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@valenting
Created October 11, 2017 01:10
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{
"cells": [
{
"cell_type": "code",
"execution_count": 40,
"metadata": {},
"outputs": [],
"source": [
"%matplotlib inline\n",
"\n",
"import numpy as np\n",
"import pandas as pd\n",
"\n",
"X = pd.read_csv('X.csv')\n",
"Y = pd.read_csv('Y.csv')\n"
]
},
{
"cell_type": "code",
"execution_count": 41,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<table><tr><td>Column name</td><td>Mean</td><td>Maximum</td><td>Minimum</td><td>25th percentile</td><td>90th percentile</td><td>StdDev</td></tr><tr><td>all_..idle</td><td>9.06498055556</td><td>69.54</td><td>0.0</td><td>0.0</td><td>38.621</td><td>16.1228222715</td></tr><tr><td>X..memused</td><td>89.1375166667</td><td>97.84</td><td>73.03</td><td>82.965</td><td>96.77</td><td>8.1836619982</td></tr><tr><td>proc.s</td><td>7.68330277778</td><td>48.0</td><td>0.0</td><td>0.0</td><td>20.0</td><td>8.53260553516</td></tr><tr><td>cswch.s</td><td>54045.8740222</td><td>83880.0</td><td>11398.0</td><td>31302.0</td><td>72135.1</td><td>19497.8115402</td></tr><tr><td>file.nr</td><td>2656.33333333</td><td>2976</td><td>2304</td><td>2496.0</td><td>2880.0</td><td>196.110747783</td></tr><tr><td>sum_intr.s</td><td>19978.0407472</td><td>35536.0</td><td>10393.0</td><td>16678.0</td><td>28228.4</td><td>4797.2713252</td></tr><tr><td>ldavg.1</td><td>75.8757722222</td><td>147.47</td><td>11.13</td><td>28.2</td><td>127.993</td><td>43.8624445166</td></tr><tr><td>tcpsck</td><td>48.9975</td><td>87</td><td>21</td><td>34.0</td><td>71.0</td><td>15.8711554494</td></tr><tr><td>pgfree.s</td><td>72872.1545694</td><td>145874.0</td><td>15928.0</td><td>61601.75</td><td>97532.5</td><td>19504.3211753</td></tr></table>"
],
"text/plain": [
"<IPython.core.display.HTML object>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/html": [
"<table><tr><td>Column name</td><td>Mean</td><td>Maximum</td><td>Minimum</td><td>25th percentile</td><td>90th percentile</td><td>StdDev</td></tr><tr><td>DispFrames</td><td>18.8183944444</td><td>30.3900001049</td><td>0.0</td><td>13.3900001049</td><td>24.6099998951</td><td>5.21975623821</td></tr></table>"
],
"text/plain": [
"<IPython.core.display.HTML object>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# 1. Compute the following statistics for each component of X and Y:\n",
"# mean, maximum, minimum, 25th percentile, 90th percentile, and standard deviation.\n",
"from IPython.display import HTML, display\n",
"\n",
"def fun1(X):\n",
" data = [[\"Column name\", \"Mean\", \"Maximum\", \"Minimum\", \"25th percentile\", \"90th percentile\", \"StdDev\"]]\n",
" for col in X.axes[1][1:]:\n",
" data.append([col, X[col].mean(), X[col].max(), X[col].min(),\n",
" X[col].quantile(0.25), X[col].quantile(0.90), X[col].std()])\n",
"\n",
" display(HTML(\n",
" '<table><tr>{}</tr></table>'.format(\n",
" '</tr><tr>'.join(\n",
" '<td>{}</td>'.format('</td><td>'.join(str(_) for _ in row)) for row in data)\n",
" )\n",
" ))\n",
"\n",
"fun1(X)\n",
"fun1(Y)\n"
]
},
{
"cell_type": "code",
"execution_count": 42,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"2875\n"
]
}
],
"source": [
"# the number of observations with memory usage larger than 80%;\n",
"\n",
"print(len(X[X['X..memused'] > 80]))"
]
},
{
"cell_type": "code",
"execution_count": 43,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"46.3473053892\n"
]
}
],
"source": [
"# the average number of used TCP sockets for observations with more than 18000 interrupts/sec;\n",
"\n",
"print(X[X['sum_intr.s'] > 18000]['tcpsck'].mean())"
]
},
{
"cell_type": "code",
"execution_count": 44,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"73.03\n"
]
}
],
"source": [
"# the minimum memory utilization for observations with CPU idle time lower than 20%\n",
"\n",
"print(X[X['all_..idle'] < 20]['X..memused'].min())"
]
},
{
"cell_type": "code",
"execution_count": 45,
"metadata": {
"scrolled": true
},
"outputs": [
{
"data": {
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/g/z3K42Cx9mmUznFVAhaHyQOeoagSUqu++o6AK92lT9u/JEBrw/gnPHnkDI2hZxHc/wq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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f4ddf9ed710>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# Time series of percentage of idle CPU and of used memory (both in a single plot);\n",
"\n",
"import matplotlib.pyplot as plt\n",
"\n",
"timeIndex=pd.to_datetime(X['TimeStamp'], unit='s')\n",
"X.index=timeIndex\n",
"\n",
"X['all_..idle'].plot()\n",
"X['X..memused'].plot(secondary_y=True, style='g')\n",
"plt.show()\n"
]
},
{
"cell_type": "code",
"execution_count": 46,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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IWA9cZmbrpxx2BdDt7qcB1wLXTNl/LfAfBdYo86A/kz2haywA0qkEqSpTN5RIjBQaFsPu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\nvBQWIiKSl8JCRETyUliIiEhe/x9AphX0Z6IMkAAAAABJRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f4ddf995850>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": 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"text/plain": [
"<matplotlib.figure.Figure at 0x7f4ddf995810>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": 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GxICk70laXJ1YAIBSVVLo10r632HbR7J9AIA6GF/Baz3CvvjYQfYKSSskadasWRWcDs3q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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f4ddc7a0150>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# Density plots, histograms, and box plots of idle CPU\n",
"\n",
"X['all_..idle'].plot.kde(); plt.show()\n",
"X['all_..idle'].plot.hist(bins=70); plt.show()\n",
"X['all_..idle'].plot.box(); plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 47,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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Z5KYG14eyTu5oG60Z6qrAb/jFIvLVoa+r6veHOc0YM0UcavLPsZgdgWYoE12jNUOlBn6O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\nRK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f4dc12dab90>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": 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"text/plain": [
"<matplotlib.figure.Figure at 0x7f4dc1280f50>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
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"text/plain": [
"<matplotlib.figure.Figure at 0x7f4dbd1dc450>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# Density plots, histograms, and box plots of used memory.\n",
"\n",
"X['X..memused'].plot.kde(); plt.show()\n",
"X['X..memused'].plot.hist(bins=70); plt.show()\n",
"X['X..memused'].plot.box(); plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 48,
"metadata": {},
"outputs": [],
"source": [
"# reload the plots\n",
"\n",
"X = pd.read_csv('X.csv')\n",
"Y = pd.read_csv('Y.csv')"
]
},
{
"cell_type": "code",
"execution_count": 49,
"metadata": {},
"outputs": [],
"source": [
"# Producing training set and test set\n",
"\n",
"fields = [\"all_..idle\",\"X..memused\",\"proc.s\",\"cswch.s\",\"file.nr\",\"sum_intr.s\",\"ldavg.1\",\"tcpsck\",\"pgfree.s\"]\n",
"\n",
"# Here is how you can create a single frame with selected columns\n",
"XY = pd.concat([X, Y['DispFrames']], axis=1)\n",
"\n",
"# Split XY into training set and test set of equal size\n",
"\n",
"from sklearn.model_selection import train_test_split\n",
"train, test = train_test_split(XY, test_size = 0.30)\n",
"\n",
"# Sort the train and test sets after index (which became unsorted through sampling)\n",
"train = train.sort_index(axis=0)\n",
"test = test.sort_index(axis=0)\n",
"\n",
"# Extract X,Y components from test and train sets\n",
"X_train = train[fields]; X_test = test[fields]\n",
"Y_train = train['DispFrames']; Y_test = test['DispFrames']"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": 50,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[-0.09 -0.09 -0.01 -0. -0. 0. -0.06 -0.06 -0. ]\n",
"0.0984907303932\n"
]
}
],
"source": [
"# Computing and evaluating a linear model\n",
"\n",
"import numpy as np\n",
"from sklearn import linear_model\n",
"\n",
"# coumpute Normalized Mean Absolute Error\n",
"# NMAE = 1/y_training_mean*1/m*SUM(Y_test[i]-M[i]) # M[i] = predicted Y_test[i] via model\n",
"\n",
"def computeNMAE(xtrain, ytrain, xtest, ytest):\n",
" # Create linear regression object\n",
" regr = linear_model.LinearRegression()\n",
"\n",
" # Train the model using the training set\n",
" regr.fit(xtrain, ytrain)\n",
"\n",
" # Print the model coefficients\n",
" np.set_printoptions(precision=2)\n",
"\n",
" y_training_mean = ytrain.mean()\n",
" M = regr.predict(xtest)\n",
" nmae = 1/y_training_mean*1/len(ytest)*np.sum(np.abs(ytest - M))\n",
" return (regr.coef_, nmae, M)\n",
"\n",
"coef, nmae, M = computeNMAE(X_train, Y_train, X_test, Y_test)\n",
"print(coef)\n",
"print(nmae)"
]
},
{
"cell_type": "code",
"execution_count": 51,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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maCdfSbX3jXyvqblclIWusCcyV36yuL0BPdtiAnHsKLZxhLY4an375rmUPHIi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\nFv4A5ClLfwAAAABJRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f4dbd242fd0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"Y_test.plot()\n",
"pd.Series(M, index=Y_test.index).plot()\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 52,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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EgSuBPQvO2QNc496+HLhfnXaSPcCV7mipLcA24GceltUYY8wSPKtZ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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f4ddf8627d0>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": 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pyYCQJDUZEJKkJgNCktRkQEiSmgwISVKTASFJajIgJElNBoQkqcmAkCQ1nTCOL02yB3gG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\nAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f4ddf943350>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# Produce a density plot and a histogram for the Video Frame Rate values in the test set.\n",
"# Set the bin size of the histogram to 1 frame.\n",
"Y_test.plot.kde(); plt.show()\n",
"Y_test.plot.hist(bins=range(int(Y_test.min()), int(Y_test.max()))); plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 53,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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kz7+/nq0dUd7/+gV8+vKl6nYK2FXnzGZOXRkf/956rr/zCVYtbOCipc3Mritn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\nByE9xTd5+AAAAABJRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f4ddf862f50>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# Produce a density plot for the prediction errors Y_test - M in the test set.\n",
"(Y_test-M).plot.kde(); plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 54,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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7tL0ta/+q7W1F1DLfZbbHXbZ/kL2GR23fkU2/zfaR7PU9Yvs3K5b1K9n0k9nv4PLf9NRI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\nSUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f4dc1296ed0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"def partialTrain(size):\n",
" if size < len(train):\n",
" trainSet, testSet = train_test_split(train, train_size = size)\n",
" else:\n",
" trainSet, testSet = train, test\n",
" return trainSet\n",
"\n",
"def trainAndCompute(size):\n",
" trainSet = partialTrain(size)\n",
"\n",
" xtrain = trainSet[fields]\n",
" ytrain = trainSet['DispFrames']\n",
" \n",
" coef, nmae, model = computeNMAE(xtrain, ytrain, X_test, Y_test)\n",
" return nmae\n",
"\n",
"train_len = len(train) # should be 2520\n",
"values = {50: pd.Series(index=range(0,50)),\n",
" 100: pd.Series(index=range(0,50)),\n",
" 200: pd.Series(index=range(0,50)),\n",
" 500: pd.Series(index=range(0,50)),\n",
" 1000: pd.Series(index=range(0,50)),\n",
" 2520: pd.Series(index=range(0,50))}\n",
"\n",
"for i in range(0,50):\n",
" for size in values:\n",
" values[size][i] = trainAndCompute(size)\n",
"\n",
"xaxis = [50, 100, 200, 500, 1000, 2520]\n",
"vl1 = []\n",
"for size in xaxis:\n",
" vl1.append([values[size]])\n",
"\n",
"plt.boxplot(vl1, labels = xaxis)\n",
"plt.show() "
]
},
{
"cell_type": "code",
"execution_count": 55,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"('Offset:', array([ 0.0005]))\n",
"('Coefficients:', array([[-0.0571, -0.037 , -0.0012, -0. , 0.0034, 0.0003, -0.0839,\n",
" -0.0636, -0. ]]))\n"
]
}
],
"source": [
"# Task 3\n",
"\n",
"Y_trainSLA = (Y_train >= 18)*1; Y_testSLA = (Y_test >= 18)*1\n",
"\n",
"from sklearn import datasets, neighbors, linear_model\n",
"\n",
"C = linear_model.LogisticRegression()\n",
"\n",
"C.fit(X_train, Y_trainSLA)\n",
"\n",
"np.set_printoptions(precision=4, suppress=True)\n",
"print(\"Offset:\", C.intercept_)\n",
"print(\"Coefficients:\", C.coef_)"
]
},
{
"cell_type": "code",
"execution_count": 56,
"metadata": {},
"outputs": [
{
"ename": "TypeError",
