diff --git a/README.md b/README.md
index d92d9390..ff9e93dc 100644
--- a/README.md
+++ b/README.md
@@ -41,7 +41,7 @@ Excerpts from the [Foreword](./docs/foreword_ro.pdf) and [Preface](./docs/prefac
 7. Combining Different Models for Ensemble Learning [[dir](./code/ch07)] [[ipynb](./code/ch07/ch07.ipynb)] [[nbviewer](http://nbviewer.ipython.org/github/rasbt/python-machine-learning-book/blob/master/code/ch07/ch07.ipynb)]
 8. Applying Machine Learning to Sentiment Analysis [[dir](./code/ch08)] [[ipynb](./code/ch08/ch08.ipynb)] [[nbviewer](http://nbviewer.ipython.org/github/rasbt/python-machine-learning-book/blob/master/code/ch08/ch08.ipynb)]
 9. Embedding a Machine Learning Model into a Web Application [[dir](./code/ch09)] [[ipynb](./code/ch09/ch09.ipynb)] [[nbviewer](http://nbviewer.ipython.org/github/rasbt/python-machine-learning-book/blob/master/code/ch09/ch09.ipynb)]
-10. Predicting Continuous Target Variables with Regression Analysis
+10. Predicting Continuous Target Variables with Regression Analysis [[dir](./code/ch10)] [[ipynb](./code/ch10/ch10.ipynb)] [[nbviewer](http://nbviewer.ipython.org/github/rasbt/python-machine-learning-book/blob/master/code/ch10/ch10.ipynb)]
 11. Working with Unlabeled Data – Clustering Analysis
 12. Training Artificial Neural Networks for Image Recognition
 13. Parallelizing Neural Network Training via Theano
diff --git a/code/ch10/ch10.ipynb b/code/ch10/ch10.ipynb
new file mode 100644
index 00000000..fc1d8609
--- /dev/null
+++ b/code/ch10/ch10.ipynb
@@ -0,0 +1,1503 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Sebastian Raschka, 2015"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#Python Machine Learning Essentials"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Chapter 10 - Predicting Continuous Target Variables with Regression Analysis"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Note that the optional watermark extension is a small IPython notebook plugin that I developed to make the code reproducible. You can just skip the following line(s)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Sebastian Raschka \n",
+      "Last updated: 08/20/2015 \n",
+      "\n",
+      "CPython 3.4.3\n",
+      "IPython 3.2.1\n",
+      "\n",
+      "numpy 1.9.2\n",
+      "pandas 0.16.2\n",
+      "matplotlib 1.4.3\n",
+      "scikit-learn 0.16.1\n",
+      "seaborn 0.6.0\n"
+     ]
+    }
+   ],
+   "source": [
+    "%load_ext watermark\n",
+    "%watermark -a 'Sebastian Raschka' -u -d -v -p numpy,pandas,matplotlib,scikit-learn,seaborn"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "# to install watermark just uncomment the following line:\n",
+    "#%install_ext https://raw.githubusercontent.com/rasbt/watermark/master/watermark.py"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<br>\n",
+    "<br>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Overview"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "- [Introducing a simple linear regression model](#Introducing-a-simple-linear-regression-model)\n",
+    "- [Exploring the Housing Dataset](#Exploring-the-Housing-Dataset)\n",
+    "  - [Visualizing the important characteristics of a dataset](#Visualizing-the-important-characteristics-of-a-dataset)\n",
+    "- [Implementing an ordinary least squares linear regression model](#Implementing-an-ordinary-least-squares-linear-regression-model)\n",
+    "  - [Solving regression for regression parameters with gradient descent](#Solving-regression-for-regression-parameters-with-gradient-descent)\n",
+    "  - [Estimating the coefficient of a regression model via scikit-learn](#Estimating-the-coefficient-of-a-regression-model-via-scikit-learn)\n",
+    "- [Fitting a robust regression model using RANSAC](#Fitting-a-robust-regression-model-using-RANSAC)\n",
+    "- [Evaluating the performance of linear regression models](#Evaluating-the-performance-of-linear-regression-models)\n",
+    "- [Using regularized methods for regression](#Using-regularized-methods-for-regression)\n",
+    "- [Turning a linear regression model into a curve - polynomial regression](#Turning-a-linear-regression-model-into-a-curve---polynomial-regression)\n",
+    "  - [Modeling nonlinear relationships in the Housing Dataset](#Modeling-nonlinear-relationships-in-the-Housing-Dataset)\n",
+    "  - [Dealing with nonlinear relationships using random forests](#Dealing-with-nonlinear-relationships-using-random-forests)\n",
+    "    - [Decision tree regression](#Decision-tree-regression)\n",
+    "    - [Random forest regression](#Random-forest-regression)\n",
+    "- [Summary](#Summary)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<br>\n",
+    "<br>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "from IPython.display import Image"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Introducing a simple linear regression model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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rUBPq3jIyMhzff/+946effnKUl5c7zpw545D7mEiABEiABJqTQJljabL+3ZzkyCk1t13q\nWJKkr6U6DpgvmY9LtzmUL7j797v5d3314yV7qzRgrsXhKNzgSHTVkzhvW+W10hxHsql+a+qGymuO\nQ455VlfbySscZaYr53u4d0myc0xJSxpU7+Y5iS4WSY5txZW9yF3kqs8Yi9Wx5lDltcINc9z8ln5T\nv1HkpCe570lKzzEqWzG1ch5W1LOeyl7wiATOnYD5A/t5CXjeTAIkQAIkQAKBSyACN6fMATIlokkW\nVm49jAEjuzmHW7TDbYVNSh+H2NogWC7BvCWLUHCqDdrUaq09hVPqurWjF6t4zFWYoJR3drbalm7C\n4UfUAj6qTdu+j5Fpajs3bT0K5g539ufw51jqioA4c/wgeLalm272cli0Pw8Hj0O5aTi3bZ+6Ws36\nFJ/kDVbW6Qp1d4X6dyH6xvd2W7KrV3nl6LHAbDUIxXPdriIkDBe7dgk+WVNlFPjg8wKMHCNU7chZ\nv9JVTSqG9K7fKAY8kI7UGVkQ+3bWjD/iGQzGLJdJXX1wwbh61uNqmDsSOD8C567Zeef5EqDF+3wJ\n8n4SIAESaEYCZbmVFuXkpW7r7jcrZjrUX2JjW3Ggolk65LYyw+rIOuRsc5vJsqv7o63CB9x9THRs\nKDyfLpY60rXl3GRd1+1V3atvBrwZpU08rTPXOIxRFG9zJFWvd6rLQl/xjWOm61rinM0NGkRpziL3\nHFX2cabjm+aZrgb1lYUDmwBfrlQ/gUwkQAIkQAIkUCeBiHikzFGmZkmZ/4tPiuTAhvWZC+QAUL7C\nN8bW8UWyitYh0Ty8b87qvP3fJ1HJUyPlYvUnh9RRCbavcjo/py5ZgZkS31ClNVsOqP/t2Jrl6mPS\nBAxyO0xLiYanToOlciusVudWpQZXntqp1MW7L3lEP4x3dTR3wQcoUHfYvt6k7N8qJaZixRIlsyVl\nZGGfeonUXrAdrlFg7OgrnNfq+b9lwBSsSXVBcd2Tunm209+7nnWwGAk0BgEK78agyDpIgARIgASC\ngsC14+92jTMbyzcpqWjbgYWul/2m/m4Eor1RKM/DJOWfYURH8boPwWt53qNshMVeizkuHZmRtQPl\ntq+xKlsat2LY6F/jN8OdFzNXbYPN/g2090byHdfX6vrhreuV1yy4a/FuI6Ti7t27IZvyoXZeTlyE\nYlfe7t0OVWYx4r16g4Th6t+Mc1W9ANsLbNi3fa3zvP9N+A81DmfKxCff2FCwXb8cOhUjLvNK2nWf\neWfDkYOm1UbVpYP7fzQX4DEJNAsBCu9mwcxGSIAESIAEAoFARNwIk+Bdiy0frDciZigTLSbcHOd9\niBUVENt0fdKpOgt1w4iJLsGb+SH++cE6tbCPpNGwxlgQ/+vRzhqyNuCD99e5fL+tGD+kjzPf9X95\nwRY8Omk4QlSYRNnGTnsBeSVVitR5Et7GFREkClUivdR5oyrQ7aobFDln2rT2A6zf6BxF6q/jEBFj\nRarr2sfr1DX96WHmaPSpIehLsHHxo2oM/T1+aCl4dw5SzK7jqt7MlAlYe9jVAHck0FwEAtuTxrdH\nRx9v354f9o4ESIAEPBGo9K+ujIwBk8+3p3uceRWO4kMHHAcOHHIcOlTb5rxeWg/f49LcJTX8lt2R\nTEyRT5SecJazznNUce82RUGZOmeeIzXZ6iqXXrVc7QMyrrijkCQucniJxVJLLeaIMJqnRDJxAqiM\nfKKvwTGnmpN68d41jql6jGqfnmMKkSKtmv3GkxY5cjab/L2Tl5xDn2sZCrNJoB4EaPFurk84bIcE\nSIAESCAgCMSNnmjE9DYPJjXlhnpECglDdLdYxMZ2Q7dutW3O65Y6XMWlbUu/wVCCs0oad5PL6q4i\nn4zVpmRXiaQpN1XGw1Z5+e8tNizhc9YcwuLHHsHcN7ZjxVTlopI7A6vzy6vU6+2k4pTLLebYKeVN\n3tBkwdDkaqOwjoO1mxPAFSPGVqswCSOqOKmX4OV+o5BhnYlF81QwxRqpHCv/NNjpN66uLX36HhXb\n/B41TlfBzBQs2Hi4xl3MIIGmIkDh3VRkWS8JkAAJkEBgEogZggeraLxkjLvWFVqwOUcc1ge3Vnlh\nMBFD4nU/opE4oUonMXbEZabe6cV2pmL8TfqeCPx62hSjzIZtB0xlvR/2SEhG6syZmDcr4Zz8x3tf\ne2uVDzLWcTcY4RGl1ejLroPLocbZieQ70L/KWjfRuO/AAZTuno8pY26s0dGiLS9gvCt0oPo2AHfF\niY9KBMY9tcHt4jL7xlk1VvmsUREzSKCRCFB4NxJIVkMCJEACJBAsBCwYLTG9Xck6U1nAa/gc66tN\nuQ9DQpJTKButqIglV5kilvS9ySRZrXOQaIjOyv4YhurEAehusq5HduxpFDhUerKyYB1HMQnjMHf+\nfDxyV0KDfbyNqrsl4H5TV6eYI5ZYLkOyWOFdKXXikBrfLETHxhqCv6yiume8Daufn+W6cyqWpA7X\n1QAxw7FwqdvsjY/3lVRe4xEJNCEB049bE7bCqkmABEiABEgggAic/Fmt6+5KUyado+DUFZzHPjrh\nATgqpqJc+XiERURUEb4RvcepaxXqmr3GNWnS0xo+YV16wbCTd2p7Hr1q6K3RmLbKgXvKlXtLWBgi\n1FaZlHVaRVGpSC+Hvca1ylKejyyYvKoCdyo41dlI+bi7FqPitvRzqNdza8wlgfoQMD/d9SnPMiRA\nAiRAAiQQ5ASKkLVA1kGUNBOjrQ0Nbee8s9H+D4tQYrWW2hoqVm1FzsgrR+tv8a6l5QZnR6gPDrUl\nT8K5trJV85WQrxVOzQ8rVe/lGQk0PgG6mjQ+U9ZIAiRAAiQQwATK8z/EDGfUOyTOu9NPF2Gx41Sp\nmqTsfBQqa7lOtoNfGmEJ+zarxVu3zj0JBD4BCu/An2OOkARIgARIoBEJfPXu/7pr+93t/d3H/nUQ\njctGStiTBdiytzKCydaVrxjDuD6hp38NR/U2MtzpPNMmPNLv+s4OBw+B2r6cCh4CHCkJkAAJkAAJ\nNIDAZSkvI+f64wi/sCv69a7dPaIBVbZI0StHqVU4Z2UjxXonTmXdiza7VyAlTa3umJiOUX40rv0b\nV2L9v0/j1O7XDY4fr/o7LDlAu36/wZgEHbGlRRCzURKoQYDCuwYSZpAACZAACZBA7QQiYnpjgCl6\nSO0lffuKJX4yclcchHX8bExPcq57b02eh8yFD6CFvdYbAK4cu5aPx3RXyEC5MXP2dCM+eeK8Kym8\nG0CSRZuHAIV383BmKyRAAiRAAiTgcwTixz2mIp88DJtNRWkJi4TF4m8WfIl64oBjsc+hZYdIwCMB\nCm+PWJhJAiRAAiRAAkFCQEVFsUT7m+AOkrnhMAOOAF+uDLgp5YBIgARIgARIgARIgAR8kQCFty/O\nCvtEAiRAAiRAAiRAAiQQcAQovANuSjkgEiABEiABEiABEiABXyRA4e2Ls8I+kQAJkAAJkAAJkAAJ\nBBwBCu+Am1IOiARIgARIgARIgARIwBcJUHj74qywTyRAAiRAAiRAAiRAAgFHgMI74KaUAyIBEiAB\nEiABEiABEvBFAhTevjgr7BMJkAAJkAAJkAAJkEDAEaDwDrgp5YBIgARIgARIgARIgAR8kQCFty/O\nCvtEAiRAAiRAAiRAAiQQcASCeMl4O2wlJSj88QiOHz+JEwhH+3CgQu3bXtgWUR06o3O0BUEMKOAe\ndg6IBEiABEiABEiABFqSQFDpSrutCLk7NmH9e1lYuiATuXWStyJp6miMvTUJiQn9ERsdUecdLEAC\nJEACJEACJEACJEACnggEhfAuyt+C/1v8PGYsyPLEwEteLrIyZEszyiQmz8Ejj0zByPhuXu7hJRIg\nARIgARIgARIgARKoSSDAhbcdax8dgVFp2VVHbk3E1NGJuGZAf/TucTFiOnVEVKTyM1GpoqwUpceL\ncOjb/cjd+SnWrs5Atss0np05G7Jh6lKULb4LtH9XxcozEiABEiABEiABEiCB2gkEuPC24evVLtFt\nTcK8B6djzIgE9OkW7cV3W6zZcYgfMBQjx03GI3MXw1a0Hzs2rcaShTOQKdVtO4oytaPwrv3B4hUS\nIAESIAESIAESIIGqBAJceEdj9PwsXHrB5bgpobcXsV0VSvUzS0xvDB/3gLHNy9uCTw5dAEv1Qjwn\nARIgARIgARIgARIgAS8EAlx4A72Hj0FvLwAaeqlb/FCMi2/oXSxPAiRAAiRAAr5LYPv27RgwYADC\nw51ul77bU/aMBPybQMAL77qm53D+Tny+cwcOHD2F8vI2uCllCgbEOLGUq3CDNmUnj1FhBZlIgARI\ngARIIBAJ7NmzB8OHD0d8fDyWL1+O2NjYQBwmx0QCPkEgeIW3vQAvpPTEjMyq8zDv+juU8I5WmSXI\nuKEDZqgXK9NzivHAAMljIgESIAESIIHAIvDYY4/h5MmTEKv3VVddhSVLliApKSmwBsnRkICPEAjS\nlSvL8e7va4pumZMI97dsFlw62mpM0yurd/nIdLEbJEACJEACJNC4BF5//XX89re/NSotUd/0jh07\nFn/6059gt9sbtyHWRgIkgKAU3vaCfyIpwzn7iakrcKjCgdxFydUehzAkJE0x8nKz9ymXEyYSIAES\nIAESCDwC7dq1w7Jly/DSSy8hIsIZr2vevHkYNmwYDh48GHgD5ohIoAUJBKXwLjt2zIncOgeZc8eh\nWy0ON9GXXQfjy7ZjhShuwUli0yRAAiRAAiTQ1ATuv/9+bNu2DX379jWa+vTTT3HllVdi9erVTd00\n6yeBoCEQlMIbLncS6+hB8LoGpa0Q38mjkPs1jtHkHTQ/FBwoCZAACQQrgf79+yMnJwcTJkwwEBQX\nF+M3v/kN/vznP9P1JFgfCo67UQkEp/CuOGVAzE3bjAIvOPd/8gGMRSuTrkNfBjbxQoqXSIAESIAE\nAoWAxWLBm2++iRdeeAFt2rSBw+HA3LlzccMNN+D7778PlGFyHCTQIgSCUnhbel2FRAN3GsY8+BoO\nG++POE3abcIjjSsF21/DbeMXOCdF/RJyv3PZItPERkmABEiABEigeQn84Q9/gLib9O7tXA1j69at\nRtSTNWvWNG9H2BoJBBCBoBTeiB6KtHlO6Z27IAXdw0NgnZ5lTOt06y0Y3j8EPQenOK3dKnfJn2/j\n8vAB9NBzKCRAAiRAAvUjIOEFd+7ciXHjxhk3HD16FKNHj4aEIDxz5kz9KmEpEiABN4HgFN5q+AmP\nvI0VqU7x7aZhHGQj2/AvceamrtiLyXHOt7yrluMZCZAACZAACQQ+gaioKKxYsQILFixA69atDdeT\np556ComJiTh8+HDgA+AISaARCdQSz6MRW/DZqqIxbu5GFN69HavfXYdP83Kw71Cp6m0Uunfvhfjr\nhmDUb/4D8d0oun12CtkxEiABEiCBZiMwY8YMXHfddbjjjjvw7bffYsuWLUbUk8zMTIwYMaLZ+sGG\nSMCfCQSx8HZOW0xcgrJoq82fZ5F9JwESIAESIIFmIDBw4EDD9eTee+/FO++8g6KiIowcORKzZ882\n3E9atWrVDL1gEyTgvwSC1tXEf6eMPScBEiABEiCBliNw4YUX4u2338bzzz/vdj154okncOONN+KH\nH35ouY6xZRLwAwIBb/G22wqQ88W/oX47nON0nAYu7I2EOK8Rv8+xbt5GAiRAAiRAAv5J4KGHHsLQ\noUMN15MDBw5g06ZNhuvJ0qVLcdNNN/nnoNhrEmhiAgEvvPcum4HBrogl58zSmo7i3Q8g+pwr4I0k\nQAIkQAIkEHgEBg0ahF27dmHy5MnIyspCYWEhfv3rX+Pxxx/HX/7yF4SG8ov1wJt1juh8CAT8T0Rb\nS5fz4eO8t2MbBPwnlPOnxBpIgARIgASCkED79u2xatUqPPvsswgPD8fZs2cN4X3zzTfjyJEjQUiE\nQyaB2gkEvJ7sfZeyVo96unYC9bgSFmkBF66sBygWIQESIAESCFoCf/zjHw3XE1luvqCgABs3bjQW\n3BHXk+HDhwctFw6cBMwEAl54Qy19Ex3NkIDmSecxCZAACZAACTQFgYSEBMP15J577sF7771nWLzF\n8j1nzhykpqbS9aQpoLNOvyIQ8K4mdc+GHSVFh5Gfl4f8/fuN/f6Cw7AZy8jXfTdLkAAJkAAJkAAJ\nVBKIjo42/L2ffvpphIWFGa4n4u8tvt/iA85EAsFMIIiFtx35axdjbP9wdOjcHf2sVvTr08fY9+nZ\nHVFqGflJTy5DQXkwPx4cOwmQAAmQAAk0nEBISAhmzZqFzZs3o0ePHkYF69evN6KeSPQTJhIIVgJB\nK7x3Lk5Bv1HTkWVaHr76Q5A5eyJ6Rk5DHsV3dTQ8JwESIAESIIE6CVxzzTWG68no0aONshLnW+J9\np6WlGUvP11kBC5BAgBEITuF9eC1Spme6pzJpZjrWbN6Gbds2G5/Os5amI8l9NQPW1HdBzxM3EB6Q\nAAmQAAmQQL0JdOzY0fD3njt3ruF6cubMGTz66KPGipey8iUTCQQTgaAU3rYjBdCG7vTNh7Bq/gMY\nOTQBCQlDjTeyx9z1AFY5DmFRstX5LOz+AbZgeio4VhIgARIgARJoRALieiIvV3700Ue4+OKLjZrX\nrVtnuJ5s2bKlEVtiVSTg2wSCUngj3DUpiYuQMrS2FSm7YcqfH3QWzF6Dr6m8fftJZu9IgARIgAR8\nnsCQIUMM15ORI0cafT18+DASExPxzDPP0PXE52ePHWwMAkEpvMPD2zjZZX+MfV4Edcnhg85yicNx\nCQN5N8bzxjpIgARIgASCnECnTp3w/vvv48knn0SrVq0grifyIuYtt9yCo0ePBjkdDj/QCQSl8I6I\nS0J6okxtJgb+cZlnN5KiLbjvxtnG/CeOTUBMoD8JHB8JkAAJkAAJNBMBcT2REIOyyE63bs5vnteu\nXWssuLN169Zm6gWbIYHmJxDwC+jYbQXI+eLfQOvWlXTD2+KKsUp5Z2cDGRNxXVsbMiYNAioqnGVO\nH8SLw8YjyzhLRNqkgZX38ogESIAESIAESKBRCAwbNgxffPEFkpOTIT7f33//PW644QYj6sl//ud/\nQgQ6EwkEEoEQh0qBNKDqY8lbPBbW6U4JXf1avc+VL3jxxmmIrvcN9Sso6OUrttOnT+PEiRMoLS3F\nqFGjsG/fPncFGRkZxtdv7dq1Q0REBMLDw41fRPxl5EbEAxIgARIgAT8ncPbsWTz11FPGCpfyd1GS\nhCD8+9//DomKwkQCgUIg4F1N2lq6NMJcnULAfzXQCJRYBQmQAAmQAAmcC4HQ0FDMnj0bssjORRdd\nZFSxevVqw/Xk008/PZcqeQ8J+CSBgNeTve9KR/Gop88LfliYBXy38rwQ8mYSIAESIAESqJOAuJmI\n68nEiRMNEX7w4EGIO4pEPXnwQVeksTprYQES8F0CAS+8gQhER0f47gywZyRAAiRAAiRAAm4CnTt3\nxgcffIAnnnjCcD+x2+146KGH1GtZ2YbrSXR0Yzt+upvmAQk0OYGAdzVpcoJsgARIgARIgARIoFEJ\niOuJCO8PP/wQXbo4XUbfe+89w/Vk+/btjdoWKyOB5iQQBBbv2nGWF+3HjpxdOFh8uvZC6sVHdB2I\nO0bG08+7dkq8QgIkQAIkQAKNTmD48OGG68ldd91lWLwLCgqMFaafffZZzJgxo9HbY4Uk0NQEglZ4\nF6x9Ej1HOeN01w15HkY54hs9qknd7bIECZAACZAACQQ3ga5duxr+3o8//jjmzp2rIv9WYObMmcby\n80uWLEH79u2DGxBH71cEgtPVxLYTM7yJbmvVOUxadA1frqyKhGckQAIkQAIk0GwExPVEVroU32/x\nAZe0atUqw/Vkx44dzdYPNkQC50sgKIW3bd/HrsVxgDlZuShT8bSLcxYZLBPTt8Gx24GyAxtgLG6p\ncq/t25tuJuf7pPF+EiABEiABEjhPAjfddJPhenL99dcbNR04cADXXXcdXnzxxfOsmbeTQPMQCErh\njfA2TrpqYZyZY+JV3BMgLNyZFdXGeRAROxzLN8wxMmc99Z7nZeWdt/B/EiABEiABEiCBZiIgcb43\nbNiA1NRUY0E5WYTuD3/4A26//XYcP368mXrBZkjg3AgEp/DWrKJcAlyfq31p4Qn3Wcy1o5EkZ9k7\ncajcnc0DEiABEiABEiCBFiTQqlUrw9977dq1iImJMXryzjvvYMCAAcjJyWnBnrFpEvBOICiFdzhc\ngjtrNwrtTkDh4c4lcrJnP48tJc68oh3rXS4pHREetK+hen+AeJUESIAESIAEWorAiBEjsGvXLiPS\nifTh22+/xbXXXouFCxe2VJfYLgl4JRCUwjui32BMNbAswCvrC4yjiLghSDWOsjCsw3BMmzYWnYfN\ncsJLuhSdKbydLPg/CZAACZAACfgQge7duxuhBmfNmuV2Pfn973+PO+64A6WlpT7UU3aFBICgFN4I\ni8O985yvTqaNWg6ngTsW07Kc0lv5liAjI8v9fMz7z9GMauKmwQMSIAESIAES8C0C4nry9NNPY/Xq\n1ejUqZPRuRUrVhiuJ2IRZyIBXyEQnMJb0U94KAt7lR9Y7jcp7vjcsWPmYu+aRZialIjExEQkJc3E\n0s0H8MhQp/+Yr0wa+0ECJEACJEACJFCTwKhRowzXkyFDhhgX9+/fj2uuuQaLFy+uWZg5JNACBEIc\nKrVAu2xSERD0Z86cgbyRfeLECeMrMfmlsW/fPjefjIwM3HLLLWjXrh0iIiIQHh5ufJUWEhLiLsMD\nEiABEiABEiCBSgJ2u92IevL8888bf2vlyoQJE/Dyyy/DYnG+01VZmkck0HwEgsJzuSRvLbLyihXV\nDhh910jEKOeSvO1f4iRa14O0WjL+wt5IiOtWj7IsQgIkQAIkQAIk0NIEwsLCIMvK33DDDbj77rtR\nXFyM5cuXY+fOnRAXlP79+7d0F9l+kBIICuH9XfYzSJmRraY4EdtuG4nofW/BOnh6/afcmo7i3Q+4\nXVLqfyNLkgAJkAAJkAAJtBSB0aNHGwvuiLX7008/Nb5RHjx4MF544QXcd999LdUtthvEBILSxzus\nbQO/ZurYhitXBvEPCYdOAiRAAiTgvwR69OiBTZs24aGHHjIGUV5ejqlTp2LixIn4+eef/Xdg7Llf\nEggKi7d10hJsu+rfQPtfYKAsU9n7LpQWj4IrhHedExcWaWFUkzopsQAJkAAJkAAJ+CYBeT9K/L1l\nqfnJkyejpKQEy5YtMxbbEdeT+Ph43+w4exVwBIJCeIdFxyJhaGyVybNER1c55wkJkAAJkAAJkEBg\nExgzZowR9URcT7Zv345//etfSEhIwIsvvoh77703sAfP0fkEgaB0NUHJTjzz6JPGtnY/14L3iSeR\nnSABEiABEiCBZiAQGxuLLVu2YMaMGUZrZWVlmDJlCiZNmmREGGuGLrCJICYQlMLb9t3HmJU2W20r\ncVq9+cxEAiRAAiRAAiQQPATE9WTBggX4xz/+gfbt2xsDz8zMxKBBg7Bnz57gAcGRNjuBoBTelZQ7\nIjqKwruSB49IgARIgARIIHgIjB071ggxKIJb0t69e/GrX/0Kr732mnHO/0igsQkEpfC29LpKBRaU\nlI1hKS+goL5vWTY2fdZHAiRAAiRAAiTQogR69eqFjz/+GH/4wx+Mfpw8eRIpKSnGS5hyzEQCjUkg\nOM290UORvjQZ1omZQNYM9AzfgxWb70UPj+vpcAGdxnzgWBcJkAAJkAAJ+BqB1q1bG7G9ZcEdecny\n+PHj+Pvf/44dO3YYC+5cdtllvtZl9sdPCQSl8C7Pe80put2TloHxwzLcZzUOuIBODSTMIAESIAES\nIIFAI3DbbbfhyiuvxB133GGEGvzqq68M15OFCxcaK2AG2ng5nuYnEJSuJghvIGguoNNAYCxOAiRA\nAiRAAv5J4JJLLsHWrVtx//33GwM4ceIE7rnnHiPyiURAqS199tlntV1iPgm4CQSl8I6Im6wW0ClG\ncT230vencAEd9yPDAxIgARIgARIIbAJt2rTBSy+9hLfeegtRUVHGYF999VXD+p2fn19j8Dt37jQW\n5/niiy9qXGMGCZgJBKXwFgCygE50PTdLRFB65JifEx6TAAmQAAmQQNARGD9+vOFyctVVVxlj//LL\nL3H11Vdj6dKlbhalpaWGa4osRT9nzhx3Pg9IwBOBoBXenmAwjwRIgARIgARIgATMBPr06YNPP/0U\n06dPN7LF9SQ5ORlTp06FiG1ZfGf//v3GtVWrVmH37t3m23lMAlUIBLUpt7xoP3bk7MLBYhW5pLZ0\nWl3rOhB3jIxHUMOqjQ/zSYAESIAESCDACYjryf/8z/8Y7iQiuG02G15++WWsXr0ahw8fdo/e4XAY\nVu+3337bnccDEjATCFotWbD2SfQcNdvMwsvxPIxyxCPaSwleIgESIAESIAESCGwCd955JwYOHAhx\nQRHLtll065HLapi5ubmwWq06i3sScBMITlcT207M8Ca6q/2sJC26hi9Xuh8ZHpAACZAACZBA8BLo\n27cvPvjgA1gsFo8QtNXb40VmBj2BoBTetn0fI8s19XOyclGmvhoqzllk5CSmb4NjtwNlBza4VrcE\nru3bm24mQf+jQgAkQAIkQAIk4CQgoQbF3aS29M477yAvL6+2y8wPYgJBKbwR3sY55YmLMHNMPCLU\nWZgrtndUG+dBROxwLN8wxyg366n3UPuPVxA/PRw6CZAACZAACQQZgfT0dIiw9pZo9fZGJ7ivBafw\n1nMe5RLg+lztSwtPuM9irh2NJDnL3olD5e5sHpAACZAACZAACQQhAVlC/pFHHqnXyFeuXAkJP8hE\nAmYCQSm8w+ES3Fm7UWh34ggPd/pqZc9+HltKnHlFO9a7XFI6IjxoX0M1Py48JgESIAESIIGmIyCW\n4sbeGrO3Z8+exezZs42XK3/5y18iNNS7jGqKuN5mPtIf2U6U293cGnO8rKvxCQSlnIzoNxhTFcsM\nLMAr62di7shYRMQNQarKS1NSe1iH4So+ZxQyMlye4EmXonNQkmr8B441+gCB8hLsP/AjKsKj0Kd3\ntyZ+f6EcRYd/xMkKoG1UF8REi2OXM5UXFeCrb4+gAuG4sGsP9ImNaeK+6Ja5JwES8CUCIiQlVTgq\nYD/jsoY1cgcjwyKNGkNCQs6r5oSEBMim08mTJw2rtkQ40ZusXvnzzz8bRcTqvWfPHlx++eX6lnPa\na0ZysxbeZxW3vQdLsfmrIuTsL8ZL065GRHgrd/3nO1Z3RTxoVALBKSfD4nDvvERkzMpG2qjleNjx\niAoVGItpWalIS0pTgLOV6K7kPO8/RzOqSSUOHjU6gXLkb1mHnQdPo2v8tRge363RWzBXaPvqDfQZ\nOENlJWJb6UYkeH4x33zLOR3b8lYiyTpe/TRVpplZBzB/TCzyXnsQ1pQFlRfU0aLcUkyLb6LOVGmJ\nJyRAAr5CQIvI3MJcDHp1EOyOphHew3oMQ3Zy5W+jxhKlbdu2NZaR/9WvfuVGKmM6cOCAW4h/9tln\n5yy8teDWnGR/5KcybN5ThA15hbCVKauGK32ytwjXXx5jWOHFEi9lG2ucug3uz59AcApvxS3hoSzs\nvWkfKi7s4Y7PHTtmLvauicX8RcuxrxSIiuqPO/5zJu4aGnP+pFkDCdRGwLYb9w9LcgrUxCUo3Ti5\naT/o6ZeLEaVszU2U7Psxu5rolpY25qsfrMSNSDaJ7sSkRGRnZcP1XnMTdYjVkgAJ+BoBLSbFVeKw\n7XCTiW4Zd8HxApw5c6ZZRKmI3V69ehnb2LFjG4xduEjSfGR/XH1t+Pk3xVi3+wccPFrmsc5Ne37E\nkF9GIywszBDcFN0eMbV4ZpAKbztKSmzoctkAmL75NiYjbuQ0LFYbEwk0JoHy/NcQ2S8FsM5E7vb5\niK/0uFDNmOXvqcZstsXqsu1drxy5nCl50Ta8MS0BJQX7UdalNwrWPYhc45IVS/d+jLviLCiXsFyR\nTWft9s6/xTCxYRIIagIiKEV0iyC225vG0m0GXFFRYYhSyavLN9t8X3Mca7EtbWnBbT9zFju+OYbP\n9xVjm9orVF7ToeIy2E6eQtQFIYbwljFSfHtF1iIXg1J42/JeQQfrdAN4cmo6Un6bhGvjlZ93i0wB\nGw0GAhUVLkGd+53h71zlYbNYsSR3G/YXnUBM70FNa+1udtiJSLnN6Q8ZHdvb+HZp5w/fuXrRC3Hd\nnWI7opaFKBqru175N1YjrIcESKDeBLS4FOEtoltEcVMmae/06dNuQSrnLS1KpQ86aR6yP3zsJD5S\nftvZeUU4cco7F3m38xftwzCwlwWD+1yIVjhjfJBp1crp6+0L49Rj5N5JICiFd7j7q3YgM22GsYm/\na2r67zAu6WYMUC95MQUOAbtdYkGGuS0dNUamfumrF8IRFqHKVLtYXlKEYsOHLhyWDjGwePp05ro/\nQt0vqaToMMoqVPnOqryu0Py7s4ZhJwyx8QnqLQMvqdyGImUVlhcRLZZo1Q9dcbV7VLkSW7lqX5VU\nFuToaEuNMVW7o0GndlsJCm3Orzml/hhVv6dk1x80lDvLBdWZaaO+9Vp09Xw7bIq7+C6Gh0fCEhNd\n5XNK9fbqNUde+VevkeckQAJNSUALTtmLtVtEd3MJbxGksonobilRah6/HMtW8vNpw7q9bvcRHC6u\nO35x+4gQXN41DNauIbBEtkKE+j17Vr2YKjx1nU05h6z73Al4j4Nz7vX69J0RcXdi7+YVmDPViNLt\n6qt60XLGRAzs2Rkh/SfhmWVrlQWy7offpwfKzsGWt9gQb+Hh4Vi80xUn0szFth1j1bXIyHC8kle5\nTFJJ/lo8ODwEkR06o3v37mrrjKjIEIx9dBkOVxHONrw2znn/4i078dq0/ujQ2VV+xGIU5b1m/IKP\nGjjd1WoWBndwfg0ov/irb5MWV1vpzF6AZY9OQkhkFDpLvZ2lH+EYPu0F5BVVdqQoby2enDTcKNdB\nlZE+d+6gfLhDhmPxxv3mEZ/TsW3/Rjw6tj/Cozq4eDjrl5+V17YUuOsUlw4ZUwfj5U3JVuONqjrO\ngTNc0YJyZ6G7ZjBtJeSnzaa4T+sfgigX986dOyAypD+eXFmNiypbnzkq98p/ODaaGEpvmUiABJqH\ngBaHzWnxFnEvwlTabO5kHq8cSx9OV5xBjnIhmf9uPv7fy+rvR/YBr6K7bXgI4ru2QvJVYZhoPYOr\nupxG61BnGEEZj/570txjY3sNIxCUwhvKfhY3dBweW7wKFaWFyNmwFKnJiZXkcjMxa+Io9Okcif5j\nH8Sy7ZXCorIQj/yBQGTHWFhdHV34xnZUSlVnZkH2m65Y7ZWjKdm5GB36jcKCbFeeNdFdR1baRHQf\n8QKKKou7j6YPG4iUDKf3spGpFmiyV6iXCRuQbOavFZXofnJgT0xMy6xRQ3bGDFgnvOJcUbV8J+6z\njsLsTN1hc/FsTL+xD5bln/uHyJKdryGqz41IyzKNTTehflZShvXEg+86xX3FyYaNV1eDbYdRUbIF\nSYq7G6FVz1wuZj+5BuaPTfWdowqv/LNRXFr9iXD3iAckQALNQECLUBHETZmkHXFpEcGrRXBTtid1\n63b0XtqWraDwZ7zx0XeYtmgHnnv3X9ixvwTK7u2xO6EhQGx0KEZeGop7rjyLoT1OIyrslCGy5SXK\n1q1bo02bNsYmBibJo2+3R5Q+kxmkwruSf5glBgOG34W5b2x0ifAVmDcz2V0gN2sBJk7NqvJH332R\nBz5PIKzbEDzo+mIjd8FS7K2iP23IXqFfAUzFSCOUXQGedVunk7AitxCO3Ruxu6IQWXNcFWXPwKKN\nh2sZuxXpa3KwN2cDVtxrRbR1KgoLi/HNmnmu8lZk7S1EcWGhylfbITneizmmz3264vy35mK2S+ta\npy7CN6UVcCghmbthkXKMUunYqSofJBKnzsPmvYdQZnx1WYG9WXN0VXhr3Vfu44YdFGDOwBT3LXNW\n5KBUWY2MfqxJd+cvSPordiq2lgHO8e5do9tONI33EAqLC7Em1TXYxDnYq9gUHlL5H09F8YZ3XKEH\nlc97jnDfrdopRk5WOlKnXA1nFF5psv5zZKmV/yEcUm2P7V3dD8Y9JB6QAAk0MYHqgrSJm2s20a3H\nJXsR2vKhovB4GdbkHMbMV3dh1hu78f7OH1B2uvYPGx3ahmJIz1b43dUh+I++dlxyYQVCQxyGsBah\nHRkZCQlleMEFFxibHEcofxMR3+JKo8W3WMGZfItALY6ivtXJ5uqNiPBevX+JuLgeVZvsWPWUZ/5E\nwIIR09XSSFkSnz0Ta3b8DfE6PGTJF3jdZUxOTE8yfKxLtmepRZScad62JRgXH+08CYvBmMcWYs7K\nLEMMr3wvF6nDay4+k75tPR5IcL4jEDfAeWtETAQs3S901doLF/eMqRZNJwI9olyX3bsCvD5RB5NP\nxbuLp7l8wC2IHz4NG0tvw35bmDMUZsQArFT+0OKjXpnCEDdmJhYlzcZ05dmRtedb5coxwKuvdOW9\nlUdFW5ZXRidZkovHxsW7Lqp+jHwAe5fuQT+jn5l4Y91TGKBidMcY4+3sKhdVY7zdY7s7r0Uplxjl\nv6080Y3znYe/c93TC/36ut6zCItWdT6gel6ZGjZHI139qcnfNbOVFfOIBEig2QmYRWpTN67baop2\npG5J5v0p5Uqy+7sSbFHxtT//9ie56LVpcSXpGxOK+M4OXNja6UIiwllEtPZNF2GtN7F2603naeFN\nwe0VdYteNP+lbtGOtFzjdhTtz8Wm9auxcOFsZLssjOb+JI+9KsAiTZhHF/jH3YYkIclYkxSYlfkp\nHho6xnjhsGCLtrAC94/ub4D4bvtGN5CI099jf/73zigkKrdt+Eno6Km5u79Wbh4j3THg5SZr6ga3\n6HZX4jowv9un3pAUbyfvyXYM2kadOM/5oaDKDepDYm/Ti4lu0a1eJC1RL2GWGS8mHkOB1rL7jhl9\nr6vZKm2ok4O7PnFlJWJqkhbdlaXiRiUr63uGYan+7sAxdSHWuFiv8ZZW1iNHva4brv5XnxIMv/Cx\nWLLhSdw+LL7yBVUppNK5zFG9+uOsnv+TAAmQQL0IaJEthbWol/2BH0/gIxVTe/NXR1GuxLe3FKZ8\nSX7RPhRxylbRM0r9plILCGlfbXEbEcEtexHUIrK1wJZjna/LaUs3rd3eiLf8taAV3rbD+chetwKv\nzp8NT66r1qSZePDeOzEicSC6uUNTtPyEsQfnQMAyEPdOVXJODMgZK5D73BgMsCg3k1ddbibWebje\n5XIQ3qZSzc4YZsWM2poTjVkt9YptXy3nPE5VaG/dk8Srq30D46Fae0k+Xnn2vzDdgz+4Ln4uP+yV\nPAarnwNdk2mvKtUoSo1PFKZrDTyMHpCCFTNfwfgF8uk3Cyk3qk1516cumY/UycPdPCr7BDR0jhrY\nJRYnARIggRoEtOA2i+1CtZrkZ9+UYN0XR3DUpkM31bjVndHpglD0U1/sXaa2sJBK00CramLbLLT1\nsQhtT2JbC25phBZvN2qfOziXv8U+N4iGdshesBJRPcfXvM2ajHmzJuL2m4egd4wnlVHzFub4A4Ew\nDE1WPtYZs1RnM7H6ixcw4Mov8boYV1VKnjUGLscGZ4br/8SZczCyA1DzV2gZOvdPdAtB9001C7ov\nnd+BeYEdDzWV7ERKh4FqZDolInlqX3TCj1iQ4RqkvnTO+2M4Ke8hevmNcf7rYFowbr7yj/9NJv7r\nqRQ43xXNRVqKerFzwxIUvzG5yjcMMpQGz5HcxEQCJEACDSRgFttyq5yXnbZj17fF6t2ao8qlRLmS\n1JHatQlBnw6hKgSgA5bwyhe7tSuJFtTVrdtmwa1dTuQevWkLuTRPwV3HJPjAZS9/Rn2gd03UhbJj\n5hfjXPG7R6v43b09ya8m6gSrbVYC0deMwVTMUk4RwMo1WzD2xDbXi3yJmHhznLsvFad0SMEkpD/7\nGOJb6idEGUB0T7I//xaPDa/92cx7a45bdKeuyMUc5Yft7LYd/X8MR8p5aW/dix9xUvxsqvuqqL8d\n+hWIap4jbqYNO1C+6cMn4w21/U2FFpw7QUWXEQN4ZgreUG/JPjAgGj4zRw0bGEuTAAn4GQEttqXb\n2rotx/t+sGHTl4XKd/soTtu9u5KEtwoxopLEdVKOeMqVxGFyJdFuJCK4tbiuLrrNlm3tSqKFtt5L\nnyi4hYJ/pJaSFS1Kx9J3NJYuikLXaxO5YmWLzkQzNh4WhwkqdEjG7GzkpiXBqt+gTLobQ0yatusV\nA1WnnH7Gr75fgPnqZcFGT3UYsI32wtuii6vh7FlZKHik6gI79qJ8bP42HMMSeqtS+tuZVExzi265\nWS12c55quG1H3YssvLomHwl3VX5IkRby3nL6d8vx2IResmu0FBM3EvM/2ozdHYYZH5I6tXXGNTnv\nOaoP/0YbBSsiARLwNwJacGuxLfuSn09h055CrN+tIlGdOF3nkDpbQnFZ5xD8suMZZQhxupKIONYW\nay22zX7bWnxry7cuW92yrUW23tfZGRbwKQLBGU7Q0ht3TZuM4Vwm3qcexqbuzKCxE2o0kTq9qstI\nt2vHQgeTXJA0Ri0+kw+b6xvBclsR8rasxKOTpmFlvrYE16jSc4Z7JccsLH5zJ8rLi7Bzex5KKr9t\nrHpfRBzunZfoykvDmGmLkV9kU/eVYPu7z2Bg5364MW2LEU6w0gKchizXYjZ2234snnYTZmVXrbah\nZ71/PU29mOpMGRP74Zl382BTy3zapR8rH4V1eqbr6hyMS4huaPWm8uVYqRYf6j/pGWzJL3KGSbTb\nsPGNSmF/1DC5A+c0Rx757wTXzzFNAQ9JIIgJaJGt43xLCMCf1UvqW776EbPfzMXv1QI3b33yvVfR\n3T4yFAO6h2LygFCM72fHZR1OK9F91nAJEVEt4f4k7J/FYkFUVFSVTfLatWtnXJdQgVJWi3Jt9RYB\nLmJbb0E8XX499KC0ePv1jLHz50zAEj8SKrCgO1wglKRMGlLNoh0Rj6fWpCJzlJjEc9XiM/0w3UOL\n8/7f0x5ya8+y9LjCWIRHvCYyUgaqzVl2UW4ZpsV78iMHEu5LU/7ngw03ktyM6eintipJWbPFjtI3\ncaz63+lPMmNYT8yQhWdypaWqyZPGr3ylp2pZ95llAJ5fMRNZ450vos5KsiqHnZpp6d6Z6FYz28ip\ntY0qIRQrcGxfLnJVWKFhmR5asCphr9xMjHQOc+SZvxWbi3erZe9r6TizSYAEApqAtmzLIN3CW1m3\nvz5UqqzbP+Lj/GOwn3F4ZdDacCUJQT9l3e7RTlxJHG5hrAWztmRXdyORc13GbN0WYS0iW5K2auu9\n187wol8QCE6Lt19MDTvZ+ARi8dslKryJK1lTp2Og9tLQmWofO3IuDm1bCvNippWXVVi9OUtx+2Ue\n1FobLz4MMcOx3LSgjbO+qbi2b4Rx2EY34D5QGdEJWFKYgznJegVHXUi9EJq6CHvfTjacTCzxk/HN\nhnTnojpSxCW6E2emY5FerKa7xcN7kRZ46bG7sd7j5uPA5iXw0A0kJs/B5gNluCvOA0ijBg9t6DFW\ncYOx4I70LExNrDnWxKnp2PvxY1WEfYPnyCP/wWhfuSqPe7w8IAESCGwCWmTLXi9w8+NPJ7HikwL8\nPiMHT7y1R4UDPFq76FbCuKv6lXfDJaGYorwTR1xix8UXnDZEsghpsVSL1VoWtzFbty+88ELjXPLk\nml70RhbE0eEBtQDX4lv2FN2B9TyGqAfP+8e5wBqvT41G0MvXWadPn8aJEydQWlqKUaNGYd++fe5+\nZmRk4JZbbjG+gtKrUvEH0Y3nnA7sKta13R6mvsqr+wufclsJim1lUG++IDLCon5pRtQUsGoZYuV9\nUa/6oNwnikpk+cwwWKKjUdkFVYfKlnjcnnplV/0oNPoRadznOcKlHSVFhVDfjiLc0gExqq+S7FKx\n+trSXK+RF6byzJlGae//lZcUoVgaUJI9skO0Wgio9gq8teHtmjC3qbFWqDYsqg2Llzakt/WaIz2s\nWvnrAtyTAAk0NQH52yebLOFern4/yd+/9/e9j99t+V2TNd09sjs2JW0yhK8IXhHIZRUO7FRRSdZ9\n8SO+KzxRZ9uywM3lXUJwRRe1rkOrmgvcSJ21Wbflmt7Emi0CuzZxTaFd51T4dYHa/2r69bDYeRKo\nnUBYAwRnhCVaxa/2YN02V69+odahDStLh1nUSjR0qqUAAEAASURBVIqerMPyQaCyWPWjsPr0Q0nr\n6JhuNULuhXmo2FNe9TY9nUdEx6BbHTj0fd7a8HZNmMtW31SvOdKV1cpfF+CeBEggYAhUMysa1u2z\nDnz1fSk+/fonbNtXgjNqSXdvqU1YCHpFSwhAICaypiuJFtOe3EjMriRSzvySJF1JvFEP7GtBL7wP\n5+/E5zt34MDRU+qTdxvclDIFA2KcWMpLlOVNiZmYaE9CKbAfDI6OBEiABEiABPySgFlwu47bnO2M\nD7+y4fN/F+Hncu8hAJVzBy66EGqBGxWVpIOqwOF0IxFLtHYFMVu2zaJbBLacSzmz2BahLffrTbjK\nMVPwEQhe4W0vwAsp6kW0zKqTPu/6O5TwFmtbCTJu6IAZ6h219JxiI35w1ZI8IwESIAESIAES8AkC\nnsQ22qNb6FD0Cb8N7R298dHX3t1JotQCN/26hOKKzg5EhLpcSZQID1UiWgtpLbh1xBF9ri3fspey\nZuu2WWwLKwpun3hiWqwTQSq8y/Hu72uKbpmFCPfbZhZcOlq96KVeVHtl9S4lvIe32CSxYRIgARIg\nARIIZAKhIecQ68EstgWOOg+Vb6lDrkKvVqPQvVUiQkNaecUWofy2L1GrSfZXq0l2aCOxn5zWcBHH\nZjFttmrrY/N1s9jW1m1pWItsvffaGV4MCgJBKbztBf9EkixhqFJi6gpkzhmHY69MMsUklithSEia\nomLPzVAhzvbB9thw9zIlcpWJBBpOQMWqfjAB4xcMRk7pYgyoxYPJlr8RS979HFFxo5A8Rq9C2fDW\nGvuO8vxlSOg3EYOX5GLx5PjGrp71kQAJBCEBEaSyDew0EDN+OQOlZaWoqKjAWcdZI+JIDSRabP9U\nDMeWdcCwXwNR7dVb5O1xxh6HkIrLlQD38sKMqlAi9XVr57Ru9+1wBiEO7bvtfOmxunVbC+3q1m1P\nYluPR/pNsV1j9pihCASl8C47dsw5+So2cObccUaYMldOlYci+rLrjMVDso6plarUlVp0UpV7eEIC\ntRNQsaq/k/javVzrmHkoaduJ+/vd6FoCfhYKNxfikaGmpTU93NJcWRUVNhXZXPW+tNbI3M3VFbZT\nC4HmCFJFMVELfGY3mIAWqWIhbqtW653UaxJ+/vlnI9KJiG+J+qWfab2XRuTY0dGB8p7TcfDn1thX\nGglbRbhyCvGeLOFncWnHs7isiwMXKPVjWKYdTr9tOdbCura9diMxC249BvPPhfnYe494NRgJBKXw\n1sGLraMHVYkNXOMBsBXiO8nM/RrH1EKFsVTeNRAxo2EEdAhrt0dTldttWHb/QEN0T52Tin2z0zBr\n2H24vngVzmtRyCpt8CQQCWhRsuPwDvz35/9tWAsbe5zywtm9/e9FYk/niqoUF41NODjrk+dIW5gl\nnrWEGJQkItcsvCVPnvMzKgjJ97YQ7CsOw79trZVZWa7UnsJDzuCiiDL0tpxE53YSWVWtCBnSWonu\ncENoSztmoW323aYrSe1ceeXcCQSn8HYtH52bthkFc0cithZ++z/5wLDwIek69KXoroUSsxuLQHne\nMkzMBKwzV+Clx8ahcFAkuo+ajdQ3dmLjAwMaqxnWE2AEDOufKy7yO/nvIPPLTISFNP6v9jPK9zVC\nheK8/hfXu79Cp/gOsIepGYcjz0510a2Fthbd+lye8WMngT0/nsVXRWqlX70Mby2iO0Q5e3dsXY5u\nbUrRJfw4Witx7VwHI9JYqEaO9aI1IrTNYltEuBbc8oFAW7d1f/VeUMkxEwk0lEDj/3ZuaA9aoLyl\n11XGKn/ZavHwMQ9eijXPTla9UCZtldqEO5eyK9j+Gm5zLZOtIu5rI7lRhv+RQGMTsJfbUHbxHSgr\nm6L+QDh/LLuNfEz5Oj6s8spQYitHtGtBnMZp2w5biXrmI9WiQDWCkLuuqVjanhfqaZwesJbzJ6BF\nt8QndsYodr4YZndoZXL+begalAQx2hCLpBYjco3iQxPivr4E5JmRZ1f24uIhQlfEr+TJuXYz+anM\njvzDp/FZQTmKT3qPty1tW8LP4BcXnERsZKnyo3X+DLRq1dYQ2SK2ZTVJ2WQBHWnPKcYrLd/SD7PY\nNlxRXB8QzM+5+bi+Y2Y5EtAEzuE1Yn2rH++jhyJtnvPr0twFKeiu3mq2Ts8yBjTdeguG9w9Bz8Ep\nTmu3yl3y59vg/VUNP2bBrvsAgRIsTIhChw4dMOejwir9CbN/hUlRHdAh6hZsd342rHL9XE9sOxci\nSrUXFTkbh6tVkrd4nHHtur9tr3bFf06P6fc4/KfL59xTESsiukWs6K/pz7myOm6U+rUoknaZSOBc\nCWjxqsW3Wwi3icR36qWqlV+cxPyNx7F270mvojuilUPF2q7AmF4/4z9iSxDfsRwXXtDGWKGyffv2\nxu9V+d0qW7RaLVj2snS7Xr69Xbt2hhAXQa6t4NrqLcK7uvjW/T7XcfM+EghKi7dMe8Ijb2PFT7dj\nfFp2tacgG9nyBpkrpa7Yi8lxlN2aB/dNQSAaCROT1LsEWUhbtA6pIye7X+Qt+mQ1jI+E1kT0aER3\nJ/O3Pu/lpWJavK78MFYtdH4IHXd1j6YYbJPXefLkSSQkJODKK6/ESy+9hC5d1PrOAZhE+OpNvpJv\nDuGt2zELEkFLMRKAD1gTDcnTBzZ5nr4/dgqb9xZh679KUF7hfYGbsNAQdLc40KeDHT0uKBfnb+MZ\nDAmJdFusxXotAlqLadnrY+1aYrZuyzNs3vhcN9EDwGqDM6qJc96jMW7uRhTevR2r312HT/NysO9Q\nqboUhe7deyH+uiEY9Zv/QHw3im7+nDQ9gYG3JwOzlODNeh07iiZjuBHIpBybls82Gk++f6z3F4Eb\n2sXoQbh/KpCtwmouXLVDCe/hRg3l+z/CbOODZzLGXtutobX6RPm//vWv2L9/v7F99NFHSE9Px8SJ\nE32ib03RCcPFxCW8RRg3ZdLCW/vANmVbrDuwCGjBrT8syv7H42X47OtifJh7BEdLT9c54I5t1UqS\nKipJv05nleOTjq7kXCVSRLQW2yK4RVxrgS3neqvuTkKxXSd2FmhkAkFr8dYcY+ISlEVbbTqDexJo\nAQJhvW9EuvJ+mpGdjeUf5mP4XXHqtYPdyFTCWL1uiYm/6eexV7a8xYiyTvd4TWcmq7jbb9SIux2B\nG+9NBzJUnPrZy5GfOhxx6rfBV6vfMm6zpk5EvJ9+5rz//vuxa9curF+/HuJykpycjOXLl2PRokXo\n1s0/P0zouay+1yJGxLe4gTS18NbtyF63TWt39VnhuSYgz4gk87789Bns+rYYm5R1e/d3P+mite7b\nRYSgd3vlDnoR0K5VpTiXqCRabGvBXZvQ1mJbLOtyj3YfkUb186v3tXaEF0igkQgEvfD2xNFWUmJE\nOLeHWxBd48UzT3cwjwTOl0A0Rt8/UwnvBcj433V4Wglv+9Ysp5tJ0v0Y0s3zj2p4uDNAodWqVln1\nkHLVyqttW3sOXhg9cDRUi1At4t2cpxGXUIaVM5xuJlPGJXiozT+yevbsiQ8//BAZGRl4+OGHUVpa\nivfeew9btmzB/PnzMXnyZP8YSAN6KcJGi+EG3NbgouZ2tJhqcCW8IaAJmJ8LOdbn+36wYdOXhdii\nBPdpu/cXJcNbhSA2OhRxnYBfWE4bdTgt007hLEJai21t3dYWbdlroa2FuRbazjqcLiUyCRTbAf0o\n+uzgPP8199nuNl7H8te+hhU7DqLf6OkYN0AvUFKE16bdhJSMSifvmYu24dlpCcHsk9N40FmTVwK9\nb7xNRdtZgOzsV7D98CSc/keaUX7mvSPdPt/VK4iIm6z+KE2unl2/87DeuHNeEhYoF5dZWbvw/3Ut\nVnF+JKUiaUB0/erw4VJTp07FLbfcgmnTpuH999/HTz/9hJSUFLz55puGKP/FL37hw72vf9e0uDH2\nsmZ2EyZzW03YDKv2QwJaYJufkZKfT2HTnkKsz1WL0P1caa2ubXidLaG4rLNaUVK5k6jYJkaxELWU\nvLZUa7Fdl2VbW7WrC26pkGK7NvrMby4CwSm8bdtx/6gUyGuVc26o/Jp++zMTqohumYQF0wcjsu8h\nzB0eWF9RN9cDxnYaQMDtd52LZ574k3IDkXuTcOfQ2AZU0rCibt/ytBcw+5i84wAkKjHedC02rH/n\nW/riiy/G6tWr8fe//x0PPvggStS3WR988AGuuOIKPPfcc7jvvvsC5g+xIXjONo/wPt954f2BQcAs\ntmVE8q1LmXIl2fntMazb9SP2Hfm5zoFGRYSiT0cgXr0D3S7M7rRuq1VxzGJbBLdZbOtjyTdbt0Vw\ni7A2C27dAQpuTYL7liYQlOEEbfu+MEQ31JLxU/Ry3EqMp80SKa5SYirWbM7CTNe392kvrHNF+XZe\n5v8k0DQExO96nlF1tnKTMJ7GqckY2ITG57De18MZWTMLCzKcz//vbu/fNMNrwVrvuecefPXVVxg7\ndqzRC5vNZljCb7zxRnz77bct2DM2TQL+RcBs0ZZjEdtn1Lb34E/IWLcP0xd9jpfW7PcqusWVpG+n\nUIy5rBUmWe0Y3K3CEN0ijsVVROJrS6xtCfVnUetoSOi/qKgo92YOAagXwzG7mJiFt9RJ0e1fz1ig\n9zYohbdeDUeWjO/smuH9773q9KdVL7JlLZmDkUPHIG35EufV7w5SeAf6T4KPjC964BioYCPuNCf5\n+iZ2c4rB7RLeRCfrPNzc20/fqtRjqGXftWtX/OMf/8CyZcvQqZNyHlUpW73MKv7x//3fapl1JR6Y\nSIAEPBMwC25DbKsIOj/+dBIrPinA/Ys/xxNv7cFHe46iQtZ095SUAO6qopbecEkofnc1MOISOy6+\n4LQhirVFW2JpX3DBBYbYNgttEd+yyTUR5Fpsi+XbbPEWgW0W3Z66wTwSaGkCwSm8XVGIjCXjZQZs\nO/HMRON7fWDqY7gl1umBE9FzIFSQNxVfOQcHG3HxEqmSiQQ8EgiLw72uxZ3Uw4jxg/T7Bx5LN0pm\n7xuTjZVcpbLkWWPQ9C02SrfPuZLf/va3hvV7/PjxRh0nTpzAAw88gGHDhuHrr78+53p5IwkEGgEt\ntkVoy7FEzbGdPK1ekjyCvyzLxYxXduGdbYdw/KQO7VeTQNvWIRh0cShSBoTg9n5ncVnHCoQ6zhgC\nWYSziGiz2DZbt0Vsi3Vbrks52fRLlFpwm4U2Lds1+TPH9wgEpY93ZNdYZddWelq9Stan/1dKdGQ5\nv9ZXeYum/dptYSzZsQaZMmeJo3CpXl9EzplIoMkI2FHy0zGjdmvqBMQ1g/HZXnYCrhYx8ea4JhuZ\nL1UcExODt956y7CAS/jBI0eOYOvWrejfvz+eeuopzJw50wg75kt9Zl9IoDkIiMDWSQvvM+rdgb3f\nHzeikmxTcbftdXw71CYsBL1UVBJrVyAmssIUlSTEsFCLaJZNRLT219aC2uwyImVEWGtxLXtJWmDr\nve4v9yTgDwSCUniHdRuJF9ULZMNkwRK1WqDTs1VN18ws3DNAK+wivPH4LOcc9r+o1qgS/jDJ7KMf\nEbDlYFGaM6rO/b8d1Cwdz1m5SH0IVSnpQQwJdHN3NaK33norrr/+esyYMQOZmZkoLy/HH//4R0OU\nv/rqq7j88sur3cFTEghMAlpwa7Et+yM/lWHzniJkqzCA3qzaQkR5UuOiC4F+MSG4tMNZhDicbiQi\njuWlR9nM4tosurUIlzJmsa0Ft9ShRbbeB+YscFTBQCAohbdM7NBHViLXmon/++BTHDvZFpfffCdS\nVOxit4HRdhAbDUVuxYppI9xW8GB4KDjGpiNwyggcUupec616S/nvveh61yAVI+P1h8DqpRrx3J6P\nF12xu2dOH+H9A2bFKaNhZ+yTRuxDC1fVoUMHvPHGG7jzzjuNFy4PHTqEzz77DAMGDMDjjz+ORx55\nxBADLdxNNk8CjU7Ak9i2lVVgxzfH8OHuQhQUnaizzXZtQnB5l1Bc0dmBiFBTVBKX2Nai2iy0tQCX\na3oT0W22bpvFtnSCgrvOqWABPyEQtMJbVsiJHznZ2DzOlWUAVpm+cvNYhpkk0CACkRh0fzpSE6PQ\n1fOaNojqMQpz5sSj34jkZgrpF4lR8+apVSovQfJN3kNmRva4DulzUhF1lYr9FYBp9OjR2LNnDx56\n6CGItfv06dP485//jJUrV2LJkiWGG0oADptDCjICWmzLsLV1265eiPyy4Cds+aoI2/YV42wdf/si\nzpTjkm4XGCEAO0WIf/cZg6KIYy2kteA2u5LolyF1GbPY1tZtqUiLbL03Kud/JBAgBIJYeAfIDHIY\nfkQgDAPGPYABXnrcbehdeGyolwKNfSksFncpi259UljMADzwmLfe16cW3y4jL3a98sorhvVbYnwX\nFBQYy88PGjQIjz76qLGJeGAiAX8joAW3FtuyP3zsJD5SYltWlLSV270OSdyrux/6EnFfrkafaDtC\nr0l1+W47V5PUbiLaml3dwu1NbIvA1iJb7712hhdJwI8JBLXwLj+ch3UffYLdXxaguAxQkYxqpLKy\nY4jsNQGzHxhe6YZSoxQzSIAEAonAzTffjC+//NJwM1m0aBEqKirwxBNP4O233zas31dfreKhMZGA\njxPwJLbFV/uzr5UrSe6P+F4J77rShZHKlaSzWlGykwNtXnsCOPYjMGio4bMtVmqz0PZ0LIJbRLnZ\nuq2Ftllkm4/r6hOvk4A/Ewha4V2w9kn0HDW7fnNnvRwPU3jXjxVLkUCAEJAwZgsXLsSECRMwZcoU\n7N+/3xDjgwcPxsMPP2z4f0t4MyYS8CUCZrEt/ZLzCvtZ5BaUGJbtnO9KVMx67z2ODA9Rq0mGKlcS\nB6LbiN+23bBIh6goJCIaWp2xI1zF09ZC2+xOoi3bWnBrv226knhnzqvBQyA4hXfRRqRUE92JSUno\nrmKG1khqhTsM7AkPxvAaRZlBAiQQeAQk6klubq7hZiIL7Ugs46effhqrVq0yfMGvueaawBt0gI5I\ni9KmHF5LWG7N45JjvR08ehKb9hSqrQgnTtXhSqLcPXq0D0G/ziHoGaUikrj9vJ2uJIagljjayuUk\nHGfRWv291MJb9lpwm63bZsu2CG9JLcGnKeebdZNAQwkEpfC2HfzSHUJw6qLNeHrKUEQHJYmGPi4s\nTwLBSUBWy1uwYAHuuOMO3HvvvfjXv/6F/Px8XHfddUbM7yeffNJYUS+Q6Zx11GEm9eHBa2G6/Kvl\nWPWvVU3S01ahrfDXYX9V/s99jPqbQ2DqcWmhLfvin09j+9dHsT63ED+UKB/KOlKHtiEoP7sbh0o/\nQNEPJ5Dzg8MIDRgSqvyuZVPLu8sWGqbiaf+yCKEXA6HR+xDy8cMIbRXqvK7Lal/tENWo2tSdaNe6\nHeaPmG/s6+gKL5NAUBAITrkZ3sY5uYnpeG7aUO8h1ILiMeAgSYAE6kPg2muvxRdffGG4mTz//POG\n9ftvf/sbsrKyjJcyxTreEklbFi+LvgxdIrooVwLnSoNamHnq08vrbfjVETu+jm6F8aOjPBUx8rSA\nFIvlFR2ucLocKIHlL0mLUmHyyhevYEPBBiiP40btvrRxRv0bcvEQXHLVJW6rrmbXmI3pOTXvT1Wc\nQa5yIdm8twiff/uT+Jd4bVJWk+zbSc1nzFlYwk4jacWfcLLipCG0IYZpmV5BJJucq72Ib3RW0Uvk\nPLQEKFjtLCdlzZs61UnZ3g0u9111H66+6GojXKD0uym46Da5JwFfJxCcwlvPSvYOHCiHCqWmM7gn\nARIgAe8ExK97ngrBKEvOp6SkGH7f4v+dmJgIWQVT3FDEP7y5khYxsh918Shca7kWJ06cMBYDstvt\nbhFevT/XrX8cMSe+RLuoHsjo+7fqlw1xJGJbXAjatGljjCkqKsotmnS7NW70oQwRebKJ6BYXIUP0\nOUJgV/+aIkkbsom7hU6NwUn6rZMek+wP/HgCH+35EZu/OopyJb69pVbq+YhVq0nGdQZ+0a5CaeUK\ng8tZtSqlI1QxClPfaLhEtllwu48Nwa1akH2I6k+IYiiCW5LeSzf1sXHB+Z9+DuVMu5yYLvOQBIKK\nQFAKb0v8SKSqNePTcjORnJqE7fPHMWJJUD32HCwJnD8BiWySk5MDcTMRIS6RT1566SX885//xMsv\nvwyJjNLUSYs6ETMi9kQky4tuInREmMm5tn5X74sWQFKHp5dEJV/XK8Jb+/Hq6BTV6/PVc+EgYljm\nR/ZNmaQNYS9JOOn5Odc2pe+SZK+3wuNl2LGvREUlOYLC485FrbzV3+mCULWaJBCnrNutQyTmNoxn\nQuZWNqMNiZApnkRm4a2t3YbQdl3Tlm11WkVgexDbUkQnu3oZU7jI86iZ6L0uwz0JBAuBoBTeUEuT\nTJuXirRRachdMB63YAnSJiklrn5p1kyngQt7IyHO++IiNe9jDgmQQKATEJErwvv22283fL937dpl\nxP4eMWIEJA74s88+C4kN3pRJCyhtmRbxJ0nORWhqwVa9D63CnFbZUCUQLR5eLBdhJJsISBHdkSre\nqhbguk1fFk963NraLQsiyXFTJhGXwl+4uEWtarAhnAwhrO4x78tPn8Gub4uxSbmS7P5OuZLUkdop\nVxKJSmLt6oAlvNK6r+fN/KHAaEe8LwWNWWxrwa3Ftt6b265DcOuip06dMrjo9hvCQ9fBPQkECoGg\nFN7lea+pUIJp7jnMXpCCwQvcpzUPrOko3v0AomteYQ4JkAAJ4MorrzSWmRc3ExHiIvLE6v3+++8j\nIyMDt9xyS6NT0uJF9locizAWISVCWQSgtnZrEWfuRFgr56//Vsrq6enDgdQrm4glLerFMi51m4Wb\n7oe5bl85lnFXEd5N/IKoWHaFu/DyxLw2LuaycqzP9/1gM0IAfpxfhFMV3j80hKkXHHt2EOu2ik5i\nUQYjHQLQ9XxIn/Qm86c/GBgfFNSy74bwFiFdXXBLp80C23xc24Cq5Usb2uItbevx+fKzU20IPCWB\nRiMQlMJbxUJqWOrYxohd2rCbWJoESCCYCIio+ctf/oJbb73VsH5/9tlnOHToEGQp+rvvvhvz589H\nhw4dGh2JiBcRM3pFTTkWAS7Wbi28PTUq/ZUk5du3b++piCG8df0iuPUm90i+PwgnEXnCQoTfWbU0\nelMmacfMXdr2xkgLUNnr7YeSk9iulm3fsPtHHFMRSupKndupxW26hOCXHc+qv1PObzukTfkmQ+ZJ\n5lnmTcfalnMR3VLGEN1qb/xNFC8cEdVaWOu9dMB8LOcNTMJeuOgxNvB2FieBgCIQlMI7Im4ySouT\n6v16TVikhZFPAuqx52BIoOkIXH755fjkk08gUU8ef/xx4yXH119/HevWrTMW5BFh3ljJEFhKREkS\nQSXnsjeLHC3uqrfpdjVR93tyNZHyUp8k7SIgQk5vkufrSY9d9saHEBVloylTddHtqS1zn+S69Etc\nSXK+PYbNe44i79/HPd1WJS8qQlxJQnBFZ6cridQpoftkTrTYri64tfCW6zKv0q6Ulz4bVm5PL0ae\np+DWna4PF12WexIIdAJBKbxlUi3RdBwJ9Ieb4yOBliIg4uaRRx7B2LFjDev31q1bceTIEdx22224\n88478cILLyAmRr3x1khJBJSIKdmkbRFVkrTI89SMFs6yF//t2pJZfOs29L36Wm33+kK+MNBbc/TH\nU1vmedDXz6p+fX2oVC1u8yO25hejog5rfLgK59cz2rnATQ8VlUQnmQMR2XrT30pooa3P5bo8G3ru\nxAotm3Eun6H0lwGNJLZ1/2Svx2zmYL7OYxIIJgJBK7wrJ7kch/cfwP6Cw8AF6uvWEz+hdUwsLu3b\nG9EMM1iJiUckQAINJnDppZdi8+bNkBUvH330UZw8eRJvvvkmNmzYgBdffNFYkKfBlVa7QYSXCBrZ\na1Gl99WKVjkNDXFarOU+EWd1JSknqfq+rvt85XpziD4tMPWYzef6uKi0XFm2C7ExrxAlJyoFtL7n\n/2fvXACjqq71/83kDSSQQAICGpCHASVWrBeqggYfBamEW0FbiS0UC1yvFawPir3QNrZS9LaA13IB\nq3Br0FqoJbT+sa0SAUUoghoUohAg8hCSkJAEQh6T5L/WmVmTk8m88p5k1taTfR777Md3hpnfrFl7\nbde8X4wFSX14q4O1TlxJ7BFJGKRdLdsC2pwLjHPOrwne+PkZ1n96zfD9RuJH2wbA7ToWPVYFVAH6\ndTKYRcjbuR6zxs9yrmLpqsWcZVvx6ycn6qRKV2H0WBVQBfxWgGFn/vz5uPvuuzF79my8++67KCgo\nwH333WdA+KpVq9CvXz+/63NX0AzDDHhy7K6s85wJtPwBdb7Pr3qdDQTnjgC22ZWjnF1JjpyjEID5\nyD1zwacwvciVZBjB9tXkStI91BF/nF1JCJTNsC2QbbZuu8I2l+fnJtAtjfM52eSc5qqAKtD2CgQt\neJ9+62mKbLLEq8JrF07C2l2rUbR5rsK3V6X0oiqgCvhS4Morr8S2bduwevVqLFy4EGVlZfjLX/5i\ngPjKlSvxwAMP+KrCr+v+w7HJgu2wZvvVgBZqrIC4jlPO4M3QbSPXkZzT57GTFrfZ/UURbA73n8Y3\n28+Eh1pwJS1wc1V8HQb2sMdhFzA2wzTDthm0Bb65jEA5Q7aAtnypMr8upF5PfdHzqoAq0HYKBP7s\nmLYYuy0Hz5mgOy19HXZnH8KRI0dw/PgR7HtnA+ZQWG8jZc5D+qbctuiF1qkKqAJBpgADz3/8x38Y\nq11yrG9OxcXFRtQTjn5y8uTJIFOkkw+XgdsE3Qzc+aWVyNx3Fo+uz8bSN3LwXk6hZ+im18OAnlbc\nNtSK2aOBCYOqDOjm1wmDNAN2t27djFVDeQIsrxzKoR8552PeunfvbpThUI8SZ10g3NXabYbvTq68\ndl8V6LQKBKXFu+zQdkjY7mXvnMKTExoujpOYOARrPrkDV09NwPxMYMWqt5E+bYhGNum0L3PtuCoQ\nWApcccUV+Pvf/46XX34ZP/7xj1FSUmLE/L7mmmuMaCjskqIpQBUQ0ObuOfbD0RMDQm5G3vGxWJbz\nlc+O96C42Vf3teJqsm5HhTis2y1wJRHrtqslW0Hb56PQAqpAuysQnBZvkTllNf7DBbrlEhCPOU+v\ndhyWoKL+gu6pAqqAKtAqCvzgBz/AwYMH8a1vfcuojwH8wQcfNJabz8vLa5U2tJJWUEAs2wLdlFPg\nPvS13ICxoUtwd+RmfD1sIYWOjPHYWCS5kozsG4LvJIfg+9fW4Pq+VQZ0MxyzuwhbrMW6zRZt88aW\n7R49ejSwbou7CVu3za4lAt8K3R4fhV5QBTpUgaC0eDsVj+F1cj2nS+X59otZu3C0jFA82nNZvaIK\nqAKqQHMU6N+/P/76178iIyPDmIRZVFSEt99+G2z9XrZsmeGaohDVHGVb4R4Bba7KsR+DK5AYMhGD\nQ+9GhKWn10bI1Rr9Y+yrSQ6LrcWmja/iZ9u3G6D8u9/9zhl1RPy0Xf23GarFbUTg2p11W18fXh+D\nXlQFAkqB4ARvieCUuRkfF8/EOA8hvUvPXrI/rJRJGKnQHVAvXO2MKtDVFEhLSzMs3Q899BDeeOMN\nXLhwAf/5n/+JP/3pT3jppZcwZMgQj0M+e/Ys+vTpUx8ezmNJveBTATewHWmJwwDreAwJS0VPi+fn\nIHVHR9RhJC3dPpKikkSFVDujwXz11VeGfz/DM/tmu4K2ALjANvtoC3C7s2QrcIvimqsCnUeBoHQ1\niR72NaQazygT4299Cjvzil2eWAVy3noeg1KX2s/7sIy73KyHqoAqoAo0S4G+ffviz3/+swHbCQkJ\nRh3byUKanJyMFStWOBfGca185syZWLt2retpPfZXAYZt2fge2rfSOur9rGMxNuQX+FbknzE6/Mde\nobuqtgTh1iOYeEUp7hl6Acl9KhBprTGgm4GaXUkYqCW5TpQUVxJezIgnSaoriSiluSrQtRQISvBG\n9BgsWW1Hb2QvxfhBcbBMmIq5c+dSSK+puNYShRGT5juf9Oqn79GJlU41dEcVUAXaWoHp06fjs88+\nw3e/+12jKV5459FHH8XNN9+MnJycBs2vX78eb731FhYvXmxESGlwUQ88KyCgzTknx3FPXIlrQx7C\n3eGZGBf+LC4PS4GlLsRexuVvbV0Nzl7ah3/l/wpbcv8dobbt6BlaYcA2QzYDNPtts3Wb/bQZpiUx\neAtscxkGbjNsi7Wbrdpm9xK5X3NVQBXonArUf/3unP1vdq9Hz30FmXkPIHUphS3hlJWJtVn2XfPf\nBRv2Ye4o9TMxa6L7qoAq0PYKsOvIq6++aiy0wyEI2U3hgw8+wHXXXYef//znePzxx5Gfn28AOffm\n3LlzxnmOCa7JiwIC2lzEsR9l6UNRSW7B0JB/R7T1Ci832y9dsJ3A8dK/49j5LaioPu8sb7E2nCjJ\nQM0wLWH+zODN1wSuzWBtdh8x7zsb0R1VQBXo1AoELXiDbNhTntmM/O/txBtbtuK9XbuRfewcPcze\nGDx4GK6fcAsmp96N0YkK3Z36Fa6dVwU6uQKpqakYP368Adj/93//R5EzKvCTn/wEmzZtMiym58/X\ngx+vgsm/3I0cObKTj7p1uy+rSQpocx5iCUc/y79hUOgkXGa92emH7anlyppinCrfiSPFm1Fy6Uh9\nMV6HiH87pi0sMsywbrMlmy3cElubgZst4GZXEz4nwM2VCWRLzuc0qQKqQNdTIIjB2/4w45PGYS5v\nXe/Z6ohUAVWgiygQGxsLdinhZeYZrE+cOIEPP/yw0ehsNhsWLFiAf/zjH42uBdsJgW3JeXEb3u9V\nNwxXhNxJwH0XwizdvcpSW2dDfsU+sm6/hRPn36WyNfbyDNsm4KbIgkTyQERUhPFlSNxIxFebgZut\n27xJ4n2BbMnlmuaqgCrQdRUIevAGbCguyMfZM2TtjukGlJYjLKY3Egb0R7Sq03Vf+ToyVaATKjBp\n0iQjKsbDDz+MV155xe0I/vnPf2LLli2YMmWK2+td/STDNSfOGbZrampQWFZJy7ZfQGL5Elwe2cOn\nBBerT+B02Ts4VfZPVNsoliylXqH06ydbtgm4LSH0h/YtFJub3UssYeRiEhqGq/tdbVi62eItkyQF\nuhmu2cItiY87GrilD2N6j8Gn5z81NOP+iYbS1wY5aWok6j8NoMElOZBxcR4dHo3+3ft3+Filb5qr\nAh2tQBCjpQ0HtqzC4tT5cHh5N3oWaekb8Msn7kdiZKNLekIVUAVUgQ5RgK2p7G7iLT322GOYOHFi\ng8l83sp39msCiua8osqGDw8XYgct2X7gS4JnA8g9Q3dYXRVZw4vQu/Y0wmpKgW6DaPuhM5wfQ7NY\nrTlKCQO1RCvhyZP8XPiXCZkkKX7dYtkWGGWtBXg7UnfpA4/rv7/+3ygrKwNP4q2qqjK+rPCXluYk\nrpfHzNqwFuxy0zOyp3PMZh2aU7/eowp0dgWCFrz3PD8NY3k9eC8pY8kMZCzZi+xLyzFK4duLUnpJ\nFVAF2ksBDje4ceNGr80dOXIEy5cvx8KFC72W68wXBbJ5DLwv25EzF7Djs7PYeagQldXe4dFKzt69\nrGXogwJE1/CCaXWGNdsSGmkAt4A252K5ZqCU87zPvtoSuYRzcS+RMgyaYukOFOjkfki/uJ8yDv51\ngPvKuVnfpr5ORC/WRvTiegNl/E0dj5ZXBVpTgaAEb9vptzDHBN2pi1Zi3qQxiA2vom/7QHHuB3h5\n1kKHJXwF0tK/iX3PTERQitWarzatSxVQBVqkAK9qyYvq+JN+9atf4fvf/z769evnT/FOU0aAUECb\n8zPnL5EryTlsy8433Ep8DSYmtAqXhZ9HgvUcLDVVhkuKNaKbAYYCzAKkDNwC3ZILWEoZnkTJ1l2Z\nTCnlBHB99acjrnPfzP0X6OZ5Ai0BbzPQ85cQ1kQmlwp8K4B3xBPXNgNFgaBkyUtn8pDteAIrd+Tj\nkXHxDZ/HuHGYMjMNax4YgHkZQPbuPLCXX2zDUnqkCqgCqkC7KvD0008bIQT9aZRdBxYtWoR169b5\nUzygy5hhmzvKbhAVVTXYd/QcWbfZlaTEZ/+jKYT25dFVSIwqJVeSiw64DCfYjjCsvALLbKHlfd5k\nX0CbwZH3BSB5X+CVIZPhW+6RMoEImQLHPEaGYtaTz/EYGLyb62YiD4HHzhvXzXXyJvqKHpLLPZqr\nAsGiQFCCNy1IZk8pqzHHFbqdT74/Zv90HYH3LIrxvRXHyuYiViMLOtXRHVVAFWh/Bdh95Ne//jW+\n/PJLY8vLy3ObV1dXG53j8IO8BP0NN9zQ/p1tYYsC21yNWLd5P+dkCbZ/VoBdn59Dlc0RZYQvuEmh\nNJdxcJwVw3vXol9EOViXmhorgaU9mokAtcAzQ7OAs8A3X2OIlJybMUM17wuky/1y3U2XOvyUAK+M\niaGYz/EYBLrNeje1w1wXb1K/aML1B7IuTR2nllcFmqtAUIJ3WFiEXa+s9/AJAfUYD0BdfPqEvVzy\njWQpaa7Eep8qoAqoAq2nAIPSsGHDjM1drQxNZ86ccQJ5cXGxu2IBe06AW+CP84LSCrJs5yPrQAGK\nLpI/oI/UL8aCpD4WAu4ahNCkSXtddiu2QKHAMueu+3JOQFFygUpuns9xkvr4mOFcAN1c1igYYH+4\nf9xX6T8DMlu6xdotz6Gp3eb6OEm9okln0aWp49XyqkBTFQhK8I5MmoxlKcDCrAyMfXwSStfc33hJ\n+IJt+OFtSww90x66HS7OKE3VWcurAqqAKtAuCjDwXHbZZcY2ZsyYdmmzpY0I5EnO8Hex0oZ9R87h\nbYLtI1/ZQ/p5a6dnFIO2BSMT6tAj1D45kLWwWO0Wa4FpzhkCxe9YLNx8jq8JQAs4GnU4YJLb52Nz\nkuuSC6SbywTavoxB+ipjFv0lb0m/RQ/OpR1ptyX16r2qQGdXICjBG4TRs5auxMKx84G1M3AzeXC/\nMPcGhDt+nkXVCbwwfrozzODNw4D9e/bA/uMtUIVu+NqYUY1hvbO/GrT/qoAqoAq0kwJmuON93mpq\n63CIXEnYuv0BTZa01TjWdPfQp3CKoz04lqzbZBkZ2MNm1CHAZwZt3ncFbTNwi1VWAJFzTgKKknvo\nRqNyvsp7qqc9z0sfOWfteczmZ9IafTG3wfXJcWvUrXWoAp1VgaAE74qc9Uhg6Hak7LXzMH6tHDXO\n5912vcvJFOwu3ebRRcWlsB6qAqqAKqAKOBQQuONcNo5KsuNgAd79tADnfbmSECgOYFcSgu3hcRT+\nr67aADqGOgFo8SuWnKFbQFss3mLdZuAU4OY6BA4lb8qDa849Tam/tcu69tf1uLXaa6t6W6t/Wo8q\n0J4KBCV4t6fA2pYqoAqoAsGugBm2WQt2JSm7ZMPeI4V4O/ssjueX+5QorpsFw/pYMTK+Ft1CHNZt\nWkbS6vCrZpAW0OZcYNsM3ALbDOjuYJs7EYyQGIxj9vmC0wKqQBspEJTgHZk0E6VFqbRYfHNTKK3G\n1dx79T5VQBVQBbq+AgLbPFKxbNcQcH+aR64kB/Ox53AxuZZ4X+Amgj6hhsSFkHW7Dpd1Z9i2Oa3b\nDNGyuYK2ALhcF9AW2OackwCn5MZJ/aMKqAKqQBsqEJTgzXpG09K+nlIZRwEgZWxh0YiNDFqJPMmj\n51WBLqWAGRBbe2DBCHSip8A256fPldtdScidpLRcZsu4V5ucPTCglwUj4snCHUvxpWl2Dddhsdij\nhghMC1y7QrdcN8O2ADc/D3kmkrvvhZ5VBVQBVaBtFAhaqsx5az027j2BEZPnYdpoiVlSgPVzb8es\ntbK8DrBg9W48N3eMrlrZNq8/rVUV6DAFGOYoeBqOnz9ugB13hM+1NAnQcT6o1yCQB7ET9lpad6De\nL7pxLlsJAfa/aILkOwcotGHhJZ9d71l2Bld/thUj4myITJvtLC8AzUAtsG12I5FzfJ3LSnl3sM2V\nyvNxNqA7qoAqoAq0owLBCd5le/DQpFnIIqHTb53nlHvPs/c1gG6+sGLeWEQNO4VnJvR3ltMdVUAV\n6LwKmCExfWc60t9Lb7PB/Ozmn2HJOHtYUm6kK0GfWUceGx9X19Qi+3gxRSUpwIe5xaj18UUmKsyC\nIb2tGNW3DnEPzkCdjSZK3j7F0ElAWsBacoFuvi6bGbYFuM16dyXdeVyaVAFVoPMqEJTgXXb4YwO6\nkZyO2bJyJcH40oWM4pRSFmHrL8bi7w+nYgUZv5c+/w8smjBTwwfa1dG/qkCnV0AWCjlXfo6iYlA4\nNUvLLd2uonC9heWFxtLkDIO8dfYksM3j4H3ZThSSKwmFANxOriQXKrzPnrHSLwEDyZVkZIIFg3va\n6PcA++qTDMchVG8o6RTarZtH67YZthm4+T6Bbd7njZPkxoH+UQVUAVUgQBQISvCWJeOTJ9+ABMeD\nyP3ry4643cnIXJeOiYmhuPX1dVgxYhZw7ARF+ia/8AB5aNoNVUAVaJ4CAooM3rw8dk1NjQFohJDN\nq9DLXQx+XD+3w7AoICi5l1sD7pIAt+jHefGFKuz+opBcSc7idFGFzz5zVJIRCRSVpE8dwq31UUnE\nPSTUSsuW03eT8HBatj0mxm1UEikrX2RYS9mkA51RX+m75qqAKtD1FQhO8HbM7cleugN5z0zEkLL9\neHaGI5D3nMW4i6CbU+Sg65FGeUb2Ppwg8u6v5G3oon9Ugc6sAEM3A3E1LZjFeVsmaUfgkMGxsyQz\nbHOf+bjKVoNPjhZje04B9h85T19XvH9h6UauJBwC8GqycMRG8CRJe1QSBmeBaHEhCQuxIJweh3FM\n4M05f2GRcgLbYt3mPglkS87nNKkCqoAqEMgKBCV4R/VLRDI9lWwsxdBrDyKFbN0OJxOsnvtN50TK\n4r1bkcFPL2UShit0B/LrWPumCvhUwGytZSCuqqpqF/DmdgQeBWYDFRSlfyymWa/jZy9i+2dnsTOn\nEOWV3r+shJAV+gpaTXIEuZIkxpDPtksIQNaCgZrBWvy1DdgODUEYGUVCw8jVpHt3p2YC2vLlRbST\n3OeD1wKqgCqgCgSQAkEJ3qH9J+KFZakYvzCT6LseurEgE98fLYRdgFd+ttD+qK69TN1MAuhFq11R\nBVqiAAMlg7e4mrSkLl/3mtsxQ62v+9r7uvTNDNsFpRUUlaTIcCU5c963K0mf7lYKAQja6hBmqXcl\n8XuBG7J4h5CrCYO5lVaadAVuV024rwrfrqrosSqgCgS6AkEJ3vxQxj25CdnJGXjt7x/gXHk3XH3H\ndzBr2hhEyhMrO4Fthhk8GRvn3um0gstlzVUBVaDzKWAGS4ZidjtpyyRuLeZ2AwUW2U2EpyGa+1ZZ\nXYP9uUXYeagAHx0v4Yte5ekRYcFQciUZRa4kMWSuFhgWt5AQsmIXVxUjxBJCft3hRh5mIRcSC0Uk\nqaOtNhRWG7md1BFsdwfIzRuWkIuwXDxttCtaueZ80UpxvQdED2hQzjjQP6qAKqAKBLACQQvevELO\nqIkzjc3t84kejc0+PnTc3qcnVQFVIKAVENBkKOb9tkxcv7TT1m35Mw5PffjidClFJTmL9w6dQwXB\nt7fEkyATY+3W7SvIlYRiADonOLILCUO3+Gev3LcS6z9bT2ZsqpE3DuzCORM/73Mu292OfWwCVtHG\n532kFXeswI++/iNnKQF05wndUQVUAVUgwBQIYvAOsCeh3VEFVIF2U4AB1BOEtnYn2rMtT32XsUpf\nOK8hwI6gG04VXcKS1w54utV5PqEHRSTpa8FVcXVksbZbt9nq7OpKwvAtW5mlDNZIK2qt9MuCgLbk\nAtaSc0vmfWfLnnfyL+QbbkNiYfdcUq+oAqqAKhAYCgQ9eJ/O2Y8P9+/F8cJKVFRE4PZZszE63i5L\nBS0dX0aW8fhY8fsOjIemvVAFVIHWUUCAtHVqa1xLW9ffuMX6M9K25Gx5Z2v2vtxz2HmwEFO/uoBr\nqHhNjWerP7uSDKcFbq6mBW5iwurjczPoyiRJ80RJ2WfLswHD4QTmtPEKoQZ4c/cYrl0B2/WYy/mR\namprnBNkxdotuR+3axFVQBVQBdpdgeAFb1senp81CPMzGmq+7JZ7Cbxj6WQx1t4ah/m0gM7KfUV4\nZDSf06QKqAKqQOAqIJDNPeR9Oc45WUJRSQqw6/NCCglo92svGrcA3SsvoDLMObPFGBjH0r6ydwiS\naKLk5T3qQwAy0LrCtmtkEgZvLsftMug7XUu4Zle4dj02Wm/aH54gy2EhuU0GfYXupumnpVUBVaD9\nFQhS8K7Alv9sDN0sf2SYPIRoDJ9MQQezs/HSmx8ReE+QC5qrAqqAKhBQCghgC2xz/lVRuQHaWZ8W\noIgWu3FNJ+MGNzjVL8aCpD4UBpAWuLHU2ctbyJWEYVuA2xW0jTCApnjbDL+cGIg5jKLT0i1G9VaA\nbXOnpR3unxm8FcDNKum+KqAKBJICQQnetry/IdWxXk7Koo3ISJ+Gcy89gOR5ZvN3KMakzqb14ucj\nO+swyhZP0JCCgfTK1b6oAkGugBm2WQq2MHOM7Q+PFGIHuZIcPFnqU6FeUVYM7Q1cQ1FJuoc2IwSg\nw5/bcCtxQDf3QyLGSB8bWbt99sy/AuZwjWxt16QKqAKqQKArEJTvVJfOnbM/l+R0ZDwzDf3pyHGm\nwfOKHXkzUulM5rl8FFGunt4N5NEDVUAVaGcFnCBL7fI+b7W0HTxRQlFJ8mkJ9yJU19hdSTx1LZzi\nZQ+OI+s2uZIM7OGAbXLVYCsxw6tsvqzbYgkXSzPfz9AtfeT92jrqC1u5xeLtqVPNPM/gzZtowbla\nu5sppt6mCqgC7aJAUII3HO4kyZNvMKDbo9Jl+TjGF7O/wDlaMj5RydujVHpBFVAF2k4BgVkzYJ45\nf4ks2wV499N8nL9IYf28JYLiAexKQrA9vDfBaa29vNmVhEGbodu8miTv8zm+Ju4mYt02AzfDLveN\nzzEIczJDuLeuteQatyntiEYtqU/vVQVUAVWgrRUITvCurjR0zV66A3nPTESiB5Vzd/2dlpWnlHoz\nhil0e1BJT6sCqkBbKCAgKTkD5oUKG/51uBBvZ+fjeP5Fn83G0WqSQ8m6fXVCHbqFOKKSkPXZNQSg\n+GqboVss35wzUJv9qBm0zZZl2Ze+ci77PjvZwgLt2VYLu9ry220VKCgmKxBF24qmIAANp8W2vHqt\nQRVQBdpegaAE7+jB1yGFtM3CUkx5dDi2PjeTjvjNDIgIizLyvD3r8e3pK4x9REeLkdx+rH9VAVVA\nFWgDBcywKkBZQ8D9aR65khzMJ+guho2OvaUIelcfEsdRSepwWffGriRiufbmSsKwLaAtlm3OOQlk\nS+6tL+bxeCvX3GuE9829tRPeV4Bn70zAwix711NXZ2Pz3FGdcBzaZVUguBUISvBG7DgsXZaCsfQO\nlr1iFgbQJmle8l14PTkLWYap23523U+/rZYFEUhzVUAVaHUFBFAFtjk/fa7ccCXZTu4kJeXeXUnI\n/owBvSgiSTwt4R5bQ1H8xJXEfQhAV+gW67YZtgW4GbAFsiVvdQG0Qp8K5G1Z7oRuLnwsrwD8G0Zw\nfoj7lEsLqAIBq0DQ/psd8+SfsfH8PZi+1GE+cD6ihtC9aOMhzEzSH/Sc8uiOKqAKtIoC7mC7lAB7\nzxfn8A75bX9Z4NuVpGeUBSMTQjAyvhaR1voFbgSgGahdIVvcSuSalJVcQNsM2eb9Vhm8VtI0BWw5\neCZ1aYN7sncfxiVotK0GouiBKtAJFAha8AZiMe2Zbcj/3h68ueUf+ODAPhw+xeG3YjBgwGCMuvkm\nTLr7WxjVX6G7E7yOtYuqQKdQwGzR5g7zsY2ikHxyvJiikhTgw9xiI0qJt8FEhpFVm1aT5BCAvSPr\nF7gRP2wGaIFrycV3WyzbnAtoi2W7Oa4k3vqp11pPgf0v/Qr2CLjJSE7O5uUlyFdyP45XAKMcH1Ec\n05y/ekXSs3Wb6DpNEUBkpIfrbm/Sk6qAKtDaCgTnv8Di/Xj2uTcNLZNnP4GZT47BzNZWVutTBVQB\nVYAUMMM27/MkSU4nCsmVhEIAcmSSMiYiL8lK7h4DyZVkZIIFg3rWIMSHK4mAtoC3ALcZtgW4xcLN\nzatl28tD6KhLxTvxuGONieT05Xjj1g8xdPxC6s1hnOepSQTeZQfWICZ5ntFDtystl+3B1JixyKQS\nczYcwpr7k4yy+kcVUAXaX4GgBO+yY+9h4dIlpHYyMucuan/VtUVVQBXocAUYMkOtofbVFdugN7xq\nY1iIPXYpwzaH2Su9ZMOew0V497NCnCQfbl8prhv5bZMrSVJ8DSIt9QvcMEDzJkAtoC05A7dck7IM\n2u5gm/ugwO3rSXTUdRveeu5hCgTAKRnL5k1A4rnDjs5k4aMTZRgXH43o3ol0lSLf0jb/lZ14aPSU\nBr7feVl/NKCbbxw9agBnmlQBVaCDFAhK8K7XujdiY4JcgnoxdE8VCBoFxMo7a/gsDAwfiEsVl1Bd\nXe09JvRbb9BKW2fJv6MvMPHbjbSSOhluGXqjIqNwe+Lt5EpSh4+PFmHP8TJk510gCzitKEPQ7yl1\nI1eSYX2sZN2uQ1wEw7bNAGOuVyBaLNmcm2FbgFvKCWwLcHObAtmSe+qHnu94BWy5f8KkpYzTQEr6\nC5hIcdgROtK+sBvtfpZzikiarNf9b8XCNGBGBp1c8TKy06dgtDMEbhmyXnZE6MIiTBzlvECFNakC\nqkB7KxCU1FkfTjAL42c9j+ObHkFiUCrR3i83bU8V6HgFzIDcN6ovpgyYgosXL6KioqLxUufm7n75\nDnCcwHtwL4LvbzqvsPsIJ65XQDciIgKllVbs+rgSq08fQBUFJA0JDYfFGkLlnLc6d6x0LjHWStZt\nCxJjqmFxwDbXyRAv1m13oC2wLeW4rIC2jJVzTpI7G9adAFagDBmPzXD0LxVLF4yz70f1cq6ifPhk\nieN6JO6YlQ5k8C+5mdj0/mmMnshrMlMq2Is/sI8JpdSV0zyuW2EvoX9VAVWgrRUITtykcIIrN6Qh\nmc0DmfMxKOwzbNzxA1we7k7uKqDnEIxJcryJuSui51QBVaBTKCAgymDKgMrWYoZkdgXhc+wOwvsC\n0w0GFUJxrJlfOe/WrUEZKX+hsgaff3kBH395FucrLQiL6IaQ8CiEkhXbuNfOv85q+9ACNyP7WnFV\nXA3CKSoJ18OhAUNcYJvh2hW61ZXEKWOX3Cne87+Y5QDmtNVPY4wYqiMH4eZUYmy6lrXrY1qBYowB\n4vE3TkUaloA+1bB0w7tYMvF+Iwxu7va/OlxVgLRUdkjRpAqoAh2pQFCCd8WB9Xbodiq/FtPH2+eM\nO0+Zd5JXouiTRygOiiZVQBXo7AowfDN0M8gydDNsC4QzdPPmNoWF0JKPNFkyzIo6WlRLUpWNYPtk\nKT4+fh5ffEUhAKl+K/l2h1L9nFvZyk1QbydvYnaC8OF9QzCK3AZiwjkqCcE23+P4MiBA7QraAt+u\nlm2+jzexZrvm0k/NO5MCp/HCHJ5AyWkOfjp7lH3X+BuG3oMdHt2ZecinebnR/EkeOQqz0lPI6E0e\n4Rm/x67f3o8J8WV4O8PhZpKyErfpT7smHXVXFegYBYISvJu8DGXviAYTVTrmUWmrqoAq0BIFBEgF\ncNnaLdDL+xyOjY9lk7b42EgE3hYDvGlCZs+eyKM423uPnMO+3POorK4xwDcsqodR1IDtEHIRIfcS\nayhZq2l/UO8ww5XkihgqS84n3B/eBKQFrCUX322+zuf4y4FsAtpSh4xNcum75p1TgdxNz2GJ3bUb\nCzY+iaQGn9SRuCppMA2MCxxE4SVaqdTxPfDG6d8DGLzJxv369jxM+GYuVjms5nMevFONR53z5aC9\n7mIKNPjn3MXG5nE4kUkzUVqUasQ89VjIdCE0imaNm451VxVQBTqvAgynDLCceJ/BlqHb7GJihm/Z\ntzrAu7iqBs+9mYfCMnJD4xQahTB6J3V6kRhATRZosnQnxITRJMlQXEUTJSOs9tUkQ+i8laKpcB8E\nqhmsBbQFvPnHJrITAABAAElEQVQab2bYdgVubj7QYdv5xYU7q8m3AhUH8PPpMhkyHQumDWl0T79h\nN9I5JupM+qWFnE0cEyYjk+5EOhnDGdrXZr5Frid5Bp7T1Ezcd4eGEGwkpJ5QBTpAgaAEb9Y5OlYd\nRzrg9aZNqgIdqoBAKgMsJ4Fwhl4zeIu7Cedszf7oWBEsCaOB6jicjb6M/Lcpckl4pMtY7OjdI4Ki\nkvSm1SQJtntGsO+43RouoC1AbYZtbl8g3AzbfA/30Qzc0qiMRY4DMQ+xhKCG/murFGrpeh9he9Yu\nNvy0WbNlOx52Oxkyuj+tnuRI+QUczFtMQ/0x/VHy9J6VQe4m8zCeMiOlPYgbOSKKpg5ToPjAFryy\nNQcRSZMwe8qoJv2KXnw6F2dLqxHTdyj6xwbya96G/Vsy8HZOKZImPYApo5Sz3L3gAvkJuuuvnlMF\nVAFVoEUKCLAyzPImwC2WbbHQfn6qhBa4KcTuw4UE3+T3nUQRJhxGQzu213cjLMSCwXE0SbIPMKAH\nRSWhGN52WLZbrMWKLbk767ZYtjkX0Oack/RZ8vqWA3OP+8nbnKQ5GNptqBExxvhVoY4mrtba3Xma\n2nOjTgr/YiV/H9aRwzXeNfguo53OoovPMRdsw6L5Dt8QslJ/44pS5OYWudxGH9sl9aeyPj2DxRPq\nJ/8nTZ5BMb0zHJZue7lFs241JlrW36V77atAAV5MS8VC9g5akGSAt//tl+GVSUMxn+5NXrYbn9CC\nf4GcTr45Cwt5ytzCEpyqW4z6V2Yg97p9+9bFwduGnLf+hE+7j8a3xiW1whuPDXn730bW0Vh8Z9qY\nVqivfR+2tqYKqAJ2BRjUBLAF2vj4zPlL+CCnkBa4KcC5C1VOueyTI52Hzp1+MbSaZHyIEZXE4rDs\nWsjKKxAtVmyexCnQLblYvqUsQ7YAtwGZ1EdO0j9nowG+I33nsST2SMQ9A+8xwjVWVlZ6jxrjY1xc\nL9fJekVGRqJHjx6IJjdAs2Y+qgj4yzlvrnNGIGE/7fGDBvnuc4XdhclZMP4mkNEbbPS2pzRMu1Hx\nR9ToiLxs/2t26KavRBsX3NUkazf3N2Yw/SHwHhxpX5CrI8bgX5uhuOvJjeTnNJ2KL8Gm/Q/jkdFq\n9XbVrouDdxn+sXCG8U2RfdzSVz+EqRNvwYjE+Ca88G0oyDuED97ajJdXLUEmf2OlKCepCt6uryU9\nVgUCXgGBbcnZ2n2JfLb3klV7x8FCHKToJJIsPJPSTYqJtGI4Wbavpl/7e4TyhEyycBtQ2NhvmyHb\nbN0W2OZcIFKAUoBVmuTjzpZkDDw2HiOPXcI1MjCbJ7A2dWxSN9fLdfImLjpdBb7DetALq4npvluu\ndLkjGpOdMb3p42oBWcBdvaJc7tDDtlTAhqxX5tsbSHmoy0eWCR1yC1am0AqqNMfX3Sqqbal0Z6m7\ni4N3NK6ZRgFPs/mnuywsmUeb8WSSkTZnMm74xmgMH3I5BvTqSWF57d8kq8vLUXK+ACdyP8X+D/bi\nzbUNf7Lj25PpZ70oox79owqoAoGugEA295P3eaul7eAJdiXJx+4vilBd4yGEoGNw7EpyZZyFlm4H\nBvZwxNsmMGYYNMO0K2ibrdti2eZcQJFzTgLZkjua7ZQZj4HHyGNnOJZwjbwyKO+bn0dTByh1M3Cz\n1Zvr53a4va6g3ZBpy0mf5U2VpVH58gsU6sSRZj8wpgmGJrlL81ZToOIQNjrmyqY9eEsQRJaJR8r3\n6CeXLPrJZcVGHFo6BaP0i1+Dl1MXB+9QTFi8GaWzD+DPL72AWUvY8YhTNjLW8mY/8vdv6pxleOzR\nWRjHn76aVAFVIKAVEMAT2OacXUl2HCxA1oF8lJS7/ETvOhoCyAHkSpJERshhvetgJcs2J7aEC0Qz\n9IllVyBbrLAChHydAVs2BkTZ7PV1Psu2IYTLHwFfHqeAd1RUlDFu1sTr4kQudXk65Da4ftGc62Wd\nXbX1dH9wnC9A5oqljqEuwOTkYP6p3wZbBQU6D42k10zjp2+j1Wo9XqNIR3QnIt3d2Lgqj2cK9m51\nTpZNHTPIfTlbGQryy1BNsY6jaI2A2OimkqoNxQX5uGS8RYUhOiHeHtvdTWuuYy4rLkAZ3RgWFo34\neJmk6+ZGPmWrQEExGSqoHW/lh429jQoTeNO2ee9vMWqcMhPLJ8n9b6lytYvk0f1HYebiNairLsWR\nfe9gdfoCpKb4sYJXcgrSFqRj4zv7kE8zijeveVKhu4u8JnQYXVMBgWyZMMmwV1ZehXeyv8J/vfoJ\nfrzuY2zec8ordPeKsuLfLg/BrNEWTL3KhqTeNnBsDoZJsbR2794d0fQByVtPiukdExNjbHzM13hj\niyxvAuQMi1wHQ6KAt8BqV3oaPCYBb7ZId6NVPlkX1oi1aunG9bB/N9cr+nJ7XVHL5rwuKnL+afzM\nz/emLPsOhrgBzubU2xnvObBmGsLoy19Y2FzkMEWbUs6rcx3XpmI/B4YxpbIDa4x/t1Fh07DH5Zqp\nmF+7Jz7aZS+XnI4bh7gCdTHeenYuLGExSBgwAAMGJCAuJgrXTn0aB4r9qb4M29Y8hWstYYhL4Pvt\ndcTQIl0PPLUeefS9wpyMcbEes15FXt5OPDrBgpi4BOO+hIQYWCY8ip2nXW4yKqB+Pv8o9TMKCY52\njPLXPoBX95w2N2HsRyalYJEDsTZt3ed36OZGFXXRE8H1TzI0GkNGTzC2uYv5y1sx8s+W4tKlSygp\nKTG+bXbr2Q3douhDNC4O8U3+1tlFXyU6LFUggBUQyzZ3UcC7hiJnfJp3nqzb+fjX4WLYPK1G6RhX\nBFmzh/SNJFeSOlzWvaErCUMdQzNvDNFiZRWg5lyuC1gLXHPOSaBQckezXS6T8fG4WRPJ5YsQD9j8\nvJoqgNTPOddt3vicXG9qvV2p/MEtv3cO58F7rnXuB+NOzGU8K5HTWmzP/m8kjRaLbhl2b5WfvDPx\n9scFGG2yyh7cutV+G4VpbNl0xjJkb2NXV0rJQxBn33P+3fPsPZi0kBc8sqdkgtVsmkeWnbkEW794\nGKPGePu1ohjrH4gzTaKVWux5xtJZyFj6BY5UP9P4y1fGDAxig7RrylqB8QMKsa/0FTilQjHWTI3D\nPMcw+JYUMlxmZXFHMzBjbAZO7sjHkyb9QCNNZOm5yO4vUIaJQeBi4yqm5+OgsHh7Gn5oZCz6JyZi\nSFISRo8ZgzFjRmMU7Q9J7K/Q7Uk0Pa8KBIgCAtmcM9ixdftkwQW8tuM4/nPth/j1Xw5h1+fnPEI3\nYRoGnvoEd769DD84swm3JlYZ0M3wxtAo1lojeobJYmu2bpstrwLkfC8DuFhhGQ6DCQplrDxu1kC+\nrLA+vLGuzd2kDrPWwaavr39+I2e9iH279yH70Cnc28jC6uvurnU98Rt3U1gFe3r97YP1gys7jHdM\n4Llh64H6aziN7RsclEkhAEcKq5tK+L1bcRwfOKpKvdklspotF390QHfynA3Ir67DJ5/Uoej4Pqxc\ntAjJ/bzPJMvbkl4P3anp2HeqFNX0Xlian42VadLDpXjsJfPY5Lw9T56zGofyyfhYdASr54gXQAbS\nX62/J++t55zQnZKeafRz27ZPUJS9ETSDzkgLH15NqplTNL52o1yNMF/QfVIgqMFbXwGqgCrQuRQQ\n2GbQFtguuViJf3z0FZ7a8Ake/8Mn2PLhaa+uJHHdrPhGYihmXVWG1D/9CMMOvAlrbZUTEtl9gV1F\nGKoFsiXnc2bYZoA0Q6BYvAU+OQ/GJOMXi7R8EWnN3Azcwaqzu9dWZPwQMiSxEam/TqqMvw73Ocg7\na8N2JxyWHX7P6XfNGmYvfZvW+HSk0x9iA1lqOS2YfkPLwgaTM/RZe1VApew48kuFOObYHXz1EMQ7\n/A9iE0fjkWeewcREV7cU8/2n8fpiWd00Ddl/XIzR/aON5x0dPwqPrDuEOY7imfNeQ56Lmw1fSknf\nin1r5tKvfNGIjB2CuSszaKVTe8rcuous1JyKkfmsY75Aykr8efGU+n6OmoYX30m335C9CR+6NNKt\np+MbS9ZWfGGvzF5W/yp462tAFVAFAlsBgW3JGbirbTXYl3sOK/76OR5aux/rso7heH65x4FEks/j\nNf1CMO2aEHz3GhuuS6hElKXaeAPkn5IjQ8Ma+CIzaIvvNvsnM2wzjLNfsStsC1wKbErusTNBckF0\nkFx0ao1c6pQ8SCTVYTZZgVjcwhE2OGVvwIen7QR68L1t9nPOv0vxfq7dtzlvV5Zj8aEU3P2NRGeJ\nZu3Qm4tHg3n0cExwfCnInD8WU59ajwOn/STUsjPY5fhykJI+p3HUkNAk/GCZWJwP4kx9kBvHMFLx\n309MbPjFLHIEpi9wWL0z9+M4y1F2DNuyHLfEROBsbg4OHDhg33JycbRIKs7G8XOe+t5Sd51mKR/Q\nNwWXj3dAPwrtnCqgCogCDNmSBLg5P1FYboQA3HGo0JiJL2Xc5eTpgIE9rbTAjQWDe9UQZEtUEvvk\nv9AIWqadDNLE5AiLJDcImvjny2/bnZWV4U+TKqAKBKYCQ1MYQNmvJBtv7jqFKdNisGez3f9j0bqN\nuLR8OlYQxG7deRz3DxmK9zMdluTU+3BDKwbjIEcQF4FiMWvVRrw0YroB+pnkk81bctoiLP/5IkwY\n4hHZ6Q2rHuivvdbT4kiljvb6whEtuWH73J0GRvVQDBw8mE4y0XdDmIMOnb3InIcRJj/vhpUBrus4\n1V/v4779+gJBt6fgHXSPXAesCgSuAgLcZtg+f7EKH3xeiKxPC3DynGertowqrpsFIxJCMKJ3LSJC\nHBMlyZ9b3BxkImQ4gXkYbeHEzaEcfYQs2zJRUsqKdVYsq2bINu9L25qrAqpAYCkQmngj0smQu4R4\ncm3mXqz85uXYbFhxkzF+8jcR/kkygTeFF968G6vIOL6VGZ1S2r23eLZW24s06W+Mm2ma0UnTsK/o\nEDJeWO4Md5ydsRS30bZ6XxHm+rHqY2GRh/fEynOO/pUb4f8aQrb7rldXCKwXopyN2fxzoCmlL1tG\nLjOuPjNcIAGThjsR3XQH7x5DSSPIdykSZIcK3kH2wHW4qkCgKWCGbe4bH1fbavHx0SJszynAR0eL\nyZ/be6+jyGw9vA9ZtxNoPn1ENdVhMyY0CjgzbJut2YZfdqjVAG+2eIew9ZtcSRi45R6xbnPLAtmS\ne++NXlUFVIHAUaA/7pyRSuBN5tqMf+JvqQNpOT1Ok5FM/s1h35xMC70QlWe+g7//v1KH73cypt80\ntNEQbAUH8Nv/mo+Fa4chu3QNRnliTbmTgNPpgOFhjmFobJIR7njmE7/Etv97BrfNs1vc5z3+Cu7d\n9oj7aCCmek95cLGrihDrNccHd5NcoJps1sg94PArSR6Ofjw26rz0P3X1ISyem+SmIl+n1NXEVSGy\n9wRfsuVtwQT6eXjCA0/h1bf2o6DM7vcVfEroiFWBjlHAbNHmfZksefSrMqx/Jxf/sWYffvu3L7Dv\niGfothIwD46zYlJSCGZeV4ubBlYhNrzKgGSGbPbF5gVc3E2SNPy2CbS7Ux1RvNHvqlzebPEW8JZc\nobtjXivaqirQUgVGTprqqGItpk93rF+96Hawk0b89bc7Ip9kYHrqfHu55BnGBOz6diuwf9PTCEtI\nJuhmOD0LD3bm+lt4L7ofrne4TR/Ly/cezzoyHhPmLseO9BR7HcO8rJAd1Q19HS1lLXy5UYxyVOzH\n2oUOv5CUSWhsjM7E8rU7HTU4suK9+L3D2u8MfWjqf+a8P9RPQG14p5ujMhx4z1FZys0Y7OsLipsa\nuvKpoATvS+e+Mr7xZtHPOTMmXY+EmDB6wT+FTdsIwnmVK02qgCrQJgqYgVuikhSWVuBve0/hsfUf\n46lXs/H3T87iYqVbG43Rpz7drRh/ZShmXw/cNdRGb+pV5L9d5wwByLDNEyFl0RaJSMK5TJTkSZJc\nLoKgO5xqDXWEvhOLN0O2bG0ihFaqCqgC7aZA9Iixzigf0ui02x3WW4p8MtXBunItdfbtaODeXbYX\n1xOwp6Svw2rHBMRGBmO5uUEeB2raSPZ41vUXK3JepfeYa/Hsqzudxr+yvG1Yu8RhdT58GjJ1sf4u\nxx5Nnpy7WiZPrsWIac8aEzNttNpmcd4ePH3X9Q7LPbDoybvdWM2TkTF/PCY8RQvpFFegrGA/nr5n\nvOOXALpnxq0Oz5T+mP4o+d8YaSmmzF2DnAKHDZxXscw7gE20sM4DT29xWsbtZW0ocxRLHpvYqi47\njs506iwowbtxXB8ga+1STL+NIDwqDFPnPo0tOw+AXo+aVAFVoIUKCGwzaIt1u6LKhg/IjeS5zYfw\noxf3Y8POPHxV7PFjBt3IEftrA0Jw/7VW3He1Ddf0rkS4tdZwC2G3EYZohml31m0GbgkBaMC2hAAk\n9xOeXMmWc6sJtBW4W/jA9faAUGDfvn24+eabUVBQEBD96dBOhA7Fv8tSikZHUnDTKJmUGIuU+wQu\n7b2ceufIht2N/hqOH8/HtsUzccu1Eu+6YRH3R5G45huOurO24dPi+lLV5YV0kI2FM8Ybxj9+34kZ\ndJsTmNP/a5obYK6/f/T3f40Fcpi5EMkDyIucfumLGzQWwu6YsxGLJso4pTDnPIGSuGcpLaQTF4WY\nhOvr70lZhodN9ySl/dy5CmX2WppgyStc8vslr2I5iFxy5q9AxpKchtb84o/wusPgnjx8UMPoKUbL\nwf0nKME7etidWL0oDZ7++WSuXYLU8cmIo9+gH3j0WWzZk4NiNYQH3L8UAbq2zANu0J2kQ67PRFxJ\nvjhdipfePkKuJB/i+f93GB8dO0+26jq3owolIh7SOwTfGhGC719bixv7kytJhN13W1xJBLZdrdt8\nzBtbvhm2OTa3W1cSapnfBBW23T4CPdkJFThz5gxmz56NG264Ae+//76xKnMnHIazy/xe0vIUijGp\ns+uroYgl15lM2sNuF+sxFaGl3VOSGoT7oJPRSEy031BdKV7P9dV52xs0dpLjcibe/aj+S1D06O8i\nc5lE2zbXkIKVW8mfeoIbYI4w2dkjk7D80hGsI5ZpnFKQvm4HLq2Z5sHanIp1mRuccbvl/pQFq3H8\n/z1puODIOYQOwTP7TmEDhS10l1JS52DDjvsafEko+Oh9p/U8NaWxr7y7eoLpnIVe1K3xqu6cmtFP\nJXmH9iJr6xv4w8IVzheKLNvqOqi0RSsxY9pk3ERLv0aHul5t+jFLz6vtVVVV4eLFiygtLcWkSZNw\n+PBhZ2Vr167FXXfdZVjsGB4YOIIdEuQle6H6ApbvXo4Keo5tkf5twL8hdbj9DZk11+RbAXk2nMuW\nX3LJWEHyXYpKkl/i+1kl9LBiBIUAvKpPLYX6q3E2KpFG3E6UpH8X/G9DIpZIWZkoKf9m5DkaeRFZ\nnL7u+PT98S+Bh3/qbEt3VIG2UOBHP/oRXnjhBWMSL7sFtGaqpGgTK1aswK9+9Sv6mb8eDj/88ENc\nf/31rdmU33WVl5cb4F9SUuI1588+d2XOnz+P1157Dffcc4/fbXotSJ8V7E3KUYwafYTT86igze01\nU6UH1kxF8jzQsuqbTcuqmwo02s3DU5ZB4GVokhfQojXLXeJnU5+Ki8twiRbbCYuKRmysfSGcBtUY\nfaPof5GNem0vRnUUFBcZ0UvCyBIdGx/beHxUsuzAGsRw52nNyX3V1P9Qck0pyKe2aZ5LdAJifYGN\n0dcio3wYGTSio2NpDYQGPaUDG7Y8ej1SecIqfYk59cnihiDvWjwIjxtJFlQahEYicdQ4zOTtyeXk\nG7Ufr8y/HvP5JxI2h9t/jXFKkrF0PngjDygcr34GtPidpnZWQMCOLajvffkefrbzZ23Wg2Gxw3D3\n0LuNLzrciEBbmzXYSSuWZyI5P5vK6lp8mFuInQcLkf1lKYcq8Tq6HpEUlYQmSo5MqEXP8HogYXBm\niBagZriWlSJ5Xza+7grbMimSG5ZnJ7nXzuhFVaCTKbB582Y89thjOHr0aKOeM9A2NfG/ZTYGuYNh\nd+c8gXNrfLloTv89jpc+8xuDoqM0vYdE0tb6KRHTVqZiKYFF9ooNOLR0YsMFb6hPsfG0eWvY6JuX\nAlRHfLwbC7mXWwwHcgLtWLrPa9vmOoy++ihfkY2NDN2UUh+aqtBt1s+x3xavMjfNBPapioJc7Prn\nm3j9Ty9RnE9HX12gu+EI7CtBJepM3YaytNORuC1UVVe1aYs1tTXgDw4GOoY4TgpudskFsvmI9+U4\n52QJtn9WgA++KDTg217a/d+wEIsRlSSJrNsDe9CzdIQAZI1dYVsA2wzdZtiWZ8T3mjdumY81qQJd\nUQFeRXDBggXYtm2bx+Ht3r0bFRUVXiHaHTjz+2x7JH5v5TkYvFKsu2348OHt0Y02bWP0dx9BCoF3\nFnlwv/buLzFqYmKbtteRlef94xWHn3oqHrt3VEd2JWDbDlrwtpWdxr73/4E/rl6OFZleKRspaXMw\n4NRaZGQF7HMMio4J4PEHAgNxa1hTvAnH7VXTz3+SGO6CPQlgy7Pg/KvicuzKKSTgLkRhmbvFFRqq\n1i/GQqtJhmBY7xr6OdSuL8OxlfQV4PZm2TYDN39oi2VbgJtbU9huqLkedS0FCgsLsXjxYrz44ouG\nu6K30f30p23nQsX/XnkuhTtg9nSuV69e4AnPcp3v70z/XsMi7AGuyaPD/xQ/Af9FkzuzlmZj6bNZ\nNOFxpgffa/+rbFbJanl/bryOZrPqa3RTGd4idydOyemPYZzfpvRGFXXpE0EJ3hW5ryJq6AzvDzY5\nDSsXzkLqpBuRGMsTLX6HZTkfYNMf1mL+m33Q0zTHwXtFerU1FWDQY794BmIzFLdmG1JXXW2d4X9v\nQCEBnkBnZ/qQkLG0JJdxS85ffC5V1WDv4ULsIFeSgyfJlcRHimFXkj4WXE0L3PQgv0JjgRtaTZKh\n2QzbnizbZtjm8vJMODc/D/O+jy7pZVWgUyqwYcMGPPzww2D/55Yk/jfF8GuGYIFhf3O2VAdFshXg\nrYw3cCY8Al9sZkNdNl5btR77aOGtsffejyQ/AHPCM/tQ+kSZ4cftOnWzvTSMHjkN2ftuRnVYb4xs\nk1/sozGbFiG6lwJURZOvuib3CgQleFeX1088aShLMuYsexQ/uGcyxgwxTXk2CoWif9I4PPIMbw3v\n0qO2V0Cgj3MGv3YBb4q2wRNf+QOK2xTLatuPtuNbEL25J7xv6E75wRMl2PFZPvYcLkaVrX7io7se\nsyvJlXG8fLsFA7rzapJ1BiQzHLOmsnmzbjNkyyb6c85JIFtyd33Qc6pAV1Ng+vTpxvvR//zP/2Dv\n3r0+h3fvvfcaUU5cYZojAmnyUwHbCayeNQ/iicp3LZ0/y7g5c+K9BN7+oFQowagfhO5nl5pVLLI/\nRo1uoi94ExsKjaQJoh31zaKJfe2o4v68Wjqqb23XrvPnFnsTKWnpeGjOdHzzG0mtEq2k7Toe3DU7\nAZAgmK3e7eVqwm1x28GQZJyiNeenz5Xjvc/JleTTfBRfrHe9casHQfUAciVJ6gMM7V1LriR27SwW\nu2XbbN02+2vzPoM4Q7iUYcCWjeFaNm5XYdut+noyCBTgfysPPPCAsbH/9vPPP49NmzZ5/AVw4MCB\nuPPOO4NAmTYcYuRobA6Sz4A2VFGrdigQlOAd1e86LJqzAP1uudvkSqKvic6ggAAhW6AZiNsycVvc\nBrcl7bZlex1VN4+Nk+Q83osUc2sPuZLsPFSAz09d8Nm1Xt2sGN6brNvxtQ5XErJuN9GVhMGbQZvB\nm8Ga982wzZ1Q4Pb5KLRAECkwduxY8Pab3/wGq1evxpo1a3D27NkGCrRqVJAGNeuBKqAKNEeBoATv\n0P7j8Myacc3RS+8JAAUEggUU27JL0obkbdlWe9ZtHg/v81ZDPu2f5p0nv+18/OtIEWw13q384bTs\n41AKAZhEXlmXNdOVhGGbQdts2eZ9TgLZkrenPtqWKtCZFLjsssvwi1/8AjyR8vXXXzes4By/m5OC\nd2d6ktrXYFAgKME7GB5sVx+jwGJbj7O92mnrcUj9PB5OMi7O2ZVkx8ECIwxgCa+k4CWxFXtAL45K\nAgyJraNVH8V3u36SpLiMeJooqa4kXgTWS6pACxRw54bCoQI1qQKqQOAooOAdOM9Ce6IKtIkC7mC7\njAB79+fnkPXZWRzPL/fZbhy5klwVT9btPjXoFuJ+gRt3oM3nGMRlE1cSsxuJ2aJt3vfZKS2gCqgC\nHhUQNxRePVJTV1SAVqs8TatVhkUjId7NapddcchdZEwK3l3kQeowVAGzAgLbfE6s27aaWnxyvJii\nkhRg39Hz5FrifYGMSHYl6cOwXYe+3ephm+FYrNYC25LLhEkBbc7VlcT8ZHRfFWhfBTR6Sfvq3V6t\n5W1ahEHTOWZ2CnaXbsMYjd7XXtK3uB0F7xZLqBWoAoGhgDvY5nMnCsmVhEIA7jhUCLZ0e0vsXj2w\nJy3dTqtJDu5VY7iScHmBbVfgFtAW8BbgNsO2O+u2Wra9PQW9pgqoAp1FgYriApSRXcIIoxfdXnH0\nipG5yr5QDVKmYngzoLtj+t1Znmrb9lPBu2311dpVgTZXQIBbLNucl5RX02qSBXiXgPtEIa1m4CNd\nKj6NM5/twJlP30FF6TlnaTMgyz7n5n0uLMdyo+uxnA+ovJai4uQ7evRfy4Bf/29AdU870/UUkEVv\nOHKQps6vQMHOZ5EwfqFjIKnYd2kzRrcDe9vydmJ+lr3ZOQ9NRlOjg3dUvzv/E2+dESh4t46OWosq\n0K4KmGGbG+bjanIl+Ti3CNsJuD/KPY9aOuctVZWX4NSBHTj6r7+h6MQhb0W7/rUSWlSLN02qQDso\nIP9+26EpbaLNFMjDcid0cyOlyC8i03f/tseq7LdedowqBWm3DWniCDuu303saJct3vavkC4rnQ5M\nFWhfBcwf1rwv2/GzFykiyVm8l1OEi5XeXUnqyNJmKzqKS6c+RsXpT9GdrL6jrkyAZUhfw2ot1mxx\nD+HcvM/X5Vis2pKzGub99lWnGa1VVQJv/9V+4/CracWfEc2oRG9RBfxXoKioCGVlZRgypKmw5H8b\nWrJ9FMh59RksbdBUFr44cwkT+zfD76NBPb4O8rBpnmMNzTkP4YYmmrs7rt++xhU81xW8g+dZ60g7\nqQIC3ALanBdfrMIu8tnmqCSniyp8jqxPd/Lb7kuRSeJqEW69ksrz9m1j4qOr37b4a4v/ttlv2+y7\nzZAtm3SgU4F3USHwdYqLyGn2d4GHf2rf17+qgCqgCnhToGw/fjJjrb1EcjKQnW3s780+Dowe5byT\nV1fmaemRNMncbaLrtFYZIiM9XHdzU9mBt5zAn552C5rk2eJ3v/kzJZKiUTXugM3G1+yRqhpf1TP+\nKGBfqcKfklpGFVAF2k0BgWxZNZPzymobPiA3kuc2H8LDa/djw848r9DdPdyCrw0IQdp1objvahuu\n6V1J0G0zLNYM11FRUeCIBz169EBMTEyjjc/zdS4XEREBM4ibLeEC350KutvtSWpDqoAq0NUU2Pm/\nj8Nuc07G1jdex7IU+whP5Z93DrXswBoY77P0Xvv8/mLneedO2R5MNd6HwzD31RznaV87e19b5Sgy\nB9NvcBgOfN3kuO5Pvw+smUr9jqLNguf3NOx37qanHNfCsD5HXfP8lL1RMTffZxqV0ROqgCrQDgqI\nZZubEvDm/MiZCxSV5Cx2Udzt8kqaEOglhZAVehCtJjkiAbg8uhqWOpvTKi2h/cSibc4Zqvk6n2Or\ntmxmtxIBbG5eIdvLQ9BLqoAqEBgK2PKwfukanECU1/5covnnt899AhMS/bAfn96ChxfaZzYmL1qO\niUMG4UyMvfqstw6i7MlxYGeT6N6JIFs42BY+/5WdeGj0FLIT16e8rD864J2N5APqL3jbqziAdUvt\n1vXk9PuQ5Ed3ndX52e9ht6dRvzPt/R77HCZXP4Mh3PHinfjhdIdzTcpKTE5qa5caZ8+73I75ddDl\nBqcDUgU6gwIC3GbYLiitwPs5hXj30wLkl/h2JUnoQbBNIQCv6lOLMEu9n3eII462QLXAtliv+dhf\nVxKF7c7watI+qgKqgFOBS2ewfMlSAyKd5zzsDP/eArrii2QrsOkXqY76UvDCExOM2kbcmApkkg08\nKwen6O3aAOL+t2JhGjAjg4qseBnZ6VMw2smqZch62REOEIswcZTzgofe2U8X7NoMro7To1NvsO/4\n9df/fkcOmYZ1K1Nx/Xy26S/FY6umYfMjo7HpJ+PhCKSCjS/OQdNs7X51MmgKKXgHzaPWgQaSAmbY\n5n7ZXUlq8WFuIXYeLET2l7TMs4+oJD3IlWQ4LXAzIr4WvSLYk9Ce2EotftsC3GbQFvjma2bLttl9\nhGsS0JbcUb1mQaKAxvkNkgfdlYcZfSWWrVuNvMoIcpfzNNBKVNL15N7ereJ8d9mB/8N0h2t36sql\nGOeY2NgtQcD5GErIem7n90jcMSsdyFhCJzKx6f3TGD2xP1cDFOzFH+y+KkhdOQ2J9rM+/lbgn+u4\nLk7puNNPWOfSTes3WeAfWYlFBN5s386c/ziexVgsdIw7ZdluTBvi6wsKt6rJkwIK3p6U0fOqQCsr\nILDN1Yp1m/dzTpZQVJIC7P7iHCqqvbuShFotuLI3rSZJ1u2BPaqoIjtwWyyNYVsA2wzdZthm6FZX\nEn4CmlwV0Di/rorocedUIB4TZ85tpa4XYN38eY660rBkzhhnvYOuv5n22RadiT3HyjAm1g7i8TdO\nBZU0rizd8C6WTLzfYPLc7X91Wo/TUtkhxY9UvBe/d5i7U1dPhgPh/bix6f0GfRVYtG81ll7P483C\nQgkajgV48cf14/ajcS3iRgEFbzei6KnAV4CtsP269yO3ijBU19W7VrRmzy/vfrnTP7ol9QpwC2xz\nfub8Jbx/qICAuxCFZRTWzkfqF2Oh1SRDMIyikoQ6XElYA6vDH1tcRsyQLeCtriQ+xNXLLgponF8X\nQfSwMyvgiCzifQgcpcN7idNvLa9ftGbDTxsslBPWrbfz5jMni8hk7LCAR47CrPQUMnqTk0bG77Hr\nt/djQnwZ3s6QVSdX4rZEHw07as59J8MB68n4wUQ/YZ3ubVa/6b7o0bOxddEqTHL4lHM3Fu1YYvf3\n5gNNzVbAvyfe7Or1RlWg9RVg4OQtqWcS3r/jfSMubkVFBaqrq1FTU2NYk6XV8IulsNK5WgLUqu4x\ncrpRLnWyFZgjeHTv3t2I8mG2CDe6ycsJM2xzMXYlqaiqwb8OF2IHuZIcPEmuJD5SzMVCDL8qgYCb\n3gTDbDQu+0RJsyuJGbjN0O0K2zwuA9TJDUXGKs3zsSZVQBTQOL+ihOadXgGajPhAVLLTL9rbeNZl\nl2KmJ/cNWy6em+SYWEhW3yfvTWpQVeSAK0Fe3mTvBg4eP0d/651Hbpz+PYDBm7D59e15mPDNXKxy\nuJnMefBOP1edLMabq8TX4yGM8xPW0YJ+k4MKzpywT+SkzhvpRO5ZOP1r5KTmTVZAwbvJkukNHamA\nQCPDJ8MlgycDJ4Mun5Pwe9LHm154Cr3yvkDxoCTseuy/5XSjXOplQDVbigVY5XqjG00nBLb5FO/z\nxqtHHjxRQlFJ8rHncBGqbLWmOxrvhlKAzyH5BzDi/QwMKDmKuv/5oxOUBaZl3DJ26S/nfI37LJt8\nceCck0C25I17oGeCWgE/4/zCa/xhik1ME8xCKTaxfsAE9aup4wdPxphTfvbC2++OBzJ+DudUyK1P\nNLb6RvbDjRRSMJP4OnNbNspoMqJ4fUcm3Yl0MlAvIYZdm/kWuZ7kOSdn3ndHQ4D31FVb3jtOa/uC\nBRP9hHWgJf3O25KOWRkNe5Qx6z7MuPMTWiSo4Xk9apoC+r7YNL20dAcqIPBrhu7IyEgDthk02drt\nCt4CnNYQqxGv2lP3zXUzwHK9bPkWkPUGqgLcAtucnyoiVxKKub2DopIU0WI3XhNZnAeQK0lSH1o8\nsTe5kjz7CuqO7oal7wC4RiUxW7V53wzjvM/jlU3GJH2X3Gtf9GJQK+Aa5zf7hyPAkdPM8YlRsR9T\no643rHtIXY2izXPrQYAsbE+FDbUv8JG6AZc2231ag1pUHXzHKRCdjD+fOo7S6jAyqHjqRjX9WhqG\nuAGCyi7livdgsRBoyko84ZY645AgEQGPnSBbMf1K6aymP6Y/Sp7eXEfGPIwXmE17EDf6GRokO1Nu\nSsV3xtVb051NuNtpSb/p3vmpjq8a9G9832PA9ePZ3zsbkxauR+krM03jc9e4nvOmgIK3N3X0WsAo\nwNDIQMs5QzbDMYMxn2PQ5H1xM+Fzkrgsp9CQUPTs2VNON8q5XtkYYLk+hm+BW26Dr0uSNiRn4L9Y\nacOeLygqCfluf37qghT1mPeM4vB/tKJkfB26h7IrCY2P/mPXGO51KI0xjFxexKJthm4zcHPfeJzc\nP+mnua/mfY+d6UIX5Jn4HJLpdULi+4wiI/V1WT39jPOLyJH4wQKK9LuCTHiZ85C+5XYsn2JfAn3P\nbx9zrqq3bNEdPoOziaaaqwJto0AoYvsn1n8xbEYjO19YZP+SSfemTL0Ol/JyUVQfRMqoMdQcECV7\nH04QeZtXjk+aPINiY2c0CGu4aNatfv77oCXijdB+1NScNFzviKTiayjN7zeFHvzJWOeYN/z6+xhN\nhvmNc+bZI7pkzMKKWXdi8QQ1e/t6Bp6uK3h7UkbPB6QCApcMngzGDJoMpmZrtxm8uBynkNAQ9OrV\ny+OYBKY4Z4jl+7hehl0+5nYkcf2y1dTW4dO88+S3nY+9R4pRXePdlSQ81IKhtMDNVX3qcFn3ajts\nG0BvqbdeE9+HUXPhkeEIi452gjf3R4Bb+iSgLbl5HNLfYMnluR87fwy5xbmGtl7HXlZCn46OEmSp\nxdF/ei3O2kbRJ+xNl99klBOtvd7UaS76H+eXY6VNSV+L1BX2D+cVqY/hgUubMfrsJoxdKM6rG/Hj\nMX6a8zqNRtrRoFOAfMTXGv7Z9pFnzR+PAfOboUL8TSCjt8l1Iw3TbpQ3H+/1mZeIX0ZLxPsFbS3o\nd8HO550hE5MXvYP7kyKNDk775TtIWXubMcFzyW0LMbn0FeccUu8j0KuuCvj1DF1v0mNVoCMUENBh\nyGQA5WMGUIZjcTER+JL+8XVOnPOy6L4S18n188b38Mb7nAS2OT9ZWI73yLK9g7bzF71HVTGs2KFn\nUFz5PsorsnGMFsR5+xjRNf9P7VlC7Bu7w/C+deBhWMmqYYn+CtZ3f2S/TmEELY5N7jNyOtcjrAdW\nTVqFmAj7+EQnX2PtStfNz2by65ORcy7Hv+FNchQrXw/8kTY/0o60HU745uJdQe+mxvlF9BiszCRL\nYKoR6RePL6JIv8cWOtRLxY5fT/MPEPzQW4uoAh2mQGgYyAOwaSltEq6s9zNx3BuNyc6Y3kDyArKA\n23nWZ93vm5aIn/INP7/MNrvfZXjzN/LveA7WLZpQ37/4CVi1YQ5GzFhL5zLw3uHnMXq0n+b3+lp0\njxRQ8NaXQadTgEHYAFYHeAt080BcwVugmfOoKPPvge6HLfXyVdnnnNu4UFGD3Z8XIosmSh7Pv+i+\nAtPZuG72BW6Gx9nw3TdmodxWbr/KHivM8rLRdwMGbsO/hM+Rn7fxbh9C7ionyQor5TnnJDnt8nhr\nLbV45IZH8PXLvm58SeBz3OdgSQLd/Ix4u1jl+9m0RJsLVReMduR12JK6AuPe5sT5pbgNU57AagLv\neWTkzlqx0BHqDFiw8Tca+CAwHqz2oqUKhCZhOb2fLm9pPXR/+YVLzlpmPzDGP/giy/UGRzi/lPQ0\nJPlLbC3o98zN1fhOhY0mR0c26mPS/WtQ/e2VsPEvzo5fk52D0h2/FfD3MfpdoRZUBdpSATNQmqHa\n3KYZvqW8lRaYYVcNT4nLme+TfY5K8vHRImM1yf1Hz8NGYOctRdC/qGF9QnAVwXNCVLUBaMYXA9gB\n2QnXbIhnyOZcNgFxzpmbLeR3bKEFdYx9OpbELuzC1Y682mYPpchFRBcpHgw5Py/WmUNKtnWycVxg\n2vhXF9FaXmdt3XZb1N/cOL8gz9m5q7ZSaLRJJt/VdCyZNqQtuql1qgKdWIECmhNRH45wcrJ/lmLz\nEvHfm/61dho/u3F6RkN3QN5OHesyzXhWt8sMUQfSFRVg0BFY9gt6CFDF7cRVD4FsPs/7vLEryY6D\nBdiZU4jScu8wx54oA3vyJEkLBvW0IYQWuGEI5CRghnA64FPeYNsJ3FTOBazpTP0546DhH4lhLroY\nxYPA6i3Pi/VmGK6qqmrwBaqhSq1zxFpzW/xszXq3Tu3tXAv5tjc3PjH3tOzMGRN085mTOEmhBGP9\n/Bmd79CkCnR1BSpy/ukMB5iy7DuNwxG6FYCWiH9+ieMKLRGf1Mh/xe1dejLwFVDwDvxnpD30ooBP\n6HbAJ2E6gavQbL1LioAb5yUE2LsoBOB2WuDmywLf7gq9yZUkKSGEwgDWItLqiEpCbTCQiQ86d92A\n8Ajeoc1s6RbQlly6JznfzMn12H62wV8GTgZCbluAsEGBLnzAz669wZu1dvX/9/laDMBn0JI4v0Au\nLSk9y2VUa5GWfh8+eWaCy3k9VAWCV4GDW37vHPyD91zr3Pe+E4m7Vx3BvkUl6Hn5SOc8cO/36NXO\noICCd2d4StrHVlGAAY2TOecoJB/nFmE7AfdHueeNBW+8NRYVZvfbvopm3MSTK4l5NUkBbok8wiAm\nQGiJIHoWi7cZtBmqZTM3zOeakMQKy20zEMoYOyMMNmHYzqIC36yDjN15sZV3uA3XKDqdUueWxPkl\nTfc8Xx86cOWOfYhYez3mZVCk36W3Yf13vawC2MrPQ6tTBQJdgZGzXsS+W0oQ1rMfRgzx/+eg6P5D\nMNq/4CeBLoH2z6SAgrdJDN3tegowhDHDkgOJAWR8zBtPjtz+6Vm8//k5mjTpEpTVRQYrAXRiLFu3\nKY/hGNt21xOGLQFdziX8IO+L1ZldEioraU00djUhd20nZAtYS85tmvf5uAmJ23EXx7wJVXTaovJM\n+UuOaNCWg+E22qOdthwD1938OL+ALZdCB0ps4bQNmDNuNEL7byDwnmF0e1baCtz5yWK10rX1Q9T6\nO4UCkfEE0H4GJOkUA9JOtkgBBe8Wyac3B6oCDGMNEh2eK60gVxKOSnIWp4vIEdVH6tOd/Lb7WjGs\ndw0iLfUL3LBFmTeBbQZu2WTBHYZy7gNDt+Fqwi4mklwB2/VYyjUhZxDkdgRCm3Brlygq4za0buMR\nmXVu9Dpr47ZbrfoWxPkFTuO3P5zu6Eoyti5zrE455H7sSP89xnPc4+wlWLh+Ml6ZObrVuqwVqQKq\ngCrQFRRQ8O4KT1HHYCggEGTOq/7wNj46XEgxR4ux/6V9BKfexerGriQJBNzkShIbUb/ADVuwBbjN\nkC37kou1m1sRKzTfZ0yqNLfdCrDtOhKBT9fzwXLcXuNvr3ba9Lk1O84vUJHzD2MZee5f2roMmFfQ\nHvfESsxZkgwj0u8f3sPzBN7+xW9o09Fq5aqAKqAKBIwCCt4B8yi0I81RQCCb7xUg4jz3zAVsJ8v2\nLnIlKa9kHw/PKYSs04NoNckRCcDl0dUUxc/mjFbBIG22bpuXbRfrNpeRclyWrd1ieZVjo/U2gG0Z\nlVkHOae5KuBRgRbE+UXSTJrI+x36Yukm7FjkKKypq8ZKD3GAPfZHL6gCqoAqECQKKHgHyYPuasMU\n0DTDdgG5krxPriQ7aIGbM+fJr9pHSuhBsE0hAIdTzO1wa33IwBAX2BZrthm6XWGbLeLi183NMnyz\n+4fzXBtCt49h6mVVoNUVCA2lxTU8fnq4AfJW74FWqAqoAqpA51TA41tn5xyO9rorK2CGbR4nW5Wr\nbLXYe6TQWODmQF6pMYnSmwY9KLrIcFrgZiSFAOwZUT+pUlxJBKgZts2gLfBttmwLbDvhmhoW327O\nedOkCqgCqoAqoAqoAqqAKKDgLUpoHpAKCGxz58S6zfs5p0ooKkkBdn9xDhXV3l1JQq0WXNnbiiSy\nbg/oTrGueTVISgzG4kbCQC1w7QrdZtjm8gLaAtcC2JIblesfVUAVUAVUAVVAFVAFXBRQ8HYRRA8D\nQwEBboFtzvPPX8LOQ7zATQEKS6t8drRfjIVWkwzBkDgbwmk1SU4Mx1arPSoJAzZDtSfLthm4xbqt\nsO1Tdi2gCqgCqoAqoAqoAh4UUPD2IIyebn8FzLDNrbMrCVuz//UF+W3TapIHT5Wx2dtrx2LYlYQs\n2yMp5nZ0aMMFbsS6bQZuM3QzaJthWyZGmi3c0rhat0UJzVUBVUAVUAVUAVXAXwUUvP1VSsu1iQIC\n21y52bp98AS5ktAkyT2Hiww/bm+Nh9JKkEPIlWQEwXZ/ciVhOBfLtMA05+JKYoZtgXCGbNkEtDnn\nJJAtube+6DVVQBVQBVQBVUAVUAU8KaDg7UmZdjpvhk0zhErzvq5Luc6Wy1jN4/uqqBw7jagkBSi6\n4NuVZEBP8tumeNvDe9dRmGz7AjdWC0UXcUC0wLYZtHnfDOO8L24kAtwC7aypwnZne2Vpf1UBVUAV\nUAVUgcBVQMG7A56NQKesgGfJyUa3D7IQmXMA62pOIfQygNZxQRktuHLFX9ag51eHYL32BuDmO1Db\nK9YARe52Z4NCGbfkPH6Osb37iwLDd/vzUxd8Po1eUVaKSmKhMIB16BFav5okQ7PZlcSdddsM3FKe\nNTQDt3Sgs2kr/dZcFVAFVAFVQBVQBQJXAQXvdnw2ApxG/uVRhLz+e4T85RWE55929uIm3ot0HgKf\n77dvfIpWm6v7xgTUfeeHwB2pqCXYFECU3HRnQOzKmLkzvM9bTW0dPvvyvOFKsvdIMaprzEs6Nu52\neIgFQ42oJMBl5ErCdfB4eTPDNMO22bot8C1lGMwZsgW0Oeck2kneuAd6RhVQBVQBVUAVUAVUgZYr\noODdcg191iDwaeRfnYR15c+BN/5AswcbhsGrCwnFlzUW5FdUo5rmEEbTSuPDY7ojouKivQ0braq4\n8+/GhiuTgEfTUTvx2wEJjuYx8z5vJwvL8Z4jKklJef2CNW4FJKge2JOjkgBXxpIrSV2VE7bFss1A\nLXBthm45L+UEtgW4Bdq5XYVtt+rrSVVAFVAFVAFVQBVoAwUUvNtAVHOVAp2cW/74IqzLFgIXSp1F\n6gigS1PuRtE1N+BMbF/MmDkTx7465ry+7JEluOvr1yEh9zP03LsdETu2AlW0KuPRHFh+dC9CUiaj\n5un/BfoNcN7TUTDJY+QkObuSXKCloz9gv20KAZh71rcrSVx3K67qw77btegWYlrgxsVv2wzaAt9i\n2eacIVss3ALaZl3M+07hdEcVUAVUAVVAFVAFVIE2VEDBuw3FdUL3pXKELCL3kL/90dlazde+gfOz\nH0PJiOtQUVGBixcv4lJZmRNapWB1dTVKI7uj5t9SUHLrZHR/5BeIfWMdov70e6CcQDbrTYRMvQG1\n//Mn1H79JqcFt73AUiCb+yvjrSHg/uSY3ZVk/9HzsNGxtxRBr8JhtJpkUp869O1WD9s8BrFaM0yb\n3Uhk3wzbAtpi2eack2ghube+6LXOrQA/46iQqDYdRLewbs7XVJs2pJWrAqqAKqAKdDkFFLzb4JEK\njBqTJ0tLEPrgt4CPPrC31DMO5+en4zxBtM1mg62qCgzXvNXU1DQCb65DrldWVqIuPBIVaT9C1Lfu\nR/xvFiH0g3eAwrOwfu8O1Kx8DbW3TzGsvdxYW4KmjFFgm3N2JeEQgO+RhduXKwl5aOPyWHYlsWBQ\nTxtCTAvcMEALcIs129XCLcBthm0Bbh63jF3yNnjMflUpfbml7y04UHQAdeTfzv95TfzLAW80DmPz\nUJg1tNCqnNFh0bg8+nLnmD0U79KnRWfOfzf2d/i84HPjCy3/26mtqzV0b44ArC//x6+3iIgI9OrR\nC9fFXWdU1dGvreaMR+9RBVQBVUAV6FgFFLzbSH8G5tryiwj7wV3AJ3uMVmqHj8KZp9egPDYetQTc\nDNpcjpO4Rrh+mMt5LsNwy7DO99RE98Klp9cifuPvEb12GVBdhZBHvoOaFzaidsLkNoFvd7BdSr7a\n7+cUGAvc5BU4fNG5sx5SXDdQvO1Qw5Uk0uqISkKw5Am2zeDN+wxAUpa1cQfb3LSrjh6606anuQ+8\ncR9/dd2vUFpaivLyclS5PPumdoLrZA1Yj6ioKERHR6NnRE+jLWmzqXV25vI8Zk7yehjYfSB61vZs\nVa35dcdad+/e3dDe/LrrzNpp31UBVUAVUAXaVwEF71bUW8DUgG6C6rDHZzqh20auJad++SIqKTJJ\nLcGzAJlYbA2YpnsEIqRbDFdsaeON9/k6tyPQfvaeH6A6/jLEPfMoQJMvQ36cBttr76J2xLWtAt8y\nJnPOUUg+OVqEd2mi5MdHSyhKiXdXkiiKjTic/Lavopjb8VEclaTSOX4GGIYaHpts4kbCx3xNNtFK\nAEu0cs1Fu47MuU+8cV8Fknlc/Nz4nLtfN/ztr9TLusjrQrQRLfytq6uUk9cEv2ZY58jISOPfCWvU\nmlqz3lw/1yvPuKtoqONQBVQBVUAVaHsFFLxbWWMDuglEretWAv/8i1F7TdLXcCJ9LaooakkdX3PA\nGMMSf3gz1PI5vpdzc+IPera0MUgwVHBikOCNy7MFvPDmbwJPLEPcsscNv+9QsnxX/uVfQI9oZ31N\nATKBbG6L92U7nn8R2z89i12fn0MZTZr0lqw0rkRyJUmi1SQTY2oQAnsUE+4HQwuPnXN3sG0Gbi4n\nOrE2AjsyHsm99aWjrnHfuO88Hn6O/Mx4DPKrBT+/5iSul+th/bhefm0IDJo1ak7dnfEe1oM3eT3x\nvxdOfCxam1/TTR0ja8rPUYDendbcviZVQBVQBVQBVcCXAgrevhTy87rAKcNU3ZFDCFm+xH5n7wSc\n+vkqVDmgmWFAAIFz/lDne/iDm/1RXT/AGdoYrBgmuDxfZ4BjoODyAhaFFBkl7NjniP7Ti0DeEYQ+\n+xPYfva8UZ7v4f651u06NIETGQvnxbSC5C7y2d5OUUlOnit3vaXRcR+KSjKyLy1yE1uLCIpKYrRL\nPrIC0DJ2AW7OGWQ452u8SVnWxgyS5v6b9xt1IgBOcP+47zwehmN+xnzMY5UvTaJ3U7vLdfNmhnpu\nQ7QTbSRvav2dqbyMkbVlPVgHTrLPWsvruTnj4vp5k2fJr1N3Wjenbr1HFVAFVAFVIPgUUPBuhWf+\n/9k7F/CoqnP9vyEJJECiiSZqUAFBDVaCggpeQIOtB7Al1IKtGlTUAsdawVqhoQXbaKWorUBbC3gq\nHAV7FNq/waNQj4ACKlQTJFESuQhRoJJAohkhgQTyX9+e+Wb2XDKZTCaQy7ueZ2df1u1bvz1J3v3N\nt9dSASXiSv7Rd5n9iJnyr8Zq+cBDT6DavFApSYSRCk31msl1qSObXPNNKq5UnMq5iG3ZRAyIKLBe\nIDN9f3nnVHTNfxfRu7ZZi/PUjrsXx791mdWklA2U1Hb7vrbuBD7cdQgbjNjeWvqVEY2BanqudZVQ\nklQjuM2sJEldPHHbKoZ0DDp22ds3FYxSTuropqLKd+/puXUeqb06fr2vMk55iFIx2BzrpQ9tX1lK\n+3JN+29O+22trrIQu2X8wkQ4Ww/C5gGyOUnak00+n7Lp51VZd0TezeHJuiRAAiTQkQlQeEfo7qvo\nhln6PWrjm1ar1dePwleDrrOO5Z+1CDDxltk9ZiJ49Z+6/oO3myTXVFSImLD/85fr+s/femHPVPy3\nmTHl3AfHmRgR43F+ZiZqFr5qtW9vW0W29CPHuu368hszK8kBvP9pBQ4fbSyUBOidHG1elISZUcPE\nrNc749alHxUmsheb9aFBj+W6bjoeHYfaKXtJurdO2tgPsV3GJ3sZn4xfhaD9HoQ7LG1XGcrezjHc\ndttaPf2M2FlHkrPwkD50E8a6tTVWtJcESIAESODUEqDwbiZ/Fa0qvLssnutsq81RTQAAQABJREFU\n0cRzl0142DoWkSniU7ydIrpVQMs/b6knm4ooX3N8/9lLW9KnigzJVxvE8/1N32+h2kxVGP/2/zof\nAD792LxomWEJBe1Hy8v+oKPGzEpyCOs/Kce/K6t9u/c7T+3eCf3MFIAXnWE89J08q09GG7vEJrFP\nxqebXXQHEtt2oShjkaR7v87b0AUdg45P9sJHmEvSfbhD0vZlL5v2o+fhtttW6ykP4SCbfsYjOR7t\nw85Yr0WyH7ZFAiRAAiTQfglQeEfg3so/eRG19aW7EL3x/6wWjwwdgerUNMQYsSUiVAS3ilARoCqU\npK79H3kgczRf6+i5llURJ3sR3wfH3ovzjPCWFP23haidZV70lGNji6RjJpTkg53OUJKi0qpG55WW\nUJL01Ggz5/YJnNbZ4wkXe1Rsq+DWMarwlr3kSTnZpI5uKlp895aR7eCHfVxyb2Tceq8iNTx7H9Km\nnkeq/bbUjn3s9uOWGENj7ct9/uabb/D1119b2wUXXGC9p9EStrBNEiABEiCBtkOAwrsZ90r+ucom\nolviSWNW/9205vRoVnz3NktoiegUMaqbilD9x637UM3Q8rLXY7sdYss3vS/GMTOTSmzJR4hZsxJH\nppl5vo23ffu/HSZu+xA2bT+EmtrjQbuMMQuHXHCGWbrdeLfP7W7CSHDMKi99+optFdl20a3jtAtu\ntVn30qCOIagxbTjTd3y+55EaWku1Gyn7TlY7Lc1h586deOWVV6w52VVUy17maLefO8wqtPK7KOmc\nc85BaWnpyULAfkiABEiABFoxAQrvZt4cFd7ysmPcWqeXuf6Ms3H4W4Pc3m4VpOrxFXEgm9QNJ9nF\nhbQpwlfaEhtE8Mr+q+tGILn4IxyojcbaN/Kx6cjpKK862mh3ZyfKapLR6Jts5iG3rSbZqZPTY60e\nbB2Tim69bhfcds+2jlkMsNvfqEHtpEBHHHM7uXVew+jduzc2btyIVatWeV0PdvKf//mf1u9osDLM\nIwESIAES6BgEKLybcZ9VdIu3+/g3DnT6tNBq7fAV11niUkSoClMVpCJGIyHCtA3Zq8CVPuS81jja\n3rngenwy+mxsO3cg8KWY1bDo7t7FhJIYz/YlKTDLj8usJM4XJaVd9W6rsFbBrXvpU8em3m21Sfay\nabIf6zXuSaAtEZDP+Msvv4xrrrkGH3/8caOmy+/JpEmTGi3HAiRAAiRAAh2DAIV3mPdZvdUqvqO3\nbbFWjpTmqo23Wz3RIljlOFKCW9rXvuVYk4janQeqsaG4Alu/OGziuLsBIrobSLHRJpQkOcrMShKF\ntG7mJUnjMVehrGJa9vrgoEJbz33Fto5P9pJUZOu+ATN4mQTaHIGEhAT87//+L6644gocPHgwqP0/\n+tGPkJpqpv5hIgESIAESIAFDgMK7GR8DFd3i8Y42L1ZqOtr7IrenWAWqCtPmCFHpT0W3Hv+78gjW\nm/m2N247iENmsZvGUpoJJUk/E7jQbNH1zgV7xKZO5uFAHxbEZrvQlmO5Zt9kPLpJfd2k/+aMsTH7\nmU8CrYFAz549sXLlSgwfPhw1Nc45+wPZdeDAAWzatAlDhgwJlM1rJEACJEACHYwAhXczbriKX3mJ\nKmaf5+Wp2rSeVny3CFm7tzscQWrvQ45F5NeYWJLN5gXJ9cVl+HTfN42OINFRhl4XJBnBfQKnxzln\nN4mq914QRD3ZsreLbl+xLeORcQR6kAhnfI0azwIk0EoJXH311Xj++edx++23N2jhP//5T8gm3vEp\nU6bg1ltvtX6/GqzADBIgARIggXZNgMI7zNtrF8QivDt9U+VqKQonEk+3BHdzRbeaJu0fN1uRWUXy\nXbO4Tf5nX5tQksZmJQEuOFSMS9f9F84p/QAV815Bl07djGh2Li3ekNDW6yq4dQwqtGUvSUW27tVW\n7kmgIxG47bbb8Omnn+I3v/mN37Avu+wybN261fqW6sMPP8T48ePx85//HJMnT7a2s88+268OL5AA\nCZAACbRvAoHXEW/fY2726ER0S7KLb9QccbbbJc4dgqFiNVRxqu06G3L+dNTF4o2tlcj526d4+rWd\neH97RcOi23ii0xKBoeceRVbPg7h63z9xthHd8npjdF2t5WmLj49H9+7dIXGqiYmJOO2006y9nMvW\nrVs3dO3a1VrsRz3fIsJFgAfzdttt5jEJdCQCjz76KCSW25569OiBDz74ANu3b8eDDz5o/Y5JvoSe\niEg///zzkZ2djX/961/2ajwmARIgARJo5wQovJt5g0Usi0f6RGxnZ0u1x9yhGKEIb7t41+PO8Qm4\nYEgWhv9kATYf7ou1JWaO4OqGPdynd+2Eq3vG4M4BJzCqdzV6da9GNEzc+QmzuqSxKs4o7/iEREtQ\ni7BW0S3CW0S4XJNNVtaUzVdw28cR6kNEM7GyOgm0GQLyO7F48WIMHjzYbbNMISgPrH379sW8efOw\nd+9ezJ8/HxdddJFVRha6WrZsmVVH4r9feukla/ErdwM8IAESIAESaJcEoozYC28y6XaJI7RBCTIR\n2zJf9tGjR3HkyBF0+2MuEv72F6uBvatKEHfGGdZKdSJiNVxDW1fkstc2HGaVu/wdBzH/pf9DYs8B\niI5xCXmt5LPvEhNlXpA0C9ycWY+zuta5F+uwpjY0ceDyj/30l/6CxPVvoLPxVh966R1LcIvoFq+3\n79L1Kq5lL0kFtu59uucpCZCADwHxZl911VWWV/uLL75ASoqZn9Mnye/86tWrLREusd/6t0CKyUI7\nItgnTpyIs846y6cmT0mABEiABNoDAXq8I3QXj6d44jVjD+x1C1d78/JPVjcrbtsI5NKyw3jlvX2Y\nuWI3Xtz8FZL6XNmg6DZzh+D8pE4YcVE07jEzBV5/fq0lukUci3dNxLSEiYj3WjzZSV8fwmnmDiee\nkeK+JnkMJbHfFR6TQGQIiFiWaQZFOAcS3dKL/K6OHDnSWoCnpKQEDzzwgPVALHn//ve/MWvWLCsM\n5a677oLEhTORAAmQAAm0LwL0eIdxP0U82z3ehw8fRvSGN5H6i7ut1ioeW4T6EbdYYRvysqLGRqvo\ndlTX4t3ig9asJHvKXLHhQew4rUs9Ljqj3vJud+/smW9b4641Blv6kqS29bh/DDqbh4DjGYNR8dR/\nW3Gm4u2WcBKpo15u7ZrebSXBPQmET0B+z5vyuyTLzcvsKH/+858hS9LbkyzUIzHiP/jBD6zfWXse\nj0mABEiABNoeAQrvMO6Zr/CWUJNj+7/A+WOvslo7/KNJOPLQY+54aRG4J0xAz0efVeDt4nKz/9qa\npSRY18eqHdhX+DauNZNuD728j9WWhoeIaJZNZyDRvQp8CV85cfAAzvnhtTDr5ODw2HvguP9X7lhu\nKS/1RRw0RSAEs5d5JEACzSMgf1def/11Kwzlrbfe8gpDSUtLw/333x/Um9683lmbBEiABEjgZBCg\n8A6Dsq/wrq6uhni9e979HXTa+xmO970UB1/4P0ssf1l1HBuNd/v9HZUQT3ewJOHVFbu3ouS9POz/\neAOO1x61ph+T+YI1RETEt8SN66aiW4W02CbCO/qN5Uj67VRILFH5b/6C2htGWeEn+vKkivRg9jCP\nBEjg1BCQMBR5GfOFF16w/raoFfL7L1MYihf88ssv18vckwAJkAAJtBECjPEO80YF8hZXDboOx41n\n+6t9+/HOe7vwm79vx6yXt+HNwrKgovuMrlG48pxajEw7gOK8Ofhiy1uW6BbTRFCLWNa4bZ0C0D4N\noMRzS9y2vjQpdU7bsBrm/UtEm3/U1QOGuKc4DGR3mAhYjQRIoIUIpKen49lnn8W+ffvw+9//Hhdc\ncIHVk7zMvWTJEgwcOBBDhw7F8uXLrQftFjKDzZIACZAACUSYABfQCQOoeJV101CNOqO4N1w+Bp/U\nXoKt5w/GiV0nTMtHG2w9PjYKF6d2wsXJx9E96jDEa14dwCMuHi4R3TIbiQhsEdc63Z/GeOtMJGpT\n9KEydNm01uq75ophqO+e4I4zV3sbNIwZJEACrYaAPGD/7Gc/w9SpU60XN8ULvmbNGsu+jRs3QrZz\nzz3XHYZyhplNiYkESIAESKD1EqDHO8R7o6JW9pLkBUbZPjtgZiXZfAC/ff0Alh44E1t6XoMTUdEB\nW+1kPNB9zojGd/tF4+7LT+Dqc44iqXOtFWctISMiqH2FsXi7VXSLAJdNX5DUmG8R4PqipNjXbcXz\niJL5xI0VX428ld7ugHeDF0mg7RCQ3+/Ro0dDYr8//vhjTJo0yfqWS0Ygc4TPmDHDEuD33XeftVpm\n2xkZLSUBEiCBjkWAMd6N3G8V2nbhfchx1MRtl2P9J+X491c1jbQApHSLQr+UKDMzyXF0ifZMm24X\nyzqftywnXVpa6m7zmWeewYgRI6wVJiWcRMS2XWiLUBfbdCaT2n2fI+F7JvbTrKR5/Lw+2PXcG+5p\nBtVbrvHgviLf3SkPSIAEWj2ByspK/PWvf7VmQ9mzZ4+Xvddff70VB56VlWX9vfDK5AkJkAAJkMAp\nI8BQkwDo7WJbsuX8aO1xfLDzEDZsK0dRaRVMsEmAmp5LXQ9XIH3nO+g9/Eokn3emexaRTp2cS6+L\n+NVQERHAsuCNeL3lmj2JF1w92/bZSDS8RMuKjbJ4Tvzvf2mJbrleftcUy9ut/UgdrUfRreS4J4G2\nSSApKcl6+VpCUfLy8qyXMd9++21rMO+88w5kk6Xpf/KTn0A84cnJyW1zoLSaBEiABNoRAXq8XTdT\nxbacyrFun+6vwjsfl2PzjkOoPtbwsu1ST6buS+t2DH0OFuGSRT+HUcLABek49ss/oIvxVms4icZo\nq/iWuseOHbPivMeNG4ddu3bJJSstWLDAWnBDV5yUNtRTLgVUcIvHHP/7MuKm3W3VO5ZxFXbPecHy\nhD/yyCOQ2M/evXujT58+1l6OZYoyCnALF3+QQLsgUFRUZAlwWY5e3hvRJN92ZWdn46c//Sn69++v\nl7knARIgARI4yQQ6vPBWwa1CW/ZlX1dbUwCu33bQHDceSpLaPQp9T6tFWpwDx48etjzPqS8vQtf8\nDZYY7zT8e4j66a+s2UkkZlsFuIZ8SJ81NTXWtGHy1bB9EY2FCxdi1KhRVpy31hWxLJvUkxAT8XSf\n+GQL4sZ/GzjyDRDfDaXP5uFY2vmWt3zx4sX49a9/7ffRkgeAnj17olevXn7bpZdeai2441eJF0iA\nBFo9gYqKCjz33HPWzCiff/65l72ZmZlWGIrEjOs3YF4FTtJJZdFKvLiqBF3SR+Le0f3RNr5+rUPB\nyqV4q6QK6SPHY3T/pJNEi92QAAm0FwIdUniLYJWkexGvR2tPGK+2xG0fxLZ9DskMeo+7x0WZlSRN\n3HbyCXSPPmp5rMVrLSEj0l7ssaM4f+4v0cWsHNnZeMI73T4J0ZOmWeJbPd76T0+EsyzC88033+C7\n3/0uduzY4e570aJFlvCWGU3swlvtl7r1O4vR+a7/AA5+adUrn/YUKjK/Zwl8qSOhJrIC3meffeZu\nN9iBfIX9ySef4JxzzglWjHkkQAKtnID8fXj11VctL/j69eu9rJUHbglDuffeeyG/8yc3lePJAamY\nXmh6nZqH2mdGtxnhvXJSLLIWCa1c7KufibSTC469kQAJtHECHWZWExHZ9k3EsZx/XFqJv6zegf9c\n+CEW/PMzbNtb1aDojjWxJBendMKYb0XjzozjuPLsYzitc53lNZI4bBHHOgNJ3Bln4sjPHsdpiach\n0VBO+p+FOP3Z3yLOhIpIWdlUgGvIiQrxxj5TOg4ZAwreR+c7v+MW3V/f9p84dMN3LZukXfGuy0uZ\nc+bMaaxZd75MWUbR7cbBAxJoswTkoVuWm5d4748++gj33HOP9QAvA9pjXsiUMDSZjlBe6t62bdtJ\nG6ej4G9O0Y0MLJ86qo2IbsETg1HTlrs4zcKKgsqTxowdkQAJtA8C7d7jLSJVkopV2f+78gjeNatJ\nSijJQTNDSWMp7bRO6Hcm0Ne8mxQN5+qTIpJ1E4ErScI/RAxbXmhXv6f9uxTn5z6AGEeltYpkpyuG\n4vjvnkP0eb3d4SLiJRePd1VVlRXP3ZDHW1+ylL7qjScr6r//iGh5mdJ41yV9c8sE7PvxdOtYRL14\nu3UTAX7TTTdh3bp1Vn5DP+TrZ3lRi4kESKB9Ejh48KA7DEWmIrSnb3/721YYys0339yCYSh1WPmQ\n8RrPNT1nLkDF2kk42f52+5ibflyO+cNTMUX+lLYpb33TR8oaJEACkSfQLoW3XWwLMhHD8mLkpk+N\n2C4uw6f7TBx0IylRQkmMdzv9zBNIiDXhHEZIi7AWsS1eJPUmi6CVc9lLUuEtYlrtSPjyC5xrxHe0\nEeEm6gRRXbuj/seP4PjdD6I+vqsVniJLzovwlnhuu/DWGG/xpovwln6jPtyImCdzgK2brT5Ni/h6\nws/w5a0/ts6ljJRV0S0iXGyU+X8HDRpkPRi4KnrtxFv/6aef0tvtRYUnJNA+CcgL2f/v//0/KwxF\nFuKxJ1kp84EHHrA85LKIT0RTTRHGx2dgqWk0e1kxXrw9PaLNn4zGipaMR8YEawQorH4R/eNORq/s\ngwRIoD0QaDehJiJy7ZslgI3g3rq7An96Y7sJJcnHc299FlR0SyjJJanRuMWEkow3oSRXmFASEd0i\nuEVYi5CVsA0RwbJ0u27yj0lEq5yffvrp1pzb9llIjp7fB3v/uALHho10Cm/zAmSneY8iNrMvYmY/\nAhTlo97YqkLd94NlXa84iOh/vIDY225AzB3DPaL7jFSU/fa/cOCHE6368mAgtorYlk1EuFyTMQwY\nMMCK5/RtX88lxlyWp5YXPZlIgATaNwH52yCzKG3YsAH5+fm46667rAd2GbW8DyLTFPbo0cOKAy8p\nKYkYjPIPVlmiWxrMGtzLq926uhrIBE2BkjOvgcxAFRq8Voe6GrM1mA+Tb+wIkn/hkBtduUvx6gfl\nQUoyiwRIgAS8CbR5j7eKVbvo3n/oiPFsl2OjCSWpOHzMe8S+Z0aQnnuaEdwpQO+kE4iudwptu3db\nhKx9E0Er5/KPS/biTZZNkgh+8STJi5ZHjzpfupRzbS/5/bdwxqLfAfs/97LkxOln4GiffvjHlo/x\n2aGvUGsiZBLMY9Et11yF82uqELt7u4kvkWXoXSk6BtWjbkXZhIdx1HjQpV+xQWxTb7d6yOW69C9b\nWVkZLrzwQsu7rk357i+++GLITChXX321bxbPSYAE2jGB8vJyyLdsf/nLX7B//373SOVvx3e+8x3I\ny94yE1JzUsH8MRg0xYSzZZiXE7d6Xk4sWjgGGZOdYW7zNlXgwcGeAJRdK2ag77jZVreLi6twd3pC\nmCY4sHBMIqxuskyYy6v+YS5u+zARxdULkR7Qm12KGQN6YbZ5OTQjZxXynxjRhuLUw0THaiRAAhEh\n0CaFt11sCwURnYeP1uG9EhNKYlaT3HWg8VCS000oSb+zoq3VJLvHeObnDhRKIuJaxbYKbhHd6k1W\nYSu2iG0S420X3iLARXxLnuWRNuWS17yK0/LMV5Xbi6Ra6KlbAmq+PQaVY+/B4bPOdYeNqKdbvPIi\nuO2iWz3e2slTTz2FadOm6am1sMbSpUutxTj0BSupIx6vxx57zPL0uwvzgARIoN0TkFC5v//971YY\nyvvvv2+NV77N27dvn3up+vAgOLDECN8Joq+zzVzjL94O1bU1u1ZgcN9xkIlOgBzsrH0CfWLMYeUG\nDE8eBuvtlMx5KFv7IIyfJMxUh7UzbsKNs63WsHxPLcb2lE40GUEdZQS1nGYtRtWrdyOwxLcJeGNT\nhbHJ85igbXFPAiRAAv4E2ozwVrEtQ5BjS+CeqDehJJV455MyFJh93XHjJg6SOseY6f/OlLjtepzV\n1SmE1RMs4lnFtK/QlnO74JayIkxV0Mpek9ilXm/55yWi2+75ljzpU+pIO11Ld6LLprWI+2gTYkt3\nIPrAfpOvrQG1neNQ3+siHL24P2quHIYjA68z1zpbfWhb0o7Yp4JbHxLUTmlN+tQkDwWXXHKJe6Ee\nEd133HGHZeejjz6Kp59+2i3oxfu9ZMkSDBkyRKtzTwIk0IEIfPjhh5YAP++88/Db3/62eSM38d2T\nTHz3ItNK1oJ8vDppoFd7Hm+zyZ9n8h8ciBWTojBOKpi0fGc1xvZRqe681tSfNSVLEN9vglXN1wZH\n0RIkZjjzcvL24InRDXv3Nz85BkOmmyeINvmCaFOpsTwJkECkCLR64a2CW8W27L84eMQS2zIzydfV\nzllGGgJiAixwfpKZlcQVStLJhJJIUvFrF9sqsFW4NiS2VXCraNf2ZK92itdbvNwivkXo6ibnIph1\nXFJHyok4lxcs77n7bhzcs9ua+9thIkt+/bvf4YYbbrC8zmKPinztW+yX6xpiomMQ0a1lZO+bZG7f\n73//+5AFe+TYnv71r3/hbmNHcXGxdVn6fPjhh5Gbm0vvtx0Uj0mABJpGwFGAMYmDIA5vFdbeDdg8\nzsjEnHlDMH2KM8Qkc84mrJ022Lt4WGf78diAHphluda95+Le8NhwDJsl3vAsbKp6FYMDu7utXosW\nmhcsJ8sLlo2XDctMViIBEmiXBDyu2lY0PBWvIlBlExH7tYnVXpW/HzOWbcX0F7fijYJ/BxXdyV07\n4dpeMbh7EPDdC2vR5/RaMxWgJw5allDu1q2b34uS8oKkzsUtL1Jq6IaKcRG66kkOJGz1mpSRsiqI\ndYYR2Ys4lnxJMjYNTRFxXmfG+7Vx3Jcb0S2vOKpwl72Gq0gf2ra9XbXR1z6rI58fY8aMseb3lSXp\nfdNVV12FLVu2WOEo0pbcAwlPufzyy7Fp0ybf4jwnARIggdAImMmfgmhZ00ZP5OTr36R1btFt5u3D\ncz+LhOgWM9Mw7qFsl72z8GaJWTBNUt0u/MMS3eZ44j0YFNxQq4rzRwLMsJhIgARIICQCrUZ428W2\nHIvYq607jg93HMTvXyvB/Yvy8cI7e7Cn7EiDA4szoSQDzonGrf2jcduldRiQchRdo49bXmIRuxKK\nobOS6CwkMiOJXWyLGBdRLmVVyIrIFa+vr6c7kCdZjGtIfEu7uolglvalbU12L7j9mlyXNkUE6zik\nvm9bKujtXnFtJ9D+lVdewdlnnx0oyxq/LLrz7rvvol+/flYZmdng2muvtQQ5Zz4JiI0XSYAEQiRQ\n5VoTwbd4wsB7sSonw+tyzvpZznhvr6vhn6TfPMEs3eNMLyz/yDpwFL4FmVpc0px7hjbhZckz0ZXK\n2wmOP0mABBol4FF9jRaNfAG70JRj3faUHTYvSR7Ae9srUHUkeCiJ0cM4Xxa4OSsKPRPqEB3lCSUR\nUStiVfYiSmVTMa3nkqflVFz7CmwZeUMiuyEqWl7bkr3YovaI91r7lTbE6y3hJlpP27WLbRHadvvt\ntks57cu3DW3Ldy/lG0uDBw+2vN+zZs2yphoUO8X7/dprr2HJkiWQfCYSIAESaCqBxAb9xA58+YXz\nFUtt84tdB4Ch3q8v1jhqEJMQ1wSBrK2ZfcqVEKf3BBMpsm7WP1A6cyg+f/1lV4GJGD3I05c1tWCM\nWYyswf+Wu/G1/JtqXui5zTgekgAJtGcCjSuvFhi9CmzZayhJ5TdH8fqH+/CLFz8y4SSFWP3RgaCi\n+8xunTDsghhMGNgJN19Yh94Jx4zorreErYhTEanivbZ7tn292+L9lnK+3m1fERuqkPVFJfVkU9Et\nQllsk/7UYy02qB1yzbcvHYt6t+1lpR15gJB2myq6fW0Ndi79qPc7PT3dKqre7+nTp3Pe72DwmEcC\nJOAhYASqK7AD6OK5bD8qXZlrCWL7taUTfojVntkN4ShaiPjEePxkxS57sSYcJ+DmCbmu8nOxesNa\nrFohsd3mXcncbKSryK4pwFj5pnLsEo/drlqeHUNNPCx4RAIk0BiBkya8VWyL0FbBfcyEkrz/aTme\nerUYPzGhJEvXl+Lzg9UN2hxvvs67rIcJIxnQCT/8Vh0uPeMo4jrVWaJTBKoIVxGmssCNCm5d5MYe\ntx1IbItwtYtXFc0NGhNihrZjF+DqeRd77ZvYJTbYk114a1m74PZ9SLDXjfSxer8feeQR6wFHvN9P\nPvkkBg4ciM2bdRXNSPfK9kiABNoNgYSzMcgV47G7tMx/kZrKzZhirSVvRmzm2c5fr/HehRg53SN+\nY2OdAdjndleFbAR9+X4zU1MpKs3iOKGklGvGQCO9Jw+70ZqTW+rdOe4yW/VY9DZnGWd1tl2TQweK\nNhp3uaTM64zjx3nInyRAAiTQGAFvlddY6Sbmq9jWvYhu2Xbsr8Jf39qF+xeaaape34Etu7/CCeP9\nDpQ6GY9xnzOMVzu9E+66HLgm7RiSuzgXpBFPr4hQEazi3VaRrXsV2xq3LcK1IdFqF92B7GjuNRXg\n0o+IZQ0TUREuAls2KWdPmi972aSe1PcV3L717G1E8lgYitiWJabV+y2zn0js9y9+8QsrXCaS/bEt\nEiCB9kQgGf2GOMdTuGm7jxe5Bit+McSa8URKLPvdXRg49C4sn+ga/9IJmLvW6faOS78d1VVVyBnR\n05lpPNPjU3ugb99e+MEi7zAVV23/XVx/TPCJJZdFfW6yL85jyjxVXYXNf77d56XQOjhcrvuMIT19\n8vy74hUSIAESUAItIrxVaKtnWzyjB6uqkfevvfj5f2/BzL8V4a3CA/gmiGcitXsnXN87GvcOisKI\nPnUmfts5K4kIT/UC2z3bGkZif1FSwzJEbEudUy1aVXzrXkS4XYiLmPYV0L75p0pw6wdG9zKvt8x8\n8vOf/9wag9xjCUeRmU9kOkImEiABEvAnEIdLr3b5mdetxceVnhLlG+a75+vOyFmD29MlaDoOYx9f\nYyYWdKZZN05HgRG8zlCTRDxboA