Quora.ipynb

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   "source": [
    "![Quora-1.png](attachment:Quora-1.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
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    "id": "J6rnhr2Xs5Vs"
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   "source": [
    "<h1 style=\"text-align:center;font-size:30px;\" > Quora Question Pairs </h1>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "o9fciGc7s5Vu"
   },
   "source": [
    "<h1> 1. Business Problem </h1>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "LRzmxjKxs5Vw"
   },
   "source": [
    "<h2> 1.1 Description </h2>"
   ]
  },
  {
   "cell_type": "markdown",
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    "id": "1nlaIYe9s5Vx"
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   "source": [
    "<p>Quora is a place to gain and share knowledge—about anything. It’s a platform to ask questions and connect with people who contribute unique insights and quality answers. This empowers people to learn from each other and to better understand the world.</p>\n",
    "<p>\n",
    "Over 100 million people visit Quora every month, so it's no surprise that many people ask similarly worded questions. Multiple questions with the same intent can cause seekers to spend more time finding the best answer to their question, and make writers feel they need to answer multiple versions of the same question. Quora values canonical questions because they provide a better experience to active seekers and writers, and offer more value to both of these groups in the long term.\n",
    "</p>\n",
    "<br>\n",
    "> Credits: Kaggle \n"
   ]
  },
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   "source": [
    "__ Problem Statement __\n",
    "- Identify which questions asked on Quora are duplicates of questions that have already been asked. \n",
    "- This could be useful to instantly provide answers to questions that have already been answered. \n",
    "- We are tasked with predicting whether a pair of questions are duplicates or not. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "34hYn911s5V0"
   },
   "source": [
    "<h2> 1.2 Sources/Useful Links</h2>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "7YIjqVPgs5V4"
   },
   "source": [
    "- Source : https://www.kaggle.com/c/quora-question-pairs\n",
    "<br><br>____ Useful Links ____\n",
    "- Discussions : https://www.kaggle.com/anokas/data-analysis-xgboost-starter-0-35460-lb/comments\n",
    "- Kaggle Winning Solution and other approaches: https://www.dropbox.com/sh/93968nfnrzh8bp5/AACZdtsApc1QSTQc7X0H3QZ5a?dl=0\n",
    "- Blog 1 : https://engineering.quora.com/Semantic-Question-Matching-with-Deep-Learning\n",
    "- Blog 2 : https://towardsdatascience.com/identifying-duplicate-questions-on-quora-top-12-on-kaggle-4c1cf93f1c30"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "jlNRUR4Ws5V5"
   },
   "source": [
    "<h2>1.3 Real world/Business Objectives and Constraints </h2>"
   ]
  },
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   "cell_type": "markdown",
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   "source": [
    "1. The cost of a mis-classification can be very high.\n",
    "2. You would want a probability of a pair of questions to be duplicates so that you can choose any threshold of choice.\n",
    "3. No strict latency concerns.\n",
    "4. Interpretability is partially important."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "VIam5Aaks5V9"
   },
   "source": [
    "<h1>2. Machine Learning Probelm </h1>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "jnty9Bhls5V-"
   },
   "source": [
    "<h2> 2.1 Data </h2>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "rty1PZv3s5V_"
   },
   "source": [
    "<h3> 2.1.1 Data Overview </h3>"
   ]
  },
  {
   "cell_type": "markdown",
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   "source": [
    "<p> \n",
    "- Data will be in a file Train.csv <br>\n",
    "- Train.csv contains 5 columns : qid1, qid2, question1, question2, is_duplicate <br>\n",
    "- Size of Train.csv - 60MB <br>\n",
    "- Number of rows in Train.csv = 404,290\n",
    "</p>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "v9grbSNds5WC"
   },
   "source": [
    "<h3> 2.1.2 Example Data point </h3>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
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   },
   "source": [
    "<pre>\n",
    "\"id\",\"qid1\",\"qid2\",\"question1\",\"question2\",\"is_duplicate\"\n",
    "\"0\",\"1\",\"2\",\"What is the step by step guide to invest in share market in india?\",\"What is the step by step guide to invest in share market?\",\"0\"\n",
    "\"1\",\"3\",\"4\",\"What is the story of Kohinoor (Koh-i-Noor) Diamond?\",\"What would happen if the Indian government stole the Kohinoor (Koh-i-Noor) diamond back?\",\"0\"\n",
    "\"7\",\"15\",\"16\",\"How can I be a good geologist?\",\"What should I do to be a great geologist?\",\"1\"\n",
    "\"11\",\"23\",\"24\",\"How do I read and find my YouTube comments?\",\"How can I see all my Youtube comments?\",\"1\"\n",
    "</pre>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
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    "id": "9qPVfeEjs5WF"
   },
   "source": [
    "<h2> 2.2 Mapping the real world problem to an ML problem </h2>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "JfBn0LYPs5WI"
   },
   "source": [
    "<h3> 2.2.1 Type of Machine Leaning Problem </h3>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "QEqiUD_Ps5WJ"
   },
   "source": [
    "<p> It is a binary classification problem, for a given pair of questions we need to predict if they are duplicate or not. </p>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "keZOL1las5WL"
   },
   "source": [
    "<h3> 2.2.2 Performance Metric </h3>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "YHktaBrMs5WN"
   },
   "source": [
    "Source: https://www.kaggle.com/c/quora-question-pairs#evaluation\n",
    "\n",
    "Metric(s): \n",
    "* log-loss : https://www.kaggle.com/wiki/LogarithmicLoss\n",
    "* Binary Confusion Matrix"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "FmDMBWJjs5WO"
   },
   "source": [
    "<h2> 2.3 Train and Test Construction </h2>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "l7PcvKQss5WQ"
   },
   "source": [
    "<p>  </p>\n",
    "<p> We build train and test by randomly splitting in the ratio of 70:30 or 80:20 whatever we choose as we have sufficient points to work with. </p>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "cW_MVIlps5WQ"
   },
   "source": [
    "<h1>3. Exploratory Data Analysis </h1>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "sNzZdmBJs5WS",
    "outputId": "0e1df4ed-4a74-4b0e-e84e-1b3862bbf55d"
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   "outputs": [
    {
     "data": {
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   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "from subprocess import check_output\n",
    "%matplotlib inline\n",
    "import plotly.offline as py\n",
    "py.init_notebook_mode(connected=True)\n",
    "import plotly.graph_objs as go\n",
    "import plotly.tools as tls\n",
    "import os\n",
    "import gc\n",
    "\n",
    "import re\n",
    "from nltk.corpus import stopwords\n",
    "import distance\n",
    "from nltk.stem import PorterStemmer\n",
    "from bs4 import BeautifulSoup"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "__T8jddGs5Wc"
   },
   "source": [
    "<h2> 3.1 Reading data and basic stats </h2>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "colab": {},
    "colab_type": "code",
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    "outputId": "2e17a7bc-9a5b-4c43-d35b-081cc9f92528"
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   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of data points: 404290\n"
     ]
    }
   ],
   "source": [
    "df = pd.read_csv(\"train.csv\")\n",
    "\n",
    "print(\"Number of data points:\",df.shape[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "34zXGW8gs5Wj",
    "outputId": "ab7d570a-9eeb-477a-b7cb-663ff6fd04fa"
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   "outputs": [
    {
     "data": {
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "\n",
       "    .dataframe thead th {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>qid1</th>\n",
       "      <th>qid2</th>\n",
       "      <th>question1</th>\n",
       "      <th>question2</th>\n",
       "      <th>is_duplicate</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>What is the step by step guide to invest in sh...</td>\n",
       "      <td>What is the step by step guide to invest in sh...</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>What is the story of Kohinoor (Koh-i-Noor) Dia...</td>\n",
       "      <td>What would happen if the Indian government sto...</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>How can I increase the speed of my internet co...</td>\n",
       "      <td>How can Internet speed be increased by hacking...</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>7</td>\n",
       "      <td>8</td>\n",
       "      <td>Why am I mentally very lonely? How can I solve...</td>\n",
       "      <td>Find the remainder when [math]23^{24}[/math] i...</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>9</td>\n",
       "      <td>10</td>\n",
       "      <td>Which one dissolve in water quikly sugar, salt...</td>\n",
       "      <td>Which fish would survive in salt water?</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   id  qid1  qid2                                          question1  \\\n",
       "0   0     1     2  What is the step by step guide to invest in sh...   \n",
       "1   1     3     4  What is the story of Kohinoor (Koh-i-Noor) Dia...   \n",
       "2   2     5     6  How can I increase the speed of my internet co...   \n",
