sentiment-analysis.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Sentiment Analysis\n",
    "\n",
    "\n",
    "Sentiment analysis (opinion mining or emotion AI) is the process of computationally identifying and categorizing opinions expressed in a piece of text, especially in order to determine whether the writer's attitude towards a particular topic, product, etc. is positive, negative, or neutral. [From Oxford](https://www.lexico.com/en/definition/sentiment_analysis)\n",
    "\n",
    "There are three main elements of a sentiment analysis system:\n",
    "- Opinion/Emotion (polarity:positive, negative, neutral; emotion:joy, surprise, worry, etc.)\n",
    "- Subject (the subject of the text)\n",
    "- Opinion holder\n",
    "\n",
    "Levels of granularity:\n",
    "- Document level\n",
    "- Sentence level\n",
    "- Aspect level\n",
    "\n",
    "Type of sentiment analysis algorithms:\n",
    "- Rule/lexicon-based\n",
    "- Automatic/ Machine learning\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import necessary modules\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import re\n",
    "\n",
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Import the required NLP packages\n",
    "from textblob import TextBlob\n",
    "from wordcloud import WordCloud, STOPWORDS\n",
    "from sklearn.feature_extraction.text import CountVectorizer, TfidfVectorizer\n",
    "from sklearn.feature_extraction.text import ENGLISH_STOP_WORDS\n",
    "from nltk import word_tokenize\n",
    "from nltk.stem import PorterStemmer, WordNetLemmatizer\n",
    "\n",
    "from langdetect import detect_langs # Find out the probabilities for the top languages\n",
    "from langdetect import detect # Detect the language of the text\n",
    "import fasttext\n",
    "\n",
    "# Import classifiers\n",
    "from sklearn.linear_model import LogisticRegression"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Rule/lexicon-based Approach Using TextBlob"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sentiment(polarity=0.7, subjectivity=0.6000000000000001)\n"
     ]
    }
   ],
   "source": [
    "# from textblob import TextBlob\n",
    "\n",
    "# Generate a text\n",
    "text = \"He is a good person.\"\n",
    "\n",
    "# Create a textblob object  \n",
    "blob_text = TextBlob(text)\n",
    "\n",
    "# Print out the sentiment \n",
    "print(blob_text.sentiment)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sentiment(polarity=0.022916666666666658, subjectivity=0.5895833333333332)\n"
     ]
    }
   ],
   "source": [
    "two_cities = '''It was the best of times, it was the worst of times, \n",
    "                it was the age of wisdom, it was the age of foolishness, \n",
    "                it was the epoch of belief, it was the epoch of incredulity, \n",
    "                it was the season of Light, it was the season of Darkness, \n",
    "                it was the spring of hope, it was the winter of despair, \n",
    "                we had everything before us, we had nothing before us, \n",
    "                we were all going direct to Heaven, \n",
    "                we were all going direct the other way – in short, \n",
    "                the period was so far like the present period, \n",
    "                that some of its noisiest authorities insisted on its being received, \n",
    "                for good or for evil, in the superlative degree of comparison only.\n",
    "             '''\n",
    "\n",
    "# Create a textblob object  \n",
    "blob_two_cities = TextBlob(two_cities)\n",
    "\n",
    "# Print out the sentiment \n",
    "print(blob_two_cities.sentiment)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Read data\n",
    "movies = pd.read_csv('data/sentiment/IMDB.csv', usecols=[1,2])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    .dataframe thead th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>review</th>\n",
       "      <th>label</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>This short spoof can be found on Elite's Mille...</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>A singularly unfunny musical comedy that artif...</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>An excellent series, masterfully acted and dir...</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>The master of movie spectacle Cecil B. De Mill...</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>I was gifted with this movie as it had such a ...</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "                                              review  label\n",
       "0  This short spoof can be found on Elite's Mille...      0\n",
       "1  A singularly unfunny musical comedy that artif...      0\n",
       "2  An excellent series, masterfully acted and dir...      1\n",
       "3  The master of movie spectacle Cecil B. De Mill...      1\n",
       "4  I was gifted with this movie as it had such a ...      0"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "movies.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 7501 entries, 0 to 7500\n",
      "Data columns (total 2 columns):\n",
      "review    7501 non-null object\n",
      "label     7501 non-null int64\n",
      "dtypes: int64(1), object(1)\n",
      "memory usage: 117.3+ KB\n"
     ]
    }
   ],
   "source": [
    "# Structure of the dataset\n",
    "movies.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of positive and negative reviews: \n",
      " 0    3782\n",
      "1    3719\n",
      "Name: label, dtype: int64\n",
      "Proportion of positive and negative reviews: \n",
      " 0    0.504199\n",
      "1    0.495801\n",
      "Name: label, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "# Number of positive and negative reviews\n",
    "print('Number of positive and negative reviews: \\n', movies.label.value_counts())\n",
    "\n",
    "# Proportion of positive and negative reviews\n",
    "print('Proportion of positive and negative reviews: \\n', movies.label.value_counts() / len(movies))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x11a56be48>"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Length of the reviews (# char)\n",
    "movies.review.str.len().hist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "label:  1\n",
      "Sentiment(polarity=0.2024748060772906, subjectivity=0.4518248900857597)\n"
     ]
    }
   ],
   "source": [
    "# Review of the movie \"Titanic\"\n",
    "titanic = movies.iloc[4930]\n",
    "\n",
    "print(\"label: \", titanic.label)\n",
    "\n",
    "# Create a textblob object  \n",
    "blob_titanic = TextBlob(titanic.review)\n",
    "\n",
    "# Print out its sentiment  \n",
    "print(blob_titanic.sentiment)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Label and the polarity matches for the movie Titanic."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Word/Text Cloud"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "# from wordcloud import WordCloud, STOPWORDS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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vSRLZt7MDv89FqVwjtXJrylsq13j7veuEAm4eP9CzZsYlSSKD/Y309zQwNrnMybPj64zuRqgaGleyMwD0BpI4RYWpUorlWh7dMnFLDhpcQZrdYaS7UGC6abJcyzNfyVLSa4CFR3KScAVIukPI4q3nfTsESUAzdObLaRarecpGDREBv+Ki0R0m6vQhCevPO5SbJauW6fE3EHZ4WajmmCtnqBoaTkkm5gzQ7AnX+8yD43bzuh6mZZFVy8xVMuS1Mppp4pIUYk4fje4QbtnxUMb4odQL94NwyEN3Rxywp6or2RK9XQnSuRKxqI/ezgSTsyvMLeRobQwTCXpJxgPk8hU0zaBS1XA4ZEIBD6WyypaeRk5fmEDTDLrb47hcDsrlGl6Pg97OBA3xAOWKRqlcY8dAMxYW+UKVhlgAy7JQNR3n6gujGQa/vDiCYZiEfG78bich7200hAXFqsrEUoaZlRxL2SI9jTG+/8EltrQmsBpj+NxOvvPuBQZaErTGgsSDXhwb3E21pnH9zBhz40sMn5ugZ1cHwaiPs29fIRQPEG+K0LOjHUM3CCcCBKN+Lp8YZuTCJItTK3Rta6WtvxFdNWjpbiAY8+N0r33YmqpjaAbxpjAev4v0Yo7kKk/ocCpsP9DF0myG9FKe9r4kF46P4nQpJFsjRJNB+ne0rnlRdMOsy43Alhvd/CgIAl7ZQ81QEUSBheoSRb2EW3ITVAJ4JDdD+Rs8FT+CIty6IS2eCHOVNKlagQ5vnHZvjJlymhZ3lIjTjyyI9Aea6tu3eqIoooRTUpAFCWU1chyL+nj+6UF+8Po5vvGd41y5Nsfu7a1s6WukpSlcf8Z3YnwqhaYbxKI+em4blO4GXTdZWMpTLNUoFqt879Uz6wKl2dU4RTZXoViurTtGNOIlfpvBvQlRFPB67ECsrhv174vFGrMLWfw+F6cvTDK/lFt3zOmZDKZhsbicv6/ryKol/uTam9RMnX/e9xzpWpHXZs9zo7CAauoEFQ9bgk18umUvTzUMIG5gAItalZMro/xi/jKXstOkakXAIuLw0Rdo5LnGrTzbsBW3vNaTtiyLtFrk7cVrvLt0jev5eTJqGVEQSDgD7Ai38nzjdg7Gutfw6ADfnjjOqdQof9hzlKjTx2uz57mQmSKnVfApTnp8DTyb3MrHm3fikzcOLD4sdNPgYnaan85d5FxmkvlKlpqh4VfcdPsSHI738mLjdhrdoQc+76/F6AqCQDzqr3OfTqfCtv4mtvXfeql6uxJ0d9z0LAUEAfbubMeyLERBwOd18sJTg2uO+9ThPvv3Ozrw9i3NAMQiPhBWGRpBoLMtRmfb2iAFQL5cYy6d50tP7CQW8G4odanpOljgkCSmU1m2tjXQGA6gGyY+lwOHLNORCKPqBm6HsqGXC3Zb/GEvh17aieGTKAs6Xc/2MT+yiCGC6JXpOdTF1Og8E8UcezwR2g92sLCYRtUNOre20NmQQECgWq4hKzIOl8LTnztQP8f4lRmSHXG8QQ+LUysU0qW60RVFwTbSokC5VGNsaI7p0UUWZzO4vU5mr8wyenWWjr7GehS6IR5g78525hdymKbFgd0d9WCPLEhsDQygW/rq9QmUjDKyIOGTfdTMGklXw6qna9/XlVoKE5MnEgNk1DTDxRs4JIm+QABFlHGKJnk9Q5PHx1R5EllQ8CkSGS2FIHjpC7TWr9XndfLJF3cQDXt57+QI750c5sSZMbo74mwbaOKpI310d8SR5bWR5ly+gmVZeD2OdTzoZqipOuVyDcuCK9fnuXJ9ftNtBcF2Lu6Ez+ta15abEEXbg799elsoVjEMk3SmxKtvXrh7+2r6/VxGHalqge9PnWK2nKHVG+WTzXuomRqXszMcXx5moZIj4HCzN9K5Zr+yrvLzhct8ffQYqVqRneE2Dsd7sSwYLy7x4coow4UFVEPnM6371rxPBb3KdyZP8oOp09RMjR2hNlq9UTRT50Z+gZ/PX+Zqbo5/1f88TzVsWdfmkl7jp/MXqRgaDlHiaHIQAYHhwgIXs9OMFZeQRJFPt+xF/ogkXZZlcTU3x38YepORwgKdvgTPNAzilhzMV7JczE5zLT/PbDnDv+h7lpBjvQ25G35tnu79YN3ob0d0Nt1eEO6uB74ziLEZlNsSGYB13FKxqnJ5cgGnIhP1e1jOlQj73Hx83wAXJ+Y5NzZH2OfhY3sHuDq9yNXpRUI+N62x9dI2xakweKAHgBvZZU6nF0j4fSjbI8xrKgVyuJwK/Ye7UdOLnKmsQFCgvaUJBKgJIi6vk62HetYcd/uRvvr/nW4HmaUcpXwZ07DoWB2EBFFg12O9+AJu3B4Hum6gawbJlgj+oJtg2Is34MLtda7x4LweJ5/9xG4O7em01SUNwboqQxREGt0Na9py8/4V9CJjpUnavc34lVvBxryeJ1VbpsWtM1uZJeyIMF4ao2ZWcUseWt1tpNUUhjVP2BEi6WpkvDRG2BEho6Zp9dwyujcH9I89u429O9sZm1zm3RMjHD89yrWRBT48P8k/+8qT7NrWWqd0bu4H1J/5/eD2AOMzTwzw9JG+TRUoggB93Q3rvn/QJISbvHpbc4RPvLCD5rvIJQP+zYNcGyGvVbiQmeL3u5/i2eRWAoobwzKZq2T40+tvcTYzzvcmT9LnT9aTByzLYrS4yF+OvUtRr/Lf9R7l6cQW/Ip97qxa5oczZ/jB9Gn+ZupDdoTb6PHb98GwTM6kx/nu5EkUQeJr3U/zdHILQcWDYZmkagX+ZuoUb8xe4C9Gj7El2EzCFVjT5qqhMZSb4+PNu/hs6z4SriCCAMvVPH89cYI35i7w3yaOcyTWS5NnLbXzsNAsg78YfYex4iIfa97JF9oOEnX6kUWRklbj/dQNvjH2Hm/MXWBPpJ0Xm3YgPQDN8PdqdP++4HM7OTzQzvfev4jP7WRvdwulWo1rM0tkihUO9bfhdTo4Pz5H0OPC43Qwk8rx5tkb6IbBjo5GVF3nh8evUNV0WmNBAp57vwC6aVLVdfyKk7lSnpDDRdjpJlUtM18ukK3Z06a425ZMuWSZXK2KdY/EwNbeJJ/+o+fqSgW3zw5ICIJANGF3YucmXGeTJ1bf9nZEwz6i4fXRY8uyUHUDTbdlWmDzloZhIhoKW31bECyRalXH4xIRBQFZUNBMnYJeQBQEspqdJdTgSlIzqqTUJSRBRsDEK/nwyX4U0UFaTeOWNlafOBwyLU1hGpNB9uxo5/Of3MN3f3SaY8eH+cZ3j9PX3YD/toCSPcUXyBeqrGRK9qzoHnA5FYIBN5Ik4nTIHN7Xvcr9boyPYnYbCnpwORUEAbraY+zf1bH5xg94PhOLfdEuXmjcRpM7XH/mYYeXr3Q/zvVzc1zNzXE+M8UTiX4AKobGz+YuM1NK8/m2A3yqZQ8h5dYsJuL08XtdT3B8eZjp8grvLl2vG13V0PnB1BlKeo3Pte7nM2378N9GA0QcXr7W/RRjhSWu5+d5ffY8X+l6Yk1fNLHo9CV4uXkX/YHG+m8hxcNvtR9kuLDIaHGRd5ev88X2Qw92QzbB8eVhLmSm6PUn+f2uJ2nxRNbcq084dzGcX+Rvpz/kzbmLPJvcumaAvxfuanRN02R+pUCpqtKeDONU7M3zpSozS1kaowHCAQ+mabGSLzG7nENVdfxeF51NEVwO+0U3DJPlbJHFdIGapuNyKDTGAkQDnrrnsJwpUijXCHhdzK/kqdRUYkEfbckQsiRhWRYr+TKzS1lqqo4sS8SCXpoTwbuS/xtBFAQOD7RzeKB9zfeH+td+fm5X75rPfc3xNZ//6GMP9pAHIw0MRuwOuTPWWP/+pqe4K9a0zuu+TU65KSRZwr+BPvh+8KB8lG6YnBmeIV0oE/K5MU2LgdYEN2aWyZaqBL0uwGJmOccrh7ficTlo9bTWvdWbA8jtnPRG320Lbl+/3W1yt5vtlkQRn9dJf0+Sjz+3nZGJZUbGl9H0tVP9LX2NBPxuZucznDgzxgtPD+JQ7u5zSJJIX08D8aiPC5enmV/KrQvA3d6ujwJ+r5Nd21o5fX6CK9dm2TbQhHeDhBrTtO6vc9yBwWAzUad/zXMXBYG9kQ6CDg+pWoHRwmLd6FYNlVMrI/Y7JsBQbm7D40qCSEVXGS8uYa5ShDmtzOXsNFGnjx3hNgJ3pN4KgkDSHWJXuJ2ruVnOpif5UschXNJa+qfVE6HDF1+nFOr1J2n1RrmRn+dSZvojM7qnUmNUDBVFlJgup9elEgPUTA1ZELlRWEC3TO6e8rQW9zC6FmeuTfPu+TH+2WeP0N0cw7QsLgzP8Z1fnONrnzpIyO9mMV3gWz89zXwqjyxJVDWdlw4N8LHDNkej6ga/+HCY65OL6IZJqaLS3RLjS8/vpiFiT0M/HJri7TMj9LTGWUwXKFZqbOlo4EvP7UaWJIrlGt968wzzKVtxYBgmg51JvvDcLiTH/7fr9txNdvYPLbnatCxypSrRgBcsi0K1ykq+xFw6j9uhsJIv0RgJ2APuhgq69V/e73eWZTE5k2ZsMkVrU5hYxIfbrWCaFplcmSvX5sjlyjTE/ch3BLwa4gEeP9jD939yllffvIAkCmzpbSQQcKPrJsVSlaXlAomYn/bWaN1z2THYwu7tbbz9/nW+/tcf8KkXd9KUDOJ0Kui6QS5fYW4hh9/nZLCvCafz7zZ59LgdPP/0INdGFvj5O0N4PU727+6o1/OoVFRWMiUWl/Ls29VOOPRgg21Qca8LWIHN1cedfmbKabLqbUlMlsFcOYtmGnx38iTfnTx51+PXDB3dMnAIMovVPLplEJG9RJ2bt7PRHUISJAp6hYxapvE2zl0APLIDr7zerMmiSNThQxQEUrXCfVz9vWFZFnOVDKqpcyY9wZn0xF2310wDzdThAczuXXuILEv0tcV5+8wIozMrdDRGqKk6QxMLNER8dCQjmKbFr84MMz6f5g9fOUw85OP4pXF+9M4l9g60kgj7cCgSu3qbOLStHa/bwakrU/zo2CWObO+oG12A4ZkUAx0NfPnFvTgdtiTsZjR6biXPh1en+NdfeIKOxgiFih0pvl3I/gi/fsiSyEBrgpqm43YoBL1u/B4X+/tsT9YhS1RUDZ/bgfIRPxvTtLg+ssD/8/V3aG0KE4/68XgcmIZFOltieGwJw7R4+YUd66Rjoijwyku7mFvIcurcBH/2zffoaIsSXDW6hWKVxaU8r3xsF82NobrRjYS8fP6Te8jlK7x/coTxyRQtTeG6xDGbqzC3kOXxgz30dCb+zkZXliX27Wzncy/v4Yevn+Ob3zvB8dNjhFdrJZTLKql0kVy+QnfH5x7Y6EobJAnchCJKmJaFZt1SUxiWRc3UcUkKeyIdtHvXB6ZvR3+gkZtnUA3NTq4RRGRx877glGQ7EGmZqOb64KAoiGzWaocoISCgmcaGvz8oLOzrFQSBgUAju8Ltd93eIzk2HMTuhntu3dYQpqMxwqXROQ5ubaNc1bg+uczh7R0EfW50w+T0tWl29Tazo6cJURB4dn8/P3jnEiMzyyTCPiRRZFv3ren01q4kPzt5jVJVXZNiGPA62T/YSmdTZJ3H5/e4EESB9y6O4/e46G6J4diAX/vBa2c5c3GKZCLI517eTWNDiMXlPD/71RXGp1I4nQrbtzTz2IEeggF7uqNpdtry+6dGWU4VCAXd7N7Rzr6dbfU05HtB1w0mpla4cHWGqZkVCsUapmni9TppSobYta2V7o54PfNoM8zMZ/jGd45TKFYB2Lm1hc+9vPeuXOKdSKWL/OjN84yMLQHQ2Rbji5/eX7/e2zE8tsiPf3qR5ZXNPYXuzgSf/tguuyaBAO3xClgCghQAy4lllRAEH5ZVAKsAYghBiG6oCpmdz65qX1fI5cvUVAOPWyERC9Df08BgXxOhoGdDflQURXo6ExzY3cmN0UXGJlOUKyqSJBIJe9na38gTh3s5tLerXr/jJgRBoLkxxB9/9Wl2bh3l5NkxxiaWyReqOJy29renK0F/TwPSHfv2dCb4o688ybHjNzh+eozzl6cpl2s4HDLJRJB9u9p54lBvPann74qA38WnXtxJe0uEd0+OcPHKDJeGZrEsi2DATWtThKcf6ycev//U9ptQTR0Ta8PAT9XQkAQBx20G0v4sI4sCT7341okAACAASURBVCb6ealp512PLwtSXXPrkhwIgG6ZqMbmSouKoWFZFrIg4hTv0Epjy7d0y6zLBte02dQwsXBIH014SsC+XgmRPn+SP+p95h7bs44OuRfu2VK3U2HPQAvf+cU55lcKZAtlylWVXX3NtuTFsJhdzjE6u8KJy5PAquRK1cmtGo6qqnHi8iQnLk+ynCmSKZSZXMis0YIChP0evC7nhjxjQ9jH//jlo/z4vSv8n9/6JY2xIF94bhdbu5JrON2RiWU+ODVKUzLE/t0dZHIV/uybx7h6fZ5qTUcUBY4dH+bMhSn++Pefwu1SeP0Xl/mbH58hlS6i6waSJPKr929w9LE+fudzB0nE/Bu26eZ1Dt2Y59WfXuDKtTkKxSo1Vcc0LCxseZtDkfjbn5ylt6uBT720k51bW/C4HRseU5ElSmWV90+NAjA+tcJzTw4Si95fSqRlWVwbnufnb19lbiGHokgkE0G8G2T7gK0vPXtxiqnZ9bzVTVSrGi8e3br6ycQy5rCMNJaaQ5CSYFUB2U6jFBwIghNBvFXIRdcN5hZyvP7WJY59MEw2X0ZVdXTDrEsAFVnE5XKQjAd47qktPPfUFsJBzx3Uiz2A/Ks/OEqlqqHfVKAI9ozH7VLwep3IkrjhvZUkkdbmMJ+J7+b5pwep1TQMw0QQBWRJxOVU8HqcSHeoYARBoKM1SmNiPx9/bjuqatSraimKhNvlwONW1kjDtm9p5j/+b7+NJIkbBiRFUeB/+XefwjAtQoH1XGcw4ObIgR52bWulXFFtjno1q8vpkPF4nLgewqtequap6Co+Za0zUTJUlmoFPLKTuPOWgkARZTp8Ma7l50jXSjhXk1juB82eMF7ZRU4ts1DJbVjDwbRMJorLGJZJ2OHbkIbIaWWyaon4BsqGpWoey7JocT9cYZw7IQgCHb4YJ1MjLFbzCKtJHB8l7vnUBEFge3cjbxwf4sy1aRZW8vS2xuhssi9SABoifnpaYnzisa318VMUBCKrPNQvTw/zvbfO89sv7GF3fwsLKwX+7+8e2/Bcm8V1RNFux5aOBqaXsvy3n53lT7//Pv/Hv/wkgQ280UKpyuWhWSamV7h0dRaX0y4ZV61pFIpVfnFsiO6OOM3JIN/87nGK5RpulwNdFqlUNLK5Mj/5+SVamyN88sWd60T3lmVRKNZ47RcX+d6PzrCSKdaDKS6njMNtR6DtRA+VSlUjlS5y8eoMn/3Ebj77iT2EQ551nTAa9nJwTwenzo1Tq+mkMyVOnBnj5Rfur1xkpapxeWiOhUVbOB/wuXjycF9daXAnggE3W/qS+HxOdN1EW5WVLa8Uqa6W27vjSSCICSxjGXAgCBEscwZBioKVQRADwK0gVq2mcfrCJN/4znGujy5imnbdBodDxud1IooCmmZSqapUa2WyuTLDY4vcGF3ka19+nMaGW8ZbEAQkSSDgdxO4z5oad0IQ7GzGB6UBBEHA5VI2zXi7E26XQnOjLWGyLIuqqjOXzhHyugl4XMiSeNcKY2BTOXdea75cxVyl3R4mGeB8Zor5SpYe+Va1P8MyeW/pOnm1TMzlpy9wa1bqkhQei/dyLTfH8dQIB2Ld7Ay3rcsgs1ZpCEW85el6ZSeH4j28On2GM+lxHkv0Eb8tiGdZFmPFZc5nppBFiQOxrg21thPFFNfy80Ruy1wzLYtL2WkmSylkUbonDfAgOBzr5bWZ84wXl3l78SovNG7fcKCpGRqiICILGw/ym+G+el7Q52bfQCsfXJognS/xtU8eqnuXkihyYLCNiyNzqJpOPORD0w0yhQrNq1PpxXQRr8tBT4sd/R+eXmYx/WDEdypbIlMoE/J78Lud9LXGGZtd2VR3WSrVeOvda8iyyNHHB+jvaWBpucCxEzeYW7BH3R//7AKJmB9RFHnuyS30djVQKFb4+dtDzC5k0TSD4x+O8eThPhKxtVM53TD56a+u8PW/Pk5lteB5OOhh20ATXR1xIiEvgiiQL1QYn0xx9fo8C0s5iqUa3/7+KQRB4Hd/69A6Yy7LEj1dDXS1xRgaXkDVdI6fHuX5pwc3zba6HXMLWS4NzdZnEa1NYQb7Gzfdvq+7gX//bz5OpapRKtUolmvk8hX+8zePcXlofaRaEEQEuQ1RbuNWpGwbG9W71w2T85en+bNvvsvYZAqwM7S2DTTT1hIhGvYiyxKlUo3JmTTXhueZmk2j6ya/eu86DofMH/7u4w/MW/5Dg6YbXJla4PvvX+IT+7ewp7sZzTAolGuE/W6bkzQMHLJETdORJVuCV1Y1REEg6HFiWrbB/XB4Bqcicbi//b76w+0QELiWm+MH06f5ePMuEq4AumkwVlzmO5MnqJk620OtbAneSmJyiQrPJLfy/vIwV7IzfH30XV5p3UOHN4ZDVNAtg6JeZbGSJ60WebFxO0GH7WwposzLTbs4nRrj/eVhEq4Azya3EncFMCyTmXKaH0yfZqK0zNZgM880DG4QSBaYrWT40cwZJEGk0xdHRGCmnOZvJk8xWUrR40+yP7a2FMHtsCwLwzLriSiGZdUVFhthT6SDI/FeXps9z7fGP6BqaAwGmwkobkwsynqNlVqJ8eISR+J9dPniGx5nM9z3Uzu4tZ3Xjw8hCmv5WVEUeGZvLwsref7qzdO2rEyAoNdNZ1MERZbY2dPElbF5vvHGh3aARZYI+x9sdYXZ5RyvvnsZsB9EoVLlmX29uDfhSFXNLh35yRd28NUvHSEc8lCuqHi9Tr71/ZNUqxqz81ly+QovPbOVf/JbhwmHPKiqjsvp4D/95TsADI8vUS7XgLVG99qNBf72tbN1g9vcGOKzn9jN0ccG1lABlmVRLNU4fnqMH71xnsvX5tANk5/87CK9XQ08eXitLA2grTnMQF9j3TMcn1phfDLFQG/yrvfIMEwmplYYnVgG7Gdz5ED3PV9OQRDwuO1MrTh+aqpOwHc3T3Kjzrqev11JF/nOj07XDW5zY4jf+exBHj/YvcaQWpbtEV++Nsv3Xj3D6fOTaLrBeyeG6e9u4JMv7rzvxJdMrszZC1PMzmcQBcHm07e3ElmV1KmqzvDYEpeHZqlUNbo6Yhza27WmpsJSqsCJD0dJZ0uEQ1727eqgscEuOjM9m2Z8MoXf72JsIkW5XKOzPcauba34Nik0U65pTCxlCPvcNEUCaIbBhzemsQCnItPdGGUhU6CnMcqlyQWaIgFG51dQdYNipcbRHd3kyzUuTc4zny7QHn+4JACnJLMr3M77yzcYys+RdAXRLIPRwhIz5TRbgk18of3gGqWAIAh0eGN8rfsp/mL0GMdTw4wUFmj3xnBJCpppkNPKzFYyRB0+nk7cqgUhCgKDwWa+0v0EfzX2Pt+dPMnZ9ASN7hC6ZTJZTDFVTtHhi/PV7ifX0QdgKxR6/UkWKjn+5NqbtHtjiILAdGmF0eISEYeX3+08QtSxlsJZqub51eJVilqVmqGTVoukagWqusbrs+e5np/DIdq1PrYGm9kbvZWFJwsiv9f1BHmtwvHUMP/xxi9o98YIO7yYlklBr7JYyZGqFej0xen0xR9IZXTfRjcR8eFUZLZ2JddM5wVBIBkN8Hsf38/4XJpSpYYsSTRE/HVd77buJH/wymHmUzkUWaK1IcSz+/uIh27dqF19zTTGAsQ34L8AOpuifPzIIPlSFUGwve+eTYJpNxGP+jiwp7M+jfd6nGzf0kxTQ7BuCCIhL0cf769HhxVFYsdgMz6vk2KpRi5fJl+sruGjDMPktZ9fZGFpdQrvd/PZT+zm5ed34L4jxVQQBPw+F08/1ofbpfBf/uo9xqdSZLJlXvv5RbZvaVrnyXk9Trb2N/HuiWFSK0XSmRKnL0zQ35O8qwC/VK5x7vJUfakhn9fJgT2dm+/wa4RlWXx4bpJzl6YB8HgcfPHT+3nx6OAaAwc2V+tyKezZ0Y4sSywu5xmbTJErVPjgw1H27eqgufHehexVVecnP73I2GSKjtYo5ZrG5MwK3Z1xImEvhmFy8coM3//JWeJRP36/i5+8eZFiscbHn7d1wcVilb/86w8ol1W7bOjFKa6PLPAH/+QJIiEvcwtZvvuj03jcDro64hiGyd+8eoZSWeW5p7ZsKJJ3OxWaIgEqNY1EyMeN2WVKNZWBlgSnh2cQRYFssUJrLMj0chaP00EqX6Q9EanL8WZXcgQ9bizz/jMv74RpWTyZ6Cfi8PHDmTN8kBqmZuhEHF5ebNzOZ1r3MXBb/YubcEoKR+K9xF1+3lm8xsnUKFdyM1QMFYcoE3X62RPu4MlEPwHHWmfKLTt4qWkHDa4gby1c4Wx6nGv5eWRBpMkd5nNtBzjasIWd4Y1r51oWbAk28UzDIK/NnefDlTEytRIBxc2RWC8vNe3giUT/+iLy5Qz/deQdcloZ3TTXJBi9s3SNd5auIQsiDlHm8+0H1hjdm7zuvxl4ib3LnRxfHmaksMhQbhZREAg5vLR7Y7zcvIsef8MDyzrvanRvir5Ny2RoYolqTeOZves9M1EUSIT9JMIbR1MdisxAe4KB9s0LjTTFgjTF1nJcllVFrX2IIAZwKyq7+7oAF4KgABaWlQeisElF94ZEgGQisGbKEovaBc3r092wd00BFEEQ8HqdRCM+iiU77/5m3v7N41wfXeDStdl6rv3enW08ebhvncG98x7s393B1RvzzC1mqdV0bowtcursBC8cXTutEgSBnYMtdLRESa0UqVRULg/NsbxSWEdz3LpXFplsmZNnxurf7dvZvun2v26omsGbv7xUv0f7drZzaG/nXVUYoiiwtb+JbQPNdZphfCrFyPgSTcn1BbvvRKFU49rwPDsGW3j+6UG7NrNh1mstFIpV3vzlZZqSQb78eZvaOXZ8mDffusyu7a00JUO8e2KEqZk0/+5fv0Q45GF6Ns2f/vnbXLw8w5NH7L6fyZZ57EAPLz23DdMw+d6PznDhyjQH9nYS3mB9PEWW8LudeF0OvC4Hhmnidih4nA47aGfZBrGq6hTKdvUur8tJMuQnlS+h6SaGaeF3O1F1A+khU98008AlKRxNDrIz3EZRt/lhRZQIKG5CDs8a42WaFoZpIop2RH9LoJkOb5xXWvZSMVR7CRzLVjt4ZScrC2XOXZjm4C7bgN1MYvHJLo7EetkWaiGvVlBXJVlOUSbo8OCTnRsW2QGbbxYFgf3RLvoCSXJaBc00kEURv+wi5PAiCyLlsoqq2oFyURQIVT3871u+SKFYpVrV8HidyLIdhKxWNWRZRBRFNM3AoctMT6fxeh2Ior1kUSDgoc0b5fPuAzyf3EZJq5JK5SlkSsiCRGMyQt4wmVkqsixXUXWDXa2N95WodVeja5gWk/Npro4v8P6lcXb1NdOW/Gjym+8HlqWDYNfg1I15tOowduVtbVUZ4MflegZB2DhyGfS71+Wn35xGA0iSQCLuXyfjkiRxTVGUak1bk3F0eWiuvgqGz+ukt68Bf8hl80arGVOGafNIDlGqPwinQ2bPjjaOHb/B9GyGTLbMletzHH2if112VDzuZ+e2Fq5cn6NS1RibXObq9Tni0b4NjY9pWpy9NMVSyi4T6FAknjzSh9v1cGtM/V0xNZNmcsZWRIiiwI7BFmIR3z0Np6JIdHXEcDoUdL3G8kqR2fnMfdVHDvrdbB9s5v1TI5TKKgf3dtLSFK6rCipVjdGJZZ460l8vp+j1OFjJFJmdz9KUDHH52iwBv4tCsUq5rK4G/gRGJpZ4fLX+RSTspbe7gVDAzshrSgaZPZex60VvEBsTALdDIbQ6QxxsS/Lmmeu8fXGEaMDLjs5G3rowzFsXhlF1vb6tQ5EIelwEvS58LgdvXxrFAra2NTxUyrG16u8pokSDO0jDRo1dhWlajE2lWEoXaErYtTcyuTKxsA91xSTs95HNlckVKuRNi1h3AEMrksmXmV3MIksiiagfSbqVORh2eAk7Hpyftyx7/4jTR8S5fias6wZXLs8wfGOBgS1NtHfEmB23S5YqgoTP5SIzXqKo2fWSAwE3hmniC7g4f26Sru4EeU+FcrnG5GSKRDzA4FYXkiThkhRc7iCLKYN3vjNEvNlOn5acYfCJzGXzaIZBTzxKqaYScN9b6XBPT3culef88Bz9bQlefnwrrgck7/8uEAQnstSOhYEgOJCkJiyrtlpFzI8oBdnMywV7yrrOoIpCfQooiiLBwHoFgSgIa6aJum7WJyeGYTIxvVKfwkfCXjS/xYfL0zR4VquqSTIr1RKiINAdiBF0uFavR6CzNUY46GF6NoNpWswv5UitFNetAycKAkf29/DGW1eoLGRZShW4fG2O/bs7NkwLVVWdY8eH65/7uhtoavWTM7K4cVMzVCxMBAS8speSXrIDC5aFIsqUjQoCAgnXgwUFNsON0UVU1dZmyrLEzHyGtz+4cV/7Tk6nMVdXDzEMk3yhanPt91AOyLLI517eS1d7grffv85/+vo7bB1o4tMf30UyEcQyLbLZMu8eH+bKtdn6foP9TXX5Va2mMzq+zH/9q/fqhs3hkIiGvNzkrd1OBadDWu039uq6dvnLjYO6giDQ3Rilu9FOIfY4FT5zeCvG6irSAK8c3FoP7giCwECLPftqjt4yjH2rgejNAkA1Q2W4OElBLxFWArR7m3BLDyd3MgyTY6eGiUd8xCM+MrkyQyML7B5s5fzVGbrbY1y+Pkco6GF+KYfP60Q3TManV9B1kz3bWh+aBnlQ3LztHZ1xWlsjOJ0KTodMJOqjWKgSWJXk6ZqBy+1Alm1dd6WqkUwGaW4Kk8/biyWEQ14cGyhbKqUqbb1JBvd32YXig27EWpXZbB7nak1vr/P+HJy7WtBKWSUsO/if/+kLAExNrTCWLiNLIu23retUqahMTq7Q0RHD5bKX+qhU7IX07n3DLPL5ChcvTlOtacRjAbZsaUSWJVQV3O4Wu6FyO7ci5PdZTUyWNi2rB7bndD8R4NvfpWpNo1Co1lUTXrcDt09hOJ9iqpRBESVEBAzLosUbXJcpEwq513jRxWKNfKGy4eKbnW1RejrjzC/aJRav3phndj67YTWrqdk0o+N2MoQoCjY/6te5URihw9vOSi1NXssjCTLNnkZmyrOopi15kZAIKD4yWu4jM7qLy/k6taCqOj98/Tw/fP38Qx2rWtPQdAPXfSyZoigS+3e3s2PV4/3ha+fp724gmQgiKxLtrVEO7u3ihaO3yoaKor0wJdjpwqqq8y//4OiagdflVOpe20eRmy0IwppUZUEQ7kkbbGZsb+J8doi/mnyVpdoKTe4Ev9P2SfaEB++6z2aQZYn9O9qZW8wxu5Clu83uFxYWmmYPppGQh76uBhRZQlON1frDOdqawoQ3cGZ+XZBlif0Hutd4//v2d60ZBNs71mbS2StcQEdHHLBobLpVF/fO5bkAwvEAKws5Lp8YQZIlure3UPAINAf9BNwu+hrunql3O+5KQNw0hjdx7fo8Y2NLjI+n1h5EFHG7HfWRrVLROHt24r4aYJoWY+PLzMykCQU9tohfEFhczDN0mzdiQ+BBevxNfmczCAgPVB0IoFrTUbVb2TWKIjEQS/B4spPDiQ72xFrYGW3isWQHPcEY3juKOsuShMvlqHeQmqpR3aQuqixLPHagp770zNjEMuNTqQ3rtn5weoxy1fa+EzE/W/rsZY8sy2KxukROy1E2yoSUAJeyV4g4IqimiixIqJaKZukbqb4eGsVSdV3yy8PCMK37OtZKusiJ02NcvT7P8koB07SQpFvP2O91smdnO5eGZhkeW6JUVplfzDF0Y77erR472E02V+b0+UkKxRrZXJkLV2aoVDfSLP/Dwsn0BeYqS6imxkRplqv5EaqG+lDHqtZUiuUa+VKV0cllfF4nhVKVY6dGmJ7P1GMcNwcCC3umMdjbiKJIDI0uoukfTWruvWBzxxt9L2z69/b97iwZu1EJWU3VkWS75ku8OUwo5qeq6WQrVSrag/WNu7p5oiggySLHjl23X96FHJGojwvnpxkdXaSjI8bBg92cPDXG3GyGV17ZQ7ms8qtfDfHh6XFyuTK7drWTThe5cmUW07R49tlBGhrWckmaqmMYJlu2NOHxOJiby/Lqq+dYWSmSzZTZvbudk6fGWFzIEgp5OfrMFs6cHieXr2KaJtu2ttDXt15OJXAftUwftDyeaa4ZQQVBIOb20Ry8zVMV7HItm0037Y5qT01N07prjdc9O9qIR3zMLeYoV1TOXZri4J7OegEUgHyhwtkLk2iq3cn7e5J0d8QJO934lNX10izbUHtkD2FHmJAjSMIZQxLs9b4MjI90HShVM+pG3OmU6elMEHlIvW1vZ3xdWu9GMEyTK9fmuHJ9Dk3T8XldHDnQzbZBu76wy6XwwtFBREngb187S7ms4vE42Lerg3277QL6vd0NfP6VvfzinSHeeOsysiQSi/nrcj07e05aM5hLor1U0Lq0Z25WH7PLgd3LU/27oqJXuX3kVE0N0zJhNbXVJSkbLouzERRFpqstRlNDELfLYSfZHOilUlU5sLODaNhLe3MUt8sufynLIhbQ3ryawr9aAc20LDsgj93vbWO92krr1ppqN6kaC+oLmiqChFOS68sAPSwWKwUEBMJON5IgPtRz8Ic97HlqgKunx3nre6c4+MI2tj7ZR7ZSJV9dv2LI3XBXo6soMrIksriUI5stk82WiUS9tLZGeOKJPt5++xqZTIktA03MTKfRNINw2MPgYDOFQoWjRwf5f7l7zyC90uvO73fzfXPsnBtodKORgRkAk8nhMGgYRYqSKEperaIt27VbtV9c5dryN39yrb3lXdeuvHJQWipRJMUgciSSkzGDMMihgc45vDncfK8/3EajG92NNEOx1v8qFID3vfF57z3Pec75n/+p1Uxu3Fykvz9UKHv9jZv88ldPbZxDFAWGhztZWanx//7x2zx1op/R0S4OHuymUmnyzDN7URSZI4d7qPbnuXZtnumpNSYn1xgZ6eDw4Z5trbY38DN4xnVNQdmU9LrbDHEn475bwitMzIUvh6rI2yhUm5FKRnj+9BB/+e1zALx3YZIvf/Y4qWRk4/gXLs+wsFQmIEzsHR7tpjWfRBBAFZXwwd4kn9iih0uhpJLcuM6HafY+LqLr3Srwwn9/6ReO8vypvVu96bvDc79E4aYoku8HOKaDEAS4jhc2odzUPSTw/Y2YajKq8+XPHuXLnzsGhAZS15UNWqEbeBTUEgPPJTj2/ElkJOzAIRNNUPObOKaLLEjsPZyjf/g5bMclpyWR5XuJ1aMHuxkZat+IL4uiwMsvjvDCM0PbOlL4gc/N6gTLZoGuaBvDia30vSAIsBwXzw+QRAHX9zeSrp7vo0gSrudv3LPreuiqguN5G8bTdj1URSKiKgzF+7lSuY3hmcTlKP2xbnRJIypH+Dcnvo4beMRkfcv5A7avmgQEBNEjl9XwfAFFigA++WwEiHK3A7SwXso+Z1aYKhep2hYj6VaCICCjRbiwNM9AIsdso0TVtuhPZFgzG7TqcYpWE8NzaIskWDMb9MUzlG0D1/c5kG1HROBfjHya39zzEgQCRasJQchm0CQZSRBxA3+j00xMUXedUP5u+jp/MX6R59sH+EzPMAOJHElVQxMfvaqvtFrjB3/2Dp39LXziqyfp3tPK+FqRhXKVpu1ysHN7yG83PNDoqqqEbbtIskQ0orK2VkdRZFrbNNLpKAEBrucTjWpbklO6LiPLEtGoSrFYZ3mpQqnUIJeNMTi4nTaWTOp87nNHmZsr8r3vX6KnJ4uuyxiGRCSiUijUee21q0QiKssrVfr68oiSSFtbaldC+s8KEV0hHtM2ZuimaW90LH4U1OrmRhIOQv5q/AGxb1WRee7pPXzvtcs0mjaFYoMPLs8w0JtHUSQs2+X8pZkNNkVPV5Zjh7YnMXaUT9y8pPqIZ6hsOrZxDaF2gEdEVx87uWI0LC6fn6K1I42kSMiKhNm0wtbfno/vB1iGjW042JZLNKEzMHKv9dBmWL7DeD1suTPnhfX+ju/SL7UzrPUwbxRYMot06jlKdtgDbG966wpKUeQtk64gCOja9oQtwJpV5m/mfsSl8g0+3f7CNqNrux6XphbR5FBlq9wwaU3FmS9UsByXrnyKYi3MoURUhZnVMvs680ytlIhpYbPTumHRnklwYm83r7Q/i4vHkrHKSHKQU9nDKOsKWCl1O5XNCywa7gpB4BLg4+PhBy4ghMwhoOYskNX2oYgRTK+CgIAs6ni+RVrbg4DEbKNMq56galk4vkfZNojICnXHwgt8OqNJRKGG4Tp0RJLkIzHOrEyTViNEZZW6Y9FwbSzPpeE6OL7HxcICvbE0N8orTFYLRGU1FM7xXQaTOfJajPFqAcf3yGgRPta5dyNhfT+6YylaI3F+NHeLv568xNFcFy917OFwtoO+RIYWPf5Q7zedi3PyEwcZvzrLuz+8zOlPHaLrYAfpSATT/QjDC6oaGs8gCOjqyrC8Xgyw+QW1LZcbcwvMzxcZG1vkwIFuEgmdUrnBBx9M096eYmionYCAbCZG133dUn0/YHp6jdnZIrbtks3G0HWFSFTl4sUZrl+fRxQFLNuluydLs2mvX8PPB7Is0d2ZIaIrNA2bSqXJ0nIFDj3a/gtL5Q0FMQh5wrkHdDEQxbCq6uD+Lt47PwnAO+fG+eynDqMoEvOLJSZn13A9H1kW2TfYSl/3RyP+8WEw0JtHkSUsy8WyXOYXShimvSPz4mFI5+Iomsz49QU8x8NxXFRdwfcC9h7o4vr5KRwnFLbfc6Br1+MogsxIsgddUjE8K1SnEhQ0SSGjxKkqTXRJQRND3YyE/OHKjxeNFVat3YWEHM+nVDcY7mqhUG3Qlo7TkU1ycWKeRERjrdIIO9LWDZ7e10OlYVI3bUzbRRZFXMsnE49sxDOTSpxf7vmFR74+1zcoWeNYXhkIEAUF0yshCjJxuQNJ1HADk6a7jCzGKFq3UMQYAiAJGhktpNANJnKUbIPeRBpNvG1EaAAAIABJREFUkokrGrqssC/VgigIJBQNy/OIyAppVScmq5xs6UWRJJKKTl6PEgQCXTGduhNy4wcSWZqOTYseo1WPEVVU/CBYD5NIJFUdSRBRRJGUqj8wBPHJ7n0czLRzYW2es6uzXC4u8O+uvUVWi3Ii383J1l6O5Drpi2fQdlErcywXo24SiWnsO9rL4GgXk5UqTcchFXm8Z/qhRvfppwfw/YBkMkIunyCdjiKJAooi8/TTg+SycWzb5fTpvSSToSRfKhXl9Om9KIpELKZx7FgvCwvldUWp8AmpmCYfzC3Sk0oxXS4zWSqSi0VJ9icpuhZ+XMLPK0xXKqSyUTJ9SUqehZ2TWMZg5EgXhuyFxuYxk2EfFmErGC00ujWT8alVGk3roQYlCEI92LViyBGN6Ao9XVniuyiA3UU6HeXEkT7OX5zG9XzGp1aZXywxvLed8clV5hdCTmIirnPy+MAWT+znhaE9rSTiOvVGGO+6dG2OT79ce2yjq+oKPXvCJWvfUBvTY0vosSjt3TlUXaa9Nxe2JwrANGxS2di2jr13oUkK+5LdG/+/Pza/L9G15fMPk30PgoAFc4U1q7TrNrois3+dGtaZSxHTFKKqwjP7+zeYDJbrIQqQjkUYbM8S0zVaU3HiEY2mZeN6/nq3jseHIsXJ6ftw/HCVJAsR/MBBFGQUMYYbGCSVHmQx/FwVoyhijIJ5k4AAP3CRBJW+RIbeIMxpbB6zjsi9wpwWfStH+2C2fWP7jmBrAY8gCOT06I6/w+bPslp02/c7QRJEuuNpumIpPt65h7HKKjfKK1xYm+PMyjQ/XRxnON3CgUw7T7f0cCLfQ1rd2l1Yi6pYpo0e1VhdKJFtTbHsN4GAmPoRSjtKkrhOqQiRzydouk2mmlOkBJWhvWEcI5u95xGs2WtM1m5z6OgIihAuuRIJndb7KsM0SaJqmkjZNGJS5uMv7EeRRMbXilxaXEJXZHpGWjBdj0qzitKqockSxxKtTBSLtOc1ila4fJEfTML4yHFguJPe7iwrazU8z+f8pWleOD3EkQPduz4AQRCwsFzm7MUparXQ023NJzhxuPehD42qSAzvaaOrM8P0bAHDdDh/aYaerix3JlcoVcKXpiWX4OjBngce658KyUSEUycGNmhiY+PLvPnubdpbUw/VFIZ7L5coCiTXdRMS6SjZ1iSSLBFPRTaaPnb0bm2h86jGcrft7oaOPozxbbgG881lTN/aVYBblkR6W7Ybq75Nn22eGOJ6mKRqTd1932IPvL7dErl3IQkKcWVr2W8Yr727X2bjs6376YCPsEkR7FFzGjt996Df4Un2e9A5E6rOiZYeDuc6eaVriIVmlbMrs3xz6gp/snqeH87eojee5lPdw3yh7wAJJQwlzo2voKgyw8f6mLg+z9piie7hFmRJXC+K2i5buRse2yVyA5fpxjRTjSlyao6+aB/XqtfQRI2R5AgiIlW3iuEZXKpdou7WSSkpRhIjROV7cSVNlokoCk3bQVcUWuJRZksVmo4dCh+vJxBa4jHKhkFrIk7JMFhtNOjNpLm1ssbRro6PvDvBoyAWVfnCp48wNr5CpWowNVvgT//qDLr2AoP9+S3VZUEQ4PkByysV/vrvLnD2g+lwmaTKnH5qkP37dlcAuwtBEBjsy3NktIuZuQKu6/H+B5OcONLLjdtL69QokRdODz3Ua/6ngiyJfO6Th3n37ATLq1Vsx+M//+1ZFFXmMx8/QDQaCh9tflD9IMBzfWzHpdm0mZkvoCgyh0dD71QURbKt20VRnsQoeoGPu96lIIx7CriBi4iIJIjYvkPRrqJJKikljoiAG3j4gY8syg9kAQRBwKpdZKp5P+VxO57EsDzq/Zatc9jeKqqYoeFMEFdHsNxFTHeBjsRXUKXcQ1slBUGAE7g03CZO4CIgoIkqUTmypXTX9T0aXhPbD+ObiqAQk3Vk4fEkKENFMA/Lt7F9Z4tBkwQJTVTRJXXXsuFHO75Pw7EpWgbvrczwvZkbFMwG7dEEGS3KbKPCv7/2Nn8/e5P/ZvRZTrb2ksrF+eCNMpmWJNVig7aeLLdXC7ieR0xT2df66DzdJ1qHxuU4w4lhbtRu8H7pfVJKCkmUWDAW6I52IyDgBR6GZ3AgeYA79TsU7MIWoysIAq/s27Pxb4DR9lb2t7Vgex5vTU6T0DTSEZ1nB3q3PBoV00QUBDqSiZ85DWcnCOvVYp//1DJ/9XfnsSyX9z+YYmm93cvI3jZ0PWQN2LbL7EKJ7/7oClduzG3Qw44c6ObXvnLygcUbm5FKRjlyoId3zk6wVqyzuFzhrffucGe9ICIe03jx9NBjG6C79fWu64ct2j0fw7A3qskgpH+Vyg3i0TB5I8sSsiQiy+KOnMa7YzTQm+d3fv15/vCP32C1UKfesPjDP36D985N8NzJvfR0ZTY4y57n0zTsUOxmapVL1+dZW6vxta+c3DC6HxW8wGOivsCqVaLhmjyV3Y/pWcw2VxAFgZFkP0tGgXcKl2nTsjydGyUq6Vws30ZEoD/WQat+LzfhBz6mZ9HwDBquQd1tcK1ym5lmKI0ZAGtWiQ9K1x94XSklQW+0A/kh7V9Mz2K8PovtP5iDm1MgIedxvQq63IkkxpClNCm5A0ncnkdwfJeJ+ixNzyAuR+mOthMEAW8XLvCjpbeZbS4iCRKjyT282vESB1NDKKKC4VmcL17h+0tvMFGfDZfz0XZebDnJC/kTJJVHE+B3fIdFc5Wx2hRXK7cZr09TcqrYvkNE1MmqKfYn93A0s5+98V7SSvKxjG/TtZlrVLhaXOIf5sd4f2UGRZIYSub56uBhXu4cojuWYtGo8e2pq/zF+EX+1yuv86+Pf4rDrR088+nD3L48w+BoF4MHukmWy0QUhbbkw8vbN+OJjK4symiShoBAXI5jeiYJOUGL1kLBKlCwC5TtMrZvM2fM4QQOirh9Sbnby1pyC5zs62TVKBHXoObWMFwDVVJwfQ8RhZH2LAnl5+fVybLIL372GOWqwevvjFGrm8zMF/n3f/QTMunohp5uvWGyvFLDWy9rlUSBAyNd/OavPks6+XjylqPDHfT1ZFkr1ilXmrzx7u2NmOmRA920Pkb7Fst2uXl7idsTy1i2i2k6mJaDaTo0DZup2cLGtnOLJb7xrbNk1tuD3y2v1jWZXDbOyWP9O7Y1UhSJZ5/eQ6Np8c3vfsDsQgnX9Tl/eYbzl2dQVYlYJBQyN63wGu6OE7CNgvVRwfBsrlTGadEy3KxNczwzzHuFa5SdOitmiZgcQRdVIpJOq55FF1UMz+Rs8TrH08NbPEHTs7hUvsmt2iQrZoFlq8CyuUbNbWxsExBwtnSFs6UrD7yu07kj/MGer5N4iNEt2mX+3Z0/Yclce+B2/3Xf1/hY/hSSKuC6Pvigy3tD1ocr4rg2ihKuNgRRoOE2+aPJv+J2fZrBWA9/sPfXmGrM88dT36Lu3mtWea50lWVzjd8e/CVGk0OcKVzkT6e/TdGubGwzVptiurGA6Zl8qesVpB3EyTfGJwgwfIu3Vy/w+up7jNWmwmKdTXD8OlW3zlRznrfWznMqd4RXWp9hT7z3oZNUyWpyo7zCB2vzvLE4zs3KCp3RFC93DXG6tY8X2wfIR+5NDJ3RJL89fJKEovFvr77JzfIKPW4MBHjxC8c37JYmy4yvFRlfLTLUmntk4/vYRleXdPqifeiizmBskIgcYdVcJSJFiEgRvMCjL9q3ZZD7on1klEcXypluzLAvMUQtKGBYMlWnSouWZ6WxhuEZdEU66Vf7/snKDHeCIAjks3H+2a88Q1d7mtdev870bAHPDyiUGhRKjW375LIxnnlqD5/75KF1mcbHu/721hT7hzq4ejMUwZmaDV86URR4/tTexxK1Nkybn7x1k29+74OHblsqN3nrzJ0dv9s70MLegdZde8kl4jqfefkgrfkkr71+nffOT25Q5mzbw97UeXYzZCnshzbY++jLtkdFmAHXqLsGx9L7wlBD4NEZybM33k1aiaOIMhk1QZueISrrKL7M6dxBClaFFatIix7GXRuuwT+uvMvZ4oMN6s8DCzMFzt4YQ5JFTNNGj2jIkoCqha2F9KiK63q0tKVI57YyNVasAhONWf5+8U0MzyKpxLE9B9MPJ/kFc4WfrLyH43v8cOlNSnaViKSjCDJ1t4mPj+Xb/GTlPU5kDtIf251V4gQury29zbfmX6Ps3GtuICKSkGNhe3ffxPRsAgJqboOfrLzHorHKr/S8ymhqzwON+ptLk/wf195moVnlQKaN3xt5hmP5LkbTbaTuS5jdhSrJjKRb0SUZy3OpV5pM31qivSeHsJ6oFQUBSRQxbIdCo0k6GkF/hCT2YxtdVVTpjITB9+5ouOzLKuutewSBtJqmO9qN6ZlU3AqDsUFiUuyRDUzNqbFqFWjR8piehSiEsZ0wXmQjCRJO4GB6Jrr0841fCoJAaz7Jl149ytGDPVy8OsuFyzNMza5Rq4dlsPGoRkdbmiMHuzk82s3wnjYy6SiiKNJ0bW5XVxEQSCk6i0aVVj2BE3gYrsPeZJ6b5RWO5rqQRRFJEnn6WD+vvX6D5dXqhiZEb3eOocG2xytpXpcT/LDw/eCh5cPRiMozTw2yp7+Fl54d5uKVGW5PLLO8WqXeCDm8miqTSkZoa0ky0JtjdLiTwb78jpoUAFWnwcXSHbqieUREmp5JUolRtuuUnRp90XbKTp24HCGrJqg6TToiORRRRhZkGq7BZGOB/ckBIpJGu55jtrmCIdsckvciImD7Dq+vXuQTrU8hCgIrZpFVq0xcvifwrooye+N924agbFeZaMyuV4RBq5al9wGGB2BvvO+BXXPvIqOk+I2+L7JmlWl4TRqusX4/s8w0FzcSYUbTprBaQ5IEFFXGbDrYlhO+p9kYI0d6GL+xiKrKZPJbQwAN1+B7Cz+l6Zr8Rt8XGYh1U3aqfHfhp9ypT+NtKvyYaszzQssJns8/hSxIvFe4zBtrZzE8k5Jd5Ur51gON7jtrF/juwk82DG5MinAie5DjmVEySgpREMNVc3OZn6ycYao5jxd43Kje4S/nfsB/p3+dVm17I9S7KFlNumNpfnvkFIezHfTGM0TkhydzVVGiJ54mq0fRRJVzP73O+LU5ZEXi2IsjqH1JBnIZWuIxbM9FfkQOuvCQDOcTv5VBEGAHNoqg7Bh3cXyHpmcSk6JhW+j1AfMCj6bbRBU1PLz1UsG7mdiwh1qY7JB2XFYUSw0azZDrF49p2/qQeZ5PqdKk2bQRhDDLfn+XXMf1KJYaWOuaCNlMjFh050aSG8f1PZqWSb1p4doBrheW5KqygiQLJGNRdE3eqKQCWDPr3CgvM5xq5fzaHDXHJCqrZLQoi80qx/PdXC0u8smu4Y2HxHZc1gr1cLm4Dk2TyWZiW0pll4wi88YqGTVBXI5QsKtklDizzRWqbpOP5Y9RqRnU61tLGD3PI/DDiq+w0uteVjYIAoL1mLQoiYTcToGWfAJNU+5Livncqd+h5lZJK2ls30YRFTzPp9JsYFsuviugCjptWhuKpCBLYYmtpskbhRS7jbnre7xbuEp/rIPpxhItWpqbtRmG4t3MNpdp0TMUrWpILVISuHgcSe9FFRUmGwtM1OfpibZxpzbHaGqANj1L3W0iIJBWEggI1F0D07dJyaHTUHUaBEFATI6gCOFvKQBNz8RdXw4HQegBXSrf4D+OfwPDtxAQeLn1NL/e98Vdnx8IE3oRSX9oniII1ksZAh8/uPf39xZf55tzP9xYmv9+79d4If0UCFsZGYEfIMsSrusxP12gvTtDOhunbFf5n2/8B27Xp4Ewofb13s/zuc6Po0kqXuDxXuEyfzjxF1Sc2jorQ+BoZj+/NfAVOvWQ/rZgrPAfJ77BlcoYAgIvtjzNfz/0Gzv2Vbtdn+YPx/+CicYMAdAVaeMr3Z/meOYAcTm6URJ8N6G3ZKzxJ9Pf4nzpGgEBkiDy2Y6P8/W+z6PuEMIEqNomtu+FfN7H6GdmeS4lq0lC0ZE9KK/V8dd1T1LZODeKBZK6Ri4eJRvbFirc9SQ/M0KnIAhowu6e6Aelq/z5zLf4l/t+l77ovVlQEiQSShib3Fy6uvnfD0I2E9tozbITJEkkn43DA+oHFFmirWV7lvxBWDQXWbPWKLpFeuI9VJzKxqqgYBdojwxv20cWRLJalKSq0xfPsmxWyapRVsw6SVXDcG1Mz6FgNeiWQ49PVeRdvb/NWDILpNU4M81leqNtVOw6DdcgrcYpOTVEUSCbjm3RQ7BNm4nL04BAJK6jaGHm2TLssKuxrmDUTBzLQYupBH5AYamC2N9Cx2DrVj4lAYvmAlWnQsWp0BvtpWgXEYB9+X1UnSo3qjfoTQzTG80hPmB5uBMkQSQuR7B9l7gcoe4adOg5Gq6JLMrUXYOYrJNRk0zU5xlJ9qEI4eOeURLIgsxYbRZFlOnU86iSgi6GMWQ/CFiq1PEDn4blUiKMVSqShOE4KJLLTKFMWypBXFOxXJeEroWsB8+lO5MiKt8r0xYIy7HT6kcjKC8IAhLStiW1LqncEzYIefaxhL6N+rbZ0dp3sHtHsRiAqKxzKndkw5hJgsSxzH7SSoKKU8MnQBEknssfp13Pbxy/M9JKV6SVa9U7+IFP1alTdxuklK33b3oWP173XAMgrST5QufLvNjyFCJbjaMgCKiCQk+0nV/ueZUlc5U5Yxkv8Hlz7RyvtD1DT3RnJlByvVLNdB2KThPH83b1JjNahJgs4wcmqhilLZKgUTW4dX0eVVOolRo4tsvwsT6SEZ2x5VVamnFODjx6fubnwqL3Ap+Z5jwFu/hALuFmA/tRl6l+1Kg4FeJKnIJdoObWaHpNZFFmrD5Gu3avlDQIfAIcIKykSaphF4zhVIqRdB5hE+dYEAQOZB5OKdsJbXqWkl2jQ8/hBz6KKJNW4xiuRYu2s9GulRqMnZ+kc287awtFmjWT3uFOLr9xA1mV6d7XQSwVpTBfZGlqFUkJhV+yHdvj9QICbVob/dF+Gl4DAYGcmkMSJCJiFE/yOZA8iI+33h3g8YyuIAgcTQ9t++z+56nuGkBAi3ZPui+tJnip9diOxwSwHJfriyskdI3Lc4vENY26ZW2syGRRoOk4LFTCBGkAjHa0oikyS5Ua7amfT7eO3XC/Z7fVmO2+X6fetmXyAIhIOnktuxHGyGsZWrXclglAEATSSgpVkDEDG8u3abrmNqM72ZhjrDaJF4RiS3vjvZzKHXlgfFYQBDojrTydPcTc/DIADbfJueLVXY1uEARM1or8dOEOV0tLVG1zB8WJEL+29xCncw1Md4J05FO4folrZxuszpcx6iayIqFoCmMXZ4gfasVyPWRJ/NnydD8K1Jw6C+byBqfv/w/oiHRQd+v0RsNsalbNEpEiXKlcoTV1T2/CcKZoODdQpTZEQcd2FxFFHVlMElWGEcWPRkuiI5KjXb8Xa7+LBxH+o4kIw0/vIZaMYjYtZEWira8FPa7fKzXNJojEdHr3d4MARtUg3ZLYdjxREBlJ7r933vtWKtpHEI9/FA6rJqnsiXft2Np7NyiSyP6OFuKaSj4eRRJFTMcNKzElEcfzMWwHPwirkSRJIB2JoMoSuiz9XGiMPwvk1NSO45bYRP1MK0li0vYmpiGXNtzXX9e42Aw/8JluLrBohJRHVVQYSQ5uM8w7ISrp9EY70UUV07dxfZfb9eldDd9is8p/uvke35m+RlxRyevxXbWLa9YchrOK6YwRU49iONdpVDtp7c5SLdWRZQnfD+7FzW2HimHueKzd8MRG1ws8ZhrzXKneZNFYxvZtVFElo6TpiXUymty3ZQC9wONC6QrTjXlmm/PcrIXZ8D+f+SYxeatr/rXeL9Gq5bcZi4Zn8EHpKpONGWpOHUWU6Yt1cyx9iFZ9a1XSXSwYy/zj8pu0aDk+3f4xinaZi+VrTDdmMXyLlJxgX3IPB5L7iMlRLM/mB0v/yLyxzHP5pziSPrCjl112qnxj+tvElSgfa3mW7mgHGXWrx1d36xxKHSKy6aF0/CKOV0YSEzSd2wSBj0oLtruCLvcj8dEJ+DwusT4S19l3YnDbdn37tyZB0k9QoPDzWqmoD6ET7QRFkujOhPKjqci9rh8PWqbf/Wx/x+59AB+EJXOGuJzC9i0q9hopJYfhNxAQEQWRtJKnZK/iBQ4CIm16D/IuMcyPCnE5umM+RhWVdWFSiMoRNGk7tU8SpI3f3A/8MD+zCYZnsWSuYq5zjTVRZTD2aNWUgiCQUOIklDimVcQnYNUqYngmUXn7BPBBYZ43liY43dbL1/YcJ6dFd50YW7QmgT+Pj43hXCcIXPIdGWbGljHq5sZzsO9oX9inTdcYbss/spcLT2h0Dc/kjdUzvLb0BlW3tl41Eup2ioKIIsq82v4JPt/1yY19bN/hH5bfZMlcxfBMGus8xsnG7LblhOltTe4EQcCcscifT/8tk42ZjcogQRA4X7rMO2vneLXjZY5n7qkq3UXNrXOxfI2clmUoMcDfzH2fqcYslm/jBR4iIhWnxr54aGxEQUASZN5afR9ZkOiP9pJWtxuZi6VrvFs4x2hqHwk5zPzeb1iiUnQbcyOuHiCmDmE4U6haK5rUgSjI+IGFLG6PRdu+i7UuRK1J6kONiOt7mJ6NT4AmyhvSjvePpx/42IF7r+Jn47eTtnk3d/d3fQ/TtwnWhUd2etk2ww98mm4oLKOIYTXR/Q+7HwS4gYvre6H26roHIQoisiA+tPpry3F8F2f92YDds8Dhteyc4A2CADfwcPxwbO7evyxIqKK8ZZ8PUy12/zlrTglZUJisX0cQBJpeHUVUqDhFIlIcw61TtFewfRNFVMmorT9zo6tJ2o4lzOE9hmZXFeUdvWGBe6GLUFd4669Rc+tbeMZhjD5KzdlOtdwJXuBtxOghtC91t7mj0V1oVDFch3+272meaxt44EokCDxMJ0CwZCQxSVQ9Qvxokr59nVt0ryMxDVcMqFkWr9+e5GtPH3mk64YnNLpTjVl+sPhjDM/isx2vcDQ9iiKqVJ0aM815rlfHGEnu2bKPLmr8y6HfwSdgtrnAn838LWO1cf7V8O/TG93qSen39XUqORX+eOqvuF2f5Gj6IC+1nKZdb6Hpmbxf/IAfL7/NX87+HbIoczR9YMeY0FxzgW/O/4AgCPhK96t0RTpwfIeZ5jwJJbFRNSMLModSI7Tqea5VxpjNzZNSti6fHd/h/eJFZDHcdreKm52uQxIjgE5cPQCbkgUSOwfizxZu8r+N/TWyIPH1/lf4hY5TuxohPwg4X7rFvx37Jg3X4Df6P8UXup5FFe69nEEQsGZXuFS6w5nCdSYbS1TsBrqk0hNt4WRuP09lh2nXs9smsMnGAv/Lzb+kYFX5TMdJfnPgM7tSnIIgYLa5yr++8kdUnAa/0HGKr/V9gpQSTixe4FNx6sw0VrhQGuNaZYols4jhWciCRE5Lsi/RwzP5AxxI9hO/L7a4+TyO73K7Ps87a1e5Up5gxSpjeNYuGsECz+UP8DuDnyOnbZ1Mbd9h2Szx5uplzhVvsWgUcQOXhBxlJNnLS61HGU327XotTwrLNzC8OpKjkFJz+IGHKuo0vTqqqNOu93Knfpmh+BGWzBlichJF/OgKR6pVA02Tt+XbZWG7MPv9EB9hm51gec6WgouyU+N/uva/76pTcT+8IOQB3/u/t81ZuwtBEEiqGjnt4S2EgsBGldvIKb+8LnkZKtppOxTq3FhcoWJYvDKy95Gu+S6eyOjW3AZrVpGDqRGeyZ2gdV0UuzPSxkhyL59sf3HbPoIgEFmfhXRJQ1pPGOmiTlTa/SEOgoC3195nvD7N3vgA/3zgV7aELboi7aiiwjfnfsCbq+/RH+0hp21P7JSdCrIg8V8N/uqW7w+l92/ZThAE8lqWE5nD/GDxx9ys3WEoMbBlIphszDDXXCCpJDiaPvAIP2SA66/h+3U0ZYCQ+vZoQz+S7CEiacwbq1wpT/BMbpSctnMXV8OzuFgaZ9ksklETDCd6tngDADPNFf5k6ke8vnoJAojLEaKyhu07XCjd5lxpjNFkH7/c83Gezg1voeEMxDrpiOSYqC9wuTzObHOFgfjOyQufgMvlO8wbaySVGEPxLhLy5jCLy3fn3+WvZ1+n4ZkookxCjhCVNNzAZ7K+xFhtjjdWL/HFruf5xa7nSanbJzcv8Hlr7Qr/18T3WTJL5LUkeS2NJIiU7BrLZgk38ELWRKKHrJpkb7x724rB8CzeL9zgGzM/5k5tHkWUSSoxVFGhaNf40dI53lm7ymc7n+GLXc9tKQN+Uni+T7kZ8rn79RMsVevk16lHluuSVRRW6g2QowxHXkQXZNqkBI7rY9gBsvbw5I3reMzNFZFkkXhMp9GwMAybXD6OJIqsrtW4fGmGY8f6yHRvdXYepcQ29Hkf3+i6wb0VHITesOE9Xmx0M+5qKuyEnlgKRZSYqZcZTrU+sE264dzEcueIaUew3QUcb4FM9Ivs1ADXCwJqpsV8uUo+/ug94Z7I6CbkGHkty3RzjjOF85zKHadVz28M/kcZv2t6BrdrkziBw1OZw9sC7ZqkcjA1wltr73O9epuCXSKrprcNgC7qPJ8/uaNBvh9RKcpocoh3C+e4ULrCiy2n0cRQbcgPAq5UblJz6xzPHKJdf5QYXoDpjGE5d9aN7qMjJkc4mRvhb+dWuVWbYa65SlZLbhvjIAioOg3OFm8CcDg1SNu6KtVdLJsl/mz6Nd5cvUxSjvJs/gAjiV6SagzLc5ioL3CmcJ2rlUkarkFSiXIgdW85JosSL7Uc4f3CDeaMVa5WJumLte34cnqBxztr1wDYG+9kT6Jry3bqOpuiM5KnM5JnT6KTFi1FVNLXNQAW+MeVCyybJb6/cIZ9iW5O50a3nWvZLPKfp/+RJbPEaKqPz3U+w2CsE1mUWDDWeG3pHGcKN3ADj1faTvDJ9qeIyfo2sZaLpXHcYE7kAAAgAElEQVT+eOpHTDWW2Jfo5vn8ITojeWRRomzXuVAa4521a3x34V1UUeHL3S+QUB6vjHvbGPk+t1fXcDyPPS05zk/Pc7K/h4ppslCu0ptNcWNplSNdHRQaTbrTSWZKFeqWzVBLjnhL9qFv2uTkKpcuTiMrMpIoICthY4CxsUWyuTim4dBsWARPzMh/MtzlGt+FiEhmhzDeoyKjJpF2WXUdzXfxbFs/fzN5GU2SOZzt2NDjvR+OX8R0bhEEFgEeitTObpRb3w8wHYdS89GbGMATGt3uSAcvtpzmB4s/4TsLr3G1covh5F4OpUboi3Y/NNb3OCg7VapuHRGRnmjntu8FBBJynFYtzyXjOhWnSkCwzSipokJf9NGEU0RBoC/azVB8gPOly0zWZ8hrOSQESnaZO/Up3MDj6ewRREHEsG9iOmP4QQ0BBVlqRZFaaVjn8YMmqcinN45t2mNY7jQx7QQ18008v4im7COmnUAUtifRVFHm6ewI3184w6JR5HZ9nv3JPlRpazwvIGCyscRscwVVVDicHiSzyTP0Ap83Vy/zfuEmiijx5e4XeLXzNGklvpEceC5/kMF4J382/Q9MNpb427k32ZfoQdt0rkPpQTojeaYai1yrTPFM/gD5HTzvueYad+oLqKLMSKKXjvsSnaIg8mz+IIOxTlr1NHkttcUQns6Pklbj/J/j36NgV7lVneVoeoiovJX1cLE8zryxRlZN8GrHKV5uPb