3EusVvoqfJRo+uue0hrzJZ948xc57YkwPPDk5E/B8K7BfNoj5b\n8HKe81LGRb3CizP3bpFnJEACHYRAxIS3im17KMnR2jpsLC7D7/6xDQ/+tQD/s/Fz7K+oaRBtt85R\nGJgWjdsHRGPcJSaUJKUWnW2hJOLZ1lAS9WrrXr3bvmJbvcQqclX06r5BY1o4Q/sXu+zHvt1KnpbR\nvZb3LXuyz8X7LS9ayswndu/3NddcQ+/3yb4Z7I8E2giBXkNGuizNw9tbyl3HDrz+++mu44lYnDPc\nM5qU4Xh2mdvtjY07VGx7itQd2O1a8dJc6xL6FCNx/W9Crk5vYuY5ued7/TyNuo66nGGiSXxenCzf\n8q5zJU1TJiuzr18dXiABEiCBhgh4AuQaKhHkuohtTSq8Zf+pCSWRpds37ziEI0eds4xoOd99tBGW\nF5hQkn6pUTjXeLVxwhlGIuJSvLuyaZiF7sV7rYJa9loukDdY2pGke9/+W8O5CulANgbLaw22iw3q\n/Z45cyb+8Ic/WDO0iPd75cqVWGJmPpF5wZlIgARIQAjEpV9rFoSHtSz7itfykTN8hPEYJ+DuV2vx\nI/MtaIx5oPf9x5R++0LU3jIPdeYbzzizOYrsLOvw1tyZLuGdhZxb+9szGzmuRbVGpmTej6Fpvj0H\nql6H919b4cwwoSnXeC05H6g8r5EACZCAh0BYHm+7yBYPt4QZHPjqCP7+/uf42eIt+M3Ln2Ddx2VB\nRfdZCZ0wvE8M7r0CuOmCWpzX/RjEGHsoiX1WEvVsy15CTCRPvNsatx0olMTuIfYMmUctQUC93xL7\nLcvMS5LYb/F+5+TkMPa7JaCzTRJokwR6Yuy8LMvywrnLUOz+EtSI6gCiW4coglxEt1/a/xamz3Wq\n55xV8zA4ya9EgxcqN79sPQBIgan3fxshVa0pxHJXf/6hKQ12xQwSIAESsAg0SXj7Cu7qo3Vm6fYv\n8fjyTzDl+Y+w4v29+PIr919RP8TdTSjJFed2wvjLojG2Xx36nXEMsWbebRHI4rmWP7oipu0zkkjs\ntpz7hpJIWbvnO5C3288AXmhxAldffTU++ugja4l5ua/yUPY7s+y9zHzywQcftHj/7IAESKD1Exh4\n24OuuO2l+Nvbpc0yePVTI53e7uxlmKUvW4bUYg1W/UnDW7Jwy419QqpV+uaLWGqVzMLDTfKuh9Q8\nC5EACbRzAiELb7vo/uTzr/Dsqh34TzMryYJ/foZte6tg5ggMiCqmUxTSU6Ix5lvRGD/gBAan1SGx\nc60V+iHebRXb+qKk/SVJu3db4rulbEOzkrSFkIyAgNrhRblPTz/9tDXziXq/t23bBhHl9H63wxvO\nIZFAUwmYuO1fuWYUmf3kOp/ZTZrW2IW3rEJe3hrsee72pq1h49iKOU4FDWRn4+qQ3N0OrJ7rXN8y\nI/dh3zV9mmY4S5MACXRIAgG+t/PnYBfdh8xqYY8v3wazzqR/QduVNFlN8kygbzIQjWNWjnhAO3Xy\nxG1rWIk9dluu2TdnHf85tqXBQDHRNhN4eIoJqPf7V7/6FZ555hm391tjv6+88spTbCG7JwESOFUE\nhj+Rj6pHHIiNT2iaYPYxuM/QEQjNV+1TMWEA/lGYb1adjMXZF/bziyv3Ke06TcC9b1ThVrPcREIS\nJ+8OzIhXSYAEghEISXhLAyK+JWygm3lh/JJzu+OTva5JTG2tJ3Yx3u3UTrj4jBPGq11n1RFxrGJb\nBLWKbNlrqIgc+4ptCR1x1vXM+qFdUXArida/V+/3LbfcggkTJmD79u1Q7/e0adPw6KOPWt9itP6R\n0EISIIHIEjCvVCaF5GYO0K0uexnyv7AAbcShT/+BAa77XzpkuxQTl4Akn1lObNk8JAESIIGgBKKM\noA7qulZvt4juuro6a3nw9z49iP/e8KXVcIwJVrnIhJKkG+/22d3MrCSmORHGsol4VkHtK7RVgGu+\neLbtcdpyLklFtu6ti+3khz7MHDt2DIcPH0aVWRBi5MiR2LFjh3uEixYtwqhRo6wXSkXECjfl6y7U\nRg6qq6sh3u+55qtaeSlX0re+9S0sMTOfXHGFecuWiQRIgARIgARIgATaMYEmxXir+L44NRYXpnbG\nqEu6YNLgGAzvVYdzjOiWVSZFSIsnW+fb1hclNXZbX5S0z0oi5WWTuiK+g3m72/G9aPdDkzj93//+\n99iwYQMuuugia7yffPKJNR3hL3/5S8gDCBMJkAAJkAAJkAAJtFcCIQtvAaDe7044gR8N7IpLUoDY\nTvWWUBbhLB5ZnQJQBbdOA6hTAEq+lJNN6miYid3b3VY9uu31QxLpcckUgzLzyUMPPWTNaCMPdE88\n8YQ188mHH34Y6e7YHgmQAAmQAAmQAAm0CgJNEt4iiDUkRASzzDAiXkwR0yKsVWTrXqcA9PVuq9jW\nFydVaOu+VZChES1KQD43stjO+vXrceGFF1p9ifdbXsik97tF0bNxEiABEiABEiCBU0SgUeEtYliS\nCm4JB1HBrVMAqtDWvQpuXeDG17PNUJJTdLdbYbfXXnsttm7d6vZ+y3sE4v0eNGgQ8vPzW6HFNIkE\nSIAESIAESIAEwiPQqPCWZtUTLYJZvNUaUqLCW+O25VzFtohzX8Et4l3bUkEfntms1Z4IBPJ+f/zx\nx1bst7yMydjv9nS3ORYSIAESIAES6LgEQhLegkeEsgpv9XiLyNYwEhFPvmKboSQd94MVzsjV+z11\n6lTrGxbxfv/2t7+l9zscmKxDAiRAAiRAAiTQ6giEJLztXmoV3+LNVqHt69lmKEmru89txiB5gJPF\ndt555x307dvXslu93zNnzqT3u83cSRpKAiRAAiRAAiTgSyAk4S2VVHyrF1vEtc7BLceyMZTEFy/P\nwyVw3XXXobCwEFOmTHF7vx9//HFrvu+CgoJwm2U9EiABEiABEiABEjhlBEIW3mKhXXzrsewpuE/Z\n/WvXHYv3WxbbsXu/i4qKMHjwYND73a5vPQdHAiRAAiRAAu2SQJOEt52AXXjbr/OYBCJNQLzfMvOJ\neL/lcyex3/R+R5oy2yMBEiABEiABEmhpAmEL75Y2jO2TgJ2AvMjbkPd71qxZjP22w+IxCZAACZAA\nCZBAqyRA4d0qbwuNaojA0KFDLe/3gw8+6PZ+P/bYY7jyyiuxZcuWhqrxOgmQAAmQAAmQAAmccgIU\n3qf8FtCAphIQ7/e8efOs2O8+ffpY1eVFzKuuugri/a6trW1qkyxPAiRAAiRAAiRAAi1OgMK7xRGz\ng5YiIN5vEdy+3u8rrriC3m839HKsXDgfT85fgoLyOvdVHpwsAnUoWLkETz45HyuLKk9Wp+yHBEiA\nBEiglRKg8G6lN4ZmhUZAvd9vv/02fL3fjz76aIf3fpdvWICsyVMwfcoE7D2iTB0oWrsSL720Aht2\nUQwqlZba7319AqZPn4KsjD9hf0t1wnZJgARIgATaBAEK7zZxm2hkYwSGDRtmeb9/+tOfumO/c3Nz\nrdjvjz76qLHq7TS/Ev/4/Szn2KbmYVTPGOexYxum3JiFO+4Yh2EPr0JNOx196xhWDEZNW+4yZRZW\nFPBBp3XcF1pBAiRAAqeGAIX3qeHOXluAgHi/58+fj3Xr1uGCCy6wepBpCCX2+9e//nWH837Xla7B\n5Dwn6Hnjh8IluyNM3oGXJg2wHnYeeqkkwm23jeZqSpZY448a8BCKAjzFxPS5HvMynWOZ8uIGMOCn\nbdxXWkkCJEACLUGAwrslqLLNU0rg+uuvt7zfDzzwgCWI5GXL3/zmNx3O+128zqW6MRE3XZLkuScJ\nA/Bc/iasWbMexc/egjhPTlhHjgOFVr3dB92xLGG101Yr1dYedZpeuBtHAr7Xm4LMO7OdZeYuR3EA\ncd5Wx067SYAESIAEmkaAwrtpvFi6jRDo1q0b/vjHP54y77cs8lNT04BvM1ie8YfW1dSYRYKaC7oc\nq15Y6mxk4nfQ10tdx6HPwMEYPnwo0tO8Mky/3n1Xlu/H/v37UV7ZkFr0GFpVE1B1eg2kprLcam//\n/nI4GmzSm53TBlPe05WnzRoHysXGcmkvUAFb0VD6NsV9+Ttc9crLHZ7G7Ef2YTdgwoVDbnTVWIpX\nPyi31+YxCZAACZBAByJA4d2BbnZHHGow77eEobRIqinA2NhYxMfHYpJv+EVdCSa58sYsLPDp3oEl\nY2IRGx+Psc/65vkUbezU8RneW+cslPP9a3zCTKSfKOvbgDELi9wtOYoWIjY2HrETlmBXyWpMGhCF\n5NQe6NGjB1KT4zFg/HzscotlE2IyXtpIdoezrJs+xBlyYVYXHf7YWne7clBp2ntoeBTik1Ot9nr0\nSEVifBTGzHgJ+73EqrFtrJPdwg0FWGLCWJw2mPI3LYRb+taV4qUZ4xEVn4hUsTFV2ovF8EnzUeQz\ne0vofQMWA8M/dsJLKC3dYNmc6LI5NTURUcMfwob9Tgg1Rc4Qk8RBk11jzcOQZCfXqKjhWGuzIy49\nEzkZzmIrVuUz3MRFjDsSIAES6GgEKLw72h3vgONV7/fatWu9Yr9l0R0JQYn4vN8xp6G3i/OiV97x\niEVzraZ4Exa58vKefQtevs/Kj/CCRod0ad6NqtnzEbSpi3olNNzYUbu71lVs6QT07TcSi5wRJO66\nhUunoO+PlrjH49jnzvI7WLe3wi0uKwsWItm0N9f1IICMTLg0KPJm34EeN8335uBqbfKwQZhgNyLR\nBcWI7scG9cIds10efVvv6xZNQcYP/+q2Mdy+sfQO9Oo1zGOz9rFuLob1+DEKzBNAbW2VXg2wX4eK\nKvsTRTJ6uj4UhZu2u+0LUJGXSIAESIAE2jEBCu92fHM5NG8CN9xwgxX7/ZOf/MQd+y0vXcrLlxH1\nfsf0wS25rrfp8lZhm20iix3vrfEYVbgM+bb55cq3vA3Vprd++xJPuTCO9hSpxzwbGT2CCO8gbWfm\nLMOeimpU7duEqW6l/AzWlYqgTMBdfy9DRVkx5riGmpGTh7KKMuzbtw8V88a4vOyleMrtEc7C8sIy\n1G9di621ZcjLzXL2vm4KFqy1gfCyKQPzjIe4OH8Nlt+TgViTV/LKE5jleijImLgAO6tqUW9EcOGa\nBbBMOXTUJfqb2zcg7ReXVaG6YicWTFQIS5H7UhESMiairKwCO1fNcVmcgbxiYbIP+8z1MX3sYTwJ\nuOwa13jRzKcqLz48IQESIAESaEsEKLzb0t2irc0mIN7vP/3pTxDvd+/eThekTDco3u9Fi9QX3exu\ncNnNY1yN5OHNLerXrsR7q+xe2kL888NSV7k65L+1wnWcg2u9RFvT7al16IuO7uCMJjWSNWcN3nzi\ndvRMikNC2mDMWjTPVb8Qew4524xLSkFSiglDSXRm9TYu3RRzLS0tDUlxMdbFys15mO2qOWfTYozt\nn+I8i0nB6JnPItelZVe8Vuj2kLuKW7t5m97CgyMGIn3gcIwdPdC8CFqKF+7Q+5RjFgeahD4Jpq+Y\nBPQfPglrq8qMEB6PJFO7uX1n5q5Cvmk/PSUBcUl9MGneUmS7jMtb9R4cMXFISUkyoTOnua72xrm9\nhEka0sx1JwFXltl1Pc31ALRuFbaHd1s8jfGIBEiABEigTRKg8G6Tt41GN5eAeL+Liopw//33W97v\nEydOICNDPZrNbd34gy/JdIu0Fa+5Ynort2OVxn+4upj7+gfOebRN+MQ/ZzvduJm5I9Gz+Sa4WwgQ\nTOLOC3yQhVkPDvcSjkkDPOMJXMdcDRC2snuzJ9Y77theEzteZHEX9rtKvkC1q7HCrf7hFxk5a/Dg\nYJdQ104dh7DNdZw5J8ufU0IK+qSJ7Aaa0zeQhacfGeHFAHH9ME5d/3kF2OOKd/fi63XiMtRvl2B5\n7v0u8wIJkAAJkEC7J+DrlGn3A+YASUAJiPf7z3/+M8aOHYuCggIMGTJEs5q/d4m0pXMLUTj3nyh9\nZgRSt7/jjLvOzMHyO6sxbsJcYFEedvx5LPqVboY5s9KYmy9tfv/NbUEEpD1SAiEpSr9eY7t4wlym\nDMvAFL8SrguH/DN69zzd/6KJNdEWM684zz/fdqU5fVvN+DGIwbnWtyTygNQVsWH/9TwTXSVmhokE\nSIAESKDDEQj7X0eHI8UBt1sCmZmZkC2yKQZXfG8sYIS3+YHNpblI37za2cWAb+O7Nx+zrgNL8d7O\nZ9G1YJWre585t11Xm7NrLRovc2ouRiQbx7jfYKqRajzqKqjd2f4F3VnOg9BH1uS+fXrS09qaKtfh\nQRwRd72f0Voy2H43vvYT9cHKM48ESIAESKC9EKDwbi93kuNodQTSLr/BetlPXph8Z/U/sXet89XJ\nnP9IR5yJoMgx1yX+eeOb/wQ+WOq0f+rNPnNuOy839Wft0dYRROyxIwvznpqJ/s39i2MEq45s3Yef\nYeZwn1AUG6hm9+2n62uwq8j1+mvGRTg7LNEtBjLUxHabeEgCJEACHYoAY7w71O3mYE8qgaTLcKdr\nIotFk8dhuhXfnYFhGanGjDSMdM18snTKOEx26e7c713tHVdsSpasXogxZk7tKDM/tswPPWPhBmdc\neJDBnH3pIFduFcoCrjwTpHK4WV38lCo8duTh+Tf0RdJwOzD1YrviLFf1dQaob4t15SVYu3mX9aJm\n8/rOwzOLNngbWvkB/st1n5DRB8Z575/8EbjKOFC00VU58zr0Dlu0+3fJKyRAAiRAAm2HAIV327lX\ntLTNEUjA0OyJ3lZnjEVGmtPte+lNOvOJFsnCTVd6e3D3r30M/UZORl5hFnLn5SIrYx1mTx6GHy8p\n0koB98k9+7mur8P2L9RHHLBoMy/W4agr+iJv8vMoMKs7lu8qQMEuZ59p14xxv2Q6N2s0Fq4tca9A\nWeMoR9GGFZgxfhJWlIRoY1w67tH5C833BaMnLUSJ6bOmphKbVz6JQan9cOPsDZbwbl7fGVg6ZRiG\nmwV+Ss2qnY7yAjz2g2Hu6R5z7rjBEwKvS8abCP6F/1NgbClHweYC2NbPMYzr4HANMWNIz/AiVJp5\np1idBEiABEjg1BOg8D7194AWtGMCfa75vnuxGBlmxtgbjK/bmZIuuc7MnWFL2bdigJcntBIrHp9l\nCmRhU8WrmPngTLy6cZMlZJdO+Juft9fWEuJ6XOwWvK++s92eFdJx6K9SJuHCTJ0NZq4RvmYlyb6D\nMKjvX1ApPcX1x+OrJKhGUiEm39gPibFO7318Yioyho3D7KWL8NnX9sVmnKUb+jn4x7PdYytcNBn9\nTJ/x8ckYkjXd9GCSeRCw7G9W31ZLWGcW+OllVu1MTB2EWa4oE2TOwQMj9C6awJHzLnXf40UTBhlb\nUjFoyAR4PUtUbsHLrhltMi7q5fetRkNj5XUSIAESIIH2RYDCu33dT46mtREwc2Dfb1PX99pnLEm4\nBNnuRVlMzPcd13q8qDKOmt2QsPCM3Icx2DlDnlF5gzHRClHZhi+DOYnjLsRI16TT65a9E3BlSAtV\ngPCQwDHI9hgKp8deUQ+f+jJ0LRy9lr14pDWXtpz3HPEE9m1ahuyA769mmvEsww8u0QFqC2Yf0DZz\nPWkwFpflIzdbBb+nTnaOWfDm79luj3LYfZuHncV5xmZP09ZR5tQF2PPGNPfDk3UxZbgR1bk+JYfg\n9HjPpfIt77q95VmZfT0ZPCIBEiABEuhQBKLqTepQI25FgxX0x48fx7Fjx3D48GFUVVVh5MiR2LFj\nh9tKWdRl1KhR6N69O+Li4hAbG+uK9Y1yl+FB6ydQU2MmfY6JQZzZfFOdyasLkFe3fzUG9RiJ3gvy\n8eqkge5qJUvGo9+EQqzal48RrrAVd6btYP/qGegxUl7fNCsqmrKj7WXr6lBjnMxxroVutJrYArMw\nTAAzTbRE4Dpa11FZbrUZE5eApASvuQi1CGoclahwmOlAzOc43pRLMOX8iDTSj7sxc1Bn2iuz2otH\nQlISZC2dhlIofTuKFiIxY7JpIgv5ta9iYEwdKsvLUG1c6PEJqWZcQTqoM2E2JizFALRs8aCtw8qH\nBiFLZrjJyMW+rTO9hXtDBvM6CZAACZBAuyMQ5L9IuxsrB0QCp4yAPDQ1lGJMXqBfxOovt1uhE72P\netfcv32pdeGbagnPCFTTWT7t2iwjH2ebyONCLH+zGKPv7u9pyBL6nlM9ElsaTA3U0fIJZtVKr0gZ\nzbDt4xKSkGa2oKmRfux1Y0Jpz1UhpL7tjVvTBcZYK1E2YrGzllk9M8WscumXagx/a1pJI+fvH0PR\n7QeIF0iABEig4xBgqEnHudccaRsjENv1zIAWp/SUAIgMdI9vWHRbFU1YysM6c0ojMeEBO+LFiBAo\nffNFM1u7pCw8fKvt4ScirbMREiABEiCBtkSAwrst3S3a2qEIxPVKN1LNvCtY4/2q45Gv95mrvYOH\nPbhIDZ08zfXi32ysLgoWFN6h0DY8WPcMJVVhrtXp27QDq+c61ySVWP2hIbnOfdvgOQmQAAmQQHsh\n0IjLrL0Mk+MggTZIIOY0I6/NupfT/we7pg1GHxlCXQmen269cYnzAkQ1+I0yZQTyq6vgqI214qn9\n8nnBi0DCJWNRmH8damPPwCWh8PWqHegkAfe+UYVbTdhKQlJEGgzUCa+RAAmQAAm0EQIU3m3kRtHM\nDkggpg9+ZOasnjt9Lm4Zfw4eu+MCbFowDosMionTx4UcK2y97BgkdLsDkm14yHFp6D/QM1VgwwVD\nzyH/0FmxJAmQAAm0dwIU3u39DnN8bZrA4GlLsfjf2ZgwdzqynIHCmDhvDebdnt6mx0XjSYAESIAE\nSKAjEqDw7oh3nWNuQwTScPcza5E92wGHmcUk3szi4Zmmrg0Ng6aSAAmQAAmQAAkEmYuMcEiABFoN\nAYYrtJpbQUNIgARIgARIIGwCnNUkbHSsSAIkQAIkQAIkQAIkQAKhE6DwDp0VS5IACZAACZAACZAA\nCZBA2AQovMNGx4okQAIkQAIkQAIkQAIkEDoBCu/QWbEkCZAACZAACZAACZAACYRNgMI7bHSsSAIk\nQAIkQAIkQAIkQAKhE6DwDp0VS5IACZAACZAACZAACZBA2AQovMNGx4okQAIkQAIkQAIkQAIkEDoB\nCu/QWbEkCZAACZAACZAACZAACYRNgMI7bHSsSAIkQAIkQAIkQAIkQAKhE6DwDp0VS5IACZAACZAA\nCZAACZBA2AQovMNGx4okQAIkQAIkQAIkQAIkEDoBCu/QWbEkCZAACZAACZAACZAACYRNgMI7bHSs\nSAIkQAIkQAIkQAIkQAKhE6DwDp0VS5IACZAACZAACZAACZBA2AQovMNGx4okQAIkQAIkQAIkQAIk\nEDoBCu/QWbEkCZAACZAACZAACZAACYRNgMI7bHSsSAIkQAIkQAIkQAIkQAKhE6DwDp0VS5IACZAA\nCZAACZAACZBA2AQovMNGx4okQAIkQAIkQAIkQAIkEDoBCu/QWbEkCZAACZAACZAACZAACYRNgMI7\nbHSsSAIkQAIkQAIkQAIkQAKhE6DwDp0VS5IACZAACZAACZAACZBA2AQovMNGx4okQAIkQAIkQAIk\nQAIkEDoBCu/QWbEkCZAACZAACZAACZAACYRNgMI7bHSsSAIkQAIkQAIkQAIkQAKhE6DwDp0VS5IA\nCZAACZAACZAACZBA2AQovMNGx4okQAIkQAIkQAIkQAIkEDoBCu/QWbEkCZAACZAACZAACZAACYRN\ngMI7bHSsSAIkQAIkQAIkQAIkQAKhE4gJvShLkgAJkAAJkAAJkAAJkEDbIlBfX9+gwVFRUQ3mtUQG\nhXdLUGWbJEACJEACJEACJEACp5SACm7ZV1dXY8mSJTh8+DCys7Nx1llnwS667cctaTRDTVqSLtsm\nARIgARIgARIgARI4ZQREdJ84cQL33XcfHnjgAUyfPh3p6el48sknUVNTY+VJGd1a2lAK75YmzPZJ\ngARIgARIgARIgAROKgEV0sePH0ddXR3efPNNd/8OhwMzZszAgAED8Nprr0HKiDjXOu6CLXBA4d0C\nUNkkCZAACZAACZAACZDAqSUgYlpE9bFjx5CZmelnzM6dO/H9738f3/3ud1FSUmKJ75YW4BTefreB\nF0iABEiABEiABEiABNoqAfVcy16Ed21trRVaMmnSJHTu3NlvWOINv/zyy/HII4/gq6++surY2/Cr\n0IwLzX65UgxjCo+A/abqsW9Let2+9y3DcxIgARIgARIgARIgAQ8B1U3q9ZaXJyXGe+TIkXj66aex\nceNGT2FzJOJ87ty5eOmll/D444/j7rvvRqdOndwvYEbq5csmC28ZiCb7sV7jPnQC+qGw7wPVtueT\neSBCvEYCJEACJEACJEACTgKqm+xhIyrAZTaTJ554Aps3b8Yf//hHfP75517YysrKMHHiRCxatAh/\n+MMfMGTIELcAF/HdXAEeZYzzKGmvrr1PtJjsP/jgA3zyySdWELp3KZ41hYCwlE2C/o8ePWq9XStP\nWwcOHHA3c+edd6J///6Ii4tDbGwsoqOjrbzm3nh3BzwgARIgARIgARIggXZGQPSVim3xZovOkk3i\nveVc87Zs2YJ3333XyvNFIFpr9OjRmD9/PtLS0twCXMqFq8OaJLz1yeHnP/855s2b52sfz0mABEiA\nBEiABEiABEigXRFISEjAL37xC0ydOhVdunSxBLgMMBzxHdLLleqZFeEt3lnZM5EACZAACZAACZAA\nCZBAeycg0w/+8pe/xGWXXYbS0lK3DhZ93NQUkvCWRtUlL+55eUOUiQRIgARIgARIgARIgAQ6AoGM\njAz86U9/wjnnnGNp4nBEt3Bq9OVK9XbLXsS3CO+ePXviuuuuc3esneu+I9yASI1R+crDjGyffvqp\nFeut7ffu3Runn366FdstX2nIG7ZMJEACJEACJEACJEACTSOgmkv0rBzLXuK+9+/fj6+//jpgYxJm\nIiHWssx89+7drcgP0WL2GU8CVmzgYqMx3mqkhJhIQLqsdS/r3Msmx3JNBKMYz9Q0AnrT5WFGli09\ncuQIpk2bhr1797obkpstb9TGx8dbc0/Ky5UiwMOJK3I3ygMSIAESIAESIAES6AAERGtJUs2ljs5v\nvvkGL7/8MlauXGk5lX1RiN6SFyt//OMfQ4H51OAAAB+JSURBVGZCEdEtW7du3aw475iYGLdT1Ldu\nsPNGPd6+lVXwqfiz733L8rxxAnZ+ytZeyzfffm4vx2MSIAESIAESIAESIIGGCaiGevvtt/HXv/4V\nhw4dClhYlpK/77770LdvX0toSyGtG7BCEy6GLLylQ3GryxOArPojHnB5epBzeXrQJ4om9N3hi+rT\nl3i8ha98ayB7exLW4u1Wj7c8YUXq5tv74TEJkAAJkAAJkAAJtCcCqk1FX8lWXFxsLZJTVFQUcJji\n2b7rrrtw9dVXW15tmcpZZjGRTfSY3cvtq9cCNhjgYqPCWxtW0S1zSatAlGMNM9HBBeiDlxogoMJb\nwnWEpTK2FxfBLfFFXbt2dX+1QeFtJ8RjEiABEiABEiABEvAmoLpURffy5cuRm5sbMDRaBPYtt9yC\nMWPGWI5OEdpyTTSY6C/Z5FzEtzicw43vFgsbFd46DBF70pkkORbVL4bR262Emr6XD4XwE4+3CG/h\nKjfTnlR4S0yR3HR6vO10eEwCJEACJEACJEAC3gRUdNt11l/+8peAovuGG26ALFaYkpJiaTHRtqK9\ndBPtJZtcF60mOkyFt+i2pqaQhLc2rKJQzkWEy4B0a2rHLO8M9BfhLR5vSXKsrJWPPF3Jk5YG86tA\n9y2n5bknARIgARIgARIggY5MQIW36CoJjRbNlJyc7LUy+EUXXYTJkyejX79+lpAWvWX3dAcS3M0V\n3XJPQhLeUlCFnohv2XwFtw5SyjKFRkCYyYdCkt3rba8tN1k+DCK4dS/3Qu+HvSyPSYAESIAESIAE\nSIAEPM5NYSEa6+mnn8bMmTOtGeS+973v4aabbrK0lOgs0VgqtHUvIlx0l+SLs1k21V/N0WAhC28x\nXDsUwejbKYW3EGpaUmZ6Q/WrC3sr+qCjZew33l6OxyRAAiRAAiRAAiRAAk4CEtutelU068UXX4zF\nixdbU2GLEJckmsru6daQEnV0qvay6zNf/evsLfSfTRLe2mygTgNd0/LcByYgHwjZVFzL3jcJV3u+\n3nzy9iXFcxIgARIgARIgARJwertFJ6mGkr2IaI0ikL1cU9Et3m3d7HHcds0l5SORwhLekeiYbZAA\nCZAACZAACZAACZBASxAQoSybiGcV3OIFFwEuSb3dIr7Vwy2iW663hODWMVJ4KwnuSYAESIAESIAE\nSIAE2jwB9U6LgFaBLYOSY323To7Vu20X3Cq6pby2I8eRShTekSLJdkiABEiABEiABEiABFoNARHO\nIrAlybF4vjXuW0W57iVfjrWsddACPyi8WwAqmyQBEiABEiABEiABEjh1BNRbrWJa9hJqokmFtuzt\nm+a31J7Cu6XIsl0SIAESIAESIAESIIFTRkDFt3i9dTILNUbFtv1cj1tyT+HdknTZNgmQAAmQAAmQ\nAAmQwCkjoOJbDLAfBzo/GUZSeJ8MyuyDBEiABEiABEiABEjglBHwFd2nyhD/iaNPlSXslwRIgARI\ngARIgARIgATaMQEK73Z8czk0EiABEiABEiABEiCB1kOAwrv13AtaQgIkQAIkQAIkQAIk0I4JUHi3\n45vLoZEACZAACZAACZAACbQeAhTerede0BISIAESIAESIAESIIF2TIDCux3fXA6NBEiABEiABEiA\nBEig9RCg8G4994KWkAAJkAAJkAAJkAAJtGMCFN7t+OZyaCRAAiRAAiRAAiRAAq2HAIV367kXtIQE\nSIAESIAESIAESKAdE6Dwbsc3l0MjARIgARIgARIgARJoPQS4ZHzruRe0hAQ6LIHKopV4cVUJuqSP\nxL2j+6M1/mGq3L8LB6pqkXhWX6QltUYLO+zHp/UMvKYSu/YcQG1sIvr2SQvtcxxOHb8R16Fg5VK8\nVVKF9JHjMbp/kl8JXiABEmgdBOjxbh33gVaQQAcmUI7nsrMwZfp0TF63u5VycODFkX3Rr18/jHwu\nv5XaSLPCJ+BA0dqVeOmlFdiwqzLsZhzbXkRf8xnp1zcb+Y7QmgmnTqCW974+AdOnT0FWxp+wP1AB\nXiMBEmgVBCi8W8VtoBEk0HEJOAr+humFMv4MLJ86KjQv4SnAldjb2WnvuNhT0Du7bFECjm2YcmMW\n7rhjHIY9vAo14XYW28VVMxEhf0rCqeNnXwxGTVvuujoLKwrCf3jwa5oXSIAEIkqAwjuiONkYCZBA\n0wjUYd2LU5xVMu/HjT0ZwtE0fv6la0qWICoqClEDHkJR2ArSv11ead0EYvpcj3mZThunvLgBda3b\nXFpHAh2WAIV3h731HDgJtAICNcVYPtdpR/Z914ORqc2/J7W1R52NFO7Gkdrmt9chWkgYgOfyN2HN\nmvUofvYWxLXJQacg885sp+Vzl6OYD11t8i7S6PZPgO6l9n+POUISaBECdXXynz0OMQH+ijjzYkxe\ngEybNeUfrMJS13nW4F62HJ9D01d5ZQVqjZCMjU1ASkqCTwGgSfbU1VnhBHFqX53DtO8w7cciITkF\nCWEoL0dlJWqqq1FrggwSkpNMG4HH7mtnZfl+VMu44pORkhS845D6sIvtRt2edagsL7P6NxYgIdWM\nPbDZ3nzNC4H7K6qNzeZeJCWYvDrLw+rm6Xt3hLexJa4BJubuocZ8nGJMfgPdu/oP8JmqcaDSUWPG\nYMgbe5KMPYHa8OLuY798jvsMHIw+vnbbz0Psx13FFWviqCyHw9xgZeXOb8JBjWmjQj4k1mer4c/n\nhUNuNGWWWturH/wB/YemNKEXFiUBEjgZBOjxPhmU2QcJtDMCRQvHGAEcb7YozN/sHU+6a8UMV14s\nlpQEf8Psiy3vOclk5OKaPoFEZyVWz38IUaav1NQe6NGjh9knmjCK8Xhps+cVsqbZ48CSsbGIjzX2\nFZVi85IZpv1EV/upSIyPwkMLQ/yqvq4cqxc+huEDopCYnIxUY1+PHtJGLIZPWohdPl5HR9FCJ5sJ\nS7CrZDUmmXrJOq7keAwYP9+vDkLso6bIGWKSOGiy69OWhyHJJuREwk6ihmNtuV2FO7B24QwMiIp1\n92/Zbe7n+BlLUBrE7oINCzHAPCRY9yI5EfNemWfG5OQ5P1BssWMzxki+YTLppZIAvwkOLBzjzI8d\nsxDenyZn8YL5rs/b2KXQT1R50Wo8Nn44ouITzRhS3fbEmrEuXLvLqx87d1/7FxZIi+YzMcbJaszC\nIq+6TenHUzEBZdvWYsZw+Vx4bIsaPgOb99vvg6dGoKNK8xl5yLQR72rD+dmKwpgZLyFQM3HpmcjJ\ncLa0YlU+w00CQeU1EjjVBOqZThmBEydO1NfW1tYfPny4vqysrH7nzp31F154Yb35TLi3RYsW1e/d\nu7f+q6++qq+pqak/fvx4vdRjIoFTSaB65/J68//d9TnNqd9Z67KmYn29CTN1Xs+cV18W1Miq+sVZ\nrrLZy+qr/cpW1C/QfFebmZkZ7t8N+T2Zs97ZQ9PssfWrtgbYZy8utFnkqZM1L999vXBBlpc99t9d\n69hnXFWFC4KXFzuyFtdXuXuorw+1j6r8eUHbXr5TCVfUL87We9fQ3nZPjS3B7F6w5h+ez8LUvHr9\nKOgQ9uRNddu1oNA+Mi1RW78mR+9rRn3ePr8W6nP0/iib6vz6LL3WwH5ZsY63EfstmwLf3/oI9uP5\nbEyst2PwsM2qz7fhqcj3+axkZHo4y5gD/n5VeX5nTH6FIuaeBEig1RCgx9v8NWQiARJoGoG4PmOx\neJ6RPlaajYefLbCOVvxiGNa5ri5/biKCftFdswfv5zkLZ12X7hdXW7r6KUx25Wfm5qGsth5r125F\nReFyaM/TH1hgTZ3WPHsysGBNMapMmMjO9QvM3CrOtHTCkyG+nJiJOcvXY19VNcxfdtRWFGOO6yU3\nLH0F29RF62rXvsvMWYY9JmSjat8mTNWO857BulJfr2jjfSRkTERZWQV2rprj6iIDecVlqCjbh33m\n+hjXNwqlK3MxQaIRJGXlIn9fFYzURVVZIeZlOy8D5p7+1dvzqzmyz5i4APk7i7Fm+XJcabys07Xe\n3OdR6DVeB9Y97wriRw5G9PcPETIBJrjytvtdzRdi+ZvF9q5QU7TOWONMU+/JhL2FzIlzsL54H4zE\nNuxrUZyX6677ypvb3Me+B172n9H4/CNh9zN1MYrLqlBdsRMLlBEW4cm/N8zWaWspnnJ/c5GF5YVl\nqN+6Fltry5CX6/r0r5uCBWs93/o46yXgsmv0t0NnWPEdPc9JgAROKYFW8wjQAQ2hx7sD3vR2NeQ9\nHk8kMuvnzMtxezYz52xqfKRVHq+l3YvsrFhRb2ZocLYXwHNXtibX1ZfxkO5RD2mo9ni8m0Z51q/a\n4/GMSt+Fi7Pd4/B4aD11vG2t9fPwShvVNs/2Mren2dvzmjVnjVfdik0ej/W8fLuvsml9ePo2HlTv\noRnL9tXPyVAvd3Z9oW9+bXH9RLcHOade0Xq8sqZu1gK/bzI89wP1Oav2CQJnKlvj/gbEm5sW0L3t\n3mXM8Wp//ZxM1/3Iqt9k8wjXVut91zZkb/P4Tlzu/halMfulnn774mtnuP343l/xnhvt7RyLeu7F\nYvdnxePxtn8W5myyfxZkjPvqc133MGPqKq/PkOQWLtDPrzcvyWMiARI49QTo8T6ljz3snATaMoGe\nyMlf4BrAOkyf4vZL4rmfDW58YMbRaPdeelVw7Mbada4riV1wYFcJioqKnFvJLnxmvMTOVIg9h9TF\n2nR7subNwoie3rHl/TLVYwhszN/j6qehnedlwBpHJcr378f+8krs2V3qrnDwa7XVfckcZGHWg8O9\nXgJMGpAJt1PUXtSU0pcFQ+nD/m6ledPTOzm+xHvWnOlAZu5E9PceunE+p+OeOTr+bfjSz/RMrHpu\nkt83GSnXjHHbPnvZ2+55sHe985r7G5DsLHXpe5vkPOuJsfoNSuF0vOP2+O/CP6a7PggTszHI9oGR\nFzGtZF68rTQvH+437MvL96B0t6v9HYfgZz4C2++qEXAXXj/+9xdxGRinX2vkvY89PnH09s53b17r\nPo07tte8D+D67JvfgV0lX7jHVbh1uzvm3V3BfZAQ+lzi7jo8IAESaGkCFN4tTZjtk0A7JpAw8F6s\n0re5XOPMWT8LfVQphjh2E+zgXdIuyvMmm5UA+yEjI8O59euLIeNU5AM1tqqRsCcm+Twjz5ypa/eu\n3nb5ndWhZPVCjJcX4BJdL1emJqNflsc+vyp6wWa385LfBVfJZvShfenexnXAgDS96rOvcp2fha5+\nURjmJVRfsS6l4/pjQq6L2tL/wnvlctGBt5a6wkwy5zU6R3tGluexY+lq59OBo+gtaKDKnOzr3Q8g\n0npdZQkWzhhvvXibrC8wpmZgtuvBQsr4fwwbsF8KN5DC68c05nc7Y3Bu796uXroi1t84twWxXTxP\nGFOGZZjVMF2fffM70LffEM8YD7mrBDg4M8D9C1CMl0iABE4qgSC/+ifVDnZGAiTQJgk48OUXNqVj\nxvDFrgPA0KbNyN3YOn+5c0zc8lHX/NRenFIx8iKPSBGxFwl7tIsDh77Ww4D7goUTMGjyUndephGP\nF/Y+EwfWzkWeNxZ3maYetFQfByuOBDblqKq5I9b0jX7B94Fr4ZpxdwKzxDu9Di+/U4rh/7ELz7pi\n9Cfed1Ojc7TH9Lzeio0XB3fes2+hctJAbH/1ZVdvEzH6StsbA5UFmJA8yD0VpfHfI3vihTDkMXeR\nq9MG7GzS5Qj3U1tT5er+II6IO97+0W3AsMypuRiRbD7+fvnVSDXfkjTcxG58LeI/0IOSX1u8QAIk\ncLIIUHifLNLshwTaIQGvF/Vc41s64Ye446atGNGQQ1U5GFGgQSLwfQ/Mlpe1oBgzJ6VrraD7JtvT\nxc+li5p9u9zhEYMuPLvh/moKkKuiOzMXhX/PQf8k55/UupIBiO03oeG6oeZEug8b131lgYX3sS7i\nlZWnBjOveah2mnJx6TfBxB5jlqm6KG+1CT0ptVoRUfzD74Ry/1Iw+r6JmL5ukel+GdaUfBvFlpA3\nL3Pm/BDpNgFZ9EquW3TnLC9E7tj+Lu92HQYciMWECGnvZvXj99GqMQ+lrrCZjItwdsOKGbVH9Tcj\nC/Oemon+Yf2nZqhJEz6+LEoCJ40AQ01OGmp2RALtjEDlZkzJcgUCZJlZLsyMIM5UiJHTl3hEdUPD\nTjgbg1xhv7tLy7znHLbl5U1+wUi4EFIY9uRNfhJrfSZE/mDlf7k7Oy8tiDoypTR36tR73aJbKjvM\nojSRSs3qw1f8xXfFWS7D1k1/HiV1PlYaob9ouku1Zo6E15cJPkX9T9Mw7iFXuMjSyRim4UDZ9+Ea\nm7Pav57nSvp3fuiaVaYQ4/oNwixX1v23XekpZB0plRxMcotuyaiEmUQkgincfvLwzKIN3nZUfoBn\nzTOFlTL6INk71+vs7EsHuc7z8PwbIX36XeUdKNro+gYm8zr0VvO9WucJCZDAqSRA4X0q6bNvEmiz\nBGqw4hdDoI7FZb+7CwOH3oXlZkoMKy2dgLl+U535DtbEQg9xXivc5PuSmE3EmcnkRpvFaErKXV5A\nWcWytAgrzMI64x9b6RL44dmTYfymN/a4CS9tLjUrKxrRsuIxDNOX+cz0d5npQZSLWSlR/ZJzl7/m\nWnSmDrvMAjM3DJtu3LS+4w3jPJw+dMl4c3cW/k+BWRGyHAWbC2Ctn2NenpxkJkd3pkXoN9ZMmbjf\nYa08WVm6GY+N8oRv5Ez7XqPhIb4jSr/5Dr9h50y4IfRoh5Qr8ZCa527cfxpCj0d4NvI2OIVpnWMX\nFk76tvGYuys2+6A5/SydMgzDzUI3pZU15kGsCI9N8Ey1OfWOa4MySbO9rDo3a7RZEKgEDtdDUo2j\nHEUbVmDG+ElY4bdAVR0crg9lxpCe7gfDZoNgAyRAApEjcOonVum4FnA6wY5779v6yMvWz6k3f4Ws\nLSNnjWc4tunjgGyvBUE8hTxHxe6p+7Lq1/vOmla7s968t+nuR/vz3s+xFglpmj2eqeO82/Lua856\n27R4AaebM+34LERj3n3zs9c+NaB96jj71HgWEdt0c546Te+j3twDo/l97Mjw8K0urp/ql+9T3kzF\nZ5u5z2vKOz+7PbfTHPnaaz4DvlMWepX3P9m3yjMtpdwf3+n9pEZV4WLv8WXoAjy2cWQucC8gE5S7\nZYLnM2HvL/x+bHb4sjbTJXp9smzTCdrZ7vHhEOiz6jfVYIVn+sbsxcX+cHmFBEjglBOgx9v8NWMi\nARJoCgEHXv+98ehaaSIW5wz3VE4ZjmeXud3e2Lij0pMX4KjXkJGuq3l4e4s1FYanVEwfPJG/D8vM\ntHeBUmbWRCxb/0PjlQ3fnqzcZVg2xxUe4e4k0yyoswfThjYQpO6OC0/A3c+ZhVEmZrprFpr4Zpkq\ncN7iee5FfhJjAwXoBoq/tceFaJ0w+jD34GXbQjJO44bg9HiXmXHpeKZ6p7lvvuOW/EzkLl6P6oVj\nG/CWBrLb1a61S8DNE3LdFzKmGg+4LTbbnRHkIO3ase6pCeVrg3sCTEOY0P9u7Fwzz1jrSk7wyJw6\nDwtyXFd7JASY1aQx+0177vtrQonC7Sd7MQo3LXN/BtTMzKkLsHPzNAT+ZHnb1nPEE9hn2sh2D1Jb\nkX0mJprP7g8uSbJfRPmWd93vJ2Rl9vXK4wkJkEDrIBAl0r91mNLxrBD0Zgl4HDt2DGbZeFRVVWHk\nyJHYsWOHG4ZZMh6jRo1C9+7dERcXh9jYWERFRVmbuxAPSOCkE6gzIQx1iDGfSZWIdhPqampQFxOD\nOLMFT6WYEdXLWpnQLAaC/GdGBGwP1lzNFag2b/vFxscjISHJtG1vuSn2OLBkTKL1Al7WgkK8Oqk/\n6swc3GUOM81EbDxSU5IasMH0Yb7uj/Pu2DLCXj/Z1Le0Zp0pb3J9GQgbAw4B0UidCPThMgrlJsxB\nJtVLSPLlZZUwc/KZsJ3KCmv2klgz9qSGxm6KB7Xb1ZzsSlfOQC/XdIrGc48HB3qLQ1vRIIeu+9kQ\nJ3fNOlSaeHrrc5GQjJQEp8q3bPX5bDZqfxD2ZvRh9iP1Ko19tYhPSEVSgteH1jOKYJ8JU0rmb6+w\nPp+xiI9LMJ//QL93dVj50CBkzTVPfxm52Ld1ZgMC390tD0iABE4BgcB/BU6BIeySBEigLREwojqA\nANURNCTINd+zdy6cMntKHgrnLkPx7BH+i7pIYSPAklLSgsQch2nPUee8HTFGyKeZLWiyHiQClwhY\nX8oHKC5sGkwR6sNqPyYBKSlBYtSlkOGaYriGkoLa7W6gHHlzdQ7zqbg5oxGm7nq+B8Hvp6d0TMDP\nRSBbA13ztGOOgrCXh5dAn79AbXpfk3opQT63Tgu863hZZZ3EhfL5rCnEchHdJmXdP4ai2yLBHyTQ\n+ggw1KT13RNaRAIdisDA2x50hQwsxd/eLu1QY29vg60p+T9MWeccVeacHzV5IaX2xuNkjqf0zRdd\nUyxm4eFb+5/MrtkXCZBAEwhQeDcBFouSAAm0AAETk/wr1+qXs59c554ppAV6cjdZtdt5uNu+7KU7\nlwfhEthmm4rxvh8MCLcZ1msyAQdWz3VO7ZmR+3BT169qcm+sQAIkED4BhpqEz441SYAEIkRg+BP5\nqHrEYUKsEwKGZ0SoG1czCbjtH4UYbJb1O/u8SyLbdAdv7ZIJzyH/+q8Re9rZ6NcnSEhNB+cU+eEn\n4N43qnCreU0hIamR8KLId84WSYAEmkCAwrsJsFiUBEigpQg4XwBsqdZ9203p0x8hruniW5XnQQjE\npfTBQIINQqjlsmLMS5dJfNZpOcBsmQQiRIChJhECyWZIgARIgARIgARIgARIIBgBCu9gdJhHAiRA\nAiRAAiRAAiRAAhEiQOEdIZBshgRIgARIgARIgARIgASCEaDwDkaHeSRAAiRAAiRAAiRAAiQQIQIU\n3hECyWZIgARIgARIgARIgARIIBgBCu9gdJhHAiRAAiRAAiRAAiRAAhEiQOEdIZBshgRIgARIgARI\ngARIgASCEaDwDkaHeSRAAiRAAiRAAiRAAiQQIQIU3hECyWZIgARIgARIgARIgARIIBgBCu9gdJhH\nAiRAAiRAAiRAAiRAAhEiQOEdIZBshgRIgARIgARIgARIgASCEaDwDkaHeSRAAiRAAiRAAiRAAiQQ\nIQIU3hECyWZIgARIgARIgARIgARIIBgBCu9gdJhHAiRAAiRAAiRAAiRAAhEiQOEdIZBshgRIgARI\ngARIgARIgASCEaDwDkaHeSRAAiRAAiRAAiRAAiQQIQIU3hECyWZIgARIgARIgARIgARIIBgBCu9g\ndJhHAiRAAiRAAiRAAiRAAhEiQOEdIZBshgRIgARIgARIgARIgASCEaDwDkaHeSRAAiRAAiRAAiRA\nAiQQIQIU3hECyWZIgARIgARIgARIgARIIBgBCu9gdJhHAiRAAiRAAiRAAiRAAhEiQOEdIZBshgRI\ngARIgARIgARIgASCEaDwDkaHeSRAAiRAAiRAAiRAAiQQIQIU3hECyWZIgARIgARIgARIgARIIBgB\nCu9gdJhHAiRAAiRAAiRAAiRAAhEiQOEdIZBshgRIgARIgARIgARIgASCEaDwDkaHeSRAAiRAAiRA\nAiRAAiQQIQIU3hEC2VLNREVFtVTTbJcESIAESIAESIAESOAkEqDwPomwg3UlAjuQyNbrgfKCtcc8\nEiABEiABEiABEiCB1kUgqt6k1mVSx7FG0B8/fhy1tbU4cuQIvvnmG1RVVVnHkte5c2d0794diYmJ\n6NatG7p06YKYmBhLoFOId5zPCUdKAiRAAiRAAiTQPgjEtI9htN1RqEe7U6dOiI6OtsS2iPETJ05Y\nx7GxsdZ1Cu22e49pOQmQAAmQAAmQAAkIAQrvVvA5ENEtnmzxcIvoliQebxHdck3yRJRLOQrwVnDD\naAIJkAAJkAAJkAAJhEGAwjsMaJGqYvd2q/CWtuVYhLd6wEWA20U3xXek7gDbIQESIAESIAESIIGT\nR4DC++SxDtiTim8V2SKwRWhLkjwR4Qw3CYiOF0mABEiABEiABEigTRGg8G4Ft0vEtiQR2iLAJb5b\nzyVPNxXpViZ/kAAJkAAJkAAJkAAJtCkCnNWkFdwuCSuRJHvd1CwV27rX69yTAAmQAAmQAAmQAAm0\nLQIU3q3kfqn4bsgcEd5MJEACJEACJEACJEACbZcAQ01ayb2jsG4lN4JmkAAJkAAJkAAJkEALEeDK\nlS0Els2SAAmQAAmQAAmQAAmQgJ0AhbedBo9JgARIgARIgARIgARIoIUIUHi3EFg2SwIkQAIkQAIk\nQAIkQAJ2AhTedho8JgESIAESIAESIAESIIEWIkDh3UJg2SwJkAAJkAAJkAAJkAAJ2AlQeNtp8JgE\nSIAESIAESIAESIAEWogAhXcLgWWzJEACJEACJEACJEACJGAnQOFtp8FjEiABEiABEiABEiABEmgh\nAhTeLQSWzZIACZAACZAACZAACZCAnQCFt50Gj0mABEiABEiABEiABEighQhQeLcQWDZLAiRAAiRA\nAiRAAiRAAnYCFN52GjwmARIgARIgARIgARIggRYiQOHdQmDZLAmQAAmQAAmQAAmQAAnYCVB422nw\nmARIgARIgARIgARIgARaiACFdwuBZbMkQAIkQAIkQAIkQAIkYCdA4W2nwWMSIAESIAESIAESIAES\naCECFN4tBJbNkgAJkAAJkAAJkAAJkICdAIW3ncb/b7cOCQAAABCG9W+NpcDdHAoxdZsAAQIECBAg\nQIBAJCC8I1i3BAgQIECAAAECBF5AeL+GTYAAAQIECBAgQCASEN4RrFsCBAgQIECAAAECLyC8X8Mm\nQIAAAQIECBAgEAkMxaQz6ViIKncAAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<IPython.core.display.Image object>"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {
+      "image/png": {
+       "width": 500
+      }
+     },
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "Image(filename='./images/10_01.png', width=500) "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<br>\n",
+    "<br>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Exploring the Housing dataset"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Source: [https://archive.ics.uci.edu/ml/datasets/Housing](https://archive.ics.uci.edu/ml/datasets/Housing)\n",
+    "\n",
+    "Attributes:\n",
+    "    \n",
+    "<pre>\n",
+    "1. CRIM      per capita crime rate by town\n",
+    "2. ZN        proportion of residential land zoned for lots over \n",
+    "                 25,000 sq.ft.\n",
+    "3. INDUS     proportion of non-retail business acres per town\n",
+    "4. CHAS      Charles River dummy variable (= 1 if tract bounds \n",
+    "                 river; 0 otherwise)\n",
+    "5. NOX       nitric oxides concentration (parts per 10 million)\n",
+    "6. RM        average number of rooms per dwelling\n",
+    "7. AGE       proportion of owner-occupied units built prior to 1940\n",
+    "8. DIS       weighted distances to five Boston employment centres\n",
+    "9. RAD       index of accessibility to radial highways\n",
+    "10. TAX      full-value property-tax rate per $10,000\n",
+    "11. PTRATIO  pupil-teacher ratio by town\n",
+    "12. B        1000(Bk - 0.63)^2 where Bk is the proportion of blacks \n",
+    "                 by town\n",
+    "13. LSTAT    % lower status of the population\n",
+    "14. MEDV     Median value of owner-occupied homes in $1000's\n",
+    "</pre>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>CRIM</th>\n",
+       "      <th>ZN</th>\n",
+       "      <th>INDUS</th>\n",
+       "      <th>CHAS</th>\n",
+       "      <th>NOX</th>\n",
+       "      <th>RM</th>\n",
+       "      <th>AGE</th>\n",
+       "      <th>DIS</th>\n",
+       "      <th>RAD</th>\n",
+       "      <th>TAX</th>\n",
+       "      <th>PTRATIO</th>\n",
+       "      <th>B</th>\n",
+       "      <th>LSTAT</th>\n",
+       "      <th>MEDV</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>0.00632</td>\n",
+       "      <td>18</td>\n",
+       "      <td>2.31</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.538</td>\n",
+       "      <td>6.575</td>\n",
+       "      <td>65.2</td>\n",
+       "      <td>4.0900</td>\n",
+       "      <td>1</td>\n",
+       "      <td>296</td>\n",
+       "      <td>15.3</td>\n",
+       "      <td>396.90</td>\n",
+       "      <td>4.98</td>\n",
+       "      <td>24.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>0.02731</td>\n",
+       "      <td>0</td>\n",
+       "      <td>7.07</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.469</td>\n",
+       "      <td>6.421</td>\n",
+       "      <td>78.9</td>\n",
+       "      <td>4.9671</td>\n",
+       "      <td>2</td>\n",
+       "      <td>242</td>\n",
+       "      <td>17.8</td>\n",
+       "      <td>396.90</td>\n",
+       "      <td>9.14</td>\n",
+       "      <td>21.6</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>0.02729</td>\n",
+       "      <td>0</td>\n",
+       "      <td>7.07</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.469</td>\n",
+       "      <td>7.185</td>\n",
+       "      <td>61.1</td>\n",
+       "      <td>4.9671</td>\n",
+       "      <td>2</td>\n",
+       "      <td>242</td>\n",
+       "      <td>17.8</td>\n",
+       "      <td>392.83</td>\n",
+       "      <td>4.03</td>\n",
+       "      <td>34.7</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>0.03237</td>\n",
+       "      <td>0</td>\n",
+       "      <td>2.18</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.458</td>\n",
+       "      <td>6.998</td>\n",
+       "      <td>45.8</td>\n",
+       "      <td>6.0622</td>\n",
+       "      <td>3</td>\n",
+       "      <td>222</td>\n",
+       "      <td>18.7</td>\n",
+       "      <td>394.63</td>\n",
