV8`T&Q7COGiPQuvBd
z;v$b)q2|lA8o$`B$#Uq#i78A|l#-wUr_tUs-HEf${^?JDdfj!`)vjH8!GZ;wHmyh`
z9^Scg`{kE+I{*CYbSJDYxUPNbN%`xYa($uP8Qh_?hv}iDzPD%*VX$+o_UPtNR*nCv
zs{%Pl)(HIJhm4aW)xD}<L-M3aDZF&pbj(ORx#C%EMQte#sKDNsK0TN&^A5_R%AseZ
z3H?MWJ`p$=>i|DX6h}HmBL&W|BMe}(Y?L$A6KOF;XfNFa^fWdkpj9UdTdneB)9#bY
za*pR!l>L<9TH5$a6Cpd3r4ikj?rPPlop|DjS6_Yg=FMA{EP3~{&;Iqm0~3Dsvu5ko
zwU|6PAuA;LD3tzsCmU#5w+=KEQjuUlYus3U^ieeZD0YlNj94B`X^~N%$fd){#l@qB
z4<A}Nh|aawrsl!@PTJG~09x)1dbu#x<2hgs?!X(HwmnfvM3>0b48Uvc&gOQ4IhKV5
zS)Nj<4Ee}WgPb~sj8md<{raU#cgFL_nQ@7Z)ymdctKBC?bNHUz#K?^I;lN=z{u*2)
zN``R|iz8TAsRIr;;DQS-IQ#6gUwUcrmMtGV`|PvtytDACt2&JvSKpi^cV{E%o!~yw
zdh*RT(e$HuHOgYg@`ODUD^%EHZ>mhefq0Ls9Knp-*7gSbiOf6ru$!>`CN~ChFX&HA
zs+XfHUAgdE;?7vsJ9eZOJ&~=NHNCd0nF;`ZnJjCOdwoHl)tZ+&5Ls^}p#n_0#?%D;
z+`KvEo;7o{!zxqDP)rtx`t~(np=r~mLxx;)_uUPZdRwU#@4x@Z?%lgjIN=16hI}Yz
z?524aA|>-g)#)NRQ5*pnj)&4Wl`JATDo5~-3h`$*VfWPVB@nK{EljHC?Su$TSc{1?
ztXr2+q|_%#CQJ~66^U^n@`Byw$o!CZ4lCcCyJ?1#yWMmRN^Z}dDPIAp)imI(TBW2~
zdH>SVv}g&foKB<HL6!;Z;xIN7ZL*d7OqP$WQe50YsjHOQxO3;bGiT0x?z!h$wrts~
zS+o2SVQnSKpeU>({~Jcq>GDN4-THSW^IpYRtdX!XbGfJK2{5a-$---Vw^5@~@uw2q
zotAjVjxn7I*5Y|#UYpp>3}U7o&W;LMNF`Do2*<F8mCFnBzQA=wWa7K;QVtc+ZfjDp
z_B!lagykNNE%q3as%uaj;d^R4WXL~?iuxXS;D8T5Ea}#*+cno*^OwK;rCz;yxx0!q
z$%5mU5mSLkU$c5lB)!?+8JG)Lu0$fiu*e$omBV{wCVXm>z!3Y|%tWQ+VX~t<?%FlY
z=p6Yc5GzEKM@p(E5+j)ftq~bmhO4yP9)G6-FMraiNXEgZY%FKx*Q{$5Iyk?aAFX9E
zMrG@!O6#z%v69PSY7FxjmP2nI(*Y?{^8U86dp61Mh?7q0clzno=gph9aN)uyo_ONO
zBab}hn4jh1Dl(-E(z#o-$cV}VlxtpGh34}2PuKOixe5u`EH18`>XkMLQt?K;A}RCv
zQ}Qs`(fnQ(m)G)7OryMJwlp^<trVFwKzca}*~Uv;L<+^)wdtzu)Hoh`=FAL8jeMDP
zsjDMLW~@gJBZ`{EXfAxKN^33CY1*@;p*4nfX%f4T6iJi2b?a8OYuBz9U3AgXrAt?>
zT1BeX^5x6lc;n<HOKQh(6@_VM_ugygfH~5au&t067rQ%Pv$EQB<Rnn9QSBE2ER1f#
z;3A$**05Q%f`M8CbKFLGDGh1$S;2m#US3iXExADx?!fXm`Wcp;sZ#?=L=U1-LfAom
z&AMhb9IaC%I3m;=LWloD<-*spvuT!zkdX|+iPiMVh#S&OX1N$zBYJnN+^u25hF4v6
zRbgS_oH=ud=qy?C*V9gGux3rWZQBx&TtyBkBga@vlfGcEtxMib<fAShS}#%*1_`Ug
zM90iRR4OAF$L-hfpH=p7yJgC(M)@#9Z@+%&`t^KF=XfSJB(VU}xD&i6?ztx;Cx>o3
zX;R92MXFo{nRT`GNa9stVJZ^|S&S0nA`)qn$!IExMg;^9E_#AECaGQIZfK3?-Lc#z
z(V;_!U;gr!FTcEc^XB)~t(&@V;XC)<dun-kZ7o~Y%_8$2$fxmc)^ULTW2OTsl?NAl
z)_~1QNpz&aPeWhl*~seG@bUXd2q>S~_Ou^q5<8KD^UhPNRs~{6yLKtk(6VJ9H)p*S
z(i9=+Ap@2=Nq1=7THSF+dZ!^CTFXb)-Xc=yoi)MDicdwG9P!_1J$t6o&2!}J>=$z&
z1hRYtLo260J8~j(_wIzZx^?UR`q!5>Z+`U5nKR#e@4d$#f4pD6!+!nimH_JY@d}xs
zg;CLP!iJd^4$*L_7SoK8x~whvJQ3k60h^W2rlW=19)Em>HAE`jG5ob*)=K}d&8(8H
z#(oh*Gk0z(Sea(NYSq-r_PG^D=p$hj4qX5%KrVnW1;<2*WLBbli+F_sG)spWa!DMs
ztSm?|=l1>g5o|hET5km~WTi<nkaM|?Tnw!~+KV5_Ci>F4bt@8iR;^k^bZ7G9Nh?>b
z?AEPY&6@lB6q)GMDOeyJOGFbM5;GLJnJ}iN4<J%>btFTX7O**cc4b2jlt5|l5YpuI
z>SY0oumbMHP~aJnEviguU5LyqWYrop1S|XN<yHj~Z%U)V_!6-1(w_iX&>U*eR-%@m
zu?`eHNHb}6?W)F&%MiFC!84ki9dHvSfFUbQCdG6Fg66A5B-yoV*S_qs%bGTAN+QqF
zrAwc9;)xb5S_~T0A}fph>tBO4f-MS)(6ng~z?h>GQW-F&T-(pO<a0D3nfZ}&ztZGX
zGWALW5kVs5;>D>wCUYQ7h+-e4fH2uHhD_;Lh+q~#jQ4>;kvUW5r)2oN1A(3dWMcNy
zBNq%|asW9cC5{ug{G~$ro(7qQ8ooKWrRnlMeJtzo@yFANwV1E&-2<>RpS5Xn_V?cp
z;3gOhT?u>y8C|JS%nD6vj}`MrBF}Nh9e3%amlDw_DJgm8nP=9oU;mfCoIY}7^)MC*
z-@WwpASpGM7lL+<v7~`yCAee9R2|72)hM5RCSALGB04KuaG(|>MidA2FJzpKX7chx
z3Q*ywoly(p`qYzwZpaOu7qf>5pMtp*02g)Yqz%ubMrD8q&@z;9h7x@lVR8V$V6a?<
zn}}4ehVF*mA_@&99nBCikXqElWfX&xPtK`u=n+Q*B~rDj_Vgf$f0_4k***(W#fkZA
z)~wlEZ@o2k?AY6GyX|j(``hHn|5EB{rH)e}Ag(5+ix1v>nA(R$0qvZo2mqrj?7&Rt
zzvQD5yGZ-#$F}L1m=7%TNzNAA<mCgv?^6ROYpE87^^|58xP+zP%b1j0;Gv`>P|8ri
z^Pz`=<Hn7HR6kgYX-}FEb=FjAUbA?Rzf|Zc8FAXGMDC<xPkkVavzscj12bx`(Ve(n
zB@%f$cI<e;1wEd7Ztj*XtEyCa{4am0_0yl8FlJ1`imDPxc6Y0NJdKkk1*`#LD9Vr`
z14mk^^FE^He&P*O`Km$qyX^Akcd(O~tS)(nMM+wA*d<T<lh4<}r;x@4;|NU7ixvem
zXzkkI8=^R<&;oJB3Q-rfF{FeIix@Sah-9TZS+`jjDzATNiZ+H;;;2-F3SaTOb&>3%
z(PLK{H7eY;t<Sb?TR;DN!RxQTUawy9si&S%Sa^UtIooYo^ilh?X@pVHihNyhb)-%D
zSov%^k>r;=D)SoGxben7k>Sm$O)2kWGc}nfjmXoPXQnO3k|sLBr`R|Gla@3d(@o&{
zw1({}WvTP0_8?Tz6D7!*L>J9vn^{qL{cQu<Q`nTFy`cGJjxs<lcG0--5T~71aNTw1
z9(UYvGiJ<q@4fd*OP?G#u+>pV9mN|x#@cttqqc(YmQdDQhRUB*Y4mZ#h{^-v)vup#
z9mBjglWXwilnM(#wzvSEnVQVb7tXrk2{fYzt>IH)&FZv9*luZQ(4T430>es&4k_Y7
z<Pw%wNK#xIkJG-gQk;Cro95aKOMQSbLyMUt;#jk0G(9@M>E8K@T}j@lMT-`fUw-+L
z$&*)Z*znX-PnDIGoqEbCRc^Wo>m;h|?vQQ)rs59g&Sj_~kt%JyD>Y&G@cxy6=uj_c
z^LW-VtRSUb;~KQ|WmUPi-%b|@rcFzmoHjgm^1y*9e}IfFXE0MOUBkLw81odNJ)Fa-
z;SfrvwGv^wH2<G`5@=x!8wSTcd!{@-^piCZJ!@oOzT{1FZQgrtx;lYbn9dqA8E-G<
zC2g?+6!|)}YhST;ZH;%{nUzTVYu>y$fBW0nGiDsHXQM}w-L3Mdv=chQxpNt+7^Et5
zy+7hlbSlz&RbFm!2JPCF&ZDUyT{FyUM#uC-Syc`W6gY8c@QBK=2NGx)^d(HT?B+l*
zTF_7p+FgUEs9rrdnbt-7_Ng_(J}eYxQwC%Q<1^q{(8bV0bRKMu-w4g>HK`!?gVo0;
zy?9}T>D)8!X)TI@x#3V4@~Frs5><<eep{u=iA6=dm72X_L%aR=f2K!|t`TqRKze6y
z8=8b^$yy>nE900ihn^db<wufqo_#h2%+B-{V9EJKVT)v6m_9wDsvIN{k}Rk{P_Lv1
zlGrtu<zo2-VO}O9u)@m&`4sWB^yk%AXT;Z9xvMpbhh-U0<lwOVmX~d2MvX%>vohAO
z3f?hhKZfbIB2fRFve@AxMqHuPViH(?_~9QDCe)}=qjTrZi3onNP<b>`!FO}0d=LsE
zIrOykK4Qc@{x+Q|s^Z%E^(lUyl0oaFmL}?MpvyEKR(z&>Q5+Vp=#x!0C?bxAs>E6E
z!;Nl2=QA^sP6de*Mgj{R12~GN5fS}rPXQ4SddGUv8b-|e6Wxs-WP+U0BXAYpGk5NS
zdn%PNtuTsd-&mt1L8{`-zB!wGtE@uV-dt}=HCAedQn$5lUtV5*>#eu`;upWzib4Z?
zTDCEk6q^@|S=c%3PBLjn?Fl9$nV#QXUZ@>=KEhn@N_|{8B%NDs=}&SE`)fF7I$vse
z46f0#Wne>*&RUjMAlk@!CPXq2AuZ(>&647YJr)(JRoa6P%cFUG%s}*tGrMZ{oHi{Z
zPXe*jW5;HEW9Dk*Xq1Uz`t=FC%C7@{&pN5Hb?fO<rW|+v`O`LTT()f4#PQ?1op)Yg
zqeh<6mNgv4;RZ0ZMwzt3ksEFthZqh$x!x;{%lyxO4h+e>w;7^kuZ4t}RDiw-%gLgj
zw0b5mfAU&aN>8Ud;|yJ9%~5~h$Du$KRt=6dkB=FMUZG}JzUSu6K`toh3uiNI+7!6k
zU?AtDa5GmcN0f&creDVs^Q(E;Xkv?I%`SfM!MRUAU9x%eKOTFmW$V^0#*dFj{k7Xm
zW4*QSoV268y_*@uaOm+%R2ri5;Di0S`Z4@9u<7KnV2|gFnX?C_07FZ?f<z-DN3?3?
zYEL2p{7_tnZ|2NA)M+rc79Ps54>3GGK07=rtJebmI3)JSBLkWUfKf-TR+wWZ5;{jD
zn(ja#$h#zHo1OXx9{9@}Zdf~U;=5mb@$56t6ciVqG-y!5Clw<6_UJp~6ovI2XLjn8
z4m*WO6Zu(IW|WIVuQEwGTzQlz&ZtrLl6!flI~+3tL{tv614p3urKht!o1_)c!5TF(
zs#a-eUJ_B1!P^<y0w<V>ow-m+EkVTXeDSm;Mhec(dQWC2Fp#qVOW%Z19BuYVa$K<@
zW{oX58oPE`D3@Dd$h#yMLK6$BSHEz;fc@^e>n)|;RO*9f&CWgd+=7CFT-Jq8-(@vX
z#&LiNfldxnHeyTam4@ita!Y^4VFDf%fbc6K4OVkl&{#517FITZvVdd?+9xa9(f9Y<
z6EFhcC|l_~{R!WbEK@@pj3y9I<lwDb%H!q5`C{E~5=z1yobA3R2Tw}zL7K^|nfIpn
zPMnuUPZsTw_?m9<M~=*}VZkKAiliX}yI~>oj_4OMZi%5oPcANQr_{?zy}M$?^y$-y
z?$oJMN9Cyis<rQIrDSpgo5AzX=eas)45Cxb`ECg@bh|x&O1GLdQ#sa%QXz_iu$Kx-
zgOv^o2qQG)Y7ZQ$+7n_U0}|NYgnmN3GQG=bUtiKqBg151?nlHRJrVihT#-g{-599G
zu}g<&t_A8dI))T;PFSZk=pU7Vdopr64p8Q0&YW~G5yzH5ftCBotQe#A;0kab=qg$y
zC~+dI;%M<uu%qKCup&_->ca**>k~CAvgWF(2%m<a{@q89UU21=-6u>~ym)c@_U)%k
znKFHPC-;kbI#k2@&NvPn3PJ(^CZA@WNR=m!L*84cq@x8FNJ_>(5xH?txJQ2XyZ)#m
zf=~{K+!czG!VC?<$i|HszlI^1A}h*A#@1jDVc7u&piJ)su5i<)ITh!Pqym^6Q&Z3(
zqiMm}Q48jvj1T8+oO4c)eNH{3e?59+2oyvG=pVm6GRU-GbXHnrfH)0QRuPQ)OfoDu
zIIKwf?;pfxaLF2(_aBBd;P0yOkwzTQ1wz^?{KYS>`0TTVUwyS=!-feHChWIgiwP5s
zjcQhk>Sofi_mJLm;f0n$&6PsZVJXHM-GrZZ`$RqLeW(;IW>@0~VWE^+129=?Tf8`(
z0|WLX0}It2{77^rc6~F?7+hKXOzjDkCbln6MtqRG6^m(}te&4j2Sj}7_3`3bxGMy!
z&Ye>OLZl73BQzjdmq5hL0CBen#)vWywa{dlFc}XWMi-RwE$GN^?T|*?=VVG-2aO*;
zedo?Wg9eQsKmOHMFFxs{KNl6<TvU`$-jF{2#VFAi9x6KE;)86<-pewM7t!%izQQ!G
z!}2A&MpAsO1O=ncVm}MvTwu|spFG=895CCYGjhnB`OsYILVAkPtc4Lr1;zzd8Ey6Y
z_0vmVyVZ?1rq1-lOVe~cO^Y!F3K)HOnS%;Dh&ZMI$LIrJsC5bZjINbbUKR_3vTTM*
znsh-6!gB;PS~XmJ65Zr;<kHgQ9nt@Sf`W%0dZ<~mX1CsY>%abWi&E=oCTJ)6FA&d!
z7D=9@k|n)od!ORC)T<Fm!5hEIWBxNtvKqdpJvDAz|Ns0aS1(m0W2zQh*Qk-&zCBej
zszZk$wF=-5B$cuq2Ug^;j9shWQbJpB^q}>LoXHi#PU&CM9Y$GBI+q3W<hW%`{*H~g
zVD+raKuXD5P17><oV_VD5!8pQrcN0!MRx#15i84@CEHQ+%f0!m_0E8%Br{!+b_q0I
zKSvlb<`Yjm@n=6fX3CVQyLT^8>RqL}Z{1qme~WlpDHz`&EOH=U(UE%zuGFD<rP`r~
z=30zAsxg&mI|(2pW7BE?PpeTQ*#9U$K-a>LM#oZefxMQrpJprsKk?*~Q!JVuH*jF!
zC&)$C^B1EvL`)oL2~gHs-Fat*PUKDpiI;Dgm7_pY<3`gBb<8b-Nw<CV>bj&ub72Zc
zDpo;q?X9ITUS_&V_kuMG#J05O&%e05{E!+oF4(c-mBPYPii)Nw)vzM;9cvOPvejdl
zu`67?N@OG0zS9<OAhRwPYiU&}{lY~8-Y%C5C|8b&*P1bfb_@JF?4p#`pBNj}vwLAV
zSz5BCb}M4t7!bw@vMxI5fV=TVI~g@gq=Rs9WBZP&8?rvaQ$vcF=4X_omScq=J@JF^
zC+<M}#so8nM=`ar1(lJ$HZ$*^N7aj`oOJ#5^Upo^oV9Bg*Q<9@A~7S8I4ti*PLtz+
z;%MAFM|W!;)GPX4s!=4@5Q%{hh41;-Fk5mOBRTiz`$&=O(#oKfd2^>h=Td1ECOc?t
ztSpq4rb=RQt&6DQtba`|EqunT>ZV;THI6WxARb|+BlL*%>oascYmnjW*@2IQYeJw`
zCeU(New@VLIrB^%gA<p4gaKNpY134V1@@gWz7J?6kl13NqncHxr888k4pLs8fxeXO
z{?L6IG&rbt@7`~`@y5p=m#km^{Ml!p-LPT9{BCk<sIjIv=ICzCTX5iT(fCj8d&lLL
zvcmDK$hk+dS41S3)yzR0<ffY}sgHCf?L!Sa0Fgm%z7<~N9N7v(sj~?gCR;bKvDtY@
z3QA?907|M#r<uN1I?Xx@#~zyw?}mM+RV%x}$hslie1*Q0m-OwMCTAd0L~*!FD2aoR
zPJcXUD@{aLDNil~M#!j0E38&Fo^rqe2V8Q=C2zm|_KFoNo_Xe(Q%*UhDG`8t%RAb!
zwkK#}IrAq*T1e$9K@-~><p4GEQPh1@gB)j43hkptrSZB*t75+xipEGEN9WF|ea=N4
za?z)uI??iBM+M(AD{7O82PUm%+6Y^HtzomE#R0l|Vm<b>=xKLm$D-7F0H`MEvZs+z
zYU^CLhdYCiOQ~lmmEAgKYxU~Yn?8Lyi9A!MPCf0k(<V=zd?xU)D{>cVN_p<?);vf$
zIX1hb6BURxb|kxxYLMfgni$!wTc@xAW$NRcIYCdPj{`|~VR&FMC^(`40UGZKM6Gq6
z4y?MTwGgm_V_MOrA_dgNbD22=TU6cf$CI|wL{sXa*+f*NOdif5(ypMOpjWS6pM3Jk
z!i5W;d+xb5ZQ8VG(V{{rj^@vl+IduEppsRC2(yEDigAwAR|k}wk7|&6aU0X6-L73A
zy35o@dJ4naN^=kF%{gWpY7{&SG(KzRO`IU>2wMvQ$E)Sy$$R4I#b(*<NO;m&O(W<$
z&1RoIX-K{>D99+SN|h?zyLaEVZ5ydt6DLkQCfO=tZ(1|q!YU#CmlK_Myemc|yY?U$
zH6i5Zs~m+@3e&JV0HV%jY*~-k%2uL=LB_^saN`7-(LKo8H46d9s~PX^xt2a2wLF%!
z>^8R?C0uDtL^P1KCUQ>8Dsk3XXYJUrWA5C!lO|1S-n@CMR;?;xdrEGOC4fpuG}UYk
zm%s%7wL~Y6Hr=eCLyiaK7yCw<Fs;}fM0Dsh>Eqz=Jn3nQiZziVK7))KgQdfy8a%?J
zKG0Nx?#K{0`aTl$6Dxz@t){FeB_L$F(Vvkhnw6gBQM0ZfSd2`%bJ}UA?cTk6)~s1m
zrc62f@Wa~zd9(uL9X>$hBbX*E0XX*bVO4ITlXGQ7FU>2*2ZhyjGJmxqglRhvaD}ob
zbQ%d?*a_Hsv^NeqD1!e$?p~9y`upQG541)#%&buph{YfJNfH!IykhKEDFJc3xGbsa
zqZf0R7~erU)M+zW>tIHuJKC`4wWKD|ol{RewV<Hjt+(EK@x>PpKKS7F?b}zVT^S}p
zFiltjaO^3=Rbuaz+Z&Y&tu`GWu^(jS>ByWQckreegC|`=?Bi(ED5YV^hWq>Y4`fiH
z^vcmOyS;tfVKSUjy0pUdSs6MkJ%B#3PMRx9iyq{=nZ7V~25Di_Su3hoX9Q7kvNT}W
zKsktrnG;tr;k|d<amRl9?f0AC{N}pruB%q9THn5Xqw>m#7Nz)++mYJdB|840{W*?q
z><4A7>*SM)p&eTTLZM-4X}|8MS>7t>p~Ol-&ZNwQ$ePry(blkt<a}`plc1=~Fk&Sl
zA*_Solkn{(K*)gcnM%jAtV6*$tGR#**xJoE-(0n7)uBU&_UqS=2+$Q*T;b0vW6(Xv
z@aIvI+lxfUhp&3NY<xyHUW!8)vz5{AQR%<6Co)eO6vv%YF5eoUTzol_24I@u#4C3O
zkfg29-4SmhF)Q-UEwmLaNiDkJ7=&CP&D@jgNZhwRD=e*`YggYD5^?(u88T$Yjvcq(
ze*3R}^(&$~y?XWX<dre#UfA&Gg`(|sq9c1P<^Kx8)060b4kSQ-YM*1SqnReE&F!dB
zK`67Pg((%f<rt2GX%>wqH{8LoF0Am0)CYD&$=sz}h`3`#%g#VODKAeA0(Iv6^Mi<|
zNU@+fvT}dg6vjn-R!FSv``h3Cmgvr1cinZ_WtS1%>E6A&XP3ihot65jkkn9GO?jbc
zd)JVhNAP_g-+>teGJk#q+hJIF#C+k(h5;C()C}kaz@)V>Ncq-uGUFn?_F51yqNQJO
zxd6XP)EV%J=!5CE$OZM$8jhw_>c$|TFfmb_)~$oMC${_M#qa6qUdZ|^D;LRn+Z;B+
zDW{xLR#rB5?%c_fCwJ)3;i#jI%GzbOJJj4Is7_#YMP}V|mCt*XUoWG3L0IRN%P{#P
z@@Nzxs^iw(e>0;8wrZAfa4g+4+FKLF;SA3W8-ngFTV^Sj?1;y=mUd9du;YBkp153l
z$h6`?AhnS?-=IMNkwX**6-080Ld-h3VHKJEpXGns95zBCkvRSI)3<Edg1Es`Pd)Xp
z!wz$FdCQhmU4pP!C@b|FIWnClEM1XViEyQKnddR((!C(8z)L9q#*u7@Y6~+1n|s5=
z0FD>X%t98YET~KXV=V;6O(Phxi%1V$PCeAnu*^Cz)_sD&KbDfDyi+BwRi~}B5Bbl2
z{nDN1+CHpp#fw7pCw%dU2;Ua4a56(e-c}Q?!=R~Us-RM4^aIhIv(G-ew6t{L!i7_&
zO!@iGe_mKv=#V1;-YsLHq$e<p`Z+>z9YnZNy3F$hzVG8k(e(C3i&AS>GYtE?y|rpq
zT0*101)b)KSa;Q`DQ^+VV<;Pdv6b=iTT6d4eB=nFlP+y`47=TkvRp^)+G_6H)CvFl
zzget8vE?1HPSe&6>bw?*LS3SPlJ=Cm_&t3rQ(2#-*+S?=5ZS`B01#U_@4RLtPP)}p
zOLs%<^ZNCJRN+jc57C|R<Hvvb<(DtM_#zP?l6nq3^iW5aCF+c}>irFsc@K2iTQ;4j
zb&}&TD;a+wGlnKkRCnK<`jbPT9O{D=2q0b+DqKYmg*f}rLjj&C$I;7bO%6Co$xD@y
zwSa1ZrXV08bxZ=p(5o2FmMw$ZGQ#<|ad|6VYIW8~`JM&`8TqK5s<jwVh>?u?o<bQv
zOIrrAf*6e?%BZzzlL~G`fkq9th>?bPBk=NIr3hv_RIksRg(_95TyVh!vuDp<v0}w*
zuf2B3C70~K|Nho4ckSvj^!aO$J_2H;wCTjClN`2!Vb|h4$qRzFH|tO&u_Nc3wh*u8
zIdkF|mExHIWu6YnI7D&2{4!XhQt6{ka)%{UGb;8;6<n4-)>f^o41+PD^-7ByKr>+^
zk~i}iXQ<!&CNQJ$wx>@zo-re`hGj|tVL)pv-y<U*kfSs&^X3Hu#q%Y1Lxt8a@}W)U
zGN8G*@WS9HQU+m=qFFfXu;8Uo+>y}*7%a;QO^sv5oMiRcwonTS3V!j6U%c|lD<vf*
zbLPys^wLYKSFdgk2}g9BhQ6p`fGldvdrnSurL^hfg-uyy4VH0ud&B=qTe@Al0H~NK
z4g=^)b7`dQ#ASu)DQqf|^_8srT-#cbjbjOb?r?{BjWV31i6t(x7Nq$=OW<3v!d^Tx
ztcW1VTqv5%jkx4#r^T5d*vlXV%$gP24b99)kE8jb*QWyrS+sa)gr$?~N2y)lV+p3w
zW@14z`_4P*9AhMK@N2GL?`5hr1-;Bxvt~__cP37pxN_ynWy_XbeDTFqGS_n$>^)bX
ziz)`VW&O&U29YD1${Mnz1)c!M@8+A;_1C9$E6D}IAb!Jgoj@0)(ShqLIf5@7b-38r
z#2sdh9QQHfE~NQ@4MRNH(y9fj<R8^ScfRvZRZXl15)vatV(<hJ!v``nRD%v3?A_4J
zd`KezT0X)Zu~+CAAxL`@I^4aC%?e`H=7{+WGu4_+vwn&hX>!OR)2B~A>7<iL<QXz#
z$k?%CO%~uV^hJ%k%2)j1UbN{{5Im?hRMIa%vdrzb2Xvk`59_t0{chi`rcMngwgckV
z3hbhxRZ3CS4b&i2XKA*eyGigJ4gSjX_bHVDs*qoVR+w`6?MTGnA@XDr((}#5HtCqO
z2`h+)l<xto06mcgA4V&BP55B$y*F2=v)m0*>`~H;!J9X$>C=OL%E&2J3_YE0R-kJf
zv!G{_G#ac~bK)I$OqeiX{`~n38Z_uY^gXlh?uSQ8ad7XyN~T`LE2`_DJ7FO0FT9Xp
zVWp+;KmQX5y1bF7{qRFTaiGZ1DhY%5wFYiLMV9&z*H;Rg4Y{>w{gspiiXPpcWNdUp
z!`#)XHJOG!2f}>Tzyb$23cnrojr0N<IuccAd@w|g>C;v#5HOgG(yMXFbL|~9)A)Pu
zrD}v-b4>=sJ?KuwAV_#=i?pb~$>?C(v|t?QfVxE6yGfH^tf<@c;>1gqElXcWzXEd@
znsQb(M&|m%giPe7e*J*j^7$dW?XZ6Rnl)>dtc*VWgcDAXL4%$;l)r<-vHvQKdPQ4H
z?z-IN9Y<mgV>K}pE{t|WcNQ+xeX%lP!85oXq57QHr!wm+g}EO!PbM{@HFfF)N+0!#
z`UdjXy?aI?qNG-`hz+SNbf%A=Q@fl2ed%(fo|pmf>_Jz10LE(6NEI*DrcVm!H0)A|
zfGJAr35ortuT|3%>611pJrt7yibKb;p448ZIMgffFghU00=g+h2^u93N-n9`sFCX3
zJN3#PJEjyvPc;TW+7M?*>g0eKxt1_H##+7NiYq?&;DbN>;Sarg_kR2Bw~sycSpNut
zNJ>+_;$A9J(z)f9{(0n4Zi72Ykr3yRA#*h44I5g5Zc#iWk{SX&Bjb`i-H+0~ODv{e
zKec>$&~t?O!?}h%MpvN#skB?e(hPJHI))q7?dQaL;mb*EA)fsG_vy<~my&_9I(Jro
z{j2)NKT-xx#B$J?r0<y1Cy}SY^XZ0t`=%qmrN4yQC!Yi<8k)0)1x(fx;RBMkSOjw(
ze|)GU=yu8HrNKN5t1ucJ;20h#>XIxpx_Y%b=bYdlrjo{)XVY!X#He!p7v$V%u(OPj
z%7Ucu#+-7>DXUhkTC!xxtFOL#`Q?|_s#VL=l<FTN?cRTtM!gy_qQ9p>!Be+#gS6Aq
zm$ZXUph=RTEmGN*Xv&_+IAKrE>VA}WV=yW@XU?3I0)jnBySHY|v|LMfmXXS=ssOek
z%s{|I0R)bYNz#FiIdEXA0O|JafqRGpWatFiNC@g-`H1bfnjuCaPlNo?wY*w6bBY6<
z*Q^P6KDLvrFk=?Lky;pafF8u)_VkFbdQ$O%r##o2Rvej|oHG8kSFeEFXs;PH8Y3)d
zp3jl`I4Accg9gt#@4Oi^W|Wka%$qllWS?r)s`<aD58HL`zsg5+@=|(1MF(j|-38ig
zwC-6vmIZ#4iV&_4_VlRkM{Sb1CWH|NW?2qeFpgHkj|KtKheY1v1|0#FzZ*0N=nn5{
z+AyRM$Y^<Mf~%ESNFy=5@@JoAAe_?1VDU}!uW5rIiK*;1&0rX8pnvdGRB4FMkz_?G
zRxF^&;pJ$|sE8jjuELsgD~<F_mmvl3dD()3f}j8V=g&U-?8=oZzxwJcqCt^V2%jiH
z$NsA{L}%Q%{{Q(;<oILv`mhR%a13%L5x63KqU0ML|LIS`dcFAKK%*HsGMGH-4~hLg
z^dhXvlKe7K6p~Nvm$2nvCgjN)e%K@`Sxk<DiKHE!8i_x!XLBRcK;t95eh#09Ni4M0
zDnmCxEVM8(>#MQCVtNUswcfP9`sfBxr6G<s(#Q%odJtpII;xoZQ#!mgi<Z`$Tlyq|
zPSee_*r@7PyLRm^UAjE+#1qBE#YY}_WE+%VbZ#|fyH<>B|G1Y*Lv)^fw*R%)MvFg=
zAuZz)Sa~&E2wI4^)v{&5IVA6pnhpNnqeto|H@%2t*>~&hR?Rr9_KPxdGQ;!>Yk;gY
zj%ekENvG3N4d~89hS(ZvI$WreEUjqe86fPKtJOVjrb0?o5)z-<6G-cyZ0XhwqK1;o
z^FdZr5Q?XE?o6FPd<(8|>aC-SX{43Rb{sUB_p51GS~XE)PaSf|A&nb1o;r2vYp=aV
zf)5>st;Au&Qg$S`%P^V5)Z!%zR5F*jXZ$g!E@z;4R-xl1B5@&TK4=kl>z1BNjAfYf
z)x`SZ9Ikx909iF-rCe5FKGT8FpVc@*tLca%QfU*US~6wf1`rAQUeXSB9wk>(kPU?5
z;#zqISnfeAu<JmZG%XL~Mm>?W)lEGS*GAP<v+4#>(IohMBuPP$DJ@M)b_*8<zY=|+
zyGf~27VwL-ay0HD<sAtKZPwL+3}`U9<BmIS{rdF_7cQJJW5$(NURjeUrj6nt6^lA}
z&pkmv8~urh_eg~ZD=X1a%D40yuU-Gp3{xMLx~|L~u;K|w?=K=Uv;>QaC^q6#`-RVR
zAoMF`*LO_g2+2Ow?dj7k<_VH|h#4MpOh&x~nLrC;ytO<--@a)=BWaVV<x#JiHq98`
z8Z`n=U==X^byd#{qNp`9wDXDdFd<mKK6r87ms(P;HT@;)%3(UInbL&4CW97Zs1e9x
zi=s*-5@()y=Buy1x@y&`l`B_Xa>*r$%peK&#In7fL^qz*c&oc|m!NSAwCoe{cYB#j
zMCR8qYNN>yITqRK{bjzCFBl-L&+ghUZYv|bkybR>Y8=6ZEn5cL8%YTqqsST-b>PxV
z)l*N|H@S85Fg6&oca1igTAnoCOHL40iOf@1jf9UQ;jN&@g{eFyN<o<0-l0Q9S~=h@
zrd`sEy?{25El|6lpy14sk|&ogUA}4+9d|kTWZ5?!D$`ycyH(;6G+|4}tP|iLexyY#
z(adQPWy%KqDoZJFE(`~!X(7eAheL(QcRpcbX5=AV1xJQ_3wEe;WgB@<iJpU~XpTti
z+BI~;t8B0<TCp9TL3BoQGslKEc35P6qTkq3it%yR%#hL(4HqysWQe9xURd!_R}L*a
zj@Uv}<m}nOH|A*bP%{X>?X8<75>I{i-FZsgdFP#-mn`Xq@|}BL7LkT>w_)F~fk9$q
zJMs}LF`iD=#iUv9O^ri^CyH3nMI~!8VIMel%i;aik{I#=R2t9xtR?#pqb5a(g=5as
zA?XH55>^+rhMq%9uv@n*785Htkte8dhYqPgWHvxp%P4!2X17f`YcV30C^_yX95q6(
zYl`H1BGBe=d8WU3t{l1q?hdY2EZrSZ<7$%UG~c>)S8?%drDm*J^{f8<_kZ94J`>hC
zEz^#~{rBGw-ecy>;Q12k$&?7SX&Eu%@56`RSm}w5SwNP@fNhhQW9bz(g;*=zur}FN
z=+~^?-#o$q#cp_Cn10Yh4!M??1z34u77%G8C%FdHhiSd_buc?;oMFfM$qFjWfyl7$
z(61cJt5mf335P}2(?cW+e#-jJVelqe5V^#I(1tf`|Hng)h!h&^#|BOwF=D1tB}%Po
zeDI~0U7ARq1`i~##(MV-?zC}ZU}Qr)Kcd!`FAoS1oo2<$Hm9Yyc*5}E{VOri(HvFO
zYZhYZy}3>R=uXz0OwI1d!D%M?w<c4V@iL}_#Zf0JH?+E3L?OwjwP+eMpVM4?+Rp6M
zDM)(g+czC?Z*Ei>|H`=<#DP(s!3`?Rfym!~A8c3Hk{UJ))RPzjqg$Hr_YmQmVZ(GN
zJ#ce>3Dh6IJqCc3j|d~RMGYcSTiZ#Uu1az7=}L`PHA+4#Iq<*(PdMQO%sI^`FtyJ)
zCnfxGNC0RMwe8#OzI*GmD0NRImUK)<701QWx`b^~2HLupo#{C6BoijIg7p4sQnZgz
zGlo4PhPO*vH#sRBrWxxN(sm}PX|&MHqzBX8!!jdsHQ*Bj$d);tno2^GE&(o(9uZq$
zGy~_dVvAcF@pqy)07WG-iXN_9slNHfrCmBEE=-5w^aX8+dP=pL!CP~*VW@ZPNO=S4
zn4U;P;;E;C*MSp;gjt$s8k0$rsBd-No<IM|T_0|pHDmgl-MgRvr$05OZW6_T?Fbvr
z#11o!4Q>iatFw|pTgcGiFh>;}lT44KFVM;X+>V2+WI_{wrS~_F5>4#PAV(QA_Jt$F
zMy7+B2|Lz69<4+`SXR9TQemx8VO2@AYmpuiTVQmECy_iv^wX!GrlPSWKq^sEQ#kUO
zII9jtv%tx;QD@u)7_vIrh^DMq5zHU}m1#g|@6ghPfsc`ui7F-in29Qh%La9s^x(T2
zzg_k4!}E%Z`wkvlEtttfuspP2Pi}N$MhEiIr<KZ+O@}nV5qp|>8vh!0hk)qNG|S23
zaA6^qrQeL}^k_`jba*q6buM9h+CGs%QCW@k03XO|+>h5u&oXEypPU*=Y#|^e(h@Y`
zT(zAURsu!qytWv#0=&urv8HaBl4(|Hg2fZabfB$T6*K=zShHuVpZp{pA^GjM0sVdc
zdA%o+HM%EGTSBxfiQ+_>ab8IDNh3@Y5Y8#aKLx;$2J;GGTmmXHQDz%Cz1JQ=iMHyM
z*PlOf<nv0suhh=@^G~N2rrQo1S>vX$N6@<xCj#~qH=;O1zG(YeBjHUUFF!V&MCLGQ
z#6|w<B07bImN}5=`2z>0L_`21b?m4!VP(t71ba8*adNEo>q!W}d)>x;Px;^6b&<-T
zNooRP3QPE$L3+j+y3p<ej=|5BBF3Ja<_H{{l@1MOp3ad78xCm#{v#-BQ1swSis~Uk
zoV14N*|TS0)zHu*P1dNPtcDTFs2U%=EX@%{737s>2PuItK4|D7GDbnFoMvOk!k%v4
zdP_lZaeI<Sl$y7y<cy2v-M4#8(M$FFytwoTdeMm!gPc54G)efo`R0K86NNwGh=Ag0
z#)dE<Z^VcQHl1qOme6jK07YmfL>59txXqm#3<+XSXmBCdXdxliiauFuMdj``lB|y-
zGFX>^<j(<JVg6c2C&Q4HeQ?FhNDu^CXu&e0Mg<_G)LUZdv=F&q7F_J~Cx@A+%v@GR
zER*3X5?I<1uwl*@<IKyD7VK)34LpkeB$<x0tLc~p6AnI#T?vbppPXFT6DxjL)=K35
z)uiaTAf`YUVP6U&WPozbAw>v8mSw`|d`A;uK`G)qCNX$Wr~T%&`}5+(&pm(DTJ=ch
zw!av?sEZo9n|>-N2ri<BFIp5-8>1JZr9m2-h9l+(GE?f~JjCgYNU~7;Dvy~45k1nr
z*VL|pf`TFRx*9i@VVBswV4>5e2XvjL!HoX2Is$SpGV8D4NY=;vRvJpf*q~COopdW-
z{$31C03<5I43H$p*I!SCor0yqj@G0}z}no=^j44xtM%7Z2CXR$*2w0pvZ^VbkPc8*
z7HA((hY`rjTBO@2O{d8P#u+oF)NBR!P%69H6{(t(w79b`1v;#wNHD$famS^qNcZZc
z=hFemh0jbR(o)I&i7vmoa>^h7_=VbBuD*Ti^WSXSs_xr$<6ZBZgG)<GQ}qqA8XL?!
z#fptc&MNin-cwbuJ@HtR8`JLP<;4UZ@5-#zEF);GSD(k4gW8B@Wqs6h^x44%5z)un
z$$X<QiN+_GmHy-aR)m+%o~@QF2}E>myjN)<(j#yOY<|2W;KVPuz%FRe4&*-}S6Ww}
zb*xMs)+z$TlU2X}KIl0vHziGX_%f2hSZM0f#UeFx<}lY&<rfSA47G3qpJhqUl?zFu
zICL!v11psZ)B;Wm?n%dVKD`ml;b@H-1vH4*Ib_v61w8|)WY=!$!G5>i`VV48N^Mfl
z_iFw?(U@P;?YKL%b|g5egCm>LUZfUT(iyRb1Iq*JO#++Fs#Q8SmKn^Nn}gI!7}Bf-
zp|d&;vU*`Xz1LRFTw{G^K8~`XOziO>*gI*JXgp07)&N$xFtlW4YBW|-yh4I9^hCM|
zivNWvac-MeE+K6YQrtoT{Rxi@pN72`W^i$Mo$x1#R~+HO8BCf$D#1N8c@SKaL2b0$
zQ7lWwyU4mMwU}6l{JgyBJ|vS8=K$%U?h%#Y*1LCb|C?_PJebIXCu!)dw^EUA)N`2|
zJYqxwWediPIk;`x??3;14&7n%v@g`{L;>iqeCO_M+Y&zEQ(*#nadAEpK0#BUC`p6y
zaMAG7Zdou{BRZ{Hr(qmSla%JK`}*r3vViCj7lf9~*6n-B>V=Kuz1GNqRV&nBG&#UW
zZU*5&uyoSQZ@|eC`$CIaa_@HRMxrJfK9)qZ)9Pw!rpI{Ui}92}gc@Zv19$D3VfjYs
z1&OB0qw3aJ!!m54T592T7)B+sE0Ht{9hS!p0}DGef)%K@)20RD2Iea);F~Fq=9qir
zksz2Gx()Sk`t;yTB9wrIm3DplhLtiDPy@0x&X<ZmzlBO>-X3;X=Xc(DPN@%qzxPwB
zOOnhcpkiia++kcMKUwBXQ&?$&u|}S6wdtgZ&hLNU|K*p{fd;^};dg=$2+IzYWQk_e
z(0}@A>U%_;SVLw_A8pn+uBb>(VXV)Qk7M0_VZs7}gPHLnP7-PE?3S`dEje~OY#fYt
z$2hI@Poq0#G+1n-n2#01Dw)#19zDDg#2Ksu4jp=y6)HHZ*rhQlVAqGiSRwg_%iu`h
z6d6kM)mQ4f?}8JsbWv&z;6bn_I9Bg7&jbP#v?|zl%F5ImZ=``lOh`67dh_PADTibn
zlx)QCUUQ9Q1!7o;qbG6ZtF~@4d*g+x)WWK2&dSTzR6Dgt(U?RCD3~vo$(v0_6Jbn=
zCQQajwN0B+_6Sqe=Yp9GBSLy9aWMJ@ehLZ7<t|^7)uEu2%Yfo``f4_vJ%z;ripd&c
zSjoy9Q_{0m2C+VldPVCP0y$Eju$R~sp<QLopN>j#e5@GObnvW6_F4@edLz1o<VOuE
znB8h&b|PztkaVcQuZJZn12tgc;TxWRejv->1gJa{CkESo&Ybkpl{4aAy|ZSev$<j4
zq0?}cHCdnKFL^fru*>)oa|LD&OJe!5R;tJQ>Vy(ieBD$3c<a~o)S#kz^%B-FENf7Q
zqhx8W!GSrp03(@{%rLM%hV%drp(!LT%{8issB6BVNFt0GnCl<C>SHhp%jEJ88{sf$
zj!U8y1|%&XTZ!Xw)K9uI%QbXB+z-;eybAYUwk&1e(bCiXbt`}4niUfxYn+rB!}*{8
z=up8Vhq8v{08a8mXfyRtZb$@yOPfwWmYJrgl7J~u?z5uM$>Xe$Z1jnMV_Gd5%pemi
zGc7|iTG5~mYIDiUkNo|Cu_ZfqHv96+TVEM;>Zu7In*_qG%!lO1rjrMv^X#+zNfWV}
z+{^%{Lk<a?qh>=OtRr~LTsqdkTw+k>a@0Ix{24D>@7{ZZeG7~$<SqgJ-EoPQ!>K`-
zIH+XSf;^JG%UuVZg5-ZXZqz7kBGn4u__r8i6~k;Kn+6p;YE%ls6;rHj2<dE&6q=x|
z&+_s#;6GN5R?d_gmmY~>DRbl1qqGK16HOmHcXAx85H66vm`>4jlUznr`-Btr@A->^
z4?ld$lqn0o|Nh#61NYl+KTn1(5wzLgGkbO=Av(YRegEy-Ej$Neh-SL~{(BuTe2+Og
zG{HX1=*xk*NO6Q?7~>8OkD74w&59KPF@Nl_Ks2C7&=MiShdO8eq52MYk`)gnY=v>i
zujDBXTZ!jx*VYbzDrrEl6&ZleEPxY_C{$<>PAya#h(etE>Is~rg(ifJu$Iih;XG{a
z(Y$ZecJ53$#o}4Y9C-EEI54`vR!zh%na`O4GpL2FBQ1kX$Z)+A7wphM9e5z!s#WXO
zufP8K`)k+k+`02i2t*z>V@{;w@_$LV+DbxnZn>qumFLhZ>DMn6DT}ykS|~GT2FFZy
z==j)U1Iyeu--IUEhZ%jfST981A}|a<4ak;+R05orhaXmNzNtR=Aoye}pl7IlROhj|
z!n8O^_L5|E?s%rQnjK@ruOG2ecs!AZLC7#ZN+hF(iKsC`6Rf2;u(TL!1;kD+TKe<w
z!vhm9QHu5JBcr}qrB}$IW<gYr#?Xp_SzVkkA@kIqa}M}ZB9Um*rp;rIJ+^S+!iz7y
zxB<P3pJ!60>!4GW2E=RJxc+avkuiLzImIEEh->1ax<p@y%AnCi(lM#WY^9GBm@Ct}
zz$?JvNhgOizIJUIQ%WVVL#R;)l&F43w?gELr_U>D(sRYZLHKm6g5aMkx-kS|Sm(0>
zC;1?>X|rScC>ddx7Al=aBxdT=fIrc%J$u?w?qln!R<_}e#>hJLime)ll$Mq*TC`}*
znl+bSez_L<>yQPtPAOGMK)g`*N*g|@2GfKwk0Ob2$`qG+?C*<(O((3HU6?A)v1gL`
zhlT1p09O1nGkoA6q<v8f)p1DaR)2$O&#JgQl8Tmh{D<0YaVMQ33`EhPy=vy3($c_%
zgiMi^Ez>12thB(Pt((aOj#{sdXpF3DGV}b79Xmey=%XuFt~~zu<6E?7;WKi)D#b87
zQu&Yrc__mG6Ho$Js|*)b%gwQMxn@gPH9NBpvT0Dh8b@_lwc`OQyWt?D0a&RBr3bV6
z8<1=6E{p!7G#?9Eq@i|5G-igK83|>bEGxthg0{jiDjk)M@I;^Tgi$$!Os61S48Aai
z8Q634TD5A`s8M73^y%-u`|fquT~}p~HzRE17(JM}EQaBcN(oo$08F6wBzA!SEyK)i
zLGyDPG9A_IWPy6==A=iX_K&Q}VTS5Un~Pa@JZff!Oc&xf5lI)y%F=EZoWKvYZcZP6
z1FO0<YyQaa&ps403^OFsP=_T9`^SsQj+K`ODF{eD&>()SfiALGP*|lS<xC}wT_Awd
zDVjD-ISs&5Nq*0K)8mdi?w|ksCuvxRAAUF;J4TM}OPe>V88cFmNl7cnnBl{3@SNwp
zSc1k4n1J9h;_V#f1ndQOf_>Q9<qU6-FuE1Tdey0u>H|$chYsqw=K^xmwQG?32jT)0
z2%L{HAhNee&!c;qdX{HN89td~8X?osb0r)EJ&Tr$tlYvUgvCQ5^HYT+k(wRz@9o}^
zajfx>)?z4;hB_=n$TMD4>liV24rJv-<?h{8SZJNRmgRS?(vecG?B85KJa{kA$sFQn
zylM67)&KB^KU{X%Wy6LIqfc2B)KLP5TQak_hW}LWU5sQ2m;fsVe2@%>EP?wy6YQ;`
zxFgqw=o<9_S$y;pt)XOWQ=pAEZyxM(X$9m^eY&c!Fm2UFiCzSTNN((3(OlzflO_cM
zFh}&Uvs0>jIKjac*|BQoC0RR`FIRW&IM6Ce$dx}RZ4CRmXUB-k^B|MFgK`l**M@-)
zyD@qFK|IX4@N*g*d+f0<z4X$DAAVT3Zru}4JkfvHc)M5PY7kiyi#!*Tk0>5y3#9LX
zGK-KZB+*#W`TF!pDX)kJ;YJjvb?YD>FJ5>{m>XMLf{0g^_9k@C>YHa`WX(&eIAReS
z2RiF=)<UkbRs@gwE|IWrw2l$4vey62dabq6kqXJem)yk>s+M&w9B-N~zn3`T@WY>a
z>ZwJG7F~PowKZzg@Eo@P2BO0e4V;`E1L4?TArIs5W=6OM+#?t6lqne$hv`l(;VofB
z4&<YyK?-g)UM^N&2A6O^lV#nrmZphEEMnt8`*_*>L1`VE&EYEYH<XxRoId&S)@!Z4
z5tR>2UI3n%MnGAxSbJ_}g2+!EcwqL%jqiW{IT4`KPe0xI1nwzk6RT9|i2T8oR+Mqa
zKmD>o@8AO@ij$i($%yOW7B$z6+pu97gzWg^1G@k%0)Dhg;^1iRd}#7vnWaRw=q5~b
zkjZIP{@o>U_rg?~W+{d=)pAFzu%VjyoJB(UE;Pb)!>~+XEtS+^D6uBBGJKlb!Xlj~
zG##Bg2itV^>@+AXUzj^4FPM<k2xz^z(vyl4W5(RuyLZQb|GTKDXvmNug@uLI8KX;)
zh`MZ9>Y#pol^?j$JWDzz&(V0b3Mmqhqe31-w3fvHz$FkoZuPPo4oI&SR^*^xzm!6O
z+mh0UWdvtuhYo4Psj%<~q;W_|!4Wg&A3yDoO=3)hzn4u5x#DS(q76hsYLg~qfUad#
z%{=o+tj@AFZ9>a_YS$v!4fFflb3wO(Gz!}SUzj5%FT=B$MnItagjKlBTmwxXee~UT
z-(9(KWkEr~X{ViLow1Q4Q{fh{b|SpF(xgE!(FqG4;nA5kEike`am7zrMg3ebk^vH?
z1-VOslf_!jfG6%$R+e!SAk>Z>n?hsZN%a&E!ipSV9dLCKg#4opBPn!5p=gHX9^eE{
z8ZJoGG6vsaZiq@O%t=I!hz|guC&|;wV`(8*GHu!fY3$iEZRUrr>O)mM4Tx^?<bGKc
zCg$a|aid<^fxSy+m7Yl9NTv~xxYRl4q_`B9kkgzk#u{kWtl3}x`q$;lmtTMV^=#RZ
zx0lI9$hTLjf-#cw;|hhj0m2!*2Q3%+2T&6BsaxYUO{x(UBN<{Mpqc^MgJVtLlv4eU
z9RXHTg9ZV0XxcRRq{(3X6nMc*L~*xW6QDdMKCY(saYzBo0i;IL-PmEwutdbla02?n
zdY{9w8VUU;Y!W%D0JA0wJY=OiR>eVHMFZkcI;2iVyKC-Ti>{i-dLg+u%S<f^r5)gB
zq^*qiqG^>npuYkb)&P=C=hGHz+&BoW0Z9U0C9z3bfpQJd_xIo5v17+>-MYQ?)>{ug
z_~0FP+@Wc2+7gZ@u~fXI;{et~+93xG!{;LN_rlmLS7VME+s0y|1PQ~jCUD9;i6f2(
zD9*Zd!I$5B6P$n5RRM`aBe5bzeMmEj8OisW1c!J<lUG|4U=Y#`3g{O5W1#<pm2;3;
zm-xnWB76kZFcCvz(G45gb=9aMSOvudgq3!np=;DACDl^*o_HeA5T(>?Rf`;b!y4d~
zLz4n`2OY0j6C6XOpeNF4?0z+BiXPgu>61@BxorOY0XN=Qy?S+Nq~@l{_mxk*vI1+$
za)lZd)kJ-(34p6Ht0*uo8d;GESsc`0qNvbP=-oTLXo$+Z{IUxY!Lv{azSoM8Ybgsd
zLfWUMbXe-6kDbCzEX)U)S(o_6lj3Nw#IkgERuMzn#)lo2qC2dso_~Ijff*x;H(xOU
zj&O6Rz(jXQIptR%wTo5)iEJoRF4>PY3QX%8<P@SBtX#{LQ(Bq|q@YKz!-)ojkw8>N
zP0=HYie4)&{&45ch7B5Y?9>VOkgilMgriat9e0?3>GanYN0<pf#(+k$r#RB7h_{$1
zD#Q@x%~OmGG;Uk~#1XcdeQdOveNh%8*C3M2vJ>u_(*FA@D)^QOBM%!~j7Njy(XPV=
zV`-PRM2xcE^jz9(vH}#M^<vZuvAT%C20#-%V*)fgH!TBtQ`9f!G?ah-(~@<3;f0}i
zV{UsqI5)jGjK@GV<dvgg8!~f}vACk9Xrj2d(fQ{;y?XVE6)Oe|7*MTRHEUH4i8NWs
zh>km1Rl{UrWF{-!$qNpH6*>!gmc;sE12fIUM;`_MQ0N1&7^H2@n4yCnM6%Ip_C;1I
zSq6mu<&Fo5os|>SPGN<S2MAH5M>GE?;tn|<8;0XEeR^(+X>4U@R>2kiVnZ5&a4=2u
z2Oi2AZjKZtYt}K+qukHZus1o7hm~uk(kn+RMkKwi+#eoQp>s4ZDVZ{5%9=H6jyU27
zWMgSnIe`8q6uU}gNOs%9@Ds83E?rUxj5wACGmc5}w0e3n$vSlcnMG#M;Gwdsw76+C
zUvNRHxE(;SA-!`kM6*1(XK%xzF!)mzCPY$|W=G83)|gA9(cm03pGU)l&q^^?ZoEh<
znR~@H2DeusYD9&ZGv#TJw#F_osz00az~o57LZf5a)<_j}JWGpj!hYe#4h_!Lk|n%V
zGBp!+WteBA10>V2Q01nZZu!k`etG4U_uqg2)mL9lZ^czPc#LCb?Cljv$H8+T;LV;L
z2-~Q8tKszm4M2jtA|fM(evV}dCOK+Up!1WK#0^50%!GiV3l2<Al=vwkgh4lW`}fwZ
z8PTnvc|rT*LSWkKU;w2Dv?4OatuXShzwU)2rSX3jE$LPyFmBu!Q+kyw+C(YMF=O7;
zk;8O<9~B<Tjuc>*ED4koqBsDZfPDuU$WmnUF&Bna;+BEPbi8_X-~;K{F&HH}jasW#
z=^?U-%s*|?<grgaS-W;^mo8n7JhGWvTdhp$Rlj~|@O=l*L0l7OMQ}os%B&Lx|2mLC
zvX(B;JPr)cjNG}KZc?>t2hgRmvNbT(t=SS%Mhy#o;Q%e4-e!f%)DS8)^<J|YSOYQg
zPIsiq-ycVcf8{I<nD8Z^S9W|U-&PUe@EYOBitFTHC-CuUZV)(d0RVXIwJERPci#nz
z(BE7r>u_o3OC_{hw}1d~!4DJ&(YB;PRH`0$VA%;L%$zxM>(;H8UV5o}j4P9R<r81;
z4yEUxFojxRm0OZG%Zigo#busk5onbe5hHId19r;GT_vLAi)8JsTQdq96RYqaJBcBZ
zCph=2URvpHP#iATO|JmWlr}~TMwoYSc<p^z-^r>)hNVr&YE~G!UAxjEDnwd(aksLp
z#W!{Do~pxTJzpxJbaPgwtev-^g<QL7(;xouhpAJiu3fv1gdHoWVH~}@0x1ryF#2Z}
zY%-4>nNaQ|DIbNLQFTa&l@=Vp!ZY20=Lk_-8nxm^6vt{?30o6cA$1&}Fy^gf->4fz
zKx3z#DZe6x){VsRO`ynD9GS42XeI`Z4y6NNKEN~+JLNrJO(Pued1FT0*>+*@H?*|0
zQhYSj2n!@4J8W1gOD-!;kVXojfIH5QM2n4+3kwS`zWCw^6DIWT{pdUI+~c!Ser)w>
z#EAY%<W;F~xJG}Ux{nYZ1{^^`ENe-y;H+Jn5fQ_MSzz18QiD@KD+%BZy7TuUJJv*4
zL>U7eWDVhQ?~O>=;Vgb584774IQGm<x8g?98zG($0dh3dxfG?~d;;w_hJ2@~8z?10
z%LDTs7OE2le*?WzCYo>FoSIFjnJ@yH<8NIOW-_`|d-B~13mZND_}@PL^uw>d8g$M%
zRZURCN|^@HsZ+YdEi92V&<xE7Xt=PP23F9*gz&HhWQqxpQ)H8(DWYSTfU|kONs}Pz
z3^73vE{I=+@pe+aP*s{<WYnnCu_^CFg#qR<fjGd@4TQCL%1(^za2gq&DabfJMW5vp
zt+<giN@02Aks5W6wl1$Uj`Y`dg1>xc%{7tpVy5Xx`I-kuTi)PgNKin-O7o$%IXWw2
z#Z{kpV&`LzJ$B{Fl@C1d;sX!#QYxKbA6x3FNJ;1Am;3kXm8y6T*#VAzVrW{7Uzlaw
z4f2ZAP;02DDJC#8kpNc{CkEOU*gh9*lhFu$`uNt*qvz5}f_0E6PW$%3L+NDAjgkut
zFzUv!X%nG6vBPb{f{EPLH)}k%=R+lKBx-W7xRhhE0;2SJrD3jVnQ4i*G2}a?T&QU#
zaDp|2RTG-%9QN5ol~rS#%;UyYClZPB^73iZrqM1^>PoCT3510)u1wx5WOGnUMmmPV
zH9CfpeqvyDDTF()3zPx=0A|vlK?+p>F@D6mrcMpEfmzm?n@_NoN+zJvIS4PFG$~a_
zkZ1%QOCZZgi=M}X7c_lC9pJcInr*n-Vu#ymbeT&GI+kOOQJS84<S~#KIjkA~U@+1W
zapM7cSl`-FAKe@rIh8~lO+YxMDkP*cwl>XlDrpR9-u&oMqwe3mdyP_8D^<5~;~*v!
z<42q0qmKeIRB6fz6VbW$+LV7TD_o;vD6Kj84(tM}n9#DNI{N6qN6w^yGY?Sw$m$=}
z07@J%y0Tgx3`WvZ!z~Q4Fl>3r<eH8h0}R6s9fIT<+F<m!>C=PonHWxDnf-(plrn-l
z^ghRGS^gE-bMfe_h^cB49>`iw+F@VgJ?udF;9Hp~Ou*oiu#kCgF{mxQ@^61jTy6K}
zEw|6^F@M2(O0E23)8|`WHRR1V(^r9yCqsoQTkcg>xJJ(kKIU|Wab2tEWX(K)cz?zj
z8Ow$1-f&J();*?G%cDy)(~k_R_mmVg21Pym;fG+JiIkJL3y}wgs;DbfqSk9Nf@3-6
zm~~;bFCdI9YT~Z-hMKF<nn7w^NDy0~t%Gn#P$%=PS<{yyurzXo4UU<@L=@-115?%3
zE3^bq3=uV%0-AW||Fw6$u~t=Aem?{QPD_K3^2`7YCZ@4@rAP@@Y+-B~qE7fyI;}|)
zWB)i`s;yt_#~S^jUr+)qi6$M>#8d;+jDwOimI`({*aRqTX#&`iR$lE(r8L1-ErM^>
zxxf3?U2pBZ*V$*EbD#G<&JH1$!@cM1v-jHT|N5<8yz&e8A$Q@VJNo(+S3LKVZ~xEU
z5q43KnK+eh_58qKBqKSHcY@eFoyL?_Rw=ZuIEBB#zI2U2fA8E`WGBPpL#LWP<8G)4
zN;zX>VSVzGx}TTtO<<%!<6y*mD8zBF&-57_2e|Qmm$;Oniv3c^u_?vG=yI1Gw}OM#
zIjy@XpN>yRthOM95BSIU%o<<NzxMFM8sa)-aa0Kt={P81D$VDv384>vcBQ!Llr;J`
zcW{e4`ow|-e|+=Jlc)ZD|NeiKK9VW?g~0)4Cz&jF0;%*!i!23vQI$1nJAe7h!g0%0
zZJ?EmRMD+lX`=2gf9bkrmo9a0yixpp^UcNFh^kb5aPW+Q<1nNPASuCjIF*Wie(qcs
zY0wD}QhUyRUaW-LA%+-i5<v3_ehykXl`N)U`?X5vp$?Y2aYAVR#5=ZZ>FI86dZs_V
zMkk!e556#XlxP~yJk!I12!cBKZ++lEpvfm~lkMfjQY)B?QlhO7p^uiNRS%0W2Cs1L
z^|7&6CMG`cfq&Vu<yPl<^zw&40DqwZ)9HW6T;)5dMk}$O_;F2H52^6Cfa!%SlB%hq
zbP=edtS{0S9qodu!5EZb=HUH_f)Nisa-`r>!0|(>A#E^F?9}@rvWp;S>702SJlOlp
ziH^!3B61{V3MfKwLz)R4lJqsPL)Y@U%B4(+pw<V~kI@Wm8BdRwSG_DUgOPd;vZ1Hr
zeP#PNXV#0-eb^yL>kN5=vu(qzWW>Bvvd?Wjh(rEW4+|@gopHypW7~?l!#(TPJ-TDZ
z92k^q)^uYt$BrFI->bPMd5nlTOa`y3|7yVB9$RH~$eD2Y;Q86H{$}`FPD_vaHy$uj
zgXh_pJe_Lj6cArXb(iD_5yu0WHKooxnFov=_J_=or8$7Y=+S&0h$v*A%-AvDTgk#@
zLwU)r#>mJr&u~$v^E>AP6%R)06S{6lzHc=7v}JH!&AR+mY37l<gK=2=A%lST-M4Q0
z_7zuN`P!fU^pAhM@~v;J#{+@CAX|{sGOrY8k#YD3J93+N4L-!~(5o`9--s@l-|fe<
zN|jQ&=!a}o{tl@*oJJwrQKihBCr|g}Fcz0MI<IcfhI3cmgdhB%>k)hHwZbI)``>$x
zA?`>Ki=!ED0ZdGe0V9zFH}qSWX6#gHf2;mQZBqQHE*aEG#r{>?AlBrwe$3nO0Vmq3
zrH3W>(DCs?0K+u7_12NU{cZfc_YRGZZ``=>vBz%3?Xe8>>Xq_z=9H-&lfEJoylBbl
zs%|6Z&4wiO>ks9WSP0`fHiNDx4#;MaWYkidAWZ>Ed-mjy2(^<lN9YTHg7<-V)MqqE
zVs|eJGU#={BG;|!HsM3*27ZOQN;v_8!T<)&%SFl*aC31(Qm}d5@^*^v&szqikF#~@
zZ{DUE7(Us!DfAhV*Yr241@W1?jQRVb>d5GG|K@LyI@1!ZA?-4o$#%t7;%d|0$r{mZ
z+y0}o*SmM`o}7G7o}sE^ZXaYx_k~xiSdm!(U0Tpw>WN5q93Xuj9D#JsrAv#(4+|E!
zH{a~$_(@wmy@Elqt=L(Owf9p>vR^d3Xi+g+`n+RDH;g4aQoxTRClKR1(mvuM4<0G$
z`LO(XblM3_TcV)-CBeypevL-@9anN-Inj2FcL8}){=P`QS+yBSojC_fZA)#{47XiG
zDZBB;8<#I%e&)=X!-o&ABY~GY=GJE}z8-`RYv~Iy)|*Ee(zjCTkV|wN(8b$sD<YRT
zE#LTt``-5ozQZ7aY^$e~?I;pqmkQ%)Fijg}I|irbx>+nT`@dP`mT@%p>CoSY5BD+^
zl}|yk=AgsK;5awk)a!u+zat9kN?E_**QiwfUR4RT$wnbJ8t%nd&rGEg)n}bNXX4Zh
za)8fVm2OMu=HMmi$Zy`fdFRfZ`}Xa7AD9kvoN_y>D*I1dqpBbPhF?(LitOI+e5cEC
zXz*E$^{!RKq$a<4b8!)D%fQ6Q3+Ti;3v(O|b{27*bLWbPDZYTJRH;V!fty5sYPzv$
z_qWR9zcpj2zEV@kiR+UeQ4X&a*f9%(nyk|(wM}UNry8YYgN$~@?YG}fOy`9cUZ|65
zGdDw>slajas5a5VZ-W4E-w%}b1~((<Dxx#VgDfm*o1|d+^5p5m`SV@2#dxE!t*R8z
zv6Vr@(J*KF`-2}WJc9Vb)~$t`{OsA@F;=XZk$T+}*o~ozqo)PdnSY|nooxFnBUsaj
zzLTy@*+@@vNM6ag(e!~4oxmgV@UQ&-g1juYgqFC}+%w+qo0mLpx#gDm^XHR!H8C+U
zO0<+UqR}h5{-BuLWT)N~K5D-HU}r?5SI<BHpI`i9ehFV4^PXmS@L)Gp%uV&W9vkZ=
zYXxIl2-a}BVZ?wWrZ3&lsKb(sk{hDO@J54tBr%{<ni(uJ1IYwK`-+!dDxxiKz13@F
zA(<E5gr*87Izz0Mrsvz)%)uTKv=^us%LvwFyG0bpKN`njk-;=$Ta&miejtyNKM}Y@
z3I*MY{+DDx)%0-=wyGRxXBJEQxYQs>6gg*&dGqESI&|pOS6>|)8(U3_7K(1y(sYy}
z!~dL2chuMHPw>D4#TqfTBipg1L`K_>8d#`WNSxs~RHCiUJ`q@05~knR;DRvy2rHCI
z><m|5qB6)9mh=!$O!Qh=$oFOZI+<mcqH{H6i}mB4zEVR=K?<EjCL%g9BBY7?5`=K#
z%q-4D#^W-qMcNcup1x*HktE~mEK2?7_<#h=1Taty3v1uLePm(Xb=O@DYdZBuw+te?
zgb%BXbP3||baG@lj4cy5Sw<oUmUi!x?Z=!~!wJD<<EX222{fxNmDqVTO^N9q^j~-1
z9R-#T%1PA@DaFc*0g3xPB=+p7b<au>uHe3q21geqcmOx0s<SUjC!$B3IMMqj+WQ7A
z(3C+Em#luSvw8gS0#Sk{!v)nwE|sKVF7K$}<X*C5$@cBr$-KJ%{`=X}GI)Kj^28nI
zZcD5@**_@5e_6>^SGgFhBq!pi(^S(XcGX1H^l|BuleRb6R(^Ux-ex7n5b2Duz%M;-
z>0yKvr;M>LGCRD!doh?h4qNr!d#B+9W^f!A%7Laae|~W*+t=>nQq%M@idV!~xpE}}
z=f{s9U$tsg21(9w@B0WQUtxnu97((2GoJ~!D@;+*l}VWR1)0MpVG}UDV6r)HEhw8_
zAu$E6@qPx7kZac#o@3%Q7cUlE8=vjE8)IMmGpTZ;f;j}nI_KRpmicLTreop<*_Fn`
zQuamiN@5U4k4ArT-+h^Bgv2RSqA3xikr3#n<5KfLG&8n(37byZ&Bji3;ma?-+?VK8
z-$zVj;f2?&n|9PmVjwBbP9!Te#T!Wl3dYOi=^~96Mj3dJn~~m|YBe~5jvyNRI|~<<
z$1f_ycZ?XKvCf8lWS{d&5iXzW(O&p4TD&#c&JG!tmdXWGDx<Y#`pzny#o(m8P(fVk
z4I8Gpv}7JY+NKlJdFrXB4jw#s&pr1H8`Ft7bm2eVy4C&cXPIZClwUNG3Zy6-dJVcY
zg0@KcM9!EUFK;RVH@F%qf^Jtu*!z-EMi*P>ni+tJ83sYF2gfRWE3636vT#3WxY9Fr
z!54T)p5zK980m`^<!~IoB~FUv4(Y-T2#}<T7vwweE+lXI24Y(sO$R~kp<+5QlN+%;
z5Ql0_rFxvlmZ_9q#J-Ryetnif6XVhZ9>4x|;ojqnq2QryDgoD64asFvN@e_gVP{rV
zwisOJ`b;e`!WHCt5c{%y7+qPvzN?cW&`DdC-SbaA5qV5>1<M`;+(^`3xtA6XnlSFK
ziaC6~BU|qIRrS1j$@=4U*IhR{Iyx~iapJ^@>#x6l*vzY-$l{l$A)9bsd1bU_-(RK_
zpab<e<NR|_pT2hKQt^|~(O$v2E305q$4|1S47V;QvXQj9Sf+BGR-!)R=5(tdQ>!ZW
z?MQZv>+vBZ;SVj9qf6MeyfkQ){>zq>bXG#~^pTit?UnPAY4K5fM|XxIL!Y5g8xD0j
zi~=-6e-u+4=_ot9cI_eyYY3T6Ok~NIm!&;QrZtGnk|BN()Q77Z26GVa#<seHI)x&j
z6#I*FPkA*`yv2$8!k&nx>JogX5?x7TWGU9MQQ4plA^PpIt&!e^s?@yYN(eOb1XJ6z
zspvtnd2^AiP8)<4=7!xJ?4`x7aRzUaHK_L1z|kXhJ**zBKj0R5(EPcKg9Bbu=k(_D
z=XVulLWhUYrxRqlu&~sC#PDgW!9<k3WmH^2*Dgo`BuH>~4>TGixCHlx;7)LN_u%dh
zjRw*<!5soYgS)%CyUodazq{_t-0!Y6^9Q>7bf4O%s<u?s-p^AlgLv1O`!Z2@l412W
zOrp0tfH#;rHwlJT85)F39a8k`=)fI)4l%=*9L1TB*?9BENhpF$IcHDf{Vw-bnPGvm
z92gsOUqn_-ggc(*u4NTxyL)pp7-^JwC2E63ysGsx8<g5~#EIQ-#E^zJo_sRx(=b^*
zh2qH`(oAchkk22y_Ayq~SQKCacg{GJN32RTWBwKoiISPkOd<$7FJy5UMVr*LfKRMc
z?you4NqC`I#yWb<AN{>Ow1-2Xis(+6v|5!kuEKlo(TsU%7-qr+{};!ann>1YU5)>O
zMAoVsGg-|;;ll>^5l7rgA%AMqgT6`*(ZxW25A+s3qpd*ufhR-oM+;-eAXi6I#Ejo)
zHh{U_@p#g5xid^ee~CmHiHNWm0`8VOXC+_%X?f#8t77<8IeWZSBZGb9+MaYT`j?Hd
zFBLW>Bn>0^+l9<jN1RZvT<9kDXV~8_2$nXOKJS#KxbHt8QinBa$@M8R{HCnJ8@q@K
zM9~gxf_=Nl?M?v7h=8Xv)g3qiPi;@XR#%ZFD%2lPW5CbvMvLZgoZBo4a<Dlb*Ue@N
z`aEn6BpMkRiS{^uqPB!Xpo2$UcEgg{nswIAdd|4`u$DtElYg4qt!A%T0F<`k9lpB4
zCr(@knDJ>$VX@!3!+4p1qqh{&C%YreKJ6p21(JsPPzNLL*YpmA3RV&uM;GBR7Wv#9
zC&F2yJamboT^sW?8fRZ6Fn($KW<Q7OKOsg-a1q+~o?th`JE9mYd}5mR3gl0CURxUS
z8XKsZ@IKqss~^D8B>3!RS(Fd%)s&5a<RNBnkz5a?&VjTli{=5LS4}zCcTBSqr8_-)
zwlR+QCrMY~3eHwem<(x(&s$;!B_%uo2Yo27)Rc~Ky!X4+y(OrJh%;nwVhOU$R()Hv
zYma$Jq<vo>15UfOq*|81j54Jck>lH_e<$d*Dk^>JTDqzw!WnAJ^GJOgXP2?%=Z*bU
zc@3%i134pGzD>$eb9Zj^^f+QzxgVATi?fzHWC{;%-1VOn^1E02E2Hx$M|?CY%dJl1
z30(FHFYV@8&X(JMQ&EAy*$iOh44fK#x}KHQy7{)Ldb^_WCR;wNGeNf2lJS^e>-LX`
z{2EoPvjTTKMk=GoVQ0#Zd+b~8k1>hw)hGvK>uDu3&;&$IxGsjbFbh*Zno?6fi05@H
zkA1-Ma$673uF6lNmM@!x1=(Mu2N?O}U}fG62o7F+Dq;>XPr6x0!^3~9b#o@n@vctG
zW5nk?@ZH8_H5Nd=Cr1R~ez^twwc+QmwEb!G4s%BBos-5NVcI_TgC`+G5s^{??0QRI
zD(soZYSrrTzZBLUnc$Z~X|yi12mPP84{^TJ-nH72r2pBSE%N{f@G6@RN`Rs@r#K`*
z*{s}li$tclXnxT}WFi$qNy$IW&1`1<cL|Dh1eW5!N!Sco=QBkcEG7nzsYP#s+65+t
z(RWqB>T2R8U%bb+URcs@9`F@^AA4K=Tm=RNT2+9WX${rLUK<~W?V58=+Zwtt4q>5`
z5&Q!7>?m*LUEaX*hjBbS@sl*$cxJ~i404OMlXblP&Nmhn!A#1n=$*o0w-xr@O)UM@
z86(zx&3}4xvj^7i<#Hfm82MqynN4xczU#2%d?Jz2CzPXH5b=EqJ{ziyahb6_M1wM<
zK-?9Lbuv89cy3DbGsb{@&SL*xd)6}j)#K)YDl`{Eb|jtWFbZ&;lkRs<i2Xz*fqe>h
z%oERD031)^0Kvqcfb9!dF6R~Ql_Mvcp0~za@b@IU7Z$y))J(cs*aDxPs2qr!^FNjy
zSR8|w=nH-acpDqf|A^HS@O#l;^)PGw<MXZ;3d^6Rgw6f%_K}gCXxx+A=-MZ)AqnwL
z=E%-(u?1(L8w&(W6!n5{lU+h@4l4g)Pin~-A>)_AU}aLOrn;akn}Qt#=3T4M?WqZS
zGftKkRNGTBto@6m(B>JQ3;VZ#{;MZmgdFEna^TL{&Em=3d4)*?H%!Ly!<+zSvNUcO
zCM&*q9Q)FQY`Sbg>hl#;R}yp7UydJNk>9{1O?|=%9w(arYRpf+RqDxf@aFjSdMh#d
zy3H~Tr4OBYjo}R(9Jlkdt+F5{e8la3jgUWn+B^L>+{Hmi)Z_klZLM<%2lAjBk)vrg
zbWA0}h$grrgt~I$h=lF8x*@non)m%zG$-UaVPcr`?_`xqR;dS}oA~1X@?#I=)*r0D
z_~|r`!B(%mYf*%IGBvhr(uh@-|M^g*yiZiZRx)?q7nl<8ViEXlOKCh|WYsL*n}cpA
zSw`fXVe(*WfleCVn&8N2JF(j|N2&~kp@b_ru8i<>XlJmUc}x*G9?2ykM3X)ixBL?-
z55qyV*y4L%E=+06OxMNzTq(8uO4g8>X?46aR{U-P>A;WmMZ|urm5>02^TcPBPMJWy
z<n}yY==)+zkee};q0*5>>OO#mM4ql*s^+4*L5v604V8sVyDSRn6ZU-jn=RCV;LSU}
zl6q3+eNo_-knnm`Lt~>6&W!QJq&(R-;7;m8RV~If{w-Wo{WuB_+8a(1#HsYXJ+_<f
zajdjIQLcnul+{RkcB}2ORWC|idL`?p%Wk*eHK?F!5EV{BE5N*%|99{=is8zVONMlS
zG+k`krNj`N+6WS_8~^&l&dQa*Gq7ch_Iy8jIGXi1kj@T^#rC?6)>0)A6FFJ&DA`t`
zz=}%_72mb_j_{Ut1B%`mznwEdr|Zb@fD;B#B%Fv7dRL*U7Aps>9E3G3louZgm11sG
zQqIhcU!;!vQCd76rKvO5Yy1hkLTA(NV{?ts8<N^pkH4Oy2ok<}KE;FHUgoe|Z2)?H
zH{BB{PWeF&-~HAY7rrAnib>^q5=!R#iZHe?u~>XV)a@YEBO2-XfT~-T5sSGx`D1tF
zc(vm-WP9+{?t2<4wJSQP%@mnSpPrKE@2=Qio0MHG38Z)DEX_nMF8evx+i7U<v9*yu
zy{D7G*3IC8H1Q*ZV%mVx-jUXeH<NiArXYH6+|3Tl9p|^<R_K9^$MuGriU;y$6EoMj
z!e0dAE$WRVE2D>?Lb{6BRro*7V~ztAUctQ>U!Ji|5e&k2WXT>ixHd5uzE~xU!_tb0
z(}J^?fISvTq1z^-z8I7!s-_a;nh_L?a_e8|t+BWvWL-SLa5}3L$2ul`c@a>~7DZQT
z${vAzFO6v{%sSSq%2OCM(ucI1WS`&8rp&tZZ`(Q^R2|s%J%@^kiP<WH=H95gbdJir
zSI9s7vrA&l8qt>WO7px?wI_z;>qHZ^TVf_dA(t2-Sx@1;TlQeW&mbZWm;}A78qBMM
zXE*9TZ$|>v)^Ndbnc*$jud|=<=|MA~;?A4J=<hC5eL_q&9fOre38l?8LRPMSV0m`;
z(^z_jlh+e{6P_8VnI*ft4LxEDiw)#mSfk@3VsXXP3H@C!_vmByhterxoF?U$$vF_2
z-KX|tI9tlG$z#XcVw-V|u^`kR-xrl@c227d;kbj#1Pcxk1U)E1qD>o>mK6_RBF82g
z%a->keyj`2iLUD+*wUP0Tef>s#uLKPI-Lz;EE&(T?F=-?d_6d1c;PJW=PJ2k6g5Dj
z;b7Ejox`ywfyH4v&8bSkCwI$Ra%I$^JkYYplOXBbE~Wl#R3Vs_hk|8N^x?|s-8(80
zxa^PZ$@*Ja2#rQw#wDEmMYYJ`d^myEj*|@U>_uln?$mr!YI0z|>gT8yRNEV@i@#H&
zrbaR1hYAfU&QT$v$nwgWkq<LS6s1hdA>mTPx(>47<VBWbP|RtGtxu`IcV8lZzED(-
zzTxxGy|r|uQ=t#UDpXxFr0*hu`&wayx$~J{kzf2+1pPRwW3y7uo=`A^I5Dmnry4?#
zg?z(TPGMnB{1slt!sm?VBTfQ`0{>UBVoqDekKfh&3c@J<aANF@j3h>!9g3$iM@L0L
zHsy*sC!AHFlDxW{-I@`0lwoBf`hnL$E~&vu2U4_-NXf)S#Bl@x3{{x)M6EHBGNb8Y
z&=CH7nQT$Bt%TYrD(oO!kw1amUhh;tthf~;UXZ^NmVuP*o;i8jz;Jpnd|$9nVYih<
zLeTBxYI=~%$DSRa*eZ}Q-uR_xhW4fTM5p9k$jIPQlM^137DtgBl)hWD-c3#Q@J%ps
zY~VFRlL8VnE|}1}=vxxJiO~k4#ox@mRW*MCQ}UGFT6_qDp@B^HkCsHc5V+v?T<aI`
z;n;{vXXNJ^)w+lfwQrij@<tO0Z`iI#;4tW^Ekx(H4iTcrm6L`w?m7sH(vIDjJCb;P
zm30ez_%*rOj69o9tRvU^Jv0I6P1T>1ulywHtnA6FpVD4Z(-c58_+b~{*)NW2bfIWI
z??$9#Jekcz4e%Vasn@D^r1jTAEcf?5g%gM-zngmlZT=Qtx)WGD^rd;=&i%HLQafXO
z@H@>_T_sNJ?~J>BAvRk0bngCk#2siq99g!Lg(XW7yfUwMW2lMvVXmBrCkLaVL=F%z
z*5t@L@(<wo)3;ftFSfO4yh(?RX57_iqdRpB)Gat?oOj5af@r8hkHuq5f_jx3vVC_5
zRMOFu9CJ&v(+}uV#Gu3$`JW2h9scZJT`heBXHg3$$-@k@2;8@wM;LP(TB!Z_al)Vv
zs(x`m-7hHI4V1pfzyCx(9qT)_&DR>W#*P1>Zr2E>>Wo}Mf%v@}Q11vY{ZO?<Qeftv
zc^677iX%N%eMj84W~GEQ5ddR@dQi|2+o|=Gf!N6^4Ir&l&b)Ee$u0;FUw7wybQ%?K
zJD4i&G~zv2YDOh3icen%qs)z|RR~W0Yy1SKBOXu<L(6KLtcRF9Vn*gsFvcCW(Ehmm
zcICJ$`0dC+cetfYzP^_DmU!r7>MnTel#m?XollLKde@Tvku{_~FGU4Mx`3*=m+iYQ
zPOid2JPBX(SOe>2jin;I6{;l8tl2l1L#lI(LRGf-%ZrUjmVAui-{kKfcGdE$@~l&5
z==R`>vDA_zGvsYw1*03SYXsPQAG0!W2Cinpv~A{cy_i%oGOyl}i)MIH7a!XXti>IH
zxE%{pA7{U{*X84etDf*vU$*}GI<$>(HPs9nvvT%^FG7FT1u14pMNpf@71Qw<NPVUS
z1X#%#^APr3`@)-G(*G_w=L0s(4e?PvbT5#(+N-QKn&Q@X=lfgQ%*LIAPZt@H@0`qt
z7*Wm%L$<z|fQFe}b!5{k$+lVHq_c<6@@;a-LX5XRPv#mbjlFv);Q9X82(GRxK`mdM
zA=wK0_0Bh+_ouq2m<wyv)9*K?yca585RJd;XRs+WlO0Bb-ug@X-;(~Wt)K`?v=$#S
zcb}O;dVhI)h;1|1H~p<00dE7o!PdoU_9j452F2l;9I|}{qw;~g>YJsx#9aSOf_1J!
zI%7WE7iD3&Kurz}7mS4Wz<wf?p;jU#t*7yWGc5znK9m6g34)`!%Pq8TlX*C%$%#1f
z-NICEs4q+DqeQlOG}Dhx?@qC}#J9;#>s=Lp2VBb8ylP^G&@zIA9kR@ncP*qr1AQ(k
zI|O5Y2jNr`<s=<&AE1WJSEKPS{<ENrHIDMuz=dz_)u@O!nInzaVOAIf>N)_pA>*jN
zMBsgOi)8)w_jcHvm{=seL_DtcK~-H+kFMf)%vC{)ZNzt4mx3gp2s4}Ro{D0TMD*cB
zI;4MrD5VU7FCvco#*SoCGIT4-@GavS8U^(kaKN8^7M}&30V5Z<FLn=)A8pM0B){1q
z!!yOUD>d}&aF9dhaFXB@j>0I&7Ca!cS=Kl(hSG|lC+wTOjyN<VQ@wiEzg^q2|6?RP
zZC!FsJN?AeVkMr@W77E5{>)6JwT@+?U8+NGYslIo{)%NZy^6N(2g$LyWYf>YE;ZB8
zAqz>q1y*(=FeQU<naoA5+VH;*;*BWkJZ1P6#$)`)oPoctwLeUFfgkaO_;TQ~oyrb(
zKN{MP+~9qE<(pzL>@bqm^6^17VmWF^o4m7Zj*Ios;FnAti2U#sV5+!BKOdeK(+V`v
zlXsOUEF>Gk@QzPvAd0{v=rw3Xs6ZRE8VMk&+8t#vhg|W%vdy{<{p!#9yvWM-*|e{{
zHUrFON@Ox*_?PrXUAZnApq;TWyn`TwpRys39F&bOFk@MLvlZHNpG*CGNBO7U!P;&c
zeO|M0jlM>dq(u(Si9KVatT>&{2g8+eC;%^=gpZA}>)T9g*i`%_v84@x*uQ5aAStJ6
z`qw9e|KnNZp#R?RY5ko1Uk|=8&XxuA0tp}h&_538F|Y4?u_ug?Ol*yvoE=RJZ2rBn
zGqgnFW+neb{_h1pKZ}HgwX=yMi-fg-vx%6Ak)5##i=2tAnX@@L$EVMNf=K`MOZNpE
zC3(9ga`d)a^`UXEPwzgwG#5&17b+DNI82L%Ga4p-jzEc#pWa!;BRVZ%5XVm!K5Zk|
zL1}&a<_OOp@_;7F*VRXuFbYDClF`8|%JIkQCJ1$J+n||Rt~4iHTCaLR%CY<%mUVl}
zg17GNEy={72nHeo=b5)GFsD_HA^2u^WrYoXV$`TaJc4iNB#CQK?4f9^P)4Q5$sf)o
zTB4+dVT$$fpSmr=KcSr+%7l?cX5dk@OYa6XvQ^jpOe{tZ^u;tB2lpge>u-Jah|6Bl
zo3frF7n_Vy%KRxjsV#1fGvOI;@oPlcZi=H+jn`&afrrQ@#tU>&sikNa${gg0_-fl%
z%Iq96QHS<&Ud%<w{~}!c-R0T0vAG2$^iYpbe~sB>arFlqvl!(;R%Qt`!Dt-?9~l<U
zz6+O4ZvIO2pA`-7_?!6=VT;-tUYx@M(v+Ie$~av7b;`|oPVFS+)eQ*7x8mCoz8qY*
zcz)ebrX~{E#wvR<M%q_xum#2r^PrOoc8?m&0NCS1=m|C}uqEcd8Cj11HL@xm_9iSE
z3Wk;@M$RnCE{4wk-b>pUn3=GsTNwWvU^WgGDH97bb7yik?oTYDcGh-|%Jv3ECM;qm
zt`<fnl8y!*EFu=pPKqXuqINd+cD5$A&Pd$A)qm4O(ZJEh37A0t?=O&Z|BpL4k+c4L
z;bLR!^q-a#9ZifajGXNp$=L)2|IgD7iH)6u_5VKaj+ea@ldeho&Q~3IEYYc%Qotnr
z)vYE5x%26995vw@QVy*g2$pOfy|wAp&QGv4e}3Cn^U_#IH>${LESdya%Be8cL}|oJ
z&>wbhnd4EjP~?N`=S)(4>Vt9kTShMzvu@3LcY9A>c0BYhp9O?mQ*%VnMGz>0{`2Qf
zn70w`pa4Wj>nA1859Y>z*moh)=BFsn$@;0K+b2vG-@Wu!i7Y+ltmQvNC2nh*Zc;;)
zH}+?HBi2MGV^l`vpNs09Nx=y>UR5|oJq4d9d*~gu#Kh-`7psn}9f;Hw1)*17WS9h4
zIXZr>Xt%WKS*G%*&KYN6m)~Um8dl_%J?oKfIu2xspvntq37nE&Q&&!AN<XYR>b0HT
zuWjh~j&46AY+%to2poj3iFHU8(L(I=SE;73z})L%jY8{U0_$S`MDE21^6Uy<0}#Ca
z^9<puISAMP^9>?x02Ww`9=cviHR+HrBF%~wxfw@5dk?{8uY%Klo$@iD0xR8kov>qa
zL(*c!IiVHl8Yv4ml|xxT*dQRizr~3qtM1;RbKo26&{PJtWv^PJ&Q!KzuNpf&_k}yJ
zz`7O_z<})C{-?z&FF+7PS_r}|Ed&r20gy8Y%b*nxA1vH(TA2Nmmo1nRlqke4pn7Zj
z9(=*~Ikc|sfS1zVuvM~k-B=1+6^DD^Bq`?`rjwN#8*}PX@yU!UMynt3HFb;fgad|Z
zaN-fz>C$Z<Q$2Z|x*_o-0~xW|r-Lmft}Wa0;j#h3ZLy<bUe-H+205^MDi)vZ<Q8#-
z;xAs%LbhCyN{GDTp#?N(peKCV^234JgI|NbXjs)Bd`&UHT71WHch%wuqPPD(Dr48Q
zpIES{wrN`QSgXXs+Pi2_cl4WBlJ1TwYGrJkpO)sK7_1&TScslM*%=<6uyAmlx~Ad;
zySN;fh~OSb^FbF7A2+&Zxo%fZdH(DY67n8rHVQF!vyDs_Hy~;j(GEk9z$1QJIoG8v
zw*qqgA_5kB*tL74%k}w!xJcIp%kGt@dD%~Y!hum8JB9zQ+|(ulV2DhC6ORv<xyMg2
zSh%D6i3AkG)~6D;z+R5ovYNSrozjlbjK%hLO>a+HZ~~u?E7W6m{0V4R`XuRW7)L=8
z*IQTse#9M(7A4D82Lp?;8yiCH#`~-4XZa;Jcp~<8@Aj$FLVnY0H;8-0UBMChPCM(&
z7j9Ev0|h!p6P|yteaS6-qJphO$+ke+C4J*?&lCd>X5-T|eOCX#725|iU!3VT)x$W~
z)2MTEzVbcOc{SUpm9}9rDU03u(9I!XVZRtMTz}YsPCM|}c1TE!3@IQz)eQCY@nOfM
zjfP0XfHWw?x+7S*rH_*`Oy}suoaXf))Tg%}eO6+jn+5600~d*gXS}^$)qKaXPK=%-
zMcWS~MD`O)`(GvyG3r_#PnW&YIBCr%DIpwg86AHaO}E=m=3H_(`+hn4fWR4i;jC`^
zu2Xz&UTK0GfyRpw_Zl27n4<5H9OinYd2GNFQEyy=rxJI-AufLijoJw|H>L(_dAuv~
zwz&;uOqO(=Y6Pymq6TT~ALw7c=sc!k=|DWwIYT`s#){dP6jsyhLyczt)bdOvrhpB~
zh0!E3d`|}xi)oJ`0S(K`u~21&jOodlC}r+@|4HozJ39<Rg6HflaC*H7;x%>M<SN?j
z;Xb#;mN5a#N_u$W75H(J-{+?LSvuddItrRy;AUwxj>1cI=NGe0cFG!qz)gFiFoo6m
zAN`J^D*C79fK~X9IoSR$E&QJ*4Y-tzbt4Ui2UqhnjrLX7Z<B*3c$$X$*uX-+5NYop
z+B!yL<A^w$ul-#QaWAX-cM2e};$e&j(n_>DM&}*>RKFEG)JNHdby)?>iLZU(zOkN^
zQ-JROYwtwif7AMI?f^xF|1zlm1UrAk%FF)u-Tx_NxYTyi)m3LTJ;d0_F*DZW;_vFq
zHS9n6hWjR_e5Y&BHmG$wb_H2FS7jz`tagmzhoyPQ2BY@Xt=C?tp@1BBMjd^PI%<G`
z@hjo?8ty^?MVm<pY>!@tycbDEd3MeH%GR+L&T>_obI!U9DX9|~hZMVdw<$iNO&6X<
z&q8%+<}BYj5{@;zaaL19J$Zl5IaoJW;0(%I3}44NReBA!=jnK~v9drGXr@)0j2y;O
z8wCDNvi8InO}dn8=*d`BghITQwQo?i{~ktrtWUjk+}&;0Su^E=nI3UID@aVwyuF+H
zkE?k|{gwJs##TH%cQzfre~_i6brIJUJ8fw_<@g+fvf}%-XO@l)oMM-LJjrf<KN2!O
z3uZ1y@imS2jE1)XWR0l&THvdq%{vOE{b63VatmB7XV+A*8Um(u@hp+OFU0*nUT0O0
zciSvg&`qf}C8wiJsmRR|nB_UGzO%D**?<Fz9k`{PoYnxJ)aLW&sab`xm0EF?Os9GD
zes2~5zQv;d1N~)L<6BlnU**?37bAZcOVW0U_1$RrsbY^8!eAjT+<vIUfBDY*&H29W
z&2=l@h7Uh3CsC(c1lDE@$h7>rlP5UM{G`nf91o(Zx{jWt`{5Vq8GJ$N(vOf46#eDx
zBpiJ$hl44(ulPQu=MBA^4k=8dXqLKTXMb{kcm!^w_F_wtY1_};Nj^<0q=njsBWPZL
zX2GgZ1D6KIdOoJQnaP;Yj+`{Eo+1UCNN%U~6B_pN>#6(7C7tTAIMWZA0*;3$u_!|*
z?Pcj!&=Tdlh0Agz=$X+fBI~y#2lepAn_vu*JgQ@`yy$4?OFow^SNP5#R^+OCM~W=8
zS~<z2W@I1xd|9zC>#w;7MLoUR`ju1d_1V5XE2GDJhyLb5@gX(R4)$V}Uad8*SB{r9
z*U5LBtv0R|%|sl&$vBJW@tFqwQ5m1yLD0@#u%r>$_CoQIv#O~7@;+JSNMVB=KA~3q
z3=&)M*4#+cT#-%QjDwhU)e3LSFCLQ$Za+_7*x8u>3Q*oszruoRN#y>UIPmlTiUa?*
zxK2q@ghg52z{!f7`(L=I#Pa_mQbhU}DgHMh-~ZQ`ko~`p36WTVwZ{KBC?scP=Vs;l
ze?)~@h^_Lfudk_xdYhm1nJ2_UNR0u;Q>UHKptuhxVhNxZhaYs9*`DUi`NE`>muZ@q
zXz>@3o`e-DJ8?s=r?(YGSw6QsW;s41^JWW=m%drEK3OfNJYLOSemA=<?N&bO^#D;Q
zA`FZ@CL%XH3=9Gp49u517#NtY_b@Q%f-o@O&)&em1fjvekb484uEWB7mx{;Vd`wM9
zQ2HrH@RS1s^QOvbmO0SCqKetOz?0AIq}vYz1|5CA+C;Pn`G4Hw-_IeUl;AfoH=6*L
zA^`z`-|KUOXbhKng-)SNvW$#Oz}cHM9jR-8qf{NZ>n5kKm;LcV9dM35Ns-E=%?Z#F
zJ-&WnRe>oBZp{`|QHYR^rxcGN6(D?8RL^ptvD8bBS>fLDgZ+Zt29K&HDWc-aAorRO
z)$^*mXg#qSsUG>ImJDW%qig#PK;*Qz8Y;?ukkiu2xR3S~a#EhGqv}~0vPq^-E;p6`
zTAGrZbJct<@x}`AyNmH?b{l}{2c#V`Yu6tERWeApjQC5Qro$O^0E5{6B$P#~w#cw2
z0s#GsF<M6+&n?loo6)xW+)*Eb{NArBf7eU&W=rr_%YM@<=UfZl=|rJOMTrzf-gGAt
zj@aAg3mA0%Ca2w%8Z)#ukF(DAW<zP`SCa~%jUt%s0KvkX&fU{Tj)IRVEc$}E(=!&D
zclEf@yCXJFds63v3i+~`PKji5UYk|R8y&AWEpCy1&$2qL{W-E=-09Mm!nhHa7~;kx
z|7U!D4dW8!xT&H}u4q=5!x?}|q0(E(<6;X)W7BQ42XO6C0UktP;B<hrMRqo}3Y{i4
z)1fo~3A10g2%wL=a+>>PK*VVsr@vHhv$)X{$z{95?fY~GkBpndpguAZGg`^^ZtISE
zYpdA>i-b4KVzL{VW$W2kb=e>GFeaT52?w*xbJhC~QX`)-lG!lkB=piv>e{xc$K;(f
zQmHDI$K^)$o8K%2=0FM3;bH?zn?6uy*BSWU?PyMz%MM6_0lLEZYHvJ&Miv-_s29(N
zD~rJEsZ#aWc9ED$fDyyh$@|SVM}U(zAsO?_>MNGdL)ew``NiNy+if@dXxPiM6ZJS%
zlwO<HWoU&SPAF>A5P2Ie6Xt2_=EO(@&awk*8xmeG4eAC)hcA=)NRE*H4~b|pzo#nK
z;{^aVA+bwxs^C#P3;_8~Kv)5Cg^UlgJr6)i9G+x+P@1D<%`GC`ia&CFZMh^lDL`UC
zrd@<TY)>4;MmTME6oGM%_+tI+C9%@l-gI5*QbB*eeY)^NlSj?nndoeUOzOylm_{|V
z63y@Reony)2Wm@zCJ~>*9DTV45W@3nT(XXA5#S;P6vF}-uHnhaNh~a^%7pd)BMWoU
z^~<Dsx*MgR{Vv0VRo+eboN~2xEQFsim7&$gmnv4^mx)qczS#t$z9k3N=cg-W^^yV&
zRyQ3vA-iV8V$3x;By75~zYLYNz&WAc>kE@sEk3)c-0jJ7Vqzj1nII%7$@%_b8zB6+
z-5U=~8Or!9YmA#g1+oT`-UHx~olF0wvaF`Fn{8;{m0d&$hc;(t%rw`TuRu`9ds++B
zv5y3AiM6??l$78+?&OF@$NaWSE0>h+CS*4*Ozz2?-uKiV$RvwV_P%`!{ZeHEpauNP
zn*9xn<_&-p1Je`-A}Htk`h1H<!pq@(cNP=`huJx+=M|kIbUDQC^LXR)a=&{xRs2I+
zVYWh-%=gY9VH`*-4drW!XX*`&{90t?K{25W?G6GC3{t5=-48&l&PDx+v_&|&jpXew
z3*~_Y;k%=k7Z9F#V|tP@tz>QDH(}(|Uy}FU9Ic6d+{Nz0N&6$*8L%`lTS-=`=h^JO
ziE`eBobPq}`-duK<3im%O_QUz_iYxRDw`eYqHHILK#$PNx-D4&KHpqLHefTGDTLKL
zU%bKJJ`hlP-WbDjyOfxZkq3QF_&p9Asz;qq$*y`asc>@B>2lZ&;i*tqaaK-Ikv`vO
zM1S-BOnUhjdFf$l%L%5wcjv(3*B@ekLh_9ySuGNNNmxra4koRuhoV-{bi8KMsOA&8
z$tLwD(C1Z>n|%1r9?W8>7_|7~y(}qDdpK540?o#GUr!pVL71ena*x36nXKaj$8E8?
zQlll#`$0DB*`G4D?dfgQFfe%)+gk%v60x{GkJVf9nY|?0eK9z(g7k8!&M1(#@N>o!
zvW_qOP#G1h_0meD`Eih&EjYQT@h(MMtU{;J{>_&nD*|li_GeBvcT(OYpVh1VC6hvz
z!?2)iYWs%eUY3FF!HwT<8c==D{QHkkev_EctB)km52T{avce#^hz55XA8S=PgWTM0
z7u7f{D1XYW14!-9qtw++Kt^!()8TaKhTp4G0~)hFGh0g_8xFm(bTT$m#4CB&2cL&4
z&1Po{0EEB&=Qo-}Ab<D?D7*IoI27M%#a56{uXP6U0gSS_!htOS?8ni3O|@tyz2z@p
z;)9fb4k^tF1&vO8$)q{W_Ub;YbQ+TNoPfsS3kiB6&IQ4Zh)&9)u7Bk7-U@5+vF}4Y
z5ugI8(#w{>1tSR0?M;`?{`QCU`fKL&5db!6m8>(&+yu0BAwY!|44G^9dF-f&WqsL<
z6;dnH00<dnukTA#OHGZ9pPnv9-{2fZ5v@ns6XmEUByg`Cj|bA6uD;?r?K)n#^~d4w
z#mdn_jir6R5;OBSP|B7V&$GqzP)?|ZDN0iyVem%V&lFNB-vGC^OfA!Hum!*n9&b-i
zms@UoBJnEq+M2(01|Z?k{{w8lIhyBMaW@*z7YCfZ{7Uqa4q#&IZI>5XJ)CTW>9qpY
zctg?1z=L&NlC?IAt^gnpl|<C9yfg8_=uw7XB!{BK4k2Jr1u}@<;4xouPR&w&RKO+k
z8}iAK{`of7-eA0uwS(I4ndauig$twosk#*1V5OC~wzRQP1_zrGPR-$HuBxD*;4M5-
z6sf>NIBoXcN*kZWD&RH&8!OIR=XGn#<^}+>l=!rbz5+#`Qm1>vfCo?&E&(DJHSP2g
zpj00~2G;5D^%0cU<{aqi4#zqMc~`yD9EC^j4nf)Qf8U+u=2*R+^IGf6K;}!g*CDN?