       "3   3     7     8  Why am I mentally very lonely? How can I solve...   \n",
       "4   4     9    10  Which one dissolve in water quikly sugar, salt...   \n",
       "\n",
       "                                           question2  is_duplicate  \n",
       "0  What is the step by step guide to invest in sh...             0  \n",
       "1  What would happen if the Indian government sto...             0  \n",
       "2  How can Internet speed be increased by hacking...             0  \n",
       "3  Find the remainder when [math]23^{24}[/math] i...             0  \n",
       "4            Which fish would survive in salt water?             0  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
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   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 404290 entries, 0 to 404289\n",
      "Data columns (total 6 columns):\n",
      "id              404290 non-null int64\n",
      "qid1            404290 non-null int64\n",
      "qid2            404290 non-null int64\n",
      "question1       404289 non-null object\n",
      "question2       404288 non-null object\n",
      "is_duplicate    404290 non-null int64\n",
      "dtypes: int64(4), object(2)\n",
      "memory usage: 18.5+ MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "HHHTGTzws5Ww"
   },
   "source": [
    "We are given a minimal number of data fields here, consisting of:\n",
    "\n",
    "- id:  Looks like a simple rowID\n",
    "- qid{1, 2}:  The unique ID of each question in the pair\n",
    "- question{1, 2}:  The actual textual contents of the questions.\n",
    "- is_duplicate:  The label that we are trying to predict - whether the two questions are duplicates of each other."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "ZulqVzTDs5Wx"
   },
   "source": [
    "<h3> 3.2.1 Distribution of data points among output classes</h3>\n",
    "- Number of duplicate(smilar) and non-duplicate(non similar) questions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
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    "id": "YHp64yNjs5Wx",
    "outputId": "361ddf04-d545-45f9-dbe2-8bebd695e8da"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x129e7a358>"
      ]
     },
     "execution_count": 5,
     "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": [
    "df.groupby(\"is_duplicate\")['id'].count().plot.bar()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "-usI2K2bs5W4",
    "outputId": "ff0a6a8b-65ad-487a-d5ec-df3c223ba620"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "~> Total number of question pairs for training:\n",
      "   404290\n"
     ]
    }
   ],
   "source": [
    "print('~> Total number of question pairs for training:\\n   {}'.format(len(df)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "YiPia6Pjs5W_",
    "outputId": "3cde4cec-4314-4c14-e807-b35e969bf9e8"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "~> Question pairs are not Similar (is_duplicate = 0):\n",
      "   63.08%\n",
      "\n",
      "~> Question pairs are Similar (is_duplicate = 1):\n",
      "   36.92%\n"
     ]
    }
   ],
   "source": [
    "print('~> Question pairs are not Similar (is_duplicate = 0):\\n   {}%'.format(100 - round(df['is_duplicate'].mean()*100, 2)))\n",
    "print('\\n~> Question pairs are Similar (is_duplicate = 1):\\n   {}%'.format(round(df['is_duplicate'].mean()*100, 2)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "wGX03QVRs5XF"
   },
   "source": [
    "<h3> 3.2.2 Number of unique questions </h3>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "VOKa6aU2s5XG",
    "outputId": "8f644b1d-27c0-4d63-84e2-bb2a42419be2"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total number of  Unique Questions are: 537933\n",
      "\n",
      "Number of unique questions that appear more than one time: 111780 (20.77953945937505%)\n",
      "\n",
      "Max number of times a single question is repeated: 157\n",
      "\n"
     ]
    }
   ],
   "source": [
    "qids = pd.Series(df['qid1'].tolist() + df['qid2'].tolist())\n",
    "unique_qs = len(np.unique(qids))\n",
    "qs_morethan_onetime = np.sum(qids.value_counts() > 1)\n",
    "print ('Total number of  Unique Questions are: {}\\n'.format(unique_qs))\n",
    "#print len(np.unique(qids))\n",
    "\n",
    "print ('Number of unique questions that appear more than one time: {} ({}%)\\n'.format(qs_morethan_onetime,qs_morethan_onetime/unique_qs*100))\n",
    "\n",
    "print ('Max number of times a single question is repeated: {}\\n'.format(max(qids.value_counts()))) \n",
    "\n",
    "q_vals=qids.value_counts()\n",
    "\n",
    "q_vals=q_vals.values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "plcvbd4Cs5XM",
    "outputId": "8e137cc1-e0c4-44f4-9cc2-703302206d4f"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "x = [\"unique_questions\" , \"Repeated Questions\"]\n",
    "y =  [unique_qs , qs_morethan_onetime]\n",
    "\n",
    "plt.figure(figsize=(10, 6))\n",
    "plt.title (\"Plot representing unique and repeated questions  \")\n",
    "sns.barplot(x,y)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "G-CwGaMms5XS"
   },
   "source": [
    "<h3>3.2.3 Checking for Duplicates </h3>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "YCiDBHm5s5XT",
    "outputId": "d8011926-4086-4c9a-9fcf-59663a584ec4"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of duplicate questions 0\n"
     ]
    }
   ],
   "source": [
    "#checking whether there are any repeated pair of questions\n",
    "\n",
    "pair_duplicates = df[['qid1','qid2','is_duplicate']].groupby(['qid1','qid2']).count().reset_index()\n",
    "\n",
    "print (\"Number of duplicate questions\",(pair_duplicates).shape[0] - df.shape[0])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "iaHTnnt8s5XX"
   },
   "source": [
    "<h3> 3.2.4 Number of occurrences of each question </h3>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "dPZwk-C8s5Xa",
    "outputId": "0d6d5978-2306-4ed3-cf27-f2a0b974e47d"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Maximum number of times a single question is repeated: 157\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(20, 10))\n",
    "\n",
    "plt.hist(qids.value_counts(), bins=160)\n",
    "\n",
    "plt.yscale('log', nonposy='clip')\n",
    "\n",
    "plt.title('Log-Histogram of question appearance counts')\n",
    "\n",
    "plt.xlabel('Number of occurences of question')\n",
    "\n",
    "plt.ylabel('Number of questions')\n",
    "\n",
    "print ('Maximum number of times a single question is repeated: {}\\n'.format(max(qids.value_counts()))) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "h_WdYxlYs5Xj"
   },
   "source": [
    "<h3> 3.2.5 Checking for NULL values </h3>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "r0x1gR2fs5Xk",
    "outputId": "721aef48-e628-40c6-d567-25466f4283e1"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "            id    qid1    qid2                         question1  \\\n",
      "105780  105780  174363  174364    How can I develop android app?   \n",
      "201841  201841  303951  174364  How can I create an Android app?   \n",
      "363362  363362  493340  493341                               NaN   \n",
      "\n",
      "                                                question2  is_duplicate  \n",
      "105780                                                NaN             0  \n",
      "201841                                                NaN             0  \n",
      "363362  My Chinese name is Haichao Yu. What English na...             0  \n"
     ]
    }
   ],
   "source": [
    "#Checking whether there are any rows with null values\n",
    "nan_rows = df[df.isnull().any(1)]\n",
    "print (nan_rows)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "CCYmufv6s5Xo"
   },
   "source": [
    "- There are two rows with null values in question2 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "yLBRyACgs5Xp",
    "outputId": "076046a9-1510-41ef-cf98-15b38661dca4"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Empty DataFrame\n",
      "Columns: [id, qid1, qid2, question1, question2, is_duplicate]\n",
      "Index: []\n"
     ]
    }
   ],
   "source": [
    "# Filling the null values with ' '\n",
    "df = df.fillna('')\n",
    "nan_rows = df[df.isnull().any(1)]\n",
    "print (nan_rows)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "l9Qcl5xfs5Xs"
   },
   "source": [
    "<h2>3.3 Basic Feature Extraction (before cleaning) </h2>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "RRzvPYzGs5Xu"
   },
   "source": [
    "Let us now construct a few features like:\n",
    " - ____freq_qid1____ = Frequency of qid1's\n",
    " - ____freq_qid2____ = Frequency of qid2's \n",
    " - ____q1len____ = Length of q1\n",
    " - ____q2len____ = Length of q2\n",
    " - ____q1_n_words____ = Number of words in Question 1\n",
    " - ____q2_n_words____ = Number of words in Question 2\n",
    " - ____word_Common____ = (Number of common unique words in Question 1 and Question 2)\n",
    " - ____word_Total____ =(Total num of words in Question 1 + Total num of words in Question 2)\n",
    " - ____word_share____ = (word_common)/(word_Total)\n",
    " - ____freq_q1+freq_q2____ = sum total of frequency of qid1 and qid2 \n",
    " - ____freq_q1-freq_q2____ = absolute difference of frequency of qid1 and qid2 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "Iq4DZ-rYs5Xv",
    "outputId": "d34e66da-d84b-49ea-8852-4beb9da688ba"
   },
   "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>id</th>\n",
       "      <th>qid1</th>\n",
       "      <th>qid2</th>\n",
       "      <th>question1</th>\n",
       "      <th>question2</th>\n",
       "      <th>is_duplicate</th>\n",
       "      <th>freq_qid1</th>\n",
       "      <th>freq_qid2</th>\n",
       "      <th>q1len</th>\n",
       "      <th>q2len</th>\n",
       "      <th>q1_n_words</th>\n",
       "      <th>q2_n_words</th>\n",
       "      <th>word_Common</th>\n",
