{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>\n",
       "    \n",
       "    @import url('http://fonts.googleapis.com/css?family=Source+Code+Pro');\n",
       "    \n",
       "    @import url('http://fonts.googleapis.com/css?family=Kameron');\n",
       "    @import url('http://fonts.googleapis.com/css?family=Crimson+Text');\n",
       "    \n",
       "    @import url('http://fonts.googleapis.com/css?family=Lato');\n",
       "    @import url('http://fonts.googleapis.com/css?family=Source+Sans+Pro');\n",
       "    \n",
       "    @import url('http://fonts.googleapis.com/css?family=Lora'); \n",
       "\n",
       "    \n",
       "    body {\n",
       "        font-family: 'Lora', Consolas, sans-serif;\n",
       "       \n",
       "        -webkit-print-color-adjust: exact important !;\n",
       "        \n",
       "      \n",
       "       \n",
       "    }\n",
       "    \n",
       "    .alert-block {\n",
       "        width: 95%;\n",
       "        margin: auto;\n",
       "    }\n",
       "    \n",
       "    .rendered_html code\n",
       "    {\n",
       "        color: black;\n",
       "        background: #eaf0ff;\n",
       "        background: #f5f5f5; \n",
       "        padding: 1pt;\n",
       "        font-family:  'Source Code Pro', Consolas, monocco, monospace;\n",
       "    }\n",
       "    \n",
       "    p {\n",
       "      line-height: 140%;\n",
       "    }\n",
       "    \n",
       "    strong code {\n",
       "        background: red;\n",
       "    }\n",
       "    \n",
       "    .rendered_html strong code\n",
       "    {\n",
       "        background: #f5f5f5;\n",
       "    }\n",
       "    \n",
       "    .CodeMirror pre {\n",
       "    font-family: 'Source Code Pro', monocco, Consolas, monocco, monospace;\n",
       "    }\n",
       "    \n",
       "    .cm-s-ipython span.cm-keyword {\n",
       "        font-weight: normal;\n",
       "     }\n",
       "     \n",
       "     strong {\n",
       "         background: #f5f5f5;\n",
       "         margin-top: 4pt;\n",
       "         margin-bottom: 4pt;\n",
       "         padding: 2pt;\n",
       "         border: 0.5px solid #a0a0a0;\n",
       "         font-weight: bold;\n",
       "         color: darkred;\n",
       "     }\n",
       "     \n",
       "    \n",
       "    div #notebook {\n",
       "        # font-size: 10pt; \n",
       "        line-height: 145%;\n",
       "        }\n",
       "        \n",
       "    li {\n",
       "        line-height: 145%;\n",
       "    }\n",
       "\n",
       "    div.output_area pre {\n",
       "        background: #fff9d8 !important;\n",
       "        padding: 5pt;\n",
       "       \n",
       "       -webkit-print-color-adjust: exact; \n",
       "        \n",
       "    }\n",
       " \n",
       "    \n",
       " \n",
       "    h1, h2, h3, h4 {\n",
       "        font-family: Kameron, arial;\n",
       "\n",
       "\n",
       "    }\n",
       "    \n",
       "    div#maintoolbar {display: none !important;}\n",
       "</style>\n"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# IGNORE THIS CELL WHICH CUSTOMIZES LAYOUT AND STYLING OF THE NOTEBOOK !\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib as mpl\n",
    "import seaborn as sns\n",
    "sns.set(style=\"darkgrid\")\n",
    "mpl.rcParams['lines.linewidth'] = 3\n",
    "%matplotlib inline\n",
    "%config InlineBackend.figure_format = 'retina'\n",
    "%config IPCompleter.greedy=True\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore', category=FutureWarning)\n",
    "from IPython.core.display import HTML; HTML(open(\"custom.html\", \"r\").read())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Introduction to Neural Networks\n",
    "\n",
    "\n",
    "## History of Neural networks\n",
    "\n",
    "<div class=\"alert alert-block alert-danger\"><p>\n",
    "    <strong>TODO</strong>: Make it more complete and format properly\n",
    "</p></div>\n",
    "\n",
    "1943 - Threshold Logic\n",
    "\n",
    "1940s - Hebbian Learning\n",
    "\n",
    "1958 - Perceptron\n",
    "\n",
    "1975 - Backpropagation\n",
    "\n",
    "1980s - Neocognitron\n",
    "\n",
    "1982 - Hopfield Network\n",
    "\n",
    "1986 - Convolutional Neural Networks\n",
    "\n",
    "1997 - Long-short term memory (LSTM) model\n",
    "\n",
    "2014 - Gated Recurrent Units, Generative Adversarial Networks(Check)?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Feed-Forward neural network\n",
    "<center>\n",
    "<figure>\n",
    "<img src=\"./images/neuralnets/neural_net_ex.svg\" width=\"700\"/>\n",
    "<figcaption>A 3 layer densely connected Neural Network (By convention the input layer is not counted).</figcaption>\n",
    "</figure>\n",
    "</center>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Why the boom now?\n",
    "* Data\n",
    "* Data\n",
    "* Data\n",
    "* Availability of GPUs\n",
    "* Algorithmic developments which allow for efficient training and making networks networks\n",
    "* Development of high-level libraries/APIs have made the field much more accessible than it was a decade ago"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Building blocks\n",
    "### Perceptron\n",
    "\n",
    "The smallest unit of a neural network is a **perceptron** like node.\n",
    "\n",
    "**What is a Perceptron?**\n",
    "\n",
    "It is a simple function which can have multiple inputs and has a single output.\n",
    "\n",
    "<center>\n",
    "<figure>\n",
    "<img src=\"./images/neuralnets/perceptron_ex.svg\" width=\"400\"/>\n",
    "<figcaption>A simple perceptron with 3 inputs and 1 output.</figcaption>\n",
    "</figure>\n",
    "</center>\n",
    "\n",
    "\n",
    "It works as follows: \n",
    "\n",
    "Step 1: A **weighted sum** of the inputs is calculated\n",
    "\n",
    "\\begin{equation*}\n",
    "weighted\\_sum = w_{1} x_{1} + w_{2} x_{2} + w_{3} x_{3} + ...\n",
    "\\end{equation*}\n",
    "\n",
    "Step 2: A **step** activation function is applied\n",
    "\n",
    "$$\n",
    "f(weighted\\_sum) = \\left\\{\n",
    "        \\begin{array}{ll}\n",
    "            0 & \\quad weighted\\_sum < threshold \\\\\n",
    "            1 & \\quad weighted\\_sum \\geq threshold\n",
    "        \\end{array}\n",
    "    \\right.\n",
    "$$\n",
    "\n",
    "You can see that this is also a linear classifier as the ones we introduced in script 02."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "tags": [
     "hidecode"
    ]
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 281,
       "width": 392
      },
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plotting the step function\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import numpy as np\n",
    "x = np.arange(-2,2.1,0.01)\n",
    "y = np.zeros(len(x))\n",
    "threshold = 0.\n",
    "y[x>threshold] = 1.\n",
    "step_plot = sns.lineplot(x, y).set_title('Step function') ;\n",
    "plt.xlabel('weighted_sum') ;\n",
    "plt.ylabel('f(weighted_sum)') ;"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "def perceptron(X, w, threshold=1):\n",
    "    # This function computes sum(w_i*x_i) and\n",
    "    # applies a perceptron activation\n",
    "    linear_sum = np.dot(np.asarray(X).T, w)\n",
    "    output = np.zeros(len(linear_sum), dtype=np.int8)\n",
    "    output[linear_sum >= threshold] = 1\n",
    "    return output"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Boolean AND\n",
    "\n",
    "| x$_1$ | x$_2$ | output |\n",
    "| --- | --- | --- |\n",
    "| 0 | 0 | 0 |\n",
    "| 1 | 0 | 0 |\n",
    "| 0 | 1 | 0 |\n",
    "| 1 | 1 | 1 |"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Perceptron output for x1, x2 =  0 , 0  is  0\n",
      "Perceptron output for x1, x2 =  1 , 0  is  0\n",
      "Perceptron output for x1, x2 =  0 , 1  is  0\n",
      "Perceptron output for x1, x2 =  1 , 1  is  1\n"
     ]
    }
   ],
   "source": [
    "# Calculating Boolean AND using a perceptron\n",
    "threshold = 1.5\n",
    "# (w1, w2)\n",
    "w = [1, 1]\n",
    "# (x1, x2) pairs\n",
    "x1 = [0, 1, 0, 1]\n",
    "x2 = [0, 0, 1, 1]\n",
    "# Calling the perceptron function\n",
    "output = perceptron([x1, x2], w, threshold)\n",
    "for i in range(len(output)):\n",
    "    print(\"Perceptron output for x1, x2 = \", x1[i], \",\", x2[i],\n",
    "          \" is \", output[i])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this simple case we can rewrite our equation to $x_2 = ...... $ which describes a line in 2D:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "def perceptron_DB(x1, x2, w, threshold):\n",
    "    # Plotting the decision boundary of the perceptron\n",
    "    sns.scatterplot(x1, x2)\n",
    "    plt.xlim(-1,2)\n",
    "    plt.ylim(-1,2)\n",
    "    # The decision boundary is a line given by\n",
    "    # w_1*x_1+w_2*x_2-threshold=0\n",
    "    x1 = np.arange(-3, 4)\n",
    "    x2 = (threshold - x1*w[0])/w[1]\n",
    "    sns.lineplot(x1, x2, **{\"color\": \"black\"})\n",
    "    plt.xlabel(\"x$_1$\", fontsize=16)\n",
    "    plt.ylabel(\"x$_2$\", fontsize=16)\n",
    "    # Coloring the regions\n",
    "    pts_tmp = np.arange(-2, 2.1, 0.02)\n",
    "    points = np.array(np.meshgrid(pts_tmp, pts_tmp)).T.reshape(-1, 2)\n",
    "    outputs = perceptron(points.T, w, threshold)\n",
    "    plt.plot(points[:, 0][outputs == 0], points[:, 1][outputs == 0],\n",
    "             \"o\",\n",
    "             color=\"steelblue\",\n",
    "             markersize=1,\n",
    "             alpha=0.04,\n",
    "             )\n",
    "    plt.plot(points[:, 0][outputs == 1], points[:, 1][outputs == 1],\n",
    "             \"o\",\n",
    "             color=\"chocolate\",\n",
    "             markersize=1,\n",
    "             alpha=0.04,\n",
    "             )\n",
    "    plt.title(\"Blue color = 0 and Chocolate = 1\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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5XzQ3z98wV701Zrb19viaxc+N/CZ6xoNof5thJiPtHW2K47mBXH6uUErlOoa5+U5K6ewW4rcYm9wqwLNKqWOVUvV5++6mlDo3b//3aa0fXMa6ANBa/xMzP0sDMEHlTRAZzcMyDjN3zQeYSTWr/bnP3d05SCmVm3gUpVRvpdTVwKF52vw852xmq+Qf08jCNQPzLNXEJfaZi783ZpCKe9ppmyAIVUAsYYIgxE6BkXgczA/WDTHfO03AMdEV5/a4CHOVfkfgE6XUO5iJFFfEzDlxDEs8KKu1vj3ywZ8J3Bv9WPoMMxJQ7mHiS7TW40qJR2t9U3RF/TjMMwUfYq4mK8wPpo9o3Xm6LIr1YMwPxJmYuVQ2iI7DfIylqdioTWitm6Ir7VOBnYEPlVL/w1z13xhzXF8HDmrjSrQtnIi5Ar8LMFMp9RZmpvnemKvzw0p4noPoGZVLMA9jHwEMUkp9gHn4PjfK01NRPe09XN0uWuuHlFKjMPOLjFNKvY/5UfxTzN2/54Gfd3Cfc5RSu2Fmg98WM2re9dG+fcznJtcR+zvm2MXBoZi7Jz8F3oxy0ARsgjknPwb2zbdDVetzj8nvYZgcvqWUehtj11ofc2xew9jCVog0uRG4/hvpVgHeVUp9kjeS3SFR/FsD7yil3sQc7w2A+iiuvYuMCCcIQhWROyyCIFSCXyzxty2mk/FfzMzVG2ut25qroRVa60cwk95NwVg+NsTMf3GE1rrNiR+jier6Y54b8DDPgjiYH/9DtdbtXeVdcn/HYyaYewLzQ3sjTCfoSmBzrfXMPK2P+YF4EPA45sfappg7BDcCP9Na30sJaK3fjto+EnMnYn3MDOcvAWcA20ST31lLNF/MFph5OGZhjkUWMx/OVm2MgtXWvq7GTAI4I9rHppgfseMwn5MhmLlTNlFKdWTI4bbqOwcYhrEQ9sEMEjAe85meWaRosX1+jjkvDsR0xr/B5HVDzFX+McAOWutjtdbN5cYQ1fll1OZzMdanNYB1MdavCzGfydcLlOv0z31kx9sM84zPTEwHaXVMR+UsYBvgsUg+OK/cO5jO1fuYYZPXiZ5Xyx3zraP4/x3FvyHmWZqbgf+LJgQVBMFCnDBsd54uQRAEQRAEQRCEqiB3WARBEARBEARBsBbpsAiCIAiCIAiCYC3SYREEQRAEQRAEwVpklLA8ojkBTgKOwjyM52GGebwPuFJrvbDE/awPXIyZbXkFzERctwA3ljISjiAIgiAIgiAIBnnoPiLqrDyCmZF5LmbW5WbMqCrLRa931VrPb2c//4cZvaYn8CxmdJRdon3crbU+vFIxCIIgCIIgCELaEEvYYo7DdFZeBzbQWu+utd4LWA8z1v42mKEf20Qp5QB3YjorR2itt9daD8MMV/k6cJhSar9i+xAEQRAEQRAEYTHSYVnM0dHyDK31Z7mNWutvMDYxMJNhFWMPzJjzT2utx+TtYxbw6+jlabG0VhAEQRAEQRBqAOmwLOYb4G3gxQLvvRMt25s5eUC0XGr2bK11zh62vVKqx7I2UhAEQRAEQRBqCXnoPkJrPbjI21tFy0/b2c3G0fK/bVUDrISZKfiF0lsnCIIgCIIgCLWJ3GFph+i5lEuilw+2I18lWn7Rxvu57X3LbZcgCIIgCIIg1AJyh6V9Lgd2Ar4CrmxH2y1atjWS2IJo2T2GduXzCrAWZnSz92LetyAIgiAIgiAArIv5Hfsh8LPOqlQ6LEVQSl0CnAcsAg6MHp4vRm6OlbbGinaWWMbFWkCv6G+1mPctCIIgCIIgCPms1ZmVSYelAEqpDPAX4ARgITBMaz2jhKJzo2VjG+93iZbzymthwXp7BUFAczYgDEPCEBwHXMf0jYK8bY7jVERTqf0mUVOsTBCGfP3ll3wy82PC0G/RBlE313XMXxCav1VWXpnVf/ITPC8DrmdEgY/pFzvguhAEea87U1PNuquvqfNcIKC5ObCyfZLz4po6zykzf9WPoVZz3vrcS2+cac15+edeMuJMlqaUMh719S1dh9xv3k5BOixLoJTqDjyAGfHre2BoiZ0VgM+BzYCVMSOOLUl7z7gsK+8BqzU1+3z3/Xyamn2yQUDGdekSfbAWNmVbttXXeRXRVGq/SdS0WybTnbouPbnowt8yY8YMsn6An23CCUO6ZBzqPWjyYVE2pCHjsEa/1RhxyRXssqcZGyJomg9+E3j1OHVdCJsXtrx267t2mqaaddugWaFXHfiL+Pa7JivbJzkvrlm+p1dW/myIoVZznn/upTnOtOa83HMvKXEmSVNKGbe+Kyv17UVEpz6CIA/d56GU6g08jemsfALs0IHOCiweHWyjAvt2gA0AH3izvJa2TdYPIj+aQwgEQUgQhK22VUpTzbpt05RSZrV+q/PguPFcfd1f6LXc8jiOi+eC5xqN50J9xrz+4ovPOe64Iznt1BP4dvYsHAIcHBwC8Jtavw59CP3O0VSzbks0WN4+yXlxTVn5sySGWs05cddlaZxpzbkV352258G2YxH6xX6CVhTpsEQopeqBScAWmA7FdlrrtoYnbosp0XKfAu9tB6wIPKO1/nGZG9oOGc/FcSAkNFYk18F1nVbbKqWpZt22aUot47oOhx56KFOmPMGAAXvjB5ANHQJcsqFDUxb8ACDED+D+B+5nx522Y9KkCQRhQIgLXj0hLiGhee144Hitt1VKU826LdFgefsk58U1ZeXPkhhqNefEXZelcaY151Z8d9qeB9uOheNV6udru0iHZTGXANti7qzsrLUuOueKUmodpdQGSqleeZunA/8D9lBKHZ+nXRG4MXp5VbzNLkAITrRsc1ulNNWs2zZNKWWi1ZVW6svtt9/FrX+7k54rrEKTU0czdYTRsy/5fPPNLE4+5SROOeUkZn315VLvC4IgCIIgpAl5hgVQSi0PnBa9nAVcrZQqqNVaHx6tPgGsARwDjI7eC5RSx0bv3aKU+iXmuZadgd7ArVrrRysTBYShsSL5YYgfhDiOsSEBrbZVSlPNum3TlFwmMK/96AH9vQcOYfOtt+PyKy7j4YfGkvFcPDfEcT280MdzzX4yLkx/ahr9//U8510wnAOG7Y8TZMFxF9+yDbLmz3GN/zT/dVyaSu03MRqH0I+2Wdk+yXlxTVhe/qyIoVZznnfupTrOtOa8zHMvMXEmSFNKmSpawqTDYtiaxSN7bR79tcXhxXaktX5RKbUN5o7NLsAmwLvA+cDfym9q24TR5XvfD/CDwFzJrzfv5W+ry7gV0VRqv0nUdKRM1g/w/aBF071HTy4ZeQV77zWY4b87k9nffEngugShix+aB/Ndx8FzYP68Hzn77LOYNP4fXDr8YvqtsS6hqZHQ98HPAh5hnQv5r+PSVGq/CdEEfhb8ZkLft7J9kvPimrLzZ0EMtZrzVrlLcZxpzbk1352258G2Y8HSro/OQjosgNZ6CnQsC1rrNYu89yawf5nN6jAODoTgeS4hZpmzHi25rVKaatZtm6bUMpmwsGannXfksSef4Q9/upz77hlDNjRXOFzHJwhDsoFDEIZkMvDc88+z735DOeX0czjquJPxPA8n40FYBxkPJwwI819HlZWtqdR+E6JxvQbCsA4nE1rZPsl5cY3rZcrLnwUx1GrOW517KY4zrTkv+9xLSJyJ0pRSJvdDpQq4VatZiJ3cg9xe9IB3bpm/7rlOxTTVrNs2TUfKZIpoevbswYgRIxk9Zixrrbk2YRjgB9AcOjSHLs3Rg/nZAObOX8AlIy9hyNC9eefdd8HJgOdFy/rWr6OH6crWVGq/SdG4GRwvY2/7JOfFNeXmz4YYajXn+blLc5xpzbkt352258G2YyEP3QuxUuDB7qW2VUpTzbpt05RSJrfazn633GIrHp04jdNOOwvXy9BMXZsP5v/73y+z++47cv11V9PU1IwgCIIgCEKSkQ5LyrB97pFa0ZRcJsTMdF/CfhsaunDeeRcyecqTbLTxpvhOBlw3ulNjNLl5XPxsE9ddN4oDDhjKG6+/goxh3wlzQVjaPsl5BeeCsCSGWs05cddlaZxpzbkV352258G2Y1HFh+6lw5IykjD3SC1oOlKGsGN1b7rp//HQw49y1lnnUJ+pI+sHreZu8QNa/rR+i32HDeHSyy5j3oJFyBj28WuwvH2S8+KasvJnSQy1mnPirsvSONOacyu+O23Pg23HQixhQqzYboOqFU2MlrAly9Rl6jjhhJOYOPExttpqm6IWsSAIuOmm6xm49668+OKLCIIgCIIgJAnpsKSMRNigakBTCUtYIc1a66zL/Q9NYPiIy2jo2hNcF8dxW6xhYGxi9Rn49JOZHHnkwVx04Xn8OOd7uZ0ekwbL2ycWCrGEpTXnxF2XpXGmNedWfHfangfbjoVYwoS4SIoNKu2aSlrCltTUZzIcedQxPDphKjtuv2PLSGL5NrGmrLGIQciYu+9ip51/weNPPIbcThdLWK3nvKz8WRJDreacuOuyNM605tyK707b82DbsRBLmBArttugakVTQUtYIc1qq63G7aPv5rprb6LXcr2L2sQ+//wzDj/8YM4449d8M/tbBEEQBEEQbEU6LCkjETaoGtB0liVsSQ047H/AwUyf8SIDBg4uOpKY58LECY8wZPAeTJwwjjC7qDK3kFOuwfL2iYVCLGFpzTlx12VpnGnNuRXfnbbnwbZjIZYwIS6SYoNKu6YzLWGFNH37rsRfbriZ6669iT4rrFh0JLFvv/2WU087mWOPO5qvvvoKuZ0ulrBaynlZ+bMkhlrNOXHXZWmcac25Fd+dtufBtmMhljAhVmy3QdWKppMtYYU0e/bvz7RpT3HgwYcVtYgBTJ06ld332IX777+XsNWOBEEQBEEQqod0WFJGImxQNaCpliWskKZnr+W4ctR13HHnWFbpt2bRkcQWzP+Riy66gCMOO5CPPnxPbqeXoMHy9omFQixhac05cddlaZxpzbkV352258G2YyGWMCEukmKDSrum2pawQprtd9iB8eOncuRRxwBh0ZHEnnv+OXbZdQf+evNfyQbmZrDcThdLWNXrts2WYkkMtZpz4q7L0jjTmnMrvjttz4Ntx0IsYUKs2G6DqhWNBZawJTVduzUy/MJLGf/IFNZdTxW1iS1YsIARIy7ggP0H8847GkEQBEEQhGogHZaUkQgbVA1obLKEFdJsseXWTHt8BqeceiZOpqHoSGJvvP4a+w0bxLXXjKJpwTx7bl9bosHy9omFQixhac05cddlaZxpzbkV352258G2YyGWMCEukmKDSrvGRkvYkprGLl0486xzeOCB8Wyy8aZFRxJrzma59rqrGbD37rz26qtYcfvaEg2Wt68qtoEEacrKnyUx1GrOibsuS+NMa86t+O60PQ+2HQuxhAmxYrsNqlY0FlrCCmk23GhDHnxoPL/7/cV4Dd2LjiT29tuaYfsP5Q9/vIz5CxYs9b4gCIIgCELcSIclZSTCBlUDGtstYUtqPC/Dib86hQmTnuBnW/686EhiDgGj/34bA/fahef++bTdt7jFEha/xvb2iSWsZnJO3HVZGmdac27Fd6ftebDtWIglTIiLpNig0q5JgiWskGattdfmjjH3cvHwy+nWtVvRkcQ+nvkJ+x84jPMv+C3f//ADVt7iFktY/Brb2yeWsJrJOXHXZWmcac25Fd+dtufBtmMhljAhVmy3QdWKJiGWsCU1nuNy+BFHMn3G8+y62x7tTjh53733MHhwfx5/bOpS7wmCIAiCIJSLdFhSRiJsUDWgSZolrJBmlVVW48677ueqq2+g5/IrFhxJrD5jLGNff/0VJ510LCf96mhmff2VPbe4xRIWv8b29oklrGZyTtx1WRpnWnNuxXen7Xmw7ViIJUyIi6TYoNKuSaolbEmN57nss88wJjz6GHvvPWipkcTyLWJ+AA+PG8dOO/+ccePGEYQBVb/FLZaw+DW2t08sYTWTc+Kuy9I405pzK747bc+DbcdCLGFCrNhug6oVTSllcqu2xhCtr9Bnea6/7q/cfvs99F6pX1GL2LfffsuZZ53Kr351HF9+8dlS7wuCIAiCIHQE6bCkjETYoGpAkwZLWCHNbnv2Z+q06ex34OFFJ5v0XJg+/Sn699+VMXfcShhk7b0NLpawjmlsb59Ywmom58Rdl6Xx+1XxAAAgAElEQVRxpjXnVnx32p4H246FWMKEuEiKDSrtmrRYwgppevbqycWXXs5dd4xltdV+0uZkkxDy49y5nHvebznk0AP46OMPsPI2uFjCOqaxvX1iCauZnBN3XZbGmdacW/HdaXsebDsWYgkTYsV2G1StaEopk1u1NYYimu1+8Qsee/JZjjjuZLJuQ5sWMYAXX3yRwYP34ua/3kjWzxbUCIIgCIIgFCJT7QYI8RGGxtbjhyF+EOI4xtIDtNpWKU0167ZNU3KZwLz2Q/tiKEXT0KWR884fzh4DBnPR785Fv/UGnuPjuQ6O6+GFPvWZkIwLQbaJq0ZdxvhJExg16lo2XHctCLLguOA3mfXc69xt5/xtVmocQj/aZmX7KqCxvX0d0oTl5c+KGGo153nnXqrjTGvOyzz3EhNngjSllBFLmBAHYXTp2/cD/CDA9wNzKbzAtkppqlm3bZpSy2R9e2MoNc5NNtmUfzw0gTPPOgfHrSNw6wjcDIFbR7Pv4DoOngOu4/Dqq68yYMBuXPvnP7Jw/nxC3ydwXELfJ/SzZolj/vK22agJ/Cyh32xt+yqhsb19HdGUmz8bYqjVnOfnLs1xpjXntnx32p4H245FSGEXRWfgjRgxomqVC7FxNLBmNhswf0FTi3Mn47nUuaZP6odhyzYvesg7bk2l9ptETallGrs2ANDUlLUuho7G2VBfx07b78iAvQbxn9de46tZs2kOQlwCPCfEwTF3Z0IIw4BX/vMyTz31GBtuvCmrrtoPnBAnBKcug+vVma/FMLt4m+Nap+nWtR4IWdAcWtm+imhsb18HNF27eOXlz4IYajXnrc69FMeZ1pyXfe4lJM5EaUop49XRrXtj9F+fj4HRdBJiCUsRuYeigzAkCB286KFpAM91WrZVSlPNum3TlFrGcx1C184YljXOjTfemAf+MZ7bRt/GtaP+yIKmhTQ7DoQuzaGxwQFkA3jnvffZ/8D9OPyY4znnzN/QvUsdOBlaHuxzMuAF0bIegmDxaxs0bgbH88EJ7GxfJTS2t68jGtcrL382xFCrOc8/99IcZ1pzXu65l5Q4k6QppYw8dC/ESgitHpIutK1SmmrWbZumlDK5VVtjWMY4M16GXx5zPJMmP852221PM3Vtzt0ShiF/u+VmBu61G8888wyCIAiCIAj5SIclZSRi7pEa0KR1HpaOxrnGmmtz99iHGXnZlXTt0RtcF8dxW+ZqATN/S30GPv/8E4477kjOP+8svv9uNtg0Pn0pc0FY2j6Zt6C4pqz8WRJDreacuOuyNM605tyK707b82DbsZCH7oW4SMrcI2nXpHkelo6Wqc9kOOTQw3h0wlR222U3wjDAD2g1f0tTlpa5W8beP5Ydd9qOSZMnYc349KXMBWFp+6oyVn+CNGXlz5IYajXnxF2XpXGmNedWfHfangfbjoVYwoRYscweVLOaUsrkVm2NIa44Q1h55ZW5+dbR3HTj31h+hT5FbWJff/0Vxx57BKeceiKzZn2DIAiCIAi1i3RYUobN9qBa0oglrLAGHIbusx9PT3+BwUOG4TsZcN1oAAKjydnFPBemTp7AoEG78/BD9xNmF1l5O50q1m2tbSBBmrLyZ0kMtZpz4q7L0jjTmnMrvjttz4Ntx0IsYUJc2G4PqhWNWMKKa1ZcsQ/XXHsDf73pb/RdqS9ZP2hlEfMDWv5++OEHzv7NmRxx1GF89tln2HY7nSrWba1tIEGasvJnSQy1mnPirsvSONOacyu+O23Pg23HQixhQqwkwB5UE5pSyuRWbY0hrjjb0Oy8y65MnfoUhx1xTFGLGMCTTz7Jnv13Y8yYOwnCYKn3BUEQBEFIJ9JhSRlJsgelWSOWsNI13Xv05PIrRnH3vQ+y2uprFx1JbNHCeYwcOYJDDxrG++/pzrtVLpYwO+q2zZZiSQy1mnPirsvSONOacyu+O23Pg23HQixhQlwk0R6URo1Ywjqu2Xbbn/PI+Cn88pcn4jgUHUnsxZdfYtfdduT6G66jKRtQzdvpVLFua20DCdKUlT9LYqjVnBN3XZbGmdacW/HdaXsebDsWYgkTYiWh9qDUaUopk1u1NYa44ixR09jYhd+dfyGTJj7GBhtuUtQmtmjRIi677GKG7bs3b775XwRBEARBSCfSYUkZSbcHpUUjlrDyNJv+3+ZMnvokZ5x5Lm5dl6Ijib391v84YP99uWrUH1g4b26n306nQvu1VmN7+8QSVjM5J+66LI0zrTm34rvT9jzYdizEEibERVrsQUnXiCWsfE2XhgZOPe0MHnpoIpv932ZFRxLzgyx/ufF69hywCy+//G8683Y6FdqvtRrb2yeWsJrJOXHXZWmcac25Fd+dtufBtmMhljAhVlJiD0q8ppQyuVVbY4grzjI0662/Hvc/8AgjLr6cusaeRUcSe++99znwoH0ZOXI4c+fNXep9QRAEQRCSh3RYUkYa7UFJ1IglLF6N63oc88sTmTD5Sbb++U5FRxJznZAxY+5i4IDdeOrJx5a+pS2WsPI0trdPLGE1k3PirsvSONOacyu+O23Pg23HQixhQlyk1R6UNI1YwiqjWWONNbjt73dyxWWj6NG9R9GRxD757FMOOfQATjvjFL79/gcqdTudCu3XWo3t7RNLWM3knLjrsjTOtObciu9O2/Ng27EQS5gQKym2ByVKU0qZ3KqtMcQVZ4wa13E48KCDmfHPf7HngIHtTjh5/9h76b/nLkydOnmp9wRBEARBsB/psKSMWrAHJUEjlrDKa1ZaaWVuv30M191wM7369C06kth3387i9NNP5uRfH8/Xn38a6+10yrkFn0SN7e0TS1jN5Jy467I0zrTm3IrvTtvzYNuxEEuYEBe1ZA+yWSOWsM7ReJ7LwIGDmTThMYYOHVZ0JDEImTxlEjvtuiMPPPAAQRgQqy3F9tv9YqEoqCkrf5bEUKs5J+66LI0zrTm34rvT9jzYdizEEibESg3Zg6zWlFImt2prDHHFWWHNcr2X489XXceYMQ+w4qqrF7WI/fDD95z727M49tgj+ezTmUu9LwiCIAiCXUiHJWXUqj3INo1Ywqqj2WHnXZk8ZToHHXZM0ZHEPBeef/5ZBgzYjdv+diN+dtltAlT6Nr1tGtvbJ5awmsk5cddlaZxpzbkV352258G2YyGWMCEuatkeZJNGLGHV03Tv0Z0LL7qYe+95gLXWXKvoSGLz5s/ngt//jn32HcR7773PstxOZxnKJFpje/vEElYzOSfuuiyNM605t+K70/Y82HYsxBImxEqN24Os0ZRSJrdqawxxxVkFzdZb/5wnnnyGk089k8BtKGoTe+mlF9l74B7cdNNfaM42L/W+IAiCIAjVQzosKUPsQXZoxBJmh6ahoQu/+91wHnx4EutvuGnRkcQCv5lrr72KfYfuxRuvvlTy7XQqfZveNo3t7RNLWM3knLjrsjTOtObciu9O2/Ng27EQS5gQF2IPskMjljC7ND/ddFPu/8fDnH32b3HdTNGRxN586y32HrQ3f/jDFcxfsJCSbSm23+4XC0VBTVn5sySGWs05cddlaZxpzbkV352258G2YyGWMCFWxB5kh6aUMrlVW2OIK04LNHWZOn590qk89vgMfrr5NkUtYr7vc/MtN7LPPv156cV/LfW+IAiCIAidR6baDRDiIwyNJcYPQ/wgxHGMZQZota1SmmrWbZum5DKBee2H9sWQ1pyvuc563D12HGPuuourr/ojc+d8i+f4eK6D43p4oU99JiTjwmefzOSwQ/fjwMOO4fzzL6JbvQtBFhwX/CYIHEI/a7b5TWaZez936zx/Wxo0trevQ5qwvPxZEUOt5jzv3Et1nGnNeZnnXmLiTJCmlDJVtIRJh6UISqmjgb8DO2itnymxTAaYCzS0IflMa90vnha2JowuLft+gB8E5ipzvXkvf1tdxq2IplL7TaKmI2WyfoDvB9bFkOacEzocfOjh7LLLLoy44DfMmPE0gVtH4LoEoUtzcxMNnoPngOs4/P3vtzNt2jRGjbyEHX6xHeAR1rkEfhb8ZkLfJ6xzwffBz5r3TQsI87elQWN7+zqgKTt/FsRQqzlvlbsUx5nWnFvz3Wl7Hmw7FiztSOgspMPSBkqpnwPXL0PRjTCdlfeBQl6Sb8tpVzEcHAjB81xCzDJni1lyW6U01azbNk2pZTKhvTGkPeerr74G99z7EA88cB+XjryEefPmkg2zuJ5LEAZkA4cgDHFd+OKLTznuxF+y75ChnPu7i+nT0IjrNRCGdTiZECcMCDMehHWQ8XCiyp38bWnQ2N6+DmhcL1Ne/iyIoVZz3urcS3Gcac152edeQuJMlKaUMrl/qlVAOiwFUEoNA0YD3Zeh+M+i5d+11pfF1qgSyD10HIQhQejgRQ8cA3iu07KtUppq1m2bptQynusQunbGUCs59zyXgw8+lO2234mLLrmQaZMnEvoBzY4DoUtzaKx7ANkAHnn0EZ785zMMv+QPHHX4gTheBpwAvHoIAvACcDK0PJzoZBZvS4PG9vZ1RON6OJ6/7PmzIYZazbmbWZy7NMeZ1pyXe+4lJc4kaUopIw/d24FSqp9S6k7gQcADvlqG3eQ6LP+OrWEdJYSqPeBczbpt05RSJrdqawxxxZkAzUor9eWG6/7KX66/lT59+tJMXZsP5s+ePZsTTzyOQw87hC++WJavCUEQBEEQSkU6LK0ZCRwBvAxsC7y9DPvIdVj+E1ejOoLMyWGHRuZhSa5mwN578+TTzzFsv4PwnQy4Lo7jtszXAmb+lvoMTJk0iV/8YjvuH3s3YXYRVo+xL/MWFNQgxyKxcRJ3XZbGmdacl3XuJSjOxGhkHpZE8TZwFLCN1vqNjhZWSjnAZsCXwBCl1ItKqR+VUrOUUvcqpVTM7V0KmZPDDo3Mw5JszQorLM+Vo67mb7fewaorr0oYBvgBreZvacqCH8CcOXM47/xzOfjQA5k58xOsHWNf5i0oqEGORWLjJO66LI0zrTkv69xLUJyJ0Vg+D4sThmH7qhpFKfU0sBMljhKmlFoHeC96GQDPAt9j7rr0A34E9tJaPxtzU58GdvIDc+V4UbNPczagLuPSWJ8BYEFTtmVbQ51XEU2l9ptEje3tkzhL1yxaMI9LLx7OjTfeiOvVkfFcsn5AkG2iiwf1HjT5sCgbslyProy48HxOOOlU6rv2AMBfNB/8ReA14NZ1IWhe2PLaa+iaOI3t7ZNjIXFKnHIsJM7KHAuvoSuO23KvYzqwM52E3GGJl5wd7DNgC631jlrrIcBawFVAD2CsUqpLpRqQzebZjMI8u0vetkppqlm3bRrb2ydxlq7p1r0H11xzLU8/PZ311lctNjEzaALAYotY06IFXHjhcPrvsQf/++8bS1sfgratLInR2N4+ORYSp8Qpx0LirMyxqKIlLFO1mtPJg8DqgK+1/jy3UWudVUqdi+mJbgHsA9wXd+XNzT7ffT+fpqxPsx9Q57l0qTMpXticbdlWn/EqoqnUfpOoKbVMz+W6Qhgye/Zc62KQnLfWKLUpjzwymetvuJ6bb7qOrB/gOQ6O45INA5qyoRlMxQt58eWX2WLLrTjjjN9w8q9+Rb0bglePU9eFsHkh+E3g1ePWmzvcQdP8lm22a2xvX0c0y/f0gJBZ38yr+WORtDhX6FXXkrs0x5nWnJd77iUlziRpSinj1oes1LcX1UDusMSI1jrUWn+S31nJey8AJkUvt6hoQ0KQEaMs0JRSJrdqawxxxZkSTUNDF84482weeWQym2yyWdGRxJqbm7nyyisYOqQ/r7/+GoIgCIIgLBvSYelcvoyWXStVgYwYZYdGRglLt2bDjTdh3IQpDB9xMZmGrgUtYrm/997VHHzwfvzhikuY/+McHCweJaYGR9WhnP1YEkOt5ryQdSWNcaY152WdewmKMzGaUspU0RImHZYYUUqdrJQaq5TavQ3JWtHy00q1QUaMskMjo4SlX9PY0MBZZ5/F9OnT2XLzLcn6QatRxPyAlr8g8Lnl1pvZo//O/Otf/8LaUWJqcFQd5FgkNk7irsvSONOa87LOvQTFmRhNKWWqOEqYdFjiZW3gQMzQyK2IHrQ/IHo5raKtqKZtpsqWHas0pZTJrdoaQ1xxplUDrLPuutw79iFGjryS+q7LtWkRA/jww4845NADGD78Aub8OGep9wVBEARBWBrpsCwjSqnVlVIbKKX65G2+DfCBw5RS++Vp64DrgTWAyVrrf1eqXWIPskMjlrAa0UT5cxyXI44+lklTn+IXO+5WdLJJz4WxY+9l7wG78ti0SVhlCahBCwVyLBIbJ3HXZWmcac15WedeguJMjEYsYanlTuAt4JTcBq31m8BZ0ct/RBNH/gP4ADgOMzHl0ZVslNiD7NCIJax2NLn8ZTyXfv368ddbbmfUn65luV692pxsEkI+/+ILjjjyUH598gnMmv0dVlgCatBCgRyLxMZJ3HVZGmdac17WuZegOBOjEUtYbaG1vg7YA5gKrAcMAuYDlwFbaa2/rngjqmmbqWbdtmlKKZNbtTWGuOJMqya3mlfGdRz2HbYf02e8wKAh+xYdSQzgoYceZED/nZk4cTxhq8oEQRAEQQCZh6UoWuudl/G9J4AnKtCkdilkZQFabauUppp126YpuUxIK0uYTTFIzkvQ5OVvyTJ9+qzIzTf/nUcnTmT4JRcy66svopHEjBXCc8PILgY/fP8tZ599Bo88+iiXjvwTK/ddEdM1WnwLPmehYAlLTKdrqll37BqHkKVtRrV5LJIWp7c4d6mOM605L/PcS0ycCdKUUkYsYUJciD3IDo1YwmpHk28JK1Sm/4ABTJrwGPvvf1DRkcQg5PEnHmenXXfinnvuwQ98EmsbSJAGORaJjZO467I0zrTmvKxzL0FxJkYjljCh0wlNX5iwyLZKaapZt22aUsrkVm2NIa4406rJrbZTpmevnvzxD1dx333jWLnfWkUtYj/+OIcLfv9bjjrqMD7+6MOl3hcEQRCEWkM6LClDRoyyQyOjhNWIJi9/pZT5+fY7MHHy0xx+zPE4rkexkcReeukF9t57N2668TqyzYtI1EgyCdIgxyKxcRJ3XZbGmdacl3XuJSjOxGhklDChMxF7kB0asYTVjqY9S9iSZbp178p5513I/fc/xHrrrld0JLEFCxdx8SUXMXjIAN5+W5MY20CCNMixSGycxF2XpXGmNedlnXsJijMxGrGECZ1ONW0z1azbNk0pZXKrtsYQV5xp1eRWl2G/m2++FY89PoMzzjqX0OtS1Cb2yiuvMHjwAK699moWNTUt9b4gCIIgpBnpsKQMsQfZoRFLWI1o8vK3LPutq6vnnHN+x8OPTGWDn/4MXDcaScxocnYxz4UwzHLTTdez79D+vPLy8x2/3S8WioIa5FgkNk7irsvSONOa87LOvQTFmRiNWMKEzkTsQXZoxBJWO5qOWsIKaTbaeCPGjn2Qc397AZlMfdGRxN7W7zBoyCAuvewS5s6bj5W2gQRpkGOR2DiJuy5L40xrzss69xIUZ2I0YgkTOp3OtMR01n6TqCmlTG7V1hjiijOtmtxqDPvNeBlOPP4kHntiBptvvX1Ri1gYhtx+260MHdqf5597Zqn3BUEQBCFNyMSRKSIMjeXED0P8IMRxwpYJ7/K3VUpTzbpt05RcJjCv/dC+GCTnHctfXPtdfc11uOOef3DvPfdy1ZWXM+f7b/AcH891cFwPL/Spz4RkXPjy80856siD2PfAw/n97y+mR5cMBFlwXPCbzHrude5Wfv62ZdFUar9V0YSEfrS95o9F0uJ0Fucu1XGmNedlnnuJiTNBmlLKiCVMiIMwunTr+wF+EOD7gbmUW2BbpTTVrNs2Tallsr69MUjOO5a/uPYbBrD/gQcxfuJUdtt1N/zQIXDrCNwMgVtHs+/gOg6eA67jMGbMXey003Y8Pm0yoZ8l9H0CxyX0/ZbXIWZm6fxty6Kp1H6roQn8LKHfLMcigXHm5y7NcaY15+Wee0mJM0maUsqELH3Hv7PwRowYUbXKhdg4Glgzmw2Yv6CpxWmS8VzqXNMn9cOwZZsXPSQct6ZS+02iptQyjV0bAGhqyloXg+S8fU1+/ipR93K9enHAfgey1trr8NyLL7JgUTPNQYhLgOeEODjm7kwI8+fPZdqUicz86CM233o7unfvDk6IE4JTl8H16sy/mjC7eJvjdlyzLGUs1XTt4gEhC5rDmj8WSYuzW9f6xblLcZxpzXnZ515C4kyUppQyXh3dujdG/6H4GBhNJyF3WFJE7qFeL3qQN7fMX/dcp2KaatZtm6YjZTKWxiA571j+KlW357nsv/+BTJ78JHsOGEgQ2c+aQ4fm0KU578H8bAATJk+k/4BdefDhcYR44HngZMg9QImTWbzNq2/9uhTNspSxVeNmcLyMHIskxpmfuzTHmdacl3vuJSXOJGlKKSMP3QuxEkKhh3pbbauUppp126YppUxu1dYY4oozrZrcaifU3adPH/589fXc8tfR9O27Ks3Utflg/vfff8+pp/6K4447gi+//BxBEARBSDLSYUkZMieHHRqZh6VGNHn566y6d9tjD5546lkOPuQIfCcDrovjuC3ztYCZv6U+AzOmP8XAgf25e8xoAr8ZZN6CpTTIsUhsnMRdl6VxpjXnZZ17CYozMRqZh0XoTGRODjs0Mg9L7WjimIelo5revZfjssv/yOjR9/CT1X5CGAbGEpY3f0tT1tjE5s2by4UXXcCw/YbwwYcfIPMWtNYgxyKxcRJ3XZbGmdacl3XuJSjOxGhkHhah06mgLaVdTTXrtk1TSpncqq0xxBVnWjW51Sq1b9ttt2XylCc58cSTcTyvqE3sX/96nl133YGb/3oj2Wz1rpIJgiAIQkeRDkvKEHuQHRqxhNWIJi9/1Wpfl8ZGhg8fyfjxU1lfbdhiEzMP7xtNzi7W3LSQUaMu46CD9uWtN/8rFopybSmWxGCFVUQsYenS2H7uJSjOxGjEEiZ0JmIPskMjlrDa0VTDElZIs+WWW/HI+MmccvLpZFyPrB+0sojlRhLzA/jf/95g6NCB/GnUlSxsylLLFgrK2Y8lMVRcY2n7iLsuS+NMa87LOvcSFGdiNGIJEzqdTraldMp+k6gppUxu1dYY4oozrZrcqiXtq6+r55RTT2f8+ClsttkWRS1iWT/LNddcxeBBu/PKK/9BEARBEGxFOiwpQ+xBdmjEElYjmrz82dS+9TfYkIfGT+a8C4ZT39i96EhiH37wPoceegCXjbyIuT/OodYsFJSzH0tisMIqIpawdGlsP/cSFGdiNGIJEzoTsQfZoRFLWO1obLGELalpqKvj+ONOZNy4yWy79bZFRxILw4Dbbr+NXXfbnukzplNLFgrK2Y8lMVRcY2n7iLsuS+NMa87LOvcSFGdiNGIJEzqdClpO2tVUs27bNKWUya3aGkNccaZVk1u1tX0hrLHmGoy55wFGXXkt3Xv0KmoTmzlzJgcdNIxzf3sm33//A4IgCIJgA9JhSRliD7JDI5awGtHk5c/K9kWvweHQw45k+ox/sdseA4qOJOa58PCD9zN48J5MnTIp9RYKytmPJTFYYRURS1i6NLafewmKMzEasYQJnYnYg+zQiCWsdjS2WsIKaVZddVVuvvk2rrrqWnov17voSGKzZn3NSb8+nhNPOoGvv/4GKywLttlSLImh4hpL20fcdVkaZ1pzXta5l6A4E6MRS5jQ6cRoJ+mwppp126YppUxu1dYY4oozrZrcqq3tK6BxcBg4cDDTpk1n6L77F7WIATz66Hj27L8z48Y9SNiqMkEQBEHoHKTDkjLEHmSHRixhNaLJy5+V7Sui6b388lx3/c3c+rc76bNyv6Ijic398QfOO+8cjj36MD6b+UH7tgHbrQ9iCUtFnMRdl6VxpjXnZZ17CYozMRqxhAmdidiD7NCIJax2NEmyhBXS7Lrb7kycMI1DDjmi6EhiEDJ9xnR22mUn7rhjNH7gY6WtQSxh8WssbR9x12VpnGnNeVnnXoLiTIxGLGFCp7OMVpFYNNWs2zZNKWVyq7bGEFecadXkVm1tX4ma7j26M/LSK3j4oQmsudY6RW1i8+fPY8TFF3LYoQfx4QfvIwiCIAiVRjosKUPsQXZoxBJWI5q8/FnZvg5qttn2Fzz2xD85/oSTCb26giOJ1WeMZeyVV//NwIG7c/11V9HctBBrbA1iCYtfY2n7iLsuS+NMa87LOvcSFGdiNGIJEzoTsQfZoRFLWO1okm4JW1LTvVs3zjv/Asbe9zBqvQ2WGkks3yK2cFETl11+KXsP3JP//e9/WGFrEEtY/BpL20fcdVkaZ1pzXta5l6A4E6MRS5jQ6ZRoA6mIppp126YppUxu1dYY4oozrZrcqq3tK0Pz0003Zdz4Sfzm3N9BprHoSGJvvPE6Q4cO5KqrrmThokVLvS8IgiAI5SAdlpQh9iA7NGIJqxFNXv6sbF+Zmrq6ek497WzGT3icTTbbsuhkk+Bz6603MWTQ7rz4/DPtWwsssUdQzn4sicEKq4hYwtKlsf3cS1CcidGIJUzoTMQeZIdGLGG1o0mbJayQZn21Pvfc+wAXXDCc+voubU42CSHvvf8B+wwbwkUjfs+Pc+dipfVBLGGpiJO467I0zrTmvKxzL0FxJkYjljCh02nD4lGKDaRsTTXrtk1TSpncqq0xxBVnWjW5VVvbF6PGcz2OPeZ4HnvyGbbebpeiFjGAu+68gyGDBzBj+lMF3xcEQRCEUpEOS8oQe5AdGrGE1YgmL39Wtq8Cmn4/WYPRd43l0sv/TM9evWlrsknPhc+/+Ixf/vJwTj/tRL779hussT6IJSwVcRJ3XZbGmdacl3XuJSjOxGjEEiZ0JmIPskMjlrDa0dSCJWzJ167rsO9++zNlyhPsueeAopNN+gGMvX8sO+z4cyZPnogV1gexhKUiTuKuy9I405rzss69BMWZGI1YwoROJ4RWdo5C2yqlqWbdtikqbsIAACAASURBVGlKKZNbtTWGuOJMqya3amv7Kpzzvn1XZvTou7nl1jvoucIqRW1i33wzi1+f/CtOPfXXzPr6q6XeFwRBEIS2yFS7AUJ8hKGxb/hhiB+EOI6xdwCttlVKU826bdOUXCYwr/3Qvhgk5x3Ln5Xt64SchyEMHDSUzbfejssuH8kj4x6IRhILcVwPL/TxXLPfjAtPPTGF555/jvN/P4L99hmGE2TBcWmxGgRZ8+e44De1fh27JiT0o+3Lsp+Kt88SjZXtcxbnLtVxpjXnZZ57iYkzQZpSyoglTIiDMLrs6fsBfhDg+4G5DFpgW6U01azbNk2pZbK+vTFIzjuWPxvb11k579GzFyMv/yO33HInffuuRuDWEbgZArcOP3QIAnAdB8+BeXPncOaZZ3DsMYfw6cyPCH2fEMf8+T6hnyX0fQLHbfU6bk3gZwn95mXeT6XbZ4vGxvbl5y7NcaY15+Wee0mJM0maUsqELH33vLPwRowYUbXKhdg4Glgzmw2Yv6CpxbWR8VzqXNMn9cOwZZvnOBXRVGq/SdSUWqaxawMATU1Z62KQnLevyc+fje2rRs7XXnstDj/8SL6d8yOvv/E/moOQMAjwnJCMCw6O0Trw2aef8vDD/6BL1+5sutkWuK4DYRYnBKcug+u44ISLX3t15t9lTJquXTwgZEFzuGz7qXD7rNFY2L5uXesX5y7FcaY152WfewmJM1GaUsp4dXTr3hj9B+BjYDSdhNxhSRG5B2K96MHZ3DJ/3XOdimmqWbdtmo6UyVgag+S8Y/mztX3VyHnPnj24+OLLGD3mPtZYfU3CMMAPoDl0aA5dmqMH87MBzJ2/gIsvvZih+wzk3ffeAycDnhct61u/jh4CjU3jZnC8zLLvp9Lts0VjY/vyc5fmONOa83LPvaTEmSRNKWXkoXshVszFy6o+jFuVum3TlFImt2prDHHFmVZNbtXW9lU551tusTUTJj3GqaeeietlaKauzQfzX375JXbbbQf+csO1NDdnEQRBEIQc0mFJGTInhx0amYelRjR5+bOyfRbkvEuXRs4//yImTnqcDTf6Kb6TAdeN7tQYjeeC54KfbeKaa/7E/vsP4b9vvIr1c0HYPq9CyueqIO66LI0zrTkv69xLUJyJ0cg8LEJnUu35GWyeP8LWYyHzsCRbU4vzsCzLfjfb7Gc8PG4CZ555DnVehqwftJq7xQ9o+dP6LfbddwgjL7+c+QubqOScBJSzH9vnVUj5XBXEXZelcaY152WdewmKMzEamYdF6HRCcKJlm9sqpalm3bZpSimTW7U1hrjiTKsmt2pr+yzLeV2mjhNPPImJEx9jyy23LmoR8wOfG2+8joF778pLL72EIAiCULtIhyVlJMkqkmaNWMJqRJOXPyvbZ2nO1153PR54eCIXDR9JQ9ee4Lo4jttiDQNjE6vPwCczP+aIIw5i+EXn8eOc77HKlmK7xSPlthnirsvSONOa87LOvQTFmRiNWMKEziSJVpE0asQSVjsasYQt237rMxmOOvpYxj86he23275lJLF8m1hT1ljEIOSuMXex8y7b88STj2ONLcV2i0fKbTPEXZelcaY152WdewmKMzEasYQJnU4ITrRsc1ulNNWs2zZNKWVyq7bGEFecadXkVm1tXwJy3q9fP0bfeS/XXnMjPXv1LmoT++yzTznssIM486xTmP3tdwiCIAi1gXRYUkbSrSJp0YglrEY0efmzsn0JyTk4HHDgIUyf8QJ77jWw6EhingsTxj/MkMF7MHnSeMLsIqpmS7Hd4pFy2wxx12VpnGnNeVnnXoLiTIxGLGFCZ5IGq0gaNGIJqx2NWMLiq3vllfty0423cu21N7LC8n2KjiQ2e/ZsTj7lJH55/LF8/dXXLKs9ghI0ZVko0qCxtH3EXZelcaY152WdewmKMzEasYQJnU5orlkSFtlWKU0167ZNU0qZ3KqtMcQVZ1o1uVVb25fQnPfvP4Bp057igIMOLWoRA5gyZTK777kLDzxwH2GrHQmCIAhpQTosKSNtVpGkasQSViOavPxZ2b4E57zXcr0ZddX1jL7jPlZebY2iI4nNnzeHCy/8HUcefhAff/Q+YgmrDdsMcddlaZxpzXlZ516C4kyMRixhQmeSVqtI0jRiCasdjVjCKlv3DjvuyKOPTuOII48BwqIjiT373LPsvMv23HzLX8kGxsRQUVuK7RaPlNtmiLsuS+NMa87LOvcSFGdiNGIJEzqdEJxo2ea2SmmqWbdtmlLK5FZtjSGuONOqya3a2r6U5Lxrt0ZGXHQpj4ybzDrrrl/UJrZgwQKGD7+AAw8YwrvvvoMgCIKQfKTDkjJqwSqSBI1YwmpEk5c/K9uXspxvudU2THt8Br8++Qzw6ouOJPb6a68ybN+BXH/dn2laMI+K2FJst3ik3DZD3HVZGmdac17WuZegOBOjEUtYclFKHa2UCpVS23ew3KpKqZuVUh8opRYopbRS6kKlVEOl2pqjlqwiNmvEElY7GrGEdW77ujY2cvZvzuWBB8az0YabFB1JrDmb5eprrmKvgXvw2muvUcjqQG6b2EkSFydx12VpnGnNeVnnXoLiTIxGLGHJRCn1c+D6ZSjXD3gBOAH4HpgI9AQuAaYoperibGdBQnCiZZvbKqWpZt22aUopk1u1NYa44kyrJlr1XNfO9qU45xttvBEPPfwo518wAre+W9GRxN56622G7TeEP/7pMuYvWLDU+0JycT35GSMItYCc6QVQSg0DpgLdl6H4jUA/4EKt9eZa6/2BdYHHgZ2B0+JqZyFq1Spim0YsYenVOI5DY2M93Xt0acnfcr270b1HFxob6xOdz6TlPJOp41cnncqESU+y2RbbFh1JzCHg77ffxqC9d+W5Z2cglrDkxek6Dl0bMyzXM9OSuxV6N7Lccg10bfRwnXTEaYVGLGG1pxFLWHJQSvVTSt0JPAh4wFcdLK+AQcD7wOW57VrrecAvAR84NbYGF6CWrSI2acQSlk5NxnPo2aORbt0b0DO/44axr3D531/ghrGvoGd+R7fuDfTs0UjGc6yNIY05X3udtbnz7vsYcdFldG3sWnQksY8+nsmw/Ybwm3PP5vsffsTsSewktsfpuh49enaha7dG3pr5I9eNfY3LRr/AdWNf482Pvqdb96706Nkd1yPRcVqjEUtY7Wkst4RlqlaznYwEjgBeBo7FWML6dqB8f8ABHtVaB/lvaK1nKqX+A2yllNpIa/1mTG1emtA0grDItkppqlm3bZpSyuRWbY0hrjhToHEc6N6jkbnzmxl5+wu8+9mcVsX/+foXrLdaT35z+JZ079FI04ImwtCuGGLTWNg+z3E54sijGNB/D3577plM/+c/CR2XLAGh08SSjLlrNM9Nf4xr/zyKLbbeaan3BXvInXsLF8xnxO3P8Pan88iEzXj4+PzAU69/w/qr9+aCwzele/cuzJlXvavAgiBUBrnD0pq3gaOAbbTWbyxD+Y2j5X+L7B/gp8uw75IQq4gdGrGEpU/T0FBHJuMx6u5/8+5nc6J3F/8BvPvZHEbd/W8yGY+GhjrrYqiFnK+22k+4a8wDjLrqenr07lN0JLGvvvycww47jDPPOIlvvvoSsZPYGWdjF4/6Oocrx/yb9z/9HpcAx2HxHyHvzvyWP975AnV1GRq7ZBIZp1UasYTVnkYsYclBa/0HrfWdS94d6QCrRMsv2ng/t70jd206hFhF7NCIJSx9mi6Ndbzx3ize+fSH6AeyucCf+8u9fufTH3jjvVl0aayzLoZaybnnuew7bD8mPPoYe+01qOhIYhDyyPjx7Lzr9jzyyCMEYYDYSeyKs75LA2+8N5v3PvmBMIQAlzBk8R8OIQ7vzvyB/747m7qG+kTGaZVGLGG1pxFLWE3RLVrOb+P93PA0y/Iwf7vU1XmsuGIPFjX7NGcD6jIujfUmxQuasi3bGuq8imgqtd8kajpSJgT69OlhXQyS86U10//9aUnn4oz/fMo2G6/Ciiv2sC6GWsp5r+W6cd99dzN14qGcevrpzP5mVotFzAlz3UzDt99+yxlnnsLj0/pz5dXXsPpq5rqSv8gDfxF4Dbh1XQia69p87TV0bbdMEjU2tO+pUs+9Vz9hkw1WYcU+3RIZpy2aytdtfib16dMj5XEmR1NKmdy2aiB3WOIld2cmbON9Z4ll7GSzeTajMM9CkbetUppq1m2bxvb2SZzLpvl+XhPOEidyodffzWuyNoZazPnegwbxwosvcfARRxcdScxzYdq0aWyz1VbcestfCfzm1taVoKn460J2lzRoLGjfD/MW4jiYu2gsbQlzMHfYfpjflOg4rdHY3j6JszrHooqWMLnDEi9zo2VjG+93iZbzKlF5c7PPd9/Ppynr0+wH1HkuXepMihc2Z1u21We8imgqtd8kakot03O5rhCGzJ4917oYJOeLNd261LP88t1Yrlt9y9UIh9ZXJvJf9+5Wj+s6fPvtPOYtbLIihlrPuR9k+P3vL2HPXQdw/m9P55OZH5ENHRzHJRsGNGVDnAx4XsicH+fyq5NO5q4x9zLqij+yxk9WA68ep64LYfNC8JsKvnbrzScgaJqfKk01665rdFh++W706maGEQ+BwHFxQ3N3OoSWuXfCEHp1NbaW775vJrtofmLitE1T6bqX7+kBIbO+mZfqOJOkKaWMWx+yUt9eVAO5wxIvn0fLldt4v71nXOIhjK74hkW2VUpTzbpt05RSJrdqawxxxZlwje8HNGd9dtpiNUphx8370Zz18f3Amhhi1djeviKaX2y/PU89/RzHn/hrsk590Qknn332GQbstRu33fY3sn52qfeFypM793bZol9J+h03+4k594Kgwi0TBKEzkQ5LvORGB9uojfc3jJbLMgJZu4ShGTHHD0P8IMQPwxZ7RP62SmmqWbdtmpLLBPbGIDlvrVm4oJmN11mR9VbrWfQ8XG+1nmy8Th8WLmi2LgbJuXnd2NiVESMuZ8q0x1h3g03A9aKRxBwcN4PnLh5NzG9exNVXXc7BBwzm7f+9BkF28Z/f1Pp1ZKFInabK7WtauIhN1u7Nev264xLk/fnGHsb/s3feYXJTZxf/SZptrhQbhxZMFQmEFiAfzQ1csWmmQ+jVBBswECAUA6Z3bNNMT+jNDdwwzfSaUCMgIWA6GBvbW7wz0v3+0Gg9tnfl2Z2ZnSvpPXnm0dWdc+e+RyczWKujKz8Stun6ndlioy401jdEUqdWnHaYW7nJ0BkZTj5jyhgJkxOW4mJGdruXbdvLHVvbtn8LbAt8WapnsKjsnxBd18P1PP+vu9lQ/Yp9peKUc27dOPmOybj6ahDPl+3XNzTS0JBm5EHbssk6nfHw/3gfvDxgk3U6M/KgbWloSFPf0KidBvF8ec52f9ye6TNmcvIppwEpPLMCz/S3rjLwPDANA8uADz/4gEGD+nPzDdfQUFeHcl08w0S5LsrN+Nvgn845fXHglLu+uro0SxuWcsbBW7Hpup3A8zCV2/RCKTZdfzXOOnRbGhuWUl9bH0mdOnFKPbfnZlBuOvY6o8TJZ4xi5SvR7QW5h6WNyJ6AdAB+dhznZwDHcb6wbXsGMAi4BDg/y+0I3AlYwHWlqsnAAAWWZaLwt0EcYsW+UnHKObdunHzHpJS+GsTzZftKQX3dUjrWVDF2xK78+4v5PPf2PBYsWcrqnarot/36bL7hmtTVp6mvW0qlZWmnQTxfmdOhQw2nn3YGwwYP4a/njObfn3xMRmXAMDENF08pMp6BpxSQYfyt45kzZwYXj72abbffCZWyQFVAysLIfrCR26e86HM0qK+2Pk2n6mouGdGLD79YzItv/4fFS2rp3Kkjvf9ks/Vm3WhYvIjFC5eClcKMqE5tOCWe27RSKFWBkVKx1hkpTj5jgh/PMkBOWNqO+4HewMXAmJz+U4BXgL/Ztr034AA749+/Mh24tVQFBc8k8JTCUwZW9pkFAJZpNPWVilPOuXXj5DvGMg2UqacG8XxlDi7U1tZjofjDJt3YYuNuZDIeqZSJacCSJUuprW0gZZraahDPl+cE378//GFLHn9iChPvnsi466+hrrEBV0FaGaBM0jk35juffc7wA4bz52NO5KzTR9OxKgVGiqZnFBgpsLzsthI8b9l+FDka1Ocpi8W1jVRTye82WYstN16dYKlVz6qiti5D7ZJG/GdFRFenNpxSz21aGJYLhhdvnVHi5DOmjM9hkUhYkeE4zn+BHYF7ge7AnsAC4FxgP8dxMiUvQuFftFMhfaXilHNu3Tj5jAmaumools4YcVBQV9fIwgV1TV0Lfqll4YI66uoamzg6axDPl6M19aWsFMcfeyLPTH+Wnf5vZ5RhkKai2RvzlVJMvP1Whgzqx6uvvoqg9FAK6usbWbhw2aPOflmw7LunVMhggUAQacgVlhA4jtOnje/NA44uQUmrRMb1sv/t9S/ceZ6/l9tXKk4559aNk/cYhf+sCA01iOd5cLL++W1Dv/pKwNG9vlZxcr5/uZwNem7EY09M5R9/v48rrrqC2vp6MDPZq6IeYGCZisoUfPvtPI455nCGH3Aw55w/lq5duxI8twA8cBuX38/etBopjpb1Wfj5+gyGGWedcfXcvz/CiL3OCHHyGSM33QuKhZRlYhig8B+iZWbjD7l9peKUc27dOK0Zg9JTg3jeOv90rU88D+e05F/KsjjqqGOYPuNZ+vTui8quNpZRBh4mGWXQmAHXA1A89PBD9Oq9MzNmzkBholAoTLAql983LDCsaHE0rY+E6Iyr57TjXOJ5kY6FRMIERYXyz4VRIX2l4pRzbt04+YwJmrpqKJbOuHKCpq71iefhnGA3hLPO2uty6213csP141l99TVbjIgB/PDD9xx11GGcOvIkfvrpZwQCgUBQHMgJS8zQXPTB89RyfaXilHNu3Th5j1GQGwnTSYN43jr/tKxPPA/n5OmfYZjstfe+PPfC6wwdtg+ukQLTzEbE/DGWSdNrxjNTGTasP5MnPY7KLMXAwyAbf8k+22C5vmwUQ1uOpvWREJ1x9Zx2nEs8L9KxkEiYoFiQqIgeHImEJYcjkTC96itWJKy5z1lrrW7cdPMt3DJhImt177FSRMz1aHotXLiQ088YxRFHHc63335LWSIeMY/NkBCdcfWcdpxLPJdImEBHKDCy2xb7SsUp59y6cfIZEzR11VAsnXHlBE1d6xPPwznBbis/p9/uuzNr1vMceviRoRExgDlz5tB/QD8efPDveMpb6X2BQCAQrBpywhIzSFRED45EwhLCyfFPy/rE83BOAf516tyFK668nr8/8DjrrL8hmCaGYTbFwsCPiVWmYGlDLZdcchGHHTyc//7HQasYSIRjMyREZ1w9px3nEs8lEibQDBIV0YMjkbDkcCQSpld9pYyENXcsdt55Z6ZMncnRxxyPYfhxsJZWEnvjrTfpt3tvxk8YT9r1KHnEI+axGRKiM66e045ziecSCRPoCAVGdttiX6k45ZxbN04+Y4KmrhqKpTOunKCpa33ieTgn2C3wWNTUVHP+eRcxbepM7M1/HxoTa2hoYOzYi9hv3z355JOPEQgEAsGqIScsMYNERfTgSCQsIZwc/7SsTzwP5xTo34r722y7PTNmvcCo087CrKgOXUnsk48/5ID99+b6666koXYJkY+KSCQsXhyJhCWPI5EwQXtCoiJ6cCQSlhyORML0qq+9I2ErjqmuqmLkqNN54olpbL3VNqEriWXcDOMnjGPg4H688847FD3iEfPYDAnRGVfPace5xHOJhAl0hAIju22xr1Sccs6tGyefMUFTVw3F0hlXTtDUtT7xPJwT7JbgWGxmb8Zjj0/mwjGXkaruHLqS2Geffc4BB+7L5ZdfzJLa2pXeFwgEgqRDTlhiBomK6MGRSFhCODn+aVmfeB7OKdC/VY0xTYtjjzuJadOfY4f/2y10JTHTUNx//30MHbI7L74wh8hFRSQSFi+ORMKSx5FImKA9IVERPTgSCUsORyJhetVX7khYc2N69uzJ3ff+g8suvZpOHTuFriT21bx5HHTwcE47YyQLfl1EQRGPmMdmSIjOuHpOO84lnkskTKAjFBjZbYt9peKUc27dOPmMCZq6aiiWzrhygqau9Ynn4Zxgtx2OhWkYHHzIobw093X6Dxy8ygdOPvzQAwwc0JeZs2as9J5AIBAkDXLCEjNIVEQPjkTCEsLJ8U/L+sTzcE6B/rVlTI8ea3PPPQ9y07jb6NqtR+hKYr/M/5FRI0fwl1NO4Mdvv0HrqIhEwuLFkUhY8jgSCRO0JyQqogdHImHJ4UgkTK/6dIyErcixLJOhQ/fi6amzGTZsn9CVxEDxzPSn6bP7bjz++ON4ykPLqIhEwuLFkUhY8jgSCRO0OxQY2W2LfaXilHNu3Tj5jAmaumools64coKmrvWJ5+GcYLdMx2L1NVbjxhvGc//9j9Bt7fVDI2ILFy7krLNP57jjjuLbb+at9L5AIBDEGXLCEjNIVEQPjkTCEsLJ8U/L+sTzcE6B/hWrvt799mDGzJc48JCjQlcSs0x45ZW5DBzYj3vuvg3PTaNNVEQiYfHiSCQseRyJhAnaExIV0YMjkbDkcCQSpld9UYiENcfp1LkTF465hAcfeIyeG/QMXUmstq6Oc887h332Hcp//vNfWhXpiFFshoToLAtHImHJ40gkTNDuUGBkty32lYpTzrl14+QzJmjqqqFYOuPKCZq61ieeh3OCXY2OxZ/+tBNznnuZk08ZhWtWhsbE3nzzDQYP2YPbbruFdCa90vsCgUAQF8gJS8wgURE9OBIJSwgnxz8t6xPPwzkF+leq+qqrazj//It54sln2MTeMnQlMc9Nc+ON1zJ83z356P13SFJshoTo1DYeJJGweHEkEiZoT0hURA+ORMKSw5FImF71RTUS1hxnq6235rEnJnH66WdhmqnQlcQ+/OgjBg0ZxFVXXUl9w1KSEJuh2HNpqrMsHImEJY8jkTBBu0OBkd222FcqTjnn1o2Tz5igqauGYumMKydo6lqfeB7OCXY1PhaVFZX85ZRRzJr9Iltuu2NoRMx1XW67fQL77DOQt996Y6X3BQKBIKpIlbsAQfGglB8tcJXC9RSG4UcPgOX6SsUp59y6cfIe4/n7rtJPg3jeOv+0rE88D+cU6F97athwk8148NHJ3H//fdx0/dUsWbQAy3CxTAPDtLCUS2VKkTLh66++5NBD9uOgw4/h3HMvpEOFAV4GDBPcRr8d7AcRj9y+FTltGVNyjoFys33FmktLnWXilHxutcy/WOuMECefMRIJExQDKvsnOtf1cD0P1/X8P9k101cqTjnn1o2T75iMq68G8bx1/ulYn3gezinUv/bUgDI49LAjmDRlBrvushuuMvDMCjwzhWdWkHYNTMPAMsA0DO6++y769t2VuS/MQbkZlOviGSbKdZv2FYb/yulbkdOWMaXmeG4G5aaLOpeOOsvFKfXcuf7FWWeUOPmMUax8Zbe9YI0ZM6ZskwuKhqOAnpmMR119Y1OiIGWZVJj+OamrVFOfZRgl4ZTqc6PIyXdMTYcqABobM9ppEM9Xzcn1T8f6xPNwTlVNJdB2/8qlYY01Vufggw5h3fXW59U33qQh7ZL2FCYelqEwMPyrMwoWL17EM09P4btvv2G7HXeiY4eOYCgMBUZFCtOq8P8JojLL+gxzec6K+/mMKTGnY4dKQFGfVsWbS0OdZeOUeO4O1dYy/2KsM1KcfMZYFXTsVJP9JeJL4F7aCXKFJUYIbt60sjdsBtvctmUaJeOUc27dOK0Zk9JUg3jeOv90rU88D+cU4l85NViWyaGHHs70GXPotXt/vGysLa0M0soknXNjfsaDpyZPYuCgPXh6+jMoLLAsMFIEN9ZipJb1WZXh+/mMKTXHTGFYqeLOpaPOcnFKPXeuf3HWGSVOPmPkpntBUaHInhWH9JWKU865dePkMyZo6qqhWDrjygmautYnnodzgt0IH4sePX7DhHG3MX7c7ay55lqkqWjxxvz583/m+OOPYcSI4/jpxx8RCASCqEBOWGIGeT6DHhx5DktCODn+aVmfeB7OKdA/LTQAhmEyeMhQnnvhVfbd70BcIwWmiWH4z26xTH+cZUJlCp6dPYM9hw7giccfRmVvWo/isyoo9lya6tT2mRzyHJZ4ceQ5LIL2REvr+cvzGfQ9FvIclmhz5DksetXXWo7Oz2FpLadbtzW59robmXjHPaz9m7VRyvMjYTnPb2nM+DGxRYt+5ayzR3PQwfvz1bx5RPFZFRR7Lk11loUjz2FJHkeewyJodygwstsW+0rFKefcunHyGRM0ddVQLJ1x5QRNXesTz8M5wW7MjsVuvXozc+bzHH30cSjDCI2JvfTSC/TpszP33j3RX41MIBAINIScsMQMEhXRgyORsIRwcvzTsj7xPJxToH9aaGiB06FjJy677BqemvQMG260SVNMzL953+cEcbGlDXVcdvkYDjvsQD7/zIlMbIZiz6WpTm3jQRIJixdHImGC9oRERfTgSCQsORyJhOlVX5IjYc1xdvq/nXn66VmccPzJmBhkXG+5iFiwkpjrwT//+Q57Dh3IjTffTGPGoykGommUhWLPpanOsnAkEpY8jkTCBO0OBUZ222JfqTjlnFs3Tj5jgqauGoqlM66coKlrfeJ5OCfYjfmxqKqq5ozRZzFp0tNsscXWoRGxdDrN1Vdfzt57DeTDDz9AIBAIdICcsMQMEhXRgyORsIRwcvzTsj7xPJxToH9aaGgF5/dbbsXkp2cy+qzzsCprQlcS++zTf3Pggfty1ZVjqV+yGB2jLBR7Lt0jO7rFgyQSFi+ORMIE7QmJiujBkUhYcjgSCdOrPomEhXNqqqoYMeIvTJo0ne223S50JTHPc7n9jlvpP7APb775JsvFQjSIslDsuXSP7OgWD5JIWLw4EgkTtDsUGNlti32l4pRzbt04+YwJmrpqKJbOuHKCpq71iefhnGA30EACMwAAIABJREFUgcdio4034pFHJ3HZ2Kuo6dApNCb23/9+wUEHD+fii89n8ZLFCAQCQXtDTlhiBomK6MGRSFhCODn+aVmfeB7OKdA/LTQUwDEMk6OPOYHnX3iV3Xr1bXElscqUHxl76KEHGTKoH3OenYEOURaKPZfukR3d4kESCYsXRyJhgvaEREX04EgkLDkciYTpVZ9EwlrP2WCDDbjn3n9wxeXX0LVzl5VWEgsiYqD45ttvOezwgxnxl5P4+ZeFKBQSCYshRyJhyeNIJEzQ7lBgZLct9pWKU865dePkMyZo6qqhWDrjygmautYnnodzgl05FhgY7LvfcGbMfIHBe+4VGhEDePKJxxg0sA/Tp09DrXhQBQKBoMiQE5aYQaIienAkEpYQTo5/WtYnnodzCvRPCw1F5nRfay1uu/0exk+4k9W6/6bZiFjwWrhgPqefPpKTTzyGH7+dh0TCYsSRSFjyOBIJE7QnJCqiB0ciYcnhSCRMr/okElYczuAhQ3hm2mz22++AlSJirrfsYZOgmDV7Fr369uLhhx/GUx4SCYsBRyJhyeNIJEzQ7lBgZLct9pWKU865dePkMyZo6qqhWDrjygmautYnnodzgl05Fs1yuq7WlWuuvoEHH3ySHuv2DI2ILVr0K+eedxZHHXU4X335v5XeFwgEgkIgJywxg0RF9OBIJCwhnBz/tKxPPA/nFOifFhragbNLr948M+NFDj3yWAzToqWHTVomvPHGawwZsju33zYON1Pa+A1F+hwt4ji6cSQSljyORMIE7QmJiujBkUhYcjgSCdOrPomElcbzjp06cN55F/Hww0+wycabtPiwSVDU1Tdw0ZgLGLbXIBznUxQKiYRFjCORsORxJBImaHcoMLLbFvtKxSnn3Lpx8hkTNHXVUCydceUETV3rE8/DOcGuHIu8dW6//Y7MfvYlRp1+Jp5ZFRoTe/fddxk2bCDjxt1EY7pxpfcFAoEgX8gJS8wgURE9OBIJSwgnxz8t6xPPwzkF+qeFhjJ4XllZxdlnn89Tk2ey2RbbhK4k5nkZJky4iX33HsS/3n0DiYRFhCORsORxJBImaE+UOzagc6xB12MhkbBocyQSpld9EglrP8+32HILHnvsSc4861wsqyJ0JbFP/u2w57A9ueyKS6mtq0ciYZpzJBKWPI5EwgTtDgVGdttiX6k45ZxbN04+Y4KmrhqKpTOunKCpa33ieTgn2JVj0WadKSvFySeewuw5c9lm+51DI2Ke53HnxDvYe+8BvP7aKyu9LxAIBC1BTlhiBp1iA7rFGrQ8FgokEhZhTo5/WtYnnodzCvRPCw2aeL7BhhvzwCNP8bcLL6djpy6ErSQ2b97X/PnPB3L2WaNY9OsCJBKmIUciYcnjSCRM0J7QMTaQRI5EwpLDkUiYXvVJJKx8nlumySGHHc70Gc/St0/f0JXEXA/u//t99Oq9M88++ywKhUTCNOJIJCx5nKRHwmzbHmzb9pO2bX9k2/bbtm3faNv2hqsYM9e27Uypa4stFBjZbYt9peKUc27dOPmMCZq6aiiWzrhygqau9Ynn4ZxgV45FUXWuu856PPDg49w87nY6rdY9NCb2/fffcfwJRzN69Eh+nj9/pfcFAoEASnzCYtv2GGAasDfwO2A74FTgY9u2z1jF8JV/2QShUMq/dO8qhespXKWaLt3n9pWKU865dePkPcbTV4N43jr/tKxPPA/nFOifFho09VwpGL7/QTw940X6D94HTCt7FcbAMFNY5rLVxFImzJw+jSEDezP5yYdRbga87CsbS2na9zLgNoKXWcYL4eTzOQWNiSunHeZu8i/mOiPDyWdMHCNhtm33AS7EP/GYAZwBnAv8E6gCrrFt+37btst3fSlmUNk/b7muh+t5uK7nH/1m+krFKefcunHyHZNx9dUgnrfOPx3rE8/DOYX6p4MGnT1ffY01uPraGxg37k7WWKMHnlmBZ6bwzApcZeB5YBoGlgGLfl3IKaeMYMTJR/HDt1+jXBeF4b9cF+VmUK6LZ5h4bgblpkM5ufv5cNoyJq6cUs+d61+cdUaJk88YhVGaf8DmgVQJP/sv+BeKr3Ac5/yc/qts2z4BuBE4DOhq2/aBjuMsLWEtiYCBAQosy0Thb4NL9Sv2lYpTzrl14+Q7JqX01SCet84/HesTz8M5hfqng4YoeD5w0AB67foKl11+CU89+TgZlQHDxDRcPKXIeAaeUqRSMOe553nnrbc4/czzOOSI4zBNEyNlgaqAlIWhPEyrCqUqMFL+P6uAlTgqdz8fTlvGxJVT4rlNK7XMvxjrjBQnnzHBF7sMKGUkbCdgCTBmxTccx7kD6AX8CAwFnrFtu2MJa0kElt346N8QGWxz25ZplIxTzrl147RmTEpTDeJ56/zTtT7xPJxTiH+6aIiK52ussTpXXHktE+/6B+uusz5KebgepJVBWpmkszfmZzxYtGQJf7vgPPY/YB+++N8XYKTAsrLbSjBTGFbK38/eHLwSJ3c/H05bxsSVU+q5c/2Ls84ocfIZU8ab7kt5haU78L7jOJnm3nQc523btncBZgN9gGdt2x7kOM6vJaxplbBtew/gPGAroBJ4B7jScZyZeY5fH/gqhPKK4zi7FlxoGBT+RTsV0lcqTjnn1o2Tz5igqauGYumMKydo6lqfeB7OCXblWLSrzp123pnpM+Zw4/VXMHHibdkb8k0yeCijkVy8+uor9O27K+eeeTZHH3kkqfL9e0kgEJQRpbzCUguEXjVxHOe/wC7Ax8CfgJds2+5RwppCYdv2UfgnUDsDbwKvZeubkY2x5YNts9v3gQeaeeV14tNW6L5Wf1I48hyWhHBy/NOyPvE8nFOgf1poiKjnNR06MGbM5UyZMpNNN9sc10iBaWav1PgcK3tTfrqxgauvvpRDDtmPf3/yUdPzIkCeyRHl55PQjnOJ5/IcljB8Amxi23bXMJLjON/jx8PeBP4AzAXWKmFdzcK27bWB24Bfge0dxxniOM5A/BOWRcBNtm2vm8dHBScsVzuOc3gzr0tLo8BHFNbqTwJHnsOSHI48h0Wv+lrLkeewlFfnDjvsyOQp0xlx8qlYhknG9ZZ7dovr0fT64IP32WuvIVxz3XU0LE3jf1I7P4ciKRx5DkvyOAl+Dsuz2c8/aFVEx3EWALsDzwObZF/tjVPxVy+7wXGcD3Nqewu4GqgG8rnKEpywvFP0CvOFAiO7bbGvVJxyzq0bJ58xQVNXDcXSGVdO0NS1PvE8nBPsyrEoq86qyipGjjqdKVNmsNVW25GmosVnt2TcDDfccA277bYLb7/9NgKBIBko5QnLJPzfpdG2ba9yHsdxaoHBwOTsuPbGoOx2UjPvPZXdDs7jc7bFX2zg02IU1VrELTYQVY5EwhLCyfFPy/rE83BOgf5poSFGntu/+z1PTZ3O2edeQEV1RzBNDMNsioaBHxOrTMFnn37K4MF7cvllF1G7ZDESD4peVIqE6IwMJ6mRMMdx3gX2AEYAHfIc0wgMBw4FjilVbSvCtm0D+D3g4UfZVsSn2fe2yHJb+pw1gN9m+WfYtv0v27brbNv+1rbtO2zbXqcE5S+HuMYGosaRSFhyOBIJ06s+iYRF2/PqykpOPOFkJk+ezp92+FPTSmK5MbHGjB8RU8rjzrvupG+/XZn78lwkHhStqBQJ0RkZjuaRMEMpVbbJdUH2RGM+8JPjOM3eP2Pb9g/499Z0dRxnUQuc3fGjcABp4EWgEdgBf9W074E+juM4xVXAC0Bv1/P/erU07ZLOeFSkTGoq/YXg6hszTX1VFVZJOKX63ChydK9PdMqxEJ1yLHTXaZnwwH33cvbZZ1PXsJSU5d/j4mUaqbag0oJGFxoy/r9jTjj2CMaOGcNqa3bHrKjGSzeAuxSsKqwq/++m7tK6pr4VOW0ZE1eO7vWJzvIcC6uqA4bZdK3jRaAP7YSiXGGxbXv1AsePKEYdBSBYzawuhFOf3XYK4QT3r3wE2I7j9HccZ09gQ+Ah4Df4K4WVDJlMTkxFNRN9UKXjlHNu3Ti61yc65ViITjkWuus0DJPjjj+e9z/8kAGDh4SuJGaZ8MDf/84uu+7CM09PA6/llcSa+lbktGVMXDm61yc6y3MsyhgJK9ZzWN63bftwx3FebM2gbETqHvzo2C1FqqUt8LJbFcIxVtg2hxuAJ4DFjuP8HHQ6jlNr2/Zx+Kuh/dG27f9zHOf1QgpuDum0y4KFdTRmXNKuR4VlUl3hW9yQzjT1VaasknBK9blR5OQ7pstqHUAp5s9fop0G8bx1/ulYn3gezunUtaYg/3TQkATPa6q7csuEiUyaPInLLrmA+T99j5U9ockoD9fz/9PtevDD999z8CGHMmjPvbn4wovpvkZXsCoxK32O11gHbiNYlRgV1ah0Q4v7+YyJK6fUc6/RxQIUP/1cG2udUeLkM8asVKzVI3Tx35KhWPewrIv/4MfLbNvOK+Bm2/ZhwIdA/yLVUAiWZLc1IZzq7La2JYLjOK7jOF/knqzkvFcHPJfd/WObqswXKntWpUL6SsUp59y6cfIZEzR11VAsnXHlBE1d6xPPwznBrhwL7XUaGAwbtjczZ73IXvvsF7qSGMDkyZMYOLAPU6Y8hVrRdIFAEDkU86Z7EzgHeMW27Y1aItm2vYZt248B9wPBadr8ItbRFizCP2npZtv2Sledsn3dgAbHcRYWMM/32W1eixC0BUlYSSYKHFklLCGcHP+0rE88D+cU6J8WGhLm+Rprrsm48RN58JFH6b72eqEriS1etJCzzx7Nccf8mW++/hJZMaoVHFklLHmchKwSdhz+P/gN/BvM/2nb9pErkmzbHop/VWW/LNcAHgG2KFIdbYLjOAr4GLCAzZqh2PjH6oOwz7Ft+yLbth+3bfsPLVA2zG6/bmutq0JSVpLRnSOrhCWHE/ina33iuawSFkfP9xwymFdefpmDDjqMsJXEQPH8C8/Tq/cu3HPvPbjK/xxZMar8q2eREJ2R4Wi+SlhRTlgcx7kb2Bp4Bf8kpBNwt23bD9m23dW27U62bd+F/4yVHlnON8BejuMc4jjOT8Woo0DMyG73aea9oO+ZVXzGVvjLMh+44hu2ba8FDMBfPez5NtaYH5R/gFEhfaXilHNu3Tj5jAmaumools64coKmrvWJ5+GcYFeORfR0Al26duXyy67iicenskHPjUJjYrW1Szj33DM59OD9+N///otAIIgWihYJcxznf/g3lZ+Hv5Svgf8P938B7wNHZfsAbgN+7zjOtGLNXwTcAzQAf7Vtu+keE9u2twfOxl8l7Jac/o1t297ctu3cu49uz25H27a9Sw63E3A30AW403Gc7ykRJDagB0ciYQnh5PinZX3ieTinQP+00JBUz3O822nnXXn2uZc59riTUVZF6Epi77zzFnvtNZjbb5tAZmk92sRxdONIJCx5nIREwgA/WuU4zpXAjsA/8U9Qfgv0zLY/AXo7jjPCcZzFxZy7UGRPuEbjn1S8Ztv2dNu2ZwCvAp2BExzH+TFnyBx8PfvmfMYs4Hr8m/dfsm37Jdu2nwS+APYE5gJnllKHxAb04EgkLDkciYTpVZ9EwpLjee5vZ6eOHTnvbxfw0ENPYG9qk3G95SJirkfTq7Gxkauuvpyhew3m448/QaEoexxHN45EwpLHSUIkrBn8AnyZbasVXo0lmrNgOI5zCzAMeB3YDf9+nJeB/o7j/CPPzxiNf2XpFfznsgwCvsO/SrN7drWw0kL5Z4eokL5Scco5t26cfMYETV01FEtnXDlBU9f6xPNwTrArxyJ6OoPmCpytt96GSVOmc8aZ50CqJnQlsffff5+99x7CDTdcS8PSpSu9LxAI9EGxnsMCgG3bBjAKuIRlD2NMAz8D6wC/w19F7GbgfMdx6pv9oDIiG1NbZVTNcZyeIe89BjxWxLLyRnOX04Hl+krFKefcunHyHqMgNxKmkwbxPA9Ojn9a1lcCju71tYpToH9aaGgHjpb15Xi3IqeiopJRp53FgIHDOPf8s/nXe+9gGCaW4ebExBSVKVAqw+2338KMWTO4/Mob2X67bfBPjZbFX4L4EjmxmdhzSj63gWL5KFI8dUaIk8+YOETCbNveEv/KxHX4N90b+Peu7IB/onJ/ts8CTgM+sG27b7HmF/iQ2IAeHImEJYcjkTC96pNIWHI8X9Vvp725zUMPPc55f7uQ6qqq0JXEPvv8P+y97zDGXHIhi5fUomVkR7d4kETC4sVJQiTMtu1LgXeA7Wk6DeMqYAfHcT5wHGex4zhH4d/v8WOWsxH+wybvsG27SzHqEGSh/AOMCukrFaecc+vGyWdM0NRVQ7F0xpUTNHWtTzwP5wS7ciyipzNoruJzLNPi2KNP4LnnX2GXXXuFriSmlOK+e+9hr70GMfelFxAIBPqgWFdY/oYfLzOA/wC9HMc513GcdC7JcZzJwJbApJzuY4GPilRH4iEryejBkVXCEsLJ8U/L+sTzcE6B/mmhIame53iXz+dssMGGPPLoZC6/4jo6dF692ZXEKlP+SmLffvs1xxxzGKefdjILF8xHm1WcdFsxSlYJixcnQauEGfjL+m7tOM6rLZEcx/nZcZz98Jc5XpQdt04R60g0JDagB0ciYcnhSCRMr/okEpYcz1v722lZJgcdfAjTnp7F7v32WGklsdyImOvBQw8/RK/eOzNjxnQUirJHdnSLB0kkLF6cJETCgG+BQY7jnJzvKliO49wP/AF/eWBBMaH8s0BUSF+pOOWcWzdOPmOCpq4aiqUzrpygqWt94nk4J9iVYxE9nUGzDZ/To0cPbr/jHm699W66rPmb0JXEfvzxB04ecQIjR57CTz/+uNL7AoGgfVCsE5Y/ZJ9B0io4jvO14zj9gZFFqiPxkNiAHhyJhCWEk+OflvWJ5+GcAv3TQkNSPc/xri2fAwZDhu3NjJkvMWTY8GYjYrmvWbOmM2hQHx5/5O/4MRlNYz0RikqREJ2R4SQhEuY4zoICx08oRh0CiQ3owpFIWHI4EgnTqz6JhCXH82L8dq6x5upcfe0N3HnHfay11totPmwSFAsW/srI00Zx5FGH8fU389Ay1hOhqBQJ0RkZTkIiYQKdoPy/H6FC+krFKefcunHyGRM0ddVQLJ1x5QRNXesTz8M5wa4ci+jpDJpFmqtvv9157sVXOfDwY0MjYgBz585lzz0Hct+9d+Epr1mOQCAoLor64EhBeaGUfxncVQrXUxiGfwkcWK6vVJxyzq0bJ+8xnr/vKv00iOet80/L+sTzcE6B/mmhIamel+C3s6ZDJy66+AoGDNmbC887my/+42QfNmlgmBaWcqlMKVImZBrruWzshUyeNo1rr7uJjdZfB7wMGCa4jX472A9iNLl9unNKPrdCudn+WOuMECefMVGPhAn0gMr+qch1PVzPw3U9/09HzfSVilPOuXXj5Dsm4+qrQTxvnX861ieeh3MK9U8HDUn1vBS/na7rsd122zNpygxOHnEqyrDwzAo8M4VnVpB2DUzDwDLANAzeePMNdt+9F7ffcjONDQ0o18UzTJTrotyMv8XwXzl9unNKPbfnZlBuOvY6o8TJZ4zCKP4/XvOENWbMmLJNLigajgJ6ZjIedfWNTVe6U5ZJhemfk7pKNfVZhlESTqk+N4qcfMfUdKgCoLExo50G8XzVnFz/dKxPPA/nVNVUAm33TwcNSfW8FL+dufvVVZXs3qcfu+8+gLfeeZeff1lI2lOYeFiGwsDwr84o8DyXt958jZdffpEtt9qGHj3WBkNhKDAqUphWhf/PPJVZ1meYenNKPHeHagtQ1KdVrHVGipPPGKuCjp1qst8SvgTupZ0gkbAYIbiJ0FMKTxlY2ZsMASzTaOorFaecc+vGyXeMZRooU08N4nnr/NOxPvF81ZxC/NNFQxI9L8VvZ3Njtt12O56a/Ax33HE742++jqWNDaQNA5RJWnm42ahZxoMPP/6Yffbdm2NO+AunjRxJTYUFRoqmG5WNFFhedlsJnrdsXzdOqec2LQzLBcOLt84ocfIZIzfdC4oKRfasOKSvVJxyzq0bJ58xQVNXDcXSGVdO0NS1PvE8nBPsyrGIns6g2Q46KysqOenkU5g2bRbbbbc9aSpavDHf9VzGj7+RoXvuzttvv41AICgO5IQlZpC1+vXgyHNYEsLJ8U/L+sTzcE6B/mmhIame53jXXjo33nQzHp/0DOdfcAlVHTqDaWIY/rNbLNPnWCZUpuCrL//H4YcfyMVjzmPJ4l9Bp+dtaPJ8EhKiMzKcJDyHRaAPUpas1a8DpzVj5Dks0ebIc1j0qq+1HHkOS3R1Fvu3M58xVRUVHH3McUyZMoNddtoVpTxcj+We39KYAdcDUNx3/3306bsrz7/wHNo8b0OT55OQEJ2R4chzWATtDgVGdttiX6k45ZxbN04+Y4KmrhqKpTOunKCpa33ieTgn2JVjET2dQbNMOtdbf33u+/tD3HD9eDp3WS00Jvb11/M45JADGH3mSH75paDnbAsEiYWcsMQMEhvQgyORsIRwcvzTsj7xPJxToH9aaEiq5znelUsnGBx08GG8+NLr7DFwCK6RAtPMLgrgc4K4mGXClElPMGzYAGZMn4LKLCXy8SCJhMWLI5EwQXtCYgN6cCQSlhyORML0qk8iYcnxvByRsOY4a6+9NrfdOpGbbprAGquvScb1louIuR5Nr/nzf2bEKSdz3AnH8uOPP6FQRDYeJJGweHEkEiZodyj/bz+okL5Scco5t26cfMYETV01FEtnXDlBU9f6xPNwTrArxyJ6OoOmJjoNDAYOHMysWc8z/ICDQyNiANOnP8Me/fvwxBOPolb8P6VAIFgJcsISM0hsQA+ORMISwsnxT8v6xPNwToH+aaEhqZ7neKeTztVWX4Prb5jAPfc+SI91fhu6klhd7SL+9rdzOPLPh/DVl/8lcvEgiYTFiyORMEF7QmIDenAkEpYcjkTC9KpPImHJ8VyXSFhznF69+zBt2iwOO/zIVa4k9vIrc+nTd1cm3nkHGU/h/y8C8SCJhMWLI5EwQbtDgZHdtthXKk4559aNk8+YoKmrhmLpjCsnaOpan3gezgl25VhET2fQ1Fhnh44duGTMZUye9AwbbbxpaEysrq6OCy44l4MO3IfPP/8MgUCwPOSEJWaQ2IAeHImEJYST45+W9Ynn4ZwC/dNCQ1I9z/FOd5077LgTs+fM5eQRo8CqDF1J7F//fJd9992TCeNvpLG+Fq3jQRIJixdHImGC9oTEBvTgSCQsORyJhOlVn0TCkuO5zpGwFTkdamo486y/8uijk/j95luEriSWTqe57vprGDJ0IO+//z4KhZbxIImExYsjkTBBu0OBkd222FcqTjnn1o2Tz5igqauGYumMKydo6lqfeB7OCXblWERPZ9CMmM4tttySJydN45zzLsSs7Bi6ktjHH3/MvvsN4+qrr6C+oWGl9wWCJEFOWGIGiQ3owZFIWEI4Of5pWZ94Hs4p0D8tNCTV8xzvoqYzlarg5BGjmPr0HLbabsfQlcQMPO6+eyJDh/TjtVfnolU8SCJh8eJIJEzQnpDYgB4ciYQlhyORML3qk0hYcjyPUiSsOc7Gm2zMPx58lIsuuJSa6prQlcS++N+X7LvfMM4+5ywWLalFoSh7PEgiYfHiSCRM0O5QYGS3LfaVilPOuXXj5DMmaOqqoVg648oJmrrWJ56Hc4JdORbR0xk0I67TMkyOOPJoXnjxVXr36bfKB07ef9/dDBrYjxdeeH6l9wSCOENOWGIGiQ3owZFIWEI4Of5pWZ94Hs4p0D8tNCTV8xzv4qBzvfV+ywMPPsHV19xE59XWDF1J7McfvuWkk47ljNNPYf6PPxDVqBTtOJdEwiQSJtAMEhvQgyORsORwJBKmV30SCUuO51GPhK3IsSyT4fsfwNSpsxk4aEjoSmKgmDR5En367crUqVPwlEfUolK041zl1BkZjkTCBO0OBUZ222JfqTjlnFs3Tj5jgqauGoqlM66coKlrfeJ5OCfYlWMRPZ1BM4Y6u6/VjVvG38HEiX+na7d1QiNi8+fPZ+SoUzjllBP58fvvVnpfIIgL5IQlZpDYgB4ciYQlhJPjn5b1iefhnAL900JDUj3P8S6uOgcMHsKs2S+xz/BDQlcSs0x47rlnGTCwLw/84x6UlyEKUSnacS6JhEkkTKAZJDagB0ciYcnhSCRMr/okEpYcz+MWCWuO03W1roy9/CruvftB1lt3vdCVxBYtXszoM0/ngAP35csvv0Kh0DkqRTvOVU6dkeFIJEzQ7lBgZLct9pWKU865dePkMyZo6qqhWDrjygmautYnnodzgl05FtHTGTTjrjO7v1uvXjz/wqsce/xJZIzK0JjYyy/PZdDg3bnnnrtwvfL9RVwgKCbkhCVmkNiAHhyJhCWEk+OflvWJ5+GcAv3TQkNSPc/xLtY6c/Y7dOjIJZdcycOPTabnxnboSmLpxgauuuoyDjpgL5yP30fHqBTtOJdEwiQSJtAMEhvQgyORsORwJBKmV30SCUuO50mIhDXH2f6P2/PkU9MYMeJUyEbDWlpJ7N33/smAQf258cYbWNrYiE5RKdpxrnLqjAxHImGCdocCI7ttsa9UnHLOrRsnnzFBU1cNxdIZV07Q1LU+8TycE+zKsYiezqAZd50tcKqqqhh9xl95Zvoc7C23C42IpdNpbrr5evbffyjv//Pdld4XCKKAVLkLEBQPSvmXkF2lcD2FYfiXmIHl+krFKefcunHyHuP5+67ST4N43jr/tKxPPA/nFOifFhqS6nkJfju11LkKjv37LXn0yWnceddEJtx0PfW1GSzDxTINDNPCUi6VKUXKhP9+/hkHHLA3fz7mJM4881yqLQ+8DBgmuI1+u6X9IApUNI5Cudn+ks8Vwinn3Lpx8hkjkTBBMaCyf4pxXQ/X83Bdz//TTDN9peKUc27dOPmOybj6ahDPW+efjvWJ5+GcQv3TQUNLVVo/AAAgAElEQVRSPS/Fb6eOOlfFMTA5+ujjmTRlBn/a8U+4ysAzK/DMFJ5ZQdo1MA0DywADxa23TqBfv51549WXUW4G5bp4holy3Rb3FYb/KhLHczMoN90uc4Vxyjm3bpx8xiiMov67tTWwxowZU7bJBUXDUUDPTMajrr6x6cpxyjKpMP1zUleppj7LMErCKdXnRpGT75iaDlUANDZmtNMgnq+ak+ufjvWJ5+GcqppKoO3+6aAhqZ6X4rdTR52t4XRbc00OPeQwunfvwWtvvEVjxiPtKUw8LENhYPhXZxQsWvQrU6c8yS/z5/PHHXeiproGDIWhwKhIYRrm8vtWhf9PVZUpCqdDtQUo6tOq5HOFcso5t26cfMZYFXTsVJP9fxxfAvfSTpArLDFCcJOelb0xL9jmti3TKBmnnHPrxmnNmJSmGsTz1vmna33ieTinEP900ZBUz4v926mrztZpsDj66GN5Zsaz7LJbH7xs3DGtDNLKJJ1zY37Gg4cffZiBg/Zg9pw5YKTAsrLbyuX3szdgF41jpjCsVPvMFcYp59y6cfIZIzfdC4oKRfasOKSvVJxyzq0bJ58xQVNXDcXSGVdO0NS1PvE8nBPsyrGIns6gGXedbeSsu8563Hb7XVx37ThWW20N0lS0eGP+Dz98z5FHHsaoUSfx8/xfEAh0hJywxAyyVr8eHHkOS0I4Of5pWZ94Hs4p0D8tNCTV8xzvYq2zAI5hmOyz734898LrDNlzb1wjBaaJYfjPbrFMf5xlQmUKnnl6KsOG7sHUKU+iMkuR57AkjCPPYRG0J1KWrNWvA6c1Y+Q5LNHmyHNY9KqvtRx5Dkt0dSb1OSyt5fTo0Z1x429l/Ljb6d5tLZTy/EhYzvNbGjN+TGzBggWMOu1UjjrmCL777jsUCnkOS0I48hwWQbtDgZHdtthXKk4559aNk8+YoKmrhmLpjCsnaOpan3gezgl25VhET2fQjLvOInL26N+fWbOe59BD/4wyjNCY2OzZs+k/oB8PP/wAnvIQCMoNOWGJGSQ2oAdHImEJ4eT4p2V94nk4p0D/tNCQVM9zvIu1ziJzOnfpyrXX3syjj05mvfV7NsXE/Jv3fU4QEWuoX8KYMRdw+KEH8MV/P0UiYTHnSCRM0J6Q2IAeHImEJYcjkTC96pNIWHI8l0hY2zm9evVm+vRnOfLIYwB/JbHmImKgeP2N1+nbrxcTbplA2vVQKCQSFkOORMIE7Q4FRnbbYl+pOOWcWzdOPmOCpq4aiqUzrpygqWt94nk4J9iVYxE9nUEz7jpLyOnQoQPnnns+jz82mc02+11oRKyhoYFLL72Q/YcP5d///gSBoL0hJywxg8QG9OBIJCwhnBz/tKxPPA/nFOifFhqS6nmOd7HW2Q6cbbbdnqdnzOHUkaMxUlXNRsSC10cffsD+w/fihuuvZmndEiQSFiOORMIE7QmJDejBkUhYcjgSCdOrPomEJcdziYQVj9OhpobTTh/N449P5Q9bbrVSRMzNPmzS9SDjZhg3/iYGDdmD9957F4VCImEx4EgkTNDuUGBkty32lYpTzrl14+QzJmjqqqFYOuPKCZq61ieeh3OCXTkW0dMZNOOus505m/9ucx5/YgrnX3gpVlWnFiNiAI7zKcP334crrryU2rq6ld4XCIoJOWGJGSQ2oAdHImEJ4eT4p2V94nk4p0D/tNCQVM9zvIu1zjJwLCvF8SeM4Onpz/HHHXeBkIdNmobivnvvYc/B/XjpxeeQSFiEORIJE7QnJDagB0ciYcnhSCRMr/okEpYczyUSVlrPe264Iffc/wBjL7mKjh06tviwSVB8NW8eBx60H6ePHsXCRYtRKCQSFjGORMIE7Q4FRnbbYl+pOOWcWzdOPmOCpq4aiqUzrpygqWt94nk4J9iVYxE9nUEz7jrL7LllmBxy6GG8NPd19ug/MHQlMYCHHvwHAwf0ZdbsWSu9JxAUAjlhiRkkNqAHRyJhCeHk+KdlfeJ5OKdA/7TQkFTPc7yLtU5NPP/Nb9bh3vse5oabbqHLmmuFriQ2/+cfGHnqSYw89SR+/O5bJBIWEY5EwqIF27b3sG37Odu2f7Zte5Ft28/btj2wlZ+xmW3bD9m2Pc+27Trbtt+3bfsvtm2X/HiX+xKyzpe4dT0WEgmLNkciYXrVJ5Gw5HgukbD29dyyTPbaax+enjqboUP3IuO2vJIYKKY9PZW+u+/Gk08+iac8JBKmOUciYdGBbdtHAbOBnYE3gdeAXYAZtm2fkOdnbA28BRwMfAnMANYHxgH3F7/qZqDAyG5b7CsVp5xz68bJZ0zQ1FVDsXTGlZNtWqapZ33ieTgn2JVjET2drPDdi6tODT1fY83VuenGW7j33odZ8zfrh0bEFixYwOgzR3HCCcfw3bdfr/S+QJAv5IQlC9u21wZuA34FtnccZ4jjOAPxT1gWATfZtr3uKj7DwD8p6QL82XGcXR3H2Q/YDHgfOMy27eGl1KHTJWTdLnFreSwUSCQsWhzDMKipqaRT5+om/1ZbvSOdOldTU1MZaT8T53nO9y/xxyICOlv67nXpWkN1dUX2X9fR16kDJ58xfffoz4yZL3LAwUeGriRmmTB37osMHNiPe++5A89NI5EwDTkSCYsMTgWqgBscx/kw6HQc5y3gaqAaWNVVlv7AVsALjuP8I+czfgJGZHdHFrPoFaHbJWSdLnHreiwkEhYdTsoy6NK5ho6dqnC+WsD4R97j8nveYPwj7+F8tYCOnaro0rmGlGVoq0E8l0hYFHWGffc++d8vdO5czWpdO2CaRFqnLpx8x3Tu0pmLLr6UB/7+GBv8doPQlcSW1NZyzrlns+9+w3A++xz/kyIQg0oKR/NIWKpsM+uHQdntpGbeewoYCwwGLmrLZziO84pt2z8Cu9q23dlxnMWFFBsKlf1DkwrpKxWnnHPrxslnTNDUVUOxdMaAYxjQqXMNS+rSjL37DT77ZtFyw+e+/x2brtuFMw/fnk6da2isb0QpvTQUjaN7fflygl05FlrrXNV37+X3v8NefzXOPmJ7Oneqoa62IZI6teO0Ysz/7bQTzz3/CtdccwV33nUHyjDI4KGMRlbEG2+8zs477cz55/6Vgw8/joqKlSgCwUqQKyw0Rbl+D3jAJ81QPs2+t0WW2xK2yG4/bOF9B/+Y/76Npa4Sul9CTgpHImHx41RVVZBKWVz7wDt89s2i7LvLXgCffbOIax94h1TKoqqqQjsN4rlEwqKoM5/vnjNvIVfe9xYVlSmqqysjqVMnTlvGVFfXcMEFl/DY49PYxN4idCUxN9PI2LFj2X+/oXz0/rtoHYNKCkciYZHA6vhxsPmO46z05wDHcTLAz0AHoHPI56yd3X7XwvtBf4821rlKROESchI4EgmLH6e6poIPPv+JT7/+Nfsfaf+PjMEr2P/061/54POfqK6p0E6DeC6RsCjqzOe7B+Bkv3tV1alI6tSJU8jnbrPttjz2xGRGjRqNYVihK4m9/8EHDBoykGuuuZr6hqVoGYNKCkciYZFAx+y2LoRTn912wr8Jvy2fk/sZRUdFhUX37p1ZmnZJZzwqUiY1lb7F9Y2Zpr6qCqsknFJ9bhQ5rRmjgG7dOmunQTxfmfPiO/mtcvPSu1/zpy3Wpnv3ztppEM+X5wTfPzkWeuvM97s395/fsK29Vpv+W6iDTl04hX9uRy655EKOOPxATjrxJN57772miJihck8zwXVdbrl1HC8+P52bxt3CTr36+f1LLXCXglWFWVGNl65o2reqOhSFU6rPjSInnzFBXzkgV1h8eNmtCuEYK2zb8jn5fEZByGRyYkaqmeiDKh2nnHPrxtG9PtHZNs7C2sblYihGzit3f0Fto7YaxHM5FlHUuarvXtD3a21jpHXqwinW59r275g5ew4Xj72Cmg4dCVtJ7LPP/sOggQM5bdRIliz+leVWEvMal99v7gGUbeGU6nOjyMlnTBkjYXKFxceS7LYmhFOd3dYW8Dn5fEabkU67LFhYR2PGJe16VFgm1RW+xQ3pTFNfZcoqCadUnxtFTr5juqzWAZRi/vwl2mkQz5dxOlZXssYaHVmtY2XTXyMMlv/LRO7+6h0rMU2DX36ppbahUQsN4vnKnE5da5q+f0k/FrrqzPe7R7ava8dKDAMWLaqnfmk6Mjp14xT7c4cfcBg77bQb559zBi/PfZGMMjAMk4zyaMwojBRYliLjwbjxE5g0eSrXXnElu+2yM1iVGBXVqHQDuI1gVWJW+v8P8BrrmvrawinV50aRk88Ys1KxVo+ulANyhcXHIvyTjW62ba90Epft6wY0OI6zMORzvs1uf9PC+6u6x6U4UNkfcBXSVypOOefWjZPPmKCpq4Zi6Yw4x3U90hmX3n8MfRRTE3pttx7pjIvretpoKCpH9/ry5QS7ciy01dna795u26zb8ndPY51acor8uRv8tiePPjaZa68bR03nNUIfODlv3lccedRhnHvumSxYuGCl9wXJg5ywAI7jKOBjwMJ/yOOKsPGP1Qer+KhgdbCVVgHLri62OeBm5yo6lPJX7XCVwvUUrlJNl2hz+0rFKefcunHyHuPpq0E8X57TUJ9mi427s+m6XUK/h5uu24UtNu5GQ31aOw3i+Qocr7DP0UJDAjzP97u32bpd+N2Ga9JQn46kTp04pfpcpeCQQ//Ma2+9zR6Dh4JpZVcSMzDMFJa5bDWxlAlTJz/JkIF9mT71KfAyy17ZuNJyfW5j6zltGRNXTj5jZJUwLTAju92nmfeCvmcK+Iydge7Ay6V6BovK/hnDdT1cz/P/wpQN+a7YVypOOefWjZPvmIyrrwbxfNl+fUMjDQ1pRh60LZus0xkP/w+IwcsDNlmnMyMP2paGhjT1DY3aaRDPV+bkfv+Sfix01bmq754CNluvK6MP+yMNDWmW1DZEUqdunFLOvdZaPbjzrnu47oYJrLZaNzyzAs9M4ZkVuMrA88A0DCwDFvzyM8efcCyjRp7IT99/j3JdFIb/cl2Um0G5Lp5hLrefD6ctY+LKyWeMwij8H6tthNzDsgz3AGcDf7Vte6bjOO8A2La9fba/HrglINu2vTFQAXznOM6v2e4XgY+A/rZtH+84zsQst3vO2OtKJcDAAAWWZaLwt8El2RX7SsUp59y6cfIdk1L6ahDPl+0rBfV1S+lYU8XYEbvy7y/m89zb81iwZCmrd6qi3/brs/mGa1JXn6a+bimVlqWdBvF8ZU7u9y/px0JXnav67vXfcQO23rQ7i2qXsuDXWkwzmjp145R67goFw4YOo+9uvbjkkgt4+umpZFQGDBPTcPGUIuMZeEqRSsH0GTN4643XOPOvFzL84CMwDAMjZYGqgJSFoTxU7n52slBOW8bElZPPmMDAMkBOWLJwHOd/tm2PBiYAr9m2PQf/bwH98I/TEY7j/JgzZA6wAXA0cG/2Mzzbto/JvneHbdvH4t/X0gf/WS8THceZWioNwbronlJ4ysDKrokOYJlGU1+pOOWcWzdOvmMs00CZemoQz1fm4EJtbT0Wij9s0o0tNu5GJuORSpmYBixZspTa2gZSpqmtBvF8eU7w/ZNjobfOsO9eyjSor29kyeJ6lIKUFV2dunDa87vXvfuaXHfDOPYcti8XnH8238z7H66CtDJAmaRzbsz/ZeGvnHn2aB6fMpVrrrmRdXt0B8vDv2u/Ejxv2X7wzBAj1TKnLWPiyslnTBmfwyKRsBw4jnMLMAx4HdgN2AF4GejvOM4/8vyMN4E/AU8AmwIDgC+Bk4CTS1D2ylD+mdZyJ8Ir9pWKU865dePkMyZo6qqhWDpjxEFBXV0jCxfUNXUt+KWWhQvqqKtrbOLorEE8X44mxyIiOlf13VMx0akNp52/e71692HmzOc58shjUIZBmooWb8x/4YXn6d17Z+679y48z0MQf8gVlhXgOM40YFoevJ4h730M7F/EsvJGxvWy33//wp3n+Xu5faXilHNu3Th5j1H469VrqEE8z4OT9c9vG/rVVwKO7vW1ipPz/Uv8sYiazpzvnpX9x2wsdZaJU67vXsdOnbniiuvYe5/hnHX2mXw5bx6YmWwiwQMMLFNhmbC0oZaxYy9kxjNTGHv5NWyy2eYEzwwh55khy/W5jeH7+YyJKyefMXLTvaBYSFkmhgEKhWGAmb0Em9tXKk4559aN05oxKD01iOet80/X+sTzcE4h/umiIameF/u3U1edcfU87Lu380678PTTsznu2BMx8E9qMsrAwySjDFyPptd7773NkD0HcPP48TRmPBQKhQmGBYaFwlzWZ1WG7+czJq6cfMZIJExQVCj/XBgV0lcqTjnn1o2Tz5igqauGYumMKydo6lqfeB7OCXblWERPZ9CMu864eh7shnCqq6s586y/8tST0/jd7/4QGhFLp9NceeVY9t1nMB999CGC+EFOWGKG5i6/ep5arq9UnHLOrRsn7zEKciNhOmkQz1vnn5b1iefhnAL900JDUj3P8S7WOuPqeSu+e1tutQ1Tp89m9JnnYlXWgGliGCaWCZbpcywTKlPg/PtjDjxgX665+nLq62pBuRh4GBgYObGnFvezzx5JJCefMRIJExQLcb+EHBWORMKSw5FImF71SSQsOZ5LJCzanrfmu1dTVcWIU07lqaeeYdtttkUpD9djuZhYYyaIiWW49bYJ7NG/F2+8+QYSCZNImEBXKDCy2xb7SsUp59y6cfIZEzR11VAsnXHlBE1d6xPPwznBrhyL6OkMmnHXGVfPg91Wfs7Gm2zMI49OYuylV1LToWNoTOw///kPe+89hAsvOpclS2oRRBtywhIzxPoScoQ4EglLCCfHPy3rE8/DOQX6p4WGpHqe412sdcbV8wK+e6ZpccyxJ/Lc86+yy659cI0UmCYpy1wuIha8HnrgfoYNG8iLL8yRSJhEwgS6IO6XkKPCkUhYcjgSCdOrPomEJcdziYRF2/NCfzt79uzJffc/wOWXXU2XTl3IuC2vJPbdd99w9DFHcOrpI5m/YCEKRdkjWLpxJBImaHcoMLLbFvtKxSnn3Lpx8hkTNHXVUCydceUETV3rE8/DOcGuHIvo6QyacdcZV8+D3QI/x8Bgv+H7M3PW8wwcPDQ0IgbwxGOPMnBAX2bMfBq1YkECrSEnLDFDrC8hR4gjkbCEcHL807I+8TycU6B/WmhIquc53sVaZ1w9L/C7tyKn+1o9uGPifYyfcCerdf9N6EpiCxf8zGmjTmXEScfx47dfo3VMSyJhTZATlpgh7peQo8KRSFhyOBIJ06s+iYQlx3OJhEXb81L8dg4eMoRnps1m3333D11JDBQzZ82gV9/dePTRR/GUh5YxLYmENUFOWOIIBUZ222JfqTjlnFs3Tj5jgqauGoqlM66coKlrfeJ5OCfYlWMRPZ1BM+464+p5sFuCubqu1pVrr7mRhx58gnXX+21oTGzRol/56zmjOeqoPzPvqy8R6As5YYkZYn0JOUIciYQlhJPjn5b1iefhnAL900JDUj3P8S7WOuPqeYHfvXw4vfv04/kXXuXPRx7b4kpilSk/MvbGG68yZMjuTLxjPG4moSuJSSRM0J6I+yXkqHAkEpYcjkTC9KpPImHJ8VwiYdH2vD1+Ozt37syYMZfyjwceYaOeG5FxvRYjYrV19Vxw4fnstfcQPvvscxSKsse0JBLWBDlhiSMUGNlti32l4pRzbt04+YwJmrpqKJbOuHKCpq71iefhnGBXjkX0dAbNuOuMq+fBbjvp3P6POzD16Vn8ZeQZeGZV6Epi77zzNnsOHcCECeNoTDeu9L6gPJATlpgh1peQI8SRSFhCODn+aVmfeB7OKdA/LTQk1fMc72KtM66eF/jdawunqqqas87+G09NnsFmv9+62YhY8PLcNOPG3cB++wzh/ffeROsol0TCBFFE3C8hR4UjkbDkcCQSpld9EglLjucSCYu25+X67dxiyy159LEnOfPMc7CsihYfNgmKjz/5hCFDh3DFFZdRW1ePllEuiYQJIgsFRnbbYl+pOOWcWzdOPmOCpq4aiqUzrpygqWt94nk4J9iVYxE9nUEz7jrj6nmwWyadFakKTj7pL8x+9iW2/uNOoRExz/O4Y+Jt7LPPQN5847WV3he0D+SEJWaI9SXkCHEkEpYQTo5/WtYnnodzCvRPCw1J9TzHu1jrjKvnBX73isXZYKNNePDRSZx3wVg6dOxMSw+btEz46qt5HHbY/pzz19NZvGgh2kS5JBImiCLifgk5KhyJhCWHI5EwveqTSFhyPJdIWLQ91+G3M2WZWKbJoYcfwfQZz9Knd5/Qh026Htx73z307rMLzz03B4Wi7FEuiYQJIgsFRnbbYl+pOOWcWzdOPmOCpq4aiqUzrpygqWt94nk4J9iVYxE9nUEz7jrj6nmwq5HO9dZdnwcfeoKbbr6Njl27hcbEvv32G4497ijOPPM05v/yy0rvC4oPOWGJGWJ9CTlCHImEJYST45+W9Ynn4ZwC/dNCQ1I9z/Eu1jrj6nmB371S6VQK9j/gYGbMmsseg4ZByEpilgnTpk1h8MDeTJ30KH58StO4l0TCBLqh3JdWdb7EreuxkEhYtDk6xBrE87ZzJBIWXZ0SCYu25zr8drY0pvta3bjxpglMGDeRNdboHrqS2M/zf+HEk0/ixJOO5YcfvkfLuJdEwgRaQoGR3bbYVypOOefWjZPPmKCpq4Zi6YwrJ2jqWp94Hs4JduVYRE9n0Iy7zrh6HuxqrnPQ4ME8/9Jr7H3A4aERMYDZs2czeEh/Hn7oAZRSzXIEbUeq3AUIigel/MubrlK4nsIw/MuhwHJ9peKUc27dOHmP8fx9V+mnQTxvnX9a1ieeh3MK9E8LDUn1vAS/nVrqjKvnmvx25jOmU+euXH7l9Qzecz8uuuCvfPW/z7EMF8s0MEwLS7lUphQpE5bWL+HCC87mqalTuOaaG/jt2muBlwHDBLfRbwf7Qbwqt6+cnHzGSCRMUAyo7J8EXNfD9Txc1/P/RNBMX6k45ZxbN06+YzKuvhrE89b5p2N94nk4p1D/dNCQVM9L8dupo864eq7Lb2e+Y3b8v/9j8pSZHHvcCXiYeGYFnpnCMytIuwamYWAZYBoGL788l379duPeu24n09iIcl08w0S5LsrN+FsM/5XTV05OPmMURjH+udomWGPGjCnb5IKi4SigZybjUVff2HRVM2WZVJj+OamrVFOfZRgl4ZTqc6PIyXdMTYcqABobM9ppEM9Xzcn1T8f6xPNwTlVNJdB2/3TQkFTPS/HbqaPOuHpe6HevXDpraqoYsPsAevXpy5tvv8P8hYtIewoTD8tQGBj+1RkFrpvh9dde5vXXX2Grbbene/e1wFAYCoyKFKZV4f/zX2WW9Rlm+Tj5jLEq6NipJns0+BK4l3aCRMJihOAGMk8pPGVgZW8eA7BMo6mvVJxyzq0bJ98xlmmgTD01iOet80/H+sTzVXMK8U8XDUn0vBS/nTrqjLPnOvx2tvVzd9zhT0yeOpMJE8Zx2y03k3Y90oYByiStPNxstCzjwXv/+hdD9xrCSaecwV9GjKDSssBI0XQDu5ECy8tuK8Hzlu23JyefMXLTvaCoUGTPikP6SsUp59y6cfIZEzR11VAsnXHlBE1d6xPPwznBrhyL6OkMmnHXGVfPg90I66yqrGLkqDOYPHk6W221HWkqWrwxP5PJcN11V7H3XgN4/1//RNB6yAlLzKDTeua6rSuv5bFQIM9hiTAnxz8t6xPPwzkF+qeFhqR6nuNdrHXG1fMCv3s66dz891vw1NTpnHXO+VRUdwTTxDD8Z7dYOc9vqUzB5599ysGH7M8Vl19MXe0S5Dks+UNOWGKGlKXfeuZJ5LRmjDyHJdocHZ4lIJ63nSPPYYmuTnkOS7Q91+G3s1ifW11ZyUknjmDSpGfY4Y87oJSH67Hc81saM+B64HkuE++8g779duGVV19BnsOSH+SEJY5QYGS3LfaVilPOuXXj5DMmaOqqoVg648oJmrrWJ56Hc4JdORbR0xk0464zrp4HuzHT2XPDDXnw4Se48orr6NipS2hM7Msvv2T48L04729nsWjRYgThkBOWmCHql1bjwpFIWEI4Of5pWZ94Hs4p0D8tNCTV8xzvYq0zrp4X+N3TWadhmBxx5DG88OJr9Om7B66RAtPMLhbhc4K4mGXCY488yLBhA5jz7CyJhIVATlhihjhcWo0DRyJhyeHoEGsQz9vOkUhYdHVKJCzanuvw21nKuddbbz3uvOs+rrn6elbvuhoZ11suIuZ6NL1++OF7jj/haE459WR+/PkXFAqJhC0POWGJIxQY2W2LfaXilHNu3Tj5jPn/9s48WpKqSve/yHuLQYpJmRtEse2DqCgitErRKlDIIKMtoAj6RES0pVWW0ErbIIumVZrXNAgyKCAPniLSJRSFhQODLU6FvHYCjm0riKAyKChj1c2M90fEKZLiZt64mRE3dpz4fmvVihORX2TsHV+duHny7IgMTas5lJVnrJrQtBqfPB+uCas6F83LMzRjzzNWz8Nq5HkmJOyz7/4sve5G9tnvgKElYgCLFi1ijze8jsWLv0K66glrORqwREZsU6tN1agkrCWaPv9MxifPh2vG9M9EDm31vM+7qPOM1fMx+15j8sy3PWeDDfj02Z/l3PMu5tkbbwZDniT2p4f/yIc//CHe/a63c+89v0YlYRkasERGrFOrTdOoJKw9GgtlDfJ8dI1Kwpqbp0rCmu25hWvnXB974e67c+01X+egg94y9ElikPLN67/J37x2Jz5/yefpptn7qCRMxEUKSb4cuK0qTZ3HtqYpsk9oWs2hrDxj1YSm1fjk+XBNWNW5aF6eoRl7nrF6HlZjz3MazdrrrM2/nHoaV3zpKp675fOHlok98sifOf74D3HoW/+Wu+76FW1GA5bIaMPUahM0KglriabPP5PxyfPhmjH9M5FDWz3v8y7qPGP1fMy+15g8h2h2WvA3fPP6m7RcPEcAACAASURBVHnnu95DOjEPhjxJ7JZl32fffffkgvM/w9STj6OSMNF42jS1almjkrD2aCyUNcjz0TUqCWtunioJa7bnFq6ddfswf/5anHDCP/F/v/BlXviCFzLVHfwksSeffJJ/+cQp7LPfXtx22+2kpKgkTDSbFJJ8OXBbVZo6j21NU2Sf0LSaQ1l5xqoJTavxyfPhmrCqc9G8PEMz9jxj9Tysxp5nQc3LX7YdVy1eygePPZ50Yo2hTxL70Y9+xH777cUZZ5zOk8uXP+P1WNGAJTLaOrVqTaOSsJZo+vwzGZ88H64Z0z8TObTV8z7vos4zVs/H7HuNyXMWmtVWW50PfPA4rlr8dV607XYw5EliaTrFueeezX77LOSWZd9FJWGicbR5atWSRiVh7dFYKGuQ56NrVBLW3DxVEtZszy1cOy36sPWLtuaLX7ySf/jIx1h9tdWGPkns5//9C/bZdy8+duIJPPLYE6SkqCRMNIcUknw5cFtVmjqPbU1TZJ/QtJpDWXnGqglNq/HJ8+GasKpz0bw8QzP2PGP1PKzGnueImsmJSY484iiuv+FmXrPTzkOfJJamKRecfy577bELN998M7GiAUtkaGrVhkYlYS3R9PlnMj55Plwzpn8mcmir533eRZ1nrJ6P2fcak+eYmuc9bysuv+IqTvnn03jW2uvDkCeJ3Xvv3RxxxGF85B8+xMN/eACVhAnTaGrVhkYlYe3RWChrkOeja1QS1tw8VRLWbM8tXDut+9DpJExOTPCWtx7K4muuY9fX78pUd/CTxCDl8i9dzut2+Ruuu+46UlJUEibskkKSLwduq0pT57GtaYrsE5pWcygrz1g1oWk1Pnk+XBNWdS6al2doxp5nrJ6H1djzLFGzySabcN4FF3P22Z9l7WdvPPRJYr///e94z9Hv4gMfeD/333//M15vIhqwRIamVm1oVBLWEk2ffybjk+fDNWP6ZyKHtnre513Uecbq+Zh9rzF5lqyBhDfudwBLr/sWe77xQBjyJLGJDixduoQ93vBarvjSZaS9KVQSJsygqVUbGpWEtUdjoaxBno+uUUlYc/NUSVizPbdw7bTuwyDNczZ4NqedfgYXnHcxm2y8ydAnif3xoYd5/zHv49C3Hcy9995LSopKwoQN0mwcTjpkW1WaOo9tTVNkn9C0mkNZecaqCU2r8cnz4ZqwqnPRvDxDM/Y8Y/U8rMaeZ8WaXXbdjZu+9V3edvg7hz5JDOD667/Jwt134dJLL6GX9p7xunU0YIkMTa3a0KgkrCWaPv9MxifPh2vG9M9EDm31vM+7qPOM1fMx+15j8pwDzfz56/DJT/5vLvvClfzFc7eCIU8Se/KJRznllJN468EH8sv/vh2VhIna0NSqDY1KwtqjsVDWIM9H16gkrLl5qiSs2Z5buHZa92E2mle96tVcdfVSjjjiKHopQ58k9oNblrHrwl34zGfOYfmKFagkTNRDCkm+HLitKk2dx7amKbJPaFrNoaw8Y9WEptX45PlwTVjVuWhenqEZe56xeh5WY89zjjVrrrkGH/3Ix7jmmq/xAvfSoSViTz75JJ867V84+OD9ue1nP3nG69aYrDsAUR5pmk0VdtOUbi8lSbKpROBp26rS1Hlsa5rC+/Sy9W5qLwd5Pjv/TMYnz4drxvTPRA5t9byCa6fJPGP13Mi107oPo2pevO12XHnVVznvvPP4zNln0OtOMZF0megkJJ0JJtIuq02mTHbg53fcxoH7780RR/0d73/f37HGJJB0WFn+1ZvK/vVvqwHNsEREmg+5u90e3V6PbreXDcGn2VaVps5jW9MU3WeqazcHeT47/yzGJ8+Ha8b1z0IObfW8imunxTxj9dzKtdO6D6NqOp1JjjzqaP5j0bW8YrtX0E0Tep159DqT9DrzWNFN6CQJEwlAjzPPPIMD9lvIrbcsI+12SUmyf90uaXdq5ba6mDjppJNqO7gojXcAz5ua6vHY48tXzhBOTnSY18nGpN00XbltIkkq0VT1vk3UFN1nzWetDsDy5VPmcpDnM2v6/bMYnzwfrll9zdWA0f2zkENbPa/i2mkxz1g9H7fvNSVPC5qNNtqQQw89jHXXXY/vL7uF5d2UFb2UDj0mkpSEJNs3gYcfepirr17EHx5+iFfuuBOrrzYP0imSFJJ5k3Qm5rHW/DXzd+Yu4GLmCM2wRES4GWsivwErLPvbE52kMk2dx7ammc0+k0ZzkOez889qfPJ8uGYc/6zk0FbPy752Ws0zVs8tXDut+1CWZt7kJEcd9V4WL/kaO/z1TvTyUrwVacKKtMOK/Ldbpnow1etx0cUX8/pdFnDjTTdCMgkTE9myxpvudQ9LH865g4APAtsAXeA7wMne+x/M4j12Br41RHKZ9/5tYwU6E9lAWTev1a0psk9oWs2hrDxj1YSm1fjk+XBNWNW5aF6eoRl7nrF6HlZjz9OYZsvnPo8LL7qERV++glNO/kceffwx0qTDFD3SZDn93H33rznkkDdx6CEH8dHjPsr6G2xEnWiGJcc5dxJwOfBi4AbgJ8BewM3OuT1n8Vbb5cvvAJdN8+/mkkKeFj3P3Iamzt8SMJlnrJo+/0zGJ8+Ha8b0z0QObfW8z7uo84zV8zH7XmPyNKhJkg5vPvgQrr/xO+y6+150k8mhv93ylf+4gn322Z3rvnoNdd50rxkWwDm3PXAiWT3eTt77e/LtewNfAS5yzm3lvX+swNuFActx3vtKByfTMTnRoZd2yZ7PnU0HAjz1zO6kMk2dx7amKbpPkkD/bwlYykGez84/i/HJ85k14/hnJYc2el7FtdNinjF7buHaad2HKjWbbbYp5517AdcsuYaTTzqBB35/LxNJQpJ0mEp7dHvZkKfbgwceuJ+j33cUu169mC9feQWrr57dQzaXaIYl49h8eWIYrAB475eQ3VC0MXBwwffaDugB/1VmgLMihSRfDtxWlabOY1vTFNknNK3mUFaesWpC02p88ny4JqzqXDQvz9CMPc9YPQ+rsedpXJOQsOeee/O1r93IgX97MCuYN/S3W5YsWcyyZcuesX0u0IAlYw8y+66e5rVF+XLGsjDn3Gpk97/c4b1/tLzwiqOpVRsalYS1RNPnn8n45PlwzZj+mcihrZ73eRd1nrF6Pmbfa0yeDdGst/6z+bczzuHCiy5jo822gE6HJOmsLAuDp8rEpqamqIPWD1icc5sC6wP3eO//OI3kjnz50gJv9xJgHnCnc+4U59ztzrnHnXO/cs79q3NuvZLCHkiYKs+m/Fj59Ij+bVVp6jy2Nc1s9ukva7CUgzyfnX9W45PnwzXj+Gclh7Z6Xva102qesXpu4dpp3Ye5Phevfd3ruWbx1zj00MNJ0x7dHkylCT06TKUJ3V7Vn2KHfL6t79Bm2DRf/nbA62H7xgXeK9y/shfwWuAm4DfADmRlZ/s45xZ47+8fMdahzJs3wYYbrs2TK7qsmOoxb7LDmqtlFj++fGrlttXnTVSiqep9m6iZzT4psMEGa5vLQZ7Pzj+r8cnz4Zpx/LOSQ1s9L/vaaTXPWD23cO207kMd52Ld9dbinHPO4p2Hv4Ujj3w3v7zzrpVPEiNZTiehFqIcsDjnLgO2LyBdBFybtwfdUP9Evpxf4P3CgOUm4M1hYOKc2wD4IrArcC7wpgLvNRJTU31T5Wk2DQg8bVtVmjqPbU1jPT7lqXOhPHUulGc789S5sHFsa5pV13dasDM/vPVWTj7lVM769NmQLs9mZaiHKAcswJaAK6DbFAgTXOkM2iIefRA4E/it9/7PYaP3/gHn3OHAz4EDnHObeu8HzeiMzIoVXf740GMsn+qyottj3kSHNeZlFj+xYmrlttUmJyrRVPW+TdQU3Wed9Z4FacqDDz5iLgd5Pjv/LMYnz4dr5q+75lj+WcihrZ5Xce20mGesno/b95qSZ5M0g/b5u/cfy4Kdd+MfP/phfvaTW2f8sFwVUQ5YvPcLimqdcy/Lm2sOkKyRL2e8id57v4JsUDLda/c6524FdgZeASwpGuOsSfPRVTpkW1WaOo9tTVNkn9C0mkNZecaqCU2r8cnz4ZqwqnPRvDxDM/Y8Y/U8rMaeZ9M0A/Z5yUtfyqKrlvC5888mV8w5UQ5YZkl4jPEmA16f6R6X2fC7fPmsEt5rWqZ7MgTwtG1Vaeo8tjVN4X1SsulXgznI8wKaPv9MxleBxnp8s9KM6Z+JHOZAYzK+Pu+izrMmjfW+15g8G6SZaZ/JyXkcc8yxrLN2PUOH1j8lzHv/AHAfsLlzbu1pJC/Klz+Z6b2cc2c65xY55zYaIHl+vvzN7CMtRt1PmKjr2NY0ekpYezTBP6vxyXM9JSxWz8u+dlrNM1bPLVw7rftg7Vx0OglrrbVWVR9hh9L6AUvOUmAC2Gea1/bPl9dO89qq7JTrn/E+zrmXkN2U/yDww9HCLEgKSb4cuK0qTZ3HtqYpsk9oWs2hrDxj1YSm1fjk+XBNWNW5aF6eoRl7nrF6HlZjz7NpmoJ9rw40YMn4DJkVn3TOPT9sdM7tDbyDrBzsC/07OOe2zv/1l3edly9Pdc5t3afdELiIbFD0Ke/98kqyYPopvV4vfdo2/QCToXORQn9Zg6Uc5Pns/DMZnzwfrhnTPxM5tNXzPu+izjNWz8fse43Js0GaIvv0etlaHWjAAnjvvwecBmwO/NQ5d7Vz7gZgMdADDvXeP7nKbrfn/3bs2/ZZ4MvARsCPnHPfcM5dBfwP8ErgS8DpVeZifTqxLRqVhLVHY6GsQZ6PrlFJWHPzVElYsz23cO207oO1c9HpJFV+hB2KBiw53vvjyWZTbgd2A7Yhe5LXq733NxR8jx5wEPAe4MfAa8h+e+V24EjgEO99t/TgVyWFJF8O3FaVps5jW9MU2Sc0reZQVp6xakLTanzyfLgmrOpcNC/P0Iw9z1g9D6ux59k0TcG+Vwd6Slgf3vvPA58vqE0GbE/JSsPOm+71qrH8hIk2aep80o3JPGPV9PlnMr4KNNbjm5VmTP9M5DAHGpPx9XkXdZ41aaz3vcbk2SBNkX1UEiZKw/p0Yls0Kglrj8ZCWYM8H12jkrDm5qmSsGZ7buHaad0Ha+ei00lq+3yrAUuMpJDky4HbqtLUeWxrmiL7hKbVHMrKM1ZNaFqNT54P14RVnYvm5RmasecZq+dhNfY8m6Yp2PfqQCVhkWF9OrEtGpWEtUTT55/J+CrQWI9PJWHla0zG1+dd1HnWpLHe9xqTZ4M0KgkTc4r16cS2aFQS1h6NhbIGeT66RiVhzc1TJWHN9tzCtdO6D9bORaeT1Pb5VgOWGEkhyZcDt1WlqfPY1jRF9glNqzmUlWesmtC0Gp88H64JqzoXzcszNGPPM1bPw2rseTZNU7Dv1YFKwiLD+nRiWzQqCWuJps8/k/FVoLEen0rCyteYjK/Pu6jzrEljve81Js8GaVQSJuYU69OJbdGoJKw9GgtlDfJ8dI1Kwpqbp0rCmu25hWundR+snYtOJ6nt860GLDGSQpIvB26rSlPnsa1piuwTmlZzKCvPWDWhaTU+eT5cE1Z1LpqXZ2jGnmesnofV2PNsmqZg36sDlYRFRJpmU3rdNKXbS0mSdOX0Xf+2qjR1HtuapvA+vWy9m9rLQZ7Pzj+T8cnz4Zox/TORQ1s9r+DaaTLPWD03cu207oO1c6GSMFEKaT4M7nZ7dHs9ut1eNiyeZltVmjqPbU1TdJ+prt0c5Pns/LMYnzwfrhnXPws5tNXzKq6dFvOM1XMr107rPlg7F3WhGZaISEgghYmJDinZMkzhrbqtKk2dx7amKbrPZGo3B3k+O/8sxifPh2vG9c9CDm31vIprp8U8Y/XcyrXTug/WzkVdaMASEeGmqV6a0ksTJvIbpgAmOsnKbVVp6jy2NU3RfSY6CWnHZg7yfHb+WYxPns+sGcc/Kzm00fMqrp0W84zZcwvXTus+WDsXuulelEuaz9qlQ7ZVpanz2NY0RfYJTas5lJVnrJrQtBqfPB+uCas6F83LMzRjzzNWz8Nq7Hk2TVOw79WBZlgiw/ozvNuiqfO3BEzmGaumzz+T8VWgsR7fXP4WhIkc5kBjMr4+76LOsyaN9b7XmDwbpCmyj266F6Vh/RnebdHU+VsCVvOMVWPhtwTk+ega/Q5Lc/PU77A023ML107rPlg7F51OUtvnWw1YYiSFJF8O3FaVps5jW9MU2Sc0reZQVp6xakLTanzyfLgmrOpcNC/P0Iw9z1g9D6ux59k0TcG+VwcqCYsM69OJbdGoJKwlmj7/TMZXgcZ6fCoJK19jMr4+76LOsyaN9b7XmDwbpFFJmJhTrE8ntkWjkrD2aCyUNcjz0TUqCWtunioJa7bnFq6d1n2wdi46naS2z7casMRICkm+HLitKk2dx7amKbJPaFrNoaw8Y9WEptX45PlwTVjVuWhenqEZe56xeh5WY8+zaZqCfa8OVBIWGdanE9uiUUlYSzR9/pmMrwKN9fhUEla+xmR8fd5FnWdNGut9rzF5NkijkjAxp1ifTmyLRiVh7dFYKGuQ56NrVBLW3DxVEtZszy1cO637YO1cdDpJbZ9vNWCJkRSSfDlwW1WaOo9tTVNkn9C0mkNZecaqCU2r8cnz4ZqwqnPRvDxDM/Y8Y/U8rMaeZ9M0BfteHagkLDKsTye2RaOSsJZo+vwzGV8FGuvxqSSsfI3J+Pq8izrPmjTW+15j8myQRiVhYk6xPp3YFo1KwtqjsVDWIM9H16gkrLl5qiSs2Z5buHZa98Haueh0kto+32rAEiMpJPly4LaqNHUe25qmyD6haTWHsvKMVROaVuOT58M1YVXnonl5hmbsecbqeViNPc+maQr2vTpQSVhkWJ9ObItGJWEt0fT5ZzK+CjTW41NJWPkak/H1eRd1njVprPe9xuTZII1KwsScYn06sS0alYS1R2OhrEGej65RSVhz81RJWLM9t3DttO6DtXPR6SS1fb7VgCVGUkjy5cBtVWnqPLY1TZF9QtNqDmXlGasmNK3GJ8+Ha8KqzkXz8gzN2POM1fOwGnueTdMU7Ht1oJKwyLA+ndgWjUrCWqLp889kfBVorMenkrDyNSbj6/Mu6jxr0ljve43Js0EalYSJOcX6dGJbNCoJa4/GQlmDPB9do5Kw5uapkrBme27h2mndB2vnotNJavt8qwFLjKSQ5MuB26rS1Hlsa5oi+4Sm1RzKyjNWTWhajU+eD9eEVZ2L5uUZmrHnGavnYTX2PJumKdj36kAlYZFhfTqxLRqVhLVE0+efyfgq0FiPTyVh5WtMxtfnXdR51qSx3vcak2eDNCoJE3OK9enEtmhUEtYejYWyBnk+ukYlYc3NUyVhzfbcwrXTug/WzkWnk9T2+VYDlhhJIcmXA7dVpanz2NY0RfYJTas5lJVnrJrQtBqfPB+uCas6F83LMzRjzzNWz8Nq7Hk2TVOw79WBSsIiw/p0Yls0KglriabPP5PxVaCxHp9KwsrXmIyvz7uo86xJY73vNSbPBmlUEibmFOvTiW3RqCSsPRoLZQ3yfHSNSsKam6dKwprtuYVrp3UfrJ2LTiep7fOtBiwxkkKSLwduq0pT57GtaYrsE5pWcygrz1g1oWk1Pnk+XBNWdS6al2doxp5nrJ6H1djzbJqmYN+rA5WERUSaZlN63TSl20tJknTl9F3/tqo0dR7bmqbwPr1svZvay0Gez84/k/HJ8+GaMf0zkUNbPa/g2mkyz1g9N3LttO6DtXOhkjBRCmk+DO52e3R7PbrdXjYsnmZbVZo6j21NU3Sfqa7dHOT57PyzGJ88H64Z1z8LObTV8yqunRbzjNVzK9dO6z5YOxd1oRmWiEhIIIWJiQ4p2TJM4a26rSpNnce2pim6z2RqNwd5Pjv/LMYnz4drxvXPQg5t9byKa6fFPGP13Mq107oP1s5FXWjAEhHhpqlemtJLEybyG6YAJjrJym1Vaeo8tjVN0X0mOglpx2YO8nx2/lmMT57PrBnHPys5tNHzKq6dFvOM2XML107rPlg7F7rpXpRLms/apUO2VaWp89jWNEX2CU2rOZSVZ6ya0LQanzwfrgmrOhfNyzM0Y88zVs/Daux5Nk1TsO/VgWZYIsP6M7zboqnztwRM5hmrps8/k/FVoLEe31z+FoSJHOZAYzK+Pu+izrMmjfW+15g8G6Qpso9uuhelYf0Z3m3R1PlbAlbzjFVj4bcE5PnoGv0OS3Pz1O+wNNtzC9dO6z5YOxedTlLb51sNWGIkhSRfDtxWlabOY1vTFNknNK3mUFaesWpC02p88ny4JqzqXDQvz9CMPc9YPQ+rsefZNE3BvlcHKgmLDOvTiW3RqCSsJZo+/0zGV4HGenwqCStfYzK+Pu+izrMmjfW+15g8G6RRSZiYU6xPJ7ZFo5Kw9mgslDXI89E1Kglrbp4qCWu25xaundZ9sHYuOp2kts+3GrDESApJvhy4rSpNnce2pimyT2hazaGsPGPVhKbV+OT5cE1Y1bloXp6hGXuesXoeVmPPs2magn2vDlQSFhnWpxPbolFJWEs0ff6ZjK8CjfX4VBJWvsZkfH3eRZ1nTRrrfa8xeTZIo5IwMadYn05si0YlYe3RWChrkOeja1QS1tw8VRLWbM8tXDut+2DtXHQ6SW2fbzVgiZEUknw5cFtVmjqPbU1TZJ/QtJpDWXnGqglNq/HJ8+GasKpz0bw8QzP2PGP1PKzGnmfTNAX7Xh2oJCwyrE8ntkWjkrCWaPr8MxlfBRrr8akkrHyNyfj6vIs6z5o01vteY/JskEYlYWJOsT6d2BaNSsLao7FQ1iDPR9eoJKy5eaokrNmeW7h2WvfB2rnodJLaPt9qwBIjKST5cuC2qjR1Htuapsg+oWk1h7LyjFUTmlbjk+fDNWFV56J5eYZm7HnG6nlYjT3PpmkK9r06UElYZFifTmyLRiVhLdH0+Wcyvgo01uNTSVj5GpPx9XkXdZ41aaz3vcbk2SCN9ZIwDVgG4Jw7CTgR2MJ7/5tZ7vtXwMeBBcBzgF8A5wPneO97JYf6NCYnOvTSLtn0XbJy+u6pKb2kMk2dx7amKbpP/7S4tRzk+ez8sxifPJ9ZM45/VnJoo+dVXDst5hmz5xaundZ9sHYuVBJmDOfc/sAJI+77MmAZcAhwF7AU2AI4C7ikrBiHkkKSLwduq0pT57GtaYrsE5pWcygrz1g1oWk1Pnk+XBNWdS6al2doxp5nrJ6H1djzbJqmYN+rA82wrIJz7r3AGYxwbpxzCdmgZB3gMO/9pfn2DYFvAIc65xZ5768sMeSnYX06sS0alYS1RNPnn8n4KtBYj08lYeVrTMbX513Uedaksd73GpNngzTWS8I0w5LjnNvaObcEOBt4GPjzCG+zENgWuDEMVgC89/cD781Xjxk31mFYf8JEWzR1PunGap6xaiw86Uaej67RU8Kam6eeEtZszy1cO637YO1cdDpJlR9hh6IBy1OcC+wFfB3YHvjDCO+xR778yqoveO9vBu4DFjjn1h41yEKkkOTLgduq0tR5bGuaIvuEptUcysozVk1oWo1Png/XhFWdi+blGZqx5xmr52E19jybpinY9+pAJWFPsQw43Xu/GMA5N8p7vDhf/nTA6x7YCNgG+P4oB5gJ69OJbdGoJKwlmj7/TMZXgcZ6fCoJK19jMr4+76LOsyaN9b7XmDwbpLFeEqYBS473/sMlvM2m+fK3A14P2zcu4VjTYv0JE23R1PmkG4t5xqpZtazBWnzyfGaNnhLWzDyruHZazDNmzy1cO637YO1c1FkSFuWAxTl3GVlZ10ws8t5/pMRDr5UvHxvw+uP5cn6JxwT4S4DVV5tkk43WoZem+bcX0Emy/1yrbqtKU+exrWkK7zM5wfxN1zOZgzyfnX8m45PnwzVj+mcih7Z6XsG102SesXpu5Npp3Qdr56KPv2QOiXLAAmwJFKnp2nRmyawIv7GSDng9WWVZFvMBkvw/0kTyzLdfdVtVmjqPbU1jPb6yNNbjm0uN9fjK0liPby411uMrS2M9vrI01uObS431+MrSWI9vLjVF9umj7C/fhxLlgMV7v6CmQz+SL9cc8Poa+fLRko/7K+D5+fF/UfJ7CyGEEEIIAdnMynyyz55zRpQDlhq5F3g5sAlwxzSvz3SPy6hsV/L7CSGEEEIIYQI91rhcwtPBtln1hfxHJbcGusBtcxmUEEIIIYQQTUUDlnJZmi/3n+a11wAbAt/23o/yo5RCCCGEEEK0Dg1YRsQ59wLn3NbOuXX7Nt8E/AxY6Jw7sk+7IXBOvnr6HIYphBBCCCFEo9GAZXS+CdwOHBA2eO97wDvJbn4/3zn3Pefcf5D9YOS2wAXhhymFEEIIIYQQM6MBS8l4738A/DVwJfBCYHfgLuA9wNE1hiaEEEIIIUTjSNJ00E+GCCGEEEIIIUS9aIZFCCGEEEIIYRYNWIQQQgghhBBm0YBFCCGEEEIIYRYNWIQQQgghhBBm0YBFCCGEEEIIYRYNWIQQQgghhBBm0YBFCCGEEEIIYRYNWIQQQgghhBBm0YBFCCGEEEIIYRYNWIQQQgghhBBmmaw7ADEazrmTgBOBLbz3v5nlvn8FfBxYADwH+AVwPnCO975Xcqgixzl3EPBBYBugC3wHONl7/4NZvMfOwLeGSC7z3r9trEAFzrndgI8C2wKrAT8EPuG9v24W76F+VgPjeuec2wL49RDJzd77BWMHKobinHsHcBGws/f+27PYbzOyv40LgU3JvLwU+JT3/skKQhXTMIp/zrlJ4BFg9QGSe7z3m5cToejHOTcBHA28HXgRMAH8EvgicJr3/omC71PZ3z0NWBqIc25/4IQR930Z2QfedYCbgWXA64GzgFcB+rBbAX0DzD8D1wPrA3sBb3DO7eu9/2rBt9ouX34H+NU0r988Zqitp+8P7ZNkXk2Q9ZGlzrmjvPfnF3gP9bMaKMM7nupjPwZ+Ms3rvoRQxRCcc68m6yuz3W9z4LvA5sD/Rn+WcgAADZZJREFUA24FdgJOBnZxzu3uvV9RZqzimYzqH9mXeasD/wN8b5rX/zBOXGJ68sHKVcDeZAPG7wEryP5WnQzs7ZzbxXv/2AzvU+nfPQ1YGoZz7r3AGYzgnXMuAS4h+890mPf+0nz7hsA3gEOdc4u891eWGHLrcc5tTzZYuQvYyXt/T759b+ArwEXOua1muhjkhA9Tx3nvNTgpGefcpsC5wMPAAu/9T/PtO5D1kX93zi0JHg54D/WzGijDu5zQxz7lvb+ssoDFtDjnDgQuBuaPsPs5ZIOVj3nvT8nfby2y6+xuwDHA6eVEKqZjTP9C37vIe//PpQUlZuJdZIOVHwN79X1G2QC4Gng18DHgI4PeYC7+7ukelobgnNvaObcEOJvsD/KfR3ibhWRlEjeG/0wA3vv7gffmq8eMG6t4BsfmyxP7Pyx575eQXdg3Bg4u+F7bAT3gv8oMUKzk/WTf8P1b+MAL4L1fBnwKWAN49wzvoX5WD2V4B099aPph6RGKgTjnNnfOXQJcSTYz9vtZ7u+AN5J9O39q2O69fxQ4gqwM9/2lBSyexrj+5ajv1cM78uUHVvmM8gBZmRjAITO8R+V/9zRgaQ7nkpUQfR3YntGmRvfIl19Z9YX82/r7gAXOubVHDVJMyx5ASvZNxaosypd7zvQmzrnVyKbM78j/CIvyGdhHKO6V+lk9lOEdZB+aHgF+XkZQojCnAIcBt5CVj9wxy/3fACTA4lVr5b33vyYrD9vSObdNCbGKZzKuf/DUgOXWsoIShXiAzK/p7qcN18HNZniPyv/uqSSsOSwDTvfeLwbIvkyaNS/Olz8d8LoHNiL7UPz9UQ4gnk5eprI+8Bvv/R+nkYSL+ksLvN1LgHnAnc65U4A3Ac8Dfkf2rdYp3vuHxg66peRT2tuQzWDdPo3k5/lrL3bOJd77dMBbqZ/NMWV555x7NvBcsg9MH3LOHQa8EHgIuAY4yXt/bwUpiOxa+HbgUu99b4S/cTP1uzuAHciutbeNFKEYxlj+5X345WR/z/Z1zr2b7ObvJ8hKik7y3uv+sQrw3u8z5OUd8uVMD3eq/O+eZlgagvf+w2GwMgab5svfDng9bN94zOOIpyjznIdvn/YCPkD2BI9vkw2IjgW+n9eLitFYn6yk6EHv/fJVX/TeT5F9E/UsYNi3ROpnc09Z3oU+9gqysqL7gBvIvtw7EvihG/HbIjEc7/0nvPeXjPEkIfW7GinBv63I7n/YBDiPbKByQ748BFjmnNuplGBFIfJB5Mn56kz3nlTe/zTDUgPOucvIyrpmYpH3fuBNTiOwVr4cdHP34/lylJvlWsNs/AOuzduDznl4VGCRcx4+TN0EvDmvDQ03xn0R2JWsdPBNBd5LPJOZ+gc8vY/8acT3UT8rn7K8C33sZ8A+3vtfwcobty8A3gJcBrxyrGhFFajfNZvQ9+4B3ui9/y9Y+ajjT5B9KXe5c+4viz5iV4zNqcBrye5HOm0GbeX9TzMs9bAl4Ar823TQG4xI+OZjUClLsspSTM9s/JvpnAeKnPMP5u+7TxiswMob4w4HHgUOyMvQxOwp4lWRPqJ+NveU5d2/kX3T+7owWIGVN26/i+zD1PbOuVeNEauoBvW7ZnMlWTnmjmGwAitnR48juxH/L4D96wmvXTjnTgb+gewR8Qf1f+YYQOX9TzMsNVDjj449ki/XHPD6GvlSN3QPYTb+5c8lhxLOef77AdPeCOy9v9c5dyuwM1k5y5KiMYqVzNQ/oJhf6mdzTyneee+7TP/7RnjvH3POXU92Y/H2TP87EaI+1O8aTH5f2d0DXus5564l63fbk1UUiArIZ7TOJnui4hPAgd77YT9WHai8/2mGpV2Em0U3GfD6TDWIYvaERwTOxTn/Xb58Vgnv1Ub+RHbR3SC/aD+NfNsGwBMzPNxA/WzuKcu7mVAfs4v6Xdyo71WMc24+sJhssPIQ8IZZ/Kh15f1PA5Z2EZ7e8IzHOuY3V21N9qx6PUGlJPJyrfuAzQc8zu9F+XK6X9R+Gs65M51zi5xzGw2QPD9fzvQ0DzEN+Td8t5H9hsBfTSNxZNfMmbxSP5tjyvLOOXeic+7LzrlBT+1TH7PLwH6XU/haK+Ye59z7nHOXO+d2GyBR36sQ59z6wI1kjye+G9i54MxKoPK/exqwtIul+XK6GtDXABsC3/bej/KjlGIwS8k+SE336MDgxbXTvLYqO+X6Z7yPc+4lZDctPoh+dGschvWRol6pn9VDGd5tS/bQioNWfSH/omB3YAXZ04uELYL/+zrnnvbZxjn3XLLr413ee31RYJOtyPrd21d9wTm3BvDmfPVrcxlUG8h/4y2U3N0GvKb/x3cLUvnfPQ1YIsU59wLn3NbOuXX7Nt9E9vSbhc65I/u0GwLn5Kunz2GYbeEzZDeifdI5F74lwjm3N9kvzP4W+EL/Drl3Wzvn+qe/z8uXpzrntu7TbghcRDYo+tR0j3UVhbmIrG73eOfcyifBOedeSXbj5+M81VfUz2xRhnehjx3b/wjVvFTiQrLHrn7We/87RG04556be7dB2JY/JGEp2WzayX3atYDPkl0f1e8MMJ1/wOfIvoE/1Dn3pj7tPOAssofdfNV7ry/kyudksh/7vJvsgSNDZ7Hq+ruXpOlMDy8SFnHO3UnWgbeY7j9X3+v/y3t/cd/2HYFvkj1a7vtkdYevI/sdgwu89++uNPCW4pz7JNmHpsfIzv/aZI8LXAHs4b2/YRV96Jiv997fmG/rAJcDfwssB/6T7Aa21+fv9yXgrfmNw2JEnHPvJbvpcAWZVwmwC9lDSg733l/ap70T9TMzlOTd6cCHyJ56czPZ77fsTHYPzH+S9ddhj08WJeCcu5HsGrmz9/7bA177uPf+pL7tW5F5tglZiYon+3Z3U+CrwL75U6dExYzo3zHAGWT9dhnwa+Cvgc3Jfpjytd77++Yg/NaQ/1jub8hulr+V6X94FwDv/dvyfe6khr97mmFpGd77H5BdAK4k+wXn3YG7gPcAR9cYWtR4748nm025HdiNrM5zCfDqVQcrQ96jRzZl/h7gx2R/iHfN3/NI4BANVsbHe38OWdnd98g+qO5A9gOdC/s/8M7wHupnNVCSd8eS9bObycqI9iCbBT0O2FWDFbt4738J7AhcTFaCsjfwR+AjZE870mDFMN77M4GFwHVk1803kn3J98/ADhqsVMKOPPVkr1cAhw75N5Sq/+5phkUIIYQQQghhFs2wCCGEEEIIIcyiAYsQQgghhBDCLBqwCCGEEEIIIcyiAYsQQgghhBDCLBqwCCGEEEIIIcyiAYsQQgghhBDCLBqwCCGEEEIIIcyiAYsQQgghhBDCLBqwCCGEEEIIIcyiAYsQQgghhBDCLBqwCCGEEEIIIcyiAYsQQgghhBDCLBqwCCGEEEIIIcyiAYsQQgghhBDCLBqwCCGEEEIIIcwyWXcAQgghxDg4554N/ATYLN90qvf+hAHadwKfy1fvBbb13j9YfZRCCCFGJUnTtO4YhBBCiLFwzu0BfDVfnQK2997/eBXN84AfA2sDPWCh9/76uYxTCCHE7FFJmBBCiMbjvV8KnJevTgKfc85NhNedcx3g/5ANVgBO02BFCCGagQYsQgghYuFY4H/y9iuBv+977ThgQd6+BfjYHMYlhBBiDFQSJoQQIhqcczsB3yL7Qu5RYBtgPWAZsFq+bTvv/X/XFqQQQohZoQGLEEKIqHDOfQI4Pl+9CtgSeHm+foT3/sIB+z0f2A3YMf/3YmAC+Lj3/qQqYxZCCDEYPSVMCCFEbPwTsCewLbBf3/YrBg1Wcv6ep5eRCSGEMIDuYRFCCBEV3vvlwGHA8r7NdwNHzbDrA8A1PDXgubKSAIUQQswKzbAIIYSIkTvJBiDht1l6QHfYDt77U/rXnXOHVBKZEEKIWaEZFiGEEDFyJk8NViC7j+Xfa4pFCCHEGGjAIoQQIiqcc/sBb89XbwfuyNvvcM7tW09UQgghRkUDFiGEENHgnNsQOD9f7QFHAO8GwiMxz3fObVBHbEIIIUZDAxYhhBAxcR6wUd4+y3v/Xe/9f+bbATbuawshhGgAGrAIIYSIAufc4cAB+eqdwAl9Lx8P3JO3D3TOHTaHoQkhhBgDDViEEEI0HufcFmQ32geO9N4/Gla8938Cju57/Szn3OZzFZ8QQojR0YBFCCFEo3HOJcCFwLr5pgu9999YVee9Xwxcnq+uC1yY7yuEEMIwGrAIIYRoOu8DdsvbvwWOHaI9Bngwby/M9xVCCGEY/XCkEEKIRuO9/zTw6YLa+wA9JUwIIRqEZliEEEIIIYQQZtGARQghhBBCCGGWJE3TmVVCCCFE5DjndgKu6ts0H1gdeBx4rG/7dt77u+cyNiGEaDO6h0UIIYTImAc8Z5rta+b/AhNzE44QQgjQDIsQQgghhBDCMLqHRQghhBBCCGEWDViEEEIIIYQQZtGARQghhBBCCGEWDViEEEIIIYQQZtGARQghhBBCCGEWDViEEEIIIYQQZtGARQghhBBCCGEWDViEEEIIIYQQZtGARQghhBBCCGEWDViEEEIIIYQQZtGARQghhBBCCGEWDViEEEIIIYQQZtGARQghhBBCCGEWDViEEEIIIYQQZtGARQghhBBCCGEWDViEEEIIIYQQZvn/Q0DO//C2Sz4AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 284,
       "width": 406
      },
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plotting the perceptron decision boundary\n",
    "perceptron_DB(x1, x2, w, threshold)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Exercise 1 : Compute a Boolean \"OR\" using a perceptron?**\n",
    "\n",
    "Hint: copy the code from the \"AND\" example and edit the weights and/or threshold"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Boolean OR\n",
    "\n",
    "| x$_1$ | x$_2$ | output |\n",
    "| --- | --- | --- |\n",
    "| 0 | 0 | 0 |\n",
    "| 1 | 0 | 1 |\n",
    "| 0 | 1 | 1 |\n",
    "| 1 | 1 | 1 |"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Calculating Boolean OR using a perceptron\n",
    "# Edit the code below"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Perceptron output for x1, x2 =  0 , 0  is  0\n",
      "Perceptron output for x1, x2 =  1 , 0  is  1\n",
      "Perceptron output for x1, x2 =  0 , 1  is  1\n",
      "Perceptron output for x1, x2 =  1 , 1  is  1\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 284,
       "width": 406
      },
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Solution\n",
    "# Calculating Boolean OR using a perceptron\n",
    "threshold=0.6\n",
    "# (w1, w2)\n",
    "w=[1,1]\n",
    "# (x1, x2) pairs\n",
    "x1 = [0, 1, 0, 1]\n",
    "x2 = [0, 0, 1, 1]\n",
    "output = perceptron([x1, x2], w, threshold)\n",
    "for i in range(len(output)):\n",
    "    print(\"Perceptron output for x1, x2 = \", x1[i], \",\", x2[i],\n",
    "          \" is \", output[i])\n",
    "perceptron_DB(x1, x2, w, threshold)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Exercise 2 : Create a NAND gate using a perceptron**\n",
    "\n",
    "#### Boolean NAND\n",
    "\n",
    "| x$_1$ | x$_2$ | output |\n",
    "| --- | --- | --- |\n",
    "| 0 | 0 | 1 |\n",
    "| 1 | 0 | 1 |\n",
    "| 0 | 1 | 1 |\n",
    "| 1 | 1 | 0 |"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Calculating Boolean NAND using a perceptron\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Perceptron output for x1, x2 =  0 , 0  is  1\n",
      "Perceptron output for x1, x2 =  1 , 0  is  1\n",
      "Perceptron output for x1, x2 =  0 , 1  is  1\n",
      "Perceptron output for x1, x2 =  1 , 1  is  0\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 284,
       "width": 406
      },
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Solution\n",
    "# Calculating Boolean NAND using a perceptron\n",
    "import matplotlib.pyplot as plt\n",
    "threshold=-1.5\n",
    "# (w1, w2)\n",
    "w=[-1,-1]\n",
    "# (x1, x2) pairs\n",
    "x1 = [0, 1, 0, 1]\n",
    "x2 = [0, 0, 1, 1]\n",
    "output = perceptron([x1, x2], w, threshold)\n",
    "for i in range(len(output)):\n",
    "    print(\"Perceptron output for x1, x2 = \", x1[i], \",\", x2[i],\n",
    "          \" is \", output[i])\n",
    "perceptron_DB(x1, x2, w, threshold)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In fact, a single perceptron can compute \"AND\", \"OR\" and \"NOT\" boolean functions.\n",
    "\n",
    "However, it cannot compute some other boolean functions such as \"XOR\".\n",
    "\n",
    "**WHAT CAN WE DO?**\n",
    "\n",
    "\n",
    "Hint: Think about what is the significance of the NAND gate we have created above?\n",
    "\n",
    "Answer: We said a single perceptron can't compute a \"XOR\" function. We didn't say that about **multiple Perceptrons** put together."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**XOR function using multiple perceptrons**\n",
    "\n",
    "<center>\n",
    "<figure>\n",
    "<img src=\"./images/neuralnets/perceptron_XOR.svg\" width=\"400\"/>\n",
    "<figcaption>Multiple perceptrons connected together to output a XOR function.</figcaption>\n",
    "</figure>\n",
    "</center>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Multi-layer perceptrons\n",
    "\n",
    "The normal densely connected neural network is sometimes also called \"Multi-layer\" perceptron."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Learning\n",
    "\n",
    "Now we know that we can compute complex functions by combining a number of perceptrons.\n",
    "\n",
    "In the perceptron examples we had set the model parameters (weights and thresholds) by hand.\n",
    "\n",
    "This is something we definitely **DO NOT** want to do or even can do for big networks.\n",
    "\n",
    "We want some algorithm to set the weights for us!\n",
    "\n",
    "This is achieved by choosing an appropriate loss function for the problem at hand and solving an optimization problem.\n",
    "We will explain below what this means.\n",
    "\n",
    "\n",
    "### Loss function\n",
    "\n",
    "To learn using an algorithm we need to define a quantity/function which allows us to measure how close or far are the predictions of our network/setup from reality or the supplied labels. This is done by choosing a so-called \"Loss function\" (as in the case for other machine learning algorithms).\n",
    "\n",
    "Once we have this function, we need an algorithm to update the weights of the network such that this loss function decreases. \n",
    "As one can already imagine the choice of an appropriate loss function is critical to the success of the model. \n",
    "\n",
    "Fortunately, for classification and regression (which cover a large variety of problems) these loss functions are well known. \n",
    "\n",
    "Generally **crossentropy** and **mean squared error** loss functions are used for classification and regression problems, respectively.\n",
    "\n",
    "<div class=\"alert alert-block alert-warning\">\n",
    "    <i class=\"fa fa-info-circle\"></i>&nbsp; <strong>mean squared error</strong> is defined as \n",
    "\n",
    "\n",
    "$$\n",
    "\\frac{1}{n} \\left((y_1 - \\hat{y}_1)^2 + (y_2 - \\hat{y}_2)^2 + ... + (y_n - \\hat{y}_n)^2 \\right)\n",
    "$$\n",
    "\n",
    "\n",
    "</div>\n",
    "\n",
    "### Gradient based learning\n",
    "\n",
    "As mentioned above, once we have chosen a loss function, we want to solve an **optimization problem** which minimizes this loss by updating the weights of the network. This is how the learning takes in a NN, and the \"knowledge\" is stored in the weights.\n",
    "\n",
    "The most popular optimization methods used in Neural Network training are **Gradient-descent (GD)** type methods, such as gradient-descent itself, RMSprop and Adam. \n",
    "\n",
    "**Gradient-descent** uses partial derivatives of the loss function with respect to the network weights and a learning rate to updates the weights such that the loss function decreases and after some iterations reaches its (Global) minimum value.\n",
    "\n",
    "First, the loss function and its derivative are computed at the output node, and this signal is propagated backwards, using the chain rule, in the network to compute the partial derivatives. Hence, this method is called **Backpropagation**.\n",
    "\n",
    "One way to perform a single GD pass is to compute the partial derivatives using **all the samples** in our data, computing average derivatives and using them to update the weights. This is called **Batch gradient descent**. However, in deep learning we mostly work with massive datasets and using batch gradient descent can make the training very slow!\n",
    "\n",
    "The other extreme is to randomly shuffle the dataset and advance a pass of GD with the gradients computed using only **one sample** at a time. This is called **Stochastic gradient descent**.\n",
    "\n",
    "<center>\n",
    "<figure>\n",
    "<img src=\"stochastic-vs-batch-gradient-descent.png\" width=\"600\"/>\n",
    "<figcaption>Source: <a href=\"https://wikidocs.net/3413\">https://wikidocs.net/3413</a></figcaption>\n",
    "</figure>\n",
    "</center>\n",
    "\n",
    "\n",
    "In practice, an approach in-between these two is used. The entire dataset is divided into **m batches** and these are used one by one to compute the derivatives and apply GD. This technique is called **Mini-batch gradient descent**. \n",
    "\n",
    "<div class=\"alert alert-block alert-warning\">\n",
    "<p><i class=\"fa fa-warning\"></i>&nbsp;\n",
    "One pass through the entire training dataset is called 1 epoch of training.\n",
    "</p>\n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 720x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import numpy as np\n",
    "\n",
    "plt.figure(figsize=(10, 4)) ;\n",
    "\n",
    "pts=np.arange(-20,20, 0.1) ;"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Activation Functions\n",
    "\n",
    "In order to train the network we need to move away from Perceptron's **step** activation function because it does not allow training using the gradient-descent and back-propagation algorithms among other drawbacks.\n",
    "\n",
    "Non-Linear functions such as:\n",
    "\n",
    "* Sigmoid\n",
    "\n",
    "\\begin{equation*}\n",
    "f(z) = \\frac{1}{1+e^{-z}}\n",
    "\\end{equation*}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 254,
       "width": 376
      },
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.lineplot(pts, 1/(1+np.exp(-pts))) ;"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* tanh\n",
    "\n",
    "\\begin{equation*}\n",
    "f(z) = \\frac{e^{z} - e^{-z}}{e^{z} + e^{-z}}\n",
    "\\end{equation*}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 254,
       "width": 389
      },
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.lineplot(pts, np.tanh(pts*np.pi)) ;"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* **ReLU (Rectified linear unit)**\n",
    "\n",
    "\\begin{equation*}\n",
    "f(z) = \\mathrm{max}(0,z)\n",
    "\\end{equation*}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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DPSUYAAAAMfDul/v16abDjllGepoWzStTXk6my1Wlhpic8TfG9JU0WsGHfX3ZiSFyJd0i6W+MMU4fXm4LbbmlJwAAQJzYcaBWv1u9NWx+y3XjNXxATxcrSi2xWupzQWj7pbXW+aatIcaYfsaYYmPM6Qu9VknaLWmkpF+ELhRu3f8OSd+SdFDSU1GtGgAAAJ1S19CiR5ZulM/vfCv5KRMH6fKzB7tcVWqJVePfuhRnRwf2/V+SNkt6rvWF0NN9vyepTtLfSbLGmJeNMV9Kejr0+k3W2pqoVg0AAICI+QMBPbFik47VNjnmQ4vydct1412uKvXEqvEvCm07vQbfWvuRpHMV/ECQJ2mWpP6Sfi3pLGvtx12sEQAAAFHw6ke7tHHHMccsNztd999YpqzMdMcc0dMtF/daa6e2k/9U0k87ONaPJf04TLZVwYt8AQAAEIcqdh3XsvedH9IlSXfOKNGA3tyI0Q2xfnIvAAAAktTx2kY9tqxCzqv6pesvHKbzTFGYFNFG4w8AAICo8/r8emRZueoaWhzzcUMLddMVY1yuKrXR+AMAACDqXnx3u7bvr3XMCvIydd+cMmWk04q6idkGAABAVH1WeURvrd/rmHk80r1zytS7Z7bLVYHGHwAAAFFz8NgpPf365rD5jZeP1oQRvV2sCK1o/AEAABAVTc0+LV5arqZmn2N+9pi+mj55hMtVoRWNPwAAALosEAjouVVW+6tOOeb9CnO0YFaJ0jwelytDKxp/AAAAdNnarw/o44pDjllGukeL5pWpR06my1XhdDT+AAAA6JJdh2r127e2hM2/d814jRxY4GJFcELjDwAAgE471diixUvK5fU5P6br4tKBuuKcwS5XBSc0/gAAAOgUfyCgJ1ds0tGaRsd8SL8euu16Iw/r+uMCjT8AAAA65Y1Pduvr7cccs+ysdC2aV6bsrHSXq0I4NP4AAACI2Obd1XrlvR1h8ztnTNCgvj1crAjtofEHAABARKpPNumxZeUKOC/r1zXnD9UFxf3dLQrtovEHAABAh3l9fj26rFy19S2O+ZghBfrWlWNdrgodQeMPAACADntl7Q5t3VfjmOXnZmrhnDJlpNNixiP+VAAAANAhn9sqrVy3xzHzSLp3dqn6FOS4WxQ6jMYfAAAA7TpcXa+nX98UNp8zZZRKR/VxsSJEisYfAAAAbWpu8WnxknI1NPkc87LRfTTz0pHuFoWI0fgDAACgTc+/tUV7j9Q5Zn0KsnXPrFKl8ZCuuEfjDwAAgLDe//qAPthw0DFLT/No0dyJys/NdLkqdAaNPwAAABztOXxSz7+1JWz+navHafTgAhcrQlfQ+AMAAOAv1De2aPGScrV4/Y75hRP666pzh7hcFbqCxh8AAAB/JhAI6KnXNuvIiQbHfFDfPM2fXiwP6/oTCo0/AAAA/syqdXv15dajjll2ZrrunzdROVkZLleFrqLxBwAAwJ/YPdV6ac32sPnt040G9+vhYkWIFhp/AAAASJJq6pr06LIK+QMBx/yqc4docslAl6tCtND4AwAAQD6/X48tr1DNqWbHfNSgAn37qnEuV4VoovEHAACAlr6/U5V7TjhmPXIytHBuqTIzaB0TGX96AAAAKe6rrUf12se7HTOPpHtml6pfYa67RSHqaPwBAABS2JETDXry1U1h85mXjNTE0X1drAjdhcYfAAAgRbV4fXpkSbnqm7yOecnI3pozZZTLVaG70PgDAACkqN+u3qrdh086Zr17Zuue2aVKS+MhXcmiW568YIyZL+kZSZdZaz84IxsmaU8bb//QWjulg8fpJekfJc2TNEzSYUkvS/qJtba2E6UDAACkhHfW79Harw44ZulpHi2cU6aCvCyXq0J3inrjb4y5WNJDbewyKbTdIGmjQ247eJwCSWslnRV6z6uSzpP0fUnTjDGXWGtrOlo3AABAqth1sFYPv7QhbP7NK8dq7NBCFyuCG6La+BtjbpT0a0n5bezW2vj/wlr7QhcO94CCTf8Tku6z1vqNMRmSnpZ0ayj/qy6MDwAAkHQamrz6t998ruYWn2N+fnF/XXv+UJerghuissbfGDPUGPOcgsts0hVcchNOa+P/eReO10vSAkm1kn5grfVLkrXWK2mRpGpJdxljeJ40AABASCAQ0DOvb9aBo6cc8wF98nTH9GJ5PKzrT0bRurj3AQXPsq+XNFlSZRv7TpJUJ2lLF453uaRcSe9Ya//sihRrbZ2k1aH8ii4cAwAAIKm8tX6f1tsqxywrI033zy1Tbna3XAKKOBCtxr9S0u2SLrLWOq3blyQZY/pIGq5g0/99Y8zXxph6Y8wBY8zjxpjBHTxeaWhb3kY9kjSxg+MBAAAktW37avTiu9vC5rdNMxrav63V2kh0UflIZ639eQd3bV3mc66CTflaSfskXSDpbkmzjDFTrbXtXeA7KLQ9GCZvfX1AB+uKSFZWhoqKenbH0O2K1XETFfMVGeYrMsxX5JizyDBfkWG+wjtxskmPraiQzx9wzK+fPEJzrhzvclWJJ9F/xty+j39r418hyVhrr7XW3iBplKTfSRooqSMX/Lau3a8PkzeEtnxsBQAAKc3nD+j/fWG9jtU0OuZjhhbqnrkskkgFbi/i+k8FLwA+aa092vqitfaUMWaBgmv3zzPGTLbWftLGOP7Q1vljq+Q5YxtVzc1e1dQ0tL9jFLV+wqyqcn7IBv4c8xUZ5isyzFfkmLPIMF+RYb7atuS9Hfp661HHLC87Q/fMLFHNiXDnUiHF5messDBXWVnRbdVdPeNvrfVZa3ee3vSfltVLeif05XntDFUX2uaGyXNCW+dL1gEAAFLAhu3HtOKjXWHzBbNKVNQrXDuFZOP2Up/2HApt89rZr/UxcwPD5O1dAwAAAJDUjtY06IkVFWHzb149TueM7ediRYg1Vxt/Y8yPjDEvGWPCLSQbFdrua2eo1rv5lITJJ4S2Ye8wBAAAkKxavH49srRcpxq9jvlZY/vp5uuLXa4Kseb2Gf+zJN0k6VtnBsaY/pKuk9Qi6d12xnlPwQt4rznzIV3GmHxJ1yi4HOj9KNQMAACQUP7wzlbtPOi8Hr0wP0v/+5bzlJ4ebws/0N3c/hN/LLT9gTHm0tYXQ83605IKJD1prT10WjbIGFNsjGldviNr7SlJz0rqLWmxMSYjtG+GpIcl9ZL0+JkP9wIAAEh2n1Qc0jtf7HfM0jweLZxTpt49cxxzJDe3L+59U9IvFbwo9z1jzHvGmFck7ZR0g4Jn6P/3GW/7N0mbQ9vT/bMkK+k2SdYY8+JpX38p6Ufd9X0AAADEo/1HT+nXKyvD5t+YOkbjh/VysSLEE9d/x2Ot/YGCS30+VPC+/tMUvAj3h5KuDt3dpyPjHJd0iaT/lpQpaZaCt/n8haQrrbV1bbwdAAAgqTQ2e7V4yUY1t/gd80nj+un6C4e5XBXiSbfcx99aO7Wd/EVJL3ZwrPmS5ofJjkv6m9A/AAAAKSkQCOjXb1Tq4DHn86f9e+XqrhsmyOPplkccIUFwVQcAAECCe+eL/Vq3+YhjlpmRpkXzypSXk+lyVYg3NP4AAAAJbPuBGv3+7a1h81uuG6/hA3q6WBHiFY0/AABAgjpZ36xHlpbL5w845lPOGqTLzhrsclWIVzT+AAAACcgfCOiJFZt0vLbJMR/WP1+3XDve5aoQz2j8AQAAEtCrH+5S+c7jjlludroWzStTVma6y1UhntH4AwAAJJiKnce17IOdYfO7bijRgN55LlaEREDjDwAAkECO1zbqseUVcl7VL027cLjOHV/kak1IDDT+AAAACcLr8+uRpeWqa2hxzMcPLdRNU0e7XBUSBY0/AABAgvjju9u0/UCtY1bQI0v3zS1TehrtHZzxkwEAAJAA1m0+rNXr9zlmHo903+xS9crPdrkqJBIafwAAgDh38NgpPfNGZdj8xstHq3hEbxcrQiKi8QcAAIhjTc0+LV5SrqZmn2N+9pi+mj55hMtVIRHR+AMAAMSpQCCg51ZVav/RU455v8IcLZhVojSPx+XKkIho/AEAAOLU2q8O6OOKw45ZRnqa7p83UT1yMl2uComKxh8AACAO7TxYq9+u3hI2/9614zRiYE8XK0Kio/EHAACIM3UNLVq8pFxen/Njui4pG6grzh7sclVIdDT+AAAAccQfCOjJVzfpWG2jYz6kqIduvd7Iw7p+RIjGHwAAII688clubdh+zDHLyUrX/fMmKjsz3eWqkAxo/AEAAOLE5t3VeuW9HWHzO2dM0MA+eS5WhGRC4w8AABAHqk826bFl5Qo4L+vXtecP0/nF/d0tCkmFxh8AACDGvD6/Hl1Wrtr6Fsd87JBCffPKMS5XhWRD4w8AABBjL6/drq37ahyz/NxM3TenVBnptG3oGn6CAAAAYuhze0Sr1u11zDyS7p1Tqj4FOe4WhaRE4w8AABAjh4/X6+nXN4fN5142SqUj+7hYEZIZjT8AAEAMNLX49PCScjU0+RzziaP76oZLRrpbFJIajT8AAEAMvPDmFu2rqnPM+hZk6+5ZJUrjIV2IIhp/AAAAl7339QF9sPGgY5ae5tHCuROVn5vpclVIdjT+AAAALtpz+KSef3NL2Py714zT6MEFLlaEVEHjDwAA4JL6xhY9vGSjvD6/Y35RyQBdOWmIy1UhVdD4AwAAuCAQCOip1zar6kSjYz6ob55un2bkYV0/ugmNPwAAgAtWrtujL7cedcyyM9N1/7yJysnKcLkqpBIafwAAgG5m91Tr5TU7wubzpxdrcL8eLlaEVNQtHyuNMfMlPSPpMmvtBw75dEl/K+kCSfmSDkp6Q9ID1tp9ERxnu6TRbeySaa31RlA6AABAVNXUNenRZRXyBwKO+dXnDtVFJQNcrgqpKOqNvzHmYkkPtZH/g6R/k+SXtE7SYUmTJN0r6UZjzOXW2soOHKdQ0qjQ+1eH2c35yhkAAAAX+Px+PbqsQjWnmh3zUYMK9K2rxrpcFVJVVBt/Y8yNkn6t4Fl8p7xE0gOS6iRdZ639OPR6pqRfSVqk4G8KLu7A4c6R5JG0wlp7d5eLBwAAiLJX3tshu/eEY5afm6lFc8uUmcHKa7gjKj9pxpihxpjnJL0sKV3Bs/BObg3lv2xt+iXJWtui4NKfKkmTjTEjOnDYSaHt550uHAAAoJt8ubVKb3yyxzHzSLp7Von6Fua4WxRSWrQ+Yj6gYFO/XtJkSeGW6jRL2iDpvTODUPO/M/Tl4A4ck8YfAADEpSMnGvTkq5vD5rMuHamJo/u6WBEQvaU+lZJul/S8tdZvjHHcyVr7I0k/csqMMT0klYS+7MgFvpMk+YJvNQ9KOktSQNIHkv7FWrsuou8AAAAgClq8Pi1eslENTc73Fykd2VuzLx3lclVAlBp/a+3PozDM3yt4bcBn1tq9be1ojMmWNEHBZUO/kfSZpHcllUmaKel6Y8z3rLUvRaEuAACADnvhra3ac7jOMevdM1t3zy5VWhoP6YL74uIpEcaYGZL+ScG78PywA2+ZqGDtJyXNs9a+fdpYfyvpPyX92hjzgbX2ULTrzcrKUFFRz2gP2yGxOm6iYr4iw3xFhvmKHHMWGeYrMvEwX29/tkfvfX3AMUtP8+if5l+oMSP6uFxVePEwZ4kk0ecr5peRG2Nu0P9cFPxP1to17b3HWrte0iBJE09v+kPZryQtldRD0vxo1wsAAOBk54EaLX55Q9j8ztmlKh4ZP00/Uk9Mz/gbY+6U9Fiojp9aa/+9o+9t50z+CklzJZ3XtQqdNTd7VVPT0B1Dh9X6CbOq6qSrx01UzFdkmK/IMF+RY84iw3xFJh7mq77Rq589+5maW3yO+QXF/TXZFMXNn2k8zFkiicV8FRbmKisruq16zM74G2P+RdJTCp7p/7vQhb/R0vqhIC+KYwIAAPyFQCCgZ97YrMPVzicFB/bJ0/zpxfJ4WNeP2HL9jL8xxiPpCUl3SWqSdJu19o8RjvFtSXMkvWqt/a3DLq2Xynfk7kAAAACd9tZne/W5rXLMsjLTtGhemXKz4+KySqS4WPwUPqhg018raba1dm0nxugv6buShkn6s8Y/9MHiltCXq7pQJwAAQJu27juhF9dsD5vffn2xhhblu1gREJ6rS32MMdMk/Z0kr6QbOtL0G2MGGWMe8w9TAAAgAElEQVSKjTGDTnv59wp+cJhijPm70/b1SPq/Cj5EbKOk5dGsHwAAoFXtqWY9srRcPn/AMZ86aYguLhvoclVAeG6f8f9xaHtY0n3GmPvC7Pcza23r4+7+TcGHgz2r0F16rLVVoQuDfyfpl8aYuxR8iNhESeMVXON/k7XW+ckZAAAAXeD3B/TY8gqdqGt2zEcM7KnvXj3W5aqAtrnW+Btj8iRdEPpyiKSb29j9SUnhn3MtyVr7sjHmYgXv/3+5gg3/AUkPKfjkXufFdgAAAF209IOd2ry72jHrkZOh++eWKTMj3eWqgLZ1S+NvrZ3q8Fq9gnfwiXSs+QpzP35r7eeSbop0TAAAgM7asP2oXv1oV9h8wcwS9euV615BQAfF/AFeAAAAieJoTYOeWLEpbH7DxSN09th+LlYEdByNPwAAQAe0eP1avKRcpxqdLyEsHt5Lcy8b5ZgB8YDGHwAAoAN+//ZW7Trk/OTWwvws3TunTOlptFaIX/x0AgAAtOPjikN698v9jlmax6OFc8pU2CPL5aqAyND4AwAAtGF/VZ2eXVkZNv/G1DEaP6yXixUBnUPjDwAAEEZDk1cPLylXc4vfMT93fJGuv3CYy1UBnUPjDwAA4CAQCOjZlZU6dLzeMe/fO1d3zpggj8fjcmVA59D4AwAAOHj7831at/mIY5aZkaZFc8uUl+Pas1CBLqPxBwAAOMP2/TX6wzvbwua3Xmc0fEBPFysCuo7GHwAA4DQn65v1yLJy+fwBx/yyswZpylmDXK4K6DoafwAAgBC/P6DHV2zS8domx3x4/3zdfO14l6sCooPGHwAAIGTFR7tUsfO4Y5abnaFF88qUlZnuclVAdND4AwAASCrfcUzLP9gZNl9wwwT1753nYkVAdNH4AwCAlHe8tlGPr9gk51X90vSLhmvS+CJXawKijcYfAACkNK/Pr8VLy1XX0OKYjx/WSzdeMdrlqoDoo/EHAAAp7Y/vbNOOA7WOWUGPLN03p1TpabRMSHz8FAMAgJS1bvNhrf58n2Pm8Uj3zS5Vr/xsl6sCugeNPwAASEkHj53SM29Uhs1vumKMikf0drEioHvR+AMAgJTT1OzT4iXlamr2OebnjO2naRcNd7kqoHvR+AMAgJQSCAT07KpK7T96yjHvV5iju2ZOUJrH43JlQPei8QcAACllzVcH9EnFYccsIz1N98+bqB45mS5XBXQ/Gn8AAJAydh6s1e9Wbwmb33ztOI0Y2NPFigD30PgDAICUUNfQosVLyuX1OT+m69Kygbr87MEuVwW4h8YfAAAkPX8goCdf3aRjtY2O+dCiHrrleiMP6/qRxGj8AQBA0nvt493asP2YY5aTla5F8yYqOzPd5aoAd9H4AwCApLZp13EtfX9H2PzOGRM0sE+eixUBsUHjDwAAklb1ySY9trxCAedl/brugmE6v7i/u0UBMULjDwAAkpLX59cjy8p1sr7FMR87pFDfmDrG5aqA2KHxBwAASemlNdu1bV+NY9YzL1ML55YpI51WCKmDn3YAAJB01lce0Zuf7XXMPB7p3tml6t0z2+WqgNii8QcAAEnl8PF6Pf365rD53MtGq2RkHxcrAuIDjT8AAEgaTS0+PbxkoxqbfY75WWP66oaLR7hcFRAfaPwBAEBSCAQCen6V1b6qU45534IcLZhZojQe0oUUldEdgxpj5kt6RtJl1toPHPLxkn4iaYqkvpK2SXpc0mJrrT+C4/SS9I+S5kkaJumwpJcl/cRaW9vFbwMAACSQ9zcc1IflhxyzjHSPFs0rU35upstVAfEj6mf8jTEXS3qojfxsSZ9J+o6k3ZJWKti0PyTpuQiOUyBpraQfSvJLejW0/b6kj40xhZ38FgAAQILZtu+Enn9zS9j8u1eP06hBBS5WBMSfqDb+xpgbJa2SlB8m9yjY3BdIutVaO8Vae6Ok8ZI2SLrZGHNTBw/3gKSzJD0hqcRa+83QOL+RVBLKAQBAkqurb9bPn/1MXp/zooHJpQM0ddIQl6sC4k9UGn9jzFBjzHMKLrNJV3DJjZNrFWzW11hrn2990VpbJWlR6Mu/7sDxeklaIKlW0g9alwdZa72hcaol3WWM6dG57wgAACQCfyCgX/3+Sx0+Xu+YD+7XQ7dfXywP6/qBqJ3xf0DSrZLWS5osqTLMftNC26VnBtbaDyUdkTTFGNOzneNdLilX0jvW2pNnjFMnaXUov6Kj3wAAAEg8qz7do08rnNf1Z2em6/55ZcrOSne5KiA+Ravxr5R0u6SLrLUb29ivNLQtD5PbUE0l7RyvvXFaP3hMbGccAACQoOyear20dnvY/I4ZxRrUl1/+A62iclcfa+3PO7jroND2YJi89fUBLo3TKVlZGSoqau+XEt0jVsdNVMxXZJivyDBfkWPOIsN8hXe8tlGPrdikQMA5nzlllG64fKy7RSUgfsYik+jz5fZ9/Fs/djsvxJMaQlvHi4O7YRwAAJBgfD6/fvGb9TpxsskxN8N7685ZZS5XBcS/brmPfxtaL7cP8/lcnjO23T1OpzQ3e1VT09D+jlHU+gmzqupkO3tCYr4ixXxFhvmKHHMWGearbS++u00VO445Zvm5mVpwwwSdqHZ+iBeC+BmLTCzmq7AwV1lZ0W3V3T7jXxfa5obJc0Lb9v5rjdY4AAAggXy5pUpvfLrHMfNIumdWifoW5jjmQKpzu/E/ENoODJO3t3Y/2uMAAIAEcaS6Xk++tjlsPuvSkSob3dfFioDE4nbj33oXnr+4a0/o4V7FknySNnV2nJAJoW1bdxgCAAAJornFp8VLytXQ5HXMJ40v0uxLR7lcFZBY3G78V4a2cx2ySyQVSfrgzHvzO3hPwQt4rznzIV3GmHxJ1yi4HOj9rpULAADiwW9Xb9GeI3WOWb/CHP3g5vOUlsZDuoC2uN34r5VUIelaY8zdrS8aY4okLQ59+eDpbzDGDDLGFBtjWpfvyFp7StKzknpLWmyMyQjtmyHpYUm9JD3egQ8QAAAgzn2w4aDe+9p59W56mkd/f/sFKszPdrkqIPG42vhba/2S7lTwbPzjxphPjDGvKPjgrrMkPWGtXXHG2/5N0ubQ9nT/HHrfbZKsMebF077+UtKPuu0bAQAArthz+KR+86YNm3/7qrEqHtHHxYqAxOX2GX9Za9dJukjSy5LGSbpO0m5J90laGME4xxVcHvTfkjIlzVLwNp+/kHSltdb594EAACAh1Dd6tXhpuVq8fsf8guL+uvq8oS5XBSSubrmPv7V2ajv5Jknf6OBY8yXND5Mdl/Q3oX8AAECSCAQCeub1zTpS7fzcnIF98jR/erE8Htb1Ax3l+hl/AACA9rz52V59vqXKMcvKTNP988qUm+32c0iBxEbjDwAA4sqWvSf04rvbw+a3TyvWkKJ8FysCkgONPwAAiBs1p5r1yLJy+QMBx/zKSUN0cWm453cCaAuNPwAAiAt+f0CPL69QTV2zYz5yYE995+pxLlcFJA8afwAAEBeWfrBDm3dXO2Y9cjK0aG6ZMjNoXYDO4r8eAAAQc19vO6pXP9odNl8ws0T9euW6WBGQfGj8AQBATB090aAnX90UNp95yQidPbafixUByYnGHwAAxEyL16+Hl5brVKPXMZ8worfmThntclVAcqLxBwAAMfO7t7dq96GTjlmv/CzdO7tUaWk8pAuIBhp/AAAQEx+XH9KaL/c7ZmkejxbOLVNBjyyXqwKSF40/AABw3f6qOj27qjJs/s0rx2jc0F4uVgQkPxp/AADgqoYmrx5eUq7mFr9jfp4p0nUXDHO5KiD50fgDAADXBAIB/fqNSh06Xu+YD+idqztnTJDHw7p+INpo/AEAgGtWf75Pn1UeccyyMtK0aN5E5WZnuFwVkBpo/AEAgCu27a/RH9/ZFja/9XqjYf3zXawISC00/gAAoNvV1jfrkaXl8vkDjvnlZw/SpRMHuVwVkFpo/AEAQLfy+wN6YsUmVZ9scsyHD8jXzdeOd7kqIPXQ+AMAgG61/MOdqth53DHLzc7QonkTlZmR7nJVQOqh8QcAAN1m445jWvHhrrD5gpkT1L9XrnsFASmMxh8AAHSLYzWNemLFJjmv6pemTx6uSeOKXK0JSGU0/gAAIOq8Pr8eWVauuoYWx9wM66UbLx/tclVAaqPxBwAAUfeHd7Zpx4Fax6ywR5bum1Oq9DTaEMBN/BcHAACi6tNNh/X25/scszSPR/fNKVVhfrbLVQGg8QcAAFFz4Ogp/fqNyrD5TVeMlhne28WKALSi8QcAAFHR2OzVw0s2qqnF55hPGtdP0y4a7nJVAFrR+AMAgC4LBAJ6bqXVwWP1jnlRrxzddcMEeTwelysD0IrGHwAAdNmaL/frk02HHbOM9DQtmjtReTmZLlcF4HQ0/gAAoEt2HqzV797eGja/5brxGjGwp4sVAXBC4w8AADqtrqFFi5dslNfn/JiuSycO1GVnDXK5KgBOaPwBAECn+AMBPbFik47VNjnmQ4vydct1hnX9QJyg8QcAAJ3y2ke7tHHHMccsNztd988rU3ZmustVAQiHxh8AAESsYtdxLX1/Z9j8zhkTNKBPnosVAWgPjT8AAIhI9ckmPb68Qs6r+qXrLhim80x/V2sC0L4Mtw9ojAn398SZrrTWrmlnrAxJdZLCPfd7v7V2aATlAQCANnh9fj2ytFwn61sc83FDC/WNqWNcrgpAR7je+Et6oY1sjKTJkmol7ejAWCUKNv3bJX3ikB+PuDoAABDWS2u2a9v+GsesIC9T980pU0Y6CwqAeOR642+tvcXpdWNMnqT1oS9vtdbu6cBwk0LbZ6y1P4tGfQAAwNn6yiN687O9jpnHI907u1S9e4b7JTyAWIunj+S/kjRB0hPW2uUdfE9r4/9595QEAAAk6dDxej39+uaw+bzLRmvCyD4uVgQgUnHR+BtjLpC0QNIRST+M4K2tjf8XUS8KAABIkppafFq8ZKMam32O+Vlj+mrGxSNcrgpApGKxxt/Jf0nySPqRtfZER95gjPFIOkfSIUmzjTH3KPgbg0ZJqyX92Fpru6leAABSQiAQ0G9WWe2rOuWY9y3I0YKZJUrjIV1A3PMEAh29yU73MMZMl/S6pH2SRltrnW8T8JfvGyNpW+hLv6QPJZ1Q8LcAQyWdlDTdWvthFMtdI+mKKI4HAEBcW/XJLv1/L37tmGWkp+kXfzVF44b1drkqIKWslTQ1GgPFw1Kf74e2v+xo0x/Susxnv6TzrLWXW2tnSxol6UFJPSX9wRiTE71SAQBIHdv2ndBjSzaGze+ZW0bTDySQmC71McaUSLpGUo2kxyN8+8uShkvyWWsPtL5orfUaY36o4Cej8yTNlfT7qBQc0tzsVU1NQzSHbFdRUU9JUlXVSVePm6iYr8gwX5FhviLHnEUmHubrVGOLfvbMZ2rx+h3zi0sH6LyxfePizzQe5ivRMGeRicV8FRbmKisruq16rNf4fzu0fcVa67x4MAxrbUCS4z3FrLV+Y8zrCjb+5ynKjT8AAMnMHwjoqVc362hNo2M+pF8P3XZ9sTys6wcSSqyX+twY2v6hG8Y+FNrmdcPYAAAkrZWf7tFX2446ZtlZ6Vo0r0zZWekuVwWgq2J2xt8YM0xSmYLLfN7uxPvvl3S5gvf9X+2wy6jQdl+niwQAIMVU7q7Wy2u3h83vmF6sQX17uFgRgGiJ5VKfi0LbddZabyfeP1rStyQ1K3j7zj8JXdD7zdCXb3a6QgAAUsiJuiY9urxC4W74d815Q3XhhAHuFgUgamK51Of80HZdezsaY4YbY4qNMf1Oe/kpST5JNxtjbjpt30xJD0kaIekNay1P9QUAoB0+v1+PLi1X7almx3zM4AJ966qxLlcFIJpi2fi3LsXZ0YF9n5O0WdL/an3BWrtJ/3Mr0JeMMeuMMS+FxlsgqVLS/KhVCwBAEnt57Q5t2VfjmOXnZmrh3DJlpMf60kAAXRHL/4KLQttOr8G31v63pGslrZI0TtJMSfWSfibpAmvtka4WCQBAsvtiS5VWfrrHMfNIumd2ifoU8FgcINHFbI2/tfaqCPad2kb2tjpxcTAAAJCOVNfrqdc2h81nTxmlslF9XawIQHfhd3YAAKSo5hafHl5SroYm53tslI3qo1mXjnS3KADdhsYfAIAU9cJbW7T3SJ1j1qcgW3fPKlEaD+kCkgaNPwAAKej9DQf0/oaDjll6mkcL55apZ16Wy1UB6E40/gAApJg9h0/q+Te3hM2/c/U4jRlc6GJFANxA4w8AQAqpb/Rq8dJytXj9jvmFE/rrqnOHuFwVADfQ+AMAkCICgYCeem2TjlQ3OOaD+uZp/vRieVjXDyQlGn8AAFLEqnV79eXWo45ZVmaaFs2bqJysmN3pG0A3o/EHACAFbNl7Qi+t2R42nz+tWEP69XCxIgBuo/EHACDJ1Zxq1iPLyuUPBBzzK88dosmlA12uCoDbaPwBAEhiPr9fjy0rV01ds2M+alBPfeeqcS5XBSAWaPwBAEhiS9/fqco9JxyzHjkZWji3TJkZtANAKuC/dAAAktRX247qtY93h83vnlWifoW5LlYEIJZo/AEASEJVJxr05IpNYfOZl4zUWWP6uVgRgFij8QcAIMm0eH1avKRc9U1ex3zCiN6aO2WUy1UBiDUafwAAkszvVm/V7sMnHbPePbN17+xSpaXxkC4g1dD4AwCQRD4qP6g1Xx1wzNLTPLpvTqkKemS5XBWAeEDjDwBAkthXVafnVtqw+TenjtG4ob1crAhAPKHxBwAgCTQ0efXwknI1e/2O+fmmSNdeMMzlqgDEExp/AAASXCAQ0DNvVOrw8XrHfEDvXN0xY4I8Htb1A6mMxh8AgAS3ev0+ra884phlZaTp/nkTlZud4XJVAOINjT8AAAls2/4a/fHdbWHzW683Gto/38WKAMQrGn8AABJUbX2zHllaLp8/4JhffvZgXTpxkMtVAYhXNP4AACQgvz+gx5dXqPpkk2M+YkBP3XztOJerAhDPaPwBAEhAyz/cqU27qh2zvOwMLZpXpsyMdJerAhDPaPwBAEgwG3cc04oPd4XNF8wsUVGvXPcKApAQaPwBAEggx2oa9fjyCjmv6pdmTB6hc8b1c7UmAImBxh8AgATR4vVr8dJynWr0OubFw3tp3uWjXK4KQKKg8QcAIEH84Z2t2nmw1jErzM/SvbNLlZ7G/9oBOONvBwAAEsAnmw7pnS/2O2ZpHo/um12qwvxsl6sCkEho/AEAiHP7j57Ss2/YsPlNU0fLDO/tYkUAEhGNPwAAcayx2avFSzaqqcXnmE8a10/TLhzuclUAEhGNPwAAcSoQCOjZlVYHj9U75v175equGybI4/G4XBmARJQRqwMbY26V9Fwbu/zMWvv/dGCc8ZJ+ImmKpL6Stkl6XNJia60/GrUCABAL73yxX59uOuyYZaSnadG8MuXlZLpcFYBEFbPGX9Kk0PYtSUcc8q/aG8AYc7ak9yQVSPpQ0meSrpT0kKTJkm6JSqUAALhsy55q/f7trWHzW64br+EDerpYEYBEFw+N/x3WWufbFLTBGONR8DcGBZJutdY+H3q9SNJqSTcbY5ZYa1+OVsEAALih9lSzfv7cZ/L5nR/TNWXiIF1+9mCXqwKQ6GK5xv8cSYc70/SHXCvpLElrWpt+SbLWVklaFPryr7tWIgAA7vIHAnrwt5+rqrrBMR9alK9brhvvclUAkkFMGn9jzChJvSR93oVhpoW2S88MrLUfKrh8aIoxht+DAgASxqsf7dIXlU4rYKXc7HTdf2OZsjLTXa4KQDKI1VKf1mU+h40xD0maLmmopN2Snpf0H9baxnbGKA1ty8PkVlJ/SSWSPu1auQAAdL+Knce17P2dYfM7Z5RoQO88FysCkExi3fjfIala0vuS9ks6X9JPJU0zxlxjrXX+PWfQoND2YJi89fUBXaz1L2RlZaioKDa/SIjVcRMV8xUZ5isyzFfkmLPwjp5o0BOvbpLzqn5p7hVjNG3KaFdrSjT8fEWOOYtMos9XrNb4tzb+f5Q0zFo7x1p7hYJn8b+WdImkB9oZo0do63xzY6n1Q0N+VwoFAKC7tXj9+vfnPlPtqWbHvGRUH91+Q4nLVQFINrE64/8NSaMlbbPW/ulvOWvtLmPMfElfSLrHGPMP1tqWMGO03qM/3MkRzxnbqGlu9qqmpq1fRkRf6yfMqqqTrh43UTFfkWG+IsN8RY45a9vvVm9V5e5qx6wgL1N3zZig6uOnXK4qcfDzFTnmLDKxmK/CwlxlZUW3VY9J4x9av78pTPaVMWafpGGSxkuqCDNMXWibGybPCW35mxIAELc+qzyit9bvdcw8HuneOWXq3TPb5aoAJKNY3s6zLYdC27auYDoQ2g4Mk7d3DQAAADF18NgpPf365rD5jZeP1oQRvV2sCEAyc/2Mf+j2mg9K6iPpO9Zar8Nuo0Lbtu7xXy5phoJ37VlzxjE8kool+RTmNwsAAMRSU7NPi5eWq6nZ55hfUDJA0yePcLkqAMksFmf86yTNk3STpCvODI0x0yT1k7TRWnvgzPw0K0PbuQ7ZJZKKJH1grWXxGgAgrgQCAT23ymp/lfNq1P598vT9756rNE/UL1MDkMJcb/yttQFJT4S+fMgY86dnjhtjxkhaHPrygdNfN8YUG2MKTxtqrYLr/681xtx92r5Fp43xYDd8CwAAdMnarw/o44pDjllGukf/eNsFys/LcrkqAMkuVnf1+RdJl0maIskaYz4IvX6lpGxJv7TW/vG0/d+WNELB+/7/WpKstX5jzJ2h7HFjzF0KrvufKqm3pCestSu6/1sBAKDjdh2q1W/f2hI2/9414zV2WC8XKwKQKmJycW/owVxXS/oHSbsUbPgvkfSJpJustT/o4DjrJF0k6WVJ4yRdp+DTf++TtDDqhQMA0AWnGlu0eEm5vD7nO1FfXDpQV5wz2DEDgK6K1Rl/he7f/++hf9rbd2Qb2SYFnwsAAEDc8gcCenLFJh2taXTMh/TroduuN/Kwrh9AN4nX23kCAJBU3vhkt77efswxy8lK16J5ZcrOSne5KgCphMYfAIButnl3tV55b0fY/I4ZEzSobw8XKwKQimj8AQDoRtUnm/TYsnIFnJf165rzh+qC4v7uFgUgJdH4AwDQTbw+vx5dVq7a+hbHfMyQAn3ryrEuVwUgVdH4AwDQTV5Zu0Nb99U4Zvm5mVo4p0wZ6fyvGIA7+NsGAIBu8Lmt0sp1exwzj6R7Z5eqT0GOu0UBSGk0/gAARNnh6no9/fqmsPmcKaNUOqqPixUBAI0/AABR1dzi0+Il5Wpo8jnmZaP7aOalI90tCgBE4w8AQFQ9/9YW7T1S55j1KcjWPbNKlcZDugDEAI0/AABR8v7XB/TBhoOOWXqaR4vmTlR+bqbLVQFAEI0/AABRsOfwST3/1paw+XeuHqfRgwtcrAgA/hyNPwAAXVTf2KLFS8rV4vU75hdO6K+rzh3iclUA8Odo/AEA6IJAIKCnXtusIycaHPNBffM0f3qxPKzrBxBjNP4AAHTBqnV79eXWo45Zdma67p83UTlZGS5XBQB/icYfAIBOsnuq9dKa7WHz26cbDe7Xw8WKACA8Gn8AADqhpq5Jjy6rkD8QcMyvOneIJpcMdLkqAAiPxh8AgAj5/H49trxCNaeaHfNRgwr07avGuVwVALSNxh8AgAgteW+nKveccMx65GRo4dxSZWbwv1gA8YW/lQAAiMBXW4/q9U92O2YeSffMLlW/wlx3iwKADqDxBwCgg46caNCTr24Km8+8ZKQmju7rYkUA0HE0/gAAdECL16dHlpSrvsnrmJeM7K05U0a5XBUAdByNPwAAHfDb1Vu1+/BJx6x3z2zdM7tUaWk8pAtA/KLxBwCgHR9uPKi1Xx1wzNLTPFo4t0wFeVkuVwUAkaHxBwCgDfuO1Ok3q2zY/FtXjtXYIYUuVgQAnUPjDwBAGA1NXj28ZKOavX7H/Pzi/rrm/KEuVwUAnUPjDwCAg0AgoGde36zD1Q2O+YA+ebpjerE8Htb1A0gMNP4AADh4a/0+rbdVjllWRprun1um3OwMl6sCgM6j8QcA4Axb953Qi+9uC5vfNs1oaP98FysCgK6j8QcA4DS1p5r16LIK+fwBx/yKcwbrkrJBLlcFAF1H4w8AQIjfH9BjyytUfbLJMR8xoKe+d804l6sCgOig8QcAIGTZBzu1eXe1Y5aXnaFF88qUmZHuclUAEB00/gAASNqw/ZhWfLQrbL5gVomKeuW6VxAARBmNPwAg5R2tadATKyrC5jdcPELnjO3nYkUAEH0xuQ+ZMSZd0kJJt0uaICld0g5J/397dx5lVXXmffxbA8U8KSioiCjyIIMGpziA4oCiNBEw4U06mthpIQ4r6XTspDvJm5XEZafTppN04gpxjGlbutPxNTigwSFRFCKN0UQZHxxQQQYZZKYoanj/2OdKUZ57qy5WnTuc32ct16HuPvfUU9t9z3nuPvvs/Wvgh+5e24ZjDALeybHLQncf2w7hiohIGdtf38gvHlrK7tr62PLhx/ZhyrghCUclItL+Ek/8o6T/YWASsAtYBOwHzgJuBiaZ2YXuvqeVQ42Jtq8CS2LKs6+vLiIiEvn1H15j9fqdsWW9e9TwxStGUVWpG+QiUvoK0eN/LSHpfxW43N3fBTCzfsAjwNnAt4FvtHKcTOJ/q7vP7qBYRUSkjC1atoFnXn43tqyyooLrrxhF7+41CUclItIxCtGFcU20/Uom6Qdw982E4T8An27DcTKJ/0vtF5qIiKTFu5t386t5K7OWf3L8CQwb1CfBiEREOlYhEv/NwEpgcUzZqmh7VBuOM4YwVGhVazuKiIg0t3dfPbPmLKFuf2Ns+ZgT+3HpmYMSjkpEpGMlPtTH3SfnKD4j2q7NdQwzOww4FngZ+KqZXQ2cCGwD5gLfdfd17RCuiIiUmaamJv5j3krWb4l/lOyIPl3520knUVFRkVVGmM4AABi4SURBVHBkIiIdq6KpKX5J8qSZWQXwDHA+YWafr+fY9yLg6ejH/cB8oI7wxaE/sAEY7+7t/YDvs1F8IiJSouYueJM75sTNCQE11ZX829+dx5CjeicclYhIVvOB8e1xoGKapuD7hKR6I/DDVvbNjO9fBpi7T3D3ScAQ4L+BAYAe+BURkYP421u555GlWcuvv/JkJf0iUraKosffzG4mzOSzD7jE3Z9rZf8qwlCfndFDwc3LuhHG/R8NnO3ui9ox1GeB8+vq6tm+fW87HrZ1/fv3BGDTpvgp5+Rgqq/8qL7yo/rKXzHU2c49dXzvVy+ydce+2PKxJw/kC5eflHBU8YqhvkqJ6it/qrP8FKK+evfuSk1NNbRjj39BFvDKMLNq4OfATKAWmNZa0g/g7g3A6ixle8zsD8DVwGmEdQJERCTFGpuauOvR5VmT/kFH9OCqCcMSjkpEJFkFS/zNrAfwADCR8FDuFW1J+ttoQ7Tt1k7HExGREjZ34VssXb01tqxr5ypumDqKmk5VCUclIpKsgiT+ZtYXeIrQI7+GsJBX9kGXH37/d4DRwPfcPe4Jrcza6jlnBxIRkfK3dPUWHl4Qe5MYgL+dNIIj+6qfSETKX+KJv5nVAI8Tkv7lwKXunm+CfjIwDVgBHJT4m9kRwCWE2X6e+cgBi4hIydq6o5Y7H1lOtqfZJn78WE4d1j/RmERECqUQs/rcDJxF6Okf31rSb2YnmNlwM2s+zcId0fYmMzu32b49gF8CvYC73X0DIiKSSvUNjfzioaXs2rs/tnzYMb258vzjE45KRKRwEu3xjxbe+nL04ybgJ2YWu6+7XxX98/fAYOBvgF9FZU+a2Y+BrwLPmdlCworA44B+wPPAP3TMXyEiIqXgN8+8zhvrdsSW9epew3VTRlFVWUyzWouIdKykh/qcCXSN/n1q9F82V+Uow91vMrNFwJcI8/pXAa8DtwL/7u7xXTwiIlL2Fq/YyNN/ir+hXFEB131iJH16dE44KhGRwko08Xf3eUBea6C7+3E5yh4gzAwkIiICwPotu7n3dyuzlk8773iGD+6bYEQiIsVB9zhFRKRs7KtrYNacpeyra4gtP+WEw7nsrMEJRyUiUhyU+IuISFloamrividW8u7m3bHl/Xp34drJI6isyOvGs4hI2VDiLyIiZWH+X9bxwrKNsWXVVZXcOHU03bt0SjgqEZHiocRfRERK3ur1O/ivp1dlLf/rCScyeEDPBCMSESk+SvxFRKSk7dq7n1lzllLfEL9M1zmjBnD+KUclHJWISPFR4i8iIiWrsamJu+cuZ8uO2tjyo/t35+pLjQqN6xcRUeIvIiKl6/EX3ubVN7bElnWpqeLGqaPp3Kkq4ahERIqTEn8RESlJK97aypzn38xa/oXLT2LAYd0SjEhEpLgp8RcRkZLz/s593PHIMprih/Uz4fRBnD78iGSDEhEpckr8RUSkpNQ3NPKLh5eyY8/+2PKhR/fmUxeckHBUIiLFT4m/iIiUlAfnv8Hra7fHlvXo2onrrhhJdZUubyIiLenMKCIiJeMlf48nFq+JLasAvnjFSA7r1SXZoERESoQSfxERKQkbt+7hl4+vyFo+ZdwQRh53WIIRiYiUFiX+IiJS9Pbtb+Dnc5ayd19DbPno4w9n0jnHJRuUiEiJUeIvIiJFb/aTq1i7aVds2eG9OjNj8ggqtUiXiEhOSvxFRKSoPffKOhYsWR9bVlVZwfVTRtOja6eEoxIRKT1K/EVEpGi9vWEn9z+5Kmv5Zy4+keOP6pVgRCIipUuJv4iIFKU9tfuZ9dAS6hsaY8vPGnEkF4w5OuGoRERKlxJ/EREpOk1NTdzz2Ao2bauNLR94eDc+N9Go0Lh+EZE2U+IvIiJFZ97id/jza5tjyzp3quLGqaPpUlOdcFQiIqVNib+IiBQVf+d9Hnz2zazl11w2nKP6dU8wIhGR8qDEX0REisa2Xfu4/eFlNDY1xZZfdOoxfHzEkQlHJSJSHpT4i4hIUWhobOSOh5exfXddbPmQgb2YfuHQhKMSESkfSvxFRKQo/Pa5N/E122LLenTtxA1TRtGpWpctEZFDpTOoiIgU3J9f28TvFr0TW1YBzJg8gsN7d0k2KBGRMqPEX0RECuq9bXu5e+6KrOWTzz2O0ccfnmBEIiLlSYm/iIgUzP76BmbNWcLeffWx5SOP68snzh2ScFQiIuVJib+IiBTM7Kde452Nu2LL+vbszIxPjKSyUot0iYi0ByX+IiJSEAuXrOe5V9bFllVVVnD9lFH06laTcFQiIuVLib+IiCRuzXu7+M8nPGv59AuHMvTo3glGJCJS/pT4i4hIovbU1jNrzhLq6htjy88YfgQXn3ZMwlGJiJQ/Jf4iIpKYpqYm7n18BRvf3xtbPuCwblxz2XAqKjSuX0SkvSnxFxGRxDz14hpeWrUptqymUyU3TB1F187VCUclIpIOBT27mtnFwDeBk4Ea4CXgB+7+RB7HGAZ8DxgLHA68DtwJzHL3+PvIIiKSuOWrt/DAs29kLf/8pcM5pn+PBCMSEUmXgvX4m9k1wFPAOcBi4AXgXGCemc1s4zFOAV4EPg28DcwDBgG3Afe1f9QiInIotu3cx7/e9ycaGptiy8ePOZqzRw1IOCoRkXQpSOJvZgOB24HtwOnufrm7X0pI/HcAPzWzo1s5RgUhue8FXO3uY919GjAMeBX4rJld2ZF/h4iItK6xsYl/m/0ntu6ojS0fPKAnn7loaMJRiYikT6GG+nwJ6Az8i7svzbzo7i+a2a3ALcBM4Ds5jjGBMEToWXe/v9kxNpnZDcAC4MvAgx0Qf6KamprYuHUPtXX1vL91d6HDKQl7GkKvouqrbVRf+VF95Wfhkg288trm2LLuXaq5ccooOlVXJRyViEj6FCrxnxhtH4opm0NI/C8jd+Kf9RjuvtDM3gPGmllPd9/5UYItpFVrtnHXo8vZkqWnTESklF37VyPo16drocMQEUmFxIf6REN0RgCNwIqYXVZFZSOjfbMZGW2XZil3wt834hBDLbhde/fzkwdeUdIvImVp0tmDOWVov0KHISKSGoXo8e9LGOazyd3rWha6e72ZbQaOAHoSxvzHGRht12cpz7x+5EeINVZNTTX9+/ds78N+yGuvrmNfXUOH/x4RkaSdPLQfM6adQlWl5uvPJYlrTTlRfeVPdZafUq+vQjzc2z3a7smxT2Zll1zzurV2nLYco6j17tG50CGIiLS7w3p15h+uOk1Jv4hIwgrR45+ZWz9+TregosX2UI7TlmMckrq6erZvj191sj0d1q2aIQN7sXp9tpseIiKlpaZTJTMnj6S+dj+bavcXOpyilelV3LSpZB9RS5TqK3+qs/wUor569+5KTU37puqFSPx3RdtcT3N1iba5psxo7ThtOUZRq66q5Kb/cwpzX3ibN9fvYO++BhoatCZZW1RVhZtZqq+2UX3lR/WVv07VlQwe2IsLP3YUxx5Z2rfKRURKVSES/x2EpL2fmVW7e33zQjOrBvoBte6+Lcdx1gEfAwYAK2PKW3sGoCR069KJ6RcM1TfzPKm+8qP6yo/qK3+qMxGRwkt8jL+7NwHLgSrCYlstGSGuJa0cKjObz4dm7YlmAxoONES/S0REREQk1Qqyci8wL9pOiSnLvPb4RzjGOUB/YEEpz+EvIiIiItJeCpX43wvUAv9oZqdlXjSz04GvE2bkmdXs9RPMbLiZ9W52jPnAMmCCmc1otm//Zu/9Ucf9CSIiIiIipaMgib+7vwXcBPQCXjCz35nZPOCPhLn7Z7r7e83e8nvCYl9Tmx2jEfgC4XmBO81skZn9lrBw18nAXe7+aBJ/j4iIiIhIsStUjz/uPguYDCwCxgFnAAuACe5+fxuPsRj4OPAgcCJwCfA2cB1wfQeELSIiIiJSkgoxq88H3H0uMLcN+x2Xo2w58Ml2DEtEREREpOwUrMdfRERERESSo8RfRERERCQFlPiLiIiIiKSAEn8RERERkRRQ4i8iIiIikgJK/EVEREREUkCJv4iIiIhICijxFxERERFJASX+IiIiIiIpUNHU1FToGErJWuDoxsYm6usbEv3FNTVhkeW6uvpEf2+pUn3lR/WVH9VX/lRn+VF95Uf1lT/VWX4KUV/V1VVUVlYAvAsc0x7HVOKfn21A70IHISIiIiKpsR3o0x4Hqm6Pg6TIamAIsAt4vcCxiIiIiEj5Ggr0IOSf7UI9/iIiIiIiKaCHe0VEREREUkCJv4iIiIhICijxFxERERFJASX+IiIiIiIpoMRfRERERCQFlPiLiIiIiKSAEn8RERERkRRQ4i8iIiIikgJK/EVEREREUkCJv4iIiIhICijxFxERERFJASX+IiIiIiIpoMRfRERERCQFlPiLiIiIiKSAEn8RERERkRRQ4i8iIiIikgLVhQ5A4plZV+AmYDowFGgCVgK/An7u7o0x77kY+CZwMlADvAT8wN2fSCjsomFm3wW+Awxy97Ux5eOA53IcYra7X9VB4RWl1uos2uf0aJ8zgB7AMuCn7v5fScVZbMzs28DNOXaZ4e53JxVPMdK5qe3M7Grgvhy7/LO7/9+k4ilGZnYNcC8wzt0XxJQPA74HjAUOB14H7gRmxV070yBXnZnZIOCdHG9f6O5jOzC8gjOzKuB64PPASUAV8Cbwa+CH7l7bYv+SvRYq8S9CZtYDeAY4HdgKzCdcLM8CfgZcZGZXuntDs/dcQ/hQ7wP+QGi0FwDzzOyL7n5non9EAZnZFOBbrew2Jtr+EVgdU76wXYMqcm2pMzObADxGuFM4H9gDXATMNrOR7t5anZerTFuaQ6iTll5PMJaio3NT3jLt6SngvZjyvyQYS9Exs7OB23KUn0Lo1OlFOI+/SGhvtxGuoanq0IHW64wDbe5VYElMubd7UEUkSvofBiYBu4BFwH5Ce7kZmGRmF7r7nmj/kr4WKvEvTt8iJP2/Bz7p7tsAzOw44EngCuBa4I7o9YHA7cB2YKy7L41ePwN4GvipmT3m7u8m/HckzsxuAP6d1tt25kT3dXdPVZLfUlvqLLoDdX/04wR3fyZ6/QTgWeCbZvZbd3+pg8MtRmOAWmC6u9cXOphionPTIcmcm/5G9XIwM5tGuOvdI0t5BeFuSS/gane/P3q9P6G9fdbM5rj7g8lEXHit1Vkk0+ZudffZHR5U8bmWkPS/Clye+dyZWT/gEeBs4NvAN8rhWqgx/sXp89F2ZibpB3D3t4CvRT9+utn+XwI6Az/JXFij/V8EbgW6ADM7MuBCM7PhZvYY8HNCkrGzlbeMARpJce9ZnnV2NXAEYQjUM5kX3f0N4J+iH7/cUbEWKzPrAxwHvKKkP1bqz02H4GPARiX9B5jZMWZ2H/Ag4Y7Rxiy7TiAMJ3s2k/QDuPsm4Ibox1Scp/KoMziQ+BdtstrBrom2X2n+uXP3zYThP3Ag5yr5a6ES/yITDfN5DVjs7m/G7LIq2h7V7LWJ0fahmP3nRNvL2ifConU7cDnh9vhphCFSscysBhgBrHT33cmEV5TaXGfkbmOPAg2UfxuLk/YLZmt0bsqDmQ0B+qD21NIthITrT4ThFyuz7Je1vUV3dt8DxppZz44Issi0tc4gnMd2cSC/SJvNhPpZHFPWMucq+WuhhvoUGXffBZyfY5czou1a+ODW5ghC7/WKmP1XRWUjzazC3ZvaMdxi8iLwI3d/FMDMcu07CugEvGVmtwBXEnptNxB6R25pfqeljOVTZyOj7dKWBe6+w8zWAYPM7Eh3z9WzVG4yif9uM/sPYDyhN2gVcDdZHsRPA52bDkmmPW00s9sICcQxwNuE4QUfesgwJVYS7oTf7+6NOc5VWc9TESd8PkcA/9uuERafNtWZmR0GHAu8DHw1erj8RGAbMBf4rruvSybkwnD3yTmKD8q5KINroRL/EmJmnYHMbA6ZMYp9CbfSN7l7Xcv3uHu9mW0mnOx6AjuSiDVp7v611vf6QObiejnhS9Z8wof6DMJMSpPNbGx0e7hs5VlnA6Pt+izl64FBwJHkvqVcbjJt6WvAOkIyMZBwB+VnwHgz+1RKk3+dm/L3wfh+4H3geeBdwjNfNwMTzexid99boPgKwt1/0MZd23KegnCeKmt51FmmzZ0KjObg6+EMwvVwvLuX9QO+caLOi8yMbZmcq+SvhUr8E2BmswmJQGvmuPs3shyjAriH8E18RfRvgO7RNm42kYzMRaIHJXBxbY/6akXmRDcf+FQmwY8e5Pk14en82wl3AkpCAnWWaWfZEo7mbaxkHUI9ZtrST4Gvufv+6DinEB4KmwbcSO4ZNcpV2Z2bEpBpT78BvpAZihhN7PAQcA5hCMdNBYmu+LXW5sriPNXOMm1uGTDZ3VcDmFl34C7gM8BswpfPtPk+oXNwI/DD6LWSvxYq8U/GYCDnOIrIwLgXo6mm7gY+S+gFmubu+6LiTE9irtvkFS22xe4j1Vcb/D2hN3a9u3/wQKu7bzazzxGGIEw1s4Hunu1bfbHp6DprAHINxyi1NpZNvvX4cWAIsKx53bj7K2b2ZUKyltbEvxzPTR3tk8DxwOvN75K4+1vRtKgvAzPN7J8yXzLlIK21ObW3D/sJoTd7Z/QwKwDuvtvMrgXOA04zs7PcfVGhgkyamd1MeFh3H2HGtswIgJK/FirxT8BHWfgi+tb938BkwsOXl7h784d0dkXbrjkO0yXalsSDrB29UEh0wYx9iMnd15nZy8A4wq3PxzoylvaSwOIqu4E+ZtYlyxjjkmpj2eRbj1GPbLbxxI8TLhJmZt1T+CB52Z2bOlr02VqepewvZraWMIxgGKGHVg7WWptTe2shWg8obi0b3H2Pmf2B8JDwaYT57cuamVUTZrqbSZimeZq7N1/ss+SvhZrVp4iZ2ZGE4SiTCWPuzouZG3YH4WTXL2qwLY9RDfQDalPywGp72BBtuxU0iuKSebhrQJby1sY9pk70BTMzU1Ia25LOTe1P56bcdJ5qf6lpc9Gsio8Skv5twKXu/rsWu5V8G1PiX6TMbDDwAuFb9hLgbHf/UA9PdLtpOWGe3mFxhyL8f45bjS+VzOxnZjbHzI7IssuQaLs2S3kaZXq1R7QsMLNehKnONhXrLAYdwcwGm9k9ZnZXlvIeQH/CmM9cU6WWJZ2b8mNmPc3sTjP7f3FflCKZc5Pm+I+X6zxVAQwn3IWLvauSRmb2najNjc6ySyquh2bWl7AA10RgDTCuRU9/RslfC5X4F6Foeq2nCR+45wgNMNeHbl60nRJTlnnt8faLsOSdS6iXD03hZWajCA87bUFzaTeXq41NJiR3aWtjO4DPAdea2dCY8quj7dPR7fQ00rmp7XYBUwmTCnxoSmczm0i4Q7Kk3KdX/AhytbdzCF/EFzR/tks4mdDmprcsiDrHLgH2A8+0LC8X0do+jxM6WpcD5zRfcLCFkr8WKvEvTrOAocCfgYnuvr2V/e8ljEX7RzP7YEYSMzsd+Dqhx3FWB8Vaiu6Itt83s+GZF6Nl3e8lfHBvjZuCMMUeJCx+c42ZXZ550cyOB35AeJjuxwWKrSDc/X3ggejHe6JVfAGIPoe3EB42/H4BwisWOje1UXSHJHP36DYz+2CRRjM7gQP1dEvSsZWQ+YRnHyaY2YzMi9G5PVN/PypEYEUscz28yczOzbwY3bH8JdALuNvdN8S9uUzcTFjkbA0wvpWO1pK/FlY0NWnNlGJiZicRTlwVhN7+NVl23eTuf9/sfTcQHkjZD/w+ev+FhAe4P9d8+fI0MLO3CDO0DGr5ITazSuB/CDNo1BHmyt4NXECYT/w3wF+nrZc2V51F5Z/gwPLv84GdhKlPuwHfcvfUJbhRQvE8YdjKFuCPhGncziN0rHzF3X9WuAgLT+emtjOzrsCTwFjCHYAFUdEFhDURfuzuqZ/K08yeJdwVGefuC1qUnUloZz0I62qsIyys1xe4y91nJhpskWilzn4EfJXQUbGQsJLtOMIdpucJHZC5puUtWdEIi7WEB8JfJn6xQQDc/aroPSV9LVSPf/E5nwPTQJ1HmMIz7r+pzd/k7rMIt5kWET6wZxAuGhN0YT1YtJjSdOA64FXCLeCLCB/4GcCn05b0t4W7P0Jon08ShkOdT6i/6cV+ouso0RRvZwL/QhjHfynwMUIdXZj2pB90bspHtDDXRYRpBN8iJPznEOruSiX9rXP3xYRpdh8krHtzCWHl4+uA6wsYWtGK2tV0QtI/hjDOfT3hrtxF5Zr0R87kwCxQp5I95/ps5g2lfi1Uj7+IiIiISAqox19EREREJAWU+IuIiIiIpIASfxERERGRFFDiLyIiIiKSAkr8RURERERSQIm/iIiIiEgKKPEXEREREUkBJf4iIiIiIimgxF9EREREJAWU+IuIiIiIpIASfxERERGRFFDiLyIiIiKSAkr8RURERERSQIm/iIiIiEgKKPEXEREREUkBJf4iIiIiIimgxF9EREREJAX+P4YDeolzSFafAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 254,
       "width": 383
      },
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "pts_relu=[max(0,i) for i in pts];\n",
    "plt.plot(pts, pts_relu) ;"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "are some of the commonly used as activation functions. Such non-linear activation functions allow the network to learn complex representations of data."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-block alert-warning\">\n",
    "<p><i class=\"fa fa-warning\"></i>&nbsp;\n",
    "ReLU is very popular and is widely used nowadays. There also exist other variations of ReLU, e.g. \"leaky ReLU\".\n",
    "</p>\n",
    "</div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-block alert-info\">\n",
    "<p><i class=\"fa fa-warning\"></i>&nbsp;\n",
    "Why don't we just use a simple linear activation function?\n",
    "    \n",
    "Linear activations are **NOT** used because it can be mathematically shown that if they are used then the output is just a linear function of the input. So we cannot learn interesting and complex functions by adding any number of hidden layers.\n",
    "\n",
    "The only exception when we do want to use a linear activation is for the output layer of a network when solving a regression problem.\n",
    "\n",
    "</p>\n",
    "</div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Google Playground\n",
    "\n",
    "A great tool from Google to develop a feeling for workings of neural networks.\n",
    "\n",
    "https://playground.tensorflow.org/\n",
    "\n",
    "<img src=\"./images/neuralnets/google_playground.png\"/>\n",
    "\n",
    "Some concepts to look at:\n",
    "\n",
    "* Effect of activation functions\n",
    "* Effect of network size"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Introduction to Keras"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### What is Keras?\n",
    "\n",
    "* It is a high level API to create and work with neural networks\n",
    "* Supports multiple backends such as **TensorFlow** from Google, **Theano** (Although Theano is dead now) and **CNTK** (Microsoft Cognitive Toolkit)\n",
    "* Very good for creating neural nets quickly and hides away a lot of tedious work\n",
    "* Has been incorporated into official TensorFlow (which obviously only works with tensforflow) and as of TensorFlow 2.0 this will the main api to use TensorFlow (check reference)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "dense_11 (Dense)             (None, 4)                 12        \n",
      "_________________________________________________________________\n",
      "dense_12 (Dense)             (None, 4)                 20        \n",
      "_________________________________________________________________\n",
      "dense_13 (Dense)             (None, 1)                 5         \n",
      "_________________________________________________________________\n",
      "activation_3 (Activation)    (None, 1)                 0         \n",
      "=================================================================\n",
      "Total params: 37\n",
      "Trainable params: 37\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "# Say hello to keras\n",
    "from keras.models import Sequential\n",
    "from keras.layers import Dense, Activation\n",
    "\n",
    "# Creating a model\n",
    "model = Sequential()\n",
    "\n",
    "# Adding layers to this model\n",
    "# 1st Hidden layer\n",
    "# A Dense/fully-connected layer which takes as input a \n",
    "# feature array of shape (samples, num_features)\n",
    "# Here input_shape = (2,) means that the layer expects an input with num_features = 2\n",
    "# and the sample size could be anything\n",
    "# The activation function for this layer is set to \"relu\"\n",
    "model.add(Dense(units=4, input_shape=(2,), activation=\"relu\"))\n",
    "\n",
    "# 2nd Hidden layer\n",
    "# This is also a fully-connected layer and we do not need to specify the\n",
    "# shape of the input anymore (We need to do that only for the first layer)\n",
    "# NOTE: Now we didn't add the activation seperately. Instead we just added it\n",
    "# while calling Dense(). This and the way used for the first layer are Equivalent!\n",
    "model.add(Dense(units=4, activation=\"relu\"))\n",
    "\n",
    "          \n",
    "# The output layer\n",
    "model.add(Dense(units=1))\n",
    "model.add(Activation(\"sigmoid\"))\n",
    "\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### XOR using neural networks"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from sklearn.model_selection import train_test_split\n",
    "from keras.models import Sequential\n",
    "from keras.layers import Dense\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 320,
       "width": 332
      },
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Creating a network to solve the XOR problem\n",
    "\n",
    "# Loading and plotting the data\n",
    "xor = pd.read_csv(\"xor.csv\")\n",
    "\n",
    "# Using x and y coordinates as featues\n",
    "features = xor.iloc[:, :-1]\n",
    "# Convert boolean to integer values (True->1 and False->0)\n",
    "labels = xor.iloc[:, -1].astype(int)\n",
    "\n",
    "colors = [[\"steelblue\", \"chocolate\"][i] for i in xor[\"label\"]]\n",
    "plt.figure(figsize=(5, 5))\n",
    "plt.xlim([-2, 2])\n",
    "plt.ylim([-2, 2])\n",
    "plt.title(\"Blue points are False\")\n",
    "plt.scatter(features[\"x\"], features[\"y\"], color=colors, marker=\"o\") ;"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Building a simple Keras model\n",
    "\n",
    "def a_simple_NN():\n",
    "    \n",
    "    model = Sequential()\n",
    "\n",
    "    model.add(Dense(4, input_shape = (2,), activation = \"relu\"))\n",
    "\n",
    "    model.add(Dense(4, activation = \"relu\"))\n",
    "\n",
    "    model.add(Dense(1, activation = \"sigmoid\"))\n",
    "\n",
    "    model.compile(loss=\"binary_crossentropy\", optimizer=\"rmsprop\", metrics=[\"accuracy\"])\n",
    "    \n",
    "    return model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 350 samples, validate on 150 samples\n",
      "Epoch 1/300\n",
      "350/350 [==============================] - 1s 3ms/step - loss: 0.7545 - acc: 0.4143 - val_loss: 0.7368 - val_acc: 0.4200\n",
      "Epoch 2/300\n",
      "350/350 [==============================] - 0s 74us/step - loss: 0.7379 - acc: 0.4029 - val_loss: 0.7255 - val_acc: 0.4467\n",
      "Epoch 3/300\n",
      "350/350 [==============================] - 0s 96us/step - loss: 0.7260 - acc: 0.4000 - val_loss: 0.7164 - val_acc: 0.4400\n",
      "Epoch 4/300\n",
      "350/350 [==============================] - 0s 124us/step - loss: 0.7157 - acc: 0.3857 - val_loss: 0.7084 - val_acc: 0.4533\n",
      "Epoch 5/300\n",
      "350/350 [==============================] - 0s 98us/step - loss: 0.7065 - acc: 0.3914 - val_loss: 0.7010 - val_acc: 0.4467\n",
      "Epoch 6/300\n",
      "350/350 [==============================] - 0s 82us/step - loss: 0.6976 - acc: 0.3914 - val_loss: 0.6935 - val_acc: 0.4400\n",
      "Epoch 7/300\n",
      "350/350 [==============================] - 0s 72us/step - loss: 0.6890 - acc: 0.3914 - val_loss: 0.6867 - val_acc: 0.4267\n",
      "Epoch 8/300\n",
      "350/350 [==============================] - 0s 79us/step - loss: 0.6810 - acc: 0.3971 - val_loss: 0.6803 - val_acc: 0.4267\n",
      "Epoch 9/300\n",
      "350/350 [==============================] - 0s 98us/step - loss: 0.6737 - acc: 0.3971 - val_loss: 0.6745 - val_acc: 0.4333\n",
      "Epoch 10/300\n",
      "350/350 [==============================] - 0s 96us/step - loss: 0.6666 - acc: 0.4143 - val_loss: 0.6689 - val_acc: 0.4467\n",
      "Epoch 11/300\n",
      "350/350 [==============================] - 0s 102us/step - loss: 0.6601 - acc: 0.4314 - val_loss: 0.6639 - val_acc: 0.4400\n",
      "Epoch 12/300\n",
      "350/350 [==============================] - 0s 104us/step - loss: 0.6541 - acc: 0.4400 - val_loss: 0.6591 - val_acc: 0.4933\n",
      "Epoch 13/300\n",
      "350/350 [==============================] - 0s 94us/step - loss: 0.6482 - acc: 0.4800 - val_loss: 0.6542 - val_acc: 0.5667\n",
      "Epoch 14/300\n",
      "350/350 [==============================] - 0s 98us/step - loss: 0.6427 - acc: 0.5657 - val_loss: 0.6497 - val_acc: 0.5867\n",
      "Epoch 15/300\n",
      "350/350 [==============================] - 0s 107us/step - loss: 0.6377 - acc: 0.5886 - val_loss: 0.6456 - val_acc: 0.6000\n",
      "Epoch 16/300\n",
      "350/350 [==============================] - 0s 94us/step - loss: 0.6330 - acc: 0.6086 - val_loss: 0.6419 - val_acc: 0.6200\n",
      "Epoch 17/300\n",
      "350/350 [==============================] - 0s 118us/step - loss: 0.6285 - acc: 0.6200 - val_loss: 0.6382 - val_acc: 0.6333\n",
      "Epoch 18/300\n",
      "350/350 [==============================] - 0s 99us/step - loss: 0.6240 - acc: 0.6229 - val_loss: 0.6346 - val_acc: 0.6333\n",
      "Epoch 19/300\n",
      "350/350 [==============================] - 0s 92us/step - loss: 0.6196 - acc: 0.6257 - val_loss: 0.6312 - val_acc: 0.6333\n",
      "Epoch 20/300\n",
      "350/350 [==============================] - 0s 116us/step - loss: 0.6155 - acc: 0.6314 - val_loss: 0.6279 - val_acc: 0.6333\n",
      "Epoch 21/300\n",
      "350/350 [==============================] - 0s 113us/step - loss: 0.6113 - acc: 0.6343 - val_loss: 0.6245 - val_acc: 0.6333\n",
      "Epoch 22/300\n",
      "350/350 [==============================] - 0s 133us/step - loss: 0.6071 - acc: 0.6371 - val_loss: 0.6211 - val_acc: 0.6400\n",
      "Epoch 23/300\n",
      "350/350 [==============================] - 0s 114us/step - loss: 0.6033 - acc: 0.6371 - val_loss: 0.6180 - val_acc: 0.6533\n",
      "Epoch 24/300\n",
      "350/350 [==============================] - 0s 111us/step - loss: 0.5994 - acc: 0.6400 - val_loss: 0.6148 - val_acc: 0.6533\n",
      "Epoch 25/300\n",
      "350/350 [==============================] - 0s 112us/step - loss: 0.5958 - acc: 0.6400 - val_loss: 0.6119 - val_acc: 0.6533\n",
      "Epoch 26/300\n",
      "350/350 [==============================] - 0s 100us/step - loss: 0.5923 - acc: 0.6400 - val_loss: 0.6089 - val_acc: 0.6600\n",
      "Epoch 27/300\n",
      "350/350 [==============================] - 0s 96us/step - loss: 0.5889 - acc: 0.6429 - val_loss: 0.6061 - val_acc: 0.6600\n",
      "Epoch 28/300\n",
      "350/350 [==============================] - 0s 96us/step - loss: 0.5855 - acc: 0.6486 - val_loss: 0.6033 - val_acc: 0.6600\n",
      "Epoch 29/300\n",
      "350/350 [==============================] - 0s 112us/step - loss: 0.5821 - acc: 0.6543 - val_loss: 0.6004 - val_acc: 0.6600\n",
      "Epoch 30/300\n",
      "350/350 [==============================] - 0s 88us/step - loss: 0.5787 - acc: 0.6543 - val_loss: 0.5975 - val_acc: 0.6600\n",
      "Epoch 31/300\n",
      "350/350 [==============================] - 0s 93us/step - loss: 0.5752 - acc: 0.6543 - val_loss: 0.5948 - val_acc: 0.6667\n",
      "Epoch 32/300\n",
      "350/350 [==============================] - 0s 99us/step - loss: 0.5717 - acc: 0.6571 - val_loss: 0.5920 - val_acc: 0.6733\n",
      "Epoch 33/300\n",
      "350/350 [==============================] - 0s 113us/step - loss: 0.5685 - acc: 0.6600 - val_loss: 0.5894 - val_acc: 0.6800\n",
      "Epoch 34/300\n",
      "350/350 [==============================] - 0s 95us/step - loss: 0.5654 - acc: 0.6629 - val_loss: 0.5869 - val_acc: 0.6733\n",
      "Epoch 35/300\n",
      "350/350 [==============================] - 0s 94us/step - loss: 0.5625 - acc: 0.6629 - val_loss: 0.5846 - val_acc: 0.6733\n",
      "Epoch 36/300\n",
      "350/350 [==============================] - 0s 100us/step - loss: 0.5595 - acc: 0.6629 - val_loss: 0.5822 - val_acc: 0.6600\n",
      "Epoch 37/300\n",
      "350/350 [==============================] - 0s 75us/step - loss: 0.5565 - acc: 0.6629 - val_loss: 0.5797 - val_acc: 0.6600\n",
      "Epoch 38/300\n",
      "350/350 [==============================] - 0s 133us/step - loss: 0.5537 - acc: 0.6629 - val_loss: 0.5774 - val_acc: 0.6600\n",
      "Epoch 39/300\n",
      "350/350 [==============================] - 0s 91us/step - loss: 0.5506 - acc: 0.6629 - val_loss: 0.5748 - val_acc: 0.6600\n",
      "Epoch 40/300\n",
      "350/350 [==============================] - 0s 91us/step - loss: 0.5480 - acc: 0.6714 - val_loss: 0.5725 - val_acc: 0.6667\n",
      "Epoch 41/300\n",
      "350/350 [==============================] - 0s 91us/step - loss: 0.5453 - acc: 0.6743 - val_loss: 0.5703 - val_acc: 0.6667\n",
      "Epoch 42/300\n",
      "350/350 [==============================] - 0s 104us/step - loss: 0.5424 - acc: 0.6743 - val_loss: 0.5679 - val_acc: 0.6667\n",
      "Epoch 43/300\n",
      "350/350 [==============================] - 0s 109us/step - loss: 0.5394 - acc: 0.6771 - val_loss: 0.5654 - val_acc: 0.6667\n",
      "Epoch 44/300\n",
      "350/350 [==============================] - 0s 117us/step - loss: 0.5365 - acc: 0.6771 - val_loss: 0.5628 - val_acc: 0.6667\n",
      "Epoch 45/300\n",
      "350/350 [==============================] - 0s 100us/step - loss: 0.5335 - acc: 0.6771 - val_loss: 0.5603 - val_acc: 0.6667\n",
      "Epoch 46/300\n",
      "350/350 [==============================] - 0s 106us/step - loss: 0.5305 - acc: 0.6743 - val_loss: 0.5577 - val_acc: 0.6667\n",
      "Epoch 47/300\n",
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      "Epoch 59/300\n",
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      "Epoch 60/300\n",
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      "Epoch 61/300\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "350/350 [==============================] - 0s 96us/step - loss: 0.4753 - acc: 0.6886 - val_loss: 0.5059 - val_acc: 0.6733\n",
      "Epoch 62/300\n",
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      "Epoch 65/300\n",
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      "Epoch 67/300\n",
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      "Epoch 71/300\n",
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      "Epoch 73/300\n",
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      "Epoch 74/300\n",
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      "Epoch 75/300\n",
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      "Epoch 76/300\n",
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      "Epoch 77/300\n",
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      "Epoch 81/300\n",
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      "Epoch 82/300\n",
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      "Epoch 83/300\n",
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      "Epoch 84/300\n",
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      "Epoch 85/300\n",
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      "Epoch 90/300\n",
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      "Epoch 91/300\n",
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      "Epoch 93/300\n",
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      "Epoch 94/300\n",
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      "Epoch 119/300\n",
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      "Epoch 120/300\n",
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      "Epoch 121/300\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "350/350 [==============================] - 0s 90us/step - loss: 0.2838 - acc: 0.9171 - val_loss: 0.3063 - val_acc: 0.9133\n",
      "Epoch 122/300\n",
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      "Epoch 129/300\n",
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      "Epoch 131/300\n",
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      "Epoch 133/300\n",
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      "Epoch 134/300\n",
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      "Epoch 136/300\n",
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      "Epoch 137/300\n",
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      "Epoch 139/300\n",
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      "Epoch 140/300\n",
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      "Epoch 141/300\n",
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      "Epoch 142/300\n",
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      "Epoch 143/300\n",
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      "Epoch 144/300\n",
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      "Epoch 145/300\n",
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      "Epoch 146/300\n",
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      "Epoch 147/300\n",
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      "Epoch 149/300\n",
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      "Epoch 150/300\n",
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      "Epoch 151/300\n",
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      "Epoch 152/300\n",
      "350/350 [==============================] - ETA: 0s - loss: 0.2170 - acc: 0.906 - 0s 97us/step - loss: 0.2293 - acc: 0.9429 - val_loss: 0.2518 - val_acc: 0.9267\n",
      "Epoch 153/300\n",
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      "Epoch 154/300\n",
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      "Epoch 158/300\n",
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      "Epoch 159/300\n",
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      "Epoch 160/300\n",
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      "Epoch 161/300\n",
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      "Epoch 162/300\n",
      "350/350 [==============================] - 0s 88us/step - loss: 0.2155 - acc: 0.9457 - val_loss: 0.2388 - val_acc: 0.9400\n",
      "Epoch 163/300\n",
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      "Epoch 164/300\n",
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      "Epoch 165/300\n",
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      "Epoch 166/300\n",
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      "Epoch 167/300\n",
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      "Epoch 168/300\n",
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      "Epoch 169/300\n",
      "350/350 [==============================] - 0s 99us/step - loss: 0.2065 - acc: 0.9486 - val_loss: 0.2302 - val_acc: 0.9333\n",
      "Epoch 170/300\n",
      "350/350 [==============================] - 0s 103us/step - loss: 0.2054 - acc: 0.9486 - val_loss: 0.2291 - val_acc: 0.9400\n",
      "Epoch 171/300\n",
      "350/350 [==============================] - 0s 91us/step - loss: 0.2040 - acc: 0.9486 - val_loss: 0.2281 - val_acc: 0.9400\n",
      "Epoch 172/300\n",
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      "Epoch 173/300\n",
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      "Epoch 174/300\n",
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      "Epoch 175/300\n",
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      "Epoch 176/300\n",
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      "Epoch 177/300\n",
      "350/350 [==============================] - 0s 103us/step - loss: 0.1969 - acc: 0.9514 - val_loss: 0.2215 - val_acc: 0.9400\n",
      "Epoch 178/300\n",
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      "Epoch 179/300\n",
      "350/350 [==============================] - 0s 85us/step - loss: 0.1947 - acc: 0.9514 - val_loss: 0.2193 - val_acc: 0.9467\n",
      "Epoch 180/300\n",
      "350/350 [==============================] - 0s 84us/step - loss: 0.1934 - acc: 0.9514 - val_loss: 0.2184 - val_acc: 0.9467\n",
      "Epoch 181/300\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "350/350 [==============================] - 0s 91us/step - loss: 0.1922 - acc: 0.9514 - val_loss: 0.2174 - val_acc: 0.9467\n",
      "Epoch 182/300\n",
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      "Epoch 187/300\n",
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      "Epoch 191/300\n",
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      "Epoch 192/300\n",
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      "Epoch 193/300\n",
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      "Epoch 194/300\n",
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      "Epoch 195/300\n",
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      "Epoch 196/300\n",
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      "Epoch 197/300\n",
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      "Epoch 198/300\n",
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      "Epoch 200/300\n",
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      "Epoch 201/300\n",
      "350/350 [==============================] - 0s 94us/step - loss: 0.1716 - acc: 0.9657 - val_loss: 0.1985 - val_acc: 0.9467\n",
      "Epoch 202/300\n",
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      "Epoch 203/300\n",
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      "Epoch 204/300\n",
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      "Epoch 205/300\n",
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      "Epoch 206/300\n",
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      "Epoch 208/300\n",
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      "Epoch 209/300\n",
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      "Epoch 210/300\n",
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      "Epoch 211/300\n",
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      "Epoch 212/300\n",
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      "Epoch 213/300\n",
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      "Epoch 214/300\n",
      "350/350 [==============================] - 0s 108us/step - loss: 0.1595 - acc: 0.9743 - val_loss: 0.1878 - val_acc: 0.9533\n",
      "Epoch 215/300\n",
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      "Epoch 216/300\n",
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      "Epoch 218/300\n",
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      "Epoch 219/300\n",
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      "Epoch 220/300\n",
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      "Epoch 222/300\n",
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      "Epoch 224/300\n",
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      "Epoch 226/300\n",
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      "Epoch 227/300\n",
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      "Epoch 228/300\n",
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      "Epoch 229/300\n",
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      "Epoch 230/300\n",
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      "Epoch 231/300\n",
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      "Epoch 232/300\n",
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      "Epoch 233/300\n",
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      "Epoch 234/300\n",
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      "Epoch 235/300\n",
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      "Epoch 236/300\n",
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      "Epoch 237/300\n",
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      "Epoch 238/300\n",
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      "Epoch 239/300\n",
      "350/350 [==============================] - 0s 83us/step - loss: 0.1418 - acc: 0.9714 - val_loss: 0.1724 - val_acc: 0.9533\n",
      "Epoch 240/300\n",
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      "Epoch 241/300\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "350/350 [==============================] - 0s 114us/step - loss: 0.1404 - acc: 0.9743 - val_loss: 0.1717 - val_acc: 0.9533\n",
      "Epoch 242/300\n",
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      "Epoch 243/300\n",
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      "Epoch 244/300\n",
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      "Epoch 245/300\n",
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      "Epoch 246/300\n",
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      "Epoch 247/300\n",
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      "Epoch 248/300\n",
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      "Epoch 249/300\n",
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      "Epoch 250/300\n",
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      "Epoch 251/300\n",
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      "Epoch 252/300\n",
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      "Epoch 253/300\n",
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      "Epoch 254/300\n",
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      "Epoch 255/300\n",
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      "Epoch 256/300\n",
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      "Epoch 257/300\n",
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      "Epoch 258/300\n",
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      "Epoch 259/300\n",
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      "Epoch 260/300\n",
      "350/350 [==============================] - 0s 91us/step - loss: 0.1298 - acc: 0.9771 - val_loss: 0.1626 - val_acc: 0.9533\n",
      "Epoch 261/300\n",
      "350/350 [==============================] - 0s 89us/step - loss: 0.1292 - acc: 0.9743 - val_loss: 0.1625 - val_acc: 0.9533\n",
      "Epoch 262/300\n",
      "350/350 [==============================] - 0s 93us/step - loss: 0.1287 - acc: 0.9771 - val_loss: 0.1620 - val_acc: 0.9533\n",
      "Epoch 263/300\n",
      "350/350 [==============================] - 0s 93us/step - loss: 0.1282 - acc: 0.9743 - val_loss: 0.1615 - val_acc: 0.9533\n",
      "Epoch 264/300\n",
      "350/350 [==============================] - 0s 94us/step - loss: 0.1273 - acc: 0.9771 - val_loss: 0.1607 - val_acc: 0.9533\n",
      "Epoch 265/300\n",
      "350/350 [==============================] - 0s 91us/step - loss: 0.1270 - acc: 0.9771 - val_loss: 0.1598 - val_acc: 0.9533\n",
      "Epoch 266/300\n",
      "350/350 [==============================] - 0s 94us/step - loss: 0.1267 - acc: 0.9771 - val_loss: 0.1595 - val_acc: 0.9533\n",
      "Epoch 267/300\n",
      "350/350 [==============================] - 0s 93us/step - loss: 0.1258 - acc: 0.9771 - val_loss: 0.1595 - val_acc: 0.9533\n",
      "Epoch 268/300\n",
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      "Epoch 269/300\n",
      "350/350 [==============================] - 0s 89us/step - loss: 0.1245 - acc: 0.9771 - val_loss: 0.1589 - val_acc: 0.9533\n",
      "Epoch 270/300\n",
      "350/350 [==============================] - 0s 94us/step - loss: 0.1241 - acc: 0.9800 - val_loss: 0.1583 - val_acc: 0.9533\n",
      "Epoch 271/300\n",
      "350/350 [==============================] - 0s 96us/step - loss: 0.1236 - acc: 0.9771 - val_loss: 0.1581 - val_acc: 0.9533\n",
      "Epoch 272/300\n",
      "350/350 [==============================] - 0s 100us/step - loss: 0.1231 - acc: 0.9771 - val_loss: 0.1580 - val_acc: 0.9533\n",
      "Epoch 273/300\n",
      "350/350 [==============================] - 0s 101us/step - loss: 0.1225 - acc: 0.9800 - val_loss: 0.1574 - val_acc: 0.9533\n",
      "Epoch 274/300\n",
      "350/350 [==============================] - 0s 91us/step - loss: 0.1221 - acc: 0.9800 - val_loss: 0.1567 - val_acc: 0.9533\n",
      "Epoch 275/300\n",
      "350/350 [==============================] - 0s 107us/step - loss: 0.1217 - acc: 0.9771 - val_loss: 0.1565 - val_acc: 0.9533\n",
      "Epoch 276/300\n",
      "350/350 [==============================] - 0s 101us/step - loss: 0.1211 - acc: 0.9771 - val_loss: 0.1562 - val_acc: 0.9533\n",
      "Epoch 277/300\n",
      "350/350 [==============================] - 0s 93us/step - loss: 0.1205 - acc: 0.9800 - val_loss: 0.1559 - val_acc: 0.9533\n",
      "Epoch 278/300\n",
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      "Epoch 279/300\n",
      "350/350 [==============================] - 0s 94us/step - loss: 0.1195 - acc: 0.9800 - val_loss: 0.1552 - val_acc: 0.9533\n",
      "Epoch 280/300\n",
      "350/350 [==============================] - 0s 95us/step - loss: 0.1191 - acc: 0.9800 - val_loss: 0.1545 - val_acc: 0.9533\n",
      "Epoch 281/300\n",
      "350/350 [==============================] - 0s 96us/step - loss: 0.1189 - acc: 0.9800 - val_loss: 0.1541 - val_acc: 0.9533\n",
      "Epoch 282/300\n",
      "350/350 [==============================] - 0s 92us/step - loss: 0.1185 - acc: 0.9771 - val_loss: 0.1545 - val_acc: 0.9533\n",
      "Epoch 283/300\n",
      "350/350 [==============================] - 0s 95us/step - loss: 0.1179 - acc: 0.9800 - val_loss: 0.1539 - val_acc: 0.9533\n",
      "Epoch 284/300\n",
      "350/350 [==============================] - 0s 96us/step - loss: 0.1175 - acc: 0.9800 - val_loss: 0.1540 - val_acc: 0.9533\n",
      "Epoch 285/300\n",
      "350/350 [==============================] - 0s 83us/step - loss: 0.1168 - acc: 0.9800 - val_loss: 0.1531 - val_acc: 0.9533\n",
      "Epoch 286/300\n",
      "350/350 [==============================] - 0s 64us/step - loss: 0.1166 - acc: 0.9800 - val_loss: 0.1525 - val_acc: 0.9533\n",
      "Epoch 287/300\n",
      "350/350 [==============================] - 0s 64us/step - loss: 0.1163 - acc: 0.9800 - val_loss: 0.1524 - val_acc: 0.9533\n",
      "Epoch 288/300\n",
      "350/350 [==============================] - 0s 64us/step - loss: 0.1156 - acc: 0.9800 - val_loss: 0.1519 - val_acc: 0.9533\n",
      "Epoch 289/300\n",
      "350/350 [==============================] - 0s 70us/step - loss: 0.1152 - acc: 0.9800 - val_loss: 0.1520 - val_acc: 0.9533\n",
      "Epoch 290/300\n",
      "350/350 [==============================] - 0s 76us/step - loss: 0.1150 - acc: 0.9800 - val_loss: 0.1513 - val_acc: 0.9533\n",
      "Epoch 291/300\n",
      "350/350 [==============================] - 0s 68us/step - loss: 0.1143 - acc: 0.9771 - val_loss: 0.1511 - val_acc: 0.9533\n",
      "Epoch 292/300\n",
      "350/350 [==============================] - ETA: 0s - loss: 0.1343 - acc: 0.968 - 0s 68us/step - loss: 0.1138 - acc: 0.9771 - val_loss: 0.1513 - val_acc: 0.9533\n",
      "Epoch 293/300\n",
      "350/350 [==============================] - 0s 69us/step - loss: 0.1135 - acc: 0.9800 - val_loss: 0.1500 - val_acc: 0.9533\n",
      "Epoch 294/300\n",
      "350/350 [==============================] - 0s 69us/step - loss: 0.1131 - acc: 0.9771 - val_loss: 0.1500 - val_acc: 0.9533\n",
      "Epoch 295/300\n",
      "350/350 [==============================] - 0s 66us/step - loss: 0.1126 - acc: 0.9771 - val_loss: 0.1503 - val_acc: 0.9533\n",
      "Epoch 296/300\n",
      "350/350 [==============================] - 0s 67us/step - loss: 0.1123 - acc: 0.9800 - val_loss: 0.1497 - val_acc: 0.9533\n",
      "Epoch 297/300\n",
      "350/350 [==============================] - 0s 69us/step - loss: 0.1119 - acc: 0.9771 - val_loss: 0.1490 - val_acc: 0.9533\n",
      "Epoch 298/300\n",
      "350/350 [==============================] - 0s 65us/step - loss: 0.1117 - acc: 0.9771 - val_loss: 0.1489 - val_acc: 0.9533\n",
      "Epoch 299/300\n",
      "350/350 [==============================] - 0s 69us/step - loss: 0.1111 - acc: 0.9800 - val_loss: 0.1490 - val_acc: 0.9533\n",
      "Epoch 300/300\n",
      "350/350 [==============================] - 0s 68us/step - loss: 0.1108 - acc: 0.9771 - val_loss: 0.1486 - val_acc: 0.9533\n"
     ]
    }
   ],
   "source": [
    "# Instantiating the model\n",
    "model = a_simple_NN()\n",
    "\n",
    "# Splitting the dataset into training (70%) and validation sets (30%)\n",
    "X_train, X_test, y_train, y_test = train_test_split(\n",
    "    features, labels, test_size=0.3)\n",
    "\n",
    "# Setting the number of passes through the entire training set\n",
    "num_epochs = 300\n",
    "\n",
    "# model.fit() is used to train the model\n",
    "# We can pass validation data while training\n",
    "model_run = model.fit(X_train, y_train, epochs=num_epochs,\n",
    "                      validation_data=(X_test, y_test))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-block alert-info\"><p><i class=\"fa fa-info-circle\"></i>&nbsp;\n",
    "    NOTE: We can pass \"verbose=0\" to model.fit() to suppress the printing of model output on the terminal/notebook.\n",
    "</p></div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The history has the following data:  dict_keys(['val_loss', 'val_acc', 'loss', 'acc'])\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 269,
       "width": 392
      },
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plotting the loss and accuracy on the training and validation sets during the training\n",
    "# This can be done by using Keras callback \"history\" which is applied by default\n",
    "history_model = model_run.history\n",
    "\n",
    "print(\"The history has the following data: \", history_model.keys())\n",
    "\n",
    "# Plotting the training and validation accuracy during the training\n",
    "sns.lineplot(np.arange(1, num_epochs+1), history_model[\"acc\"], color = \"blue\", label=\"Training set\") ;\n",
    "sns.lineplot(np.arange(1, num_epochs+1), history_model[\"val_acc\"], color = \"red\", label=\"Valdation set\") ;\n",
    "plt.xlabel(\"epochs\") ;\n",
    "<div class=\"alert alert-block alert-warning\">\n",
    "<p><i class=\"fa fa-warning\"></i>&nbsp;\n",
    "ReLU is very popular and is widely used nowadays. There also exist other variations of ReLU, e.g. \"leaky ReLU\".\n",
    "</p>\n",
    "</div>plt.ylabel(\"accuracy\") ;"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-block alert-warning\">\n",
    "<p><i class=\"fa fa-warning\"></i>&nbsp;\n",
    "The plots such as above are very important for analyzing the behaviour and performance of the network and to tune it in the right direction. However, for the example above we don't really expect to derive a lot of insight from this plot as the function we are trying to fit is quiet simple and there is not too much noise. We will see the significance of these curves in a later example.\n",
    "</p>\n",
    "</div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In the example above we splitted our dataset into a 70-30 train-validation set. We know from previous chapters that to more robustly calculate accuracy we can use **K-fold crossvalidation**.\n",
    "This is even more important when we have small datasets and cannot afford to reserve a validation set!\n",
    "\n",
    "One way to do the cross validation here would be to write our own function to do this. However, we also know that **SciKit learn** provides several handy functions to evaluate and tune the models. So the question is:\n",
    "\n",
    "\n",
    "<div class=\"alert alert-block alert-warning\">\n",
    "<p><i class=\"fa fa-warning\"></i>&nbsp;\n",
    "    Can we somehow use the Scikit learn functions or ones we wrote ourselves for Scikit learn models to evaluate and tune our Keras models?\n",
    "\n",
    "\n",
    "The Answer is **YES !**\n",
    "</p>\n",
    "</div>\n",
    "\n",
    "\n",
    "\n",
    "We show how to do this in the following section."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Using SciKit learn functions on Keras models\n",
    "\n",
    "\n",
    "<div class=\"alert alert-block alert-warning\">\n",
    "<p><i class=\"fa fa-warning\"></i>&nbsp;\n",
    "Keras offers 2 wrappers which allow its Sequential models to be used with SciKit learn. \n",
    "\n",
    "There are: **KerasClassifier** and **KerasRegressor**.\n",
    "\n",
    "For more information:\n",
    "https://keras.io/scikit-learn-api/\n",
    "</p>\n",
    "</div>\n",
    "\n",
    "\n",
    "\n",
    "**Now lets see how this works!**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "# We wrap the Keras model we created above with KerasClassifier\n",
    "from keras.wrappers.scikit_learn import KerasClassifier\n",
    "from sklearn.model_selection import cross_val_score\n",
    "# Wrapping Keras model\n",
    "# NOTE: We pass verbose=0 to suppress the model output\n",
    "num_epochs = 400\n",
    "model_scikit = KerasClassifier(\n",
    "    build_fn=a_simple_NN, **{\"epochs\": num_epochs, \"verbose\": 0})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Let's reuse the function to visualize the decision boundary which we saw in chapter 2 with minimal change\n",
    "\n",
    "def list_flatten(list_of_list):\n",
    "    flattened_list = [i for j in list_of_list for i in j]\n",
    "    return flattened_list\n",
    "\n",
    "def plot_points(plt=plt, marker='o'):\n",
    "    colors = [[\"steelblue\", \"chocolate\"][i] for i in labels]\n",
    "    plt.scatter(features.iloc[:, 0], features.iloc[:, 1], color=colors, marker=marker);\n",
    "\n",
    "def train_and_plot_decision_surface(\n",
    "    name, classifier, features_2d, labels, preproc=None, plt=plt, marker='o', N=400\n",
    "):\n",
    "\n",
    "    features_2d = np.array(features_2d)\n",
    "    xmin, ymin = features_2d.min(axis=0)\n",
    "    xmax, ymax = features_2d.max(axis=0)\n",
    "\n",
    "    x = np.linspace(xmin, xmax, N)\n",
    "    y = np.linspace(ymin, ymax, N)\n",
    "    points = np.array(np.meshgrid(x, y)).T.reshape(-1, 2)\n",
    "\n",
    "    if preproc is not None:\n",
    "        points_for_classifier = preproc.fit_transform(points)\n",
    "        features_2d = preproc.fit_transform(features_2d)\n",
    "    else:\n",
    "        points_for_classifier = points\n",
    "\n",
    "    classifier.fit(features_2d, labels, verbose=0)\n",
    "    predicted = classifier.predict(features_2d)\n",
    "    \n",
    "    if name == \"Neural Net\":\n",
    "        predicted = list_flatten(predicted)\n",
    "    \n",
    "    \n",
    "    if preproc is not None:\n",
    "        name += \" (w/ preprocessing)\"\n",
    "    print(name + \":\\t\", sum(predicted == labels), \"/\", len(labels), \"correct\")\n",
    "    \n",
    "    if name == \"Neural Net\":\n",
    "        classes = np.array(list_flatten(classifier.predict(points_for_classifier)), dtype=bool)\n",
    "    else:\n",
    "        classes = np.array(classifier.predict(points_for_classifier), dtype=bool)\n",
    "    plt.plot(\n",
    "        points[~classes][:, 0],\n",
    "        points[~classes][:, 1],\n",
    "        \"o\",\n",
    "        color=\"steelblue\",\n",
    "        markersize=1,\n",
    "        alpha=0.01,\n",
    "    )\n",
    "    plt.plot(\n",
    "        points[classes][:, 0],\n",
    "        points[classes][:, 1],\n",
    "        \"o\",\n",
    "        color=\"chocolate\",\n",
    "        markersize=1,\n",
    "        alpha=0.04,\n",
    "    )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Neural Net:\t 486 / 500 correct\n"
     ]
    },
    {
     "data": {