4R8uiLtpGQoywaRe7U57lTn+fLyvZV2KpV5odL7zPdWKYrkuefD7zK8czQltDJkfQebN/lTOE6/7h8niPpPRxJ7/lIwgwN2wl1TDQVx/NYKFcxnLufaQRBQLlpULcs/PXii7VGg4F85sGcL2B6ao1EMkIqFWFltUYqFeHAwW7+4htnECWRp54awDDsj7hZ08MhidKWVVRCifE7g1/dUaPlUaCLKnl1ZyrkUrNG3bE4tzrLTL1MfyJDTN6eXwB4tTvJU/lTKFIbohBBFnMIgkizZmKZDun8PX2FPS1ZdEUmFdm5lHg3PKHKWIxPtr1IX7Sbt9be51L5GnfqU7y1+h6D8T5eaXuRoXj/Q4UoHgWWZ6/TTQSiO1BTIOwIoIkaAQEN1wiD9veNgSAIRKRHZwZktTT7k0NcrdziTPEDjmYOIooq4/Up5pqLtGhZhhNh3NrxlvCDJqZ9i6h2HMO+SjT+a8S0kxj2NRr2eRSpHcuZBAIi6mEsdwrHmyemnaJpXUAWs0TU/duuQ0CgJ9rCcKKHy5UJLpRu81LLEfJiamulXeBzZu0abuCxN9bO3kT3FqOxYBQ4X7xFzW0ykuzl813PklTuJe7C8I/Gcy0HmWwssDRX4FJ5nFu1GQ6n78Xnk0qU5/OHmGwscrUyyXRjiZy6VSs5CALeK1yn4Rq0ahkOpfdsMdx3kddSOxpsCF+iY5l99ETPMtFYYNksYvsOUbYa3cn6Il7gk9dSDMQ6ttyzIAj0xtroiOS4XZ9jsrG47Tx+4DNeX+B88RYBAZ9uP8mR9OC2WHVXtIVf6Di1HustcKUywfB66OdJIUsSox2tOG4oD3h6sAddlsjENAREYqpCRyqBJsu0JuN4vo8s3ssDPAo1bc9QG2+9eQtRFEkmIijr3WAiEZVEXOenP72B0bAZPfBPyy5RBHnL+6gIMvsSA2TVnZ+HD4MLa3P8dHEc2/eYqZeYqe8uLH8s9zIxdXRjMhPWTaTnebzz95do1kwOnd5L33AHEV1hqHX38uPd8ERW8S5l42jmIPuTQyxbq5wpXOBS+ToXSle4Xhnjd/d8neOZR+xh8wAoG9nRYFcpOy/wcIKQ86tJyi6zv/BQr2AzJEHiYGqEM4ULXKvcZMlcoU1v4VZtnDWryBe6PkVsUzsXWcohiQlUuRvTuU3DOofp3MT1yyhiHllM07TPocjtaHIfNfMtGtYFfN9EEOQdvVxYV8hXk5zMjXKtOsXl8jiLZoGcltoyr5TsOueKtxAROZDsZyDWvsXbn20uM9MMOZFPZ0e2cC03QxdVDqYGeXP1CqtWmQul21uMriLIPJMf5XuL77JkFrlYHmck2bel4WHdNThbvIkTeAzGOzmQ6n/kcd983xFJJa+lmGgs0PTsDVbCZjiBu14OKm1L/IXXKyGve9A7xfws3+FaZYqmZ5FTk4ym+nYsJxUR6IzkaNMzzBtrjFVnabjmhzK64CNLVRRZwA2KqKqN7dfxxQppbYQAD10MGTZZrY27idfHEVfv68vR2vp0WMUmCWGHb0nil756EkkSse2wM4WmK9S8+oe4l8dDXI6S3xTqcwKXRWP1iYxusK7D7Psho0m5T/jpc30HONXa90jHymsOK9U/pOlcR5FaiKqHyMd/lVgiwqlXDjI/ucobf3cB568cnn7lIEee24emP553/qFcUUkQicoRBuRe+mM9fKrtJb41/wPeWH2P7y/+mGOZg7vEd4UNT/RhXWjTSpKkEscnYM5YYji5NVMYEFB3GxSsElEpQlpJPXIG9GHoinQwmhxitrnAu4XznMoeZ6w2QUTWOZY+uCVJJXCXRiPg+zUcbwFV7kEO8vh+DUHQSUU/hyxmqZlvosq9IR1FfwFJjK3HjnaGLqnsT/bSFckz01zhTOEGw4neLd7je4XrVJwGOS3JofQgsfuoM0W7RskOBUX6ort3LhUEgTY9Q1KJsmgUmKovbvu+Xc9yOneA7y+e4czaNT7Rdpyo1LZhBC6W77BgFIhJOqdy+7cY5PsREFB3DAp2laZrYvkOrh9WqRXtKpV1ClGwy5PSE23dSJrNN9foj7VvCVMsmgVWrTIC0B1p2ba/47tMN5aAMAR1pzZPw905obNmVTauoejUthH+d7/HneH6TebqPyCqtCMgIYtRvMDGcJeQxSgBPlXrDpqcpV18DkUK8xmP41mJokhsB2H7yHo2fksnam/bZj8zJJQY3ZF2ZEHCXWce3KxNMJp8/JCN5/lM3FqkWbdIZWP0D7VtOUaLHqNFf7R+d4Z9E5MRZKkFTenD9dZC6cmmxfWzE6wtljn9yUP0Dnfw1nc/oFFtoumPN1E8ttENA+DBhhDFXQgI5LQMB1MjXChdpWiXdz+pIG5w+2rug2fXuBxjMNbHtcoYF8tXOZ07vqWYwvFd7tSnWDJXGE7sIaOmPpI4G4TLtxOZI7y9do5rlVvktSyzxgKjyX20bFI1UuUewAdkFKmNROTjSGICy51EFtNIcj+K1IGoaWjyAA3rAprch6uWsNxxZDGHLLUisnP4BELjMpLsY6a5wtnCTb7a89KG0bV9h3OlW9iBS3e0hf3JrbN6EARYnrNRARhXdj8PQETSUAQZn4D6DhnluBLleGaIt1YvM91c5nZtju5IC7IgYfsOl8sTlJ06XXqeE5l9u56nbNe5UpngcnmCifoCBbtKwzWwPAfnLnf3IZPy8cwQ31ZTLFslfrj0Prqk0httRRYlVqwyry2dY6K+iC6pPN9yeNv+XuBTc8NEyKJZ4D+Mf+eB57sLy3MeqNImCeIWyuBO1ZeSoJDWRtDlPF5go4oJbL9GVO5AFZPYfoWMfgBNyjwy2+W/FEiCRG+0gzY9z7wRVqderYzxsZaT5HYpU98NgR9QKTVxbJd8+4MNYMhR393Vk6VW1MBGkfI07ctoUi8AvuczMNrJc589Cuv7P/XxUeKPqYkNT9Sux+Na5RazxjwDsV7a9VaiUgQncFkxV7lQukLdbbAn3rcriyGhJEgrYabyzbX3yGtZMkoKN3AxPJOsmt4IqAuCwOnccT4oX+VG9Q7fXfgHTueOk9eyWJ7Fpcp1frT8BpqocjJ3jOwuwfQnRV+si4FYDzdqd3h37TyO73AwNbyFm6sre9b/DrUAJLGTimWhKoeJb2paZ5gdzBRrdCVfRJY04trHqVoWgigiiw9epmbUOCOJXt5du8aiucbt2hwnc2EMeKqxxHRjGUWQGEn00qpvH4N70isfPmUiCSKD8U72JXs4V7zFm6uXeS5/cJ0xUGC8Po/nexxMD+was102i3xn/h1+svIBq2aZpBKjP9bOcKKHmKyjiSqWb3O2eJMFo7DjMSCcjL7U/QJ/Nv0aZ9e5te2RLLIoUbAqTDXC+vzPdJziqexuE0A4JnE5Qlckj/YIHNiOSHbHOPVdhDFLjZrbIFivmPIDf4sXLok6rdFTW/aLbiqXjvJkuhv/pWBPvJfhxACLxio+PhP1Wd5aO89n2l94rGS8IApEYipBEBDZRQ1wxajz04U73CyvYPnurmyNz/ft51TrfiBAEVuQpSyCIJJIx0isi0MtTq3iuh7NlIxtNujWUo9V+v3YRtcPPKYas3xn4YeklCRRObruFfk0XYNVq0BSSfCp9o/teoy4HONIepTr1ducLVxkvrmIJmn4gU8QwH879Ju06/eWgm16K7/U/Tn+n6m/5O+XfsKF0mUiko4beBTsErbv8Mm2FzmZPYr6EZLGARRR4VTuOBfL1xirTdChtzEQ60Xe5Hk4nseZuVlmq1VOd/cgiwIXF5c41tHBTKXMeLGI5/v0ptKsNZs4fhh/ao3FOLswz5G2dhZqNe4UC1iuy4nOLpYbDW6srtCRSPDywCCyKDKyHmK4XZ/j3bVrPJ0dAeBi6Q5Fq0pCifJ0bmQbrUoQBDRJRRMVTN+m5uwsHH4XTc/CDlxEhC3asZvREclyKDXAlfIEVyuTLJlFBmId3K7NMdNYQRIkXmg5vOMLYHo2b65e5u8W3qXhGjyfP8SXup8np6aIyhqKICGJEitmmZnmygONrixIfKr9KSzf5hvTP2bWWGHWCNvupJUExzNDPJs/uGscWxTEjfBHh57j1/s/uWMY4n4oorxR6LET4kqMrJpmZb0F+4K5wlhtkuHE9jLr+6u1duWs83Dt4m17PEhQ6uesEZGQYzybP8716h2WzDWqbp3vL76OIsq81HKS6ObE9w7+QiictcBEZY5gQQVXoFJqkL1PC6RkNflPN8/wnelrFK0m3g5joksyg4kcL7RpNKxFYtpxNKUfgHd/eIUb5yc3OpkXlioce2GYyFNtlE2TjkQC8TF6NT620VVEhaeyR3ACl4n6NGt2kZpfRxFksmqa07njnMwdoyuye4wSAp7KHiGpJPjpyjtMN+dpuE1icpROvQ1FkNbVuEIjDwL7k3v5V/t+n7cLZ7lRvU3JrqBJKsfSB3km/xRD8X60HbzFu9cV8cwnjvUeTA2T13PMNxcZSe6lO9Kx5Ue1PI/FWp32eJxcZL0ARJaoWRYzlQppXUeXZT5YWkSXFW6srfKrBw8RVRRiikrVslhpNIgpKp2JJK+N36ErmSShacibiNyD8Q5Gkj2M1+e5UplgzQ4LPq5XpjA8i/2pPkaTOycMWtaZAnPGKuP1BT6+g8oWhAZgyShQtRvIosRgbGdvSxFljmWGeHv1KhONRd5cvUxOTXKtMkXZqTGa6mdvfGfh6jWrwrniGA3XoCuS57cGX6U32rrNCBTFGqb34D5gAbBgFnh95SIJJcrv9X2e45l9yELYYFIVFXRJ21VHVRVlBmKdnC3eouo2iMk6PTtcy+OiRcsylOhnrDaFj0/RKvNHk3/DFzpfZl+if2MlF6zr4TY8A11UadVzSDsQ8cNtw1Y9a1aJpmdgeCZNz8TwLAzP5Fr5Np5/LzD7TuEiq3aJqKQT2fijEZHCfmNt+kffleNxIAgCh1PDfKHzZf5i9vtUnDorVoE/nf427xY+4HTuKPviAyTWNSCcwKNiV1k0V5lqzjFWm2bFLNCptvLF3GdYnakh7dBz8NzqHP8wf5t9qRZ+a/gkU7USfzt1hd8dOU1bNMFPFu7w04U7fH3oOKfbMljuuyhS2FNOFCIkszE+9sUTZNvC1fnUzUVkRWLaMHDXk3ePg8c2uqIg0h3t4KvRzz3urhso2+Fyoj/Wzpe6XiQixfHXs9ARKUbDK1OwTQQEys4aIBCXk7Tp7fxyz+cf61yD8T7+x9F/8cTXCmxoS2TUFCOJvSTuK/tVJYkTnZ388M5tVFFiTzZLyTSJKiqW59IXDZfYtuehiKFKUdEI44hl00AWBUzPpT+eJqIoRBSFkmFyp1jgVw8e2li6KKLMqdwob69do2jXuFAcI6elmG6uIAoiL7Uc2TGDD9AXa2cg1s68scbZ4k1+qeclMjvouxqexeXyBKtWmZiscywztMPRwuXvUKKH0VQ/k40l3i/e5FBqkBvVaQQEPtZ6dNcEmulZlO0wlt8VaSGtbO8s4gc+i8YaM42V3X4WIIzpf2/hXe7UF/jF7hf4RNvxx2IUaKLCofQA31s8w7JZ4kLxNkPxHiLSg0XrH4a4HOVU9jA3q+Pcqc/gE3CnPs2/Gfu/ScgxEnJsvQrLoukZ2L7DL7S/yK/3fWFH/YDwXh2+Nf8a31t8/ZGu4XzpKudLV3f87lTuCP/DyO898f19VFBEmU+1P4/p23x34ScU7QqGZ3G1cpurldtA+KwJCFuKKTYjJ6URRJHRY71hOfN9mK2XqDkmf3DgVU629OIGd4jICt2xFEfzXRzItKGLMt+dvs5o+gStcpG1+p8iCipR9RgHnv7kluMNHwu1pN16ibJhPrYr96Gi82dn5xlbW0MRRY52drCv5dFmTjdwqLtl/j/y3jzIrvM88/ud/Zy7773vaKCxAwRAgKRIUBQlUdS+2PJux6MZx/OHUzNTU3FVUomTqrhqqjKLK47jZFKe0XiLJVljWotlUSQlLiIJcMW+9YJG7923737v2U/+OLcbaPQCQFsS56kiJd5z7u1zz/nu+33f+z7P89bdCqv2MroYDjJZVIhKcWZbkxT0XlJKngXzFqGxhk5UThL7CacP7gc36pOsWKvsjg+zK75ZUeZ4HhXTZDCVphCL0nQcVEkiIKA7HifdXv0eKBTIRaLossxivY4uy8hiqF3viEbJRAwUUSKl69Rtm735POeXFhlKp9e3L3sTA3TqGa5Ub3K+Mkm3kWPJLJFSYxxOba8Bz2spHs7u5UJ1itnmCv955hU+1fPYOsc2CMIA8PLyOd4oXsIJPA6lRhiNby8oWRNuvLZygRWrwg9XLjLTXKagp9mfGNzWTlEVFWLtgDxvFqk4DRJ3BN4gCJioz/ON2ddpeDurfcLgHG7hG65JxW6gaPKGbiQ7QRRERmI9nMyM8f2l9/nO/Bk6jQyPZvdvkB0HQbDuzVCya/RHO7actO7EnvgQn+p+iufmXmSiHXghlNHf2bjy/++QBIlnO0+TkKO8vPwWN+rTNO947sE23BUBgYyaZDjSz+LFEoIj0NG9WXFq+R6apJBQNERBQBFFvCCg5YXsE0NSeLjQz7dvXWaqZrOv/1chCHcMwhaNGBrVFp7nk44aRJTNhlL3wo8VdF3fp9wy+eblq/zqQ8F9B92YHK784nKabmMYZ93eTccNbKJygricxpBjocAiaOclxZ2r7j8NmJ7FD1feQhIkxuK7yGubW05EVZUjXeE2PHRXEhjJbD6vM3b7RzqUTm/43ztxoNDB+aVFIopCy9lY9Y63mQNXqtNcqU6zYK5i+jaPpw5uUKDdDVEQeDS3n/H6HN+ef4NvzP2QBXOVsXg/KTWO5duM1+c4u3qFmdYyA5EOPtP7+LYtUNavNTlIX6TApcoUPyxexPRtjqR3UdDT2w7GtBpntC32mG0u8+c3v8ep7H4SSgTbd5hrrXB29SqT9TkGo51MtSldW0ESRIaiXbxTusbZ1Ss03BZ5LbXOHBAFAV1SyWspDqaG6N+CLpfTkjzbfYp5c5Ur1Wn+4+R3OFcep9fIt6l3AXXXZMWqMN8q4uPz60PP3DPoyqLMqewR0mqSM6vnuVIbZ9lcpeG1cH0Ptd2eJ6nEyWlp9sSHdhQUyYLEycwRCtrWNqb3D4FOY/NvVRVUHvKO0GX3oOkqI8EQy7M1NMWkXjVJpCJUyg2G3EG+kEuyulLjVH4/K9N1jC6DWtWk1jbPEZcMTvMoluDgzYIxsPNvV5NUTudPMhof4mLlOjfqN5lqzFK0y7Q8E9f3kEWJiGS0UyNZeo1OBqI9DBt9+FGBXCFJ4G/2o9AkGdtzsf3QUU9vdxVftRrrHrxri6SKvUrdXML1V/GCKro8wq3z/dy8Mo/Qzuku3iqy62Afzd0x6rbNcCbzkyukBUFAgB8u7gWxPdusSWwFTg30cbirkzPTM/f9BwGicoKoHOZH7pzBtmrL021s71XgOh6v/937CAKMHOxjebbE4q0iBx8ZxXFcLr5xg+7hAsP7ejn74kVUTWZoXy+Xzk7gWA57TwwzsGdjftYP/HX3o6bb5PXi21yoXCWvZXkkd3xbV/ufpHF1VzyOKkk0HYesYWzI6wI8ktvP12deZqa5zHyriCSInMrtv2fFN6XG+fn+D+IFPt+Zf5OXFt/lTPEyuqThBR5Vp4kf+OxNDPAL/U+xN9F/z+8VUyKczO7lYmWSJXOVqKRzKDlMYgdfgqhs8GThMNdrM7xfvsFLS+9yrjwR/hja1xGRNH518KMU7eqOQVcWw4LdmdXL3Gou8drK5u20IkjEZIPBaCef7zvNqey+Dc9cEkJByZeGP85fTb/IO6XrPL/wFqqooIkKASHly2rTvnbFetYNm+4FWZTZl9jFQLSHZWuVutvE9h38wEdq20XqokZUjpBUYpsMiu6EJEocTO3mYGo3RfMKMaULSdCw/Qau30SX0wSBjySoOH4Tx29geiUSSj+yqOPj0XJXkEWDiLTFAskWiE6k2a8V6MlmuXF2jvwBh7mVVXaNdXHr5grzsyUi0ThPHdjLdHGZY5lR3rh4lUzc4t0zE4iSiGO7DAx3kaql6epOM1lbJBu5N6tIFkMaWbde4FT2MKt2habbwg7cdeaHKipEJJ2YHCWpxEKWi+nw3VffJpGM0tWfYfeBng3Pt6DHUCWZ8eoKB9IdpDWDmKLx0twNjuX6yGgRxqtFqrZFRI5hqAX8wMRyp/D8Eo1qjnQhQTofTrKSJKIZKnu6u7i4uLRjsXLL77nTwQCPujNNgIcmpgCBmjNFXB1GESKIgoIkCtsKvfwgoOU4tBwXCNBkmZga5so836dmWeiyjCbL6zepaTu4nk9CDw1mAh+ajoPphlr0MOcZnj9xcQZBFDj02G5uXV/AbNkcfGSUM89f4LFPHiXTmeTmlTmMqMbSzConP3yQdCEBQUD3UIHOLdp7W77NV279Lecrl/ECn5Znoosan+t9lq77Mrf58aFKEl3x7VdRPUae45k9vF8eB2AwOshIrPu+CoUFLcVvDn+Mk9m9vLZygfH6LBW7QUSJsC8xyInsGA+lR8lrqW3zw3fjA7mDfOXWSxQth9F4L6Pxvh1broiCwK5YD/9s7Od4dfk8bxYvsWCu4gQeGSXOyew+nu44tr6C7Y90EJeNTROA7Tu8vHSOv555mZrTpM/Ik9NT6KK6Xuy2fYeSXWPJKvN+eRzTd9aNfO6EKikcTA0zEO3gvdJ13ixeZqqxSNVtEhDQIafpixTYnxzkUGqYPqMQ8p9bNoqm7NiJWRAEopKBpnRiuTZqREFR5U0rMttyaDRaRBO3Xfc8v0EQOEh3yb7L9gQlaxzbr6FKCaJygZI9jiFlqTtzqFIMSdCo2reoObNYXhUBEUnUyOv7NgRdz2/hBy0EMUIsbqDrCp7jkSskqVSa+H7A9OQKoiigaQq5XILico3lpSrzsyWKyzVicYNIVENRwueezsZoNS0iMRUCsC13oxBjB8iiRFpNkr5PdZosS+za10O+I0E0sXlFvTfdwViqwOXyEh/rG6M3muRUoZ//eO0tzq8ukFJ1btZLDMQyDCeSON4Cnl/FcZcQBZkjj+1GkmVEScRzPLoGciAIXF0t4T9Am571693p4Fq/roY9w5L7JqIgAwK2VyGq9JBUty6yQBhwb6wU+eq5C1xaWsYPAoYzGX7j+FF2ZTNULYv/5jvf47GBfn7hyEGkdiD+92++xVy1xv/4kQ+hSCKXl5b52vmLXFteQRQEjnZ38QtHDtGTTISda4MA23TwHA/f9bEth2a9xVsvXMAyHXzXR9EU9j08zJvfPcejzx4hktDxXA/XcdGMjdtnSRDpNjooWiWcwKWgZXk8f5LR2PB9Wd3dD2x3GVlKt+/ng8OQVH537y+vFxYEhPvyPoW2hFuOcDK7jxOZsTvyZQIiAqLQLls8wEByA48gCHO8B1PD9EXuTbmSRIluPcsX+k7zud7H169hzSpwzQHqZHYvxzJ7EO94DcJd2PeX3uP/HP8WAQG/MfQMpwuHtyyi+UHAayvn+XdXv8ZUY55z5Yn1oOv7Aa7trm8do4HO47nDnIiO4Xk+elTF9wN818exXIyojqrIuI5HaaXKq998l1MfPUS2M4nZtJFVGVWT8T0fx3ZxHQ/NCAsvb3//MmdfuMDpzxxn9HA/qiYjiCK+6yErMuPnb/HCV9/kn/7+FxGk9la29mfUrDcZzf8xArd3MqqYxA1apOQ8hpzF8RuoYoyE0kvLK6KK4U4ypnShiBEMKQOCiOWVEYWNO6Ji829Zrn+N0dwf8sTT+4A7FPN3sNQEAXwf1jZee/b3IAoCz3z6IQSBde6rILR7mfWmERB44ul4u3v0TwcBUK+2WJotsfdIP0Zk4/cbimf4rw8/RU6PYLS51Z8dPETFtnh1YZJVq8muRI5f2vUQ+9Md2G4ZkDCUPejKLmRJxfMCJi/PsjxTIpYy6N/dha7IGIpC1TRJG/fvsXyPla5PgI8oKESVHmQxih/YSEJoyCEIErC1FLJu2fz5u+9TbDT5ncdOEdc0vnruIn/42hv89x9+ikzE4FBnB6/fvMXnD+4PvT0bTd6fX+DZsd3Ikshivc5/evs9RFHgX55+nKZt85/eeY//fOES/+jhYwyN9bAwtcLU5Vm6BnJ4ns/4hRkeenIvzZpJeaWGHtEQRYFmzaRvtJNIXGdwrJuJi7OUlqobZsa6aVNttng4dopdwWFiuu2xiowAACAASURBVErC0Ki0THwfbpUqOK5HJmYwV6rRk0mQjNyu0Ie+uRU8v44kGkhigiBwcf0qipRBFBRcv0rFPEM68gQCEfzAwguaCIjIYpKAANcr4wcWkhhFFhMIW/BuwyLVgzVzvPP9AjxwM8itEBDw6vJ56m6TLj3LweQwiiivm/uE98Ai9A1QcbxZVKkP1y+F3SrEBEFQRxB0fL9KgIAidSMIYXpCQEDdYrJreRZvrFyiZNc4ltnDM10nUbax9gN4JLuf/136Bqt2rd2sMoTdsrn01gSaoSDLEo7tkelMMnlxBlES6d0VSkpvXpkDQSCeirL7SD9X3p6kVmqyMlfCdTxuXp1n8VYREDj6xB6KCxWuvjuFEdXZ89AAjuUyfXWedD5OR1+Glbkyvu+TzMZYvFVkcKybvceHee3b7911fz38INwp3one2KNbfk8Bgf7Y6U2v3V2IulO4FAQeQdu7ZKcVO9wOuHdC2maHtW7Qv8Nz+YkgCDCbNvWqSa3SpNC9MZUhCgKjyY272u5ogn9x6DSfHzpIw7XpjaYoGDFcv4Ii5YmqR/D8Oq5fRJZSrMyXefeVqwzu7mLy0ixm0ya6P8f78wvUbZuHerrR5ftcye90UBRkkuoukmqouAp5sw6Wt7o+k259DwKqpsl7c/N86eHjnOwPex/9whGRf/mt73BxcZHHhwZ5YniQ56+PM10uszuf4/zCIrbncairE0kQGC+WmKlU+S9PneBIdyeO7zO+usprU9Ms1RsMpFM8+bkTBAGIokD3cIH2gmn9OkQx7NTZO9IBgrBOcO7szyGIG1d0pXqTi9OLdGfiTCyW6EjF6EonuD6/wmNjBgulGrWWSVzv5PzUPLn4xrylF9QoNp9vG9ioJLUTVK13gQBB0MkYj+N4JWx/hSDwcYM6FfN1/MDG82tkIx/G9WtUrXfwA5OouoeE9hDcZ/5wK/iBTd0eRxBkInI/UpvLHAb3Ki13BkPuRRIjNJwpJEEjovTf9+cvmiVeL17EDwJG473sbrMdTOddZLEL0z6HJGUIzV06aVlvo0S6cNxJEGTwRETBwHGn8YIaAhJR/YOo4s7yyqZn0fQsQCChRHYMuABLVhkv8FAleYMvhW05XHjjBrFUBCOi4Xk+c5PLKJqMEdW4ce4WekRlZnyRPUeHGD9/C81QuHl1nuH9vbiuR6VY471Xr2K1bBani3QOZCkvV7l1fYHHP/EQRkRDliWynUlc1yOejnL5rUlc20WSRKavLdA9mEePbE11E+749+3Xtl9VbXXs/83dtNesBew1qbogoAoyqqRsshvYCoIo0NGTJt+ZJNe52Ut7DVXbpGg1sO9qwR6RVVatJqtWk4Q8hyHMIBsFHG+JpnMRTR7GatlE4zo9IwWaDRPfDyi3TIYyGUZz2QfK697n/vb2Y5cElYi8k/AhRMO2sT2fzvjtinra0NEkmcVayNHclc3SGY/x6tQ0g+kUF+YXGUynyEdDF/aqaXGzVObfn3mLr50PDbFnK1VkUaRmha05BOF2TlkQhA1jc+3mC4Kwvl1bw1bbnaD9JVdqTXKJCIaqUG2azJeqrNZb5OIRVFkkl4iiqzKev5E36HpVTPcmEWUMxyvTcm/iBQ2i6l4q5pvY3m5UqYDlzrdNksH2ljCUYWxvBctbxPVrOH5oPRcELj/qajZ8f0DDmWKu/g0iSj+yEaFh3sT2i2SNRyHwaTg3kcUEkmhgeUsIiPcddEt2jedmXmW8PkdCiXC6cHjd2DvUuNfxgjqCrwEekhxvU3AkRDGF79cJaOEHXphTRAwfwLYNsm8jKultHnDAVGOBifocw7HNfst+4HOzscj/Nf0iNbdFp57ZINoQJRFBCFd4ZssiEtWxTZvBvd3rAdGxXXqGCxT6MsxNLVNdrRNLGnQN5EhmY1gtG93QGNrbw7En95HOJ6iXm3T25xg+EE5Csu2SzMdwbY9o3ECSBJwgwPPClNiOz5GAUvMFquarBAQk9UdIGR9Cak9M5dYr2O4sUe0gpebzWO4tdGWQbPTT6PL9PUsBmZp1lmr1DEFgE9eOkTY+jCyFq8a69R4N+zwx7SGqrR/SdK6iyl1kI58kom7vr7EdLM/hZnOB67UZZprLLFll7LbZEQjI7T52BT1Fr5FnNN7LQLRjSzZN4Ac0GxbDuzswopuLyVXb5NvTl3l5YYKSFSpCt8Ovj+7iRHqKSut5PL+EKIa1lc6+LHOTy7z3ylWiCYOBPV2QUjFdl5Su/3RlwPcLpa0MMd3bChnPD3B9H629DFdliccG+3lpfJKH+3q4USzy4dFdJPRwyy6LIrlohEcH+hnK3KZWJXWN3uT2Sfbb1newnev7VuhMxUhGNMKHLiIKAj4BXZkEcUNDADqDUGL4xL4hItrGB6xIGfzAAQLSxmNY7gKSYCALMQRkgsBFECQEQcQPbARBRhLjaFIXtrRMEPgoUhrHK5LQHvqRBvPdUMUMUWWQpHYQRUqjB00sa4Gy+T5p/Wg7RRTeJ1FQ24F+ayy0Vvm7+TdxAhfTs5moz4c+C4HP6cIRjqX3rJ8bN54iCAIiiCCsNZuMETc+giTG0ZUxAlxAhMDFEI6sNzeVtmjQeTd0SeXR7AEuVaa42Vjk9y/9OfsSg/RF8uiSiuO7rNo1brWWuNlYZNEsEZE0nup4iEOp4dv3R1PoHi4Q+AHl5SqpQoLO/hwv/+3bqJrCyME+FFXGbFqIYmgb2DNcYHZ8iZe+fpbqaoN8d4Z6ucXU5bBoO3KwD0mWUO+y/JNlaT1L0D1U4Bv/4QdMXZ5DNRRajdDFanZiiTPPX+Do6bH1vly2O0Op9ffE1MNY7gxzlT8GBDKRjyEIEpY7xWrj7yibLxNTDxJVDwLCA0mGXX+VYuM54vqjON4Si7U/x/PrFGK/iCjq2N48xca3qLReJaLuJaoeIiAc6/cLPwgwPYs3ipf57sJZbjWXaLompm9jb+PYpooKuqgQlXX6Ix18tOsEJzJ7N4hXwoJ7wPm3bzIwUqBveGNN4eX5Cf748utIgsiBdAdxdXvXu7zRTTrah+VMoco96PIIizMlpq8u0GpYiJJIEIDv+kysFKlYFj2JxM8u6K479gShasu/QxKX0DT6kknemZ1jf0cBWRR5e3YORRI38Hk/MDjA3166ynMXLyOJInvyufWe9D3JON2JOBnD4ERfD4ooYboOsiiS0LfeigVBQBCYON4yipxv+9Te3w1RZRmlLUK4c4sS1TbPnqno5iqp69dw/SpN5waCIJPUH2a1+T2KzedRpDyKlKVqvoXjlSmZr5HST6GIKURRQxZTiIKG65VxvTJ1+yKKlEHWUutGyg8KQRDanx1DFmM0nSlK5rvIYgTXr+H5jfCagwZeYOJ4VSDAD+xNxRaAhmfy2soFZlthYVRq+xacyIzxywNPbzCAkcS1vNpG0bwsZdnYI+7O4/c/cAVB4InCIezA4W9mXmXFqvDS0jtbdhTWRJWhaCfPdp3iw53HN1ynosk88aljQLhiEiURURL4wj/9cNj1WFPaRaLw2JOfPY4kizz7a4/juh6iKKDqCh19GSxzDFEU0SMqe44OMHr49ipTViQOPnJ7Es33ZPjlf/4sAWEwlhWJTCHJocd2h0bjyp07HIHe5O+EbnaBy3T5X7HS+Hq42hXCcWi6kwwkfo+k/kT73gYPNG68oElX4h8T1Y4APvPVCMXmN8lEnkEVQw665c2QiX6cfPSz7R1LgHCfOzHP97jZXOQr0y/xw5WLNLdtHLoRth+mHKpukwWzxIXKJI/lD/BzfR9kIFJYzxf7ns/qUpV8R3Kde7uGxVYN03P416c+xdFcz44sH1GwEbGI6acIAgs/MCktN7h4dhzf88l3p5EVCcdxsUWPmmX9ZCljO6HYaHJuYYH5ap2Fep13ZueIKAq7shnGCnmSus4XDu3jq+cu8r+89gZRVeHS0jKfPbCP0dxtcncmEmEsn+N718f52Nhu+lK3qTGjuSxPj47w4o1JJksloopKxTI51NnJM2OjqNuYTDjeAqXm35A0PoKh7nug73W/Fcitzqvb58kYTyKKEVwvTBF0xH4e8NaDTMp4lJRxuwhiKKFXgi734vktVhoXycc+3g7g9bDAsQPLYaFVYaK+zP5kD0l180QgIqPLXe2tqIAqpRAECUVMYXmr+IGN45WQBAPHLyMJOo5fQ5M2E/DTSoynOo4y01rB931SapS9iQGOZ8aIbGINCNv8//s9fm8oosxHOx/maGqU98o3uNVcouLUsX0XWZCIyjo5LUl/pIO9iX6SSmzTc9vK9BrCFttbQVTDtJSqi6goG16X7/gcSdzooCAIwoZAKooCRixcca0Z2ciqiKxqm65RkXKocg+ioICgEFH3UTXPtHcKa+d0YCi72t2mHxyymEaT+9YnW0MeZcX7G7zAXA8qipgloowiiduLcLaCF/hcrd3iTya+zfvliU1yXl1UickGmqSsM4Q838f07dDqs82PDghoeCbfW3yHZavCbw49y1iiLzQx93yyhQSitHks7U93MprMM1lbpS+WIqHo2zanDNxbeN51EsZT2O4cTecivcMf4YOfPUFpucr4hVvMvLdEvidN50iShr2zN8hW+JGDbstxmK1UWW40eWZPSB2bLldIaBqjuSyaqvL40CApw+D9uQUcz+OXjx7iA4MDG5biuiLzyX17yEUjfGBocMOqUpNlnh3bTW8yyfmFRUzHZVc2y1ghv0kwcCcEQUVAxg/CQsuPipK9gipqSIJM1SmRVnMsmjPIgkxEjlN3qySVDFE5zPvE1ENUrYs4voMoDeIGEXzfRhREPN9an4HD3LiE7bsYkro+0ERBJaYdxnJvIYkxosrubTtKrOFcaYYvj7/Gf3voE1sGXUk0SOttcxsJDLkb7ihOpPTbHrNRZWd3/YyW4JcGnr7Pu7cZoZTWZdms0xv98bvpQliZ7jQyPGM8/BP5vJ8lgsCn6syw2HpnPZ89GH8KRbg7vXJ3EU2EtlBpDaIQue9V533hTs7Y+t/QEXjwHmZLZomvz7yyIeAaksZIrJuRWDedeoaMmiAq68iiRBCA67s0PJOiVWXBXGW8PstEfR7TDzuInCuN83XlZb40/HFycpJoXMe2XWqVFuXVBqnMbVn54Ww3p7uG+bPrb/PqwiR5PRrK77e41g91JxmNLtGw3sH1V4CAuallzr5wmUY15FCPHuwj25kk2ubSP6gw6kcOur2pJL/y0JEdzzEUhYf7enm4b3v9viyKHOvt4Vjv1o5UhqJwsr+Xk/3311QSwgKUKEYRhJ0HyMJcmR+8eJnlpQqqKrPvYC/HT46gt3NxJWeFmBQnrqRYNGfIqHlqbgVdNFi0ZrF8E9NrMhwLfW0VKcctuxtJEJEFmWpzPCwLBT4pJU5ciTLbCg1cdElFQORA8nZ+URAkououour2Hgo/Lu6mn/2sca26yA+Xb/Bbu5/8f/Q6fppwXY/rVxY4e2acSqlJLK5z8tFd7DuwcQx7gUPZnqDqzBCVO2i6K7i+hSxGNrANHH8V1yujyh0EBJjOFLKU/pHTTlvB8ys4XjHsYCKA7c4hihFEYfPK+0Hg+C7vl8c5s3p5PeB26hk+33eag8khuo0sEWn7xo5+ENB0TWZbK5yvTPDXMy+zZJbw8Dm7eoUTmTEeTx9kZbGKIISF0XKxTixhrAs1LpUXeX3xJjONMmW7RVzZvvB1OHuKw5nD2O48ohDBUPYg9WZ55KOHcB2PtUnIiGikDIOU8eA7i39YdvTr8HG9FVxvdVN+Zw21Wos/+oPvcu69aUzTQRQFvv/iJX79S6d5+qMHcbEp2yvYUgsEgVVniZbXICrF0SWDlt/ADRwi8h2mKATMNJdIKTFUSaFoVcioCUzPwZN9lq0Sy1aZrJKg6FbbgXfnAb22tXMDP/QbZk0MIW7YIvlBgO25YVcPITy+9tkB7Zx7+/3ieqHw9vsd310Xw3hBSKmR2jnR8I4Gbbe1oH1MQBKk9cG79vrdf0cSQ4tFL/BxfI83Vya4WS9iem1eqHBb2LFWI3ADn2D9e2z8G07gIRFK0td6nkmCuN5IMmi//+5ruB/q0cW5RZ577xJfOHZwy4aDQRDQtMOOEfEdago3ri3wb/7Vt1hYKOPYLrIs8e7bU/z2f/Vh9u7buLiQBZ20OoyARNWfwcfbQHsECHyL2cofkDKexvbmKLdeJB/7wj0XFQ8CQVCZq/5v5KKfw/WKFJvfIqk/iiz+eI0i626Lt1evrbdASsgRfnvXpziR2bujCfwaREEgphjsUfoYjHbQoaf5N1e/QtVphp9dusqx5G5SmSilYg3bcsLcvHj7Bp4rzvH2ygy/tfcRTneNoEmb1YBrKOhRIqqEJveDICMJMbScSiq3vUL0zpzu/UxQDxR0TdNhealK4P+sGza3uXidyfuSEopinIh6kAAHP2ggCZtzUGffmODi+RkajZB65nmwvFTjW8+9w5Mf2oeqahxInggJ/ILIqczTKKLaDrICGbWAH/gbDEpERD7SeXI9SKwVmwLC9kY+AfsSQ0iCyLnyDSAMdjsNvoCA69Ul/ubWO1yvLmL5LgnF4BO9h/lYT9j4UxDg1aXr/MmNV1gx6xSMBJ/te4gTuUEkQWKitsTXpt9msrZMy7NJKAaf6X+IJzv2rAer3z//LUYTHYDAq0vXaDgWpzv38GvDjwICbxUn+bvZ88w0V3F8n75ohi8MHONQug9JEHEDnwulGb41e46J+jKu75PTY3xh4DiP5nfx+vI435h5j7eLUzi+x5de/w8AnMqN8BsjHyAqq6xYdf5+7gI/XL5Bw7Hoi2X5TN9RDqf7UESJumvxP53/Jsezg1SdFmdWJnF8j4/3HOJzA8cIgoDrtdv3yvE9MmqUT/Yd4bH8LlTp9rOyXJeaaeH5AZIgkIoajBZypCMR7DbjpuU41EwL3w9IRnQc1+P1ienwuof7iWnqptyg43h8/4XLTE0ur7/meS43ri3w0vMXGdvbfVs0IKh0GEcJcKnY02hSEk3c+OOWxRSK9wlqZYP55T8mFpPIxT9JNvrp9ZWuJCZx7Sy2LaDJwbrMPlQWQst0CIKA6DY8YFlMkIt+GlXqZKn+l/hBi6T+KPnYzyO2Ux2iEA3zyuL26S7P9TCbNnpEXfe1bbgmV2rT6+d8vPsRjqX33FfAvRuapHI8vYdPdD3CX0y/AMDlyk3MwGZwtIN0LoYgCnT3ZzcE3YIRZzCe4Ui2h92pAqoobbvSdb1VFitf3tSYcrs0ZdgKy6Vsm0QVlbhyb1vRBwq6Vy/P8d/97ldoNh48efzjwoio/Nv/9dcYGd2+qeIawsaQS4CMI88iiXs2nbO0WMF2NtNUFhcqeF7IwlDvsHWT1n+w7R/ZFmouoe1odT841u76cC8smzX+50vfISKpfHHoYQp6gmWzRrdxewVStU3OFif5xcGTRGWd5269w59Nvk5vNE1fJBNKMo0UjxdGick635x9nz+58QojsTxD8ZBes2LVmZhb5lRuhN/c9Tiu76FLSjsHLeAHAftS3Xyy9zBNz+EvJt/gqzffojeSIafFmKgt8a8v/T290Qy/OvwoGTXKklmlUw9FNIfSvQzGcvzhlRdwfI9/tu8jAEQklYis0HAtvnbzLd5cmeAz/Q/RG0nzwvwl/ujqi/zzvR/hQLqXIAiYa5b4jlXj0fwu/snoaSzfIdXmBtu+x59NvE7NafFPRk8jiyITtRUyanRTcJxaKfH1dy6SiUZwPI+n9+1irDOPIkkIQuh9fHZylvdn5hEFgZF8ho5EjJevTaHKErqscGKol4i68XMDP2BxYXN/QMfxWF6sEgS306WhslADNHL6XsrW5CYJdkz8PN/8/hkEEVLJR3nk4RG6khsVV7nop5gdP0JFjBEzwlzx3FwZw1BIp6K8+95NLNvlqdN7txxj2egn1uX0jeXHMWI66USC8nwNq7VArisF1lGkxWEsN4aUsFiZLSGIAumOJM1qi0a1RRAEXDozTv/uTgq9WTKdKdzAXW+IqooKxzO77/s3shV0SeV4Zg9/PfMylu9QcuqYts3suSK1aovO3jT9wxs9Ug6kOzmY6eJPrp3hamWZrkgcVZTYKpAORFfJa5sbU27fzQNmGhXeLc5yIt/3kw+6/1+BInUS0Y5CICCLW9vgGREVcYtinGHce8v/s8S50gyrVoPfOfw0B9Nb57XdwOOLAw/zZOcYkiBScZr86fgPqTpmaDMZLzASDwdiEAREZY1/sfJXzLXK60HXDwKSSoRfG3mUqLx54DxauJ1n9gKfZbPG38+dp2w3SasR3lqdAgF+c9cH2J3YLJ5JKAYxWSMma1ieS29kYyFtoVXhbHGSD3aO8fGeg8iiRFIx+L33n+NceYZ9qXBb7vg+u+JZfnHwJIa88ccbEKBJMlUnvCd74p0czWxfHIzpGp8/tp+Lc4u8f2uesc7b/M5Ky2S+UuWDe4YZyqX5d997jd9+8iSnhvtIRQweHx3c+kMFgcgWK0pRFNDv8PkIgoCidQVJUKjYNwGBhda7HM78BsYdHVAq1Rau6/Erv/AIkYjG7FyJ986FKTFJEkmnIiSTEcrVFrlcHM/3mb5V5MUfXCGVNDh6eADb9rg5vcIrr10jl4szMpTfcse4Ml/ipb9+k2NPHcBsWFw6cwPbcij0ZukZ6eDca9eIxHT2HBvktW++y76HR/A9n/ELt5AVmXQ+jtmwmJtYZubGIic/ehg/Eqx3TY7LESLy9vnb+4EgCBiyTlyJYFkVvMBHkASG93ahGyquu7ml8dnlac4sTbPQqvLmUrjq3u4KfvfwcT430LOpMeVOEAWBlutQd+wdA/Qa/sEF3VChZhBRN3d+vRMHDvaRSOg02+mFNTx8atddHMmNqLstFs0SMUmn3G4PnlSiVJ0mmqSgSyqrdo0OLUXFaZDTkli+w7JVodvIUrEbJJUoSXVjhXpxocLbZyYwTYdkKsKhI/3kCwluNopEZLW99d8aUVmjO5JaN4TR23aEnu+3PUJbvF2cYry2TNVpUbKb7RzrbeqOLIj0RNNbBtyAgPlmhXdWp7jZWKXumEzUl3F8v53DDZiur5LT4gxEfzSv14ZrMdcs8+rSdW41Vtv32mShVaFkNdfzt5ok0xNJbQq4a8c+03eUb8y8x59OvE5ei/NIYYRHcru2ZHasKQrvXH2uYT0f3s5Vh2LHtbTR9oomWRY5dLSfF757Ac+7fV40pvHQ8aEN216AmjOHG1hoYrLthbHxeCYTpZBP8Nw332Xf3m5K5SbVaovxySWGB/NM3yry5BNjXL02T8RQyefjoYmP6yFLEpIk4vk+jYaN63m89/5N0qkInR2bc7WxRISRg/3Yps30tXl8L6BrsIDZtCgvV8l2pVieKWI2LSJxncOPj3Hz8iySLHHkiTEcy2F+apnDj49x9vnzmE0LKRraapadOj+JpqhwWx8AEJcNBB/mb61S6ErSatobmAsQUsa+NHZyu4/bgKO5HiJqmgBvQ2PKnSAJbSHVDuPiTvyDC7r3S1QeGMrxK//F43z9K2dYnK8QiWk8+oHdfPrzx5Hl2ze5XKzx8jfeJdeZYs/RAebKK6ykavQYOWaaK/REsrxevIQsSMiChC6pHEoNY8gaE40FIrJO1Wky21zGD3wsz0EWJRJBZMPAePftSf7yT1+jUbcYGMrT0ZkkX0i0c8JhIW07qKK8RY4qvA91x+LL469xrbrA8dwQ+6PdNFyb90rTG84WBNpbrs24WS/yJzdewfJdjmYGGI13IAoiF8uz6+dIgogf+Fs2/bsfhKkZmbwe30AnG0t2cSjVt05oD53/t75OAYF9yW469ARTjRVeXx7nLyffpGQ1+XTfUSJ3Bepio8lfvPk+giDwobFhXp+Y5t1bc1RNkyd3D9GTTvDClXG4Ms7DQ31ENIWIqvL9q5O4ns+jIwMY6sbVqygKPHxqhC/+8iP84MXLlFbrZPNxPvHph3jkA7fFEYIgkNX2EFd6kAQNSVCJq92od6nxNFXmqdN7GZ9c4uq1BYqrdY4dHWRpucrgQI5r1xfJZmJ0diSR5bC4Wsgn6OvJMDyYp783w8zMKiPDeU4cG+K5b75Hs7l1erBebVKcL7M867P76CDLM6ssTq/QNVRg8tIs9UoTWQ6Lnlqb4ZPuTHH+9eu88tzb5HsyiHLIV14b27qk0hPJUa7UqblNmq55X6vB7RAEAXW3SbW94OmJ5DFkjaZoc/X8DAO7Ni9O9qY72Jve/PrN+VVKtRaaIqPIEqvVBuMXV3C7fAQBonqK3o7tPWYg3CFWHZOUaty3C+EDBV1VlSl0hLPJ/aLZtKhVzQ2vCQKh/2bk3rmdtSBqRDQkWcT1/R05ukEAL16+QVRVqZs23ekE48ur1FsWo51ZlqoNyo0W+USMzO4MsdOdfGTXwxzq76InnwzTC3cMiFe++R757jSTl+dI5eLcnJ2n8wN5inaVVbuKLimklBg+AVk1gelbjNfn6DFylOwasiBSdhqIgkjTNSk7DQQh9LVda2fjeT4X3r/F8lINz/NpNMz1bdL+VDffmHmPVxav8XhhdJ0JIAjClqvSu1F3Td5cmeCJwm4+2/cQkiDyxsr4AyVQphtFrlcX+fWRx3isMErLc7hSnV//DEkUOZDu4a3iFGeKkxzPDIZWnYGPJIh3rEoFDFmlaDWoO1bIoCAMohk1yki8g24jxTPdBzEkFT/wcQOfqKzdFxfS9T2aro0mKexP9tAfzdLyHM6XZvhQ195NQbc/k+SZ/btJGDoLVoW3S5P8+gcP0xVJcq2+yNGOPsY684zXVliyK4iiwPGhHoY6UyQ0nUAIf3AC4cR3sTxHQU+QT8b4zC+d4OBHBknIOlkjBjoEKji+d8ekISAIEnV3PuRxE8qg74TjeviBT3dXioXFCjdvFZEkAVEMA2xAgGk6mKZDs2lhWg6SGDJBqrUWLdNBaPvgrrNAtllxjW7hZwAAIABJREFUFnqzfOiLp5BkCc1QGTnYj+/5qLrCvhPDeJ6PJIuoukrvrjCFFE9FeOrnTuI6HqqmgBBKq5/8/MOouoKNy/H0Hq5Up7Hb9LG9iYEHarF+J0zP5q3Vq7iBhySInMjsIWXEyB1M4joeirY9M+Fu6JrC4tQSg90ZpuaLVOsmkiSSSRhYjstKpU5vx73N1yVBJKtHyOv3lq/DAwbdvft7+D++/I+x2nw1VZER2Jkm8bdff4s/+oPnN2y1NE3hN3/rST7x6Yfu+TdrtsVKq0lnNEax1eRScYlD+e0NdwICbhUreH6A7XpMrpSI6xr5RJQ3btxiIJfmg/tGeP7iDXZ1ZDm0q5vPPnZwy/wuhLk4WZaolRqsLJTJaAkeyuzGD3wMSeVwamSd7nQnBEGgJxLKnW97QWx9nxbmy8zcWt1wj9ZwMNXLY/ld/MXkG7y7epOMGqXhWozEC3ym/973z5BUdsULnCvPoE0ruIHP9eoCWe3+VUUFPUFej/PK0jWWzBplu8nFyixxJdyyS4LIyewwby5P8OXxV3lzeYKkotPwbI5m+nmqMyzgCMDRdD9nVib5w6svkNNijMQLPJofoSuS4tN9R/irqTP88bXv02UkaXo2vu/z0e6DHEhvzeO+E0tmja/ePEvDtchqMequxXhtiQ917iOubKy6R1SVgWyabCyCrihcrDUouQ0UVaTqN/newmU0WWJ/qpueRIKlYpg/nDFLvLp0g+5IktDUP+wyPRTP8frSOHk9ziOFYUQEJoIiJ5KDCKrAt2cukNGiHMn0td8bOvbNN86yal0jpoRS26Taj3SHb+7yco0Xf3AZ2/Ho6UrxxGO7SSQMenvSxGI6+VycazcWKJYaNFs20ajOoQN9dHelOH9xBkkSicd0TNOm0bQRJYHVcpPoUgXH8TB0hUw6tu4rkczeZk/cqdRb84G4+5ggCETim1M3a2o7A4lHcvt5u3SNi5Upvj33JkdSuziYenB/atf3eK98g+/MnwHCfoEPZ/aGHsoSWyoLd0JHJs6HT+1BAEb78+tsDwh/sz2FewdcUQBFlLhcWsL2PJKqji7vzMyQfu/3fm+n4xsOBkHAaq3JS+fGadkOpu0gSxKavL2B9tXLc5x9c2JDUJJlieMnh9k9tnV77zvheD7vLs0xWSlxvVwkqij0xnc2u5lYXg1XxJJIXNdwPA9VlunLpgiCgMF8mmsLKwzm00wXyxiqQkRT130X7oQgwOSVOUrLVWKJCLuP9KPGwxVmXI4gIOIHAU7bLs71QkNvx/NwgyCsCnselutRNlto8uZUwIX3b/HK969Qr4U7gnQmyolTI3R2pZAFkQOpHpKKGm7PnDpJReRwKkJez2C7i2iSyMHUIFE59DRYW00dSPeS02IMxvLYvkvRqqNLKp/uO0qHnmQ4nl8PvnXXYiiWYyi22YA8qUboMVLUHJOS3WQgluWZ7oMUjDhDsTwxRceQFA6ke4nKGlWnhek55PU4RzL9pNXbNo1S0WXu/TmuXJ3CFQN2dXbRH82iiBI9kTRD8Tx116RkNTFkhf2pHsaSXehSmKdueQ67k52bCnHQTnG0c9hVxyQqqZzuGOOJjt3E7gq6SUNnJJ9FkUIq2oXyHJbn0vJs9iQ7KdstPtl3kIisUnVMFlpVBmJZqk4LQ1JYaFVpuDbHsn0kVYOK00KVZB4pDDMYy6CIEiW7SVRWWGzViCoaT3ePbQj+QeBSdxfRpRRZfS8xpQtdSm3IISbiBocP9rHvQIFYt01HV4xMJkK2VyaZ1OnsjRDNiOw9lOXIoX4yeR1N0unuSnHkUD893WmSyQjLqzWWVmpUaibF1TqVaovr44v4AXR1pjblmn+SSCgRorLBVGOBRXOVm80lorKBLqnoorpj8A3alo+zrRXeKF7iz6a+y7JVodfI88X+D3IoNbLB3P5BEdaA1v7Z/Pq9EAAlq8mVyjIJVaMzklgLuv/Ddu95oKnBDwImFleZWiohyyILpRqiIJAwbm9zS0sVjJi+rTfog2CuXuVicYmabfHK7BSFSIx92Z1b5oiCwPHBXkzHQRAEYppKsd7EDyAV1SEAQ1U4PtRDNhrh2FAPrufftr1vY3ZiifNvhu1wmjUT3dBo1EwmF4tUY2ELIdv1cHwPTZZJaBoV02Sp0aA/GQb3eluXXWq1SBk6sihyVNM2pEcCP+Dm5DKl0tbdYcNODwHPFBSeKRRCw29piMCdIvBX2GMssacvAsIFPKuFrD3FaKJjQ+FtOJ5nOL4xmO5NbpzwvjBwfNt7qogSR7MDHM1uZAIcuoNNIQgCeT3Op/uObvs5QQDXXr7GR3P7+FjnAfrHuum4gzEgixIHUj0cSG29qtUlhV8ZfmTbz4/IKqc79nC6YzNFcCesmHWars3jHbt4Y3kSJ/AQBYF3irfoi6a5UVtmor5Cfy3DhfIcTTd8roogYcgqth/u/FKqwYXyXLuabXOtsojtufTHMlyuLKBJMkOxHHk91r5nMqoUY6l1HqdtbWnI2S07irS8BvPmDDE5iSIq1N0qXuASleJktQIL5gyWbzIa2+w1Ighh8FYVmVQigq7LRAwV2/FIJgyk+wy4tuPiuh6CEPZBW2NASLJIo2HhuB7JuBF6IfgBmiojSSKKKHMquw/Hd/ny5N9zpTrNH11/jrFEH32RAh16mpQSQ5c05PbE6fguTc9i1aqyaJWYbixyvT5D1WmSUmI81fEQWS3J5ep02w5y51rCQLSTrLZzfvZBMDVTDFfDXSn8ICAmqwzFs6S0eyvUHriQJksiTdtmvlTF8wP29hZ456WL2FZIC7l1bZ5Hnz1K7+i9PXfvBUkQ0SSJjmiMZ4f2IIsiyg75XAh//IP5jaug7nRiw3GAfT1hUNrXXQiVS3fNalpEJdeV4vJbkyQyUfpHO5m7ucLEzUVaOYWKaWK6LlFVRZUkqqZFzbJwvNB5aC0Im66L6/vUbJtCdHPOp1ZrMXNrFbO1k6eqhe9egMBBEDMgNgj8JQRPJ/AXEIQYATYIcX4cr4mfPgLMls2u/iyRhEE8/WDGKT8t5PUYH+zcTaeRIKtFybX/OyAM5CPxPHk9TkGPk1IjWJ6DIkrtXF6UrBalNxqyR4pWg7iiEZFVnugcJSprFPQYsiCiSvJ6uxgIC38pdYioHIpSIBRMbAfHt2l5DSqOiShImF4TAQHV0UODdlGj6pRJqRsZJLqmsGdX54Z2OnDvtNfdmJ0rceXaAkMDOVZLDUzLJWIoxGMGfuBjmi5Ly1VUVSaXjbF7VwfRiMbNxgLfnn+T6cYi9XbPuaJd4bWVCrIgEZF1DElFEcJd4Jqy0fEdmq5Fy7PW29dDmNf94coFXl+5iBu47eLtzkH3Hw1/nMdyB1haqbFabtDdkUJRJSzLpVhq0N2RJGKoNFs2s4tlCpkYibiB7bjML1UxdIV8Jobr+SwsVblwZZZUMkJXIYkXBKxaTeqOueM1rOGBgq4oCOzuzmPaLvOlGru7c/RkkzTUIrFUGFBWF8rr/aZ+XOQjEeJqb7ialOS2H6cTbuNtF78tYkCgvT0StqR77TSo1gxo7ka2kCSdizM7scS+48N09mdQVJl6w+R4f1/oqRkEyJLY9ldY62IcFpYkUWw7yodbpFCdJmxq6TE/V+bWdHHnGyEkkfSPtf9DCeWJUjegICm7uW10Lm4p2rA9l+lmkfHaEofT/XToiR+LK/mjQkAg05Hk3RcvICsyh57Yy+D+vp/5ddyNmKKvpx/6YxkA+toMCkEQ7koJ7Byo1lIpITvh9iQ7mihsep8XOBStKxhShrS2s99GQklxOBWqHYM1mXBw+/O6jV5EQW57Em+NTbS4BxwDgiCEZu+mw3KxRiYVxfMCJqaWGR0pUGw20HWFVDJCudzEslyiEY3p5hLPzb62zte9E2H358Y6G+F+YPo2N+qz9z7xDtTdFo2mzXPPv8+BPT1EIxrNls0b707S353hrXM3+cgTe3n1rXEkUeT7P7zG5589yrkrs6ys1llZbfDMk/tYWa1z4+YylWozbG0kQFaPMJYqhGY93HvZ88ArXctx2dfXwcO7+9YD1t7jw6wulnEsl6F9vcTT91fFuxdEQWSlVeVicZFTXf20XIcfzEzyxT2HuHFumrmJJRKZGK7jEktGSGRiDIxt7h7wo0AQBSRRotCb4exLl9AjKq2GxeFHRompt1cjawN3K/21KklbFtjWzvf9gOmbK8zOrO58LYIEwloee03OtJa+WcuXbu9JKwoCluegS/KmCv5WCIIAy3Jp1C2aDQvLcnBdv90ItK2kkkUURQ63qlGNWFwPTbp3/CIQS0W5dW0eURTWWyn9KBNA8H9T955BluXned/v5HNz6ts5d0/cCbuzOSGRCCRIkARRIEgIDCqWZZUsl1y2qlS2/In+oLIlW0XTJdKWXCyRImkCIkEQWCxyWITNO2EnT0/neG/fHE7++8O5nabDdM/OUtJTtTXbN5xwzznv/43PI8BxXGrVNs2GjWW5m8enKDKapmCaGvGESSxuoqrynvsRgcB1Q9mcDW5b2N8g3Vc65gjfkyQZIQLW7ZtsXLekPoyyB6eCIqlE1fC52qF5tum9HnxcQRBGGY2GTbvthNfU9Qk6I/2yHBpUvSOsaUZ0YjEDY1s3wMhQjqGBcMLx7CODm0Y8EAJZkpmc6AnPQgqvz0baIvRa9yfH/7uCoYfplVKlyVBfBtfz6c0nOXWsl/VKkxtTqywsl4maOoEQzC6WmJot4PsBnu9TWK+zWqxz/tQAa8V6Z9xasNSs0fZcUgeQo2/H0XK6geD2chEJeOr41qSG7/vcuTxPvdSguFwmmY2TyMQeijelyQpV2+Z2uUjb8/CDkKTad/3OAwvpfBIhBMlcGK4KIVhdqXLxrZkj729ssptjx/s2CwvPfuws46cHqJYaROMmyXR0nwfo4IctCATttkOt0qJaaVEuNykW6rz20zvUqu0d36nXLN54dYqVpd3jpPtBkiXGxrv3LE4KBGWnyVK7wmnfI7HHVRdC0Go6LC+VWVwoszC/zsLcOosLZdaLddpth3bbJfADVFXGjBgkUxG6uuIMDGWZPN7LxGQPwyM5YvG9bz4hBLPXFvjUf/0xbrx+h/JqhaFti6Tr+ty8tsTC/Jbnn83FOX1mkHjC3NxGs2lz984aN68vcfP6ErMzxc4xhhwDpqERixvku5OMjHYxPtnDiVP9jE9275rEsi2XS6/dIZGKkulK0DuUPfC+FUJw6+Yy03fWDrwe92KDxa63b6siLiGjyVEK1jUsv4qEREztQdmHl2B7TLb5//d5xFzXZ35unZmpNWZniszNFlldrlIqNWjULVwvlMcJFymVVDpKJhsnn08wOJRjYChL/0CGvv408YS5Tbhya8fy5r97H0yPmeFneh4/+EDfZ/RHcni+z6nJXq7dXuby9QWGB7Ksrdc7hjU0wMP9WbLpKJlUlL7uNOulBpIskc/GGR3M0bZcpmaL1JsW3bkEgQgo2S1s3wtbOQ9xLEcyupIUskJ978oUi6UasiTxwqlRMrEI554/juv4vP6NS/j+7lG8B0U+GmM4mWa+Ee7vfHcfiiST6opjdEZ5N3Sotj8st2+u8K/+xdeOvL9f+3vPMjHZg+cGiEB0+g9V8n1p5u+ssrZQ4sIHDsebADB1Z5XpqTVWl6sUi3VKxQbr63UKa3Uq5eamp7EdxUKdv/yzV4903Ioi89nfeHafjhCJhBZBRsITu69NtdLi0juzXHpnlrt31pibLVKvte+tLW7CcXwcp0Wt2mJhbp2Lb8+i6wqj4908+vgoH/rIacYm8rs8XwmJgeN9vPWty3iex8S5nYU5x/b4xkuXePlrlzZfO3mqn3/0332ck6f7CQLBwvw63/jaJV5/dYqFudKeY59Nz6bZtFlbrXH1ygK6rjJxrIcXPniCj//8eVLprW6KQAhaDZt4IrJJ0nIQgkDwyvdu8P/9h5/e97PbkUpF+Mf//Sd2GF1ZUsibZ0hqw7hBk4iaQ5XfewE6PM6Audl1Xv3xbd56fZo7t1ZoNPbLOQpsP8C2XKqVNnMzW4tePG4wPNrFmXNDfPQT5xgd393dsh9sz8MVPicSQ/wPJz/7Hs9of1TKLVKpyIFpTUVSaLdd2pbLyECW3u4U9YaFLIfpmjMnBhgdzOF5PmvrdZoth0TM4MRkLzPz69SbNooic3Kil+tTKyTiBoO9GVQ1ZLjb4Cg5DI5odCVGurM8f8pH67SJ6ZqKY7lcf32KRq2FpqvEUgcruR4FqizzdN8gFSvkEUgbocczMNGD3XYxTO19yU+uzpewWg63Ls1SLtTRDJXCUpmJ04fn9QX45kuX+OH3blCttHDdh7cYHQUS4WIpddIMG3kn3w94/ad3+PY3rnDrxjKrK9V9De394Dg+t24sMztd4NqVBX77dz/ImfNDOyW9JTj97HFW7q5htx2iyb2jhu2oVJqsF0PClLt3Vvl//+j7XL44i20fPlx1HI/rVxeZn1unsFrj0599ir6BMGerqQr5nhTNukXOD96Xe2k/CBFQcaZZbr2JKpsIETCW+Bi68t7Sc62WzU9eucU3v36ZG9eWjjTMdC8aDZtr7y4SBIIPfHhvwhwIF6/lZo2y1WYwkcZUVX6yMkvTcTif72Mokabh2Cw0qgzEU5iKStN1KNktYqpOdzR+ZDLwDSzNVlCGFWJxY5MLey/EIjpPnBsBQpXwq7eWGe7P8uT5EUwj/N7x8R4mR/Ob98FQX4aBnjSEpSNiUYN8bqP7RML2PRCCqKph+d77w72gawo9mQQINotDhaUSsVSEyfMjaIZKLBl5aDdv3bH50q2rvLO2RMYwOZbp4vOnziPL8r6SKkCn2VtBBBu6aWJPr3I/5AcyBH7A6sI6Jy+MkkhHmb21QrDHAMNBKKzVWV9v/Cehw9yAJwJaXkjG8W5lgR4zRUTVkWWJYqHOpXfmqFZa+34/zL9u9S0KEXpSe/2etu1x9co8f/gH3+Kf/k+fYnxiq8Vvba7IzLUF6qUGhfl1TjwxTtdA9sBjr5RbFFZrzM2u80f/13e4/M7srv0qiowkh37GQcfWqFu8/LVLBIHg87/9AplsDM/3aTUtxk70E0sejpBaUcOcsejoAwaBOLJOFoSFtIa7QsaYJKkPMVP/Dp6w0ER0h87bYSGEoFJu8ld/+QbfeOkSlXLrvsclbSRhEfsuuKoqc+JkH6Nj+3u5FbvNX9y+zIV8PzHdIEeE6VoJmdAwlawW3567Q0zTeWVpho8NH+MHi3eJqTpzjQqfP/EYPdEH62bRNIUbVxfp6UsRRBVMXWV6tYwQgudOj3JrocBCsYqmyPRkEpQbLUq1FicG8vh6yBW9/fncGHaSJGnrPgrCYnjgC5BCzTvRoW7NR+KYqkbOOJyzeSSjK4BircnbU4s4nsf0aplfe/4ct165Qa3U3DRIY2eGSKQfTjFtrdWgJxrjXL6Xs109TFfLh6oQjo7n+c3f+QD1hkWjZlGrtahX27TaDq7r49jeJo3jXjA7EziPf/AUuqGGI5CKghE5Gg9orivO4FB2b4MvBPW6tSunq2kKqXR0c779MFAUmVR6b6Ohywo9ZgqrozW1XUX1sSdG+f53rnH54hYXgyyHTFmJpEk8YdLbm6YrnyAWN1BVhWbTZmW5wuJ8iWKxTr1m7Xi4hYC7d9b48z/5Mf/0f/zFzTxqIhvHaTucfGqSZDa+2fFyECzL5caNJS6+PcO7l+c3f0ddV0lnouS7kwyPdpFOR9ENjXbLYWmxzPxskcJabRfPgGW5fOeb79I/mOEXfukCkiRRr7Z57XvXOPf0BMN7zO5vhyxLPPn0BLquUq9Z1Gpt6rU2jbqFZTm4bkCjbm165wduS1LQlRhlewpfOATCQ+n06FbdZYTwyRqHk1AHqFbb/Nm//zFf/fI7e6ZdJCm8r+NxMyySRTRisfCa2rYbnkMnd99s2DiOixCQ60rwxNMT6Mb+5sJUNSKKStFqMZnKEdcN+mNJEprBeDLLpfVloprG071DfGf+DlPVdaKqxrmuPjwRsNysPbDRDfP8FpqWBV1loVhFkSRkReat2/NEDR1dVehKxlithNdFVRWiEZ1Y1ED4gmKhRBAI7JaDGTMI/HDhdiwHTddwHQ/HdoglIpRWqwxO9hKJm+gRjS4zxu1qAVWS6Y7c/xyOll4A+tIJXjg12jF8U0iyxOjpQe5cmmWTtOohOnUZM0pPLE7KMLm2vkZCN8IH/D5tYAODWT73hed2ved7Ae22w8x0gd/7n/+K0nrjwP3fujTH8LEepq4usDpf4vST48RTUQJ/CUQLWT241efXPv8Un/rlEyDFEaIFwTrIXUhSFNf1ePlrl/jrL76x4zs9vSl+4zef5+Tp+4++bp00JPfx1GRJZiTexUi8a9d7g0M5nnxmgts3V5Ck8O/h0RzHTvRx5twQQyM5IpHdHQ9CCErrDV776R2++dJlrl9d3LGwBIHg8jtzXL28wGNPjALhaOj5D54mmUuQzB7+Afv+d64hgjBSkSSJ7p4kTz83yQsfPMmpR/oxjJ0ppo1C6je/fpnvf/sqC/PlHYtCs2nzw+9d58KTY/T3pTHMkJfAMLX7hoeSJHHm3BBnzu1udXNdn0bd4kc/vMnv/8uvH3hOIVOWS9Y4gSzpWF6J8eTH0eUEkiRh+TUqziJuYKPKOll96EC2K8fx+PrfvsNLX9nb4CaTEY6d7OPsuSFOnu5n8ngvyVRkW/QisG2XlaUq83NF7txaZWa6wMJ8icGhLGfODd23wPhs3whvri7wxtoCQ4k0Cc1gvlFhtJUhY0S4Wy1xq1Kk4TocS3cxWy8zVV2naLWI6w+eyw47bDwqlRbnRocZzu8c391vTB9guDtDs9bi9jszGFGDW29PE09H6Rvr5u6VeXzfJ92VpHc0jwgE0USEtfl1yms1jj02QvdwF7bv4nTaWg+DI3u6hVqTK7MrQOhdxSMGw4+OdHplZXK9KfQjeGj3Q8aMkDL6CIQgH42RM6ObhNRCCERQIAiqKOogIIPwETiIoI6sdIdH3eldDIICstJLPGGS64rvzDfug6l3FzCjOpd/eofB8W5mby4zdrKfwP4+IigjRbsR3hxCVJGUISQ5QeDeRJIMJHWUTGqOTNJCMUYIfJfAmUXWupDVHLbtkdoj/63pCvmeJEMjOYIgoFmz0HQF3ws2CUUqhZAcJ9sT9tzO3lxmYbWKN5wj253EsV1KazVS2TjRuInddmg3bdpNm66+NKq2Nbr94odOsbJcYWAox9lzQ4yOdW16+vtBkiRyXQl+7hceZXiki3/z+9/i1o3lHZ+p19u8/uodHn18FEkK27PuvDPDox95hPXlUC05kd2t0HsvfG8rGukfSPP533qBZ184vtnRsNex9fal+eyvP8PAQJY//eNXWJjf2ZZ359Yqd26u0J1P4nk+jXqbdjNUknB9r9N/LVFqtlBkGU2RMTUNLwgr1Koi47hhbSMQAlNTOxFKhHT6cGHmWvtdYloPCa2fvLlzkiym5qi7a1TcJXQ5QkYfOjC6u3Jxjq//7UUcZ7fBHRzK8qlPP85Tz07S15/ek2dEkiRMU2d0PM/oeJ4XP3SKaqXFndurKIq072+9ASfwKVktRpMZxpJhvvxEpouS1aLqWJxI5ynbFsV2g5OZPL3ROBcFVB2LiVSWgdiDT4v19qVRVYXMAR1TB91juqExfnYYM2aQ7U1DZ+HN9qSRFQlFUYhnYlSLNVRVIT+YxXM89M5i73fUI/z3g9pRAnKJKOdGe5GQiEcMklGDdq3N3StzpLoSFBbXOfn4OPnBg1tvDoOFepUfLs5Qs0PO24Sm88GhsR2fEfh47mVEUEUIC9+fRzeewvfm8P15An8FSUogSQoCG03OIkmHX1UjMZ0ffOVtTl0YJdWVoLRaRQgbEawjYRK4VxH+MpLSTeC8iqSdInAvohgfAVQkBIG/Qli714GjsSu5tseb371GLBXBd300XWXoWA93ry9RXW+Q709z4tER3n19Cs/1icR0YskIF1+5iet4NGptfvYzT3H9rWnuXl+iqzdFKhvfMUTS15/m7/32i2SysUMtRNshSRKnHhng0599in/1L76Ku+2hd52wXanZsIgnTOZvLvGTr7zB6lwBx3I5+dTRBDgjUZ3PfeF5Pvgzp3e1flWabZZKNZIRk7Vqg0eGezAjOs++eIy5uSJf/uIbO1INjuNx7eoiTz87yeBYN+urVRRNodJq8/rUAr3pOKoss1SuMdyVpmE5rDdatB2XdDRCRNdIRU0W1qtEdJVnj42gHum3E8w3X0GRdSJKjojSGcqIv4gmRzHkGLKkYcpxDCV2oMEtl5t86+UrrK3Wdr03MJjld/7Bh3j2+WNo2tFKOKl0lMefHLv/B4G0bvLhwQkEYpMLocuM8QtjJwEJWZK4kO8nEKGM0HKrTk80zrmuPobiqUPbCiEEi+0iP1i7CMAL+XPcvrlGNKpTLjUZGtmtbXc/aIZG72iYr07toYW2sb10Pnwv3Z3EatnEk1F8EbDabmwVAQ+x76N5ugJWKw0cz2exVOXmQoFffe4sER+aNYtIzKSwVGbskaNV+PdDxozwbN8QTifvWmw3+cHCNL9x8vxWWBQ0CUQFRRojnMhSCYIGnj+NJAIEAYoSJQjK8AAS1c9+/ByninW6B7O4jkffcI7AvQFCIKQA4b6BpIwhq5N47lUk4SHJWWTteBiqylngFkL4QACE/x628BIEgoW7aySzsZDxTFPpH8/TqllMX1vEbtmcf+4Yma4ERlSnf6yb1fl1Lv/0DrIiUS7UOf/cMaqlJvFkhHPPHsPYRqnpOh6u6xONajTrFrIihUXSQCArElbbxYxomwWqcNBg5+8oy2HIffrMIJfent3xXqXcYm2tGkYX/Rme/uQFekbyqJpCpufwDxvAM89N8sIHTuypetC0HKZXQ6KjbCxCqdGiL5MolT0wAAAgAElEQVQkGjV44YMn+ckrt5i5W9jxnTu3Vmi1bFYXSji2hwhCbpHZYhmrw60RCEFXIsZssYLSKSi6vo/T8rFcj5bjkIlHDvOs3QOJieQnWGtfxlSyRNV859Xwt626yzTdIqquUbNWyerDexK7CCG4cnGOdy/P76pPRKM6n/vCczzz3NEN7pHPpjMoVXLqTDcWiaomhqxT95qktDgNr93hqjC5Xp3meGKUdMyn4q2jWR7L7SJJLUZUDflRrMCh5GwIuzoU7DKnkmPkjDQzzRX+Yu67QMip0NefJpuL3zc6ux/uN224yREcNTCjBr4I8IMAx/cYjWfJHIJ3AR6A8Ga5XGO10sDQVHLJKC3HIZdJceHDp5m+usDxx0bp6ss8lO6FmKYzlgo9ACEESd1gvl7FEwFaZ9xVUUeIKL8OnSkejQuAgrqZa5U6o7EBYYLkcFMjG0h3JUh1WkQ2uiV866cokU8CGn77PyL8Bfz2ErIygiQnQO5wP4g6vnsJ4c8jvBuIoIbwbhFIBorSy2EWAVmRiCVNAj9ACAlVh5kbSzi2w6nHx2g3bWRFIZaMYLUcfNdHVmQGJ7oZO9VPMhOjeyDL9PUlDDO2S9X05tVF5u4WUFQZRZVJpaLIiowZ0enuS3HpzWlKxQaxuEkmG+PcE2PEE/f030oSiWSEM2eHdhndsEATRiqxVBQzavDOd98l8APOvnCSyccO50lFozqf+pUniMX3jlKihkbM0GnYDtW2TczYegDHJ7rpH8gwO13csdjNza7j+4J0LhEuKIkI57M5Hhnq6YzbhrUDVZZ5ZLBnWwGSTvdOp3ddlh6o3SlrHCemdiNLOpocQ4hgk+xGQiIgoOEVsfzGvh5UvW7xzlszu7xcWZb4+CfPdzzcozsbD4qG2wQEVadOxW1wIjHC9do0/ZEuhqO96LLGHXkeU1GJaxqj8R6KdpWm36bolOnSQwKZFauIpmgU7QpZPYWh6LhB2JJV91q0/I6grPBJpqK0miFv92FTO3uhYNcRQLeRuG/+uunZfGvlKh/oPsHT3cNIUngPPPThCFmW6M0kuDq3ylPHh2jbLhFNw7XDSaWP/cbz3Lk8R7tpY8aMh9rzKEkSKcPkmb6hTRLocPvapsEN0fn/hypPvfM81MjPb/0R+TTCm0HWHwsNLhJ0DL4kJ1Ejv7jju4qxnSXr/r2mqqpy6vEx6pUWnuuTyERJZeMsTRdxLJeB8W4kYPh4Lz/823e48+4CZ5+eYHVunduX54klTHpHusj1pPZsHjcjOulcHE1TCIKAvqEsiiITjRk4jocZ0Tl1dpBEMkqpWN/Fxra5HVOlf3A33aLr+rTbYVgvhODqj28yemaIldkCVutwmlIAZ88P0zeQ3veznh8gyxJxU+ex8QGS0a3FVVFkhke7ePO1uzjO1m/ebtk4tke5WMd1XLr70+iqgrEHy9eDREkHIfQMVSLqVnGz4syR0PpRJIOEliel9WL5dYai56k4iyS1XrR7hicW50vcurG8K3LqH8jw7AvHSaWjCOHj+TUEDrIUQZZiBKKNwENCQZbiCGyCoIEih0Wo8D0NIRwkSUXgEQgLCRVZiu1b1AsQlJwaeSPDgBql7rUYjPSQ0uNEFANZkkloYVie1ZNEFBM7KBBTI2T1JE2vjSqpnElNUnHrpLQENbdOO3AwFB2BoO3tlNgqrNWoVloMDGZ3SC/5fkCz0gydES8IZYaSJpGoidWyw/qIqYMQpPNJXlm7ye3aKr898QI95t5RWCAEK+0KX5x7g7+ee4tz6cEjcVPDAxDejPfmGM5nUGWZtuOiqwpTF2d57RuXqZebrC2s81hyN73cYRCIgPnWDUDQbY4QUXaeTFTTyMckys4qGb2HQASsWHfxAofh2P77FEKwYk2T0Xsw32Pj+b2Q5CyoKkgm7wfD12y1wuSjw5RbbSRJwlAV1hpNzvzKWWzfJxUxKbRazAdtXvj1p/CDgOVGg3MvHuexF08gy2EP68T5IdZrLRqWjSLL4Q1pO4wc62byZN++oVW+J7X53sjE/n2aYSFGQ1aksJexA98PNodCJCROPj1JpifN4p2VA7Xo7sXps4P7jhcD2J5PIKArGUPbI7eaSkV2ccYKEQ4SuK5Ho2bRvkcvLxABRXuZmluiy+hDkw1WrXliaoKokmChPUVcTZPTe4iqR3vw7idMWXGWaXjrpPU+ivY0qmTgBha9kRM7trG2WtuTu+PUIwOMjHYhhMD1V2naP0YIF10dJmo8TqX1N2hKHk3pR1dHaNmvEwgHRY5jqBM4/jKGNonlvIum9uO4MwSiRSAsEuaHUeS9o9mkGuOR1AR9ka3FZLv/JxA8kT2947WTyVEAbN9huV0kpcXJGlv3HVLf5nZs36Xt77xO6XR0c2Hffo3bDYvLP7pJvdykVW8jSTKqrpDtTVEvt8j3ZyitVRk/M0Q6n8QLAl5auowiyfzusQ+R1nbOGwQiYKpe4E+mf8x3V67RG0k9kLLxEXO6gqX1Gi+/fTN06GSJjz92AjNq0DvahR7ROPbY6JFzdQC+8Kg4a1yr/Zic3k/eGML2W1TdIoYcJaFl8IVP0V5Cl8OHTwJsv81s691No2v7LWreOmmtG002sPwWda/EdPMyhvLM+2B0Ex0P9/3BjbUi/akk660WjuejKDLT62Hl3/F8BlIJkqbJ7eI63fE4c5UKQgjSERN9OzEPsFptcHWhScNyyMQiqIpMMmp2ZMcPbpO6H6ROX6SiyATbx8C3s+5JkM6n6B7p4plPXiCVPxzbmaoq9A2kN3O5QVAG4Xa6U0JkYhEGc0nW6y3cPXqv95tc9Dyf/uEcqqbQ1bvTk/aEy1zrNr7wyOjdLDSvU3YKSEgMx44x3bjOmdTT7ymi20+Y0hcuvnBZaF2h6a7zWPaXqbjLwJbRdV2f1ZUKzcZOI2QYati7nIkCAs9fxfHmUeQUgWgCCm3nbWKJ/wpVzmF7t3H9VSL6GZr2ax0y/jV0ZQDbu40k6TjeLLo6iusv4vhzROQ0ezkZKT1BUoh9R2L3en3jNU1WGYh2o8lbqhT3QiB2Gd3SeoNGzSIaNXZETpquMjDZiyRJuI5LIhOnUWmibOi8RXVWZgv0joQLxLnMEKdT/Xx18SJJLcLvTL6Ivi3quV5d5g9vf5c31qc5nernC2PP03VELxeOaHT9QDBTKCGEIBU1aTsutufRP95No9pkbb6EENDVlzlwWmwvBMKn4ZURIiChZtFkk5pbpGgvsGbN8mjmZ4iqSSruGrps0hcZR5JkMnoPc61rAFh+i6vVVzCUKHPN65xKPsPtxluAxJo1x8nk4RRB/3NBqdlioVxlqVqj0GhSaVt0xWM0bYehTArH84loGpoioysKtu8hIVGzbeqWTWyb0bVdn0KtQaHWRFcVlss1comDF6AjT1nd5/NCCG6/M83II4P0Tx6ebzmdCWXGN7yYwF9GBDUkecvzbloO88UKi6Uaw/k0qehOr3g/ZQTPDZVyx071oZs7HwdVUhmOHmO2eZOSs0rJWSOlZdFlk5SaJaYm6TEH0OXDFVC2I6R+3BKmVGVjhzBlRh/EFy4SMp6wWbFu0WvuJGe32g4ry9Vd206movT2pjdZ3BQlQyBamMppIto5JGQUOY2phduz3BsochpV6UWSdISw2ZhS84MWIJAkE1XpxgsKCLHF/XzvPaJI8gMHfIqkoOxBTbododHdOfCS6fR8B8HOxdaI6Ix2et03DfhI1+Yx10oNnvr4eSLx8PodS/Twj078LP/H9Zf50twbJPUInxl+ElWSuVie