{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "<h1 style=\"text-align: center;\">Data Mining Project 1: Frequent Pattern & Association Rule</h1>\n", "\n", "<p style=\"text-align:center;\">\n", " 呂伯駿<br>\n", " Q56074085<br>\n", " NetDB<br>\n", " National Cheng Kung University<br>\n", " pclu@netdb.csie.ncku.edu.tw\n", "</p>\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 1. Introduction\n", "Frequent Pattern & Association Rule 是 Data mining 中的重要議題。 本次報告實作 Apriori [1] & FP Growth [2] 兩種經典算法,並使用IBM 合成資料集與 Kaggle Random Shop cart 資料集來比較兩者的效率。" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "---\n", "\n", "## 2. Environment\n", "\n", "這個部分我將會說明實驗所使用的環境與使用資料集。\n", "\n", "### 2.1 System Preferences\n", "\n", "實驗環境如下:\n", "- <b>Operating System</b>: macOS High Sierra (10.13.6)\n", "- <b>CPU</b>: 1.3 GHz Intel Core i5\n", "- <b>Memory</b>: 8 GB 1600 MHz DDR3\n", "- <b>Programming Language</b> : Python 3.6.2\n", "\n", "由於所用資料集大小皆不超過 4MB,實驗環境記憶體足以符合實驗需求。\n", "\n", "### 2.2 Dataset\n", "\n", "實驗使用以下資料集,來自 IBM Quest Synthetic Data Generator 以及 Kaggle。\n", "\n", "#### a. IBM\n", "\n", "本次實驗使用 IBM Quest Synthetic Data Generator Lit 模式合成了四組資料,參數設置如下表\n", "\n", "| ntrans | tlength | nitems |\n", "|----------|:-------------:|------:|\n", "| 1 | 5 | 5 |\n", "| 10 | 5 | 5 |\n", "| 10 | 5 | 30 |\n", "| 20 | 5 | 30 |\n", "\n", "- <b>ntrans</b>: number of transactions in _000\n", "- <b>tlength</b>: avg_items per transaction\n", "- <b>nitems</b>: number of different items in _000\n", "\n", "#### b. Kaggle: Random Shopping cart\n", "\n", "[Random Shopping cart](https://www.kaggle.com/fanatiks/shopping-cart/home) 包含隨機排序之購物車資料,適合本次尋找 Frequent Pattern 的實驗情境。 <br>\n", "此資料集有 1499筆資料,總共有 38 種商品,由於每筆資料中物品有可能重複出現,後續仍須再次處理去除同一筆資料中之相同商品。\n", "\n", "\n", "\n", "資料範例如下:\n", "\n", "1. yogurt, pork, sandwich bags, lunch meat, all- purpose, flour, soda, butter, vegetables, beef, aluminum foil, all- purpose, dinner rolls, shampoo, all- purpose, mixes, soap, laundry detergent, ice cream, dinner rolls, \n", "2. toilet paper, shampoo, hand soap, waffles, vegetables, cheeses, mixes, milk, sandwich bags, laundry detergent, dishwashing liquid/detergent, waffles, individual meals, hand soap, vegetables, individual meals, yogurt, cereals, shampoo, vegetables, aluminum foil, tortillas, mixes, \n", "3. ...\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "---\n", "\n", "## 3. Implementation\n", "\n", "### 3.1 pre-processing\n", "\n", "#### a. IBM\n", "\n", "IBM 所產生之資料格式為 id number(每一列),為方便運算我將相同id之資料轉換至同一行,中間以','分隔。 如 (1: 255,266,267...)\n", "\n", "\n", "#### b. Kaggle\n", "\n", "由於 Random Shopping cart 中的每一筆資料包含商品可能重複,因此再跑演算法之前需要將其過濾。<br>\n", "已過濾之資料路徑為: /data/Kaggle/cart_dataset_v2.txt <br>\n", "原始資料路徑則為: /data/Kaggle/cart_dataset_v1.txt\n", "\n", "\n", "### 3.2 Apriori\n", "\n", "<b>概念:</b><br>\n", "分為兩步驟,先迭代找出 Candidate Set,再篩選掉不符合 minimum support 的set。\n", "\n", "<b>優點:</b><br>\n", "易實作,適合用在少量資料集。\n", "\n", "<b>缺點:</b>\n", "需要產生大量 Candidates 也要重複 Scan 資料集造成效能瓶頸。\n", "\n", "### 3.3 FP-growth\n", "\n", "<b>概念:</b><br>\n", "分為兩步驟,建立 FP-Tree,再從中尋找 Frequent Pattern。\n", "\n", "<b>優點:</b><br>\n", "不需產生 Candidates,整體資料集僅需 Scan 兩次,速度較 Apriori 快一個量級。\n", "\n", "<b>缺點:</b><br>\n", "需要額外構建 FP-Tree 來儲存各 pattern 出現次數。" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "---\n", "\n", "## 4. Analysis" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "實驗使用2.2 敘述之資料集,比較兩種演算法在改變 minsup 數值以及資料量的情況下其時間的變化。 <br>\n", "其中 FP-Growth 之結果為十次運算之時間平均,Aprior 由於時間上之考量為兩次運算之時間平均。\n", "\n", "### a. IBM" ] }, { "cell_type": "code", "execution_count": 67, "metadata": {}, "outputs": [ { "data": { "text/html": [ "<div>\n", "<style scoped>\n", " .dataframe tbody tr th:only-of-type {\n", " vertical-align: middle;\n", " }\n", "\n", " .dataframe tbody tr th {\n", " vertical-align: top;\n", " }\n", "\n", " .dataframe thead th {\n", " text-align: right;\n", " }\n", "</style>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>data</th>\n", " <th>minsup</th>\n", " <th>fp_num</th>\n", " <th>algorithm</th>\n", " <th>time</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>t1_l5_n5</td>\n", " <td>0.005</td>\n", " <td>366</td>\n", " <td>fpg</td>\n", " <td>0.375859</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>t1_l5_n5</td>\n", " <td>0.006</td>\n", " <td>196</td>\n", " <td>fpg</td>\n", " <td>0.092633</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>t1_l5_n5</td>\n", " <td>0.007</td>\n", " <td>196</td>\n", " <td>fpg</td>\n", " <td>0.099796</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>t1_l5_n5</td>\n", " <td>0.008</td>\n", " <td>101</td>\n", " <td>fpg</td>\n", " <td>0.024959</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>t1_l5_n5</td>\n", " <td>0.009</td>\n", " <td>49</td>\n", " <td>fpg</td>\n", " <td>0.008303</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " data minsup fp_num algorithm time\n", "0 t1_l5_n5 0.005 366 fpg 0.375859\n", "1 t1_l5_n5 0.006 196 fpg 0.092633\n", "2 t1_l5_n5 0.007 196 fpg 0.099796\n", "3 t1_l5_n5 0.008 101 fpg 0.024959\n", "4 t1_l5_n5 0.009 49 fpg 0.008303" ] }, "execution_count": 67, "metadata": {}, "output_type": "execute_result" } ], "source": [ "%matplotlib inline\n", "import pandas as pd\n", "import matplotlib.pyplot as plt\n", "\n", "ibm = pd.read_csv('./results/ibm.csv')\n", "kaggle = pd.read_csv('./results/kaggle.csv')\n", "\n", "ibm.head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "上圖欄位分別為:<br>\n", "<b>data</b>: data parameter <br>\n", "<b>minsup</b>: minimum support <br>\n", "<b>fp_num</b>: number of frequent pattern <br>\n", "<b>alforithm</b>: choosen algorithm <br>\n", "<b>time</b>: running time <br>" ] }, { "cell_type": "code", "execution_count": 68, "metadata": {}, "outputs": [ { "data": { "image/png": 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qyrj/nCZc27UeE+au5sZXF/DXvv1ZFxQREUkniB4Yh3Mb8KqZPQAs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6QxFzerxAtTf+K2sfM5kJIadCwREZHjCqSA4e5rwo/rgQlAU2CdmZUFCD+uDyJblql3PjS5CmY+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\nW7bw+eefs2jRIm6++eYyH78yRGMCIycn5/g7VQAlMALOugOSQ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vM3XUbRwTsHAovM7Hvg\nVWCccy7kAKDRJiHBeOjSHnRtkc5NUxayZKOmVxUREYEIJTDM7C/AMeDFwKoQu4V8BtU594Rzrpdz\nrlfjxo0rK8TwJafCJc+DczDtKsg+HOmIRESkJIumwRs3wd4NgPN+vnFTuZMYBw4cYN68eTz99NN5\nCYzZs2czcOBARo0aRadOnRg3bhy5ud7gjHXq1OGOO+6gb9++fPHFFwwaNIj5872nG6ZMmULXrl3p\n0qULt9xyS945SipTHTjnRjvnmjvnkp1zLZ1zT/vrxzjnJhXa9zXnXGfn3KnOudOcc29EJuqySa3h\nTa9av1Yy1z37DZv3anpVERGRyhwDIyQzuxpvcM+zXP5wuZlAq6DdWgKbqjq2MmuQAaMmwcuj4d3x\nMPyhSEckIlJ9vTMetiwufnvmN5BzpOC67Cx4/UZY8GzoMs26wi/vL/G0M2fOZNiwYZxyyik0aNCA\nb7/9FoCvv/6apUuX0rp1a4YNG8b06dO56KKLOHjwIF26dOHuu+8ucJxNmzZxyy23sGDBAurXr8+Q\nIUOYOXMmI0eOLLaMxKcmdVN45preXPTvL7h28nxeGdePOjWrvOkmIiISNaq0B4aZDQNuAUY45w4F\nbZoFXGZmNc0sA+851a+rMrZy63AO9L8ZFvwHvou5KehFRKqPwsmL460P05QpU7jssssAuOyyy5gy\nxbsX9OnTh7Zt25KYmMjo0aOZO3cuAImJiVx44YVFjvPNN98waNAgGjduTFJSEldccQVz5swpsYzE\nrw7N6vLY5T1YsXU/N03R9KoiIlK9VeY0qlOAQUAjM8sE7sSbdaQm8IE/LdiXzrlxzrkfzGwasBTv\n0ZLfRfUMJMUZfBtkzoc3b/ZmKWnaOdIRiYhUP8fpKcE/u/iPjxSS3gqueatMp9y5cycff/wxS5Ys\nwczIycnBzDjnnHOKTIMZeJ+SkkJiYtEpuPM7JxZVXBmJb4PaN2HCiM7cPnMJ97y1jAkj1L4QEZHq\nqTJnISnynKpz7mTnXCvnXHf/NS5o/3udcyc559o7594p6dhRKzEJLnoGUtJh6q/gsAbdEhGJOmfd\n4Y1fFCw51VtfRq+++ipXXXUV69atY+3atWzYsIGMjAzmzp3L119/zZo1a8jNzWXq1Kn079+/xGP1\n7duXTz/9lB07dpCTk8OUKVM488wzyxybxIdf/aw11/XPYPLna5k8b02kwxEREYmISM5CEp/qNIGL\n/wO718Lrv/MG9xQRkejR7RIY/ojX4wLzfg5/xFtfRlOmTGHUqFEF1l144YW89NJL9OvXj/Hjx9Ol\nSxcyMjKK7FdY8+bNue+++xg8eDCnnnoqp512Gueff36ZY5P48edzOnJ2p6bc/eZSPl6u6VVFRKT6\nsZK6qka7Xr16uagdff3zR+H922DIvXD6jZGORkQkri1btoyOHTtGOowiZs+ezcSJE3nzzTcjHUq5\nhPp8zWyBc65XhEKqNFHdtgAOHT3Gpf/3Jau2H+CVcf3ofEJ6pEMSEREpt3DbFeqBUVn63Qgdh8MH\nd8C6zyMdjYiIiMSBWjWSeOrqXqSnJnPd5Pls2avp20VEpPpQAqOymMH5j0P9NvDKNbBfXT1FRKqb\nQYMGxXzvC4k+Teum8MyY3uw/nM11z37DwSPHIh2SiIhIlVACozKl1IVLn/cG83ztOshRA0NEpLLE\n8iOR0Uyfa3Tq2Lwuj11+Gss27+O/Xl5ITq6uk4iIxD8lMCpb085w3j9h7WfwyT2RjkZEJC6lpKSw\nc+dO/bFdwZxz7Ny5k5SUlEiHIiEM7uBNr/rhsm3c89bSSIcjIiJS6ZIiHUC10H00bPgS5v4TWvaB\nDudEOiIRkbjSsmVLMjMz2b59e6RDiTspKSm0bNmyQo9pZqcAfwRaE9QWcc79vEJPVA1c1a8Na3Yc\n5D/z1pLRqDZX9WsT6ZBEREQqjRIYVWXY32HTdzBjHPxmNjRoG+mIRETiRnJyMhkZGZEOQ8L3CjAJ\neBLIiXAsMe+2czuxYdchJsz6gVb1azG4Q5NIhyQiIlIp9AhJVUlOgUue8wb3nHYVZGdFOiIREZFI\nOeac+7dz7mvn3ILAK9JBxarEBOPhy3rQsXldbnzpW5Zu2hfpkERERCqFEhhVqX5ruOAJ2LIY3v5j\npKMRERGJlDfM7AYza25mDQKvSAcVy2rXTOLpq3uTlpLMdc9+w9Z9ml5VRETijxIYVe2UoTDgD7Dw\neVj4QqSjERERiYSr8cbA+BxY4L/mRzSiONAsPYWnx/Rib5Y3veqho5r9TERE4osSGJEw+M+QcSa8\n9T+weVGkoxEREakyZpYAXOmcyyj00uBQFaDzCek8OroHSzft479e/k7Tq4qISFxRAiMSEhLhwqch\ntQFM+xVk7Yl0RCIiIlXCOZcLTCxLWTN7xsy2mdmSoHUTzGyjmX3nv84J2narmf1kZj+a2dAKCD8m\nnNWxKbef14kPlm7lvreXRTocERGRCqMERqTUaQyXPAt7M2HmDeD0DYmIiFQb75vZhWZmpSw3GRgW\nYv0/nXPd/dfbAGbWCbgM6OyXedzMEssTdCy55owMru7XmqfmruGFL9dFOhwREZEKoQRGJLXqA0Pu\ngR/fgnkPRzoaERGRqvL/8KZSPWpm+8xsv5kdd+oM59wcYFeY5zgfeNk5d8Q5twb4CehT5ohj0O3n\ndeLnHZpw56wf+HTF9kiHIyIiUm5KYERa33HQeRR8dBesnRvpaERERCqdcy7NOZfgnEt2ztX139ct\nxyFvNLNF/iMm9f11LYANQftk+uuKMLOxZjbfzOZv3x4/f+gnJSbwyOgenNI0jd+9+C3Lt2h6VRER\niW1KYESaGYx4FBqcBK9cA/u3RDoiERGRSmWeK83sdv99KzMra++IfwMnAd2BzcA/AqcJsW/I5zWd\nc08453o553o1bty4jGFEpzo1k3hmTC9q10zkusnz2bZf06uKiEjsUgIjGtRMg0ufh6MHvCRGTnak\nIxIREalMjwP9gMv99weAf5XlQM65rc65HH9w0CfJf0wkE2gVtGtLYFPZwo1tzdNTefrq3uw6eJRf\nPzufrKM5kQ5JRESkTJTAiBZNOsLwR2D9597jJCIiIvGrr3Pud8BhAOfcbqBGWQ5kZs2D3o4CAjOU\nzAIuM7OaZpYBtAO+LnvIsa1Li3QeGd2DRRv3cvPU78jV9KoiIhKDlMCIJt0uht7Xw+ePwrI3Ih2N\niIhIZcn2ZwRxAGbWGMg9XiEzmwJ8AbQ3s0wzuw74XzNbbGaLgMHAzQDOuR+AacBS4F3gd865at31\n4OxOTbnt3E68+8MW/v7u8kiHIyIiUmpJlXVgM3sGOA/Y5pzr4q9rAEwF2gBrgUucc7v9adQeBs4B\nDgFjnHPfVlZsUW3o32DTQm9q1SadoOFJkY5IRESkoj0CzACamNm9wEXA7ccr5JwbHWL10yXsfy9w\nb1mDjEfXntGGtTsO8n9zVtO6YW0u73tipEMSEREJW2X2wJhM0bnaxwMfOefaAR/57wF+ide1sx0w\nFm9AruopqSZcPBkSEmHaVXD0UKQjEhERqVDOuReBPwH34Q28OdI5Ny2yUVUPZsadwzsxqH1jbn99\nCXM0vaqIiMSQSktgFDNX+/nAs/7ys8DIoPXPOc+XQL1Cz7RWL/VOhAuegq0/wFv/A07PqYqISPww\ns+edc8udc/9yzj3mnFtmZs9HOq7qIikxgUdH96Bdkzr87sVv+XHL/kiHJCIiEpaqHgOjqXNuM4D/\ns4m/vtrP1V5Eu1/AmbfA9y/Bt88ef38REZHY0Tn4jT8eRs8IxVItpaUk8/SY3qTUSOTayd+wff+R\nSIckIiLy/9m77zipyuuP45+zu8Du0pbekWqXumA3NmKJYidWEAvWqDHR2GKM+RmNxhqNUYOCxkQR\nGxpLsFeURRFsCGKhCUsXWMrunt8fc1dml9mdbbN3dub7fr3ua+69c8uZhws8c+be58SVLIN4qlZ7\nLD+7HPoeCC9cDotnhh2NiIhInZjZlWb2IzDAzNYG04/AMiJVQ6QBdcvLYfyYfFas38RZD6u8qoiI\nJL+GTmAsLXs0JHhdFqxXrfZYMjIjj5I07wCTToOiVWFHJCIiUmvufqO7twRucfdWwdTS3du5+xVx\nDyD1bkD3PO48cTCzFq7m0kkqryoiIsmtoRMYU4AxwfwY4Nmo9aMtYg9gTdmjJmmveTsYNRHWLoGn\nz4XSuFXmREREkt3wiivM7NUwAhE4ZJfOXHXYTrz46Q/c/PKcsMMRERGpVMISGJXUar8JGGFmc4ER\nwTLAC8B8YB7wAHB+ouJqlLrnw6E3wlcvwTu3hR2NiIhIrZhZtpm1A9qbWRszaxtMvYCu4UaX3s7a\ntzcn796Tf7z5NY99+H3Y4YiIiMSUlagDV1KrHeCgGNs6cEGiYkkJw86C76fB6zdEEhp99g87IhER\nkZo6B7iESLJiBlvHwFoL3BNWUBIpr/rHkbuwYOUGrnnmU3q0zWXvfu3DDktERKScZBnEU+IxgyPv\nhHb9YfKZsFZDhIiISOPi7ne6e2/gt+7ex917B9NAd7877PjSXZPMDO45ZQh9O7Tg3H/NYO5SlVcV\nEZHkogRGY9KsBfzyEdhSBE+MhZItYUckIiJSY+7+NzPb1cxGmdnosinsuARaZTdh/On5NMvKZOyE\n6Sxfp/KqIiKSPJTAaGw67ABH/Q0WTIOpfwg7GhERkRozsz8AfwumA4CbgZGhBiU/6d4ml/Fj8lm+\nbhNnP1zAxi0qryoiIslBCYzGaNfjYPdzYdo98NnTYUcjIiJSU8cTGRPrB3cfCwwEmoUbkkQb2COP\nO345iJkLVvObJz5ReVUREUkKSmA0ViP+BN2Hw7MXwvK5YUcjIiJSE0XuXgoUm7e4AM4AACAASURB\nVFkrYBnQJ+SYpIJDd+3CFYfuyH9nLeHWqSqvKiIi4VMCo7HKagonTICsZvD4abB5fdgRiYiIVFeB\nmeURKZ0+A/gI+DDckCSWcfv14aThPbjn9a+ZVLAg7HBERCTNKYHRmLXuBsf9Ewq/hOd/Da7bO0VE\nJPm5+/nuvtrd/wGMAMYEj5JIkjEzrj9qV/bt356rnprNe/OWhx2SiIikMSUwGru+B8IBV8Gsx6Fg\nfNjRiIiIVIuZHWtmtwG/AvqGHY9Urqy8au/2zTn3XzOYt2xd2CGJiEiaUgIjFez7W+g3Al66EhbN\nCDsaERGRKpnZ34FzgdnAp8A5ZnZPNfZ70MyWmdmnUetuMbMvzWyWmT0dPJqCmfUysyIzmxlM/0jU\n50kHrbKb8ODpw2ialcHYCR+yQuVVRUQkBEpgpIKMDDj2fmjRGSaNgQ0rw45IRESkKj8DDnH3h9z9\nIeBwYP9q7DcBOLTCuqnAru4+APgKuDLqva/dfVAwnVv3sNNbj7a5PDA6n2VrNzHukRkqryoiIg1O\nCYxUkdsWRk2EdUvhqXFQWhp2RCIiIpWZA/SMWu4BzIq3k7u/BayssO5/7l4cLE4DutdXkLKtwT3b\ncPsvBzHju1VcNnmWyquKiEiDUgIjlXQbAofeBPOmwtt/DTsaERGRyrQDvjCzN8zsDeBzoIOZTTGz\nKXU47hnAi1HLvc3sYzN708z2rcNxJcrhu3Xh8kN34LlPFnP7K1+FHY6IiKSRrLADkHqWfwYs+ABe\n/zN0Gwr9Dgo7IhERkYqure8DmtnVQDHwaLBqCdDT3VeY2VDgGTPbxd3Xxth3HDAOoGfPnhXflhjO\n+1lfvlu+gb+9No/t2jXn+KG68UVERBJPCYxUYwZH3A4/zIYnz4Jz34bW6lSIiEjycPc36/N4ZjYG\nOAI4yD1SU9zdNwGbgvkZZvY1sD1QECOe+4H7AfLz8/VMRDWYGf93zK4sWLWBK5+aRbe8HPbs2y7s\nsEREJMXpEZJU1LQ5jHoESrZEBvUs3hx2RCIiIglhZocCvwNGuvuGqPUdzCwzmO8D9AfmhxNlamqS\nmcG9pw5lu3aR8qpfF6q8qoiIJJYSGKmqfT846m5YVAD/uybsaEREROrMzP4DvA/sYGYLzexM4G6g\nJTC1QrnU/YBZZvYJMBk4191Vpquetc5pwkOnDyMrwzhjwnRWrtePJiIikjhKYKSyXY6GPS6AD++D\n2ZPDjkZERAQAM7u4OusqcveT3L2Luzdx9+7uPt7d+7l7j4rlUt39SXffxd0HuvsQd38uEZ9FIuVV\n7x+dz5I1GznnkQI2Fau8qoiIJIYSGKluxB+hxx4w5SJY9mXY0YiIiACMibHu9IYOQurP0O3acNuo\ngUz/dhWXT55FMBSJiIhIvVICI9VlNoETHoKmuTBpNGzS86kiIhIOMzvJzJ4jUt50StT0OrAi7Pik\nbo4Y0JXLDtmBZ2cu5o5X5oYdjoiIpCBVIUkHrbrC8Q/Cw0fBcxfBceMj1UpEREQa1ntEypu2B26N\nWv8jMCuUiKRenb9/X75Zvp47X51Lr/a5HDNYldBERKT+hJLAMLNfA2cBDswGxgJdgMeAtsBHwGnu\nrpGg6kvv/eDAa+DV6yOPlOw+LuyIREQkzbj7d8B3wJ5hxyKJYWb8+ZjdWLSqiN9Nnk23vFyG924b\ndlgiIpIiqvUIiZl1NLNjzOwCMzvDzIabWa0ePzGzbsBFQL677wpkAicCfwFud/f+wCrgzNocX6qw\n969h+0Ph5atgwfSwoxERkTRlZsea2VwzW2Nma83sRzNbG3ZcUj+aZmXwj1OH0r1tDuMeKeCb5evD\nDklERFJElUkIMzvAzF4G/gscRuQuiZ2Ba4DZZvZHM2tVi/NmATlmlgXkErmd9EAiZc4AJgJH1+K4\nUpWMDDjmH5FHSp4YA+uXhx2RiIikp5uBke7e2t1buXtLd69Nf0KSVOvcSHnVDDPGPvQhq1ReVURE\n6kG8uygOB85292HuPs7dr3H337r7SGAg8DEwoiYndPdFwF+B74kkLtYAM4DV7l4cbLYQ6BZrfzMb\nZ2YFZlZQWFhYk1MLQE4b+OUjkeTFk2dBqUqdiYhIg1vq7l+EHYQk1nbtmnP/aUNZvHoj5zwyQ+VV\nRUSkzqpMYLj7Ze7+fSXvFbv7M+7+ZE1OaGZtgKOA3kBXoDmRuzu2OUUl573f3fPdPb9Dhw41ObWU\n6TIQDr8F5r8Ob/4l7GhERCT9FJjZ40FVkmPLprCDkvqX36stt5wwgA+/XckVT85WeVUREamT6o6B\n8Wczy4tabmNm/1fLcx4MfOPuhe6+BXgK2AvICx4pAegOLK7l8aU6hoyGQafAmzfD3FfCjkZERNJL\nK2AD8HPgyGA6ItSIJGGOGtSNS0dsz9MfL+KuV+eFHY6IiDRi1a1Ccpi7X1W24O6rzOxwImNh1NT3\nwB5mlgsUAQcBBcDrwPFEKpGMAZ6txbGluszg8L/Ckk/gqbPgnLcgr2fYUYmISBpw97FhxyAN61cH\n9uPbFeu5/ZWv6NU+l6MGxXxSWEREpErVrSSSaWbNyhbMLAdoVsX2lXL3D4gM1vkRkRKqGcD9wO+A\nS81sHtAOGF+b40sNNM2FUQ9HxsGYNBqKN4UdkYiIpAEz297MXjWzT4PlAWZWmx9FpJEwM248djeG\n927LZU/MYvq3K8MOSUREGqHqJjD+BbxqZmea2RnAVCKVQmrF3f/g7ju6+67ufpq7b3L3+e4+3N37\nufsJ7q5v0w2hXV84+l5Y/DG8dGXY0YiISHp4ALgS2ALg7rOIlFSXFNYsK5P7TxtK9zY5jHu4gG9V\nXlVERGqoWgkMd78Z+D9gJ2AX4E/BOkkFOx0Be10EBeNh1qSwoxERkdSX6+4fVlhXHHNLSSl5uU15\n8PRhAJwxYTqrN6i8qoiIVF9178AA+AJ4yd1/A7xtZi0TFJOE4aA/wHZ7w3MXwzJVthMRkYRabmZ9\nCSqOmdnxREqrSxro1b4594/OZ+GqIs791ww2F5eGHZKIiDQS1a1CcjaRcSvuC1Z1A55JVFASgsws\nOP5BaNoCHj8VNq4NOyIREUldFxDpU+xoZouAS4Dzwg1JGtKwXm25+fgBTJu/kiufUnlVERGpnure\ngXEBsDewFsDd5wIdExWUhKRlZzhhAqz8BqZcCOpMiIhIAgTjXh0MdAB2dPd93P3bkMOSBnb04G5c\ncnB/nvxoIfe8rvKqIiISX3XLqG5y981mBoCZZRHc9ikpptfecPAfYOq1MO1e2PP8sCMSEZEUY2bX\nVlgGwN2vDyUgCc3FB/XnuxUb+Ov/vmK7ds05cmDXsEMSEZEkVt07MN40s6uAHDMbATwBPJe4sCRU\ne10EOx4BU38P308LOxoREUk966OmEuAwoFe8nczsQTNbVlZ+NVjX1symmtnc4LVNsN7M7C4zm2dm\ns8xsSGI+itSFmXHTcbsxvFdbfvPEJ8z4TuVVRUSkctVNYFwBFAKzgXOAFwDVa09VZnDUPdC6Bzxx\nOqwrDDsiERFJIe5+a9R0A7A/kfG14pkAHFph3RXAq+7eH3g1WIZIUqR/MI0D7q2H0CUBmmVlct9p\nQ+naOpuzH57B9ys2hB2SiIgkqeqWUS119wfc/QQinYAPXKMtpbacPPjlI1C0Cp48A0pLwo5IRERS\nVy7QJ95G7v4WUPEn+qOAicH8RODoqPUPe8Q0IM/MutRTvFLP2jSPlFctdWfshA9Zs2FL2CGJiEgS\nqm4VkjfMrJWZtQVmAg+Z2W2JDU1C13k3+MVt8M1b8PoNYUcjIiIpwsxmB491zDKzz4A5wJ21PFwn\nd18CELyWDTLeDVgQtd1CKrnLw8zGmVmBmRUUFuquw7D06dCC+04dyvcrN6i8qoiIxFTdR0hau/ta\n4FjgIXcfChycuLAkaQw+BYaMhrdvhTkvhR2NiIikhiOAI4Pp50BXd7+7ns9hMdbFvHvU3e9393x3\nz+/QoUM9hyE1sXufdtx07ADen7+Cq59WeVURESmvugmMrOC2y1HA8wmMR5LRYbdA5wHw9DhY9W3Y\n0YiISOP3Y9RUBLQKBuNsG9ztWRNLyx4NCV6XBesXAj2itusOLK5b2NIQjhvanYsO7McTMxby9ze+\nDjscERFJItVNYFwPvAzMc/fpZtYHmJu4sCSpNMmGUQ9H5ieNhi0bw41HREQau4+IDA7+FZH+RCEw\nI5gKanisKcCYYH4M8GzU+tFBNZI9gDVlj5pI8vv1iO0ZObArt7w8h+dnKe8kIiIR1R3E8wl3H+Du\n5wfL8939uMSGJkmlbW845j5Y8gm89LuwoxERkcbtJeBId2/v7u2IPFLylLv3dvdKB/M0s/8A7wM7\nmNlCMzsTuAkYYWZzgRHBMkQqps0H5gEPAOcn7uNIfTMzbj5+APnbteHSSZ/w0ferwg5JRESSQJUJ\nDDO7pqpbOc3sQDM7ov7DkqS0w2Gwz6UwYwLM/HfY0YiISOM1zN1fKFtw9xeBn8Xbyd1Pcvcu7t7E\n3bu7+3h3X+HuB7l7/+B1ZbCtu/sF7t7X3Xdz95re2SEhy24SKa/apXU2Z08sYMFKlVcVEUl38e7A\nmA08Z2avmtktZna5mV1rZo+Y2Wwig299kPgwJWkccDX02hee/zX88GnY0YiISOO0PPiRpJeZbWdm\nVwMrwg5Kkk+7Fs148PRhFJc6YydMZ02RyquKiKSzKhMY7v6su+8NnAt8BmQCa4F/AcPd/dfurnpj\n6SQzC45/ELLzYNJpsHFN2BGJiEjjcxLQAXg6mDoE60S20bdDC/5x6lC+W7Ge8x+dwZYSlVcVEUlX\n1R0DY667T3D3G939Dnd/2d2LEh2cJKkWHeGECbDqO3j2AlCJMxERqQF3X+nuFwP7uvsQd7+k7NEP\nkVj27NuOG48dwLvzVvD7Zz5VeVURkTRV3SokIuVttyeMuB6+eA7evzvsaEREpBExs73M7HPg82B5\noJn9PeSwJMkdP7Q7Fx7Qj8emL+C+t+aHHY6IiIRACQypvT0vgJ1GwtQ/wLfvhh2NiIg0HrcDhxCM\ne+HunwD7hRqRNAqXjtieIwZ04aYXv+TF2aqKKyKSbpTAkNozg6PuiZRYnTwWflwadkQiItJIuPuC\nCqtKQglEGpWMDOOvJwxkSM88Lnl8JjMXrA47JBERaUDVSmCY2fZBJZJPg+UBZnZNbU9qZnlmNtnM\nvjSzL8xsTzNra2ZTzWxu8NqmtseXBpTdCkY9DBvXwuQzoKQ47IhERCT5LTCzvQA3s6Zm9lvgi7CD\nksYhu0kmD4zOp2OrZpw1sYCFq1ReVUQkXVT3DowHgCuBLQDuPgs4sQ7nvRN4yd13BAYS6bRcAbzq\n7v2BV4NlaQw67QJH3gHfvQOv/SnsaEREJPmdC1wAdAMWAoOCZZFqadeiGQ+dPoxNxSWcMWE6azeq\nvKqISDqobgIj190/rLCuVj+1m1krIs+5jgdw983uvho4CpgYbDYROLo2x5eQDDwRho6Fd++AL/8b\ndjQiIpKkzCwTOM3dT3H3Tu7e0d1PdfcVYccmjUu/ji2579ShzC9czwWPfqTyqiIiaaC6CYzlZtYX\ncAAzOx6o7chJfYBC4CEz+9jM/mlmzYFO7r4EIHjtGGtnMxtnZgVmVlBYWFjLECQhDr0Jug6Gp8+D\nlRodXEREtuXuJUR+tBCps736tefPx+zG23OX84cpn6m8qohIiqtuAuMC4D5gRzNbBFwCnFfLc2YB\nQ4B73X0wsJ4aPC7i7ve7e76753fo0KGWIUhCNMmGEyZGBvd8fDRsKQo7IhERSU7vmtndZravmQ0p\nm8IOShqnUcN6cN7+ffn3B9/zwNv6AUVEJJVlVWcjd58PHBzcKZHh7j/W4ZwLgYXu/kGwPJlIAmOp\nmXVx9yVm1gVYVodzSFjabAfHPgD/PgFe+G2kSomIiEh5ewWv10etc+DAEGKRFHDZz3fg+xUbuPHF\nL+nZtjmH7to57JBERCQBqpXAMLM8YDTQC8gyMwDc/aKantDdfzCzBWa2g7vPAQ4CPg+mMcBNweuz\nNT22JIntfw77XQZv3QI99oAhp4UdkYiIJAEzu9jd7wR+7+7vhB2PpI6MDOPWUQNZtLqISx7/mMdb\n78nAHnlhhyUiIvWsuo+QvEAkeTEbmBE11davgEfNbBaRkcf/TCRxMcLM5gIjgmVprPa/EvrsH7kL\nY8knYUcjIiLJYWzweleoUUhKKiuv2r5FM856uIBFq/Uoq4hIqrHqDHZkZh+5e9I9m5qfn+8FBQVh\nhyGVWb8c7tsPMpvAuDchR7+EiIikCjOb4e75NdznP8CeQAfg6+i3AHf3AfUYYq2ob9H4zV36I8fe\n+x5dW+cw+bw9aZndJOyQREQkjur2K6p7B8YjZna2mXUxs7ZlUx1jlFTXvD2cMAHWLIRnzoNSlTcT\nEUln7n4SsAcwDzgyajoieBWps/6dWnLvKUP5unAdF/77Y4pVXlVEJGVUN4GxGbgFeJ+tj4/o5wmJ\nr8dw+PkNMOcFeO/OsKMREZGQufsP7j7Q3b+rONX2mGa2g5nNjJrWmtklZnadmS2KWn94fX4WSV77\n9G/P/x29K29+Vch1z6m8qohIqqjWIJ7ApUA/d1+eyGAkRe1+DiyYBq9eD93yofe+YUckIiIpJBgU\nfBCAmWUCi4CniYy5cbu7/zXE8CQkJw7vyTcr1nPfm/Pp1a45Z+3bJ+yQRESkjqp7B8ZnwIZEBiIp\nzAxG/g3a9YPJZ8DaJWFHJCIiqesg4Ou63NEhqeN3h+zIYbt25oYXvuB/n/0QdjgiIlJH1U1glAAz\nzew+M7urbEpkYJJimrWEUY/A5vUweSyUbAk7IhERaWBm9kjwenECT3Mi8J+o5QvNbJaZPWhmbRJ4\nXklCGRnGbaMGMaB7Hhc/NpPZC9eEHZKIiNRBdRMYzwA3AO9RP2VUJR113BFG3gXfvw+vXBd2NCIi\n0vCGmtl2wBlm1iZ6YPD6GBzczJoCI4EnglX3An2JPF6yBLi1kv3GmVmBmRUUFhbWNQxJMjlNM3lg\n9FDaNm/KmROns1jlVUVEGq1qjYHh7hMTHYikid2Oh++nwft3Q4/dYeeRYUckIiIN5x/AS0AfIj+E\nWNR7Hqyvi8OAj9x9KUDZK4CZPQA8H2snd78fuB8iZVTrGIMkoY4ts3nw9GEcf+97nDFhOpPP24sW\nzao7FJyIiCSLKu/AMLNJwevs4PbLclPDhCgp55AboNtQeOZ8WD4v7GhERKSBuPtd7r4T8KC793H3\n3lFTfYyweBJRj4+YWZeo944BPq2Hc0gjtUPnltxzyhDmLlvHr/79kcqriog0QvEeISl7RrWsPnvF\nSaTmsprBCRMhswlMGg2bNT6siEg6cffzzGygmV0YTAPqekwzywVGAE9Frb657EcY4ADg13U9jzRu\n+23fgeuP2oXX5xTyp+c/DzscERGpoSoTGO5eVi7i/Bi12s9PfHiSsvJ6wHEPwLLP4b+Xguqzi4ik\nDTO7CHgU6BhMj5rZr+pyTHff4O7t3H1N1LrT3H03dx/g7iOj+jWSxk7ZfTvO3rc3E9//jofe/Sbs\ncEREpAaqO4jniBjrDqvPQCQN9TsY9r8CPvkPzJgQdjQiItJwzgJ2d/dr3f1aYA/g7JBjkjRyxWE7\n8fOdO3H985/zyudL4+8gIiJJId4YGOeZ2WxghwrjX3wDaAwMqbv9Loe+B8GLl8Pij8OORkREGoYR\nKdFepoTyA3qKJFRmhnHHiYPYtWtrLnrsYz5dpPKqIiKNQbw7MP5NZKyLKZQf+2Kou5+a4NgkHWRk\nwLEPQPOOkfEwNqwMOyIREUm8h4APzOw6M7sOmAaMDzckSTe5TbMYPyafvJwmnDlxOkvWqLyqiEiy\nizcGxhp3/9bdT6owBoa+ZUr9ad4ORj0Ma5fA0+dAqUYFFxFJZe5+GzAWWAmsAsa6+x3hRiXpqGOr\nbB4cO4z1m0o4c0IB6zcVhx2SiIhUobpjYIgkVvehcOiNMPd/8M6tYUcjIiIJ5u4fBWVV73R3PUMo\nodmxcyvuPnkwc5b+yEX/+ZiSUg0sLiKSrJTAkOQx7CzY7QR4/c8w/42woxEREZE0sf8OHblu5C68\n+uUylVcVEUliSmBI8jCDI++E9tvD5DNh7eKwIxIREZE0cdoe23HmPr2Z8N63THzv27DDERGRGJTA\nkOTStDmMegSKN8KkMVC8OeyIRESkHplZppm9EnYcIrFcdfhOHLxTJ/743Ge89qXKq4qIJBslMCT5\ndNgeRv4NFn4IU68NOxoREalH7l4CbDCz1mHHIlJRZoZx10mD2LlrK37174/5fPHasEMSEZEoSmBI\nctr1WNj9PPjgXvj0qbCjERGR+rURmG1m483srrIp7KBEoKy86jBaBeVVl67dGHZIIiISCC2BEdxC\n+rGZPR8s9zazD8xsrpk9bmZNw4pNksSI66H7cJjyKyj8KuxoRESk/vwX+D3wFjAjahJJCp1aZTN+\nzDDWFm3hzInT2bBZ5VVFRJJBmHdgXAx8EbX8F+B2d+9PpCb8maFEJckjqymcMAGymsGk0bB5fdgR\niYhIPXD3icAkYJq7Tyybwo5LJNrOXVtx98lD+HzxWi76z0yVVxURSQKhJDDMrDvwC+CfwbIBBwKT\ng00mAkeHEZskmdbd4LjxUPglPHcxuDoPIiKNnZkdCcwEXgqWB5nZlHCjEtnWATt25A9H7sIrXyzl\nzy98EX8HERFJqLDuwLgDuBwoDZbbAavdvez+vIVAt1g7mtk4Mysws4LCwsLERyrh63sAHHg1zH4C\npv8z7GhERKTurgOGA6sB3H0m0DvMgEQqM2avXpy+Vy/Gv/MNj0z7LuxwRETSWoMnMMzsCGCZu0c/\n62oxNo35U7u73+/u+e6e36FDh4TEKElon99A/0PgpSthoR6TFhFp5IrdfU2FdbrFTpLW74/YmYN2\n7Mh1Uz7jjTnLwg5HRCRthXEHxt7ASDP7FniMyKMjdwB5ZpYVbNMdWBxCbJKsMjLgmH9Aqy7wxBjY\nsDLsiEREpPY+NbOTgUwz629mfwPeCzsokcpEyqsOZodOLbnw3x/zxRKVVxURCUODJzDc/Up37+7u\nvYATgdfc/RTgdeD4YLMxwLMNHZskudy2cMJEWLcUnjobSkvj7yMiIsnoV8AuwCbgP8Ba4JJQIxKJ\no3mzLB48fRgtmmVx5oTpLFN5VRGRBhdmFZKKfgdcambziIyJMT7keCQZdRsCh/0F5r0Cb90SdjQi\nIlIL7r7B3a8GDgIOcPer3b1O3wbN7Fszm21mM82sIFjX1symBiXap5pZm/qIX9JX59bZjD89n9VF\nWzhzYoHKq4qINLBQExju/oa7HxHMz3f34e7ez91PcPdNYcYmSWzoWBh4ErxxYySRISIijYqZDTOz\n2cAsYLaZfWJmQ+vh0Ae4+yB3zw+WrwBeDUq0vxosi9TJLl1b87eTBvPZ4jVc8pjKq4qINKRkugND\npHrM4Be3Qced4cmzYfWCsCMSEZGaGQ+c7+69gkdKLwAeSsB5jiJSmh1Uol3q0UE7deL3R+zM/z5f\nyk0vqryqiEhDUQJDGqemuTDqYSjZAk+cDsWbw45IRESq70d3f7tswd3fAX6s4zEd+J+ZzTCzccG6\nTu6+JDjHEqBjrB1Vol1qY+zevRmz53Y88PY3PPqByquKiDQEJTCk8WrfD47+OywqgP9dHXY0IiIS\nh5kNMbMhwIdmdp+Z7W9mPzOzvwNv1PHwe7v7EOAw4AIz26+6O6pEu9TW74/YmQN26MC1z37Gm18p\n+SUikmhKYEjjtvNI2PNC+PB+mD057GhERKRqtwbTIGB74A/AdcBOwJ51ObC7Lw5elwFPA8OBpWbW\nBSB4XVaXc4hUlJWZwd9OHsL2nVpywaMfMeeHut5IJCIiVVECQxq/g6+DnnvClF/Bsi/DjkZERCrh\n7gdUMR1Y2+OaWXMza1k2D/wc+BSYQqQ0O6hEuyRIi2ZZPHh6PrlNMzljwnSW/ajyqiIiiaIEhjR+\nmU3g+IegaQuYdBps0q8fIiLJzMzyzOwiM7vNzO4qm+pwyE7AO2b2CfAh8F93fwm4CRhhZnOBEcGy\nSL3r0jqH8WOGsXL9Zs6eWEDR5pKwQxIRSUlKYEhqaNUFjn8QVsyDKReBq6SZiEgSewHoBcwGZkRN\ntRKUYh8YTLu4+w3B+hXufpC79w9eV9ZH8CKx7Na9NXeeOIhZi9Zw6aSZlKq8qohIvVMCQ1JH733h\nwN/DZ09FxsQQEZFkle3ul7r7Q+4+sWwKOyiRuvr5Lp25+vCdePHTH/jLy3qsVUSkvimBIall70tg\n+8Pg5athwYdhRyMiIrE9YmZnm1kXM2tbNoUdlEh9OHOf3py6R0/ue3M+//nw+7DDERFJKUpgSGrJ\nyIBj7oXW3eCJ02H98rAjEhGRbW0GbgHeZ+vjIwWhRiRST8yM647chZ9t34FrnvmUd+aqLyIiUl+U\nwJDUk9MGRj0cSV48eSaUaiAtEZEkcynQz917uXvvYOoTdlAi9SUrM4O7Tx5M/44tOO/RGcxdqgHG\nRUTqgxIYkpq6DIRf/BXmvwFvaNB5EZEk8xmwIewgRBKpZXYTxp8+jOwmmYydMJ3CHzeFHZKISKOn\nBIakriGjYdCp8NbNMHdq2NGIiMhWJcBMM7uvnsqoiiSlbnk5jB+Tz/J1mzj74QI2btFdoSIidaEE\nhqS2X/wVOu0GT50Nq74LOxoREYl4BrgBeI96KKMqkswGdM/jzhMH88nC1SqvKiJSR0pgSGprkgOj\nJkJpKTwxBop1+6aISNiiS6eqjKqkg0N26cxVh+3EC7N/4Jb/zQk7HBGRRksJDEl97fpGKpMs/hhe\nuiLsaERE0p6ZfWNm8ytOYcclkkhn7dubk3fvyb1vfM2k6QvCDkdEpFHKCjsAkQax4y9g74vh3Tuh\nxx4w8JdhRyQiks7yo+azgROAtiHFItIgzIw/jtyFBSs3cNXTs+nWJoe9/oCsigAAIABJREFU+7UP\nOywRkUZFd2BI+jjwWthuH3juYlj6edjRiIikLXdfETUtcvc7gAPDjksk0ZpkZnDPKUPo06E55/5r\nBvOWqbyqiEhNKIEh6SMzC45/ELJbwaTTYOPasCMSEUlLZjYkaso3s3OBlmHHJdIQWmU34cHTh9Es\nK1Jedfk6jc8lIlJdSmBIemnZCY5/CFZ+A89eAK6RwEVEQnBr1HQjMBQYFWpEIg2oe5tc/jkmn8If\nNzFO5VVFRKqtwRMYZtbDzF43sy/M7DMzuzhY39bMpprZ3OC1TUPHJmmi195w8HXwxRSY9vewoxER\nSTvufkDUNMLdz3Z3lWaQtDKoRx63jxrER9+v5rdPfKLyqiIi1RDGHRjFwG/cfSdgD+ACM9sZuAJ4\n1d37A68GyyKJsdevYMcjYOq18P20sKMREUkrZtbMzE42s6vM7NqyKey4RBraYbt14YrDduT5WUu4\nbepXYYcjIpL0GjyB4e5L3P2jYP5H4AugG3AUUFYDfiJwdEPHJmnEDI7+O+T1hCdOh3WFYUckIpJO\nniXy/34xsD5qEkk75+zXhxOH9eDu1+fxRIHKq4qIVCXUMTDMrBcwGPgA6OTuSyCS5AA6VrLPODMr\nMLOCwkJ96ZQ6yG4Nox6GolUweSyUFIcdkYhIuuju7r9095vd/dayqbYHq+Lx1OvMbJGZzQymw+vv\nI4jUDzPjT0fvyj792nPV07N5/+sVYYckIpK0QktgmFkL4EngEnevdjkId7/f3fPdPb9Dhw6JC1DS\nQ+fd4Ijb4du34fUbwo5GRCRdvGdmu9Xj8Sp7PBXgdncfFEwv1OM5RepNWXnVXu0i5VW/LlwXdkgi\nIkkplASGmTUhkrx41N2fClYvNbMuwftdgGVhxCZpaNDJMGQMvHMbzHkx7GhERNLBPsAMM5tjZrPM\nbLaZzartwap4PFWk0WidEymv2iTTOGPCdFau3xx2SCIiSSeMKiQGjAe+cPfbot6aAowJ5scQeT5W\npGEcdjN0GQhPnxMpsSoiIol0GNAf+DlwJHBE8FpnFR5PBbgwSJI8qApnkux6tM3lgdH5/LBmo8qr\niojEEMYdGHsDpwEHVngm9SZghJnNBUYEyyINo0l2ZDwMgCfGwJaN4cYjIpLC3P27WFNdjxvj8dR7\ngb7AIGAJEHOcDY2vJclkcM823P7LQRR8t4rLJ8/CXeVVRUTKhFGF5B13N3cfEP1MqruvcPeD3L1/\n8LqyoWOTNNemFxxzPyz5BF68POxoRESkBmI9nuruS929xN1LgQeA4bH21fhakmwO360Llx+6A1M+\nWcztKq8qIvKTrLADEEkqOxwK+/4G3r4VeuwOg08JOyIREYmjssdTzaxLWYUz4Bjg0zDiE6mN837W\nl++Wb+Cu1+bxyLTvWL1hC13zcrjskB04erCGeBGR9BRqGVWRpHTA1dB7P/jvpfDD7LCjERGR+Cp7\nPPXmqAFCDwB+HWqUIjVgZgzv3YYMg1UbtuDAotVFXPnUbJ75eFHY4YmIhEJ3YIhUlJEJxz0I9+0L\nk0bDuDcgu3XYUYmISCXc/R3AYrylsqnSqN02dS6lFYbAKNpSwu+enMW0+Svo3iaH7m1y6d4mhx5t\nc+nQohkZGbH+KoiIpAYlMERiadEBTpgAE34Bz5wPv/wXmDoEIiIi0nAWry6KuX5TcSmvfLGM5es2\nlVvfNDODbm1yyiU2yuZ7tM2hQ4tmmPozItKIKYEhUpmee8CI6+Hlq+C9u2Dvi8OOSERERNJI17wc\nFsVIYnTLy+HdKw6kaHMJi1ZvYMGqIhauKmLhqg0sXBl5/d/iH1ixfnO5/ZpllSU4cukRI8nRvkVT\nJThEJKkpgSFSlT3OhwUfwCt/hG750GvvsCMSERGRNHHZITtw5VOzKdpS8tO6nCaZXHbIDpH5ppn0\n69iSfh1bxtx/w+birYmNIMmxYGVkfvbC1azasKXc9tlNMsolNXq0yS233La5EhwiEi4lMESqYgYj\n74aln8HksXDOW9Cyc9hRiYiISBooqzZyy8tzWLy6qMZVSHKbZrF9p5Zs3yl2gmPdpuJyd20sXFXE\nguD14+9Xs6ZoS4XjZZYfd+On5EbkNS+3iRIcIpJQSmCIxJPdCkY9Av88CCafAaOnQKb+6oiIiEji\nHT24W8LKprZolsWOnVuxY+dWMd9fu3ELi6Lu2ohOcEz/ZiU/bire5njdtxmDY+sgo61zmiTkc4hI\n+tC3MJHq6LQzHHEHPD0OXrs+MjaGiIiISAprld2EVl2asFOX2AmONUVbWLhqAwtWln9MZeGqDbz/\n9QrWby4pt33L7KwYd29sHWS0ZbYSHCJSNSUwRKpr4C9hwTR4907oPhx2OiLsiERERERC0zqnCa1z\nWrNL123Lzbt7kOCIvoMj8vrdivW8O285GyokOFrnNCmf1Ci7g6Nt5LVFM311EUl3+ldApCYOvQkW\nfwzPnAcdd4J2fcOOSERERCTpmBl5uU3Jy23Krt1iJzhWbYh1B8cGvi5cz5tfFbJxS2m5fdrkNik/\nyGjb8o+p5DbVVxuRVKe/5SI1kdUMRj0M9+0Hk8bAWVOhSU7YUYmIiIg0KmZG2+ZNadu8KQO6523z\nvruzYv3mmHdwfLX0R177chmbissnONo1b1p+/I22ZY+r5NAtL5ecppkN9fFEJEGUwBCpqbyecOwD\n8OgJ8N/fwtH3hB2RiIiISEoxM9q3aEb7Fs0Y1GPbBEdpqbN8/aZtysMuXLWBL5asZernS9lcUj7B\n0b5Fs23G3ShLdnTLyyG7iRIcIslOCQyR2ug/Ava7DN66GXruDkNGhx2RiIiISNrIyDA6tsymY8ts\nhvRss837paVO4bpNW8vDRlVS+XTRGl7+7Ae2lHi5fTq2bLZN5ZSy5a552TTLUoJDJGxKYIjU1v5X\nwMLpkbswOg+AroPCjkhEREREiCQ4OrXKplOrbIZut+37JaXOsh83/nTXRvQ4HDMXrOaF2UsoLt2a\n4DCDTi2zK72Do0vrHJpmZTTgJxRJT0pgiNRWRiYc989gPIzRcM6bkLPtLwAiIiIiklwyM4wurSOJ\nh2G92m7zfnFJKUt/3MTC4M6NBVGDjBZ8t4rnZi2hJCrBkWHQuVV2uUFGt47BkUvn1tk0yVSCQ6Su\nlMAQqYvm7eGEifDQYfD0eXDivyFD/zmJiIiINGZZmRl0y4uMjbF7jPeLS0pZsibqDo6oQUY/+GYl\nz8wsIiq/QWaGBQmOWI+o5NC5VTZZSnCIxKUEhkhd9RgGh9wAL14O794B+14adkQiIiIikkBZmRn0\naJtLj7a5QLtt3t9cXMoPazaWKw9bluR47+vl/LB2Ix6V4MjKMLrkZdM9LzfmIyqdWmWTmWEN9wFF\nkpQSGCL1Yfg4WPABvPYn2LwOZk2CNQuhdXc46FoYMCrsCEVERESkgTTNyqBnu1x6tsuN+f7m4lIW\nry4qVx627DGVt+YWsnTtpnLbN8k0uuYFj6bkbTvIaMeWzcioIsHxzMeLuOXlOSxeXUTXvBwuO2QH\njh7crV4/s0hDUAJDpD6YwZF3wbfvwtu3bl2/ZgE8d1FkXkkMERERESGS4OjVvjm92jeP+f7GLSVR\nCY7yd3C8NmcZhT+WT3A0zcyg208DjOZEjcWRy2eLVnPji19StCVSVnbR6iKufGo2gJIY0ugkXQLD\nzA4F7gQygX+6+00NdW5lJqVOmrWIvX5LEbx8VeRuDMuMDP5pFjWfUWE+o/z6cstl85kV5i0ySUqY\nPuU+enx0Cx29kGXWgQVDLmPYyHPCDksaGV1HEWH2K0REaiu7SSZ9OrSgT4fY/cuNW0rK3b0RfQfH\n1M+Xsnzd5iqPX7SlhMsnz+Kx6d+TlZFBRoaRlWFkZhiZZmRmRl6zMqz8exW2ySqbz8ggM4Nyr+X2\ntch+WZlGRoXjVjz31m0yyp+zku3LYs2MWm/qF9e7ZPmunFQJDDPLBO4BRgALgelmNsXdP0/0uZ/5\neBFXPjWboi0lgDKTUju+bikx/7lcXxgZ6DORtkl4lM039PqyhExGjORMvARODRI7cddXFUPU+mp/\nzqqSSBn1lkCaPuU+dp1xDTm2GQw6U0jrGdcwHdLyy6fUjq6jiDD7FSIiiZTdJJN+HVvQr2PsBMeG\nzcUsChIbYydMj7nN5pJSSkuhqKSE4lKnpLSUklKCV6ek1CkudUrLXt2D7bZ9L9lkGOWTLRlGVmbG\nT8mTWAmRDCufYImVPNm6nEGmbZuwiZlwqSQRFOvckeNWkbDZ5jNZue3LnzsjZoKnqkeNKpNM35WT\nKoEBDAfmuft8ADN7DDgKSHhH45aX5/z0B1KmaEsJl06ayV9e+hIg5hfTmmb3Ktu80vWxvw5XsX2s\nbSs5RuxDVPpGZdvX9PixNq/p56xMfcRS2frqxHivt6ObLd9mm0Jvzd9aX0YGpRhOBqVkeAkZZfOU\nYpSS4aXBctk2wfqf1pUE67bulxG1n5VtQykZHnWM4P3M4uh9SzCcTI86/0/ndDIpJYMtke29tPz5\nKMXcK1lXUsVnCNb71vOXrWvsKv5JlVqkJfyn9Zm4WdDyW7cpDbYpCbYZWPwdTa38v0U5tpmBM67i\ny1kP1igmq2O71nX/+hD2Z6h7WqrubVibzzBwS+zrqMdHt0AaJTAIsV8hIhKm3KZZ9O/Ukv6dWtIt\nL4dFq4u22aZbXg6Tzt2zXs5XlsgoKXVK3CkpibwWl0aSJMVRSZGybYpLtk2KlFRIkERvH51giU6e\nVJVsqWqbktJSSrx8wqbieTcXlwbnLh9vzOO6U1xSSqlT7nMnYX5n27tjMmMnW8pe5xeu3yZRVbSl\nhFtenpP2CYxuwIKo5YVQvnKRmY0DxgH07Nmz3k68OMZfaoBSh337ty83SnCZyq7FWNtGtq/0jZqs\nxis5Qay1lcdS92NX9UZlnzVmO9bDMaoIpdLtK9ujZn/W5d/5y+JR3NTkn+Ta1tv2NnhT/rTlFNa2\niVWEK32Vazr3CgmUSpIiP21Xss38tkmY2PM/JXo8KoFS2fZsTf5UjGXb+aikTHRSKO68B8msyHzv\n4vkx26sJxWzJiv2cbJXtXMev4HXdvz7UPYZw28DrpQlrdpDeW2JfRx192wRriovbr4DE9S1ERJLB\nZYfsUO7Xc4CcJplcdsgO9XaOjAyjqaqkxOReIclRujXBU2XSJipxU1zJ9uWTJ1u3KZfMqbhNaWmQ\nXNp2m22STEGsXy1dF/OzVfYdOpGSLYER66ov9w3R3e8H7gfIz8+vt3xW1yoykzcfP7C+TiMpbu+b\n1nHFWrg8axJdbQWLvR03F49iRqsRvDt2eNjhSSPww3X96EzhNuuXWgd2u+K1ECKSxqiy62iZtadz\nCPGEKG6/AhLXtxARSQZlv5Anw/gF6ciCx0OyMsOOpPb2vum1mN+Vu+blNHgsyZbAWAj0iFruDixu\niBM3RGZSUl/kOtrMlM37/LQup0kmN+o6kmpaMOQyWpeNXRAo8qYsGHpZun3xlDrQdfST0PoVIiLJ\n5OjB3ZSwkFpLpu/KyZbAmA70N7PewCLgRODkhjixMpNSH3QdSV0NG3kO0yGoHrGcZdaeBUPTs3qE\n1J6uo5+E1q8QERFJFcn0HccqG/MgLGZ2OHAHkXJnD7r7DZVtm5+f7wUFBQ0Wm4iIiESY2Qx3zw87\njnhq0q8A9S1ERETCUN1+RbLdgYG7vwC8EHYcIiIi0vipXyEiIpI6MsIOQEREREREREQkHiUwRERE\nRERERCTpKYEhIiIiIiIiIkmvUScw2rdvH3YIIiIi6Wp52AGIiIhIekm6KiQ1YWYvAYnIYrRHHbN4\n1EbxqY2qpvaJT20Un9oovkS10XJ3PzQBxw2VmRUC3yXg0LpWq6b2iU9tFJ/aKD61UdXUPvElqo22\nc/cO8TZq1AmMRDGzgsZQGi5MaqP41EZVU/vEpzaKT20Un9ooOejPoWpqn/jURvGpjeJTG1VN7RNf\n2G3UqB8hEREREREREZH0oASGiIiIiIiIiCQ9JTBiuz/sABoBtVF8aqOqqX3iUxvFpzaKT22UHPTn\nUDW1T3xqo/jURvGpjaqm9okv1DbSGBgiIiIiIiIikvR0B4aIiIiIiIiIJL20S2CY2aFmNsfM5pnZ\nFTHev9TMPjezWWb2qpltF/XeGDObG0xjGjbyhlHH9ikxs5nBNKVhI2841Wijc81sdtAO75jZzlHv\nXRnsN8fMDmnYyBtObdvIzHqZWVHUdfSPho++YcRro6jtjjczN7P8qHUpfx3Vtn10DZV7/3QzK4xq\ni7Oi3kv5/88aivoV8alvEZ/6FvGpb1E19SviU98ivkbRt3D3tJmATOBroA/QFPgE2LnCNgcAucH8\necDjwXxbYH7w2iaYbxP2Z0qW9gmW14X9GZKkjVpFzY8EXgrmdw62bwb0Do6TGfZnSrI26gV8GvZn\nSIY2CrZrCbwFTAPy0+U6qmP76Braus3pwN0x9k35/8+S7M8hbfsVdW2jYFl9C1ffQn2LurdPsF1a\n9ivqoY1S/hqqbhslQ98i3e7AGA7Mc/f57r4ZeAw4KnoDd3/d3TcEi9OA7sH8IcBUd1/p7quAqcCh\nDRR3Q6lL+6SL6rTR2qjF5kDZQDNHAY+5+yZ3/waYFxwv1dSljdJF3DYK/Am4GdgYtS4drqO6tE+6\nqG4bxZIO/581FPUr4lPfIj71LeJT36Jq6lfEp75FfI2ib5FuCYxuwIKo5YXBusqcCbxYy30bo7q0\nD0C2mRWY2TQzOzoRASaBarWRmV1gZl8T+QfwoprsmwLq0kYAvc3sYzN708z2TWyooYnbRmY2GOjh\n7s/XdN8UUJf2AV1D0Y4LbsufbGY9arivxKd+RXzqW8SnvkV86ltUTf2K+NS3iK9R9C3SLYFhMdbF\nzM6a2alAPnBLTfdtxOrSPgA93T0fOBm4w8z61n+IoatWG7n7Pe7eF/gdcE1N9k0BdWmjJUSuo8HA\npcC/zaxVwiINT5VtZGYZwO3Ab2q6b4qoS/voGtrqOaCXuw8AXgEm1mBfqR71K+JT3yI+9S3iU9+i\naupXxKe+RXyNom+RbgmMhUCPqOXuwOKKG5nZwcDVwEh331STfRu5urQP7r44eJ0PvAEMTmSwIanp\ndfAYUPaLUTpcQ1CHNgpuX1wRzM8g8hze9gmKM0zx2qglsCvwhpl9C+wBTAkGk0qH66jW7aNraCt3\nXxH1b/QDwNDq7ivVpn5FfOpbxKe+RXzqW1RN/Yr41LeIr3H0LRIxsEayTkAWkQFFerN1YJJdKmwz\nmMhF2b/C+rbAN0QGJWkTzLcN+zMlUfu0AZoF8+2BucQYGKexT9Vso/5R80cCBcH8LpQfJGk+qTlI\nUl3aqENZmxAZQGhRqv09q24bVdj+DbYOJJXy11Ed20fX0NZtukTNHwNMC+ZT/v+zJPtzSNt+RT20\nkfoWW7dR30J9izq1T4Xto//f1DUUv41S/hqqbhuRBH2LLNKIuxeb2YXAy0RGWX3Q3T8zs+uJ/CM3\nhchtiy2AJ8wM4Ht3H+nuK83sT8D04HDXu/vKED5GwtSlfYCdgPvMrJTInT03ufvnoXyQBKpmG10Y\n/JK0BVgFjAn2/czMJgGfA8XABe5eEsoHSaC6tBGwH3C9mRUDJcC5qfb3DKrdRpXtm/LXUV3aB11D\n0W10kZmNJHKdrCQycjjp8P9ZQ1G/Ij71LeJT3yI+9S2qpn5FfOpbxNdY+hYWZExERERERERERJJW\nuo2BISIiIiIiIiKNkBIYIiIiIiIiIpL0lMAQERERERERkaSnBIaIiIiIiIiIJD0lMEREREREREQk\n6SmBISI1YmYjzeyKkM7dxcyeD+b3NrNZZjbdzPoF6/LM7GUL6vAF614xszZhxCsiIiJVU79CRGpC\nZVRFpNEws1uAd9z9WTN7Cvgd0As41N1/Y2a3AlPc/c2ofcYA3d39hlCCFhERkaSkfoVI46M7MEQE\nADPrZWZfmtk/zexTM3vUzA42s3fNbK6ZDQ+2O93M7g7mJ5jZXWb2npnNN7Pjg/VdzOwtM5sZHGvf\nYP26qPMdb2YToo7zDzN728y+MrMjKgnzOOClYH4LkAPkAlvMrC/QLbqTEZgCnFQfbSQiIiLVo36F\niCRCVtgBiEhS6QecAIwDpgMnA/sAI4GrgKNj7NMl2GZHIv+pTw72e9ndbzCzTCKdgXh6AT8D+gKv\nm1k/d99Y9qaZ9QZWufumYNWNwP1AEXAa8Ffg9xUP6u6rzKyZmbVz9xXViENERETqh/oVIlKvdAeG\niET7xt1nu3sp8BnwqkeeM5tNpCMQyzPuXurunwOdgnXTgbFmdh2wm7v/WI1zTwqOMxeYT6TjEq0L\nUFi24O4z3X0Pdz8A6AMsBszMHjezf5lZp6h9lwFdqxGDiIiI1B/1K0SkXimBISLRNkXNl0Ytl1L5\nHVvR+xiAu78F7AcsAh4xs9HB+9GD7mRXOE7FAXkqLhfF2IdgYK1rgD8BfwimfwEXVThXUSXxi4iI\nSGKoXyEi9UoJDBGpd2a2HbDM3R8AxgNDgreWmtlOZpYBHFNhtxPMLCN45rQPMKfC+18R+9eaMcB/\n3X0VkVtKS4MpN4jFgM7At3X9XCIiItLw1K8QkTIaA0NEEmF/4DIz2wKsA8p+KbkCeB5YAHwKtIja\nZw7wJpHbRc+Nfk4VwN3Xm9nXwTOs8wDMLJdIR+PnwWa3AU8Cm9k6wNZQYJq7F9frJxQREZGGsj/q\nV4gIKqMqIkkgGDX8eXefHGe7Y4Ch7n5NDY59J5ESaK/WLUoRERFpDNSvEEldugNDRBoNd3/azNrV\ncLdP1ckQERGRitSvEGl8dAeGiIiIiIiIiCQ9DeIpIiIiIiIiIklPCQwRERERERERSXpKYIiIiIiI\niIhI0lMCQ0RERERERESSnhIYIiIiIiIiIpL0lMAQ+X/27ju8ijJ94/j3IQECSBVEBYSgSAu9iQKC\nBbGAiIidYkEWy6o/C+uqqGvdxe6qawNURBCx91VZxAZBFBBQVBAi0qs0ITy/P2YSQwyQ5CSZnOT+\nXNdcJzPnzJw7h3PxTp5533dERERERESk2FMBQ0RERERERESKPRUwRERERERERKTYUwFDRERERERE\nRIo9FTBEREREREREpNhTAUOkmDOzQ8zsNzNLiDpLvDCzxWZ2XNQ5REREQG15fpjZFDO7KOoc+WGB\n0Wa2zsymR51nb+L5c5bSSQUMkWIi/KN7a3iCk7Ec7O5L3H0/d08vBhnPzZZvi5m5mbXL5f67NZJm\ndoCZjTezZWa2wcw+NbNO2fY5x8x+NrPNZvaqmdXIY+bBZpaeLXf3vBwjh2P2MLM5ZrbezNaY2Stm\nVifL8+XN7Bkz22hmy83s6ljeT0RE4kM8tOUAZjbAzOab2SYzm2dmffOw7xgzuz3LenkzezpsqzeZ\n2SwzOzHbPsea2YLwvOFjM6ufx7zdzWxXts91UF6OkcMxm5lZalhkWGdm/zWzZlmeNzO7J2zn15jZ\nP83Mcnn4LsDxQF137xhLThHZnQoYIsVL7/AEJ2NZVphvZmaJeXm9u4/Lmg8YDvwEfJXPCPsBM4B2\nQA1gLPCWme0X5msO/Ac4H6gNbAEezcf7fJ7tc52Sz7wZ5gEnuHs14GBgIfBYludvARoB9YEewHVm\n1ivG9xQRkfhQrNvysOD+PHA1UAW4FnjBzA7IZ4REYClwNFAVuAmYaGYNwverCUwOt9cAUoEJ+Xif\nZdk+17H5zJt5PKB/mKkm8DrwYpbnhwJ9gVZAS+AU4JJcHrs+sNjdN8eYMW6EBR/9bSmFTl8ykWLO\nzBqEvRwSw/VkM5saXuX4r5n928yeD5/rbmZp2fbPHE5hZreY2SQze97MNgKDzayMmY0wsx/DKwwT\n89DLYRDwrLt7Ln6PO4CuwCPhlZNH3P0nd7/P3X9193R3fwIoBzQOdzsXeMPdp7r7bwQnP/3MrHIu\n8+Vb+JkPM7OF4ZWZf2dceXH3FdlOSNOBw7KsDwT+4e7r3H0+8CQwuLAzi4hI8VTM2vK6wHp3f8cD\nbwGbgUNz8XsMJWibrwvb8jfcfbO73+Lui919l7u/CSwiuDgB0A/41t1fcvdtBEX+VmbWJG+fYt6F\nn9s1Zjbbgp6eE8wsCcDd14eZHTD+3JYPAu519zR3/wW4l1y05WZ2IfAU0Dn8jG7N+Dc1sxvMbHWY\n69y9HGOKmf3Dgp6pm8zs/bAQlNvvx0vh92OTBT1GDzezv5nZSjNbamY9s73loWY2PfyMXsv63TGz\nI8zsMwt6nX5jWXqxhjnvMLNPCS4yNdzX5yMSKxUwROLPC8B0YH+Ck4Dz87j/qcAkoBowDriC4ArD\n0QS9CdYB/97XQSzo/tkNeDbLtnPMbHZOr3f3vwOfAJeFV04uy+GYrQkKGD+Em5oD32Q5xo/A78Dh\n+/wtd9cmPGH43sxuysPVqlOADgRXXwYAJ2TJeoiZrQe2AtcA/wy3Vyf4HL/Jcpxvwt9FREQEom3L\nU4H5ZtbHzBIsGD6yHZgNYGZdwvbtT8ILDeOAf4Ztee/srzGz2gTt9Lfhpuxt+WbgR/LeLh5gZivM\nbJGZ3W9mlXK53wCgF5BM0JNicLa864FtwMPAnVme2i03uWzL3f1pYBh/9P4cGT51IEFPjzoExZEn\nzKzxHg4DcA4wBDiA4Nzomn29dxa9geeA6sAs4D2Cv/vqALcR9G7NaiBwAcF3ZyfwEGT21nkLuJ2g\np8o1wMtmVivLvucT9FapDPych4wi+aIChkjx8mpY4V5vZq9mf9LMDiH4g/pmd//d3acRdHnMi8/d\n/dXwKslWgu6Qfw+vMGwnOJHqn4s/8gcCn7j7oowN7v6Cu7fMYx4AzKwKQWN7q7tvCDfvB2zI9tIN\nBI1kbk0FUghOAE4HziboLpsbd4dXaJYAHwOtM54IxzNXIzgZuRFYkCVzRs78ZhYRkfhVrNvycB6O\nZwmKKNvDx0syhju4+7SwfcszMytLUOAY6+5Z28VY2/IFBG3wQcAKsuIvAAAgAElEQVQxBL077svl\nvg+5+zJ3Xwu8QZa2HCD8XasClxH8sZ8he+4NwH4ZvTHz6SZ33+7u/yMoDAzYy2tHu/v34b/vxOy5\n9+ETd3/P3XcCLwG1CM5pdhAMk2lgZln/jZ9z97nhd+AmYIAFE86eB7zt7m+H37UPCApgJ2XZd4y7\nf+vuO8PjixQqFTBEipe+7l4tXHKaUOtgYK27b8mybWke3yP76+sDr2ScbAHzCbpR1t7HcQYSzFkR\nMzOrQHBS8YW735Xlqd8IxudmVQXYlNtjh8NUFoUN7xyCKw/9c7n78iw/b+GP4kTW468l+BxeC08U\nf8uSM1+ZRUQkrhXrtjwcavBPoDvBlf2jgafCXpD5ZsH8B88R9JTM2suyINry5e4+L2zLFwHXUbBt\n+WbgceBZ+2MukOy5qwC/5WbY7B6syzYnxs8E34U92WfuvViR5eetwGr/YwLZreFj1uNl/T79DJQl\nuEBTHzgjS0FuPcEEpQftYV+RQqcChkh8+RWoYWYVs2yrl+XnzUDmc2H1PGs3P4DsDe9S4MQsJ1vV\n3D0pHO+ZIzM7iqDRnZTH/H9q9M2sPPAq8At/nhzrW4LhGxmvbQiUB77P4/tmzxDL1ZOcJBL08Kji\n7usI/p1aZXm+FX90pRURkdIt6ra8NTDV3VPDgsAM4Esgt7cfz6ktN+BpgoLJ6dmuxGdvyysRzLcR\nS7tYGG15GYLPPeOuYrvlJva2vHq2YS+HEEwkmle5+X7kVdbv3yHADmA1wffquWzfq0rufneW1+e3\noCOSLypgiMQRd/+ZoOveLWZWzsw6E4xzzPA9kGRmJ4fdOG8k+IN/bx4H7gjntMDMapnZqfvYZxDw\nsrvntVfBCrJM8BRmnERwNWCgu+/K9vpxQG8z6xo2+rcBk/PyvmZ2Yjgel3DCsJuA1/KYO/sx+5lZ\nYwsmTatF0I11VtgbA4KuuTeaWfXwPS8GxsTyniIiUjIUg7Z8BtA1o8eFmbUhmGQ7xzmscrBbWx56\nDGhKcAeWrdmeewVIMbPTLZhA82ZgdpYhJvsUTlx5iAXqAXcTe1t+vJm1CecBqULQlq8j6L0CQVt+\ntZnVMbODgf8jS1tuwcSZg/P4treG/+ZdCebZeikf0fPz/diX8yy4rWxFgnOtSWGPjecJzsNOCD+n\npPDfom6M7yeSbypgiMSfc4HOwBqCSZUmEIxhJZw7YjjB7Ne/EFTp03I+TKYHCcbevm9mm4AvgE57\nenF48jGAHIaPmNm5Zra3qxMPEozJXWdmDwFHEjTgPYH19se93buGv8+3BBNhjQNWEoyXHb6P3ye7\nY4HZZrYZeJvgVm537n2XfaoDvEvQ/XUOsAs4LcvzIwkmKPsZ+B/wL3d/N8b3FBGRkiOytjycf+EW\nYFL42peBO939fYDwosFvOe0behpoljHHR1g0uYSgZ8fyLG35ueH7rSKYg+oOggJBJ+Csffw+2bUF\nPif4LD4D5hJMXBqLasB4grktfiS4A0mv8E4pEEx0+QZBOz+XYM6K/wCYWTmCCVi/yMP7LSf4/ZcR\nnNcMy0sRJ0M+vx/78hxBcWY5kET42br7UoIJY28AVhH0yLgW/Q0pEbL8D+MSkeLAzCYAC7LMci0i\nIiJxRG15fDGzLsCl7n52Ll/fHXje3dVzQSRGqp6JxBkz62Bmh4bDF3oRVMb/NMu5iIiIFE9qy+Nb\neKeWXBUvRKRg7es2iSJS/BxIMAxif4Iug39x91l736VkCW9BN28PTzcLb3u6r2N0Bd7J6Tl3z8tM\n3yIiInlV6ttygL0MVTnR3T/Jxf4xnw+ISHzREBIRERGJC2b2DMG8OSvdPSXc9i+CCRB/JxjHPsTd\n14fP/Q24kOB2kle4+3uRBBcREZECoSEkIiIiEi/GAL2ybfsASHH3lgSz8/8NwMyaEUwU2Dzc59Hw\ndoMiIiISp+J6CEnNmjW9QYMGUccQEREpdWbOnLna3WsV5Xu6+1Qza5Bt2/tZVr8A+oc/nwq86O7b\ngUVm9gPQkeBOBnukcwsREZGil9vzirguYDRo0IDU1NSoY4iIiJQ6ZvZz1BlycAHB7SghuN1x1lsc\npoXb9krnFiIiIkUvt+cVGkIiIiIicc/M/g7sBMZlbMrhZTlO/GVmQ80s1cxSV61aVVgRRUREJEYq\nYIiIiEhcM7NBBJN7nut/zE6eBtTL8rK6wLKc9nf3J9y9vbu3r1WrSEfFiIiISB6ogCEiIiJxy8x6\nAdcDfdx9S5anXgfOMrPyZpYMNAKmR5FRRERECkZcz4EhIiICsGPHDtLS0ti2bVvUUUqcpKQk6tat\nS9myZaOOgpmNB7oDNc0sDRhJcNeR8sAHZgbwhbsPc/dvzWwiMI9gaMml7p4eTXIRkcKh9k/iTazn\nFSpgiIhI3EtLS6Ny5co0aNCA8I9YKQDuzpo1a0hLSyM5OTnqOLj72Tlsfnovr78DuKPwEomIREvt\nn8STgjiv0BCSrGZPhPtT4JZqwePsiVEnEhGRXNi2bRv777+/Tt4KmJmx//7768peDF6d9QtH3f0R\nySPe4qi7P+LVWb9EHUlEShC1fxJPCuK8Qj0wMsyeCG9cATu2BusblgbrAC0HRJdLRERyRSdvhUOf\na/69OusX/jZ5Dlt3BCNXflm/lb9NngNA3zb7vKOriEiu6P9piSexfl/VAyPDh7f9UbzIsGNrsF1E\nREQkj/713neZxYsMW3ek86/3vosokYiISHxTASPDhrS8bRcREckiISGB1q1bZy6LFy9mypQpVK1a\nlTZt2tC0aVNuvfXWHPdduHAhp5xyCoceeijt2rWjR48eTJ06tUDzLV68mBdeeCFzfcyYMVx22WUF\n+h6yu2Xrt+Zpu4iI/KF79+6kpqYW+vs89NBDNG3alHPPPfdPz5199tm0bNmS+++/v9Bz5EX2Nj1W\nDzzwAFu2/HEjrzvvvLPAjl3QVMDIULVu3raLiEjcKox5CSpUqMDXX3+duTRo0ACArl27MmvWLFJT\nU3n++eeZOXPmbvtt27aNk08+maFDh/Ljjz8yc+ZMHn74YX766ac/vcfOnTvzna+gT3Zk3w6uViFP\n20VECltpmZcnL+3lo48+yttvv824ceN22758+XI+++wzZs+ezVVXXZXv4xeG4ljASE8vmht9qYCR\n4diboWwOJxRHDC/6LCIiUmgy5iX4Zf1WnD/mJSjsk7hKlSrRrl07fvzxx922jxs3js6dO9OnT5/M\nbSkpKQwePBiAW265haFDh9KzZ08GDhzItm3bGDJkCC1atKBNmzZ8/PHHAJx00knMnj0bgDZt2nDb\nbcEQyJtuuomnnnqKESNG8Mknn9C6devMK0nLli2jV69eNGrUiOuuu65Qf//S6NoTGlOhbMJu2wy4\n5OiG0QQSkVKtMNq/xYsX07RpUy6++GKaN29Oz5492bo16GWWtQfF6tWrMwv7Y8aMoW/fvvTu3Zvk\n5GQeeeQR7rvvPtq0acMRRxzB2rVrM4///PPPc+SRR5KSksL06dMB2Lx5MxdccAEdOnSgTZs2vPba\na5nHPeOMM+jduzc9e/b8U9b77ruPlJQUUlJSeOCBBwAYNmwYP/30E3369PlTL4uePXuycuVKWrdu\nzSeffEL37t254YYbOProo3nwwQdZtWoVp59+Oh06dKBDhw58+umnAKxZs4aePXvSpk0bLrnkEurX\nr8/q1atZvHgxKSkpmccfNWoUt9xyCwA//vgjvXr1ol27dnTt2pUFCxYAMHjwYK644gqOPPJIGjZs\nyKRJkwBybNMzTJkyhW7dunHaaafRrFkzhg0bxq5duwD4y1/+Qvv27WnevDkjR44Egh4oy5Yto0eP\nHvTo0YMRI0awdetWWrdundkr5fnnn6djx460bt2aSy65JLNYsd9++3HzzTfTqVMnPv/8cxo0aMDI\nkSNp27YtLVq0yPw9CpIm8cyQMVHnh7cFw0YqHwhb1sPsCdD+AiibFG0+ERHJlVvf+JZ5yzbu8flZ\nS9bze/qu3bZt3ZHOdZNmM376khz3aXZwFUb2br7X981o7AGSk5N55ZVXdnt+zZo1fPHFF9x00027\nbf/2229p27btXo89c+ZMpk2bRoUKFbj33nsBmDNnDgsWLKBnz558//33dOvWjU8++YQGDRqQmJiY\neSI1bdo0zjvvPA477DBGjRrFm2++CQQnel9//TWzZs2ifPnyNG7cmMsvv5x69ertNYvkXsZEnf96\n7zuWrd9Kzf3Ks27Ldl6emcYZ7epRoVzCPo4gIpJ7UbV/CxcuZPz48Tz55JMMGDCAl19+mfPOO2+v\n+8ydO5dZs2axbds2DjvsMO655x5mzZrFVVddxbPPPsuVV14JBMWKzz77jKlTp3LBBRcwd+5c7rjj\nDo455hieeeYZ1q9fT8eOHTnuuOMA+Pzzz5k9ezY1atTY7f1mzpzJ6NGj+fLLL3F3OnXqxNFHH83j\njz/Ou+++y8cff0zNmjV32+f111/nlFNO4euvv87ctn79ev73v/8BcM4553DVVVfRpUsXlixZwgkn\nnMD8+fO59dZb6dKlCzfffDNvvfUWTzzxxF4/C4ChQ4fy+OOP06hRI7788kuGDx/ORx99BMCvv/7K\ntGnTWLBgAX369KF///7cfffdu7Xp2U2fPp158+ZRv359evXqxeTJk+nfvz933HEHNWrUID09nWOP\nPZbZs2dzxRVXcN999+32GTzyyCOZv/f8+fOZMGECn376KWXLlmX48OGMGzeOgQMHsnnzZlJSUjIv\nmgDUrFmTr776ikcffZRRo0bx1FNP7fP3zwsVMLJqOWD3O44seBtePBvevR56PxhdLhERKTDZT972\ntT23MoaQZPfJJ5/Qpk0bypQpw4gRI2jefO8ngqeddhoLFy7k8MMPZ/LkyQD06dOHChWCXoLTpk3j\n8ssvB6BJkybUr1+f77//nq5du/LQQw+RnJzMySefzAcffMCWLVtYvHgxjRs35tdff/3Tex177LFU\nrVoVgGbNmvHzzz+rgFHA+raps9sdR97/djmXPD+TK16cxePntSOhjO4eICJFo7Dav+Tk5MwCfrt2\n7Vi8ePE+9+nRoweVK1emcuXKVK1ald69ewPQokWLzN6EEMxBAdCtWzc2btzI+vXref/993n99dcZ\nNWoUEAzFXLIkKMAcf/zxfypeQNB2nnbaaVSqVAmAfv36ZbbPeXHmmWdm/vzf//6XefPmZa5v3LiR\nTZs2MXXq1Mz2++STT6Z69ep7PeZvv/3GZ599xhlnnJG5bfv27Zk/9+3blzJlytCsWTNWrFiRq5wd\nO3akYcOgt9/ZZ5/NtGnT6N+/PxMnTuSJJ55g586d/Prrr8ybN4+WLVvu9VgffvghM2fOpEOHDkBw\nweaAAw4Agvm/Tj/99N1e369fPyD4LmR8DgVJBYy9aXISdLkapt0HdTtCmz9P7CIiIsXLvq4UHXX3\nR/ySwySKdapVYMIlnQs8T9euXfd4hQSgefPmu03Y+corr5Camso111yTuS3jhAvA3XM8TocOHUhN\nTaVhw4Ycf/zxrF69mieffJJ27drt8b3Lly+f+XNCQkLkY3pLg57ND2TkKc245Y15/OPNedzSZ+/f\nVxGR3Iqq/cvelmQMIUlMTMwcurBt27Y97lOmTJnM9TJlyuzWFmW/5aaZ4e68/PLLNG7ceLfnvvzy\ny93ay6z21HbmVdbj79q1i88//zzzAkP2nNll/Tzgj89k165dVKtWLceLILD7Z5Xb3yOnz23RokWM\nGjWKGTNmUL16dQYPHvynf5ecuDuDBg3irrvu+tNzSUlJJCTs3pswI29hnVdoDox96fF3SO4Gb10N\ny+dEnUZERGKU07wEFcomcO0JjfewR+E655xz+PTTT3n99dczt2WdSCu7bt26ZU409v3337NkyRIa\nN25MuXLlqFevHhMnTuSII46ga9eujBo1iq5duwJQuXJlNm3aVLi/jOTK4KOSubBLMmM+W8zT0xZF\nHUdESomibv8aNGiQOXF1xtwNeTVhwgQg6EFRtWpVqlatygknnMDDDz+c+cf8rFmz9nmcbt268eqr\nr7JlyxY2b97MK6+8ktk+5lfPnj155JFHMtczChBZ2+l33nmHdevWAVC7dm1WrlzJmjVr2L59e+bF\njSpVqpCcnMxLL70EBAWDb775Zq/vva82ffr06SxatIhdu3YxYcIEunTpwsaNG6lUqRJVq1ZlxYoV\nvPPOO3s8XtmyZdmxYwcQ9NacNGkSK1euBGDt2rX8/PPPufuQCoEKGPuSkAinPwMVqsOE82Hr+qgT\niYhIDPq2qcNd/VpQp1oFjODK0139WuzWzb8oVahQgTfffJPHH3+chg0b0rlzZ26//XZuvPHGHF8/\nfPhw0tPTadGiBWeeeSZjxozJvNrRtWtXateuTcWKFenatStpaWmZJ2gtW7YkMTGRVq1aFbvbwZVG\nN5zUlBOa1+b2t+bx7tzlUccRkVKgqNu/a665hscee4wjjzyS1atX5+sY1atX58gjj2TYsGE8/fTT\nQDA59Y4dO2jZsiUpKSl/mlsqJ23btmXw4MF07NiRTp06cdFFF+V5+Eh2Dz30EKmpqbRs2ZJmzZrx\n+OOPAzBy5EimTp1K27Ztef/99znkkEOAoCiQMeHlKaecQpMmTTKPNW7cOJ5++mlatWpF8+bNMycm\n3ZN9temdO3dmxIgRpKSkkJyczGmnnUarVq1o06YNzZs354ILLuCoo47KfP3QoUM58cQT6dGjR+Z6\ny5YtOffcc2nWrBm33347PXv2pGXLlhx//PE5DkstKlZQ3Wny9KZmVwEXAQ7MAYYABwEvAjWAr4Dz\n3f33vR2nffv2XhT3BgZgyZcw5iRo1BPOHAdlVPsRESku5s+fT9OmTaOOUWLl9Pma2Ux3bx9RpEJT\nlOcWW39P5+wnv2D+rxt5cegRtDlk7+OkRUSyU/tX/DVo0IDU1NQ/TRJaWKZMmbLXCT6Lg1jOK4r8\nr3AzqwNcAbR39xQgATgLuAe4390bAeuAC4s6214d0glOuBO+exs+fSDqNCIiIhLnKpRL4KlB7ald\nJYmLxqby85rNUUcSEREp1qLqRpAIVDCzRKAi8CtwDJAxOGos0DeibHvWcSiknA4f/QN++l/UaURE\nRCTO1dyvPGOGdCDdnSGjZ7Bu8147n4qISJxZvHhxkfW+AOjevXux7n0RqyIvYLj7L8AoYAlB4WID\nMBNY7+4Z05SmATkOxjKzoWaWamapq1atKorIWd8cej8E+zeCSRfAxmVF+/4iIiJS4jSstR9PDmxP\n2rqtDH0ulW070qOOJCJxJIopAUTyK9bvaxRDSKoDpwLJwMFAJeDEHF6a42/m7k+4e3t3b1+rVq3C\nC7on5feDM5+Hndtg4iDYqSslIiIiEpsODWpw74BWzFi8jmsnzWbXLv1BIiL7lpSUxJo1a1TEkLjg\n7qxZs4akpKR8HyOxAPPk1nHAIndfBWBmk4EjgWpmlhj2wqgLFN/uDbUOhz4Pw6Qh8MHNcOLdUScS\nERGRONe71cGkrdvKPe8uoG71Clzfq8m+dxKRUq1u3bqkpaVR5D3TRfIpKSmJunXr5nv/KAoYS4Aj\nzKwisBU4FkgFPgb6E9yJZBCw93vHRC2lH6TNgC8ehXodgrkxRERERGIw7OiGLF23hcem/Ei96hU5\np9MhUUcSkWKsbNmyJCcnRx1DpMhEMQfGlwSTdX5FcAvVMsATwPXA1Wb2A7A/8HRRZ8uz42+DekfA\na5fDqu+iTiMiIhF75ZVXMDMWLFiQ530vuugi5s2bV+j7SPFmZtzWpzndG9fiptfm8vF3K6OOJCIi\nUmxEchcSdx/p7k3cPcXdz3f37e7+k7t3dPfD3P0Md98eRbY8SSgLZ4yBchVhwnmwfVPUiUREJDdm\nT4T7U+CWasHj7IkFctjx48fTpUsXXnzxxTztl56ezlNPPUWzZs0KdR+JD4kJZXjknLY0ObAyl477\nirm/bIg6koiISLEQ1W1US44qB0H/0bDmB3j9ctAEOiIixdvsifDGFbBhKeDB4xtXxFzE+O233/j0\n0095+umnMwsYU6ZMoVu3bpx22mk0a9aMYcOGsWvXLgD2228/br75Zjp16sTnn39O9+7dSU1NBYJC\nSIsWLUhJSeH666/PfI+97SMly37lE3lmcAeqVSjLhWNnsGz91qgjiYiIRC6KOTBKnuSucOxI+O9I\nqNcJjvhL1IlEREqvd0bA8jl7fj5tBqRn6+S3Yyu8dhnMHJvzPge22OeEza+++iq9evXi8MMPp0aN\nGnz11VcATJ8+nXnz5lG/fn169erF5MmT6d+/P5s3byYlJYXbbrttt+MsW7aM66+/npkzZ1K9enV6\n9uzJq6++St++ffe4j5RMtaskMXpIR/o/9hlDRs/gpb90pkpS2ahjiYiIREY9MArKUX+FJqfA+zfC\nki+iTiMiInuSvXixr+25NH78eM466ywAzjrrLMaPHw9Ax44dadiwIQkJCZx99tlMmzYNgISEBE4/\n/c8TQM+YMYPu3btTq1YtEhMTOffcc5k6depe95GSq/GBlXnsvHb8uOo3hj//FTvSd0UdSUREJDLq\ngVFQzKDvo/BEd3hpMFwyFfY7IOpUIiKlz75ubX1/Sjh8JJuq9WDIW/l6yzVr1vDRRx8xd+5czIz0\n9HTMjJNOOgkz2+21GetJSUkkJCT86Vi+l6GIe9pHSrYujWpyV78WXDtpNjdMnsM/+7f80/dKRESk\nNFAPjIKUVBUGPAdb18OkCyB9Z9SJREQku2NvhrIVdt9WtkKwPZ8mTZrEwIED+fnnn1m8eDFLly4l\nOTmZadOmMX36dBYtWsSuXbuYMGECXbp02euxOnXqxP/+9z9Wr15Neno648eP5+ijj853tpLEzJ4x\ns5VmNjfLthpm9oGZLQwfq4fbzcweMrMfzGy2mbWNLnnszmhfjyuObcRLM9N4+KMfoo4jIiISCRUw\nCtqBKXDK/bD4E/j49qjTiIhIdi0HQO+Hgh4XWPDY+6Fgez6NHz+e0047bbdtp59+Oi+88AKdO3dm\nxIgRpKSkkJyc/KfXZXfQQQdx11130aNHD1q1akXbtm059dRT852thBkD9Mq2bQTwobs3Aj4M1wFO\nBBqFy1DgsSLKWGiuOq4R/drW4b4PvmfyV2lRxxERESlytreuqsVd+/btvdjOvv7GlTBzNJz1AjQ5\nOeo0IiIl2vz582natGnUMf5kypQpjBo1ijfffDPqKDHJ6fM1s5nu3r6os5hZA+BNd08J178Durv7\nr2Z2EDDF3Rub2X/Cn8dnf93ejl+szy2A33fuYtAz00n9eS1jh3TkyMNqRh1JREQkZrk9r1APjMJy\n4j1wcBt4ZRis+THqNCIiIiVV7YyiRPiYMQFVHSDrZCdp4ba4Vi6xDI+f344G+1fikudnsnDFpqgj\niYiIFBkVMApLYnkY8CyUSYCJA+H3LVEnEhGRIta9e/e4730Rx3Ka5TLHbqdmNtTMUs0sddWqVYUc\nK3ZVK5Rl9JAOJJVNYPDoGazctC3qSCIiIkVCBYzCVO0Q6PcUrPgW3roa4ni4johIcRfPQyKLszj4\nXFeEQ0cIH1eG29OAelleVxdYltMB3P0Jd2/v7u1r1apVqGELSt3qFXlmUAfWbv6dC8eksnm7Jg4X\nEZGSTwWMwtboOOg+Ar4ZDzPHRJ1GRKRESkpKYs2aNfHwx3ZccXfWrFlDUlJS1FH25nVgUPjzIOC1\nLNsHhncjOQLYsK/5L+JNi7pVeeScNny7bANXjJ/FzvRdUUcSEREpVIlRBygVul0HaTPgnevgoFZQ\nJ67v5CYiUuzUrVuXtLQ04qH7f7xJSkqibt26UccAwMzGA92BmmaWBowE7gYmmtmFwBLgjPDlbwMn\nAT8AW4AhRR64CBzbtDa39mnOTa99y61vzOO2U5tjltPoGRERkfinAkZRKFMG+j0J/+kGEwfBJf+D\nijWiTiUiUmKULVuW5OTkqGNILpnZ4cC1QH2ynIu4+zF728/dz97DU8fm8FoHLo0hZtw4v3MDlq7b\nyhNTf+KQGhW5uFvDqCOJiIgUChUwikrFGjBgLDzTCyZfDOe8FBQ2RERESp+XgMeBJ4H0iLOUCCN6\nNeGXdVu54+351KlegZNaHBR1JBERkQKnv6CLUp12we1Vf/gvTP1n1GlERESistPdH3P36e4+M2OJ\nOlQ8K1PGuHdAK9rVr86VE75m5s9ro44kIiJS4FTAKGrthkCrc2DK3bDwv1GnERERicIbZjbczA4y\nsxoZS9Sh4l1S2QSeHNieg6smcdHYVBav3hx1JBERkQKlAkZRM4OT74XazWHyRbB+SdSJREREitog\ngjkwPgNmhktqpIlKiBqVyjFmSEcABo+eztrNv0ecSEREpOCogBGFchVhwLOwKx0mDoSd26NOJCIi\nUiTMrAxwnrsnZ1s082QBaVCzEk8Nas+yDdu4+NlUtu3QNCMiIlIyqIARlf0PhdMeh2Wz4N0RUacR\nEREpEu6+CxgVdY6Srl39GjxwZmtm/ryO/5v4Dbt2edSRREREYqYCRpSanAxHXQmpz8DX46NOIyIi\nUlTeN7PTzcyiDlKSndTiIG44qQlvzfmVe95dEHUcERGRmOk2qlE75ib4ZSa8eRUc2AIOTIk6kYiI\nSGG7GqgEpJvZVsAAd/cq0cYqeS7u2pCla7fyn6k/UbdGRc4/on7UkURERPJNPTCilpAI/Z+BCtVg\n4vmwbUPUiURERAqVu1d29zLuXtbdq4TrKl4UAjNjZO9mHNvkAEa+NpcP56+IOpKIiEi+qYBRHOx3\nAJwxJrgjyavDwTVOVURESi4LnGdmN4Xr9cysY9S5SqrEhDI8fE4bmh9clctemMWcNF0sERGR+KQC\nRnFxyBHQ83ZY8CZ8+mDUaURERArTo0Bn4Jxw/Tfg39HFKfkqlkvk6cHtqVGpHBeMnUHaui1RRxIR\nEckzFTCKk07DoPlp8OGtsOiTqNOIiIgUlk7ufimwDcDd11Rff9wAACAASURBVAHloo1U8h1QOYnR\nQzqwbUc6Q0bPYMPWHVFHEhERyRMVMIoTM+jzMOx/GEwaAht/jTqRiIhIYdhhZgmAA5hZLWBXtJFK\nh8NrV+Y/57dj8ZrNDHtuJr/v1McuIiLxQwWM4qZ8ZRjwHPy+BV4aDOm6OiIiIiXOQ8ArwAFmdgcw\nDbgr2kilx5GH1uSe01vy+U9rGPHybFxzb4mISJxQAaM4OqAJ9HkIln4BH4yMOo2IiEiBcvdxwHUE\nRYtfgb7uPjHaVKVLv7Z1ufr4w5k86xce+O/CqOOIiIjkSmLUAWQPWvSHtBnwxb+hXodgbgwREZES\nwMyec/fzgQU5bJMicvkxh7Fk7RYe/HAhdatX4Iz29aKOJCIislfqgVGcHf8PqNsRXrsMVn0XdRoR\nEZGC0jzrSjgfRruIspRaZsZd/VrQ5bCa/G3yHKYtXB11JBERkb1SAaM4SywHA8ZCYhJMOB+2/xZ1\nIhERkXwzs7+Z2SagpZltDJdNwErg9YjjlUplE8rw6HltOeyA/fjL8zNZsHxj1JFERET2SAWM4q7K\nwdD/GVizEN64AjTRloiIxCl3v8vdKwP/cvcq4VLZ3fd39xFR5yutqiSV5ZnBHahQLoELRs9gxcZt\nUUcSERHJkQoY8aDh0XDMTTD3ZZj+RNRpREREYtUx+wYz+zCKIBI4uFoFnhncgQ1bd3DBmBls3r4z\n6kgiIiJ/ogJGvDjqSmh8Erx3AyydHnUaERGRPDOzJDPbH6hpZtXNrEa4NAAOjjadpNSpyiPntmXB\n8k1c9sJX7EzfFXUkERGR3aiAES/KlIG+j0HVujBxEPy2KupEIiIieXUJkAo0AWZmWV4D/h1hLgn1\naHwA/zg1hY+/W8XNr3+La+iqiIgUIypgxJMK1WDAc7B1Lbx8AexKjzqRiIhIrrn7g+6eDFzj7g3d\nPTlcWrn7I1Hnk8A5nQ7hL90P5YUvl/CfqT9FHUdERCRTJAUMM6tmZpPMbIGZzTezzmEX0g/MbGH4\nWD2KbMXeQS3h5Ptg0VT4+I6o04iIiOSZuz9sZilmNsDMBmYsUeeSP1zbszG9Wx3M3e8s4I1vlkUd\nR0REBIiuB8aDwLvu3gRoBcwHRgAfunsj4MNwXXLS5lxoOwg+uRcWvB11GhERkTwxs5HAw+HSA/gn\n0CfSULKbMmWMf/VvSYcG1fm/id8wY/HaqCOJiIgUfQHDzKoA3YCnAdz9d3dfD5wKjA1fNhboW9TZ\n4sqJ/4SDWsMrw2CtuneKiEhc6Q8cCyx39yEEFzPKx3JAM7vKzL41s7lmNj6cMDTZzL4Me3dOMLNy\nBRG+tEgqm8AT57enbvUKXPxsKj+u+i3qSCIiUspF0QOjIbAKGG1ms8zsKTOrBNR2918BwscDctrZ\nzIaaWaqZpa5aVYonsiybBAOeBTOYMBB2bI06kYiISG5tdfddwM7wwsZKgvODfDGzOsAVQHt3TwES\ngLOAe4D7w96d64ALY05eylSvVI4xQzqSYMaQ0TNY89v2qCOJiEgpFkUBIxFoCzzm7m2AzeRhuIi7\nP+Hu7d29fa1atQorY3yoXh9OfwpWzIW3/g80U7iIiMSHVDOrBjxJcBeSr4BY7xGeCFQws0SgIvAr\ncAwwKXxevTvz6ZD9K/LkoPas2LiNi55NZdsOTSIuIiLRiKKAkQakufuX4fokgoLGCjM7CCB8XBlB\ntvjT6Hg4+jr4ehx89WzUaURERPbJ3Ye7+3p3fxw4HhgUDiXJ7/F+AUYBSwgKFxsICiPr3X1n+LI0\noE5O+6t35761PaQ6D57Vhq+XrufKF78mfZcumoiISNEr8gKGuy8HlppZ43DTscA84HVgULhtEME9\n4SU3jr4eDj0G3r4Wls2KOo2IiMg+mVk/M7sPuBw4NMZjVSeYSysZOBioBJyYw0tz/KtbvTtzp1fK\ngdx4cjPe/XY5d749P+o4IiJSCkV1F5LLgXFmNhtoDdwJ3A0cb2YLCa7G3B1RtvhTJgH6PQWVasHE\ngbBFM4WLiEjxZWaPAsOAOcBc4BIz+3cMhzwOWOTuq9x9BzAZOBKoFg4pAagL6H6gMbqwSzKDj2zA\n09MWMebTRVHHERGRUiZx3y8peO7+NdA+h6eOLeosJUal/YNJPUf3glcugbMnQJmo6lMiIiJ7dTSQ\n4h5M3mRmYwmKGfm1BDjCzCoCWwnOJ1KBjwnuePIi6t1ZYG46pRm/rN/KbW/Oo071ihzfrHbUkURE\npJTQX7glSd120OtuWPg+fDIq6jQiIiJ78h1wSJb1esDs/B4snFdrEsFkoHMIzm+eAK4HrjazH4D9\nCW/hLrFJKGM8dFYbWtSpyuXjv+KbpeujjiQiIqWEChglTfsLoOVZ8PGd8MOHUacRERHJyf7AfDOb\nYmZTCObCqmVmr5vZ6/k5oLuPdPcm7p7i7ue7+3Z3/8ndO7r7Ye5+hrvrHqAFpEK5BJ4a1IFalctz\n4dgZLF27JepIIiJSCkQyhEQKkRmccj8snwMvXwSXTIVq9aJOJSIiktXNUQeQ2NWqXJ7RgzvS79FP\nGTx6OpP/chRVK5aNOpaIiJRg6oFREpWrCGc+B7t2wkuDYKcuOImISPHh7v/b2xJ1Psm9ww7YjycG\ntmfp2q0MfS6V7TvTo44kIiIlmAoYJdX+h0LfR+GXmfDeDVGnERERkRLqiIb7868zWvLlorVcP2k2\n4dysIiIiBU4FjJKsaW848gqY8RR8MyHqNCIiIlJCndq6Dtee0JhXv17Gve9/H3UcEREpoVTAKOmO\nHQn1j4I3/gorvo06jYiICGb219xsk/gyvPuhnNWhHo98/AMTZiyJOo6IiJRAKmCUdAmJ0H80JFWF\nCefDtg1RJxIRERmUw7bBRR1CCpaZ8Y++KXQ7vBY3vDKXqd+vijqSiIiUMCpglAaVa8MZY2DdYnh1\nOGhsqoiIRMDMzjazN4DkjFumhsvHwJqo80nsyiaU4d/ntKHRAfsxfNxXzFu2MepIIiJSgqiAUVrU\n7ww9/wEL3oTPHo46jYiIlE6fAfcCC8LHjOX/gF4R5pICVDmpLKOHdGC/8olcMGYGv27YGnUkEREp\nIWIqYJjZAWZ2mpldamYXmFlHM1NRpLg6Yjg0OxX+ewssnhZ1GhERKWXc/Wd3n+LunbPdOvUrd98Z\ndT4pOAdVrcDoIR34bftOhoyewaZtO6KOJCIiJUC+ig1m1sPM3gPeAk4EDgKaATcCc8zsVjOrUnAx\npUCYQZ9HoEZDeGkIbFoedSIRESmFzKyfmS00sw1mttHMNpmZxhqUME0PqsKj57Zl4crfuPSFWexI\n3xV1JBERiXP57S1xEnCxu3dw96HufqO7X+PufYBWwCzg+AJLKQUnqQqc+Rz8/hu8NBjSdUVERESK\n3D+BPu5e1d2ruHtld9eFjxKo2+G1uPO0FKZ+v4qbXp2Lax4uERGJQb4KGO5+rbvneH8sd9/p7q+6\n+8uxRZNCc0BT6P0QLPk8GE4iIiJStFa4+/yoQ0jROLPDIVzW4zBenLGUR6f8GHUcERGJY7HOgXGn\nmVXLsl7dzG6PPZYUupZnQMeh8Pkj8O2rUacREZHSJdXMJoR3JemXsUQdSgrP//U8nL6tD+Zf733H\na1//EnUcERGJU7FOuHmiu6/PWHH3dQTDSyQe9LwD6naA1y6F1QujTiMiIqVHFWAL0BPoHS6nRJpI\nCpWZcU//lnRKrsG1L83my59011wREcm7WAsYCWZWPmPFzCoA5ffyeilOEsvBGWMhsTxMOA+2/xZ1\nIhERKQXcfUgOywVR55LCVT4xgSfOb0+9GhUY+txMflip8w4REcmbWAsYzwMfmtmFZnYB8AEwNvZY\nUmSq1oH+z8Dq7+GNv4Im1xIRkUJmZoeb2YdmNjdcb2lmN0adSwpf1YplGTOkI2UTjMGjp7Nq0/ao\nI4mISByJqYDh7v8EbgeaAs2Bf4TbJJ407A49/g5zJ8GMp6JOIyIiJd+TwN+AHQDuPhs4K9JEUmTq\n1ajI04M6sOa337lo7Ay2/p4edSQREYkTsfbAAJgPvOvu/wd8YmaVC+CYUtS6XA2H94J3/wZLZ0Sd\nRkRESraK7j4927adkSSRSLSqV42Hzm7D7F82cMWLs0jfpR6gIiKyb7HeheRiYBLwn3BTHUC3tIhH\nZcrAaY9DlYPhpUGweXXUiUREpORabWaHAg5gZv2BX6ONJEXt+Ga1GXlKMz6Yt4J/vDkv6jgiIhIH\nYu2BcSlwFLARwN0XAgfEGkoiUqE6nPlcULx4+ULYpS6dIiJSKC4luPjRxMx+Aa4E/hJtJInC4KOS\nubBLMmM+W8zT0xZFHUdERIq5WAsY293994wVM0skvJoiceqgVnDyvfDTFPj4zqjTiIhICeTuP7n7\ncUAtoIm7d3H3xRHHkoj8/aSm9Gp+ILe/NY935y6POo6IiBRjiTHu/z8zuwGoYGbHA8OBN2KPJZFq\nez6kTYdPRkHdDtC4V9SJRESkBDGzm7OtA+Dut0USSCJVpozxwFmtOfvJL/jri7N4cegRtDmketSx\nRESkGIq1B8YIYBUwB7gEeBvQbdBKghP/FfTGeGUorFWXThERKVCbsyzpwIlAgygDSbSSyibw5MD2\n1K6SxEVjU/l5zeaoI4mISDEU621Ud7n7k+5+BjAU+NLdNYSkJCibBAOeDX6eOBB2bI02j4iIlBju\nfm+W5Q6gO8FE4FKK1dyvPGOGdCDdnSGjZ7Bu8+/73klEREqVWO9CMsXMqphZDeBrYLSZ3Vcw0SRy\n1RtAvydh+Wx4+9qo04iISMlVEWgYywHMrJqZTTKzBWY238w6m1kNM/vAzBaGjxqXUMw1rLUfTw5s\nT9r6rQx9LpVtOzShuIiI/CHWISRV3X0j0A8Y7e7tgONijyXFxuEnQLdrYdZz8NWzUacREZESwMzm\nmNnscPkW+A54MMbDPgi86+5NgFbAfIKhrh+6eyPgw3BdirkODWpw7xmtmLF4HddOms2uXercKyIi\ngVgn8Uw0s4OAAcDfCyCPFEfd/wZpM+Cta4J5MQ5qFXUiERGJb6dk+XknsMLdd+b3YGZWBegGDAYI\n75D2u5mdSjA8BWAsMAW4Pr/vI0Wnd6uDSVu3lXveXUDd6hW4vleTqCOJiEgxEGsPjNuA94Af3H2G\nmTUEFsYeS4qVMglw+tNQqSZMOB+2ros6kYiIxLdNWZatQJVwuEeNcFhqXjUkmFR8tJnNMrOnzKwS\nUNvdfwUIHw/IaWczG2pmqWaWumrVqnz9QlLwhh3dkHM6HcJjU37khS+XRB1HRESKgVgn8XzJ3Vu6\n+/Bw/Sd3P71gokmxUqlmMKnnxmUw+RLYtSvqRCIiEr++Iig4fE9w4WMVMDNcUvNxvESgLfCYu7ch\nuLtJroeLuPsT7t7e3dvXqlUrH28vhcHMuK1Pc3o0rsVNr83l4+9WRh1JREQilq8ChpnduLcrJGZ2\njJmdsqfnJU7VbQ+97oKF78G0e6NOIyIi8etdoLe713T3/QmGlEx292R3z89knmlAmrt/Ga5PIiho\nrAiHuhI+6i/gOJOYUIZHzmlLkwMrc+m4r5j7y4aoI4mISITy2wNjDvCGmX1oZv8ys+vM7GYze87M\n5gC9gS/3cQyJRx0ughYD4KM74MePok4jIiLxqYO7v52x4u7vAEfn92DuvhxYamaNw03HAvOA14FB\n4bZBwGv5fQ+JTqXyiTwzuAPVKpTlwrEzWLZet3YXESmt8lXAcPfX3P0oYBjwLZAAbASeBzq6+1Xu\nrkGkJZEZ9H4AajWBly+CDWlRJxIRkfizOuzN2cDM6pvZ34E1MR7zcmCcmc0GWgN3AncDx5vZQuD4\ncF3iUO0qSYwe0pEt29MZMnoGG7ftiDqSiIhEINY5MBa6+xh3v8vdH3D399xdZfGSrlwlOPM52Pk7\nTBwUPIqIiOTe2UAt4JVwqRVuyzd3/zqcx6Klu/d193Xuvsbdj3X3RuHj2gLILhFpfGBlHj+/HT+u\n+o3hz3/FjnTNxyUiUtrEeheSfDOzhHCm8DfD9WQz+9LMFprZBDMrF1U2yYWajaDvv+GXVHhfd9AV\nEZHcc/e17v5XoKu7t3X3K1VckNw46rCa3H16S6b9sJobJs/B3aOOJCIiRSiyAgbwV2B+lvV7gPvd\nvRGwDrgwklSSe81Ohc6XwfQnYPZLUacREZE4YWZHmtk8gnkqMLNWZvZoxLEkTvRvV5e/HtuIl2am\n8fBHP0QdR0REilAkBQwzqwucDDwVrhtwDMGs4QBjgb5RZJM8Ou4WOORIeOMKWDEv6jQiIhIf7gdO\nIJz3wt2/AbpFmkjiypXHNaJf2zrc98H3TP5K83GJiJQWMRUwzOzw8E4kc8P1lmZ2Yy52fQC4DsgY\nvLg/sN7dd4braUCdWLJJEUkoC2eMhvKVYeL5sG1j1IlERCQOuPvSbJvSIwkiccnMuLtfS448dH+u\nf3k2n/2wOupIIiJSBGLtgfEk8DdgB4C7zwbO2tsOZnYKsNLdZ2bdnMNLcxzUaGZDzSzVzFJXrdKN\nToqFygdC/9GwdhG8diloPKqIiOzdUjM7EnAzK2dm17D7sFKRfSqXWIbHzmtHcs1KXPL8TBau2BR1\nJBERKWSxFjAquvv0bNt25vjKPxwF9DGzxcCLBENHHgCqmVli+Jq6wLKcdnb3J8JZxtvXqlUr/8ml\nYDU4Co6/Fea/Dp//O+o0IiJSvA0DLiXobZlGcNvTSyNNJHGpaoWyPDO4A0llExg8egYrN22LOpKI\niBSiWAsYq83sUMLeEmbWH/h1bzu4+9/cva67NyDorfGRu58LfAz0D182CHgtxmxS1DpfBk17wwc3\nw8+fRZ1GRESKITNLAM5393Pdvba7H+Du57n7mqizSXyqW70izwzqwNrNv3PhmFQ2b9/XtTQREYlX\nsRYwLgX+AzQxs1+AK4G/5PNY1wNXm9kPBHNiPB1jNilqZnDqo1C9Abw0GDatiDqRiIgUM+6eDpwa\ndQ4pWVrUrcoj57Th22UbuGL8LHam79r3TiIiEndiKmC4+0/ufhxQC2ji7l3cfXEe9p/i7qdkOVZH\ndz/M3c9w9+2xZJOIJFWBM5+H7Ztg0hBI11UQERH5k0/N7BEz62pmbTOWqENJfDu2aW1uPTWFDxes\n5NY35uGak0tEpMRJ3PdL9szMqgEDgQZAYnA3VHD3K2JOJvGrdjPo/SBMvhg+vAV63h51IhERKV6O\nDB9vy7LNCebFEsm384+oT9raLfxn6k8cUqMiF3drGHUkEREpQDEVMIC3gS+AOfxxS1QRaDkAln4J\nnz0MdTtCsz5RJxIRkYiZ2V/d/UHgJnefFnUeKZmu79WEtHVbuePt+dSpXoGTWhwUdSQRESkgsRYw\nktz96gJJIiXPCXfCsq/h1eFwQDOoeVjUiUREJFpDgAeBhwANGZFCUaaMce+AVizfuI0rJ3xN7Srl\naVe/RtSxRESkAMQ6iedzZnaxmR1kZjUylgJJJvEvsTwMGAsJZWHi+fD75qgTiYhItOaHt1FvbGaz\nsyxzzGx21OGk5Egqm8CTA9tTp1oFLhqbyuLVOgcRESkJYi1g/A78C/gcmBkuqbGGkhKkal3o/zSs\nnA9vXgWaUEtEpNRy97OBI4AfgN5ZllPCR5ECU6NSOUYP7gDA4NHTWbv594gTiYhIrGItYFwNHObu\nDdw9OVw0W5Ls7tBjoMffYfYESNXdcUVESjN3X+7urdz95+xL1Nmk5GlQsxJPDWrPsg3buPjZVLbt\nSI86koiIxCDWAsa3wJaCCCIlXNf/g0Y94Z0RkDYz6jQiIiJSSrSrX+P/27vz+KjKs//jnyuThSSs\ngbATAVEKIhUIaqt1q1Zr3Rfa2iq4UZfW2vqjalurP7tp6dOq7VMfrIqU2p/iUqSupdblUatlEUVR\nXADLJvtOAlmu3x9zkkySSSb7mcl836/Xec0599muuXMmuefKOffNHV89jMX/2cZ1c96islJ3g4qI\npKrWJjAqgCVmNsPM7qqa2iIw6WQyMuDsGdB9AMy5CPZsCTsiERERSROnHjqAH355FE8tXc/tz74f\ndjgiItJCrU1gzAV+DrxGTR8Y+ve6xJdXAJP+BHs2wWOXQqVu4xQRSSdmNjt4/W7YsUj6uewLw7jo\ncwcw4+UVzH5dTyyJiKSiVg2j6u6z2ioQSRMDx8Gp0+Fv18CLt8EJPwo7IhER6TgTzOwA4BIz+xNg\nsSvdfWs4YUk6MDN+ctpo1m4r4eYn3mFgjy58cVS/sMMSEZFmaNEdGGY2J3hdWmcYtLc1DJokNGEy\njPsmvPwr+OC5sKMREZGO8z/As8BnqH3npkYxkw6RGcngdxeM45CBPfj2X95k6ZodYYckIiLN0NJH\nSKpu/awa9qzuJNK4U38N/Q+Fx6fCtlVhRyMiIh3A3e9y91HA/e4+PGYEM41iJh0mLzuT+6YUU5Cf\nzSWzFrBmm/qjFxFJFS1KYLj7+mD2qjhDoF3VduFJp5WVC5NmAx7t1LOsNOyIRESkg7j7lWb2WTP7\ndjCNDTsmSS99u3XhgYsnUlpWwcUzF7CjpCzskEREpAla24nnSXHKvtzKY0q6KBgWHZlk/VvwzA/C\njkZERDqImV0DPAj0DaYHzew74UYl6eagft2YceEEVm3ZwxWzF7G/vDLskEREJIGW9oFxpZktBUbW\n6f9iJaA+MKTpRn4ZvnAdLJ4Fb/457GhERKRjXAYc4e4/cfefAEcCl4cck6Shzx/Yh9vPHcu/Vmzh\nhsfext3DDklERBrR0lFI/gI8A/wSuCGmfJd6EJdmO/5HsGYhPHUd9B8LA3QnsYhIJ2dA7FjaFdQZ\nkaRFBzWLEO0MdK27n2Zmw4CHgAJgMXChu+9v7Xmkczln/GDWbCvhN/M/YEhBHt876eCwQxIRkQa0\ntA+MHe6+yt2/XqcPDCUvpPkyInDufZBbAHMuhJJtYUckIiLtaybwhpndYma3AK8D97XBcb8LvBez\nfDvwW3c/CNgGXNoG55BO6DsnjOD8CYO58/kPeWTh6rDDERGRBrS2DwyRttG1ECbNgh1r4a9XQqWe\nQxUR6azc/TfAxcBWoomFi939jtYc08wGA18B7g2WDTgBeDTYZBZwVmvOIZ2XmfGLcw7l6BF9uPHx\npbzy4eawQxIRkTiUwJDkMeRwOPkX8MEz8Opvw45GRETakbsvDoZVvdPd32yDQ94B/ACoyoD3Bra7\ne3mwvAYYFG9HM5tqZgvNbOGmTZvaIBRJRVmRDP7wzfGM6NuVK/+8iPc/3Rl2SCIiUocSGJJcDr8c\nxpwH//wZrHgx7GhERCQFmNlpwEZ3XxRbHGfTuD00uvs97l7s7sWFhYXtEqOkhu5dsrh/ykTyciJc\nMnMBG3ZqmHcRkWSiBIYkFzM4/U7oczA8emn0kRIREZHGHQWcYWariHbaeQLROzJ6mllVh+WDgXXh\nhCepZGDPXO6fMpEdJWVc8sAC9uwrT7yTiIh0CCUwJPnkdIVJs6G8FB6ZAuXqMF5EpLMws4iZ/aMt\nj+nuN7r7YHcfCnwN+Ke7fwN4ATgv2Gwy8ERbnlc6r0MG9uD33xjP+5/u4tt/WUx5hfrmEhFJBkpg\nSHIqPBjO/D2s+TfMvynsaEREpI24ewWw18x6dMDprge+b2YfEe0Toy1GOpE0cfzIvvz0zDG8sHwT\nP5n3Lu5xn0ASEZEOlJl4E5GQHHI2rF4Ar/83DJ4Ih56XeB8REUkFpcBSM5sP7KkqdPdrWntgd38R\neDGYXwEc3tpjSvq64IgiVm/by90vfkxRQR5XHHtg2CGJiKQ1JTAkuZ30f2HdYpj3Heh3CPQdFXZE\nIiLSek8Fk0jSm/alkazZVsJtz7zPoJ65nP7ZgWGHJCKStpTAkOQWyYLzZsKMY+DhC2HqC5DTLeyo\nRESkFdx9lpnlAkXuvjzseEQak5Fh/Pr8sWzYUcp1c96if48uTBxaEHZYIiJpSX1gSPLrPgDOnwlb\nV8AT3wY9gyoiktLM7HRgCfBssHyYmc0LNyqRhuVkRphx4QQG98rl8j8t5ONNu8MOSUQkLSmBIalh\n6NFw4s2wbC68fnfY0YiISOvcQrRviu0A7r4EGBZmQCKJ9MrP5oGLDydixsUzF7Bl976wQxIRSTtK\nYEjq+Pw18JnToqOSfPKvsKMREZGWK3f3HXXKdHudJL2i3nncO7mYjbtKuexPCyktqwg7JBGRtKIE\nhqQOMzjrD9CzCB6ZArs3hh2RiIi0zDtmdgEQMbODzOx3wGthByXSFOOKenHHV8exZPV2rn1oCRWV\nyr2JiHQUJTAktXTpAZNmQ+kOePQSqCgPOyIREWm+7wCHAPuA/wfsBK4NNSKRZjhlTH9+/JXRPPvu\np/zi6ffCDkdEJG0ogSGpp/8YOP0OWPW/8M9bw45GRESayd33uvuPgC8Cx7v7j9y9NOy4RJrj0qOH\nMeXzQ7nvlZU88OrKsMMREUkLSmBIavrs16D4Enj1Tnjvb2FHIyIizWBmE81sKfA2sNTM3jKzCWHH\nJdJcN502mi+N7setTy5j/rINYYcjItLpKYEhqeuU22DgeJh7FWz5OOxoRESk6e4DrnL3oe4+FLga\nmBluSCLNF8kw7vzaOA4d1IPv/L/FvLV6e9ghiYh0akpgSOrKzIFJf4KMTHj4Qti/N+yIRESkaXa5\n+/9WLbj7K8CuEOMRabHc7Aj3Tp5IYbccLp21gNVb1R4REWkvSmBIaus5BM79I2xcBk9+D1w9gYuI\nJCszG29m44F/m9kMMzvOzI41sz8AL4YcnkiLFXbLYeaUwymrcKbM/Dc79paFHZKISKfU4QkMMxti\nZi+Y2Xtm9q6ZfTcoLzCz+Wb2YfDaq6NjkxQ14kQ47kZ4+yFYpDuQRUSS2H8F02HAwcDNwC3AKOBz\n4YUl0noj+nblngsnsHprCVNnL2RfeUXYIYmIdDph3IFRDlzn7qOAI4GrzWw0cAPwvLsfBDwfLIs0\nzTHTYMRJ8Mz1sHZR2NGIiEgc7n58I9MJYccn0lpHaoygxgAAHWVJREFUDO/N9PPH8sbKrVz/6Nu4\n7gwVEWlTmR19QndfD6wP5neZ2XvAIOBM4Lhgs1lEbyW9vqPjkxSVkQHn3AM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120u44bG3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"text/plain": [ "<Figure size 1080x1080 with 8 Axes>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "\n", "# t1_l5_n5\n", "plt.figure(figsize=(15, 15))\n", "plt.subplot(421)\n", "x = [0.5, 0.6, 0.7, 0.8, 0.9]\n", "y_1 = ibm['time'][0:5]\n", "y_2 = ibm['time'][18:23]\n", "plt.plot(x, y_1, 'o-')\n", "plt.plot(x, y_2, 'o-')\n", "plt.xlabel('minsup (%)')\n", "plt.ylabel('time (sec)')\n", "plt.legend([\"FP Growth\", \"Apriori\"], loc=1);\n", "plt.title('Figure 1: t1_l5_n5');\n", "\n", "plt.subplot(422)\n", "x = [0.5, 0.6, 0.7, 0.8, 0.9]\n", "y_1 = ibm['fp_num'][0:5]\n", "plt.plot(x, y_1, 'o-')\n", "plt.xlabel('minsup (%)')\n", "plt.ylabel('number of frequent pattern')\n", "plt.legend([\"number of frequent pattern\"], loc=1);\n", "plt.title('Figure 2: t1_l5_n5, fp number');\n", "\n", "\n", "\n", "# t10_l5_n5\n", "plt.subplot(423)\n", "x = [0.5, 0.6, 0.7, 0.8, 0.9]\n", "y_1 = ibm['time'][5:10]\n", "y_2 = ibm['time'][23:28]\n", "plt.plot(x, y_1, 'o-')\n", "plt.plot(x, y_2, 'o-')\n", "plt.xlabel('minsup (%)')\n", "plt.ylabel('time (sec)')\n", "plt.legend([\"FP Growth\", \"Apriori\"], loc=1);\n", "plt.title('Figure 3: t10_l5_n5');\n", "\n", "\n", "plt.subplot(424)\n", "x = [0.5, 0.6, 0.7, 0.8, 0.9]\n", "y_1 = ibm['fp_num'][5:10]\n", "plt.plot(x, y_1, 'o-')\n", "plt.xlabel('minsup (%)')\n", "plt.ylabel('number of frequent pattern')\n", "plt.legend([\"number of frequent pattern\"], loc=1);\n", "plt.title('Figure 4: t10_l5_n5, fp number');\n", "\n", "\n", "\n", "# t10_l5_n30\n", "plt.subplot(425)\n", "x = [0.2, 0.3, 0.4, 0.5]\n", "y_1 = ibm['time'][10:14]\n", "y_2 = ibm['time'][28:32]\n", "plt.plot(x, y_1, 'o-')\n", "plt.plot(x, y_2, 'o-')\n", "plt.xlabel('minsup (%)')\n", "plt.ylabel('time (sec)')\n", "plt.legend([\"FP Growth\", \"Apriori\"], loc=1);\n", "plt.title('Figure 5: t10_l5_n30');\n", "\n", "\n", "plt.subplot(426)\n", "x = [0.2, 0.3, 0.4, 0.5]\n", "y_1 = ibm['fp_num'][10:14]\n", "plt.plot(x, y_1, 'o-')\n", "plt.xlabel('minsup (%)')\n", "plt.ylabel('number of frequent pattern')\n", "plt.legend([\"number of frequent pattern\"], loc=1);\n", "plt.title('Figure 6: t10_l5_n30, fp number');\n", "\n", "\n", "\n", "# t20_l5_n30\n", "plt.subplot(427)\n", "x = [0.2, 0.3, 0.4, 0.5]\n", "y_1 = ibm['time'][14:18]\n", "y_2 = ibm['time'][32:36]\n", "plt.plot(x, y_1, 'o-')\n", "plt.plot(x, y_2, 'o-')\n", "plt.xlabel('minsup (%)')\n", "plt.ylabel('time (sec)')\n", "plt.legend([\"FP Growth\", \"Apriori\"], loc=1);\n", "plt.title('Figure 7: t20_l5_n30');\n", "\n", "\n", "\n", "plt.subplot(428)\n", "x = [0.2, 0.3, 0.4, 0.5]\n", "y_1 = ibm['fp_num'][14:18]\n", "plt.plot(x, y_1, 'o-')\n", "plt.xlabel('minsup (%)')\n", "plt.ylabel('number of frequent pattern')\n", "plt.legend([\"number of frequent pattern\"], loc=1);\n", "plt.title('Figure 8: t20_l5_n30, fp number');\n", "\n", "plt.tight_layout()\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "在 IBM 合成資料集中,實驗使用四種不同 item 數,與資料量數之資料,結果顯示在 Figure 1 ~ Figure 4 中。\n", "minsup由於不同資料集所含 frequent patterns 數目不同,因此有 0.5% ~ 0.9%, 0.2% ~ 0.5% 兩種設定。\n", "在實驗中可以觀察到 FP growth 的效率遠遠超過 Apriori。 而當 minsup 值升高時兩者的時間差則開始縮小,其原因是因為 minsup 值升高則frequent pattern 的數目減少,而 Apriori 所需要 join 與搜索的 Candicates 數目也隨之減少的緣故。\n", "<br>\n", "<br>\n", "<b>改變 Item 種類數目 </b><br>\n", "而從 Figure 3 跟 Figure 5 可以看到item 種類增加(5000~30000),Apriori 時間似乎會花較久,但事實上是因為後者生成之的FP數目較多,Candidate 數目也較多所導致。\n", "<br>\n", "<b>改變 Datasize </b><br>\n", "Figure 5 與 Figure 7 則是改變 Transaction 數目,從約10000筆資料到 20000筆資料。 雖然 Apriori 需要不斷 Scan 整份資料集,但可以看到在目前的資料量下,Apriori 依然是FP 數目影響較大,FP 越多,花的時間越多, 但 FP growth 就不同了,雖然圖片看來差異不大,但在20000筆資料所花的時間是略為大於10000筆資料的。\n", "\n", "\n" ] }, { "cell_type": "code", "execution_count": 73, "metadata": {}, "outputs": [ { "data": { "image/png": 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"text/plain": [ "<Figure size 432x288 with 1 Axes>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "\n", "x = [2, 4, 6, 8]\n", "y_1 = [0.014822602272033691,0.024219989776611328,0.04265165328979492,0.037372589111328125]\n", "y_2 = [1.559230923652649,3.5876978635787964,3.9564480781555176,5.6181875467300415]\n", "plt.plot(x, y_1, 'o-')\n", "plt.plot(x, y_2, 'o-')\n", "plt.xlabel('data size (10^3)')\n", "plt.ylabel('time (sec)')\n", "plt.legend([\"FP Growth\", \"Apriori\"], loc=2);\n", "plt.title('Figure 9: Scalability (minsup = 0.8%');\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "進一步改變 Data size 的結果可參考 Figure 9。 此實驗固定minsup 為0.8%,可觀察到兩者時間皆會隨者資料量增加而提升,但可看到 FP-growth 的時間改變量並不大,在資料量高時具有拓展性。" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### b. Kaggle\n", "\n" ] }, { "cell_type": "code", "execution_count": 39, "metadata": { "scrolled": true }, "outputs": [ { "data": { "text/html": [ "<div>\n", "<style scoped>\n", " .dataframe tbody tr th:only-of-type {\n", " vertical-align: middle;\n", " }\n", "\n", " .dataframe tbody tr th {\n", " vertical-align: top;\n", " }\n", "\n", " .dataframe thead th {\n", " text-align: right;\n", " }\n", "</style>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>data</th>\n", " <th>minsup</th>\n", " <th>fp_num</th>\n", " <th>algorithm</th>\n", " <th>time</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>cart_dataset_v2</td>\n", " <td>0.05</td>\n", " <td>74</td>\n", " <td>fpg</td>\n", " <td>1.402993</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>cart_dataset_v2</td>\n", " <td>0.06</td>\n", " <td>89</td>\n", " <td>fpg</td>\n", " <td>1.395103</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>cart_dataset_v2</td>\n", " <td>0.07</td>\n", " <td>104</td>\n", " <td>fpg</td>\n", " <td>1.427723</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>cart_dataset_v2</td>\n", " <td>0.08</td>\n", " <td>119</td>\n", " <td>fpg</td>\n", " <td>1.365158</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>cart_dataset_v2</td>\n", " <td>0.09</td>\n", " <td>134</td>\n", " <td>fpg</td>\n", " <td>1.427504</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " data minsup fp_num algorithm time\n", "0 cart_dataset_v2 0.05 74 fpg 1.402993\n", "1 cart_dataset_v2 0.06 89 fpg 1.395103\n", "2 cart_dataset_v2 0.07 104 fpg 1.427723\n", "3 cart_dataset_v2 0.08 119 fpg 1.365158\n", "4 cart_dataset_v2 0.09 134 fpg 1.427504" ] }, "execution_count": 39, "metadata": {}, "output_type": "execute_result" } ], "source": [ "kaggle.head()" ] }, { "cell_type": "code", "execution_count": 74, "metadata": {}, "outputs": [ { "data": { "image/png": 