      "image/png": 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dLOBYxvtKOiahY69jH9oJA6ev2FDZgt52O/4V14LhKwl/zryO+2mO/hDOeODf2tpaA3w+/t8LwF+1trZOum5nZ+fH43IHsBT4FPBP8e+c1tbWX4t/94+tra2fJpb3vx2oBr7Z2dn5i8KMotSQKctS0amhkXstpeSZg108sa8Xk/E8dfAiDWUaH7p+OW31lVO2A7mlr3z02hUsCJzh8QMXScfGxQH+w7Y1eA1t3LazT6tUn1y05bhLC0slVtEz+3P6nvYmHMfh8Y70xyrAg5sbuHXlopz6ylRLYLak+ph9pzAP7oATr07uuAlIIk/+Jd6Pfw0jL2kUuaX6aBLY8hC89b0M7Rf42u8unG+TUn3s0ACc3ZOhXenRr/0wjjBKNn3FEVlWgpGiYGNJ9pU9cpHIYw9DdJILlIEz2K9/l5G9j+J573/FE6gquj9nWqtUn/xzHVcq8WyO/0vHx6f4js7Ozt2tra1biT1BuA1YDxwB/gD4Vu6mzhZEylKWgE61zX1bUsJ3XzkyZb5094jDN3Yc45PblnBtS/0UbYo0n2emhYD7Nixl+5omdh3vYd/pS4xELLy6xpqGSm5ubaC+zJfTeEtH5+ar+aLTpfrUlPnIpZjYdSvryGUfCCG4b+MSrlpZz0sHunnp+JXUHw3YvqamCMcrlHqqjwTCb/wEDmRzvyhM9NDLeFu35WxPrqk+mrQpb7uJ4ZE+OPTEtJZ77/49PMFqnAL5NjnVJ+w2730SjFs+hzeellSqqT7e8jqyqvlXtahgY0n4ilA/kUf/DMxpnmCH+zB/9sd4Hvgimr+8ZHyrUn1yZ8ZH1NnZ+STJvwaZbbNsiu86gIdyNEtRgjx14GzGL0l+d9dpFt8XpGVB+gmCXB10aQj6DO5a28Rda5vjn6QGMXODfPhqrpMIYVO5dvVCdhzO8lE/sG1Nw/QrZUBjWZCPXreSj1zrELIchAS/R0OIyZ5IFZbMjqek86gIj/dH9j6RZdAf59Cz0Lotd3sQ5JLqk9DBax8kWtOIdeApGJjkFbqWzXiv/iDeirrC+paEBi6ezN6/LVvwbbgXT3UTTpr0leKniMS0XlYNda0T3w2ZBm/rzQUcS8xXo3semT7oT2ANMPrOI5Rf//GS8e3M6PgxNUd/COfepcy8RKYsS0WnhkaZ66jlZJynnODpvWf49M2JtLHUNlX6SuZa+SoX3VwRpLlC5+yQjVvubqulyp+cc5+7TUIIgh494/ULoSVQyqk+1mAvvPtzcmK4BxuBEFpRU32StXfF9XhXbCVy+Qx291GwLAhW4G9eh+ZfgGN4cSyzsH5OrupjpSZsZoZ202fwrroBzTJxkvoo1VQfJGjr7sB50UXgX7MarW55wfaH1HRM24Tjr7hz/rFdWNd8BAKBkvFtobVK9VGUNLE6/pRUHX/bSXyWfW3w1066f/lrT9cIA+EoFZNU1HEciYjbl+8xzzWtfJX5sS5h0hr4H7tpJQ8/cRg3bFu+gPduaJ6Tfo/NeSDiY5tkHcdGmFFwIrFymdKOpYkIfUZ0tOO5CfsjK8Ij4PHlZo85irTN2P/zNEZPVSO+8rr4RZWOZkXz2v6UOjoS+380DMEKyOJhmFFeC2ZoQh8yKsCwwBydsWMlU+1rWEto2Y1wMpNA24v3xl+edIz50jIqGD2c6Xsr44kcfR1f280l49tCa2n5wImC41epPorSQko59mMqpUgKGIqHbUuk0LDt5GDIPUfPuZtyfmy7viE2LaqeaFd8Lo4c37ucFyhfZYbtEA/8J37XVFbGb9+xgm/sOD5tO80VGnduauGqxTVjF/JzDTvuo7THlCMRZgQtGo1NoCMEIu6ImdAc2ZWXcUrHhmgoJ3usEOiOREbDM+6HgvjWDEMkhIOEpo1w1uWEXb4atGAVTnhkQh+2qeGYYaQZLfo4J9OezR/A1HQ4/lL68emVeG7/DLq3HHuSMeZL26ZGpO+cC8dfQQ5fmNT/c1U7ZhjHjCC1ufkjqAL/WYwQAoGDpgmEkPHZcouNwHYcdF3kZE/Ycp8mAWBa9qT96losmC0NH5U2yleZoWuxwD+dn9bULuCP71/Hy4e7ef7Q5XFFIRcYcO3yaq5fs5BFZQFyuUieDehxH6U/pgTC40N4veDoaLoHYUcBUTBtXTyJ2dURuxPtpvRlOpquRvP5crbNCFQgbAsRkTPiB8wI9mAvlm2C7kGvXIRmeCddX2gG0a4O7K79EIk93RB1q/CuuAYRf8I7oX0NHCSavwzfii1E3vwJ2IOZ+7XtdjSvHyYZg+4JoBkeRMguqI+y1bruwXPdQ1jrbifauQtOvAHWAKBD/Sq01lvwtGxCl4W3X/cE0LxZTu7m8aP5y4ruz5nSmseP5vEh5Nz8EVSB/yxHCIEQoGkasdQaGB9EzLSO//HXEjZl11aF38uU9bPTUO7zTNqvpom4XWLc54XSo1Gb1070crRnkIhpU+Yz2LC0ls3NtegzZEO2eqZ9NVu1pol44J/+OF/g9/Ke9Uu5d8MShk2biG1TbngI+tL96S2NseVbC00jdjEpJl9H08HjBc0HmgOGF+J52/nWkXMd2G/8GIbOkE+0DfeBEczdTk8QoUXBsAvqBycyQHj/DjjwNMRr0EQBRBm0342/7eZYNZf4+uGTb+C89qN44HoFeXwXkd3fh40fILDxrljt9+S+JGBL8PgRngDaTZ/AeeFvM3NqWQOBtdtj7UwyBuENIHUvGNGC+Chf2qgKoG37ONrWjwISx/CiJXL5DW/83YfC2iC8AbyLlmbxqwpadRN4AiXjz4L7yvCBFrsJMRdRgf+cIDVAkxlr25G8c66ProvDmLZDmc9g84p6FgYDrtqZqFNtc7f9+qU1vH7a/aTLq2sr0/QrchxPZtp24OfvnJykqkuEt7tG+AGn+dDWZm5avijjNmdez4yvZrtOV85zMi2EoNKvl4TdxdFQCuU8w4dfwtn9f8k7zZvx1S3JSynGfJTznE7bF08QefKvmPRJhxyBdx8hfOApfPf+LnrNEsIHnsPZ88MpHGDBvh8TGjlH4IZPxmyP95U6c2+weT2h6z+F/dp3pvbpgmb8d/8Ouq5DGl+UajnPUtTCsahYtokhXE7uJoIEmtcjS2gsBfeVKuepmIs4UvLswS6e2nd+wh2AR9+9wKoaLw9uXcaSysxnxs0nmxpqCIhThGTm29y8sgqfRycWTExETPpp/nAcyf954RAd59NXcDaBH75+lpHRKPe0N499LqXk2OUhdh3sobt/FAnUlPu4fvVCNjTUoIlCWz+eme1tdpIIYRXTk9nxlHQxUIBSfdGeQ4UJ+hevp+zmX0MKLRag5mozgnyU80ynrcFeok9+FSZMiZiCHCXy+MOIGz6OnDLoT+LYK4QqWihv336lXxI2MGZHYOV1ROuWED24A469PL6N4EK0tXcRWH0DctrZhBMXk/n10dzUAqHpsO526Hg8s/0JsO52hKYji27/zPqKpMVcQwX+cwKZspxaO1Ly7Rc72ds9mrbFo5ei/PkTh/mt21ewdmGVq/avkBoaZa41TfDB65r5weuT1J+eBAHcub6R9L4ofInKx/adnjLoT+YX+3tprCljQ0M13UOjfPO5TnpTrnK6hkbZ332SoHaSX7ttZQazE+dLq3KehdHZnw+zXUug2OU8rbdyLNeZSkUjbLiPwPJrkDmX8CxMOc/JdHT3T5g26B8jgnzV5cXSOz/FXncbQtMmlvNMskOrW07whk/iXPsRzOGLsZe7/eWIqhZ020RmMJ5SKucpLQt7pBfbsTE8QfSymqKXpZzMV4FN7yfUcwQuHZl+X1avIrDpvQU7FktVq3KeilmASFnKKfWje09NGfQn87fPHedL97dTF3Q7y2eqbZlsM17fuHwRQyGTX+yburSnBvzO3aupDSQmhJ6sTZHm8/zoqOXw1KG+Ke1M5el9Z6mt8PNnjx1iqtoBow58Y8cxPrt9OesX1xTE/vG6sL6aK9pNqk/m681VDcVM9bEGeuDyUfKFvvWTBFbdgCO0WF95TMspZKqPHRqEnj0uRxt1uX4E8/Tb+JdunjTVR5M2jhnFGegBKdHKqvBVNY/Z6tjuZ6MtZqqPNdRH+NBOOLwTsACwARY0QdsdlK24FqF7ip++EveVrkHZ3Z9n5OXvwNm30+/Gls0Ebvp1dEFej+/ZoFWqj2JOMWpaPO0yQH2+4xwfvmZ5gSyamnvWNdNYHeCpvV2c7J94l+q6JRXcu2kJ9WW+adtKDcHyye4zF1xvc+KyyV8/fnDKoD+Zf9h5gv/9YAUVXo/rvtxSSF/NFRIhrGJ6Mjueki4G8vzoPtqdwd3NTNDKMO74HP6FKwti59iFUIFSfSIn38mPH6bBunAcll5NaqpPtPcwVscOOJty8bHkOnzrbsdTu9Tl2BIXk5mun18dPrMf64W/m9wJg12w+58ZOfw8wTs+D8GqGbVtKl8J3SB42+dwLp8l3LkTzhwAMwI+PzSux992C0blYhzNmCbVaq7q+DE1R38IVeA/J5Apy/T61WPuJ8Z64ehlHrhqCV7D7eyfqaFRdnpDQw0bGmo4NzTK8QtDRC2bcp+H9sXVlI2rjjJVO4VNXzl93kV5uiRGXJYJfuVwN/esb3Flm3utUn0Ko/NzPsxGLYFipvo4Zja1TAAEeKugYiHa2lvxLb0a6QkUeLbbwqX6OOHs/k65xopcsd+2sAljvvFjOLpz8vVP7yZyejeR9vfhu+ahjMdTzFSf8PmjOOmC/mT6zzD61F/i+8CXoIgpSZP5SqtqJrj1V3C2ja8ylDpDcrFsLpqvUKk+ipJHpCxlWn24O7uJsU4PjLBqrGJO+vYnXiJPb1OmurGijMaKYJbtCJfru9PWDE2e9tzBC9yzfknWdmamC+uruaJVqo8bDcVM9dE8/oyfrI1jw/2Ub7z3SkoPAqfA6QYFreqjzVAg4y0bsx/HJLz3F3BsikmsEhx4lIjmoXzj3WQynmKl+kjAefHbmftjuJvI/mcpX3/7jNpZCr6ajXqup/rMzdkJFGmJmHZW24Wz3K6USA3B8kmFv/DpNwAjNkSznNzMDYX01VxB+ShzEiH/9GsBSManiOSuvc1rs7Lb17yuIPZMqRGMS/XJY/ue2sTTwsLiW7pxrF+rvyezoD/B/kewRwbIbGzJR9bM7Sfz/BEIX8x8TAAdz8ZuLM/08VRkX81OLcYt5hpz71JmXiJTlum135vdHZ+g18io/fFaZLFNoXRh01c2LaudpHZ/YTBtG6+hZWybe61SfQqjS+l8mFktgfSpPg7gIHWtYKk+WsVCqG2DvkNkTEUTeu2KIqQ8FC7Vx2jZgEkQyKy4Q1YEF6HXrR7zW/Swi6A/TqjzJYJbHijZVJ/oid3u/WJeJnrpDJ7aZSWT6jPTNswWrVJ9FLOAlKtUZFq9rqWGd3u6XLVuAEsqyzJqf+IlspttCqlFDttOr1fULGBhQEwoyTkVqWFgpgTGXu6dnb4qNX16cJgXD5zjVN8IpuVQ4feweWU9NyxbiN+jpd1Wpfq4+3sweapPLB1E2BGIjqAhKVQFEWPLA1hPZx7461s+VJS0iHSpPmbvEaI9x5BWFOkvJ9C0Dj1Y5b6vTe+Fvf+WsR9ofx8ceIJ4rZrp/bb1Y+P8xvFXMu8rwfFX0Ta/v3TTV0ayS5llpB+tpjgVclSqz0RthwYJn9wL4X7QDPTaJfib1jLXU33m3ogUU3L90nr+9Q13gf/tbbUY+uzPCksNwfLNR25YzjeeO57Rul7gptXVPHfksqs+rm4qi0/mlc0lQ+YU2lelwEAkyrd2dnLi8vhqURfDJifeOsdP3jrHhzY3cNuaxkm3L/xeKH3Oj4R4oeMcnd2DhC2HgEewaUktN61ZTHXAO7Ze2uPJcRDSjgX+0o79Q1KIih3eRavgxk9jvTJ9brZ2zS/ja9lQnIomCJJTfcKn92Htexz6T42zMQTQuAnP5gfwVTZk3H5g/Z2E+o5OrKwzGUuvJXD1/dhNbUSf/iumC/71638VX3P7mN+kY0+7zaRELmU4nsTF5NRjzrvWs7wTbHhm1s5S8FUJamuwl+hbP4au8VWubGDEWICx9X7qbv/0nP0hVIH/nECmLNNrr6Fx/8aF/Hxfb0YtG8CtrQ0Ztz9ep4ZyxualAAAgAElEQVRGxdSFT19pW1jFr25bwj/tOs1UeIHfu6+VCq/HdeB/67oG3OzvUvVVsfVQ2OR//+JdhqeJSX7ydjfRqJWnSkqldD7kpiOmzT+/cmTCfCADUUnPwYs8dfAiNy2v5MPXrkDXYkk+k6b6aAIZjSAcE0c6xB6tF+4xvnfF9VBeh/XWT6GvkwlUrkS/5gF8DW0Zt2mPXCZy7E0I9YMQiKrFeFffSPYVXK6k+oy+8yTs/8lEOxOc24t5bi/Onb+Lb3FmNkvDS+DWzxB66ydw6Jn0bbfdS+CaB5CGF2NhK3zgy0T3PgUnXpy47qJNeK6+F0/divH9Gtm++yQySnUqWvpK9dIJQWMmaFVNRZsMy62vpGMSOvEWXDwNwoZAHb7lm9ADVUVPxclFm5dOYz7+x4z/u5aENcjAru8RvtTFgoe+6nofzwZU4D/LkVIiJTiOgzNWWSb2M5tO39HayKWhMC+fmL6023+6ZzUVPg+O40zZZkI7jsR2Ep9lts1MaMeRiLh9hexrc1Mti+718+y+s7zVNT4o0oDtq6u4bV0TlT4vIHnf+noefTezOQCubgyyoroiPobZ76ti6u+81Dlt0J/gF+9eYNmiBayurRjXjuPELpCmOjdK9XzIRZu2w18+tY+u4TQ/nHFePjHA5dGD/Mdb2pCOg4wfW+PadCyEbSHNUbBDYIZiwb+0Y2kiBdDemhaM9/wOzuVuot2dYIbB8OJtWINW04xmRWPbTNOOPXKJyBv/Duf3jxu3BCKvfxdW3U7g6ve7t9McRdomoc6Xpg76k7Cf/Uus+/47WnVTRn0JK0rwqgew2u8h2vki9HRCJBSv496Od802DN0DloWUIbCiGP5KtBs/BpvfT/T8YZxIBM0w8CxcgSivndRvQtrgrYGoy/efqpZltA9kVIBhgTla8OMmWftWXkfk3Z+5G1N9O7rHl9G4CqEz9ZW0Ioy+8ygc2gHxCckSRN7+PjRsxHPdh9H8C2bU/nxoZ7gf8/GvQLqgP3msB59n+Jm/pvLe33e3n2cBKvCfxUgpx35MpRRJgdp0CB7asoKmhRfYsbeLi+GJa2xuKuO+q1uo8wdwXNTBs22JFBq2nRzkFB/biYdeWdX0c0dDWRmfuGENH7JsTg0ME4nalPk9LF9QjqHHHrcm7LijrQnTsnnq0NQ/jJsaAvzK1lUzYn+qr04NDrP/ZB9DYRNDEzQvWsA1jXV49NLZv27oDYc43OduNtLn3+1i5c1t4z6zHeKBf/rtSvV8yIVH956aNuhPcOB8mOeOdPOhugpgEl85EmFGEbaFEx5F2LG0K0cIhJQF1bq/gsDya7CFQI9/bodHMtrWHujGfPZvgImTCo5x9DlCvUcwtn8Wj+HN2DYrBJrtIN96JCMfJ4jufwzvdb/iyg8IQWDNzbDm5vF+kCDN6OR+k+BbuCZjv7FyKxx8wtVYWH4dMhqadgy2qeGY4bS2FkpruhcWbYTz+zIfU+tNM26nW19J2ySy8x9h4GT6cXTvw/zZfuSdX8CzoKEoY8lWRw89h5uZqEOvfZ+KW38DLViV8TazARX4z2KEEAgcNE0ghERzmYa/belCblxSz4nLQ5zqG8ayJGV+g03NNZR54rmI7q3Cdhx0Xbi2p5DoWizsmkmbyr0G7fVVTLxrmozgvRuX0tZUxc6ObvZ2h8Z9u6LKYPv6JjY1VF+pNFZgEr46dnmQH792nO6RFJtPDvEjurintYZ7NyxB02ZXQPvKoR7X27x7PsyIaVLhu5K3rmuxvTn1MVWa50O2RG2b54/2u9rm+f3n+fCNqxFiMh8IhMeLkAaavwxhjgICTfcg7GhJahwb87l/ZMqgP8HgGazdP8K7/dMZt28EKggdfwucYVd+5swe2PorCI+3ZHyl6R68a7YRdRX4ewms2orQjWn70D0BNMODCNkzPjbvLZ8k+vifw0gGk2LWtcHIINZwL56alqLsj0x8FXrlX6YO+seQWDv+D8aDX4qdtyVwnE2nhdDg8K4MxpaMTWjPI5Rt+5TL7UobFfjPcoQQCAGapsHYFDXJgdh0WrCqrpJVdZVMxE07CR27s6lpCZtyaSt/OhaciqQgtbj2pOrV9VWsvrWK0YhFXySC40iq/V4W+L1MTmF99faJC3x9x7E0fcd4qvMSZy6P8tnta0tqX0+nuy5nV8qwdyRCZcA31o4Wz12feuyleT5kq986mVlaWjJDNnSeG2BdS83YeTiGZoClI3QfUsQfoSDB8JLIkc+XdqRNtKsDZ3QEvD68tc0YVU1j61j9XUTPHYXoEHiCGMs24i2rm9BO9OBL4Axl7oCefVgjlzEqGzOz2RNk5PRB134GiPadwWhaXzAfZqO1slrY9huw6x8zGoN+3/+L8FVk1IfwBpC6F4zojI9N8y/A/94/JPzit6Bn/6RjGePiIbh4CBMwK5rhqvsJLr16Rm2ezlfW8CU4/XZG+wgAOUL42Jv4r7q3JI6z6bQ9fAGk+7/9kRO7VeCvKEVSg9nUO8zF0Km2FdsmUTQb+kbDvHy4hwNn+xmN2AR8Gu1N1dzcuojaYGDc+kGfh6DPKKqvevpG+ItHMws8OnrD/OydU3xw87Ki2uxG29k8yALslFz++VjOs7d//BOpTDl9aYi1LbVMOA8dByFB4KDZVkFKDcrhPkLvPg5Hx9eTjwLRmpXQuA7OdcCl8Re61p5/wapbg/eq92Msbh2bsTVW2tId0UM78V73SxnZrNkRMEdc9wEgQoMlV64RxySwaDXmbZ/HeuE76S+a/DX4tn8WvaYl4zGkK1FpXjxB5MguGOyNHW4VtXhX34Rn4Uq0fI7T46X8jt8iOnyB6KEX4OJJGOmfenKvobPw0t8xeuEeyrd8cMb2x3TlPMOHdro/4A49i1h/e6z0bAkdc5NpEXb5BC2OjGS3XSmjAn/FvCE1BCs0lu3ww93HeO3U+Jeo+02H7s4+nu3sY+vSBXzsupXx3P/S4NG3T7haf8fhS9yzvpng2PwCpU1V0AuX3OX4A1QGxz99SYSw8wlHZjdiy5HTn39jK0jyVbbPvHwG87GHgUleZIJYsJ8S8I/j4mGiz/4F5o2fpmz5NcjIqPsZWwG6D2VuPwLNE0jf1hQIjz+u8ufDnHXcHu/iNXg+9jDmmb1Yh1+G4QuxPLmKevTWW/EvbkMIEXsvIOM+BMkXplZ/F9GXvg0DZ8c7pu8I0ZOvES1fhOeW/4ivuimv4zQWLMZ7zYewQ4OE/v2/ZbazDj3FaKAa//o7Zmh/jPfVhHXOu5jcLkG4Dyc0iO4vnwH7c9NXzg13CF95VtuVMirwnxPIlGWp6NTQqJh6ZktU2o7k75/voPNihKl4/dQgl4YP8Nt3tDN+qoTi+Cpkmjx/qG9KmyfjleO93NnWOGN25qKvXVnP22fd3cWpD8DiskCONpTS+ZCdXhAYf/GTKbXlPhLn4bg2NQ0pQKLhaAaIWB3/fJTts8OXMR/7Cm5e5kuHfOXbhCtq0YPV2TUQDrko4+jFv2Q9kY4drrsxmtpKb2ZW28Ihgqbp4PHjXbIZ75KrcQwvmmXG1jO8SMtEuuwjuURl7CLvy1M7aPh8rJTje/4AT93yvI851PEsqZVwpsLZ82PsDXeRfenXPJbzDGWXAmk7JnqpHXOTaFHTAlq563dnfCtuyMovpcwceN1MceUqXpSQLhU7iqOf7jg7bdCf4EhflKcPnC26zQCnB7L743+0Z6Ak7M9Ety+upsLlLY/t65sQYvyxLV31Wzrjz0Vfs7yebNiybOHk/pok1UeTTjwdIzcdOeCugsd0WG//HN3I7q4hwbLM7bcjlC/bBFqZuz6WbMEwAnn1YV60Y6IDmm3nvQ/hWLGZjq0w5lNfy9hV5pNfQ5jhvNojrDC4TpexiB55ZUb2R8JX6dYh4PJ4i6Nr3tI4zqbRum1B++3uBqd5CVz9QFZ+KWVU4K+YN4jpV8kLjiN5NsPa/AmeOXAhXu+9uITNLGbZJDah02xBE4KP37Ii4/WXVhpsW7ZwwuczdTyVEtUBH+2L3KWh3LKyCq9Hn95fYytIklNEstHStuHg867snJYLh3Aiw1DZMv26qTRtJGP7EQjdg3bV/a668LXfm7PfCqspQLuxC8jw6T1gTT8vzRVChI6/kVd7zEunyKjSUwry7N682TC1Tr7wnmSdxvWubad8EVpgwQzZP722w4OMvvssw2/8mOG3fsLo4ZdxzOjYOsHWW1xdUAdu+ARaYLLCJ7MbleozJ0h5fF4yWpSIHRKYuVSffd2XyOxe/xWiwN5zfWxuri2obdPpMm92U9GX+YyC25ZP3b6wil+7aSn/38unmIpllQa/eee6pHcw5sr5kL1+6NrlHHy0g0ymlCjX4b6NLcTCWZnUVnxZoFQfq/couD4Lpyfc1Qnr7oZXv+1qO1/bdlepPpodxbduO6Ghi3Bkitl142g3fw69dklJpFRMmepToPQV++ALrvYHgHPoOZz2O/Jmjx3N8ulSKDQj6VnTpfr42rYT6Xjcne2tdyENL04iZatIx5kZHsR85XvQtWeCiaO7/xlWb8e79Zcw/JV47v09zMf+lOlSsvwb7qb89t9y549Zggr85wQiZSlLQKfaVmybRA7butM9/VmWi+wPQfPM2TmZXlZVgU9AJDnWy4D1SxIVW2be5mz15uZaltxfzouHunn+8CWSh7y00uCW9kauba5NKsM5vp35WNUHBPXlAX7/vla+9kQn4SmOkyovfOHedsrjcx9cSfVJarNAVX2yreAxHcIcwd96I6H9j8JwBvXbAVbegidYCdLJuKqPcBx0KQlu/TDh6sU4+x6H8CTv3tSuxtjyIP76ZRm3X4yqPgIQtg2iMJVquHzC/c4c6kJYEfJVkUY3PGT13NPvy9txn0tVHy1QQWT1djiyM0O7ayhbdR2yyMeZNdiL+Ys/A6aoOHZkJ9Gewxjv+c/4qhrRPvBHRPY8Aqd2T7KyhmhoI9jUihzthwUTn/bOdlTgr5g3pIZghUJmmbJjz8S0vNNg6Br3XN3Iz98+l/k2wDUttdOuV4rUBf08uHk5929awqVIFNORLPAYSRN1pd+XYspv5zbNC8r48oObeOX4eZ57t4fBpAyHWj/c3LaYm1Yswu+N/cRkdO6NrSTJtZKH8Pomtp8HhOFH6B4Cd36B0FMPQ2iaF+EbNxHY+jF340LE/h//PLj6RuSqGwn3dGKfPwpOFDzl+FvWY1Q1xJ6UOFbm7Repqk8s+s93H4mLySz/dsokkaM9Rs2S7N4oWbwubzZk5qv065Rd8xAjI5fhXCL9KA36Avx3/y7C8CCLeGxJ0yL89NeYMuhPMHSO0Re+Sfldn0evqKP8pk/hXPtRRo+9CSdehv4z8RUdZHcHl7o74Km/J7DpfZTf9QX08tn5GzcZKvCfE8iUZano1NComHrmUn3KAtmVtSwPepPaKp6v3nvVEp7ec27Ku7nJfPCaRgxdK6rNuWpD11gY9Ge8fna6lM6H3HXQo3NnayN3rGngZP8wuw71sPvUEH1h+Ok7Pfz0nR6ubSlne3sj1fXlzGSqj1azjEJgNK+NtV9ei//9/43wO0/C4WeZEHgalbDhXgLt25GG32UqRCzVZ1xqkJB4llyFr7E9tk68Io6To68sO4IYHUR4/RCsycnnafUMpPpkU60FdKTHl7c0Fc0bhJW3wLEXXVnhbbsJBz3/fk/jqynTinQd312/Q2TvU7H5KsyBiQavugXvNR9CM4I5H3+56sjJlyByOXNn9x4kcvkc+sKVICE6cB72/JDxf+OSkBahd35K9NSb1HzqO+iVDZn3VcKowH9OIFKWsgR0qm3FtknksK07vaWlnn99M/M75gmuWVI3o3am0+Vlfv7XRzbzxR+9Pe0drPvW1XPrqoai21wMPV9TfVL1y8fP86M3upiMN84M88aZw7y3e4CP3dzGhOO7UBN4GR5YuQ2O7ZrUrqyoW423YiFOvC/NEyR43UOIze8jcvZdrJFBNE3DqFqM3tCKLmMzETsu7U+k+mgFSt2RkRHCx17FObQDBruvjC+4EG3tnQRWXY8GsyvVZ9VWOOyy/OnKG/I7mZe08bffQdhN4N/+Pgy00kj1SWjbpLz9VuS6WwifP4rT1wXYEKwi0LwBzePDETNj83RaHnrO3T4HzEPPIAwv4Ze/C5eOZrSNffksl7//W9R+5kcIPbsbe6WECvwV84bUEKxQlPkMrmku500XteK3NJdRVkITYLUsrOCLH1jPk3vPsOvExLs+SysN7trYzFVNc+fxp1sSIex85tVTvWmD/mQe23seTQjuaWtKv9LYCSrJRzqAf+1dhPMY+Huvet+kfQndg3/p1TiagebEXhh0hAbSyc5+BMmpPrn6IVlbwxcJP/mXELk0cYCjvThv/YCR/U/gfc/v4q2oy0+/JDR5H0+8UfyttxJ2Gfj7Wm/Luz1GxUL02z+P/dzfTG/A0msp23TfDKbKCMZduKemzVgWTmQER9MRvjKEEPga16ItXg2QdHwX2s7MtLSjEydqy4TTHUSPvwEu38iwzh8m0rkT/7q73PdZYqjAf04gU5alolNDo2LqmZ3A64EtS9l/9kBGdUW8wAe2LJsx26bXMV9V+718bOtKPrjZ5kDPZYbCJl5DZ1ldOY0VwRKwc7bpUjofctch0+T7r54hU37xTg9rGxawpLL8SjsFnMBLq2qE2/4TPP/XGduYlut/DWPxuhlKbZgk1ScP7TuhAcKPfgXsoanHGr1M9Od/gnjwy+jBqlmR6qNVNkDbXXBo+gpIAKy8Bb2mpSD7z7NkM9q9f4i5+yfQ1zmxb1EGm+4n0H4bUjBj6THpUn2ivUexDjwHXW+MmRhBg9bbMdrvxBusLbht2Wg7i9KpgMuyr+MZfeNHcyLw17/0pS8V24bZxq8Cy2zbIRLJfIa+QhAMerEdychwBMeRSAlXruhFUbSUIKVAiNi/YtmRqgNlsZf9wqPmjPQb8OhsWl7N28cuEE2Om1Io0+H33ruW+jL/jPghG18ZuqCxsoxltRW0VJfFX3wtvp3F1v74PguPRtKuU6rnQz70i8fOc6jbXV61bZpsbKm50o5jxyqrOJH4MpYWgnRi6Qk5amPBIsTiNuxL5yE8WS6wD1quBiEgMklAXL0Czw3/Af+yzXmxJxMd8OkIxyESjua1/dAbP4K+zFIbwMIODeBruSrnfoUdRdg2mhabyyGf/gr4dAQO0VAYT0MbZmgILp2aemjLtlJ2/S+jCVGwfekJVOBddQMs34rjrYTqJljYhrH2TgLbPoFn0Up0ZuZ4msxXApDSIvTa93De+AEMpaamSug7jnPwWWx/Fd6alhmzM2ONwNr/2NT7Os/Ygz2U3fKZ+N/ymcHv96DrGsAp4J/y0aa6469IS9SyefPsJS70j2LZDlXlfq5dWscCf3FTUkZMk7fO9DE6EkU3NJpqy2mrXxD7Qz4FM3eqxlhcFuBLH7iKV4738HxHD5eTbv9Xe2F7+2K2rVyM34i92FVKzLSvZiOCUttrM8vrh3tdb/PaqUF++Xo5+bk69pEkn6kB3voVeO/7z0T6uzFPvAnRYdC8GHVL8S29Gmn40BwLs+8UkXOHwQwhvEGM5nZ8VbGX+ZyZTGdAkO9UH8cKw7GX3e2sk7txrvkoBBbkZsPYPqUA/rpyISqEIHD9x7FaNmEe2AG9HePHs7ANo+02PEuuRkh7RvalUbkY78Z7AMalghWnEs4VX4Ek9Nq/wNGXpj0MnN3/l5Bu4Ft1YxFsTq+F7oHa1dB3ZNox5A3bAtsEwzv9uiWMCvznBDJlmZuOWja/2HuK549MvEP2yJ5uNjYEeei65dQEfBO2Ha9TQ6PcdN9omJ+/fYq3zo6kWHWech3u3riY29Y0xK/GU9uZ2VSfhPZ7NG5vbeS2NQ0Mhk1Ctk3A0Fng8yTdNcjv/stdF8dXc1/n93wotr44nN1szaGoRZkvfvOggKk+qVqvX4GnuiX2ebwqjkxaR69bgX9xG1qiykvRJibKf6pP+PRbWe2r0ZN78K+/MzcbZiDVJ5G+Ig0vnqaNeJo2YJojyIHzIEFULsTjrSjyfi2uTvZV9MJxOJr5S8j2q9/BWrkVYwYmGnOjtXW347w0g4G/4Ysdw7McFfjPcqSMpfg4joMzVj9eknxl70aHTYu/fuYAXcPp6yLv6x6l42cH+C/3tdJQHhjXjuNIbCfRnpO1Ham6a2iUrz5xOO3rOMM2/PueHo71DPCpbWsmTLrkOBIRty8f9mSjK3weKjAAEU8ByZ9/8qlLwVezQTtO7ALJcdLvx0KdD6WgE8otMvnYciyEbSPtCNhhMEMgdJB2rCLMfNTmKNI2Y//PV5uDF7LbWSMXYvskFxuiI7H/R8OxNvPoLxkVYFhgjk5YRzf8sdx/oePo+qTrzCed7Cur4ynXh0K040WM1VtLYiwJ7WtYS6iiGYayeMk3C3yrts1omk+hUIH/LEbKWEnBWH6/SArUsuefXj48ZdCfwAK+9kQn//OBDfiNK4eRbUuk0LDt5CAnN0ZMk7+aIuhPZm93iEf2nOSDV68Y97ntxEOv4s+RVfIoX2WGHavWOKWfCnE+lAoNlQYnB9y956QBPk2/4jNHIswowrZwwqMIO/bCniMEIvbSEo4Q2EN9mBdOgG0ifH68i9YgfWXj1pkr2gqB7khkNJy3NrGzmloKHAcnPJKTDZhhiIRwkLF3OvLoL9vUcMww0owWbH/MFZ3wlRMJwZm30+7ytBx+Abns6pIYS0LrUuK9+deIPvt1iE5Tz796NVzO7elA8LqPTb/SLEAF/rMYIQQCB00TCCEZu8mdJV3Do3RcyKQOTYyQhNdP9XLb6sZkq7AdB10XOduTYNeRHlczIr5wbIB7NpiUJ5XH1LVY2JUvm+Yy88FXl0IRXu7s5kjPIBHLIejV2bS8lhuWLcLvyWzguhYL/Kf2U/7Ph1LhprWLOfmauzttt62pRteTL4AEwuNFSAPNX4YwRwGBpnsQdpRozxGs/U9D3+GxLSTEqmUt3Yq3/R6MBXVj6ydvO1u1EahA2BYiIvPWpqhfgTzoalcBoFU3x/ZLDjaggYNE85eBx59Xf+meAJrhQYTsktl/paoTvpJ9/e4PBICRAYTXWxJjSdaGtw4+8EWie5+AzueJ3ZZMwlMJa+/A1347kRe/DV17shq+d/l1eJdvzc53JYYK/Gc5sWohxFNbErfRxv+wZqpf6kiazCVDXjx4njtaE7W5r6QzaBpJ6TbZ2+RIyc5Dfa7tevVYL/e0N4+1o2kibleij+zsmQ96tvjKcSQdvQP0DoSQQF2Fn/WLq9Hj9k+2bdRy+P5rRya+JzJqcXzPeR7Zc573b1jI3euakqrwTG6Dpol44D/VcZ7f86GYWko4cL6fVw71cGEogiPdPxK6pa1xvB80AywdofuQIv4IBQmGl9GDL8HbP0jf2KnXiZ7ajXP372M0roV4/jGGd3ZrTxChRcGw89amd/kWIi+VgUx9P2oqvPhWXIf0BHKzQQK2BI8fcm0rRQtvAKl7wYiWzv6La4lDtO8MTteh2BMXfyXe5jaMivqi2JPwlfAGXBwDSWg6GMGS8G2q1gwvwWs/gtzyIKFzB2GgFzQDUdOIf+FKhBA4hjf29CkLPE0bqProXyHmyN0bFfjPCVIDNJmV7uxxX9+2Lwyjpk3QY6S0mWpbdjb1joQZzSLl5EBXP/e0tyS1KbK2Yf7p0vaVZdvsOHiOZw/0EpKMw8tJ7lhXx13rmvCOpaDFtjVth689/S6nB6dOGvvF/l6GwyYf2rJiSnvm08y9J/qH+OaOwwxmWTob4DN3raY26B/ffpqZe8PHXp866B9DYj39Fewbfp3gkg0IwzcjM4YKxyLacxhz8CKadNArajGa1hELC3JrvyAz99oWYsNdyH0/zXyHtd+JLoTrmYdT9UzM3FsKs8gm6+jx17H2Pw5D42+mRd+CaP06PNd8AKOmZUZtS/hK9waIzR7jMv2rtrnk/DxBC0GweT1aU+zz2AzDNkiBdfEE9Lp87CUMym/9Dcq2fSr7C6YSRAX+ijFCWaaBvna8l1tWLcYY9wg/P4Ss7OZKCEcnBnf5t27uUqq+Cps2f/Psu5xOk1seBZ7ouMg7py7xhfdsiF+QxvjpnpPTBv0Jnj9ymWWLLrKlOf3MxInLo7nOsUuD/NXTueXGfmr7cm5ta+LShdRa+RLGZgONfyIlzu4fuWpfvvotRl4VsPpWfO33oJVVxdsU8Z2UHy0di9GOnXDwWYjGUiac+D+TALTfQdmGe2J3t7PtC0G+y3kiBMH2uxjpOTyxzOVk1K6mbON9+bGBhCbv+4OxGxX5bDM3Pbrn53Dg0fS+vdCB+UQH3Prb+JrbZ9C2mK+EELB2Oxx8eooDYCLa2u0l5We3Onpop6vxAnhW3UD5bb/pertSZ248t5j3yKRl9jqQZZWqf9/Tze/92x5ePNaT1GaebNL1rGzyGdqENmUe7JkvulR99a0XD6YN+pPpHnH4+x0d8cpJsWpVLxyd5uWvFJ7Zm8hfz4f9xfddNjpqOXw9x6AfYP+Jvknad8CJghVC2masfKUwCJ8/ApFLWfQi4chOIj/9A8IXjo2Vw3SEkRdt2zYjT38d9v54LOgfTwgOPMrI43+OZUdy6MuLFFre7Ze6l8Cd/w8s3Ta1G1uuwXff7yN1X35s0DzYCBwtv+NxhDGuRGW+/ZWNHjny6tRBfxLmC98gMtAzY7Yl+8rXtt3dqWVU4lm2pWT8nI3mmPuStubRV5CJF+TnECrwnxOIpGX2etPSmqwtsIF/feMcTx04m7MdyXphWQB/Fva0N1fnzQalS0MfuzzMIRcvn5/oNzl0MZa+9uop9+UMzw7ZnBsaTWuPdGV/6fjRjX7tVG/qq3JZ8eapQfqHEjMcx9t3HERkBN0eQbNG0awImrSwz7yTc3/W01/F6e9Cs6Jo0kKTTs469PK3oK9z+j15/o0AACAASURBVM4HzxB98mux1Ips+rIjCMfOi82pWheC4M2fxH//H0PbnVC+GHzVUNEAa+7E//4vU37Lr6NLmb9+HRMd0Gw77+MRjoVmR/LaZrZaOCZyz89cHafm/sdmzM5kX3mCNYjrP5Wxnb67fhs9nopXbD9nq5HuZhkHQNpIM+R+uxJHBf6KMW5ubci5jccOXKTjYpZVAyZB0wS3r6tzvd221YsnfCYmWU8xOaXoqxcPpE4rPz07341t093n5qXGK5yeYrtS9FG+eamjJ29t7Tg4WQUgCY4N0mEsLcTMbl+lEt7zsyttInPS5qXTcNbFBcmlo0R7DmXXL4JxqT55sD9VGxW1lG/5EMEP/hHlD/0p5ff/D4JbP4qxoL6A/VKAdpMvVgvnr0x0tOsAmAO44tSbOJFEQFpoO8f7KrD6RowbP82UYaCnCt+9f4inprmovs1Vh0++mX6M0yCMbG49ljYqx39OIFOW2en6Mh/XtpTzxpksroyT2LH3DOvvqiT2RyY3mwBuXtPA0x0XM77zePOKSip8iZeNE+2o2Wgz16Xpq3fOuj8uD5yP3a0xrcxy+1OxHCcj2zLT+TkfZlJ3j+RvModzF4dh+cIr7WsCqes4UoBImqVWz9MPbdc7mJFhPN5yILdZQs2Ona67Nw/swNPYnkW/+Z+5t6AzwmITPncIeeBpuHg69kJvRTUsvxFv+y1ommfGZu4tpi/Mc1nUSwXCvScItmws/H6axFfeFdfjWXIVoZNvQ+eLMHwREFDThL72doylm9FtC6cEjrNstXQk1puPZLVv9OZNc6aSTzIq8J8TiJSlzFr/yvWrGAof4tCF7MpeARy7bHFxNMLClFl9s9UVfg9fuLeVv3iik+nCkPZFAR66ZsUk7QhysWF+6dL0VXahe6zsZ1WZD3B/4VAZ8KS1Z35U9ckfsVTZpGPLkQjbQhMyNgtavDKHsXA11tEX8tKnefwtfK3bYv3lUjXkVBa1v8+/i5CJ9JYiV/UpkHYGegg/9/XYDL/JDIVg34+J7vsxbPgggdU3zvmqPljZPakS4cEZsT+tr3QPwVU3oK28DkiqhIPAsc2SOM5y0VbPQYi4LwkOELzmw1ltV+rMvUsZRU4YusZv3raOBzYtJpjD0XHqcn4e1ydYWlnOf3//OtYvnryklg+4f+MiPnPr2ngd94nkP5SZu5Sir7J7zTuWLrZ5ZX1W/a1dWJX2+1L0Ub7x5bGtuorJ7uQnXWzEH8v7ll5FrNxgHogk/g5daT8r7ar+fRKO7b4vBIVO9cmHtoYuEP7Fn0wM+lPZ/wihA0/HdvUcTvWJ1bh3z5UykYW2s4R8NYM6muWTGHwL8K+9I7ttSxx1x39OIFOWuWlNg7vWNnJHWwN/+dR+Tva7f73PNBP3Z/NjE0gWlvn57Pa19IejvHXqIsOjUTy6RkNtGZsaapImnJqsndJMXylNXZq+WrPQz8Fed0+illfHSlW1VARprtA5O5T5c4NbV1dj6MnVoTKzM70WeWxrZvQNq6rZ6bIaUjpuWduY1IecItVHh00Pwt4f5t6pEchLGkhWdc8hXk1nbqb6RJ/7B+JzKE9P5zOEl1yFt6k9r3aUUqqP1rgO58jzmfkjCaNp7YzYX0q+mlEdHXW9TwAW3PBhhGfu1O5PRgX+cwKRspR50ZqApXUVnOx3/8O/IJi4Y5dfm0BS5ffFZwt2s61wuX522rIlb5/r4/XO81waiaIJweJKPzeva8Dv0Xmxo5vDPUOETQh64eqlNdzc1kBd0Fdw20rNV2719vWNHHzuOG64bf2VWaU/um0lf/Hk4Yy2qzDgnvUtY9tOZs98SPW5Ze3ivAT+K2q9NNUv4PKFoSvtp0n1AUFgw22Ehk7D8Vdy6teob8lPGkXjWji3113nNavQpXQ9AdZsSPUx+07B0BlX7nA6nkFbtHrOpvoEmtYxYiwAy8VEmEu2YBiB4qb6zHFNlsG7r6Yh9sROn3thskr1UUzJ1SvcV9TRgDW1C/JvTI6khmD5Zl/3JX7/3/bwz7tO03kxwoWQ5Pyow97uUb6x4xhfffIwu08P0R+FsIRLEdhx+BJf+vkBfvDaUWxHFtjCzCm0r7JhXX0VTRWZJ/zUBQRXNVwpUbu8qoLfvG3FtNst8MDv3ddOmc8z5Xql6KN8s7AswN1t6Scxy5TP3rU+jb+SLjaSHtELoVF2wycQmx4CsrzrVr4Iz8KVk7bvVhvr7nTdvbF2e3b9Ivj/2Xvv+Diu6+77e2e2AyB6J0CCbUmAXaTYxKreLNmO7CSKHSeOnWY7dpz4zWs/fh77fewUx/nE5XESx6+d2InjuMiW1SubxCKSoiixgCDYG0CCIPrWmbnPH7sLLoBts9jFLiD8Ph9pf1zMnHvuuXd2zp0595x8D/Xxt+82YYkwrhzBGH76mimdBPHm0ERzIRTUlY+kaIwQ7IsfmEA988dWE8mtdQtJB65Zy6fsj/zUW8q8KyFHfWaOzy0tpNwB3SYiLLbML4mq4pt5ndLj2Q1fefNyN//2+gXSxd7z/fT7Wvn45oUokZt+FvRMjednqI8Q8Cd3LuJrzx6jL0hCFCjwqXuauZWQISSnubqE/++RFna1drDz1M0RG4ZLrLClpYY75lbjsKpRbWeqL7ke1/T4w8saCWg6O0+bT9OrAh/f2sTZ7n7eunSDgDfInKoiZpUUxg/1IRw6IcG55B705fcTPHsA48QO6E7tjQ2AWPwg0mLD0ILDMqPlm+Fq/RK0kiboPZda47YyrLNuSzNcZxKE+ty8lvI4RCPovYlia8iYHvkWvmJfsBHPYB+ceCqpLdTNn0Ata5gw3fLNVhPFLbUtBG2lEEj9zaXStBJLcS0Y6e4sy29MO/6THFJKpATDMDCGnxhLRqzsx8k/sK6Jf96R2g3PAWxeWBvWxcioHuPhhiERyKzYqNcXHJfTH8GxTi87TnWwdX5NxnTLhK00XefwlZuc7ujDG9AosFtpbixlcXUJilBMyx8PL7JZ+asHF/PLN8/HTTu7st7F+1c1UWS3hfswUk6J3cYjy2fx4JKZXPf68QV1Cm0qlS4nIrzoSmWeGEZogWQY8ee5YcjwmxxBPl0PZvn7VjaxuKGXnSc6OHZt5FOAWleoZ9eiQmlnWGD1nBKu9/tj/HZ0UO2C+5bWsqpcguEHQ4LUQ6/mhTqCC6niqGtG1i3Ge+C/4dwekmLeNpyzV2IEvTFlmuVC6jg2fwzfi/8AviQZQpRCrPd/BqEHQo6D2XaDHqQeDP17HDpnlRup75UZgYAfgp6M6SEDAixaRmWOlztW3I+/qBR59DnwxNj4XLEQ9bZHsJfUTegY56OtJoILLYBY/jDywI9Snqblt70nVFXccEzJUJ+p16N3EaQMxRkbhkRKEeWsZBbu8hJ+Z91M/nNfrAI8t+AS8Mn75lNgtaPr0U5O7qEbYdcrc2nJh7HnVEfGZO041smmOdX4DYPWrl4GvUFsVpW55UVUODOZYyU+IrbSDcn29qs89/b1Uak0vew934+DCzyypp51DVWxBWUJTouFx9fM59GVGgcvXKdnwA9Iigvt3N5URaHFSsjpTixHEQo1rkgmjtA1ZKY6u24QdvwTHKNLpFAycj30BgLsO9XB7pM3idSSLFJh86JK1s6pptCW3Z/zeeXFzNtYgieo0eP3gRCU2uy4rCogCOoGHi2IzaLSOeTlmy+djivrmgd+vOc8/Y06d81WwdAwhECEByAet654lKBjBrS+QMj6o6FCy4M4FtyBDHhTkpkqV1Ur9rs+hf/Ir+Hy4dgdq1mCbcWjYHEig4G02tK8oBoSGfBlVP+MckcRmKxVBYBiT9susbgeVDCCvozKzAR31C9G1rfgu3Eeus6BHgBrAda6RViKKtCFmHCd89VWE8EdM5fg7bkL2l8hGcSqx7GW1mEE/UglCw5DHmDa8Z/EEEIgMFAUgRCSbNaZuL2hgpllBew4eoU3Lo580uoAtrVUsGl+DQ6Lim4YqKrIqj5moSohtyvTOkkp2XHyZsbk9Qbhu7taOXljbAaRuaUW7l3ewMLK+CkmM4FQIhvJzw+dZe/5+BvVfMBP37jC4FCAe5vr4x6XLRTZLWxbUEvsJ9XZh6qEWkw8p0RGrocdpzr41ZGx4RUDOjxzrItnjnXxO2vrub0x+4uwQruFQnsBI+0OdkXBbrXR4wvwrQROfwRSKGw/00O51cLKWSUoqjX0lBwRkxtBP8Ezb4L3JtQvB99QyPiKBWwORO0iHLNvQ1rtKAnkjIerNjuOzR8DTx++c4dhoAuEAa4KbPNux2JzAAJjHG1ZnEUIXUP4Zcb1z5gd5q9Hv3bU3MQpnYO1rDazelidKBYrwqvnhV2iuUDgaFiCUucenhPZmpeT3VYTwR23v59A2UyMo8/GfhNTNhdl5XtwVMxGsTpQrHaEzCMnJoOYdvwnOYQQCAGKosBweato5ydzfGZRAR9av4DHVutc7h8ioBkU2C00FBeE49JvhT0oSkSnzOuRDg+l+hRRKT8zI9/jl2kk+UuMWE4/hAqj/dOOczy6vIa7FtYl1W08tnrpnSsJnf5oPHusi7qyApbVRTbS5nasJ4origg7/onm+fivh1dPXY3p9I/Gf+6/gqqqrG6oSKs/meI7TnTEfBY/GhKBgcKuU70snVOFYnVAOP4Yi22YSyHwvPHfEK+oV1EDyspHcZTPCh0fdS5Z4kqBBdfiuzAsNpTIHgKLDcJ8XPKtLoQSAIueNf3Hy22zV+DdWwTGQAojHUbznaEMKxnUQ9icSNUGlkBe2CWf+bStbDjmb0DOX4f/xkWMKydA94N1BrbGhViK64avYWGxg2KbjvGfRj5jtDMrs8odVpV55cVJjh+tW3Z1Ss5FVuRrcuJfBT55pJMZLmvU093M9suQ8Mu950zp9MKRSyyri2R/yfVYTwyfiHSe3d4AvzrcSar44Z6LLH5fKU6bOq6+pcsDms7uFNN/CiSKkPg0nXNXepk/s2xMSj6pBfG/+A3oTZDGdeASxvNfIbjlk9jrW/ImjWC6fDKk8wSBZdsfor3y9ZTGmuoWnDOXIjKsx7s1ReW0rcbHHRWzUMpnAlGViqOuN4GBkNqUTeeZNz1yu90fAf4N2NjW1vZ6iudYgEHiF5i80tbWNjMzGk5jsmO0C5YJFNgSp3zMFp7Yf4lVDZXDb1oyiXcu3GAgSdac0bjUr3NlYIj6ooKM65OvEIRc3mzi9Tbz+0f2nr/OnQtqs6BNcpzvGcDsUlgC7dcHmd9QHjaoBCFAwtD+/0js9EchuPPbWB75KmJG1Rg5k4ojGJHOM9f6xOG2WjdiyycJ7vx24oGpX45j9QcRisiCHoIRC9A8sEv+8uS2MgJDeE7tgwsHwNMPQoWqRmzubag17neRncN2yobTkAfIC8ff7XavA5L8esREMyGn/wywP8bfMxd8ndeQoz7zhY92jXLJs5OiUlUEy+tcHLmaXnXAdDFkwPHOmywZzlOfuX6dvJzeZXP6Wj/1Rbc2y2ZKn3zh1wa9dA36EAIqCx2UVRaZlGP+eth1MkkGmRh4vbUj7PhPvI08gZHbwBNBIjCkwDAEXm1sGj59sBsuHExZHoC3bSeOtY+PkDP5+CRI5xnm1plLUR/7Or6T++D4i2BE7f+qW46y+F6s5bMw/EOgqJBhPd6tKSqzYSvPOy/C279gDC51E7j0FhTUYr/7U6iF5TnvS9ZthUAKS2jhMwWRc8ff7Xa/D/h3oDCN01eEP/+tra3tqxlTatJBjPqUecBH65ZrncQ4zk3Mtyyu58jVdiYaRy/2sKS2PKFu6XBNTy98KahFMtfkeqwzxw1pcOBiNzuPX+Fy/0indn7lOe5fPZv5RU5E0rSmEcRuT0rwBXVURWCzhG42mq6ntX/kRiTlTw7s5bCmvhkuEuqjKhKHCsqo1/Ketl0pyxrGyZ2Ile8NvwkTeRFWMFVDfSJcsRXiWnYvYsldyKAP3dCx2OwIxYohFNB8oRmi6yDyL3xFDnThPbUTrrRC0Au2AqhrwenehFpQmhXbaT2XCV4/gwwEwFmIo9aNYnPlLNTHc/AJOP504mtrqAP/U1/F+cgXUArK82b+ZcVW06E+2YHb7Z4J/DXwIcADXAOqTYqJOP5vZlC1aUxRjHbBMoV5ZTO4vbGIAxdNbHTLAIZ8JuNxUoTTkV74ks+fHX1yBU03+P5rJzna6Y359/YuH+3PneS2mQX87np31Mbx1HGhb5Cdx66MqElQoMLWRVWsnVuZtu65wqwS889vJNBY7mLMK/frZ9LQIIjW34laXHtLTrTMPOJSGgS7L2D4PUirDVtxPUo4K9BkCPUZzYUAYXOAYkEY2q1jIqMsyELb4QUe5s+VhoZn33/C+VHBAt5u6LuIt/V5mHsHzjW/nbHx8F1tQzv8JPSeH9GkB2DOOmxLHsJWWJalcYptq8D19uROfwTGIN7dP8D1wOfyYs5lj4ftlC2nIcfI5VLmK4Sc/kPA7xMK9UnX8Y+TVPndAjnqM1+4yBM9JJDdarSPr52HbrTz5uXYRaWyAZtFidIjc31ZPauSXx5IXLMhFq72erKiT674f+xrj+v0R+PNy0PYD5zmt9fOSyLz1vVgGJKfHjzDnnNjk6EP6fDMses8c+z6mCsoFVQ4Y7U9Mdxps7B2VhH7LyRfBEdCfaxCYUFDjPCBYHr5sgzDyOvQD8M3iK91Jxx7Cbg1vzSAprXYN74fx4y6SRHqkxLXNQz8KHkU6iN1De9L/we6TyaeTGdex9vfi/OeTyCEkr6eUsP7yr9AR4JnlGf3ETi7D3nfX2GtmDNhoT6B48lz249Adzv+vg7sM2ozplu+8ake6pPLJKUngd8F1rS1tZlMCAxut1sAy4FO4D1ut/uA2+0ecLvdXW63+ydut9udRMQUgoj6zBeeL3pMDFcVhY9smM8fb5tDc9Ww5zWMVQ1F3L2wnLJR29BnlVj5jVV1Y45PBU21xVnpS5HLlpY+nX2+rOiTC36hb8jUIm7v+X46BiNOXPLr4SdvxHb6R8Os0w9wx6LIxt7c2O6upQ0p6RkJ9dkwvwKnEgn10VCkgaIFwJHeRnHVah8hJ5+40XMJ35NfhmO/JtrpH8a5/XT/6C/xtO/NG53HzY0gKqDoesbbEIaGovtNn+t9+9nkTn8EXcfwHXs5bT2F5sX71JcTO/1RCL7wdeRA14TYCl8/XH075WsrAv3E9vyYW1niI0J9piBy9sS/ra3tb8cpYg4wI/zfd4E9wA5CbwF+E3jQ7Xbf39bWtmec7cSEzWahcsTmvomHES4ZWlZZRFDTkUboqbbI4f8NQ6Ibobo6ajh3fq51uvX/kK2y2crGqmLuaGnE7wtwczCAqghKi+zYrBYk8HtIvH4Nf0DH5bAMf3++a5BDF1LLm0+4Lw+snIPVqma8F9duDqWsRzQMoLSyaMJH1h/Q2dl2lTdaO+j3BrFZFBbUFnPP0lnUVhSmJfPnb10w3f9D57v53a2LRsiJdT2cuHSTfSbG2iweXjUXh11lIscg+v9llTP43MOCrz19PKmua+aW88CachRDRyoqQhogJVJRsS5cTd+15DJGoKiKiplNKMhhOdEyc8k1Tx+dz30dgskXfD0vfofy93wOR+OyvNE/XS6DKoZfoNgcCJsz422AoKSkMOXjDWngaX3V1LSSJ1+leP1DoNpM6WYIhc6nvwMDqafkBQ2j/VUqtn0067byd1wmrbQUg9dM2XzScUIPMSori1Cs8ZJGTl5M5rJkkTCfK8BtbW1tm9ra2t4DNAH/ABQBP3W73Y5cKThRCN1yIfR8MDqkJXdc5IkeueJ2h5W6igKqylxYreqIY5x2C8VF9hHfP7p6DmbwyKo6bFYlK/rPKEgvxr/YZRtXu/5AkJ5+Hz5fIOVzn3/7Ar/33df54c6znLzm5Wq/xvmbAV463sVf/OQQf/vEQTzeoGl9Xmszn01n94nrcWVGXw/PHz5nWnaq+LMH3Njtas6vgWWzK/jKbyxnQVXsn1+nAh+8o4kP370YoSjDYQhS3OKuhZtM979w6b1Ii32EnHzhPYdfTMnpj6B7x4/QVWve6J82Vy0YQkEqtrzQaeDC22AkD+EbAX8vQ5dbTbc11NmGcdH8E3X/ie0EdT3rNjGkNK0bABOgW045UzvUZzJvV34CaAT0tra2q5Ev29raNLfb/TlgC3Ab8Cjw35luPBDQ6Osz+eORYZSXh16Fd3cNomk6hhG5iCWRFetEc8OQGOEnnJFqubnWCQSllUWA5GbX4LjkZIuXWBR+e20D/7X/EsmwvK6AbXOrs9aX0soiFlY7OXnN3Pxe1lhGT9egqbY03eDgpS52negYkTWn3AGbm2tZ31SNY3iBNPLc545d5LljMUqvR+Ho1SH+8kd7+X8fWobLZklJN0MapnPRA3gN6OkaGCFz9PXgDeq8mcYm8HI7dPsTH/ORDY3Mn1FoegyyxUssCp/a1kLHoIfW6wP4fEGCAY2GikKW15WjoNN3owvVOwiGfquQDreK6ogVH0C+9bNUTASuSoyZq+jr7hkjJ9dcaH78b7+YWj8iGOjkRushHJVNOdd/PBzNhwz4ETYJFl9G2ygpdqEg6esZSPncwQunzY1DGH2XzhIonmdKz8G9v06rLYDu1oM465uzaiupjw1LTQnOYvp6+jOmW77xsvIShNTout6b8yf+xcVObLbMuuqT1vFva2uTQEwvqa2tzXC73c8RcvxvIwuOfz4goOnsOHaFp/efobPXh2FA1QwL69zV3N5YFd78OY0IRPJDcor1s6soclj5xf6zdPvG/l0ADyyu5N6WBtJIIGMK96+axclnU4yBDWNdU1Xyg6LQ4/PzrReP0+Ud+9Sp2we/PNzBC0c6+PQDi6grHHmDOtnVm9TpH24nAD/e187HNi9K6fhsmrYvkGbmIwFffXQJe9s72XWyi8HwGqnEBpsX1bB+bjUFtvx8OlVb6KKlqQoQ3OwaYHhxYMCIBUOM7BquRVsZ8g1A6/OJG7GX4bznMygWG0bOM4KM5YGuM5BGclbt3CGobMq5/uPiRDhZaGPUw6VUzu01n7gAAEMzpZuUBnS8k15bEKp9YKZfadhKLSyF0jnQk1qRvAjUeeszrFu+8bCd8t1pSBOT1vFPAZGgOlfCoyYpjl3u4Z9+2Eq/Vxvx/YU+jQsHrvDTA1f42ObZLK0NpwYbxkRykcO2R/PokIR80Cc2X1JTwuJHVnDm5gBHznUz6AvFrM+unsHqxgqsqpKSnPFxyW1NVcwrO8vpm6k5K+9dWYNz+Ml88rY8fo1/ePYYvUn8YI8Bf/dMK198uJmKglthI6+8Y+7m/XaHhx6vj1KnPaluQghqCxQ6hsw9959dEvk5jX89iBH/Th2KgGKHlfuXNHD/kkSbZ/NjHo/mEsJ9j/zNAAykqmBIAfEy2AiJY80HCVbPQz/6InSfYiQc0HwntuUPIBQbBrnPCBKLa940U/16BvI6Q1FKPI+y+vjOH4Irb6U3Fq5SU7oZ2viKOkp7YUbHPp6thHsLcr8Jx99WiqVhBYYezJhuZrju6cV/4R3we0ARWKqaUOtaMjuvmA71yUu43e4/BTYB32tra4uVj6op/Jnm8j5/cfTSTf7+ueMJ4/Mk8K+7zvPROwyW1ZVHfZvd1/yhzYyR74xxycokN4xQnHU+hEOlwueUFjGntGjM9xOhf8RWH9+ykH/efoJzvSMXl6PxQEsFW+fVmNLt2XcuJHX6I9CBn+0/wx9tbQagx+vjZFeSuJcYeK2tg4eWNibVDSQbm2v42cGro0UkxMZFteEN9/Gvh2JbevsnKovsk2LuXh/y8NrJTo5d7sPjB6cdWuqLeXTNXKrLCsN9MEDzI4I+CPaD7gcpQeqgBUI321HcXj0PatwEhm5g3LiI1DUoKMFR2XSrUFTQE/PcfOBpL/ks+d2vlHhgKPTvQPg1ZgbbkAEBFi0lG+n9NzBe/246owCAvb45VOArRd1EWgN+C9bS+oyOfTxbOWYtw3t6EdxoTUkvy4YPI7TU7ZAprg10EXjrGeg4MkIfDdDs5Yil9+GYszYzttLsYATAcEwX8MozzAE+QOj96QjHP7yh97HwP1+aYL2yCm9A49svn0x5U873X7/IXz9ajMsyMUOt6xIpFHQ92gHKPXQj7HqlV5T2XYWIrWyKyie2LWb/xWu8dqKTTs/IObe42sGWJfXMK5kR5ZQmR1A32HUm9U2OACe6/Nz0BSix2bjSn97emss3PSmP/6qGSp48eDXl4IwCBZbXlo+RP/p6sAiVVTMLOHTZXOakDYtq8nruBqXBT/a3c/jKyKecXj+8draP184eZm1TMe9bPhub0BF+L0IfRHr7kAEvIDGEQIR/1+JxxVaItTYUsqULAZqGREvp3FxyxVWS1r4RZtQjw/UM8qUvZjlBH/i9GEgw9Iy2oQcVjKAvJRsFTu5MZwRCaFyFkKD7hsbIl0h83RcRQz0gFMSMauzF4ZJEtlII9Jhvr3oxQlEzOvaJbGXd8CGC+34E15OEd1pL0PquI0pnDt/dJ2IOBbvPoe/8Tny9/N3Igz/G23UW68r3jVs3I+jDCPqRSh7/6I4Dk8Lxd7vdjYRCdm60tbXdCH/9feDPgMfdbveTbW1tT4SPtRIqBjYLeL6trW1KVfXd034dTyDxE9jR2H/uGne567Ok0WgIdMNAVQVKHm0xUJWQM5tPOuUrom2lKIKNc6rZOKeG60Neer1+VFWl2uWg0G4hncXdsY40boTAm+e6uHtR/XAaW7PQdCPl8XcoCn923wL+/oXRoSWx8cn73Vgto0PbINb1sGVxHYcut6es9wwLLK4quRUunWfQDck/bz/B2Z6xr3Ds2jXWeV6j2fsOhR2DdO23UlDpprD5bhwNzQirFYEVECiqFaEHpiQPnLmQlm0tjUsQNlvO9R8PRwEDieIoAKsjo22oVieKxYrw6gmPF0KF9r1pjQEU0KqL7AAAIABJREFUYF/xKMJmHykT8B7fASd3QLB3+MqXgHfGTMTCbdB8Lxwxv8XQuvQ+FEfBhNlKVQuxbP1jfGcPIQ/+R3zFgr1w+CcEey9hWf0YQmRgfgR8+C4cQV5+J/Q2QrVB1QIcc1eh2AuQvgH0nf+SmuHO7yNYVIO1Zcu4bKVYHShWO0JOTYdhUjj+wI+AzcCXgS8BtLW1nXC73X8OfAP4hdvtPghcBNYAMwkVCPtILpTNJna0mskHHMLrJ69zz6KZjHTSssVDTzZDTqOS4jnZ55EMQ8rwrtjc6pPPPJ6taopc1BTF2zKTuvx+T6rP0UeizxNAUQSlBell6C0tsJuak7NKCvn8Qwv5952nuDoYe7Exp9zG76yfS1WBc8S5t/jY62F2SREPLa7kmRQ3J//x3W5UNTrWNH/mCsCv3row1umXBlsHfsWGoV0jvzc0jGuH8Vw7hGYvx37Pp7AX1wESLDYIxx9POd5zkXSgeXqxlc7Mvf7j4RLQJVgdYHVmtA1hcyJVG1gCCY+Xup+YBdNSgOV9X0R1lY+QaQQ9+J//R+g/H/uk/svIAz+C+tWAHTARmrj297BWL8DI8HgktZWuIY8m2UQfwdnX8TpKca18eFy6+U7vg/0/ZszG986j+N55AhZsA6kSCvhMEUd/hWy5E2GxpW8rix0UGxjTMf55h7a2tm+53e7jwF8ScviXAheArwJ/29bWlnrpzUmCzl7zG4Zu+iF0o5bcumFnm0eQL/HHE93/ycyzayuR5msXi6oAgsbiAopt0Gdy/bB6fiTrUOo61xW6+PxDK7jQN8i+k53cGPQjBFQU2nlo9TxmVhXR09WfgswIQv++b3EDFqvKk2/FX8g7BXziPjcNxYUp6TqR3JAGhy51s+PYFS71j7opS8l9/U+wyvMaEhE+Y6wtDH832tNfwvrgF7GU1IEWGE6rN9U4enqLXSXgRZFazvUfV9+NYOhXRddBZLYNYWgoyKQ2EoEYadJSgVqEzVU2Qr4wNHyvfie+0x+NKwehYSVceovQNZAYYt1HKZizCqSR8fFIZitf+z7w3Yiv3GiceBpatqJQmJY+g+88D0efStzGqe2p6xOFwLkDOOetT99W0ZV7p2P8s4e2trYtaf7tVeDVLKiUl9BNxFJHQ0qZ0VCBC32D7D7RQXtnP0EdCuwKK2eVsW5+NUVWW+YayiAEENAMDlzq4u1zNxjya9hUhblVRdyxsJZSx9Sr0JcuMjhVxqCuNL1EWxUloafqQgi2ttQkdJpHo9QO7oritNoFmFVcyKw1c4l2giNVoNPFXe561syqYs/pDvaf7qLHByows8zGxuZaVtaVoeZhbFpQN/jerlZOXI/tTDX4T7DK83rMZc9oSCS+139A4UNfIPcp/LLIHTNM2TgCxVk4bKm86YtZPqw/WWhDMGJhGud4xV6Qlv0pKh0jM3DlBNxILQQQgEuHsW79FMG3nobec7GPKW3CufWPEAVlobShWRmP+LaSgHEyVo6UxPCe2otz6b2m9fFfPpHc6R8H9CtHYd66cdrq1sdUQ944/tNIDWWFdq73m3t64RRRv78jXBXzfNAf5F93nhzzan9AM3juxA2eO3GDbfNLeM/yWSioCWVNLJe8dPQi/75z7A/v6ZvdvHiym5X1BTy+dh52qzrq3Hcbz27qU3d5EUUWGDC3VYU1DRXDsjbNrWH/qWt0DsnEJ4Xx+IY5GbsG0udizN+KHBbuW9zAfYsb4mieL3MizKTBD147GdfpB7jd8zogo570x5IZhb7L+Lsvota4h2/AeZOKMkPcMnsV2uldcW0WGzbUupbwnpb86YtpngfpPKXVBvUrzKfynL1+jMzgCfMOcrCzHcej/wv9+mmCp/dCXzeoAkrqsCzajM1ZlvXxSGQrzdcPQ9dN90teehtjxUOm9QkefcF0W6bg92LESxGciq2YTuc5jTzCHQuq+eWhC+bOcZdzy+mIWvGb5J6Azt8/fzRmcalobG/vZciv8Tvr5iNE5Inl+NoeL3/iwFl++UbiqriHrwxx7aWjfPbepdgssavFvjv4+OdKIi6E4M7FNTx5JPUn9pvmlmC33sqTb7OofPqeJXzr5WNx4+8j+OjGWSysKs14X+QwJ4XjUz0ufa7pOte9fnxBnQKrSlWBEyEyO5Zt3QMc7YwfKy30Ptz+VkIhPslscQvB9t1YK5ryIjQlG9xeNQ/NWQFeE6EUzdtQDT0v9J/soT5oAWzN2wiYdPxd89eMkCMDHugyV9gQgAuHUG57L5bSeuyrPxCjWmz2w7kS2srba75PANogikl9tJ5L0J16coO0YHegjCNcajrUZxp5ha2Lavj1mxfRU0znCXCHuzYjbf/3gTNJnf4I3rg4iHvmDW5vNFfNNRs4fr0nqdMfwZUBg18cOsdvr52XZa3yG/FdtMxgm7uW9o4+jl9LvuFOAP3eALvOdLK2sRK7NbSYLLRb+dz9S3nj4g12He/gaqScbficrQvK2LSwlgpXdkK4Ii51rtHj87P7ZAfbT3aP2AJXbIUtLTXcMacKZ5r1A0Zj1/HEtQ1K9Zso6Vil7wZ5E5qSBS4EWNf8FsGd307NHo5SXM135Y3+4+JEOFloQzBicZngeGvVPAJNa+DcGykNgbLqcRSbY0QlaJnuXgHvUB6MR3xbCTW9hAmoLtP6aDcupNeWCSg1LSnrMx3qM428R2mBncfXz+FHe86kdPzDS6ood9oIz+aoT3O8zxfg8GVze6W3H73K7Y2VabWXSf7K2+ZquO0938cjKwIU2K0Tqmf+8OxXOVaE4GOb3PzX/tMcuJR4XkngyFUPR656+PnBK9ztLuPhZbNQFIFFVdjQVMWGpip6vH4GAxpWRaHCZQ9vBs6O/unx0UuF8fO2rj6+/Wrs34K+IPz6SCevHu3kzx9YFJV5KL22dENytCNxcoGR1XkTyRwFmUehKVni1plLCG74OOz514Q2xFJE5fs/z5CYkXOdM8LjhProioLv3FsYp/bA0E2wWKG0FmXR3TjKZ6XUhpnKvYoE5/qP4A0KuLw/8Ris+G0cC7eMqQQtHWnuFVCtOR+PRLYSpXWAE9OZj2rmmdcnzX2KZmCft3Y61CcBph3/SYh7ltSjG5If70tcZvuhJVXc01zPiFV+mnz/GfPxf5cHdK4OeqkrdI2r7fHwbo+X9m7zGTX2nuvi7oV1OdE591yM49zU+KX+QXYd70jq9MfCy2036ezz8bFNC8O58UMyS512Sp2RJ1fZt1WuQ30u9g/GdfqjMajDPzzXyv94eClFjujaC+ba9QaTl1ruUcswUBChW2ccmTFQUmE6ZGAycWFoBDpOwoVDUFQDAz2MSfGoFEDzVqrXPoTV5sLb58kb/TMd6uO/dJTg7h8Ao56g913EOP8GnpJGHJs+jjKjKmOhPhHu3PJ7BM8vQzvyJAxeG9n+3PXY3dtQyxpjyxQKFFSZj4evm5fz+Z3QVroGLXfC8WdMdcu5YBPCpD6q3WUmOad5OCtRVQvjyYw0HeozjbzE/ctmsnXZTJ46eI7n3rxE9D7JO5qK2bS4jrqCiMM9flztMZ9GFOBqryfs+OcGF3vMVUiN4PKNKZcJ1hQSuGjjxosnLvP0O9eSH5gARzs9PH/sIg8ubcyQVuYRWR7lCj/fm3jhH40hHV44donHVjWl3Z4lhQxDhlrCKfsiFvpPmJJtm7uR3IdCZIcHuk6j7fo++Lpjd95aglj9flyzViIUBYujKOS05In+mQ718Z09hLb3+4knRO9FfE99BdsjX8RWWJagDcGIxWUSnQzNj/fNJ+D07tjtDvQgVFtCOYp7G8Zhc0W5bAvvzIPxSGwrp3sT3uMvAClmXWhchVpYNiIUKhV9bLVuvJjMzW8G9kxkwgrbKZs3whxi2vGfxKgvL+CjdzXzQHMN/UMBDAxcFktU4SU56jN9nu7rOV03MqqHWa7p6ekd1CI/ShOvc+559kJ9Xjk5fqc/ghdO3ODu5pnYLPkW0hOPj14qpM87Boc415v8CXw0dp3u4ZHls9K2l92iUKiG3iAkwhuu9Sz0H0dC+Kl/LJlRKKxFrZqT81CIbPBAZxvaK19PbLBgL3Lv9wmU1mMtnYmh2lD0wLhCFfKKR4X66J7e5E7/MHwEXv4m6nu/ghCMO9TH8HvwPf81GLgSv8nrrfie/hLqw/8De3FDTDm2+RvwHX6SMW8r4qFkDmrV3JyPRzJbKa5SLPd8Fu2lv0vep+LZONd/JK05Ku1OmL8V2s1nR0oJUklpPiS0FVM71Cf/kkRPwzQUoVDosFJos0VVJo1e3Y+flxakl5u/tNCeUT3M8mKnNS29i4c3hE68zlOV9/kCPHkkM04/hH6rD1zsyklfQu2bOT6zbR8+ayI7TBSOX+tJu10hBNtaqpO2ccm+mDeddyBIZKNb39g3fQxVynAohIYijSnB8Q+ivfKNlMcm+NK3EHoQRfcjDD3n+meMG0FUQNF1gsdeTtkeAHiuo187GbcNYWgouj8lPXy7v5fY6R+Ggf7018A/GFOOxWLBft9nGT2XY8JWinPbH+XF/E7FVo7KJqwPfhEq5sfv04JtOB/8HKqqpq2Pc8k9oBalOgvMobxm/LaKDvWZgph2/KeREm6fbz47j0uBeWXpFa7JFOaWz8CRwu/zaNw2rzL5QVMYaZgsKfa0p56+M1VcuD6QcZmpIhs2ShV9HnNP+2+dl14F2QjWz0vu+CMEzxf/BvtcGxPaSLGXYXvwf2ItrQUkI8NCJj/3nt5HymETAMFe/BffAkRYVv70JRNcSh3azFdiDbTtSCA3ejEZv22t9wp0HjPRqg9v+964Mq3lM7E9/GUoT+AgN6zA8cj/RHUWJdRt4nhqtrKWNVJ475/jeOhL0PIQzL0D5m+BVR/C9YFvUrj6MYRqTSonEVedRTju/3NQMu/8WxdsHpduIYgRH1MN06E+UwJy1Gfm+cwZBcwsUrk8kPoK+M6WKkJRR9nXLx5XFcGWheW80BonvjYGyu0wtzQqTnCCdc49z06oz7729J5SJ0JA0zOi28RwkTFZapqPbEKZjtJvt9Bm4U+3zeE725PsLxAKu4ofY/0DH8LZ+iqBS9vRBroRFjvWsvk4F92Nc9ZSpKJjBP1AHoWmZIjL1h2JbRQD2sndGKvunZKhPjLowdRCKIIbl+PaItVQn8CJXaablSdeQV98FzKOTKW8Edf9n0Prv0rg1AHwdIEiYEYD9nm3o7pKMCw2DC2Y+zEwYasIV0rqcKz+DZSI/hYbaMExmY7S5UpJPbYP/jWBt1+CE68wJqPQ3I2IqgXIfamGhgHFs1FrFozb5lM91Gfa8Z8SGLVKRWaFP75pPn/3bOrFS1qv9FJbXsCS6tLwQjq7+sXj25pncvD8Tbq90c5MfPzWHXMJFR6bWD3zh4txnBuf94zvYXNMFNqtGdczVZ7LrD51FUVwtg+zaCwvGLcOi6pK+fTd8/j3XafpjTOmjcUWfn/TAioKnNDQQmnl55ESem8MhrK8+PtRggPIYCj0AETeZKHJBJeaD3xdpseH7gvhUB+D8RQgyiceyepjpJsDX+pxbZFyVp9Oc5vNAQj0IHwDCIdI2E9bUTWWVY8Mf28IBQJejP4ODMWK6igKPSHP56w+OeIW1Y5txQPIZfcR6L0CvkGEakWpmIVFDf22Dw7cgGO/TmHAHDg2/0FGsidNZ/WZxjTCaCgq4DP3zOebL7WTuFZqCKdvBji98xwLKzv4/Y0LOHVjgK5+L9KQlM1wsKy2DKs62jHKPFxWC1/+wO186WcHuJHE+f/9O2axsLIk6zrlO7IxKtnI47C8qSLDElNHZHmUC9zeWMFPD5irT1FXqNJYXJj8wBQwr3wG//u9K2nt6uONU53cHAqgCEH1DAcbm+tomFFAtHUEIESMWTX8lST3WU8yyPV0Z3owZJToUJ9c92W8PDy+wpFmWEdBBrL6BPwxBCeHDPoRztT7HLh2isDxV6Hj7WEZAYB5G7Et3IatuCYlOdnhKdoqB1woSqhytxF6I2QoFjA0QOJa8RBeixV55BfxB8pVhePOT2ApKjedZSi+raLMNcUw7fhPCchRn9njc8uK+Jv3LuH109d48eh1UnmIe7LLx+d++c6Y7wUX2DK/lAeXNuKwRl6pZUfv0hkO/v7D6/n1G6fZfvwa/aNCpO+YU8zWRXVUFzmzpsPk4dkJ9amfoXKxP3Ouf7kD5pUXZUS3ieGjlwrpc7tFYev8Una095Aq7lxSN+52o7kQ0FxVTHNVcYzWRv4mSWC4uJeiIAVIlNANXoTCtXIdCpFJLu2RYmkmYStmymb1sRdA5WLoMhNrD8xeM+5QH+xO0My/IZOOwrihPtFcV1Q8e38IZ16PLej0awROv0Zg+QdxLb5zUoT65BN3Lr4XY8F6fG0H4OxrMNQHQoWqWaiL7sTSsAxFz1wY0nSozzTyGlJKpATDMKJSboZus9niTqvKXQvr2HH8OoFUHv3H0x3Y0d7D2xd7+Oz9iymypV9cKBk3DInVorBtQR1b59fSMeRh0K9ht6jUFjiwWUKXgmEYWdNhsnDDCBVfyvR82rCwmosHrpIpvH/N7OH5n0k9U+WGEVogJZozhiHRjch3mZ1bDy9t5My1Pi72J78IN8yeweqGipzNb2kYyPDcwtAQuo7U/aD7IOgN3WClHgoNmSp89ho4/0bSsRmBubdD0IPUgyFZ+dKX8fDAUOjfAR+iZStypznH3zF7RVxbyIAAiwZBT2I9GldC63PmxqJkNopQMYLepP307/khnNuTXOaRn+JRwLFoy4SPR8q2ylOuKDYcizejLFwHQsVQ1VD2LKFiaMnHyJStNDsYATAc06E+08gvSCmHb6ZSigkphR3Bsa5eBsfh9Efjph+++eIx/uqBZSixwgEyAN0Iu14GgKDG5QJXtIOWlWYnJUbaKnO4rb6CX3CV9PLRjMQH19TTXFk6oXN+NHSDsOOf4BhdIoUSrmeR2bmtCIVP3rmYnx06y8EEFZDvX1TOPS0NOZ3jerhtwwAMiQgGELqG4fMg9NCMMIRAhFdxU4Fb5qxFM+n422atRvMOoBoSGfDlTV/Gwwn6wO/FQGIpayRY3QLXjqdmkGWPIaREBrwx29CDCkbQhwwGEuqhzroN3azjP2dN3HajefDmhdSc/ggO/xStrgWrzZlQ50zzVG01zSVG0IcR9COVqekYTDv+kxhCCAQGiiIQQpJCYc2MYCgQ5Od7z2dU5nUvHLrUxdrZKaQLTAOqEnK7JspGkxnZspVNUfiTu+fxzZdPpy1jWa2Tu5bUM6skSzmgTUBVQo5/YjsJdMNAVUVW5p5dUfjQuvk8tMLP620dnOrsxx80cNpUls0uZ11TFU6rSqYXHWYRyUIUsoFAWG0IaUFxFCCCHkCgqFaEHpgy3FI1B61pHZzbl5qRFt2PpaQKi7MIoWsIv8ybvoyHo4CBRHEUgNWBZdNH8b72b9B5NLE9lv0GjsXbErahWp0oFivCqyfUw+qsQ5+3BU7vTG0sCmpwzl2LUNWk/dTO7E1NZhS0i+9ga96YVbuna6tpHkCxOlCsdoScmg7DtOM/ySGEQAjChbsiq9Pom3yId3tCjsGe9pt4woeVO2DjwhrWz6nCZbPEPXe0nK8/d5yBNLKyJcPPDlxl/ZyalPQwy0PVjEVUVePMyp9KPJu2ml8+g8/eO59/ebmdoTgPU6zA729uYmFVMae7+xn0azgsCrPLiiiyW0cdnTtbKYoIO/5KguON8LHJjhsfL3c5eGRFE8mRG3sJRSG0mBSgWEBTEaodKcKvTZBgsUE4/niqcOf6D+PVBVxM4hwufADnykdDJrO6EEoALHrO9Y/FjYAfw9eDYbFhtc9AWGyJj/cOEDhzCIJDoe+LK7Bv/hj+a6fh+IvQ1TbSFrPvwNqyFWtpQyiFZAKdJAr+vk4CvT0ojkJE8cy4xzvX/CbewCBcPJR4LFyVWB/6fxDWwqS2kAi4dDixvFg4sweW3juh4ydsTqRqA0sg53Mo37mw2EGxgTEd4z+NvMVoB02O4C+3XuHXb4+tmNrtgyePdPLkkU7+cMscltSUjjk3mgc0g2+8mB2nH0AjtLAodzkS6pEeF1mQOVV5dm3VVDqDv3lsBUc7e9nT2klnnxcJlBfaWbegittmVgznm19UVZonNhnLc5nOc/JxGK7iaxgICQIDRdfyKr1gNrhz0++iXb6N4IntcH1UiEv9cmyL7sRS4w4dL0VepvMUhk7g6gmCx7dD160+aABz12NdeCeWkroR5+q+fnwHn4CLBxgN/4H/hHmbcW39Y9CDBH39qEJBLShHWh1hW8Tvv9Z9EV/ry3hOvzZScEENyqI7cc5fhwIjz0VSsPGjDLU3w4kXYXD0PdEOzdtwLb4PrM6U5iX+oTF9Swmevgmf9/mYzjNf+XQ6z2lMarx4/DJPH72e9Ljv7jzLH22dw+Lq+Kks956/Tk96WdFSxp5THbxneVNWZI92waYRH9m2lSIEy2rLWFYbTtWX0FnOT0SWR9NIjrgjOvwHSa5TCmaLC6Fir2/GXt+M5vcih24ACqKoHIvVATAyBSGCfErnKQ0Nz+vfh4tvxh7DM3sJntmLtuIDFDRvBSHQB27ife5rEEyQSef0LjzX2nDd95dYyxqHUznKJDr5zh1E2/v92DKHOjEO/Zih9t047v4Mit054lwBuNwbEfPXod04T6DvGsLQUF2lWOoWoYb3mKWcElKkGQqiRFyviRxLwYjf2TyYW/nLw3aaHLci05h2/KcE5KjPEL/a70nJ6Y/gezvO8vXHlmMdURY0JFNKyY5jmcvIEg8dvV7i9Wd8PDspKqcmn7ZVdvjopcK7h0vg3ZTOM25aycIyVEcoBW38qq75k85TYuDd9QO4HMfpjx7pt37GoGrH3rwF/4vfSOz0RzDQiWf7d7E/9PmUdPJfPoYez+mPRt8lfC98HedDX0BYrGPkKBLUyrnYaxeNqExrtuKrsBUATsZUnU2GkvoJT6s5mdN5Tvy8n07nOY28hxj1GbrN7mrtMCVFBw5evMH6psgGWzkss98XoDvNwovmEHkqcavtzPBsyJyqfNpWqfCJCPUxJBy/1svuE1e5fNOHpsMMp2DNvCo2zK2mwJ69FLiZ5fBuDfUxy/Mp1CfQ2Q6XTWQmOvQf+BVhrmpx9yn0zpNYK2Yl1ElqPvQ9P0xd7uBVfCd3U9CyNXs2QoeWO+H4M6nrBSgt26ZDffKYT4f6TGNSwjAke86ZL1iy52RnlON/C0NalgL7R6Gzz8sXnniToSA4LdBcP4PNzTU0Fo8/i8toF2wa8TFtq+SILI+yhc4hL//0Uis3R4XXeT2Sp965xlPvXOO9y2u4c2F9FrXIDEbOJzlclfPWHyS5f72fJxxBvoT6BFpfNTfQAIefMn2KduIV2PTRhDoFrrRCoNeUXHlqO7J5CyjZs5HTvRGvGcffVoq9YUlIxoSOq+DWBZf7uTURXBo6RmAIFCvSFimql6qtosw1xTDt+E8JyFGfMBRML1t614A2Qk6E29WJeeXV5b3V9oAGb1zo540L/bgr7PzBJjdOmyWmfsn5dPhKPtnKkJL2G310DfgQQHWxk7llRYiIw5OldnPLU+/btQEvf/1sK8mySP/qSCdBTee+xQ1Z1Hv8XAKhUB8jVBhH8yKNIIZQYTr0IC9DfaQWhKtvYxqaOeccgEvHk4ag6KfHbhJOCk8Xgf5O1IqmrNlLuEph05/C7u+kpJL17k8irQ7TYUXj5e+WUB9dsRLobAstJq8cuWV4pQha7saxaAO4yhPbiulQn2nkPcSoz4jjNl55cpiXOm0UqDCkj0tw2mi74ecfXzrOX9y3eLjKbrR+ybkwefy7mWfPVpou2d52lVePXRuTzrPEBluaa9i6oA5Vyf/xymaoz/+/qy2p0x/BM8e6cM8spamkMGN9yzwP3UwxDIR/CFUfAj0IiLx5vZ8vPF9CfaS3f/RUyyJ8yUNQvD3piR7qRSnJrr1cjcsIbvoTgrt/AMSJibWXY7/zT7CW1GHkInzlXRDqIw0d/85/gcsxUqwaA3D0l/iO/gp126dx1i6Ib6spHuozNasTTINCqzWt8yqLYk9yRQi2NVeZllflHO34pI+rgzpPHbmY9vmZ02TqIxu28gV1/vHlYzz1zlinH6A3EEov+53txwnq+V8xMVvz6UzPAB0my2JPxMb78SCynAxBhm6o0gARtUiY5gwvlqJDfXKkj5igt7wAqJFQzgQ6Kek5YMJimRB72RuWUPBbX0es/yhULYSCKiiohvplWLd+Cudjf4O1tC5t+RIIXGvHc/RFBo88i6d1F7qnx4Sc6KswT+Z6BrmUkqHd34vt9I+ARN/+jwSun0kgU4z4mGqYekuZdyXkqM9Qhcz1s2ew97y5pzYbFtaMkBPNN8yv5rmj10n1of+yWhePr5/HP7/ayrne9EKPRmPn6R7es1zDZonclGLrOpZPh/rk0lZSSr67s5ULKcyDUzf8/HBPG3+waWFGdcg9Fykd9/oJ80784cuD/GZAiyrEN15dM8tD7qwERSBVFUMKyIOsNfnJ8yPURzhmECqnZ/a32wKY3BPWuCxuCIo0dLytu+F6q0k9QlBK6ybOdqqKfcFGlDlrQ99bbOPKGoQEiYH39H5466kRm6YNwH/4J/irF2Nd/f6k4UxTPdQncP4QXEk9NC2w6/uoH/hb5Lsw1Gf6if+UQKwVvWBLi7lNfyqwurFijJwIL7TZ+PR9C1KSVV+o8OH183FZrXzm3iV8dOMs5pXbRxxjM6XdLRy6fDOmftM8+9wb0Lg66OXKgBdPQEv53NfPXae9O0CqOHLVw6W+oZz3NxGXpo5PXW5Hn8nUgGF0+/wZ61vW7GVIhK6hCIkiDRQtgCK1aR7NdT/C0HOuj4oBi+40PxEXP2D6FGvzlph6iIAH7yvfhsP/BSkHv0WhYQUWa0F+jGsaXBga3gP/DfsD18mWAAAgAElEQVS+Hz9T0rVjBJ/5MtrlYwllCkND0f150a9scOPEdnNzI9CDduVEbFtFh/pMQUw/8Z/CqCty8dDiSp45llpqtY9tnTMqh/9YNJUU8YWHFvLz/ec4dSN2Na/Nc4t5z/LZ2K2h1bIiBCvqy1lRX0FA0xkKBrFZLOw908mvj4ytKJwMN3o9ps+BW67INJJjtK1O3+xnx9ErvN0x0vZLal1sbaljQcWMmHLevnqTF45c5lK/+axQO49f5UPrU1to5gKC8IOiDMNIM8rJMLKhTWYgYv5Lkg+ZP/KOIxgR6pNDfZzuTXhbX0h9oF2VOJbej+/KUeg5l9o5M1dgLWuMyvR0Sw/v6/8G144nERAftua7x8jMJjf8Q3ja90H3GQgGwVmIZdZK7PUtoKimZXpObIdTO1Lqq/bK19He87+xFFXEkRm9IM/93Mok1z290N2ekp2iETy7F+vMxXFsFWWuKYZpx39KQI76vMXvbZmJIgRPJSnk9fHNs6Oq9o6VE81rC1186q4WugZ9HDrXRa8ngKoIakpdrG4ox6qqKErs820WBZsl9OTf0KP/ljp0Q5Koz7H5dKhPOraSUvLLw+fZ0R57Y93RDg9HO06zaW4Jj61qQkTFS6ZaNToe3ro4wIfW54tNMsFHLxVi89ICG1cHzT/1L3HYMqhrZrkEpkN9JleoDxJEYQVs/BN47Z9IDhXbXZ8EmxPHnZ/E9/zXYKgz8Snl83Fu/GjMtoM9l+DiwRTajYOVj2OpnI8xAfbShcDzxk/g9O4xamjn96OpRbDhw7gal5uQqSCP/MpUl30nXsG19vF3XahPMN2N6P09ccKrpnaoz7TjP8khpURKMAwj6olf6DYb4XctqmfF7ApeP3mVPe29wzkHSm2waVE16+ZU4rJaw+ePPDcRL3fZubelfsT3hiHRDSP8nZHw/OLC9IJ9ZhSY19UwJCKsXyrHv5t5tK2efvtCXKc/GrvP9GJRzvHoiiZAcuDijXE5/QAByOvxMozQAskw4s/z0PUQ+S7x9RDht8+v5Pg1c5vY55RYKLJZ8tZe0jCQCAxNRwQDYPjBkCB10AKhG+w0D/GgB6kHQ//OA30cjUsI3vGH6K//OxD7LS+uKmyb/xCLqwwj6EVRVRz3/QW+oy/DqVdg9M4wpQiat+Fs3oKQIIPeMW0HW1+K3VZSWBCrfhO7ewMEPVm3kTQk/pe/Db1n4qukD8Du7+BZ8UFc7o0pyfefO4TpvRKnd2EsexgcBWP1DAiwaBNik4nmwkhzD6ECxJh7UrOH0g4bjimZ1Wfq9ehdBClDKQUNQyKlSPiqv9Ru5+Fls3l4WTwHJTM66bpECgVdj3ZyYmNpTRlw2XQbK2dVjtF3KKjxxrnrHLvUg8cfxGmzsKiuhHXzqimy2dCNsOuV/8lico6Ira55fLzcdjPl87a393L7fC/VTju/Pmh+XGMhn8dLNwg7/gmOMXE9RLCkugwHF+MlBYyJjYvr8t5WEFrIiaAfJRAAQ8MQAiFDv1vTPMQ1L6iGRAZ8eaGPIQS2qnnIR7+E79IxuHAwlFpTCiipRZ27HqVyDiogA97hcwVgW3I3csF6ghffCTlYihWluAJb9QIMRQVNQxJnHpxNo4YA4Hj0KwhFRfcNTYiNvId/kdjpj8ZbPyVQXIlSPjupfC4eiSclIQKdp7DULBgjUw8qGEEfMhjIuk0mmgtLQVq2orAUI8Y8MYI+jKAfqeTxj+o4MO34T2IIIRAYKIpACBkVXpNLCHTDQFVFUn0cisodTTN4/Vzqr+mW1ToptlmH/21IybPvXBzroHo0zvXe4LkTN9jQNIOP37cUVUmu0zRAVUIu6p6THabP3XOyg5aGUgYysCdqQbk1r8dLVUKOf2IdU78eIlAQfGRLE/+y81xKxy+pdrCytuxWJro8RGTrkKIIhNWOsNnAUFFUK0IPAGKah7nFWYTQNYRf5oU+ES6w41h4B8r82wGBoVpRwscYCc5FATF7OYqjEKyO4XNI2nZ6m9yFzY5QJmZuSU8vnH/DlH7aqddwbG1JKh/d/L4oAKSO4igYI1O1OlEsVoRXz6pNcnPNFKLVLoGOo6ZMZV2wNaatFKsDxWpHyDy+AY0D047/JIcQAiFAURRuZT2I9gAmmoeebCpKRKfE57xn+WyOXX6HVLJ9OoDH1sxFUcLhAxJ+vK+dg5cGE56351w/Q08f4bOPrBw+N7c2ym8espFg/2nzFTj3n+1DzZC3vm3JzLweL0URYcc/0Tw3dz1E+OKaUj62SfK93edJhOV1Lj6yYQHqmE35+WGjYaYoCECxqGC1gWIHxQCLjUjl3mke5lYXQgmARc8PfcbLJaBLsDrA6jRxfhppQQGsBaGpNwF985191bx+V9/G0AMoFldi+fZC87IB4SyOaWdhcyJVG1gCWbVJrrhlyb1oZhz/kias1fMwYsgUFjsooYcTUxHTjv+UwGjnSOYBH61b7ONcdgt/8eASvvXSMa57ZNweFtvgz+5tDm9gDJ27+0xnUqc/giOXB3ny0Fm2NlVnuJ+3+JA/yIGLN+gZ8GEYkvJiJ7c3VlJgVzPeVna5AKSpUJMIgoAnMP6aDdUuhebqkiR65pZns3IvwLK6Mr76aBF72jt59XjXiOjqJbUuNrfU4S4vQgglZZm5/D0Ync4TI7eVafOV50vl3kxxjGDoV0XXQZg4v3ohXDuGKZTMQUUHOUF9u5liiM8o6NfPYat1J5QvZrYgL+03LdtePiumzKleuddROZfB5vvgRIpZqGwOtM42LDXusbaa4pV7p16PpjHpUOKw8fkHl3P4yg12He/gQt+tpzx1hSqbm2u4fVZVONVoaHEgpeTlt80VOnrmwGU2NlZiSZKy1CyGgkF+eeg8b1wYHbLUwxNvXmV1QyHvXz2HwqgQpXzHaHfVDBzW8f2sFKrwibubUfI5doXI8ii7KHbYeGBJI/cvnolHM9B0gwKrimW4qmq2NcgMRMx/SfIhFWDecQT5ks4zI3x4rDF1vrV5G0GTjr9o3hrV3gT0TUsvHEdqgaR6OppW4t33H4TSHKSIeZtQrDaMmDIFU/3aK1j+CEOqE46mkA3peiuBV1oJuO+hYNV7EWNsFWWuKYZpx39KQI76zBc+2jWKzy2q4PbGSm5vrCSoG/iCOnaLis2ixDhHcupGX0rhQdHwSzhytZtVDRVp9mcs7/MF+Ltnj9GfQJeDlwZp63iHzz24mBJn/qZcvMVD2WpqCgSdQ9F/S45KJyyoK2H3GfNhQhCq9vyba+dSZI8skvLFJpngqV8Po7kQggKrClazFavzg0tgOp3n5EvnmRGuaxj4URQVTJxvqW8mWFCTPCVoBLZSbPPWh7NsTVDfnEWp6TYartKkaTWl1QnLH4EjP09ZrK35nrjzZiqn8xzmQuK47RFwb8D38j9B39nkRmt7iSHFguu2R9816Tyn5s6Fdx1E1Ge+8PTPt6oqRQ4rNosa95hLN9Mr4tVx05OBvoW4IeHbLx9P6PRH0K/B/3nlBIaU4253ovim5rrkHRuFTS11LK0txWXyl6XcAV99dAkf27yQIvutcK58sEM8nq3KvVOVT1funVyVezPGjSAqoOi6qfNVCfZ7PwO2UpJCuHDc92lUQ5/Qvlkbb0uu22iohdjLZ6Ykv6B5GyzYmpJYy12fxVZUHlfmVK/cG83xDaTm9EfQ+hxyoOtdU7l32vGfxqSEoaeXZiuY5nmxcPxaj6kn4p1DBkc7kufEzwcIYO2sSlOvBBVg3awqFEXw4G31ptr7yJYFFDvSq+uQK4jkh0wjjOilwC0mGRkKMs1B4r9xgevbv8/gL7+A58efZvBX/4vBQz9H67+eEfm545g+Ry0ow/Xw56FhBXExaznOR76Ipagqa33QvQMEus4SuH4afah3+Htb3aLUFibRWLQFoUR+WRO3KwQUrPoAYs3vgrMihjCgZjH2B76IrXZhEpnRV2G+zInscF/bzrF2SgLvqV2jbHXrY6phOtRnSkCO+swXLrLWRqEzvXj5IoeVTNlr5zFzewwAdh6/yrK6snG1m30eCvWxWRQ+cfc8vvHyaVLBJ+6ci8MaepaweW413b0etqdQ/OtD6xpoKikkU+OSvzx710O+cwlMh/ok5obfg2/nvzJ4/Tgj4PFA26sE2l4l0LQO57oPI/NE55R4mqE+Ea44S3Bt/mOCwSGCJ14DzzX+L3tvHh7HdZ15/25V9Yp937gDZBMAKYqrRJHirt3yIjleZuw4ju1MHC9xEk+SyUwcJ+MvceLEiZKZjJN4EttZxrGteNNG7QslSuImigRBcF8BECB2oPeq+/3RDbDRQHdXdTeIBtjv80j9olh17rnnVnWfe+vcc9AFFFZQvW4Xalk9Q32DM1KhN9B5Av3Ys9B7Y0zCAOXLYdV9uBbehnLnRzFeNVPZGLCV4WzZbTl8xdG8E9G0hdC1U4R7LkaKPTiKsDWuw+YsNSXnlgj1Geen3zA3HrHo2I9xx3+K2Ir5HeqTd/znBeJmqZFvi1nm8bplt43b6yv4t3SKfy2t4oYDlpkOHdcTVLFMgtN9wYzbnXl+wz5NFSX85r3L+fZLpxOGNBVp8KmdTTRVlEyS88j6ZVSXdfP0O1cZmmZ/2sJijfdtWsLKyuIc6rt5PtNZfeYXj/yY5rP6TM+lfxTfs38BgykqNp/fj887guuez6OMhw3mgP4zktUnjttsBThuuwcQGEJBkTr2QjcyGr6S1fEIB/C+/QM4lSBdZ/9pePU0voVrKdj6KbybPoF8+7vJx85WivOB30KzuTDS0Q0dtaYJo3bFxPGIHcz1fb5n9bkxdn4i+eUswhiJhvrks/rkkUdOwu3Q2Liw0HQ6T4A1CwqpcDlmUCtzkFIiJl4p5iZitVtWXsTXHllH27VBXj/ZxfWRyISnotDBluZaVtWUoSToztZlNWxZWkN77xDnugYJ6hK3XWPVojIWFI/nqZYz2peZwvj0KI/UENP+JZntLCCzzaWE8EAngYP/kdrpH8e14/iOv0Rh645Z13+cSyDQ1UHo5EvQewmkDgXFsPgOnI0bEEKNDHvW2xYzcj/5Dv8ssdMfi8tHGNv/Pdx3fwa9op5A23Nw6eDkc9QiaN6Js2UXms0xi+M0M7bKOU66q/QizlYx5ppnyDv+8wIy7jNXeLxrlF3+8NrFvHO5zfTc/he3ecCYCX2sIeLz5+qYScZ/yGOPKwJW15ayuraUxJhephDQUl1CS3WJqfPnL5/Z5yGXuQTyoT4xYRfo+E6/AcefhlGTWWticexp9NZdCCFmvS/hkW6Ce/8a/L2TdRwahnev4H/3cWh5D861D6UV6pPUjjMQvqJ7++DEU+bH4sLbBFruxVGxDPe2XyGsfwKj90IkZae7BHvZAoRQMDQ7Rjg0a+N0K4T6hIe7CI0Nga0UQhYzy5Usjnk+86E+eeQ8RNynzAEer1v22yh3O/jye5r5qyfb8cX6GnFQgT949HZqKwoZ6B1/Q5C5DmvqCzjaOZa44WmwutaVcbszz0UG1946PB/qY+37IB/qE+Ei7Mf74reg5wRpQx8m3H0SR61nknzDN4QxNgiKQBRUTlThnqm+GMM9BH/2x4Avub4nnsCPgXvd+7Oqx0yEr3hPvmx9ONqeRdn6SUCgqQ6UmqbIeERDksiBYmzzNdRHSkmg/Q1kxwsweNny2I1Dbd6ZD/XJI4+5gIZCN1/9wG28dqqLF0/04o1J2mMHdjZXsG1lPUvqSxEkmR2kgR2t9RztPG3pmp2t1lNkzgbi3dU8pmJ8epRHaohp/5LkRGjATQ6J8b767cyc/ij0gW6oXYEkGmbT/iJ0vTv5pMYtODy7USsWzki//K/9Iymd/nGceIrgglU4q5ZaaiM82I3/3Fsw1g+qiiiqx7H8LhSHGyYWKrLYt3MHTI/BBC6+jdz6SUSW7ZtdPgO2mmUuQ2HGXv476xWe4yHcOJeuR+ZDffKYO5Bxn7nC412jmeEFdo37Vy3k3tYFXBv14Q3puGwqNQUuVOXGF53McrtN5YU0lds502+usmJjmY3llcVZ1WFmePZtledws56HmeTBcJg3L/ayr72b7lEDAyh3wJYV1WxtqqHAYZv2WgnkQ30kgd5z0BnnnKcJQ0p0wPfqd+BSgiwmZ18ncPZ1WPefcbfsyG5fBi7DgIVc6UD4+AsYu37VVBvhoasEX/tnGDw7SYYE/Ecfh0WbKLz3U4iC8uyGr/jTS7ls6GGE5siJ++xWCPXRFRuBV/8mc6cfsN3360jVng/1yWNuQEqJlGAYBsZE/HrkZ3Y2uGFIdGP8mHHT9agpcE06HqniKDAMiYjql812P7N9BY8910bnaKyzMxV1BYLP7Fg5MV430yZW+UzZajreM+bj1fYuLvWPEdYNipw21jdWsa6hAk0VGcufSW4YkQnS+D2W6JzZfB6yydt7BvjWyxeIv9P7A/DzYz38/FgPH1xfy7bGuinXSsNAIjDCOiIUBCMQ2W8j9UjmF6HeEtx492myBcVViO+Nf4JLb6c++fC/4pVh3J67s9YX/fjz1pXuPIQx1ofiKEzaRqj3LKFn/zy5rEtv0/3981T9wlchFM7eOGEH/Nb7ZkSvz4H7bDougwK0MIS8OaFPpjx45djUN1xW4axA3f4r2EpqJ42dDDvACILhzIf65JFbkDISZ2wYEilFjKM2e9B1iRQKuh7r5Mw+dCPqemWvfhcADlXjS/fexosnrvD8yb5IjucYaMAuTxl7WhdhV5ScGKNUmClbxWIsFOKfXz/DyfiUqCMB2nuv8H2u8KE7G9i0oHrmlMgQukHU8U9yjonnwTAkR3v6eevkNa4NBUBAVZGdO1ZUc3t9Jaq4uc+RP6zz5sUeOq4M4guGcTtsVBbaefXccMprf3SoG0PCtmV1k46P180zDIkIBVCCQTDCGEIgojPh+c4lErqOprShaSgKnDfh9I/jyL+jL2hF2t1Z6Rd91tMpA4QHO7GVLUwoNxzyE372MXPCRnvp/dlf4Nr+2Yz7M8HLF0C/udolE7CVQjiEJDTr91kirocUjJAfGQrmhD6ZcGnoyJdN1k6YBCcUVYCrDGXFZuzVyzGEMsUmRsiPEQoglRn8EZxF5B3/OQwhBAIDRREIIVFyog6zQDcMVFXkiD4RqErE7ZoJnZyKwoO3LeK+VQs4fm2Q68N+kJLKEheraspQFcilSVAqzKStAMaCIf7syTaG4mdJMQgD//bmVQLrDbY31s6MIhlCVSKOf3I7JX8ezvQP8/fPn52yvjjQF+TU/iv8gCt8etcyPJXF2VM8AQwpeerYJZ492R/3Lzr0mF8B/Y/D3ayqL6PSfSN1rqoAUqJgIGx2hN0Ohoqi2hB6pLbFfOcynEZu8URYcQ+GFac/isClozhX7UlPf0MSvHAELh0C/xiMmEw/Ggeh2RB2R8L2wmfexlIe9r5zhEd7sJUvzMo4Kc07MF636Pi33he5p3PgPkvEVZsLRbMhfHpO6JMJ9516G9CtjRGAw4X7od/BUG0oUZlMdw/YnCg2B0LmkBOTReQd/zkOISJlvRVFIRJKAJOdzPT5xcExXjnRxbuXRvADNqC51sWO1nqWVxYTyUUff21kZVNRxnXKXI9s8PHsFspEzH/221IUlbUNFdBAEsyuHczwmbbV9944k9Tpj8Xjh7porC5iUUlhzNHcsJWiiKjjn+w+T/w8nLo+xF8/Pzl+OR4B4H+/eI5f27mMlprxVKrZ74+U8M9vnObQFfN1MZLhtY5uHl2/9EZLQkDYhyr9SKGD4gDFAM0O0ZjjucxDA5cJnXoDhrsABYprsHnuwla6YOIcoaXhqEwHVyW2dQ8T+v4XrV97/m1Y+7DlPno7XoeD/4+0nK04KEU1oDmnbU9iQPtzlmWGTu/DtuWTWRlLx9IN+N76IYSHTLauYm/ZDooto3Znmgu7C6naQQvmhD4Z8Y4XLN8jAASGE957k2ylOUCJLE7MR+Qd/3mBeAdNZsSDYYN/2neKY93eSa2EgHe7fbzbfZbFpTY+u7OZQoeWQE68bpnplDkXOaDDXOEzZ6ser592C6vHAC8d7+ITW5bPiD6Z8EzSeQbDYf42hdMfi7996RzfeHQNLps2I/158VRn1px+gFdPD/DIuiUIEZ3sGGEI+VCCo8jgGIoMRdrOobSA6fBw/2X8+74D/XFjee0YodPPE6powr71E2iFlZFrC2phLI28/eMoacC154sY0dAEy/AOTaQsNNtH76GfwLGfpK9zLMqbsLmKSJTe0gj6rOdfB7h2BiWLKTPtD/wWwZ//T8y8ebDv+RKaouV8msz5ks7TGLya/jOkukzdJ/M9nef8fI+RR9oI6wZ/80LbFKc/HhcHQ3zj6XfxBk0u3eYA4l2wPBJjpmy176T1L+wDl0dy8j7LxEZvXuidsh8kFfaf78mgxcQwpGTv0Qyc0WmgA77QjdVhASAlSB2EjB6VMLF/Ye7x8EAnwZ99darTH4u+MwR/+lXCg12Ra5v3JD43GcobUbZ8hoIH/xuqsxihprkSqaqW+hjobM+e0w8oLbuTtifDaU5oQqGEMtPhWmkdzof/B5QtSdymqxLH/b+LvaZpxu4zKSX+y0fxvvwtRvd+k9Hn/hrvoR+jjw6kIVMwadKfQ8+SFa6PpZd1CYCqJSbbEpM+5hvm31TmloSM+0yfP99+lfMD5uIr+/zw48MX+M93NiWQKbKiU3Z4PkVlLtjqQu8I6eDamI+l9tivq1yxlRV+43l4tb0Lq3i5rYtdK2Iz5mRHv7bugUn1L7KFyH686J0kIg6nRMFQNBA6kBtpAdPhMhzGv/cvMRf6ouPf+xiuD38dx8q7CRx8HNO57+3lOB/5QxTNgaHZkeEQEol0lQEu83LGUVJvqb+h489ak58MNauxNd6JoYcTt+0oSE+2w4mR5fSwSkk97od+j8DgFfT2l2CwJ7ICXFKJtvxu1PpWVD2EMUP3mffSMXjtH8GIexPXcwJf+9NQfzv2HZ9BU+ymZM6XdJ5GBivwasseU/dJPp1nHnMAcbNUZFrcMCTPH48ru54C+y8M88h6fZoQhHjd0tMpe1xkcO2txmfOVnqaWY1Ceu6lwswk1Kd7zLod+gMg5fiiVPb6c6XPWvVps3DZ1RttyYijLzBQ9PCcDzcIXHgbdAuT2PAQofOHsC/bhOOB3yDw9Ne5sScrAZRCHA/+VzRVY0pojB6Elp1w4inzOgC2mOqkqfooR3qht92S/ISoWYVr26cQeih526oNKpZDn7XCiCxen9VQn1juKF2Acud/AmIq8SIw9Jm5t4QeYOzAD+HMq8n73PkOwZ/8IdpDv4tid98yoT72wiqr090oNJy1KxD5UJ+845/HDbT1DKaTvZi3Lvayo6ku9YmzjHgXLI/EmClbFbvtmN7ZG3udyz4D2mSG8enRzcR0U4lMEQpnadNpDLYsLUERNzQVcKN+xcTh6CxGzj2un3zFsk3CHS9jb7wDW/lCxEO/j//178Hg+WnPFQ2rcG76GKKwIrI/YhodXCu24bPi+DsrsDe0xFQnTd7H0EB66TonoW4NqudubGWLEJpmyr5K8y6MfdYcf9eKbSn7Mxe49+RrcPD7mM5q5O3B+/p3KNz5WRPyBZMm/enc995hfKf2wcXDEBgDmw2ql+NYuQNb+cKbYiu1oBSqmi1PStXdX4gmJDHTVtRO89RpyDv+8wIy7jM93jOUPK4/EfqGfAl0iHeNZpPnQ31ywVbrllVyrMtaGsBKJ9S4nTOiz83lN54Ht4Ll8BoNmEi0lEX93E6bNUVMYHvz5JCkmQz1kWE/vjNvQfvzMBx1VpUi8GzG1nIPNldp1tqa4BYr1gLQd2ZCjlLWgPP9f4Dee5bQ6ddhqA9UoKSOyjsewFFYycDgWPJQlMIKlK2/grHv7001b9/zOaSimQ+p0DPbV6Pu/BK2xbdD0IfhHwVFjazop2jb1ngHgePPwaA5G9tue2+kem+2x/gmc++Rn0LbE9YN3fkuIe8AoqQ++XOSQaiPRMd38KfQHqdfEDjfS+D8GwQqPNjv/Rya6ppxW6mte9BftuD4lzViW7gGIxwy2d/5HeqT39w7LyBiPtPnMu3p7XQys6NTns8vvq6hkhvZ3c1h+6r6mNSxudMXaen8yX/ftbw8Zb/jsaWpbEb6s25xpWVdkuHBlirqi8djtaNtTRPqo0gjGnaSPg9f68D3/S/DW9+54fQDGCPQ/iyhx/8r3iM/RUg947bGuQgHpnbaJOJlOkrrKdz4Idz3fI7Cnb9K4br34yisQBi6KX3ci9ehbv88CFfiRguqsL/nD7CX1Fnqr+YsTCzTBFSbLSLTCKECim5uDFQ9jHvP56BkYco2bJ6tVN39C1m7n2aLBy8eSs/pjyJw8pWUbQkjjKIHLOsmjDC+/f8y1emPR18HwR//EfhHZtxuroZWWL7TnHHUIlw7PmNJ/qRQn3mInFnx93g8vwT8E3B3R0fHPgvX1QN/ANwD1AGXgH8B/qyjoyP9b+hbEBVFVl2yCEoL07vuZkOkPiWPKGbKVqoi+OjWxXxn30VT5zcUqWxdWjND2mQGweS1cyu421PH8x3xhbKSY1vzzITTlbscNFc7LadZnQ4PtlTywOqpDpuArIf6BHvPEn7uz1Mr1fYEYzKMe/2jabcVy4VQATuR5U4rsJtrCxH526Q+jkVrEB/+Bv6Lh9HPvAG+gUhVuaJa7M07sFWvQKpawpChRNxW3UhQuEGm9yZYK6llcsYUTLetONwUPPDbjJ14CdpfnJris2QRastuqm/fAeMpYzMc19nk4eN707LxBK5fMtFW7ITfvG6Bi4fgrEmXzH8d74H/R+HWX55xu7nu+Cg+mxNOPJ1Yn8I6XHu+iOouwbAkP2qneeo05ITj7/F4NgN/k8Z1C4D9wALgCHAY2AL8EbDL4/Hc29HRkcVyibkKGfeZHl9dU4qK9RItm5ZWJtAh3jWaTZ4P9X/rHhMAACAASURBVMkVW21YUIFvY5h/P3CVZKgvVPjCPS3Y1JgfqxnQ5+bxG89DhdvBQ61VPNlmbjP9PZ5yagpiw52yq98HNi2h/YmTpnSByW6vAmxtLGXbyjpqi8ZXnifrme1QH4lO+Lm/Na0vJ54hsGQjjvJFGbU7wZdthHOvm28fgCDes2/hXnZHCvl2FD1oKUuNIsHReCeGZxvKeDiDZkeJZgFKK6uNBJp3woknLfYTWLgeHEURmXoYgwCKyVCfCa6pONe9F7H6PkI9Zwj7hlGQqKV1qJVLI/1UtTmfqSYw1AkD563bOBbh0Ixl9Qm3vWhNl4sHCG38EKKwakbtJjU77nWPoLfsINC+H86/Ad7hSCG12mVoLXtQG1Yj0si8NN9DfWbd8fd4PI8A3wHSea/4t0Sc/t/v6Oj4WlReAfATYA/wReAvsqNpLkPEfcq0uKaq7PKU85yFlci1DQUU2cc3XsbKjNctPZ2yx0UG195qfOZtdXdjDUuri3jpeCdvXRwmFtVuwfbWerYsqUKbyFc+2zaZyjPJ6gOS+1ctRDckz7RfJxl2rSjnvbcvntH+1Be6+dK9y3ns2dOR38AEUIFfv28Fy8oKCeuRCWJkYpairSxn9QlcPhYJ57EA/fgzKHf/ckbtjnPHyp0ELDv+wBvfJqgInIvXJ5Sv6AGEYTBTWWqscHfrbrxn3oCgtdzpjtZ7J/THCEW+VXQdRBp6YKBWN07OqDOPMtXo3UnqQJiFZkuZrSkdW4X7LpreaxGL4Kk3cN32QFbsI8MB9OsXCA92YxgGWmEp9poVKIAidRRHCeraB1Fuvw+4kXnJCPrwvvMkXD4M/qHIhKCmEYdnB2r5ouS2ymf1mRlEV+v/GPg44AWuAabf6Xs8Hg/wHuBsVA4AHR0dYx6P51PAOeAL3BKOf/Zw36qFvHt5kGsmdh4WqPDBTUtvglbZQbwLlkdi3AxbLSgq4OObl/PB9SF6fAFCukGxQ6O6wB09I5kLOvsYnx6lfb0QvGfNYloXlfHKiW4OXp7syK6tL2DHqnoay4szbMkcmsqL+cP3reKlE1d55fTApGSTKrBjRRk7WhooczoAiabGhFikgICshvqETpuOBr2BSwcwQh9DsZkMuUkWBlPWQGDZZji337Ia4X3fRS5cA5pjevkIrIT6zCRX7C4cD/42gae/AQGTC0JrP4qtYtENOYzLZAZ0jZt05kjojiUeykJQQu85jKAXxebIqq0iheesQw5fzdg+UoLv1OsYJ56FsWsTskNASCmA5l24W/dMeZ4lMHZsL/LdaQrPjXYROLsPKlfi3vFpFEdBElvFmGueYTanMl8j4vQfBH6ZSKiPlWDeyPQOft7R0THJS+3o6Ljk8XgOAxs9Hk9LR0fHiSzpnKOQcZ/pc6dN5Tfub+V/PXeCKyOJg37K7PDr97VQ4rAnkRnvGkkMQ/Ju1wBvnr5G/2gARQjqSl3c3VzHsvKirPRhep4P9clVW7nsGovnfHEuM3zq8wCwtKyIpVuK+E9hncFAJICmxG7HYVPjZMy8ruUuO4+uX8p7b1/M5aExfKEwbrvGwuKCaRx98/KzntVnOL0qxmH/MJq9JivhBq7Nv4jPF4SuQxa1COI9fxjHym0J5FsP9ZlJLkrrcL7/q/jbXoDjP03SLw1x58dxrNw+OXtKuqE+Jvh8KEpFQYmJeyYVAvhP7sN9233ZtVW6+V90IyOb6IpK4OW/g8sHp5dvjEHbz/FePIjzwS+Du2LiWt+B/4COZ5Lrd/0k3ie+jvO9vwfu8qm2Ih/qM1M4CXwC+JeOjg4jsoBvCa3Rz+NJ5G8EVgPz1vGXUiIlGIaBMVEcSTJpZm+RuzWNL9+/muPd/bxyopvTfTc2sS0sVtneUsu6hko0VcEwJhdWMozxIk2CSIGaG/92omeI775yHl+sXwBcGRnlwOXT1LjgM7tXUu12ZdyHeG4YEhHVL1sy5yvP28ocN4zIBCn+GTD7PMRzTVGodDkmjieTO9NcFYIlpYWTjmeij9RDSCOM1AOg+yHki/yoSj0SDmKVpyp+lQihYOT6dNuN4UKo2O76CKHHrTr+IM+8Do0bp5cf8iL1UNb0zAZXkLhX3YN+232ELh3FOLsfhntBVcFRDEs34lp8O0K1YYR8k+UExyJ/B6Obx7OonwwK0MIQ8s66jdLl9tomy9vEp8WxZ5DNd0c2n2fJVorTnd6T5i6JPONp2iSw/98SO/2xGO3Cv/cxHA/+Nhg6ga6TqZ3+cfiv49/3PZw7/8tUW4UdYATBcOZDfbKJjo6Or2coYjzFRaJ3UePHczMlSBYgZSTO2DAkUooYRy0bEKyqKWdVTQVSSkKGjk1VEROrlgJjmm8EXZdIoaBPqrQKR7v7+ad9yfO3X/PB//fESX7nwZXUup1Z7AvoRtT1StNfuJVwM21lSMlIMEgISaFqw6nNnQzDukHU8U9yToLn4VaDrksIBQn2nUXvOgeGH7WgDKVqWfQ7BQwhENF4oJTcVQ5j1lf9hc2BDPom5EgjTCjgQ1M1hN2JIRTzOgD6sLVK5xMYu47hH5tWZtgHqiGRQb81m9wELoXAUd0E1U3oQqCO20EI0MNIPTzlWkJ+CPgwkGDoWdVJDykYIT8yFMwZG1nlCAEL10Vi0TOBHCV0/QK2kvqs2UotqsZQi0EfnqbBxLAtWJvw/k45pmMDcPZl840NXSJw+k1ci9agH3/Okp50vkOorxO7u3iSDkbIjxEKIJX56TDM5anMeKLoRLnGxqs6Z5aMOAHsdo2qqqKZEG0ahmEgMKisKSYYCiOjK5BiFv9vGJKwLtFUgaJEYgpHxgIpnf5xSODbL53kLz+5FSEU020jDTr7vAz7gzhUhYbKIuw2Ne5MqKwpmnUbzY3/z6ytRkb97D1+mb2Hu/DLG+PvqXZw/4YltNaW0TcSWSGsKHbidmo5YJMpdx0yaicrz8Ps631z/29Iyeihpxl97TuE+k5gI5IJSAd0WzG2NfdQvGY3wlGA0MMgJVKzJeX2dbsZetZ8FiIAlm6ioq4OaRj4es4ydHQv8sIRiOqCVoRj9R6KmjcjBRgYaDYnqr0goT5BtyT51uwEsDupqKlK2scyhyulHeYClwEvemAM1eG2NMZmuaLZKLO7Zr2fmfDw1l+g9/8dw3TF3gQocmu4KsuyaquhdffiPfAj80pUNlLVuDJtm/Sfecl6x8/vx1Fdja//jOVLtWvHKdv0nkk6KKoNxeaguqIkum9ifmEuO/7jUzGZ4N9F3Oe8hBACRYCiKMjImgpwY13+ZnMFAxWBooBQFATwbJu10u/XfXD88gBrllSmbC8UDPPs8cs8ffASA3FVG3Y1V/DQ2iXUVRTOul3mCu8d8PL6mW683iCaptBUV8rtS6pQFZG1tt69eJ2v/6yN6dDRE6DjqY4px9ctKuLB9UtoXlCeM7ZK93mYbZ1uJsfQ6fn+HzLc9mM0jKk/OKFhvAcfx3v+CNUf+go2W+RNn1TtCCWYkBc03cnQC98D3Xxmn7JN70EqKtde+AeMM29OPSE8QuDIj+k58uPJx+tXUbLhIQoWrkYIMUkf1d5guv1JGLpKzyv/Rvntu7GXNaTs75zmgDAMhK0QbK7c0CnHuL1iERUf/gp9//6HQPoVk1VnIcLuyqpuJesewnvmIAxcMKVD1Z5PZaSDPzoZt4S+sww+9U3r1wGBwU5E3PeOUDRQ7ORj/HMPo9HPRCULx2NFxmai8WAwzNCQL/WJM4iKishLj77eUcJhPSdisg1DYhiR+jGKIpASnj6YPF/7dPjZW2dZWDA+hNO31+/181d72+hPUKbtxfY+Xmzv4xNbFnPvuqWApL93dIqcPBdcHBrlP948x9mB+BWnLlyinftur2X3ivpoko702zrdN8Jjz53GKg5fGuHwpWNsayzlgxuWosQW7Zklu5VVRV4PD/QOJzwn/nnIlfG+WXz4+f+Nt+1nqbcI9p2j59+/RsH9v4kQyuTUjQm4tvuzhJ/9s1SSI2i+B7+thoGfPwZXLDoWnccZ+tlxhupWU7D9V5CaY5I+NN0NZ16zJhPQ2/bS27YXZd1HcLbsmpBZUlaIMAwGh7ym7JDrnLAfGQwg7BI0f1bbKC1xoyAZGhiZ9X5mzLUqHI/8MYG256DDYshKFD61DP/ASFZthX8EympNOP42tPt+C59ahi+BDqa4d0ZctsTw+xgaGJ6kQ3lFKUKG6e0ZnPUV/5ISF3Z7dl31uRNQOxWd0c/aBP+eag9AHjcB3rA+KZTDLC73Ja8i6guG+ebTiZ3+WHz39Yscv3R9wh3JYzLaegb4xtMd0zj9Efgk/ORIN9994zSGTGMwo5BS8r1XrTv9sXj17CA/OXwhIxnZQv5+So7wyFW8x/7d/AX9ZwhebQMkk9NATs/tNcux7/kNwJZcbvMDFKx9hMDld607/bHoOsbYy3+PnHgGIvo4V+5KXyZgHP4+3oli9dGJU2w6z5i25jZnBuTGTaZzop/pc7WgjMINj+B6/59gGct3IVRbEvnWbaWP9eP9+dfg3DRvyGLhrMD1ga9hr260JH9aPlEX6CahoHwafcSkj/mGuez4j2fzaUnw783Rz2M3QZdZRswPUc7wyN9hI3FK0GQI6cnbeKnjKoMWwiH//tnj0R/s2bZLbvGeMR//50VzBVoOXh7h6WOX026r4/rwlHCsdPDi6QG6R71p6TB7PFf0uHncd+hpIhGZ48dTI3TihYk0lobQUnKttgXnR/4CNn4CCmLXgFzg2YP2yJ/gXv9IJJXh8edN65EQ3cfwXTk+SQeldAFs+Fhmcg98j3AwEJVpRwrFkh1ymis2dASGkv02YlNUzno/s8hFYQU07rR0C2m33Z9VW+lSEnjqGxAwUbzN34fv4A+z0ndqlqf5EKUHdcXWqbZC5NN55ijGcza91+Px/G5sLn+Px7MIWAtcnP85/OHGtHT2X+3HT5ELtBSrcQlQ4IDzg6PYFYXqAie2mAquhgEvnbC2pa7PB+1XBqh1Tldl+Nblzx+zFob19Inr7GleEM0xb62tt05dI1t4tb2LD21stKxDNnmmlXvnOw9cfZ2p/U+B3pMIPThRlRNEyuqeimrH7dmCsuJOQESzzUSyKEVe3Ycx/KPQN3XvSFo4vhdlQcskHQo9WxmzOZD7/4l0U40GTu2jYNWenKrcmw2eceXeJHw+VO5NxF13/AK+kW7oaU9579h2fhGbqzSpHazaynfuAAT6zN/Alw9i9F9GK6rMqO8Ozy4C560XxksLxfU4yhdNscl8r9w7J1b8PR7PIo/Hs9Lj8VSOH+vo6DhPxPn3AH8Uc24B8G0ixSbzVXtnGZqqsLrOnfrEOPQH4C+eOcWfPHWS3/jhO/zw4Dl6xiJ7Ks4NjkypBWAGb3R0pj7pFoI/pPPGBWtp2gD2X0yveNKgNyvZqiM6nB3Mmqx0YdGlveVg+IbSuk4GfWQSLiFi939Ej+veLN4vfaciE4m4dl1Nd1Lw4cdQNn48LbHy/BtMTJzyoT4muWDSpHOW+iaBQHcHowd/hPf17zB64If4Lx9FSiNtmUK1UbD7c9B8f0wf41C6FMf9v42jvjnrttJPvjh9m0kQbH8p7f6Oc1vFAqhcabntdODY/PEktiKh2ec65spU5nvAduAPga/GHP8c8Drw3z0ez/uADuAuIvH9TwP/5+aqOVuQcZ+5wiM/Xttb6jjWdTZeaUt45cwAr5wZ4JNbF6ctY3AsSG7YJTf4xUHzGVFicfrqIDuaxsMqzLeriOx9i4YAKQ3EuINkUofZ5XNJ18x5JFNG7HFzkHYXMssVUg0lu6/sQ74hbK6SKW0pNrAv24D/wD9bFzrcNxHqk0uVezPm87xyr//CAYy3fgChyZPL8KkXCeOC9Y/gat6W1j2tSHCvfwR9wyMET+1D9l+CUBicxdgaN6BWLUMJhzCybCsZ8sPgRZM3bgwuHsW446MZ29Z+7+cJ/vR/wlj23hLHQ93zm6hVjdPbinyoT86io6PjnMfj2URkxf8BoAk4B/w18FcdHR3p58WaU4ibpSJzgN/QzVNZwpo6N0e7EpVcMI9/2neRh1dXpXWtXVOYfbvkDveH0wxJCI8Xo7LWbn2Zm47rWQjyjyKysjt7NsyH+iTntrJV6P2XsYTCGlRFw8hi6IQI+wl3ZbapPB6qUBKG4oh0lwlVO4o08qE+cyjUx3f8eeQ7P0gyqD449K/4RrpwbfpwZEzTaQuFgqbNwF2TM+HMkK1kIM3f6pA/K2OhKXa0h/4b3kM/gLNvpKfLdFAKoGUnDs92bM5CSPgMz+9Qn5zpUUdHx440/+0y8MkZUCmPLEEIwSe3ruAf953i3Sw4/y+3p1ctc3FNccZtzye400wR5rKntwqydWUtL502sVHMBGrcsx+lOD71yWN6uDc+hP/s05auUTy7AQlCTKzCZcJD188T2vsYyMy/d2KhFlQmble1gSgAaTEtYVkdExOn2FCfLNhhVjnjnBloQzBp0nkT+xbobEvh9Mfg1Iv4ShZQuGLzLI6HeVuN57W3DMd4dvXMdVZsDgrv/Djh9b9A4PR+5Eg36DoM90Cf9Ym89sB/x1HWgBACQ9HACCfRIWqnNOfwuY6ccfzzyAQy7jNX+A3XSFMVPrPNw5Gr/bzc1sm5BKkjzWAkDA2FCldHra1Y72lZQGAsdsU5e302pOTEtUG6+scIG5Iit511CypinOtcGI/JfHFJAemgeUFZjCzz7dYUuFheYed0X+ax/nc316Slw+zy+KnC/Oa22tuw120g2HUAc3BiX745ayEuof7LhJ5JIy1iKizZGglHSqSDkNByD7T9xJJYZeWOfKiPRT6boT764ScsjS+Hf4K+YkvE8ZyF8bBiK8XuhqIFMGKt+CYNq7M+FrjLcLXeEzmu2ZHD1wj8x+9Y06vuNrSa5chwKPFzG2sr8qE+eeQ8RNynzAEer1tk0926BZWsW1DBUCDEgD9I96CXf9lvMRwAKHTZYTR5rv9Y7FhZgdttJzA27nRmp5+GNHjxVBfPv9vNaFzm0u+/fZVNi4p4eO0iylzJi5HNBrdrKjuaSnn5jPmNjwLYuKiKG06stXZ/6W4Pf/zzY4yll+UViOza37ykJm0dssXnQqjPleExXmnr5N0rw3h1sAPN9QVsb62nqbxw8kbYLOsgBJS+7yv0P/4bGL2nSA4N20NfRlM1Er1+t8KFoRN69Vsp2kwPttadKcMZHJ4tBKw4/qIA18LViHyoz5wI9QkPdcOgxX1r+jDhzjYc9S2zMh5WbaU270J/+3uWumhv3j7zY+EqJrBgHVw5bF6v1ntQrNhqnof6zP778jxuSZQ47CwpKcRlS29GLYCHV1ebOrehSOWXdrRMccEyhW5I/uGVk/zkyFSnfxxvXxrhaz9to3tsdqs8J8KulgZLXwLvu70Gm5r+10aJ085/e3g1C4rSX0n5wj1N0XSis4ts30/ZRDBs8H9fO8nXnzrJ/ovDjOkRlzwAvNM5xmPPnebPnznGaDD9N29moLjKKf/gX1Pc+jAKyvQ2q/LgfPgr2MoXRw9IMs22Euo9A6PpZZ9K2p91H8VWviilDmpBKdqWT5uWa7vviwglslIKgkmhPinamjucGZArmDTpvEn9CV4zV/skHqHu0zdVz0xs5Vy2CRzjxa1MoGENWlnDTemLe/PHwG3u91/c9gHsNU1p2OrGx3yD+tWvfnW2dZhr+CVgia4bBAKzu3fY7bajG5KxUR+GIWMqSs4epJTI6PMjROqnps/r5+AF66n2aotsfGRTI4UuQXvnCIl6vq6hgP+yfSUlpZHYQ783e5tLf3jgLAcuj6Y8TwcOnull6/KqjJzmmYDLpuGpL2T/2f6U5+5cXsZDqxdiYliTwqmp3NVUg9MGQyNjBEORFYgSe6RwW6KxLNbgc3uW01iRG3s1XAWRmhD+JGlKrT4P2YBuSP7XCyc40ZP8jdhQwODF9muUFqrUF7tRZkg9odmpun0bJa3b8IUcCGcplNZD/Rq0rb+Ms3kPwlmIodlBghQiYx44/GMYtBimkKofd34Cl2ebaR20kjr00oXIi4dIeFdrJdju+zJq7YqJax0FhQhp4AuEs2qT2eLSMDDCIbA7kZojq204XQ4M1Y7fF7ypfQt2n4Hu49OPaTKULUZbsHpWxsOqrVBU5LINGB1vgkwRnlnWiGvXryHtrpvSFzQ7wrMVvecijCXZ87fpl3C37LYs3+VyorpLCeJAKLP7m+102lAjfsNF4DvZkDn/3mHckoibpSJzgMfrNv15DcXpxZkvqiwEYFtjHXctqeHA5V6OXuhn1B/Cpgoaa4rZsryWMpedGysd2evnkD/Ea+fM5yn3GvD6+R72eOqzpkO2+LLyYr7ycAs/P3SRI51TNyRWueC+2xdx5+KqjNsK6ZIX2q/yQlvPlFoMgTDc31pFQ1UBb53qpWfIhxCCyiIHd3lqaK0p40b6ztm3W66G+rzYcZWz/eb2URjAv755hR+9eYX719Swe2VDdAKQRd0MHRHyYrPZKVy1EwNxIytJtMAWWQ4HYNhagb8JbPxF6DkJA5chGISCcpSld+BouhNVVbEahuReeBv6R/8XwXNvoZ95A0aug6pCcS3Kyu24GloQQp2UnSUf6pP7oT6q00Va0Yp216yNazq2srtKsb3/f+A78mM4/+Z0HYLm3RSseRCh2rKajSsV1xQ79j2fJzx8DX/Ha3D9AuhBsBeiLN2Aa9kmpOZI696Y76E+869HecwplLrstFS7ONFjLRTmruW1E1xTFTYvqY7GfcNU5yOCeBcsE7x+utvyNS+1dbN7Rd1NW/m1guoCF5/a5mEkGObcsJehsQDhQJglVUUsKyskG9bzh3T++vk2Lg1N/6bMa8CTbb0sLh3kC7tbcdoSpV/NDYxPQXIJhpQ8f9x67usA8NOj17h0fZRPbl2R1ZoLAEgZ+RGVBiga4YEuQr3n0cM6mtOFvaEVnEVRg0oyzmKSpvq2inocKzYDYCgaihGe4MmzgCTmQrPharoTmu6cIlNMJxPBpFCfbNlktjjjnBloQzDpO+Im9c3e0EI6wZuORbfN4nikZyvVVUzhXZ8gvPHDBM8dxPAOgFDRSqpxLF6L1BzT38c3iWvFNbjv+Mik52qcy4xsFWOueYa84z8vIOM+c4XHu0bT812r6znxgvmNUrfXuyl12izqJKNHstO3dy+lDo2Jx1AQ+n0BKtyxqdJyYZxu8CK7xo7mSJzmQO9IyvPNciklf/dye0KnPxYXB0P8wyvtfH53S1xY0ezb52Y8D5nwkz2DGW2cPnJ1jOqjl3j49sXZ000RSFVFCoHvShv6O0/C0PmJM8LR/1i8FcfaB1ELKzPPCFJUCX1nLPdflNZNm5UkONZH+PjzcO5tCA8DdqhqQqy+F2ddM9nN1JLP6mOWz1ZWH+Eqgbp10GV+gylFC1ArlpoqtjUTPFNb4S7DuXJH5LhmRzGZIWcu8nxWnzxyGuMxxIZhYBjjP7iS6VdKZ54bhkQ3xo8Zpq5ZUVHMg62VPNWW+vV8mR0+ekdjtK/m9TMMiYjql41+egPpeVdjwRBlTkdWdJgpnm1bgeB037ClNJ4d1wOcuT5EY0WJKfndYz7arw4QCunY7Bqt9SVUF7izpv903DAik0nDSHyfp/M8ZMK7BzPPV7/3ZB+7Wxpwamp2dDN0ZChI/74foR9+PHHDF/cRuLgf2z2/gVrdGAkTESpI3TLXGu8kfGG60IQkqGpBUe0Q8k7IkeEgvkOPw5lX404OQu8J5Isn8LmqsO/+NZSS2ox0nuAhL1IPRf7ORE6u8OBY5O9gdM9JFtuQQQFaeNKY3ay+aWsfIGzB8RcbHpkVPXPBVnONy7ADjCAYznyoTx65BSkjccaRjb0ixlGbPei6RAoFXY91clLj3uaF2O0qPzmSOExBA4aD8Ls/PkahBhuWlLCluZ5KR+piI7oRdb3SK1Y7BS67AkHrwuyamjUdZgrZthXAK22dlq95ua2LpVtLkp5zsm+IZw5f5kLcm4QfH+lmaanGA+sXsaJsZjYD6wZRxz/JOWk+D+kiW98A+8/3sL2xLjvCDMnQ4b2MHn6c1OtnOqHn/hLj/t/D7o6MmyEEIpq4wCzXiusJuyvBaz7WX1m+GcM/NiFHB0Jv/xtcOZL8Ql8vwSe+jnrfl7EXlKat8zgP+0A1JDLoz0hOrnBCfgj4MJCR/R5ZbEMPKRghPzIUvOl9U1xlKFs/g7HvH+LviKlY+xFs5YtmRc9csNVc40bIjxEKIJUc/7FOE3nHfw5DCIHAQFEEQkhmefN5FALdMFBVYVmfXcvr2Lqshjcv9nLkbC9j0VX1bm/0BzHm3JEwvHRmiJfODLG9sZQPrFucNC5ZVSJuV7Zs1NJQytUOa+E+bgGVLseMZU/JFrJtK4B3u61HxB7t9iXV4bVz1/jhwcQTivODYf72hXN8ZGM9dy2tSXheulCViKOd3E7pPw/poLzQkfokEzjTOcjO5dlx/KWuM7zvOyac/nHo6GdeR2x4HyBQVBtCD1riih7EdvenCe39urkml23BsXANUrshJ3Tp3dRO/wQC6Ad/iLjnc2nrPM41VxFCDyMCMiM5ucJRwECiOAvA5oyuPvsiWeg0J0JV025DtblQNBvCp89K35wLVhF+4H8QPP4MXD449baoXoVtzX3YKhZjzPJ4zLat5hJXbE4UmwMhc8Kpyjryjv8chxACIUBRFCKhBDB5ZfFm88jKpqKM62TteqeisKOpjh1NdQz4Anztp22kwitnB/GHdD62uSm6cXaqfEURUb3EpOPp8m0r63jOouO/e1U1mhrr/szmOCXm2bZVJkikw4lrg0md/lh8/0AnVcUuPFUlJvU0xxVFRB3/ZPd5Zs+DVb66tgwbF8k0O78/bEzcB5nq5r90GN3fb8HxB868iNzwKEKzgWaHaFyyFW6rWobxnq+gP/ENSLYVc8W9uDY+ihACgn6ZxAAAIABJREFUGSun7QUrGkPfKcLeYbTimrR1Bgk2N0IJgqZnJidXuAR0iR7yETr+Ipx4DoiE/YQAFqxFab0fZ9Uyy20Iuwup2kELzlo/tYpFKLs/D6MDBLvbMcIhFFXFVrscUVSDEg7N/hjkiK3mCheaAxQ7GPkY/zxyFvHOkcwBHq+bdVnf23cGs1n337o0QsuiftYvqEwgU2S1n2UuJ5uXFLP/wrAp/ZwCtjaNr6DO9tik4tm11dR7wgqml/nzQ5csSXny8GU895Wa1NMcz8V0npqqsru5kmfa00xnGUWh04bV+0CGQ+jeTjDCKM4KFGdkohU6sy8tHfTeM9hrPRml/HOULkB85E/xnz+MfvJlGLoQle6CFZtxerahlNRHzpc3rg2P9EzagGwWwVP7sK//QEY6z8d0noFLR5EH/2V6o105gnHlCN6ld1Jw58dRIOfTeU7LHU60xeuiKWojx29mesuZsFX4+gX85w9CcAQUO1rFIhyL11oao7nI8+k888hjFnBtzMfpPmvFtl4+3hl1/KdHJu7ndPjwxkb6Rk9w6npyPVXg1x9cSYFjvDJn7iPbtmqqcHDG4ngur7BPe/zK8BiXh60Vzzs3EKR71EdtYer9IGYx7hbnGu5pWcCRi31c86avXfNC8xU7w8OX8B54Et+pJ5D+IcbvHnvdHbg3fBDda77eRSyMoBeQZJryT6g2XE13YKzYMm3KP2Oaa/V06wAMd2WuM4JcTedphAP4z7yJMXgJwiFwleJYsh61cknCawNX2hI7/bE4/yZj0sC1dbzisRmdBJMmoDlgo9zl5m0Vun6RwIEfTcmMFQbCbzhh1f0U3HZv5Dad9X7NlK1izDXPkHf85wVk3Geu8HjXyDx//aT1PPnnB0P0jHqpLnRNIzO76TxBoqmCz+1qYe/xyzx34vq04RWra108unEplQVOcnecZt5W21tqOfPaRaxgR2v9tDLbLg9YkjOO41f6qV05vcybw9N/Hqxwh03hN+9bxd88f4IrIzpWoQAbF1bEyE3clq/9VYae+wPQQ1POCXa9SfDnb6IVVqX1QyPtBbOW0jLtLX2GzILOmafzNMI+/JeOwdggQtNQahtxFNenbRMZDuM7/B/Q8dyULgfan4GiBWhbPoa9ctmka3WhoO/7R/P2u/A2/qZtuGo9pvSbrXSec5GbtZWvsx35wjeTDJIfjv+Esf7zuHZ9FplDfcyarcin88wj5yHiPmUO8HjdrF3fPZROeRToGQtEHf94mYKZ6KeqwIO3Lebe1oW809nHtQEvuiEpcNnYsKSKEod9RtqdSS4lXOge4til6+iGQanLTmttGTZVSVvmmroKKp0XuR7N6JcK1W7B6rqyaWX6g9ZW+8cRDI07wdmxVS6G+ozzAoeN335wDce6B3ju6GUuDJq32Xtvr8U2sRclcVv+cwcZ2vv7MP6aPMH5xmiP6bZj4ShbkDLcRYQD+C8eQe+7BEYAHCXYl65HK6md9nyz3FZYll5l1uKqjEN0Mgn10Yev4X33CTjz2oRKkkiGIm/ZUrRV92JfvN6STBn04nvuL+H66cT9HrlC+JmvI3Z8AduC1RPX+s68DRZ3nMhje1FqGk3pl1OhPjnOzdgqPNKbwumPQedRfId+jHv9oznTx6zZKh/qk0ceNx8yzesMmfjKeBcsm9BUhQ0LK2FhMqcvtyGl5PULPbz61FE640JpFC6wY0U5961aQIHdZlm2ogi+eG8rf/ZkG6MpPKoiDb5wT2vCLE1Oe3pfW3Z7dldvxqeSuQpFCNbUlbOmroxzg6P85TOnUuq7a0U5uz2ps/lIPczwS18HOUPp7pbcieKIvrmb5lW8lDred56Etr1AcNKlwbYnCJY34tj4QdTqpinXmuFaSR0U1cOItTS0jqbNltuawhGkE+oTGugk8NSfMb5xdgoGzhN+7e8IX3+AwnXvNS3ft/9fkzv9MQi9/DeI9/0x9sIyQCJPv27JfgBcO4YR9KPYnSb0E0yaaM56iEgu89S28p943tpYte/FuO1BFM2eI33Mpq1izDXPkHf85wVk3OfM8T5vgAPneugbDaAIQVWJi83LqmKcwdjz410j87yi0AE9JpeHY1Dhji2OFSsz++Er84kbUvLPb5zmwOVRpoMBvHiqn4Pn+/nyg62UuxLZOTEvdzv4vfeu4odvn+fI1bFp21nXUMAvbFpGkWO6+ynCPQ2l/PyY9VXklXWxxcDM6Zxdnv7zkClfVlrIV97TwhPvXOLQlaljXOMW3LdmAZsWV5mSGTh7AGO4O+k5N3js3+agrXkoYUiC1HV8L34Luo8lFtB/lsDeP0Xs/k1cdc3TyknFab0f3rQQplK8GLV8SRYqs1oP9TF8Q8md/li0P82oqxR3y86U8nX/AJy35rwHT7yEdseHAAkj6e2VCPuH0RwF+VCfLPJUtjJC/mkK1aWG//QB3C3bc6KPWbMV+VCfPHIeIu5TZp1fH/Pxg7fOc6InPgRniJ+8082mRUV8cP1SwoZBfyCIqggqnQ5ckyYE5tvbvKKGfeesbQqsdgsaigsSyBQJjuc5CH72zsWETn8shkPw2N42/vt71mLX4laQTPBih4NP3b2SkUCQty700jfsR0pJVYmbTUuqKHJoKeUsKS2ktkChe8z8avPCYo0FxYWm9TTDcznUZzpeVejik1tX8KFgmKOdA4z5gmiaytLqIpaUFFiS6Tv2tIXzrS2bads+i72oJmFIgvfQj5I7/TGQL3wT471fQyuqtPza39W0Cd/5t+Ba6pTCoGDf9smshJykE+rjPfECppz+cRx+HOHZkjI7i/dkGhmZTj+PWP8wQrWDlp6LoaCY6n8+1Cd7oT7hfmuZ0ibQ9S5K85ac6GPWbJUP9cljrsIbDPPGuW46OofwBXVcNpWWRWVsXlKD02a+MEXXqJc/faKdZFHCb18a4e1L7045vq6hkB2r6llWVmhJ98WlhdQXqnSmiguJwfbW+qT/bs39uHUwEgjxfEef6fP7/LD/Yg/bG9MvilXksLHH0xD9K95ZTI33rF/Mt189b7q9B9ctsqSfGYxPJXMB3mCYHq8PXUKp006Fa/qMSAAFdht3LakmHbuPwwilt8GaFTvh1JtMm1e/dCn2jY9ir26cNtMOyEimnw5r4Qj+9hco3PQhrL72F0KjYMevMrbv/8LVdxI3oBTguO9LqGUNYIRNy89WqI80wtD+siWbQBD/+YM4lm9JLr/HXIjPZEjCQ93YyhdCaS2MdlmWoLpLE+s0icctPsx6iEgu8+S2kiFrWdcmEBoPtcuFPmbTVjHmmmfIO/7zAnLSpyElP3vnAs9PKTAVor23i8cPdfFgSyUPrF5IpOBVrIzJPBgO85dPJXf6k+Hw1VEOXz3FA82VPHhb6vZi+S9ua+LrT3WYaqep3M7WpePOzHQy86E+ifjrp6z/ML98/CrbllUjJn0x3jydb68v431rvPz06LWUun5gXS2ra8dz+N9cPSfz+KlC5vz8wAhPH77Mid7Jq70LilR2tNaxaVFVtBBXdtsVxKemTcZv/G1bfjfK5o8ROvc2Ru85COngcGNbug61ahlKOJQ0VMZ/+m0s4/RL6OsfQdpc08pMxhUbuHZ+lsD1Sxhtz8DlQzfkFtVD6324lqxDaI4shhtYC/UJDlwGaT0Zgn75KMbKHcnlh4LJRCSWrQdRhYrSci+G6erHUSzdhrQ5kSbslQ/1yV6oD44Ca+M0DlvBvLN/PtQnjzkAMfFpSMk/vtbBO53epFc8deI6/WNB/vOdTVHnLdYhv8HfvHgdbxb27z3dfh27XeWe5gVT2kjEFxQX8lv3r+CxZ04lnXi01Lj49N0rUCcqo04nUyQ4nudH00iP2euDIX+I0kmx/jdX/3uaF1BZ7OSJQ5e5Ns1NWlug8PCGxaxJkB0oUz6boT5SwnffOMXBBOFZV0Z0/uXNK7x1updf3bESh02bVk66XCtfSrDroMnzxUTPbQUVoOvYFt0Oi9ZaL3bUZS7EJx56/yW06uVphwA4KxejbPs0kk9jGBJFgBDKDf2zWGzLcqiPz1wRwSkI+lFSyXe40xKt2dwo0sBRvQyfuwq8vaavtbXuMB26kw/1SS/URxo6wYvvIAc7kRiIggqcC1YRwomlkDFALL5t3tk/H+qTx5zCCx2dKZ3+cbx5cZiF1deShmy80mZ9NTgRfnr0GncsrabYaUt9chRLS4v4+qNr2H+hh5fbuuiL+U5aVeNix6p6PJUlcSvP08PEKbckxgLpvc/xhsOU4kh94gxibUMFaxsqOTswwqkrA/hDOi67hqehhKVlxdGzZFIZ6WJ8KnmzoRuSP33qKJ2jqWfkp/sC/N0rJ/n87sRZktKBa/39eNt+aOkau2c7is2eMIzHFA+nF44gQ0HrbU3DBSA0GyIbIT1ZCvURtjSfQZs9pXxl4e0YJvdTTEArQS2pjegmVOzbPkPwmT82dalY+yFsZQsthEzdmFjO2HjMGy4wwiFG33kSTrxA7FsiSTT4rqQBhq6aGqsI7DiXbZiH9o/eU/PUacg7/vMCEgDDMHj2qLXCV8+9kzhkI6wb066kZoJ9p7t5cPXCiTZi20vEnTaVncvr2Lm8jrBuEDQMnJo6jSOTTE4+1CcRd2gKBKyPs2Mir//N0TMZbywrpHHKPpLc0O0G4qcK6fHvvXHKlNM/jlPXA/z9K+0sqSqidWEZC4tjX+mnp4OtYgX2ujsIdr1p4vzI36Vr72csw+JWaGmGIziLZj18wDy3FuqjVC9LzybVzSnlO5ruwHfgn63JbX0AqdgYD9VRyhei7PkyxivfhtBg4us2fAzXym3ZDV/J8wmuh3W6fvwn0Hsq8RhYcvqBtY9GCu2FQznRx2zxfKhPHjkNKSOv/Q3D4GhXPz6Z+ppYDIbgZM8gnqrScYmMT3N9IWuFV8xg/+le7m9tgNhVGgtcEeBUVZBgSMP0tYYhEUgMQ5o6/1biy2uK6LKYQckJlDhst7Q9DSMymTSMxPehYUh0Y/yY+fs1Eb84NMqhK9OnQk2G490+jnf7eOJYD/WFgvduWEJLdUlG+hQ9/Nv0/+CL6INXUpwvKNv9JWxldTAwClKHcDDyo2qVL1wN3RZjxnGhFVUhQ770272ZPORF6qHI3ybOV1QVlm2Fc9Yy8Dgb10MKmwihwpoPw9F/NyfUVop9xUYIeW/ICY6hucsQ7/t9gr3nkCdfgv5OCIehuAyW3IF9xWY0RTPd53EugwK08OT2Znv8cpR3P/fd5E6/Vay4F7dnC8Zcea6s3FdhBxhBMJz5UJ88cgtSRuKMDUMipeDq9dTpGKfDpeujLK8onXLcrmT/9hgKgjFDNX+SQTeirtcstJ3r2NJSx6sWHf9dLZUgBRN+/y0I3SDq+Cc5R5dIoaDrsU5/+nilzVpBqenQOSr51svneXR9HXcvrU1bjnBUUfrBxxh68s8SxvsLVynFWz9H4aq1GEOdyKAPQwhEtNCeVW6rbyaEQmQSZRKerRAOYuihtNu9mTzsA9WQyKDf9LW2xrsIWXH8G7cjJOj+sZTy7U0bCXp74PRLyWWqxdh2/CoYEqkHJ+QQ8kPAh0Riq1yKumUJALoQqNE29LhrzNpLDykYIX8klCtDu89nrg91Ic8fTj5+8ShbCgPnpx4vagDPLlyLbsv4ec5VboT8GKEAUpmfDkPe8Z/DEEIgMFAUgRCSkJ7eTarrBso02T0VRbCsVOPcYLo5faZCg2nbmmmoSsQdmY22cx11bhera5wcu2ZuU5cCbFlee8vbUlUibm5yOwh0w0BVRVbsdTCN1f5EePxQFzXFTlZWTZ30m4VSUE3lh75B6PpJvAefJjx8DkkIhQpcLXtwrrwLoWioNi+Kw4WwB1FUG0IPAsIyV9VCQrd9AN593KSGTpzNuxB2R0bt3kyuuYoQehgRkKavVWwNGHf/Kvpr30ptkprVuDY8ghDmdXOvfz/+miaM4y/A4Lk4gRqs2ImzdReKowBdKGDoE/JRwECiOAvA5syqvVSbC0WzIXx6zoxfLnL/kfGQPAswwP6hbxK+dAzDPwaaA1tlA7bSeowc6ddMccXmRLE5EHJ+/sjlHf85DiEEQoCiKBS5E+fuToZCtyOa8g8mr0oKdq5u4NxrFzPWcxwLy+0okzwgcVN4pH8iYT9vdf5LWz1889ljXB1JPXn80r3LKZmSJz53+pJtHtIN/n/23jQ+jus68/7fquod+w5wxUI0AYI7JUpcxE0LJdmyLNtxNHbiZGLHWRw7jh3nN7HHWSZ7xuM48Xgmbzx24sSxs8i2bNnaKVGUKFIiRVIkQTb3HTux91513w+NBhpAA93V6G40QDwf2A8bVfeee2rpU7eee87b17p5/Ww7Hf1hJFDqUnhw0zJ2rKxJcD5HZvoVhRmf93oGXq88e+wGzXtjsx5NbUP3cICOIR+GAaX5NmrynKPb2CqasT3SPEUvBsJiRVisoNlBs8KIJjsV7lj9EL6QF848m2B0drTHvoiSV5ZyX7PCLU6EEgRNN7WvbdkGAvlfQn/ze9B7MY4/LNDyCLaN70Poumnb7MvvwmjYinH7GuHuaxhSotrzsFa7CQUG8Z96GTz7gZEF2FoBND2AdcVdkeNusYPFkVZ/CasDqVpBC+bO8ctFfqM1wbUSB/2XEfYC7A1bAImhWVFGtPw5M64McaHZQLGCsaDxX0DOIvLjvH5ZOT86njiv+USsX1I62sbEV/Vrq0tZUnCT6wPpmfW/b7TIVrZ12SKLfc09brNofO6hNTx9/Cr7L8RP79lQYuXn7q2jJt81ZTvTcW8gxJtXurjRPURI1yl02thQXz6yKDc3/DCRn+3s5f/uuzQpnWzbsME/7b/MP+2/zMe2LuWuJeUJ2owidZvSmZUnist9IdqH/FTl2eP2K6XB8bZeXjl1i0u3g+P2rXIp7GypYcuy8piHmjj2GxJh6AgpTVWjnZKjk7fhfXhLl2Gcfj6+HKFhO47VjyJcJXMu1WAqlXuj3FayFOWRzxPua8N/5RgE+kG1IkqX41y6FqFaMPTwjOzU8sux5ldgCAVh6HhPvYh89weTj0F4AE4+RfDkU4iNH8Gx4l4Q2a1Gu8AjnBSLcwlvP4rNOev2L6TzTC/m34juYJQ6bDRV2DnTmXwe3vWLXOTbLEgp8XT3c+FWH4GwxGlVaV5SzLKifH5zTzNfff4kHV45I/vyNVhfXTKjNmaC9IdN8wtWTeFDm+r42B4HBzw3uXi9l7AhKXRY2FRfQaXLQSSYM4dg2OAH71zm9UsTM3p42X+hl3IHPLm1gcaywrSMI10429XH1/dNlDVMxj+9cQ1jC2xeWp5w25lACEGFU9A5w+twIlrb+qhaMVnrb0jJ9w9f5OCV+Hni24cNvn/4BkcvdPLru5qxavFmx2TkxzN2EXiaUu/ZazeiLFtLqPcWwZ5riHAIxeFCW7wGbeTHOpo61AgM4b14BIY6Iu24KnDW34Viz0ubPbOVzjMe14qqcW5YgmJEHlkNRctIClLvyeeRJ38U9/wYdxYc/S5+qzUye5xWfwnGPWjmwvHLRW5zgD+5NN+xEFb7HerbkXNqngYNC4H/vIAc/Xz/puWc+dnZpPYSwGPrl3HoSifPvHODvvETevz0dBfVLoXH717GFx5ew0utN3n5TDfBuK0lxm8+6I7ROcuYv2SDL6TzTJbbrCr3r17KxqpixkPG3X46HgzrfPWFU1wf0JkKXT7425cu8Cvbl7F+UWlax5IqD4Z1vvFyPLlEfPzzwWu4ywsoclinaV/M2L77mqr4z6Ppq60BEAhEs3eN7+vpY1emDPpjcb4nyDdfO8uv72pCiNgxSgj7EXoAKQ0kJJ2i0gxXS5Zgq6gflSEYmnU0vWA47CN48Ltw9fAku73H/x2WbMKy5aNYLK602TNzbi6d52zx4FBXUkF/FMab3yZctxktjak3F9J5JskXNcPFA0kfKwAKl8/LVJ1JnVfM73Se6h/+4R/Otg1zDb8ELNd1g0CKhY/SBafTim5IhocCI5l9IN9mpbHaxeFLt6fdVwU+97CbY1e6+cGxdvxTxGVDIcmRK32U5Gvc37SY+5urWV5up7bcxerFhdzfVMHG5UWcvtpLSMZvo8wh+MxDK1lcECvpEFnlDlekyI3fG5o1G+YKT6ev/vngec52Jfea+di1fjbVFuGyWrM63nj84JUuTt00VxHVrkpWVEZTZEbakRKkFCNrcQQzta+6wMm+M+1mctokxOolRdSWRq/NSF89viDfOng16Ta6hsPUV7koc0UlQwKMMCLoRQkN41D8iHCIgD8I0hh5nU5GueHrJ/D0n8DtC1MbPnAL4+xBtNqNqBZ71mybjjtsKsIwsuqrVLj/2E/g9uWkzxEA3VKIrWxx2uxw2FQEBkGfP2f8kpPcVYp+fr+pY6Wufz+WoprcsD/L3Om0odldBAwLYpalPna7BVVVAK4C/5iONufnkuU7HA2lBfzx4y3c7y6ZdICtwENNZfzx+1u42evl2dbupNr8/ls3OdfTj6oIWqqK2VFfxfa6SlaUFdBcUcxffHADv7J9GU3lNiqdUO0SrK9x8en7G/iDx9azKD+10u/phEi8yQJGkA5f9foCHLlhLsXsvjSkq0wFhpR0e/1cHxim2+vn9TPmZ9VfPduVAcvGw2ZR+fSDK9La5qqayVl93vCYH3/8Kt8SpB55AoqVr0T/liEupcT38t9BMP56lXHQB/C99DWk1LNiW0KOyKqvUuFSSjj/KqZx4bU02yQYu1vNvl9ylVuKq1Fr15M0XBXYlq1H9/YSHuzBCAdyZizZ4WLcx3zDgtRnDqOjz8uPj1zhpWNXGRjR3yzKV7ivqZq7l5Xz+PrlvGfNUm4MevEHwzisGovynWiqgpSSZ45cN9Xfs+9co/GBlpH/xU7vSxQF1i8qZW11MYZBnCwmk/fJLl+Q+mTbVwfPmasiDfD6pX6e2BCO0YtndrzDwRAHzrfzyulOhmc4je41IKwbaGrseR/br5iRrVFeV5LPFx5u5FuvnKM7+eU8cVFfbKHSFaPjHcFbF3tMt3Wy3YshJaOJsxQFKUCiYKgKiqFnTb4SunEK+m8kb/xQO74bp7HW3Z1x2xLz3Jf6GLoPU7UUohhoT6ssZ0HqkzyveOBTtD31Z9CTSMLogKpGvP/2WSBGClDegrr6Aaw1K5E5NK5M8Pku9VkI/OcgpJT86Og1njpyddLfbg4afO+tm/zgrZt8am8jtUV5LC/Kj+5J9BH2TGcfZhP1nO8J0jUcoNxlg9hZlrg8ikTbZYuLGex7p/H0+OpC5yCpoG3Yz7LCvAyPEW4MDPE3z3rwy5TMjIvIGtbMXw9LC/P5g/dt4OLtAd4810nXoB+kRFMUzvUkn8Hj0U3LiHe8h1Is2u0L6rhslsh/DAMhQWCg6DrC0ElLVp8kuN76kmnbjdaXUZaum/XMIjPJ6pM1LlO9aFTSmYFnIatP8lzVnFR/6Eu07f83OLMP4q3WK10BPefhYpxicF2n0Pedwrd4A477Pj6SZWz2xmX4B/FfOowx0B651zhKsNffheIsnvl5tZDVZwG5hn9/6wo/OTb9bH0A+Mpz5/jdvY0sGw38x3Du1sQMK8nhbEc/5XUVKe0725gYgi1gaqTDV8Hw1At6p0Mgxf3MoNvr569/5iGdPSlEMiNlC0IIGkoLaLg3mg0pErzvO9/GD44mlkx95J7FNJbGz6RkERBOIbabdvyx8pWRGbaM8Y7kEhyMQ7cnO7Yl4ojs+ioFLlQrEeGoyVQPRdUjJF02jQSfaW1zvnKBolnJ2/A+5Lr34Lv+LkZfW2QbZymqKtDf/MfEx/DGO/j2/wOunb+KmIWxGMEg3mM/nCQ1k4DvxH9CzTocd38Y1VU0w/Nq7GO+YSHwn2M419afMOiPxT/sO8f/eP96RKyODfCHUgzKgtHXBDLm26m4SHK7bPAFqU+2feW0aTApA35i5Fm1mLYyM8YfHbmc1qAfYGtdbAAer9/sXA+7V1RRkW/jmaPXuDE4eZS1RRbeu3EpjeWFU7ZTW26n1URaYIAiK1hUZaydWZT6kOLR1RUt87Yl5Lkv9UFIcO8BT6IiahOwcteC1GeW+DhfqSrWhi2jmbB0RSXwnV9L/jjePIb/1llsi1ZldSxhPUTw2b+CwZtT23brOL4fncX6vi+iFC9JzVcsSH0WkEN4weTix74gnO0eoKm8iJE7NgBOa2qHXo6+4o19kIjHSXK7bHExg33NccOQ+MNhbJqGqmSv3yntkZLT7b0cOt/J7aEAIKkucrJ1ZRX1JfkZ89Xa2jLOdJrQWROp9VCd50ybDfH4QCDE8Vvmc1onwo7mRSNs9q+HlqpiWh4t4cbAMGdv9REK61itGquqi6gaXWg/dTv3raqhtTNxDYNY7FwVnc0daWcWpT5gY7SCbNIQqHpo1qUZc0Lqg8DWtJ2AqcBfxVG7aUHqM0t8Ol/5zh8GzOn79NYXUWqasjqW4Itfnz7oH4Wf4LNfwfGBPx1Za2jSVwtSnwXkCryBMG9fSi4LTywOnm0fCfzH0LykmBfOml/A9/SJDpaX57Mix4otJYOJIVg6YRiSd9tus7+1jfMxGutF+So7mqu5a2n5yGxodnGup59vv3KBwQkT79cHBnnr2iCVTsEn9jRR5XKM+3s6fHX3knK+f9hc4L97VWXM26nM4Og18+d9IjzSXE5VXmoFzjKJxQUuFhdEH6QgWftWVRRR7hB0+ZLbXgW21CaQAGZYvmIEfARunET3DkFRJfRdS8r2USxelzHbTHFExn2VDq7ml6Ns+gjGke8m5V51xycRmgXSWkhMMO7czgG/mOFG0Euwvw0RDqLY8lBKlmZwLFP7Sl44mNQxHIeOVozgMNgLsuKr0O3r0NWavH3BPnwXD5O34t4UfRXjrnmGhcB/DqFnOIA+WgEzeXT0+xg5m0c/60vyKbVDTwpZQb720gW++J6VIzPlof42AAAgAElEQVSzY21O5mKav2WbZ07qMxgM8vUXT3NzcHKWi5uDOv96+AbPHL3Bb+9tomJScJg53trRxzdemX7WtsMr+dOftPLfHl1JTb6TdPrKqgme2FDND95JLjVkoQW2N1TNuN9EfGg4tfL1U2FvUykPr14c08dcuB6m50LAb97fxJ//pDWpefNPP9CA0zohE1OWpD667zaBoz+BK3EWJJqA1nT/rMsxInwOSH1GuH3lTrxChbe/M71z7/04lsWrMytfySG/JOKB/pvo7z4HVw6N95OSD6sewL5qJ1gcWfMVQ6lNhoT9gyiu0qz4LXB2n3kDTz2P0bjdvK9YkPosIFcgE28SD4YhMQw50sDYE/9jm5by7ddNzoqN4MdvX+ETO5omtWkYcuThRBBJ9xY70zh73DAkgqgf0te+PxTmK8+eTJhWcSAMf/HMGb743iaKHbHpEzMz3qFgKGHQH4UE/u65s/yP969FUZS0+mpnQyVeX5Dnzkz/w5KnwmceasKqirQfo4l8UpbZFLF7ZSkPrVmKAzlSqGvyNZar10MyvMRh47+9p4nvvHGBS73xZQAVDvjF+xpYWpg/+R5jhBG6jtQDEAQZ9kdkOFKPfAp1xjzcc53gc/8LmGFe03I3lpKlyJAvbbalzENepB5Ku68yxZ31dxNeupqg5wBcPAi+7sg5kF8NK3ZiXboaDAOCI8cojX3LoAAtDCFvTvgiGe67dAT59j/HPw+NQTj5A/znXsWy93dQ7PnZ8ZWSYoBrhCFb18zNFBbsezuQvgGExWbOV2EbGEEw7AtSnwXMLkryrCgimjIweZTn2zDipFxeW1XK+9YHePpYh2lbTnb46fUHKbRaxn2v6xIpFHQ9NsiZfejGSOiVzpKnwPOnriedSz0I/Mdbl/n49qb0GhEHb1wwd0wHdTjWdpv11WVp99XeVUtZXJHPyyducLlvvOZIA3Y2FrOreTEuTYsJ+jOHihIXYH6G679uW0JDeRHSAIemUFpRiAT6uqau8Jur10OyKLLb+PSeFtq9Pt70tNEx4McwJCUuK5tXVlFbkE/kASfOzoZEhIIIPUzYH0ANB5FBP4YQiJG1QjPh4aCP8PNfY8ZBf/5ibPd8FEJ+jHB6bJvRuHygGjKtvso0RwgcjduhcTu6EKgj3+tCQMgPAR8GEgw9rX3rIQUj5EeGgjnji+l48MbJqYP+WARuE3rmr1Ae/l0Uqz3jviK/AobNF+1TNCeGfzg7Pgz6TNsHoHv7UB0FpvoyQn6MUACppDlgyBEsBP5zCC6bhY21ZaZ1/veurJpylnPPihrauod46/qwaXvevXmbHfWVE74V6IaBqoq0zaymA6oSCbvSaVNYN9h33lxa1FMdfgZDQQpt1vQZEgevtpqvIvt6axsbF5VlxFdrKotZ82ARXd4A13qHCYd1CpxWVpQUoKmx2tPMY31VCd/jmqlEhA4Ba6pLUETUKRJVicxvT++n3LwezKImz8EHNtYS/w3BVBAIixUhNTS7hghriIBEUS0IPQiIGfHwuTdAmr9vjULJg+Zd2FoeQB0ZT7psmwnXHPkIPZxWX80mRwEDiWJ3gcWe1j5UiwNFsyB8+qyPMxEXQsV450fJn5/6AIFLh3GueTDzvlp9P0b7ieRtA6jbiuLIg2z50O4Cv/mHfNVVhNAspvpSLHYUiw0h5/BNexosBP5zDA+sqjEV+OdrkQwfo8U2RjHG8x1WwPwPqD8QnlCdd0zOMLlyb/y+s8X9gTAnb/bQ1jWITVNZUZFPqdM+uk0k800f+1tvcbkrQAAo0GBjbTE7VlZTNlrddKzN1rb+lNRXR690c3/TorSPMcqDYSOlKrQ3esMoikBRIueKMlqCNX22VeY5qMwbv5B4PDJ/PigKPNBSzk9PJf9w9MDqSjQ19nV4xD+RwH+68zw3r4escEWDsIpQbWBVIou2NR00K4zojFPlEh1Omy/SRc06qGlBFFVhr2xACAVDs8JIWsN02DZjbnEilGDafDXrXAK6BIs9oltPYx/C6kCqVtCCsz/OBDzY5oFQv7nz9ex+5Ib3gGbPqK8sNasJ5NfAYPJZA63NeyJ2ZcuHy9aDx+Q1X1iPsBeY95VmA8UKxoLGfwE5gOZFRTzYUsMLp5K7QD++q2HcLOXYD/MYt2qpndwWS/T0mW4WcHb1yu2DXp579zpHrg9Nsn9luZ2H1i2h2G7h6y+doXtCBpOBMLxyvpdXzveys6GIJzbWjTxARdrv8aa2SLR3ODhiX2bGHk6xqmZExR19QJyd45Ut/lDzEi51DHKmK/EMUkuVc+RBjXHtyLgB71T9xt+u1xege9iPFFDmtFPisGZszFnnGUznGR7oADn5mk6IkJ889zYMoUTazMGUmXMlnWeyHCMUuavoOojspajMNS4vv2n+fDUGMbouoZY3ZNZXehD7zl/H/5M/Iak0uBs/irWwOqvXj929Hb/JwF9p2ZPSubGQznMBOYePbq3Hoir89MTUqRIV4FP3N1BfWpCwvWUV+dBqPk1obXme6X2yibPd/Xz9pQtT/73Lz9kXzyfV1qsX+vCFLvDRexoimfYAZVJQlxyU1HZLGvYUH+SKbGM8wybOOhRF8MmdK/mPI5d54/LUs3Db6wr54Kb6mAe+MUz+JjlIGUn9+srpNi70jP+RrS2ysHP1IjbUlIyeZ/MGUR+mIbWfTFHvS8iXNhsyxhFp9dWsc6KcDPQhGPfQmStjjse7U0ukYfiGULPgK62gDPtjX8T/8v+G4anWiAnUe34R24ptaU7Lmphr+RVQey8k+wBVuBjbsvVTjjexr2LcNc+wEPjPQShC8OS9dTy2eTk/OXKFF965MapXLrXDjuZqttRWYLdMSK83BW+uLMKlYEoeUmaHuuJo4D9V+xNDo+zxtqHhaYP+VHD46gBNi7vZtKQMkFQW2BPuEw8lBXbGbE3/2BUB62tcHLtlTr51T33ZSDt3RpVjTVV4cnM9D68J8sa5dk7f7MUX0nFYVFYtLmZbYxWFduuE/WfSr8CQBt998wKHrw0SD5f7Qlw+cIUTi7r42NZGxpd+yB3fJcUzmM5TWqeTi00DzTYH0mSmP52nrlgIdl4gfOEN6OsBVUBxDVrTLqx5FZkdlx7GIICiqJDmPuZKOk+JAQPmappEYWi2rPlKKajC9sG/RL95kvDZfdB9HaQEZzGs2Ia9biNKmuxJhdu2f5yAdwA6Tk/vNEc5tgc+g7Q6MEZkfAvpPMewEPjPYSwtz+cTD6zisZZFBINhpJQRHS2ScU/2CbgiYO/6Gp46mry+b+/6JQihTNN+FMnbkU7+7PHUbrKJ8MrptpHAX7CyoginAl6TevrNyysYeyjKjB92ttRw7FZybzOi2NpYxdisUHaP12zyIoeNR9cu5dG1y0ztm4rU56mjV6YM+mPxzs1hrIcv8NF7GzMy5qzwDEp9LHnlBHEAJmf+q1fmvIQm3VKfUM91Age+BcPt433R2UrY8xLhskYc234JJa9sQeqTIR5sP2fuPI2Brbgmu77Sg1gqG6ByxZgkDpEb8jgDXHt+A2/rq8jTL0JoYnINK6zcjXPNXhSLHSNVXy1IfRYwF6AoYkbpF3c2VHGrZ4g3r0ydmjCKXSuKuWdZgiqds4jBQIh3bqSg/00CV/tCdA77qHA5UYTg/pYKfvxuZ9L737usAKcl85ddfUkBGxfncTRJP7ynpSJmdntiuLqAeIg+HiWL9mEf+y/0Jr39oauD7GgZZkm+y7RtOYk0yleEosCqPXD6GVMmOEeK+eSE9CMLUp9g50WCL/zP6Z3SfQ7f03+C9bEvYc0rTv+4iHIy4C/BuIfOXDh+cXjwVoIZ6qlQvBzFnodxB/kqERdCwbVqN3rLA+jtZwj3dmBI0PIKsS1ahdRsKDOWIY34aZ7+EC4E/vMCcsKneS4E/JfN9ZTl3+QnJ+MHsirw2PoqdjdWJ9n+xNAoO/xMu7kUm2bRMeCjwhWRGuxZuYjWG31cuJ04OWSpDZ7YuJxs+eEX7m1Af+Mcx295p7XrAXcJD62KLl6VwJ0h9ck2f63VfJ7s/adu8tF7V2TFvmR4r8/P6+faOXKph4EAWBWorXCws7kad3lhzBtHMl6519JyP6HTzwI6SaF+NziKMGbY71yR+oT1IMEXvpqcb6SX4NNfQj72R1gKKhekPunmwenvwVOiwp0+edxc8VWSXGpWtKpmtKomDM2KEg4h03VesSD1WUDOQ0z4lClxIQQPrVrMLvci3rrehedGL4GwgdOqsnJREZuWlKOp08l7xrhhSE609fDOpW76fSFUAcvL89naWEWZ0zZjW6fjw4HxRaLSjUiNqUhfqgK/sXsV//zmOY7dnFpTX1tk4dd2NeGwamRy7LFcUxV+ZftKjrfd5tWTN7k4ofrqmmonO1dV01hWNGFfMWWbC3yMm5X6HL5o/oH07auDfPTe2T8eUkp+cuIqL5wdX/gsZMCpdh+n2i9R5VL4jT1NlETT5GZQ6gMCi8WJsvd3CTz3V0TSpk6DqhYc93wo5yUhkD6pT7D1NZJ+KIocMEI//u/oG57E3rxrQeqTTm5JbT2YWlqTtmsml30lDZ3ApbeQbSfB7wOrHbWqCXvtXSiQdXsWpD4LuONg1RS21VayrTaqRYeJQcx0ONbew7+9fm1SPc3zPT28eLaHlkoHv7itMWOSF5sls0/pZXm2cf+3apEA++bgMK+1tnHiWj/DOjgUWFmdx47mKupKCsbPhmYJQgjW15SyvqaEfn+I2/4AClDmsuOyWKa0J7kjfWfD7NFMJfmrDhhSZjwTVCL8+5FLHEjw4NI+bPBnPz7Nlx5fTVGMbGwUaZT6RLmldBniPV/G//Z/QsepOFbZYfWDuFY/hFS0rGciSYkj0uOrMy9Oe7ymgvHO9/AqGnnuLekZF1FOBvwlGPcblQvHLw63VDUROmu+7oS1wj2vfSUBb+urGMeeZmIFbv3qWwwf/ldY+xiulvsRWbVtxE/z9IdwIfCfF5ATPmePH7zcwfffnn6R8KkOH3/xzAl+75E1uGxa2u1oqMiftv+ZoMIhWFTgitvvonwnT26u58nNU+09u8ep0G6h0G5Jwp4FqU9meGqIlAmb+JiRPX7iVk/CoD8KP/BPBzx85oGWjEt9RjORFNVgf/jzyP52ApfeAV8/qBpK6SIs9ZtRdYN0SQCyw2cu9ZF6AAK3kzpmcXHknwkvX4diz583Uh9pGARvtaJ3XYOwH2z5aA2bsDqKM35ctcWrCCn5YCRe2D+KijWIvNK0SdNyQepjhLyE/d0oegjFVYL/+HPgeW4aJ4TgxFMMD3XhuPcjyCzZuSD1WcAcgJjwKWeF3xocThj0R3E7AN85eJ5f39WUdjsqXA4aSm2TcqSnAztbakbY7Po6s1zMYN87h5uV+iwtULk2YEZ6EUHYgMhLrNkZ574kiwVGcb4nSMeQn0qnllGpzyTuLMTSsnt8JhJdT7odffg24eFeFClR80pTznIjpUSEDRQpUxpLOqQ+Rnjm976A5wCuNXvnvNRHAv6TL8Gpn0Jg/OL68Il/J1zRhG3jE6ilyzIqIxF3PYE8/E9J+9+y4dG0ynJmU+oTvNVKuPVFuHEs6fGPw8XXCBQtwd60Y0HqkwbMvxEtYNaw79RNU9uf7vCNZshJN/auW8zXX76Y1jYby2xsq61Ma5u5CpF4kzse0cejZLGpvoJrx8wv8H3rWhdba2cni1a318/FJBauT8TBc+28f93i8V9mQOozUy6lxH/tHYwzL0P3hPS3pSvQmvdgW7I2kvN4mnaMwDDe8wfB8yr4R9ZBCAc0bsXq3ok1vzR52xAz9pVIUVMeC3nxIKx5aOZ+JsrJwLEUjHtgnbCNlDB86F/g4htTD7TzDIFn/xR1z2dxVDWm0bbx3NG4De9gJ7Q+m9D32pZfwVJel2Zp2vS+ygSXEoaPPgVnU5OdxcI49SyyaUeW7B/x0zz9IVwI/OcF5ITP9PArfUO8euoWJ28MEQAswKoaJztX1VBfko+Iuan7QzqHrpp4jTmCA2fb+cDG2rTaDbCyvJAPbKjmqXcSB1qrqxycafcx3ZLg5go7H7/PjTJaUCm9vs4tviD1yQTPd6R2u73U1j8S+Gff7o7B1CrktvV5syb1SZVLQ8f3xrfg6lvxB9FznvCB84SX34Ntx69O2Y7/6jGM174xeX/pA89LBD0vEVz1GI71jyYpVUhDVh8hoWodtB9P6fgB4O1Mz/GaRamP//jPpg/6Y6C//FWCj/0JWkFFxs4554Yn8BVUI489DYGeyUYU1qHd/QTWysb0y1dmQerjP/KjtAT9AARu428/h6PKnXH7F6Q+C5gDEBM+5Yy4P2TwrQMeWjvH/+iHgOO3vBy/dYG6Yiuf3NmEy6YCgg7vxKW8yeFK11Da7J7IdzXWUOSy8sO3rnI7zptvu4C966rZ01hFICw5eKWT/adv0RMzlDXVTu5bVYO7NJ9IwbL025l7XMxg3zuHm5X6hI3YwDp5BPVoxprsj9NI0WYZ2Tm7Uh+T3Hv4u1MH/bG4coiAZidv84cntRO4cjR+0D8Rp3+MTxg41z2WFakPCLSW+wnPJPBHTcvxmi2pjwwMw8kfmxpxsPV5rJs/ktHzz1V/N3rDZvT2cwTbz0MogLA5sC1dg1K0OLJ9Bq6TbEt9wn234MxPUzjvpoZsP49SWZ9x+xekPhmE2+2+H/h9YA1gBY4Cf+HxeJ5Pcv8lwLVpNnnD4/Fsm7GhdxBCusHfvnSKa/3Tp8S81BvkK8+/yxceXoPdohHSU6selup+yWL9olLWPV5KZyDMO5c66On3YbOo1FUVsK6mFFWJBLl9wQBVhQ6e3FJHWZ6DPIsFmyYmBPt3DkTiTe54tHUP8cLJaxzydOIPgd0CTdX57GxZxJKCyUW38myp3W5dNstMTU0ZJXmpSUZKXDZAjkkVgFyS+oT7bsKFA8kP6MKrhN33oRVVj7Yj9TD6q/8n+TZOPUNo+QZshdXT24lIi68sVY2Ea9bArXeTtzEWRTUztiHTUh8pJd4rJxg6/hIM90ayuhZXYF2xnWB/N+bSmQIXDmBs+CDYnBk9/4RQsVY2YK1swFC0kYJTpKlQ11RcMO7h3mQ7+nAPXs8B6L0Bug55hVhrN6NWN8VtM3jmVXO+TwZ6VHaY6XvEiJ/m6Q/hrAX+brf7l4BvE8lyt49IfahdwHNut/uTHo/n/0uimfUjn+8CJ+P83ZMGU+cA5ITP1PkLp24kDPqj6PRKnj52hQ/fXU+BNbVTqcBhSdnWZLkQsHJJMSuXFNPbNSZHMgzJ65c62H+6jbbh8Q8gK0qt7G6pYXV1SUZty01+50h9dMNgKBiZkcyzWlAUkXDfYNjgOwcnF0YLhuDwtUEOXzvLynIbH79vJVZ17JejsayQVLB6WXHS40k3r8lzUO6ALpOKn3tWlIMRhLAPaYQwFEdOSX2CrfvNDQjwe/bjvOcjo+34Lh+CaQWCkxFq3Y9ly0cT2JmeAl5Ss+K475P49n0DOlOoHNuwI6elPsHO8ww99U3wT8he1H+F4JUk3uRMgeDt62iLW2b9HM0VqY/hH8L/+rehbcIDZCcELx0ESzHajv+Ktco9bl88r6d8DKaErSgrUqUFqU8G4Ha7q4H/C/QD2zwez6mR7+8CXgK+5na7f+rxeG4maCoa+P+Vx+P5bsYMzmFIKZESDMOIeS0vGfdknyQP6wYvnOk21f+BS/28d12YUoeNEiuYXQe4bnnpOLulhLPd/bx66hZnuwKjVq6tdrJjVTX1Jfkpjc0wJAI52lcwHObv93s43xPf4PM9Qc7vv8L2ul4+uLF2ZOJqfJu6ITl+q4dbPcOEdIM8m8aGunLKHHZTtuUan+ir2bYnE/z6wDCvnrrJ2zfGF13buryA+5qrqM5zxd03rEv+7uXTXO6bPuA72xXgfz57gt95qAVVKIDAogq2Li/gjSsD0+4bizwVVpYVYhhGWsdvht/XXM1TR5NflFxuh2UFDqSvGyXQB3oIggZSD0WkH1KPfAp19vilFIKSC4dg04dG25HnUmjj0n7kXU8gprMt5E2br4QM49j1q/jOHYJj3zNlqn3ZuvQcr+Bw5P/BEQ1lGo5f8NZpjH1fM+//JGD4+iDkm5GdMuQncPVdjAsHwXs7cjnlVaG6t2GtaZqV814GBWhhCHmT3tfwDRD86Z+PLViPh1Av4Ze+gtz+SSxL1421Q4rViqeBtrTZlP0p+ypsi0xcGPYFqU8a8VuADfjzaNAP4PF43na73X8F/Anwq8AfJGgnGvgfzYiVOQ4pIzpjw5BIKVLW40ZxoqPX7ItRAN663s22ZZXsWFXJD491JL2fAmxYVIoxMtk+GAzxf/ed4ebQ+Nl3CRxv83K87SL1xRqf2NmMXTX3JK4bkXuvYUT89s3Xpg76Y3HgUj8OyzUeWb109DtDSvZ5bvL8qS5CE7Z/5nQ3K0qtPH7XMhbl5ZmyMVcQ66v5Biklz56+xgtn4+c4f+PKAG9cGeCxNRXsblw06e8vn72ZMOiPot0LPzl+lfetr0fXI4H77tWLOXillWSv1Mc3L0HKSOXc2cKW5ZUcu9jFpSTH/ZEdKyIPjyE/SsALRpiwD1RDIoN+DCEQI+OZLW52pj6CADLoG2unP/l7XSyMwdso9rwpbcuEr6x1GwmpCvJIkvNjWz6OkHL8eFO0gZAfAj4MJBj6jMdjBL0ZC/oBhKJh+IdTttPf7oGD/8LEglR4u9A7T+LTChHbfxl78aKU2k+V6yEFI+RHhoJJ7xvc93+mD/pjoB/4e3joi1hdRYk3TgUlK1AsDlP2p3yOhfwYoQBSmYc/gsxe4L935PNHcf72QyKB/8MkF/gPAefSZ9rcgRACgYGiCISQMRlnUsPt/tQW6Pb1+1AU2FZXyRtnOuhMspkPb16E1RIx2hsK85VnT9M3MZKegIu9Yf7uxVP8zkOrscTIKBJBVSLBrKJAa2c/Z7uTfzXxguc229xVFNltGIbkmwfO0to5dZ7s8z1B/vq583xqTx2NpanJO2YTsb6ab/jpieu84Elc2OjH73YihGCPu3r0O8OQvHja5Buxy4M8siaEw6KhKFDusPHbDzbw1RcuJNz38bUV3L24zFR/mYCC4Nd2NfMP+89w/vb0F+hv7a6jrjAPjDDCYkNYrWCoaI58hB5GBCSKakHoQUDMGk8Vwmoba0dRIppys23Y7OPbmWBbpnzlaLgbv9WOcfD/TWOdhrrjk1gWt6StXxQwkCh2F1jsM243cOFN8043AWtVI9LqSM22G61w8JvTdxDuR77yN4Qf+DyW0qVZO+9ViwNFsyB8elLbh3s7oO+SKd/pl99CbHgPICB/MQzeMLX/dLBsfD+K3ZUVXykWO4rFhpDz8EeQWQj83W63AJqJ3DLPxNnk3MjfVrndbuHxeOJOdbnd7hJgKfAO8Dtut/sXgBVAH/AM8Icej8dc5Zk5CCEEQoCiKIz9CsX+sJnlKRmBoghsisZn9q7ia8+fpjOBJviJDVVsjcmJ/+NjVxMG/VHcHDJ4ofUG7127LNaIaXlEux2x87VW87nUD57v4D1rl/HDY1emDfpj8fWXL/HHj62ixGlL2s5c4LG+ygV7EnEpJVf6hugeDICAygI7SwvzJm3fOeTj+SSC/iiePtHB3bXlFNqtAJzq6Md8Rns4dquXbbUVI9co1JcU8EePNfPcuzd4M47sp7HMxoNrF7OyPPahcXZ97bRpfPqBFk539LH/dBtnusae7vM12N1cyZb6ypFK3ICigsUKig0UAyxOhBIETQfNCiM63Vnj+UthcLq8EHFQVAuafaydwiroSV62NepRZzEIZWrbMugre91mwnV3ETz7Opw/AP3tIFQorIamnTiWb0SoGkY6+5WALsFiB4tj5u16zK/PSBor9iBsecgUbDMCPvTX/z7prkIvfQPtya+kxydJcGF1IFUraMGktg8mmQZ1HM69jNz0BEJRYdWDcOhb5tuIA3XPZ7FUNoyel0agH3/7BURgEMXqQl3UhKI50+crzQZKZNJiPmI2ZvyLich8ujwez6TfUY/HE3a73d1ABZAPTHVnjcp8NgCrgf3ADeAu4BPAe91u906Px3MHLPCdGKClpuktKXSk1HuByzbSliTfauXze9dy8Eo7+0930DvhCG9YlMeulmpqiwtG+/aFdA6a0D0DvHKmh4dblqCNSn4SjTNin25Aa7KvJGLw1qUedjUt5pXzvYk3jsH+s228f8PyBLblGhc5YENirhuSVy/cYv/p9knpWssdsKtlEdvqKok8vwheO9uOWbxxvp1HVkceMDv6Ustp3zvgj7E98lnqtPORe1bwgY06Zzp7GQro2DSFutI8ylzR6zB3fA0gBLRUldBSVUJY1/GGdayKGif7lQBDIvQwipBgGGlLUZkuLlbtQR769rTHbSLUpp3j7Fcbt6O/afJl84rdqEimS9eYaV9pQsHq3grubeOrHEd5mvtNZzpPoQfj579PCxQcq/ak7HffudfMdSe9BC8fwVa/OTvVaM2m8+y+nIIPDeRgN2pBOY7aTfgOfQ9I7b4JQP02rKsewppfBtIg1Hke/8ln4fo7ERcSydukA8H6LdibH0IprF5I55kAszGi6Kq56VZ+RM+UPBIH/qeB93o8nssAbrfbBfwD8CTwXWDTjKydAlarRnl5fiaaThrGiAi7pDyfUFhHGnLkZze1f3cXO/mX16+a1vn/8J02fGGDvWuWUlroQDfgAzWFfGirm1s9Xvp8ASyKYHFJPg6HZVLfL7eafx0YBK75/GyqrTIxQnDmp5aecDgIxzr6TO+3/9xtPnb/KjRVndGxyf6/kfMqFyyJ928wZPDXTx/ldFv820iXD/797Ztc7Bzktx9di6qqvHnR3EMbwKGL3Xx0dwsSsF21mt4fwOqwUFyeP5I6dvJ4amoKkxrznPpXDyGGfSjDXtDDSAQIQVGhE6moCGmAlPw/heUAACAASURBVLPG89feR9s7P4Bgf3IH0VJA5ZptKKpltJ2CNVu4dehfQSYf2JRvfhhbYd70duaYr2bKZUjFCAgUqx1hdcysrYBgOLGbU4BC6Qe+hKNqScq2ec2khx2BfvF1ijfuztrxAEFRUYLzb4QPpbQOBgqcKtaRczfwgd+l+6k/Tmq/4se+gILACPrA6sBeXotqG7sGhi++zdBzfzt1AxcP4r/4JiXv/32cNStn5isikx3l5fkoFtvUfc5RzIaAKapHkdNsM3EKOx6+CtQBO6NBP4DH4xkGPg7cBDa63e57ZmDrnIAYdaUEYlMxmuOaprB3zZj8JllI4Nl3O/jMv7zNwXO3gEhWGIRgUZmL5iUlrFhUjNOhxe27sye1W3l7r9f0OC3adKfU1LBqcPqyOX03RJYQ3ugaNG3nAp+GS8nXfnpsyqA/FkevDvD3L57CMHQC091xpsBt/1i/FQWpvREryrcjcsV32eJCQaoqUojRNIJSKDnDsedR/sQXmP4nJgqF0p//EsJiH9eOsNgpeuLzSewfgevej6KVLp/1sWedqxqGUJCKdcZtYU3tGgQ7FFTF/Yuo20T5R/8c6+LVKdumKwp4U3gT0Xkld47TBI41tfVpwlk42o6t2k3pz/0x2Iqn3sFeQumH/hB77V3Yl67B2bAZe+0mFEf+aDvetnP0Thf0j0Jy+4d/SqD31sz8wEI6z3RjaORzuis4Oi07ZUTo8Xh04PIUf/O63e59wC8AG4FDKdg5LYLBMP39M3iFlQaUlkZenvR0DREO62lJv7izoZLD5zvp9qUQJQHfePECPn+I9dUlo1rxRH37fKkop2F4KMDtrujpNP3YisvzAclgn49CC/QnuZ4giiXFdga9qdl56/YgBUlLkmafR32VrG+zzc9393PsetS2xHj9fC9bG7uS3n4ion6ozUst6Ggqyed211DS18O84EYY4etH9Q2BoVNYnIcwDPr6vfHlJbPB1RLs7/ky/te+BQPXiYuipdi3/TI+WUSgd2ByO44lWPZ8ltDLX4dJOb7GoKz/eUTdNvp7ehPalpO+mgEn7EcGAwirBM0/83br7oFLJn/SV92Pa+2jhLovEey6jgj7EbY8LMvWYrHn4ZMCI4ljMxXXjVTy4QHoSZ0T6eBFhU4UJP29g0ltryxeh9EZbxnmNChcznBQgeDgWJuWChwf+gvCt1oJnXsNBrpAEeAqRV1xH/aalQSEgm8aP3hf/VdTZnS99n3ydnwyZV+VlBYhZJiuzr5Zn/EvLHRgTbFO0lSYjcB/gEjwX+Z2uzWPxzPufZLb7daAMsDv8XjMayvGEBX0OmfQxh0Hh1Xjc3tX8/WXTnNzMLWb2XcPXGXtB4tjFodOj6IU5TcleeYuSAEIIdjZXMXTJ8zpvXeuquHV021M9+M+FVxpvmizgdTei2QHr542v2b/QGsbBRYYMHn4qlxjnrBZVHY0FLP/QvKSofU1TvIss1d5d3Yx4WFgmmq0od6bBM7th962yPeOAiwNWyM5zxV10vbp4lphFc73fRm96yKBC29AX1fkPXhBOZaG7dhKlwDTV1S1VTVi/fBXGL58BM69Cn03AAMcpdCwDVvjdix2V/K2JfDVnOPRcyByA55xu7bG3QRMBv62lfchBFjL69AqG8cq5SraWHXpmdimWiLHLfJF8nAWZ/F4TJh4SLC9veEevO+Yq/0gmnfHbV8IFVv1SmzVK8dXKlY0RAL/h/tuwu2LpuzgxjF0/yDCWTQDX8W4a54h6xGJx+ORbre7FbgbaARaJ2ziJnLrjVeJd2wjt/sPiCzq/SOPxxNv29qRz/Tlk8pZyAmfM+P5No3fe2QNJ9t6eeXULS6YrMoVBI7f6mHT0vKk+rt7aRn/ecRcMKcAa2tKkmo/wsdkCFvqKkwF/mV2aKoopKPfR2uidEUTYAVq8p0m7MwFntuVe08kIfGZiKOX+3lgdQXPnOw0td/2pupxNjyyZgnHr/Ym9cbIDrx/0/KY/ScGBvOYKwKpqhhSgFCnrEYb8vURevnvoXdyetPQjXcIWQpRdv0a9ooGIHPVOtWyepxldRiaFSUcinyvWTGiPFE7FhV7826Uxu2j+6bUDumr3JszPM2Ve9Wy5VB7H1xOcjHt6scR+RUmj4H5KsnUb4OLJnX+9VuzdjxMV+61qrDxF+DoPyc3lsI6rI1bMXQjrfYHr56K210i+K+fxNa8OzVfMb+lPrOVpPS5kc/H4/wt+t3PErSxBvgA8HMT/+B2uyuAB4lMz76Soo1zCCLmMz1cEQpra0r4he0rUrLo7QtRaUXi/pxWC5uXFWAGu92laKqSVPsTuctm4dN76pPqxwZ86sFVCCG4t9b8+oc9zWWjizozcZzuNK6nWE8lAGxriK/xnQoKcO/y8nE2uKwWPv9IC2UJXlIVWuEL722iyB7NeDX7vssqj8nqo8hoVh89wsNBFBlGDnYR+s//HjfoH0WoH+OFvyR0/cS4fec1j+OrOc2NECqg6Hra2nVseRJqk1i+17QX15qHsjJOm3tnYnsmwNG4JWvHQxhhFD1gal9n03ZoeW/igRQvx/ngb6LqetrtJ2A+ZS6AvH4c/AOp+So2q888xGwF/t8mUtbu99xu98bol263exPwBSJZfb4R83292+1e6Xa7Y1ebRBPmfs7tdm+N2TYP+BZQAHzT4/GYz+G3gFH0pai/7zOph3//huUUJPn+qdKpsLdliWmbRAxvLC/kc3sbKXeIKbevK7bwxfe1UOaMRHl2i8qjLeVJ92cF7musMW1nLmBqr8wuklSPTYIK5Nks/Mr2ZUnv8xt76rFqk2d8ih02vvTedXxqr5v6svFys2qXws9vXswfPLaeCleqCxHnC6IHS0Z4jHxFAr5Xv8Gk6qZTILT/6+i+AcZLR+Ypn+CrWbcnbZy0tSUUDdeWj1Hy6Odh8WomYfF6tAe/QN6G90XOwiyM01JcA+49k22ZAmLdh1Dt+Vk8BrEP58nvm7f2Eaz3/w5UtkwehLMc1j+J66HPoVidmbFfSy2bGm3v4v/+pxl6+z+QRjgFX419zDfMivjY4/FccbvdnwP+N/Cm2+1+mYiLd4/Y9Isejyf2nfzLwDLgl4F/HGnjBbfb/b+A3wFec7vdbwDdwHYiawQOAMmnXJjTkBM+08fVFCOtsaK6yfWXZ9P43UdX8bcvnKZrGjXN0gKV37y/GbslNm94Mn1Nlq/UFuXx5cfWcfH2IIfOddAzFEARguoiB1vdVVTnOye0Jdm7ajG3hwJxCy/FQgU++7CbfHv0Ekv/sckcz12pjxCwKF/h5qC5qf+6UisgWb+olE/cJ/jma1fGtRwLC/Bre+pxjxbQmmyPpipsWVHFvSuq6Gzvxx/WsakqVm3iXErsvmLasc0rnkDqE247C/03MQOf53Xsdz0x0lUOSVnSzhekPslwRYKjdj3VDZvpvXkN3duHoalYbEUoNmcKEquZc8ddH8KnAxdenv5kXvMhHC0PYmTRNtNSnxiuVa1EWbwGOdCJPtCBLiWaPQ+teBFSsyHDIWSG7BdVjcjWn07vz+lwbh/DPTewPfoFSHLs813qM2urDj0ezzfcbvc1IjP824m8jX8d+FOPx5Pgqhlt43Nut/sQ8FtE8vqrwAXgr4C/8Xg85ldizklMeEpFpo2nOmu5pDRaOTX5/oodNv77ezdwov02r568ycXescPXXOFgR0s1zeWFTCoWlBQXcb8XQtBQWkDDvYVJtSOE4L9srmdZZRcvnrhBT5wJy7uW5POe9UsodeZmIaZUfZUrfEdzDf962NzSnZ0ti0bHtbamlK98sJi3rnVy6FwnXYMhkFBVZGXryio2LCpJqjCcHOEWVcGScPsocsePGeUJCniFz+7DNM68jFj/CMpIPvJcKASWCZ7pAl4yHEAO9WAEhpEWO6qrGKFoGRtXOgt4TVWUSrM50WyujBUhM8Od93yYcO16gmf3jRaaGkX9FmyNu1BLl5F0Ia00+2pG/TrysTgKYvwsMTJsv73Gjc9aDEHzdVhG0XOOwBvfIe+eJ5Pz1UIBr8zB4/E8AzyTxHbLp/nbfwD/kUazFhADp0Vj4+I8jt5IPn0iwLbm6pT6UxTB+ppS1teUAALDkCgKjA8sUsPEECzldoRgW20lW5dXcPH2ANd7hgnrBi67hbWLS3FZtBnZmQtIl68ygbuWlvHjIzcYSlJ+WWqD1VXj80hbNYVtdZVsq4vq/icGr4kRfTxaAEgpCbUfJ3TtPDLsR7W7sC6uRVWj14JgnHylZ4r0mdPBGEb6vWBzjLUzMlM3r/hEX6WpfRkM4bt4CKP1RfCNpbcNCic07cTWtAvFnpf+cRHlZMBfgnHXbS4cPyGwVtRjragnrIdhuBcUBeEsQlUiEwTTZYnKHM9NXyXiQmgoax7BOPJdZoSLr2GsfwzF5krSVzHummeYf48ydyTkhM/08t0tNRy9kXxp+mWFKovyYme7U+9bURJvkxxPv3xFCCJvC0onLkzO7PHIPM9dqQ9EZtg//fBK/vKZswmrTNuB33qweWRtwGwfl4mPCvOD+868xvCxfyTcMXaPUDCwYhAqb8Ky6QMYZWXj5Suh1F7G6kKipCBVmFs8/VKfkK+f0DN/Cf449SykF1p/RqD1Z+gPfh5rReOckPogmZF8JRscuwNFs0e+nwXp0Vzy1XTc5t6Bb+AWnHtl8vlrAl7PQZxr9y5IfWbbgAXMDFJKpATDMNJSwCseX1Lg4n1rKnj63cSpEK3AL25fMWLLWOnrdNtklhtGpJpwpnw0n/hc8FWV08HvP9rEd984z6W++KXlV5bZeHJLPcV2a0bGYhiRByTDmPo8NwyJbkS/y53rIV188LVvM3zsW4zNJspRLgGj6wyhZ/8Er/gsriWrItIPqYOrAPr7MQtF1SDkjfwgSz3SXpJcGiEC106i97WBBOHIw7Z0DTgKTLWTcR7yIvXQmK9m2KYR8hP68Z9BOLG/wy/8T8SDv4davjx94woOR/4fHNFFptFfMihAC6d8TtxJfC77SoSDONY/gc9RASeeJtmkAJPQeRbCuxP7KmwDIwiGfUHqs4DcgpQRnbFhSKQUMcFN+rGrsQaLVZs23/7iPMEv7Wik0GpH12ODnNmHboyEXimmg7yTkIu+0g3JiY7bdPd6MQyDvDw7GxaV8un7W+j0+3nrXAddg34UIagosHN3QwWl9shbp0xdF7rBSOA/zTa6RAol566HdMB76nkGj/0TkeRw4x8IBJGHoqjn+3/2VdTHfx+pFEdkDovXQL9JuU+ZGxkKIWWkVUMIRBIcQ8fv2Q+t+4gNGCTgP/JdWLQe65pHUR2FSbeZSR72gWpIZNCfljb97z6bVNAfRejwvyHu/1TaxkXIDwEfBhIMPa3+0kMKRsiPDAVn9ZjNBT4ffGVtuAuldj3+1udTm/0PBZBBX8K+jJAfIxRAKjn0I5hGLAT+cxhCCAQGiiIQIlYWkxncV1fJPUvLOHSlk6OXuun3hdEUWFaWx/bmapYXukZmOA1UVWTcHjNQlUhokks25SpyyVdh3eCF1hu8eKZnkqznP4/c4q7FLt63cTmPrVvO5BnpzEJVIr1M7yeRk9fDTCH1MN53/h8KkWI98QL/WEUxQP/Jl7Hd9REU1YK9cTv+0+YydajNu1HsLoQeBASKaknIhRD49/0f6JhYJzIGN48RvHUO68OfQymuNtV+JrjmyEfoYURAzrhNgn648IYpP9N/BcPXh1ZQnpZxoYCBRLG7wGJPq79UiwNFsyB8+qwes7nA54uvFD2IZdldhFIJ/O15CKstYV+KxY5isSHkPLppx2Ah8J/jEEIgBCiKQkRKAON/btPL7VaNnY017JwyP31kZlNRojZlxg6zXBkpoqWMpiedXXtymeeKr4Jhyd/ta+Vy79R68LdvDHP61mm+8GgzZa7YqlqZt1NRxEjgP915npvXw0y5/9Jx6G9DAbrVMt52bsFja8KvOLAaARr9Z9k+tI8l9Izuo188jLHxw2AvQHFpsO7n4fj3SQpVq7EuXYfUbERT8qFZE/LAwe9OH/RHIYcJvvA1rB/+SxTNmXT7GeEWJ0IJgqbPuM3grbNE6liaQ/jKCawbHkvPuCSgS7DYweJIq7+E1YFUraAFZ/eYzQE+n3ylVdYRwgUMmzqvldq7QLMn9pVmA8UKxoLGfwE5i4kB2uQZuOzzibbNtk0iB2yYKzw3fPWdg+emDfqj8BrwN8+38uXH1sUU3Mq8nXKUk8T2yW43N3jw0iHCWPhJ4Qc47dwyboRhFU6qZVy2NrNm6C32Bp8lWuYs3H4e69L1KFInb9VOhsI+OPU006JiFa6dn4i8gjeRClD39sGl/dO3Pc7wfoJnXsPatD2p9jPF05nOE2+KKRB9PWlLN5mNdJ7ZTo05F/m889XqB+Dkj0yc1DYcS9cikriuFtJ5LmAB8wQTQ7AFTI3Z9lX7sI/jt7xJb98XhLeudcWk58w8oo9HdyLCfh/fK/4YV+1rQErG0jbGQEouOxr5kbDzROCHWAAZ8hObPs+5/j2EFzUSPP0S3Dg2fv/SBjT3Diy1dyOikiITqQB9ntfMD6z1BWTTdoTJvtLKEaQtnaeS4ozlaLAzcxvCA92RnPbXTo6sNbBDRR1q827sNc2Roabch2Dcg+lsHbM5weeXr+zNu/Cf2w+B5B5uxT0/H6lXkbSvYtw1z7AQ+M8LyAmfucInhkazyXM7RWUucCklZzr7OHP8Cn3DAYywwbLyPLbUV5Jns2TVntdap15EPhVeOXWLbXWVWbXTHM+l62Fm/I1gA1fttUwV9EsEBgqGDm3aIvYH7uF+DiGtzknp87TyRrSdKwjrAWTfLaQhEfllWOyFkW1STYN44/QkuxLC303YN4jqLJoX6TyV0iUJU97GRXHtjNM+SnR8b34/ThVbP3S2one2Muwox/rQb6MULUqtjzmcojLbfL75Ckch1r2fI/jTv068eH3NB7Gt3JX0fWQhnecC5gDEhE+ZA3yibbNtk5jBvvOfn2y/zfdev8zAhMyYJ9q8/PjdTu5dXsCHNtVj1UTSbc6En77Zh1l0eCX+kIHdombUtii/U6U+hpTsDy8fGU78KTGBRBESVQXFMDjvaGGn7zAFFXVTvmbXVBtKydJIH0KZuSQh7ItrWyKIoBdlJMXnXJf6aIXVhIqWQd9VUz6w12+ckf+FoeN98x/h8uHpO/J1EXz6T7E9/mUUZ6Hp/uadfGVB6mPu/M4rRXv8y3hPvwRnXmFSms/yZixrHsJW5TYlE1yQ+ixgAfME8UOUBRy62sm/vDl9asU3rwxw4/ZJPvvgKqxa5m8bvmCK++nhkcA/8xCMTBTdYWjt7MOf5NUU9U9AsXNtyaOssTqzV7XU6oDk1WKjEJZZrgyMIG1SHwla8/2ED/6/5B3QuAvF4gAjnHK/gatHEwf9UUgvgQP/iOWhz6TQn2Dcg2kOSFByl89PXyk2J85NTyDWPkqw8wJ6YAhFtWApXowoqEBJ6Twe8dM8DRoWAv95ATnhM1f4xNBoNvmC1CcevzkwnDDoj+L6QJjvHb7Ix7Y2Ztw2uwW8gaTMGge7qsS0lTt+jiD718PNgWGOXOyi3xdCUwTVpS7uXV4R83Bkvs22nkGmg5AGQkYKChqGAEMhYKgMlq5OazXahLx6FfRdm9bWSbAUg7MYQzAvpD5IsNZtJtx5JY7kJg6K6nBs/OCM+w23vmLO791nCQ52oxVU3tHylQWpT+pckWCpaUbVrCgzrJC8IPVZwByAmPApc4BPtG22bRIz2Hf+8pdO3sQM3r4+xOOBEIU2a0ZtW7W4iAMXzcl9Kp0Ch8WSEXvi8VyW+lztG+Tf3rzEtf4J2q0rAzx1tI0dDcU8vn4ZFjX5LEih7jN43/4p3pt9kPd+4kFIA02GsMgAVsKoQkdRDKyAcLjSIl9Jljvc2/CdeTaunVOi5SFUdJCZtW06nk6pT5T//+y9d3wcV3bn+723qjMAIhIEwJzALIoUSYnUSKIkahTGownyeKLz2t7g9cx6n+fZ+3ZtP3ufd+3nMLve8T6vdz2e9Xpm1hM1o5FGkVSgIimJGcw5gUQggE5Vde/7o7rBBtCpGt1AE+rf50P2D9W3zj339O2uU7fOPSd056eIhSKw/8ncY593B5GtX0BIw1NYxHhu95+D/hPe7A4kj76Kf+PHP/DhK7VQH+9cjfRhXT2JtuLoYB3BtkVuSs5SbVUL9anhVoPtKPZe6ONy3wiWo5gVCbBxQStNIf90qzatGO+CfdARTdq8fW7Y83m7j13mkTXzK6DRTdyzssOz43/v6o4KaZMdgtRCUZXhSO8Af/lCfsdr1/F+Tl29wRcfWpuRAjU7tNYMv/o1Rt75G0AQCa7P2VagMbSNqZMY2kakLCSAlrrUzeIUhQAYkWZYtBVO7S5gsRSMeoLd6dSkU6fnBI6gnKE+oBHCoG7dI6gV9xE99iac3wvJETAC0L6cUPc9iIbZiEmE96S5c6O3OHuPx41LJfQnGHOTWkUhKNXHZ56trOvnSBz8CZzdM2YqjSBh+b0E1jzs7tcpyVYZ5pphqDn+MwIaANtxeHr/OZ47cp3xhaa//95lVs0O8sSWRcyOhMacVwpXWjGStFFaU+f3YUiRpf1412g6eS3UZzy/OOSt+EkaJ67cgDVpWcX3azmKo9cGGYrZBHyShc11NIUCWdt31IVY3xkuOqVngw/uXDDbkz5Tzyv/feiPxQs6/WmcveHw968f4xc/1J1X5vArX2Nkz9+MHu+OHwA1DLJuolCt3dUzrVAKHAUCiSN8rJo/GyuRnNIQgMCHfonEcB/0HilsEGeIxPXzyPZlU6LbVIX6jOGBOoLrH0GuedA9XoawiAm81PAIpT2PeaaHr5STZ9rKGbmGFRvGQGE0tEG4uWr0LJZHT+2FV/8q12SCoy+ROLoL9cj/ia9lYS3UJwM1x39GQGA5ir984RDH+3Lvijx0Nc4f/vAw//rRbuY3RBhz918kvzoS4+XDF3n5+MCYm4tN8+q5d3VHSu5Y3bz2URkuJnHuzORJJ9PRKx6Wk5ZVXF83EhbPH7zAS0f7GN/jqtkhHlzXyfLWxgnn/uzW5Qy+cLBgEa+whC89vBq/KYvSp1y8GkN9Xu65jBfsvTDC47EkLSF/Vpl2/wlG9vx3Mj9vHzYbo6+zp27HRIFCoISBEBIhwRAgtGLjogbqA5KBuLfwleG4w8mrfcQsm6ApWNI6i7qIUdS5IEBB6J5fIvadfwMU3jGun/tjnId/G1/LghkV6jOV3BduKMLSWdDQ5nnMMzl8pezcSTLSs5voO09D/0nArStuAXRtwL/6AfxtS6ZfzyJ48vLRPE5/JhTW03+C+fjvIevbaqE+Kcy8EX1A8Q9vHM/r9KehgL/4cQ9/8Im1RPzeQn92nbjMP76dPSb87XNDvH1uiHuXNPL47QuRExye6Uf1aTS9qA/6CjfKgjoP510cjvJnTx0hnuMe49DVGIeeP8EnN3SwfXnnmPf8puQ3HlzDTw6e47lD17CznL95fj2Pb1yYsedg6pB2hasFtqN46ch1z+e91nOJj65fkPW96NtPZT1+3/Dz9ARWM+wb+5lpBI4wsYQfU/pwtI9ZPsEjdyzES/hK32CMXT2XOH75BpYjMaQGNI66xJI5IbYtbWfu7FkF5SAEsUMvUozTn0bizW/ie/S3itLzVgn1mUpuNs0lOasLBr3tHwosvauE/gRjblirYPzVyLVlc/FHf44+ty+78S/sJXlhL8m1jxNe/5Gq0DkfT77z7RyzKBssYgd+THjrz3ucVzdfZhpqjv8MwPXhOG+euVF0+yTw6rErfHj13IyjOi9/9eSVnE5/JnadGABO8cmNi5joGk0nr4X6jOdd9SEiEkbGx4UVwPqFLRmycssfilv82Y+OjM+snBXf2XuJSNBk8/y2MXJMQ/DYuvl8ePVc3rvYx9WBKEppGiIB7pjXSjiQUWG0gD7Tzyv7feiNxbPeHBVCz6VBWD9Rptaa2Mmns/YV0jF+se+r/F3zrzDoS+330BotJLY2UVKiDEHYF+Tz9y+lvnUWOjGEkmbBx+wnrgzwD69fRGoLgZsRyI+DQJBQBscvxzh55TSPbVjAuvmNOeWAxpESDr/gzSD9p0gMXMJoW5xXz8rxCob6TBEXKx5Av/n14m3esBCjeSHKY3+1UJ/C3JE+Ejv/Aq4UUdBu/w+I+uoJr7q3avQfz61rJ2GwuEx0ozjxGvaWz2FKX1F91UJ9aqhqaK3Z1XPJ83nP77/KAys6kemVpTxhBFHL5ptvFb96s+vEIHcsGWZBY11euVPJldIINErpScmpBNdac6LvBvvP9DGSsPEZgkVzZrGhqwXTEJOWn4/ft7KVpw5eo1gYwPrO5pQd88t//tD5opz+NP737nNs6GpGCjlBphSwoasFulrGHFdKFRxjpbhS7s1kPh2U0jgqfayyusaSpbj97nnZxqCtGE7sRs5+69QQv3btzzkUXMcb4W30Btw0r1oaNAcN7ls1ly3zW/BL0KZAWXFAgp0AJ7WJVDtgJ90LrHbo7xvmyTdOYDgSn3RSfZr4GMe15ql3TlHvm8uitoYJctI8eb4HL6v9aVgndmM0dmSVWXFuRdGO5f49lf2WkQcXbiR28h3oPVSUvc0PfQGsqOf+dFKAaZd07geFJ8++X5zTn8bev0ctXIf0h6tC//HcOrmn8BiyIHn6Xcx5a4qbV3YAVBJUsBbqU0N1QWs3zvjg+X7P58aA6/EELYFgwba7T171LH/ngQt8YWt34YZTBHeTISiPq9uVxoHefr7/5mmujfOQXzs9xDc4z8OrW3lw5VxEhZ453r1sDq/0XJtQsTcXntjciUAUtKOtFS8d9TYv48D7l/q5bU6Lp/OmC44i5fjnaeO4q+COk+lYVwYhX2k/53UBM+sYNAaK/PsmBLA6/j6r4/uw8BGXAYLheuY98Y3RmV6z+AAAIABJREFUNgqNI0DIII7wo5UGlURoB5WMI7R7M66E4LWes0gnQVCBKRQaiaGs7NyxeOPgWRZuXYISYoycNNeD3n+7ABi6hoqPZJVZaW7HwFAanYxPab/l5IbWBLd+nvjuvy/g/Acxt/8aMjgLbSU99+dYEmXFSzr3g8L1/mfz2D874j0vE+q+tyr0H8+Jefd3ABi+XvQ8UVYcZSXQssochjKh5vjfwhBCIFAk7dImZ9J2kKHC7d44esWz7D0XY3xB6NQThemHIV2XRcqCTacMu09d5ZtvX8z5vgM8dfAalwZi/NzWZVTClBG/yZceWcmfPX2YoQLO/+O3tbNt4eyi5J6+NjQhs1Qx2HfqGrd33hqOvyFd1zb/nBI4SmEYouJzb3Y4wCwTxqfuL4S1C1uy6yb9+Bo7cAbSczT/U4cACQIqjq9++Th5AiMQRkvcndhO3F2hVQmkthFWFBAkE5qeq0mU8GFJE59wV/nz8fNDDtdGNC2tdQgnCQik4RvlBLNkHioG/hAyGMkqs9LcDNUjHBuR0FPab7m5IEBgx7/AunQYdejFsavO9R3Q/QChBesRPr8bHlRCH4YvhDR9iJhTFWOuNq5sC/qPeZ//5/ch1j407fpn4/jD3scDEGxA+P1F9SV9QaQvgNBV5DCUETXH/xaHEIKQ34QRx/O5Eb8PKTNDScjK+2Ol6ZawNZFAWl7+PirN0+OUsjr0OdU/lNfpz8TeCyN0HL7AI2vmVUSftkiIf/vRdew8cpEXDl1jfMHcVbOD7Fg3l2WtDePeyS1zJOl9PgLELFU1n1EhLqVIOf4yT3uValuoXTm4wf1r5vC997xl9rl7cXtO3SLdjzP85lcz3tMFed26j034XRGmiTQaEAGNkPWQjIG2wRoCYYK2OXX5BnHtQwAJDHQqRUAhfvR6grs6Zrty0GD6R7nZvrikfQ80LQJfKKvMinNfGCGTYDpT228FuDD9+DtWoZrmI4J1YJhIpRBCoEw/IpVKtNQ+hD+ENvxgJqtmzNXEdaL4MM4xiN4AMzjt+mfjsn056vguz0Myu7rBDBfVlzADIP2gajH+NVQpbpvfzOl+b3H+jX5oCgZwL875V/IMAZbOIqQAjDEX//x9VJ4XHudU8mf3ect48cyBXh5c2eWp0qoXHvb7eHTdfB5eM59+22EgkSA6kmR+Y4RZQZ9nmQFfaT+YU52SczK8GtN5blsyh5cOXWYgSVH4qbWzCfl9OWWG1j/E8J7/4cblF6GDrGsluHwrE79voIUEmbrA+uvQtuV+M+0kQmmiiQRm6tG6H4rmjm0jc6Tn89e1YTcvgb4TxRkkhfCSzdOWsvBWT+c5nqNSn7NyQBpIFOWqjFxL55mf61IfM/p8VTv/QvPXMbI7BNrDimTrcvyRlqLnyUxP5zkzn2N8wHD38jmez9m+eg6iyNiRzkbvFX9NIGBW1/QqbrSVx2A8yf5LxRWmSsMB3j5b4uqNB0gpWNrVxKbF7ayd08SsYGnVnhc0lRZisbh9/FMF70jaDq+dvML33z3Fd/ac4rnDFxiIF+kJe0C1zKdMBH0GX3x4NXVF3Hfds6SRh1bNzdvGCLfS+PAfgCjiu2wGaPzI7yLMiXNGMN5eAkwfWphoGUDLAD6fHyUMtEgnAy7uf0MKbsbB6QncXJ2l3kA+LLsX6Q/klVlRTno8U9xvxTkVkJs5s6plnNXDZaAeKCKedzwC9cTP7MW+kd4jM/1jSXMhTcTaRzwNx7f24RLm1c2XmYaZdyvzAcSskI/ty5p56VhfUe3rDNi2uB33IpNGbr5tRTsnX/eWPuveZY2pG4vi+qg8r550nieuFZ96NRPvn77G6o7GDGe8em1V7zc9Vd5N465FpVffTdqKH75/hpeOTdz89YP3r7BmTognNi2iNRKccO7U8Kn5PrSGA/zbj67lJwfOs/NY/4S9Fp11kh3r5rJpfuu487PLDC67i8af+lMGn/tddGwwaxs5q53GR/4t/o7bssrRgEjNrTHn+sNuMJR2WDDfT3Tf9dTqm41fJUmn8MxM5zmeL5hdnzc9n3/BRuzeHXDkOQqiYSGhjZ+a5lSat346zzHcsVEkkNKAMvdRS+dZgAsNq3bAoSezzfbc6D+J/epfA5BsWopc+xD+xZurZlzBdQ8R67sI598oPJbbnsDXtcbbvGJmp/M0fu/3fm+6dbjV8PPAQsdRJBKlpc8rF8JhP47SjAwn6G6fxcW+G1weKlzl9DcfW5VyHjNXS3LzOfUhXjp0GS9R2z+3bTFhv1l0H5XmoUgAgHjUmnZ9TvePsP+8d+e/d8TmxSNXOXCuF9Mv6WyIZDy1qT5btTSG2H28+IJS25c1s25ec0l9JSzFnz+7n315nqRcHbZ55Wgvty1spH60eF3ptgpGgoAgHk3kbKM1aC3cjfhCFCW3HNxvSlZ2NvPgyjksaAuypD3C7fMbeXRdB4+tXUDXrIgnmWZzF5HbPoHZuAQdjYJPIMNN+JrWUX/Pr9Ow/TcwG7pyyglF/ClbJce1EWD4QfoJhiP09N6gP+Zg4OAXFlIoBG5IjxR6Am9v8LlPL7VOPaIHtJrAfZ0rsUQQrhwmJxbcQej+f4ZhGDnlTAUPBQyEUiTiyWnToZxcOEmE4yCla9dy9hEKGAgUyVh82sdZrVw0tuMcLuKmNxfifegzb2NHR/B3dLthItM8LonAWLQeWwm4eoKU5z4WvlmILZ+nrvtuz/LD4QBmMEJC+RDTHOoTDPowDAlwBvhaOWTWVvxnCKQU/OKHutl57BLP77uUNT3jXQsa+MiGBZ6rnJqG5J/tWMqfP3e8qPY/s6WL5lCgaPlTBVG4yZQgEpjc1+7soM3/fP0cu3uu8E+3ryLoK39IVTlstWBWHZ/Z3MU3iqgB0d0W5GO3Lyy5r7/bfZTzQ4VvTR3gL358mN//2G2E/JNbzRF4+RZND3yGZF1H+mYKJqOx8AUJrX6Q0OoHU3IyZeafMQXflQYgeOC2xfy35w9hSgtH+9zVf52Wn95VcZPf093u/iXE6KpdtkqcAgjf9jCs+BDx42+izr4LdhSMAMxeSqj7QxiRJpQ0Qdk55UwJRzAm1Gc6dCgnJ82pQB8iY3ZV0ZiriBuRJnz3/nOsXf+FSeHoc0SDESJrP1wV4xLCpO62R9FrHiJ66h301R6wEuCP4Jt3G/6u1WjDV+L3OTWnqsVpKDNqjv+MgAbcIkf3L+9g+7I59PQOcr4vVeU07GN9VwtBnzGm/c3XwnxJSwNfemgZf/XssbxFmb5w51w2zm1NnTfeNZpOXj2hPstbJh/HDnCiL8lXXzzIF3esGZc+sXpstW1xO/UhH/9792kGcjyMun9ZE4/fvhBDjj23WH5peCTvSv94RBXsPnWFB7o7Pfc1OV5N34ep5e6vgc54L1t7wbI5bTy+ZSnfe+MElvYjlIPUNkEdQ2qbpJKjoT73ru5i+dwWT9VepT9CcNV21LoPI1MZZdLZZbxWja0cr4X6FMtroT7Fcd+89dR97N/Q/9RfgpUZrucNet/3sVfeB6FQVYwLDZgGgZXbkcvudo+bfqRtoSczr6iF+tQwFj8P1Rfqo5R2F8YQCAGtkRCLWxtY0lbP3MY6zFGvKnOFxBtvDgV5YFUH7Y0+RkZiJJMOBtDRYPLQ2jn80l2LmddUNy2hDYV4NYX6mIbB4EiUcwPjE2d6R39c0VJvMndMleTqslV7fZjtKztY0h4iIDQtYYN5jQG2Lm/lF+9awtq56Wq9pcn/8fvnONvvpUYwXOkfZvuKOYhJ9FvNoT7VxnOH+oznkvkts+icXc/ZgQT9lg8hwCccpFBIFB0NPh5d18n6RS3THkZRC/WphfrcKryhbQ51dzxGPDIXpRyQEYgXty8wE46/AX/rgqoZVyV4LdSnhhoyYEjBpnmtbJrXljriruWluVI6x5nTD1G4yZRhx9q5vHaq9JWXTOw8eIk7F7aXRVYa5baVEIIVsxtZMbspdWTsvJkMjlz0bsf+BAwnbeoDpWUtAlf76p3t1QWv82lNRzNrPtLC6YEbHDt5HjUyQJ3hsKjFx7wmP4JUFc/USl01hB7UQn1ycNKcCvQhGPM7Ui1jrkouEEIS6FpFoGsVVnyIxHd+C6/QZ9+FNTuqaFyVsRUZLzMNNcd/RkCPe60WPt41mk5ePaE+4GZe+fjt7XzvXe9Vkcfj/JBDfyxO0+i+ipllq0I8lsw8XjwStkN94IP6fZharoHCoT4T+cLGehZu6IZkFKFttDWEToygUShpjF6wpz3coKy8FupTLK+F+pRuK9vy9pR0FPEb0z6WituKmR3qU3P8ZwTEuFddBXy8btOtk5jEuZXhHY3pzCqTx2DSpikULJNu1WerfDwSkAxHxyetLIyQmc46VVq/1VjAq3o5KXuVMrdkRsEvgdACtI100oV3qGjhoKTSnLk0yEAsgUTQ2hBkXnMYKcrfF8zgAl6OA6K8fdQKeJVuK0ManjL1jcIXylkwb6bwmV7Aa+aNqIYacmC8CzbdaKsLFm5UJHyyvKOrNlvlw9p5TVzpKT5tKMCciJx0dqW0C1tDYUx+Pgm34Bd+lBkBldp/omxQSSrx2D9qO+ztuczes/0MxxWG1IDGUZKWOpMNi1rZuGR2qgpm+foFQS3Up1guuDm7qmjMHrgTGyTaswuunAA7DoEwcu56AkvvAsOomK2McDOIMGhvtVZoX1wVdquF+pSOmuM/I6DHvVYLH+8aTSevvvCVtkiQ+bNMzg5OfpN4SzCQ0cfMs1U+/qEV7Tzv0fG/d1XHlOtZXd+HqeUaKCXUZwI3g2jpc/9WYZwREARQ2gHLbVeOR/3RpMXXdp2hf3gEARMKh10btnlu/2VOXEvwxOa5mFJ8IEN9tNJYV49hn9oLiQEw/dDWTXDxRqQZqIX65LOdbRF76x/gxKuMh7p8kNg7/wtue4LQ2h1uWsoy20r7/LBiOxx+akL/+eBfsb1qbFgpXgv1qaGqobWbzUcplbGx1r3MTgdXSuOo9DE1bXqM50ppREq/atAnze9b3cHXd3urijwem+ZG8BsyNbaZa6tcvCkQ4M4F9bxxZohi0OSHTfNaUWpy81Mp9wYpn5xq/T5MNddKoVNza/Iyb6YlFv5GhBMH68bNfN2WAjvhXrS144abeOC2leQfXzvFwI0kfumk+jTxMZGfvHCd5/daPLx+bkl9ZeVWFO1Y7t/lklkBnjj3PvrNb0H8GmNw6nXib30N2lbhv/9XkI7tnpNMxZSXUQ+dFGDaYEWrxi7FcO0kiD/7FbheoDbO+98mFh8geMcnK2KrwLKtJLw4/nPWYoaaUFZs2m1YSa7tgPskUQVroT41VBe01qMXU61FVWTUcRyNFhLHyXRyyocRy+JqLEE0YVMfNGkJBYmYvsJ6qZTr5T0UvKK4vbOF/Z3Xefeix8etGbh3TWdZx1WttsqHn964hIGRQxy5lszbLiLhX3x4FaaUk/6+OIqU4z/2uEITtxz8Urhr/BX8PtwqcFI2Ku+cMkD4U1ExMRB+0BbKthCWBSRRQiDcPMdF8zOXBhjoHySowBQKjcRQVk5+9OwI9yyow18X9NxXmicGLqFPvArXThN1LAjPgo41mIu34PMFS5JZSR47tQfe/Wb+j6f3EMlvfRHWfBR/11oUGpQzRpZz4yrW2b0QvQGmDxo68C24HWH6i9LDsSTKiqOtZFXYpejP+70fFnb60+h5nnhDJ6F5ayfVbzZbGaYf4+5fxXn1/yusR6iNwKZPoZOxqrBhJbmy4igrgZa30EXQA2qO/y0MIQQChZQCIfS4Ik7TBYGjFIYhyqaP7SjeOd/H0++epz+LX7ek2ceOtV2sam+a+GYKhnTdruqwUSYEP7d1OYG3jvPG2WHPZ39yYwfzG8q3SRiq2Va5IaXg17av5KWjl3l+/xVGsvxef2jxLB5ZN5c6f+kpPDNhSNfxlxKU1hy8MsDOAxc41nezUllrAD60ajZ3LZyN71YyaJmRLiNSXhMI8AdAmeCT6EQItIVQCURyCJSFlCbCSQICafiK4m+dP0dS+LCkiU+4K/uF+J5LMbatbfHcl44OEH/5a3C9Z+zQ4v3Qdxr74I+w1zxOaPUDSLM4mZXm9rVThZ3+TBx4kuT10+DzQywKPhMizXD1DAyemtDcev/bWMseIHT7TyF9gbw6Gb6Qa5eYM+12KZZr24aTLxdvP4CeXYglG4uSr6wkKjEEjsKINCID4by2CsxdTeKhL+O88Q9wI8fT50Wb8W/+DEaq7sl027DSXPqC7tzTM/M3u+b43+JwCwOBlBI3lADGrixONXdXNqVM6zQ5ucMJi688e4BLI7lXZ0/0WZzYdZqtC/v49JYlqWJQY+VIKVJ6iTHHq4FLKfj81uXcvy7Kywcv8dapQfKvW4MEPnfXPLYsaBv3Tnn0qVZb5eMSgx0ru3hwRSeHrw5yeSCKozSNkQC3dTbhN8fHa06uXyndqPW4rfjqi4c4PTBxr8a1BHzv3av8+N2r/OZjK+isD5el71uNCylxbyZF+eVLCZhghgANKoEeMRHYoBXYFuC48eWp+GZMf1YeH7zMyesOQvtIYKJx9U5g5OWHehNs84UKys/kSiVIPv1nEO8lLw78gJiTJLjlZzzJrxS3Dr2YX99suLTPW/tjLxC7dobAR76cVyfhD6ENP5jJabdLsTx24hXv9rtxBnv4Ombj3Jzyk1dPoPY/PdHW87fiW3M/orU1p618nSsJfPTfYfWdxTr+FsSuuyEujYsILt2MDEZQpj/1XZp+G1aaCzMA0g+qFuNfQ9VivIOWPT5Wa8Xh3hu8fOgSJ65EsTTUB+COhc3cvaKTllAg57ne+XjdvMtK2oq/ePYgl/M4/ZnYffoGPvM0P33HoiwyRRnHVhneWRfh01uW8uktN4+fGxzmlcOXOds3jGUrGiMBNi5pY/O8FkzD8CS/eF79tsrHhRCsam/MeAJUmb40Att2+MpzB7gwlP+RcAL4j08d4f/66GrawuVKu3orcSg9nWeRvzcyHfInINgEThKpEgjlbtDFyZ/CzxnpJ/rDP8Os/ywAfuVgph71+yEvt62k5xSHyV1/W9jpT+Pw01hzlhPqXFG0/EpwNdIH594pTufJov84iZf/G3V3/0JOnW7JdJ4Dp0syh3P9HP5ZnRNkCmUx8ta34HiOpwhnd2Od3c3gpp9m1pbH89oq0NSFb/Mnb37eQrp8hqSWraXzdDHzRlRDVlyPJfgvzx7kamysE92fgOd6+niup48Hljfz+O0L3PzUVYCdRy9xOVvMRh7sOt7Ptu52Ousnhr9Ux6i8Yd6sCJ+9c2nqr/FOT+VwK9pqqiGAH713pqDTD4AzRLt9jlefOslj2xbjm712NI75g4ApnU/SQBsBkCbaAmkqd6MeuBdyNNlS+MXe+z4mA662WqV0dm9UCnG/aWSVmYs70QG4sNfTsJwDP4HOFUXJ146NNXABrBjSF0Y2z0t9CIXPzcet62c96TxpnHkL5/aPY0Qac+iUvpmc/NimjKuboYBeoB07q8yRt7+d2+nPwPDb/4j2BRCLtlWHHaqap+bUDL0Q1hz/GQE97nUs74/F+aMfHKRQnb4XjvYxkrD43J1LEaPOf3aZxXFR8vlKa148WFpV250HL2Y4y2mZt1aKyunlNVsVw7VWPPNOfkeo3jrLh4ZfZk18D34c6IO+b4GsayG0/GNENj+ODDVXxXgqyTVQlnSeRXEBph+tFPgCqGRwtOovqeJf41P4qdgwnH4LP9Acv0Kvf86EFJ75+PzWek/pAmNHd+MZvUewogMYkZac8q3YANa+Z+HoC9wM/QTww8odBNdsh0hr0XqO58407PiPHXmF8B0fmznpPAO596LlgwjNmpDiNTFwDo69VLSMkd1/T7BzPTLYMP12qGJeS+dZwy0AMe5Vj+F/v/tEQac/jTfODLFyfj8bu1omyPHGx+vm7fxTfTcYdopUevwYTt/gs3eOlymY3Hg+SLxmq2L44QsDDOVZvFsRfYPHB7+VsujY/Tdq+Doje/8HsZPP0Pzx/4DZuHjax1NZ7l5Mvcwt29EMWzZCa+oCfgzpZV7KVOy/zqj6C8JOIpSeEPYTP7tnVM+11vvsCs4uGN7jco0pNVsWNXsL9RksLYWvGriEL9yUVWbyynGs5/80x5lJOPwU8SMv4XvkS5hNc4vTcxw3fQEmX3XEI672kKuKcSmhPtqKkTz7HvaRl+HaWUBBfSMs3Ep4+VZkIFLRMJLAoo0kDv3YoxEkwY7liHF2cA4879mc8Z5XqFv3yLSH01Qzr4X61HBL4+pIjJ7eYt1+Fzv3X0g5/tOHgWih7a25oQCldMbmVBcie/MasqBmq8LoHY7lfG9+fD+fGPwGACq1ETQb1MBF+r77W7R+9q+RweYKaFkd8DKfTg8Os3P/Bd45PzbL1YcWN3LPqjl01HnNYiXcqr/KRMtAhkYCtANCoKKDo61Xcox37U30y2bSNypi9JzxXLBsTj3NDQGUl1ACp8RVjRyhSlbfeZI5nf4M6CjWj/8Y8/Hfx6hrKqznOO5rW4RNEIpeSioDnHgendI3k8WNweo7T+K5/wz24Ng+bsRg33eI7vsOcsNnCK6+35NdvHBfYyeJpkXQf6p4Gyy/H2H4xsjRWsOpEp4cnXwd1j1c9nHNLJ6aUzP0Qlhz/GcE9LjXm/y1Hu/hMqcGLK4OR5ldF8oqs3guimw3kY932r1CiPEya+ErxfOarYrhORO9ac3DN55EolPr/DrjvImy1OAlRt74LvX3/dKUj2GquAYKhfporfn+u6d44Wg/2fDKyQFeOTnAx9a38+CKLu/6+MNuzjHtoHUSnYgisN2qv+JmLRAf8FjsO3zD/zlsXxihIaGzh/q0+Bwe2bgQJaS3sIJIaeEeRBqzVvRNvP0dD0IsYu8/SfjuX/QcCuFWe70fjnhdsZ4EzHDOKsZeQn2svnNYT/9hwe7U3m8QdTThtQ8UlFkqN7Z+HuepPyhu/EY9/g0/NWGMysm98JAXI323RFXo6eQzPdRnZiYp/cBBZLyO5ZcHS/txuDKcGCNHa/fRe76+xvJi22XnXY3ptIfe0RIEkco3PBkdarzG8/GuWXVkQ0fyKLOdq+Pa55cVO/qkm9+7SsZWCa4LtPnBe2dyOv2Z+P57V3jx2OUSdJBu2I+vHvyNbuYfXz1SGPha5o7po5UEP5v8W1YmDuOTNn7pZvgxpMIvHcJE2RB9my+skUQMjdQqFepjF8X9izcXHOcEBJrxN86dIFMNXIDeI95knX4DEsOedE7z8Or7waj3rn+JEPNuy6mTUDbSSRTUW9gJrJf+qvhO3/8mTt8Zz59rsTzQNA/f/V8srEegieBj/wemGZogxxAlum/CX7FxzRQ+JtRnBqK24j/DobQu3CjHebaj2Huxj10HL3Fm4GYw86rZQe5b08XKtllUKgHQ7EiIRY0+Tg14z4Bw76o5WY9XSNUZiZqtCmPRnAba6wyujNuMsjruMWc5oEb6sK7sw9+1oVzqVRUKzaeLw1Ge7+krWt5391xk0/wW6gNeMyMJkAYg3Mw/AHYM/7x1WP5GSA6MtpyF5mH7BYaGX+AYKxkRdUg0dXqAFRwnYNQTWvALjD7R8RLu0bqYZGQOjFymaKzckfq9HSszedJbdqA0Euf2EVi2rWid01wGIwQf+VfEf/TvYQoi/kNL78qjU+YNXm69kxePQLTI1KkpJI68iO+uL3j6XL3wQEc35uP/ntiRXdDzImNs6W+ClfcTXn430h/MGkYmpAHmrIlhS4XQ0lX2scw8nppTM/RCWHP8ZwT0uNebvKUuAFe9x2NKNL/3g/cYyBJqf+hqnEMvnmBxk49f276SsD9zGmXqILLqVCy/f20n//2VM570FsDWRe1ZZNbCV4rnNVsVw4WAR++Yz9/uPEUmQiqW0U5n/MsvV40kCra5VbkG8oX67DxwAa947dhlHl4zr0TdhJv5RxpgSJQG1n4UZ8/XRx+Dp6/59cAGDo8VA7D6w2hfEJUqauQprEBo5D2/jCoi9ASAhvkE1j7o6jlOJvGh4mSMgxOLlhwKIRu78P3M/4u182/gyoGS+i8Kqz8GwQZUDj2KDfVxDnuslAtwcjfOls+hfZULKRH1bQTv+ixiw8ewBy/jKBvT9GE0dqDNINhWzrGDhlU7YN+3PQ1LrtheC/WphfrUcOsj9+Ptu5a3e5bWHISvv3w6q9OfiZP9Fl957gBJW2fRYfKhAbd3tXLfUm+xsL/+4FKCPqNsOtR4jefiGsH9a+azuj1EJhwRyNK+sFzhDxRscyvzfKE+r5++gVfsPtY7Sd2kW/RLBiHYRGjDExjz7yK1fXaCnz8GHWuJrLl/UmEFwZb5xYV7NC0m/OHfwNA6qxyM0pwTaRiT0t/nC1P34D8n8Mi/gbZVJemQF0vvIXT7I/lDMooM9WHoYkkq6JG+KQkvMaQg0NRJsHUh/llzMDTFzaHurd4G5GsgNG9dVYTTVDOvhfrUcEtjQWMdnXUGFz3kxvQbkj5dXL7mC0OKF49cGLfyVj58cuMiwgGDHx+8lrddRMKvPLiMJc0N5Lpki6xHa8iGmq0KQwBSCP7JPd18862TvHHGdV7P+rvY4HVrjTQwZy8ou47VgnzzKWmr/E52DvQlCrcpCqmCX0KaBB76Muz+71iHfoiECav/ACy9j8imn0YIgS5HuMcn/phYz8tw6EXQ0Zv9NC9BrLiP8IINCClzZg2SrQsoJbu+2Tq/JJ3Hc1/zXIyHvwSJEez+8ziOhekPIwIR4k//KSQL79sYg4a5GKt3EFy0GS0kaJVHj8wbuTy6llp/QJdul6ngMliP795/gbXrL4saTusnfou4NKpG/+rlqTk1Qy+ENcd/RkCPex3Lv3DPUv7jj3uKkjS/weDsDW93uc8fuMpDq7pSmXgydRj/t3cuBDy6dj73dnfwytHLvHa0l/7UkwgDWNTs54HhqKN9AAAgAElEQVS1Xaye05iqOJzLFrXwleJ5zVZeuGlIPn/XUh5cE+PlI5fYf/JuEjeeJKBTaQhH/+WWFZx3L0a4JW+bW5lrIFeojxyTgat4GGXTU4wW/BL1bQS3/wb+zZ8h+f5P4Mzb4IyA8MPcNQRW3Y0It6LtJJoyhXuEGwluegKx/iOo2AB19QFMI8RQUqJMP9q28vblW7yJxCt/B3i424zMwZi9uKwhEtIfwWxfjjT9yFT4k/+JPyT53jNw6DkmpADt2Ii5ZjsqGUPFhhESzOb5GLOXIAuM2WuoD+FWiF7FMyKN0x52Uoj75t2Guv9LOC/+1UQbpxFoofXj/wpf+3Ki1wemXedq5zM91Kfm+M8IiHGvegyf11DHv/rwcr7yk6Pkc+lXtAVZMrueswe9bYKKA4d6B1nT3sjEW+TsOnnlEb+fh9fMTz1ZKEWO8Nj+g8xrtiqG61EOIJhTH+ZTmxbzqU2CoV2fZmTv17hpzzw2FYLwtifyt7nlOeQq4GUaBhEDRjw+Ve+oT4f0lUPPsQW/pBkmeM8vI6zPIZwkqCTCsUBo9GjxL8pbOAiNEWokWF+HUAqZiBZ3rgNi3cPofd8r2nbG2ocxtEZVuCCSKX2Y63agurfhDPWirATS9OFr7ITQrNF2Ssib3EuhpSILeMnuu1HXDhVtHwA6b8M0/BW3UTl4qKMb53N/jnVmL/ax12G4FwwJ9e0Y3fcRnNNNsDGMToVFVYPO1cxrBbxqmBFY3FTPH31iHa+f6mXXoUtjHpNnZun55lsnSpJ/fTgB3rcTTClE4Sa3DK5F45wbiGI5ioaAybLWxlRl0/JgJtmqUki7sNlQd/cvYF89Sez8KwWlNGz/Hfzt68qsXXUhcz5FkzbH+4aIJhxCPsnStga2r5zNjw54W5G9Z1VHeZUchRib+UeaaMdAGEFQKadJOaAtQILOXlSrZI5w//ZwbnjNDkb6TsP5dwsPb9n9BJdsKa/O+TgghMTXtgjMAFK52Ws8FTzLydM3k/nbBxasJ7Y7zJhQqgLwr7p/6mxUBi6kSXDBBliwASXNm3aWJiI9b4uwVY1z004z9EJYc/xnBPS41+w87Dd5oLuDB7o7SNoKSylCpjGmWFbJ81xn02G8azSdfGaErxy60s9z+85z7PrYndc+YPuKFh5Y2UUkkCvL0gfLVtPJhWHQ+In/G7nrawwf/g4khya0M5rnUb/tiwSXbq4avSvFNXC1b5hv7j7Km2cnZqFZOyc04Vg+mMCm+a0V1RnEaAgQvqD7igYVwUkMAwqtLUhEAYWSxqgDMblwAz/SSXrKvCI1hO77VWJv/wB6ns5tuHVPELj9I2inuFCaYrgT7yNx+DXo2XUztWSkHZbfj3/VPUjpQ5FASgPKHZJRZKiP9vkxtv8Kzot/kds2mVh4N+acVXkz6txq3Euxsw86r4X61FDV0FqjNSilUCp9wdKMubPPwk0JpnQntVJq9Hhzvde82C7a6gOjOjgq3c9NucXoVEmulEagPdmo2vgz+8/y48PXyQYLePbIdV47ep3ffGwlraFgyX3NBFtNBVfKvUHK/P6MaSMkkXt+Af+Wn8Y6/BrOhX1oO+bGdHffi3/+be6mzVznzyB+4Ewvf/Rk7lCL/Ze97Yb+pw8swRBM0RyVuHk0U06AlBDwg3IQTgxtAtoGx0o9EdBgK3BSTwK0A3bSdSKK4VYU7Vju3x7OFcIgeMdHYfV24ifehItHwEmAPwSd6wgt2oDwBVB2zJs+eXj0wPPwfpaqwSNX4N1vkHz3G4it/wRf60JIpuLPy9Q3dhKdFGDaYEULtg+0LSK+7ZdRr/1N/sm1eBuBOz9TlMxbiXuxVVVwx3Y56ubmbHtquNYOKKsW6lND9UFrN85YKY3WIuMiWDo2LZzNk/u8xfhHJCxtnoVS4DgaLSSOk+nMTD8c5WpTanKH6cauU5dzOv2ZGFHw508f5nceW0vIV9pqxa1uq6mCo0g5/nnaOBrhqyew9iGM9Q+T6VRqBEUmz7qlcWF4hD955mhZZPmAX31wCUsaG8rye1caBGC64UDKQcmI6/irKAgbtONmAVQWQjuoZByReiKqhCjI7RgYSqOT8aLaj+cGgtCSO3GW3oWROu4IAVqhk7GSZGbj8SM74dBTBa2ld/83krf/DP55a92bpTL0neaOJVFWHG0li2ofaO8m+ci/wzn5OhzdDXrkpqKd6zG778bXtAAnEUOXUc9q4F5tNb1cIgwTLUTqCYWbX0spMSXcET4UgvSz75mGmuN/C0MIgUAhpUAI7e5NmyRmBX1s6Aqz90LxsZD3r56NaaTDegSOUhiGKIs+5YIhU2t3VaRTsbAcxff2XCq6/ZANu09eZsfKrpL6u5VtNZUwpDvj89upOr8PU4kf7Tnjqf2CWSZblrXxxtGrXB920Bo6Gn1sXTmHDZ3NGFVjSAH+IJh+QIMKoxNRlwsNVgyUu5lSOEnQCilNlyOQhi8rN0P1CMdGJHTONtPN7RvXinL6R/HutxCLNiCCkUn1jZ2kdyDOoUvDxBJJ6kJ+Otvq6ZoVIGgWJ8cXqiMw6yPo9T+FQiGScYQ/hDbdEKupt6kfoRUIjcREaKci3AiEkYaBSMiK9zVpLgLujbX0gy+E1qnvvCHRjqo8Dzcggg2IeLX81pQXNcf/FocAhLZdpyJdbELKSfFP37GA01f2MZAUiNQdryY7X9wS5MHudiROhhyNRCClWTadJsuldh8bSqpDHy/83dNXMJSV8zPIxl85dJEdq7pSKU5h7NOX/Nzd8yEy9n4Uf+4HiUspUo6/zNNepdoWajczeX8sweFebwn3zwza/OycRu5ZWmjzbjWM0UhtBAbwgenuVRBSQDLmhizoJDoRRWCDVmBbgOPGu6firTH9N7kvjJBJMJ2xx6uIJ4+/ilckzh0guHoHGKkwRMMEp3h+/lqUZ96/xJUbcWwl8UmFJoF9bISIT7NpYQvbbpuHv0j5Ao0wTGTAvWZpj/qUhQvAX4cWvtR0MtHpELFy88aIu1HdGqyM/LLy1HdDGEh/KBVqB0LK0UesleSyvgERCEIiR3rUWxzT6vh3d3c/CPwOsA7wA3uA/9DT0/MTDzKWA78P3A20AMeBvwa+2tPTM7MfpGsNdhzhJCARRzius6gFpFNjl8IjwG/fv4C/3nWUy8PJ0a7SPmSar+6s41NbujCtoZtyHAdhJRE+P9jGpPQoK487gEAkhqtDHw/86MkzhJ2RrJ9BLm4rydX+PuY0t+JeXTQ3HZZC3Gv73Fxr1/EVonwyq4WPT+eZv32x7WYWf/+Cx+JNKbx/9joPrZo37fp747iVgNPH/XWpDcEOiAA4SaRKINSIe4aTPaWgdBJuOk+tpj2tYTYulA1Hd43/yArj+G7k6ocQZhgESCERQhXFD17q5xtvXEIIifCFUFqSFKm9SFoyLDQ7z45wOn6On71zKaYhPMmfNi4NhK8ObYRTK9wCnQ5hKzNXkTqEkOiQr2D784PDvH7kMleGEgihaasLcmf3HOY1RCqiW3bOzcepo4tQOuNGu4Lc8N+8qM5ATJvj393d/fPA3wIJ4EXcnVPbgWe6u7t/taen56+LkHEb8DLQALwGvJ2S8Z+BO4HPV0T5aoF28xgLK4a0U7F7wo2xF0pPiteZmi8+sJCT1wZ5+/g1TlwdxnY0QZ+gu6ORu5a1MqchjNYxRPLmudp2810L7UOY/knrUS4uEwqERCaHJy3THrlOvOdVGLkKwoD6DkLLtyLDTRXR30kMEVQxlBYIQepil59rIYnF3Tzg6QsKGT+q+bh2LISQkEoH5+VcgMFEkt09l3j1WC9DlvsEojEA27rnsG1JO5GAz7PMquSOhfuXytPeSf0t3M+hojpJJt5oTC8SCbuk80biVpk1mQ6kPnMy0oJaIE0Fyg3zAIn7VMgAkeIiVVNAyrHHq4RrO4ryWCtYA8n+y7xz5BpGXR2rOluYFQkWtfDROxTl796+hpahggsfh3vhhwev8/jGRVWzcJOfp+aG6Yf0gssYJ7d8XBg+BBqkmbPN5ZEYX9vZw/mh1BPnNHqH2XXqOF31Bj9373I660IV0zMrr6GsmBbHv7u7uwP4r8AgcHdPT8+B1PFNwPPAV7q7u5/q6em5kEeGAL6O6/R/oaen5+9Tx9tSMj7X3d39vZ6eniwpB2YKXEcblUDZUbATQCq9m2NPmmvDx5JZJks2tqOMuRPaaGtkwrnYcVRiBBmsQwtVFj3Kwi2NEibaHilZjh64TGL3/0Rd2Tfhkxh552vIuXcR3PpZZDBSVv0jOo5pxbCVxJTuBbcgF4KIM4JI9OFeXIybT4QKcBmNoTERscGi2mfy109c4Qd7L2KkrnARLTCExrbgpXf6eXnPIT69ZT5rFrYXLbNq+ciI+yQjHs/dxkrefALm81dQJ4EORECmHIjRFaw09LRwwyjtRiRgGhXXbeq4yEgLGkAlgwjtoLWNHv2tvRnmoOobEdrGSYbHHK8aLvyMTSacHzr1bwDJ948nseUwen+UpW0hPrpxHgsa60HK1NMRJvBn91whatRTbKjjC2dtdmwMEQoGcsqsLp6x2jzmtdw8f5rmC0Mj/IenjuR1sy8MOfw/PzrMlx/pZt6sSIX0rAY+s282pmvF/9eBAPBHaacfoKen5+3u7u4/Bv4Q+BXgd/PI2IEbIrQz7fSnZPR2d3f/M+BV4F8CM9fx1w5COwjHQtpJcOKAAGXffDQ7xdwNPbIQVhy0mjY9xnNhSaQ0kCXayO4/T/KHfwTEyLnd5/zrxJ/sIfTYl5GhprLpv6AxyJnLA0gkZmqlrRAPSIMmXxIjdg1w1+rSehficngYjcCIRT2du/fUdXbuv0iDcjeau1lrJvIf7R4gmJjHiq7GnDK1UtiJYYRtuxsCfYGi9Z8yboRBaORwLGcbnCQymUT4A2D4KqeTMHDsGJhhd2XZF0zFm6dWEacpDGbJnFnAFbxiYXvDtOlcGZ5ewdc3Q4Ak2cMcwg1ordDR0NjjVcIxZ2H5G9HJG0WN3c2OIrhkdpEwI2jhzt6e6zZ/8uwpfvmeRazvbCJb6EUsafH6uRg3Q6iKwyun+nhoVVdWmVXJKzDn4paiL5EArWkMBGjK81tgOw7/6Zn8Tn8mvvJ0D3/007fjM2TF9J9eXtqCxa2C6XL8H069fj/Le9/DdfwfIb/jn1NGT0/Pa93d3VeBu7u7u+t7enomVoyZcchYLZjOqnejOjC9ekzgmV9mb+dqxyb59J8CReQaj/cRe/GvCD32O2X7PG5b0MRrR6/gJh5wf7zdkeTm6+Y3ETAlqFTIhDQzQncKcMefCksp/txo3OGFfWcxtMZREokGcvMn95xlfmuIsE+OkePEh4ke3glHdoLKSLXXtgLfqgfwd612N34VO5ZKcp0O49H526skbrL3SurkIKSJsAFpoKRAY4LpzWEqNxY11tMellyJFh8aMssHK2fPqqBW0w2Rcvp01tAGkQ7xkWbONtPJhWkQ6P4I0f3fpFjHXyHZE9k86vRn4m9ePsWXH/Uzr6FuwnvnboxMOFYMTly5AatKy2p2q+Nk/xAvHbjAuxfG2m7D/AYe2TCfjmBgwjl7zl9nxJlwOCfiwNvnrrF14exJalvDdGDKHf9UiM4q3MWqw1maHE29t7q7u1v09PTkugldnXo9kOP9HmB2qq83S9e4iiEMNBItpBtmkxlyk3Icp5w7dsWqNE6GT6ZqYezUmzcrUhaD/tMkrp4gOHtpWfQPB2H13BbeODmIHweBIKGMnLzO0Ny5pG1KbfX26X6SjiioW5pHbcHB84NsXNw6Kidxfj/Ozv+U3aa9R7B2HcFqW03gw/8SqqD6pBZu3tOq+T6gkXYUBG4MMaCV6RZwGt0ol171S6Py/MPru/j67nMUiw/f1pmxGXzq9KwW7rrNOuO96tEtzcN3fITo/m8V3X5YRDga2kgu/OS9c/zyPSsmnJ+0S8vPkbDSXmx12GsquNaap/ad4Zkc9V72nr3B3rMHuGtBA5/ZsiSVtc09d9eh4tNFp7HzwAW2Lmwrm/7VxTP/nnmYjiSlTbhhPtd7enomhAr29PTYwDUgDNTnkZPO9ZZrxqaPt5eoZ/VDOwgUQrux9FLbSK2QdnL6uLIwAOk406vHOC6UjXQSJZ2re17y/NGoA8+UrLMeuobdewz7ylH04GWkttmxbg5L5wQxpcKQCr90svKgqfjEprnUhX1Taqv3T18tqNt4vv/s9VE59uXDuZ3+TPQeJPHMnyFUctrnlVDu5vrq+T7EkU4caSeQyRvI2HVkvB+i/W7mLztJ2q28uSJbeb55/mwe31AoNaeLe5Y08qElc6ZMt2rlugp0yMfNxkXUb/2Noto7GHy38TOQTlmZBe9djHIjYU04PxIobW2yPp25pkrsNRX8JwfP53T6M/H6mRv8455TY849O+h9E/7FYVWxsVQPn5mYjlCf9I6QfBWi0jEVdcCNEuVkyig7/H6TtrZ89yWVh7Lc/NjNzXUkowptGaA1WhoIraaFa8tAJQTSH0T4Q9OmRzYOgsbGOk/n4lgMD5zx/uFcOeGpr+TwNaJH32T4+Dtw7cQYUYn2bprWP8SvPXQHz+49zdvHr+Ek0w7czfCejuYwT2xeyNzZDVNqK9txGI7bGHKsPoX4UMyicVYYJSQXv/f14m17rQff1fepW373tM4rtAINTY2RKvw+mKOVL/GbaBO04QN/CGH4M1aVp+b/n2lbTktjmG/uOkE0S0hBQMAT2xbw6PqFIMSU6lad/0NTW31VaJLr/+aP/AL9DSEuPfsnN0PPxiEugnyj8We5GFyd9f1MXE5YLJjbMqaPxpYIkReOMeLRL922di7NbfXTbqNK/3+lb5gDl/rpH4rzowO9RdvnlRMDPLZRM7+9ITXbSkNja10qCqwarFG+/9Noa6sfV39lZmA6HP/0s7t8802Mey1FTjEybm2kQn2QAiV9ICwQoIxUBcJp4CjbLbct/ehp0ENrhZImhrLLM55kiQU8nOGMUJDs8h3DR6znFQb3/AR6j+aWdaWH/p/0wKItPPrwP+WB9Qt5/0w/F64OkHQ0dZEQGxY20tlShzYD6Km2uaCo8J7xPKQlWhqMXDgEI9c8mXfg3eeJrLh3Wud6dX8ffEjHAilRVtTd1C5ibsrdVP5DLc2bKRFJX/Qqxx9cPZftKzt559RV9p28xkjcIhwwWbOwlc1L2jEMiXtbqKdEnxqfPG+659PUrbmHwTd/SP/BJ7EGLqEQXJGz2RPewnuhLSDDFINo3IJxn72Qgg/f3sV3386Z4G8CAgI2L55dNTaqBD907hrf3n2cnqulF5h65v0z/MpDawEICohrb+cbMLodrRpsUl7u0Ri3GKbD8R9OvYbytAmmXvPt7CkkpxgZJSOZtBkcLGKzZwXR0uhHoOi7NoQVGwY7BgiUkKNZYaaaY8fRyQTCr8GMT0nfKjFC9OhuOLoT4n2ucWQEurcS6L4fX6SRxllhJJrB/iFP8oXysOMpE2YDg/03csrXymHklf8B5/cWL/PUm1z6sabu7l+ke3aYlW2BMTJvDAyjRHTS9izFVm0RyUjC3Qzsh9H0ovl4c8RksH+I4T3Pebfv9ZNcP3sS2dA+bXO9ucGtwjk4MFJV34fsXIPhwzGHpyXzT1NbHRrB4PUoyxoiLFtfN6bNjb5oxXW4VXjaVv29Q1WhT2HegLzjs7Tc8Tm01vzj2yd4+aSHPVEpWEmb/t7hCX1smdfCT/ZeKHrz6cc3z53R82nnsYt8e4/3mPzx2NnTxxO3u27UhgUN7D6dK7giOzYtaMj6ec0E3tLuJhfo7R2a9hX/WbNC+P3lddWnY0Q3cJ321u7u7gmjSR1rBeI9PT0DeeRcTL3OyfF+oT0AMwz65uuY7DqFuVau86yseMZ9rnc5WbP6jHtPOzax47sZfv1/MvzyXxN943+RuHjIrfBaQn/xM+8S/fa/hn3fven0g5sR5vBzJL7/24zsf86NXsG7fCGAOWvwjIUb8sofeet/eXP60zjzFsnek5P8bApxgVdb3T6/afScYv+/bV6zK2ektOquKjZQYTsU4CJlp0l8H6aOK3Asd2+EHUXaI6m4f3eVtdLInFE15MetbCshBN1zm0o6t7utIevxiN/Hlx5dRagIo/zU2tncvWjmbut7/1JfWZz+NJK2ezd13+pOz+feW8I5NVQHpnzFv6enR3d3dx8CNgPLgUPjmnTj3pDsLyDqAPAobtaenWMEuJmDVgBOFvkzB5PI6uNIH8nLPdiHnocL72XIrIM1Owiu2AaR1oJyis1i4kiT6N4fwsEfcTNKy4V1fBeWbxbc+TnCC24vur/ouf3wasECz+h936U/6GfWXZ8sKauPsfJBnMu5kkdlh7lmR86+rL6zcOJ1T/IykTzyIrJzlffPpkheSlaf25d18MqxXpTSRYX6CMNH9/wWd1W8xPUHJX21rD4F+OBQjJPXh0jYSSI+kyVdBmG/GwY3lZl/3HU0nfFeeeXPJH6r22ptRxMhcZqYh/vJdR1hGgI+co15TiTIv3t8Lc8dPM/OY/0T6gYva/GzY91cVrU3ln081cS//9Ypyon0vqzO+jB3LWzg9SJX/TfNq2NeQ5hcn9etzz1M3lsQ05XH/xlcx/9jTHTMP5Z6/XERMn4r1f6r497bCrQBu2Z0Dn/tuJuq7CTaGiF55j2cG72gBbKugUDXWreNnQRhjHKtILH7v8KFLCvOehj2f4/4/u8h7vt1Qu1Lx5xbkCdH3L/TsfF2Eo0g8VKO/tKwBuGVrxK98QThlfcV7E87NhSTBSaF6FvfJLT0NnDC3sZjJ/HPXkysaSn0Hy+uswWbMCPNYEWzyrQOPV+03llx9h10MgqO5XksxXCdFGDaOfXPxoOmwWNrWnlm3wXAxIdb6TMbF0Lz8TvmEdIJsA1o6oDrPZ7NYIYbwIqVdexeuLZsd1iW5en7MBX6nbrUz+vHLnHpehRLmfik+xk4e86yqivCfctnU9dsQ3wYjAAqGUIYQbQwwB8EJVI3ATDm6U+JXCuFRqDSdQ+q5JF+NfKZYKuPbOzkH99JP5QvjIfWdabGO1aW0orj14e4OhRHaM3q+c08tnYeR68PMhS3aWmOsKS5Hmm5twJKZWaaqQ5blIuf6B+mt4zRxXWGKz09zz51x2LiyaO8ezFf3hVYNyfEZzYvzvp5zRSulHb3Qs1QTJfj/7e4TvuXu7u7/3/23js+juu89/6eKdvQO8EGgg0gWEWKoiixqlDFKnGTnThOnOJESd7PdfqNc+M4yc1NfPPGcXJv4jdx4sR2Eid2XKRIsToliiIlShSLWMEONoAgetkyuzPn/WN3wQWwbbZgFyB+nw+J3+6eOec5z8zsPjPzO8/zUnt7+/sALS0td0be9xETzLe0tCwBdKCzvb09Kh7cA5wAHmxpaflse3v730fa1sVs++WpmEyhIC0Ly/AyfPCHmIefIzbBkQX4UGHxFpwrH0DRXVhCgGXi3/ct6D6Ruv83/i+Bbb+Io3YplhCIyImQjBP0Q8CHhQTLREiJ79SryYP+WBz9HoGyWtSGpUnH852zf8e8770f4Vr9RMo5xOPalp8m9ObXYDBFPvK6Vlx3fATLP5rYRxfftW37RJgj/SiaI6O5pOJmUAnLvoKGrW1XNXpQQ7W8cqILIU0kCqoVRBPWGHfpgodWz6OpSh3rX1m0EevcG/Yc0LASIXTMJH7ON7c0J1IByx+wdT7k2773LvTw1qluNNXCMWEfmGaQS9dG+VZXH09uaGJ+fUX4oiHoBsUJqgPLcCF0T/giQHNw60cxc5iRW7RWZmnZbyvMBF/du6iBwWE/L7f3pWz701sWMr+sdNx8Q9LijTOd7D7ejXdC/FWmwc62erYvm0tNfTkCGdGaz2ycupzal3awY0XdOJ8LBD+1eTlt13vYe6KTy0PjF1XMLxVsWz2PjXNrEWJ6H5+pYEmBJZmxwX9BAv/29vZLLS0tvwH8DfB2S0vLa4R/Xe6L2PRT7e3t3TGbvAY0AT8DfCPSh9XS0vKzkc++1tLS8nOEdf87CNcK+Pv29vbnpmZGhYEMGVz519/BvPh+ghYmXNhD4MZpXLt+FcVdiXHxYFpB/1gPb38HPvKHKJoDYRqAQFH1hByFcCEhVwnorvCdzlOv2ZqXefoN9Ka1ycfrOGirTwB5di+s/xhCJJ9DPK65S9Ef/FV87Xvg+G6QE35o3DXQugv38rvDxYcS9CMUjbACLTuoJeXh1I1p2m+Hq7obRdMRPtP2tm1L5rCoaQ4fXOzh+JU++n0SVZiUl+isXlhH2/xyPHpYmhbd1tGwGH/NMug9m/b8tVUPIBzOnM/dDldcDpASxaWlfT7k26YzHTfZc7afoOJGF+G7/EFFm8wtyXfev8HT2z2UloWPSQUDaYVQVAEmSNWJRI2585851EgXMzAzXs4xU3z12Nom6ird/Nf71xiIk46zuULlibuaWVJVBjHSCn/Q5G9eO0nHUPzvyeEQ/OcH3Zy40s/vPXUXmqZMe1+lA69hP9d+Mty7tCGO3wSbFtSyaUEdPV4/3cM+EIK6Ehd1JeFkEmHMzIA4CkVIFEGkkODMQ6Hu+NPe3v7VlpaWy4Tv8G8FAsBbwP9qb29PK1Jsb29/t6WlZRPwR8BOYBVwFvg88A95MbyIcPXffhcjYdAfg9Eb+Hf/Lc4f+yJW+5v2BgkOYHRfRG9aR7RaKpojMZeAKcNBv+7Gf/E9wOYXVs9pTP8wirsq8XiD6ecsjoWUIHRX8jkk4EJoeNY+hrn+w5jXjmMO9yJVHa28Br22Gak5EdHFkgn6EZG79FnBUYUZGEUoGsJVBprT9lySceFwI1UHaEZG/bhcDu5e4ebuFfOxNAdKxCexfOK2zu2fJfDDP5p8QRUPK38MR+OqnM03Uy70SJEgQ0v7fMinTZbq4LUzAwRxECC8/kdAYh5S2XNhkA+tLw/3ExAN2FwAACAASURBVAohhERaARQZRJoBpK4S/rEXoEQ5MX/T40IJjxmuFmpv29uNzyRfbW5u4K6FtZztG+Zi1xAhU+J2aqyeX01DmXvSNlJKvr73TMKgPxbn+4P8zYvH+LUn7oj4amrnNtXcncPMLk/vaKbMFftbNHnc+lI39aWJkiYWh0/yxRVFzNigHwoY+AO0t7c/DzyfRrtFST47CXwsh2ZNCwRvnGX4UErX3cJgB4FTe2zdVY3CPPcmznltY+kBCRkJOVYwHCaYJggDefOC7fEAzK6zOBetTzyeokxcI5wWFCSKtJLOISU3g+gNS6Fh2a20iVJipdkPVYug/1JGfgHA6Cf0g98JX06V1EHLLjzL7kLRnNnNK8KFFYr4KZRVP7a4uwzlyd/Ft/v/g6HEciqx7uO4Vz04tbYl4MJSABmpnpve+ZBPm6529jDs86Mq6adUbb/ez32rGnBDuB9TgGXeSv85auUo/SfcqkZbPFre4uQzy1dCKCytqmB5TcWEi5nJ25zrG6a9J0C6ONgxxKXOQSo0teDzzDdfOq+SV9KQTqVCuQ4vHLlKx9xhtixvpMLlKOi8ipPfOhdnIgoa+M8ic3jf+3f7G53endlgo4OMVepAJudEOeHXZvpf4rGQISOmr/AY5mgfvrNvw0gXiSpFJoeO0J2p55BvvmB9doF/LEZvwqF/xXv8R7ge+jWUysYc2Dnhx3mK/KOWVOF+4guY3Wcw2vdA9yWwTPCUQ9NdeJZuQnF6wtr5Qu27WB59bed8yKNNF28MEw0WwyOn5kFTcr3Xx5K5zgl9muH0n4qBCAGKiqUIJBpoerIjMi5m9s9obnE7++qNE+kvCI7ixaMdfGLD4jxYU1xoq6ukQofBoL3tVMaLS4eCMDQQpGOghxdO9rB1SSUf29AcyfAzi9sBs4H/NIVxbp/9jZLcSU0OJfN0nnpZRiNKd8VYOsmgMUpwz9ehK1WG1+TQ1zyMFBoy1RzyyL0XD4ZrDuQaRj/+576E/tQfozvKsrIzk3SeueJSc6DVt6DVL58kEyIUxCrgviv2dJ7DIUHIEtipnuzAJBCyEvePRAl5QZBV+k8JTOcUlVPJZ7avkh8rR6/br7d54Gw/n9hQDHPLLxcCPrR+Pt8+cJV0sbBcnbRIdyL2nh+gd/gUT+9cgRJ7syIHNk9fHvt65mE28J+msAJTmMWgqj6pnCGZtMHRtAaj/WXbQ7rmLEORIUzvIMH//BMw7VUVjIfKtTvwWYWTiPivfABv/V3W80gML8F3v4Pz3s9kZWdBpD7TkBeb1KdElWnJeyZyp0qSOdyqNgwSEQogFQdWyB/O/GNFM/9EVwlKxj0tirl/PZPkK/nlM9VXUcRvN5Yd0iaMJH3ONH5PcwM9gz5ePt1LKqye4+FYV/LUnFGc7Pbz2unrPLhifsHnWBx84jE7s3AbrIWfmRAOT2YbzltrexPnsu1kWrlXr1sCZYmKKyfA0m0ougsJ+Hb/dU6C/vItn8ZREbUj3TnkjkvLJLT3G1nPIyUuHcAKjGZpc+wX39T7atpwEfGTjfMhnzYtqisN2yMljYGr7Bx+kZ8Y/jo/N/rXfHz4n7lreD+lZlQOFP5fUQSNNSXpjWVZCMtAmD4UK4AIjoYzClmhsCSLxJHbzP8pzR1uV1/duttsD7ebr55Y18RP3L2AigR5IuaUKHx2WxPnu9ML+qN45diNmNz8s5jJmL3jP03hWLQR/5Fn7W1UsQht1UOErh21tY1auyhjqY8iQb3zE5iv/1WaA2o41zwSrnDbeQoG03+smRAbPkXZuocLJl+xVB3jwgGQNquvuOvAZz97kbfjCJ6WbdNS6jOdeLFJfRbOr6Pm0Gm29f+A+fSPOybKGWYOR9joP8L7/lXs92wjIFVWzinF43SkOZYGoRAoFlbIhyIC4SJvsgQh9EjxLw/j7yfJsf9nrnwlt3xm+0okbbeoQuPSoL31WysbYyvI5tLW4uX3LKpjc1Mtp7oHOdc5iD9o4nGotC2oZnF1GUev9+G1mfzCa8Gxrj7Wzq2e0rkUJ499PfMwG/hPU3g2ftJ24C9WPoCrtpmRlR+CE/+VxhY6ru0/bysLTjxpg3vuCkbv/lnkO/+YYjwN58O/he6uwgoZmCft5f8fB3cNLN2Cq2UrmrOk4PIVK4OCY5kE/QB4B7LKXFRoX00XXmxSH2Wkh4/1fxcHiYuVC2Ajx9G9Qd4se5Bty2rtHyuxmX8UBcv0IhQHUnGGE205Ik8eIiFsFDNTvpIPPlN9FUXidttXz+PSWx3YwcMbmgo0n8JyIQRtDZW0NVRNanO9z/5aCYDOfm8k8C+OORaOTzxmZxZmA/9pCsf81Xhat+A9/VZ6G3jqcC1aD0DJ2scY1Vxw9PuJ25fU4bj/c2hlNYAk46w+kc/cyzZjVtYTOP4SXD08YTABy3fgXPkQuqfiVl83zqc3twlw/8RXUSO2WIoWyQAUezLbmE+uuD9acHoKoKhZ2lxgX00XHierjznSi/f063D5MHhHQHdA3SK0tvvR57Tm1aeBfd9MGvRH96wE1tHOggUbqalwZTGuGSnfKRBRpbUMhjNyKWrMwt+Z/jOaW9zOvlo/t4Zn9Q4G0sxc01imsm5RHQM9mQW6MxWWKTPazjRncDneWYxhNvCfxlj4mf/D2a98EvPGmeQN1XKcD/060uHBCgVBSFx3PA7L7sHfvg/OvQW+4XCavqom1NX345jbitRc4fbYkB0kkTaotUvw7PglgsFRzK5zSMMPngpcdc0IzYmlOcaPZ9qUxkRgAmKCTKXg8pUpXE6jVM7FEuqs1CfPPFbqI00T37v/Buf2jN8ZQR9cP0ro+lFCFUtwPfjLKK6ynNtkenvSznoVDSxrrr2FtX57DmyISIAIIIMjiFAAUJDOElCcoKhIYObKV3LLZ7avRNJ2qgKfe7iNLz13klSJoD0K/N5H7iRcaCl7+7xGiB6fH0tClcsRyW+fXZ+F4iUenUxQ4o5uVzxzKQyPfT3zMBv4T2Oo7lIW/cLfcumHX8Y8nkC603QX7g0fQ3WXTy4w5fRQumYX1tqHx96/VZCKtAtS2ZU26HoJzvmrJow3WW6AqwL89guWqIoySaZSaPkKNfNg8LLtuYAL8Nto78Y9fyVihkp9pGViXDqENdgJ0kKUVONeuA7F4S6Y1EeEAvj2fg2uHEq+awbP4//Rl/B86PMolObUJm/7PhvHSNSeS1gD19EqGnLjl5AAayBcF1iomKEAuCrDVaClDF8ojQV+MU+UZvkEDrer1AegrsTF7z25iu8cuMDxBFlp1sxx84nNS6ksd2dt38WBEV4/fp1DV8dnymuu0tmxci53zKtGEamyVhUX37Cwlu+934ldbFhYF2HFM5fC8InH7MzCbOA/zaE63ZRv+RkCax8ncG4f5lA3SIniqca1eCO4y1GsECCZEvkDUU7SdqGBGwQuH0UGRkHV0OuXoM1bFbO9gKYN0P6KPYc0rkEIFeTEOceezFPkixjuXL6VwAWbOv8l94YX+B5/Jv1tVu1CzECpj7RCjBx5Hk7uJvZCSALeA9+EJffiuuOjKE731NkWee0/uz910B+F9ybe9/4Nz9ZfyK1NQ/YLHwGYw91oFfW58wuAFQRMhBa89dTODN6SoM0iKWZ2yJEeqtxOnt6xgn6fn3cv3KRn2A8S6ircbFpcT4VLJ+qpbPz10omrPHesO+5nF/uDXHyrgwP1N/js9hXo6vRJgljm1Fk/r5RD19JP+71+XillTp3Zc3TmYzbwn84QKhIlfCfNWYpz2TZAji98NNVSiBRZTII9Fwke+C70nxs3leBJCCplcMcTuFdsQ6o6jtbtGDYDf7XtgYLKVyz/KP4LB2GkB5BQWoej5W602mVQvhCG0r/rr694AKVmIYHudug+lXqDuhU4NzyBZVpZzaXYpD4y5Mf3wl/CYJI1H+f34b/4Ac4nfxe1pGbKpD4SifzghdT7JhaX3iN090+iaZ7c2WRlps01LSsrWVhSLk2U4EhY7uTTEZoKRsB28a/bjUvgdpX6TORVbicPrZxPfIR9JGPa27HptfbrCYP+WJzs9vP1vaf5xe2tOZMVTQV/8s6FfHDtJOnkSFKBJzYsjNm+8PYXlse+nnmYDfynM6QZXrgaMiDkD6fWE2r4/ZBRGG6Mhl8bkbuyMZ8FOo5gJitgZQ3D+/+Kb+ASzk2fQnOVYSzdCuf2puePulYctc3IoG+SfdIQoIXy5iMr6MN/6Bm49M4ks4z3/wVj0Sb0u3+c4MtfIVpyJinWPYVeWocVMnBv/wV87/0bXHo3cfumu3Df9anwwsos55JvX9nlvr3/mDzoj8IaJvDil3E/9oUpsU0GQwR6LoEvdfAwEcbJN9FWbMudTZ6q1IPGgeouC2+fL3+ZofATuGELqagwamIZboTqiqT/dBMOOyAa8t7uXFoWEhHJqV54e7LlliUxreh7Vk7HsCyJiIxhZ1tv0OSHh7tIF8e7fBzr6mdVQ2VO7c8nr3I6+Y1HlvMXL5wh2VppB/CrDy+j2uWcMcdcLo4rKWdu8D8b+E9jSMtCmkGkFUQGvBAMB5SWEIjIQTvVnKAfAj4sJFjm2GfGUDdWulVrz+8j4KzE3boD1+rH8I8MQtcHybepaMK5+Sch6McKTbbPDCpYQT8yDz6yvAMYr/01GAOJ7bt0gOD1dtj+NLz/XRhJ8qOz7hO4F9+JNHxYQqBKiXv9xzFaHsQ8tw+6ToHPC54SmLMcdckWHCVVYBpY/mDW88qnr+xyc7ATrtuoO+HrwXfubRyL78y7bZbmxOi+kr5tsejryKl/tQV3EDr7uj0b9ErUktqx4yyf/jJHQVE08BkQdIcX/aqRQGMs/ecsAKKJVTJ8iFN0MM3w+o5wxpjc7mfTilxO2PTV/vM3bI/15slO2uoqbW9XSDSWlPAHT67i7fM32NN+k+GYKwA3cP+qOjYvmUOJrs6Y4y0XsKTAkszY4H828J/GEIqCUHWEoiOcbhDhq1VF1cMVNQvAUcBCorhKQHeNfWad2WNvcidfgJUPoKgqzvuexjj3NvL06zA0oaCXqwZa78O5YhvCN4Lv3NswchOwoKQOfekmhNODqrtRNB3hM3M6ZyFUAq98JXnQH4UxAO9/D9ejnyfUe5HQ6Tdh4BqYofBC5sWbwgG/0xN3LN09H2fVh4GPYKk6StS3Od6X+fJVJtx/+IC94wbgwn6Utu35t9Opg8hQ96tqCIcjZzY56hcRqmiCwY70bVj1MIrTmV8fRbjqLkGYJooS1v8rGEgJQoSQVjCcUWwWAESl5Mr0kZSngMC0LFRV5HxOajixlu1+3ztvv0bK6ZsBQpaFQ1NTNy4ilDk1drXN56ltrfQOernWO4Rb1ajxOCYsWp5FFIqQKIKItGvmYTbwn84QKigaaA4wI7pZZPh1RJ895VwCpgTdBbobhIZljMDlJDKVuLDwX/0A95LNCM2Be/k2WL4VY+gGVv91kCaiogFH5QJkcBj/3n+Fq5NlNsFjzxBsXEvpQz+P6q4AzcjpnI3rp2DoWvrTGrlO8OYFnPNWoi1Yd2stRsy6DFnI/Sc0hMMdzsSSY19lxK+lsbZhIgYvh2UlwpNzeywzhP/cATj5Cp1e+3cNx1DaAFpu7dN2/DyhZ7+Q3vjVy3CueTBye3kK9qXuQSgGqKHw+6EQAgNhelEUBRkKRqr+Rn9oY39wby8uFCUSzIqisCd7Hr7TryigjIvQsx8j6qPw3/S3HfJlFuh6QyYuRzRsKhb/psdVVaG+uhQ9bo7/4rGzGLiiiBkb9MNs4D+9IU0EFkJaKGYIcpx+UYYCmL0dWP6R8GLbijkorjLb6TyD3Rcym96NdpTFm8aN4ShvQCmrBcKpQOVoP/4X/gz8vYk76jxK9zf+G9Uf/QKKqzG3Pjr+su15mSdfRZm7ouCpMRPxYkrnSdBOKtMYeIfCT51yaI/ReZrQS1+BlBnGU8OxdGPO/esoa0B9+PMEXvorkPHTIAJQvwLPjqfBNKdsXypmAGFZjKsSHBLAECIUSFH193bjcDun87THM/ORrpHWUquJ0GOK0hWHT/Pvq9uTTzxmZxZmA/8ZA3nrbxpp98zRPrztb8Lg9fBdv7IqnIvvRq1fijSCeM/sRZ58FYz+sRFCAPPWoa3ahau2OX7/YzYw9pk0kgQhyWD4ks5BAr7X/zp50B/jn77v/xH6fb+Ks7ElLR+lxbtP2J/XjRPZj5tXHvvFV2B7nG7w2a/KGeg4grZkE0LTcmJPsPcyoZe+ZNuOuKhbgVbeEKkonVt/6TULUT/+p3jPHYD2V2E0ZuHxnNWobffjamxBEFmTM1X7EhH5fpjwvmVxq+pvCGlZkbSfty9mdsiRe2Tir4U1HgY67f0uOYAS5/QOmWaPrVnAbOA/vRGTzjOaShNSpJv0DePf9w3onLBYtgsCZ/eAqw4sE4wEhbOuHSF07Qgj6z6BZ9X9aaXzlM7SzObn8CRNNejvbIcBe4srg7v/Eh79Amr9koQ+ssMzRd5SKOYifWYxpfOct3JyNdx0cPBfGD34ffRHfzPrfS0tSWDP1+zbEBcqjnt+Ir++c6i41uxCaduJNA1MLFRFRwgFS3MgQ0HklO9LB4ppxDnuo1V/g0gZIlJ3m1t3J6O4fXj4MknGfFY8tmXPc71fM0vnuXXFHD7otPckemdrDco0SueZK1+lw42QyY1RH4GQSYlTY06Je1qlPo3nq5mM2cB/OsOm1Mca6cH//J8kr4brT3PR05HvEHC6cS67J6XUx1m3mEzu+WtzV46XBkyYjzz+Uga9QvDAv6M/8lu5kTNM+iFLB2rSeRWaF5PUx9WyHX8mgT8APoI/+mN47AtoFZlLvEJdp8DXk6ENsfDgfPTXx9K0TokfFRUh9DAv4DEXV+oT28aS4WJ+whlZX+IAple11NxxmJX6pMsz81FrXSV1bsFNG1r/LS2NebB/Knnuj6ebo35eP3mNN8+PT25RocOOtga2Lm3Epau2+iwOPvGYnVmYMXkDZiFv/R0nubnF/bv/NnnQbxPmgW8jrWh5kInjMvZa0R2wZJu9zoUH54K1cfqN4TdOZ2Z471lCA9FKp0n6T4fPW2d//Plrsh83rzz2iy+9bUMD1xg59Cwj+/4R775vMHrydSxjNGt7tMpGmH/HJBemD0nwzX/Kygbj3L4sxgfcNXDHJ3B98n+jV83L2I5pzRGMk/pMbGNZCEyE6QtnArLMyX5ME1JKvMEQA36DkDn9chTO/LAjt8jEV0IIfumBFWnf+fy5bYuocjszGKm4kMvj6oOuPv7wuROTgn6AwSA8e/QGf/zcEXq8Ga7TmkXeMHvHfzrDhtQn2HsJ+s4l7882DHwdR/A0b7w1VoLKvdq6xwidfzP9ru96Cqk6SCZJIK2ahAksv3gI7Y7HJ/VplyurHsK6dtjW2FrbAzNG6hPs7SC4/9swMP6xuQS8h78LzVtxbXoKRXNmbI9768/he+ErMHDelp/HMHSFQN9l9JrmzGwYSmcNyWSI+38DR818ZCiE4i4F3Y1VqIraBeeJpD7xKv0GwnkaFZXxd+JIyocNg7fau9h98iaxN3Jb65zsWDmXlQ2Vt6450uyzEDx8mSRjPise27LnIs126fLM5Sv1JS7+x+Nt/P3rp7k+kvgC0S3gW29e4rvaJZbPKWXHykaaq8pyZP9U8txJfc71DvG1Ny6SCgMGfPlHJ/i9x9dMWB9RLD5JxGNfzzyof/AHf1BoG6YbPgMsMk2LQCDzwDMX8LgULMOLd2gIafgRlhH+mZRWWLIRw/2HfwgDV5N3mAmCBs7mDWNjCdOI5OtWETBmh6K7EQ3LMS+8nbrPlY/hWfnApDlM5MaxV8k4+C+rwzl/VdL+0+FKSRWhznPgTVMKUtuCe/Uj4UdtWYybT+52qggsDJ8/uf+72sMLXv23FoBPwsBlQleOoTffhRBKRvYoQkVdtpmQBdzsIKwDtwcLgXPe6ox8Ejy3H3z2n5TprTvR3OVxz4dC7+Op5m6nirAsAn4jcXvLRAkFUDCRQgPNDSK60Df2PvhkfvLGAF96oZ2zN72TvhF6vCYHLw1w6eYA6xbUoI6llEzeZ6G4uyRc38HvNbLqp1i4lCBlOD1iWPeduzHcJeG78H5vMKN+ShwaW5c3smJ+GZZhoEiLUh2skCQYif1ChBOSGhZ0Dhm8fb6PM9d7WT2/OpLTv3h8nU9fxfK/efUko8nKAcfAsEDIIK2NVVM632y4p9SFpioYgRCFTuvpcumo4eIeHcA3ctHnrNRnxiDm7lC8R+kZptRMicBIgnGZZIejfimux74I8xJIN8oXoG35LKXrPhR/DhN5cxYSENWRuv80uBAKnp1PQ1Vz6jGrFuF64Fcim2c3bqGlPpZ/mNArX049Z4DBq3j3fj07P6s6peseQ9/5dHpjTsRQT0bjIgRU1GY0pFoS3S7++XBbcQRJpT5j3AJpEilKkhbO9Q3x1ddTf7+d6vbzd3tOh6sFFzFiz75ZpEYufNVcWcan71nGbz2yhppSF6MpDr9zfQZ/9sIH+IKFvflnF7nw1cWBYW547Z1Dr7b34Q9NL1/NZMxKfaYz7GT1MbLPPR7fBn28JCSB1Cdqh1I5D9eDn0MO38S4cgwZCD/W1+csRa1fghIKYiWawwSurnwQ82IaTxDioWZRzrLWKI4S3A//Jr4PXoETLwG+CYM5oW0X7rUPI50lRS/3SEfq421/CzvBGZ0fYIzcxFFSl51tWoY6W8vMWF6lLd9G6OLk4nBJ0bAaSquxDF/S8+H24WlIfVQdpABFRao6KJELBYj5O55LafGN18+mvVvab/p5p6Obe5rrE/ZZaB6+TJIkm7tpSQ5f66H96gC+YAi3Q6N1XiXr5tWMVf4thrlM5iLNduny3Gaq2XO2k2Nd6aWi6PXDvx84x89sacl63KnhufHV++diUgSnCQn85veOcv/yKnasmDthvUSx+CeWx76eeZgN/Kcz7GT1KamAgcHc21C/eFz2l3hZfeLa5CpFX3YPVkT+AcJ2phNn5Xy889fB1SM2jRa4F63PbdYaRcVzx6OINQ9gXD9FcKgXISVaRS3a/FWokXGnLJtLFjxVVh9hheDUbtuHSuj4q7ju+nhWtuklVZmJu3rO4D/zFq7lW2yP66xpJlTeCEOdaQ+nt92HYud8mOE8ZVYfBJhBFFVDKg6E0JAWMcH/hKcHEd7eM8RAmpKDKPac7IwE/vH7LDyHZFl9Xj/XyfMHr08qI/f2pSGcXObxO+eyY2ljkcxl/LzCyGW/yY8PO1xKi90n0j/HAd6/OsqO/hGaq6KF53I1r3zw3PhqyJ/5nfvXzvTz2pl+PvfgMpbVlOdhjrniE4/ZmYVZqc+Mgbz1N86jdLFwfV5GdbZuTzAuce3INS+59+egdI49o1sfQOjROw65tUcoKs75q/Csup+SlTvC6wgULWf9Tw2P/eKb3MbyDoA5jG3ciN6Zzdw2taQKapfbHxuw3v0W3jNv2R5XCHBt+Wz6Ay3biXNua0HOh6LlCFJLfVQk4cBfChWU1D9PB87Yv/t4bdike3TiU7niQbKw45nDF/l+nKA/igDwvYPXefbIpbzYVozIVYh2vm+Y/gwejH/5pTMM+DMoA1wA5MJXmpJ9L3/1ylmujWRY2HMWWWM28J/OmCD1sYQ29ih9Ine2bs39+HPXISobx4+l6JgILCW+HbnmUnfhfvKLULEkPZtrluFe/5EpsW268lipT7w2ppnhj1zAm5VtJiq+zjMQyCJoe/dbBH3Dtm1QquejPv5FUFIUo2t9FPddnyzY+VC83BH+nkp1PmtOLM0FDnfEoTE3NOLw3pHMUgX2eY2EfRaaywTvv32pm1fb01tk/srpXt651F3wuYznU+svu/z6QOaB6POHO3I+r3zwXPhqXk3JpPlngu+/E12XUzz+mcxnJmalPtMZNqQ+il4Cq56E48/mZuzyRjxbfmbSWIWSNrgf/218R1+AY88kNFlr2Y7zjo8hxPSQ3IDAGu3D8o0gdAeipBJFyryPm1Lqo3syO2bcFRnLq8yB6/hf+78wbO9RfDwETu2mdP2P2bbBWTEP8dSf4u84jHnqDRjoAExQy2HpJlwt21DKG8LtZWHPh2LjaUl9rCBCcSCEirRkROYD4R/h+FxN46lAPKhiwlOtouIwUeojpeSFw/aysr1w+Cqbmuq5VUG18PMKI5f95m5uMotY752OYT56p4lb13JmTy550JS8fa6Lzr5R/KMBKstcrJ9bjVPXbPe5ubmBHx7uSumTVDjTE6Bn1E9tSexFfuF9deu4mrmYDfxnDGKuVoWIvBzP3es+hC/ohfZXkndVUg+GAcHJhTkAWLABz+ZPo+gOrIljjdlAQjvywYVQKV3zENaKnXjPHYCrhyHoA0WHOS003LkLrayGwf6hKbEnGy6tIL7z72K2vw4Dl2/5XS1HtN2Hu2ULuCvyaMOEoGhCG9VdBhULYPBKwkMoLpqicjN79pijfQSe+Z9MXjSdIU69AuufzMg/QtVwL96ItXQz1eUukJK+4QBKpJBdpueDlGB0niZ4/TSYPtDdaPNW4axfgiiS4zIrjmCc1CdeGynC/2xgbpWHs7329Rn1pS7b20wV4nngbN8QfTan2RuAc33DES118WDQb7DvbBfHr/ThD1m4dJW2uRVsaZlLpcuRuoMJyFWIVl2SXYGuI9f72NxUnyNrcgMjZPJfH3Tw+pn+SakY/pUrbFtSyWNrm/A41Ljbx4PHobGluYK3Lma/ZvBQRw+72hZk3c8s7GE28J/OsJPVB4nUHHg2PkWgsRXzg5eg78z4/ly1sPIR3MvvAVXg7zyDPL8fRodAaFDbhLNtC6K0ARJl30mR1Sfv3KHiWrMLpW1n+H3NgRIKopV5UmaqKQZu+vsJ/OjPwRtHu2wOIY89g/fYcyiPfB5XTVNebEgnq49o2Yl891upj9EYOFZuz8j/N/HsawAAIABJREFUvr3fIGdBPwAmppThzDFZ+EoKBQTJ26dxPngvvg/vfRcC42UcoZMvEnLVomz4CI5l9xTNMZoZTyerj4aiKOHYX1GIfBBBfH5PSwN7ziWpIxEHK+pcVLgcafVfCB6+TJIxn0kudA2RCS50DbKspjiKTZmW5LsHL/HWhYkBY4jLg728eKqXexaV89TGxWjjUhMl6zd3WX3aGirRAZtrxccwMhogdp9la0+23BsM8hcvHqNrNPbz8Xjz/ADHrw7wm4+sonzcRVfy/j96ZzMdvce4MmQm7DsdDPuCKccqDE/ss5mA2cB/OkOaYIXCkoKQH4JeEGr4/ZCRkDvntGDNa0MO3CA0fANLCjRPKVrlAqSmIUIGSBXnnGUotU0gVCxVDWcpESpW0Je4f2M0/NqIaG9jPgv1Xsa4cgwML6g6av1itPmrwQymZXc2XBoCtFDaPioEt/wjBH70Z+BLVQzMxHrhjwk9/Hm0ynkF8ZWraT2+4y/Gv0CJh7ZHUYRm2/+h3ivQeyZ1/3Zh+MJ3m7PwlQyGwr8PwWBG5wMhA+/xV+DE84nt9Pdg7fsa/qEePG3bC36MZsyDXqQZ8VOiNkIgpYJUnMhQKPy0DoiGwvF4Y4mbpgqVjsH0A5DtKxuxLGtcP1KCL2hiSQuPrqOkyCaUTy4tC4mI1BsIv29kmC/eCIbG9TPVcwGJZUmCpsXX97Zzuif5+qD9l4boHT7JL+1sS2sfWJZERMbI1laBZGdrNS+ftl+sD0AoouC+HjuGpMXf7j6VNOiPoi8Af/XScT7/2DqUNGVhqoDPPbiSb79znkPXRlOOkQiqICf7LtfcsiQyG+1XkWM28J/GkJaFNINIK4gMeCEY/lK1hEBEDtpkXHWW4HAuxhQCVUoI+rFC6W2biBP0Q8CHhQTLREiJ0XMR8/B/wvB4jap5GkxKYM0uXEs3Ywklq7GTcTOoYAX9SJs+ygUPhQxClw7C+bdg9GZ48loFNG/EsfRuVHcFlhAYx15KI+i/BePAv6Pu/OWC+Urd/jTmG19NbfOiu3G17sDyj9q2xzjzZtr+sANpmRnZE8stzYlUwPIHbJ0P0c8Clw8nD/pjcewH+DxluOatmtJjN2fngA9USyINf+L2poni0rGCBlJP/0f3p7e28KfPn0zrTu19y6pora0cCzb6/AH2tV/njbMD4+pB3zm/hK1tjTSVl8XvKI8wI5oMK0ab4XLq8RungMupj+unEDBNyRtnu1IG/VG09xq8euoqD6xILQExrXDIlqs53rdiPnvb+/BlEPM1VpcU3NdRnOsf5kJ/+s8ubvjgaFc/axuq095GEyo/tXk5jwf8PH/oMu9ncAHQWFtaND6LhSUFlmTGBv+zgf80hlAUhKojFB3hdIMIX60qqo4wjYJwFLCQKK4S0F0ELh7E2v/1JLMYhQ9+iH+kG+fdn0JYwbzYp+puFE1H+Mwp9Uugsx1zz1cnTzs0CGdfxTj7Kqz9KM62HTCWajJN9F/E9A2hVM8tiK90dyn6o/8d/6ndcHI3TEwyWNEMK+/Hs3A1ICCT42k4/QuhtLFoE4rDlZE9sVxxOUBKFJeW9vkQ/UwoGvLkq7bMlqd2I5o3FPT8zpRr7jKEGUIEZOL2mhuh6ii6A6mItHPO1XqcfP6xVr766ml6kiT5qXeFf8jP9w+xtLqUdy7f5N8OXIvb9uDVUQ5ePcfWxRV8dEMzOchgmDaiKhdFCdv73pUe3m6/kVFfaxdUp5MVNa+wJLx8/KatbV4+0cMDK+antF0Nq+1yNkePovL5x9v4/f88aWs7AXT1jeLRVZoqcpP1Jhu8eeK67W32nrjOHY3pB/5R1Lid/NQ9y7jw7BH607u2A0AH1s8t/PEZD4qQKILIwviZh9nAfzpDqKBooDnAdEcKqcrw64iWesq5BEwJuovgcHeKoD8GF/YRqGjCs3JnXuwTDjdSdYBmTJkvAjfOxg/6J+Lo9wkMdQP2UxMaHYdwNSwpmK8UoeFZ/2HMjR/DvHYSc6QfqbtwVNSiVc7H0hwQqVSckT15gGP1o6C5svaV0CN3YQ0trfMB3T32WbD3MozaDOaGrhAcuolan9v9PSVc9yAUAzQzSRsXQnUiVBWhaTD2+D32xzc+ry/18MUn76D95iBvnOzk0k0fIxPUP91+6D43wOvnBnCT3qqRvRcGEeIST21cnNKGXHGhKOG72FLyj2+d5YPOzNJMttQ6qS+NZkzJr83J+KnOPtu6eQM4dqOfO+bVJB0jLAcSkb+5sbva4+Kh1hpeOt2btr0S+OGRG8ANGksUHlo3nzsX1ObEnkz4qS77vyXn+oJj/sxk3F1r5/Gd9+JfSMfDrlV16FrsouLUY4VMydXhUXxGCI9DY16ZJ7IeJLc+VBQxY4N+mA38pzekjcq9U8Rj0xeah5+zN5/D30O0bkYo+pSnqMw1l4YX89U0gv4oLu6156so/H0oxeArE/SGpdAgGKvGnCx1Y7rHU3k12LtZmBzLH8RRXp8T24QVXoCazP+J0nla1+zdUYwidP00evWCgqfntMttpfOUYRkjSjQoCD/JTMWFUGitr6KlroK/29PO8a7EAbOdpeJvnh9g49Jhmqui2XHSsydzDhLBtw+czzjoB3jizqY825ke7+zPbGF+Z783EvgnG0Pkxe5H1yzkYs8IZ3rsZ4zqHLX4xr7LdK3w8tjahTm3LR2e6QLlkCnR1NjgP/1xtyxu4HL3MG93pF6IvnqOh4dWRqVcqfvv9xm8fuo6r5/pI1Z8owM7W2vY3tpIhcuZsp/0+cwN+mG2gNcMgrz1d1wawcJwKzACVw7anIOBv+Nwnmya8GWW5/n7Lx4kkzv4tqHo095XybhjyT1pOiINLN+J+66P585OEfFTWu0Z/zqUQYlQQIYCRXF+2+ZjfkrSJoN0nvHwL2+fSxr0Z4I3MpBOZAoBdHQN8e7lDKpjR/CLOxbTVJmi2NwUwcxQxG2ZMnUj8hOiqYrgl3e2sXlR5qlQXzzVwxtn49cdMUImXaM+ro948RqZLdxOhkzv6I7PpmQPQgh+4u4lPLqyLmm7HUsr+ey21shC4tQ41zvE7z97nN0Tgn4IX+C8fLqX33/mOB2DI5kZfhti9o7/dIbNdJ5TwiPpC4M9lzKaknnjDNbiuwuSojKXXJ7ak9H8baNs3rT3VTKu1i2Hkgb7sphYVC1D3/wJ9OqFWJoDKyI9yta2bNJ54sgwoNBLiiQ9Z3Gk85zILw2McCCLgDkR3r86yqdNMyYwsm9bulwCLx69lJGdd84vZde6+cwtjRbZy5+d6fISV2YLk0vcsVmdEo2Ru3SeE7mmCj5191IeXRvgrTNdnLo+yNXB0KR8+Mnw7PudbF0yBzUiReoYGOaNE9d578r4IHVFvYsdKxtpq69kfDycmf0rGz0ctfm0aGl1bIrbzMYVQvDo6gXsbGlk/8VuTl7px2eEcDk0WhsruHdpAyXOifs1cZ/Xh7z85SvnUtpuAl9+oZ0vPNZGXakrY/tv8fQuOqcrZgP/6YwilvoEjQzzrgd9yeUA00Tqw3Au9SmJ4Vl61/T3VTKOiXPrzxB48UvpOaRhJe4dT2MaI6hCQ3GXIoWaM+lRrqQ+jgWtGEfs72/XghU5l3ZNBZ8KqQ8I/n3/BftOTRMjQZNKVbVlT2Yc9p+xn1JSBz6zZXnO7Rn0B7g84CVoWpS7NJqryiOBbHr9bFhYy/cO2a/0eufC6J3jZGOkb4fXMBkwDBSg2uXEoaW3L6vcTh5fu4hdbSa/8X17J20QOHStj40LannpxBWeOxY//fGpbj+nui+ycUEpP7l5uS3/xuM7Vs7laGfqgDkWO1bNw44/k3G3Q+f+lrnc3zIvq35+ePBS2vZbwHOHL/OzW3NxDoy7+ppxmA38Zwxirp6FGLuLNuU8YoPi9IxLj5c2HCV5si/2ZJ4CX0wFlm5FcbgmV4udbr5KwfWaJsxdv0no5b8EkjwWn7OGku2fRWgawlE7Vk1X5su26Os0zoewS299plXOxahZBr1n09jREdS3opXV5WF/T8X5EPFXsjZZSn2GAgZXh3Ivm4hCTVOakDWkTHaUJ0Smuu5EON83xKsfXOPYBNmUS8B9K+rY3tpIiSP13fwSh876eR4OXUv/DvQd80ooc+mk812abK9IKTl+o583jl+nfYJe/+6mcnasbGR+eXqSqPP9mT1JOnWljyGfkTDoj8V7V0ZQlXP85OZlGY0VxdKaMhZXO7jQl16anQaPYM2cqqzGzDV6fQFOdduTyx66NsLHA0HKnPlJDDFTMBv4T2cUsdRHnbMso8Bfmbc6hRyguOUr5vBNAu1vgJlZpU2a74aL76RuV9KI686nprWv7HBt7iq0j/85/jP74cRLEIqp/tm4Bm3lg6jzVofrWkyRbdlW7tU2PEno5T9Pva8j0Nb/WF7299Tw/Et9TnUNpO3LTFCiR+8Qp2dPxjzrC4zsbdh9ppMfHIqvT/dL+NHJm+w9c5PfeLSNWo8rZZ+P3dHE0Wun0vpNUIEnNzSRnq8TS31CpsU3953l8PX4+eXf6RjinY4hPrxuDve3zk05ljeQ2aXVoNfg3QS+TGTX5pYhllSXJbUnGRdC8PSOVr784jFueJNfPFU64L/tWhlJqzkFx3ea/PClzJ6aH77Sy7alc7K0IfUF53TGbOA/nVHEUh+hODAW3wsX9qU/H6UM97w2xDSU+kgp8e/7dzi7O/P9uXQrJXf9OKOuKjj1QuJ2jWtwbf95NFUj1/KVopP6xHKHk9JV92GtfgCCPjCDSGcJYUGIwDKn1p5spD6EDFx1S/Df8/OE9v9DykND2fI0rpqmvOzvqeBTIfXxBjN6xpgWdiytRFFi9f2p7cmcw6JqB5fSvFs7tk1l9O57dja8e+VmwqA/FsMh+IsXTvJ7j63F41ST9lntcvFbj7bwVy+0Jy2O5RLwq4+0UOtxj9s+MRdx35dS8q39iYP+WPzwSBearrJ9SUPSsVyOzMKlAZ+9/Qiw52QnS7aUJbUnFfc4dH77kbU8/0EHb5ztjxvKbllcyeNrmyhJsf8KwUcDmT29G/UHc2DDFD3dKxBmA/8Zg5gr9SKQ+iDAseoRjAv7Y2xLDnHXRxFCyZN9sSdzbvuXEkbf/iZcPJDWPBPB1bITIQSeOz8CbffhPbsPOo6AMQIOJ9Qtx9W6Da1iDpaigRWadr7KFVc0B2iOPPshBc9C6hPlruYNBMprCB75L+g6PvmgaFyDc+2HUOsWF26eueBE/JWsTZZSH7eupm6UIbataMxb3xMhgF3rF/K1V+1ptLetzN5Gy5L8x/7LabcfCsKbZzt5eNX8lG3nlnr4ww+vYd/5bnaf6GI4Jq4rUeD+VQ3cu3QOJQ57+zHeEdPeO8QhG5Vk/+O9a9y1oAZ3EunS4qrMqjgPZ1AG+NDVET5jyZj6BJnBqat8dEMzj69dxLnhUbr6RvF7DSpKnWyYX4NLj4aA9m3MN/QMK3vpWWQmul0wG/hPZxSx1EdRVJTq+Wi7fiM9OcPqD+Ns3ZGzjCsTeT7lK8aFA1kH/az6CErVAqxIn4q7Es+aR7HW/xhK1CeaAyUUHGuTr31ZjFKfYuTZSn2iXJ2zAv2BpZjeHgJd58E3gnB60BasRHeWF3yeueH5l/q01GeeejEZPn7nXOpL3LbtyZRL4N5lc/jmq+cm1sJOiBIlXAU1WxuOXO9Nekc+Hl470c2utnkTKrDG79/j0HhwxVweaG2k1xvAFzJxayrVHueE9I7p2h1f6vPG8Wv2JgHsv9jN/S1zY94Z36fHobJxQemkjDzJIAAjs2ym+IKhmAw4k+2xwx2a4N7l4QvD/pvDKdsXA59bU0ImRVwW1JbmwAabJ8E0w2zgP51RzFKfiLTBVbeE0GNfxH/kObh6aPIcKpvR1jyMa8FarDzal0/5inXytax2o1jzY7jWPFQ00pqilfoUGc9W6jORK+4q1MV3jb0fLoQ2M/bBVEh9qtwu2hrcnLyRYUaxOPjkpvlsaW5I2wYp4eTNAY5e6GE0EELXVJrqS9m8qAGXnjqLjJQSr8/Ab1g8/eBS/iqNVIYC+NyjK9BykHHo6KX0q9VG4ZPQMTiSRoGzWxYLIagtcWVlK2NPJse/HzQlx7vsHwMHznanzELz4Jr5vHfldNp9Prqqjt0nbhLKII5UldiKtNnt10S+Kma+prEat7hk60K0ygnLa8tzYMPEY3ZmYTbwnzGQt/4WidQn+plWMQfPzl/CuPwBoQ/+C/q7wp+X18DSe3HOWTEF9sWezLnrPzTYCf0XM9tlrQ/hbtmGWlqNJRSQVuH3Xx59NeN4DqQ+tw0n4q9kbXJQwOvhdQs4+dKZtNvvXF7Fwroy3jrVReeAQciCmlKFTcsauLe5Lqn0YyIOX+vlP97pYGjCGtCDV4b5/vud3L+8mifWNY3ldI+F1wix/0I3r5/oYjBm+3o3+IOQKFnRgnKNz2xfRkOJJ34Dmxj2Z7aAdSTD7XKBid4cCWZmy4A39a35uWUePru9mb/fk/o7f1NTOQ+vnM/ZriHbFYDdApxa7iUr0ymcVRTBrjVzePZo+qlgH1wzDyGm0ywLg9nAfzqjyKU+UWmD6e0l8MpXYThGOyqBQS8c/BdGD/4L4q6fwtl2X97sy5d8xRi2f4cMgAUbcN3944gpkO4Ui69mGs+V1Of24FNTwGtxVSmf3ryAf377CqmwuamMj65vBmDjgtoErdIb981zXXz3YPLqvq+d6eNq3yi/fF9bTPAv6RgY4S9fPBM3HWd35MZ1lQOWNpQy5Au3qit3cW/rHBaUl9iyMxV3ZKiP1rXYjEfJxhJZ2TeZT5b6qBnGfdq4rDaJ+drGKn5tl8YPD1zk0uDkKzK3gEfWzWHn8kaEEGxpncOZtzps2bJzRW0kgM2vr4qd39/aSMfNIY5cT50K9u6mMrYurs+RDbGvZx5mA//pDGmGF/yFDAj5IegFoYbfDxmF4cZo+LURzr9rDt0k8Pz/Bpl8oZV891v4AwE8rffkxT5pCNBCufdR0N6dnDGYZjgzTaH2UyF8NcO4DIbCvw/BYOI2xihG91kY6QehorhLcM5bCdJdFHOYMh70Is2InxK1EQIpFaTiRIZCoETvtkuw8bh+44Iaytw6//neJa4OT76LW6bAhzbM5Z7mBixLptVnMn6hfyRl0B9Fe0+AZw5d5MPrmwFJ56iP//fF1E8o+g04f2OE33lsDa6YwlO5sD+WL6or5XgGUql5Za6ILZP7tCyJaUXfs3Jma3T+IjJG9H2PpqFERrKDuZWOhHOYyJsrS/n1h1bROerj6KVeRnwGTl1jYX0Zq+dUoggFKUFKizVzqihROhi1YdDmZQ1YVv59lcv+88U/c+8ynjt6mdfO9JMID6+o5pFVC8d8ngtfSTlzg//ZwH8aQ1pWOG+5FUQGvBAMpw2zhEBEDtqp5gT9EPBhIcEyMfZ+nVRB/xiOfgejZg565fyc22cGFaygH5ljHwmHK7N7A+4yLP9owfZTIXw107ilOZEKWP7ApDYSif/8u3DyVQjeyi9vAT6ARffiXLkLxekpmvnkk4d8oFoSafgTtzdNFJeOFTSQenY/usurK/jNh9ZydWSEU1f6GQ2EcGoKzY0VtFRXIARYGS66nIhn3r1oq/3r5wZ4aJWJS1P4zr7zaW/XZ8BrJ6/yyKomuyamjU1LG3j+uL0FlevmenBrekJ/mqZECgXTjA3IcgPTilxOjBtbsHN5VdJAMR62tDXaPiYa3B52rfAwLniUEmvc4Sv4pV3L+fM0LvAAfnrLQsr02IuQ3CC+r4oXHcMj7DvZycWbI5gWLChXqXBpBEIWRsjCpausWFjF3U0NuDQFKcMZ9nIBSwosyYwN/mcD/2kMoSgIVUcoOsLpBhH+8lFUHWEaBeEoYCFRXCUEh7phwN4jztD5/Tg2fzrn9qm6G0XTET4zp3N2NLYQcNeCr8fWPB3LtqK4Sgq2nwrhq5nGFZcDpERxaePPgZCB/+1vwuU4i9mjuLSPwLXjOB/6dZSK+qKYTz655i5DmCFEQCZur7kRqo6iO5CKgBxInBeWl7BwZbQy68Q7e9mhvWeQb791gX7D/rYHOrppm1vBhQF7ucp3n+7j4VULyVfGwgqHzt0Ly3jncvpVau9fPTGjz0QITMtCVUWKdvahhtV2k/rd2tJoK/Cv0GFVQyVZZs9MiIXlJfz3h5fxd6+dZSDBEgQd+MzWJlY3VsdvkCUS+arYMOA3+NrrpyY9reszTK4MmQB87M5Gti2eWHchd1CERBHM2PUCs4H/dIZQQdFAc4DpjjzblOHXEX32lHMJmBJ0F+aZ/fbn1PEe1uZPg+7OqX3C4UaqDtCMnM5ZaA5YsQsOfTv9OVYtQatdhFXI/VQAX800LvSIFMXQxr3vO/hM8qA/iuAggVf/D86P/QlonoLPJ69c9yAUAzQzSRs3QvMgVA2hadz6UY/98S0O/v7VXv7Jpm47Fh3dw/gyWBAbJHzBsWpOVULbjJDFu5dvcryjL/ykQ1dYNqeCe5c1UOrQJ7WfyD+xaQmdg8fpiKNfn4hP3jWP5kn57Sf2Gb7TryjEFEGLP7ZdHs5zL2Ly3Yf/1pa4+Ol7FvLNNGoSCOBXdrVGsiLlzraJfEFlGf/zI3dw+uYgb53qomvQhyUl1SVO7m5pYP28mgkLv3PLE/mqmPig3+BLz58g1Trr7x3sJBSyeKA1moUp976aqUE/zAb+0xuyyNN59qVeYBd3WkPdqJpr2qSoLGnZzOjlg9CT3qNc1z2fSp7WsMB8Np1n5uk8rZEeaH8p/YPd30vg9F5KW+4t+HwKms5TKIACio5QnUhLQswC2Fs/zoXn14dHswr6AYKmpH80g0cFQL83ut1k2149fY1nj3RNuv95+mY3zx3rZuuSSj66flFMkDt5jrqq8qu7VvG9gxfZd3Ewrg1lGjy1uYk75tUm7GcyjyKX+0MkbLNxYS26pvLNNy/GXTgNUOOCX3qwjTljdRryewwJobCivooV9ZV5H8uOr4qF/9ObZ1IG/VE8c+QGS+ZU0FxZlgd7Jh6zMwuzgf+Mgbz1t5Dp+8ZsgHB6ygxgmXmwL/Zkzu2chaLhuf9X8L7+Neg+kXhewoP+8K+jVTbm3u/TxFczikdfx7zvP/1W4v2fCCdfRrbciyj0fBJwyz+KceMcZtCHqjnQG5YhPFX2+iHir4RtFKRQQXWE/yoKprcb36EXMK4eReJDUI5z6d24V9+P4sxPsa508Nox+8WhJqLMpRPNsWIXg8PxEwo8e+QSr5xOnmVs7/kBugZO8v/cvypuWtEodFXhxzct5Yk7guw7d4PLPSMYpkWZU+OOJXW01edPFmMXycxYN7eaVR+v5P2rPRw4203/qIEiYG6Vhy0rGlleU0a4WvztgSLZZXFxfdjLuT57F8OvH79O85aWPFk0czEb+E9nFHs6T1cFxL9hlBylldMvRaWu4nzktwhd+wDz+KvQ9cGt+bjroG0XrmWbwFWet+rE08ZXM4THS+fJlSPxj+lk8HYT8g2jenJ/3GfDQ0OdGId/BFfeGTM1FPnHnHXo6z+EXt2Um3SeikA6PaC7kZbF0I++jO/McyDNca4yOt9i+J2/pnTdZyi591O37jUQ8X+eudcIccCG/j0R1iyq5lrvKDBke9sL3dFtbtn2/pWbKYP+KM72Gjx/9BJP3rFoUj8TeYlDY1fbPBLDrg9Fmu3S5alTVGqqYFNTHZua6iZZnxsbpgsv7nSee091YheHro7wyUAQj1PLsT2xr2ceZgP/6Ywil/ooyzZj3Thqb07VS9GdFTmv4jsl8hXTQG9YBg3LMQWIgBehOZCq45YUpAhkF0XhqxnA41buDaaZwWoCRGAExV1eNHMLdp4i+NpXEhvcdYTgj44g7/l5HDHVhjOW+kgdaVpYIZP+H/w+Ruf+8Ofx5AnBACPv/R3WaC9luz4Xk+88pk2e+JWh1PnEU6FMg7aGKtwOFY51295+JBC9GApX+n39bCc/OGQvaHq1vY9HVjfh0KI+hvz7MIpc9jt1+34q+EjAYCgUwiEUqlyOSPXeXPVf3L660pfZd+cNn59mZ2mO7Zl4zM4szAb+Mwby1t8ikfo4F67Fp5SAlf4JrbVuz5N9E37g8uwLITQUhzs8WpFINorVV9OWx5H6oGdWQVXo7sLPJ8JDA9eSB/0xCO3/ByipxFW/JHn/RPyVsE34tfftf8bofDutsb0n/wN94TrcK3ak1T4XCITM1I1S4GN3N6EIEcnHbx9mJM2jJSXf2HeGQ1dHbPchgXev3GRLc33KtsWO6R6iWZbk4LUe9pzopCMm5Y8C7FxezbYVjdS4nTkZq5h9ZWaYvjTT7W5nzAb+0xnFLvXR3ahbfwZzz1+nN5/aNvTmu8I50mflKwXjs77KQurT0AqDHfGP70QQpVBSnZfjPhNuvP+cLfNDB76H9cT/SNF/CqmPAMsK4T3xAyIfxvxNzL1H/w33iu1pt8+Wl+iZBetRfGxDIxvm1wCSCqcjoz4qPToAzxy6mFHQH8W1m8PQXM9U+O0WRJrt0uXFLV9Jxb1GiK++djJuBWCLcLXn18708XNbm7hjXk2W4xa3ryo9Dq4M2S8eV+nU82BP7OuZh4IF/i0tLU8Bvwa0ASawH/ij9vb2d230sRV4M0mTf21vb//JrAwtZhS51Adh4J6/Ev+9nyW07++Tz6VhFe6dT6Nigcy9fdNJvmKN9GL5RxC6A1FShSLllNownXxVbFIfR9t2jDM2svoArN6Vt+PeLjf9Q9CZRirSWAxdwuy7jF41Lyupj/fsfmRgiPC9zsgxD0l5sPMEwZun0OtWpNU+W95UWYYOCbPEJMLcUoWn7lnC0uqysT5LnTrLa52c6bFX/XtTSwMjgSC7z9orUDURobE7pfn323jksl+RxbaF5SFT8jevnUwrberX93bwy/dptNVXZjFucftq49J6jnWDfrMNAAAgAElEQVR1JHZCHMwtVanNS0amicfszEJBAv+WlpY/AL4IDAO7gSrgUeChlpaWJ9rb219Is6s7In/3AxfjfL4vS1OnEeStv0Ui9Yl+5lq0HqP2TzBOvw7tbzDuZ7O2Fa1tJ875q5GqDlYoT/bFnswF9lEcLq0gvvMHME+/AYMxuafVcmjbiadlK7grpsie4vZV0fA4Uh+trA5j4Z1w+SDpwYWrZWvR+Nq4cjxNu8cjcOUoetXcxP0T8VdCG4CeSxmNHeq+Hgn88w9NVdjZWsPLaS6kBVCB335kLZoavaC5he0r53Jmz8W0+3IAG+bV8uqpq2lvkwhlLj11o2mA6RqivXXxRlpBfxT/svc8/+sj62/9zGaAYvbVurnVeJSOtNN5Amxf2Zg/g2Ywpjzwb2lp2UA46O8A7m1vb78Wef9DwDPAP7W0tCxub29PZxVVNPD/7fb29tsoyI+g2KU+MZ8pVXPxbHwKeedHCPlHEEEfiqsMPFUooSAyz/YVs3zF/P/Ze+/wOK7r7v9zp2xDL0RjAUGCXJBgF0VSLCJFFatblkscO7Ed20ne9OKUX/LGqbaT2E6cxD+XJHZiO48Tx3GJJVmdEimRVCfFBhJgASsAgiD6YuvMff/YXWDRdmcWC+wCwvd5SHyxuPfcc8/M7Jy5c+45gR6CT34RhibY6Gf0w4mfMHTiCZT7/ghXWe2065PLtsolPlGoj6nquLd/DP9AD/ScH388R0Gg3/d7kFeSM5mejKC91edhBIcmD+PBWqhP/Psr9seEn8m5DIdGfd7jD3KwpYPTbX0EQgYeh8qaJaXsrK8kfzgswLr8sfyOhhoOnLmJVUu9d3MNmhpfbR0tc11VMeuq3BzvsBbi8NHbl6IqcPLK1Fb7ATYtS8zBzwxxYbGdVZ7b4SuTcSkl+0+2YQf9YWjq7KGxsiTNcXPbVooi+LldS/mXAxexgroSnW21CxJkZfa8msvIxor/p2I//yzu9AM0Nzf/1Ov1fgv4JPAzwL9bkLWRaChcGjn05gBmQajPRO2EuwjFFd2FP1NZbnI1fIXQEMGnvgBDN1IcbAPzqc9g3P+n0ZXVd6Ctco1PmNUnEkJRVfLu/R18R34Czc9NfDgXNOLa+gG0osppuQZkJJBWiJii6aRVfUNzTB7Gg7VQH9wl0c9shiQoBVEHNmJI/vuN87xycUyKTH+Eiyc6eeJEJ3d5S3l4w9JYDnpr8sfyApfO7z64ir9/4nRK5/+BNRXcXl81qUwhFD6+q4FvHWrm7bbka10f3bGE9TXROO+hkPWV4omwuFBjUWH+OH2ml8eRSbn2zpVc4R2DQ3QFsI03znXFHP+5aat11WV8ZIfJdw4lr7hc7gZNEXzm8bcRAioKXexcVU1jRXHsjUgmzqu5i2w4/vcSte5jE/ztx0Qd//tI4fh7vV4H0f0BZ5qbm9PLAzWnkPDUm2OhPlnXScLoizkX9InyoZPPgi+V0z+C4Ov/hf6u331H2irn+AShPnEuFBXPlg8g196H//wr4LsZ/dxViGvZrYjCCpQMhrVJaRC4chzzzAvQeWbkhKlZj7pqL66qlVgpEOaoXE4a/gh65dSz+ugNW+Dod22NK9xFOJeswzAlX32xKWW8/PPN3fT4QnxsxwrEFGImFuZ7+PQja3nm+GVevjC+WEldsc49GxaxtqqU4QlOAk1V+MQuL2e6+jlwqp2THSMPACpwZ0MZO73VlLpHNgO7dRVI3/l/323L0u6ba5iNLtpI9WV76PWl+UYuhtlgqy2LF1D3UD4HTrdz4FzPqKun3AXdAejyQ5d/xIadQ35OdlygyAG/tHcltcUFM6/4LMKMOv5er7eaaDz/1ebm5oneVcbvWGstiFsD6MBFr9f7GeC9wFKgA/gh8Jnm5ubeKSudy5hFoT7ZDmHIxfAVQ1Gg6QV7x7zrLKGBGyilS95RtspFPlmozyjucOOo24LizgfdjTINIT2Gr4fgs1+CwQlyubcdw2g7hq9sBa67fg3c7qQyldJFULQU+i4mPw8ToRai1axJkZUodaiPWlyLo3oHgfZDMcGJt/yJuXvFAwjdyVPHLlneJPvW1UGWnuvgjhWJ8cGpxxrLi106P7NlOY9sNDjd2Ud/IIRDVakry6eywD2mT3KZQghWLShi1Z4iCko8+Pxh+nqHyHNqKKMeUKLtVy8s5mJfl6X5jsUv717K8pL4ar/1+WaGC4vtrPLcDl+ZjGtplj0eqbY8t221IM/F+zbX8cjGWroDQcKm5Fq3j++8coVk6AvBF55u4VPvWkFdSdz5tzZux4CfQy0dXO/z43I5qCkrYGvdAuoqslclfLow0yv+8W/ayaqNxD+vtCArHt9/P7AbOABcBW4lGk70kNfr3dnc3Gx9OXW2QRrRzbCREEQCEB4CoUY/j4Syw0O+WAhLbN0wW3qM4TIkQIvkho1iPHS1GdJYXw2dPYRr04J3lK1ykctwJHrvCIezdj2Y/j5CT/w1hFLEfN88S+DJL+K8//eIrr5PLlOsfwD50lesn5AbHkZEAsl1Dg8hjZidJmqjOpDhEJ67fgH/948jgwOQ4rW8WrIQz20/Qyhs8Mxp65ttAZ57u53bl1UyUfGv1t5BXjrVTnOHj7ABeQ7YUFvMroaaWD710e11VWFddcmoz03TTKn/pPNSBIV5TiJDIZCSkTTlI21uW1HBk032HP9qj+ATd3ipyHNhDgu1p1s63DRlLNe6IBqZm7kxTFMiYmPM1HwywSvy08vLX13sTvvcmo22UoSg3O3EF47w1ymc/kR8+Zmz/M1718f21iQf6+ZQgP84eJYLvYlv0AIcudzPE0evsbKqkE/cvoKFpXmWx891TNnx93q93wVusdD0x8CTMT5ZMGPcC8q3IC/u+B8A3h938L1ebznwPeBO4OtE3wRkHA6HxoIF2X2dZAT9SCNMcZGDQL8EoSAViYmBqmWHA0hhIpwG6NnTYyw3wn7McIBCV27oIxUJoZ5JL4RkUEM9FDjeWbbKRW4GIkgFChwiI9dD2HeD3rdfxmg7CX4/6A60RaspbNyNWlw1Yd/rr30/tdMfx8BVjFNPUXLLg0nnlrd8Fb6BRxg6+r8pReoNeyheuw1VyqQyI/4BVFNS4GbCNqgCM1/DrFlH3kf+kcv/+fsYvvgL2/E3bEfFMmo/+EW08iW8cr591NqdFfQbcD0cYWV1aTQECoHPH+JLTxzhbNfoMIxgCF4428sLZ3u5p7Gcn93ZgKIosV6Z/z9ekKiwrGDSNkXkc+eqMvZZfOBRgE+/fysej2NadE72v2GYmFJBESaqqmRUumFGz4yisoIZntXU/i8in42L8zl6xV4dhoc211NU4n5H2Uogeenti7bsFAKaB4bYvrwqqeSuniH++onTJAu8auno5y8fO8bnf24b9dVFtvTIVWRixb8W8FpoVw3D+8ZSfU9beQ/2O8A/Ae3Nzc0D8Q+bm5u7vF7vR4AW4D1er7e6ubnZXj3zWQKhKAhVR1EdqM68aOiBlCiajjAiWeFSUTEEqE4PwpmXNT1mA1fd6a0gCN2F5i7Iuv7zPDPXA0i6Xvw2kbMHRx/oMERaXqa75WWo3UTV3k+g6s7hvkZgEFrfsHXuRE7uR2x7D4qiJdW7eMtDaCUL6D/4PxCaIGJSeMi/7T0UNO4B3TF1ewkVHE6EKvDUbmLFr/0Xva89Qfex/yHS2078luCsWk7Zpg9RsOldqK5oleSrnQPj9bOASzcHWLWwFAH4A2H+9L9fpStFcp1nT3XR5zvOr9+/HoQgm+uiP797FZ19RziRYlOwAP7i0XUU5juyoqdAEDFMNFWgKDM7di7zB25ZylEb6XPX1nioKPPEnNbM6WOYkmAwjKap6LqaM/ZJ5M+/ZX21P47nj1xi14qqSWWaUvLXP3kzqdMfhy8Q4c/++02++au7cTlmf93bKc+gubl5p9W2Xq93fYy6J2niiv1MuVm3ubk5TNS5n+hvbV6v9wiwC9gE/NSqjlYRCkXo67NfZS6TKCtxg6LR3R8iFDQhZAISU5MokexwQmAGJIoUYGRPj7G8pNiDVB309vfmhD4gCeall4M44l5Iz9BombK/i0hvByagufLQShcjNW3O2CoXeVl+9OuqezCc9vUgMfA/82W4cSr5Qb90hI7/7cV93+8hHdHjOnTantMfRZDrZ0/gWrw+9TxrbsX5szuIXDmGcfUYBPzgcKFUe9GX3YppmPT5JWY4tb2KyjwIJURPr2/iNorEFGEI+4Eg4EHb9AEWrH8UY7ANGfIhnIWoBdFaAQM+Cb7obWJwML0Nj4N9AfpuRldc//1gS0qnP47XLvaz8NWz7KmvRkkzVjsZShYUIIDuG6kfaH5xp5enT13huVNdExYVW13h4tFb6yhxaPTeTL/K71RgmiamCYoCStzzzxBKFkQLovVYsFWuocrl4P7GBTx5KnU0cr4KH9q6fPh8TQeJtpJS0tzVz4FTbZxISCVbqMGe1RXsqK8kz6FPLmwGYZiS3jT2Ql/sCtF7c3JX8lhbt+VrHqBrIMDjr15gz6qZrR1QVOTGkeGHjZl+dImn76ya5O+p9gDYQUfspycDsnITcnam88wGn84UlcKMYAYHkaaBcHgQmtNSX2dhDUPFS6A3eeqysfDUbxmWE2w7Q/jk86McxzAQ9pRDw93krdyOUPWcsdVc4pOm87RxPfhO7Uvt9MfRcwH/0f/Fc8v7onJ8Han7TADZ14WyaPLUm6O4EUKvXgnVXkyhDH9uGoYte1lJ5ykNA2lKork2o+tyQtXRipaQbD2wKC+9eOmCPAcgGAiGeOuqPYfqR0c6+NGRDlZXuLhj7UIayosylEYw/puI/Z68vaII7l+7hHtWL+bt9m7abw5imJJ8j4NblpRR4nZN2nfmeRyZlJvaRrnM71+7BKdD5cdHJ7+W64p1PrnHS4HTMaqvfR61VdiQ/PvBZo63j39T1B+Bx4538vjxTn79rnq85YUzbpOx3DDTSjAcC7RkUvkvn7b//bmvqX3GHf/pwIw6/rEwnE5gkdfrLUgM0YkhXn7xRCpZXq/3n4DFwC83NzdPUPmIutjPqZc4nBWQIz/n03lOwONffJmTafj78Te/BKdfBDO6shAEqFyDunovrppVpEqhqK6+C+Pwv1k/zPW3ozhcGMDg0cegaZIi10NdcOS/8LW+iueuXwdXYVZtNSd5knSeVq4HaUrkqUny/E+G0y8gNzwMigoivdXTqFozbC8E0owQ6e/EMMNojnxUT2FCm3g7+9i0tJyfHLN/E19fUwrAK+cnun1YQ1NngKZ951ld6eaTu1bi0KZ+Sx3rJluBpipsXlQOi8oSJKRnz9mGdOyVS7jTW8OO5VUcbu3kyLlOBoIRdFWhtiyP3WtqWFKYmIFpapBS8q8HztDUmXypWwJffv4cv3vPCpaVZjerjUNL73uuxJH87xdu2E+s0XpjENOU0/KmbyaRjWClp4GPAA8B/znmb4/Efj5JauwgGsbzBPDNxD94vd41RDf/3gTemoqyOY35dJ6WeaZTVAavnMDY/08TH5frJzGun8RXuQb33v+D1CdPoeio24b/egucPzixrETk1+De/H5MoeI/+eLkTn8ieloZeu4rON/9f0Fam/t8Os8MpvNMcj2ErzVBeHwO+OSI4L98HE/drZBXYbNvFKKoIkWV3cxyw9dF54mnCB9/EmK558MARbXQeDfupbdE/X9VS1jtJ+Fncl7mdrBqgYvTNm7k22oLYrnwJdd709liPxpN1/187cXT/MadjQlOgTX9x/LYYxJ27TA7uLDYziqfPSkqk3GXprB3RRV7V0wUDJGp80ByqKU9pdOfiG8dOMtfPLIxoeZFduyzrbaAVy/ZC+faVp+8MnV6lRQgbJg4FTXN3rmBzAbcWcPXiFr/b71eb3xVHq/X+wDwMaJhPv+V2MHr9TbE/iWG7fxz7OfnvF5vQ0LbBUSLf6nA55ubm9M9vrmPMaE+ioygSDMWepBZLiIBwlfeZuiFrxD44R8z9PhfMLT/a4QvvY0wwyPtzTAqoBjGtOiRLhdmBMUIZkRm5OrJyZ3+RFw/iX/f1xCRwKQyVQzytn0YVt+fXFb1OlwP/hGqqkHQB2//V/L2iei9QPjcq1mx1VzmwoyFRSVrn+R6MLuvpTx0E6L7CoqM4KmzkkxtDIQHV7V3xmwUuvQWwR//EeHjj8HYglN9l+DwN/A/9XkIDEbDoYbTPsLoN0/J+Xu3Dt9KUsIl4MENtcN9TSlTdbGEszdDHLp4w7LOybicQt/c5bmixzuXP/3mJeygOwhnbyam1s2O/rvXLLSlN8DOlfGQnIllFqSx7O3UlLTfQOQSZnwGzc3NrwJfABYBJ71e72Ner/dF4HGiWX8+3NzcPHa31unYvy0Jn30D+AFQARzzer3Pe73enwDngc3A94G/m9bJ5BQSVgVGhRhMnYdunMf/vT8kfOAr0HYMBtqg7ypcPYrx4j/g+9GnCd+8xIShDdOkk32eePGnL0dKk8iBb2AZnacIXIhvwpxYpgA8t7wHz3u/iFj3HiiuA88CKFoEK/bievAvyN/7Kyh6NF7Xf+4V6+PHYDbtszHfzNhqznMRs5Ol9oz/m0wvdjXeT3G4YJnl3ApRNN6FUNQUumaGhzrOEHn566l16mll6Ll/REbSX6OpynfzqXtXpnyFnafC7z2wmuKEKrilae4RmAgHTrVNWUbi1TeP1Ji3lTV09Q7R2m3/Gnu15fo0aGMPiwvyuH15seX2D6+rpMiVPNZnc12pbT0215UzlYrfuYKs5CVqbm7+Q6/X2wT8BnAXMEA0886fNTc3H7Eow/R6vR8Afgn4JLCd6IPDKeBfgW82NzdnZiknV2Ez1Cc01IPsvhZ1HArLcRRUI0TyUIVQ+xkiz6d4fgrcJPj0X6Pe/yfoxQvndKhP8NoJCNsrCC1P7cNcti2lfMVdhHvdfZib3s1whVfNgRIJYybO5WIa0Ws9F4gE/Siu/PlQnwzxqYb6iPwS0vqCyqsYPjaObR8k1NYMAQt1CouX4dz0cKyAz9Tnbwx2EWw5BN1tQAQKylBX7sJZvBiJSeTAv1uektnTSuD0AVzbPgTDVkm0TmpeV5zPXz2ylpfPtvPCqa5RpfEKNNjbWMmO+ko8ujaq75b6Cp45Y68A2GTo8Jm0DfioKfDY1j/OJTAf6mOVz41Qn5ngNwbSy0LYNRBIkJU9/d+3uY6IcZ7DF/uT6nvf6nLuXlWTUuddDZW8eLY7qayxuKuxxlb7XEXWEpI2Nzd/G/i2xbYTPmLFHPt/ZiTs550FC1l9ZCRI6NJRIqdfgK7R2U/9BVUoK+/E2bADJb7OlNDXDPQTef4fLatjPPlFHO//LCrM2aw+xoVD9o9T30Xk4A3UvNLMzCuU/ItvUgT6UJye+aw+OZLVx71oDb5xjlBquOrWDx8bTXGgPfAHDO37KvS2Tt6pshHP7l8C05zy/M3AIIFD/wEdx0eP0Q5Gy4sMFS9BLN8FYYuFxWKIND2GvPWDCDX2RiLxrZNFXuBycP/aJdzbuIQbQwECkQgeh0652xl7GTG+b2W+m/pSB+fSWA2dCN1DoZjjb19/hn+Lr/unZ4fc5HFkUu5cs9H0cSXNlWpVUcgFOytC8KFt9Wyqj6UhHZOVaPPifHY31lBXnG9JZkWeh931JRw4Z+17alv9Auors1u0NVOY/ZUI5hFDwtNtLFOGlCb+174H516auMtAB+Zb38V/6Q08e38NRXcM9wVJoOVVxsXlJkWAQOtRXEvWxb6TR2Rln8dvElOU47O7GTMKY6gbNa8kM3NJ8wtcqE6LY2XIVmO4NCP4L76JceF18PdFV8BLF6F79+Asrs7oWDPCE4/HZG2GbTi+ndAcsHIPtLxo/SAu3oTiLgIzMixHceWTd/8fELjegtH0PLSfIbp91glL1qKtuhNX+VIAzCnO2fD143/ybyCY5GbZexn51netzymGSN9lIr3n0MtWpW6cAooiqMx3M/7GPzE+uKOezz7elKSFjbGnGAowtd7vPMzbyxqqi9PLbF5VNFnZpeygobyIht1F+ENhesNhBIISh45zzJs8K3jvpjqGgmHeSFFB+ZalZfzSHu+cCPOBecd/diNFqM/QkR9N7vQnoquFof1fx33PbyMTXufTZDPVICCbX8JYsn7Ohvqkuy1GKnpGMqlEeq7AYDrpB3XIL8WMnyszYqsRHmh9A/PQvzHuQbL7AuFzLxEurce595cQ+RU5c86k4lMN9UGCa8NDBC4cgYiVB0oXzls/MPF4QqIvXo+zenX081iIWJybcT6FORuKTvDFf07u9E8R5mA/lMVv3Ik38OnlVXkufu/elfzD0y0TFsKyg4rhPQPp6RN9VJEJf5s5O0w/FxbbWeXzoT5WeWG+k7U1eZxoS1kfdRR2NFQmyMqNuQC4HRruUYWt7MtRFPjI9hU0Xr3JiyfbuNQ3+v60bEEe96xdxPb6ilmfwjMR847/bEaSUB852A2nLaR7jKPzNJFrp9AXrkGRRvTysBI3PBYDl+d0qA+l1XBzwoLRSaHnlzNp8SKL3By8SeipL9k/JgCr70FFggUdMh3qEzx7CPO1byfXr/scwZ98FudDf4Lizs/IuLke6qNIA0X34Hz4jwk+83fgS/JApxXiuvdTaO5izCzNOdRxBnovWD7l0oFQYcQ5TFypn35eW1zA5x5dx+GLN9h/qp2eNAoCLyt1UJ430ZsGOxzmQ32s8rlmo+nkggc2L+XEYxYLBgJLijQWFebniP7Tw4WAzYsXsHnxAjp9Q1wfCFJSlMei0nwKdTXj1aZzAXNvRu9YJDyRC4G/xcJK/xiET7/AcHjClFLcydj3TMJFlnUev0lMTY5zxe1WjTCChRujsfVTnEugaR8Y6ZWmd67abWOszNgKJJHeNoxUTn8c4T6C++PbdXLhnEnBRcxOltoz6d/UvFLyHvo06taPQtHi0TbJr0LZ/CFc7/ssWlFFVuccOR3PDDV9UItrp32MZHA7NO5cWcNfvecWPv/oOmqLdVv971gz9c1/iVffPFJj3lbWsa62nDtWlFhq6wA+vnvl9CqUY6jIc7O2qoT1SxewsDw/dYdZivkV/9mMZKE+F9+0L6/jJIaigNRASKKlEIxUvcbolI+BmLOhPmrpYiheDr3nLZtEWXvflMeVMpJW6BWAesdvIQoqLId7ZDLUJ9S0356y3ecI9rahlyzO+jmTimci1CfOFQnOFTswV90B/n5keAjpKkBTHNE2sXAdY6iLYOclCA2iOPLQlqxFU5wzMmfaz9o7lhYhY/8cNTtQ8xfEfiPhZ3a4x6Hxyd0r+cxPTmFl8X/70kI2VJcwVf0lMB/qY5XPh/rYtdWjm5bi0lSeOt3FZCh1wq/fvYpyjyuH9J9Jnvj73MO84z+bIY3oJr9ICCIBCA+BUKOfD6W3MiwHboLujMpZvhPOH7AnYPnWqD6hWDK9SGhEpyxyGRKgRUbbyKYcGfQTaH0TQjZiJFc/gKNs8ZTGJRIifPMypBN9XL4CZ+UKzLB/Rm1FJIQ0Dbiw37bK4dP70Ld8KOvnTMrzIRyJ3h/C4cnbh3y2rwcFCY58TKEMH4NQxxnM409B5+lhO5lEq0+Glm7B0XgPSvHC6Z1zeGrR7ybQSiWnWMuQno8UCu7wIF55hjou4771IcyImVC9N/vhAEVOB3/44Cq+8txpbibx/veuKOHdG5YgJUgpx8mxw6VpIhHDaVdzwQ5T4aYpMYYLs5kZHcM0JSI2Rrbnmes8bisp4b61i7ltRQWHWjp4/UI3faHoCv/Scie3r65idWUJihCYZmaP12zhpilj1/HcxLzjP4shTRNphJFmGBkcgnA0HZ0pRHR10bCft9eMhJGxi0Cp24pp0/FXl26C4BAmEkwDEbt4TCGyyo2wghkOIBNsZEdOoPMcHPwGtt6AND6I27sbI+Cbsv6mz17tgETIkH9GbRXnkf50NiEDN6/a1jkb3NScSAXMQHDSNoQDEPRP6Xrwn38Njv1gcntdfJ3Qxddh16/gXrBs2uaMywUBexsD42gnj/2OexjUijEM0FQTiUKPWsplfSWq6uAR53JqUaK+YQ6h1OXiTx5cT9PNPg6eaufCjSBhoECHzUuL2dFQQ6nTiZRyahGSMRix+Zs5Zod0YRgSKRQMI9GJzJBsM/Y4MU22klJyddBH50D0wb2iwMWigjzELAwwGmurQoeD+9bUct+aWsY5v1JgZuBcnq0wY/Ofq87/vOM/iyEUBaHqCEVHON1Ew3MEiqpD5VJoO55KxGg4S9EKShFGCBA4KusIrLgTzlqM7V39AHpJJWbQj+LKA901LEtR9axyVXejaDrCb9juG+o4CwctlopQC6HxLhwrt6Npesb0V1x56flDugvhcM6YrUbxdLMg+Ads65wNrrgcICWKS5u0TdSPlWlfD6FLbyd3+hPx8tcw7v80WuGCaZkzy26DpifsHcvSetop5oe+DQgtmp8/rGjowgAkQeHCUFwEhJsv77/KL92VT8MC6xU6Zw6CtRUlrK2Ih/JMtFqYGaixnXdzZ0+hwDBNVFVkfE5qNNou43JNU3Kw9Tr7T7XTFRj9t3IX7G6sZlfd7Mr0Ml22motQhEQRzJn0nWMx7/jPZggVFA00Bxju2EqZBM2BtvpuInYd/1V3g+6GWGw3mgP3lg/gFxJaXkjR9z6ct74fwn4wJOiucbKyyYXDjVQdoIVs9ZWmJLL/X63b0OjHuXwzIr8cYjH1mdBfq1huq6LCMCpXg+aaEVuN5UpheToaQ6gHqeg5df5MxIUe2/gZ0iZvL0n7epCqA+PI/9ozXdNzaDs/Pi1z1tfeSdiu4994L080afgUiQMDAQSJ7k0SQBgVU2gYIpru9qsvtvLF967HpasJQsQ7igtFiTloIif0mTqPrvQrCmMypEx9jLiNRhzwqcuMGCb/fKCZ0zfGePwxdAXgh2+1c+pyN+BIpukAACAASURBVL+8ZxW6qkwoJ9f4dNhqrnJFEXPW6Yf5rD6zG2PSeSoygiJNlEgIZ0X9+AwhSeHAs2JbLDXhiBwVk/xbP4B29+/Dog3juy2+Bce7/oD8Te9GNUIoZhgVUAxjnKxscmFGUIyg7b6h1tfB0ta+EQTPvJhx/TXNCct22NIDwLNi64zZaizXnYVQvMy2zgCh1jezfs6k4sKMpT5N1n4K14PR3gRD1+0Z7uKrEBycljnreh6svs+6LmUraaWGQCiEQzHQFBNVMUdxTZEoQkaTJBF9rX7oYjxELNHxfWdxmQM6ZJ7nih7J+XcOn5vU6U/EmRtBvnP4bE7oPM+ni89NzK/4zxnIkZ9CIAS49/4a/sc+B0Z/yt6Od/02isM1aWVPR9VKXBXLMEMBIv6+aJ53Tyk4PSgJVURHdGBU/+zzxIvZel/j7EGbxwE4fQB5y/ttj5WKOxrvIXThkHU9Vt6N4nDPmK0m4qJhD/JV+7nfjXOHoX5bDp0/E/D478naDNswRbsJeOTyMdt2Awi1n8ZRt2Va5py38WF84SE4eyC5EmX1uO7+dY68chmIn02CuEs7no/g5aYO7lxRndbc5wLmtsuReWTSXpf6BjlyLXkV10QcvebjYt8gS4vyMqjF9GH+3JoHzDv+sxspKveKvFL09/0F4f3/CtebJpZRuBh198fRihZhYiGtnzMPJa9kpCpomukLZ5qnnaKyuy2NAxPCMCMIRc/oXJSyWpTbfxXzpa+mVqFyLc7bPohpmDNnqwm4urAhvRCl/s6snzOpeCbTeU7ECQ6lYzkiAT/aNM1ZkeDZ+mGGFq6HE0/BzTEpPp1l0HgP7obbkQ4313vDGKZC0FRjoT5iFA8T30Q38hjQ5Y9m1Bj9pl2+Y7gE5tN5WuWZTee5/+Q17OLAyWss3bEyYzpMH59PfWrHVnMZ847/bEaSyr3xapu6nofzrt8gMnCTwNlD0N8GpoS8Upz1t6GX1WIKhUxVaU1VqTRbPN1qtCMrnvagRIIousj4XDyL1xK661OEXv8+9F+ZaGRYfS95Gx5EGsaM2moirkqZnuOviqQVcXOBZ6Jyb1KuO9KxHKrumHbbeRatQVm4ishAF+H+GwgzjJpXilK2BFVGY7rNSAghjGh4D6Ap0e3piVxDEhkT6gNx51eM+u2dw+MPQSKNvrnM48ik3Mza6MgV66v9cRy5MshHMz6v6eBz7XyablvNXcw7/nMGcuTnBK/qtYIyPJsfjYXlgKloo0N05OR9bfFhHcis3CnzxIvZRt/Ccui1m8JQIHQP0U1tmZ+Lo7Ie7d1/inHjAsFLRyAwAJqOWroUx7JbUZXoxkg5jbaSUhK6cY7ImReg6xKEQpBfjKi9Fad3B4oeLfyiuAps2i6G/MocOGdS8GkO9VEq6jFbLGbUSoCjYvmM2UIrKEcpqhr5XhEKyJHzPs+h0ecPxc6mqOMxMR+BDiijl/vfUXjnzjw9ZNJeNpI1T6lPtjB/bs0D5h3/2Y0UoT5Z4XMt1Gf5Tnjrkr3jUr8LqTstV8pNl6vly/CU12FqjpHQq1iF1+m0leHrJvj8V6D/8uh59/Yhey8ROPYDWP8+PGvvQeoOWL4Lzr9sy4SK9/asnzOp+HSH+ujLtxA8+B3ARj2OilWIggU5Y6M1C4t5qaXLVqjPbcvj6TxlwsTeOVwC2Q71CRsmQ5EITkWNZVjKlHxhsZ1Vnunwlakgs8egxx/kjQs36PYFUYRgQbGbrbUL8Di0NGWmtlUoYtJ8o4+BQBiHprC0NJ/yPNe0zTF3eabOidzEvOM/mzFBqI8x1Eeg5SBcOw5BH2hOqG7A5d2NUlQ97eEPcy3Ux1O/haG3/hM7XwSuhj05H6aSrq2M/k6Cj30OIn3JjXDsBwwZATwbHsbpvYOgHcdfK8K9eC0yh2wyEZ/2UB8DxIaHkG9/37LpHOvuR5Fmztho09JSDp3vtBXqc/uqmthsEt6YvKN4/CFIpNE3fW6aJkfbe3jpVBvnu0PDupQ6YU9jNdvrKnHpii2ZY+cVRSb1zqyN6kp0WnvsVaiuK9YzOq+b/iD/81orJzvG7vHp5YdvtbN9aSGP3rIs4YFs6rbqCwR5/tQ19p/tGXenW1nu5J71i2hYUISUcK57gJeb2ujoC2BKSUmeg20rK9lQUxarQZHt6ydT59XcxbzjP0cgpYHv9e/D2RfH/3GgjUDLC7BwI3k7fiGaf3y6Xv8T50zfGBNwM+zH33IIefMC+PqiVUoWLEdbshFn2SJGX8zW5SuaA23nJ4kctJjLv/FBtKLqSbMj5Qo3BnvxtxyAK8ch5AeHEypX4WrYDUXLJrVV8KVvpnb64zj5BKHqBlwVywmtew/y+I8tdXPs/UWEEFMIVZohPs2hPgiBp/EOfIPtcM7Cg9Pmn8NRWZ99uyTwfI/O7SurePF0J3HHI2qVsTyKu7xlVOXHMlHNUbT2DvDSqXYu3BggYkKhS+OWZWVsX16FR9ey4nL4QmG+/Nwprg6MD1zpDsKPjrTz0yPt/NZ9K1lSlGb43jQhk/a6vbGG1oOXbPfJFNoGh/jCE2dI9uhx+GI/5zqP8al715HnUJO0HI+JbNU2OMTfPXF60qTVLV1BWvad566VpRy/2kPn0Ohrs8MX4HTnJVziEh/bXceaqlJbOs1j5qH++Z//ebZ1mG34GLDUMEyCwbS2LWYMHrcDI+hnsK+fgWf+ES4eSt5hoINwWzNq/Xak7gIJUgjMWKGhTHBpmtFQE4cLqTmnZQwkhH1dhPpvYPi7CRz/KZGX/wU6TkFfG/i7Yegm3DiLee5lwqcPIt0etKqVBAJh22