4/+8+S2uVhZ5umuC3538ICdTfejy0QuURya8SURMhvMZvMBncb2G5wc0622u/OQ2fWN5SitVrJZ9ZMo+VdLJ6r1k9B56I+PIkoInHFp+nVV7Bjtok5LyxNU0dXdv/tk1a4Zl6y4ROU5AwFL7DoEIGI2doeoUeNjhfxAENNoOiiITM/UwwW45BELsyCk+KFIRk89dOIepqfQlEwRC8M7CMkOZFJbr8fzYSJi8l6AvmURXFQZTKXwRYNzD2xvRNZ47MYog9ELCzgoJ456qdhAIHMfDtlzaLYfl5QrLS2XWC3UqlVZI99h2cGwX1/FxHK/zbzjR5O7RJ7oFCcdy+Iv/9W+IJSOceeEk42dHDvh8iHjC3DFTL6HiOm/iuZeRpARG9NNEDI3Jvi76MkkS5uEXfFmW8IKAy69NMXl6gIFto65OYFNxi9S9Ckktw1BkgunmDbrNARRZo8vo5Ur1dSbjZ0jruwdP7gdJkjGUrf7UcDCiI1YqXCrOEi2vjCZHeCT9MXR5Z5HIcbw9h3tS6SjdvVvbDYI2rjdPGwVFSaEoGVR5K/8e0c9RaX2ZSuuvUKQ0Uf0C1fZLlFtfwg/KgIoXFKm1v4pAEDOeByS8wGfVKjPVWMQOXCbiAwxF85ue6gYs36HiHDyEdFi0fZuSs7NoaEY0kqnontwmBzECJtIxkpn4pllQZYVHUv388zO/xP/y7lf446kf0fYcUnqEP5t+laJd55eGLvD3Jz5A3kw8sEzQEXO6MmeGe5nszbJcrjPUlaY/myJou4yeGiCejrJ0d43AOxo/AXTCU0lFRkGVNGy/yXzzBhE1QVbrQ0LCCdo0vQoNr0LLq6PKOlW3QMur0fAq6HKUXnOcgcgkESVBQEDVLVJzi7iBxVFG5VxvQ/MsQFcVPD9shG9ZLrIsETE0WrbLl3/0Lomowa+8eJZACH58ZZq7S+v8w19+/si/wb1QZJnkhgHp6Lc9MzpEudUmEzFRt2m6beq77eMoyLJE1Ni/uOj7AcVCnbmZItevLnLl0hy3b63sCl3fCyQJnvnkBRanVjEiOvnB3P2/RChkuj3/K6vDaObHEEEJSckBKtVmgzduzyOAfCpGnMMZXkE4tBF4Ye5ZCEHFaRNVdRxfZjJ+lsn4GSRCPt7R2EkEIX/s6eSTBAT39c723K8QlJ07BNtY3xaaP+Zk6tOYaoa6u0ZMzXA8+QEUSUWVzF0GxHV86veMkIe/V8hxHMLDcq+SjX+BQFj4QQUhXPLJ/2bz84qcJBv7AmE7Yxh65+Kf7xCiy3hBAdcbxNTPYKjjm0W0u40l/uD2X3O1OgNAr5nlvz3+aZ7O7ayvvLp+jd+7+u+P/BsdFivLVSrlJoNDOwtp94O8R+5flmTG4l388zOf4veu/A1/dPt7eCIgrUf5wvjz/Ob48w/k3W7HEb8tqDRa/OjaDP25JJl4BNfzkIKAyfMjKKqMEdHJ9h49pwuht9tjjqLJBqqkkTMGqLhrmwUwO2jhCx9DjtLya0SI0/JqpPVuGm6ZnDFAzStSsOaJa1mGo6fI6L1U3SJZox9DPnw7yUqpTrVpsViocHa8n7m1Mr3ZBHeX1vEDwYVjA2STUc6M9bKwFvLeKrLM+ckBZlfLRz73w0KVZfLxh5uXbjZs3npzmh985xqX3p6huseD/FAgYOrSLLX1Op7roRkq8fT9eZc1TdkxtBH46/jeDRAOwp9DiQzhBcFmzvsoWZHAD3Bsl2jCZHWhRKYrziu1aSZTeRaaFU6me1BlmW4zgdphapOQaLhtTEVHlR+0q0EwU//Oju6FujtPQGiEdSVG2VlgsXUFTY4wGD2/Kx8aBALb2d0Bo2kKxiZPgkJEP4fl3kRCwdROIku775/wGmw3B9Km1y1LUQxtElXO7OhaWGwVmaovbf5dtKu8vn5jl9F9vzE6lsfuD/PxD0NgU5IkRuI5/smpj/Fvbn2Xt0ozPJYZ4TPDT75ngwsPQZiSgW5W35nDtT1OPj5GMhM7FC/pXjCUCKPxs5vsTaOxM2E4vI2r8rHMz2x+3g8C1OI4jw89ycpajcWgRso8jtxskUrHuXW7gCJ3c2LkVCgXf4SFoFRrcXVmhWKlSdTQmV0tM71cYrFQpdJsEzN1nj59eDKS/xQQImC+dZ0uY4iouveY5dpKlZe/dolvfv0yqyu784N7wTBUDFND01V0TQn/1VVaLZulhf0XHIFgeXqND3/ueW69OUXtPrwXG5CkncqsIlhHkgxU40PYrb8E4WNoCoamUm/bR2KTk2UZBBRXqgyMdiEE1F2b1wqz6LJCpKExGEtzb5S00C6S1mOktFgoAX5kSEwmP0lS3+JwKFgnN3O6phzHCdoIESCUvTXAhBB7EjbJirxNAUPG1I5jasexLZeluTK6XiEa06lX23R1J1ldqeC6PulMjEq5RTIZodWy0XSVvv40qhonop/ZtR9D0YgoOlYQ5lgVSSb6QL/Fe0O51KBUaiLLEn3995dNB1izaiy19hcJkCUJTVb4xcHHsHyXu4013lyfpsvYWTTfUKo+Ct6zMOXJgTytusX0u/Obnzn/4gmMI06H+J6P1XJQVBnHciksVRg+3ovn+kgSeK5PNGHumBsXgWB+sURfT4pCsUah2KCnO8l6qUkqGcOyXBRFRr1PdX4vRAyVSr3NxEAX12ZXGe3LUqm3efRYPz2ZBN2Z+3MGPAwIEdD2m6iyRiDCAQ9dNqm56zT9Khmtl6iaoO3XqboFsno/umzS9utUnDUW27dIaDmi7Da6a6tVvvjnr/Ltb1yhsU8aIZ2JcexEL8MjXfT0psjmYkQiOmrH+9z+39tvTvPv/vB7+56LJEkcvzDOj/7qNYyoziPPHU21dwOyMkAQrOC0v4KijoCkkY0bPHtihOsLayHPwyFrCiIIsKyQlznXnSSVjZFuRrhRXWM0niWm6vRFk7s8WkWSmWmskNCiPJIaPfI5SJK0aXAFAoQgo09uClO2/RoSMnEth+U3wlD/3jTGPp5dEOxtjJeXyrRbDtlcHN8PWFwso6gKM3cLaJqK5wasLJURArK5GIPDuQM9x2OJIX6u/xleKVzG9h0eSY3xoZ791aABuo00E/EB1AfMh3oiYKqxyJq9ZTB1XSWfT+w65yAQIWWpECTT0R30pD8t3OFPp3+y734kwqEYGYmCXadg1fn9G9/aRWrzvz/+6wzFDpcm28B7FqYc7s7Q8/FzjJ8Zonc43Ll5xM4FgKWZAtfemEbVZBRFwXN9Zm8uUVqtEUkYHD83zPgjg1vaIMB6qUlxvcHcYplypR0+aISeWLncIhLRaTZtPC84MqdANhkjk4gy1J3m8t1lPvV8H4vFKm/fXGCpWOOjTxxnfq3CT6/OUm1anBjuJpuM8v2LU8yslHn71gJnxvvQH9Dr34AvfOaaV0npeZzAwhcuXcYQb5e+wVDsFKYcQxBwsfxtYmqKmcYVTief51bjdUw51una2C1bXq+3eekr7/CNly7RvkeJWNMUxsa7+djPn+PUY4MkEiaarqJoMrqqbnIMCECTQhFOIcR9td5c22XoZD/ZvjTVYm1Tb+1oEECAoo6jao8T+IuARNN2uLNSxHa9I3EgKKpMMh2luFJFVsPvfbBvkmd7xtBkGU1W9gwpFUmm6raoui1OJ0ceaAEOhMdq+x1u176KKhkIBBdy/xBTzRAIn0B4NL0KLb+8Z6JSlqU9R6I9NyyE3gtNU1kvrqN2pg/XVqokkyZW2yWRjLC2WgUkfN8nnoiQzERB2hBd3VRl68ScElk9wWeHPswn+57BFwGarJLQojs6Gu79XR7NTPJbo594oP5WCAtpfzz9Mt9efWvztTu3V/E8n2wutoMHd3pqlb/5yzd49PFRNE3h2Kl+evrC1Gfbd1i3D1/cS+tRGp5F455szmFJbrbjoQhT6pnYe9ZE890A3/WJJU1EIIinIiBJZPKhh9Y3lt+V+O7OJ/jVTz0eelCToShe+IMHHY9Y7CmvfhjkklE+++GQ4+HUSDdqhwD57FgfSKHiAMA/+NQzHRE+GUmCz3zwHL/6gbMosvSQPGGBJxx84eEGNr5w0CSdqJqk6VXI6QOsWbPUvXUCESBLMqv2DLpkMhw7Q9Ut7tpiEAhuXl/m5a/tNrixuMHPfvwMn/ncMyRyUb50913iNZ2aY9H0HPqiSQZiSdqeixV4nMn2MBTvTDHdJ5l68XtXqRZrIEmszhQ48eQEfWPdB35n168hPHzvFkFQQNUewXUvIqvj+L5EJhYlE4vuKDDef3uQ6Upw7ukJND2MiOLa/Z2GHjNDRDGYbR5NK207AuHiBi16IxeIqb2UnTudg4KsMYQvHIr2DMOxC8jsXkg0TSGR3N0lY1kezdbuyKV/MENPX5owWJQYHe9mYXad7t4UtuXy5DOTYZeIFD5HRadGvWFjKhqarOAEHpYfThEmtSh1t01E1YkoBhW3SSB8ZhpzjMbzxDqy6vc+AVk9SVqPP7DRNXyNtLazNzaRCEfUtxMaBUFApdQKmfpE6PE629RGfnnocT7Rf+6BjmHHvrWjdyk9FGHKh4F0PsHTHz1Dtje1+dp2Hsz9Wj8UJXx9uyievBkKPrjR27HtzbHjUFF0Oyptm5bjMpgNj1tVHm7KQZLkziTUDG5gEVfTBPj0R46x2LrJfOs6A5Hj5PQB8sYwMTWFJhtMuW+zat2l5e8m07Ytlzdem2K9uHOl1zSF5144zq99/jny3Uls32M4nkJTFFK6SURV6YrEiKsGJbuFL4LNtpmw1exgOaLB4/0MnxpAVmRWRwrEH0Q1WjgEQQnfW0QIB0UdR5IM6u06UytFEhGT/uzhaQKDQFBaq+EHAb0DmV3cFPth1SpzpTJDfp9x0cNAljQS2iC6nMTyS3hBm417tumV0OUoSa2Hsj1HlzGGco/h1XWVzB6/Yb3WprhWh0d2vi5JEqq6/VglhkZzZLvimyrA28/lcmWOglWn20zySGqQxVaJ5XaFmtumy0gw11rn8ewYo7EuputrTCR6WLNr1FyLY8lehqK7w+6EGn1gWR4In/OIunNRHBrOsbhYpqdna9hGUWT6+tNcvTSHbVVIZ2PEE1vUBKaiHTkX+7DwUIQpc8n3Xk3f72b/u9SsuhfLlTrThRKaomCoCtl4lOm1EgPZFF2JGFfmVyjUGuiqStN2KNSbHOsJq9FTa+vEDJ2IplJtWwSB4ERfnnTsaI30Mgo95hh++zYxNUlG6wMk3MAibw6T1nvI6v00/DKO30YTJlmtnx5zjLZfZzh6CvOecWrLcnn30vyufWVzcV780Eny3aHRMhS1Q9cXYuNKCCHoj+28Xr4f0NxX+DBE33g3xYUSN964gxHR6J84WKVhL0hyDE1/GlV7FEUdwvcWAZl8KsaTk2EXg6kfvmiq6Qr9x3qwLXfPFqL94AY+I7Fu7MA9ck86dNSXvSIpfQSQaPslomoeTQ5145peiaX2VRJqDw2v1Mnp7tyGYWr0bHNSNlCttjp6d/c/LlmWSexDfm8qOscSvfRF0mT1OC3PIavHCBA4gcdANMNQNIepaPRHMxiKxkAkixP4mLK2p3GNq5F9VYMPA0mSdhUue/vT9O5RQMvm4vzcLz1GpdwiFjf3Pc+/azwUYcr3C7WWxe3lImvVJudGe+lJJXA9H11VjpyjfRBMra6zXKnjBQGVZpvx7iz5RIyrC6sosoSpaWiqwtx6haYdhjZzxQrHeruYX6/wzOQw15cKYQpGVXh3YZUXTowe6RgkSSIm5ci0dUxTwxAqTtNHrA8wOJAm8MH3Bb3KSVquTVQ2aDc94v4w2Y5GHK6E5bqbAwau6+2QOd/Ahtz5rmPY45juheN4FNYOlqgRAi798Bp94z0UF4qsza+TfQBGuiAoIUQdhSE89yK6/GHaTsCNxQK26/HksUHMQ3arKIp8X9n1Pb8nybR9+4Eb5EGw2HqV48lPIUkyMTXPavttkvoQitBJ6/14wiajD9L0ejojwjuh6yo9fWlMU8PalsO12i5zM0WqlRbpB4kmOjidGiShmqhS2KM8nujuHHkn+tx2Z5xKhZNfWT1G23eIqXuH3XEtsi9ZzmEgsdvo7gUhBMtLZeo1i8HhLG/8dIoLT42R79m9SP1d48gWMx0zefr4MIoi43p+R8L46Cv9QQgCwbWFVf7vb77GUrlGrWXxzz79YVRZ5os/ucJILk1vPE4yZhKPGhQrDcYHctyeKxAxdWaW1smlYoz0ZenJPTgvgh8EZONR2rbDcqVGy3EZyWe4s7ZOpWnx4olR6pbN1YVVkhGDvnSCmKEjSdCdjDPSleHGUoHBbJhHm1rdaegkae/xVCF2TtQ6rsfqao1qrcXySpWnnhhjbq7E5SvzqKpKb0+KkeEcd6fXUBSZbDaObbusrtZACsUiR0dyHJvoQVFkHMfbpRArSZBMRUimju4NCCFoNmxu31y+3yfxHA9NV/G9gMJ8kVx/hlxf5ohthgG+exsR2ATeXTA+hKrIJCIGcVNHVw9/W3si4G6thKbIRNSQN3iuUSYfiWN7HsfTe5P8GLLGSLSHmLp7aOGwsP0aTlBHlaMIEdBwlwlEmHeUUMjpI7T9Or5w2StVttEiNTiU5c7t1R3vXXt3gem7BR69cH/F5f2Q1fc22DsIbIQgwA+HR5A6hcdo570AkDiZGOSfnfp1JEnmkeQIEgJfeJ08tYQvPEAgSwqyJHe2GZKdbwylbN/3YdrSPNcnGjO4/M4cr/3oFucujJC8j0p507WZb5XIGjG6jMS+aZCiVWemWaQ/kqYnkjrywntk7oXXbs2RiUcYzmd45eo0j4z0MNx1uN64w6LlOPzpD95mOJ/mn/zii/zBSz/G8Xx0VSUQAW/cmuczz5xlqDdNo2VTLDdQFRlZlunrSjK9uE5vV5LlQvU9Gd2EaeALgaHKjOez9KYTvHTxJn3pOGcHe3nj7gKGpnJhdADH8ynUmuT6o+iqsnljJiMmsc4kWDKyc/VXFHlP8cmNMdwN6JpKPG7SaNmMjeaRZAnX8+nrzZCIG7TaDo2mhWV5dOXiVKuh3HvYiiTRlYsTjRoEgUBRNgqL4Wz9JiQJTTt6ax2ED97cTJGbNw42uhISiUyc+ZtLWE0LJIn5m8uk88kjGV1FnUCIFoG/jGZ+vMPwZtO0HZqWw4R/eKl7XwgKVoM1q8Fco98qStYAACAASURBVMILvaNUHYvuSJy6u/84++3GIp7w6dKTXMgeO/T+tiDREznP1fKfkzEmsP0aEgqqFN4jFWeRqrtCIFwa3jq95klkaXfxaWAwy4lT/UxNre1QtJ2fL/Hqj24xPtlN8iGqc29HIALqboGys4SuRMnqA9TddVJ6N5bfIBA+hhLDCZaZjGvkjGECYbPUvokTtEnpvcSVDLfqP+mkws7RY45RdpYpOYvE1Ay95k51EVmSyBlJxmIh81hM3dtJ+PH3b1Jeb1Cvt7Ftj7u3V5k80Xeg2OtUY41/ff0bSJLE/3bh18juQ2ZzvbbMv7z2Ek/kxvjHJz5KWj8ah+8RhyMCbi0VURWZhfUqK5U6Z0aOnpe7H1w/YKXS4Hc+8iTjPdlNz0VVwpYlRZHpSsfQNQXL8UL+V1NnvdKkUG4w2J0iHjH2Vfo9LC6M7RSGFAKemtjSqjrRl+807od/h8KJO8PvZ49tFRxH8zv5ZhVFDnteVRlv2+h0vdqmtN7YfOA1TWFiPM/E+JbXNTaS31VknBjr3vHafkVIVVOIRsN2us1zC0JlYrvTs3oUVCttvvrlt/ZsU9oBCWKpCMlcnFa9Te9oNyOnj64yEgRFPPcKstxD4M+iqCPYrk/M0FHlo02kqZJMVzSBAAZjKbJ6lJpjY/ke2h5aYhu4kJkkppq7eAYOC0mS6DbPEFW7qDnzxNQ+MsY4WsdLNJQ4llVjIHKGiLJ/sS6VjnLusWFe++kdioWt9I4IBN98+Qrjx3r4yEfPvC9E5m2/xvXaKwT4lOxFnsz9EiV7CSuoU7IX6TKGKdgzTDcvokoaTa+EKuusWnfpNseYaV7kZOL50DjLUaKd0f2Z5ju4wsHYgxFQkiTOpsf51xfCMWZT3rsL4rGnxvA9f8e9cL8obrldYba5zhNdYwdOGvaYCbqMBO+U5mh69vtrdGVJDrXig4BsIsqpwR76MgezrD8IZEnC1FSWyjWO9W+NSbZsh0KtycmRbvrzYW5muDfDcG9ozEb7Q5WJjePJ3iecOCokaWdode9pP8gIYjJlkkpHd3QSNBoWd26tUKu2SXWY8A8i7tjvtf2ui6oqdPemmJ7a2e5ULjWZvlvg5On+Qx+/Zbl846VLvPPWzKE+r2gq3/kPP2RgspeRUw8m6yT8deiE4b57C01/joiuoqkyfhCKSB72nlQkiaF4erPtDWA4sZuM/V6k9QeTC98OSVJIaAMktN2qzwktT0/kBFZQR5aUTRmfvXDu0RFOnxngRz+8uYPLuFG3+OKfv0o0avDci8ffUx1krwXc8hsE+MTUDEm1C1OOE1PTrFnTCARRNcWaPUNSzZHU8iS0PC2vSl/kOBm9j5X2bWRJIaZmkCWVpB4KyQ5ET1G056k4ywxGTrE9tSIhoUoK8X083A2k0lGKazXe+MkUdNJ4T79wbJORbC80PIumZzMR7z5w3DepRcnoMW7XV3GDw0dVGzjSVZAkuDAxwJPHhnhkqIfhfHqzX/VhwtRUnjo2xJ98/23+7bdeZ65Y4Y07C/y777xBtdnmwvje0uT3jov+l4CufHJXBVoIeO3VKd55a2ZP5qT3CsNQOX6yb9frhdUaP/rBjR0e8EEol5r8zX98ky9/6c1d/b77QVUVPvH3P8Kxxyfwj5AG2A5Z6UdW+gj8RRR1HCSNtu0S1XVMTcXzD69Bt4GNe+c/l/un6iyz1r5FzV2l7CxyEFlTrivOR3/uHNncboMyO13kj//tD/jyl97odDQc/hiEgKXFMl//6kW+9fIVrHt7utU0CbULN2iDJBFV0yS1LmpuAV2OEFUydBujeMLFEy4RJYEiacjS1nBNuJ0MC62rzLeu4gYOlt/EDSxWrakjUFTtROAHzN4tsLRYplG3KKzWaDbsA+8LN/BxhU9MNQ7ssNBlFUNRaXlOZ0r0aDjQ03U8j8VanYiqYqgqbuCzWKmRiUZw/YBUxEBXFIQICc29QJCKmO+pDw/CSv8vPnEKTVX4yY1Zyo0WV2aXOT/ax299+AnGurd007ZDkiT8zgMXDtFsTdEIIUKCEMul3XaYnyvhebsf+kqpxcJciXjCJBLRUDtdGhvXIDw1qVMEk98zwcbAYJbxiR5uXFvawRmwtlLlj/+f71Ovt/nQR05jRraRcIvODds5P98PaNQtIhH9vlLZAKapc+GJUb7/nWs7UgLttsO3Xr6Cbqj83C88SiYb20qfiC1GLstyuXRxlm99/QqXL85Sr4WtYpqmgMSB9I5CCKYvzzF+bgRF/f+pe9Mgya7zTO+5+82be2bt+1697w0QALETFAlSJEVqtFC2LJmSHKNwyDGeP7bC4wiPIzwRM+Ef/jETXjSLqJG4iKIkLhJJAARIrI0GGo3eu6uX2resyso9737841ZXd3VVd3U3QFl+IxDourmdvHnvd875vvd7X+Uhi7ABijqIph9FktKAhmWETC6vUWk69LXuvFJ9UNy8fkQoNp376LxEbbeO7VGrOdtqWATBusvDTBEzphOLaShqVHzi5uV127XlhTaWmgURUg9XCUWwLYMBouv+8NEBPvu5Q3z7L97GcTa3Tc1MrfDnf/oGb/78MvsP9rHvYC8jox1YcX1j5yYQ+F5IYbnCwkKJyevLTFxeZG52jeJqlSPHBvnE46Pcvr405DjDiaM0gmg1rkgqSa2Vw9kX0WQTXTZpM4c2KItxNUtcjX4bVdKwlDSGbNFi9KHJBoacQJFUcno3CTXLQPzQXcXQd4aEpqv0D7YgyxLXJ5bwd1jAqLKCKslUvAYBdw+mTuhhBx66/HA1kHsGXS8MWarWKNTqzKyVeXZ0iIrtkInFqDoOa40G11aLWJoOEvSkUxzuuf+t6d0gSRItqTi//vgBvnB8D0EYVTJ1VSGmaxuBzrU97KZLGIaoqkoyY/HW61c4fWqKWrVJo+HSbLg0Gw719X+HQUgoBGEQiVffiVdfPs/bb1xBkiVkKVKfj1k6lmVgWToxSycW00mmTF74zAFGxrYX43Y9HxFGgSgKLGzcpJIEjuujayrxhMFjT45y9sNppiY3d4/Nza7x//zbV/jut95lcKiVTDaOosp4XkCj7lCr2pRKDcrlJmEQ8k//6AWeenb3judXViR27+3h0cdGeONnlzYF++JqjW/9+dv87KcXOXi4j4GhNixLx/cDalWHmelVLl2YY3GhjG27G9vZjs4Mn/3lQ1w6P8fbb05s+7lCCK68f52WnjyzVxYQoaC9r/WBe1jCsEAYzCHLbQjRRJJMUpbB0ZFuQhFN2h/3itVxfH7wN6eYnlpZv6ZcGg2Het2h0XBxbC8KykJsO+nU6zZ/9h9e55v/+a0NNSzD0IjHDcyYFl1X69dZd0+WTz4/SEdLniV7gqzee9eAexOGofEr/+Q4pVKDl/7+zCYKmRBQrdicOzPDpYvz/O1330PTFExTw4qb0fXo+DTqDr4X4Achvh/ge8HGtRFsI9cqra9uY8r6Tk2Kmhcy+q17QpMMcnrPxuO3B1FtwwFGJr+uQyFJEgk1B2xOFT4oZEVi974eRnd1sjhfIp2xaG2/d9NMm5Gi1Uzy7soNvtR7FHMbt5FQhNyoFbhRK9AXz2PKD95gcc+gaygKbYk4YSjoSqWIrXNym54XBS4BvZkMCUPn2kqRULDhoHq/EEJsmAoqdxQuAiFw/WCjIOZ4PtWmQ9zUScYM6pUm775yjjAQDO3tZteRQU6dvMGPfnh6U2HqQeC6/ibzwrtB11XGdnXdNehOzxRZXavRkkvSaLrkc3EqlSZxK6K7TM2scmBvD6lUjMNHBvj0iwf4xp+9Ra26ucGg2fSYnSkyO3NvXQNNU3YQEL8FSZJo70jzhV85ytJimYnLC5sCr+v4TE+uMD25tYV4O7R3pPnN//Jxnv+lfXzzz97ixNtXt1X6kpDItKVZnS8SBmFkm/IQOwVJShIGi7jBD0BKYMS+gCQZD0QVe1C4rs9Pf3KOiSuLD/V6IaKdRPM+VDP7BloY3GdSsVZxwjqmnOR+tKATyRj/1e89hWXpvPKTc6yuVDelE4SIdiGbr5P7U5a7F3YKjA/6+McxYQoB9bqDbqgMjbaTysR2zGkPJ9sYS3Xws6XLfGfqJF/qPUKbmUKXI853zXeYqCzy3en3mK6v8l8MPv7xtwGrisJgLstg7tZ2bSC3devm+D6yJJEyjQfKGQE0mi6Ts6ukEiY960T5MBRcW1zhpQ+vslCq4PrBpmvuuQMj/NKhMYyYhhnTSWbitHR+vLS1j4pQCIrFOguLZRzXJ5U0WS5UOXKwj3K5yUqxtvGVNF3lxV8+TLnc4Ec/+JDKL0rP9jbIssTe/T189bef4C+/8Q4Xz889MNtDliUGh9v40leO8dyn96PrCt29OdJpi7W1+tYXSPDIZw5y9fQkiUycnrGH2xXJSjdG7FcRCMJwmY/bqfcfAxr+Gq1Kkv7EUS6VX8EOquhyHPUu1XqI1jrptMWv/9Zj9PbneeUn57hwbm5nVsl9QFHlh5og/79CvWZz6sQ10hmLrt4c7/z8CsceG6Z34O4OH52xDJ/u3MflyiJ/fuNtrlaXGE91ktJjIGDZqfD+6iSXKgvsSnXyXMceLPXBxb12XBrcz6xjqCoHuu7f8+p2uF7AtakCXR0ZejqjgG57Ht988wzzxTKHBruj7qLbXtOWjnJEQSBo1h3a+1po6fz483gfBR1tKZIJcyOQNZsuI4NtdLSnaTZdOmppYrdRsxJJk1/7zcdob8/w6svnmbi8sCU3dy9IkvRA7roQUcceeWyYbC7O3//gNG+/foVSqXEfnwX5liSPPjbCM8/vYc++HnQ9+uzunhyt7altg64kSVgpiwNPPbzItRAOgT+DCFcQwibwLqLEfwe2tU3//y90OY4dVJmtfxh1VzUvkDcGyOjbF5FvRzIV4/kX9jE61sGp9yZ5580JLl+c35RyuB/IskRXd5ZDRwZ44unxTZZJDwshBJ7wWWgWKThr1H0bN/TXlcweHAczw3TEcluOu47P8mKZwnKV0loDM6bf000aItbUk23j2IHP16+/yc+WL/Oz5csoGw0bAl1WOJob4LcGH2cs1fFQ9StphyrvXR8UQjA1vco7715HiJCjhwdwHJ+Ja0uIUNDf10JfX46/+ev3OX9+jkMHe5meKbJvbw8fnpnm2ef2kMvFSWUtzl2eJ5eJ88SxYQBK9Sb/7D9+nz944VEOD3Wj3pF2kNdzYoX5NT74+SXiqRhPvHgIgKnJFVYLVeqOy+RiEU1VqNkOa9UmuaSFokTFi5rt0NeawfF8wlCgqwqOH1Ao12hNJzZSGaNdLeSTW6lnkizRP9BKvuXe1KFb51falHXZjoIjRORPtjhf5trVJS5fmGPiyhJLCyVqdRvH9iPRHUMjnbHItSTo6EjT3ZNjYKiNvft7yOYevO1TCCiuVrlxvcC5D2e4cmmBudkilXKTZtNFViSsmE4ml6CnN8fuPd3s2d9Nd2+OTMbapHHcbLpMT61QWy+u6YZGT19uW2GWOxH4ITMzq6wWNrcTJ5Imvf0tWJaOEC6+e5ownEeWO/DcE8Tiv490mytIZIXT4LvTp6h6Nv3xHAfUHkoLdSpOk7R2qxw0vruLeMLYcXHh+wE/PXWBN2Ym+GLvoY3ja26DM2uzHM33k3iIVc92MGM63f1JlJiLWLfzkSQZQ06gKw9Gg3Qcn6XFEnOza1y9ssiNa8sszK+xUqjSaLgEfoAsy+hGVLtobU3R2paiuzfH8EgbHZ0Z2jvSpG4zBn0Y3Ay27xUv88riKeaaKzQCGy/0H0oe8Sb+2fg/4bGWvVuOe26UIhMioo8Zpko8Yd4Xbc4JPK5Ulzi1OsnV6hIltwEStJkpDmR6OZjtpcvK7uQicdeT9dBB17Y9vvu377Nvbzf9vXnMmM6rr12kVnd49qldaJrCB6enKJWbDA+1ceLk9agS/9Qufv76ZVKpGAf29zIw0ILjRQ0ObevdY+WGzf/0Fz/ia88/wsGBzrveELVyg8r6Nr37DonAatPhg2tzmLpGqdagXLejjrVcEgmJG0tFZEniM8fGmVsp4wchhUqdkc4WTl+fQ1cVdE3l0GAXLemPLugTiIC6H6UNLCV2T/K1EOs2Muv5Zd8PNoo0EAVqWZFRlUgXVVOVDVnCj5IPu2lK6TrRZwa3Ua9kOfpMTVUwDG3j8/6hIUSICEsIUUOSDMKwgqIOIt220g1FyPurU1wsL/Bi9350RcWQVa5WC5xbm+XFngPEFI1m4OGFAbIkkdRM7MDDD0MEYlsVqkvlRV5euMB/u+u59bEISl6Thu/QZqYAQd2PjEllSSKhGnhhgB14IEmRjsE9Gi5+URDrtRbP9XE9H98LCYJwPe9+2zUlS5EgvaqgadF/8scgURoKwaK9yrenX+ONwlnKXo3woclgm/E/7/1tnm47tOV4xGC6ee0++DkXQuCGwfoqPJoUFFnBkFW0+7NouutJe+g9mW17ICCdskilrA0dgdZ8gnw+QbPpsrBY4vqNFWo1m9aWBHFL59qNZXp781y+ssBjnxhBIFhdq9Pfc2uLoKsKIx15vvXmhzRdj7Rlbppl88k4LUmL0kqV8+9eY2ibriZFjhgQddslbhpYpoEsRWrwAkFPPk1Pa5rp5RKu72O7PqmYQdoySFsm1aaDrvGx5bEqXp2fLL7NmlvhC93P0BXbvqcf1tXcVDnSKrUeTnf0YSDLEqapfSzbyF8UhKjiOj8lDOYBgaqNo6gDm54jIZHWYxSdOlP1IoOJFup+g5/Mn2O6XiShmTzSMsgHq9NcqxVwQ58v9R7m7cI1Vp06GT3GvkwPB3P3bt7wRcgr8xeYqC7zB2NPUbCr/NXU+2R0C1+EPN0+zmxjjdl6kUCEPNk+xoHswzWEfBTc7JI0TO2Buw0/Dqy5Fb4x9VN+vHiSQGwu9krwUMaet16/fUD9qAsQSZIwFHWLU8THgYd+x3jcIJWKcfHSPCsrVXp6buZUoy+qaQpjox0oikx/XwuZjMXqao23T1zjyU+OMTtXRFWjzqHr0yu4ns/hvZEweigEK9UGZyYXmFhYIZ/YLEz9mSPjfO7ILmqlBoap4Tpb5fUsQ2d3b7T6vXMLf+exO3FkpIcrcwVURd7Uz36/cEOP6foCK24JIQQHM+Nk9RSP5vfzwdrFjefV/AZXqlPYgUNSjTOeGiAQAddqs9S8Orqiszs1hARcrc1Q8+voks6u1CDWHSpOQggKzhozjcXIgl3RGU30oUgyFyvXaQY2lhpjJNGHLmtM1KapeFU0SWM02YelmEzUZih7FTRJYyTRS0KzmKzPU3CKxBSTwXgPoQi5Xp/FDwOQYCTRS1pLMlGdouLVaQY2HWaegXg3C/YKi3YBQzYYjHeT1hKcr1xFkzXKbpWuWBtdsbYHyouJsAyiiRn/GhDiNL6Opj8J0m0W7ZLEQLyFx9tGeH91ihu1As927OKTbWPcqBX4XM8BgjCkO54loZmcL80xXV+l4bvsSXfyVMfYfYmYaLLCY23DVLxbhc+0bvFbQ5/g5Mokbxeu4QQ+g4k8ngg5uzb30EE3DEIW15XhugaiFvDSSpW15SpDe7uZvb5MPGmSaYk6RF0ncq1W1xlHlbU6lWKd9t4c2jZuE3eiVrW5fnUJSZbo6MjclW4l1gXCK+Um3b25Ldt3Pww4U7rOmytnNwKuIskMxDvotdpIa3FMRedh+bg91t0XL/9Y8dBBV9MUHn1kiGvXljjzxiW6f+0xxsc6NmhfqqowOtKBJEnY6xzG4eE2ZFlmaLAV09BobUni+AG247G0UqVYbpDPxNEUmWf2DvPJ3YObP1QAEgy155BliZ6Rdqqlxl2TIPfbOnsnUpbB/oEOBFF33INACMFMY5ETxbN0x9rWZ/HtBxgKgS98/DDgtcJJOmItNH2bnxfeZzTRR0ZSQAhW3Qo/W36PsWQ/aW379xMIbtTnOLF6hmO5vZwvXkWTFAbj3XjCxxMBJ1bPYikxWo0sLy++zXhygIyeirRdA4eXF99mLNkfHUOw5lZ4a+VDBuNdXKxfxw08LNXkpcW3+WTrEa5Vp/FDn/2ZMV5dPsmB9ChnyhM0Apu8keXNlQ/otzqZaUzT8Jscze3l9cIphhO9JFTrNgOYB4GMCNfwnFfWv3jAnTs5IQSarPBIyyAD8Tx/Nf0+q04dSYpWpwALzTLvFK7TF8/SCFwCIVBkmaRufgS5RsjoMRRJRpFk/DDAFwG+COmIpeiMPTzDJghCrp2fi7SM14Pu4vQqF09NMrS3O2raEFEHi+8H3Lg4TyJt0T3YunFOwvD+c6eltTqXL84zMNSG7wfMzqwyeb1Ad0+OltYkVy4tICsyA4OtVCtNLl+YJ5O1WFtr0N2TY3Zmlda2FIHuc7p0lbIXFVZlSeZT7Uf5pc7j9FsdJDVry/m+Wl0iq8fJbyM4M1FZRJdV+hO3WAhRWiggcRt960plHk1WyRkJrlUXsRSD4WT7fWllCCG4UJ7nzNoMq24NN/R5sfsg48mILNAIXJq+S1KLPdRK+AGdI0KWJgsUl8qk80naevN0Zyy03V10dKSplxuEYfTjNipNJGDPWAeLkwVilk5LLkHbuv1Ofr0HemmlSrFUJ5U0ia2rcemqyvMHRu42jNvGI4inY/QMtX2s+UVJkjD1h9uGhQiW7SKGrPNk65G7bp2EEDQDm8XmKgLBVH0BJ3BJanH6rU6W7SJJLY4sySQ0i4F4F8tOkYRqbbRR3glZkmgz8zzWcpCSV2XZWaPdzLPYXCFEMNtYouY36LHaGE32s+qWiSkmiqygSipjyQFW3RKmYqBKKpONBS5UriMBBWeNlJqgW2kjp6d5PH8QP/Sp+00CESAhMWcvk9fTjCb6KbplLpSvI4Sg6FbQJBU39FAlhbFkPwPxnavwlbrNe5dmcDwf2/UwNI0wbDDceRTXDwjDkNHeF0DaXMASCE4XZ3m7cBWBIG8kyBtxFEnifGmeH86eYSDRQtGtIRD3TXMMRMAHq9P8u8uv0hnL8Eh+gJcWLnB6bYaUHlvP6966DttjKSxVZ75Rohm4tBk7K941qjY//tY72A2X4b3dHHxijDf+7jSlQpXSSpXRg32899pFpi4vYjccYgmD6atLvP6DDzj27B7iKYtLpyZ5+TsnSaQtHnl+D8N7u3nz7z5EMzRau7LIisxPv/sepZUq3UNtdA+1cuHkDWqVBrqu8sn1onSj7lBYrtDZlUE3VDw34PzZGXbv7eHyxXmefm4PhqnhOB6SHFE9lxdL1Gs2heUqqVQMV3a4Ur0lmH8gPcRX+z9Fj9WKHXhUvCYFu0JGj9Nmpig6NQp2hbQWFQydwGO2UaThu7QYCSbrBUIRiah3WVlUSebtwgRO6LE/ExW4JCQ+XJskbyQ5W5oipuqUqNNqpsjv8BvYgcf3Zj/ge7OnmKkXqfsOgQjZk+5mLBmJe72/eoNvTJ7gCz2Heb5zzwPbsj/Qs1fn1njp6z+npSeHEPDCbz9F4Iec+dkFjr5wgFKhwvUPJ9nz+Djv/OB9Dj2zj+tnJikulSnMrPLi7z1P5x0BUpElDu/rRZakDQrVzYaJG8tFTkzMMF+sYKgK+/s7eWS0l4SpE9npyCxNryJJEql7CFlsh6ggU0SwXUPBTct3GSQVSTKAnSvcEHXkGIqOHTiU3CqarEWpACFo+DZO4NEMbJzQ5XJlEjf0OJLdzQdrlxBEUnVHsnuo+XV+vPg2/VYnLUZ2/ViDl5feodfqoMfaqu4WiJCyV6Pq1al6dXJ6mhv1OUpelSdajnCtNguAJmsczu6i5jd5vfA+LUaG8dQgh7LjVP0GbxY+IKenSWtJumOtPN5yCIEgq6dYtovElOhcbAR/AbqssTs5RFZPkdVTVLw6HbFoAgBIawksxUCWZGLK/Wn23lydBaEgETMJRUg6kSOUTJzAo1Rr0h/0krhjYpOQGE+302WlI/1VVSeuGqS0GH+063l0RcVSdP7rkU9uFL1iis6eRBdyKFGtr7c1qwqmcWvyHUm28b8c+iKqLKPLKgnV4PM9B/l0115MRcNQVA5le4mrOp9oHdx477rvIgHx+2A3aIbKsWd2s7JYZnpiEUW5yvJskSc+e5CTP73AykIZ31vl0BNjTF1ZpFqq096TxbQMmjU7sqnpzzOwq4Pe4XbGDvRiWjpdg61MXlqIDF+vLNCo2Xzyc4c489YEH7xeZGm2yKe+cpzpiSXOvHOV0SMD5FuT7NrThaLInDszQ+CH2HZUZE2lY3T35ghDwWohwPMCNF0lkYzxzhtXOHxskGTSZN6rs2Svrf8u8JnOR+gwo/rNYrPESwvn6IvnuVY7z+8OPwXApcoCWT1OzohzvjzH2dIMbuBzINuHFwZMVBep+w6T9RWO5QaZbRQxFBX/tpW8JqvUfIcza1PszfSSUM2NXc7d4IU+fzX9Hn96/Q3swKMzlsYJfCbrKxsC1xISuqKy0Czxk4VzPNIytO2K/F54oKB78yZIZuOouoqmq3QNt+N7EZ+0vb+F0z89x8ylOUqFCqqucP3sNGEQmU6WCxU6hzazDGYX1mjNJbGdWxzCIBS8dXmKf//yu8QMjZZkHMf3ef/6HCeuTPH7LzxKazqB7wcUl8pIksSuIwMPtNoVokpp9csE/lbbGiQFSbKQ5SyK0o+qH0bXP4mijSDLGXbqWx1N9LHqlPnW9EvUXJff6H+BhUaJnxfeo+zVWHXqPN16HBEYTJQXWaxXyapZdEnn7OoMP1l6g7hm0JdoJ6FaFN0yP1z4GRISnWYLKW17NoWMRMmt8NezPyWuxhhN9OGGHqfWLvJu8SxZPUlaS1Dxavzt3KtRh5iWJGdkqHh1vjf3GiGCjJYkrSdJq0n2pUd4eekEqqzwZMthLNWkbf2mSWuRwWDZq+ILn3eL56j5DXanBnmi5TDHc/v42fJ7IEk8mttHVk/Rbrbc98ogFTd59uhWrdqbSsCRIej2KaS4I6+6vgAAIABJREFUamwJcrKk0B67lZs07rAxuji5yInTk+i6iixJ9HfneO6x8Q2qn6Fo9MQ388FblO1vuNtJ8w9CoJ+fWuHl77yLGTOoV9b5pSmLls4MmdYk8zcKJNIW+Y40jZpNtVTHMHVMK5oIJVnCtAyshEkyYxFft6ixkrENzeLiUoVMPkF7dxZVU6iWGrR0ZGjpzFAqVFmcWUU3NBzb59yZmXUrdpm6bZNKmViWTr41WjE2Gw7TUysUi7WNdttE0iSeNFE1hcANqfvRJJbS4vRarRvpBF+EtJkpDmb7KLp1Sm6DDjNNXDU2AmQoQiQga8TJ6nFqvs3R3CAZPc5cs4gmK3TGMmT1OH23WaE/2jLKxcosX+p9hIJdJqVZG6vnu+FyZZEfz58lFII/3vd5nm3fzV9Nv8e/vvB3G8+RJIkOM0O3leVyZYGG75J/QKbgAwXdZC7BylyRZC7B0U8fwLU9Zq8sUFwsMXtlno6BNtoHWjn5o9Mc+dR+YgmT3vFuFFUm35llcP9WM0tNUzl7aY7ujsxG0Gy6Ht87eYHn9o/wq4/vJ2EahEJwcWaJP3n5JG9dnuKLj+yNaGY9OZKZ7U9m1XGiXm59OwaAQAgX2Kq/gAAhmgThKoF/Fdd5hab8dUzry5ixX0NRR7mb5YgkSSS1OC90fII98d28tzjPbNnm0kqVIfMgrak4q80GxVrItaLDmHkERZIYzuYx5RhnF24wZhxlf2sHY7noIkpqcX5v6Cs7/j6yJDOeHOBLPc9tOv4Hw7+68e/p+jKLzTIvtD8JCEpunYrbRJJsPpE/ih26WIpB3XMpuwUGrD52JTcT0PusSKHsQGYMgA/WLpHVk4wk+lloFjZSKp/IH+AT+c2Oq5/vemrH73H7uVT+AWlpR/f1USjWGBtsw/dDZhdLbBQS/oGwulDGMDR6R9qYnlikrSfHuXevcf7d6yzNFMm1pWnWHS6+P0lxuYIIBYszqyxOrxD4Aa1dWbKtUTHt2oU5UrkEqWycyUvzLEyvMHVlgfbePKd+folTr1/Gbri09+aolZsb4jsAbe0pvvIbj26M66bQz00WUW9/lFONJ0yeeGqcJ54ax3N9rlxeoH+wlc7O6H6WuKW3YMgairSZaqjJCooUcef9MGTZrrDYLGEpOgPxFhKqydXqEoeyA2R0i9nGKpqsRBPveloorhrcqBXoiEXBEGCiukDVbVLzbMaSXexK75zOmqyvMFVf4fM9h3iybRxD0bZN5SU0k5QWY8WpPZS04wMF3RtnpxnY20MiG+f6h1MkM3E8x+PQs/twmpE18/jxYeqlBqNHhkhmE4wdHWL60hy1Un3bRH5rPkGtbpOI35ougjCkWG3w2Hg/CTM6LksS/W1ZhjpyLJYi8rwQgoXJFXKPDG873ulSCVmS2N22k823hqrtRVGiSUHgI0QDERQIgimEqCHCZZr1/0QYzGMl/zmqOsxON2NSN9jb0kYoBI/39KHKMh3xBGcLS4znW2iz4iiyjCbLJHWDmKrxWE8fMhJdyQdzvJCQ6I61kbzLKvgmJqrzFN0qXbEcSS3G1do87WaWqtfADlxCBEnNosvMUfObVLwGMUXftuvnJkYSvTT8JjW/QVpPMhzvIaY8eE/6Pwb0dGSYmisSBCGtD5iy+jgwtKeLylqdmGWw7/gwg3u6kGWJZt1h9EAvHb157IbLymKJ7sFWrISB5/gM7upCM1R8z8e0dMYP9zN7dRnX9gj8gHgqxtDuLoSAjr4cYwf7qJUbDO/tJt+RprhcicxCB1pJZuPYrocqy9iej6GpuF4k0BSE4PkBiiKvi/ZHIXW5VCNpRmJQbW3pDbU7RVJIaRarbgU7cDc1QuT1BGOpTuKqwYFsL0nNpOo1GUt1Yik6zcBltlFkb7oHXwRcrS7Ra+WJqwaarBJTdHRZZSTZQd13sINbC6iiU+VApp+0Hid2n3bvNc+m5juMJtsx7rEbU5BQJHmdv/vgxeAHCrp23UZRFdymixkziCVj7HpkhF2P3Cp6aYbG07/22MbfPWOddI2035Kwu2PlslioELeMTTKNkeGlwvxahfHuW5QQ2/VZqdQZWxc2DwNBS2eGytpmK/GbcIKA9+bmuFAokDIMPjU8fBdGg4kR+zyG+YX1IwFCuAhRIfCnsRvfwnPfBGHj2C+hqrtQ4l9Dku99U6YMk6S+ee8hSRKPdUfBPWvENo7dxFg2v+XY/UCSJDpjrXRybwpNXDXpi7eSN1IYska7kcVQdCpeHU1WkSU5srlWdOqBjSqpW7iVdyKpxXmi9fADjXcnCCGoNhxOX53nykyBQqlGY50GFTd0cqkYfe1Z9g110pG7u5/V5veExWKFH7x9gcXVKodHu/nUsTHM2yhU7S0p3j0zxe7hDnKZ+La/Q7lu853XPmRidntBIENXefbwMM8ciu6LunsBIUISxr7bxhJStt8kE3ty02szLUme+eIRQuERhHUUWefg41tTLKMHejf93TvSTihcKva7SFIHg7u6GNx1S9viic9s3nEcfHwUL1il2HwVUxtlvCvKvXf2t8BKhVOXZlEUmXQiRtNxMTSVIBQ4no9laDScKChPLRZJxAxcP+D47l6GRjbXGkxFp8dqY9WtUAtslp01RkUPiiRHKQMjWiTsWV+JZnSL3vU0gR24OKGPJEk4oY8uKwwkWjeaE1rNaGHSIid4rmNzV1rOSHK+PIsmKwwnOxhNdjJVLXKiMIUbBuzNdHC4ZTN9T15nnXhhcM9Q2ghc6r5DUns4pssDBd09j43Tt672b8YNYjv0Mt/EvTpCiqU6QSBI3fZeMU3liV0D/PtXTnJ2apGefBrH8zl9Yx43CHh03QJHNzXS+QRtPduvwoZyOVRZRpYkDOVeBGwZWcqiqFu3IKp2EFXfR630x+uBt4nT/AG6+TkUafsgfjvu9fjDUto+Cg5kBjEVLSoVSrdM/m7Pd90cw90cXX/RCMOQ9y7P8ic/OMH00hp1243U5tYlPhVZQlMVYobGP//1p2nLjCErO5831/N59dRV/vRH7+F4Pq+fuc6egXYGO2+5AZ+5PEdnaxo/CJlbKtHdntnS/uq4PqevzvPOhalb2s23wTJ1BjqyPLPeKOX6S7jhCkI4GFofmtxC07u2QZkTQuAFKzT9G2hyFlMboO5eoOZ8gKH2kjajRUzTv4GEgqn2I0saTe8aIR6qlMJQe2h617i5+xJCEIgqTe8asmRgabvwwlWa3g00OUNMG0KRk0goeEFh0/ht12N5rYauKfhBgOP6aKrCaqWBqsgc29VLbalEKmWiaypeENCw3W2v3aQW43B2hLOla4Qi5K3COQ5nRknukF+FqDj7VNs4jcCN/PW0yJn4Ttxcbd9EKEIOZPqxA4+Se0sD5N3CNPuynSQ1Y1t1sDYzSYuR4P3iJM937CW7jQ1PIEKuVpeYqC4xmuwgpj5489IDBV0zbmDGP57+8pt44tjwhsbsTWiqwhcf2UvCNHjt3DXevTqDKssc6O/gVx7dR/+6SLVp6Rz85Diysn33SdPzOL2wQE86TUxVGcnntzxnJ0iSgqIMYsZ/B899Bwjw/cuEwXzkWvAPmO/7OHBnUedBJ4W7IRQBTmgT9QjJBMJHkRQCAkIRYinxHTVhb+LSdIF//Y1XmVwsIgTomkI6Ht3gEHFWHc9HCOhpy2xbTNsOXhCwUKzQXC/aFqsN1mpNbmeD65pKudKk0XRpzSe2VSmNGRqP7eknYeqU6zbVpkO96bK0VsXeRhZUIGh6V5HRaHhXaI1/GVk2KNdfJxt7CoFPsfkSQgRY+hgm/QRhlUDUkSQNQUDFfhcnWAAR4OmrxLXdzFb+T/LWZ1G0OFGCSaFsv04m9gShaLJc+ys0JY+utAMCNyjgBgss177FQPaP0ZQ8irw1sPR1ZOlqTa9f2ZFmiFgXa5eIOPgtmQSyJNHTFnGPRSi2dZExZZ3juV2cWL3IpcoUr6+c5XB2lKfbDmHusO2P2rNjJLUHc6hecaqcL89Q921WnCo9sTyjyU5iqsZSs4YbBiiSTMbY/L7jqU72Z3r4+dJlhhJt/FLnPqrrjS8132GhWeZ6rcCfXn+DNbfObw89sWNxbjvsGHTvp4Pro2C7H0qSJNKWyZc/sY8vPboXd12bQZXvsGPeQVlrthwxG4QQXFpZ4ZGenocqzEiSgqoOIck5RFgAAsJwAQjZyfFIhA2CYIYwWESIBhCApCFLWWS1F1luQ3rANkghBCJcJQhmEeEaQjQR+JGPlmQiy0kkOY8styNJ22+R7/q+okoYzBIGBYSoIwiQpBiynEFWuu86Xl/4TNUn8IWHLhuUvSJJNY0sKdT8MntTx3fYbURwPJ/v/vwsM8slhIB82uKZQ8McG+/doARZhs7kQpFQCNqzSRwvwPV9VEVGVaIiSygEuqpuCpq6pjLS00ouaVGzHQY7c3TkNufO94528uOFNVw/4GBb9/arN8vgqy8c2fg7CEOuzKzwb775Kh9end/yfAmZpHGMmNpPzT1HIJoYSjc3rx1ZUrG0URreFYKwgoSGrrRjqL3E9d1IyLjBIhnzcbxgBTdYIVRddKWNvPXpjc8x1O71sAheWESWTFLGcQy1kyBsEISRdm4o7LtQJSMosrxF2/pO3NxZ6DvoEEiSxHCii6/0PMXXJ3/MbKPAn1z/IRW/wZHsGF2xPKasf6zxJaPFSagmu1M9FN0aN/OunVaKE8vTdFhJJKArvtkmqzOW4Vf6jrFkV/iTq6/xo/mzG6/94dyH/GThHBfKcwjgM137ebJt7BfTHFFrOtieTzZh4fkBqiLjB2GkDyDLW4KmF3pcrV1hKD6CcR/+9Dfq12k3OrDU7WcMX3ioqnxPgZhABMw35zBkgzbzVk6pNR5nslRitlKhLR7/iDZCKrIUJyDaiglhc68kuhAevncB134Zzz1J4E8gRBkhfCTJRFY6UbX96MaT6MbTyEor97NqFqKBa7+O67yM750nDOYJwyoRC0NFkhPIch5F6UFRR9H0R9HNT20ShLnbeD33BK79Cp73IaE/TSjWQPjr79mJqu1CN55FN19Alje3hWqyTkrLUPbWEERBGCTSWh4/9O7a0HEnFlYrXJtfwQ9CdFXhmUPD/OGXniCTiPH9ty/gej4j3S1YpoYQ0LBdLk4vkbJMvCCgWndIxU3aMgl6Wjd3gOmqwif3DSARrXL3DXbQlklsuuEvXVukJZdAUxWC++zgUmSZmK7eU8zmdmPJUDjU3Ckcf46ae249XRBDlTOU7DfJxp5DlmP4YYmKfYKM+SS60kHFfpdQuMS0IWRJ3/SeQWhTc8/i+HPU3YvoSmTyWLFPoKvtmGo/dfcSimShSHFAYPuzNNzLSJJMwt+PprTelZWzHYpOhenG0g7PkpAliaye5KnWg/z9wglW3Qp/euNHvFU4x3iqj65YnpyexFT0iN1w3yOAgXgHGX3zxKkrKkOJdrJ6nLhq4IbR7qPmOSQ0nTYzgYS0rVXU8fwgyvjz/PX0+7y3OsmKExXtP1ibijo8k6081TbOL3cfosPcHLTvFzsG3XLdYWK2gKoqFEo14qZO0/HIJWMMd7fQmUttynn5wudC+Rw9sd77CrqmbN71hvRDn6n6JCktRbt5d73eUITMNKZIaelNQdfSNCSg4ji0Wh/VGThYD7QRJCna0m0HIZo49ivY9f+I554G7Dse9wj8KoE/gee8gWd+QCzx+yjKvbnGYVjFbnwDu/5nBMENtgZ9FxEWCcIigT8Bzs8Igjl084UdvluI0/wuzdqf4PtXgM1bZBGWCMISgX8RzzlB4E8Qi/8BsnIrly4hkdXbSKoZJEkmq7UQV5NIkrx+I93frbS0VqPaiIwxU3GTQyPdZBLRNtAPQlw/4PzkYrSSVRRWK3UW16ocGu5ibqWC7XhUmw79HdltUwP5dJwvPLFVCvAmKjWbno4MbfnkJr3jjwJL3wUIFClO0jBQ5DiKiNMa/zKKZCGhIEsxdKWdtviXARldaSWpHyUUDSTJIGkcoelNAPJGPjZvvbjxGZIkoclZWuNfQpZiKHKKtPkYbrCMIlmocoa0+QmCsIqh9qBISaBOyjxOlELQedBU2cXKNP/pxt/v+DxJihx8ZUnGCaPUTjNwOVO+ztnyDSzVIKla6LK2Th+7f/w3I1/gWG580zEhBJcrcxzJDbPqVKl4DbJ6grlGmbiqM1Nfo9VMAFvNWRVJ5lhukB4rx0RliWW7QtWzkSQ2uMDDyTZSD5jyuB33IWIe8Wb9poOqythuJH93Y3GN9vWtmRCCJXuRU6X3kICyV6IZNDlZfIe6X6fX6ielpblcvUggAjJahiPZYyzY85xae4/n2l7AkA0W7QXOlc/ghi5DiWFCEfL26hvE1SQH04foinXz/tpJGn6dgfgQw4lR3l59Aye0Kbtl9mcObhp71XWpuS4ykfXPw0KIkDBYIgxvVqsNFLWXrY4FAiF8XPs16pV/RRhMA5HTgW6+gKqOAAZCrOE6J/CcNwnDJezmtxFhkUT6X4Lcum3gFULgOj+lWf8PhMEMEEPTD6PpjyIrHUiSjggr+P4kvneewL+CEM1188Z7yUiGOPb3qFf/D8JgCtBRtYNo+uMoaj+gEIbzeM5beO4ZwnCeZv3rIClY8T8kJIYgsjI35BiGvE7GVxLcvIlN3brvG6nedHHXDQRNXd0kq/nUgcENmUIBqIpMEAiECLFMnf72bLSbWU9PPQxacgkmJgtcm16hvzvHoYe0ib8dhnprwaAq0Q5BlUewtFusn5g2xE1OcFQYMojrezaOKbKJKh8HbqX6bmdDyJKBpY9j6eO3vecgptq/8Z6Wtmv99VB2Hd5YWOHJzsOcXJ5lOAU9iRBExI0O10W7gzDk4lqBhKYzlM4hc6t+UvUbXK8vfKRzIxDUfXujgeJBcVMu9XZM1Qu8UbjE1eoimqyyL9NLKEIebe3nreUbaJLCaPru0gEREyhDWvO4UXmHuDaIJluYsknaaKfuz1N2IaF2YwdFNDmJG5ZRZQtF0tHke9M2dwy6HbkkLanRTWuq1WqdWtNlqCOHJIEnPKabU7TorXTFullz15huTGKHNvvSB7hUvUjFL+OENk+3Psf5yllu1K8zltzFufJZfOHjCY9L1Qt0xroYio+gyip1v8ZgfJieWC/98UEmqpfxhc/e9H4uVM7jhA5e6PFI7jFOFk9sGbupqliahu372P79uzDcCRGuYDe/zc0VoGY8iix3bVlJCQGBf4N69d8QBpOAim48RTz1xxEHWNKIAlGIaX0V136VRvV/Jwgmcey/R1GHsZL/HbC1wCDEWhSkg1lAIxb/Tazkf48kxbgV/AUIH4FLGCzjue+gm790r2+G752iWfu/CYNpJDlPLP67mNZvIEuZ29wYAoT1O9jNv6RR+3eIsIjd+EsUZYQza0cpNm32traz2mygK5EGaxAKFFnCCwJkSaY9HqfFuvfFKITAW2cpwPoK6TbVqnwqvvG8m4/fmksFcfPWeXvYHKGpq5RrTVw3IJf+qLuj+0c0Xumexx7mO92eLtikwqdqtBhxvj1xhr5kBjvw+c61cxiKysF8J5PVNaaqa4yk83hhwHKzxopdZzTdQkvso+tL/yLRY+X5Qs9xOswMgQixVIPXF6/z3soM4bpJbHs1SV8ie8/FQChcFEnCDebwAxVF66XQ/IBV5xxChLTGDlJyJkho3ZTdG2SMUeJqJxljK8XvduwYdBVZRtE3b/+785tzGWEY4IUuGS1LSkuhSiplr8RMYxpV0ogrceJqnIbfIK4m0CQNO7DR1jtUIMoFhyIkpljE1BgSEo6socoq+roAS9lbY6YxhYRESksjEMRUi4SaxNpGUd8NAkq2Tdm2Gczer53PTfFjlzCsEAbzOM3v4jR/CIAst2PGfg1Z6WDrdizAsb9H4F+OTq62n3jqf0RVd3FnhJYkAyP2eQQ29cq/Wg9k38SIfQFVG+dOiLBKGK4SUT1UNP0JZHmb7yTpSFjIcgZVG7v3Nw3rOM3v43sXABXDfBEr8U+RpDtXiRqSYhKLfw3fO4fT/D5hMIfrvILn96AreX58fYK662FpGkPZLHXPpdWK4wYhqiyTNjanmhzXZ7VSp9p0sR2PpuvRdDw+vDq/wS5oOh6nJ+Yp17eugmRJYnd/O23Zm1zprUpjhVKd85N3N5LUVIVj472beLo3ZlZ5+vgIbfkk6jZF3vuFEFGBTRD9X16fIG5eBqEQaIqCLEnMFkpcnV1BVRWOjfegqQor5TozyyVsxycR0xnozG2kWYSAQqnK1FIJ2/UwdY3ulhRt2eSmSepe0GSFrkSK95ZnyZkW51YXON7WgxsGvLM0jSrLPNrey3A6z8szV/lgZY7nekbIm7fuszYzy+PbuDb8QyJvbM2rqrKCE/g4gcf7xesMJdt5pnOEotPgeGsfa06Dottg0w8CuIGPHXrEFH2DmmaqrVG+3JvBVPPUvFlC4aJKFqHwCfGpewvocoqmXyCtb9+otWl8H8cXVyWNtJZhujFFI6hjhzY9sT6aQZN2s524ksQNbQrOMmdKH1DzawwnRphuTFFwlrlRv85IYpS0lma6MUnNr9FutpPVIsWg67Wr6LJOt9VH1a/SbraTUFNYisWJ4ltcqJyl6K7SYmxtDOhOpehMJmn6/l0bOoVwcJ3XosIRgAgRok4QLBJ4Z/H9CcBDljuIxX8X3XwaSdom3yds3OZP1v/QMMzPoCiDWwLuTUiSjqY/jqYdwXVeJgxLOPaPtg26kmSu55EB4eO576AZjyHLD5fMB/D9a+s5Zw9JymLGvrxNwN08Xt14Dqf5IyDA9y6RNVaoB5082tVDCDQ8l85EktVmg85EkqrrYioq5h1OvTOFEt9+9UOuzBQo123KtSa1prupeLVSrvNv/+bNbceiqwr/8muf4YVjd59YPrw2z//wf/3wro9nEjH+4l/8FuZtDIZ8Ns6VyQJzS2U6WlOM9D+cXmvVcbi+sobj+zRdj5sdtk3XR1Nk8gmLgXwWWVF448wN/s03X8PUVf78X/wWrh/w5y+d4t0L01TqNi2ZOC8cG+MrTx+gM59iYrbAt149zZtnblCu26TjJgeGu/jqC4fZP9S5I/PgJlKayWimlYSm024luVpeQQA5I4Yvwg1mgiLLtJhxyo7NmtMktx54D2dGOJS5d5Bx/YDptTKqImPpGjXbJW7okUqcEFi6xsxame50CjeMlOOSpkG5aWNqKookE9M1mq5H3XWxdJ24rlFq2vRkUnetFVwozzDfLFLzbVadGiIJ7bEkl0rLuKFPp7VVH3iyvsLLi+dpN9O82HWrmSRv7qPoXCAUASl9iIa/RELrJq0PEwqXknOVpJ6n6S9jKDvLd348QVdW6bcGUSQFRVI5nnuUXqsfVVaxAxtVVnCFREJNEFMsWoxWOs0uVtwVDmQOkVASqJLKeHI38/YcQRggI6PLBqOJcVbdFVRJJW+2EAgfJ3BQJIWsnmNv6gCB8NmT2kerubndd6VeJ2kYHOrs5C/PnbvHN7Bx7O/j2N+/y+MKqn6cmPVVdON5ZHn7ZowgmCYIohyXLLehaGNI8r1zi4rShaKNgvMa4OK7pxDC2xLUJTmLqu3GtZMIUcVu/jUCH8N8AVU7giw/WNtwNN4ZgmBqfbwpFG33jq+JrHGUdUuhBYYzEkasZwu1cCCTRQixTakiQqnW5MOr81yduz+b94dBPKbT05qmbrtRg4W3c598R2uK6fkpFgtlFEV++KBrO5yenadqu4DADwWGqqBIEpqi0JLcyqaxXZ/JhSIvvz/BT05e2ZiAZgtlvv3qh2iqwqePj/GfX3qfl05eiVyygUK5ziunJmi6Hv/r1z6zsSLeCaaqsjvbSkI36E1kuFQqoEoy3fEUNc8la8RQJZkD+Q7259qxA3/TmKPc871THk3P4fpKkXTMpFCr05/LcLWwiqlpdKWTKJLO9ZUiOSvG9FqJzlSKmu0yVSxRd13yloWl67i+T931cIOAfZ3tTK2W6MvePcDty/RR9x32ZyJlMgk43trHVK1I0/fImVuplNeqy3xn6iSHcv18qmMPlpomb+wlprYxkPwcMbUVVbaQnfXmImTy5n5iaju6ksIPB1Glnc/9x+ZFkdSS7NY2bzWGE7dyG9dqE2T0LLtTezfYCt2xHrpjmwsV49rmGagz1kVn7FY740hi88pmNHn3lc5gNsv78/N87+JFdrW2PnQbg2F+Fiv5R+tCN3cndAf+DSDaGstKG7K8Mw1MknQUuRtJSiBEiTBcRoRFJKV9y/MM87N47mlc+8dRnrn+F3j2ayjaGLrxzDqVq2P9+dzzs4UICMMlRFgEIAwLVIq/BztQhkRY22BxCFFDiCawlXoTjeHunz/QkeMPv/Q4lTtSB+cml3jp5GXKdZtcyuKLT+ylv31rGkWWZfYObJW3vB37Bjr4337/RbwgwPMD6k2XN89N8t2fn73ra65Pr2IaKlZMj9ID29CK7gctCYvHh/qZWF6lJWExX6qwt6sdfZ2rnI6Z265I//bN81yYXOLxff30tmU4PTHP5Zll6rbLG2dusLBa4c2zk4z0tHBopIvZQpmTl2Y2UjNnri3w1MGh+xrj/0vee0fJlZ7nnb+bb+Vc1V2dExpxEAaTMMMZksNhHJIyRYkUdUjLsuR0Vt7VypaDdr0r7/7jY1myvaacvbIsaS2RoiiSI4bhkJycMQAGwKDRjc65unK8ef+4jQYa3QAamKGkc/yc0zjo6lv3fnXru+/3fm94Hk2S6YtsNjcAJ9L+cyYIwra4bT60u2rEXuC4LoVGC9fzvd5ys03HsglpKt3RCGFNRZUkOpZv0LtjEWaKJeodA8N2CMQVpgpFehNRap0ObctmqVpjoVKl2u4QC+zu1MSVIBO1JdJahKCkUug0mWuUKHT82HQ2EKYvtN1o16w2davDWCSHKskoooqi+uGrTOCaDlsucD9XO7oEBAKE6Jo3AAAgAElEQVTynS3Meza6bcuiYZrEdR1lc+K0LAtJEFAlnznIsG2aloUoCEQ1bduq2KX1EZNyey4dugqfVEK44xpb23XRZJkT+Tym4zBfqdz8YCGArn8KRfNZlfya1Zcx2t8CbCzrAj6f7q07aNzr+HkFIYwg7C0RI4hRBEHfzMybuG4VUdppUESpn0jsn9JW9tNp/RGus47jzOA4c1jGC7Qa/w5Vewwt8CSychBBiN6icsHEc2uwOV7Pa2GZL+xpvNdgc3WRuVOkokEeOTK0o+hNUSReODdNtdkhpKs8eHCAY2O7M0TdSp1WEASiIZ1DQ/4idJWjudLs3NLoIsDYYJazl5YIBpS7NrqaLJOPRciEQ0iiwFgmhXpdjPhm5zx9eZHPfeAYP/vEvSiySKHa5Ff/7beYWtpgYqHA1NIGDx4a4Jc+8whdyQjVZof/8M1X+OZLFzAsmzcmFvZsdG/42DcNg70byKLIQDLGvX1+o4nf1eY/z6rs240P7R9FkUSG00lUWWJ/LsNIOoXrucyVKgymEhzJ57Acn+ZREkUOdmUIKDcv6TtdniGuhJhrrhNXQ4xHeil0GvSHE4RkdVd7Im1SBkQVHekWTU+7JT3v6J7s9cBLGwX+45tv8ssPnWJss5327bU14rrOaDKJJAi8trTEi/Nz9Mfj/MT+AwSvuykTGyUurK/zs/cc3fNwHddloVolpKpkQneWMV1rNHhx3k8I4HlMbGzwYF/frscKqCjq/ejBz229JitHcO1FLOsNXGeBdvPfE47++jap7xvhU0VePanEzpKym0CQN48HcPB2o5vEz0QLUo5g+H9GC/wERuurWObLOPa87yE7c3Rav4vR/iqK9gECoS+hqPfvulh4ngPXjVcQIkjKPu50Monitdbqq4bNf90/j+O4XBUJ9DYzx5537SpXS7+uGqHrCUSEzfPsNTl0K1wdw+0W74dPDCEIAs22SSyi35WS7PXXvGpo9xpnDeoqn3n0CLFNLpIBXeXUoQGmV4o4rouuqXzy1EEGu3zJqqCucmS4m2fPXqHWNJhbLd/1eH8ciOgaJ/t7Cd5CiSWsbZ+fmiyjyf7zP5CME9F8rmBtm7W6temKKUE2jBqO55JQw4QVjUe7RigbLVRRIiDvHE9Oj5HVoyy1ylieg/reBQK2Yc9nPd6d50B6fmvSbjSbtCyTrnAYD1is1Xh1cQFdljne1Y3neby+tOQnViIRLMdhtdHg+blZYrrO/nSG+WqFtUaTgCJzKJNlrdlkoVrBcT2O5HJstFo8dXmCbCjEqf5+BmLxPU/emKZxX0/PVgInot9Z3aas7EcNfALbnsLzKpidH2DqT6DpT3AzwySIm50uAJ7Brly9u8HrgHfVY5Rv6yELgogsDyFFfgXXWcEyX938Ob1Zn9vC7DyFY08Siv4TNP0Du5xDgeuSZpI8TDTx29xpxMlP5Pn3o9UxWS7U8DyPgKZgOS6djrXVvltvdQjqKposo6oShmlTaXQ4OpYnFLh1K6jruphtE1mRMTomVsdCEHyJmEBEx3VcJFlClEQsw0ILqFuk3XtFodSgUmsBIEsCldrOGtAfN9LRIJnEdva6we4koiDg4tGXiZNLRLZ5+YlIgLCuUW10qDTubMyO62LYNqos+/p+lSoxXScVujYH6x2DUrvNwC1iqLbrMrVRpDcW22ZEJVEkqN7dwiWJItE7fG6vYjTSRUTW0SWVkU1ts9PFRWbrJWzXZTyepe8GQvr90W7uTw/zVnmexWaJfdGuHwv9wV2bchd4Y3kZ1/PoCof9YmrPw/E8HM9lrlrhtaVFBuNxXpqfJx4IUO10aFoWV8plyu02s5UKmWCIpbpBy7LojcawXZfJYsnXtgoEaVoWjufh3qEqb0hVCanqlocV0e6MqEcQNDT9I1jGc5jGs7hugU7rj5DlA5uNETshilmuereuW9tsz709XLe8ycvgVynsWgq26xhFJLkHSf4Mqv5hbOscpvEDjPaf4TpzOPZl2o1/j6Leu6NtF5RNgykDNp5bRxQSCLcp7L4VDMthfrVMs234vAnxEItrFSzbQVVkXM9DUyRS8TCO41KsNnEcl4NDt47NAqzOrFNeq1Ir1rFMm0QuvrUbzg1kmLu4iOd66CGNTsvg8CP7Cdyh0S2WG6xt1JEkkUq9dUctse8VMvGddKHR0DXDk4wGCQW2e4aqIqHI/litTe24vXjojusyW6owVSjSFQszkEhwZmmFVDDISDpJNuyT2syUytiuu2V0S80Whm2DIJCLhGkYJsu1Gi/PLPDh/doOz/UvAhO1ZR7J7EeTrnm0c/USA+Ek0/UNOo61Jad0FWk9zE8P3M9/nX6R35l+gSd7jjISyRGSby7VFbwJ0fmtcNdGNxsKMRCPoUgSkijSH4sxlkqhy77X+s2JS0xsFBCAitEhoCj0xmJ8cGiY70xN8uL8PEOJBO8bGGC6XOb5uVkUUWKpVqfSabPaaHBProuRRJLD2ewdM4TdSIyzu3rErSFKfWiBz2BZb+O5G1jGKxid7xEIfXHXLbusjIOggQeuu4LrrnE75QHPbeHYC5tGV0KUehGEO09ciGIYRX1ok4y9j1bjy7jOEo49gWNdRNQe3Ha8IIiIYheilMF1VnC9KrZ9BUW95yZXuD2iQY0jY3ksy8FxHSJBnUhQo9Ux6e9ObvF0qLKEZTuYlo2iyERCt/dmbNNh9vwCruuRyMWwDAtFU2iUmyiawtzFReLZKI1qi3b97jzUvu4E+VzMl6ZpGbTae9ypvIeIhvQdeQ/lOqKgkK6i3aBQLQnXwjPupoOylw2h7brMVypcWi+AAL3xGNV2h7V6g5lSmZN9PYxlUqzVG7QtixO9fqLt9948Q38izkAyQUhV+N7EFLIoMV0q7XqduyXNcjyXxVaB+dYatmsTUYKMhXuJKrcncapZLf5k4TUiSoDRSBfj0TwP5gYJyyorrSpxNbDjqbxQWeIHq+9QNOqcLS9wsbpESougijdvY//fj3yafPDOVJ73ZHQ9z6Nj2xiOTcuyMB2fVq9j27Qti45t7zBqmVCI4WSSJ0ZGUSWJpXqN+WqFUruF6TjkI1EMx6HcaVMzOliOy+XiBoPxBGwqtEqiiIdH3TTo2DaaJP1Y3P2bQRBEtMDHNr3Hb+B5ZYz2V5GVIyjqyR2ekCimUNSTmJ1v+zLh5muo6iMIYuKmrb22fQnbOge4CEIIVXvklm27tx6vgCDEULX3Y3S+h+ss4XkmjrPKbhE1SRlBkvfjOit4bh2j/SfIyv7bJgxvBlmWyMS3e8qRkIZ7A+3f1d3HjWO/FfKjORK5GKIkboYNPARBxLEdJFmkeziHokjMXFhgdWYds20R2IMxvx7BgMryepWXTs/gui7HD/bddSLtbnGjQQU/v3V1BIq8OwPY3YxQkSQyoRD9yThHunNokkxYUxnPpql2DMptX8KnKxLm9OI19rRqx+DhoQEimsaVjSICAkd7uliqVne9zrpR5k8XX8TD47HMMQbDXbekdXQ9l9VOiT+Ye4Y3S5cxXF+VRhIlYkqID2aP87HuB4irNyevfzC9j6JRJyCpxFV/Tk5WCyw1q7Rsk95wfMf3erm2yjcWT2N7LrIgUTQaFI3dBRKu4iqXxJ1gT0bXcBxeWVyk3GnzZ1cuUTL7EBE5t77qZxBFjwd6/WB5x7WoWR0OZbNMlUp8e2qS3kiUfCRCRNX44wsX6Y5E+MmDB3lmZprfO3eW/miMzx85whvLS5xdXSGsqfRGw0RUlXwkwquLiwiCwInuPA3TIKio25J0e4Htumy0miQDAdQ7oGMTBJ1A6G9gma/hOkvY1tsY7a9sUj2mbsj4KujBz2MZz+N5DYz2N1CUo2iBJ4Ht9Xue5+K6q3TaX9s0un4NrKZ/dNdxuG4Dzy0jiHF8usbdXRn/vBt4mzR+CPI2YprrIUnDaPrj2OZZPK+E0f4WkjyGFvg4ghC76ULheS2/rE0M7wiF3PgeWZJ2zSfeqSGTFZlwYrfQhz8PtKAfPtp//yjj943esrLhZhAEgTPvLHH/Pf20DZsr8wX68wlURb6r890NpNskDf2F9b25ligIBFUFx3UptlqENRVFkojqOobt4Hlg2DbldodKp0Ol3SGqa+iyRFTX/ISXomC5DuVWG9PZycrmeR7zzXWeWTtNyazxZyuv8KsHvsDD6cO7jMg//kpjhd+e+joXqjPb5H0Aymad35t7munGCj8//HHygdSuc2m9U+VseY4j8X4EBJJqGNNxSGlBekIx0trOuXQyNcQ/OvzJO7qHGf3Od6V7sj66LPOBoSEeGejnmZVLyIqHIsCTR0YwHYeEpvHmxjyHunK8tjHLTH2DI8kevnj02Lbz3Fg90BUJochd3NuVJxEI8LGxfX6plyRtcSWc6uvnQCaDLEq0LJNnF2bpi8TYl0wR0TQ8D4rtlk8fpwewXRfLdWhZFjFNR5dlmpbJcr3O8wuzfGxkH93hO2kkEJCVfeiBz9Jq/D/4jFzfQtUeQ9U/inDdLRQEEUU9iRb4NJ3WV/DcIs36v8Bz65t8DWlA9kUvnTmM9jcx2n8M2AhClEDo5xFuEs91rAu0W7+DKPWjKIcRpV5EMekn3QQZPBvXq+HY0xitr2Nb7wAgST3I8u6tmoIgoeofwTLPYrT/FNddplX/TRznCor6IKKU3+R2ALzOJtduAduawLYvowU+iR54ctdzm47N+dIq+VCMgCSjywqT1Q0iikrNMhiLpVloVFFEkZLR4kiye0uG5abfxB6szbupNgBIRANcWShiWTaNlsHkXIGhntQ2Db8fJ/78fGof2XCYqK4xVSjRF4/Rn4iTCAS2FFdalsVGs0VYVVmt1QmpCke6u7Y8zIFEjLlSmdlSmf54bIczZLk2s81VNkzfCRAFiZ5A+qbjKZo1vrb4HBers1sGV9jUJHM8Fw8P07V5qXiBXCDB5/sfJ7oLkfhkfYXuQJylVpGOZjIUzmK6Npok4wtS7bzT/aHUNkXhHxfuWIK9ZnaIyDqL7Qq6pFA2m0RVHWOTszKlhe6I2PdyqQj4NIzD8SQbrSZjyRRn1lY5ks0xVS6y2miQCYYYS6aoGR3eMU1mq2Ue6R0gqKqcWVuhYnQ4ksnheR5vF9YIKSpxXedotptnF2awHZeV5t4SWzuhoQU+iWk8g22dx/NqtJv/1Q8xSNspJwUhRiD01/HcKobxfVxngUb9n6F0DiNK3fhGt4VjT+HYlwEPQUygB3/GN+I39WA7WOZbuM436GxKw4tSDkGIIAgynmfjuiUce2qTFMdFEGLowS8gSjefSJKUJxj+m3huDdN4Gtddod34jxjSN5GkPgQhjB/uaeO6ZT8U4VUQxDSq9sGbntdyXb45d5F7M72cSPey1mnw1elzJDSdUqfNXxk6wg+WJxEFgbl6mV+55zF6w3cWG/txYKQ/zUapgScI9OAT4Px5ebl/EQhrKh/aN4q3mVS6Gre9Hp+55+C23z964FpDkiJJPL5vBNe9Vhp4PVqOwZXmtdDEWLiHlLq7d2i7DqfLl3mjdAl7U5cvo8V4OH2ELj1J1W7yUuE8c601TNfihcLb3J88wNH4TtmssUg3k/UVREEkrUX9nXK6j5VWFQHhrsjHb4Y7jVnf8ZVVSaY/nKQvlMDDp2YLyxqdYJyoojMezSHfIvB8IxK6Tj4c4fzGGrosU2g16YvGmK2WGUumWG02uFzaoC8aQxQEFFGiJxJlpVFnvlZlJJGk1Gnz1uoyYUUlpCgYjsNQLMR0pYQiSlQ6He7J+Ab8biAIApI8iBb4LLY9DV4LyzyN0f4mgfAv7nLsKMHIryBKPXTaX8Vzi5tSPzdCRJJGCYR+Fi3w6c0E2s3L0UQxiess4LllbLd8y74ESRohEPoSWuD22yVZ2U8o+vcRW10Y7afw3A1cZwnXWbrZ2ZGkHiTpZk2+EJAVfmLoMJOVDd4prxHTdPLBKMfSeVRRwsPze/9jaR7ODe0Q8PyLQi4dpbZYonswi6xK4IHZNmlaNpZho22Wtjm2gx7S7rgs7S8jrtJhvqtz3GSHYbgmS61rrd6HYkOo4u6hwQ2zyssbFymZvnMUlHS+MPAhPpA9TlgOYLgWByL9/NbEVylbdVbaRS7W5tgf7d8RI05pEeJqCNt1iG1qnY1G04xGb+5lnysvcLG6zGA4zUg4S1oL39aQXq4tUbfb6JLKoVj/LY+9ijsyurIo8WBmiJweuWky5Krg226DtV2XhmmiShKBzfrZfDhCKhhElWRfccB1ads2ddNAEAQe7umnJxzlmdkrfOHQUeRNufKK3MZ2XU5vGtuD6ewWc39POEIqEGShXqVtW+iSTETTbiBdEfxOMDeGIMbgJskjz4OmYSIgousfwjZPYxrP0uxYdMpfJyN/AF33eVFNy/ZbF1Vl0/D+XUTlCeqN79LpvIznriEJNroWR1XGULVHUbSHsNw8bUtDU1w6pk/MoykysiTSMW1sx0GW9hGK/QaOdcavx3Wu4NhrGFYdyzbxUJCkNAH9IKHgg6jafUjSIAg6pm1jWM4Wm5Zh+WVFAn7MVVUkJHmcQOjvIYqfxLZfw3VOYxozSGIdx3EQxDCS3I3HGMHgg0jyYQSpC9u+2iq7Pd5YNtu8XVyhbLQJKgrjsSzniiucLS7TE4rxaPcws/USF8trRFWdY+mdHtbWvLEdREFAEAVcx8V1rzLBCZvfkYcoCoii8J4Ywem355m7uES93EAPaqi6woEHRpk6O4fRNAjHQ8iqxMEH9xGK3j2Z9f8IMF2bdeNaw8ZouGfXMJLruUw3ljldvrz12snkOE/kThLY1PXTJZXjiTHenz3Knyy9gIvHO7U5mvZ924