"evalue": "'numpy.float64' object is not callable",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mTypeError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-56-75412b52ffde>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m 4\u001b[0m \u001b[0mTN\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m1\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mY_testSLA\u001b[0m \u001b[0;34m|\u001b[0m \u001b[0mM\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 5\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 6\u001b[0;31m \u001b[0;32mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'True positives: %i'\u001b[0m\u001b[0;34m%\u001b[0m\u001b[0msum\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mTP\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m;\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 7\u001b[0m \u001b[0;32mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'True negatives: %i'\u001b[0m\u001b[0;34m%\u001b[0m\u001b[0msum\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mTN\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m;\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 8\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;31mTypeError\u001b[0m: 'numpy.float64' object is not callable"
]
}
],
"source": [
"M = C.predict(X_test)\n",
"\n",
"TP = Y_testSLA & M\n",
"TN = 1 - (Y_testSLA | M)\n",
"\n",
"print('True positives: %i'%sum(TP)); \n",
"print('True negatives: %i'%sum(TN)); \n",
"\n",
"ERR = 1.0 - (sum(TP)+sum(TN))/float(len(M))\n",
"print(ERR)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"p = sum(Y_trainSLA)/float(len(Y_trainSLA))\n",
"print(p)\n",
"\n",
"import random\n",
"\n",
"naive_Y = []\n",
"for y in Y_testSLA:\n",
" naive_Y += [1*(random.random() > p)]\n",
" \n",
"print(\"p = \"+str(p))\n",
"\n",
"naiveTP = Y_testSLA & naive_Y\n",
"naiveTN = 1 - (Y_testSLA | naive_Y)\n",
"\n",
"\n",
"print('True positives: %i'%sum(naiveTP)); \n",
"print('True negatives: %i'%sum(naiveTN)); \n",
"\n",
"naiveERR = 1.0 - (sum(naiveTP)+sum(naiveTN))/float(len(naive_Y))\n",
"print(naiveERR)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import numpy as np\n",
"from sklearn import linear_model\n",
"\n",
"# Create linear regression object\n",
"regr = linear_model.LinearRegression()\n",
"\n",
"# Train the model using the training set\n",
"regr.fit(X_train, Y_train)\n",
"\n",
"linearM = regr.predict(X_test)\n",
"# print(linearM)\n",
"linearSLA = map(lambda x: 1*(x>=18), linearM)\n",
"# print(linearSLA)\n",
"\n",
"linearTP = Y_testSLA & linearSLA\n",
"linearTN = 1 - (Y_testSLA | linearSLA)\n",
"\n",
"print('True positives: %i'%sum(linearTP)); \n",
"print('True negatives: %i'%sum(linearTN)); \n",
"\n",
"linearERR = 1.0 - (sum(linearTP)+sum(linearTN))/float(len(linearSLA))\n",
"print(linearERR)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Task 4.2\n",
"\n",
"# Generate all subsets containing either one or two distinct features\n",
"subsets = []\n",
"for f in fields:\n",
" subsets.append([f])\n",
"for f in fields:\n",
" for f2 in fields:\n",
" if f != f2:\n",
" subsets.append([f, f2])"
]
},
{
"cell_type": "code",
"execution_count": 112,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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IK3wuxszMzJrITV/7x3jgREmvknIfCytjJwE3kB70nwsMLbt3800DzpO0gvRc2mokHQ0Q\nEecBX5S0D2ll7RHgV/00RzMzs0FB6bEkawZJw4EbImLHFk+lX9V73WPHjo25c+f2y5zMzMxaTdID\nETG21jjfpmyu7kDvVk+kv+Q2Hb8E/trquZiZmQ1EXhkboCTNAtYt2fzJiOhqxXzqse6wkTHs099r\n9TTMzNqasyc7R70rY35mbICKiHe3eg5mZmbWey25TVma4Zi3LenF8TaXNCt3iN+zKZOsfc4lzRjT\nbJIulLR9jTEfrjWmjvMsyf+OyN35l9fYxczMzMpo5TNjzcxw3Bfoioh35azEbo1mLQ50EfHZiHik\nxrAPA2WLMUkNrZZGREuzOM3MzAa6dnqAv5DjOD7nON4o6XFJ5+U+XEj6jKQnJM3OmYtn52ig7wAH\nFWUtLpd0pqT5wO6Sxki6S9IDkm6RNCwfb4Skm/P2e8o1NJW0Qz7fPEkPSxpZPN86r2nDfD3zczbl\nxLx9iaTN8uuxkmbk15MlXZzn9DtJEyR9R1JXnu/alU4oaYaksfn1cknfyue9X9I/S3oP8CFgar6m\nEXmf70maC5zQxOs3MzOzGtqmGCvKcQQYBxxHWr0ZAUyQtAXwNWA3YA9y89Ec93MKcEVR1uKGwKyI\n2BmYBfwAOCQixgAXAd/K5zkfOC5vnwScW2ZqRwNn5dWfscDvy8y31jV9APhDROyc2z/cXMdXMgJ4\nH6lwuhS4MyJGASuAep/u3BC4P38PdwP/HhH3AtcDJ+bv67d57Do5tujMkmP0+PrB2ZRmZma1tOsD\n/LMjYhGApMuB9wKvAXdFxLN5+5XA2yvsvxK4Or9+B7AjcJskgCHAUklDgfcAV+btsOavEwHuA06W\n9Bbgmoh4sgfX0wWcIWkKqR/XPbV2AH4VEa9K6spzLhRwXdSfTfkKqakspGzKf6kytlI2Za+uPyLO\nJxW9rDtspH+6a2ZmVqJtVsZKlP6fdgAqN7CClyJiZX4tYGFeBRodEaMiYn/StT9XtH10RLxzjYlE\n/Iy0OrUCuEnS+xq+mIgngDGkQupUSafkj15j1d+gbDZlDgd/NVb1IGkkm7J4v55mU/b6+s3MzKyy\ndi3GxknaOj8rNhH4DTAb2FvSG/ND5gfXeazHgc0l7Q4gaW1JO0TE88BiSYfm7ZK0c+nOkrYBFkXE\n94HrgJ3KjHms2gTyLdYXI+JSYCqrsimXkIo0GrieZmgkm7Lm9ZuZmVnPtWsxNgc4G3gUWAxMj4hn\ngG+TirKZpEKm5kNIEfEKcAgwJT/QP490exLgMOAzeftC4CAASR+S9M08ZiKwILfh2BG4pPj4+QH8\nWqt2o4DZ+RhfB07N278BnJUfnF9Zaec+8HNSVuZDkkaUftjI9ZuZmVnvtKQDv6pkGUoaD0yKiAPL\nfDY0IpbnlbHpwEURMb2Pp1uVpAOBbfLK0aAlaXlEVA06dzalmZkNJmrzDvzdGY4N9qiaLGk/0vNV\ntwLX9snsGhARN9Qe1bnyytrVwJ9aPRczM7OByNmUA5Sk6cDWJZu/EhG3tGI+9XA2pZlZbc6m7Bzt\nvjJmvRQRH2n1HMzMzKz3OiKbshfzuEnSpjXGHJ5/DdnTcwwvdNavMW5JL87RsmxOOZvSzMysVzol\nm7JHIuKAiHiuxrDDgbLFmNon97Jl2ZzOpjQzM+uddmptUchxHKaUTTkv5zjumbd3r7xIOkTStPx6\nmqQf5uzFRZL2lnSRpEcLYypRzobMq1ePKuVdLpR0q1LG5SGkCKDLtCr3comkKZIeBA4tOd7eedy8\nvEq1EenHCs82cP2dlM1pZmZmNbRNMVaUdfhx4Ja82rIzqS9YLW8Edge+BPwS+C6wAzAqFyv1GAmc\nExE7AM8BB0fEVcBc4LCi3EuAv0XELhHx85JjTAKOyXPfE1gREU9HxIRaJ+/EbE5wNqWZmVkt7fgA\n/xzgIklrA9fmYqOWX0ZEKOU4/ikiugAkLSTlONZzjMVF53qA6vmPlXIcZwL/I+kyUo7j7+s4bzkd\nk83pbEozM7Pq2mZlrCAi7gb2Ap4Bpkn6VOGjomFlcxxJuY0vF21vJMexeL+e5jieDnwWWB+YWe62\nX506KpvTzMzMKmu7YkzS24A/R8QFwIWsynH8k6R35uen+rOtQyM5jiMioisippBW+LYr+XxLSbfX\ncagBlc1pZmZmPdd2xRgwHpgn6SFSIXJW3n4ScANwL7C0H+czDTiv8HB86YeSjpZ0dH77xfyjg/nA\nq8CvSoYPI91urGXAZHOamZlZ77RdNmUnk3Qs8FREXF9lzHgGUDZngZxNaWZmthrV2YG/VStj3dmU\nLTp/S0TE2dUKsTpMzt/ZAtKKWcuzOXN7jHk4m9LMzKxHWvJryoh4Gtiqv84naRZr/lLwk4VfXbaT\niJgBzKjw2aR+nUwdIuK3gJu+mpmZ9VA7trZouoh4d6vnYND1zDKGn3Rjq6dhZtbWHBQ++LTjA/xm\nZmZmg0bbF2NyqHi5cUt6eo5mkTQj/xADSXfmCKaaDymamZnZ6tq+GMtaHkY9kEPF+/rcEbEPKTbK\nzMzMGjRQirFSDhWnZqh4aVj4vvk8Xfma183jdpV0r6T5SoHgqzW4lbRhPv78/B1PzB89m+drZmZm\nvTAgizGHilcPFc/bi8PC55Ka106MiFGkH258XtI6pJzNE/K4/UixR8U+APwhInbOfeFuznOYkH8V\nW5UcFG5mZlbVgCzGiswBjpA0GRgVES/Usc8vI3W67Q4Vj4jXSR3oh9d53maGih8PbBoR9XTmL2d2\nRCzKWZSFUHFYMyx8cUQ8kd9fTMr/fAewNCLmAETE82Xm0QXsl1f49oyIhiqqiDg/IsZGxNghG2zS\n+NWZmZl1uAFdjDlUPB2qwvvSsPByVGb/0nk+AYwhFWWnSjqlh/M0MzOzMgZ0MSaHikP5UPFSjwHD\nJW2b338SuCtv30LSrvmcG+WopeJ5bAG8GBGXAlNZ9R2bmZlZEwz0pq/jgRMlvQosBworY4VQ8b+Q\nnpeqmpnYRNNIoeIrSM+lraYQKB4R55FCxfchraw9Qu9DxbcF7iRlVq4mIl6SdARwZS625gDnRcQr\n+YH8HyiFoK8g3ZLcGLgwIg4ARgFTJb1OCj//fB1zKmvUlpsw180MzczMVtOSoPBGOFS8Z6Hi/S33\nR5sUERVbXDgo3MzMBpN6g8IHwspYd6h4q3uN9aeIOLvVc6iXpDuBbUgrZ2ZmZtaAtl8Z628DKVR8\noFl32MgY9unvtXoaZmZtzdmUnaOTVsb6lUPFzczMrD+1/a8p2yWbslRxNmOVMdPyc139RtLRRS0+\nKo0ZLemAXp7H2ZRmZmZNMFBWxnqVTSlpSFHPrY6Wf6lZy2hSdNNNpR9IekOjDWgjYh/VEXBuZmZm\na2r7lbEKBko24zLglXyc0yU9IulhSWfkbdOUMi0L51pedF13SbpOKUPzdEmH5fl1SRpR6YSSJkua\nlF/PyJ3zZ0t6QtKeOQLpm8BEpVzMiXmfn0qaCfy0iddvZmZmNQyUlbHVlMlm3B74HSk3cQJwFauy\nGf9D0nrAk8C+EfGEpEtI2YznkuKKJkbEnNxfq1I24wcBJG2S51BPhuQJeZ83kZrPbhcRIWnTOi5z\nZ+CdpKJnEanv1zhJJ5CyKL9YxzEA3pD3OwD4ekTsl7voj42IY/P8JpO+w/cWZWoW9Pj68/ijgKMA\nhmy8eZ1TNjMzGzwG6spYsbbOZsyeB14CLpQ0AXixjn3mRMTSiHgZ+C1wa9F8hjdw7mvyv7UyNK8v\nU4gVzudsSjMzsz7SCcVY22cz5gJvHKk4PJC0ggepw37htqqAdYp2K83NLM7UbGRFs7BfTzM0nU1p\nZmbWhzqhGGt5NqOkSySNqzRBSUOBTSLiJuBLpFuQAEtIhQ7AQcDadVxvMzSSoelsSjMzsz40IJ8Z\nK9EO2Yw7AUurzHEj4Lr87JqA/5e3X5C3zyetlpVdneoDdwIn5XYhp5V+mAuwpmdTmpmZ2ZravgO/\nqmRTqg2yGXPh9uOIOLRVc2gHcjalmZnZalRnB/6BcJuyO5uy1RMpJz/0P9gLMWdTmpmZ9VDbr4xZ\neZJOBkqLwCsj4lutmE89nE1pZlaZMyk7T70rY53wzNiglIuuti28zMzMrD5tfZtSPcylLOpkP1zS\ngh6e+0JJ25fZfriks/Pr7m73VY4zWdLhNcYcnhuv9oikQyU9mm8X9rni+Ur6kqSnCt+JmZmZNWYg\nrIz1KpeypyLis/19zl74DPDvEbFaWw/1IGeyURHxXUl/J2VdmpmZWYPaemWsgkIu5VBJt0t6MOc1\nHtTMk+Rcx7H59RE523E2sEeF8SMk3SzpAUn3SNouf7ScNSOWSq3I4wp5ledJmpvPeWDevoGkXyjl\nW06XNEvS2NyE9b3AjyVNzatW10u6A7g973uipDlKuZjfKJrzJ5RyK+dJ+pGkIWWu63itytT8eel8\nzczMrHcGwsrYaopyKV8CPhIRz0vaDLhf0vXR5F8kSBoGfIPUnHUZqUfXQ2WGng8cHRFPSno3cC7w\nvog4o9Y5IuKKkk3DSR37RwB35ma1XwD+HhHbS9oRmJf3/aak95HbSuRborsAO0XEs5L2B0bm4wm4\nXtJepKJ2IrBHRLyqlNN5GHBJyVxOAraOiJeVMzXLzLciZ1OamZlVN+CKsSICvp0Li9eBLYF/Bv7Y\n5PO8G5gREYUVuSuAt682kdRh/z2kprKFzev24py/iIjXgSclLQK2I61+nQUQEQskPVxl/9si4tn8\nev/8X6GAHEoqznYiFZhz8pzXB/5c5lgPA5dJuha4ttELiYjzSYUq6w4b6Z/umpmZlRjIxdhhwObA\nmLyyswRYr4/OVauIWAt4ronPtpXL26yUr1lOcSd/AadFxI+KB0g6Drg4Ir5a41gfJIWq/1/gZEmj\n+vo5NDMzs8FkID4zVrAJ8OdciO0DvK2PzjMLGC/pzZLWZs3eXkTE88BiSYdCCv2WtHPpOEnHSjq2\njnMeKmktSSNIzVQfJ2Vu/ls+zvakmKJ63AIcmVfvkLSlpH8iPU92SH6NpDdJWu07VMr73Coi7gS+\nQvrOh9Z5XjMzM6vDQC7GLgPGSuoCPkUK/W6IpC0k3VT0/ialXMZuEbEUmAzcB8wEHq1wuMOAzyjl\nTC4kBX+X2g74Wx1TewqYDfyK9BzaS6Rn0DaX9Ahwaj7HsloHiohbgZ8B9+Xv6ipgo4h4BPgv4NZ8\ny/M2YBh0t/UYCwwBLs37PQR8PyKeq2P+ZmZmVqe27sCvKrmUA5GkG4AJEfFKlTHTSNd8Vcn2IcDa\nOfR8BGll6+3VjtVf8o8GxkZE1VU/Z1Oamdlgog7pwN+dS9mKXmPN1stA8w1Iv6xcm/Qc2OfbpBD7\nEnA0cHWr52JmZjYQtfXKWLNIej8wpWTz4oj4SCvmM1g5m9LMrDJnU3aeTlkZa4qIuIX0ILuZmZlZ\nW2nrB/jVw2zKKsfbPHeuf0jSnk2ZZO1zLmnWmNzctjt7swdz+aak/cpsH5+fZ1ste7PKcZxNaWZm\n1iQDYWWsmdmU+wJd5XInJQ2JiJVNOk9biohT+uCYzqY0MzPrhbZeGaug0Al/vKS7Jd0o6fGc57hW\n/uwzOddxtqQLJJ0taTTwHeCgnMW4vqTlks7M7Sh2lzRG0l1K+ZK35CikarmT3STtoFU5jw9LGlk8\n33quKR/n2nyehTlKqGmUci8Pya8/IOkxSQ8CEyqM31zS1Uq5lnMkFXI5nU1pZmbWJANhZWw1RdmU\nkPIWtwd+B9wMTJB0L/A1Uj7jC8AdwPyImKcUqt3dgkHShsCsiPiP/CvFu4CDIuIvkiYC3wKOpELu\nZMnUjgbOiojLJK1D6tFVOt96runInCm5Pimq6OqIqKc3Wd0krQdcQLqG/wUqZU2eBXw3In4j6a2k\n5+7e6WxKMzOz5hlwxViJ2RGxCEDS5aT8xteAuwrZjJKupCRLsshKVrVkeAewI3CbUlbjEGCp6s+d\nvI8UF/QW4JqIeLKH13S8pMKvPLci5Ug2tRgjNZ9dXJijpEvJBVOJ/YDti657Y0kbRcQL9Z7I2ZRm\nZmbVDfRirLcZji8VPScmYGFE7F48QNLG1JE7GRE/kzSLlOV4k6TPRcQdDcwFSeNJBdDuEfGipBm0\nLm8T0m1Vity2AAAgAElEQVTs3SNiRR/NwczMbNAbiM+MFRsnaev8rNhEUn7jbGBvSW+U9Abg4DqP\n9Tgpbmh3AElrS9qhgdzJbYBFEfF94DpgpzJjakU2bQL8PRdi2wG71Tn3Rj0GbJ07+QN8rMK4W4Hu\nrvr5uTszMzNrooFejM0BziblRS4GpkfEM8C3SUXZTGAJ9WU4vgIcAkzJD/TPI92ehAq5k5I+JOmb\necxEYEFuw7EjcEnx8XNbilqrdjcDb5D0KHA6cH+teZdT0gqkkDPZLWddHgXcmB/g/3OFQx1Pyv98\nWCkT8+iezMfMzMwqa+sO/KqSTZlv6U0qFzEkaWhELM8rY9OBiyJieh9PtypJBwLb5JWzjiJnU5qZ\nma1BdXbgb/eVse5sygb3m5z3WUBaMbu26TNrUETc0KGF2JeArwLPt3ouZmZmA1Fbr4xZZZJGAT8t\n2fxyRLy7FfOph1fGzMxsMKl3ZWyg/5py0IqILmBAPVDf9cwyhp90Y6unYWbWlhwUPni1+23KjqSS\nzE3Vl025vGjfBT0874WSti+zvTuPUtJkSZNqHGdyfk4MSVMl/bHWPmZmZlaeV8Zap5mZm3Upl8nZ\nhGOeKOkfzT6umZnZYOGVsfZQyNscKul2SQ9K6pJ0UDNPImlGoc2FpCOU8zuBPSqMr5TJuZyUT2lm\nZma95JWxNlCUTfkS8JGIeD73Jbtf0vXR5F9ZKAWgfwMYQ+rBdifwUJmhZTM5I+KMBs7lbEozM7Mq\nXIy1FwHflrQX8DqwJfDPwB+bfJ53AzMiorAidwUl+Z0NZHJW5WxKMzOz6lyMtZfDgM2BMRHxan6w\nv1XZlGtRRyanmZmZ9Y6fGWsvmwB/zoXYPsDb+ug8s4Dxkt4saW3g0NIB9WZympmZWe+4GGsvl5Gy\nILuAT5ECvRsiaQtJNxW9v0nSFsVjImIpMBm4j5Tf+WiFw5XN5DQzM7PmcQf+FqiWuTkQSZoMLK/1\nYL878JuZ2WDSKdmUnaqnmZttR9JU4BOAe42ZmZn1gB/gb4GIeBrYqjfHkPR+YErJ5sUR8ZHeHLdR\nEXEicGJ/ntPMzKyT+Dal9Zt1h42MYZ/+XqunYWbWcs6hHBx8m9LMzMxsAOiYYqw0fDtvW1LHfm0Z\nwF1lzOH5gfmWyd/VjPx6T0mP9PS7MzMzG+w6phjL+j18G1IAd0Q80t/nrYekPn0uMCLuAQ7oy3OY\nmZl1sk4rxkp1YgD3ijwOSdMknSdpbj7ngXn74ZKul3QHcHtu2DpV0oJ8/ROL5vLlvG2+pNPLzHVv\nSfPyfw9J2oj0a9BnG/uWzMzMrJyO/jVlJwZwR8QVJZuGA+OAEcCdkrbN23cBdoqIZyUdDIwGdgY2\nA+ZIujtv+zDw7oh4UdKbypxyEnBMRMzMeZUvRcQLwIRacwUHhZuZmdXS0cVYkY4L4C7yi4h4HXhS\n0iKgsMp2W0QUVq/eC1weESuBP0m6C9gV2Bv4SUS8CFA0vthM4H8kXQZcExG/b2RyDgo3MzOrbrAU\nY50cwF16vsL74iasojyV2X/1g0WcLulG0nNhMyW9PyIajmkyMzOz8jr9mbGCARfALelYScfWcc5D\nJa0laQSwDfB4mTF3AxMlDZG0ObAXMBu4FThS0gb5nGvcppQ0IiK6ImIKMIdVK29mZmbWBIOlGBuI\nAdzbAX+rY2pPkQqrX5GeQ3upzJjpwMPAfOAO4MsR8ceIuBm4HpibW4JMytd2tKSj875fzA/+zwde\nzecxMzOzJumYDvwdGL59AzAhIl6pMmYa6Zqv6reJlZ/HcOr47h0UbmZmg8lg7MDfMeHbABFxYLVC\nrF1I2hP4JfDXVs/FzMxsIOqYlbFmaZcA7k7kbEozs8TZlINDvStjg+XXlHWLiFuAW1o9DzMzMxsc\nOuY2ZU+zKfta7s4/vMaYaZLG1zHmkKJj1qy0yxzjQ5JOqvBZ3RmdzqY0MzNrno4pxrJeZVNKGtLM\nybSbiLg+ItaIPOrlMZ1NaWZm1gudVoyVKnTCHy/pbkk3Sno85zmulT9bLunM3Lphd0n75gzGLkkX\nSVo3j9tV0r05w3F2zmjsJmnDfPz5uRVEIf/xWdKPC6pZBrySj3OKpDn5GOerqE1/byllVp6dX28t\n6b58nadWGD9EKdNyjqSHJX0uf+RsSjMzsybp6GKsKJsSUn7jccD2pBzHQrbihsCsiNgZmAtMAyZG\nxCjSM3Wfl7QOcAVwQh63H2sGen8A+ENE7JxbPNyc5zAhIp6uMc8TIuLe/PbsiNg1H2N94MAeXHo9\nzgJ+mK9zaYUxnwGW5e9xV+DfJW0dEU9HRN3ZlEpB5nNXvrisOTM3MzPrIB1djJWYHRGLcj7j5aS8\nRkirPFfn1+8g/XLyifz+YlK3+ncASyNiDqQu+hHxWsnxu4D9JE2RtGdE9LTy2EfSrNyg9n3ADj08\nTi17kL4HgJ9WGLM/8Kn8HN4s4M3AyEZOEhHnR8TYiBg7ZINNejxZMzOzTjWYfk1ZKcPxpVygQe8y\nHJ+QNIb0/NSpkm6PiG82MkFJ6wHnAmMj4mlJk+m7DE2onaMp4Lj8C1MzMzPrA4NpZWxcfk5qLWAi\n8JsyYx4DhkvaNr//JHBX3r6FpF0BJG0kabVCNkcjvRgRlwJTgV1KDy7pEknjqsyxUHj9VdJQ4JD6\nL69hM4GP5teHVRhzC+k27doAkt4uacM+nJOZmdmgM5iKsTnA2aS8yMWkvMbV5FzHI4Ar823C14Hz\ncif8icAP8oP+twHrleRVjgJm51t6XwfKPRS/E5WfzyIingMuABaQCqE5PblQSRcWWl+U5EwWOwE4\nJl/nlhUOdSHwCPBgbl3xIwbXaqqZmVmf65gO/NXyEXMPr0kR0VcPw9ckaWPgxxFxaKvm0FecTWlm\nZrYmZ1O2mfzQfycWYs6mNDMz64WOWRkbjCTNAtYt2fzJiOhqxXxqcTalmQ00zpC03nA25SAQEe9u\n9RzMzMysdzrpNmWfKM28LORdShqbu+Svk9+PkLQoPxvWcYozK3OiwbT8eqKk/5V0Q0snaGZmNkC5\nGKvPGpmXETEXuBuYlDedA5wcEc/39+RaKSKuAD7b6nmYmZkNVC7GGveXotf/CXxW0peBtSPi8gr7\ndJO0RNJpkublmKBdJN0i6bfFLSgknViUCfmNvG24pMckTZP0hKTLJO0naaakJws9zCRNljSp6FgL\n8r5l8zMljZF0l6QH8lyGFW2fn9t5HFN0Ga+Q8jTNzMysl1yMNag47zL3BZsCnAZ8oYHDPJVX2u4h\nZWEeAuwGfBNA0v6k2KFxwGhgjKS98r7bAmcC2+X/Pk6KdppEKg6rWSM/Mzd0/QFwSESMAS4CvpXH\n/wQ4PudxFn8H90bECfVcqLMpzczMqvMD/L33r8CfSAHkj9e5z/X53y5gaES8ALwg6SVJm5IyIfcH\nHsrjhpKKs6dI2ZldAJIWArdHROTmrcNrnLcLOEPSFFJfsHsk7QjsCNwmCWAIsFTSJsCmEXFX3ven\n+VobEhHnA+dD+jVlo/ubmZl1OhdjvSDpQGAT4P3AdEm3RMSLdez6cv739aLXhfdvIGVCnhYRPyo5\n3/Ay418u2RfgNVZf9VwPyudnkpIIFkbE7iXn2pTa2ZVmZmbWS75N2UOS1ifdLjwmr1RdB5zcpMPf\nAhyZ8ymRtKWkf2pg/yXkbExJuwBb59fl8jMfBzaXtHses7akHfIt2GWS3puPWSm/0szMzHrBxVjP\nfQ24NiIeye8nAx+VNLIksxJJN+VCqC4RcSvwM+C+fPvxKmCjBuZ2NfCmfBvzWOCJvH2N/Mycu3kI\nMCU/qD8PeE8efwRwTh6vBs5vZmZmdXIH/hrqzV0czOrN/nQ2pZmZDSaDMZuyr7R15mWr5fYY5wJ/\nb/VczMzMBiI/wF9DRDwNbNXqebSr3PT1ilbPw8zMbKBqWjGWb+c9Cjxe6FYvaUlEDO/h8TYHbgDW\nIfW6uqc5M616zprzLYyRtBWpA/+YiHhW0huBB4HxEfG7vp5rK0haHhFD8996WkSMl7Qn8CPg9Vq3\ncrueWcbwk27sh5mamfWcw8GtvzX7NuUasUG9sC/QFRHvKi3EJA1p0jl6LK+Y/RA4PW86HTi/Uwux\nSvLf5oBWz8PMzGyg6utnxv4C3cHSd+consclnSdprfzZZ3K0z2xJF0g6W9Jo4DvAQTk2aH1JyyWd\nmX/xt3uVCJ8Rkm7O2++RtF3ppCTtkM83L8cNjSyebz3XlH0X2E3SF0ld8M+stbOkGZK+m7vSPypp\nV0nX5DijU4vGfaJojj8qFKD5e5gqaaGkX0sal4+5SNKH8pjDJZ1ddKwb8t9giFKU0gJJXZK+VO07\nk7S1pPvy2FOLLmMl8Gwd35WZmZnV0KfFWHF0ECna5zhSp/oRwITc7uFrpCigPUjxPkTEPOAU4IqI\nGB0RK4ANgVk5mmcWlSN8zgeOy9snkR4uL3U0cFZexRsL/L7MfGteU0S8CpxIKsq+mNtE1OOV/OuK\n80j9yY4hdcE/XNKbJb0TmAjskee4klV9vjYE7oiIHYAXgFOBfwE+Qo5TqmI0sGVE7BgRo0hxR1D5\nOzsL+GEeu7Toup+OiAl1XquZmZlV0Z8P8M+OiEUAki4nrSS9BtwVEc/m7VcCb6+w/0pS/yyAd1A+\nwmcoqUfWlXk7wLpljnUfcLKktwDXRMSTvbiufyUVKjsCt9W5T3Ec0sKIWAogaRHpxwLvBcYAc/J1\nrA/8Oe/zCnBz0f4vR8Srqi8OaRGwjaQfADcCt9b4zvYADs6vf0rK4WyIpKOAowCGbLx5o7ubmZl1\nvP4sxkobmgWNNRJ9KSJW5teifITPxsBztZ5bi4ifSZoFfBC4SdLnIuKOBuZSON9o0qrUbsBvJP28\nUFjVUE8c0sUR8dUy+74aq5rDde8fEa9LqhWH9HdJO5Pim44G/g34ItW/s141onM2pZmZWXX92Wds\nXH4GaS3SLbjfALOBvSW9MRcSB1c9wiqVInyeBxZLOjRvVy4+ViNpG2BRRHyfdJtwpzJjHqs2AaVl\npB+Sbk8+RYoXOqPO+ddyO3CIcgSSpDdJelsD+y8BRktaK//qc1w+zmbAWhFxNfBfwC41vrOZwEfz\na8chmZmZ9YH+LMbmAGeT2l8sBqZHxDPAt0lF2UxSEbGs1oFqRPgcBnwmb18IHAQg6UOSCs9UTQQW\nKDVy3RG4pPj4uWiptWr378BTEVG4NXkusJ2kvfMxupvESrpQUs0OvEXX9wipWLpV0sOk25/D6t2f\n9F0uBh4Bvk9quQGwJTAjz+1SoLDyVvY7A04Ajsm3QLds4PxmZmZWp6bFIalKbJCqxOVIGhoRy/PK\n2HTgooiY3pRJ9ZCkA4Ft8sqZ1VDtb19s3WEjY9inv9cvczIz6yn3GbNmUZ1xSM18Zqw7NqjBXmOT\nJe1Heq7pVuDaJs6pRyLihlbPYaBQavp6LvDXWmNHbbkJc/0/cmZmZqtpWjFWLTYoImYAMyp8NqlZ\nc7D+l5u+jmr1PMzMzAYqB4WbmZmZtZCDwq3fOJvSzAYCPzNm/a1pK2OShktaUfIrwiX537E5gmed\n/H5Eju/ZuFnnbyf5u1iQX4+XNK2O8TN6cb7tcmzSQ5JG9PQ4DZyve76S9pT0SOF6zczMrDH9EhQe\nEXOBu0lROwDnACfnHlfWex8Grsqh6r8tbMw9w/o68spB4WZmZr3QL0Hh2X8Cn5X0ZWDtiLi81s6S\nlkg6La/6zJW0i1Io+G8lHV007kRJc5RCv7+Rtw2X9JhSMPYTki6TtJ+kmUqh3IVGqJMlTSo61oK8\n74ZKwebz87aJ+fNKAeVj8tj5pKzJgleo3TutO3hbKeT7OqXw7yckfb1obl9TClr/jaTLJU2SdACp\ni/7nJd2Z5/64pEuABcBWkvZXCvx+UNKVShFIFa+l5G+wd/7+CytvG+GgcDMzs6bpt6DwiHiOlG14\nGvCFBg7zVF5tuweYRmr2uhs5FFvS/sBIUpf50cAYSXvlfbcFziQFkG8HfJyU+ziJVBxW8wHgDxGx\nc+6fdbOktakcUP4T4PgcZF78HdwbESdUO1GZ4O1xpDSCnYFD823esXnbaFIe5ti8702kwPHvRsQ+\nef+RwLk5TPwfpAay+0XELsBc4P/VuJZik4Bj8t9gT2CFg8LNzMyap78f4P9X4E/A9qRIo3oUh2oP\njYgXgBckvSRpU2D//N9DedxQUjHyFLA4IroAJC0Ebo+IUH2h2l3AGZKmkBqa3iNpR8oHlG8CbBoR\nd+V9f5qvtadui4i/5XlfQyogAa6LiBV5+y+r7P+7iLg/v96N9H3PzHNehxSUXjZsvcyxZgL/I+ky\nUqj67xu5EDko3MzMrKp+K8ZyV/tNSCHV0yXdEhEv1rFrPaHap0XEj0rON7zM+JdL9oXKodpPSBpD\neh7qVEm3kxICygWUb0ovA7VLlAtVb2QV8x9Fr0Uq7j5WPEDSKMpcyxoTiThd0o2k72GmpPdHRNXc\nzpL9HRRuZmZWRb/0GZO0Pul24TF5peo64OQmHf4W4Mii56C2VA7YrtMSYJe87y7A1vn1FsCLEXEp\nKQR8FyoHlD8HLJNUWMEqG6otaVx+lquWf1EKB1+f9HD+TFKw+v+VtF6+1jWipSq4H9hD0rZ5DhtI\nenulaykz5xER0RURU0j5otvVeV4zMzOrQ3+tjH0NuDYHYANMBuYptXz4B3BhRBwAIOkm4LMR8Yd6\nDhwRt0p6J3Bfvt22HPgE6SHzelwNfCrfxpwFPJG3jwKmSnodeBX4fES8IukQ4Pv51uQbgO+RwrWP\nAC6SFKRYp3LeCqyoY06z87zeAlyaf42KpOuBh0m3eruoL1T9L5IOBy6XtG7e/F955a/stSj/OCIi\nzgO+KGkf0vf5CPCrOuZvZmZmdeqXoHBLJE0FfhoRD1cZczgwNiKOLfNZIVR9A1KrkKMi4sE+m3Cd\n6v3bOyjczAYCN321ZtEACgofNCLixF4e4nxJ25Oea7u4TQoxB4WbmZn1QtNWxsxqGTt2bMydO7fV\n0zAzM+sXrVgZM6vK2ZRm1u58i9JaoV9+TVlKVXIsWyl3vR9eY8w0pbzJIblz/V5Fn90q6dC+nmer\n5O9nbH69JP+7fu7O/4qkzVo6QTMzswGoJcVYVjbHsl6ShjRzMo2KiJWkJIFzcluIj6XNcWUr59Xf\nImJF/jvW9etXMzMzW10ri7FSfwHIq053K+VCPi7pPOWwa0nLJZ2plP+4u6R9c15il6SLCq0bJO0q\n6V6lrMjZOU+xmyrkTpLyFmu1xFhGypskImYB95JadXyb1TMpy1LKwrxY0j2SfidpgqTv5GsoRC5V\ny8CcIem7Slmdj+ZrvUYpb/PUPGa4pAVF55wkaXJ+fbykR5RyPH9e9H1cpJTv+ZCkg/L29SX9PJ9n\nOrB+6d/LzMzMeqdtnhkrzrEkZTNuD/wOuBmYAFwFbAjMioj/kLQe8CSwb+6ZdQkpLPtc4ApgYkTM\nkbQxa/b2KuROfhAg99minrzFMjmTXwWeBr4XEf9b5+WOAPbJ13gfcHBEfDkXPB9U6nj/A+Cg3Cds\nIik38si8/ysRMVbSCaQGumNIheRvJX23xrlPAraOiJeVkgMgNeC9IyKOzNtmS/o18DlS49t3StoJ\n6P71Zsnfy8zMzHqonVbGis2OiEX5VuDlrMpmXElqhgopW3FxRBSatF4M7JW3L42IOQAR8XxEvFZy\n/C5gP0lTJO0ZETWbp1axF2m1rJH+ar+KiFfzPIaQCs7CvIazem7kPFLQ91uK9i/O61wYEUsj4mVg\nEbBVjXM/DFwm6ROkKChI2Z4n5XPNILXOeGu+tksBcm+0iv3RKpF0VF7Fm7vyxd58zWZmZp2pXYux\nctmMAC/lAg1S5mI5KrP/6gdLBdwYUjFzqqRTejJJSRsC3wHeR4oWOqDOXV/O83gdeDVW9Rcpzttc\nGBGj83+jImL/0v2pnNdZNm8z+yBwDineaY6kwvkOLjrfWyPi0Ty+V71PIuL8iBgbEWOHbLBJbw5l\nZmbWkdq1GBsnaev8rNhEUi5jqceA4cqZi8Angbvy9i0k7QogaaNccHRT+dxJSsZcImlcjXmeAvwi\nB2d/Afhuvn3aW3XlRlbxJ+CfJL05P0d3YD7OWsBWEXEn8BVScPtQUr7ncVLKk5L0rnycu8k5m5J2\nBHbq9ZWZmZnZatq1GJsDnA08CiwGppcOiIiXSHmQV0rqIq0KnRcRr5AKuB/kB/1vA9aTtIVS7iWk\n3MnZ+bbc14FTy8xhJ2BppQkqdcL/COlZLiJiHqmo+Ur+/EKtagNxtHLeYz3yNRwCTMnXMA94TwP7\nvwp8k5Rx+WtSgQrpluil+ft6CPh+Djn/b2Bt4OH84P9/5/E/BIZKejQf74F652BmZmb1aUkHflXJ\nMpQ0HpgUEQf287SK57Ax8OOI6NieYc2m1HdsbERUjEVyNqWZtTs3fbVmUpt34G/rHMuIeB5wIVYH\nSeuTfhG6Nml1siJnU5qZma2pJcVYRDxNhV/9RcQM0i/6bACIiBVA2xXUZmZmA0Xb9BmzzudsSjNr\nd75Naa3QdtmUksbmrvjr5PcjJC3Kz3F1nOJu+UrpA9PqGD+jP+ZWYx5L8r/OpjQzM+uFtsumjIi5\npJYKk/Kmc4CT83Nc1iAlffZ3djalmZlZ77RTa4virMP/BD4r6cvA2hFxea2dJS2RdFpepZkraZec\n6fjb4rYSkk7MGYwPS/pG3jZc0mOSpkl6QtJlkvaTNFMp83FcHjdZ0qSiYy3I+5bNulTlfMkxeex8\nVs+zfIXUzb+alaToIyQdLuk6pbzKJyR9veh6HleKiFoAbCXpY0r5lwskTSm6hg9IejDP5/Yy3+sw\npazQeXnfPfNHzqY0MzNrgrZ5Zqw46zAinssFw7mk/MZ6PRURo5XyGacBe5C6zy8EzpO0PzCSlH0p\n4HpJewFPAduSfkF5JKnP2cdJMUwfIhWHH65y3jWyLpUCvyvlS/4EOC4i7pI0tei67yUFj1eUf/xQ\nnKE5jhSd9CKpo/6NwF/zdX46Iu5XanI7hZQ68HfgVkkfBmYCFwB7RcRiSW8qc8qPA7dExLckDQE2\nyPNwNqWZmVkTtE0xVsa/kjrJb0/qSF+P4szGoRHxAvCCpJeUArD3z/89lMcNJRUtT5FyLrsAJC0E\nbo+IyA1Sh9c4bxdwRi4gb4iIe3LH+kK+JKSGq0uVQsk3jYi78r4/zdfaU7dFxN/yvK8hFZDXAr+L\niPvzmF2BGRHxlzzuMlLu5Erg7ohYDBARz5Y5/hzgolxcXpub29ZN0lHAUQBDNt680WszMzPreO10\nm7KbpANJUT3vB6ZK2qDOXWtlNgo4rSiDcduI+HHJvqX7F/aFCpmPFbIuK+VL1szObFClHM9/FG3r\nTY7n3aTC7RlgmqRPNTQ5Z1OamZlV1XbFWG4ieiZwTF6pug44uUmHvwU4UtLQfK4tJf1TA/svIedY\nStoF2Dq/Lpd1WTZfMscPLZP03nzMw8qdSNK4/MxXLf8i6U35eyvceiw1C9hb0mb5VuPHSDme9+Xt\nhetY4zalpLcBf46IC4ALKZPjaWZmZj3XdsUY8DXS7bBH8vvJwEcljdTq+ZJIuikXQnWJiFuBnwH3\n5duPVwEbNTC3q4E35duYxwJP5O1rZF3WyJc8Ajgnj6+0avVWYEUdc5qd5/UwcHX+NepqImIp8FXg\nTmA+8GBEXJdvWx4FXJPneAV0txe5MO8+Hpgn6SFS5udZdczJzMzM6tR22ZSW5Af7fxoRD1cZczgp\nD/LYfptY5bkswdmUZjbAuemrNZOcTTmwRcSJrZ5DPeRsSjMzs15pu2xKq19ETCO18GjlHJxNaWZm\n1gvt3NrCOoyzKc2s3fi2pLWDdnyA38zMzGzQ6PdiTFVCwmvst7xo/wU9PPeFktbo6J9jhc7Or1eL\nPKpwnMn54flqYw6XNLkn8+yp/CvI79cYs6mkL/TyPN3XL2mqpD/W+s7MzMysvFbdpiwbEt7XIuKz\n/X3O/pTbWqzR2qLEpsAXSFFTq5E0JCJWNnjOEyX9o/ZIMzMzK6ddblMWYnqGSro9B1d3STqomSfJ\ngdpj8+sjcrj2bFKGZbnxIyTdrBT0fY+k7fJHy6ndA2xFHoekQ3PI9nxJd+dt3atx+f0Nksbn18vz\nitNCSb/ODWBnSFok6UNVrm+8pBvy68mSLira7/g87HRghFLw99S8zz2SrgceLXPM0yU9ohSsfkYD\n129mZmZ1aIsH+ItCp18CPhIRz0vaDLhf0vXR5GZokoYB3yBFGC0jNUN9qMzQ84GjI+JJSe8mrSa9\nLyLOKDN2NRFxRdHbU4D3R8QzShmZtWwI3JFXnaYDpwL/QsrpvJhVGZy1bAfsQ2ps+7ikHwInATsW\nViZzAbhL3ra4eOfckf8jwHY5p3PTfG01r7/oGM6mNDMzq6ItirEiAr4taS9Sz6otgX8G/tjk87yb\n1YOzrwDevtpEUmTSe4Arpe4m+ev28HwzSbmOvwCuqWP8K8DN+XUX8HJEvKr6QsuL3RgRLwMvS/oz\n6bssZ3ZpIZY9TyqQL5R0I3BDA+cGUjYlqahl3WEj+7/DsJmZWZtrt2LsMGBzYEwuPpaQw7j7QK3C\nYC3guWY82xYRR+eVtQ8CD0gaQ4XQ8ezVotXA7tDyiHhdUiN/s+Lw85VU/nuXfeYrIl6TNA7YlxTt\ndCzwvgbOb2ZmZjW0yzNjBZuQQqlflbQP8LY+Os8sYLykN0taGzi0dEBEPA8slnQogJKdS8dJOlZS\n1TgiSSMiYlZEnEJ6Pm4rUuj4aElrSdoKGNfrq6rPC9SZx5lXBzeJiJuAL8H/Z+/Oo+WsyrT/fy8i\nMoVBhbZDZBLSIoMMiSBiNGgaB/iBTI0v2MgkHRVw6KC02BAVVAQbQVBeoCHNpLQCyiTBRkJCgJAA\nGUGwDYgCryIIEubh/v2x70oqlZrOfE6d67PWWafOU/t59n4qLtlrP7vui5Xu38zMzHpmsE3GLgPG\n5cWGiwIAACAASURBVOO4Q4DfdPUCaiNMPIOzp1BifGZRZ+N6Ohg4QiVEezFQ7wsFWwJPthjWafmF\nhEXA7ZSw7lnAQ8B9wFnAPS2u0Ssi4klgVn6h4LR6bao+s7WB6yQtAG4DvtQfYzQzMxtO+j0oXB0W\nEp7fXtw3Il4e6LEMlKyntrTVxv5x48bF3LmtKm+YmZl1BrUZFD4QK2PLQsIHoO9eFxF7DvOJ2GnA\nJ2mw78zMzMya6/cN/L0VEi7pw8CpNYcfioh9enrtwW4w3XtEHAcc19/9mpmZdYp+f0xpw9dqo8bE\nqE99f6CHYWa2jIPCrS8N5seUZmZmZpaGTFB4H4zjhlbV8DOyaMNmbVqcv6mk6W20e7iNNoMyKF0O\nCjczM+uRYRUUXi0iPtZGs0OBRcBjtW+oG6HaA60vgtIdFG5mZtYzg+UxZSWWaJSkGRlivUjS+Dy+\ntNJQ0v6SpubrqZJ+JOnODMP+QIZj319p04ikhyWtnytN90s6XyWY+yZJa0jaHxgHXJbjWSPPOVXS\nPdQUis2+5+XPvZLWpnxz9Kku3H8nBaWbmZlZGwZFHFJVUPhBwLSIOEXSCGDNNk5/E7ALsBdwLWVi\ncSQwR9L2EdFOCY0xwP+JiE9nfuR+EXFpVtafHBFzAVQyKp+MiB3rXGMy8LmImJWV61+MiGeBfVt1\n3olB6VV9OSjczMysiUExGasyB7gwI4p+3uZE6tqIiKza/6eIWAggaTElVLudazxU1dfdNA/jvqLB\n8VnAf0i6DLgqIv7YRr+1Oi4o3UHhZmZmzQ2Wx5QARMQM4P3Ao8BUSYdU3qpqVhscXgnDfp0Vg7Ff\np/3JZruB2tA4VPs7lBW5NShxQ1vWa9dCdVD69sCfGARB6VU/7+yjsZiZmQ1bg2oyJmkTSlD4+cAF\nQOVx4J8kvVPSKkB/FjbtSqj25hGxMCJOpazwbVnz/mhJN7e4zJALSjczM7OeGWyPKScAx0l6hbJJ\nvLIydjxwHWWj+1xgZD+NZypwrqQXKPvSViBpEkBEnAt8ISdQr1HCv39Z03wU8GqL/i4Drs1HrnPp\nZlA6cEHl26IqoelHRsSyb4RGxOOZJ3kH8DSNH+UeDPxI0teAVYGfUELOu2Xb0esy1wUWzczMVuCg\n8H6SXwZ4JCKuGeix9DY5KNzMzGwlg7kCf0cFhbcrIs7u0ImYg8LNzMx6oOOzKSXNZuVvAf5z5VuX\nQ5UGUVh4u5xNaWa9wXmSNlS0uzI22PaM9bqI2Hmgx9AXImIaMG2gx2FmZmY902uPKdUkc1LSuKyo\n/8b8e/OsmL9Ob/U/mKgqP1LShDbSAFpmWNa55nXdHFvdTE5VZVJmssH+La4zVdKEfH2ZpKdanWNm\nZmYr6+09Y3UzJ7OC/QxKlXqAc4ATsnyC9aOI+FhEPN3L1zwY6Lj9cGZmZv2hrzfwP1H1+qvAkZK+\nDKwaET9udXJmQX478x7nStpR0jRJv6uUlch2x0maI2mBpK/nsU0l/SZXcB7M1ZuJkmZJ+q2knbLd\nshWh/HtRnruWpOslzc9jB+b7YyXdmnmN0zJaqHJ8vqT5wOeqbuNlSuRQM8syLLPvmSr5lPdIem+r\nz6kr8jNdP1+fkJ/NbcA7GrSve7+Ue3q5N8dmZmY2HPXpnrGqzEUi4mlJp1LyDbfqwmUeiYjtJZ1B\nqfu1K6Uq/WJKDbDdKdmSO1HihK5RiRN6BNiCUtD0cEoh1oOA91FyLL8KfLxJvx8BHouIPQAkrZsF\nUn8A7B0RT+QE7ZS8/kXAMRFxa37DsHLftwO3N7vBiPgDyzMs/wz8Y0S8KGkM8GNKYHmvkjQW+ASw\nPeV/B/dQoqCq2zS834j4fJv9OJvSzMysif7ewP9RSsTPVsADbZ5Tefy1EBiZ4dvPSnox9z7tnj+V\noOuRlMnZI5RvF1ZnVd5clWO5aYt+FwKn5wTyuoiYKWkbYBvgVyp5jSOAxyWtC6wXEbfmuZfkvXbH\nqsDZkranrJj9Q4v23TUeuDoingeQVO8x4zuoc79d6cTZlGZmZs3122RM0p6UuJ8PA1dLmlaZCLTQ\nKntSwLcj4v/W9LdpnfYv1ZwLpSp+9ePa1QEi4sFcPfoYcLJKlNHVwOKIWKEaf04Ke2ui8UXKhHW7\nHNeLvXTdelqNWdS5XzMzM+s9/VL0VdIawPeAz+VK1S+AE3rp8tOAwyWNzL5GS/q7Lpz/MJmBKWlH\nYLN8vSHwfERcCpyWbR4ANpC0S7ZZVdLWuSH+GUnvy2seXK8jSTtJurjFeNYFHo+I14F/pqxG9YUZ\nwD6S1pC0NvD/1WlT9377aDxmZmbDUn9V4P934OcRcV/+PQX4hKQxkjZUyU8ElpVe2LDdC0fETcDl\nwB35+PFntBnuna4E3pyPMY8GHszj2wJ3qZTqOAk4OSJeBvYHTs2N+vOAygb7w4Bzsr0a9LUx8EKL\n8fwQ+FRef0u6UdlepZTIBVV/r5R2EBH3AFdQsiZ/SdlTV9um2f2amZlZL+i1CvwappmTXZEb+y+J\niAUDPZbeplJL7bqI+FmjNs6mNDOz4UQDkE05LDMnuyIijuvQidhlwAfo2/1tZmZmHanjsyk7maTD\ngNoSE7Mi4nP12g80Z1OaWW9wNqUNFe2ujHV8NmUni4iLKPXNzMzMbIjql2zKFuctrTp/UTf7vkDS\nSoVkJR0q6ex8vUKl/QbXmSLp0BZtDpU0JV+fJenfq947QdI53bmHoUAr51dOyNfOpjQzM+um3l4Z\nq5tN2dci4sj+7jN9DZiXe6YCOBLYYYDGMmAi4mC1CEM3MzOz+volm1LSSEk3Z9biQkl792YnkqZL\nGpevD8u8xbso0Un12m8u6cbMW5wpact8aymtS0+8kO3IoPMTgLMp4ecntgrhljQhsx5/IWmJpO9I\nOljSXfnZbJ7tNpB0pUrm5hxJu+bxKZL+K8f9e0n7SvpunnujSoRRbQblOEnT8/UHVLI+50m6N2uM\n1c33zOON8iudTWlmZtYL+iub8kVgn4j4W04Q7pR0TfTytwdUQqy/DoylTBZuYXlMUrXzgEkR8VtJ\nO1Nqe30wIk5v1UdEXFHz948lHQu8FhGXtDnU7YB3UsLBlwAXRMROkj4PHAN8ATgTOCMibpO0MaW4\n7Tvz/M2B3SixUncA+0XElyVdDewB/LxJ35MpxXdnqRTKfVGN8z2fo0F+pbMpzczMekd/beAX8K38\nD/zrwGjgrcD/6+V+dgamR0RlRe4KarIdcwLyXuCn0rLarKt1t0NJbwP+HghJIyNiaRunzYmIx/P8\n3wE35fGFlEkWwERgq6oxrlNZxQJ+GRGvZJHbEcCNVedv2qLvWcB/5KPVqyLijzkZq5fvuTat8yub\ncjalmZlZc/01GTsY2AAYm5OIh8kMyD7Q6j/4qwBP9+LetjMpiQLvpFTqP66Nc9rJzFwF2CUiVnhs\nmpOzlwAi4nVJr1StMDbK3Fz2WUfEdyRdT8ncnCXpwzTO9/wCvZe5aWZmZnX0VxzSusCfcyK2G7BJ\nH/UzG5gg6S25d+qA2ga5z+shSQcAqNiutp2koyUd3awzSR8F/g64GPgmJetxpW91dtNNlHimSl9d\nnTw+THlcC7Bf1XU2j4iFEXEqJQJpSxrne7aTX2lmZmY90F+TscuAcflY7RDgN129gNrIsMxHf1Mo\n+6hmAfc3uNzBwBEqeYuLgXpfKNgSeLLJeFYHvg98NorngC9TNvMjaZKkSfl6hazINh1L+cwWSLoP\nmNTF878OnClpLiUdoeILkhblvb9CeeRZN9+znfxKMzMz6xlnUzYg6Tpg3wzLthbkbEozM7MVyNmU\nPRMRe3oi1h45m9LMzKzbBl02ZW4oP7Xm8EMRsc9AjMd6j7MpzaynnEtpQ0m7K2ODLpsyIqZRNpSb\nmZmZdbymjynVJG8yN6UvkvTG/HvzrCi/Tp+OeICoKjtTpYr+1DbaT++PsdX0e3sbbb4gac0e9LHs\n/iUdKOl/c4+dmZmZdVE7e8bq5k1GxFxK6YNK+PY5wAlZOsIGSES8t41mXwDqTsYkjehif1dQMjnN\nzMysG7qzgf+JqtdfBY6U9GVg1Yj4cauTMzPx25mNOFfSjpKmSfpdpRREtlspKzFXm34jaWrmJV4m\naaKkWZJ+K2mnbDdF0uSqay3Kc9eSdL2k+XnswHx/rEpe5N05llFVx+dnGYjPVd3Gy5S4pWZeo8Qd\nIWlrlezJeXk/Y6pX2rLNZElT8vV0SWfk53O/pHdLuirv8eQWn+/S/D0hr/Oz/MwuKyXVdCywIXCL\npFsq50j6Xt7nLjXXO6BSCkPSjC7cv5mZmbWhy3vGqvImiYinJZ1KyXbsSrHTRyJie0lnAFMpgd6r\nU2p+navGWYmPAFtQirkeTql7dRDwPmAvyuTw4036/QjwWETsASBpXZXisD8A9o6IJ3KCdkpe/yLg\nmIi4VdJpVfd9O9D0cWBE/AHYN/+cBJwZEZflY90RlDioZl6OiHEqeZW/oBRwfQr4naQzIqJhDbQq\nOwBbA49R6q7tGhFnSfoSsFtE/CXbrQXMjoh/rXONE4EPR8SjktbLe2t5/xVyNqWZmVlTvVHa4qPA\nn+jaZKyScbiQMgl4NvMkX8z/4FdnJd5DKcA6Js95KCvIv06ZvN2ccUDt5DIuBCZKOlXS+Ih4BngH\nsA3wq9wb9zXgbZLWBdaLiFvz3HZDwOu5A/iqpK8Am9RGHDVQ/RktjojHI+IlSrD4Rm32e1dE/DE/\nq3k0/nxeA65s8N4sYKqkT1MmkV0SEedFxLiIGDdizXW7erqZmVnH69G3KSXtSYk6+jBwtaRplVDp\nFqqzGGtzGt9A46zETeu0r5frWJ3LCJnNGBEPShpLyWU8WdLNwNWUyU7t47n16KVcxoi4XNJsYA/g\nBkn/AjxYb4xVWn1G7ag+77Um570YEa/VeyMiJknaOcd+t6Sxba7KmZmZWRu6vTImaQ3ge8DnImIh\n5VHaCb00rkZZie16GNgxz90R2Cxfbwg8HxGXAqdlmweADSTtkm1WlbR1RDwNPCPpfXnNg+t1JGkn\nSRc3G4yktwNLIuIsyuf0Lspq4t+p5GiuBuzZhfvrqWeBtdtpqJJlOTsiTqTsF2x3Vc7MzMza0JPH\nlP8O/Dwi7su/pwCfyM3pLXMkm2mUldiFsV0JvFnSYkrY9oN5fFvgrnwceRJwclbZ3x84NTewzwMq\n30g8DDgn26tBXxsDrR47HggsyutsA1wcEa8A3wDuAv6HbuR19sB5wC8rG/hrSfqGpL3yz9MkLcwv\nG9xOyak0MzOzXtK0Ar86LG+yL+TG/ksiYsFAj2WgSJoATI6Ipqt7zqY0M7PhRL2UTdlReZN9ISKO\nG+YTsQMp36b960CPxczMbChquhE8yzN4j9AgI+ktwM113vpQf2+uz6KvV/Rnn2ZmZp1k0GVTWms5\n4VopFWGwW/joM2x6/PUDPQwzG8IcFG6dqNvZlP0pvwCwXos2h3blSwJ1zm8rS1LO5nQ2pZmZWS/q\ndjZlf4qIj2WpiWYOpcT8rERdzFtsc0zO5sTZlGZmZj3V7WxKSaMkzVDJW1wkaXweX1ppKGn/qhWU\nqZJ+JOnOXEH6gKQLVbIXpzbrUCXPcv1cnblf0vmSFku6SdIakvYHxgGX5XjWyHNOlXQPJT6p+nof\nyHbzJN0raW2qsiTbuf/kbE4zMzPrkS5PxqqyKQ8CpuWq2XaU+lytvIkSRP1F4FrgDEp24raS2l19\nGwOcExFbA08D+0XEz4C5wMERsX1V3NCTEbFjRPyk5hqTKcVqtwfGAy9ExB8iYl9aqM3mBE4Fvg18\nts3xQ2ZzAjMp2Zz7A++h1B1DK2Zzbg+MVcnmhJLN+T1KRNSWLM/mnEyZHDZTyebcLsuV3Kjl2Zz7\nR8RY4EJKNieUbM5jI2K7ms/g9oj4fDs3KumonHTOfe15z9/MzMxq9aTo6xzgMElTgG0j4tk2zrm2\nKkfyTzUZk5u22e9DEVGZ+N3d4rxG3/KbBfyHpGMp+ZOvttl3Pc7mbMLZlGZmZs11ezIWETOA9wOP\nUoKkD6m8VdVsIPMWAZ6rdzAivkPZ57QGMEvSlm32vQKtmM15mqQ12zy13WzO7fNni4j4z5pza89v\nK5sTGEuZlJ0s6cTsa3FVX9tGxO55vFeyOc3MzKyxnmRTbgL8OSLOBy4gsyCBP0l6p6RVgH16YYzt\n6mre4sKIOJWywrdlzfujVULEm13D2ZxmZmbWYz2pMzYBOE7SK8BSoLIydjxwHWWj+1xgZE8G2AVT\ngXMlvUDZl7aCyub4iDgX+IKk3Sgra/cBv6xpPoqyutRMvWzOefllhOeACyLiY9n3DcCREfFYOzcS\nETdJeiclmxPK5/vJHG87rgQOUcnmnM2K2ZynSXodeAX4TES8nF+AOCsfTb4B+D7lEehhwIWSArip\nzb7NzMysC5xNWYekoymb7K9p2dicTWlmZlaHnE3ZfRFxtidi7ZGzKc3MzHpkUGVTSpoNrFZz+J9z\nT5YNQs6mNDMz65lBlU0ZETsP9Bis7zib0mzwcuaj2cDpSZ0xMzMzM+uhAZ2MyUHk9do93N0+uqoS\nM9Xdc/P3Ghnt9HJ3r2VmZjacDYaVMQeRD2ER8UL++7VVtsPMzMxWNBgmY7UcRL78Ol+WtDDDur+T\nx46VdJ9KgPhP8thCSeupeLKShiDpEpUg8RGSTs92CyQdU9XfMZLuyfdWSiJo9O/AioHpZmZm1k2D\nagM/1A0iPyVXntqJGqoEke9FCSLflRJ7NEfS9lWZls2MAf5PRHxa0n9TgsgvzdpjkyNiLkAWY30y\nInasc41KEPmsrKL/YmZ3th1ELumjwMeBnSPieUlvzibHA5tFxEtVj1Zn5b3+HlhCCT+/mBI+/hng\nKEoV/h0i4tWqawH8JSJ2lPTZHPeRNUOq++9QHZjejKSjsn9GrLNBO6eYmZkNK4NxZaxiuAeRTwQu\niojnASKisqq2gLJC90mWpwTMpOSEvh/4EbCtpNHAUxGxNK91bmUcVdcCuCp/N7rX7vw7LOOgcDMz\ns+YG7WTMQeQNg7r3AM6h5ErOkfQGYAZlNWw8MJ3yCHF/yiSt2bVg+f3Wvdcm/w5mZmbWCwbtZEzD\nPIickgV5uKQ185w35z1vFBG3AF8B1gVGZnHe9YExEbEEuI3yyHFm1bUm5cSNmseUre6l0b+DmZmZ\n9YJBOxmjBJHPk3QvcCBwZh6vBJHfDjzej+OZSgkinydpjdo3JU1ShpFTgsgXSZpPCeTuchB5RNwI\nXAPMzdIfk4ERwKWSFgL3AmdVfQu0OhB8JjCaMimDMol6BFiQYzqoWd+Sxkm6IP+cQP1/BzMzM+sF\nTYPC+7xzB5F3TP5l1h0bFxF/adTGQeFmZjacqM2g8IH+NuWyIPKBrjXWnyLi7IEeQ2/JVcI7gFUp\ne/PMzMysCwZ0Zay/yUHkA2q1UWNi1Ke+P9DDMLM6nE1p1vuGyspYv3IQuZmZmQ02wzqbUr2Qzdii\nzfTcF9dvJH1D0sQWbSZIem8P+3k4fzub0szMrAcGw8rYgGdTdpKIOLGNZhOApZRvpK5A0hu6UqQ2\nIl4Atu/PSbSZmVknGYylLTopm/Ep4LXsf2qev1DSF/Pa0yWNy9frV602HSrp55J+lat3R0v6kkrO\n5Z3N6oRlP/vn64clfb36/nKlbhLwxbyn8XnOubmn7ru9eP9mZmbWwmBYGVtBJ2UzRsS+eS9jgdGV\nEh5V425mG2AHSsrA/wJfiYgdJJ0BHAK0uxN+hfuLiCMlnQssjYjTczxHAG8D3hsRr9Wc72xKMzOz\nPjQYV8YqOiKbMS0B3i7pB5I+AvytjXNuiYhnI+IJ4BlK8DmU3M1642yk1f1V/LTORAycTWlmZtan\nBvNkrGOyGSPir8B2ObZJlIr4UCaTlX+DRjmbsGLWZldyNquv092cTWdTmpmZ9aHBPBkbMtmMkm7O\nlbhG11gfWCUirgS+VnWNh4Gx+Xr/dsfUC7qSs+lsSjMzsz40aCdjQyWbMSeIW1A26zcyGpie93Ep\n8G95/HTgM3nt/iwLcS2wT2UDf+2bXbl/MzMz6xlnU/aQpG2AwyPiSwM9loEkZ1OamZmtQG1W4B/o\nlbFl2ZQDPI5ui4hFw3kiVin6irMpzczMumVYZVN2EknnUMp5VDszIi4aiPG0w9mUZoOTcynN+ka7\nK2ODrs6YtSciPjfQYzAzM7OeG+jHlMOeavI5NcSyOeVsSjMzsx7xZGxwGLL5nBHxQo79sYEei5mZ\n2VDkydjg00nZnGZmZtaC94wNMp2UzZn34WxKMzOzJrwyNnh1RDansynNzMya82Rs8OqYbE4zMzNr\nzJOxwWvIZHOamZlZ93kyNkgNlWxOMzMz6xlX4B9gnZDPCc6mNDMzqzVUsiltiOdzOpvSzMysZ1za\nYoDlfq+NBnoc3RURLwBDsmCtmZnZYODJmPWbhY8+w6bHXz/QwzCzGg4KNxtYQ+YxZW2GYx57uAfX\n20DSbEn3VlWV71PtjLefsymnS2r5LLvJuZvm61skLe3utczMzIazITMZS72Z4fghYGFE7BARM6vf\nyErz1qaI2A3wznwzM7NuGGqTsVpPAEiakPmJ10t6QNK5WZMLSUdIelDSXZLOl3S2pO2B7wJ7Z+bi\nGrmy870s/bCLpLGSbpV0t6Rpkkbl9TaXdGMen9kgz3Hr7G9eZkGOqR5vO/eU1zkkz58v6ZI8dkBm\nRM6XNCOP3SDpXfn6Xkkn5utvSjoyX6+Uc5kOyLE+WG+FUNJa+bnOz34PzLeeonz5wMzMzHpgSO8Z\nq8lH3AnYipLPeCOwr6TbgX+nFCp9Fvg1MD8i5uWEZVxEHA1l0gHMjoh/lbQqcCuwd0Q8kROQU4DD\ngfOASRHxW0k7Az8EPlgztEnAmRFxmaQ3UuqDtZXnWJVNuTVwArBrRPylqlDricCHI+LRqmzKGcD4\nfMT5KiWnEuB9lLpkjXIuAd4QETtJ+hhwEiU6qdpHgMciYo8c17o5zn1b3Uu2dzalmZlZE0N6Mlbj\nrqw+j6QfUyYirwK3VrIYJf0U+IcG578GXJmv3wFsA/xKEpTJ1OOSRgLvBX6axwFWq3OtO4ATJL0N\nuCoiftuN+/kg8LNK3a6qPMlZlFii/2Z5ruRM4FjgIeB64B+zcv+mEfFATojq5VxC62zKhcDpkk6l\n1EObWadNQxFxHmUCy2qjxrionZmZWY1OmozV/oc+KJmM7XoxIiqP3QQsjohdqhtIWgd4utW+tYi4\nXNJsSo7kDZL+JSJ+3YWxVMaw0uQlIiblitwewN2SxlLCvMcBS4BfUaKRPk2ZYDW8VmqVTflg9vEx\n4GRJN0fEN7p4L2ZmZtbAUN8zVm0nSZvlXrEDKVFAdwEfkPQmlVzG/dq81gPABpJ2AZC0qqStI+Jv\nwEOSDsjjkrRd7cmS3g4siYizgF8A76rT5jctxnAz8E+S3pLt35y/N4+I2RFxImV/2UYR8TLwB+Cf\ngDspK2W12ZQr5Fy2+TkgaUPg+Yi4FDgNZ1OamZn1qk6ajM0BzgbupzyuuzoiHgW+RZmUzQIeBp5p\ndaGc3OwPnJob+udRHk8CHAwckccXA3sDSNpLUmXF6EBgkUoZjm2Ai6uvL2l9WqzaRcRiyj61W7Ov\n/8i3TsuN+IuA24H5eXwm8Kd8FDkTeFv+bpRz2ZCkDSXdkH9uC9yV550EnNzsXDMzM+uaIZNNqSYZ\njpImAJMjYs86742MiKW5MnY1cGFEXN3Hw21K0p7A23PlrCNImk75N2hY4sLZlGZmNpyozWzKobRn\nbFmGYxdrjU2RNBFYnfK47ud9MrouiIjrBnoMvUnSLcDbgVcGeixmZmZDzZCZjDXLcIyI6cD0Bu81\nfSRnPZdFX83MzKwbhsxkzIY+Z1OaDU7OpjQbWENmA796OZuyG/0/nBvvu3VuG22WZT12s48fZ7X+\nL3b3Gl3sz9mUZmZmvWCorYz1ZjZlx5D098C7I2KLOu+9ISJe7cv+I2K33MBvZmZmXTRkVsYaqM5x\nXCl7UdKxku7LFaOf5LGFktbLGmFPSjokj18iaaKkEZJOz3YLJB1T1d8xku7J9+plUo5Sycicp5Lj\nWMl6bCeTclnWY64ynSFpsaSbJW2Qx9+dY5on6bQsbwHliwmj8/j4XLX6vqS5wOclbSDpSklz8mfX\nvN5aki7MY/dK2rvOPTmb0szMrA8NtZWxFVTlODbKXjwe2CwiXtLyHMdZlOzG31Mq1o+n1AF7D/AZ\nSo7iZsAOEfFqTYHUv0TEjpI+S6nVdWTNkA4CpkXEKZJGAGtWj7PFvVRnPa4FzI2IL6pkaJ4EHA1c\nBBwVEbdrxbDvvShlP7bPzwPgjZWv00q6HDgjIm6TtDEwDXgnJfvy1xFxeH4+d0n6n4h4ruraPcqm\nNDMzs+aG+spYxUTqZy8uAC6T9ElKTiWUQqjvz58fAdtKGg08FRFL81rnVh7tdTHHcQ5wmKQpwLYR\n8Ww37+d14Ip8fSnwvpwsrR0Rt+fxy1tc44qq1xOBs3O/3TXAOpLWBnYHjs/j0ynlPzauuc5CYKKk\nUyWNj4iWRXOrSTpK0lxJc197vkunmpmZDQudMhlrlL24B3AOJcJnThZ+nUFZDRtPmYA8Qam2X4kO\n6kmO4wzKJO9RSpj3Id24l3q6mrMJUL26tQqwS0Rsnz+jc6IoYL+q4xtHxP0rdBzxIDCWMik7OVfq\n2h94xHkRMS4ixo1Yc90u3oKZmVnn65TJ2ErZiyoZlRtFxC3AV4B1gZFZr2x9YExELKFkWNbmOE7K\niVtXcxw3Af4cEecDF1AnxzH3gI1ucalVKBNEKI8+b4uIvwLPSnpPHv9Eu+Oi3NPRVWOofAliGmUf\nnPL4DnXG62xKMzOzPtQRk7EG2YsjgEslLQTuBc6KiKfzlNnAg/l6JjCaMimDMol6BFigkgl5ULO+\nJY2TdEH+OQGYJ+leSj7lmTVtVwG2oGx+b+Y5SvD5IuCDQCXz8gjgvLzHtWgjZzMdC4zLzf/3MpMf\nZQAAIABJREFUAZPy+DeBVSn3uij/djalmZlZP+qIbMqhQtI2wOER8aUW7ZZGxMg6x0fmvjYkHQ+M\niojP981ou0ZtZFOuNmpMjPrU9/tvUGbWFhd9Nesbcjbl4BMRi4CmE7EW9pD0b5R/t98Dh/bGuHpK\nbWZTbjt6Xeb6//TNzMxWMGQmY82yKTtNvVWxPH4FK35LclBwNqWZmVn3DZnJmA19zqY0G3z8iNJs\n4HXEBv5qGuAMy0bURvakpKmSJvTTeKZImtyDcw/N16dJ+n/dvZaZmdlw16krYz3KsJQ0IiIc9dOG\niDhO0nOtW5qZmVk9Hbcy1sATAJImZHbk9ZIekHRulpuo5EF+L8tZ7CLpQ5nXuDDzG1fLdu+WdHtm\nNd6VleyX6WGW4zPAy436kbR1vp6XZSrGqGRyHpvnnCHp1/n6Q5IuzdcfUcnUnC/p5qr+tsoVuyWV\na9SS9B0tz/c8PQ8vBV5o54M3MzOz5jp1ZWwFNdmQOwFbUb6NeCOwL/AzSt2u2RHxr5JWB34LfCgi\nHpR0MfAZST+kbKA/MCLmSFqHlScl3c5yrJSpkPTGBv1MAs6MiMuyzQhKosC/AmcB44DVJK0KvA+Y\nqRIyfj7w/oh4qKaI7ZbAbsDawAOSfhQRy74RmW33AbaMiFDme0bE6ZiZmVmvGC4rY9Xuiogl+Rjy\nx5RJC5RVqyvz9TuAhzIKCOC/KDFH7wAej4g5ABHxt0qGZZUeZTlW9V+vnzuAr0r6CrBJRLxAyckc\nmyt0L2WbcZS4p5mUAPQZEfFQXqu64Oz1EfFSRPwF+DPw1ppx/A14EbhA0r7A8129ETmb0szMrKnh\nOBmrrXJb+fvFqn1ijXIgm+VWlov1MMuxWT8RcTmwF2WV7AZJH8yVrIeBw4DbKROw3YDNgftbjPml\nqtcr5W3mBHAnyiR1T8pKYpc4m9LMzKy54TgZ20nSZrlX7ECWxyBV+w2wqaQt8u9/Bm7N4xtKejdA\n7uNaYQLTTpajpIsl7dRkjHX7kfR2YElEnAX8AnhXtp9BiYCaQZmMTQLmRYlXuAP4gKTN8lpdydoc\nCawbETcAXwS2a/dcMzMza8+w2DNWYw5wNiUj8hbg6toGEfGipMOAn+Zkaw5wbkS8nBvyfyBpDcoK\n1cTc03VBRHyMkuV4mqTXKRXpP1NnDO8CHm80wEb9UCaPn5T0CvD/gG/lKTOBE4A7IuI5SS/mMSLi\nCUlHAVflBPTPwD82+4BUcimPpKyo/SL30ImepQeYmZlZHUMmm7JdapJhmTW8JkfEnv08rOoxrAP8\nZ0QcMFBj6G2SpgBLW23sdzal2eDjoq9mfUcdmE3ZrkGdYRkRfwM6aSJ2GuUbl99r1dbZlGZmZivr\nuJUxG7zGjRsXc+fOHehhmJmZ9YvhvDJmg5SzKc0GHz+mNBt4HfdtSg1wNqWkhyWt391z22jTMuOy\nr8nZlGZmZr2mU1fGepRN2UkkvaFOYdpe42xKMzOznum4lbEGnqi8yCzHhZnT+J08dmxV/uJP8thC\nSeupeFLSIXn8EkkTJY2QdHq2WyDpmKr+jsksyIWStqwdjKRRKhmZ81TyK8fXjrOJZRmXKnmaZ0ha\nLOnmjD6qrJ59X9Jc4POSNsn3F+TvjbPdWyVdnZ/FfEnvrTNWZ1OamZn1oU5dGVtBJZtS0keBjwM7\nR8TzVQVQjwc2i4iXKvmLwCxgV0qG5RJKvNDFlHihzwBHAZsBO0TEqzXFVP8SETtK+iylGOuRNUM6\nCJgWEadIGgGsWT3OFvdSnXG5FjA3Ir6Ylf5PAo7O995Y2TQo6Vrg4oj4L0mHU3IsP56/b42IfXIc\nI6v7krMpzczM+txwWRmrmAhcFBHPwwo5jQuAyyR9Eqg80ptJyaN8P/AjYFtJo4GnImJpXuvcyiPA\nmszHq/L33cCmdcYxBzgs63NtGxHPdvN+XqcEigNcyvKcTaqOA+wCXJ6vL6lq90HKvRERr9XJ0XQ2\npZmZWR8bbpOxRjmNewDnUKKL5mTV/RmU1bDxwHTKI8T9ycr2Ta4FyzMfV8p7BIiIGZRJ3qPA1Moj\n0F5QPZ5m+7jaqmfibEozM7O+N9wmYzcBh0taE8pjuIwI2igibgG+AqwLjIyIPwDrA2MiYgklw3Iy\nyydjNwGTKtmUXcx83AT4c0ScD1xA/fzKm3MlrplVKBNEKI8+6+VsQgkQ/0S+Priq3c1kXFPugVun\nZgzOpjQzM+tjw2oyFhE3AtcAc7P0xWRgBHCppIXAvcBZEfF0njIbeDBfzwRGs3wicwHwCLBA0nzK\nZKghSeMkXZB/TgDmSbqXkjd5Zk3bVSjZmU/R3HOU4PNFlEeO32jQ7ljKY9EFlNDzz+fxzwO75b3f\nDWyd/d+gEni+NnBdnncbzqY0MzPrdR1Xgb9ZNuVQIWkb4PCIaDr5kbQ0IkY2a9MfnE1pNnS56KtZ\n3xnOFfgHdTZlOyJiEUNkFcrZlGZmZj3TcZOx3Ou10UCPoz8MhlWxiDgOOG6gx2FmZjZUddxkzAYv\nZ1OaDRw/jjQbvIbVBn4zMzOzwabXJ2PNgrrzG4WLJL0x/95c0pLakgqdIj+LRfl6gqSpbbSf3h9j\ny/6W9uDch/P35hnr1O1rmZmZDWd9tTJWN6g7IuZSiqlOzkPnACdExN/6aBzWxyLCoexmZmY90F+P\nKasDsL8KHCnpy8CqEfHjVidLeljSt3MFZq6kHSVNk/Q7SZOq2h0naU6GWn89j20q6TeSpkp6UNJl\nKkHfsyT9VtJO2W6KpMlV11qU564l6foM0l4k6cB8f6ykWyXdnWMZVXV8ftYe+1zVbbwMtMoDeo2s\nLaYGQeS1wd3ZbomK9SS9Lun92XampC0kjZR0UdW19qu6z1NyvHdKemudz35rSXflZ79A0ph8q51Q\nczMzM2uhXzbwVwdgR8TTkk4Ffghs1YXLPBIR20s6A5hKCfFeHVgMnCtpd2AMJb5HwDU5KXmEUkD1\nAOBwSi7kQZR8xr0ok8OPN+n3I8BjEbEHgKR1Ja0K/ADYOyKeyAnaKXn9i4BjIuLWLPtQue/bKZXw\nG8pvglaCwFcKIled4O6IeE3Sg5TPcjNK8dbxkmYDb4uI/83P+5mI2Dbv4U3Zx1rAnRFxgqTvAp8G\nTq4Z1iTgzIi4LB8vj8ixtgw1z76OynthxDobtHOKmZnZsDJQG/g/CvyJrk3GrsnfC4HZEfFsRDwB\nvChpPWD3/LkXuAfYkjI5A3goIhZGxOuUydvNUardLqR+kHe1hcBESadKGp9h2u8AtgF+lXvjvga8\nTdK6wHoRcWuee0kX7q9WvSDyRsHd1aHm36ZMNN9NmXhWrnVO5cIR8dd8+TJwXb5uFGp+B/BVSV8B\nNomIF7pyE86mNDMza67fJ2OS9qTkP34YOE2ZE9mGSvj261WvK3+/gbIa9u2I2D5/toiI/6w5t/b8\nyrkAr7Li57E6QEQ8CIylTMpOlnRi9rW4qq9tI2J3moeHd9VK12oS3D2TEmi+E3ADsB4lcmlGo2ul\nV2J5BEOjUPPLKSuILwA3SPpgt+/IzMzMVtKvkzFJa1AqtX8uIhYCvwBO6KXLT6OEgI/MvkZL+rsu\nnP8wGdgtaUfKIz9UMhqfj4hLgdOyzQPABpJ2yTarSto6My2fkfS+vObB9TqStJOki1uMZ6UgcjUO\n7p4NvBd4PSJeBOYB/8KKoeZHV/VfeUzZkqS3A0si4izKv9e72j3XzMzMWuvvlbF/B34eEffl31OA\nT0gaI2lDSTdUGmp5WHVbIuIm4HLgDpXg659Rgq7bdSXwZkmLKROXSkD4tsBd+TjyJODkiHgZ2B84\nNTfqz6NMhgAOA87J9mrQ18aUlaZm6gWR1w3ujoiXgD8Ad+a5M7Ptwvz7ZOBN+QWE+cBuzTqWtJek\nSuj4gcCivJ9tgFaTSDMzM+uCXg8KVwcEdfe13Nh/SUQsGOix9Ba1EVo+bty4mDt3bn8NyczMbECp\nzaDwvlgZWxbU3QfX7ggRcVynTMSURV8pX8gwMzOzLur10hbDKajbStFXwEVfzczMuslB4dZvHBRu\nNnAcFG42eDko3MzMzGwA9WtQeH9QiU5av7vnttFmet7j2ipxTGPy+KoZN7Rzd/oeCqo/Wzko3MzM\nrFf0a1B4J4mIZ4F/Y3ll+8nA7RExe+BG1f8cFG5mZtYz/R4ULunLuYI0X9J38tixVeHXP8ljCzP4\nWpKelHRIHr9EJei7bpB2OkbSPfnelrWDkTRK0oxc0VkkaXztOJt4ivKNUSLiv4HXVULPJ1EmZ02p\nBJb/SCWYe4mkD0i6UNL9kqZWtdtd0h15Hz+tKmbbMjRd0gRJ11Vd62xJh+brFYLG89gGkq5UCVmf\nI2nXPP4WSTdJWizpAlasm+agcDMzs17Qr0Hhkj5KCeXeOSKeVwm+Bjge2CwiXlLJmQSYRQkD/z2w\nhBL3czHwHuAz1AnSruryLxGxo6TPUlasjqwZ0kHAtIg4RdIIYM3qcba4l31rDn0BuB84KvMj2/Em\nYBdKzNC1eZ9HAnMkbQ/8kZJ3OTEinlPJhfwSUCnE2jQ0vVGnqhM0nm+dCZwREbdJ2piSZvBOSpHb\n2yLiG5L2AI6o+hwcFG5mZtYL+vvblBOBiyLieVgWfg2wALhM0s+Bn+exSvj174EfAUdJGg08FRFL\nJdUL0q64Kn/fDdROnqAEaF8oaVVKIkBPaqJ9BHicUp2+XdfmZGgh8KeMhiKr/28KvI0Soj5LEsAb\nKYHdFdWh6SPzkemzkl6smmDVUx00fj3LQ8InAltlXwDrSFqb8vnvCxAR10v6K10UEecB5wGsNmpM\n71YYNjMz6wD9/W3KRoHVe1D2Xu1IWR16AyXkenz+TKc8Ftuf5XmLzUK5K0HgjcKvZ1AmGo8CUyuP\nQLtKJa7pWEpA98cktZvb2E7o+a+qgsi3iogjunB+o9DzRkHjqwC7VPU3Oid40HvB52ZmZlZHf0/G\nbqKEea8Jy8KvVwE2iohbgK8A61JWe/4ArA+MiYgllCzGyawYfr1CkHa7g5C0CfDniDifkgG5Y502\nN+dKXDNnAN+KiD9SHiOeo6rlpR64E9hV0hY5ljUl/UMXzv89ZaVrNUnrAh/K6zQKGq8NEq9syJ9B\nhp3nI+a2A8bNzMysPf36mDIibsz/0M+V9DJwA2Vf0qU5aRBwVkQ8nafMBkbk65nAtymTMiiTqH+g\nBGm/ApwPnN2ob0njgEkRcSQwATguz1sKHFLTdhVgC8pm/UbX+0dK4Pd/5r1dK+nTea3/Ugk9PzIi\nHlMJ3Z4bEdc0ul61iHgiN9z/WNJqefhrLA8vb3X+HyT9N7AIeAi4N99aG/iFpNUpn/WX8vixlInk\nAsr/JmZQvpDw9RzDYuB2SnB5t207el3muvCkmZnZChwUXoekbYDDI+JLLRsb4KBwMzOzWnJQePdF\nxCJPxNojB4WbmZn1SK+vjJk1stqoMTHqU98f6GGYdQRnTZoNfgO5MmZmZmZmbRoSkzF1M+9SmZeY\n5y/qZt8XSNqqzvFDJZ2dr6dImtziOlMqVfCbtDlU0pTujLOraqv0d+Pcqfn6QEn/291rmZmZDXf9\nXfS1JwYkAzG/fWkNRMQVkv5EKTtiZmZmXTQkVsYaeAJK7aysCVbJoty7NzuRND3LYiDpMEkPSrqL\nEkFUr/3mkm6UdLekmVqejbkUeKFFdy9kOyS9VdLVKhme8yW9V9Jakq7PvxflqtROkq7Kc/bOFcQ3\nSlpd0pI8voWk/8nz7pG0efY3UtLPJP1G0mX1aqRJOiD7mi9pRh5+GXim/U/RzMzMGhlKK2MrqMpG\nfBHYJyL+Jml94E5J10QvfzNB0ihK3a2xlInILSyv31XtPEo9s99K2hn4IfDBiDi9VR8RcUXVn2cB\nt0bEPir5mSMp0UuPRcQeOaZ1geeAHfKc8ZTaYu+m/NvOzuOXAd+JiKuzxtgqwEZ53tbAYyzPAq3U\ncas4EfhwRDyqjFqKiNspdcdakrMpzczMmhrKK2MVAr6VBUv/BxgNvLUP+tkZmB4RT0TEy8AVtQ2y\nwv17gZ/m/rb/C4zqZn8fpGRyEhGvRcQzlCzKiZJOlTQ+Ip7JiKP/lfROStTRf1CinsYDM1UyJkdH\nxNV5rRcr2aDAXRHxx4h4HZhHycWsNYsSGfVplhfgbVtEnBcR4yJi3Ig11+3q6WZmZh1vyK6MVTkY\n2AAYGxGv5Mb+1fuor1arbasAT/fV3raIeFDSWOBjwMmSbo6Ib1DSCT4KvEKZkE6lTJwmUyarjVTn\nWjbK8ZyUK3x7AHdLGhsRT/bG/ZiZmVlnrIytS8mZfEXSbsAmfdTPbGCCpLdIWhU4oLZBRPwNeEjS\nAQAqtqttJ+loSUfXHq9xM/CZbD9C0joqweTPR8SlwGksz9ScAXwBuCMingDeAmwJLM4x/VHSx/Na\nqymzQdshafOImB0RJ1L26W3U7rlmZmbWWidMxi4DxklaSMmF/E1XLyBpQ5UsycrfN+TEZ5mIeByY\nAtxBeXR3f4PLHQwcIWk+sBio94WCLYFWq0ufB3bL+7qbsrdrW+CufAR6EnBytp1NeTRb2WC/AFhQ\ntW/un4Fj81Hu7cDfN+tY0jck7ZV/npZfjFiU585vMW4zMzPrgiFRgV8dkHdZLWty7Zt7z4Y8SROA\nyRGxZ7N2zqY0M7PhRB1WgX/I511Wi4g9O2gidiDlG6N/HeixmJmZDUVDYmWst0j6MHBqzeGHImKf\ngRjPcONsSrPe42xKs8Gv3ZWxTvg2ZdsiYhowbaDHYWZmZlYxJB5TqpvZlL3Y/8NZULZb57bRZnru\ni2vVppIE0K3xSJok6ZA6x5dld6qNzEo5m9LMzKzXDKWVsQHJpuwkEXFuH1zT2ZRmZmY9MCRWxhp4\novJC0pez/MJ8Sd/JY8dKuk/SAkk/yWMLJa2X9b+erKwSSbpE0sSs53V6tlsg6Ziq/o7R8vzLLakh\naZSkGZLmZZbj+NpxNvEU5UsKSPqRpLmSFkv6evc+mvokTZE0OV+Pzc9rPvC5Bu3XknShpDmS7tXy\n3E9nU5qZmfWSobQytoJKNqWkjwIfB3aOiOclvTmbHA9sFhEvVTIVWZ6/+HtgCSUy6GLgPZQCq0cB\nmwE7RMSrVdcC+EtE7Cjps5RVoCNrhnQQMC0iTlHJklyzepwt7mXfqj9PiIin8ho3S3pXRCxo60Pp\nmouAYyLiVkmnNWhzAvDriDg8P8O7JP2PsynNzMx6z1BeGauYCFxUyVuMiKfy+ALgMkmfBF7NYzMp\nuY3vp+Q+bitpNPBURCzNa52beY/V1wK4Kn/fTf0MxznAYZKmANtGxLPdvJ9/knQPJYR8a2Crbl6n\nIZWA8fUi4tY8dEmDprsDx+devemUmKmNu9KXsynNzMya64TJmKifGbkHcA4lMmiOpDdQKtSPz5/p\nlEeI+1Mmac2uBctzHBtlOM6gTPIepQRrr7RRvuWNSJtRVt0+FBHvAq6nb3I2m91nbbv9ImL7/Nk4\nIholD5iZmVk3dMJk7Cbg8EreoqQ3S1oF2CgibgG+QsmvHBkRfwDWB8ZExBLgNsrkZ2bVtSblxI2a\nx5RNSdqEkpF5PnABy3Mjq9vcnCtxjawDPAc8I+mtlPDvXhcRT2cf78tDBzdoOo2yV04Aknboi/GY\nmZkNZ0N+MhYRNwLXAHPzcdpkYARwaeY63guclRMQKDmOD+brmcBoyqQMyiTqEWBBbmw/qFnfksZJ\nuiD/nADMk3QvcCBwZk3bVYAtKJv1G93L/Bzvb4DLKXvcukxV2Zo1OZPVDgPOyc9MDS71TWBVyuex\nKP82MzOzXjQkKvB3QjalpG2AwyPiSwM9lt7mbEozM7OVOZtykImIRR06EXM2pZmZWQ8MiZUxW5mk\ntwA313nrQxHxZH+Ppx3OpjTrGudPmg1tnbYy1rbejk6StIGk2Vn0dHzrM3quzfHeXfmWI3AhsBqw\nGNhV0vF5nWVFXvtgjMsinCTdImmpMq7JzMzM2jdki7620JvRSR8CFkZEbZFXJI2IiNd6qZ+e+Cww\nMSL+mH9f05edZUHaZSJiN0nT+7JPMzOzTtVxK2MNPAHLAq5nSLpe0gOSzs1vOSLpCEkPSrpL0vmS\nzpa0PfBdYO+MOVojV4C+l9+23CVjhW6VdLekaZJG5fU2l3RjHp/ZIEJp6+xvnkr80pjq8bZ5T+cC\nbwd+KemLkg6VdHadvtoZz6GSrsp2v5X03ar3VrhvqiKczMzMrPuGxWSsJpJoJ+AYSmX7zYF9swzE\nv1NikXYFtszz5gEnAlfkI8EXgLWA2RGxHaVMxg+A/SNiLOVx4SnZz3mUuKGxlHIbP6wztEnAmbmK\nNw74Y53xNr2niJgEPAbsFhFnNDmlnfEAbE8pzbEtcKCkjfL4svuOiNsiYt+s22ZmZmY90KmPKZu5\nKwu+IunHwPsocUm3VuKPJP0U+IcG578GXJmv3wFsA/wq66KOAB6XNBJ4L/DTPA5lT1etO4ATJL0N\nuCoiftvDe6urC+MBuDkinsnz7gM2Af7Aivfdlb6dTWlmZtbEcJyM1X59NGhc9LSeF6v2iQlYHBG7\nVDeQtA7wdKt9axFxuaTZlOimGyT9S0T8ugtjadcq7YwnvVT1ujr66cXu7I+LiPMoq3KsNmqMv7pr\nZmZWY1g8pqyxk6TNcq/YgZTq+3cBH5D0poxC2q/Naz0AbCBpFwBJq0raOiL+Bjwk6YA8Lknb1Z4s\n6e3Akog4C/gF8K46bX7TjXtcQbvjMTMzs/43HCdjc4CzgfuBh4CrI+JR4FuUSdks4GHgmVYXioiX\nKUHjp+bG9nmUx4FQ8h6PyOOLgb0BJO0l6RvZ5kBgUZbh2Aa4uPr6ktana6t2zbQzHjMzM+tnHVf0\ntVl0UrPYHkkjI2JproxdDVwYEVf38XCbkrQn8PZcORvUsrTF5IhomHfkoq9mXeOir2ZDW7tFXztx\nz9iy6KQu1hqbImkisDpwE/DzPhldF0TEdQM9hnZIuoVSXuOVZu22Hb0uc/0fFzMzsxV03GQsyy1s\n1OC96cD0Bu/1SaX64SAidhvoMZiZmQ1Vw3HPmJmZmdmg0XErYzZ4LXz0GTY9/vqBHobZkOE9Y2bD\nQ8etjLUKCpd0mqTF+XuSpEPy+FRJ+/fRmB5uo82y4O1u9vHjjFT6Ynev0cX+HBRuZmbWCzp1ZaxZ\nUPhRwJv7I+Bbpdx9b5WmaNbP3wPvjogt6rz3hoh4tS/7d1C4mZlZ93XcylgDlVDta4CRwN2SDpQ0\nRdJKG/cbhX/XtJki6cJcIVoi6dg8vqlKCPnFwCLKlwnaCf5eFrydq0xn5ArezZI2yOPvztWvebmy\ntyjPvQkYncfH55i+L2ku8HlJG0i6UtKc/Nk1r7dW3sMcSfdK2rvOfa6lEqw+X9IiSQfWjtfMzMy6\nb1hMxqpCtfcCXsjQ7yvqtZW0Ko3Dv2ttCXyYEj5+Up4LMAb4YURsHRG/bzP4uzp4ey1gbkRsDdwK\nnJTHLwIm5apf9URoL3I1MCJm5rE3RsS4iPgecCZwRo5jP+CCbHMC8Os8vhtwmqS1aob2EeCxDAjf\nBrixznjNzMysmzr1MWVP1A3/btD2+oh4CXhJ0p+Bt+bx30fEnT0Yw+tAZbJ4KXCVpPWAtSPi9jx+\nObBS8doq1ZPNicBWWh4Svo6ktYHdgb2qVgdXBzampBNULAROl3QqpZjuTLpADgo3MzNrypOxldUN\n/26gUaj2c708pq6GmdeOYRVgl4h4obpB7mnbLyIeaNhxxIOSxgIfA06WdHNEtB2f5KBwMzOz5obF\nY8ouqhv+3VsXzz1go1s0W4WSeQlwEHBbRPwVeFbSe/L4J7rQ7U3A0VVjqHy5YRpwTE7KkLRDnfFu\nCDwfEZcCpwE7dqFfMzMza8GTsRrNwr+zFMak7l5b0irAFpTN7808B+yUG/Q/CFRWoo4AzsuyHWvR\nRph5OhYYl5v/7wMq9/BNYFVgQfb1zRznhpJuyDbbAndlnycBJ7fZp5mZmbVhWAWFDzRJ2wCHR8SX\nWrRbGhEj6xwfGRFL8/XxwKiI+HzfjLZr2gkKHzduXMyd2/BtMzOzjtJuUHgnrowtCwof6IHUiohF\nrSZiLeyR5SsWAeMZJKtU7QaFm5mZ2co6bgN/s6DwoaLeqlgev4IVvyU5KDgo3MzMrPs6bjJmg5ez\nKc26xtmUZsNDJz6mNDMzMxsyPBkbImoD0AdL+LmkNXIf28uS1u+LvszMzDqZH1MOLY0C0Acs/DwL\nyW5fPTk0MzOz9nllbOgaCuHnZmZm1oInY0PUUAg/z76PkjRX0tzXnm+3Rq2Zmdnw4ceUw8OAhZ87\nm9LMzKw5T8aGh8EYfm5mZmb4MeVw0afh52ZmZtZ9nowNA30Zfm5mZmY903FB4Z1qMAegw7K6Y+Mi\n4i+N2jgo3MzMhpPhHBTeqQZlAHql6CuwKvD6QI/HzMxsqPHKmPWb1UaNiVGf+v5AD8NsyHA2pdnQ\n5pUxMzMzsyFgwCZjtVmLeezhqtcDlrfYos30HPvakn4naUweX1XSQkk798XYBgNJD1fyJ6uyKTfP\nbMqlAzo4MzOzIWqg64w1ylqEAcxbbEdEPCvp34BzgN2BycDtETG7D4Y4aEXE7yjZlJ6MmZmZdcNg\ne0w5FPIWn6Jspici/ht4XdKXgUnAv7U6OVf2fiTpzhzPB3J890uaWtVud0l3SLpH0k8ljczjD0v6\ndq5GzZW0Y9777yolKiRNkHRd1bXOlnRovv6OpPskLZB0eh7bQNKVkubkz655/C2SbsoVygtYccLq\nbEozM7NeMKgmY0MhbzEi9o2IP1Qd+gJwKnByRDzV+i4BeBOwC/BF4FrgDGBrYFtJ2+ejwK8BEyNi\nR2Au8KWq8x/JFcWZwFRKDbH3AN9o1qmkNwP7AFtHxLuAk/OtM4Ez8v73Ay7I4ycBt0XE1sDVwMZV\nn4OzKc3MzHrBQD+m7IkBy1us8ZHstyv1v66NiJC0EPhTRCwEkLQY2BR4G7AVMCvv7Y3sf3aQAAAg\nAElEQVTAHVXnX5O/FwIjI+JZ4FlJL0par0m/fwNeBC6QdD1QWT2bCGyVfQGsI2lt4P3AvgARcb2k\nv3bhHsnznE1pZmbWxFCejA143qKkDYFjKStu/z97dx59V1Xf///5SkSmSAChfkOKBgIVkcgUURQw\nKAVRfqAIpkJFQKBBEYyFllYbUaEyWQYRESjEAAIKoggIWEoIhClhyMDcQoAiFQQBU8bA+/fHed/k\n5OZOn8/nfqZ7X4+1su695+6zzz4fXMu99tn3/bpR0r9HxPwejOetqrG9lWN7E/hdRHyhl+cvYflV\nz1UAImKJpG2AT1Csph0GfDzbbhsRr1TdH4AnUGZmZv1oSD2m7KF+zVuUdIOksU2anQL8a0T8D8Vj\nxB+ptLzUB7cDH5W0UY5lNUl/1YPzH6dY6VpZ0miKyRe572x0RFxD8Yh082x/PcXEjGxX+VHFLGDf\nPLYrxeNVMzMza6NhOxnrz7xFSSOAjSg269dr89cUe6j+PcfzG+BPQKUExzW5coak70ravdXrR8Sz\nwP7AxZLmU0zONunB+U8CP6f4YcIvgHvyq3cAV2Wft7BsH9rhwMTc1H8/xY8RAL4D7JCPT/cEnmh1\nDGZmZtaaQavAryGctShpM+DAiPhG08YGgKTFETGqURtnU5qZWTfRMKjAPySzFgEiYqEnYq2pFH0F\n/jDYYzEzMxuOnE1pA8bZlGY942xKs+FtOKyMmZmZmXW9IZlNKWmipIWS3p6fx2e1+jUGabj9Kv8W\nC/P9pHIl/gbtZ5Y+X5yb76fmjwV2yuMzJTWdkfdyzIvy1dmUZmZmfTDYdcZqZlNGxFxJsyjyHv+V\nIv/xmxHx0kAPcKiT9P+AD0bERgN0vZHlvFBnU5qZmfXNUHtMWc47/GfgIBW5jytFxMXNTm4ltzHb\nHZUZjPMlfSePjZP0oIrsyIclXSRpJ0mzJT2SxVIrWZdHlvpamOeuLulqSfPy2OT8vmZ+Zh6fl2U5\nvlq6jdeBZrlBb7Ks7Mb1wNi85+1z/HvV+NvUzLqsajNd0umSbs2VyL3y+CRJN6vIDH0gmzub0szM\nrA2G1GSsnHcYES9QZD5+H/hKD7ppmNsoaWeKTMptgC2ArSXtkOduBPyAoqbXJsA+wHYUK3T/3OS6\nnwR+HxGbZ7mOa9U4P/N84PCI2LzcSUTcGhFHNLpQRDwZEXvmx93JFcaIuLlWezXPuiwbQ3HPuwHH\nl45vBRwREX+VY3A2pZmZWRsM9mPKZnalKJmwKUXF/VY0y23cOf9VCqGOopicPQE8VpUTeUMpQ3Jc\nk+suAE6WdAJF/bSbs17ZCvmZWRV/zYi4Kc+9IO+1v3yYxlmXZb+KiLeA+yW9q3T8zoh4rKcXdjal\nmZlZY0N2MiZpN2A0sAtwhaTrIuLlFk5tltso4PsR8ZOq642r0f61qnOhfu7jw5K2Bj4FHCvpBuAK\nauRn5qRwICcmonHWZVn5b1COdupTjqeZmZnVNqQeU1ZIWpXiceFXc6Xq18A329T9dcCBlT1TksZK\n+osenL+I4pEdkrYCNsj36wEvR8SFwEnZpmZ+Zj6CfVHSdtnnvrUuJGkbSTN6eoM19DXr0szMzPrJ\nkJyMAf9C8bjs/vx8DPA3kjaWtJ6kayoNVcqAbEVEXA/8DLgtHz9eRpHZ2KrLgbXzMeZhwMN5fAJw\np4pSHd8Gjm2UnwkcQBEsfi/Lr0CVvRt4pQdjq6lR1qV6mJtpZmZm7eVsyiFM0knABRExf7DH0oyc\nTWlmZrYcDYMK/EM2m3KoiIijhvpETM6mNDMz65NB28AfEU8C6w/W9a09KkVfB3scZmZmw9WQ/TWl\ndZ4FT73IuKOvHuxhmA0bDgo36w5DdQN/v1ODbMx8f5Kk+/J1iqT98njNCvdtGtOiFtrMzP12gyZT\nCPbP9ydJ+t9yKoGZmZm1rttXxmpmY6ZDgLXLOYz9RUUl1nq/qOxr32+LiCX90TcU+9okuQaZmZlZ\nL3XtylgdzwJkBuMo4C5Jk6vzKCvq5U5WtTlG0nm5ovWopMPz+DhJD2UdsYUU++dayXt8nuLHD0ha\nLOmUXMG7QdK6eXympFMlzQWOkPSe/H5+vr47271L0hXKjExJH6m+mKTjJd2f556chxfThpIbZmZm\n5pWx5VTyFiNi9yzVsAUUE6rqtqXcyT0i4lkVweDHAQfW6HoTYEeKemYPSfpxHt8Y+FJE3J6fm+Y9\nljIpAVYH5kbEVEnTKOqbHZbfvb3yc1pJvwFmRMRPJR0InA58Jl9viojPShpJMQEt3+PawGeBTTIW\nas0cw8m0SNIhFKuMjFxj3VZPMzMz6xqejPXee6mRO1mn7dUR8RrwmqRngErm4+OliVhvvAVcmu8v\nBH5Z+u7S0vttgcok7gLgxHz/cWA/gHwcW53k/RLwKnCupKuBq3o6QGdTmpmZNebJWO+JGrmTdZTz\nHt9k2d+93XutypOdRn23NCmKiCWStgE+QZEkcBjFBM7MzMzaxHvGeq9m7mS7Os+9XWObNBtBMUkC\n2Ae4pU67W4G/yff7ltrdABya1xspaY2qMYwCRkfENcBUYPMe3YSZmZk15clYLzXKncxSGFN627ek\nEcBGFJv1G/k/YBtJCylWrL5bp93hwAGZS/lF4Ig8fgSwY2Z03gW8P69fyft8B3BVnncL8I3e3pOZ\nmZnVNmjZlINtKGdjStoMODAiGk5+WsmDHAj5A4fFzTb2O5vSzMy6yXDIphxsQzYbMyIWNpuIDRUZ\nZv63tH//m5mZWVfo2g38nZCNORRWxSLiKOCowR6HmZnZcNW1kzEbeM6mNOsZZ1OadYeufUzZLJty\nsLSSPZn5mJPy/fZZgf9eSWMlXZbHJ0nqcV2wFsfobEozM7M26faVsUbZlE1JGjkQ2ZVN7At8PyIu\nzM/9EmJeIWm5/804m9LMzKxvunZlrI5KNuUkSbMkXZ35kWdluYlKHuQPspzFtpI+IekeSQsyg3Ll\nbPdBSbdm5uOdkt5RvpCk1bP/eZIWZpwSlLInG3gReF3SQcDnge9JuihX+xZWN85rnSdpTo51jxpt\nxkl6QNI5udJ2vaRV87vlsi5xNqWZmVnbdPvK2HIq2ZRpG2BT4HHgWoo4ocso8iDviIi/l7QK8Ajw\niYh4WEXo96GSzqSII5ocEXOymGr15OWTwO8j4tMAkkbnGPakiYio1Am7VdJ2FCU6LmvwePObwH9G\nxIGZL3mnpP+IiOoVrY2BL0TEwZJ+DnyOImYJSlmXZmZm1j5eGavvzoh4NB9DXgxsl8ffBC7P9+8F\nHouIh/PzT4Ed8vjTETEHICJeioglVf0vAHaSdIKk7SOiOheynXYGjs79cTOBVYB312j3WERU9tDd\nBYwrfXfpis2bk3SIpLmS5r75cn/eopmZ2fDkyVh91dVwK59fLe0TU51zVeP85TsrJnBbU0zKjpU0\nrbcDbYGAz0XEFvnv3RHxQI129TI0oZd1xCLi7IiYGBETR642ujddmJmZdTRPxurbRtIGuVdsMrVz\nHx8ExknaKD9/Ebgpj68n6YMAkt5RvfE944Zezo33JwFbVXcuaUYGdffVdcDXJCn73bINfZqZmVkb\neDJW3xzgDOAB4DHgiuoGEfEqcADwi8x3fAs4K3MrJwM/zI3+vwNWkbSepGvy9AkUe7fuBb4NHFtj\nDB8Anm7DvXwPWAmYnxv8vwfFhLA0HjMzMxsE3sBf30sRsVv1weqq9xFxA7DCSlPuF/tw1eHFwKfy\n++soVqxqyk3/j2RSQF0RsX/p/SJgs3w/k2J/GBHxCvB3Nc79fWk8S8/NzyeX3k9qNAYzMzPrvW6e\njC3NpuxLrbH+EhEvAXsP9jiayWzKzwI/aNZ2wtjRzHVFcTMzs+UoouE+c7O2mThxYsydO3ewh2Fm\nZjYgJN3VSlmobl4ZswHmbEqzxpxFadadvIHfzMzMbBB11GSsWfh3hlrfl69TJO2Xx6dL6pdMx1bC\nx1sMB58paWKlT0nr9GIsS++56vjSGKVWAsazzfR8P1nSf/VXKLmZmVmn68THlI3Cvw8B1h6IcO+s\n6VWvKOygiIiz+qHPSyX9ATiy3X2bmZl1g45aGaujEv59JTAKuCtXc46RtMIEQtLWkm6SdJek6ySN\nqdHmmAzeninpUUmH5/FxKoLFZwALgfUr129iaTi4pB9nfNB9kr7T67uuoXzPeZ/zsg7aV+u0rxcw\n/jpFWLmZmZn1UcdPxirh3xGxO/BKxgHVzFmUtBLwQ2CviNgaOA84rk7XmwC7UASKfzvPhSJs+8yI\neH9EPF4VPl5vjHuW6ol9M3958QHgY5I+0Nqd9tj5wOERsXmDNpWA8Q8COwInSVo9Im4thZU35GxK\nMzOzxjrxMWVfvJei8OnvMjloJPUr4F8dEa8Br0l6BnhXHn88Im7vwxg+L+kQiv82Y4BNgfl96G8F\nkkYDa0bETXnoAmDXGk13BnYvrSBWAsZr5VrWFBFnA2cDrDxmY9dRMTMzq+LJ2PIE3BcR27bQtl6o\ndq8CtQEkbUCx9+qDEfGn3CS/Sm/7a3QpmgSZl9p9LiIe6ocxmJmZGV3wmLKHHgLWlbQtFI8tJb2/\nXZ1LukHS2AZN1qCYzL0o6V3UXq3qs4h4Ia+xXR7at05TB4ybmZn1M0/GSjLgey/ghNzYfi/wEVha\nFmJKb/uWNALYiGKzfr3rzwPuAR4EfgbM7uW1rpG0Xr7/rqTdazQ7APhRlgGp96vPmgHjZmZm1j4d\nFYeUtbquiojNmjQdcJI2Aw6MiG8M9ljaTdIk4MhawepljkMyM7Nu0mocUqetjC0N/x7sgVSLiIUd\nOhGbDJwJ/Gmwx2JmZjYcddTKWDeR9E7ghhpffSIinhvo8bRi5TEbx5gvnTrYwzAbspxNadZZHBTe\n4XLCVS9pwMzMzIaJjnpM2SybssF5i0vnL+zltc+VtGmN4/tLOiPf16z6X9X+GEn7N2mzv6Rj8v26\nku7ICvnb5+b9Ncv31W7OpjQzM2ufTlwZa5RN2W8i4qCBvmb6BLCgdP2b+/Nikpb734yzKc3MzPqm\no1bG6qhkU47KOl93S1pQyllsi8ypnJjvD5D0sKQ7gY/WaT9e0rWZgXmzpE3yq8XAK00u9wqwWNIW\nwInAHpLulbSqpEWS1qlxvaMyY3J+vczLPPc7pb/RJnn8GEkXSJpNUa3f2ZRmZmZt0okrY8spZUO+\nCnw2Il7Kycrtkq6MNv+CQUWw+HeArSkmLDdS1A6rdjYwJSIekfQhil8kfjwiTm52jXK2pqRpwMSI\nOCw/1xrTzhSZmdtQ1BS7UtIOETGrRvd/jIitJH2FYrWrsuK2KbBdRFQmirc2G2de+xDgEICRa6zb\nyilmZmZdpeMnYyUC/lXSDsBbwFiKPMn/bfN1PgTMjIjKitylwF8tNxBpFEUx2V+UJk8rt3kcZTvn\nv8qkcBTF5KzWZOyX+XoXsGfp+JWliVjLnE1pZmbWWDdNxvYF1gW2jog3cmN/f+Q+QvPcxxHACwO4\nt03A9yPiJy20rWRulvM2oQ+Zm2ZmZlZfN+wZqxgNPJMTsR2B9/TTde4AJkl6p6SVgL2rG0TES8Bj\nkvYGUGHz6naSDpN0WBvGdB1wYK7IIWmspL9oQ79mZmbWR900GbsImChpAbAfRf5jj0haT9I1pc9L\nMyArIuJp4BjgNopsyQfqdLcv8OXMwLwPqPWDgk2APhdwjYjrKbIub8v7vwx4R717MDMzs4HTURX4\nh3I2ZW9k7a49M8B8yHI2pZmZ2YqcTdkBImK3YTARczalmZlZH3TUyli7SNoFOKHq8GMR8dnBGE+n\ncDalWWPOpjTrLM6m7IOIuI5i07uZmZlZvxr2jykb5VHmd/sM0DjqVb5f1Mq5Pem/t5mTkr4raaca\nxydVsiXLWZoN+ilnY06V9ESzc8zMzKy2TlkZq5dHOQ7Yh+KXhF0vIqb1Q5+nSPoT0HQZ1szMzFY0\n7FfG6ng2X48Hts/cxqmSRko6OXMX50v6GixddToxj98paaM8vrekhZLmSZqVx2r2UZH5kNdKOrhq\nLK2MF0m/yrzK+zJKqG0kTZe0V77/pKQHJd3N8pX2y+3XlXR5ZlrOkVTJ2XyFIkPTzMzM+qhTVsaW\nU8qjPJpSyQVJhwIbAFtGxBJJa5dOezEiJkjaDzgV2A2YBuwSEU9JWjPbHdKgj1HAJcCMiJhRNZZW\nxgtwYEQ8L2lVYI6kyyOiz7XGyiStApwDfBz4L+DSOk1PA06JiFskvZtiH937ytmYLVzL2ZRmZmYN\ndOrKWD07AWdFxBKAiHi+9N3Fpddt8/1sYHquco1soY9fA+dXJmK9dHgWgr0dWJ8iQ7LdNqH4degj\nGZR+YZ12OwFn5H68K4E1JL2jJxeKiLMjYmJETBy52ui+jdrMzKwDdeTKWAOifm5kVL+PiCmSPgR8\nGrhL0tZN+pgN7CrpZ9GLmiFZPHUnYNuIeFnSTAYvPxOKyfq2vQkINzMzs9Z0+srYn8nYn3Q9MEXS\n2wCqHjFOLr3elt+Pj4g7cuP7sxQrVY36mEYRX3RmrcFIahbBNBr4U07ENgE+3PwWe+VBYANJ4/Pz\nF+q0ux5Ymo0paaCCzc3MzLpGp0/G5gNLcgP+VOBc4Algfj4KLJe9WEvSfOAIYGoeOyk36i8EbgXm\nNekD4OvAKpJOLB/MshRqMt5rgbdJeoDixwe39+x2l16rXObjXEnL/dIxIl6l2Md1dW7gf6ZOV4dT\n5HnOl3Q/MKU34zEzM7P6hn0F/nbkUWadr4kR8cc2DavWNXYDNoyI0/vrGoNF0v4Uf7/DGrVzNqWZ\nmXWTbqrAvzSPsk6tsSEhIq4a7DH0h1xxnAJcPthjMTMzG46G/WQsIp6k2MvVlz7GtWc0A0fSBOCC\nqsOvRcSHBnIcEXEKcMpAXtPMzKyTDPvJWLeKiAXAkF0JrGXBUy8y7uirB3sYZkOWg8LNulOnb+A3\nMzMzG9KG/WSsUVB4k/MWl85f2Mtrnytp0xrHl4ZtSzpG0pFN+jkmN8E3alMO5/5Mreu2WzlAvMFY\nHBRuZmbWB53ymLJeUHi/ioiDBvqa6TPAVcD9g3T9pRwUbmZm1jfDfmWsjmcBJI2SdIOku7Ne2B7t\nvIikmZUaXpIOkPSwpDuBj9ZpPz5DxO+SdHMWdoUidLtZlftXgMWSPgLsTlED7d7scyNJ/5H11O7O\nY5MkzZJ0taSHJJ0laYSKoPPpKgLQF+SvIanVR9XYPyjpHkkb4qBwMzOztumUlbHllIK3XwU+GxEv\nZdHV2yVd2ZuookYkjQG+A2wNvAjcCNxTo+nZwJSIeCRjls4EPh4RJze7RjmcW9KVFLXVLsvPdwDH\nR8QVGQI+guIXptsAmwKPUxSU3RN4DBhbqctWCkC/qE4f5ATwh8AeEfEE8GgP/jYOCjczM2ugIydj\nJQL+VdIOwFvAWOBdwP+2+TofAmZGRGVF7lLgr5YbiDQK+AjwC2lpIf6V+3rhDO4eGxFXwNLq+uQ1\n7oyIR/PzxcB2wA3AhpJ+CFwNXN+kj/dRTCJ3jojf93R8EXF2ns/KYzYe3hWGzczM+kGnT8b2BdYF\nto6IN3Jj/2AFb48AXuiHvW2NIpaqxxQR8SdJmwO7UBRr/TxFhFM9T1P8zbYEejwZMzMzs8Y6dc9Y\nxWjgmZyI7Qi8p5+ucwcwSdI7Ja0E7F3dICJeAh6TtDeACptXt5N0mKSGsUKUAtCz3/+R9Jk8f2VJ\nq2W7bSRtIGkERQD6Lfm4dkREXA58C9iqSR8vAJ+mWGGc1PJfxMzMzFrS6StjFwG/kbQAmAs82NMO\nJK0HnBsRn8rP1wAHlR/ZRcTTWerhNorJy721+qJYqfuxpG8BKwGXUISPl20CzG4yrEuAcyQdDuwF\nfBH4iaTvAm+wbDI4BzgD2IhiH9sVwATg/JygAfxTvtbrg4j4g6T/D/itpAMj4o4m46tpwtjRzHVR\nSzMzs+U4KHyIybpee0bE633sZxJwZETs1paBNb7W/jgo3MzMbDlqMSi8Ex5TLg0KH+yBtENE7NbX\nidhAytIY/wS8NNhjMTMzG46G/cpYu0jaBTih6vBjEfHZwRhPJ1p5zMYx5kunDvYwzIYsZ1OadZZu\nWhlri4i4LiK2qPo3qBMxVUU95a9BK8f3GaAxLMpN/yscz9fxWXzWRWDNzMx6wZOxoa9W1NM4YEAm\nY81ExKBEUZmZmXUKT8aGl2fz9Xhg+1yRmpoRRydnvNF8SV+DpataJ+bxOyVtlMf3zjikeZJm5bGa\nfVRIWlVFlNPBVWMxMzOzPuj00hYdpRTzdDSlX0pKOhTYANgyIpZIWrt02osRMUHSfsCpwG7ANGCX\niHiqFId0SIM+RlGU05gRETOqxmJmZmZ94JWxzrATcFZELAGIiOdL311cet02388Gpucq18gW+vg1\ncH5lItYTkg6RNFfS3DdffrGnp5uZmXU8T8Y6g6gfxxTV7yNiCkX1/fWBuyS9s0kfs4FdVQrVbFVE\nnB0REyNi4sjVRvf0dDMzs47nydjwtDQOKV0PTJH0NoCqR4yTS6+35ffjI+KOiJhGsfdr/SZ9TAOe\nA87sh3sxMzPrap6MDU/zgSW5AX8qcC7wBDBf0jyW/6XlWpLmA0cAU/PYSblRfyFwK0UkU6M+oAgT\nX0XSif12V2ZmZl3IRV+HsL5GPWUtsIkR8cc2DqvetRZHxKhGbVz01awxF3016yytFn31rymHtqVR\nT0O1lpek