z(!gAL{QXU5Z?VTk7~jcSYLLq5qTRB5PzgGPhn8lTq6~!vB@)I$)Y)|5#W%qI7_Sy+
z#rOR9UJIPd8{cx;tt<h)kw2YEbg>wTT*P^QqQ{9wGKLtGQJ0lroFuneAe}gs0C@lB
zhr`OeBl;kgPbck}i<43pyw2#OLM#$7wbgHI4%L{m#&Z4d_NdPLsfbM~^+$2uGVOhp
zQj*D+00!w!9S~%HZKG#n*-aPYYM9GRIvGwQ><n;SWPV|^InJsUcb5ty!*K@_Uz6wz
zTxn|E5c)RbaTwGTA+mM==eHUZoCWY4)R~WO*dWDwPi+hx9)mLZ+-!Aw0D^0xSO7U3
zC`GVD^dWaMKbA*t|DH#b=B=M2sIzyKY1gf7r#V@95=y3_Fgejpn`7TXY%yMI!B|Eu
zh<(4}+fvw<i-hr4vVvZWuF!rEH$^X;QLLz7%#A`st8cCf8+zuo;89&s8?RP=haF!|
zJ>c`|6TkmXr%bB{*`(ThUBp)&(!cB41&4aG51@$l|Lw*CY&8JiVHZ8bnG4wLb)fE>
z5b$<wdBOh(1pr;@kEa4n=~f$uSA5Iq*NQ7qDg>B)Nt!bmLqecAzRmg_zO~@{LqN6N
zkj3fivD-DIZ0`a{Ih~Evi@Vyg26&jXx&zrn)~7(@KT*(@H0q;N6=WvI@`#n)j_D6(
zcm16M--pU0d_cmG0jUHdfZlyIja$)S5tH%~v?n+Fz3=fw{m3Y`s_3h+(*p<D*J7o7
zCo^z-EFn#A^ri#dRia)`ghON}OGj8JsbOk^BMsNrgujz9W93y2RI{;C;E;lD=6?n3
zAt*!u*pV7gyz!5N9S9dM>Ywj6V}UErfEoZH6ch>trgK`p)<icD5POyXDPxkF21XOm
z(oKryj~um@(=tS_75W|Rbux$WZUcP37?Xn;HGa$cf<XLZOnWiDsmxXE`H$Yigty$N
zJp+kl9}CKD79A&boGsTTCp9wZ?;ot(D7pwgSIm@MKip(go)*oUY7`=}d>l{qJ0oAl
z+-O*BWGNVW`+S{>&k+Wzhk3qeYs4<A=d>zl>t28y2U)uub;1TS{8S8A$=2K7hXlNg
z`Zbo}Atd#u77_~i^3Nw#RHrrB^#ZUY5D35Z+^4|NsY-pn{fWZ%ozhnObrAsc2jIv_
zNUJ+A`C?LBJ?Yzh@p=l7DS2<jOB%Y~(yQY#X;(jbU90-O#`?iwklVMrzj))qSI%QP
zrWRlnxYZ&?;2usHh+<nf?rf2_g0ogn&0i<deN|0`k{cx7qjn6`0Gd~*kh*$4==Tsg
zn|b8Py11dxGm<6#BKV|u$`BjN<S0ro8zvOtIv}4A*LIyUR~ybm8t#6&vV<7w9PLd;
zg8eL0pQ@70Xb|}B=|k=|5Sljh>jZu$$5Z2|)}04sf5!4n5FLuAk_7DK2SH_Eu>wGr
zF-%M1Uw6DdSAw&6>5r~m`hoD79i0my2VUNH%XX&R2rrT9JzvK6w@R7>{)S(WtWhYx
zPVgzsDG06;>sIJME~C94@p@{G7i+!^KT=mo$6^-WPGOd7cVw2^Wr<T^ot!S%s|esq
zDpijPQc;ZFj4aOUd;6m-?&d_2`EBKw!y~J;2+aE2IwbLXa)kCfH%slCXI%`)Z%7GO
z@m8>z%*orW3_-)>9DYccUqo80vxgtGn>`UAL6m*CNO3?)1s`;YUr^^Y?&6kyNj%ai
zqEIdizd1Tv=f^dAa%cZ4)R(c9ob4&&eNWSphj9Bl^OJwg5S66kK?<|(MH4)dy})d*
zd`)bIt~9mUhJ`?3`18FP5LEI?fmN02t%J4d=;JQROH6*EQ|LZj9s9%bp#N0NMCI}G
z)v?_P47ZvmzAQti9YF0eLt$GM9=G*uo_Br^61_cHTKnu(Kfgjm&2hQo@G6)vSYy@{
z%-WxS?_Xm83#IB0Xghn$fHBYENki-88R!a<H9}stE|k3-tU=F{OD+-#qV`@;32y-Y
zpV+l_9zzxQJA1Iy%g(suk@~5Dl_pU9LF=>ycT6??BXAf;>J$kf$7Bqi)AWNCSuyND
zx<fM+RbV0x2n1n4DH6rBF`4^xGJo4o1)(i0k0T7p{2@6%l*}@E9Mz4&)wr<VA2W`6
zCU@+cm05wluCCw2lvj^#5KU+cYP~lF6*>3AzO$EIZM$`hQdn^F`Na1r0vA|X#Z35(
zu!<;zeprtrbaqN%8brBwTN%K}p7n-L|E$!;Zr0f6Xs?PU<$STdDcmieVb6k&Id<l7
zP46Zm=n#M->B39_l}1gyqE90%rk+M2Y#e+V6bUNfssm)WW)2!ATB^2Ct^llYH?tn2
zp@rjt1~mv&%%~eieiFoz@_0@ldb^*h??^pYMa2acxK>GliRlhlnp|mH>SRiaA=GXD
z^}*$!dwz7fbUrvZ+-=3chKA>Y<JXKz$c<K*Vowj5PxmwIy^?&HX46F{pH+Kn(@x{h
zy6kNAwy$0scW)LE^^dSY2yc^5@IOaPlGwHHgWW!mjjXR<?Nk2V5YRJU&Y&rlC#n~+
znnj9zYX9tNcVUcM==)6RDy_#NcJ~|hf_F_MXm)dBi-{rUWwQ-xaWB-<l(2m{Q@LFs
zpj?K3V)k8;>+jwK2{N9eES^WI#bhJH>GwA?*1<<B%`Qum!L@e7goi{O{VAmU{nw58
zGh$$(kK)kOk~xR1{v0B=QG$!r1W8w})jJbC)v!3ZO4lR@Ey9Nm7zBOilX+Gn_x6Eo
zfu@GjUk3c1Io1nP#cH;}Gjb=@n(Ve5+I4>ghAU~tDiXdgdD&Ud??36vI$MpGh$D|*
zGgO(554^j-ZB(WiOl?8Jz-5Mrzh_YS4UvcG!sF>LPGTCA_cgvMQQhQZ7_|Emfy`xx
zE1!9xF)N?RSel@~R&4+q+8kcxmX1x|Z6HXnG9!TGbEElVF@)3cSs0g6r5IC}LWCxm
zP7Glkc0ELAE{24EFQAr1P8k-Mk>aWcQ|nQGN3uRPr=@+*zt}R4cd2#%Ae<6I3@|DX
z?<bzLgjgIPVdyn?N0-s7UaWW-_I}W-_~!z}C`1s*5Z=?sM&RjxIQ=M-yusRG96(q7
zay6+ayFem1wB!8y`==om>wQh#wv4(;RfdrQul*S+SwjnC4Byu_KIR!*N+7N!!JCxD
z9e9H-i2gWLKFBE_b51D0Tc8@x+Uyw2WEmx@<~D#ia_oH2y-y-^Nj^E5D+2FZ3@GXu
zJu^^nq#O2$iD|G1b2S*@O%R$`P9G`OVuKU?ef^?XB(8A9))EgX-AtaSMsvRgDhcSm
z{$yG6=l0egQwk=k9J$JR8(rBLGPk~-D`o94#Y}pTI;wa+g?DFe2zsx(@bAXJ5eADH
zb$th}echIz1~%N6bb-uJOJg;RShT%wx5t~jTy<8VGj!4zwv#`f5+DMDP^JB~B+!yM
zolY>qvXVw|f>0^q5uJB7{ZA6YW7UPNE^7p`t}ogAI=>HQw0RyDt7+2y-c`1CasJi6
z&Yd%SavwQ}+b`7@O6v++kp}NjI9?^_G?L$(x@mVctnN@(_m`<_)Rpit|GdR0C4FPq
zcfP+hRNCCR7o2>tJzrO6H_DC+&s{3e*YqCMp25IdAi3IXSF}PcdXP%#mx7kDK&{fm
z=g+6R$qY+v+J!PZqi$JN5dqSkb;Su9Bk00+0lik+EagySm7S>tk#xr8^;)aG>o?LJ
z2UCcQ50Be;9y^<-ak5G1>}HN$%l+|?t?-~3U`Z+qbY4%-;j9%hhN%T5htMW*7*!u$
zT&)LFbY)8SzQ!{C`n*}w>)LOB@5*$taf)CTB>0o^TzMkF6=2_K$xXjo)CE|XOmE$9
zqBWpS940F03`XLO<_A(onqNAEo!wu5KiAHp`I+AlE4Tr+4t(celrxaYP!SyKz77xW
z6?~vW1}7<_0`ZlpF3qe7g~+s6qo+#>VBNycJSjxJiLSF3N7;K*BqVf^Jdk=tSrHWJ
z6zZ>2g@4e`Dz#69Fupw7x$15`hM>h33QPiA>yLKKVsy{s&)&zM)hW7kni`9h9)uq}
z2AO7tmOfBYdH2ut0w^JXk#2O*shb*l4GLoU{JCAS`tRR6qrMC8c5KgHI%=~k9rf6o
z!PK7D=Nvv}wP1vS2K9=gn8*Pl!<~413_+W~th0U>xYXa`MOHXGPL4*ezx|gMRvN27
z>yfM6>gRtMATHz$d}iR*rO0p{UVpuXe-C88EkkO~n3pNCC>ACXG>$14cmyZ1tdVP{
z&*`)EeMoLXsta$vxag0F7NOgAI)A!VXYV#KNeOx$J(BBx7NNV~>}F+3WDI&hgS+-Z
zno}C3xCKiC=-GULeoZfyh>ptPV)P!)TwE`bj9PQ8xpF@FCh2_+@8+7$N@aU^kxbWF
zsaCGjk<AafIdYKAt3c125XPpAVp$K|tVsza+i~7)bnp#2y|N(viPP_`S|uA#$$t=o
znr`ebw)O|j<CA{k$EC?0RKQ%l4oZ*)BWy>C_-KZGQpjcm83Ffk0L;`UAHV@`XAp66
zL^uWi5+%yn8ldI*^Ekw=)4APp>W&ECk;OaDihIQ<c6Z!x^08~aW;qx^`cdT(Sc;t#
zUy^$xxE$L&2tE#hy3})F-UEZ;*q)^4ap^eUE}wZdv06#C_y-Zo`fBu}f2mruKAK>E
z$6t{knfLoVxlCe3^2yvk&?!W8-^cTDjk-vGG`}>>xtv&#y92fZHBV^eF@;|GC$on#
zdT<O}x*F{BfJGLoGhIXMJeF$MFDEk(x2Avgaj8C<HYuT?(8PkWb^V?8PZ{(B-bhK{
zBtzc#VW#35<n0_gXTb<;JMbr^T*cVl5Ps&K&deS$U?CtGXmaAOl{3cOB;=nXnDrN?
z5CH{8;sL(lXMr)O-!fGs2*HKK>_H;xn^lboa;n`0HC13F%A$2Q_Q3N{`cp`Eg@e(|
zGvI>J=qPCE*jf-fUU(b)dCM946@<?3;%CYQkT6b`4^QL;TuNHd4XzJtL1j8TU}Ox+
zDHr1BhjRg-WPo2~s&unP8N$%?>`Hno-oS;Z|MH~M|NX<--MROu?c8rWU>&moIqi+!
zIB6kqW*w9NC_6m?%Q8sbvt?Xyp8J;{*iQ~dNJh0p#+XP~<<%(HYQ?4$D>~%f`+KyQ
zS{KW#J6f6)6wCbW5#F`Hb}*%>*Yo;hSoOn9Io9pcpGyq=_90}+7;G}{-<#cx*zIJ#
zYZ9=I^Q@62nb+riA@B`OTlUhKV*eam0z7gxHl@mO;Acw>_GPjlG!2m~UdZ<20Yf%K
z>h_6t-D-9uB$QF|R0as#u-9h1JrffseIKLoQkaLG+E{e@+XNZIuVRVUzt|sXq)g2P
z1<Ym(=$=ZS!=_n$!J%w@^}TiZQNk1#`O_@yUxP~c?9qMZn@*I~oMgKByz6|vki)<^
zmZ@psbIraOYh}86CXF!~Z|5c;9_lr_G!^n`l@}r1>w+h>C8~)#PHw%UY<NltEmfq@
zkjzTyWdZziGX2+J1ZCx#0-1dEFmxMl@Ky*B_iU3@YAUxn!Xq-7Fd>)IM{O*nwx9C4
z%A^g}0|p>O!@Vv^Q<)UC_!3LRj@OZVIWJqp!1s}<`mSxK3(3K!#|=&MZX?9Db;vln
z6kS5ETX!O#F-k;&j9Wp(YD!YLY)TfNgPOrX+11WfJeqe>3sRdqqj*rTLyq9QdomY`
z6&d0xLuJr{S>O<MaPz>x64TZY!ANk(2vzLa0xHP{Gfm!?kIql->aQm8^L6PJp(U!i
z2MTsq;W1+aIXQeWCG9_|(eo@R3naVmFI#nhj&oo=mYGAs5V?a89am`nna!-<U*_qO
zl$4h^&2qc*Io~+h#xDJ;A;I<IUglYkVpXM@y1{&W(qxK`)u$Q$QLaR?OqZzSY}p~S
zbT!`-3A0@5{0V|N(ambq<#E#nG{DR4#@rwrb{LFMCm)o;|ApKe6;CjN^X6n*EL80-
zApxSa5<Rq>K;yY?H527GK}CVRPKDRxScw1W`yalIZbampn>p6mOx4mxZNss+=c$sF
z+trqpxU=Gz=>2``5dcF&5{L3=JkU=y7(vzlWxmFPhezoU$x`$V!r%>ekCAnjOm7mK
zeg89GH(c#*PXJzHt3*V)M+xF}PRRv9A?UhEr`#I@X-!?5F7vud33CO<KdY2!bZ);;
zvki;;e-uCt_qc472Jj`#ofU3W!^Wxy5{$I(;-*m;oJD^Y>}B3t&wI_|0k|3BcVWHJ
zJQI}LNX?#;6NSXPE3QPVU0X&mWDV9id~T-F3pJ8(S(k}7HFo;8tOWupalgMdN~gj#
z=_VY_>TS-3Vb~Jk%NtXO$V#8l@P5r<-PHOBkA(eQDtNz2=I7`@7nIpv*Id6tkgGkh
z(VPO7uBO(Pf=?pkJljj>kVPZz@m+!!(y^ZXDN&V=7tCs5IW28Vs44X4#i+%kJ4fUR
zZly-N6^HYFS9MWYsqViv;@;7UwLIRoe7wO-pKtpUN<!^TSwp*-1oAYa4YjaYOfjJ#
z$a#6LVVrMGr~0JedbGv~uo(=@ls~Zx2oyXg{rMvW?f)5{3T%GDw9Bp^G&xn&%f@Tg
zr@!>{O27S#0*RLXgv!aPoY58>dlYUxiIKkC&8+K~ayDJIZ_@Y{B)dQ1xt^%gxaPZB
zK@$7-EJ#GW*}|FK<DJU!!J6>x@ArrxknkJ#5eOs^rT=`NeEpIyLN0ntz&T8O(xD3q
zzyaqn1uG!9)gD6H8GW0s)okT%qd2BT1D06B<K-3($oX6cnQR>>jOfI^HHGaA^R#)~
zlzD|S63c$A=^qk#IulJ9D)U%$Kc~D6KhL!Ox@=ORvo0NNEuDXA3NO~eNy`*ndMt9s
zU;H(&g|#%M&+!E5J}wUN4T0CmVjohGe86O-zIVI{IQHSX>GovN&WV*aF_5`nV4-6*
z*`8LNS#nBdJ<$SX!sIh!7ff@w#qAcc81<x5<FmwHV@F)34VHw`<(fY8{I^6%IsLp;
z+|FaZdly9Lg5_t@er~&2PZH@Y(>>~zSOa9~EH)j1r|m|xMLTVAac3sRzX&s0L;ogW
zNXA~&7n~KB%dVGdCHwh4*MZjg{IXR~tNOZ2D;)!W-Z$3yc&#iZVXws1i0JBC6WuGS
z_s&8Ua(aF6XUtP8tkYM?*9Wr}+v{dwg=4-ck27luWu1Xol{zcM>f85&My)!eE*yZ|
zd?hm8tpYsDVar3MyS+*pna11hv{b!~oU2!wL(erB1%7fis210~PLlYCEE?~>d*XwK
zd3>^PMXHgfUp(*z+8NCUankQmnop!awQQ2HaJ*kHY!zpuX}z~f5)!WkZ2Vs42S570
zX7Odz+8%E!I6BEJY;D=g+#yU*C3f8ov8Nm8QjVg(9ye43w|S-upefxM7aTFJ!`8cb
zZ+QBOqAtR0JWLtMI22$m`|^cWv6bc$=P-8&Ye|=weY!FkCDps{46JY!tvrvab(dK%
z4shR7yj???_+i(M_$4;mE9Ta6Q)0Gq=Y6Y^hP>p%qypy?9_>RkmjlsU8k-6CtCXNP
zQceX{NFv4hVs-E;vEUKmi1_*G-{hq~RG+75yn7<f&EIF8-OIp_d7O-OZc_?9^a~IV
zrfveB=>2r@K&+H2&i0_wmFHI2JRyCp1*V{;IWPIv8ZPfvwMo|c9sf1z07{D%QTl3b
zOMor4F=uU;B*nq~Wrb!$_jT0!;@_1W&6vp3rB05bJS>=up6>23bj!8j3XIP#<CLjq
z<Hac+1{AIMTnpwhNFDRP{7ZZKvsvpbn}o<)GRJ;Kf0ap1>dve1v?Jw7m^}?S+Pst6
zk$NO|MGbd_g16Iq_fUh*K=dkWJFTZ3ZlSN(-%LkGx>6xWT1w@FZt7|yvLU_MU2e0w
z{0MT3i<%7H1O!W!QncQ+ra0*<4)19_>Y9uSCEGtrru0Kp2UD0yQm+p2eoND7CWO`%
zQBEw@pO4J_2oZm~p@XnmD63N>n#&S-wo#%YlV@*0IxgG=U09IH4*FT0=<K9t6}I=$
zg>saR56$DDm_pRuUObszv&(GRS-h60K+v+KJ+4PUyUwxBLh5Y^g1m@=ev+3)C8^s<
ze2<LH;a#gG*@s-+;MiQc4_#1kLcpjk(|qXPi{DUuDm{S(c^4o%+B1N#_v2?BMVdP?
zx9&*Ye_v;F=9w&TynsY?5eb-=yGgdX_Jp>%-X?d^iYtREGjRTR*D`QXw+IZnDgciE
zmw?HjBSXT6GIH;TDU}<(0+|vcDvS5QwV7NkizCKQQaAD<xKRN!5T0i~K^qs?tTSoH
z?TyMJ`#CH&Lq}3)-~?JOd#Gi$$?4m!KWD9xBrZ;U&6)HMJln|kd<4k-_gpjOn|i2w
z9L3QWGD&Oi8viEi%JvT@GY+spC4R+o#xv4yv(X(CVhQq0#sqP$haXAXl6zBk_EL?u
z--jfF8NC05z&nwei_(G{OWEC%Eglha<ZR)RRu$=gwr{A<Zi&=V_lnfYcD--e^%$M>
zVSdhnoCc)mG;dv4*(_ctB#0EkQyVnz^VXq`q*hKL<s7gvv1MXU=kPj@`F)l7wEU(0
zB)d0SR~$wZGNOYEqskQM_*>@-u)j!_igdh5HHET}lumH|E)|cjE0lXSQK_&|>ASO8
zHIIU+ok{45S&j=+eBQ})X_<1n=l92w5#US!7p;&t3&Ouo*!YovwJY*09{ESC2P$g;
z38}z>?gNLIK1FbBZT8LX-T61-oFo6aAClH6NY!Q03K?Rs3cDbu?2i~?@bi)%x_0;P
z)GA$h1dDaF)|F+q&?A3HMwbwH;<T^-@j%K`Ep2@Yr<P#w&OQ$L=juGhmG9`_vbo2f
zt-=DAlg|cVa)L&Gn|kKcgT`!&p4=JZzL6VaDAOqPsEEVFPQb%=$u&9hujEQ&K@gt>
z{B}N!1_M3wl!VluO;=58f4*^4zK0v%nOm7ou{G<m)!&%|W%0Z*I9bj#`Ffpp33(PB
z!OB0*6nV=uT+CdbJbfUFCz#;UEHC|{G7iT+mMB{7XaWgp#xVC}k5jpZY6oVk!o!`d
ztqZ^X5=zWQvFnnQBH#RpM@OBBqKU#G3C(=EXLRm7K5k$j>SEVynrc+avhV?4A^ZR0
z>>Z;-d4fQ{wQb$CZQHha*S2ljwr$(C_pWW5*?)3!^4`fyPEI~ef9RR+s%gx0)vxN(
z!YhwuvU6_nZv2~e!#wJg35ES;s}fz~3^p(%ChSoS3NIYrU3!E3s)deJW`9L48BuZ5
zmv2_zXllJt``YeU+S@%djFUGJ3RM+5tU(z+VvJmoGDEqh%YFS+VlOd9?$_4lza(b;
z!v(yTxbCPK_~{jmbxpC;_0Ba*V$lgjkq1JN6dV_|0PGhNqpaS+$1+%}c7(;Tz_OpY
z?Ky(*==F6km0K9X_Yt*-Z{fyREifIA!ZAw5JnbUT*(+~VsI0}#i=Z<~7A&_x-jO8%
zYrM^Gk&ZHoO!mpndo=L<fOjpuT8-_S^Lx}HZ^6|`&UT`b#6wo&b<HtzqWKLFr%4w)
z`?p2<jm$jJOwMT7i^hT0u%lGsn>#+cQiXi0oe7?t(`ifxicfF=J?^|bwp>{>Z0nJ}
zmNx9qo>GbonV04$neqJE-S1uko$KU9?4=vL(1<v#^m^L)cHhn-?o1;s_rm1I*7lHR
z-T!$0DqeNQbgw-QZ%JmduxE)2Pk~h7+KMA8k%4W#2R)Fdfq_4G=f8%+8xG^#922|k
zDPv&POG_fHI#kGFM#h~8O!FHSMnN^6dDv0{)ge;ywTX7hD_h|q=4MxbFUEEZf%fuq
zUhJBrA}O%Q$-FU>%UuzVSB!Zn+5;W=KSdRlNdy+6$Ux?n9y~1fm40<tRVM;-oOZjE
zMIoVFO<_o-gr({|Z$oOhR6U^ZPF{p0a7raoA^*|+v$NSw>q?r>w&76v^%3d*5U}Zi
zU<^WZ{z?rV123HV`NrYF;~e2zkwd~cN=Adt(X&_@eO##$Bj+Opl|FS&nPg#Rif6mN
zprST`j3kr(MkLi{8k-6qg?HmfERhEfVBYC)WWe!|VUt9w@@2lu?<%kY&%5ihOFZ4r
z@;b>8Oz};lyE#ESN6rK97wbvXzkH&K-FJ?I_eD&TcX~O6>1}6<@8tl3&9i={#uB9#
z;+9DIb_r7!*h2MW_GoqkJYca@M>?MmlKq^*dftqeTlH01puEB0JeJM%kvpLMp`&rD
zGGC2-VKSb;t&l~8gD0qi06kB;6}K@%evYeZk;X=+hMcFx!?NsKR*A|>fo8O6!~nD&
zW6~qYcvgkd6Aaz$%g=%p5(cGdEGn{k`g}KfP3T>b>M)aajgTC(#WepL(CS+}SyU>r
zRAePzqeB@613`B0A$cU)uJXUeoQtN+gXJ)pom0o&6^OyX5?{+$#^N+WIuz5yUyX1Z
zeSN=LKB01%^?EXXy3xj<fR2p#em1)mf?Kp2GRCe>6;@$Xe_frz?{TU`^Xpxta}ci0
zbf9uotq#Y~0ySA$9J~O8|D=1(g>>Y&gCX@eo=k;wY$S{CEk*}Gh`$ycx@VJqR<{ag
zvZ6un(DlB;I6GTyoo6tr&+JuJPLMEa0Xq+r;`?_xkVv(Mx?E{vVd?Hg#wP$mI14Pr
zmq<{F=75$cms#$<I&EniVJPJXpexYl4iWPZo)Jgip1<OHj5Q?Y#Z;Opuo?hH=d5yS
zXLq>s@*NIDqH9d>NF9PRi?X|oqCbVNAe;x35qajC1<zI<6!u-+^a9&?>2%nEQfiz_
zl6irB`E;Knmg}Z*dbx4-=nuMahuDvHAd#>;*lr}nTHYC~&YS0={qDP%C)Zm#N=P^V
zMi+6TFbBz0%kx(wz;b{!1pzg50Df181Nqe7E#lauBwCI@Wmf5R2?Hk41Eu7<TQ@j%
zCIrU0)A?b&F^E??4*;E_Z4d0F4aK^dmg8-HySqjYn|b$|>v1jat41u^fd_+l1E#jw
zFb?_CqsB6Q7Z<?JOIJuok(R;>EM|5!Eanx7mlG;|b1r6X&i)p^btjv9920W+O#v-)
z9&`pNxh-zGobgl544WVJJyZ+xJOW_!ToZ2NmA@;fgR%QB?-C0F??Zl#?5i2FN@c`W
zXEh(>d7Q-SX?HqL7E|cUs3lB|WksTC+6o1#yenKY<=bvxJW|hT04py7(ZC7Y($l$d
z05b1GjGT=cB%X%CgEs)MV7j)0-3Be=VCaMJS<mhAeVN6+<t%-0hi<SO2k#^qW9(La
z2f&b2*@enL0JO7h2L(Nk;&&u#JVFkdCy7fF<kep9V?ruo+i>JZdgEpzX(bZxhVRz8
z{D7%D5SdHk@4X^rl{mTn)l6oz3tQ(6*p2Qy3sAZKU7Cbm#L}EwFEE}G6<ts}(QF-(
z&z`8fnCA=c&P=to8;2f-*1s9JDxv*$JhyGZO?n4R{ak_pL7g0Sj81ghpX{LV%oA;x
z!%A`8(IQj4zM3zhX~hWmer^X1-ks<MDnbI%Yylyb@A~%v@olB5^_U=>1Ce>FlR%ol
zwsL~xUXxPdP0m=)%f)(v<Up$jLmiLR+6C8XDWnn?CcM3Q9P(>So3Qo&2&<wGh25H)
z3NYDu^`FJC5+TOjo=vH}2F>!n@chXY2nb#gLK5t!t`<f!oAb`$-IaQ!hIfu%2JMTz
zH6Mj><K%U|06TeY+G&`$E0_2)i2f|e{qNO_BJ~s5Tel=+8NE5Tl<&-`Q3}208%beL
zC=1%}cSUwmk*qGbcx@VwHklr97UXv3liw>$e4A$D4n_d&LZv(nmV0>`i6)};8aoHt
zJ$r?r)(ZNgHzcSN7!5}3bue<bgwyWWNhwdGG0<xPz3vH>OITp-_3PK`6f^6vx5(M!
z=YfC0E3G95;fn{`8&hi_vU+Kj!~I6wi-L*yuFG30cM&Xyde8KYu1@Wx8Hrx)ZwVlb
zXl^IX*|Q6yHWwpD<`{49<x}jwK%Dbu9Ht^Lvi2#Jg6O()R;4b_(=`%)=oB+QlM?;c
z2QHtTx0Y!eY-^;nlY)rIYb5jc{|*oJ=^|5eIfKP#MGmo^Q#8@QLZb{@WL(G<4IwlC
z`uBh~<S4_*E0xX@uNSfD;2{a79T@DuH1N0&P%X}~EA+lLKdDQtw9Na1+R;|C<_ywg
zUmG!cDug;zh2QfLV94)Qr6#MmFH2(d#FE;2E@oEAsfbg#*5=E-?agF0YS$!44)fpL
z!I#)ePhFS7Z11gmwP>&zI{p$%Pc+fy$jRhgFe=jkn@7`y+LmxU6jKWQrYN+bGhm(Q
ztKjT;NP#wGCAMm$fmhVM`jWz{gZ0ROS{M5d^SV67r#}Wr`|mpmC9<8bb1|Lwp%UJU
z^l%Vz1@q#ehk1WT6pIPF={+guAf)jE@Cys>Q03r7Jr~`&qxkh+6>fLfi(ThI&qs#_
z(A<kmJAf_*cQC@q?Pu8btWD!wPP)b9>KOwV1@~haK+m$$z&#IP!Lz{kYBarwtQbkZ
z9WUW&@WjZ1#`iiF+S&you3ih?fBm(ToYbSL&hlC6zH+<rwz2k%{dGA68_)&VgI0!?
z-tvb;WAAyo5J@B@1bQj!#~n$%O~<N~vZY4ny@JxG0SuB0ic1aSzsT7MeA;x^%eN^M
z{YC2y)L)f<wez$Do)q-9%#vsch93d4luZ(mk&IE^;t}|Fv+DqxM^icrV{ibyi9Q|k
z2@wt*lAj$X*XEqgQe+fqIwI|Wsm+)tcy<+-Nc!PNlgNuxpD{+XyCQ965!B*+X`rcz
zdD919b8ed3IKph6Flr(>gEGfAXA*HX3lluwhCkzU@@{xwn=~VdX)>{E#6`{;+R?`E
zl+j4sjRt!Qyihb85}CR&x_<HR_2eCB(M`K!<iiOOxz}Ed(ANa{gra|2UxGS|aB~DG
z{84zV84bSq+d*vIqqH>r9bNbgGKg`xpJNamgvcBc^dDhv^W}+vL1|f+*>P-wJ?MHe
z$FT07!&SLl-e@i8A;y%pYJ{!eDPAHCGl4QHAk3!94<r6?M8kCvY`idg0|6U@#k=9k
zTTn{H6|gb01K>G%2tDE8**tj3A-@CQnf?U%)dUreAQ0er0dPZreIRz;NTDtAm@<(v
zXg>)9ed?g<AozS*07Pi#K)Cutbj18%Vy}iDD9RE5fXj$-k6*|9U$Zz;UNrpg5dP88
z>kVK>=54wIEZw_BsC2n9gU(%Lz&3jV23T9U6W9fSNJ0?11aqBbu_9-+X^Pd4D!To>
z)ldHIip{bE0lY$cGBwh0^wJTFz{}<!{CD^GC@K*(?R39~;8G!vExIMytpM#)c;S`>
z3WE2;7}(R$`Gp4BPV{jHAri0w07f}@iP-?ccqu488^saDMkk9U1zvyg0${M)D$Z^X
z4z?xldkF>;m|rM>EIy6={n_~u^K?T6g%ac!E*PSjOci6c3H}cUFer7dHJ()GQ#+jj
z0)R*}7~a=vp3epaK%jTNh>+uJb_hnC8PDT_252GNg!=WQB_t4l!NJ%uh5TLW`~V=7
zG=d;PJASndAiSfdS`PHz)4R+YQn=m!Pm%!uTEGDqKnpS5hhU?+N}GaK=NCf&$oe%p
zLpc9J4>7X<Ear5iO(1;{*ia|<4Fe|>`f1@f0K3$qsWcabWY{^v0Bey5)6xb7ZC)JT
z!Li)gr5zH5WT~DRyjLe@-Pge9wdO7vY$$$dsYPRUyMAFdZ}CH?U!GA$3N!-oKmeCw
z?GF37KPZuC9CEKtKqpkXq8gL{R%cX&{{TW7lz&x;D7pvDPzv}v<)iBZ50tP<H!cNl
zW`IV)`Eew|k%MRPxc~veVA@U$2$!0NL<pBa`%(xa55WX=0C3RH2k!ds00GiNZvVUN
zIhOp&7T^G5PwFKSaqE@*%Xat$uC>T0S3&GK@*&KPfcsf{7dv<<guBoGmSw^gt8A1F
z9Fuau0oY|r2!2&+rr5<UxJ3;Q@;kH;@Zt^UVr*w{;`#W3=q7*Qe|5g5s#|<3B2|KT
z4g^r(cR7HR%&wM)hX>F?(9_T%$47I%f;ye?Ute&L&rN1=S(cH-Z2$mp{u?xXksNSA
z9lrvPQ~k$^jQ+AGCFNRdw654&8UO$S_aoQ^p04b?0Q`?(UVJ@G%<*h=|J5rODZ}32
z5aIh3WiSZ->lGku?;lgo3*PTs>9fzL{jIXg{`<1=A%4fx{{gXB9aH^^vKF+Y{GFbF
z)J{|I+dupX7##9nNP)*g*g)&|%c|K@WWM0nQ8E{Gow)_`svaOn^7*pBbQZ$ea{o8c
zVPKiU;QBzRva|$-6D$CBB2j4DMGgD*PaS)Ym4?ud6|W|cI6~vFEfF9<-`>E9%0SV4
z;v_JOw}rL2x%n#@Ai#d#9q1lZcSK2_x(<L}cF=4sxUe%CfZtZoY!TzYuYUF=UInGK
z`+iFXny*PCzrUKNTN=B+P!0o)SI$C6k0$^CSxCo9hd2O0owV-$Oh!DM#moBZD#Rr}
zd(Xv-VGr+LpY|P+dN0yx-}%=>FyE(lGA8IhZdeC*0splGfTP!iC)Z+SraP2AWImK0
z^gBlYw7C9*ef}%`hX4(PD*YemkKc6tgOL!qukDrj5XTUxdFEo8St<6y1*U-i2ZCH-
zO9E?Odef-6bluL|{X13P@6(#5&0)TbM(S_fo!ig-0qn30ucS>TLF{c#(Tz@5OTWla
zMiW;mki+KH&~g;B#!t8BUC?;7wb-NC6WOC(p#S%o2k%cKOEg0)TiA{|Qv0=%4J}3b
z-bWI=7{+uKv=}C;`rA={?Ekv?{{Sh;ibc<^cCXUheI}}8{#UJl)K@nZT+?c>wk*E`
zhMp9$6ksR=-(#6Y2i`|-qx9m)mK&yT73ov#SIuETXUH0nCYC$cz6-mZfaaKwa0B3e
zPax=19#F+DJOXTo?&n03L4v<EQLB}MW=k{*ihi5mFDsGb%9n0aYP$7+0~v=!a7>dh
zczv&2oz0w=apN)7F?5>)1VH$m9RCwe^*<9=3I{*H$UO!0{|nNM_5UK>{-0x6Ffh^o
zkMtN#6c6+OKZ3BQ?{JdlrU{>eYiD&?p3W|3z2c?d*pDrDVHXA1F@rk6ZcLs9`~im{
zs7Trd>nD|j3o(J~2Ss7bkb;9%3MVppB_InUJaM(G;)EJ*O(VT5bjeJ;ej7v(1v|(U
ztV$`jNs*%Ek89JaH{H{%8Cw>1$rkwD3}MLsT?__B`u|Ig|1(&_e_S4e(Eke-$I8L>
zKgm_>4ymrR`g{{EK4Y%ZR5Y5?lp$=LeKDu0MYVz{fLIO%ML2<g&xkDQPY(*AK>#wq
z4I1aJs*sLSrFmgh-K=UWqV<n+`*~&idXT5<rrVpp%G=|nbLYo<{vo*WMR(BlYh}ao
z1|n>}5YnF?zh%2lKl5vYBPO$zqwV9P(~~0La(fF0Wp@VNXR1e}E)nGNupWd8b69d(
z{m$&#u;xim<44n)HywX!q$?!1aOvQ!bQI*1czYz)+(2r;3xxoG3REM24wMfnhdQfu
zH!xj&xQfH^xCRa?22SPmHDnx(R--}q7?H-4lTeFDjnWIqc=0Y3qTN^C&X1ekD#t2P
zn-=;Zd9n-j+hP4&25zS1`ND{4b)9QsWgPhE_Y0aa)DGS^xO7T(j{;tK!gRbVP|)~$
z(07bG5U3kPKO&F+h<jbKcuJ=PCdHHTme4(^lsgSCjS4y?6pTDc@>8coe!@#|3wZE1
zV0RsUI^o<R-)5v*?2acmXvlbgP~fc_0B(N)PC$~r<gC1iT^fv~le!^~m0?<~Cq}JW
zdEa0IzAg76)fiLO%kjE;S|_1A1$3fV@SO+uAtPNd<Jt}n*2Qo^fDoz>X%H2vKd-Mv
zE){h0M_Z`J+hnqkgS+zgqug`W>UDqr_q>p0V2BCNVl9Ny#8>yp7iQp(hVpHkRS+#B
z7v^kgUvCc)M1PRNkg~x@S^GgM;$(R8aZAwF>9oneFr6T-Bhz@PCW96b1QQUEF2OFz
zC?e{xyCIN$mVpEW#9Cx#hIqHYzYq*}q<3fx;c*Cz)Ye#ctaoq>*=Yh(q}wFNh!5eQ
zLxTI>28efn^=YqR>H<}Ss>qgM%|R^nF?9r1;ZH$->f`IkSP|idN{37gpzD+Eh_k}1
zhg=5I?Fa_KKn!4KmlcMr`%(I_1R8dWc0B>vB5+x}m50h~r&-gUVqGx5`ZkBV)Y=r*
zD7=Jch^xnOC*G3RYm0h1?52_!_@bI|Kc3nop{XLP?wCe@9D%x651N5fu2b>}6;a4K
z-a8_Ef>q_5GTeS(OWD3hrqB}i4eqkpmTehqjb$3L4!JbwIVA3DiqtxEun1)u{{G~b
z!?V^?$@t<o&OxXAT#6BlU1s|zfOYWg%8FqeJW<{P?{x3w=Wa0idn()fm1<91`99t|
zJd$>-Ne1DH){O5_8U%?eXzo1)tnf!-xe3&~YCTitX_u)Tn==~JV6YGoWbBN^PBrn#
z6gCpLl8}H+s(2AOG-Wsyn#quz*halZImtr<Z;l6NT5eI4eJi-7q~>mg0`QstyT>IK
z<k`40UvTLc7w6Y^9@o*4ao(}9yMbR0D%Aj0g!VZ@c`DkZlM47sL<`F%A{-<pDxo+|
zYSYQF!C`MCO-4ItxQD0^FE>gpKiH?4xC_}C%Iog@GknZd0W?C!tI){9YFxhHGD;>A
z9tGOr_#T7FIm0nuc@yFk`iXI;vd?CRZF8hc!KLS2S#oiHRc2wb!5v$DuR*|C!BU4~
zq_WtF-m=E*;r#*`oLVU3Iu&E@^LX^&ECb7<$88~ul2>o1<4kf`@EJd9x_B)XOs~dQ
zW?!vopqbkV#v3pkK1rk+N)5AK|IS1Gmkmn}oJ^#YeYheuwC2Z2Rv&0l_qlcFCAYep
z_IUWin}nZcEViaM&4T_UBDkM5MA<P?IT1jH-^7XEPM)7%MqbCzT1LoQzM2p0imk=%
zFMPKIyZBcv*KuhuS`}%7G5-=_*@f8Amr0r1Uz+LZ*8LI3a>ZR;irnq}g?$IYQy6)(
z88$P*2?2qNT)V{L`JTr@;6`?q(zsqe-<Hu@@j$N)8=gKWX6_TPk_>7E{@yXW_^V>a
z;4t3e#WQ~J{i-P9Vki2HAZtj-m<$@BepmhnyT^w-0LnZKsql^aW}3O0KZI+l-Uhgj
z5$ky4?1LV|8GfKX2D-uKwc*{KTS!|!3P$8LMM7=_nRRih()lf_(gcTP5_F;3<U@*%
zOqA@5&ZQ~;(n3~<#E$1}pK|+IpyDTM><Gs#?82Sww<l!`&VxX+hf6gpX#-woOk*O8
z>ju0@OlP8t-QqUpgH4jW=aII)W{z@BR|l;qxA3WX;#a*|+R=wjKC&)V+1IM$NreJr
zizAmA?qt^~KBOjKSn$!dI+w4ScK>*0x-Vq!Dxk<$R>`Y~{(+Y@nz)MQ)tK>~7fblJ
zOZvHaw?2IQoIo@Ox)mSlRUs%f7&XI@bh?^pdd{`ycz!)G%fU3xX&1RsALs$a{Jx7b
zV=J|^L{}jsGnS$&Fi;Z2p86OqG`E4zAZm;!T5O0H8<93!^~VF<TM#e{O^=8#)WNzs
zq(iWyL`0w}W&t8uoD#(7H)qcruI*n{Fvse^9C!WWF{ha0&!92*FPoXcd4Hc?>c4v3
z)*-@|d}hL>*>eV(*f7!_6c`N0!GEX7WmxE`7B-t=>QG(IRE<hX7&$$+Xtf>CERW4x
z(iPKcYBC0J^Kf%?M-j8uL!Pqu&mm@t?U%WF4U_DXGatU7T*EC5;Yt65MA{W!H(t}l
zu;~WWQW$;T$;gtg1M*fxZwu|23%be`8YJf3ktO+#M(Hrug+0@zADkezd#Ao$iD<Dy
z?~*EQCS=~=W65|uVrK-2Usx&;&miPE*vW`5SRf}%##{dw8aZa|^X}5l@8C3Mb69sy
zlSR7fir7l#lQdG-78SHx>aocafpopGNhu8cI#Y@n(`woS9Arh?)6R~t<IUj+2IP>T
z&#5;7dXFw8;=~_UeqpHkN7}fU7*H1GMw*fc0{NK$s?bgG>T@SrUxF|&Lh_`(`ck&y
zIaIj&*|b#kHXL9n>TUQ4IiYLhk2Z-dZ3o>#v~w@OwkX){XvR$0Ya;#dBNN*#e$bO$
z77MTXaxS_-$SS$6WMvFQYczRPfUGQ!`L0RpBPKC<vzqlnvn%3ltowAiU7UF$wP~)t
zIfLt}J~LUb?z$qxD57_8)o5RB*GmFr3U#k^!fO}`a_HsdW`dM(l^uFEhQ>29T3*#~
zZ@-<cFbD&ABnnRQen%U&t)5KnZXb?W;_=2a5Bhg46wt{rjAx;e^@$9-peIWcUif1=
zfisJ!xTU-wb^2km?3f>rN&TtiertZYbVZ6({!JDv3_16jnoR%b_UL$XjvX?A)e(Af
z@MF^Ux98aw`j``DC(=@5Dg;^FL9p++0r^Z|3r&^mkhn0kSG^8y4X5QcfFvV;T-FXF
zeG6HKZwj~MB0*-$ods6p5B~Rg+CMM~H_;D{s@B$UNH9<k6v~Uj3F!2+h9n^JkdlCe
zUY+hQ>t#T832$5HJL_@UTNwwaeBsu4s=z;V6pO&d+=FqS_*^?i6pVF_;5cS{`j6M@
zH%aPbHaLSC@G}wcQBiPm=$@8S_64<Vp>Jwa54C=?jsdfdw%xV5$*{u)<mBAiK26Vr
zfg8cuI2!vo<pIPD#6x95i#nm_Lr~1$XYVZyrWdI1aZ^D^JH6rHC;8*dNco0i30xtI
zc;=LYaPH}Ig=k66^m*Gz>3sZmzOCOfNf~}VSatav<zV<|1$D%-6{SF9sxoStC0JEM
zf&Rp9WTB(QLBx#uMP?QQM^O$sm^EMSaj{fC??IVoewEKEZSouQk8k-YQnw^Hkyfdz
zW52G#xkgWis>e$y3ctCfU6q#gPm6u(E{@3|*>XEYBD0X-j1KuRx68LVF9<UYtVg5c
z!35Ps&CrIo@n}3E%VsL3%s|d#X>()ItR0Sm7P|9*eqbgH`Z(@i``Uy8BKW4jwl7K(
z9oRfH#c079TB9?=5+^a&2?!easc?VJ&q#E(_|IcZTF`XWwL2^RqEZLM5?Xb!=TDcE
z_jQZdf1mQ$a7LtV23QuSYSmv7P9wrE_N}$-5@e}o@(H_;$v9zzlSjFcBpED8)nAaX
z)tv<U>4t{N=;?l+Paq2S;h{8~5S!tg5Gfcc72Y+q&unaMY_ZGMY}cubLNd{JfU{&{
z2_aEq3$oEuaFeDZvE<iZ-Z(Yqi}<1xx+^)8R}hv{*D!Z@iXB;`0dATNgSW(FYO`J)
zP3e<?5fDf2A86wBjXqrxEZK#{UbKN1EJgUQJF(Rgq{rqGL8_WttTN{kY)E&<DU?=%
z0xu2tSZz`g56de5y(nS5rOqe{Rni!B^rX<!_mK9;Vx=)|o#3NQMrs!>Q!AzovPQ^8
zOcTi`ZD1|bT?Nz=iY{Ru$C$vtnYswh@Kgtbhd=$z^A=S8uSp`KsL9CQ$suk8ZBrm$
z$w+=onAIrtly*>pJ$@stiB2RJN`L7c4M`Te;~}Qa==A`W4CUD`bOnwGJ<1xHvQLH$
zIW+qUdqW`i#Rc(RjP2|P&+@!c$~jkvElb>7+-E)mV9O@X95dBP&kBWP0rZ}NF;FhP
zS1>E1OR3QiVxJ|~wCU92PTQHT$%0P~Z==`sTq)WPn9Yw5jztSOG}>0^`w|q(1|ur_
z-G*n|S=D@iVPqz6se0xWo)Sk?8AG=KC=p1>Kv?9~x7r{m;+uhep6;~qZk_<VP|e@m
z;B=nQzXgrT$qc7N;(2dsWK%3DmwsELa=xNj(_S)<lM$3@I8Rc&=y=5McYn+&{Ao~4
zM-ua5y)f$az;D2Y5H&p0*Q5-=$@muzJjQkCu@4%me)-WFk|p~^u>E<|CVw?t4E47>
zhYIV`unnoj_Duc>3BGq&_K$mtp4(N!Qc-q>&Y;D23{x9OWj)n^lJAMsF|Do)oEMeJ
zmUuiI3*7f#8zd8SV^mXARJ^g;X<FfxDo`c$R6UK2D9b^$*#WkW#OVtZ=e+T@pX~yv
zfNIPifxBnK;XiZ=ml6$tfnR(h{G_K%>ju_2D-dl!&)m)*5I4_D1n@8W!%RQtd3YQc
zGv7uw4D!hCX8coX!Y$|UOfK(!Ow%Ivj_v8%^e53Jj*fBIP(rzR#pD?7j*LTg`3v3G
z%$na+rv2{rv=8lSb_y-~Q}fDA;MSjSkna$-O}%hO|Heg~tbXiz@)PugBjbPiggb`S
zQ`e)U*z70VuLV6jMHV2!rXd)P-|0UTp_@s$8Q)$!%`OLxa~%oIDQ&WqHEpjPtbao=
z<IRxG6K==D$%KxS1tkBl4OWX`<h&z=z*A#H1Pq7CfcEa5kN;b;syeauaIhHNf2-s@
zA}_~E;4hX6H@#PWZ}L*nkK&~v!(F%UGa@;N<>aEEqEHKdn=76j=EABO;8<&F^z!<A
zWYDNUqD6Y5Cxfbd$e{H6XAwI<alW^4un7`gMl3oeGbEs}D;b;}e>c;_hxc`oRgY~i
z`qJQ39mdUgJ3`Rk<KtAb6SXy<x-gfzp5^IfUZ{1gn%Aa*b4U9Uf}#!ObsmuU#bt<F
zUPIPItQ@K$b_gDjrP*fi#XPR4?t<UU^G0|;wj?41Q`9%}c#G1Sm|v<~YDcb0Fu60&
zrFg2y0;=-C+Kz5l7|9;Fza?Xrea`3k4n7WOCRGz|y^)Dt2|u+lrCtXtgplccJuH^|
z3I1AUfa4apLgl1imx-nQj{agLx>@Nt&x0m+HK%f#NzSaN4(%?%X<UN3h>yZx$9v$n
zcW7~i3o(FdQi5(GO8xxIqff|wzH&;A*eGkdmbC|ZF$_B}g?(I*-r?=e(FV4sO7)ux
zRDq7)1bz)P+XUG04d~@Z7WJBPTt>rmvU^a1%{xRlHBao&R{@P<lY)+Ft7GRPZ!|ef
zZ<no2dwYI;S;ZbU4wKfVUZTW5n6#hY3@KZqh;9k<@z=TD5Y{8rTE9Wk(UFo7E7V$Y
z=y_^6@~yj9Br&xj5-|{rSrc{?W`to=tjhD%Jer|h<B}|Dt!>pB`+AhjE;S9as*qGK
z6TID^Y;3xG9D~w7t5_8Fx<ih!u1QlSo0N}WR4i%)<`~@y%(^D?wT!-WzOA+jy4v6h
z^AYRCanbYr5KXo(cd&gm2I`f}+e!XFdLR9c%=MN{wt;tYglp`eWDHK{Cq`DsiZ;U&
zH?<0=hZs9g(laQwLM=ow)g%a)VS~Tm?Ap66{vT`(<UKtZp9zmfHLV%>&BXLbXu*eF
zpH}Z*6Xrn~sI5z=Ncx9V=k~3*B*AdrfBgB7r_P*ngR<A4Nu{>`^eLLsHP0t2B6z7j
zN#Sqq#SUWY10DjPzsz=COw6ABz_X1un_cA^RJzztK{PV55%B*!66hDQs&}`r2Xm}>
z&=;}P!w-Syadvi{rq-qrMbotIQU;fyww8!%_mglE8(pA{_%Nd!r=X>-to$K22e0V0
z&5pwEHecVjroyJOt9A^#G(OW6c!D>%WOj-Pkrncktvun~;f7xW)GA;dU%kH9g}zbR
z)Pg@QU?b_atEO%(>x!44zK?m7y6>MZU9aE@dpkHi!z&ic<7!}L{)bD3<PmZENIRKw
zGr#Af0NLr+T-<sI&G;@zPp%}Wa&u$E3hq`LFkII((lXk}Q77e$D=nXHJ~kAJE^3s@
zi8oqER;ZpD8+#rsAA>wXi@mz$aP_cibBX^3BwLWsZ(gYH{p+MgJ+?f&gIe}-WmCu?
zZuhwvyYOWvzg$2aJE<nX7@~u@9-5UoA3+Le`9V@Wv4syWwE?^Tw5yK3guGl-Bf}?>
ze>N~_r|!!kr&c!U?*$c4Rcl{JvR$Wjl&`(tVM-(o+(|f!^(Ign`MsJ4yAgF&%;J6o
zny#@_N*=PEQnU2F`^_SJV&+#f6%lZLPZVm|kq=p+QrwT~*#P{+`bo2S%R0H^w@-TJ
zYxi<S@4&PEy#GXOA%gtcIm@Q&@Di>iUDFIm;_cpxY0Rp{>T|FZ`}yw7CfwUNTm`DL
zoRcyuBlA%6(qH=bj&=?AB!6=s{S@fAuEFtRbzJpX+Q6y;YBe`+A?><Nq$VmXQYz*F
z%-5n{hsz5;GjN2PA5HIyy+d_?2FXV0cvU>VFHKD+-gs<mY-(!RWN-S1{xP|E)tOlZ
zCy1Bc94cXw!e&w8UGXGk_+o48vbex^qwPtzcYDQNc7x?qoag~q|AMb>gI<Nw0NtAN
zxfzAC;vakp3brK^?Sm^GTgY`%Ivus|shGr9LBOTI#v|mO9TqB&$h38vOnZ_-VdcTU
zB+jyV-F?UHD@=<XqtW0TI$B)&Z{dOFy>`7*=Rq170?}8-*g9}NHt3n~c%pFKbq|Ly
z48-%W)1wxcju$cxnz?ws+IE^7{6_b$0?Na_iL;`qE5v{MS&H9X&Tv0c>Z?&fEGNTt
z*N3**(7Fy6o9MQZedpA<kC8Z(dix)Br=UcwM{u8ar`L9otoxloD46mTrf%w{x)PKG
zbwjoOjl#7Iyfl!YuS;@26Hd)5hinH<Spqg|yB(90zRAFY*Ixa`+adS9wu9G*FB5uB
zH*c3Mujp^gCGn(_y)T6T-?`#?6FoAmka{7RUqaouuixFpXcf&TZ+#s<EOn)^t=4+9
zdn;eg>tQ@JbmIjlsrtNYvA6lby|b?&xfkzNHQ;YAa<fyZulvZNPE+^JQ9GcqSeCaT
z_@AXuV8^T!3Ea?Mxk1A-uu7_<GmCSCorth9Kd1*iWFjO#IdX$Rt=I%!i@u~?pq}gv
z;W`_{W?i8k*XjA9ssfISrM=OajTEjY_*m0Ih2Q**^IDC#cWPIjr@3^~yQKDq&&znT
z4c=i|=k;4BzJ1DGQ!J~6Mn0~CJzq<)oIU6~6JJagfw$bBgS)*TZ*t@Br=rf_O9IZS
z_OwVo(>PVv@31opV^-fUZc5K!xkG7dBvfgkMP>_35W?SPkfs7c`2@9jMK#skm*ny<
za{puxME`j;$#<Y~OLlMAH?wmX?Mt|5rvluleb`BHz5z`a8w@Tic`ooB!FqZi4*XO&
zk$lnN$Pn&HuY4zbDY!>P{ya}?wY&X5fb1Nu6~X^WhsE`@vZw3owl`$Rz^4dJm;xet
z3gUwtp%4l%Os=4BM!J!E-pRK&TMBoCFXov;+Cd7lPJlxUX#2yF#Gf(YJL#BN%UI#e
zt$!i?$>H2(a!Llt=G}oy8#WJh|JYy1li)!fOZ8kh5zJv`CFtU0@*7{GkB*ekDU#*^
zo5CQ+ut3EK`$Ip`_+Kioc<yuasV-NBXkarb{(7V94WKjGb=6MW%aI`kGqH+jx?sig
zVXt085&IfQ_R@UH1|4z1Bzo*yhvcAz#1_RCuXmtQxm*i)%Qdc8^?HXq6c;5*HX_TN
zH$$oc7t$o59n;MzwO(Lq^~Q>W@`kKt9`>CnXza%{Zet4bs%(wbeJ5>yA9h1S=Ld$0
zgqgOL1xi@Afi#bza*!^WV~k^>($q_$o%oIgH+t?UxgoIfclbMi4ho38<+yRrF=E@r
z2g!G&^&n_kEiR;&91vY~4_*QSE;<St8p<L131|;O61ozaT9u6-5z0BKUY4!Mzy|ft
z!d!&kS3TLAqYF3=byazXTZliub<{{Q(sXxNVp|xV2SYf`8k0q$pS7{tV)J3iqnR}f
z@3`;<tFgR*k*7!fN*YL;b&LY|D0rKk{yy+Q;31y9p*&ZjVj0D5EwN)mq%#kuGz|xI
zE?~i}Y3NL_-e#6=l8yK{^e_rWmcEa2no5EeET;Pt=rkvPZ2?e8L1o?2&<>TZQl>h#
z)u3S3u2dUMeMEHDrI7NdnZs?5RZsztVe&P8tv=}rLO7pLX%v0~Yd32*euG8&E8eEg
z1MQw+(`sC9Yi*l`6=x8sJ^U0`@cPk+q+#&cLj+U0M(P|;$+v-zL~u_=YBR{>z7D8*
z0bWCkq!tGJyn(oQtegsmBO@sXH^+i4q*%eAw07f0l6GKX0l(pf*5FK)46{arQQ05k
z#kLu*tmN!e4WRvj4H7}G0QGJhg=50dwTK`f5hgJa#*n!CM@4H9ZJR8N=p)s=_h!-T
zB1s95yX{@Bfy=I_sn3P|jr|I%gVoq{zP8~GCr1TA*(`?nvJzn`5Bu$t-8GU!11(RM
zR~a76irCd*Y~+X`q3T5uKu(WPxSd>i%&kU14F(DlbCJ(j5BCwtEuZbxbvYtn`plPR
z=5DB5y$G*i@jle$nn{%u&d9;bFWyIo<UkDYown96MaX2T38YA+Nq#RxxYp|*+$#g`
z^gzDkW#uzV_jJ|S+s1uk)dO`DQh_Xtr85chaFid<e#jQT9Mfj2{;Uht7d$V|ul_IN
zogS)TZ_<AYWc)jIG){P~+u==N%&==|K?!Qp_uFqfhI;1i&A!r+^MC12fAe`?%}!C`
zaKWAF{qW^i=Ar$s>~W0$KkRYLOzh16qn$BZ9a<fEwIv6_NVIaEu_W8vI>ExSTCqmN
zI@ZI=Ti%Oc9~ln?lK9t_#vi~B;2ug$^=nsiLk2++h{G>a@3&aCXl_x}vb?JHF6*|w
zGWUGns6S{wxfZBwIca(EImwKDOSshV{JPNl(X@mI8dyz5fB?B%2m_5=0hQBg<s{Uu
znIA*7us&siZpH%bWgq<@-^<3o<z>ydl@xU==H0&Q(FSmROwH0ee&-QIAd=-7+j$td
zcYR~`A}iU?bPML&V+%k)@Ck6}=L)b4xXFdh;<bcwxqECf2aIf@&@ex5bL+lbUvD&>
zG-xn#RW<6D%;u!kV8!%Zt5`BqN=O=i=U>MvRxF8Cn^3lhR%|gdejO>+k2F?73$0G1
zQ6;5XJjBv)^N14tLj|oeN}1?9QEE(SogwY`5lO9@StWn5ykVr1XXP(TntG;~RBvHb
zG!>;%atjUEfMTqfpjkSYYiyBCO)HaArABGwQ7xT~Qz@Z#dwq99Y2Bckl29^yA>jsU
zi(0dM=3H-nj?xX|2?Gz;9<6{!xBB4YY|%x%SaF*OWo^ZYH@(Hut({$!c$S>*u`L&+
zSR#L+$b3(IiEf*mP=Xs0SEEEWy7hpIFQqgxzKDWVY~!Yvlk9mnejTZqlTa{@)v^r7
z=e*jhS*v8B?Em6$dt(erH|~=hNv%9`an#C(7{rf4zrT(+i|7~zIz+fnVgQOLTZJ}8
z5pA1cTjGa5f<S0My(c#}<hHNBuV6rU2f~mJL7YQkg1{6Wyhe(l6^sxWPBNrufY}01
zq`J?eiuh}58{#zJK@_keW<`{{7sM7AyA5>M=jjLP$0o|bi{E{Slgqea?B(x;7Q`1z
z#61VCa5owo3oIPKO?&UdE<A~5jg60e3nf>799}!EM<0>-<9ga}4&U;|NB16=iT@42
zv*uiI0xZOXoSrD(M%zlxZoZL{*I^w6;T=IZ2hoqfNx*87F7=yHhDj!P1jiIH&>}d-
zMNQ6IP9K$M!Vx--&qI(}t^Dvl=k?n=C+FKo#kNZMkR_elqLnW~6lN>xH+g8dwJNrH
zzj-~X5R$Vc3FLVCy5s{Pav`CdhMWere?cKv)wFzct?Frmyptm=P7{nKIehy-O_TA8
z0C*&Ag5A2yM!45Q1%_->IuR<B13@))BW3cwcL%J^%{AWFJoZ|3;WTl{;JjDo4j{pm
z09ax>szqK-@YM>|zZpgF!R{1WmxAEa;YM4J?O>)$R{``k`vki%#;TZ^eG?{JrGtMn
zG5#&UeK}jL#`P%2RJvJgv6++8`gL@J47DG&->1a)?zqu~v({pZ!^sLRI@6op8agC_
z{P~z>+=O#PitG)|d@iP%Un}?I;3B>b<+if`Sp~cvcTeF7%?O#TlmdgGjGjR<C)}NK
zfX*JapBqOC43Wim15===qOR_V)?9Uu?JuFM)48qsSAyo;WtNJq7nzQki{Ni*utH`(
zr9kj5Xd$9+ZpLh-VZhUf8k4NNPXnZ~09i05oqC&J4znSK<tGf3cgxsjgoJ86*!ZyE
zBJaoaC#}u3F1VLm?kyicN&d)`G|o>jtw~%oy!C8W?^8ptma3GqUN81yKNpG$xEV1(
z^N)PuoSau5wSsNyLu8=Ae;#D*KNhETSt1bO?FJfvv;a}+<CM~JZ8c^i{c>WBT>8dK
z%|f98=9EaY+_QDGQZ6=HB9AJ%eP5u_jw_%PB_AA7LCZ(HY|g5UqXhKFq}j;sxGw8j
z{1zqWAO2O#$cNXGnw=Oco12GZcSW&oz^-&@n*GfmP&IbF7V3e)3YCrY-KG3&Gs{0M
znYb-^IcMkxWL`cwPM95XU!Gi=gqid^LbFHlaX`)ZY0Z_7dnAN0!QDDd8@@#CuvyEb
z^pw(`ddy*|N$0me(nffJNbNgQz$X*J|Dtp2e(v_kB3FKY*JR^l38E%T!u9R?ckt-8
z<d%ql*XXLH7g$)=JC|{jHTwu*IX{#DmCUnP0iX&mk<y$JY(hh9Xdrk%urU!`r$D)2
zh(+F<+6VKzqKULLl9g33L@g@k6G&kNSfM43?rVD9h%+MEUn^Mp6tuNxq)k31N3g^P
zj21a;rGLgMBj9j`6$TjW92hNV)3CMjcMEPwL!WNx<e$<c8>6=%GGQO6>W7+DRh>~Y
zhi9e&+@+#E(4k;RG}2ds((d-L!c+Mso436>k={$rNW4ss`7JKePKJbF+%S$t1tWu#
zIJE>{(8yn}EevIGHXAUrsPF>d5AkH4hAD$U%PKa%pvvcaf5zDCyEtSSfVWqmG$T1U
zip`ALlaw7K25p3ngw>RKx_7A*Z0iZIZkF+hV?WUS>fSxj8<s(3B$bHla%ms+zwldq
zGnsBDHk;~j>Gg!C<CLDt>Erz$IU<CEG}(C8Rb%#NSgn8IGT~BG+_z60O$YAs)!++C
z2$rul^HEPB-#MJXbkI7dRO_@fW#FV}^z_(x#SD93|LlJjH}S}gK~CwXc&$YQ?gCx*
zj=oTFq-DhETRi`D`rvYZ;xj0C9~1wWbaBMgHtt?G(s3{JCX-it76F}(j)q7^+Y3|r
zgGMh<-kj&xzkej)F|QMpIWNrF_rZ7{5!>9fa&@}4ta0J*y|g;Xz$(Fe%5N>vJk?TU
zm<p?jyR*bMi0;AWtkYX^{XJxy*nA}O{D)tM3x`7t>SD3Lz{#A$e1v9YpBn6}-S+Er
z?!tv{!(&Ym5|^nZx{{oH_3C(>1?TD8PTdYHkd??c7n)oW=0VEvUadFN&f%Yk^qGOM
zkAF_wKy#Rql8fB{oQZ&v0U1uoOt;Hv!yyEW(No2QDbtsr+oFA$Lu!47#sZc?Gs^0I
z7KJ0nrmtx?)_XD9_~8`8pe(Fa2^zH?msA5rinEz6MGxoZ1fSLzd`vvv>k9#*2Mmv2
zq4yI_y?t=uSS*|yFgoZ?GGS%@wnKuNOaf2$l)ABdur(=7C(V_c_Epn$%d@7X-E4VY
zO*J>824QHLSF(>cA+n$giY;D)T9eu($TQ8_AJbeOqR72Zr9zZj91*RPN3mYIbvRgK
z71?uWX3ebhiV0~TN`^+~S`@I%t&>c!3e23MR-r8so5$FT(_q{mON<h<w?%#OYm=n5
zGA{)Whx`sB6-aC`Tn6jbBvgKOPfJS33mGb@6<a!k({WE%Lw!8yf2pl_VhugWE}X6`
zkIlH3R<@YuHyt3U<cCZ@_XkLz#3!ScgRBgQJ^wm8u-^SUoa~O26gSu@EY1XRlnsO=
z>0~w9_PF1Vy+Z!&1d8oA-4L0HYx|7MtGMU2h2+n6Yxg!&!zJa>tQ?7&rR6<^W9yR+
zPq%R+%Ot?~m)<A090yA4Cv&oGrNLY->Zc-pxxegUo&h`>uRWnsg&ura!140C;qeZR
zcEZDF84(v`cE#{Tr6R{;%2rbqbHlC)i_=OoP)8W4QYonpQb%MyZaXn6E&x5H9pmxp
zM?HL<BJj2+%57v+(Q-dw(R`Lcl=pq*r{keQvPMsz1{G3Y?F*9)E5uhc_bZ$r+->Cy
zo{)UxlNC~VxV`3xJ%r2+83NVf{6YHR{=p$j8WOUwmvkvg(WnsDg#={3h0hg}lZpu}
zJ(pg67LwN0F|tt9r>;Y+EB~d)T|6Zs-%z1=sJY>Qk47yhXlsPDDXINYEMr*aC)Rq+
zuebn5^I29{K|77!9Ft!rYwatCgWTlfU28EsuA)&{W;#Sop_zreCB#&{K_6Jm`-E7*
zkp%Seq_yh;)^W75Kz)k~#+Rz1K7*_z#dKX>?X8KcSkNk`7)*S?LW$<HanH(*I_DBU
zVPYDJ)nQRd(JgD5b{jE+H|E$EoC}uPr;<#<0aXuq23TP8Q+wSm@EKv=ZeAS-up++$
zMo97T-o<X<YHQc*NlTcKU9nmq?(zR&Eh1_(;;f#$=7Ay?=T%6U$yiU6QEeuocg0YU
z({4jyBCMvdG+ED>g!F{VL6pY{PJg=57|anepF0lu;H<N06d=no4DYQ-nm1H#Dwb7N
zxu~&eR?rVP+LaXd<`(!~UskOH3$<BPN@bGMI>>t&sXgiiDk)zOA%WBCn%(eF7Tb2%
z(jXEkh&|uOkst~P?0sy<1(ddT7S{9>l)~ihzi!VW)4nFH?y0q1qZw{s$K96*c9HWm
zljj@hFsSbNl@D9d9ahrj1UZioMsxV2&B_yx$ka~4hEm(!WfOg?Y2*_-$oa1NH(uR2
z60?Ilo!W02pTAvCJbb60`9K!XBhjKhm$Z~#N0t8rk;tPO-)40M4Wx~K&9k}B)AP{W
z%*`wR&do2!3EcY%OXv#UPhCKUb}DutvIHtDUK893O<3HHMKbJyjq8TbQ{!LQJ}MK&
z$&MpWbuE8+a&>)qA#)lP5B2u&Jw^1273L*3PYe4g{u=t(q4gmU>5alHagFy(``6z3
za~h(019FF#S4<SjVQuFBm7>I5O3@;$b@oTL^Zn&9+e~dH(e9y6#$2I5!|~rSIQ0dq
z&qA|-hcD|FE$YjZ9VKCJ-#>Ub4a&To&rW81iA^d-3811>j8~A;*wL1dU7*f~CqfNH
zUWsZN4NHTYaneC=HI@~=$n&54q;@2m(caReNMfEnUMn$}&@N;+$oDxAVliYdc|28U
zHp3AZ8!+_OdFe;u4EHa`M;xd$@8C?r>3`HcbAP6IhQpf>BhkyZMdb1r{)o-)sc{dr
zbCbVYI~~32)dPI>b~7c_<h|-Yl|#dnzz$pW4Bvdei+^WRTl9gQ5+F~uVn#2kkBi;m
z9safHE=SE}eP-_QZtAMq+#~1WT6zG~E<GLR>$Y=W=;nArl&EYIyT()fEK1nUR&LWN
z_z@1!_uJXSfx&;uUKnExMyvN#MP~U6{zx7xtsmKfIk|`Ni=aIw%zW2$C$O4LRW3KH
zGQbhjlK(TA1XYDjcb`b3$xN>m_Ys6s>Uz03#XLL+{Kn2j&K55+n@g%4twoNd)qY7r
zNqHXIVeslm=1AN5>*q$vNNXNVjD&QK#53X;C8$2d<HK#3cM4HA_LJziBefSW@lfUI
zLR45bRTC;(QQLV{Rs;`cw82!NdRKcHLe(a<m(b;;sJ)b=An!LjVr3-zm4|}*EzG%d
z7)0VjCeChfQ-L#4ZD7E};*QODg}5TUCRt6KD*92JXgcxPX2CdC_$j>rX^om(vv;um
zgziqE#2w6L7lUqqCDet?i-P?V0Dg+9txj7O0A|Lf<h0ZaC%-8UHK8Xd*B4a#O0%~p
z(P6ISH1)o$^|Be4x4{*Q>64o}e0W%BX(R5^4`-_L2h3^c5G@ZT@l{Fxi(f_SN%r&j
z->kAtZG}~xD|?!*Lxy=^GlDX!s>E}KB~9|M{Fq=4MJq8`ROSdu3AznSnMs~<NnUkR
zOz1UrZR7L5Is!6Q>p-hCtI-teV=%|)e1?p#r(UQwp*^-G@ggKgB6f`s+Dd*yljYpR
zPB87KWmwZ1MyK!AO>RDqZ}7IL%nxeWwc*tIk(_xHUv3CcdOae2Q#qCP(FqSA@tEBb
zQLloFX^ZhP(*tO9qsoYE`=~)pf0k9y=yt9a`9Y<p27oO=ecrMyF0Cj)#z6qGJAjw8
z3+m-{XGt{Bt&9S_LcKq=s=%30S~~B4-ioDF`)0b(45908`YgrZ3l+>7*;2~4HCE*<
zclr}i%ozPgux1jMjB8rkBJgvAksN#0+0z~AXA6z$GAN}{trQZ=kxW}hR5VPBg)y%)
zA2usD1;@$pRug}4cpU6dus-_o8V3}PDIH^QqenO^aAxKW<GeqA05r`eF8)`iF8@RR
zy8rK9&}^(s|Kq7kv<A33%E?L&YT6%c%7Y9N1}5P`(iRM@B{Ksxgs=cfVNiiHnRmsM
zZTN226x}0(%Lp2BT8o5cQc?yPBpIgh?r~qzx3<{_Un#_{_VJ%V#t-QaS>IVtUZbdd
z0;0t6q+^%km6U%c>Y5sFzlo7L{^mqSYSpzW`F(FIo^gm~P$uT!kwAhan5~(LIrV;g
z#oIh`4~{vI&J#WGAwQ1JlAg!bE)F^cLmCn%)_ZmZ4xrEI)&0HW><!ch#I?B=Fo8y=
zR%f=w<)LMBgk#d;+L;<K?@iS+%S#PSPrLzSihNUjn6CN~JQIO$O#Sql&eGY;?$Vk5
z)ShnX?6PhdbP0^EUMJ0Lb+KypcK`OZ;BvCLLf<_&lxfE1l3^8kUPxleD$b9dr!{#Y
z7aIwG$mpX7iy|0gbTDCN3c;L=G8|zVnF28(WC~}{cQauu{98glOD{Q+<euUllRm*c
z3lTLYZCJuY$H<hHK1F>@a%^nE$`qSE+ciM_SJxPgAu^q0O7WP*1hX+fW4y{>`>!gK
zWxDf}hY4_F{Q8iU$rL8^NJ70qReEb$^%Uz!NWF0j6IWVx)Sm~|#>je87p9d$3`~_7
z&5?<7G2_?{%iAu2P(Z9612e-@TXoylHM8n@_mvPHIz39h-|*9lV%Rad9Gu2YHd93m
zouivroz9n?2T2DJBaH`+QKcAM7<d`GjC<QdN63++)tQft;q4O8KioS^?(2tn<I#z|
ziWn64sJ_zmISUU{le0#Y{Y!mI<Bz4Ldfv~4CoMdwJj-qthf89!p?tn-^ynMI(fg(*
zcP8CyCQ-Tj=IO+et0?KEJog3spgj!fF~dU&>A2~b3lPq6I`O(Q@y2a8H@WmHLA#c0
zcE_;VSpOSm?-VOqv~2B8+qP}nwr$(CZQEE=YudJL+qQMr+kbYlZ*p>;#(PhtdK;}*
zeT^!4$)eW6J=~%{r@Ajw%zUlL&0<v_L3a%)#oQePO(dv-o#gY6sCd%-1p4pgGW(i^
zyMw!ZIMKZ?Tg((o1AfmP9JU~t01^PY8d43S7C|FraXCzE>hD(-HvS7WVG^oE%^dxU
zQLoj=q?dyh{GX@2Cp=AWb@^5Mr&$2q66q*!@D8`lP8;p+3*1fS+;I9`RE1{^MMO<J
zDiEO{D$1;-U+-WhPBvCZBo0+gkx~Wdc2v#KwYyI;Q#C;`wj-z0Pe0`?$LpQuN!QCP
zR$T4byz8?b;siZP`Tg>T=r}djJPxgS9UYzhLmC6^{GSU!ujc)s$ceC`qKV+RAjcRq
zduzw2l@i7DMbzEp%Eq&xn@!!8?SBiRlu}-b^GFbg1JpJq0B;_vLhT+7-KR;x+{G+2
z>caFtU;i*+uw}6C4&8m!ng4#b%p&XPSJjL$@q2Bht+J>X25?`Td|ofu<!e6`IN-&#
zwc*k$;(C2C+g`KyI2KR9_wq0DNyxY>i-`JAb>xOoEjOy<P?vQ3K_}4CPN$?U3p7dY
zLV<h+x7^mn^5i=KJaPPE+I95xeJPBca}u`L_4Yd!X(eHEgs+yt5NA-(a{2PH70)%1
zhVF|qE`Zk^j{|cUYvwT}PPkDNvJz0$M9BPl&}9K(_sa$;4jOk;P*2PRDbv$cj&G%3
z5J|_;17dietv+j@t#3D0fYkGKQ^~Oo`CY)?sNJXmMsL11zOQCBt2%)Kp(S8q(Fq$E
z2)RMi5j80_fzTKxQzMSrK@;omB@xDlPeWE%_xV(edJJL=Z!smj=ad1D=ie*N0gtmc
zzN8b*)zFU7PK8L*L1jNdnH+V+d_TPBOtRo@^YZpE$ph>=Jj3`6Yp9mrD-r;7EL1c~
zASwrj4nR_DRguiBU4&GKLbAw$ZxI2>cch->nZflJIqqz%gW0T9lsCL9p0(gtE3xHZ
z5it|MbyOWiDVzVkguw{gV@T{Tb^KfqEjn;%gIwP<QJt;3Fv<Ru2smJ6&WHtjbIX1Z
z0hS%Ic>4cf1Is1t<K<We#9yLp0N--lz(3qOSwdMLI}xz+vJ)>@$&0u+*+^+1g*YXa
z?jKk8>I^9g^?HndN1kO5Hb=0pm!;*I1iX5o;PO4|(E;x4)J*?WhhJ)Gff$cJziI-5
zqQF2VMk*dP`rE6JiIe~iIUAPls|v9mrJbN0n)lkPoTQZkPZMYTD&?uUsfFLp9o-F2
zLN(<D&L$`hIiPu_X&_wck1=yV!TJxF&kL<-loQZvCNNg1Y3^b8K$>6R?Y8?Knlec<
zfK{9P6+KkI{0EZ%n+*<S9;h;tCYT#v5HxgFVv%0z##p%&(UhQacL+q4Bo!*fk>z~A
zu-a-A$U}tC<$F<}GDOqDfB9_v$3k}I)hJ*_eY8wsM;!I}L8|=40X#Kd;6TIA=(YX!
z7LR}6KEB2+w*xHSfy3@Z*GOjMuTIZWGvgl}W#t_T0YxQ89lP_2McP{+_ut~~XpuF8
z-f181d-9cuwznY?1?L@C1+u&Kw}cfPZ9BiJa`CVjYl~V|&iPL@dq?`X`RmpD9wmet
z)xK%o?UQNUUyCjf^mcCRU2-QTyTO~-JlBg#z(CSSP|8eNT6RMGHu#wi&WJtab3cZ)
zNq&N5+GR3Mq`vfG3nj7~qxYcEQ#Sj?mS!D-n{alhZ+Ua`(-Q$y^{PrJ2&lLxt+zq8
zoPb%M`@6)Q(J_nC(~9`pGewoC(QRNmz0lqUTS!m5Abe~HNlV2-5z!xo5m7;^X~x;F
z5H!F>NaJC+P7mka=x6$9wXrR12M*26A_71fuswP)uTSl!HW6y?f<YE6CHp4Dudk7K
zlbAiYILMiPoDGAZuW_o9tY?{sM<mHo9+)7lP^htq8EOj)UM<1?o1gJ!wAxz#OV)Y#
zmZSIX?A+XRX;~X6D(J|kNbb6;M_7x9WIX>^u<rn#N^n?jhxV1Df$3*_<A+f<uAio(
zP;Qhlx(m``5bio?cL%zD^q%ft060NS88@VF$q)v*I-;hYn#!*B#p?r`te)AV`=P+0
zqWz6gpkLMrzb~B8NbE8i##T*ojlRGc1zQ_iG1?RdUiBHjxAV|xYmmbrg!2Y9HzD^(
zEgTYGmOZ@(%xn!bu=_qwfjTLdq$+|p1#T5KD2<sU)}e)991x}`<zRDfPfx?-gjie-
z_*JXUDk>(XvyboblHqQaOqWep4Pp4Ntw5Cy>-lc6N6Eo)xwfHgeXsZ!(nCG%b|HXl
z<xakTxTx$Z;eQDG<*h66Xa}S$9-q75I+%D0o4+`#P(GVlRmr-ioijc@w+nXM^&wrw
zYZi8$GJCdZneOLJ>9u~3cS)tL{$%vG{csoZxGOu_{>i_oUXL9>$dQ3|bc`A8F&&Y<
zbqPUpo*epq4;D0;Z5O4S2;g?-gr=J7>zoQR%VDbT;2~k39;$yw$595z6>P6URq-iX
zZtFB#j7~q@dpg!#r8(eY1x*WaW}ETG)cd{U;x#PzVziR>^9`e2uzxToe(-NilkHJ&
z-<vwx-akFxQajzD6uEU&67M4^FF+#Bz*jDA>5B+MMPKbv6fjfKlcnPd+L6u+8Cp5u
zsbsBHL=lB>N=hmY$9x;~G1D<n<smVAn+O1VN$4h$+NM~v224{$S)2IRw%g<hxpbHm
zn>vHE2;w!HsYB1$ctw8u(VEZKXLSbM<avF5`^CH$ym1wX4vbq$gyDC;&)|mdL+jg*
zq+JSeX-IkD+8|V25UvfLynyfsPo#w486mx7WrC@SCqsP3Wa!67Kil*(^NUxrJ5)dE
ztDl6*{{rrQ;O#OcAZ4F-NP^C<M`rdTK$>)^l*VxXm3sx#j|kvEXX=qJRuM)Z`7=U7
zHX?*_stkWTKyM>TOS`M!fNcF^8X~=(buMOCh(cslDD)LdTf1sJK;@$?m~!R)){}@A
zAk=hL!1^v0p4hR?^;3cpR7)+;`uPzxVZBT~BMk4p`N7ACAOTVJQkD`qP`Nf&#y2Jg
zGUs~vd?R7}PQcQ!;4SZi8c&teJ!K9}Uj!^?^d8?+-tvIL$`XGKZ=*|tfG4bA;5kU2
zwaOI{TzwG%*J){6=}=lki(q8&7DTYjzJH7wIMYX~@qj%ulHU7Kt=<K6dqb<4h?$2t
z52M3vQ7M9Q{9R1}lVp^Pq?Am^O4ZlsXurJKhX~4eW$&(?;CF~})N}RS(feE8-0;cu
zw*YDIt_TR7zJGm4g<wYbB_(Rv_hN6K*~RQW6Wk(9nqn?tesc`uB8D^R!y7HOBZ6}O
zsmiDh4Y!$$|LhKPTXGu@_cuJnO=X^YZ9_;XG`0jP_#U&JS)>U+=t63Ley<zCJn+R>
zJdHo9f_ohhV?hP7^_Vsi6i;Z^f=%hxrJHEl*DBwznI!R89QKKoYDf7u@w;fSLZu6^
zv^_Ga5S0wJcX6fc1;=phYd_8~JdY#wyLLjhw3SKR;tzPfs1{FQD(;FQFKSu<WR#hI
z1j^VNp^`2co^f$x;dfqcU$qA^W=`lM-}@<VJ#GzHDl!={4|&HAzYzwIBx54p&Ge^q
zuEo<xw?rQC$<TxT5Xxv&`kscfDl9roA@Ie%l&39hQ-pkj`b#R@b_JDXFbS!E(kd)A
zyHYLJ2H)2F{&}Z-mW6IUmw|{hF87xTfUJ%?5)wn#!rpdaju2|9z3}m?cTgcP?qJ%2
z(_((~(VYWE;<+-i+0gs`&uQLY;zdIuT`Hw~KGlLkMF`l!^JI#O&LdJ-VQ4yBwZ>xk
zYc3zMSmS(?VJF%&OIbSC>&uT4=&}X+9<hlcs<41`f{Np6tP5d3NoRoNwKOn(P2AJo
zOb&|T?E@cV-*y(ZmnR|`sp-gSSOBi5wU<r02iYAg3)GJ#r&@$Nw0E0sjs2eh=C@aH
zv{(6b^qX$67IV>KP}EKn@}nViQWQbPl%wNRlM|mnPEqu>Cq93*UXk7npR4b(t@m<h
zVG4XH468Nz7?>n|ENPn3l(UwxsbGT+OA(jndvB+g&4@cmzjGR`mr|6@^wQ;EOJ~Is
zL&b@TWcV>0fFe3vHig;FOU}po1TiDgzd~?HhesJF1X_iruE2Q6Y$w!xP%EQX5%tgT
znh?<h0?58>A2>dsw?gnf3Lcehouy}dTcIy)3)edHcp9)PUM}L&w0|s2Q3#YiJ;$tu
zi&^!N+XaGtD*F@}bNKlzB<E4uH}(082%4OCM>q$dGaj%%zwcwY!!LJ3)B@XQB-Kj~
zdy}c~DYc$0u%zc%ro*G<U148?mC-@kpgbK}=Vb-4TnR^iKlH>)(c+x+dpz?O1RC~#
z)uHlU*Q6}M)cE6mi@898`Xl4Q_)_(b`C<{n13I-ncMKD0hWx3na9d~*RtisP({6n|
zj4p2k<+F2o?FzZ8g%wYoa$o=OxP$`LwBtt8x$!wMxT_9FL6s^eN^|F&%(&*YQ|@6<
z<fb3yV(MgQu!3Ms0L~RGaq#Snw)=`F0)xT^FH@&S)xD>@n+H<xmmf)EBlAOt4(Dw5
zR8xcU*UfJ1?ky$w&{Pvu^HdK&bMP`mi%`Y~1ekG29;Y0&;@!`S=>WsEYr}hmE8Y8U
z3x8M>kj4TmpK8p$dB{6fe?ijd2`ut8=lWpat;7hZM}bY05vIfmHgs~u6keuDzk=N>
zGZLYXwf@a)&)t@be*7igYn^t=f~KARF8y6nj@M+`uTuB>IIjxl5bPVBerVw0Wf<`%
z0=1B(noE)qpNyYR`jwc#L1H%E^Mxg4uur%t&#h!fe#!h(FL^zF?1<Y)s!ZBjtIJFg
zv&$8Hj1SBA3xDz2^8l32WC64YRvz*Nry_+q;cxLF>PPo(B@5-~h(l-BKm0{pA)w#o
z6-g~#f_f>@6VobJC;eE=gj{<q_}P@xmPIfjYfOk@6s(N&lE5pehT==WUCp##lxy+_
zCvb)OdBXdXMW`oZX6K@c>41O2tu~a1D_vDkP}S^4G-iTpptMmF4qZ~i%gRoG_h@0W
zIbqu`+NpdX>^I;lhA<?P4*+Lbfm)83@xqzfLG$lOQ%jat5Ksw+FNQh`O^--N>{uGW
zyT4m-KhXNXLfoot31&!>>BerEvLm31&e=>qpvlecqT@{joe3X<&9`^-7B~4zLcmGz
ztyE1XYC^hBLXt4wuWvF_1wjuFM%&!j9RuP)qY*Rm{iDCVlG*4<s5-E^s+QipNRF0y
zz%3W0-@jY2QDva6#rc;vcM8|hj&F@+?DB&ay67vLHd^G`jdo`#D`(j-Bu-t@ls4)I
z?f|5I{XP@NYtsJaEvKu^9_;V)yvPz%>0Czs4xs1xxhf{<`S1te$Kd16rDy|TsLUFu
zGY6m9p=z{#2;A8?@AklbRaN(F2`*SoqF0{NvOg6?KqwBx;hRyb1Nirl*|E<VE+SX&
zRUf7%jw<-A#e6xwmh+P@ayKf6;eI?lL}x$Y)L^!7G~nkd9P&!CFQ*Mfhys(MDmjiT
z7v8FrwBF00`<SvzZezdEAk{0lIR)rtkI()^?xZz3v==45rD>}x#+o%jdy&li8FZu&
z$j~3M5Hhifm%o5^=N;I)IhG=vd}W^C2SYBeQm(WP%~=nBHW&K?|N2GC=!%T^as$gM
z@H`p67ZCH$UD^dbbNCnA5zm|!mwci5W>$iNQ-*&hAD9qct{>^N#24k-%;*e6`;cY6
zJzwpH&~dEHtqt9;*cI>*ZJB)<2272vqWE-H4>qu`^tv{_eqyiM>s&T%*RX@v_;`Sl
zTg$!$F9Z@gpYh}*6+roUi|63mYFEvQ`Hb&#swqR_j{U>J$PgNzP7xuC^aNx4@*ty@
z^L)GJ*5Q>=N|0(hb$)JR3zUThe4&4S>c*py&yzGQecz=9n$YR0=y>81>G>Pq8X2C_
zc=if<alB<dP2mWJT7_%x$Xp2?B0d}XWOcvtu|SW%gKtz>O<;i<Be{XD>Uh0E^>_}S
zMoBK3rp}0kXS@ESC%bOaJtAXLiYrlBsnBwVVARQPUiRM`XG93SPIUK8YxickMfo1*
zl~To}GcqDN_U{4TkOIEX<K#0q#sb>}Vc3n=uDCGeeOb?<1NDx;a1G4#Y$K#zs(e#9
zV6S;Z1ii&bNQ9gr#pR<RD<`SJ0%&5AYJz4~ga>m4dV74T)sE5Yz)m~L?#E^oZTvlW
z$_ZwL1no;*MPZFJVP;fV<KWu3cHr_A+^aR$=W;yw8aDQJgBn99a?t6vU-`Q{Di5KM
zxVxdHs-6bl8h9y6tg*2B$d4iQdChYDNLdYC&~^}!cr|N}uTN<M1`eMZC4Bjt^ldeY
zIJimFB692e(Qp=ly_SE2!{=T&bW0l;f`<{I9Q;$^`7Iv_oY?)C4C(dNq&8|@x*Z(j
zj<th7rp9C|!V@b2kI5gjkb_y!@dd;rTdBqZrvCX33jX=73eL`^rp^u(?8%&VG7Yt5
zyJ;HT30ra{2)RS!iRu2&dcbauH&}TTM=(sIA$5+uTLny}4ZE*yl(*#vx)y`sd&zIe
z-|DYFN)2Re&%ccE%u2ZI4B6Z>5c~t9N?JW+h%Z-|qQ`jPlQF5}-C`|E=d!Qy9e$e<
z_4(eK9Uz&qP28z)2Y5=+3!E7KMa9z(?XwivkMnD3<xCL*csk5y*<B)jgT{mr3l99U
zXezQra?0%;pU0=AjATcM(5%CX($Fc-`@|j(Q?;Hi{WabzL#DS1Rr;9^R+85{PTF(+
zJN5@qqq}5~t2_RAjMwtVn5zwY#y?Vq=*!*N5hd>9#+wC^?S$$?yIQYt<l?5-{$HA%
z=VjI~P!s^5mXD}D#CJ#jTw!LM`cFIXU3|8Zv`!vf;7G5^X6AQxTwgZKYlz^v`-eM&
zRXJez*h?TyeL7tOFtAHov_su^`%c(F^FB<}s>wlXqhrb&V9v$dlEcuXA4sr=ufwEU
zA&aP?aj(xiRy4Lge^=q>(akvfR46&)fF~ErTtE635Ty`SRe2O&i;M?$ONedj5#0ED
zJeZU7l0$>Pl$TnJ#CH;6sd2$>q38YP&8=fs0-gq*7PyY9s^*}AhMi@-kTn740SV|=
zGzE*^dUjMwk7jn%9&Sxf^D*~6GsXS*lRW$^ggxfbaR;GR-d{Ochb4}nD6Ai^IVW~R
zy?55H!Vc!2WQw)%_6{#PgqHwY!@lm4s3W0YK`K7)fXGu}O)o|9vdz#XF|3f#>ipdE
z)iYqNsHK<CrFrf-5k){O_t~Zd2g2zIGB$`d_C2562*);?A+VQ*&#PqEjN|C=&KcmO
zftmNd>z7>l_*W=k-!^j-<bQEc1HyGLDl^`7KBraGMcryb+EoFp{$d(QvorIcJ7{qO
z^FeW9al6SkrGZd;V4n?+o)Ii!)K$Z}ua4<~DNIlDl1yG4O0hJFiJc;0?y*?nP5twO
z4y#i|IXqMk<v_3*YC^CmEs8Jq)eW8qK1uvT&RUw7H$N@^mfSI;P6Qy3BXVG&ON6Wn
zl&(zN6P^EIY@B?TD$a@x)s>;PDPh$GVM_NuBNK3>EZcDNm@jbF)L?*8Ii5PgqS}PK
z8iSaK;{(nS$EIrMR(K`o$N&C0;@+$Yj+u#~hpH`D>I7z*Y^ba?sQs7L)n+N&y&dpk
z!o8tipr2+Tf0`U3n6o>ZG~Nb6Im*U00B7$JgI9D4dytiB|6`-VFETZL7MLgiPb02@
zBdkY+JdRZSO{4#M#1)6<Q0O;!Ae}QOO@{Ff3g7>I=0}<GS=qE=aUkKHwNirTo;dyL
zNPKgU|6TR=ml#j6Cejdc=VQNuNiJD|6CYV@hKe|NdEkKM!CQ@{1Rw(lqzesIAT#XN
zt#xUP1+L*STGY=~S3@+Q51$_c@8s6S!oTbdIY-1)jj)I4+7D^T?{u8Y)gxska945o
zc%N1bU5R$|a;>65YBt1aQMkVVX{<O<(DnANKr0^8$tl+*EaXqAKwE`4s|wTtNDEgu
zoI+`iqfB8_4T3tV8EV4-#^ANzq1b60Ht^wy)#%v>9^Itm>aU5q>2VD9YR&lAglw%0
zecPHEgI$*j@4G)BZvjT|e|y0CA2_1_^#c|YGwXj~e00?yRg~4S{0!SYDO~q4hZnu{
zxooRrqS~63#H~?>O6gD?tC~q@AQ6%?0!W}h1q+Y(0MUa|0E7|>fB;L1QaW&}aMO(o
zSzQt}%(b{%v^qIoUFn$3?SP<c)4XS<zjuHAe;<6=kw#|Y6D3Fv*=W}5TnvwnR@MK`
zS-RGi$-!rLD3nFM;&hYnO-Ia13_<b{J)w@iJ=+rD|JuF9Z~FrcO6*y5Fu{v^t>h`@
zxzg!|eIpFLNs<`+3mb@;tE<zDcJXQ?lq{7?orQqT=5(df9Xyt5^f4^=$t0o^O}CSg
zOrmbOe=sei7CNSOJ%1V{c_+zLtC3NlW{GstE%LD)0P~b_Im)u|T*^{qtSS~3`VDb)
zkamRi4}>&DW2Vk*p4yBV;E$Yt!}OU(!7~tJqGwvnSf2tiBW#R_G$5+bMQM!D6s%Tf
ztjc81u=r4cX^dMR0{LV>8i}uGMkiS$bsPWn$$NHui+f!D^t(mhcVEhX)<5}$C<#tp
zcwZoM<Gh4`fTI|MCDuFdC-$x@1dqQ+Tyk%9-!0bH&mqc7+>YvQa|||m-qqu5KN)1c
z)&D>`Xu>yHKA|FBD$cp~c1xQ0oq?E{r5h7&x3Ma%0OOhqtUM5IyLe3xivH^zJrDRI
zwA<P+o0`5_t6r1EUu(O(-e{ZN8*rrV7*E4ES!b=TX6u~XJY&^-v5dg}be%|Qf_Yy1
zl6IoxEh$;Sh!`i}5d=~@i0pHJjor{<60TJ!FBN4y$JVXCx+W2?%Q5np$m4Jb&^mZc
zZ5F-WU{~SnZi#c143xZ*RL*5pv3KnYb+`xji>k5&J!6(@aA52R0|zis6qANLkZ9$h
zu4T)Kl%9}Z7(x<&m$?IGInaV^D`Xo>o>T51*>gQmuiImkw?_Kt5ikp!9LH5t7B~4h
z004<zyz6*Fu@N;w4{BB|ntE2J^3cLJ9M`{1AAIi~We5=dDw;m5u5mH{_D5Lsx+}vl
z*$d_^Fv5);%<f8l(c4|Zt5|TLCdY$M=mW%n8_YLK<2;$cV<&+}6!Zl@3ntT3o9Ish
zfTng%0^~Sq0;ahlmAuA;hYx5<F2cqeqPI@8ZrdR0lOTN=I<=OGT(v=%R*GhZ_Lx!V
z(_#j<$wAubjxX!DT}?3Ck^)hV$^89*lLxLV29$d8>cjY*s|D1gsklz;2P|8m^rt+x
zhMi#bkBT6(5%!gLE|2DmLYT~0N>0Diup~Ds1$w<T@@2Ni($BFXPfJEA#a(#Kt*#)c
z-4gcA2xhlV*Bji*#NAmPxD!V70aJ1;P2RG8Dnwr>?9=)P_a{?;B14Pd%><)MflRrT
zq~&zAIL*uZJvdJoJU+Mc`-CZCmiBd>w%3o&=rHza(TR|Jmvc=49eduD+<WGyq|AW8
zN;#UFF!BV|7~_d)DRmmL+fI^ot30k}i1;Ip_XyFDs`*mkJ)E~G@a*@}qs_kA!-f=-
zA(-dI=P6@&zV+8I*yrT>jU=dg8=|lCZG@WEa`gaQITCZ908sGVxJE&RB#o>9%{^qM
zkO_~vgqT12gZ)~hRwYg|O{-I_oxU5jQKz73_uN!+yT_jx>y3gO{C1WTuUmF?di~FG
z-$OBbDM0r6AyP!u_+&|8&Ua4Bb9>tf)&f$CamhznH1?nktEIS7X@8PK34<{arl^>&
zcTgTQ{zb=I2!3q&ltkWwuZi_VfAO;Uca}?<mBBu>RpN9I`kQA|eRTc=U0=^yPM@9Z
zMZ+tX3Q6sPhlc|?OIYBOY8RUBr^djjVi%fLC^J5oRwI6cN12K|Im&x$K=m24)xn}E
z;f}+I>WV6hz0Y8sp&vnw$bK63*ZQ_n%@a=?x#88%Nil+F{eqW|-^?bYd_o)keb`ZF
zLy#!a2IH`e6qI1WWF>13zpj>-H)E|s+Z6rr>F*q}LHyU?5I8mv_*>-!L*W<pe&Z~q
zfW(>t=xz8;JU8cDBh$ve{VIQh)<}4D0)T}I;YdxaliimtcNYn07jC6k8@H;rm&y8L
zG-A=coJ{ddg{xFdC#2PIi+6g#P+fx>4bAJLMx9G_PSs<tX;Z=7#e|EJ?OHP)6rie{
zG;Hn~{W@{K-Sk<T+0}8+TBZDrRB!6JxKJ*7x3agXUfJzxRB13Jv|^k0R$Z_68L);+
zv1Dmlhm<-yS@7yz$XuI}#c7XuUL%v6DnC(YJN?n+fpv1H7v*Vf3$Vh2JK3}3+%;e?
z2_!eDp3x?g;oty|^+5FeBxgDu3(NNKh@6%ENPdR=CrAbo8DGhb^{>2t`A+t7z8lu9
ziHeI>T(;YN-yfPU)783VT%`{O<U8*u?UB0epUIvN67=Za;FAibWZ3Gq9@DaLswSZI
zFGKAQ+l^=VqP--*%K)?8+4vp`%1|{xw34*lel>{+Mm<e}@TriS61;UG@lZUHuxGsl
z2u-2@8!rAXGVf=f#l}2et}{`RzrrQrI6zNr0I|+4dxv1UC9g#EZvx!n9}Pxh0NBBF
zIKSj)P@1$u7{|VkC1yn#>%rYRpX&(bw>k7gYtvHQYhZebj4YF&%m0PgX_2=VWu+mn
z<uv=na)Ra)HXRvIA?#+zg|f{ysNJ_$U`PdpV>R`&lk=+?Knp*+a?DRwUo@-oV0mtR
z^3J^O)YY_r&MsLAU!=05ki6be&Q-4z{JUVp@}OKwCU_Y_Iu-`U>SDnKePNMlhW{&G
z)>>9Z5vu26oOjXZ#Utm2v5tC=X3W(zWHYrMw?=yQWQbR`Pq{#@j+FU8S>CcVqIg>2
zFz-|0K8BN0ub+TxprkG&FMnHc3ue-on)Z%`yFP74Croo6bs|^(P{-fOf(LhAmuv&3
zb4p!nYF$hN2R?Khe;Z0pd`04Ep!C{;w-%!X$EMb}b@DwzC0(j6imM_jjxhLKiV<bs
z7PNuqG+iY6H5otwuxDA+YJP0MMwuNl`2`QFSO4@i&L5~1aD1fb_wq7!)pUOjnWch^
z6eR^U?@d!yyX&V>#b~=~=QxrV3a?0xr*AS$c<^BG8IWV5J<O@bGpc0*>X5OkrEAdl
z(`QKG&}N<C?IA9kxaWzgj&=<VbG1PY@p)czQQ*LEM_KPR^$VyLNbURIG#LB;TiyEq
zM?x~O{HNwp=(ieVfa(6EcDceJl@#}83QRO$pwZUUa&xmGWo2n`%n^Qhj^3UdghM(z
z=arP#9JaP|3AnndwqX>gjeX!)UzZMn1GklM1SmCKt4ukjz!|R;Ym;XrGQpQs{;gYW
zd~1${Xyt`Js5$=eG8~3Pd4az%b%Y47K5^}o0Tj3Mf=)pSxbnv31?}y-(OrkrCwe(X
z^|A$9)2(77yuRAP6=wl-QcAPbx#y763Qa~v>Sh2&$~q-g7?BDR`@Xb|s!3<xhvdao
zH~Sfhi|i~^M2+S1oml^Lwf_)vLP@s{o(<=`ETS65UvP7@{coqn`TtJsf2~{@8QEC>
z=jfHMlCA@?D$0&6v`7Rhc4Z`U3%XBW;h(+&iWp_?G^P}^Ndi+bYSZkk66}`F&23mq
zAeL|W?8TBlu#(tWESAcW*0zEIm7s#+ARzb(h#m>(_&6>&;o$!!%rLiA=GjkYe4J1G
zPJMQHPw|q*#t9{od^#E`D{cJWKddH(RTY&6AGt}R3S}`XMaGF!glG#1lr(co|Blmn
zFx;21Nzb3!$jkryObIO}nL`p)y^O=$_!0$kt5-_cs1cs(;T4V(Nf=QkO8+IwBVtR!
zPh1_>Mj2#R$UwIYGiGN}G49ShIB)*rS{^f^2B*4Nt%A7GWH=Zmx@F=MdzcGkYnw3l
zOH%P#L^Aj62AQ3h%W4cL35#*P)(wB%M}tNoR$ZaqDU{j-*+?X2jZG4{tEF@0z!9Ui
zVs`{$T`$>eR3}Q+@(`nFm9~PZSP?-%@~|dSh19kl;u(3eMl@txn209ID%KS77iicl
zhZJu9Z)AOTy;bdwHiw!}S6_us@kF`Oat_5cD<lg&p`5_7>G7BlbEb`y5Lq1RGMmVd
z=8_Dvc>}vq^A94qA`3MUB)c?bzE{jt(-?D#^0R8GN1&chkZy_QrT>knM4y@z1G9$C
z(zJ}|GSgxv+mH<5<E#Kho_S7m^On&{?@tqn(TLI1U+{@5@DFc(ylQB{1WuGd$gsWT
zIpLRt?viwkr1y6kqr5F)z3Bs|RFg|u&wXkpM{;e)pCo(3p*Ug(l&zl@4eu%68t(@4
z67~h`OrB&ed`Xy5d0p5Tw@{w`nutH6tNX8SCFxom(#>U;5qoZ&rq!n=t1_QBtNs{Q
zdfAgB5iciX<i!=6E)?BBEtWQk9rZ!*d)@OD_0EmX&dSV%r)0Yvu#g#8nR2=uwepR<
zCygwd6gXyCZEoZiS{>o!Q2Qg0nHfF&EV(kI1fYcG1#-z%8d~%dsb$C4r{iPr>rr|$
zGw8akZ?x&CFyIAFKWni&hX_jYZT4g6av!IL04MucGM>PI-*+;Wwb%?8T!lrmK>g75
zllXaDoP5kIz>5sqoK$cwVLHL$#(dxF(o+>1^;irl0-<%c*Sf4KmeP{(jjND!92hnE
z94w*yuKHp0drBAmw$G~Y+m7Zw`>x`7bbOaH@nGS*l5l?HwfUcldSv$RFY(J>+k(B+
zOI|=p4B2nz*)Qci<HKA-EpQATeZbZAaSpE@o1dr;!oJBdSUokccW#Xa{42y4#=yz@
z1TuX?frs8=F(2rQA|I(PzdyEFTcOp}_|)y{J6%X_N0jti^QC@i{*&-<L$!JL{%~Oo
zBHfN_Np>U0+KU|qoj)%_SKZc5^X5*Og6`@&+vk-V$vcppFxGOdGN?X46@~Ql?n6&S
zlefmC=onV<r8Ve63$JzrtWN$%pL^?!7?;?O;)IT0A_B$Ip$FeRYi(lgt;|!c_!f<U
z{nw~nK;<YG(!D$y;I%;Z#_ZNkxr)aOV`*y(E>Ls>0}~-O!KR>-jl!fGqhL*wpH0+a
z>AO0NSl9SVR`y{78>6PbmAwF#EqKQNrNuEb{vTQ#69?0OOqb@WS-M~^qvEY6GC@{A
zE4t3WfLcO@0zqoZYY9QhUhIVoL6T)RCapElP^Xb<CJ|4jRWC0{xU(dYYPOJSKLptp
zlIR&Htw}ndK|w0GIS~{JSW;4^2o=Uml}suRd=&dv;@`%c^t|?X_uh8i#sXwB0wY6!