OpxTWECxfB5TeT27fxEdwbHgShZHy+meImJv5XvkfklW/AjXMQGgAjAKFB6L5IpGU/A12XcddvJRgyR9t8oAPjqMUqsjGYPh/a8m3olfWE1QLoOJGktY7yrj/EUbEiJ2yVirudGgiFodhWmem4HoQEbdFaImohdJyDibZKuxeg7PgFnN5dOWGXsXxReR5Ot5tz7X1IGQ31EUikFISkiqnqRITOjoYaHtlUN/K8NMfQ6w/xD8+e5JmmLtr6Q/gjEDSgP2hypsPHc6evo6smjbXlCMA/ZK9+SLoIRQz+7ukTtA0mrw1uAIfO3WRTXTH5DutrhlJK5PCzcmYPrjsvugE+kCFbVeW7OdzcQdBimfQCDT60pS4jFXxDEYPPPXaKoIVnXl8YWq/3cNvyCsvyJ7LVQDDM3zx2ylKlmgs3/fiSPJFEgDcv9lJd4qC60G1Zr1yEJ9+JpiqEgpGMn7N24XLpqNFXKZeAb2VC5rzjbx8fI1ccf5eCGRri5qs/xDj5uLVOgR4i4SCOqoZoWAeANDPGhRFCGAaKoiIgo2NgRgi0HCL8yrcw3v4x8tzLmOcOQc+YWPOxMIOELh5lsOM8joVrUWIbEO3ooJXUoNXvJKK6oasdZPxVuAor7sC146M4lm5CJfM2zdixMSMMHfgXuPRqcnv1tuO7cAxH3eZRtvIffxJutibvOxa+6+grbo++OamoG7FhXxcY/qj9impRN7wbz66PRzeL5oCtrHCPQ0VIk4A/NK3XgwJoFXU4Gu9EFi/EdBZDSTVUetE2vhf3LY/iKKrMGbtMpP+aujIaFxcQCIbp9gVQhRkr3KWyZkkZ7966nG0rliAUjZF1STFneF8gzF8/cZKbgeReXfN1H6YZZvXicgJD8e+Y6dXt+aY2jlyznsCgraufbcsrLcuXEqSMVkKNOlGZm4M7zwlAYCicEZmKUFizpITDLTdI5ftrwO89sJpCZzz71tTmcrC1k2PXBlKMOoIev8HqRQUUu5yW5E9kq2dOXqW5y8YeIgs4ermP7fXluDQ1qT65zD35rjnt+M+H+sxySCkJHnvMXqczLyI3PgJKLEZwCq/zpWkQuHyUyLnDMHgzGmKTV4bWsAd94Zq4llMaAyEwg36GXvgq3DyXvrEuHcUn/oP8Hb+Qlg6qu5D89fdjbnwYEY6ukUjdmZDRZGpznG7uP/MSXHvbmq1uXsB39HHyNz86Iqe3PS2zRwau43Dlj7Ph+AxT5H54TyKP/56sDXGeop0FLhQVV+0mzLoto2wnpiM7V6Y5gkVlhTy4UWHveoHpD4EEJd+D21mI6ciPNp2j+PbBFnwW07/875ttrFlcTpVTT914ijBNyb6mztQNE3CuO0Snz0/FqE2f2UOm3bLKfDd/8vBq/vPweVq6Jl4LX1Hm5MM7llPuydzK9oFT9mvGHDjVxtIdXsvtE20VMUxeOHPT9phWcLilg/vXLbbcvi8Q4mBLO6+e76I3CAqwpERn1+pqNtWUoqn2QprmkRzzjv9shlDxXToO/R02Oxr4Lx7Fs3wbkH7mjmBbE8YLXwPGlDfvv0ak/TgRVzmOu34VrXjRlLKDGIpC8PmvQs8UnP44Lr5OsPFe1AXL0tYnXsgoqpuGGfCBMJDukinJnE5uKDrmqWft2ar5OYyNDyN1d1SOYfH99xiYEkyh5oQdZlNWn7nFHShGCFOoOJwOFDW66GDqTqSiIAXRRQMS3f+5wa8P+id1ICfDT99s5RM7Vky7bi03+xlK47J+7dx1Hlpfm8a4Im1dJ+bTU5Sq3OPiN+9qpHPQzytnr3NjIHqPW1Dg4raVlVTkuS3JscpDkci42HkrONM+aGOs0bZq7RlMr8aKBexrusF9axeNCdsbr5uUkmebrvL4idEPnwbQ2hOm9dBl/kdc5jfuXcmSovxJ5WSe2z8Wswnzjv9shjQId7am17f7CsqyzYAYn60kEsAY6sGUAsVVgOJwj2sTvvw2RqoKsoEuQk98BvHA/0UtWZx2dhB/y2uZcfpjCDe/iF6yaErZSuTADfwt++H0i4yKu16+HefKO1DLanMiq0qch9pOQbDbtq1CF97AuXJnVE5BEaSxQKS5inIq08ysyeozh7hiBBGmOf48MMMIJbqXQZpm7C0kRG+8Yk7wg812F2bg6JUB+gJhilz6tOrWPZhebHyPLzEMyeq4pNEnFRdT6JuaV+R7ePfGpdMmP84DaS6qBCLYGGu0rQaD9rIX2UEQ8IcMPMNvrSbW7ckTl3mqqSupLL+Ezz/Vwh/e38DiQs+EcjLPx56zcwvzjv9sh5HmM7sZf+8sib+SD/dcI3j6BWgd2SQcAqheh7pqL65qLwKJGRggnMrpHxmI4E//FtfPfAFFc4wazyqXTfvSm+NkuHAEbvuILR0S+VDLIXj92xPLPn+Y4PnD0PAu8m55BJGG/OngkW77r5EBjIGOYTl63W2EL75uT0DRYrTC8qzOfa6E+sxqTsxeYz+XIvpvDuNaz9i0jNZw3eePOf7Th3T3pOZSgbXc0SR9uLX0XLE8m6dHoq2c2vSGz4RT7JJo7R1I6fQn4l/2neEvH9lItmPu5wLmHf/ZDKGiFC1Ir69nwagCTUPHn4Fjk2RsaT+O0X4c39JtuHd8lEDzYZuDhQj892+h7P41XIvX2woTMPzd0HcxvTlOBtOXdtiC/9zhyZ3+RJx5Bh8Cz+ZHcyLUIho1mQYMY1iOtnA1Yb0Ywr3W+ze+y37RtFnCsx3qYwR6CF6/CMEBFN2NtrgRTfNk3S4T85FQn9GfayhzPNTHSHM1d6Tf9OlWXexJS7eKonRDXYTFdlb59IT6zDTXVcHSYo2LvfYW8jbUFtsYa7StaovzbI1lF3la8kJjL5y4ZkteTxBOd/axurI44dPpOi6Jv889zDv+sxnSIH/5Fq4LJ0h7r2z1urUQHgKhEmjaP7nTn4iLr+LHhIun0lLXPPAVAjs+iWPpLdEwCKGCNJJyc2A6Nh8pEPZb1iHOTf8g8tV/tz7MmacJ125ELV9ie6yMc1eam9BchcO2EkJFuf3jmPv+3lrfci/O2g3D51nW5j5NXIYj0ftDODx5+5Av+nsotg8mA2OHu1oxjv0U2o8Pm9ok+nYutHA9+pr7c+OcS+ThIaQRs1Pi50IgpYJUnMhIBJTkoQGzkRe5dbBZFAqgyOXANM1p1W1xoYdyF3SN2aaVCluXLbCsm2lKDDP+WWbnY5oy+hbalFOSkwt81+pqLh6+gh3s9FZanvtYW7l1lY01Ho62pfdGKhnWVrpQBJPqFoiYHLWRSSqOl5vaqC8toDcYwjBNCp0O3Hr8ASOz55WUc9f5n3f8ZzGkaSIUBbHqDmTT09Y7LliFUJ3IcAgzNARH/tN6X7uhHmNgHvoGkdJadD2aEcIUAhG7wCbkaa6WJUXpcsyAL/m4E/BQi43qszGETz+PuPVnbI+Vaa6X1yUtCjMZHDWNo2ylFdcQ2f5xzMP/lrxj+Upc238eMxQY/gLN1tyni5uaE6mAGQhO2oZwAIJ+TCSYxpTHDlw5gXzjO5Pb/doxwteOEd72cdw1q7JuoziP+EE1JTIUGN3GMFBcOmY4hNTn5o1284oKjrRdtNWnukChwuNOcJymD3vW1vCDN6yHAm5a6CFP0y3rZhgSKZTYG4zMhmkYZuxxYhpuEzONDVVlPOu+wnWL2TU3L8qj3O22PPeJbLV33SKOtrXY1jUVdjXWJNWrK5De3pKT1wP87g+PjfrMW+5g1+pqGitKEBk6v0wpMCVz1vmfd/xnMYSiIFSdglveR/+51yDUY6GXhmPrB1BceQgjRPDcK9Ou51hELp/EsWo7IFBUHWGEJuVaaU1aDmsyqKvvGJ6/FR3inMtv2R/s8huIHR9DyIitsTLN8YcmUTAJqtagFVdgjpHpWrIeo+KzBFsORzc3y4QVowWrEKvuiO4HEQKyNN+Z4IrLAVKiuLTJzxkFTCSKKw9015TGC18/m9zpT8Sr/0bknt9HL12cE/bS3AUII4IIytFtNDdC1VF0B1IRaUek5TLsVlcFuHdzbTTyaQawa2kFZ6/2cKw9tcdZqMEHtiy3qZvAME1UVWR8Tmo02m7GbDWdUBTBb9yzmr9/uonuFH5xQ7mTD22ttzXviWxVW5jHh7Yu4j9fu5qWzhNhY40Hb1nhmIw+o5Guez6RG97cFaL5pUusr+7ko9tXoqlTd/4VIVEEc3Y/wbzjP5shVFA0lPxSnI/8KcHHPwf+G0k6uNAf+AO04oXRqppCg9apreCnheYXYO1dgIS4HpNwoUlYvgfO78/M2J4KHEs3ITVX0nEn5P3phR1JAagee2NlkEsMws/+g02tVRxbfxY014Qy1Xw3ri3vR2x8GBkawJASVXOhaM5oxdlIOGvznSku9FhYSkibvL0EDAm6C3T3lMaLHLVYpC+G8Ns/Rb/nt3PDXroHoYRAM8Z87kKoToSqIrSxxbuY9fxKv48vPXceO1hZ4WLvqkX0dftmTM9P7Grg+29e4OCFvkn1Wlqk8ct7V5E/nKnFqvzoSr+igDLKU5263tGKuSKhcm7uHPt0eLHbxR8/uJ5nTl7hheZuxpZ+KNRg75oq9q6snqBacHq22l5XQaFb5/uvtE76wHHnylLcDpUnTibzL2BDjYeP7lgZLzg1qT6lLmdSOengWLuf77xylk/s8iY47OmfV3PV6Yd5x392QxoITIQ00d0laO/+NIELb2Kcfh4GEl7dukoRDXfi9O5E0xyQmFJvaPIv+mlD4AZKbAXcSipAx+q9hDLh+GuFuO/5bVQJZhqpCVE1xn0TW4BimijC+nwzzQNXjqd4IByP/F0/B/mlo8+VieRjgiMfRSjRz6c5bac0TUKtb2I274eui0AInKWwfAtO7x4UT9GM2XYm03lGuq/AzbP2TrzOJuTADZSCBfPpPLPApYSvP9c87rAkQ4VH8P89ckuszoGYMZ0VRfDBLcu5e22Ql890cOTiTXxBcOqwojKP3Y0LWVZSMMWx4sjkHGbORjPFXbrGuzfW8cC6JZzq7KN7MIgioKrYw8rhlfTM2mpNVQmNj5RwrnuAoxduMBiM4FAVFi/IZ1ttBU49WnejYXEp+09e480rgyRiZbmT3Y01rKsqRghlnPyxPM+hs7rSTZPVuCaLeLttKLYBuCSlDqltNXcx7/jPGUiEquNecRumdxf4+zBDQ0jdg+bKQxCt8snYKp+qmpYzmymdraQC1IqrMbZ9DOPVb6U9krJ0A+V7PsFQWLM87jheXG3f+RIehOYEaaQ/7hR55PR+2/YabD1C/pJtWdN5Ih7ubiP4/JchNCarULAbmp4m2PQ0wdX3k7fhoWiewunWLf57sjbEeYp2KXjo0shGXjsIXjmJq3Fv9o8fMXuN/XwOp/M8db2XPptxisUeBy5n9m7LZW4Xj2xcyiMba0nuvOcOclezqUFTFdZXl8Z+G+ucpodkthJCsKKskBVlBUx27JcW5fOxHV4+GI7QHQxhSEmxQ6fA6bCt257GGpqu23sbZgX7T7UlOP7zmAjzjv9shlCRKEihDKcOhFhlWVcRiqsQU3MgI2Ekk6TaK14InTbSMwLghLpboNVuWs8YtCLbKR6d9TsIODyYL/0b4yoFx1G6ApZsgo4WMIbAXQgFC3Gu3Eb5wsWYqgPzZq+tcRO58N6OPGzT8V+1B6k5MGOhL1lJp3jjtD2dAa6eyKk0nKGBTiJPfoaUN5amJ/EZYVy3fXjadZvJdJ6EbaZdicEMDeXE8Zs8naeOoupIIeZcOs+fvNGKXbR0BekfDFKY70yQlf25ZI4Li+2s8rmRznNmeGZsZUrJ2a5+Xmpq59LNAGETCl2wdXk5O1dUUehyWJKzakEh22oLePXSAJlEU2eAzz5xhMpCN9u9VayqKEoI27Fuq7mMecd/NiMh1EcxImAjfCbO9YbdhDttpudsvJu8dffiEyZceNW+3qvusBXqE+eexeswPvwlwpffJnLhNfD1g65DyUIcDXfgKCgHBOaaO4f7mrEQFGFGUJBpjRvnniUb8L2aB6b1NGRu7y5EmiESRmCAQMthuN4MRhh0N8qSDbiXbUEBi+Eo6b/OmYqtMhreA0T2fQXLX8bNzxFZvA5XZf206jajlXvV9L6qheZIql9WQ32EAiig6NEsY6ZMqCglmXjVcXZwKU3afek5D539fgryXYw4ybkzr6nxODIpd67ZaDr51G3VORTgK882cXPMOsTNADx5qosnT3Xx0NoK7lm9KGVIkhCCD22tR1UucKg1syHH7YOS9sEh3m67QIkTPrHXy9KiPBvzHXvOzi3MO/5zBnLkp43X8I5FjYQdJRYzAkXhbLgdoajkbfsoQ1WNyMPftKWpc+XOtHQFiVA0XEs2wJINmIqGYkYLnkwYxjSKJ17M9sdFCISq4bjr1wg9+3lL81Rv+wVUT3E0raONsaQZYfD1/4Jz49OHmh0n8L3+Xdj4AfJX706ts4BoqhS7+e4S8qlnOdQnfKN19J4VC4icfA4q66dXtxkM9dGq64mcsGUCABxV9Vk/fsM303GhPgpSqKA6oj/nQmqWGIbCaVZUj2Fuux2ZxbytrGMqtrrhC/C5x5tIdWY/fqKTYNjg4Q21KWUqiuBnt9azvWGQA6faeP3yyOp/oQ7+MFPO6tcThC8+1czv3LOC5aWFU5Q2NzDv+M9mJAn1id9wU3Ih0e79XSKPfdrSkMrOX0YUVERDV4TEuXIXAb8Pjn7Pms6bfw5RUD7joS9SUaOhPlMMX9Eq6onc+38xn/kSo9JYjsXOX8G5dBOmXT1lBP/T/wDdyXIrm3D0ewz6+/FsfiS1/EXr4epRa8cnBrF8a86E+oTOvmRLdwA6jhMJ+lFc+XMi1EetbiTiqYChTus2KFyEWl6X9eMX5ROE+igC6fREsx0p8Yw+MmECs5cHI+knlq8odMfkxOXmzrymzjN9jHMn1CcQjnDuZj9DoQhuXWVZaSF5zuTVa2eWT81W39zfnNLpj+PZMzdZubCYhgVFluTXFuXxke0r+PnbJMGIiaYINFXh6/tPc7IjMxuAv/zsWT7/vvU4tMQEApPplvj73MO84z+bkYFQHxA4CivRHvhTAvu/Br7Jsr+40Hd/EueiNeMy4uSv3o0PE3n0+0nVVTb9LB7vzrQy6kyVZyLUJ85d5bWID/4twcvHiZw5AP2dIE3IK0Wp346rfms0LCeNsQaP/CiF05+AM08SqqjFsWRjUpmOhr2EbDr+pRvvIpgjoT5pp1EdvIHi9MyNUB8jhLrhQYxUhdMSoK1/YHwWnXj4VCRMoOUgdJ2FcBCc+eiLN+KoaZiW0KAJQ32kjjRMJLGQHySj3srNYu7R07+1FuQ7kRkKzcgtHkcm5WbfRjd8AZ49cYVXLvYzFpsX5XPPhsXU5HuyrudUbHWpb5CrA/bCRl882UbDHcW2xhJCwaWPfBfcvrqGkx2Z2QAcAV67fJNdyyot6DP2nJ1bmHf85wzkyM80XslrxdXkvfsvCXaeI9L8IvRfh4gB+aVoy29DX3oL6kRZOWI8b/UdhJZsIHR6H7QcgPjagHBDw+04vXvQ84rT1m/qPPFinrpMoWi4lm7CXLZlVLjRME9DphkKwennbR31yKnncNRuSipfr6gnVLEaOpssyVRq1+OqWkGwp3+ajoVNLtNdPTWnV7cZDPVBCNx1tzLY2wFNT6ae+pp3R0PixsiR0mTw6GPQ9DQj3xlRhM+9RNhZirLlA3iWrM+svYjZa9TnjNMhVzEUinChZ5Bg2MCtqywvK8SpTV6Yy6WrLCnSuNxnL+RnQ03eHHc5Mo9s2qulq49/ev7cpH9/8+ogb149zS/urmN9dfYzzaRrq8NnOmz3OXXdz0AoTIFDT914EqxaUESpk5QFzazi5ab2mOP/zsa84z+bkYlQn0QuJNqitTiqGqKfaw6UeEiOhcw0SkkNrs3vw1xzL8LhBN2NGm+X5cw2mQr1mU4euPgGth2hm+cIDXTiyK9Ielwdd/8moSe/AD0pVk9K66l812/mlq3yS6DbnlkAyCubMItMpvhMhvrEuWfTIwwVVsEbPwBj/AojejHilkdxNtw+7nqTGPif+xpcPza+XxzBbsyXv87g5g/jadg9vaE+AqSqxTb0xs/7xPM/+7xtPWJziAAAIABJREFUwMdzx6/yxpi85QA7lxVx95qFlHlcE/bd01jNdw5fGdcvGR7dXBszjSRXbTI1Liy2s8qzF+rTNjiU1OlPxL8eaOV379FYVlow43qO8PRt1daTJKw1CW4M+ikojbuZ9scVAn717lV89onTo/6SLtoGjYQxkttqLmPe8Z/NyFCoTyb5cGgDCkiZ9Wwi0xHqM222676c1mlgdLai5JUmla8JBf1dv81Q0z7kyX1gjk2hpkLFKvS1d6OoGhjBnLGVXr+T8OU37Rml3IvuKmA6C4rNaKhPAvfUb0Ms20zoWhPh9tMQ8UcfsmvW4Kr2IoQyYTjd4NHHkzv9iXjzu0RKqtEqV05zqI+RkM0n9jQAOcGPtd3kX1+6OKmJDl7o49CFPn57eNPgaDm3LFrAvoI2rlkMkdi9vJhSjzvWWzDiJOeOTabG48ik3OzZ6PE3L2EHP379Ip+6d92M65kJW03lnWu640oJZ7r62H+yLcNuuBV9xp6zcwvzjv+cQcJTbDZDM4Z1ILt6jOOJF3Mu6DMBN9PMBGLE8x4kly9Ujby178JYey/B869innkReuM3LwM6TxLed5Jrb1ZReMvDyOqNiGzbRIKjykvYVQ6BLssmURvvtmSTKfEZDvUZdSyFwLmoEX3J+lGhZmKSzFYyHImF91hH6OSzaFXezOhMzF6jPo//LfdwvmcgqdMfhwS+9OxZPv3QairzXKP+piqCX7+zkb9/5jg3UuxPvGVRHu/dvAyY6y5H5pENe/UFQpywuem0tTdMh89PVZ57mrRKjXRtVZbv5FKv/fw6JW5nWuMNBMN8dd9prvRPLTvWWKSnzdzDvOM/m5HpUJ9M8AyHNmSKz4ZQHxzxjVA2kVdqa16h3quYr/4Hk5Zs7uug/4V/gfo9uLf9LDLb9hESde+vYDz5V9bsUXsr2tLNmMb0hpZlI9QnXR68+DK2U7q2Hycc6Ed3FmZAh9kV6vPDVy9gB08cucQndnnHySlwafzh/et54fQ19jV1MTZUudIj2Lumhu11FbHnRBkzjSTXbJIZLiy2s8qzE+pz7Fp6CQeOXeyiqnHRjOk5mqdvq60rFnDk6vhwt2RYUqRR5nYkyLI21lAowheePJGxuP5EbF1ebFGfxN/nHuYd/9kMaURXiSMhiAQgPARCjX4eCWWHh3zR30OxCh/Z0mMMlyEBWiQ3bDQJ12vXEj5jYePmKKg4Kuosz8sIDmI88VkmdfoTcW4/flchrvX3Zd0+ekkN4s7fIrLv/0+ue91tuG/9IDLin3bdZDgSvT+Ewzl/PcjOZpvnVRTh9rPoC1dNXYfwENKI2Sn+uepAhkNIzZjRUJ+IYfLmlRu8dvYG3b7oKmZNiYsdDVU0VhTR5gvY3pR79JqPszf6WF4WzxM+Mq5DFdy7ZjH3rF5Ec1cv3b4QqiKoLnJTWxyN+ZYyGtoAIE0TicA0JTNlk+nkpikxzPhnZkbHME2JiI0xk3Pz+dPLLj/gD2XtuE7FVg3lReQrMGhj7eD2VZVpjfXjt1qnxekH2LmqypL9TVMi4xfkHMS84z+LIU0TaYSRZhgZHIJwCIhmlBGxkzaRh/0DGBdege5r0fCQvAKUxZtQq7zDp/1kfa1ywgEI+jGRYBpTkpVJboQVzHAAmcJG2eRaXjnhgkUwcDXpcR+F8hVR58niWMGTz2HJ6Y/j5GMYy7YiNEfW7aMXLUJ9+M8Jnn8Tzh+EQMKq2+JNiPrbcZUshEgI0wiPkyOliamoGdPH1JxIBcxAMOevBwJp5sL29Wbkmon4QTUlMhQY+dyQmHoI6Zy5G+yx6918++VL49599FwPcOr6RQpUWLs4vSI//7jvPPevLufuVYtGorxGQeAtL4byRKd4fCsj9tlEf5uNMAyJFAqGkej0Z0i2GXucmGFbqVp6xeYcmpK14zo1Wwk+umcZX3nB2puwVQucbFpYbnssvxHhlUtj959lBruXF1HhclvSyZQCUzJnnf95x38WQygKQtURio5wukFEn1YVVUcYoWEuAwMEXv8eXDkyWkA3mFeOYGpFmNs/hLNm1bi+djkKmEgUVx7orinJyiRXdTeKpiP8Rk7oMxnXtv4Mkef/zvpJ0HWGcPcVtMplqY9NKADnDts5xQAIXzuNvuLWnLCP6nDi2nAvytq9SBl1LtX4Ks3Y+UZChLtaiZw+AG0JdQxKlqN4b0ev24SQZtr6KC4HSIni0nL+esCTWEjHOkRRGcLhmLIOmrsAYUQQQTnyuaKj6DpSEdE0/tOMt6508e1Xkj9UDxhweIJ87FbxZFMXEsl9axanLUON2WLuFDIWGKaJqoqMz0mNRtvNuK3qKwuB67b71VYUZu24TtVW3vJC/s+eOr6+vzVpu7WVLj6204umTvyQ1zUU4MTVHvzBCJqqUF9dxLKS6Juvt1qt7+Oygx1LC3nPpqXRF4sWoAiJIkBM/AQ/6zHv+M9mCDVa8VJzgOGOhfDK/8fee8fHcZ333t8zZRs6QDSCJMAGECDBLopNJEVJpIolS9d2HMftdfpNtZ3c+4mT6/u+aTdO7nWcchM7TnWJ7cSJ5SJbki2JpCSqi6JEEiDYOwiAAAkC2D5z3j92F1jUnVnMFkD7+3xI/Hb3zDnP+c2Z3WfOPOc5sdeJmG9pEH76CzDcPX090UGM579IcMcv4lq5ffTY5HoscwkYEnRPbEfO2dTlIBcuL1J1gRbOC3um4666FqKNO+Hii5aHgTz8NeQHPhebZZyh/ujAJSBsud5RXD0OrbvzQp9x5xQZexIRT1uZXEYiCR78MvScmNyfm2cxXzlL6M3v4nrgt9BKa9KyQeh6rL6wZut6kJhEbl+HkUGEoqIsWIKi+TKql7r0DoyzL9g88QLX4nUgxext0H0IJQyaMfa+IhC6G6GpxFwSMa5tJ/mtQDil0+8UnuzoZ9OyGuqKE4s47dksFCXuoGVWk+zx+M21Aso4r3P2bSQ0UkY9uuz0bVlFCbU+QY/f+oxwkQLt9RUoIjfn1Qmt1tRV8CePFfPSmR6eO9HLSNLseXudl92rF9KyoHQKh1lw4dYw33v9Aqf7J/wGHe+j2gsPblzCzaH0Yny2N5Vyyx+mozc47v22Gg97Vi+krXbi+rnUWs1Xpx8Kjv/chkydzjN88MszO/1JMA9/GbNqMVrJgkn1WE5JmYH0hU7wuZDOU5FG7NHiteP2xkF4AON6J666lhnrN0JphntE/Hmv2zgNTYPQj/8K+k+l6NdNwk/8Cep7/wdKUVXG03lKY5jAyUPIrucgMH5xYHjxRtxt96FWL8+ILp6alYx4qya1OyOa70aVOHLuc53O8/Bp+xsQzQaHOq7xwS0rLNk2mUMhnadVnjuN7l+/mK+8ZD0F8/3r61FE4sYnFzY7o1WJ28X+1YvY17aIUNQkYhr4dB11hms4VWrcvgB85fAlFpem55JWl3n5ma0rGQ5FGAgGQQgq3W6K3Vqa/Z04ZucX5s3DxALk2N/4naox3A9X35r+kCkQ7kjsHDtWT3qcWR7vNE++mPPBnql5dOAyRG5hF+ELr6WsX3G5bNcLgCsxc5l7faxwf+eh1E5/AuYIgVe+mV5biZk7C+WNwCCBJ/4IefTbUzvfl48QevpP8XcczIguQoB+xwetaQIgfHjX7HfOhlGdkt9PfJZZSCk50JmZEILp8MK5wbTjg+e/2+EssqXV9eEAP3j7Il9/+TTffPUMw8EIe5srLR27fWkZe1bWZdjC1HBSKyEEHl2lxJ1w+qfGlaERS6lxgbTTd1aWxFLpFrt1lpQVs6S0mGK3nlZd7wYUZvznMlKk8wycsvtoHzhzCGPz+xGau5DOMwc8GrSXMm0Uw7dS7lKrlDcSu9e3ueJq4dq81y3BpSmRozevFtH9DpHAILqnzF5bFtN5GsYwkZ/8Jfh7U9ty5BuMuN14l29zXCN9yXoiW38WXvmnFEa40B/8bYSvwkEbcpfOMxiNEsz8/cUkhKIGHl2dZE8qHpNGkklNcseFxXJWeebTeV4d8vOtl85y/uaETD7nBwFoKte4eivKVHl+FODhtTXc29oQDx3J5TnITerTp95Kb2NKq1CBdfUVlu2xxnPwhZFFFBz/uYxUoT599nJRJ2AOdqNXLimE+uSAK0LYybkzBl0fH0YxVf2qAi13Q9eztqr2rLwj73VL8GjvKQjftC1fpOtF3Gv322rLaqhP8Nxr1pz+OOQr30I0bUIBxzXyrdiKUV5N6NjTcPXohJY1aNmDe/V+dG8xTu56nMtQHyNHWVTGYrnt2gyFUB+rPLManR24zRd+fJqZcOFWlPoihbvbGzjbPchIKILPpbFiYTlbFlehqck3f7k8B9kfT0OhKEev+ckk9q6qQlMVx2we02r+ouD4zxvIsb9CxF4aabmQSCM8vh47fNQG0js+Yzz5Ys4He6bmWsWiKWeOUqJiiaX6va17Cdhx/NseQNG98V2Fc69PKm4MpRnSMZxwzG20m3g9QxkJcPp5e7bIAKGLb+FavjUjGulVTeh7folIcBjjxnmIhJCeEjzVyxGahqlozp9v4nqNez/xWWbh09P7mavxwp3La/jBces3bQnogEtTU5abCvPb5XAemdJrKBzhr1I4/Ql0j5gcv9jPL+5uZXS8A9kY33aQ7bHV1TeY0fprfQr7Vy/OaBvzEQXHfy4j1c69Ll969XrKUoaNTMsLoT6z4sJXDtXt0HfM1ilzN++0dM5EcTXs/RQ894XUlS7ejHfje3OuiR1uijR/2qRie2xYCfWJDlyG4IBtc6IX3kBrviujeomSalze2E6WpuZCRiPIjJ2b3IX6KApsbCjiyNWRFKqPx9aVtexra8DlUvnPI9YSJCSwp6XStp0JHpNGkklNcseFxXJWeebCV17s6rb19PWdbj89Q35qS7wZsSeftZqOB0Lpxey313m5Phigb4Z8FEtKVX713jY8evKC6fTsnEqr+YyC4z+XkSLUR126GaPHngOJrxq9eEEsv3kh1CcnXFu/n+hPbJy3JVvQPSVYCc0whgfgwN+nrrNxA0U7fg4hwcwDTaxyvbQmnYSlUFJte2xYCvUJpDnjFfLPWO9c47nO6rNnTQNHrlpc8B3HjuW1gGDHsjoeP9Jta2XMXS31s7AZCqE+VnlmNDKlyYHOPuziha7rvH/zUsftyWetZuIed3ouZrnPxS/sauXt6wMcPHaVs0nrK9pqvOxeU09bdRkiY1mSJo7Z+YWC4z9vIMf+xh+le5ZuYuSVrwE27rpb98UjdsbqscVHbSC94zPGky/mfLBneu6qa8FY+yjyne+mPl+l9Xi2f9RSv0I9p4n85M9T1wlw8S2CC1/Du2xLXmhiOYxlQRNh3wLw2wv5cTdvs6ThOG4h1Eeoui07RqFoeaOpI5y4XuPeT3yWeSyrKGFbUykvW9yc632bFlIUd1pcmsIv372Mvz1gbc3UT9+5iEqfO21b57fL4TwyodfNQAh/GmtDTl1Pf/O3bCDbY6ulOr1dsFsaKlAUwYaFVWxYWAkITFPGNx9LdtILSAcFx38uI1Woj6rCto/Dy/9orb7ietxtuzHjG/YUQn1yx71rH8LvKoY3vj79+apfi2fXz4GnBDO+idV0dQZ7z2JadfrjMF7+FsayLcg80cQKVySwah8c+Yb1ji5chyipSalhOqE+orzBluajqF6aN5o6w3MX6pPgH9qyHNM8w6uXhqYRPYb3rqvl7tHUi7Fj22rL+ZW7l/HFA+fG1TwRP3PnIrYvrZmVnTFpJNnQJPtcWCxnlWcmfCWU5orwYCQRHJQPWmdHq5l4qVtnbb2Pd7qtL/D1Clg7RZaesb3fsqPVfEbB8Z/LSBHqAwLf8jsJBAaRR/9j5rpK6/He9ymEnN2GPYVQH+d4cctOjJXbCJ97FeP8mxAaju16WtOEq2UPrqL4TEiKesyQH/PpP01jgAWIXDyCq+mOvNHECi9q2cHI5aPQ15G6i3o53m0/g0ijLSuhPoqqEl6yCS69aUt5b/OOtGzKV57rUB+Ixfp/dHszd6y8xcHj1zjeMz6AeFtTKXva6mkoLZ6ynrbaCj7//vW8crGXwyd76BkyMIEFPsH25lq2L63BN5o7fDY2QyHUxyrPjEZFWnquUdFoaEuutc6eVqn4AxuW8E73Sazi4c0NE3atzpVW8xcFx3/eQI79nfC4vWj1PYRrlhE+/jRce3v8Ye5KWHUPRS13IfT44kg5dT2W+KgNpHd8xviEL5Kc22ONC82Fd8U2WLENU9FQzFjYlp3MK4FTLyaND3uIXjqCa+mWnOtghwtFwXvvrxB48Z/g8pHpO1dUh/e+30T1lKY37i2E+gDoK/cQseP4L9qAWlQ5+2sxnzhxvca9n/gsu1hVXc6qu8sIRk0GQ2FUAWVuN7qqpLTHpansWl7HruX18XfifRvls8f8djmcRyb0KvO6qPEJev32zun6RmsbeuUKuRhbi0uL+Lm7GvnHFy6mLLtvVRW7lteRi++FdxMKjv9cRqpQnySu1axA2deGHOojeqsbU0o0TxFa5WKk5nYuo0ch1CeveGxDq2fTHWHg9+dNX+xw6fbi2/1fiQxcItJxAC68AQRjfappRW27F61xI8KIYqarrcUNvERZHWz5GLz21dR6+2rw7PhE+lm18pbnPtRnIvdoCh7NM8Vn2bNhKh6TRuaNPc5yYbGcVZ658JXdbfV8+41r2MH2FbWO2pArraQ0uXBrmBtDIYSA2lIvi8uK0rZhQ0MVn9rn4nuvX+DcxI3QgEo3PLhhEVubapKOz7wm4ajBKxf6ONx1nZ4hE0nsCd79W5ayf23jJDvnC/LC8W9pafn/gP8XWNzV1XXF5rHNwO8DO4Eq4AzwZeBvu7q6crR1S5YgjdisbzQM0SBE/CDU2PvR8JRc1Vyo1cswVRUlGoZoBBl//J7qWEs8PBJ7HY47WU7VO0suwwK0qCWN5hOX/kGIzmLBmapCJJAXfUmH66W1qDs+jHLnB0CoY+NeqJjR4Oy0jURjvyORSMrrwdO4gZC7GPnS18GY5nws2oBr+0dRkLE68kjHWfOIH2lExvplREFzISMRpGZkJdRnrnBpmkhiixnzwZ7ZctOUGGbiPdPRNkxTIuJtOG33nY3VPPPONW5aTBN2b3MFRbqGaTrbR6e4Fa0MU3LozDUOnujl1gT/fIEb7m5fyI6lNbHL1aYNS8uL+eR9a+gZ9nPsyk0CoSi6KlheX86KyhKEICPncTp+vOcWXz50gYno8Uu+cvAcXz94jl+5dxV3rqiZVGauI+eOf0tLy6PA76V57DrgeaAUOAy8DtwN/DWwFfiIQ2bmJaRpIo0I0owgQ36IxL6hTCEQUuaEEwlCKICJBNPImR0TuRFRMCNBZB5olNXzERxmVqhswAyO5EVf8o2bmhupgBkMWboe9JplKI/8HuHuU5gX34jdFCg6lDfgWrEV1VuGYZhIaeS8b07zaABUUyLDQUyhIFQNGTUxTZDze3rGNhLrSs15oothSKRQMIxkh9ihus347UQGtNKEwm/sb+PPf9TBkDFz2dYFLsJRk78/1IkQ0FBRxJYVtVS4Xc4bliZSaRU2Db50oHPKGXmAGyH49hvX6Lg0wCd2NCftSGwP1T4ve5sTewzFnHEpQUo502GO4ljPzZShRwbw18+cBCG4c3l1dgzLEnLq+Le0tPwK8Bfp2NHS0iKArxJz+j/a1dX19fj71cAzwIdbWloe7+rq+k8HTc4rCEVBqDpC0RFuL4jYRaSoOsII54SjgIlE8RSB7smZHRO5qntRNB0RMPLCnqxxMbsfWs+q3eApyo++5BlXPC6QEsWjWb4eFCOMp2kd5vLNKPFypqqP8nzpm9Nc85YgjCgiJFE0D6CC5kW4fYg0d7idr1Dj2UvGspjMdQgM00RVheN9UmPRdhnTqsrr5vceXsOPT1zl4Ombk/ZyKNNjDnXnjTCdN8YeDbxzPciTnf2srnHz4e0rKXbp5BozaSWl5J9f6JrW6U/Gid4g33ztDB/f0eK8kVlAMGJYWm+QwN8918WqhWWUefPnJm62yInj39LSsgr4PPAgcANwAyU2q7kPWAscTDj9AF1dXX3xG4oXgd8A5q3jj1Bj+b41Fxje2FNUZOx1PJY961wChgTdA7o3d3ZM4MLlRaou0MJ5YU+2uBAaVLdCX6f98bVsB0rRAsw86Uu+caHHf8zD2py7HrLOdR9CCYNmgO5FKG6k5kXxFAMJTyT5JvXdy4WixB00kRf2zJ7HZvoVBZRxXufs20hoFPubmT4Ue1z8l01LeWR9Ix29t7g1EkZVBFJKvvX6zGsATvSG+NwTx/md96yh1JPsOGb/fMyk1ekbI3T2habrxiS8edXP/pEAC0t8WbPfKf7qJXsbs4UNk0Mnr/PIhiW2jstn5GpO4UvEnP6fAJsA+3vaw/3xv5N2Oerq6joM9AI7W1pa7N5QzB3I8ek8FRlFkWY8vWCOuBlBBRTDyK0dE7gwoyhGKG/sySbXWvfYH1tVyyja8sG8sD9fuTDjaWLn4PWQdW6EEKYR42YEgUQIFUZjv5OdkQKXeWCD8zxf7EiPa6rK2vpKdq2oZ33DAv49hdOfwO0o/N2BRDrL/OjLRH7whL1FzACHOrrzxn47/PDJHqtdHMXBzuu2j8ln5Mrxfx14pKura19XV9elNOtYHf97fJrPu4j1ry3N+ucEgtfPMPTyvzJ86EsMv/xVRk48ixkeiX8qGZ9iM5ucHLY9FU+++PPBnuxx96J2WGDjsWxVE/Xv/2x819nc25+3XMTH1Jy8HrLMR3WSIEXs3zyAaUqujwQ4f2uI6yMBTAfilJO/qQpIjVxo9dK5nklhPzPh4mCUswMzbxyXDUyn1ds2NthK4M0Lg7MzJkfoGbG/IKT3djBp4fHcR05Cfbq6uv6bA9UkEil3T/N54v3aaT6f04hcP8nZr3yO4Lk3xr0vgeDRb8OyHXi2fDAWXiBjn2QlfV8hnWf+cSFx7f8k4Sc/DwNnZh5Yy+9i4f2/hNTcmCPTp4ctcOvpPPPxesg+T07nqaEoSsz3Vybmzp8bfCgU5vmubp7ruEFygIRPgbvbqtm1sj5pMyd79cduk2TSZ7nvr3NcOFxv9nejldLkuRP2Z4APnrjG8ruSAxCyfQ6m1iqa7i7FMrmufBhb1njyKzswpUSZJ7fks3b8W1pa/pVYuE4qPN7V1fWZ2baXhKL43+luVRPbMhZP8/ms4HJpVFfnJopo5MzrXPinj2GGZrhLP3eY4K0r1L3/99B8HkAiFXV0t9FMcRnWMVxuVJeOcGkZb88yj+/cW1nqzg97csD5qd9lqON5br/1JAxNeNxZ30b5pgcpXrIWpIE0Qu9qraxxA5BUFU8/zvP2esg2j+/cW1nqRuou0D2Y7iLw+EBNxD7HXN585xe6b/H73z1GeAoPwm/CD4/3caCzj9//wCYWVpek1ZZEUFFdmrG+mKbkyIU+zvcMYppQWuxi+7Jaykq8GWnLNInH+ItZ1TUdj2nlbJ3T8WAwylAU27jYP5JVO61qlW42HQ0yOkYzxWuKFHptzvpXFrupryuzdUw+w4kZ/0bAShxBfeoitpA4c9ONWjHh77xAdOgGF7/08zM7/QkMXOT6019k0Xs+HVcp/rg9w1wkS5+F9grcGheqTumavRS330u07wLR4X6kquMqq0EvrYndHEgz53bON164HibwxDe2HFUlj9yCmXnPwAif/Y+jKcM8/AZ89t/f5P989E4qSz15Y78hJd9/4xw/fP0y/gkpKr/+/AU2N5bykbtWUVPhdazdxGvyoP9O8FAkRW7PaRCK5OdYRwgaK3QuWsjok4zWhbG511zbb5fvXruIb79sL8L8vrWLbJXPd8za8e/q6trphCFpIJGg3DvN54ktGUem+XxWCIejDA4GUhd0GMOH/gXDf8v6ARffovfyebTSOkzNhRKNADJjnHAYMxhE8WoQ1jLenlVeUe7DVF0M9t/KC3tyzl21UFmDqbkIRSNwOzgrraJmlHDn83D1GISC4HFDfTuutl1oiiv3/c0AryrWQUD/cGTOXQ/Z5mVVFShGmJuDI5i6QFFVTF2BgM7ojspzAP9woNNybHfQhH959gT/z85mW21UVJcgkNzsczYm3DAlf3ewk47e6fV+4+Jt3rz4Gr91fzNN5c48LDdNM2nG39llhRXVJUjgZt8sNim0iXA0Pcff54KBLNo5ETNptaO5louv2to7la0rqrOqu1PYWFfGt22UFwK2NlXR5/D1aBVlZV5cLmej8nO+gdcscA1YD9QBJ6f4PNUagDkHaRr43/h328eFTx7EdcdPQTSMImN57DPFMSOxWR7DAJH59qxyEQ/1UWQ0L+zJZ25HK2FGGXn7h3Dih+MH3SDQ00H46L8Rbr2fog2PoETJmz46wRNhLMocvB6yzZV4qI8izZgmigshQZomKIk8/vkwHzg9vxkIc6LH3mTPG1eG+UAoSpE7kcfd0hwsY1l9Zi5vmBJ/JIKmKHg0Nb6eeury3zlyYUanf6xt+IunTvGHj62lxK1btNlav2Jw8tyk1shp7tI0VlS6ODNgcUvfODY1VWXVTjta3bFkAd974wojFu9pKt3QXl85Y52z5YkNkNPZJXgmXuJ28VN3LLSclelDW5dRXepJXXAOYS47/seJpQRtAw4mfxDf3GsVsc3XOrJuWYZg3u7BvG0/FRW9ZwDJ2GP3DHISnOy0Z5knvvjyxZ585ta0koD/5a/C+Vcmj7lkdD7FiL8f712/ML/0T7yek9dDljlxvZDM1aw+Ry71p3XcW1f72bmsznL5VMpIKTl1Y5BDHd28k5SNpUiFu1dVs6O5Pu6wj2EoFOHQmZuWbYgCL5zq5sH2/M9dnouRtGv1Qs68cMHWMTtbnI52to/ptNJVhd98oJU/e6KTVMsXPAJ+Y38binBe+YFAiBe7ujl0sn900bxPgT2rqtneXEe5x5mN0HYtr0Mi+Pbblgg+AAAgAElEQVTrV2cs98EtjTywtsGRNvMJc9nxfwr478CjwN9O+Gw7UA0c6urqyn0OLYcgI2mGFoWD8Wwahaw+78qsPhnSKtB1OLXTn8DF1wlUNeNr25MXfXSCF7L62OFzP6vPkN/6BkfJGPaHbbUVu02SSZ+NlYkaBv9y+BRHr01e4zViwBMn+njiRB+/vGcpa+oqRo89fMp+FppnT/Rx/+pF8QW5TunpZF0xjaSt8s7w9fUVNJZd4eKgtVW+9zRXUO5xZd3O8XxmrRYWe/nd97Tyr4fPcHaaeP9V1W4+vH0FFV6343Y+d6qb7xyZHKDhN+FHHX38qKOPn97SwM5ltZOOTYfvXl7Lmvoynj95nUOnBsbd8DywtpaH71hOsaogMnCDk2vMCce/paVlOaAD3V1dXYnksYeAE8B9LS0tv9DV1fX38bLVjN0IfD7rxmYQwpvmqnJPaewReyHUpxDq45BWEuDo9+yNw7d/gFi1AyGUnPfRCV4I9Xl3hfroanrx6aqq2GwLpgr1kdLkn148NW6Wfzp86eB5fu0ehVXV5QCcuGZjXVgcIeC6P8jCYq9N+6fvVwxTl5MSOvpucej4Nc71BYlIKNLgjmWV3LWqngU+9xTHikn1ZIMriuBX97bx1892cPn2zM7/tqZS3rth6Yx9zw5PrVVNsY9P7W+nZyTIq6d76LsdRCiC2lIPW1fUUOVzPusTCA6cntrpn4hvvXYVIQQ7ltY60m6Vz8tjG5t4bONSQhEDicStqSyoK8fjUhnKwTrObGBOOP7As8SyB30C+BeArq4us6Wl5Wfjn325paXl54jF/e8BKoC/7+rq+kFOrM0Q1OIF6A3tRK4es3WcWLwekOQi1McYuY0ZGMTUNHRfFYruyo4dk3jiiy/b7c5FnlqrcHcXRGw6E8Ztwtc6cTe05UEfHeCFUB/rnLheSOZqqE99VTFww/ZxDZVFqQslYTpljlwdsOT0J/DPB87yJx/YiCIEwbDF4O0JCKaZwcYu+v0h/vaZTnr845dO347Cs6cGePbUALuXl/O+zcsmhZhkayT1B4KcuHqTUMRE1xVW1Zfx6f3tHDrdzXPHrzPR/19SpnH36oVsXrwgPmsss2Tp9LCqVW2Rl0fWNzF67QKZsv9mMMR/vml95+BvvnqFdQsrKXY76766dZXx/Z2/mCuO/5To6up6raWl5U7gD4C7gTXAaeAzwD/k0rZMwbflpxl83J7j727emdVQH4MgkQtHoOMQDJ4dtSMCsHQbavt+3KUNmbVjAi+E+jirVeTGVWuDbwIi/VfQF7XnvI9O8EyF+hhDfYQud0AkCKqGvrAZtXpZXvQ5XX51KMI7Z64xOBjEVN1U11SwpsmLaw6F+qyrr8DFBews6SzRoLWmzFZbMddDJn0W+3vwuHXnCGDEhBPXB2ivr8TrUmNv2IRXSzzlcErPiQ6w5FYgzOd+cIJACr/y0Nlb+MOn+Nj2lUmOdOZDfc7fHOKJNy/RdWNiqNc1lpbrPLhxMX/8vg2cvznMwEgIVRHUl/moK/ZOqiu34zg3YVGp+Asn7edfefHMde5fvSiDtqUYjHMceeH4d3V1NaX7eVdXVwfwfodNylt41jzAyKvfIHrthLUD1rwXzeWGLIX6yPAIkUN/D/2np7bn/MsY518muPFD+Fp351X4SoHb0EqmF++MGSJbYWdzLdQn0n+R0Jvfh77x+Qgib0GkfCn6uodwL1qTN/23wvsGhnm68xoXe4NoSszxDOMienmY77w1wJa2Jh5cvxxNTdwAJM8u5hdXFYV97TU8cawXq7h3XX3cSbXTFkwM9bkVDHP+lr086wCvnO6jvb6K1YsqOHfTut0QW1RZW+yLv3JCwwTGf/b1l86kdPoTeP3yMK1X+tmyeEG8njGNZm/fZP7mlRv884sXp7Xn/K0If/PcOdrrvJgSQlETn0ulvamKBV4Pmmr33GeSZ1ardPmLp+wvmj/c1Rt3/DOp1fyFs0l1C8g4hOai4sN/g1ZnYc+0lrvxrn8o/kIyPvTAeS6lJHDoH6Z3+pNgHvkmgbOvZNymMT7hCzhr7c5FnlorxZ3ertWKuzRP+ugATzh0lsozY7ng5XcIPfW5SU7/KG6dJ3Lo/zJy4tn86X8KfrF3mK+9dI6rN/zx0TT+fwN4tqufv3mug6hhfzY6F9jXuojWGmup/TY2FLN3hf1MLlO5HQOB9G60B4Zjx+1cWZui5GTcs7omI5lbktHnD3Kyz94+DhOffGTKwvO3hmZ0+pNx7HqAEz0BzvSHeKfbz7++fJnf+vZbHDidX9nE882dNaXEn8alf9NeJtUCJiAvZvwLsAe1eAGVP/s15Dv/Rt+Br8DIhLjTsqUoa/fjadyIqbkwExvqZPhxfuDyMeibakuFqWG88hWMldshC+E3hVAfZ7VyLd1A8I1/tXyuE9CbNmYv7CzD3KlQn9DNyxjPf9GSfvLot/GX1uJbsi7n/Z+J+wMhvv7yZTBBoBAyVVwYCAQRBKYEKUEiON0f4t9fP8vPbF1BvKJEb/OOKwr88p5Wvv3GOV48N8h0uKe5gvduaBq7D7LRlgQmhvooaXpssfYlRS6de5orePaUtZSebhI3C2M2zGSzdS7GvX6py75jfGkwyvXhQDyUJnPhK0+8Yc3pnw4G8J9vdjPiD/OedY2O2pYez79Qn9nfiGROq/mMguM/R6G4fVTd90uUbXov1958hsBAN0JG0SoWolQsQomGY6EG0oj9FWrm+TtP2uyFJHTyBXzLNmfcPhkWoEUh4s+OFnOYW9FKUT2wcANce8v66a5tR9Xd2R+XGeIyEo39PkQi05cPj8Reh+OzmlOUM15/3N5l8+o3kfUrEXmkxUT+xqkeRDSErhjERNLQiXETBQODMGbcwYWXLtzmoXUhStyu0e8GHH107xwXSH5q8zLubw9z+NR1Oq8NEowYuHWF9sWVbF9RS7FLAxmb0bRbvzRNJALTHDu2ypPQxR7qSj2j9Ty8rpH+oRBHUywQVoFP3r8Sr6aNs8Gq/RO5aUoMM/GeOe6z7lvWFysno/vWCDW+WN9EvI107ZuK3/AHpojpTw9PdfbTsKCYdfUVjtiWLs+UVrPlPgF+m352hSu2I3QmtZJy/jr/Bcd/DkOaJmCi165EFi2ASOyLygiOIOKD1hQiK9wMjcDNM/Y7cepF5OK1GbfPiCiYkSAyEs6qLnORW9VKWXM/5rW3if2Yp4ay9sF5pb+puZEKmMHQtGWIBCEUwESCaUzWOnAbeuwt1ifUT/haB67qFXmjRTKPGAYnLvXgMU00YSJRUM3IKBdIglJDw0DIsbFz+PR19rXl/4ZRCRTrLvavXsz+1QmbxxyIMefKPhJRT2bSZeXVdNbUuDnea88Z3dZal1SP4OPbm2k8c41nj/cybEwuv7HBx0MbGqnyuMe1PxsYhkQKBcNIdtRiSNe3ikqJaca0EuCYrQkcSyP96Uz4ydtXaK+tsFR2KBrh5dPXeenUDRLLOup8sKO1njuXVONKM61sprSaLXavquLJTntx/jtX1WS0H6ZMPJWcn85/wfGfwxCKglB1hKIj3F4QsR8eRdURRjirXPrT/KIMDSNcrozbp+peFE1HBIycaGQMXCU62I2JQPOVoFc1oWiurJ8nJ7VyVS/B2PdbRH78lzBjrhMN/d7fRK1pyps+OsEVjwukRPFo05ZBAROJ4ikC3TOpXOTyBfvXDGD0nkM0rI61EQ1jBocwjQiK5kHxFOVUl8H+2wxHVSKKG13EZvkjijbKQ3gwhE4UNRYuFcep67e5f01acswrJPw6ZYJ/t7e9gePPnrNcT60XlpdPXIsjuKdlIXubF9LZN8iVG8NIU+L16WxaVEmRK/mJi1MQGKaJqopJfaoqdkOvvRh/gJpiD4oS00owWavZIhKytjGXVVy6bdDj91NfPHNq14Onu/nOWz2T3r/uj4UNfefNbn55TxOtNdZuIpKRKa1mix3NdbYd/x3LajPaD0XEwuvm4+ZdUHD85zaECooGmgsMb3ziVYLmQqISuXGeaO9FiIyAuwR96XoUT9lomURsvSPcbW3B2yRoOmi+zNiUxIXLi1RdoIUz3lbyOQhdfAPz2I9h8MJol6NAVCuD1ffjbb0LobmyYk8mtNJrmlHe/8eEOl+AEz8Gkn/E3dC2D3frXajecsw86qMTXOh6rJthbfryEjAk6B7QvZPKyWiaDoYRxjQNgp0vQcdTo3sqRAFKFsLq/XibNiM0Peu6hJQgEVyEUOMz/IzjEVTM+GuZNAMcMWR8l1hg3Mzwu4sLRYk7aOOX+TZXl7O/tYqnLThJKvBL97bOqOeauor47r5Twcl+xWb6FQWUcd6aYFtLHc/PsFZiKlS5YUl5MUKIUY2cHjfDDjv+AJcG/DSUFk/b7rNd13h8Cqc/GRL44sEL/NpehVU15VPWMx3PlFaz5eVeNz9z5yK+8eoVrOATO5ZQ7NEnvOusbYoi5q3TDwXHf25DGghMhDRRjCjE0y8GTx/GfOcJGBq/cCry5teJ1K3BvfFR1Moljqbv030VtvJbj6K6MSspNrOdzlNGQwRe/TqceWHqfkcH4e1/I3DxZXz3/SYKJXmRfjEdrRR3CeqG9yDW3kf01nWMaBhVc6GV1yNVVzz95/xI4el0Ok/V7WGKiIvUMIIE//13gSl2lhy6Bq/8M4FjP8S771MoRVVZ1aVIFaiKiQtGU3gmcw1JVMhYYqSkmeUit8bYj7B8F3OYaudekLxnbSMuXeMH70zvIFa44Vfva6OmyJMHfRnfrxjGPltcVsziUi3l7rfJ2LNmYVKK1MkazZaPhKIcPONsqA9AJDp9THqvP8jjb123XNffPXeO//2BDTbThTqvlVN8+9JaFAFff2Vm5/8TO5awaXF1FmybOGbnF/LsoU8B6SP2Azr81ncxD//9JKd/FNePE/rRHxG61jF2nAPp+4Siwsq9tq3WW+521I7p+YQvyAynMgwc+d70Tn8ybl3C/8zfIs2E+5d52zKllVA09MpFuOqa0SsXxcZEzvuSQZ5wPiyVZ8rPXHUrSAvnX2VKpz8ZI70Envo/mOHEAsrs6FJW4qbM64qPoJn/T0Z7Y9XM/XmXYCa3QwjB/rZF/Mlj7TzcXkONT+ACilRoq/HyS7uX8vuPbpyweVR+4yO7Vlouu7zSxa7ldePec9pFe+mcdQfcDkq8E2epx/B8h73sRhHgjSv2d5HOZ3d2a1MNf/q+tbx3XS2V7rH3qzzw2IZ6/uy/rGPT4gW5M3AeoTDjP5ch4o/LhYKp6vg7no099rcA47m/IPzw76OV1TuWyk9rv4/o6ees21+2FLWuOSvpRrOZztMI3oKOH1nX4eZZApeP4WvckHHb8k2rucydSOcpiquhejX0WdyQzy4C/QSPPolvy/uzposiYV1jFU8dvzGawnOmdJ4J3NlYTbyiON6dXAJT7dybzEvcOvtXL2L/6kVMjemPzS0Xkz5rKPby2/c385dPnWKmLcpaFrj5xd2rGFvbGtNoTLXZ2yelyXMnZg63SRdtNeVTtmtKyaEz1tKsJuPFzm622rpm8i+d50RepGvc19rAfa0NTI9s2JP8ev6h4PjPZSSF+ohoEI5819bh4RNP49r2Mcce87t8VRhbPoZ87aupG1dL8O79RUQ0jDFwmVDXATj7GqOLRCtWoLXuxt24AQXmVKiPv8vCTP9EnPgxypL2jNuWb1rNZe7Uzr3axkeIPp0hxx/g1LOIje9BxBcCZ0Oj9UsrOXLhJsPBmCuXKtTn0Q31uDSFcU+a3rU8cUMk0jg2n3kCkz9rKi/mT963npcu9HLo+DX6k5IXra71snv1QlqrSxGji8ETxzqrUSgqGXI+vJ9dy8tx6+qU7QbCkbTczN7R8CirfZxv4ymTfOKYnV8oOP7zBKFLR8EYtnfQuZcwN70fPCXxG1wJQsyKe5dtJiAE8tV/Y/xCz2RoYBoEHv8sMM237M0zRF86Q/SNClz7P4mrtGaW9iVfzLPv54z8ko3c9gkMnMGMhFB0d2ZtS1Or6EA3wfOvwHA/KCqU1uNu3oniKc6hnTnmidczlRnVcPpyrpplyO0/i/HSP6UeJwta4EZX6nLjECXU3YlnUXvWNCrSFT6xcxlfPXyGwUA4PppijscYj2HfqirubVlIvJJ3Pea3yzE9PLrK3pX17F1ZR9SQhA0Tj66gjHP2J8NJvSLS+RyRHgH72hdP+3m6oz6dTJPv1rFVwHgUHP+5jKRQn+jl9GYMQ33n0Zs2jf5wz/qRvxFFb1iD+NBmQlc64fQL4B8EacDQDSAEJBYiW0D4JuEf/D488gdopTVzI3zFfzutc2GE/eDy5TyUJVmr6K2rhF/4Cgyen2Rv6Nj3CDVswLPjo+CrzKnNueBO7dxrqjruZdsIF1URfe0/ptQazwKUTY9i9p5Jw/GH6PBg1ndM9pR5+djuZl471cvLF4eIRkIIBBGhYqCyqMzNjo3LWL8oOVwh2Zt5d3IJpAr1mbtcWCqnqQJNVS3U62z4ik9TcRIe4NMPrqJ8NAvN5HZ9enpuWFWxFX2Sef6H+uQPT349/1Bw/OcykkJ9iI6kV0doZMZQBbt8NLRBCnyN61GWtGOG/fi//8fEnP50YBI+/BVc9//W3AhfcbmYMVh1GqiqjpIH2W8SWhk3zhF+6nPM+CV49S2CP7iE56HPoLiLcmZzLrhToT4J7qlZjvnwZzAHrhC6chwZDoDuwlW3ErV2Jao0Ge6znsc9GYqq5GRsFbsEe9fU8dC2ZVzqvkn3gB88RdTW1LGgYgFSdTPu6VKBx18JxpzkXNvjXL9icLJeZzVSFYXVtV5O9KRYOJ8CHgF3ty5gV0t9yt2oFQW2N5Xx0gV7aU23rUoscs6NVvObTxyz8wsFx3++wOVL6zBF9+DoY34SnNHP/K9/C0IDs+oe/aeJ3upGqVyUpn3JF7MD/ZyJ162As332+qeVIfImbEZgBP2EnvoC8TdmRqCf4LNfpPjB37bVljQlpv8mhmmgubyxG4ec990GdyjUZyLXKurRKuoxFQ3FjD0ZM4UC0kQpa7C4T/J4aBX1OdXLpamsXFRJdYkPw1UKnnKkUPNvN6E8wPx2OZyH03rtXr2QEz1nbR3z2YdbGQpFCUYMilwqS8pKUJWEo50au1bX23b8ty6pTl1oAgpjqwAoOP5zG0mhPsrCtZinD9quQq1b4exj/gmhDWbgNlx43ZHuBs++hKf6w2nZl81QH73lXiJnX7bXubb9SKEhM2ybVa0GTx5m+jUaU+DmWUL9F9EXLEtZvxkaJNh5OLbhl4w9qQoDlDXBmn14GzcglOyGpcw21McMjhA88yrc7gGi4FuA3rIN1VVsKdTHKnctv4Pg61+zfl4APAtQF6zAFCnCkjLKXShGGFMRSLcvtpmZksjbn+wcFbgE3u2hPta58+ErrdWltNV46LC4o/D+1ipqi7zUTtqU1/r5W1Tis7w5G8Andjbi1qda+5BdreYvT349/1Bw/OcykkJ9vI1rGRE+kP7UxyXQdCea7sPMRKhPPLQhcNqmAzwTBnvSDkvKZqiPu6KBSFUz9J+y2DEdT8u2/MmiY0QJHHnC9umJdD6He2fTjPVHr3cRfuYLMNW89eAFOPxlAieW4Lvn11G8ZXkR0jNTqI8RDhB8/qtw7vBkPY59h0h1K/qGR1FcXlKF+ljhiuqC5nvg1LPWT8zaB1ExQOZOL8UIIUwTRYI0TGQsVxcJNzeGAmf0lWDMSc61Pc71KwYn63VeIyEEP7+rhS8dPMmpGzOHp+5eUcF71jY60q/3rG1EEYInO2bOz//x7UvYtCiRzz63Ws1fPnHMzi8UHP95AiFUlA3vxTzyTcvHeFbvBySZDPWRI71OdTEGBzPVONZnCWZwCP+pl6DnZOwGSNHB4o2Ya9+nYtlxzGhGbLPdl7Af/NZmnsah5+yM9Uf6LxJ55vOp67l1Cf8zf4H3gc+AquaFJlNxIzhM93/+IQzOsPlOXyeRH59G3P/f0aqXOtK2d9NjBPrOw00L8f6Nd+Bt3gnSyK1eiPHfDwVMi/ntcjiPTOjl0lR+be9qXr7Yy8Hj1+geGT9R0bzAzd2r62mvT2w6N/sxLYTgobVL2LS8hhc6u3nhzM3R6RGfAnvbati+opbS0YXCabQxaysLmA8oOP5zGRM28HK33k3AfwNO/iT1oXs/hVKxGBOHH+1PymLi4I98aa0jmWoc6WecG4qC/+V/gzMH0+hPE/pdH0WrWJw34StIMAwbIT7JCAVnzBwTefEr1usavEqg4xC+tfvyQpOpePePPj+z0z+KKOGn/hLxkT8HB8afdHvx7P8UwRf+Ea4enb7Z5vvwbnkfUnNlZZO8mXk81EeAVDUYF/+c/B1R4LHbJMn81EdYLGeVZy58RVFgx9Iadiyt4fpIgIHhEIoiqC32UOF1W67HLq8r8vCBzUv5wOalRAwTRYj4egFSHpsrrUAipeTczWHO9dwmEjXxujXWL67MqFaZ48mv5x8Kjv9chjRis8TRMESDiGgA3/r34i+ph2M/hOAUs7YLmtE3PIJasxwifhBqrJ5o2BkeHom9DsedR3epY911LdsCkUBa9smwAC3qaJ+lhNBzX4IbnamNL2uEsgVgSPBVoC/bgrpgCUo0HKvXyXMwS66405xR8hVP25fIzaswdMVefceeRLbuROSBJhN5pO8sXD9pozMjhDqex7dyiyN2KNLEd9fPEx6+QfTkQeg5BcFA7Bw0tKO37EJ3eSEaRcr0rhlHecSPNCIQjSAjYaRmJDn/+fJ4Pz+4NE0kAtOUs6pnKn55aIQ3TvcyFIygKQr1lT62Lq3Bq2sZ65dpSgwz8Z7paBumKRHxNpy2O5nXeD3UeD3j2s3GmFDjT8lMc/a6ZVKrVy/d4OmjV7gxYc7oP49001rt5s6V1ZzvGebGUBCJpK7My9bmOuqKPI7Z4PS4kk5OWuYZCo7/HIY0TaQRQZoRZMgPkdiut64la1EWrSFy4xxG33kwIuDyoTS04y6uBMAIjiDiA9sUwjFOJAihACYSTAPXknWEO344+86WN6F6y9O224gomJEgMq6RE30OHH/amtMPMHgRlm/HtXQTaryeTJ2D2XJpKrBgBdw4Y61vCTS0I8OBKeuMnEljrYccJnz9JK6qZTnXZCI33nnafn9OPIVsWu+oHYq7GO/ah4CHMIQYG1tCODrWZ8ujAVBNiQwbmHoY6Z6/P6qzhRGP7zAd3Evqwu1hvv3SOa4OT6j00hCPH+3hrmVlPLKuEX00d75zMAyJFAqGkey8WoMpJR03bvHm6V5ujoTQVIVFlUVsb6mlxuvDMOO3E87vuzXvkCmtvv/2BZ47fWvazzv7QnT2jZ/06egN8dzpWywtU/nIrmaq3B5njZolTCkwJfPW+S84/nMYQlEQqo5QdITbCyJ2t6qoOooRxt3QhrlkHYoRBgSmqiPiXMkQRwETieIpAt2DouuEa1ZDb3objMV7imv7RxEud9r2qboXRdMRAcOZfoYDcOqgvW6cPoTSuivj52C2XNW9lG5+iNtP/aWt7rlbdk1/jsI2d5WOQ4aCszrvmeL0nrffmVA/CIHQXXnRh2xyzVuCMKKISAhF15GKiK3tLWAS1LguTmU67ei9xZcOzjxeXzg3yPneY3xyfzsux51/gWGaqKqw1aeO3lt89dB5/ON8L4MzA7c4eOYWzVUuPv3IRop9eiErrAWosSRkjmp14Ez3jE5/KpwfNPiTH3TyOw+toqYof5x/RUgUAULYu1GdKyg4/nMZQo2lxNNcYHjjiVJk7HU8ljjrXBILZ9E9sZR9QsO94yOEHv9Mmn0sQnvkM2gltbOyT7i8SNUFWtiRfgbPvQZY3H04gaFrRAa7cZcvzv15SqGVr3kbt1/7IQxYnPVvuR+1rH76+tU0v2p0D2ienGsyiRvpbfAjBYh87E+mue5DKGHQJEJ3IzSVmBuS/MNa4EBsQgdQlNnrMxAIpXT6E7gyLPnmq2f5xM6WWbc7nsdm+hUFlHFe5/THvHW1n3984eKM9p7qD/M733iFP/3w1rhWTtg6f3liPDmllWFKnjhyndkiCvz1j0/yh49tyJvzqChi3jr9UHD85zaS0nkqRhTyIB3kVDuVKr4KQrXt0HPMdhd9H/hf4PLOOtWl0+k85YDNePU4jN4LKGX1OT9PqbRSkfju+RX8z/zf1Nljlm/De8ejM2oryhYjsb+fg6usLic7zqbknpKp19CkgKq5EdJERoNELh8ncvFVGBkGTYOqRtzNu1C8JfnTT4f4+HSeBtKUhRj/aTk4lc7z+ZNWFp+P4c0rI7w3EKbS68pIv2KYudyNkUBKpz+BWyH4/BNH+Y29qx22dT5yZ6+3I9cG0tmgfkoMRuDt7gE2NKSTptQ6v+EPcX5gmHA4SrHXxaoFZfH9EKbSav6i4PjPG8ixv7lMdzhqA+M+09Y/QPRpm47/yt0ouiu2dmDW9iVfzA7000zzKy+aeEqQ4/NkQSvF5aXo/k8z0nEITj47effl8qVobXtxN22O7cIqp09H6l25Df8737GnVcUKtLKaPNFkAl+yEU6lzp41DlUrEapG6PI7RF76V4hO2Kmz+xih408QarwD350fAbcv9/10iiPGfz8UMC2ccjmihsmBLvs7pr/Y1c0j6xsdssI+Dtm8WTnVG+TS7WGWlBZnyKL5Ayfd2a7LNx2sDQ6e6E5y/J3FyRuDPP3WJU73hyd9dteyMvatXUyFxz3FkfMTBcd/LmNCOk+MmOOV03SHk9J5xj5T69qIli6G25ctd8+1au+M6SFzms7TXWbzZMVRVJ6V3YMd00pV8Wx4CLHmPiI3zhH130ZBopXXolQ1oUQjWNpt2FsGi7fC5Vesa7X2wXFaRW9eJnzuDRi+CZqA0kW4Wu9CUz1Z10prv5eoXcd/zQOMnHsN+fI/z1zu4uv4B67ieuR/ouX5WLHO8yedZ38xgPcAACAASURBVNQwCUQN3KqCS1NtHZsNHrtNksxWnx5/EAP7ONU9COvTb3dmLmYsFzVMDp6271A+f+IaH9m20mFb5xt3Np2nP+TUfH8M5wbCjtmWzJ89eZXHj04fkvTCuUFeOTfIpx9sYXFpEQmt5jMKjv9cxhwJ9QEBRhjv3b9M4Pt/jJVNrcSWj+MqrQGHwjycDvXxNG0kePx7tk+Zp6HVMRsyGeozSSsM1AVNmEIZLW93x2fP9g8R/NEFGLIQF9p8N77Fa1BklMjNq4Re/ibcPDupWPjovxNeth3f5g/G9oLNklYubwVK6x7CnQetnfiyRvSiUiKH/spa+aFrhA99GdfuX8hKf+Z7qI9pSo5e6+dQRzdnB8Zm/Wp8gl2tdWxrqsWtq1mzZ2YOToT6hKPpuP0QNhJOj/P9SlXvQDCclst1sX+E2eo1/7mz+vjcOpDeWqepEDvvzvb9tYu9Mzr9CUSAL/yoi//53jWUe91Jdc1PFNbCzxvIsb/jQm5yxZn0mVpcifeR34PSxTP0Q0fb/nN4W3Y6bFPii8+ZOrWyGljQMkM/psCKXSh6InNBvpynzGuV4IrLh2//f4OaRDzuNFjzMEWbfwqEIHzjHKEf/dGUTv8ozr2E/8nPYUYSP0LZ0ap698dQl22auS8ARTV49v5XIp3PpS6bjCtHMEZuZa0/GeWICa+zh+FwhP/91Dv804sXxzn9AL1+yX+82c1nHz/K1aGRrNo1HZKvvtmgyJXevJ7Pnbv5wEia6RMj0UI+Tytw0p1d2VDuYG3gdKCNKSX/8ar1CIMw8GzHVYetyE8UZvznMuZQqE+inCipxv3YH2L0dBLtPAA3LgMmeCth5Xa8jRsRmp7Z8BWH6tS3fJDIj/7A4sny4V7/njwJu8i+VgmueErx7ftNwkM9RE/8BHrOQCgIvnJYsgnXml1oigeJJBoNEH76z63JO9RN8Lkv49v/yexppenU3v9Jrr3+DBz7EQT7JhjlhlX3ojVvw9RdcP4la31JQuDkC/g2P5rzMTF7nptQn1DE4AtPHaNnfE7ISfCb8Kc/PMlnH26jelxawczZNh2P3SZJZqtPtc9DmR5bOGkH7Y0Vs2p3Zi5mLFeqpeeSFHsSG5DN1r75zJ0N9dnUUMk3uJRWONlUuGNpmWO2geRY9wB+m/eDB0/f5JH1xoS65h8Kjv9cxlwK9UkuZ4TRq5pg58+ODx1J8AxkcXE61EeRBu6Khcj7fpvoTz7PjF8UaimeBz6N5i61HR6TN6E+TofKlNTiufND48/7KI+1Gz5+AOz8rPSdwLx5Ga18YVa0EqaCQOJr2YFYuZVI3zkit3sR0kAtqkRb2IowIshwiOhAmjNJA+fJy6xGcyTU55nOqymd/gRM4Fsvn+XX712TMXuscXAi1EcI2LumnsffsrdYdsfSujhzvl+p6i3xuFhcqnL5tj13ctPyGmar1/znzuqjqSoPb6jnuzbH13TY1VbvmG0gOHbR/sJ2CZwZGKJuYaXtY+cSCqE+8wZJsx358GhfkCd2JHjii8/Z+l11LXgf/V/Quo9J99F6OWLto3je9wdoZbV5okPutLLDpZTQ8Sx2Eew6aLmtyI2zDB/5PsOvfgP/6/9B8PI7SGlYt1PEdRICIcBVswxv8058K3fgXtiKULTR8tJIcyGckZQpyYpN+coRE15nHqYpea7jhq1jum6E6A8EM2SRNSRffbPFjuW1lLusl39kbS0e3fnde+1gz+qFto/ZtrQmA5bMPzg1rhK4p7meXctnH/KzramUhSU+Bywaw0jI5j47cQynedxcQmHGfy5jDob65IpnNHyleAG+Te/HuOODmP0XkJEweIvRi2sQQmBqLsxoZMpjpRl7rCjfJVpZ5UboJhi3Zxz+U+L66ZSZoEJXT2C89m8wPH6mKtoFUaUYNjyGd9VdCMGM9UgR2wrTyvVAUZo/jm6fY5mtcsuzH+rT2XeT0IziTo3XzvTyQHtiHVJmbJuJx26TJE7o49EUPrm/jc8/2cFQCn9mz4oK7mtd6Ei703ORstwdixfwfEc3FwetOWAf370Uj6Zk2O75wJ0N9QGJEPCBzUupq+zhqaPXuJ3G/Ma6eh8/vWW5I/Ykc5eW3m2OR504RucfCo7/XMZcDfWZp+ErmKCXLwSSw5am0Cg4RODUYeTJAxBOpK7TYOVdeFbtRilbOP+1SsHNcJqzrkZ4xtCYQOdB5Kv/Mv3x5jC8+TUCgxcpuvNDKClCfUDOWCZxPahF1UQ9VbY3/dKaNhdCfdLk/cOTc3Zbwc2RMGMOamZsm5mDUxt4gWBBkZf/8XA7P+m4ysGugUn7jS8u1bh3bQObFlVlvF8xzFxOUeBX713N3zxzIqXz//6ti9m3dgk3+4YzaPd84ZkZ00LAruX13LWsjs7eW1zqGyYUNfC6NNY2VnK6Z4iDxyeH3DWUqOxZXc/WxtgEmdO2La0r480r9hfsN1WWJtU1P1Fw/OcNkmY7hIi/zAEftYHc2jGJJ774cmtP8PLbmIf+ZorzF4XTBwiePgCtD1C08RHEu1grxV00hUYW4CmZts5w37mZnf5knHmeQMlCPGvumd7OxGsL14NQRCwc7K1v2ukM7sYN0/ZnTnHE+O+HLEBR0vvxFukd5hgy0XyRW+fRDUt5z9pGOntvMRiMoCkKiyt9NJQkrrXsnBcr8Okan9rXzovnrnOw4zo3AuNtW1PrZe/aBu5ctSj2PVmAJWRyaAshaKstp602eXG4oG6Zj7uW1XB1yE/fUAiBpKbMS32Rb7SM05BSUllsP0/QunofJW7dcXvyDQXHfy6jEOpjmedD+EroyrFpnP4J6HySEQm+zY+9a7XCXQIlS2DoUmq9krFow7ShMeG3n7RVlXn0+xhr7wM5tQ52Qn0URcW95m5Cpw7AiIV9DAB2fhypurG0QVre8+yH+tSXeWeUdzrUlHkzbttMPOYKyYzYoKmC9voKxiObfRQWy8Vs3bOynt0r6ugeDjAYCKMqgvpiHyUefbScTFFPgSd4brVqKPHRMC6OPzPX2M1AiL95poPrI8mfWcN96xaR0Go+o+D4z2VIA8xoLKQgGoSIH4Qaez8azg0Pj8ReJ0I1cmXHBC7DArSo4xrJiJ/oUD8goaQKBWXK8jIcxDj4Revn9uSTRJasQa1eNm+0ss3b9oLVGfo4PMu2xI6fUKdxuw96jtmqC+kndPoVfIvXTH1OI9HY70MkYul6EC4P7nt/ndAzfwUjPTO3vf6n8DSuz/05cIpH/LEFztEIMhJGakbGQ32ayooo1+GWzbjjLUsXYJqJH//M2DYTl6aJROTUBie5aUoMM/GemVZddUVe6oo8o++bZqwe05SIeBu57me+83eDVoOhMH/0vRNpre356S0NLCn1YZoxjWSae0rMBRQc/zkMaZpII4I0I8iQHyKxmFZTCER80GabEwlCKICJBNPImR0TuRFRMCPB2MJbB+qM9l8geuoFuPb2uHMSrmtHbd6FWr10XPnw+deJ7Q9oHZGOZxB3fnjOa5Uu1xeuIlJSZ22nX4DldyNUDRkOTKoz0n3SWh0TcfU4sq55SjtNzY1UwAyGLF8PqurCc8+vEzzzCpx+ESK3xrdXswatbQ96ZSNGcCRvrp/Z8mgAVFMiwwamHka6s/GjKtizppbvvpXiJisJmxp8eFUdM4f7QRnxtnNpg5MwDIkUCoaR7PQ7VLcZv52YJ1plEu8Grb724mnbTn+5Du/b1kh7TeXoTZEpBaZk3jr/Bcd/DkMoCkLVEYqOcHtBxO56FVVHGOGccBQwkSieItA9ObNjIld1L4qmIwLGrOqRUhB++0dw8qmpT8r1YxjXj2GsuAfvpkcQIq7LhTftn+ArbyF2/iwiPks217SaNRcqom0/8tVvEttXcQYs3Yb7zg8gzMiUdab/BS4RLteUdSoeF0iJ4tFsXQ/C5caz4SFE+31Eb14jGh5BVVS0snrwlaHkyTXjJNe8JQgjioiEUHQdqYisJJPes6KO01cHOdGberF4hQs+sGUZSo6TXKvx9jNph5SSczeHOHj8Gl3XAwQBL7C+sYRdqxfSUJxemNTUEBimiaoKx/ukxqLtcn7O5gLmu1Z9/iCn+lP8TkxAtRv+xyPrEGK8KIqQKIL4ouP5h4LjP5chVFA00FxgeGNPUZGx1/H47KxzCRgSdA/o3tzZMYELlxepukALz6qewNEnpnf6k3HmWQKaC9/mx2LHDtvL5pKAaYRR3aVzUqtZ6XzqBeQbj4M5NLNAJUtQ1u7DvfQOpOaGeNrUiXUqvjLSmuhyl4Lmm7JOocfjjMNaWteDEBp6zXJUzYWSSPea4+skY1z3IZQI6ApC9yA0lZgbkvzDOsajhiRqmrg1ZcKP8tTlp+MKKr+wexXfeu0sr1ycfiw1lWv88t2tFI9b2GevLae4UJS4gza9PrPhI6EoXzpwkvMTYqACwMsXh3j5YhfrF/r4+I5mdFVxoN34xIUCipL+uZyKJzQaW8idm3M2F/h81+qV09af7CXQF4L+YJiaosSN7phW89Xph4LjP7chC+k8s5miUg7fgOPfs35+Tj6JuXIrSll9PJ7ZPhTDyHpazVyn8/S/+R3otHBz1fYAvo2PxdOmyhl3RfbUrsSfhv7akrXT6mAnnWe+XQ/Z5ooRRigaQond8EiTSTH+w6EwL53t5UBHz7ic83csLubu9gaWlBaPK2+Va6rKR7Y1s2+tnxdO9nD04gDDYfBo0FxXzK62epZXltqqM7McnEznmcwD4Sh/9uQx+lM8ADl6zc/wc538+j2r408gnOlXDE7q5bxG85fPb62u304vBXTP7QA1RYlFx8lazV8UHP95Azn2N5fp+0ZtILd2TOLJF3N69QS6nrd9VoKnDuLb8jNQWgtB+1uIK57iWdmcK63S5YEzh605/QAdTxKqa8Fb35KyfsXtg6Vb4fwr1sX3VaPXNYM0pq4/8XpOXg9Z5orK/8/em4fJcZX3/p9TS6+zL9Is2kdSS6PNkixZkrVZtiwv2CxmCcHsEEi4wCUOISG5P+DecG8wgQtkIUACJD8ICWAwYONNsiQv8iJr14zU2rdZNBrNIs1Mr1Xn/tHdMz1rd/V0T/eM+/s8tr7dU/We97x1Ttdbp97zvlJRkYqCFOqweINjrR18b8/5ES/D/ss97L/sZeO8Et5969yU03ROczt5aPVcHlo9l+iFi/5FpiQvU8iky/H4oQsJnf4YzlwP8Ly3ie2LazOo0fgxtV209GIq22pg07LF83Jr+k8I8o7/ZEY+nWfSPC0pKk+/Yv0aefdhrv8gimczZtsJa+fO34y0uUat+pvTtkqlXUzk/sctmcg49ARmzZKk5NuW30/QguOv3Pou5FhVly2m88zWfAiH+giefh18XaDYUMqqsc9cNrHVohUdRXdgaibYYq/VI3fcxqudozr98XjpXBeGcZb3rZ8f962ccjzySCLj/pYe+b5QiJfPd2MFO49d5c5FNUNeWI5HH5HkccnyfDrPvK0iKHPbiASsWUOZ2z6CTDnK0VMDecd/MiMf6jOh4SuErd00Iwggwn6cM5bRqxaCkSBmPQ6ORVvHDCPJZVulwgPNJ8CwaOPOM5hdzWjF0xPKtxVUYm7/POHnvp5QrLLqvbhmLh8zfCjXQ33kzXZ8B38Jlw8O6psJ+BQ3LNmOe9l2hFAzH+ojwyAlQqhxVXsjaRl/uOdc0pf7lYs3WDW/m8WVJcRc5AimEofxhPqYEkJhE5sm4vZHSF672I5V9Jpwsq17WFGmVPsVQTrtlZqNpgoPGwb7r1zn2IXr9PhDOHSNuqoibq+bRoHdNuT4qW2rdZ4qXjxn7f5R4YAZRa4RZA4ds1MLecd/ykAO/JsP9RmBx0/mxMeHOi4TOLkbrjRCqBfU8WS5UBCKiu3OTxJ8NrHTCSBWvBOtuDqSEjLHbZUuHrp6KiXrBttOoxVPS6otW9VCtLd8Bf+xJ+Di/uHCKhejrbgXx/QFiXXO4VCfUNcVQk88CowS12H2wrHH6b12GvcdfwyaPbO6yZHVONTcgX+Uv42GvQ0tLN5aYu2kSYRUXI6wYXKg6Tp7jjdx+YbR//3CCjtbltSwrKqE692pxUC3dvviHP/cw9R20UbH3rOt/Gp/E8agb0M0tvn43dGrbJxXwjtXz0VTByw0lW01u7iAmgKV5h4j8cFRbFlSw1TexDsa8o7/ZEY+1Cdpnmz4SlgaBHf9E7QOKfZkWKwCFINrWn+4iDZtAeEdf4n5zDcYMz3lqodx1m/BzHFbpZ2HraVii8EMBket1jsSV0pqcG36OOEN7yd86SimzxdxUnvbwQwSvnScnu42HHVrI5l4RpGTq6E+ZrBnbKc/Hq0N9L7+cxybPpJZ3RQNRShIQTS+P+Ltv+q1nonjeGsffcEQLpvWLyeCqcElYCXUp73Xz7eeaaRrhOlzqj3Aqb3nmVGoMrPMPfyAJBBJhWu9L92+IM03+wiGwhS57MwqcgFqSrJG51M7fGU0/vtjF/l9w9hvcF4610VLVwOfvrMeTY3Mualuq/dvms/XnvKSDOaW6GyaN20UmXKEM6YO8o7/ZEY+1Cet4SvSNAg+9U3oSj70IBGURXcOCgVxVM5F/MHf4T9/AOPkXuhuAsJgL4OFt+NcuBHhLJnwMBurtsoIt7lIBardiSJNy+1qqgO1ch6+/T+HpsPD5Pr3/wTmb8W1+u0omn3ShPr4vC+RlNMfw9kXkCveguIszJhukVAfEyEVpGmCogLQ3pPaKnRXv+Mf91ZlynBINtSn2x/ka0804kvgp1y5adDec2Psg0ZBecFAxdxk9Pde6+L54800XB0cb12kwbalVWyeX41NU5KSlZgnttFU48daOhI6/TGc7Qjym8MXeWj1PN4MtppZXMDn7l7A3z97mrjEYMOwoNzGJ7bWo6kqI8sUo547FZB3/KcM5MC/+VCfEXj8ZB75mN4jT6bV6QcVx/zbhrUlVB3n/HWYCzeimJGfJ1PRBvgksFUmuH3GMgLHn7BsZVtNfUrthrtbCT7xtyDHSPR5Zg99187guvsRcBQMlpODoT7SlMiTz1u2YcD7Avot949bBwkE288R9O6B9ssgTXCWIJZtoWD5DmDww53od0asQZnCN2YrPXts//mETn8MVkOqIOIgLK1KPqzq6YbLPHGsbcS/3QjD44dbef3MNT6zfQkF9vS4H1N3JIyMZ49esXT87tOd3L8iEv7yZrBVXVkh//uhFew718bu4y10x72sXzzNwZYlNSyZVszQol1vJuQd/8mMfKhP0jxR+IoMh6FxZ1ovj3rXZ8Hmynrf022rTHG1Yi4U1EBPc/JGrroF4S6zHBYlw36CT36dMZ3+GLqv0LfnX3Dc//nBYyYHQ33kzXYIdCZvvxiuHMdc+dZx6WD0tRN47h/h5hDHpLeN7t2n6Nr7E9TNn8ax8m1ET6aq2EGbz3omjpL+Qlvx3uzU4BJIJtTnZjDIwaYeMok7PGX9YSIj6RDPd59qHtXpj0dzj8F3njvOn9+7PCp7bLlj86kfvhLP23p8nO+0Hnb62oU23lZd/KaxlUtXuctTzV2eagIhg6Bp4tRUC+Mt/vPUQ97xn8zIh/qkLXwlePkQkGIc/1BoRWh3fALHtHmQQghKtnk2C3ipa9+N8fy3kja1vvotKenZd+a1xFWB43GtAaPtNFrpjH45uRjqYwStO9EAhH3jut6y5zqB3/5NZNPwKDBMH4E9j4JUcax6GwC3L67iaOt5S6reOrMAhx6L7497qzJlOCQT6vP6eetZeoiTmgiVTsGOpbPizhpd50AozGMHW5LWobnH5NWL19g4r2pMuYn52DaaavxC5+jzayxcae9homx1sbuXvQ3NnGq5SdAAl11wy6xSNi2qodxpH7d8q9yua9gtnzswF6ci8o7/lEHc6lA+1GcEHj+Zhx9jdCdeqRoRjgqwRVc/XeVonk3YZyxFqjYwwznS9/TaKpPcXruEwG0fwHjt3xOaXr/jM+hls1Oys3lqVzJXdxBCJ/Zg3/DwgJwcDPVRbA7L/QLi9lek1q7vhR+M6fTH48ber6POqUcvW0j9tBJKbdBpYV/3liU1/by9z8+hi+3c6Ati11RqKgpYUV2KmmKRr1xAspp39QRSkn/3onJePXt9UAjEUNQUKHz67qU49KGbcUfGKxes/37uaWiOc/xTx+S90tYRCpupnWdEzsukrXqDIb6/+wRnh7yR6PNJdno72OntYFNdCe9anXoRvjzSg7zjP5mRD/VJmicM9Ul8bxsZdRtxrYzERpuaDSUcQuZAfzNpq0xz+4JNBIuqCB96Atobh9u8ejX66vvRS2akJN8M+eFG8quT/bjSMMgmuRjqI9wVYCuFoMVwn5oVKV/vQOdl6LCyN8ak79XfUnzfIwgh+KM7PUln4rivvoK5JQU03ejhsdcvcKp9qPPbjp0L3L1sGtvra1FEbJUzhtznErBewCt5uJw6X37bLey/1M6ehmaaewacybpSna1La1hRXRZ1zpLT4dA5628fWnslnT4/pU57Um2MzCOhPqY0OdbSydEL1+kJhLFpCnOnF7Fh7rTow0uq8nOLFzlSc9kKHToxW2VCt75QiEefPMr1BM+iL57t4qbPy0c3eRDxCyNp1ifGQ4bJoSvXab/hBykpKXSwekY59mEPtMPH1VRG3vGfzJBGZLUzHISwH0J9INTI9+FgdniwN/I5GM3WkS09hnAZFKCFR7WRcBWlNtVdhblh9wm01Xi4DAeR4SCGaSA0O0LVRzzeVj4LbcenkV1XCbadRYZDCN2OrWYhwl2GEg5GzklFB19q2U0I+QbZRIbCkftDKJQz80GEg7DoDjj6K0td0xeuS/l6G8efS6qN2O3UBHrPPY078EkU3UltoYtHdizgH545zVh+wwPLKtm+uJaT17r4h12jP2gEgN8da+N82w0+tsmDogwUsWKE1/v+UJhXL17j8Pnr3PSFsGkK86YVsmlRFVUFrjHPzQSXpolEYJpyzOPLilJ7u1NZaEcVgnWzK1k3uwLDhIBh4FCVQbYy+58HEuvc3ZdamGSXP0ix3ZZUGyNx05S8cqqZHz53Zlgeq0NNvfzqYAvbFpTw4IrZ/X0zpUljWzdN13sxDAO3y86qmeUUToIsUQvKi0kFt8wtxzQlAhkdV+nV7eevnU3o9MdwuLmPvWdb2TyvKq06xPNA2OT3Ry+x+0zXsPb/47UrbJhTyIMr5+AaJWTQNGU0je3URN7xn8SQpok0QkgzhAz0QSjyvtwUAhEdtBPNCfkh4MNEgmlkTY+h3AgpmCE/chQb2aoWjul0jAat2jOqzMnKE9kqFW6G/ATOvg5nXoRg5Mc4BFA2HxZswlGzCCGUYeeqdjeOmcsxhECNfm/4e8elj0KK2Rw05yCbmJodqYDpD+TUfLDPXkng6NNAEhuXAeZuQChayteb9ksJm4jdQk2IOLWhIOGeFrTieQDMLCzgq29fzsHWDl5ubOFyt4EJFOuwvq6M9Z5qinUb7X3+MZ3+eBy/6ufxQxd428p5ox6z+0wzvzk8tJaASVNPNy+e62ZRpZ0PbFiIq3/VOPOIRmXEOd4j49ZZFTx2wNqbKwewqKJkkGyBwKFG3/akFkmCrkEqP6CapqTcJsCTRy7wny+NPf6eP91FU2cfH9+ymBfOtPLckasM3Qnz2IEWVlQ5eWDNHCrsKYbLZQBhaXKw6TovNDRz5WZqhqp0wOyiQgwz4t6Ox94joTcU4o0r1vYe7D7Wwu2zpw9EQ6YRfeEw3372OFfH+Pnbd+EmjU3H+Ny9Syi26cP+bkqBKZmyzn/e8Z/EEIqCUHWEoiPsThCRp1VF1RFGMCscBUwkisMNuiNregzlqu5E0XSEzxjxGEXXoW4jnH0p+QswbwNaYXnO9HGibGWVh9vOEHru7xmxaFnHGXjtDP6y+Tju+ASKw53xPipGEErnQafF1K1zViNstgE5DhtIieLQcmo+qDY72v1/RvjJr8MwF2cIqpfhXPsepGZLuV2SuHnHDlEAgUTBRJghlLhnMJuisG5mJetmVjLaat7eExYyPgF7znZzz/JwdGVvMJ46domnTlwf8/yT1wJ84+lj/Pl9S3Hqwx2ETCCWeERJ8HzqVjTWzS7k1YvJb1K/c0kFmpZ+b2teZSEtvRY2y0cx3eFI2M/R4G3vTuj0Dxwb5G9+e4SuMV5MHGn1cex3J3jk3oXMLEytpkg60dLTx3eePkXvOB3196yfi6JExpUg8biyitfOW9/fcT0Al272MLekIK26SCn5we4TYzr9MXSF4Ls7G/mL+5dHQwIHoAiJImCqVvXNO/6TGUIFRQPNBoYzspyGjHyOxutOOJeAIUF3RKqeZkuPIVzYnJENt1pw1GMcq9+B/1IDhJKIj7aV4rj1nTnVx4m0VbI8dKMV47lvJLZnxxn8u76L/cEvTkgflcV3Yu6z5vhry7eD5uqXI2KOYFDLufmgVc5DfcdXCBz8FVx4dXhn1EJY/gDO+i0IIZDjaddRBjebEtovtmU84ndINHdlXGhJ7IjRecgweeFsd8J2huKVc21sX1w7SGbj1e6ETn8M1wPwn6+d56ObPUnpOV4uFCXqoImExz+0ei6nWxPHVgPMLdHZXj/Dks2T5VuX1vLyhZOJlYjD1vkl2PrfpFhve+fRxGMuHmM5/TGYwLefOsX/fPsyCvpTxmb2eo/Er/X4efT3pzASqzwmPrppNoumlwID42lgY216dL7Wndom86vdfurKihLKb+8NcLy5E3/QwKarLKoppqbANeLxZ67f5Hx38lZr7ZP88o0LtHb7uOkPoasKcysLeMeGhcypTr5+xWRD3vGfzJD5dJ7pTFGp6C7sD3yRwM7/CzfGWFl0T8e5/XOougszy/3Klq2S5cYLP05+PHedI3BsJwX1WzLeR+fsFfQeq03KYQVg3npsrrJBNsnFdJ6DuLMQ/fYPEV77HkIXXk7HFwAAIABJREFUD2H0dYPQ0ctrsFUvQipa5Hg5vraUhesxrx1L/joDtur1qK7RV/ZH4k09vv6QISs41dLN9sUzBsncdcya03iouZduf4hiR+rx6MnzSDhU5PPYxzttOp+/bxn/vPskF8bwbJdWOfnIRs8YlUrHx6sLXCyqdHDyWvKVmLcurkm5vet9PrzDNnanB0HgxVOt3Lsslso009d7OP/JvjPjcvqr3IJ186eztKos+k1y4ykVbqYYDmP0nzay/HOdPTx58BLeoWPqYAuzS3TuvWVGXP8i5+5ttJ604aXz8YsJBldudvPiuf2snlPKx7YspLB/8/nUwZu3dNmUgxz4d1AawWxxckSPGI/98I19vOouwX3/X6Ft/KNI/Hk8yuej3f4xnG/9Eqq7OEf6lT1bJeKhzibrlZAbn42O5Mz2USgq9rs/B+5piXWqWob7tvcNlyOidsrx+aDYC3DOX0/B8ntwrdiBvWZx5BV2muTbZ98CinssCw6Da/U7LB0P4A+GLZ8TOW+wC9XhC6TkNO4705pS+1YRP/uSQYFd55Edy/js9gWsqHYRc1OcAtbNLuIL9y3ik1sXY9Myu0/hw5sXUpakj/SRTXOocKceS3/+emYLlz3feC1lh3a8aO31cbZjhLDIMTB0vLT2Sh4/0sojvzjErw6cJxg2RjwuHSh121I6r6xg9PMOXG7nm8+cGu70R3GxK8Q/7znP7tODHf2GptTqHIyow4VO/uY3R+kNpPa7k8vIiRV/j8fzZeBLwEyv15t0PWqPxzMTGCvI72Wv17txnOrlLvLpPJPmVlJUKhJsc25Fm78BEfIjwwGkoxDVMCLHaDbMcCgn+pUNW4WuXyB0Yhc0n46MOacbZq3F7tmA6irpPz5wboQQk0QIdBDsuIRePjfj/RWFFTju/yv8x56CE88SjZUbgK0Ult6Pc/HGgTkWJycX03lmg0vdhrLpQ5h7/zGpS2yfsRl73W1EBcT9OzZ3prjB1mGLT90naepKzTm43N6DVZ1T4RKwms5TCFhQXsiCLYtG70AGdQZw6xpfuG85P3nlNMdaR95bUqTDwxvnUT+9ZNj5VngwxXz2ycInoa3XR1WB07Ju4+WveK2vWo/2iGICz5/upLG5i6++bz0Ou542PWN87fxpPJ1k2FwMdmBRRSxD0WCZ5ztv8qOXk9u78diBZkpdNm6pLQPkSLvIxoWmLh8/evE0/+2uxWmWnF1k3fH3eDxvA/4qxdNXRv89Coz0rjm55NCTFflQn8yHryBBs2Ma4ZzpS7ZsZQZ68e/+Plw/NXgc3uyGhscJNDwOi3fgXP12FCmh19rNIIbQpSPYymaNGUKTNq7bKFj1NoyVDxK6fBSztxtTqNhKKtGrFiKFGg2HGV6BOedDfSaQu2Ysxbf+oxiv/OuY19Zeux7Xg3+NUOJzq4uk+IyiAnSs19eun1kWlRWRE05xJTcsGSTHqv7Jc0g21CfXuNuu84mti+nwBdl3upVL7T2EDJNip86t86exZHoJQoydXjUZXphiPnsr8IWj43uC7XjtZvpDmFp7Jd984ghffOjWtOs8ze1kfrmdM8nm8wTuXFKJqigjyvz9ocsWega/PXAx6viLlH4fEuH1s9e4vm4e5QVTJ+Qnq46/x+P5E+Bb49Aj5vg/6vV6f5oerSYr4laHhOhfjZtw3q8D2dVjGI/dSHNFn1zmw21l+vvoe+pr0HuNMXHiGXyBXtzr3wcixUjCht/T292Cc/PHo2Mq830Xqo5j1goATEVDMSOvd+VY58Y+T8r5kH7unHcrwWnzCJ7cA97dwMArcqV6GVXr30pfxbrIRucUoCqCbYvKeeaktQfK9XMHh3OVuFILTShxpnaeVYjEh+Q8ypx23rJ8NhDJG2+akWwy6cqS4qlMLZ+9FThHyAQ1EcjU9W9o7uVMUxfltvT0qycY4rovgEDy4K2z+NYzp4e+Mx0RFQ5YUFvCG1fakVIyrcjJrCI3QsB1X4ATbcnvEwFo65Oc7bhJXVkRS2vdHEpjuA+AKWHvyVbecevstMrNJrIysj0ezyLgG8B9QDuRNz+FKYiKOf4H0qTa5EI+1Cdpnu1qtJOJj2Qr/+v/ldjpj+HcS/iqF0FRLfBGcucMxZVD+Hb/C85tn0SIBOE0WeJTMdRHYhK4dh6z8TlovwDBIBSWw5zbsC3ehKboY8pRSmtxrXk38tZ3EPb3IEI+FGcRpdUzUXQnfT1E8wnKuIudPN+yqJrnT15PelXv/iWVOLT49iSzi90UqNBjcffkrXUVg+Qkq7NVLgHrlXsnCxdJHjc2t2kqm+tKeOHs8AJN6YADmOa0k41rUFFoh5Yka3BYxFOHL/Dw2rqUdQNJ49Uudjc0D3PQ5xRrXOwODzp6KNxKxJH+9rOnB31fZoc7llST6nPhicsd1JUVsqW+mkNNZ1ITMgaudKT3YSLbyNaK/z8DW4DngI8BL5C6498DnEp04JREPtQn86E+b0I+1FbGzXa49LqloWk27sK59RP4jv/G0nmD0HKQ8JWj2GcszbpNRuJTLdRH9lzHt+e70DUkvrazGzrPETz0M8Kr3otr8ZakZApnMYqjABCRsSRNhFSQpgkphPoAFDlsfO4+D9/8vZdEW+42zC3mnqUzhslRhOCO+mn87pi1/OMFTp0BxzV5na1zmKyhPon6FUF65N69bCb7z3fhy0C4/7YlFQkrPmeKr/dUs+tUEimlU8Dh8908vDY13Uxp8ov953nx3MgPWxe6IzNydonGDV+YzrjIn1lFKiFT0tJj0jtCRFBHAB472EJpii/V+oKR35y6siLmleqc60xvwE/IyOyekolGtrL67Ace9Hq9d3u93uR2cQyBx+MpA2YRcfr/1OPxHPF4PH0ej6fZ4/F83+Px1KRT4dxH3MpElrKH5EoWk5F57EaaK/rkMh9sq8DpfVhG53lksA9qbrF+bhxCJ58fQ88scxG1U4bnQ7innUBzA4HmRsLdrRnpj9HXie+3Xx3u9A+BefBn9B57xnpbkrRhVlEBf/3gElbPGDmTUJkd3rN2Bu9dWzdqaMnWhdVMc438t9Hwt0+cpLU3QUG0NCB+9uUxOkocNr7y7tU4kjDW/LLkPUod2LSwOnXFxokqt9OSvlbgH8c8/PXBC6M6/fG42BVm1ewyHn3Hcv7X25byjYduobLIQUtPYue5M8XdubGN/0IIPnnHYqrc6XVtUw0PzFVkZcXf6/V+Pg1iYmE+q4BlwF7gCrAG+DjwgMfj2er1eqfuBt98qE/SPB/qk7qt6LRWLTWGYHcrti0fIfjYlyCY4grW1UbCIT+4SnPGPjGeyVAfiYnv4mE49gx0Rl5dG0Q3rhXUIpZuxzF/HTJN/Qk8/30wk0uRKI/+msCMJdjLZifflqKhCAUpGFeoT4xXuOx8eKOHdwdDHL7SQY8vhK4pzKoooK6scIjDP1yOXVf57zuW8PUnjyftbJjA93Y28v89uHJISML4+jKUSyAf6pMcr60s5NsfXsfPX/Ky+1TnsBjzW2pcbF8+gxlFbn6w9yTHr4794CaA/37PQgozkP3GCn/49vn8ze8aE77VsorIQ5J1fVpv+th9Ovnf8F2nOtiwcDrTC5xcvtHLgSuZDZVZUFtCTGeXTePz9yzjiaOXLOk8FtbNr0yLnFzBuB1/j8fzU2B1Eof+2uv1/uV424tDzPFvAB7wer3no/q4gR8A7wV+Ctyaxjb7YbNpVFamEp2UPpihAAKT8lIXwb4wMmgCEqmo/WEIE81lUMew2VFtOsKmZU2PYTwavlJWZM8NfXKZD7GV3y6S2rQ1FEVODXd5GeH3foWrT/49tJ9OfNIIKNQC2ApyaCz1cwOQlI+hWyrzgbCkbfcPwfviyAbpaUK++mMCLYeZfu9nUZXxjelAx2X6us5auibS+xxlOz6VfFvRUJ/SskJwuECNraBF3NxUeSmCGbXlKZ9bP6uUl88k7xxc80F70GDhjMGFg9LNJYLSyqKMyA+Hw7x4qpWdhy5xuSOICVS6FTYtrWHH0lm4XektUha/uXdwNeL0tFHgtvORHSv4wF0mp5s7uRkKYVdVFlQW4XLZ+4//i3et5XeHLvDEqxfpHWF/x5o5RTy8cREVpa609j8VXlpZxKPvc/G/HnuDTmt7XcfEyrkl0XFlTZ/fNSSdZb0f+y9e5/1b6/nVkZSCOpJGpUth/eJaGJIt6mM1pXzorjCvn79GS2cv0pRMK3HR1Rvgv/Ylr9PMcjdbVsxM28b0XEA6VvxnA56ER0G63539X+Ax4KbX622Pfen1ens9Hs/HgM3Aao/Hs87r9aaQUHwSIj4rSxa56P/RyK4eU5WbwQA9Z1+j5/TrmH09oOo4q+ooXLENvbg67e3q7nJSSTCnFkRTrBWUUvuuv6bp3x+B3vaE5w1FJPVj7tg/0/Ph+ks/IzSa0x8H8+Jhrj77XWru/fS49LtxbLfFKwLm2dcw/B9EjcbwJ2wrtoAo+y2RZdcKAiHDktMfw3NHL+GZUZZ1/VPhjZc7+NrjR4dtjm7rNXnstSs89toVPrh1HjuWzUxbu7HPZLhvmqr0p26V/f+P00MIHlw1lwdumc3hS+2ca7uJEQpTXOjg9roqCt22uHMzp2eyvKrMzbc/spn956/y9BsXOR0tZqUDGxeVU11RwH+8dBEruHfl7JTm3t4Ga/thAPY0tPGBrfXs81r/zbeCd22a3++UD9Vf11RuX1AdNx4EobDBvsZmLnclfp+iAP/tvqVTyumHNDj+2SqQ5fV6DeD8KH/r83g8zwPvJ/I2Iu2OfzAYprs78/GeY6G81IlE4XpnH8FAEIJ+QGJqNpRYgakJ5gSDmH4/ilODoJY1PYby0hIXpmqj+3pXTuiTKvcf3wlv/JQBTyoCX5sX39HfQ9UtOLZ8BEV3pM1Wxuzb4MgT1ganVkyfewa9PaF++ZTOT8nxv2m4kHFycuValBfoIOD6GLpZnQ/Bvg7Cx59N2jbmuddpPduArWJeyv0JtKa2Itfe2oReNjuptoorC1BUG52dfeDTgTQuY6aI5p7UMqecb71Bx7UbadZmAKWVhQgkndduplXuqWvdfGdX4jc7/7bnHN1dfdy1KD3b5EzTjFvxT2/sdWllIRLotHg95rgdzJk7kFY21Begoy/9+fPTAU+xG8+d9cO+N03JLtclrvbJEc4ajvpqF3U1JSmNXX8Kr3z9JnRcu5HSucniviUV1JcW0GFxrnzqjsV857kGmhPsO/j03Yupddu5lua5aAXFxU5saUrBGkO2NvdOBGL11V1Z1SKTGJLVR5FhFGlGs4xkiZshVEAxjOzqMYQLM4xiBHJGn1S4/8Bv4I2fMNTpH4TWw/if+lsI+tJmK1vRdCibb2loivptqJJB8rXFm6yP8fmbUBU1J+w/lAszmgEpjfMhfHynZROFT+waV3+QFvNaRiGMJNtCoigKUtERqj2Sz2/QWnB2eIp1vDAmQH+ZZplhw+R7STj9MTx+uJXWXn+a+5Xd6z3VuKIofGb7EkqTqCtVU6DyyAOx6OhU2k0VqZ/7yTvmUVs4crXucgd8YP1M7ls2m1RsWGDX+fN7V/AHt82geshGYAG85ZZqvvfx21k9pzxl/XMZWa/cmyo8Hs+XiGzq/YrX6x2pau/c6L/Wg9MmJeLepfe/as8C79eB7OoxjMdP/lzQxxoPtnrh2K+TGwo3mul7/Se4Nv1R2mzlWPeH+H//P5Nrv2gmrsXbhsnXpy8gXFwL3U3JyQEcnjtS0H+CeOxzOufDWWtpUwG4uB82fiT1/riLoMN6s6qzJMm2FKRQkZoNKcLRzb3ZR7E9tUwdZQWZzfAxHjdrNBy8ct1yuN4Ljc28e01d4gOzjEzYa7Kg2GnjL9+ygt8dujhixh0F2LqglLesmI3DrhMLf7KKKrdCa6+1pfuYQz2/zMaZDmvpemYWqSydXsrS+0u52HWTk1e66A2GsWsKC2pLWFBWNPDzmiI0VWHj3OlsnDuNTn+Qm8EQuiKocDqpqinFYVO5meWojkxh0jr+wHLgHcAJYJDj7/F4pgF3E0mCsXviVZsg5LP6JM0ne1af4PHnrI2NC/sJ3fY+dN2dFlspZTPR7/lLQk8/SiS/zCgomYftvs8hFQdyiHyp2bBt+WOCv/0KSRVWX/NBlNKZmDlg/5F4JrL6YKQWQmKYJlJPrT9q3UaMy4esNVg8B1FQkdy1UQRSLwDdBVpsFTneAckOL7BpLCi3cfq6NafktoXTM6qbBNKd1efFk61YxQtnu3jnajO6ITddfUz3tZdxVkuXzMnFXbrKe9bO460rDQ5euU5Xjx+BoLzIwcoZ5ejqwKbXVG21eXEVP3/DWna3zfXVkXPrqzljcS/Clvqafh1mlxQwu6RglCPTY8NSh41Sh22U46YeJoXj7/F46ojsaWnxer3d0a+/R8Txf8Tj8Tzt9Xpfjh5bAPwQKAK+6/V6rf/iTRZIA8xwpFBQ2A+hPhBq5PtwMDs82Bv5HIzG8GZLjyFcBgVo4X4bSSNAsO0Mpq8HqTuwldSgOouzrudI3LjRDlePWx4eoeO70ZduG7etYt/rJbWId36NoPclOLkHQnErTKV1KIu2YJ+5Aim0Ucei5iyE+79A8Pnvg2/0DWNizfuxL1yXG2N6FC5D4cj9IRRK33xAgVRyKJkGhHwp9cc2fQE+UQAyuXSeACzelvy1UW1II4iUYJpE+xf3NiSLfHN9NadfTN4p0YBbqksxzXinPL26SdNEIqJtpEdmS4oFjW4GQhTatXHpYJoyLjwqvdfeNCUi2ka6ZE5WblMF62ZXDvveNCM2H4+t1s6q4JdvNCf9y6QBa2aWY5qS5VWlFGkXuZFkblK3AqtqMzvHkhlXMtVYwEmASeH4A7uIZA/6MPBjAK/X+6zH4/km8KfACx6P52WgHdgEVAAvAn+WFW0nCNI0kUYIaYaQgT4IRVauTCEQ0UE70ZyQHwI+TCSYRtb0GMqNkIIZ8mP0dBE4vQdOvUD8ynUAYNoSRP02HGWzckLnGA9fvzD00ieHtjPI0MaUbSVHGE8IgXPB7cgFGwgH/CiGH2FzInUnqpQQDmIaoTHlq/Zi9Hs/j3ntDMbpfdARjcZzFcOs1TjmrEJoNgx/b07YfzRuanakAqY/kLb5QOkc6Dxn6TJTMB1CAcxwMOX+sP69sO8HybVXvhC9ZtGI42NEbkjMYADTIaOOf+5g6bRSlk2/yrGryW02/sDm2QhERvsRKxKaC7YKy/FfM8OQSKFgGPFOf3pgmNHHiRywVa5jPLbSFJVP3VXH3+9Mbp/In2yfj6Yo0bYEn96xiEefPJnwPa8CfPqeRShCyeo1NaXAlExZ53+yOP4jwuv1PuLxeF4FPk0kr78KnAEeBb7l9XrTW7c5xyAUBaHqCEVH2J0gIk+riqojjGBWOAqYSBSHG3RH1vQYylXdGamA+vQ3Rg+naGtAtjXgW/VeXAvXZ13nAZuOvMEp8QABYbNZbjfc10nXkT0Y5w5FVqwVO8xajL5gK+q0uQgjiECgOQtRoueaFvulqjp6bT3ULomEEsXJUXLA5slwxWEDKVEcWtrmA0t3wIvftXadPdsQNvu4+uOYu4qg+Djmywmc/2mLcW7+GNLuSl6+oqPY7AhV5Ep4fxwEH964kH/fd4bDLWNn+fnghpncUlUGGQ4DiEVmpNNWFQWCKz3W9S6y6WnQQ2CYJmoGrr8aibbLwXGVexivrRaUFfKnOxbwr7tO0z3K6n2RBh+/cwGziwuInyfT3Q7+6oF6/mPfGU6NElo3r0Tj4Y0LqHA5yPQcSwRFSBTBlEvjGUNOOP5er3dOqn/3er2/AH6RZpUmBSQKRiiywipVO0KLfItmg1jV1YnmEjAk6A7QndnTYwg3wmGu//yrYCSRluvgz/C7i7HVrcsJ/dWC8pQKaOEuB82VdFtSCPr2/oC+KwcHyzGDcOF1QhdeJ1RRj2PbJ1Bsjqxf02xzoesR+wS1tM0H+7w1BPb/EvzXkrzINpwLNoBmH3d/HHPXYlTNJ9D4Ipx4Foi7QVfWR6oF19QjhEBaka8IhGpHqNqQAk7kBLcpGh/d7MHbfoM9Dc0cbx3Y0KcDdywqZ5OnilKnfUw56eJCUaIOWvpsdfviGv5rf/Kb6gFum1WIrg31ElPRIbLSPzyd5/j7FrNR5N/0yJyqPB22mldayN88tJITbd3s87bSdiPypmxakYPbF1WxqLI4zlkefG6F28Fnti+lvdfPK6ev0nbDj0RSWehg/YLpTCtwZqzvVrmiiCnr9EOOOP55WINx4yp9b/yCtoOPYd6McxDqNmL3bEUtm4UiDUBAODihHDOEAIRhgJjYtsfiNw8/DeHkc/Gar/0nYvYtKFJmXX+1bCYhWwkEh2dtGAva/LWRdI1JtCVNg8Czfw/XvWMLbW/E/+zf4d7xeRTImeubDR6rSqukcz4YYRzbP4P/if8DMlGeeYF+7+dRVQ2kmZa+KY5i1NUPIlbei+nvwTANNJsbRbdjCiVyvLQoX+oIwkgZC/WIzinIGS6EwqLKEhZtLSZkSHpDYTQBLpuOIiZaZxhI55kembfNruQX+5ssLSBsWVqTVh0GkE575eZ4GouHDJNufwApBEW6jl1XJkiH9NhKCEH99BLqp5emJKfC7eCBW+ZMQH/Hw4eO2amFvOM/yeA79nu6f/3XYIzwuuzsSwTOvgSL7sG9+q0IJPl0ngJpGPQdsZgfPdhFsKkRZ83irOsvhEAsvhN55LHk9XdVok9fSCRHe+K2+hp2Jnb6Y+i6TO/hx3GtfW9OXN+s8djnNM8HragC54N/he/lf4P2UyNfg9K5ODa8H6VsZmSDf9rHnILqLEIoGsq45UPKSfOzAF1VKFFtRC/uhLefiRZtmsJHNs/lX144n9Tx2z1lzCoaLZNKbmGyuGiXu3vZ29jCqxcHh5quqHZxx7Ja5pcVZVyH/l8kKWm42skLja00tvn6/7as2sXm+mo8FfEr93lMNeQd/0kEf8OzdP/yCwzcTUfByafpReC69e35dJ6qTvjqKTB6LdvbaNyLWbss6/ojwb54C/5TL4AvuRAQdf37kZoNM1ZFday0lKaBtFo4yvs8xpr3EAvryLZ9ssEzkc4zxkVhJY63fBHz+kWCp1+CjqsgTCiuRPVsxl48I+v9T5oLkEIHEXlDEkH8b1iex/OIySTpttUtNaV8+HaDH708dqXmHYvLecvyWWlrdzAXSR6XLJ8c6TyfOn6ZJ4+P/Nt9pKWPIy2n2TSvmHetmYcyyN9Ov61uBAL8w3ONw6rWSuBoSx9HW84yt0Tnj+9YjMuupVmHycLjP089qF/+8pezrcNkw4eAOYZhEggkmZ8qDTADfXT86MNgJFmGpf0MyowVqPYCFGlEnvSlmXEujCDCMFAUFQET2vZo3Gi/iHnpDetG771K6PJxpM2JVjQ9q31RFA0xdw1G03EIjJ12Udv0SZy1S5KWH246jnn+JcvmMZ0V2Mtqsn59s8VdNhUhTfy+YMbmg2Z3YaupR5t/G/Z5t2KrXYrqKJ7wPiNNwp2XkZ1NSH83QnegCpKTI1QcRUWgu/D7Ypm0BAPrj3kez53uyIZ8f19wXHJG4jXFbjbNr8ClQ/P1XoJR/0YFNs4r5gMb61g9szL6sio97UoJUkbfXAoxLllDudMd2Xfh7wulTWa6+c6Tzfzu6Oipi2O41BnAF/BTX1OWEX2cbjt+f4iv/PIAbb6xHdsuv8nxK9dYN68SVVEzok8uc1eBA01VCAbCWX/z4XDoqJEd/xeJZrUcL/Ir/pME/mNPIgPJx6gDBLy7cW34ELEVuDdrqI/QxlFps/M84Ze+T/jaBdy3vp348CnTf5NAx2VEyIdiL0CdPp/+Vc0M9EV1leC+94v4zr6C2bgT+uJvJgp4tmDz3ImtsNyS/HD31ZRMI2/Ezsvu9c0az1CoTy5xM+zH17gX6X0eAp2DB0DdRhyL70QpnZFADkymUJ9sI9NuRqFDZ8eSmexYMhPDNDEl0SJPcgJaTz9yWePuQJDHDydfSmjPmS7We3qpLXRnRJ+fvXyKjiTXDlt6TJ46dpm3rpybEV3yyB7yjv8kge/Ib62fdPZljI0fHbiJv0lDfdTy2cnUiR0b3mfpdRThWnY3gY5LGIefgKah1U4V8NyNfdmdiILKjPRF0cGxaCvGkruRXU3IYB9Sd6C7yhCajplkeE88l6kmTDaZtJWQ08EzGeqTC9zobSfwxNcGF2qLx9mX8J99CTb9Ca7ZK0eXKciH+ljgEZNJJsJWqiJQB/0t030UaZab26E+L5+yXj90T0Mz71s3P+36BIIhdja2W9LleW8H9y+fhRZX/TdVHaQ041bPc+cajczjP0895B3/SQKjuyWl80RfdyT1IuLNm9XH7kKtuxXjbArhPvE48hg+zY488NNRDjDB+zSB0y9hu+8RtOKqzPXLCKIUlAHlcdlWUsvsohWUkFLQmruUZLMGTUWekaw+OcIJ9BB46u9Gd/rj8eI/EbJ9DnuVZ9Jm9cktDunO6pMbPIZ0ys1tG71y2pqjDfDKhRu8b136+7XvjPU3uwZwpLWL1bXlw2S29Pp4obGFQxe68BlgV2DBdBdbltawoKwIIeB8Vw97G1p44/JAtMKsIpUtS2tZXVuOpgqyfY1GH1dTF3nHf5JAiBSLOClxT+pv0lAfJJSuegvt43X8kWM4/XEwewg+8SjqO76M6izKet8TcfuMFYRRia9knAwcdbf22yVX+jKhfAqH+vQ1Pg/+60mPhdCrP8X21q+AMpJMyIf6JI+p7XKkH9mwV0tPHxfbewhLSbFTZ1FlMbo6/B7dOXKtqoQIhk1sWnp71taZKD3wyOi44YPagc9hw+Qnr5zmjSuD95r1mbGNymeYWaRR6NBobBteEfvSDYP/f98lHtcu8Zl7FlFdkJmwpjxGR97xnyRQy2djdDVZPEtDOooxDWuhH+PiORraYJ9BkVpyAAAgAElEQVS+gIJtn6Tn+X+2aMNU4cPXsAvXmndmve+JuKIDnm3gfS757s1aA+4yy2FFU4lP1VAfQ1GgYVfyYwGgt43AtXNotUuGyxTkQ30s8IjJJFPTViLJ45LlExvqc6jpOs8ducKlG4MXSRRg64JS7l46gwK73n+89eWUCPoja9Kov0z14VsOjEXTNPnenhOcuDbcoY/H5RthuDH2e+SbYfjbJ07yPx6sj1brHa5z9niKtpokyBe6niRwrX7I+kmLt6EaQRQZRpFmNCQhw9wMoQKKYUxMe0lyYYYprt+ItumTYC9NaLq0wLsbEQ5mve/JcNfy+8BZkVy/tGKca96ZdZ2zzYVpIMzwpJwPY3Gj+STgSzQKhiF84bVRZIIgDP2hPjD4dXqeD+UyB3RIP88VPaxzKeHxQxf51xcvDnP6IVKb+PnTnXz1d8e41uvvP7e22PraaqEGav+b+vT1ZXpZaivrZUXOfjl7zrYmdPqtwAB+8tIZMnntxsenJvKO/ySBfdE2lKLpls5xLtzC4HCDieRkse2ReGQyO2atwP3QV2H1Hw43WNoRInj1VA70PTFXbA7s9/0FFM8Yu0v2chz3fR7VUZR1nbPORfQGMSnnw+jc9A0uMJQ0+m6MIhPyoT7JY+q7HenFRNhql7eZnd7EoW83w/DNpxrxhSKr3ZsWV1lu6456a/f5ZLGhzrouKrCiugwAKSW7j6W213AsnOkIcuVmD+19frr8QUwz/1uRaeRDfSYJhKpT8u5v0vHjj0A4cT4ucdsHEIWVEx8ukKOhDVJRMVVbJAuNkNiXbCNw4L9I7UVs8gj7e1FzyA5jcVFYgfP+v8bWcYLuA0/BtbiqscWzEYvvxLZwA4opMXNE53yoT/q5tDlHGsqJoeojyxTkQ30s8IjJJFPTViLNcjMf6hMIGZZSct4Mw0unW9leX8uamRX84tUrWAn1Xz9/Wpwe6euL3a6ybXE5z59Ifu/O1oVl0Q24krMdN1Pes5AIf/vkQNV4Fdi6sJTNi6spd2YrBCj+89RD3vGfRLDNXEHZh35I18//FPPGGDv0134Q55xVkewc0oj8K9SJ4cHeyOdg9HXgRLY9BpdBAVoYQn0gVISqwoKtcNpiLLNFCEVAyJczdkjEhRHEPWsZrnmr6GrvQIYCSLsbVZog1Mh+kRzQMxe4DIUj94fQGDbJ0fkwFteKKlNLf1sya+SxrtqQRgBphKOredGnAcjzEbg0TSRiytjKNCWGGfvOHJesodw0JSLaRqb6sO+89Ww4O49fZZunGkXAn2yfz7eeO5PUeR/dOAu3pmXk2pum5L0bFnDgzHW6k5jg05xwz5Lafts2d/Ym1YfxwgB2nepk16lOHl4/g7UzK5josWua49gTMQmQd/wnGWwzV1D52afxn9xJ4NAv8bechUAA7G6Yuxb73DVImxMZijyam0IgogN4IjghPwR8mEgwjQlteyxuhBTMkH+QXWx1txHMsOOvFVZh+ntzxg7J8JitCIcRQsUMBfp/BLOtWy5xU7MjFTD9gUk3H8biQrVDhQfaB1bhkoF91vKRx7ohMYMBTIck1ZIRbyYYURtNFVsZhkQKBcOId/rTJNuMPk5k0FZHLyS/Qh5DrwlNvT5qXE7mFBfy2bvn8/1nz4y6c0YDPrRlDksrSzLWF8MEm03nkfuW8E/PNdLaN7pjO7tY5Y+21qOrar8+4Sw4wj955Qq6qrCiqjwj8q/09HLtpg8hFKYVOahxRd52mlJgSqas8593/CchhKbjXHovMza+lWD3Na5dvkwwcBOCfYDAVHWEEQQEygRzFDCRKA436I6s6TGUq7oTRdMRPqP/e0XXMbZ8CmPvPyY2ekkddJ21dqGmL0ErmZbV65EuW+WKbrnEFYcNpERxaJNuPiTi2rIdhHdbcPzn3Y5aUDryWFd0FJsdoYqB7MJ5jIpYRpeJtlWnL8CL3lYOX+qkxw8OHRZWFbC5vopZxYXjkCwwTBM1A9dfjUTbZdRWvYGUqpzgC4RRCiK8rqSQ//OuFRxr7eLlk620dkXCdSsKddYtmMaqGRXRIlmZczRjtiqx2/iL+1fQ0NrJ3sYWTl0P9h9TP83B1voqPJUlRIptDehTWWDPmG5j4d9fusTXHyqNKyI2Ppim5KXzrexpaKV9yD7lKhfcsayW+6uKUITKQMGxqYW84z+ZIVRQNNBsYDgjb1GRkc/RiqoTziVgSNAdoDuzp8cQLmxOpGoDLTjoe/vMFQTu/x8Y+/4DOkdx7BfvwL723QRe+yWcfCrpy6OtuA80R9b7ni5b5flgLnQ9cqGD2oTOByPYg79hD1y/AOEwuAtR596GNntl2vpmq11GePk74egvEw/04jk41/4haPrIMhWBUO0IVUNRhm5dzfOhXChK1JmdGFuZpuSxA+fZe3ZwsTZ/CF6/3MPrl88wv8zGx7cswm3XRpUzOo+s9CsKKIM89PH3IWajyL/pkTmUO20a9FoPfnPa1EH9VVBZWVvOytqxVq8zd73jbaUgWFFbzoqoLlLKUZzcge+WTC/DzkUS7zBMLwzgSEsna2bFh/yQEg8ZJv+85wTe9pF70doHP3utiQvtfTzytlXjVT1noX75y1/Otg6TDR8C5hiGSSDFlYB0weVQMIN99N24gQz6EWYwMsyliSKNrHBhBBGGgaKokZtXlvQYyp12FYFJ0OcfdozqLMG+YD3KrNWE9QIomgYVdYj5W3Bv/Aj22qUICbbpdYTaL0FPW+KLs/phXLNX5ETf02mrPB/gLpuKkCZ+X3BC5gNBH337foS570dwzQs9V6HvGnQ3IS+8htGwG4qnoxdNS0s/bdPrCDnLoekko26Cn7cO5x1/jKoqo8sUKo6iItBd+H0xOfFObZ7Hc6fbBgj8fcFxyUmGSyn5t32neeXi2JmcOnwGhy+2cdu8ymihquTbkhKkFAghos5l+vrgdEdWof19obTaJZ539Po5026t+JUA3rlyFqqS3v5mylYRn39sOUKASZjTbakVAhsPgoEAa+dOG1W3ZLiUkh++eIqGEYqKDcWVTj/dvX6WzyjL+qq/w6GjRt52XAR+nA6Z+RX/KQM58K8Q0Y9Z4P06kF09hvH4H4KRj9FKqnGtfhuKGXmgMxUNYYb7jxGKivuOT9J76Hdw8pmRL4OtFG3tO7HNXQtx5+aOHdJjqzwXA58nYD6YYT/+Zx6F7qaRxx1A+Aahvf+Iuf7D2OdvSEs/nZ6NiHlr8F88iHHxYGSzsmqHynk4F96O6izCVLQEYx3y6TyTx0S6GS+fb+PAkAqso6HdJ/nFG+f5wIaFGdbKGjJtr9s9VTx1ot3SOVsXlKJnOHQnFYzXVncuquXQhQ6aezKbDW8oOnuDiQ9KgHMdNznSkvxDyzNHW7hjUTUzygrG3XauIe/4T2YIFYmCFEp/6kDIckrAHE1fOCid53hkqiqOde+FFW/Bf+Y1aDsDhg+cBYjZa3FUL0IIkfX+5oStpjifyHSe/hf+ZWynPw7GKz8iOG0etsKqtPRTkWCvW4fp2YwSq9Ss2RDhUBJpXQUoKlKxkU/nmRyXwESl89x1LLkxFcPrl27y0K1B3DY9xXZFyrqOzDOfzrPEYWPNzAL2X07uAQlgy+JqJuL6WePjt5VNU/js9iX84/ONXOoePeLBrcCsCjsn2tITGCQG6ZGa/nsarNcgeL6hhQ9sWmD5vFxH3vGfzJAGAhMhTRQjDDIMCAhHQg+ywTFDkcVNwwCRHT1kqI/gpaMY3VeRUqK6SzCXbkBxSJR02UjTKFi0EXPx5v7vTaFEuMzuNRgvF2YYhTTaaopyYUYcWSXD88Ho7YDmA5Z+GsLHnsax/uHs2sgIoagaUrEhVCXqdsQcv7i3IXk+hMNA5d7MtXWxu4dr1gs088r5a9zlqU2pXxGksz8TM57ee9sCrt08zoWuxLH+n9w6lwpXrBZGLoyn9NrKbdf5s3uWcfxqF3sbWvDGVfKtcgu2Lq1l7cxKbJrg8o0+9jY0s//ijXFVzKktc49b/0PN1tORvnG+Pe/455HLiHsafpOG+siwQe/xp+H4TmDgx8gAWl79MXr9Xej196DY3dm1Uc7z2E0iV/TJUT5BoT6+Uy9iGedexrz13Si6LYs2UpFEHH8pNBCq9X68CSESH5IWNHWmFqvd1pnC00IGMRH2iqx0L+Xxg8M3QcdQW6jy7g111JXmbmhIumylCMHyqjKWV5VhShN/2MSmKNFiXwO/ezOL3Dy8fgEPr5f93//by15Lb08gtQrI8TBSrAZ8059SRZOcR97xn8zIh/r0cwNJ4NlvQcfohVJCjTsJnTmA/YG/QBROz56NcpznQ31yLNSn7eJoQ3pMhG62opfPzaqNFMWOqTlAd0efg2I34PgbcZ7H84iLJMm0rcwUE8aHTXMcuokkj0uWZz7UJ8Z1VfCuNfN44BaDVy600dzeS8gwKXLqrK6rZHZJQVJyssczYytFCFy6mvTx25bWsv9y8qmCp7sE80oL4mRZ11MR8Z+Th12bmosVecd/MiMf6tPP+3Z/f0ynvx/BTgLPfBPng19CESLr9spFng/1ya1Qn8i8tg4RDqJIM3s2MkMIxYYQKpFqBhoDjl/c25A8H8JhIkJ9ygocpIKyAnuKusWQzv5M/Hhy6Bp3LKiBBbkwVnLbViPxmcVu3rayiscPtZIIGvCJOxchRCwtamrtCiGodiu09Fp72J03bTz1K3IX+XIqUwZxT8ODQgyyxZmw9sJdzdB0cHTTDEXPVfwXYjHTuWKvXOKxm0Su6JOjPPbgmOn54HCRChR7YWb7n4hLEfkvigH25kNLbx/PNF7h1wcv8NSxS5y81jVqVdD42ZdJeCqKSaUk05q6aYkPmkC8mceVVeSKre7y1PKuNbVjHlPhFHzxgXqmuZ1jHpcstiyptnxOXzBMa3duhbalA/kV/8mMfKgPSPB791g2nTy+C3PeuuzbKwd5PtQnfaE+Zl83wRMvwIXXwdcOaFAyA+q34Zy9CqFqCdtTZq/BbDoy5ngeBns5orgGM4F+meUaiqJEfH+hEvlDzNmNd3qnLj97/Qa/3n+BC11D3to0XKNYh/tW1nL7vOmDzpXARIT6qApsW1xhKVVlXanOdLdjHLqJJI9Llk9cqM/k57llqy1101k7s5xXLrTx+ulrdPsMNAVmlDnZXF/NospiRPxCwjjbXTurkt/sb8IX/6cEONPWw5d+dYgv3L9sSq3+5x3/yYx8qE+EX2mwbrsbFxBhP0LVcyZ0JFd4PtQnPaE+vsbdyNf/bcjAC0LXOdh3Dt8+B7a7PoVW5RmzPefsW+jdZ4ucmyyW7EDFIKsZpmKhPhKQBpJY4SfJwNrj1OUHmzr44YsXGA3dIfjZ6020dfl4+6o5cefCRIT6ANxZX8uBi9dp60vOG/qDDXXjaDeGdPbhzTOepqKtnDaNbQtr2bYwlSxR1rhNU/jsfYt49MmTWAn46Q2E+bunjvO/37WaEpfNwpm5i3yoz5SBHPg3J0IhmLj2Aqm9ipOhQOZ1m5Q8dpPIFX1ylI8R6tPnfXEEp38o/AR3foNg26kx2xOKilj/cAJZcSiqxunZkJk+W+Fv4lCfS909Yzr98dh1qoPdpwdyjMfPvkzDoat8bscyagrG3sRoAz5/r4fqwtTCzjKJN9O4Gi/e7LaaUejmiw/UM6PQ2qbdG74QuxqaM6TVxCO/4j+ZkQ/1iXDdDkHrqemkzYUp1JwJHckVng/1GV+oT/hGG+YbP01qDAKEn/0Oxnu/jVBHH4t2z2b8/j449J9jC3NXYb/7c0h7AWas2FY+1GfC+dOHr2AFTxxoYUtdFYoSCfKZqAJeAIV2jS/ct5wDl9vZ09DMpRtG/9+KdNi2ZDob6qbj0rUx5STHRZLHJctzK3wlt3neViCpcju4dV4FV45cxQqeP9HKW1fNQlMn/3p53vGfzJAGmOFIiE3YD6G+yA1WGpHvssGDvZHPwWge/Yloe8YiOPeKNdsVzkSYIZBmdu2Vg1wGBWjh3BhPaeam/wb+M69Db1vkfuAqw1G3GlxllmXKUDgiIxQa9H2w8XmLEzmI/9yrOOeuGbM918L1hMprCTXugubDg0XYSmHRNpwLNiA0O2bIl3VbIwRSKkjFjmmEENgwzZhDmzvhBunmNwJBjrZYW4gIAAearrO6thxpmkjEhNpKALfOLOfWmRX4Qwa9oRAOXcWladGXOKnrY5oymkddAGZa9TZNiYi2kWkbTXaet9UAb7jcgVV09wVp7upjVnnu1mlIFnnHfxJDmibSCCHNEDLQB6FIDLApBCKaMWKiOSE/BHyYSDCNCWlbm7OesFXHf/4GZA7YKxe5EVIwQ/4pZR8Z6CNw9DdwaXgFXP/RX8H05dhWvRXVWZS0TFOzIxUw/YH+7w0knN4zrI1EkKdeQNYuTTzWi6oQ6x9G+N5K+OZVjHAIzVmAVlKLKRSQJjLoyw27GwaKQ8cMBTEMiRCQYur4SQXvtRspnXe6qYuV1eUYURtly1Y2VcWmKoBASkbNPpQsDEMihYJhxDv96YFhRh8n3gTjarzI22oAvpCR+KCRzgumdl6uIe/4T2IIRUGoOkLREXYnkV10AkXVEUYwKxwlkrFbcbhBd0xI27ZpcwhPXwZXjyVnOEc5ds/txF6qZ9NeuchV3Ymi6QifMSHtIg385w8i+7oADaWoHPvMpSiaPT3yg34CO78D/jGyl1w9SvDZC9jvfQSleFpS8hWHDaREcWj934twEFIpTt/djrDZk+6bouuoReWYqo4S62cOjSEQKJoToeooug1VFQgFlMn/ljwhAik6Ff5QGEWBWCRBNm1lSklPMETYlBToGrZxFTISGKaJqooR+2RKyYm2Tpqu9xE2TFwOjVWzKiiyJ95IqUai7d4U42q8yHVbdfQFaOv1I4Fyl41p7sztJ3Hqkdoils+zTY2CXnnHfzJDqKBooNnAcEbHsYx8jsZnTziXgCFBd4DunLC2bds/RfDJR6Hz3Ng204qx3/tnCEcRRGOgs2qvHOTC5kSqtv/X3nmHx3Fdh/43sxWdAAmCANiLLkmIEotIi2oUSfXqWLbiF8ntc40dO3GcKIkdx7Jj+7k892fHcZPiWIkcW8+yZdFWpagu0aIk9stOsRMkSIJE2zLz/phdagEusDOLXexi9/y+j9yD3Zl7zz1zZ+bMnXPPBX8kr3VZkV56X/vtOSPkFtBDCNquo+KCazF8/qzrsrGIrPrm0E5/kngnfU98j9DtX3VVvhEIOPtF/G9+b2Q5ommdpnv9H/FNXUSoYXJR9INhy4Ewhi+E4fM5DwSmgWkOnLpaenJVKEA2VAYDmKbhDOhAQWx1oqePp7ceYrXuIDUB6dzxYa5sa2HO+NQUi27Ld0b6TRPMFK/TtmH19kM8+vphzgx4Vvp/rx5hfkslty6aSmNVOKWs/nUkbeR8ZtfmbOXj3RGe1Yd4/Y0TdEdswgGY3VzHsrZmmqsrXZczUnIhbTWYbNuw/tAJntxwgJ0noqTSWmOyvK2FJVMaE4tt5q5eI4vrdH1lkJYxxTe5PRt8d999d6F1GG28F5gaj1v09WW3omauqAybWJFuujs7sSO9GFbE6dq25aRlLIBsxCMY8Tim6XNuXiNUt2GYBKcvJmrZ0L6HdE/zfrWM8BUfwF9RV1AbFbtcEfJhYBHp6c1bXfGuDvpW/W84suWc4+QQh3ZN7KAmOHURhmFkVVfs8DbiW/8wSB1piHVjV08gOKYpY/mVQR+GbdHbE3nze9NHbP3v3deXSvt27O1PE937KlSPw189tmj6RFayYWCYfjBDhGuqwfTT2x1JNNbgzRtxacm1oQCPb/U2cRDg2gtbmFBTSUVVEDBG3Fav7DvGNx/bwa7jPedcPdu7Yqzdc4IDHZ1cOGlsigOfuXwnXMhZPdVIZMKybLj32W08ue0EkUEiiQ6fjvL0tnbaJtUyJhw6p1wwqKhyvu/tjo6IjcAJmfmftbv4+Yv72HW8h64YRGzojsEbJ/t4Ztsx9h07xfmtDfh9xrDqyqVcCFsNJVs23P/STn77+hFO9J57vz4dsVm/v5O97SeZP3kcPg99bih527FOVm1qP6e+TNx44UTaJtZ73m+4hMMBfM5rwL3Avbkos0hf+gjesd/8LIp0h4x43YbPT/WCW6n4i+/gv+T9cP7N0HYT5pI7aX7/v9F09Ycww9VFZKNilVMvlLkv37Zi9D36Teg5TkY6dtD1zE+zriuy9YnMdQzAOjs5N0P5CScm9XvD8MGMyzzX2Y9T+4mu/g49O190p0exymWazrM6FGBha5WnfSpNmDfBcSpSz76RYsPhDu557o2M260/1M3PntXDjvv//et7WXegK+N2NvDtP2g6eyODbjOStrJsm58+s5Vnd50ccrsNh7v51qMbiMSKK6C+mM7B3722h+f3ZJ4Ps/loL//x3LZh97kkT270npazOuxnRRYr/xYrEuozmpF0nmllO1BBcPrFBLGx/EHMWBSzolJSVLqU853Os/eNV+HM4SG7dj8Ovk7fqQOEalu913tgq/t6kpzYQRyfMxl1iPIHS+cZmLOC6M5nvdc7APuFn9FX10Rg3LSC94ns5PJN53nd/Ems89D3br2oFScCw0m5OJLpPOOWxb1P7Xat6/pD3bx28DgLWsdmUa9BdyTKo1tdPPQniAJP64PcdOGUNOWObIrK1dsPuc7YdOB0nN++uod3LJ4+Irpllosnnefxnh4e1+4z67x2sIudHZ3MHFs7rHpP90XYeNh76u/5kxuoqyiNxbtAHP/RjS0r97qVZTXa4rGVtXW1564e3/gY5tI7s6i3z3NdAGa8L+OqzoOt3OuvayY69wbYvCqrulOJblhF6Mq/LHifyEoegZV7LQvWHz7BC/owHV19GBiMrw1x2Zxm1Li6s6koc1GXF7mlppKPLJ/OD1dnmHME3NDWyKXTm1LKgZFauRdsXj3Y4fksWbP5cMLx91KXw/O7jnqsDVZvOc718yanhHskyx0ZG4GBbds8scHDgAWwZudJbplvEQokH3xHvi8WwlaZ5GeyCIVbs/kQMy+vHVa9R7p6PdcLzuq9pYQ4/iVDyuiQYST+LIB8Vgcy7mPbcXr3rXccwWNvgNXr5FOftpjQ7CsxwzU51C954SsCGxW9nD9b2XELjm/HM29shKVZ1OurhvgZ7/X5/Fi9p+ne8TJ0HQbbhspGKmYswVc1hrOhPjZp662afzNdGLD5Ye91p3LgdeI9nRhV9UXUP1zKeQ712dZ+ip8+tZOuARNDD57p5rWDOxkbhg+uUEysLUze7fOb6rnrBsVDa/eypf1ch2NClcF1CyZz0cRxJIwG5N5OmXh5u3dHfMfxPk73RalxkXlnIJv3n/C8Tx9w6Ex32mM5UvbadryTzmjm7Qby8r5jXD69KfcKZcFI963BWLvL/RufJK8e6MK2bbKZmJskbtmZN0pDLJ7dfsWKOP6jmVEc6hM7eYDII9+ByICbQHc7bFpF36ZV9M29mYqFN2GPgvCVUpLzaqs+769ZAYidzk6fyfNgt8c1HsbOpuvpn8K+P53zU8+G38CECwhc/h7sukowBgkHMmzCS95BbNpCYhsfg70vnlOWW/qO7iQ4c2nR9A/3cv5CfTYfOckPMoymH++Fr6zS3HX9eUyuqz6773Dq9SpPrq3iYyvn0tHTx+v7O+jrjREI+JgxoZapYwbqZJ/9fyRDfTrOZDcKerI3Qk2/DEZu6jXoznL01MmhPrDckQtfOdiReU5COo6e6GKkjuXQcvGE+pzK4gEKoC9mEQ6kOv7e6q2vSO2v7qmvKp0wHxDHf3QzSkN9Yh37iDz0JZzozSHY/BA9Vg+VF91e9OErpSTn01aGL8s8yP7qQfWxTx+j7+AWrEgvZjBIsFlh1k7AtOOE1Ar6vDr+x/fC8Z7Bfz+8nuiv/pnYHf9KoK7xnFCfVDlY00hg0W1w8R30PH8P7Fvnve09nUPWUbRynkJ9uiLxjE5/Kv/3kW18+bYFKRlWsqt3OHJDRZjls1pcbg8jGerjhM8MeG3igv7pRt21C6Ay5IfT3utzcqgPLHdkbARGFlnfHZxB5pHTc3C5GHRw5CAw+HTtwQkkFpbLtt7xVZVMqDI53OXtaF4ya3wW2hYv4viXDCkjCkUe6hNZ82MyOv1Jtj5OZOIFhJtmDVO/ATepYgiFKFo5f7YyTB80zICOne6Of5JJbefoE2nfTeS138PRTWc3s4BegHGzCS64keD4mfS1zIeDr3mobAinP2WbIw9+hZY7v+rqfDBMA4LZhZxYr/w33WaA6vOWpq+jWGWPoT6xuMXL+47z+u5jnO6JEAz4md5YzaXnTWBsZfjsds/v9BYf3G3BuoPHWTJpnKf9CkUmO7klEovz4t5jbN1/gq6+GJUhP+e1jGHp1EbCgTdv/S1jKjlw+rTn8seeTbHpjTmt9Ww75i1WPggpufH7kyt7ZWJMZXajvnVZ7pcPRspWmZg6LsS2Y95mlowNg88cfguWtbXwy5f3u96+eUwFba1jhl1vMSGO/2hmFIb6RI/vgVOZ08alElv/ONY1c4aln4T6FI+tDHUl9gveHH9f29X99OndvRbr2R8NvsOxrUQe20r0Le8htPKj9K36eua5BcEGiLjPNEHXMc7sWofVunjI86Hv1B7sHc/B3pfclz2QtT/nTCxCZduKgvcP97L7UJ9ndh7hV2sPDBhVjbPjeB+Pbj3O/JZK7lw6i3DAx+rN3pxGgKc3HUw4/v3rLUbZBoYT6mPbFo9sPsDvNwyM3Y+w4VA3D7xykOvnjOP6CyZhGgaXzW5i7T5vjv9FE6sJn52w6l43MLhkxnh++7q3Y7h89tiE0zew3JELX3HSre71oLXD4mmp/a6Qfat4Qn2umNPMtmf24IUr5kzIiQ6XTG3k5e1H2H0i8+CjCbz/ilnDmldQjEge/9HMgFAf045h2lYiLKBAshXFB/Uf0NIAACAASURBVJjxeNrtotvWeG/n0fXQc2pY+hlWDDPeVxw2KnI537aqnLoAKhrdH/+m8wmNmXi2nNjBLUM7/amnyEv/QezgVqqu+QTGhbeBr/bcjcwqaLsZ6r3nae587dEh29u7dQ32E98cntOf5NX7sU4dLHj/cC1bUQw7nhLqk/om6U35sS0H+OU5Tn9/XjvYzTce2UB3XyyrCZb7TybjytPrUGzyYLbKJNu2zf+s3Z3G6e/PH7Yc4+fP78C2baY31DKhypsrsHxeaxbtcv6uCga4SjW4rssPXDG7JYv6cisH/T6unOltAae548PUV4QLprNbORKz6IrEsM6ehPmt94KWBuo9vAjxA5dOTzr+w9PBZ5p8bMVcZo0d+o2VD/jkdXOZ3VJao/0gI/4lRMqIQjGH+pzynkECIN7VjhmuGoZ+qSd/gW1U9HJ+bWWYPkLXfYq+VV+FvgwZPuqnUrnsg/3Kia377dD7DCD+ym8wbryLqvOvIn7+NcQObSF+6igWBoGaBoLNCtsfovs/P+6pXMBZbffsH/3b27t7Lay733uZQ9Cr11D5lr8oor4yhOwi1Gdb+yl++7q70J1DZyzuf9ljiFiCbJPxHe7qYdvhU0SiMcKhAG0TxlCf53ze6ezklud2H+WZDItLJfnTvtNM0odYObuVD62YzZce2uwq0v/WCycwpS6xEGKW3DJ/Ku2dvbzuIif+X193HnXh4KD1Dcde6eiKRHlux2Fe3dPB6d4YAR/MGF/L5XObuf6CSbz6xglOuQhQ9wPveMv0jNuNJKm26o7EeGF3O2s2H6IjJepm7vgwV57fypzGZDrc3GMaBn917Vy+7LLPffyaWWneMGVPOODj41e18fqhDp7acICdKaP/AeCmxRO5edE0/FlmASp2xPEfzYzCUB+yPJHigM/wFW34SinJI2Ero24C4Zv/md51D8Cu59MccR+oa6hYcBP4A2f3jZ0+DMc8Lsp1cjd9pw4RGDMR2x8k0NJGoGXu2cXd7KRuZJfdxDJNDMvo10bbjmE9d29W5Q3JttXEl94J9mjox5lDfR7fcMBT892s9pqOukBSSr3+DC5vOXqSVa/uSxMOsJ+548PctGhy3jIF2UA2oT62bfPo697s+cj6wyxXzYyvCvPpm+bwg8e2cHyI0OvbFjaz/Lxmz7q9iXOemAa8/wrFk/ogj64/Qnea1z3nT6jgzy6aSlN1xRD15S58xbZtHt20j4c2tp+jS/veTl7c28m0MQE+slLxkyf1kHYKAZ+8QdFYFc6JbrmR37TVzo7TfPfR7Wmd7s1He9n85E7mNIb5wBUqsQZB7vVpqgrz2Zvn8rOnt/HGqfSP5o0V8L4rk1m5su1z6WXTgAUtDSxoaaA7GuNMX5SAz6QuFKSxeQzhoJ/Tp9zM9xp9iOM/mhmNWX1qx4CHMOokgXA9pm0VZaaaUpNHzFahSqqXvpvY4j8nsvtPWF0nwDDx1Y4jPGUhti/obJ9y3GP7NnvvPEBs30ZCdS1D911fDcS9T3L0WdY5ZfbufgXXE9i9cuaE8/arCPrKkHKGrD4neyNsPur9Yau5yuSQ56wcyYm9KW+yBpGf2nGYX//p4KBlbT7ay+Y/bOODy6ZxYXO9qzK9yZBNVp8dHaf7jdy6oduCjUdOcsGEBpqqK7j7rQvZ0n6KpzcdZP+JHqJxqKswecusJpZOG09l2sw67tvl4PxtGnDV7ImsUK1sPNzBwePdxOIWFWE/i6Y0MubsKP9Q5Xqz0VDyg6/u5oltQ7+B3H0yyr8/ofm7G85n48ETrNl8iENn3uyLY4Jw5dwJXDqjiYqAP2e65UZ2bLWvs5tvPZp5LZUt7b38YPUWPnHV+SnzK3Kr27iqMHddfyEHOrt4dushjnQ614OGqiBLZzczo75mROxTGQhQGQgMsFXpIo5/yZDyNFzEoT6B6ZcS3bPWW9PGzsJXVTdM/VJP5gLbqOjlkbWVGaykctYlAFimH9NyRn/sNNtb0ezWAbAjPZn1mXoh7HzWU7m+aQvTlhnb/XJWeroj6WgUQ18ZQs4Q6rP/VHaj91VhP3R5SwZ46Xnu5m+8frBjSKc/lR+v2c1d1weYXFfjSZdMZOty7G33/tAKsL/9DBdMcGLuDcNg7vgxzB1fB4M6TLnFNAwuaG7gguaxaerLTC5ctA2HT2R0+pOcjMAvX9rFh5fN5rLpE+iKxOiORgkH/FQH/InbYHE6jgbwn0+7X0BxZ0eE53Yf4YoZEzJvPAxaa6v48yUzGPqBUcgV4viPZkZhqI+/ZQ5Rr9lTzr9u2GEnEupTIrbyp0/plxF/GCtDqFhgzgqiHh3/2guvpyddmd1ZrBbslqqxWJZFURyPDLLpCzgPcGlCfaIx77ncwcnlfcnUWp7f0+lq+5vOb2RMOBnrk+q49pdt2+bBtXs86bJq3T4+snzOoGVmIzsuT39budk3Gs3OnpFY3HNd2cuGy+3cyrkJ9Xlivfv0jgAbDnXT0d1HQ2WIqqCPquDA9UnybcfsbLXr0CkOnvHWT1ZvPMAVM5IrDxdLW/Itp/5deojjP5qx42DFnDCFWC9Eu50brB13viuEHOly/o4kXuEP2M4wfJjL3o/12NfdtbF1PqHWOcNumx0xwB8rDhsVuVzMtgo2Tctu4ZcJ05yyhig/UDWW6KSF7hfZmjCbcONUerp7zi3Tl6eEaZMXYcQjRXM8Bpf94Lex8GEbAWwrhk0Ay0reVA1qzjrj3qgJ+bn9oulEY9tZu3/otwZXnVfP1XNa+9XrcK68s6OTdo8hvRuP9NDR3ecyLMWdbFsWNoYrnVPlinB2t/PKkN9zXV5ky7KJW8nvrJzWYVk2RqKObMs53tPHjg7vV5Vn9EFuvnBqztqSb9mybFZv2uepjQDtPbDvVBetNRVF05Z8y5blDASUKuL4j2Jsy8KOR7GtKHZfN0Sdi5dlGBiJTjvSMtFe6OvBwgYrnnY7f20T8cs/TPyZH8FQT9ZmLUSj9L14P4EZS/DXNGWtXzxqYkV7sYvARpnkWLQXo/c04IOqOmfRqxHUoZht5aush5qJcNrDCF14HP6aZuxIT8byw4veTu+Z03Aiw+vwmomMX/kBrFgvVm/fuWWOmQAdO9zr6BL/9EuweruK5ngMKptx4jELgkFs20fc9oFFSrpAmFxbTQjwGJbO/BmNgMFfvGUm82ee4pmNh9g6YDGgC5sruKKthRljarBtAzf38M373IV6DGTDoRNcOqUp84YuiSdsZHlcKvaC1gZ+/cohz/VdMGWs57q8EI/b2IZJPJ7q9OeobCvxODEM/fdnOYHzwInuvNot18QtOJplW9u7emmuqsixRsWLZRtYNiXr/IvjP4oxTBPDF8AwAxihCpxZdAamL+CMChZAxgQL25l8GAgPup2/dS7WbV+ld/ta0E9AX5rQH6vTWZX1KER3PU20YQb+S96Fv7res36+QAWmP4DREy+4jdLJhuknsn8D8S1roL3/BNbo9MsIqisxG1pHRJ+it9WiW7Gf+r77E2XR2zCCQVflG4QIXffX9G1+CrY+eW6/9NXC3OWE2lbir64C28YM+88pJzh7OZFd3sKGMjJrOcEJs7CK4BhklM0AZrgKOxDG8Afx+RyHz0x5EWJisHLuWFZtPu7aBNU+aDubZtBg3vh65q2opysS4URfFMMwaAgFqAikC+0Zmu5Idkk/I5F4v3YNl+TLIq9ljgkHmdcUZsMR9xOmZzUEGF8RzrzhsDCIWxY+n5FTO4FjKwPvtupHls5d3LJz3p584jOd+RTZ4Ddyf+yKGdNwMk+V2sJdScTxH80YPjD94A9CvCIx5892/k7EZ4+4bANxGwJhCFQMuY/pryC88EbMC64h1nmYyO++yJDjfx07if3+85g3fRb/mIme9DOCFdi+IPgjhbfRANm2YvQ99VM4+Kf07d71rONELnkvleddmnd9itlWYBOaupjeBe+EV13kyL/g7YRnXQKxqOvyDX+QynnXYs+7mr5jb2CfPAC2k4I01DgdwzCw/EGMpHMZ8Z9Tjr9hEpFxbXBsU2Yd3XDetVQsfhsYhre+hYVt+rDxOc7ySB0n08AIhBMPXKYzSAGYZurEcVgxZyIv7jzuOhvNnZdPw3c2jOrNcmrCIWrC6RbkMVzLFcHsboehoO+cdg1HHsxWbuS3Lp7Ght9vca37bRdPT9QzPJ2Hlp2RftMEs5/3OPw6kjYaThsaqoZeyGkwxlaHR8B2uZNN02BSYw2vZ5ESt7mmMufHrphl0zRK1ukH8N19992F1mG08V5gajxu0deX7bIwuaEybGJFuunu7MSO9GJYEafb2k56wULIRjyCEY9jmj7n5uVmHytG7x+/BhF3k/Xiu14jOPtK1+WbdpyKkA8Di0hPb8Ft1L/tcbqf/QnsfyVzww+8hlU1jmB9a151K1ZbpcqhxsnY9VOwju+HSJqJtJWN+N9yB5XnXZZ1XSbgq6onVN9CsGESvuqx+LDOblMZ9GHYFr09kbTlmJPnEdv5MsQzj8Aa898B46ZD+wH6pQGdcRmhpe8mNHNpv7qH0htsooc1fX/6JbFnf0L89d8R2/AQ0TfWE7cNgnWNmIaZ3+Nk+LB9Fc5kbMNHRZXzxqW3OxlL7Thsfp/B/KljWb/3KD0ZLqfvvnQyC1rHnt03tZxcyDET/rTHe7jPbRdNoursG4bh6zOYrdzI1UE/s1treGnn8YzvOv7qqplMr6/Jic5DybYNtu04Uo4zlbs6KhJOe293NOty6kJBXtx+iF6Pc6NvWzyJhsriX5U3KVdUhWiqCfPIBndZq5JMqw9w1dyJBdP/TF+U430RInGbsM/EMMy811tZHcbvM4n0xQr+ABAOB5KDHXuBe3NRpoz4lwC2bRM5up3Y8b0QtzCqGwg3z8bw+Si2dJ7p5MiBTXDGw4q+sVN0PfczwkvfjRkIudQv9cQeIVu4afthDW+4cPoTxF+4F3vyAghW5FG34rTVQDk0aR6BKQuIt2+nb/9miPRBIESwZTa+pvPwJZzgvOmQ/HuQbcxwDeEb7qL3mXvhmE5/QM0qzEvupHLKfACsBbdiRHqx7Th2sDLRhsT8GRc62ZEoXWt+BEc2nlvXyb3YL91L17oHCV/z15gNE/N4nBJ/Jxjq1llfEeLTN87n2Z2HeWrzYU4MmGd5ydRals9rpbmqsl+ZuaZt/Bhq/HDaw3jOjPoA4ytzG/s8XDdjen0Nn7/1fJ7cfIA1208wMAz9ihljWNHWyrjKgYtLjU6Gay/DMFjW1syDr7qfHzG+0mBGQ27TuI4ETQ1VzBobYvtQq48NYPn5rXnUKD1xy2btvmOs2XSQfZ1vnpABYOWccVymmlMydQleEcd/FGPbBifW/paTT/0nnOq/YmMXfphzFcEFN+M3nYwTxZDOM50c3fKk98bvW0fvvnUYF7+PiplLR22KyujmJzw23KZr11rCbSvzplux2mow2TduFpXjZp5diRdsLH8QKynnqV7bcAKMh9y+YgzBKz6AFe0ipp+Hjj0Qi0O4BnPWWwhNno8dqOinqxlw9rc9tiFumvQ98T04nmFicvQkvQ9/Cf9tXyJY0ZAfGxlg+5yQH7ATX9kkNkz5dORQwGTl7BZWqGYOn+nhdF+UoN9Hc1VFysqh6ffNlWwYcOPCVu5/uf+1dCiuXzg55/pkspUbub4iyG2LpnHL/CnsPXmGnkiciqCPyXVVBP0jY8/0spHjcnOTznPZzCZe3n6Eg2fczda949IZiZHgkbRdbmz17stm8sXfbnI1qX7plFoWtNSnlJV/Pbv6onzv8c3sP33uK5go8Mctx/jjlmP85fJptDXV50mf1L9LDwn18c57ofChPrYVp+NX/8CJp34GfekWbrHg2A7i21/GP2UhvkCIYg31ib7wX5B28XAX7H8NK1xPoL511IWvWNEeYi/e673NfT0EZ1xc1qE++Zbt7hP0bHqM2NM/I7ruN0Q3PkZs33rsQAh/bSOGbWcM9Uk9H/wVdQQmzSM04y0Ez7sU/4wlBOuaMDFypnfPptWwa43LTmRhte8jNGNJfuzoMtRnoGwYJjWhIGMrQ4wJh/D3i+c/d/tcy5Prq4jFIuw8ljn7yTsWt3LRxNyHHg0n1Geg7DNNGirDNNWEaagM4zNH1p5A0Yf6gIHPNFk4ZRzbDx3nVN/Qzv9HV8xANY7Jif4jKSdtRcTiohkNbHqjne4hXJiV5zXw9sXTnLDAEdIzErP51qMbOeBirYE/7TnJeS3VNJydnJ47fSTURyhKTj/6DbrXPZR5w95j9Dzxbapu/Az4QyRH5oop1Md7Qr/+xF/+OfGJbZgVtUPUlXpi57H9HmS7N8tFnro78qxb8dlqpGTbtul+fRVsfLC/za0IHNPEntHEQg0Er/441M109s/1+ZCl3mx+LGPX6ccxTazzKP7a8XnQLfF3gsLeOr1xy/ypjK2r4OFX9tOZxjFqqjR465KpzJvgvC3JNaPJVsVAruxVFfLzt9dewCsHjvHUxoO8cerNgx8CVswdx2XnTaAu7STy0UHSVmMrwvzLLQvY0t7Jmk0H2XW0h6gNNUG4aFoDl81uYWxFIox2BHl65yFPC4zd98wO/uWWBW9eagVXFMTxV0pVAJ8Cbgdm4vSurThPM9/XWrvOjquUugj4HLAYqAY2Ad/RWv9XjtUuGuKdR+h+8T73O3Qeonv3OkKzrzh7gy6mUB/MGrCyW24+Sd/WZ/EtvGXQuooyfCUQzK6xyZWa86RbUdpqhOTuV38Dm1cNbf++DiK//zx9d3yNYH1r7s+HLOTo8d3QeyxDxzmX3h0vUbnorQUP9Sk2+dJp47lkaiObj55kx8FT9EbjVIb8tE1qYPrZ2O78tGW02cqbbOS43NyE+iTxmbBk0jiWTBpHVyTGmb4oQb9JXTiYkgqzGOw4fFsZhsHc8XXMHV9Heka2/9m2zZMbDw+iS3rae2x2dnQyc2xtjvVJ/bv0GHHHXylVDawGLgI6gDVAELgY+C6wUil1m9Y642OfUupq4GHATJTTDawE7lNKtWmtP5OfVhSW7ld+Dba30Bh762rMmRcnsn4YEIvkRcaKOoOb8TgYLveZsQi2PzU8o2x+HHPBDYPWZVgxTGxMO5bX9nuR/cFKovgBjyFjDRMx86hbMdpqJOTY0W2Znf4U2ld9j9Z3fnHIY5HN+ZCNbJ9u99aHknQfw7St3NvUDmDH49iWnXD+wT77JinxZJD4tlhlw4C2pgbamhqGVY53efTZym27HHJZbv5sVBUMUBUM5K38kZeLuz/tPXWGzpSEZm5Zu6M94fjn2lalSyGWZPgMjtP/BDBDa3291nol0AZsB24FPpCpkMRbg18k/rxaa71Sa30zMA/YD3xaKbUoHw0oNJHtz3jf6eRurGgybtWmfxhCPmRc7xNWV3pvzzn0YMdiQ9SVejKPRPszy4bphyzaHlRX5lm34rPVSMgRrxOtT+yj7+jOnJ8PWcvZYORLn/46lf6tNHeIrbwhtnJPMdvqZFck80bp9uvObr9yphCO/3sSnx/SWp9Mfqm13gP8feLPd7oo513AeOA+rfXqlHJ2Av+Y+PMTw9a2CLF63OW7H0g8FsEy/FiGD8sXyI9sBohjYJnu9zHHtMKMy4dvF4NB60oNX8lr+z3KwdlXemtkZRO+5tl51a1YbZVPORbvg/2veu5zJzY+mfPzIRvZqGvxrDsAVRPyZFPOCfVxHgSSDwMiDyaXrq3EXoWWi9lWviy90bNrqOVFt9JkREN9EmE+24F9WutdaTbZlvh0cxe7LvH5YJrfHsJJE3O9ZyVHAUawMqv9fKYv7+Eb2YY2VC15J119XbB/XZZWMTFNP8YgYQvFGr7irx5L/OL3EX/xHjdHkPCKj+GzbSwJ9cmpbJ90n8M7lXjHEUJFEOoTqm8hWt0MZ7y1o2LWUgn1KTq5VG2VJJfllpqN8ikXt61a6rLzayY2VCWkXNuqdBlRx19rfQZYNsQmixOf+10U15b4PGelGq11p1LqIDBJKdWktT7iTdPiJjhlIbHDW73tVNWIr6LOecVjOJ3btH05l7EDELDA7wOfz/3+fpuqFR8jsvMFomt/DZGTaZsxKGoFPn9w0PJNfxDbMDB9g29TKLlKXUp3MEzs6XuAQVZ5rWslfOVfEawbn3d9itlW+ZJNI5nb3CMGmEP086zPhyxk37zriL9wj3vdWy4kUJun/mT6sE0T2zTANDFNJyWg81l4J6OY5dKzlY2TytPGNI1hltVfNkwTAzthq0K3s7jlYrdVY3UlqrEC3Z45lW4ql53XcvacyZU+hmlS6DSe+aRo0nkqpULAPyf+fMDFLs2Jz8GGuA4Bk4AmoKQc/8rFf073S96SFgUX3kagYSJG3BnFNX2+vMjEItjRCEYgBP6A5/2DdX+G1XYtp350O85J6I7qi/8XvtoJg5YfrK/GNvz4rJN5bX+2cvXCSXD+dfRuf5rezU9C1zEnC0z9ZKouvJ5Ay/ng84+IPsVuq3zIoVB1VkllKxon4a8f/Lwa7vngqQ8tuZ3Oo9uwdz6XWfHKcdTc+I/4wrX50ccwMUPVTgphDMJB51YTDGb5gFVGlJqtbNvGtsEwyLkzVZGwUanYKp+MBltdN38y+rFBVjlPw6LJdTSNye5NwVCEAyZ+X+k6/8N2/JVS9wFuJtH+Rmv9T4OUYQA/BWYBWxJyJpLvdwZ7PEx+X+2iLM8Eg34aGwu0ZHfjfCILrqfz1T+42tysbWL6jR/EVzmGs9mADF/eZNuyMExzWGVVv+/bHLjnr121b/zNf0fjoitdld9S35j39g9LVnPg5o8WhT5Fb6scy3vmLqNr8xq80HjFndRMn5n388GtbH/sRxz81b9y8oX7B9U5NLGNKR/4NwINrfnVx/QzMMF2a2s9gjtKyVa2befViSolW+WbYrZVa2s9R7qi3P98ukjw/kwaW8k/vmMx1eFAXnQxDIPx42vzUnahycWI/xRAudiuOd2XSikf8BPgDuAE8DattZvBtzhgaK0HGxY2BnyWFBPf9X/YffIwPbuHnpDoqx7L9I//nEDtuMQ3qYc833L2+zcsfismsP8//x47NsisfcNkwq13Me7qjyRuKiPZNpFLTW5c/j5Pjn9w/DRq25Y7Tn2ezwfXsr+aSe/6KuOv+RDHn7mPztcfId51CjMYpnLaQhqueBfV6tKEziOgjyAIggfeu2I2tVUh/uOpbfRF42m3WTyzkbtunU9tZZZr4ZQ5hm27D6fINUqpKuC/gZtxcvpfo7V+xeW+J4AxQIXW+pzAaKXUS8ASYIHW+rXcac1TwLJIJMapU95i0XLN2Do/h3/3dTqe+yV234BVYA2T0Ozl1Fzzd/gbJhVGwRwQP32MnnUP0P3Kr7FOOVFdZlUDFfNvpeKid+BvmOyqnOTbmfb24S0UVg6Uq61s26bzwc/S81q6fAED8AWZ/jf/TdWMi8rOTtlQrn0qG8RW7hFbuWe02aq7L8az246wdvcxzvTGCPpNpjfWsKKtmUlnJ/Tmh2KyVV1dBUEn/G8NcGUuyizY0IxSqgln8a1FOJN5r9Nab/JQxEEcx38CsCfN75nmAIx6zGAFLW//F3wXf5jejX8gdmw3WDF8NeMJz7seX13alyyjCl/NOKqXfZjqZR/GjkXBtpx4aUHIMYZhUHvL3eAL0PPKrwbfLlzLmHd+m6oZF42ccoIgCGVEZcjPNfNauWZea6FVKTkK4vgrpaYAq4FpwAbgBq21m0w+qWwE5ib+7RlQfi1OStD2Usvokw4zVEnlotsKrUbeMfz5ieUThCSGz0/dLZ+jctFtdK+9n96Nj2AnFr7zjZtG5UW3UzH/FsyKwZa5FwRBEITiZcQdf6VUA/A4jtP/NHCL1vpUFkX9EbgdeCuwasBvNwO+NN8LgiBkJNB6PnWtX6T21n/FjnRj+ILy4CkIgiCMegqxcu8PgJnAqzjhPRmdfqXU7MS/1LxNDwBHgfcqpW5I2XY68BWcXJDfzKnmgiCUFYZhYIaqxOkXBEEQSoKRXrl3Ds4oPcBp4MdKpU0I1K61/mTK31sSn8txJtcmF+n6IM4DwO+VUmsSZa4EKoHPaK3X57wRgiAIgiAIgjAKGelQn2W8mV7ziiG22wt8cojfAdBa/04ptQz4F+DiRNnrgW9qrQefnScIgiAIgiAIZcaIOv5a6x8CP8xiv0Fz8WutnweuG45egiAIgiAIglDqFCLGXxAEQRAEQRCEEUYcf0EQBEEQBEEoA8TxFwRBEARBEIQyQBx/QRAEQRAEQSgDxPEXBEEQBEEQhDJAHH9BEARBEARBKAPE8RcEQRAEQRCEMkAcf0EQBEEQBEEoA8TxFwRBEARBEIQyQBx/QRAEQRAEQSgDxPEXBEEQBEEQhDJAHH9BEARBEARBKAPE8RcEQRAEQRCEMkAcf0EQBEEQBEEoA8TxFwRBEARBEIQyQBx/QRAEQRAEQSgDxPEXBEEQBEEQhDJAHH9BEARBEARBKAPE8RcEQRAEQRCEMkAcf0EQBEEQBEEoA8TxFwRBEARBEIQyQBx/QRAEQRAEQSgDxPEXBEEQBEEQhDLAsG270DqMNvYDrZZlE4vFC6pIMOgHIBKJFVSP0YDYyj1iK3eIndwjtnKP2Mo9Yiv3iK3cU0y28vt9mKYBcACYmIsyxfH3zkmgrtBKCIIgCIIgCGXBKWBMLgry56KQMmM3MA04A+wosC6CIAiCIAhCaTITqMbxPXOCjPgLgiAIgiAIQhkgk3sFQRAEQRAEoQwQx18QBEEQBEEQygBx/AVBEARBEAShDBDHXxAEQRAEQRDKAHH8BUEQBEEQBKEMEMdfEARBEARBEMoAcfwFQRAEQRAEoQwQx18QBEEQBEEQygBx/AVBEARBEAShDBDHXxAEQRAEQRDKAHH8BUEQBEEQBKEMEMdfEARBEARBEMoAcfwFQRAEQRAEoQwQx18QBEEQBEEQygBx/AVB3gEHMgAADbRJREFUEARBEAShDBDHXxAEQRAEQRDKAH+hFRCGRilVAXwKuB2YCdjAVuBe4Ptaa8tDWRcBnwMWA9XAJuA7Wuv/yrHaRYFS6m6c9k7SWu/3sN8k4I0hNnlOa33ZMNUrKrK1VWLf84DPA5cBY4EdwI+AH3jpn8WMUup24JPAXCAOPA98QWv9socyLgeeHmKT+7TWdw5L0RFGKXUV8GngAiAIvAJ8RWv9iIcySr7/wPBtVabXpfcC9wCXa62f9bBfC8717GqgGcduvwC+prXuy4OqBScbWyml/MAZIDTIJge01hNzo2HhUEr5gL8E3gPMAXzALuB+4Ota616X5ZTEtUoc/yJGKVUNrAYuAjqANTg3jIuB7wIrlVK3aa3jLsq6GngY5y3PGqAbWAncp5Rq01p/Jj+tKAxKqbcC2bZpQeJzPbAhze86y3KLkuHYSil1IY4zWws8B6wFlgPfw+mno8qRTUfKQ9Fp4EmgHrgBuFYpdYvW+g8ui0r2q+eB3Wl+f26Yqo4oKY5GH45dfDjH/o9KqQ9rrX/kooyS7z+QG1tRftelpTj9wOt+E4EXgInAq8A64FLgC8AKpdQ1WutoLnUtNNnaCmcgIwTsBF5M83vHcPQqBhJO/2+BG3Eecl4EojjXly8ANyqlVmituzOUUzLXKnH8i5vP4Dj9TwBv11qfBFBKTQUeBW4FPgD8+1CFJN4a/CLx59Va69WJ72cATwGfVkr9P631K3low4ijlPoo8G2y79/JG+zXtNb35Uar4mQ4tlJKGcDPcS6E79Ja/yLxfSPwOHCHUuo3WusHcqjyiKKUWoTj9O8FLtVaH0h8fyPwIHCPUmp6pptGgmS/uktrPaqc/IEopZqBHwKngMu01hsT3y/GOfbfUUo9nLTXIGWUfP+B3NgqQTldl96G81a7Oovdf4Dj9H9Wa/3FRHlVOOfrVcAngG/kRtPCM0xbJfvUPVrrL+VMqeLiAzhO/3rghpRr+Djgd8BS4LPAPw1WQKldqyTGv7h5T+LzQ0mnH0BrvQf4+8Sf73RRzruA8TihBKtTytkJ/GPiz08MW9sCo5SarZR6GPg+zk32dJZFJS+GJfEglI4c2epqnLCFp5IXQgCtdTvw0cSfo71ffSrx+blUx0xr/TDOzbYJ+HOXZS0ALOC1XCpYID6OM1L4raQjC6C1Xgt8DQgDH8pQRjn0H8iNraA8rksTlVI/Bx7AeStyxOP+CrgJZwT7y8nvtdZdwPtxwvQ+njOFC8hwbZWg5PsU8N7E598MuIYfwwn/gcx+VEldq8TxL1ISYT7bgZe11rvSbLIt8dniorjrEp8PpvntIZyL4fWelSw+fogTgvEYsIjsX1MuwHkluC3ThqOYXNhq0H6VGNE+ClymlKrJVski4DqceTW/S/PbbxKfGc8dpVQQ57X61oQTMtoZ6pri1i7l0H8gN7aC8rgufRFnoOpPOOETWz3ufy1gAA8NjLnWWr+BE/YzRSk1Nwe6Fprh2gredPzX5UqpIuQYjm3Szcdy60eV1LVKQn2KFK31GWDZEJssTny6mYjZlvjcOPAHrXWnUuogMEkp1aS1zmbUoFhYC3xDa/0QgDP44w2lVAMwGedC+LdKqXcBs4CTwO+Bu7XWB3OmceEYtq0Yol8l0DhvmuYCL2VTQSFJhGjUA/u11ifSbJK80c5zUdz5QADYo5T6InAbMBU4jDNi98XUt3rFTOK191yctxdb0myyLfFbm1LK0FrbgxRV0v0HcmerMroubcV50/0LrbWVxXUpU5/ainPvnAdszkrD4mFYtkr0zfk416BblFIfwpn42osTvnK31nrUzxvRWt88xM9u/aiSulbJiP8oRCkVAv458aebmLLmxOehQX5Pft80HL0Kjdb675OO7DBIjoAsxHlVfBRngrUf+CDwisrSSy4mcmSrUu9XuWxfsl/dAPwNTkaJZ3EeLD4FvJSIFx0N1OOErhzXWkcG/qi1juGMslUCQ42AlXr/gdzZqlyuS1/RWv98GBlSyqFPATmx1XScmPUJOPMEe3H6VC9O6MtapdSlOVG2CEk8+Hwh8WcmP6qk+pWM+I8gSqn7cMIqMvEbrXXaiSaJzvpTnNGeLQk5E1WJz55Bfk9+n83koLyQC1tlSfIGuwm4WWu9O6FPFfBj4H8B9+FMui4KCmirZL8abGLrqO5XwKqEPFj7king3LQv2a/WAO9IxIYmJ5jdj5Nh64c4bwKKnUzHHfof+84syym6/pMFubLVqLsuFYhy6FO5ItmnDgA3aa1fg7MpPr+CMyDxS6XUTLfpLkcZX8aJqjgCfD3DtiXVr8TxH1mmAG5GZZrTfZlIS/UT4A7gBPA2lzmJ48BQr9yNAZ/FwLBsNQy+hfP0fzox+QdwJocppT4AXAEsUkpdrLVOl/6sEBTKVsmRplLtV5nal8RN+z6Jk4L3kNb67ERqrfUxpdS7cUI+/kwp1ay1HmxUqVhwYxc3x3409h+v5MpWo/G6VAjKoU/ligdwwsfiqWFiWuuYUuou4EqcQZK34gxOlAxKqS/gJDbpA25PDsQMQUn1K3H8R5DhLK6SGNn5b+BmnImY12it3U7m6QLGKKXCgzy5h1O2KwoKtRBNYk2EdDnW0Vp3K6WexJlQtYj0eY9HnAIu2nMm8VkxyO+jul8l8jZDDtqXyBuedlKm1vqgUmodcDlOKMfDbnUsEJmOO7izzajrP1mQE1uNxutSgSiHPpUTEgOB+wb5zVJKrcLpT4soEcc/8Tbj+zhZtHpxBk+HWlQxSUn1K4nxHwUopZpwQgRuxpmEcoXHnPvJp/kJg/yeKX5NeJPDic/KgmpRHJR6v0qmfhuJ9o2mftWJcyMcl7iR9iPx3TigN8OE5VLvP5A7W2ViNPWffFIOfWqkKKk+lciU+BCO038SuNbD4osl1a/E8S9ylFJTcFYhXISzWuNSrfUmj8UkZ6Kfk8JMKVWLk8qqfZRn9MkJSqnPKaV+rZQaLFPLtMSnm2xKpc5Q/coAZuOEmY3K7BmJkIqjwMRB0rTNSXymW0W1H0qp7yqlfqOUGj/IJqOmXyVGCjfj5A4/L80mCufekskuJd1/IHe2kuuSawbtUwlcn7OljlLqY0qpXyqlrhpkk5LpU0qpepzFSq/DectxucuR/iQlda0Sx7+ISaRwexznBHwap7NmcxL+MfH51jS/3YxzU1qV5rdy5AKcCZa3D/wh4bRdg7Pc9+qBv5chQ/WrS4BG4NnUmPZRyB9xzo90KeGS7XZz7lya2P6ccpRS5+NMtDvO6FlIZ6hj79Yu5dB/IDe2kuuSO5K2vkUp1c+/UUpNxjnP9mqtR4WDlmem4/Sn9wz8QSkVBt6R+PPRkVQq1yTWUEmGLW0GLkldSM8lJXWtEse/uPkBMBN4FbhOa30q0w6JFVlnK6VSX889gDNy+V6l1A0p207Hmb1vA9/MqeajAKXUjISt6lK+/vfE56dSU5klXhP+DCf92U+01ocpIwax1RqcLCNXK6U+mLJtI07fBfjGCKqZD/4N5/z4qlIqOQKGUupGnBUhD+HMvSHlt3TnYLJffVkpNTtl20bgHpyHi6+lS/lYpNyDEyP7D0qps1mSlFIXAXfhZLn4Qcr35dp/IDe2kuvSAJRSkxN2Gpf8LpHt6I84b1K+kLJtFU5iDB+l0ac8kc5WOBkB48AdSqnbUrYNAN/DSYTwB49hxcXIF3AWONsHXJlp8LQcrlWGbWdKWCEUAqXUHJyOZuCM9qedhIMTovPJlP2SB3S51vqplO9v4c2lvdcAp3FSCFYCn9Fan13evFRQSu3BuXhNSneyp/z+Pq31vSnffwP4W5yZ/M/h5Nm+HCcW9xmch7Ch0vONOoZhqyXAEzhpzF7CiYW8Eid/+Y+11h/Kq+IjgFLqqzgOWjdOW2tw0sBFcfrC6gHbn3MOJkYffwm8HYjg9KMuYHmivP8B/iIxiXNUoJT6KM5EuSiOXQxgBU7SiHenLm1fzv0HcmarcrwuPYVzrl2utX52kN8+r7W+O+X76Tj2mYAToqFxRmWbgT8AtyTWTygpsrTVJ4Bv4/THtcAbwFuAiTgLhC3TWh8dAfXzQiJqYj/OpNx1pF9EDwCt9Z2JffZQ4tcqGfEvXpbxZmqoK3BSeKb792duCtNa/y5R5qM4rzuXAetxUlmVnNM/HLTWn8J5Bfocjq2uwxnZvQtYWWo31+GgtX4Z50bxAM7aEtcAe4GPAH9ZQNVyhtb6H3BG97cAV+HEeT6MM9/GVWhFYpGd23Hssh7HEVmZKPODwDtHk9MPoLX+AU7o0os4DuhinEXJrk51ZDOUUfL9B3JmK7kuuUBrvQtYAtyLE4JxI07663/CyeJSck5/tmitvwtcDTyCc/7dhDPA8SVg8Wh2+hMs4c1MPAsZ3I+6I1NBpXStkhF/QRAEQRAEQSgDZMRfEARBEARBEMoAcfwFQRAEQRAEoQwQx18QBEEQBEEQygBx/AVBEARBEAShDBDHXxAEQRAEQRDKAHH8BUEQBEEQBKEMEMdfEARBEARBEMoAcfwFQRAEQRAEoQwQx18QBEEQBEEQygBx/AVBEARBEAShDBDHXxAEQRAEQRDKAHH8BUEQBEEQBKEMEMdfEARBEARBEMoAcfwFQRAEQRAEoQwQx18QBEEQBEEQygBx/AVBEARBEAShDBDHXxAEQRAEQRDKgP8PqLydj/x9VnoAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 363,
       "width": 383
      },
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "_, ax = plt.subplots(figsize=(6, 6))\n",
    "\n",
    "train_and_plot_decision_surface(\"Neural Net\", model_scikit, features, labels, plt=ax)\n",
    "plot_points(plt=ax)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The acuracy on the  5  validation folds: [0.97 0.95 0.96 0.97 0.96]\n",
      "The Average acuracy on the  5  validation folds: 0.962\n"
     ]
    }
   ],
   "source": [
    "# Applying K-fold cross-validation\n",
    "# Here we pass the whole dataset, i.e. features and labels, instead of splitting it.\n",
    "num_folds = 5\n",
    "cross_validation = cross_val_score(\n",
    "    model_scikit, features, labels, cv=num_folds, verbose=0)\n",
    "\n",
    "print(\"The acuracy on the \", num_folds, \" validation folds:\", cross_validation)\n",
    "print(\"The Average acuracy on the \", num_folds, \" validation folds:\", np.mean(cross_validation))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### NOTE: The above code took quiet long even though we used only 5  CV folds and the neural network and data size are very small!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Hyperparameter optimization"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We know from chapter 6 that there are 2 types of parameters which need to be tuned for a machine learning model.\n",
    "* Internal model parameters (weights) which can be learned for e.g. by gradient-descent\n",
    "* Hyperparameters\n",
    "\n",
    "In the model which we created above we made some arbitrary choices like which optimizer we use, what is its learning rate, number of hidden units and so on ...\n",
    "\n",
    "Now that we have the keras model wrapped as a scikit model we can use the grid search functions we have seen in chapter 6."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import GridSearchCV\n",
    "# Just to remember\n",
    "model_scikit = KerasClassifier(\n",
    "    build_fn=a_simple_NN, **{\"epochs\": num_epochs, \"verbose\": 0})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/tarunchadha/anaconda3/envs/mlw-2/lib/python3.6/site-packages/sklearn/model_selection/_search.py:841: DeprecationWarning: The default of the `iid` parameter will change from True to False in version 0.22 and will be removed in 0.24. This will change numeric results when test-set sizes are unequal.\n",
      "  DeprecationWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.894 {'epochs': 300}\n"
     ]
    }
   ],
   "source": [
    "HP_grid = {'epochs' : [300, 500, 1000]}\n",
    "search = GridSearchCV(estimator=model_scikit, param_grid=HP_grid, n_jobs=3)\n",
    "search.fit(features, labels)\n",
    "print(search.best_score_, search.best_params_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/tarunchadha/anaconda3/envs/mlw-2/lib/python3.6/site-packages/sklearn/model_selection/_search.py:841: DeprecationWarning: The default of the `iid` parameter will change from True to False in version 0.22 and will be removed in 0.24. This will change numeric results when test-set sizes are unequal.\n",
      "  DeprecationWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.8119999953508377 {'batch_size': 10, 'epochs': 30}\n"
     ]
    }
   ],
   "source": [
    "HP_grid = {'epochs' : [10, 15, 30], \n",
    "           'batch_size' : [10, 20, 30] }\n",
    "search = GridSearchCV(estimator=model_scikit, param_grid=HP_grid, n_jobs=4)\n",
    "search.fit(features, labels)\n",
    "print(search.best_score_, search.best_params_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# A more general model for further Hyperparameter optimization\n",
    "from keras import optimizers\n",
    "\n",
    "def a_simple_NN(activation='relu', num_hidden_neurons=[4, 4], learning_rate=0.01):\n",
    "\n",
    "    model = Sequential()\n",
    "\n",
    "    model.add(Dense(num_hidden_neurons[0],\n",
    "                    input_shape=(2,), activation=activation))\n",
    "\n",
    "    model.add(Dense(num_hidden_neurons[1], activation=activation))\n",
    "\n",
    "    model.add(Dense(1, activation=\"sigmoid\"))\n",
    "\n",
    "    model.compile(loss=\"binary_crossentropy\", optimizer=optimizers.rmsprop(\n",
    "        lr=learning_rate), metrics=[\"accuracy\"])\n",
    "\n",
    "    return model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Exercise: \n",
    "* Look at the model above and choose a couple of hyperparameters to optimize. \n",
    "* **(OPTIONAL:)** What function from SciKit learn other than GridSearchCV can we use for hyperparameter optimization? Use it."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Code here"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Exercise: Create a neural network to classify the 2d points example from chapter 2 learned \n",
    "(Optional: As you create the model read a bit on the different keras commands we have used)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import numpy as np\n",
    "from sklearn.model_selection import train_test_split, cross_val_score\n",
    "from keras.models import Sequential\n",
    "from keras.layers import Dense\n",
    "from keras import optimizers\n",
    "from keras.wrappers.scikit_learn import KerasClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "circle = pd.read_csv(\"2d_points.csv\")\n",
    "# Using x and y coordinates as featues\n",
    "features = circle.iloc[:, :-1]\n",
    "# Convert boolean to integer values (True->1 and False->0)\n",
    "labels = circle.iloc[:, -1].astype(int)\n",
    "\n",
    "colors = [[\"steelblue\", \"chocolate\"][i] for i in circle[\"label\"]]\n",
    "plt.figure(figsize=(5, 5))\n",
    "plt.xlim([-2, 2])\n",
    "plt.ylim([-2, 2])\n",
    "\n",
    "plt.scatter(features[\"x\"], features[\"y\"], color=colors, marker=\"o\");\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Insert Code here"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### The examples above are not the ideal use problems one should use neural networks for. They are too simple and can be easily solved by classical machine learning algorithms. Below we show examples which are the more common applications of Neural Networks."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Handwritten Digits Classification\n",
    "### MNIST Dataset\n",
    "\n",
    "MNIST datasets is a very common dataset used in machine learning. It is widely used to train and validate models.\n",
    "\n",
    "\n",
    ">The MNIST database of handwritten digits, available from this page, has a training set of 60,000 examples, and a >test set of 10,000 examples. It is a subset of a larger set available from NIST. The digits have been size->normalized and centered in a fixed-size image.\n",
    ">It is a good database for people who want to try learning techniques and pattern recognition methods on real-world >data while spending minimal efforts on preprocessing and formatting.\n",
    ">source: http://yann.lecun.com/exdb/mnist/\n",
    "\n",
    "The problem we want to solve using this dataset is: multi-class classification (FIRST TIME)\n",
    "This dataset consists of images of handwritten digits between 0-9 and their corresponsing labels. We want to train a neural network which is able to predict the correct digit on the image. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Loading the dataset in keras\n",
    "# Later you can explore and play with other datasets with come with Keras\n",
    "from keras.datasets import mnist\n",
    "\n",
    "# Loading the train and test data\n",
    "\n",
    "(X_train, y_train), (X_test, y_test) = mnist.load_data()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Looking at the dataset\n",
    "print(X_train.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# We can see that the training set consists of 60,000 images of size 28x28 pixels\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "i=np.random.randint(0,X_train.shape[0])\n",
    "plt.imshow(X_train[i], cmap=\"gray_r\") ;\n",
    "print(\"This digit is: \" , y_train[i])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Look at the data values for a couple of images\n",
    "print(X_train[0].min(), X_train[1].max())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The data consists of values between 0-255 representing the **grayscale level**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# The labels are the digit on the image\n",
    "print(y_train.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Scaling the data\n",
    "# It is important to normalize the input data to (0-1) before providing it to a neural net\n",
    "# We could use the previously introduced function from SciKit learn. However, here it is sufficient to\n",
    "# just divide the input data by 255\n",
    "X_train_norm = X_train/255.\n",
    "X_test_norm = X_test/255.\n",
    "\n",
    "# Also we need to reshape the input data such that each sample is a vector and not a 2D matrix\n",
    "X_train_prep = X_train_norm.reshape(X_train_norm.shape[0],28*28)\n",
    "X_test_prep = X_test_norm.reshape(X_test_norm.shape[0],28*28)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**IMPORTANT: One-Hot encoding**\n",
    "\n",
    "**TODO: Better frame the explaination**\n",
    "\n",
    "In such problems the labels are provided as something called **One-hot encodings**. What this does is to convert a categorical label to a vector.\n",
    "\n",
    "For the MNIST problem where we have **10 categories** one-hot encoding will create a vector of length 10 for each of the labels. All the entries of this vector will be zero **except** for the index which is equal to the integer value of the label.\n",
    "\n",
    "For example:\n",
    "if label is 4. The one-hot vector will look like **[0 0 0 0 1 0 0 0 0 0]**\n",
    "\n",
    "Fortunately, we don't have to code this ourselves because Keras has a built-in function for this."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from keras.utils.np_utils import to_categorical\n",
    "\n",
    "y_train_onehot = to_categorical(y_train, num_classes=10)\n",
    "y_test_onehot = to_categorical(y_test, num_classes=10)\n",
    "\n",
    "print(y_train_onehot.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Building the keras model\n",
    "from keras.models import Sequential\n",
    "from keras.layers import Dense\n",
    "\n",
    "def mnist_model():\n",
    "    model = Sequential()\n",
    "\n",
    "    model.add(Dense(64, input_shape=(28*28,), activation=\"relu\"))\n",
    "\n",
    "    model.add(Dense(64, activation=\"relu\"))\n",
    "\n",
    "    model.add(Dense(10, activation=\"softmax\"))\n",
    "\n",
    "    model.compile(loss=\"categorical_crossentropy\",\n",
    "                  optimizer=\"rmsprop\", metrics=[\"accuracy\"])\n",
    "    return model\n",
    "\n",
    "model = mnist_model()\n",
    "\n",
    "model_run = model.fit(X_train_prep, y_train_onehot, epochs=20,\n",
    "                      batch_size=512)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "print(\"The [loss, accuracy] on test dataset are: \" , model.evaluate(X_test_prep, y_test_onehot))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Optional exercise: Run the model again with validation dataset, plot the accuracy as a function of epochs, play with number of epochs and observe what is happening."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Code here"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Solution:\n",
    "num_epochs = 20\n",
    "model_run = model.fit(X_train_prep, y_train_onehot, epochs=num_epochs,\n",
    "                      batch_size=512, validation_data=(X_test_prep, y_test_onehot))\n",
    "# Evaluating the model on test dataset\n",
    "#print(\"The [loss, accuracy] on test dataset are: \" , model.evaluate(X_test_prep, y_test_onehot))\n",
    "history_model = model_run.history\n",
    "print(\"The history has the following data: \", history_model.keys())\n",
    "\n",
    "# Plotting the training and validation accuracy during the training\n",
    "sns.lineplot(np.arange(1, num_epochs+1), history_model[\"acc\"], color = \"blue\", label=\"Training set\") ;\n",
    "sns.lineplot(np.arange(1, num_epochs+1), history_model[\"val_acc\"], color = \"red\", label=\"Valdation set\") ;\n",
    "plt.xlabel(\"epochs\") ;\n",
    "plt.ylabel(\"accuracy\") ;"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Adding regularization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Adding l2 regularization\n",
    "# Building the keras model\n",
    "from keras.models import Sequential\n",
    "from keras.layers import Dense\n",
    "from keras.regularizers import l2\n",
    "\n",
    "def mnist_model():\n",
    "    \n",
    "    model = Sequential()\n",
    "\n",
    "    model.add(Dense(64, input_shape=(28*28,), activation=\"relu\", \n",
    "                   kernel_regularizer=l2(0.01)))\n",
    "\n",
    "    model.add(Dense(64, activation=\"relu\", \n",
    "                   kernel_regularizer=l2(0.01)))\n",
    "\n",
    "    model.add(Dense(10, activation=\"softmax\"))\n",
    "\n",
    "    model.compile(loss=\"categorical_crossentropy\",\n",
    "                  optimizer=\"rmsprop\", metrics=[\"accuracy\"])\n",
    "    return model\n",
    "\n",
    "model = mnist_model()\n",
    "\n",
    "num_epochs = 50\n",
    "model_run = model.fit(X_train_prep, y_train_onehot, epochs=num_epochs,\n",
    "                      batch_size=512)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "print(\"The [loss, accuracy] on test dataset are: \" , model.evaluate(X_test_prep, y_test_onehot))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Another way to add regularization and to make the network more robust we can add something called \"Dropout\". When we add dropout to a layer a specified percentage of units in that layer are switched off. \n",
    "(MAKING MODEL SIMPLER)\n",
    "\n",
    "### Exercise: Add dropout instead of l2 regularization in the network above"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Adding dropout is easy in keras\n",
    "# We import a layer called Dropout and add as follows\n",
    "# model.add(Dropout(0.5)) to randomly drop 50% of the hidden units\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Solution\n",
    "# Adding Dropout\n",
    "# Building the keras model\n",
    "from keras.models import Sequential\n",
    "from keras.layers import Dense, Dropout\n",
    "\n",
    "def mnist_model():\n",
    "    \n",
    "    model = Sequential()\n",
    "\n",
    "    model.add(Dense(64, input_shape=(28*28,), activation=\"relu\"))\n",
    "              \n",
    "    model.add(Dropout(0.4))\n",
    "\n",
    "    model.add(Dense(64, activation=\"relu\"))\n",
    "\n",
    "    model.add(Dense(10, activation=\"softmax\"))\n",
    "\n",
    "    model.compile(loss=\"categorical_crossentropy\",\n",
    "                  optimizer=\"rmsprop\", metrics=[\"accuracy\"])\n",
    "              \n",
    "    return model\n",
    "\n",
    "model = mnist_model()\n",
    "\n",
    "num_epochs = 50\n",
    "model_run = model.fit(X_train_prep, y_train_onehot, epochs=num_epochs,\n",
    "                      batch_size=512)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "print(\"The [loss, accuracy] on test dataset are: \" , model.evaluate(X_test_prep, y_test_onehot))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Network Architecture\n",
    "\n",
    "The neural networks which we have seen till now are the simplest kind of neural networks.\n",
    "There exist more sophisticated network architectures especially designed for specific applications.\n",
    "Some of them are as follows:\n",
    "\n",
    "###  Convolution Neural Networks (CNNs)\n",
    "\n",
    "These networks are used mostly for computer vision (EXAMPLES) like tasks. \n",
    "One of the old CNN networks is shown below.\n",
    "\n",
    "<center>\n",
    "<figure>\n",
    "<img src=\"./images/neuralnets/CNN_lecun.png\" width=\"800\"/>\n",
    "<figcaption>source: LeCun et al., Gradient-based learning applied to document recognition (1998).</figcaption>\n",
    "</figure>\n",
    "</center>\n",
    "\n",
    "CNNs consist of new type of layers like convolution layer and pooling layers.\n",
    "\n",
    "###  Recurrent Neural Networks (RNNs)\n",
    "\n",
    "These are used for time-series data, speech recognition, translation etc.\n",
    "\n",
    "IMAGE HERE\n",
    "\n",
    "### Generative adversarial networks (GANs)\n",
    "\n",
    "GANs consist of 2 parts, a generative network and a discriminative network. The generative network produces data which is then fed to the discriminative network which judges if the new data belongs to a specified dataset. Then via feedback loops the generative network becomes better and better at creating images similar to the dataset the discriminative network is judging against. At the same time the discriminative network get better and better at identifyig **fake** instances which are not from the reference dataset. \n",
    "\n",
    "IMAGE HERE"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## CNN example"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For this example we will work with a dataset called fashion-MNIST which is quite similar to the MNIST data above.\n",
    "> Fashion-MNIST is a dataset of Zalando's article images—consisting of a training set of 60,000 examples and a test set of 10,000 examples. Each example is a 28x28 grayscale image, associated with a label from 10 classes. We intend Fashion-MNIST to serve as a direct drop-in replacement for the original MNIST dataset for benchmarking machine learning algorithms. It shares the same image size and structure of training and testing splits.\n",
    "source: https://github.com/zalandoresearch/fashion-mnist\n",
    "\n",
    "The 10 classes of this dataset are:\n",
    "\n",
    "| Label| Item |\n",
    "| --- | --- |\n",
    "| 0 |\tT-shirt/top |\n",
    "| 1\t| Trouser |\n",
    "|2|\tPullover|\n",
    "|3|\tDress|\n",
    "|4|\tCoat|\n",
    "|5|\tSandal|\n",
    "|6|\tShirt|\n",
    "|7|\tSneaker|\n",
    "|8|\tBag|\n",
    "|9|\tAnkle boot|"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Loading the dataset in keras\n",
    "# Later you can explore and play with other datasets with come with Keras\n",
    "from keras.datasets import fashion_mnist\n",
    "\n",
    "# Loading the train and test data\n",
    "\n",
    "(X_train, y_train), (X_test, y_test) = fashion_mnist.load_data()\n",
    "\n",
    "items =['T-shirt/top', 'Trouser', \n",
    "        'Pullover', 'Dress', \n",
    "        'Coat', 'Sandal', \n",
    "        'Shirt', 'Sneaker',\n",
    "        'Bag', 'Ankle boot']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# We can see that the training set consists of 60,000 images of size 28x28 pixels\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "i=np.random.randint(0,X_train.shape[0])\n",
    "plt.imshow(X_train[i], cmap=\"gray_r\") ; \n",
    "print(\"This item is a: \" , items[y_train[i]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Also we need to reshape the input data such that each sample is a 4D matrix of dimension\n",
    "# (num_samples, width, height, channels). Even though these images are grayscale we need to add\n",
    "# channel dimension as this is expected by the Conv function\n",
    "X_train_prep = X_train.reshape(X_train.shape[0],28,28,1)/255.\n",
    "X_test_prep = X_test.reshape(X_test.shape[0],28,28,1)/255.\n",
    "\n",
    "from keras.utils.np_utils import to_categorical\n",
    "\n",
    "y_train_onehot = to_categorical(y_train, num_classes=10)\n",
    "y_test_onehot = to_categorical(y_test, num_classes=10)\n",
    "\n",
    "print(y_train_onehot.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Creating a CNN similar to the one shown in the figure from LeCun paper\n",
    "# In the original implementation Average pooling was used. However, we will use maxpooling as this \n",
    "# is what us used in the more recent architectures and is found to be a better choice\n",
    "# Convolution -> Pooling -> Convolution -> Pooling -> Flatten -> Dense -> Dense -> Output layer\n",
    "from keras.models import Sequential\n",
    "from keras.layers import Dense, Conv2D, MaxPool2D, Flatten, Dropout, BatchNormalization\n",
    "\n",
    "def simple_CNN():\n",
    "    \n",
    "    model = Sequential()\n",
    "    \n",
    "    model.add(Conv2D(6, (3,3), input_shape=(28,28,1), activation='relu'))\n",
    "    \n",
    "    model.add(MaxPool2D((2,2)))\n",
    "    \n",
    "    model.add(Conv2D(16, (3,3), activation='relu'))\n",
    "    \n",
    "    model.add(MaxPool2D((2,2)))\n",
    "    \n",
    "    model.add(Flatten())\n",
    "    \n",
    "    model.add(Dense(120, activation='relu'))\n",
    "    \n",
    "    model.add(Dense(84, activation='relu'))\n",
    "    \n",
    "    model.add(Dense(10, activation='softmax'))\n",
    "    \n",
    "    model.compile(loss=\"categorical_crossentropy\", optimizer=\"rmsprop\", metrics=[\"accuracy\"])\n",
    "    \n",
    "    return model\n",
    "\n",
    "model = simple_CNN()\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "num_epochs = 10\n",
    "model_run = model.fit(X_train_prep, y_train_onehot, epochs=num_epochs, \n",
    "                      batch_size=64, validation_data=(X_test_prep, y_test_onehot))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Exercise: Use the above model or improve it (change number of filters, add more layers etc. on the MNIST example and see if you can get a better accuracy than what we achieved with a vanilla neural network)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Exercise: Load and play with the CIFAR10 dataset also included with Keras and build+train a simple CNN using it"
   ]
  }
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