Using_Topic_Modelling_to_Analyse_10_K_Filings_v3.ipynb

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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7GIPtxjml0Px",
        "colab_type": "text"
      },
      "source": [
        "# Using Topic Modelling to Analyse 10-K Filings - Auquan"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "W6YLH48isWKu",
        "colab_type": "text"
      },
      "source": [
        "**by Wian Stipp**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "irF5jnBqsaR7",
        "colab_type": "text"
      },
      "source": [
        "## 1. Getting The Data"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qnfnCFV9se7H",
        "colab_type": "text"
      },
      "source": [
        "### 1.1. Install Relevant Packages"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CO48nkWJsjtW",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#prevents printing the install messages\n",
        "%%capture\n",
        "!pip install sec-edgar-downloader\n",
        "!pip install html2text"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5r1X7lpWsoNF",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from sec_edgar_downloader import Downloader\n",
        "import textwrap\n",
        "import html2text\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "%matplotlib inline\n",
        "import os\n",
        "from tqdm.notebook import tqdm"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4QbGyMsRs96E",
        "colab_type": "text"
      },
      "source": [
        "### 1.2. Download The Data"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "VsBrNNx3tCID",
        "colab_type": "text"
      },
      "source": [
        "We use a tool called SEC Edgar Downloader to scrape the html from the 10-k reports. For more details: https://pypi.org/project/sec-edgar-downloader/.\n",
        "\n",
        "We will download the data directly into the default working directory that Google Colab uses. We also need to specify which companies we would like data for."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WlnJRquVtNGI",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "PATH = \"/content\"\n",
        "dl = Downloader(PATH)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uPkjObWhYRCf",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "SYMBOLS = [\"GOOGL\", \"MSFT\", \"AMZN\",  \"IBM\", \"NVDA\"]\n",
        "           # The ARGS variable holds some hardcoded information that we might need to reuse\n",
        "ARGS = {\"Type of Report\": \"10-K\",\n",
        "        \"Companies\": SYMBOLS,\n",
        "        }"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "pm3fUPJbuHGX",
        "colab_type": "text"
      },
      "source": [
        "Then we can simply download the data by looping through each of the companies and downloading using the SEC Edgar tool. The data will download into the \"content\" directory as we specified above.\n",
        "\n",
        "Throughout this notebook we also use the tqdm_notebook tool from tqdm. This is essentially an awesome progress bar that helps you see how far you are through a loop and the expected remaining time. https://github.com/tqdm/tqdm"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "iRx1xAp9t-Lu",
        "colab_type": "code",
        "outputId": "14dbc91d-8033-457a-f124-bbb2d836703e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 117,
          "referenced_widgets": [
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            "fde3b40ca860411cb151d722cc048384"
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      },
      "source": [
        "for symbol in tqdm_notebook(SYMBOLS):\n",
        "  dl.get(ARGS[\"Type of Report\"], symbol)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:1: TqdmDeprecationWarning: This function will be removed in tqdm==5.0.0\n",
            "Please use `tqdm.notebook.tqdm` instead of `tqdm.tqdm_notebook`\n",
            "  \"\"\"Entry point for launching an IPython kernel.\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
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              "HBox(children=(FloatProgress(value=0.0, max=5.0), HTML(value='')))"
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        {
          "output_type": "stream",
          "text": [
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6yWy1wcEwNQ7",
        "colab_type": "text"
      },
      "source": [
        "### 1.3. Extract Relevant Text"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NTViChS-wR4u",
        "colab_type": "text"
      },
      "source": [
        "Now that we have downloaded the data, we need to extract the relevant text from the files. We also need to extract the year of the 10-k filing. This is easy since it is included in the document name.\n",
        "\n",
        "To extract the text we just use a rough approximation to take a portion of text near the start of the report.\n",
        "\n",
        "Html2text is another tool used here to convert from HTML, which all the files are in, to text."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XTrW9YQUwXyU",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def createDataframe(company_list):\n",
        "\n",
        "  df = pd.DataFrame(columns=[\"Company\", \"Year\", \"Report\"])\n",
        "  start_index = {\"AMZN\": 49257}\n",
        "  end_index = {\"AMZN\": 185190}\n",
        "\n",
        "  for company in tqdm(company_list):\n",
        "    try:\n",
        "      reports = os.listdir(PATH + \"/sec_edgar_filings/\" + company + \"/10-K\")\n",
        "      for index, report in enumerate(reports):\n",
        "        opened_file = open(PATH + \"/sec_edgar_filings/\" + company + \"/10-K/\" + report, \"r\")\n",
        "        full_text = opened_file.read()\n",
        "        full_text_length = len(full_text)\n",
        "        opened_file.close()\n",
        "        try:\n",