zm<SsFi^1SNwcS?HQ7KYOPpN1MedV*EQ8W*xX67GeYK35Go#|&<$cdq07oJMcJO7-`
z!TAyVIj`N)uSVL5GUWVbHx)mPZ!EqLV=>|v9?WTJ&tL#YHV_hQ+%xS5528n-*J!$;
z195rn@_c-3=4#X{{mY;foldVWmzq^kOT1(iDp<Ot;9e9C4ArcrUOI<?u8@riHK9>h
zFrjJ%n))j-9~aJEQ+T7*=t$ixR;YL;y`-WjTcJf-2Bqp0I#SS<O|^PK`TWeEqEkhw
zjH#*>YY5mpA&{y7mMc^v6piwaTTh3bA%BkYL6zDckVlyssB|ICA|{Gyx}-!EtD~!&
z7Ak%_*^EV8x@fJ~1tmJCCaM)YPq13$A_hf>R!!HIS6p8-lrhX&u8^^iR-9?7<tf{e
zWp6x!lsY1SiP}6uBUcebrHtuvfy6u@s(t=cX-&C=p@Mbne)dS&Eu>jQuS^ZI`ZC?{
zpzFKzT1hv5zI0-3-Mnh%8(qtUg>vx#x@&p&TM;C*#Qvucs^YJ;$^(AS5JP{C&tCv<
zjLisC!|Xa*4aqnmg+NRvt3+A9xNo3NaP0`%ku;;px?Q99H7gc(*sN~Q%_!SJHv{KJ
zu!;T&(2~B~Mv7A-JDZVfW;zIWsQ9R~D80;z?03|f9MP$c1orMZp49i(l)bgj_Dt%`
zwi1059(2Ek?>Bp;Y%TUaCyg3^3wHj?K=HTrzTN1@&7LDhyT&JCkJ8iGQ|&Ma@3S4*
z;S*GWhv;<QA=I&eN#3vV##3^#{7YYGy}&Z88LIoPDU{s5(oW%{?dBu0<Lw97?epi^
zq`xpZ;f&zMgoDx{-9#@!XDTiwBekX%1xw<0lifo9+<hmp{yifva$)OPFK=H(@TJ7L
zy$Tog>MkvTC0Y>u3UNtfylX=-3O&vd9FvX$WThTOe7YO2iBY(}=B_csFp@}FnH0zv
z2QVP)(o67`yCto&;v{jNLa*L<_eD?>qNDA4Y%?IiicIm|^w|$Ap$*O7#$G)pM9?==
zL7iDG^RDj@TMZ#@la?TI)}i2{2dU~Bg>9Emv3cu(MlBom@V?O>WmAGgELbpcU|^a-
zxCmS#uXC;jVZJc!JM>f8a%)K|M^D!fHu&cGderUtXd9xiL|8O2i52D$t!{Ih`<HH_
zxF%CrRiNZ=0Wb3wPaj=;WwXb{>3YiuE3mS1rQ)*1?>cmwP%u6+$^C&yYBNMoaES<J
zML%byGMI!n2YhWG9d{*XQYR+p<WJyXnoeu@I0$$Mw1w&Q16qmN+<|YFj)J^YnKdtL
zKOa`WiRt?_l<TZxzG*qxhP&vC`UFlhI^?4{s$$n_wG&j0W8#zN3xAmoB{G}EGguZO
zaV0VtOQz~6OQyJ5vg*SNqeCE&jgallFCYzo#tniX8)$7d6`Z&70lpfMz0vP9eilj_
zuoyrmH@~fn)^c3R08UAC;Mk)(MZxmX<nGz$x>CqI3jMKzA#~qjy)ail4-D8w8+Rw~
ztzVbuAH0t9?44<{1U$L}qJ!}l2lz-1<kU`k?Q$WI4Y_xw$<E-(fOCK9_=j3bvyy-O
z`hGpSY?i-J;kW5Fsf%p4rhOc}R<XM>P|&;kpazS|C3vO;I_dX^H$Oxl3TXdgo<g?+
z9>_>nx|ZLb-#=k`pzUb$kDq$Ids_?y>GaQ<-PdOJnDy06O_%HHA$LOx+pSD+eK+%U
zn)5OeT%6u$A8B7`A65z6gg#F(neho{ynlvJDqdw8xKr%l5M>J!PY*`?#Vj&a=T;n+
zKy~82p3m#tp{FE+gIt1H_p8Shtwi}&KuwWIq_vbYyWw{v;AFq6%oy9sRFu9-{5C7(
zP{pc(J`;c@Yprn%_VQ2lJKZ6*@`L-^_d22iX~`br5|4w2kIC!yQ51%boX1s*dEw|N
zDqF%xb{$CUYu|fzL6o)Cw0c;vq=|#phbx=th6xgAs=+hRO;@oG+B{r&Altj~W<k2Q
z@~6cQlZU-;8mnu``%jrOl2^f^^HR`4$|e~TJQ?U#uio_W-^F7j>!voX^D+0tJm3LI
zMf>O~IWW5X=~b80Wl5mtIB<(D8>e5)bA}u#p3I-KVe~fbKw^U-MyuF%qF6y_jDm>@
z+qIfa{pNkZ9PMX!N&RfcGME*74Yg)F|G+LM>OJo#-a{r29tH$VIh~7K9Bh7<w+a*{
zb3L=XYn77~<e*Xd6DR-UAA)nl(0q7U@ei2#C<#s;u&R{oR{QmeHdwpC(^&gn5?<Kk
zapqAuJi;8wXkP#HA~?$qP39LgF2(x(S@{^3Vedi9zIfPWEE7*mBvkL!HUhUq<*vL*
z-4iF$unyZOT{6;yPS(NMf|#`*gNtjDdBih&Po9sPyE8|Qc=_@L?vl%ZSy~Eh{pRAR
zUci+D>bM(ERWG<1xUXZ_9Cn9G5?p%He3@t0k;UqAbR|gTQkWODE4meH?39+#D-XQ%
z6J>NJ{5&*=V$6Dy1c+yrR&Sw@No$LScQ+tS`ildzp<QeYPPilFIr~UVEg%twog_H&
zV8$Z85<9ja*bB?DQ3LIsd5iLjdbM>nO*{fAS+cU7{Tf2LXq@b-w*x0XV_85;x=JN#
zKw8MmPHdi#(Y3nC>xedE7nHcZ^QW_+##*=DFRWM2O)Nafc!C{EaVF&$AA?VPJckb)
zK6S`l35$16)2|8OP(ti`C7zk-D-SkSQM~T2_tC?ER#BW-CWcOwneWZ_>k#4dia@O`
zgl3WwUxuaU*43+A5!c*5EhA>&3(F{x69u7=?C$)dq7f%O23TBocG{hql|iwe(5<Q#
z7gOF*GG0W~TN-BfYg>`K*Lwo*-uK~_H*}HS-pZl2vk~ug#t3qK_cO}IWg{J7`y@Fv
zGSybQ=wx`2D{g&=J{N0=&Pw;v;s!%FtNYqsW?*2z-}B0*khUKDD1#%Fx}6U9;mcgv
zcqw(9y6-}Vpwx~_nC!xsJ$AD@%NDkv<;5F%U%8oQLCyq8b`x7P>r?QB&_mMN<7CyV
zKx#Vqw9W`%$@Y2?%}Dob-lxV^vEwGKfaeb3+NN`>ew@SG^KN->N+`S)cjaAB1c&NH
z<XrNbMj_*sjg{42KY~WLfmYe;*R~?2^19r@QMI}bx4txvm^@r{I^Z98`C%Vc*g%dQ
z!n8~fpT#yYmSkQWf?34*duAO(Ls?v(Zj(^oSkHD17V}ZdPJOvI65%i#-?sguvyR;$
z*sra^*m(|3Y!b2s9CcRND<UB>;GJMx#r+ToiFJcy!11#;k0=l1khO!>0bni<n8qh1
zPe2a*2WDM^l8tl?ytBTJ_xtE4QDq%F=>g-LYbxi>9~Sx4Co$ey`v@_A7Ld`~HAR{V
zuG4u<g$3LO`{RNuki{Xzg)~cBmw+t;N6@?%66;_)l~3d&`;n1nUtdcQ)#ol?v^Ic6
zu5_)(`ndv9f_2&r4d2m<ERlRgH2CLXQ3Y9-IB;X*cf?)R@;P#B<N-V(I;6R8;_L{H
zuwSZGRjnE!S_0TDun8Vy#$`;CHj_~#%}>Q{-)3_(i5qvj_7xDmvw)pX9IoVlpp9uq
z0sou0X8u2jYc^(%|7jn{#_38Z8)A&^y6?{TzRj1H+n$nTaGfe5GB3@ECKZ&bfgm=W
zjK^+}J+5V*FqJYM4;yP)A#cu4E@X3nfX<8NvkUHsp`VDNr@DUTT%%edm)iN=`Ty|n
z|6o|MKp5NH*j#qZ?I`VEpVgQ>hI8?wHbzdhsc{CS6l>I1p@eI3me&)9Fb#X!X+D0b
zrJUB|sJu!ifEf?&-H}YLcxkkZV4vehfqeq;96qEoF2lk;+x-bM#m-1WK!^SCHT*O9
zk35wSKO34YBR_<H^hP+cjIsCrgRH6=MBw352q;gov8r=o7D9k;(9sS^=M{ZvDkna`
zvv>~eICWz-OV^#K@Jlc&ni(fSj}A*5(oCllm^EkWLdxC<fSCx%sX%Nc=7l;S5nNNG
zt`R@G$Csl(Ml(T3MQ83!@^#vc#dgMvjX1t^heR8eggqx(kR)))YynD*!mmZ3EW$EH
zHXLFcqtcpNT$)~Pl~O6mYnfGc!mjd}sKvf{8ewRrmE-#3Jb0b+#Tmi&a7>pm8~j3}
zartkaPnalEhGQHgQwILqt-H86#3>}NOHO{CNXyAQMJ5SS*ocesFoSrP-#=hswLlAi
zb)7+jDL$Tl5E@fYN@IS)suU^AwysjTG?HXkVoOrpP&S>&Qjnm&Ex2Z$)t5&MgiuyV
zZj4knXNV-SL^&=83T|>Q@qSaLl<P3Gwq@9N@`ZkTXSGQAU6Ov|<%@Xc8owjaHy<=l
zL2;(T6h=%F7QWmfd9v|_Hgm&W?CG~Tw7rFm*{;~_cO|ZK3*If))Is!~PSC^8OjNv<
z_ADQ36<QrHOXf0Z{EETqf>asM2Ja2}Lxw2#zm+uW|DmK=nE%5OGd3|WG&C?VIeK)3
zx_fe%cYA@2V_^Y^Ts!)Pet+ISwOPSug5fv8^dDpVPB8x=oFRIhi$3OuPo@C|9@G~^
zcn`)G@k0Vgv=Rt%j6wjWCME{=w|y)r8cLZu8o~hvHs%EeCO-WNnkbrjia`R6HIW4s
zmX>zL1tkUsmIfW&S|Tdw-~rtoN{U(98bV4S3*GxVnp#Sd0)@?vNlF5u_Wgc%aOxh~
zGV1;+a^aT64SkqOf(b2HI#?=dSV949{pvyj?H$@u0gb?aB1m!evlFzGv`P%J_mi?x
z#GCV!fq?-PD5EpBF*h(XF`3VKD-OOr-MUrH#=QN0oB2}y_D(&^Y2SW4Rd-V#|6Lzb
zQ(f29<7@Sf<Jq09`V~U^g36oqI6ehB!B6tnkN$CcCtPRhtnMt<Zm9bADbyU*EUG~q
zyWX?7w9X}AulJsrvP>qI%V4$LIJ!;r*C8cSl|Rc{;H~PkKPbuMFY@D*kjYx#-rb&L
zZ1%M3)^T>1)^=tE*S|l=e}P{2la@D5KQv;@pB?0k46h0g_hw!-yP0nE^a#j!{S-z7
zC|W4=`fqjcKMdvnwKdAh!SNrxty<GlL)m<#7qGqCPNgt2Y-1<d)z-GUxurRJQLEMJ
zQmx)D0;qvrFfBv?jet7mijaf^Z~zVoVXHvPx;bWK1@zje)xN`T(U*%k_oe&AHSgA2
zxM?l(+~?OP;gnC_Zl~*(v-gqv^lK(I0E;~i2?C_m-qUXK!8~oo+wWS-vFEKH^tF~w
zZf05v*7wQV4{Lara_=^3Z(Sk}d(^LijOo~)OjJjCKe6ie%O<8Hx(KA9qLA8`x?L$>
z9#S`-Af4MkrmzSEh>NjIqQ&7S2sAn^651qG6Ek!8{@=o?$Jx*4_;dXmisl#d<KZev
z*-1pHD5vKA_`CPP>Xbe=zhAC~l%3h|Eb8fKS&1px>7UW)#Y5zUmJ$1%rh`z~`zk$+
zUeCcl-Y37w^Xxk{$ml1er$~)|Wa2ZZ+iufFD@d0X#!?Y4%VC3LgK(E-B2380J93l|
ztclAdtZ-QKvIJ!R6jf+fP%f!Zg+-Ox75`DBab;%>&XSxiI#F=0@Gb{02d@ZSBDyfV
zki2+ZQeIkIVqRjh;%pAmlB_PuD^*jrE<dekUgEOiZ4TU!=oYXoLMoqLie5sqqHd1e
zkRw%+TslNd3Bbt|=%y-1%85r1B@r1T?n1m39TA@x8IEy|_hyfZZCIRlf&JcXrNBU3
z<n>$eSPTJ0?#awxs&_w-Prk`|dMas5#?QQp_hb3EtG=}RHjp}As!*mMnu>oXSwZ`J
zv3+m#C6z({dFtHSJhXd_P&zalirq@&v&DfD8GQ9mzq2j@Qh7+1@Wb|3vT(z!HZaVZ
z(0<h5Em=~r@yGHftz^WQ0K{<QUTU0H0FRR+L+7X1QdglzjE;7C)H<5R)vf+qvv^X6
z<dSMhCZ0z?5+#~~hn96~1ip$<7j2ONea^E|#gao(tt!NVfeG-yUtc(A@WLn&t`Lfz
zv;edAc*avO$3U_4SgloS4d86z+MpMV*Ar@_FBC5ciubf8>BYB2e=!Y;k}Z%gTvsHw
zRL6fx#751@;Jb6r29Gg_wodpgu|c#b`KYwzkYhPz=l4ueGZi#5!*6R_Jp<w4rV3Ou
zjoT?d>^Q4DczQ1|lJc1VYeBt&MO&q&kbC;J-})uKz6j-m6E9vKm{W5WgN@N6Z@(Zm
zP>mCLkY<A}>{+m5xPIn$M4Q7AVCECEo);szTjHwTGri*a-O+s`%?XgNt1I+#-3@C=
zNxtk1ux$Of-g>TnyEmzF{rVQ-sOHG`?6{jdVSewEphks)OHu0<_w+O#J-nmXJCDBS
z>Vgy>tEzNSn4!pt`Xl&+Bqcz;X2ByE#KEE(&@lfb5fTR(zL@pkhE{SU#ls1%NuAMW
zgJQo<I<0P9CEZM5xYRsMD{OHQ&Y$$ws)tm-;U`GqkC6)pS{yWNzYWk3Cu|=Y7!fx2
z!%4x0<m;=sMs+DSxlX*3J5PDZfv~naDps4SW19e_ckqkh>AJAYDU@F%I4~igT)3#$
z_-&k9E@jCe080Fr!dkCa?z&;^Ik|i)>Q=T4<|^YNn&1us8sUeW+l?`vzmJbtG>-k-
z1~EGf?Etys`&!?=`wv`(I3b*QMb(W!KLjI}S>Li(4L?uCf>-_Zc=r<vy>0>VfIRy(
zN3qum5|RsAV1m9VF~%LMmK<9*Y-EAj=rmWkT&enq^mK5sVATwh5;LK;)4wuE_;}xw
zxr&D}U3?6hKzjqg9!}F#h6|+QG7P$df^tWOR26MeUso|jXe?c*RFy2LW+T4?xSnuM
zRZnlNirF|N9zNTIYbSuLC)NrMC~N%lvSx}Ch|U*q*h+;H<<<h%W$+4Lp#8JrgOC9|
zSTV_=(ME#VNN+<UL$y=KX6d~zg|sIXZG*3Q2^ij6QwC~fh<~N-qJl@kU%&$bljQ^_
zdrKMys%VN}3JQ9wCq^Hu5$TKnKsrD@n@cRt_x99<8>J<Ry$Od_HofX}^EhJM7XxE*
zwefY8oN=;XAqseO<SxYPp77J=C_EzLvzccqEe)QF1E4YWM~t2&8S6l~`RH2hmd@kr
zA&x!)fY%3YxY5B=-ahkmaZsFY0F!J8wh2o%7V_ShIw}@-AAg@5+8ZkSae=vU;V~N;
z7`QtS@P*RHD}~eM@2}Z|!@R|0>2C+gTNr)x(eA0S5DAU>{c96bY(gEo$bfZ|H$I^u
zSC9N!I;znbt5zoWgaUndPuVtbF%0Yv!ckdr3RCjgZD_X*?Jm|bc`%R?5s5-xEbi)t
zsHp+~SFnt-GEQY&?w;YVls7k_K##%@?k^z}uaNp|!l2%q>L{hF7Al1h>yotEI~N4<
zok^2sgeXf^Bprrz(O9Mgl89so_KS3)5BCOX$mHfn(!~s`FGk30W@D%P?x+zc?XGvO
zqAW{X&!32ij!0uHEZKJ~QNRAwBDnpP5K`u~%qPbbl+Zs5`2$$2t+2Pqblz^y!8CED
z;fW+r+oJw^I-TjAc`W=18NZSGWLM&^7}I3n8HX23JxDD$E*nsysZf=w4}bgcZs82>
zjJ_atf|DJdLf0b%oqI)Edp8Q4(i~6~;IJgY@YvPUfTxTt@xKsxLp7R!MB!gcd1eEf
zI>4G_{Lt4VZaQRN<j7Jip$?kvok)Jk_~qX$$*|(d8#%bIETNwqiv3Q)zpr54GbNHi
z@}d{!+3Y+s5q6^~yP!tvA1zR(sf&6F6-$8S$00%yO~dtoeyH@Rp8QRkGOi3FFHw?x
zdeE4`OuaECma^kA<U2bhD8{Ai(0!3i#S1N6n#IV$VU-POFU`37O`LnJ+%`63<fOZ+
zQ(b8DD{Dfg!>yXFxP!~)Yr}d>id;!kDN8&fh3VJ@a)(#2DizNP39w+Gewc#jkmuxy
zblb-EB8}&-N*izVJ5M|f)Q<E#;|V;#z0efXac8fpSyBMpkwZvyiX6H!u<sXCR4po-
z{-p_PDvNmoMXmyS@XZW!->0zu)5q>mVO-i+u9%;FfQHFob5`>`X$qcL#)h<oeh}pi
z43(c2@P@WyQa53#)kn~q4oWC97^dsJnutLQ=wR&TFm^1CFk1}_h|Fi`btF`;`YR5r
z=Z?>AKfw$li=#6mhqcF|%X62W)jj&KRRna;{J9^8)8R#sK6)vMe9p4gbc_@5f;q|m
zusj?uiA1-kp}d%&hwlVT2n<-eFr{KOch5)E8AkO!Hk)a8g6Hgx;v8TijNeFJd{DB5
z^G*44CHZZpO#79`@>52)e`X1Um*Ss31Bb4+EEc<MSwMR|z<odENOprJLTU<)EjYUd
zeY&dQ<PGj^BvP-U=ry{LRtJd8?iES1raB72FJgG#&=OS<FF<8zbywl4an>Gs7ro=y
zpIvc<sWVvh454~z73eDFK2XV7WWmY+r2;DosD(l+uXoCAyy%|5Iogm!IB$CL<iB$x
zfKHhG*U{is%=av#s(YK3#b=9)pcA{$uL+hdPwQ!SIBkn`XK?8_#&bL$zr=<86==;0
zyR@ViLR^49T}N)knf-_7m=-vjQMCpXbpOB3&MGJluSxq50t6>OAh^2>?gWAl?(R--
z3C>`H2Zsa+?iSqL-3E7e88pBEVY3&z^}hT4Yrm?kbJG{+s-LP;r@No7-~O+jJA4k)
zqrJ5IseO~d#R23pw&X}8W+Y}4(mK~;h}2Z&7vZVk`8|$#4w~a}C4ULFVx+ux7U%_A
zQYUiZ6=^rY6Q<=n>1%%pfp(GE;BGOO&Abw)0Mqy_WBKk(cMgp}L2X#`u!=w#hs<m}
z$`^-5E2#H-oY0WAZ8{^iju!TBR!FTcn{<ymTl0i(Yeb%5vY$Nl`F~up5V3X&QlpA+
zH=-FS{s_e3e)M`2<&FH}{#MwbB(qt!9_E7yZaSe5%IzTtwRxPMpd(g2IjAPK2;sPx
zD7eahO?@9q-g><8Wd-Zb1ZtI6HF$#@`tbX{;ee@xvwxda`GSqhLEtP>f_W;J(N1t5
zkjZd4Ahk=+hVo!t!@yE<mBCkE6)oWud-8=%k66+P7y2=;1~V^BTwc)*pBX7cF0&Yr
z;*^N9t~+bC+ignqdZ^Mdyn_Oi;nZs$(_t(b#n2RZs$&I*rKT&P^7BDcVhEZ|y^bSh
zZ8aMTeu8(WU5B?F(dV+?ARqG`IZ}4+tBzM}7PcJ8sFLSKTEkA05|^qe8KRb452Z8|
zqDt|<qIJpfcY!B@-K9KcOmD#q6)UN#^#`+~&!^q!v(Z;FCnWK~oQ_(~yOxlVJXyYj
zr4%~KB7&WW6#e~xk7BCX`7S>&>x&&soV@%O-;MqWzCCIR?Su@vHooE3>-fwxhQIr6
z=fUg&1*P-piQWL=;g&5_TDvriswf9|4E^179`JQ&+fpaO{E~aTrg|oOCCQiJ?yb87
zdAk`&#l0RNVWnqnPYXTh;o%jLqcv0+mXZ+{HxLl7)OQ~~6Z;e(_7qDIMQK$-Ez5PC
z@lhBl;tLlMaSNP3%H#s(!v?gW3lns3yO3*rsj(Blk|ovdBzL?dRPXi<33UEqK1^!>
zYC19Hjl1xLPMG&~-!>+{PYae0@pHmf#6S)E1MYmmRh@pO-0rF9Ha}BP`Bk=(JJJYa
z=H;A<Ej%TcHTG7Ha$;=uMD8yYv)0I9;Y}hGR`5)yV==Df6SY<Gi}feQ>^eNVCUe0Q
z2s4rqjs8R`KSf*LFbf*qX>8E<M-HrUpF0wmKK&|rPQaJkP~QaeU5+<Bilk`$_^pNi
z4RuYxHn|a%WWsY1`{}N<E&!oL955!3Y6hUH=t7j)_8OlQsM}^`gL}Ey(Bg>2Io1?V
zHiMs0`JiIOnLfgPM!`6%p`&7u__S8cKAgAg3=_IX84LyCD+PR%VKp~Dyhudv2!5NL
z6EQZ_k)hUNT!I_&iMW(7vLUvn817ciu=IH#_$Q8u#ylpU%%FEtc}C<*ZR0ruPOsg#
zex$$M@<hCvP7(jNtY=m^j2Zp|qN5K5Rwug!Mul-ll={(?4sa9xgq0H9?Ea03RG2tP
zmz8DqbPHvyi;OwR%VfjCunE)KwxZM0+uw&AvH}neoF}%b_7T}oBh5xi9(t^gEo&S7
zK^>s3zRjDUlDlvB6g5Kxo@V!dChCm3ckAZyZTVJ_w2J3vVUsykD5*%=mW0Tfuri`P
zv!7HDMhv2OP5`D=Cv4G_J2yNgW^+K4#vX8&jF*h}mlyKG9VSH7D)IaB%bR5(>ZzFa
z=dTs7+BuBwoE`1%QiAN7wBH@sej8ru+T|c@EUmN@ZtVN*1`oJiTNIC2gKN`?LO$zi
zmckLL&*^OVt+}>3$U@qZ6ob!0?X;n5dIoxVt-(q!9zdA2332y%Ef2Y5v}DkU@_hVo
zKP=ZkTY_nHWmgvvuo$hg_;nYNA&$mk2BQt_b1{Uu9_Kh}z1yZK!&jg>ct$PG2J}=F
zJJb}D`ARSNXGP6lpyV@g(~fITWY_O}p>~T`q%0~Xo11F$)VZfX?2~c>OS()|wCQ)1
zu~tx(-O_`_ZGNsqNZL4^IBesYC9~>Q_Dz9!R=t9$4Kt5j?pt>?2`MI!q-0p{Nm+2>
z{9`JeoN2v-NAQeRK-pc!qRI$;cfF1UEr|tzXrVS<6?&!GYq;iU!P(Q;-)}O?fpR92
zd|ShScfQ6=v^3SLKyuW1B6@E?Wsh``dQ)BB#-0tKB*!Jwjr2;-*E2TRl)~aM<k{0G
z3f$jv7R&oBQHm-9-&`xv#@T#}mv=E>fi8q&b0j5SZs$)vG|S*&61$0zS77}5L0nZ2
zg50#eifu9v1=+f4TDrw2f1@8xYT)FQlTGcvsK^CWNsu_Q{(S`GQ9C2iokf3<frym*
zaWCD+C&-Pi-rFiCjo`6o7)q@qnZT3X&n@hj(@*nr0=wMGu0q!f&_5u}Aml&I#pGm1
z9^%gdo>{KeTyYXb%v?;bn1OV(G!nTwHBH<yS5z8n&ZB#-?kjq0^e_Di=DD#55<D)^
z5#%il=HEzdHd1R9^Wt(@2T!u<ak0Zo$Y(kjve51(0#<nk(Wwmu6RY}(`+RJ0V=i{$
z^iF^&_5I!{YyvY#dfwi8KpCSaEj8L9(=d$@!rX)LJFzaifG5m$-TG&`=*I9u7}2L2
zKD|bn-XxEKA4wp8M1QvSX5mY8#sNV0LCmQCGAXcooVma5j`AZvj($^ROJkQ~Ns{a_
zT}LVctH$YmxpTNIfP6ljZ=f;6y}3!y*p~c+kRQ{LUu9h*Z@zR+g+KiP1Ag(!I2dMt
zEkgWl&(h{Q_NR>X_@kvgg(eAtxs|pzosLjA{>V?KK~^o&<|To0bmj7g_9vGlf(c8W
z<XI^-6ltM5D;=bXn59$Bx-H7StvHYzg@h#9F$P7BJ>0ugI7Cq0@;Tz>QJ=cE)%<tw
zqser*n!AcCjV)Q3MOA*zGA1^p89~N`!XH$RWyId9Z((u`3W?3k-UN&-IQsNy6x|kA
z?LjA!#$gg<j-l}GLpIgpE-}|WgoGYEi1V7q4@NVJv}YnP-8JHdyXjwivNGZ`nO!-T
z)5BGv-^BGRl13MCF-q9I3>V|t1NHj!W_s&^q;j=N)V~(a2IZwzcF=`SZB1(DNY_c+
zg)qrJ?;3VkfWLATLxj{1i%oJZk|#1vT=d&JxhgZk6CTl8QfeB;rL6K9MxBHnoYBe_
z=ye^_ryGlg^bX8qfg#fis+$K;&rGTh9QO9;K8}(?!574lFNs(=cD<m99+tEUgVDs^
z!Um1>d1=J=$F&-30iMCa)!CsNu-qVM+7bqD)jUSb_}KA_K6Evhem{n24jr?tA$NZr
zgq0?Da-OadP*%E-%voc2>}IJit7z=*asT0VPxbcqv<+)q!k`})dfyR57xHlxgwyI9
zmm6*ZzUqw>G3DZ>s{|S|BcL2naNu&ax{jpp;6ZX4ZIM<4Y#Ke6pT7cvN-wIR!NU-h
zRrz|)j<U|5tVo#!tx*c+RGHia%0M_PR+4*{>Zyw+zWOfOveU-Lobbm7?<Bz{I$|C6
zkMe@(u|(Dwdm$Res%gfW1#B}ot1l;Nn)qi{R)&lM;}_wMPES3t=KwpsEt1=g1x`Ow
zU{APlUJ$?$^?4&6Nj2GVEg=bBLCF@)R{mn;rBN$UiAttvSrdBa^U`3Hi0xP@^{Ybd
zhs2|e^gJnd7s$#w;7taqEu?oRSgetYIQ6)^n!=LXSEzfZcJ9eobb}C%ZdxT(qPD51
zR<Dg1fXisSR!IRvOUCr~SMMnGzLkkB>ZNj7{+-#O52!tN?q9}zS8#8;I<m&VA2*2c
zt!x4sf?XuhPvB2rZhWxF5W0Eo34SE>%*<X9&%?-izrPD4^coa5Y1AUO-ODL%>^fAp
zZexa19JgaM9J%q>DGgmbw)N2O$kG;s0aIM`nV9%XxJ#JPW)B%W#Bgr9PX+qUDBh;C
z(vAE~iL0P7Q?Z=&c_y<#G%*WtkZjSQC0=kKS#t}M+TAwCTYc8GS&<x)0Klsyjw`L<
z`{?AAq+60u<kwa9?QjX5lNJfug*41SZ0u@1L(q_-GWLE27I%$(J!6M;46p17MiphG
zuCSS70X-3$@GdK_^L!Jppi=u&tfdC3eKu%cT>H<rT>n%GrXge&i5Xj!(9q_H)6b@y
z$Tz4dl9i<T&QzR;9erjHvicypt#+gxloPK2eJpfS4A-q*)?>|5;#RV7sP_~ny|MNq
z$78sb3H9xGYD*Vkv5{C};*IQGJ+)X7S360BMUBrBqm9kZdn2ae*rGCd5OIeJT~q=q
zO-D8NEqA8XkE*sRUb`uc^9OcCoLn+ag!?+>>7KTW#9lbux)NM`PtseCq69$=A6Q+|
zVz`A&4T8NOUQY!m?6zjky~F*KaU8eS918*!Npu}n^*V}smCHwU%IV{Lo4>mv(5QC)
zsjL2fF&uO8{u^N9zosk65!d$^*PgS#ukI$M#`w6WG2|2h6F`<XBWW-3rVo^34-&D*
z;lejqh~GuGy8zGQuk(FOW8K}Cn%I({9pnV)Td@M3coTO5GZS+QJnV^Iju$K}696FV
z<hcA4aGsfwmm9*x&h9b^C}9~@m|{?6=VWJJm`6<fjtWdcRq{bKiX2&f+#gs@Lyh!i
zL^bm1^g#tZ?u+#(AjUikhZf9z>;K6e`pg{~Kld(l+GH`u1o58Ybvn>QQN(?*?*a5E
z)e<QnX3bHC5b1$LCoPS?3AKCZ3}AWcEH^mvy)b{O>qXg&du?W1X{mNK0`kca>jSkl
zHLX#?FTsx4j7I(&A_M2P8La}}?|<o2h^83o<lc*I=At0`(A_JXXN~tPvY&_F)I%F*
zVZ}8gE;Zo28)v1R?URG;Pd#6Qe0R?xMJU)!L9dczSzpn7p6Kt!Pt$yyCp5@5$kC+W
zd}_i-{y{+Q|BIcHi}&Ab_x~jzp68uw%<b*We+ziW0vsPvKmsoR5B>04!i~m(uimMD
z=3YB5Z&s?vv~-b>!Zw2`u8~C}o~hudDudqU*I^?NQ|AZTcxbB2nA(DXUtFw=e3Z(J
zfHk=ZSwTUOZlKSWs#ZSIk--T;K{dbF;_EeJ%QT!J>X&i~lam0z<fMk|xV-8m<Yr#|
zatwfsYQ<!;cgRRnXhm~Sz_?e;2riIj03)>_Au=~3KZYZ{wL8i6elvJC1{hTwhi{Ei
zC6%q`V&~wj<qt6KyMHlXzBk<eCfXZd@IG_6v)ae8)PPvu!`d7u@3G)y9XUA|8+L&d
zn?AxLzo+GK4^8E_o($4OJ|sKJSmP_SMnyL6t0&LG?OauYo|-1@nxTvwZvX7>o1f_O
z4?cZ!AFC@uF}=y<aQ&7nt54(w*C%MLGfN=J%PW8d4<4>!39S4N;_>{;xc+}xlHla~
z*ZI7mN}J=~C5g4W4<xg>)n~beZxKQf5XfL+nSOyRGqHgYI5ggun~Q4S&`D*3WynU%
z=P?e06oOCa`FYHOm*X)%t`p()f}<(5_;O<8WVG6AA$4U>$$#Yaz^K4=hp4;LJIJC_
zjtfWwkRf9e>&G9buV90mPWr9~u58m^N5M2?HrxZZ)8b6X20aX)->|KQV)x{QlvVA`
z<VxeWa`HolVh*2Dc3Hi6lu3PT|4mMsmnskTBHeWhlRWQDA%RVa3z6yZQApP498qs)
z(-9!*+EwDgFTwLuUDONw2!s9JKP2j37J4<joh<>%W?$6Z9RNxc9PFF`X<Jt}cM5(U
zj=zz;yQM2Y+TPUNQqt1g$-)w#VCiV>ZbQMv_3ufX*IRa79mMgU(jM0@xc*>0lqlCC
zXP2i_`vDkx#_tm-tJ8}CWPm&3e}wt^z?o5(s3aPu5D)ajr5^A9%*q;sgK+jFTR`NB
zgo{Eg^*HnKX2W!+*w49?eaGJ&L<bU%y~$1ez|x;KuYjt|{tPHAo=E^o9a1Jq%N-C8
z^T6Nu)A|2Ijv5go!rv0cqqNxi=)*FnfRg_)UG6PBI8l*Z3{{5~;kI6Z@dGD!O6?~!
z7emCbP<VBsL<xgMq8Id7vWq8vGi)$pzDZ^8j96t(nA1$4ByJ)qLjxHL?#I&vv}y&!
zC_!^~h7kc23p_{Q78Ro7cA3_ZEWI9r03$cWEPQ?l7P<3!FTFT~nw0<EFHtJBmoJ7G
z-qD7FYzqkk!)0Ae`(0N-y%=d2VGdvh4V|!ifCa`NPJEa?o_v%O^lph0Nfg_v;>{Vh
z$+kcRN7kKAPTJLnv_P~^(@E@*w_(q{mDtvjtQ3hVNV5fCUc_u#s(v3h%{Ol&z+Xo>
zmEYU*Un#(q67}mhGjFGbzkcL6LJW5FW|y1&pi_87&6rgm)Wc7aPV?;&W2c)_v>>+m
z!#i2pav&aJR%lSp!h3iX6F>suB#?0!qcYrRYl9lm2F#wxKy_P?`#sMXXn-qKptBU8
z_dNoA)E4d!2V#LlD2?u$s7tUrh4iHU7eu_lFl=l*nS~zk(d!>yQ1T?2U?8PJl^f`-
z!5ESaGqH2K{R8_l4|a!TNM4<N%TjrIVg*YEn1jc-bI)2=urneW>-lUe!gw$n4I~`x
zxchQltxXnNW!(8(WU1vK>0n&h)>u^7T2sv1>d@Q9$M+gDMDyS!jSCyo;oWj{a8hP;
z4f2kHG@>ia6>iJxf0_J}tW~O4coUs;<CHW@YXO{ftCqQ#5jnFnjQL%>v$6p8!xawN
z?`fGX{!{v}kWhE>#gnMo8d+c=UTxrN+?@C0;BzUc;?1(jorw=&s)RA^B2Nh>6C`<)
ze3-K&<oOUMa9;UNrRu!=%C7J=<#sK&GN{&NCoOj=@m9?2i72fU1E);*${;65-K0yw
zFPo;WU|7^X@|?BBf5KR%ZYdKH6J&r!!q>6!#aEuMW8ULPr>{W;<Cb?-zwy|iQNo(>
zSExr$Q&YzW?v9T0X^#4Rr|?2xkH-@4u_ot;C>T?%tbV=c{np7@*(S2O>-EJ;6<)sd
zTWau=A4jH9z|H3-C*4Y!lBY07LQ5Ghx)Mk4TK_2m*Mbw@PMb}M9%1oHopp84VHPxw
z!d8baYWXwni}Tw6q<un0>F&~YWOvM+_TlxAuxN}{_cXj&uT!c_wkVp+!&N^50>A^-
zvE?Y17{8QC^1w~62v_{sVg%(q%}f%d%o$bsx^mJZ>r-kW7yg89@;Z$azGXlSFp)sl
zb+`UDxdfipTK9QGr0F)91Qc86k~lQ}6nl<|I`!ITbxhAm1;ewBa4?8C!1ahqdk;O6
zS>`@JCMbAi7`ZkpO~{t?KBwMPCaAWT;o&{u#RhFk^QfIUM=hrn-dK#+@^^*na>$F;
z-uUw>)m~9C`tUAcBK4HXq9Csr-3?|mVwtXnSD%O2yFaj!7aTFqQBOy9N^xclo_&&s
z=C9fW3UxVnri?PDebFo;YOpmyNpDKSeIk{xp{py2Sa#}Krx#)E3H;6}USKe<c4AT(
zt81`Um;^HLWo<6wDFT=4;b<}5ubK*UW3{Cg+PcV~E9Oz19M3xV1UG)VGKtWHDdamb
z_SXyi))&`Uh6+#4O7X^WtdGTaKA(OHdsuCThiLQ#E|9b|3U^(^X}k7a$)U&H^O8Ct
z51lIBoi_ey^)=cyOLw7dbS!1~@$ML|;2h2KGW?wV^sHJ$==RbpVD>Nj&WoCtlpHgz
zi8z+;0EPeiw5>jwLB#CEWIUxuCAHC1ys1pZhI6}S$Zm(?-Ky!!MiETp&WmCU0{-2#
z)}z|fZc5SNoJk3qiZ^j?cj6_1V#(A)odiMrc?b=Te$%An=uhMi8L{_dRN{B%9+D51
z=*Y0#T#tB(DvQdBCmU<X{zpP#8X#<ciK(2`dY`l8d!ymatXHC>@9`lP&3d2Des~#9
zYxZBbhm}74jOBCeOB$Z%8KzGy%b($k{43}$`|g`=3y#>jN>7eE@t}y;;B!}YtsR7l
z9)09ZMoNSVB{`pJ1~1CJCUL(u!xL{e@+r@uxfUj=xWt_mZ?|+Mvp>9Xn=GhvLC%x&
z=Z^I0FjfHHDKVu=$f4Ns(20PeQ>nte0nhKBqUU3+mE9%Mjtgv}yCmKbEA9n~cG1XS
z#XqV6`>v^nsFJ(ag;>XA9Y|t5`{rh<tF5H(AUk!5tRvzQe>uY8skduP7p5M1E2c=O
zoza5AWw)d?W{-9We`IM<xIjk+6jHo74kE5UCQCk0cpR7U6h!ehn4*%^e#^t~ZVh{?
zk5+K{``XL9k-uucxw3JsFQWgBkI@ikcI@JY_cd<c809-u%RzZG>z1hh8vm>Q`CRx?
zkY14dA<<OZTeM8TZdfe*F90E1p_(!g(3AK=zE`*zh_0`_UD4P%C|Z;I{Pm?Dfm#b1
zE#q}--^I+W{h%H<fnQGDs=WYwruXZoPN*ytoC-c!^D=TuWcBp{iLD|SrD>+K<ol}U
ze;cpel&Dd5u~V7<cu(;;p<>O8c|l<<2PiZ@o@*FM@o~HLi5BfywHh8d<G22OaoN>{
zYzFI8^8M>2fsF{H@+TyAc4+)HcNp5gi{R4c$QyxNAo>X{!up^a)+A~7*v9g=F_~=4
z;)p7k%baLTo3z8`=@c?HiWIl{<f$6Qlt9*Ks1Ey1J~?y@&iZ{^$#Az8eZ4dy<a2j&
za<?7bbf|4T<4&ZQcx!PeoGGQW`uS&E-5!}{x-Z6>JvGaOtzZsL>nn$nBNP=676m;p
zXX5tXpW)52gO-S0n<vYUczJ?^V$!w<q=cVB5yQw8^Yw)(`O|zg*e+!EaKGg&4o96}
z`=2|K(axqCp=uMqzl2h;H9ic1Uu$Hey<E9ii3xRkp`~k?gXHB}-Y!2Ux5(rs+Po%J
zo#>9ihqDY_y8%PGqqE-Q(W2@`YnCqKqIq87x5^w-NmIgcqJQ}}iBqaX;tSlFyVop?
z`d8%95iB|9ls_-7MLMUj9w<e3hMvLM*Ne{>qPu@BaK_h7&ryJbB>u4>Tl|}d%}b7K
zXNLWz=UDn@&rkbYpAH$j4g)T~9ky?w{fT@X5xwbpm@pHJLHiUvNa*1HUQna!mu0=o
zdb(`cZ{>7Z&Y}-;a-Xo}hP2zBoUmG>?jgq(JjvsOx#0Lx31GeW2vSHcO$u2Qt*{={
zhH{LFd9IOIzKmF2;%Wv16*V*wYB=rtPETG=o6!EjZVADYwQ(V+ky5{P==()=;g?2s
zvreI@Bv|{v%GGJo7y*58@v(4<*`aW!p+dfW3U?caZ7p3)I)-hvbp$tnWNTapWm4tv
z>2@GI-slI{?0!Yo^Uc5+5-wBA*SQ2EvWe-@{6`CZQaD!UpRbe9;^^mcx^UZ{hniu{
zg+q({r!*1d4{;&D9fE?$`)9g7Y8M0kQ>ST`n6h8rvxKRCJg-L%_XIAnQ#goHylmgr
ze4^NVdARU`Rzm{Rx`VfAc0`283i;Z}j(fbglO>L*8DX4?;p<+vtyj(8I+%iE#g6v{
zL1DF$LK5h^SL~!ej#mZp9C7;UU4N9&F&w?eYeY#b+lEKQ`W0*RQ!1diSk<LTUA$p5
zq>B;DY(zY1SSma))7j#OK#vy5L!MMHpgSG<i90`Tz7-E_h?;l&V%Wd?B;2!><VQ5&
zNcX<@2M<qeADJWmk>0KItzZxv8B>eWq!MPpd6A!52dcwma-#gHR>aeWAo#`PohcA|
zwzx>P<@P1(%*3E$t%PI4^Xr7&j|#T(oTB*7^4<rxHWuC8yD;Z+kS-Nbv&4Gf6D&M?
z^H2Sg+3T%Al%`nU1R9p%;kRk-K)IjxonflYtpGyXjEN|=@2x*L!|>^w=u+gRt>JA7
zU-0`KHu=Eww<aI#5_^s}mapQFvHh<=aN5jSfu}1h6ce(ksM^!Q*oCR5Lb*@%X{c|z
z^rviB5k0<-!<&Ug5X;Qs>yiH81&^IpIkt)%pCgs8&X<>mbgWx_HB8d1e1^<_57G23
zBd;bkQPj~i{QMZ{*>qC2fwC|>m^~z0-Z!vVm)*XCr7^}?%96;i`Fs|Y|JCh{)H*+K
ztqHBpTEy(Lm4STR(~U#w7=}b?7v^a-3T=JHexDXJGo7}>r%-2TuF6SiYLn!}4aI4q
zs|38bErya!PvfDnSBrQ<bkEzj903Tw*;WpR#wq{tzE}y^UgL39Zw~KZS*K)JIA{*p
z+a3Ct<WF))j+YhvZo`V}0G5FBJ%pGT*!v1+E;ss5?n<8jSz+m@Sy}<qoSfV#{<FcL
z1^_x*IZ^y4j`eRSsV{75&dbYd$<J<P`uFg#vvUgYS(uvfa++FMm|2=}@O=9J-!KHI
z+xl3da`5m0wDmbCxF|Rc|Ca)eo9Ew(U>=CPy@Ctg(3RW8D}>`q(MHwxM)b0*d7nAA
z`Z87+C%PV9U#~QkDJ4Wl7VOBvWM;b9FKuQ=S(CElMPpbRlafeB^`5xYw^N|*aqq|G
z<Kc#{>njx%7gvI83dx|bV?WthA3Vzzp>TMy%R9hUb(DqFaGuNWutVt-4GD;Bk<Gzi
zhxS?lgx|`4R==+JX!adn`K_|9=)+9A;k9jdB@^|2Q-8DK{+|fGI_I|05Z{gQLAsQg
z3X$`XYJt1a;txv|oG(kTt`^Djwu@Q3#u9ww_OuWo&2Z4lh}wqVrX5JmW3J>eCR<^b
zEk^Ichl}8T%QEKrH?1mVY7^)oP0oJ)XwvhP{A-%IZT4he2a(9rt|HRCE%%=(rA`If
zuu0x*E0?aY3!+oBM#GP+uT@MHJ%#+_Kem|+0-0S<*!sU1#4<a-XB(Vf3cS;a{@M(`
zxI<(RCtwgB?u>TPz3zpLWujj@28Kd@Z4dBF$#A#-*=dpJJ3MT>v&iI=;qhbLY*ND5
z)D!WvO$D8_==W7!lkyjB-z!cH@hElO_=)e1hG)qyMZb9AD4V{Y+M&oAiuTgnRR|Y7
s#4(y_hJ_3V5N)aG{=cQZo4cv2yO*n_6)GpM01qcSDlM&)vNY;{0A($h^#A|>

literal 0
HcmV?d00001

diff --git a/exercises/Solutions7/.ipynb_checkpoints/Likelihoods-checkpoint.ipynb b/exercises/Solutions7/.ipynb_checkpoints/Likelihoods-checkpoint.ipynb
new file mode 100644
index 0000000..acb7876
--- /dev/null
+++ b/exercises/Solutions7/.ipynb_checkpoints/Likelihoods-checkpoint.ipynb
@@ -0,0 +1,457 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Solutions 7\n",
+    "Maximum Likelihood method"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 205,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from __future__ import print_function\n",
+    "import numpy as np\n",
+    "%matplotlib inline\n",
+    "import matplotlib.pyplot as plt\n",
+    "from scipy.optimize import curve_fit, minimize, fsolve\n",
+    "from scipy.stats import norm, chi2, lognorm"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 151,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "measurements = np.array([97.8621, 114.105, 87.7593, 93.2134, 86.6624, 87.4629, 79.7712, \\\n",
+    "91.5024, 87.7737, 89.6926, 133.506, 91.4124, 94.4401, 97.3968, \\\n",
+    "108.424, 103.197, 88.2166, 142.217, 89.0393, 102.438, 95.7987, \\\n",
+    "94.5177, 96.8171, 90.903, 132.463, 92.3394, 84.1451, 87.3447, \\\n",
+    "92.2861, 84.4213, 124.017, 90.4941, 95.7992, 92.3484, 95.9813, \\\n",
+    "88.0641, 101.002, 97.7268, 137.379, 96.213, 140.795, 99.9332, \\\n",
+    "130.087, 108.839, 90.0145, 100.313, 87.5952, 92.995, 114.457, \\\n",
+    "90.7526, 112.181, 117.857, 95.2804, 115.922, 117.043, 104.317, \\\n",
+    "126.728, 87.8592, 89.9614, 100.377, 107.38, 88.8426, 93.3224, \\\n",
+    "138.947, 102.288, 123.431, 114.334, 88.5134, 124.7, 87.7316, 84.7141, \\\n",
+    "91.1646, 87.891, 121.257, 92.9314])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 1-D Maximum likelihood fit"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We have a set of measurements which are distributed according to the sum of two Gaussians (e.g. this can be signal and background).\n",
+    "\n",
+    "$\\rho = \\frac{1}{3}\\frac{1}{\\sqrt{2\\pi \\sigma^2}} e^{-\\frac{1}{2}\\left(\\frac{x-p}{\\sigma}\\right)^2} + \\frac{2}{3}\\frac{1}{\\sqrt{2\\pi \\sigma_b^2}} e^{-\\frac{1}{2}\\left(\\frac{x-p_b}{\\sigma_b}\\right)^2}$  \n",
+    "\n",
+    "where for one of the two peaks the parameters are known already\n",
+    "\n",
+    "$p_b = 91.0$  \n",
+    "$\\sigma_b = 5.0$  \n",
+    "  "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 228,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def likelihood_point(x, position, width):\n",
+    "    return 1.0/3/np.sqrt(2*np.pi*width**2)*np.exp(-0.5*((x-position)/(width))**2.0) + 2.0/3/np.sqrt(2*np.pi*5**2)*np.exp(-0.5*((x-91)/(5))**2.0)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "First, we assume the width of the peak we want to fit is already known: $\\sigma = 15.0$.\n",
+    "Perform a 1-D Maximum Likelihood fit for the position of the peak $p$.\n",
+    "\n",
+    "Complete the functions below which return the likelihood and negative log likelihood (NLL)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 347,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def likelihood_1d(params):\n",
+    "    return np.prod([likelihood_point(x, params[0], 15.0) for x in measurements])\n",
+    "\n",
+    "def nll_1d(params):\n",
+    "    return -np.log(likelihood_1d(params))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Minimize the NLL and give the best-fit result, including asymetric errors and plot the NLL."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 355,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "position: 117.72333147980623\n",
+      "negative error: [3.31211666]\n",
+      "positive error: [3.39091994]\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "solution = minimize(nll_1d, [100.0], method='CG')\n",
+    "min_pos = solution.x[0]\n",
+    "min0 = solution.fun\n",
+    "scan_points = np.linspace(110.0,126.0,50)\n",
+    "plt.plot(scan_points, [nll_1d([x]) - min0 for x in scan_points])\n",
+    "\n",
+    "nll_1sigma = lambda x: nll_1d([x]) - min0 - 0.5\n",
+    "print(\"position:\", min_pos)\n",
+    "print(\"negative error:\", min_pos - fsolve(nll_1sigma, min_pos-0.5))\n",
+    "print(\"positive error:\", fsolve(nll_1sigma, min_pos+0.5) - min_pos)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 2-D Likelihood fit"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now we perform the 2-D Maximum Likelihood fit, fitting for both $\\sigma$ and $p$ at the same time."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 350,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def likelihood(params):\n",
+    "    return np.prod([likelihood_point(x, params[0], params[1]) for x in measurements])\n",
+    "\n",
+    "def nll(params):\n",
+    "    return -np.log(likelihood(params))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Minimize the NLL and find the best-fit result."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 353,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "position: 118.31548192622421 width: 13.629783202046086\n"
+     ]
+    }
+   ],
+   "source": [
+    "solution = minimize(nll, [120.0, 10], method='CG')\n",
+    "print(\"position:\", solution.x[0], \"width:\", solution.x[1])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Create a 2D contour plot of the 1, 2 and 3 $\\sigma$ contours of the NLL and plot the best-fit solution."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 354,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "scanA = np.linspace(110.0,130.0,50)\n",
+    "scanB = np.linspace(5,20,50)\n",
+    "minValue = nll(solution.x)\n",
+    "Z = [[nll([a,b]) - minValue for b in scanB] for a in scanA]\n",
+    "p1 = plt.contour(scanB, scanA, Z, [0.01,0.5, 2.0, 4.5])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Compute numerically the error matrix of the NLL for the 2-D fit."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 301,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[[11.95694692 -3.06065748]\n",
+      " [-3.06065748  5.72672173]] \n",
+      "sigma(position): 3.4578818544955507 sigma(width): 2.3930569834299082\n"
+     ]
+    }
+   ],
+   "source": [
+    "from scipy.misc import derivative\n",
+    "\n",
+    "# compute the error matrix\n",
+    "A = np.linalg.inv([\n",
+    "    [\n",
+    "        derivative(lambda x: nll([x, solution.x[1]]), solution.x[0], n=2),\n",
+    "        derivative(lambda y: derivative(lambda x: nll([x, y]), solution.x[0]), solution.x[1])\n",
+    "    ],\n",
+    "    [\n",
+    "        derivative(lambda x: derivative(lambda y: nll([x, y]), solution.x[1]), solution.x[0]),\n",
+    "        derivative(lambda y: nll([solution.x[0], y]), solution.x[1], n=2)\n",
+    "    ]\n",
+    "])\n",
+    "print(A, \"\\nsigma(position):\", np.sqrt(A[0,0]), \"sigma(width):\", np.sqrt(A[1,1]))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Binned ML fit"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "With the same data as above, we now perform a binned ML fit and compare with the unbinned fit.\n",
+    "First, create a histogram of the data using np.histogram."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 374,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[ 1 19 26 10  7  5  5  2  0  0]\n",
+      "[ 70.  80.  90. 100. 110. 120. 130. 140. 150. 160. 170.]\n"
+     ]
+    }
+   ],
+   "source": [
+    "nBins = 10\n",
+    "histoMax = 170\n",
+    "histoMin = 70\n",
+    "binWidth = (histoMax-histoMin)/nBins\n",
+    "h0 = np.histogram(measurements, bins=nBins, range=(histoMin, histoMax))\n",
+    "print(h0[0])\n",
+    "print(h0[1])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Compute the binned NLL:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 375,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def nll_binned(params):\n",
+    "    # params is a list of [position, sigma]\n",
+    "    expected = [likelihood_point(x+binWidth/2, params[0], params[1])*(binWidth/2)*sum(h0[0]) for x in h0[1]]\n",
+    "    return sum([-np.log(expected[i]**h0[0][i]) for i in range(nBins)])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Minimize the binned NLL:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 376,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "     fun: -138.93433719876123\n",
+      "     jac: array([-1.90734863e-06,  1.90734863e-06])\n",
+      " message: 'Optimization terminated successfully.'\n",
+      "    nfev: 60\n",
+      "     nit: 6\n",
+      "    njev: 15\n",
+      "  status: 0\n",
+      " success: True\n",
+      "       x: array([116.43876363,  15.33581135])\n"
+     ]
+    }
+   ],
+   "source": [
+    "solution_binned=minimize(nll_binned, [120.0, 10], method='CG')\n",
+    "print(solution_binned)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Make a contour plot of the 1,2, and 3 $\\sigma$ contours for the binned NLL and overlay it with the unbinned contours."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 377,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAbgAAAEoCAYAAAAqrOTwAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMi4yLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvhp/UCwAAIABJREFUeJzs3Xd4VFX6wPHvOyWT3kjovfeuIiIgWJAiiBXsWFbFdde2rmVXd3Xdn7rW1VWxASpWBBFUQAEREOkgvZcACSG9T3t/f9whhB5CkknC+TzPPDPnzi1nYDLvPV1UFcMwDMOoaWzBzoBhGIZhVAQT4AzDMIwayQQ4wzAMo0YyAc4wDMOokUyAMwzDMGokE+AMwzCMGqlUAU5EPhCRAyKytsS2Z0RkjYisEpFZIlI/sF1E5HUR2Rp4v/sJztlDRH4P7Pe6iEj5fCTDMAzDKH0Jbjww6KhtL6pqZ1XtCkwH/h7YfjnQKvC4C3jrBOd8K/D+oX2PPr9hGIZhlFmpApyqzgfSj9qWXSIZARwaMT4cmKiWxUCsiNQreWwgHa2qv6o10nwiMKKMn8EwDMMwjuE4k4NF5F/AzUAWcFFgcwNgT4ndkgLb9pfY1iCw/eh9DMMwDKNcnFGAU9UngCdE5DHgPuAp4HhtaUfPB1aafawdRe7CqsokIiKiR9u2bcueYcMwDKNaWr58+UFVTTydY84owJUwCZiBFeCSgEYl3msI7Dtq/6TA9pPtA4CqjgPGAfTs2VOXLVtWTlk2DMMwqgsR2XW6x5R5mICItCqRvALYGHg9Dbg50JuyF5ClqiWrJwmkc0SkV6D35M3AN2XNi2EYhmEcrVQlOBH5FOgPJIhIElZJbbCItAH8wC7g7sDu3wGDga1APnBbifOsCvS6BLgHq3dmGPB94GEYhmEY5UKq03I5porSMAzj7CQiy1W15+kcY2YyMQzDMGokE+AMwzCMGskEOMMwDKNGMgHOMAzDqJFMgDMMwzBqJBPgDMMwjBrJBDjDMAyjRjIBzjAMw6iRTIAzDMMwaiQT4AzDMIwayQQ4wzAMo0YyAc4wDMOokUyAMwzDMGokE+AMwzCMGskEOMMwDKNGMgHOMAzDqJFMgDMMwzBqJBPgDMMwjBrJBDjDMAyjRjIBzjAMw6iRTIAzDMMwaiRHsDNgGEbwqbrBnxF4pINmgT8XNBc0D/Vbz4fSaAGoB9QNHPWsHsB/1BXkqKQdCAEJPI55HQG2SJBIkAjEZj0jkYHtcWCLA1u89b4cdX7DwAQ4w6ixVNUKSP4U8CVbD38K6ksG/wErkPnTraCmuac4W2ggsASCjISBhIItCnAGAlPgWZyAvWROjs4ZqDcQFN1HPqs7EED3BYJpLmg+esw5SnKitkMBLxD0bLURex2w1YESzyKu0/53NKovE+AMoxpTdYNvD3h3g28P6tsNvt3g3QP+ZCtYHC0QALDVAmdjsMUhtvgSwSEOJLZECSockeD9VKj6QfMPlx79OYGgbJU49VCQPvTwrAPfHJTCY88lsWCvC/aGYG+I2BtZrx2NwN4AkbAgfEKjopzyWysiHwBDgQOq2jGw7UVgGOAGtgG3qWqmiNwAPFLi8M5Ad1VdddQ5nwbuBFIDmx5X1e/O8LMYRo2l/nTwbgPvVtS7BbxbwbvLCmIlSzcSAfZG4GgO9guPKsXUDZRiQoL2OcpCxGYFWiKP//5xtlml12zwHQiUYFMCpdcU8O8D7w4o+gWl6MjjbIlgbwyO5oijOdibg6NFIPjZj3MloyoT1ZMV/UFE+gK5wMQSAe5SYI6qekXkeQBVffSo4zoB36hq8+Oc82kgV1X/czqZ7dmzpy5btux0DjGMakXVDd5N4FmHejcWBzX8aYd3kghwtAR7U8TR2PpBPvSwxZv2qFJSVfAftErAviTwJaG+PeDdCd7tVgmxWAg4mgYCXkvE2QYcbcHeyArARoUTkeWq2vN0jjllCU5V54tI06O2zSqRXAxcfZxDRwGfnk5mDONsouoB7xbwrEU9a8HzO3g3Ax5rB4m0ApnrIsTREhytrLStrgli5UBEwJ5oPehubSvxvvozrJKedzvq224FPe8GKJp5uE1QwlFHG3C2QxztwNkeHG2qXSm5piqPivUxwOfH2X4dMPwkx90nIjcDy4CHVI+4XSomIncBdwE0btz4DLNqGMGj/hzwrEDdy8C9DDxr4VAVmUSBsyNE3Io4O4KjQ6B0YAJZsIgtDkLiIKT7kYFPC6xStWeTVcr2bICCaahOCuzhRJ3twNkFcXYBZxewNzb/l0FwyipKgEAJbvqhKsoS258AegIjtcSJROQ84D1V7XSC89UBDmI1HjwD1FPVMafKh6miNKoT9aWCZ9nhgObdhNV93gHODuDshjg7g7OTqeqq5lT9VjWndx3q+R3cq8G71hpOAVanHWcnJKQrOHuAsytiCw9upquZCqmiPMnFbsHqfDJQj42S13OS6klVTSlxnneB6WXNh2FUFerPB89StGghuBdYd/lgdal3doGIe5GQnubHrQYSsYGjMTgaI6GXA6DqDVRBr0Y9a8CzBs19A+u+3oE6O0DIOYizJ4T0QGwxQf0MNVGZApyIDAIeBfqpav5R79mAa4C+Jzm+nqruDySvBNaWJR+GEUyq/kCbzALUvRDcy7Haz1zWD1fUSHCeA872iDiDnV2jkok4wNnOap/jeuDoauqlkDcB5T1AUEdr63sTcj6EnIfYooP7AWqA0gwT+BToDySISBLwFPAY4AJmB+qVF6vq3YFD+gJJqrr9qPO8B7ytqsuAF0SkK9atzE7gD+XyaQyjgqm6oWghWjQLiuZaA6XB6lEXfjPi6mPdjUtocDNqVEliiwJXP8TVDwDVQvCsAfdS1L0UCiaj+R8DNtTZCUIuQFwXWu15QRyLWF2Vqg2uqjBtcEYwqBZA0Xy0MBDUNNfq4ejqb/1QhfRG7InBzqZRA6i6rSrNokXgXgSe1YDf6oQU0tsKdq6LzsrvW6W2wRlGTaZaAIVz0MKZ4P7Z6iwgsRA6CAm91PqxMV3BjXImEhKopjwH+BPqzwL3r2jRfKsqvGgmIKizC+IaAK6B1rg800PzuEyAM4wAVT+4l6CF30DhD9a0ULZECL0yENTONdVERqUSW0zgpmqQNTDduwmK5qBFP6G5L0Puy2BvhLoGWgEvpIdp7y3B/LUaZz31bkULvoGCb61pnCQi8KMy3LqbNlM0GVWAiICzLTjbIpH3WtOOFc1Fi+ZA/iQ0fzxILBp6MRI6CEJ6nfW1DCbAGWcl9edCwVS04GtrvBI2COmDhD0MoQPNpLtGlSf2OhB+PRJ+vTVExb3Aaicu/AEt+AokBg0dGAh2F5yVJTsT4Iyzinp3o/kfQcFXVhWkoy0S9RiEDj0rG+6NmkFs4RB6KRJ66eGevoXfQ+Fs6yZOYtHQy5GwK8DZ/axpszMBzqjxVBXci9D8iVA0D3BA6OVI+E1ISJdgZ88oJ6o+wFdcLae+/SAuaykgQD0bwBaN2BsE0mutyant9QPpjYF07cDxKSCRiC2CQ73Nq0NgEAmB0IuQ0IsCwW4BWjgdCqeiBZ+CvSmEXQlhw4s/e01l5gYyaizVAjT/MzRtKJpxm9XlOuJeJHEuttj/mOBWxagWWRMcH0oXzkWLfi5O+3NeQvPeP5xOvx1/9jOH90+9GM362+F02jVozkuH0+m3oHnvlkjfhOaNL7H/1Wj+hEBeFE3tW2J/L5rSFs19uziv/oPD0IIpgXQB/qzH0KJfD3+Wgm9R755A2of6s6yOTJVIJAQJHYAt9mUkcRES839gr4PmvoKmXoQ//Va0YJrVa7gGMiU4o8ZRfy7kf4LmfWAteeJob/1hhw4xKzoHkXq2gD8NcfWy0rlvov4MbNFPWun02wAbUutjK533DkhI8aBovFtRW+3DEx87WiP2esXnl4jboWQ66nFrcdND6dj/WGvjFadft9bIK06/ai05ZF0dif6HtTrAIRH3QEj3wNtea909ibLS/nwoWmjNXAPgS0WzHkKi/20tpurbix682Poeho1EfXvRrL8jkWORkO5WYC9aZM1gYk84/X/cUhBbBISNRMJGot49VnAunIpmPQzZEWjoYCTsWnB2rhYl1dIwA72NGkP9WZA/Ec2bCJoFIRcikXeDs2eN+YOtytS7B3w7EJc1S5/mvoW6F2OLt0pF/swHwLMaW+IcK539HPgPYot92dq/cCYAEnqZlfYlB6oY4yr7o5wxVY+1zpwtHrHFWgGsYCq4+iKOFqhnC5r1KBL9GBJyDupegqbfiMSNR1y9UfdSNPMBJPZNJKQL6t0FRXMg9ArEXgtVLZfvtDU0ZqnVTlf0gzXe09EGCb/eupYtqhz+NcpHWQZ6mwBnVHvqz7aqmvLHW7OMuAYikfdYM/Ub5Ur9uYjNWllbC2ejBZOR2P8hYsOf8wLkTUDqrEHEjuZ/grpXYYt90drfuw3UjzhbBfMjVEmqheDdA/b6VpufZyOaPxGJvA+x17eqEbMeRhJ+QBzN0YJv0OznkFqTEUdD1LPZGiMXekmZp4lTfy4UTkfzPwPvemuS8NDhSPiNiLN1OX/i02cCnHFWsaoiJwaqIrPBdalV5eNsF+ys1QjWLBorAhP/hqP5X6DZT1ptOfYENP9rNP8jJH48YotBvbutGwxHW7P0TzlTVatWQqKsmwf3CrTgayT6b4i4rNJy7itI7VWH/68KZyJxbyESgvoOgDhLXRpWz+9o/qfW2FCKrO9A+I3WzWOQJjswU3UZZwVVvzWGLfcl8KeCawASeT9Ssr3EOG3qS4aCL627dkdjcK9AM/+AxE+CQ8v8RD4AgeAl4SOR8JHFx4vDLEhcUUTEmiruUDqkO3KoPRAgYgwSemmJZZj8gP9wj9Lc/1qz89RegoighXMBd3F18DHXc3ZCYjqhUY9A/ldo/iQ0849gbwjht0LYVVabXhVnSnBGtaLuFWj2s9bgbGdXJOpxaxFJ47SpPx3NeREJHWa1+3h3oAcHIbGvIqGXW0u7eLcElvsxqyNUZ+peBb4kJGwoAP7020CzsdWabKWz/w8kHFvU/db+6j+iFK7qg6KfrNoSzwqQaAgfZQ21CQyrqGimBGfUWOpLRnNehMJvwVYbifmPNTjbVIWVmqoHzXwIcfW2OhFIJBQtsFaYBrA3RWovL25jE1vU4V6DRrVm3QQevhGUuHcOL/UE4E8DW1FxUtOuREPOxRb9hLXBlwSuAdhCL0Xdq6xAl/cumvcBGjYMCR9TJdrpjmYCnFGlqboh7z2ry7j6rXFsEXdWi+qRqsCf/TRIBLaoRxBxoppj9ZQjMCA4cX5xbzyrGiwyiLk1KotIyBFDKA51BIJAe5+rH+JoEUh70INDIfwGJPqv1tp04deiEXdD4WQo+Aot+BoNuRCJvBcJ6VHpn+dETIAzqix1L0WzngDfTnANQqL+gjgaBjtbVZrmT0I967HFPBvY4AV8xe/b4j88Yn8zfMI4moggUQ+W2KJIzHPgaG4lfXvQjDFI9D+Q6L/hD78Ncl6yZgtKH4WGXIBE/vHINsIgMQHOqHJUi9CcVyH/A7A3QuI+sFbKNo6hnjVowXQk6jGr84DvIPiSUPUhYj8c6AyjjERCIGzY4Q32OkjcBAiU8MS3DS2aAXHjEO82NPdtNP161Hk+EvVAUNvITYAzqhT1bECzHgHvZggbhUQ9WqJnmKH+fGvAr6u/1Vbm3WZNHB1+EzgaWXfOplRmVCARF7jOP7wh5AKk1mRwtEJc/VH1Qu5L4FmPpl+LOrpZy09F3FTpQwxMgDOqBFWf1daW+zrYYpG4cYirf7CzVSWoFoG6rU4f3o1o1oNIzMsQNtSafix0SHF3cBPcjMom4gBnp8PpiDHgugDszax16nJfg9yVaNECiH4ItAgktFLGq5phAkbQqXc3mvUXq/ux6zIk5h/FM8Cf7dSfh6b2h/AbsUX9yRoD6FkJzm6mB6lRLfh9aZD7PyicFhisHge2WtgSvwNA3avB0QyxRZ/0PGUZJmD+Qoyg0oLv0LQR4N2MxLyAxL5+1gc3zX3XmvYKa4JcibiruA1SxIaE9DDBzag2bPZa2GL+hiT+ZE1YrQXg24k/+zn8vgw08x40+6ni/UuuKHHG1y63MxnGaVD14895Ac36MzhaIrWmIWEjzsoqNlUf6l5xOO3fC949h9cgi7yzSnW9NoyyEFs0tqgHrEAXNhLyJ0LqpeAaAuG3AYHJBw5cgOZ/Ui7XNAHOqHTqz0cz74O89yBsNBL/ydnd/T//I6vXmXc7ABL1d2xx/z0rg71R84k9EVvMs0itqRDSAQomQNbDaOFPqAoSeT84zwWsXsL+g0NQz/oyXeuUAU5EPhCRAyKytsS2F0Vko4isEZEpItYkaSLSVEQKRGRV4PH2Cc4ZLyKzRWRL4Ln6rYdhlIn6ktH00VA0B4l6Eol+ChFnsLNVqdR3EH/mA8WLYxI6zFqbzN4IwFQ/GmcFcbZF4j5E4sYBNjTzHsh6GEIvPbzihHrAVgvKuPJ4af6SxgODjto2G+ioqp2BzcBjJd7bpqpdA4+7T3DOvwI/qWor4KdA2qjh1LMOTbsafLuQuLeRiJvPmlKKqqKHpkayRYJnLfiTARB7LSR00FkX6A1DRBBXfyThW2uBWs9K9OBQ/NnPW0szhfTAFj8RscWe+mTHccoAp6rzgfSjts1SVW8guRg43fql4cCEwOsJwIjTPN6oZrRovlVyw4HEf3bWDQHQzPvR9DsCC1WGWut6hV0Z7GwZRpUg4kQibkUSZkHYCMh/Hz04CC38gTPp6V8edSFjgO9LpJuJyEoR+VlELjzBMXVUdT9A4LlypqM2gkILpqMZd1uT+db6EnG2CXaWKpyqGy2YyqH7QAkbioTfAAQ6jog9iLkzjKpJ7AnYYp5D4r8EW4J1Y5h5N+rbW6bzndFAbxF5AvACh7q87Acaq2qaiPQApopIB1XNPoNr3AXcBdC4sVlvqrrRvI/RnGfAeY61+KItKthZqhxF89CsvyASA6EXnXDdLcMwjiUhXaDWV9aCxrmvWZM9l0GZS3AicgswFLhBA2VIVS1S1bTA6+XANuB4ayikiEi9wHnqAQdOdB1VHaeqPVW1Z2JiYlmzawSB5n2E5vzTWpA0/v0aHdxUFS2cixbOsja4LkbiP4azrCrWMMqLiAOJGIMkfIdEPVymc5QpwInIIOBR4ApVzS+xPVECdS8i0hxoBWw/zimmAbcEXt8CfFOWfBhVlxZ8bZXcXAOtwdviCnaWKpzmvYPmTQQODcg+96zpRGMYFUXsDQLV+6fvlFWUIvIp0B9IEJEk4CmsXpMuYHbgD3hxoMdkX+CfInJojY67VTU9cJ73gLdVdRnwf8AXInI7sBu4pky5N6okLZyNZj0OIedbq0PX0N6B6t2F5r5pDXWwRUDsq1aX5mDmSZU8t4esgkIKPR7cPp/18Poo8vpw+7y4vT68fj82ERw2Gw67DbvNhtNmw2G3Y7cJIXY7US4X0aEuIkNdhNhNm6FR/Zi5KI1ypZ7f0bQbwNkaiZtQoxcmVfdqNOM2JO4dJOScCr2W1+9nf1YOe7OyScrMYm9mNml5+WTmF5BRUHjEs8fvL/frhzocRIW6ioNebHgotSMjqB0VSZ2oSGpHRVA7MpI60ZHEhYdhMyVXo5yVZS5Ks5qAUW7Ul4xm3AO2Wkjs2zUyuGnRz+Dbi4SPthrCE+dby9aUx7lVScnJZVPKQTampLIzPYOkzGz2ZmaTnJ2Dr8TNqE2E+PAwYsNCiQ0Po0l8HF3CQ4kNCyUuPIyYsFDCnU5C7HacDjshdjshh57tDhx2G6qK1+fHq37r2W89fH4/RV4vuUVucgqLyC4sIruoiNzA65yiIlKyc1m7L4W0vHyOvkV22mzUjYmiSVwsTeKtR9P4OJrEx9IgNhqnKQ0alcQEOKNcqD8PzfgDaB4S/zliTwh2liqEFkwB7w4Iu9ZqBC9jcHN7vWw6cJCNKQfZdCCVTSkH2XzgIJkFhcX7JEZG0DA2mu6N6tMwNoYGsdE0DDzqRkdViUDh8fk4mJtPSk4uKTm5HAg8783MZld6JiuT9pPndhfvbxehYVwMLRLiaVsnsfjRKC7GlPqMcmcCnHHGVH1o1kPg3WSt4+Y8XsfZ6knVDwVfWAuM2usi0f+01rI6zYUbc4vcrEraz7Lde1m2O4nVe5Nx+3wAhDudtK6dwGXtWtGmdgJt6iTSpnYCUaFVv2OO026nXkwU9WKO30NWVUnLy2dXRia70jPZlZbJzvQMtqSmMW/LDvyBUml4iLP4s7etk0Cn+nVpUzuhSgRxo/oyAc44Y5rzfGBuyb8jrr7Bzk758iej2c9BxAEk6v5Trll1SJ7bza879rBsdxJLd+1lQ/IBfKrYRWhXtzY3nNOFrg3q0a5u7RpdehEREiIjSIiMoEejBke8V+jxsiU1UIpNSWVjSirfrdvEZ8vXAFa7X8f6dejSoC7dGtane6P61Iowq7sbpWc6mRhnRAtmoFkPQPhN2KL/FuzslAtVBc+K4iVq1LPFWtLnFEEot8jNvC3bmblhC/O37qTQ6yXEbqdLg7r0bNyQno0b0LVhPSJdIZXxMaolVWVvVjZr9iazem8yq5L2sy75AJ5AabdZrTh6Nm5A72aNOb9ZY+LCw4KcY6OylKWTiQlwRpmpLxk9OBgcrZD4j2vMcADN/wLNftKaMzOk+0n3zSksYs5mK6j9sm0nbp+PxMgILmvXikvatqR7w3qEOExFyZlwe72s3X+AZbv3smLPXpbt3kdOURECdKxfhwuaN+HCFk3o0qCeqdKswUyAMyqNqqKZ90LRAiRhBuKo/tOoqfoQsaNaBAXTIOyq4y5d4/X7mbd5O1+tWsuC7bvx+HzUiYrksnatGNS+Fd0a1q+xVY5VgdfvZ+2+FBZs38XC7btYnbQfnyoRISGc17QhF7ZoyoDWzakbXXNnzjkbmQBnVBot/AHNvB+J+gsScUews3PGNP9ztOBrJP4jRI5fhZiSncuXK3/ni5VrScnJpXZUBIPbt2FQ+1Z0aVDPBLUgyS4s5LedSSzYvosF23aSlGlNfdupfh0ubtOSi9u0oEVCvJlVppozAc6oFOrPtKombXWs1QFOs0dhVaSFP6IFk5GYF46YM9OvyqLtu/ls+RrmbN6GT5U+LZowqkcX+rdqhsNmFietSlSV7WkZ/LRpKz9u2sbqvdaae03jYxnYpgWXtG1pbkaqKRPgjErhz3oKCr5Aak1GnO2DnZ0yU8968O1DQi+20qrFd/ken4+vV6/jw8Ur2JGWQVx4GFd16cB13TvROL5siy8alS8lJ5c5m7fx46Zt/LZjDx6/n7rRkQzv1I4RndvTPCE+2Fk0SskEOKPCqXc7enAIhI/CFv33YGenzFQVzbgFfAeQhOnFpVBVZdbGrbw8ZwE70zPpUK82t57XnUHtWpnOItVcdmEhP2/ZyfR1G5m/dSd+Vbo2qMeILu0Y3L4NMWGhwc6icRImwBkVzp8xFtyLkIQfEXtwJxYuq0MlNfWng/qLZ11ZvmcvL/74CyuT9tMqsRYPD+xDv5bNTNtNDXQgJ5dv125kyur1bElNI8RuZ2CbFlzZpT0XNG9iqp6rIBPgjAql7pVo+nVI5J+QyLHBzk6ZaO5bqHcrEvNicQ/J7QfTeXnOQmZv2kpiZAR/6t+bK7u0Nz9yZwFVZV3yAaauXs+3azeSWVBInahIru3eiWu6daROVPnMM2qcORPgjAqjqmj6aPDtskpvtuo5o4Tmvo16tyExz5GW5+aN+Yv5YsXvuJwO7ux9Dree153wkJoxns84PW6fj3mbt/P5yt9ZsG0XDpuNgW1aMLpnF85r0tCU5IPMrCZgVJyieeBZjkT/o9oFN1W31dbmaAgRf0CAb9du5J/fz6XA4+H6Hp0Z27eXmQbqLBdit3Npu1Zc2q4Vu9Iz+WzFGr5etY6ZG7bQvFY8N53blRGd25sboGrElOCMUvGnjQJ/CpIws9rNWOLPfATcvyEJ31PoC+HZH+bx1aq1dG9Un38NvcT0pDNOqNDj5Yf1m/l42Sp+35dCbFgoo3p04ebzuhFvpgmrVKaK0qgQ1iKmVyFRjyMRtwY7O6dNPZvBu47tuX358+QZbE5N4+4LzuWP/c837WxGqagqK5L28eGvK5i9aSvhTifX9+jMbb26U9u001UKU0VpVAjNmwASAWFXBzsrpaZaAEW/IKGXIs7WTF3v5envJxHqdPLe6Cu5sEXTYGfRqEZEhB6NGtCjUQO2pqbxzsIljP9tBR8vXcXV3Tpyx/k9aRBbupUmjMpjbl+Nk1JfChR+D2FXl9vK1ZUi7wM0834KCzfz2LRZPDptJh3r1eGbO280wa2CbEo5yM9bdxSnp6xex2vzFhWnX/ppAQ9MnlGcfmzaTMZ8Mrk4ff+X33Lbx4fTf/pqOvd/+W1x+snps3n2h7nF6bcXLOGTZauL0z9v2cHKpH3F6SKvtxw+1bFaJtbixRGXM/PeWxnRuR1frvidS9/8kMemzWJHWkaFXNMoGxPgjJPS/E8BLxJ+U7Czcnoi7mK/7TWuHr+YKavXcU+f8xh/09XUia5GQbqKWbY7ibcXLClOv/TTAvq/9l5x+uOlK3l82qzi9Oq9yczfurM4HR7iPGKpoA716tC9xBpxvZo1pm/LpsXpTvXr0rF+3eJ0REgIYc7D7b+/7tjNij2HA9q/Z//MhN9WFqeHvfMRj37zQ3H6mR/mMnX1+uL01tQ0couKSvXZj6dxfCzPDL2E2feNYVTPzsxYt5HBb03g4Snfsycjs8znNcqPaYMzTkhV0dT+4GyDLW5csLNTKlowHVwXsS2tkBsnfokA/7nyci5o3iTYWavy8txu1u0/QM/GDbCJ8PmKNbw8ZyHz/3wnLoeDt375jTfnL2b5o2NxORzM3riV1Xv389CAPogIezKyKPJ6aZkYnAkAkjKzsIlQP8aqKpy4ZCX1o6O4uG1LAEaM+5j+rZrz54t6o6p0+b//MrpnF/56ST9Ulb9Om8ng9m3o16oZAAUezxEB9VQO5ubxweLlfLJ0NT5VbjqnK/dceC7RoWaGlPJgOpkY5Uo9m9G0oUj0s0j4tcHOzimpdxfISNf5AAAgAElEQVR6cBBZ9tsZ9nE0oHxyy3U0MXNHHldKdi4zN25haIc2xEeEM3nVOh7/dhbf3XMLLRLi+XXHbmZu2MIDF11ATFgoBR4PDputRqy55vP7+WnTNhrGxtC+Xm1yCou48r1PGNOrB6N7diEjv4DzX3qbfwwZyHXdO5Pv9jB741Z6NWt0ysHfKTm5vDp3EVNWryMmLJT7+vbi+h6da8S/WzCZAGeUK819F819EUmcj9jrnvqAKiA1fT6jJ20gu9DPxzdfQ6vaCcHOUpWRlpfPR0tWcXn71rSpk8DyPXsZPf4Lxl0/gn6tmnEgJ5cNyamc06ThWT/WKyO/gEnLVjOgdXPa1a3N6r37ufaDz3jzmmFc3LYlu9IzGbdwKXf07kmzWnHHPceG5AP83+z5LN65h6bxcfzl4gsZ0Lq5GTBeRmUJcKYNzjghdc8HR5sqH9zUm4S6V5Gam8eNn24jI9/HBzeMPOuDW5HXy7iFS1m0fRcAAry7aClr9llLyHSqX5f5f76zuEqudlQk/Vo1O+uDG0BceBhj+/aiXd3aALSvW5sZd99Mr2aNAKs69MdNW/H5/QDM3bydEeM+Zk9GFmCtQt62TiLjb7yKt68bjk2Ee7+Yxi0fT2ZjSmpwPtRZ6JQBTkQ+EJEDIrK2xLYXRWSjiKwRkSkiEhvYfomILBeR3wPPA05wzqdFZK+IrAo8BpffRzLKg/pzwb0cXP2CnZVT0px/4cu4l7snfcaBnDzGjRpBh3p1gp2toJi/dSdzN28HwGm38+Hi5SzasRuA+IhwljxyD9d06whYM3eYuRZLx2m30zKxFpEuFwAXNG/C4ofupkVgkoAQh51akeEkRkYAMHHJKnq99DZ5bg8XtW7O+BtH8vglfdmUksrIdz/h2ZlzyS4sDNrnOVuUpgQ3Hhh01LbZQEdV7QxsBh4LbD8IDFPVTsAtwEcnOe8rqto18Pju9LJtVDj3YsCLVIMA5418hqcXXcnmg3n877or6N6ofrCzVGn8quws0TX9f78sZtyipQDYRJh93xgeHnhh8fsRIcdfrdw4fSJSXN14QfMmvD96JKFOa2hxh3q1ubprx+Jeo6//vJh3f13GD/feyrXdO/HRklUMenM8MzdsCVr+zwanHOitqvNFpOlR22aVSC4Grg5sX1li+zogVERcqlr2vrhGUKjnd8AOzs7BzsoJqWc9ONry7x/X8OVaB69eNYjzmzUOdrYq1b9mzmPa7xtY+MBdhDgc/OfKy6kdKEUAR3TLNyrP+c0aH/FdHNapLd0b1ScuPIynBw9kVdJ+dmVkcv9X07msbUseHNCHpidoyzPKrjxmMhkDfH6c7VcBK08S3O4TkZuBZcBDqmpGSFYlnnXgaImIK9g5OS717kHTrmVD7kgmLYvj9vN7cHn71sHOVoXbkHyAf8+ez4sjBlEnKpIRndvRvWF9CJQkGsbGBDmHxvH0atroiPQf+pyLqpKUmcXr835l1sat9GnRhHdHXYmI4FfFZjqjnLEz6mQiIk8AXuCTo7Z3AJ4H/nCCQ98CWgBdgf3ASye5xl0iskxElqWmmsbZyqCq4F0Hzg7BzsqJ2RuyXx9k7LfhnNe0EQ8O6BPsHFWYjPwCUnJyAauKcX9WNkmZVmeGTvXrMqRjG0JMF/Rq5fL2rRncoQ13XXAuX90xmvox0fyybRd3TJrChuRU+rwyzlRfloMyBzgRuQUYCtygJcYaiEhDYApws6puO96xqpqiqj5V9QPvAuee6DqqOk5Ve6pqz8TExLJm1zgd/hTwpyGO9sHOyXGpevH4fNw51Y5favHKyME1dtJkt9fLoP+N5/V5vwLW7Bmzxt5GjxIzgBjVW9s6ifz4xzH8/fIBrNizjxsmfEHjuBgaBea2XLs/hbs/m8qudDM7yukq06+CiAwCHgWuUNX8EttjgRnAY6q68CTH1yuRvBJYe6J9jSDwBKYzqoIlOPXuRA9eytTln7LtYDrPDL24xq3jtjEltXhKrBCHgycu688t53Urft+Mo6p5bCLc0LML0/5wI23rJLAyaT9vLVhCVkEhB3Pz2J6WQUyYNSPKkl1JfLVqLW6fL8i5rvpKM0zgU+BXoI2IJInI7cAbQBQwO9DN/+3A7vcBLYG/lRgCUDtwnvdE5NAgvRcCQwnWABcBD5Tz5zLOhG+n9exoEdRsHJcW4aE+7y7ex6B2rejXslmwc1TuZm/cyoeLl5NZYHUjv6JTO1qf5WP6zhaN4mL56OZreGTghczdvJ0r3/2E+PBwZo29jdhAgJu+diOvzVuEPXCjsycjE48JdsdlZjIxjuHPfg4KPkdqr6qSpYXnf5zP+MUrmH73zcXjkKozt9fLhCUr6dm4Ad0a1qfA46HI6yv+QTPOTmv2JvPnyTNIycnlkYEXcst53RARVJUDOXnUiY5EVRn2zkfUj4lm3KgRwc5yhTIzmRjlw58CtrpVKripFqF575Oac5BJS1czrGPbGhHcADx+PxOXrGTOJmuAdpjTaYKbQecGdZly5w30a9mMf8/+mXu/mEZmQSEicsSqGA8N6FNchV3k9XLTxC9ZsG1XsLJdpZgAZxzLlwxVbXquop/RnOeZueYrPD4fY/v2CnaOzsjGlFT++f0c/KpEhITwzZ038tDAmtsT1CibmLBQ3rx2GI9f2o9ftu5k5LufsHZ/SvH7IsJFrZsXr5aRnJ1LTmERtsC9aXpePsv37KU61dSVJxPgjGP5ksFWtQKchF5KeuiXPP+zmxFd2lf7FQLW7k/hu/WbSQrMXRhfwzrKGOVHRLjlvO5MuvU6/KqM+vBzJq9ad9x9m8THMuXOG4oHmX+1ah2jx3/B7sD37GxTHgO9jZrGfxDsVadTg2oBImF8vDIPr9/PPX3OC3aWyuRgbh5Jmdl0bViPq7p04NK2Lc1aYUapdW5Ql6/vGM2DU77j8W9nsSMtnQcH9DlmQHjJpoUbzulCi4T44hvCl35agAg1etxoSaYEZxzBGproAarGDCbq3Y0e6IO/8Cemrd1A72aNaRRXPWfrePSbmTzw9QzcPh8iYoKbcdriI8J5b/RIruveiXcXLeOByTMo9HhPuH9ESAgD2xzuDZ1ZUEBGfkFxel9WdoXmN9hMCc44igcAkSoyh6HYwTWQNQcS2Ju5hj/16x3sHJ0Wvyp+VRw2G09c1h+3z2dmHTHOiMNm4x+DB9IkPpYXfvyFlJxc/nftFaWq5n5m6CXF7XF7MjK57M3x/GPIQK7p1qmisx0UJsAZR1K39VxFApzYGyCxLzB5wWzCnU4uadsy2FkqNY/Px9gvvqVFQjyPXtKX5pXQ61NVycnM52BKFu4iL36f4vf78fv8+PyK3+fH77N+4CJjwoiJjyAmPoLwCFeV6jVrnJyIcPv5PWkYG8MjU7/n2g8/Y9z1I0r1HTv0/xwbFsaf+vemb2As6caUVHakZXBZu1Y1Zh5ME+CMIx0KcAQ/wGnh9+DoiJt6fL9+C5e0bVmtFuN02u00T4ijYTlXqXo9Prau38veHQc5sC+DA/uzSN2XyYH9maTuz6Qw333qkxzF4bQXB7uY+EjiE6No3KK29WhZm7qN4rHV0OnQqrPL2rWiTlQk93z+Ddd/+BlvXHsF5zZpWKpjo0Jd/KHP4VkSv1q1lqmrN9CneROiQqtGE8WZMgO9jSOoLxlN7YtEP4OEXxe8fGgBeuB8CB3KnOTbGPvFNN4fPZI+LZoELU+l9fu+ZBIiIqgXE1Vu50xNzmL5L5tY+vMmVi7aSkHe4UU6YmtFklgvltr1YwLPcSTUjcEV5sRus2GzC7Yjnq1AlZOVT1Z6XolHLlkZ1uuDyVmkpRxunwkND6F5m3o0b1eP5u3q06pjA5q3rWeCXhWxJyOLuz6dyp7MLN68ZljxKu2nw+f3syMtg5aJtQB4+rufuKhV8zKdqyKUZaC3KcEZRzq0PE6Ql/ATCYOEmYCyfPdmnHY75zap+hMMu30+/vTVDJrEx/LhjVeV+Txej4/1K3aydP5mls3fxM7NyQAk1ouh/9AudOvdimZt6pJYLxZXaMWUavNyC9m99QC7t6SwY9N+tm3cz5xpK5k+aTEA0XERdOvdku4XtKLbBa1IrFs9O//UBI3iYvj0tusY88lkxn75Lf+9eigXtW5+Wuew22zFwS2zoJDfdibRND6Ofq2aoarVsgrblOCMI6i60ZSOSOSDSOTdwc4OANd/+BkAn912fZBzUjpr96cQFxZGg8Bs8KcjP7eI7z7/janjF5B2IBu7w0bHHk3p2bcN5/RrS+OWtYP6Q6OqpCRlsH7FLlYs2sKKhVvISM0BoFHzxOJg1+W8FoSGB7+a+2yTXVjImE++ZmNyKq9ePZSL25R9Plmv34+q4rTbmbdlOxN+W8nzwy+jdlTkqQ+uAKYEZ5QDJ+BANY9g/YyqNwnNfRWJvA+PNGLd/gPceE7XIOWmdHakZbB+/wGGdGxDx3p1Tvv4zLRcvpm4kOmTfiU3u5AuvVpw95PD6HZBKyIiq85wAhGhbqN46jaKZ8Dwbqgqu7aksGLhFlYu3MIPXy7lm48W4Qpzct5F7eg/pAs9+rYhJMT81FSG6NBQPrhhJHdMmsKfvprOq1cNKXPHrJJLUOUVeSjweIgNDwOoNguymm+dcQQRQSUcDq+CVPl826BoPkT+mQ3JB3D7fHRtWO/UxwXR/35ZzK87dtOvVTMiXaUvuezfk8bXH/zCrMnL8Lh99L6kPdfc2Z82nRud+uAqQERo2rouTVvXZeRtF+Iu8rBuxS4WzlzLLz/8zvzv1hAZHcoFl3ak39CudD63OXa7aberSNGhobw/2gpyf548g5dHDuaydq3O6JxDOrZhcIfWiAhev59RH37ONd06cm33qj28wAQ441gSAZoXvMu7+kHtRYg4WJm0AqDKB7hnhlzC/uycUge3zLRc3n/xO+Z8sxKb3cbA4d24+vZ+NGxevRf1DXE56XZ+S7qd35K7nxjGykVb+XnGKuZ/t4aZXy0jLjGKAcO6MWT0edRrVCvY2a2xokJdvH/DldwxaQoPTJ7Ba1cPPeMhNoeqxnOL3NSNjiS+RGlOqJrrFJo2OOMY/oMjwJ6ILe7dSr/20Y3Z//h+DjPWbmTJI/dWel5Oxe31Mm7hUm7v3ZMwZ+k7euzamsLf7/yQ9NQcrrjxfK687UIS6tTsDhpFhR6WzNvI3G9XsmTeRtSv9B3cmWvv7E+ztlX75qU6yy1yc8ekr1m7/wDvj76S85qWf83ApGWr+WnzNl67auhp1V6cLrNcjlE+7PXAtz84187/GH/aVag/F4C03DwSIiOCk5dT+HXnHt785TeW7Eoq9TFrftvGQ9e/hcfj45XP7+XOvw6t8cENwBXq5MJBnfj7mzczYc5fGXnbhfw2ZwP3Dn+Np+4ez/oVZnmXihDpCuHt60fQJC6Ge7+YxobkA+V+DafdRpjDSUQVHKNqApxxLHt98O0LzrVtcWBvhNisnlqpeflVNsD1a9mMH+69tdSris/9dhVP3P4B8bWjeeWze2jZoeoPe6gItepEc/tfBjNh3l+56f5L2LhyNw+NeotHbnyH5b9sPmuXdqkosWGhvH/DSKJcLu78dGq5zz95TbdOvHHtMESErIJC7vx0CptSDpbrNcrKBDjjGGKvD5qL+nMq/9phQ7HFvlqcPpibR2IVW0pmX1Y2G1NSAUq1bI+q8sW4ebzw8Ge069aYlz69mzoNa8ZirWciKiac0WMHMmHuX/nD40NJ3pPOk3d8wB9H/pff5m4wga4c1Y2O4t1RIyj0eLlz0hSyCwsr5Dq70jPZfOAgfvVXyPlPlwlwxrFsgTaRSi7FqT8XVd8R2w7mVr0S3Is//sItH31Fvttzyn39fj9v/uMbPnzpB/oN6cKz799OVEzVCtjBFhoewohb+vDB7Ef487+uoiCviKfvnsCjN49j67q9wc5ejdGqdgJvXDuMXemZjP3iW9w+36kPOk2dG9Rl9n1jaFe3NgAfLVnJij1Bqg3CBDjjeOyBqjPf7kq9rOa9hab2LQ5yXr+ffI+nys2L97fLB/DKyMGlmhfz87fnMuPTxVx9e1/+8p/rzHiwk3CGOLjs6nN4Z8aDjP37cPZsS+VPV7/B2//6lvzc4M6sU1P0atqIfw27lCW7kvj3rJ8r5BqHVsso8nr5aOkqvly5tkKuUxrmr804lqMlIODdDFxSaZeVkF4gUYhYfyB2EQTw+qpGdcehwa3x4WH0bn7qOTFXLNzCR6//yEXDujLmkcurZDfqqsjhtDP0hvPpP6wrE16ZybSPFrFw1lru+dsV9L64Q7CzV+0N79yOjQdS+eDX5XSpX5cRXdpXyHVcDgdf3zEaAlNGpGTn4vZ5aRR36mr98mJKcMYxxBYB9kaoZ1PlXtd14RHTg4kITrsdTwVUpZTFxN9Wcvukr0tVNZmanMXzD31G45a1uf+fI01wK4PI6DDGPjWClz+/h6iYMJ4Z+xH/uGcCB/ZlBjtr1d5DA/pwbpOG/P27HyukZ+UhkS5X8dCBp777iRsnfonbe+IFWsubCXDG8TnagLfyApz6c1HfsX9oIQ57hbQVlEVYiINol6tUVZPjnptOUaGbJ1+/0czJeIbadmnM65P/yO1/GczKX7fyhyEv891nv5lOKGfAYbPxysjBxIaF8cevppNVUDGdTkp64rL+/HPIxYQ4rIpDn7/ia2ZMgDOOz9EGfLtQrfgvPgBF89DUPqhn4xGbQ+xVJ8Bd170zr1w15JT7rV22gwUzf+eaO6r/zCRVhcNp5+rb+/LOjAdp160J/31qCk/fM4GMg5Xf07emSIiM4LWrh5CclcMjU3/AX8E3DI3iYoqH1Py4cStXv/8pKTm5FXrNUgU4EflARA6IyNoS214UkY0iskZEpohIbIn3HhORrSKySUQuO8E5m4nIbyKyRUQ+F6kiS0gbAIizDeCHyqqmdHZGop4Ax5Gzn7scDoo8lVelcTzp+QX8umN3qX8APn59NrXqRHPV7X0rOGdnnzoN4nj2vdusacAWbmXs8NfMIPEz0K1hfR67rD8/b93Be4sqb5Yop8NOfEQYcYHpvipKaUtw44FBR22bDXRU1c7AZuAxABFpD1wPdAgc8z851GvgSM8Dr6hqKyADuP20c29UHGcX69mzqlIuJ47GSMQtiBxZ/VcnKpL92cG9S5+6ej23fjyZnWkZp9w3My2XNUt2MOiacwgNM/dsFcFmszH85gt4ffJ9hEW4ePTmcXz/xZJgZ6vaGt2jM5e1bcnr8xaxMTm1Uq7Zr2Uz3h89khC7nSKvly9Xrq2QKudSBThVnQ+kH7VtlqoeurVeDBxaJ3048JmqFqnqDmArcG7JY8VqcR8AfBXYNAEYUaZPYFQIsdcFW33Us6JSrqeezag//ZjtDeNi2JOZVSl5OJEbzunCu6OupHnCqQdnL56zHlWl9yWmt19Fa9q6Lq9+OZauvVrw+t++5r9PTcHjDm5pvzoSEZ4ecjExYaH8ddrMSm8SmLpmPU9On83qvcnlfu7yaoMbA3wfeN0A2FPivaTAtpJqAZklAuTx9gFARO4SkWUisiw1tXLuLoyAkO7gXl4pjfmacQua8/Ix2xvFxrA/KyeoPSldDgd9WzYt1b6LZq+jbsN4mrUxEwhXhqiYcJ5+51auubMf3332G4/d+p5plyuD+PAw/jHkYjakpPLOgsotDV/brROf3npd8Yoh5dkWeMYBTkSeALzAJ4c2HWe3o3Ncmn2sjarjVLWnqvZMTDQN9pVJnN3BfwB8FT+bhEQ/h4Rfd8z2RnEx+FVJDlI15StzFvL9+s2l2jcvt5CVi7bS+5L2Z/WwgLycQrIzrOWW3G4vU8YvYMMqa9KAnKx8/nztm8yZttJKZ+Zz35WvM/+7NQBkHMzh/pH/ZeEsq7k/N7uAia/NYsdGa/LvokIPW9YmUZB3eOC33W5jzMOX8+jLo9i6bi/3X/UGOzaVf2mgpru4TQuGd2rH2wuWsH5/xQ0dOJqI0L1RfQA2JqcyfNzHbDt4bG1OWZxRgBORW4ChwA16+DY/CSi5JkND4Oi5Wg4CsSLiOMk+RrCFdLeePcsr/FISehHiPHbxxIax0QDszqj8akq3z8ecLdtZuy+lVPsvm78Jr8dH70s6VnDOqg5VZe2ynWxZa62o4HZ7ufa8fzJ1wkIA7DZh3L+ns/wXq7NSaFgI4REuwgJDJ8QmJNSJITTCSttsNmITIgmLsGavyUjN4fO357J7u/WDu3dnKvdf9QZLf7bOl7wnnVce/4pdW1PoP6QLz398F36fn4dHv2U6n5TBE5f1Jy48jL9OmxmUWhOv+nE57OW37I6qluoBNAXWlkgPAtYDiUft1wFYDbiAZsB2wH6c830JXB94/TZw76ny0KNHDzUqj9/vVV/yOerLeLhir+PLVn/RKvX7co55Ly03T1v/82Udt3BJhebhRPx+vxZ6PKXad+Jrs/TyNn9Vr8dbwbkKrmXzN+nPM1YXp2/q95w+9+dPitPTJ/2qm3/fU5zOycpXv99f5ut5vb7if9Pc7AJdMOt3TTuQraqqa5Zs01G9n9FNa6zrLZ6zXod3flJv6vecXtXjKV3z2zbNzS4o87XPRrM3bNHW/3xZJ/y2IijXP/Rd8fv9uikltXg7sExLGa8OPUo7TOBT4FegjYgkicjtwBtAFDBbRFaJyNuBgLkO+CIQ/H4AxmpgckER+U5E6gdO+yjwoIhsxWqTe79MEdqoMCJ2cPWDop853FxaATxr0PRrwLvhmLfiI8JpGh/Lyj3BWZ9ORHA5SjejXWZaLlGx4dgdx+s0XH2pKqnJh0vQX70/n6kTFxSnn/zvTdzz5BXF6SGjetGqY8PidGR02BlV2drttuJ/04ioUC64pCPxiVEAdDqnOZMWPkmrjlYTfu16sVx6VU+eff92IqJC+dtdH3LNOU8Xt8sl70knZe+pe8OezQa2aUGf5k14fd6vpOflV/r1D31Xvlj5O1e++wm/7yt7dXNpe1GOUtV6qupU1Yaq+r6qtlTVRqraNfC4u8T+/1LVFqraRlW/L7F9sKruC7zerqrnBs5zjaqa2VSrIAm9BDQT3Esr7iLOdkjcu+Bofdy3uzasx6q9+yt15gq/Ktd98BmTV60r9TFZ6bnEJURWYK6C4+P//shdl79U3O714P9dw/MT7yp+v3WnhsTWCu7nPvSj2KxtPe79+3Aat6jNv8ffgSvUSWi4qzjvX70/n3uGvYrXY1W/HUzJKn5tWESExy/tR4HHwytzFwYtH0M6tOGhAX3oUK9Omc9hZjIxTs51IRCKFs2qsEuILR5x9UNsx1/ZulvD+qTl5bOnEtvhcgqLiAsPI8xZ+vnIM9PyiI2vWkv7lMWOjft54vb32bc7DYDzL27P7Y8MLg4iiXVjcFaDVRHqN0nghY//gDPEwWO3vseBfZkMv6k3f3nxOhxOq0T44sOf8+hN44qPKSxwByu7VUqLxFrcdG5Xvly5lrX7S9cGXd4iXS7GnN8Dmwjp+QVlOocJcMZJiYRZQa5wNlpBixiqLxl1L0X1+D8u3RpatdorkyqvmjImLJS3rx/O4A5tSn1MZlouMUEuyZSHyJgwUpIySA1MatyyfQOGju5VLefUbNKyDv/6YAx5OYU8duu7RESH0Wvg4dnzr7ytDyPHXAhYVbF3DnqJ957/LljZrVLGXtiL+Ihwnv1hbtDn/cwrKlsFnwlwxilJ6GXWcIGK6k1ZNBdNvwH8x58lvmViPNGhLhbvrNz16U5XUaGHEFfVL9kcz5TxC3j1CWvehcR6sbz7w0N06dXiFEdVDy3bN+CZ98aQdiCbf/3x4yMGg/ca0J4LLrV6vXo8PgZffx7dercErOEO//nL5+zaGpwSTLBFhbp44KLerEzaz0+btwc1L2VdYscEOOPUXANBwtGCqRV0/ouQuIlwgipKu81G35bNmLdlR6XMQA7w+LezGPvFtNM6plbtaNJTq+cg4/zcQrIz84t//GvaOL52XRvz4HPXsH7lLt59fsZx9wkJcTDqngH0uNBqC96xKZnf5m6gqMBaHinjYA7pB7IrLc9VwZVdOtA4LoY3fv416KW4sjABzjglsUWA6xIo/KFCVhcQe13E1QuRE6/cPbB1c9LzC1i1t3KqKVskxNOmdsJpHZNQN5q0lOrzAzhvxmo2rbEmHRp17wD+9sZN1aJtraz6Du7MyNv68O3HvxYPJD+Zjj2b8smCJ4p7aE4Zv4BbB75QPIj9bOCw2bj3wvPYkJIa9FJcWZgAZ5SKhI0AzYHCOeV+bvVnokULUf+JO5Fc2KIpTpuNOZsq54/s9vN7cn//3qd1TK06MRxMDu68maVVmO/m/Re+Y9pHiwBrgHVNK7Udz60PDqJ1p4a8+uRkUvefeuHUEJez+N/lsqvP4c/PXkV0nNWR6Itx84oHnNdkwzq1q7alOBPgjNIJ6QW2OmjhN+V/bs8mNOM2OGotuJKiQl2c27Qhc6rwXWRCnRjycgrJz626I17cbi+qSmh4CM9/dBcP/vvqYGepUjlDHDz68ih8Hh8vPPwZPl/pq7wbNE1gwPBuAHjcXmZNXsbyBYencaupEz1X51KcCXBGqYjYIewKKJqP+sp50mtneyT+U3C2P+luA1q3YHtaOttS08r3+kdJzc3j3Bf/x/S1Jw64x5NYz2pDTE6q2PyVlaryzNiP+OwtqxRev3GtGjcovTTqN67FfU9fydplO5k6YcGpDzgOZ4iDd2Y8wE1/ugSwhlbcOuB5NqysmdODDevUjkZxMby7sALHw1YAE+CMUpOwqwAfFEwu3/PaopCQHogt6qT7XdauFXYRpq45dsaT8uRy2Lm8QxvqRp88P0dr17UxAGt+q5p3ud7/Z++8w6Oovj/8nt1N74Uaeu8d6SAgIL0oIiCIoiiIBbHrV0XRn9jAiiDSRMFCEaSD9N57C70H0nu23N8fG2KEhLRtCfM+T57dnb1z5ywD9ysAACAASURBVEx2d87ce8/5HKOZ4qUD8fG3b5HJwkCH3g1p2q46v363lpjI/FWV1hv0+Ph6Wl+IUKVOWEYF96SE1EI3nXc3DDodQ5o2YP/lqxy75jgh5oKiOTiNXCOGSuDeDJX8u81z4lTqFpTx0F3bFPP1oXXlCvx16Jhdoyn9PT0Z160jTcplWcEpW0qUCSasQij7tobbybKC4eZu4Plxfen1WN7WFosqT7/endQUIz9/tbrAfVWsXpJxPwzDL8AbpRQfPDebT8bMtYGVrkOferXwMOiZt/fuv1NXQnNwGnlCvB4F8yVIs62Ej4p9G5X4c47t+tSryfX4BLafu5hjW2fQsGUVDu4841LrMcY0E1+9M58r52/m2PbC8ctMHjOTl9q8w7xPFnLDRadbbUHZysXpMagFK/7YmVGOxxao9IK3t9INlFIkxts++tjRBHh50r12dRYfOkZCauFQfNEcnEbe8OwEumBU0jybditBkxG/V3Js17F6Zfw9PVh44KhNj5+ZyMQkmnz6PX/syzmU/HYatqpKSlJaRv0zV+DC6Qg2rzzEuVNZJywb04xs+H0rr3R4n+G1XmLx9ytIjk/hp7d+ZXD5kbze5UPW/rKJ5MTCf5G+ncHPdcTH34spHy+x2ZSiTqej12Mt6fxQEwB2rDvGsI4TOHO88FcEe7RxPZLSjHlen3YWmoPTyBMi7uD1EKT+gzLbTuFB3Goi+uI5tvMwGOhaqxqrj4cTn2KfaEUPg4E+9WtSMSQoz/vWb1YZnV7H3s25K5LqCCrXLM30Na/R8oHad7x35uB5BpcfyfhHJ3L9XATDPx7ErxenMGX/58w69Q2D33mIK6eu8smQrxlQ6mnmT/zbCWdgP/wCvRnyQicO7DjDjnX2WdstVS6E1p3rUK6yVTTYbCq84s71SpekZolizNtz0Nmm5ArNwWnkGfEaAFhsOopT5suopF9zlUjev2EdUkwmFhzIvdJ/XvD1cOedLu3zvAYH1nIu9ZtXZu1f+1ziQnb1YiRKKfwCvLN8P/p6DNHXYxn24aPMCv+WR9/oS1BxazRo6coleXzcAGaFf8vn696nXrta/DB2FnM+/NORp2B3ug24jxJhQSyYscku/ZevUoIXxz+EwU1PSnIaLzz8Lct/32mXY9kbEaFfg9ocu36DMzaqum1PNAenkWfEUA48OkDy3GwFkvOM8Qgq7n0w5TzyqVu6JA3LlGLOrv12DTYx5bPv7o824+a1WKcnASclpDKy5yRmTcq+EkSd1jVw83AjPjIenS7ry4FOp6N+u9qMW/QanYa2Y9Z7vxUpJ6c36Ok+sDmHdp7l3Mn81x7LDSajmVJlgykRlvfZAVehcw2rVueq464ZTJUZzcFp5AvxHgKWKEix0ZSVeyuk2Dow1M1V86H3NeRCdCwbw8/Z5vi3MXjW74zOoxblLZp3qElIcX+WztthY6vyht6gY+Q7vWjbNfv/qYeXB3Va12DPmpynnPR6PWN/GkmHQa35edzvXD9v43xIJ9L5oSa4uRtY8ss2ux7H19+Ld74ZQqNWVQFYs2gP+7a5vqPITEl/PxqWKcXKY6ecbUqOaA5OI3+4twBDVVTibJsszovOB9GH5VouqlONKpTw82X2zn0FPnZWdK9dnc41q+ZrX71Bz4OPNGXPppNcOus8J+Dh6UaXh5tSqUbpu7Zr9EA9zh2+SOTVnCtd6/V6hv/fYBBh8fcrbWWq0wkI9qFd93r8s3ifwyIezWYLi2ZtYaGdpkbtSecaVTl6LYKL0TnLnTkTzcFp5AsRsY7iTEdtVkZHpW5GJc7OVVs3vZ7HmtZn69kLBSppnx2DmtSnX/07gzJyS49BLXBz1/P71PW2MyoP3Lgaw7ol+0lNMebYtnGnegDsWp67m4XiZUNp1acpy39aS0qS68qS5ZWeg1uSkpTGmoV2Kgt1G3q9js9/eZZXJjwCQHxMUq70MV2BzjWt05Qrj7n26FNzcBr5x6s3SCAqYbJNulOp61GJP+Q6iXxQk/oEeHrw1Xr7TCvFpaSQZspfPltgiC9dBzRj7aK9nD7m+PDwvVtO8ekr8zKKlt6Nyg0qULpKSb5+bhqLv1+ZqxF5SOlg4qMSSIjOnwqIK1Ktbhkq1yrNxuWOixD09HbPEG+e8vESXn50MilJrp9jViYwgFoli7Mh/KyzTbkrmoPTyDciXojvCEjbhEoruEad+L6AFNuISO6+lr4eHjzbuhmbTp9j6xnbagDuvnCJpp9NZveFy/nuY/DojvgFevPNewuxOKiO3S3adq3HD3+PoVS54Bzb6nQ6Jm0eT4MOdfhm9DTGPfw5cVHWunYmo4nYm3FcOX2NU3vPsH/dYVb/vIHF36+k6/COhIaF2PtUHErDFlU4eegSaak5j3xtTf+n72f4q10LTeX0ZhXKsP/SVVLzeRPoCKQw6aU1adJE7d6929lmaGRCqWTUjU6gL4sE/+rwkitpJhMPTp6Fv4cHC54ejM5Gx49LSWHOrgP0rFODskFZF2LNDf/8tY/PXvuN58f1pdujzWxim72wWCzMn7iU6W/9gsloxsPLndTkrEcTwSUDmXrwCwJC/R1spX3ZvvYo40bN5tM5I6jbtJLT7Di27zxJCakZaiiuyNoTpxn1+2LmDO1P0/Jl7H48EdmjlGqSl32KbnVDDYcg4gW+o6wh/mkbwaNdgfpTSX+gzOfQ+b2aq/buBgNj7m/FK4uWs/jQMfrUu3tFgtzi7+nJqDYFd0jtezVg1fxdzPhiOS0eqEVQaN4EnPPLzg3HQcF999fI9T46nY7+Y3vSoH1tjm0/xbWz1/EJ8MEnwBtvfy98Arwz/kpXKYmPf9a5dYWZWo3KA3Bkz3mnOTilFLMmriQ6MoHvW7zoshUfmpQLQ4Ad5y85xMHlB83BaRQcr4chcRoqfiK4t8n1FGNWKNMpMB5EKZXr0WD3OtWZsWMPk9Zt5cGa1fB0s83X2mg2c+zaDWqXKo4+mxyxnBARRr/fl5G9JjFtwjJe/WyATWzLifnTNmI2W/Lk4G5RtVElqjZy3ujFmfgH+VC+agmO7D4LtHeKDSLCO98OISkh1WWdG1i1KauXKMau85ecbUq25PirFZHpIhIhIoczbesvIkdExCIiTTJtHywi+zP9WUSkQRZ9vi8ilzO162a7U9JwNCLuiO8L1ojKlOUF68vvDXQh8/I01akT4fUH2nI1Lp6ZO/YW6PiZWXbkJP2nz+XY9YKF+pepVIz+T7Xjn8X7HJb8/b/vhvDmxEEOOVZRo2bD8hw/6Fwxb19/L4qXDgRg+ufLWfmHa9Zha1o+jP2XruZbFMHe5Oa2dCbw4G3bDgP9gI2ZNyqlflFKNVBKNQCGAOeUUvuz6XfirbZKqWV5tFvD1fDsCYbqqIQvC6Rukt/RX7MKZelUvQo/bN7Btbj4fB8/M22rVODLvt2oFJJzoEZOPDqyPRWqlWDi238SE2X/yENffy9CShSt9TFHUbx0IAmxyaS5QEUIs9nCmWNXCT+a/2Ane1KzRHFSTCYuRsc625QsyfFqopTaCETdtu2YUiqnW9GBQNEqiKSRLSJ6azUA80UooEalSpyFJfLRPCeQv96pLWaL4rO1+avSfDtB3l50r1Mdb3e3Avfl7uHGa589SnxMEl//b4Hdi2GeOnyJ36euL1JFNx1FQLA1bD8uOtHJllhz5d6dPJRR7/YGcLnPs2oxaxRt+A3XLKtkzzSBAdzdwY0WkYPpU6CFV5hN41/c24J7c1TCdyhLAUYpOn/QlwbypihRNiiAp1o24e/DxwsU3p+ZxLQ0/tx/mLOROat85ETFGqUY9nIXtq05yqo/7RsNfHj3OWZ8sYKE2GS7HqcoEhDkOg4OwN3dgIgQeT2O14ZMdao6zu1ULmad3binHJyINAOSlFLZFdSaDFQGGgBXgS/u0tcIEdktIrtv3HCdD1bjTkQE8XsVVDQq8cf89+PVF13gl9YIzTwyolVTSvn7MX7FOpsIMacaTfzv7zWsPm4b3b2+w1pTv1klfvh4Sa4KkOaXB/s3ZeH+D/ALLHqRjvbGP8j6P4uNcg0HdwtjmonI63HcvOY604E+7u6EBfjfWw4OeJS7jN6UUteVUmZllaz4EbjvLm2nKqWaKKWaFCtWzA6matgScasLnt0hcUaB68UpS97LcXi5ufHaA204dv0Gv+09VKDjAwT7eLNs5OM83bJpgfsCayj+2AmPYDDomDB2HinZ5JkVFC8fDzy9CkfCsKsREOwLuJ6DK1k2mKnLXqZBiyrONuU/VCkWwql7xcGJNUqgP5DtQoyIlMr0si/WoBWNIoL4jgHMqIRJ+e5DJf+NimiJMuVdoaRrrWo0r1CWL//ZQkR8wQM6KoYE2TSBvVipQF7+v/6EH7nMh6N/tlsww5pFe5ymhVmYMRutdfxcMUTf4KZHKcU/f+1j5Z+uEVlZJtDfZoFdtiY3aQJzgW1AdRG5JCLDRaSviFwCWgBLRSSzrHhb4JJS6sxt/UzLlFLwqYgcEpGDWJNNxtjkbDRcAjGUA++hkLwAZTyav07cm4DPSJC8T7GJCOO6dSTVZOKD5etssjA/fdsexq9cV+B+btHigdq8OP4h9m4+xadj59qlOOqhnWfZuuaIywUmuDrRkdabosAQHydbkj3/LN7HhmUHXeKzDfbxJjYllTSz8wv83k6OGbFKqYHZvLUwm/brgeZZbH8q0/MhubRPo5AiviNRyfNR8Z9A0Kw8j4BEXxLxezHfx68QEsQL97fg87WbWXjwaIEqAwDcSEjkamw8FqVsJgfW+aEmJCWkMOXjv5n0znzGfPxwtkVH88Nz7/XG3aPgEaD3GjHpDi4oxDGqM3lFRHhj4kC8fDwcLo2XFaE+1pvQ6KRkSvj5Otma/6KJLWvYBdH5W5O/07ZD6up89aGUQqXtQxnzlxz9ZPPGNC0Xxocr1hW4btVrD7Thu0d62cy53aLP460Z8kIn1izcyw8fLbHpHbnm3PLHLQcXGOpaF+vM+Pp7odfrSE5MZd9W5xYeDUl3cFGJSU61Iys0B6dhP7wHgKEaKu4jlCU/C/ZpqOgR+Y7I1Ot0TOj9IDoRXvtrZYHUFm7dKcckp9gkOjMzA0d1oN+TbVgyZxuzJq2yqZNbt2Q/o3pNwugCScuFheibCRjc9Pj4eTrblBz56bPljBs126kBMcE+1mjnm5qD07iXEHFD/D8Ay9V8BZyIeCBBUxH/9/NtQ1igP+917cDei1eYvGlHvvsBOHo1graTprLymG3vmEWEp17rRtcB9/HbD+v4+avVNnNyvv5eFCsV6LAq1Y4kLjKelTPXcfOyNYLPmGYkKT65wP+7i6cjKBFm28AiezF4dEfGTxuekZzuDAI8rTcC8SmuV/xWc3AadkXcG4H3YEiajUrLXcXo/+7fENEVbKqoV92a9KlXk+837WBnAYRha5QsxtD7GlKrZPEC2ZMVVlHmPjzYvylzJ/9js5Fc03bVGTdlGIEhrjvdlhfmfbKQ/3vsK8wmM+ePXuLzJ7/n/FHrZ3p020l6Bwxl3z/5D8o2Gc0c3HGa+s0r28pkuxIU6kedJhUA56mc3LoRsLhAwMvtaA5Ow+6I71jQlUTFvpUvnUplPIwlahjKkn81kXe7dqBcUACvLFxOVFL+1D10IrzSsQ0VQuwjvKPT6Xj+g74ZI7lpE5barFBqbFQiF09H2KQvZ2KxKCwWhd6gp0azKswO/5ZaLasDUKJ8MZ6e8BjlaoYBcPbwBVKT8zaqOH7gAslJaTRqVdXmttuTP6dt4L1nZjrFyd0a57qgf9McnIb9EZ0vEvABmE+jEibnowcPMF8A04V82+Dj7s6X/boRlZTMW4sLNjq6GB3D1C277HIx0el0jH6/Dz0fa8GCGZv5+MVfCpwMrpTi1cem8PW7C2xkpfMY9FY/3v71JQDc3N0oVakEXj7WKbKSFYrzyKu9CS0dTEpSKm90Gc9nT3yXp/73bj6FTieFZgR3C08vd/wCvElLdfxa660RnML1PJzm4DQcgni0A8/ekDgFZTyet33dqiKhaxD3+gWyoXapErz2QBvWnTrDz7uyK3KRMxvDz/H1hm2cjypYZGZ26HQ6Rr7TixFvdmfr6qO89thUoiLi8t2fiPDs2z0ZPa6vDa10LMY0I+H7zua6vae3B2OnjWTw2w8B5HokvHvjCarXL4uvf95l4pxJj8EtePWzAXh4Oj5y9lZksTaC07inEf+3QBeQPlWZtztNER1KWfKlbJKZIU0b0L5qJT5dvZGDV67lq4+HGtRh7fNP2m2qEqxOqe+wNrz73RAunonghYe/5UQBapQ1alWV8lVK2NBCx3JqzxlGNn6NFdP/yfU+93VtSMW65UlLNTLj7bkc3nzsru3PnrjGqSOXad7BNlXhnUHElRhuXLXPjVd2uEKyeXZoDk7DYYguCPH7H5gOQ9KsPO+v4t5BRQ1CqfxHBIoIn/TuQnE/X176cynR+ViP83QzZCS0JqTaN3KsecdafDH3WQwGPa8OnsKq+fmvQpCcmMo37y5k65ojNrTQMVRpWJExU56h09B2ed43PiqBP75YwuXwu9/QTP9sGb7+nnR9JFtpXJcmNcXIiK5f8OdPG3NubEPi0qMnA7w8HHrc3KA5OA3H4tkVPDqi4iehTOfytKt4DUD83gIKNg0T6OXJpIe7ExGfwMsLlmHMp8TQtxu20WvqHJKNxgLZkxOVapTm6/mjqd24AhPf+pPvP/wLkzHvNrt7GDhx6CKXzhS+qhzunu50e/oB9AZ9ngNvgksG8nfiHLoMa59tm31bT7F700kGPNuh0FZg8PB0Y+yER+g9tJVDj3vrJjHI2/WmdTUHp+FQRMSa1ybuqNh3sBaUyOW+7vURr+6IFFwEt17pkozr/gBbz15g/Mr8FQZtVqEsPWpXR7B/vpR/kA/jpz2RkRD+5hPTiL6ZN4FbvUHPpN+f45ER99vHSAdw+sA5nqo9hl0rc7+GKiIY3LJXJbRYLPz02XKKhwXS67EWtjDTabR5sC6ly4U49JgZDs5Lc3AaGoi+BOL3Bhh3QvLvedpXKYVK+h2VXPCIwIca1Obplk2Yt+cgs3bmPUevafkyvNyhNZ53uXjaEr1Bz9Ovd+e1zx/l1KFLjOw5iS2r85bzZXCz3hycPXGNGy5UVyy3FCsbQkhYMKUq5i0Xcd28LUx9dXaW761fcoDTR68wbMyDRULe7PDus2xaftBhx4tO1kZwGhr/xethcG+Bip+AMue++raIoFKWolJW2cSMlzu0plP1KnyyagP/nDydrz4OXbnGx6vyNwrMD+17NuCr+aMpViqQ8aPnMGHsPGKicl8WKDE+hVcGTebnr2zzP3Qk/sF+fLbmPcpUKw3Azx/8wV/frci2fVqqdfo4LSWNXSv33/EZxcckMXPiCqrWDqNd93r2M9yBLJi+iR8nLHXY9zEqMRmDToevh+vVH9QcnIZTsE5VjgcEFTMGpXK/jiWB3yCB+cmnuxOdCJ/2eZBapYrz8oJlHLma9yKtB69cZ9mRE1x1YE2s8lVKMPG3UTz2/ANsXnmIZ7pN5J/F+3J1UfPx8+SNLwfx1GvdHGCp/UiMS+LA+iNEnLeuKSqlWDZtLZFXrYIAP772M8/UH4tSis6P38/IL4f9R37LZDTz0Yu/EH0zgVHv9bZpJQdn8sL4fkxdNtZhUmMXomMIC/R3SWkzceUQz9tp0qSJ2r07/1FkGq6HSl6Gin0JfJ5G5/dq3va1JAJmROdfYDsi4hMYMGMeRrOFP54cSKmA3JdKsShFQmoq/p7OEec9d/Iak96Zz4kDF2narjqj3+9L8dKBudrXYrFw9UIUYRVC7Wyl/VBKISJcOH6Z4bVe4oXvnqLnyC5sXriDMwfOM/Ctvri5u92xzzfvLWT5bzt5ZcIjdOzTyEnWF366T55FueBAJg/obdfjiMgepVSTnFv+S9G4ZdEotIhXN/AaCIk/olI35Ho/pZJRNx9EJXxlEzuK+/ky5dE+JBuNjJi3KE/h/zoR/D09UUqx+fR5h+cFVahWki/mjmTEmz04uPMMz3b/ksVztmI25xzAM+PzFbzU/1uibrhmRebccGvkUK5GGNOPTaL9wNYAtO7bjKHvP3KHcwNYNGsLy3/byYBn7i9yzk0pxZ/TNrBzfd4EFfJDmtnMuagYqhZzbGBLbtEcnIbTEf83wVAdFfMaypy75GsRL8TnCcTLdneN1YqH8tVDPTh9I5IX5y/Nc3mdpUdOMPzXBWw9m39Jsfyi1+voO6w1U/4eQ81G5Zn84WKe7/s1uzeeuKvD7TawGY+PeZAgF659lhfKVg/DN/Duyvo7Nxxn2oSltOpcm6EvdXaQZY5DRFg8Zyu7NtjfwZ2PjMZksVDFRR2cNkWp4RIo02lU5ENgqIUEz0bEMZGJWfHHvkO88/caBjSqy7huHXO9tmCyWFh59BRda1ezeWHUvKCUYuPyg8z8ciXXLkZRv1klho19kBr1y911v/jYJHz9vVxyLcVWnDp8ideH/khYhVA+m/MMnt6uFxhhC1KS0hxybsuPnuSl+UtZ+NRgapWyfZWNzGhTlBqFFjFUtubHGXejEnIvkKtUGpb4L1HJS2xmS/+GdRnRsim/7T3EpPVbcz3laNDp6F6nOjoRElLTnFq+pF23+kxd9jIj/9eLc6euM+aR73nv2ZmEH806YvXaxSie6T6RRbM2O9hax2A2W/h96npefnQyPn6evPv90CLr3ACHnduhK9dw0+moFBrskOPlFc3BabgM4tUHPPtA4mRUam6Lk+ohbQfKeMimtozp0Ir+Devww+adfLtxe572vRgdQ7fJs/jr4N21D+2Nm7uBXo+1ZMaa1xj2cheO7j3P832/YfzzP3Pu5H+ngouHBXJ/9/o0al3NSdbajyvnb/La4CnM+GIFzTvU4tuFL1CsZICzzbIrO9Yd48cJS+1+nO3nLtKgTCmH5YLmFde0SuOeRfzfQxn3o2LHQuhiRHf3O0MRPQTPRsS2Ong6ET7o/gBmi+LbjdvRifBc2+a52rd0gD+tK5encjHXuKv18vFgwDPt6TGoBQtnbmLBjM1sWXWEJm2q0fOxljRpWw2dTseIN3tk7JMQl1zoFPVvRynFsnk7+HHCUtzc9Lz2+aPc36N+kZ6CvUX4kcusXbSXp1/vbrdjxCancPRqBKNz+btwBtoanIbLoYxHUZH9waMVEjgl1xckZboElsgCl9XJjNli4a0lq1h08Bhj2rfi2dZ5F+K9FcbuKsTHJLF4zlaWzttB9I14SpYNpseg5nTu1wS/QG/++nkLv09Zz6Q/Rxfakc7N67FMems+ezafpGGrqoz5+OFCey6uypoTp3nu98X88nh/mpQrY/fj5WcNThvBabgc4lYL/F5HxY+3Vh3wGZbjPkopVMwLgBFCFtvMoeh1Oj7u2RmLUkxctwWdwIhWuXdy327Yxo3EJMZ162gTe2yBX6A3g0c/wCMj7mfrmiMsmbONaROW8fNXq2nfswGN21ajZafaBBfCyEqlFBuWHuC7cYswGs08914fug9s5lI3GEWF7ecu4GkwUC+slLNNyRbNwWm4Jt5DIG0bKv4zcKuDuN/9xk1EIGA86AJtfjHT63R80qsLFgVf/LMFEeHplk1ztW+qyUyq0YTZYkHvYkoZbu4G2nWrT7tu9Tlz/ApLftnOusX7WPHHLipUK8nvU9dTu2lFSpUJplip3CWOO4sbV2NY+9c+1i7ay6WzN6jRoByvTHikUCewF4Sd64+zY90xRr3bG73e9t87pRSbws/TpFwY7vqCi5/bixwdnIhMB3oAEUqpOunb+gPvAzWB+5RSu9O3VwCOASfSd9+ulHo2iz6Dgd+ACsA54BGlVHSBzkSjSGF1WJ+gIh9BxYyGkPmIPuzu+7j9W6hSWRIQne1GIHqdjgm9u6CU4vO1m9GJMLxFzrMlYzq0QsDlRxCVapTmxQ/78eQrD7J20V42rzzM7K9WA6DT63igbyOat69JgxZV8PJxjbpfKUlpbFl1mDWL9nJg+2mUUtRpUoH+T7ejY++G6A2ue+G1N8f2nefA9tN2cW4AR69FcC4qmidbuHaSfI5rcCLSFkgAZmdycDUBCzAFeOU2B/f3rXZ36fNTIEop9YmIvAEEKaVez8lYbQ3u3kOZzljX4/SlkeB5iO7uSbwAlrgJkLYBCVmEiG3DpU0WC68uXM6yoyd5o1NbnmjeOFf7XY9L4KNV63m/aweCfQpHvbGoG/EsnbudvVtPcf7kdZITUzG46anXrBKNWlWlap0yVK5VGh9fx0mUpaYYObbvPP8s3sfmFYdITkqjZNlgHujTiA69G1KqrGsmHDsDe679frJ6A3N27mfzy88Q6OWYz98ua3BKqY3pjivztmPpB8zLsTLTG7g//fksYD2Qo4PTuPcQQyUI/AoV/RQq9lUI/BaRu9+VikdzlNjnR2fQ6fisb1cU8MnqjSSlGRnVJuc1nhuJiew8f4mTNyJpXkgcXHAxP4a80IkhL3TCmGZi8c9bOXfqGsf3X2TahGUZ7cIqhFKldhhVapWmSu0wKtcqjV9Awc8xJSmN08euEH70MuFHrH8XTt/AYrbg5eNB2271eKBvY2o3ruDyI2RnYK//idliYemRE7SpUsFhzi2/2GMNrqKI7APigHeUUpuyaFNCKXUVQCl1VUTsmwKvUagRj9bg9yYq/iNUwleI35gc2rdDPNrZzR6DTsfnfbviYdDz9YZtRCUl83aX+++qXlKnVAn+eX443lnoIhYGRIS/526nZJlgflwxluib8YQf+df5HNt/ng1LD2S0Dwr1JSDYh4Bg62NgiC8BQemPwT4gkJKYRnJSGilJqf95TIxL4dypa1w6cwOLRWX0V6V2GC061qZqnTAatqqKp1fRTdQuCFE34vn2vYX0f7odNRuWt3n/O89fIiI+kZ6dati8b1tjawd3FSinlIoUkcbAIhGp2MYFRwAAIABJREFUrZSKy2+HIjICGAFQrtzdpYY0ijDeQ8F0wpoEbqiKePXIcRdlPIhKnIEEfGLzPDmDTsf/9epCsI8307ftISoxiQm9u+BuyP4ndcu5rTx2CpPZQvc61W1qkz0xuOkZP+1JfPysd+xBoX40bVedpu3+PYe46ETCj14h/Mhlrl2KIjYqkZjIBE4fvUJsVAIJcSl3PYaXtzuePh54ebtTplIx2jxYlyq1wqhSJ4yQ4q5ZjsUViY1K5PL5mxjTTHbpf/GhY/i4u9OhWmW79G9LbOrglFKpQGr68z0ichqoBty+cHZdREqlj95KARF36XMqMBWsa3C2tFej8CAi4P8+ynQWFfsWGCoibrXvvpM5EtJ2g/kSGGz/Y9SJ8PoDbQnx9uaztZuISU7hm/4971r40aIUc3btB6Bb7WqF6qJ9KyJRKcV34/6iVqPydOjVMON9/yAfGrWqSqNWVbPc35hmIjY6kdioRETA09vqzLy8PXD3NBSZemzOpmL1kkxZ+rJd+r6ZkMjfh0/Qt34tl1UvyYxNLRSRYliDR8wiUgmoCpzJouli4HHgk/THv2xph0bRRMQdAr9FRfZDRY+EkAWIPvswcPFsDx6rETutx93iqZZNCPXx5q0lqxg6+w+mDuxDqG/WwTA6Eb7t3xNPN0Ohcm6ZSU0xcj78ep4rELi5GwgtEUBoCS3h2l5Y0itg2Otm4edd+zGazTyZy+AqZ5Pjf0FE5gLbgOoicklEhotIXxG5BLQAlorIyvTmbYGDInIA+BN4VikVld7PNBG5FQHzCdBJRE4BndJfa2jkiOhDkKDJYIlBxYxGqbS7txdrnTaVOBtlOmc3u/rUr8XkAb05ExnFwJm/cSEqJtu2AV6eeBgMpJpMfLJ6A1GJSXazyx54ernzfzOeYuCoDgBcOnODuOhEJ1ulAXBwxxkebz+Bs8ev2rzvxLQ05u4+wAPVq1AhJMjm/duDHB2cUmqgUqqUUspNKVVGKfWTUmph+nMPpVQJpVSX9LbzlVK1lVL1lVKNlFJLMvXz1K10AqVUpFKqo1KqavpjlP1OUaOoIW61kID/A+NeVNy4nFX7LZGohG9QyX/Y1a52VSsy87GHiUtJZeDM3zh6NduZdwBO34hi3p6DbD5z3q522QODmx6dTofZbOGD0T/zwXM/O616gsa/eHi5U71eGUqVt326xPz9R4hNSWV4y8IxegNNi1KjEGOJnwiJkxHflxDfUXdtq0wXQF/WIdOCp29EMvzXhcSlpPJF3660r1Yp27bX4xMo4Vf4JLEyc/zABUSE6vXKOtsUDTthsljo/N0MSvj5MnfYAKfYoNWD07inEN+XwLM3KmESKunPu7c1lENEUJaoPJTiyR+Vi4Xw2xOPUiE4kJG//cUPm3dmO7q55dyOX7/BhDUbC+UoqEb9chnObcGMTUz5eAlmk9nJVt17HNt3noS4ZLv0PXf3AS7HxDEilxJ1roLm4DQKLSKCBHwM7m1Qcf9DpazLcR8V+x4q9kWUss+F4BYl/H35ZdgjdK9dnYnrtvDS/KUkpRmzbb/h1FmWHTnBjYTCvZYVeT2WG1dj0NlJIkoja1JTjIwbNZsv37T9NHxkYhJfb9hGq0rluL9qRZv3b0+0KUqNQo+yJKKihoApHAmejbg3yL6t+QpYYv6jW2lX25Ri+vY9fL52M1WLhfDdI70oG3RnFKFSipjkFIK8C3cNNgCzyYzeoCfqRjybVx6ix6DmWgqAAwg/chlPb3fKVCxm037fXrKaRQePsviZIVR2YuVubYpS455EdD5I0I+gL46KHoEync6+rb50hnNTpqwyWGxsW7oo89SBfbgaF8/DP/3K9nMXs2wX5O2FUoofNu9k6eETWfRWOLglcrxq/m5++nQZ1y9rOur2JC3VOjNQpXaYzZ3bwSvXmL//MEPva+hU55ZfNAenUSSwpg9MB9Gjop5Cma/ftb1K3YK62Q2V8o9D7GtTuQJ/Dh9EqI83T86Zz+yd+7JcbzNaLGw+fY7NZ845xC57MuCZ+/l6/vMZAsjH9p0vlGuMrkxSQirPdJ/Ikl+22bxvi1J8uHwdob7ePNe2mc37dwSag9MoMoihnHUkp2JQ0U+jLPHZN3a/D/EdDe7NHWZf+eBAfntyIPdXrcRHK9fzxuKVd6zLuev1TB3Yl496dgYo1A5BRChftQQAJw9d4uVHJ7N0rn0DfO41lFLUbVqRyjVL27zvObv2c/DKNV7p2AZfD9cokZRXtDU4jSKHSt2Mih4B7k2QoGk5lsxRKg1UAqJzzBSMRSm+37idbzdup0JIEBP7daNmyTv1xmOTUxj1+2JGt21Oi4qFW4fVYrGwesEe2nWrj6e3OzeuxRIY7IObu+vLPbkiSimMaSbcPewj3n38+g36/zSXVpXKMXlAb5dQ3dHW4DQ0sFYfkICPIW07KvZVlLq76KyKGW2d1syhna3QiTC6XQtmDnmYxLQ0+k+fl+WUpcliIdloJM1c+EPudTodXR5uiqe3O0opPn7xF956YpqzzSq0zJ60ijcfn0ZK0t2VfPJDitHE2AXLCPDy4OOenV3CueUX7fZJo0giXn2sCibxE1C4Q8AniGRd4Vm8BlpHcOLYn0PzCmX5a8QQ3lq8ko9WrmfrmfN83KsLwemRlCE+3vw5fFBGGZ64lBT8PV27/lZuEBEeG/1ARnCEUooLpyMoX6WEky0rPFSpHUZiQgoeXrYfwU1Ys5Hwm1H8NKhfoSnOmx3aCE6jyCI+w63J4Cl/WfPklCXrdp7tEa+eACiLY1Xjgr29mDygN+90uZ/NZy7Qa8rP/4myvOXcdp2/RIevp7Pz/CWH2mcvGrepRosHrNUgNi4/yMgekzi8+6yTrXJ9biVyt+pch1H/s/3U4doTp/l19wGebN6Y1pVtX0vO0WgOTqNII76jwGcUJP+JivvgrkEbyngMdaMTKnmpAy20jmiG3NeQP558FF8Pd4b9/CcT/9mCMdPUZJViIXSsXomqxWyvMehsGreuxpOvds0oznnm+BUSE+5eO+5eZN/WUwzrMIFj++yjXXopJpa3lqyiVsnijGnf0i7HcDSag9Mo8ojvi+DzFCT/ap2yzM7JGSqDZ09wb+RYA9OpWbI4C54aTL8Gtflhy04GzvyNkxE3AQjy9mJC7wcJ8vbColSOQs6FCV9/Lx4e3ha9XofZZOaDUT/z8Yu/ONssl+HW97VyzdI0bVed8lVL2vwYcSkpPDvvLyxK8WW/bnct3FuY0KIoNe4JlFKo+A8g6RfweQ6d34s5tkclIjrnCCEvP3qSccv/ISEllZFtmjGiVVPc9NY1xCmbd/LNhm0sHfk45YMDnWKfPQk/chmTyUyN+uVISUpj8vjFPDy8LWUr3xlpWtRZOnc7+7aF8/ZXg+0W7JFiNDH81wUcuHSVHwf1ddmI3fxEURYNN62hkQMiAn7/Q6kUSPwOJV6I74hs26v4TyFtK4TMQ8Tx8llda1WjWfkyfLhiHV9v2Maq4+H8X8/O1CpVnIFN6uHv5VEknRtYAyhucfrYFTatOESXh60iv2mpxoxSPfcCJqMZY6qJ1GQjnt53T3fJV/8WC2MWLGXPhct82a+byzq3/KKN4DTuKZQyo2JfgZSliN87iM/QrNulbkCl7UZ8xyDi3Ivp6uPhjFu+lqjEZJ5u2ZRRbZvhkT6FdDE6ho3h5xjcNHv9zcJOcmIqXj7WROPZk1axbe0Rvp7/fJHMoTObzPz500aq1A6jcetqWCwWq6i4HUZvSine/ns18/cf4X8PtucxF/8OaXlwGho5IKJHAj4Fj46o+PGopHlZt/Noh85vLCI6lCXeqYoinWpU4e9nH6dX3Zr8sGUnfX/8hf2XrBWbf919kK/Wb+VmIa9CcDduOTeAyrVL06h1tQzn9sePG9iy6rCzTLMZt75fZrOFtYv2smuDVYtUp9PZzbl98c9m5u8/wqg2zVzeueUXbQSncU+iVBoq5jlI3YD4vYX4DMu6nSUaFfkIePZE5/eCY43Mgg3hZ3lv6VquxcXzWNMGjGrTjPjUtIzpSqVUoU7MzQtms4VRvSZRt2klRr/fB4A9m05Su0kFPL1sP51nL1Yv2M2CGZv5ZsHzGNz0xMcm4Rdgv/wzi1L836oNzN65j0cb1+P9rh0KxXdGG8FpaOQSEXck8Dvw6IyK/xiVMDnrUZoEgkdHxKON443MgnZVKvL3s0MY1KQ+v+w+QLcfZrPz/EUsSvH73kOM+n0xKUbHKLI4G71exw9/j2H4q10BuHzuJu88NZ3lv+0g8nocv01ZR+T1OCdbeSfxsUksmrWZ2CjrqDsgxJcyFYtl5LjZ07mZLBbeXrKK2Tv38XizhrxXSJxbftFGcBr3NEqZULFvQspf4PM04vvKXX/wynwT0Yc60MLsOXYtgg9XrGPPxSvUKVWC+8qX4UxkFN/275kRcXkvYTZbOLTzDOWrluDovvOMHz2H7/96kYo1ShF+5DIHdpzmwf734ePnWDUYs9nCyYMX8Qv0pkzFYpw9cY1RvSbx6mcD6NCrocPsSDOZeHnBclafCOf5di14rk2zQuXctBGchkYeETEgARPAayAk/oiKH5et4olKWYO62RGVtt/BVmZNzZLF+eXxR/i8T1ciEhKYvn0PgZ6exCSnkJiWVuirg+cVvV5HgxZVCAr1o1WnOszd+g7lq1nlv/ZvC2fGFyvQ6awX9DWL9vD5679jNls/68T4FMwm22h+KqVY//d+9mw+CYDFbOH1oT+y4vddAFSsXpIfV4x1qHOLS0lhxLxFrD4Rzttd7md02+aFyrnll6IXhqShkUdEdOD/Pkq8IeknlCUZAj66U5vSvQl49QNDVecYmgUiQs+6NehQvRKTN+1g5va9rDl5mtIB/iSlpbFs5OMZEZf3GoEh/+YwPvxUOzr1a5IRsBJ9I4GLpyPQ6633+D9+8je7N51kzsa3APhr9haiIxMYNqYLAFvXHCEt1cT93esD1mKuyqLo0t+avvD5a7/h6e3B6Pf7ICL88u1aylcrQeP0gJjx0574Tx6frQuT3o2L0bE8O28R56JimNCrC33qO6aavStwb37zNTRuw5on9xrofFEJX6FIgYDPEflXzFZ0gYj/e0B6iR1LDKJ3jeRjH3d3XunYhoca1OHjVevZGH6OIG9Plh05Qc+6NTHcI3ljdyMg2Cfjef+n29H/6XYZr1t3qUvVOmUyXl84HUHElZiM10vnbicxPiXDwa1fegCT0ZTh4IJC/f4jfPzxzKcIDv3XwdZrVtn2J5QLVh8P5+0lqwCYPrgfzSqUdYodzkJbg9PQuA2V+BMqfoI1uCRwEiJ3Fnu0xIwB41EkdEmO9eYcjVKKjafP8dW6rRy5FkEpfz+aVyzLRz06odccXb5ITTFiTDPh629N+jebzOgNrrvOmWoyMWH1Rn7ZfYA6pUowsV83yhVyYQC7rMGJyHQRiRCRw5m29ReRIyJiEZEmmbZ3EpE9InIo/bFDNn2+LyKXRWR/+l+3vBitoWFPxGe4daSWuhYV/RTKcmcknngPRnyecjnnBtbRaLsqFZn/1CC+ebgHiWlpLDxwlN5T57D2xOlCXSXcWXh4umU4N8ClnduZm1E8Mn0ev+w+wBPNGzH3iQGF3rnllxxHcCLSFkgAZiul6qRvqwlYgCnAK0qp3enbGwLXlVJXRKQOsFIpFZZFn+8DCUqpz/NirDaC03AkKnmxNcLSUNFaGVyftcitMoWDrgSi83OwhbkjzWTiz31HmLlzL+ejYqhdsjhjO7amZcVy90Sgwb3EwgNH+GD5OjzcDHzSqzP3V63kbJNshl1GcEqpjUDUbduOKaVOZNF2n1LqSvrLI4CnZDW/o6FRCBCvXkjQj2C+jIp8BGU8eUcbZUlCRQ1Fxb3jBAtzh7vBwKCm9Vn67FDaVK7AiYibPPnLAvpPn8uKoycxW7KOGtUoPEQlJTN2wTLeWLyKOqVL8NfTjxUp55Zf7Blk8hCwTymVms37o0VkKLAbGKuUis6qkYiMAEYAlCtXtIRANVwf8WgJwb+iop9GRQ2EoMmI+33/vq/zBv/xLhVZmR1uej3DWzSmWvEQygYGMGPHXl6cv5RyQQEMa96YfvVr4eVm+wrRGvbDbLHw5/7DTFy3lYSUVF68vyXPtGqqrbWmk6sgExGpAPx9a4oy0/b1ZJqizLS9NrAY6KyUOp1FfyWAm4ACPgRKKaWezMkObYpSw1ko8xVU1HAwX0QCv0A8u2TdLnkZeD7gkmtzt3M+KprJm3dy+kYkB69cJ8jbi8FN6jO4SX2CfeynpqFhG7advcDHqzZwMuImjcuW5v1uHalW3DVECOyBS5TLEZEywEJgaFbODUApdT1T+x+Bv21th4aGLRF9aQiZi4p+BhXzAvi/i3gP/k8blXYAFfsSot6D295zRRYcOMqa46f5+5khXIqN5adte/h243Z+3LqLPvVqMahxfWqUdFy+lkbuOBsZzYTVG1l36gxhgf589VB3utSsqq2nZoFNHZyIBAJLgTeVUlvu0q6UUupq+su+QOGXA9co8oguEIJnomJeRsWNQ5kjEN+XMi4s4l4fgmaAewsnW5o7Xrq/JQ/Vr03JAD9KBvhRPjiIVzqmMnP7HhYeOMpvew/RIKwUAxrXpWutatr0pZOJSU5h8qYdzNm1Hw+DgbEdWvN4s4b3bCJ/bshNFOVc4H4gFLgOvIc16OQboBgQA+xXSnURkXeAN4FTmbrorJSKEJFpwA9Kqd0i8jPQAOsU5TngmUwOL1u0KUoNV0ApEyrufUj+HTy7IwH/h8h/9Q2VJQaSF4L3sEJxZ73syAneWrKKn4f2p27pksQkp7Do4FF+23OIM5FR+Ht60LtuTQY0qkvVIjwN5opEJiYxY/seftl9gOQ0Iw83rMOL97ekmK9PzjsXIfIzRaklemto5AOlFCRORSV8CYbaSNBkRF/i3/cTf0LFT0RCFyGGKk60NHdExCfw07Y9vNKx9X+EmpVS7L5wmXl7DrLyeDhGs5nGZUvTv2EdOlavjL+nY4WL7yUuRsfw8679/LbnEKkmE11rVePZ1s2oXuLevMHQHJyGhoNRKWtRsWNBfJGgKYhbbet2pcAUjri5fnTl7SSlGflg+T8817YZZYP+TRCOSkxi4UHr1OX5qBjcdDpaVS7PgzWras7ORiil2Hb2ArN37mf9qTPoROhRpwbPtL6PyqHBzjbPqWgOTkPDCSjjcVT0s2CJQgI/uyPCUqVuRKVutBZWFdcP395/6Soj5i7kq4d70KLinak5SikOXrnGiqOnWHHsJFdi4zVnV0Cuxsaz7OgJFuw/QvjNKIK9vRjQqB4DG9ejhL9vzh3cA2gOTkPDSSjzTVTMKDDuR3zHgM+zGWtvlvhJkLoeCZ6D6ArHxSohNRVfD6tGw6bT52gQVgo/zzs1G7Jzds0qlKVNlQq0qVyBSiFBhWId0tFEJSWz8thJlh4+wa4LlwGoV7okg5vUp2vtalrwyG1oDk5Dw4kolYqKfQtSloBnLyTgI0Q8rNOVKhnReaOUGZBCMZIDa+Re+6+m0b1Odcb36HTXtkopDl25zvJjJ1l/8ixnIq0CSGGB/rSpbHV2zSuUxdfD9XME7UVCahprT5xm6ZETbDlzHpPFQuXQYHrUqUH32tUpf49qRuYGzcFpaDgZa/DJD6iEieDWEAn8OiP4RCmFinsTlEICPik0o5oDl69SJjCAEB9vElJT8XRzy1X5nUsxsWw6fY5N4efZdu4CSWlG3HQ6GpULo1n5MjQoU4q6pUsU6elMi1KcjLjJtrMX2HzmPLvOXyLVZKZ0gB/dalWnZ50aVC8RWmi+C85Ec3AaGi6CSlmBin0DxNtacidd3kslfAsoxPd55xqYD5RSjP5jCXEpqcwa8jC6PFyU08xm9l68wqbwc2w6fY6TETe5deWpFBJM/bCS1AsrSf2wklQrHvqfSM7ChNFs5ti1G+y6cIld5y+z9+JlYlOsaoWVQ4NpVak8XWpWpVHZ0nn6/2loDk5Dw6VQpnBU9Ggwn0f8XgHvJ/9zp67M10AXemflcBdm2ZETxCSnMKiJtfCnUipfo4/4lFQOXbnGgcvWv4NXrhGZmASAQaejevFQKoYGUykkiMqhwVQKDaZCcCDuLrIupZTialw8p25EcioiklM3bnIqIpLwm5GkmswAVAgOomn5MJqUC6NZ+bKUCnDNahOFBc3BaWi4GMqSYF2XS10BHp2tU5M6X+v2mz3AoyW6gI+dbWa+2Hn+EpPWbeHLft0o6V+wi7dSisuxcRy8fI3DV69zMiKSMzejuBz7by0+nQhlAv2pFBpMCT9fivv6UMzPh2K+1r/ivr6E+HrbpHp5mslEREIiNxISiYi/9ZhARHwi56KiOXUjkoTUtIz2xf18qFYslKrFQ6gfVoom5cLuuURse+MSWpQaGhr/IjpfCPwKkmag4j9DRT4Egd9a8+N8R4BbfWebmG/iklNINZltsoYmIpQJDKBMYADdalfP2J5sNHIuMprTN6M4czOKM5HRnI2M5uDla0QlJd/ZDxDo7YWXmwEPgwFPgwFPt/Q/g3WbXqcjzWQmzWwm1WQi1WT6z+u4lFRiklPu6FsvQqivD+WDA+ldtyZVi4dQtVgoVYuFEOBVdNcRCzPaCE5Dw0GotF2omJdAJSD+HyFePf59L2U5uLew6l0WIm5NUZotFt5dupYhTRs4TKDZaDYTmZhkHWnFJxKRkMCN+ESikpJJMZpIMZnSH42kGE2kmswkG41YlMJDr8fNoMfDYMDDoMddn/5oMODn4U5xP1/rqNDPh+K+PhT38yXI20tbN3Mi2ghOQ8OFEfemELIQFfMiKvZllHEP4vcGWGJQMa+B90DE/y1nm5knbq2/nY+KYd2pM7SoWNZhDs5Nr6ekv1+Bp0c1ii7aCE5Dw8EoZUTFfw5JM8BQHQmcCCrJ+lzuTKYuLCSkpuLj7o6IsPp4OB4GA22rVHC2WRpFhPyM4ApHtqmGRhFCxA2d/5tI4BSw3EDd7AvGI4A7SqVgiXkFZTrrbDPzjK+HByKCUooZ2/fw/abtFKYbaI2ih+bgNDSchHi2R0KWgHtTVNx7qJjnUMbjkLYVjMecbV6+ERFmPvYQXz3UAxEhITWNKZt3kmw0Ots0jXsMzcFpaDgR0RdDgqZZ1+JSN0DM8+D/EeLVDQBlSXSyhfnD3WDIEAled/IME9dt4VREpJOt0rjX0BychoaTEdEhPk8iIX+AeEPMs1jiv8CSdgh1syMqdaOzTSwQPevWYPmoYdQLKwnAooNHORlx08lWaRQWDl6+xttLVudrX83BaWi4COJWCwlZCF4PQ+IUiHsHDHXAUM3ZphWYiiFBAKQYTXy+dhPTt+1xskUahYWTN26y9ez5fO2rRVFqaLggVi3Ld0ElIr4voryfQNI2gUf7Qi/MG5OcgslsJtTXh4vRsfy0bTfPt2tBiI+3s03TcBEuRMVwLT6B+8qXQSlFktGIr4eHFkWpoVEUEM8HkdBl4NEelfA5RHZHxTwLqaucbVqBCfTyJDRdxmrPxcssOXwck9niZKs0XIk3Fq/kf3+vwWyxICL4uOevxJKW6K2h4aKIPhQCv4GUpajYcYABZboAyoxI4VTbv50+9WrxQPXKGcVV316ymkqhQQxvkacbdY0iQFRSMj7ubngYDHzUszOe6bJqBUEbwWlouDAignj1QIotA492kPAZKrI/luiRKEuMs82zCbecm8liIT4l5T8ixtFZ6E1qFD1iklPoNWU2k9ZvBaxrtraovqA5OA2NQoDoiyGB3yMBn4PpDKSuRSV8i1JFJ7fMoNPxdf+evNCuBQB7L16h7aQf2Xb2gpMt07AXUeklkgK9PHm8WSN6161p0/41B6ehUUiwjuZ6IcVWgXtbSJqNiuyHJeUfZ5tmU24F0ZTw82VA47oZ6QUHLl/l4OVrzjRNw4bM33+E9l//xKWYWACebtmUGiVsq2OaKwcnItNFJEJEDmfa1l9EjoiIRUSa3Nb+TREJF5ETItIlmz4risgOETklIr+JSP5WETU07jFEXxxd8DQk8DswR1jz5qJGoCyxzjbNpoQF+vNOl/YZAQbfbNjOSwuWYrZoASmFlcS0tIzCtq0qlWNQk/r4edhPfzW3I7iZwIO3bTsM9AP+k4UqIrWAR4Ha6ft8L1mviE8AJiqlqgLRwPDcm62hoSGenSBkERhqQtoG1M0HUclLiqz+46SHuvNd/57odTosSvHYrN9ZeOCIs83SyCUmi4U+U+cwfsU6AEr6+/F6p7Z2raWXKwenlNoIRN227ZhS6kQWzXsD85RSqUqps0A4cF/mBmKdg+gA/Jm+aRbQJ4+2a2jc8+gMpdCF/mVNENeVRMWORUU9jjLlLzHWlfH1cKdmyeIAxCan4OXuhrveGgielGZk65nzWIqocy+spJnNbAi3CocbdDpGtmnG0GYNHXZ8e6zBhQEXM72+lL4tMyFAjFLKdJc2GhoauUTcaoHvy4CAcT8q/mNnm2RXgry9+HFgX7rXsVb/Xn70JE/8soADl6862TKNzKw+Fs6IuYs4fv0GAP3q16ZhmdIOO7498uCyklm4/bYqN22sDUVGACPSXyaISFajRlsTChQVsTztXFwTO5/LIWCq/br/Ly7zuTR692VbdOMy52MDXOJcatrmc6me1x3s4eAuAWUzvS4DXLmtzU0gUEQM6aO4rNoAoJSaigN/qQAisjuvkjCuinYurol2Lq5LUTqfonYued3HHlOUi4FHRcRDRCoCVYGdmRso6yr4OuDh9E2PA3/ZwRYNDQ0NjXuU3KYJzAW2AdVF5JKIDBeRviJyCWgBLBWRlQBKqSPA78BRYAXwnFLKnN7PMhG5NQH7OvCyiIRjXZP7yZYnpqGhoaFxb5OrKUql1MBs3lqYTfuPgI+y2N4t0/Mz3BZd6UI4dErUzmjn4ppo5+K6FKXzuafPpVABge8oAAAEN0lEQVSVy9HQ0NDQ0MgtmlSXhoaGhkaRRHNwmRCRQBH5U0SOi8gxEWnhbJvyi4iMSZdSOywic0XEfnIBdiAbebhgEVmdLu+2WkSCnGljbsnmXD5L/54dFJGFIhLoTBtzS1bnkum9V0REiUioM2zLK9mdi4g8ny4zeEREPnWWfXklm+9ZAxHZLiL7RWS3iLjqslAGIlJWRNalX4OPiMiL6dvz/PvXHNx/+QpYoZSqAdQHjjnZnnwhImHAC0ATpVQdQI9VPq0wMZM75eHeANamy7utTX9dGJjJneeyGqijlKoHnATedLRR+WQmd54LIlIW6AQUJun/mdx2LiLSHqsaUz2lVG3gcyfYlV9mcudn8ykwTinVAHg3/bWrYwLGKqVqAs2B59IlIPP8+9ccXDoi4g+0JT2aUymVppQqzAW3DICXiBgAb7LJM3RVspKHw3rhmZX+vNDIu2Ujdbcqk5LPdqy5oC5PNp8LwETgNbIRbHBFsjmXkcAnSqnU9DYRDjcsn2RzPgrwT38eQCG4Diilriql9qY/j8c60AgjH79/zcH9SyXgBjBDRPaJyDQR8XG2UflBKXUZ653nBeAqEKuUWuVcq2xCCaXUVbD+CIDiTrbHVjwJLHe2EflFRHoBl5VSB5xtiw2oBrRJr3Ty/+3dvWsUURTG4d8LMUK0iYioIEQEaxMRRAJCYiEi+gcopLCySwqrVNaCrTZWGgQ/gqRPYRnRYGKhoGAwQRQ7QRvBY3FmyQcpsrPCzQzvA8PuMrCcy+6ZM5yZufelpDOlA+rRJHBH0ip5TGhKpwAASUPAMLBAjfx3gVvXB4wA9yJiGPhFc1pgm1S96avAceAosE/S9bJR2XYkTZMtmZnSsdQhaQCYJttfbdAHDJKtsVvAE3UWqGumm8BURBwDpmjQ88aS9gPPgcmI+FnnO1zg1q0BaxGxUH1+Rha8JroAfI6IH5FLPs8C5wrH9D98l3QEoHptTPtoO5ImgMvAtWju8zonyBOpJUkrZKt1UdLholHVtwbMRnoF/CXnc2yqCTL/AZ6ye5893kTSHrK4zUREJ/6u898FrhIR34BVSZ0JPcfJ2Via6AtwVtJAdfY5TkNvmNlijkxYaPj0bpIukrP5XImI36XjqSsi3kXEoYgYioghskCMVPnURC/IpbyQdBLoZxdMVtyDr8D56v0Y8LFgLDtSHbMeAO8j4u6GXd3nf0R4qzbgFPAaWCb/6IOlY+phLLeBD+TCtA+BvaVj6jL+x+T1wz/kQfMGOaXbPJmk88CB0nH2MJZP5LJSb6vtfuk4645ly/4V4GDpOHv4XfqBR1XeLAJjpePscTyjwBtgibyOdbp0nDsYxyh5c8zyhvy4VCf/PZOJmZm1kluUZmbWSi5wZmbWSi5wZmbWSi5wZmbWSi5wZmbWSi5wZmbWSi5wZmbWSi5wZmbWSv8AwxgeuO82f7YAAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "scanA = np.linspace(110.0,130.0,50)\n",
+    "scanB = np.linspace(5,20,50)\n",
+    "Z_binned = [[nll_binned([a,b]) - solution_binned.fun for b in scanB] for a in scanA]\n",
+    "\n",
+    "fig1, ax2 = plt.subplots(constrained_layout=True)\n",
+    "\n",
+    "p1 = ax2.contour(scanB, scanA, Z, [0.01,0.5, 2.0, 4.5])\n",
+    "p2 = ax2.contour(scanB, scanA, Z_binned, [0.01,0.5, 2.0, 4.5], linestyles=\"dotted\")\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Repeat the same for 50 bins:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 373,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "scanA = np.linspace(110.0,130.0,50)\n",
+    "scanB = np.linspace(5,20,50)\n",
+    "Z_binned = [[nll_binned([a,b]) - solution_binned.fun for b in scanB] for a in scanA]\n",
+    "\n",
+    "fig1, ax2 = plt.subplots(constrained_layout=True)\n",
+    "\n",
+    "p1 = ax2.contour(scanB, scanA, Z, [0.01,0.5, 2.0, 4.5])\n",
+    "p2 = ax2.contour(scanB, scanA, Z_binned, [0.01,0.5, 2.0, 4.5], linestyles=\"dotted\")\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.7"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/exercises/Solutions7/.ipynb_checkpoints/Solutions_7-checkpoint.ipynb b/exercises/Solutions7/.ipynb_checkpoints/Solutions_7-checkpoint.ipynb
new file mode 100644
index 0000000..154f625
--- /dev/null
+++ b/exercises/Solutions7/.ipynb_checkpoints/Solutions_7-checkpoint.ipynb
@@ -0,0 +1,457 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Solutions 7\n",
+    "Maximum Likelihood method"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 205,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from __future__ import print_function\n",
+    "import numpy as np\n",
+    "%matplotlib inline\n",
+    "import matplotlib.pyplot as plt\n",
+    "from scipy.optimize import curve_fit, minimize, fsolve\n",
+    "from scipy.stats import norm, chi2, lognorm"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 151,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "measurements = np.array([97.8621, 114.105, 87.7593, 93.2134, 86.6624, 87.4629, 79.7712, \\\n",
+    "91.5024, 87.7737, 89.6926, 133.506, 91.4124, 94.4401, 97.3968, \\\n",
+    "108.424, 103.197, 88.2166, 142.217, 89.0393, 102.438, 95.7987, \\\n",
+    "94.5177, 96.8171, 90.903, 132.463, 92.3394, 84.1451, 87.3447, \\\n",
+    "92.2861, 84.4213, 124.017, 90.4941, 95.7992, 92.3484, 95.9813, \\\n",
+    "88.0641, 101.002, 97.7268, 137.379, 96.213, 140.795, 99.9332, \\\n",
+    "130.087, 108.839, 90.0145, 100.313, 87.5952, 92.995, 114.457, \\\n",
+    "90.7526, 112.181, 117.857, 95.2804, 115.922, 117.043, 104.317, \\\n",
+    "126.728, 87.8592, 89.9614, 100.377, 107.38, 88.8426, 93.3224, \\\n",
+    "138.947, 102.288, 123.431, 114.334, 88.5134, 124.7, 87.7316, 84.7141, \\\n",
+    "91.1646, 87.891, 121.257, 92.9314])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 1-D Maximum likelihood fit"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We have a set of measurements which are distributed according to the sum of two Gaussians (e.g. this can be signal and background).\n",
+    "\n",
+    "$\\rho = \\frac{1}{3}\\frac{1}{\\sqrt{2\\pi \\sigma^2}} e^{-\\frac{1}{2}\\left(\\frac{x-p}{\\sigma}\\right)^2} + \\frac{2}{3}\\frac{1}{\\sqrt{2\\pi \\sigma_b^2}} e^{-\\frac{1}{2}\\left(\\frac{x-p_b}{\\sigma_b}\\right)^2}$  \n",
+    "\n",
+    "where for one of the two peaks the parameters are known already\n",
+    "\n",
+    "$p_b = 91.0$  \n",
+    "$\\sigma_b = 5.0$  \n",
+    "  "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 228,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def likelihood_point(x, position, width):\n",
+    "    return 1.0/3/np.sqrt(2*np.pi*width**2)*np.exp(-0.5*((x-position)/(width))**2.0) + 2.0/3/np.sqrt(2*np.pi*5**2)*np.exp(-0.5*((x-91)/(5))**2.0)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "First, we assume the width of the peak we want to fit is already known: $\\sigma = 15.0$.\n",
+    "Perform a 1-D Maximum Likelihood fit for the position of the peak $p$.\n",
+    "\n",
+    "Complete the functions below which return the likelihood and negative log likelihood (NLL)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 347,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def likelihood_1d(params):\n",
+    "    return np.prod([likelihood_point(x, params[0], 15.0) for x in measurements])\n",
+    "\n",
+    "def nll_1d(params):\n",
+    "    return -np.log(likelihood_1d(params))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Minimize the NLL and give the best-fit result, including asymetric errors and plot the NLL."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 355,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "position: 117.72333147980623\n",
+      "negative error: [3.31211666]\n",
+      "positive error: [3.39091994]\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXcAAAD8CAYAAACMwORRAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMi4yLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvhp/UCwAAIABJREFUeJzt3Xd81eXd//HXJ3uSAAkEMggjiOwRwnbgQrRuy7AKLorVqv3VW229b2u927u1rdWqrVYUFaviwIEVB6LIkBX2CiuDhABJCIQMsq/fHzl658aMEzjJdcbn+XjkwUnOxTlvwpd3vnzHdYkxBqWUUt7Fz3YApZRSrqflrpRSXkjLXSmlvJCWu1JKeSEtd6WU8kJa7kop5YW03JVSygtpuSullBfScldKKS8U0NoAEQkBVgDBjvHvGWN+c9qYYGABMAo4BkwzxmS39LoxMTEmOTn5zFIrpZSP2rhxY5ExJra1ca2WO1AFTDbGlIlIILBKRD41xqxtNOZ24Lgxpp+ITAeeAKa19KLJycmkp6c78fZKKaW+IyI5zoxr9bCMaVDm+DTQ8XH6hDRXA685Hr8HXCQi4mRWpZRSLubUMXcR8ReRLUABsNQYs+60IfFALoAxphYoAbo28TpzRCRdRNILCwvPLrlSSqlmOVXuxpg6Y8xwIAFIE5HBpw1pai/9B9NNGmNeNMakGmNSY2NbPWSklFLqDLXpahljzAlgOTDltKfygEQAEQkAooBiF+RTSil1BlotdxGJFZFox+NQ4GIg47Rhi4FZjsc3AF8ZnSheKaWsceZqmR7AayLiT8MPg3eMMf8WkceBdGPMYuBl4HUR2U/DHvv0dkuslFKqVa2WuzFmGzCiia8/2uhxJXCja6MppZQ6U3qHqlJKdaCnv9zLusxj7f4+Wu5KKdVBMgvLePrLfazPav/rTbTclVKqg7yx7iABfsK0tMR2fy8td6WU6gCnqut4Nz2XKYPj6BYZ0u7vp+WulFId4OOt+ZysrOXmsb065P08rtxLTtXw3sY86uv1MnqllGcwxrBgbTb9u0eQ1rtLh7ynx5X7VxlHeeDdrazP1htglVKeYUvuCXYcOsnNY3vRUXMqely5TxnUg4jgAN5Nz7MdRSmlnPL62hzCg/y5dmRCh72nx5V7aJA/Vw7twac7DlNeVWs7jlJKtai4vJp/bzvMdSMTiAh2ZlIA1/C4cge4MTWBiuo6Ptl+2HYUpZRq0bvpuVTX1vOTDjqR+h2PLPeRSZ3pExPOe3poRinlxurrDf9al0Na7y6cExfZoe/tkeUuIlw/KoH12cVkF5XbjqOUUk36Zl8hucWnOuzyx8Y8stwBrh+ZgJ/Aok26966Uck+vr8khJiKYywbFdfh7e2y5x0WFMCkllkUb86jTa96VUm4mt7iCr/cUMDMtkaCAjq9ajy13aDixml9SyZoD7T/DmlJKtcUb6w7iJ8KMMUlW3t+jy/3ic7vTKSSAdzfm2o6ilFLfq6yp4530XC4+txs9okKtZPDocg8J9Ofq4fF8tuMIJadqbMdRSikA/r3tMMXl1dw8NtlaBo8ud2g4NFNVW8+/t+XbjqKUUhhjeGV1FindIpjQr6u1HB5f7kPio+jfPYL3NupVM0op+zbmHGdn/klmT0jusHlkmuLx5S4i3Dgqkc0HT7C/oNR2HKWUj3tldTadQgK4dkS81RweX+4A14yIx99PeFf33pVSFuWfOMVnO48wPS2JsKCOm0emKV5R7rGRwVx4TiwfbDpEbV297ThKKR/1r7U5GGOs3JF6Oq8od4AbRiVSUFrFin2FtqMopXxQZU0db60/yCUDu5PYJcx2HO8p98kDuhETEcTC9XrNu1Kq43205RDHK2qYPb637SiAF5V7UIAf149KYFlGAQUnK23HUUr5kIbLH7MZEBfJ2D4ds4xea1otdxFJFJGvRWS3iOwUkfuaGHOBiJSIyBbHx6PtE7dl00cnUVdv9MSqUqpDrc0sJuNIKbdavvyxMWf23GuBXxpjzgXGAneLyMAmxq00xgx3fDzu0pRO6h0Tzrg+XVm44aAuoK2U6jCvfptF57BArh5u9/LHxlotd2PMYWPMJsfjUmA34D5/gtPMGJNEbvEpVh8osh1FKeUDcosrWLrrKDPSkggJ9Lcd53ttOuYuIsnACGBdE0+PE5GtIvKpiAxq5vfPEZF0EUkvLGyfq1ouG9SdzmGBvLX+YLu8vlJKNfb62hxEpMOX0WuN0+UuIhHAIuB+Y8zJ057eBPQyxgwDngU+bOo1jDEvGmNSjTGpsbGxZ5q5RcEB/lw/MoEvdh6lsLSqXd5DKaUAKqprWbj+IFMGxdEz2s7sj81xqtxFJJCGYn/DGPP+6c8bY04aY8ocj5cAgSIS49KkbTA9LZHaeqOrNCml2tWiTYc4WVnL7AnJtqP8gDNXywjwMrDbGPPXZsbEOcYhImmO17W2gka/bpGkJXdh4fqDGKMnVpVSrldfb5i/KouhCVGk9upsO84POLPnPgG4GZjc6FLHqSIyV0TmOsbcAOwQka3AM8B0Y7lVp6clkn2sgjWZukqTUsr1vtx9lKyicu6Y1MdtLn9srNWZbYwxq4AWkxtjngOec1UoV5g6pAePLd7JwvW5jO9r7QiRUspLvbQqi/joUKYO7vjFr53hNXeoni4k0J/rRibw2Y4jFJdX246jlPIi2/JOsD6rmFsnJBPg75416p6pXGR6WiLVdfW8rydWlVIuNG9lFpHBAUwbnWg7SrO8utwHxHViRFI0b+mJVaWUixw6cYol2w8zPS2RyJBA23Ga5dXlDjAjLYkDheVsyD5uO4pSygu8sioLgNkT3GP2x+Z4fblfObQHkcEBvLkux3YUpZSHO1lZw8INuVwxpAfxbnbT0um8vtzDggK4flQCS7YfoahM71hVSp25t9fnUlZVy52T+tiO0iqvL3eAn4xNorqunrc36EIeSqkzU1NXzyursxjTuwtDEqJsx2mVT5R7v26RjO/blTfXHaROpwJWSp2BJdsPk19S6RF77eAj5Q5wy7heHDpximW7j9qOopTyMMYYXlqZRZ/YcCYP6GY7jlN8ptwvPrc7PaJCeH2tnlhVSrXNuqxith8q4faJvfHzc7+pBpriM+Ue4O/HzLQkVu4rIrOwzHYcpZQHmbciky7hQVw/MsF2FKf5TLkDTEtLJNBfdO9dKeW0jCMnWZZRwOzxyW610lJrfKrcu0WGMGVwD97bmEdFda3tOEopD/DPbzIJC/LnlnHutdJSa3yq3KHhxGppZS0fbcm3HUUp5eZyiytYvDWfGWlJRIcF2Y7TJj5X7qm9OjMgLpIFa3J0vhmlVIteXpWFn8Adk9x7qoGm+Fy5iwi3jEtm9+GTbMzR+WaUUk07VlbFwg0HuWZ4PD2i3Huqgab4XLkDXDOiJ5HBASxYoydWlVJNe+3bbKpq6/np+Z5x09LpfLLcv5tv5tMdhyks1flmlFL/V1lVLa+tyeHSgd3p1y3Sdpwz4pPlDnDzuF7U1BkWrj9oO4pSys0sXH+QklM1zD2/r+0oZ8xny71vbASTUmJ4Y91BaurqbcdRSrmJqto65q3MZGyfLoxI6mw7zhnz2XIHuHVCMkdOVrJk+2HbUZRSbuKjzfkcPVnFXRf0sx3lrPh0uV/Qvxt9YsKZvypLL4tUSlFXb3hhxQEG9ujEeSkxtuOcFZ8udz8/4dYJyWzNK2HTQb0sUilft3TXETILy7nrgr6IeMYEYc3x6XIHuH5UAp1CAnjZsS6iUso3GWP4x/ID9OoaxuWD42zHOWs+X+5hQQHMGJPEZzuOkFtcYTuOUsqSb/YWsi2vhLnn9yXA3/OrsdU/gYgkisjXIrJbRHaKyH1NjBEReUZE9ovINhEZ2T5x28esccmICAvWZNuOopSywBjDs1/tp2dUiEdN69sSZ3481QK/NMacC4wF7haRgaeNuRxIcXzMAZ53acp21jM6lMsHx7HQsfitUsq3rDlwjI05x7nrgr4EBXj+Xjs4Ue7GmMPGmE2Ox6XAbiD+tGFXAwtMg7VAtIj0cHnadnT7xN6UVtXyXrouoq2Ur/nbsn10iwzmxtRE21Fcpk0/okQkGRgBrDvtqXigcSvm8cMfAIjIHBFJF5H0wsLCtiVtZyOSOjMyKZpXvs3WRbSV8iHrs4pZl1XMT8/v61GLcbTG6XIXkQhgEXC/Mebk6U838Vt+0JDGmBeNManGmNTY2Ni2Je0At03sTc6xCl1EWykf8uxX+4iJCGJmWpLtKC7lVLmLSCANxf6GMeb9JobkAY3/P5MAeNxqGFMGxREfHcr81XpZpFK+YNPB46zcV8Sdk/oQGuQ9e+3g3NUyArwM7DbG/LWZYYuBWxxXzYwFSowxHndPf4C/H7PG92JtZjE780tsx1FKtbNnl+2jc1ggPxnrWUvoOcOZPfcJwM3AZBHZ4viYKiJzRWSuY8wSIBPYD8wDftY+cdvftNFJhAX5601NSnm57XklfL2nkDsm9SE8OMB2HJdr9U9kjFlF08fUG48xwN2uCmVTVGggN45K4M31B3loygC6dwqxHUkp1Q6e/WofnUICPG7ha2d5xwWdLnb7xD7U1Rvm6967Ul5p9+GTfLHrKLdO6E1kSKDtOO1Cy70JSV3DuGJoT95Y1zBhv1LKuzz39X4iggO4bYLnLXztLC33Zvz0vD6UVdXyxjpdZ1Upb7LnSClLth9m1vheRIV55147aLk3a3B8FJNSYpi/KpvKmjrbcZRSLvLU0r1EBAVw5yTPXPjaWVruLbjr/L4UlVXx/qZDtqMopVxgx6ESPtt5hNsm9iY6LMh2nHal5d6CcX27MjQhihdXHNApCZTyAk8t3UtUaCC3T/LeY+3f0XJvgYgw9/y+ZB+r4POdR2zHUUqdhU0Hj7Mso4A55/Whk5deIdOYlnsrLhsUR++YcF745oCus6qUB3tq6V66hgcxe3yy7SgdQsu9Ff5+wp2T+rAtr4Q1B47ZjqOUOgPrMo+xcl8Rc8/v65V3ozZFy90J142MJyYimOe/OWA7ilKqjYwxPLl0L90ig71yDpnmaLk7ISTQn9smJrNyXxE7DumEYkp5ktX7j7E+q5i7L+zndTM/tkTL3Uk3jelFRHAA/1yRaTuKUspJDXvte+gZFcL0NO9ZZckZWu5OigoN5KYxSXyyLZ+cY+W24yilnPD1ngI2HzzBzy9KITjAd/baQcu9TW6f2JsAfz+eX67H3pVyd8YY/rp0L0ldwrhhVILtOB1Oy70NunUKYWZaEu9tzCPveIXtOEqpFny24wg7Dp3k3otSCPT3varzvT/xWfrp+X3wE9G9d6XcWE1dPX/+fA/9u0dw7Yh423Gs0HJvox5Rofx4dALvpOeSf+KU7ThKqSa8k55LZlE5D142AH+/Ftca8lpa7mfgrgv6AfBPve5dKbdTUV3L01/uY3RyZy46t5vtONZouZ+B+OhQbhiVwFsbcjl6stJ2HKVUI/NXZVFYWsXDlw9AxDf32kHL/Yz97IJ+1NUbXtC9d6XcRnF5NS98k8mlA7szqlcX23Gs0nI/Q4ldwrhuRDxvrjtIQanuvSvlDp77aj8V1bU8OOUc21Gs03I/C3df2I+aunrm6V2rSlmXW1zBv9bm8OPURPp1i7Qdxzot97OQHBPONcPj+dfagxSVVdmOo5RPe2rpXkTg/ov7247iFrTcz9Ldk/tRVVvHSyuzbEdRymftyj/JB1sOceuE3sRFhdiO4xa03M9S39gIfjSsJwvWZFNcXm07jlI+6U+fZ9ApJJC7zu9rO4rbaLXcRWS+iBSIyI5mnr9AREpEZIvj41HXx3Rv91zYj1M1dbyox96V6nDfHihi+Z5C7r6wL1Fh3r98nrOc2XN/FZjSypiVxpjhjo/Hzz6WZ0npHsnVw3ry6rdZeuWMUh2ort7w+092Ex8dyi3jkm3HcSutlrsxZgVQ3AFZPNr9F/ents7w96/2246ilM9YtCmPnfkneejyAYQE+taUvq1x1TH3cSKyVUQ+FZFBLnpNj5IcE86PRyfy5vqD5BbrjJFKtbeyqlr+/PkeRiZF86OhPWzHcTuuKPdNQC9jzDDgWeDD5gaKyBwRSReR9MLCQhe8tXu5d3IKIsLflu2zHUUpr/fC8gMUllbxX1cO9OlpBppz1uVujDlpjClzPF4CBIpITDNjXzTGpBpjUmNjY8/2rd1OXFQIs8b14v1NeewvKLUdRymvlXe8gnkrM7l6eE9GJHW2HcctnXW5i0icOH5sikia4zWPne3reqq7LuhHaKA/f12613YUpbzWE5/tAeDBKQMsJ3FfzlwK+RawBjhHRPJE5HYRmSsicx1DbgB2iMhW4BlgujHGtF9k99YlPIg7JvVhyfYjbM8rsR1HKa+zMec4H2/N56fn9SE+OtR2HLcltno4NTXVpKenW3nv9lZaWcOkP33NsIRoXrstzXYcpbxGfb3huue/Jf/EKb5+4ALCgwNsR+pwIrLRGJPa2ji9Q7UdRIYE8rML+vLN3kLWZfrsESqlXG7x1ny25J7gPy47xyeLvS203NvJLeOS6d4pmL98sQcfPkqllMucqq7jic8yGBzfietHJtiO4/a03NtJSKA/P5+cwobs4yzf632XfSrV0eatzORwSSX/dcVA/Hx0XdS20HJvRz9OTSSpSxhPfJpBXb3uvSt1pnKLK/jH8v1MHRLHmD5dbcfxCFru7SgowI8Hp5xDxpFS3tuYazuOUh7rv/+9C0H4zysG2o7iMbTc29kVQ3owMimav3yxl/KqWttxlPI4X+8p4ItdR/n5Rf3oqZc+Ok3LvZ2JCI9cMZDC0ir+qVMCK9UmlTV1PLZ4J31iwrljYh/bcTyKlnsHGNWrM1cM7cGLKw5wpESnBFbKWfNWZJJzrILHrhpEUIDWVVvod6uDPDxlAPX18Jcv9tiOopRHyC2u4O/L93P54DjO6+99c1G1Ny33DpLYJYzZE5Id80/rtARKteb7k6hX6knUM6Hl3oHuvrAf0aGB/P6T3Xpjk1It+O4k6j2T++n8MWdIy70DRYUGct9FKXx74Bhf7ymwHUcpt1RVW8dvvzuJOqm37TgeS8u9g900the9Y8L5nyUZ1NbV246jlNuZtyKTbMdJ1OAAXTrvTGm5d7BAfz8evnwA+wvKeGuD3tikVGPZReU8+5WeRHUFLXcLLh3YnTG9u/DU0r2cqKi2HUcpt2CM4ZEPtxPk78dvfuSTSzG7lJa7BSLCb340iBMV1Tz5ha7YpBTAok2HWL3/GA9ePoC4qBDbcTyelrslA3t24pZxybyxLocdh/TSSOXbisqq+N0nuxjVqzM3pSXZjuMVtNwt+sUl/ekSHsSjH+2gXmeNVD7sd//eRXlVLX+8bohO5+siWu4WRYUG8tCUAWw6eIJFm/Jsx1HKim/2FvLhlnzuuqAfKd0jbcfxGlrull0/MoGRSdH88dMMSk7V2I6jVIeqqK7lkQ+20zc2nLsv7Gs7jlfRcrfMz094/OrBHK+o5qmlenJV+Zanlu4l7/gp/nDdUL2m3cW03N3A4PgobhrTiwVrstmVf9J2HKU6xPa8El5elcWMtCTSenexHcfraLm7iQcuPYfosIaTqzrvjPJ2tXX1PPz+NmIignn48gG243glLXc3ERUWyENTziE95zjvbzpkO45S7er55QfYmX+Sx68eRFRooO04XknL3Y3cOCqR4YnR/OHTDEoq9OSq8k4780t45qt9/GhYT6YM7mE7jtdqtdxFZL6IFIjIjmaeFxF5RkT2i8g2ERnp+pi+wc9P+N01DSdX//DpbttxlHK56tp6fvnOVqLDgnj8Kp1ioD05s+f+KjClhecvB1IcH3OA588+lu8aHB/FHRN7s3BDLt8eKLIdRymXembZPjKOlPLH64bQOTzIdhyv1mq5G2NWAMUtDLkaWGAarAWiRUT/r3UW7r+4P726hvHr97dTWVNnO45SLrE19wTPf3OAG0YlcNG53W3H8XquOOYeDzSeuzbP8TV1hkKD/PnDtUPIPlbB01/usx1HqbNWWVPHL9/dSrfIYB79kS6b1xFcUe5NTQTR5LV8IjJHRNJFJL2wsNAFb+29xveLYVpqIvNWZurEYsrjPfnFHvYXlPHE9UPpFKJXx3QEV5R7HpDY6PMEIL+pgcaYF40xqcaY1NhYnYi/Nb+eei5dwoN4aNE2XbVJeawN2cW8tCqLmWOSdAGODuSKcl8M3OK4amYsUGKMOeyC1/V5UWGBPH7VIHbmn+TlVVm24yjVZhXVtTzw7lYSOofy66nn2o7jUwJaGyAibwEXADEikgf8BggEMMa8ACwBpgL7gQrg1vYK64umDI7j0oHd+evSvVw2KI7kmHDbkZRy2uMf7+JgcQVv3TmWiOBW60a5UKvfbWPMjFaeN8DdLkuk/g8R4b+vGczFT37Dr97fzpt3jkFE57tW7m/J9sMs3JDLXRf0ZWyfrrbj+By9Q9UDdO8Uwq+mnsuazGO8se6g7ThKterQiVM8vGgbwxKj+X+X9LcdxydpuXuI6aMTmZQSw+8/2U1WUbntOEo1q7aunvsXbqbewDPThxPorzVjg37XPYSfn/DnG4YRFODHL97eolfPKLf13Nf72ZB9nN9dM5heXfUckS1a7h4kLiqE3187mC25J/jH8gO24yj1Axuyi3lm2T6uGxHPNSP0XkabtNw9zJVDe3L18J78bdk+tuWdsB1Hqe+VVNRw31ubSewSxuPXDLYdx+dpuXugx68aTLfIYH7x9hZOVevcM8o+Ywy/+mAbBaVVPDN9hF726Aa03D1QVFggf7lxGAcKy3niswzbcZTirfW5LNl+hAcuO4dhidG24yi03D3WhH4x3DohmVe/zWbFXp2nR9mzLe8Ejy3eyaSUGOZM6mM7jnLQcvdgD00ZQL9uEfzHe1s5UVFtO47yQcfLq7nrX5uIjQzmmekj8PPTG+zchZa7BwsJ9OfpacM5VlbNQ4u26cLaqkPV1Rvue3sLhaVV/OOmkbr4hpvRcvdwg+OjePjyAXy+8yjzV2fbjqN8yN+W7WPF3kIeu2qQHmd3Q1ruXuD2ib25ZGB3/rBkN5sPHrcdR/mArzMKeGbZPm4YlcCMtMTWf4PqcFruXkBE+MsNw4iLCuGeNzfr8XfVrnKLK7j/7S0M7NGJ310zWCeyc1Na7l4iKiyQv88cSUFpJb98Zyv19Xr8XbleZU0dc/+1EWMML/xkFCGB/rYjqWZouXuRYYnRPDL1XJZlFDBvZabtOMrLGGN45IMd7Mw/yVPThpPUNcx2JNUCLXcvM2t8MlOHxPGnz/eQnl1sO47yIv9ckcmiTXnce1EKF53b3XYc1Qotdy8jIvzx+qEkdA7lnjc3U1yux9/V2ft85xGe+CyDK4b24P6LUmzHUU7QcvdCnUIajr8Xl1dz38LNOj2wOis780u4f+EWhiZE8+SNw/RGJQ+h5e6lBsdH8d/XDGLlviJ+v2S37TjKQxWcrOSO19KJDgtk3s16AtWT6NRtXmza6CQyjpTyyupsBsRFMm10ku1IyoNU1tRx54J0Sk7V8O7ccXTrFGI7kmoD3XP3co9MPZdJKTH854c72KAnWJWT6usNv3x3K9sOlfD0tOEM6hllO5JqIy13Lxfg78dzM0aS2DmMua9vJLe4wnYk5QGeXraPT7Yd5uEpA7h0UJztOOoMaLn7gKiwQObNSqW6rp47F6RTXlVrO5JyY2+sy+GZZfv4cWoCc87TKXw9lZa7j+gbG8HfZ45k79FSfvH2Fr2DVTVpyfbD/OeHO5g8oBu/v3aITi3gwbTcfch5/WN55IqBfLHrKH9dutd2HOVmvt1fxP0LtzAyqTN/nzmSQH+tB0/m1N+eiEwRkT0isl9EHm7i+dkiUigiWxwfd7g+qnKF2yYkMy01kee+3s8b63Jsx1FuYnteCXcuSKd3TDjzZ40mNEgvefR0rV4KKSL+wN+BS4A8YIOILDbG7Dpt6NvGmHvaIaNyIRHhd9cOprCsiv/6cAddw4OYMriH7VjKoszCMma/sp7osCBeuy2NqLBA25GUCziz554G7DfGZBpjqoGFwNXtG0u1p0B/P/4+cyTDE6O5960trDlwzHYkZcmRkkpufnk9AK/fnkZclF7L7i2cKfd4ILfR53mOr53uehHZJiLviYjO3u/mQoP8mT97NL26hjFnQTo780tsR1IdrLi8mlvmr+NERTWv3ppGn9gI25GUCzlT7k2dLj/9UouPgWRjzFDgS+C1Jl9IZI6IpItIemFhYduSKpeLDgtiwe1pRIYEMGv+BnKOlduOpDpIcXk1M+etJedYBfNuSWVIgt6k5G2cKfc8oPGeeAKQ33iAMeaYMabK8ek8YFRTL2SMedEYk2qMSY2NjT2TvMrFekSFsuD2NGrr67ll/noKSittR1Lt7FhZFTPnrSWrqJyXZqUyvl+M7UiqHThT7huAFBHpLSJBwHRgceMBItL4jNxVgM5U5UH6dYvkldmjKThZxez5GyipqLEdSbWTorIqZs5bR1ZROS/PGs2kFN3J8latlrsxpha4B/ichtJ+xxizU0QeF5GrHMPuFZGdIrIVuBeY3V6BVfsYkdSZ538ykv0FZdz08lpdh9ULFTn22HOKy5k/ezQTU3SP3ZuJMXbuVExNTTXp6elW3ls17+uMAn76r430i43gjTvG0Dk8yHYk5QKFpQ3Fnnu8gvmzRuuhGA8mIhuNMamtjdNb0NT/ceGAbsy7JZX9hWXMmLeWY2VVrf8m5dYKSiv/t9hna7H7Ci139QPn949l/qzRZBWVM3PeOoq04D1WZmEZ1z//LYdOnOKV2WmM76vF7iu03FWTJqbE8Mrs0eQUlzPjxbUUlmrBe5rNB49zwwtrqKiq4607xzKub1fbkVQH0nJXzRrfL4ZXZqeRd/wU019cw5ESvUzSU3yVcZSZ89YRERzAorvGMywx2nYk1cG03FWLxvXtymu3pXGkpJJr/7GajCMnbUdSrXgnPZc7F2ykb7dwFt01nuSYcNuRlAVa7qpVab278M7ccdQbw43Pr2H1/iLbkVQTjDE899U+HnxvG+P7dmXhnHHERgbbjqUs0XJXThnUM4oPfjaBntGhzJq/nkUb82xHUo1U1dbvK8URAAALJ0lEQVTxq/e385cv9nLtiHhenjWaiOBWJ31VXkzLXTmtZ3Qo78wdR1rvLvzy3a08u2wftu6TUP/rSEkl0/65loUbcrnnwn48eeMwggL0n7av0y1AtUlUaCCv3prGdSPjeXLpXh5etJ2aunrbsXzWhuxirnx2FfuOlvLCT0bywGXn4OenS+MpJxbrUOp0QQF+PHnjMBI6h/HMsn1kHyvn2Zkj6Bapc4F3FGMMr6/N4fGPd5HYJYy37hxDSvdI27GUG9E9d3VGRIT/d0l/npo2jK15J7jimVWszdRFPzpCZU0dD763jUc/2sn5/WP58O4JWuzqB7Tc1Vm5dkQCH909kciQAGbOW8vzyw9QX6/H4dvLvqOlXP/8t7y7MY/7Lkph3i2pRIXqsnjqh7Tc1Vk7Jy6SxfdM5PIhPXjiswzuXJCu0wa7WH294ZXVWVz57CoOl1Ty0i2p/OKS/np8XTVLy125RERwAM/NGMFvrxrEin2FXPHsSrbmnrAdyyscKalk1ivr+e3Hu5jQL4bP7z+Piwd2tx1LuTktd+UyIsKs8cm889NxGAPXPf8tf/osg8qaOtvRPNbHW/O57OkVpGcf5/fXDublWal6Y5Jyipa7crkRSZ1Zcu8krhsRzz+WH+CKZ1ayMee47VgepbC0ivsWbubnb20mOSacT+6dyE1jeiGih2GUc3SxDtWuvtlbyK/f305+ySluHd+bBy7rT1iQXoHbnNq6el5fm8Nfv9hLZW0dd1/Yj3su7EeAv+6HqQbOLtah5a7aXVlVLU98msHra3NI6hLGH68bogtGNGF9VjGPfrSDjCOlTEqJ4bGrBtE3NsJ2LOVmtNyV21mbeYyHFm0j51gFlwzszkNTBtCvm5ZXQWklf1ySwfubD9EzKoRHfzSQywbF6SEY1SQtd+WWTlXXMX91Fs8vP8Cpmjqmj07kvotTfPLu1hMV1cxflcX81dlU19Yz57w+/OzCvnrYSrVIy125taKyKp5dto831h0kKMCPOef14c5JfQj3gZkMj5dX8/KqLF79NpuyqlouHxzHg1MG0FvnXVdO0HJXHiGrqJw/f57Bku1HiIkIYta4ZG4a24su4UG2o7lccXk1L63M5LVvsymvruOKIT34+UX9GBDXyXY05UG03JVH2XTwOE9/uY8VewsJDvDjupHx3DqhN/29YM6UnfklLFyfy/ub8qioqWPqkB7cOzmFc+I8/8+mOp6Wu/JI+46WMn91Nu9vyqOqtp5JKTHcNqE35/WPxd+DbrUvq6rl4635LFx/kK15JQQF+HHlkB7cdUFfneRLnRUtd+XRjpdX8+b6gyxYk83Rk1XERgZz+eA4pg7pwejkLm5Z9DV19WzMOc5HWw6xeEs+5dV19O8ewYy0JK4dEU90mPcdalIdz6XlLiJTgL8B/sBLxpg/nvZ8MLAAGAUcA6YZY7Jbek0td+WM6tp6vth1hH9vPczXewqoqq0nNjKYKYO+K/rOVm/wKS6vZvmeAr7KKOCbvYWUVtYSEujHlUN7MiMtiZFJ0XpJo3Ipl5W7iPgDe4FLgDxgAzDDGLOr0ZifAUONMXNFZDpwrTFmWkuvq+Wu2qq8qpavMgpYsr2h6Ctr6gkP8md4UjSjkjozoldnRiZ2JiqsfabANcZw6MQpduafZOehElbtL2Jz7gmMgZiIYCYPiGXygG5MTInV9UtVu3FluY8DHjPGXOb4/FcAxpg/NBrzuWPMGhEJAI4AsaaFF9dyV2ejorqW5XsKWZd5jI0Hj7P7cCl1jnnkU7pFMKhnJ+I7hxIfHeb4teEjNMi/xdetrq2nuLyaorIqisqqKCitYu+RUnbmn2TX4ZOUnGqYythPYHB8FJMHdGPygG4M7hml0++qDuFsuTuzexEP5Db6PA8Y09wYY0ytiJQAXYEi5+Iq1TZhQQFMHdKDqUN6AA179VvzTrAp5zjpOcfZkH2cj7cd/r7wvxMZHEBggB/+fkKAn+DvJwQ6DusUl1d/X96NBQf4MSAukqlDejCoZycG9ezEgLhOrf6gUMomZ8q9qd2R0/fInRmDiMwB5gAkJSU58dZKOSc8OIDxfWMY3/d/56ypravnaGkVh46f4tCJCg4dP0VRWTW19fXU1Rtq60zDr/WGemPoHBZETEQwXSMafo2NDKJreDAJnUN14i7lcZwp9zwgsdHnCUB+M2PyHIdlooDi01/IGPMi8CI0HJY5k8BKOSvA3+/7wzHQxXYcpTqUM7sjG4AUEektIkHAdGDxaWMWA7Mcj28AvmrpeLtSSqn21eqeu+MY+j3A5zRcCjnfGLNTRB4H0o0xi4GXgddFZD8Ne+zT2zO0Ukqpljl1vZYxZgmw5LSvPdrocSVwo2ujKaWUOlN6lkgppbyQlrtSSnkhLXellPJCWu5KKeWFtNyVUsoLWZvyV0QKgZwz/O0xuOfUBu6aC9w3m+ZqG83VNt6Yq5cxJra1QdbK/WyISLozE+d0NHfNBe6bTXO1jeZqG1/OpYdllFLKC2m5K6WUF/LUcn/RdoBmuGsucN9smqttNFfb+GwujzzmrpRSqmWeuueulFKqBW5Z7iIyX0QKRGRHo6/dKCI7RaReRFJPG/8rEdkvIntE5DJ3yCUil4jIRhHZ7vh1sjvkavR8koiUicgD7pJLRIaKyBrH89tFJMR2LhEJFJHXHHl2f7fMZAfm+rOIZIjINhH5QESiGz1nc7tvMpcbbPfNfr8cz9va7lv6e2yf7d4Y43YfwHnASGBHo6+dC5wDLAdSG319ILAVCAZ6AwcAfzfINQLo6Xg8GDjkDt+vRs8vAt4FHnCHXDTMULoNGOb4vKub/D3OBBY6HocB2UByB+a6FAhwPH4CeMLx2PZ231wu29t9k7kaPW9ru2/u+9Vu271b7rkbY1Zw2kpOxpjdxpg9TQy/moZ/fFXGmCxgP5BmO5cxZrMx5rsVq3YCISISbDsXgIhcA2Q6crWbNua6FNhmjNnqGHfMGFPnBrkMEO5YYSwUqAZOdmCuL4wxtY5P19KwEhrY3+6bzOUG231z3y/b231zudptu3fLcm+jphbwjreUpTnXA5uNMVW2g4hIOPAQ8FvbWU7THzAi8rmIbBKRB20HcngPKAcOAweBvxhjfrCEZAe5DfjU8didtvvGuRqzvd1/n8vNtvvG36922+6dWqzDzTm1OLctIjKIhv+GXWo7i8NvgaeMMWUiTX3rrAkAJgKjgQpgmYhsNMYssxuLNKAO6Al0BlaKyJfGmMyODCEijwC1wBvffamJYR2+3TeR67uvW93um8jlFtt9E7nabbv3hnJ3ZgFvK0QkAfgAuMUYc8B2HocxwA0i8icgGqgXkUpjzHOWc+UB3xhjigBEZAkNxy1tl/tM4DNjTA1QICKrgVQa/nvfIURkFnAlcJFxHJjFDbb7ZnJZ3+6byWV9u2/h77FdtntvOCyzGJguIsEi0htIAdZbzoTjbPgnwK+MMatt5/mOMWaSMSbZGJMMPA38jxsUOzSs0TtURMIcx7fPB3ZZzgQNh2ImS4NwYCyQ0VFvLiJTaDiccJUxpqLRU1a3++Zy2d7um8tle7tv4e+x/bb79jpjfDYfwFs0HOOsoeEn2+3AtY7HVcBR4PNG4x+h4WqBPcDl7pAL+E8ajtVuafTRzXau037fY7TvVQNt/Xv8CQ0nu3YAf3KHXEAEDVdX7KThH91/dHCu/TQcW/9uG3rBTbb7JnO5wXbf7PfL8nbf0t9ju2z3eoeqUkp5IW84LKOUUuo0Wu5KKeWFtNyVUsoLabkrpZQX0nJXSikvpOWulFJeSMtdKaW8kJa7Ukp5of8P+LHYXg94TcoAAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "solution = minimize(nll_1d, [100.0], method='CG')\n",
+    "min_pos = solution.x[0]\n",
+    "min0 = solution.fun\n",
+    "scan_points = np.linspace(110.0,126.0,50)\n",
+    "plt.plot(scan_points, [nll_1d([x]) - min0 for x in scan_points])\n",
+    "\n",
+    "nll_1sigma = lambda x: nll_1d([x]) - min0 - 0.5\n",
+    "print(\"position:\", min_pos)\n",
+    "print(\"negative error:\", min_pos - fsolve(nll_1sigma, min_pos-0.5))\n",
+    "print(\"positive error:\", fsolve(nll_1sigma, min_pos+0.5) - min_pos)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 2-D Likelihood fit"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now we perform the 2-D Maximum Likelihood fit, fitting for both $\\sigma$ and $p$ at the same time."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 350,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def likelihood(params):\n",
+    "    return np.prod([likelihood_point(x, params[0], params[1]) for x in measurements])\n",
+    "\n",
+    "def nll(params):\n",
+    "    return -np.log(likelihood(params))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Minimize the NLL and find the best-fit result."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 353,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "position: 118.31548192622421 width: 13.629783202046086\n"
+     ]
+    }
+   ],
+   "source": [
+    "solution = minimize(nll, [120.0, 10], method='CG')\n",
+    "print(\"position:\", solution.x[0], \"width:\", solution.x[1])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Create a 2D contour plot of the 1, 2 and 3 $\\sigma$ contours of the NLL and plot the best-fit solution."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 354,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "scanA = np.linspace(110.0,130.0,50)\n",
+    "scanB = np.linspace(5,20,50)\n",
+    "minValue = nll(solution.x)\n",
+    "Z = [[nll([a,b]) - minValue for b in scanB] for a in scanA]\n",
+    "p1 = plt.contour(scanB, scanA, Z, [0.01,0.5, 2.0, 4.5])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Compute numerically the error matrix of the NLL for the 2-D fit."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 301,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[[11.95694692 -3.06065748]\n",
+      " [-3.06065748  5.72672173]] \n",
+      "sigma(position): 3.4578818544955507 sigma(width): 2.3930569834299082\n"
+     ]
+    }
+   ],
+   "source": [
+    "from scipy.misc import derivative\n",
+    "\n",
+    "# compute the error matrix\n",
+    "A = np.linalg.inv([\n",
+    "    [\n",
+    "        derivative(lambda x: nll([x, solution.x[1]]), solution.x[0], n=2),\n",
+    "        derivative(lambda y: derivative(lambda x: nll([x, y]), solution.x[0]), solution.x[1])\n",
+    "    ],\n",
+    "    [\n",
+    "        derivative(lambda x: derivative(lambda y: nll([x, y]), solution.x[1]), solution.x[0]),\n",
+    "        derivative(lambda y: nll([solution.x[0], y]), solution.x[1], n=2)\n",
+    "    ]\n",
+    "])\n",
+    "print(A, \"\\nsigma(position):\", np.sqrt(A[0,0]), \"sigma(width):\", np.sqrt(A[1,1]))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Binned ML fit"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "With the same data as above, we now perform a binned ML fit and compare with the unbinned fit.\n",
+    "First, create a histogram of the data using np.histogram."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 374,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[ 1 19 26 10  7  5  5  2  0  0]\n",
+      "[ 70.  80.  90. 100. 110. 120. 130. 140. 150. 160. 170.]\n"
+     ]
+    }
+   ],
+   "source": [
+    "nBins = 10\n",
+    "histoMax = 170\n",
+    "histoMin = 70\n",
+    "binWidth = (histoMax-histoMin)/nBins\n",
+    "h0 = np.histogram(measurements, bins=nBins, range=(histoMin, histoMax))\n",
+    "print(h0[0])\n",
+    "print(h0[1])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Compute the binned NLL:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 375,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def nll_binned(params):\n",
+    "    # params is a list of [position, sigma]\n",
+    "    expected = [likelihood_point(x+binWidth/2, params[0], params[1])*(binWidth/2)*sum(h0[0]) for x in h0[1]]\n",
+    "    return sum([-np.log(expected[i]**h0[0][i]) for i in range(nBins)])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Minimize the binned NLL:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 376,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "     fun: -138.93433719876123\n",
+      "     jac: array([-1.90734863e-06,  1.90734863e-06])\n",
+      " message: 'Optimization terminated successfully.'\n",
+      "    nfev: 60\n",
+      "     nit: 6\n",
+      "    njev: 15\n",
+      "  status: 0\n",
+      " success: True\n",
+      "       x: array([116.43876363,  15.33581135])\n"
+     ]
+    }
+   ],
+   "source": [
+    "solution_binned=minimize(nll_binned, [120.0, 10], method='CG')\n",
+    "print(solution_binned)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Make a contour plot of the 1,2, and 3 $\\sigma$ contours for the binned NLL and overlay it with the unbinned contours."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 377,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "scanA = np.linspace(110.0,130.0,50)\n",
+    "scanB = np.linspace(5,20,50)\n",
+    "Z_binned = [[nll_binned([a,b]) - solution_binned.fun for b in scanB] for a in scanA]\n",
+    "\n",
+    "fig1, ax2 = plt.subplots(constrained_layout=True)\n",
+    "\n",
+    "p1 = ax2.contour(scanB, scanA, Z, [0.01,0.5, 2.0, 4.5])\n",
+    "p2 = ax2.contour(scanB, scanA, Z_binned, [0.01,0.5, 2.0, 4.5], linestyles=\"dotted\")\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Repeat the same for 50 bins:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 373,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "scanA = np.linspace(110.0,130.0,50)\n",
+    "scanB = np.linspace(5,20,50)\n",
+    "Z_binned = [[nll_binned([a,b]) - solution_binned.fun for b in scanB] for a in scanA]\n",
+    "\n",
+    "fig1, ax2 = plt.subplots(constrained_layout=True)\n",
+    "\n",
+    "p1 = ax2.contour(scanB, scanA, Z, [0.01,0.5, 2.0, 4.5])\n",
+    "p2 = ax2.contour(scanB, scanA, Z_binned, [0.01,0.5, 2.0, 4.5], linestyles=\"dotted\")\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/exercises/Solutions7/Solutions_7.ipynb b/exercises/Solutions7/Solutions_7.ipynb
new file mode 100644
index 0000000..acb7876
--- /dev/null
+++ b/exercises/Solutions7/Solutions_7.ipynb
@@ -0,0 +1,457 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Solutions 7\n",
+    "Maximum Likelihood method"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 205,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from __future__ import print_function\n",
+    "import numpy as np\n",
+    "%matplotlib inline\n",
+    "import matplotlib.pyplot as plt\n",
+    "from scipy.optimize import curve_fit, minimize, fsolve\n",
+    "from scipy.stats import norm, chi2, lognorm"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 151,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "measurements = np.array([97.8621, 114.105, 87.7593, 93.2134, 86.6624, 87.4629, 79.7712, \\\n",
+    "91.5024, 87.7737, 89.6926, 133.506, 91.4124, 94.4401, 97.3968, \\\n",
+    "108.424, 103.197, 88.2166, 142.217, 89.0393, 102.438, 95.7987, \\\n",
+    "94.5177, 96.8171, 90.903, 132.463, 92.3394, 84.1451, 87.3447, \\\n",
+    "92.2861, 84.4213, 124.017, 90.4941, 95.7992, 92.3484, 95.9813, \\\n",
+    "88.0641, 101.002, 97.7268, 137.379, 96.213, 140.795, 99.9332, \\\n",
+    "130.087, 108.839, 90.0145, 100.313, 87.5952, 92.995, 114.457, \\\n",
+    "90.7526, 112.181, 117.857, 95.2804, 115.922, 117.043, 104.317, \\\n",
+    "126.728, 87.8592, 89.9614, 100.377, 107.38, 88.8426, 93.3224, \\\n",
+    "138.947, 102.288, 123.431, 114.334, 88.5134, 124.7, 87.7316, 84.7141, \\\n",
+    "91.1646, 87.891, 121.257, 92.9314])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 1-D Maximum likelihood fit"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We have a set of measurements which are distributed according to the sum of two Gaussians (e.g. this can be signal and background).\n",
+    "\n",
+    "$\\rho = \\frac{1}{3}\\frac{1}{\\sqrt{2\\pi \\sigma^2}} e^{-\\frac{1}{2}\\left(\\frac{x-p}{\\sigma}\\right)^2} + \\frac{2}{3}\\frac{1}{\\sqrt{2\\pi \\sigma_b^2}} e^{-\\frac{1}{2}\\left(\\frac{x-p_b}{\\sigma_b}\\right)^2}$  \n",
+    "\n",
+    "where for one of the two peaks the parameters are known already\n",
+    "\n",
+    "$p_b = 91.0$  \n",
+    "$\\sigma_b = 5.0$  \n",
+    "  "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 228,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def likelihood_point(x, position, width):\n",
+    "    return 1.0/3/np.sqrt(2*np.pi*width**2)*np.exp(-0.5*((x-position)/(width))**2.0) + 2.0/3/np.sqrt(2*np.pi*5**2)*np.exp(-0.5*((x-91)/(5))**2.0)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "First, we assume the width of the peak we want to fit is already known: $\\sigma = 15.0$.\n",
+    "Perform a 1-D Maximum Likelihood fit for the position of the peak $p$.\n",
+    "\n",
+    "Complete the functions below which return the likelihood and negative log likelihood (NLL)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 347,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def likelihood_1d(params):\n",
+    "    return np.prod([likelihood_point(x, params[0], 15.0) for x in measurements])\n",
+    "\n",
+    "def nll_1d(params):\n",
+    "    return -np.log(likelihood_1d(params))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Minimize the NLL and give the best-fit result, including asymetric errors and plot the NLL."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 355,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "position: 117.72333147980623\n",
+      "negative error: [3.31211666]\n",
+      "positive error: [3.39091994]\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "solution = minimize(nll_1d, [100.0], method='CG')\n",
+    "min_pos = solution.x[0]\n",
+    "min0 = solution.fun\n",
+    "scan_points = np.linspace(110.0,126.0,50)\n",
+    "plt.plot(scan_points, [nll_1d([x]) - min0 for x in scan_points])\n",
+    "\n",
+    "nll_1sigma = lambda x: nll_1d([x]) - min0 - 0.5\n",
+    "print(\"position:\", min_pos)\n",
+    "print(\"negative error:\", min_pos - fsolve(nll_1sigma, min_pos-0.5))\n",
+    "print(\"positive error:\", fsolve(nll_1sigma, min_pos+0.5) - min_pos)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 2-D Likelihood fit"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now we perform the 2-D Maximum Likelihood fit, fitting for both $\\sigma$ and $p$ at the same time."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 350,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def likelihood(params):\n",
+    "    return np.prod([likelihood_point(x, params[0], params[1]) for x in measurements])\n",
+    "\n",
+    "def nll(params):\n",
+    "    return -np.log(likelihood(params))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Minimize the NLL and find the best-fit result."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 353,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "position: 118.31548192622421 width: 13.629783202046086\n"
+     ]
+    }
+   ],
+   "source": [
+    "solution = minimize(nll, [120.0, 10], method='CG')\n",