8cDlwB+atXVQuJmZ2Yo8GRvCIuJJiv1cvT1/XPtGU/ca/w0MyYmimZnZcODJmA2YBU+9\nyLijrx7sYZgNWX5Madadum4Df3XeYx5bNIhDqoxhZu4Ra9RmuqRJ+f7rklYbgHEdI+nIJmO5SNLz\nkvbq7/GYmZl1mq6bjKU+5SlKGtm8Vb/7OtDvk7FWRMS+wJWDPQ4zM7PhqFsnY9WeBZA0SdIsSVdL\nekjSWZJG5HeLJf0gyz5sK+kTku7JEhHnSVo5231Q0q1ZduJOSeV6YEhaPfufl/mQlTpgz1Ns2G/k\nReB1SYcD6wE3Srox+/2kpLuz3xvy2DGSLpB0m6RHKrmSksbkfd6bY9i+Xh9VYz9Y0m8lrVoZSy/+\n1mZmZlbiPWOskLO4DbAp8DhwLbAncBmwOnBHRPy9pFWAR4BPRMTDkmYAh0o6E7gUmBwRcyStAbxS\ndblPAr+PiE8DSBqdY9izhXEekW9vlfQNYMeI+KOkdYFzgB0i4rGqgq0fAD6c479H0tXAF4DrIuK4\nXOVbrUkfSDoM2Bn4TES8RlG3zMzMzPrIK2MrujMiHo2INynyHLfL429SlHAAeC/wWEQ8nJ9/CuyQ\nx5+OiDkAEfFSJeuxZAGwk6QTJG0fEe0IbPwwMCsiHsvrLpcrGRGvZK2xGykmm3OAAyQdA0yIiD83\n6eOLwK7A53Ii1jJnU5qZmTXmydiKqqvgVj6/mhM0KHIca2mU71h0VkzgtqaYlB0raVpvB9ridVe4\nn4iYRTF5fIoiMHy/Jn0sBMYBf9nTgTmb0szMrDFPxla0jaQNcq/YZOCWGm0eBMZJ2ig/fxG4KY+v\nJ+mDAJLeUcl6rJC0HvByRFwInARsVd25pBmStmkyznI+5W3AxyRtkOeXHzHuIWmVDAOfBMyR9B7g\nmYg4hyIGaasmfdwD/B1wZY7fzMzM2sR7xlY0BzgD2Ijisd4V1Q0i4lVJBwC/yMnWHOCsiHg9N+T/\nMDe5v0LxSHIN4NyI+BQwgSIb8i3gDeDQGmP4APB0k3GeDfxW0tMRsaOkQ4Bf5iTyGeCvs938vI91\ngO9FxO8lfQk4StIbwGJgv4h4tkEfRMQtWeLiakl/PRARS2ZmZt3Ak7EVvRQRu1UfrM5djIgbgC1r\ntJtDsf+qbDHwqfz+OuC6ehfPidsjWX2/roj4IfDD0uffAr+t0XR+ROxXde5PKfa5Vfe5Qh8RcUzp\nfcOxm5mZWc9142RsSOc9RsRLwN6DPY6ekHQR8BGKX53W5WxKMzOzFXXdZKxR3mNEzARmDuR4+lN5\nVaufr7PvQFzHzMysE3XdZMwGj7MpzRpzNqVZd+q6X1M2yqbM7/YZoHEskrROreOtnNsfY+oJZ1Oa\nmZm1R9dNxlK9bMpxwIBMxgZSf2dpOpvSzMys97p1Mlbt2Xw9Htg+MxunShop6eTMn5wv6WuwdFXr\nxDx+Z6XemKS9M+txnqRZeaxmHxWSVpV0bSU3sjSWpuPNlbwHc2XqAUmXSVqtNMYTJN0N7C1pC0m3\n5xiukLRWtttI0n/kmO+WNL5qfCNzFWxh3sPU/MrZlGZmZm3gPWMsl015NHBkpbSFpEOBDYAtI2JJ\nVSHUFyNiQlavPxXYDZgG7BIRT0laM9sd0qCPUcAlwIyImFE1llbGC0UE05cjYrak84CvACfnd89F\nxFZ5L/OBr0XETZK+C3wb+DpwEXB8RFyhInOzeoK+BTA2IjbLftbMMTib0szMrA28MtbYThTFXJfA\nCnmNF5det833synihQ4GRrbQx6+B8ysTsV56MiJm5/sLWZalCUVoeSWMfM2IuCmP/xTYQdI7KCZa\nV+TYXo2Il6v6fxTYUNIPJX0SeKkng3M2pZmZWWOejDXWauZjAETEFOBbFKUz7soIokZ9zAZ2lVQv\n67IV9bI0Af6vyblNrxsRfwI2pyj5MYUiPqn1wTmb0szMrCFPxpZXznsEuB6YUsmXrHrEOLn0elt+\nPz4i7oiIaRT7utZv0sc04DngzFqDkfRgC2N+t6TKytwXqJGlGREvAn+StH0e+iJwUxaY/R9Jn8nr\nrVzZc1YawzrAiIi4nGKiuUKWppmZmfWeJ2PLmw8syc3sUylWgZ4A5kuax/K/tFwr92EdAVQ2tZ+U\nm9wXArcC85r0AcW+rVUknVg+mJOgVlbMHgK+KukBYG3gx3XafSnHN59iH9h38/gXgcPz+K3A/8vr\nV0p/jAVm5ucLgX9qYUxmZmbWIkXUe4LWmSSNA66qbEjvZR+LgIn9GZYtaTdgw1lu1IwAACAASURB\nVIg4vUGbcfTxXtpF0vQcS91IpJXHbBxjvnTqwA3KbJhx0VezziLproiY2KxdN/6ackhnU1ZExFWD\nPYZWOZvSzMys97puMtYom7IHfYxrz2j6JiIWAYO+KuZsSjMzs97znjEzMzOzQdR1K2M2eBwUbtaY\n94yZdacBXRnrpJBuSetLeqxSqkLSWvn5Pe0e71AhaXG+jpM0M99vL+n+/AWpmZmZ9dBgPKbsiJDu\n3Hv2Y4o8S/L17Ih4fPBGNfAi4mbgU4M9DjMzs+FqKOwZG5Yh3ekU4MOSvk4RQ/SDZidLminplIwI\nekDSByX9UtIjko4ttfvbvL97Jf1E0sg8vljSSZLuy4DvbbLPRyXtnm32l3RGqa+rJE1SndBvSePz\n73CXpJslbZLHN5B0W7Y9tnQbbwLlWCczMzPrpUHfMzacQ7oj4g1JRwHXAjtHxOst3vbrETFR0hEU\n+ZRbU0xu/lvSKcBfUFT2/2he40xgX2AGsDrwnxFxlKQrgGOBvwY2pcicvLLBdWuGfgNnA1Mi4hFJ\nH6JIBPg4cBrw44iYIemrpft+EtizxXs1MzOzBobCylg9wyGkG2BX4Gl6VmKiMmFaANwXEU9HxGsU\nodzrA5+gmKDNyf11nwA2zHNep5j8Vc6/KSLeyPfjmlx3hdBvSaMoaoT9Iq/1E2BMtv8oy/7WF/Tg\n/paSg8LNzMwaGvSVsQZ6HNKdqzqfpgjp3rpJH5WQ7p9FL2MIJG1BsSr1YeAWSZdExNMtnPpavr5V\nel/5/LYc908jolb00Bul8S49PyLeUuZfAktYfqK9Srb5k6TNgV0oQr8/TxHH9EKDArh9imiIiLMp\nVt5YeczG3RX3YGZm1oKhtDI2rEK6JYliA//XI+IJ4CTg5BbusxU3AHtJ+ou81to9/JXmImALSSMk\nrQ9sk/2sEPqdYeGPSdq7cl85YYNiwvo3+d6FXc3MzPrBUJqMDbeQ7oOBJyLid/n5TGATSR/LPsrl\nO86V1DSbqiIi7qeYLF2f9/k7lj06bMVs4DHgfuB04O48Xi/0e1/gy/k3ug/YI48fQRFCviDPNTMz\nszYb0KBwdVBIty3T6n/XiRMnxty5cwdkTGZmZoNNQzQo3CHdHUbS9hSrgv02OTYzM+tkAzoZ66SQ\nbitk0dcJgz0OMzOz4Woo/5rSOoyzKc0aczalWXcaStmUE7My/Nvz8/isKr/GQI5xoOTfYmG+nyRp\negvtZ+b7LSQNSASRMo+ywVicTWlmZtYHQyabMiLmArOAI/PQj4BvZukFW94WDJE8SGdTmpmZ9c1Q\nKG1Rznr8Z+AgSf8ArBQRF9c5ZykVWZXfV5HhOFfSVpKuk/TfkqaU2h0laY6KjMrv5LFxkh7MvMaH\nJV0kaSdJs1VkRVbqcx0j6chSXwvz3NUlXZ3lOBZKmpzfby3pJhVZj9dJGlM6Pi9LSHy1dBuvA83K\n078JPJ8rh98FJuc9T5Y0StL5WpbB+bm83mIVOZj3SbpB0rp5/PBczZov6ZI8VrOP0j2voyKn8tM4\nm9LMzKxtBn3PWFXW4wuSTqD4dd6mPejmiYjYQkWu43SKGJ9VKGpmnSVpZ2BjiuKnAq6UtANFDbKN\ngL2BA4E5FLXItgN2p5gcfqbBdT8J/D4iPg0gabSklYAfAntExLM5QTsu+z8f+FpE3CTppNJ930pR\nG62uch6kpGkU5T0Oy88nkHmd+XmtPG11YG5ETM1zvg0cRpEDukFEvKZl+ZT/UqcPJL2LIsLpW6W6\nas6mNDMza4NBn4zVsCvwB4rJ2EMtnlPOehwVEX8G/izp1Zxs7Jz/7sl2oygmZ08Aj0XEAgBJ9wE3\nRERkodNxTa67ADg5J0NXRcTNkjajyKn8nSQocjKfljQaWDMibspzL8h7bYedWFYpn4j4U759C7g0\n318I/DLfzwcukvQr4FdN+liJIhHgq6Wxt0zSIRSB7YxcY92enm5mZtbxhsJjyqWy2OpoiuzEkySt\n1uKprWQ9fj8itsh/G0XEv1edW31+5Vyon/X4MEWg9wLg2Fx9EkX4d+VaEyJiZxrnZPZVq31X2nya\nYk/eVhRh5JW/Ua0+lgB3Ufw36bGIODsiJkbExJGrje5NF2ZmZh1tyEzGJK0K/IBiBWYB8Gvgm23q\n/jrgQEmj8lpjlbmPLVpEMXFB0lbABvl+PeDliLiQIptyK4rVvHUlbZttVpL0/oh4AXhR0nbZZ82s\nR0nbSJrRZDy1cjwPK/VRecQ4Atgr3+9DEWY+Alg/Im4E/pFi8juqQR9B8Yh1E0lHNxmXmZmZ9dCQ\nmYxR7Fn6VeYyAhwD/I2kjSWtJ+maSkNJ1+REqCURcT3wM+C2fPx4GctPZpq5HFg7H2MeBjycxycA\nd6oo1fFt4NiIeJ1iAnRCbtS/F/hItj8A+FG2r5d9+W7glSbjuRHYtLKBHziWIq9zYV5zx2z3f8A2\nWXbi4xQb/0cCF+bf4R7g9Jwo1uuDiHiT4hHmjpK+0mRsZmZm1gPDLpuy0+XG/gsiYn4b+locEaPa\nMKxm1xlHC/9dVx6zcYz50qn9PRyzYctFX806i5xNOTxFxFGDPYaeUA+yKSeMHc1c/5+NmZnZcoZd\nNqW1biBWxZxNaWZm1jdDsbSFdShnU5o15seUZt1pKG3g7zU1zrwcJ2mfARrHIknr1Dreyrl9uO7K\nkv6jtKG/35X+vqvmdV+vde9mZmbWWEdMxlLNzEuKwq0DMhkbRFsCZF2zS8tfSBrZnxeOiFfy7/77\n/ryOmZlZp+qkyVi1Subl8cD2uXozVdJISSeXMhi/BktXtU7M43dK2iiP710p9yBpVh6r2UdFrhZd\nK+ngqrE0Ha+W5WVeJOkBSZdVit9K+lR+d5ek0yVdlfXSLgQ+mPc4Pu/lBEl3A3vnsWvzvJslbZL9\nrSvpchWZnXMkfbR6UJLGSJqVfS/MDfut3pOZmZk10bF7xkqZl0cDR0bEbgCSDqUo2rplRCyRtHbp\ntBcjYoKk/YBTgd2AacAuEfGUluU4HtKgj1HAJcCMiJhRNZZWxgvwXuDLETFb0nnAVySdAfwE2CEi\nHpN0cZ73jKSDqu4R4LmIqBSqvQGYEhGPSPoQxa8fPw6cBpwSEbdIejdFcdz3VQ1tH+C6iDguV9lW\na/WezMzMrLmOnYw1sBNwVkQsAYiI50vfXVx6PSXfzwamS/o5y7IdG/Xxa+DEiLioD2N8MiJm5/sL\ngcOB/wAejYjHSmM8pEEflwKoSB34CPCLnKQBrFy6j01Lx9eQ9I7M9qyYA5ynIgD9VxFxLz0gZ1Oa\nmZk11MmPKetplOMY1e8jYgrwLYqSHHdJemeTPmYDu6o0w+mF6r6D+hX76/m/fB0BvFDKytwiIt5X\n+m7b0vGxVRMxImIWsAPwFMWkdL8e3YizKc3MzBrqhslYrRzHKSrCsal6xDi59Hpbfj8+Iu6IiGkU\n+6TWb9LHNOA5ikeBK5D0YAtjfrcy2xL4AnAL8CCwYVa7L4+1oYh4CXhM0t55fUnaPL+uzqNc4QcQ\nkt4DPBMR5wDnkhmdZmZm1h7dMBmbDyzJDfhTKSYUTwDzVWQwln9puZak+cARwNQ8dlJu1F8I3ArM\na9IHwNeBVSSdWD6YpR9aWeF6CPiqpAeAtYEfR8QrwFeAayXdRTHJfLG1PwH7Al/Osd4H7JHHDwcm\n5o8Q7gem5DgnSjo320wC7pV0D8UE8LQWr2lmZmYtGNBsyv6iNmReZt2siRHRNNanD9fYDdgwIk5v\n0GYcde5F0qiIWJyPQH8EPBIRp1S3Gwyt/P2cTWnWmIu+mnUWDdFsyv4yLDIvI+KqPnZxsKQvAW8H\n7qH4deWgkrQqxSPdlYC3GrV1NqWZmdmKOmJlzIaHiRMnxty5cwd7GGZmZgOi21bGbBhwNqVZY35M\nadadOmIDvxpkU/ayv3Ul3SHpnlLF+X7Vyni1LA9yTUlf6e8x5bVmSlphVi9nU5qZmbVFR0zGUr1s\nyt74BLAgIraMiJvLX6ifsx5btCbFLysHnbMpzczM+qaTJmPVKlmPkzJb8WpJD0k6S9KI/O7Lkh5W\nkUV5jqQzstbWicAeueKzqqTFkn6QpSG2lbS1pJsy6/E6SWOyv5oZkGWS3p/XuzdLSmxcHm8r90SR\ntzk++zgp+/2HLMExT9LxeWympNO0LFdymzz+sTx2b67+vaNeH6Vxj5D0U0nH9mC8ZmZm1kTH7hmr\nyk7cBtgUeBy4FthT0q3Av1AUMf0z8J/AvIi4V9I0ijINhwFIWh24IyL+XkUs0E3AHhHxrKTJwHHA\ngcDZ1M6ALJsCnBYRF0l6OzCyxnib3dPRwGaVlUBJuwKfAT4UES9XFaFdLSK2kLQDcB6wGXAk8NXM\nvhwFvNqkj7cBFwELI+K4VsdrZmZmzXXsZKzKnRHxKICKgO3tgCXATZVcSUm/AP6qzvlvApfn+/dS\nTGh+V5T7YiTwtBpnQJbdBnxT0l8Cv4yIR/p4b1BkTJ4fES9D7bzNiJglaQ0VYeezgX+TdFGO4X8k\nNerjJ8DPKxOxnpCzKc3MzBrq5MeUZX3Nenw1It7M9wLuK+U5ToiInWmcAbnswhE/A3YHXgGukVS9\nctYbreZt5hDieOAgYFVgdj5ObdTHrcCOklbp6cCcTWlmZtZYt0zGtpG0Qe4Vm0yR9Xgn8DFJa6nI\nmPxci309BKyrzI6UtJKk9zfJgFxK0obAo1mF/9fAB2q0aZZfWStv80BJq+X5K+RtStoOeDEiXlSR\nt7kgIk4A5gCbNOnj34FrKFb9umU11czMbEB0y2RsDnAG8ADwGHBFRDwF/CvFpGw2sIgWsh4j4nVg\nL+CE3NB/L8XjSaiTASlpd0nfzTaTgYUqynBsBswo968W8isj4jmKFa2Fkk6KiGuBK4G52e+Rpeav\nqsiVPAv4ch77ep47D3gD+G2TPoiIfwPuBi6o/ADCzMzM+q4jKvCrcZ7jJODIiNitxneVrMe3AVcA\n50XEFf083IbUQn5lD/qaSXHv/V72Xs6mNOszF3016yzqsgr8vc2mPCY3rq9C8ZjuV/0yuh5oQ37l\ngJKzKc3MzPqkI1bGbHhwNqWZmXWTVlfGvPfHzMzMbBB1ymNKGwYcFG5Wm/eKmXW3YbEypjYHgfdh\nHNdk0dRGbfaXtF4frjEuN943a7eo9P4kSffl6xRJ++Xx6ZL26u1YWrm+HBRuZmbWJ8NpZaydQeC9\nEhGfaqHZ/sBCagRnSxpZKh7bTocAa/dT38tRES+wtPRGRLwCbDEYk2MzM7NOMCxWxuqoBIGPUREE\nXgnD3j6PL640lLSXpOn5frqkH0u6XdKjKkKzz5P0QKVNPZIWSVonV68eUBEufp+k63OFaC9gInCR\nloWML5J0gqS7gb2r+qsV2P0m8HyNy9e7/yuBUcBdkiZLOkbSkdWNVSfcvKrNMfm3mJl/m8Pz+DgV\nIeszKCaa6+OgcDMzs7YYtpOxUlD1PsB1uWq2OUUR1mbWArYFpgK/AU4B3g9MkNTq6tvGwI8i4v3A\nC8DnIuIyYC6wb8YhvZJtn4uIrSLikqo+KoHdWwDbA69ExJMRsWezi1fuPyJ2z/O2iIhLa7VVEW7+\nQ2CviNiaIjC8Xs7kJsAuFOHq385zK/d7ZkS8PyIebzUoXNIhkuZKmvvmy01r6pqZmXWd4fSYsp45\nwHk5afhVRLQyGftNRISkBcAfImIBgKT7gHG0NqF7rHStu/K8empOkqgR2N3CdXujZrh5nbZXR8Rr\nwGuSngHelccfj4jbe3rhiDgbOBuKoq89Pd/MzKzTDduVsYqImAXsADwFTK9sXmf50OvqgOvX8vWt\n0vvK51YnqOXz3mxy3v/VOlgnsLs/1As3r6XefdW8BzMzM+ubYT8Zk/Qe4JmIOAc4F9gqv/qDpPdl\njuJnB3BI1SHeddUJ7C5/P1bSDW0YU81w8zb0a2ZmZn3UCY8pJwFHSXoDWAxUVsaOBq6i2Gg+l2KT\n+0CYDpwl6RWKfWnLkTQFICLOogjs3pFiBep+4LdVzccAS/o6oIh4PX9ccLqk0RT/3U8F7qsaj5mZ\nmQ2wYRGH1CgIvJNJOgx4IiKuHOyxNNNKULjjkMzMrJt0WlB4b4PAh7WIOGOwx9BMT4LCzczMbEXD\nYjIWEU9S1LYaEJLuAFauOvzFyq8ubZlK0dfBHoeZmdlwNSwmYwMtIj402GPoRM6mNKvN2ZRm3W3Y\n/5rSzMzMbDgbFpOxZkHhgxmU3aTNzPzxwYCR9F1JOzVpM0nSR/p4nUX56qBwMzOzPhhOjykbBYUP\nWlD2UBMR01poNomiDMit1V9IeltEtFxOw0HhZmZmfTMsVsbqGA5B2c8Db0oamat0CyUtkDQ1+50p\naWK+X6e02rS/pF9J+l0GjR8m6RsZJn67pLXrXbC8GpjnfkfS3XndTXKlbgowNVe0ts9zzsofLpxY\n1V/NIPYW79/MzMyaGE4rY8spB2VLWlxZNZN0THXbUlD2HhHxrKTJFEHZB9boehNgR4oq+g9J+nEe\n3xj4UimfsWlQdiXwW9LWwNhKnTRJa7Zwi5sBW1JEOf0X8I8RsaWkUygK257aQh8Af4yIrSR9BTgy\nIg6SdBawOCJOzvF8GfhL4CM1VhcrQezHSRoJrJb31nJQOMXKJSPXWLfFIZuZmXWPYTsZ66FBC8pO\njwIbSvohcDVwfQvn3BgRfwb+LOlF4Dd5fAHwgR5c+5f5ehewZ4N2v6jzmLc3QexLOSjczMysseH8\nmLInBjUoOyL+BGwOzKR4RHhufrWEZf8N6oWZw/KB5j0JMy/309sw83pB7GZmZtYG3TIZ69egbEk3\nSBrb4Pt1gBERcTnwLZaFmS8Cts73/fKrzzp6EmZeL4jdzMzM2qArJmMR8TrFZOcESfOAe4GPQBHc\nXQnL7g1JI4CNKDbr1zMWmJmlOS4E/imPnwwcKukeYCDLQvwG+GxlA3/1l5ImSqqs3k0C7s0xTgZO\nG7hhmpmZdT4HhfeRpM2AAyPiG4M9lsHkoHAzM7PltRoUPlxWxpYGhQ/2QKpFxMJunohVir7ioHAz\nM7NeGRYrY7YiST8CPlp1+LSIOH8wxtOKlcdsHGO+1GpFDrPO4NxJs+7VaStjLWkUm5Tf7TNA41hU\nKxqoxQilpm0AIuKrpV+HVv71aCJWLjrbU+WoJ0k3Slrc277MzMy6WUdNxlK92KRxFAVMrc0iYkfA\nm8HMzMx6oRMnY9UqsT3HA9vnLwinZkTRyRkTNF/S12DpqtaJefxOSRvl8b0zDmiepFl5rGYfFbmf\n6lpJB1eNpZXxImm/7HeepAsajOMaSR/I9/dImpbvvyfpoHz/DznOeZKOL11v77zPh+v8snJ1SVfn\neQszvQAy6qmF+zEzM7MGOr4Cfym252iKOKDdACQdCmwAbBkRS7R83uOLETEhC5yeCuwGTAN2iYin\nSnFGhzToYxRwCTAjImZUjaXpeLMO2jeBj0bEH0t91xrHLIqJ5iKKQrKVvWTbARdK2hX4DPChiHi5\napxvi4htJH0K+DawU9WQPgn8PiI+neManeNsVM3fzMzMWtQNK2P17AScFRFLACKiXCfs4tLrtvl+\nNkUF+oMp4pSa9fFr4PzKRKwXPg5cVikVUeq71jhupqiSvx1F3NIoSasB4yLioRzn+RHxco1xluOS\nxtUYxwJgJ0knSNo+Il7syU1IOkTSXElz33y5R6eamZl1hW6ejAmo91PSqH4fEVMoquevD9wl6Z1N\n+pgN7KoMw2zX+OqMYw4wEdieYpXsHuBgiglW3b5Sw7ikiHiYIiVgAXBs5RFoqyLi7IiYGBETR642\nuienmpmZdYVumoxVRwBdD0yR9DaAqkd3k0uvt+X34yPijoiYRrGva/0mfUwDngPOrDUYSQ82Ge8N\nwOdzsrW071rjyISBJ4HPA7dTrJQdma+Vez0wV8uqx9mQpPWAlyPiQuAkHIdkZmbWVt00GZsPLMmN\n6FMpchafAOZnRFL5l5ZrSZoPHAFMzWMn5Qb4hcCtwLwmfQB8HVhF0onlg1n2ouGKWUTcBxwH3JR9\n/1uDcUAx8fpDPoq8GfjLfCUirgWuBOZm2Y8jG11b0nqSrsmPE4A787xvA8c2OtfMzMx6pqOKvqoN\nsUlqIdanryTtBmwYEaf31zUGmqSZFD+QqFviwkVfrRu56KtZ92q16Gun/ZpyaWxSnVpjQ0JEXDXY\nY2gnSTcCGwJvNGo3Yexo5vr/mMzMzJbTUZOxiHiSYi9XX/oY157RdI8s+mpmZma90FGTMRvaFjz1\nIuOOvnqwh2E2oPyY0sya6agN/GqQTTlA16+ZSdnquS20KedB/nNvrtNTkqZL2qvJWJxNaWZm1ksd\nNRlL9bIpO82ATMZa4WxKMzOz3uvEyVi1ctbjCvmMkg6XdH9mQF6SxxZIWlOF5zIWCUkXSNpJjTMp\nvybp7vxuk+rBSBojaZaKjMyFpTzIVnIrnwfezLGvmn1clP3WyrGcLumsrID/cP6KE0nvV5FHeW+e\ns3G9PqrG/r3scwTOpjQzM2uLjt8zVsp6rJfPeDSwQUS8pmVZj7Mp8h0fBx6lqGw/A/gwcCiNMyn/\nGBFbSfoKRT2vg6qGtA9wXUQcJ2kksFp5nE3upZIHebSkwyorgKqfYwlFxNE2wHjgRhXB51OA0yLi\nIklvB0Y26YOslTYaOCCKeijOpjQzM2uDblgZq6iXzzgfuEjS31KEbMOyrMcdgB8DEySNBZ6PiMU0\nzqRslvU4BzhA0jHAhIj4cxvurV6OJcDPI+KtiHiEYmK5CUWqwD9L+kfgPRHxSpM+/gVYMyL+LnpY\nmE7OpjQzM2uomyZj9fIZPw38iCLmZ05GG82iWA3bHphJ8QhxL5bFC/Ul63EWxSTvKYrA7/16cS/V\nWs3ZzCHEz4DdgVeAayR9vEkfc4CtexKjVLqYsynNzMwa6KbJ2Ar5jLn3af2IuBH4R4rHcKOyXtk6\nwMYR8ShwCytmPdbLpGxI0nuAZyLiHIo4pRWyHiXdkCtxjbwhaaV8XzPHMu0taYSk8RSFWR+StCHw\naCYA/Br4QJM+rgWOB66WVM73NDMzsz7qmslYnXzGkcCFkhYA9wCnR8QLecodwMP5/mZgLMWkDJpn\nUi5H0kRJ5+bHScC9ku6hCCI/rartCGAjig3yjZyd17+oQY4lOc47gd8CUyLi1bzuwvw7bAbMaNIH\nEfEL4BzgSkmrNhmbmZmZtcjZlEOMpM2AAyPiG23oazrF3+OyPg+s+bVm4mxKsxW46KtZ95KzKYdn\nrbGIWAj0eSI2kORsSjMzs17rqMlYO7IpO0lE7D9A13E2pZmZWS911GTMhjZnU1q38SNKM2tF12zg\nNzMzMxuKhuxkTA1Cv/O7hr9gbOM4aoZ/q7Vg76Zt2k3SuZI2bdLmM83atHCdRfk6PmOVFvelPzMz\ns241ZCdjqV7o9zialJPoVhFxUETc36TZZ4Cak7FK7bQeXK9bgtnNzMz6xVCfjFWrhGkfD2yfKzJT\n6wV356rWiXn8zsxlRNLeGdI9T9KsPNYo/BtJq0q6VtLBVWNpOl5Jq0u6Oq+3UNLk0vjWyfcTszwE\nko6R9FNJN0t6XNKepfu4tlTsdQWSZkqamO8XSzour3u7pHdJ+ghF9f2T8u83Ps85VdJc4Iiq/mqG\nird4/2ZmZtbEsNrAXwrTPpqiptVuAJIOpX5w94sRMSFjh04FdgOmAbtExFNaFg7eKPx7FHAJRXHU\nGVVjaWW8nwR+HxGfzvG2kgs0HtiRYgXrNuBzEfEPkq6giHD6VQt9rA7cHhHfVBH0fXBEHCvpSkr1\nxyQBvL1OLZQVQsWr7q0hSYdQ/G0Zuca6rZxiZmbWVYbbylg9jYK7Ly69bpvvZ1PkQh5MTi6a9PFr\nipDxGb0c3wJgJ0knSNo+IlpJzP5tRLyR546kiCSq9DWuxeu+DlyV7+sFl1dcWud4rVDxljmb0szM\nrLFOmYy1GpQdABExBfgWRU2yuzKPsVEfs4FdlUtIPRURDwNbU0ykjpU0Lb9awrL/BqtUnfZanvsW\n8EYsi0p4i9ZXNMvn1QwuL/m/OmOvFSpuZmZmbTJcJ2N/BsqB1Y2CuyeXXm/L78dHxB0RMY1i79P6\nTfqYBjwHnFlrMJIebDRYSesBL0fEhcBJLAsHX0QxSQP4XKM+2qz671eXaoeKm5mZWZsM18nYfGBJ\nbkyfSuPg7rUkzafYmD41j52Um+EXArcC85r0AfB1YJXce7VUbsBvtmI2Abgzy3R8Gzg2j38HOC03\nzr/Z4r23wyXAUZLukTS++ktJu0v6bn5cIVR8AMdpZmbW8YZsULjaEPqdtbAmRsQf2zSsWtfYDdgw\nV466lqTFETGqUZuJEyfG3Ll1c8TNzMw6ijogKHxYhH5HxFXNW3WuXFm7HPjDYI/FzMxsOBqyk7F2\nhH5HxLj2jGboyRIXG1Qd/seIuG4gxxER/w0M2cmymZnZUDdkJ2PWWER8drDH0FMOCrdu46BwM2vF\ncN3Ab