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Fixdz2WWXER4ezqWXXnrYej799FOGDh1K27ZtAbjqqqtYtGgRo0ePPuIyIiIi\ncuzWbd3F5Lm5DOrahqnj0ggLq/+vBlJRWJ2kdFj1OnzzGXQ8y+80IhKKjnJFj4dTAk1HK4k9FX46\n77g2WVhYyPz58/niiy8wM0pLSzEzLr744sO+g+7g46ioKMLDww9bV3VfEn2kZUREROTYHCgtY8KM\nz2kSEcaUsam+FISgPoXV6zYCwiIg9zW/k4hIQ1UH/ZhnzpzJ1VdfzcaNG9mwYQP5+fnEx8ezePFi\nlixZwvr16ykrK2P69OkMGjSo2nX169eP9957j23btlFaWsq0adM499xzjzubiIiI/ODx+Wv4PL+I\nB8b0pH1s1QPN1AcVhdVp1gq6DIacOVDN2XIRkePWaxykP+ZdGcS82/THTqgf87Rp0xgzZswh0y69\n9FJefPFF+vfvz8SJE0lJSSE+Pv6w+Srr0KEDDz74IOeddx6pqamceeaZZGZmHnc2ERER8XyWt4PH\nF6zhkt4dGdmrg69ZrLqmQaGqT58+rtZGwFv6LMydAL/4CNppqHURObrc3FySkpL8jnGYhQsXMmXK\nFObOnet3lONW1b41s2XOuT4+RQo5tXqMFBGR41K8r4SLH3ufklLHGzcPpnnU4SOPnqiaHB91pfBo\nEkcCBrlz/E4iIiIiIiINwOR5OeRt381D41LrpCCsKRWFRxPTDk7rr36FIhLyhg4dGtJXCUVERBqC\nt3M2M21JPuOHJNAvobXfcQAVhccmKR02fwGFa/1OIiIhoiE2zfeb9qmIiIS6rd/vY+KsbJI6NOeW\nC7v5HaecisJjkZTu3epqoYgcg6ioKAoLC1XE1CLnHIWFhURF+Tcym4iIyIlwzjFxVjbf7yvh0SvS\naBoRPF/rpO8pPBYtToW43l6/wkE3+51GRIJcp06dKCgoYOvWrX5HaVCioqLo1KmT3zFERESOy7Ql\n+by7cgt3jUqmW7sYv+McQkXhsUrKgHcnwXcFEKsPJSJyZJGRkcTHx/sdQ0RERILEuq27uG9uDoO6\ntuHaAV38jnMYNR89VkkZ3m2uBmkQEREREZFjc6C0jAkzPqdJRBhTxqYSFmZ+RzqMisJj1aYrnJKs\nfoUiIiIiInLMHp+/hs/zi7h/TArtY4Ozb7yKwppISoe8D2GX+gmJiIiIiEj1luft4PEFaxjTuyOj\nesX5HeeIVBTWRFIGuDJYNc/vJCIiIiIiEsSK95UwYXoW7ZtHMSmzh99xqqWisCba9YCW8ZAzx+8k\nIiIiIiISxCbPy2Xj9t08NC6V5lGRfseplorCmjCD5AxY/x7sKfI7jYiIiIiIBKG3czYzbUke44ck\n0C+htd9xjkpFYU0lZUBZCXzlual+AAAgAElEQVT1H7+TiIiIiIhIkNn6/T4mzsomqUNzbrmwm99x\njomKwpqKOxOad9QopCIiIiIicgjnHBNnZfP9vhIevSKNphHhfkc6JioKayosDLqPgjXvwL5dfqcR\nEREREZEgMW1JPu+u3MIdI7rTrV2M33GOmYrC45GcASV7vcJQREREREQavfXbirlvbg6DurbhpwO6\n+B2nRlQUHo/T+kOzNpCrUUhFRERERBq7A6Vl3Dw9iyYRYUwZm0pYmPkdqUZUFB6PsHDoPhK+ehMO\n7PU7jYiIiIiI+Ojx+Wv4PL+I+8ek0D42yu84Naai8HglZcD+XbBuod9JRERERETEJ8vzdvD4gjWM\n6d2RUb3i/I5zXFQUHq/4IdA0VqOQioiIiIg0UsX7SpgwPYv2zaOYlNnD7zjHTUXh8YpoAokjYNU8\nKD3gdxoREREREalnk+flsnH7bqaOS6V5VKTfcY6bisITkZQBe3bAxg/8TiIiIiIiIvXonZzNTFuS\nx/ghCZyT0NrvOCdEReGJOP1HENkMcjQKqYiIiIhIY7H1+33cMSubpA7NueXCbn7HOWEqCk9Ek2Zw\nxoWwci6UlfmdRkRERERE6phzjomzsvl+XwmPXpFG04hwvyOdMBWFJyopA3ZthoIlficREREREZE6\nNm1JPu+u3MIdI7rTrV2M33FqhYrCE3XGMAhvolFIRUREREQauPXbirlvbg4Du7bmpwO6+B2n1qgo\nPFFRzSHhPK9foXN+pxERERERkTpQUlrGhOlZRIYbU8amEhZmfkeqNSoKa0NyBnyXB5s+9zuJiIiI\niIjUgccXrCErv4gHLulJh9hov+PUKhWFtSHxYrBwyNUopCIiIiIiDc3yvB38ef4axvTuyKhecX7H\nqXUqCmtDs1bQZZD6FYqIiIiINDDF+0qYMD2L9s2jmJTZw+84dUJFYW1JSodtX8GWlX4nERERERGR\nWjJ5Xi4bt+9m6rhUmkdF+h2nTqgorC1J6YDpaqGIiIiISAPxTs5mpi3JY/zgBM5JaO13nDqjorC2\nxLSHU/tC7r/9TiIiIiIiIido2659TJydTVKH5twyrJvfceqUisLalJQB366A7ev9TiIiIiIiIsfJ\nOcfEWdns3FvCI5en0TQi3O9IdUpFYW1KGuXdqgmpiIiIiEjIeunTfN7J3cIdI7qT2D7G7zh1TkVh\nbWrZBTqkqigUEREREQlR67cVc+9rOQzs2pqfDujid5x6oaKwtiWlQ8ES2PmN30lERERERKQGSkrL\nmDA9i8hwY8rYVMLCzO9I9UJFYW1LyvRuV87zN4eIiIiIiNTI4wvWkJVfxP1jetIhNtrvOPWmzopC\nM3vWzLaY2RcVprUys7fNbHXgtmVgupnZY2a2xsyyzezMCstcE5h/tZldU1d5a03bbtAmEXI0CqmI\niIiISKhYnreDP89fw+i0ONJT4/yOU6/q8krhc8CIStMmAu86584A3g08BrgIOCPwMx74K3hFJHA3\n0A/oC9x9sJAMaskZsPEDKC70O4mIiIiIiBzF7v0l3DLjc9o3j2JSZorfcepdnRWFzrlFwPZKkzOB\nfwTu/wMYXWH6P53nY6CFmXUAhgNvO+e2O+d2AG9zeKEZfJLSwZXBKjUhFREREREJdpPn5bKhsJip\n41KJjY70O069q+8+he2cc5sAArenBKZ3BPIrzFcQmHak6Ycxs/FmttTMlm7durXWg9dI+17QorNG\nIRURERERCXLv5GzmxU/yGD84gXMSWvsdxxfBMtBMVcP6uGqmHz7Ruaecc32cc33atm1bq+FqzMy7\nWrh2Aez9zt8sIiIiIiJSpW279jFxdjZJHZpzy7BufsfxTX0XhZsDzUIJ3G4JTC8ATq0wXyfgm2qm\nB7/kTCg7AF+95XcSERERERGpxDnHxFnZ7NxbwiOXp9E0ItzvSL6p76JwDnBwBNFrgH9XmH51YBTS\nc4DvAs1L3wSGmVnLwAAzwwLTgl/HPhDTAXI1CqmIiIiISLB56dN83sndwu3DE0lsH+N3HF9F1NWK\nzWwaMBRoY2YFeKOI/gGYYWbXA3nA2MDsrwMXA2uA3cBPAZxz283sPuDTwHz3OucqD14TnMLCoPso\nWP487N8NTZr5nUhERERERID124q597UcBnZtzXUD4/2O47s6Kwqdc1ce4anzq5jXAb88wnqeBZ6t\nxWj1JykdPn0a1rzjfU2FiIiIiIj4qqS0jAnTs4gMN6aMTSUsrKphTBqXYBlopmHqPBCiW2kUUhER\nOSZm9qyZbTGzL6p47lYzc2bWJvDYzOwxM1tjZtlmdmb9JxYRCT2PL1hDVn4R94/pSYfYaL/jBAUV\nhXUpPAK6Xwxf/QdK9vmdRkREgt9zVPF9vGZ2KnAhXteLgy4Czgj8jAf+Wg/5RERC2vK8Hfx5/hpG\np8WRnhrnd5ygoaKwriVlwr6dsH6R30lERCTIOecWAVX1nX8YuJ1Dv5YpE/in83wMtDg4wreIiBxu\n9/4SbpnxOe1imjIpM8XvOEFFRWFdSzgXmjaHHI1CKiIiNWdmGcDXzrnPKz3VEciv8LggMK2qdYw3\ns6VmtnTr1q11lFREJLhNnpfLhsJipo5LIzY60u84QUVFYV2LaArdhsOq16G0xO80IiISQsysGfA/\nwF1VPV3FNFfFNJxzTznn+jjn+rRt27Y2I4qIhIR3czfz4id5jB+cQP/TW/sdJ+ioKKwPSemwuxDy\nPvQ7iYiIhJbTgXjgczPbAHQCPjOz9nhXBk+tMG8n4Jt6TygiEuS27drHHbOySerQnFuGdfM7TlBS\nUVgful4AEdEahVRERGrEObfCOXeKc66Lc64LXiF4pnPuW2AOcHVgFNJzgO+cc5v8zCsiEmycc0yc\nlc3OvSU8cnkaTSPC/Y4UlFQU1ocmJ0HX872isKzM7zQiIhKkzGwa8BGQaGYFZnZ9NbO/DqwD1gBP\nAzfVQ0QRkZDy0qf5vJO7hduHJ5LYPsbvOEGrzr68XipJzoSVc+HrZXDq2X6nERGRIOScu/Ioz3ep\ncN8Bv6zrTCIioWrDtmLum5vDwK6tuW5gvN9xgpquFNaXM4ZBWCTkahRSEREREZG6VFJaxs3Ts4gI\nM6aMTSUsrKqxueQgFYX1JboFJAz1mpC6KgeHExERERGRWvCXBWvJyi/i/jE96RAb7XecoKeisD4l\npcOODfDtCr+TiIiIiIg0SMvzdvDY/NWMTosjPTXO7zghQUVhfeo+EixMo5CKiIiIiNSB3ftLuGXG\n57SLacqkzBS/44QMFYX16aQ20Hkg5M7xO4mIiIiISIMzeV4uGwqLmToujdjoSL/jhAwVhfUtKQO2\nroStX/mdRERERESkwXg3dzMvfpLHzwYn0P/01n7HCSkqCutb0ijvVlcLRURERERqxbZd+7hjVjbd\n28fw22Hd/I4TclQU1rfmcdDpbPUrFBERERGpBc45Js5awc69JTx6RW+aRoT7HSnkqCj0Q1I6bMqC\nHRv9TiIiIiIiEtJe+jSfd3I3c/vwRBLbx/gdJySpKPRDUrp3u3KuvzlERERERELYhm3F3Dc3h4Fd\nW3PdwHi/44QsFYV+aJUA7XpCjvoVioiIiIgcj5LSMm6enkVEmDFlbCphYeZ3pJClotAvyRmQ/wl8\n/63fSUREREREQs5fFqwlK7+IyWN60iE22u84IU1FoV+S0gGnJqQiIiIiIjWUlV/EY/NXMzotjozU\nOL/jhDwVhX5p2x1an6FRSEVEREREamD3/hImTM+iXUxTJmWm+B2nQVBR6Bcz72rh+vdh93a/04iI\niIiIhITJ83LZUFjM1HFpxEZH+h2nQVBR6KfkDHClsOoNv5OIiIiIiAS9d3M38+InefxscAL9T2/t\nd5wGQ0WhnzqkQexpkKtRSEVEREREqrNt1z7umJVN9/Yx/HZYN7/jNCgqCv10sAnp2vmw73u/04iI\niIiIBCXnHBNnrWDnnhIeuSKNphHhfkdqUFQU+i0pHUr3w1dv+p1ERERERCQoTf80n3dyN3P7iES6\nt2/ud5wGR0Wh307tBye30yikIiIiIiJV2LCtmHvn5jCwa2uuGxjvd5wGSUWh38LCoPtIWP02HNjj\ndxoRERERkaBRUlrGzdOziAgzpoxNJSzM/I7UIKkoDAZJGXCg2OtbKCIiIiIiAPxlwVqy8ouYPKYn\nHWKj/Y7TYKkoDAZdBkFUC8jRKKQiIiIiIgBZ+UU8Nn81mWlxZKTG+R2nQVNRGAzCI70mpF+9ASX7\n/U4jIiIiIuKr3ftLmDA9i3YxTbk3M8XvOA2eisJgkZQOe7+DDYv8TiIiIiIi4qv75+WyobCYKeNS\niY2O9DtOg6eiMFgknAdNTtYopCIiIiLSqM1fuZkXPsnjZ4MTGHB6G7/jNAoqCoNFZBScMQxWzoOy\nUr/TiIiIiIjUu2279nH7zGy6t4/ht8O6+R2n0VBRGEySM6B4K+R97HcSEREREZF65Zxj4qwV7NxT\nwiNXpNE0ItzvSI2GisJg0vVCiIiCXI1CKiIiIiKNy/RP83kndzO3j0ike/vmfsdpVFQUBpOmJ8Pp\n53v9Cp3zO42IiIiISL3YsK2Ye+fmMOD01lw3MN7vOI2OisJgk5QOO7+Grz/zO4mIiIiISJ0rKS1j\nwowsIsKMKWNTCQszvyM1OhF+B5BKEkdAWITXhLTTWX6nERERERGpE68u/5o/vbmKr4v2APBf/TsT\n1yLa51SNk64UBpvolhA/xCsK1YRURERERBqgV5d/zZ2zV5QXhAAzlxbw6vKvfUzVeKkoDEZJGbB9\nHWzJ8TuJiIiIiEit+9Obq9hz4NCvYdtzoJQ/vbnKp0SNm4rCYNR9JGCQo1FIRURERKTh+abCFcJj\nmS51S0VhMDr5FOg8wBuFVERERESkgWl1UpMqp6tPoT9UFAarpHTY8iUUrvU7iYiIiIhIrSnctY99\nJaVUHmM0OjKc24Yn+pKpsVNRGKyS0r1bfZG9iIiIiDQQzjkmzl7B/hLHbSMS6dgiGgM6tojmwUt6\nMrp3R78jNkr6SopgFdsJ4s70+hUOmuB3GhERERGREzb903zeztnM/45M4obBCdw0tKvfkQRdKQxu\nyRnwzWdQlO93EhERERGRE7JhWzH3zs1hwOmtuW5gvN9xpAIVhcEsKcO7XTnX3xwiIiIiIiegpLSM\nCTOyiAgzpoxNJSysco9C8ZOKwmDW+nQ4pYdGIRURERGRkPbEwrUszyvivtEpGmE0CKkoDHZJ6bDx\nQ9i1xe8kIiIiIiI19nl+EY++u5qM1Dgy0zSQTDBSURjskjMAByvn+Z1ERERERKRGdu8vYcL0LNrF\nNOW+zBS/48gRqCgMdqckQ6sEfTWFiIiIiISc++flsr6wmCnjUoltFul3HDkCFYXBzswbcGb9Itiz\nw+80IiIiIiLHZP7KzbzwSR43DIpnwOlt/I4j1VBRGAqSMqCsBFb9x+8kIiIiIiJHVbhrH7fPXEH3\n9jHcOjzR7zhyFCoKQ0HHM6F5R41CKiIiIiJBzznHxNkr2LnnAI9ckUbTiHC/I8lRqCgMBWbeKKRr\n34V9u/xOIyIiIiJyRDOW5vN2zmZuG55I9/bN/Y4jx0BFYahIyoCSvbDmbb+TiIiIiIhUaWNhMZNe\ny6F/QmuuHxTvdxw5RioKQ8Vp58BJbSFHo5CKiIiISPApKS3j5ulZRIQZU8elEhZmfkeSY6SiMFSE\nhUP3kbD6LTiw1+80IiIiIiKHeGLhWpbnFXHf6BTiWkT7HUdqQEVhKElKh/27YN0Cv5OIiIiIiJT7\nPL+IR99dTUZqHJlpHf2OIzWkojCUdBkCUbEahVREREREgsbu/SVMmJ7FKTFNuS8zxe84chxUFIaS\niCbQ7SJYOQ9KD/idRkRERESEB17PZd22YqaOTSW2WaTfceQ4qCgMNckZsLcINiz2O4mIiIiINHIL\nVm7h+Y/z+NngeAZ0beN3HDlOKgpDzek/gsiTIFejkIqIiIiIfwp37eO2mdl0bx/DrcMT/Y4jJ0BF\nYaiJjIYzLoTcuVBW6ncaERGpRWb2rJltMbMvKky7z8yyzSzLzN4ys7jAdDOzx8xsTeD5M/1LLiKN\njXOOibNXsHPPAR65Io2mEeF+R5IToKIwFCWlQ/EWyF/idxIREaldzwEjKk37k3Oul3MuDZgL3BWY\nfhFwRuBnPPDX+gopIjJjaT5v52zmtuGJdG/f3O84coJUFIaibsMhvIlGIRURaWCcc4uA7ZWm7azw\n8CTABe5nAv90no+BFmbWoX6SikhjtrGwmEmv5dA/oTXXD4r3O47UAhWFoahpjNe3MPc1cO7o84uI\nSEgzs/vNLB+4ih+uFHYE8ivMVhCYVtXy481sqZkt3bp1a92GFZEGraS0jAnTswgPM6aOSyUszPyO\nJLVARWGoSsqA7/JgU5bfSUREpI455/7HOXcq8ALwq8Dkqj6JVXmm0Dn3lHOuj3OuT9u2besqpog0\nAn9duJbP8oqYPDqFuBbRfseRWqKiMFQlXgQWDjkahVREpBF5Ebg0cL8AOLXCc52Ab+o9kYg0Gp/n\nF/HIu6vJSI0jM63KhgkSolQUhqpmrSB+sPfVFGpCKiLSYJnZGRUeZgArA/fnAFcHRiE9B/jOObep\n3gOKSKOwe38JE6ZncUpMU+7LTPE7jtQyFYWhLCkdCtfA1pVHn1dERIKemU0DPgISzazAzK4H/mBm\nX5hZNjAM+O/A7K8D64A1wNPATX5kFpHG4YHXc1m3rZipY1OJbRbpdxypZRF+B5AT0H0UzLvVG3Dm\nlCS/04iIyAlyzl1ZxeRnjjCvA35Zt4lERGDByi08/3EeNwyKZ0DXNn7HkTqgK4WhLKY9nNpP/QpF\nREREpE4U7trHbTOz6d4+hluHJ/odR+qIisJQl5wBm1fA9nV+JxERERGRBsQ5x52zV7BzzwEevjyN\nqMhwvyNJHVFRGOq6j/Ju9UX2IiIiIlKLZizN562czdw2PJGkDs39jiN1SEVhqGvZGTqkqSgUERER\nkVqzsbCYSa/l0D+hNdcPivc7jtQxFYUNQVI6FHwK333tdxIRERERCXElpWVMmJ5FeJgxdVwqYWHm\ndySpY74UhWY2wcy+DAyxPc3Mosws3sw+MbPVZjbdzJoE5m0aeLwm8HwXPzIHteRM73blPH9ziIiI\niEjI++vCtXyWV8Tk0SnEtYj2O47Ug3ovCs2sI/AboI9zLgUIB64A/gg87Jw7A9gBXB9Y5Hpgh3Ou\nK/BwYD6pqM0Z0La790X2IiIiIiLHKbugiEffXU16ahyZaR39jiP1xK/moxFAtJlFAM2ATcCPgJmB\n5/8BjA7czww8JvD8+Wama9iVJWXAxg+geJvfSUREREQkBO3eX8LNL2XRNqYpkzNT/I4j9ajei0Ln\n3NfAFCAPrxj8DlgGFDnnSgKzFQAHT010BPIDy5YE5m9deb1mNt7MlprZ0q1bt9btiwhGSengytSE\nVERERESOywOv57JuWzFTx6YS2yzS7zhSj/xoPtoS7+pfPBAHnARcVMWs7uAi1Tz3wwTnnnLO9XHO\n9Wnbtm1txQ0d7XtCyy4ahVREREREamzByi08/3EeNwyKZ0DXNn7HkXrmR/PRC4D1zrmtzrkDwGxg\nANAi0JwUoBPwTeB+AXAqQOD5WGB7/UYOAWbe1cJ1C2FPkd9pRERERCREFO7ax20zs+nePoZbhyf6\nHUd84EdRmAecY2bNAn0DzwdygAXAZYF5rgH+Hbg/J/CYwPPznXOHXSkUICkTyg7A6rf8TiIiIiIi\nIcA5x52zV7BzzwEevjyNqMhwvyOJD/zoU/gJ3oAxnwErAhmeAu4AbjGzNXh9Bp8JLPIM0Dow/RZg\nYn1nDhkdz4KYDpDz76PPKyIiIiKN3stLC3grZzO3Du9GUofmfscRn0QcfZba55y7G7i70uR1QN8q\n5t0LjK2PXCEvLMxrQvrZv2B/MTQ5ye9EIiIiIhKkNhYWM+m1L+mf0JobBiX4HUd85NdXUkhdSUqH\nkj2w5h2/k4iIiIhIkCopLWPC9CzCwoyp41IJC9M3vjVmKgobmtMGQLPWGoVURERERI7orwvX8lle\nEZNHpxDXItrvOOIzFYUNTXgEJF4MX70JJfv8TiMiIiIiQSa7oIhH311NemocmWkdj76ANHgqChui\n5EzYtxPWved3EhEREREJInv2l3Lz9CzaxjRlcmaK33EkSKgobIjih0DT5pCrUUhFRERE5AcPvJ7L\nuq3FTBmbSmyzSL/jSJBQUdgQRTSFbiNg5etQWuJ3GhEREREJAgtWbuFfH2/khkHxDOzaxu84EkRU\nFDZUSemwZzts/MDvJCIiIiLis8Jd+7htZjbd28dw6/BEv+NIkFFR2FB1vQAiojUKqYiIiEgj55zj\nztkr2LnnAA9fnkZUZLjfkSTIqChsqJo0gzMu8IrCsjK/04iIiIiIT15eWsBbOZu5dXg3kjo09zuO\nBCEVhQ1ZUibs+ha+Xup3EhERERHxQV7hbia99iXnJLTihkEJfseRIKWisCHrNgzCIiFHo5CKiIiI\nNDYlpWVMmJFFWJgxdVwaYWHmdyQJUioKG7KoWDj9PK8JqXN+pxERERGRevTke2tZtnEHk0en0LFF\ntN9xJIipKGzoktKhaCN8m+13EhERERGpJ9kFRTzyzmrSU+PITOvodxwJcioKG7rEkWBhGoVURERE\npJHYs7+Um6dn0TamKZMzU/yOIyFARWFDd1Jr6DwQcub4nURERERE6sEDr+eybmsxU8amEtss0u84\nEgJUFDYGyZmwbRVsXeV3EhERERGpQwtWbeFfH2/k+kHxDOzaxu84EiJUFDYG3Ud6t7m6WigiIiLS\nUG0v3s/tM7NJbBfDbcMT/Y4jIURFYWPQPA469VW/QhEREZEGyjnHxFnZfLf7AA9fnkZUZLjfkSSE\nqChsLJLSYdPnsGOD30lEREREpJa9vLSAt3I2c+vwbiTHNfc7joQYFYWNRVK6d5s7198cIiIiIlKr\n8gp3M+m1LzknoRU3DErwO46EIBWFjUWreGjfU/0KRURERBqQktIyJszIIizMmDoujbAw8zuShCAV\nhY1JUibkfwLff+t3EhERERGpBU++t5ZlG3dwX2YKHVtE+x1HQpSKwsakvAmpBpwRERERCXXZBUU8\n8s5qRvXqQGZanN9xJISpKGxMTukObbqpKBQREREJcXv2l3Lz9CzaxjTl/tE9MVOzUTl+Kgobm6R0\n2LAYdm/3O4mIiIiIHKcHXs9l3dZipoxNJbZZpN9xJMSpKGxskjLAlcKq1/1OIiIiIiLHYcGqLfzr\n441cPyiegV3b+B1HGgAVhY1Nh1RocRrkaBRSERERkVCzvXg/t8/MJrFdDLcNT/Q7jjQQKgobGzPv\nauG6BbB3p99pREREROQYOee4c3Y23+0+wMOXpxEVGe53JGkgVBQ2RknpULofVr/ldxIREREROUYv\nLyvgzS8389th3UiOa+53HGlAVBQ2Rp36wsnt9EX2IiIiIiEir3A3k+Z8yTkJrbhhcILfcaSBUVHY\nGIWFQfdRsPpt2L/b7zQiIiIiUo2S0jImzMgiLMyYOi6N8DB9/YTULhWFjVVyBhzYDWvn+51ERERE\nRKrx5HtrWbZxB/dlptCxRbTfcaQBUlHYWHUeCNEt1YRUREREJIhlFxTxyDurGdWrA5lpcX7HkQZK\nRWFjFR4JiSNh1X+gZL/faURERESkkj37S7l5ehZtTm7K/aN7YqZmo1I3VBQ2ZknpsO87WL/I7yQi\nIiIiUsmDb+SybmsxU8elEtss0u840oCpKGzMEoZCkxg1IRUREREJMgtWbeGfH23k+kHxDOzaxu84\n0sCpKGzMIqOg2zBYOQ/KSv1OIyLSYJhZNzN72szeMrP5B3/8ziUioWF78X5un5lNYrsYbhue6Hcc\naQQi/A4gPkvKgC9mQd5H0GWQ32lERBqKl4EngacBnXUTkWPmnOPO2dl8t/sA//hpX6Iiw/2OJI2A\nisLGrusFEBEFOXNUFIqI1J4S59xf/Q4hIqHn5WUFvPnlZu68qDvJcc39jiONhJqPNnZNT/YKw9zX\noKzM7zQiIg3Fa2Z2k5l1MLNWB3/8DiUiwS2vcDeT5nxJv/hW3DA4we840ojoSqF4o5CunAvffAad\n+vidRkSkIbgmcHtbhWkO0Kc8EalSaZnjlhlZhJkxdVwq4WH6+gmpPyoKBbqNgLAIbxRSFYUiIifE\nzMKAnzjnPvA7i4iEjiffW8vSjTt45PI0OrVs5nccaWTUfFQgugXEn+v1K3TO7zQiIiHNOVcGTPE7\nh4iEjuyCIh5++ytG9epAZlqc33GkEVJRKJ7kDNixHjZ/6XcSEZGG4C0zu9TMatT+y8yeNbMtZvZF\nhWl/MrOVZpZtZq+YWYsKz91pZmvMbJWZDa/NFyAi9WPP/lJunp5Fm5Obcv/ontTw34ZIrVBRKJ7E\nkWBh+iJ7EZHacQve11LsN7OdZva9me08huWeA0ZUmvY2kOKc6wV8BdwJYGbJwBVAj8AyT5iZxq4X\nCTEPvpHLuq3FTB2XSmyzSL/jSCOlolA8J7eF0wZ4o5CKiMgJcc7FOOfCnHORzrnmgcdHHVveObcI\n2F5p2lvOuZLAw4+BToH7mcBLzrl9zrn1wBqgby2+DBGpYwtXbeGfH23kuoHxDOzaxu84/5+9O4+u\nujr3P/5+CASSIIRgQJkHmQ+DijjVWayoKCQKbW9ba716rd5WFNvSYrXt71ptUbG9QyeptcNtAS8C\nVgWptQpqtSoJJGAAAYWAEGQmQELy/P44B4kQIYEk+wyf11rfdc53n+/hfFhLQ56z93c/ksJUFMoh\nA0bD5mWwZVXoJCIiCc2ivmhm34uddzWzhijYvgo8H3veGVhX47X1sbHa8txqZm+Z2VtlZWUNEENE\nTtTWPRV886kl9O3Ymm9d2S90HElxKgrlkAHXRB+1hFRE5ET9D3Au8IXY+W7gv0/kDzSzycAB4I8H\nh2q5rNbdwtz9V+4+3DdKq3sAACAASURBVN2H5+bmnkgMEWkA7s53Zy1lR3klj40/nVYttPJbwlJR\nKIe07QKdz1RRKCJy4s529zuAfQDuvg1IP94/zMxuBK4B/sX9422i1wNda1zWBdhwvJ8hIk1n5tvr\nmVf8IROv6MvATsdcWS7S6FQUyicNuBY2LIbt6459rYiIfJrK2KYvDmBmuUD18fxBZnYl8G3gWncv\nr/HSXOBzZtbSzHoCfYA3Tyy2iDS2Dz4q5wdzizm7Zw7/ekGv0HFEABWFcrgBo6OP2nBGRORE/Ax4\nGuhgZg8Ai4AHj/UmM/sT8DrQz8zWm9nNwH8BJwELzKzAzH4B4O7FwAxgGTAPuMPdqxrlbyMiJ2z2\n4lLOe+hFLpzyEuUVVXx2UEfSmqn9hMSH5qEDSJxp3xs6RqJF4bm3h04jIpKQ3P2PZvY2cBnRe//G\nuPvyOrzv87UMTzvK9Q8ADxx3UBFpErMXl/KdWUvZWxn93saBKfNXkJPVkjGn17o/lEiT0kyhHGnA\naPjgddi1KXQSEZGEZGa/d/d33f2/3f2/3H25mf0+dC4RCWPK/JKPC8KD9lZWMWV+SaBEIp+kolCO\nNOBawKHk2dBJREQS1aCaJ7H7C88MlEVEAtuwfW+9xkWamopCOVKHAZDTG5ZpF1IRkfows++Y2S5g\niJntjB27gM1EN4YRkRSUmV57y4lO2RlNnESkdioK5UhmMPBaWLsQyreGTiMikjDc/UF3PwmY4u5t\nYsdJ7t7e3SeFziciTe/vJZvZU1F1xKYyGS3S+OZn1bRe4oOKQqndgNFQfQBWzAudREQkEY04fMDM\nXgwRRETC2bqngm8+tYS+HVvzUN5gOmdnYEDn7AwezBusTWYkbmj3UaldpzOgTZfoLqTDvhA6jYhI\nQjCzVkAWcLKZtSO68yhAG6BTsGAi0uTcne/OWsr28gqevGkEAzu14YbhXUPHEqmVikKpnVl0tvCt\n38D+XdDypNCJREQSwb8BE4gWgG9zqCjcCfx3qFAi0vSeens984o/ZNKo/gzs1CZ0HJGj0vJR+XQD\nr4Wq/bByQegkIiIJwd1/6u49gXvcvZe794wdQ939v0LnE5Gm8cFH5Xx/bjFn98zhlgt6hY4jckya\nKZRP1/VsyMqF5XMhkhc6jYhIwnD3/zSzCDAQaFVj/HfhUolIU6iqdu6eUUAzMx4ZN/SIDWZE4pGK\nQvl0zdKg/zWwZAZU7oMWrY79HhERwczuBy4mWhQ+B4wCFgEqCkWS3C9efo+33t/G1PFD6dIuM3Qc\nkTrR8lE5ugGjoXIPvPe30ElERBLJ9cBlwIfufhMwFGgZNpKINLal63cwdcEKrh5yKmOGaWdRSRwq\nCuXoel4IrdpGdyEVEZG62uvu1cABM2tDtHm9biwSSWJ7K6qYMH0xJ7duyQNjIphp2agkDi0flaNL\nawH9roKS56CqMnouIiLH8paZZQO/JroL6W7gzbCRRKQxPfT8ct4r28Mfbj6b7Mz00HFE6kUzhXJs\nA66Ffdth7cLQSUREEoK73+7u2939F8BI4MbYMlIRSUJ/L9nMk6+/z1fP78ln+pwcOo5IvakolGPr\nfQm0yIJlc0MnERFJGGaWZ2aPAl8HeofOIyKNY+ueCr751BL6dmzNt67sFzqOyHFRUSjH1iIDOgyA\nd56E72fD1Eh0R1IREamVmf0PcBuwFCgC/s3M1LxeJMm4O9+dtZTt5RU8Nv50WrVICx1J5LjonkI5\ntiUz4MMl4NXR8x3r4JlvRJ8PGRcul4hI/LoIiLi7A5jZk0QLRBFJIk+9vZ55xR8yaVR/BnZqEzqO\nyHHTTKEc24s/hKqKT45V7o2Oi4hIbUqAbjXOuwJLAmURkUawbms5P3hmGSN65nDLBdpcWBKbZgrl\n2Hasr9+4iIi0B5ab2cEdR88CXjezuQDufm2wZCJywqqqnbumF2DAo+OGktZM7ScksakolGNr2yW6\nZLS2cRERqc19oQOISOP5xcvv8db725g6fihd2mWGjiNywlQUyrFddl/0HsLKvYfGWmREx0VE5Aju\n/nLoDCLSOJau38HUBSu4esipjBnWOXQckQahewrl2IaMg9E/g7Zdo+fWDK6eqk1mREREJKXsrahi\nwvTFnNy6JQ+MiWCmZaOSHFQUSt0MGQd3FcHn/xzdhTQrN3QiERERkSb10PPLea9sDw/fMJTszPTQ\ncUQajIpCqZ/el0KrtlD0f6GTiIjELTO7sy5jIpI4Xl5RxpOvv89N5/fgM31ODh1HpEGpKJT6ad4S\nBoyGd/8ClftCpxERiVc31jL2laYOISINY9ueCr45s5C+HVvz7Sv7h44j0uBUFEr9RfJh/05Y9dfQ\nSURE4oqZfd7MngF6mtncGsdLwEeh84lI/bk735m1lG3lFTw2/nRatUgLHUmkwWn3Uam/HhdC5snR\nJaQDrgmdRkQknrwGbAROBh6pMb4LNa8XSUhPvb2eecUfMmlUfwZ2ahM6jkijqFNRaGYdgPOBTsBe\noAh4y92rGzGbxKu05jBoDBT8L1TsgfSs0IlEROKCu78PvA+cGzqLiJy4dVvL+cEzyxjRM4dbLugV\nOo5Ioznq8lEzu8TM5gPPAqOAU4GBwL3AUjP7gZnV+ysTM8s2s6fM7F0zW25m55pZjpktMLOVscd2\nsWvNzH5mZqvMbImZnVH/v6Y0uEg+VJZDyfOhk4iIxB0zy4v9e7bDzHaa2S4z2xk6l4jUXVW1c9f0\nAgx4dNxQ0pqp/YQkr2PNFF4F3OLuHxz+gpk1B64BRgL13Yryp8A8d7/ezNKBTOC7wIvu/pCZTQIm\nAd8mWoz2iR1nAz+PPUpIXc+BkzpB0SwYfH3oNCIi8eYnwGh3Xx46iIgcn1+8/B5vvb+NR8cNpUu7\nzNBxRBrVUWcK3f2btRWEsdcOuPtsd69XQRibWbwQmBb7cyrcfTtwHfBk7LIngTGx59cBv/OofwDZ\nZnZqfT5TGkGzZhDJg1ULYO/20GlEROLNJhWEIomrqHQHUxes4OohpzL29M6h44g0ujrtPmpmPzKz\n7Brn7czsP47zM3sBZcATZrbYzB43syygo7tvBIg9dohd3xlYV+P962Njh2e81czeMrO3ysrKjjOa\n1EskD6oq4N1nQycREYk3b5nZ9NhupHkHj9ChROTY9lZUceefF3Ny65Y8MCaCmZaNSvKra0uKUbHZ\nPADcfRvRpaXHozlwBvBzdz8d2EN0qeinqe3/RD9iwP1X7j7c3Yfn5uYeZzSpl05nQLseamQvInKk\nNkA5cAUwOnZou2aRBPDQ88t5r2wPD98wlOzM9NBxRJpEXVtSpJlZS3ffD2BmGUDL4/zM9cB6d38j\ndv4U0aJwk5md6u4bY8tDN9e4vmuN93cBNhznZ0tDMotuOLPoMdizBbJODp1IRCQuuPtNoTOISP29\nvKKMJ19/n5vO78Fn+uj3GkkddZ0p/APwopndbGZfBRZw6P6/enH3D4F1ZtYvNnQZsAyYC9wYG7sR\nmBN7Phf4cmwX0nOAHQeXmUociOSDV8Gy2aGTiIjEDTPra2YvmllR7HyImd0bOpeIfLpteyr45sxC\n+nRozbev7B86jkiTqtNMobv/xMyWAJcTXc75/9x9/gl87teBP8Z2Hl0N3ES0QJ1hZjcDHwA3xK59\njuhS1VVEl+Lo29d40mEg5PaP7kJ61r+GTiMiEi9+DXwT+CWAuy8xs/8Fjvd+fBFpRO7Od59eyrby\nCp646SxatUgLHUmkSdV1+SjAcuCAu//VzDLN7CR333U8H+ruBcDwWl66rJZrHbjjeD5HmsDBJaQv\n/Qh2lEJb7dAlIgJkuvubh21QcSBUGBE5uv97p5Tniz5k0qj+DOrUNnQckSZX191HbyF6798vY0Od\nAa0XlKhBeYBrCamIyCFbzKw3sY3RzOx6QLc+iMShdVvL+f7cYkb0zOGWC3qFjiMSRF3vKbwDOB/Y\nCeDuKznUMkJS3cmnwalDtQupiMghdxD9IrW/mZUCE4CvhY0kIoerqnbunlGAAY+OG0paM7WfkNRU\n16Jwv7tXHDwxs+bU0hZCUlgkH0rfhq1rQicREQnO3Ve7++VALtDf3T/j7msDxxKRw/zi5ff459pt\n/OC6QXRplxk6jkgwdb2n8GUz+y6QYWYjgduBZxovliScQWNhwX1QPAsumBg6jYhIUGZ232HnALj7\nD4MEEpEjFJXuYOqCFVw9+FTGnq49ESS11XWmcBJQBiwF/o3ojqDaWlsOye4GXc+O7kIqIiJ7ahxV\nwCigR8hAInLIvsoqJkwvoH3rdB4YG+GwTaFEUk5dW1JUE91e+9dmlgN0ie0KKnJIJB+e/xZsfhc6\nqL+PiKQud3+k5rmZPUy0766IxIGHnn+XVZt384ebzyY7Mz10HJHg6rr76N/NrE2sICwAnjCzRxs3\nmiScgWPAmkWXkIqISE2ZgLY1FIkDr6wo47evreWm83vwmT4nh44jEhfquny0rbvvBPKAJ9z9TKKN\n7EUOOakj9PhMdBdSTSSLSAozs6VmtiR2FAMlwE9D5xJJddv2VHDPzEL6dGjNt6/UqiaRg+q60Uxz\nMzsVGAdMbsQ8kugi+fDMnfDhkmibChGR1HRNjecHgE3urub1IgG5O999einbyit44qazaNUiLXQk\nkbhR15nCHwLzgVXu/k8z6wWsbLxYkrAGXAvNmqtnoYikul01jr1AGzPLOXiEjSaSmv7vnVKeL/qQ\nu0f2Y1CntqHjiMSVum40MxOYWeN8NZDfWKEkgWXmQO9Lo7uQXv4D0G5eIpKa3gG6AtsAA7KBD2Kv\nObq/UKRJrdtazvfnFjOiZw63Xqj//UQOd9SZQjO792jfaJrZpWZ2zae9Likqkg871sH6f4ZOIiIS\nyjxgtLuf7O7tiS4nneXuPd1dv5GKNKGqaufuGQUY8Oi4oaQ10xfWIoc71kzhUuAZM9tH9FvPMqAV\n0AcYBvwV+FGjJpTE0+8qSGsZXULadUToNCIiIZzl7rcdPHH3583s/4UMJJKqfvnKe/xz7TYeHTeU\nLu0yQ8cRiUtHnSl09znufj5wG1AMpAE7gT8AI9z9Lncva/yYklBatYG+V0Dx01BdFTqNiEgIW2Kr\nbXqYWXczmwx8FDqUSKopKt3B1AUruHrwqYw9vXPoOCJxq673FK5EG8tIfUTyYfkz8P6r0PPC0GlE\nRJra54H7gaeJ3kP4SmxMRJrIvsoqJkwvICcrnQfGRjDtcyDyqerakkKkfvp8FtJbR5eQqigUkRTj\n7luBO82stbvvDp1HJBU99Py7rNq8m9/fPILszPTQcUTiWl1bUojUT3pm9N7CZXOgqjJ0GhGRJmVm\n55nZMmBZ7Hyomf1P4FgiKeOVFWX89rW13HR+Dy7okxs6jkjcU1EojSeSD3u3weq/h04iItLUpgKf\nJXYfobsXAlo2IdIEtu2p4J6ZhfTp0JpvX9k/dByRhFCnotDM+prZi2ZWFDsfYmb3Nm40SXi9L4VW\nbdXIXkRSkruvO2xIO2+JNDJ3Z/LspWwrr+Cxzw2jVYu00JFEEkJdZwp/DXwHqARw9yXA5xorlCSJ\n5ukw4FpY/heo3Bc6jYhIU1pnZucBbmbpZnYPsDx0KJFkN+udUp5b+iF3j+zHoE5tQ8cRSRh1LQoz\n3f3Nw8YONHQYSUKRfKjYBasWhE4iItKUbgPuADoD64n29r3jWG8ys9+Y2eaDK3NiYzeYWbGZVZvZ\n8MOu/46ZrTKzEjP7bAP/HUQSyrqt5dw/t5gRPXK49cJeoeOIJJS6FoVbzKw30W21MbPrgY2NlkqS\nR48LICtXS0hFJGWYWRrwJXf/F3fv6O4d3P2L7l6XPoW/Ba48bKwIyCPa1qLm5wwkumpnUOw9/xP7\nbJGUU1Xt3D2jAAMeGTeUtGZqPyFSH3UtCu8Afgn0N7NSYALwtUZLJckjrTkMHAMl82C/dmUXkeTn\n7lXAdcf53leArYeNLXf3klouvw74s7vvd/c1wCpgxPF8rkii++Ur7/HPtdv4wXWD6JqTGTqOSMKp\nU1Ho7qvd/XIgF+jv7p9x97WNmkySRyQfDuyFFfNCJxERaSqvmtl/mdkFZnbGwaOBP6MzUHMzm/Wx\nsSOY2a1m9paZvVVWVtbAMUTCKirdwdQFK7h68KmMPb3W/wVE5Bjq1LzezLKBLwM9gOZm0Sl5d/9G\noyWT5NH1bGjTObqEdPD1odOIiDSF82KPP6wx5sClDfgZta2P89oudPdfAb8CGD58eK3XiCSifZVV\nTJheQE5WOg+MjXDwd1QRqZ86FYXAc8A/gKVAdePFkaTUrBkMGgtv/DLatzCjXehEIiKNwszudPef\nAt9z90WN/HHrga41zrsAGxr5M0XiykPPv8uqzbv5/c0jyM5MDx1HJGHV9Z7CVu5+t7s/4e5PHjwa\nNZkkl0g+VFfCu8+GTiIi0phuij3+rAk+ay7wOTNraWY9gT7A4TuFiyStV1aU8dvX1vKV83pwQZ/c\n0HFEElpdZwp/b2a3AH8B9h8cdPetn/4WkRo6nQ7tekaXkJ7+xdBpREQay3IzWwvkmtmSGuMGuLsP\nOdqbzexPwMXAyWa2Hrif6MYz/0n0vv5nzazA3T/r7sVmNgNYRrRN1B2xTW5Ekt62PRXcM7OQPh1a\nM2lU/9BxRBJeXYvCCmAKMJlD9ys4oCYwUjdm0dnCRVNhdxm01jd6IpJ83P3zZnYKMB+49nje/ykv\nPf0p1z8APFDfzxFJZO7O5NlL2VZewRM3nUWrFurEInKi6rp89G7gNHfv4e49Y4cKQqmfSD54FSyb\nHTqJiEijcfcP3X2ou79/+BE6m0gymPVOKc8t/ZC7R/ZjUKe2oeOIJIW6FoXFQHljBpEU0HEg5A6A\nolmhk4iIiEgCWre1nPvnFjOiRw63Xqj5CZGGUtflo1VAgZm9xCfvKVRLCqmfSD689B+woxTaqpeQ\niIiI1E1VtTNxRiEAj4wbSloztZ8QaSh1nSmcTfSehdeAt2scIvUTyYs+Ftd6e4yISEIzs9/HHu8M\nnUUk2fzylfd4c+1WfnjdILrmZIaOI5JU6jRTqPYT0mDa94ZTh0V3IT3v30OnERFpaGeaWXfgq2b2\nOw5rMK9du0WOT1HpDqYuWMHVg09l7OlaaSTS0I5aFJrZDHcfZ2ZLObTr6MeOtbW2SK0i+bDge7B1\nNeTofgARSSq/AOYR3Z37bT5ZFGrXbpHjsK+yignTC8jJSueBsRHMtGxUpKEda6bw4PKXaxo7iKSQ\nQWOjRWHRLLjwntBpREQajLv/DPiZmf3c3b8WOo9IMnjo+XdZtXk3v795BNmZ6aHjiCSlo95T6O4b\nY09vr2Vb7dsbP54kpeyu0PUc7UIqIknL3b9mZkPN7N9jh1bWiByHhSvL+O1ra/nKeT24oI96HIs0\nlrpuNDOylrFRDRlEUkwkHzYXw+bloZOIiDQ4M/sG8EegQ+z4o5l9PWwqkcSybU8F98wspE+H1kwa\n1T90HJGkdtSi0My+FrufsJ+ZLalxrAGWNE1ESUoDrwNrptlCEUlW/wqc7e73uft9wDnALYEziSQM\nd2fy7KVs3VPB1PHDaNUiLXQkkaR2rJnC/wVGA3NjjwePM939i42cTZLZSR2hxwXRXUj9iD2MREQS\nnRHt8XtQFYftRCoin27WO6U8t/RD7h7Zj0jntqHjiCS9o2404+47gB3A55smjqSUSD488w3YWAid\nhoVOIyLSkJ4A3jCzg01ZxwDTAuYRSRjrtpZz/9xiRvTI4dYLtWGvSFOo6z2FIg1vwGho1jw6Wygi\nkkTc/VHgJmArsA24yd0fC5tKJP5VVTsTZxQC8Mi4oaQ10wS7SFOoU/N6kUaRmQO9L4Pip+HyH0Az\nfUchIsnD3d8B3gmdQySR/OqV1by5diuP3DCUrjmZoeOIpAz9Fi5hRfJhxzpY/8/QSURERCSgotId\nPLqghKsGn0LeGZ1DxxFJKSoKJax+o6B5Ky0hFRERSWH7KquYML2AnKx0HhgzGDMtGxVpSioKJaxW\nbaDPFdElpNVVx75eRCTOmVmamf01dA6RRPLQ8++yavNuHr5hKO2y0kPHEUk5KgolvMHXw57NsHZR\n6CQiIifM3auAcjPTPvoidbBwZRm/fW0tXzmvBxf0yQ0dRyQlaaMZCa/PFZDeOrqEtNdFodOIiDSE\nfcBSM1sA7Dk46O7fCBdJJP5sL6/gnpmF9OnQmkmj+oeOI5KyVBRKeC0yoP/VsHwuXPUwNNeyERFJ\neM/GDhH5FO7O5KeL2Lqngmk3nkWrFmmhI4mkLBWFEh8i+bBkOqz+O/S9InQaEZET4u5PmlkG0M3d\nS0LnEYlHTy8u5dmlG/n2lf2JdNZqa5GQdE+hxIdel0CrbO1CKiJJwcxGAwXAvNj5MDObGzaVSPxY\nt7Wc++YUM6JHDrde2Ct0HJGUp6JQ4kPzdBh4Lbz7LFTuDZ1GROREfR8YAWwHcPcCoGfIQCLxoqra\nmTijEIBHxg0lrZnaT4iEpqJQ4kckHyp2wcoFoZOIiJyoA+6+47AxD5JEJM786pXVvLl2Kz+4dhBd\nczJDxxERVBRKPOlxAWR10BJSEUkGRWb2BSDNzPqY2X8Cr4UOJRJaUekOHl1QwlWDTyHvjM6h44hI\njIpCiR/N0mDQGFgxH/bvCp1GROREfB0YBOwH/gTsBCYETSQS2L7KKu6aXkC7zHQeGDMYMy0bFYkX\nKgolvkTy4cBeKJkXOomIyHFz93J3nwxcBlzi7pPdfV/oXCIh/Xjeu6zcvJuHbxhKuyy1nxKJJyoK\nJb50GQFtumgJqYgkNDM7y8yWAkuINrEvNLMzQ+cSCWXhyjKeeHUtXzmvBxf2zQ0dR0QOo6JQ4kuz\nZhAZC6v+Cnu3hU4jInK8pgG3u3sPd+8B3AE8ETaSSBjbyyu4Z2YhfTq0ZtKo/qHjiEgtVBRK/Ink\nQ3UlLP9L6CQiIsdrl7svPHji7osA3SwtKcfdmfx0EVv3VDB1/DBatUgLHUlEatE8dACRI5w6DHJ6\nQdFTcMaXQqcREakzMzsj9vRNM/sl0U1mHBgP/D1ULpGmNntxKVPml1C6Pdp7+JohpxDp3DZwKhH5\nNCoKJf6YRWcLFz4CuzdD6w6hE4mI1NUjh53fX+O5+hRKSpi9uJTvzFrK3sqqj8deXL6Z2YtLGXO6\n2lCIxCMtH5X4FMkHr4Zlc0InERGpM3e/5CjHpaHziTSFKfNLPlEQAuytrGbK/JJAiUTkWDRTKPGp\nwwDoMDC6C+mIW0KnERGpFzPLBr4M9KDGv7Xu/o1QmUSayobYktG6jotIeJoplPgVyYMPXocd60Mn\nERGpr+eIFoRLgbdrHCJJ7+STWtY63ik7o4mTiEhdaaZQ4tegPPjbf0Dx03De10OnERGpj1bufnfo\nECJNbV9lVa0zDhkt0vjmZ/s1eR4RqRvNFEr8at8bOp2uRvYikoh+b2a3mNmpZpZz8AgdSqSx/Xje\nu2zatZ/bLupF5+wMDOicncGDeYO1yYxIHNNMocS3SD68cC989F60SBQRSQwVwBRgMod2HXWgV7BE\nIo1s4coynnh1LV85rweTRg1g0qgBoSOJSB1pplDi26Cx0cfiWWFziIjUz93Aae7ew917xg4VhJK0\ntpdXcM/MQk7r0JpJo/qHjiMi9aSiUOJb2y7Q7VwoUlEoIgmlGCgPHUKkKbg7k58uYuueCh4bP4xW\nLdJCRxKRetLyUYl/kXx47h7YtAw6DgydRkSkLqqAAjN7Cdh/cFAtKSQZPb24lGeXbuRbV/Yj0rlt\n6DgichxUFEr8G3gdPP+t6BJSFYUikhhmxw6RpLZ+Wzn3zylmRI8c/u1C3fsvkqhUFEr8a90Bel4Y\n3YX0kslgFjqRiMhRufuToTOINLaqaufuGYU48Mi4oaQ107/PIolK9xRKYojkw9bVsLEgdBIRkWMy\nszVmtvrwI3QukYb064WreXPNVr5/7SC65mSGjiMiJ0BFoSSG/tdAsxbqWSgiiWI4cFbsuAD4GfCH\noIlEGlBR6Q4eeaGEqwafQv4Z6j8okuhUFEpiyMyB0y6Doqehujp0GhGRo3L3j2ocpe7+GHBp6Fwi\nDWFfZRV3TS+gXWY6D4wZjOm2DpGEp3sKJXFE8mHFPFj/JnQ7J3QaEZFPZWZn1DhtRnTm8KRAcUQa\n1I/nvcvKzbv53VdH0C4rPXQcEWkAKgolcfQbBc1bRZeQqigUkfj2SI3nB4C1wLgwUUQazsKVZTzx\n6lq+cl4PLuybGzqOiDQQFYWSOFqeBH2vhOKn4bMPQpr+8xWR+OTul4TOINLQtpdXcM/MQk7r0JpJ\no/qHjiMiDUi/VUtiieTDstnw/iLodXHoNCIitTKzlkA+0IMa/9a6+w9DZRI5Ee7O5KeL+Gh3BdNu\nPItWLdJCRxKRBqSNZiSx9BkJ6SdpF1IRiXdzgOuILh3dU+MQSUhPLy7l2aUbufuKvkQ6tw0dR0Qa\nWLCZQjNLA94CSt39GjPrCfwZyAHeAb7k7hWxb1t/B5wJfASMd/e1gWJLaC0yoP/VsGwuXPUINNcN\n7iISl7q4+5WhQ4g0hPXbyrl/TjEjeuTwbxf2Dh1HRBpByJnCO4HlNc5/DEx19z7ANuDm2PjNwDZ3\nPw2YGrtOUlkkH/Zth9UvhU4iIvJpXjOzwaFDiJyoqmrn7hmFOPDIuKGkNVP7CZFkFKQoNLMuwNXA\n47FzI9q/6anYJU8CY2LPr4udE3v9MlNDnNTW62LIaKclpCISzz4DvG1mJWa2xMyWmtmS0KFE6uvX\nC1fz5pqtfP/aQXTNyQwdR0QaSajlo48B3+JQz6b2wHZ3PxA7Xw90jj3vDKwDcPcDZrYjdv2Wmn+g\nmd0K3ArQrVu3Rg0vgTVPhwHXRovCyr3RJaUiIvFlVOgAIieqeMMOHnmhhFGRU8g/o/Ox3yAiCavJ\nZwrN7Bpgs7u/XXO4lku9Dq8dGnD/lbsPd/fhubnqm5P0IvlQsRtWvhA6iYjIEdz9/dqO0LlE6mpf\nZRV3TS+gXWY6Bfw1hwAAIABJREFUPxo7GC3SEkluIZaPng9ca2ZriW4scynRmcNsMzs4c9kF2BB7\nvh7oChB7vS2wtSkDSxzq8RnI6qAlpCIiIo3gx/PeZcWm3Uy5YSjtsrSpm0iya/Ki0N2/4+5d3L0H\n8Dngb+7+L8BLwPWxy24kup03wNzYObHX/+buR8wUSopplgaDxsKK+bB/V+g0IiINwsx+Y2abzayo\nxliOmS0ws5Wxx3axcTOzn5nZqth9i2eESy7JZOHKMp54dS1fOa8HF/XV6iuRVBBPfQq/DdxtZquI\n3jM4LTY+DWgfG78bmBQon8SbSD4c2Aclz4dOIiLSUH4LHN7KYhLwYmx37hc59O/gKKBP7LgV+HkT\nZZQktr28gntmFnJah9ZMGtU/dBwRaSLB+hQCuPvfgb/Hnq8GRtRyzT7ghiYNJomhy1nQtmt0CemQ\ncaHTiIicMHd/xcx6HDZ8HXBx7PmTRP/d/HZs/Hex1TP/MLNsMzvV3Tc2TVpJNu7O5NlFfLS7gmk3\nnkWrFmmhI4lIE4mnmUKR+mnWLLqEdNWLUK7bTEUkaXU8WOjFHjvExj/enTum5s7dn2Bmt5rZW2b2\nVllZWaOGlcQ1u6CUZ5ds5K6RfYl0bhs6jog0IRWFktgi+VBdCcufCZ1ERKSp1Wl3btAO3XJs67eV\nc9/sYs7q0Y7bLuodOo6INDEVhZLYTh0KOb21C6mIJLNNZnYqQOxxc2z84925Y2ru3C1SZ1XVzt0z\nCnHg0XHDSGum9hMiqUZFoSQ2s+hs4dqFsGtT6DQiIo2h5i7ch+/O/eXYLqTnADt0P6Ecj18vXM2b\na7by/WsH0TUnM3QcEQlARaEkvkg+eDUsm3Psa0VE4piZ/Ql4HehnZuvN7GbgIWCkma0ERsbOAZ4D\nVgOrgF8DtweILAmueMMOHnmhhFGRU8g/o9ZbUkUkBQTdfVSkQXToDx0GRZeQnn1r6DQiIsfN3T//\nKS9dVsu1DtzRuIkkme2rrOKu6QW0y0znR2MHY6ZloyKpSjOFkhwiebDuH7B93bGvFREREX4yr4QV\nm3Yz5YahtMtKDx1HRAJSUSjJIZIXfSx+OmwOERGRBLBwZRm/eXUNN57bnYv6akdakVSnolCSQ04v\n6HSGdiEVERE5hu3lFdwzs5DTOrRm0qgBoeOISBxQUSjJI5IPGwvgo/dCJxEREYlL7s7k2UV8tLuC\nx8YPIyM9LXQkEYkDKgoleQwaG30smhU2h4iISJyaXVDKs0s2ctfIvkQ6tw0dR0TihIpCSR5tO0O3\n87SEVEREpBbrt5Vz3+xizurRjtsu6h06jojEERWFklwieVC2HDYtC51EREQkblRVOxNnFOLAo+OG\nkdZM7SdE5BAVhZJcBo4Ba6bZQhERkRp+vXA1b6zZyv2jB9I1JzN0HBGJMyoKJbm0zoWeF0WLQvfQ\naURERIIr3rCDR14oYVTkFK4/s0voOCISh1QUSvKJ5MO2NbBhcegkIiIiQe2rrOKu6QW0y0znR2MH\nY6ZloyJyJBWFknwGXAPNWmgJqYiIpLyfzCthxabdTLlhKO2y0kPHEZE4paJQkk9GOzjtcih+Gqqr\nQ6cREREJYtHKLfzm1TXceG53LuqbGzqOiMQxFYWSnCL5sLMU1r0ROomIiEiT215ewcSZBfTOzWLS\nqAGh44hInFNRKMmp3yhonqElpCIiknLcncmzi/hodwU//dzpZKSnhY4kInFORaEkp5atod+VsGw2\nVB0InUZERKTJzC4o5dklG7lrZF8induGjiMiCUBFoSSvSD7sKYO1C0MnERERaRLrt5Vz3+xizurR\njtsu6h06jogkCBWFkrxOGwnpJ2kJqYiIpISqamfijEIceHTcMNKaqf2EiNSNikJJXi1aRdtTLJ8L\nBypCpxEREWlUjy9czRtrtnL/6IF0zckMHUdEEoiKQklukXzYtwPe+1voJCIiIo2meMMOHn6hhCsH\nncL1Z3YJHUdEEoyKQkluvS6O9i3UElIREUlS+yqruGt6AdmZ6fwobzBmWjYqIvWjolCSW1oLGHgd\nlDwHFeWh04iIiDS4n8wrYcWm3Tx8w1BystJDxxGRBKSiUJJfJB8qdsPKF0InERERaVCLVm7hN6+u\n4cZzu3NR39zQcUQkQakolOTX/Xxo3VFLSEVEJKlsL6/gnpmF9M7NYtKoAaHjiEgCU1Eoya9ZGgwa\nG50p3LczdBoREZET5u5Mnl3Elt37+ennTicjPS10JBFJYCoKJTVE8uHAPih5PnQSERGREza7oJRn\nl2zkrpF9iXRuGzqOiCQ4FYWSGrqcBW27aQmpiIgkvPXbyrlvdjHDu7fjtot6h44jIklARaGkBjOI\njIX3XoTyraHTiIiIHJeqamfijEIcmDp+GGnN1H5CRE6cikJJHZF8qD4Ay+eGTiIiInJcHl+4mjfW\nbOX+0QPpmpMZOo6IJAkVhZI6ThkC7U/TElIREUlIyzbs5OEXSrhy0Clcf2aX0HFEJImoKJTUYRad\nLVyzEHZ9GDqNiIhIne2rrGLC9MVkZ6bzo7zBmGnZqIg0HBWFkloG5QEOy+aETiIiIlJnP5lXwopN\nu5ly/RBystJDxxGRJKOiUFJLh/7QMaIlpCIikjAWrdzCb15dw5fP7c7F/TqEjiMiSUhFoaSeSB6s\newO2fxA6iYiIyFFtL6/gnpmF9M7N4jujBoSOIyJJSkWhpJ5BedHH4qfD5hARETkKd+fe2UVs2b2f\nn37udDLS00JHEpEkpaJQUk9OT+h8ppaQiohIXJtTsIG/LNnIXSP7EuncNnQcEUliKgolNUXyYWMh\nbFkVOomIiMgR1m8r53uzixjevR23XdQ7dBwRSXIqCiU1DRoLGBTPCp1ERETkE6qqnYkzCql2Z+r4\nYaQ1U/sJEWlcKgolNbXpBN3Pg6VPgXvoNCIiIh97fOFq3lizlfuvHUTXnMzQcUQkBagolNQVyYMt\nJbB5WegkIiIiACzbsJOHXyjhykGncMOZXULHEZEUoaJQUteA68DStOGMiIjEhX2VVUyYvpjszHR+\nlDcYMy0bFZGmoaJQUlfrXOh1UbQo1BJSEREJbMr8ElZs2s2U64eQk5UeOo6IpBAVhZLaIvmwbS1s\neCd0EhERSWGLVm5h2qI1fPnc7lzcr0PoOCKSYlQUSmrrfw2kpUORdiEVEZEwtpdXcM/MQnrnZvGd\nUQNCxxGRFKSiUFJbRjacNjJaFFZXh04jIiIpxt25d3YRW3bv57Hxp5ORnhY6koikIBWFIpE82LUB\n1v0jdBIREUkxcwo28JclG7lrZF8Gd2kbOo6IpCgVhSL9RkGLTO1CKiIiTap0+16+N6eI4d3bcdtF\nvUPHEZEUpqJQJD0L+l4JxbOh6kDoNCIikgKqqp27pxdQXe1MHT+MtGZqPyEi4agoFIHoLqTlW2Dt\nK6GTiIhICnh84WreWLOV+68dRNeczNBxRCTFqSgUATjtcmjZRktIRUSk0S3bsJOHXyjhs4M6csOZ\nXULHERFRUSgCQItW0fYUy5+BA/tDpxERkSS1r7KKCdMXk52ZzoN5QzDTslERCU9FochBkXzYtwPe\n+1voJCIikqSmzC9hxabdTLl+CDlZ6aHjiIgAKgpFDul1EWTkaAmpiIg0ildXbWHaojV8+dzuXNyv\nQ+g4IiIfU1EoclBaCxh4Hbz7HFSUh04jIiJJZHt5BRNnFNI7N4vvjBoQOo6IyCeoKBSpKZIPlXtg\n5fzQSUREJEm4O/fOLmLL7v08Nv50MtLTQkcSEfkEFYUiNXU/D1qfoiWkIiLSYOYUbOAvSzYy4fI+\nDO7SNnQcEZEjqCgUqalZGgwaCytegH07Q6cREZEEV7p9L9+bU8SZ3dtx20W9Q8cREamVikKRw0Xy\noWo/lDwXOomIiCSw6mpn4owCqqudqeOG0TxNv3aJSHzSTyeRw3UZDm27wdKnQicREfmYmd1pZkVm\nVmxmE2JjOWa2wMxWxh7bhc4phzy+aDX/WL2V+68dRLf2maHjiIh8KhWFIoczg0gerH4J9nwUOo2I\nCGYWAW4BRgBDgWvMrA8wCXjR3fsAL8bOJQ4s27CTKfNL+OygjtxwZpfQcUREjkpFoUhtIvlQfQCW\nzw2dREQEYADwD3cvd/cDwMvAWOA64MnYNU8CYwLlkxr2VVYxYfpisjPTeTBvCGYWOpKIyFGpKBSp\nzSmDoX0f7UIqIvGiCLjQzNqbWSZwFdAV6OjuGwFij7V2RDezW83sLTN7q6ysrMlCp6op80tYsWk3\nP7l+CDlZ6aHjiIgck4pCkdqYRWcL1y6CXR+GTiMiKc7dlwM/BhYA84BC4EA93v8rdx/u7sNzc3Mb\nKaUAvLpqC9MWreFL53Tnkn611ugiInFHRaHIp4nkAQ7Fs0MnERHB3ae5+xnufiGwFVgJbDKzUwFi\nj5tDZkx1O8ormTijkF65WXz3qgGh44iI1Fnz0AFE4lZuP+g4OLqE9JzbQqcRkRRnZh3cfbOZdQPy\ngHOBnsCNwEOxxzkBI6ak2YtLmTK/hA3b99KqRTP2VVYz998/Q0Z6WuhoIiJ1pplCkaOJ5MH6N2Hb\n+6GTiIj8n5ktA54B7nD3bUSLwZFmthIYGTuXJjJ7cSnfmbWU0u17cWBvZTVpzYz3ynaHjiYiUi8q\nCkWOJpIXfSx+OmwOEUl57n6Buw9096Hu/mJs7CN3v8zd+8Qet4bOmUqmzC9hb2XVJ8YOVDtT5pcE\nSiQicnxUFIocTbse0Hm4diEVEZEjbNi+t17jIiLxSkWhyLFE8uHDJbBlZegkIiISRzplZ9RrXEQk\nXqkoFDmWQWMAg6JZoZOIiEgc+cLZ3Y4Yy2iRxjc/2y9AGhGR46eiUORY2nSC7udD0VPgHjqNiIjE\ngX2VVcwt2MBJLdM4tW0rDOicncGDeYMZc3rn0PFEROpFLSlE6iKSB8/eDZuK4ZRI6DQiIhLYlPkl\nlGzaxRM3naUm9SKS8DRTKFIXA68DS9OGMyIiwqurtjBt0Rq+dE53FYQikhRUFIrURdbJ0OviaFGo\nJaQiIilrR3klE2cU0is3i+9eNSB0HBGRBqGiUKSuIvmw/X0ofSd0EhERCeTeOUVs2b2fx8YPIyM9\nLXQcEZEG0eRFoZl1NbOXzGy5mRWb2Z2x8RwzW2BmK2OP7WLjZmY/M7NVZrbEzM5o6swiAAy4BtLS\ntYRURCRFzSko5ZnCDUy4vA9DumSHjiMi0mBCzBQeACa6+wDgHOAOMxsITAJedPc+wIuxc4BRQJ/Y\ncSvw86aPLAK0agt9roDiWVBdHTqNiIg0odLte7l3dhFndm/HbRf1Dh1HRKRBNXlR6O4b3f2d2PNd\nwHKgM3Ad8GTssieBMbHn1wG/86h/ANlmdmoTxxaJiuTBro3wweuhk4iISBOprnYmziigutqZOm4Y\nzdN0942IJJegP9XMrAdwOvAG0NHdN0K0cAQObufVGVhX423rY2OH/1m3mtlbZvZWWVlZY8aWVNb3\nSmiRqSWkIiIp5PFFq/nH6q3cP3oQ3dpnho4jItLgghWFZtYa+D9ggrvvPNqltYwdsf2ju//K3Ye7\n+/Dc3NyGiinySelZ0G8ULJsNVQdCpxERkUa2fONOHp6/gisGduSG4V1CxxERaRRBikIza0G0IPyj\nu8+KDW86uCw09rg5Nr4e6Frj7V2ADU2VVeQIkXwo/wjWvBw6iYiINKJ9lVVM+HMBbTJa8GDeYMxq\n+55aRCTxhdh91IBpwHJ3f7TGS3OBG2PPbwTm1Bj/cmwX0nOAHQeXmYoEcdrl0LItFM069rUiIpKw\nHp5fQsmmXUy5YQjtW7cMHUdEpNGEmCk8H/gScKmZFcSOq4CHgJFmthIYGTsHeA5YDawCfg3cHiCz\nyCHNW0bbUyx/Bg7sD51GREQawaurtvD4ojV86ZzuXNKvw7HfICKSwJo39Qe6+yJqv08Q4LJarnfg\njkYNJVJfkTwo+COsehH6XxU6jYiINKAd5ZVMnFFIr9wsvnvVgNBxREQanfZUFjk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+wx+L\nhsT6jk4+O38h+02ewCUnziw7HElSHTwvtCQNlePnVabQ9jSmvTLeqszJiDdvwWLWbtjElXNnMX6s\nnz1LUjPidWIjAAAL00lEQVSwKZSkofKW0+GD36wcGSQqtx/85sg7mrozzMmIdtui1SxYuJrz5xzE\nrOmTyg5HklQnP8KTpKE0EqfUDpQ5GZFWr+vg0lse5/AZkzjvuAPKDkeStBM8UihJkgakqyu58HuL\n2NyVXHn6LNpG+/ZCkpqJf7UlSdKAXPfA0zy4/AXmnfRm9psyoexwJEk7yaZQkiT125I1L3H5nUs5\nYeZU5h45vexwJEn9YFMoSZL6ZdPmLVxw40J226WNy045jIgoOyRJUj94ohlJktQvV9y1jCVrNnDd\nObOZMnFc2eFIkvrJI4WSJGmnPbT8Ba6+fwVnHj2DOYdMLTscSdIAeKSwhlsfXcVX71zK6nUd7D2p\nnYveezAfPnxa2WGVypwUmZMic1JkTorMSfPq+dqNCpg8YSyXnjiz7LAkSQPUNEcKI+J9EbE0Ip6K\niIuHaju3PrqKz9/8OKvWdZDAqnUdfP7mx7n10VVDtcmGZ06KzEmROSkyJ0XmpHn1fu22JGzYuJm7\nnlhbdmiSpAGKzCw7hh2KiNHAMuDdwErgYeCMzPxFrefPnj07H3nkkX5t652X/ZhV6zoK47uMGcWx\nb9qzX+tsdv+97Fk2dnYVxvubk2Tg+9xg7LYDWcX9y55j4+YaOWkbxbvetEfNZeo9/UK952mIOtdY\n9/oGuN0fL1lLR439pH3MqD6nltWzL9T7Wtf9vDpf+frX17f+7CfDpazTgdzXR06mTWrngYvn9Gud\nEfHzzJw90NhaRX9rZF/1cSCvnSRp6OxMfWyW6aNHAU9l5gqAiLgROBmo2RQOxOoaBQ9gY2cXTz//\nymBvrinUagi7x/ubk0Y5QV1/z5RX601t9/jKF4v70GB/+NKIDVCthrB7fOnaDX0uV88rUFajXK++\n9qOd3U+GS5kfBvaVk77+9qpx9PUa+dpJUvNrlqZwGvBMj8crgaN7PiEizgXOBZgxY0a/N7T3pPY+\nPwm989PH9Hu9zWx7nw6bk21Nm9TOf33qXSVEVL7t5eTuz/xRCRGVz/2kqK+c7D2pvYRotDP6qo++\ndpLU/JrlO4W1Pobf5qPuzLwqM2dn5uw99uj/tKyL3nsw7WNGbzPWPmY0F7334H6vs9mZkyJzUmRO\nisxJkTlpXr52kjRyNcuRwpXA9B6P9wFWD8WGus+A55nxtjInReakyJwUmZMic9K8fO0kaeRqlhPN\ntFE50czxwCoqJ5o5MzOfqPX8gZxoRpLUXDzRzM6xRkpSaxhxJ5rJzM0R8UngTmA0cF1fDaEkSZIk\nqX5N0RQCZOYPgR+WHYckSZIkjSTNcqIZSZIkSdIQsCmUJEmSpBZmUyhJkiRJLcymUJIkSZJamE2h\nJEmSJLUwm0JJkiRJamE2hZIkSZLUwmwKJUmSJKmF2RRKkiRJUguzKZQkSZKkFmZTKEmSJEktzKZQ\nkiRJklqYTaEkSZIktTCbQkmSJElqYTaFkiRJktTCbAolSZIkqYXZFEqSJElSC7MplCRJkqQWFplZ\ndgyDLiKeA349CKuaAjw/COsZScxJkTkpMidF5qRosHKyb2buMQjraQmDVCPdn4vMSW3mpcicFJmT\nosHISd31cUQ2hYMlIh7JzNllx9FIzEmROSkyJ0XmpMicNC9fuyJzUpt5KTInReakaLhz4vRRSZIk\nSWphNoWSJEmS1MJsCrfvqrIDaEDmpMicFJmTInNSZE6al69dkTmpzbwUmZMic1I0rDnxO4WSJEmS\n1MI8UihJkiRJLcymUJIkSZJamE1hDRHxq4h4PCIWRsQjZcfTCCJiUkTcFBFLIuKXEfEHZcdUtog4\nuLqPdP+8FBEXlB1X2SLi0xHxREQsjojvRMQuZcdUtoj4VDUfT7TqPhIR10XEsxGxuMfY70XEjyLi\nyertG8qMUfWxRhZZI7dlfazN+lhkfaxohBppU9i34zJzltdMed03gDsy8xDgrcAvS46ndJm5tLqP\nzALeBrwK3FJyWKWKiGnA+cDszDwUGA18rNyoyhURhwJ/DhxF5f/OSRFxULlRleJ64H29xi4G7snM\ng4B7qo/VHKyR27JG9mB9LLI+Flkft3E9JddIm0LtUETsBhwDXAuQma9l5rpyo2o4xwPLM/PXZQfS\nANqA9ohoA8YDq0uOp2wzgZ9l5quZuRm4F/hIyTENu8y8D/hdr+GTgRuq928APjysQUmDwBq5Q9bH\nrayP27I+VjVCjbQprC2BuyLi5xFxbtnBNID9geeAf42IRyPimoiYUHZQDeZjwHfKDqJsmbkK+Brw\nG+C3wPrMvKvcqEq3GDgmIiZHxHjgA8D0kmNqFFMz87cA1ds9S45H9bFGbssauX3WR6yPfbA+bt+w\n1kibwtremZlHAO8HzouIY8oOqGRtwBHAP2fm4cArOM3rdRExFvgQ8L2yYylbdb77ycAbgb2BCRHx\nx+VGVa7M/CXwFeBHwB3AImBzqUFJA2ON3JY1sg/Wx62sj0XWx8ZiU1hDZq6u3j5LZQ78UeVGVLqV\nwMrM/J/q45uoFEBVvB/4v8xcW3YgDeAE4OnMfC4zO4GbgXeUHFPpMvPazDwiM4+hMj3kybJjahBr\nI2IvgOrtsyXHozpYIwuskX2zPm5lfazB+rhdw1ojbQp7iYgJEbFr933gPVQOb7eszFwDPBMRB1eH\njgd+UWJIjeYMnBrT7TfA2yNifEQElX2lpU+4ABARe1ZvZwAfxf2l223A2dX7ZwMLSoxFdbBGFlkj\nt8v6uJX1sQbr43YNa42MzBzK9TediNifrWfIagP+IzO/VGJIDSEiZgHXAGOBFcDHM/PFcqMqX3UO\n/DPA/pm5vux4GkFE/C0wl8oUkEeBT2TmpnKjKldE3A9MBjqBz2TmPSWHNOwi4jvAscAUYC3wBeBW\nYD4wg8obptMys/cX7dVArJG1WSOLrI9F1sci62NFI9RIm0JJkiRJamFOH5UkSZKkFmZTKEmSJEkt\nzKZQkiRJklqYTaEkSZIktTCbQkmSJElqYTaFUski4kMRcXFJ294rIm6v3n9nRDwWEQ9HxIHVsUkR\ncWf1mkrdy9wdEW8oI15JUmuxRkrDw0tSSC0sIr4K/DQzF0TEzcDngP2A92XmZyPiCuC2zLy3xzJn\nA/t4bTJJ0khmjVQr8UihNEQiYr+IWBIR10TE4oj494g4ISIeiIgnI+Ko6vPOiYhvVe9fHxHfjIgH\nI2JFRJxaHd8rIu6LiIXVdb2rOv5yj+2dGhHX91jPv0TE/RGxLCJO6iPMU4A7qvc7gXZgPNAZEQcA\n03oWu6rbgDMGI0eSpNZkjZQaS1vZAUgj3IHAacC5wMPAmcAfAh8C/hr4cI1l9qo+5xAqxeWm6nJ3\nZuaXImI0laK0I/sBfwQcAPwkIg7MzI3dv4yINwIvZuam6tCXgauADuBPgK8Bf9N7pZn5YkSMi4jJ\nmflCHXFIklSLNVJqEB4plIbW05n5eGZ2AU8A92RlzvbjVApSLbdmZldm/gKYWh17GPh4RHwROCwz\nN9Sx7fnV9TwJrKBSQHvaC3iu+0FmLszMt2fmccD+wGogIuK7EfHtiJjaY9lngb3riEGSpL5YI6UG\nYVMoDa1NPe539XjcRd9H6nsuEwCZeR9wDLAK+LeI+NPq73t+KXiXXuvp/YXh3o87aixD9QvzlwJ/\nD3yh+vNt4Pxe2+roI35JkuphjZQahE2h1AQiYl/g2cy8GrgWOKL6q7URMTMiRgEf6bXYaRExqvq9\nh/2Bpb1+v4zan8SeDfwgM1+kMgWnq/ozvhpLAL8P/Gqg/y5JkgbKGikNnN8plJrDscBFEdEJvAx0\nfwp6MXA78AywGJjYY5mlwL1Uptf8Rc/vSgBk5isRsbz6PYqnACJiPJWC957q0/4B+E/gNbZ+cf5t\nwM8yc/Og/gslSeqfY7FGSgPiJSmkEah6hrXbM/OmHTzvI8DbMvPSnVj3N6icgvuegUUpSdLws0ZK\nRR4plFpYZt4SEZN3crHFFjtJ0khnjVQr8UihJEmSJLUwTzQjSZIkSS3MplCSJEmSWphNoSRJkiS1\nMJtCSZIkSWphNoWSJEmS1ML+H0/8+TT375gdAAAAAElFTkSuQmCC\n", "text/plain": [ "<Figure size 1080x720 with 2 Axes>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.figure(figsize=(15, 10))\n", "\n", "plt.subplot(121)\n", "x = [5, 6, 7, 8, 9, 10]\n", "y_1 = kaggle['time'][0:6]\n", "y_2 = kaggle['time'][6:12]\n", "plt.plot(x, y_1, 'o-')\n", "plt.plot(x, y_2, 'o-')\n", "plt.xlabel('minsup (%)')\n", "plt.ylabel('time (sec)')\n", "plt.legend([\"FP Growth\", \"Apriori\"], loc=1);\n", "plt.title('Figure 10: kaggle running time');\n", "\n", "\n", "plt.subplot(122)\n", "x = [5, 6, 7, 8, 9, 10]\n", "y_1 = kaggle['fp_num'][0:6]\n", "plt.plot(x, y_1, 'o-')\n", "plt.xlabel('minsup (%)')\n", "plt.ylabel('number of frequent pattern')\n", "plt.legend([\"number of frequent pattern\"], loc=2);\n", "plt.title('Figure 11: kaggle fp number');\n", "\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "觀察 Figure 10 與 Figure 11,在 Kaggle 資料集 Apriori 依舊比 FP-growth 慢許多,雖然資料僅有 1499 筆,但平均每個 Transaction 有 15 項 Item,算法需要花費極大的Join 與 prune 時間,為此即便FP 數目少 Apriori 所花費時間仍為IBM 1000 筆資料時的 20倍。" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### c. Association rules\n", "由於IBM資料集是合成資料,探討其 Association Rules 意義較低,因此以下僅提供 Random Shopping Cart 出現頻率前五高的 Rules。" ] }, { "cell_type": "code", "execution_count": 72, "metadata": {}, "outputs": [ { "data": { "text/html": [ "<div>\n", "<style scoped>\n", " .dataframe tbody tr th:only-of-type {\n", " vertical-align: middle;\n", " }\n", "\n", " .dataframe tbody tr th {\n", " vertical-align: top;\n", " }\n", "\n", " .dataframe thead th {\n", " text-align: right;\n", " }\n", "</style>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>rule</th>\n", " <th>confidence</th>\n", " <th>support</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>eggs->vegetables</td>\n", " <td>0.851536</td>\n", " <td>499</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>yogurt->vegetables</td>\n", " <td>0.845217</td>\n", " <td>486</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>laundry detergent->vegetables</td>\n", " <td>0.840909</td>\n", " <td>481</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>aluminum foil->vegetables</td>\n", " <td>0.826307</td>\n", " <td>490</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>spaghetti sauce->vegetables</td>\n", " <td>0.825312</td>\n", " <td>463</td>\n", " </tr>\n", " <tr>\n", " <th>5</th>\n", " <td>sugar->vegetables</td>\n", " <td>0.824468</td>\n", " <td>465</td>\n", " </tr>\n", " <tr>\n", " <th>6</th>\n", " <td>ice cream->vegetables</td>\n", " <td>0.824027</td>\n", " <td>487</td>\n", " </tr>\n", " <tr>\n", " <th>7</th>\n", " <td>dishwashing liquid/detergent->vegetables</td>\n", " <td>0.821070</td>\n", " <td>491</td>\n", " </tr>\n", " <tr>\n", " <th>8</th>\n", " <td>waffles->vegetables</td>\n", " <td>0.819699</td>\n", " <td>491</td>\n", " </tr>\n", " <tr>\n", " <th>9</th>\n", " <td>sandwich bags->vegetables</td>\n", " <td>0.818841</td>\n", " <td>452</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " rule confidence support\n", "0 eggs->vegetables 0.851536 499\n", "1 yogurt->vegetables 0.845217 486\n", "2 laundry detergent->vegetables 0.840909 481\n", "3 aluminum foil->vegetables 0.826307 490\n", "4 spaghetti sauce->vegetables 0.825312 463\n", "5 sugar->vegetables 0.824468 465\n", "6 ice cream->vegetables 0.824027 487\n", "7 dishwashing liquid/detergent->vegetables 0.821070 491\n", "8 waffles->vegetables 0.819699 491\n", "9 sandwich bags->vegetables 0.818841 452" ] }, "execution_count": 72, "metadata": {}, "output_type": "execute_result" } ], "source": [ "rules = pd.read_csv('rules.csv')\n", "rules.head(10)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 5. Conclusion" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "FP-Growth 算法比起 Apriori 有顯著的效率差異,原因是它透過建立 FP-Tree 來減少探索時間,相對的 Apriori 則需要不斷 Scan 資料集來確認是否滿足 minisup 條件。 雖然這種FP-Tree的結構在資料量大時仍具有優勢,但當 frequent patterns 的數目減少時(minsup增加),兩者的時間差異會越來越少,這時建立 FP-Tree 這種資料結構的時間花費便體現出來。<br>" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 6. References\n", "[1] In Proceedings of the 20th International Conference on Very Large Data Bases, VLDB ’94, pages 487–499, San Francisco, CA, USA, 1994. Morgan Kaufmann Publishers Inc.<br>\n", "[2] J. Han, J. Pei, and Y. Yin: “Mining frequent patterns without candidate generation”. In Proc. ACM-SIGMOD’2000, pp. 1-12, Dallas, TX, May 2000." ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.6.2" } }, "nbformat": 4, "nbformat_minor": 2 }