yu7Tq8U13kZGqE1XaF2UaBpBq+bWnX5doq/3nqWVRRRpcUcnqU0UiO0WiW0XCOfDCBKsrIgp/MFBGIKgGCsrYj9nwr3JHRFWDL4MLuhvVWK8OlQoEvfe2P+cyBg/zjxx4jpKgI+G3EST3AQDTOVLnI92YmAQHXc7m4UeRKpUhU01AkCVkUeX1lCdt1ONHVw0qjzguLs4iCyL5kGlWS/HpQWSKu6RxMZ/nO9CTPzF5BkSSUTcMsCFHiqT/GrzgWbsmu9b/97rcxbYd//Tc/RST+L2h2WvyDP/xT1qst/pdP2zy+Gbr+5msX+dpL5/m1z3+IA70ZinWZH50L8N3ToyyXksgiDHUl+fjJA5w6MIweCAMyv/O913jryjJPPnCAP3zuLPV2h598+B6ODeX5bz94k7fnVrl3tJe/9qGTDGR/Gj34V7Bsi8tLa3znzUu8PrlItdkhHNB5YN8QH7/vMGM9XciijO24fO/0ZX7nmTf4+SfuxzBtnnrjHZZLNWRR5FB/js+cOsLxkTzFosTrr2vkch8ln/8sMzNrHD3ay+TkGpIokkxFeeWVWU6cGKVS6QAFJEmk07FIJkO4Lui6TDCokc5E+ImhI7ieR0D2J+oXxk5gOjaqKKNJEp/oP0DHsZEFcVeV16uYOr+ErEjkehPMTqywsVajVe+gqL4GWrPeIZYM0T+aY+Tg7i3Dd4JgJIDZsegZyWGZNpVCnUalidEySeUTRFMR1hc2cPZAoPM/OmzXobFJNB6WAyTUyK674JZt8Hzh7a1jVVHhUz0P7fBgdUnlRHIfX1/yqyHmW2t03O2lfQutIheqC6x0yoQknWOJwT3tvO9NDfJz7vuYaawz3Siw2qky3yrx3ZW3MV2HkKwxFskxFs0xFsnRE0wQUwI8u36evmCG/lBmT9e5M6P7LrcgjudRMwzatr8vPpq7tj3Nh/04z189fHyraFkQBPLhKA/29G0F9j+7/zDupoctCgL90Rgnu3uQdokn9UX9GOGXDh/DxdvGeu97ZbdPqAkCdCWinL6ySKHaojsZZbZQY6MmYlg6E0ulLaN7eWkD03boTUVZqzT4T999lR+eu8KB/ixPHB/AtGwmlgr8sz9+g7/+YYHPve8osiRg2A6Xlws89RqMdqc4P7fKH/zoLZ45M0kuEeFAb5Yfnp1iIBPnS4/fiyjoXFwo8xtfe4NGx+Bw/yDJSIiNWpMfvr3E2dkyv/wT7+PYcB7wsByXxY0q/+0Hb6JIEkNdSQ7155gvVHhlYp7LSxv8xi88SRCJbDaGrqsYhkKtJtJqaThOENP0iMXSJBI1VEWl066jaTIraxUOHMiT64px4cIShYLJ4cO9iIJAWLnxgZHRr4ulqZK8p0qSdFeMhSvrSJKIZdrEk2EisSCyLCKIApIkIkoi2fxeOZNvjUc+fXLr/6uzBVZnC2T704wdH9qaPyP37G0r+ZcRnuex0F6h43RIa0mSapzVToGIHCIoBfb0nLuei+M5KDcJFWxdCw9rM98TlDQUcWfZp+d5bJhVXiu+s/Xa0fgwQ6H8jmNFQSSpRgjLAep2i7JRx3a3CxQMhbN8suckbcc3xmFZ39NnGgpnGApn8PAbvVbbVRaaJRZaRRaaJVY6FYqdBt9YeIui0UARJb58/xcZDt9cSmw3/Hiai98FBEHYptgrbhLqXI8b6eXk29xQQRC4M7m77RjtTvHKxByLxSrdyShXVotIksi+njTzhQqGZSOJIosbVTKxEAFV4XtvXeaZs1M8emiIv/2JU3QlIriux/Rqkd/8+vP80fPnODHSy4E+n46y3GjzwHg/P/XIUf701Qv85p88R186xi89+TDNjsnUapHFYpV620RXZP7o+bPUWh3+zidO8diRYXRFpm3afPXFc/yn777Gd09fZrgrSSTgb806pk250ebvfOIUHz0x7hNl15p8+Vsv8tTr73B+dpX3HxqmtzeJJIm4rkdXl1/LmM8n6XRMRFGgtzeBpit0dcfwPEgkQiSSYRRFQlEkTFMkHn9vFRfSXTFS2SgIMDh+lUXt2vfp+frgCLdIeF1VpTAse9trbdPCdtxt/A7Xn7t7KEv30HbK0OuP8Txf0qZj2de4gK9ey7RRFOldCqK+93BxeXb9ZcYiQ4Rlv3SqYbfQRW1PdCGe51EyK5TMKvsiQ7c81ie38SsPbuUFnilPUbH8mlhJkDiVPkxI3n33KQkSQUmjbrcwXGvLCbsec80CruchCyIh+Q67UfFtUD4QJ6mF6Q7G6AummGkWOF9ZpGa3KZtN2o6J6dqEZA35DnQA/9IZ3b+MGOlOYdkOS8UaJ0dhZqVEJhbmQF+Wt64sU6g2UGWZaqvNseEeys02Z6f9jO3HTu4nu9naKYoCo/k0Txwf48vfeonnz09vGV2AI4NdaIpELh4mEtA41N9FNKjjeh7ZWJhWx8KwbGqtDm9MLnJsOM/7Dg0R2CQTCWoKJ0d7+fYbl3h7doVivbVldAHuG+vjof39qJuy9ZlYiCODXTx7fpqFjSrBoEokfG2id3dv0v5dl50dH/d3J5lMZOs1AMuySaXCDAzsXjf5bnErgypc3z2A3976wrkZJhYKdDYVKTqmjWHaLBSusc21DIt/+ZXniAR1VEVCV2R0TSES0Lh3vJcjwzsThXOrZZ5/e5pa06BtWLQNE8O0qTY7W2QzhmXzzJuTzK6WUSQJXZMJqApBTaG/K8EDBwZIRP5iYsFNu8WF6mWmGnMMh/uRBInF1grnKxPcnzpKTIkw21xgtrkEeGS1NN2BLG9VLmA4BoOhPmJKhOcLr1EyK9StBuORYcLK7u3joiASlDSqrk3L6WxVJVwP07V5vnBu6/e+YIZ9kT7kPTQJ+UVkO1G32uyP9ZJQQwTvwOiajs1Ku8JkfY3J+hqLrRKFTt1vlDAbqILMYDjNvckhBsNpcoEoVzWj9tpI866Nrud5lNptvjc1xRvLSxRbLSRRJBcK80BvL6f6+3cwhHkevFNY588uX+ZiwZc0P5DJ8OS+ccbT6a2OG8/zsF2Xc2trfGfyMlObbYZjqRQfH9vHwWwWRRR5Y3mZf/PKK/zcieO8f9CnC3x1cZHfeuklTuTz/LXjx8mFw6zU6/yX06eJ6Rqf2DfOv3r5Ze7r6eFUfz9PXZ7g3OoqHdtmOJHkE/v2cTyfRxZF+tK+wu3iRoVau8PiRoWBTJz9fVmeOTvF4kYVVZZpGRb78mmqzQ5LpRr5ZJR0dCdZ9YHeLKIgMLFU2JaMjG5yg8qSiCKLRIMasuSHVWRRxHFdXM9jdq1Eo2Pw5tQi/9O//ZNtBqdj2ixuVAnpKoa5fds1kI3vEGwM6SqqLG3zAG/EjS3Vu0GWJbq6Yrct7v/zgON4/OD0FM+cnsRxXRzH21VO3bIdnj83A/hhJEn0EyShgIosibsa3ZnVEn/w9GmqzQ6O42JvJfWuu77rMbVUZGrJr5YRRf/7kySRk+O9jPWk/8KMriaqDIZ6iatRxsJDRJQwrufSdts0bZ//Y6m9RtEsc3/yKBE5TMmssNha4VTqXlJaAkWQyWgpApLOYKj3lqrfqiiT1eJUrSYNu0OhU2FfpG+LTc7zPM5UppjZFLgUgBOJfeQD6Zs25zieu9UJpgi77yQyepSJ2hKiIDIa6WJ/9Pax/ufWJ/i96Zcom02atkHHsQjKKsPhDO/P7edgLE8+mCAi64QVnZCs0XEMXi9OktX3Xu74ro1uzTD4R08/zfn1NfKRCIlAgKZp8sbyEjOVMplQaJvRFQWBs2urzD9XpWEapIJBCs0mf3T+PK8sLPBrjz3G8e78VmH2Vy5c4PfOngGgLxbD8Tx+MD3N01eu8Lfvu59P7/cVThdqVS6uF3iotw8PeGN5iTOrK7Qskyf37SMXDlNstXhhbo4PjQwjiyJnV1eZKZf54cw0a80mmWCQtm3z7clJXl5c4JcfOsUTIyMENIWeZJTlYo25tRJr1QYPHxqkL+3f6PlCBVWR6Jg2+3oyWLZDx7QJasqutIRXBRSbneupFf3s+1bMGQFR3C5H7W3+U2+bCAhEAhrp6E4PozcVIxLUiNxgYIOaukP3S9iKc9+9WvLV8d9MU8xxXGrNjh+GEUTSiZv3zocCKnms+GUAACAASURBVH3ZOLoqk41H0G9BCdhoGSiyiKrINywMEAlq5BI7yWP2Ak2RCWjbr2u6NsVOkxWjQkPqEI6ohBRt6/txPY+q1aFudXA9l5CskdKuLbiu51E2W7iKu2NhioZ0hrqTeJ5HMrozNBPQFAa6ktiOQyYW2vH+gKbQk4nheh496dgtOyxkUSapJghKAbL6tfKpkBzcNvfSapLeQDcIoIgyg6FeXim+xbH4QcYiQ8SUCJIgktJuHUcPSBpD4TyTjSU8PJ4rnONwbIikFsX1PIpGlW8svUjd8g1+PpDm3uQ+IvLNF6WWY1DfXCB0SeNG0XbLdTgY68NwLIpmY8+BxcVmideL00SVAI93HeThzBh9oRQRRSci6wRkdVteCKBm2bQck5Zj7PEq74HRPbe2xpnVFT42NsY/eN+jBBWf+LnQajJbrnAgs51V3fU8FqpVHukf4BdPniQVDFJpt/nt117ld8+c4fm5OfanM+iyzCuLC/y/p9+kJxrlHz/6GAez/lb8wvoa//zFF/nXr7zMvlSKZCBAXyzGQrXqS64LApcKBYYTCRzXY6PVwvU8Kp0O5U6b3liMgKJgez7fw1hqnH/ygQ/SF4th2ja/f/Ys//zFF/jhzDQne3oIyypjPSlm1spcmF+nZViMdKWJBnVy8QiLG1UEAQKqQjYeptxoE9J8PShrl9bIWqvj1zcHtOsm+t4RDWpIksD94/38w5/6wC1jhrZzbTt3wy78zwWe53FlYYMXzkxj2T4Jzk9/5DiytPtITh0e5NThQVptk/mVMvtvoQ5xea5AKh6kL5fYVmaqyBJ/7/Pvf08/xwtrU/y3K6/i4jLyiQh/dfQUj2RHt8oUr9QL/O7UK8w2i3iex33pQX5h7GECsr/AVs02X770I+JqgN7sdq/o4w8e4OMP3lwq6cS+Xv7w//ziTf9+dDTPv/yln7irz+V6LiWzQtmsstoukFb9lvGriWzP83A9l249i+d5LHfWGAj1oEkqM80FlttrpLXkTWtvw3KAY/FRnls/S8c1eb5wjowW497kOKZr8aP1M5wpT+HioQgS9ybHORQdvOmibHsOS63CVolWSo2giNvNWNls8FZ5Btt12DBq5ANJxvfg6fYEEzyQHqFkNHl+/TKvbFyhL5hkKJxhNJKjN5QkrYVJqCGiSgBFlLbxP+8V79roXlWNaJgmxVYLLRJBEkWyoTDZ0O6exv50hk/t30866K/qiUCAU/0DfHtyktlKhY5t43oery8uUe50+LsPPcT+TGbLuBzJdfHJ8XF+86WX+M7UJL9470l6olHf6FomiiixWKtxvLub2UqFmUqZ++1eVhp1gopC7rpxdUci/OShQ/THfIFHTZZ5ZGCAP3j7bVYaDSqdNvGYxkh3mnMzq1xaXCca1IiFdIKawlAuwVKxSse06U37rGvxcID+TILvn5lkvdJgpCu1ravpwvwanucx3nt3+luD2QS6orBQqLBeadCVuHN9tDuF7bjMlivUjQ65SJhUMIjjeliOg67Ifn2yIlNqtVEliVhA93crHYtzk8v0dyV4/8lRJEmiXGvxzvQqpuUw3JcGPBZWK7iuSy4VIZuM8PqFec5cWsKwbAZ7Uqxu1Fgv1ZEliaPjeZotk2qjTTYZxrIdZpeLFMpNzM3jB7oTe+Zevh08z+Mrs6fpDSX42eH78TyPrmBsy+B6nsfL69NM1tf5hbGH6Q0mUCV5y+ACaKLMA+khArLynnEn3C1EQWBEHOTl771N11AKI9khq6VxcWnaLfKBHIXVMq+eu8CRB0Zoyx0uLl9h6vwijx09STAXIK/nWOtsUDTKxJXoTY2uLErsj/ZzOD7EG6UJbM/hKwvP8u2V1zavd02uqSeY4dHMPUR2aeu9Cst1mKxfa9rJ6n7t7PVIqCHyeoLuQIKy1dwR/rkZ3pcd50i8j8VWielGgdlGgeV2hYvVJX649g4e0BtMMhBK0RdM0h2IcziRJ6zoBG8RYtlxT/Z85E0wnkpxMt/DD2dmqBsGJ3t6eKC3byveuhvy0Qh9se0qtiFFIaxqdCw/G1k3TabKJbrCYfqi1yb4VRzKZImoKudWV4lqGj3RKK8vLdEwfe+yZZqc6M7TtCwmNjZ8ddZymWwoRO66cEdc1xm/gTZSlSQSAb+pwHZcJFFkIJOg1uowsbjOcFeKkK4SUBWGupK8dnmBjmXzxLExZFkkqCmcGO3h+Ysz/OmrFxjMJehJxXBdl0uLBZ5+a5KgpvLooVtnfm+GbDzMI4cG+dG5K3zj1Qt89uF7SIT9Uh/DsrmyWiSgyPRl3psSKvAZ5d5aWqbQbPJAfx8t02atXme2VOHBwT4m1jfIRyNb3KsPDfTRE4tSbxk4jkc+E0OWfUFLURRQFIn1coMzlxbRVJnJ+QKHRrp469ISx/f3Ypi2f5wsIgp+nFsSRaYXN4gEVbozMd6ZXiWoK0RDOmcnlmkZFrlUhJfOTNOdOkpAf294XTuORdFo8KH8AfbHdpYH2Z5LoVOnKxDlcKKHrL5zEdRlhcfz+9+T8bxbWB2b2GyS5753hic+ex9DmW56Q93oAZVWo0MwohONRPmjF55h5FAv2a40H8ydov39l9D7g0iCRFKL83ju4T1dLx9I8fHuB1lpF1lqb/jMgZvhgasISjof636Ag9HBW57LcE0u1Ga2fh8I5dBvMHiKKHMo3o8AxNXQpuTX7SEKAkktRFILcU+iD9t1KJlN1js1Cp0aa50aM40NzlcWeXrlArok8+8f+Dn2R3qJKsE9J5DftdGN6Tr/66lTPHU5zVOXJ3hzeZk/eecdHujp5a8eP85gfCeFWlBWCN1AjCEIIApsthaD5TjUOh3Cqoq+C91dVNeRRZFyu4MkinSFw7Qsi1rHYH5T4ucq09nplWUapslMyY8xZzfVLsA3sNEbCc4FX9bl6lhAIB7WQYCZtRKPHxsjpPvJlq5ElHrboFRvMdKd8vmFJZFTBwa5slLkG69e5Nd+9zsMdyUxLYeplSKr5Rp/6+MPMdJ9d+QaqiLzuUePsVSs8ofPneXNySV60lFsx2Wj1qRQbfKTp46QT8WQ3iPeAF2RSQYDBBSZkVSCuXKFi2sF2pbF5cIGy7UaTdNkLJ1iqVpjulgmFwkjbnaYup6fcPKAUrXJlYUN6k0D23EY7EnSm41z6ugQX/vBOQzTZjCfpN2xODjSjWU7zK2UWC812Kg0KFSaHB7L052ObsXMdU2mtyvOweEcv//Um9jO7b0bz/O4Ui/wlbnTLDXLxNUgT+QP8FBmGEWUuFIv8LX5t5isFZhtFPndKy/z7cXz5IMxvjTyIH2hJP995nVe25jlcnUNw7X5+69/lZga4Mnee/hwj89Z8P/NvM6zq5exXYcP9xzipwfv3TGOy7U1vrt8kcnaOiIC+2I5PtF7hMGwrxV2pV7g6/NnmWuUiKk6n+y7h/vSg1vb2z+eewsBGAgl+dOFc5TMJgdjXXxu6CQpbfuO0+hYlNZrROIBEpkIhZUKxbUao4d7OfvSJMcf2UcyGyUUvcY5G4kHiadv3xa7GxRR5r7kfsDj92e/z3RzZVvNQUIJ81P9H+AjXfdtIx3fDdONFVbafoJSl1TGwr270kRe3RXLdyCbdaNHbLkOdavNeqfGfLPITKPAVH2dtU6VjmPSsDuAQFfgzpybd210BUFgOJHgF++9l586dIhza6t849IE35ma5NJGgX/4vkc51t19w3tu32ghiQK6LFNqt7d0t66HsRmC0GUZAeiJRIlpGkv1OudWV8mGQgwlEowkE3x3apJiq8V8tcJjg4MkAgFK7fbW+G9XRykIEAloHO735Tv25TPom8mbXDzM/t4sCxsVhnLJLSMXD+n8/Ifv41B/jqfeuMTbs6vIkshYPsUvffJhToz0bG1/owGN7mTkmgFRZLLxMGFdBUHwu8EiQQKqjCT54x3tTvFPfuYJnr8ww0sXZzk/t4ooimSiIT54dJT79vWhbHbnBTWF7mR0K4F3PQKaQi4eJhq8NfesLIqENY3OZpVDWNWoGwYHshnOLq1yX38P1XZnMxl4rZU/HgkQ0BWm5gtbMjSXZtaIhHRyqQhXFjZwXI9Srcl6qY4oCAQ0n26w0TaoNTsUSg2W1iocHstj2w6u622Wa1k02yZtw/LbmWU/k+0/PLc3utONDX797FOMRNJ8qPsAS+0KX770I6pmmyf77iGjR/hozyHuT7dYapV5LLePU9lhApJKLhBFFkUeyg4zGs3ytbm3KBtNPjd0kqQWojd47UH8cPcBBkNJ/t3Ec8w2tkvOu57HS+tX+A+XnycbiPBYbgwBgY5rbxm86foGv37mKYYiKT6U3898o8RvXXyGv7v/Azyc81vQ55tFzpaWyOkR7ksPIgo+M9duMcdQNED/WA7LsukeSDP19iKNagvbcihv1LHfo047y3aodQxfvklROJU+zKHYEO/U5phvrdOxTEKEOZEeYSCSQbsFx+5VXK4vkA/4oZDxSB+jkZ4dibS7xWKrzOvFaWYbG0zW11hoFTE35dsF/OcwpYZ5LLuf0c2utOu/573iPanTFQSBgKIQUBS6IxGeGBnlv5x+k9948UV+MDPN0e7uO74tYUVlOJnk4sQEa83GDmmNqWKRpmlxtKvbL+CPRMiGwsxVykwWizzY10tAURhN+moW59fXqBsGg4nEXRWrZ2Jh/u8v+Vy3pWKDSqlJLBFiX0+Gf/U3P73rPQnrGo8fG+PxY2M7/n49vvjBe/niB33vx3Fc+iNR/vFHHsHzwDH9jPX/9cWPbHuPJIr0pGJ8/tFjfP7RY7udFgARgY+cGOcjJ8Z3/fujh4d59PDe1GNHUgnOr6zx2vwiDw70cyCXZSgZZ7Zc4Vi+m7V6g1fmFpBEn61KliQECR4+NsSP3pjiq0+fIRkLcni0m5fPzhKL+IZXFkVWN2p8/5XL7BvI0N8dxzAdXMfjuTemuGdfD+GQzvmpFYK6QiwSYGJ2nWKlRatjIcsS6USIWFhHliT6uxN7Kl376uxpNFHm1+75+BYfgCJK/PfZN3g8v5+YGuAetZeGZRCSNUajGR7MbL9X+6I5BkIpXilMIwkC96YGSOvbPcuUHmZcEEjswuFaMVt8f+Ud0nqYXzn4Ibp3USH4+sJZArLCrx7+CBFFx8Nj+c0KX507zUPZka35vNqu8n8c/QRDkZuTusBmCZsiIcv+TyCo0qi2WZ4tUCu3cByX0nqNernJ+nKZSCJIu2lQLtTRgxqdlokWUG7pOLmux/OXZnhrbhlRFDk+mOd944OktRjvy/jqJDPrJf7zj94gfbDN8IG9maKfGXicnxl4fNtrnmfjeg6isLe4quu2MZ1FZDGBLF27V8+tT/BvJp4mogSIKQHygQTdgTgjkQwjYb/9N6NF7srbvx7v2uguVKu4nkc+EtmSFXE9j0QgsJn5vLtSpLCmcTLfw7cvT/L9K1c4lM3SF43hASv1Ot+7MoXh2Dw+7D8EmVCIbCjEZLFIodVk/2bVxEA8TlzXeWVhAV1R6I+9e/rAC2/NA/DgY+OIt+GAvRN4nkdhrcqPvnseWfHDFJlcFD1wZ4TtPy6kQyF+7r7jgG/0P7bfX0z2Z/17nQoF2Z9Nb4VnriIZC/GZx49uO9eRsWsENy+emeb4gT4+/sg1CkFZkfj0Jw6R3pzk+VyUlm0QUa61qR4b76Fo1LE9h9x1dZKfev8RANq2geHaxHaJt7mex0xjg+FIehvvw4FYF08tvE2h0yAU/vEzn1XNNovNCvdnBknvEgsGmKqt07RNvrlwrYGgaDSpWwama6Nvbsl7QwkyNznHjYglw/SP+pUhub4ksck1Sms1BsZyOI7LxkqFXF+SarGBadhUNhqEowFs06bTMtA256RhWCwvlOjKJwgEr3mqxUaLV6YW+Pn3nyQX88c0vV5ktVpHQODYQDdD2STHB/ME1JuZIQ/XM7GdDURBRRITgITrNXHcCpIYQxQ02uYFXK+NrowjihE8z0YUNFyvhSgEcD0Dxy0hiRFEIYbrtai1v4OuHCAa+NDW1cajXfzcyPvoDSYZDKXpDyUJKzvlk94t3rXRfW52lufn5+iNRkkG/DKKcrvN8/NzDMTj3NfTe1dDlkWR+3t6+NT+/Tx1eYKObXM4m8X14ML6OqdXlvnw6Cj39/YCoCsK+WiUVy4sgOBXSIBPHdkdifLi/Bx9sdhWlcJ7gUatzelXpzFNi8GRHO2WgdGxaDUNZFliZLybRr3N0nyRYFjnnhODrC2XmZ1eBw+O3TdMMn3NK3Icl5nJNd/g/PT9yIqEZdo89/QF2m2T7p6Er9/V6NBsdFAUmZ6BFAIwN11AlAROPDBCYbXK8mLJb+XNJ0hlIrx9eg7TtBk/lMdxXKYnVrEdl2xXjAP39BEI7C3pdH1FgOU6VI0OUVXfatW+GxLxwXwK84bmjOV2iadXznB/eh/9wTSLrSINq8OJ5DAlo8Hl+jJxJYThWnQcE8u1kQWZqtVkw6gzHM6x2CpypbHKieQwA8HMDfFC7z1/mO4W3iYvyK3+3rZNpuqFrdf6Qkm6A9t5aQOSsqdPJAgC2Z4E2R5/a6wHNd73iaO4rkexUOPyhWVi8SAf+8Ip9IBCudhAC2t87AsPIcsStWqL5YUS2a4YnZbJ26fnCId1TMMiENRQNZnFUpXuRIRYUEcQ/IYox/WwbJfp9SK6InNi6NZlXP57KrTNc9hOgbD+MLKUptL6FrKYQFPGUKQMTfM1BE9ElpKIXgPTXkFXxmmZpwmqR7HdEoZ1BdtZIRH6PLKUQpMHEbg2Vw2nykjI4J74SVRp58JluU1kQedupbSux7s2uoPxOE9PX+FbE75hFAQ/Fnskl+Nzhw9zX8/dsz5lQiH+2onjdEXCfGtigteX/FKRnmiULx07zqf2j28lwQRgIBbDdB0yodBWdYQoiuxLpfj+lSnSwSDZ0N2r3d4IRZXpH0qztlLhysQKaysV+gbTTE+uk0yHMQybWrXF6HgXaytVzp2eZW2pjKLKjB3I7/BgHdulUmrS05dE2Vz9RUmkdyDNxnqNuZkCpUKdnv4UExeXGRjKUCo2kGWRbHecVtPgtRcmcV2X0kaDY/cNEQprXDq/SLtt0tUT59XnL5NIhpmdXufkQ6NMTawQSQV5Ry4jCyLD0aTfhReO81ZhmZ5QlDcLS4RVFU2SOZjIIQkCM7USqiQzVy/zQK6f2VKZI8kuFhoVXDzmahU6jsWDuX4K7SbvVNbJBsKMxJL8aGmafDDK8UyeqKrTk925EKqijOHapNQIqiijigob5rpPil1fRUQgrUVYaZeZb25guw5pPcrl+goSIsvtEsPhHCICcSW0I7YpCiJDkTSTtTVsz0XZfJguVVcJyioZ7e4aK+4UEUWnOxDjcm2dktH020pvwGgki4jI39j3CNp1pVmSKKKJe3+Er1JiXt+EcxVXG2Q21mpMXVqhbyBN31CG0kadN16cIhjWyOXj9A2kefGHl4hEdaYvr3LwmF8lMD+7Qb3S4pHH/d3Ktdi6D8d1WK3WWa3UKTc7rFb3KCjgOdhukabxKqoyhOPV8TyDoHYcUQgjCBqymEQSEyhSL6Y9i+UsoymDmPYcurIfzzOx3XXqnWeJBT+NyPaSNNezqRgTNKx5BEGiak6hSXFcz8TxTDQpzkbnDAntICIyhlNGkxIYThkPD1WMYLo1ouowmnT7nfS7Nrr39fayL52mbVtcWSpi2A7DXUnWCnUOpLK8c8X3qCJBDVyXv9V/nCPJbr77yiVyyQiqIrFRaRIKafzC2HFiwQDnJ5bpdCxSsRCSKHIokKb/YIRSvUUqHqLZNIhrARJKAFEQsB2XWqvDvdk8X/7okxiGzVKhusU78MTAMAei6S0NKPA94N//yc8iS+KOCdgTifBbH/sYeJAK7qwZdF0XSRYprFV59YXLOI6H2bFwHJdcd5xioUEqHWFlqczi3Aa1SgtJEkmmwhy7f4hXn5/krdeu0JXf/gUJgoCqybRb5lYfd7Xc5NUXJvBcaNQ7uK5LtjvuexrdMc6/NU+91mJpvoQeUBgYyeIYDt09CfYdyNNsGpQ26oyOdzN2MM9LP7xENBakfzDD+KEeZqbWKNYaLKlVHssPYzg2y80WuUCEs8UVZFGkbhl8oGeE5VaNV9fmSWgBXM+jOxhlolzAdGxMx+bZ5WlEQWC+UeFAIkuvGuV7C5MktAAxVeeeVBdr7QYVo80Duf6tbfFuCEkacSVETAkiCxIBScFwbNqOSce1yAcSxNUQq50KG0aNiBIg4gT///bOpLeN+wzjv/+s3BeRkihSEh1J3pfENdwGaVEgBYreChRFD732kySXAr32WLQfokAXFEXbBEmbOm4SJ5ZtWbFWmhIXiaS4zT7TAyW5srwkhSsXyfyO8x9yMMTwmZl3eV483ycfGRlY65JKZr+Q/Uleqj8uX+XtW7/jF7f/xNWxGapGhz9v3eUn5WtElRcT0rE9l6bV4+GgQ8ceokoy9zo1kqrOVDRNVo/x5tRZfr38Pr+891deH59DIOg6Btdys5xNF/jhzKv8/LM/8pvlv3M1N4NAUDU6lONjfL94dKqD7wfstfoEXoBju6Mqgf1uwcAPaG63yeSTyLJEfupoZZEkCbK5BIVSloXzUyTTUT764AEL56eQFZntSot6tcN4IUVpNsfixxu0mj3au31Wlmv89GffRd4fBT83Mcbvby1R2d1jMp2g0e2z2mhxoThxmAS3HBfTcTFsF8t1UR5LpAWBycC6iSLliGjnEEKBwEcSGpKII4SKQEUWKQQSQigIoQIunt/H87s4Xg3beYCunMFU7oMAP7DxAxOQCQIHgUxUyRPg4fhdVClJy1pEFXF0JYftdZHQUESUgbvN0N2m52wiCQVFitKx7qFICVQp8YVEV37rrbeetf7MRRjdbeOaRjoSwTE9Wq0BSU2n1R6gKjLLlQYT2STz03nSsQi26VIcS5OKR+gOTO6t15EkwdCwmclleG2+yOZ2m6Hp0BtapBIRDMPBNl2un5klqWlsVtsE/qj2M6qr7A1N3v1slfuVBqVsGjkQ2K7HwLL5vLpDu2uQ0jWiivrIfEYI0pEISf34THtZkkjqOkldP6wP9jyf1k6P+laHpTtVZl7J09jew3N9ynPj9LoGtu1Snhun2eiSSERQNQU9qnLt9XnOXiwxU86PKgwmUzQbXVzXozjzaGikkASu43H7kw3i8QiDvkllfYe9jsGZi0W6HQPTdDg1N0Ftu8P4ZBrLdMiMxblwZZoLV2aYOzNJdbOFHlGZnRsnCAJ2d/q0W30cx2OvPSSdjSFJErOvjPNgaZuJyTSFQob3t9fRZBnDHbmmLXUazKVyuL7H5fwUuUiMvzxcoWUN+V5pgZ5jcb/TpBBLko8m+O3aXUqJFGktMjJ6FhL1YZ+sHiGrRyknsyiSTN+xWdytU4ynnjoxIgBadp8to4Uuq6z261SNFuORNDFF53ZnA8OziSo6xegYKXVU3iSEoOcaTEYyZLUEW8YuPdcgpyePmWdntRiXMkUWO1v8a3eDoWvzo/I3+EHxwhELQi/wWdqrcSlbYjZ+fMhnQMDDYZuIrPLq2PSRm8l6f5dfLb/HO7VlvCBg6Fp8sLPKhzvrvDExR0zRKcfHWEhP8HDQOZwxmNIiXMyUSGtRsnqMy9kSK/0mN3bWWO7W0WWV6/nyYQy3MhhNSbkULXDvxhqe57Nb77Jyp8rdm6s8XGmgqDKu7bF4c5WNpW0WLk8fMX0XQmBbLrVqG0WRyeaTtJpdTMPBdTw8zyeRjGAMLRRZYq8zZLKUpb7VIZWK4ro+hdIoiakpMvlkjD/cus+nGzWimoLnB6w2W2iKwmQ6Qc+w+GS9Sqs/RJNlpnPH33hcr47p3CHAIapeQFfnGVofYdi3EUJGlQsgBH3zXQQKurqA4dzFcu6PEvzaJRy/ju2uAT4x7Rq2u8HAvonnt1GVIrKUIWBUpaBKCUxvB0WKIQkNP3CJKVN4/hBJqJhuE1nSGcWbLTQ5gy5n0OQ0SXUGRTpsX377iRc2IJ7TrfGlsmC1Vo8gCJjIJri/2WRg2Hi+z+xEhomxJK7r8dnKFrl0nJiu0RuaGLaDQJCI6miqTGEsyXKliblfHH+QkJElMeo/933WtlokoxqzhdGk1lZvyOJ6DU2RuXiqgGW7NPb6SELgBwED06Y4lmJoOZwuPTuz+zQs02Hx1gYry3XyE0muv3GabmfIP99bJpWOoukqge+zcG6KrUqbZDqKHlExhhbrKw0iEZUzF0o0antsVXZRFJlvv3me1GM2iKYxipGtPWig6QqvXX+FTz5cJZGIoOkKvh9w+vwUm2s7TE1nMYY2mqbw+dI2koDzV2bodQ00XeX0uamRV2mjy8c3VrFMh4uvzUIwSoAsnJvi9scbRCei1PQhtuexkM6z2WvTc2wUIXE5V6BjGVzJj8r+btQrmJ7DtyZnWWo3uNOqcy47znwqx41GhZl4mulEmhv1Cq7vcy47jum5xBSV2USG6qDLp7vbxBWVV/NFsvrTe+xHdb3BE59S/SA4FNnja/6hp8SoMP75ZYFfFVzbZfNBHdfxcCwXSRYM+ybN7Q7z50vYtovneKRyCcpnCsd+P8tyuHurQq3a5pvfOY2qKfzjnSUURWL+7BSlco4P/raE47hMFrPMny2wfHeL2VN57t1+yOVrZVLpF2ftOdInn9HswAM7TR8IDrcd7DMaRiAxmunnA9J+K/PR/Z97zMdkTyCObevaq7j+AC+wyUeuIsSxYZdPPdALFd0jH3zCsLYvuu3Lfs/BKRzscnz90Wn8L2wHvwr8pzgdCBriv081HTRCPC52QRDg7yexvi5CeNI8fv3blsPebv8wnPDc/9x+7FdIj/wXRrHgR23Pvh/8XzjKvSyCwMf1B8hSFMFxY3ZehuiGhISEfI15quh+fW9VISEhIS+B51UvhO9/ISEhIS+Q8Ek3TLumiQAAADNJREFUJCQk5AQJRTckJCTkBAlFNyQkJOQECUU3JCQk5AQJRTckJCTkBAlFNyQkJOQE+TfUJEkp3/QCeQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Generate the word cloud\n",
    "titanic_cloud = WordCloud(background_color=\"white\").generate(titanic.review)\n",
    "\n",
    "# Create a figure of the generated cloud\n",
    "plt.imshow(titanic_cloud, interpolation='bilinear')  # the interpolation argument makes the word cloud appear more smoothly\n",
    "plt.axis('off')\n",
    "# Display the figure\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can remove stop words from word clouds using `stopwords` parameter."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Define the stopwords list\n",
    "my_stopwords = STOPWORDS.update([\"movie\", \"movies\", \"film\", \"films\", \"watch\", \"story\"])\n",
    "\n",
    "# Generate the word cloud\n",
    "titanic_cloud = WordCloud(background_color=\"white\", \n",
    "                          stopwords=my_stopwords).generate(titanic.review)\n",
    "\n",
    "# Create a figure of the generated cloud\n",
    "plt.imshow(titanic_cloud, interpolation='bilinear')  # the interpolation argument makes the word cloud appear more smoothly\n",
    "plt.axis('off')\n",
    "# Display the figure\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Bag of Words (BOW)\n",
    "\n",
    "BOW is a simple but effective method to build a vocabulary of all the words occurring in a text. It creates a numeric representation of the document.\n",
    "\n",
    "Types:\n",
    "- Unigrams : single tokens \n",
    "- Bigrams: pairs of tokens \n",
    "- Trigrams: triples of tokens \n",
    "- n-grams: sequence of n-tokens"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Read data\n",
    "amazon = pd.read_csv(\"data/sentiment/amazon.csv\", index_col=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>score</th>\n",
       "      <th>review</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Stuning even for the non-gamer: This sound tr...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>The best soundtrack ever to anything.: I'm re...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>Amazing!: This soundtrack is my favorite musi...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>Excellent Soundtrack: I truly like this sound...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>Remember, Pull Your Jaw Off The Floor After H...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   score                                             review\n",
       "0      1   Stuning even for the non-gamer: This sound tr...\n",
       "1      1   The best soundtrack ever to anything.: I'm re...\n",
       "2      1   Amazing!: This soundtrack is my favorite musi...\n",
       "3      1   Excellent Soundtrack: I truly like this sound...\n",
       "4      1   Remember, Pull Your Jaw Off The Floor After H..."