+    "print(\"position:\", solution.x[0], \"width:\", solution.x[1])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Create a 2D contour plot of the 1, 2 and 3 $\\sigma$ contours of the NLL and plot the best-fit solution."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 354,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYcAAAD8CAYAAACcjGjIAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMi4yLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvhp/UCwAAIABJREFUeJzs3Xd4FNX6wPHvuy2b3ukdQu+EKlUQAQFBUMECKoq967X3n3pt13pVsCNNRakiigKiCCJNegs1JpBAEtKz7fz+2EVzqSEkO5vkfJ4nz+5OZue8C9l555Q5R5RSaJqmaVpxJqMD0DRN0wKPTg6apmnaSXRy0DRN006ik4OmaZp2Ep0cNE3TtJPo5KBpmqadpETJQUQ+FpE0EdlcbNtzIrJRRDaIyA8iUsu3XUTkLRHZ7ft9x9Mcs5OIbPLt95aISNl8JE3TNO18lbTm8Ckw6IRtryil2iql2gMLgCd92wcDCb6ficB7pznme77fH9/3xONrmqZpBilRclBKLQcyTtiWXexlKHD8brpLgSnKaxUQJSI1i7/X9zpCKbVSee/CmwKMKOVn0DRN08qY5XzeLCLPA+OAY0A/3+bawMFiuyX7tqUW21bbt/3EfU5VxkS8NQxCQ0M7NW/e/HxC1jRNq3LWrl17RCkVfy7vOa/koJR6DHhMRB4B7gCeAk7Vd3DiHB0l2ed4GZOByQCJiYlqzZo1pQ9Y0zStChKR/ef6nrIarTQdGOV7ngzULfa7OkDKCfsn+7afaR9N0zTNIKVODiKSUOzlcGC77/k8YJxv1FI34JhSqniTEr7XOSLSzTdKaRwwt7SxaJqmaWWrRM1KIjID6AvEiUgy3uajISLSDPAA+4FbfLsvBIYAu4F84Ppix9ngG90EcCveUVDBwHe+H03TNC0ASEWaslv3OWiapp07EVmrlEo8l/foO6Q1TdO0k+jkoGmapp1EJwdN0zTtJDo5aJqmaSfRyUHTNE07iU4OmqZp2kl0ctA0TdNOopODpmmadhKdHDRN07ST6OSgaZqmnUQnB03TNO0kOjlomqZpJ9HJQdM0TTuJTg6apmnaSXRy0DRN006ik4OmaZp2khKtBKdpmv8o5QRPlvdHZYEnAzzZoPJA5aFUHnjy/n6NKgTlAJygnL5Hh++5p9iR5X8fxQJiA6y+R5v3UWwgwSChf/+I6fjzMDBF+36iQKJAgvGu9qtVJjo5aJqfKOUAdyq4D4EnDTyHUW7vI+7D4EkHTyao3LMcKQhMoSAhvhN2MN4TeyiYjp/srb5H8/HSjwdR7LXLl0Ac/yQTlQseB6h8X+LJBVXAmdeLDEKZosAUA+ZqYIoHU3Xk+HNzNTDVAFMcIrqxoqI4a3IQkY+BoUCaUqq1b9srwDDAASQB1yulskTkauDBYm9vC3RUSm044ZhPAzcB6b5NjyqlFp7nZ9E0wylPFrj2gPsAuJNRroPgTgb3QW8SOPE0K8Fgqg7m6mBtB6YYxBTlvTKXqH+u0E1RvkQQgojVv59Juf9JFp4cX20m01e7yfR+ZpUFnqPgTgfnVvAcQZ2UUmwocy0w1wZzbcRcG8x1wFwXLA0RU6RfP5d2ZmddQ1pEegO5wJRiyWEgsEQp5RKRlwCUUg+d8L42wFylVKNTHPNpIFcp9eq5BKvXkNYCgVLKe6J37QJXEsq1B1xJ4N7jPUEWZ6ruOwF6T4JirgPmGv9cTUtopWySUcrl/bfwpIE7DdypKE8KuP/yJcu/vM1lxUk0WBqCpQFibuB73hjM9f2eECub0qwhfdaag1JquYg0OGHbD8VergJGn+KtY4EZ5xKMpgUapTzeq37nFpRri/eq2LkVVOY/O0kUWBpBUD/E0hjMjcBS33t1LEHGBW8gEYu3NmSuDr7z+okpUHnywZMCrgPg3oty7QXXPij6FeX5ptieVpSlEViaIZZmYG0OluaIOd5Pn6ZqKos+hxuAL06x/Urg0jO87w4RGQesAe5Xqvi3TdOMoTzZ4NyAcqwD5zpwbi7WB2AFSwLY+yOWFr6TVGOQ6Ep59V/exBQCpiZgaeJ9Xex3ypML7n3g2o1y7QLXTnCsRhXO+2cfUzWwtkGsrcHaBqytEVOMfz9EJXZeyUFEHgNcwLQTtncF8pVSm0/z1veA5/A2wD4HvIY3yZyqjInARIB69eqdT7iadhLlOgiOP1DOdeBc720qAsAMluZgH45YW4G1FViaIGIzNN6qQkxhYGrtPeEX2648WeDcAa7tKOcmcG5GFS3heF+OMtcBS2vE1gGsHcHaUjdJlVKpk4OIjMfbUd1fndxxMYYzNCkppQ4XO84HwIIz7DsZmAzePofSxqtpAMp9BByrUI6V4Fjpbf8GkHCwdkDCLgFrB7C29Q7f1AKKmKIgqCsEdf07aShPLji3gGszyrkRnJtQRYt8v7WjrG3B1gGxdgRbR93xXUKlSg4iMgh4COijlMo/4Xcm4HKg9xneX1Mplep7ORI4XQ1D086LUg5vzaDoZ3D85m2eAG8ysHVBQq4DW1ewJOhhlhWUmMJOThjuw+Bc520edKyFvA9RuAFBWZqDrSti6+b9GzCFGRl+wCrJUNYZQF8gTkSSgaeAR4AgYLGvrXWVUuoW31t6A8lKqT0nHOdD4H2l1BrgZRFpj7cuuA+4uUw+jabhu5Is+hlV9BMU/QwqBwgCWyckbCjYuoO1lbfTVKuUxFwdzIMR+2DA1/nt3AjONSjHasifjsr/FDCjrO3A1gMJ6gnWdsjf94ZUbWcdyhpI9FBW7XSU+ygUfY8q/BEcvwNO701ZQRciQf0hqAciwUaHqQUIpYrAsc7XvLjCO/AA5R15FtQLCeoLQT0RU7TRoZaJchnKqmmByltDWIwqWOBtMsIN5gYQMg6xDwBre30VqJ2SSBAEdUeCugP3eTu6i1b4mh+XowrnAyaUtT0S1AeC+nmH0lahUWk6OWgVilJFULQMVfAtFC0Firw3mIXeiNiHgqVplfoCa2VDTFEQfAkSfIn33hbnJlTRMm/zZO7rkPs6mOuhggYg9ou8gxcqeR+VTg5ahaBce1D5M6BgNqhsMMVCyBXehGBtrxOCVmZETGBrh9jaQfjd3vmvipagChdD/ueo/I/BFI+yD0TsQ8DaqVImCp0ctICllNP7pcyfBo5VgBXsA5Hg0d7RJrpDWfMDMVeDkDFIyBiUJ8dbmyj8HvK/9v5tmqqjgoch9uGItbnR4ZYZ/e3SAo5yp0HBF6j8L7xz85hqIWH3QfBoxBxndHhaFSamcAgeigQPRXnyvE2chfMh71NU3ocoS1PEPty7j7mW0eGeF50ctIChXEmovI+gYC7gBFsvJORZCOqjO5a1gCOm0H/6KTwZULgIVTAPlfsq5L6KsnZBgkeCfVCFvKFSD2XVDKecm1C5k6BoMWCDkFFIyPWIpb7RoWnaOVOuA1A4H1Uw1zs/lISCfTASfKX3znsD+sf0UFatQlGOtajcd8HxC0gEhN6KhFyLmGONDk3TSk0s9SDsdgi9zXuXdsHXULgQVTALLC0gZAzYhwX8ndm65qD5nXJuQ+W8Ao5fvYvbhNwAIVcF/JdF00pLeXKhcIF3xJ1rm7c2ETwSCbkGsZy05E2Z0zUHLaApdyoq53UonAsSiYQ/BMFjvVM3a1olJqYwb40h+EpwbvSOcsr/ApU/FWXrhYSOB1vPgBoSq5ODVu6UJweVNwnyPgMUhE5AQm9BTBFGh6ZpfiUif99Dodz/goKZqPwZqMwbwdwQQq7x1igCoBYdOGlKq3SUcqPyZ6LSB0DeZLBfjMR/jyn8XzoxaFWemOOQsDuQ+KVI5KtgCkPlPIdK74Mn53XvCCgD6ZqDVi6UKwl17BFwbvDeQRrxmHfFLk3T/oeIDYKHI8HDUY4N3uHcee+j8j9FBV+JhE7wzjLrZzo5aGVKKRfkfYzKfQskGIl8GeyX6uktNK0ExNYesb2Ncu32Du/O/xyVPw0VPAoJvQmx1PVbLLpZSSszyrkLlXGl9yagoD5I3EIkeIRODJp2jsTSBFPUK0jc9xA8Cgq+Rh0ZiCfrQZRrn19i0MlBO29KuVG5k1BHR4ArGYl8HYl6BzHHGx2aplVoYqmHKfJZJP4nCLkWCr9HHRmM59jDKPdf5Vq2Tg7aeVHuQ6iMa1C5r0FQf19t4RJdW9C0MiTmGpgiHv0nSRQsQKUPxJP9Qrl1XOvkoJWacqxFHb0MXNuRyJeRqDf13c2aVo7EHO9LEosheDjkT0GlD0DlvuddCrUM6eSglYrKn4nKGAcSisR+pfsWNM2PxFwTU+SLSOx8sHVF5b6OOjIQlf+Nd7GiMnDW5CAiH4tImohsLrbtFRHZLiIbRWS2iET5tjcQkQIR2eD7ef80x4wRkcUissv3WDkWaq0ClHLgOfYUKvtJsHVDYmchliZGh6VpVZJYEzBFv4fETAdzDVT2w6ijl6GKVp33sUtSc/gUGHTCtsVAa6VUW2An8Eix3yUppdr7fm45zTEfBn5SSiUAP/leawFOeXK8d3IWzIDQm5DoyYgp0uiwNK3KE1siEvMlEvkf8GShMsfhybwL5U4t9THPmhyUUsuBjBO2/aCUcvlergLqnGO5lwKf+Z5/Bow4x/drfqbcqaiMseBYg0S+hCn8Qb3GgqYFEBETEjwUiV+EhN3jXYjoyCDv/RKlUBZ9DjcA3xV73VBE1ovIzyLS6zTvqa6USgXwPVY73cFFZKKIrBGRNenp6WUQrnaulGsP6ugV4E5Boj/0LmCiaVpAErEjYbchcQvBdgGq6KdSHee87pAWkccAFzDNtykVqKeUOioinYA5ItJKKZVd2jKUUpOByeCdsvt84tXOnXLt9XY840ZipleqNXI1rTITSx0k+l3vcqZ8dc7vL3VyEJHxwFCgv/ItCqGUKgKKfM/XikgS0BQ4cRGGwyJSUymVKiI1gbTSxqGVH+U6iMq4FpQTiZmKWBOMDqlCcnk8ZBcUklVQSHZhEYUuFw6XG4fbjcPlwuF243S7cXk8mMWE2WTC4vsxmwSL2YzNbCYsyEZ4kI1wexBhQUGE2qx6hJh2VqVdorRUyUFEBgEPAX2UUvnFtscDGUopt4g0AhKAPac4xDxgPPBv3+Pc0sShlR/lyfB2PqsiJOZznRhOwaMU6Tl5/HUsmxTfz1/Hckg9lsPRvHyOFRaSlV9ITlFRuZRvEiEsyEZUsJ34sFCqhYcRHxbqex5KtbBQakZGUDsqAptZ9w9p5+asyUFEZgB9gTgRSQaewjs6KQhY7LtyWeUbmdQbeFZEXIAbuEUpleE7zofA+0qpNXiTwpciMgE4AFxe1h9MKz2lClCZt4D7LyTm0yrflOT2eNifkcWu9KPsTDvCrrQj7Ew/SnLmMZye/x1THhVsp2ZEOLFhITSIjSY62E5ksJ0o32NksB27xYLN4q0N2MxmbBYLNrMZi9mEx6Nwety4PQqX243Lo3B7PBS5XeQUOsgtKiKnqIicQgc5RUXkFjnIzC8gPTePbYfSWZ67jzyH439iMolQMyKcutGR1I+Jom50JPWio2gcF0OD2GgsJn27k3YyvUyo9j+UcqOy7oCiJUjUW4j9YqND8iulFPsyslh3MIX1ySlsSU1jd/pRHG43AALUj4kiIT6OBrFR1IqMoHZkBLUiw6kZGUFYkM3YDwDkORyk5+SRlptHSlY2BzKz2J95jIOZWRzIPEZmfsHf+1rNZhrHxZAQH0vTanE0qxZH02px1IgI001WlYheJlQ7L0opVPZzUPQTEv5ElUgMTrebTSmHWXvwL9YfTGV9cgoZvpNnpD2INrVrcE3D9jSNjyWhWhyN42IItloNjvrMQm02QmNtNIiNhvon/z6nsIj9mVns9tWEdqYdZfX+ZOZv3v73PrGhIbStVZ02tWrQtnYN2tWuQYTd7sdPoRlNJwftH/lToWC69wa30GuNjqbcFDid/Jq0n8Xbd7Ns1x6OFXr7BBrERNE3oSEd69aiQ51aNIqLwVQJr57D7UG0rlmd1jX/dwGZYwWF7Eo/wvbDR9iccpiNKYdYtmsvCm+NqUl8LB3r1qJr/bp0a1iX2FC99ndlppuVNACUc4d3Er2gnkjUewG10HlZyCooZOnOJBZvT2LFnv0UulxE2oPo17QRFzZtTGK92vpkdwq5RUVsSjnM+mRvrWr9wdS/O9ibVouje8O6dG9Qj8716wREk5p2aqVpVtLJQUMpJ+ro5eA5jMR9i5hijA6pTLg8Hn7ZvY+v1m9m2a49uJWiRkQYA5o1YUAzb0Kw6lE858Tl8bA1NY2Vew+wct8B1h5IweF2YxahXZ2a9EtoSN+ERiTEx+o+iwCik4NWKir3HVTuW94FeuwDjQ7nvB3MzGLWhi188+cW0nLyiAsN4dK2LRjcsimta1bXJ60yVORyse5gCiv3HuDXpP1sOeS9ZalOVAT9EhrRr2kjOterjc2iW7CNpJODds68zUkjwT4YU9RrRodTah6lWLpzD1P/2MBvew9gEqFPkwaMbt+aPgkNdQ3BTw5n57Js9x6W7tzDb3sPUORyE2Kz0i+hEcNaN6dn4/r6/8IAOjlo50Qphcq8DpzbkPjvEVPFmzldKcUvSft5Y+kKthxKo0ZEGFd2bMuodq2oHhFmdHhVWoHTyaq9B1myM4kftu8mq6CQqGA7g1s2ZXibFnSoU1PX4vxEJwftnKjCJaisW5Dwx5HQcUaHc87WHEjm9aW/sebAX9SJiuDO3t0Z2qa5vqkrADndbn5N2s/8zdv5aUcShS4XdaIiGNq6OSPatqRhbMW7MKlIdHLQSkwpB+rIMECQuPmIBPbY/eK2pB7mjWW/sXz3PuLDQrmtV1dGd2itp4ioIHKLHPy4fTfzNm9j5d6DeJSiS/06XN6hNQObJ2C36v6JsqaTg1ZiKu9TVM4LSNQkxN7P6HBKJC0nlxd/+JmFW3cSFWznph6dubpzu4C/KU07vcM5uczduJWv1m/mQOYxIu1BDG/TgjGd2tIkXq9HXlZ0ctBKRKlCVHpfsDRHoj+pEO2+P+1I4rEFi8l3OLihWyITunci3B5kdFhaGfEoxe/7DjJr/Wa+374bp9tNtwZ1Gd+1A30TGlXKmxH9SU+foZVMwTfgyUDCbg/4xFDgdPLS4uXMWLuRFtXjeW3kYBrrK8pKxyRC94b16N6wHo/lFzBr/Wamr/mTW7+YR+O4GCZ0T2RY62Z6SKwf6ZpDFaOUG3XkYjBFITFfBXRy2H4onftmLyTpSAY3dOvEvf166JNDFeJ0u1m0dRcfrlzD9sPpVAsPZXyXjlzZsY2uNZ4j3ayknZUqXITKuguJejtgJ9bzKMWU39fz6pJfiQq289KlF3NBo1PMIKdVCUopVuw5wIcr/2Dl3oOEBdkY07Et13XrSHxY6RayqWp0ctDOynN0DHiOInGLEAm80T0Ol4t7vl7ITzuTuLBpI54fNpCYkGCjw9ICxObUw3z02xoWbduF1Wzi2s4dmHhBZyKD9YyxZ6KTg3ZGyp2OSr8ACbsHCbvN6HBO4nC5uHPWApbt2ssjA/swvkuHgG720oyzPyOL/y5fxbxN2wi3B3FTj85c26W9Hrl2GqVJDvpuoaqkaJn3MSjwhq4WTwzPDOnPdV076sSgnVb9mCheHjGIOROvoVPdWry25FcG/vcTvly3CdcJq/NppaOTQxWiipaAqSZYAmvZT4fLxV3FEsOYTm2NDkmrIJpXj+f9MSOYNv5yakdG8MS3PzL0/Sks2ZlERWoVCURnTQ4i8rGIpInI5mLbXhGR7SKyUURmi0iUb/tFIrJWRDb5Hi88zTGfFpG/RGSD72dI2X0k7VSUKgLHb2DvF1BX5E63m7tmLWDprr08PeRCnRi0UkmsV4cZ113Ju1cMRxBu/WIe10/7mt3pR40OrcIqSc3hU2DQCdsWA62VUm2BncAjvu1HgGFKqTbAeODzMxz3daVUe9/PwnMLWztnzo2gChBbb6Mj+R/Pf7+Mpbv28tTgCxnbqZ3R4WgVmIjQv1lj5t18DY9f3JetqWmMmDyV15esoMDpNDq8CuesyUEptRzIOGHbD0opl+/lKqCOb/t6pVSKb/sWwC4iekByIHBu9D5aA+cEPH3Nn8xYu5EbuydyVWLgxKVVbFazmWu7dGDRbddxSetmvL9iNUPfn8Ly3fuMDq1CKYs+hxuA706xfRSwXilVdJr33eFrlvpYRE47JaOITBSRNSKyJj09vQzCrZqUcxOYaiHmwLi7ePX+ZJ7/fhl9Expy34UXGB2OVgnFhIbw0qWD+Oza0VjNZm6aMZt7v/6WI7l5RodWIZxXchCRxwAXMO2E7a2Al4CbT/PW94DGQHsgFTjtKjNKqclKqUSlVGJ8fPz5hFu1OTeDtY3RUQDehezvn72QutGRvDpiMGY9xbZWjro1qMu8iddwV5/uLN6RxCXvT2Hepm26w/osSv2tFJHxwFDgalXsX1lE6gCzgXFKqaRTvVcpdVgp5VZKeYAPgC6ljUM7O+XJAfcBxNra6FAAePWnX8jIK+DVkYP1NAiaX9gsFm7v3Y05N11Ng5hoHpyziDu/WkBmfoHRoQWsUiUHERkEPAQMV0rlF9seBXwLPKKUWnGG99cs9nIksPl0+2plwH3Q+2hpYGgYAElHMpi1YQtjE9vSumZ1o8PRqpgm8bFMv+4KHuzfi2W79nDp5Kms3HvA6LACUkmGss4AVgLNRCRZRCYA7wDhwGLfUNT3fbvfATQBnig2TLWa7zgfisjxO/Re9g133Qj0A+4t48+lFec+5H001TA2DuDNpSuwWy3c2rOr0aFoVZTZZOLGHol8ccMYgq1Wrp/6Na/+9AsOt9vo0AKKnj6jClD501DZzyDxvyLmaobFsfGvQ1z+8Qzu7N2NO/p0NywOTTsu3+HkxR+W8eX6zbSuWZ03Rg2hbnSU0WGVOT19hnZKyp0KWMEUZ2gcry35lZiQYK7r1snQODTtuBCbleeGXsTbo4eyPyOLkR9M56cdp+wqrXJ0cqgKPJlgikbEuP/uXWlHWLXvIBO6JxIWZDMsDk07lYEtEph909XUj4niti/n8d4vv1f50Uw6OVQFygFi7An5mz+3YjGZuKxdS0Pj0LTTqRsdybTxVzC0dXPeWPYb98/+rkrfWa2X1aoSHIBxycHpdjN30zb6JjQkJjTEsDiM5nK6yTySg9PhwuNWeJTC4/bg8XjwuBUIhEeGEBkdSlCwNaDmwKoq7FYLr44YRLNqsfxnyQr2ZWTy7hXDqRERbnRofqeTQ1WgHGDgLCY/797H0bx8RrVvZVgM/nIsI4+t6/aRejCD9NQs0g8d40jqMdJTs8g8klvipgpbkIWI6FAior3JIiY+nNoN46nbKJ56TapTu34sZkvgLdZUGYgIEy/oQpP4WO6f/R2jP5rOf68YTrvaNc/+5kpEJ4eqQDlBjPuvXrhlB7GhIfRq3MCwGMqL0+Fi2/r9rFuxi3W/7mL31pS/E4A9xEZ8jUjiakaRmNCM+JqRxFaPxGazYDKbMJkFk8n7aDaZ8ChFTlY+2Zn5ZGfmcSwzz/s8K4+Nq/fw09z1f5drtVmon1Cdhs1q0KRVbdp1a0y9xtV0baMMXdi0MV9eP4ZbvpjLuCmzeOeKYZXyb/h0dHKoCsQOnkJDilZK8ceBZLo3qIvVXDmudIsKnSydv56VP25l4+o9FOY7MJlNtGhfj2vuHOA9UTepRlhEcJmerAvyikjem87+3YfZt+Mwe3emsuaXnSz+Zi0A0fHhtOvamPbdGtOuW2Nq1I0ps7KrqoRqcXx5w1gmTPuGW2fO5T+XDWFgiwSjw/ILnRyqAgkFT64hRSdnZZOWk0fHerUNKb8sZR7JYcH0VSyYvorszDxq1otlwIiOdLwggbbdGhMaVr7rGAeHBpHQug4Jrev8z/ZDBzP4c1USG1Yl8eeq3SxbsAGAGnViSOzdlF6D2tAqsSFmsx5/UhqxoSFMGTeaiTPmcPfX3/LisIGMqAIDK3RyqApMoaCMmYly7cG/AOhUt5Yh5ZeFfTsPMfvTX1kybz1ul4eu/Zoz8vpetOncMCCacWrUjaFG3RguvrwzSikOJKWxYeVuNvy2m8XfrGXB9FVEx4XR46LW9BrUhtaddaI4VxF2Ox9dfRm3fzmPh+Z9T57DwdWd2xsdVrnSyaEqEG9yUEr5/WS27mAK4UFBJMQHxlTh5yJ5TzqT/72AP37eQZDdysWjOzNi/AXUaRi4swOLCPWbVKd+k+pceu0FFOQV8cfPO/hl0SZ+nL2Wb2esIio2jN5D2jJkTFfqN9HzW5VUqM3GpDEjuPfrhTy7aCmFLhcTup/TTccVik4OVYCYYlC4QWXB6ZfOKBf7MrJoEh9T4abl/uPnHbx473TMFhPj7hnIJWO6EhEdanRY5yw4NIjeQ9rSe0hbCvMd/PHzdpZ/t4mFM39n3ue/0blPM0bd0Ju2XRsFRC0o0AVZLLw5+hL+NWcRL//4C3aLpdLWIHRyqArMviYddwqY/Jsc0nPySKhWcWoNSinmfLaCD1/6lobNa/LUe+OJrxFpdFhlwh5io9fgtvQa3JZjGXl8O2MV86b+xsPjP6BJq9qMuqEXPS9ug8VaOQYOlBer2czLIwZR5HLz7KKlhNpslbIPomJdzmmlY/Z1Brv/8nvR6bl5VAurGFfcLqebt5+azeQXF9Ctf0tenXZLpUkMJ4qMCeWq2/vz2dKHueu5yyjMd/DS/TOZMPAVZn/6C4X5DqNDDGhWs5nXRw2hR8N6PDr/B5bu3GN0SGVOJ4eqwKDkUOh0kVNURFwFSA45x/J5fMJHfPfFaq68uS+PvXU19pDKPwdUkN3K4Cu6MGnhvTz13jiq1Y5m8ovfcsNFLzP38xU4Ha6zH6SKCrJYePvyYbSsWY27v17AmgP+v/gqTzo5VAUSBRKKcvt3UZOsAu8qW9Eh5TvE83y5nG6evf1ztqzbz/0vXc519w3CVMH6SM6XyWSi24UteWXqzbw6/RbqNIrn/f+bz+2Xvsn633YZHV7ACguyMXnsSGpFRnD7l/M4mJlldEhlpmp9A6ooEQFLM3Bu92u5xzuh3Z7Ant1y8osL2PzHXu59YTQDRujpxFt1asBLUyby9PvjcTrdPHr9Rzx/1zTSUirPia8sxYT0s0YwAAAgAElEQVQEM3nMCABu/WIeuUWVo0lOJ4eqwtocXNvxLtvtHzbfHdGBvMLWkrnrmT9tJZfd0IsLh3cwOpyAISJ07deCSd/ey7i7L+KPn7czcchrzHx/qW5qOoV6MVG8MeoS9hzJ4F9zFuGpBNN96+RQRYilhfdGOHey38q0+SaGc7gCMznkZhcw+d8LaN6+HjfcP8jocAKSLcjK2Nv6M2nhfST2bsZnr3/PPZf/l6RtKUaHFnC6N6zHIwP78NPOJN5attLocM5biZKDiHwsImkisrnYtldEZLuIbBSR2SISVex3j4jIbhHZISIXn+aYDUXkdxHZJSJfiBi84EBlZ23hfXRt81uRf9ccXIF5pfnFpGVkZ+Zz+5OX6hlOz6J67Wgef+sannp3HJlHc7l79DvM+vDnKr8gzomu6dye0e1b896vv/P9tordV1PSmsOnwImXVouB1kqptsBO4BEAEWkJjAFa+d7zroic6pv3EvC6UioByAQmnHP0WslZmgM2lGOd34o0m0xEBdtJyzVm6o4z8Xg8LJm7jm79W9CkVcWf98lfuvVvyaQF99K9fys+euU7nrvjc/JyjJnUMRCJCE8N7kfbWjV4fMFi/srKNjqkUitRclBKLQcyTtj2g1Lq+CXhKuD4bGCXAjOVUkVKqb3AbqBL8feK91bMC4FZvk2fASNK9Qm0EhGxgbUdOP7wa7n1Y6I4EIAjOLau209Geg69h7QzOpQKJzwqhEffvIqJjwxl9bLt3DXqbfZuTzU6rIBhs1h4beRgXB4PD8/7HrfHf/18Zams+hxuAL7zPa8NHCz2u2TftuJigaxiyeVU+wAgIhNFZI2IrElPTy+jcKsoW2dwbUX5cYbWetFRHMg45rfySuqXRZuwBVno2re50aFUSCLCyOt68tKUiRQVOLn3ynf5cc5ao8MKGPVionj84n6s3p/Mx6sq5r/LeScHEXkMcAHTjm86xW4nNkyWZB/vRqUmK6USlVKJ8fGBO+FZRSC2zoAHnBv8Vma96EhSs3MCqt/B4/Gw4vvNJPZuRnCocSvkVQatOjXgnTl30bx9PV576Cs+enkhngp6pVzWLmvXkoHNm/Dm0t/YdijN6HDO2XklBxEZDwwFrlb/9EwlA3WL7VYHOHFowxEgSuTv5clOtY9W1qwdACuqaIXfimwYG4NHKZKOZJx9Zz9JPZDB0bRsuvTRtYayEBUbxvMf3cDQq7ox66PlvPvsPJ0g8NaunrlkAFEhdh5bsLjCNS+VOjmIyCDgIWC4Uiq/2K/mAWNEJEhEGgIJwOri7/UlkqXAaN+m8cDc0sailYyYQsDWDYp+8Nsokw51vevurj0YOLk/80gOAPG1os6yp1ZSZouZ2568lNE39uHbGat0gvCJCQnmkYv6siU1jZlrNxodzjkp6VDWGcBKoJmIJIvIBOAdIBxYLCIbROR9AKXUFuBLYCuwCLhdKeX2HWehiBxf9eUh4D4R2Y23D+KjMvxc2mmIfRC4D4Jrq1/Kqx0ZQY2IMNYG0Lwzmene5BAdF25wJJWLiHDDA4N0gjjBkFZN6dGwHv9ZuoL0ABy5dzolmrJbKTX2FJtPezJXSj0PPH+K7UOKPd/DCaOYND+wD4DsJ1GFCxFrq3IvTkToVLc2fxxINmSxoVPJPOrtkI+OCzM4ksrneIIAmPXhzwDc9uTwKjdXVXEiwpODL2TYpM95afFyXh052OiQSqTq/o9VUWKKBlsPKPzOb1NpdKpXm7ScPA5kBsaopWMZ3qu38KgQgyOpnE6sQXzy6iKjQzJcw9hobuqRyPzN21lzwH+zFJwPnRyqIAke7p1Gw7nGL+X1blwfgCU7k/xS3tmEhHlHKOXn6pu3ysvxBHHJWG8ntR7mCjdf0IVq4aH8Z8mKCnFnuU4OVZF9IEgYKn/W2fctA3Wjo2hePZ7F23f7pbyzia/h7Yg+khoYNZnKSkS45bFhtOvaiLefnM2e7YEzKMEIdquF23p1Ze3BFJYn7TM6nLPSyaEKEgkG+yVQuMhvN8Rd1Kwx6w6mBESH3PFRSumHdHIobxarmYdfv4rwyBCev2talZ9qY1T71tSNjuSNpb8F/MytOjlUURI8GiiEwgV+Ke+i5gko4Mcdxtce4mt6l/5MTw28aT0qo6jYMB5+/SoOJWfyxmNfV4gmlfJiM5u5s3d3th5KC/iJ+XRyqKqsbcHSFJU/0y9f1qbVYmkYG838Tf5dcOhUouPCCQ4NYveWwBleW9m1TmzA+HsG8uv3m1j1k3+GUQeqoa2b0SAmio9XBnY/jE4OVZSIICFXee938MN0GiLCqPatWHswxfC7pc1mEx16NGHtrzur9FWsv112fS/qJ1Tn/RfmU1hQOVZLKw2zycQ1nduzMeUQG/86ZHQ4p6WTQ1Vmv9TXMT3t7PuWgRFtW2Ixmfh6w+az71zOEns3Iz31GAd2B+6cN0opdqxJ4o1bJnNf3yeZ9Z/5HEkJnGlIzpXF6r2LOu2vLL6cvMzocAw1sl1LQmxWpv7hv3nOzpVODlWYmEIh+DLvPQ/uI+VeXnxYKH0TGjJn4zbDlw7t1LMpAGt+2WFoHKeSm5XHnHe+45aOD3JHl4f58fOfycnIZdIDU7iq7i08OOAZFn28hLzs/LMfLMC07dKIvkPbM+vD5aTsL/+/uUAVFhTEZe1asXDrTo7mBeb/o04OVZyEXAU4oWCmX8q7vENrjubl86PBw1qr1YqifkJ1flu8xdA4TuXZy1/jv3d9jNli5q53b+KLlMl8sPE/fLztDa55YjRpB47w2o3vcVPr+ziwveL1m9z4ryFYrGY+/c/3RodiqKsT2+F0uwOiJn0qOjlUcWJpBEF9UflTUaqo3Mvr1bgB9aIj+fT3dYa39w8Y2Ymt6/YH3Pj76vXiCIkI5u2VLzDsloGERoYCULdZbcY9fQWf7niL15Y9g8vp4r7eT7Br3R6DIz43sdUjuGRsN1b8sJnDyRW3mex8NYqLoUOdmny7ZafRoZySTg4aEnIDeDKgYE65l2U2mbiuWyf+/OsQ65KNPSlfPCqRILuVeZ//ZmgcJ0oc1IH87AK2rz517UpEaNu7Jf9Z/hxBIUE8NeJlnA6nn6M8P8Ov6Q4izJu20uhQDDW4ZVO2H043fJDGqejkoIGtK1haofI+9st8S5e1a0lUsJ2PfjN2KF94VAgXXtqBpfM3/D2NdyDoOKANJpPwx6L1Z9yvTkJN7nr3JtKTj7L8q1V+iq5sxNeMoufFrVn05Wryc8u/xhqoBrVsigDfbQm8vi+dHDTvsNbQCeDeC0XLyr28YKuVazq356edSYavkDXqht64nO6AGj0THh1G825N+fWb38nNOvMd5Z0Htadei9rMfutbP0VXdkZe15P83CIWz/bPHF+BqHp4GJ3r1wnIpiWdHDQv+8VgroPKfQPf8hvlalyXDkQF2/n34uWG9j3UbhDHRZd1Yt7UlSRtC5y+hxF3DObgjhRubv8Afy47fad5QU4BecfyMVvMfoyubDRvV48GTWuw8seqfVPcRc2bsOdoBgcDZNbi40q0noNW+YlYIew+1LH7oHAeBI8s1/Iig+3c2ac7zy1ayk87khjQvEm5lncmEx4cwu9Lt/PGY1/zxpe3BcSJtt+YC6jRsBovjXubB/s/Q/1WdWjeuQl52fnkZReQn+1NCsfSs8nNzOWZOQ8ZHXKptOvWmEVfrsbpcGG1Vc3TUfcG3lWVf99/kLrRkQZH8w9dc9D+YR8CltaonDdQqvwnSBvTqS1N4mJ46cflOFyuci/vdMKjQrj18eHs3vIXsz/91bA4TtSiawLvrXuZEXcOxmqzsOaHPzmw7S8KcgoIjQyhYZt69Li0M49Ov4dmiY2NDrdU2nRpSFGhk52bKsYaB+WhSXwscaEhrNp70OhQ/kfVTNXaKYmYIOJhVMY1kDcFwiaWa3kWk4lHBvZlwvRv+Gz1em7q0blcyzuTXoPbsGxBSz5/azE9LmpFrfpxhsVSXHCondveuN7oMMpNm8SGAGxcvYdWnRoYG4xBRIRuDeqyat/BgFktEUpQcxCRj0UkTUQ2F9t2uYhsERGPiCQW2361bz3p4z8eEWl/imM+LSJ/FdtvyIn7aMYQWxcIuhCV9z7KfbTcy+vZuD79Ehry7i+/czjbP9OHn4qIcNtTl2Kxmnn1oS9xOY29g7uqiIgOpV6Tauz4M7Cumv2tS4M6pOcGzmqJULJmpU+BQSds2wxcBiwvvlEpNU0p1V4p1R64FtinlDrd5CGvH99XKbXwHOPWypGEPwiqEJX7ll/Ke3RgX9weDy/8sMwv5Z1OXPVI7nz2MratP8DUd340NJaqJL5GVEANJTZCixrVANhxON3gSP5x1uSglFoOZJywbZtS6mwDc8cCM84jNs0gYmkMIVdBwRcoZ/mPv64XE8XNF3Rh0bZd/GLwCll9L2nHxaMT+XLSMtavNH7tiaogKjb073W9q6qE+FgE2JkWOPNNlWeH9JWcOTncISIbfc1W0afbSUQmisgaEVmTnh44WbWyk7A7QMJROS/6ZajpTT0SaRATzbPfLaHQaVznNMAtjw2nTqN4XnnwC7KOGtfUVVVExoSSlVG1/52DrVbqxUSxM738m3JLqlySg4h0BfKVUqebUeo9oDHQHkgFXjvdsZRSk5VSiUqpxPj4+LIPVjslMUUhYXeC4zcoWlru5dksFp4eciEHMo/x/q+ry728M7GH2Hjk9avIPVbAv++bgaOoYk1NUdFExoRRVOCs0ms8ADSNj6sSNYcxnKHWoJQ6rJRyK+9cDR8AXcopDu18hIwFcyNUzgt+mZSve8N6DG/TnA9++8PwO6cbNqvBXc+N5M9VSTx/1zQcDmNrM5WZ2+Xt/DeZq/bI+rrRkaQcyzZ8Qsrjyvx/Q0RMwOXAaeeAFpGaxV6OxNvBrQUYESsS8QS4D0Dex34p87GL+xEdYufBOYsocBp7xT5gRCfufHYkq5dt54W7puHUCaJcZB7JJSwyGFsVvQnuuLiwEIpcbvIcgVGDKslQ1hnASqCZiCSLyAQRGSkiyUB34FsRKT4xe28gWSm154TjfFhs2OvLIrJJRDYC/YB7y+TTaGVOgi6AoIt8Q1tTy728qGA7/x4+iF3pR3nxh5/LvbyzGXJlV25/agS/L93Gi/dO10Ncy0HGkRyi48KNDsNwsaEhABzJDYzFf0oyWmmsUqqmUsqqlKqjlPpIKTXb9zxIKVVdKXVxsf2XKaW6neI4Nyql1vieX6uUaqOUaquUGq6UKv+zjlZqEv4wKIXKfsYvVd6ejetzY/dEvli3ie+2Gj8h2dCrunHrE8NZ+eNW/n3/DJ0gylhmeg4xcWFGh2G4uFDvuh1H8gJj5FbVbuTTSkQsdZHwu6FoiXfeJT+4p18P2tWuweMLFrM/I8svZZ7J8Gt6MPGRoaz4fjP/d+dU3UldRjweDyn7jxJXM8roUAwXHWIHIKsgMKYw18lBK5mQ68DaEZX9HMp9qNyLs5rNvH7ZJVhMJu6atcDw4a3gnWL69icv5fel23hy4qcU5AXGl7gi27MtlayjubTvXjHnhipLZvGejitth7RWOYmYkciXACfq2GN++QOuHRXByyMGsf1wOs8uWlLu5ZXE0Ku7c/9Ll7Np9R4eunYy6YcCZ7qDiuiPn7cD0KlnU4MjCQC+KZU8OjloFY1Y6iNhD4LjFyj4yi9l9mnSkFt7duXrDVuYFSALsQ8Y0Ykn3x1H8r4j3D3qbbau2290SBXWmuU7SWhdR3dIA+LLDrrmoFVMIVeBrZv3zmmXf6ZZvrNPN7o3rMuz3y1hS+phv5R5Nl37teD1L28jODSIh8ZNZsH0VQHzpa4oUg8eZduGA3S7sIXRoQSE45OxBspfkU4O2jkRMSGRLwKgsh/xy5rTZpOJ10YOISYkhDu+mk9GXmAM9avfpDpvfHU7HXo04b/PzOHlB2bqfohz8PEri7DZLQwcnXj2nauA3CLv/Q2hNpvBkXjp5KCdMzHXRsIfA8fvkD/VL2XGhobwzhXDOJKbz21fzguIDmqA8MgQnn5/POPvvZjlCzdy9+X/Zf/uwKjdBLKNq/fw6/ebuHJiX+KqB87qZ0bKyC8AICY02OBIvHRy0EoneBQE9UXlvIJy7Tn7/mWgdc3qvDJiEBuSU7l/9kLcnvKvtZSEyWRizC39eP7jCeRk5XP36HdY9NVq3cx0Gm63h0nPz6da7Sguu6G30eEEjMx8b404JkQnB60CExEk4v9A7KhjD6GUf67kB7VsyiMD+/DjjiSe/35ZQJ2A23dvwjuz76JZm7q8+fg3PHr9R6QcCJxZNgPFoi9Xs2d7Kjc+OIQgu9XocAJGRp6v5hASYnAkXjo5aKUm5mpIxNPg/BOV+x+/lTu+a0du6NaJaWv+ZNKKP/xWbknEVo/gxc9u5I6nR7Bj40FuHfo6M979SU/c57N13X4mv7iAdl0b0XNQG6PDCSgp2TlE2oOwWwNjjimdHLTzIsGXQPBYyPsQVfj92d9QRh4c0IthrZvz+tIVfL1hi9/KLQmTycQlY7sx+bv76NqvBVPeXMztw99kQxVfPCh5bzrP3PoZcTUiefiNqwJmreRAsSvtCAnVAmPtctDJQSsDEvEYWNuijj3st/4HkwgvDB9Iz0b1eWLBYpbu9E+55yKueiSPvnk1z31wPS6Xm0eu+5CXH5hZJW+cyzqayxM3fQIiPPfB9UTF6LmUilNKsTPtKAnxsUaH8jedHLTzJmJDot4CrKisO1Ee/ww1tZnNvDl6KM1rxHPP19+yPjnFL+Weq8TezXh/wb2Mve1Cflm0iQkXvcKkF+ZXmXWTC/MdPH3LZ2Sm5/DMpPHUqh84V8eB4lB2LjlFRTTVNQetshFzLSTqdXDtRmU/4beO4rAgGx+MHUn1iDBumTmX7YcCcynZILuVcXcP5MNF99NvWHvmTV3J9f1f5oN/LyAjLdvo8MrN/t2HeXj8B+zanMxDr42hebt6RocUkLYf9v7dNq2maw5aJSRBFyBhd0PhfCg40/LhZSs2NISPrroMu9XCuM+/YlNK+U8MWFrV68Rw7wujmfTtvfQc1IY5U37juv4v8+5zcytVc5PL6Wbm+0u5Y8RbpB44ysOvX0X3Aa2MDitgrdp3EJvZTOuaNYwO5W8SSEMBzyYxMVGtWbPG6DC0M1DKg8q6GYp+Q2JnINa2fiv7YOYxxk+dxbGCQj68aiQd6tTyW9mllXLgKF9OWsqPc9YhIvQY0IrBV3ahXbfGFbbDNmlbCq8/OoukrSn0HtyWW58YTlSs7mM4k2HvTyEuLJRPrhlVLscXkbVKqXO6FV0nB63MKU8W6shIQCFxsxFTtN/KTj2Ww3VTZ5GWk8eksSPoUr+O38o+H4eTM5gzZQU/zllH7rECajeIY9AVXbhoZCciY0KNDq9EHA4XM99bwpeTlxERFcLtT43ggoGtjQ4r4KXl5NLrjQ94oH9PburRuVzK0MlBCxjKuQl1dAzYuiPRkxAx+63stJxcrp/6NclZ2fz3iuH0bFzfb2WfL0eRk18XbWbhF7+zZe0+LFYzPS9uTb9hHWjfo0lArrNcmO9g1ZKtzHx/Kft3Hab/iI7c/MhQwqMC42auQDfnz608NO97Zt94NS1rViuXMsolOYjIx8BQIE0p1dq37XLgaaAF0OX48p8i0gDYBuzwvX2VUuqWUxwzBvgCaADsA65QSmWeLVidHCoWlT8Tlf0khIzDFPG4X8vOyMvn+mnfkHQkg7dGX8KFTSveYjL7dx3muy9+56e568jNLiQo2Eq7ro3p1KspnXo2pVb9WMOanlxON+t/283S+etZ+dNWCvMdVK8dzW1PXUqXPs0NiamiumXmXLYcOszPd9+EqZz+P8srOfQGcoEpxZJDC8ADTAIeOCE5LDi+3xmO+TKQoZT6t4g8DEQrpR46W7A6OVQ8nuwXIf8TJPwxJHS8X8vOKijkxunfsO1QOq+OHMzglhVzQRlHkZMNq5JY8/MO1v66k5T93ik5atSNoVPPprTr1pjGLWtRo040JlP5jTFxOd3s3JTMsgUbWP7dRo5l5BEWYafnoLb0G9ae1okNyrX8yuhoXj693/iA67p25MEBvcqtnNIkh7PWUZVSy30n/eLbtvkKPJeyirsU6Ot7/hmwDDhrctAqHgl/COVORuW8AOZaiP0iv5UdFWzn02tGMXHGHO77ZiGZ+QVcldjOb+WXFVuQlS59mv99RZ5y4CjrftnJml928tPcdXw7YxUAwaFBNGpek0YtatK4RS0at6hFzXqxhIQFnfN3NT+3iL07UknalsKebSkkbUtl385DuJxubEEWuvZrQb9h7enUu1lANnVVFPM3b8fl8TCyXUujQzlJifocTlcjEJFlnFxz2ALsBLKBx5VSv5zieFlKqahirzOVUqfstRSRicBEgHr16nXav1+vulXRKFWAyrgWnDuRmM8Rm39P0PkOJ/d98y1Ld+1lQvdOPNC/V7lV3/3N6XCxb+ch30k8laStKezZkUphvuPvfSxWMxFRIUTGhBIZE0ZkTCgR0SEoj6Ig30FhgYPCPO9jQX4RedmFpKVk/X2vSkRUCI1bepNNk9a1SezdjNAwu1EfuVIZMXkqZpOJr2+8qlzLKbcO6XNIDkFAmFLqqIh0AuYArZRS2Se8r8TJoTjdrFRxKfcRVMYVoAqQmK8Qi39HEbk8Hv5v0VJmrN3IoBYJvHTpoICZ4KyseTweUg9kkLQ1hfRDWWRn5nMsI5djGXne55l5HMvMwySCPdRGcEgQ9mAb9hAb9mAbwaFB1G0UT+OWtWjUohZx1SMq7LDaQLYx5RCXfzSDJwb145rO7cu1rHJpVjoXSqkioMj3fK2IJAFNgRPP6IdFpKZSKlVEagJpZRmHFnjEHAfRk1FHr/TeBxEzEzH5b91gi8nEU4MvpG50JC//+AuHc3J594rhxIRWvhE1JpOJ2g3iqN0gcKZi0E426dfVRNqDGNE28JqUoIzvkBaRePGNWRSRRkACcKoZ0eYBx3snxwNzyzIOLTCJpQkS9Ta49qKy7vHbGhB/ly/ChO6JvDnqErYeSuPKT2ay9+hZB8lpWpnblXaEH3ckcW2XDoQFBcayoCc6a3IQkRnASqCZiCSLyAQRGSkiyUB34FsROT5Xc29go4j8CcwCblFKZfiO86GIHK/W/Bu4SER2ARf5XmtVgAT1QCKeAccvqOznDFmsZ1DLpky59nJyixyM+WQmaw785fcYtKpt0oo/CLFay7056Xzom+A0Q3hyXoW8yRB6G6bwewyJ4WBmFjfNmENy5jEevbgvYzu11W3rWrnbdzSTwe99xnVdO/LQRf5ZJrU0fQ56ULJmCAm7H4Ivh7x3UXkfGRJD3egovrh+DN0b1eOZ75bw0NzvKXA6DYlFqxqUUvzf98sItlq5vltHo8M5I50cNEN416B+FuyDUTkvofK/MCSOyGA7k8aM4M4+3Zm3aRtXfDyTfbofQisn323dyS9J+7inXw+qhQf2ZIQ6OWiGETEjka+ArTcq+0lUwQJD4jCJcEfvbnxw1UjScnIZ9dF0ftxetZf01MpeTmERL/zwM61qVKsQN2Pq5KAZSsSGRL8N1kTUsX+hCpcaFkuvxg345saraRgbze1fzeflH5fj8ngMi0erXN5c9htHcvN4+pL+WCrANCOBH6FW6YkEI9GTwNIclXUXyrHasFhqR0UwffwVjO3Ulo9WrmXclK9Izqo8i/BoxlixZz9T/9jA1Z3b07ZW4CzocyY6OWgBQUxhSMyHYK6DyrwZ5dxkWCw2i4Wnh/Tn1RGD2ZF2hOGTpvLNn1sMGXarVXyHc3J5YPZ3NImP5YH+PY0Op8R0ctAChphikJhPwBSNyrje0AQBMKxNc+ZNvJaWNeJ5ZN4P3PnVAjLy8g2NSatYXB4PD8z+jgKnkzdGXUKw1Wp0SCWmk4MWUMRcA4meAqYIVMZ1KMefhsZTOyqCz64dzb8G9GLZ7r0Mm/Q5y3ad6qZ/TTvZ2z+vZPX+ZJ4e3J8m8bFGh3NOdHLQAo5Y6iAx07w1iMzrUI51hsZjNpmY0D2RWRPGEhMaws0z5/Lktz+S53Cc/c1albVkZxKTfl3NqPatGBGAU3KfjU4OWkASc00kZiqY4lGZEwxPEADNq8fz9YSxTOjeiS/XbWLY+5/z8669RoelBaB1B1O475uFtKxZjScG9TM6nFLRyUELWGKugcRMKZYg1hodEjaLhX8N6M3U8VcQZLEwceYc7vn6W9Jyco0OTQsQW1IPc9OM2VQPD2PymBEVqp+hOJ0ctIDmTRCfg6kaKvNGlCMw5tZKrFebuROv5q4+3flpRxJD3pvCjLV/4tEjmqq0XWlHuGHaN0Ta7Xx6zWjiwkKNDqnUdHLQAp6Yq5+QIP4wOiTAW4u4vXc35t18LS1rVuPphUu46tMv2Jl2xOjQNAPsO5rJ9dO+xmo288k1o6gZ6b/1SsqDTg5ahSDmar4EUQOVcQOq8EejQ/pbw9hoPrtmFC8Nv5i9RzMZ+cE0Xlq8nOzCQqND0/xkU8ohxn76BS6P4tNrRlE/JursbwpwOjloFYaYqyGx08HaDJV1Byr/K6ND+puIMKJdS7677TpGtG3JJ6vWctE7nzDtjw043W6jw9PK0c+79zJuyiyCbVamj7+iwg1ZPR29noNW4ShPPirrTnD8goTdB6E3B9w6DNsOpfHi4uX8vu8gjWJjeOiiXvRp0jDg4tTOzzd/buHx+YtpWi2OyWNHBOxMq6VZz0EnB61CUsqJOvYIFM6DkHFI+KOIBFZFWCnF0l17eGnx/7d35+FV1Xcex9/f3Js9kIWEnUiACBREEEWDiiii1rqvrXZ0RudxxrpUWjoOY6ejM0/7WLHt46PjVlHUti5VW607D7ZqlWUgQCBGIEiAQICsZN/u/c4f52DT7DchnHuT7+t58uTek5PDhzz33O89v3N+3/MpRRWVzH2mAUoAAA7eSURBVM/K5N7FC5g2KsPraKafAsEgv/jor6xYs5H5WZk8eu0lJMXGeh2rS1YczJCiGkRrHoT6lRB3CZL8ICLhdz/e5kCAlzfm8djHa6hubOJbM6fxvbPmMXmQDD8MNQera7j3zQ9YW7SP78ydxX9cuJAYn8/rWN0akDvBicizInJYRLa1WXatiOSLSLDNfaERkcUislFEtrrfz+tim/eLyH4R2ex+XRxKaGMARKKQYcuQpKXQ+DZaeTsarPM6VgcxPh83zZvDh3fewi05c1m9vZBvPfkCS15/h512ZVNEeTd/O5c+9SJ5+w/y00sXc//Fi8K+MPRVj0cOIrIAqAVeUNWZ7rLpQBB4Cliqqhvc5XOAQ6p6QERmAh+o6rhOtnk/UKuqD4cS1o4cTFe0/lW0+ifgn4akPon4wrctckVdPc+tzeU3GzZT39zChdOz+d7Zp9twUxiraWziv9//iLe2fsnJ40az/IpvRtQVSX05cvD3tIKqfiIiE9stK3D/wfbrbmrzNB+IE5FYVW0KJZQxoZKE65yZ1Ed+gJZfA6lPIdEzvI7VqbTEBH646CxuyZnL8+s28eL/beKDgp2cP3Uyt+acypzxY+zEdRhZv6eYe998n0PVtdy14Az+9ezTI+JmPf01kP/Dq4FN3RSGO0Ukzx22Sh3AHGaIkLhzkbSXAB9acUNYzYXoTGpCPPecO5+P7rqVOxecwfo9xXxn5Stc9+zLvL3tS7sE1mOltXXc96dV/MMLv8cfFcXv/vF67jwnZ0gUBujlCWn3yOHto8NKbZb/hTbDSm2WzwDeAi5Q1V2dbG8UUAYo8D/AGFW9pYt/+zbgNoDMzMy5e/bs6TGvGdo0cBituh1atiHDlkHCzRHxSbyuuZk/5hXwwrpciiqqGDkskRvmnsz1c2eRlhDvdbwho7apmWfXbOC5tbk0BwLcdPoc7lqQQ0JMZPZIggG8WimU4iAi44GPgH9S1c/6uu3O2DkH01uqDWjVUmhaBQk3IsPuQ6THUdSwEFTl08Iinl+fy2df7SXW7+OSmdO4ds5MZo+zIaeB0hII8GruVh77ZC0V9Q1cND2bJeeeycQRkT+wMSDnHEIMkAK8AyzrrjCIyBhVLXGfXgls62pdY/pCJB5SHkVrlkP9CjRQDMm/RKLCv99NlAjnZGdxTnYWhaXlvLB+E29tLeD1zflMTk/jmtkzuXzWdEYkJngddVBoDQZ5L38Hj36yhj0VVcw7YTw/WnQ2s8aF70UNx0NvrlZ6CVgIpAOHgP8CKoBHgQygCtisqheKyI+BZcDONpu4QFUPi8gzwJOqukFEXgRm4wwrFQH/0qZYdMmOHExfaP3LaPUD4MtEUv8X8U/xOlLIapuaee+L7by+OZ9NxSX4o6I4N3sSV8+ewdlTJg6ZcfBjqaGlhTc257Ni7Ub2V1WT7d7jeTDOZLdJcMZ0QZvXo1XfB21Ekh9C4hZ7HanPCkvLeX1LPm/mFVBeV09aQjyLpk5m8dQp5GRNIMYfGcNnXtlVVsEruXn8YcsXVDc2MWf8GP55/mmcd+IkogZZUTjKioMx3dDAQbTqTmjJg8Q7kKS7wq7lRihaAgH+snM37+Zv5+PCIuqam0mMiWFhdhbnT53MgilZJMWG34xxL1Q3NvLR9q94I+8L1hXtIzoqisXTpnDjabOZO2HsoDtSaM+KgzE9UG1yhpgaXoPYhUjyw0jUcK9j9Vtzaytrdu9j1fZCVm/fRUV9AzE+H/MmjufMrBOYPymTqSPTB/2bYFtHC8J7BTv4bNceWoJBxqUM5/o5J3H17BkRfSOeUFlxMKYXVBUafodW/xR845HUxyPyPERXAsEgufsOsGp7IZ8W7uGr8goA0hMTmD8pkzOzTiBnUiajwrSDaH+U19XzaWER7xfs4K9f7aUlEGBs8jAumn4iF30jm1ljRw+pAnmUFQdjQqDNG9Cqu0EbkOEPIPGXeR1pQJQcqeHz3Xv57Ks9fL57L5X1DQBkpiZz0tjRnDR2FCeNHcX00SNJjImsYaiSIzXk7jtAbvEB1hftY0dpOYAVhHasOBgTIuc8xBJo2Qjx1yDDfoxEDd5LRIOqfHmwlM9372XL/hK2HjhESXUN4FxCOzk9jWmjMpg6Kp2stFSyRqQyITXZ85PcrcEgeyuq2FVWQWFpOTsOl7F5fwkHjjjZE6KjmT1+DDlZE8jJymTmmFFDviC0ZcXBmD5QbUVrH4W6J8E/GUl+BInO9jrWcVNWW8e2kkNsPXCIbSWHKDhYyqGa2q9/HiXC+JThTByRyglpKYxMSiIjKYH0pEQy3K/UhPh+XenT2NJKaW0dpbV1lNXWUVpXz+GaWorKK9lVVkFReSUtweDX649LHs6scaM5ZcJYTpkwlmmjMuxy3m5YcTCmH7TpM/TIUgjWIcP/0zmSGKKfPqsbGymqqKKovJLd5ZVff99TWUV9c0uH9X0iJMfHER8dTYzfR5zfT1y0nxi/nzi/nyhx7mvR1BqguTXgPm6lORDgSEMjtU3NHbZ5tChNTh/BlIw0pqSPYHJGGpPS0yJu+MtrVhyM6ScNlDoFonkNxF3qnIuIGnwnbvujrrmZ8tp6SuvqKKutdz7p19ZT2dBAU0srja2tNLa00hxwvje2tqIKsX4f0T4fMX4fsT4/MX4fMT4fw+NjyUhMJGNYIumJiV8flaQlxOOzo4FjwvP2GcZEOvFlQOqzUPc0WvsI2rIFkh9CYk7xOlrYSIyJITEthswIup+BCZ2VZWPaEfEhSbcjaS+CBtCKGwjWPIxqx6EPYwYrKw7GdEFiTkPS/wTxVzlHEuXXoC3bvY5lzHFhxcGYbkhUElHJP0NSnoBgKVp+FVr7a1TtRjxmcLPiYEwvSNwiJP0diF2I1i5HK76Ltu71OpYxA8aKgzG9JFFpSMpjSPJD0LodLbsErVuBaqvX0Yw55qw4GBMCEUHir0DS34XY+WjNz9Hy69CWL7yOZswxZcXBmD4Q32gk5Qkk5REIlqDlVxOsWY5qo9fRjDkmrDgY00cigsR9E0l/D+KvhLpfo2WXok1rvY5mTL9ZcTCmnyQqxbmiKfV5QNHKmwhW3YsGyryOZkyfWXEw5hiR2Bwk/W1IvA0a30bLLkDrVqLasReRMeGuV8VBRJ4VkcMisq3NsmtFJF9EgiJyarv1l4lIoYhsF5ELu9hmloisE5GdIvKKiFgnLRPxROKIGrbUKRLRc9Can6HlV9hQk4k4vT1yWAlc1G7ZNuAq4JO2C0XkG8C3gRnu7zwuIr5Otvlz4Feqmg1UArf2PrYx4U38WUjqM0jK46AN7lDTPWjgoNfRjOmVXhUHVf0EqGi3rEBVO+slcDnwsqo2qepuoBCY13YFcfognwe85i56HrgixOzGhDXnhPX5SPq7SNLd0LgarXnY61jG9MpAdGUdB7Q9hi52l7U1AqjSv80e6mwdAETkNuA292mtiAxEc5t0INLOHlrmgTcAebcBvzi2m/x79jceeJGYeWqovzAQxaGzu6O0v2lEb9ZxFqo+DTzd31DdEZENofY695plHniRlhciL3Ok5YXIzRzq7wzE1UrFwIQ2z8cDB9qtUwakiIi/m3WMMcZ4ZCCKw1vAt0UkVkSygGxgfdsV1Ln93J+Ba9xFNwNvDkAWY4wxfdDbS1lfAtYAU0WkWERuFZErRaQYyAHeEZEPAFQ1H3gV+AJ4H7hD3f7GIvKuiIx1N3sv8AMRKcQ5B7HiWP7HQjSgw1YDxDIPvEjLC5GXOdLywhDJHFH3kDbGGHN82AxpY4wxHVhxMMYY08GQLw4ikiIir4nIlyJSICI5XmfqjogscduWbBORl0QkzutM7XXRbiVNRFa57VJWiUiqlxnb6yLzcvd1kScifxCRFC8zttdZ5jY/WyoiKiLpXmTrTFd5ReQut9VOvog85FW+znTxupgtImtFZLOIbBCRed1t43gSkQki8mf3vSxfRL7vLg95/xvyxQF4BHhfVacBJwMFHufpkoiMA+4GTlXVmYAPp1VJuFlJx3Yr/w6sdtulrHafh5OVdMy8CpipqrOAHcCy4x2qByvpmBkRmQAsBsLtPqYraZdXRM7F6aowS1VnAOE2hXwlHf/GDwEPqOps4Cfu83DRCvxQVacDZwB3uC2NQt7/hnRxEJHhwALcK6VUtVlVq7xN1SM/EO/OEUkgDOeHdNZuBecN4Hn3cdi1S+miRcyHbWbxr8WZjxM2uvg7A/wK+De6mFjqlS7y3g48qKpN7jqHj3uwbnSRWYHh7uNkwmgfVNUSVc11H9fgfNgdRx/2vyFdHIBJQCnwnIhsEpFnRCTR61BdUdX9OJ+s9gIlwBFV/dDbVL02SlVLwHkBAyM9zhOqW4D3vA7RExG5DNivqlu8ztJLJwJnux2aPxaR07wO1Av3AMtFZB/O/hhuR5QAiMhEYA6wjj7sf0O9OPiBU4AnVHUOUEf4DXd8zR0nvBzIAsYCiSLyXW9TDX4ich/O4fpvvc7SHRFJAO7DGeqIFH4gFWcI5EfAq25jznB2O7BEVScAS/B2jlanRCQJeB24R1Wr+7KNoV4cioFiVV3nPn8Np1iEq/OB3apaqs4dZN4A5nucqbcOicgYAPd7WA0fdEVEbgYuAW7U8J8UNBnng8MWESnCGQbLFZHRnqbqXjHwhjrWA0Gcxnbh7GacfQ/g97TrOu01EYnGKQy/VdWjOUPe/4Z0cVDVg8A+ETnasXARzszucLUXOENEEtxPV4sI4xPo7byFs1NBhLRLEZGLcGbyX6aq9V7n6YmqblXVkao6UVUn4rzxnuK+zsPVH3Ha9yMiJwIxhH/H0wPAOe7j84CdHmb5O+77wgqgQFV/2eZHoe9/qjqkv4DZwAYgD+eFmup1ph7yPgB8idP7+UUg1utMnWR8CeecSAvOG9StOC1SVuPsSKuBNK9z9iJzIbAP2Ox+Pel1zp4yt/t5EZDudc4e/sYxwG/c13MucJ7XOXuR+SxgI7AFZzx/rtc52+Q9C+eEeV6b1+3Ffdn/rH2GMcaYDob0sJIxxpjOWXEwxhjTgRUHY4wxHVhxMMYY04EVB2OMMR1YcTDGGNOBFQdjjDEd/D9zM/Z/JpXIyAAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "scanA = np.linspace(110.0,130.0,50)\n",
+    "scanB = np.linspace(5,20,50)\n",
+    "minValue = nll(solution.x)\n",
+    "Z = [[nll([a,b]) - minValue for b in scanB] for a in scanA]\n",
+    "p1 = plt.contour(scanB, scanA, Z, [0.01,0.5, 2.0, 4.5])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Compute numerically the error matrix of the NLL for the 2-D fit."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 301,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[[11.95694692 -3.06065748]\n",
+      " [-3.06065748  5.72672173]] \n",
+      "sigma(position): 3.4578818544955507 sigma(width): 2.3930569834299082\n"
+     ]
+    }
+   ],
+   "source": [
+    "from scipy.misc import derivative\n",
+    "\n",
+    "# compute the error matrix\n",
+    "A = np.linalg.inv([\n",
+    "    [\n",
+    "        derivative(lambda x: nll([x, solution.x[1]]), solution.x[0], n=2),\n",
+    "        derivative(lambda y: derivative(lambda x: nll([x, y]), solution.x[0]), solution.x[1])\n",
+    "    ],\n",
+    "    [\n",
+    "        derivative(lambda x: derivative(lambda y: nll([x, y]), solution.x[1]), solution.x[0]),\n",
+    "        derivative(lambda y: nll([solution.x[0], y]), solution.x[1], n=2)\n",
+    "    ]\n",
+    "])\n",
+    "print(A, \"\\nsigma(position):\", np.sqrt(A[0,0]), \"sigma(width):\", np.sqrt(A[1,1]))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Binned ML fit"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "With the same data as above, we now perform a binned ML fit and compare with the unbinned fit.\n",
+    "First, create a histogram of the data using np.histogram."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 374,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[ 1 19 26 10  7  5  5  2  0  0]\n",
+      "[ 70.  80.  90. 100. 110. 120. 130. 140. 150. 160. 170.]\n"
+     ]
+    }
+   ],
+   "source": [
+    "nBins = 10\n",
+    "histoMax = 170\n",
+    "histoMin = 70\n",
+    "binWidth = (histoMax-histoMin)/nBins\n",
+    "h0 = np.histogram(measurements, bins=nBins, range=(histoMin, histoMax))\n",
+    "print(h0[0])\n",
+    "print(h0[1])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Compute the binned NLL:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 375,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def nll_binned(params):\n",
+    "    # params is a list of [position, sigma]\n",
+    "    expected = [likelihood_point(x+binWidth/2, params[0], params[1])*(binWidth/2)*sum(h0[0]) for x in h0[1]]\n",
+    "    return sum([-np.log(expected[i]**h0[0][i]) for i in range(nBins)])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Minimize the binned NLL:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 376,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "     fun: -138.93433719876123\n",
+      "     jac: array([-1.90734863e-06,  1.90734863e-06])\n",
+      " message: 'Optimization terminated successfully.'\n",
+      "    nfev: 60\n",
+      "     nit: 6\n",
+      "    njev: 15\n",
+      "  status: 0\n",
+      " success: True\n",
+      "       x: array([116.43876363,  15.33581135])\n"
+     ]
+    }
+   ],
+   "source": [
+    "solution_binned=minimize(nll_binned, [120.0, 10], method='CG')\n",
+    "print(solution_binned)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Make a contour plot of the 1,2, and 3 $\\sigma$ contours for the binned NLL and overlay it with the unbinned contours."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 377,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "scanA = np.linspace(110.0,130.0,50)\n",
+    "scanB = np.linspace(5,20,50)\n",
+    "Z_binned = [[nll_binned([a,b]) - solution_binned.fun for b in scanB] for a in scanA]\n",
+    "\n",
+    "fig1, ax2 = plt.subplots(constrained_layout=True)\n",
+    "\n",
+    "p1 = ax2.contour(scanB, scanA, Z, [0.01,0.5, 2.0, 4.5])\n",
+    "p2 = ax2.contour(scanB, scanA, Z_binned, [0.01,0.5, 2.0, 4.5], linestyles=\"dotted\")\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Repeat the same for 50 bins:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 373,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "scanA = np.linspace(110.0,130.0,50)\n",
+    "scanB = np.linspace(5,20,50)\n",
+    "Z_binned = [[nll_binned([a,b]) - solution_binned.fun for b in scanB] for a in scanA]\n",
+    "\n",
+    "fig1, ax2 = plt.subplots(constrained_layout=True)\n",
+    "\n",
+    "p1 = ax2.contour(scanB, scanA, Z, [0.01,0.5, 2.0, 4.5])\n",
+    "p2 = ax2.contour(scanB, scanA, Z_binned, [0.01,0.5, 2.0, 4.5], linestyles=\"dotted\")\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.7"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/exercises/Solutions7/Solutions_7.pdf b/exercises/Solutions7/Solutions_7.pdf
new file mode 100644
index 0000000000000000000000000000000000000000..c77d5b460e9f6514ad5bc44d36ea1cba3630ab64
GIT binary patch
literal 125204
zcmce-WmsKJwl0bVg1fuBy9Rd)?!It$hXfDq?he7--Q696I|O$)E8o|ByZ7FG?m73*
z{juh=YR;OY-g;})kgA&GilXB5%na-><a@u*A7PkTiI|A&j4WaJ_!z}4tepXljN;aY
z&Hzz>v7HHkQ5Ilp=4?*H%Ff0rAOPd!><BQlfpK4L(9yKp<3RD<(w||3t&eBxd-)ub
z%#U(r#IbA*xg5g9wAyG5JEz{de>ML8O#cH#tkDFq)HDWKt3oYN0OPcrMo;gxlL!{>
zBQ_}SHzMOjV|Z7)zV{2bCQ=X;viH@yp-{aL&gp=(Jz1nid)J4{)0pqg`uxkrvAFKr
z{d=~2AQ34<0|{*~1ad`STv6hq#YfHRrrJ<BI=VY`r$aUx>ITi@i`Ro`D-j+ugEqT+
z4H#T#of>$H7IMVZZ(GHl2^J9l*q&hLI29ss722n&yJF9mc9*HH@-Hgs&lX)>Zb*%)
zHxNtspREug`ggv3c^=!%4;Y-(_otqfZM1tFy09dt(tpQcr-;j$*BdlK3t#%gC4NtX
z@_60`S|awiY}k+g>%JChT*L;9N2Nw%t<Rw=a~kS>nmt?gbk=A+Aw50h@Zd>fV|`pq
zvYDW~qABF9hFhJR1B1(g&Yf$q4U*~n;%61aM!Fe23G&<yJQejl3k<VIk<CLpzju}m
zjd)BR&0@2_L=T4sKZF}rsqp%T9Xg~w<<mC2Bs@z*lhy}cQ_HpNjOzv-$BU`*76lh%
zVvT*r+I*X3qlkTQ^FbjV$utKW48FixdCOuRmggs7w*sFmCq+r{xDky7!#N$iz+Ygj
zY80P8waUBQZL_Wtrg>_ARHaQYh{aIwsAb}WmdFJhHeX~#6PBlkv_1_MhS_vjAvfv|
zTyNKJd1qY4!4S;WM=*oyPNo!_Ze*Bh&nCfDd_Z`{i?nX*#=r);z3evDt!^mGwT(@@
z&%85HhT#SmkgQ1YA(4J3BfAnb$mrh+&HlFDsFh9BXIrj2O`7W9F^u<Z=Mt3%91P@}
zK<M6gwpiFcUhy0R^6nTc63XcP#i8Bjo4aY77K31aY^orn3UYGjzz_xuo&MRa@j;SF
zYuOwE3X#-DNCc6gN)BA8KI?UwiWrn&A8c4~J8YLrZ#v-!ZOG&VrY!$~(3-=^!QL9Y
zCfPoIDb#9vhM}|cXt5)Qv7BgwTN9LOEO_Fg1YHP@-6ZPf;?HuSuItX&oV~N+yQils
zDXmzeOghcChM!Tm3X7j%=wmW%aKATL>E?_pNiip)xVJsGe~#nu9-R0-)4`@f0g)mg
zn<Th1Rv?INh~#7sgMv#Io)cp|PiBuSucXr-GK?aL6^1T!-p4I3`9**WLx_04L@Ehd
z{3OtSHe~R#ly)}yFQx%snvv@;vsf-9hVGruFbQH5;e%q;?gUV)Z2e9ob)h<7W8Hhx
z3>jcpiPHNdTzttQ=~!3+<f?R8<K)#`91_05$YwktlVbMn0#K_t1WMxNc)}7I(#&ZN
z{r(EN^{AqyaN<zCpRn+SW9=1U@x(EEcChh{YP#woVjYpG&{{j_X+>-Tzk$mnvEap3
z{-g`|)DD<u!8&3xbsvq9jO2a%P8^F3Wb-H@Jb@3M+?IO@@QWcoNl0#lOM!$V%W*?#
zpQA~{Y)e%|CQj%~uo<KV+-6!CPdtLVpW*ueD(^Jp9eJ<R@l5dvibk*szAG}HEtizs
zf|&iI3jR*D#mIDqiY{2V%tFl=OQnZ=8N>I!;*(_x@#4p`m@-njLZya!W0D2+RE=xA
zzq?^hnnDy|fsh|=a20`2kDXquBrXM>FJpj>g})l~?Kn{i;EaryHIpkxCJoaplEwfE
zkethv;!>#=-zzvK(eO?~m@rx*!Jd$sLrzk%&~h*-IOBsP{poaa3B+D1LUm8U%3T0N
z9?vrkS&A&;k}LuQ4yh#J3hFOqZ7HP-5kl#b1OG$TP*g3A;oCsrakU*4i$@>>SRX3S
z84g%s;c!_S5cg;J(v^$W6F&8@1SwsGBcSYDaGO9yEO}G-5+L-zsA|C?WK9=0XgcuR
zJ_ZyfwqCx(Y2Y9VOK^N6i8n=Vg}{(Fs_X2@*q`43a@ES)|NXp~%jySRuTsgyLCUI8
z8!pRgp-ATBk>aDHc*tr!dF)7O+nB*IHooCEBWaYZo+A<szWjWKT57FAdC%mY8CIS_
zP=*}VlM50>?+ttx#AZZ=5fK5o<eu5$W5smC(3t|&X92k(4VXd;gewfb#`3mrM11)d
z2tlwhJb~ZORWc`uU6ARr=7}+2^d=+`N`aJ10u||HM$<BlA)pV)*Wv+YQXQQxgiLzT
zZs)cn;0%N&AWz{bRt!xkVt5{3umA}o`<j%_04<J%_P`BfKtp-}!xAa>=hl!Q*%TI7
zVDBd*nVbm>xL^}&7CP|4En_T8(gdP37O>w|mSWY9><uL$2A{{`ER%iZ=o8-0pHE)*
z#_d6%`s%<~XwFYG=Lxfj%ez{P8T(SP9&5RIQJo^0ZS;86!66GK?LNE!!?m?TmcOqp
z-fB8X#u{F@R3qD#zcec}`P}^|0W<|QBzk$2U1sqJJ><9f>1U@I`@F2HJZx0CxxL(f
zd+~fY%Cfk>k7r0PzF44i8#Z${yQy`<{8E{%Q0#e*03W-5S1TNC+f~YMVnSM^y=j|u
za-lP`;dvP0pxrX1{MM{(w|?ijq<P}%^RSI<FtY#d;pXQP#8V#TF2db=!3J&A+i|$e
zZ)RP^4BfS=yUb*+4r0C>Z#7XB^R;=cJv@{dHmc;f5&f3n_X`*bg$fNL(iu`wOdkYX
z8@i4)rXUU6x52FyI$zD#_3Rs*1jO$}TOGTQ#-hTJ!kDg`*`4=l3`j#H-sKy0@Rk%?
zKZLOR7I$1xNl}``hLBBdqi&Ge{lAuOfBJrZf;Ia?g3;bh&msx4eo$Th%UvcLAC%ov
zUftWa<tGIDYkm-LlfnX6yU$$BIT*eL(qclL@ZVxU!qRt2J>|akAuruaK|y1MP;Q!W
zuA@xPQ*KQ&iE8(mLwS=E{e~g!W?J>cCA<&~##ZqXswOn9F(j!9e1<ZDbtG`>xP>QB
z=|Qhg=yStN6M#Pt>K1c(iMi9=z~(Zy`3bcdIK=Qv%W}2wzPb=%%^23Xwv=Srs@^5P
zxwO~<4CiM^qn_OD=Mcpnn>skZ4U^xjOpqiD25!+l{9<M$%_1Cg%vERoe5AxxIgk*b
zS7JPfafi-}F(6NC&J=K#c=Y)X(rvz#`lQa&?zBJgSwYKY^WXRI2m+G@)pj}h8s{-d
ze8bxAw07I6s<m+AMlEp%wu~BhV3Bg2Dr2fu<Ephg)<SHHOAs00EC^T_neCMHvF%YP
z7<L7|PAlQo8g?s4S_#4{s@R{RUwcU%ankRIqoQEPSlW828_j+zRG$A7-9%1SfTYU-
z;SX1b+t+gpFt}evSVhqZ3eIswW3vcmVnU+oL0hW9mq)E83X(on3@y1KHMVHeva74V
zsQz9vYyvj}rU&CNn2{>rT&NlzwLGB!64g5We60en8mB36)&{=tqN`q5pNQz)nl`Lg
zPmZL6S9TXNw-Oz4=g?7+cC!3djgaWKe6HV$W8UHL<>%!W1rL6>{sh{5(P@|aTq0yr
z3q*P1byck<<!{P8-Rg9~A)VPQa1cZhnup{}<2}mknDe7Zy`n1XnG}Z5sSx32Jt+8P
z?Q`Z}aV+N)87%VoK}HEDjM?I_pBTO<5m>fW5ui*Figy!PH}vHzE-+EGaG#y(j)wKT
z|1vE(@O&ywkNI+@(7+G3>Ely+nO=Nae(5-+>-qEiwlsgg#xg6qO$Jd8^~VMmql%oc
zLRx7uH&oQEuUE~YZ#{t}Ald4?2y?6ddC>ezLfbF@yaaqzFc+$!Pv9+cmqSIg8I0Un
zSY-vtt1DBIfm&UoTw6MWlL;)GF9{ZAUU`1=UqU=NBHoeAD%qb?Qxz-W>FQ#aD?ktL
z-%fTmgLBx8BtR=1*FD&FODw54Wz7J6M@uy<yi-#N$qxIjWqfmyYw_qd`#8g@*$6lJ
zLD}wz?3E1X7F~RpmY+s-em3d*Or7(H4Yjv5w)i$~)MHDms19E3P$9V_4ti(bpF*Ts
z#K7{{4!Y={>i8Fbq1R#aZ%*y1#hF;Pb=DD<lT_kHXupA{NFiW7I1Nu<B20J}zNx*F
zR&C}%`w_+aUAIz|Ql%Ev@jfGpqENU4WH$P{wQzHCN<`*P?TI3bPBTk_Y=jpBF@=o$
zpbzWTpwcump|UmIZL{5hda%;ALxrP5N}P0rhqnVqVj7vroOi_u;^(*7y`@N$xancm
z!w~bJr)5J_JY8fRmJ}*!(@&_?{ah(JD!eqz%pdj+=Oux3dYm)^gSs)ZE|x~F+j*^D
zv3b8LE}P&ejFS|c<@lRXp6sl^&5-$#h${;S&8`TPuJoTt^mh79>@ABhl=`!)`*kB}
zg@-*wgK13+Q^)@>sMsrick1Lb)bCyW64Z~hXlwKu^6Xygw}&A9IEL94ku%`v!^3a4
zX0=Jr*Bz8jYT;+6PS?A2CM@=#QoIA`uxbstznD8$wD7a$wC=93j|8)2YVy6>nBM^b
zFPGV5gp+6Zw1#DOWX>wix5XHFmPG`%Jrc6chJNw=`3Gw2tDB2=wO_@gW^rQe!&*?N
zK|`<2!ZyWb*^RAZxxCjgW<c<MrY+v{=&YjEQP_TlE5eV#Q+u;>6iJ8)%XEV#dYHmI
zj#rm1e(SO#9xsOvGfjmzg!GB`0p0txZ@*^mOnIv|(|TmGFV(Ko9b6Z7TT8#(N1qew
zuWZ!69&fbymdkg^qclXZ>?Cm%fZ>?qGj_kf7Jq<CzWRv40BlYEk>LUV{mBWLx&D<L
zs(9D~7&R1(ECI&OjLI%X&VR~MHil*ZMs*95KRF^NJEJ7P!pz*6h=rYnQN+&L&QaOk
z&=|ld3UIYB21qy>dN2xGI6El<97XJG?Cop;w$3nIz^8vQO+`aT8z*26`rj)Mas4gH
ziHP~n9~T>2r@vZKbOe}K7(3fJ60ryf{O>bY7-mjp=6}yz&o}&J=UwoIt`I>D&EHe>
zl*b7DOk~73z!B3YHHvhSxgBUIkA<x+UR75m#kJO#@o>iT>|xEgdc?SEEFJ?;4y(X!
z?B9^oI3h6K1-A!r;&2FjqF<1UH+;C<9;nxE?xygYk_k5jUN2xdA0LswBME;d4g_AF
zx(cVlN<#THSK~`|nv)VBm&!A{Ay_9`dG(=B*DyRz?ktJb`qSh_sH;Or*sngWjc!Z#
zi-St4Rm5~=TzV^m`P&b!DzH~~xkd6z6wDX1=(Q<!a$ueo1b*Kv90r!i%`V^3>ztP@
zi!I*q*|J!!xcD^)-0mMZ606BGr5`Z-k}1QvU(Vb{Je!<sytq_mnK5kzX=_a%-}r{_
zx(t0*jk;_|S>1=gdsf{iZxE8%jlc_h0QRQ{ya(~8N=Lkp=#}D2qxZSgPN)9BruFhd
zH6pI-{dO9+3y#+_U8QRwXR6O9{hrDUpWCpMFKqX<gEGerQPC?Cx!*gst|Gk}Lf-E=
zjNh>;UB%8~_tCZ2r*L=D4b^)I8s{2|PDiGT<lnNsTIYT36S&)pcD-7ZyqYQ7$M@^J
zg6G8EWa#-9u4TgzAZo+^4)YIIAZiq<HZx8Mjm{eWbGkFjW&Sqqi1JA2C+O<(wh`y^
z11df>8ejQ#xHcNsL))lpxnfa3uxfR>2z;9r=g>F2NFQBz^)?*W2U`SN7Mc~{+j<%R
z*+;>tWEJ+atPMQFPr>)(YRevN5dUBZNX|dt{6*MbT>XQ|zp0bTITNd^9ONSCh&u4+
zpFf*h{gHXLF%|01zsH$6vtRuYa+a0PZJ<89d2WutF;imf*6+jyr4Gbb1-y;O?q#tP
zQ}lC~7x;E&lgvLx3P{F(qu`%7{xO*RW^R`Uzj@NY?-lFX+??F;CqTW1?N}<e9`By@
zZw%E3-lt~ps%cFwUD=@8xSL1T56|=@oVp1t|MdJ%!-wZ6e-Heh7XNAts38As41fv(
zY*q^7Eco^Jd3ya&{d#zw0WXmBzNsm5zPf68<7oYXh02IG3z@iKH16thK(?8?DxLgv
z|D0oGrvA&Hs)v*oXU_}5aJFq0dcS|}lDj%lwU2OJ+%$sfoLerFPAYV{Q#7cB8YYW5
z4w@{Z`+!?`e`z;2ZglaRJVRK!&i)TT|DgS^I{inn|EM_VA1*}EhZ+jlx+{Y>>e7wH
zey${BfB^@5icw2lql~2ThzF>W7b_DElhafusA+!}zNQz{oN7b-MO4!tHK3(Tk#oZQ
zO|Ck_8FR6L=OazADvSAVP~yD|yYADMLPXNMu0uQ*VNQ%X3fQIH6h5>TAAdD`Z`HW8
z?E!+ipj*ReS5w41-JlniocUSKY}5((xk)2>K-;8W9@AiI?fH8#U82w`EY>B7&w{qG
zy31!4UW(T7MHzX3yK`yO=SuA@PHp9OGRfL=Nh|JHmwszbhP9%COFW_hvq*cOS?TRJ
zsls|vJq3sEk5YG)#v~-b{hUXwTK~$x7NTvU&*1z@(NSwZWOhZW1TRN8+~1<iWH>tN
z_RTy!G7jSj=iWV|vISK&%E;*H4JZ2SMzx%ZbnM3BeZ-AV@(9KonAkl87fjN#lO(eq
z;=Da2WCVQFWq3OB_wAlRmSM}>!lDt+W9z0^zffwIU&dk|JKnQ9L>~bwE&iZOCFpQX
z*AHYASR>EgoQYGoG#@E;tACXv1bJDcvH2YUP`bZ*F<Ts49DQ+Z5}VBGbX4-%50;j)
z8vRi(M48?7tGGeH*S7etRy*?l$hhY8`EAD%jjwy%;9L<;+AsLR`zbDIB!*(MGnTdu
zuS`aCW_zPkn=!q(X{`9tQtBVSr*r|3`>X7u5cQ}QdJA~IEL{qH85zXNd!a;SJAB_u
z|ANG;z1btyB0}#*IPI(!ih?;_?b_o^`agtq9RsN4@MaSLFN#agx^Ws^^~Mv#npPqQ
zjKbsXG}>bizc#ePS$GjE;~SpOu`nA+m)vMs%fwXD0e=b6wUR1wYw%W3_}f<y?^V9S
z+Ys&Y{|ArE{V$LFKRm3GgfOGBoS~By(O-^RiShq~&xZNqv;Twh{lE3jtpC|N!!Wb|
zzqw~3E@o!7|LUHhG|<f6q3qpObd!Pr$C-tsN`*8e_o8%R{EZr@@$Q~sZc_7YIwOTT
zJB8TFY+spOQDlsM)IhY&LQv;9V<hP%>0va!O0;~rikVDGUAa8m-i{?<y-9cLdg)Z&
zG^9!#{R#qN*(VAO0zwTAB8d$GQUC`+1o*4up8)#Ll1kvh5+WAdV7|V9XPEt$FP;fO
zK|ytOtkQ6TULzwTZ|h>+Cm&x>Kk8_eL#O!Pr5~c7g+<5D*P3d;LCmW@s0{Ao83Wn}
zN<Th<fQZWm{D{^=End2ieQ*mDCO&}$`S~6qvr&5W7+U)AA@&&>TF3{~;Fpu^TOjP9
zGzimcf*~JU3RMprJMe#}tXsb`{&x>Hhv`^Q{~iPQ&XCzRd}lonL4Xf7$g~iz-<T9t
ziP$KJzZRMb=jAIsXN}@_U`HCb(iF#ySdxOdNdO>%cdI;ml(ymd-ZnoUb$~rC*;KFq
zM4s2G?q;eya!SRrz}sX}#>=3=WVC`!QxqzBQ0eVi*q|L4i8m#*B&a^EhSiJ4LbPEE
zhAR!+xyj&$MMYRv1(S-6iassl9M{ROGxXR(PUrPqi3Q_zd=f$^d#fvu#0HPr)bU%>
zzf5Y91T4>iXc1c+KXP|?8^(ZNB`NeJ5Ys_Bn4iad#C?s*gwH_-;^Q^>VW#T2o~%QS
zO2+2Wz{D3OgzC>hg*HkGEoR7Ins4)1FNX~oP(!s${FMxOq23@F+<bXq-Lj^@CJ~2+
zC128|V?2)tio$rl6wTQTgzem3CpIYdU6v3xtvDjmANSbMgQi6^3KsZClFp(dL$`rs
zJho3shA8HAb&H%B0RVsQ@YJw;1%l(mmYXtSKu1_y8P+<I$ih0Pf$OA688-lYmNv6V
z{c+LgK8QuD{q3840bFtgr`^xDmNs-CiOX*wIKQ1dTM7z|7u?P6=)XI=$)E&Kl*(Q?
zd1AYM@o%M090liu<^LH$vOeKL1$aICl(i^M8ShX8EqERme!?<_vn0+{?t)`CTL@Rv
zAoCsn5^d>Fl|2_3q-5b+I*BoZl~7KHR<iKm6W{=9^^_T#ayk=KvAFiR(J2*utT&Jk
zL8bzh#CTNW`mQ*t@l~Kug+e`t8l+?-_K~DeskG?SXC#XyaG)njRc_X*Wx*T<<Yao2
z`b(C^YhY7DKDL5{vT0g!Vh}PV1v2I&NXbCz{A}yuWCN-WTQyx=5nRZC4%!(~x~l5J
zaug(JjuxVVL3q3M<F4)mTXnNQ^z$uO-QCd**~?Qb3_UEmuS>%(QOA*@(hGA85E?Xn
zqLDK5)tX@i)~Q@mgcOes3Yn$o))I>e9Xhx^-3jBkAIzs*{+2#Y<<kj6S)7Y{tl`GM
ziKGjiH&ATENJxn$vub;=l%ok!@<U_R^Ga|3nu$2MqBxZt!8@zHN<j7D!K+{K;UTCu
zc5{b&ZYWK1eK%|%mLSO-4}`{nt25L+R-pVHbvZyTwKK2al(z#?@xd-QFfhV?|5np8
z{5xJ}3wfDsoYwW-i~xvuNrMPRyawLBv9Xy^PE8O?2r@H2q_QQye?8s!*CItDFn*9k
zu0*BPWd?2B?VlsS3Nzt`d<7TY$2+zIh)x`&v+Cg?As|OWxr*XjRJU$Ofn2GUjx8br
z3^$LOlrCNGVHe}sT{7XcC7HiWzXgLaS$Azz(fzboY2Z_2rL2aMPxY-8>D&0R;@fEV
zo`M}uB|Zug(cg(1bWZ_U=VQaxu0!!8(+4=EhjZj|YZ9KF{Ud|#nEWm*D9#0Wl8#@l
z>m)US3W?eLB!*Ug7mKOe{=7qPc*%?&-evwZuQZlLk7MwgaQrS8Bpef6;hl{c>u8vp
zl|XW2!tvZSlcBGROk4>9J)}Vi!a5b1Kvksu%;+q2Jd4wtP1yTpO(01<mOrtD^<{(<
z%G6a|!_u!r_XJsNf{+HP<!x(mF}XUlb4Y{VopNZLG5}l>-G&A*Ss3)wm8)KamYp>+
zS({Hx8kvp*-_nD2b;e)uAS6@2wP;^aHm4HGPz?sugF+r#i+gB_v|;eFpvhqOp4b=9
zJ~uAEB$@-)4V7pTUn((MwQ%HbYK2GP79*G!8^JEN-SNO7u{4Bp$@1&BTf!J*DMZ4{
zg=!KEkD1j2p{ouTf%Y*U9`x4kvH^@$WD;m6&MP6~9fP!S4u%(vb9lk0yMF!{T>e?s
zfSSS1K}q1FH27mG^16XPRcCgUSVRWwXiQxl)*Y{7VOw5HAC&@qOaiU4RM(ZF0gfIz
zN8L#3G};0%))z>@hoEH2C7dDKVf`4g^TS>nRj}%Qi^_yWoS*PmI5;uaWRwXjVqtso
zVGK7u{Lf>BhKbpff}JPhbDK|)CK`kkVy9eRrlYE+NLpFVl|cCM+R5Wa(HP}j$w!_Q
z*#WJ#R?l~~9xkZ1R$!01bt<k*l}A!`R&KNcN~Q&BLA)-biOgXG<{BUyq3WU{+J`dn
z8^d+dX!6*cGWn8-;rY-5nU0@UF+hZ9;x9Y&m^K&a6wRfx8jO@$i%v;0XmQr2OcU}w
zK#?ZbsmrYsY%_M`%Np`rM`f?-9UGV;I=_Z0uYx!RIZicr+iCAg5$t)cLNzD{kC-5B
zq}Im;Cm3bSLh8C*>4^!sj15xT;`pwkOehCeQ!V$TjIDmyYbk?dia8&4wZ~nWOr{rJ
z4bjhh0l4EWUOV(UxzYB=sj;z7YpOey&B@^CrJWT$4bj7wOqt2t-~C#B_bWiR>dk!6
zW#mJn^fZp*)WSV7l{l!A@o)vJT%`2ClGi`DDmIFb$qWOXk%{o&3Sz;*JIZN(UERxT
zjZ|LeqGO4YPkGyuQSZN9P(6PC!~!wxgsPiXP#fRGe8qK?r(o<<@%Stwj5pHa`4iO7
zxP*B2?L|0O(KsEMiVYqm4X>G83;p8fkV($Uw+l(S1Tr+~cY!7C(>hgnRzNGM@oy;o
zRzFrUGSPVLTHm~}17kO%OLc)`<A5Zn*4;Xp77XzWbVK3UN@C>(<b1KO0vy1(Jj3ZA
zjq5Ba?XYpTo(3OV35mx)Te;VLnDYDss}_JH6WwC^eXeWD6p+OJxC}l0E=bA4Qqxkz
zcVD-gIouuxv?n45z7w;G+ml<44_j2PkEu20eu7@u<nYOL<@(v&tBToT2X|*q`_EFS
zvyhS9{vBk?+Zsk!jZ}<Q#td4-)9s{A)JrL2Bz>>jZ^pAfFQ(+26KNA>nzuoYE_^+9
zgS-+?Npv1{IF!Wm0}tA{-(4k831s&If<Lp}Kkf68`Yb+w#=NegiT7RbbBMD7sv{ey
zG8niGK%Fgff=ZvYN2=R6-p|G&g{AZUJ5c?OaP#ivxzk9eXDKnABDj76yPTk*O+o+A
zGdN<)J~2B-U1%O9_yrhbOajz?W*-R$Q??%4js~`+zVLqT>`Y=*H$dReF_aKD==Nop
z4Wglyok}od!eW$jMO|pjn))f255XfvT^T-6=4lvL&YUcl=wdqSVm5wT<50u2?!-<A
z1UJFMauA#WoafZ_lfa0ApTbzAz{*ofM8}stjbd6vSm^w29Vaha{yN;pg&go1HsFjt
z;?_-nLgEwxhsblEP<Om$=QRGcl6*rNn?c*|P1KC!<uZvQClYL7W4v6BF2OqOME+o{
z%7p>&nRbX9`?#*NRn4CRX8v;6WDUA`tQU{E>6%Re`x~g&KG!zmF+i+=-n>*$7dT=U
zXiFastsw$3(T=oJ1#cG~wt28X9e9C>ngj{rWg*lbM^3xKfZ|njcstH!Q5#%GH|&ic
zAt!fnH$YXK1qLBN;`FmFdKUBdV3SH=d5;H1DrF(TH?VW^<Xd(EZdQQd1}xY)bt^Pa
zEK4I;fSE9UXB$03xt&P)Crjd08zH%|#Z<j{A1)|m3D<=|>WluvD*JOdMQN)L>qEPi
z+{vQ3A&swYw9e#!GdVaQ5IhAGb&kgTb1+rWEvlckakkm}Vh15@Bmm!s5b6t{oj`15
z6-16pisDy?&AM$waAnEA4L(vF<pOpCausOpD=mfHAgO-c$awUJT3F4Sm)U(*E_Pi5
z1`R;}pr-=^S3sFmPY}lFNzp&E@gq7R7sU*=4`?f+`!Sc;WM-nijSIA-^ch5~vh>A6
zuJz70WHwo(wW&~Sx#34EfnK+&FFyKM9=CqR#0AACpe=Hu-ZizjRq@}*<%%sR1h2pz
zKK^o87twMS8NYE<!!6<~&k;mmzLq?p3|uOteR<aEx!2S)pBCh3y0)q`QU7ph#3ax9
z&XSjKOjuala$9{c1zY`cptk)SHmT;#cZ4A+TB+FiGUnrj8UVEM^(#~q^iC#%+Q4n_
zy-`Mn{aj7((hO<p5jv}Nj<N8u#rz3z_&Ub9CHEgR8T4@3&@OyQ8n503<@wl%k+{P-
znlvxJyj^3a_o)4}d2E(`OUU}@Ua!CT<SI%Y(EE9$4UO!@0j<^-9Tfk2c$4ZwX<wK4
z+f&t^gwAY(&!k1Z*M;RDvv*Hdj#MAng@xp)P3z*_x~Lb<JcVRcWzgri^_2ycE|+)T
zJ&e!6f{NJI>WPctc60S^f*d*I9XJh6eY}~W<nZckhgz(6ZWgI<vL5grW$*JXW_{3t
zPlf=kDcFu#(az+MazK0IivY&vJe>rzwb`?ydRYgyc1AfO)N|_I(`V~*H?a&*yP|}8
z!vf6$a5nh$K&#sMcEeBG`5x}v0%uo1N|CnhwBnbt<H}yllM51^19ASyTtTO*^YJ`B
zGw*3creykAEreGa_Y`#lo9`<wTUCWf=dMmtRmT<3legNEDjoEYQhjhukV>hzkr(4E
zfWWVLb>y_?zFj)Cr^-U=HX&2rbh8pi3A1iS>DTSSIGYJN^~WtTR_#sP*I^%8%I{8y
zj`DRIGB=4wPL`STq|%AA3mR{}KM@$C?b<oDA9timrhcUDn&FbMJ^dDz4_67c^oP`$
ztZcQfx{jXPI6rc~$Dy8sg$%eF#JfD9I95-oLY=SbP<}s%POZcUo>Fiwd>aH#3(PYU
zo}sP#Q>!O*_!M8fd8^BY?|G|>oZ0up;-`|>8@2qSGPziH+d<X|VDl1m7H>P!n{@i=
zQdvo@)g3?x+vbD7i1XDxDjHG+e15MB%IDDYboibG;JM1Su+i`TcER}I5KB-ZvLv)M
z!PWtQre>uA1HY%8<G0;kLa6{<09l9|-TOG<Il?{8<JAU+djq3a@(dWk9|Gy*@wAHY
zin$$2FrtWv&nI=a&SvGYEFA`?%uQ3*qve(YS%KlVf&fu=`fT0V+<;EWR8YQS8~(|G
zv-oiCy9#$})DnNCQt#amxF~{~Y=x(z)kac)=a2zY=%S$LxCACsJV8l!X~Jx}M}xMR
z$IPxntR(ir??zTV{0nck+#_4+Nz1;_c{Z7?r+vNZ_~TKgTm)MIrJ^r>p1pJ9F*nhy
zZs^VGxZ=Muvo9MY0hf%o`B%3HlBtF$Sk+Bp9))O2+0xWP>;~?Y2Pz*iT8$1r+Ctd%
zIcfb9u)Fh!LWpulUb;-#>s|72m{cn8YdJfo?CaRp*%su?!huyv1Z4-cZ@f>ncm;aE
zRSk|pO_i0F?l*<~GFt^9saZ{c{wr}e17RTSB9R>^!5ygbFLr1Qh^WCWDaW_k>}kCQ
z?e}};sG9+O-OVk~kWv)t)?YGh4GX$_l9Hp6!TyW`ftU9D(VCb`v9k16c&vyG^=sBK
zoUOD&QYHgZfPrEX1aFiJo5$tS&tNq?$Y4;DB=)n`gclWm-GnOoGUnf?)?N|Fo_qw_
zZsJI?6uX&Eoj0A$Pn%T_Tl#y^Mc_WiG1Pi*43StyU3c|}W#2l-ryo~gv$Bs+;>kJ_
z^#d#g^<4)JOjX%Bx~8Y*V*)I1lUd%}loAm6Hyd(0=vnzP$36V`Gl2=N#Ob%8!mv}I
z&(p^{WAce$|0B#E%l>v#`xj-I&Bl{9Lm4PP(j|XYmSo+|>Bla$ol1WCN%s53MjJBB
zv4`<8gj;waag<jT=J;I``Jke?$n<$W?23o#M95+Yw$n;UFphGOR|z@_ittj|o6CKQ
z%!=xod|~zqTF<PA9}<Biiq!oEl@7CfiB-HxDJ|fc2fUCJg702i;l-LSt@cBFJ3M}4
zIwGe*<bE}b4N4h&2mkW|mi4If;c)(to5Q|U6P12CWk<!A&J@<Up~012q)#&rc5lvA
zz|7IR&z;l<ql|eu;h0ttiEU&1<=%S>n^5;{>^Q<0ckp<%g~NS*CE_}T><Spb0(0B1
zkwe=ly}vbaojzE+WOanjX9A5>M9dNwn{2)$68tj1KBVOP3SSU$LJ#W_aa-;Fko*$F
zPG1s?5I2IZZFE7Eknc*5J2vP6QV^jvI%MKq&E?-`^nSdi!q+YZ<>SlED5df+>8(%c
zgqJQdS`r!Q|8fDgrQ-0GtbY9!J~NIi?(D~eE|&S1FVh4If}<r%I(}_HgDns_A;(xM
z!R{<7Vs%J6bP4pRn`xKfmP7!y?TTU_U?kh$Tv`GCz&VqxRQfvha12cOb;rOQPH?F6
ziNOe7`p7|_7U+2=0l7H!ZhT+8Ir4WJQuD#_<P?EjIS_5u{uyrnevoveCC^?aWZd8U
zI6=rt%1@^k>MX_{!;;oWKRUKCdUO|W;oi!jyU^fl%@=1hvP3y3-TL&IvmheLVa1a-
zIjX|uV%e1jA5&^BS|`_ge{CLU8k1=Qm_*Q(#I3x~KdqU0WKR8z=%f~AH$RwL)-mFG
zJ(v^V!V5^4sXe4<<=eJ-+gy2Z`*ErWzuKIk+Ca<U=1|>t%_N}$PMr^cgWT;K0ObI?
z0LI$({=o!5i%!2)<hh2^#kz&*1~j*lLfyuGH6$fvrEd$DvuH4QIk}DZ@=r1nalmtM
zS!Mw(*8FG?)FK%D({GV0#zu8z_j-(i>?Pr!lQ}!P(lr+!M`mk;od{sQClDvIqh^@B
zth^q2-`m7OEaa3_RiXo3xraw9RF;%~0x_u($j3X0ACz*y#L$JL77&i-q?y_oS2Fw1
zmg|@^A`q~M(%?E2lzC{V2l)X)QE*Pucx^B}bLl$*1tC3j_a}ErY^9q9%y#~*Lr>d@
zHnYoo9F?yN%kQr}YZQF<o2pzpyh_cPDyT`pFm*I(2<vDbE5F11@`=cnHmSGlk6eMt
zURZK(MMMkL=VzAr--Bn&u>=@?FWa^#Q4sOz!nxjeqFSC7eUpX8K4-sGz7)U(@kDx#
zPbEWfr(NsuWY8tm=Mfb&uyQ;k-(H`8)l+z0&cTty{(+1X@XAh%@Azp3*DiKc5ebDS
zO|hPq;~^C(1e3y04XOZ+y+sqpr-p>8xb|3Kt7s?9pq4xRt2~@Vv4>_yTQ_18G7*5v
zI<9yh*QuRxz5Mmj2%$`fpy}GOvA+NkS}v>!l<!7#AO!aLNmGBS@*MdlMG{c5D8Y58
z!UmijQ;UIZjv1&hc`iVU+t0K((97g0R{{!kSizpi!k-uTSSyi6q!S!Tb-)YjU7lX(
z_px2sLQFZjguy<O_x<Fc0SZOVJtN?>4}y;aChr(I{+c*JYClwfdxn+p)cpGTYQFhZ
z3r$p;24_|MT!HM};jx7Y7nC7#<}uWwo72SEQvCZ-szepMZY||SkVuoU9$vHzE1SaX
zYz+@jZf67OL6E(=3@KWmGqp&dOx4hKTNgFj4#^TIQpc^xR1p31^<vslfiiC26`BgX
z#Q2YnE3t+OsKwxCZlWEjB%uA(YxToHLHYiSFsx=E2r;q4?@*qPN~kk6DN(G8YMEY0
z`#_GiSbwIC%F?p|_US9_fq!4#5HDSGf?&gX#bf`Pg=)UDjkA)76(vmGp9~7!i%@j@
zojn)0@<kQ8sC00}DjiKBUv&)Lhe5@*@n)^4r+PE`c9lY-@rI15Pi7IOhs!eq-hpKk
zo$}1%64}>r!UU*aP%r<k&le}#K0)B)i%0N~kFX$zNFuJYgwsKR22NfZks=sh<f(bC
ziyx=H(e7>MZBvK#>1#z<(fx<`xRxlKl6Z*ReLdNxDXf)ND1jXOcX1b(vUJSzPF1Zf
zR|A`62+lusn&fV)xvDwpC1-12Pu=rEqQ6J1XYRBPc<pEvUhm=#hP0*T6G>uYiQOKC
zHLb_BHUjjARN3nDiAWu&%j1r){;WOQWcX7~kpO<V^=n`B+!>1Y(>-3Bs)>=pu<&hd
zVAydboS6b7MI-7yF?foOc7MP)F~3|C+6O7Mn7FM^wBw+eNG3?{A0|6CWyxm$G9|bX
zZYI|!WEUD0Wb{6R*!m#uo$NP|bKWW4LqRi%DPVchmpAivCpHtxc>{9XZx8*aYIbNS
zu_v2o*TDj+MfSkKj!_{fa^HBN{=g7f7^i~Ehq)9;NRW6XVAvf739GtG6SMKn`B?(f
zN%~}UQ}b8&$oezT$OG-?>>}~njWrU*h0tg>5|Z6;|5|MTwVk?kWl7_H9s0NCs#>VL
zWH+wPql)Yc?zF2YJFG*)2?;qSqwl%E9N5pd_a?p&)PnrZ$#o-?v3p&;%jz=0jz9H1
znl&|^pWbg7?OUjjK4h?+P)<Nog~9I39=g}Ae)jR*4w*V%FLzU|#aL5mDj>2-_JK%1
z5vc$!V%{A0JvyDzmNyX?Y%kix?(Lclo}5`Xzyw_zWE{Qe*TLOszNm$a|85k&5ghfm
z#5%4M&?mq;u)d#Ud^qrSeG5ky;&E5C5)*kjrJSogT$H`31+D-Ss`nEC1P6dSENRe_
z?+D+429s{xCmf5XgKr~XY%#Ie`!zxBzKQU{br|iE;kPa_MZdL#B(`F*VD9lsGHDpW
z1kg23nW?>+IS5wAQ+IH-v`R@^U#8Bp<h|UFu!5$b<57cKQvRy>qJhH1*?mz}%!+Z@
z(qQ|2%!-xY0}tmyGgSw;B7C$9%YDxBl!Q%?Uffz|zk#F#$!1zxy#Mq?ZuQDa?RKJf
z>q5P=YdYE#m7N?z`rYBcR!vN~AY>q2;iWkjHXs3Xf_2ucgMZ5{s_VQ_`Iz>e9O$n)
z{y4@T*HweS$?z~`n%SugL~`N5VO|ssAJq%&cluykTDBJeWhQ^churUA(rQc?Uq5wQ
zXa+XuRGT-?4o!Ru&1D#^|M3V>dBdC=MRXM84j6iI$hqj_oIGk;U(5vVTwW%0GL5*1
z6qFOY*veH}9WVRk|4!G=GcC1Fs+w4&V#FLr0OG5yVVeC|;@fIKW+2R%T7A;Kh<(gs
z-=3r$;!TZxIsOdH%YBw$5jIg8oQDq|SKb|3+ma)h?<tfZhr^G+yjxhf3Y()or$sXy
zc>8kO8OFQ;^Js<ZjpKODMWlEtflQ^@RqE_>vzKeO=oyY?d}wNLG-NV1{(1qrrdsOf
zqEL5iLIpGlPmQ0^_NjXorN!rMzM;%Bz|AM%3Z(@(JRXKqW%fBR$ZFm|rUp+CZFk<%
zK5B9~I5G|!2-y)m?UbtBJDs|2Am%~HoixRXN|$dTX(_powLFe41@!+Ko?AW2R`6i_
zT+g|^?cmm$z!IIs=<WQYk>C)S*$cNJ1;-Ic+7{BW=eFL!l*RrQMHu1$I<f=m{v0}I
zk!8oc-0Z9adE1GS+QLG{Ns%jd@<hg|T@#-y78zU5lSQUXDnVPuW9<^9S^jslI|J(-
zI|=W4NkG5VyS4aJn90@lBZ)}Vrbez*^)%b`2;1<C^PZa*>UlwZCw<*d=DPZAalHwA
z2mYBTx(I^X+#(N6pt<WWXm1rTI+0F#pWC|v0fS+lH{(Y)0Lpb81}-m9qRmraF8Mf+
z>sPv0D*(5Nyw4)e<={hO$NQ!b1>m?kw&GWam63E4(@d$azAlhWl$w+D?(2CNi)d3`
zDH{nMSTVW-TA`2`I$goW!2%Mn&(_ddS?hatt3#1duY_LHkA4*-&KILCY%r9`pe(#4
z=xw6&=srHvafFu+nN+91?Kmxw^^0~o7fNyZV9bpLE4&Jk6o(=*;_i0tK*z9%%w_Tl
z=OWz|+CrIqPZxW*DF8T%0pjcHD=X?d6CZWvWN(44^kR5#6e=5CI!MW4bQfut$SEHx
zoBj#UNyG;jsIO0aCY4Lo<7L^%FOG>pnop*v5RG18SHoc+g<AkYFCI656y>4<2YE9<
zVBu{==;6cR;UWPCAw3`CBVapf2k{?$W&!!Lbt^au0-^*-{?`_+VhWC(ij2Jc;EF8>
z(O6_uTGzf3EvLsxuc=X(28p;VNY2-Pypd=KMgt<K4+&BtJEvUfn!V=hT{(53r>b9<
zR&EhSlTGKtwD$N|Wl>v3s!vr~#$!;rP%m0xoj<4*1>;73{LB(PWm%cJKkKzu((B6I
zURGxD<^IMT9-i(iEBS)=n97qEv2r->^c|qeYHR6Xp{Ac;3)|xqZ=9p-o}KBjtgNgt
zKqn7|6O}%sl^zPQbh=kNq>&+PQ7zgu!FF6KFi9j88C6Q$^453T!q*tsG>dg;>(cgB
zSJg$*+!O}9opCISmRJ0xUI|4szr}lV#B)-~Q^)-y%0dT1$GcBMV}@QbSMS>}$<tU?
z67pPrq~*JqaKPL0lHS}N3Ogb5i7mHfSs8|H56<uX;-Z^aANsHR{IfmQ`Ic)iPvvE>
zYoSRZNx=PM-GE)-M^b<9hyz&$iTS(qH|HSEBOjm`myluq^MnEm)4xtA{O>;~V`gLh
zFZ=!tTH1E`;%L5?Rf0aH6@&3Dh@vMljBTryZL^iv7}|-P6bLksjK6GF^|!vIehYAD
zH<nGrCG#dUn|vE5dqjx=IYmUFD*NK_yV>f38KtMV=^Ri4@7sxJyH)LPfVLe9@u2Xm
z5dJJL+Vgf`c+Zf|2(e!m@^te4@T=(s6-4@UjNpgE-SZ7z{xgg~SF2O>ZVrZ>)u(>7
z)q`27G2&FkP?|;3wSEQ4<$8v*(UD`95Ukjk`;^(eDlWOK8}A#+6+9;d?9gA5EGzjM
zllP}a9kj_#)uLU6+~24qz(U})k<~*;ZejwBDv|^^)|$Bb*Q&HW+j}Xo1|k=foYW8_
z5~m33{3fv0-%H;=1Eq4!rqpc6=vbfkrTE!MF!*D%1g{;{Pg`a1NN}k<eMb2AXXX)4
zYf&OXZZ!O58}6=o2aRFKd(Y8uyRIx_3p^|keI9zr!Y*V}Qt0)pgzxdw{oF({eBQ~d
z%+oy#ddVRqn**Avl?>{kGAQ4P!uHXmz?c9QC&~DsdWe|(XAmnwFrtBc@%Ri;uF1S0
zH@%em!vuS2&c-*81~*ci8Srdz2Wx;Y;chrhw$3lXf=NMoP<ic9pt&2-oNRt%nzb_T
zk%!O8?rQvN;fR-8>ESMo%$RF?r&l7`yR}+$%teVUj0d6<JJ&I4JiA{Bj#J!a5!1=m
zv?{N&b#>53@>?RqA!9HLJz9Mh1SZOJ`2-c-7qTQp$-0`?!Ld+#^Ebh}tA8On*jPX{
zvnU{{#+l{&z=lwf8D`&7ZhiM88=q_)m_6`MC*9Dc!7G$qg5UIm?pZE&R1}J4vo4&O
zyv?u^Pi7gHo#iQKjHtIv)0+0S3iax8SP#4($<I~t!;;&?%)o<w8(S+aaHSvAES<W=
zLNbe={!U|?FMX=bbvZlnm3wn86`mJ|SnQR5Bau3^jpvMv10R{}Zsb@A0rQH5r#n=s
zufYQXa|S-qLB(GidyL(ssu3qTW97HTldxf?U@}YD!oEeVq~<=@@&bm_0$QA>>b8Az
zaB)DaF)}hQk;^j;;ZiC#*kGHdF&MFnY4N7*MH*aaTb`LkGW^jrIVov(p_JMMaPzz}
zqLQpNmU9ujKJZZ$WzZ%JVtJ+xX<=b25HCTrSdmc5giU9_U1}8#b~!K_E4TjYlfTW*
zB6iQd&FRcbJq~R?#tc_s*$w&CU7qVHSog`hJ#_w<3uTe|jL0KwMgfCrk?mv}GHR!z
zLzon3Wiku@5Mfz-)|Har9{V24#wfffA^*OsDt`4-M9Vn^C<K^Og%)DC`L3bLG2vHE
z$D|S0CW34h-7n>L!~3*0nZ@+0Z^VL0m2TObR?!e8p$*0%J}s=3JT=9xjLpMed6(G>
zJ8qU(jdV6MB}`SI3RdNpE((=|kp?+Z(O0qgSz^w0h|rR%nCaWCFo+Hx=47ENjZZ4r
z7~@-wThkBFZ6xf~URm)$`&TBu@}fj+f#lz1rG1vr#1N#cqx+SyTI<gf-gH{Fl3JRG
zg(;>d3ozlt%FrF5UXK;usP^3$muRWiF7{ugTxr3Bc2MvPd_1}5NHb!MjM<>L;U;D@
zC3iM|L~Z#Eq2y*S7qWWu`w3lWP*}7Y2i*jOM>7U=vNQm$vR*3t!!9OMd&Eizl4;RT
z-@)3U!q{y{;8*4sJ>A`)>LDusg|V8pKC&a#eB9Ct73gUXGE59;uSQ7wH|a(LPn_r?
z!hCa|`5nLS(*(_68c!J?9oEGi7tmzYT$Z>vha?5^g%QlXs%S=1ngRVCg+1T<b$_xk
zJa!|IwqDCnt5f<j4F`97;(zT|w=LAKhc!Qh{!~0c41(?%hx7xgeY0o2qvUg`t+eXf
z7HHF@E7v1CqQENs)O!>NGbmM}t&vDPajDnX(B(UL(pT}#|6t$$?StX}^Z7Dv=KsIW
zgacdpzdaMq!otP<Uk{!6$tEu04_yVU)Sv$ZrAhx4{Z*+DroNtN*+vzI9_6Q#%}izf
z7g6lOqR@#wb%%J1d2LNLDw|RvV{IkS0m+#$CSiwqP;Eu)b2xS^8J3jSW|CvY;h?rd
zk4o$Zj`9cd{q^bgFMi$ozwf>sb~;Y(w>)_g5dVGH4;yynLB*_kx!t<i3D72?XZ<lc
z_iV8I-ZoLnk%i}y`f>i!W2BU=!Ar@_LC(R#s6_Zm*U-~m9!Du*_!<p)nq6z!{%m^=
zI)k!69C$f|_~S5NZ@cB|MD>1Qxq>r3yAMejD^Ad7PI{DRGrmGUPHL&ZE4+;n=a+S7
zj`fDCWsXSV4yn+VHMZc}LQWX>$mV<tm@e!-#^_IeQ+W^pG{LEuqNCrN$*Ts$FMdo9
z2`F#_Hf@51@cX`%j3jdoLL&IZzcNO@ro0D=&*0JYK5Xfx8U+Y#Tiat}8Tf-j{-+BV
zI2;5d)@L~5ke;K=ab2F}v}xRIihhx34yyoezcUoO6hp-!gIjs=C5<gK>{WH*eb8rv
z$tFKzjuPDj+j?q#e9S7Jb<45fo=iA&a<SE+MZh6kaH3D7Un^LEWANE;vwAb$TBd%i
z7qKRrx=Wf}V*Z~n*n0AM%T@fFD-CzN@qsHjF~p`r-x<!gn;V}nBbm1lt~~cH|8CY)
z?g$xvgk+RW0hgN2{FP|l^*9j$tJyo?fA;1x91{|j0TENTpn?ClZYEc3JWq|csivby
zP_1^ESH_AQU0LD)5yVqTt6A4@>N#3)$e{39NyO97A~Gr=b~Z$CC)hO7KVG|@TkS+L
z-^Hqmk<{BG?B@7N<Ag2^Gnr~A&m<MIS;6asi2EW5)QByYIBTg|!h1IRPK>c(N?>cu
zja&GOWG|hu=9#%}s|DIdc<O6}FAY1YoW<81Vc~Q-dqBY9n7aSV<uB_03EMWjC)eXz
zYVNV89>*sJUQm^li%mhmv4y|cvaF|jGQJFxyck}Pzx55$z@9X~2YkADPi)W{YXv0X
zKq?XL9$qj&;D_zP5FQ5zfP>1Uno~i(T<Y<bHy#2q2=4wsYAQZ`^vu{Aq$~~CjHAfn
zC*nL_;Jag-|4l9asS*E#<x3PG@=!3W#b4hjP1xSG{Bj!}z*J&vf0UKsMAUoP|C89J
z`qti|XT{Axv6(8;S-~{SCXc3A?{jn|_2Q5tMqf@=Ze9UZSFYq)1s_@eUNBVut+a8<
zfm9)!=fj@%9Zo=t?B{IH$R502YsCozX9XawsXd$g*tdp(`ggK#<j)404n3H5eK}D*
zSNF#&<v&kpg1Bq=bCzGmVZoW~pCJDSt$(rnFK(OqfC|hom5M(@wKEs!el%m+GZ}w^
z9kDsG(szl|WP<)jsVN)zR640v+6K<t2GO-`R+yTzb6r-r1HNTaZj2yln%kwA97;8@
zoSm(n@WBP(VhuJ-beTug+!R<s=DW?-t`#?V)P%B3%!x01#QmQ+#R?F)DfoE8@Zlt+
z-z0xeR@3<=gb%#8C#f+0Z`v6!{{Lv_h2i7LX(znPv&O$PGtIEKKdYKl$Y&C69C86}
z$1htmVSV`5KlD+5|2~DKV-7fxeogZ|ol%@l55jbKTzAf5+l<lwXWss80)c?H&_jkC
zj8Mw$Ld{v;3b|&fO#G2Eynp|ztP~Plo1jcDA>%oL?=OAi716mSG3JOk<AV<H49<zp
z)4pR4$)r3sRGnn1px9PEG&BwHg#23t&-hCJT?PLy=rVX@X5wP2`SEi>#H#$Z=Ow&D
zp^NKt01spc#UeKp$*cT0&Mc%1A|_^r57b|?I$tSinWD_`B<S~1nvoBk88e)%;jkSQ
ziWe;+G;_-~D09cG`I?M0$~XFt%WS!e&LBd5%K_udZj#@6m~eIrR&WO_#NH?DkWRyL
z(Tnc1jF0(heD?^iJ_8Jx<XH185I$Jj>io)nVFDUzI==_jm`Z3}+kHImX--?E4dty7
zsqO`Cdb{PKlj;vKd~Zw8qdUO2w3vwlBTC&dAlK9u#uhrW%wJfxTSarP27laCsOX>_
zGT>Xw9VOCX82MBxG@00pS}htM$3Y>`H;=U*uqHRmZDq5xQ!3oQ7F_G;Za;c?7D^ZU
zvK(radUS~v{!@s=A0u5mVC1<SV7TGfpkTs?{peA=_bvMJXM|j|qvq+U*Yjr4Rz(8e
zmsD9ET%tFEq50sDm#8xy2IaDkt26H<Fw6n1tf=s0dV%^DzIN@(M&~K$mb$s{^7}--
zu3JQ<{r#t>+C$3NY5ouIX6m~yjsvCImH5%g0+fed?#-qpRMc^F@8`YqGPQ6M`^-yA
zehePJR#XXgeeWXYq2EcH<?dg}P&R*`Dtq|&C6(RLcvU|9%FX*~G>qUkNTKdMH8i7M
zh&{PbFi9VqdgXal84sY1!;pH#Kvl5iUiX5+E@FEOA7V|GTwsXSq}NMpGIho%OpeFA
zmm%OTXw{dsihgXG|Mp>9hF)>gy5}2Sp2ZPe9<Q_Xervvf@N^R|rE4TJIW=1#P5Lm=
z(b(nCL>KTE`$K0M)8rlN7O`3IBffsAs{A6392tHMuG_9lAK_cO{9`=Ce!fbK2tm7y
z6q4^NcT0yq&Hfi_?-*rC)I|way3$r9D{b4hZKKlGOQX`Zv(mP0tI~LhO51k7Z@Oo?