        "          if company in start_index.keys():\n",
        "            start = start_index[company]\n",
        "            end = end_index[company]\n",
        "          else:\n",
        "            start = 44800\n",
        "            end = 200000\n",
        "          text = html2text.html2text(full_text[start:end])\n",
        "          t_len = len(text)\n",
        "          relevant_text = text[round(t_len*0.003):round(t_len*0.08)]\n",
        "          yr = int(report.split(\"-\")[1])\n",
        "          if yr > 20:\n",
        "            yr = 1900 + yr\n",
        "          else:\n",
        "            yr = 2000 + yr\n",
        "          df = df.append({\"Company\": company, \"Year\": yr, \"Report\": relevant_text}, ignore_index=True)\n",
        "        except:\n",
        "          print(company, report, \"Failed\")\n",
        "    except: pass\n",
        "  return df\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RbxhAwd3xLMH",
        "colab_type": "code",
        "outputId": "2223c940-0af7-4142-9616-6c607c0a3010",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 117,
          "referenced_widgets": [
            "8a28b3400b1a462f85b0a22fb150b4b0",
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      },
      "source": [
        "dataframe = createDataframe(ARGS[\"Companies\"])"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:7: TqdmDeprecationWarning: This function will be removed in tqdm==5.0.0\n",
            "Please use `tqdm.notebook.tqdm` instead of `tqdm.tqdm_notebook`\n",
            "  import sys\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "8a28b3400b1a462f85b0a22fb150b4b0",
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              "HBox(children=(FloatProgress(value=0.0, max=5.0), HTML(value='')))"
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          "output_type": "stream",
          "text": [
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rukBnhbYxOrK",
        "colab_type": "text"
      },
      "source": [
        "### 1.4. Data Cleaning"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Bv_8wkNdxQov",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import re\n",
        "\n",
        "def clean_dataset(text):\n",
        "    # Make Lowercase\n",
        "    text = text.lower()\n",
        "    # Remove some remaining html\n",
        "    text = re.sub(r\"font\", \"\", text)\n",
        "    text = re.sub(r\"size\", \"\", text)\n",
        "    text = re.sub(r\"pt\", \"\", text)\n",
        "    text = re.sub(r\"px\", \"\", text)\n",
        "    text = re.sub(r\"padding\", \"\", text)\n",
        "    text = re.sub(r\"family\", \"\", text)\n",
        "    text = re.sub(r\"style\", \"\", text)\n",
        "    # Remove HTML special entities (e.g. &amp;)\n",
        "    text = re.sub(r\"\\&\\w*;\", \"\", text)\n",
        "    # Remove tickers\n",
        "    text = re.sub(r\"\\$\\w*\", \"\", text)\n",
        "    # Remove hyperlinks & URLs\n",
        "    text = re.sub(r\"https?:\\/\\/.*\\/\\w*\", \"\", text)\n",
        "    text = re.sub(r\"http(\\S)+\", \"\", text)\n",
        "    text = re.sub(r\"http ...\", \"\", text)\n",
        "    # Remove whitespace (including new line characters)\n",
        "    text = re.sub(r\"\\s\\s+\", \"\", text)\n",
        "    text = re.sub(r\"[ ]{2, }\", \" \", text)\n",
        "    # &, < and >\n",
        "    text = re.sub(r\"&amp;?\", \"and\", text)\n",
        "    text = re.sub(r\"&lt;\", \"<\", text)\n",
        "    text = re.sub(r\"&gt;\", \">\", text)\n",
        "    # Insert space between words and punctuation marks\n",
        "    text = re.sub(r'\\[\\[(?:[^\\]|]*\\|)?([^\\]|]*)\\]\\]', r'\\1', text)\n",
        "    # Remove characters beyond Basic Multilingual Plane (BMP) of Unicode:\n",
        "    text = \"\".join(c for c in text if c <= \"\\uFFFF\")\n",
        "    text = text.strip()\n",
        "    text = \" \".join(text.split())\n",
        "\n",
        "    return text"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FLtvAkYdyZuL",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "dataframe[\"Report\"] = dataframe[\"Report\"].apply(clean_dataset)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0aieYaEExvP1",
        "colab_type": "text"
      },
      "source": [
        "## 2. Data Exploration"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "DbNrC0WGx1VB",
        "colab_type": "text"
      },
      "source": [
        "### 2.1. Word Cloud"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Ay_msP3pyCH5",
        "colab_type": "text"
      },
      "source": [
        "Word clouds, as basic as they are, can be useful to visually represent the main themes in the report."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "E6sSt7jMyBop",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from wordcloud import WordCloud"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dgWHEpRyyRVN",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def make_wordcloud(series):\n",
        "  all_text = ','.join(list(series.values))\n",
        "  wordcloud = WordCloud(background_color=\"white\", max_words=5000, contour_width=3)\n",
        "  wordcloud.generate(all_text)\n",
        "  return wordcloud.to_image()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gePKYppox6uh",
        "colab_type": "code",
        "outputId": "59655edd-d263-4b6a-b063-56e01c99227a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 217
        }
      },
      "source": [
        "make_wordcloud(dataframe.Report)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<PIL.Image.Image image mode=RGB size=400x200 at 0x7FEE2AC19400>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 46
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3q94CRSHyp_x",
        "colab_type": "text"
      },
      "source": [
        "### 2.2. Word Cloud by Company"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Bm5MW8dSyAp8",