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "amazon.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Count Vectorizer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
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     "execution_count": 10,
     "metadata": {},
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   "source": [
    "# Build the vectorizer, specify max features \n",
    "vect = CountVectorizer(max_features=100)\n",
    "\n",
    "# Fit the vectorizer\n",
    "vect.fit(amazon.review)\n",
    "\n",
    "# Transform the review column\n",
    "X_review = vect.transform(amazon.review)\n",
    "\n",
    "# Create the bow representation\n",
    "X_df=pd.DataFrame(X_review.toarray(), columns=vect.get_feature_names())\n",
    "X_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "toc-hr-collapsed": true
   },
   "source": [
    "### Extra: Unigrams vs n-grams Example\n",
    "\n",
    "- Only unigrams: `ngram_range = (1, 1)`\n",
    "- Uni- and bigrams: `ngram_range = (1, 2)`\n",
    "- ..."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Comparing Unigrams\n",
    "\n",
    "The following texts have opposite meaning, but their BOW representation are the same."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['I am happy, not sad.'] \n",
      "    am  happy  not  sad\n",
      "0   1      1    1    1\n",
      "['I am sad, not happy.'] \n",
      "    am  happy  not  sad\n",
      "0   1      1    1    1\n"
     ]
    }
   ],
   "source": [
    "happy = [\"I am happy, not sad.\"]\n",
    "sad = [\"I am sad, not happy.\"]\n",
    "\n",
    "# Build the vectorizer and fit it\n",
    "happy_vect = CountVectorizer()\n",
    "happy_bow = happy_vect.fit_transform(happy)\n",
    "\n",
    "# Build the vectorizer and fit it\n",
    "sad_vect = CountVectorizer()\n",
    "sad_bow = sad_vect.fit_transform(sad)\n",
    "\n",
    "# Compare two bow representations\n",
    "print(happy, '\\n', pd.DataFrame(happy_bow.toarray(), columns=happy_vect.get_feature_names()))\n",
    "print(sad, '\\n', pd.DataFrame(sad_bow.toarray(), columns=sad_vect.get_feature_names()))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Comparing Bigrams\n",
    "\n",
    "n-grams helps us better evaluate sentiments."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['I am happy, not sad.'] \n",
      "    am  am happy  happy  happy not  not  not sad  sad\n",
      "0   1         1      1          1    1        1    1\n",
      "['I am sad, not happy.'] \n",
      "    am  am sad  happy  not  not happy  sad  sad not\n",
      "0   1       1      1    1          1    1        1\n"
     ]
    }
   ],
   "source": [
    "happy = [\"I am happy, not sad.\"]\n",
    "sad = [\"I am sad, not happy.\"]\n",
    "\n",
    "# Build the vectorizer and fit it\n",
    "happy_vect = CountVectorizer(ngram_range=(1,2))\n",
    "happy_bow = happy_vect.fit_transform(happy)\n",
    "\n",
    "# Build the vectorizer and fit it\n",
    "sad_vect = CountVectorizer(ngram_range=(1,2))\n",
    "sad_bow = sad_vect.fit_transform(sad)\n",
    "\n",
    "# Compare two bows\n",
    "print(happy, '\\n', pd.DataFrame(happy_bow.toarray(), columns=happy_vect.get_feature_names()))\n",
    "print(sad, '\\n', pd.DataFrame(sad_bow.toarray(), columns=sad_vect.get_feature_names()))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### n-grams"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
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     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Build the vectorizer, specify token sequence and fit\n",
    "vect = CountVectorizer(ngram_range=(2,2), max_features=100)\n",
    "vect.fit(amazon.review)\n",
    "\n",
    "# Transform the review column\n",
    "X_review = vect.transform(amazon.review)\n",
    "\n",
    "# Create the bow representation\n",
    "X_df = pd.DataFrame(X_review.toarray(), columns=vect.get_feature_names())\n",
    "X_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
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       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 1000 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   able to  about how  about it  about the  about this  after reading  \\\n",
       "0        0          0         0          0           0              0   \n",
       "1        0          0         0          0           0              0   \n",
       "2        0          0         0          0           0              0   \n",
       "3        0          0         0          0           0              0   \n",
       "4        0          0         0          0           0              0   \n",
       "\n",
       "   after the  again and  ago and  agree with  ...  you think  you to  you ve  \\\n",
       "0          0          0        0           0  ...          0       0       0   \n",
       "1          0          0        0           0  ...          0       0       0   \n",
       "2          0          0        0           0  ...          0       0       2   \n",
       "3          0          0        0           0  ...          0       0       0   \n",
       "4          0          0        0           0  ...          0       0       1   \n",
       "\n",
       "   you want  you will  you won  you would  your money  your own  your time  \n",
       "0         0         0        0          0           0         0          0  \n",
       "1         0         0        0          0           0         0          0  \n",
       "2         0         0        0          0           0         0          0  \n",
       "3         0         0        0          0           0         0          0  \n",
       "4         0         0        0          0           0         0          0  \n",
       "\n",
       "[5 rows x 1000 columns]"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Build the vectorizer, specify max features and fit\n",
    "vect = CountVectorizer(max_features=1000, ngram_range=(2, 2), max_df=500)\n",
    "vect.fit(amazon.review)\n",
    "\n",
    "# Transform the review\n",
    "X_review = vect.transform(amazon.review)\n",
    "\n",
    "# Create a DataFrame from the bow representation\n",
    "X_df = pd.DataFrame(X_review.toarray(), columns=vect.get_feature_names())\n",
    "X_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Extracting Features from Text"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Word Counts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['Stuning', 'even', 'for', 'the', 'non-gamer', ':', 'This', 'sound', 'track', 'was', 'beautiful', '!', 'It', 'paints', 'the', 'senery', 'in', 'your', 'mind', 'so', 'well', 'I', 'would', 'recomend', 'it', 'even', 'to', 'people', 'who', 'hate', 'vid', '.', 'game', 'music', '!', 'I', 'have', 'played', 'the', 'game', 'Chrono', 'Cross', 'but', 'out', 'of', 'all', 'of', 'the', 'games', 'I', 'have', 'ever', 'played', 'it', 'has', 'the', 'best', 'music', '!', 'It', 'backs', 'away', 'from', 'crude', 'keyboarding', 'and', 'takes', 'a', 'fresher', 'step', 'with', 'grate', 'guitars', 'and', 'soulful', 'orchestras', '.', 'It', 'would', 'impress', 'anyone', 'who', 'cares', 'to', 'listen', '!', '^_^']\n"
     ]
    }
   ],
   "source": [
    "# Transform the string to word tokens\n",
    "print(word_tokenize(amazon.review[0]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[['Stuning', 'even', 'for', 'the', 'non-gamer', ':', 'This', 'sound', 'track', 'was', 'beautiful', '!', 'It', 'paints', 'the', 'senery', 'in', 'your', 'mind', 'so', 'well', 'I', 'would', 'recomend', 'it', 'even', 'to', 'people', 'who', 'hate', 'vid', '.', 'game', 'music', '!', 'I', 'have', 'played', 'the', 'game', 'Chrono', 'Cross', 'but', 'out', 'of', 'all', 'of', 'the', 'games', 'I', 'have', 'ever', 'played', 'it', 'has', 'the', 'best', 'music', '!', 'It', 'backs', 'away', 'from', 'crude', 'keyboarding', 'and', 'takes', 'a', 'fresher', 'step', 'with', 'grate', 'guitars', 'and', 'soulful', 'orchestras', '.', 'It', 'would', 'impress', 'anyone', 'who', 'cares', 'to', 'listen', '!', '^_^'], ['The', 'best', 'soundtrack', 'ever', 'to', 'anything', '.', ':', 'I', \"'m\", 'reading', 'a', 'lot', 'of', 'reviews', 'saying', 'that', 'this', 'is', 'the', 'best', \"'game\", 'soundtrack', \"'\", 'and', 'I', 'figured', 'that', 'I', \"'d\", 'write', 'a', 'review', 'to', 'disagree', 'a', 'bit', '.', 'This', 'in', 'my', 'opinino', 'is', 'Yasunori', 'Mitsuda', \"'s\", 'ultimate', 'masterpiece', '.', 'The', 'music', 'is', 'timeless', 'and', 'I', \"'m\", 'been', 'listening', 'to', 'it', 'for', 'years', 'now', 'and', 'its', 'beauty', 'simply', 'refuses', 'to', 'fade.The', 'price', 'tag', 'on', 'this', 'is', 'pretty', 'staggering', 'I', 'must', 'say', ',', 'but', 'if', 'you', 'are', 'going', 'to', 'buy', 'any', 'cd', 'for', 'this', 'much', 'money', ',', 'this', 'is', 'the', 'only', 'one', 'that', 'I', 'feel', 'would', 'be', 'worth', 'every', 'penny', '.']]\n"
     ]
    }
   ],
   "source": [
    "# Tokenize the reviews\n",
    "review_tokens = [word_tokenize(item) for item in amazon.review]\n",
    "print(review_tokens[:2])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "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>score</th>\n",
       "      <th>review</th>\n",
       "      <th>n_words</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Stuning even for the non-gamer: This sound tr...</td>\n",
       "      <td>87</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>The best soundtrack ever to anything.: I'm re...</td>\n",
       "      <td>109</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>Amazing!: This soundtrack is my favorite musi...</td>\n",
       "      <td>165</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>Excellent Soundtrack: I truly like this sound...</td>\n",
       "      <td>145</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>Remember, Pull Your Jaw Off The Floor After H...</td>\n",
       "      <td>109</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   score                                             review  n_words\n",
       "0      1   Stuning even for the non-gamer: This sound tr...       87\n",
       "1      1   The best soundtrack ever to anything.: I'm re...      109\n",
       "2      1   Amazing!: This soundtrack is my favorite musi...      165\n",
       "3      1   Excellent Soundtrack: I truly like this sound...      145\n",
       "4      1   Remember, Pull Your Jaw Off The Floor After H...      109"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create an empty list to store the length of reviews\n",
    "len_tokens = []\n",
    "\n",
    "# Iterate over the word_tokens list and determine the length of each item\n",
    "for i in range(len(review_tokens)):\n",
    "     len_tokens.append(len(review_tokens[i]))\n",
    "\n",
    "# Create a new feature for the lengh of each review\n",
    "amazon['n_words'] = len_tokens \n",
    "\n",
    "amazon.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Language Detection"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### `langdetect`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[de:0.9999970729529929]\n",
      "[tr:0.9999973965203564]\n",
      "[de:0.8571404875236557, tr:0.14285894819212433]\n"
     ]
    }
   ],
   "source": [
    "# Import the language detection function and package\n",
    "# from langdetect import detect_langs # ind out the probabilities for the top languages\n",
    "# from langdetect import detect # detect the language of the text\n",
    "\n",
    "deu = \"Ich komme aus der Türkei.\"\n",
    "tr = \"Türkiye'den geliyorum.\"\n",
    "mix = \"Ich komme aus der Türkei. geliyorum.\"\n",
    "\n",
    "# Detect the language of the string\n",
    "print(detect_langs(deu))\n",
    "print(detect_langs(tr))\n",
    "print(detect_langs(mix))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> [Note](https://github.com/Mimino666/langdetect): `langdetect`s language detection algorithm is non-deterministic, which means that if you try to run it on a text which is either too short or too ambiguous, you might get different results everytime you run it. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The detected languages are:  [[de:0.9999972083375406], [tr:0.9999977855242717], [de:0.999994334217459]]\n"
     ]
    }
   ],
   "source": [
    "sentences = [deu, tr, mix]\n",
    "languages = []\n",
    "\n",
    "# Loop over the sentences in the list and detect their language\n",
    "for sentence in range(len(sentences)):\n",
    "    languages.append(detect_langs(sentences[sentence]))\n",
    "    \n",
    "print('The detected languages are: ', languages)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### `fasttext`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(('__label__de',), array([0.99987382]))\n",
      "(('__label__tr',), array([0.99999571]))\n",
      "(('__label__de',), array([0.87219495]))\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "# Load the pretrained language model\n",
    "language_model = fasttext.load_model('data/lid.176.bin')\n",
    "\n",
    "# Print results\n",
    "print(language_model.predict(deu))\n",
    "print(language_model.predict(tr))\n",
    "print(language_model.predict(mix))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Preprocessing (Tweet Data Example)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "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>airline_sentiment</th>\n",
       "      <th>text</th>\n",
       "      <th>negativereason</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>negative</td>\n",
       "      <td>@VirginAmerica it's really aggressive to blast...</td>\n",
       "      <td>Bad Flight</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>negative</td>\n",
       "      <td>@VirginAmerica and it's a really big bad thing...</td>\n",
       "      <td>Can't Tell</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>negative</td>\n",
       "      <td>@VirginAmerica seriously would pay $30 a fligh...</td>\n",
       "      <td>Can't Tell</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>negative</td>\n",
       "      <td>@VirginAmerica SFO-PDX schedule is still MIA.</td>\n",
       "      <td>Late Flight</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>negative</td>\n",
       "      <td>@VirginAmerica  I flew from NYC to SFO last we...</td>\n",
       "      <td>Bad Flight</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   airline_sentiment                                               text  \\\n",
       "3           negative  @VirginAmerica it's really aggressive to blast...   \n",
       "4           negative  @VirginAmerica and it's a really big bad thing...   \n",
       "5           negative  @VirginAmerica seriously would pay $30 a fligh...   \n",
       "15          negative      @VirginAmerica SFO-PDX schedule is still MIA.   \n",
       "17          negative  @VirginAmerica  I flew from NYC to SFO last we...   \n",
       "\n",
       "   negativereason  \n",
       "3      Bad Flight  \n",
       "4      Can't Tell  \n",
       "5      Can't Tell  \n",
       "15    Late Flight  \n",
       "17     Bad Flight  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Read data\n",
    "tweets = pd.read_csv(\"data/sentiment/airline_tweets.csv\")\n",
    "tweets = tweets[['airline_sentiment', 'text', 'negativereason']]\n",
    "\n",
    "# Drop rows with missing values\n",
    "tweets.dropna(inplace=True)\n",
    "\n",
    "# Print head of tweets dataset\n",
    "tweets.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(9178, 3)"
      ]
     },
     "execution_count": 111,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tweets.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Stop Words"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   00  000  000419  000ft  000lbs  0016  00a  00am  00p  00pm  ...  zone  \\\n",
      "0   0    0       0      0       0     0    0     0    0     0  ...     0   \n",
      "1   0    0       0      0       0     0    0     0    0     0  ...     0   \n",
      "2   0    0       0      0       0     0    0     0    0     0  ...     0   \n",
      "3   0    0       0      0       0     0    0     0    0     0  ...     0   \n",
      "4   0    0       0      0       0     0    0     0    0     0  ...     0   \n",
      "\n",
      "   zones  zoom  zouowgv3q6  zrh  zukes  zurich  zv6cfpohl5  zvfmxnuelj  \\\n",
      "0      0     0           0    0      0       0           0           0   \n",
      "1      0     0           0    0      0       0           0           0   \n",
      "2      0     0           0    0      0       0           0           0   \n",
      "3      0     0           0    0      0       0           0           0   \n",
      "4      0     0           0    0      0       0           0           0   \n",
      "\n",
      "   zzps5ywve2  \n",
      "0           0  \n",
      "1           0  \n",
      "2           0  \n",
      "3           0  \n",
      "4           0  \n",
      "\n",
      "[5 rows x 10559 columns]\n"
     ]
    }
   ],
   "source": [
    "# Define the stop words\n",
    "my_stop_words = ENGLISH_STOP_WORDS.union(['airline', 'airlines', '@'])\n",
    "\n",
    "# Build and fit the vectorizer\n",
    "vect = CountVectorizer(stop_words=my_stop_words)\n",
    "vect.fit(tweets.text)\n",
    "\n",
    "# Create the bow representation\n",
    "X_review = vect.transform(tweets.text)\n",
    "\n",
    "# Create the data frame\n",
    "X_df = pd.DataFrame(X_review.toarray(), columns=vect.get_feature_names())\n",
    "print(X_df.head())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['zira2z3udc', 'zkatcher', 'zkoe6clgiu', 'zl4bvexmcj', 'zombie', 'zone', 'zones', 'zoom', 'zouowgv3q6', 'zrh', 'zukes', 'zurich', 'zv6cfpohl5', 'zvfmxnuelj', 'zzps5ywve2']\n",
      "['attendant', 'bad', 'booking', 'cancelled', 'complaints', 'customer', 'damaged', 'flight', 'issue', 'late', 'longlines', 'lost', 'luggage', 'problems', 'service', 'tell']\n"
     ]
    }
   ],
   "source": [
    "# Define the stop words\n",
    "my_stop_words = ENGLISH_STOP_WORDS.union(['airline', 'airlines', '@', 'am', 'pm'])\n",
    " \n",
    "# Build and fit the vectorizers\n",
    "vect1 = CountVectorizer(stop_words=my_stop_words)\n",
    "vect2 = CountVectorizer(stop_words=ENGLISH_STOP_WORDS) \n",
    "vect1.fit(tweets.text)\n",
    "vect2.fit(tweets.negativereason)\n",
    "\n",
    "# Print the last 15 features from the first, and all from second vectorizer\n",
    "print(vect1.get_feature_names()[-15:])\n",
    "print(vect2.get_feature_names())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Specifying a Token Pattern\n",
    "\n",
    "Specifying a custom token pattern instead of using the predefined one is a nice way to limit the size of the vocabulary and use problem specific tokens. Default regexp token pattern in CountVectorizer,  `(?u)\\b\\w\\w+\\b`, selects tokens of **2 or more alphanumeric characters** (punctuation is completely ignored and always treated as a token separator).\n",
    "\n",
    "- `(?u)`: [re.U](https://docs.python.org/2/library/re.html#re.U)\n",
    "- `\\b`: matches at a position (zero-length) that is called a \"word boundary\"\n",
    "- `\\w`: word characters. equivalent to `[a-zA-Z0-9_]`\n",
    "- `\\b\\w\\w+\\b`: 2 or more alphanumeric characters\n",
    "\n",
    "Important: This pattern ignores words like \"don't\" or \"haven't\". However, they may be useful in sentiment analysis."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['hashtag', 'mention111', '0tohero', 'am', 'happy', 'great_work']\n"
     ]
    }
   ],
   "source": [
    "my_string = \"#hashtag @mention111 0tohero i am happy... great_work\"\n",
    "\n",
    "# Split my_string on given pattern and print the result\n",
    "pattern = r\"(?u)\\b\\w\\w+\\b\"\n",
    "print(re.findall(pattern, my_string))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['hashtag', 'am', 'happy', 'great_work']\n"
     ]
    }
   ],
   "source": [
    "my_string = \"#hashtag @mention111 0tohero i am happy... great_work\"\n",
    "\n",
    "# Split my_string on given and print the result\n",
    "pattern = r\"\\b[^\\d\\W][^\\d\\W]+\\b\" # words with 2+ characters without digits\n",
    "print(re.findall(pattern, my_string))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['#', 'hashtag', '@', 'mention111', '0tohero', 'i', 'am', 'happy', '...', 'great_work']\n"
     ]
    }
   ],
   "source": [
    "# nltk function\n",
    "print(word_tokenize(my_string))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Original tokens:  ['@', 'VirginAmerica', 'it', \"'s\", 'really', 'aggressive', 'to', 'blast', 'obnoxious', '``', 'entertainment', \"''\", 'in', 'your', 'guests', \"'\", 'faces', '&', 'amp', ';', 'they', 'have', 'little', 'recourse']\n",
      "Cleaned tokens:  ['VirginAmerica', 'it', 'really', 'aggressive', 'to', 'blast', 'obnoxious', 'entertainment', 'in', 'your', 'guests', 'faces', 'amp', 'they', 'have', 'little', 'recourse']\n"
     ]
    }
   ],
   "source": [
    "# Tokenize the text column\n",
    "word_tokens = [word_tokenize(review) for review in tweets.text]\n",
    "print('Original tokens: ', word_tokens[0])\n",
    "\n",
    "# Filter out non-letter characters\n",
    "cleaned_tokens = [[word for word in item if word.isalpha()] for item in word_tokens]\n",
    "print('Cleaned tokens: ', cleaned_tokens[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3     @VirginAmerica it's really aggressive to blast...\n",
       "4     @VirginAmerica and it's a really big bad thing...\n",
       "5     @VirginAmerica seriously would pay $30 a fligh...\n",
       "15        @VirginAmerica SFO-PDX schedule is still MIA.\n",
       "17    @VirginAmerica  I flew from NYC to SFO last we...\n",
       "20    @VirginAmerica why are your first fares in May...\n",
       "24    @VirginAmerica you guys messed up my seating.....\n",
       "25    @VirginAmerica status match program.  I applie...\n",
       "26    @VirginAmerica What happened 2 ur vegan food o...\n",
       "28    @VirginAmerica amazing to me that we can't get...\n",
       "Name: text, dtype: object"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tweets.text[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Last item in alphabetic list:  ['VirginAmerica', 'seriously', 'would', 'pay', 'a', 'flight', 'for', 'seats', 'that', 'did', 'have', 'this', 'playing', 'it', 'really', 'the', 'only', 'bad', 'thing', 'about', 'flying', 'VA']\n",
      "\n",
      "Last item in list of alphanumerics:  ['VirginAmerica', 'seriously', 'would', 'pay', '30', 'a', 'flight', 'for', 'seats', 'that', 'did', 'have', 'this', 'playing', 'it', 'really', 'the', 'only', 'bad', 'thing', 'about', 'flying', 'VA']\n",
      "\n",
      "Last item in the list of digits:  ['30']\n"
     ]
    }
   ],
   "source": [
    "# Create a list of lists, containing the tokens from list_tweets\n",
    "tokens = [word_tokenize(item) for item in tweets.text[:3].tolist()]\n",
    "\n",
    "# Remove characters and digits , i.e. retain only letters\n",
    "letters = [[word for word in item if word.isalpha()] for item in tokens]\n",
    "# Remove characters, i.e. retain only letters and digits\n",
    "let_digits = [[word for word in item if word.isalnum()] for item in tokens]\n",
    "# Remove letters and characters, retain only digits\n",
    "digits = [[word for word in item if word.isdigit()] for item in tokens]\n",
    "\n",
    "# Print the last item in each list\n",
    "print('Last item in alphabetic list: ', letters[2])\n",
    "print('\\nLast item in list of alphanumerics: ', let_digits[2])\n",
    "print('\\nLast item in the list of digits: ', digits[2])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Length of vectorizer 1:  10836\n",
      "Length of vectorizer 2:  8917\n"
     ]
    }
   ],
   "source": [
    "# Build the first vectorizer\n",
    "vect1 = CountVectorizer().fit(tweets.text)\n",
    "vect1.transform(tweets.text)\n",
    "\n",
    "# Build the second vectorizer\n",
    "vect2 = CountVectorizer(token_pattern=r'\\b[^\\d\\W][^\\d\\W]+\\b').fit(tweets.text)\n",
    "vect2.transform(tweets.text)\n",
    "\n",
    "# Print out the length of each vectorizer\n",
    "print('Length of vectorizer 1: ', len(vect1.get_feature_names()))\n",
    "print('Length of vectorizer 2: ', len(vect2.get_feature_names()))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Stemming and Lemmatization\n",
    "\n",
    "\"**Stemming** usually refers to a crude heuristic process that chops off the ends of words in the hope of achieving this goal correctly most of the time, and often includes the removal of derivational affixes. \n",
    "\n",
    "**Lemmatization** usually refers to doing things properly with the use of a vocabulary and morphological analysis of words, normally aiming to remove inflectional endings only and to return the base or dictionary form of a word, which is known as the lemma.\" [Stanford NLP](https://nlp.stanford.edu/IR-book/html/htmledition/stemming-and-lemmatization-1.html)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create a string\n",
    "morpheus = '''\n",
    "Have you ever had a dream, Neo, that you seemed so sure it was real? \n",
    "But if you were unable to wake up from that dream, how would you tell \n",
    "the difference between the dream world & the real world?\"\n",
    "\n",
    "'''\n",
    "# Initiate PorterStemmer and WordNetLemmatizer from NLTK\n",
    "porter = PorterStemmer()\n",
    "WNlemmatizer = WordNetLemmatizer()\n",
    "\n",
    "# Tokenize the string\n",
    "tokens = word_tokenize(morpheus)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "import time"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Time taken for stemming in seconds:  0.0008540153503417969\n",
      "Stemmed tokens:  ['have', 'you', 'ever', 'had', 'a', 'dream', ',', 'neo', ',', 'that', 'you', 'seem', 'so', 'sure', 'it', 'wa', 'real', '?', 'but', 'if', 'were', 'unabl', 'to', 'wake', 'up', 'from', 'that', 'dream', ',', 'how', 'would', 'you', 'tell', 'the', 'differ', 'between', 'the', 'dream', 'world', '&', 'the', 'real', 'world', '?']\n"
     ]
    }
   ],
   "source": [
    "# Log the start time\n",
    "start_time = time.time()\n",
    "\n",
    "# Build a stemmed list\n",
    "stemmed_tokens = [porter.stem(token) for token in tokens] \n",
    "\n",
    "# Log the end time\n",
    "end_time = time.time()\n",
    "\n",
    "print('Time taken for stemming in seconds: ', end_time - start_time)\n",
    "print('Stemmed tokens: ', stemmed_tokens) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Time taken for lemmatizing in seconds:  1.5948243141174316\n",
      "Lemmatized tokens:  ['Have', 'you', 'ever', 'had', 'a', 'dream', ',', 'Neo', ',', 'that', 'you', 'seemed', 'so', 'sure', 'it', 'wa', 'real', '?', 'But', 'if', 'were', 'unable', 'to', 'wake', 'up', 'from', 'that', 'dream', ',', 'how', 'would', 'you', 'tell', 'the', 'difference', 'between', 'the', 'dream', 'world', '&', 'the', 'real', 'world', '?']\n"
     ]
    }
   ],
   "source": [
    "# Log the start time\n",
    "start_time = time.time()\n",
    "\n",
    "# Build a lemmatized list\n",
    "lem_tokens = [WNlemmatizer.lemmatize(token) for token in tokens]\n",
    "\n",
    "# Log the end time\n",
    "end_time = time.time()\n",
    "\n",
    "print('Time taken for lemmatizing in seconds: ', end_time - start_time)\n",
    "print('Lemmatized tokens: ', lem_tokens) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Lemmatization take much more time than stemming. However, stemming produces roots of the words which may be meaningless, whereas lemmatization produces actual words."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "deutsch = [\"Immer weniger Deutsche wollen auf dem Land leben, obwohl es dort genug Arbeit gibt.\",\n",
    "           \"Aber die öffentliche Infrastruktur ist schlechter als in der Stadt, und oft funktioniert das Internet nicht.\", \n",
    "           \"Das soll sich ändern.\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[['imm', 'wenig', 'deutsch', 'woll', 'auf', 'dem', 'land', 'leb', ',', 'obwohl', 'es', 'dort', 'genug', 'arbeit', 'gibt', '.'], ['aber', 'die', 'offent', 'infrastruktur', 'ist', 'schlecht', 'als', 'in', 'der', 'stadt', ',', 'und', 'oft', 'funktioniert', 'das', 'internet', 'nicht', '.'], ['das', 'soll', 'sich', 'and', '.']]\n"
     ]
    }
   ],
   "source": [
    "from nltk.stem import SnowballStemmer\n",
    "\n",
    "# Import the German SnowballStemmer\n",
    "GermanStemmer = SnowballStemmer(\"german\")\n",
    "\n",
    "# Create a list of tokens\n",
    "tokens = [word_tokenize(string) for string in deutsch] \n",
    "# Stem the list of tokens\n",
    "stemmed_tokens = [[GermanStemmer.stem(word) for word in token] for token in tokens]\n",
    "\n",
    "# Print the first item of the stemmed tokenss\n",
    "print(stemmed_tokens)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Applying to tweets:**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['@', 'virginamerica', 'it', \"'s\", 'realli', 'aggress', 'to', 'blast', 'obnoxi', '``', 'entertain', \"''\", 'in', 'your', 'guest', \"'\", 'face', '&', 'amp', ';', 'they', 'have', 'littl', 'recours']\n"
     ]
    }
   ],
   "source": [
    "# Call the stemmer\n",
    "porter = PorterStemmer()\n",
    "\n",
    "# Transform the array of tweets to tokens\n",
    "tokens = [word_tokenize(tweet) for tweet in tweets.text[:10]]\n",
    "# Stem the list of tokens\n",
    "stemmed_tokens = [[porter.stem(word) for word in tweet] for tweet in tokens] \n",
    "# Print the first element of the list\n",
    "print(stemmed_tokens[0])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Tf-Idf\n",
    "\n",
    "_Term Frequency (TF) :_ How often a given word appears within a document in the corpus.\n",
    "\n",
    "_Inverse Term Frequency (IDF):_  Log-ratio between the total number of documents and the number of documents that contain a specific word."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Count Vectorizer vs Tf-Idf:**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "   afraid  can  feel  know  now  out  re  that  there  you\n",
       "0       0    0     0     1    0    1   1     0      1    1\n",
       "1       0    1     1     0    1    0   0     0      0    1\n",
       "2       1    0     0     1    0    0   1     1      0    1"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "neo = [\"I know you're out there.\",\n",
    "       \"I can feel you now.\",\n",
    "       \"I know that you're afraid.\"]\n",
    "\n",
    "# Call the vectorizer and fit it\n",
    "neo_vect = CountVectorizer().fit(neo)\n",
    "\n",
    "# Create the tfidf representation\n",
    "neo_count = neo_vect.transform(neo)\n",
    "\n",
    "# Print the result \n",
    "pd.DataFrame(neo_count.toarray(), columns=neo_vect.get_feature_names())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
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       "     afraid       can      feel      know       now       out        re  \\\n",
       "0  0.000000  0.000000  0.000000  0.406192  0.000000  0.534093  0.406192   \n",
       "1  0.000000  0.546454  0.546454  0.000000  0.546454  0.000000  0.000000   \n",
       "2  0.534093  0.000000  0.000000  0.406192  0.000000  0.000000  0.406192   \n",
       "\n",
       "       that     there       you  \n",
       "0  0.000000  0.534093  0.315444  \n",
       "1  0.000000  0.000000  0.322745  \n",
       "2  0.534093  0.000000  0.315444  "
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "neo = [\"I know you're out there.\",\n",
    "       \"I can feel you now.\",\n",
    "       \"I know that you're afraid.\"]\n",
    "\n",
    "# Call the vectorizer and fit it\n",
    "neo_vect = TfidfVectorizer().fit(neo)\n",
    "\n",
    "# Create the tfidf representation\n",
    "neo_tfidf = neo_vect.transform(neo)\n",
    "\n",
    "# Print the result \n",
    "pd.DataFrame(neo_tfidf.toarray(), columns=neo_vect.get_feature_names())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Tweet Example"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
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       "    aa  agent  airline  airport  americanair       amp       bad  bag  \\\n",
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       "   baggage  bags  ...   ve  virginamerica  wait  waiting  want  way  weather  \\\n",
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       "\n",
       "   won  work  worst  \n",
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       "\n",
       "[5 rows x 100 columns]"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Define the vectorizer and specify the arguments\n",
    "my_pattern = r'\\b[^\\d\\W][^\\d\\W]+\\b'\n",
    "vect = TfidfVectorizer(ngram_range=(1, 2), max_features=100, token_pattern=my_pattern, stop_words=ENGLISH_STOP_WORDS).fit(tweets.text)\n",
    "\n",
    "# Transform the vectorizer\n",
    "X_txt = vect.transform(tweets.text)\n",
    "\n",
    "# Transform to a data frame and specify the column names\n",
    "X = pd.DataFrame(X_txt.toarray(), columns=vect.get_feature_names())\n",
    "X.head()"
   ]
  }
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