zr>9rX{Fr~S){7SrH|~jh_Bm(obJ1YP;9@v6V!1YDhQ*zhLvrz25t6C4Xh)3}{puK9
z9erc=19flxoPFh!CfBzYeYxqjxX8Wy{v$vGsnAUJdwm@O<o*|!j_tq4?Ef280n2}&
z-~Sg?!T*K&{(n>z{EztV|4miE%+A5e`F}%rIWT${qrg$iTFy3+E0RBD->J%suDXq4
z=$C&*&Xq}jMlesa6*AAcvS;FWGp_R1V-H1wgTql#5!5)_ZD~vS#Gr|H`^{dlURt*E
zdRSUo^uF=2Hr^@Wm_mXF3UQD^V}N~!1g8lUB1Tbyg#-sn0UdlV1N{~t13EBMsn5M8
z5LagBWp9Kbe1G{jI4W5F(7W<V5BgI^pT@vakP1Yg&~pAf>9<!#LVls8!4c(Y0VM>`
zuk*p)L5fei07t5PB^*LRBz7=x?k)zsHYIG<gS9d>8<>T=6X8P>Q`4lRr2GLP;xJ8u
zIWSX4VwAC#djz#t#u?=$k*`ZnfRTE}-xJVHX8!O@BY658jeo%d+JuSYwaf0++7@4f
zVTi!9goxw4__<L2+erSPfe!wskwB_}hATw=|2*YCqyDdh{m-cXhf_LH;3L18Ne*a*
zMIX2hnCV7Fr+lZC9x$^}TRtbP64r`}M@k@C(6xJry~#6G`syv#3g0Cjs@ZZrrq6i}
zM_VR~RBj{{L9lX5B#tB#sCq+C1Lwx{Xc|)rKzNF;N5Q=^h3FO!eH+sXi3U;aKKH(y
zP~}mC*~Olh!cj>i5S><+bS<WLY-Vm|dwPCot@n<LBiWPjR#-PtqiB4MT36`FT@S_o
zETJ^<Bo4(^ZxnUtMjgy4=!0TLPRV7@=6rohY(tsByLbJDnk1PQgU~+a2y=EfhJ}0k
z3pzhc2VNLq5T3akg*P%FpumZ^7%}z7<ZljxUpUHa0J#~-!R-*1iAwp#25Z)8gfF}D
z6k<`-d40(dlXu5ZR2WuY)P*>*$gNjWi*F`SstC-BZQ8cQVaE60*y3Qgz;FVstzaQJ
zU$dn0e#1uGYmddtZ?7VY6^;@)=_Gik7hIWL^~xjfN@$Pk7leC>w-fJ+SRU@o5_Plj
z`~DniAhpQJ|Au#$u)`p~T7j4ToQ~`#Aq|^_Qb{;mrP+-hlrDT2P)@3Pf@<^q85|&i
zfNM1<nHKp_-XtuH#_)F3l1i-MJ9Hs7=V9SH1XnN(7im+fDWgoco?KYnE}8K=hDMCI
zm~!MFeO2%8`&Q*8Slf-)*(Y1MiNK|J6Lzt4$04}Vbi%*RQ(Lw7ZN^Z}zv8OUP8dMV
zqvjJ_FY*M4IZS^4G{BBrDtq067fxQ321HSZeq$CvrM)-SF)jqhwI}tZD4`!kDB=%e
zkMQ>&z7{(#!Qc(DMdb}L84Hk^70Qql@IpG~<5Dv+BHoyFmm~2doX;I)3uTx``aw_O
zbxvyFAGrycFa%e8jLPsALM;N+SSWPn%cTPUWIjOv@vH_dbFYGwo!=(iv!UvH<N`u{
z7OA`Gk<*jbY=1%r({X}9Ddjsc2M3l;MNYz)QgoAL-6I>xxMOKYranq1F8QfwOE`fv
za#?V0peovsojBS<U;2D8$p?E?XOOF=(^AiAoL4$Q1lKf`2$h1%=p}D0ZHl${Jo>p~
z_@A?1WCJ?u6cw-VR*G|el86yp^wj-a9Z{K>(1SZAY$b%D$vXl!;a0s`-eA;e#%9EV
z{X;(vnhLm_TruOfd0|sNX(ONZ7=U}(6>cnGV+_V(xj=RQor*!TsJiT2tf7c}F<(^5
zTu)0^Nyf2__=3)PZ{;q7vXZxKjz5X@kN7Ip!xflAh9l=xLss%mKoWP1ho>ewnBsZX
zcB?pl@X*AZw_oIC)`H9UBI}L!_rF3BZU9Of4B^1cf+MP+Dsu@~6wX+($e!Vfc#h)r
zIDv>{LDK`p@I)hf-bf&-6Ho=Ae9zmFxU@bR$>f1yU7j=-ypa$sI^R<tm3N()Z~#f1
zDl)3=27{9@Kf~4EYiZp2>lJ7DTr50LI>m{X*q#z_7})JaFGQ~NpxR0UTj^0)QTSbb
zt`YZltx{J{_|mrxN8b8;vheM&-KYH0h&h6N>89;!t#Bz5XcK$Q$dwqv*t(=1!9R&E
zcT#yV>Syr)yn&SPolwZZUpRBAsC{D!h*Z$KV#?W6`?pvhaK=lhRovlfm35~cL)U#>
zml(C2CZnrdxdAdWR`@5!j}<z>O_s;nu|@T_VSi0|{(4_Xg_r<H`W<DM@bM0sPKwsz
z)O3$!EIp0=1S4~mu#nWi*S^J#-KlniRdW2jvSaq4o|79Q5@FuVH^X1=brF8=!RTe=
zps~`_Z$zioG&*3B<8w9ef2A#9RDw0bw4{fpO(T6k0?5TI#G?6xwh{b62995oy?4Hi
zYellX)$yRxVi6C6PuWY~z>pXy;$?iosX2oap46BOqp7*Y8@Nd^C8I@`Y6&aR&BRdR
zqD2=$lGY1gTe+J&An9?#kN5U<8DM;o1KfMmd9eazkfup5psJpstl~%~FkuZ#AsJQW
zIH|Dye(gnIhKs4jUn2ru+W_$M=l!SC;Y;6PLrDz;ZKi*~&~R7yx<X9$3a_Kz%UBh7
z9K#e2cBg$~!OaRc2bmxj^XdUa(b{kIGsgo;DFLE87?q$oj7k9|*n1N`77U{ok)pLg
zD&~V*!riz(l#szfZnx)RO(|m8T$`AHCWU6ON)1y5UKuVdr*$1$0WhWUKu@z|(*v}k
z_ap-tQLY5E9tmc>d@Z3_bDp`R+`1iqsqS2koCyQrDI+qRnD7TPze2pSeH6X{=%OvL
zEUC6=gVix}=x!6X@ApBTCLN@6SyEgXgmtPP5eIps!z6M(JUd#AHBvBY4gO>&jgG@~
zMV(@^Yi>F6wPxQze%N)F*v~(5o5+T7E=e{ys@@vvuvZM#JNpDm4V>SE<RszNqv|;@
zR*51v&J*IFJZe1d%}_)Mm!OF+1^--#Uz9oGA#zDb5|zd`C0Ml>vQ2rt7lC<w@eTXr
zv(h>g{xK0-liw<d4t0Js8VsVR^jh7pcdsf1)EJe7O5+*wWDwmHKkY~&sBT1t&JZck
z#&&JtiiYxx=kT;%cvY%LGxgI21JLBzrNbzc#<$|l+QKF+i8rB!E~MN2z#B<~m(9=@
zvYbD@!BZL^INYzNpXO*WMWS^-2Knv?k#~wDK7upkMb`ZQBnY((QN&qu#FSomEXe@e
z+Z@r*qJ1wToQ25Wgwg7gXayzZ3}sppE0CC>!Pn5Te5h{p=+Xl)x98wz%9n`&;*lXo
z>E18*zrdSAp*_hO7y!2Umz4hS01iT9`kHsN@|C%(SJ|H6@CRa`IXZ82;%8*@xYj2-
z|BphQ2)(V+HYbawlw7#N(oe$sDJhl-rxy%>7sn2n3>*3~A=nyN7FWGgOM>5uX3w{@
zn0{BHV>%PLZmJYQFgP6EW#Pp9Z)x%(ueLILaRy*8kPX;OsG~94*f$*-isib|QC|g`
z6ii{&_iB<?POPWBQ{>^tAFiOeb~@1tNlA9a9kxS+0&fuIxQRbT96F0TFq~6{I-?k2
z%?+-=<Ud|BRoV65-vL|r0xje8dOXlS`N?(RrNGxXv$Q@DP8gH#kr#k6ap~g7Fh%c-
zHs4=Ua<Qeo+V0Im+>&kChml+oE0^(NQmp0MbyUmC=Z_zBx`l0+evQZW|6#=$Yn6-a
zH)GOTiWvW+B}Wl9j^V`3Kz7qc!k{^3Eb85;&#~n7ve}_>;-q*Xd~N>=hC(8-KG}c^
zTvNG09VwKIbVS_wU^8;!{nWKp#6amMqjCieTc&f0gn{})(5R&nMqqNt>>Ua$zrL86
zZa3*Y2CVN;jUSq(2=gzamfF%;1W!mFWFs}&>d!M>r5plXb_Q=m+)I@POb$Gp(pVOp
z81CW&&L|lcxF`oKEK7_OkTU2mZVa-Y(DlyV=`ID@qDzv@3m<bHW%*X(kZC;^-8x1A
z`kEz+wp{33xSK?9fA~YwNyzw3B?6#bqOz=lM2wWztg>uAwR}+=?jXind=#TKMdxC0
zilh`iJ~G%0tpPDvMr6Z$e{twGDAbZj4RHELw_4D1wK{mf*PRU4@kOqvQ%>6N&#1-)
z<JYshm6wdc=P>U}In?hAaq^*;0wRU)s3;*`Z60<wyRx=e^_8`&zi}9cYM4Dzzo&$|
zX_P4)rfD9grW_@!@28{?NllXNSG<5_jg#5J-8tI)JK6fXkYn4eFMiF|tV|O~=P$dU
z+FFUE%0Q&tMfjL#m(%aa{`L90s}uVU?t8RM<&`^g9B^^g+ttw(0=bn*(Zd;dw`N;u
zrZ~3qeUod5SmZ1Alw?KiAUVZS1L%bNoBYW6^1Lfm<1Qsj3FYK$X>o;jewKG)7ML;`
zJOZoctWmB(`k(`-D7rQW&Q|G^@+PFXFwwJ~Sh)WVy+p2{Omanlw<r7uoZ4>f>5Mi8
z9)`ll;qB~+&j%~gEM{hj4_iG&)gEm3Ci*j;9MbFA9sCBgB}7?cVxVB-3k5)3V3h5%
zg(fl(O!i7_C?p@->CRZT>$tFyo*C&ZkDuem7%18nC?KAC*rTA~9a~;`RHe^GA>LD#
zA1aEgW7dN)ZsjBP@t5P`snjGQNBCnC;iDKb)``dWLTxDiK^u$g$wJcX3d5;M_N*Bm
zkm5_am)N_R0%kE7kftVpnL23B#ap22<IOXf@E(_?hEK*IBaxLK-qvDc<o2X^zq(&`
z?9=+^c8?X6QTgX7821wlo`!j~2@7H<jqeoc1>LVuk1JZ}=YPB;YijhosGKfOmj8Ax
z?o?u8sPU>DK@8v9Q+xUu)gOLe*qLEq2W*h3RUDtB3?V@}Z<SO?+-G#Uz|hEBjiv|x
z;G%2G50Hu7>7@3{-%Gyj04mf(;}A`c8TfwAm+Q78M4qDPTqWhA5)1^bDF|+dO`0sN
zDzWmmb{m#UO-xZhHQ($msUI=Ia@huz`l0UFi3a`o6Cm?=)@e-l6=`t1{2&S(pTDWf
z7MD~k*`g)OUDqYhMUJ)JJ%DrautB9xP0N%_ELuL9*_hx>OmHKnck!_92$?0tGXE-x
z$$WVWZ~?g^PIUtD^@ZfgFbq*^_%-R7?#Aom^8Jm@A=-?UUdio}9j3uLhTX=sp<vRJ
zJ%gMG%ho%c^_Iz%75z4m$a)7cPZ6^{BTnjMG^KF{1akV+S<$IiDW-wb3~BndUaFJD
z`Y9kG$qr>V53&^&7^5X$SooesFr|3PqmqurZZn>&Vy>ajuHGc^SN=3fM_Aa+s^chF
zimi4<^O$2)B3(XJ3#N-QmF5)x^-As;?$DKIt8Aw-eL)rb-dqCNt}|M}k0e*anpLNR
z*OI7}t<9b*_$}q#WyH7$gogke6d`h5wq!P~b{r=iQhP~Si#<oc^0_{Yy3WO!ovwUz
zoYRg|zO@`JBlg@r2fd-y=I;4L*EDu*wPOC;@ZKp@wB1sbsg^JFJrC%Sq)w~Hv}8sf
z8&x{h&Rp-1KrHzk8B)eL2bf2=z|vN3@ncn1XHMN>3R|5lpO**3h@Y1Y5jXW8BtHgx
z!2cxD<H$hsv=H&4)mPua*{^pOWB?z{Fw+#mqcY~o**7q;lH~gGHcEz{+YaAqw)Nb3
zUV?LAu*h@pfekOR(^*0JaB2+|6-Xqa3<tM02Z-dPBeb&ydXD=cA+vA3i+?yCg8y*P
zF>)lW)mnLOhlf%&x}@a@d`<7{_2zQ%c6sf$wH*?Y{7gnx8voIf=nQ27{nK6(K2Ni9
zMRB{~$WcP$2>PM-d?kmkHJ_o1>Q>Gg4^3>oIp}Rtk^IZ=aG>H2+t7Y48b{TnN0m|S
zn_2@`B7(S7nn*;6E>|d;@ciO*XQKU9=2G|IYU;F+d)@`rz^Kj6KY<0=Nq+%%i<guA
z80=aF>&@MR)HwxS9MD+0-crRJ+H<OcinzNw5#l03Wa@;W#>wu`0Se)9+OGHeppOZq
z)5`koS$Ix9+mVEJYNNTWTFniT*#~9PxpEtgOdf)4En=XX4+~jQi`Bg>Z}p*YGwhYY
zz>AD@a!teiclcJSI9iR!x@x%KUlXL{86#pT9>CplX*VxK;<<Qz;OE&$w<{T`cmEE0
zt%FoFGSYLMB;`?t_qgXs7M$Qil4MUcYEz>>u@jUr`>>TmJ-T1JU<C0=l0lB-keKid
zFk#H4Qg$$YNK?{(wQ%ntXdtAx5}S~_eEB4MT?1e(b~PG9`bROROl+tHg?O35(7<D7
zg7&7K)D%qGoth)*#d*@gH<K;f<9Ky9>Bzmf#CN_@_U$h~L8jR9N&Z@OvrnhjxgvZ^
z%L5Yx@k_`-8h>t&@ccz)I!}uu4o7cbvzbVL7Z^W`1pABY?2hzV%Y*%fk$YV1Wh`&w
z$y(XHr^A@bQykwvHCx*%^k&9Q_91)gz&o`vb_WL_Ix!}r)bVi0k`%s((*rmfW!&e5
z+HEc6Oio9NY~Lay$;MAhPd<OKE6OYpl(Gga6ug7`WcyDg_-vd0oxNh2hgwF&fF~S$
zX?!djnYT8=m;s9`zURwFUl@Bczpd8F#T|>m<p=6lIVo_QB?eK0hv2^R9B}R7;w;AE
z*zg!1t_GAbV~o?C7Wa)hkIp99jtqXIlhcJ(=bT}U3RAHw$?N;;)^8=FWv;ca!vE|p
z?|l64$>OE~-FCE8&XSln6NXj<ctrS2E@xXem96m~Rs_1dQzE`7bHE@LEHzFlqQ>0)
ziB0df1(wp5xZwCmq*@9A10Y3ArZ@3PtLk{E!vXz|4PGG{ClX|&``rO(5poWbwyX!b
zZ9?v!=+~pe!$N_)!I1cmN4}jOTtyBFRxf?q%Y+KEpe$VOxCV_VLb>*y(iR<^fD5mK
zi4UEayBm!gh*%k2LY%Q56BE%(C}?w)3#T-mIFg8^eE55Kd2&S?VdPKQh|=mz$+vRP
z=~ZK2(gAGp9Dp~_pfeW=$p%_WMJHrRiae*kZtpZE*0`U=`%-!S48eTZIY+~R&4_eF
zA~+!xj1;at9Ll6Te$sMKNTzzXUK<__z;W;Tb~!c<EH>{&IJ>$y1qa0pOByVD=lQ)X
z4rx^C!G&3?vR-P*G_|E03}dwPG*pZon#y`g!tN81n2PQ~TzaXMYR5Fiu;h1+H}H?&
z%dSvyP7kp6N6fgro2UlP#_e=M0d1%*R%5r#g2-n?2S=tJnqX=TWGSxUET(~1(()eY
zaO=Mp8xL5BCH}@laZ^(|lw`MMc=7i_3-N)C4(gs?l13of9Pg02-=Vp1(LpEm8>t{b
zcawJx*(at|(o9OJXe3jg<TxLsxU7YF=v8sEWDUZ>f!4`mg7qOr8U`|^p#>}XiWx)^
zj##44^-uy+4f{_kEkQg7G$HacficgqGG^=>CV@O}${vFNJBha1`QzBjF3;D$ZUd+>
zGP2<dR6LINi#2QlBK+bt??)8jI@)eW8p;ZXAHb6*2XhQEqFJuC2k;lY(;c)W8ZsV+
zkn90op+j3c`l$uqy%1FN6<c55kJu~dhs!N0gfhsA6d4gSwe*u!j|p1%gNegHzZUm7
z^Wj^4o7s3VTl_qs)*9I9tX9~lXn52}xkfg2!!g9DYs!;;%BNcy#=P+kSBkUSjHx31
zv@y!9&xX}U#PtW;buy0hao1E-LKCi9d{(pDjtd97HU3t^C&)Kk2rwFLC4`>?GOPq|
zB3t?mF|gOoIg{NvP7c6GbV)SEANL~~IRh}U4l7OKbx>V2Cb$WUroZ4#1P?7R4R~_A
zPShxv|9r<SzARYYB>A38qQeE1tLN2qPzWu*mi}z8>sPG7TOrN{3ZK)fLsH@x_pH#0
z_YXQ9+l^c5^>%X33yKY|c)m1w(nc?ti^K<3IX?RdHK-gS;ou%ftokPH!V4vDU3|8e
zM1c(F1|AZ-6xQ3k4Nun^`?{m{t|Ca5<JTd$x{p3BS7j2lc{mO*vaa7!=sc5{%y4i~
z7$lPy??a`nyvo9+u(0TVa(->#+GNm;q@QfQAvhe+G)I^RfUeIZmMBro|E0NJ->vyn
zC79KAsuqQW2&4FmXsx0L!Xw5-yXq?J{$=~o_YRrhs9vkZff+1?j>@7L)KpgzVL`QA
zss`b%sUcr%s_()-SC7NN!?A6avN!hO8VhJdehr!J-c2H1L8XpXNbU4c{xtfDwLau<
z9pY~1&1POt;9?KL@7Z~O8)Y1bvC!*tZM))N58ZVWWtQ~kLOTz++!I7jtd@phA9x1i
z@@#c^JT&#vM&OsbEV85kS|%Be`^gOcguedkN5SD>>{k~L=$-zJcEe5$S7k;zTx}j_
z|N4;K3Ar*85Y@ml(qw+*7#OIV50k4NU{)YgGHiG5Y{=HSXMYS*nZ&Kecnq38{aAJ$
z&&ADq=NVa0>~Oujgxx&6(sENNUE(dGecJo$7vsyfz1~2}{Zuo3h+bwUtc)fGTi<_g
z<C;>rWJZgrMTx*xpD2*kK*LUcbm1jBI(<G~Hwfh=ox8`w`DyV`3BTNPXt-^9gUwrt
z@cg-3o)x<SEv`2+j|$;CT%*cCahQ?<k&;a)gYjDnF@a(FUP6ogJA%Yn7qvoHi_MY4
z^s}|p3S_6DK(Vx3w-+s?e}`w4g5|T(`z>Q>HT4vuN1Zzr`gIzTA~7$>`Kqa{6?B3?
zL7fXx?7bBZp1k*Brug?}=->m!<FVK%-j!@ZwtW4g&uRR_ow*9tLh??_Xs=4(OYc0z
z)2I8#ZXwd)U#JPPDCnng28Iui1xNsOA0f-k*05%+#T1L!Hkg@HDz;^oPU+>z#ft$a
zE1d&{5R_e;3;ouSgXVFbg_DW=tfoNDcbA>d6H)(}j)W^IY#btdu1fE)L0oM?vYeLY
zuKL_vC$+QQAb{ZQPsoRw;OfQjty2f$RO-TiA9l1S^dEfNb!6oYau34|u(7WVJ!yIO
zgG3T}+&&*+iY?vj`=w5-0EFDtEn^Fg7<<$l_ul6fbW1!h@B6x9?1S7jjSBWyp=y8d
zXlrk{5cE(ZyGX0J2{R@#AA@`mt8rQ8A2E{dMMG{@80$l`^|bUgvU}v67DFCZ4ewdq
zdA;p2)GJ&gZV7mgAbdjN+by`^c5Uxf?5lKw`sbhZ(rY7S$OzO&rxuqhm&1iu5)ro)
z#d!01{KP|Y_B@H#+m-?<?S?qX^S`%*B+Cc>v}6vu>$ui=IQ$`H^ZFiwRapnNp1ihg
zBer#^nknAaH`vfb7m52C`|tGe&deM%RH0)blAxGc<(6*!#>r<}9Vu<8!nAFHPq{2`
zZ=K`Usi4Q}IxgeK?O`?~433M~68|>Q<MDm}3`B2UJ5#wEd}mQ&c{DL)%&|ON`~o<w
zzi@|aIB>a04ed%ne&zIG|HKma>0<xWrv3u&_V)~CRx8M=@>&$%0EZn8+rZ=`C;xX|
zcW%KmRK!T@Y**H|x5Co(Wn}C93#$df4fJ@m(JhkeJ!{lX+?TLQh0MpYetQ<u5fB_n
zZ4VFHm`@y-NF^A^sRJjsFJMelJmzJwzq>W<^-2BJggbg9KGnzV+aUwlwbqHW9z-<i
zSyHtUNmilb_OzL;hanok<y3YWU=>xS{jHnt4oa5W3FNXH9<EckEXi54r#J-&nf6nV
zI#+U>5SYdx-?cY+O0Y0$dL4&L%I_VOuEN^y{>_ed4%&P$#~;rH9XyDI@)h;k51772
z=z}r@*mMuhs@kaX<mH`FcN!$(@}8xoq=DrgNzm275WT#BhOvg=_P8@?Ulcd5biq%&
z@24NGHys5NarG9+!}cZvS!5J_S3Z{ntkpD6R4FnTOKND`F6_$z5$CoBkJxKHH%|VY
zm!>3XcTTl4AE{ND9@ncuf!*hW!4MfU5lJKpHbrYT>zaxt;2&@Y6B`Y1@OmQR)E``K
z3E#83X^0xqKeg&+w-_l`<$228&Sz|_#@j*BJmR0k6dl%H!H=y7hP9AUTOu^E7D*O5
zR4W7Gg0L+$I7Bsb#PrwbKfJxjcD+VdX?NCHsksaTSFzbm1+@@r4XNlW1j-~;>$U@;
z&{E_(281vREcEfd$POKOoo#=>ts9`_YNs!;mnuHvh#V#r8n0FkJZ+cpE_C$ACIwU7
zK#5t~i}W~8-*^im4lBA6dqh!{+gtj-y4tL-8m`D?_pMsChi~wb9pO-h3c99OwM7WA
zvESn(u?hIis}CRpWHelNLy3!oGxbY~R6KF4B*W&X?HRnD|HA*n!b}$B7l<E{7wdDq
zQdlck(>({2g$<f1*b(`E80>uFEo(m^m6`F^lcH9FX;gC>!`uHm!8)hd^kSdh@Jj3G
zy#Bp0wb-c?gG@`(vbB1$sd(NCIJ|c2?1}F|MLZiP>&<IX;KWrL&&)whHX`G<k@gr5
z8O6un+!rzEX{YpJ&4{+B7G=yH3wR-l`}OzPUD((ls&~Cy8R(=2P7%!9A%36o?yY&b
z1wq4|;R#Xk*0qv;9S@rj145svvMW<LPavof1xMq|3Quon3;FwGTsz7@m#1-WG^-b=
zoo#5%t3F|}u+Rg4mFl3EX|`hLVx(pY7S{dxPEMQrGrm9b5KT1vB{<%ifTK&1c~jn5
z4mqLDRO|YYmb1jeTD9><b_T{m3)mV3ks@VD;INFGat&Y8>+04)SR><+;|qQsN-g<S
zmEP-RMVW$pN3`vVy;Sz!rB~H565ZviRZhTNgQ-4x<{NZ7&}t9EsXe$L(^jhe)u8p!
zUL-1&W>sbx8r^9(9;tjK>pERDOFWrMJ#30kyl+8M#MAZEsDeD|tTBK4lkdWVB8vMW
zO&aeu?bH3BbK8OL$H!d!z{akF;ftHf(9{N7*6}Iez`r|Uu4%XP!+n!Wa#-R70UCaF
zQ-~a|5Uo;R*L|pB2}d*IH2p&46iQ~8cBUk~bD{LCFa3;~J2g9h5A&G8@!-qzwbHNF
z4LkDW*#ivmW5kEYR{Rxtn__zI?4A|q@5(y-`V(ifOYbc@oY(8$`Uh|EBarK?a}tz%
zIaiqeM#G)o&IX1nn*iu6M;bF@8W|qe>+xnhi1qTCGj*g(8&%MAn=moQ1U6<=sN}d`
zDznBHl#M;c^zuG(YxaGR8TTkuHUgR-i3k)MQrZri^f_k?V(^MK85>sNxP{2GP)jjQ
z*!w58<)3vnK(Pm!fWr&*UF>;>6l4wz8*j&*%JcvQmeO5~JTAiNdrwre-x<-NC|_p*
zFZn1(PCcL+@GQ0d7{1zilP#lc-fP!rxd)9Q;;p(6D5GFOiwa9gGDxqMVpd^#yLLWD
zUw~|Bw%KO6#oW-3zJBHg9TmgbE+xL>gs5Q4UrAN#`w583oePY4Z~3SLVp+TYYiog&
zhG``SI_%v$qc(rPZLLEGrK1pU0K_GN8gv7zX3U!-O|}wK#kI(UtTtDPhWt<V!6s;$
zc`aIPO(C4#2gOIdKR#v-igb9^mz<j$M9fJO{tOg?pRr#H_>T2hs=31>Pcu+Y(>87Z
z#hF<9B&NtY(58nlSraHwZ(uU_(@g{%tizsXp9uJ~bC=lp7JV}Ep6#z1B^R)!w;F!!
z;bHX&m+shg1@j-pyxeRuxCCWBhqZoJ*QiKNu~sLaE{rUY5zjE0d3>@hvMAgY2}dto
zI9<c{?q1nFgNnac%kKJCj$=!1$(jZr)qqSzcNqsY!$krmrrgq|d0nTf!07bJf>s32
z^D+P)APk>MT_I6D;3uDv$JC77zLbQlH`DEumM$eyPuyl@bOuH*pvBv5Y#Sc4XuXiy
zul_@SWQq3?$Rx_YvGF^Iwe_}7!&$^7B2aI8w74+AnH)`?L;ivVr?JB$yUqP9?}#Cj
zy-cCgKt9Qx-QZ!ce|a-g%VLYzaLn6u&J$R>TvFd}=6#^&o|F!72eOp60l+NnkF+f^
z7ATcZV0XG~5U*o$aWt^EQY%y+Ty#wAEvxjRpM$AOvZQ0zILOLWwXPXQXCLE!?H|yv
z7!LaGC4tpGe%kkY;~mukQid%=?3TyWX%G+|<a)C$S2X7ua=&U8?>8DEi8IJ|CHZ=*
zL9B#k39N58{6`lTw+sxIVVHtSyWAx_hcKJGkNOR_l@QX~$R{>E*`3}IrG6a0wx--F
z{dw5zC6LP|U~aEdK?{0FyWZg`vD3*@6rCvfh-&`bKSiNv^0FZ~;h+SmZm=^s4pA~C
z3a@7QNncKTLe9I>Ac8ls2OP9cC+Yd&g$$(>WHKl8WjYu8UQmvnv_w+ExZYgBa?7un
zQmol^D|}0lo^38ms~ckSh~$K*#CeahIaa<a^Kos9l-H18&@`)l-Y{$P>ved;VQcex
zGPae>o}^5kq+U+TprY<f_Fv=L{#vTCnn}h&_c6`}`bKLhPXPPv^>sk#TP>)M0M(pY
z5&!Rs*v!*(e5|vDtsIw08mv4n%jKy%$ZTzRNgA3(!&$x3>Um*14OMBi;^nmmXIU{s
zz72W{+6)_uK8RzXLLWj{31SOhy03|l+Vc?@m`8^npTP3F!`uskjlbUOM-ZUE^VNC%
zN3{lWiq+o*R#RNLx7^>LRM3&8n$BUYUw$GW$RF&di(!?~TJo9XSbS7+Z`O5Pqql1!
zoadGOcIk4)O5{l<3hP5-;8EgyPiBk6(Y+B3tw#8e57KCl<xX1^q&gs2#RfL&>Vy@7
z@Cg28-`Gq*;OD>I5~P4!Fu4tmbYL|gRS10eo_U=41Y7C#=|9%F0pahvTw1ApxBXVE
zAp!B}HS^fcy6ji?E17i`lZrl-SLpaI^Y?ZnCJkzu4Lgl6m%~}`_<^t928`y6aH667
z@8_1E!nH%A47@pV_#L1XFrEF&S8@b5X2eiX@%{C}?aH5bz^Y$OgDh>$|JcRjXLqK0
zy^8j%y!8Sh{#ciq36v6pLw*J6{iIXL!o5Ou;d>VdJ~Ndro;xMI#@X|GX>2FG)%r5K
zHM_X`p)(vECAYmvoqvdgUn#a6Sj$#TfU7mw!H)cNchP&{=18Yogv=V(<0?ar>Q8U+
zN|UgBRLBFNV1*aYzO_P5f!r>U>MG|JxTzjxC4++SSmgRAcw-fWs232g3CNK~MM0h$
z7LTMk#bF$Lozym?c%0$hd)(+NN#}^^D>>ZD?{K{2vUb(owYWBLk^2=DJFSWC)jtC?
zX+D8b16O}@5>7tWFXE3~%#!n5Mau85ZMADYP&u`Ts>0l6|9QA)II_{M|GxZ>sJ3*U
zOYwo6X!3j1wMW2~Jskhhl-Qrd!dE9!R7#}DBa?1w?4k7ap9}(A{~Eg`Rp2jm|B>jp
zSvNWE;raZ0>SmVpsOH~8*!v0PMb61^Fydyvk7B{w-7J>_mU3Po%mk|4cF3#Eu<WdF
z$wBV{1G~+^`?Ip#Q)$$(+T#Bx)rx18u-Op2-eq6hZEIg$Z_4%hH^vQ`x)GtBeOoCg
zE3l-2<;MSmc1*;?{6UqrCgqJ)t^u+}dy#&{!B<RLH3wU{CZSo*gea}+lihk#%X_Yt
zqX}J5@HXCcRi^HKp!XQ@p=p@8F}Epu{w+<lLM5qMZVZMdkPywEiGW+-6LC3<WlK9G
zZiN$;pK+7g(;G+_JohVtW7G)2zj`69S1~!Qa_h(KLXZ3r{%jU2PPA;##>ojKE?a)L
zsh)C1WJ`%TIOy>V{qV)x{Q{{7Ydn!nRoB`1@i@~#Y4H5}D#j;DZ3L9Ln|wjLtmQhZ
zz9%C44^aI3zJVrLQRRy_YhH@kb!+dy5O;y2sa8?dk^-6f{Q^;~fl+WK$@v50gu!&!
zqlEu<Rb!fRXt+wcr)z)TD@s2$EA4DU(9r8{A>^LctM*C1?J{jJ_J-pM6v-94c!)Pw
zY`0jdhFN*Pq&PFj*({qo5`JI4XyIhKc{94;F47BKQ^uH0mKEp$HtQ#dv|k@fBPTdI
zx}s0&A>AClMu*!hwa?Ahx>vt{{!18=RTaP$nxcBE{=?fEdD!(?+S&U2Ax=tgqW(AQ
z_pov9C6+a&V;+zwUC)x#ovg-lyNN&!A@a4t|Ec96SYX}l`j<otdF|My?0f%Hp}?12
zmw`h%yZzd`$1?E5Z1NUuSTgC8c!r}LD`LtCtDDr%SHf62JuJ&y0fYoZX_WD0ZXrZ$
z^#m~S<hcuGjahn$<~cWjrmv}+*jM|y&mAT}PQ5Ohk#J5PqHPa1lU9dRpXP<0)XwFy
zCeU6&mf8tEGl})7AVvuDW~JI#WyOzxYDfTDdU{W?+0S{)-HA9kpdkIOvmYMw-%0iR
z^;ZI`r${;<eLX_&Dc#Mq3-3<V-s7RA(ov6$FW^e23_Pt}uY@zpRj=S<Yu3tT*VD!I
zHI=09J@<vcCfX0rAkf8G(%bB)=5(bR(aTpd9>+k&dYcdoicQkhvuX~v8{dn)UM4Mk
z*v-Z-aqYZ0HhXRt+ZgWrUwp!*UtIw?#-L1w1I4>iyzcGiK*EiF7MFo3uM2P7>Kv-T
zRw_5ay*scmr1s~gxZVr7iKNoG6gnZ75RC!9#p>!()@b!sgYABw#b-}yFV~k*oW~&|
z9(VEN2)QIES=Q+>*uJcvbq^>vSJMl>xF@mP95a0GKdd@?x$qY6`Be6oMEv7O!%NBW
zca{ah(TH}fy<My~;ug`wbGN~2Rm);M7@esamvu^?D!mSH){b8ZRrHAHn)P`QfAF?_
z!pp*NusE%~mnwhya&dStT17Z1Rk>9A{@JH1>PLcf7$z8-+x)AjkfdCxW{ir)N`Kqg
zo_bZr9f=|oQrf?de9-BoQ>r7RK(2kOFmBa+DLsEr(;=hD9XLDq+KLF<uxyT&ce#Z8
z8sp!26ydw_Xv@{<^O-A%edz!sEl};Xn5!10?;>jfr3YR1d2ZI7?YFKH|GqD2`|6W{
z!`PdaHn@zp$k!cMZ=0cZ6P>vHr!)kC-bwBJ@m<p|`T*3R5?S8cQ4t#zv4maM5LmYt
z_S2V#v+nTJ9G{`{F2I5e75{wG@$ot~8I-VX_i<ONj8At*G+F1kMs#M)b17ZHWyqc2
z>$uxJ8C-!)?|LtCNsYBx`?;}$x7vfp$*&)>vqiXEszUH!wh`~}vm}7Lj|?aFp*8*#
zc?#iAh$-ma8A{hjlcEONoo=Tk`tZ<?EKk<kZpupOD>g)rdDr%}vX%{rUoyRpBzt;j
z-EEm30nv5TptN{VeQj%RazB$W3{;>p2oOqlG7td0f49YuXc~<6%;mg_OAalwj^Ehn
zk;^RIQ~~#N*e$iLB`lUusZd&mmizZ_2{|_UZ);H&(stcgFZ#)Swj$?(DQkfOE@!zc
zDF8FuWO_Xo=A7_y71Qedqtqh&St)^BVT7{Hvzd6sXA%1k$Gq1VACVs~iTmis6evz-
z^8xCS6}l%-+MKtI4*QmyRBbs<aRCv7=FO*z;eAvkzM|f7`S)_`Ui9(~Y)H}8w&O`X
zzg`4>T6j!Tewi}}i#edHMwk$fgX_JrF!<w*YbfKR2a66JFyL|jI$Yj#c_eICHMmho
zb4Snxh`m5}yu?Oc@zNNSE}_S&u@)1b0Y3YyvE3^azBZTF<TN9M=+Gn0NgCWz;ObB-
z)pcTYDwXGTn=53r%RxEFTC0xz(V;^boCOND0mc?z(fshEkPGX#gELeCgZ$LYB|8@6
zno!0#$cW0$Gi*FOcsXX&^k=CCoHV4tgW8{K6p;rG>wEBA=Xs$IUnbjg2c?V~^tIWX
ze@cf#BhMH(!Z5LR6ji44Bns<^Ta*8l9~*d?XH*oZ33o9yBxL|v3*@BxCeWRq!5QLa
zcVM{)w-WnM?4SDUAtfI0UOK$ndD>kZ$-lN@-o%L>&dql28Vo`t6)*WJb}+bnv7pcw
z2&B_?6Vn`DDy3R-P0~Hve_;jRNY~DQsocDaj$Tb-f~rA`=<D~_=)wKhl}8F_O)*D*
z6VZ=``0Pc@Ol4Qrqn_=NtH{a(@xak(lM~;=?^_*TTo)Jy@BeI3j*R*Vw#`0x|7QHC
z|J0DiO>P%GwJ^Q`xwSN14s<7OEP;n8Py)~mynk;yhz^dz-e#W%1jj~F@<>+{r<LJB
zZt&XO&C7Q%cE`W!%S-EPRY;J^U6M6fAs4%Dtx#7?_j7TUXs&aaNJz{<7c9w$iesPn
zKxHakg<)aZDe{+pUJQDuqE4&tM#VJI{V^8_1sb6UQX!Dsyi;{ywAq@Wc`UJw;_jbN
z3lhZ~FkBFW+P{C|z=B6SK&`>}{rdSe5M(M|4x@g}3NXW$-$*EivEP!OZ6879b&c_F
zO5;xDVzqZ4xVsMnw_aM!{c@J5cV4oglyVm+d7cj=b=xc3q4pGSD_du{qmA~8SBvWl
z!#oa;U@7=`a&oT^`U>8!z8DV-F3mq90gb*J4x}(x6PUqiwfxDcu;dQCi8d8IJlC4&
z4vMrhDNr=reCb@t_mV@g!<b)#nT3bE%AN55T|->VbBde>t0T~C-DAP-Lb9~?DDtRR
zDfz9J!cPLO7QUrqgUR{GbU@Y*IK8m(parh&Q#$GMy03<(kAZA0TD$#3!3zqb$M?j{
zedhEoO`<tG_Wq&8gabq^1)~TJPox)>^VfT%S=MLYj^LsLRlYmvq>fVzv^M<5Ppi{Z
zjoFqBK;#KeOW>9ga;ry2Q?tDS=&ukIxa4mT%tdXkUE2L(iy%J1?tU7cxA&9`ACucc
z4M@8>u{|QrXCFk?tkC+)kw^eIk-#1(GXqIc!bz?lfm74wioa~Yan>Hr`2<?jK3AIc
zRSv8DWHrb)r$TRA%|D)3PC%t>fs3W#0mfbM6rpgF7u5##reQUSzGRbS7iQdO5N{%j
zGBzF-s<vkP=H^mVrW&iF58924)Mh-w1ys%?6Ju}r{SJCRe@$-l%_>m`A=2d8IMc&H
zY=9Oye9)du7N!$Reo>kqZg*H}f%4SkjQ)qq{GgHueJ=-yA$jkhsnBvu#H7#kbOL%W
zUmY!m{O&ku!<k)vZ};?_@CR^ZP`XOi{U4yBx>tL6Dw1LyzynPQwok0Dse(sff>tZb
zHjs4$`t>56&srz!dGJKL+h4!^a=@GvVOCq66F!QvYK`XbMkAVVmjP{EVFdJ65h!ZI
z`dqibEy*Mnk{_KSYJ23~e=9!W<F=cKN*FV%g{ZrB7TLaM(%Vwp#mxo#4l!_~aQ;b5
z3LbC#A-C1$3cifrFeM$lS>zu}$XDwhr%P@Q;jhbj^{-wg2Ksa$u_M#pVJH$2Kx)7Y
zaEzBTu~~zl0ZZqjZfy?VL`mz7P|u=sRf&`dx9)n1aRMa-zqnlTfr|cB-2T#*OhRs2
zLjLAkKN>;qY|wz$J)M3E;u}q%wJD^i#H{IVQ$BX2DqYK}t!+lYmwB%}sE}@bd~mbY
zQ}~fqrO=Uq=+Zx30Wv5<!`|Rk$+OXJwQYTitPDud75d2j6h)!d3TpU-9T=L}wtLus
zDjsNImn&(nc+7roAF;4DEhU?bK|h$=5hV6NUxEcW;V*SYqleY}Wu0Qs7N_A`e_#wS
z@gj~)LlMd~j7+J~{N<JLWpgr5H;9Yuq`DulYu@iyPC(p?H?ff3xfb(q`B&`6GVhez
zC--wfP<dWVZXbr9$2nb2cZOc<9-3dz24SBCo_V9*1dKz2cpMpH&>3Q6UjuicK^sJi
zO3meK<#@Thw6W^`!RcA3U&*8xu5>H1o|O4Ho;wH#PI#UaEg-cO9HfYXc{1D<$*SXI
zQkG)FWhT29aVjp1cV!-KIxfIM6S(r%2lk>7y^UrhFO8=?>dhX{QGuNH#w3LGkv92?
zns3XM)b3IfmEe^F8er|8qi(gyA}Upzj^Z<<MxqB52W+TvqG7^Mm^6g`4z)>3AqtGH
z&EkuMa&$un;3?>P4=AoIwUv__pA0+F+jL_%Qur#abT^dETNkjuXiGbXk{%2BkZI;f
zqa1pJUgJsk%t3J>=q6M>ks|v{-F0jBAuIy_1jV|eqwI!Wmn70*^NpaY(P)dcL=|O=
z-BYxdCWtv%P*}=g*nvfbiB2f~_O`0|X_zcCYe&NW92XcE_&3@XL+;#hzXdj}H+Hi0
z?}vO)y+(cbu+P>a(hd5qX^UZd{jKc;5coL5>uPHzw<yb;z+y85)nHCt(bSu#KrvIh
zY(z}X(d^q5Fxb*r>G_j}zaIl&{bvG3Fgzya_lQ}i5r_xBX07*$l<F^z&$G{xRZzuh
zPrL6ZF)R@i9>JIE^)~B59hIo?0ob+O4!;I5$O=Au{y+#@GTF+tzb@3X7Sp)RN*J^9
zG?yPyG%whAePTVDU7hbdcM!ipqYq0a64k%s73stxdc;5TMjAJ-l;B`2{iI7Tnkqos
zT_88;u0K$*4)3&;avcg7ldu;FMAwMl`n8djd9zo)0OG9D2fuG$%rnf-0&8lI#Yq8z
zL8W1&fWf`Vz`mhBdfS6l#CqJI2^5~sqt+;MYXtFK9<}pozRuSDt{z;$DRvipz4ygR
z>s~=CS`&P=<?u6pYO+o#5J2trk^%2ANju25k%~a{_b1yb*d_0oF56Hrs0UFf?>0k7
zGbov^``w1>#wT)R+MPv0u9QAa)kCjcxj+q6V`4xQ?O;a$@gD!x;k)EIYB-&QCes2L
z3~LwF*zcaairu1S5Kmccb0yte^Ns8|kF|9$OUa#q61}6k0#NX!N(JTjL$Y`%%sE@2
zN3|&Bkmhu_ke1(0%B(-xW&atrz)aU>GZ_Dv-%r9Y3O3QJQplZ{+x;-{(AavniXhQ?
z{TIa0GX?h{m|_G2@nulu<Q&L!MfJFg-f_=V6Fk9&9h(!n>NDh=3loc$&J?#Nc++#e
zxcF^6ta-x5ZMA26<sFsIUIP*NH!m&;YXaxC@Ng*`on0jg^M+Ec)1E6hn$_??QU-nA
z>?plnv2%*H+sRY=62q#k>HBaHyKsS8pwuHRxyvW0bT5PZ4vB!&upJ&ML_S36<^ck<
zYXYJ=$!-kcY*+V{#7M-sUQYi%T6eE@^TynmLC_~a(7AwB1__u!0w==(m9|ejyU0Zo
z&hR6*x1sOG&6so`Uy?#P3WMNYKm<+@$u8FNfXqOibQ@Y`Sl+ig>lKi&I0j7e^wF4Q
z8Yk=jHIeh~(t5`dDKkdN7Of-33lR;{?5Wm8phs=A?B3$G?GjY+i?;hnG243BZb_bt
zbP*;-<CJdFl7w{lv<46dw0V{RW`x5#={6<{^V7WR<bIq}AjU^dZ)(?Y{&10<Y>bp;
z{XdA)jGOdhwed-_P`2((y^6ghJ2(A-AQU^pkqsggbmOES+n1<o-H@s<jC9CKz4H$v
zHQw-{e`9~V0a0UTO@80%^nX;oJdHo<&E({)%oDy(8Sd)L%Ksos@{ANCBnj-4I#$jB
ztr)C`o>s1}B)=2MoNGvmE0k*5^i?E`#9Davwq><nBJ>O2eMUxy*^sZv9bFU05rxR(
zGrG934p<;ubmp;C$Bp)1QFoE+3rBg4<wxsY$(G)=%jY&2=qbC})KxSZ<!8Dp=TAQm
za8#1hXZIS{@UfCcO>S1{Ow<zypno?(i>7S-A&=OaOS1c|Q&=`H5F|d(iQ>@;B1`38
z+W#bMc)euNy(S8jAs#hBZ$)&LE$Ua}#<QCJ%P)_Rz;6&v8;5;zCO<z&MAN*E_Pat#
zI+_5}?utsy+}bTgp?2;pf}n`~FL1YlCK@$u*o-x!#)lzIT4{J+?aKT6Z$btUkA`ZF
zHZyR6B(TXo5Nt`kKIDtP_bAef#N0OBG$+edbjsu%DMl5QpxT36pEV(Nr~k$=*0hD6
zgx0Mf=`sMV$1_i57*LND2?bI*YG=u3V<o>S3=xR|<?lP3PLwE!LFDFjBa?8P-P&U)
z`WW#xGZc;YQ05SCh$ZR2wU8{7S)TeRrMNZE-)Vb<)g|LS35SEkKZT5F1FLM=#;L{?
zlRX9IOZRqrwA%4a`7z+X+mgqt_lqfq=YeX}ZMUj)d0~s43D5A6n;<SKWDD9@g{=ud
zHPMiI{Ao-pKo%PBVB4rF&;%^78nU5fZs_bc*Nv6{5w-tdCdAl-OC%l)IRv;ateH9o
z$k|<I-zx&>4;q+9ZlCJ%asJOCZFo-jVxuWYWa_uI@oLocX~7JG10rFbq%e}gwp**%
z>*h&mZv}4sC_?za_?P=UlP7u81#vX}geBco97Gt10!3j=y0j__a<3`-zN?jL48fHY
zO;U|Bey^+~=r~z*M2>_iQ|kl?GHNuc1-;*0zIQ?bN=EU9&t%%K^2HY751CRK16aV2
zXY>F;`S(mM-;DqGpZyTo7NG{wjp)$<?i^^P*bQLfVB$B6wq0c~mi2N0AYm$XxtRK-
zPsx9jr^uJevPVwGi#Q=-akB_zFGb7c5FnN9y{jlp^beHp`@abB|C@3(esJP)1)=Wu
zlTKB77-bix*?9J<1xN06KKb2sNNWRrrv#y(6+z<;aZ4hm_bA=Qv0WFNEj^wFk!^kX
zp>YZypTbe8#b8hQ{g~=l2c|;`ULOaTS#?L<8gK8RsI0}TMC9%Z!-lXe`6<!b0HPJV
z2NXhe!6-#Jm*ra%)Z~p5(c^duIAL~#&5uLNH7Vs)3vn$@&E*TX>bEt5Kp776s&%Q(
zu+QXC7n5O@`kX`K2MeXNN2_g9NA~Vlj$1Se97&GfMWbOm*>%7<g{;8M*^AH&uoffn
zbqfwcF9BV1u8j<v{c_^ZTH8K6%Ku1yAzeC!|EmP~e-`8ZN4N{J-tUmkW*<bfEta4E
z3l%u~e^-J3Z#uVeu!6R9gZpnA#d@y)r*j*S1H*5-7Uh)%uTFbQ9P?O|QFkt%{_oFK
z(F-FbG;-9ED4WS&9fpE3Mf;?4)zoXE_G(jiH+wrf?Q8uj0gzh$3B*OOcrdMA*RM35
zpS+0Qg%eQN+B#Nh8DW<dl^xw$Gf~BkUUBzu^?Py{%ljIUPXwh?1yT=kybMg`DqkL!
zh<-#sHq*t1ix=qR-a?<KzR1rW&7JF->~oAi_G}y;<^!6(E>d4`1dCq;zQ!}*nQG>D
z`}wTpSlg&8C1quWbmWo}RjdH))tk(Rr~`m63lY7IHR_+)1;QjF=h@TDD8F&6G$She
zL9M6lCDk)2%M!jWD!2)w#jcM!?c02r!iJzLFn=b}*#6zdzAOIY(_(F`2PSi7tQZOV
zzW0J(2!K2??B%MfI^ov}T5YfkrPoF3XZwz*jeO3~E*MlIzi^q2zuh_zPPHyNfmFi$
z&2<<S9u>QbvNzXm*Dm{N*`-df*SFbL#O^?j;}@`K-2on%0>dWf-aJa4GR*TyHR)R%
zaPN(Ts>m1_F-8dn1!z0j`C#kz_jEgpg}&n9+)5cQqi$VCzIdX{b$O_?oJE2_-;gnS
zEpJjv9Eo(dp9(D#^@LStc61$u5(Cs*j;R@*w_U(;p#D#3Fp^oBKdOZTh9}=lknCuH
z3Nq_qB5p7E{w>+oMXPi!#*%%Ih>j(y)z|nlc{Y>~O~d(pNx|Rj@9~`hNyI!OG{gS8
z<PzJj%V9#t#c@{&z8Nm~aSy~OGoyEoht}k@M9Y0SM6UKRzfcU*07T)=-Pknoeb9TR
zIz?Wu4lLhW^8|dDf_XAMv_*L@OE$?mt*ve|4!w9Ocj(qG8m(nQC=y99=jwm{h-B_p
z82v@{586tzrrTJ_Uen@@hCXUMGyqeDXY(Ym=RPJFf~B2*$j#Rf8)7)dT%x|I1X`mk
z?ID~e*~vpQYFw@<$?Z|b=hrd1O_$46ksTkXUkmEKfN3yMteyg+{%lbx+fCui(zmD2
z1rzjj2+9KF_cOD9!bpMozFl4()XR1iH>!uDD#?Gl`xf?yVBHFa%H=0q;oLI=(Ne#m
z=+A=YReU9ZAvOKnRG@Q;xy7lab-_7p+3$GRJe63loyO~MCl%78)sAtay(V#a_%S|7
za;k{Tm78DwDT=LO2cj?LHbUqU;FO!9Ec}Iy&g-cV@Il{QNyuns#8yy)#g7nez30*N
zOLVgxdL+b=+Y1LLY4w0@vrkLP4gmMh2d>ibm)>#&LzbpcgNGwl%+~jEU4|f9G-YY(
z(jh%9@2%-{CbH(s6r`URE)gkwsXAy81+OLb9jIo@08{7wHW@i=+Lj9%V08z5RK*5x
zW`Tm%4qST+nMKYdOT#rjw!t)q22EV*H{}oY`5t@Ra|C`%V@?Qxx_9GGpw4dcd78``
z!D9G1HfWv{Eqj%W<R0M<+*_S)tSfR_QPIF(&3e+?@wf`n{tIjG0A$(o?1{H++qUg#
z+db`R+qP}n*7Qu<wr$%s|M&ZQ|F`eGc)PK?5nFNNMs=M&bt<#6va<5#`6MYGG{c*{
zxhM$2VO_(D&E7f~1tRHb@W2ZSluWq(Qp{g!nu3eHi;wQ%;F$4m#(vSPYA4Y?+Hb-c
zJA9Ie7I-^ZQi&Y;Zh`gGl%=>|`!?7kx_zm@8&uj5j%f`hK(u@(!3FIxqf0-~oH!-;
ztIJpuqh`2t<jdg*Np0u#s8+BY)9eW0xD}$h_pI}>Hxu&{5Z>IqWkANzHJb#wr3woL
zXmFf7g{~^*wU_<oJy9pE^h@iK#^?s@!^4TX&Me=v6s;RA9~vVuIsbt2sq5A#37O}8
zl|CiMa0WWYvU{ZN>n?)#cHlhZw1*{fs_;I@!TC!HTzb%u-V=bMS%VBb{^U;=N6yKI
zoL9h(4=+0la5%~hi(_azGcAwOSZ-?TnaEz=Ww04hU59UNO_QsFdDo{{?+q}_s?NLu
zvC#>27mzP=$x)obz_KzDvZ>Lco#7DgTIrtGMzLuG1pW2)6`5BDMKZ$@@$ipOTWG2H
zkPt9;vld3(nB$ZqMHDh$r|5<LaN7mKd7cw<+xI66htvlxy_c&d!Q)sgc6H!khT?`!
zpKF$2q)wju?Y~H<>hUE&*}Km2Vy)dYJmxZbo*LtUc_)`)_mom&73Gs_J-^O1-i1iB
zWxT%dIB6#CyqZ0{$l>!Qe7Th}vne6Fp-{s%$a<z$Q>?Fql%yk>gGW;$o3lRk{W0z*
ziS~^BKiR1MQxeqw^+v_Q%FX(pH>yr;?YR9WbO6LC(^}ZdX>_WaH0%R0benMGCI~&m
zNRSCwCiZl+qPDB?^p&-r4|PIetn4M66tx%!?bzI_v%CA%0fVpp4!H97ZV1^KqSnUc
zx>xeeV7FgW|C__@{^Z^!WLpo_sA4E4C*s+xi_>uZ;N++0t4w3tQ84{-)#RighF>=}
zF;Ou#LOE66wr^fRF?R1W=XLw?Y@xyREK@jK^W~rYRAD9tRFYGmivocQiJfGoiG^D(
z)v+9p?5nZ;Wn(~MC&Sl%Iv2(9&s)VjMr2*#U1v$R?waxG!PbwC#s=u!w{ug2U7umZ
zQX`Nei@+9Zhws9)m#aU50(s4o@cOxV6Th^V!?X~e2U{8Rw~pH1!G<G*3O@%B`G+UI
zwpli|V=ap$QKb%m&h6}+;tRpbjD^(jjWkElpwL189N1v6WGFJD_5H4n+%%%YO*fQ4
zBk=x3gF3Y8UtKM5JXBb_KB!#S$F|$yQ%U%<O1-;Y=5svlGmQUI2+HPBnVxEna1H%^
z{eAC}8i{Pd5y3)#%PObZm6tswjn>DF=OTCduplypdC>a?9r0B;@S#rVXlWKsaZeWA
zi>UykRhM3<Y4xCr)@D@&kOE+K437dQG$n4-G!c$8LwR8LjtG%}YWWSG+^B(EkATlb
zee_nkH$RZaN|KL)isdpFNVEQ?#zDozrDo=+X)n55vVX4{YsQydoiBtk5vC>^nNNwN
z!3nHkKljkeOm03WTG#w$mh4vnaqaC<^4>upB%zgZoxB4NQP%XN{ZwO9D0C=nwVXFb
zuf0@*Af)*U<*^jLoO0r<=}27zV%-oN=bGs3js)k!-K#amJVe?6Ha(ed#=NCbOPwif
zMEBSB1%@yCfITEjwsk8KJY+gU@jIwBry|#q`!RBCqaIWk$i#9&EXS`=UYi`wSi>cA
zme>X<A-;vLiYh79$HEkF?><Uw@db0>{H%EReOM~A__!Z!F|&8vox*9Tkdfswk;e(~
zlaa)D2$E5{)RPIAKNen5jKXmev46BNkj=B5!*wB!zRgM=u8Z9Pp*F`<s8&A8Yo{CO
zsuTDSu7`sZzutva`D&suN&k7RO4;2>yZr)|j*$ah5p0i|C;2O5&LK+R$vP2@A`*Ik
zhPzVWjmj)4kP`P!qECQWR>!>K{pg#Eh)fsL)j^T}_luG}7ih+%6m8RmMB1V;1Vk0_
zTjT|N^7Q$wy1@*e5H>tQ4QQbs`I*_6D0M~g7pV*uCYs4_ESq0(92A>n#J)pl(e%3S
zoYLPV9fE?BrMEtCu=DQt{Wsx5_0TxeSO`n5rPZPm@7U~J3Ms(l3W{o}dne`%L(gJH
zSeSUs66@{d%L1e{Dx5Kek2w2e`y?uV6Hgx7OQBID?MIdI5k1lrpVesG&0J!TCB~1)
ziBlj;a8bo{>yW!QeG@8$=v2t<r+${6L6OOYx)$7B#z`BnMxqT<tF<bGDzmJZlSH|S
zj6t5|&?U~1k++@4psv|R&D;&wRW?1-l0!FqJSgcq1&7B@UJL9*eKHkVq7_=b>1$-u
zkKZSej45}@h@kIPtYpplI50evb7h=g?%u0wLJk_g4tA*Fcb)V)Xba+<b$Y1i+roQI
zwiwj5T0SjE)SD<}m`#StDb@bbv3*Qpq&J!*9ba)Wnb|P?0h_^AYch^#;=`kFA+<p*
z`F8_jQXR=avtRB0Ud4llkl@^~)((*1Y0b9+`y_(1=vYOQ|IENemJXlE>^ets>}Mb{
z{AOgZv1(oOGYN43ASnBio<UNBx{*h2(>TySiPa4JKpwHk|Fj}trwzw;`Bvo?rPFWm
zFb~Q34Na}}_)nzC2wq8@E!go-_4#8cA+u~!A^1$gF%%0JqC*5<8@{s#Y7oUZ`c+>P
ze}=r@YSLr~yvqa^k`+em#pkJns$K=7b(O=7gTO0p1fwh_S8SePjy9{{U+>U^j?@{y
zeJZ4x%b#=GDx|#%p|#IUojQi^R!5t*CX`oK5*eS@_a1cYp4ZR3St?F0!Te;wV&ajJ
zGZ<j~<1@uN?oJ4z)9CpXbyefmD}E0wVF=F`MrWQFS2$YHmCjwltx{wmccPRgHZPdF
z{i+2^q}iJBtn&*BC+J$@5GL9T?m||v;E6fM6`sp521arSrvu8_gs)dHATFfGu+r_z
zka`Mfl_;Hv*KKuhAU|5`4C5%uvby9Xlb+D7z&Dwc^c#)+gOAKx=sIu_zMk-h^f6}g
zCw+ieq6M#X{3_W;1+NRcWT~BS$lY`t;kHEaR0p&e7n>7=s_I=cs@cug!w+wu3*Yx0
zk5Idh9gR5EOV{oIoRRxKstUC<OCn<Fs26R)gzfdw%!&3+xzpM0^EX;f(oXabCS}=`
z1nh_EPP%S~_vloKoWPWOEs81rO(U1%4qk2$ju8S05JEo6!szmNrc3R2g6q1k&RiSc
zu6$H}X^cfleNB)sz29f^oGDkvr4-{$R?x!RatM36jzCKM9HMKNY?4mk=uEz8zj=+p
z3VgKc6I<p_p7JT~jGqUITt~WA{L5ZEd!8tXx1I^S?G5pe^T#RAjn==j)G%A;#LNN-
z+D4cOGk!H>bN6~4->PxHn{H=Ap~3^NoqAj|(+|n&;8m{&<3+<_TWda{4Kz2ba59u-
z(1wNm_~d{DLCxV7k=~hJEMBvL#-HxJq#_JtTBaEv3xW^TrO*0<6<Bse=bo;O9GEe`
zV=v56aBdLfUSB`SF5Ss5xU5!Pdj5L?yg04&xdJ7BBJH+O{27s)C|7|rjJ2N3pP`G@
zK;cM;EAL2jIV{k*z2C^TzS^ZVV%n^21I*t~aM5KQW4NlkmW`We6RfCC0+={=8G_Xz
z&9Z0!@9N|tKm_XW@Puky=V<(=goGOOsksui&8DiJP|Q>OP$ci}n(A+D&DYALYcZR3
zf{KLF%H_t(vO1dQL%(nTJ$0&X@?fYMTVm{#y~{fcl;8_k;kK*dRTFfa3nIBiu)m#^
zH48Z(Th+1%V@63H!|Q*Iufj5cf<O=uMAoXLjv*j*^8J4d2C;LBtlCX0gq@UFUEVC3
z+zWIEi~@0$@GuG8T$}QQ&gH}htq6Ydp&r8#YiM@C+RMc=bdA%LkI^y5oz{uWeaww`
z#Xf9=dGW7!>x_}fHM%u=EquSte*-NW{jB@%<{2x?fA|spw+O*(Y)t<HA()%vpCs~j
zjw<#BMkb7+CaxAnCK8SY9{(*bJq#D%s*#<siK2m{jT0Z=fBOd_u7B&viHP~1ii?e{
z6VX3U`aebpX60u7&$i>`rjMeU2|>>-p?uY1CMa2ZAft?|G)yvi;H9l}Q9)3vZHc-S
zV<BBal41RrObcyQ(N^)SQpJeyaHN!w1KUF#mvqa5e~6vQd3=!31l?~COk*wdBNDHv
z*2ygyRtN>|x9m6HZzPY0(=8`KC;4ZVPaYD{f67C>&H6qjsmbDm73MMfn~iJ>-*tK`
zm+d?L)Pk)ZUQE`@q*nq<xh_VWl+@2;q;}>doe#Fgq1K9MOo?NWXR%Cu7Kx4z`9p+?
zC|*XCk6E8P>kXL>j<xn0-`%+)xpzFm@q!1f1oL4u53)}OJzU;wyXM-J?j?H}UK$qP
zn%z{6ejR!FREG}n=7yvEYp3_guaR>dN)JCrD_$HQ-R1-{<Jn@(d3~wyO*Et5K20a#
zPW7mqF`KRf@LhjMh(FerslT7bb6mOqs>?^GoLssk)1M8=9pBe1+xN%V_rr5;KymiL
zbN2mvedC{I(ha}|iu2#=McP-UG4w|hC)EnpS?#h1Ut#iCQLBEoiObn778ig^LmDmm
zY0mrY<(&pchEJS!_FWdfIUlZg#}S%NG5X8SX1k`e&2xny`^#?`WpvFRmyLcFjbG`n
zoHd(ug?Vi9RVyD<R+r0@n^l3wPF)t)PljU$<3rb+Y&EY_7U5Y_w)0C0mMp&OwFY~d
zR`f3&9XRiBwm9vYMo}4{TE!G%dw8hPDIXnc&%>^HUZ;Gv-d7ZWvHu%Y|DxufI)K#w
zpz41RJ~uRJ_C^2Tob#&RjRTGmlAARjnKfi9aBsJj;P1j9AuhyY%cD=(d2grJ+}3j6
zo3h)o+Edd|PV_KdyOySrvEA?*)c~<m+MUJy&GGBkcnd%BCI4o+_QLAr`YUI`SV^M}
zrOp0Ph99h>ONg3SYjk5a1|V}T4q*7N6W#ocbDNEY3)z0{?e3!e)|me{BkzmxP0pI$
z*fWa#${DAb5W8A0>-^{MAWR(6@h~mhjc>X4bK|)8r&snc=N(f@&S{@_(-PUgQ=Hd6
z@AmU+lTO=R;fVX^lk9o#Pt48b^;u@N4nM{AD7U%cxwoBN?<eTr2=z4gH21rF)&PU0
zXPD+AMe$2`f4XhW`VYzZhpGQ-Ub~;#7vUZpv9?fUz_wweWko%sK@fO#{Uf2?5SYUk
z;n3V%mE%9@wsh0o?rb)kyW~9~F<DJ&lFc-tUfAIs1he_kIdNN3FwhT{&_<T(bV-7&
zu2ObQmRRoXJlivZqU_XhZKqHY@-6!(u?JsEJG0nZMqK7>ZF74E!x0Td5MtQ2(=O<!
zC*D8kH`rY7v2~KGU$4kb_GB&LXyN$2bRhq4SUxv6iQ|`*!_QNye#C&<`dxt9=ksp8
zV;a*P)Ackpr*;3Y6@Yp5UHa`Z#~Q10>YzQJ6OskLd1jVdx6xvkSF>Sw&(CVN(LAkZ
zi<`OhpmXG1AEezK8evw${;&CC|2kU3TW~e39N?zFqTOhYo8An@wQboIZ$SophvEW0
zwty<2E%@K-|7sR**&UJz!`;N<%xfK-nRhvo<BbN15}BSfAdoNvw256)4fCXtk`m!_
z=F}!_wIbAN>u$X6jM;Bk?>!xAT#i>wG;;VQECFp??EV)q-je(JPB|~m*$C9pHBeJx
z*l+(&@=Gu9|I4p-$NvNQZ7%<>^4qNNkNkSzbsxneY+M|3(3(WLba`Aik9=goZ;%K?
z*K#g<Up0@smUgfIi5yVyWY{+a<^1lamEp76uv7B-hZBbxW}<tHj@@DcZm9ppKLDcu
zEc_3i{)?CY@b}-E0pE-{p|%2`_KB_eRt~ML?g|Dwe~)<`=2s@ofNUpX)8m7R+M);d
zHbXopw*iXuyx2{MNM%Xd!?iJ7e;(<%e^teyK1|zIF2I_8ot&lRNDo^NBO;4`W2>5D
zBb%H=cri4Kb?jsQz!<u=eM`{6zWy%hh1#Z73K7+J8L?enuUqTUtz58#zWFJ`W0OPx
zut3=6y!Gqqi<z$Po9*XcGG5_&q?Wv&6ImQ6*hA4F2>gD7a;^*1Uo(1)PkTlUO-F}6
zj0g(WdHFWZrD`O#bxQ*DVz=EV62l*=hmCCY5_)b{o^#ao<fd@CNoY&;^|}-E=P$O#
zAnYu+cCXzspAF`ZPU7VzYgOK_Ew5&p?(ZBM8s1P}z9Z?sPF~xbKLvxej~$J{ifL^k
zYVErjEVYf@zkK#HQ=0fP**o_&Fc%SydP3`h8(IXepb!LHmuCg1RCvDhaGhCXA1SGS
zSP__S*Pr_LA>99vo^J1XwuqWVtv{-?S#7kmbFQCD-J^$0LD=3<>8d$d#r9KPY2f74
zxP0-lx=8iow`Y6omiAKC(C22-cvM{f5Yqxx-IR`kNpAWua6ID{Yew}d)75>;SnG{u
zcs_q#uSI|QV677d+)mqe<R-z<^sQzhBQ!d1!|R5=0oPo8LfNj?wDGF+L9W)Uf?ly7
z)t5{l&VkR6k;GRrtqnn-*V%qK)COMxog%?fT4kNVZxhhLWUwpOBHxwH@>(}nB#Pfg
z*N<cLRdh%7ZLcc*8B9@P<~rKFem$4pb@ejXb@x~~yltf><+9qXQ(8yry8c4DZpBI4
zcG$qh0y3Z6B&#i0A$6-1tvcPD9N!(o!zOa+&HL$ic;MpmDG2x0d9FnQ=-|D3=k7gE
zPSwlr?YV4oIEm-&>$!%o?~<BemTA4Df!s+bYN43QX0`Ib)W*YaePyZy-4arz`L?MF
zHtxw`_`0gAg`yT;iN}}Z-^yF>CnDRQrT+;IG3$TIE%DzhL1hUMMin^&Co7_V8JPbE
zIK=;9Tf(paY)cUfXD3Ay#~*e!_I9==w$1<}@&D9{Wc{C8k^eg!Viqno?*D>A3_vy#
zLX*JE?bK`CKT$)AHxx!g^SbVQCWzTApCz*_q$tHo`e}HfYjoX78`=3wSgbd&@WHop
zQqU`EK%W|N10QlC?U7~U^3o>b^~j6WZi|#C7zh+lihj-9(D(dPL?Zx^KLHUa|Foi#
zLjVeeG#sGFCSw80Ax5-_04Y=;jUt1nhu=}>>nrc8FaNdzGZPaNO=)2;#7=wYKew~^
z!T&@OJNkP(jFL!WWH{K<Q@&H`Mm)szuMWrh*h-~NSgwh@1-7@h2?e16{g3ZoZMF~n
z?2NAC>F!8^g@$f*I!5sQ1$bs^SVCQ#`Wq<<g#S0;z{Gs+iq<fozmZZmf?*#$st;{<
z5J&(8bx0-&-f7ne9(IOp1AyyTJ+w}MQ86(o8U6y4LO|R9j=FTJf9Uvsct^i<Pfs&|
z!6NK4W>`(mK=uB5Fhpy{@pL2QEdDS+>_C^$0-j>mK%Wv4Z&Z1#!jS&pQ2kGS-~##3
zKmi;A!Wb{c8FVWkIBR4F@8Sm`L;uW<ffhk9+3IPMKSRRCgH*h?9EC-~2Fr-TJtX9T
z19mSs*qaIz(7cdGs74?(5dXECz?U3QBZPq3_p`KZxcxS6I;M>y)ScYvL@-+<8+?b9
z6WNKp{}Gr#2MQRjpvphfTrwZp)-3c6(J6xO=>yte7a)*eTpnjYL?l!XDyuLc0rpBc
zDBa|b<frJ(7x*jvUiC4Y*6FZPV7O<wxH1IIO(pAyc}CY?=+#eO)J)P1!r83>*%`5}
zN2t~otk4k-7S$y8h0<$3?rXy9-s#+b?LMj2?+Y0sbhHJ+A@T`)W=O;a&J%o%D-reH
z7)LoUTmowmsPj~!aAK8c?5BzE9hflbDHfD~k02p;HoWsFgjlw5eE-QK|4bK1B`s3<
z*Lu<s&`IuzC-hxn6>w;C0RL%A?oTZx--$Sn4T;VqMs!2p0j9rN3j#k#U@eQNeZk%u
zzPS>1L4Q6%hf+#mEQlwm`u7Ulp+F($M*!V?21|`Tl_-#-H!u*`#3#?y2HfF^j<I3V
zjoh%rmm>B)fWrALIK_j-7|;}ch_V`v^;5x`E&!3Cvnwg0g0sc>KXG%1h$x5n7mK;d
zfON8WkFrd*qR9Klr^&hzaVkE4L!qr+NZMKIi)_j~z%(cXoH<{)Q}qVH3HHDdVFCMN
zLt*(Y5&fpkETkI^qVqSq7r1+P7>EPjC$-{%G`|vl#P{bIh)cGRf&#9m*oh;Wp)Cp(
z#@O}P4P3{c#P~C#{!DsVK?L4%giSrft`M*X70e5l$3%?EY>LziCsHdJx{qrGQuy&f
zfnn%|X)&l00?4WNfkng1eWvVxB+tN2vY+YPVgO_c--9d3WCdzUZ_SjdRcAlCK2j}q
z&?LZbQDa5;qj-K1(ch3T*^c#sGSr+<9lxg<GTj9V2XVX25Y>eE?hg{d06v;H@R$+;
zTXVq~jiN-nVOTY-j`G3{B~mrghV<k_7F*AaC~ohmxp|FNKhiL0xd#**Vu5prfk-s4
zlKvnJc{;JbS{xCi6thxlI5{k_@B=cz(!fjg`vUIJI7vRv?Y?fmKWUbAWS^ctHcWZ|
z*lB9Gfyb8U8^1qZ3mE8^-@`sfyiLF_{xxux$|QxzMWjDCNNl*W$$5!8l-4h;HJ{ur
zN9RhxN)fM0@TwXGXK;$TWcVGUp2vvp{ZOu`%9TiX-ci+2oE9QIw(@_naC37|EMy1L
zkrGM`1(WR{{X-|5dMsuCWF-b=iI&_9nxa|o;u`7u0b(1Iunxci&sbD(UXZ0nD=b}M
z>t6rjARma*i?<?W;q_E1yy+_86R=FHE&IRW#Mgw@rT8O>Jg^}f)oH6=m_=$F{YnoY
zOu@{*V^a)EIxm7|-T`brmy*5%xm1T)Y?=_~ylDOvih1A*%LQjF-0_7xa%)GDx^>#X
zu2yqq^?jO20L3Od80VSa1)dTXaEI1P5qAW{#Sj1SvpeypS;_<-CiYu5Lh)s}4}>aa
z{uy8L7x;|$v;V~snMLA04Z86R_VTZ2WtCzM3>pN`K_y3B{9ew6ysP4eo~UD*kO?xN
zc|x1uUm&eb$}Y&23_!04RNH`54GwEWEa26D?rdcN%9n~-@T^400g46z$Oa92C3!J;
z#}}Tf#=$&vOeWKrvIke_1|q&o{|w5}UCRT8<_TB?Olgn^yWW9&F&!{^X(Hn7_^;7&
zU?SA4T?MFa&};^GUx<Cu{%lCDODOqvoiNBlseC?}r;6gC3rYKSVAIVz_SlFBlzwwS
z_JZ<S;aBziHqj+K)qRg2(CSECtVgZuJV4ILgD@`8{=bs`?kH;Bqs>@DwPh9Hi9vkj
zx8sb>hSn7se3J=5LE&^y;6IMsA2*P3(ZfeaupKeyi>i;v3q6Bu<od7Y@OYd<>S2T$
z$QG#yy)vXPftyByWkMe8VTVPr-4dpgM>!#?8CSzpR(C+D?W$_#-BTvpjL?_GEt!2M
zw<}2Fra$`Fa@R0Uys|cD3*e0{`A{K-)J+mToq`@}mw&;P0uY<yFeqMsoLZiNJyhrx
z>=8W*J!)<XFFtOds^EpJHKXzV@WJ3_G=mr-<Ek;=>xK%m3O?Z!atoXzXDTTX@Bt&k
z%%%MUn1ks_ZAS1rzXEGgY?V}GNp%N#GdzR#KfI+8z;A4U_~NN0-$ahZs}t<;Ym&t>
zQ0<q-noSTydra=mNxf_qv8{=5N;&!MfY@q3wh>xbv!n#iin<p>mOrMp9^BY^tj!(C
z(R5~9;C-gR*x2D9etf?cOT=YIPO^h%ghm~Wk&~(lkesQRtRM{TX|X>wo)Jo$i1S#Q
zF+~rn388vL1J%-s-51#%<q$Tz7Sy>3@w0`7m%GR{aiJ%wYlS%o9eI#9eEl`oc>tIc
z$yne@#u)K-A$2vz2-xZ>B1{NOKW;3mzON7yf3<0VwPyq%X$U{UXATgZ7}F-4P~b5f
zZ2hn#N0IXG|0abMwIt-h&AhDqzTnNW`&thZJm_X=p7%59XY{4w{H&d@xEF4p1+wc2
zV@X2tO&L}Uz8u?N*^IUC$bOe=LpAV&!MgAiLQN!{n#k8-At(Pc)pNwmWQAkUjy-c(
z@WwHbEgt?Y`~<cz1iT2|a~g_}ZpU=phn2J|3{@u7OXj#5w+Ez15ER+zzblc;$3r9+
z4iMBaYL+avlY^|$L$-^h;9q>$ePxd0mMpMX7A$!ok)Oft<Rqky`EpI*T>NC6`Q{wP
zhR~ItMXBGt&Lyi8U*RKb-x^;;h&8|mY-~kqkSvYh9|^4{rHlrCL~fL14;QhZmVC$-
z%A!jP8#zb76R(RzF_->#;R5fNeYS$V_F<XF#ZKg9HVNk{Qd(x5L@V(5UtWo{hwZ<f
zK;rLt51c98e?XbK02seyVQ$BcwYKWN8Ozq54xFMYS9OhDD0q|ajX04Di}yQ0y-XlX
zb_mD`h?t~B^2NEJ@DQb=*ZiF*xr25Kp7=HPmAH#%J`d?L@g2B~w!)s;iD30HyQvjS
znkSEuc;$KalGqNQ=?vN<iGC`kMRzI8IS5d+5-m4M-b0PKfeZ?luYk{z?!Xg!o!9Br
z`vW*91TP6jjUarQCzJ=WQUleCMq(FVmAC<{OCnTCh{6J=Fp$V}FC$V1stCLdHt$c2
zL(zL4(u5cs4l1u=K{L>5qOKc=11Gi<N5XX}05<mHO)HPrhw(E@r{&N+Dw?6R1Iqu+
zsNgdo+zPo+OOXq8<XQL?A)Py8B6kQExTSOSRt~@xlOg1A%sR|O-~Dj3k-ud;DdOCv
z{n>+H)KNIV<^FT%p&KZ<;e^nm43+}H4PVtmKy-p&V-SSN>;^LtttPg1%4I6dLkHRk
zyCTRX0`Hs+gmPRVXY~pE@|E(vSfVY(Tq!oe+^_-8+z0Sv`~d7bfKsHRFG5lQI#gou
zXgEQfv+F2?L<v&_Wi=x*CDUdk2C9T&$>g-)_C$_#tN1JFNI|TCXOJE*G%CQ=j7wp?
zd@$*pAhl=SdP-)Xr7%*dP@nPRs~}3^2|i{Xq4GM3RHV*@%)+r9r9%mWpld)>)56Av
zJ-&wWQ#F2eGEeAcIJrRA2>7J_7&K4;b&3p%BAB~pr$u5d2qBF+z~~PNc#GjrMVGoe
z?_Be}LBdLMvL6OG3skEmC9c37y}^^s#yc{4f+K{G$?u~B=#ilD4+H5!f6fdF2bl6|
zd~i)Cf!mh-Hv^ESNU*NZ?^zCc^N*nA8fI?7IF{$Z*=0sc<1%So-Ro$c$e?x~Iv^#d
zaFkf@+M1b+&<yJR|Ger=<3c-rkzq!ZAO=?t1^;3(@O_PosH=mPLZ8rtIlvut)@{Na
z#o9-|0AWWg*ux_Jh|Ol0DM?)YT@Xuj(&`+c)I~<wY;{HGOxfoO%YG$wq9?TI@T)y_
zrpteBk;w<hn1d{MeuAedHlmK@Z}AcJM5dJK@DT!8@V09Dg|2Zm&LTLAH;)`p1LKG}
zen)}-uCOIM9TqK=MICUv!4;vT*xDr|oPOYDpllSyC#rdF&x3C~vTW$fJ|cS<BQnL9
zGzpo@L3pt<cYXx)0(!l+Dx3>kerC4&tayL*Y48bS&!x!kKV+Dib$>K<9i)<kOSekK
zK9u}7v1Y0gk(o-y{n=0|N&F|_{LdhInF}zP06q33u5mJ1Va#lRUOWYx1EU9*epE?f
z%US>fR`#yyT$NNEDpQctC402AXr^pWwNxsY5m7T{{BN)^Ql=V6B5UBuSbHvEb@WHj
zo^q|Ri3BcBMKLp}LFl?I^E}kTZRp#(yQxIQHu=1J-h6e2`(b6H>f&|$3>P4cBWZY?
z%~*=JTzf8sA>aU*e6N~mK(@MZ|BQWurw+tVD+u*uth}G4o>ZTpnj6JCD4%M+Wlf_F
zpp&4Mrl@Dzz`8i-egR0)y$aa-Dlt;^#KH4Kc2E`(<INzi!abM5axe+!u!&hfS==7y
za?DEVo~aAtqbF*WPX4^%LhMC4$L@)CLEPOy?<!zLIK&^ov=Y&Ur-@fW;TVa|8TYt~
z*1%`>R38v5=O}^H{+fh}TgaN*$c2L?<YOHHH{L;Hegw*=NupaJevt-xsSD(N(GEhm
za!|)0TQhfDWxy^KOvh${>v6w0$w(Pzaz@i(RsUea!I7ROo+4UvCeUSk;<EGrS5Z?_
zQV+|P1GDk|a`t}QqaGu9{X6o>JR#4VIn1;#17s3>dFII3E~ieJE{sq-2PT9H4)+%m
znx}FG|0ZdW3R;8YOSq<|iy7Nbs|~NzHK1cR)O_F(YE&IHeJjr%FE#iu$XqpT)SNb$
zo_JM^>2qxR)l43^&8^2)5PKPRb&YG6KGfnQ5&DG`s0=p4L#GRvCJrR4wHP&gmL*o{
zjFkw+Q27j>j)s1iCDN!E7pXt(kFI{KBS~V_R0<{BEf*G8Gwh7atXF7eh$c#Zv9nY&
zrD(U1tzSBS+(qIleZVB5De>4eHDes)D7w+0QjSO1-NS&xr>rn%-6hj8We*%VX|a|j
z)+b4RTkuN`k!Yy1&*g2=028BbU7-J_0E<wgGd8w!|AFXhg!@F%U?3Z*SPuB;Xh&^W
zaPUr;hGpu8cpX_gJF%V?1B+jRLf(W&6*1Q(P&+*FYHrpE3}`<}DpydnbA+c!9SQ>T
z5iMI0;%7yK92U@x$lto=7DVq~qlbr7f+Wc`#H?wDZBfg^jZpo2sV!Nz2q|LikUq*m
zPDQAGqG^Pv=E8Y~=~1@HW(G^vJ(lJ(iAz^>(2Wj?Z-@r0anU~1deU!Z>6*=B3z>8w
z$`!NaE<BF&XR2DNGlhZbwPnN>!bQv{Y&<bTWU$UWBsM&{Bc|*2D<BBCDGvDkKq1qf
zm^S?A9~8Od&0yE&1icg3{+jdgn1A@prF<w(L2EBze-==TiiVFc5Yry(s=Imveomjb
zV9VfB`8}dYb+5<n>0=Q)ZNL<a$1#{?NHDC_<Aw&49SkBtysboJUx&PM*TPl}LBpzK
z_>)&AQSWenH0675QJo+gud*-T3cnXuG`5IWr*w`!S%`q*P%@9aM4YmuLw-7ZdP006
zJw2!B9grWmFY%_ukU?T%Up-+3;+CsCI$U<(@Frch7(N4z$J0cq))@jN@g!X<M-TS#
zpelF6*|Sr+Dy4FPTUw1{O_ipiAihYM+(f2mi;|e(>n8{i{uv#L`d@U7g!rtq0gTSJ
z2%Geuk%j=1)az_^+_7_vynU3dQr)*HGDzqh#{NA4K}fv5SgXn>HQJo{QPUA-Z}@n$
zA_Io}F(JtRnxU!UeD{n2gIL{>(|U?S1a>A#yi6u#4B4cJe)T5ug@=Kkl|?|fR9)qL
z2D)!k{lJFt*3B`W15q0pBln+ubDUQZvfdQvl9i>dxRC;i?Vx+}2gG~{FDi#%VlQwE
zR9R&4#^*~gF97Gq1ND1Hw@^!h!cNHZPg{}CJCMGhgqI*C<U{1dZe1J0z#sz&@tOc;
zhotq8i80Asw9L3@CMN~?rSb%Ar|`|)Pua7B&zuXZR-Pi3)NH`RCnkisqx{c6unGG9
z^i4|_C<L!|1NpKLXDho?36Tx_?)HpIzjfVcFN+u*NfwW!>sfWMH&?{x#8joibc`l~
z@&YEbdF0!;+fub`V5Wvp%1%bfNp{@3t=DxU1nHkKlhY_8;ynK4rcoSq`NF4D#jIqQ
zz7)-m05B{SF+Yfx+&v|QEUpq@Elz*?M=xKigu8d3-QPXTr;q=wP0^x{l9w0&#1?-d
zDKzt3F@U&=g?En|u*Ca{YHv+?q@{e`=#rp}OLEzDy7=`!wx4`fx;C9u;;KDb|6H6;
z9Ue@<LnRkKHJ1!CUVHY(mWAqWoKsP~2w8OsFvjeylwtA_{N|IWXuHWi7^Y>^BOPB8
z?zu+Qsjul)&~?MaC<<H_FXq`CBx!J|6lUnxuxeC-9(CL5Xn%g%te|A}A}xxy7MHGf
zXrz(rtO9Fo{dND(<D>>~CiH;vLy_p4MAD>aR*839DS?2~^N#xG&mPlqyEohfY4#3r
zW8{y?(BR57+JEFT*74I`(m|eK#c#%N0Ex_gMtJ6--LhPrjkmaH*wx0i44VDLac|{(
ze6m#Q%&d<=TJ{52ctgT2IOS8gbN1SsXYN$n@aT~K#<-_bqv!9p5>l&koe6PIqT=L-
zlK3GXy~M)dgb6x*^*!Iq2o?<hPMT*e(9vmEbiDEqB$y6FElc_^a(NV_R;l0$=5hFL
zZ9SoMgaiDo;=QuP)4{8PAmRXbi$Tn>L_H{51o5Np)k+~jpB3ZwMceyL-#sP|RF=EO
zYk^S}Mwc2g=}ws@mo-;*)0cqx4~l{DhUoig!)bo6zXyA24IWYhHJ=@`5-nxDq7ey%
zN=J0SZ*VNWI0j;IaF)41n_hwQ5H<&fPCw?wQXc~#<2W~4o@wa&Q-|@f)ocAWwQ^CI
zeZvRA+wvquJlB^A*INYU_RGhGqgx+M4?@q~a%{jht-v43@qbS01=^tjT*aV-7W#<-
z5gh?wtTT~QFvQ24`7#ta<j@f4bZ4^87y6hadSzxy_6|B5ML)uTwqXRtjIg<&!9mZ#
z3*fPjcA0DA_+$0lE~hf9n$KHWAE01P&`v1#9B*{{L^Veb5AurpIs6LM{*qM?4$1&8
zsE|Bgt-D1{>qbN?+_?PFX3h6<%Byr|o0JXpSl0S7mzhc+h=~+<*%|#n#q{OIkCna4
z6%L50QtJ)EO{bzIm#HKTIHMy@moAO{o2ZibJ8<nMk(R6pv|mQ%*K$aeS>7NI)C+8T
zgV-c`vl?uV5*5}ald<a0&f$pBFakc|VvoB~Co;bG#hOLV77Cgj?cj*Ixj$qUTVDv~
zPyU>9nNv|rqAZIg(&)4d+bd4j$XTQ5YlXAn5H22c6uy=iJ}6*H^MlYf^=lmM4<CnD
zBNBdL`uZ-16e#IIB7r!n4#RB(s3zq8XDBlp<klES4R!j5;uOuZ<fw3NJ>Mwo0%Qih
zd>%>#vjDmFGOTCnK&Z9uEu3Eme<`GA;CEW?41>H96!>H-&CD@2|K#Dfgxo`bLp2Fe
zPp)h9zd~s7sopcLI|6!?`{k!xVMNd8VzK>M-bV0S(9I%>Qm+O^jcm_ujV)`fW43~F
zx_P^`d*>j;B<(kRH6~lN@E@?+IKa68SX22vJUOXvRlN=&Y$p!*LY#3vS^skTn~&T^
zXa6k;@KS4k!V)w>(kv;}AFoS)uiI-*-R@?Zy`$8u=lCZ=Z1rViM18};>Yt8|uq0(H
zX-P;l{%0_~77|QVgr0>tevXh~qdJ1+W-4}!AJNr#V8RRc_CK_(rPlx=!m-`a{Q2}L
z(97O!j<rY8)cXvHYVv9(rHi7N3)Ue_9tCMDJ;EV!x=nMN0B)v7=zhbsFn>9no0Dx#
z(@x`&nw*)QG`~?sLqq6&^CBm{w15$#_&R-$e4C*u<LM@T2mmaVA4{;s+rUjJyWr0(
zQntepNiDnLg&@}1YNn-`=66l)|21(rgOZ{lv4P^a67(Zbk(=)*1wIY`;wo~CrY2RF
z$4!+GvVD-BN8WbHezS4i>zaSf=~}5-3)l!ZOueyw&}I_rSNo_|gmM=u5PdJM)>By@
zVV02glG#1HDCB+?+X`QULW$uhuiYN9pOT4aAbo00+1R?B#FT(zD2<?dnmhLsyV}v;
zbPwh|%NDN1Po!Ly>Pj=#JRNww@x|7ouoWwBjHvMme67~6v&qip>x#Qtv;O9d=;ATm
zW$X8&8|uoB`HCojI2VjbQcA1Q?AH#ekbG6htk)-r!^2hhd^k9lscCl!xZl}`Q`p-6
zR7zDy4Mw6u0CByqi}3?S9|meajAA>Cx2c5!;()taY|%=I^qe-aeB2K{KvLku5x(ep
zzNv-PVDw{$IBi*Xxc+MOHZuOHtNQ!$2o9b>d!!hDz2)l$!lmMR>Ux3anrlCcV+4@&
z_LDJkjwndJ_(rVg+i}%z`L~Cj$N0>5sOA#lSw?dvF8=W251@i}yDjKd>&nCjt#3x2
zi)NdwqB}rd2{)Jrt1B5Aoe*(Al83y0F|zFSuEUn0N6xL5_v=f@vfLp!_!`zO3m;qr
zz4f4oI&WcI&<AL+cDl5^_zIJHQ`1?AxE0&}eB^J0YyFbGnq~R1)ZEzEQ{{ezzT+HE
z6xR2X4AlKFuXPzdn?ZcgDq8m9US5)FhCOx~HZt_8z_;IFH6zu>M+{%rFa2kb$0>+0
zlkh9mVK;FE_!XPQG>@Cj*FhUPirhbbo4*{Nf#+yH*J^A3UdoP%g|9lhZT)WT^cSp>
zV;y*uVboi9-oFB<W=eKgT*X=rgun>7>pM_2u2-$n3<4<e6t<;I-}Vrk9NYPwE%Z?G
zd1FUW_~}GtC2j%Q^W*t6f=&gI!_qCaC4`e*LnK@HDo>IG$(mg8xe1=uAo-$3*c*6f
z2T>E^Fdy5wa=uxfR*(KQh<O1Da+jrDmAEbPLPs6XqwFFx{Sp&zZzR$DZdWTe`-xwE
zlwl!dj4qm;mml?*T+3_}V~&QNRg9pac~UI;X_7KSKOR5PDy&DQFtHsshk84`@h`_8
zKUFOnt!lTro>&mDFwkq-UEg=T5_v6Kc}U+gSv2qgVcq3Gv8zpdR|BR7z*A%a5InpD
zgq(2H(sddm6Fkp!>SmJ8zPGRA1Xj)6lOrIZLg(S+oQS8LZGLXX7YZv*#xGBwvr^P8
z*f=Jh$2H!CZD|S^B8NkwFW+u=pPpTLhG;`7p45ul;2%mRt{?z3=|#jLe1e?pq7|kV
z4S38lM^8$|;V$uxnL1B)GUx=C)pq$9@x@9a+V`c2TW<{R?Y_c!+NVr&=UoAWR$>qw
zjVec@A+E{^!<kDkK1kpU1mE+T_wr*dK@YYzrGP1fvRpoWY=^4lHb2M^2sw)_Bt#ty
z;pA7&f{&NK@q@+bgfbwH@Qz(sn;q4@`m`^n!$w)TWn)VS7Y355in;k=H*%h(X}#wr
zhbg$_j)hNg!E>upMRycM)T!0MBUY8+Z1+cpfHnVAp_0_bJ>G8@Jlw^{ZZJ?Mv)r`V
z=0|e2+96-F4*9YtKFXAcDp=MrO-@!SHV?%!NeXo0uh~xL`v(2%<=gXWcZ+IhZVMkr
z{gKYi?HV97v3#DME^9vla6=N)v9S=d!HUdtJei>I@T-Isj)0A|+)8xl{X@RydYE#+
z#8`gSt-7;&`axZ;gQj`7WYo0t-egqa!VRt{-vbUy!1xXuw!Zj@UiSQ1b<JuRe|;AI
z+y$;$PtSH`e&9fVT2F7I2(+%qYXPDB{0asR{dzyb2+j!U=rBR!t4_*i!Fr#yK2^WX
zmx8A$!Y#!G)T<C9hRut7LPUwL@MB|)!2zkPgVEE|a}Vat?=$9!nF_T%>nazhOo1<R
zqh}nH8tS}T1KTlVEq(?$V-8w#wH(01VUGr8>7b6>x(tOy==L}r59H^;*dYC8TOQBh
z01td^H4d*1EYa6iJ~~k*-!27V-eABfRQi?hK}sl3oQW>y4p^i{9C8S+@Ep3Gypu=M
zuN<~fecj;jh{IPv(9PzY8aXe63k6b-4fP`jZ%U4!@&2k`a7q`u!$iSFf)}x$=e9h!
z7=3rS+iJN`np`s6*xHK<Lnc++Lfx}{{SQx!W`CXdvsL=q)y!rNqw>7p+1yYjsSMzx
zrU(lIKzh!8q7UVS8hV^WVG~9IY&x;t+ymRA0xSXsY6CTNAuMV%UKy#v9$8zFQQK~U
zFwh^;WmNZ;Ta;??Z}&!yAL-13Uuesli9*k7H$OY*t0>iQcG|9n-VuP=%c&66qv~h>
zuG?)BD%LiokDsd30xe_8^!2NE*!<j<<GiBpbZ<Ov^GkcWiVo&`oc&vV62;8uwM0Q*
z(2Sii#nA`T)K_*<R+(_>b3BEw1M5V8eWCAf$hqsj-M2YW=#<8}-Qs4w!DM$H)fZKL
zLElXBgKsl%Qx_d4M!E`hKRU=HGVB_qd(DrJT7swEMTP8)Ixg)1FC!@i2)*t1>Jd$y
zChs4K*Iz>W008_kJS(FyG9jlXEE$pVz&dzp?h0O8!4s|d?R6TX6sw1~nr0G4J}+oE
zSQuuRRBp?5ZbCd(ENsH=T=={{$C9NY5ti0^vK81hnTkJ1Xlh(fmO7Cc_h|ZdKz+~D
zO+6_=WPa<YhV*BPl@VUepE#ptg=jA3gv(36-X4JS2YX|j-=q_>YH{{9_IncTIewik
zySTO=X&CB1YdzbkiYVs_S!s6S98Tt@(@!c@nup2@>QZ#SKk2@@pbm&y_=rl?<0+wJ
zyPjruM)fW72ZGetT%j~}qAKJL<1UfP*dMV}v1x^ImbV*;`YmL@Z!~y)e-7te_*<f(
z<R){zcfH3!+HBnq-LQ?5+NZNyv39BeZ;BvO5vd}9xp@`KRxQWb&Gksz3RS9N<Ynj%
zZ<e?z#<+e7xYzuB%-V(Q!eaZ}iCC4G?vR$r+o~KLzB=&;k_`G0)Nx6!YW8vb3iaUH
zdb2nfIfnyWKmY^}l=?FXw9)RDBWmZ}M?!g7Or>WgZ(HH*%hAoL1!QcM3(vw3lT0cs
zQD?WWV(piU6+pk6t$poF$2{HpywIszb?fNI%7&2q_?<s?U0?SXaQbIp3zc6XsY-<A
z`PJ;3ZvD-0zq@c0Go~k1qz57b5yMgD13QpNhbPeFty#*p7ARkn@ayAqBc&Mcbj)`X
z2Alr1LPp#255O)lefQ&FJlSZov#0;(q&Cji7|=&2oD8-ZgY?}cxJMT^IuW&|Yx)@^
z&*?C&WSr?U-;@DeA6&GQo<yx6H>O<G;EBILdNSjO^!dZD5Fd=>w`89LZrt>cZFfnH
z5*TxvL3sO5w)>e;-z?V}{F$P738_sR<}}_$u_9B5<;|1gWDN`KOHl6`Ct_|2>2p%)
zU#^A0V@du>D)2h;0ujP=nn$yi8P1;UUfR#f>EBv03{Ru&Zneru+h$~x#w8uovhpLc
zQX{Hki{k8{6gh^n38F*}NS>a&iCJ+)^<lxGsA-sx;iuWbfw%35h${_NV%@fnRV|ox
z_3{1x=*Y^BYu|+eNQErbs=%!Q(Fmo6Q+TNRIgDVK^hf+1=hj3n7Ksv?^+)=+Ey}g~
zR|b;ZW*ZqmHh6O8^Q#oI=Kee}y(qO2$9v?+OE3fmLj}7qPdRUVU7O1sF>8jTSg1Jp
znJ+K$Q$|Xe+!TiQKT`E9BYjyb#rI)XN@069KNmlAD#7z77n@79+5Iplvcw8w+sAX2
zK!>mR*v_^=zaK88!Q&ZGwi|5(@@;J*$&d56w&$8N6Z8S+2k08iANnwAN1w=nuontN
zd9Ck9+=-3=;!+k5D_z<u-eWZq!YizohoXilpY^u8K0c-n&1&<Ku<`M9MCseWz&Y<~
z&W@w05+|u`6iAb3wg)J>BcwbZ>2}4O`$9+)hFv#=uGd%Yf^c6KY%Xv&R+f9QhwW*R
z2$~9WNZ;e$+`b1oCBCobOH3Xu#eBgZja8YRWJB(b&fOU!=t;)<_5+Oys~bcu!dYMs
zO~3JZGB$}>zLe!KB`}I6&VZui^|zX%=+mqYz8ovg2ClNgI3$Y6&E~NiG9Np{r!TP9
zHV9WoN^Ru5fhqH2Dc1zd?bFfnZV&V}I<1(We-8x{`UyD-vqMHQ2jRGr?ncmFQzAKW
zXN5h87dqZ;70J9iTkId(d|<}K;!00G0rvMbM^;Cr=cibcoWG!`C2GJ@#1O$O_m2g}
zdrYUwB1}?0-1+1szqoE2$Zu)r_oYM}k28JQV`N5>GAQ^v9?$;bFPE&2<uhG7IJJ|6
zia47Vd_@su^OKodE%IZr@3f?oiKZoJ7|CCD`na#N8!vO3W6u;iDR!Fr(qY&te)op<
zuX434WAY-%Z@O7qTu%*6V;_mVCRMP(#E4Ijw$IhUV@2mxu|rD-`h!h(L@Ganlrw<#
zYAB#4B^+u4G?46XXMi!Ytlufzq?Uk<gR)hMzv6y4@x_bN_*-ecr^`s|W$}q>-RwYZ
z6WgX!oI|aOE$(_ymmlBdaG_|Nk^sh<D`tJsdaFeK@@?_NnJ+nGJOvjbLFaYxYv1qj
zOm)M)yPWn?wTP$fSgX@_r*zXN$m*#|n<G{<ovLQ~EtG|oAa@gwOOF&v+?bDBUrAl`
zRpqz|dX+n*o=iCb7bCC5o-0wjtnrFq!_3*~*%CqhvHGXAsr?VahM25*_B$=>m)rMn
z28HW(qjMtbMfR5ad<X^t*A{VD?hE)JFOuKcP2Vqd0=kjsQR48Gl4_P_xB&-24P<l#
zgUF~QDFLRi4;Dvt?eIVL!^xGn8TeHO479m4tJdQ6y_}1X7?Ly_XN=Vx{z?f(uO{av
z&KtS!=aOcv@oGL!KnF=7+FXTkq$oR`D?bbtC4@DJ<864CXqK&g4mFgYT`P~$Hu_KM
zDV&Xg&Ld>6FqNBu)*7yZ88RGScj;E#{ccB`7WDgD?S?5s5#29pVUphtw5jccuhXSl
z%O|>GOKcv<Rx$DQBUB^`@D_0Mw@$0(_+;zNO%I}7*Nej*2S!+{@iSN+j_u*e>0j=N
zQhdt-dV1>jnbh!oUOOG0r+i<T?=K^5h@~3k*4D&EPocQpEVqAb^7lVoz*Dj00%%F{
z`z-0+<2>Jc320WN(i-_*cH*tl&q8z&k<=`$hY={Z`xVfnFlM7s64%q%ol1M=^~TFD
zcY}$9{Zpf+57qM83_iTh((5N9oU9PPGr5f?H)*G7op_XsVL0Kg$c^iVFZfh5$+rMg
zUURaHt|_hwbC{k33Gi?OybI1mNJ=8q3&-h)I~xpax5|~-G%4IanOw0gu>dEA9wg7I
z_=Es{E5^^S$KU?HrUl(%ogCAWpUbGxZXx8XI}i7o^{1{9UpRcOFmWlL-`<Y~%TiJq
za%_UnA)XX18;o4>Ce&$UJQ`XaCIGH#PnKjjim@?>Oas1gxI&b(5~Q!RcWO0DC9&3*
zjg;N>r_sgCK-Zb`$8S2Wj?-zCNnS|$4JI5V*$|InJc6p>8w%X%sgJYcEtm|;uNL}k
zIBneb7v00^ovzp?Nko#^td?VQ>ZF84S0@2p3pA5KyLNPs>q{@zCK*{dfZc){Ew_m~
zk6L1lE^8s~P@-O&X2q7Oqx$YMtS;l}LSwA-dbNldcik~-=3KAo_fQ+Z6)uZW<dZkU
zK3t~oQSiQwNv-l;{qyr)sZ~`C`rJJ|ZFpLDeKRuZ`%%`N8c|FvL)oHn-FAB$7u%%E
zX()+X9U>HwB_ZI68^}h^+O571h?oqj&TtmK3tcM*;J6x7_}RD5QK`OTJ=9snyq=n8
zwc0pUo`Fy6ibe>Lv}I!7kD+^Rt?BqS(qyGEEgJLPrCr3qPNU6!t;b8m!)>zv*e?8g
zia$^~YcO~67v75@(yBd><XCE@ix#3bYS{U3<XoH3%!~gOF#EKupRp?-9)zJ;7D<*z
z*C>H*LfcL}cFQ3jsfd@H*=jagH&-B5#rHx@$}k7ox&`(4@x|)dy&(L3e!vEH$WF>P
zPjj{MEm~dfOvB-Occ2yEh^J^NvF13WXS*JFCDwkfY}ArXM%Pn)1FngPNW{xu_|$hX
zW<E)k(wH`3wqCgqiN0#=##5nQbV@_4u%<2j4B|_>&g1h`FB>`ktMB@CKJYs$#WFEW
zxQPCAQEK8)e#?zcrhskH;JCr=*65f+6$~_I%a{X|Lb{4fS{E-^T7^Kx1-g_iAoJuq
zJkgf8@It@&DRC<Y_Kodqsw#Xw?#<W0rOKAhG~yY1(|Ypu6u4LIE7#5b>xCY>f;;8-
z?h^b1@Fp*%>EQ&Qv$S2IJiwF}ea*{>ioL8iFjJ0QU{k1MGP(@H7j<#YOBc)QewFe|
zzy87!TOW}Xgn9td+^!}{!cW%BLYe}O{w8Sw{J5DSZnXp}l}4LOUcz>BM@5#}`*dh_
znQiSEXRDr(*OHWV+4o2qXWQLna61~YnKt?9Wqx=okLc?5iD1!_gt?^O&f4Tq4jngP
zJVWmNn?BAeu?Sro5TNOnr)baxDw|Fi^&JfVQQm4NeP`m1^07N0K^%}192jhUADQ+8
zF*kap>)S<3T$tCmREluRbFsjVDolH|KY7-?;i?~paa%BA+{};F`f`-NnP$2Xu~MTQ
za5}w{SuR9Xk#+xBOv?#(7D7jvqTbHQQkg6&%9@B;A$b4!*L!;yTT)uy(%DSNDJ0X2
zf{$W>FYN6hZrh<5y>pVyF7dFpKp3#x>Bt<$Vqg^%CoLA5Abd~IIx2Y?H+lccr$66a
zBNF|8Y<**6UR|_pleDqzG`4NqwrwYk(=@hi+qR9yP8!?x-S0gg?)`9n!Gpcl+;fgO
zAgxodq!tb>k9f!3g5<~v<W1T}@8k+2<V^CqJ@$2oIHzrqS>*g^_=TVF-^_}BOR3@d
z`!yMAfO{Zv$A2>&k@nr5LY#){<~dEQ#5kRHo3Q>e<K-7x{XkPgOx7l;>W9uX*vub=
z!HgcW!DP~sr)rp(2+8yY@7k)a+J60}z!BZQZBv81;!7eM=^LGSmIzz)t;scic+n^-
z1_fy&Y&|n7PkjCV`~kDW&)G6D)6JC!=Sra6`f1}!`0In;cVs{<kV-axwepG-qdMFB
zwlfybM9Fl&X+_KyY@+Fga6-|{Jl61L!Rn%K3Q7Jdf%=D2{l5Og+fjd<M13Y@&VuvH
z0Sba5Co!wJR83D!HsMEp(Q0_r#B^u4oPcSP5()}hPRd<~TuIbx`yRU=T%^ytozxXt
zO*W}oFxU3*OovyM{~}ULHVH{aD=U(SE`qv>K(WtL)4G0PaYhyef7(_ZW`&xVXjpEW
zb$xrkr6j`_{cc&u3V2ud7(>KE6l^IBBR#NqM5~|4nwV+C)-7meomfr~WaU4dwjQ)b
z<$Sch-piv^mvk#Yvi9S3dxAb$9@?q@<;}MI??U7+q!&3-h<vgoOZtjl??D1gK9iD@
zTTS=#A*m$vm0rnDp-wzGU-lc@*Z9@9U84jo7kI5kpGv)wFVDZ_<WIX)!c{r*-9fZ5
z5wH%-9?w>J*;BbGG#uKDq4htLSpqpoBH7y+BQcR~zydMLiDc%RA2=00zMX!}$oDVD
zj-E&w)pN?_0MjNGw~16lzfCE7-B{_`GlWNC1ccMmxU&>mzVqkDS=Jky4jFyIQ0lc$
zOR()|5UC5e&((#;AMz%U;KlGBo7en2!>*{_U~a#M>({9dMNN{OuU0pc&0&*7dj$It
zVnu#-OYuh(IlK5a>fi-f(kv|{WM=yi@e0&JKVP$QR7$n(*t>xh*%-Snk%MsEGawf5
zJ-RBJwB)k$?Jw>~&B<<~`+@*@-nY8>yZzlE+<dHoNZ{&j<>bsf?sqUBcJ1Q8xb)h=
ztLz}A8*%90Ar?RVSS$Hrry4v07E*EcD3fjg*!s88G|f^eFE!+OD1FFS%BXr*Z1R_d
zwOtSE(^cNykz2ud7DVvN(xj|z6i1y#6JFZN!T~Xr%|>(a;BQ<Y?Ly0TWU;NAb&T{Y
znr_#vOBme_Gs^AR_q@0}?~amQx{wQav#5-wZ?DaCNH$ST(Cf;lj7_P~&YoqDit{di
zydC>64zpkesiPWGDGOd1C~9&Ux6!KLgP*SZ>z>xxnte0ufM+Q8yLH2JPobvYmB~g@
zLD=b)?f7rq(;;U#zp8nnr0w;al|SIZFNeGKWIQ^Qb5tYyQ(M1|gndQnLF@AHigJux
zX>r@MA7N8*XL(9r=Bx)<^Ab93d`T}<<E=k#l$YJlfZ?P}Qqw5x8yZ!($WOBt>n+=h
zM^cQM&xN9@G~Xg;IQ}##<$=~gw;{GJMjB{0<!>AcuEXDxV#o+rrl8hJR#5@;WZvYq
zr5L|>QnB2|Lx-*bG9-MGMHBx)13^!ed=jf`2jHOKK-8Cs&zy@mCjV&&)pTQw(y3Va
z>i&>+0o{($$a&{$c>62Xfc{i6>Ugu<(Sy~Vye3<wpza@jbz#8X=$q9Qr>F=eY4!>Q
zlEGVU$crslIDwW*G2yj5PTHpAfu77oN4Y>M^k56Qn1e$4v%xE4<rUqLvjW29N@bvi
zB0n+wyi!gPT4bZ}ITBSk6dDLnrTf!gY4XDpji4llJv#n?q6eog+I|4eIt$0K<Y<(v
zON_M$wv`d@Q>W)x<VWc{N=C5cb7xQqUHUpzQduONb4b`+dezlqVS>WRQMW3F;j2OJ
z7Ykoh%%8#Ff$p1E*)*LjsWQX5D$?<7pj1g8of9V&MEu$;!xA*yQqv$nbo^@cRnc;K
zoJnXoddUxIg0`_qMVhm4$pUAAv=cex5b=H;soVsb@q@-gpuM-G#mS?4f4@sH`ykva
zs58uOG&}2H{rOA5v2<a?QgX%aM^4(73w9NNC`vQd$Y7fMFnpY4MtQ=ebYkZg&$vgm
zhWbXBLe;>0fcmC!cW*3!H*?Z+?}LohVmEsu&u3JJ2{zJFzMO~~=KDZ%1N4^*3m#%t
za-L3ZZ5404f2@A3g{uYXx5Ssh%Ot8*QdoNT+QB52NMT8a=5nK)GX#v1XM3ETF3uf<
zzg@{#`T)C$cifAJjj63wGTO38oa>@Jglt|CXFd4Lzo&0#!f6MD^_~rKO<qwpPA+e*
z<Bq)g3$Qn@>_+gcuF-?Hp-Vl04gUcoEVixCJh*&<(j+YvK&^tT-hceY06{^Yc=%Hx
z2TD~vk1t0uqj*bC2K%C)S$Sz_7EZXh(L$v$-vzmd%q(fAOLcq^$CuyHWS!N(;Rn6A
zuMKgrd#T=p&&kFICDf1LH`BVb1+=Csl-I@nEw<+U&eiOu6pEHdr{C30pq1h(M){3)
zwf*0w!tPP1;8<o1h9#~Q8OC`aInQ79E-}XQ=bPP;$D9&P<5|S2mhziNcIwb=akxL0
z`3|a;ejdC{>4UjqZ(iwN&7mdD3`|XQt3Dajqn9?BKW?|-#C23Jyb{}KB$Wba+-7)u
zE|?vAZab`NQK-NWEC~tstaUd*bNS^56YYnnRJx=bhbUD=&#waj@g#b3aUdL#LD`$9
z@s>ss{bX@muU0LTaDQlQFpGH-@}Ksed(T#M*CSXnI=`hAsj-fh$KgtHk@H5&V#r_2
zFNs^~W<m~=LeQRVuV?`%8mN{&Pv8O&sBhA~-_v6a7KLkGeAoDTQ{)gs@cCHBR_n3u
zqgmfGZNuSzp(unzVn19L7Yzld6*#+~-PyJtbQ%530+0s#5&k3Q`AE3kD9<0={=yHv
ze5{+LrMD6b+81kxBvc{6OKo3tJ7peAYrsGxATm}8t^t>8ByWjJHO444Rq&pa<-a|+
zes*0@LYDT{yyJlGVuiPRqPCwccZqC*CZ-qB)Vay!Avj|vjIHY5&UlzHO;u#JUf(eE
zVWi4`^rJ!?Qj|v|j$dx$b-gi5%_|DnH|KA;FL#Lpyq9p)BrtzE{L-4_Op1m>fz{@K
zGa~_|==<Tb7WD4we`0(H_FauD23W6orp`}2G1Ls${TavZ6~(rn<tH1uJ$N#8&#3}X
z|8_1`Oce%{z3dft)}+`FXG?A2#>*g+su(VVLnlsj2Cha6N|aPJtGNNX0Di9}j0G~q
zwyINklddRa58d(nlJ6E9?qeT>yp&(DC6mYK6?K%1D8a^xI;FZYBQC8!JAFK{9KR=C
z!ade+tNtgQxAw*N+28tjnjTVd1se=rBmb!OdMv-@U;Jt)Ow{li%&!NRym2nm^`ny~
zy^CipBy?KN@B+r!>G|+xJ!dD5pO)OBz)lyFD_DnNy)zsUzcnauDkOe~aq<FN6)Tk{
zI+i2l1ZB>Rm0yJw8|gg_A>adW;y_$COF1;Wa&ZL#Rc>u#H+ZXmvIX;(qC7RaJ?TH}
z3f%qAhMgN?yIyWG=m~~>);~)k3u(;hLuPwmt@z86stS%jA_g0@2WD(xzHE=3s{NV0
z1;4iD+cnHL5yL%%OB!`w_6$Xw4XZ$<ohdNnJIF4qJUtW8WJ(q_XIRv2)sMcBhX8CM
zqT#`C<N}AY4t}hZl0smJ-4c9>7Q$VA17B9LYJ$M$p*!$^Ua4%m(8gvKgM!@|3h3{g
zA&3^4n%iJ!*s;Z))5)nN5fW1(Ga5tUhg(7JFzRjLMUu{E*X_ze;>>sdY~|>2yX#$K
zZnpWd%voHDlKVL0Zd=7)aVbh1Dcjv+aW%9DPpDQYExFe;<b|4s3^-OT8J;gpOd*Aq
z`;T{vL^^;{s|_+VJ!7nm`D4mBm6yN*W^zhlVv%!Y)Aa4)jVx_$7TYIw8VtLT%q(pk
zMvG20>Z;vkV$NHLanWXX#)R2<&M6k@wM`Ryp>sGuxM2<6y4!TiuHrz7<bw+V*30E=
z@$9Hr5<744Scius)Vt~kd;KNBdSymrBzY0aAfJX`XAB4W{zSl|l0@H(6;K&~id6}@
zVX5)|M<zH}SuWz4>C=9cVKS;>52{rIaf@afao=QNPWn4MbFoxu%$G8ASdXN>#j9>0
zVat`pXFTw%_9xv&yOx}iZiwqltYeE%27T|iXIa*X<u{!B<+gB{#Q72YW>hD>_94cx
z4Y3H`%Sj32f7gqoBiMfj##CFH!AAb?^Y_iFYgJF%4<7(A238=_9_d-u&%mmeRS(Yf
z7{l{v{O(!ZZ2u=)$ZEH|1*_k(H-FW7@#9%(izO6Rki)^TJ_Iq$_2~Hgk|wJNvhUq=
z6FqJi%SB_8TnT^U!D*io&I1{8Qnx&E!nT>oe>A?tbHv!v1wKm6f2^h90wmg_@G@LJ
z5*B}()a}F3h`yX*p`lLER3cS2b-`Ip>W{sa^qA&-q$Ni?f8BRWl~G!Rc-oFp2CS3A
zoDt&kp5r`0^0w`CDpl7{15S7j#6>zjm;ZE8@Dt=pK5njBYcL+bf?eW+XlPr$D}u)^
zJ)M-8659i^FlvX61j&)Y3MrAqlw2Fv9lf*AJrUf;dg-F3fAvXHO-mYA5_MU*#$Vt^
z2%BCv3pCM`00jH~`9Md7Zr^=fy?1cB*Xh0MNrDNZc;;3epXpM@B5akVTHs0RekHIx
zK`xt_95G<52*SGb?&R|7uNoRLaSFOQlrLaDa=oJdKUGdghylH{M!>aBy$7HZ2M(A9
zP@IRsteiW_V(WO^ysk~e*MN54k@30QlylpM$0d4S7cL1gUR`-WJdj02O6F;ftX0Iw
zI^QIsu7F!K79qi$HI~0T6+XbczQ<+BiH$8-HENcvUg?tzZ+Llp87-{Z?u3eQN88z6
znVB~&61V2f+=<^<B@N?3TyYl<9jD<<ux)vP(LcT2KvCIZ1>W`gCLX|b_6am&9lrk{
z;SNW`ip*WbY{04S1_(Z1Z*9~N!@uUFP(3Q!3qAlkOk@mR)KAo#Vc&Jro660S$Bj#t
z+}~lU967o}Es#gb+TyeQ1>2YUPTqUxeqQS_M$Ve$Gq*DmnL`DBcqUV-1!BB4J~(vz
zUeC>Hr6(Usv%H0Nd-J0lZ7QO)hjk1>yqo_q&aQQhEYRFxz5o`%EjiV11JIMRf^M1O
zM%v^D+HDxu8q5B)pz{GEhT<lR_lLX$aeUqj#SRkqNjBW+z~UjYj^xeL%vC?H`5bbb
zW~jy{jP&d1P3RbK#0C2u{XkpMf`_cxCb<c0dHX?<ZmHdfXK$n!^8wMC1wgXb4L6g1
zCT6fnb^LI%$>EG>YKIo!FcCkGWjU7wSE98=%sV&_=eo(hw+p<cF$>~ezpZ+vHhiy5
zN4Jym)0${vG>N{-znyohK502Q+$_SVafK|MHcT!Iws2`L38Q4y!98IymPg2%D}3P{
zao2A5oefKic+0r9yU_R3ESblUOholQQxn$atioVx_9l77-}VT4#cOz&AKX9_j?1S>
zaODO(_-Dp;+;lq8(zZNo!?3qLTP=M)r$?^*ZXag&`zc&aO+orlQ(0js<U#F@jJy?}
zs~88vX)Xck7EZJ6_F=};Fl(8905i|c^GoGG$)g`*shOh^d5605@tf#fRs~(-u@ZS>
zTTYqX^3xY=QT>Pt`1kMXVH~zw0^FcD)xC(oBO@T+EMIB4!m(03>j+=p@>105$`))m
zewP|UGsS;W%uM}HRm+0Gq5Ing43H0U*h+4(BpXImX~T8$ti8TeMz^jOxcM{ok@5>N
z6V&v0x|N%eQ(j^~MB00Nyzh}ZKUVQDsnQ^&HXEvgbF_ySX+k$?O)xos5|GU4^T{fV
zs$BdQ4P)vZQ!HETo*HFh%Jwf4ppJ*Q`y8w^m(7ot69b#;`Z1*Q0cWaxZwVIZk+|Jx
zTRcB7bVMxW;MvSWSHJB+W9Ha1YQ$_#iyxanssH%_qP*CWTKWuNzN+zS)bX*^Jm?an
zVkPsw8mu<Jyhd9Y;*BR!^JfKB8ltZtt&R5xR5|0M&k9__5MY8YV2m`xn1LNve?#r^
z9T-r%7cGc2R-7Gjx*z)^Q?=#Jf2yh_=GdbF%zlISkIOT>oDEmel9$R0S<4xpJV7pM
zML(sqVUS1}NJd3vxoIiAmi{)G>1sSMcH)tk3MCdT9tEvz4tpc@&CjDG=s{g|I*`en
z!I_>x?ywm53|)k=5xe8BbKL*?Lk=UayYV&j(~~hIPzC$qX=GZ~dey>a&nU*}5qEa~
zvFLcQ2e(SKr`#ItO=+tX-ba}Gv1Vqc9snw8>qw6d8s=zIGSv!}h^l2|C!K0e4SwhB
zHE7roW!#D<ifUc{02ugqLPA0eXGyWtX-z*%m}6}NIt7Qqm7yL^zE>TCh|nc)R>;m<
zLdXF$FAD_;g}P~3fFgy-zadRT*XIgAa##_svd7IX0Pq^o97Rhy;`nktu1qQt{99KV
zxu_w;<Ra_@w)lXo&j<J1XguXaH&ixi$`;14=$oY^ULqYWxHMIC-@cfF^}<?4^zX%i
zPM*(p2UgCK73rt)wbkTSY>w8@lCL%P$D58EGF#;oF7?4SVfU$)<c;#&`PevK+VhS-
z8ptQvFd@Y9R}CWVBJOpvMsCqf6yu##pB}p6SKc<(ewSz}v0sS6Ct>kBP#y#R-o*QG
zq^ZL%Aq_&`{CIvw-e!p*I0}|5{YRL)*&ph8bECXu-u(4(`F4KZb|JmHv|KA2RR-fl
zpVoEtgvH&X%~Z+~^)*|i4h)YzBP=!7u1o{5af&u0E${OB9LQxMwYpb`&f6v?l_tPa
zAS+t5MaQp~V$O|ixcl=Rk4Mx_P>~a#>Tw`*+y4UH_>@A>oh(o}ioZK0>mm^y?u&!K
z@p>o0DCh@hFv~WSCTHnKq75sFm8(;k(-i$k^7JPEMG9YIeEYO)=5p4MnOg9;Kv8v5
z6N*+lGV)-HG`Q1?4UCKEqj!DGOe{4Z9f;8Vsh6hw85VCQ{F-z04yj@eQj(>(#C|%=
z@)TD}k}CG%8UtN{fq!>eqKqNTyBXdT>ZO)ZTx7$^8_XUjtqK78qEQa-8+N4JTBL?!