+       "      <td>2.94</td>\n",
+       "      <td>33.4</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>0.06905</td>\n",
+       "      <td>0</td>\n",
+       "      <td>2.18</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.458</td>\n",
+       "      <td>7.147</td>\n",
+       "      <td>54.2</td>\n",
+       "      <td>6.0622</td>\n",
+       "      <td>3</td>\n",
+       "      <td>222</td>\n",
+       "      <td>18.7</td>\n",
+       "      <td>396.90</td>\n",
+       "      <td>5.33</td>\n",
+       "      <td>36.2</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "      CRIM  ZN  INDUS  CHAS    NOX     RM   AGE     DIS  RAD  TAX  PTRATIO  \\\n",
+       "0  0.00632  18   2.31     0  0.538  6.575  65.2  4.0900    1  296     15.3   \n",
+       "1  0.02731   0   7.07     0  0.469  6.421  78.9  4.9671    2  242     17.8   \n",
+       "2  0.02729   0   7.07     0  0.469  7.185  61.1  4.9671    2  242     17.8   \n",
+       "3  0.03237   0   2.18     0  0.458  6.998  45.8  6.0622    3  222     18.7   \n",
+       "4  0.06905   0   2.18     0  0.458  7.147  54.2  6.0622    3  222     18.7   \n",
+       "\n",
+       "        B  LSTAT  MEDV  \n",
+       "0  396.90   4.98  24.0  \n",
+       "1  396.90   9.14  21.6  \n",
+       "2  392.83   4.03  34.7  \n",
+       "3  394.63   2.94  33.4  \n",
+       "4  396.90   5.33  36.2  "
+      ]
+     },
+     "execution_count": 1,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import pandas as pd\n",
+    "df = pd.read_csv('https://archive.ics.uci.edu/ml/machine-learning-databases/housing/housing.data', \n",
+    "                 header=None, sep='\\s+')\n",
+    "\n",
+    "df.columns = ['CRIM', 'ZN', 'INDUS', 'CHAS', \n",
+    "              'NOX', 'RM', 'AGE', 'DIS', 'RAD', \n",
+    "              'TAX', 'PTRATIO', 'B', 'LSTAT', 'MEDV']\n",
+    "df.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<br>\n",
+    "<br>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Visualizing the important characteristics of a dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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840uKCdDnk1I3+DzU65NF59COSw7Ibf4TElFs+OxalGV5TJblN2RZ/ksAxQDe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\nZ6Brlf/Tt8xGdXnij1LKpWrddThd+Nd3LuJMX3BrhXKzjVhVno2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J193RVZNnc1Pz1D8A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zivd5sjCIIgCIIgCIKQ/qSMeyeQDxz3e9yhlMrQWnf2VoMEQRAEQRAEIZXxZGWScXwbrixP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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x1090e5be0>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "%matplotlib inline\n",
+    "import matplotlib.pyplot as plt\n",
+    "import seaborn as sns\n",
+    "sns.set(style='whitegrid', context='notebook')\n",
+    "cols = ['LSTAT', 'INDUS', 'NOX', 'RM', 'MEDV']\n",
+    "\n",
+    "sns.pairplot(df[cols], size=2.5);\n",
+    "plt.tight_layout()\n",
+    "# plt.savefig('./figures/scatter.png', dpi=300)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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NJ4j0jY/m84IyvKYZuLvcXuNC1/wTnBJt/m6vnMpa+tadn11Rg89sOHkpEnSL9X82a8uq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\nqkIIIUSASFAVQgghAuT/Ac215gKzigiqAAAAAElFTkSuQmCC\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x10d8de908>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import numpy as np\n",
+    "cm = np.corrcoef(df[cols].values.T)\n",
+    "sns.set(font_scale=1.5)\n",
+    "hm = sns.heatmap(cm, \n",
+    "            cbar=True,\n",
+    "            annot=True, \n",
+    "            square=True,\n",
+    "            fmt='.2f',\n",
+    "            annot_kws={'size': 15},\n",
+    "            yticklabels=cols,\n",
+    "            xticklabels=cols)\n",
+    "\n",
+    "plt.tight_layout()\n",
+    "# plt.savefig('./figures/corr_mat.png', dpi=300)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "sns.reset_orig()\n",
+    "%matplotlib inline"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<br>\n",
+    "<br>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Implementing an ordinary least squares linear regression model"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "..."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "collapsed": true
+   },
+   "source": [
+    "## Solving regression for regression parameters with gradient descent"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "class LinearRegressionGD(object):\n",
+    "\n",
+    "    def __init__(self, eta=0.001, n_iter=20):\n",
+    "        self.eta = eta\n",
+    "        self.n_iter = n_iter\n",
+    "\n",
+    "    def fit(self, X, y):\n",
+    "        self.w_ = np.zeros(1 + X.shape[1])\n",
+    "        self.cost_ = []\n",
+    "\n",
+    "        for i in range(self.n_iter):\n",
+    "            output = self.net_input(X)\n",
+    "            errors = (y - output)\n",
+    "            self.w_[1:] += self.eta * X.T.dot(errors)\n",
+    "            self.w_[0] += self.eta * errors.sum()\n",
+    "            cost = (errors**2).sum() / 2.0\n",
+    "            self.cost_.append(cost)\n",
+    "        return self\n",
+    "\n",
+    "    def net_input(self, X):\n",
+    "        return np.dot(X, self.w_[1:]) + self.w_[0]\n",
+    "\n",
+    "    def predict(self, X):\n",
+    "        return self.net_input(X)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "X = df[['RM']].values\n",
+    "y = df['MEDV'].values"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "from sklearn.preprocessing import StandardScaler\n",
+    "sc_x = StandardScaler()\n",
+    "sc_y = StandardScaler()\n",
+    "X_std = sc_x.fit_transform(X)\n",
+    "y_std = sc_y.fit_transform(y)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<__main__.LinearRegressionGD at 0x1090e5ac8>"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "lr = LinearRegressionGD()\n",
+    "lr.fit(X_std, y_std)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x10e7550f0>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.plot(range(1, lr.n_iter+1), lr.cost_)\n",
+    "plt.ylabel('SSE')\n",
+    "plt.xlabel('Epoch')\n",
+    "plt.tight_layout()\n",
+    "plt.savefig('./figures/cost.png', dpi=300)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "def lin_regplot(X, y, model):\n",
+    "    plt.scatter(X, y, c='lightblue')\n",
+    "    plt.plot(X, model.predict(X), color='red', linewidth=2)    \n",
+    "    return "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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UZ8nalEl4zi6ysr6mZesraNqk8cUW7YLgAV9bvjcPqBTlmFB5cAmBJTY2locn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\n/Bt1BAUBjNSjdwAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x10e75ca20>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "lin_regplot(X_std, y_std, lr)\n",
+    "plt.xlabel('Average number of rooms [RM] (standardized)')\n",
+    "plt.ylabel('Price in $1000\\'s [MEDV] (standardized)')\n",
+    "plt.tight_layout()\n",
+    "# plt.savefig('./figures/gradient_fit.png', dpi=300)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Slope: 0.695\n",
+      "Intercept: -0.000\n"
+     ]
+    }
+   ],
+   "source": [
+    "print('Slope: %.3f' % lr.w_[1])\n",
+    "print('Intercept: %.3f' % lr.w_[0])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Price in $1000's: 10.840\n"
+     ]
+    }
+   ],
+   "source": [
+    "num_rooms_std = sc_x.transform([5.0]) \n",
+    "price_std = lr.predict(num_rooms_std)\n",
+    "print(\"Price in $1000's: %.3f\" % sc_y.inverse_transform(price_std))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<br>\n",
+    "<br>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Estimating the coefficient of a regression model via scikit-learn"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "from sklearn.linear_model import LinearRegression"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Slope: 9.102\n",
+      "Intercept: -34.671\n"
+     ]
+    }
+   ],
+   "source": [
+    "slr = LinearRegression()\n",
+    "slr.fit(X, y)\n",
+    "y_pred = slr.predict(X)\n",
+    "print('Slope: %.3f' % slr.coef_[0])\n",
+    "print('Intercept: %.3f' % slr.intercept_)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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G6bDdn4ctx4UQASK3YbajR7+n+7OjaP1oL4/rl5q0bsuK2TGE1q3HqR9/cHn8wIFvyMjI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\n5Nf2IcwAAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x10f3ab908>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "lin_regplot(X, y, slr)\n",
+    "plt.xlabel('Average number of rooms [RM]')\n",
+    "plt.ylabel('Price in $1000\\'s [MEDV]')\n",
+    "plt.tight_layout()\n",
+    "# plt.savefig('./figures/scikit_lr_fit.png', dpi=300)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<br>\n",
+    "<br>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "collapsed": true
+   },
+   "source": [
+    "# Fitting a robust regression model using RANSAC"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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YMXezdevdlJaWsnv3QHzXJ7lcx+kiWlVn4aaS/x67jLstsDRyzVGqYaiPRIVw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\n6ZySJEmStuN/ThdrIxh42yIAAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x10f14c710>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from sklearn.linear_model import RANSACRegressor\n",
+    "ransac = RANSACRegressor(LinearRegression(), \n",
+    "                         max_trials=100, \n",
+    "                         min_samples=50, \n",
+    "                         residual_metric=lambda x: np.sum(np.abs(x), axis=1), \n",
+    "                         residual_threshold=5.0, \n",
+    "                         random_state=0)\n",
+    "ransac.fit(X, y)\n",
+    "inlier_mask = ransac.inlier_mask_\n",
+    "outlier_mask = np.logical_not(inlier_mask)\n",
+    "\n",
+    "line_X = np.arange(3, 10, 1)\n",
+    "line_y_ransac = ransac.predict(line_X[:, np.newaxis])\n",
+    "plt.scatter(X[inlier_mask], y[inlier_mask], c='blue', marker='o', label='Inliers')\n",
+    "plt.scatter(X[outlier_mask], y[outlier_mask], c='lightgreen', marker='s', label='Outliers')\n",
+    "plt.plot(line_X, line_y_ransac, color='red')   \n",
+    "plt.xlabel('Average number of rooms [RM]')\n",
+    "plt.ylabel('Price in $1000\\'s [MEDV]')\n",
+    "plt.legend(loc='upper left')\n",
+    "\n",
+    "plt.tight_layout()\n",
+    "# plt.savefig('./figures/ransac_fit.png', dpi=300)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Slope: 9.621\n",
+      "Intercept: -37.137\n"
+     ]
+    }
+   ],
+   "source": [
+    "print('Slope: %.3f' % ransac.estimator_.coef_[0])\n",
+    "print('Intercept: %.3f' % ransac.estimator_.intercept_)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<br>\n",
+    "<br>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Evaluating the performance of linear regression models"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "from sklearn.cross_validation import train_test_split\n",
+    "\n",
+    "X = df.iloc[:, :-1].values\n",
+    "y = df['MEDV'].values\n",
+    "\n",
+    "X_train, X_test, y_train, y_test = train_test_split(\n",
+    "    X, y, test_size=0.3, random_state=0)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "slr = LinearRegression()\n",
+    "\n",
+    "slr.fit(X_train, y_train)\n",
+    "y_train_pred = slr.predict(X_train)\n",
+    "y_test_pred = slr.predict(X_test)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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Wj/diOjsfTeO+KIVABUrJiEJPbIwUyAlYDgsARwJX0dU1lW3blubo6v8HWIPH\n2z+m1WflbZoKWMLd3T2DqVPPZPnyK+KKVPC+Bbua4gvlWQT8FwCWUO1JwTnB4h2HlOv9gehtA+O2\nDsNbeitWLGfWrEW89dY7GNOfL33pRRYtWh7ytkuXtc+vxBLlaUFLYstVEZlVOPoFqbPzbKqrq9hn\nn0Z6eg6l1+Xf6h4M2hX8zQbssT1tMbkbFSglbySbKJnOREqnbiGv9xr8/pFYrt+tBAL2xM5A5Pmi\n37yDE2SjiR4n6qUT+B8C/hbCxchDGwE2A7dF2PXoo9dRVTUQkbns6o5u9USfOxar5VRli0crUGe7\nfE+L2TfWM28gAf95BPz3AEvtrTMI+I1tJ7ZTxB4C/s9xmhsF0NHRETFmF53IMDh+19x8dMqBW4Ps\n3LEzqdD29NzE5MmXcMwxR9HWdoHj89+58y527nwda45aalTVVcXtelRcQDpxkQq1oLH4Ckq8pHuZ\nxC5LFpAz1YCdTnHVnFJMNDWNjThfPLura6oj4tZV1VWZW7tuDSXcCy7BoKi9KSM6THiw2FGj/tMh\ndlyDsYKwjnHIo9QvZFf4tSP3qTJW4NdD7evWmXhJA4cOHWk8nmBw12BA2w4DbcbjaTCwn4EjY4K9\nhp8n1saquPHuUkmHkQyncsTmkwre86WmomKIQ1Da3nQi1v3pjSdYUTHE4dnHXkNzQuUeNFiskk0S\nCVFfoj/3NXK0UzbbUaMOS5p9dtSoI+2K7iy74o4NNhotVv36e2MqQ49ncNx7EhnxvM7AJFsoxjpW\njJao1RuPd2BIHKL3sQQleM5qA7W2cEWKXXTFHbtPVUwFnVgQ9jHQFrfyzkbOLufrh9vc3xbojjAx\nOtREBrUNCrEVUT066WP476al5Szj7d/f8UXB6XemEc6zhwqUklVykTQwW60vK/tssy0Wbfbbcp1p\namq2l7ERFcuCBQsMDIqptCsGVMS1zRKY/Ux46opkrYSgnVaLLtjqaTNQn0AMlhoYElfEnFpell1H\n2MecZaDNIZq40/Uitzvv01vhezyD4z6f2N9Hm2loGGVaWs5KuSXlFFXeSlnf+7JhtUIX2OUcY5e5\nwy5/Xej5wyCzYMGCFH9/bSaYDmTUqP9MKV2KilTfKEqBAjxYrlF/Ap4GRkV9n/07paREugKVKAVG\npuc0Jn5l4XSupqaxEfsGRcuqvNscK+REtlkV4iBTU7N/yl2TQfEUqbEr1zF2ZRpPMMKv5dyiCK7H\ndgF6bNEN/gaxAAAgAElEQVRtNB5vRYyYBbsr4wlU7Pn6m/DWSjDlh1N5m5qaTUVFsMsxPAdVZD6q\ndCt35+cQnldqSEhAR4060jQ0jDINDaOSilPkuTtsIXa2UVPDZ590BcotThJnAhXGmK+KyHFYwbbO\nLLBNCulHgM7VvJJEc50APN660MTW9euDzgdTAR/+wA7Wrw9GGG/HieDEUiuSQTTDgAvp339+zDwc\nICpaxHM8+eS36devAr+/HTgfy6Psb4DHwYmhIspzbphjBAiIvJex87R2Ad8OuaAnfgZW6KQr976S\ngL/XtdByU/8hAf/PsMI3WREZFi2KfN7RHnQVFVfT1HQPGzd+QFdXcBLvJMIn9GbHy/NgIudKzaOy\ncgN33JF6Rlyfz2dPS9gEfIY1PWEFcEEoULCGO3IPbhGosUAHgDHmBREZXWB7FJtU0jykSya5jZKf\ny0kYfcAlKcWKC04s9XovAdaHfTOTYNTy4cP3c7ShNy35fcCNGHMXfn+vm7NVxh/Q3f1jAv7gHKlN\nwFsE/P0Ipj+Hq/B4P48bwii58D/maF/4/h6vJxRRwjkm4MsEg982NHzEAw/EPu/ol4WeHmhsXEFj\n4z50dsY1ISHRER2qa6rtSdHTQtui3eCD9p197tkpBaKNFNZXgXuwRBS7LJHhqLL5O1UyQ6xWV4GN\nELkHeMQY02GvbwQONMYE7HUXWKkoiqL0BQGMMZJ0Rxu3tKA+BWrC1j1BcQoyL+zzOHtRFEVR3Msq\ne8mYdAascrUAZwH32p/HAE9EfZ+1QTrFHZDAUSFdWlrOivHM63UNj/3O4+1vz2eKdr9usz3Cer3C\nwgfOm5rGhg3UOzkyBOcf9Q6qW44ZR4QG5T3eiojrOjlDODtBRHvxeWKcHcK9AoOehtHzncLX483J\nSgUn9+tEzgcLFixI6BEX7144O5REzj1L9FuK9cocZD/bWCcO6/nGOkWos0T2oEidJB4DWkTkOXv9\n/EIao7iHePHWoiNBrF79LD12kNaKiquZO3cGCxfOpLv7QAL+O+kdD1pGwH8l0OUw/vJLILhtBk1N\nR7BoUe8YzFtvvYuV0n0qTlEcRGzJCPGq/fcD+28rAX9PVFy/SDxeKyhr73o/An4rLUasvc7jIS0t\nK1j7/Gdce21vgNigU4V1XBtwDwH/7cCreDxLOfDA/Rk0aK/QfU02zpg4d1Ir0eNYyUJjxQsmGxtr\n8Cr73FacvYkTp1JdU51mINoraWo6kkmTromIbp9JmCYlt7hCoGxlvajQdijuwikg7Zw5sanb58y5\nFNhNbyry3YwePdpOFT6f9etnhJ11Jtb7j5M33y2EV2SNjSsiKmG/v3eEPjZrbi0B/3dDoZU83gsJ\n+D8neEh4jqXZI2dHiFA4A/rX0O2/M2Sr5ZX3AR7vdMe0Ek5OCStXPoJIbNT0IC0tG2hu7q2cm5uv\nYOHCn/H226kH/nUi1qlgQ8jJIlnln0owWZ/Px+TJl9DV9VPCha6lZYWjd2V8DqaxcTBz5sxhzpzI\nb+I5RaizRIFI1sQCjgK+itX19kfgG+k00bKxoF18JUem86Wcwhk5bQvvgrG6biKjRxCnayy666+6\nptqe7zPWwF4mMnpBg4Fq4/FGRp6oGFAR5/xjIrquorvyqmuqw7oRx9i2dtifBxuYZDyewRHREaLv\nY9Bep+vH60rNZhdWvMgL2Zr0mq6tHR0dDqGf4kfGSGS/RpToO6TZxZeKOKwFjsaK2X888Gw6F8jG\nogLlTnL9T5stgXKqHKPDGVXuVWlXZPGE5VDTO75iRTMIH89Jbfxkv9Ax0d9F2hgcH2kLE8TkE1+T\nxZpL50UgF2Ms2fi9ZCJ0CxYssKONWBE9NCJE4ciFQD0NDAR8wfV0LpCNRQXKfeQjDEy8QKSpbIsX\nfihR5RhvwN06b70tEr2VeHirJDWBajPxoknEDtC32a2m4LbkIhIuNNEtQSdhSnSf812BpyNemQid\ntoDcQS4E6o/Ab4EZwHeAlelcIBuLCpT7yPZbd7wuv3iR1FPZluzcTjgLS1CQGiIq8UQCFes5ONAE\n49t5vN6o76pSaBmmJ1DpPpNCVuDRcfeiPQ8TPS+luMiFQDUCp2HNsToJaEjnAtlYVKDyS67i6SXC\nqZLvC+EVbirnDpY51vW6NlQ+kXrT1NQcCoQK8V3CI7vr6u2WUzC+3ZiYe9fU1BzlDl1rhg4dEVZx\nR3bxeTz1aQU3TUek801TU3PU/cjub0FxD+kKVFwvPhH5XtSmC+y/X8KK1aKUKKnE03NzGJho779g\nCvVEOJXZ46kn4L8FWIbHcwU/+lEbc2y3r6OPHhdyCXe6V93+LXi83+XrJ51Jc3Ob7XloxberqPg7\ncHUobXow3t2LL77IddddgTXR/v+xefOReL0zGDXqVjZseI9AoAW4C4/nn/zoR20xXnaJwlLlKkZi\nNti48f1Cm6C4lERu5kOx3rYUJYZcxOjLFrFzX6ZldJ5A4LvAXYj8g332qefmm+/lkUf+wKJF17Fx\n4/v2/KrYc4fPcwq6P48ePTp0r5qbr+KRR/7Axo3zGT58v9Bcq/b2xRhzCOHpyv1+GDlyBXfc8WP7\n+GG0tc1znA8WnJuU6XOIjocXL6Zdts83fPi+dHVdlfF1lNIlrkAZY+YFP4vIMKA/Vjdf6vmUlaIm\nfL6OiMRUMH2pDKOJN1EzFaIr6mg83kzTev8U8GHMZDZvngtAV9dVTJhwDvvvPyytSjV4r6Jbd93d\nM9M6PojTHLFM5i6Fk+1WVqrnW7ToOiZMOIeeHmseW3TE91Sel5NYp/Kd4nKS9QFiTa9/HXgX+Ah4\nPJ0+xGwsaB90Xgkfr3D7WECqnn7JBv6jx2gsJwdn5wQ4wtTUHGBEamIcHqJzL0WTaOzOchZwTlce\nXt7eZIipjwGmMgaV7eedzvn6mp3Zum9jDIwxFRV1cTMcO/0W3Dw+V2qQrTGoMI4CjsCapj8HuK2v\noqi4j/C3zN88+BtaW1sRSTnocMFwCqGzevWKtLsfw1uGodaJfxlWWoxoNrFjh5Wygj2XU11dyZ49\nwhe7P2DXtl0RqS2S4fFeTGfnrtC9rq6p5vDDD2fjxvnU11cxaNBhEVEYeltNTnYlL1+w2237J9sd\nW8WFoi+t8Vmz5tPT48XqGoWenquYNWt+qNs0UYglcPf4XLmTikBtNcYERKTaGPORiOybc6uUvBKv\nuygV+jpukavul75UeGefezbd3dsJji95vH8Oy0N0OTCdYIVnDOzceRdW5XgVVk4oKxbd109aEXPu\naOeSgD/WyaKxcR8AXnn1eQL+PQB0dj5K/4r+7O65x772voTnL0rVSSVRZdyXblYnsn2+eGzc+AFW\nd+zUsG3zc3ItJb+kIlAvicjVwCYR+TVQnWOblDwT7y0zXmbXcPry9pmNcZRceBM6lWnUqHbefnsT\n4NQqGkZv5TgP+CCuHdHOJU6x9KzkiXcBiRIttgJTaWiYz/Dh+wKHphzoNRyP1xPRUs5miypfLbPh\nw/ejqyt2G7jH21THwTIjqUAZY2aJSA3QDZwKrMu5VYoriJfZNVuk0v2SjETehE6tu988+JuMKopN\nmz6kN8DsZfbfI+ltNVk0NHzEMcesCNmRzNPOuRt1KlYa8lgqK2eGVbb3ceWVscFz0xH5XD/jfLBo\n0SwmTDgv5LZfUXE1ixYtB1LzNs2kpZeO4OTCoaVsSDZIBVwftcxNZ5ArGwsuHJwvJeINJOMwyB09\niO20T6rkOgack22pOE84HRfrKLGfEak3Xm9t3PNlMkDf65zhfP/DnQkWLFhgO0sEA8um7yzRl+eX\nTfoaySKfkTDSDQ2l+aR6IQeRJC4EvoeVDuMeYEk6F8jGogKVe5z+wZ0rz+xFKMh1DLjkQtNmGhpG\nxVRq0WXqX9E/poKpqdnfdHR0xNy38PXYCAnJI2+H349EIX9iE/HtY4KR2tOp/NwgUH0J5hovmnsu\nhSpdwVGB6iXrAhVzAHSke0xfFxWowpLsH6yvLsKZHptMHBMLVIcJd+dOVClagVzD02w0mqamsY5l\niRAYz+CkFVOmFazTM4ExaVXuCxYssDMPF87F2ikdhhW3sC+xE3Mb9NYpfUs6Lx7lHE09XYFKOgYl\nIgeHrQ4DDkh2jFI+9KV/PdiP//HHWwBv2oP8yRw0oscWqmuq2eMPjuHcRbjnV6LxL8urbgy940JT\naWzcELNf9JhaIPBqKIEhOA/QO5UhveR7vQSz16Zy/xYuXMi11/4E+IW95TIWLLgmFMqpL6QzPtPe\nvphAIDJRZMA/rY/jYsmfaab4fD5ee+1N4CZ7yxQqKvy0tf067jFujrridlLx4ltMb8ijz7HyRStl\nRCJPqEwdHXqFbQrwDJZYZHcA2cmLLFh5vvTSRzGeX/HoLb8lwql7gh3JUUcdRmNjb1rxbFVMTs8k\nVXECuPHGxcDthAvDwoWz+ixQpe4Q0N6+mJ6emwi/b4cffm/S8mUz6ko5kYoX37g82KG4jGgPuOqa\nalpaslfR9grbCpK1ZLLpohusKBYuXMjcuYlbN+HHpPIG7CQawTh72aavb+Xd3Z87bvP5fH2yN90X\nluh7Ft7iTIXoVrLH24+A33qO0c80V67ejY2Ds3KeXJLtOIt5I17fH/CqvbwOvIWVWfefwAvp9CFm\nY0HHoPIOKQ6eZ9q/3juGknx8K9MUEolyTFnnbDMwxng8g82CBQsyvVUx9yOdMbVChdkZNeowE56+\nI1kq9FTJxCEg+p711fEmlynni3U8KdX/53zYYbLpJAHcCxxifx4F3JfOBbKxqEBlTqZOCOn8oDPN\ncNorEvGdFfriARWvDLnwqiq2eG7WNILqtAb7Uz1vtoQgm9542XrmHR0dpqlprGloGGWamppdUbZU\nKFaBSmUMapQx5h+2SrwtIiPSbaUphSFf4wGZ9K+Hd1F9/PEhwL00Ng4u2gHkYovn1trayoIFP2Du\n3HYCgQtJFP0i3fP21SHAreNYmUaiT3QOt5TNtSRTMKxsb/OBbwI/Ae5PRwGzsaAtqIzoy1ujW1oE\nfXkjJ85bYy66aeJdy+0UMtV7PHLRws3GM8+GXYWaE+WW/2dy0IKagjVZ9zSs8ajrsiePiltxywBq\nX97I44WwUbffXsrFu6yYn3kqzh3J9nHL/3PaxFMu4Cv239aoZXw6CpiNhSJ5G3UbxTqg63ac3kbd\n8oZaCrj1d5sN55p0y5bK/m69X06QLScJYKb9dymWo0RoSecC2VhUoDLHjV046eK2MlCk3XnFhNue\neRArLFN9XDFIxe50ypZKFJd0k1cWknQFKlHK9xvtv9NEpB9WuvevAi9kq/Wm5J5i78LRQeXyxK2/\n29WrX46IfPHF7os55ZRTQt9b87CWAPF/q9kqW+//xoF9PpdbSSXU0W3AG8BwoAnYQvg0akXJIdlI\nyZGMop3EqBQcp4ST2fytphbFZV/Cq+RC5bzKBak4SXzFGDNDRFYZY8aJyFM5t0pR8ki6LuL5yhSr\nuI9owcg1qTl3WBmcYV5a8RiLgVQEyiMixwAbRGQAUJNjmxQlhFsyooaTrdaVttyKj1QyIltikdlv\nNVmCy3Bi/zc2lJQ4AYg1bpVgB5GLgWnA+cD/AK8aY5bk3rQIG0wyO5XSJdfpskUkpgWVj99boa6r\nZA+nmJXHjxkPpP9bjR5vraycmXS8tdhSyYsIxhinNNLO+6fyDyEitcAI4G1jzM7MzcsMFajCUmz/\nBOlSqJaMCpQSzvjxk+jsnEDveNIyWlpWZJx+xY2kK1CeFE74LWAVcB9wpYhcm7l5SrERfKvr7JxA\nZ+cEJk6cis/nK7RZWWVb17YI19Zci1NdQx0iKf+Puh6fz8f48ZMYP35SQX8bwfsaXOoa6gpmS3Z4\nlZdeeqXg97WgJPNDB/4EDASexhqzejkdP/ZsLOg8k4Kh6aqzD/Y8qqq6qqKf3NvR0RGTmr66prog\ntpDB/DQ3zbeKnHDbZsKjzbt58m06kINQR3uMMZ/bTTO/iOS9i09RSpEb3rkBKO6uvfb2xQT8e4oq\nUG4Qt82xa21tZc6cS7n55vl8+ukO/P7ehJK5mF5RDKQiUGtE5EHg/4jI3cCfc2yT4iLc6EWnKNkg\nH3Ps0sFKonkLgcCXgP4FscFtxBUoEekPTAA6gQrgZaxJuqfnxzTFDRRzkE03Ee2IceXeVxLwW6lj\ni3keVVvbBXR2PlpoM4Dinp/m8/ns1Ce32FuuBi4LfV+uL4ZxvfhE5LfAbmAoVsqNfwG/AG43xtyQ\nLwNtW0yxdoEoCpS2x17NoBp27ujt+S+W+VyZuHXnCicPPvgpDQ3dHHPMUSXjPZuuF1+iLr6RxpjR\nIlIBvAT0ACcZY97oq5GKopQOOz7dUWgTMsLtvQMez2YeeOB+V9mUbxK5mX8KYIzpsfdryYY4ichE\nEbk/bH2MiDwvImtEZG5fz6+kj1vchJW+o88yPVpbW1m58hFWrnykoELQ1nYBlZUzsVpOy/B4ruBH\nP7qirMUJSJhu42mnz31ZgGDg2QfCtq0HDrQ/PwH8p8NxWXJyVKIpplwyxUw+8kXpsywenH4PbnJ5\nzxWk6WaeaAzqQ+BJrDQbJwN/7NU0MzkTMRSR7wAfAt8zxpwrIoOA540xh9nfXwZUGGN+GnWciWen\n0jfKYfZ6uaDPsngo5THJRGRzDOo7WOouwN1h25PeRRGZDkRPhphmjPmtiIwL2zYIuyvRZgcwMtn5\nFUVRlNInUcLCVZme1FjBZFMJKPspkdHRBwGO7j/z5s0LfR43bhzjxo3L1DwlDJ3nVDros3QnTrEe\ny4VVq1axatWqjI9PKVhsNrFbUN8zxpxrr68HJgEbgMeBecaYP0cdo118OaTUg8GWE/osMyOX982p\nO6+2vrYsU63kJJp5NhGRZiyBmmyvHwfcCvQDfMaY6xyOUYFSFCUn5Ho+VLmONzmR9Wjm2cYYszrc\nycIY84Ix5nhjzLFO4uQm1IXXveizUTIlMuSRJVTB1pRSWFKJxafgvsCSSi/6bBQ3U8whmAqNClSK\nuC2wpNKLPhulLyRzLulrQstyGFvKFSpQiqKUNclCHm3/ZHtRphMpBVSgUkRdeN2LPhulr7S2tmqL\n24WoQKWI2wNLljP6bBSlNMm7m3kmqJu5oiiFoq9jUEovrnczVxRFKSa2dW3DGENHRwctLWdx7Oiv\n61SGPKEtKEVR8k6xtUrclNywmHF9JIlMUIEqH4qt4lIyo1iiKwRDIL300it0dZ0JBBMtaKT4TMhm\nNHNFyTvq0qu4hchW0wTgKqAF0FZTvlCBcjnaolCUwhA9AdxiHvCBTmXIEypQLkdbFEopUqzhfxoa\nPuKYY1boVIY8oQKluIpirbiU9HBjL0B0yg2nCeAPPKDClE/UzVxxFUGX3uDSl4pMI5wrqRIcb+rs\nnEBn5wQmTrS69R57zHKGaGlZoV57BUC9+FyOjkFlhroFK+kwfvwkOjsn0DvepF56uUC9+EoMFaPM\n0AjnilL8qEApilL2aMBhd6JdfEpJol18SrpEO0nobyX7aCQJRbHRCkdR3IUKlKIoiuJKNJp5maIu\n1YqilBragioBdLxFUZRiQLv4yhCdw6EoSjGgXXyKoihKSaDzoEoAncOhKEopol18JYK6VCuK4nZ0\nDEpRlLJCX86KBxUoRVHKBvVgLS5UoBRFKRvUg7W4UC8+RVEUpSRQLz5FUYoW9WAtbbSLT1GUokad\nJIoHHYNSFEVRXImOQSmKoiglgQqUoiiuQ6PzK6BdfIqiuAyd21S66BiUoihFjc5tKl10DEpRFEUp\nCXQelKIorkLnNilB8tbFJyK1wH1ADVABXGmMeV5ExgC3An5gpTHmRw7HahefopQROrepNHHtGJSI\nzAO6jDG3i8jBwIPGmGNE5C/ARGPMBhF5AphjjPlL1LEqUIqiKEVOugKVzy6+W4Av7M/9gW4RqQEq\njDEb7O0+4BvAXxyOVxRFUcqInDhJiMh0EXk1fAEOMsZ8LiL7AsuBWUAt8GnYoTvsbYqiKEqZk5MW\nlDFmCbAkeruIHAk8CLQZY54VkUFYY1JBBgHbnM45b9680Odx48Yxbty4LFqsKIqiZJtVq1axatWq\njI/P5xjUYcCjwLeNMa+GbV8PTAI2AI8D84wxf446VsegFEVRihw3O0n8L/AfwEZ70zZjzEQROQ7L\ni68f4DPGXOdwrAqUoihKkeNageoLKlCKoijFj0aSUBRFUUoCFShFURTFlahAKYqiKK5EBUpRFEVx\nJSpQiqIoiitRgVIURVFciQqUoiiK4kpUoBRFURRXogKlKIqiuBIVKEVRFMWVqEApiqIorkQFSlEU\nRXElKlCKoiiKK1GBUhRFUVyJCpSiKIriSlSgFEVRFFeiAqUoiqK4EhUoRVEUxZWoQClKmVLXUIeI\nhJa6hrpCm6QoEYgxptA2JEVETDHYqSjFhIhwa9etofXLGy5H/8+UXCIiGGMk1f21BaUoiqK4EhUo\nRVEUxZVoF5+ilCl1DXVs/2R7aL22vpZtXdsKaJFS6qTbxacCpSgljIqQ4iZUoBRFCaGOEIqbUCcJ\nRVEUpSRQgVIURVFciXbxKUoJo2NQipvQMShFURTFlegYlKKUCT6fj/HjJzF+/CR8Pl+hzVGUrKMt\nKEUpQnw+HxMnTqW7+0YAKitn8thjy2htbS2wZYoSH+3iU5QyYPz4SXR2TgCm2luW0dKygpUrHymk\nWYqSEO3iUxRFUUoCb6ENUBQlfdraLmDNmql0d1vrlZUzaWtbVlijFCXLaBefohQpPp+P9vbFgCVY\nOv6kuB0dg1IURVFciY5BKYqiKCWBCpSiKIriSlSgFEVRFFeSN4ESkb1E5HcislpEOkVkmL19jIg8\nLyJrRGRuvuwpBlatWlVoEwqClru8KMdyl2OZMyGfLaj/B/zZGNMM3AdcY2+/CzjXGHMCcJyI/Gce\nbXI15foj1nKXF+VY7nIscybkbR6UMeY2EQkK4nDgExGpASqMMRvs7T7gG8Bf8mWXoiiK4k5yIlAi\nMh24PGrzNGPMSyLyFHAEMB6oBT4N22cHMDIXNimKoijFRUHmQYnIIcATQBPwvDHmcHv7DMBrjGmP\n2l8nQSmKopQA6cyDylsXn4jMAt43xiwHPgP8xpgdItIjIiOBDVitqnnRx6ZTIEVRFKU0yFsLSkT2\nBpYBA4F+wExjzFoROQ641d7mM8ZclxeDFEVRFFdTFKGOFEVRlPJDJ+oqiqIorsT1AiUiE0Xk/rD1\nkp/YKyIeEblLRP4kIk+LyKhC25RLROQ4EXna/nyQ/WyfEZE7RaQkxx9FpL+ILLfL+YKInFEOZReR\nfiLyS7ucz4rI4eVQbrCGOUTkPRE5uIzK/LJdhz0tIkvSLberBUpEbgNuAMIL8XNKf2LvmVjzw74K\n/ABoT7J/0SIi1wD3AAPsTTcDs40xJ2I9928WyrYc81/AR3Y5TwHuwHrOpV7204GA/f97Ldb/d8mX\nW0T6A3djOYgJZfA7F5GBAMaYk+xlOmmW29UCBTwHXIQtUCIyCBjgMLG31BgLdAAYY14ARhfWnJzy\nFnAWvS8hRxtjnrE//4HSfL4ADwHBHgAPsJsyKLsx5nfA9+zVEcAnwDGlXm7gJqyX6832esk/a+Ao\noEpEfCLylIiMIc1yu0KgRGS6iLwatRxjjPlt1K6DiJ3YW5s/S/NGdDn3hEXhKCmMMY8C/rBN4a3l\nnZTm88UY85kxZqcdTeUhrNZE+DMu5bLvEZGlwG3A/ZT4MxeRaVit5ZXBTZR4mW0+A24yxrQCF2I9\n63CSltsVKd+NMUuAJSns+ilQE7Y+CNiWE6MKS3Q5PcaYQKGMyTPh5ayhNJ8vACKyP/AocIcx5kER\n+UnY1yVddmPMNBHZB1iHNfUkSCmW+3zAiMg3gP/Emm4zJOz7UiwzwJtYPSQYY/4pIluxgjMESVru\nonorN8Z8CvSIyEh7cG088EySw4qR54DTwHIKAf5aWHPyynoRabY/n0ppPl/synklcI0xZqm9ueTL\nLiLn2ZP2AbqBPcCLpVxuY0yzMWacMeYkrDij/w10lHKZbc7HHj+3s1fUACvTKbcrWlBJMPYSJNhU\nDE7s/XNBrMotjwEtIvKcvX5+IY3JE8Fn3AbcIyIVwOvAw4UzKafMxuremBvmjToDuL3Ey/4wsFRE\nVgP9scr8d8rjmQcxlMfvfAlwr4gEReh8YCtplFsn6iqKoiiupKi6+BRFUZTyQQVKURRFcSUqUIqi\nKIorUYFSFEVRXIkKlKIoiuJKVKAURVEUV6ICpZQlIjJORD60oyz/UUTWisj3MzxXm4hMFZGjRCRu\nwk07Mv/QFM85WkTuzcQe+/ilItKa6fGK4gaKYaKuouQCAzxpjJkMYE8c/IeI/MqOWJL+CY15BXgl\nwS6XYU1O3Jxgn2wRPcFdUYoObUEp5Up0wM5BWEFr94jIKhH5jYisFJEKO4/Najt/UTOAiJwpIi+J\nyEqskFvBVtmD9ufpIvJnOx/OPBE5DTsOm50L6lKx8n09JyKX2sccYq8/CVwdY7BIu4j8t/15XxF5\nUazcYb8QkQ4ReUVE5kceItNEZJG9MlBENtifj7Rbjk+LyMMiMkhEhoRtWysiR2X1jitKmqhAKeXM\nyXZl/BRwH3CpMeYzrJbHA8aY8cB0rEjUzVh5uu4QkX5YeW2+Ye/zsX0+AyAiQ4CZwAnGmKOBCmA1\nvXHYvgR8ByutyonAmSJyMFZKhrnGmG8ATzrY+wtgqv35POCXwP7AWmPMKcBxWKHAwonXiroHuNiO\nD/d74BrgK3ZZTgUuAfZKdPMUJddoF59SzvzRGHNunO/+Yf89EjhBRI6z1/sBw4DtxphP7G3RAS9H\nAn8zxnwBYIyZDWAnDxXgCGA48Ed7/zos0ToECMaWfAb4avhJjTFviIhXRA7AEriv2199RUROwoqC\nP4D4hLcYvwz83LapP1bk6T/YdvwOKz/VggTnUpScoy0oRXEmmPbjDeBBu6XxTeC3wAdArYjsbe8z\nJuNr+rYAAAEjSURBVOrYt4FD7XEt7O7CYfY5PVjBUV8LZhoFlmNFrH8dOME+x/Fx7FqC1dJ6zR4r\nmwZsM8ZMwWrVVUXt/zkQdMw4Omz734Hz7OvPBv4/YByw2c7fsxAr262iFAxtQSnlSqpOBHdjRV9e\nhTVOdYcxZreIXAT8XkS2YSVeC57LGGM+FpEbgdUiYoAVxphNIvInrFxArcBTIrIGKxfS88C/gSuw\nIn23Ae9jpaKI5mGsRH9n2OtPAg+IyDHARqzUFcPCytgBXCQizwIvAdvt7y4ClouI197vu0AX8Gu7\nbF7ghyncH0XJGRrNXFEURXEl2sWnKIqiuBIVKEVRFMWVqEApiqIorkQFSlEURXElKlCKoiiKK1GB\nUhRFUVyJCpSiKIriSv5/jOO17+5aqksAAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x10f8284e0>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.scatter(y_train_pred,  y_train_pred - y_train, c='blue', marker='o', label='Training data')\n",
+    "plt.scatter(y_test_pred,  y_test_pred - y_test, c='lightgreen', marker='s', label='Test data')\n",
+    "plt.xlabel('Predicted values')\n",
+    "plt.ylabel('Residuals')\n",
+    "plt.legend(loc='upper left')\n",
+    "plt.hlines(y=0, xmin=-10, xmax=50, lw=2, color='red')\n",
+    "plt.xlim([-10, 50])\n",
+    "plt.tight_layout()\n",
+    "\n",
+    "# plt.savefig('./figures/slr_residuals.png', dpi=300)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "MSE train: 19.958, test: 27.196\n",
+      "R^2 train: 0.765, test: 0.673\n"
+     ]
+    }
+   ],
+   "source": [
+    "from sklearn.metrics import r2_score\n",
+    "from sklearn.metrics import mean_squared_error\n",
+    "print('MSE train: %.3f, test: %.3f' % (\n",
+    "        mean_squared_error(y_train, y_train_pred),\n",
+    "        mean_squared_error(y_test, y_test_pred)))\n",
+    "print('R^2 train: %.3f, test: %.3f' % (\n",
+    "        r2_score(y_train, y_train_pred),\n",
+    "        r2_score(y_test, y_test_pred)))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<br>\n",
+    "<br>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Using regularized methods for regression"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[-0.11311792  0.04725111 -0.03992527  0.96478874 -0.          3.72289616\n",
+      " -0.02143106 -1.23370405  0.20469    -0.0129439  -0.85269025  0.00795847\n",
+      " -0.52392362]\n"
+     ]
+    }
+   ],
+   "source": [
+    "from sklearn.linear_model import Lasso\n",
+    "lasso = Lasso(alpha=0.1)\n",
+    "lasso.fit(X_train, y_train)\n",
+    "y_train_pred = lasso.predict(X_train)\n",
+    "y_test_pred = lasso.predict(X_test)\n",
+    "print(lasso.coef_)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "MSE train: 20.926, test: 28.876\n",