       "      <th>word_Total</th>\n",
       "      <th>word_share</th>\n",
       "      <th>freq_q1+q2</th>\n",
       "      <th>freq_q1-q2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
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       "      <td>0</td>\n",
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       "      <td>What is the step by step guide to invest in sh...</td>\n",
       "      <td>What is the step by step guide to invest in sh...</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>What is the story of Kohinoor (Koh-i-Noor) Dia...</td>\n",
       "      <td>What would happen if the Indian government sto...</td>\n",
       "      <td>0</td>\n",
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       "      <td>4.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>How can I increase the speed of my internet co...</td>\n",
       "      <td>How can Internet speed be increased by hacking...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>73</td>\n",
       "      <td>59</td>\n",
       "      <td>14</td>\n",
       "      <td>10</td>\n",
       "      <td>4.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>0.166667</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>7</td>\n",
       "      <td>8</td>\n",
       "      <td>Why am I mentally very lonely? How can I solve...</td>\n",
       "      <td>Find the remainder when [math]23^{24}[/math] i...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>50</td>\n",
       "      <td>65</td>\n",
       "      <td>11</td>\n",
       "      <td>9</td>\n",
       "      <td>0.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>9</td>\n",
       "      <td>10</td>\n",
       "      <td>Which one dissolve in water quikly sugar, salt...</td>\n",
       "      <td>Which fish would survive in salt water?</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>76</td>\n",
       "      <td>39</td>\n",
       "      <td>13</td>\n",
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       "      <td>2.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>0.100000</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   id  qid1  qid2                                          question1  \\\n",
       "0   0     1     2  What is the step by step guide to invest in sh...   \n",
       "1   1     3     4  What is the story of Kohinoor (Koh-i-Noor) Dia...   \n",
       "2   2     5     6  How can I increase the speed of my internet co...   \n",
       "3   3     7     8  Why am I mentally very lonely? How can I solve...   \n",
       "4   4     9    10  Which one dissolve in water quikly sugar, salt...   \n",
       "\n",
       "                                           question2  is_duplicate  freq_qid1  \\\n",
       "0  What is the step by step guide to invest in sh...             0          1   \n",
       "1  What would happen if the Indian government sto...             0          4   \n",
       "2  How can Internet speed be increased by hacking...             0          1   \n",
       "3  Find the remainder when [math]23^{24}[/math] i...             0          1   \n",
       "4            Which fish would survive in salt water?             0          3   \n",
       "\n",
       "   freq_qid2  q1len  q2len  q1_n_words  q2_n_words  word_Common  word_Total  \\\n",
       "0          1     66     57          14          12         10.0        23.0   \n",
       "1          1     51     88           8          13          4.0        20.0   \n",
       "2          1     73     59          14          10          4.0        24.0   \n",
       "3          1     50     65          11           9          0.0        19.0   \n",
       "4          1     76     39          13           7          2.0        20.0   \n",
       "\n",
       "   word_share  freq_q1+q2  freq_q1-q2  \n",
       "0    0.434783           2           0  \n",
       "1    0.200000           5           3  \n",
       "2    0.166667           2           0  \n",
       "3    0.000000           2           0  \n",
       "4    0.100000           4           2  "
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "if os.path.isfile('df_fe_without_preprocessing_train.csv'):\n",
    "    df = pd.read_csv(\"df_fe_without_preprocessing_train.csv\",encoding='latin-1')\n",
    "else:\n",
    "    df['freq_qid1'] = df.groupby('qid1')['qid1'].transform('count') \n",
    "    df['freq_qid2'] = df.groupby('qid2')['qid2'].transform('count')\n",
    "    df['q1len'] = df['question1'].str.len() \n",
    "    df['q2len'] = df['question2'].str.len()\n",
    "    df['q1_n_words'] = df['question1'].apply(lambda row: len(row.split(\" \")))\n",
    "    df['q2_n_words'] = df['question2'].apply(lambda row: len(row.split(\" \")))\n",
    "\n",
    "    def normalized_word_Common(row):\n",
    "        w1 = set(map(lambda word: word.lower().strip(), row['question1'].split(\" \")))\n",
    "        w2 = set(map(lambda word: word.lower().strip(), row['question2'].split(\" \")))    \n",
    "        return 1.0 * len(w1 & w2)\n",
    "    df['word_Common'] = df.apply(normalized_word_Common, axis=1)\n",
    "\n",
    "    def normalized_word_Total(row):\n",
    "        w1 = set(map(lambda word: word.lower().strip(), row['question1'].split(\" \")))\n",
    "        w2 = set(map(lambda word: word.lower().strip(), row['question2'].split(\" \")))    \n",
    "        return 1.0 * (len(w1) + len(w2))\n",
    "    df['word_Total'] = df.apply(normalized_word_Total, axis=1)\n",
    "\n",
    "    def normalized_word_share(row):\n",
    "        w1 = set(map(lambda word: word.lower().strip(), row['question1'].split(\" \")))\n",
    "        w2 = set(map(lambda word: word.lower().strip(), row['question2'].split(\" \")))    \n",
    "        return 1.0 * len(w1 & w2)/(len(w1) + len(w2))\n",
    "    df['word_share'] = df.apply(normalized_word_share, axis=1)\n",
    "\n",
    "    df['freq_q1+q2'] = df['freq_qid1']+df['freq_qid2']\n",
    "    df['freq_q1-q2'] = abs(df['freq_qid1']-df['freq_qid2'])\n",
    "\n",
    "    df.to_csv(\"df_fe_without_preprocessing_train.csv\", index=False)\n",
    "\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "-zLujovVs5X3"
   },
   "source": [
    "<h3> 3.3.1 Analysis of some of the extracted features </h3>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "zRIFQTkCs5X3"
   },
   "source": [
    "- Here are some questions have only one single words."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "jSS0X82Ds5X5",
    "outputId": "5dacd7b2-d955-4435-9639-f1c6acd9b580"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Minimum length of the questions in question1 :  1\n",
      "Minimum length of the questions in question2 :  1\n",
      "Number of Questions with minimum length [question1] : 67\n",
      "Number of Questions with minimum length [question2] : 24\n"
     ]
    }
   ],
   "source": [
    "print (\"Minimum length of the questions in question1 : \" , min(df['q1_n_words']))\n",
    "\n",
    "print (\"Minimum length of the questions in question2 : \" , min(df['q2_n_words']))\n",
    "\n",
    "print (\"Number of Questions with minimum length [question1] :\", df[df['q1_n_words']== 1].shape[0])\n",
    "print (\"Number of Questions with minimum length [question2] :\", df[df['q2_n_words']== 1].shape[0])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "kFzTIHW3s5YB"
   },
   "source": [
    "<h4> 3.3.1.1 Feature: word_share </h4>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "s4rwGLFDs5YD",
    "outputId": "0103aaa0-3f5a-4eb4-cd22-164a57d7aef0"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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Xl2Bsbsz8GpsGoKSkxHESOZHly55nZnWKsnDu7qAxf0SCVDrNqlWrXEcRERE5LpVqHykrK4V0wnWMk2LSSULhMOFw2HUUOY79+/dTv6uBeSNyc+nHgOlVKcrCfdcciIiI+JVKtY+Ul5Vh0jnyY/p0gpKSUtcp5AReeOEFAOaNzI0XascTDMA5w+OsXLGcdDrtOo6IiMgxqVT7yKhRowjFOyEHDrsIxjqpqRnpOoacwIoVyxlTZhlbmhtLik5k3ogE7Z1dbN261XUUERGRY1Kp9pEFCxZAvJtAzOf7P6cTBLubuOjCC10nkeM4ePAga9esZf6ImOsoQ+KcEUlCAbSNo4iI+JZKtY9c2F9Sg+3+PgI81LkfrHc4r/jPH/7wB6yX5i3j86NUl4Utl42J8egjf+XQoUOu44iIiLyKSrWPjBo1ikmTJxPu9HepDnY0EikuZu7cua6jyDG0tbXx54eXcunoGCNLcn/px4BrJveSTqdZsmSJ6ygiIiKvolLtM6+79FKC3U2Q8umODdYS6djDhRdcoJ0/fGrJkiUkEkmundzrOsqQGlXicfGoOH/60//Q1tbmOo6IiMgrqFT7zGWXXQaeR6h9t+soxxToPohNRFm4cKHrKHIMBw4c4MEHH+CS0XHGluXPLPWA66ZESSYS/OIXv3AdRURE5BVUqn1m1qxZjBgxklBbg+soxxRu20UwGOSSSy5xHUWOYfHixeCluP6MqOsoGTGuzOOK8b38+eGHqa+vdx1HRETkMJVqnwkEArzhDZcT7twLftuz2lqKOnazYMECysvLXaeRo9TW1vLkk09y9cQoI4vzb5Z6wP+Z2ktxyPLjH/3IdRQREZHDVKp9qG8JSJpQx17XUV4h0NsKsS4t/fCp+37+c6ojcO2k/FpLfbSKsOUdk3t4cdUqNmzY4DqOiIgIoFLtS3PnzqW0rIxQxx7XUV4h1N6X5+KLL3acRI5WV1fHmrVruXJCD8Uh12ky74rxMcqLYMmSP7iOIiIiAqhU+1IoFOLiiy6iqLPRV6crhjsamTFzJiNGjHAdRY6yZMkSIiHDG8f5dNeYIRYJwhXjoixbtozGRn9vQSkiIoVBpdqnLr30Umyil0BPs+soAJhkL4Hug7zu0ktdR5GjtLS08OQTT7BwTJSysH9ehGXam8fHCBp46KGHXEcRERFRqfarCy+8EGMMoQ5/zMIF+9d3X3TRRY6TyNFWr15NKp1m4djCmKUeUB2xnDsizoplz7uOIiIiolLtV5WVlUyfMYNw5z7XUQAIde6jvKKCGTNmuI4iR9mwYQNlYcPE8rTrKFk3szrFgYPNHDx40HUUEREpcCrVPnbhBRf0Lf9IJdwGsZairv1csGABgYC+ZPxmw0vrObMyTsC4TpJ9M6v6tp3cuHGj4yQiIlLo1JB8bMGCBWAtoS63s9WB3nZsoqcvj/hKd3c3exr3Mr0q5TqKE5PK00SCsGXLFtdRRESkwKlU+9icOXMoKooQ7NzvNEewfwnK+eef7zSHvFpXVxcAVUX5e9jLiQQDUF70938HERERV1SqfSwcDnPOOWcT7j7gNEewaz+jx4xhzJgxTnPIq/X29h30UhIqnF0/jlYStESj+Xksu4iI5A6Vap+bP38+JtqGSTo6Jc96FHU3cf5557kZX05ooEwWBwu3VEeCaZVqERFxTqXa5+bNmwf0zRa7EIi2YlNx5s+f72R8kddiAOujQ5JERKQwqVT73MyZM4lEigl2NTkZf2Dcc88918n4cmIVFRUAdCcLcOuPft2pEFVVVa5jiIhIgVOp9rlQKMTs2WcR7nGzD2+wu4maUaMYNWqUk/HlxCorKwHoSRXut3JPyhx+cSEiIuJK4T4T55BzzjkHE22FdDK7A1tLUbSZc885J7vjykkbKJNdicKcqfYs9CTs4RcXIiIirqhU54C5c+eCtQS7sztbbRLd2HhP3/jiS6FQiOqqStrihfmt3JEweBZGjhzpOoqIiBS4wnwmzjFnnXUWAMGe5qyOG+zuG2/27NlZHVcGZ9SoUbQWaKlujQUBtDxJREScK8xn4hxTXl7O6DFjCURbszpuINpCMBhk6tSpWR1XBmf0mLG0JsKuYzjR0v9iQqVaRERcU6nOEbNmziDcm91SHYy2MnnKFMLhwixsuaKmpqZgZ6oHlr1o+YeIiLhWmM/EOejMM8+EWCekEtkZ0FrCsVZmTJ+enfHklA0fPpzepCWRdp0k+zoSAULBoC5UFBER51Sqc8S0adMACMTaszNgOoFN9GrpRw4YNmwY0FcwC01H3FBdVUkgUHh/