zJo%{WFf?EDi4EmK#o@l!WUX<)I5Rpeka)t>MjD1Jj!=&H?*St6l;O?1l-&tcAC7rg
zXqAf?LB3=b-Sx^}We2EIO+!%E!Z%vQLYnEYC}8A`r>x~Cvlq)PG`z}2(g(Y<Zz%1w
zO&V4kOaSYl2$n31em`*$%0|Q6sV}g~EK4^2T2f3dX^hrG7ohnD0>2?}Dn=m3V80Sx
zuoh~)DR9br&#zzBhPlSL!pnZUrd_GC5l$XCK7&?QJN@TpsBKT`Vwv&RYlpi2M)SGN
zSS#`g9t1`m+F{iv=Bg!U3A?_GkGR~4)dIFhbbc$w+oNcGLWV6C{1ANhg6GcbY~prD
z+RxqCO@ZjSe56ERZbq#(StKQ{9cvMCEp&W?Kxy37q}#Omk2zg?J3gg76ejiW>YSm{
z*CDsT_{0;`Xzm>!?=|>R$2HHSovE>HJAjpV&<&K@+pWWw`}r)^l&gyFQu3XkKrLUr
z{!iOyXNQHxeh|f8Ou^zY{+2fq>Y;A%vXM1?CD!hbNls}&S1`|s=>P*@>eHc21+kcB
zux^e^!_Fg<%Hab&ji~!|7!qB@V=Jmpz>`MD?v@9`w9;d@xlpk{?QF@iwol-t)b8|=
zzefsl6Cop1*j9Pbck+N}>jJ*n^Lu7#1?_j#=x^f^DtKdbxEt7?C|}*-o&`#%w7K~N
zor<H2DoVBWL){NMcikH{WYvWW^+Dzlz(4XczYq#?Hb&s7|5yjfCf5rvnuz>@;){4t
zuaOHSH7B6VuJp=?YkNGsP-?~<Op;J{!<)PAEe_Q${;W_zrKbgXeMl2tqWRkKZ{lqg
zZ19>|y+Na5(3DBBlGEX{)Z+5ue9Tzg+)~X?tmwAwakv@v3+I{c3dO{ZqR<wV{5@a_
zCCbw@PD9+^#uxgHS}GEG8Ho4oJ%Kl|&LwL<&PwYJ{NM~w?IdMq)yXt#vh-v;JZN5j
ziu(Gmt)CdE87WF(<3`&Zzj5w}`l!BZYLGXB)6B@u)U5<(vy)qvgz?mxxtl9xt&0;S
z`Mq<!!n_niz*Ev9=!ITfaHZpWh_P=7mlxj-Ysca2=*KofGY#ITV{TgsNZkjL*cw1&
zO&EHc#2g^tgu3D?Ty7ay8s((7t$K30-v1tZM|GVo@bG&irceqQ6Lz0*=AR&Z!TP*0
zc}V4;&ER*les+w~RPFAW@FGwjUQXORm}RZ~%Ddh?b%(9istDIAT4gaeR~el5e)4#r
zVLZI)s8##D<m@_j#XJ?)S|{V(2s|Agu#IFyUxY?Qf$pJFs}x?0VuXNN%=y01a^JJ9
zEk%lFtL+=uJ#cDH8SsM#GSxJ*dZtR(e>vPEjF|Unf0i>>^&>fiq0>&U2;lX&pH{H(
zljR*2Ev>t4xF<^!@i|ZCy8Q5cFf#m(8Xa$-e#4a*_q?hpA*f1Ctwj4>t~;#mi%+vn
zx<|C4>-Gb>O`?E#q+0EP%p!%ic8jIGNAkc15Q^+1l0&ED$CJ!V;w;~Pfnp`hx7yVG
zHSXFSx_OMUoi|pW)e}$h<%%=bW9L59eq5D{n6B6~Y<sH(EjRSal{<oU9F=m$Yk#p^
zj1P-Rrgx3&FD}IQ8}M0fwI;KuXWnA0Nl~?>)d)=7XEYXS-fD-*_?Z^j*KcXK`u%~g
z6fX5W<Hg^gud*$O{Fcn!><7}u^moN`(}pJ9EDRSr=ZnRRppq!Ki1pry0%<sY(A3F^
znc?IfhYyKyypN8!GiIfT=p=s)%aiP|kKyLp?4yU>zB76&nGT;<-?*5hIDKehk$QlT
z;NpVfjx+bS{M2wBzDb8yHfNJPg`y3ZHKb;eT*!r<Y5#73hVzPQZ$sYlDkz?x{M&N1
zinK(nKf4^DZHaW+g00%+>rzdKC#9hdd>;0m_gEqTGr}K=xoW>ZUCqZsC`&Z}b}z*w
zW|{*7PCyp8b3&^M?b_n~ZgqvVx!uS9l(aZO=dWAe!A5wsRwZU&<w#9Vke*6`rU596
z5I6Yz7ZDF>0@~e0rO--w;FHXyu8BjRftK&xjqDwes)+d>%)SlkPp5LQ3r|$LCST<2
z|0B}luzbtM>R_6WfP^NWQ1s=sSZp>m2Oi0)L}0|slyeQhonQCe@OsWs&bca3i`zr9
zD#C`EiLuv}EB3yNSMFxrNO!Nc4RXEi9i~1*$78=}kV~P&WJ&k8ot2mbDt<FaH{u6@
zncSb+P(wkXcXmQO)p%$T#Ga>P#N%C#kKV8mD9K^(gPpfRl$adSRIWk;0EH&)Acsxc
zAltn!py#G-b0DdYeH_DZ>i)3)i_yTPxk#K~Izgc0nTeYs1gJ^QW=cq7CqR~_)*em&
z!rseu?9dK>u@cz_<*OCEd7SoOmCZrjwa;5Dtt6MwoHsoV!p_r`c_+l3kTus`GXaWm
zv~+4Ld=ho5ON=(~%-lFugi}+69Ni85ox0SDH+S*kzbPH?<R)mceqd*de2l)W6{ExZ
z%MmuGeF<ytJ_hv>B_=nMn?Oy?eD4-`O8k513ycq4dS92W<w-pB-EdbiFn2u~EZ>v1
z`Mi2e&QGmF?DCuh22N=qSZ1vnkFgeQmAUPEO%CKk2sVD*0NCrNpjx`TC=HUsFS2Cc
zI!a4Gk_4j<Nj4rnowOn6&y6^`B&hmp$4jWxzYH(eMH9+*NRdK=IX6V80ITCn0Z{Bo
z1g<%7lxB%{hBAl9XR5m%0UQc~H{Fzw7QN`R_vN^YWtVnP^G3E0$J2^#>N#bn%Hue!
z#`uz+_2Cj}ZFrl2aam6`Otv0^yRQZ<{57YC{Y&kS9-G;Aaovh4-{Y4{Z35pjrN%P&
z(xnqXL!{J(`Jc9}ac9*V0@Ihl8GV5ieT}c}yB|s4g<idW*`-BKrF#knrKR!{0XrG$
z`pw9U*cFGv#UhTg4pWvKP&LQWjpsp6)ifXG<HmSXw%gum^p!4IYWuD&@oX-t7&U@J
zaxw%m{V=n&cV^Ep@a#9LhihxrG+j4|Ar}-k;n}eihl@;e*AD&w9t5ySNy4FVCslw)
zFpPe+@U=M7Wc&;i%T0BCuCsL^iXV?ho<!LSVeP9vFE?wYl~~5?m~OqpN2z@Ztjg%(
zcFgMi=C$iA0^flt20{n07*X(=9*a*&seNZuyy=l$*QD)zI1~M-a<;fNuH<Dj-tELE
zYXEXmXDecO0nrtZB0G?AU%9aHd(JPsK55}KvnO}gV$8SH-l3{4O2~cNtMPuWsow+O
z9Jp4WgiJb=N!c#N>P&I}sN!Yxd4Jl}8zGTBYjN6YovM@Pa|M&|&iFJvA`NL4(RguQ
zIJpP#nshz`H2u*R!vlr{gs(KPV%{<FE0i=AE!Kz_#a$J|q+sec-rQ5by(XZNAz(8X
zu>q~rUZ|9$Rag2^cwUQjT7g1Md_zcuqs;?}?yo&F6NLRLcbDlNzMz>-;iL&;I~=e~
zH|I402a3Ok(@am?ESHIscFpPZ0(`XHE_&9VQ?@d@Dfw$3oztA{b6{z*T<B9JdFPeN
z%25sFc(*(M-zj@3Zjg+2+k03C1b4-*Lqj5AH(xrI%m`MO1C?04pAv!|U1OwN@m1dy
zZ-W{oNIqBEqT~?Nb-I6#1H@jm`i6>8aIEt!_+&&#9lnmQ6xt40t6vUxNnja7)!81n
zbB&(Mgi%82s^Z}1D_89b9g+=bONjnQU#5IKb!Ug|q9bWCua*4ZjmO9;(Q}f+e7^3V
ze|ZxSO_POQNC-WY@>rn(fnQlz{4m6B5d&cO@`@h1jJmKwksP@8D_5S{4zZc1T4$vw
z3jZ`EM89O-VlN#Pvkb)NReIUY!^PnDV@#x4A!!GKZREnijV}9<PuuFM@SLfFMu#3D
z03cmAS9mSyXtmqN|7jSN%GR<{TAEvZyAtC;usi1l=7A+=BQc}vLN*KLu{^sT4+s#*
zic`SnZD3Mo4EB4!y}`5)Q!}EU2q7}KnH-1!*m-nfWG4mqf^aJ!Bg6(7W}_o2p9;S|
zqhxvkc(}CA1c>DMOY8PF5!Ofe1e?A-ANeTj$ytC8qFk5RB2H5kVO_MO!#!eu4F9uv
zA7PXjyQ5yrG#HM`bLYP3>ZUyhte7o32Ef%lgs`Vj&=28^0NktkpeY4ZkOdkM{|**e
zriD`boh|7h^yOew=kEb;G_BETsw%mn+0%Q^_8%pVX*6PYa)K6KSRbFqJ?wP&VLVMw
z%|O)w=J`d)s?%LMM+c;oF=8pnQ64`IWX;N(8m0HMU%p+XIT<NFoK<!$bfVjx&o02E
ze{!$HtGQsS6BY$?<6=B}lH3yx8pc!;q`(|cz@9p@4U!`|PxG!ES&y1T0`&<#|JpYx
z38?A?-KiD4lxF1UY3;D<W7_^>Wgee=Ir4$7?iJt!qc$Ejszrs(lt&FIOl`ZK%6EzR
zY=Sr{51y0)T!?ruNEfY)Y}WxT@CD1`fdg%#*F2@_Zcnn)jXW;|yG{Z2Dzi}aZN@2W
zcZ6c*<AauN-l$076lJMokzett?_9jbDg*U5)$~>QG?^f@%%_2hHr0h;{ufkEwM@pi
zvFAi1fL%SG7m`8<ScmX;MImwo@Tf<x82D1Mci&6s-SIfXhuh7l@T;6e+K-pXq8B`E
z$!AkUbw!HZ2h6SSwkMRFBko(R?zD&FW8&tIn}!T0O*kqQ>nqDG#U1Xs)cOXyK>TeH
zc-<I4uth4tr`)$PM;QAjS*7iY;C546@h!>rx$%+gCY&D|ZK`l~4I(kh9CSb;dO}^)
zDABmuL<MZulwl7hl@%pa9Z}>HkFO8@kBFrQh~vR-HlE^-*Nb!0HZA5n%iqSfluWBt
zt<Oi4mWJ>3umaSMi11l+=(gBRj{sGy#u&Nasz<ly`1=E8eSnWu>&y4{eD%SMP(A5f
zTzwuNg13F$b7Lp3J0j7VdE(i%O63bS-m}HhBh@}^trL3jl4SzF_sX)El++2z1ZjiR
zQ*>`S+A{ar(#EaR2|Lih?Y_*MJQpm&OtoIVsj)!Qq8f9SH3D94U>kR^vpjzg&UvBz
z_qGC!RX3AP27k(!v8p79VBKS2Rk!|fAu@6yb7iFM{PEsb=lKR=fv@=yG%k=nKDNe)
z`}gS5HPsFnD21t2A!3q~>Q)7fc9;eGr)jtzLfI8jv+YWIu)S={zu|6Nx{82B#=<G}
zN$wSQnLGb~R7ITdr+f*{Cc5jdDT_g=z4yhxVk3E#Gk=frJl+G&aG9!R|KPyZ%nLEN
z22%dHQC?G8;|r~l3EgX3zEdNqf38*W)|avC^<>Z`(4TH)4~>?*D$;noT1KjsT<Of$
z*w`a5wPREh3}HCI7LvjK$2dJLIEU3dPk0Jt0rs5@a{y^$4nuy+yhyre^~IcE_f`X!
z52veL-PvL-FIhe}Ltgg3b$2)|@!u>DjFj*|56T$l{aIzch{bZxG}B#LPQ}AnBf<2z
z{ON#G!L4Os09?{fDv~u}Ca}`)t6cS0Ofp!a=)IK$JLML@K)@_2FOv}Uoi9$qi)nd&
zPXegf0G;&?uBSZ2p@R^^t^|8=B2&Ed#7sr^OVfd))-FkHGob9r*5<_(=Pmyt;RA`%
z#B>x<Fc;<MTEcAqc7+Ch;1$C(15EWJa~U^BZ~RZ8l<7!*_5&<*<9h%u3IG6OJK1Qa
zcvYK#+(4%;!I(utxIsXA^4U>*FCjI^A%&cWMev!W7|fo@@VTF?t@2t1<XZIq{H9B&
zU)@Bj`aR2&rOLu0WSPiD5fg@L-I4R))M=@VwNAW3ZCkFaUD_3Y$bcm?DjqJ*&QPC?
zsJV6PiR1vzB8xhbEzB|j2tV<HM;{_8-%r7?1d&HuK-`!Ec2vjp&yi3qCidhu{cQdn
zZsLGH+$MUTRzLSVR)f)n7evhH_3X8Ba}kNQvnUGI{npJ7#nv8NA##(AB%Li~lw~9%
zYu;xTEAS-~iR?1y6RI@?K|6<}ph7<dNc24~W}N*c0t#(lkI^q*hwcD%f2$*H^{box
z?vU4TD6>nbN-yh|uT%<rvYBkAPV9hb)e2Xb82QLu!nybF9|)F-zxEfR?xk1xX*M1n
zhM}sf&aQ~`id8NpB+vFP3}XwggUQF2<v5l+`z9Va@F|7|lVtRN>VS|s8;q$7)K)&S
zXEq~BErVUa^uMEg^wf2foHa8Jlnh^p>$Fjx>TaVq6)ugB(}zpTJ~g%Aq5hlSmo1c*
z1eUEm*$*88gKvCpR7+r|;29IQc6%%RqV(h?yvz2I`{BoQg0gSgfG{S~uQeVAWU?4+
zl}@2~M;}$*y59<Tt=N~^+nlZJ&fE~ExxA)cbRaX=TkH9++&xbsl=jA}=FVF>tHb7W
zGJrPhTkp&-d5pc*z0w?1G_BdDR5!ajSsWb$=2L%|bjH3g9l(W8ur<0n9$_%2(aK!I
zb9dtfyJt6tt&SiHv^2wuuXwH}+9p`xkhq<lw5~5B-jm2@6b5B1@s7_ogCi$u-OpB|
z7(-aB;P5$F9gno48%2tbWMgNusOi#_jt<p<*JPAjB4j^iFLV%Tj<CmWk1jZ=zQC9b
zq$&7|5;q9HTmL8||J7pH5&ss7esv|Op~>6_gt*=2L`t$^c^kC8#BENPrOP{_YKTzv
ztJUx$0snfD`dxBTjkMI|>EXt}eAAd85`6e?#VG1Dfkt@rsG!}|{eJigTiRtvJ}@mt
zK};G8;*>F+2RlC#iyAK1OF_e%8Da=d&CYr&o+9gzxC=0B&Z7@&MvonVLb?1DX+__Y
zwmo+6=*tBjAAhTWDMPw-9>tlJL;h^On631P0WT%PWUwb{F*y9fG$izlZ=2}ofbnIe
z7F{m)v5=X7YcojF>hW}6j~JYXniAK03qL)dx6TEPk)(+y@g2|SA@6Bp5}l4{e|F+_
zH}@@33nl{o_o9GCD4usMuJ*NsH?kA?oU1lpXPptZsGKx(6w+awdGyWj+LeIM-=Yb*
zhoTqYt-CxlnBNXc;GVnkF00(cW189aWZ#Wv9w(N|Wu?j+*PJyGW=#1DwVb=yn?~As
z2~LePHrkTsGj;jKi^9v0DiDUq&YV$b1GjNy8xQZyf`b(H-2wlqXN+nSjs#Cq)PdbG
z1#Rs$Yc=rzewbQV+?4GhrAB8X6$JT~gOM-2U)RqQ%E>fbAIqigDe593C8$`0LCZt)
z@TyF9JZaok!tc@2Q*JA{zEH>iPA?0Vi5>{ltBIqWe6JMvGGybngTj#awsZW7`3bcU
z7Sa@WP=rk3+zg2FNi29=psgZcpS!%-&Pw9q2!dU?Vtvhz4_CNn?#ktEG-R*^B}{G^
z*WF3kW#k-u{_PxH-`pDd1jjO>+~r8$Z|~XYpoT@-F*{Fhpb>koFY|vN-o$v>&f!^~
zpLb<Q+UN%ybr;5x&h7TOT5pLGy>IO~`|^IFTjmK1$AcOTXl`=b<8XJ%l;PHqR;OcC
zFIkyVB`FDW_TXYiq4RZug^UQz4Npev-uE3IGOIM#NIu)Vn|ZV;<z$Sio_cu2K(?Ky
zj`}9vsti?ZVb<(PT_QTk%biF;YWTzc6sd6(MD<q+V0hO!T^K*<O{|N6#}_wy4m!_F
z72HWigV|Oy>)f0zaQ(d|weJKHBHSiM9Lm`6dYsIPXvsG|?tD|reBrpevs^DjW$yd{
z9oKTZpEengzb%Gfdn;R^Q84)HZDflk{a!SM;O7T*Uh}J_oV+fw!vOX_uAgQb*IWNc
zcX+2w`LP>sp2`;;@TB#|Jm#!7lP5UkDOM~5^YpUbVrNsQab<J(9HyocA&R+pT%r$j
z-(F^(y=(cb{*`%Y`V|+-o}(^IZ3bEOAOYtx!;#bvoFN0mq{DD*xyk-cee?H=PaaJ-
zVPiub-ugv4NBE4H?6c3)>HLxE7v?5Dn_5@bCXX#e787A<#`2*2>lx9q*|tyk0-Z88
z<}%$z&R^?;ENf2Bt)noF`eTKFny!n?Y+Z_HW?)$E<W!#qH%st8m!E^J8A}2@B*MN|
z29JO28{2od&%Ft+zkWD<IxRG3VR`&G&ixV7;dIS?tPHbrVeYYD!`|)B&BGNbS7N!4
zUSs}_&&<8VGW^F5w)Ks}<@oYq*Rp1&N^1Ys(h)*|{FB2)plT=czl|w^BGENJNGD*K
zhfblZzDE;5_8y<x^3yrMx(JT&Tg*^o*m;YY#vqF0*k9Q?{+u?AA}u>EKhynWJWf#~
zD)ygQg8KEolPfW=0pX3D6&M%3nMvk_e+Yd?h7KBx71d#ACehy!hxV4YW5lbK<hMPp
z4GnMFcffQrSdz8ydLuY7?MIS&ff(U~+;ejY2dK|PE!3#FEB{;2+HmH7_#-K^+so90
zLpK@5VQ2C2D3@l{q31{I{cKdNQT`Kdb*PG+AiVEX31H@%9-faYQP(>rtq?^*Lk4qE
z)^7z`MT(RQ&}mXLKH*kXZqr-O3*v1ODd*=>o0Sazoh9w+LZkpwe-x0S3WL>0Ejj~t
z&~t`w*nd@5<|11>H3sF0iC`wigTEe+fTs&EfK(KIay_(a;A+Q-1?*nKSfP2j#Z7NB
z*a`1W_F0!*xs)kdyV=Re@TUAejWg!*dztvftBFimD7`12Jw2*-D>nn;yQT7Z0eo~R
zg!<g?Ywkkz{y9^B1EfyH8e~(`T`?zqgg$!?`_}F%UanRS-;CJ%iI)lTOH-nXOjw7s
z`0k_EDF=d?bS$z`@M_J2j={|t!8`><&HaH^YvuSq5vt3+pNYt-DL0}8v)OBYO|1gh
z3scg59~hf=A@fI%Iz6n!EG9A`&8Sy2l|YS%p-v05pfzw0A(b{<ZPvmEvt>*R5pVAn
z96|(m@$m3ptX2|jN~~7a8}-Iv2_4_B!ttw&C5=~|H=Am{_J1Jd5_!LleUvTJnVd$K
zWKYFTB+Allg>_LfgI9?*vqen36WYBt4#mB0E5jJd2+bA4P!<HNc`tM^KfFN#lXh2I
zylAK|__=Q_3%C@!iaDv!_mwUvHw$8kxy^xx>&Ci*KVZhnbKvjwA5PQ8_qd;~55ti?
z@Z+k(jbX6>)5PhQ+&%e+BJY!7U%aGt^kt{YbN+X%&f^9~Qp%*ntFJ3gNhGXqDi%ZT
zE={wX(&;d8{~&;Bhe05<;`_4p9P6fke`ShVw_A4-m4~4YI*U#50xP8sM)BkQh5o3J
z_&W@BleDa0)fgTeBwt2XqhAfu#d{BDa_j!}SnC1~o-Qtu?wn;{9V=xX^Vmfejk-<7
z{;s9_o7QwQL5%zP*4nsStOqswh0+o?G1sY56b`yX4iFD!L$^z)Me|Fqp7F4@x4&{Z
z*X_7Mk#V7gV)3-iD<-=&<;C!6nps#llC%JPiGuOoyA~_Zf%Ko)?|RwV`-=28x7>FU
znq#=6sJh-=W1x`x?w1pO+!fBzVknE^Mr)AKD^r!BV}HOkO*Cim(o{~x8FugWBV*7z
zLF_;5Pge2c7O2+pGtH^fKEu@El&2((pQXfsu7daxbXRNV0LUZEur1f)GMYkMjMsdf
zhpOZxTo7`bx73F3U_8(kOhTWTh<Jq5iiPFztIzrWla{y^c(GTY2J1hlIe#<z0d}d-
zdu_uKMZ=WqN^jo+XHIY1@$11vEcuUuLbVEh8ejGl!_q8WefcCgpSRLex9}fVDs#u~
zdq*|=E+V9%Vta&=%&F(SNg@^g+7EBXJeU4V2bCU&+hnD@OC-TpFp_i0-&&Y_0WK0+
z4<~Z)F}!CJgr#jpeq2Es9Hki;2}2sqFJR<P<1VSMftyumF%hKmyN~=d$ThgAd?bID
zTid^(kOmtbL#6sbTX<9G!u-aCRF9)1bt$nR61o-*y3Hp+k@#ziBZ$w8ZtZ*Z*U<LE
z82OVg;pK0@2&dDTo{2xgz_T(dBrUc2ow&?pba?d^El=<KwxYUUVp7Ue<J=9O?}%uj
z&1vZE)iX<zR28vQVX2yEP3S7y*j#&j=z3r)d+ZANr5PPxqCb`P76JC$_}Fne9`L1h
zTI2aq5HrK!qNRRn2_x3DDhZ8&=C;FCBE&IgM1iVPN9c>gaV9)C*zN`eL7*&sW^Ifu
zTE6slyCh85px{3Gkq;vDc(#;x-yb=uUZ!ooa|x!jf)c*YgQzaAtia%?umx##-43R$
zB(i*=yH$k1I~2bc`?nB}m!E)bS{+U_K(WU7V^?Fk<_s}twzEW#wbk!aYw$pY`jS}$
zeC6zRAuJ$ZN0C+x8@eMbGd!skia9?Ib~moG=X9seH(1ltw;aZ%W=t98=U1rLnHUpw
z<XTK`5xeW}`~5BZ*wEY5aA}WgF6_oGnnr1gZb;=$$5ST~BAFa3y3U{NHDVU5flU#p
z4H$coVj|Tf%Uwlcxf?E=@a4<q<<WK5?`XQXUBvRkyKiX8TJF%sP(L~pNRbWlv25w)
zOCL33VvEKGn&hccKbQ+RPI$H4XyRYJhVOn14iIrKZY*uJ4kRk3olG{32elTsG@5hI
zN_Ci#vv{C;B~dW0_OA3b21e&wNZM|=9a(v^R*orH=%NSpK&wN;llt$<z}?^$)7V_n
zpqF!8D`~{)(W|N?Gb`(+mr1bN#2qBrFFtH6Jl}r^JYcczmLm${`vkTvQYTrei>J%c
zPkL=?@;HaHqKby6L2hkGau;Z9?BfYjCdZKzsQaZ=pO3@SK^UbrBexhj<qkK7C@e#t
zu==fV6Es(F2#CEM({!D&dk{6hzI+P}7Nk($pBy_$Bb;4SBmU=*|L!gKHKhZmtEbxq
zSamlt*)uyblk}e-X6Mfm;<pjtMS`p(AtNEj5b%dcNKE(9zH@SVbLO_BR5gvgvvh^l
z!w1!!Lb8ozu&U4D`7gq2@SuhBg9a4s-<vK%Di#ZljeLIx?R2S)t7E?awD6iCCDA@a
zaqr2Quk&dU8FV^WbHnZ~3I^6*I2%6p(GWpH)bg2<ojIFIBpTc_L>nXXgZS^SD5iKc
zW!w?!P7G8wwn()b;6u)1MGms~eP0^vPLOIpYgeE(WU&x;_V9Isf4r{3Oes3nHKuNB
z*IgK$0%wc6X3Ju|e#lD@<in?{mRFB`km)73A?35xDQn7L@@&!8TY`Jf^}Gf_vAff<
z#SYm!jnVZ%>m!6Aon9ST=cg8eboX~toZLes#Oj}rP8Cdjmr~K_RhXlXAX|aM1PsSG
zu691a6sGM$vGe3e+>N#3u6OnK<D|vCc<cGxpg%mYun&aqstMtmg^+qqfr5s5+mn%%
zja0T+1<wA6ze5}YXFQ~tnMbi(ji8cuV1XZ^jw|ejij>j+AI^X7-UNAEAv#-mUkTl+
z%@E1eWP7lTJn2%M21m(PP4@F^(JU>1ZYMDIagV!M+9MGsB^}@WVfaeD8iJEE(&A`)
zeLX9n%n^qmCSs^m_HTzso*HqHmsSg+JvWM9+`lYN%&3xXkWZDtU<D=wba*gy94NB1
zDYR=c<VuG86Ak3PrOYo)ekX2&Hl+vPP%sY&WzvY?>EJ4A>Zo<oXR!=p;L8}yS?F%+
zZr$A2WJiWI=IlenH0yVNK7H6=3&P&p^EPRVyIW5jUatd<A`6|alP8!bE=@K#Oo9Kg
zR{)9}GDX5YdO$dHDdJ(oC<TseU}R^&JeKC4_2-}Y3wU)bd!p{}*(;P3A18OlEms5`
z2{%t45K~c%Ig(N_8{I8WuT_%)MAG;eWwDBaw&&m951H-$`yeiLCT*~aO$8pXaQ^S_
zoV_+$2Mf)&R0_r4G~h$Yyg=8vnmIlcS#o4AmH0CrYHcUb4{@92@ZfD$L6hX)0@aKs
zW7XvH@T-J2wc9i`Q&&2@72D3Avm&1}o~n7qMZawK40@ate);j!C+JiIu6u&K)rHz%
z6We(vNLV-OS*l2BQMz&L-q3=3f~94-#l^GKt~8X%WBhBb9R1r~tG(TeWQgUMcf<1-
z;z+t?JQ{Jaji4k=s8EQy`EzASC^=J1TU&8di6xh&L)-1{eB9zyi$r^#zzs+8-J%=D
zngIo>J)<*eA9%m&eqxae>jDpWzU%%id>=9Ua54=zBIr(wY-w&-Yo@Jn2xoX8+F;&4
zhO|YYM#9y##zt{d!JJxhBPb>Q8M8y&PDgS&R=l#bi!ANnRt};MmfpT;3L9tIKwv-s
zE`HX;bX)wLNZt|$<P>>eW6Zt%g{^rMuLdw6vJk2x`}5;5=;WCY(s-vtP7a?+;1_Ze
z33rYW$Q7~$gri0NfQU{RMf<|z&U~aNBIJvLh87Vmo7M4ozqdIQ9?Sr$jB7T5om%p5
zw8U_}E}N>f#IW3gIg3l4nV#F@v82q6iWp~7rypFQ_ajehEl^K4Hdp%s1NgqcYP?h^
zCDUD^tNs@{4K~8$Q9{JOcg=QXg*)88mHJ^RTc|eCm2xIw^Pe|J)qnalUAXEy6kiJE
zEy{Ep_fGB_b?P(6JX`5M*j_qkEw-AMs+W@O)X+oYH!5khHN!Qcm<~cn{M3{@`FpN?
z^>#kqLGRNv6-rAbe$I~1<>$nhGnD<{ZbD4oV+8UC{>1ByUH?pRCy6jPk;?59!`@`!
zTaYr`q3FF7f;3K7>jd#lGIXRr;?Yd_^*NFUcK)Nr9K{*FBO(KaiYtCRDY5W~ujl1l
zR7`KDX+$1jyR)JL_NJyl!XI;fI@5lFy@8RvVJBfId8TFe?i49rw@mFwa^kkg)T5|f
zp0b>WhDr>TQdNblr-_yQuD%W*4>z>YY%qrGoFW=n{WP`$yQuu8TyJWckL>$;)DIB`
zUJyTM18^nrQRFn-GJ}UyXA%iE)O@9(Hm0OgLqkV3K}9yIpdke42&5?VqzSzj>Te=E
zbSYy7%$T?;sc1ohpDA($l#AFVC)5j8D&?oZ^HK8E?}p0yS@~i{mz)pOyX<DQY__Fr
zHZ>tSw~P^yFvCKfFd40eFn_FXLj(i)I|8+t|Dvi{a1O*lDwx}BJ0)jJn#Aqx-APKs
zDs-{^tZYJ>AELAROqqPnZ52IUa-|9=X{gB``sV6+%20iXP7^B@oSNEqm4o<5nSKyy
z(gxaaWsT)FP(z|RvS$d2lXY@tm|9PhqbWHTCgjU^|A>hw!3N4gDz0ul*S88E1s)u!
zLm^cv!)`>2A%%C!f1*eMqKO91-<-k+!=4(rJGije0AN>6T<jxb<pC2oZ0Wq}bGE+^
z+%($58wZmp11(q$(O@vQ7UKR+tuYxMerHBMq4C(YBU3u-=h51UW;XomfMTF92Wvj}
zJqIa2N+1IPX6r|}_P5%fiPl4Rd9NU7B>N4k?F?Gl^u?b?=c2ejz=0qpF_c--5^_UV
z$Xc%6c>3I5WGb6K#$g0pL!<)bY;XXvbM5a^B2^0Z><${`GKUcWi0ZX4VcrO?U6Gkq
zp^Eo^7Iq2>kv=R8USkTE7&T&W*&ze~Pj&L_o*ea1uc>?gBjDQD2hWc#I;fFTWP=UP
zH<~Gb>mWds2k*avbi;JrkA$_jxH34hGW~t}HgEs7w(hy((1R-Gd3l$mZf_6;9H-;>
z6KRbLu#KHu8)6DgP|Q#N9Z^<7pt`A-sb_I@kaD6uVJF4K={xKQ!UJ3Dggq(j7&0BX
z&?S&5=3E)@`afD#(BtyTwuC_gCb-#bzP?v>{;=S5FSny*{q;^8KB#>KL^lVS<fTkB
zf3L0^9f3bzCXtTw3UNvNTL*b!HE0l$0uLMoaw%D1=tN!h3P$QH$J5`b4y!11cU;lh
z>wh;h2ssNDP2UBNaXYuZ|B6N)fNYDmgA+OX&4Vx1Eo6*YP5@F$2+8$B6mM?0C8ni=
zZunqVm>jtH`P_RVkFx&xPH9*XV`dD=;*WbO=O8{6{QsTcOdUx?fFV&UWH6@m^8<f8
zMyMT+P-k|39oJfDB;Km3S%t0!T3Qj%W&U$?<Y$8i+Kk+&bz(4Y^n0lJDdHa5+LI{5
zg>nE8c|{=9`LVDn;0P_{7Z96o7YWGkHbX~a+7mn`YxsPzo6VGO|A@Cj;ON9vrs4tm
z%01yV1o-Q+Cm*2ZVs=?cIzS+?LHVPBkGBl;=V`rIN>TTJTS>dyhJm)2pTl3AcgiU`
z;v4x`L0obEP@8e>0R%Y;K29m{?Jk24-ZNdz%6<Hj?fTK`T20d$XMO1<-=^=QW^2of
zXbs!2#^nXZkmr`;7ylso$o~yoNNg^Ws3qwvsqW@GF>*5mi0uS_Ej6qeVaF4t+N5kv
zhtEzAdAK<Kb{d@u%O5YYFP7FWjn1SJFk;8har*pT^zFR{87I-WhEK%H|Gd^_rrAA0
ziVG!eLX3X7(tn$?+won10C1Jj8b2ED<j~Vc(7PW#1F*>64RraHY@-H0thy=>9W$dK
z3nT@M8*?=Ye(KI!KK|FNM1`E<Qo&4`H@GNLk}`3o44KSI64&UIwZ~=^3`q^+2$#*0
zn8wd4VX~~<|BZDdynyAY##ZsHlrv(9vKqVp@!n<<>9FXkfo+V+q<!6*k-rsnO<pjD
z*Ohf6c)r!RVctr;jYxh8_}<lhT}F7T5MUoKPtW$%KG`!p<)*00Rr(sSkl1~@u3#MB
z^gYqp@UZ`p18!XxOe&?@+dZtpcJ@3>w&YlApuw<Vk%(FuCu5%vx^g_++smNZ8Rw-S
zi@(rCP=3JSyBbld_C@$#`kFHFIQuNLwVtx;cj_b{6Rj8obRG-HQWM+k%>el?vh1Fr
zD@umF_%vFw97=6udZ8F?M&o<Mo<RbP+DNj|Mn+TkO>jV3x|n2B+IK1*x#1N%RE)@U
z>BI#B8&ssqL$8CTfHX)&HLo}&hg^lDS*GNYP_7RPoz4R8m3=1VhS_rP#r1G=D{Rsm
z1d6w7<wuW!2~Ycn>4N$h&LVset8y6DBZ|{Y1shNH)6rhNqG7vzY()em1(k4~?1$6>
z2Fm=x`Y;hwA6a%gif0`~kGZPo3(Q}&SVM0GYci&r9~MLvTkVU~#@q7Yf<DQWZP!uc
zgBb(4On4#A=lCYEC85yMbf_4W>0|-glc6SF7u5UP-v_w=Z@!xzSSK@(|MtJ7)dBV4
ze~ojS)w$vKHj@7PzhqCd{J-pJF-sd4Qzv>c8$%b<{|Wk|moc?7cd_`%$jrg||HnI5
zY3kaqv!VKI*C0MJL4;!YDnr+XplgS~Uj(E3mgAv@6O*6XtTyd8uhwiANk|qR3^m&9
z4>USo?d%-1rKCvRvl1Z%(<4(tZ*!#A<S!>2AHqdIib2UquxN^~h{catRPrNB{vX!P
zIx3E6%hxmzB)A9n;O;KL-Q6{~yCy(zcPF^JHxk_4-QC^cb?(fpJG17#x8AHZ^+&Dh
zf~u}Qwa-4k+I!deSUpmrFoE0mmbArRB4NW7yE%=+$ZFyOYs-K`*<hdjgnr__FdVHe
z7p>Sl^Ww@5Kwe_()R-)N89AZx$8z8G^W*@QcJ(J%vJ>o5(~R6Q0V@z}*O8BvFd<LL
zVkVP1HPGx7!Kq5iyEf9P_4hssRXgZerTI&1&!`!usl6D>N7lR%Z|Gb{_E@+%Ny+mI
zAG;Hbsf#s#7DtAO-j9iiZZCJmII3&zsu~-SIQ-d_hWi%C1WvDGKT%pfZK=MVwzq{y
zb6+vaO}A<S9I=${M~UZOM}nI>r65LifC4v&Ye=U;HYWKR7-~jAZ0#%Yg#<Z@DP96#
z9Qn%sMghcO!HZV9Vb;f*^K4?XV;^xb6*~#?sK_ekYAIx7$+J$tMA|1cqrf=NC)=k?
z8rx3}b|;tkx{9-SBpuMKVfRqjJ`qk*ck_04o>{QYsNpx#IC8txHbi%K;c=HQE*WWr
zUim<K<>so+x`>{;bjkSq;-h3vFGlkGD9*FWPcW#T83r3fzK~;A(OZNt`XkV;w&KP}
z>aCM+Kq6Y_1-KGbQ0NwttPn!I-;#J!?>^^Avi^R^#%+gG$wJ&H=i=T;U@pXXQp3Rh
zjSIQk@Fg(#7r2DNYIq+r!>}y(gjrsf3AzwdD4z<r-@pK@VuTIskxo_cb&duJs&Y0o
z+zz0?+z+<L!(ZWnLp(4Bng3y5g#`9O)wp7F;ec8LI${R|WCi+SO;*FXg@x#L!j&!t
zVE~*Hjv0)rT_r}J!}9>!7NU+QI5v8oT`*JCXY#xQYJ)#lrk7x!X3V`F;Ss(>oi)Zn
zKKFBKj8=r<bsp7g(9;orXP-WKDg6BfwWqMKnI!^3c#xbiuS35@@7#IA-K_pgUZF_q
zB7h6prfE%W0IAifNZh{>Y=z)A0sD;;lK8f-jB}M!27v6>RMU>fMxPLeR)~Li=(mS^
z#%K{iypZT5tN{yq;ostG@v^!R7jluGux9rcOKLWyZYA@-83USf-m?OGg$>rDduY&t
znV>P!-<0gXFP`YUOH|qjd<10q+1mSKQuFQ<T-0f*1#sD$-3)fYaRJ?}Zw9Yu+#a4=
z6~~<~bk$Z^gW*5m*Iqq2QiytJi6j^#T#)E$$cKcis%*rmq0hui+wEv<dwz==iPRH+
zTYxwCnr$F>y2l}iRr8(G+4f+t?ko<k4cVd|I(5gK!I(wa$ohQN!qw({Jp1&-`I73>
z0%>~DTKyzTyng_7e`B<>PH+>R!Y>)cn-EB6gUoq`u7FrRG91SrhbF!#hzfe1v3o)_
z-;p#1>U=up#D*>Yu-~3~hYJa(WM|k#t%zQab@5Pb;C1dinc-H~&Cn~pE%i)C2_0B6
z&pY7Lvr)eQza+U<NHBDN3wdY%Rgjf?Z{G?N-8kVq`BN#{copkDa}Ie`zOL3&&umY@
zNO$Hzsr3hI%F+F?K3ny}kl|^{ZNSG(LA9y=5z<Miqb-xUED>c*`>XUW($mnpFI!^V
zNItMM%xR^sP>ERv8KaB*^Z3HEjKkV+(cQyRF)-0LGUrWg|AWR1QaiLk1~M+=(67Ne
zk`tONf~sunI|)is7)x_53FbFQNEOmlN*nJRSJAwU`6=kfQfv>Ta@ar_aZ$&aa-UIl
zO|4YufxCE1KFXOy{5koWJ3)h+&wX`mcUhOHAHy=E1v=D}(GI5q0Z*4Reh3&lIRC<3
zbNtg?t9aO(GHA#fS(+L<Gbp<lIsg4EX=7+^%Ajsx0`l7b#JLjv#ku}BK&-f<p~ruN
zyng=whrBZVYsf1RGe1AW|8*?+%*?{Y^q<F)vo#+DRa1gLSA=q9b7>G{z@H4#GE$!t
z$bX*MO6C0yXt6C&vtr1ljg2>|8<uXSDa%{WpHeIt6dDMV6m(#{t>u(zp7js1Gd+n3
zFdn6?1jjPb!q_MA8gCg}mu7*I=X%a~_WeNixIJ2T5^$1xV1DB!5&65^*4eD=V3C^6
zjap$Husz$zH1l4hv~b$K;E&JR>fuGEKaP3DG8gM&MoUU+Bp?Hs=5=0K8~R&HBCx~{
zgdarH^qIxmUgh=>OTu{=P+zBfuB}(4+t`<Z6~0^3d9p8fgd@LiwPL>ypnH(LIq2c?
zWY{%TEq2b^OY=}O`&MivvG?f6$t5|oi8a;l=Uh0wjJ*$@=umk0Ia=}H_~<spnww1J
zYtHCPhOD6*SNb#_hB(#z1+lyKB6L)f5Wg-jP<`BwWV&+IsL4en9-g@+&>e#a*Ebal
z_C1mR1ip9w$M)*q-5~fo=_(Aze{APzpPNO}?T;Ro%Uh=dWp>|#<-UY3`Ps%TWHeix
zg8tO6-mIVOyz>uGy3sv{oqdOeZ|18j-a)9QQ>6Zav-y@8P1AHP_|C#}Y7uRd$614)
zMZ<f_6Gz2bZEiNJT-o9)rPbNO*jm}o1E&s)i+iKt-I4wa4%UjNaf^`jaod@Bc}r&B
zl`6w+O)I*`wl<s>cv~Exrg3;GgjW7n(QQ1mh{RVA5_`Zk+v|w;e?))pA2WZayLHZd
z((lCigc+2TJ`<MSZ_9sUw;t>7LN6{R$ZgB5Ptks3r`OcleAAV<)x7klqQ02ucBE=K
zSv_^5{wcg3YO}C2o$KSjFxHpOR-QOwhYK2XC~S82Q~lr^U4m3aTOu0%6KcJa@841D
ze!%|))cQ)%e?_f-$o@sGzuZ5u1v_t=QE*K7yqFcp)Qodn_`KN9ERQ*Dbc7)7oQ$z$
zzuYr571yPk+d627Zc}V<A#iOtyWWh_Jrn9_Zfovz_$&jTM(OA$cu7(HVqfmBTGIb%
zIe$&%@29^O^bgDVyNv*{oqt%6`;mPf?(RNIGi54VD`s+f_yal=fmg?03-yM=8aM@!
z&^b#-G-%g#Q{1j?R-HTKJfX2zOe+%1)x#gz5F7+D_%Jwdn-eiHcIVLt=WBII0<6vx
zw@l}mZ|ppQsR7}3YPh!JsIfVgJ!9B?PlfI2Y|Vo%)3&x*U40=)M#6}ZtQ*Ovv{a)n
zuXL-dt~b~^3FS}c<fhv)mhd$2yze^D|084R8^iHS&*bASRNJRVYx($%*6s6Ry=fNN
z8QF0^KCN~0j~;+^{!#ehGR+dDeB_`#lNpqbuy$;oRlC}3mtC=Hbi>DDx7swJXN#LQ
zf2*_aT^FF;`76}Cg6;h8{bPIIU&fntH7f1p`uc@twJCaH?VnhC5Xb&6(7yQ}c>BQr
z*!~N-|BJfsmk!2V`@)gk(l<HdvM<XM0Uj<qF=j|0ZVuWfdO<nBolHtfgwK&#6~Eqs
zSf#DI`m{N0ziPdGzo&jSQa0Ma?iV``wtBktU(9%2_WdLABtLWYr;e_nsv`Xk@ITov
z9sj@EuXfx2g8eoX|5y8MlK*SJ9(bMmF^H?D2kbPaVJ;mW7fpk&=?JSN{1H_g3*P5V
zgHMH>E7f7W@}BfNW)K`7Jv7q1R;zZ3UVmF+FV$RRo58VDl-~`__CFd5)P(<R(|@#O
z^WXOS$8OM?A@i3lKZJc;OOBO8ON+a_;bzSNkHgI3m^qm3XjDo}KwfJ^-}YLN2gL@+
zk)Gtc2@)yKOL@39gy_#8KlCgqJJbbh+sgj1rduIrZr<0!*24@<=UZJbXJ5@AClQ+a
zmCiE!Is;OsRls!t2m89~_(v+67D*&D-vuO~oL;Baom;U$0bSF5s>d1$Kd6JSPJ8Ru
z*5xx^+%(zGJf=Ru|B;;ceu!gs_{#PR1B$?}5`trezwUy;V`RcJyniAh<aSU%pw`Q`
zVLC}YzO_>V<QF^b-jM05DQ{OZ)C%ahSh!EnRuURRXvbhJ)mCZ`F&^Go8v?M?-GE+O
zMLw%cukFMOjn*nW@9SPo)SVxhHq<=7e0le!YMi__I5Y$TwGSLkAoFQ#LaXdM=`FQQ
z+~0k6(h?ha)7aW~)UoCe_y7E=4XkhGKZil&cU_ng7+2<g*TZ#Ymbs&#`esG&eWUKk
zzZ>!9o74pG&x1wy6k6SWDX2u=($2YVI%%5@IuUVWRk@?$a0%N_X|bMzL;dX0%jz`A
zkI$a<u2afONnM|dQT<L~<yBM*LS;=V{BuI%tD)mDmuM53SCOvnbLw(e1pULw!%7v#
z{VPkY5a@84)_pf|_QnrYQ)$7W85<rqj8*ui@<R%sYUApY;w!molQKrhPIz|$ffzeJ
zeQG>!!GtyxfnGcCtiKhZ1U6Bexv<PSmCxo!8>8WtY_nWP3iDI#be;%)D{T*s@q6Ai
z<%hkB)LY=!0&~})&XtSloR0IyzK-j=(t!;tRY{knPMyM9QrDG7niVS!n%2E~PG<0#
zghm-{ffC6p#R!#&ri7TzNN!f)GjE<Z$Gu$_pEm*c=k^mV63_`=IybLhvSn4g{GK0*
z)&}CapWh!U2)oay2&NcU3hK$71jA?ZDQ%WYcg<`({8kpn3otC9m7A^_%iyB#9R@DS
zI-0*$;VW|clKk89-1UaUT0JlGFO(zaKb7NuQ-Vt3!VJo?hE7&QEPn;262t#MTYmnl
zErl(dofJ$RzuDQ?+u54hf@sYQ{}w?|Fm$wW;^qBU|L~8ia3W&)*DCTe6DZ>Ff0vO&
z%%FwwKkLZT%unv<3(Er&sV>n#6jEYDSV%WF%D&$c(lw1LIeOoz3g`+F8k<DJfj}Ne
z32+Bgn80Q)*E;S4p7`7SYd~LGqV@PksyTD&(i#_gr^iMfGBISx-{oBsT@w6n^MXtL
zKi0WlY3^Ang<wvQj)BN*uPgS~LoD)d&&pM(C@3hYMWUg{B1(~<v-w{iv2O|x*Z#<g
zPK&u4fGh_oQL}STfgWs%zh}?!PKsQjY_@(xiIvzrI1qRI84@yh85SAogHn}jQ+~+?
zNzcp6`}ONDlP@SJv0HaP%?R%<{7~j$Uq7Tqp;qGEPeA9}KlS8bJRb@$_X}s;L`Fej
z!&2Z+c9VY-?2-H_4f>KBWbD*$e<iS><o|5_zjS~}ny^IpVcK)UO-v=a#reCBo_YxD
z&<EeRXj*<}^bvFZ$jgPBDvX5$4m($kBq}n>C9y8bJDeihG#R)H>t>)}j#+V0C`5)m
zn2Mkm)^`>#9xyQc95^sta7uGXR{u(H!BBs6L6{g+KUAUcR>BI?C@OKV7XM1HVkn27
zsm3;6|E!|J0~Fv{uO7dI6>b^lcK~9*!!|x8U?4(3;tZZBK`&8*1tG^BB>2aLNL)~g
zz?arP{CMW}CkZKy;fDQg1Q-DjaT!pS81c_ve%kx-LGVp*0Zgh}Cyy4e3lWBt1E#?4
zzt@9D`YERjFcJwMha%xO12S$HN0ot@BKYGRh5<^AI>KkcF2NGO=S=Yk_U9i9RrTK)
zGKPEZI7Iz}7WGE)8;(X{;gyF8O6UzEgok)xJS6a;DX~qOh*G6Nx9#?`j0%gOiYU`P
zJ~c!dx__j|C{NDohWQsvNx%*`9M}M~{UVqum>i@Dkc6^V1Dw_?WD7);LL^SxvgTuA
zGCXqhAHE_0o(ZZCBV%S8O6dM5?flqiLO;RI!|;oZBYqbgbQB#n_D6#uzxYNexQJ)g
zBA7%<1q%gE-fK7btLcjLgFVChRHlc5@Z~u^IV1!pl5H_^ZZ)VCzED`f1we$~lLj?|
z90s=chqGnXH3l~@C*mrE37IPk*{e&Q?j8!BHxM0-oygbq%kk(Ib^mDr+>c7+3zT)M
znG}|=v*K~g{&^-PYmv!c9vs!YKkUiY^Oo`b1tL&ytE7uROK8S`8xw_JEAq2fSG|ua
zD@r^{%nr`ye#Iyr2iN_YBmHBapeLxaC4^20!2nwD-CcwWq80wKRj_d)kHLD8)p$$<
zK$;Z?*u2pYNjV6o2eew}jCA2nn*n5*C-*)kz*Ti~_c|elF^VTh-eD-SLMeHQxe=T3
ziAH`T6pMlF8MBiL5yUbL76X>O1mgn}D1O`%&xC=q`Xy`25p6zzEnEKy5B!O;=x9w@
zS-q5xgl3vVSNCK711V?Gd;!E^gvj^uf5_?3)oVebdI0<p7lduj1nXqTE&p--1$`_K
zk5Fk5XC~J>G2#_^oL$G!$%%AH@HT`)7lE}Yh8xO&(?}ck@K~-63>|z5HwFzfSB%3?
zszw^gGC%KgO6fm++9%@H6IzA367j!Mq*gd{IwEJ)-#hYUzpYBZE97*5nRF`SA@#Cy
z*QBNaa@8UB;_u=nE`ix6eG7zv!NKv?-_Px5F(71R@bM-fxDPJkU<>?f1LNcY6fgjK
z6U_y6TH!doDx+E$d**F~_qW@rh^9(ev17=bDCh>S<7XCt0(aOx6bp2khKbKlBOb<n
zD)cwyM0&v9NNVuJ=4QM#IZ0_99)=)Vq>B2FW7f&N`9usrj&<rY=h6f63TF%iaZ!uM
z%&+aQpB8Q1172GT3Gyq-wnN0zApJRPGRyR)1NI>qb<>==2KV9nYE6y>sjMVIVJP*Z
z;wJowIz&p@LbKd`rUkvAiz|udxC()6IjO!u)(l`){>~`75B>D)SC0{0vh16WI|x6S
zKC14&;p;-n;!{TDFLlTqC|GN0T?8kfTM#n7#iT_RJiVR^MNeHy>c2=j^EZg2D6;J5
z;S^Uguf0`Yg`kg8B~jF2js4he`l1#$ZKSlCPv*aXaosw<ml;>XG&d!KBx?f&W~nlZ
zSp)IooPRSAe1z>wtwcKL3!TP=?wx!Nu&%1IdB2THk`w}jTwv!2454ztKF!497K2ss
zB+&n&)%H#-;Ur~tF^)%Swz+~j(RW|uQ`haO;@b}@>Ie&4CYFPo`Ap^sC$(PXb<AdF
zVHOw5CE~f6ca2xd2VhM;PJ^K=G?H!vx94HZB>Y&34pyjz6^@5%Mv1`%kX?|pq88_+
zC?*a|%V31P%4_*#V#8If2Ggo3wn>17yWFDdSg7N42@hmS`PoP#Gr;?a!Xy?k!i>Qf
zZ23*|&?I-A!-T2-2GaElF_qCT*phMLIZXv%VvE`n)7V81e7KUhP`Ezv%ve0gkrFAT
zM_D3)_xbvVrJykXd>>UnZbg^k_fLpX;Y$Oi!U6`6>=byzv9(LMC+cUZ8kvme0Jy3Q
z1ABn%;9?w@BGxre8E~mw^*vHu2umcQKHD5%&*Q?WK@J@NQ$X=Gi_9U7idFMokQ3_j
z0HaUmta{8fx<aPNa66biGI@}-eD4{IiaOW|E_=fjIAnrIm4UDsao>Kc-moK7>?)$d
zJt@o0mqrpzb~2xkY6`%k4^iiY9}h1c^e{{DD;=1LOdSCSss`v?AE9CsoNyS*(9y7H
z7{DD{{$k)7&9CTJAq7s9@3sKBlg^XhFyx^GZlf*!8~E421r)TE6mkD+Rt0@Aa?mJ_
z1)?uw3JCk^gWC5-(1fm-FJw2}pB`U9%^x4;5S&t5X#pNbcsls7FJuYg+KWhQ{Bft@
z<%bcSa{aC!AYYgkI;RyzbS72t81y4#kz$<@6s$$B?_6$p9sM7DDAT%ce+PIozb79i
zwmG68NBI9lkw_ogRPq2*^@RaT_P}@Ifw1KfJOB(s2@?Rw72^d>$Q7)G*>C~H;LVV5
z0}y>sgC~Ot252<BBC>HQ*6NfV(4hy6J-(I?fISBVe%KmqBM3A>Ks}d)prpxmNV)Je
z!k6$->Q4%gixpI5E~B4(k>WT-BG2NK{TC3gmx-U@a(I8D^Z1_=vojtIGyE{XbDj8Y
zK(^aJkweDJCXTBe`z3tNe+5*8z}VxZc~52<a(fXM{&B9AFDVjs1_nyh@qld-4};s2
zrgRBNz*12G$YT{zLcbR(n~cf((?ePN<+MZe#T%|gFq1iiz)Q)<At!ty9ugO<fgWmN
zwzHk%G6P3!YvPAlItMkFSZ@9^W~ml@y`=vhT!k-mhMJ&9p)H>k35hXwN|NB~S7g~w
zrFAmexz{lF1F2I8xss#sX9)<qjbA}mi%#P5srXquhZjDFF6R_L?!T(A|I+|W5|3nO
z2etP7yty*U*uF#~9Q`B}HfxE0(i0kVtpINjM!rh)S{2OV$E6KrR+mbyaz1z5AX(@T
zLeOfW%eQWJaBFo-+bPo-e|yxJkI&>c#7%Ud1NptrzN<W8e-o4A$!P&B*~(n7Q~T$`
z$4c&?VyPTqN$_G(x-#~byQ2xBOz#WR8$8#1BVbYG0o_yMFLBf=?ckL2fMFD^O6b)n
zK@W7+ASM$L5x<?dTCnY=AK#f3tiQ393C)40mGH!Yujv~22+Hpe!BD27k+Zoe?1_(3
zQ<f+B(?ezmnl00G5ydfsOf6XfOk~F2fVQ}=S*Irg&$tC*4F3;;P@2de#P6$p$)JNn
ziA#(v$*k1CDFc!~$_Js?j`;*16@|^_L<vS@23yDI1U31xR&jJ1JxznsH@b!!mNHcD
zXWTNa3RiaOK6IyJ%Na2!d@=47mq5&nr4yFYA=2$2SQAu34~S&F5=<W|CzMQPwo*q{
zK;;XLYZy?NJCHGqd-G?Nbcrt@uH7Y$P2F~cLXHx>b`h)rB*GieLbQS{>KXld10C@e
z0F=#3GhN&<oBx$aAJ``GhfoSx(&W530VJ=;GW;!l0S&;^1_7%F;w=pYAl4;x+cgo$
z3$hfDRT!96DP;mcym`$#3yMuqR4G(8ES*~G-_}e4BgX~Bo=h=S(9Zlh7z(@%u|i9i
z3UgJHZ;LGV`7ig2!s`<qQu+E)$Db9RRz}@zh7JTlj$!~J30e{}*DP2%im>_>u9zXW
zwBWG@taKz~17T7X?2xD_l7Mdb;QFd1n_`-WoNcJs8!E>NYz1iRsXG|4_LamktGstF
zdjVn5*<?SvQZrNv3NZYm#lY*hz_Uns!eG_-1GTIgRzb-HHZjt8-A_$Bm0wUnVmdGc
zMi>#j9bh8|=wcK+U{t;>8=tICN;T3Y#N2b@yQNT+j*#k6ti6YsL4fgEO7c^(=IDJw
zlJp?S{pboag)o(lF<>oy{;b*tvG_wG`Oc1R*xdL^vggJ$Tk=sK0}Csj)*4c3xG85C
z35*B*236(b@iUjO$Ske>5W<gnubf@u99d|InMce$<9=fZl8+&?a_y!E!m*)WRUqZz
zf%L&g&XIR)2UbN<uK`FXk}!89$u-Z77#npSx`0!`0!x`p!Q6|dcWvjBpv6MFY<^%$
z*scH6U4nd_W5Bo@<KZ6?l64Zw^KI+V&q|Sf^MVO+{^KQo;O#p@uKN8zs9sv!@J#Ww
zGUz>^;?F96k)Xm7H+uiVVx<=0N+qDC=B8uwi!17TQ*?HdR?&*{O=FbUx1X<b+-GTJ
zOxBoejk1jCx$mt6G#Hl@O$f$y8l8F(waFm|&Y=m{9#*+VyydiFO^E%S9z^_oLSI8~
z?fV8)r3z98@+6jQ)l}F#wb@<Ujg5>X<k~)yvxOEr^KL#0k3^1!g-U>4)f}UE?qj>o
z2$RB%rEIFTyct_Tk{Tvi)ul#n;9z#LoWmLA_b&f^<mUv38K_3`J8)oxx~du@oZG8q
zMY8uxxy@?Efy>Ev1`Gy%hmJD&aKkPc=<?fbC5lv)!M&KcrTo=PEF&W9)EN`1p<C)~
z*c^;aBxr>}cxSeYM-mkc-MSmK?Sk9j4%c}u4Ar5hAji9pvWP0BQd&swQD&6j9Z;cT
zG`V+Hq5_Ef)<TOJJl5@H`<B($k4gS6TM;!+lD0H6T9{{%sHUB#q)wyyxKOH6n$`+|
zTazRFo7e3D&?tYU9Mt3A^qj6YGRU2ga%@f9XNZe>$N`kjuh4kB3%%+2244N?dG-oN
zL{<(mK{g(zKI2F=YFCgILWRLd%TC7NY^PsUQPo9H^{#!p7R~K^-8+SJvzjKc^jZ3o
z>Re?~tv!c1RrbdG+9yJwh6H{-Dq?uSg@T21YaGreG4}Pa<NFp2)l^fArrMj!xo?ss
z?s+LF1DCdn7981_^CVl>PU?PN=9|=|FK`m68>-3vOv(KrR-*v9;&mGu{24Tf^!`!W
zp)+{Su1OtAO$E>tL;49Z>h|1sdY+P+Z;}Mz<lBziUS5IRJkQHZk4q~?JX0FPy|k_g
zl9%`SUEz^qqt~C|$3~~af=&0_!lmBZ9OEnNC<PYXRyVrTs5kZ7l56Y*qZfoBZfMHv
zO!jZ@9{S5EMAQ7^B#?5--?KD#G#<YiG?n81p8G8+UX<H-Km1mn|N3(Tl-JxE{5iQY
z&pq1b;BH?wh-tfM!1|VimVv@<gtETe%*~Mjf~P-(NmpZ}s%d0sX?WAnU}fvLpRP_T
zC!dn#mS0O{r1E*UU(TH%)omOvs*Uc%ZOaNu&VETK=lhI~>u<oTkSV+j`MsefR4iYt
zpu_&6Ybw9q>49L=uZ31ECZ4ZwwA$bU0H7O#!1hMu85{!GA*h+#nyL!h3VGXhot?I|
z4IfWLX8<-M3KWRcOMI*=csx9GTCzjeo-EE*Vk~9<3cG^*N-$L)04Hd=Nvg?pXn$Kr
z#bUM6@iiZRAEna_qo!Fk+HP+=yh9F9H%zwKFF>iA=o9|M@%r`8M`^=y{%dYZ`KtC(
z%T#I$Apz%)sqe&_b(-Z0Si#8V1G-J9Oy5ofWIzFoTcd>DQmtP2);RCa#L)JyX$qSS
ztUiqVrpmWxFXVeTnR0&&ftp&h*wZ|teI-k-61@z+<}2mgA&9_><dyWc5}{Mo2~GM<
z4<a|3wR`ur11$_qLj^X867fRhyP!dJv%5k>Ib@!NDio<y?xHh7Pq-4-T<B5OsK4pU
zOP73j92U;VP8GNP#G4Rt2&SnQhkl4El1YC&;3=sThm)@FJBOAlQQPs3c1^ZrWW}cF
zFWx0LydyT2eCpfj{?>80fS<m#=!E3(6&9qwZu7Y$)pJU#06btJ>rfJK?wK;Ye;z$L
z?uo2?R8Hq2WM1tk7#<$HoZ%fEFKcT^TAr*WpI+B}*WG*QdjqDg>nx7Ur_V{j8vA>V
zy;vrP-X)ZlYYpF^pr4F+&{uX$B9H;(Q+`64Z!9?@zxTAvrjU+`3h`7<H7`PB7wvb{
z<U}!M-n3~wo3w)$y1^yCc9atZKDKY0&fHf-+0^j)o}c^3jG_&>T@?_H>vgE>gst6V
zHla1i`r^OrleK^Z10Q(<lHeIVkvqbWI$+9r@QQ89p2V4&?X9RTnPr6L6W%@2x0idn
zJ*#_J)?cyhBD{^VFzeOEzE|r#q(dcEm3;<vYf40a!0^9p20w5I>cNpSkbw{HTgkmc
z5)oc{1`J30+m~8EF|+fyZB5m=g-e4hZ{uYCcLh)ym%}{H?b&`58v1FgqPTV6Ug`M#
zF+p@-!q4qEPUcS?O6adoO-9NphN_Y6Dl*ey{<U0+N<O&7a^J3iF&m$*=!FJZ8j$%Z
zskD91kB{n|y?Lo=?YI21tf$9w{FWqpXO@HIBdz0g6r73p_Y}4>d4l@*)GBdtPCK9W
z&yMg(KsTDKL_~0jmoilK4I;-HxrxWXOo*LMDrt7mkKfNlUxzt+#G}Z{E7d<*NRrY*
zLI?%||Llsr(=8d!jqtQBv-#PQ^^>8D+WB4Z(zw!4QpzK!D;bo=)sy6~>X*=i$<iYG
zfeq_HZ!Zb*&N%=4C7V(eon56^0*N<%l^QSU_0yrxwC4HoWs|P1+-RTz^7THg%_BNf
zL#417e}?;ugkXhOk9pmYKOwl3Q$6J#NY8zO84v~uah^1Z;!DzpyBen#)D-$j<>iL{
z5V8bT{1$CZ+QYfMwMu-s{M9v7G<6kH!aBo&MRwvN@`47-v1NxISQ=Q$vGBMw5o<Z&
zp(2@NqOI>}uyb$Q({A^8k_=RE4vvew7U&N3vdZ3wUK$c*zq{U>9IxNQTJHuDaTj;t
z9b9QEV~sg@sY%N!fN)2(a)K5F`@3Rbq9L^q4dQ^#PO9=Ndm?_*t*bh3#G<Os34gVY
zz__pU*1U}lF8$h`4o9VB9MNP2cdsr!6G?gl%ICgIRWFYr3UMN!5ho+ARXB$iF>uHK
znsIth_#G?{xDp<fxsn}&x@+?BQcRrxAUPA)RB;#G^E`1j?K{?R=;JVi9@yk`&Ewvh
zOk>G1G7K`OcsBxo=?r9n5f7LaWyavF;<RXgg?g*L(*1cBDaZ@gyv%n5;r=#3OH5Df
z;|TDaIL2!^_Z+R%4i%4|(V;c@3}VR%N`p&*4}X=A3=}-`{d4kp$#(RE7Mc_a%FAYl
z<Gh=F@cD{#HANK5ge)Mf9?vvx;>FbuXuNW_U<?AC&k)ydzcyfJk|breGgH$t_AbSe
zp#V(LV($rZZU7H-ttOD@6F*<|`aesUY4+uhd6g!Xx&d7d50B&=`>jn#F~=_EOdWyq
zDRF+f(Q7_DC;;UBZIHVWTyoBD9X#a%jLupr7?dmQs;afum`1A=olJJqMWnLF4z8Lo
zp!jj*PvkfLmX_CVeP{HVOZ*P^1B_?KLX`Z%*+#zq2u&fa@uZ>ya*>Y8ma=-;q?{3b
zVQ^!yt*BJ3!D8A95Oq(cJgV`I=!*L_qRpORCzvJK{ER!$#|cCqt@09&W1fIE{dUr&
zjP+G))V_6Kg7@x3zFq2;R%)6}5Faiu9z{v`dj|RQFh+wi`^8X6@wHR6jfI^wbKhwY
z;o^BmR%Y9(+nSV)2UBTv?K5x&YvUOC{VkQIj=SNmw&81zcdGfLg}1j@0PWUJnS>8M
z7+nsnf+AV<G611tH$bZ>z2(Nh%ww<qek(~BV1Z=Azg?}dLn;gn^<2T}=Q1+Z{0-KP
zm6fki7FiPe9*+4+a+l9S7?qYiO0+~0(*INQ89*0&G3yu7AGad<_3<HnCP#x0wGJ|#
zwBNLluR#!=9hjJpPg^r?pTj}(grC`(dOW><<B$%&+~;=D%bQc=PmGIxx)5O?lVvvW
zQeHLi=LoR!^S*9Oafgi#&c=+x7}NX_bq_3xd^_<xt-`9(A6fi!U@xqT2TQ46PqNcu
zu6JLwy1FXy`_=CP*M@o1RjgG<kPEvx+e6BCN_}Ab7dOu}CoM&4f(i6hFGvbBg47Ub
z;Eoy_FDq5uS66ddDsLMu{na-NOPOs)z~5uwea-T5`{VM`UWw#`q7`|vU=%W;Z|%4B
zC*&`-ySZ9rZ<l`@;C^fh<OOSl2a?f*9Pe2eH<(_ZtQeyuWw!9T&zT88r|-Vz!d=KD
zvr%O4h@KqjhsAyLPg~#nx_z!YX#6o9<Zd^}Q`L&&)0sayv98v=6X#9z?3dzi@9jSL
z8yn60tUQm!Nqc!|HoHo#Gz}=aB-lC-qyBM`XYZYm60JvG?u~Ioz$_UoB#G@@^1|F=
z<fBsF6sam1i5C+=>Y1iPN5?LYbGLA(O*5{K-(hxAe{Z9v)7<EJTFr=cLl|*cu6AMW
zcT+<6ol{cFPES@s9V`dzXc$cCTT*7%Iq)mBSjk)^9^2xWQomu1gcvfPDs5QWI&oVC
zaKy_i+Slv)XJ<fWRRw}Wdni18E3yJ&jwOF-hF!H6=E7jpdc?`r&XK9HD5d1B&L<w8
zQ$Htf4#J82*KQ;vyaBzzZL|ZB8U#Hi!|!`yfh9qp`$6{A!Mje(sprjcueEp~q27F!
zKtyg2F#&(Ss6u+LmtO$s!QhIeRtowfHCH_+=l0MDC@EV|yoF^E0j|u_wf8GL%;jBO
z6RB})sGbgT_t2SEa0_R9-9d<&I8B3=H*;Be_neZM?-?3oLgBt=GGhSNoP+<FVak2R
zSq-dKTGm248vO>@Q>b1S!Ul)W>dQ;CkLRO`l5baRe*KV5&pxP^>)4U_z!KwtAqYyT
z+;c04V}p;t;7hgcSlDRlkWw7U-&)q9!Y)MNGPSx)Hl{#Ahq{9t-c2|upg>YmIhjIj
z(h*_vOD@`8#p&v5Sn1E>v>wTl{mj}h997k)4Tga3)$5Fi?Pucx;fk55{M|S0iG*BC
zghQ@Q^#fpE!D2maJk7jL_;^V%StUm-zl;>yx{wsOhc1FsPXMf2Al`V1rt)Z^9MyxN
zq>!9uy4<KU0d7)ub9xGkPZwB&6wr6+YP)UNLt5<kd%eTcQ0eEs(TU&?)1P+&1j_T4
zYUh~{#IH_Q#F)W%`#*m?0xe*dl%#*>m9bGJGEiptgT{TJ#vnhpy?1Y`&0GU@kk*ZD
z6Cx<|<Ov|p1-U2$&L0XBPU&U!%R5vLo-ZES;pzAOeJhGb3v%Rlc=OyGdApa5#V=GI
zT{e8-aMU+-QVlq3r@v`%`CAO8(|+wRY&@53xl2qQFw?|}RqS20B$_&MFaUeB^K|%Z
zu&UCf6laewqTHW&wl*lpZ(EK}E{qvaCRRO=J3UuCnzvcB(%1u1pJ6CvC+-UC6qn>D
z_rDl;o!prvUzn?vses(H<WtZO*@|``W6|mD`=V_?Ph2h`-m)VdX~pJX<H=nz88d+W
z%UYZDit}}iQ*FIN%oQXaCKLgj(#Hx*w5>}4xban|wyY_a6Eaq!@BJS-?}q#wN0am*
zI&XYO7U|mQ@Kv8PKJPOu84VT#86NO4bIRF1hlSZ1I~xH;5ZmP<_*U2D?rS5BSK}>7
z4w)tGMbN-06~*@!l#4r`rp0od*V2V!XUmGpl6++437D;(`d88wJ01S4>bs`8$Nb2E
z`i<g{d3+!3dgp4F`pqk@k8ApC9P+Qm^X=Uc^{+1_Pm8pX?T%P>sHvctoeEaR;iGQ1
z9hbt*Y`k~gd>1{*$sd?nw1ykyvH4t1)}Nr|-Ip~&)89`4kO^NuNh#{;B7O4sb*SsW
zL%}dO<U5eSBunmbT{mI|uQ9$~ZMjEn{ROJtHTrakI=3u4y-9b+brmi2>HW8@`wLJE
z@^^Q}b%J(kQ{@`a<!-$OpX1W(!l*>e@|=9{5vmn^Hz891kHcGv0*<D3`)Q*V`I1BC
z3zy4D2hWq}9Ce{wiAv@7);c8=g|vifs$MZ?Yz5s<8EP@Lvm2SS+`5jwQ-L80GxF%C
zL>sM+7hAfgI^q_yTJiz|3x#g5Cx-zJW;qX7fryoA?k7yF@ZVT&^oaZS&dMH8^H}D6
z{j6_>XHsUdOFjiqFe6|+Qa5sEuG&agBwat93`GAyd<1$$jh@cBjy0qRh#?(KvSRUl
z&0!@EKoy1z5=;&rE)pE+1e1>**<7Qd>@Mfca$MNxa+Qebgw>8M&>ve&@#%yibUQ_3
zJRY;93($>B7boaIbjM{XsM9Kt>`3&uGnw8)3ti;P=Cbz~;QzseP41CepiS>5tT<bz
zo?!X4T!$mdaL<o^<8bo`^K@0E=!HComG1)gI%kgk&-{fa8%Ts*tq+WA^>a)esUz98
zrhR!MI+ppIZxF0kQRqFn(Kk~+AFzAPX?WItb$@RjV5cwL>CSpST@$Dk)LOYP6$&t3
zd4C<sa$S4VPhW4~O8yR;-DLY}KRRrMRGZ6O3w-h1PIit@C(87n?hRbCQTf#3nSIAs
zS1#OtP=U>}${J7n-v1_$j8{m6$KJAYNtJ1HD(mS^p57C7xQuE?U9i6&G<y?lN4V&0
zx}l$ZnnBBEX$Dy@S2i+1?_@D0gY}Ma<8Flwblhn38F+5G=JGgLze=^^)MhPc5Ce5r
z8Cvn2G2v2ae@A~jKAgk?Hb>l=LDm_ft^PMVGrOe0K>aGu9)@}<x{}@<C+-KhZfy(0
zxgU}0Q6MVmQSwX)>=HkKe#KV+=zSp58DrKeoRBi`<}_M;h3lFn3lzvRUMUon6#T`Q
zMnIXF!aW?;QtTS7ccDzHPRX{6V%3<Ya%z$GeqGVYpISRUEcbk}&LtZj7d^bJ$8GcO
zABn}E^(c2SyP~P>$76h9t*NURj@*i$n01rWjE!jn2k={aL{Z}R^RgD!GgVd76fMya
zq6IzjqnL0J6G3C9|ISjsr8lINGNgg#nF;0jH5er@F0L<A=MeK&|7K&Y$bPrltN4Wj
zco0IBDSz70;`V0xig<bJR!*&M@ST3;-j#)R9Z_j0t){2nc9G5Jgug{>$J`ERIm&3H
zh}tgvi<7xIpO#3v^G=hTEsPPX2qd)9B-$FZ&g|fjto1xvl{<49iuhR}q}W3kb`jH8
zE#8iz*I!)om44Gx70(v49SyZKIL@83Va-v?)=_WryRvt-^K+K9DGeX^Oy;41hIYNN
zJ2T7_@#B-?dw+rwkr@yky!#+ku1z4l_I)u5m$cCNdH0}(mtf$PIjP6=!H3RhZtB6@
zkE@=t>UwR#SNr;W{%$0yh~j{)iJ9PN>-lVx9~2|1L5lil*H#hMvCB;!L$<S40N=yT
z4~2StM?MZREpJ`-fksnemKyQHS>HH}?Qpr%>mGxSvQ?YGf@i&m3bsbHntZ%pg9^5N
zecGKm<Dj+f#V}=id>w-;0XZbp@cr71=ShQ)+1%|r#TpP($ClhWi<P>2EyzFaQ1lZO
zict$VWnkk0U~&OQ6P^??+!RC#O%a6N-2ORyA+Wf0Zv1gGt~r+P)u=(8@-|nU)uUDQ
zYP7*o#}VvyB^xz%y2|i<J#HaQUU9(J4HP+9f6N~`>jr7j&=c9)T8645!3X48ygMlb
z`t-TsE?cD)#ys(dlhky}z2OOUIph%cG3x9$;Z@Lp%L6j8-b46Ixr1YiVB|P;nmHF;
zhOy2R<=F{RD2>lS<`?`5#cb}%mNFdMy}7KC3Z>)IVVN;|;CJj{u_Hz$F8$f8l%U>u
zo3M&Whc9C@8`;HswNU8N)ws)`f2#ekQ`4IQEfHKwjV^GOGGvt6iDFEO`F6ehCluGs
zulmDX(M2{UzNaa`i-S*rU4!*e^x?4c!@2f-G+y8DST)yA##K%l^1DJEssqmvQUWjg
zi{o?8Q2K*)#&;PvyO#^|ic>PUkBrp}r`7c($E#(k;3koZmn+WXiF1Kc8>bwT_HLaF
z{-c$%lbwTBcMp(0V_UNCaQ5XL1`a4Zs;I|aUKRAg8s7h8Qn=3~Ae0OJ;<b_4&KvP%
zDr`C}Fdh3%-_Gj5WNGv!Be(c>Ja^?PkGL5XclFURXx7|#V?xl~CzDP8@Z{fFhJ3iY
zi5ecP#Z0r+#=QF}Q~vQ#GV68xWX5kX+*wPhi8xx`-s%i<_g$@7W4QW+ul-vyim~Uu
zAT2$Ys~jfT%*o!?Nwv|#>7+zeqW<a21P3$&zI<_F*;wbFZ_V}w45|JfSHI)6jkga7
zISqf#C;FaHW}|FD#kIDy#8T`!IA}x6A+&mO?TQKNnft7E%?Z2Ga><rVRcq23(^)va
zS$_^5xkt4BioH%0D${H~hr5`x+P<HwA5x}OfD+OihLfm4&A6kX4f`n^>OQUgWHNQu
zIqauYh1wscijH~)gyDW>4BRg`1O##f(F16yGdNVyV1066PlEC}^$Alj4NqGFWdn#O
z-y>R7_SlB&12c%xmc5?2V)6V`XffIDdfv0%<!JcVZpH^UmYuI<BYgeoWSwv-Zu31G
zZgFsS{m7_X9%gF_#Uw9lT@s?%F1|#Rj!os7l!rel@Jj1fx~<*P`MiRFHvO9IMEgo<
zYI!G<JTbfX%5ydbgSBUNq&T{qI0uCLw(FRt?ffSzV^U<VyO$-~$7ys^4zd^tA0m<(
zMObbiUH)g%4mRhLg8sq=zPEKMMwvF(vd0pR`O)$1u1?#8uY!o<OZM*Cu*cK5=Dh3l
ziMjH5(QYD!I@W$ds)M&F_<Q#|-*lK%2e+&1&=4Gw^hi1sCGO?Jm(RHGKD7-8C4^te
z-}g>9G(fcqlnj%5PBBKo{I);z*9g=wn`*DGsmmfz2Zousf=67P8@?{R!7p)XdN*y!
z#s+>IB{XEq>OS0^j_t1@uGbZ)Q^-HyA~`k6;OrUN2>TFcsfs_5sBOh(TX24h820Ov
z^0mw7g>h~Hqq=hrSHK#GcRRm(KdVZ4s)N_3$uCyqZqW`F@r-Tr1*pk=e19#S&Mj>T
z|D~9~<yO<{I#=Y=im2Ic>L)B^#c_a>C^gCwdZlFolUEXE(X$0%ONrk8yND;Sg>%lb
z-U-;qF^pT0t72<NPV)3h;^^3<S!>AsYjF7@8e>njSFfY2STWVg1T?P#MI!<zsBu)}
zr0)54ub!Hl1af!SRce49sXdFp?}4|>ro>R>>pW8A!j&jE*l>=+adxKj3o595>gVhC
z8<pAARchnemj}6;xEybe!x5xBK&Sjf33n>KJU<$Jc2iUrH_(&Qsn5eFXB2AuQ0jw|
zoriXtihS?&f*0(mRIXpqnQCQD&JDG-@o^EpQ?HhLuVc$47^aRC94D|&o@ov;<lgIT
z=lDu+(%hb{(z3>h3gA-8_Z?o~l#u=iEoZk^H?(2L+iTm-me2JzFP3b-vdmYymWJnt
zx`)+-ub=w%2#@dI;}lbmMAOx>=8GFzkfcZ13PIVGRa(<d-3ZXU5cM}|;GoE^fObLx
z%%vy98U)egLMsWM51*>m+CV$dQIi&8V7y;QtBLYwlF6-f$%y|ZiZVC;I_llkE?ah6
zjaUKeL?YQDI$N0&5ER!Onb7S^7q;2`j;fHxPQyf^$X3c1#Omo$;(C9*si0yjiZQZj
zQ!;;FdkWpx>D!RKdp&X-`S9`t)nby55s^6;FmRA!#R`g?8Rs!~mOk&6b3`ab%7LOy
zy^)X!d@)rb1;!|AkN5=S-g??d$siM|XvZ|(JlA2rXBNrZY|QtXln(C!y$i*UN#uOK
zX_Fbnu3px*UfiaxgA+eSOOwxH39SS!HPc8*3<vWIOcybRzH7gL-*&2Jfd-g+4YC7f
zcpl{LdBtB3S?`Es$@)Taw)0Myi1J|`A+py;KFO*{Lt}Hr&J76_9ga@R^FA~TVkn>t
zbk7R`EEvL(H_Y9(4=7pl`*<Jnw!Vhhr0w-vs%D;ThCJIsu+GLY6Dw`5)kwJb4&GXM
z;^&)Fwb01nP?X<S9-mP87uF@>QIQqcQ50(V08RKxr#V*d!po!mO{cxN<Lh*CMQa?@
zR>l`bdxw?{7JVhgJeBDZN?4}JtC8&!?jGE0C6QRWiG^~P99ZzP#V+IL?3_$v4X;RW
zwtdERCg5Gom&l;RiaBngF<2(FWe0RA+%r}9d(a%4a^q=x!uw>*mF3J*@|a=6*}nPb
z&<Nyb>&-a%XP+0_q6xKet(TpI4P&|fog176F>~;6I6l=%J=cK#27#Ivv+XhHqWk_7
z)2(FKM+gWl0;x9T0u}NDEL2dQz3#CcVEU;UKSzSWYUI}Xh<wW-9xm*2Ss4O$MRgRv
zR-~Nm>6ANpOTj^t{!{TmZm{IiVwlQ&aa09YzyzVD&-3a0h`4%o>LybeYv(F!c>m)P
zpQuvTwY=-c_0#(6sZt&7yYVt4&1@PdGDT41s_p6#<UkF82}<)9g>8{wL>*ip)g?Ar
z;&{HeSv&=0rI31yZf`h16?4RAZA72s&mu8lTKIp^i%G5=qBRNNKH{@e;PYA+(P1yR
zSt3dEXs*BgoJpt8TJm-{>P^()AK)nuN(>0CR41-Mmh&!p@ay2`yt(Px=f+U3jt}T;
zKLf?D34`zISd?VRYbevh9{6y;a8$iT)JJCqE}}ofsT?`KY{%1BCPIZLNOU8G-g5hK
z`ko;TL?l+L7R;r@eLJ|_NO@mLi4HcF+K!jgLyX6Y>^D!!)c-QiLP>sno@95A_xdI>
zYpCpO2OVK+bz$wXrxN#s^VweHyC5j_n3KonTY?5^cqdImAV3c(ClY!T4?A+hj(%{6
zc(m_oAzbmBx()lT=SD!k*fe+R$B9j3Vs)li{mvz)HTxZCf~rRui3NXA=>3PRS(lk^
z-E_?Jwt9+T$PuXUF&s{I<}0)Q>4&B&mUSBOs^gu5F_m9r1!s}c?8AW_ps0CpVs|Lm
zoUpGt%-yw8<}7=;R>Ar1p6y`PK`h^E#aX^N()xQ58~+_$mOs|w(8YAeN%#P*pCy<E
z?L72}+5MM=lGbvl^wh7zv9Vit^gN_=8#+aIE-_M<;jbVMXC5q{DJKo86?!&zSb=Cn
zinGgYcnYz7r9K0tvF=eF^h_Iw+i|TC?!5R&_LY{s@_eJ)y%#8tmRr^B9n>u5GgpV>
zilTkG%QNoR+$9u)KNzQm#!S?5D<J~Huc`aT&P^-(I-|2E*0$sBcEn}u>;m^-U@6n1
zL_>ojrEES^9tG{5;J!U@(ar~k#8_c#;@YjYreqm%UU=zvAttM)XjhiAjpNMQctY>I
zUA92bTAO~{V)Lk{v-|vfsNzk+OEd$a-Y*{!rGlPE-!+`h{%j0nUv(&d4vVa9g$J0%
z#~D!89ex?X|2%NGb1)DTEa9=5i1oL|z^a;4QzINYBD9=3nWq)IXlkBjwr6N%YoX-?
z@zwJ+oLJfp52&i9;Cs-|C%YEGb}i*$XjUUv6t5-8N-gUl6l8&kut9`Xr6%{np_97m
zQ=*o~(ph_9L~ONZ;P$>AKscepA8YjeAi%%Y0y`0$(iiS<I<>+1mWU~uqt3}BQj4!a
z7x!Dg)oVtUlJwLt!-Fm-s=A(%&7rw}u9}V}-ZJUsgWCdyUWc-thQpoUEzg<2Si5?R
zTc^QE)63WT6c^{a)El14`;5tgnM>EN+PvM%UdiYxQJ!^WEpqWw<2~fWO`FbI&zV1s
z{`x2>P67qj;$xmxBS0>$-<8s-50AD)a&~9w{80i91Rp3`b6F7$y(T;O<nUhkExihx
zUy<(2Bpk`_bv`;i!EsN1=KaxW9m@N9Ea!pZWpu6m6q*0s<z5R;`)G78Ubf{sWc}6T
z(cz|R7C6cT?TBw+Ivjn{2K*sgmAE*>Mw&CHou?Q%@`SJ|+>d*H<Xh-A+sKy}9d<9h
zrYKN0dHrSKE&%+E|9EbR@#>hL>~?nj5E#l8&@YNZZdjwsbsW(3dkx}Tr&B#fkuok+
zg!#7u^`Dn#1mFQl$z;NaSlRr!d$v7k&-l>v`qK34XTRoj7Dys)o+e#5b<nRrki5i+
zsp^XY#p9<2CXnE*pB5q)L4^W>;$8uLu)>_?TbHTd)XMc;jm{T(0N~!l3zw27Gs|dp
zvh_L=bfV|{_aGVw(2@Bny5}NQzYFA(>CSBOm}`};!=WG_x3OBTgq9}jRW@2qr;-XO
zCU`yHYpMi1LJ##>g5Y{D7-^!YqDgJ71|6+URz7!sYHyWUZljo~-m%o>wGo%SXnl)z
za!@>q<<lP1WzE>V8yOZDed~^GhIJZij8_N*-c&C35}5~67YG%>xVwbc{_wez@uvO^
zOL#~AZa}0TT#5-GgJ!270><*ix)M~jYkPh;JaLcrG@e&h=w`yBRWPvt#WQ8;M=L@O
z=i^MSX$HrKgi92}Q~CpCJU!4BS4x~)`vN?hs@cD<f#2yMvcF0fn@fq2!%6W&7XK}5
zMT0vMg7b8nnmUXg`J3Itq1DqNFn^CWX0<#k;R98hapTKgKksEI)1a&u^_!NA)o-l~
zBprQA(z8DFh@<2anD7fvFyIp`fUC<ztUFB6(=z}SlW_2eQ8p~USHN=@D8LB}aI9b8
zx_sRmh;|((>>Son$DYNaq{CLAPXU!z^zf?J-qo6p{$wdRf_Pul&`_O$)VVl#USqfx
zrRCt@Q0mZU+9N0YJsrCU5T}Kfx)#dY*sh2|dD&6!;Pcq3f+bx-C*Q&?o*BC2;1<=F
zXm@0$VqAro>W%h*m845`eVVd!1I0qpOd&;eD(hslbVwhnYgH@|%-K%L=3Z)WM$Ns6
zczH7tF4RrPDPXRk!S@seHA$vaB-k7)BK><(H_C26K_$<(Mqds4GKOatj+pdsiiF-D
z&*j84mZg>eNUg}IJ`ic{+NhF)MK>=vIzZ$r>jJ0eiWZiNDq$K^%~13D1SK$E12-gb
zyY+~w1s^vv?ylR{EkC$Stn6fNI_G3~nOm#vW9@I?I^4|m)q1%V-!FsI<Beo`4ndEW
zuAF&9G2+g#CdNns=dp0&g5Z|y`Z87A=ZwPZ)0^@Kac(~}`3eFo1w@~x79XsV(ct}e
zXK24E|EvIYn{^!g=uk=0-duRrv@)M6e>be?*QXgi*KtgC#0p8h&X8g0R7+rk0Nx2d
z+32vEQIbPFDlDifs0P>dS1Tv|;andhhpE7Vn=i`8^AJmZ=&HZio{S0*OJ!6<5YAdo
zs5F)k3CU{2t+hel^?vwNH`p2GjmxvROYet|*e@E(gt8<&$>yh=gX;Nf%K~h#gvi`5
zZiSA!AtGtqnwI`4vEBp#11^o5bWg>Ekk~$}kAjj?;fKfFf*>cXvNB;)3eM90c6)^M
z1L6JE&HSOW4uqTrm$f5>Xrv`5;%eD&KfecD>_4652r+rC6I${T!DYEWC_fS8;^*z%
z=o&ZRAdfdn3Hm>by<?DMUG()??6Rx6Y}>YN+f`k*ZChQoZQHihW!s*7CSv0K$ILqu
z^EE$Y-poAb+<n*jt-XT_fj7}9<Uxz(MrV!PK0fll+^t=GRpCT2JEj07wDHi=Oj<12
z8kgq#ulD#Y-_#3Tr9mUb<rHY<*uoSE{})onppgXs1)6B7v4DkI;Osam{=7*t!lhT{
ze_YxUz300JYOXskrF1j=fDRQa7!0oV@1MY5;;qvX)BN?+86{^;!Pbdq^2>uw-r2Ey
z4ObnQqlzOZ2VX0vulbd&h^v{Wq!t4<F~J7`M$`r#ARVxEI9Ol50q|Tc-`ExoF8QG&
znMm+2Zf*=Z*QsR8LByDrABSr_Dch;xCj6EzG}JxG`!7Wc){dgT(Xl?DF|43T{m`I0
zo01|sEo#3yDS?YI(FpD!Bs^j<ONGxWfqd3`#dYL*bgCWdlQB75*N$>Op6n(154i0@
zU2bb~m7-4-i$}~Utys4qLNYEucKy;GI(&jNC;t%)!dZ~lv;y8u3f6QD*w4R0o#>4$
z7^{~&JA6`?&qzSD`hHQkG?ui~GzHVylW<DPS{*)T?)$m<@n&aJEnfTisY-VE4UOdD
z0)SjIQ|;~yFHB8(_~45e%=xkc#HU)Op^L~@VF0iV!ea6Pr1v4#E9zhP^{JQL%j-c%
zMQrt_?q?Mgj;j#e><Hj1adc=^YkJZ==qb7s7Zfy&tjq_R@Tx7Pn{?FzDvO&xN-lX@
zC}~}LBh@~OqbN~{rzskgUrn~aO!R#*bBnZOV!^I2cT4BagF;%E!+99rVkh)|xf)6u
z<3`HP+XlA)#mj8Rg`K#X;KXpeZJjrWSnIEw%mwqt;zmPK>0X6CB*pYBaBJ`aJs_T-
zU@g+&?eh<`(DH1q=UHhy>?8I>O(k2Bbs+E&aQgoz#lnYu&A+N*`|`cbgzw5-#aPU|
z^>~gO)+h5@D~bQkG~HpW&TVMTl#c>HM*#Qc9ew`=qARD&lauNiH2LGn0RkWe4_kpo
zRalYPZhT$u6mp`agvJ$@<}OZjJArJtUw%G#3t+#*`heU?W~;5jGUIS>JFMP({OXjV
znesbSYA<N%0mE9_C0Ah-E<~2@Km@|et3oR6&bdauJgoa;P`fozLAQf9!*n2XPTGu0
ztH*5$*cp<H2<X!rDoP5Rego&*R&;EYmn9aS54GmO$av44nZn)|f}CEfd&Q-2Y4c#o
zW)o7Wyve>Gr#xjn8tk0idL_{Km{_-FVYg0h_|{z>+<ff(om-G_b8Cy<PW^pvlOtQw
zQ|Y{y=<sniCtF<`u5>uDvl7#}56idyD|pi&$<447VGym*ZZ2$VvXYgpZ&J5d3V4F5
z(gF80hoPW%us&OdUwGMgscPPC=R~d{0EJXN2GcCL%@rT#{OqR9`n%@!;)T|RgfKzR
zLGcB@<R~t8F=~+YL`d(ia?AasPfbpXF2pbPBu8DcT2tjE2wrYXM;`8ais?;Y{i(;<
zWfFpJQx@<&gu!=kj*aCC)}+l)OvWC7n8AOO#cF6<OHGbq4*U8x^}QC1jX;sgnPy)t
z_Ojh|MOAF}uT5ewuAe=%VS*aR6dBHD?kO3tVgOl5b&8MQd5&h7Ox==WN1uS>vRQ-U
z=ifNMt|gW%Z%Xhyuj8b#7W(e6t0~M>y*%m^f~sw*t5B<XKYHkXTQ4?CZD*1FImrwH
zxTiOCc#O_QqsPh>i&4@cIC5(XyU>+zt!D7R#qEts3_y@0Aa7V&JvlX6MWr*LuE>n8
z9agLvP?@eibw95b#?Ba)@nN-t@|)&>GdZv5Z<rl|PS4&+5_;^AI^e;bQrksqduy*3
zFlXH9G1~S^tl5U|NV9s8m!=ZZzRs&rI5=AafCVnOR{{R=HCn+ZdI1mkxp%Fs+0~ij
zw1gmNs>*uoox#<yMb$cuCBXf;nu}?+xA`M~di64!){@HVPTJ@L|AeiU$L5#rqMeR}
zPkhGB{DSX-mrsVO;S3G!>VmVEeCtbx8-Yu*NDQ>pSlDmsbH`0!1y3Bvt?|jP3&HNh
zho~M-2fp5hbAtig1F@~0VavOXa{M12wc-WeyJP)a8NDQ|w3IYWtsnn+=Oi5#9mT@P
z_T&!hZ#qDI&wulG+q;77=ebjQ;uf!S=U2@RVfD^*P>MA@fp##}N#VIZ@FW(bn1B?$
zQ$FB%bb_i<$A^}Sv6i8<prNrfEJeX!DGltBi=arj*8iZ>*7oCz#<h>QqeVu3JVi6|
zSYGFAXFmb3_+%;Ymo2ZF`1sXQmo+vgd)|$i^v7&$NjVv7{o(d5x5Lt4ubG)x{QVQl
z>JtbtLRD7%_vMG?g202{Gh_|);$n}Qn>+2uV{A~YbpTGwpeaK7T>i@~TUd+EX5;k&
z;7<Y^-iewKm)V#fTqlAKqMEV-*a*sceEC&nQks?D7X8-%!TX=9UTzIW*x)W~)ExkQ
z6O@A|9t7d?!gS8z1)zUJQfKNCC=jPoLl>p|BnpVWyme`@VgTC2dYA|uCOvee`#08~
z$5R-Qu6gDT1gurxpU}yV?+ztu^{<}OzJE}-HM!F5_8hj8N3+33ctT@f4(S+Q|B}Y7
z2x0EuvH*MC85?zhCyk@JO10z=efkVJ0v8?d`~aW>0}_#eqtMEnQOn+gQ@!C7!4FSO
z&y}Yp0Hy1ExE_Ev3P~wW3NaCp05ZjzZIKwrWoUhwi@>dS@o*lEsWr2%b85Nd>~i@c
zxC8ik&rpt-+>K!oCh1*b5dl44{3p&_Ty-umGLqU5nvJx5^P279n*C5KWhzE^aR5Tt
zuIjJ}BoQ2tM3{>fOh(ke4^W`52BwMBVroN(nadwq>KCLf?`ZxfI|`qu0XaH*_Vlj3
zU6U=z*F}0hwsKMWGUAGh0dlT2C8(=Bn;V7jf`N66zHe3~Q1j`i|2W*F)vi<tXvxKD
z{cp{6dNJO6=w@bLVJQ+u(f8SP8*Z~P(7)kLk^`x%(EpqWk$xV96kDU+SwbHxAO*cv
z88szddv0?UJ2SN;2^oj3^THpoi&tWryX)!wO}JH6O4ipEvxQFkl{1Imdy{?%E~ueH
z$l*vu=>mRd?Rkm4Vgb3~#jZ_>y@%rL_-zRw5vTUo;D2#!tPhr?qicT_bZ6yY?p-r=
zN_VQkKbP!#vZIE-%bCZe#*xpmWO?8J$A_876e63)SW{B@;4aaKc0uM*17{iV^8UD7
zY&{RR_wIkPB2dG`%!r%_JUlvizGNaP^Iy6F;02j7+1{)X@@i|+yVV<*U>%QU$0PKQ
zA*Jt{O#QtF#<T@{huG7NhWXD6%cJ50sA}%_F&a^Ic`{wz6z97?Jbd^V*NkV~e7s?y
z>0hWx^aaU*1pxY6d9xMXjd>t(_-CcSn+;gD(J~8CiOJv%f(kc8iX4~=dm<IMAJHd;
zA@bc&gI@<Zws@;r|B$V;U_q0NwLg$B0V+)PI=HoA-%@5U6J41;SAwURJ#@I_b8rg)
zfuIyJcWH56>G%L=W<BL?y>DW|Sl#v2PgR7yBsv!T7V=617*Zu=iiV|nG-yjI%XFE&
zl`r9JxF29@$;$?at*{Q$9qJmD3Y(o8&v-k(yuOaR#nvXz?Ukrt2DTzC+qG!-u{!io
zKfp0xRHzfnpc)geplEyrj~0P85$@hkS@jvI{cv{h(esK+>CsdtUq;B|Y*2V=JI~<d
zM_NpB6ZYH}%bLgQ5en%`cDmwSE$wJ_ZdJwEtpF4YV3wgL1jndo@qyokVEbXW&6>Ra
z?IMQv=lm<bFi|(z)KyFzxtsn*vEOae8|dxb8vHJ7?t;fu^fji%+sn<Nnw^66+mo}I
zCsI$cM{V7O|3AIK&A;yAq=gdVi~rG;w5DGBiN~(214do{{q|GBehX_PV#Q|-iK|c&
ze4@*{AaD2*JScO;@x`@J-wf1ga5uBYjVt<bHdp5+N~|ffh+&iPHa9V??EH8tdP><j
zS!p?mfGOBfFYGfvyw?;jhT~KH{F7Bf&oilz^8mX&%0}jM_&yQqa?BV8DXEE})q8q@
z_;qc0@p@pdZM%|wiT<f@ap7;vmDjIVFs?9{$32*~A`L!Oc4ntr>gN+A&<=MmdkwL&
z@IPRyojE~uHJT4-kQc|tViN)LBw1ih7O)hm=)e`=Y+-~;tBsMGZ`<)bYD9bQWou|B
zwZ8mKH`i^4zTbQ+1ts;K0mU(3otto~NvcQpkI6i+(}7tH6HFeW7H~Ve^5fm^EcxG{
zt+mDYn+}D07a)Xb^!t0=owYu-d)3|AoL&%I+MALQn0G{6$?oD~dyltI`%ck|-=F7h
zHhug2>(RIAyh2z#cmVSaaB0y}S=<XD5}Cj(5d!xC-xQkk|EI8^0TaTX)2#!@PUtA8
z(5X+IZS+u*WiKf$brd4(;Bm)wKTSAthLXu?^~4hv3XC1xuTR@3xDr<siTQFcs`edS
z_GEL+OE~2;{46tz@mM(-kfLjRhDwP8z3GSZWALB(JI-0<$lV~?5+nP1z9xq?AC=V!