+      "R^2 train: 0.753, test: 0.653\n"
+     ]
+    }
+   ],
+   "source": [
+    "print('MSE train: %.3f, test: %.3f' % (\n",
+    "        mean_squared_error(y_train, y_train_pred),\n",
+    "        mean_squared_error(y_test, y_test_pred)))\n",
+    "print('R^2 train: %.3f, test: %.3f' % (\n",
+    "        r2_score(y_train, y_train_pred),\n",
+    "        r2_score(y_test, y_test_pred)))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<br>\n",
+    "<br>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Turning a linear regression model into a curve - polynomial regression"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "X = np.array([258.0, 270.0, 294.0, \n",
+    "              320.0, 342.0, 368.0, \n",
+    "              396.0, 446.0, 480.0, 586.0])[:, np.newaxis]\n",
+    "\n",
+    "y = np.array([236.4, 234.4, 252.8, \n",
+    "              298.6, 314.2, 342.2, \n",
+    "              360.8, 368.0, 391.2,\n",
+    "              390.8])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "from sklearn.preprocessing import PolynomialFeatures\n",
+    "\n",
+    "lr = LinearRegression()\n",
+    "pr = LinearRegression()\n",
+    "quadratic = PolynomialFeatures(degree=2)\n",
+    "X_quad = quadratic.fit_transform(X)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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s0FprpdSXwDZMpw3HPFicsishIYFTp07h5eVFiRIl0ryWlJREz54BrF+/GSenonh6JrJ9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\nSFKghBBCGJIUKCGEEIYkBUoIIYQh/R8s2tuwIStbFgAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x10f853588>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# fit linear features\n",
+    "lr.fit(X, y)\n",
+    "X_fit = np.arange(250,600,10)[:, np.newaxis]\n",
+    "y_lin_fit = lr.predict(X_fit)\n",
+    "\n",
+    "# fit quadratic features\n",
+    "pr.fit(X_quad, y)\n",
+    "y_quad_fit = pr.predict(quadratic.fit_transform(X_fit))\n",
+    "\n",
+    "# plot results\n",
+    "plt.scatter(X, y, label='training points')\n",
+    "plt.plot(X_fit, y_lin_fit, label='linear fit', linestyle='--')\n",
+    "plt.plot(X_fit, y_quad_fit, label='quadratic fit')\n",
+    "plt.legend(loc='upper left')\n",
+    "\n",
+    "plt.tight_layout()\n",
+    "# plt.savefig('./figures/poly_example.png', dpi=300)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "y_lin_pred = lr.predict(X)\n",
+    "y_quad_pred = pr.predict(X_quad)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Training MSE linear: 569.780, quadratic: 61.330\n",
+      "Training  R^2 linear: 0.832, quadratic: 0.982\n"
+     ]
+    }
+   ],
+   "source": [
+    "print('Training MSE linear: %.3f, quadratic: %.3f' % (\n",
+    "        mean_squared_error(y, y_lin_pred),\n",
+    "        mean_squared_error(y, y_quad_pred)))\n",
+    "print('Training  R^2 linear: %.3f, quadratic: %.3f' % (\n",
+    "        r2_score(y, y_lin_pred),\n",
+    "        r2_score(y, y_quad_pred)))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<br>\n",
+    "<br>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Modeling nonlinear relationships in the Housing Dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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HRkayc+dOm+cbgzglJiaSmJhY6/vtiVM3YJ4QIgF4V0ppNi+WUtoOu7GBlNLk\ndBZCfAU8DrwqhBgkpfwaGAZYjWGtKk5XCvtO72PGFzPYev9W2mjb1NheudoaF87OfOoyU7IkIiKC\n06dPU1FRgZubm9k1X19fioqKTMcZGRlmYgRw+vRps9eWQlLTGJZ069aN48eP06tXLwDatGlTbc0m\nLS2NmJgYAIYNG8bevXut9jVw4EA+//xzq9fKy8sJCgoyO3fs2DEmTJhg176qJCYm8uijj5qOL1y4\nQGpqKn369HG4D3DcrdexY0czd2hOTk4112SXLl345ptvTMcajQa9Xm/zfGPAchLx7LPPOteBsYqq\ntR/AE7gX+Bz4FngE8LV3j6M/wFfAVUAnILGy/w+o3Bhs0VY2S3JzpZw0ScrMTKuXh8YNlSxBDvhw\ngCzVldrtKi8vTyYnJ8sjR47II0eOyOTkZJmXl9cQVrt0rMZKY/+drKiokN27d5ezZ8+WhYWFsri4\nWO7bt09KKeWAAQPkM888I3U6ndyxY4f08fGRCxcuNN0bFRUlr7vuOnn27Fl5/vx5OWDAADl//nwp\npZTt27eXu3btqnEMS5YvXy6nTJliOi4rK5ORkZFyxYoVsqysTH766afSw8PDzI6aOHfunFy/fr0s\nKCiQOp1OfvHFF7Jly5YyKSnJ1Ka4uFi2atVKZmRkmM5NnDhRTpo0yWqfer1ehoSEyIsXL5rO+fr6\nyoSEBJmenu6wbc5QUFAgu3btajru1q2bzMrKklJKefLkSanX62VxcbHs06ePqU2/fv3kyZMnZUlJ\nidXzlxNbfxuV5x3WCLvzcSllmZRyk5RyODCmUkhO27vHCVG8RUr5m5TyhJRysJSyv5Ty0co3cWUw\naxasWgX9+sHx49Uurx65mnBtOPvO7GPq51PtujxcmTHcGHBh3BSs1psaHxqNhq1bt3Ly5EkiIyOJ\niIhg48aNAKxYsYKtW7cSGBjIunXrqq2hCCF48MEHuf322+nYsSOdOnViwYIFTo1hyYQJE9i+fbsp\nIs/Dw4PNmzezatUqgoKC2LhxI6NHj3bqPQoh+Pe//027du0ICgpi4cKFxMXFma1dbd26lVtuuYWw\nsDDTubNnz3LTTTdV6+/w4cPMmzeP4uJiNm/ebDo/efJk9u/fz86dO52yz1H8/PyYM2cOy5Yt47nn\nnmPOnDmmiMN7772Xn3/+GW9vb5YsWcKiRYtYsGAB06ZNo2PHjnh5eVk93xyoMX2REMIbuAcYj2FN\naKWUcqWNmURPAAAgAElEQVQLbKtqQ/PUrKwsiI2FAwcgMBD++1+4+WazJj+k/8DNH91Mia6Efwz9\nBzP6zrDaVWpqarW0R35+fgQHBytXXwOg0hc5z/z58wkJCTEFQbiCvn37snLlSlOodllZGT169ODw\n4cM1uiIVtaPBc+sJIW4BJgC3Ygjtfl9KWbvt4XWk2YoTQGEhPPAAxMeDpyesXg3jxpk1+U/yfxj3\n6Tg8NB7sHrmb7u27VxMZawljW7duTXZ2ttk5NcupH5Q4KRTWcYU4JQLvA59IKa2nxHYRzVqcACoq\n4K9/hbffNhy/9BLMmQOVUXr5+fks3LmQTi07MajNIJPwGGdKxhmRZUBETk5OtdmURqMhIiJCCVQd\nUeKkUFjHJVnJhRChwF1AMHAG2C6ltBrq3ZA0e3ECkBLeeAOeftpw/NhjBrFyd7fqsquKrRmRvfui\noqKUQNUBJU4KhXUaPCt5pVvva6ADUAzcAPwghKi+kqioO0IYAiQ2bQIvL3j3XRgxAiw241nDVvCD\nZRqjqmRmZtbZZIVCoWgoanLrjZVSnqtyrg2wRkp5m2vMM43b/GdOVfn2W7j7bjh/noru3Un75z8p\n8refOMPPz89qluT8/HzS0tKqnddoNGb5vOqDK2mflZo5KRTWcUU9J1FVmACklBkY0hcpGpL+/eG7\n79BHR+N26BDtxozB+9dfAUMiy5CQEIQQfH/ue7ambbWb2FWr1ZqlRjEipXS4xpNl2Xhr106cOOFU\nDSl7fSoUCoU9cbK1zVjFX7qCTp04s3Ejhddfj2dmJh0mTEC7axdubm6EhIRQ2qKUx/Y+xuKDizmr\nOWt3lhIaGlrNvSeldKgIob3ChVWvWVbqtbfPShVDVCgUNWFPnDoKIV4QQrxY9QfDGpTCBeiDgjj1\nwQdcjI1FU1xM5MyZ+P/73yAlPaN6Mr3PdMr15Ty49UGO55hv4jXOTE6ePElmZiaenp5WBaqmjbr2\nNvdaXnMUV24YVigUTRN74rQIOA78WuXfXyvPK1xAcHAweHvzx/PPkzljBkJKWr3yCkyaBKWlvPKX\nV7iz052cLz7P0DVDSc83JOesOjMpKSmhtLS02symJoziVlxsNe9vjagaUgqFoi7YE6edUsrVlT+r\njK8xCJWiHrG1/mJKE9SiBcUzZlC8di34+sLHH8Ntt+Gec4GNYzbSJ7wPablpjNwwEr3UOzWj8fPz\ns2qPUdwsk0hWFR1r0YDe3t41pjRSxRCbBk888QTLli2r97au4ptvvuGaa+qnRprC9diL1vtKSnlL\n5es4KeV4y/OuojlH61nL7GA3i8NPPxki+c6ehago2LqVU6FaRm4ayd96/I27r7ubrKwsm1VFLSNp\nrI1nbX+UEAJfX99qUXi1jdBr6pF9jT1ar3379qxcuZJbb731cpvSJEhMTGT8+PGcOXPmcpvS5Kmv\naL2aig0aaedohwrncLo2Uo8ekJQEI0dCUhKyf3/ESy8RNzAOIQSnT5/G3d3R/1bHazFJKa2KSG2L\nGjZkMcTGjJSSrKwsU/2ewMBA2rRpU+81u2oST51O59TviULhampXiUxxeWnTBhIT4b77EAUFRD71\nFCEffACVqeYrKiqs3iaEwNPTs8bubbnYVNBCzeh0OlJTUzl69Ci//fabWd0kMNQGOn/+vKkswMWL\nF8nOzjZrI6WkoKCACxcu1GrNb/z48Zw+fZrY2Fi0Wi2vvfYap06dQqPRsHLlSqKiohgyZAhgyHrd\npk0bAgICGDRoEEePHjX1M2nSJBYuXAgYZhbt2rVj+fLlhIaG0rZtW1atWlWrtufPnyc2NhZ/f39u\nvPFGFixYwM0WCY+NGO1+//33CQ8Pp23btrz++uum66Wlpfz1r38lPDyc8PBwZs6cSVlZmcmOqnWq\n2rdvz+uvv0737t0JCAhg3LhxlJaWUlhYyLBhw0hPT0er1dKyZUsyMzNJSkrihhtuwN/fn7CwMJ42\nZm9RuAQlTpeZWq+/+PjA+vVcnDkTISWhb75JxNNPoykqwsPDw+paUGRkJGFhYTWOp9Vq8fLyqtsb\nc4DmttdJSmlyier1esrKyjh16hTl5eWmNnl5edVmypbvPT09nbS0NDIyMkhJSXH6S0FcXByRkZFs\n27aN/Px8Zs+ebbq2Z88efv31VxISEgAYPnw4J0+eJDs7m549e/Lggw+a2gohzH5XsrKyyMvLIz09\nnQ8//JBp06aRm5vrdNtp06ah1WrJyspi9erVfPzxxzXOHBMTEzl58iQ7d+7k5ZdfZtcuQ03S559/\nnqSkJA4dOsShQ4dISkqyufYlhGDTpk0kJCSQmprK4cOHWbVqFX5+fnzxxRe0bduW/Px88vLyCAsL\nY8aMGcycOZPc3FxSUlIYO3asM/8NijpiT5wGCCEyhBAZFq/7u8i2KwJHaiPZfIgLgfuzz5L29ttU\ntGiB/5df0uHBB2lTWEhkZCR7z+9lW/o2oqKiiImJMbnSHKnF5G+RkaK+gxaa416niooK07f2qlSd\nPVlzpVU9V1xczKVLl8yKrmVlZdmcDTvLkiVL8PHxMX35mDRpEn5+fnh4eLB48WIOHTpk9v9QVUg9\nPDxYtGgRbm5uDBs2jBYtWnC8Sh0yR9pWVFSwefNmnn32Wby9vencuTMTJ06scf1u8eLF+Pj40LVr\nVx5++GHWr18PwNq1a1m0aBHBwcEEBwezePFi4uLibPYzffp0wsLCCAwMJDY2lp9//rma7UY8PT05\nceIEOTk5+Pr6Ol0JV1E3bDqdpZQ1+38U9YK99RfLgImioiIzQdFqtTBhAmdiYmjzxBN4nzxJxaBB\nHPrXUqadnAlAkHcQQyKGmNaMrI2Xn59PVlYWZWVluLm5odPpzK63bt26XteInF5rawJoNBqrD7mq\ndYNCQ0MpKCgwRUEKIcwK4el0OqvrRY6UQneEqm4uvV7PvHnz+OSTT8jOzkajMXxXtfX/EBQUZGoD\nhnLvBTZyP9pqm52djU6nM7OjXbual7Srto+MjCQ52VC9JyMjg6ioKLNr6enpNvup+ln7+PjYbfvh\nhx+yaNEiOnfuTHR0NIsXL2b48OE12qqoH+wlfl1U+bPY4kftc3Ihjm5YLQgP5/d168gbPBi3vDx6\nPPRXZpbciF7qeerrp9idutvm7MQogCUlJej1esrLy6s9HO1lRa/aT3Ny0zmLRqOhdevWJheVEMIU\nWm/E09OTmJgYwsLCCAsLo1OnTmYuVG9v72qfvUajwcPDwylbbLnJqp5fu3Yt8fHx7Nq1i9zcXFJT\nUwHzWYQzgRqOtG3dujXu7u5mUXGORMidPn3a7HXbtm0BaNu2LadOnbJ6zRms2R4TE8O6devIzs7m\n73//O2PGjKn1vj+F89hz603HUGzQHcis/Mmq/FHUExUVoLeVKMpBjGKlb9GC0ytWkDV1KkJKXn1x\nPw9ntqNUX8qT3z5J0rkkcnJyzLJHnDhxgjNnztQ5LNpZN11z3esUGhpKZGQkrVu3pk2bNkRHR1d7\n8Hl4eBAUFERQUFA10fHw8CAqKso06/Dw8LDahyN2/P7773bbFBQU4OXlRatWrSgsLGTevHlm141u\nRUdwtK2bmxv33HMPS5Ysobi4mF9//ZW4uLga39+yZcsoLi7ml19+YdWqVdx3330A3H///Sxbtoyc\nnBxycnJ47rnnGD9+vEM2VyU0NJTz58+Tl5dnOrdmzRpTsIq/vz9CCLPZoKJhsfdJtwFmAh0x1HQq\nA9ZKKd91hWFXCp99BlXWoKth7SHu5+dne4ai0ZD9xBOkvfkmej8/Pnj3LBNPaimpKGFO0hwKSgqq\nZY+w3GhrSU3CkZ+fX03gakpJ5OjaV1NEq9USGhpKq1atahUi3qJFCzp37sy1117L1VdfbTVxb03M\nnTuXZcuWERgYyPLly4Hqs4MJEyYQFRVFeHg4Xbt2pV+/ftV+1yyPbeFM27fffpvc3FzCwsKYOHEi\n999/f41RpIMGDSImJoYhQ4bwt7/9zRRtuGDBAm644Qa6detGt27duOGGG1iwYIHTNl9zzTXcf//9\ndOjQgVatWpGRkUFCQgJdu3ZFq9Uyc+ZMNmzY4JJAIYUBu8UGTY2E0AKjgHuBQinluBpuqVea8ybc\nCRPgL38B45e9bdsgOBj69v2zzblz50wPeq1WaxbxZayKe+7cOcuu8UxJIWrGDDzSTjE91p0hN02l\n66inbG7QtcTDw4OKigo8PDwICwuzKh6Wa2JVsVXGoznQ2DfhNiX+/ve/c+7cOT766KNq106dOkWH\nDh3Q6XRq1tJEcEXJjKr0AgYAUcBZRztX1MxHH8H99xteSwkLFpjXFzx9Op/s7Gz0ej16vZ7c3Nxq\nM5S8vDy8vLyqfXst69CB39evp2joHbwdr2PknDcJfO45RJXQ5qoY3RYeHh54eHhQXl6OXq+ntLTU\nppvOVqqk5uKmU9Q/x48f5/Dhw0gpSUpKYuXKlYwaNepym6VoZNgLiOgjhHhDCJEMPAhsALpLKWfb\nukfhPG5uYIwk1uvhiSfAmHFGp4M+fXxIS7O/GG50zxlFIiQkBD8/P7y8vPAMDib7n/8kf9kypLs7\nQXFxRD/8MO4WlXCNaYwiIiLQ6XRme3PAuczhGo2mWbnpFPVLfn4+o0ePpkWLFowbN47Zs2dz9913\n22xf39kzFE0De7n19BiykO/AsN5kREop51m9qYFozm49eyQnw9SpRbzzTgoAhYUa/vGPUObNy8De\n36txHcfS1efz889Ezp6NR1YWusBAzr78Muf796SVbytTmLm1vHpV+7V00zmdG7AeaAx5+ZRbT6Gw\nTn259eyJ06TKl1UbCAzitNpxU+vOlSpOAHl5+Zw5Y3j4f/ppIN98o2XduhJycnK4eFGg00FQkPkG\nTXsPTreLF2n3zDNov/2W1d1h5igfvpi8mxsjDItctsTJnui4Uiwuhxgax636Hlu2bKnESaGwgivE\n6SUp5TN2DLB7vT65ksUJ/nwwpqW54+vbihtvNETrvf66Lzk57ixYkOFchxUVBL/3LrOy/sX668Bf\n584XY+Ppe90wqwEOXl5eNgMiXI018WzowAtrgti1a1clTgqFFVwhTueA/2GYLVnjVillqKMDVfbp\nBrwPXIVhRvY4UAqswlAWPhmYZqlEV7o4WSM/P5//+79y7rnnAtdea4i+e+edEHr3LqR375o3zHp5\nedH+9xM8sHYUn8SU4Vsu2NzjRYaO/nu1WQJgdWZU04ypIWZUl0OcrI153XXXKXFSKKzgCnEaXMO9\nUkr5taMDVfY5AoiVUj4qhBgEzKq89LqUco8Q4l9AgpRyi8V9SpysUPXh7+sbzNVX+/LJJycJCzME\nMxw44Ev37sV4epp/dlVdYbrTp3j0+T6sbnsO9wpYrx/FmMX/gcrNobbcaIBd91pDud+szey8vb1x\nc3NrMJeiLXFSKBTWaVBxaiiEEG5SygohxETgFmCIlLJd5bW7gdullE9a3KPEqQrWZiR6PXz/fSFa\nrSEFTU6OO3ff3YkvvzyOn59hk61Go8HHx6faQ1yWlzPn+UG8V/odX6+C66P7wbp10L49J0+erLYv\nyigG9mYwDTnDMb5/nU5HWVmZUwJYm9nc5VrnUiiaEw21z6neqBSmVcAKYC3mbsMCwN/afQoDttIE\naTTQr5+fKZtAdrY7kydnm4TpxAkvZs++iuDgYHJycsyySwgPD15d8i3JgzdyvVs4fPcdXH89bNpk\nNcu2tXOuRKvVEh0djbu7u1NZKWqbCb05Z7NQKBorl6UUppRykhAiFEgCquZm0QKXrN2zZMkS0+vB\ngwczePDgBrSw8VJTNu+WLVtSUlJC586GHyOffx5Ely4VphnA8eNelJefY/hwTPdGDL0XDt0KkydD\nfDyMHUv4ffdx9umnkT4+ZmP6+flRVFRkNpswplUCrF6/3Jty65IJ/Uqt3KtQ1JbExEQSExNrfX+N\nbj0hxF8wiJgGeAtYKKVcW6vBhBgPtJNSviiEaAn8DJwAXpBSfi2E+DewS0q5yeI+5darxJ67zF4q\nobIyDXq9B97epQD87W/t6NmziAcfvISPjw8BAcEEBmoNbq/sbLRxcQS98AKirIzCmA6kv/QKpVdf\nberPmDbJaIufnx/Z2dnV0ioZr9tyodUlaMJZd9vlCKZQKBQGGsKt9zzwG4Ys5QMwRNjVlk+A64UQ\nXwNfADOAJ4FnhRDfYhDBT+rQf7OnavkFMJ+R2EolBODpqTcJE0CnTqXceecl9Ho9hYWF3HmnYNu2\niwa3V1ERmaNH8/u6dbx1RxAj+qcQMnEcQatXm1KoSykpLCwkOjqa6OhoCgsLq81Kql4HqiWrrWvB\nQWfdbc01E7pC0RxxxK1XBJwDyqWUGZWZI2qFlLIYuM/KpcG17fNKIj8/35TC30htiwBOmfJnPzk5\nbqSmetGu3enK0gfw4YfBjL6/mGWDNJwrhYHjdcS//xrtv/6aP5Yto7xtW4qLi0lNTa3xAW+tYKKn\np2e1ulG1KTjojLvNKGaXO7uEQqGoGUdmTnkYZjkbhRDTMAiV4jJgbWZU1U1lOTNwlODgCrZv/w1j\n1YKDB33ZujWAAF8fVg1eTZRfew6HQe/HBIcyDxAzejQB8fHoKypMMx4/Pz+bsxJraz2OlOqoDTUV\nPDQGU0RHRythUigaMY6I01hgipTyY+Br4KGGNUlRW6q6uZytO+Pl9ae4BQbqmDMnEyEgokUEk2U8\nARduIdtXcsvDgv9GFNBu/nwinn4at4sXTS68+ohoq6ioqHUV3bq6CZs6V3olYkXzwl5W8q+EEGuA\nFlLKXwCklMlSylJb9ygaFkfWTKqGWVtibVal0WhMYhISEkJkZCTXXefBbbfpTG1OHG7D39p8yIMx\nD+Lr3ZKLPedS5t0C/y+/JOaee2ixd6/Z2JazEmdmdCUlJbUWFUdL2jcHLIXoShdmRfOjpgwRGVLK\n4y61yLotKlqvEkej26xFpnl7e5uV1jCeCw0NrTGSTqeroLS0hHNF2Tz2YD+efzSJ4Rtn4nfwIACl\nkx/B6x9vgA17jH1VVFQ4VOywNlF0V0o0nrUoRS8vr2qfa3N874qmS32mLxLACGAIho2xl4A9wCeu\nVgolTs5jLaw8JCQEHx8fMjMzKS01nwBbpgCyFMGcnBwKCwvR6+Hrr7UMGpSPRlbQ6qPVBK94G0/K\nITIS3n8fbr/drm0nTpyoNr4ltXmwXimZHKyJsEajqbaGp8RJ0ZioT3F6B0P2hh0YMjdogWGAu5Ty\n0Xqw1WGUONWOc+fOmdV0Mj6sjUJjDVtl30NCQsz2MRk5fdqTrS+eY3LFRHp/Z6g79b+oR7j14Gto\nWgVYnenZ249V1c7aiEpd9001hUg+a+Lk5eXldConhcKV1Kc47ZFSDrRy/lspZf862Og0Spxqhy03\nF2BTnMB6VmFj2YysrKxq7qMPj3/Im8lvMqesH8++moRneTmEh1P0xhscCOnGxYsaYmJKzR6Y+fn5\nnDlzptq3fY1GQ0REhMsfqk1p1mUvGW9TEFfFlUl9bsLVCCHMxKkyk/jlTaymqDM1BShY+yJQXl6O\nVqslJiaGqKgoNJo/f3WKdcVIJC957uMvr/fkVN+e8Mcf+I4di9eUhXweJ0z9Gh+eWq0WnyopkYz4\n+PhclodqUwqmsLX5WIXJK5oT9sRpEjBbCHFWCPGHEOIM8DTwfy6xTFFnbEX3VX24eVSWxqjaxvIc\ngKdxExSGh2NERISp7ye7PMm7N79La9/W7LnwPX1H/8H/Xn4MvZcXfX/bxFu7+6HdtQuAZctasXWr\nffsUNaOESNHccahkhrHMhQvssTW+cuvVEnuFAy3z4Rmx5tbz9/dHp9OZ+jG65jIzMykvL8fT0xPp\nJ3ls52N8deorBkQMYMf1b6KZMgW/H38E4NLg27jp4EoSjkbi4XGO7OxsEhK0DBhQQIsWEk9Pz8tW\ncbcpufUUiqZIfa45dQReB24AKjDMsg4DM6WUv9WDrQ6jxKk69VGXqLYYgyZyc3PNou6EEIS3C+df\nh//FA9c9QFRAFPm5uZT+4x+0eu01NAUF6P1aUPj3OaTFxnLugjcjR3bif/87jq+vYe1Jrxf4+Hji\n7u7u8nWTphIQoVA0RepTnL4CnpFSfl/lXF8MVWsH1NlSJ1DiZE5tvuXbCkCob2yGL//xB8yYAZ9+\nCkBx58788H9L2XmhH/fddwGA48e9ee65tqxda4j6s1Zdt77Fw5E+lWgpFHWnPgMivKoKE4CUcn+t\nLVPUG84u3hvFrKGFCQzph6wSHs651e+Que4jytq0wefYMW6aPZYZqXPQFBQAsGtXS/r3LzDdcuiQ\nN5s3F5q9B8sMCHVJ2eNIVgWVeUGhuDzYy0p+WAjxEYZ9TnkY9jndicG1p2hCZGVl1dmV5yhVx6k6\n4wgKCmL8lvEcyTrC0g/mcfemHwmOiyN47Vr8v/ySjLlzeeLx2ygr//P70qpVwQwcWA5AZmYm+fmC\nFi2kaZz09HR0Op1ZtnNHakgZcaT4YF0KFCoUitpjb+Y0FdgK9AFGA30rj6e6wC6FHRyNcsvPz+fk\nyZMOpQuqL4w5/SxnHMm/J/PHpT/IKMrg0QNPMfWWAn5eu5Ki667D49w5ImfOpP3UJ9Cmp5j6uvnm\nAiZN8iI/P5/S0lKmTo3i22//rGdlreTGuXPn1CxHUStU4tzGhU1xklLqgb3Ad8B+4FvgO7X4c/lx\npMieURxcKUzw5yZfy9laS4+WrBm8hhldZuCh8eCT1E8YfvoZ1rw+lfR586jQatHu20ene+4hbPly\nfMrL+etfW9KunZacnBwuXXIjP9+N3r0NsyK9Hl5+OYyiIvv7tey5O+0VbjQ+qCzdlJcz3F09PBsO\n5b5tfNgLiHgUmIJBoPIxuPUGAh9KKf/lMgtRARG1wVp2CFeg0Wjw9PS0K4onck+w8MeF/HLxFx7v\n/DjTrp2G2/nzhL71FoGbNyOkpDw4mJy//Y2Ld96JcaVMSjBOGA8c8OOFF9qwefNJhIDSUsHFi26E\nhenMxtJoNPj4+FRz8VmmdgJDiqaQkBCrUY1CCJMwhYSE1O1DssDRoAwV6t5wXClJgy8n9Rmt9y0w\nSEpZXuWcJ/CtlPKGOlvqBEqcnMeR5KqXE51ex+ZTmxnVfhQemj83/fokJ9PmxRfxPWxY2izq3p30\nuXMp6dLF7P6sLHcyMjzo2dNQtHDHDn+2bQvmnXdSbK6vGTOwA6SlpVW7bnwY2RP2+hYFR0VHPTwb\nFvX5Njz1Ga3nDvhanPMDGj7kS1En8vPzKStr3Fmm3DXu3N/pfjNhAiju2pWUuDjOLltGeVAQvocO\n0fH++2m7ZAlu58+b2oWG6rj++mJTBOKZM56MHJlN69at8fPz47PPWvHFFy3N+i4pKSEtLY309PRa\n2+1IZKQzrjdrARdZWVnKfediVLaSxoc9cVoK/CCE2CGE2CCE+Bz4HnjONaYpaou1cu5VqU0p9/pE\no9EQEhJiM7R9V8ZXLOrwOz9v+Q85EyeCmxutPv2Uq4YPp/X77yOKi6vdM2VKNn/5Sx6FhYW0bx9N\nXFwwrVv/6eJLTvYxrU+Vl5dXux/+zKBR23L39bVuUVJSUq0P9fBsWBxZx1W4Frvpi4QQHkBnoCWQ\nC/xa1c3nKpRbzzku13qTo1iWd6iKTq/jroS7+KPoD0K8Q5h13SxGll9D29deQ1tZcbc8NJSsp57i\nUmwsaMy/X2k0Gry9fTh50p+2bTMASXm5YMiQq1m9OoX27a3PKI21royzInd3d3Jzc6u1s+V2s7XJ\n2ZZryLjOpNPpbH4Wln001GZgtclY4Qrqbc2pMaHEyTnqK02RNTw8PMz2FtUGa7n7qpJ8IZkXfn6B\nIxePANAruBdzr59Lz2MXCXv9dXx+/RWA4muuIXPWLAr79bM6hjHF0qlTelatCmbu3AwAcnM1PPlk\nFKtWpeLm9qcwOfKZGUuHVH2A2/u8rYmTtfbGYo9lZWXVZnYNufahAi0UrqI+AyJeBCSGgoNVkVLK\nebU30XmUODmPs2XRHcXPz4/g4GCrdZ3qE73U89+0//KP5H9wofQCHbUdSRiZQElhEf5btxL65pt4\nZGUBkH/TTWTOmkVpp05mfRgj9UpKSsxCwjduDOTAAT9effUsABcu+HH2rDfdup3HESwf4LZmqs4G\nNwQHB1sVuaioqAYTCxUIoHAVzoqTvQwRWRg23D5fZ6sULsdY3wf+FKri4uI6pTCqWnLDXsHA+kAj\nNIxqP4rb2t7GO0ff4eawmwlpHQKtIe3uu8m9/XaC1qyh9QcfoN27lxbffsulu+/m3OOPUx4eDoBe\nr7cqGqNGXeS22/JMx2vXtqCszNMkThUV4OZm2zZHskTUpmiitbVCb29vNYtRXJHUtOa0FlglpfzS\ndSZZtUPNnOoBW/t3PD098ff3Nz3I/fz8qu0BMrrJLPf4uHJ9y/iNvmqYvMjJJuzd92i1aROiogK9\nuzsXR48me8oUdA7uR9q0KYi+fcuIiDAELyxZ0pbevQsZPjzXtL/J3lqSs64xW+1zcnJcPotRbj2F\nq6jXNSchhA+GBLCX6sk4D2AlEAV4AcuAY8AqDCHqycA0SyVS4lR/5OfnV3PJWXsgGWs1WZbEsGxn\nbTNrQ+Ht7U1MTIxpzMLyQsbtHseo9qOY6DGAqPc+wn/7doSU6L28uDBuHNmPPEJFYKDDY5SXw9Ch\nV7Nx4+8EB+sQQrB9ewT9+6fj72+I/rNWFt3Pz4/c3FxTbavQ0NAas8RbBiHY2xjckKiACIUraLCA\nCCFEG8BfSvlrHYybBHSTUs4SQgQCh4CfMJTh2COE+BeQIKXcYnGfEqd6xNF1hpraNWTghTW8vLzo\n1KmTya7PTn3Goh8XARDsHczkqyZzf0V3ot/9CP///Q+ACl9fzj/0EDkTJ6Jv2dJe9yZKSgTe3ob3\nlJnpzujRMXz11Uk8PQ1i5eMTQuvW3lZnodaCHBx94Del9R8laApnqbdNuEKIGCHE8crXfYBdwHtC\niK2gNJkAACAASURBVGfrYN8mYFGVscuBnlLKPZXndgBD6tC/woXUtJ+qvjEmlTUGN4yMGsm7N71L\nl8Au5JTk8MrhVxj02xP8e+YQTm7YQP5NN+FWVETIe+9x9R130Pq999A4sO/IKExGFi5Mx9PTMGv6\n5Rcvbr9dS0ZGZrX3bnls3K/UGPO0NXSpEYWirtjbhDsLeE4IEQXMrjyeANwkhIiszWBSykIpZYEQ\nQotBqBZY2FAA+Nemb4XjOLqhszFt/Kw6dtX1kf6h/Vl/y3pW9FtB91bdySvPI8w3jJIuXUj7179I\n+fhjCnr3xi0/n9C33uLqoUMJefNN3C5edGjcsDAdd9zxZ/DEDz/4cccdueh0hnDvAwd82bbN/q+s\nlJIzZ87U+AC393nXZ9LXuoqLI/XEVJJaRV2xF0q+E9iNIWpvDvBS5aVpwD+llKtrNaAQEcDmyj5W\nCSHOSCkjKq+NAIZIKZ+yuEcuXrzYdDx48GAGDx5cm+EVlTjqlrHXLj8/32qOOmvY2ttU054nMOyt\natu2bY2h2wBHLx6lc0Dnahke/L7/ntbvvkuLAwcA0Pv4cOHee8mZONHhwAkjUoJGY7D7r3+N4Kab\nChgzxiB2mZkeBAeX424lDtbRisWZmZlma1dAvQYtWPv8nIkudNbdq4IsrkwSExNJTEw0HT/77LP1\nts9pKLAQOI+hVMZLlXufoqSUD9TGWCFEKJAITJVSflV5Lh7DmtPXQoh/A7uklJss7lNrTo0Ua/Wi\nLAVHCIGXl1et90UZ15qM1GatK7Mok3k/zOM+0Yvx/zlMyNffAqD38ODiqFHkTJ5sCkF3hr17W3D9\n9UW0aGGI5nvwwQ489tg5Bg4ssNremB6nquBXzUzh5+dHdna22YPd09OzWhLfuqxFObsvy5KaxKcp\nrZ0pXEd9R+v1AIKB/0kppRDiIWCzlLKolsatAO4Fjlc5PQN4E/AEjgL/p6L1mg7Wsp8bN5RWfQAD\ndQqesMzM4OzerRXJK/jg+AeAobbUPdqbeOJ/F+m9dT9CSqSbG5eGDydn4kRKr7rKdJ+/vz9eXl4O\nRSQWFGiYMSOSd989hbu7oebUM8+0Y+HCdLRag40eHh42c/s5Q10e9s5mtLDVh60ZtRInhTVU+iKF\ny7D1kDOWprCWfy49Pb1OD+eoqCiAGnPgWVKkK2L7me1sStnE0UtHTecXhD/KrPgsArZvR1QGWhT0\n60fOxIn4jhxJSGhorfdy7d/vx2uvhbFp0+8IYYgC/P13L7p0qVtmjfpwkzmbC9DZvpVbT2GJEieF\ny6hN3aO6btr19vamtLS0xhmYcdNwVReZkV8u/sInqZ+w/cx21t+yng4tO9Di3DmC4uLw/c9/cKvM\nel7SsSPMnEnmbbdRoNNZGwY3Nzf0er1VewoLNWRkeBATY5hZbt3qz+efB/Dvfzu2TmeN2mSesEVD\niogKNa9/mvpnqsRJ4TJqEhpH905VpSa3l0ajqdGNZ3yAA3ZzABbrivFx9zHlrktNTaU4PZ3ATzax\nNvlD7jxUwLXZoA8KIvu++7hw331UtGpld2x7bNwYSFhYuWk9as2aIDw99Ywdax45GBISQmFhIUVF\nRdVEr77dY039gecIzeE9NofZaL2LU+W60xTAu/KUlFJOrr2JzqPEqXFSU2CCoxm5q5ZAN4Y328LL\ny8tuhd+q2RscWeOqmoHBKJzHLx1nzK4xAFx3yYsHfihlXDJEFnmSe8cdXBg7luJu3UDY/jszbsC1\ntSYmJYwcGcOSJen06GFYwv322xZ07lzC9dcbAjOsRUK6ImOEq2lI8WgOD3VoHut4DSFOh4C3gLOV\np6SUMqH2JjqPEqfGi626RPYeAjWFp9sSFWulLYzuO+MfrrE/R9yHljYax07NS+Wj3z7iyz++JL/8\nzz06jxyED+INr4s7d+bC2LFcuvNOpK+vzX7thdtnZroTFlYBSMrKDDWn1q5N4ZprPCrtKbQsV2VT\n8GvzcG8MMwpHxKMudjaHhzo0j/fREOL0hZTyjjpbVgeUODUN6uthV9NivSPjWIsitIblH7ix76Ki\nIkp1pezL2seOMztIzEjk/8JGs+BbDwK3bMG9chNvRYsWXIqN5eJ99yGvvRZ3d/dqNlkLtzfi7e1N\nSUkJOTlubNrUiieeyMbPz4+LFzWMGhXGli0n8KhSyd5yzam2M4PGMqNo6D1TzeGhDo3n/6su1GfJ\nDCOnhBDPYMiBB4aZ085aWado1lQt0wG1FyutVouPj4/NmY/lONaobSl6Y79paWl4unlyS9tbuKXt\nLRTpiijXl5PV159z06bRcudOWm3ciN/PP/NmxnqS163nLnEVt98xDe3YRwHz928NY1Tj6dOnCQ6u\n4Iknsk3uzYQEd7p2LTYJ09mzHhw54suwYbmkpaWZ3Hu2sjXU9PnU9j5XU1c7g4ODzdbuHM1y0hhm\nlVX5//bOPD7K6lz83yfJZJ8ESCIgSwDZBNlVEEVBBK8bVqvY1pXbutxrtbZVfy6t8unv96u22t7a\nza1Ve63VunCVioKCxAUFlNUFWTSyExICyWTfzv3jvEMmk5lkJplk3ujz/Xzez7z7ec6ZmfOc5TnP\n4w8j360yNTXBypUwYACMHt21aYUgkp7TU9igg0cxxizoQplCyaA9px5GZ1t6HRnugWYT84aGhnZ7\nTrGwKEzZupVz1y9gR3Lz8N+IUuH0pOFcPfYGep80J+zclH+uzev10uBYA/orHWOgqMhHRYU16Pjd\n7/pSXy/cdtsBAKqqhNGjB3PoUOswG/75rsD3BeOWHkV3LOiNVtF8HXopnWLbNvjb3+Dpp2H3brju\nOnj00U6/NpaRcD3GmHoRSQm+Zoxpf7wkhqhy6nl0daUSzrAisEIJ95tJSUkJOfzWluxtkdA7gbe/\nXMqSVU/xTuWn+JLscOTu30Jer8EcmTePI/PmkTh0aNhQ7G0ZOhQWFrJ4cQLDh9eSn18HwN13D2Dq\n1EZuuimlVUUaXCahKlY3VcDRfM/dIadbFHe3cvgw/POfVimtXt18fsgQuPFGuPXWTicRS+X0rDHm\nuyLyFS17TsYYM6xzYkaHKqeeR1f/wSM1eAj+3QS7QgpFNO6RgpVKQ1MD695/kXde+iO3/GM7Hsez\nhBGhcupUmq66gkmVDzCi92jG9R7HuD7jOL7X8aR70hk7dmxYeQKNKhob4eKLh/Pcc0WcdFI+Pp+P\ne+9tYP58Hzk5tRG7OnLb0FU4/DHI6urq8Hg8LTyF+K/HMh/fGOVUXw9vvGEV0uLF4P/dZGbCpZfC\n1VfDjBm0ssrpILrOSXEFXd3i7ahyirSSidSgwr9GKpCjyqSxkczVq+n1yitkrVhBQl0dG/rB5Bta\nviNREpmSO4Vn5j5DYmIiGRkZrawPd+/e3cITRlMTDB1q0965EyZPhj174MCBQioqKjlwwEP//vUR\n5dntSqqt31JX/M7c1KuMOQ0Ndh7p+edh0SIoLbXnRWD2bKuQLroIMjJinnRXGEQoStR0ZAI3mkoy\neKI7FMaYVkN9gZPhbaWXlJQUkXIKNTl/1AgiMZGKU0+l4tRTSSgrI3vZMoYtfZ3Nf/6ItQNg7QBY\nM0j4+JhGkksPU3v4MCbIEGRH8Q5WHFrBwMSBjMweSf/0/ohIi8as1wvPPgtpaTYfa9Y0cfvtA1my\nZDsJCUJGRgaFhYUh8xlcEVdVVbmuIm7LKKIrDDviYnzQlTQ0wNtvNyukQCOd44+HK6+EK64AZ+G6\nW1DlpHQZkVjV+Ym2kgxVgQSHlQdITk4+GqQwsJJpL71QVl6RelZvCOHqqCk7m8Pz53N4/nw8xcWc\n88YbfGfZMjJe3UClB0rStzPwl2fgO+MMfLNm4Tv1VJqys9lwaAO/Wvuro+/JTMpkiHcIcwbM4aaM\nm/B6vfTpA3PnNpfLkSNJXHllBZmZ1gP6yy9X8eWXqVxxxaFW+ewpVnvdQXBjpUcP49XVWYW0aBG8\n9BIUFzdfGzUKLrvMDt2NHdvmYvJ4EpFyEpGRwHBgM7DPGNO+G2hFiYKOVJLByq+kpKSVckpKSgq5\njqm6urrN9EIpP2gdVymUWXJ7ZuwNeXmUXn45pZdfjufAAbLeeIO8ZctI3LyZXkuX0mvpUkxiIpVT\npjBh1miuGXoRnzftZ1vZNkprS/nk8CeM6zMu5LsXf7KYld5nGHjyQFYUH0/2wWyeemkcZ5+acDSf\n779fxtSpXnr1alNM19CWOXhHTcWDibZxFI+h0HbTLC6G116Df/3LziUFBnkcMcIqpPnz4YQTXKuQ\nAmlXOYnITcC3gD7A08Aw4IddLJeiRE17FVW0caBC9fxCDfeEMmkPRkRIT0+nsbGxRe+rvl8/Dl11\nFYeuugrP3r1kvfUW3oICMtatI3PtWs5eu5azgZrhw/HNvJjC0ybx+cA0stP6tMpbUVERSz5dwvM7\nnrcnNzoXp8NxQxZgg1nD7bfncf/9cM45UOmpZGvZVvJS8+id3JuEhISYRTuOVQXe1jBbrIbgomkc\nxWMoNGSagwbh3bULXn3VKqQPPrB+sfyMGwfz5tkeUjvuttxIJD2n7wCnY2M6/VZEPupimZRvILFo\nAbdXUQVXQIFEml7gcJi/Bxbo+byqquroMGIw/veHc2dUP2AAh668kkNXXklCWRneVavwFhTgffdd\nUnfsIHXHDvL+AlO8XupnzCD1/H1w1ln4+vZl1+7dGGOYM2AOfdP6sqdyD/uq9rG/ej97KvaQn30s\nANXVCYwYkXB0GPDJT//Gb1c/CEByQjL9M/szIHsAt0y9hUvHXtpKxrKaMjyJHtI96VGZgHe2Am9r\niDhWi78jJR5Dof40E0tKyFy7low1a0j54APYv7/5puRkmDULzj/fbkOGxCTteBnMRKKcBAgcxutc\nMBpFCUGsWsDRzHOBdQeUlpYWcXrBlW6wxaAxhkYnLlTw+V27dpGXlxdRwMGm7GzKzj2XsnPPRerr\nSV+3jqy33ybr3Xfx7NxJ4muv2SEcIHXAAPpPm0bFtGmMnTqV0cPmH31PSkoKIkJtbS3Jycnk5/fj\nySdr2LXLusos3ukltXwsqcfs5UjNEXaW72Rn+U4WTAy9zv7OFXfy8EcPk5aURq/kXvRO7k3vlN5c\nNeIqzhl5zlFv8P7KtLTWWoNlJ2fHrALvCqUYq+HBmFNRAe+8Q59Fi+j37rukbdvW4nJDnz74Tj+d\n1EsuIW3ePGsdE0PiaTATiXJ6FngHyBeR14GXu1Yk5ZtKtIolWkJVQNHGRmqr9+XHH+MpGGNMRBF1\nWz3n8VA5bRq5l12Gx+uFr76C5cvhzTdhxQo8e/fS56WX6PPSSxgRao87jsrJk6maOJHqKVOo7d8f\nRKirq2vV0/vp1Iu5+rjLGDw4AW+Ol6de8vHKiv2c+5PhgDVZD7QMbGhqIDkxmeqGaqobqtlfZVvu\n8/LnUVZWRlVVFcnJyUeNQh7Y/ACv7noVQchOziYvM4/c9FwWzlzI3OPmtsrrjtId1DXWkZueS05a\nDokJiS2ut1dZFhUVdahXE03jqEsVWUmJHZ5btcpuq1dDQwPZzuWm1FQqJ0+mcto0KqZOpWb0aEhI\nsMsFuuC/E0+DmYjWOYnIGOAE4HNjzOYul6p1+rrOSYkJnR2iaG99VVueKTpL8Joqn89HycGDJH38\nMZ633ybzgw9I37CBhLq6Fs/VH3MMVZMmUTlpEjVTplA1fDiEGXpcv/44Bg5M48wz7fH999vPO+5o\nTrO4uJhDvkOUVJdwuO4wh2sPM7rXaPqm9W31voXrFrJ833LK68oxAWv5X77sZS4cfWGr+7/9/LdZ\ntGURAILQJ60PeRl5/OGcP3DWsLNalX+hr5C0tDSmjJpCYn0iu3btavXOrlhAG5OhLmNg61arhN5/\n335u3drynoQEOOkkOOssqqZPp2jYMKqbmrokgnEoYrkguSu8kl8HjDTG3CoiS4F/GGP+O2rJOoEq\nJ8UthFqgGRiyIxKffm3RVjDFQK/soYIoejweUozhmN27SV+/nso33yTlo49ICgpj35SSQs2oUVQf\nfzw1Y8ZQPWYMtccdh/F4WlU8kyfDn/8MY8f6OHDgAK+8ksK4cdUMGND2sGQwjaaRhqQGMvIyKKkq\n4fjc48lJz2l13w9f+yHLv1xOSVUJpdWlRxXam1e+GVI53fDeDawqWgXYObM+KX3ISc3hjgl3MDFn\nItBSqVfVV5GWlNZhx8AdpqkJvvgC1q+324YNsG5d8yJYP2lpcPLJMH263U47jWCzyu5cJBzLtLpC\nOW0ATnb87HmAd40x06KWrBOoclLcRFut5s6EoW8rtDxY5ZSbm9umxWFgRezz+dj11Vckf/kl6Rs2\nkLFhA95Nm0gMYZDR5PFQM3IkDePHkzVjhl2cOWYMFWl5NJkKdu/eRU0NnHXWKJ599gsGDrTKqbpa\nSEuL7L8Z7fxeQ1MDpdWlFFcWk98rn8zkzFaV5b3r7mXTkU0UVxdTXlt+9NmnZz7NxJyJrdxVzXxq\nJmv3ruVY77Hk98pnaK+hDOk1hGsmXsPArIER5aNdKipsD+iTT5oV0caNLU27/fTvD6eearfp02Hi\nRGvY0A7daaQQq7S6Qjl9iFVORmxzY5UxZnqHpOsgqpzcjdvd33Qn7ZmrhzKG8HsRDzRND+4Z+Vus\nJSWtvZAHEhwLKZRPOt+uXZStXEnS5s2kfvYZaVu2kBLGgpA+fagZNoyq/HzKjj2Od0omMO2aXOr7\n9uVweTIXXTSCZcu2kpLSOr9tDXEG5zkawv3eDpYeZOOOjRRXFzMiewQZnoxWrfxJj05i44GNrd65\n6YZNjO87vtX5h1Y/RFJCEiNzRjIyZySDsgeRIAk4buNhyxb4/PPmz88/t568QzFgAEyaZLuj/s9B\ng3qciXdH6Qrl9DPg34C1wGRgqTHm/k5JGSWqnNzL19oPWRjaU8bhgiWCVR4ZGRktng/njTxUOu31\nzAKH/iIJOeJXXik1NfQvKiJ9yxbb4v/sM1vhlpeHTMckJVGe1Z+dCUMYNCOXumOPZX/qED4sHsXZ\n1/UhdcgQyqqqqK+vxxjTpgl/LH8vkTSUfLU+9pTvYWfZTr468hXbDm5jwfAFZHoym59xlE//p07g\nQO2ho8+mNiUwotzDa88mMLCoOrQQyckwcqTtfQYqozDf8zeFLnH8KiKTgJFYg4hNnZCvQ6hyci/f\nGA/ODpEq41Dh2UMN23U2zlXw+/3vCve95ObmRt7LNQb27aNq3TrKVq8m+YsvSP3yS5J37sQT6A4n\nDPVZ2TTk5tKYm0NDTsutMSuLpsxMGjMzScnNZdDYsZCVZedcYtmTMMZ62/b5Wm7l5VBcTM3u3VR8\n8QWJhw6RVFpKUmkpKWVlJBQXY+rr+c102JYDW3Ps5wEviIHq/wcp3l5WAR1/vA3GN3o0t1S+xJDB\n4xjffxLj+44nN90F5uguIZYhM641xjwuIvcFXTLGmLs6I2S0qHJyL9805RRNfn0+a0RQX19PcnIy\nffv2DTks15k4V6E8mIeTMzU1ldra2laKEWi/txHcI/F42LNqFQ07duDZu5fkvXup+LSI3mV7yCjZ\nS1JpKRLGsKNNkpKalZTHE35LSLAOTcNtdXXNiiiEr8OIyMmB/Hy7mDU/H/LzKR90DF/lJDJ+/Bxr\nqBCgSEuqSsh7IK/FK4b2Gsq0gdN4+qKnW5nFf9OIpVdyv03mdqD1qkJFwcWLF2NEJK6JwhFq3VZb\nYds7895gQn0vwcNr/oXB/n0Iv8gyVJrZJ5/Mrn79WrzTrw6b6hu5dUEW992ymT51RaT5fBQ8V80p\nw3aTXb2fBJ+PhMpKEisrSampIaGiAsrKbC8n2IKts3g8dnFqVpb99G95eZSlpFDt9doeXZ8+NOTk\nkDxgAINPPBFSU1u9KgtoPTPlJJPg4U/n/onNRZvZXLSZTUWbKDxSSEpSSkjFVN9Yz57yPQzt/fVs\nyHWWSOac3jTGzOkmecLJoD0nF/N1NYgIZzYe62G5tqLgdlb+wO+lPWMKP9EM/7U1v+ZHRGhqyufM\nMzPZswfq6nwUF5ewaVMKs2d7ycoKeHdtrR1yq6mxwfACtsojRygrLkYaGsj2eknPzrY9rcDN42n+\nzMwErxdfXV1cIu02NDXwWfFnlFaXMnPIzFbX39v1HjOenEF+dj4zh8xk1pBZzBo6i8HZgzudthvp\nCoOIfwL/ALbiuDEyxmxr86H2hZwK3G+MmSUiw4GnnHd/AtwYrIlUOSnxINJ5G2h/WCyQgwcPtvAU\n0V1GJJE6vvWvAYq0wm7rvQkJCQwaNIikJC+ffgonnmjPr15twwht2xbZFFNHlUikhiGRfn+xbIg9\n/+nz3PDqDRyuOdzi/IKJC3jiwic6/F630hXBBvsCtwSdmxWVVAGIyO3AFUCFc+q3wF3GmHdE5GHg\nQtRFkuJiAoe4OuJ7LJQ/vu5wCeN30dNeTydYybQnX1vvTUtLO/qcXzGB9Vd6663Niulf/4IPP4Rf\n/CK0TB11oxPJc5G6zYq1n7n5Y+dzyZhL2Fy0mZWFKynYWcDbX73NmLwxIe+vqKsgw5PR/QuI40Sb\nyklEsoDzjDEdW1UYmh3AxdjwGwCTjTHvOPuvA3NR5aS4gEjm07rC91hXDpN6vV4GDRrUqqfTWbdL\n4d7b0NCAz+drlYeLLmr5/OOPw7e/3Zz3DRtSGD06izFjMjssU6zpiu86QRKY2G8iE/tN5Men/JjG\npkbqGutC3vuTZT9hyfYlnDfiPM4feT6zh84mIzn24dTdQkK4CyLyQ2ATsFFE/i1WCRpjFgGB5jOB\nzYAKOOrjUFHiir9H4F+bFKuht9zc3Bat30Cl52+dV1ZWUllZya5du/CF8izQCYLzlZ+fT3p6etj7\nowknMnjwYFIDDAlqa2sjysNjj8E55zTn/Wc/681775UcfS4nJ3yZtUVbZe1GEhMSSfOkhby2qWgT\n+3z7eHz941z43IXk/DqHC569gG2HOjXL4lra6jldDozCGqj8HVjaRTIEjgN4gSOhblq4cOHR/Zkz\nZzJz5swuEkdRmmlvyKcj1optecDuLi/QofIVmA/omBcHr9fbyiIxkjz06weFhTbvtbXClCmVTJtW\nQUmJIT3dy/TpXl5+OZ/ERLu+KlKZYhWKxf9sPC1TV39/NZuKNvHqtldZsn0Ja/as4fXtr/PEPHfO\nTxUUFFBQUNDh59ta5/SWMeZMZ3+FMWZ2h1Np/e4hwLPGmFNEZDHwG2PM2yLyCLDCGPNC0P1qEKG4\nllgOw3XVurGIPCfEKB8dzUO453bvHsqPf9zIiy9as/fU1FwWL/Zy/fUdEq9THDx4MCLvHt1BUUUR\nq/esDundvb6xnhWFK5g9dDaeRE8cpGtNLBfhrjTGzArejwWOcvqHMWa6iIwAHgeSgc+Aa9VaT/mm\n0hnT5rC+9GJkLh2p8gqVXnJyMklJSVE/518kvGXLHtLT7XLLRYt6s2bNMSxZYivdmhqorfVRWtq1\nyxni4aqrow2GpTuWcs4z55CXnsc1E6/h2snXMiJnRPsPdiGxVE4HgeXYOaEzgbecS8YY873OChoN\nqpyUbxIdqZDCmXPn5+fHzCtFNBWzPw+NjY0hHdi29xyE9ye4Zk0G2dkpXHKJDT3/85/Xsn9/Obfc\nUhRRGu3lMxqP813pDaUzynDx1sXcsfwOtpRsOXruzKFnctdpdzF7WMwGwaIilqbk8wGDVU6PBpxX\nLaEoXUhHIgKHi9AbC48Uod4fiXm5X7EEEulzbTF1aiUZAUZqy5c38aMfNTuo/ec/e3HaaWXMnRt9\nzzBeIclD0Zn5x3mj5nHByAtYs3cNj617jOc+eY63Ct/i8nGXd6XIMSWscjLGFHSjHIqidBHxnsiP\nhlA9l/bkf/rpA1RXWw/hNTXC73/fl7lz9x29vm8fHHts+2m3pwyiLcd4e04REaYNnMa0gdP4r7P/\ni2c+fobLxl7WrTJ0hrCm5Iqi9ByCTab9NDY2UlJSQl5eXqdM4jtqkh3Nc+HM6Nsz6e/bN5eEBL9X\nC1i4cB8TJvQG4OBBGDsWQnlt8vl8FBYWUlhYGJG5fqAcKSkppKSkUFJSEvLZWCwJiKUZfHZqNv95\n0n+GXBdV01DDrL/N4q/r/0ptQ8ejOMeaiEJmxBudc1KU9gk0iEhMTKShoSGmk/fRGER0xL1TZ+Z0\nwsn2xhuwZAk89JC977PP4O9/hzvv7LjfxEjmgmI1P9XVvS+fz8ejax7ltlW3AdA/sz83T72ZG068\ngV6pvdp5Ojq6JJ5TvFHlpCjREa9QJp2ZxO8OmX/6U0hJgWuvtWnt3eshLa2JPn0aI3Z4G4mcPSGU\njP+7qmusY+mepTy57Um2l20HIDM5kwfnPMj1J8bOXr8rfOspiqJERGcm8btjbuy662zkDL8B4UMP\n9WXChCouv9yG6Qj2m+g36Ii219IT5vn835UnwcMFgy/g/EHns75sPU9/+TQrClcwMGtgXOVT5aQo\nX0N6QuUYTCy9OYRj1Cj76fPlUllZhTFw/vllR8vniivgrrtg0KDwlnuRlG135CXWiAinDzidq0+7\nmk0HNjGu77j4ytMThst0WE9Roice1mJdvVA1lnkKfldxsZdp02D3bti3r5CKikreesvLzJk+EhNb\nDsvF2xIvFnT3omKdc1IUJa50VcXd1ZVpUxN89RUMG2bnjN57z3DPPQNYvHg7IpCensGwYe6ZM4oF\n3alkVTkpitKlxKvX0J1GBj6fjxdeOERxcSLnnWeH/davH8bbbydy9917gZ7bY4oXahChKEqX4TYv\nCl2F1+vl0kv9JvDWiu/uu5OYNq3oqIJcvlwYPz6BqVO/vjGV4okuwlUUJWLCWeN1B90dm8nr9TJ0\n6FCGDh2K1+vlvvv2cvbZZYBd7Pvgg33Zvr3ZbVKAC0ElBqhyUhSlR9BVwR8jJTXV4PFYxdzQAOee\nW8Ypp9QcPR450hpTKLFBlZOiKBET78iywb2Z7iQw7x4P/OAHhzjmGJv3DRtg6FAYNMjeW14ORKXF\nGAAADy9JREFU99xje1hKx1CDCEVRouLrYEbdUdrKe2MjJCba/cceg6VLYdEie1xejmOO3t0Suwe1\n1lMURYkzGzfaz4kT7ecvfgGlpfC738VPpngTrXLSYT1FUZQYM3Fis2IC+PRTuPrq5uNf/xrWru1+\nuXoS2nNSFEXpRqqq7NzU5s0wYIA9t3EjjB8PCV/j7oL2nBRFUVyMxwMvv9ysmPbvh1mzrNJSmlHl\npCiK0o14PDBjRvPxl1/CTTdBZqY9/vhj+Pd/j49sbkI9RCiKosSRU0+1m58nnmjuVQFs2WKH+/we\n1b8p6JyToiiKizh0yH7m5NjPyy6D00+HG2+0x8aARDxz4x50zklRFKUHk5PTrJiMsb2o732v+fp5\n58H69fGRrTvRnpOiKEoPYfduOPlk2LXLzl0ZA488Aj/4gT12M9pzUhRF+ZoyaJCdg/IronffhT/+\nEZIc64GGBrt9HVDlpCiK0oPo1at5PyMD7ruveQ7qxRfh8svjI1escYVyEpEEEXlERN4XkZUicly8\nZVIURXE7U6bAvHnNx8uWwbe+1Xy8aBGsWtX9csUCVygn4FtAsjFmOnAH8Js4y6MoitLjeOIJmD/f\n7htjPaPX1zdfLyqKj1wdwS3K6VRgKYAxZg1wYnzFURRF6XmINHtGb2qC226DM86wx/X1MGECfPFF\n/OSLBrcsws0CygOOG0UkwRjT5D+xcOHCoxdnzpzJzJkzu004RVGUnkZiYktns1u3wtSpcJwzaXLk\nCNx+Ozz6aNesmyooKKCgoKDDz7vClFxEfgOsNsa84BzvNsYMCriupuSKoigx5OGHYeVKeP55e1xc\nDHV1Lb1TxJJoTcnd0nNaBVwAvCAi04DNcZZHURTla81558Hs2c3Hf/wjHD4Mv/99/GQKxC3K6X+A\nOSLitytZEE9hFEVRvu4MHtzy+MgRu5jXz513wpw5cOaZ3SuXH1cM67WHDuspiqJ0H5WVMHCgDZJ4\n7LGxeaeGaVcURVE6hTGwbVtsPaGrclIURVFch/rWUxRFUXo8qpwURVEU16HKSVEURXEdqpwURVEU\n16HKSVEURXEdqpwURVEU16HKSVEURXEdqpwURVEU16HKSVEURXEdqpwURVEU16HKSVEURXEdqpwU\nRVEU16HKSVEURXEdqpwURVEU16HKSVEURXEdqpwURVEU16HKSVEURXEdqpwURVEU16HKSVEURXEd\nqpwURVEU16HKSVEURXEdqpwURVEU16HKSVEURXEd3a6cROQiEXkm4HiaiKwWkfdE5J7ulidWFBQU\nxFuEdukJMkLPkFNljA0qY2zoCTJGS7cqJxF5CPglIAGnHwa+a4w5DZgqIhO7U6ZY0RN+HD1BRugZ\ncqqMsUFljA09QcZo6e6e0yrgP3CUk4hkASnGmELn+jLgrG6WSVEURXEZXaKcROT7IvJx0DbFGPN8\n0K1ZQHnAsQ/I7gqZFEVRlJ6DGGO6N0GRmcD1xpjvOj2nD4wxY51rPwKSjDG/CXqme4VUFEVRYo4x\nRtq/y5LUlYK0hzGmXETqRGQYUAjMBRaGuC/iDCmKoig9n3goJ+Nsfm4AngESgWXGmA/jIJOiKIri\nIrp9WE9RFEVR2kMX4SqKoiiuo0coJ7HsFZGVzvbLeMvkR0QSROQREXnfke24eMsUChFZH1B+f423\nPIGIyFQRWensD3cWZL8jIn8WEVfMNwbJOElE9gSU53wXyOcRkaedclsjIhe4rSzDyDgp6L8d17IU\nkUQRecIpt3dFZKwLyzGUjK4qRz8icoyI7BaRkVGXozHG9RswHFgcbznCyHYx8ISzPxV4Od4yhZAx\nFVgfbznCyHY7sBl43zleDJzu7D8MfMuFMv4A+Em85QqS8Rrgt85+b2AX8IqbyjKMjN93U1kCFwJ/\ncfbPcMrQbeUYLOPLbitHRzYP8D/A58CoaP/bPaLnBEwBBojIWyKyRERGxlugAE4FlgIYY9YAJ8ZX\nnJBMANJFZJmIrBCRqfEWKIAdWAXvb0VNNsa84+y/jjsWZQfLOAU4T0TeFpG/iEhm/EQ7yguA3/1X\nAlCP+8oylIyuKktjzCvA9c7hEOAwMMVN5RhCxiO4rBwdHsAqof3OcVS/R9cpp1ALeIF9wC+NMWdi\n3R/9Pb5StiB4IXGjiLitXCuBB4wxZ+NYR7pFRmPMIqAh4FRgV78CFyzKDiHjGuBWY8wZwJfAvXER\nLABjTKUxpkJEvFgl8DNa/r/jXpYhZLwbWIv7yrJRRJ4CHsJaErvxNxkso6vKUUSuAYqNMW/4TxFl\nOcZ1nVMojDF/BVrMiYhIGk7lYIxZJSLHxkO2MJQD3oDjBGNMU7yECcM2bOsfY8x2ETkE9Af2xlWq\n0ASWnRfbKnQb/2OMKXP2XwZ+H09h/IjIIGAR8CdjzLMi8uuAy64oyyAZnxORbDeWpTHmGhHpi630\nUwMuuaIcoYWMa4Dpxph9ziU3lOMCwIjIWcBE4G9AXsD1dsvRFa3nCLgHuAVARCZgx6rdwirgXLAe\n1rFzE25jAfAbAEexZ9Hc1XYbG0TkDGf/HOCdtm6OE0tF5CRnfzbwUTyFAXAqqTeA240xTzmnXVWW\nYWR0VVmKyJUicqdzWA00Ah+5rByDZWwCFrmpHI0xZxhjZhpjZgEbgauw33XE5dgj1jmJSDZ2KC8T\n24O60RizLb5SWRyLkz8D451TC9wimx8RSQKeBPKdU7cbY1bHUaQWiMgQ4B/GmOkiMgJ4HEgGPgOu\nNS74kQbJOAH4E3bOZD9wnTGmIo7i+T3+XwpsDTj9I2wL2hVlGUbGO7ANJ1eUpTNK8xTQDzuhfx92\nQt81v8kwMu7CZb9JP46V6/VY5wsRl2OPUE6KoijKN4ueMqynKIqifINQ5aQoiqK4DlVOiqIoiutQ\n5aQoiqK4DlVOiqIoiutQ5aQoiqK4DlVOSpuISKbj0/B9ERnnnDtNRG4Pc3+BiIzqXilbpD/DL2eY\n6yki8v0uSDfR8Qb9noj0Cjh/gojMcPa/EpHkWKfdUdqTR0QuEpH+ItJXRP4Ug7QKRKS3iBwIcT1N\nRJ5y/D++KyIviEgfEZkT4Gm7LmB/kvPcYhH5l7OfF3D9sFjP5ytF5Gbn060Lz5UQqHJS2mMu1ivz\nf2I9HwPcDPwuzP3BkY67m+8Dbbm36o/1Kh5rBgBeY8xpxphAtyyXAGOcfUNL/2Lxpr3v6WYgyxhT\nZIy5MQZpzTXGHA6T7gJgvzHmbGPMDOA94B5jzJvGmFmOp4FD/n1jzAYRGQxkAFkiMtQYUxxw70bg\nSuf49845pQehyklpDx+QBqQDlSLyPWCRMaaurYdEpJeIvOp4SV4lIrNEZJ6I/MG5foeIvOLsXy4i\nd4rIIBF5zWnlviYiA0VkiOMAeKWI3BaUxpNiY8N8KCJXiMhk4GzgV867fijWC/tqRxYP1tnoGBH5\nuYjcKyLXO+8aLc3xmv6/I/OaUD1ER961Tgv/CccDxyPACBF5OOC+AdgwET8OcC3zcEDrvpfYGEd/\ndcrpXWl27+J/xxCnx7FEbEyuXwScf8t5rkBExjvnN4nIi458j4tlYah8BqRxgtNjWS4iG0XkFBE5\nD8cnmoiMEJEPnHvnOOVZICIviUi2iMwUkddF5GUR2Swid7X12wjDAWCuiJwvIlnAH4CftvPMv2P9\nyP0d23gKxk0NASVa4h3zQzd3b9g/+H/R7P7oBWxP4BHgthD3r8TGbnkQuMk5dyzWU3IqsMk59xrw\nIZAIPOc880/g35zrs7GVTj5wEEgKSseLdWab42zfdc4/ie3tCdYno98LylJguvO+D5xz9wLXO/uj\ngZXOfiEwGOtm5bqgdHOA7UCGc/xb4MbA9wbdf6//Hc57pwfIeSnwH8D9Ae/+JOj5Ic5z6U5ZfQBM\nAl4ELnDumQB86OyXAf2c/ReAi9rJZwowHzjBOfdd4LGA73KkI4O/zL4E+jv7N2PDIpwBfIpt7KYD\nR0KUQyGQ7OzvD/Nbuwgb/6fESfuEoOv7A/YTsG6QemN9Re4CUoN+hyPDPa+b+zftOSltYiw/NsYs\nwFZcD2HDMdwFDBbrCy8Uo3EcOxrrLbkcW4lsE5ETgTpgNbZiG2yM2QqMA+5yWvY/B45x3lVojAkM\nWYExxod1Bvw4VqmlBMuN9TP2rIj8BRiI9cIfrjUdeP5y4FfAMqBX0H3DgE+NMZXO8TvA2DDvDMU6\n5/MAtiI/ATjXyfOLQKKI9Al6ZrUxpsoY04j1QD2SluW7CRjk3PuZMcY/p7PKuTdcPsEOse0Dfi42\nBMMlhIlWICK5QLkxxj938y7Nef/YGNNkjKnCOiONChE5BVhujLkI6736KWcLx9nYBso/sN+/AN+L\nNl3FvahyUiJCRI4BRhlj3sNWqv65pfQwj2wBTneeHYBt4ZZgW8YPAm9hPVT/Engz4Jn/Y+z8wA+x\nlQ60DKPhl6cfNgjcxcD5wK9FJNG5N9EZ5rrQGPMdbAs/AVuBNdH8u6/BzkEBTHbemwxcaoz5LnAm\ncI3YMA9+CrHDgv58z6SlI9NgmrA9Hj/B8y2fA886eb4QeB4b4C6QCc7wXyJwMvAJLct3Is1e5keI\ndZQMtqf4cah8BiDYBse9xphrnPv95dNCdmNMCXZ+p59z6oyAvHd2nvE7OJEHnIaFX+5w/AD4vjHm\nHGPMOcBl2B6s8jXBdfGcFNdyN/B/nf0/Y4fJdjqt9mAMVuk8ISKXYOesrjXGNInIEuAJbNDDvdje\ngj+q563YOZlU55mbA97XMgFjDohIPxFZhQ1r8ICxAdjWYL00fw87R/YOVimuxw4vrgaSReQ+4FHg\neWeeZ519rakTkVIRWY3tASwzxuwOSLdERO4FVopIE3aI73bn3aEq6HXAAyKyJcR148jwuIgUYHuW\nf3Iq5+D7/oUd9nvWGPOpiNzqPHcr1jO131ilBvhvseEp3jPGvCYinwXnM6hc/w68ICK7saEW/Irs\nfWwcnusD7r0WG56hCSjFzqmNC8pbe4oqR0Q+DDh+EPv7+qOIbMAGx6wMyFOL9zp5Owk7LGovGPO+\niKSKyDTjIo/7SsdRr+SK4mLEhur4gzHmggjv/9gYE9aUPl6ISCEw2hhTG0cZ9htj+rd/p+IGdFhP\nUdxNtKb5bm5tLhOR3t2dqIhkOD1TN5eNEoT2nBRFURTXoT0nRVEUxXWoclIURVFchyonRVEUxXWo\nclIURVFchyonRVEUxXX8L1ZRyCpzsLLxAAAAAElFTkSuQmCC\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x10f72f198>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "X = df[['LSTAT']].values\n",
+    "y = df['MEDV'].values\n",
+    "\n",
+    "regr = LinearRegression()\n",
+    "\n",
+    "# create quadratic features\n",
+    "quadratic = PolynomialFeatures(degree=2)\n",
+    "cubic = PolynomialFeatures(degree=3)\n",
+    "X_quad = quadratic.fit_transform(X)\n",
+    "X_cubic = cubic.fit_transform(X)\n",
+    "\n",
+    "# fit features\n",
+    "X_fit = np.arange(X.min(), X.max(), 1)[:, np.newaxis]\n",
+    "\n",
+    "regr = regr.fit(X, y)\n",
+    "y_lin_fit = regr.predict(X_fit)\n",
+    "linear_r2 = r2_score(y, regr.predict(X))\n",
+    "\n",
+    "regr = regr.fit(X_quad, y)\n",
+    "y_quad_fit = regr.predict(quadratic.fit_transform(X_fit))\n",
+    "quadratic_r2 = r2_score(y, regr.predict(X_quad))\n",
+    "\n",
+    "regr = regr.fit(X_cubic, y)\n",
+    "y_cubic_fit = regr.predict(cubic.fit_transform(X_fit))\n",
+    "cubic_r2 = r2_score(y, regr.predict(X_cubic))\n",
+    "\n",
+    "\n",
+    "# plot results\n",
+    "plt.scatter(X, y, label='training points', color='lightgray')\n",
+    "\n",
+    "plt.plot(X_fit, y_lin_fit, \n",
+    "         label='linear (d=1), $R^2=%.2f$' % linear_r2, \n",
+    "         color='blue', \n",
+    "         lw=2, \n",
+    "         linestyle=':')\n",
+    "\n",
+    "plt.plot(X_fit, y_quad_fit, \n",
+    "         label='quadratic (d=2), $R^2=%.2f$' % quadratic_r2,\n",
+    "         color='red', \n",
+    "         lw=2,\n",
+    "         linestyle='-')\n",
+    "\n",
+    "plt.plot(X_fit, y_cubic_fit, \n",
+    "         label='cubic (d=3), $R^2=%.2f$' % cubic_r2,\n",
+    "         color='green', \n",
+    "         lw=2, \n",
+    "         linestyle='--')\n",
+    "\n",
+    "plt.xlabel('% lower status of the population [LSTAT]')\n",
+    "plt.ylabel('Price in $1000\\'s [MEDV]')\n",
+    "plt.legend(loc='upper right')\n",
+    "\n",
+    "plt.tight_layout()\n",
+    "# plt.savefig('./figures/polyhouse_example.png', dpi=300)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Transforming the dataset:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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33ltKly6HfHq/LzURExISSE1NbXXTSHc1G6N9/ZX0EKOXLz0uv3tSLu4GVmit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\n+BsGAYCyxrWWDwAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x10e779588>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "X = df[['LSTAT']].values\n",
+    "y = df['MEDV'].values\n",
+    "\n",
+    "# transform features\n",
+    "X_log = np.log(X)\n",
+    "y_sqrt = np.sqrt(y)\n",
+    "\n",
+    "# fit features\n",
+    "X_fit = np.arange(X_log.min()-1, X_log.max()+1, 1)[:, np.newaxis]\n",
+    "\n",
+    "regr = regr.fit(X_log, y_sqrt)\n",
+    "y_lin_fit = regr.predict(X_fit)\n",
+    "linear_r2 = r2_score(y_sqrt, regr.predict(X_log))\n",
+    "\n",
+    "# plot results\n",
+    "plt.scatter(X_log, y_sqrt, label='training points', color='lightgray')\n",
+    "\n",
+    "plt.plot(X_fit, y_lin_fit, \n",
+    "         label='linear (d=1), $R^2=%.2f$' % linear_r2, \n",
+    "         color='blue', \n",
+    "         lw=2)\n",
+    "\n",
+    "plt.xlabel('log(% lower status of the population [LSTAT])')\n",
+    "plt.ylabel('$\\sqrt{Price \\; in \\; \\$1000\\'s [MEDV]}$')\n",
+    "plt.legend(loc='lower left')\n",
+    "\n",
+    "plt.tight_layout()\n",
+    "# plt.savefig('./figures/transform_example.png', dpi=300)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<br>\n",
+    "<br>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Dealing with nonlinear relationships using random forests"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "..."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Decision tree regression"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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JEaSuW86poCAZxdohE86Eso6rrJ63gH9jqdXzGvAOluDuO8AIrfV3HjFIsnoK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\nSCSqjP8BMoIlGmqeaNsAAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x10cb98f98>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from sklearn.tree import DecisionTreeRegressor\n",
+    "\n",
+    "X = df[['LSTAT']].values\n",
+    "y = df['MEDV'].values\n",
+    "\n",
+    "tree = DecisionTreeRegressor(max_depth=3)\n",
+    "tree.fit(X, y)\n",
+    "\n",
+    "sort_idx = X.flatten().argsort()\n",
+    "\n",
+    "lin_regplot(X[sort_idx], y[sort_idx], tree)\n",
+    "plt.xlabel('% lower status of the population [LSTAT]')\n",
+    "plt.ylabel('Price in $1000\\'s [MEDV]')\n",
+    "# plt.savefig('./figures/tree_regression.png', dpi=300)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<br>\n",
+    "<br>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Random forest regression"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "X = df.iloc[:, :-1].values\n",
+    "y = df['MEDV'].values\n",
+    "\n",
+    "X_train, X_test, y_train, y_test = train_test_split(\n",
+    "    X, y, test_size=0.4, random_state=1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "MSE train: 1.630, test: 11.065\n",
+      "R^2 train: 0.980, test: 0.877\n"
+     ]
+    }
+   ],
+   "source": [
+    "from sklearn.ensemble import RandomForestRegressor\n",
+    "\n",
+    "forest = RandomForestRegressor(n_estimators=1000, \n",
+    "                               criterion='mse', \n",
+    "                               random_state=1, \n",
+    "                               n_jobs=-1)\n",
+    "forest.fit(X_train, y_train)\n",
+    "y_train_pred = forest.predict(X_train)\n",
+    "y_test_pred = forest.predict(X_test)\n",
+    "\n",
+    "print('MSE train: %.3f, test: %.3f' % (\n",
+    "        mean_squared_error(y_train, y_train_pred),\n",
+    "        mean_squared_error(y_test, y_test_pred)))\n",
+    "print('R^2 train: %.3f, test: %.3f' % (\n",
+    "        r2_score(y_train, y_train_pred),\n",
+    "        r2_score(y_test, y_test_pred)))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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SJC666CIqKiqYPn06paWl+P1+Jk2axNSpU+3nXXnllTz99NNcccUV+2yPPmrUKJ588kkm\nT55sZ/ENHDjQvv/hhx/m+uuvZ+7cuZx44olMmjSJjz/+2L5/9uzZXHnllTQ3N/OXv/zlgLahVxTl\n4KhU8v1T+0EpKU19xopy5EjpUkeKoiiK0lkqQCmKoigpSQUoRVEUJSWpAKUoiqKkJBWgFEVRlJSk\nApSiKIqSkg77dVCqqoGiKMqR6bAOUGp9jKIoypFLDfEpiqIoKUkFKEVRFCUlqQClKIqipCQVoBRF\nUZSUpAKUoiiKkpJ6JEAJIcYLIT5u+fpEIUSpEOLjln+X9ESbFEVRlNTS7WnmQoi7gClAqOWmMcCj\nUspHu7stiqIoSurqiR7UN8BEILnCdgxwvhDiEyHEc0IIfw+0SVEURUkx3R6gpJTLAL3VTV8Av5BS\nngpsA+7v7jYpiqIoqScVKkn8TUrZ0PL134HH23vQ7Nmz7a8nTJjAhAkTurxhiqIoysFbvnw5y5cv\nP+jn98iW70KIQcAiKeXJQoiVwG1SytVCiFuBPCnlr/Z6fLtbviuKoiiHjwPd8r0ne1DJiDMdeFII\nkQDKget7rkmKoihKquiRHtSBUj0oRVGUw9+B9qDUQl1FURQlJakApSiKoqQkFaAURVGUlKQClKIo\nipKSVIBSFEVRUpIKUIqiKEpKUgFKURRFSUkqQCmKoigpSQUoRVEUJSWpAKUoiqKkJBWgFEVRlJSk\nApSiKIqSklSAUhRFUVKSClCKoihKSlIBSlEURUlJKkApiqIoKUkFKEVRFCUlqQClKIqipCRnTzdA\nUZTUMHHSROqb6ve5PSuQxbLFy3qgRcrRTgUoRVEAqG+qZ+arM/e5/bFLH+uB1iiKGuJTFEVRUlSP\nBCghxHghxMctX39PCPEPIcQKIcRTQgjRE21SFEVRUku3ByghxF3AXwBPy02PAndLKX8MCOA/u7tN\niqIoSurpiR7UN8BErGAEMFpKuaLl67eBM3ugTYqiKEqK6fYkCSnlMiHEoFY3tR7SCwGZ3dogRVEA\nK1uvvYSIrEBWD7RGUVIji89s9XUACLb3oNmzZ9tfT5gwgQkTJnRpoxQlFXRn6veyxcvafb36pnom\nTpqoUs2VA7Z8+XKWL19+0M8XUspD15rOvqjVg1okpTxZCPEG8IiU8hMhRBHwoZTytb0eL3uinYrS\n0047/7QOU78/fuvjw/71lKOLEAIpZacT4XqyB5WMOHcAfxFCuIGNwNKea5KiKIqSKnokQEkptwP/\n3vJ1MTChJ9qhKIqipC61UFdRFEVJSSpAKYqiKCkpFbL4FEXpQHenfqtUcyWV9EgW34FSWXyKoiiH\nvwPN4lNDfIqiKEpKUgFKURRFSUnfGqCEECcIIf5dCPEDIcRHQghVK09RFEXpcp3pQRUBUeA3wD3A\n/V3aIkVRFEWhcwEqilXhwSWlXAnoXdskRVEURelcgJLAy8D/CiEuARJd2yRFURRF6USauRAiGxiH\ntVfTBGC9lLKu65vWpg0qzVxRFOUwd6Bp5h0GKCHEDR08R0opnz2Yxh0sFaAURVEOf4eymnl/9lQc\nVxRFUZRu1alKEkKIAYALa/fbAVLKf3Z1w/Z6fdWDUhRFOcwd8v2ghBAvAD8A/IAP+AK44KBbqCiK\noiid0JlisScAx2Kth7oH+FOXtkhRUlR3br+uKErnAlStlNIUQvillNVCiH5d3ipFSUH1TfUdboeu\nKMqh15l1UGuFEHcCu4UQi7GG+hRFURSlS31rD0pK+WshRABoBs4FVnV5qxQlBew9pFe8tZiVX6zE\n5XQxdszYHmyZohwdOpMksXftvROB33ZNcxSlc7pjPmjvIb3ZP5/N4DGDKVlbckiOryjK/nVmDqoS\naz2UBoxGbdGhpIADnQ9SCQ6KcvjpzBBfUevvhRDvdF1zFKVrHIoEh7T0NIouK6KqpIrPh35u3662\nQ1eUrtGZIb7hrb4dABR0RUOEEP8HNLR8u01KeU1XvI6iHKy75t8FWEHt47c+7uHWKMqRrzNDfM+y\np+RRFLjjUDdCCOEFkFKedqiPrShK11NDqEpX6MwQ34RuaMcJQJoQ4l2sNt0tpfyiG15XUTqUFchq\ndwjwYIf0juSTuFojpnSFDgOUEGJDy5cOwA1UA9lAnZRy/CFuRxh4SEr5vBBiGPC2EGK4lNJMPmD2\n7Nn2gydMmMCECRMOcROUw8mhDh7tWbZ4WbtBpb6pnomTJh5wUFEnceVos3z5cpYvX37Qz+8wQEkp\njwMQQrwI/EFKuUUIMRR44KBfrWNfA9+0vG6xEKIWq5p6WfIBrQOUohxocDjYgKaCSufNnTaXSDjS\n5raqkqoDCuYH2ss8knulR4K9OxMPPHBg4aMzc1BDpZRbAKSUW4UQgw7oFTrnKuB44OaWyukZQHkX\nvI5ylFInq64XCUeYvmh6m9tK1pbw+aOfd/CMfR3oBYG6gDiydSZA1Qgh5gBrgFOAHV3QjueBF4UQ\nK1q+v6r18J6iKKkp2YMp3lpMem469UGrN6MJjczMzB5unXK460yAmgJMB84DNgL3HupGSCl1YOqh\nPq6i9LTGxka2bNkCgGkceddcyR7M3Glz2bphK9VbqwGIRWL40/y4nK4ebqFyONtfksRJUsrVwI+A\nr1r+AZwKvNcNbVOOYq1P7CNHjiQQCPRwiw7c2rVrKSoqQtd1AMp3lfPghQ+SlpYGQPGWYnCCQ3Nw\n2vl7VlgcjvMnd82/i9k/n83wMdayyZK1JZw8/mQAPuXTnmyachjbXw/qdGA1cBn7bv2uApTSZfY+\nsTudTm688UZGjx59SI5/IBPrB5tc0djYSFFREVlZWXZAysnJIRgM8sgjjxAIBDjt/NMOu/mTvS8c\nFKUr7S+L748t/08TQjiwtnv/d6wddRWlS7R3Yo9EIhQVFdkn9u/qQCbWD7Yns2XLFnRdt98DQFpa\nGtXV1WzevJmTTjrpoI7bk9q7cIhE9mTtJUtBAW3KQR1I6v+BXhB0x3IDped0ptTRn4BNQCFWJfNK\n4MoubpdylOroxP63N//GmRedSUVlBYZp7HmCDsNGDDvkw2Jdkb78+v+8TjgWZuNdG0lLS6N4azGz\nfz6btPQ0u4xST9rfe57/7Px2LxxqamrQdR2n09nmPRxsOagD/dkebkOh7TkShrO7SmeSJE6SUt4u\nhFgupZwghPiwy1ulKHsxNIObF9zMpi2bGDxmsH170WVFzHx15iEdFps4aSKr1q1i6pNt83YikQiv\n3/l6m/kiaD9ojRgxwu5hJE/oMSPG1c9czahjRuF0Oln5xUoGjxls9zp6Wk2whmufuxYAv9+P02md\nHh679LEOLxw0Q+P3F/0eh8MBgNfjRXNoqgfTSV09nH2460yA0oQQY4ASIYQHUOFd6TLtndgjkQhC\nCPz+g9/MuXXvINlzAdrtvdQ31ZM7OLdNIATY8OkGcgfn7jM82F5wzMjI4MYbb6SoqIjqaiuzzTAM\nCgcVsm79OhJ6glAkRH2wnnA43OMbIa5du5aysjK2b98OgBCCQYMG2aniv7rvV5TsKsG93t3mefFE\nnPze+WRlWQFJnWA7rzuGsw93nQlQLwNPYy2m/SPwTJe2SDmqtXdid7lcZGdn21f0B6P1vFOy5wIc\ncO/F0A2CwWCbHkZHRo8ezSOPPMLmzZsB2HbvNjIzM0noCQaPGczWdVZadmNlI+8+/C5VJVV8OvTT\nTvc+DnYYcu8hJSklRUVFOBwOfD6f9T4Ngx3bdzDqmFEAbP56Mxn5GQhN2MeRUhLcHWTQoEH06tUL\nUCfYA3EkzlMeap0pFvuUEOKvwCDgN1LKUJe3Sjmq7X1iHzlyJBdNuuiQHd/ldNm74laVVNk9oP0F\nBj2hI01JPB5n+/bt+/QwOhIIBOwTjeZou9fn0O8PZeu6rfs8p7O1/g6misIZ557B9tLtSGkl5iZ7\nppGGCA6vw36cw+EgHo8TCll/7sIluPX1W2mONCNbknqlKZl72lw7OMHRc4JVJZa6R2eSJH4G3NPy\n2NeEEKaU8ndd3jLlqNb6xH6otR5G+3zo5986mS+lJNJsZasJTeDz+fbpYRwswzDIHpzN9EXT26wd\n6opU88bGRraXbieQFyAei9u3x4wYDdEGzCaTisoKTKwFxaZpEtoYonhrMbFoDKfTiT/gx9ANXrjh\nBaLhKFJKFr+x2D6WS7gYe3zPDFN2p0NRYqmj4WyXy6VS+Ft0ZsxkFnAy8Dbwe2AVoAKU0q2S6cTb\nSrbtk8X32KWPfedJ+b3nqBJ6gvvH34/m0MjOyyYej1O9o5qB/zYQ2NPDCAaDRCIRVq9e/a0ZWMn3\nULy1mNzBuQCUbysn/9j8g2pv67m0yh2VmIaJaZi4nC47kaP1Ff2WLVuQUhKPxbluwXX2sXRDZ9eX\nu1gyYwkLb11In4I+IK0A5fa4KTimgOL/KwasHpfT5SQWiXHD4hvYsmIL+f3z7SSJv1zzF3WC7aSO\nhrOnT5+uhkdbdCZAGVLKqBACKaUuhFBDfMohcSDptV09bNL6injN2jUk9AQAC25egFf30lTbhGZo\nNFc28/x1zwN7kjfcws1TTz2F0+nkiiuuwOv1tvueku+h9QLd2T+fzXXP7wkWYFUF3/LVFvJH7hW4\ndBg3dhzLFi+jvqmeqU9OxXSYGIZBLBLD7XPz15v/SnpuOglfgrvm38XDP3uYM849g1A0ZPWIIiEc\nuoOdm3YiTcm7D72LHtORUqIb1jCmYRhoTo2f3fcz3vjjG9TX1GNKk8cveRywepG1O2qtr4VAT+jE\n41aPzDCMLj3BHmlDa+0NZ6vgtEdnAtQ/hBCLgDwhxDNY1SUU5Tvp7vTab1vQWbxlT2+kDR3e/+/3\nueOOO9pkWwWDQd555x3O+Y9z7DmYbdu2cd111zF27FhcLhemaXLmmWcycuTINiee1m2pKqmy58OS\ndetKNpZwyZ8uIe+YPGt5fIsXr3yRVetWMXHSRPs2wzAoHF1IuCGML9NH9uBsphZN5dUbXyUYDFJW\nVkYoFGLSE5Osdv8hiMvjQiDI/34+mkPj5r/fjGmY7P5yN7FIjILjC3hx2otkZmaix3Wueukqdqzb\nwYBjB9iv+/i5jxNpsAL0qGNG2XNV+Xn5XZrBd6RUL9/74uxInq/7LvZXi88FXAS8j7Vh4f9hLdK9\noHuaphypeiK9tr2r6+RJYvXq1UiH5NqXrTVADqeD5659jmg4StSIctZ/noVpmtTW1mJEDY4beRwA\nw4cPt4NTY2Mjq1atIpFI4HQ6cblcfP7556xcuZJx48bh8/nsANy6Laedf5o97zR32lzeDL9JNBLF\n5XUhNIE30+qNCQROr5Pcwbl2D8LldBFqDBFpiGAaJkbCwDRMNKFR9nUZ/3X5f9EUbsKUJu/Pex+A\n4O4gucNy2wQ+IVq+Sf6niT33S0gkEpimScVXFfbtelyn7ps6emX0wul02j+HvRNBlH115cXZkbbo\nd389qL8CCayNA5dhVZN4Dni8G9qlpKiD/QNoPTQTiUSorq7G7XbjEi5+euFP95v9dbCvub/nrVix\ngiuuvQJTM0FCNBol2BgErGDQ3NTMNX+9hq3/3Epun1wSiQRSSl6+6WWEEJjmnsrku3fvZsWKFVRV\nVQHw2Wef4XQ66d27N5qm4fP5yMrK2icAT5w0ka0lW5k+2tpDKZFIMOnxSZTOKrVO9AI0TUOask16\nd9LYMWNZ+cVKsnplUR+sx+Vy4XA4yMzMRDgEU5+fyu6vdiOlZMjJQ3A4Hfzxh3+kdnst8eY40aYo\ntTtq2fF/1g46ieYEpjQp21RG3e46TNO03qe02uFxeezUeiEEBXkFDBw4sFOfxcF+TqnqUNZoPFQX\nZ0fiot/9BaghUsqxQgg3sBaIA6dJKTd1T9OUVHOwfwCNjY2UVZZx84Kb8fv9hEIhtm/fjs/n47lr\nn6OszNo4OZFI7PO8v//97yxbtgy/34/L5frW10wGwmQZnmQ6tVM6WbxgMaNHj+aTTz7hhhtuIKpH\nufzxy4nGoiz91VJe+8VrxCNxpCGp21XHc1OeIxaO4Q/4OeOWMwDw+Xxs372dqB6lsbGRjds3Eg6H\nkVJiOAzSnGl4PB52796N2+2mubmZRCLRbgDeUbaDyfMmI6XE6/Wy5L4ljBy/b3JBtMnKltNjOuW7\nyqkSVcTrouetAAAgAElEQVRiMd7/6H0QUB+sxzAMwg1h4s1xEnErmEpT2u8/qXdBbzxpHpb+Yin+\nHD96TCd36J4elaFbCSiJaIJ5k+ahmzplX5WBiZ0Ikbz/2aufpXfv3m2Of6BruFp/TlJKZEIy5945\nXHzxxSkdqFKtRuORuuh3fwGqEUBKGRdCaMBZUsq67mmWkmr2/gOIxWKUlpYyZ84cnnzySQYMGNDu\n85JBrbq62l4/lJ+fjxCCSCRCY2Mjq1evRtd1dF0nGo3az3v88cdZuXIlbrcbt9vNSSedRGZm5n7/\n6GqCNUwrmkZJSQlpvjScLiemaVI0rYg5c+bwhz/8gcceewyn04nTdO7pmQgI1YS4+uWrqSquIhFN\nkHdsHqZpMn/afOJ6HGlKdF1HuATXv3A9DQ0NdpsdDgfzp88nt3cusViMcDjMtm3b0DSNtWvX4vF4\niMfjbNy4EYCVK1dSUlJCY0MjEkljqJGEkeDZa57F1E3izXEqv65Ec2iYhonD7cDlcRHoG+A/7vwP\n3n/sfRbdvgiBoN/wfnZmo3AItnyxBT2uU7bRCizJ95hcvzSlaArPX/E8p918Gq/d8Roevwenw4mu\n60QaI2jCGqaL1kVxBBxIXeLz+eyhQMMw8Pq8vP362x1+7t+mvqmeWxfeylcbv8LlcqEndIINQRbe\nspA5c+bw4Ycfcttttx3WV//d6Uhd9Lu/ANV6TKFKBaejV2NjI3/729+oqKggOzub3bt3s2bNGkzT\nJBwOc9NNN3HfffftczJpHdTcbre9fmj9hvU4XU6i0SgJI0FlUyUA6Z50FixYwJAhQygqKsI0TdLS\n0ujVqxeJRII1a9Zw9tlnk0gk2v2jS5br2bZtG6FQiObmZtJ8aUSaI0SjUdavX89ll11GIpEgGo1i\nSAM9YQVGf7Yfp8f6c5BS4vK58GX6MHQDl9dFv1H9qNhY0eb13G43fXP7UllVicfjsYbWhGD37t0k\nEgnrRO71snPnTiorK/F6vei6zuzfz6Yh3IBwCHBaQ4oIQEK8OU7OkBzee+g9TNNEaAJpSoJlQYQm\nKPh+AfnH5XPVC1ex8IaFVHxdgR7V7TYFy4L8/c6/IxA4hAOn30k8EqfkixIQUPl1JX/88R+J1EVY\ndOsiEtEET/7kyT1vSlr7Uxlxg4KBBeyq3MWHD35IQ0NDm/c+eOBg/H4/q1ev5lf3/Qpd6vvMP31b\nZl0oHEJKiRCCYEMQh8OB0+kkLS0NwzDavRA53KuXq7VPB2Z/AeoYIcQrWH86o1oy+QCklHJy1zdN\nSQXJHlBFRQWbNm1i165dNDc3k52djctlZZ0FAoE2J5PknMLGjRtpbm4mLy/PPp7D4UAiKTi+gHg8\nTk5hDpN/Pxm3282LN7xIIpFg4qUTqQ5W272smkgNAIlIgpqamnbbmQyGDocDj8dDc3MzAkFNTQ1e\nn5VoEAwGqa+vp6mpyaqgkOu3F+A6XA6qt1ZT+mUp0pBoTs2em6nbVcfCGxda64xM0xrOMww0odG3\nX1+CDUFcLhdut5tRI0dRvKOYjH4ZdtuklMSIEQlFWLFiBdInueTRS3h/3vsMOGaAFaA0rDknp8bZ\nd5yNEIJYOGYFKEPy7sPvctX8q3BoDgTWHJhhGvTO781/3vOfeL1ePG4PRVcVMf648Xy69lMk1rBZ\n/gn5VvKEQyNrYBbn33M+C6cv5Mrnr+SN+9/gpmU3IRCEG8KUf1WOS3OxeMZiMjIyyG7O5p6Z93DW\nWWfx/vtWosVZZ51FeXk5d9xxB7quU7KrhKueuWqfyhqPXfpYh2nh20q22V/H43F7KNIwDCKRCA6H\nY58LkcbGRn59x68JhUK0LHtBCEF6enq7J/dUnNvqqrVPR2rg21+AugRro0JB2/p7e29eqByhWveA\nsrOzKSsrIxqNUlNTQ05ODolEAofDQX5+PhUVFWzevJlIJMJjjz1GNGrN0ZSWltqBDGhzggdr+Cm5\nbigpakSZVjQNj8dDdXU1DocDIQQv3fgS0WgUn8/HgAEDWL3aWvEwcuRItmzZQiQSobamllfufsVa\n09MyryGEINwUJhqK4kpzkZ6Tbr22Q+B0W38Cp99yOst+vYz84/Ip+6qM/OPy7ZNmINcaVltw/QLi\noThev5dQKMSAAQMIhUJkZGRQXl5OJBxh3bp1uHwuLnn0EoA2c0BLZi2hoqKCjH4Z9vuXUiKRaGg4\nXU5qS2qJR+IITVhDc3LP0BwSO0vPMA1ryNHQqa+rBwFerxdpSqqqqog0RVh02yKEJnC4HbQUh8A0\nTBLN1lyfmTD3jJO09OBM0yQStXqcGzduJBAIUFhYyIABA7jyyiv3+b1IS0vDvd6Ny+WyK2u0rlHY\nUVr4TWNvwp/utxNODMMgGo0Sj8epCdewbt06CgoKAGu+qqyyjJqaGhLxBM3RZut3KGaQk5nDyJEj\n6du3b5u5yVROGOiKtU/fJfClYiBP2t+Ghcu7sR1KCtp7XPukk07ik08+IR6PU1VVRSAQYOzYsXg8\nHsA6KcybN494PE5DQ4M1sa/rvPHGG/Tu25uiaUWEw2F0Uye7MBvDMPCl+ezXM00Tl8tFIBCwr5B7\n9epFMBi0M8qcTiennnoqs2fPbnPyGThwIKtXr0Y4BVe+cOWeAICkdnstL1/3MsIhcLgcZOVbw0EN\nuxt49+F3AagvrW+TnefN2BM0XR4XOUNzrB5TVl+C8SBNTU2UlZXR1NSEntBxOB00R5sJ1gcR6WKf\n5IT2OJwO5l81H7ACdbAsiNPtxJfhI+84q9cZCUYo+1eZFahMK5glFxHbhJVVF41GaY42U19fjyfN\nwyXzLsHpc1LwfetEL4Tgucufs18bAU6Xk6cmPmX9rKSkflc9pmESj8Qxmg38fj/Dhw+3X+qin13E\njrId1NfX2/NS9Q31VNdUk5mRSSgUalObb3+cLieDBg1i06ZNNDc3278DDs3qPRUXF5OXl0dNsIYL\nfncBhmHYJ19d11kyawlut5uysjJ7WPiRRx6xi9+mcsJAV5TyOpjAl8qBHDq3UFdRAOjfvz/nnXce\nS5cuZejQoXz/+9/H4/EQiUSQUvLmm28ihLDmflpODM3Nzei6TnNjM16vl+y0bJqMJi7//eUYhkFD\nQwORcAShCbsKwYy7Z9CnTx/Ky8txOpwE/AF0Q6dfv3787ne/Y/bs2fssml24cKHVU4tB2b/KMKUV\naJI9qKy8LE679TRcHhf9j+kPwPyr5nP6racDsPj2xQgheHHaiwTLgvQd3td+3640l9W7kCY1NTXE\nifPyTS/jdDjt1xFCIOOScDiMP73ttiAOpwPNoeFwOcjol4HD5eCjJz7C4XTYr+/yuXj9rtcRmqC5\nsZmtK1uKyLbEufrSeh47+zF65fWy56wcbgf1pfW8OvNV+70mIgl2Nu7EcBssut0alW9TgdyULJ6x\n2JrvisQ551fntLnvlZtfIdGcQI9YFyaFhYWUlZUxYMAA1q5dy1dbvuKyP11GKBSy1z+9cs8rmJgs\num8RRsKwkynqdlnT1tPHTKdo7V5V41tKVBmGwfbt22mONGNKEyNmgAEVFRWMHz+esrIyYrGY/bsi\nEAgh7NcoLS2lX79+bN++HcMwWLFiBTU1NfZ8adKRkDDQGQcS+A6HzL+UCFAtWYJPAccDMeBaKeW+\nZZ6VbtXeuLbT6WTs2LFkZGRQUWElDbhcLs444wyWLFmCYRhIKe2U5ORJLLmXU3Iop7a2FofDQVpa\nGtk52QQCAfLzrGG1sjJrOMfpcKIbOrm5ufjT/Xi9XoqLi+1e3Wv//RpRPWoFwHgzPocPhDWMJU0r\nMAX8AZoiTXuGyTrg8rrIys/C5XEhpeS0W/ZsSigNSdlXZZiGSVNTE6ZpEm20Ur+Tc0+GYVjza+30\nnDSHRv9j+pOVn8Xpt55uB8n5V83nb7/+G5rTWvNkJAz0qM5rv3iNPoV97KQD0zDJHpJNzbYau6eX\nfJ1ENIEe04mH4nYPMy7jZA7IJHdYLuf88hwKxxTabQnXhVk4fSHVW6sRmqBwdKH1s2kZPuwzqA9V\nxVW4DBe4YUf1Dm656xY8Ho9VlSIS4s2H3uTMW8+0khuCQRJ6gkQiQSKe4NoF19rDhmUbrKHSef8x\nb5+fybARw/j4rY9ZvXo1v/3tb9myZYuViCEBzQq2TU1N9uN13RqyRVgXCsmh4lAoxDfffENdnRUM\n169fT2FhIeXl5ZSVlXHSSSfRv3///X72h7uDLf+UHCFxOBz2Uo/s7OwOk5B6QkoEKOBiwC2l/Hch\nxHjgkZbblB7U0bj2b37zG4YNG9ZmKGHz5s1kZGTss4g1edLMzs5mw4YNAMTMGAtvWQjs2RjP7/eT\n6c9sk+gQj8cxEyZbt24lq1cW1dXVzJs3zx4KrGusY/Ljk61adLEY2dnZvP7b13G6nWhSQyBwu91o\nzZ2vbqDHdfp+ry9Dxg0BrCHCaFOUsg1ldtCDPXNLuq7b2YnJFPnk+2odrJJX/S6PCwSUbyonWGYt\nDL797dvtxy28cSGaQ2Pyky15SNJ6rd3/2s3iGYv56R9/itvtttY6tQTdJbOWEG3c89rJ4c360noS\n0QQ7/2/nns/DMKnbWUc8EmfRrYusqhKtuH3WhoQOhwPhFVz73LUce+yx9tq1+sZ63n3oXXvoVdd1\nHC4Hr935Gkio3lptH6szw5xgrS2rq6vD7Xbj8XhIJBJEIhFKSkqorq7eM1/ZKs197/eanKv0+/2U\nl5fb+3UlMz8NwzjsEwY68l3KP9XU1LB+/Xr7b1bTNPLzD7x4cVdJlQB1CvAOgJTyCyHEkV+v/zCx\nv3Ht1ldYI0aMIC0tjdGjR/P+++8TiUQwTRNd1xkwYAANDQ12QkEvnzVHEY/HCYfDPPDLBzjvvPNY\nvXo1Tz31FOgwb9I8a8iqZfGu3+8nIy2DQYMG8dZbb9lrjJLVE+LxOE2NTUhTomnWluP1QWs+BbCH\nhmBPsLADBnuqdNeX1dOnsA9P/+xpGisbrZOstHo3hm6QnpNOPBK3A4KUkmg0iqZp9vETkQRLZi2x\n73e4HPQe2Ju0rDR7eBGgV14vgmXBNgE9GZAAu+170xN6uz1CIayArOu6ncGXd2wevsw983xGwkqu\nSPbOrii6wk5nT7b30TMfJR6Pk9E7g8GDB7e7MaPH4yEnJ4empibOv+t8Ppr3EZFIhEFjBtmP2bG2\npUJFPMEjP3ukTRp6Mi18xIgRxONxMjMzaWhooL6+HiklpmkSj8e5++67qYu2VLUwzDaBf+8AmLxI\n2LVrF+PGjbPX2W3YsIEBAwYc0VXC506bSyQcaXNbVUnVfvcV69+/P8XFxWRkZLQZ4kvO/aUC0dmr\nnC5thBB/AV6XUr7T8v0OYLCU1gC/ECIFWqkoiqJ8FwKQUu5bt6sDqdKDagRaX9poyeCUNLvV1xNa\n/imKoiipa3nLv4OVKj2oicCFUsqrhBA/AO6VUp7f6n6ZCu1UOic5VOd0Olm9ejW9evVC13U2bNhg\nJ09kZ2fjdrsJBoN4PB7S09PJyMigsrKSnTt3kp6eTv/+/amoqEBKidPpJD8/H5/PR0NDg7W40ydx\n9nJy5QtX2kM/bo+bXV/u4vVZrzPn13OYOHEigUCApqYmNm/eTCgU4upbrmZa0TQ0TUPXdSorK/no\nyY+o3W7tcXTpvEsBeO/h97hq/lVoQqP0X6W4NBfRaJTFMxbTVGklS2RkZODz+Whubsbj8XDeeecB\nVmZhKBTik08+wZ3uxpPhsdPbkxxOB7U7apnx3gwAXrnpFc675zwW3riQyx6/zH5cotmab1p822Im\nzZtk324aJoZusGTWEhorGu3bNU3D7Xfj8rr2mWMCa27qkkesdVrCIRAIXp3xKoH0ALF4DFM3GTFi\nBN+UfMPPH/k5CCtRJFkCaeEtCxl37Dhuu+02ioqKWLl+JZc/cTlvPvSmXcsPoH5XPZqmEY/EcSQc\nmKZJv379OPXUU8nMzKS4uNiuvrF161bWr19P3759iUajVFZWEggECIfDVikkXSeRSDB48GAryaax\nFleaq83W9VJK4pE4xKFfv36YpsmECRMYPHgwYG2HsmrVKns7lFRLqYa2e4WBNZz71cavWHDzAiZf\nbM1LRiIRgsGgnWl32vmn0WA2MH3R9DbHKllbwuePdrxjdGNj4z7byOx97IN5/P7WVbV8ToddD+pv\nwFlCiM9avr+qJxujHHpOp5O+fa3U7ebmZvvr6upqIpEI4XCY6upq+vbtS3p6Oj6fj2HDhjFkyBA+\n++wzDMOw696dcsoplJeX8+XmLwk2Bin9V6lV2qcl+Om6TiwWY+nSpfzjH/+wT0LJObPc3FwcmgPd\n0GloaMDr8XLmLWfy6qxXSTQnWHzrYhwOByYmlZsrrfkpzUnf3L6Ew2GcTie+TB+edA8ulwshBB6v\nBykl/1z9Tyb/3DqRlJWVsXr9ahweq/pDUqg6hB7XycrLIh6JM+/seXY6fLQxSiAnQM6QHGq219iV\nzAUCzaGx9K6lRJuieANeaz5JWvNJGf0y7Lkx0zSJh+L4Mn1Me34aNdtr7HT1RDTBR3/+iPzj9yxE\ndrvd1nxewoXhMLj2xWutxc3/FebtuW8jTUntrlpE8ryiQ+/evXniiSfYvHkzmqGxeMZiYrEYgD2n\nZkQNok3WwmrhsBZku91u1qxZw6mnnspXX33F+PHjGTp0KP369aOuro5YLMa//du/0dTUZG8IaZqm\n9XmYJpWVlXzve9+jWTQz8Y8T7bk2gbWw+dVZrzJy4Ei8Xi9ZWdYFwbp163C5XKxbt85O2iksLEy5\nlOr2JMtBadqe+bu9U+azAlkUryu29xVLSu4v1pEDXdzbmXp/h3pdVUoEqJbu0Y093Q7l0Eimp6en\np6Npmp3oEAgEGD9+POXl5Vx77bU89dRT+P1+wuEwXq8XwzCorKykd+/exONx+5e8d+/eHHfccWRn\nZ5OdnW1n+M1/dj6/uO8XZGVkkeZLAwFVVVW4HdYJ99hjj7VrulXUVdDUbKUtl5WX8fqc1612aTD1\n91NpamqyTthxSMQS+LP8pOekM/bEsXYbd2zfYU/gu3wuLn3sUhyaA7fHbd/+1r1vEYvFqKmpYfv2\n7Tg8Dq5+9mrCzeE2hWnfe+g9zr7zbNw+NzmDc1h400L0mM4/nv8HoeoQtTtqSUQTuL1unB4nQghy\nhuQw+anJPHH+E9z61q2Uf1VOIrZn0W7rbD5N00hEE8y/aj6NVY30KewDQPW2agZ+fyCegIdYUwxN\naNZ6LtO0Aokm7KzEc2adQygUwuFw8NKNL+HX/FYpoliElWtX0pxoJp6I4/F47Gw6I2YQrrc+T5/P\nR1yLo2ka4XCYXr16kZGRQSgUYtu2bZimaVcR8Xg8nHLKKXz88cdWwPd47N+LRCKBpml4vV67p+Rw\nOPB4Pei6bgdEIQQuh4vs7Gzy8vJ466237GryyYXAPp+Pzz//HI/HQ//+/Y+ItVHLFi9rs69Ya5/y\n6X6feyirWiSLRwcCAfLz8+01kt/lIiAlApRyZGl9ZZafn9/ml1/Xde69914Mw6BXr14EAgFCoZC9\njigWiyGE4Ec/+hHXXXcdUkrmz59P375996kxNmbMGAKBAMOHD6f462KCDUH7ZGaaJnV1dfZJqLK2\nknv+5x5gz5buhmEwf/p8Xrj+BRobG3FrbgZ9bxChUIjjjjuONevX8NBPH8Lr9ZLmSyOeiLNr1y68\nDi+60BFCYJgGzZFmemX1Ii0tjVAoxKuvvorD4bBOirKl1pwhwbDS1j9++mMaKxt57+H37LTuuh11\nVmUHj5M+g/tQMLqA5oZmfBk+TMNk5//biR7XqS2pxdRNaktqrRN1yyJgwF4IDFa6vMvrapvtJ6zg\nO6Voin2TpmnohnUh8IMf/IBPVn9iHcvhQJrS7r3szcDg8icut9fImaZpD38KIeweUTJ4SCkJBAJ2\nRmAsFkPTtDaLafv378+4ceM466yzOPnkk3nppZfQdZ1gMGgXDU7uuSWEIDMzk0gkQjwet6taeLwe\nvir+ilX/WkVCJtDSNJzSiT/gJxaKkYgnCAQCdvp5qmtdDiqpvRp736WIbmcX9+6v3l9tbS133nkn\nW7dupVevXmzcuJGxY8d+54sAFaCULtH6yiwcDgPYRT0DgQCrV6/G7XYzfvx4e7O/ZGmkY445hjvu\nuMMeFkhusdHeMERWIIu/XPMXSktL0RM6sXgMp9OJw3R0eBIaO8ZaxaAndF5Pf51xx45jw4YNdtX0\n5uZmNmzYQH1VPUbUIC0tjfQB6ezcsZOETOBwOjANkw/+9IE19ObUOPO2MwmHw9ZWG41hxowZQ0FB\nAW9/8rY99CWENVRnxA0mPzmZnKE5pGWmIRB88/k3LLp1EbU7aulT2IcnL34SaUprZ10hqNleQ96x\neeR/Px+H20H+9/PZ9vk2hBD0P6Y/AkFWfhZnzbB2/11611IuefQSe+PDvGPyEELw6FmP2sNhQrPS\n0pNt23tBq9AEab40Erq1v1SyiGt6ejqRZiswJHtHLqfL7kV6vV5yc3PJyMiguLiYAQMGEIvFcDgc\nViWOeJzevXvbveak5Ilv4sSJSCnZtm2bXYU+WYS2pqbGTuf3+/12tfqCggLKy8vtSuiTH5pMPB63\nj685NF6d9SqYUFpXCsDiNxbjEi4eH5k6e7C2F2gikQjosGOHlbbf3jDcwe5PdSA6GhI86aSTmDFj\nBolEwiplpluL6w/FRYAKUEqX2d+VWfJqLDMzk4svvpjy8nIqKytxOBw8++yzbfYZ2nsYYs4f53DH\nvXfY9ycDQCAtQI4/B6/Xi8vlIhgMUlpais/nw+tpW5AWrFpw2dnZhEIhQqGQfXuyrE7yROtyuawE\nj4CDqX+eisvtIhwNk3dMHi63i6cvfRqnw4nm0NA0Db/fz4YNG9hQvAHpkPz93r+3WbMTC8XQYzo1\n22oo+H4BUlhrrfoO60u8Oc4Vf7kCsLbGyBmag6ZpPH/F80x5ekr7659aSh9pmkZGRoZdrd0hWorE\nOqwejNtlDUU+P+V5JJJ4cxynwzoFxJvjlJTsmcMwDMPah8tlFYL1+/340n32PJC3t9c+QSZ7wOnp\n6TidTgK9Anbyy5lnnsk333yDEIITTjiBeDzOxIkTufjiiykuLt7v/Mftt99OUVERw4cPtz/7UaNG\nUV1djcvlsnphQmP48OFIKamrr6OhqaUGpNQRToHT6bQrufce2JuzZ5yN2+O2fz/f++17KTX/1FGg\nSSb5QM8WdG39txgKhYhEIsydOxdN0+jfvz+RSARN06iqqiInJ8f++zvYBdIqQCk9ovXVWCKRQAjB\nwIEDmT59erub4LUOdk3NTW0ynYLBINu3b+eV215h7NixrFmzhnA4TDgcJhQKMXPmzDYBrbW0tDT+\n/Oc/c/PNNxMIBHA4HPzzn/+05znS0tLsWoNgldlpHWyS82sut8u+Wne73dYmhi4HU4umggYFowvs\n5/zh3/9A3rF5VBZX7jN8JjRhL6xNBjx3uttKoAjtWRwca4ohEEixJ4nC4XAwaNAg+0rbznBD2IHI\n5XFhxA0Mw6BxdyO9e1k74ub0zmHVqlUkSPD0lU8jhLCH32pqanALN2PHjKW0tJT09HQqGiusK3ta\nsgbdbgoKChg+fDg3TL2BZcuWkZ6ejsvl4sQTT+SMM85g5MiRbU6u3zb/0VEvPC8vj6nXTOWte9/C\n6/GiOTSr8sSOEvJG5BGLxsg/Nt/exHH3V7uRpqSxqpH3//S+nXDg8/mo2dV2+5ZUrezdFcVlD1Yg\nEEDTNF555RUqKirYunUrzc3N9i4Du3fvJhaLUVdXZ//9HezPUQUopcccqgna1uP0/fv35+yzz6a0\ntJRQKLTf3X7B2pdoyvVTiEQibNi6wRriizUTM2MMGzisTRUFIQR+v5/m5makIe3swfqyev5661+t\nK/eojuHZM2ylOTRikVibUkTSlNa2F9EExf8sBqyt1mu312Lohl2aKBaOUfavMpweJ/Wl9bw07SWr\nFp1u2kVtAbILspFIMrMy6de3n13nsE8fKzEiFA5RsakCj9eDJjU8cQ+NjY30zerLTy/8qd2ub775\nhrPOOotRo0a1+SxaX71XVVUxc+ZMu4oHtATMWIw0Xxoul4upU6dy8cUXd+pz/bYTb/L+ferNadbF\nRbLeXGNjIyeeciKXPnApL//yZVwuFzIu9yRQaII+hX2YWjTVCthOJ+n+dH7/w9/bh0z1yt6ponWR\nWYCsrCwSiQSlpaUMGzaMoUOHUl5ezsCBA7/17+/bqACl9KhDcWWY3LbBMAy79+Dz+Zg5c6b9x9HR\nJLIRN5j56kzmTptLhsfap8kX81G7s5aKxgoq6irIz823SyP5/X78fj/RqFWkdtiwYSzRljCg1wDS\n0tJoymhi586dVtIHDgYMH8DuLbvx+r0suGEBsbC1zmjxLYuJN8dJT08nLT2NE688kdyhuVRtreLl\n61622ydNq0CqHtVxepycO+tce0hz6S+WIoTgqkeuIhaLMWjQIPtnAdgBpE9WHwoHFZKZmcmqYauY\nO3suTz31FIWFVhHZ1//ndRIyQTwep7is2J4AT578W39GH39sramRcWnXU0zOHX5W8BkD+w8E4Mrr\nrjyoAqYd+bZ6cxkZGWRnZ1tloFq2JXG5XbhcLrweL9F41C4FpWkaSGiONNu9zMOhsneqaJ1u7nA4\ncLlc9OvXj9LSUiuL1u3G7XYzZ86c7xScQAUo5TAzcdJEircWs/KLlW1udzld5Oflc9NNNwH7XrV3\ndFI87XyrankkHOHGxdZKB13X2bxyM5rUWHT7Iurr6zn++OPZVrHNDg4OzcGQ4UPIzMykX79+NAWb\nqK+3TshZWVlMmTKFooVF+AN+hGZdscfDca5fdD3FnxXjTfMSi8Twp/lZcPMCtty2hdyhuVzy6CW8\n+9C7XP3S1UgkZf8qw+VxEY/EWTJzCa/c9grxeBwAp3SS0SuD5697HsMwyM/LtzP6fC4fb937FoA9\nDLLemiMAABVhSURBVAZWgNg7GyshE0wrmoae0NtsONheQPf7/YwYMYLdu3fbQ5oOh4O8vDzuvfde\nO5B9lwKmBystLY1Rx4wiIyuDBdcsQHNY9RErtlYQj8cp+H4BDq1lSFW0Ha7tzBofZV8ej4eTTjqJ\nNWvW0KtXL/r27Ut2djazZs3iRz/60Xc+vgpQymGlvqme3MG5DB4zuM3tJWtL0BzaITmROJ1O0nxp\nfPTERwiHILN/JqbHRApJ0dVFmAmr0kLypN9Q30B+Qb69PUQgEODjLz6mOdRsZ5wBViEyYc1XDR8z\nnJK1JZw8/mQ+H/q5Vdy2qZ43fvkGkeYIu77cZW3tbpgkTGueS0pJLBqzdsiVkJaeho6OMASD8wfz\nwdsfdPo9ts7Gisfj6AmdwkGF7RaGbW3EiBH069ePoUOHtpkXikQiKVEp3Ol0cs/Ce9rc9uCFD7J9\n+3ZOvf5Udn7ZqrK7adrVMZTO2/sCp3///vz/9u49SKryzOP49+mZYYaB4WIG3GiMjEg0RkoiRImX\nOK5ujElMBCtRVkGQJEYS12hqcdcK1qzJJiXqsm7irQBjFvG2RjdJbSJqzCiSgQBJjBEFxJFSAgES\nuY6BYebZP87ptqen59JDX053/z5VFN2nL/O+c3rO0+c97/s8Z599Nps3b+a6665LLP/IBgUoKTq1\nQ2q5d1rXAnjbW7dz2oTTsvYzzIxDBw8x675Z3RZALrh0QZf0ManpaeJmj5/NLZNv4cDfDiS+sW94\nYQPe6bSube2y0j9+hrd27VqmXjGVKguztHOQMR8dE1xD+eD7qKqq4trHr6V1bSsnnXgSEJzVfP/y\n72fUv+Trf+vmrutWqr0nyZNb4tdq2traIpEpvKdh3PcNfx/barYxom4ENTU1xGKxIDNJ+yHGNowF\nel/jE4XAm4lcTPRIvQbY1tbGzp07iXXEOOv0s6iqqmLevHlZv16nACVFZ+4Dc7ttW3DpgrysBcnE\nySed3C2QXXDeBYn7a9auoWVVCxs3beTcz5xLR0dHUEZ+z97E2P7BjoO0t7czqHoQMYtREatIXA/r\nb2n1nsSvLdXW1vYrOMVlM/tANqXb//GJD6+88gp3zwhK21dXBymq6uvrOfrIoKxEpml/ciEbgSVX\nEz3SDdkeOnSIW6fcypw5c3L2GVCAkrLWuqmVOZPmcODAAV5e/nJie4zsDv2ku3a2r20f7z/p/QwZ\nPYTJN0wOCi8ePMDSry/lQPuBLkEjXhsqVhELamu17evyXhs3bey19k+2xYPbnj178hKo+sqUkK6q\nbEdHB9ve2sbFn7mYWZcHE0l6m91ZyMCbjcCS6USPvgJi8uPpapNVVlZSW1ub0+tzClBS1hrGNiRm\n8a24e0Vi+/bWIKVOXwk3++udve8w5uQxLLt9WWLb1je2MmTUEEYeOZKGiQ0caj9E27ttVFVX8dgN\nj1FhFbR3tDPyAyOpiFUw/IjhtB9op9M7qa6t7nIdbnTD6LSz5vpjoGlyejuoHk7qnXT6CrzpvuHv\n2rWLpk81JQ7W1dXVjB07ls2bN7Nly5Y+19vlS7ZmEGYy0aOvgHjehefx5ttvJiaR7Gvbx7yp86gb\nVpd2BCNXFKDksKX79goDn1Lcm2wf+OJS/+jiPyOeFikbUn9G0xeaOHPOmYw/ezwQlN8AGHnUSDp3\ndzLlgik88tNHuOzblzF+/Hhqampo+kJT1toTN5B91NdBNWrDrZnK52c63zMI+9p37s6bb7/Jlxd/\nObGQfMu2LRwz/hge/MqDWW1LXxSg5LDlc0pxPg98/Q2GAwma82fOZ+sbW+ns7GTD2g2J7bFYDO90\ndvx5B4/+7FHa3m1j8dWLE9kP9u/ez+3n3s6gwYM48tgjE6+rHVIL7773/v29njHQA3GUp2XHy5+7\nO/va9vHQ/z4ULCy2Ki4878J+TXwoxDT5w9XfiR597TsgkUklzsxwPO1QXy4pQIn0oL/BcCBBs21/\nG/UN9Rx98tHUHfFe8Nj8283UDK6hekg1TU81MXTo0C7XouIHyN4OnplczyjGA3G6oLpx00bmz5wP\nwKaXN1HfEKRpGnH0CCpiFVTVVLFvyz527doViRmHybI1gzCXEz1ixHjrD2+xY/OOLp+Nwx256IsC\nlEiW9HY2kk7NkBruv/J+Bg8dnNi2bf02YhUxOjs6eXX9q4ntVZVV/RpuzFdGhEJOy04XVFtWtSSu\n711+9+Uce2qQJaNtdxsjho/g7i/ezahRoyKZFWLYsGG0vt3Kcyuf61IhuL6+niu/fGVGX4D6M9Gj\nr30XL54ZL4EDMKp+FIfaDzHuuHE9VujNBQUoKRvpAsgbrW8wZ8Icxp0wrsv2gXwz7O1sJHUYcHvr\ndqbfNb3LQQJg0ZcWcdV9V/HkLU92mQSRXC21tyHFfA29pX5bf3HVi3RWdFJfX8/nLvtclzblY1i2\nqrIqMbHlQNsB2naHiWwtyCZRUVlB7aDavAWnTIdOO2Id3Lb8tkRW/fiZ80DOZPua6NGffffugXd5\neN7DXDIvyNUYsxjHjjk2sTg9XxSgJK/yefE5VW8BJNffClP71lMF1CXVS/j46R9nWe2ybo/19F7J\nVq9e3eV+cp69dXPXdcuzl86atWsSa7OSpb6my2LfN9dx45M3dltPla+hwkkTJ7F8bFA9dmjtUEaO\nyO3QU18GMnRaWVl52Gvb+quvfbdm7RoWf2Vxt3RZuR7SS6UAJYctk0kCxXjNo5gMJM9efFJB3P79\n++nwDtoHt3eZeZhuHw10sW/U5Wq2aJT0tu8mTZzE8uOX53U4L53S+URJwRTrlOJ46ffUs4V8nM31\ndAAkmNfQLZ3T9tbtiZx96STP2psxYwZLlizpV569kXUj+c3vf8P0u6Yntu1r20dtXS3P3PrMYfQw\nOwaSXaGqsqrLkCgEv78xE8b0++cW62e61ChASdlqP9ROw8QGRjeM7nJWl4+zub6yq6dbl9XTt9l0\ns/ZmzJhBTU1Nn3n2nnjkiW7DjS2rWmiY0MAzFDZA9TQbsa+zm+Xzl3d77LQJpynoEN2CjD0peICy\nIN3z20B8MUiLu99UwCaJDEg2hoUyfY+eZu0tWbKEO+64o9eht/j1wG4pmPbv63d7c6W32Yg/Wvij\nyB9YM5WPIcWeAn6UFTxAAWOBte7+uT6fKXIYUg8CGzdtZHTD6GCRaxZk4xt6pu/Rn0WXPYlfD2z6\nQlOXGYPJOQn7K9sH2CgvBO6PTH8fuT676y3g1w2ui+z1tigEqInA0Wb2HMFa+OvdfUMfr5EiVciL\nz+lm0qWbsFFKevt995S7zzBa17ayvXV7vxdlavisq6j9PnoL+PNunBfZgJ/XAGVms4FvpGyeA3zX\n3X9sZmcCDwLZK+wjkRK1P9xi19eiy95+3/HrXekmZIwbO66g121KqT6TDFxeA5S7LwYWJ28zs8GE\nc5fcfYWZpS1i39TUlLjd2NhIY2Njztop5aEUphJnI71NJhMy8iUK9ZmKXfKaw3itsYrfVVBdUc0l\nF12Sl4Df3NxMc3PzgF9v8dQahWJm3wP+6u63mdkpwD3ufkbKc7zQ7RSJsr1792Zcx6inIc4oBKi4\ngfRLAqn7d/fu3Wx+czP3X30/53zsnETAz3YV3N6YGe5u/X5+oQ/8ZjacYFhvKMGZ1NdSr0EpQIlk\nXyGzekjuJM/OHN0wOrG9dkgtNyy6gVun3MoP5v+gIAE/0wBV8EkS7r4buKjQ7RA5HMV4sI9qu+Tw\nxGdntqxq6TI7895p9+alCm42FTxAiZQCpXAqLsX4haIcKUCJSNnRF4rioAAlIhJBh3uWl5qTML6u\nrZhmqSpAiYhE0OGe5aUWuFw5dmVkZmf2lwKUiEgJKYX1fXEKUCJZUEoHBSlupTTJQwFKJAtK6aBQ\nDvSFojgoQIlI2dEXiuKgACUiEkE6y4tAqqP+UKojEZHil2mqo1guGyMiIjJQClAiIhJJClAiIhJJ\nmiQhIhIhSmT7HgUoEZEIUSLb92iIT0REIkkBSkREIklDfCIiBZZ83Wnjpo20rGoBgpIZqVnJy4kC\nlIhIgUy9bCo7d+3k9Tde54ofXEEsFuNPt/yJI44/guHDh3ep51SOFKBERApky5+38NnvfJYdN+1g\n9ImjAairr2PR9EUMGTKE7a3bWTl2JVBeKY7iFKBERApgz5497Ny5k6qqKixmVMQqcHem/ec0dmzY\nwRmTz2DBpQuKrshgNmmShIhIAaxfvx53p6KiIrHNzHCczs7OArYsOvIeoMxsipktTbo/2cxWmtmL\nZnZzvtsjIiLRlNchPjO7E/gk8LukzfcAU9291cz+z8wmuPvv89kuEZF8O+GEEzAzOjo6qK6pZuH0\nhQB4p7Nn2x5WH7+6LK87Jcv3NagVwJPA1QBmNgyodvf4VJVlwPmAApSIlLRhw4bR8IEGFs5eSLyc\nkJlRX1/Phz/64bJLa5ROTgKUmc0GvpGyeaa7P2ZmjUnbhgF7ku7vBY5L955NTU2J242NjTQ2NqZ7\nmohI0Xj2F8+yd+9eXnvtNQBOPPFE6urqCtyq7Glubqa5uXnAr897wcIwQF3t7tPCM6gWd/9I+Nh1\nQKW735HyGhUsFBEpckVVsNDd9wAHzew4MzOC61MvFLJNIiISDYVYB+Xhv7ivAkuBCmCZu68uQJtE\nRCRi8j7ENxAa4hMRKX5FNcQnIiLSEwUoERGJJAUoERGJJCWLFREZoOQ6TslG1o3UQtssUIASERmg\nd/a+w/WPXt9t+4JLFxSgNaVHQ3wiIhJJClAiIhJJClAiIhJJClAiIhJJmiQhIjJAI+tGpp0QUe51\nnLJFqY5ERCQvlOpIRERKggKUiIhEkgKUiIhEkgKUiIhEkgKUiIhEkgKUiIhEkgKUiIhEkgKUiIhE\nkgKUiIhEkgKUiIhEUt4DlJlNMbOlKfdfN7Nfhf8+ke82RVVzc3Ohm1AQ6nd5Ub+lJ3kNUGZ2J/Bd\nIDkX06nAXHc/N/z3Qj7bFGXl+gFWv8uL+i09yfcZ1ArgGroGqInAVWb2gpndbmYVeW6TiIhEUE4C\nlJnNNrOXU/5NdPfH0jz9GeDr7v4JYCjw1Vy0SUREikvey22YWSNwtbtPC+8Pd/fd4e0LgUvc/Usp\nr1GtDRGREpBJuY2CFiw0MwNeMrMz3X0LcD6wJvV5mXRIRERKQyEClIf/cHc3s9nAj83sb8AfgYUF\naJOIiERMUVTUFRGR8qOFuiIiEkmRD1BpFvZONrOVZvaimd1cyLblipnFzOxeM/t1uHh5bKHblEtm\ndrqZ/Sq8fXy4b18ws7vD65Qlx8yqzGxJ2M9VZnZROfTdzCrM7P6wn8vN7CPl0G8AMxttZm+Z2YfK\nqM+/TUrCsDjTfkc6QPWwsPceYJq7nwWcbmYTCtK43LoYGOTuZwD/AtxR4PbkjJnNJbjuWB1u+g/g\npnDZgQGfL1TbcuxyYEfYz08BdxHs51Lv+2eBzvDv91sEf98l328zqwLuA/YT9LHkP+dmVgOQlIRh\nNhn2O9IBipSFvWY2DKh299bw8WUEM/9KzZnAUwDuvgqYVNjm5NTrwFTe+xJyalI2kV9QmvsX4H+A\n+AhADGinDPru7j8Brg7vjgHeASaWer+B2wi+XG8N75f8vgZOAWrNbJmZ/dLMJpNhvyMRoDJY2DsM\n2JN0fy8wPH8tzZvUfnaYWST2Vba5+xPAoaRNyWfL+yjN/Yu773f3fWZWRxCsvkXXv8dS7nuHmT0A\n3AkspcT3uZnNJDhbfjq+iRLvc2g/cJu7X0CQgGFpyuN99rug66Di3H0xsLgfT90D1CXdHwbsykmj\nCiu1nzF37yxUY/IsuZ91lOb+BcDMjgGeAO5y94fNbH7SwyXdd3efaWZHAr8BapIeKsV+zwLczM4H\nJgA/AkYlPV6KfQbYQDBCgrtvNLO/AB9NerzPfhfVt3J33wMcNLPjwotrnwRKMbnsCuDTEEwKAf5Q\n2Obk1e/M7Jzw9oWU5v4lPDg/TZAo+YFwc8n33cymm9m/hnffBTqANaXcb3c/x90b3f1c4PfADOCp\nUu5zaBbh9XMzO4ogID2dSb8jcQbVh8TC3lD8VLECWObuqwvSqtx6EvgHM1sR3p9VyMbkSXwffxNY\naGaDgHXA44VrUk7dRDC8cXPSbNTrgP8q8b4/DjxgZs8DVQR9fo3y2OdxTnl8zhcDPzSzeBCaBfyF\nDPqthboiIhJJRTXEJyIi5UMBSkREIkkBSkREIkkBSkREIkkBSkREIkkBSkREIkkBSsqSmTWa2fYw\ny/JzZtZiZl8f4Ht908yuNLNTzGxeL8+bYmbv7+d7TjKzHw6kPeHrHzCzCwb6epEoKIaFuiK54MCz\n7v6PAOHCwfVm9t9hxpLM39D9JeClXp7yTwSLE7f28pxsSV3gLlJ0dAYl5So1YecwgqS1HWbWbGaP\nmtnTZjYorGPzfFi/6BwAM7vYzNaa2dMEKbfiZ2UPh7dnm9nqsB5Ok5l9mjAPW1gL6loL6n2tMLNr\nw9ecEN5/Fvjnbg02u8PMZoS3/87M1lhQO2yRmT1lZi+Z2be7vsRmmtn3wjs1ZtYa3h4fnjn+yswe\nN7NhZjYqaVuLmZ2S1d+4SIYUoKSc/X14MP4l8CBwrbvvJzjzeMjdPwnMJshEfQ5Bna67zKyCoK7N\n+eFzdobv5wBmNgq4ETjL3U8FBgHP814etnHAFwnKqnwCuNjMPkRQkuFmdz8feDZNexcBV4a3pwP3\nA8cALe7+KeB0glRgyXo6i1oIzAnzw/0cmAt8LOzLhcDXgCG9/fJEck1DfFLOnnP3aT08tj78fzxw\nlpmdHt6vAI4Cdrv7O+G21ISXxwF/dPcDAO5+E0BYPNSAk4FjgefC548gCFonAPHcki8AZyS/qbu/\namaVZvZBggB3XvjQx8zsXIIs+NX0LPmM8cPAPWGbqggyT/8ibMdPCOpTfaeX9xLJOZ1BiaQXL/vx\nKvBweKbxeeAxYBsw3MxGh8+ZnPLaTcCJ4XUtwuHCo8L3jBEkR30lXmkUWEKQsX4dcFb4Hh/voV2L\nCc60Xgmvlc0Edrn7FQRndbUpz/8bEJ+YcWrS9teA6eHPvwn4GdAIbA3r9/w7QbVbkYLRGZSUq/5O\nIriPIPtyM8F1qrvcvd3MrgF+bma7CAqvxd/L3X2nmd0KPG9mDvzU3f9kZr8mqAV0AfBLM3uRoBbS\nSmALcD1Bpu9vAm8TlKJI9ThBob+LwvvPAg+Z2URgM0HpiqOS+vgUcI2ZLQfWArvDx64BlphZZfi8\nq4C/Ao+EfasE/q0fvx+RnFE2cxERiSQN8YmISCQpQImISCQpQImISCQpQImISCQpQImISCQpQImI\nSCQpQImISCT9P5O8/2dTktUuAAAAAElFTkSuQmCC\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x10d83ce48>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.scatter(y_train_pred,  \n",
+    "            y_train_pred - y_train, \n",
+    "            c='black', \n",
+    "            marker='o', \n",
+    "            s=35,\n",
+    "            alpha=0.5,\n",
+    "            label='Training data')\n",
+    "plt.scatter(y_test_pred,  \n",
+    "            y_test_pred - y_test, \n",
+    "            c='lightgreen', \n",
+    "            marker='s', \n",
+    "            s=35,\n",
+    "            alpha=0.7,\n",
+    "            label='Test data')\n",
+    "\n",
+    "plt.xlabel('Predicted values')\n",
+    "plt.ylabel('Residuals')\n",
+    "plt.legend(loc='upper left')\n",
+    "plt.hlines(y=0, xmin=-10, xmax=50, lw=2, color='red')\n",
+    "plt.xlim([-10, 50])\n",
+    "plt.tight_layout()\n",
+    "\n",
+    "# plt.savefig('./figures/slr_residuals.png', dpi=300)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<br>\n",
+    "<br>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "collapsed": true
+   },
+   "source": [
+    "# Summary"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "..."
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.4.3"
+  }
+ },
+ "nbformat": 4,
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+}
diff --git a/code/ch10/images/10_01.png b/code/ch10/images/10_01.png
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diff --git a/code/ch10/images/10_13.png b/code/ch10/images/10_13.png
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diff --git a/code/ch10/images/10_14.png b/code/ch10/images/10_14.png
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diff --git a/code/datasets/README.md b/code/datasets/README.md
index 08dbfb00..697cc6da 100644
--- a/code/datasets/README.md
+++ b/code/datasets/README.md
@@ -19,6 +19,11 @@ Sebastian Raschka, 2015
 
 ### movie
 
-- used in chapter 8
+- used in chapter 8, 9
 - movie dataset converted into a 2-column CSV format: The first column (`review`) contains the text, and the second column (`sentiment`) denotes the polarity, where 0=negative and 1=positive. The first 25,000 are the training samples and the remaining 25,000 rows are the test samples from the "Large Movie Review Dataset v1.0," respectively.
-- source: [http://ai.stanford.edu/~amaas/data/sentiment/](http://ai.stanford.edu/~amaas/data/sentiment/)
\ No newline at end of file
+- source: [http://ai.stanford.edu/~amaas/data/sentiment/](http://ai.stanford.edu/~amaas/data/sentiment/)
+
+### housing
+
+- used in chapter 10
+- source: [https://archive.ics.uci.edu/ml/datasets/Housing](https://archive.ics.uci.edu/ml/datasets/Housing)
\ No newline at end of file
diff --git a/code/datasets/housing/housing.data b/code/datasets/housing/housing.data
new file mode 100644
index 00000000..5b93504b
--- /dev/null
+++ b/code/datasets/housing/housing.data
@@ -0,0 +1,506 @@
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+5.87205   0.00  18.100  0  0.6930  6.4050  96.00  1.6768  24  666.0  20.20 396.90  19.37  12.50
+7.67202   0.00  18.100  0  0.6930  5.7470  98.90  1.6334  24  666.0  20.20 393.10  19.92   8.50
+38.35180   0.00  18.100  0  0.6930  5.4530 100.00  1.4896  24  666.0  20.20 396.90  30.59   5.00
+9.91655   0.00  18.100  0  0.6930  5.8520  77.80  1.5004  24  666.0  20.20 338.16  29.97   6.30
+25.04610   0.00  18.100  0  0.6930  5.9870 100.00  1.5888  24  666.0  20.20 396.90  26.77   5.60
+14.23620   0.00  18.100  0  0.6930  6.3430 100.00  1.5741  24  666.0  20.20 396.90  20.32   7.20
+9.59571   0.00  18.100  0  0.6930  6.4040 100.00  1.6390  24  666.0  20.20 376.11  20.31  12.10
+24.80170   0.00  18.100  0  0.6930  5.3490  96.00  1.7028  24  666.0  20.20 396.90  19.77   8.30
+41.52920   0.00  18.100  0  0.6930  5.5310  85.40  1.6074  24  666.0  20.20 329.46  27.38   8.50
+67.92080   0.00  18.100  0  0.6930  5.6830 100.00  1.4254  24  666.0  20.20 384.97  22.98   5.00
+20.71620   0.00  18.100  0  0.6590  4.1380 100.00  1.1781  24  666.0  20.20 370.22  23.34  11.90
+11.95110   0.00  18.100  0  0.6590  5.6080 100.00  1.2852  24  666.0  20.20 332.09  12.13  27.90
+7.40389   0.00  18.100  0  0.5970  5.6170  97.90  1.4547  24  666.0  20.20 314.64  26.40  17.20
+14.43830   0.00  18.100  0  0.5970  6.8520 100.00  1.4655  24  666.0  20.20 179.36  19.78  27.50
+51.13580   0.00  18.100  0  0.5970  5.7570 100.00  1.4130  24  666.0  20.20   2.60  10.11  15.00
+14.05070   0.00  18.100  0  0.5970  6.6570 100.00  1.5275  24  666.0  20.20  35.05  21.22  17.20
+18.81100   0.00  18.100  0  0.5970  4.6280 100.00  1.5539  24  666.0  20.20  28.79  34.37  17.90
+28.65580   0.00  18.100  0  0.5970  5.1550 100.00  1.5894  24  666.0  20.20 210.97  20.08  16.30
+45.74610   0.00  18.100  0  0.6930  4.5190 100.00  1.6582  24  666.0  20.20  88.27  36.98   7.00
+18.08460   0.00  18.100  0  0.6790  6.4340 100.00  1.8347  24  666.0  20.20  27.25  29.05   7.20
+10.83420   0.00  18.100  0  0.6790  6.7820  90.80  1.8195  24  666.0  20.20  21.57  25.79   7.50
+25.94060   0.00  18.100  0  0.6790  5.3040  89.10  1.6475  24  666.0  20.20 127.36  26.64  10.40
+73.53410   0.00  18.100  0  0.6790  5.9570 100.00  1.8026  24  666.0  20.20  16.45  20.62   8.80
+11.81230   0.00  18.100  0  0.7180  6.8240  76.50  1.7940  24  666.0  20.20  48.45  22.74   8.40
+11.08740   0.00  18.100  0  0.7180  6.4110 100.00  1.8589  24  666.0  20.20 318.75  15.02  16.70
+7.02259   0.00  18.100  0  0.7180  6.0060  95.30  1.8746  24  666.0  20.20 319.98  15.70  14.20
+12.04820   0.00  18.100  0  0.6140  5.6480  87.60  1.9512  24  666.0  20.20 291.55  14.10  20.80
+7.05042   0.00  18.100  0  0.6140  6.1030  85.10  2.0218  24  666.0  20.20   2.52  23.29  13.40
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+15.86030   0.00  18.100  0  0.6790  5.8960  95.40  1.9096  24  666.0  20.20   7.68  24.39   8.30
+12.24720   0.00  18.100  0  0.5840  5.8370  59.70  1.9976  24  666.0  20.20  24.65  15.69  10.20
+37.66190   0.00  18.100  0  0.6790  6.2020  78.70  1.8629  24  666.0  20.20  18.82  14.52  10.90
+7.36711   0.00  18.100  0  0.6790  6.1930  78.10  1.9356  24  666.0  20.20  96.73  21.52  11.00
+9.33889   0.00  18.100  0  0.6790  6.3800  95.60  1.9682  24  666.0  20.20  60.72  24.08   9.50
+8.49213   0.00  18.100  0  0.5840  6.3480  86.10  2.0527  24  666.0  20.20  83.45  17.64  14.50
+10.06230   0.00  18.100  0  0.5840  6.8330  94.30  2.0882  24  666.0  20.20  81.33  19.69  14.10
+6.44405   0.00  18.100  0  0.5840  6.4250  74.80  2.2004  24  666.0  20.20  97.95  12.03  16.10
+5.58107   0.00  18.100  0  0.7130  6.4360  87.90  2.3158  24  666.0  20.20 100.19  16.22  14.30
+13.91340   0.00  18.100  0  0.7130  6.2080  95.00  2.2222  24  666.0  20.20 100.63  15.17  11.70
+11.16040   0.00  18.100  0  0.7400  6.6290  94.60  2.1247  24  666.0  20.20 109.85  23.27  13.40
+14.42080   0.00  18.100  0  0.7400  6.4610  93.30  2.0026  24  666.0  20.20  27.49  18.05   9.60
+15.17720   0.00  18.100  0  0.7400  6.1520 100.00  1.9142  24  666.0  20.20   9.32  26.45   8.70
+13.67810   0.00  18.100  0  0.7400  5.9350  87.90  1.8206  24  666.0  20.20  68.95  34.02   8.40
+9.39063   0.00  18.100  0  0.7400  5.6270  93.90  1.8172  24  666.0  20.20 396.90  22.88  12.80
+22.05110   0.00  18.100  0  0.7400  5.8180  92.40  1.8662  24  666.0  20.20 391.45  22.11  10.50
+9.72418   0.00  18.100  0  0.7400  6.4060  97.20  2.0651  24  666.0  20.20 385.96  19.52  17.10
+5.66637   0.00  18.100  0  0.7400  6.2190 100.00  2.0048  24  666.0  20.20 395.69  16.59  18.40
+9.96654   0.00  18.100  0  0.7400  6.4850 100.00  1.9784  24  666.0  20.20 386.73  18.85  15.40
+12.80230   0.00  18.100  0  0.7400  5.8540  96.60  1.8956  24  666.0  20.20 240.52  23.79  10.80
+10.67180   0.00  18.100  0  0.7400  6.4590  94.80  1.9879  24  666.0  20.20  43.06  23.98  11.80
+6.28807   0.00  18.100  0  0.7400  6.3410  96.40  2.0720  24  666.0  20.20 318.01  17.79  14.90
+9.92485   0.00  18.100  0  0.7400  6.2510  96.60  2.1980  24  666.0  20.20 388.52  16.44  12.60
+9.32909   0.00  18.100  0  0.7130  6.1850  98.70  2.2616  24  666.0  20.20 396.90  18.13  14.10
+7.52601   0.00  18.100  0  0.7130  6.4170  98.30  2.1850  24  666.0  20.20 304.21  19.31  13.00
+6.71772   0.00  18.100  0  0.7130  6.7490  92.60  2.3236  24  666.0  20.20   0.32  17.44  13.40
+5.44114   0.00  18.100  0  0.7130  6.6550  98.20  2.3552  24  666.0  20.20 355.29  17.73  15.20
+5.09017   0.00  18.100  0  0.7130  6.2970  91.80  2.3682  24  666.0  20.20 385.09  17.27  16.10
+8.24809   0.00  18.100  0  0.7130  7.3930  99.30  2.4527  24  666.0  20.20 375.87  16.74  17.80
+9.51363   0.00  18.100  0  0.7130  6.7280  94.10  2.4961  24  666.0  20.20   6.68  18.71  14.90
+4.75237   0.00  18.100  0  0.7130  6.5250  86.50  2.4358  24  666.0  20.20  50.92  18.13  14.10
+4.66883   0.00  18.100  0  0.7130  5.9760  87.90  2.5806  24  666.0  20.20  10.48  19.01  12.70
+8.20058   0.00  18.100  0  0.7130  5.9360  80.30  2.7792  24  666.0  20.20   3.50  16.94  13.50
+7.75223   0.00  18.100  0  0.7130  6.3010  83.70  2.7831  24  666.0  20.20 272.21  16.23  14.90
+6.80117   0.00  18.100  0  0.7130  6.0810  84.40  2.7175  24  666.0  20.20 396.90  14.70  20.00
+4.81213   0.00  18.100  0  0.7130  6.7010  90.00  2.5975  24  666.0  20.20 255.23  16.42  16.40
+3.69311   0.00  18.100  0  0.7130  6.3760  88.40  2.5671  24  666.0  20.20 391.43  14.65  17.70
+6.65492   0.00  18.100  0  0.7130  6.3170  83.00  2.7344  24  666.0  20.20 396.90  13.99  19.50
+5.82115   0.00  18.100  0  0.7130  6.5130  89.90  2.8016  24  666.0  20.20 393.82  10.29  20.20
+7.83932   0.00  18.100  0  0.6550  6.2090  65.40  2.9634  24  666.0  20.20 396.90  13.22  21.40
+3.16360   0.00  18.100  0  0.6550  5.7590  48.20  3.0665  24  666.0  20.20 334.40  14.13  19.90
+3.77498   0.00  18.100  0  0.6550  5.9520  84.70  2.8715  24  666.0  20.20  22.01  17.15  19.00
+4.42228   0.00  18.100  0  0.5840  6.0030  94.50  2.5403  24  666.0  20.20 331.29  21.32  19.10
+15.57570   0.00  18.100  0  0.5800  5.9260  71.00  2.9084  24  666.0  20.20 368.74  18.13  19.10
+13.07510   0.00  18.100  0  0.5800  5.7130  56.70  2.8237  24  666.0  20.20 396.90  14.76  20.10
+4.34879   0.00  18.100  0  0.5800  6.1670  84.00  3.0334  24  666.0  20.20 396.90  16.29  19.90
+4.03841   0.00  18.100  0  0.5320  6.2290  90.70  3.0993  24  666.0  20.20 395.33  12.87  19.60
+3.56868   0.00  18.100  0  0.5800  6.4370  75.00  2.8965  24  666.0  20.20 393.37  14.36  23.20
+4.64689   0.00  18.100  0  0.6140  6.9800  67.60  2.5329  24  666.0  20.20 374.68  11.66  29.80
+8.05579   0.00  18.100  0  0.5840  5.4270  95.40  2.4298  24  666.0  20.20 352.58  18.14  13.80
+6.39312   0.00  18.100  0  0.5840  6.1620  97.40  2.2060  24  666.0  20.20 302.76  24.10  13.30
+4.87141   0.00  18.100  0  0.6140  6.4840  93.60  2.3053  24  666.0  20.20 396.21  18.68  16.70
+15.02340   0.00  18.100  0  0.6140  5.3040  97.30  2.1007  24  666.0  20.20 349.48  24.91  12.00
+10.23300   0.00  18.100  0  0.6140  6.1850  96.70  2.1705  24  666.0  20.20 379.70  18.03  14.60
+14.33370   0.00  18.100  0  0.6140  6.2290  88.00  1.9512  24  666.0  20.20 383.32  13.11  21.40
+5.82401   0.00  18.100  0  0.5320  6.2420  64.70  3.4242  24  666.0  20.20 396.90  10.74  23.00
+5.70818   0.00  18.100  0  0.5320  6.7500  74.90  3.3317  24  666.0  20.20 393.07   7.74  23.70
+5.73116   0.00  18.100  0  0.5320  7.0610  77.00  3.4106  24  666.0  20.20 395.28   7.01  25.00
+2.81838   0.00  18.100  0  0.5320  5.7620  40.30  4.0983  24  666.0  20.20 392.92  10.42  21.80
+2.37857   0.00  18.100  0  0.5830  5.8710  41.90  3.7240  24  666.0  20.20 370.73  13.34  20.60
+3.67367   0.00  18.100  0  0.5830  6.3120  51.90  3.9917  24  666.0  20.20 388.62  10.58  21.20
+5.69175   0.00  18.100  0  0.5830  6.1140  79.80  3.5459  24  666.0  20.20 392.68  14.98  19.10
+4.83567   0.00  18.100  0  0.5830  5.9050  53.20  3.1523  24  666.0  20.20 388.22  11.45  20.60
+0.15086   0.00  27.740  0  0.6090  5.4540  92.70  1.8209   4  711.0  20.10 395.09  18.06  15.20
+0.18337   0.00  27.740  0  0.6090  5.4140  98.30  1.7554   4  711.0  20.10 344.05  23.97   7.00
+0.20746   0.00  27.740  0  0.6090  5.0930  98.00  1.8226   4  711.0  20.10 318.43  29.68   8.10
+0.10574   0.00  27.740  0  0.6090  5.9830  98.80  1.8681   4  711.0  20.10 390.11  18.07  13.60
+0.11132   0.00  27.740  0  0.6090  5.9830  83.50  2.1099   4  711.0  20.10 396.90  13.35  20.10
+0.17331   0.00   9.690  0  0.5850  5.7070  54.00  2.3817   6  391.0  19.20 396.90  12.01  21.80
+0.27957   0.00   9.690  0  0.5850  5.9260  42.60  2.3817   6  391.0  19.20 396.90  13.59  24.50
+0.17899   0.00   9.690  0  0.5850  5.6700  28.80  2.7986   6  391.0  19.20 393.29  17.60  23.10
+0.28960   0.00   9.690  0  0.5850  5.3900  72.90  2.7986   6  391.0  19.20 396.90  21.14  19.70
+0.26838   0.00   9.690  0  0.5850  5.7940  70.60  2.8927   6  391.0  19.20 396.90  14.10  18.30
+0.23912   0.00   9.690  0  0.5850  6.0190  65.30  2.4091   6  391.0  19.20 396.90  12.92  21.20
+0.17783   0.00   9.690  0  0.5850  5.5690  73.50  2.3999   6  391.0  19.20 395.77  15.10  17.50
+0.22438   0.00   9.690  0  0.5850  6.0270  79.70  2.4982   6  391.0  19.20 396.90  14.33  16.80
+0.06263   0.00  11.930  0  0.5730  6.5930  69.10  2.4786   1  273.0  21.00 391.99   9.67  22.40
+0.04527   0.00  11.930  0  0.5730  6.1200  76.70  2.2875   1  273.0  21.00 396.90   9.08  20.60
+0.06076   0.00  11.930  0  0.5730  6.9760  91.00  2.1675   1  273.0  21.00 396.90   5.64  23.90
+0.10959   0.00  11.930  0  0.5730  6.7940  89.30  2.3889   1  273.0  21.00 393.45   6.48  22.00
+0.04741   0.00  11.930  0  0.5730  6.0300  80.80  2.5050   1  273.0  21.00 396.90   7.88  11.90
diff --git a/code/datasets/housing/housing.names.txt b/code/datasets/housing/housing.names.txt
new file mode 100644
index 00000000..16064561
--- /dev/null
+++ b/code/datasets/housing/housing.names.txt
@@ -0,0 +1,52 @@
+1. Title: Boston Housing Data
+
+2. Sources:
+   (a) Origin:  This dataset was taken from the StatLib library which is
+                maintained at Carnegie Mellon University.
+   (b) Creator:  Harrison, D. and Rubinfeld, D.L. 'Hedonic prices and the 
+                 demand for clean air', J. Environ. Economics & Management,
+                 vol.5, 81-102, 1978.
+   (c) Date: July 7, 1993
+
+3. Past Usage:
+   -   Used in Belsley, Kuh & Welsch, 'Regression diagnostics ...', Wiley, 
+       1980.   N.B. Various transformations are used in the table on
+       pages 244-261.
+    -  Quinlan,R. (1993). Combining Instance-Based and Model-Based Learning.
+       In Proceedings on the Tenth International Conference of Machine 
+       Learning, 236-243, University of Massachusetts, Amherst. Morgan
+       Kaufmann.
+
+4. Relevant Information:
+
+   Concerns housing values in suburbs of Boston.
+
+5. Number of Instances: 506
+
+6. Number of Attributes: 13 continuous attributes (including "class"
+                         attribute "MEDV"), 1 binary-valued attribute.
+
+7. Attribute Information:
+
+    1. CRIM      per capita crime rate by town
+    2. ZN        proportion of residential land zoned for lots over 
+                 25,000 sq.ft.
+    3. INDUS     proportion of non-retail business acres per town
+    4. CHAS      Charles River dummy variable (= 1 if tract bounds 
+                 river; 0 otherwise)
+    5. NOX       nitric oxides concentration (parts per 10 million)
+    6. RM        average number of rooms per dwelling
+    7. AGE       proportion of owner-occupied units built prior to 1940
+    8. DIS       weighted distances to five Boston employment centres
+    9. RAD       index of accessibility to radial highways
+    10. TAX      full-value property-tax rate per $10,000
+    11. PTRATIO  pupil-teacher ratio by town
+    12. B        1000(Bk - 0.63)^2 where Bk is the proportion of blacks 
+                 by town
+    13. LSTAT    % lower status of the population
+    14. MEDV     Median value of owner-occupied homes in $1000's
+
+8. Missing Attribute Values:  None.
+
+
+