2ZmZtYRhuxkTA2Cwgd4HNeUqvTXa7N/lq/o7TXGVaKQmrRbVGo/JILS5aBwMzOzPhmyk7E0\n6CHUEfGpiHihSbP9gZqTMUkjax3vo3EMkaB0B4WbmZn1zVCfjFWrBG+PkTQrV2QWSto+jy9dnZG0\nl6Tp+X66pB9nWPajkj4m6TxJD1Ta1FNZGcrVqAcknSPpPknXqwgP3wuYCFyU41k1zzlB0t3A3lX9\nfSzb3Zt1vt5B8cvR52tcvub9M3yC0s3MzKyJYbWBvxROvQ9wXUQclytPq7Vw+loU2ZS7A78BPgoc\nBMyRtEVE3Nvo5LQx8IWIOFjSzynCuy+UdBhFcPlcWBq8/VxEbFWjjyOBr0bEbEmjgFcj4s/Ans0u\nPtyC0nNsDgo3MzNrYLitjFXMAQ6QdAwwISczzfwmcxoXAH+IiAWZ+3gfrQdvP1aatPU2eHs28G+S\nDgfWrAST99GQDUp3ULiZmVljw3IyFhGzgB2ApygmFftVvio1qxm8TRG0klNN5AAAIABJREFU/Vrp\neE+Ct8vn9TZ4+3iKFblVgdmSNmnx2o0M6aB0MzMzq29YTsYkvQd4JiLOociUrDwO/IOk90kaAQxk\nHa6eBG+Pz1W5EyhW+Dap+n6spBt6eL1BDUo3MzOz3huWkzFgEnCvpHsoJhmn5fGjgasowr+fHsDx\nTAfOqmzgr/5S0hRJU/Lj1yub54E3gN9WNR8DNHt0OWSC0s3MzKxvOjoofDjKHwM8ERFXtqGvRfRz\nUHrpWg4KNzMzK1GLQeFDeWVsaVD4YA9kIEXEGe2YiA2UStFXHBRuZmbWK0N2ZWygSboDWLnq8Bcj\nYsFgjKcTrTxm4xjzpVMHexhm/cp5lGZW0erK2LCqM9afIuJDgz0GMzMz6z5D+TFln2iQsy3rZTq2\nem4LbWbmvrpKRf0HJN0oaaKk0/P4/pLO6M0YWrj+dEmT8v1Fkp7PNAIzMzPrgU5fGeuW3MQvAwdH\nxC35uV93yasqbzMi9m0WK2VmZma1dezKWB1L8xQl/UOWd5gn6fg8drj0/7N351F2VXXax78PYRAI\nBBnaN8YhTC0zSAKKTAFpBURUBGKLjRGEDoIgvtFGUYy02ii0yKAg0phGpjBFmZqhIyEkTEkgMyD9\nhiigrWAACYNg+L1/7H1Tp27dqapuDffW81mrVt06Z5999r1hLfbaZ9/fo6U5m/GafGyRpI2U/LlU\nYFbSLyQdUCfT8YuSHs7nuhR3VZWMTRrLfVwBrJJ0BrAX8B+SzpY0TtItFe61maQbJM3JP3tWaDMu\nr7hdL+mxvOKlfK48b/NF4PUGxmlmZmY1tPvKWCelPEVJBwEfB94XEa8UCpyeBmweEX8tZDbOJuVY\n/hZYBuwNXA68HziB2pmOz0XErpK+QMqk/HzZkCpmbDaS+xgRpSzLMyXtT87GLD06rOA84NyImCXp\nXcAdwLYV2r0X2B74feG9l1bcinmb19QbIzib0szMrJ6htjJWcgApa/EV6JTDuBC4UtJn6Ci8ei8p\nemkf4CJgR0mjgBURsZLamY435t/Vcix7krHZUwcAF+Y9dDcBG0qqlBrwUEQ8nXM759N53NXyNqty\nNqWZmVltQ3UyVi2H8SPAj0nxSnNyNNBM0mrY3sAM0iPEw0mTtFp9QUeWZcUcyxoZm31hDWCPiNgl\n/4yqMvmrlb9ZMW/TzMzMem6oTsbuBI6RtB6kHMacZ/nOiLgb+BdgBDA8Ip4CNgW2johlpEd2k+iY\njNXKdKxJ1TM2i22m55W43roTOKnQ71D4YoOZmdmgNyQnYxFxO+lR3dz82G4SMAy4QtIi4BHg/Ih4\nIV/yIPCb/PpeYBQd+6jqZTp2kktPXJr/HEfljM1S2zWArUib9XvrZGBs/pLBUmBihfGYmZlZP2vb\nCvztkG0paQfgmIj48kCPpZ5c2uKWiLi+WhtnU5qZ2VDSDtmUvdXy2ZYRsbhFJmJXAvsCrw30WMzM\nzFpN266M2eDjbEobCpxNaWYlXhkzMzMzawFtOxmrl02Zq9Uvyb8nFirrT+mrjMXuZk72td68V2dT\nmpmZNUe7V+CvlU15PLBxRKzq60HkSCH19X0GirMpzczMeq5tV8aqeBZA0k3AcGCepPGSJkuaVN5Y\n0hhJ90iaJ+kOSSMrtJks6bK8orVM0sn5+GhJj0u6HFgMvJNuZE7mPg7M2ZYLJE3Px/bNWZbzJT0i\naQNJP5F0aD4/TdJl+fWxkr6TXx+dy1oskPSLwv32kXRfHnuXlS2l7M0pStmZiySdmk85m9LMzKwJ\n2n1lrJNS5mNEHCppZWnVLMcRdSJpLeAC4GMR8ayk8cB3gWMqdL0NsB+wAfC4pIvy8a2Bz0bEA/nv\nhjMnJW0G/AzYJyKeLBSTnQScGBGzJQ0nfYOxlBJwE6kGWmnSuBdwjaTtgdOBPSPiubLCtCNzu23y\n9eWlKXYBRpVKhJQyOyPilHrvJbd3NqWZmVkNQ21lrDveA+wA3JX3nX0DeEeVtrdGxF8j4jngT8Db\n8vHfFiZi3fV+YGZEPAmdMi9nAz/MK3Ab5UzMe4G9JW0HLAX+mFfx9gDuA/YHrs/jK8/P/GVEvBkR\nSwvjLloGbCHpAkkHAn/pzptwNqWZmVltQ2plrJsELImIPRpoWy3PsTdZjhUzLyPiLEm3AgcDsyV9\nOCIek/RW4EDSKtnGwJHAyoh4Ke9Zq5efWbpn+f2el7Qz8GFS1f4jqbw6aGZmZj3glbHqHgc2k7QH\npMeW+XFfUzSQOXk/sK+kzXP7jfPvLSNiUUR8H5hDerxYav8l0mTsXjrnZ04HjpS0SbGvBse5KbBG\nRNxAWh3skp9pZmZmPeeVsSoi4vW8of18SSNIn9WPgCWSJuY2F/ek70YyJ/M+teOBG3P7PwH/AHxJ\n0n6kFbilwH/lS+4FPhQR/yPpt6TVsXtzX0skfRe4R9IqUvbmhDpjnJ/31I0Cfp7HAPC1nrxnMzMz\nq6xtK/AP5mzKVsqcbJSzKc3MzDpzBf5BnE3ZKpmTjXI2pZmZWc+17cqYDT7OprR25TxKM6vEK2M1\n1IpKyuc+3U/jWJ43yHc53si1hdcnS3o0xxIdKum0fLxiMdtmUCG2SdLdklZKqvsfnJmZmXU2lDfw\nV4tKGg18Griqf4fTK18ADoiIp/PfN/XlzSQNK/4dEftJmtGX9zQzM2tXQ3JlrIpSVNFZpAKq8yWd\nmuOAzslRQAslfRFWr2r9IB9/SNJW+fgROTpogaSZ+VjFPkokrSvpdknHlY2l7nglXQxsAfxXHu8E\nSReWN5a0Zb7HPEn3StqmQpsJkm7M7Z6Q9IPCuZWS/l3SAlIx2dWxTWZmZtZzQ3llrJNSVBJwGjAp\nIg4BkHQCsDnw3oj4W1mNrhcjYkdJR5PKXhwCnAF8OCKeKUUHkeKAqvUxHLgGuDwiLi8bS93xRsTE\nXBl/vxx1NKHKJZcAEyPiCUnvA35CqsxfbhfgvaRisI9LuiAingLWBx6MiP+b282qN0YzMzOrz5Ox\n+g4ALs6xQ+VRQlcXfp+bX88Gpki6FrixgT5+BfwgIq7so/GTMyw/AFyXivEDsE6V5tMj4sV83VLg\n3cBTpFWwG3pwb2dTmpmZ1eDJWH21ooSi/HVeqXof8BFgnqQxdfqYDRwk6arou6+2rgG8UGWPXLlq\n0U6vRUS3H0tGxCWkVTnWGbm1v7prZmZWxnvGunoJ2KDw953ARElrQpcoofGF3/fn81tGxIMRcQZp\nX9c76/RxBvBn0mPDLiQ91ts3FBF/AZ6UdETuUzlv0szMzAaYJ2NdLQT+ljfgnwpcCvwOWJg3rxfL\nXrxV0kLgFODUfOzsvFF/MXAfsKBOH5AyJd9S3DAPq3Mhu4R399BRwLH5/kuAj+V7HCrpzCbdw8zM\nzLppSBZ9bUZUUq7zNTYinmvSsCrd4xBgi4g4v6/u0Sy5tMWkiKiad+Sir9auXPTVzCpptOjrUN0z\ntjoqqcF9VAMiIm4Z6DE0QtLdpPIab9Rqt+OoEcz1/7TMzMw6GZKTsVyq4Z297GN0c0bT+iJiv4Ee\ng5mZWavynjEzMzOzATQkV8ZsYCx65kVGn3brQA/DrOm8Z8zMemNIrozVCgrPr8+WtCT/npgr7CNp\niqTD+2hMyxtoUwzn/npfjKPCPSu+ZweFm5mZNcdQXhmrFhQOqWL8xj0pctpdSiXxe1K+4uvA95o8\nnB5xULiZmVnPDcmVsSpKwds3kfIi50kaL2mypEnljSWNkXRPDt6+Q9LICm0mS7osryItk3RyPj5a\n0uOSLgcWk75M0Eg4+ApglaSzgHWVwsyvzH0enUPIF0j6RT42RdLFkuZK+k0ulYGk7ZXCzefna7au\n1kfZ+/nX3OcaOCjczMysKYbyylgnheDtQyWtLK2aSZpc3lbSWsAFwMci4llJ44HvAsdU6HobYD9S\nVf/HJV2Uj28NfDYiHsh/NxIOflh+eZqkkwpj3B44Hdgzh4UXK/yPBnYHtgTulrQVMBE4LyKulLQ2\nMKxOH+SCtCOAz+XYpsMwMzOzXvNkrGfeA+wA3JWDt4cBf6jS9taI+CvwV0l/At6Wj/+2MBHrrf2B\n60sFaMuCyK+NiDeBJyQtI00O7wdOl/QO4MaIeEJSrT6+CTwYEcd3d2AOCjczM6vNk7GeEbAkIvZo\noG214O2XmzyeRsLMASIirpL0ICnM/DZJ/1ynjznAGEkbl03S6nJQuJmZWW3eM9YzjwObSdoD0mPL\n/JivKSRNlzSqTrM38uNSgOnAkZI2ydcXHzEeIWkNSVuSquQ/LmkLYFmOWfoVsFOdPm4HzgJulVQM\nUTczM7Ne8spYD0TE67ncw/mSRpA+xx8BSyRNzG0u7knfeXP8VqQN8rVcQgoefzgijpL0XeAeSauA\nR4AJud3vgIeADYGJEfFa3uP2GUlvAP8LfC8iVtTog4i4Lk/EbpJ0cES82pP3Z2ZmZp05KHyQkbQD\ncExEfLkJfU0hvc/rez2w+veaQZ2g8LFjx8bcuVVPm5mZtZVGg8KH6mPK1UHhAz2QchGxuBkTsf7U\naFC4mZmZdTUkH1M2Iyi8FUTEhH66j4PCzczMemhITsZsYDib0lqVsyfNrC8N1ceUZmZmZoPCoJiM\n1QruzucWV7luRl+GU0vapBCCfWHZueUNXL86TLu/SDpT0gF12oyT9IFe3md5/l2KZXpd0qa96dPM\nzGwoGkyPKWsFdw+U10jV53fIP4NeRJzRQLNxwErgvvITktaMiL91436vArs0Mjk1MzOzrgbFylgV\nXYKz8yrMNZIelTQNWLdw7qIciL1E0rfzsYMkXVtoM07Szfn1sTk8+yFJPytf+QKIiJcjYhZpUlZ3\nfBWUgr2H5YDtxZIWSTo1j2H1yp6kTQurTRMk/VLSXZKWSzpJ0pclPSLpgfLcyLLPaEqugUa+9tuS\nHs733Sav1E0ETs0rWnurI1D8QeAHZf2NlDQzt10sae9uvH8zMzOrYzCtjHVSCu4ucwLwSkRsK2kn\n4OHCudNz4dJhwPR8/i7gp5LWj4iXgfHAVElvJ6147Qq8BPwaWNCE8ZW3OQxA0hhgVKmumaSNGrjF\nDsB7gbcA/wP8S0S8V9K5wNGkIrONeC4idpX0BVIdsM9LuhhYGRHn5PEcC7wD+EBErCq7/tPAHRHx\n3fzZrpffW933n/t2NqWZmVkNg3llrJJ9gCsAImIhsLBw7khJD5Mqx28PbJcft90OfFTSmqQsxl8B\nuwP3RMSKiHgDuK6Px70M2ELSBZIOBP7SwDV3R8RLEfEs8CJwcz6+CBjdjXvfmH/Pq3PddRUmYpBy\nKT8naTKwY0S81I17ExGXRMTYiBg7bL0R3bnUzMxsSGi1yRhUCLOWtDkwCfhgROwE3EpaUQKYChwJ\n7A/MyZMJ9dNYAYiI54GdgRmkR4SX5lN/o+Pf4C1llxUDxt8s/P0m3VvRLF1XDCmvpGJweUTMJE2C\nnwGmSDq6G/c2MzOzOlptMjYTOApWxwbtlI9vSJpMvCjpbcBBhWtmkB5HHkeamEHKatxX0lvzitkn\nezMo1Qn2zt8yXCMibgC+kccDsBwYk18f3psxdNNLQEOB35LeDfwpIn5GmkTuWucSMzMz64ZWm4xd\nBAyX9ChwJunRGxGxgPR48jHgKmB26YL86O0W0gTtlnzsGeB7pEnZbNKk6EUASYdKOrN0fd5U/0Ng\ngqSnJW1XHJAaC/YeBczIpTuuAL6Wj58DnCDpEaA/y0LcDHyitIG//KSksZJKq3fjgPl5jOOB8/pv\nmGZmZu1vUASFawCCuyUNj4iVeWVsGnBZREzrQT9NC/ZuZXnSOjYinqvWxkHhZmY2lKjFgsIHIrh7\ncr7fYuBJ4Jc96aQVg72bqVT0FViLtJ/NzMzMumFQrIxZ90n6MbBn2eHzIuLnAzGeRqwzcusY+dlG\nK3KYDR7OpjSznmh0ZWzQ1hmz2iLixIEeg5mZmfXeoHhMqRrZlP08jtvqFWTN1fHf3ot7jJY0o4F2\nywvtB202p5xNaWZm1iuDYjKWDXg2ZUQcHBEv1Gk2Aag4GcsV6ttJKZtzUrUGEfFq/nf7fb+NyszM\nrI0MpslYuWehejaipJWlhpIOlzQlv56ilFP5gKRlkvaVdJlSnuWUWjfMWY6b5tWoR5UyK5dIujOv\nAB0OjAWuzONZN1/z/Vz9/4iy/vbN7eYr5UpuQPqyQq0yGJ3ef1l/rZbNaWZmZnUM2slYIfuwlI24\nC6mKfSPfuHwrsAdwKqmm1rmkiKQdJTW6+rY18OOI2B54AfhkRFwPzAWOiohdIuLV3PbPEbFrRFxT\n1sck4MQ89r2BVyPiqVJmZS31sjmBb9FRMBZSNudYUiHcfdWRzfl+SevnNuXZnO8nfQlgm3rjaXB8\nXUg6Pk8S56565cXu3sbMzKztDdrJWEFPshFvjvQ10UXAHyNiUUS8CSyh8VzHJyOiNPGrl+s4tcrx\n2cAPJZ0MbJSzMnuj5bI5nU1pZmZW26CfjNXIRizW5KiW61jMdCz93eg3SIvX9TTX8Szg86THibMl\ndXsFqlK35QcGezanmZmZVTfoJ2M1shH/KGnbHEf0iX4cUndyHbfMq3LfJ63wbVN2fpSk6d2496DM\n5jQzM7Oea4U6Y+OAr0h6A1gJlFbGTiNlTT5L2sc1vJ/GMwW4WNKrpH1pnUiaCBARFwNfkrQfaWVt\nKfBfZc1HAt15dHkR8HOlbM5HKWRz5uzIx4CnKMvmlHQL6Vugn83HnpFUyuZcka9bnc1JijU6I/+9\nnDTZW1vSx4EPRcTSbozZzMzMahgUFfgHIptyMJB0EvC7iLhpAO7dlGzOQn/LcTalmZnZaq1WgX91\nNuVA1xrrTxHRpZxEP5os6QDS3rI76WE2p6R1gftxNqWZmVmPDIqVsf4m6UFgnbLD/xQRiwZiPEOF\nsymtFTmX0sx6qtGVsUG/gb+n6kQsjQfWzLXCSj+LcpsBjxiqc/2M/Fh3wEiaLGlCfn22pP+VVLVK\nv5mZmVU3WB5T9pUBj1iqoBQxtEP+6VOS1mxCfbOqIuIrkiqW9jAzM7P62nZlrIp2iBhaQdpjR15d\nOzePb7qkzfLxGZJ+JGkucIqkd+fzC/Pvd+V2b5M0TdKC/POBCp/PWZKW5mvPyYdXAq+WtzUzM7Pu\na/eVsU7qRQzlCKGHC+dOj4gVSgHg0wsRQz+VtH5EvEzXiKFdSbXIfg0saML4ytsUo5TWB+ZGxKmS\nziBFJJ2Uz61dek6dJ4uXR8R/SjoGOB/4eP59T0R8Ir/HTuVBJG1MquG2TUSEpI3yGM7BzMzMmmKo\nrYxV0nIRQwVv0lHI9Qpgr8K5YkTTHsBV+fUvCu32J9UuIyJWRUR5eORfSCt4l0o6DHiluwN0NqWZ\nmVltnowl7RIxVHwftfZxNfQV2jzx3B24ATiENAnt3oCcTWlmZlaTJ2ODNGIo7+0aVafZGsDh+fWn\ngVlV2t0HfCq/PqrQbjrpMS2ShknasGwMw4EREXEbcCqwc7fehJmZmdXlyVh6TDc8RwydSSFiiPR4\n8jHSI75OEUOkKKaD8m8i4hmgFDE0G1hOIWJI0pml63MJix8CEyQ9LWm74oBy3uZWpM36tbwM7C5p\nMWmV7swq7U4GPidpIfBPwCn5+CnAfpIW5fe9fb7/bXkP3AbALfm6WcCX64zHzMzMuqlti74ORMRS\nsyKG8grdMRFRc/IjaWVE9FcmZ61xTAZW1tvY76Kv1opc9NXMeqrV4pD6wkBELDUlYigiFtMiq1CS\nziZ94/Lf67XdcdQI5vp/bGZmZp207cqYDT4OCjczs6FkyMchmZmZmbWCdn5MaYPMomdeZPRptw70\nMMy6xXvGzKyvte3KWJ2g8AHTSNC3pCmSxhXad1nilDShUtxSM0n6rqSnJK0sO+6gcDMzsyZp28lY\n1qug8BwRNJTdTCr6WlVEfAW4uH+GY2Zm1n7afTJW7llYHe49U9Ktkh6XdHGu7VUK3/53SQuAPSR9\nUNIjkhZJukzSOrndbpLuywHbD0naoHgjSevn/hdIWixpfD61Oui7hheB18sPSvpcKYgc2LNw/KOS\nHszj/O8cAL6GpOWlPMnc7n/yuS0lPZDf03fKV75KIuKBiPhDhVMOCjczM2uSITUZKwvi3h34IrAd\nsCVQCuBeH3gwInYG5gJTgPERsSNpj90JktYmVd4/Jbc7gK6TkwOB30fEzrnW2e15DIdFxFN1xnlK\nRNxXPCZpJPBt0iRsrzzuklnA+yPivcA1wFcj4k1SZuYn8vXvA5ZHxB+B84Dz8nt6utZYqozvnIiY\nWr+lsynNzMzqGVKTsTIPRcSyXE3/ajrCs1eRshgB3gM8GRG/yX//JylY/D3AHyJiDkBE/CXnOBYt\nAg6Q9H1Je1cI4e6u9wEzIuLZiHidzkHg7wDuyJX0v0KupJ/blFbkPlW4Zg86gsxLAeJ9wtmUZmZm\ntQ3lyVh5gbXS36/lCRpUD/9Whes7d5YmcGNIk7LvSDqjpwOtMMZyFwAX5pWuf6Yj0Px+YCtJmwEf\nB25swhjMzMysiYbyZGx3SZvnvWLjqRyy/RgwWtJW+e9/Au7Jx98uaTcASRvkCKTVcrbjKxFxBXA2\nKVicsjaXS6q5Qb7gQWCcpE0krQUcUTg3Angmv/5s6WCkir7TSDmYj0bEn/OpB+gIMi8FiJuZmdkA\nGMqTsTnAhcCjwJOkSUsnEfEa8DnguvwI8E3g4vyYcDxwQd7ofxfwFklvl3RbvnxH4KFcWuNbwHcq\njGEnoNIG+S7yRvrJpNWu2XncJZPzGOcBz5VdOhX4DJ0fa34J+HIOAN+KHGgOUFYK5AeSngbWy4Hm\nkxsZq5mZmTWubeOQagWF5xpekyLikH4eVnEMGwL/ERFH1G3c/HuvB7waESHpU8A/RsTHetHfZBoI\nCncckpmZDSWOQyoEhQ/0QCrJm/77fSKWjQHm55WxLwD/t6cd5aDwzwAvN2lsZmZmQ0rbrozZ4OOV\nMTMzG0oaXRlzNqX1G2dTWityNqWZ9bV2fkzZUsqzNEs5mvn44irXVMytbOKYNpF0d04luLDsXGl8\n60qaL+l1SZv21VjMzMzalVfGBpdeZWn2gdeAbwI75J8uIuJVYJfBEMJuZmbWirwyNng9W34gr0Jd\nI+lRSdOAdQvnLsqxQ0skfTsfO0jStYU24yTdnF8fW8q5lPSz8pUvgIh4OSJmkSZldcdnZmZm3eeV\nsUGqLEez5ARSIdltJe0EPFw4d3pErJA0DJiez98F/FTS+hHxMqk22tRckPabpEK0LwG/BhY0YXxm\nZmbWTV4Zay37AFcARMRCYGHh3JGSHgYeIWVTbpfzMm8HPpoTAj5CCg/fHbgnIlZExBt05FQ2nYPC\nzczMavPKWOvpUotE0ubAJGC3iHhe0hQ68imnAicCK4A5EfGSpGqZm80fbMQlwCUA64zc2nVUzMzM\nynhlrLXMBI4CkLQDKU4JYENS0dUXJb0NOKhwzQzS48jj6IhEegjYV9Jb84rZJzEzM7MB4ZWx1nIR\n8HNJj5KyKecBRMQCSY+QAsyfImVXks+tknQLMIEcIh4Rz0j6HmlStiJf9yKApEOBsRFxRv57OWmy\nt7akjwMfioilff9WzczMhgZPxga5iFhOLiuRy0h8qkq7CTX6OAk4qezwVRFxSV4Zmwb8Mre9Cbip\ncO3ono/ezMzM6vFkbPBYnaXZT7XGJks6gLS37E7yZKy7JK0L3A+sBbxZq+2Oo0Yw19XMzczMOvFk\nbJCIiKeAd/bj/SY1qZ9XgcFUqNbMzKyleDJm/cbZlNaKnE1pZn2tZb9NWZ7lmI8tL5wbtHmOda6f\nIWl0Hw2v/F5TJB3ei2vH5ddXSlrR077MzMyGslZfGRtsWY7QQJ5ju4mIo3JtMzMzM+umll0Zq6Id\n8hxXkDbzI+lASQ9LWiBpej62r6T5+ecRSRtI+kkuSYGkaZIuK4z3O/n10ZIW5r5+UbjfPpLuk7Ss\n0sqWpGF5FWyxpEWSTs2nXgReb+D9mJmZWQ2tvjLWSTvkOUbEYQCSNgN+BuwTEU9K2jg3mQScGBGz\nJQ0nTfpmAnuTSlKMAkbmtnsB10jaHjgd2DMiniv0RW67F7BNvv76siHtAoyKiB3yuDbK4zylO+/d\nzMzMKmu3lbFKWi7PMXs/MDMinsxjX5GPzwZ+KOlkYKM83nuBvSVtBywF/ihpJLAHcB+wP3B9RDxX\n1hfALyPizVzI9W0VxrEM2ELSBZIOBP7SnTfhbEozM7PahsJkDGrnOX4wInYCbqVznuORpEnMnIh4\nCei3PMfSEKkw7og4C/g86XHrbEnbRMQzwFuBA0mrZPeSxr+yMPZquZB/Lbtn+f2eB3YmxSpNBC7t\nzpuIiEsiYmxEjB223ojuXGpmZjYkDIXJ2KDMc5Q0XdKoGk3uz/fbPLffOP/eMiIWRcT3gTmkx4ul\n9l+iYzI2Kf8GmE5aBdyk2FeD49wUWCMibgC+QfpczMzMrEnaas9YFYMuz1HSGsBWuZ+KIuJZSccD\nN+b2fwL+AfiSpP1Im/yXAv+VL7k33+d/JP0W2DgfIyKWSPoucI+kVaTHshNqfWiFJIBR+fMrTdy/\nVus6MzMz6x5FVHt6NbjlWly3lDaW99M9h0fEykKe42URMa0H/ewAHBMRX276IAdILm1xS0SUfwFg\ntXVGbh0jP/uj/huUWRO46KuZ9ZSkeRFRt7ZpK6+M9XeWIzQpzzEiFgPtNBG7EvgAXb+J2YmzKc3M\nzLpq2clYf2c55ns2Jc+x3UTEUQM9BjMzs1bVspMxaz3OprRW5MeUZtbXWvbblLWyKfvp/svzNw17\ndG0DbVZnVFa7l6TJkvp0tU7SZZL+pLKsT2dTmpmZNUfLTsaywZhN2W6mkOqXVZUfU97UL6MxMzNr\nM60+GSu3OvtR0ldzluICSWflYydLWpozGq/JxxZJ2kjJnyUdnY//QtIBOZvxnNxuoaQvFu73RaXs\nyEWStqGMpJGSZirlSC6WtHf5OGtYnVFZ1ufpSvmYs4D3FI4fJ2mW2UF0AAAgAElEQVROfr83SFpP\n0oi8qrZGbrOepKckrSVpt/x+5ks6u3zlqyQiZlK5BIezKc3MzJqgrSZjpexHSQcBHwfeFxE7Az/I\nTU4D3psr7k/Mx2YDe5LikJaRMh4hxRE9ABwPbF647srCLZ+LiF1JtcwqPS78NHBHXr3bGZhfHGed\n93JY/pLCapLGAJ8i5UUeDBT7uTEidsvv91Hg2Ih4kZSfuW9u89E8njeAnwMT89i6TPoaGN8pEXFf\nd68zMzOzztpqMlZwAPDziHgFOmUxLgSulPQZ4G/52L2k/Mp9SJOqHXNl/BURsTL3dXHOgCzPdbwx\n/54HjK4wjjnA5yRNBnbM0US9sTcwLSJeiYi/0PnR4A6S7pW0iJQ4sH0+PpUUdg5pIjdVKex7g8Jk\n6qpejqsqZ1OamZnV1q6TsWpZjB8BfkyK9JmTi7fOJE1y9ibFID0LHE5HlFAjuY6rqPDN1PyIbx/g\nGWBK6RFoL1UbyxTgpIjYEfg2HTmbNwEH5QikMcCv6cecTWdTmpmZ1dauk7E7gWMkrQcpizHvm3pn\nRNwN/AswAhieHwVuCmwdEcuAWXTOdbwTmJgnbt3NdXw38KeI+BkpYLtLrmMDGZVFM4FPSFpX0gak\nx44lGwB/kLQWOYsTIK/uPQScR6qQvyqHf78k6f252acafU9mZmbWXG05GYuI20krQnNz6YtJwDDg\nivwY7xHg/Ih4IV/yIPCb/PpeUh7jrPz3pcDvgIWSFpD2gVUlaaykS/Of44D5OQNzPGlCVGxbN6Oy\n7H09THrsuICUSTmncPqb+X3MJuVmFk0FPkNH6DnAscAl+fNZn46czbdLuq0wxqtJIeTvkfS0pGMb\nGauZmZk1xtmUA2ggMypLOZv59WnAyIg4pRf9TcHZlNaGXPTVzHrK2ZQtYIAzKj8i6Wuk/wZ+C0zo\naUfOpjQzM+u5lp2MDUQ2ZTuJiKl0fmzZm76cTWlmZtZDbblnzMzMzKxV9NnKWN7T9SjweOkxoqTl\nETFa0lhSKYZdI+J1SVsCdwG75PpZbaW4vy3nOU6IiAl12k+JiHG5/aSIOKRCu+XA2Ih4rumDTv1v\nQyoOuytwekScU7x3/rfcErgB2Coihtfqz0Hh1oq8Z8zM+lpfr4xVzI6MiLmkMg2lqvU/Jv3Pvu0m\nYi1uBXAycE61BhHhfFAzM7Ne6O/HlMVMxq8Dn5f0VWCtiLi63sU5Z/Hfcp7iXEm7SrpD0v+TNLHQ\n