zFjI@Jowd}b?Zu#0k1QOYa=^*Dz1X`nvuIWD^?J=Ek-jR@f0TEJ=X6P#EK~sk&a8oP
zf=9}LOn0&`Z1BOO=cplJHBCsM+Y4TLoy7}1F$5it@AWi1QukMvq#hzKS-Vy@FIkV9
z^bKI51e+1V+U!%6{yAve`vFN@CNec=wf`lz3MWFHHleK@XxDDXd(#@jUNa^)yv%0H
zt{_#ytp^Euc+S#J+o4pkrGbI;!LOjDOvJCWl)wN17L-5Ad5-LjwuNdpjmcph9JZOC
zTyfLB8d`eVtHbw7?}O(ZM^_QybxkbYdn2?+X^r<n$qqL308vR?gVGn_Hmo26t;~oG
zU_>=(zV{TSRX@#88f$52&un?vzqME1TylOMtWS~<ho6UJUjwQ)fI9GFD}jHoa`2zU
z0U|}Sj@)+zt8xP52Wilb^{?bv>28Hx4<BRBad|njUk;v`yLph@Ldj+EQk6>m({WEr
zT)x$?zq;;05C8!t8IeM0Re@cq<vkP34FN>?1vEf^MG$!N_1o_UFPGSviR}6V0RV$s
zl^v$0-!0it$O+?x!AivnHix>C0bu6j>k^R#Ct3YX!zOk_Ob--?y?&M94YXVW@LPb9
zJ6T(S*KC_9Rf%>d@gVo0Dx}xH(^kZ0J40(tqe(Ugo&qrTEY^%Pg(si<jX<^FMVMjL
zc$d~Ejc1!2T8!qOqG_t-!hms;Qk<x{;|?Vnk=Y1)IemA;XmnW3PEUL~Glk*-Ip6Pq
z6l?a88Zaq^M%ejf2^U<#YT)Y&Thn@{a{y1QCoQL|V}Co;(5T+S<r%7?$p+}26Fd0(
zXlzcPe%u`m@Fjkxq~r*iUeW$6Dz2ld@L^K0VIB7v;k~r3Ld;$vdr&Yo%jo@wHbV==
z$f;}S3IkB(6fKR<!82(gc=FD!1!o~qGY)c3GHQ=(e`~0UECdch35E>=Towk*6K5Oz
z>+AdZfP<S$UM>Lt=qP&C^7nO+nQTLHh(v!%RVwB=$Y+!$`12}BTsso11m!;<hBq!H
z&`_ZP=+D03GEs&a0pR)f&)^o<!r;?CZ4_>g4j-?3A#1hkzij=%iO1D|Q@kWCnHF<t
zb$LR_TB_P(sDQ*1SS_*y5JqOD_NPOzFd`Vmw<m^mvZBua7dJ25Dl+QhcYF`Xmps#M
z>Cl=0^wXX?zuDAhHWI5TN%O^PyEs#_a^<t8lHY8Ab&76W?EYKGlHdp7-xDZK&`dN0
zVb&TO&DONXPLHvj^$s)37=Gs-5nh-{BqwEQ!TsW3sTSv2_inc^IX!|h4Bmh6O;*Yc
z$h>nN7odwp>2ieg6SOdV?~_~ICcDpP_?$mzgqw(t+}AyKuTI<V>~!IVlJZKa@~j5a
zS~YQce8#*+HyD&GmuQV-5qiUQ;5%{1WsqUdk_jbsAdw`XCMFG_V4a!7ejhF`4;n6C
z+o6nV!2O#uBTI}Jb?;ipvGh!;e7>jqz(CEz6oy(FP)97nk^tOQX@L9+E+hp=l7^cJ
zPE6cZ&OF&2ZO&{TWuly@>&84cH=gYsM92fG$P_BnY}r{;V$YT)7GAsEdF^$N2QSy_
zOPh|$3cxPxrlF=J(4DGN`o<3yluQFv0Vm02Jb@#nPPjcu0EGZ+(sBO0T!Z7@y+pjr
zYoeiJ|9QMDAJI|{IYkTJZFoSy;dguKNB`=O4~Jwu%1cxdeT1$u0bWP=n*t~o|G5Iu
zkbBSE_Mcvz#AII=m(qQEJek2*O~!e;jwBD|yygdUgMw%~#?-6f&r2)*=a$Bj?V2j9
z==<TLAOLd8^Og09gETODjM!y#Aa~?b=LF8+{zS)Jz@~)%=y5k%{rqp##b*Js5)ol*
z9)O7s6ee%C{@ubZ$(CMq`*GQ+jLyE@#S6&J=wupb*N6Kjj_GKQga7U=jC>KCc4J8d
zUAE}61*TQQ;3LuP*2eMh+6UzHT1STPCFL?#dSr9CV2;epV!K5yZ7Z%or_}&}GJp#S
z@xnY96z02G&9;Q5wo14b6#0ovfyI0i4)7+;s{-;Xsuv<UzU#T%HQs%6$XQ9fC@>PQ
z-F4vRw%e<3gn`c?ST|8pvTDA$;*6w>=m!3jD$E#r4+1Rw_p~Y#(DfHcXu&JjK3DOs
zSOFB!wQ2&>;QCNNWI>y}*O!FyDy%VW#CmOgch_7;7bQTbdL2LaQ%i#&P8Pb0QMFiQ
zX3gQBn(Mc@PiKq;?vM@Fy5I!de6Kkvco7Vy8r2G+F1SK2#HXP`%a}w!Kv2-9XVc^7
z!F>$es-3QHJZGtNkxJjs`;bTUJRp`v(i>fTc|XDEcSVsvy>qkUptM<6g60m`RwMl=
z@lz1V7|Wj^97qB@t8IHo4(<5Qmpn`?_T%L+fOrN#jh(r|!o0lnr?-iVX+?#6Z@wpO
zH<QSgll{58rNqidBCIZPfwXhn1K4ktJZh2Vpkw08yFnH7f7Trp9(Yl^W~wl3Nt)|g
zba>6yia!wiIQ_0xqgLys`C6q9zh6-y%7gtE43sRI7hA5QW+ve5>$Dji^RySOwT<~J
zw^q-_^U>vTqwm)0cFpxknY48xOBk!Fw7@3FL3Q~Int?_@O?@~o&$kPr(jZ#?sOd#}
z`u#ZQYhUcpcH0NgRHXKHzKorGbwF@!Bt<VK;Uy<eZ@SSm*I{5kVUu^0Su|V_d}&7^
zqqmtya}jw1CSah(LMQ}M@dMMa4_ySoKonFGkh0RzeY}1EJZcx3x9i%p0zMvlqs8gA
zenH=qW+}|vIQ~8v@u4GoNoDy$OTfzb*iQ`MxyCOHbAGuCmMseSC@;{9cPNva<yb6I
z4k%0LC&3t~Y86KJZ<ueo$D+dYKg^8Aqxvms$bskMwXiSPEq!=zC$ER-$O#D8a%Fsf
zKNj~_w*0ZkIMgjHlTrNG$sq<jsfzGi_p_4&>MP50?>|lIr^+#-qn|IOdjIu-0iT4`
z9KQgnYRSK67aYdvvqEUQk|i;~`E8}5@o$cx*XNG%qIR^W^QA1axGKsm>JEwUvcpvI
zWthD6#oj4dQi7}}5RmqvkIQLKO(0b--~F@t{)LA-T`0ZM8#74VOMx|q38>&A(>xjy
z4-l%{@*6Cs7{vP#`|boM4*WO4l}7MuL%S~-Z2}n?XN$J=(a{=7xn`6o`f}Xm^=D_{
zbA4;I!!WZq7F_s}C~2s+RZvqBp3e4^JyMqWTGTShWA2-ob16@M&?s^Z8Q8K2={y?E
zNGG8*?8J|l6^6MbSg^-_{Qpa=jfw=0R%FKJ$K|?NyIyyct?kn3>O0YP_)+h!(8;(>
zVk0#2b2?i-tr;Fa_FV|S8vb02R1Zd!Q-X`iF^!A!a?4ub+nOPKR|ef<CRg=JX*q`f
zO%%`ss)U=0w|}$q=ZA&bHOX*8v>CHl&(TgC+}62u?Jcq%d|CsAa*HJ^4dLPzYq7UE
zGSKE%!P%6es&1*2l<Tq4QC*_?aXNb+$!yg`^qnhQJajcQ<NQ2(+YZX1!Hg5URWw?`
z$M$7XCrUCSMFon|S6miS=!5}+qy0io?u83B&NFC}3|X8UInNaA=09D36A+r1SsUW7
z-2x=Y`E%NN0EvkAi~6^eWsKxkV?bOvmI-S4GU_EFxpOy*gI>Kd1?wtapclc*rG<T$
zm1%vJp)}XZj{ks!kv!s?pL$*39nPS=Amd2j0Z0KE$g~7g1qS#h#<dy{SC1N>*hb2%
zJ5bv{K5b(_AI%s1UZzS3pTuo66gQ`jBMtgsnXMYSiBM|)lyKa5{tMPDw>ms|Fqo4)
z?JFv?;1E4#k4J|%3uJAl@&=qI>ObuKAa<VVe}zC90Ts$NunlgPJC62IAK*dlu-;A$
zxYe%gELMlbnY-zmeML-Y9Ss2}<FmzPpD43=Cs1wvQsu?nbtf=v!L@lj)YBKWQAj)O
zi(^DLKwFeSknmlUI@i4WR>d<75?!#0Uf@qYg_b1~P-{|TIV*fV)&^NXtrwB(MSi^n
z;8^VKbR5O$UsmYkjPTGm|K{1w=QJxaK6C))6eg$Z*Z8=;K5iEvhiGN%DUt0vhC)CX
zh-^p?PnT{1Xcf$?6dZnoZV1&W5LFm|160IW&{^z#Q|Mo2;f;Va?#ua>z!c0269;O@
z^wi7_9!+FNdOGnw<J=HQ%<OdTJY#_*!c+S*!Gnq=*K_$#ZMPFbY(>Yx>&^8?ccyjX
zxN>+O0g*ve3aqGfsq<1r6Gyc1BnNHN52|%_GvK8azN{4}6>Y>K1_M!UwO`a6{m|is
zNEH^t5_O>h$dDw#!LkiEy3Y>pot=r2jf`ci>y4Q=<}+LCTie-RM_W!kFJ3vV*K2ov
zF3$Jq>qo_LbZRe2x&EEYf)s*m&f?l4A>6@ka3OAq;bgeIq`R|{?4tG6$-SV43XGW@
zGrd!e>kHqVKD7_pM{=E18Omh6UL{f57cW~H!sXMEo?rd=bXmD5tHn-zqwM~}jb=Eu
z7w-4dp=|Q_#UpLkx*R_%>5Js|dUuFY@wCH?iB8oUrkL*0`?y84)74+knvCY^$&Q`|
z^D*WST~F1e=khr_i;w+IH#nx5oTUxc#$TJOYN_3BEuD)O(taUNOBQU^U50qYZ11T<
zcF(V%omjg+8&hX{8kgyzBiS*dcwScuGzm0LYdzY`X`)H8q-J#jfUnscYQ0OY+kDF>
z5y3prAd_u`>6J^7e`<a*$FFqe=<9TiBZrO>L2TXu_<W;nou7C9MP~jA7#O^*FKB7Y
z3-DPqaQuvhO}3k|be*RQZFEJ;+L7|%YfHC&jnT6xkwhWfl^cW*zo+~D&D!>gHM8wi
zZ==aQKRdo9#Mt3CW^3$Tmge^o%tbzZ)mTaeL@9z8oeWzwAJZS+r5{|wpNMdrqC;@1
z3T1QDBdlN5sYh{(^61<D;zTk#I(9Eq7O|^K+ZbzB*r(G+8c)bNVW_g<D+PBKobm@O
zdhOJ=?=#w8{7n1L(SICv@LwmlrQedQ<=)0ZW8<pP-(j_6Ri#XlgrdvzBS@?Wiqk3|
z<kzA@AKjg=0z6m#{O*yUarL<>*)-Q>?JjzKecMrwYrVGPLsIV*QKd7Nby;>%SDDIG
z2@$;pTy~|}c}c|}_YUiMNpEP#q0TfFrAQ)0Nz0rQO6NGo&xB+a1*^(1+-+g@_UKHu
z-Pv7T{9SI?26s!hOE7Egk|FoT@|VIkupNgHD;9i9Q^Wk`{M;Y5(*>b+vsu1M#a^5A
z_sf=I?Vky)=b`MVx2HtfSv^gaHrsL9&-aoTd7qkws%Pw0`$FHNhfedfG`e)=Ms~!{
z&4dzmxfVOWi)@r`y!>-JW<{rnZO^Zw2@BF|PBhc1xO;xu-$GCVQq-($j$a8vn<2fa
zU8NybprZP!?DG^`3ESN?{FQRQY;&Bjyf@rAesdq4nOCljj4$q9pV?XPt)oLfXkR}4
z>eXnxmTb(q!8C!ixY_v5lB|s)MdtnPi_>58V$2C2+Q)Z(xqW-Q7_`BCSh2-|6n~AX
z&Hi&DQVtcjBe7)4=;yNnt2SaWEV>xEE!PMo3NHlZs46?w5%}0R)E6hRtHeZ4{MB4^
zXf8chwK_{dyT|fK5|u}eaSjFM_0^vv;2%emUFDzP&f^k&r&zlDUWmS)j@7Kyk|l;q
zUOi}1Vb2N6mA(CkqJDFYu)&;0F|Gw93R5=*u=Cs;EnY#|f9QT)7tg6R&=7Y}AnPHy
z>(tk(y{wK|J!;vM56(ah3$WF#;>F8TH<L>3Et+V`<(r3?mxo^#k5QjPA^;&u0fn**
zK4Fc{qOO`rJtz8Qzk7>cq4Loe*g+$Se*;010;L@;Jem%FAv+jv{dh6fQDORMKi1~W
zEskL_%;d!(OYFeXmjqU$dP>#RMzbb7L72RyTGA%zDdA`^g8ZWZmS7)bjdTgw-;s<A
z3B(rp7h%Fg^PUQG13HESBo5Vw*uJD+9UJ<Apa0J+5|8Kr9qo%X`05cDhZN|iFa;}W
z<Sl&v9ffp8$sT)Hz7mu>VcW<cMdDXHKTs^PzqF%8NBZL`)b1crU24I{c%8pUJSwx}
zuRcW(1`+CJ(MYTA{ZG(LDQH{8W+mJmD3b18#0c9%1ph)Xg5s1&q2tXl2KiUK0)gsq
zJc5U8)OF!wZY6DPkpC_$Y^6cN!A2#^igHJ+Cv5zkS1Rr{*tPh%84L)p>L#?Pj)$cb
zaWT;q47zW(5~A6{`+gy9g-!t<%s=8~IVNy|8sFA$6X#5MN8J|LF|@2Sh)}MP5PgK)
zurK+sr>B;Al{%G6XBDpbEs67k#M6p%%0P=}P5$x$J0MRhsihM28o#%R?2_<ti@24(
zq#o|`AzBZc0yZX19%PX#!niX|KVbyMFcAl={utw!ecynlqX94eVXd*ZvI0sK7)$q7
ziT8L>gD%`7(|~NS^#tj!WvRV~$b&RgJEd(>^kMg~jzh*rSSAQ>=ms|4KV?V3Utq@L
zog#ickDjVjzywJO4IsYB<v7HRoD=kB>jwl3V4*TeOmz;Hp460cr@w$?M8r8M{L;Dp
z_V6>LF9t1`i)HbH0Rg>{CF75xnfaRC!pe{W5%4Pzm;(VZaA^!c0s;MqFLIA|)mKrI
znZj5nr<AY5(W`@CADOIxVw*}b)*Oc$dQbJ;Pb*HWBoPR^ObmM`@i1_PQ5?}*CxWGV
z=(k%Ncn~EI+7==IM)d$Vic3TuHO97Qa_=T9#;MGUzhIWh;plg9iXEXWsC%29;08T&
zPjkp0vYg5*{IgsM8{>=~VIoUj+EQVssHUI9Y~ghUp@dV4JbfeiBD;{K(laHPKAN4|
z6YBKFx6arm9AQygvfBJ$FUoPgxQ@rm+&mwtR#%fi-c*^K{$b<pqvR_9tRg2Fgm)Rs
z1lS~95(MkND1xy^3f5`eA>Jwm8tI@wKxx3#PC!6_6$A9&Rs-@^009CDMg{^RNCv!`
z5BR+R8w^NB<r@YnaKGYz)4Z4Se>Ly@f1RvAz{$?>KO5p!YiQeTFrfPG*490X6z{)Z
zn=IgIT@_s0pvZ%(02kvbWhfvwJ?-9*$OY;)8YPDR&RW^MJ2|+~f%OzbMRHdx!u&*R
z@~p{$N<CeDac%PwVn=a)iXoP=mobVYi@U9Lcq1|XZ$^*pVpP3}5li=9dEdO+Bx0bE
ziTt4bj32)qf;OD>hm+ilGq9A$*t<k5=4o7w9<8EPTI8tb)~&)PcjsKj;YjMyos&fx
ziaop!=gkz*Vi0LUTq_QLFyb8gMA-~G^vWmerS#|$j)DZ^l!3olWJ8b|DFXp|u1Edh
z11v^#2zVnwFm^E8fJrgCc(JgAgu2&_QP9jK^?ex@7yx@$;Z{~3oEZ@*Vn(eF99y72
zwb=>Py8o9-ovG@t`1y@ZVe{p=a4&9S;0lXnMz9DrJR9*~i8Q68Nm?CheaiD-kNBJI
zFUO>ki?Ep)q6AI`JVPRvx?t=ljk+@y*~E+)YZhG{x)at1x^Jud@a8^_J|7`q`s2QP
znsxL{nyWL)nRLm>)H8pM1D#CJW`nqoM<AL_Af*?u<vLAW4l~rd&%3`@AS+>i%2aHC
z(;)CE;OtNlM<BvR4`_^_`0`@wWj!;3^I)BhCW@uH9-O`iNSGa5YVd<o@j^t^`NJhV
zN|E*<B!f?atPX<efzC1>3-h`Dp89U_%u6qR(|$MXpb7P#NuuE$Oaxdb6)=PL%U)_6
z3t1+I%Uo*IdG~ISX(r5yRnkmHZ5x3y`sC8+HTsx)X5B8rdy41VMGupKYmSh0esPhg
zGR#WLUYlzzx#X*amBVOnxC|OpK^Ocr{FNIJ7+#cbkpEuRa2818M40ghIu#t06m{K~
zotd*3{x4NaMLo{;a_-{H5#%+Ws=ZdF5Wfs4m6C|ed`tXxC-fM%DzeibFcyvKfiQ#q
za;xwV9at>~gaM%YaqZGb0)7_LB@i~<i8hzSWqljXU5rAJ{qS3-ps7$eB-55|r-7-F
z?AXSR(Q{ne`e${7D0cYl^7aGGxoaWg3I5@_a4QlnSOm&1W7sI}X&FweQ1M0Cjg>$m
zdBlAfFFh+rx}1wHci9yWbt4<E7u&V~7Qobtiq?s`hqQwh;&bkw1BI59+Q+aeimE87
zT5yuW5>vmlPsXhK*~;-`q2V3bCULK|5-BWWI?X?^ffJTJ#SI2;H<z>4&TVcz1US+*
zJyER09MVeXU^S+7LCefCNFX#w1MaLb!_X+j;9=4f$4G4g3V+rQEbo`En8o#(9#Qnr
zh|hPl%G-Za!$Hw7*<@I@eSY2|csi|<{6CKcR<{3jEc{;sVE*ge%_#i;(KwrpftBTd
z9uwIbP|nJ#tv&f9FBQ#}gTvUvB#}&zL=*)S66sPf#VUEp@UXZGY|av!&Ug!yHj9@m
zvPm;G653n~NECCxMd7f4uz>^<^l0Fs0lN{S?N6@VFEcjf`$0eYc3;$*Zlo_+jbS&N
zUb#N8{uul@h>&2&tuB+^I=bfWHBa}q2na6-N)=mr+Le*FClI@)x#Vk-AkW}lfhn=Z
zrmj^cJTG*sZ{qX#(joMSLv7l#x3kfuujYM=uaaHS`1DKM$O8_+{)o_#V7XAXkiNOh
zR?pgtb&+SxW>Y#9ky4S0p}`{)CsVon`grjqz5GP_B?{ESL`TZEaqgmRR8&GpnB3Am
za{2M2p;U+c6ZRqAXIKb!>Mv;b7Svn3N7SeZkr+Sr?czEDZA=Q+C9o<Y6=;h`{S_D<
zuc!DPc~ndin3XD`73SmUl}F!>F3Cg+5$Kf2plFJpeKH=&L}WtcY6rWH#aejC2l$wk
zkcp!ehZAFcw`~M^yuSEVk<lsQ!)3SbV0irnJSyiJE(Xy|RBExTo-|H)&5UeI{Hd#z
zYWs%mR2Z|cO5h`RJD;4tFKVMzCXLY|>br7)LU<&~W1(N<2R!S`$}o<?p&TqtO~&*$
z%qNUd3BMTy_n@D`b?{PpKa=?XHT$WkxG#)pEss_4KT#VmN8bL^^mDk)tq%-4G~Od}
zOM19NetU5PBecg5mqD4rVgI-*1~CuV<Ula0?u%LzdQ~<Yz=$Lu3Lgz0jUb1D*r(jb
z97sS!L4nNpE5Rep5FG3t>z?DD?jG`2N`_dE*bJ!_#VMRUqJ4-v7^OZ~LyCqt6=h}!
zdLYD}R1Mj7$Z{ZCL)aY=AMqmmDVV%I;FgFDc_$cQPkQRW*a7D*Mu2RMY7TM&taAiv
z1U#nGq;>jw<hAXNJ(xNqO>|NOssN@a)Ulw)o#yq99iRV8B>oHM<3N6dKmCMZy#Mm{
zKsmLvSYDk6<$21FJCyUAN{_{GA-D$W<uo!P7KIkY&zmi(-k6I-E|EUh$;H5Lxmjd=
zZMem2jPAJUzz?qAkIBnKUK`LC$D#bF_CgF6WYFAU+g}8o^f8-^>3>phNi%x`9QQi;
z@fRfhvZboWb_<M1Mec92V*bDKhOW5tB!5i#mW4bqQ^2{Hvy$`VNQkCEw~{;t>?A}|
z*?nkiz$#tEy)}`7c8;^oXdY7A;4JX8A}XS`l*?RF9L~c+R*m;apMY)~;bC@J$awyK
zW&F-0E+`}>!Y;_n#Kz9ZEXYt{f^fciW#xAMyD325jkk^OL;NjtjD8EJL(AFwwHR6%
z<CE)`wALq+gI1@<$h#rh2>Uyr%U9|r3qEZnuXeZe*0Je0V>?U-!{>VOxFs7xZp&5_
zmB6d5iWA>Q%z^t~;W$5;7;|p#@~JxiZcBp+W*6uK6Z$ZHOb*Ib=OY}A0kvmj?1a?B
zdVGa0YU6znCK{&9%d_9swoAiVLohnNtDrxZ0>-u@*(%9lg@__uiB7(7jjVr;n5(l$
zG8IvQ;_}wUQd^_wGAf;ufeRF_@IJiY13?95ld!7rq{a0U_?EIx4Q$(}Wr_2v^DB;`
z4+v_;9e74~WP;_<%@#~Uk;41)^;uS`GS!av*RsEUJbZ|=?pH+G=pz4P;xv*dY^1FD
zny_;O<h(fCFHj5tC6)N%R5<d4e<=xj<=X56K@b@s5fmYgWg)J-q}No7ZUU{o!52>>
zQ%W2phIa85B`Fi8NR5LeUC`PMG2OoFNMG*4))f_ng&u@fb@6gjX6oc-C?4ncR(W7l
zbxVnXTCr2xMJH+}`6**b?O)oH;`R|jhXW5;8Xc(K$lhH^?UFK$WnHc3f6Tuy<8?m9
zoN{2ZKGB_oW^(og&&5hN6W*eUJHDG|W$)-e)^t2e$2^OgzDzy{<Gs1+`lrVSZPID5
znP9PmD*_~U4QAF=ySJAhk?*D}zp%}E_ph0xTcALwh%=U&GdhL_71L#6I!TMhya+7(
zT+wv`@ICps7I$>J@!#|K4eca+qFGn=b!8&_+V3T$>2V5c==nz0CDr?mMfCO0`Q}cm
zl{uDi6gL6U{boY-jfh}YObfoZO7T1wLFDKO_-LtFcsw*H{KFnGd}A?AoHQMxIKh}6
zN%3s*uX|*|Wq~shOA-`dN4hq%b2)a#sKU0Hy}ktv!iMaEb_OJ9VYd(-9NyDwPT-GM
zcEGH@Zz*D~n{Yhw`thMd?fyW<S|I5F*3}`7Bbybk0narfd#b5vQ`qZJT8@vCGqcl{
zHOId_JtL8z0^3|Yfy5l0s??kTQz%{47I2w8W=j?>R+9r%EfN_@FD6$(%=SHRh2JP)
z+Oq?q&``VG0}(|%hy|LsJVNr=9lkiK>tNNpg$-7&ms|Pm&h}5yb^gII+9dR|x}%&x
zFCfFyz4y=s^Fy1x`I6zzJ&y%T*hDONzL=VxK4PwLj<{VxI`GO{+FP3MQ3$%FvOA~u
z-(9k~g(oI+if!FZ9WC8<;`|E6!0t!*vHQ`)q|WtX6s{XF4s6C*R#O-!+a28!_WxcM
z#{h%X^_gi&#1BKxcSA`38M`+#FsCuj3^g(lf(o!_S|X*#Y49;>dkO|x7xcaD;Ztmh
zXIbJGY~pIqeCP>nG-CH6>x;w8;c^i$auU<&<x8x=>Mc#GqZ1O!N$;yaHlfyl-5iL7
zQQRHf@LO!djFwwX#`cWyGP=!6D{0J^z1q@-h$`$nN-opk-xlq-I{CQmydSU^-+DOR
z4C8fKe;_n%&eZYR$8&A(I(<L*?vg5LV52lG_zhuUQ}G4({y|x*0q8PUwxn}oHI2R3
z_BU6R_(aHaPN-V1*LFCAvVR#<ykd#zyT3Y{a9z{1sQI}kOq^gifa$1ew9BlKIT2)w
zz4B=5+2*j+jzKfaSJ|t4e|TpfdVd^G4_B4KbVTw=M4;xB<0>>xXI>$8nlEw6jf8ig
z!oqU4sLQ&|*&jy;vQ-<n<~R+JBsl18i9$!Q+X;~z1?1d(p<okfw<WRQf4iz><*YUp
zc|%Qwxb5o(awJS<r1^X0><`RiZ=ojBbwj6Bop4cx<x6dU18v6psH;Q~BYzKdY@u}e
zSQi>giWtsZ^g*i{44YQIS9<q3#F6z?x6IDya`fUO*~bZ8XQA?rhe$N%1(5LAl(hO*
z`clWD_8Mr|{SyIK0c#gutz<Us7aF%GflAPHpj=OGgIjNOJNu;^c1Pe1ug>CyV%6AE
zs+siebCEir@^6HC*YQc`pph&cWq=HlKAdiV1}=IjILT{%rW<K(gWL~!R&?(adRt}A
z=rkZ|uB_2a(+DFetWLD3Gr~BY9WR<+8L2>r-Y1ognqE6Gxq6{Jke>o>0t9+}?04g`
zJ?#6Q#LUbkF6W3&c(Iymy#3aae#dSp!9X3_q2~xWKdMoka;OANa;D{w+|z@c=^)5>
z9XS+?V<Y>Sx47j(gE2U6sIYs@Gotf(+)<oLk?e>Lj5nzR29?YjH_2XviLh@ScEP%%
zj@jN|K~LmhmhQ#K{)_*<RQpRirA{<#Nz*LnnePlHQh+-OFT%;he334ymcW3Vu%M)&
z6I7D=k5~`<fFLpyCqtU}dqoRhF9*z0A|*-L!{S2|W`xy(PQ3#D3=$&+qz4g5jJTCa
zn1SC+&0}H2Q+-}vAJO&>!RFkMqD-LzhgU{utxT$(aW7Ko3$iYbdjNYGoHWe$@f$|=
z9;FP8$Ht$LZW%_guHT~pXa0ClkFppLacVs*@=O^oXf3H*B?BU7JD%Bkm8;T@M9ol~
z?d~;X#x<#4AVLJL!6*D#tSvIWnMZI97pC-|B(B(zLeDHTV~g)cl;ZX|>rs*?4$s{8
zh%+=LhtkRpwGQ-l-$b|qOhrc2JIszLgP^eL)9_tk`XzxTkF>6qKe>#Nt8`P-8%3K;
z700<8FctM!tx`NsbiMu7d&{VUX~?e3lx%q`dpE&s3GEGK6=e;UZkkE$Ae@cxGNMkJ
z+0D2otKi#zsSmu?7xWwuJb~Q^FJLw6dj9QF>~NH{oghVmO0pTb--<z_1`67xo=QLc
zrYKkgec9fb?fhB@4;y#CYwG335Q87B-#zfTPD^lpZ$tBGvtr3sc$(e!pz!`fv~6fX
zXFaNm<gny*jN{hY-rz1l8{U!}%)sqP{m901Kd_xS_PsJ-oTZT(Wz{W)U!`9ZH}s5k
z@QRZ@d2u=abV`Gz@#*I@<Uu3uh@V2-C=TR=(OCXzu0!LVxj{?YCEJVtl>PFah`=FL
z5<)YF;kb~l6vJ36qnuzgMJ*?8i^Cwg{#wXtj%J$jhrz{X1bNGUuRu9kmU~<n(@chP
zLB$E`6v(NlAit^lT0wnLkV9%)%lEIB7FCTLk^>5(uhiNIH}OgKnQwd<M3w`#)5tBP
z`PxSCm+<U0+9ifa>94Cb7i(NN(2y(AuwAQdod!JeW9L}+7%#P7d`E=SidTExl&!33
z%v?-ZM%eq4L=Lty4qBX=?|3v@*_+uup-?E8i3H&0A0?k{S=0EVILkQ0Dl~$zZu8Vk
zr;APHSVEhhlkImYS;EUJY;7`dIXvIl&g{;n>?JLvX(d?1Q7cUFenn>Mp~q`r!i<{E
zhQ;**2280MS~M}ItSv#54XLQBznfx1706uuN)!iHZ{c+<Y6wL^xaQ7WaG0}3>~HG6
z9Y$SJFI^@7Quw;uo6&bM^G#=)mNevaF!@j>OXRf_wbdJtz;TLa|Nex#K{v3F?J(2T
z6mtj%<7L!Q>-qginn?;FI$WKKyZJGPypk<AYtP@*Z8&xP56oM9iq}b@kN+%akjoCa
zSK?<ajX!i80!O<x-=Eru-TBRm%PmKN=RrA(?Z{h`L9pAGe^yK(U?bi<02H|?QbhR^
zf<vkU4TKrB9(&+#ib+t5x4Eo-1ZFEa|G>4u!KH;HEIXxHHU89aGTr6oXUY=vvRGrp
zfOs^Nvx$Eu5oB54T#XOYX@g6Xb#3iu*?yFgjtD}D-xxF>S{9xNcTcl$Ej;*~W-9IY
zW&?VWw2HVyB`2{E8;;O`rBAY}hbnwysgu03`7w}$t8h?z$^8k;)b~nk<t=9jMQJ6y
z)K?!lC70(H*N6{~O0q^}mF#q9WoG~ujt1e$NUbKvtd)ZmEnmDl369a3aA4@o<=Xfu
zDqMHT_5MsBZhkTFKbQ;TRf<$QC_Q*PrL714X^~k;g#O|y(iGNe88cIRAq_Q1TcXQU
z9pS0&pi#TA=YyFoEMr&m+gN-2JxZTg+}#47pnXeMwhO7+&HRR7A=EM_BNjVbqZKhQ
z`T&nxy~2xtzUMK%=~9EB4k2a2hR*TuZAbl4Zybs;rsTbnc955S4z%eu_oY*Oo~VYw
zLt&1H$}sY{u#~s-42_gr`8Iq7yAGeA9D%^WQrj`temvQ*4zg+)&~*$~7l6)UD=udP
zvhu{NrQ9Hel-Z0%Lfzk}=|WI1yP52lETk-QVvu{MQ=UJd0Bibc-}H*iu7?Gy%?f<}
z=DVpjXkuUaXsvR&Zcp}!>@1j8yt$#F1qcL-`1mXRsr7E=1A8Hy|0R#|h_;f-zI3^}
zx`tpgmsc;gY|AW;rTcnt^QZiz7JNweXFSAzbS=I6Vvm{oTtxl>9b}eoUkIpGOy@){
z`Ds1ws;q9i{wWGBh{AgAHszI0dzfqPY1_keOWLEKmaq*!#X2Cy0{Gm;FoEwMe3Tcv
z-jv?I8IzJ@%Ujp{hLXq9=_lhM9W7TahG6B!+D<~ojDVb1C{CSQ-Zq_yX*7@WNi0(Z
zTS}WUS|&79)ZeM6QMbRE5Pr3|vA;AeUk3o!olP`aQP@t8KCbV$JgB|fxu!+@OqxFA
zPo{Nx%8kE=@>-B=;db6vgIDS$My$76@pVxI<%C}IE~}=N1Uh|E9#$qq{XPypa0{Za
zQqZx*%aFrIiYZY%@>ybwvrRFF!GfFqR9lCQd2jTdvHM18R%keR3_vk|_UffS>bTuP
z5gR;h(F#D~8zsi+o23m@Mn04*7+vw>LB_b-cr6mGf$ew$G$POY^4tF6&=YoT{5AYE
z7Nk0-8G?P#y})+(>4Jvx!mgDTGq3f@P@VZ&`S(7o)@UujS^JmT$?HenTH!TPPh4aU
zuWQKRJQ!IU-v(Lq8y)E|naPn%YetAm5f)M9pzA*0EhbqJaUsuo=&AnSo|%yb*Q{9O
zan;Jt7O)WdJA*Y@g!ZXTg@;<3HLhd5>A`E>zuWMB@h_aRb)4T$h@!Xp_45-wGHwxg
zA(<bvg-EY>4TVrxkGc0l??2gV2VtFTbcv4C-#yk-IkDm<^FfpLJZ*8q!s47cAK?6}
zH?!@)hy47nhb|Tr*N#a_zpD)f<RDnMgc-_i^-ol%*o!pY$)EhdX<ThtOyDkQaA{q!
z7w4XTBHQdF9iDn)OXvOFuwh<X{P1t#FMER7p1auuFJztnmEc;Mrm(P^PE594xZl0Q
ziWF-8+2Vbq^7MVT`ChQX{u*2_-y(_KYJ1#1t7)B+9_#s8ys@X)+1t=oVC(HP-0i^^
z;oXh2OZyz&VBmrLp?HlG_y%ntEmz$Hot{%rQ4JJ9Pv?d6>FuY4Uhn><-AN82Bw;3X
z=|ORdLY03<0de5SnB|;PFr=cbwY@&y@kK1dkL#2>9_j327H`1=sS@07(;5@&Bkz&K
zfKt=l?v18Qg8_&8#mA9h^Vxn#xjiPjWoF!iBqtq@;^95p54!NXOtKQX&{AZ0jBhYY
z<u6zwB&2jioaV{Ir&we<gob|n+;GqK@!FFnB-61j>h7gjl7;<>s0jp~)K2x)@YQ-F
zAl^nCl&$F!(Q^83V``^1TN~LRNbgctcowYUeLWSH@7u6OwqU<hH`wMB#5#oIU)?#b
zIJq*L5MWo`z}~~?LvUnmDS!OzzJ=uJNu9c9D&55eKhYrx@u>PP*`F=g$z4S}?UEF=
zIY1|UVGve6w#DD~r^0wa$q7XeIoXJtKse<Oh^F^nFGBIQsNn7qxykZJC9!O*e5hF#
zF$`qFKrJ~sShSG?_=^*OU|KjFJG%ty`x7Rv*3l{%F0DK~QlVmKA&3ZFv5<Uap}LV>
zmn-yjIQVe)=Hj8)##+K=q<%j7*<JzhtirOA`xYD^;S)I%hdj#@gi?}-@5y0^vnJof
z2D~{=SDZP<`X>2~x&=>;xq_-d)xg~=Wj7A8RZ#M$aDn(!-(-P!Q8m)7ja-2}NjaZ-
z)ZBl;$SGw{0C-VEZuH8B==&AR`*ry0E#<8wYXJiXWKcN-LR55_6iz{5Lutnr=#hiU
z-138}ss+S5r?aTJo^CCalOz4u=|U-`lJjk|EPWpBfOifg<tWv25KnKArHT;PPYm9G
zs)4eXvX>{{H~&!mx}#3s8G<x(Lds30S=!obp5&U`uy#zxxV&*`BX-$WNyGW~w3*gS
zvg#@pw!7v-J<X1)|Jw+X)x{IqSv*NV^yi~a3_H1b!$AYez_Am_#%NKqmCbq#2jzrI
z%b}R7mU79oB8~X=n9M<zZ|j(jmIhp6WXT)RRyDnDFh%|vaWh+?&iafZR5WD=*=|Lx
zEVX13ftM{*4ccUrW=57wJc+n2O1d-*^kkK)tm>25<S_RXuWb&YKE(`$T%(CrQ}Wsw
z;>@?vl&%<5%TqjjuLYrT^jWC0v{<o8kt`>%07UJ)LnqUDB7Bt)%-^i@E~#&-&Lt)D
zwc(HVb00Gxy(6FI<JE&kA=5H2sJ4j`G*ZjTR+1Io%PRtYa0!OD)I%vLVkK^y*{X?c
z_(bNqTBp_4KQP!6mv6pa#@+Q1Y3={yKY!mcVKbnkX~vTQ+xV*K9e;7p!mBOpQPddv
zteZN+wDa%_)dI~JnB23UFFIwF|5jXIAHb?SBa)aiQT%K<T%#a;;X|J&BVJ7G=7~hc
zs9(GpA=-j4d{JzKKZkbx`#~z|GXSZ$0Fu8(Nk6X74VwFwgU(5^7OG=SbavocT>BaK
zWsDn#z&CHmm@YFS<n_@%Pyqcwp5QR>17bPydicMQ{{P1~&;Q@jKQr_HP5M`dQBhW3
z=>bmks{YH>F6+hIyr)HNOzTp0`lvn;Ze-Y$SEiwrFEJJbCWM@LE75lkFF7L%DWc#J
zl12cH3{8Pn6=&Jp+$>{r<@9c4)yZXJUQ(Ey6Sd09O#a8G>4)D+ZB@6On*AiFyZ7tL
z?xt~YP)LXYFOGaYxr@(sId!zfT1qm}qM0c~Ecf=+vRe&FL#LlFyxz(EQaB%j8qR1i
zaQ#E54f6W6Inhe-GmL@e&!KvN*I9Q!r!`xLG0eP;YY*fBgdtD#Y~{gMf?K-VQG{qz
z8m#~wp3h}{xJGxs(UM8|^drh-8ht4<?c+3>XUsy;_`>|&sdRu+CHo<j%dzOAS#cs;
zWlED&q3S|Cc4CfxCtr>pVy`a@ZtW6f3=?g3a&GzZ*cn)iPB}9rezB@2adr2dz~mEG
z$Vk=vg-q0`Dw&p8CahHZFB_%b<0+wiLSQxuSWc{DDDQGsGR~=rCCt=lO`7sn&W<)c
zME~%<Y8Ec0n)=JcFWUcrr%NqlPLZ`M8Z#`8882kCVPi~NrgQ#VEfSn;(hJ#9IX!v{
z_+wk0Zi<y1!A2c51<ao=rIpW!*~A2MA0>ksonZm_MixVN(eeVhQ8tx`iKbO-v0>|<
zvUxY}CPK@M(Uf}D{eISJ`?mU5R;t=^{tD-_p(>B`UA%n`%2|Nk?I+lf#}M(E<0c}H
zyncBNizc8IGszG-CnF8T2%1VrXnZ($t@|$|as3AabY|G}DBkFAL+SZVd1Hq)Eo<CV
ze^R51dN>W_)M%UjoZ*~NJlJq5$j*VJTOyBcK@$b1+uFcg==o6ZX@2rGg0{#TGNFhs
z<^i+ES%aJOtnzG594x#JpHzFbnD`La-VumizwTi-Bs!h@>Qicp;ER7kwtT3G5R+@J
zY~nhU;nTBp>KhCG!7!nJfneLUFWfvbwYszOa9Pw}G&3&B?HsIZ+AF~hft=cz%vFg}
zsA?PhOT%G#W|cHjb!GnE#_@R9U_*?zP^Azr3YObDx?rMq05b{2XPe>z0flcu@z=PT
zwp*U}LqT1J_PhIF`{m}9)}Mwj)Br(fTc>D+y^n4ivFBP-Jp?$MaE?BTRZkv+kCM*T
zs}CL8(hzftlU=Mv$HKx!N1Gs+ghK6+{sjqg7yn0DKfPZ@r>c&&PHj!PBL#i^+uaiz
zA`^ldi^8xx4np)6sa~cyas1aZap8A=0@89kkIdKxXmEtVuc9&6wgg^cS4O%%pxQ-M
zaL0%XZ2LTFDm5;nE69D!W7x&Wz{ykNjea?2mjGXK!Im2kdT#P8Nc#fqGb&3EA7po6
z2>kY)+kyCn|AVu43eqHQ+CJO1t!caOwrzJ$+cu|d+qP}nwmogz=FT2$eD53kY{bTM
zREHH&|E#RctMd9KL<DMyV;hMBH{%Kh>mf35E?;Hr)=xK7Pt2xA$CV)fdZg_w6s(9)
z^D2hE4wgk)0J3p%S@|Hp4b=pJK^tDJ=c(jJ$6ob*E8$_&+5$zQ)pnxJev+!U`^m;`
zNBxDE$lc0G%L^+^u_`L&xS$Ok?cT&Y`RPj(Onz{eV$a&K0hAu&M#44IMKoITy^;b5
z_}88{QeZCv=qQZXA@9g>K!+2epzf#sYabSe@TbL*6{2*af0vU{ONy2VK2nqH`%5nH
z5H3S`Vy@(D)IZBdgzEBNFK3$?1R`RJ9sUMZ^7r<`WdqB+_2g9p+nwzkeg~Rk{96L#
z^_dj;Y4ksTuDr7QpxBtDxBd!}H%3#{7HwW&;Y)xQ1923Fr-E%a<_Je`4+?-hfHyC1
z)?dgvn|J|^O-LK?^9U}TT8Q2!H~S**msyw~0$;NH1m|SneHV@iIpm;9eXRK7_XBhB
zf``R_`ygk*pA4wC`Tg1x0wBxD@shD|9%K#cbI<u>rj=?_j{1D4M|a|gNYzjr2pG{&
zl~2_xn0|IyAuhsZ#I<zBP0kn25R~FNw4y=y0uP6E3<#fJSJ_U-P|p55Uu|{UF$fY|
zp0Dvjg@iGnB03Z$eXzPpZc-456FV6H{dBirn1ZJPHi=r6t+NYtYR^n#E2<34g)^<y
z?2tt#NTETJ>&EPx8L-&Rx2NYu^_O$|OTl(vk3f<K-xP2#V;fZR!%C3_O=R~t;F&P~
z2a-OJux~X4F*kS3jk+SzGj0!qyCBQOnD0vTcfw|&?LLK~>E8%x>Uiw7sBY40M|S3H
zpcx>-Omd~smgdnwaH7pN0PE9RwbeZ*A0r5?QI#lkFl#N@M~U0uVpRgf+5|#mzsZPm
z+saKzh=ZItq(C4&#jem+5MDcv_8>=JLN3yW8k!UZ5d>J#3HL|c19}ki8Rgn&mZN9a
z?_b*eYEV-TZ$+bu@!IK7#sb4dZBTp73649FKFf6z?C1ff>329E5Fp5WCMKi9tEv0?
z;fwLAg$>HaS$GsTp^b5gz{Z@mK*?-bCKO|$gKZPpsuxcFlPGK;khz@~BOrZWuY*vo
z*d0J=Co97$Xi4eqW;L)>w$PtVO`Y~E2eT;jOS*7VGW5Tw1n0}sJ6}awNUoO8SjW;Q
zX2fl-zU@j9gLAygWTPP8aj<I~OIJ5|fp;eD&AV;)6I<RB#FQ`CL`42hHp73vfWm3T
zHmd3EWs8Dw=TW}$$Y2JhWlS~5-k9e4+<_?R@eK6_#0L3KQkh{~?tytPRkUPz@1h&t
z550;n80k*|iYm0^qM`aSbMEjxZMe(%or+niz`<G#1<^ubeAefK_%Dd*3U!;G%>}9J
z&(jZ07ToG;<UQNYAI`S#*3Si&XI>RpqGsYlcVz&;1iTb2WFFLS#zPl17^Y?dYTSa2
z4^BjN+O9s;)&1`33o+a;+@6ZQmj=(w!c#Q@?I@}2c(ADx0;cH`pVkQIp)V@Vcn+&C
zD=$FfoOS$@-8>6I4h3CK;IH_C02(pCcB+eC$_50(9XcF3=23ZsBXGR((vuQan)yrj
zB3nx$Ox)uW%u@e4<jlYhVuYAt=4yUnP4tsYaU{)!;e#`J#(DH|0_|s7_n?LrLIP<P
z62s#;F1)iC(Kv7iOyIxaw~Zne>wjAYY_~ll_uwoj%*AYLb&IT-ya4n_)~^<7xQk&p
zvXzaP$=ceyQWl<hM`k~6V)4;gw@n<pvdLZK-YAAU_-;q|XM&9CYq`j$J}a*JkI1jL
zl{~5tqr?0|GL9hSIEO0O5sQyYn!&c9VMJ|XvAIF9Qt}|agX`Zh4i*2%+YGHj2&7c{
zR<(Ef4tCC^2aLdkNLgS;&b8O%ZB={@(RnynX(;5O`J291IDFu80rc%wzz%;<^xacI
zn!KVLFk{Uv{3ob_IoSAX0i7Pw=p-gn+h_J_L`pT4H(ZgZC9(V8xw%7(nEZ?jzNgi?
zPcT$mU*kcE5@6F3U|*ez!(OJniO6jPkc7YCj-HO6jM;3!+xvd&d>Z~H0q5_V@3(mw
zv<l4jXA*tH#!;XIyYWnmJWecq_%k8P3dkHnu%4l&aDbGO^QDjqx`PlH#|P3NkZYLj
zv%H%P4F)2+1QoX5BD%3tz=CTmZGUYSxIW(hc?1!Kf8}qRBfl6g&5R&rG!;<TrM9hm
zZ9B>HzxVife|~0Jk=7-&+Fn;$1ta|kY5M?Y8D-Ce8<{RgQFRXldOjz*kphyuW)L6H
z9SmMIl|A8FVb4A-!zFAej?Dh`?fb8&E<xQ~fJWe&1w*m6KWekIC2#XR?7J=3HEBwU
z6!WyC{)4xH>seDEw-W8RwGiz175GKb(bf%rMsh`C?6V)%ND#&0Q}A!^5SNT}qo7YS
zYaYadq`Mkk2D2TXZeQiavBl%^xXZW=Jm$NtWZIj&;umH^bb_O&Pr5b3?qL>p9e!g>
z<DU`tL3@Oxe$IY-EXmZ!sHj7dc2QqVn3KU`Juhfq$X_YG6Le5Cvgo;l>Vsb)sOQT2
zMk+T0;=5Hn2Z<7ilI&AmkFKvAa0nqkL4@Jm3T5W=E4-1%50GkhZ?ahG+>{@MPfEZ~
zjvD_665&l%o6`#{aa&0Tt+Glmg6Qlo#(U~VOf2N#^m4k;peHKd6kd~BRKqd*rggDc
z1gCuT_z<BHo5Aq7R7E@&rDb$P>)!U7+Uu(7x-l`#lA4kaB3M<gkk{UcsJ|cN_jc1d
zeMjT3;FG!}wBEJjxr3=dz=t5bY#{gv`L!lbT=u0uCuWO21YEyjM$Qh-Ba{7|kjpAW
zck-O7#m*v(PP}Maz7Q?*a!Kx30wCr<Jn+HNzaJnXs9U?PEgn~g4CSl38rdBp9U5$`
z#Eu&?VGz821$Wx@m^M>verfU$_;@|AFLdk2ww9l*jD=SOMEP4NkR1nbbj%r3t#XlO
zFx+(;MVE2S`pCtb_#PC@|9&G*ko%}xgnfo6Ds;wdDVL}BM_y`GsI#lfpP7~TMCJ%}
zfK{f?2%*gmo<?yVt}s+dW{<pQeCmUL7rc1p{R?G~Z%4h+I!_U|jk?M4qnFQ2e(nD<
zlXbh|X^Csqou7ZJD<3$D%LUtU^T8gD=*IH@ZnxMN|BFd0RyIcV|2b(DtPZLETWzsN
zy4@39^r*vB@_vg9N7|aHMIE&d1_q}u1YGe<Hf8&7zyNLci>_z@GIvW}n@FKm^hg?c
zcuD$Ur2BD(aLx(;WI-ImPHL{B>&^8#vzh6(`$={(qnKEf2uXacvwDw7^>kCSiwp2K
zu-$aV-tIB-{zb)7H@lu!FtT29*be{+Tv*XysWsd@7yL7y?HhAYqF`i>W|3XW^~<a2
zy5k@HF57qnOPchOZD)7v-wmcSI%)Oof)*wRnb!=Tt2F+YPUWozV|0?xR+(w8bzDC~
zL&4tPHP}Dx7#VOc+Li=+3t)NAPZ~=3^$yds%d6?RMgqCQQnXHE;Nq!qaA;OJs<#?+
zAK)_oJT%STT!AL})bu<$l^mppPoXsyM<w@DkA&14tuV<=iG?@j!yZ`9FowWQ!Wo4y
zlTSgJk{V4!8HzC*VKIl8jQ{|qcn>JQ%%;*))77y2v1euUb<Ii`mDAIwXiW(o*c$yb
zrf3aP05^;&QLK$o8Z%Y;s({M0<)s9=#t;qZt99kbi}V*P$dP!>?LKQ`Hm1yUx+vzx
z<a!o;(kNt|`$Xdy(gpn@|3v?dUGKok5Qq>fIGSi(R*#)ml>_aSs3*hT<PNFG;#^0B
z&1M7D!5TP1?$3yEr$l2^2azp_!T6h)-!VKKN>=BF9u7%wWnGH$;&#}L4pS<PXQs}d
z)_iW4G)502+ezIN+}ySsZHo-)y+uA}YVH+pR>xZm@*}FspK$V0BEfDNDGm!X>=*<6
z{CtDOVQpi}^Vs>(3XmE{=YSa6zU-f_0VhGkn4A7DUXFb6g_Y&SC+;tH5%#-)%k7++
z>KWS`4f?OXyWc1Ik*;8Hz9clU^suM_0_Zs?;^#lw#5<{R86ed3T`UzsWx-kZQy|uE
zEmAq@xjEpRo9jJ0CaqhYt=0~Db|%X`-;Za{*MC9I(Epgw?*ThF>_v}qO=Lz!OnkXO
zuWgiiIx~1T@2%BGgbWNsbkhe=X<ERGX2Js~6-~s@66W=+#s3KWdVNEpzCE&&hIItw
z2TjVB!#z@Phi4r^3&_ZIEw>zHJ>idP?w&VN|3NQ#?O8#}HEe~OGyY)DDP`YoxP$?b
zOUFwo@;e1lS@F>pASE*<SQ{{QEg-6$ijb3#@;9fa;;yQJb}hhrI5i;QV?%4rZ-txK
zzgJqGXi%y4A0Bg>g#8oYm=`b^RI&wv-A&uA`^ChPej-@e%I-!1vs>FDiAXvnexKI$
zTLBmo0~fj4?>69&1YK%n61tI34m}gj;P|8Or_Qg?t^}fF-LKc3#I3bOh{m~s>gr(0
zBRbXd$ayuS%xMg~rlG7W$V1grC%|A8epupMCzE0b+L%X9A2xq_5tZj`3k;x(BcAKg
zBA5>{nh0%;=ho6JsFm-BYTwlq$ET785);46mSr3!e{P(?OW1aa@ciIlrPKnm9#=Rc
z8Qby~1HF~Oz*ht&$YGu=-UQNzHx@#u*M0sW_zd?luN?PMuZ6%U?8U?Q%IAsCR!UFr
z<+u+t^juRDOnLrm-I%vRNB}(>Pnz%NPq8+7GDO^vXp)F3%yOJ^f=*B_zZTUjl?v#g
zW62#3liDxN>S07mj?cRSxGRc%=^;NXS#kzcEyEugcWC&zQ!_7Yo8V+{0AM%;POoDL
zBlG|*tH|5&W95l`%(x$orJx)_c91kp$hE;Pr5Q{xiBX!?NIHK;9xC*(Es3M`EpvZR
zxp3j%uPwiSV|z(Z=yUPjmq`C)x)c^_D6g;cJQ4qNYe{?-8Yj=|)wJv@(c%@o$s0^>
zZsVr~n6l^lpj#k+(uU8|Yi9mUPLH$$Lf<9leZc04{ZJA2P!gP8gME*uk(Y*wtVSVF
zFqLRHCmDrYix<>pVCUZxO8wvmPc}KyxV1^t!~|uckca9(CI8t0aoXAD<3oIfxiS5B
z6Q`9l{0p5`t)<Ap=FaA>UaY7UKwEwF-;(x=uMt@LoK73>WQp-GbS*WH)#2ag@E8;)
zH?!sz?ZEDRI^0+F!oWWBIiA8^Aj#zf<p>`Iz@MBunwEE9lPCO~jqkjkqs@(IoqO2<
zeTf<xoa+kX%OPsifx-pera!L)F$1YRUXSXSM#e1rz2;GN7iFR{1J&=048x_?mDH=!
zQ2K#HWOQs%L5ci_;q`G+Ovl-!mdK2ej4qe@VQMd->MsVt_<)y3V2TwwK!LGhcrVVy
zr``N6PxN6v#-h2D_q^cqBsYKjUI0Z~nM<(!sUP)Oq>cpZMYYtUVjPSG7#Jba1TixW
zd+(lr_?sxs<H1TxWNNLa7BY~PBPie0#Kd$yJsWsxh}Z|&wZ`o*49ftMe2<{8pxH_u
zWVDBU{>bHK^il49k374@TVesCZ5%OoX(|>}roQrFjLz`uhASe$fFNb`tW-Y#I8r}&
za_I7^N(!s1M^>)X-Uk3%(j^XzrNM{fb|>$!l)*m8@Tkq54Pg>YYi$v`M6yh_HrTM{
zWZ_JnkGpk4ig6kdoxzfHv<b6pw0~d6+im>B+R*}x6!-+3cn?O+f+GmeIxs>Ecv9U^
zbj2gSTL7*N>(-WrhQ=JFp$^|HueqeNfrmHydIyml-nLYKfy<&kMECW~gL13>s->c1
z$G!`d-X8II=NGy0&|hf`=v{lm$x?qj-$8)Wv`5`x313d2(cI<yAYm}LoBFZ-i6Px>
z^@_`A=Zs7C`{Q<Sbtc;IA?Ugk?kG6OO<iA!bY=VNd}}OyRr8Jb{Q!Y=>D`jyRreA5
zYbi^HnsJ&8T=Qc1UJ*Ep%;n&EQqau6dcy%ewDIEt)glox&r*JIx`xKO!pO4NZwTr^
zk}t1`)B|;?y~0zAtl(l-6FFQw4B8F1r{`7n4qF)Ue5N**sDpM;&-(!b&Qc@VCTwH6
z`O}*w(T?bEIg_@tcTlO&_=5DRX?#Pl`=DrtQm3_w!a@GE0ad9`;||5l))?v^{e!Nv
ze-_NDJ_>6GPkt3kpT)4+U`ZVNEyT7N1sU5EK}9JaZB<7xC=pO>D%A;8#NjgUe!>Ex
z`RJ(2%7jq?M$*y|3JMAV+7mTIlYv@mcRFx4EH_F^ALfxa>uE5fSVm2eW`&y9WV*^#
zm1(6-+Fy>(MqQRwH1i7Z*K=v!mBZ#&s&u~EaKpoZIos}rvVF&=`o_7ztp;BKIB=AS
z5wL*V7&kO{Zzj)mO0un1-4hX<Nyx&x;%(1});i&q$x+jaTdwOqCR*Q@NlR`!QS|Ai
zhn%NB-~&dKFtm1$CXG)B!$9eTD~9Z%b{zEa%@BfJaT489Pio!ul;)WBjmKXx%6HMV
zzBek`0f?=b3=?58v6c`u@D<ePWpU4m&R|8PMP*7af|gYaoerKyrn_NLSWoOhtP+F{
zc#e8cK8uLeoH#rs@pub{ysQ;M$yJ<41>lAQVl?HhNr!0SYaE7$T{ge$Bez8%C=Q}N
zetJsH17iawp%Mww@zs6^pM{CZ_%<dR-Ce_JntAq}?_uhRt$cqSn-{bJiD%GQwIM-S
zaYHSmgV4vXtO!W^HipehFlOZp_`BqO3o{zR0j!~?6Rvdy5$fkV;#M!P;?5$8r{X?@
zzb>3U6wdpVqSu3KvUOfMZ&lv=R>RVrW)*Ichp^84sV!r3SF4K1@z!<f7|5CIS=WhU
zA0!^-pHCLnf)fAKF|u;!t3osNaXyO+^|QaJ_5<fLh@GRt^uX{*iw_Ne@;=<FJ!L{x
z6oda|KrNkG+uS8-rB!iJ%CA6*$m3vd<@og>Jw?wmTK)l|v4!y^GD87($r$%ZPscU~
z<#x{X7C$FuD1IsQl2CXkv8>xP*t`tyCZe8B0m`jT&`Z3{#OTB}1#F_SdDg~!%jyNi
zYhS%9qUb8U2igW|6HX?ly~kDu=4ngRqt+aITAQCnLOy9Lsg>~19n_<yep5th7yJZ7
zr>X*TGS|gmn)nC~GG|%0WnI7-Z)dDm#VXsuK+S4;)RrRC*POer`CXGnyuCPRUce`)
z(p0oVyj71oxTA8#B5$O;@tjQ7B9A_O^f|+p0l%dQ-s?j$$xopE(*ttkZSrPYJO_3R
zuK?_RMTt3ibaUZomhl*TTFXS!*B;K%3Vw1QJM4wOdO=H%ZJpp;1^Y%luG>sS=eHr#
z(*>Ws*nnykK*dZhfJGW~sb=%*uhOOY2r*5ykh4#sIdLuSqf_|xZI4fMT&Y4M`v-OO
zTc<`&vx0<*Alads>0#FB+SFiH#`LB`j!2pEpQNWa;hmb`s+P>8To@b(5v1r3vT8ZE
zNMig3aZnga78!LhoZ?r3s4)hA>oInIERa2rxA$J-LIEj)M^RD{VE}2GrwS+?E;ZJ>
z$qdMJBXpB>lG8(7H0YJ>dm}W5_Z*^Oq1}~lP5iql1s>X*DUf5qhbs2r_2f>jY)@XI
zl$4K>29(O-d^|FY#DvV4#0bN;HrcGySaekhNoP^jXK*_fmx$LXwP%YMBC(|b63yaI
zmOu$&dn+~vl|~inn8-IG2&cwDQ9mvYbA?%9lUJMi2FXROlA6Sr)Wq(bnupOK=HXK|
zYX3$pb%iRW{|JvW_hH3y^4ah?Nw)UKVbzTzxJG?$)CR~R2cOT8cuOzL)m-AAhTsFb
zN^kjMe2|*@jDj%Quhu9ftJ55)7>4_>&HP13mrewqJinRR4F^MV@z}stR`aU(&$3}_
zoL%$B<h`<W{%&&XV0>iV9=wh7uT}!mp9Al8Ypft8$I2>HF6~%y0w4r3exFz#75YWh
zwHo#Fx7#Fp=?(9@P4QL+%~^CO)oV-A)+VeaE6R>Sk(E=#aUnF)qKbh8qrp}UV0Pd2
zCp&r80yMqycOVpW%h?DluDNB^WY(<=AnvduuKRO5z<y`Ac}PSce=<DSea7{rsd_B4
znx)#l44>+h)qJu^RGsjY>5q#&AoIy~L!~28sl|B1X@KSx4)C5?-V3W^UzA@TesIN0
zbX;d2NmsonYDeBfckma_mDbJdtpm54U%S`w%dxaDaR$HbahwdVlIh?j!ZW8+czM-F
z#ck{uJ{&kHhE`tOdw37Kr2=FlUgrT7TU@3@mb?(ni<{}o1Z6_O=RT9mfHNjF9;W40
z^(A!G-$mDC`+|#LkN`d=pGdp%>&}V`Ir79L6+?Jw-?e2HolP#~nY)PQM^9q5i1;ht
zr7l)xfPe*GiLiuX-BT@JV=c9{w7Q|BNs*BZId$%QAT;%MOWG%ODuX`63csmM6W7fl
z_qjXr(_Cx{<ESCyHU(aYhojWyW&d?g=C{Auu*<F~ZEIGoU$)6M*&ZH)!G0l%a33O1
zWU{*hUM-GIGpMQ{5Bp6y@3MhW?z0!wtt!SGfrG~%Ve1&HC#}9}{X`;+gazki0=22T
zgM)*}#R^~|=57vV{$EcD_7wK9wp*7fw~ci9ob^-<^%Zq&jS|`2tzyZD+8VM3i2+sQ
zApl6sR9ReY`5e?&8V$@Cd~VP0v6g5MK+u>rt*cy)wfkcl(2+YN!ta&j<9@nbk9n-)
zC)YWvS%Vu9gIsZ~(uDg6Sh2=lYrYN<b$ITsfkDouABNLd2W*m{<GSIFv_(W3ZF}&K
zhbv*T8Fa`hREaeL?;ISbO4G+$rBi0l4>RV+_jS!ltD$O8fIbi3Tw!t)VS>ETg0L80
ztLTmV*#|#=WE5tlNi^{?FfcJOFz_;QboBCaM^cCtCHl=WHpg=0eXg3hyWRYlD0vOu
zZr%DN?CEY>4>qkxfL7j#F1wx-$~RIfL3YiIpVQbLPT-_apfJf7IN#k?ZXPH_PwX#x
z*iZH5*FoR<%F#;b;R}+@^R=TeE%@Oo;8`z?dkEViLv71VHj(2gyi0>C+I0C6+ymt=
zVjD>J=0=7Qcc{r3yrISiv&*l)=0e*`(px^EA-xLX48K!G`=aDWc+CP&h)-v?I4Ww5
zg|7*_u+Gc*<(#n2giyB#cc?I)tY{E{m5Y-7F0K0!U4|hp!}+ijuHpH1F;US5?sALS
zLIuI>`7R?d<UNWA@<A!x%3gA93;R@*dKy~~jKyUgimC++i}q&o9Zwh{zYWHBnLe0g
zR6c(|m}+@}$U1+df_Q)Y40_If1nYBV7|iSCFpYFZv6vsA(Raj{OXHY=etfua^7>!+
z^U~`8;%tjE3lkI9GY59H+U*Vx4Og@<%Eb^nOX&JP>#-K|LX(p}+wQLvvgO0JzI5Fq
zKkk=0m;6p7+tziM@anx={J~2KkO(Lgyi!@i4^_HtA|244nJ&xRb9@FY@z2nJ_s(+j
zyfz4mjY><7{R18mc6x$WL&Fvhx#Qg~`~2jqCp!Y2)$QItpZK_YqA~U%81T)vWc<=m
zD6+=M4&oS4nFg{!i<;MYNxhY$CH+Ke{J(f3Lj-!=B+jtTOR83TFTR1rVb1d!>bQ0*
zXrys-N{>n)CRsxYU}NI@?L<_M5}PX&u5;Tfkob1b5u3aiyby+cu>H@+9>-Gr^TjH2
zEdox{;wl`=9sTXkU-x%|31HkFEDj6PW{N)Q>*D{ozT}WugN}k6btm0>gZuZLk>(gy
zu)riu3lSt$!2adtohfEpAf4j7eJlS))hR5N@XnP5?FODUK2kV3%f`T=Dq80x1p|FS
z$f{P>C!xo>XPpj!yoCK~v%l7F^^|yST&!{5?D$%r*A?9P@eE=6Iqg6G$&&jCaA!!l
z@v>6Pnnf|^CDNbXNzXp4Uu+U=4MI2_v$H#VwXw{lri8Q=Kuk*sju>Q3(A>NSYQ>89
z7+!^3Gg;Fw!O<Wgb`1l&!D2_S=pBSOt4SB7a@RgpfmYGi1!h&4lTw*=y*~!NUw?tl
znU|1qxN!Yqp_WTxg%k`DD6$qoVpRe~kC1bL<uu9~xvq@mMP5a|oBsxpr+yhE&YAey
z5?R9(JNhQ)G1@Huu_VG8K%eE;6_wQ{=}zy*o)7K+WHdfZwmK)Mh-9uPADi7+k_#~*
zM;A*`4&On*E>2%gtyA09E+*7<p0^b++Yg#KIUUq=D)|#<>38DVXiPgJj8Te3Xc~!|
z6X;CDvOJepm+^SbF!J;}<^0k1hua@?$T4kr&lm<<_89xd^sNK4_JGkF%6afqjiVwp
z3V;CRF84C{UQOvFJ4-OFZO*WqwtRrm@D5xvME5!QL&HBArdH+Y7&M{AUZg^*T25_R
zV0kLT!lFS(8$&S*K-K5><Mg|~QYTa4_?9CmpSQp>K0>E*o9qa4Ebb%R!o@~D^{{s>
z-8bWi`6pX>DQ~`$lz;UnI$72St_TW*UheM(9g0;HyMc38Qd`{6;iknATu>BU1)?hP
zmPn7IFJR-Ov=EX!p(b!dRL_(IYC@ag=n>J=^c91rgI6(bj#23k)ny?M8U%%#wyhj0
zShQ%qB&wvR^BRh1sd$*ibO>ep_OSX6Nz{8>{s2Ff*Yo@b?HKT1XvY91W~TqC9qXw4
z-=jJh-g^)C3K7ZD3ThYL>Gehi&}jTJCfY4N0=GYcx8nGN4A4VhD16}oL_|{8%tYvM
z34Wv!uaHQhaoEl!)%g5I#jH~@(o5+zE|==n%+A+;Y8qF%z^dgEn`bmB+HW?!pE*7G
zk&h09!$k_PB{NZ4ZFe~Aw%(s`noV`CKgVv-aa1xsYW41G6+bUXd|EIOyWfa%OS#7>
z<ePr#-)2fE73Z9Ym0n8P4_+&;+eDyzjs+&fNedDi?=eI~#OO84$Jxx29Yt%L1dR^|
zZtaQ1q?O?o(2*Bd8<uEg{pqEtlFJv1tVmYGCD_ejf(VlrkfJp+5^Cbg0Yw>bXDg3a
z|2FTg(Lpb?(cz-9lLd)=f?Y)Z?i&_S7+<QTO0P{&dNc2aCyB@qqbp8Pq$x}O4TmmW
zU9exqy5wOA+8n<jVpD=rR{fhKS>&q7MX_HuDeOM~7>X=Hw5Nb)p0InryK39;2JXfs
z7c5t+N3h3qBljBVBjRB=Z9S$F6e)*pK+>&+y~kS^kY=Ga){s20Wb1sB-jYLrRmb!r
zDeNDnRw6lf7t!&H)%a6#X)5A9P?j@n9vkLf{kNu$A@%6NPjrTcj&>z~Fji)9doVl^
ztX<?Q?LGyz%uxhUyAWnEj<S**F0Q&)ur0?ld;@T3R%Mpk5Vo5Si19;;Pq^?jb{-CT
z`h*NzN;toCm-O*<&Ywe_;^{ev>9Wog?#VoQ<zHPfVofEo#%~4yR_I`+Jkn#!O~g7(
zZ*694v%ZFv%RkB9VP`G)&UMDq!q5x9yiJKgE0RA<%HWu^m^VB%SRPXm@U*9)C~xhu
zcDO*Tbj(RMf?+8HaD0I($=o~+U6tA(i!n^jvAQHKDQo3}nOMS>Dx<OQ5en0tdFYCq
zvuu$9XtGbgAZptAt@5hynbt1)`R$Q#@w@D=LRgt97(#c`UKfVr5$&|2v_Q6Abetfz
zo%Y;os>F9ab;B(rV*_YHJ$`FzR8&&Z$5o3?ExcN_&SrZ=l{WglRmV%)P+1E%Ubd9j
zVtDa?Gb;l$di<w!1$5t@;o)wxa(pxPNVA2nRqKX>gBUpaR-vn|kDdiN^(6SMcEP<+
zWGbt$pU%RFb@L5G20&2LzhzH*_bAWpT3P}6gNoDj3BW^9^RidkgwiRy!6<W`yhqt<
z%i*gOJsloz8{)#;E^eClox!4V$PB*QG6dAT+8JuM+cN;V2=O~J_*Se<;ntQsVp`oV
zzbT^c{6FKB#v6rtyZWE+W8KQuXgM^*p{FA>m?1xYA@jvu9ilQoj{CqTMx{`-8Mnu)
zcPC_OJA9U5x*b-0o6LGuYHAZlS7NXqtk_{i@Sf54Q3%9?rW*_WobS($5Ns%j`h**s
z^hN7gIb?iakIAshS9KMqK_#$ED{Rv2-0tI{YwSp+`X2GHUIRDRiEV_Td#~@LHefJM
zH0U|+v134Y9&&~DTNbm<v|*%iwaHGFT8#}&nkuT5EtQqc&9E>>?&))aF_W3IXy=w3
zyrjyxvw<RASWr3dzr{+rl6DH{K<>?ejI#ZEaXsB0_OP&yGT%wJ-Yq$YJnqJ&E-`^w
zDZ53o+iQ70K+%;7F9X{lAiOlT3r^s|6y5X8*i@h_v5+R8(9`eb^55|{7DhNbUeqQv
zx<uf_dQY*>?YMgQfl;D4cFJl6G@>?bSq5h*X0I}NUPNyc^d3GEOJRI?mQVSX?*L1^
zoe4UQt7`O&eXD#{n*Pg=y$K-&1|U+?%V*gCmL=2G$_q!;CE^$);E#x5c%2i0!VteG
z5FLu&YO$Czcb=4EK$P{(Kd!QSgo>XxNybsWMDQB7|LxCrDunbG*DH{ww{}Fm#sENh
z3V4OvsY3yio__OOMt9%ri~`Zu#EaO3M@Rm3e7L@vOs&=VaXa|S=vS8Y%1Xg^Vqv)7
z4SfWLkPXEAGmD}W0ceJE8#Je@PW0TZ<h`AyJ$2$?rPj^Z)oNOA#3gK>hlQ&u`e6Q;
z^gTyL0&115WB3~+aHwe3feVh{uqV0$jTGU=FDZR@E7S&SL%-P)ZGi+@ZWdm|qHe07
zMy)=VW5CYV&jdgc#$5^^2#OkJWq7<LFR?%hy4HQgEs=o>o7aFZmD8ZhCV(gWW0~cj
z%XMc!u{Kn?s}JoVW2(+vd7^20j5k>$tO_Afo!!EXMEilqdMeYd?O!gyxFI-WYGMba
zpFw>EBlu=g96D)y{G0TW;1$)Kok41*;Fb{P8XZ2FJ96Gutb8m6Lw!>>liQ`NxIi~J
z4XsdaG$isVYXjIS9y|M#Krvdi1)4?F`OA5UdGSv`(6*C->aD+xTgV}4!}vl}2Z*`M
zrea2Hnj4E(!mvAs%w<ZH=Z-qcGxnCDwJdtX$w2Z=7_YP`CfbKtLo7mjIv*dO!*!-v
z3n$T>m#q4_F(&1q6>mv9;l$QCL#2EmgY24Mf(7Gy<q_FAN8uKN!@<MLKrGEA5xUcu
z`V7aV@YkrPCRUtwp%ojOyY>=&n>o<Du3j{8?>aWeE7Ieg*!Rh-jDN|N@Z(ffd<+RZ
zaa;cj*jE9ehZ3gks8w-&5)Yqt0-;s{dnJ)ha;3PoN;R~1pWo&4XQE{?pI-Oh`V$bZ
zFtNL;3NE~f+H4EYbvcni`aRaY>65I~PhIQmB1y>QLqDwCeitp+3L)7>IjeRaiR-yD
zNW_xH&S7`$nKR}w(?E<Wl*GoNnn_XdI3HEZ%6S;~4BN`Rs<8pEq@}yai`)H<3N9SE
zyY-5@m&&``fX`B{(zcQq^-7MH(vI@;wBDcTLOiu!#?zzi2j8`~bb>#iEDZq!|DjG{
z`7i1ePBzZ}S*Ca^>B!)zU}bBxI0YwTtVHYYJN_Lh`MpWjyq2`|8`+{PW#KiIZTX7P
z)AiZb)U1Tr)t{{TGl|k3lae3ALgQQNN;7&I1FbGN&%#19ifP{3s^DfB)5y@&p5PH7
zqUX&K(0=mqZ{-j(;pEyC++;&60ljbYee^wrRh!rwRH(4Lih-O+mnc5VJ=WlXag@*s
z5Sx)!$VvYZe>U5%o$rFTe!|%5?CnR6!L<rUI@%q>J^yO7^lr4vcK0b$+(7`g40+=8
zr7^!L=DG=ZIkASYP|)9DOn(?KaO@=!@EWX3Dk>_CQQmD#W3mzw;-LN?w0T$f$}+YH
z10a!Npx?j<g7#(atoBe+3P&Ov%F>v?48I7ZaKJi+uxL#9^t`?QEl4cT)j;>+X+|+X
z`T!Gs+=tLU1fR9?1I++&7a`LJMMRG>F2|x&h$fOP+ySx>P~aSQ903TRFo=Q1Z7jaz
z(Hi8H0giqp<two^gy`+56zLOH=9LtnfW@1u{K2|yD4J(wA%e6p5jZYHmiS=jm?D-0
z#bVQ+EX4=_T0o1BRMev`E;IKIDhnaq<9{*C6Y^IEC0SZ=uKr!73Z!9Wu3GpiPn1;<
zR{x~y-hHm;8^lTp(Y+!HbV1s;Ls9QQ3^%xeCT4>DtkaTxrGL3%*TST>uJmj=qp9&=
zCN^`0+8TQP5Vg=m^A`Et>6vHuIpg#~r^G0Nl6_#DyADJ%DYGhI_5G4syVYBX>6@>H
z)^>fiY!fXWldf`D9B@_MTI+!!>F8#1wrSq8vbIIGC#Gb7Z+$;01XzCZv2#2ujVFXO
zB_B8TYTy<m-|k$yuqT5Y_3RL1;M9KE2uWIL)f_0wD_6ey4P7j-xw&&gF^fz_d*ytH
zbu~>@$TIAcy0Rl__Q`WY5W0$kF@~a%lac*q=`J@*+RI6R*W0}FCnAgMAeM+${5*(x
zp1fI9*=k0WU2U|5J-w#{A9LnN{wX%6cVRL9QYPNw-o~}VZa!bwDAe;v_@_sxL2L8a
zmsi!bgGvZbeDDuH8jAO#v-jlm*<OG6wscU8W2t*TrBwYdi?0F85QiOeXPRoemf&!a
z7*sO)5n&yiJJcAtyQ%s_c_ijlgX5V&m08+!>`tbav#CodM-xk`JNUOCI9Ir4M4u&x
zU>PJFsh?rpj4}6&us0f~ov*GVjYjiU_uVtp`_EOUs7njpi;<^jCPV1LnA84w8D>e0
zv5ggIUKuve1^@&i7vcFI)8XuOBaO;+qwpM`(m&4jV{diaUt@atw;lh%Z@~87poW;3
z0RI~`lwkEQJD`o4VVJiD=LxaTh^=?~n1ce>dzB$8R|dfxtrx?D1$1zGx?-heW1B)?
zP{*2JP8^7>+3H&ubWRrbO$;ibg$XRF;}TjdL(t(6S{N2E$u1#AGrO=lV>F$r-GnB3
z&i>>)aGz`k8qLpR#T>i`lt#*<{Vqz$p=3|T9<eTN9|(OQWkyBLRU+t1f^;CGLQsao
z8OAC`nh(h(zZtR^5Fbdj%k9WUPYg21IQ}3}?V>O|^HhoCEy^$_1CE^6=fDWe9jV>p
z-)#jfAQDKa3R0DqTdd>lkk0*X1_J<?nf;5QKdb!;6^f`Z@D$kL<M!ORjf<Ub;erGz
zLi)s*k&Fr1RSwZl0uDF#mEvGEDATa@D_R*@pkXXf8c1Y-+2nwxo`K`=tcuApjE0@!
z$UvBp+$9z)k*rCH36U3o5W%_WBF+isNb<m#X#{EFI0PC=mF=Q;ReGb1$ogQjD*md7
z%9`wpNRop8V;o4a;7_mDC|0mZBiBe&0(Py>Wog7$#5>w6B~3HX{lRdHp+-UclQG;!
zHj5mWe|HJkB`Yv2&^8OpgMt)I1tk0Lt8R}~UOqy8FE^*3q?c5y+g_~b@X>|G)feA<
z_WyjKtAPB#!Bc|8m*<cFa52Hfw&)`r)QdUOHq_%)fmN;%z``F7AKW>^<JQK_s2oWk
zrT%P&CH(biJ=^d)a!#V@6y8}zYHYDGtUD+|c7EjY<VO6g7khLmq+hCTu37FfQx~v<
zOfj?d@lcTy0{GK+VEhf?hS21lmJo4aU%kiEGh*PXN^bO-m(7XKVJ`>>5q|#6z-wqh
zv#1QZBm2d*!_QlX^y*=F6<yr^<|;&76s`7X$vZCndRqe58<x-;U+A<y4tNa+6nd9(
z*#l)QvztJDXOnpSD215)u%)H#3Fcd<SztyF@j{E4sK;2Nx!hBClKB{tS`OLscCvUM
zz1XOuJh}MH1y2=DJP|VuA&+B2vtPoQmtIaj0$CN})U#Gw-`Z19xYj5Rkh(ttBeVF$
zq_MqKihCL(`RTa8yLly?8rSvla1^`qX|KDIcd;Q~gg&7!X5xI5oMI`CZ;}yb6wdck
zIcP3{YyY7M2M@*ePme<wxJbj#<~w<SBl^l)T{aZCCJ>kRlzb(h_vyOJ`!#6ut1G*3
zJI`#Tu(t$nC3^hBC-LS7!Ga?6yfW(40f49VTohPs(?SFRz1<Nn(L6(QUp?Jb2_1eD
z<5Mdfle03jk<+q$y&YrlBC(2;Asm^XzAWpGxHrl9aN70+vJjiE@ndc79c&Ly**W(K
zv%p@s(*K-Cv#ZU4Zuy?<=I#A7vGfYJ5SvHbTKZ<q&*W7&eR2MWm^WOa;uQIBqsWP-
z%)a^G*-voP3gIVPHLG!>px&?Mx7~H1h1q<jNd93pkvLHX!a}QdsQ&ex0|z<OyO=yB
z+@#8YPB_m(uaBFdtM;~+$~Df|4!M`n;c@SeZxG^Mw3h!+)%-U`DOL`^|Ey|6V{{~x
z4e&;GT+K2U*-pd@3wS*30>ZgsBhuo7=^MZ%Sqdgsc-j^J8WV6YYMZi@eBOm6Yz(>F
zvvr3fqlsX8Z-cqx>ZD-`5~4Xh@jpBj$R^qP-16Lhx=}J^LKiR1FE1`%UsT;WzpzOJ
z7jX79R~ujF40Fgt{aPvmU>E4)F{&X8eWzf)ZfWeUHh{z)8*pg}P9<%Gh?kO<kJCs-
z_nhuZ(6S)?PNPtm#?jQ$veYOfZEkq3!-EC?L|(a43vgVkJsgx>!GsSoHVJZ<14B2t
zb@T47sL=Iw8^eu;&#J01v6O&r&~=xH*-H3WPd67aIQcS2zw&y%p2LJkM4)kuau)wM
zQV&9+b~A^!nAD>sCmWIzF=RrPnmVL*myA5+G-_Dl7;7HY^5*(_kn4l@*#XCqiKTyV
ztZMVe1ik>`&M}1Ks{h<0kqI=-l(vv`0Kwt%bUQ+~#W?|K68H<^+I5_QxY@qWirBJ_
zZ8P(=6W#i7Z&ud6`=H~bEi?)4#O9j?qy<Qp>M%el0sQLawR1<c$9^rp_+()?KzAK6
zfFZXTPhYCyK-}T$9-UA#*AxL}`<L#;M+pudXnW?$4aQ-_@MAjcmosMkYDS6ojw{<t
zWrf95h;xwMaP(-Th6GiZ<6MmRG*FRNoyUrXyI>Ga<}-`!9G!(`LiMzzJyWjqR<@QC
z{=SYgbh>;QU0)Hd)br|{-A3$bjf@RkMkMQL(RwH~^w=HwZ}rst#WxIZ7j4BnIR5s<
z;`wR(e@7<!e=}3c!p!<VBh$!O-@riM*!b`v>^|HvX36ClK8A$_D17bk3-;}4@5FkA
z5CF$-jO{zd^c83JO*}*LG8cKokC;dc3OcAKi1ZdnDB^<x6mKaI;1GcXO7rU%q_53G
zQNd8s)Zq{wD5w!HDCn<~&wz=7smCZ3@MvRMaA9fbKltFkKtZL!M>m&9@;Z3HcZL$8
zmNth_;>d${KM$vt5~RT4Goum`Kxn+b?(ZGDhc*qnKMS0=W$}aWrxM_T^Op9P3L2Ks
zz*|4NP{4bKHh+VM5k3+n{qeCCw2-un53uu*vQ#9PbC-dG0~aWzH?lU<H#Ih%&w4Ek
zygAvtQOU%<`FfrCRQ~czKFw<1dN@&YQ5*kRA5&FX*U{x`^^D=!nXUX0!uW*AnRPol
z0XZg2@YRd_c6lRS2Xt0<7HTzA{=4LBjA|6rAdg+`nqOFDld{))&P-V(QplyT+H4%&
z#QSQKk*mm`<}C15cG~S1rSli~a7xHztZ(gXO)@pRTXt*zah2Bk!wRW)x1akAyY3?`
zZ<Kmq$do%f$Qd4585-)zylQ$q-RSP-m-g}@j12T(ng1WyuIx<zr|tUxX1fAd{<j>g
zAZR(rh}3m~?kPhxoPbyiZU7|*PftqcER<MCTdgBV2d%;P`qFHiD2%A-qQ*QpWoqJG
z7L5?+?LmVJH)m1E6XSA^l0U4AJK(6G8Db-)f-n$Y#RKDt0xI<E)9I7@9D8(kHF3Il
zq+M^OuvQ)SaEWwpQ-e6o1A%9`sIi*`WULptkM#-H+1)_LH?$S1cG`Sq;7XO*WI=x7
z;~;?Qmrx+#X#GT5JunX)uA4>xUD^bss{$@q`2BT6Q+Rdw52D-{R>a~<m6B8aWwmb;
zR)*BLT{toRF)Yj_A%z)j9{7^Uc|k(c&956^m-QcB9_Ih^)c$|_Us(bF3y`Z4D{GFb
zh!%W8w#X4}Q8ipJM(V!RjI|)yoPvgIX$~b7VLs$?k|smpPmW5iEUciV1etJVgTeyc
z5=IK{aDoVd)}d^_H<L{R?s`5F{YuUQPH{Cc{B_N1_Gd;EZV~`R{4!iTd;$BXbfEri
zIB(9?^!~FJkN>u-l~>;bYcWzLcY4U>f#bpei^Z);{#hb-s!nvos{a_z01pa28?Ul8
z=TJmtqDEqJBe<!{x6*=h88g71iwH{DfJ|+(_14W01X4I3n?DC72q|Y$!jg;F)InxR
zNl9#pwmzViI=ra}{qD6rspQC%f&w;JNMPy^G=e%e_x3<YQ+kg^Nmt2EQW8(G0Y?{!
z8HMka(!Op$ioCT#k~)S;QgT}JXr9qf0b2!&Tc}EII^yr=%*zN@*MT%{{3%7}Tinf?
zcxC4F0)~BOjHXfLSzw1EtL1&9)s5kh7&eML5>P>)dwNtHJ_`9mGtN@pM6;9nON~D;
z?#m~!j5jj%P96md7rYaO`5Eg~98vcJ%iBG<b<2vYExXfo@28#WcX}_sxW7~3A+HWe
zr7Iy_?}Y2)2SgRH+J;{O;>45VvRjG=?vH>?Q2re~cplehxB{lPOyOgK0Tz0heKC1c
z*I$KT(}U5IouQZbF}pCc)GTnobUUmKq<rL+u;)zo;1(o(Dyy(Ah1PHFz{87r-^Y(F
zrvDIQtp7!fF|+-zmgoOT&`Du1&W})$3(t2?)3cN8EOYQuvJBIbv>@Z@UvUPn#FMYW
zF=wGX4~XC?{Cm9&pHn}}gVd9KeYYw|B0l?&@i$-sMXYi9PMC)JM#gAJ(>1o&w6xO<
zl5{gu(zBAwG*oOX3(Sm+4l@j8v=cJ3l!}Z1M#hz8@Prf?$z&Kg4;bC>@%6W(;q`Qw
zaCa&gU5_3Q7@xNzfqohA=ue*DqNS*zESBI;mf*N0sNgyM)jWOh7owjzKYdwVr`5q%
zpEuF=a83a;rV7w-7YK4OF^ruXtJfZ1+K-+}?NiTdqmRY`=$+V~HmZ&GI!9eec5z%S
zNi`J}Gw9IUAlqCjUGHt);VX;G4$hR98Z9FJWF7VV7lEC8Xh;w87n!S^ss2^QtI&t0
zo0i4*()w|S`k=!Hd%69+^W#0ferJErgUfJUB1QwB9}$8aXIPI9il?cIbPxOK-vrx)
zu%bW_Ng)*f;r!VDi}M4pu`&M7FRZsJtb&#bP7krYr!+Dh7W?mR_T~UvtMwX{zcn3}
zge^-~sAb*?lti_+P(g8!hl2j3LHzO{q{RM3JScGj(iX5*#grz0)phbMojWh8w49rm
zYZ@=At|dJ$E(!9Qoy|JNr_$Sf<-UKocYd;OdhtjA3A^?jy>1pe&rR^2YM-`$*%Z@H
z|AAmjBpRe69L;>+ZC(XQK9MLs?M2d#L((PQCsb``d<{u#&DYYc2=&;-cx8~XhKAk9
zYIe9gecX30-)VIXTZWJo@etA#waeWA?#mUWY+)1V`vn0);k*CvEvmKJ;j7L8c)|MK
z4f<9tw)GOK8rq~|^q#VHtnOM3`kfLXfuaGUq9y@ZT?3a1UjCGNaVf28aXB3pt`+(Z
z*Z61L%D(BJ{CRe~wwU44jna&5rzS(FL%{n)@v?^~?(t;thBbJ<qDZfabAI_qS5dH`
zFra3J(}}>;C$EWFQ@9{;hwF$?$BPxAdWLxqfg*!J+VPKu?^+);i83NXT!YjwIGP-W
zDz+<{NuC81feS@QFVlC~<<omOKlUmxu26_t(H9g&o?>I4*cs5Ud!b#Oz@rC*$o-;F
zR9gC5WCRsrpO=ChKib>c9EfZUjV~1&RTBh}-eXIc+2?y%(_2N%DypE)0ful)n?6D=
zS{$D-&@O<xD83?jpC~A-;=Y9M#LnWXj%TI-@<Zqv{*&wkH-I!aQh>H?T`_tLG}17y
zG?X`tM=B!S4wZc@d*m*Sm~xUN@i$W3oF=W$8m&8njHYrG=TI|F>4`3(hN<awk??e_
zIp1nh2=7tT4U%2EAr=$KKf@GNvgvuFfwC#X=~>X8ZKRIM7`-e%KWgjMh`LwOY%U-L
z1-wtPFsWT<YWlZN7IgIWUVilIf-GAh`V{noq_TtwCzRRGK%Tgy%XI8y!P>Ixkdg#p
zTV8u^#B#_SSyXt&aftX_A&<1OD>7Yxn5=iXZjDSbFqzeP1Cu`c-i_zjaBI^nLHzv+
zN2LX}xh2j;L@W%QW1hMpRcDroYS#J_A2WC_|MG_AveQu){K1^_!1X0u2YJ6-z193L
z9+!Tcl#Tdh#w%gP+<eiK*meN(nU(-Sj^IFT``)%RPi$9XNKG(Ab&p~RQp6fmlnLD@
zu0zk)H_Re1LAUU79=A4P^6z%FtBaQ>JnxIXad_I;Xd^;nY#f(2$A7NGLpcF{xPI(|
zvPqln)6m_`_|zZ;JbJO&ypZ%x3@ym8QzjhlbRu`;Z42z@Pxrbvkk_(5o}(*`kq-H8
zY{;p1$FDRa2r>P)vy)ZA2-_p#Eb@!RhjT}W^76o}YbrYZLk;%|Zj3<~p&Ukf`Mtiw
z@baPZw_*_i7pLNa-R;)`fph1TTcS3^$>?obKAYA2ah>av02jfxd(gN%;OzQs<NEZe
zIk_jb7S&WqnhAio=*@iU?%~l2-%I%l)5jXPQ})lBjUcbQCg?N;t(;cv@*h0P$X>}=
zKiyhTR<g6Q>+`E;_=U#4xkh3Z{D{mP|MXYikX)af(6+s$w-|~kXKi4qWL-s`ghlOe
z?jFBrLvY%!wYO|SCP9v4yi1IFMX&CXSI!hrRt4B)^1^lV5$~53x8b9NiUc*jP(>WF
z{&u@;)Fe=y6FTpV=na8U{v0xV?m!MTg(1j;>m_YdU3f}rw9uY3i;VErkWstvM!ZMM
zZ0+{at~MCT=>BWtJb0NHAL?&Q=~>(`NVD=-KuF8{S=_H0)ohX3yJ*!!c<ZIj>DTEi
zX-2UI2X=v45YSUpW83CX1i$LZ73L@cUsM}$QU&3b3MmKxVBM2dh<dxCdcIsh?g|m3
z=Wbf^(eI_il))Q2bcy*p{gpp>+12m&8S66lpU8i#4(iNqIF`<s4R282J=a{<Sbd(H
zpm1UFMO4>D*x)A&4gM|A6;&(Gm(fB31_k_>!DYCmvx^Lz)do(l?2dY1<E5PWf!3Z(
zp?OfJE+U$)j8z)DTIyO9>BE`|zuBF6{zwY#SQDKHRvmZP2rMU@ZYi2%8!oUUmtOZc
zU>YnsdXbQqpD3JXA0-`^#LQZW{^-<4Rt3b3AX&mBc`sKuI){T#d;^jp=u-Lh(mEY5
z_uYy=zcZc0=maR7{>HYAa#6%;`#D&WQ^d)d3HXR=#LxWVPljk$psrM9eIA?EECb1|
zs%J~}4nYJ8>A0^*0;B{%NG(CKZGsj~v#x(lYy5TwFQbnvhycz%1n-u*zrU<`|7n!Y
zrqb5PHi+{J@Yuss_o&vXZnY-+L@POzgD!W1S@y92PKj%oUzzZ0T;92Cc8xCJaEtU{
zw=?F>25*n}^g1&4(IP_=3jR%8MWt9QgwMKK!^g;-0ebA>ItH3FsID{XS0ZUwwljoq
zdqO6<m6X&xcxQS-v84t<{~CjsjSV?*QHBbLOP>~_X>5kcMwtzZ`Yx!iWT(n$?SI8C
z8cwkm^4UhDQf6p~Tr+o}nk1FCd52RxC1?U8o6nTxNokCT_A;nFd<2X5#Bt!ux!m-I
z+A(&3`m&^#{|na<BX27>>ZWJh)r#DD$Qmy}%b94QB+M2jS^?u;Z;80H&6PB6fVkF?
zW%t1-Q>Z>FPzX#I8Qq<Kh`Ff>sVUfE6bYMTWHnAIU#J!x;@<@=Auxn!&WXavka@j-
zceTLSvSkm?_9{P@+I1G)|Fov$BK%JObRpzRu0S(no#2B38wl;P5z~Zcg$oO`yuTcY
zH5VXQyE*m#_J1{YmSIt7TLUIUI;BHEx`%EM5rz~6q=p>w&_ly8bV^G)!qDL$jliL#
zTT(g&Wkd!Ux(2+S@7F!g`JQ|4zx`wV+s|IlyZ2gazac!lZAZ_dZkOJvlH~8Pu`oU)
z<YSc$s%}=$S5A00HVNlqQyacCnCPZ?0(!DmkH>Sol~k5W26c4{lRQ<^^|hIvJA4>t
zqQpeFmC#sR+(1uZqycL;srz_4@N{VUfjhVt>CVh8LKpVVX7>;5<J<Q0HM@~$*O!ot
zlE;WSvp7@$bJop-QzeU17HG~2Df}MlPY_ZikkHU=pHGH3zUacO+#Q<sBl5`o!Vs0T
z=DjWgF^RhK?s@{x$S#PjQFkQ;*y3lF-&%bh`lv8<(^*b9LzCn-30KS(C9t|r{w=OO
zG(cYUDxgv{6SUM@)y3DiF*x}OjIY{Ie?55<f%<(mW=69VF|4N2?QF8T-D&M8R9Ebe
zjOF<to9K~g?*v$4T{dlfq8Q#w<HJMeqD>Va5|M#A-2iXH%&W+?udF$I1)0V;!q1Xc
zeY+JSc;?+_TrtWs><cC+ks9sz=+7&OQ34@U_Y3MST1P{;`vG{$VWw$^dG<7`v-<Ug
z)*Agbo4Bpu0KBTN9Tc<-I4^|{`%-t^<z4rO9&N4Oe4@x*Jvjf+24N|%NHVlCwZWJ6
zRd8Vuw~-6Sm@yZS21_)QDe=40;eGIv93Oo%m(MOtku)A?r$lv6aEViN2=1ofKK&3Z
zi<yT~yu+O0f3P|{Zr1|>_?pyf42`>fZEbD*(a!Mf1I4}QEA<uYA}UlGzfWFbjR$F^
z*~|$=Q$|@S*W6Qa(zo^{oK7$JL!4&JIw0rv;w{TH;^NLJ^wq?w@D#*u9<&em^OsmI
z`qbi|;41e&>0Trx{x)4~^}NHgA3(NtM#nl=T#qicx`!W&kN@!caY1N=@Jwn%0u6UC
zW^>UnnT$;<3ixo;ex4i^sTcKwPfi*fwVY1DxXw)57n8(kB3qQA12pMuoNYqhWZmgn
z#E@vOd%QZ^Kbu|jDuRR=i@gQ9q=w3+f%IGm;cP^`ILJ+ynVwzrTztTTZU?lY3t1e!
zoK;5y$Ig#iHW5R-$(5GmYIYQVrFh6YfR;r%@g#@}wkg4VTNePRFkg4DEeYOKl;2eU
zsXRR~Hj5bh%-q-A`d#eRv5)#?Xhxt))2mzDQGWOTrzj=<Zx^(`ycc2yU{@nQ53y$)
zGLpXy7(Tv!98$9K|G|YJAu0X07lC;*H>1@Nn%nM=;BFCPl3jrmKR2P5N>6ggsa^s5
zN-sIky$eG*2|Rr6VYi4J2heNB+RSt$bSFYna!z@@vZFFBBaXO&JM%tep}>^A1%U0o
z9&{oz%5GyZjw#KY4N#g#E-VQrpvjCQP;k&}>=`bbilWNoBrOLcZ0)oJ^JpW%ytalR
zK^DAp%`QrKy=7BnaWds2c!j}vvSA~%Ey0)NoIhg$L^q+rA#vKIoGz~yek)BLOX$TR
z90AtEHUP5hjMu?sT8z88ay+Bd71>02lziEJN)#MI-bD*vnbF7YJw;|v&J9>dpIgPK
zRgwp%FlOr`8PmO#UTT&65>sM67*RiQK6#<C1-WesCgQ;f9v$AjUD6eOlV0NlH_D#i
z30W#$E|_jmpBx%DJxuE`$6R2G7eT^-PveHu&=(vQDc2-30LE$Ba*E)?IE7<-DQCGW
z2`AL6rY>>Ggb0}{81UOnC~TeOBZho%qA?#CEhM`0vm6-X>TFKtv{w`ZuS8VWRks*^
z3ZUY5T(-`~hCQ$3I-$4Mt#FZXzNwl_+GU-K`J0=MR%zT6!}VO)H(V&|DFe7-hxhyb
zTDn~=hb2<3A_4d(BL9+OU(_dQf6f<7bH{Wqi^H{>L8u~yXm)uNH**I%K;0ewD4E4)
zj&}LaVi|qA%eW1x!ANdzkls=CJ_|C?@l2+qP!CwzvFVwOtTX4?C2i@B7+bxcI&Yy~
z$S~qzN&St+eKpV=F>_#ksUgV#QSo8%aVum48XN^FIF(*zvJCc19W=$uZGKf;@}JzS
zU=Y}hm%Im*f@nX><!AH9Q~Dm}@y_^p@M*r_hxb4Hw!5cOx}FQIx(4oWLtp`+pNn7A
zO}DwP1uMXJA#jI@7C)j{2$ex&sME-ctWRWLpnJ6Kt*2Ppj%iC`0@7kvOOjEa#W&-#
zdu1pIzZ~${b|H|^A76HE@12g|=n;HfM|nIuuI;tZfWMJtq9-I=A5zDwvRUx**}I&X
z!)+BZQR5~FXfDM9Sh@A(lE}{*Dp`Gqhr(crnUNCKv(=xPYWx0_*7f`b*ey|7sQOQd
zFRAPuVXOxg2CWXrw7!;N<mUr@%dE=Gl+y2gRt<F!6$kZvz?(M0bH+23FGc$8@2}?&
zZ{02BS9n^~uUpMYxKJ0RYaZxdK;lG%u#!Lu{g3SQkEBO8PQjBmz~6b%#n-=pb>PbC
z%P?dm5G&z(Sb4?&Y|8|)O1)g-(X@;d)tPeUpff{j1WV}<`W;<GJ-ISuPpK$M8p2`T
z&OSu)hZ?-UF#72k_MV7|S`m#S(%6ltyKJja-_Fxmf~i46pYs}g>U{mAc|I8INd@*w
z7Wm8v_5tLk;t1IbX3a-Rk?7RMonjKJGpacn@84Pr!F!PgF%(bJ^Z56u@L)Y+W&`8l
z^gS|Ps-bOI+?0XC$NT3>8Lhm}r&v@JBLe1!9nsFE97N-62%AyrrDlma>nN!tliMpp
zRsSyVAC4_8*Ig=2W8LQn8?2E!dNCB<Ruk=h5t18I6&&d$*>rj)y`ledS%IkVQ~MBE
zMh(Z3<`Mlz2*|91?fn<W2Fls}pTENIioUiI+le)=whep_e4`=cv$wdrc=GbXvbHAI
zKGvqG+rUQd^6FHjk^!E9wO^ZxXS#JPLL+UHq5Up+Pt;->7<I}o-<IW`?>O|hk8%W-
zPSCBSPF{FXP}*1_U`i>PrB&>eK#=pEoZuwhpNIO}K&pG!t36RilkVp4OUYI5_O^Se
zVz`Y%`Sz*bG>lb>5e)|E61-+FF&*wIZ~*w|VNTXkRO@>%(J!P%dK|P+qSV<UdpF$0
zkZd8>r0ZHUr37H=ijj8B7HhY<2PQsGl?KZEXM88C{s_&6?y&d0P^a%S6owBfVRxVO
z4H3MlC>tXLCEw%`Awd57<#rIg>d^$O_h%m+A|mARIKTeo!%Y++Rz!7s7#mY~EJ=k;
z3x%JqbMKcw80HF_f_6?7r%_&}4`Uy&ABh_Tjj><FI4hxy?qb8g8DQD(Jen}^ZIfeE
z%{{;Ui8q#8gFku-t@2sXmNEwG`oP<OE&PUnE$Xe{Ee#Qj^)yq^J3rH}YCl_=tKdOZ
zU7BB`%SUAUSCieD#;syf`vOuKUxdON1a^*%lZFKy7LtHVui|Cavxy=d_(r#{Zc4bP
z&g;@BY}nY?E42j&7v8Yw0cRWp06qsjBf+*VZA4@fdnC#10Rw}-|Ci3W&)Iw`tIK&C
zVi`J<JdXMU-f?%E(D%gIf055t?Q^3679#aYp<0?nV;K|gwg5wDa&<@FTT=h_@Dahr
z3F#fKKeYdl?=oQLxUURKHlB%Gp6zr_x;fMg4iD?@>A-I{QMqc&HW>P~5qj+%5)3)(
zR=DGj$1U)$4O1v6+l2M*Px?t1@8ZsmauoP2Y4OeLRSL4uLoBG_Z979AaV0ECXOD_O
z8R?B-i#lGBmo^;?gzSMf1)7_)JN)dP*;T@vwTl{-6Bz3?WC`w>N!!7vSBurtEhYC`
z=@QaD2-9Ak*RiaBmKCYDS7F+=PA1%`inDAr*ymj2<+H1I$_%;K{fb`qKM|~m&MlHs
z?R(Z-%Tsq|%IO*{`jkfv84C>Lyw1uX*8`D#d$@PC<RVe61frY{n3DzLR*AVK23;>s
zZmgO@hn)?D?RIN6m(Du3)SG(rwjFQp)R?~0g%1$DIeUs<YXUk%(`#h%*Pe!$EU;Qq
zwg-gMpD~+|e!uIKZ(r_++dbK*Kx5f^JROg8nnd(s*&Z!txC=ggQZlNeO}-=}m*H`?
z0m;XyA~r-WMj*dTpO{&d<!UFt_M6B#sKlE>p3|9mX}079E}F6%1b8pJt){@Blh&b4
z$&xKxJG2VPg3n8x>}y<$Doo4PJe8^(**1PGn*_Sm_%&7N6ZR8;8K=Cui-aQyj@6br
zNxVb@@37t{Hs*HxcY$WbX9{L^^qC6>N}1BOrgE(h2f|pubsZh=owO7e`52(4Njn`k
z{+HV^9aW(R)iQl4E3$S3kmrDM`hgZ=hy>eaG}B;o+4dYc0_5CEZ8jG*DSA>7()44f
z@@dp#%S22V7PIwSS2=fG5OdaXn57om{5nnwONz>prT-_s%KzKI!~bEKnW3YTn4y=K
zAID#{E<-VGPbV*qzk+@*pMsQR0WttPaVZB$IeST2dueGq2XT9OIXi&7jFX%UKuTWa
zzeiY!8T}FBNFpyOW(pGLkmQiC`cKp+^Ec>|o*oxfe?c36bS72<-Jflz_5Zk7Qw1V^
zaA?bGtp>JT%NTe%5R*^|092_F>c=F=S8UVijomdWTzwn2;JZQlPD~)@6U_Ur#Z%bi
zVkPC`PudcM+TzCBYKvxgVH-h#+ZDoy+d>eMc7pxNWQj)z9ebrFVWxK@6D6>gQ;-X@
zj$d!A-oaqkJ%a49<E>{)Ts&fZN=C#8dKk-~lY5V*dmm$qts-wd-6|jzmN#I{nm*}j
zqs(t*4RtQNsk%r%tv^&2&JODYmibITjgd;hY1&iqU+%;{sLF_TEj&Y{Z-jO6m0ti4
z+46_XC${lK?I=ve(^%SsVELEwAhYytU#D>RwP620x6;?o&c`pv$I*%8cPp2ygaio>
KkA{IJ$-e+zo>Nf(

literal 0
HcmV?d00001

-- 
GitLab