dxER8Rc9E+WIMWPGABBIdGdlvEC8C4CxY8dmZTw5dQOlurMQS3UywLDhw13HEBERUanOFaNHjwbAxHuyMt5AeR8YV/xrYM17ugBP6vYshMNFrmOIiIioVOeK8vJySsvKsjZTPVDeB2bIxb88zwPAFOD5LwHAs57rGCIiIirVuWT48OGYZG9WxjLJXgK6ACynFOI3szFgbQFO0YuIiO8U4vNwziqOFGO87GzxYGyKoqIIphCnP3NMd3ffTy8iwcIrl5Ggpbuz03UMERERQq4DyMkrLikGLzvLP/DSRCKR7Iwlp2Xnzp0EDYwpLbw99caXpVm1q4lYLEZxcbHrOCIir7R48avvW7Qo+zkkKzRTnUOKIxECNksz1V6KSEQXgOWCuro6xpV7hArwu3lieRprLfX19a6jiIhIgSvAp+HcZfoWkGZnMGsxRl8efmetZUfddiaUJl1HcWJiWQroe2EhIiLiUsZbkzHmKmPMNmNMnTHm1hM87h+MMdYYsyDTmXJVLBbDCwSzMpYNhIjH41kZS07dvn37ONTSxplVKddRnKgp8aiMwEsvveQ6ioiIFLiMlmpjTBC4B7gamA28zxgz+xiPqwA+CazMZJ5cF+2NQSCcncGCIWKxWHbGklO2bt06AGYPK8yZ6oCB2VVx1q1ZrV1ARETEqUzPVF8I1Flrd1prE8DvgXcc43FfA+4C1OJOoLe3F5vFmepEPKai4nNr1qyhuhjGFeBFigNmD0/S0tbO7t27XUcREZEClulSPR7Yc8Ttxv77DjPGnAdMtNb+5UQfyBizyBiz2hizurm5eeiT5oCe7m5sMEsXDwaL8DyP3t7s7Istg2et5aX1azmrKl6QB78MGJilX79+veMkIiJSyJxeiWb6roT7LvCZ13qstXaxtXaBtXZBTU1N5sP5TDqdprOzAxsuycp4Xv84ra2tWRlPBq+5uZnWto6CXU89oKbYozJi2Lp1q+soIiJSwDJdqvcCE4+4PaH/vgEVwFzgaWPMLuBiYKkuVny19vZ2rLXYotKsjGfDfeOoVPvXli1bAJhWWdil2hiYWh5ny+Za11FERKSAZbpUrwKmG2OmGmOKgPcCSwfeaK3tsNaOtNZOsdZOAV4ArrPWrs5wrpwzUG5tKDsz1VYz1b63detWggGYVF7YpRr6Xlg07N5DNBp1HUVERApURku1tTYF3AT8DdgCLLHW1hpj7jDGXJfJsfNNU1MTAF6WZqq9orJXjCv+U7tpE5PL04S1nThnVKaw1h6evRcREcm2jB9Tbq39K/DXo+778nEe+4ZM58lVjY2NAHjFVdkZMBTBhCOHxxV/SSaTbN26lTeOSbiO4gtnVKYwQG1tLeeff77rOCIiUoA0x5Uj9u7diwkXQyiStTHTkUqVap+qq6sjkUwyvcAvUhxQFraML/fYtGmj6ygiIlKgVKpzRGNjI+lIRVbHTBdVsnuPSrUfrV27FoAZVYV56MuxzKhKsHHDBp0EKiIiTqhU54id9fWkI1la+tHPK6mi5VAzPT09WR1XXtsTj/0v06vSVEd0OM+ABTUJemNxXnjhBddRRESkAKlU54CWlhY62ttJl47I6rgD4+3YsSOr48qJ7dy5k527GrhktA7mOdLsYUmqI/D444+7jiIiIgVIpToH1NXVAeCVDs/quAPjDYwv/vD4448TMHDhKF2keKSAgYtG9fLCiuV0dXW5jiMiIgVGpToHDJTadJZLtQ2XYopKVKp9JJVK8bdH/srZwxNUFmnpx9FeNyZOMpXWbLWIiGSdSnUOqKurg+KKrO78AYAxJIuHsW37y9kdV45rxYoVtLS1c8X4mOsovjSlIs3UyjQP/+lPWKsXHSIikj0q1Tlgy9ZtJEuyu556QLp0BLvqd5JMapcJP3j44aUMK4ZzhuvzcTxvGNvLzl272Lx5s+soIiJSQFSqfa67u5sD+/fhZfkixQFe2QjS6TS7du1yMr783cGDB1m1ahWXj4kS1HfucV08Ok5xyPCXv/zFdRQRESkgemr2ucPrqctczVSPBGD79u1Oxpe/e/LJJ7G2b92wHF9JCM4f2cszTz9FIqGLOUVEJDtUqn2uvr4eAK8kuxcpDrCRCkwwfDiHuPP4Y//LGVVpRpd6rqP43iWjE/REe1m5cqXrKCIiUiBUqn1u9+7dmFARNlziJoAxeMVV7N692834AsCuXbuo27GTS0bpAsWTMWdYkkrtWS0iIlmkUu1zDbt3ky6uAmOcZUhFKqnf1eBsfIG//e1v/XtTa+nHyQgG4KKaXpYve57Ozk7XcUREpACoVPvcrl0NWT+e/GheSTWHmg8Si2mW1IVUKsWjj/yVc0ckdCz5ICwcqz2rRUQke1SqfSyRSNDacggvUuE0hxepwFrLgQMHnOYoVC+++CJt7R0sHKsXNYMxuSLN5AqPv/5Vu4CIiEjmqVT7WEtLCwBeUZnTHLZ//EOHDjnNUYistTz04INURuDcEdqberAWju2lrm4HGzZscB1FRETynEq1jzU3NwNgi0qd5vDCfeMP5JHsWb58OWvWruXaST2E9N06aAvHxhhWDD+4+27S6bTrOCIiksf0NO1jAzPDNux6prqvVGumOrvi8Tg//MHdjC/3eLOOJT8lkSC874wuXq6r45FHHnEdR0RE8phKtY+1t7cDYMPFboMEQphQhLa2Nrc5CsySJUvYf6CJfzqzW7PUp+GiUQlmVqf46eJ76erqch1HRETylJ6qfay7uxsAG4w4TgKEiujp6XGdomDU19fz61//igtqEswdrrXUp8MY+MD0brq6urj77rtdxxERkTylUu1j3d3dmGAYAu4/TV6w6HDJl8xKJpN8/ev/QUkgxT/P1L/5UJhUkeYdU6I89thjPP30067jiIhIHnLf1uS4uru7IVTkOgYA6UBYPzrPkt/85jfU1e3gX2Z0UVmkfamHytsn9zK1Ms13v/PtwzvriIiIDBWVah/r7e3FBsKuY/QJhIj29rpOkfcaGxu5//77ef2YGAtqEq7j5JVQABad1UVvtJt7773XdRwRGSqLF7/yl4gjKtU+5nme0+PJX8EYvLTnOkXeW7lyJZ7n8X+m+v8FzP3bS2noCtLQFeTOtZXcv93t1o8nY3xZmotrYqxYvqzv+0tERGSIhFwHkOPre9L3R6m2GNIqIRm3Zs0aRpVaakr8/2+9uztEb7rvdfnW9tx5fT57eJLnDvRQV1fHjBkzXMcREb84epZ70SI3OSRn5c4zYQHyPA/rm5nqAJ6nwzMyKZVKsX7dWmZXx11HyWuzh/XtprJ27VrHSUREJJ+oVPtY3wlwPinVGNJa/pFRsViMWCxOJKiLEzMpFIBgAFpbW11HERGRPKJS7WOe52H9UqqN0RrUDCsvL+eyhQt57kAJvSnXafLXU3uLSXtw1VVXuY4iIiJ5RKXax3x3oaKWf2Tce97zHqJJeG6/41M081TSg8f3lnLBBQuYNm2a6zgiIpJHVKp9zFcz1Rg8T8sSMm327NnMmTObv+0tRf/cQ29lUxHtcXj3u9/jOoqIiOQZlWofa21rxwb9cfiLDUXo7OjQEpAs+Id/uJ7mqKG21Sd7lOeRp/aVMnHCeBYsWOA6ioiI5BmVap9Kp9PsbdyDV1LlOgoAXnEViUScQ4cOuY6S917/+tdTWVHO0/sirqPklcbuIC93BLn27ddh/LKsSkRE8oZKtU8dPHiQZDKJV+yfUg2we/dux0nyX1FREVdd/TbWtkToTKj8DZVn9kcIBYO89a1vdR1FRETykEq1TzU0NAD4p1T3z5gP5JLMKisrI+3B/mjQdZS8sbcnRFmZ/099FBGR3KRS7VMGsR4EAAAgAElEQVSrV6+GQJB06XDXUQCwoRKIlLNq1SrXUfLe6tWr+dUvf8kFNXFmVGlvvaHyf6f1EO3u4qtf+QqplP5dT5cxptoY86AxZqsxZosx5hLXmUREXFKp9qF0Os3jTzxJsnI8+ORCRYwhUT2FVatW0dnZ6TpN3tq/fz+3f/UrjCtL8+Gzun2zo2I+mFqZ5l9ndrH+pZe49957XcfJB98HHrXWzgLOBbY4ziMi4pRKtQ9t3LiR9rZWUsP9tY9ucsQ00uk0zz33nOsoeWn79u3cesvnSMd7+OTcDopDrhPln9ePTXDlhF4eeOAB7rvvPuJxHQl/KowxVcBC4OcA1tqEtbbdbSoREbdUqn3oySefxATDpKonuo7yCl7pCCip4vEnnnAdJa90dXXxve99j498ZBHtTXv4xJwORpdq68JMee+ZUS4dHefXv/41//av/8LKlStdR8pFU4Fm4BfGmHXGmJ8ZY8pchxIRcUml2mfS6TRPP/MsicrxEPTZPsXGEK+ezEvr19PR0eE6Tc7zPI9HHnmED/zTP7J06Z94y/hevnlhK7OHa71vJoUCcMOcbj43rxOvYx+33HILX/riF2lqanIdLZeEgPOAH1tr5wM9wK1HPsAYs8gYs9oYs7q5udlFRhGRrFKp9pktW7bQ2dFOathk11GOKVU9Gc/zeOGFF1xHyWk7d+7kEx+/ibvuuosa084dC9p5/4woZWEdo5gtc4cn+Y8LWvm/03p4ccXz/L8PvJ/f/va3uojx5DQCjdbagWn+B+kr2YdZaxdbaxdYaxfU1NRkPaCISLapVPvMsmXLwARIVU1wHeWYvLKRmEgZzz//vOsoOWv//v18/KaPsbtuMx8+q5svnNfO5Iq061gFKRyAt0+J8Y2L2ji7qoef/vSn/PjHP3Ydy/estQeAPcaYmf13vQnY7DCSiIhzKtU+8+xzz5GuGAMhn56mZwzxyomsfPFFXeR1ClKpFF/72h14yV6+fF4bl42NE9AOH86NLPb4xNldXDmhl4ceeojly5e7jpQLPg781hizAZgH3Ok4j4iIUyrVPmKtZd/evaTKRrqOckLp8hoS8TgtLS2uo+ScX/7yl2zevIV/ndHFqBJdjOg37zkzyuQKj29+4060DvjErLXr+5d3nGOtfae1ts11JhERl1SqfSSdTmOthYDP91IzfV82yWTScZDcUltby29/ez8Lx8a4eHTCdRw5hnAAPjq7g3i0m2/9512u44iISA5RqfaRwxdIGZ9/Wkzf0dm6oGtwrLUYDJ3JAJ6uR/StzmSAtAWrz5GIiAyCz9tbYRmY+bU+L9U2oJnqUzF37lw++e//zvpDRdy/vUylzYf2RwN8f1MVY8eN50tf/rLrOCIikkP83d4KTCQSIRAIYFIx11FOyCT78pWV6ayHwXrHO97Bu9/9bh7fW8zf9hS7jiNH6EwYvrOhmmBxBd+86z+prKx0HUlERHKISrWPFBUVMWHiRIJRf1/vE+xtI1xUxLhx41xHyUk33HADCxcu5Hd1ZSw7UOQ6jgC9KcN3N1TRnizizm98k/Hjx7uOJCIiOUal2memn3km4Xi76xgnFOhtZerUqQSDQddRclIgEOALX/gC8+ady0+3VLC6WcXapXgavruhkoaeMF+9/XbmzJnjOpKIiOQglWqfmTZtGjbWBSn/7g4RjrVz5hlnuI6R0yKRCF+/8xvMnDmTH9VWsLHFZ0fSF4ikB3dvrGR7R4jPf/4LXHrppa4jiYhIjlKp9pmBHzsHEt2OkxyHl8ImerX0YwiUlpbyn9/6NpOnTOX7myrZ2amZ/2xbvLmcja1hbr75s7zpTW9yHUdERHKYSrXPDFwcZVL+PK1wIFdVVZXjJPmhoqKCT3zy30mkYU+3z/cnz0N1XRHmzZvHNddc4zqKiIjkOJVqn8mVUq2dEYbOiy++SMDAeSP9u+QnXy0Y2Uvtpo10d/v0J0MiIpIzVKp95nCpTqtUFwJrLU8+8TizhyWpKNLG1dl20agEyVSaZcuWuY4iIiI5TqXaZ+LxvtLq2wNg+nPFYv7eSztXPPTQQ+w/0MRFo/z5IirfnVGZoqbE8tv7f0Nzc7PrOCIiksN82twK1+7duwHwiv25Zjld0pdrz549jpPkNmst9957Lz/84Q85b2SCS8eoVLtgDHxwZicH9zfysY/eSENDg+tIIiKSo1SqfebvpbracZLjCBVjikoO55TBSyaT3Hnnnfzud7/jivExPnF2F2F9Jzoze3iKz89vJ955iJs+9lE2btzoOpKIiOQgPZX7TENDAyZSBiH/HgiSilSya5dm9E5FPB7ntttu5bHHHuP6aVH+eUYPAeM6lUypSPPl89oos9185tOfZsWKFa4jiYhIjlGp9plNtZtJ+nWWul+6ZDjbt28nmUy6jpJzfvKTn7B69Ro+OKub66b0YlSofaOmxONL89sYXxLja3fczr59+1xHEhGRHKJS7SMtLS007tlNumKs6ygnlK4YSyIRZ+vWra6j5JTly5fzxz/+kbdO7OXycVpD7UcVRZaPz+mEVIw77ridVCrlOpKIiOQIlWofeemllwBI+b5UjwFg/fr1jpPkjkOHDvHNb9zJ5AqPd58RdR1HTmBkice/zexi69Zt/OIXv3AdR0REcoRKtY+sW7cOEyrCKxvhOsoJ2XAxtnQ4a9etcx0lZ/z4xz8mHu3hxtkduigxB1w4KsHlY2P813/9VjuCiIjISdHTu49s2LiRZGnN4b2g/SxZPprNtZtJp9Ouo+SE+h11zB4WZ1yZ5zqKnKS3TIhhLezatct1FBERyQH+b28FIhqNsruhgXR5jesoJyVdVkM8HlPhOEmtra1UF6lQ55KqSN/nq7W11XESERHJBRkv1caYq4wx24wxdcaYW4/x9huMMRuNMeuNMc8bY2ZnOpMfbdu2DWst6bIcKdX95X/Lli2Ok/hfKpWio6ubKpXqnFIRthijUi0iIicno6XaGBME7gGuBmYD7ztGaf4va+3Z1tp5wH8C381kJr/avHkzQM7MVNtIJSZcfDi3HF9PTw/WWsrC1nUUGYSAgbKwoaury3UUERHJAZmeqb4QqLPW7rTWJoDfA+848gHW2s4jbpYBBdk86urqoLgSQsWuo5wcY0iWjGDb9pddJ8kZ2pJaREQkf4Uy/PHHA3uOuN0IXHT0g4wxHwM+DRQBVxzrAxljFgGLACZNmjTkQV3b/nKd7w99OZpXMoyGhm2kUilCoUx/KYmIiIj4ly+akLX2HuAeY8w/Al8E/vkYj1kMLAZYsGBBXs1mx+Nx9u1txBt7rusog5IuHUaqKcnevXuZPHmy6zi+ZW1efbkWHM/TWniRjFm8+JW3Fy1yk0NkCGR6+cdeYOIRtyf033c8vwfemdFEPtTQ0IC1Fq9kmOsog+KVDAdg586djpP4W11dHQCjS7X9YK4ZXZJiR52WOImIyGvLdKleBUw3xkw1xhQB7wWWHvkAY8z0I25eAxTcM9hAKc29Ul0FxqhUv4a1a9cSNDCzKuk6igzS7Oo4W7ZuIxrVKZgiInJiGS3V1toUcBPwN2ALsMRaW2uMucMYc13/w24yxtQaY9bTt676VUs/8t3OnTsxgRBecaXrKIMTCEFJNTt27HCdxNfWrl3DtKoUxb5YbCWDMXtYEs/z2LBhg+soIiLicxl/mrfW/hX461H3ffmIP38y0xn8rm7HDtIl1TlxkuLRksXVvFynUn08Bw8eZPu27bxjSsJ1FDkF06tSRILw3HPPcfHFF7uOIyIiPpZ7LS4P1dXVkcqxpR8DvJLhNB9soru723UUX/rzn/+MtZbXj4m7jiKnoCgIF4+K8fjjj+lrXERETkil2rHW1lY6OzoOX/SXa9Klfbnr6+sdJ/GfVCrFXx5eyjkjktSUaAeJXPXG8THi8QSPPfaY6ygiIuJjKtWOHb5IsTQ3S/XAxZW6WPHVVqxYQUtbO1eM73UdRU7DtMo0UyvTLP3T/7iOIiIiPqZS7djARX65tvPHAFtUhglFdLHiMTz11FNURuDcEfm560dvylBcXMz1119PcXExvan8PTPysjG91O9qoKGhwXUUERHxqUGXamNMaSaCFKqdO3diImXYcI4cT340Y0iVVFOnUv0KyWSSF1YsZ97wGIE87ZrRlOHaa6/lpptu4pprriGax6V6/si+F0bLli1znERERPzqpEu1MeZSY8xmYGv/7XONMT/KWLICUb9rF8lIjm2ld5R08TAaGna7juErL730EtHeGOeNzN9dP0pDlj//+c/84Ac/4C9/+Qulofw9OXJEscfUyjTPP/+c6ygiIuJTg5mp/h7wVqAFwFr7ErAwE6EKhbWWPXv24BVXuY5yWrziSnq6u+jo6HAdxTdWrVpFOABzhufn0g+AkpAlFovx0EMPEYvFKMnjUg0wb0ScLVu2ahcQERE5pkEt/7DW7jnqLp27fBra29vpjUbzoFT35W9sbHScxD927drF2DKPSNB1Ehkqk8tTWGvZvVs/lRERkVcbTKneY4y5FLDGmLAx5mb6TkmUU7RnT99rlHwp1Sobf9dQv5OxJfk7S12Ixpb1zSHoYkURETmWwZTqG4CPAeOBvcC8/ttyig4cOACAV1ThOMnpsUXlwN//PoUuHo/T1HyIcWX6QU4+GVXsEQzoxaOIiBzbSR1TbowJAh+w1v5ThvMUlObmZgBsUY5vqBIIYCJlh/8+he7QoUNYaxlZrANf8kkwAMOL0de5iIgc00nNVFtr08A/ZjhLwWlubsaEIxAMu45y2tKhUg4ePOg6hi90dXUBUB5Wqc435aH04c+viIjIkU5qprrf88aYHwJ/AHoG7rTWrh3yVAWiubkZr6gsYx8/svsFgtEWAEq2/hWvdDjxSRdnZKx0USkHmlSq4e+luizPd8MoRGXBNF2dna5jiIiIDw2mVM/r//2OI+6zwBVDF6ewHGxuJh0qydjHD0RbMem+i+VCXQdIZWwksOFSWlu01hSgp6fvNWe+bzFXiEpClqZuzVSLiMirnXSptta+MZNBCtGhQy3Y8HDXMYaEDZcSjfYQj8eJRCKu4zhVUtL3QimWzt8TBgtVLG0oLcvcT5dERCR3DWamGmPMNcAc4PCZ2tbaO47/HnI8nufR0dGON2q86yhDwgv3FcnW1lbGjh3rOI1blZV9J2R2J1Wq8013KkhNZW5vgSkiIplx0qXaGPMToBR4I/Az4HrgxQzlynudnZ146TQ2nOM7f/Sz/aW6paVFpfpwqR7U2UqSA7pTQc7o//yKiE8tXvzq+xYtyn4OKTiDeda/1Fr7/4A2a+3twCXAjMzEyn8DezrbDF6omE020rdXdVNTk+Mk7o0YMYKAMTT3qlTnk5QHrTGoqalxHUVERHxoMM/6vf2/R40x44AkUNhTkqdh4Ehvrzg/Zr28SN8BNjqqHIqLi5kwYRy7uwe1ukp8bl9PkLQHZ555pusoIiLiQ4Mp1X82xlQD3wLWAruA32UiVCE4XKojuX2a4mGBEKa4XKW635nTZ7K7p8h1DBlCDf0vklSqRUTkWE66VFtrv2atbbfWPgRMBmZZa7+UuWj5rbGxEVNcAYH8mc1MFlWwZ49KNcC0adM41Au9mdzHULKqsSdIOBxiwoQJrqOIiIgPDXb3j0uBKQPvZ4zBWvvrDOTKe1u2biUZqXYdY0h5JcPZseNlkskk4XDunxJ5OoLBIAABbQCSNwxgMIc/tyIiIkc66ZlqY8xvgG8Drwcu6P+1IEO58lp7ezt7GxtJV4x2HWVIpctHk0wm2L59u+sozsViMQDCulYxb0SClkQySTqddh1FRER8aDAz1QuA2dZaHRN3mjZu3Aj0ldB8MvAiYePGjcyZM8dxGrd6e3uJhIxmqvNIcbDvv75YLEaZDoAREZGjDGYebRMwJlNBCsnGjRshECRdNtJ1lCFlwyVQUsWGDRtcR3Fu65bNjIh4rmPIEBpR3Pf53Lp1q+MkIiLiR685U22MeRiwQAWw2RjzIhAfeLu19rrMxctPq9esIV1WA4H8W5uZKBvN+pdeIp1OF+za0/r6ejZs3MR7zoi6jiJDaN6IBOVFsHTpnzj//PNdxxEREZ85meUf3854igLS3t7Ozh07SI0/z3WUjEhXjiN6aDvbt2/nrLPOch3HiaVLlxIKwMKx8dd+sOSMoiBcNqaX/33+eVpaWhgxYoTrSCIi4iOvufzDWvuMtfYZYDXwXP+f9wNVwPIM58s7a9asASBVOc5xksxIV/adB7R69WrHSdxIJpP87dFHubAmTkWRLj/IN1eMi5FOezz66KOuo4iIiM8MZk31s0CxMWY88L/AB4BfZiJUPluzZg0mFMHLs/XUA2y4BFs2omBL9bZt24j29nJ+TcJ1FMmA0aUeEys81q5d4zqKiIj4zGBKtbHWRoF3AT+y1v5fYG5mYuWvzZu3kCwbCSZ/91pLlo1m69ZteF7hXag3cJHmzOqk4ySSKTMrE9RuqiWV0sk+IiLyd4Mq1caYS4B/Av5yCu9f8BKJBLv37CZdmt9rMdOlw4nHY+zbt891lKzbuHEDY8sslVr6kbdmVCeJxePU1dW5jiIiIj4ymH2qPwncBvzRWltrjJkGPJWZWPmpoaEBL53GKx3uOkpGef0vGurq6gruSOfm5mYqwzocJJ9VF/X9BKa5uZlZs2Y5TiMiTi1e/Mrbixa5ySG+cNIzzdbaZ62111lr7+q/vdNa+4mBtxtjfpCJgPlkYGYr32eqvZJqMAFefvll11Gy7oILLqSuI0RPUqe+5Kt1LUUEgwHmzZvnOoqIiPjIUC7feN0Qfqy8tHfvXjAGG6lwHSWzAkEorijI5R+vf/3rSVtY3xJ2HUUywFpYc6iY+fPnU1GR59/HIiIyKFoTnUVNTU2YSHleX6Q4IBUu5cCBA65jZN1ZZ53FiGHVrDoYcR1FMmBPT5CDUcNlly10HUVERHwm/9udjzQ1NZEKl7qOkRW2qJwDTU2uY2RdIBDgLW+9inUtRTT36tsr3zzeWExROMzll1/uOoqIiPjMUD7raxHpa9h/oAkvXO46RlZ4RWW0t7WRTBbe1nLvete7MCbAY43FrqPIEOpMGJYdKOatV11FdXW16zgiIuIzQ1mqvz+EHyvvxONxDjUfxCsujHWYXqQSa21BrqseNWoUb3jDG3jmQAm92so4bzy5t5ikB9dff73rKCIi4kOvuaWeMeZh4Lib7lprr+v//ZdDFyv/7N69G2stXskw11GyYuDvWV9fz+TJkx2nyb53vvOdPPnkk2xoKeKi0TpdMR+sbO67QLEQv55FROS1ncxM9beB7wD1QC/w0/5f3cCOzEXLL/X19UD/dnMFwCupAmMO/70LzfTp0wFo6g06TiJDwbNwMBpgxowZrqOIiIhPveZMtbX2GQBjzHestQuOeNPDxpjVGUuWZ+rr68EE8CJVrqNkRyAExZUFW6pLSkoYPqyapmjMdRQZAm3xAEkPxo8f7zqKiIj41GDWVJf1n6IIgDFmKlA29JHy07r16/HKRkKgcHaESJaMYN36l/A8z3UUJyZMmEhT72AOLRW/aurfyWXcuHGOk4iIiF8NpuF9CnjaGPO0MeYZ+o4o/2RmYuWXzs5Otm3dSrKysJ6QU1UT6OrsOHySZKGpqKyk1yucF1H5rDfVt7lRVVWB/KRJREQG7aSm0YwxAaATmA7M6r97q7U2nqlg+WTNmjVYa0lVTXAdJavSVX0/Kn/xxRcLci1qIBBAO03mB9v/eTRGn08RETm2k5pGs9Z6wD3W2ri19qX+XyrUJ+nFF1/EhCJ9yz8KiA2XYMtG8MLKla6jOGGMwR533xzJJQOfx0ABLd8SEZHBGcwzxBPGmH8wmqoZlHQ6zfPLlpOoHFcQx5MfLVE5gdpNm2hvb3cdJeuCwSBJq2+XfJDsvywgGNRuLiIicmyDaXkfAR4AEsaYTmNMlzGmM0O58samTZvo6uwgNWyK6yhOpIZNwVrL8uXLXUfJupEjR9IWQ7PVeaAt3vdf5YgRIxwnERERvzrpUm2trbDWBqy1YWttZf/tykyGywfPPvssJhAsuPXUA7zS4VBcwTPPPus6StaNHj2aRBq6kpqtznUtsSBlpSWUl5e7jiIiIj41qP2+jDHXAQv7bz5trf3z0EfKH9Zannn22b5dP4Jh13HcMIZE1SRWr15NNBqltLTUdaKsGT16NACHYgEqi9KO08jpOBQLHP58ioiIHMtJz1QbY75J3xZ6m/t/fdIY841MBcsHDQ0NHGpuJlU9yXUUp1LVk0inUqxbt851lKwaOM56T7f2qs51e6JFTJ4y1XUMERHxscGsqX4b8BZr7X3W2vuAq4BrMhMrP6xatQqAVGVhn8KWLh+FCYYP/3sUiokTJ1JZUc72DpXqXNYSC9DSC3PnznUdRUREfGyw21FUH/FnnYLwGl5ctQpKqrGRAl+HGQiSLB/DypUvuk6SVcYY5p59Di93RlxHkdPwcv+LIpVqERE5kcGU6juBtcaYXxpjfgWsAb6emVi5L5FIsH79ehIVhXWK4vGkqsaxf/8+9u/f7zpKVs2dO5cDPYb2uC5WzFVb28MURyKcccYZrqOIiIiPDaZUXwvcR1+ZfhC4xFr7h4ykygPbt28nmUiQrhzjOoovpPtfXGzYsMFxkuy6+OKLAVh7qMhxEjkVnoV1LcVccOGFhEJaxiMiIsc3mFL98/7frwO+D9xjjPnk0EfKD5s2bQIgXa4dAwC8kmpMqIja2lrXUbJq6tSpjBs7hrWHtAQkF9V3hWiLwWWXXeY6ioiI+Nxg9ql+ir7lHl8CfgosAG7MUK6cV1tbC8WV2HCJ6yj+YAzJ0ho2bNzkOklWGWO4bOHl1LaFiaa0BCTXrGkuIhAIcMkll7iOIiIiPjeYLfWeAJYB7wG2ARdYa2dlKlgus9ayYeMmkmU1rqP4Srp8FA276unp6XEdJasuv/xy0h68eFBLQHKJZ2HFwRLOO28+FRUVruP4jjEmaIxZZ4zReQUiIgxu+ccGIAHMBc4B5hpjNA17DPv376ejvY10+SjXUXwlXT4Kay1btmxxHSWrzjrrLCZPnMCz+/Xtkks2t4Vp6YW3vU07hx7HJ4HC+mYWETmBwSz/+JS1diHwLqAF+AXQnqlguUzrqY8tXV4DxrBx40bXUbLKGMPV11xLXUeQvT1B13GGzKTyFCVBj5Kgx6zqJJPKU64jDaln90WoKC/jda97nesovmOMmUDfOQU/c51FRMQvBrP84yZjzB+AdcA76NsJ5OpMBctlmzZtwoSK8EqqX/vBhSRYhC0dzsYCW1cNcOWVVxIMBnh2f/5csPj+GVEmV6SZXJHm8+d18v4ZUdeRhkx30rDmUIQ3v+VKIpH8+ZwNof8P+BzgHe8BxphFxpjVxpjVzc3N2UsmIuLIYJZ/FAPfBWZZa99srb3dWvtkhnLltA0bNpIsrQEz2LN18l+yrIbazbWkUvk1q/lahg8fzsUXX8LyphJSx60h4hcvNBWR9OBtb3ub6yi+Y4y5FjhorV1zosdZaxdbaxdYaxfU1Oj6EhHJfye98aq19tuZDJIv2tra2LWrnvT481xH8aV0xRjiB7eybds25syZ4zpOVl199dUsW7aMja1h5o9Muo4jJ/DcgRLOmDaV6dOnu47iR68DrjPGvI2+yZZKY8z91tr3O84lfrN48StvL1rkJodIlmgqdYitXbsWgFTleMdJ/GngEJg1a044yZWXLr74YqqrKvNqCUg+2tMdpL4zyNW6QPGYrLW3WWsnWGunAO8FnlShFhFRqR5ya9aswYQieGUjXEfxJRsuxpaNZNXq1a6jZF0oFOLKt17F+kMROhLas9qvntkXIRQM8uY3v9l1FBERySEZL9XGmKuMMduMMXXGmFuP8fZPG2M2G2M2GGOeMMZMznSmTLHWsvLFVSQqxmg99QkkKsayubaWaDR/Lmw7Wddccw1pC89pttqXEmlY1lTCZQsXUl2tC41fi7X2aWvtta5zSAFbvhy+9S145hmw1nUaKXAnvab6VBhjgsA9wFuARmCVMWaptXbzEQ9bByyw1kaNMTcC/0nfATM5p76+npZDzaQnX+o6iq+lqyaQPrCRNWvWFNzxz5MnT+acc87m6boNvG1SjIAmrH1lVXMRPUl4+9vf7jqKiLyWr3wF/uM/IBiEujrYvRve9z4IZbTaZN7Ra9FB69FzRKanUy8E6qy1O621CeD39G3Hd5i19ilr7cCU5QvAhAxnypgVK1YAkKqe5DiJv6XLR2NCkcP/XoXmuuvewcGoYWNr2HUUOYK18PjeUsaPG8v8+fNdxxGRE1m6FO64A/7xH/tmqq++Gp5/Hh580HUyKWCZLtXjgT1H3G7sv+94Pgg8cqw35MKep8uWLccrG4ktKnUdxd8CARKV41i2fDmeV3j7yy1cuJAxo0fx4M5yPP200jfWHgqzoyPIe977PozRjxBEfCsahU98AubMgfvug5ISeOc74Yor4KmnoMAOGBP/8M3CX2PM+4EFwLeO9Xa/73na3t7Oli2bSVZNdB0lJ6SqJ9HR3s7WrVtdR8m6oqIiPvihD9PQFeCFpiLXcQRIe/DAzgomjB+nvalF/O4b34CGBrjnHggf8RO/d70Lxo+HX/0Kmppe+T49PfDII/C978GXv9xXxtev1zpsGVKZXni0FziyZU7ov+8VjDFvBr4AXG6tjWc4U0YsW7YMay2pYVr6cTJSVRPABHj++eeZPXu26zhZ96Y3vYnf/+6/eKh+JxeMaiXsm5e3hem5AxH29RjuuOUGQrm+HlMknxy9vnjnTvjOd+D974fLL3/l28Jh+NCH4NvfhjvvhOuvh0gEduyAz34WOjv7HmfM38v0/Plw663w7ndn/u8ieS/TT+WrgOnGmKnGmCL69jRdeuQDjDHzgXuB66y1BzOcJ2OeeeYZKK7EKxnuOkpuCEVIVYzlqaefwRbgTEEgEGsRNsYAACAASURBVODGj36M5l7Dw7tKXMcpaJ0JwwM7y5k9+6yCu3BWJKf09MBPfwoTJ8IPfnDsx4wbB1/8Yt+M9f33w89/3rfW+u1vh8ceg+ZmiMf7Lmz80Y8gmYT3vAduuw0KcDmiDK2MTslYa1PGmJuAvwFB4D5rba0x5g7g/2fvvsOiPLPGj3+faQxVmlgoSrGgsbfYuxtjYprpzZKYWFI2iWmb3+77ZnffjZutKZtdN9lseq9q7CXYa+wNEEGUKiAdpjy/P26JDWWAmXmm3J/r4iLCMHMCOpw5z7nP2amq6veIdo8Q4ItzfYw5qqpOdWVczlZZWcmuXbuob5sqXgFLDrFGdCIvezNZWVkkJSVpHY7bDRw4kIkTJ7J49SoGtK2nU6hN65D80vvHgqm161mw4FnZSy01mxzU4AaqCunp8M03cPYsrFgBVxt5GRkJTz8tEueQEGjbFubPv/g2yckwZw48/DA89hi88goMHw4PPODa/xfJp7n8Oqeqqj8AP1zysV9f8N9ev2Fhy5Yt2Gw2LBGdtQ7Fq1gjEiB7Mz/++KNfJtUAjz32GDt3bOffR+z8z4BSDLINxK22F5rYXhjArFnTSUxM1DocSZIa7NsneqaXLROJdHk5BAfDgw/CwIFNf71eD926NX07g0FUrMPDRWKdmgqDBrU+fskvyV/hTrBmzRoICMEe7HkHKD2ZagzCFtqeNWvX+mULCEBYWBhPPf0MORU6FmfLNhB3qqhXeD89lC4pKdx9991ahyNJEoDVCjNmQJ8+8MEH0KYN9OoleqhfeQWGDHH+YyoK/Pa3kJgIH38MpaXOfwzJL8gTOa1UXl7Ojh07ZOtHC1kik8jN3kxGRgZdunTROhxNjBw5kvHjx/P92jX0i66ns2wDcYv3jgVTbdXz/AsvyMOJklfx2ZYTi0UcGPzmG3juOXj2WffNnTYYRDL/u9+JXuxL20UkyQGyUt1KaWlpovUj0j/bF1rLEtkZFB1r167VOhRNPfHEE4SHh/Pvw2FY5FkZl9taINo+ps+YQXJystbhSJKkqvD++yKhfu01UZWOdPPB/3btYOpUOHBAtJ9IUjPJpLqVVq9eDYFtsAdFaR2KdzKYsYZ1ZNXq1X65CKZBWFgYzzz7HCcrdXybJdtAXKmsTrR9dO/ejbvuukvrcCRJAtiwAbZvF20Yjz2mXRzjxkGHDvD551Bbq10ckleSSXUrFBYWsnfvXuoikmTrRytYopIpLipiv59vwRo6dCiTJ09mSU4QmeWyHcEVVBXePRpCnWrghRdelG0fkuQJTp6Ezz6DHj3gxRe1jUWvFyP2iovFkhlJagaZVLfCmjVrUFUVS5S8fNwa1vAEFL2RVatWaR2K5ubNm0d0VBT/PhxGvWytdrrNBSZ+Kjbx8MOz6dSpk9bhSJJks4kNiEFBMHMm6DwgLUlNFQcif/c7WL9e62gkL+IBf3u914oVK7GHxKCaw7QOxbvpjdSHJ7Bm7Vrq6rxyoabThISE8Oxzz3O6SuHrrCCtw/EppXUKH6SHck3PHtx2221ahyNJEsCaNaJSfffdEBqqdTTn3XMPdOki4srP1zoayUvIpLqFMjMzOXEii3p5QNEpLFHJ1FRXs3XrVq1D0dygQYOYMmUKy3ICST8r2xOcQVXhP0dCsClGnnv+BfR6vdYhSZKUlQXffy/G5/Xrp3U0FzOb4YsvxIzsoUNh0yatI5K8gEyqW2j16tWgKFgj5cIIZ7CFdUQxBYqZ3xJz586lbdto3jki20CcYXOBib1nTDz08Gzi4+O1DkeSHLZo0eVvPuPpp0W7x113eea5pF69YO1aEduoUfCb34g52pJ0BTKpbgFVVVm9Zg3WsI6oRjmpwSkUHXXhndmyZQtVVVVaR6O54OBgnlnwLKerFL47If+OtcbZeoWPMkLp0SOVW2+9VetwJEkC0av8zTdw3XXuH53XHNdeC3v2iOUzL78MI0dCXp7WUUkeSibVLXDo0CGKCgvlbGons0YmYbFY2CQvswEwePBgrrvuOpbmBHGiQrYrtNT7R4OpUw0899zzsu1Dkhrh9mq4zQZPPQUJCTBhgosfzAnCwsRhyk8+EQn2c89pHZHkoWRS3QJr164FnR5ruJwe4Ey2kBgICBGtNRIgpoGEh4fz7tFQ7P65yb1Vfio2sqMogOnTZ8hpH5LkKd57D376CRYuBJNJ62gcd9ddYob2hx/CoUNaRyN5IJlUN5Pdbmfd+vVYwmLB4EVPBt5AUagP78yuXbuoqKjQOhqPEBoaytx588kq1/Pj6QCtw/Eq9Tb4KCOUhPg47rzzTq3DkSQJoKICfvUrcfjPG/9dPvssBAeL/mpJuoRMqpvpyJEjlJw5gzWis9ah+CRLZGdsNpucAnKB8ePH07vXNXyRFUKlxQMP83io5ScDKaxWePyJJ+WSF0nyFK+8IkbU/fWvnnk4sSnR0fDLX8KXX8pV5tJlZFLdTBs2bABFhzVcThBwBXtwWxRTEGlpaVqH4jEURSSG1RaFr4/L2dWOOFOr4/vsIEaNGsnAgQO1DkeSJIDsbPjzn+Hee8VyFW/15JNiasmXX2odieRhZFLdDKqqsn79j1hDO4BBXop3CUWhLjyBrdu2UVtbq3U0HiMlJYWpN93E2tNmTlXJw3ZN+SIzCHRG5s6dp3UokiQ1eO45kYx6+/rvyEjxomDFCsduX1wMJ064NCTJM8ikuhmys7PJyzuNNSJB61B8mjW8E5b6enbt2qV1KB5l+vTpmM2BfJYpq9VXc7xcz+aCAG6/4w7at2+vdTiSpBmPmnGdmQmffQYLFoAvzIqfNAl27ICmRsBmZMDvfy/aXeSMa58nk+pm2Lx5MwDWcJlUu5IttD2KwfTz91sSwsPDue/++9lTbOJQiewRboyqwqcZIYSHhXLPPfdoHY4kuYXdDqdOwf794n1dndYRXcJuh88/h44dxUE/X/CLX4gnnMOHr3ybDRvgb38T//+1tbJa7QdkUt0MGzduwh4cjWoK1joU36bTUx8Wy8ZNm7Db7VpH41Fuu+022sW05eNMOWKvMTuLTBwpMzBj1kMEB8t/p5LvOnUKFi8WnRTz50NcHPTuLd4HBcGwYbB0KZSUaB0pYnzeiRPwf/8nJmf4gkGDoE2bq4/We+01CAyEF14QhzKvloBLPkEm1Q4qKyvj8OFDWNr4wGUrL2ANT+BsWRlHjx7VOhSPEhAQwJy588ip0LH2lOzrv1CdDT7JDCGpc2emTJmidTiS5BIHD4pOgpdfFkmzTif2p7z1ligG/+tf8PzzotNg8WIxve6990RbrybsdhFo+/ZiK6GvMBjEN/7QIVGxvpTdLlac9+wp/t/j4+HIEffHKbmVvIbsoK1bt6Kqqpz64SbWNnGgKGzevJnU1FStw/Eoo0ePpl/fvnx1cA9DYuoJNcmSNcDS7ECKaxReelKO0JN8z5kzoiV5714ID4dbboHhwyE0VHx+9uyLb//734vpdStXwsaNsHOn+JqHHhKJuNvs2SPK6jNngq9tNJ00Cb76SowI7NDh4s/t2SMuE3TvLv7cvTusWeOBvTmSM8lKtYO2bNmCEhCMPShK61D8gyEAW0g7Nsm+6suIEXtPUGPT8aUcsQdAUY2OpSeDGDt2LH379tU6HElyqp074X/+R3QP3HqrSJivu+58Qn0lkZFiCeDLL0PXriIpHz/ejVVrVRVV6nbtRLuEr5k0Sbw/ePDyzzVsBm4oCnXvLtazp6e7JzZJE7Kc4wCLxcK27dupC0vwrmH1tnrMZjM33HADS5YsodJWr3VEzWJpE8/xzB0UFhYSExOjdTgeJTExkVtvvZWvvvySER3q6NLGf0+Vqyq8fywEvSGAOXPmaB2OJDmN3Q7ffgvLlkFyMsyaBVFXqOtcbbJHZKTou968WSTWgwfD99/DNde4Ju6fHT4Mubkwfbqby+Nu0rmzeMFw6JBoBbnQmjWi9aNNG/HnlBRRqZctID7NB/+WO9/evXupranxutYPxVrPDTfcwPz585kyZQqK1buSatu57/eWLVs0jsQzzZw5k+joKN49GorVj89zbi80sfeMkVkPPSRffEk+w26HRx4RCfXw4WKJ35USakcoirifH38UgyiGD4erLa51yii+tDQICQFfXsDUowccOwYWy/mP1dWJyR/jx5//WEAAJCWJ20o+S1aqHbBlyxbQ6bGFdmj6xh5ENZhYsmQJqqqydOlSVIN3tQrYzW3AHMbmLVu46aabtA7H4wQFBfHEk7/kpZdeYlmOmRs7+9+ynCqLwkcZoXRJSeGWW27ROhxJcgpVhblz4e234frrYepU510kHTIEtm2DsWNF98LKlc65X+Di7Lu0VDSAT5gARqMTH8TD9OwJ69aJedQNrR5btkBNjfh/z8s7f9uEBPFCw2bz7v7yS19lXdrQ78dkpboJqqqycdMmsUVR72VPDHoTtbW1fPXVV2I7od6kdUTNoyjUh8Wxe9cuuV3xCkaMGMHIESP4NjuYgmr/++f8eWYQ5fUKC559Vh5OlHzGM8+IKR4vvODchLpBfDysXy86FyZNgpMnnXv/AGzaJMrto0axKK27ePOEJTTO1qWLSJAvHK33zTdgMsHo0RfftmNHUdHOznZvjJLb+N9v4WbKycmhID9fLnzRiDU8HovFwu7du7UOxWM9/sQTGE1m/nsspNHJTr7qaJmBdafN3DZtGl27dtU6HElyir/8Rbw99pg4kOiqYzxxcaLA2qYNvPGGKCw7jd0uRo706AFt2zrxjj2Q2Sz6pRuS6poaeP99uO02CAu7+LYNE0LkvGqfJZPqJjT081rbxGkciX+S2xWb1rZtW2Y/8igHS4xsyveyqxEtZLHDu0fDaBfTlhkzZmgdjiQ5xaefwtNPw7RpYha1q8/Fx8XBDz+IHuvXXxf5oFOkp4ssffhwJ92hh+vRQxzILCgQw8LLyhpviWjfXry/2sIYyavJ66VN2Lp1K2pQJGpAiNah+CednvrQDmzZug1VVVG8afqKG02dOpWVK1fwccZhekeVEObjs6uXZAdyukph4a+fISjIu84KSL7txAkxsSM8HHr1cvxw4eLFcP/9MHIkfPCB+1pue/WCRx8Vy//ef1/kgq1+mt21S/RR9+rV/K/1xt6QwYNhxQr4xz9Ei0e3bpe3foDYJhkWJpNqHyYr1VdRXV3N/v37qQ+L1ToUv2ZrE8eZ4iKyZR/aFel0OhYseJYam47PMnw7ycyr1rE4O4hx48YyZMgQrcORpJ+tXy/Wht9yizgImJgIf/oT1DcxeGnlSlGd7tcPliwRHQXulJoq5l/v3n1+vHKL2e1iLXmvXmLihT+IjIQ5c6CoSHwTr/bKpEMHmVT7MJlUX8VPP/2EzWbDJls/NGU996Jm+/btGkfi2RITE7nzzrvYkG/mSKlvXoRSVXjvWCgB5kDmzZuvdTiSF3DXGefly+GTT6B3b1GoXbECRo2CBQvEPOilSy//Grsd/vhHmDJF7AZZvvzyNlx3mTBBJPVff93K/SQZGVBeDv37Oy02r9C1K8yYAUOHwoMPXvl2HTqInmp/OgDjR2RSfRU7duxA0RuxhcjZt1pSA0IgKIJtMqlu0gMPPED7djG8l+6bs6u3Fpg4VGLg4dmPENWaob2SX1ixQsx3PnXKtY+TlSUGPgwaJFop+vcXUzWWLBE9yzod3HCDSFzffFMsYXnrLVHNfu45uOkmUeWOjHRtnFejKCIXjI6Gf/8bzp5t4R3t3t3y1g9vN2iQ+OFe7bmpY0eoqHD9X0pJEzKpvoqf9uzBEhIDOi+eJ+kj6kPac2D/AaxW/90c6Aiz2cwTT/6SU5U6lp908zVkF6u2KnycGUq3bl258cYbtQ5H8gK//a2oBru6c2ztWtGycd99l/dCT54M+/bBn/8sirjz54vze3PnwvHj8M9/whdfQESEa2N0RGCgeFFQXS1mZNtszbyDhtaPnj3d38PiLRomgMgWEJ/km9eInaCiooKc7GxsHftpHYoE2EJiqCs8zPHjx+X4tCYMHTqU4cOH8d22zQxvX0dEgG9cZvwuK5Dyelj4y6fQe/PiBMktNmwQo5IB8vNd9zjl5aLdY/ToK+eRJhM89ZR4y8qCPXtEm0hSkusnfDRXbCzcey/8979ilXmzdiodPy4mXwwY4KrwvN+FE0AmTXLe/TpywFMuaXE5mVRfwaFDh1BVVbZ+eAhbSDsADhw4IJNqB8ybN58Ht2/n88xgHulRqXU4rXa6SsfK3ECuv34K3bt31zocyQu88ooYkayqYtKZq2zYICq6Y8Y4dvvERPGmFUdyr6FDRV/1ihWi6OzwU+7u3WAw+Gfrh6NCQ0V7iKxU+yTZ/nEFBw4cAEXBFuzjg+u9hBoQghIQwv79+7UOxSt07NiRO++8i035AaSf9e7XzqoKH2eEYA4M5KGHHtI6HMkL7NsnepmfeELMYnZVpdpiEVune/QQ2wl9yR13iBcl774r2kGaZLeLpLpnT9FHIjVOUcT3aN++1t3PhespvXEMoY/y7t+2LpSZmQmB4d63mtyH1QdGkp6RqXUYXuOee+5h2Q9L+TjDxq/7l3ncZWZH7S8xsu+MkXnzZhLhCY2nksdrmLTx6KNisd/+/aKa7OyuoeXLRbfDvfde/PHGchxvu/JuNsPMmWI6ySefwJNPNvEF27eLhS833+yW+C60KO3yq1ezRx1xexwOGzpUrM2srgY5Z9+nyEr1FWRkHscSEK51GNIF7IHhnD6VS31TQ18lAIKCgnjo4dlkntWzvdA7Ny3aVfg0M4SOHdpzswa/rCXvdPq0WL8dFSUqyDYbFBc7/3HWrhWDLlJTnX/fniAxUUwt2b5dJNZX9eWX4lVL795uic2rjRghLnPs2KF1JJKTyaS6ETU1NRQW5GMPklUxT2IPjMBut5Obm6t1KF5j0qRJJHXuzBdZIVi8cMTehrwAcit1zH7kUYxGedVIckx+/vkhCw1tGa5oAVm/Xhw29OW/mtddB8nJYrdJTs4VbqSqIqnu0UNWXh3RsL5948bGPy9nWHstmVQ3IufcM4fdLCvVnsQeKF7kZGVlaRyJ99Dr9Tw6dy6F1QprTnnXiKs6G3x9IoQeqd0Z3djKX0m6gry880l1w7AFZx9WLC2FvXubcYjPS+n1YqeJzQYPPCBapy+zc6eYW+hvC19aKiJCbATasOHyz23aJIaX//CD++OSWk0m1Y3IP1fSsJtDNY5EupA9QPw8Clx5lN8HDR48mP79+7E4O5gaLxrzvTrXTGktPDpnLoq3NoRLmsjLO59MBweLgQvOrlRv2CAKiq1Jqi89a+ap583atoW//x1+/FEshrnMl1+KqR99+rg9Nq81YoRYFHPhMPAlS8SGoOpq+O470XcjeRWZVDeisLAQALspWONIpIvojSjGgJ9/PpLjHn54NhX1sOKkd5zKr7IoLMkJZsjgwfSWPZpSM6jqxZVqEC0gzn4t/uOPEBCg7Xg8d5oxA8aPh2efFT3rP1NVsb1mwgTxCkZyzIgRYrNiw0SrjAyYNk1MBvntbyElBd57D06e1DZOqVlkUt2IoqIiFL0B9AFahyJdwm4MpqioSOswvE5qaiojR4xg2ckgKuo9v+q7LMdMlQUeevhhrUORvExFBdTUXJxUt2/v/Er1+vViiIMv91NfSFHE9sf6enj88Qs+8dNPYqPNtGmaxeaVRo4U7xsuecybJ16lLV4s2kPmzBG9N+vWaRun1CwyqW5EUVERqinY81ZdSdiMQeTny/aPlpg5axa1NliS49nV6vJ6hRWnghg9ejRdunTROhzJy+TlifeXJtWVlVBV5ZzHKCsTuaSjC198RUoK/OY38NVXojsBOD/1wx+m8zizXychATp1EluK5s2DlSvhd787/xc3JET0qO/aJV7JSF5BzqluRFlZGTZZpfZIqsHM2bNntQ7DKyUmJjJhwkTWrF3F5Pgawj10ffmS7EAsNoWZM2dqHYrkhRqS6oaearh4AkhycsvvuyGH2rdPFBfLyy9O3p3BY/qq09Iu/vPsUQA8/bQYrzdvHowdoxL2xRcwbpyYXyg1T8MA8LfeEgn03LkXf37IENiypfWLYiS3kUl1IyoqK1H13jnX19epehNVlU4qN/mhBx98kDVr1rA4O5D7uzqyJs29Sup0rDkdyMRJk+jUqZPW4UheqLFKtbOS6gaZmaDTNa+f2mOS5VYyGsVhxWuvhTsmnGF5RgZpQxZwZBGQ1t2zl654mqFDYetWMVovOfny7UTdukF4uLiN5BVkUt2IiopKVL1nXyL3V6rBRG1NDXa7HZ1Odi81V1xcHJMnT2bFsqVMTqgl2uxZw6sXnwjEjo7p06drHYrkpRpLqhsWcTrrItepU6ISbvLT2svgwfDYY/D6a5FsYhgn+t2idUgeq9Ftjxdu11SU8/3Vl9LpxDd79WooKhJjWCSPJrOSRlRVyUq1p1L1JlRVpcpZzZF+6MEHH0TR6fkuy7NeOBbV6FifZ2bKlBvo4Oxr6pLfyM8X573CL1gzYDKJtdsVFc55jNOnITbWOfflrX73W5UOugIeDnifqiAvSPa8YXZhY669VgwH//RTrSORHCCT6kZYLBbQ6Zu+oeR+534uFotF40C8V0xMDFNvupkN+Wbyqz3nKeDbrEB0egP333+/1qFIXqxhRvWl58xDQ52TVNfUwJkz0LFj6+/Lm4XmHOQN+1wO1yWzdq3W0Xi5qyX8sbEQFwcffKBNbFKzeM5vVA9it9vl5A+PJX4u9kbXekmOuueeezAajXyT5RkrhU9X6dhYYObmm2+hrbzEKbXCpTOqGzgrqW6Y0ezvlWq+/JKb+I4+qXUsXgwlJVoH5MOuvRZ27ICjR7WORGqCTKobYbfbUZFJtUdSZFLtDFFRUdx62zS2FgRwslL7qzJfZwURYArgnnvu0ToUycu5Oqk+dUq89+ukWlXhs8/I7zqKO+4LwG6Hzz7TOigfNniw6K+W1WqPJ5PqRqiyUu2xGl7s2C5c7Sq1yN13301wcBBfHNe2Wp1Vrmd7YQB33HknEQ0nyiSphfLzG0+qw8Kcl1QHBEBkZOvvy2vt2QNHjpAx6B6io+GGG8SH9uX68zfFhdq0ERsrP/pI9FdLHksm1Y3QGwwoqvyL64kafi5Gf1lj5kJhYWHcfc+97Ck2cbRMu0FAXxwPJiw0hDvvvFOzGCTfUFcn2hAunFHdoKFS3dqc5PRp0U/t18OHPvoIjEayBogtihMmiBcyn+5Mobpe+ytfPun+++HECbHKU/JY/vy0cEUBAWawW7UOQ2rMuZ+L2WzWOBDfcNtttxEVEc7nx4NRNdgFc7DEwIESI/c/8CDBwcHuD0DyKQ2ryK/U/qGqrduqqKqiUu3XrR82m1haMnkydcGiMm0wwD33wJkqMy8vGaBxgD5q2jSIiYE//9m59+utU1E8lEyqG2EOCECxyaTaEykyqXYqs9nM9JmzSC8zsKvYvWMk7Sp8mhlKu5i2TJ061a2PLfmmxmZUNwgNFe9b0wJSXi6Scr9OqtPSRLn+3nsv+nDXrjA8OZ9XV/Zmxwl52NjpzGaYPx9++AEOHtQ6GukKZFLdCHOgrFR7LJsVnV6PwSD3FjnL5MmT6ZQQz2eZIVjd2PW0Od9EdoWO2Y88SkBAgPseWPJZV6tUh4WJ961JquUhRUTrR0iIaKS+xLT+mXQMr+bB/46h1iLbQJxu7lwIDIS//EXrSKQrkEl1IyLCw9FZa7UOQ2qEYq0lLKyN1mH4FIPBwJy58yioVlh7yj1XAOps8EVWKN26dWXs2LFueUzJ9zVUqq/UUw2i2txSDUm1386orqwUYz5uvx2CLj/gHGSy8fb9aRzOi+DX3w+8/OvT0s6/yXaD5ouKgpkz4cMP5QxDDyWT6kZER0ejl0m1R9JZqomOitI6DJ8zZMgQ+vfvx7fZwVRaXD/5ZllOIKW1MHfuPLluXnKavDwxuCkm5vLPOatSHRZ2PkH3O59/LhLrhx664k1+0TOX2SMP86dVvdmc2c6NwfmJZ58FvV7OMPRQ8rdZIyIjI1Hq5RpsT6S31hAdLZNqZ1MUhXnz5lNtUfjaxevLz9TqWJITxOjRo+jTp49LH0vyL/n5IqFurDssKEgk3K1JqgsLG6+C+42334bUVBg69Ko3+9O0rSREVjL9v6PlNBBnS0iAX/9azDDcv9/1j3fpQUZ5deGqZFLdiOjoaFSbBWxyFban0VmqiZKVapdITk7mxqlTWXsqkFwXLoT5LDMIdEYefXSOyx5D8k/FxRAd3fjndDpRYW5N+0dhIfjtws+DB2HLFlGlbmKPQ6jZwrsPrie9MJwXvxnspgD9yFNPiYMDn3wi5khKHkMm1Y3ocO6Ui672rMaRSBex1qPW19DRbxsaXW/mzJkEBwfzYUaIS0bsHS0zsLUggLvuvvvnf2eS5CylpVdfytKarYoVFeKtsdYSf7DviXew6Y28r97vUMFybLc85o85wN/X9uLgabnUyalMpnMzDM/Ad99pHY10ATlCoRGdO3cGQFdThj34CmUPye10tWXA+Z+P5Hxt2rRhxsxZvPbaa+wuNjKgrfOu1thV+DgjhOioSO6++26n3a8kNSgpgas9PbQmqc7MFO/9sVKts9XTZdsHZPe5idpQx78BC2/bxvpjHfnP5m68dP1uIoLqXRhlM3l7G0PXrjB6NKxdCwMGQHKy1hFJyKS6UbGxseh0up+TOMkz6GrEz6NTp04aR+Lbpk6dynfffM0nmSfpHVWC0UnXszblB5BVrudXv5pDYKBr+7Yl/1RaCv37X/nzoaFiKV1LZGSI9/5Yqe6Uu4nAymKOjLjyAcXGBJlsfD57NX1/exvvbOrOL8fvQ3/u+WRRWvfLbj971BFnhOs8np5433qr6Kv+4AP41a9AbhrWnMvbPxRFuU5RlKOKomQoivJ8I58fpSjKbkVRrIqiTHN1PI4wGo106Njx5yROvG7W5wAAIABJREFU8gz62jL0BgPt/fqkkOsZDAbmzn+MwmqF1bnOGbFXa4UvjoeQ2r0b48ePd8p9StKlSkog4iqdBq2pVDck1f5Yqe6euZSKyAROpU5o9temdijj3sHppBeG89VPSS6Izo+ZzXDffWLszdKlWkcj4eJKtaIoeuBNYCKQC+xQFOV7VVUPXXCzHGA68IwrY2mu7t26cXrjNuRgPc+hrzpDcnKyXPziBkOGDGHIkMF8u3s7IzvUEWJsXYP1DzmBlNXB/z3+hByhJ7lEfb3Ydni1pDosDGproaZG7NBojowM8fX+tsw1pDKPuLyd7LrhN6i6lh1gvjapkBMloaw5EkdseBXDkwsc/tpGK9qzWxSGb+rZE4YNgxUrxGWahAStI/Jrrv7tNhjIUFX1uKqq9cCnwE0X3kBV1ROqqu4D3LjLrWmpqamodZVytJ6nUO0Yqovp2aOH1pH4jUceeZRaKyzLaV0WUWFRWJEbxKhRo+ghf36Si5SWivdNHVQEKCpq/v1nZPhnlbrb8eUAHB0+o1X3c3v/TFLbl/LR9i4cK5ALvJxq2jTxl/u998Bm0zoav+bqpDoWOHnBn3PPfczjNfzy11e24NlXcjpdTSmqzSKTMjdKSkpizJixrDwVREV9yxfCLM8xU2uD6dOnOy84SbpEQ1LdVPsHQIHjhdKf+WVSraqkZK3idLt+VEW2rgKq18HDIw4THVLLmz/2JKckxElBSgQHi2kgubmwZo3W0fg1r7kOqyjKbEVRdiqKsrOoJWWGZkpJSUFvMKCvKnT5Y0lNa3hxk5qaqnEk/mX69OnU20T7RkuU1yusPBXE2LFjSUqS/ZSS6zRsbb5apbphq2JhM5/Wa2rENkWfOaR44brwtLQr3qztmcO0qTxFeuJEpzxscICVJ8ftJ8ho5e9rryH/rDyw7DR9+0KfPrB4sRi1J2nC1Un1KSD+gj/HnftYs6mqukhV1YGqqg5s64ZygclkIrV7dwyVLShpSE6nr8gnPDyC2FivuNDhMzp16sSYMWNZmxdIXQuuKv54OoA6KzzwwIPOD06SLtCcSnVzk+rjx8V7f6tUd8laiVVnIit+lNPuMzK4jifH70dR4G9re3OmMsBp9+337rpLLOb5+GNcsmhAapKrk+odQBdFURIVRTEBdwHfu/gxnaZfv37oqorB5kGzNf2RqmKqKqB//34oTWzykpzv5ptvpsYC2wub98vPrsL6/CD69ukjZ4tLLudIpbqlSbU/jtNT7FaSs9eREzcUi8m5rRrtwmp4Ytx+ai16/ra2N+U1chScU0RGws03w4EDsHGj1tH4JZcm1aqqWoH5wArgMPC5qqoHFUV5WVGUqQCKogxSFCUXuB34l6IoB10ZU3P07dsXVBV9haxWa0mpK0etqxI/D8ntevfuTXxcLOtPN+/A4qFSI0XVCjfceKOLIpOk8xypVAcEiLeWJtX+VKmOy9tJYF0Z6Z2d0/pxqfiIKh4be4CyahN/X9uLqjo51ckpxoyB7t3h889bdnhAahWX91SrqvqDqqpdVVVNVlX19+c+9mtVVb8/9987VFWNU1U1WFXVKFVVe7o6Jkf17NkTvV6PoSJf61D8WsP3v0+fPhpH4p8UReGGG6eSftZAXpXjTxkb8gIIDQlm5MiRLoxOkoSGpDo8/Oq3Cw1tWVIdGSnOg/mLlBOrqTWFcrLjEJc9RnLbcuaMPkheeRBvrO8pE2tn0Olg+nQwGODtt6GuTuuI/IrXHFTUgtlsppvsq9acviKfsDbhJMj5m5oZM2YMAHtLTA7d3q7C/tIAhg0fQUCA7JmUXK+kRBxEbGqMfUuT6pSUlsfmbQy1lXQ+uYGshDHY9Y79m2+pHh3KeGj4EbLOhHHLW5Ow2GSLX6tFRMCMGXDyJLzzDtg9amKxT5NJdRP69O59rq/aqnUofstYVUjfPr1lP7WG2rVrR2zHDhwscaz3MbtCT2U9DBw40MWRSZJQWnr1fuoGLUmqMzPBn4bXdN77HUZbrctaPy7VP6GY+wans+pwHE9/MdQtj+nzeveGO+6AvXvhyy+1jsZvyKS6CX369AHVLkfraUSpq4TaCtn64QEGDR7CkbMmrA4UPQ6WiuR7wIABLo5KkoSmVpQ3CA1tXqup1Qo5Of6VVKds/4jKoBjyY3q57TFHpOTz1IR9vL7uGt7Z2M1tj+vTxo0Tb2vWwLp1WkfjF2RS3YRrrrkGRVHkYUWN6M+13vTu3VvjSKS+fftSZ4WcyqZXFR8rM5IQH0ekI6VDSXKC5lSqi4ocvyJ+8qRYUpeY2Lr4vIW5vJC4QyvJ6DwBFPemCAtv3cbE1FzmfDyCbVl+dCrUlW6/Xcyv/uwz+N5rhq95LZlUNyEkJIT2HTqiqynVOhS/pKsuQa/Xk+gvv9E8WHJyMgCnqpo+THSqxkRKl66uDkmSfuZopTosTFSfy8ocu9+sLPHeX56Cknd9js5uI73zBLc/tkGv8unDa4gNr+L2f02kWM6wbj2dDmbNgk6dRDuIoxsXbTbYtQu2boX0dKiqcm2cPkIetXVAclIiebsPUKt1IH5IX1NGXHw8hqZOH0ku17FjR4xGA7lVV69U11qhqBo5m9qHKYoSD7wPtANUYJGqqn/XMqbmVKpB9FVf7faLFon3DeN+t271j5F6Kds+4kxcb0ojki/+xKWbF0c5byHMhSKD6/jykdUM++NN3PvOOG7pk4VOlv9aJyAAHnsM3n0Xpk6FJUtg7NjGb3vqFPz1r+KA44WvPA0GuO028XXyfNMVyb+qDkhMTISas2BvwUo5qVWMdWdJ9qdmRg+m1+vplJDAqSbaP05Xi8/Lqws+zQo8rapqD+BaYJ6iKD20CkZVm9dTDY4fViwuFjmEP3QyhRVm0C5rKxmD79U0jgGdinn9zk2sPBTP0gOdNI3FZ4SEwKpVkJAAEybA734nLtmA+Ad0+jS89564JPO3v8EvfiE2M/7mNyIhT00VLSRvvSWq2FKjZPnPAZ07dwZVRVdbjj3IgWdtyTnsVtTaclnx9CAJnTpzIO/4VW+Tdy6pliMQfZeqqnlA3rn/rlAU5TAQCxzSIp7qarBYHEt8w8LEe0eT6jNnxP3qmz5K4PVStn+MqihkDLobDmRpGsvDI4+w+Xg73t/SlcTocq7pKFswW619e9i2DebMgf/3/2DhQvGxM2fEpR6jER59FJ56Chp+7y5aBB07Qs+esHq1mCSyeLHY3ChdRibVDgg/t01Asckh6u6kWMV6+PCmtjlIbhMXF8e6GrDawXCF61wF1Xp0ikKHDh3cG5ykCUVROgP9gG2XfHw2MBtc/wKrYUW5qyrVUVEti8trpKWBqpKy/m3y2vahSqOEelFa94v+PDChiDWHY/nPpu68OHk30SHyd3CrhYXBhx/CXXfBihWwbJkYwp6cDIMGiYS6MYoCEydCXh4sXy4q193klJZLyfYPB5jN59Yz271rVrU9KBJVb0TVG7GGtsce5GXXL899v3/+/kuai42NRVWhqObKTx35NXpi2kZjMrl2aYSkPUVRQoCvgCdVVS2/8HOqqi5SVXWgqqoD27q4GdmRFeUNgoNFfuDoWL3iYoiObnls3iK65CjhFSfJSHT/AcUrMRnszB55CLuq8NraXpytcWxOvtQERYEbb4Q33oDnnoOHHhK90iEhTX/tnXdCTIzoz7ZYXB+rl5FJtQMakjrFyxbA1CVciy0oCltQFDXdr6cu4VqtQ2oWRSbVHic+Ph443zfdmLxqI3EJsg/S1ymKYkQk1B+pqvq1lrE0VKodaf/Q60Xl2ZFKdX09lJf7R1Ld5cRqbDojx+PHaB3KRdqF1TJ/zAHKagL465reVNTKxFpTAQFw993ileymTVpH43FkUu2An9cse1ml2uud+37LiqfnSExMRFEUciob7xyz2uFUlY4uXbq4OTLJnRSx3vQd4LCqqn/ROp7mVKoB2rVzLKk+c0a89/X2D8VuJfnEGnJir6U+INSxL0pLu/zNRVJiypk35gDFlWb+uLIPBw647KEkR3TvLtpFli+X1epLyKTaARUVFQCoBpncuZVefL8rKys1DkRqEBgYSFxsR3IqGk+qT1frsdohJSXFzZFJbjYcuB8YpyjKnnNv12sVTHMq1SCuXjuSVBcXi/e+XqnuWPATQbUlZLhpLXlLdGt3lifH7afWomfIEPjoo6vfflFa98veJCdRFLjhBvFqdssWraPxKPKgogOKzz2zqsYgjSPxL3aT+H43fP8lz9Clazf2bTnV6OeyzyXbMqn2baqqbgQ8ZlhtcyvVMTHw009N385fkuouWauoM4aQE+vZLYIpMeW8dP1PfF94LffdJybEvf661lH5qdRUMX5vxQoxYu8K43Ea5r03mD3bDbFpSFaqHXDm3DVA1SSTarfSGVH0xp+//5JnSE1NpbgGSmovf/pIP2sgOCiQuLg4DSKT/FVpqfidHupg54KjleozZ8TOi4YxfL5Ib62l88k0shJGYdN7/gbDNoH1rF0rJsJ98AH07w95ZwO1Dsv/NEwDKS6GpUu1jsZjyKTaAcXnpv+rBnlgzq0UBdUURFFRkdaRSBfo27cvAEfKLr/QdeRsAH369kXvD0N9JY/RsPjF0UVvMTFiWVx9/dVv1zBOz5c3+nXO3YjJWuPRrR+XMhrh5Zdh/XpxkPSPK/tyJF+OXnW7vn3FP7zXXtM6Eo8h2z8ckJ2dDeY2oPjwM6uHsprCOHEiW+swpAskJSUREhzE4dLaiz5eUqcjv0rh1r79NIpM8leOrihvEBMj3hcWwtUuqvjDOL2ux5dTEdSO0+36ah1Ks40cKXaZDO9bz9/XXsPDI47QP8E17YKN9WTPHnXEJY/lNfR6GD0avv0WDh2CHpotVfUYMkt0QEbmcSxm+SpYC7bAcE6ezMFqlZNXPIVer6d3n74cPnvxpeLDpeI1ekMlW5LcxdEV5Q3atRPvm2oBOXPGt5Pq4OpC4vJ2cizpF15bNOrcGZ6dtIfEqAr+vbE7e076+KgWTzNypBiz98YbWkfiEbzzX5Eb1dXVkZ93GnugTKq1YA+MwGazkZubq3Uo0gUGDhxIYbWCxX7+Y4dKjISGBMtDipLbtaZSfSVVVWL9uS8n1V2Or0RB5VjSdVqH0iqBJhuPjTtAQmQlizamsv+Uly0682YhIWJu9fvvw9mzWkejOZlUNyEnJwdVVbEHNqMMIjmNPUh837OytFmbKzWuf//+AFRZxFOIqsLBMjP9BwxE58sNqJJHam6l2pGkuuFzDbf1OapK1+PLOR3Th4rQWK2jabVAo40nxu0nNryKf6b14OBp+TvbbR57TLwKffddrSPRnPzt14SCc7ts7Y4OxJecym4Sa1MLHTmqL7lNp06diIoIp8oqToblV+soqYUBAwZoHJnkj1xRqW5YY+6rSXVM8UHCK056fZX6QkEmG0+O20+HNtW8JRNr9+nfH4YNgzffBLu96dv7MJlUN6Hk3FYB1ShH9mhCb0LRGX7+OUieQVEU+vYfQLVVPIUcPStWB8t+asnd7HYxyaM5leqQEDCbm65UKwq0bdv6GD1Rt+PLsejNZCWM0ToUpwoOsPLkuH20C6vhjfXXkJbeQeuQ/MPjj0NGhtiy6MdkUt2En2dUy6RaG4oCAUFyVrUH6tmzJzYVLHbIPGsgNCSY+Ph4rcOS/MzZs6L9qDmVakVpelZ1YaG4T6Ox9TF6Gr21luTstWQljMbig0vNQsxWnpm4lx4dSvloexc+3p5CrUWmOy51663QsSO8+qr4B+mn5N+yJpSWlqIYzV57MtoX2PQBlDasTJM8Ro9z45NqrAoZFSZ69LwGxdFBwZLkJA0XsZpTqQbHkmpfbf3ofHIDJksVR5Mnax2KywQabcwdfYCJqSdJS+/A/y4ZyOK9Cf6c77mW0QgvviiGh/vxMhiZKTYhPDwc1VoHdpvWofgtvbWW8HA5fcXTJCcnoyhQbdVxulL3c5ItSe7U3BXlDWJjISen8c+pqm8n1d2OL6c8uD15MX20DsWl9DqY1j+LBZP2EmC0MfUf1zH2Lzew9biP/mC1Nns2dO0KCxaAn47BlctfmhAfHw+qiq6uXE4A0YLdilpbIdsKPJDRaCTAFEBlfS0q4vCiJLlbQ6W6Oe0fACkpsHKl6Mm+dGBNcTHU1PhmUh1ckkNs/i5293rAb67AJrct56XJu1FReHlpf4YuvJmb+2bx+5t2sDGj/WW39/ulLi1lNMIf/wg33wyLFsHcuVpH5Hb+8S+qFRqSOV2tnL+oBV1tOYBMqj1UgNmMVRUtH3FXW00nSS7S0kp1SopInE+fvvxz6enifcOSGF/SfdN/ADia5LutH40x6FWMeju/mrybqb1PsPxAPNe8fDuf7UyS/dbONHUqjBsHzz8P2f63DVlWqpuQkJAAgK6qGCI6axuMH9JViZWzDT8HybMEBJzfqhgb6/2zbiXv05pKNYiBBZe+HmxIqn2tUq3YbXTb9A65HQZRGeKfUzHMRjtTeuUwustpvt/XmbVH49hzMprpQ4/Srb0snrWaosDbb0Pv3jBjBtyxWlwKSks7d4NzVwFmz9YsRFeSL8+aEBQUxLDhwzEXHUGx1Ggdjn+x2wjM30fnzokkJSVpHY3UCJPJ9PN/BwbKCTmS+7W0Ut2li3ifkXH5544dE3mAr21TjDu4nJDSXI6k3KB1KJoLMVu5Z3AGCybuwai389e1vfnhQDx2eZCx9RIT4a9/hXXr6PHjW1pH41YyqXbAI7Nno9itmE7v1ToUv2IsOgq15cyZ8yh6vV7rcKRGyOq0pLWSEggMFHOnmyMuDkym81XpC6WnQ1QU+NrTTuqGf1Md1o4TccO1DsVjpMSU8+Lk3QxMKOK7vYn8Y31PSqoCmv5C6epmzYLx4xm4+NeYqvxnepdMqh3QqVMnpkyZgqnoCMq5Hl/Jxaz1BObtpV+//gwePFjraKQruOOOO7QOQfJzpaXNr1KDSJiTkhqvVKen+17rR1DZaRL2L+HY0OmoOtn5eSGz0c6s4Ue4a2A6h/Ij6P/7W9l5wscuU7ibosCf/0xAdSn9lv2f1tG4jUyqHTR9+nQCTCYCT2wEm0XrcHybasecvQXVUsOcOY/K2cceLCwsTOsQJD/X3BXlF0pJuTypVlXfTKq7bX4Xnd3GkREPaR2KR1IUGNstjwUT96KqMOLVqXy8PVnrsLxbnz4cGzqda9a9RmhlntbRuIVMqh0UFRXFggXPYKgsICh9lUysXUW1Yz6ehrEkk5kzZ9K1a1etI5KuQvZRS1orKWlZpRrOJ9UXLgTJz4eqKh+b/GG3023TO5zqNpbymBSto/FoidEV7PrV1wxJLOTed8bz0rcDsdsbv+2itO4XvfmtRYsufrvAjqm/RUXhmiNfaBSce8mkuhkmTJjASy+9hLGqkOD0FWCr1zok32K3Y85cj7HkOA8//DAPPPCA1hFJTbjwoKIkaaE1leouXaC6GvIuKKLtPXd0pv3l44u9VuyRNYQVZ3FkpG9OXHC26JA6Vj35A7OGH+H3y/oz7V8TqbPKdKklqiNiyel9A8nZ61D8YImebKxqpvHjx6PX63n55d8SfGwlVV0mgkEeamg1u43A4+sxlGYzZ84c7rzzTq0jkhwgk2pJayUl0L9/y772wrF6HTuK/163TuywSPahK/+pGxZRGxxFVt9btA7Fa5gMdv59fxo9O5byzJdD2JndlsfGHqBNoCymXcmVqvWZA+8kafdXtC/cCwS5Nyg3ky+9WmDMmDH87//+D8aaM4QcWYquskjrkLyaUltO8LHlGEqzmT9/vkyovYhMqiWttbanGi7uq163DoYMEZNBfEHg2Xw67f2OY9c+gN0oC0DNoSjwywn7WTJ/BYUVgfxpVR85GaQFcnpNwWIIJCV7rdahuJxMqlto5MiRvPrqq0QHGQg+sgRT7k7wg0sbTqWqGAsOEnroW4JtFbz00ktMmzZN66ikZtBdut9Zktyovl70P7e0pzohAQyG82P1ysth1y4YO9Z5MWotdcMi9DYLh8b438poZ5l8zUmeGLePilojr67sQ1FFM+c3+jmbKYgTccNJzPkRrFatw3Ep+RuxFfr378977/2XydddR0DePkIOf//zBkDp6pTacoKOLsOcs41BAwfw/nvvMWHCBK3DkppJTmaRtNSw+KWllWqD4eKxehs2gN0OY8Y4JTzN6az1pKb9k5xrJssDiq2U3LaCpybso86q569resuK9VUcOACvvgpLl57PoTM7jcNcXw6HD2sbnIvJpLqVQkJCeO6551i4cCGRZh3Bhxdjyt0lq9ZXoqoYCw4Reug7Qqzl4nv3yiu0bdtW68ikFpBLeSQtNawob2mlGi4eq7dunWj7GDq09bF5gs4/fUPw2TwOjpmvdSg+ISGykifH76e63sBf1/SmrNpHeoScpKrOwOvrevL662KKzvffw+9/D6dOQW6HQVh1JjhyROswXUoeVHSSIUOG8P57/+XNN99k+fLlBJSdoCZ2ANbwTqIxS0JfkY85dwe6yiIGDhrMggXPEONrw2D9jKxUS1pqbaUaoEcPWLUK0tJEUj10qNjQ6At6rn+D8ugkTva8TutQfEZCZCWPj9vP39b04o8r+zJ/7AE6tqm+7HYZGfCPf8DaH3tQWWvk2qQCRqbkXzEdaOyQ3+xR3pOAWmwKizamkl7YhmnTxNWew4fh/ffhvffgheEmiqK60SEzU+tQXUpWqp0oNDSU559/noULFxIf3YbAjLUEH/0BXWWh1qFpSqk5S2D6aoKO/EB0gJ0XXniBP/5xoUyofYBMqiUtOaNS/dxzYtLHjTfCTz/5TutH1Mk9dMjYyMEx80CefXCqpOgKnp6wD4tN4Y8r+rIzOxqLTTwXZmbCU0+JF2tvvQXFlWZqrXo+2t6V/2zuRq3FN38Wv/x8GEfyI7hvcDoTJ4oJOr17i39X2dmQWRRGQdtekJMjDkP4KFmpdoEhQ4YwYMAAli1bxtvvvIPu8BIsEZ2pixuIavafDXSKpQbTqZ8wFR/FbDZz30MPMW3aNMxmecjD18jkWtJCQ6W6NUl1dDSsWAHDh4uDir5ySLHH+jexmII4OmyGKMNLTtUpqpLnf7GHN9Zfw7839iDQaOWN9ddw5BHxGmbmTHj5ZVi8YDd2FZYdSGDx/k6U15qYZwNf6px7d1NX3lzfk4mpJxmWXAB0+/lz114L334La4/GMi7xGjj0sciyfZRMql3EYDBw4403Mn78eD777DM++eRTjAe/pj66O/Ud+6IafTixtFkwFRzEnL8fRbUz9aabePDBB4lozW8+ySMpikJgYCAPPvig1qFIfqihUt2a9g8QU0DWrIGvvhLJtbcLqCqhy7aPSL/2fuqD5fOuq0SF1PGrybs5nB/OzuwYwgLreeS5CG6+GTp3Pn87nQJTeuXQJrCeD7Z1ZeFCePFFzcJ2qvSCMB77bDhju53i1r5Zl30+IABGjIDVq6I51HMg14Eo5/somVS7WFBQEDNmzGDq1Kn85z//4YcffiCgJJOaDn2xxKT61mU5VcVQcpzAUzuhrooRI0cye/Zs4uPjtY5MchFFUfj+++8xGo1ahyL5oYZKdXh46++ra1d44YXW348n6LbpPxgsNaL1w838bV23Qa/SK7aUXrGlogd69pW3Vg5PzudIfji//nUMY8bAsGHui7PRnu1WLti02BTu+884jHo7701fz7IDjf+uHzsWVq+CFSe681S7dhcPhvcxMql2k6ioKBYsWMDtt9/O62+8wa6d2wg4c4yauMHY2sRqHV6r6arOEHhyK7qKAlK6dOHxxx6jd+/eWocluYFMqCWtlJRAmza+dSm9tRS7jR4//oPTXUZREiefgz2JosC9g9M5o4/h7rthz57WtS5p7eUlA9h+IobPZ68iPrLqireLjITesWfYfiIGtV8Kyt49oKo+OcRBJtVu1rlzZ/706qts3ryZ115/nYJjK7BGJFAbN9gr+61F3/QuTEXHCA1rw6MLFjB58mS5FESSJJcrLfXupMQV4g8sI6w4i223LtQ6FKkRgSYbn34qqtQPPQRffgmelFouWnTJB9K6NzqFZO2Rjvx+WT+mDz3K7QMuaftopIe/R4cO7MmNJqPtULpUbYKjR6G7713VkJmPBhRFYfjw4bz/3ns8/PDDBFcXEnrwGzHf2mbROjzH2O0Y8w8SeuBrzGcyuf322/n4ow+ZMmWKTKglSXKL1qwo91U9v/4tlYFtOVERJZIbeUjR4wwaBH/4A3z9Nfzzn1pH03yF5WbufWcc3dqV8cbdmxz6mm7tywBYZxslPrB1q6vC05SsVGsoICCAe++9l0mTJvGvf/2L1atXYzqbQ2W368Hgwdua7HaC0leiLz9N/4EDefyxx+jUqZPWUUmS5GdKSmSl+kJt8o8Sn7edHb1noerkr3dP9tRT4nDs44+DckcqOkW9+hdcVkLWht0OD/53DKXVJlY88QPBAY6tHW8XWkObwDrW53dnttEI+/a5OFJtyH91HqBt27a89NJLTJw4kRdefJGgzHVUd5nkmYcYVZWA7M3oy0/z1FNPceONN8pxapIkaaK0FOLitI7Cc/T48R/YdAaOpNygdShSE3Q6+PRTuOcemPPxSEYk5zE+9RQdwqqb3WrszsUxf17Vm+UHE3jrng30jitx+OsUBbq1O8u6Y7GoHWNR9u51SXxak0m1BxkyZAgLnnmGhQsXEpCzhbpOwzyukd+YfwBT8THuu+8+pk6dqnU4kiT5MVmpPk9fX03XLe+RFT+amkDZE+MN2rQRq7x/NWUPC1f0ZWNmB8ID64gIriPQaGXL8XYkRlcwsFMR47ufIsBo1zTebVltefHbwdzW/ziPjDrc7K/v2q6M7SdiOJoyjO57V/jkYUWZVHuYyZMnk5uby0cffYTd3AZL+2u0DulnhtJszLk7GDNmDDNnztQ6HEmS/JiqyoOKF0re+TnP/TQIAAAgAElEQVQBNWc51OUmrUORmkGvh1du3U54YB0H8yI4VhBOZZ2B6noDKw/HcbosGIAwcz13DMzkpet/olNUpdvjLK4M4M5/TyAuopK3709rUS7crd25vmplHN3PfAx5edCxo5Mj1ZZMqj3QrFmz2L9/P/v2bcca0Rk1IETrkEC1E5iVRvv27XnhhRfkYURJkjRVVgYWC8TEaB2JZ+i+YRGl7buTHyPH6HmjyOA6RqbkMzIl/+ePzR51hJp6PeuOduTL3Ul8sLUL/93cjbHdTjG1dzYmg3sq1xabwrR/TST/bCAbFiwmPKhla8bbhtQSF1HJuoqBzAHYu9fnkmqZGXmggwcPcvDQIWxhHVBNQVqHIyg66iMSyc/PZ+PGjVpHI0mSnysoEO/bt9c2Dk8QcWo/7Y9v4cjI2T53Od3fBZpsXN/rJP958EfSf/sZQxILWXU4nv9b1o+TJcFuieGJz4bx47GOvPNAGoM6F7X4fhQFxnTNY/3pLqjgk4cVZVLtYfLy8njxVy9hMwZRnTwOFM/5EdV1Goo9tD1/+MMrHD7c/H4qSZIkZ2lIqtu10zYOT5CatgirIYBj1z6gdSiSC8VHVvHAtcd4ctw+qi0GXlnRj40Zrn1VuepwLG/92JMFk/Zy75DWb0IcnpxPUWUQ2R2HiUq1j/GcjE2iurqaF154kcrqWiqTJ3reWD2dnurksVgNgbzw4osUFhZqHZEkSX5KJtWCvr6aLts+IKv/NOpCorQOR3KD1A5l/Pr6XXRtV8YH27ryyY5kbHbnX6HYvh2+3J3MHQMyeeWWbU65z/4JxQD81HGKrFRLrpGbm8vbb7/N/fc/wInsE1QljUENbKN1WI1SjYFUpYznbEUVM2bO4rXXXiM9PV3rsCRJ8jMyqRYaDigeHjlb61AkNwoxW5k/5gATuuey/lgsf1/bi8o65x2T278f/vtfMbHj/RnrnDbht1dsCXqdnd0BQ+HIEaitdc4dewh5UFEj1dXVrF+/nqU//MDBAwdAUbCGxVLfZRK2NrFah3dV9sAIKrteR33efr7+9ju+/vprEpOSuH7yZCZOnEh4eLjWIUqS5OMKCsTkhCg/L86mpv1LHFDsMlLrUCQ30+vg9gHHiYuo5MNtXfnD8n48OvIQ8ZFVDn39okXApTOuR4kC8r/+BbGxMGfIQaeO8gs02UhtX8bu6u5gs8GhQ9C/v9PuX2syqXYju93O3r17WbZsGevX/0h9fR0EtqEubgCWqBRUk3sOHTiDPTia2pSx1FrrMJYcJ7MgnTfffJO3/vlPhg0dxuTJ1zFkyBAMBvlXTJIk5ysogLZtPXNHlrtE5u6jXdZWttz+F3lA0YkaW6aiBUfjGJpUSPuwGt5K68EfVvRjau8TzBpxFL2uiS2NjdixA959F+Lj4YknIGinrVWxNaZ/QjErM5PEH/btk0m11Dx5eXmsXLmSpT8so7AgH8Vgoi6iM5bortiD23r3k6EhAEtMKpaYVHTVpRiL09m0fScbN26gTXg4v5g0ieuuu46kpCStI5UkyYfk58vWj9QN8oCiJCRGV/D/rt/NR9tT+GZPEkMXhvDqbdsY3TXPoa+32+G7fZ1ZfhCSk2H+fAhy0fCx/gnFvL+1K3mGeDp8+CHU18Ns32hfkkm1C6iqSnZ2Nhs3biRtwwaOHT0KgC2sI/VJo7GGdwK9733r7UER1CUMpi5uIPqzuViK0/n8iy/5/PPPiY9PYPToUYwYMYJu3brJ1eaSJLVKQYF/J9X6+mq6bJUHFKXzQs0WHhl5mO0nzrDiUBxj/nwjE1NzmTfmIDY76K/wdVnFoXy5O4mMojaMGAF33QVGo+vi/PmwYuR4OuRucd0DacD3MjuN2O12Dh8+zMaNG/kxLY3Tp06Jj4fEYIkbgCUy2TOWuLiDToctIgFbRAJ1lhoMJVmcKMvm5Ecf8eGHHxIZFcWokSMZMWIEffv2lS0ikiQ1W0EBdPeMq/SaSN7xGabacnlAUbqIosCQxELeuHsTb6zryV/X9OLmt35BmLme1OPQtSuEhYHBAKdOweFN13AwL5JQcz0PXHuUYfd1c/nF877xZwDYbR7G9blfiPWoPkJmM61gsVj46aefzlWkN1JWWgKKDltoeyydhmINT/CqPmlXUI2BWNr1wNKuB1hrMZSdpKA0h+8WL+Hbb78lKDiY4cOGMXLkSAYNGkRgYKDWIUuS5OFUVVaqUxs2KMoDilIjAk02FvxiH7+csJ+l+xP43Q/9OHQohm2XTMaLCTUz5ZpsJvXIxWy0gdLNpXE19GK3C61mVflgXqqqgrNnXfqY7iST6maqrq5m27ZtbNiwgS1btlJTU42iN1IfFos1qRfWNnGeN1/aUxjMWKO7YI3uQq3NiqH8FJayHFav38CqVaswGo0MHDiIUaNGMnToUDlFRJKkRpWXQ12d/ybV8oCi5CiDXuWmvtkUlAeijoyhqAiqq0Ubc7t20GbvTk3iio+s5EhBovhDbq4mMbiCTKodpKoqK1eu5LXXXqeqqhLFFEhdWDzWuE7YwjqATn4rm0VvwBrRCWtEJ2pVO/qKAgyl2WzZvY8tWzZjNJqYNWsm06ZNk+0hkiRdxN9nVMsDilJLKArExGgdhZAQUcnO7BjOEEmUTKodpyjKdcDfET3yb6uq+solnw8A3gcGAGeAO1VVPeHquJqjsLCQP/3pT2zfvh17aAy13UdhC4nxqBXiXk3RYQvrgC2sA3XqEHTVZwg4vYd//vOfrFm7lheef15OD5Ek6WcNSXV7125o9kiGuip5QFFyK1eMGEyIrARgs3kcN+ZmO/3+teLSpFpRFD3wJjARyAV2KIryvaqqhy642SygVFXVFEVR7gIWAne6Mi5H2f9/e/ceH1V953/89UnIlSTcVUBugQAKKkISIiCCUopQkVq0ivoTV7FslepaXbuPta3rr91ftbbWll0qtUq7tquttha5eKloQSU3LuGiQNCgQhAQhBAgCUm+vz/OQSMCmZBkzszk/Xw88sjMmTMz78MMZz75zvdSX8+iRYv473nzqK45SlXvkRw94xwV063JjPr2XTky4DLa7Stja1k+s2bN4sYbb+T6668noTWHJItIVGjLLdWZxX/yBiiO/VbQUSQWLV8elqfp5RfVKxPHccX2H4flOcOhtVuqc4Gtzrn3AczsGeBKoGFRfSXwgH/5OWCumZlzwQ4H3bFjBw8//DAlJSXUZXTnyIDRuOSMICO1LWbUdsnkYEZ3kj4sYMGCBbzxxj/43vfuY3BbHvIvIm26qD5nxeN82v0cPh4wxtsQpiJIWtD8+UEnCFxaUi1d2lex2kZ4/6GrqiA5OehYzdbaTa49gY8aXN/ubzvhPs65WuAA8KXvtMzsNjMrNrPiPXv2tFJcj3OOu+/+LiUlJVT3HM7hgZOitqCuT+1MfWrnoGOcNpeQQlX/cRzJvIRt28qYM2cOhw8fDjqWiARo1y5vJcWuXYNOEl6dPyrhzLIC3h1zmwYoStTr1amSjTUDvJVn3nmn8TtEgajpx+Ccm++cy3bOZXfr1q1Vn8vMuOmm/0NCQgLJu98lfv9Hjd8pQlX3zqO6d17QMZolrnI3KeVrMDOuv/56UltrmScRiQq7dnkFdfzJVrOIUccGKJZepAGKEv16d65k+5GuHCQNSkqCjtMiWruo3gH0anD9bH/bCfcxs3ZAB7wBi4GaPHkyTzzxBP169yB1699J2vYW1B0NOlbb4upJ3LGG9psW0y0jmccee4yZM2cGnUpEAtYW56huV32IrIKnKRtxNdXto/fbR5Fjjg1WLIkfAevWBZymZbR2UV0EZJlZPzNLBK4FFh63z0LgJv/ydGBZ0P2pj+nTpw+/njePa6+9lsRPtpD+7ovEHfok6FhtglVV0H7zEpLK1zDhsst46sknOf/884OOJSIRoC0W1f2LtYKixJZjRfXqDuNjpqW6VQcqOudqzewO4GW8KfWedM5tNLMHgWLn3ELgt8D/mNlWYB9e4R0xEhMTmT17Nrm5ufzoxz+Gdxbi2nehJr0ndR3Ppq79GV7nPmke54g7vJd2B7aTcGA7cYf2kJKcwnfvv58JEyYEnU5EIsiuXdC/f9ApwmvwivlfHKAoEuU6pNSQkVzN6na5sO5X3lKpUT5WoNXnqXbOLQGWHLftBw0uVwFXt3aO5ho+fDgLnnqKRYsWkZ9fwIaNG6j/eB3WLpGa9B7UdehJbYez2/yy5E1hR6uIr9hBuwPbSTxYjqs5gpmRNXAgeSMnMWXKFM5sa81RItKoNtNS7c/s0fnTrZxZVsDbVz8a9UWHSEO9Ox9i9aEhcGAvlJdDz+PnsoguWqquCTIyMpgxYwYzZsygsrKS1atXU1BQwMr8fPZtewsAl9qZmoye1HU421sgJq6NjaQ5FVdP3CG/NbpiB3GVuwFIS09n5MWjGDlyJDk5OXTq1CngoCISqSorvWWWW7KojvQZzoZs+Su18YmUugGaQk9iSq9OlbyysydHSCalpERFdVuVlpbG2LFjGTt2LM45ysrKKCwsJD8/n/Xr11P38XqvFTvtLOo6nO21YielBR077OzoEeIPHGuN3ok76rVGDxw0iIvyJpObm8ugQYOIb2vD+EXktLS1OaqTqg+QVfYqpf0mUp0UnVO7ipxM784HqXPxbGAoOevWweTJQUdqFhXVLcDMyMzMJDMzk2uvvZbDhw9/oRX7kw/e9nZM6UhNRg9qM3pSl34WxMfg6oD1dcRX7ib+wA4SD5Zj/sDO9IwO5I0bw8iRI8nOzqZjx44BBxWRaPT++97vvn0DjRE2g7cupl1dNRsGXhV0FGkhrbHsd7Tq4w9WLOr8VXJiYLCiiupWkJqaypgxYxgzZgzOOT744AMKCwspKipi7dq1HN31DsTFU5d2BrUZPajNONtboCUa+8o5R1zVAb9vdDkJlR/j6o4SFx/PueeeS27OVHJzcxk4cCBxGtApIs1UWur9zsoKNkc4WH0t5255gR1nXsinndrYyExpEzq3r+bMjMPkp17Gt9f9Neg4zaaiupWZGX379qVv375cc801VFdXs379eoqLi8kvKGRb2SqSWIUlplCT1p3aDj2py+iJS4zgBU5qq2lXUe61RleWQ5X3l2b37j0YOX4KOTk5DBs2jPbtNWhTRFpWaSmkpkL37kEnaX39PlpO+uFdrMy+I+goIq3CDC7K3EX+h+fBzs1Rv1y5iuowS0pKIjs7m+zsbGbPns3evXtZtWoVRUVFFBQWUVHmfbfpUjtzNL2HV2SnnwlxAb5Urt7r0lFRTkJFOXGH9oBzpKSmkp0zgpycHLKzs+nRo0dwGUWkTSgthQEDovOLvSapr2f4+t/zaUYfPug5Oug0Iq0mr99uXljbj710oEtxMYyJ3mkjVVQHrEuXLkycOJGJEydSX1/Pe++9R3FxMYVFRaxbt466XRuwhGSqO/en5ozBuOQOYctmNYdJ2LOZpL2lUF2JmTH4nHPIzbn8swGG7drpLSQi4VNaCuedF3SK1tdvzfN0PlDGa6O/j9MsUl/0pela1Ec5JBE6c0xepjcTWAF5TH7tNRXV0jLi4uLIysoiKyuL6667jqqqKtauXcvSpUtZ8eabJO7aSF1GD2q6Daa2Y+/WWXTGOa9Fes8mEvZ/CM4xIjubr02ZQnZ2Nunp6S3/nCIiIait9QYqXhXrY/bq6xmx6D/4NKM37/ceH3QakVaV3WcPcXGQf+Y0Ji97Gn74w6AjnTYV1REsOTmZvLw88vLy2Lt3L0uWLOFvCxfyyXvLsMRUqroO5GjXgS0zVV9tFQmflJL8yWY4UkFaejpf++Y3ueKKK+gZ5fNGikhs+OADr7CO9UGKA1f+js7lG3lt1P1qpZaY1z6plvPPh5WfjoOVt8OhQxClY7JUVEeJLl26cOONNzJjxgwKCgp44W9/o6iwkKSdJdR26EVNt0HUpzR9mrq4mkMk7NlM4qfbcPV1DBkylGnTrmTs2LEkJSW1wpGIiJyetjDzR/LBPeQ9fw87B4zhvb6XBR1HJCwuugie/l0/6o7WEf/mm/DVrwYd6bSoqI4y8fHxjBo1ilGjRrFz504WLVrEi4sWU1H66mk/ZnJKCpOmXsHUqVPJzMxswbQiIi2nLRTVF/35bhKqDrLihvlQuifoOBFJ8zzHnrw8mDevHZvanceQZctUVEv4de/enVmzZjFz5kwKCwupqKho8mOkpKSQm5tLamoET+EnIoJXVKelxe5qigMK/kBWwdOsmvJ99nc/R0W1tBl5ed7v/H7XMeS1PwcbphlUVMeAhIQERo/WlEsiEttKS71W6licTu+sLcu55Pf/xI5B41kz+f6g44iEVVYWdOsGy5Inc8vqf4PycojCaXpVVIuISFQoLYURI4JOcZqOn85s7NjPLp5VuoKJ86ZR0TWTV7/1PPXtEsMcTiRYZnD55bBo4bnUuTji58//clF9223BhGsCrRstIiIR7+hR2LYt9vpTD3zrKaY8ehlV6d1YOvIBalat9wrwCJ1TWKS1TJkC+/bHk593Fzz+uDfVT5RRUS0iIhGvrAzq6mKnqE48vJ/xv72Bcb//J3YOvIQX7sunMq0NrL0uchITJ0J8PCzuPRs+/hjWrAk6UpOpqBYRkYi3ZYv3OxaK6i77tnDVjy6kf/EzFE19kKVzllLTvlPQsUQC1bEjXHwxLHq3P2Rmwuuvg3NBx2oS9akWEZGIt3w5JCTA+ecHnaR5st5/iYsLfkZVhzNZeO+b7M7MCzqStCQtod4sU6bAvfcaHz74A3r/YKb3H/+SS4KOFTK1VIuISMR7+WUYM8abUi9aDdn8PONX/j92dRvKXy6dy+7tNZ/3n1YfahGmTPF+L+lyIwwdCn/6E3z4YbChmkBFtYiIRLTycli3DiZNCjrJ6Ru66c+MLv4lZb0uZun4h6lKbvoKuCKxbvBgr+fHc3+Jg5tvhvR0mDcPdu0KOlpIVFSLiEhEe+UV73eULrJG/8I/MmrVXN7vdQl/H/MA9fEJQUcSiUhmcOut8NprUPJpb/j2t72pfx56CPLzg47XKBXVIiIS0V56Cc46Kzr7U3ff/AbjFsyk/IxhLBt9Py5OQ5lEvmT+/M9+ZicvoH3SUR559QLo3Rv+9V8hNRUuvRRefDHopKek/90iIhKx6urg1VfhiiuibyXFTuUbmThvGgfOyOKV0T+iPl6LusSy+cs1KLEldGpfw6wxm5j7+hD+c1ohvc44wyusn30Wpk3z5rC+9dagY56QimoREYlYxcWwb1/09adO3V/O5b+8nLqEFF6as4SajR8EHUnakGgv8O+6bD2/en0Ijy0byiPTCyAjw5ti7+qrYdYsqKnxuoZEGHX/EBGRiPXMM14L9YQJQScJXUrFLib/4iskHd7H0jlLqOzSJ+hIIlGlT5dKrst5j7mvD6FoWzdvY1oavPCC97XV7bfD3LnBhjwBFdUiIhKR1qyBX/3KmwSga9eg04Qm5cDHTPn5paTv3cZLty9ib+8Lg44kEpUeveZtzupwhK/P+wq7KlK8jUlJ8NxzcOWVMGcO/PKXwYY8jopqERGJOLW1XrfJrl3hkUeCThOas0pXcNV/jvAK6jlL2DloXNCRRKJW17RqXvjnV9h3KJmrfv2Vz2fVS0z05q/++tfhzjvhwQe9wRcRQH2qRUSkycxsEvAYEA884Zz7SUs+/i9+AatXe5+dnSJ8Be/U/eVc8PJDDHl9LhXd+vPSHYvZW1YBH2tBF5HGnKj/921jNwEwrNdefnfz69zw5KWcc473B/YNN0BiYqI3cPHmm+GHP/QWT3riCejbN8zpv0hFtYiINImZxQP/BXwF2A4UmdlC59w7LfUcW7bA1KkwfXpLPWLLsPo6kir30mHPVrp8tJZeG1+i5zuvEFdfx+bRt5A//RGOpmRAmQpqkZZw9YgyhvZ4ntv+fg233AL/8i9w+eWQk5PA4Gv/hx79p9PxoX+jY+YIMq6aQPxVV0JuLvTujUtIpLoaNm2C9ethyBAYPrz1sqqoFhGRpsoFtjrn3gcws2eAK4EWK6rnz/cG+IdzGr2rfjScpEN7MVfv/dTXw7HLrp64uloSqyq+cJ+Krv14d+y32HDZXRzs2i98YUVi2Ilar//xD1i61BuruHix11ANBkzzf4DnIfn5I9QRTx3x1B/3GPfd17pFtTnnWu/RW4mZ7QE0P1HougKfBB1CYpbeX03TxznXLegQzWFm04FJzrlb/es3AiOdc3c02Oc24Db/6iBgc9iDeqL9/Rnt+UHHEAmiPT8EewwhnbejsqU62j+Qws3Mip1z2UHnkNik95eciHNuPjA/6BzR/v6M9vygY4gE0Z4fouMYNPuHiIg01Q6gV4PrZ/vbRETaLBXVIiLSVEVAlpn1M7NE4FpgYcCZREQCFZXdP6TJAv8KVmKa3l9tjHOu1szuAF7Gm1LvSefcxoBjnUy0vz+jPT/oGCJBtOeHKDiGqByoKCIiIiISSdT9Q0RERESkmVRUi4iIiIg0k4rqGGdmk8xss5ltNbPvBZ1HYoeZPWlmu81sQ9BZRBo715lZkpk9699eYGZ9w5/y5ELIP9bMVptZrT9PeMQJ4RjuNrN3zGydmb1mZn2CyHkyIeSfbWbrzWytmb1pZucGkfNUQv3MN7NvmJkzs4iboi6E12Gmme3xX4e1ZnZrEDlPREV1DGuwlPDlwLnAdZF4EpCotQCYFHQIkRDPdbcAnzrnBgCPAg+FN+XJhZj/Q2Am8MfwpgtNiMewBsh2zp0PPAc8HN6UJxdi/j86585zzg3Dy/7zMMc8pVA/880sHbgTKAhvwsY1oW551jk3zP95IqwhT0FFdWz7bClh51wNcGwpYZFmc84tB/YFnUOE0M51VwK/8y8/B1xmFs5F0E+p0fzOuW3OuXXwpZWXI0Uox/C6c+6wfzUfb37zSBFK/oZr1LcHIm2mh1A/8/8v3h+VVeEMF6KorltUVMe2nsBHDa5v97eJiMSSUM51n+3jnKsFDgBdwpKucbFwrm7qMdwCLG3VRE0TUn4zu93M3sNrqf5OmLKFqtFjMLPhQC/n3OJwBmuCUN9H3/C7ET1nZr1OcHsgVFSLiIhI2JjZDUA28NOgszSVc+6/nHP9gfuA+4PO0xRmFofXZeW7QWdppheBvn43olf5/BuowKmojm1aSlhE2oJQznWf7WNm7YAOwN6wpGtcLJyrQzoGM5sA/Dsw1TlXHaZsoWjqa/AMMK1VEzVdY8eQDgwF3jCzbUAesDDCBis2+jo45/Y2eO88AYwIU7ZGqaiObVpKWETaglDOdQuBm/zL04FlLnJWP4uFc3Wjx2BmFwKP4xXUuwPIeCqh5M9qcHUKUBrGfKE45TE45w4457o65/o65/ri9Wuf6pwrDibuCYXyOnRvcHUq8G4Y852SiuoY5vcbPLaU8LvAnyJ4KWGJMmb2v8BKYJCZbTezW4LOJG3Tyc51ZvagmU31d/st0MXMtgJ3AxEzxWgo+c0sx8y2A1cDj5tZRJ3LQ3wNfgqkAX/2p0KLmD8cQsx/h5ltNLO1eO+hm07ycIEI8RgiWojH8B3/dSjB69c+M5i0X6ZlykVEREREmkkt1SIiIiIizaSiWkRERESkmVRUi4iIiIg0k4pqEREREZFmUlEtIiIiItJMKqolopnZ2828/0wzm9uM+28zs67NyWJm08zs3NPNICLS1pnZODNb1Nr3EWkOFdUS0Zxzo4LOcEwzskwDVFSLiITIzOIjIEO7oDNIdFFRLRHNzCr9393NbLm/YMAGM7v4FPe52cy2mFkhMLrB9gVmNv0Ejz3Of+zFZrbZzH5tZl/6v3Fsf//yfWa23sxKzOwn/rZZZlbkb3vezFLNbBTeik8/9bP3939eMrNVZrbCzAa3wD+ViEhEMLN7zew7/uVHzWyZf/lSM/uDmV3nnz83mNlDDe5XaWY/8xf1uMjMJpnZJjNbDVzVyHNe4p9j15rZGjNL929KM7Pn/Mf5g5mZv/8P/PP1BjOb32D7G2b2CzMrBu40s27++bzI/xl9sgwiKqolWswAXnbODQMuANaeaCd/+dL/wCumxxB6C3EuMMffvz+nOIGb2eXAlcBI59wFwMP+TX9xzuX4294FbnHOvY23xOq9zrlhzrn3gPnAHOfcCOAe4L9DzCgiEg1WAMcaPrLxCtsEf9sW4CHgUmAYkGNm0/x92wMF/jm0GPgNcAUwAjirkee8B7jd/4y4GDjib78QuAvv3J7J5w0tc/3z9VAgBfhag8dKdM5lO+d+BjwGPOqcywG+ATzRpH8JaVNUVEu0KAJuNrMHgPOccwdPst9I4A3n3B7nXA3wbIiPX+ice985Vwf8L15BfjITgKecc4cBnHP7/O1D/Zbn9cD1wJDj72hmacAo/GV6gceB7iFmFBGJBquAEWaWAVQDK/GK64uB/Xx+jq4F/gCM9e9XBzzvXx4MlDnnSp239PPTjTznW8DP/Rbyjv5jg3du3+6cq8drjOnrbx9vZgX++fpSvni+bvi5MQGY65+vFwIZ/nlc5EvUX0iignNuuZmNBaYAC8zs58653zfxYWrx/5D0u3ckNnyK45/yNGIuAKY550rMbCYw7gT7xAH7/dYUEZGY45w7amZlwEzgbWAdMB4YAGzDa3k+kSq/YeN0nvMnZrYYmAy8ZWZf9W+qbrBbHdDOzJLxviHMds595DfWJDfY71CDy3FAnnOu6nRySduilmqJCmbWB9jlnPsN3tdvw0+yawFwiZl18b9uvLrBbdv4/GQ+FUhocFuumfXzi+1vAm+eIs6reK3mqX62zv72dGCn/7zXN9j/oH8bzrkKoMzMrvbva2Z2wSmeS0QkGq3A65Kx3L88G1gDFOKdo7v6gxGvA/5xgvtvAvqaWX//+nWnejIz6++cW++cewjvm81TjVU5VkB/4rc6Tz/Fvq/gdQ089jxqEJGTUlEt0WIcUGJma/CK3sdOtGbO5UsAAAEfSURBVJNzbifwAN7XjW/h9W0+5jd4J/MS4CK+2BpRBMz19y8D/nqyIM65l/C+Biz2vxK8x7/p+3hF/Vt4HwjHPAPc6w+e6Y9XcN/i59iI1z9bRCSWrMDr2rbSObcLqAJW+Ofo7wGvAyXAKufc346/s98yfBuw2B+ouLuR57vLH3S4DjgKLD3Zjs65/XifBxuAl/HO/yfzHSDbzNaZ2Tt4fxyInJB5XZVE2i4zGwfc45z7WmP7ioiIiJyIWqpFRERERJpJLdUStcysAEg6bvONzrn1QeQREZHWZWY3A3cet/kt59ztQeQRaUhFtYiIiIhIM6n7h4iIiIhIM6moFhERERFpJhXVIiIiIiLNpKJaRERERKSZVFSLiIiIiDTT/we4RwIhGx8dPQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 864x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12, 8))\n",
    "\n",
    "plt.subplot(1,2,1)\n",
    "sns.violinplot(x = 'is_duplicate', y = 'word_share', data = df[0:])\n",
    "\n",
    "plt.subplot(1,2,2)\n",
    "sns.distplot(df[df['is_duplicate'] == 1.0]['word_share'][0:] , label = \"1\", color = 'red')\n",
    "sns.distplot(df[df['is_duplicate'] == 0.0]['word_share'][0:] , label = \"0\" , color = 'blue' )\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "RcwMI4xps5YJ"
   },
   "source": [
    "- The distributions for normalized word_share have some overlap on the far right-hand side, i.e., there are quite a lot of questions with high word similarity\n",
    "- The average word share and Common no. of words of qid1 and qid2 is more when they are duplicate(Similar)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "K0AbOS65s5YL"
   },
   "source": [
    "<h4> 3.3.1.2 Feature: word_Common </h4>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "_mCFvztcs5YM",
    "outputId": "008ac763-a832-4c11-88fa-5da52cdb9305"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12, 8))\n",
    "\n",
    "plt.subplot(1,2,1)\n",
    "sns.violinplot(x = 'is_duplicate', y = 'word_Common', data = df[0:])\n",
    "\n",
    "plt.subplot(1,2,2)\n",
    "sns.distplot(df[df['is_duplicate'] == 1.0]['word_Common'][0:] , label = \"1\", color = 'red')\n",
    "sns.distplot(df[df['is_duplicate'] == 0.0]['word_Common'][0:] , label = \"0\" , color = 'blue' )\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "9Ej1ouEVs5YR"
   },
   "source": [
    "<p> The distributions of the word_Common feature in similar and non-similar questions are highly overlapping </p>"
   ]
  }
 ],
 "metadata": {
  "colab": {
   "name": "1.Quora.ipynb",
   "provenance": [],
   "version": "0.3.2"
  },
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}