7is5p3GhpG/nY6MlPSZpSs52vDJnT86W9ISk3XO7yZImFfpanK9dX9KtOftxsaTx+fwYSfdImpfH\nMrJwfEEuh3Fi4W28Ti4jUcMqKpS7kLSJpDslLcnlM1Q498s8hiWSjs/HTpD0g0KbCZIuyK+/Kelx\nSbMkXV18zyUR8aeImAO8UWGMjeRrmpmZWR39OhkrZjLmGl/fB/4N+EI3uvldXom5l/So83BSjuSZ\nAJI+BGwN7E7KcBwjaZ987VbAvwPb5J9PA3uRVui+Xue+BwK/j4idczmN23OB1QuAwyNiDHAZ8N3c\n/ufAyTkrsvgZ3FevhEREPBURh1U49S1gVkRsD0wD3lU4d0wew1jgZEmbkL7dWOxnPCkOaSzwSdLn\nc1C+plsaydc0MzOz+gZ6A/9BwB+B7bpxTSmPcRHwYES8FBHPAq/lzMUP5Z9HgIdJk66t8zVPRsSi\niHgTWAJMj1RobRGVsyWLFgEHSPq+pL1zCPd7gB2Au3Lx1G8A75A0AtgoIu7J1/6iG++vln2AKwAi\n4lbg+cK5k/Mq3AOkb5lunT+XZZLenydn7yEVhd0L+FVEvJrzMm9u0vi6cDalmZlZbQNW2kLSIaRI\nog8D0yTdUQr2rqOUB/lm4XXp7zVJj+7+LSJ+Wna/0RXa/7XsWkgB4sVJ6lsAIuI3ksYABwPfkTSd\ntDq1JCL2KLvXRlTPkOytLv3mTf4HAHtExCuSZtCRTTkVOJJUlX9aRISkfs2mBC6BVPS1v+5rZmbW\nKgZkZUzSuqTHhSdGxCLgV8DpTer+DlIu5fB8r1GS/q4b1y8nZ0hK2hXYPL9+O/BKRFwBnJ3bPA5s\nJmmP3GYtSdvnR7AvStor91mxDpek3SVd3o2xzSz1Jekg4K35+Ajg+TwR24b02LbkRuDjwD/SUVds\nFvBRSW/Jn1OXb2qamZlZ/xiolbFvAr+MiKX578mkDMcpwMvApRFxMEDOSfx8RPy+kY4j4k5J2wL3\n5wWglaRcxlUNju0G4GhJS+icWbkjcLakN0kb2k/IZTkOB87PjybXBH5EegT6OeAySUEKG6/kXcCr\nDY4L4NvA1Xls95EyMwFuJ4WZP0qaID5QuiAinpe0FNguIh7Kx+ZIuglYSHpMvIiObMqJuc3Fkv4P\nMBfYEHhT0pdyP/7Wq5mZWZP0WTal2iA7sq9JOhv4RUQsHIB7D4+IlZLWI624HZ+DyHva38p6dcac\nTWmtyHXGzKynNAiyKVs+O7KvRcRXBvD2l0jajrS37D97OhErFH39Y722zqY0MzPrqs8mY86OHNwi\n4tNN6uf/kUpkmJmZWQ8MdGkLMzMzsyFtwEpb2NDjbEprRd4zZmZ9rc9WxnKE0Ku5GGrp2PLCucVV\nrpuRK8T31bg2kXS3pJWSLiw7t7yB62fk8W+gFMO0dT6+lqRFkt7XR0MfcEpxVJuWXuffWyrFU60c\n0MGZmZm1qAEJCh9gr5FKa3TJYuyOXLn+a6SQc3J/90XEg70bXmtxULiZmVnvDGRQOJAKwEq6RtKj\nkqYB6xbOXZSjdJaoI/D7IEnXFtqMk3Rzfn2sUgj4Q5J+Vr7yBRARL0fELNKkrO74KlhBrlkWEdeS\n6m99FZhImpzVpBRUfpGkByQtk7SvpMvy+59SaPchSfdLeljSdYUitnXD0vNnckuhrwslTcivz5K0\nVClE/Zx8bDNJNyiFq8+RtGc+XjWYvMHPyszMzOro1z1jVcKlTyBVtt9W0k6kPMmS0yNihaRhwPR8\n/i7gp5LWj4iX6Qi/fjtpxWtX4CXg18CCJoyvvE15gPeXgEdJdbpWNHirtwJ7AIeSciH3BD4PzJG0\nC/A0KefygIh4WdK/AF8mh6GTw9IlnUsKS9+TVKJiCXBxtZtK2hj4BLBNjkXaKJ86Dzg3ImZJehcp\nxWBbOoLJz5T0EeDYwufgoHAzM7MmGAwb+PcBzgeIiIWSigVQj5R0PGmcI0nV3xdKup0U53M98BHg\nq8AHgXtKEyJJ1wF/3w/jPxD4AykwvFE358nQIuCPORKKXFl/NPAOUnj67JwisDZwf+H6Ylj68PzI\n9CVJrxUmWJX8hbQieKmkW4HS6tkBwHbqiKzcUNIGpH+bwyAFk0t6nm7K/37HAwzbcLPuXm5mZtb2\nBsNkDCqHX29O2oe1W470mULn8OsTSY8M50TES1L/hV8Xxvh24GRgd+BuSf/RYDX9emHnq4C7IuIf\ne3h9tbDzv0nanTRxPRw4Cdg/t90jIjpFM+WPtFcRDQ4KNzMzq20w1Bkrhl/vAOyUj29Iyql8UdLb\ngIMK18wgPY48jo7w64eAfSW9VdKawCd7MyhJ0yWNqtPsXOB7EfE06THij5s0KXwA2FPSVnks60nq\nzirfb0krXesoZWZ+MPczHBgREbcBpwI75/Z3kiZm5HalDfnVgsnNzMysSQbDZOwiYLhSyPWZwDyA\niFgAPAI8BlwFzC5dEBGrSI/YDsq/iYhngO+RJmWzgeV0hF8fKqm036pUluGHwARJTyvFAlE4vwaw\nFWnlrSJJ/0AK+v6PfP+bgeeBo/P52/LKGZLOlHRoox9IRDwLTCCFgi8kTc626cb1TwHXAouB60if\nI8AGwC25z1mkCSSk1b2xeVP/UtKXESAFk++TH58eRkcwuZmZmTVJWwWFqyP8ek1gGnBZREzrQT87\nAMdExJfrNjbAQeHWvlz01cx6SkM0KHyypANIe6TuBH7Zk04iYjEdq0ZWgxwUbmZm1ittFRQeEb0q\n5Grd56BwMzOz3hks36a0IcDZlNZK/HjSzPrLYNjA3y2qkXnZw/42k/SgpEck7d2UQda/5/LutFGV\n3Mdczf/w5o2s4j1ul/RCsaJ/Pj4j7wtEHVmffZYpamZm1q5abjKWNTMP8YPAooh4b0TcWzyRK/8P\ndWcD/1SrQUTsB8ztn+GYmZm1l1adjJV7FlZnMs6UdKukxyVdnMtUVMytzPW0fgB8TCnrcd28wvPv\nkhYAe0gaI+keSfOUMiBH5v62zKtG8yTdK6lL6QlJ2+f7zc9lI7YujreR91TWn/K4H5f038DfFc6d\noZQruVjSJbnttpIeKrQZnctaIOlgSY/l8Z9fvvJVEhHTSfFS5VZndJqZmVnPtcVkrCwncXfgi6Q4\noS2Bw9SRW/l+Uo7jNvm6+cAZwNSI2CVXoF8feDAidgYeBC4ADo+IMcBlwHfzfS4BvpiPTwJ+UmFo\nE4Hz8ireWFLmZKMZmJXafAJ4T35vRwMfKJy7MCJ2y6VE1gUOiYhHgbUlbZHbjAeulfQW4KfAQXn8\n3c4piojD8pc0zMzMrBfacQP/QxGxDEDS1cBepHigRnMrV5FKNUCa+OwA3JUL6w8D/pAr2X8AuK5Q\ncH+dCn3dD5wu6R3AjRHxRC/f2z7A1bno7e8l/bpwbj9JXwXWAzYmhYbfTCr+eiRwFmkyNp40GV0W\nEU/ma68m50c2m5xNaWZmVlM7TsbKq9gG0J2IotfyZId83ZKI2KPYQNKGwAv19q1FxFWSHiSFmd8m\n6Z8j4te1rmlApRzPt5BW5sZGxFOSJtM5x/M6STemIcUTkt7byzE0PlhnU5qZmdXUFo8py+wuafO8\nV2w8Kfanp7mVjwObSdoDQNJakraPiL8AT0o6Ih+XpJ3LL86PB5dFxPnAr+jI3Sy2eawb720m8ClJ\nw/Letf3y8dLE67m8arf6G5a5Dtgq0mPaUo7nY8AWpW9Dkj4nMzMzGwDtOBmbA1wIPAo8CUyrlVtZ\nS0S8TprYfD9v6J9Pxz6to4Bj8/ElwMegSw7meGBxLsOxA3B5sX9Jm9K9VbtpwBPA0tzX/XmcLwA/\nI2VR3pE/g6KpwGdIjyzJe+O+ANwuaR5pg34px3OspEsLY7yXlG/5QaUczw93Y7xmZmZWR59lU/YV\n1ci8lDQOmBQRh1Q415TcymaSdAiwRV456+97lz4PAT8GnoiIc3vR3wzSZ1+1xIWzKa2VuOirmfWW\nBkE2ZV/paeZlU3IrmykiKpaT6CfHSfossDbwCOnblT0i6W5gC+CNWu2cTWlmZtZVy62MWesaO3Zs\nzJ3r2rBmZjY0tPPKmLUoZ1NaK/FjSjPrLy23gV81sinzucVVrpuhPsxOlLSJOjIaLyw7t7yB61dn\nPfbw/lfnKv+n9rSPbt7P2ZRmZmZN0KorY83MpmyW10jlI3bIP/1G0v8BdouIrSqcWzMi/taX94+I\n/fIGfjMzM+umllsZq6JSjuO6kq6R9KikaaSIoNK5iyTNlbRE0rfzsYMkXVtoM07Szfl1l1zL8vtF\nxMsRMYs0Kas7vgpWZz3mVaZz8/imS9osH98tr37Nl3R2YRXwTmBUPr53XrX6kaS5wCmSNpN0g1J2\n5RxJe+b+1pd0WT72iKSPVfgc11fK+lyglHtZqknmbEozM7MmaNWVsU6q5DieALwSEdtK2gl4uHDu\n9IhYIWkYMD2fvwv4qaT1I+JlUo2wqerItdyVVI/r18CCJoyvvM1hhT/XB+ZGxKmSzgC+BZwE/Bw4\nPiLuk3RWof2hpHIfuwCkahWsXdo0KOkq4NyImCXpXaRaZNsCpwO/johjJG0EPCTpv/P7LzkQ+H1E\nfCT3NaLCeM3MzKyH2mVlrJJ9gCsAImIhsLBw7khJD5NKOmwPbJcf5d0OfDTXIvsIqWr+7uRcy4h4\ng1QAta+9SUe1/CuAvfJkaYOIuC8fv6pOH1MLrw8ALsz77G4CNpS0AfAh4LR8fAap7Me7yvpZBBwg\n6fuS9o6IusVyiyQdn1ch5656pVuXmpmZDQltsTJWQ6Ucx82BSaQ9Vs9LmkLnHMcTSY/g5kTES7ko\n6kDrbr4mQHF1aw1gj1x5f7X83j4ZEY9XvXHEbySNAQ4GviNpekScWa19heudTWlmZlZDO6+MzSRF\nFiFpBzpyITckTVRelPQ24KDCNTNIjyOPo2Nlqae5lhXlPWCj6jRbg458yU8DsyLieeAlSe/Pxz/V\njdveSXrMWRpD6csPdwBfLE04VSFAPD+mfSUirgDOJn0+ZmZm1iTtPBm7CBgu6VHgTGAeQEQsID2e\nfIz0qG926YKIWAXcQpqg3ZKPVc21VOccylIJix8CE5RyHLcrDkgpvHwr0spbLS+TAs8XA/vn8QMc\nC1ySHyuuTwP5mtnJwNi8+X8pMDEf/1dgLWBhvte/5nG+XdJtuc2OpL1k80l7177T4D3NzMysAS1X\ngV81sin78J5NybXMK3THRMSX67RbGRHDq40jvz4NGBkRp3R3HH1Bzqa0NuOir2bWW2rjCvw9zabs\njabkWkbEYqDmRKyOj0j6Gunf7bfAhF701TRyNqWZmVmPtdzKmLUuZ1OamdlQ0s4rY9ainE1prcSP\nKc2sv7TzBn4zMzOzQa+lJmMawiHhxfcgabmkTXswzomSjq5wfPVnpxQDdUudfsbl+mxIGi/pf+pd\nY2ZmZpW14mNKh4T3UERc3Ad9TpX0R1IhXTMzM+umlloZq6LdQsK7jK9ZJE2WNCm/HqMU/r2AlDpQ\nqX21IPHXabzGmZmZmdXQiitjnbRhSHiX8eVszWb7OfDFiLhH0tlV2lQLEr8PuK/KNZ1IOh44HmDY\nhps1Y9xmZmZtpR1Wxipp5ZDwLuNr9g0kjQA2ioh78qFfVGnaSJB4TRFxSUSMjYixw9Yb0dMhm5mZ\nta2WXxmroeVCwuuMr6m3osLnU6VdzSBxMzMz6512XRlr1ZDwWuNrmoh4Id9jr3zoqCpN6waJm5mZ\nWe+062SsJUPCa42vOyTdlve7IelMSYdWaPY54Mf5EWS1FcCKQeJmZmbWPC0VhzQUQsJbkaRxpJDw\nQ2q1cxySmZkNJY3GIbXaytjqkPB+vOfkfL/FwJP0IiS8TSdi44GfAM8P9FjMzMxaUUtt4I+Ip4B3\n9vM9B2UxU0mbANMrnPpgRPy5v8YREVPp2GNnZmZm3dRSkzHrkCdcgy2JoCYHhVsrcVC4mfWXVntM\naWZmZtZWWmoyVisovM51KwvXVwwTb6CPS8u/IZmPTyhFJBXjhmr0M1nShDptJkiaXKvP3ryXRkk6\nIscyvalC0LqDws3MzJqnpSZj2YAEhUfE5yNiaX/fd4AtBg4j1W2rKO8Z+3y/jcjMzKzNtOJkrNyz\nkEpQ5KKqD0taVAi1bgpJM0qrQ5I+VwoPB/as0n5LSbdLmifpXknb5FMrgVfr3O7V3K68z4rh3nmV\n7N783h+W9IF8fKqkgwvtpkj6pKT1JF0raamkaZIeLK58lUTEo1Wq7zso3MzMrElafgN/IYj7NeAT\nEfEXSZsCD0i6KZpcSE3SSODbwBjShORuUqHWcpcAEyPiCUnvI5V/2D8izql3j7zaVEm1cO8/Af8Q\nEa9J2hq4GhgLXEMKPb9N0trAB0kh6icCz0fEdrn+WbdKhTgo3MzMrHlafjJWIOB7kvYB3gRGAW8D\n/rfJ93kfMCMiSityU4G/7zQQaTjwAeC6QrzlOr25aZVw71Jc0lrAhZJ2IdViK43nv4DzJa0DHAjM\njIhXcwzSeZDqn0kqBqk3VURcQpqYss7IrVunwrCZmVk/aafJ2FHAZsCYiHgjb+zvi5BtqB+yvQbw\nQpP3ttUK9z4V+COwc773awB5pWwG8GHSCtnVhb7MzMxsEGiHPWMlI4A/5YnYfsC7++g+DwLjJG0i\naS3giPIGEfEX4ElJRwAo2bm8naSTJJ3UyE3rhHuPAP4QEW8C/wQMK5y7hpRDuTcp+BtgFnBkHsN2\nwI6NjMHMzMyar51Wxq4Ebpa0CJhLCtvulhyufWlEHJz/vg34fET8vtQmIv6Qy07cD7xA9f1WRwEX\nSfoG6THiNcCCsjbb0L0w8M8Bl0kK4M7C8Z8AN0g6GrgdeLlw7k7gcuCmiHi90P4/JS0lfU5L6AhA\nvxS4OCLmSvoEcAFpxfFWSfMj4sPdGG8nO44awVwX0jQzM+vEQeEDKNfmOqwwSeqv+w4D1sqPMbck\nxSr9fU/H4aBwMzOzrhoNCm+1lbHVQeEDUWus2epNXvrQesDd+TGrgBN6MREbD3wLmNfE8ZmZmQ0Z\nLbUy1iySPgx8v+zwkxHxiYEYz1CxzsitY+RnfzTQwzCry7mUZtYMja6MtdMG/tVqxSblR52bRMQu\nZT9Nn4hJWp5rnnU53si1hdddCsDm41MkHd6bMTYwjtslvVAed5SL4I7Or++WtLJS4VgzMzOrrS0n\nY1m12KTRwKf7eSyt7GzSNzSrioj9SF+aMDMzs25q58lYuWfz77OAvSXNl3SqpGGSzskRSgslfRFW\nr2r9IB9/SNJW+fgRkhbnWKKZ+VjFPkokrZtXmI4rG0sj4y32I0kXSnpc0n8Df1c4d4akOXlsl+S2\n2ypFNpXajC4VeJV0sKTHlOKazi9f+SqJiOnASxVOrSDt4TMzM7NeaLUN/D1WiE06jcI3/ySdAGwO\nvDci/iZp48JlL0bEjrlkxI+AQ4AzgA9HxDOSNsrtjq/Rx3BSWYvLI+LysrE0Mt6iTwDvAbYjpQss\nBS7L5y6MiDPze/oFcEhE3CxpbUlbRMQyUuHXayW9BfgpsE9EPCnp6i53qj++w7p7jZmZmXU1lFbG\nqjmAVFfrbwARsaJw7urC7z3y69nAlLzKNayBPn4F/Lw0EeulfYCrI2JVrn3268K5/ZQCvxcB+wPb\n5+PXkgu8kiZjU0n1zZZFxJNl77PpJB0vaa6kuatecba4mZlZOU/GascMRfnriJgIfAN4JzBP0iZ1\n+pgNHCSpWRFEXe6TV7p+AhweETsCP6MjCmoqcKSkv0/DjyfoxzikiLgkIsZGxNhh643or9uamZm1\njKE4GXsJ2KDw953ARElrApQ9Yhxf+H1/Pr9lRDwYEWeQ9nW9s04fZwB/Jk2WupDUnaSAmcCn8h61\nkcB++Xhp4vWcUkj56m9YRsT/I+3t+iZpYgap6v4WpW9DFt6nmZmZ9bOhOBlbCPwtb8A/FbgU+B2w\nUNICOn/T8q15w/sppDBugLPzRv3FwH2kiKNafQB8CXiLpB8UD+ayF91ZpZoGPEHaK3Y5eYKYcyt/\nBiwm5U/OKbtuKvAZ0iNLIuJV4AvA7ZLmkSaopTiksTkSqTTGe4HrgA9KejrXaDMzM7Mmacuir82I\nTcp1vsZGxHNNGlalexwCbBER5/fVPWrce3hErMyPT38MPBER5/aivxmkL0ZULXHhoq/WKlz01cya\noV3jkBrVErFJEVGxnEQ/OU7SZ4G1gUdI367sEUl3A1sAb9Rq56BwMzOzrtpyMhYRT5H2cvWmj9HN\nGc3glFfBerwSVtbXfvVbmZmZWSVtORmzwWnRMy8y+rRbB3oYZnX5MaWZ9ae23MBfL5syb76vdN2M\nvsxXlLRJIcfxwrJzyxu4vpgH+fU+GWTXe1bMv5SzKc3MzJqiLSdjWbVsyoH0GqnExKQm9NUvk7FG\nOJvSzMys59p5MlauUtbjupKukfSopGnAuoVzF+XK8UskfTsfO0jStYU24yTdnF8fK+k3SjmWPytf\n+QKIiJcjYhZpUlZ3fBWsAFZJOgtYVylf88p8/6OVcjEX5Dik0qrWxfl9/CZ/exNJ2+dxzs/XbF2t\nj7LP619zn2vgbEozM7OmGDJ7xqpkPZ4AvBIR20raCXi4cO70iFghaRgwPZ+/C/ippPUj4mVyvJCk\nt5NWvHYl1ez6Nan+WG/HV96mlAd5mqSTSit/krYHTgf2jIjnyorOjgZ2B7YE7lYKPJ8InBcRV0pa\nGxhWpw9yjbQRwOci1UNxNqWZmVkTDKWVsUr2Aa4AiIiFpIKwJUdKephU9mF7YLucPXk78NFcbf8j\npOzJ3YF7ImJFRLxBKpLan/YHri/VRCvLxrw2It7MMUjLSLmU9wNfl/QvwLtzEdhafXwT2Cgi/jm6\nWZjO2ZRmZma1DfXJGFTOetyctK/rgxGxE3ArZVmPpMnLnIh4iX7Meqyi0XxNSPmUVwGHAq8Ct0na\nv04fc4Ax5atljXA2pZmZWW1DfTI2EzgKQNIOwE75+IbAy8CLkt4GHFS4ZgbpceRxdGQ9PgTsK+mt\necXsk70ZlKTpkkbVafaGpLXy6+mklbxN8vXFSdMRktaQtCWpMOvjkrYAluXK/78ive9afdwOnAXc\nKqmY62lmZma9NGT2jFVxEfBzSY8CjwLzACJigaRHSIHaTwGzSxdExCpJtwATgM/mY89I+h5pUrYi\nX1fKejyUFKt0Rv57OWmyt7akjwMfioilpf7z5vitcj+1XELKwnw4Io6S9F3gHkmrSI9WJ+R2v8vj\n2hCYGBGvSRoPfEbSG8D/At/L++Oq9UFEXJcnYjdJOjg/2jQzM7NecjZl8+5ZynpckxTofVlETOtB\nPzsAx0TEl5swpimkz+H63vbVwL1m4GxKaxMu+mpmzSBnU/Z7NuVkSQeQ9pbdCfyyJ51ExGKg1xOx\n/iRnU5qZmfVYW07GmpFN2YN7NqOQa1NFxIR+uo+zKc3MzHqoLSdjNjg5m9IGMz+aNLOB0vbfptTQ\nyKk8IqcI3C1prKTz8/EJlZIAmiFX4h+XX18paYUqZFiamZlZbUNlZWww51TukH9641jguBy1BH2c\nE5lTCVbL3+ac0pf3NDMza1dtvzJWRTvlVJ4B7AX8h6Sz8zhuqfD+NpN0g6Q5+WfPCm3G5RW36yU9\nlle8lM8tl/T9nEpwBKl0x+sNjNPMzMxqGCorY520WU7lmbmC/qSImFt6dFjBecC5ETFL0ruAO4Bt\nK7R7Lyn+6fek+mp7AqUVtz9HxK759TUNvRkzMzOraaiujLf8TY4AAAmpSURBVFXSLjmV1RwAXJj3\nzt0EbFilmv5DEfF0RLwJzCcFjZdMrdC+JmdTmpmZ1TYkV8ZqqJVTuVtEPJ/3RhVzK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"text/plain": [
"<matplotlib.figure.Figure at 0x7f4dbc6dde10>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"(\"['file.nr', 'ldavg.1']\", 0.10207216930752588)\n"
]
}
],
"source": [
"errorResults = []\n",
"for s in subsets:\n",
" coef, nmae, M = computeNMAE(X_train[s], Y_train, X_test[s], Y_test)\n",
" errorResults.append((str(s), nmae))\n",
"\n",
"errorResults.sort(key=lambda tup: tup[1])\n",
"values = []\n",
"words = []\n",
"\n",
"for r in errorResults:\n",
" key, value = r\n",
" values.append(value)\n",
" words.append(key)\n",
"\n",
"y_axis = np.arange(1, len(words) + 1, 1)\n",
"\n",
"fig = plt.figure(figsize=(8,20))\n",
"\n",
"plt.barh(y_axis, values)\n",
"plt.yticks(y_axis, words)\n",
"plt.show()\n",
"\n",
"# This is the minimum\n",
"print errorResults[0]"
]
},
{
"cell_type": "code",
"execution_count": 113,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[('ldavg.1', 0.70937839668916114), ('tcpsck', 0.64107643510095624), ('file.nr', 0.62371684783642523), ('cswch.s', 0.57239216798702708), ('X..memused', 0.34078681954222106), ('all_..idle', 0.32006290177769048), ('sum_intr.s', 0.2629863192078799), ('proc.s', 0.049392824896153278), ('pgfree.s', 1.1735954018529034e-07)]\n",
"[0.1059390718046009, 0.10422456686658928, 0.10127980197335251, 0.10127953684210818, 0.10074059722870765, 0.10005897679161894, 0.10010097937636944, 0.10007545076454935, 0.09849073039317284]\n"
]
},
{
"data": {
"image/png": 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NZ+UysEdW1KG5JOWJJIYnEuc+tW3PAaa+uZY/v7mabXsOckrvDtx4Vh9Oz+3o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\n55xzteKJxDnnXK14InHOOVcr/x/XtUJrCvGHNwAAAABJRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f4dc11dbd90>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# Task 4.3\n",
"\n",
"featureCorr = []\n",
"for f in fields:\n",
" stdevX = X_train[f].std()\n",
" stdevY = Y_train.std()\n",
" avgX = X_train[f].mean()\n",
" avgY = Y_train.mean()\n",
" \n",
" # this is the sum\n",
" val = (X_train[f] - avgX)*(Y_train - avgY)\n",
" # this is the correlation\n",
" corr = 1/float(len(X_train[f]))* val.sum() * 1/float(stdevX*stdevY)\n",
" featureCorr.append( (f, corr*corr) )\n",
"\n",
"# sort with highest first\n",
"featureCorr.sort(key=lambda tup: -tup[1])\n",
"print featureCorr\n",
"\n",
"resultList = []\n",
"for i in range(1,10):\n",
" # get the first i features\n",
" selectedFeatures = map(list, zip(*featureCorr[0:i]))[0]\n",
" coef, nmae, M = computeNMAE(X_train[selectedFeatures], Y_train, X_test[selectedFeatures], Y_test)\n",
" resultList.append( nmae )\n",
" \n",
"print resultList\n",
"# plot the error to number of features\n",
"plt.plot(range(1,10), resultList)\n",
"plt.ylabel('NMAE value')\n",
"plt.xlabel('number of selected features')\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": "2.7.13"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
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