        "colab_type": "text"
      },
      "source": [
        "Let's see if we can see any immediate differences between companies by just using the word cloud. Since we wrote the word cloud generation into a function called <code>make_wordcloud</code> we can easily reuse it! Let's look at Alphabet vs Microsoft."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GBHtlPUFao-V",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import numpy as np"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9Eeru3Pez2rp",
        "colab_type": "code",
        "outputId": "cbbad346-1af9-4e0c-a99c-0dcb205a25ab",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 217
        }
      },
      "source": [
        "GOOGL_condition = (dataframe.Company == \"GOOGL\")\n",
        "make_wordcloud(dataframe[GOOGL_condition].Report)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<PIL.Image.Image image mode=RGB size=400x200 at 0x7FEE48A604A8>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "juTapbEfyy9R",
        "colab_type": "code",
        "outputId": "a810a262-7b6f-457e-9d06-cfc2ee866249",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 217
        }
      },
      "source": [
        "MSFT_condition = (dataframe.Company == \"MSFT\")\n",
        "make_wordcloud(dataframe[MSFT_condition].Report)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "image/png": 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9Qyp6GACs9o8kovss9vdRRICiIopyB0iLVPSQxf4egnAAwE+08YSz2L4cNisTACjwA4CfaJeJH7c6PkFRCYJwUVR6UU/zfxgGFEk9d+P7LDZGktTiW6cAQHyaRqmVPnXdu3K1OCF9YLkkkvL//uhX+m7z2JlZsclqq9HBoABA3oSUZ294H4AaMyMrqygx4uUQFJm2eNRjy97WxMppNRkAFKRFv/n48s0Hzm49UpUYrahq1gFAT781WilmVOdxWA6XFwDae000Bccxt9cf8VrhTYWrUYxWZ6xaGrreD5oLjES/BnB+pT2MJTUFEcqQS1IyBIYQ1cGJWOja/0RD+2Of/UBSlEzIu2vm2Bk5KXEKCX2zAZIsfPadoa6CjezeQ3H9hLy/bDlQ3tbT3GdM0SgA4MfSWgBg49jCURl0x2weL5/N2lRena5WVOn6HF7fg1PHf1FcVhQX7Q8EAOBMZ88dE0Z/evy0WiQYEx+TIJcG2/cFAuXdOoIkNSIhzbynrvGeSWMB4I39R+kGgwLL4fbWd+gbOw13LRlP3y9TYMm5eSZPpc3XKGanyrn5jFIWnmq1f8TljMMwtcO1CceiSMpBz8VIym53rsNQJSCY3bWBJG0I4IGA3k80AYDPV+X1V6LowLcORcR+ooHPyoELoexA1eZ3dtadbvF7/dHJmtmrJ1/70HwWN/LXCQDsZufmd3ee3H5G16YHgKgk9dTlY1c8soAnHPSpeXLOK9UnGhfeMePx9+4Mb8Rhcd6Q9AjhI37/xUPTrxsfpH/8u+82v7szlPOjkr8lZscOfwtN5W0/fXqg6li9ocdM+AmJUqyOUxROz563ZmpUEtNAYYT9DweCIm9ufnQQBUGee3dNKKW6pEWplf7uvVtDieGUGx6cc8ODc0IpDF0VjQdeWhH6r8XufumTXXwe2+X2PXbT9ASt/GRl2+8/+MnnDzx6wzSTzbX2h+Kalt6PthybkJc4b0LmVztP//HDHSqZkH6pkmMU/WbH797/6ZrJ2Xmp0Z9sPVHRqPvUe3J8bsKyaXmhTXX0RtAum20ujWKQajmoMDLZXQytc0TIBAMbcBaXOyKDlyDcPj8ASAXMrbpLgGPwBCoIm3uALgpZV76z8xhJURwW/tVDNzAMwRhrusvH4tFZb23/2eX1bzlV/cySaQCw40w9AMzMSRHzuAAQoEgZj1fS0Z2pUdm9nlGx0WWd3XTdFoOptq+/WqdXCYXbKmo1IiEFlGDwXsGs9OQHN/zwwaqlQeZkhfyzk6Wj42OCDQaZFRKBzx+YXpgS/BQxBVaj5Uu9u1jGye527lNyx2TIBr3PAt4CgAAAxmGPCrUzAgCr4xORYNU5ykCRXPI7+kQlfwcAOKw8AJAI7zHb/snnzXF59rBZGcM8u03v7Pjk9+voc56Q21bb9ekL607tOquMjrzV3VDa8ocVb1oNdgDg8NkUSbVWdbZWde799uirPzwbKh3mrZlWfaLxyOZTD76xhh0m/g5vKiZ8hFAqmLRkdCg9b3KG1WC3Ge2mXkvT2fZhek6DIqlPXvhu87u7QomGbpOh21RzsjFUFF5s/68qGis6Oxp7M4sSu5r14+fkFO+rdru8boc3IV2bPWZAk7V7/Um7xaXQSvPHp5zYW7V4zWSnwTFTrswqSvS4fQ0/N7pTNUtyU9rqdSnZMV69o7Gx6abJuZM1qolzc4/vrmyv6VmSEJtZlOhyeH4s6W6r1wUIcp5Gk5Id01nft7e4bbRWuuzmOR2NvZnxWgSBoG4LAOK1simFyQCglovefmI5TVw8NedIWXPoXaRqFfRJRUdvThxzkz4cmTFqWkXV0BN5K7NRZ6QnX5nRkZVcFwW9zeHxE6GKbRot+gF9TdI5weQjAvSib1J6QrjZau+5ReKVgoDDXlSYuam48qfS2ievmVLdpe82WQFg+bkVYn60NkerxkJslfKiNQBw2/giAPj70vkAkBOlHsocioPj/1m9AgCSlfIgM91OfrSWYfzU028lKWp/aUN6/MAzZy62da7Dk6Pey5Y/NDnq/T7XsUh3FNQUDqor5F8bQgk/GQSp6AEEWBLRAxFLadSeavr0D+sBYMrysd80vrO17+Nt+k+e+eTehrKWgxtPhPOb+6z02z5qZs6Hp/76Q/+nPxg+fXv/H+MzY3Qt+pdueIfwB4LM01eO5/DZTpvrxE9l4U3t/fYoAMy4fgKLM2g8TVxc9Mwn976y5akXv3tsmJ4H8cUrm2lpNW5+was/Prux84OtfR9/euYfj79/58pHFzCmZhfV/0tDzthkxmQqnAIACIrYrW4un63rMABAT7tB125YuHpiQ0VHkMfvC/BFXK/Lq9BKCD8BAMd3VSy9fWpaflx/jzl3XEpWUWJNaeuCGyeUH28MNogAVJ1qDhCBICU5KyY5OzoxI+rwT2cEIm5DRWew5SDPSG7tw83HWrqNoZSipGgOCweATScryBGoyiR8bmFCNAAcqGqKOG3ZfbaePpmckTiSLg0PioITDRG+eUdqWgGAx2ZlnBOLHv/ASjncLpTu7eV3hoFVk/IBwORwnWzs3HO2AQBUYsGk9PPG99gIbJUuzRyK0SCtw1o08fwO0rDawYtZ/2Io0xyJpMiIa34AQBAOm5WBnJvfReRc/+ZPFEnFZ0T//osH6SkVh8ees3rKA6/fEnHb/utXt1oN9rj0qFc2P5WUEwcACIJkT0h7eeMTKIa2Vnce3HBezPGE3CnLxgLAvm+PMtrRtehri5sAYP6aqSO++wjQdxo3vLUdABbeMeOVLU8VzcoVy4U8ITcuPWrh7TPufXX15fT/qiIQICVyQdWpFpGEv29zidPu5gmYzmUIiqAoCgjS2axvru5uquqKTdVs+fRQ/dkOAKD55Wrx/i2nlVpJsMFxs7I3fXRwwtzcIAXDUVO/vbNZn5wV7XZ6s0cnBlsO8oykz8tn5GsVolD1k5DLWTEuBwAadIZXtx4kI6mmfMSgb8AdM8cAQJ/V8f5u5qOu7dZ/d+wsAExKTwjO3S4TH+4t9hJEKOVgdTNtUzq/IJ1zbvIl4nJpUVXd1ce4i1a96ZP9p65IZ0KRFaOm56RHaluO1rUBwOKiyOZXAGB3e1v7TU6vT29z+IhAj9nWrDeeae/x+Amjw9Vnc2wvr6OPPiLQZ3N4/MTOinqD3QkA20praIrR4aLr0jzBxlt6jFWtvWcauoI3zpyRqnkTjukelnFyzN5qDW/yJd+zJ+D/WV9bJE8iKUrC5hu9DiHONfrsMTy5zm1WckTOgFfC4lt9LhGLR3MqOOcVDQEiULqvEgAW3D4dG2xEM3PVxPef/NLnGaSgJQPkgXXHAWDpfXMY06KoZHX2+NSq4w3Hfyyde/OUIH3emqn7vzt2el+lWW+VqSVB+r7vjgFAfGZM+ughTTRHgkMbTwSIAIfPvvuvN16Q+RL6f/WQURCfmhOL4SgAhO4DLr/zvCXnNTefNwR/6s3VAJCaG0sGSBRDMwoGbPxmLC0KVqcb7GzWj5mRxWLjoZe445lrEBSJS1HT1dPPVQ/WGkmf0+NVwVVDEI8unHy8vr3dYFl3/Ozplq4lo7NSNAocwwx2Z7fJerKhg83CP71vZZB/Zk7KsjHZ207XfHrgVFu/acW43Gi52OnxHatv/+JwqddPCLmcF6+bfRGPcmiIedyarr41/1p/+4wx6VFKfyBwpKb1kwOnAIDPYT00f2KQE0Fgdm7qttM1LX2mFzfsvXPGGLVEaHS4DlY1f7y/mKIoKZ8b3OK8Ulg1Mf9PnXsP1bT0mGwAsGxo8ysURWq69XsqG6UCnojL4eA4j42jCNLWb95UUvmHZbMQBNGIhQiCrD1yusdsy4/Tuv0DYlpvcxyubcmPi9pUUlmUGMPB8ca+2h6z7dnF04UcNgCI+ByPxT8uO35IHVam7B6jp9zua9Hyp8q5eZd8w7t6yj0B/9au0zqXeZwylY3i4xQpddaeKkunze/2BHwSFt/id+lc5lxpnCfA3B7qbu6jRVLaKKb5D4fHjknRtlYPcgRtr+122d0AkDk2gp9gdLKm6nhDR113KLFgWpYmQdnXbjiw7sTKR8+7QexfdwwA5t96WdMrAKg91QwAORPShRL+BZkvof9XFUExEXGLzRcImLxuEYvt8PvEbM6+juYJUXEqnsBNEhigKCD9bqeKJzB53RI2x+z2RAlEdIMcLiso6cIvgYbbB4xMWg0FIZez9sHrn/zip7PtuqZe49vbmbPpMSnMDZOXVs3FUHTLqap9lU37KgettjQS4Xt3LY+RS+BKoDAxKj8+6r3dx5/7ZkconcvC/3nbEu3gXYLHr5lS0tzVY7ZtK6neVlIdpIt4nH/ffe23R8t3ltdfkV4FsWhU5us/HKGlVW6cht4ujAi9zdmiNyWqZEqRQMbnHaxtXjIq62RzZ7PexGXhrf2m5j4jfYxXSNO1SqvLY3ef33Cwub2lbd1cFp6skh+sbY6WitO1Sv45Pb2Ay3bzONHK8888gvOzgluo4BYCgN3fJmIlXtoNYwhqJ9xRPFmqUBPFkx3R1yYJ1W0OfaJQxUIwGU+m4Ag5HlaqUGPzu+0Ec1/GZhyYFkrVzB1xABDJhQyKqc9Knzwy7c9Ddcludob+iyDInNVTvnl1675vjwYFVvWJRl2LHsOx2Tde+uyShllvBQB13IiWD5fQ/1D4iIDR5pQIeBiKEAHS6fEJuGynxycRcO0ur5DHsTjdLo/P5vIWpkQH+RVigcXhFvO5FqdbxONwWLjb5zdanfFqWY/RqpaJLA63VMgz2pxR8kG/wkeVpzrt1jiRRMblYQjiPreu2dPeqOILTR4XB8MbzIZOuzVfpc1VaKIFA6+fOuaczpjyUWQ/IBwAQBABRTmBIijfKYQ9jvKdQnlLR/LQRgK1WPjVwzcerG7eVV5/tkNnsrsoCmRCnlosGJcaP6+AaQWOoehLq+YuHp214URFWWu32eHmc1hJavns3NQbJxeE68gvGRaX576540clRX/985ny9h6H26cU8SdlJNw9a1ysgikTlSLB+idu/mT/qUPVzTqznYWjGolockbCXbPGKkWCys7eKy6wuCx8yehMehUcakEajiSV7JF556fb6VFKFEFWyMTIOXsPupQ+MlwO75k5jqSoICddN1RbLxHyjla0tveaF00cMAke7geoM300VvPqpd3wktjRoZ1LFWlRBLk/fe727jMIApNU6RIWP/wGziNoRBNJdYezwrxYyQEtqUgmGMouRiRjGhDOu2Xqt3/f1lLZ0VLZkZwXDwD7vzsKAGPm5sk0l/shpRVtIzQCurT+B3GwvInNwl1en1IsqO3UC7lsh8cn5LKJAMnnsnz+QGac2uH2BtUQNH9TT22P0ZaToMmMU5fUdwp5nHEZcbWd+jPNPdkJmi/2nu4x2iZkxrNZOENgRQlEGTKl0+9T8YU9DpvNN7AeISjqpK5jUWLG3o6mGKGY5ilURYV3mPTsBoRDkf0U0Y7xlgdc63DJyxQgCKalLkp1CgAAW8/UTEyJF3E5KIKgCGJwOEVcTr/dqRELnT6flMfLiddMyUw0OJxKocDgcEZLxQDgIwIGh9PjJ+weL0lRJW1dszJTUATpNlsxFvridbP77c5oqdju8fLZLKfPF5RW2/dW2uxulUL03IJpc6MT05LV5VWd3b2WxFgFyxQ4+Pt7OrtNjS36qtru1GQ1m4Wvjsu8cfnY6MHP0OcnAGBcaty41LiR3KOUz31mybRnlkwjKQJFBr22a6YWrZlaxOCnl7okRVBUAEGY70vEKgzQmnU2ji0sHG4rn4HhHeDCS8PPQyk2p6emrXdaQUpQRJy/83rLZxnSO6qM7wYpZm/NyDs6fOeCnbgmZtTwN0AjOIei9dCMUofVxaBIVQOj4Z1Df4pJ1cLIoE1U5U3JrPi5dt+3x+59NZ7wBw5vPgUA89dMG2ELw0CsEAGAods0EuZL638QiVr54YpmrVxU2tjFwjG7y0uQpN3lFfE5VqdHLuLnJmq7jdZTdZ2QcZ4/Si5Oi1E6Pb7cRC1FUdXtff1WZ2uvKUEt6zZYtTJRWowySi4+XNE8s2DQQvW6tNyI/vfXn6NnylWRv0PngOAppHcfgmcCDqSvBBAeRTSTRCNCNJNEIwqBkM3oC0Nvdxyqb+GxWSqhwOxyc3B8QnJcjU6/p6ZRyueZne5ui21SCh1fSMfBcVpg7att4uB4r83OZ7OmpyejCLKvtkklFFBA0YKvRqc/0dLBZ7P6bA4pn7ckP5N2DPT5CQGf4/b4XS6fUi6sqO5SyIWFObExUbLaxt6vNxY/ft/sj744kpSgrG/qvXbRqNQkdVICc0tqJHF+/aTTT7rYmMgXsHIwqS9gp4Cst6zLk9/jIx1cTOIOmHiY3B0wsVCBhzAKWFG+gB1H+QTpYmFCgnSRVEDvLksUzR/5w6QRIEna/GpGdjLDZ5sBj8cfICkMRZwur0DAsdk8KIqUV3RMmZR2vLi5MC+Ow2FRFGUyOdRqMU0hSUqpYC6SIkIq4kUrJXb3ebf28wJLzEoGgH53cYpkYANL7z55sfd5pRCTqmVxcL+XaDzTmjvYwThABDrrexj8iTlxPCHX7fA0lLVe1As/b83Uip9rD286ec/fbiw/VO2wOMVy4fiFhZd/Cxmjk07tKq8+0ehxerlhu2wMXHL/B64Vq0qLGZhO7yltmDc6nT6GSo0YhWTZJHE4P12alxSVlxQFAA8umQTBWBcUhSJIWkyEcBQj+UgOs7GNsLIxVua5TWqK3pDGRU8GjxcLm9vLxrGStq552WkH6pqTVfKWflOiUqYSCrg4nq5RxkjFB+qaZ2WmHKhrnp2VAgDJSvmBumaFkG91eSwud5PeGCeXlLR1rSjKOdnSqRGLWvpNGrHQ6vJES8UqocDt99MCC0UQeq5aUdMlEnJRFAUAHpcNABRFLV9UuOdQTUqSyuH05mbGYBhqMjs7ukzxsRfthddo3cJGhRRQck5Gl/MIjvCU3DwJJ6XO8p2T0Gn54zCEbaB8GMLW8MaavPW97hIc4bkD/WxUouTmNtm2jVU9gyCXogfcW9FocrgA4PqJTOtxBn7YUU57UPJ4bJ+PSEvVZGVEoSiy72CNx0PsO1jDwrGEBIXJ5Kyo7vJ4iO27Knr7rA/eO0swAoMVFEFSYhR9JnuQcl5gRQmmA0CCaGm86Bqaonf/Qvvo4cBZWNGs3OKd5bs+P7zs/rmh6tjjP5a5Hcw9EZyFzbph4vb/HFz/xk+Tlozm8EbqvD11+dj3n/zS0GOuPtF4dNtpAJh14yScfQVUFTOun/D1q1tddveXf9kcbsTAwCX3P4igjFgwJgMA6CNczPR7UGthUS6uIErrO0dnxIWY1Fxu+/dOGxeqW0jXKlEEeXT2ecVKKD1dOyB/M6NUNIUupfnp8xVFYgYl9HLLQr5njNLrl44GgMQ4BZwT+gBwz61TL+0ZkpTfR9r4uFrBzTF7G3ykjYNJeJgcAKScVAGu7Xb+HCOY2u38WcxKtPnb+JjaR9oEuJaLKfo95TjCsfnarL6WiKvCYeDw+N7bdRwAsmLU41Pjh2d2Or0SCR/HUZvdLZPyszKiOjqNre2GaK3U4fRQJCWR8FUK0ZmzHTRFq5EkJyp5vCGdVYKgKLA63A63Lzvp/Cc8PIBfaIWR3meXq/64Yeuq+OcAYEPHPyYpl8fyM37u39jiOEsBJWNrl8U8AgClpt1V1p9JIBMFuTPVqwHg2/ZXkgT5Ha5aB2G+JfHPHPS800PV8Yan5/2VoqhpK8bd94+bldEywkec3HHmnw+vdTs8hD/ACOBn7rM+OOmPpl5LSn78zc8vz56YxhfxrAa7UWeuPtFwdNvppz+6NzYtwuTlzfs/3fPVkVVPLt779c9mvfX946+kFjAD1DLQ1264NftJuJBrzvtPfvnDR/sAYNrK8cvum5OUF4eiqLnP2l7XfXpv5eJ7ZtH2VpfZ//863P2P9Z8+d8Ov3YtfDXQAv4xo1aYnbxmes92+J140Bzkn2SkgGeehxzAe6qK+BK16k9Pr57LwdoP5432narr6EAQ+e2DV6OSRxnELl+xDUUYYE5QkqX2nG4w2p93lvXvJhMi+hKE3f1FSORxnLQdXxj0dxU2mjUJNvt5K65Hbkv6CAPJl24vd7sYYXhoA4Cjrhvjnw6vnTkq/7cWVn7+06ciWU0e2nOKLeF63L0AExszJSylIWP/mTwx+mUby95+e+9Oqt5srOl5e/W54g0OJ5vlrpu756sjuLw9bDfbkvPihpFXJnoojW0657G6n1UV7zwDAq7d9IFNL+GIuX8QrmJ7NsJO67x83e5zePV//fGRz8ZHNxYwGZ66aGPrvJfc/FCRF9rgtaq7Y4nOJWTyr3+UkvDa/O1Mc7SS8bBQ3eu0qrthF+FTc8xvna7cXn6rtICkqViV98fZ5ALDlSMWeU/UkSRWlx96/fBIAvLPxSI/B1mOwWp3uZ1bPKq3r9PiIiuaeGaNS95c2Pn3TjLGZ8YxalS26z3acwlHUaHNGKcSv3L2opcfw2Y5Tte19j7+7FQDeemTZFZ++/V9Cgmhe6L9IiJk3fR56DOO5uAf73bFyek8wiMcWThm5tIJIM/GhKCP80VEUmVaYsvNkrYDLGdIO64Tu8UlRA29Lce/TjPAyIwEJAxteN8T//rhhi9nXN0l5bbpoTL+30+TTfdX2J7rURw6YMsTzh7RJu+nZpamFiVv+tav+dIvf549Lj5p7y5RrH5p/ZEtk696ErJiPTv1t9xeHj2473Vbd5bS5RDKhIkqaNT51yvKxsUPohnInZ0SnaHqa+wBg3i1Dml/VlTTv+eoIg9hW09VW00WfBwiSIbBwFvbUR/fMvWXqrs8P1RQ3mXotACBRirSJ6nELChJzmFOzS+t/KLZ1lWVLYr5sOdrjNmdLYjLF0Q7CgyJIu9PwfefpRzLm1dp69vdWS9j8hdEFAnxAs7b9RM3f7r0mI15N2xN39Vt2Fdd99PQqBIEH39xU09abFqv6+WzLpr/cbnN57n9949T85NK6znHZ8fEamcPtfeqGGSer26MUYkYtAGjs7N/81zvYOHbPP9a36owpMcqX7lpw9ndr//no8vDOb99e/v33p3U9FplMMHly+h13TuMO7eU+QpSf7SgsuMCi5n8AgCiZOFouNtpcOIZmxapvnVY0M+dqpT4YOVweX0OHnsseOoBfEBSQToKp2x4KbIznDFgAIEAF9J42mihna5fFPOoOON5vfOjpzC9UnDgJS3VLwp9RBA1QAfScLnCYEJoAMHZe/th5TLXfzFUTGdOTIDg89tL75y69f+4Ie07jhj3zj/QfHycfvTzm/H5Kmflsl7tnafRC+t81L1y75oVLid6VPzUzf2rmhfkA4FL7HwQbxXtcZg1XkirSuAhvrjS222U+ZWxuCfRzMVa/197q6E8UKBUckSfgDwqsNx5a9tWukm6Dbc380VPyk1t6TF16y0NvbaJLnR4fC8fGZMY9+8GPAHDTnIHtcJmIb3N6OCyczcJ8/kB4LS6blRGvpqN9ysR8pydySEwau3ZVvPXmQCQMnc6yadMpq9X1/O+WXNpzCGLtFz+/+9bVDYHv9wesNjeffz51VXFp65jCRAAor+yYPjnDanOHbop9+fCvsBz+ru3Y9p6yWZrcO1NmBolH9LWtDv1tydMB4I4ZY+6YMWboBs6j1NQyWj7ICSS0nSsLPpd97bT8M41dwQDw5wVWv/t0jekDi7dmV/siAAhQ3ljhvKEaYkDFiRPjyrUtzwtxmYoTDwAUUJ+3voAjLAqosfJFACBna0fL53/Z9kcUwSiKXJ3wRxZ6gb2zXwwLtHNYKMvuH+T4XiQrKJJFSB/yW8Y1MYUkRaIIen5zkC9bxhuNIAMfhgfT54TXilVJXrxjvs3pue6Pn+956/7kaLlWLnrviZUoihABktYcm+yuh1ZMSdCejxbA+M6E16pu7Q13QEMQxOsjwrUY3285zeDct6/6oYfnikRcACg/2/HZl0cxDPV4/G/+44bWNsP6Tade+uNyAPjTK1tvuG5cdlb0N9+dKC1rIykqOkr27FMLW9v6v1l3sqGx93d/3AQAf3t5JYIgX317/HRpGwBMmpB6w/Xj/v3xQa/XX1XdPXVy+uGf6x95cPaowoSfdpQfOFgbIKnC/Lg7bpsKAL/746aiwoTK6i6Tyfnaq6v4g7dE6PRWUVpJMHWVSik6fqpp3swcBB1IfvXQPSPaFLt6uClxMgfDLb5B9kDT1FnT1CPNRxXER437Ph5/7+W3MxJw2TjD6eq8wFLxxkyPWVvS9/sx6lcAaJPNkW6IIoCsjHsKAH5YX5yYqolNSOzrsdyV/A8GW6F0VqF0VihldcIfR977g7sqZy6I4C3U12PRREuHqrW1e3ultQYAimT510TNb3K0bOvZgSGY1W9TshUPpt4VcYq3t+/gkf7jOZLMG+NWAkDEWgf0R04YS0iKzBKnXxc7XAjgXxL01HWozcFwkBR13+sb2SyMpKhVswoBIFYlXTG94IE3N2EoQlLUPx+9liRJiqL+9tU+DEXcXv9Ldy0Ibye81hDdQ+aMTb/tL99GKcWvPXB+AtXdzYxyRVGUTmcRibQkSf39je3vvX2LUjlcWKvde6v++PulaakaWtmXlKj63TPX3Fzd9eor19EMVdVdVdXd/3xjNQA8/4eN+XmxADB6VGJsjNzh9Dx0/6yS0jaNWrLvQM3br69GEHjquXV19brMjCgAYLPxl1+MfEfB9FbB1FUAYHd49AZbW7shJlo2wk2xEeLv1VvbnQZPwDdBmX5f2pxyc9s3bUfZKK5zm8cr0iJSIrazsePkT92lYxWpD6cPrCpKTS0fN+3HENQT8L039i4+xmZcq9nR93nzoVpb9xOlXwDAm0W3oggS3s7a5oPFxiYAmKrKvCVpapWl8/OWQxiCmnwOLVf6csGq4RdVDDASuIVFHJXdOUJde0NNT0eLPjMvzm5ztzX3paRHYRhaX9OTU5jQWNvzw/pTK9dMMhnsuk5TdLxCpRGXl7SmZET5fYSuyxSfpGJx8JqznSkZ2pxzKoZDuytdTm9iioYgAjQPIAjdMuEPtDb2moyOk0fqJ0zLOHmkPi5Refp4U1Z+HIIAfa3ElAjhohrsTQ2Opj9kPw0Ar9e/myFKB4AOV9cb+X9hofjLNa91u3WxvAgxv+dqZvIwXpf7vPseoxYLYR03Fr+Q9TQCyN9q32pxtiULEkfy3H5rQBFk7e+Y7tmLJ2UvnnRet/jt3tJxWQl0Yrj3txytaNY9vmo6ABSmDShl6RNGrbzkqKBICpVNz64e9NEaBgQRAAA67+ZQ0oo8FwrmlT+vWL+xWNdrveG6cRPGR9C/tLUbM9K0A14gaZrmln4AkEj5NrubzcbYbNznI9o6DN09lqefH4jC5nYPLGPzwhSOQSyYfd6MtqggITSx1V1rpkKkzbLLwVNZS1goRlLk0sOv35s2GwC6nMZvJj8KAHec/GB+dEE4JVEQIYDX9fEThDi32TEQXpmkyJcrN306/n4VVzzUtVKEmj/nX7/iyBtvj75tqHbOmtvPmts/HHc3ADx++otCWSIANNp1G6c+yUbxe4s/bnXok4UXDk9GQ292fLTt+LSClKmFyZF3CcXsVACKogYGwTDCC0URu93D47OLf65fecukDV8cXXXblNQMbUqGNhAgk9M1iSnq6jPt9TU9KIYKBByFUlR1pl2uFOYVJUbHyf/zr72JKeqGmp6gwNL3WlfdNmXD50cVahHNs/nr43TLQMGq26ds+PyorssEAD2dpo6W/lW3TwGA4LUidrLbrUsSJNASPUmQ0OnqiuPHJPLjWSgOABKWyBMYqZs7o5be39/r0b9a+zZd6h5xO0PhrLG7QHERmzI0vmoqyZCox6kSupyWWIGUUfpjR/WS+AuHdb3g1afkJ//tq30nqloJkpIKuHcsGnex/bwgYmJlTY2DYpNjGBoXpwAAuUxotblNJqdcLgAAigI+n20xOwGAIMiWtoHkETHR0mefWmS3e267+9Mt6x8GABRFvF6Coija2yk5SXXkaD2911pX3zthXEpnl4khSBLjlRq1+PVXb0BRhCBIDBsoH976Mjx11chnuBcFH0m8WfuTi/ByMNzhd9ObJAkCFX2JFKGm22US4BwGJaLAYqDfa5ew+KHSKvxaI8nU1+rQZ0li6DcuUxLTZO9NFWnTxdFsFAcAOVvoIoZTZTIwnKU7jRrTB+32H/yknaJICSdtRsyXQ7UVCJASKb+yrE2uFO3fflYZ4qWMYajJ6OhsM5iMjnlLR5WfanG7fEIxl9Zo8PhsAEhO0zodnpz884ZILqd3zw9npHJBkCfYMuEP7P2p3Ov1y5XCvT+VOx2e1IyozV8fzx2VkJETQ18rLjGCQXYcP+aUqZS2q2h2tBXG5sGlDiBGrRhetJKteD7zccYewkVhY2u5xefW8ETjVQlVZl2BIqbNYTqiax6liHUHfO0Oc6pYiQDSYNVnSbUA0GTrL1TEWn1umoKjaIVJN0YZX2XWfdV4+q6MCemSgaFZZuiqs/YZPM593Q1zYtL3dNd7A4ST8KaJVTIO/4S+LUeqbbT1D3X1PPkgd6h4jezDp6+/hBscOVauGPuPfwwyVVm8ZBStwMJx9JknFvzpla0sFhYgAn95eWVCvFKlEj/yxDcKuSAxQQkAFEU9/vR39MJ2+dKBbQEEQWZOz7z/4S+1WslLf1yenRVdkB//+NPfAkWNG5eSkx1z5GgDoxvR0dIl1xQ+9dw6FEUoknr1L9dxhsgW8augxNhs87teLVxt87v36CpoYotTT1IkANJk77steYbZ52BQRtKygiO0+FxGr52O8kQBFfFaCCCeQAQVZBCpIu2Bvir6jauxdk1RZQAANmLlEgNWh1sq5Kml57csmAKrz3ViQcKOM/pXcpWPVxn/OUxbGTkxqZlRdILpYNija1cPbN7d+dAcBEXW3DcTAHIL42FwZCUAmLkgjyRJNCS/iEQmmLu4kMETrBV+EqxOXytiJ1OFyVni9L/UvEEBVSjNTROmNDmYAeE8Ae/atq86XT0Biuh2626Iu1bGln3c8lm3W+cOuI1e04rYCHtVGq5qtmba3+reQgGlgHom4xE2etFaVV+AEOIcN+HX8EQ+kgSA3V1192VOAoBt7ZXjVPEJQvnahuI708d/XHdisibJ6vcIcNZBXSNNuTdzYrZUmyXVBCgyU6oJSisAKDF03Jc56aO64wgCJf0dAZLscJgfyp7yWcOp0cpYDVdUYujgY6yhrh7E+qazVp9bwxflybXHe9tz5JpOh9Xp96VJlXIOj6YggNRb+nk4K0WsKO3vypZrmq1GulaqWElTepw2uhYHw7+sL70na1y6dNBnf978PD8R+P770z3dFpVKtGTJqBUrxwZLx4xOGjN6UKChP4RtIL73zwh2mI8+NGi/9eYbJ9x84/kkqQ/cOxMA8nIHlnv0SWyWaoaioCA+yury/FhRnxWluu2B6Z8cLbuVXRSgyG+Pn7192uh+m6PTZE1QSDUSUXFzR1a02kcEOk3WZLWcg2Pl7brMaNWohAFVQ3VXX5PeWBAf5fb56SIiQNLMJodrVnbKvuqmvDjtgermmyYWtBssRxvaCuOjUBRhtAMAOZLYz5oPPlH6hYIjShUNmLlIWLwXzq7v81gmqzISBEqzz8GguAK+v1dvbbb3+alAq0P/YPp8NVf8cuWmVke/k/D0uS13p85OEChfyL32+fJvWShOkIE3i9ZEvBaKIHO1ebef+CCaJ/v7qNUBimS0kyuNK5In3X/qU4qiJqnS86TxVZZBkaAuChfOS4ijPATQAOVloxIPETm4NRkgERShznkeQKSYBEEKQQQQBKGn5dhgZRs6OBvSzPl5w7QTfhKsPnxEhKXRi5ZGLwr+mypMfixtIDRz8OTBlLsZtR5IYWYPCq81VTlpqnISXAYQBKGn2c12Y62lt9rcmyJSrm0oHq2MAwA+zgYAFVe4tb1SwxMRFClj80/1dwQpwXYwBDV4HM12Y4poIJqNgiPY0lbhDRAzotIeOLbhvUnX9TaV0kWlhk4xm4sh6DBXL5APvCQ+MiBkcdyE3+H3aXiiEn2nLxB4OG/y2rqSMapYmqLgCrg4LmFzf2qvyZCqKo06HMXoWkGK0++ja92WMTpLpmZIKxrXXFN4zTWFwzyuqvKOzV8dY7Fxvc5SND7l1gdmAUBFaduXHx7AMNTj9v/937fxLnszDkEQm9vLZ7MO17bcNnX0fw6fvmv6mMxoVWa0KkCSGVHKNI2irK27qrMXQxAhl6MWC0vbulUiwZikmHiF9K2dR9O0iqquvqCgCTa4paSaLpLyuTTzroqGsx06IkBqxAMpZPZVN941fSwAhLcDAFK24NMJ9zM6rOZK/lpw4zAUH9G8TFudlrkGQfB+dwkH6WywHL89USFmT1FwCxotXyYIlBZvnYp19u8F4+nQUgDg8Hf8Lksq5+ZTVMDmQ2y+Ooe/gyCddySnYEhqs3WdzddEUsRtCZSEM9lF9BKkU4h1Wbz9ecKDK2LXBCif3dfi8LfnShP+Pmo1AHQ5dt+XwhGzTXZ/oN9dImVnkpTPQXSKWckowmq2rkuTrhGzU0Nv5MJ5CYWsBJLy4yi/uO8ZP2kPLfJ6/C6nly/gFB+szRubVFnSmjc2yWHzUBTlsLoT0jSmfrsmWuqwe3h8tsvpFYp5LqeXJMiKktb8sUkVJa0TZmayODhFUqxIznr0YvD/ANqcTYmCVIvfVGo6PluzOHgSzrk65XySi9fGLQWAHNlAHP6gyFgSnxOcgefKtBhC5ygaoNyRPqBOeipvZugs/bqkApqn2W6crk1hoxjNSR/Dp/SMqwfpKCD0FUv0nRI2F0VQ/rnYPkGKiMVpshqzYjVZMo3N5xmtiq009tK1gpTT/QPmtRiC9rsdzTZjivhSAg33dJo+XPcgADx628czF+bHxMtf/9P3b6+9SxkpbtqlIUCSMj7vdGu3Siz8oaxWIz6/HsFQ1GB3tfabDHbntWNyTjZ1unzdYh6HfmJ02LnMaJXN7QmVMsEGg0Wt/SaaeWZ28iNf/vCvW5e29pvqdP013foklfyLn0uLEmPC27lkSNjpEk66mJ1q9lbzMJXBU4YhnCTxdQ2WzxTcAcOdLsceMTvF7K0JCqwe5/506R0A0Gj5Kk26psHyOUn5M2X3NFm/SRHfSDdYZXyXrkWQTkaRxVvrI204cj56pYvQpUtvb7B8puKNo7vBxZRK7mghK46iAnQtRs8vnJdwlOoFAMhXPtvvPiXn5IYWdbcZdm4sScmM8nj8xQfrPB4/ADTV9EjkAhRFUBRpquk5c6KJx+cY9VaRlJ9VEL9zY8lDf1yKoohCI0ZR5NjeaoGYWzQpQu7s/3bU2SoNPr2Ko9VwoztdrYmCVClLTtsqBU9GiHDVZlC4YOc0ZSN3g+Bi+E0hYnEo5qGufnP6+XBAoWLuzsyxQcp7lcdmxqTu7258IGfiQPoTxXkVWCiFrvVM4YxL1kPHJioH8lSnaHRdJi6PJZbwrqC0AoC8OG12zEBWmOAt3zplQC/2+ILJKII8NGciABQlxkCY9F9UkEGHW4nYIF2UGzvwBnJw/OM7VwBAkkr+t+vnA0B2jJrmyYvTMtqJiEJZIr0TNwwFQTAPYbT724zuMyxMjABKUK52+zYOJrf5mq2+Bqu3XsJJ95P2oPwCABErqdHylZI3iourOuzbebjaGzAzGgzWMnrKGUUUBDiY1OApixUOmDsQpJO+aLAbAICj/NBaoRFDAyR5rLIVRZDmbsMD1w4E1IzsS4ghbC1/CmOGVVXaxuGyUBx12tx8Icdpczvt3s4WfVbhmPKTTUqNpLNFr9RK7FaXOlomUwpp/s6W/vamPvo4fVH+6Z8bJ876DWkxrxQMPn27sxkFNEOUQ1CXm97mCiKGf2VC+sLQMvGe7PHtdsu92eMhkrQdRv5eAjpa+kmSRABpbeq74Y4pMoXQZnGZjQ6ZQggAwQ3By0QwK8zIPwyDq4dT0KGKIl0dYZxcPnIUDyOAiqSJQWfh4MkY9V8AQMLJCPUjBoAowQyaIuPkhhalSm4+3yAr8RxPDqMIAKTsLADocuymK7IxWbxoCePqjB6GUigKitJjhTx2etx5BQIzWsO2lvGj1S/TQvG47tGgXyGEjAZa7R2qRI94Hj56yo43yZXCxPSrGHLASXh5GPuXd6ndoducIcptsFcXySbs7fthumoBjuL7en+cqVmEIRh9Esu7QBCI/2F4VJV3rH13r0Il0vdaR08Y0GGVnmz+5uNDLDZGEORLb90kFF+BLKf/w9WAN2DiYBcXFKzf4jhS3hKnlo7LHjB+YgqsY7qHBKxYIR6XKr2FIbB++9B7bB827p+mzpymzvxfGID/e6gq7/hhffHvX726Bhb/w28HLo9v3b4zWoV4yJjuCGCFyt/Vm9dWGt8Oj+NK+AN9PWalRmKzuCQyvsPu4fE5GIYQBOl2ehWXoUpobOytrOhsaelvadFbrS6n0+d2e1EU5fFYPB5bKhXExMriYuUJiar8/DipNHIeGimbH82T2f2ei5VWPh9RWtpafqajqamvp8dst3u8Xj+LhQuFHK1WmpKizsuPmzAhhc//5ZwfLRbXsWMNZ892tLX2Gwx2l8tHUcDjseVyQUyMLCVFnZsXl5sbe/nxDP7PgCCdBOVko1IfaaGPLFSsdx2Sc8dwsAg2ekFQFFVT011V1dXQ0KvrsdBP2+v14zjG5bK4XJZSKYqKkkZFSdPTtdk5MbKhg+tfFH6Do+63BpfXr5IJzXZXUFHInGEV9z0zXvM6AHQ6dpTpX16WPChK8t4fztitbg6XlZoVVV/ZzeOzMRyVKYRNtT0CEXfWNQXciwyV6Xb7Nm8u2bunqqtrRLHPAQBBIDFJNWZM8pw5Oampg2z8y83tPpLodpmXxY4eocwyGOzrvju5d2+VIyyKKQNsNj5jZtbq1RPjRpYIZygsmP+aPySH8zffPKCNkoYy9OosX3x5dP++6kCk/MOh4HDwsWOTn37mGtrAkgGXy3vdyne93kGpOh97fP7Sc3aVl4zP1h75+utBWcFFIt6mzY/g+AWcul74/caTJ0eaqXjs2OS//+MiAhs0WT5mY1JfwOwmunl4jJvolnLyA5QnSrBgKIHV1WX6fsvpQ4dqLRZmloBhEBMrnzQpberU9Ozs2Eubx//yo+6/FF4fse1oFY6hK6YPhGxhzrBoaQUAccJF4YlUMQxlsTAen52RG9tcp7PbXCIxv6K0jc3C7Db3BccrA3v2VH7y8UGTacjsVRFBUdDa0t/a0r9xQ3FSsuqZZ67JyBjYkxKzeHqPbZwiZYTxDDesL/7ii599PuKCzADg8xF7dlce2F99/fXjb79jGn55KfOC0PfbQwXWrl0V7/1rb9CFbXh4vURVVZdQGPkLzOdzpkzJ2L+/OpS4d0/VZQosioJ9g9sEgFmzsy/217/i4OHRHEyBIhwRO81P2kXsNH/A6idtEZmNRsennxzau7dyBFERmejuMm3cULxlc8m69Q/Lz9ni+EgXC+Vd0K33Vxl1Jw/WdjTpV90zXPiX4kN133ywXyjirX5gZu6YpGE4LxYVp1ryxw0KRzOS/tDgsHHaIT+I4YKXe0kzGxu0x0SSFIIgYyanAcCi68YGte+Hd1dOj2T2ORQoCj78cP+mjZebZbuzw6QJycclxLlf645NU2dG8aTDyyy73fPSn7ecOdN+sVckCPK7706cPdvxyl+uG2plelHo159/oz7/7MhXXx0bhjkc48anDLMvNn9BHkNg1dR0d3ebY4L5AS8e1VVdvToL80LzLz3n7pVCjHDA0m2o8MFBHDva8Prr2+32y3L/nDgxNSitHIThWP9XKcIJKaIJw8isX2vUTZiZNWHmBcK/7Ntadv/zi7OLrvy+0Jfv7nvj60HhaEbSn6FwcXkJ5ywpDP2XllAIisxYeIHUGgxcEWkFAFOmpIf+fiPUYVmt7ice/7q9PbId/0hQU9P95BPfvPX2zZcvs/r7B2xHvvry6MVKKwCYOIFpaxeKoqJElVocKhMBYN++qttuu/S81nv3VjIoiYnK4CT3t4ChwgfT2LCh+KMPD1z+Va5ZXBg852ESCUvjJR3DSKtfa9T9+O3JvVtLCyek3vnkfACoO9u5/uNDGI6ajQ5NtPSZf6zq7TJ/+e7e6tI2s8EhEHGefe0GgYj73YcHy443AcCEmZkr75gKAH964IuC8Sk1Z9pN/fa/fnLHNx/s97j9tWc6Js3JPrqn6v7fLy4Yn/KvP2/tbjd4XL7RU9LXPDKnrbFvwyeHGmu6//TAFwDw5/dvRVCE0R8AYFwrvIehn+SrmJdwKJwt79i8KYK0UqnFE8anpKdHRcdI+XwOm427XF6Hw9Pba21t6W9u1tfX9xDEILXO4iWjQv+1+FwytsDgtQ/jnOn1Er97fn3EcZOZGT16TGJGRrREwhPwOXaHx2x2VlV2Fhc3h0dram83vPjHzW+9vfoyl0L6fhsAHD5c98UXPzOK4uMVmZnREilfIuGRAcru8HR2GJua+gznIsrjODpm7HCzdwRB5s7N/fab46HEfXurL1lgEUTg8OE6BnH+gpF+rh58aM6KlWOsVrfV6rJa3TarK3je12d1Or0XbuLysHFYaYUgkJKqSU5Si8U8iZSPIOB0eA0Ge1ubob3dELqIU6nFY8eeX+bY/YZk4XirvzfihA5+1VG3ZPUEgYjbFhIJo6Ve9+mOJ1ls/Jk1H3c06xNSNc+9fsPLD3990/0z03JjAKCmrL2mrP21L+4GgBfv+yJ3dGJGfhwAsDn4H945H7511MTU2ESlw+659/lrSo81FYxPeeCFJTgLIwPkbXNev+Xh2Ylpmqdevb667I2X/n3bUP0Jv1bEHgar/wp5CT///AhDccDnsx95ZN6cubnhASpD4XL5ysvbTxU3Hz5cZ7O5Y2LlhYWDZrACnOMgPAGK3KOrXBAd+S3617/21NfrGMS8vLh775uZnR0hvsr06ZkPPjRn796qjz48wFDNVld3ffbZkXvumRlea+To19v0etsbr+8IPhORiLtq1fh58/OGigDV3m44eaJpz55KmUxwwS2k+fPzGAKrp8dcXd2VM3SAp2Fw4kQTYyWFosicOblD8TMQEyMbajW6du3hb74+HrHoSuHkyaaPPjoYsSg+QbF69aRx41IkkshmXH5/oLq6q7S07fDhuu4u08KF+aGffQlbU2XZjSHsoWJe/qZGXUpWNO0bJ1UI3c4I2tKOZn1abgx9g6k5Ma31vbTAyilKDGWTyAV2q4vFwdkc3O/1+7zEh6/+5HF5WWzcaXeTJBUMzjMMwq+VmK4dpoe/dF7Cfr2tsnKQ9zaKIn/926r8kDgzQ4HPZ0+alDZpUtrDj8wrKWmGkPBDNDwBv8nrjBPIh5JWJSUtO3ecZRCXLi16+JG5GDakOhNBkHnz8nJzY599Zp1usPpmw/riOXNyk5IuHG9oKHR0GF/92w8u18DkYvbsnMceny8YNvFqQoIyIUF5w40TRqKFiY2VZ2fH1NR0hxL37qm6NIG1d28VgzJ2bLL8v8EJ1G53v/nGjvDMQxwO/sgj8+YvyB/+Y8liYYWFCYWFCXfdNb2hoVelGvQtMfu6cZRr9HZErPtbG3XDXJRGYrr26N4q+lk1VHWNmz6Q45KhpGa8fWeLm+0W1wv/XG23ug/vGAhHgyKIz0MwIrVc8FrD9JBZkCw5v5E8WvXS8DcWEQQVCFBD7sdX13Qzxsy48SkjkVahwHF04sS0iRMj+CTGCuR0QJ9wkCT13r/2MohTp2Y89vj8C/6EABAdLfvzSysYnCRJff4ZM5XORaGry1RRMSDBV9886fcvLGVIKzo8ccS6Ea0ZwhG+ZDt0qI4O5nlRsNvdxSebL9j4bxNffH40fD9aJOK98ebqhYsKhpdWDKSnaxmmWAJMliKckC2ZHc786466QIB8/bkNW788dnxv1T+eWd/VOiINWmZBXN6YpGdv+/TZWz/JH5uUVTiitEMZebGmftuL93/xnzd2JmUMeLMgKDJtYd5jN3zw18e/jdifi71WeCJVyuZrIciBWaicO+Tuj4vwuAJeCUtg9TtFLL6DcPNQtivgBYCzluZpqnyr36nkMB3ZNm069e8P9odSbr9j2po1TPuJS4PBaz/e32jzu1cnTgrXYe3fV/23v/0QSpFK+V9+df/w0xkG/v3B/k2DFXAIgnzz7QOhm5XDg2GHFcSUKekvvbwSADxuHxmgSIoyG+zqKOnJQ3X5YxJJkqLtct1OL4uDBwiSjpxhs7h4Ao7ZYNfGyGwWl9Pusdvcyena0KgYTqf3+uuYBlkvvbxyypT0kd84APz445l/vr0rlCIScTduepTFugIGDeFLQtoOy096SCBRQD0BZ6uzPElQKGIpAKDCsj9JUMjFhD7SzcUEDsIsxGUuwsbFhI32U/GCXCF+fu3Z329fc8u/GY8dQZB/vnNLbu6lzDQZICmixXHK5u8vkjND+/8yo46ifCSpB8pFklaclUuRZgSVUZSDIs0kacVZBRRpQlFFINCJYmoEuBSQCPJrJsUIh4vw06pnm88LAMW6jvFR8cW6jmUpg9IAMncJS/pecBN9HGzgxx4mL+GPPccFOM/ic/R6TKnCGB7OsfjsApw3RzMaRZD1HQd7PaYHU5fx8UGzALfbf2XuLxJEONcd8A3lS7glLC/LypVjL2rcAMDSpaMYQ4eiqP37qlfffFmBsSQS3tPPDKzEd2wswVlYfLLabLRXlbZ5Pf5dm0/39ljue2YRX8g5uq9aIOJqomU7N5ckpWlTs6LjRdym2p6je6p6eyzTF+ShKHJ0X7VQzCuaOLCBKBBwJk9OP3Bg0BbK3j2VFyuw9u5hrgdnzcq+ItJqGJSZd2EI7gk4rH69jB3V6DiVJ5nJQrkOwtToOBXDyzhj3h3Pz8FQdr+nzerXR/My/CRTeb9jR3n4R+L668ddEWkFAATl95LOKF6E5/nLjDqfZycgHAThA6Ae1yYWKw9Is8f1DZu7AAB1O/4dCHSy2OMp0oKgUgQVsznTLqoDvwC+qStno5jN5+10WF8cPwtBEC1fGG6yw5yUugjdtJj/jNe+Qf8NcwE/GbD7XWqudKIyB0cxu99FU/ReS5uzl6bzMOYPIw7zTa2u6rqkG4yAfq+9UJYgZwvIsDVpV5eprm5QmkUURZYsHQUXiZhYeWoaM4R+cTFzoXSxWLKkKLi+c9o9FEUpNeKOln4MRx12j1IrmTAjkw5NF5ek6u+1VpW1cblsHp+dkRdr7Ld3tPTTPNHxit4uc3pObE+HkRPitTMvzE7q5MnmizJE0uks1dXMXyq82SsOb8BJASVmqdJE4wDAE3CgyMBX1hNwdLiqWShHwYnVe1ppHhTBvKSD0cieMFErEnFvvyPyS+vw+vrsDref0NnsHoLodzitbk9dX78vEOizOwIU1WG2NPUbyzrPDycf6exyVTkJCzXYm+0XG3UYnhrw12J4IhnoQFA+zi70+4oRhEdTUCyazZ2LIBggLJydTxJtCPKbcxG3+7wUQJRANCc+pc9lb7AYmq3GBouBIAe9y8wZFgdTkJQPHcF0MZqnmKkeFYwmSlHUQf0ZmnJH0kIYItZHcjJTU3j6dEtJSUvoJvElI5on3dZVxkbx8Ajrx44xo3dnZ8eIRJfys6WnaxnpEurqdARBXrLtO4IgS5edtz5f89CAKuS2h+dAWGyMzPy4zPy40Gcbm6gM5Zx/7egzxc2jBwcdGzMmSakUBe0hgDZQOFTLsAsZBvvC1O0JCcrMzCsQXm54TFMP7KNTQKWLJlBA0bZOk5TXU0AhAHSsMTU3CQGELg3y0GhvN4Rbus6Zk8vhRDZCXFdaIeSyKQpyotT765v5bNaMtOSz3b0HGlq6rbbCmKjcKI3J5QqZxVMIYBHtsH6xUYezcnBWFgCK8W+kZyE8wR30kzlHIT2uLQAIGehjca58xtPLx5NFA1nT6YXh00VTAYA+hiI8Hhaxp2O5lJNJb9AOM8mapTkf6p8+BimhdAYyM6MFAk6oxQ1FwZ9e3Pz4EwvmzbvczzWKoNfGRc5ee6aMaV48avAe7ciRnMzM0EMQgfZ2Q8oQmXsuiKQklUIh/OHbkzw+29BnE0v5NrOzt8dy37OL+EJO0Do3tErEZxvkDC4GQ/nnzs397rtB275791aNXGDt3cd0x/mFrdtpWRAqEcLPw3kAoKysLby1BUObOvsCAZvbqxUL86O1Nb16q9vDwlAeC48SizLUSofX12WxZmlVJ1oHtkoooDpdFWyUZ/X3MWTlLzvq0JAjhMSMHKBz+ddd2qV/YQxv9R2Wl1DKjGV+ZcFiYYsXj1q/fpCFl9dL/OPvP23bWrbm1snjx6dejcAwdXVMK5j4+Ev0Jg1f1QJAX5/1kgVWdk4MAOA4are6NdFSmVLE5uKJ6drLD08eivkL8hgCq6qqS6ezRA12vY6Impru7sHe6SiKzJk7UvOrXxeNgycmACAUclNShkyNFy+TLspJp1+bG4vySYqq7OljY9iCvHT640/HUF1ZOBCbBAE0RTih1nZAjAoYsvK3POouiK+PlP1QUjMnP+3eueOHYStp6hqbOkgVeLim5cPdJ0U8zn1zJ4xOuejMdcODKbDk3LzQvIRXAzetnrhvX5XRyFQ01NX1vPD7jfEJiuXLx8ydm3MJUTV0bkudTddo6707dVAQXqPRYbe7GcyX7E8XUWN6sS7coaANahatGjdUTMQrgrg4RbhB1r69VWtunXLBuuHrwTFjkhUKYUTmXwx1uv4NpyvTtcrsKPWxpvZui+2W8YUbTle+uGRQotbWFj2jYlqaZpiP4uLcjNB/UQQpiNEWxGjh3MefjqEaOsBYKCdfupDRzq836oihXe4CAOgIA3bfMq2IjeMWJ/MWGHhv5/EvHlkVSvmhpObZ5TNGJV0VdcGl5yW8ZIhE3D/8cflzz66L6LDe0W58953dH390YMaMrAUL83Nz40Y+4VJwhL6Af7qGGb1PF6bCAIAHH/j8Yns+DDyei0gPyUBQ3Y6gCElSOr2VzcLPVnVOnZjmcHr5PBaKohRFHT/VXJgbJ5cJdh2oGl2QKBJy3G6fUMA1mp0KmcBic4uEnCBPxAvNm5/HtCAdgcAiCPLQoVoG8bfg7Xy6vZvHxvlsVn6stqXflK5R0hQGm36wKyUApKZd4Zi3AcqPAIoiqDtg52EDM69fZtSRlJ0inRimIohOHE8AQJyuLVzONBSVkJQDRSQBUo+hapI0oajE7dnDYU/EMOak7Exr9xeHStkY3mO2TcyIf2hB5C3vVzbub+83u3z+yZkJDy2Y1KQzfrr/VE1X38OfbgWAd+9a1m2yvrfzeFlLt8HmEvHYr968UMTjfLy3+ERDOwBMz065feZoAHj4063j0uLKW3v6bc4P71vx4e6Tbh9xtq1nVl7q3oqG55fPHJc2pGHmpeclDEWVZYeGm9HrqVFx0zqcpTZ/b4F0eZV1e4pwMgKozlNj8/dOUz/ARgecNvPz4956++aXX/o+fDDR8Hj8u3ZV7NpVERMjW7SocMHC/JE4fNZYu2UcQY21O02kZcywRngjlwzCf+lzUqHwvOXHjn2VGanamGQpgiLb91bwuGwMQ+Uyga7X6vUNWIQYTI4TJc1Z6VE/7anIz4lls7A97Qad3pqdHh3kiYiZM7M+eH9f6Heiu9tcU9Md0TskiFOnmq3WQZ9ZoZA7afKvn0nk5vGFwR95+ahskqKQMOUeQZBWKzPWlUx2BcJshMLm72txnEIRHAGkUDaQMPGXGXVO55cIIkYAZbML6alTINDr9uzlsIsczq85nPEIwnX56wiik8MuIqkhw351GCybnl4DADf/87tFRZlJ6gixjJ9fMYOFYSRJzf/Lpw/On5Qapfjr6vnlf+157+7lNEOcQvqPWxY9tvaHe+eOz4nTAMCZ1p4zrT1rH1wFAA9+8v3o5Ji8BC0AcHD8rdvPZ5ackB6fqJba3d5nl804Xt8+jMBibmyNJC9hOHCEZfPrWChPy80U4apk4cQedyWOcKXsGKtfR1NY6KBFeFZW9H/W3r18+ejhjYy7u82ffHLwxhvee/217R0dxmE4AUDM4pEUFR4P64Jh0i4fVFh01pGDG5InksXCdH3Wji5Ta4cBxzCb3RMIkOVVnRiG2B3ndyrsDk9FTReHg8fHyJvb+tUq0eRxqQyecEQUNOHLPQbC3XFmzsxiR0rU9guDIZtQJMJWhMvlDXcTEIRoG8jAgCMBQQQuGDFxKIhZWi4m4qLC0EH+y4w6ivKTpAUQDkF0AAz0nyQtXu9JBOGx8DS/vwbDonm8uYBgFBV5fgAAiSo5iiAogqRqlZ0GaziDlyBe3XLw+a93vrRxr93tDbccioiWPmNOnAZBAEEgO1bToOun6aOSBn0j5UIen81WiAQcFuYlhosUdhF5CYdBpmRu0E89SzIvdL85R7owfKeZBp/PeeTRecuWj/7i858PH64bygEFAPz+wK5dFbt3V86alX3nndO0Q+iJ2SguYwsabLooniTUsoFh5P1bxvyZOSRJoShy981T4NwWb+gRAG65bkLobCIlSRXOMxQWzM8/dHDQ+u7gwdoHH5o7lE2G0+k9eYIZI/S/xR0HACKqHYL6IK/bX3ywJm9sMkmSAFBxqnXc9AyTwaHUiGlXAZvZKRTzbRYnX8Ch6S6nVyIT9HWb1dHSYHpNd8BKkD4pOzpBcH7X9ZcZdTiewOctA0DP6adALHoUgARA6BeQxcqizRpCjhHQ0mckSQoQaNQZ7po9NpzhVGOn1eV587bFVpdn55l6moggiMc/XOb6tCjl3opG+s2u7uydlj0QXITBP3K1z0XkJRweoX7qQ+03R0R8vOKPLy6/o8u0ceOpvXuqvN4hFzUURe3fX/3zz/W33jblhhsmhE/NhrLDIsmruIdwxRF6X2iIfjf0Nw4/D+eJiNFjkhQKYehqxWZzFxc3Tx5iiXf4UB3jnY+PV2RlXXXzqysFiozwFUTPueYd/PGMx+3btfFUX7f53t8tRlEExdCm6u76sx0CEbetobev25yWG5uaEyMU85qqu4/trhRJ+Rwuy25xLb31vD8ZG+W7AzYWMcip45cZdXzetedOQ10O0LBzNIw+CBI+99mvdugstqnZiYlqmcvrf2XTviad0R8ItPSZHr1mcl689pO9px76ZKtKLEiPGrCmRBFkfmH66re/i5aL37o9Qqrg/ISoMSmxd36wgaJgSlZiQeLlxk1jCqxe1xEtfxqdl9Dh79C5fo4RzMLRq+6OHxsrf+KJBXffPWPvnsodO862tvYPxenzEZ9+cqj0dOufX1oRqv0BAD8ZiObJSk2tDJEfcf3yyad3XcGovldcLXKVQJsjrF83yKxk376qoQRWeLg+2ro9QLT6vYdw9migiABRj7FyAv5qkrSgWBQAQlEOHM8gSTObO9fn2YtiGo/zc57wPoryE77TOCuHggBJtGF4Gs5mJnm9soiYY9x1zgwQxVCnzaOJlSWmRxn19vbGvpTs6M5m/bgZmQ2VnaooaWJ6lNvpzciP62rt72zWxyapZCqhxeDA2Xjod8VBGCRsLSNaw3/XqNNIRK/duij4L5/DevVm5r7nl49GCLH/uxXMQDfv3Lk09N+7Z4+7e/a4UEpQ50XjqaXTIGSRyFgtMsB8puX9/2BhH6RKVieIlp7W/0HFG1tueJVOtfgLQCTirlg5dsXKsXV1PTu2nz1woGao6OZnzrQ/9+z6f75zS9CXjQLKQXiNXnssX86YaERMLSOR8H/1jfkgPlt/XCETJsUrJCJeWWVHWrLG7ye6ey0JsQqgoKm932R25mXFdPda4qJkKoWQwcNh4ZX13WlJ6rzMC5u9LJifzxBYJ443OhwehvQHgL4+KyMWEG2ACgA+z06e8EEA8Dg+5grvdTs+QBABgogoykWRFp7wQbfjA9q9liTa2NyZOCsbwzNctr9hrEzCX4GgMpw9AcMTL++xXRgRf/qg3fK8lWNCHQlufXweANDHzMJ4CDEuiU1S0XQA2L+1FAGwW93icxtBdLQGDXeQ0L/io85kcQEAn8dyuf1cDs5m4xHnj/+3wZwisjHJtOiPdc4jAIAjvBz5I96A5ZfvVmZm9JNPLVy/4eEHHpytHiJ7WF1dz8cfnY8eSVFUqanV4nfr3BZysDos4hAJ3zz6FYGi6NJ5+bWNvU63TykXVtR06Q32guy43IzoszVdy+cXcNh4d6+lrrG3p88SzrP/WJ2Qz6lrYlpIRkR8goLhUuP3R4gjCgD799cw9Iq0iw8AYHiKx/Ex4StHMLXXvRnFogBQBMEQQCjK4XWtR1EFgki9ro0kZQfAyYA+QDRhrGyKdODsMQCAXP1pOwBwuSxeWCYnW4h5VERHAkYpA7OXj15880RxyLa1wdfe667rdlWG7r1c8VF3uLjh222nOnvMn208fri48URpS+AKrTpHJcWETq9+y2DOsASsGBYqBgC7v40CEgCCvqahICkCRXD6GLFdkgqgCMaoAoCgCEYBiZzTCA4PgYBz3XXjli8fs317+RefH2FsrgPAtm1lK1aOpW21UQSdps7c0XNWiHMYM6yIsV/6++3h7g6/FnjnvsaVNd0iIYdebvB5LACQSfg7D1Z7fYTXRyyanVta0e52+xk8qYlqh9OTO2LPvvkL8hhOufv2Vl1zTSGD7fDQ5lds7nyAAACGQyFDletx/ofDv/4cZaCIL34eAMXw1IFarIIRdjUIinIHSDOGKgOkiSRtJGXlsHJJ0oEgnACpRxChx3tcyF8RXlGlEjH2l1uah1Q4XBq4qMhBGuIFo0J1tVd81CllQgSQs7VdXA4rM0VTfKZt6rjhIvqHo+xIna7dEJOkjkvVnNhdsfi2qYe2lrqd3oR0LSBIa223ud8+ZkZWW70uJSfGbnHRzAIxt6OhN7MoMeZcz+vK2mienlaDy+FJyIjicFk1p1tTcmICAVLXZohL04ikgrPHGlJyYzEc+/HzIyvumyWS8BkXxXCsra6Hw2PPWD5SzQBT3PDx6L0d16p4Y2uM77FQcYt1QzA2FgAEKK834KAgUG3ekCNbVW3eUKi43U0Yxew4h1/Hx9XegIWPqwCg2b4nhj+GhQpRBA1QfjdhZKF8nbs8QTi103FMyxvlI+18XOUnXVxMYvf3CllaDImcExTH0WXLiqZPz3zpz1uCse4G+hMgf/zxzL33DqyiHYQHR1Cb383QYUVHyzgcFkOdX1vbM358yggf09XGqiWjg0dG5xfNzg2l5GfFhPPMmZpJ7y2O8HIzZ2Z/8P6+0IgrlZWder0tdDLb02NuGjxlEwg4kwdFpAl+kAbN09kDm1aMouDJJWpw7K6NHFae3bmBCHQKeUsBQf1Ei835tVzygtdXIeSvGEqjHB+vYAishgamx8xlQsLWOAhjpWX3ZNWtQZl1xUcdLZ4oChAESs62jy1IvNgWdO2GhrMdGIYWTkkn/AGactNj87d+esjvI65/cM7GD/bVnG5dce/MjR/s44u4NHNqXpzd6uKGmNoHeQCArmW3uBIzoxoqOsQyQe6ElOhEVcPZDrlGUlXcfO09M5OzYxIzogCAcVGhhM/msYQXk1OD+RvnKZ6YHbehUPX78do3xqhfJikfvW9Io976U7ermCA9Mk4KfUQANXgbGq3b/aSr0vxtmfE/ftIJAC7C0OE83uE40ueuDJA+g7eBj6sQQJpte1yECQAM3oYay8YO59Fm+74u5/GhgmEHIZXy//bqqvB0kqWlrcHzoeJhoSjCSLkKALWDbb5p+ElvueW4nbBY/SY7YbUTVj/ptRNWL+m2+c3B0uG7epkI3+kbEeVi/HhEIu6kweEcKAoOHhwUMOvI4XpGrZkzs1AWMkw42XM9GS7N8iUDRQQc9igcj+Zz57LwJILocHtPIggvEOjzE41+oslP1FMQwZIg3K7daHT09UUwNbpkUBTpJExSdlToDGvko24oOJzeji6Ty+0zmBwer//A0TqT2dnZbXK5faNy4zRKUUeXKUj3+giP12+2OPuNjn1HmFNjGuZ++7xVE4y91s6mvubqrqbKTt45MSRRCPdtPOX1+OVq8f7NJcooaZA5ECAlcmFVSLDZII/L4dmz/qREIUzOjnE7vNljkgGAbrP6VLPD6kIxFMNRk97W2dQXflGBiKvvMquipSN/JswZFkE6g3uCOCpIld4SWooiuDdg42ASHianjy7CYPW2idnxdr9OgKvl7GT8nO2cL2DHcFav64wAV1u9bVZOu8XbKmJH+0i7j3RYvW0SdjwPV7gJE4qwwgPChIPHY99x57SXX/o+lNgZ8vG0+F05kthcaYSobGPGJDHCOVVUdNrtHkaU4TLzUR/lLTYeMPn6Y/lJRm/faNnUk6b9YlwmwEUooD4qgmVmq7N9b9+BRVHzPAFvl6s7QRAXoEj6JEmQuEO3e1HU/Ave3S+M+QvyGXqrQwdrb7hhQvDfI0ciZMcx+xxnzK2TlBlGryOOr9B5zGqOxOJ3CjCuK+CVsPhGn12Ic41eh4ojpgPS9nrMWq6UhV6uoamQvxIARPxV9DJThN8UNDWSiZ8LHsNRGCnq7p49VYw4twHSiSIcCkiCNGGIKEA5cFRsdu0Xc8cDUBgqIgIWNq6NqMrwkk4/6fGQDoa94QhH3VBAUaShpe/QiQaJiIuiKD1Za2jpq2noEfA5owsSGlr6ztZ0eb3+02fb5TKBRMz7aU/FE/fNGSpV5S1PLgSAnHHJAPDU27cAQGpeHAAsv3sGhOwwhPqx0sypuXFYiKXejOWjaZ6t/zk09/rxNDMZIFEMTS8YeNrX3jMz2M4dzy+hTxgX/e6d3eNm5xTvrUpIH6m5A3MYHdU9OCPmi6G4MyXLaAPRWMF4AKCPRcp7YHD2SgAokK8JpdA8oZz0OQA02XYBILQcvGB3GWlyAMDrJbxego5tJGHxf9bXdzgNi2IKGWyTp6Qz8mj5fMTu3RXXXTdowxVFMDfhkrNUUdx4evC1OOvYCEfGVolxqcVvdBMRlKbx/Lh4fmwsL2anbs/CqHnbdbtQQOmTJEHiBW/qV8HYsckMg6yGht5enYU2yu3X2xhZXmjfaQBAAUEBrbd3V1haM8Wx3+gO6zzmaJ5cwuILcR4bxcfIU+vt3Yf0lRIWn4OybH7XdfFXJgT2OVzApIiB7OwYoZDLsDvftavillsmh77XZtcuDBW5/A1eoouDx7JQGSAYSblNrp0owqcoL5+dy8Yjv1dslO8lnRQVqLcdyhSf3+Yf4agbCgaTo63TGB8jl8sEeoPN7vQ6XN62TuPEMcl1jb10abRWYnd6BXxOeVWnTMLncHDaTSIQIEcSMz4UQSEVvtWAhdkV0zwzlo0OMqNhlxumQRor75+lazesvC9CLPyhwBRYXOwCqTiGWruF560cKpMlg5IqXjCyrgIAhCcOwnE0aPBi87sRQPo8tnDT25QUdXq6tqGhN5T4/ZbTixePCt1+HiufwQgCx/hmRjTZxxDU6rf1uHulbOkxwwkZS4ogKH3S7e7pcHW1uzoT+BeXaONqg07PxYjz8/PP9devGg8APx9lRp6jza/anf2tzr50UXS7Ux/HV/a4TRquNEUU5SK8co5QyhIc7a9JFKjbnfp4vkrOEZq9DhzFL2jOelWBYejMmVk//ngmlNirs2zbVro8RNfLZSU7fBUcLIrPyghQTham8hE9AdLGQlUEaWFhSiFnyI0CgvS6CIuMHRMqrWDEo24oxMfIaYcHGvSopik5GdEAwHCHCDo/hNa6GghQVLfFqhYJ/VzU6HQBAAfHnT6fjM8zu9x2j9fm8aaqFAaHk6Qom8ebHaW2e7wiDsfsdisEfIPDGS0RAwCbw+JGi7Y3NMxISep3OLUiodPnE7I5KIoQJIkhKBtDMXSQuGAKLAW3oMW6QcEb2O8Qsy9uG+Jqo7PTxKDI5MLg6zB8XsKVK8e++uqPoZTeXuuH/97/+BODJCbDNJ8hnoYy2V8VtwIBJJqnDZd096dc3RBjl4z5C/IYAuvosQZaYB0bLLCC5lcJAtU9KfMAgD6SFIkiaOjnIUUYhSIIXQoAO3WlCIDN75awfk3D2sVLRjEEFgB88vHB8eNTguHAhJxRQg7tWBO66XkBj5YgpOxoQUjaiyBGOOpGgqHk/gidHK4gtpypzo3WbG6s4rNZHoLoMFkVAr6IyzY53d0W26LcdARBMBSp0enlAh6CIJvLqvhslpcIZEepyzp6ODhOCywA0AiFKIJgCFLdp9/d0Cjl8QQsllIooCl3jWXuHjIFVr+7BAB6XUfpfydFvQu/JezaeZZBSQ7JzibAObclD5nTePacnHXrT7a2DNrS/vHHM0nJ6mUhEYovDeECbhhvpIhoaOz9flvZquvGEgRZXdOdmqKWSgWnSlpysmNOFjcrFILEBGWf3uZ2+xITlCiKtrb1p6ZoMtK16zeeuuH6cTW1PTSFJMnWNgOHg3M5rMmT0n4+1jB1cuRkEwkJyoyMqNClX011Nx3onWEvWlSUyMjER4PWPA7lMwQAC6OuriH7CJGaqpk8JZ0hhT0e/3PPrnvttRvDXFOH8msZEhRQOMq2+HXhE/CrOup+LbBxrMtixTHU4vbEyaQIIGaX2+r2RklE6RplvFx6oqVTKxY1G0yFcTknWjppToWAnx+jZWHYgfrmOZkDW6XNRlOjwZitUTcbTclymVIo6LXbT3V0ZWlUfBbLFwiwsUHbykyBFVFCbfzX7usfmb/h3V3KKGnmmOToJPWWD/aueHDuxn/tlqrEOAsTSQU2k50uajjTZuy1dDf3cfkcFEMRFFm4ZkCIOByeJ5/4ZuGigjlzci4hsvWRI/Xbt5cziDNmZI2wOoIgTz658LFHvyIH2we/+87uvj7rHXdMu6gEMARBnjzZtHtXxaRJaQsXXbRVUThSktUpyeqkRNXH/zmclKisb+j1+YibbpgAAKdOtyxeVLhpy2m3x7dm9aRNW05TFHX9yrHrNhRnpA9sgVXXdNMUmUzAYeMiIZcgyIrKzuEjEMyfnxcqsEiSOlPW5g+LWzB/wa8f/eoycd99s06XtDAckru7zY888uXvfre0aHTiRbXW1NRnNjuDiQg4mMBPelgIN/wr9RsfdZeGZflZdNjV4OR6e1X9wpz0oPv9ylFiFEEemzkJAOjzIGeWVpWhOb+PnKKQPzF1EgDQRxrDeFNH2LsxeSptvkYxO1XOHVhVuezuPd8ekypFAMATcAFArpXs33BSESXl8jmdDbrknFibyU4XVZ1sEkn5KIoiKIKizE9Tc7P+vX/t/ejDA+PGJY8dlzJ2bLJWe2Fdu9nsXPfdyc2bTzEMr8Vi3uSLyVWVnR1z661TPv/8ZwZ9/bqTBw7U3HLL5OnTM4aRpBQFXV3G8vKOM2VtZWXtdDzJcVfImAvDUJPZ2dFpTElWORzenOwYo9GxcXNJXm4sjztgqx08kcsFe/ZVK5WitnZDS6u+qVkfpHC5rPYOY0qKOj5O8Yc/bX7pxWuHvibMmp3973/vDzXIOnu2w+EctBPK53OmTMkIq/pfhpgY2T33zgzPaWoyOZ955ruiosRVN4wvKIgfJmyOzeaurOisqOg8caKxu9u8cFFBUGA5/KYYXo7V3xvcdArFLznq9nY2NVkMD+RNiFgaEWtrTm9qqlyYkPFIwYVT1T18+IfbMovGamIZYVevORemdShH/WGm4eEYhoH58zRavtS7i2Wc7G7nPiV3TIbsTgCQKERzb5wUquqfsWIgnu93b24fNy/v5O6KGx4bWJOveGDOBcP7+v2BY8cajx1rBICoKGlyijo5SRWfoBSLeQIBh8djE/6Ax+Pv77d1dZnOnu2orOyKmKn4wYfmXGyKtzW3TunsMu0PS6nQr7e9/dbOf769Kz1dm5yilkh4YjGPJCmXy+dx+4xGR2enqbvbdFVjhtxz5zQEQeLjFEErUPokMyMKAK5bMZBfgz4JJs753bOLASA1RU1Tvvr2+IRxKcdPNKEoMn5cyvAfcJGIN3FS2pEQ+4aqqi6LZdBO6MyZWUMlmBkhXC5vU5Pe6fQ4HV6n0+t0eh3OgZNzFE9/PzOW0dmzHbff9rFAwBEIOAIBWyDg8vlsgZAjEHAEfI5AwBEIOUqlKDZe0W21qoVCm8cr4nAsbrdCwDe73ABQ3Nm1NDsz2OC1145padHv2M7UKgBAWVlbWVkbm43n5sVGaaUSCU8k5hH+gMvls9vd3d3mrm6zod82VAAkCVtTZdmNIeyhtqR+sVE3Ny51btzF6Z3vzB7DwTCT5wKhkH8jYA5EnevwtOhPARAA6kj33bTAmrFyXLgAoikrH56na+2/7qF54UUjhE5n0eksx8K2pS6IefPz5l5SHoTnn1+CYeie3cw4BABAUVR9vY6xo/+LIWg+E7QCHcYcNNzWhqbccN24Hp3lhuvHGQyOpYsLL3jR+fPyQgVWU5hD4uUnH6yr1T3zzHcXW8vnIzo7LxCysaAgfsLtBXlRmo0VVXwWy0sEcrTqLVU1XVbbC7Omh3+on3hiYSBA7d5VMdQVy0rbLrafAIAAmhcW052BX2DUfVlXtqmpcnJU4nOjpwPAmf6eDypOYChqcLtihOJ/TluCAJzs7XjrzFEcRV2E/5t5NwhYTEfLM/09H1ed+vfM5QDwwMGt9+aOG6WK/mf5scPdLVECkcE9EEX+24byH1tqAxQ1QRv35KghFcdXHMN+Oc/93PRiMCLYHFbC1U9OF46lS4sefewSrTFRFHn22cVJSapPPzl0yUEmf7Ngs/HEBCUAaDSRncYZGDsuWS4XDJVEIyZWfqXSI18lsHGs02LFUdTq8Sj4/IIobbPRlKFS9jkcjQZjgCRD98VRFHnmmWvi4uSfrT3yC//0v8CouzWzSMTiNFjOBwquMesPXnsvG8NW7fym0WJIkSieOrp986JbtPwh3+hwtFhNB7qatl5zKwUwd+unANBut2xrqVm3YDUCcPPudWcNugLl5Qa6GiGYAkvNm3BM97CMk2P2Vmt4V9beD1gsTKUShU/+Lwoqtfj++2eNXNceEQgCq1aNHz066d13d1dVXnrq6YyMqPR0pufHfxcwDJ09J3fjhuKIpfMvO1nk1cbyHKYCeEVuNn0eqscNAkHgppsmjhmT9N6/9lZdRtZx7OJzGv3yoy5HrqF32RRcgdPv07vsMg5v5NKKdsNqs5szZCr62aZLlQDQaDG02yy37F5Hszn9l56B5WLBFFiZsnuMnnK7r0XLnyrnXuHByuGwvv7mgSNH6vftrTpzpj1i+NphkJ6unTM3d/HiQg7nwkZ3I0FKivqdd9aUlbZt3lJyuqSFIEb63UtKUo0dmzxvfl5S0gXsbP8rMH9+XkSBhSDIvHn/BckHw/NuXVCtm5amfefdNWVlbVu3lp4qbg7ddhgebDZeVJQ4f37eJafh+CVHHTbY403FE5o87n63U8UTAAAVyc9IwGIbPU4AIEiy3twPAPEiab25nw7Z1GI1AUCaVBktEH857wYMQQiSRBHkwE/lmflx0fGKtW/vTkzT2CwuuUqkjpLqdZammh6a4nZ6b7pv5vdfHQeAa9dM2vLlMYvRQRctv+XC+n4ayDCR1MNBUh6CtOGolCDNtNs9G1MCAAUBL9Ft9ZyW8abgqIggnRjCC1BODBEEKOc5frB4itWCgWiEXi/R0KBrbOht7zDqeiwmk8Nmc7tcPp+PIEmSzcb5fA5fwFHIBcnJ6tRUTV5+XGxshEweVwp2u6e0tLW6uru1Rd/ba7Va3V6vH0GAx+PweCyJhB8TK4uLUyQmKgsK4mVD5NG6BNQ197V2GXPTo9wef2V9T1qiSirmlVZ1pieqfUSgu9eSGCuXiHjF5W256dHHy1oUMkFSrCI/MwYAKCAtvj4RS+EJOGh1L46yfaSbj4ldAZs34PQEHFG8NFfAJsIvMX8nAFz3xtf1Pf0A8N7dy6ZnJw/PTBCBS4uoSRABBEEu1pvkisDl8pWVtdXUdLe06Pv6rGaT0+PxEwTJZuM8HovHY6s14ugoWWysPCc3Jisr+gqGDL2Co44gyWeO7WiyGB1+b54y6vGCyVafJ1wb9XNP6zvlx9kYRpCBT2avxBH0hRO76y0GfyCQo9A8WzQ9Wih+7MiPPQ6bmi/wBwIP5k8cpYp+vezIMV17nFDiIfz35o4fq4nd3FS1samSntiunXPdiV3VoyakyJSiQzsqHHZ3ek5MYpr2pce+/tM7txzfX0NTqs90XLtm0vdfHRdJeBRF9XWbYxNVdFH6iNUOFyewemxfYSjfS/R6iS4hJ9fj71ALlwvYWb32DSJOnsl9EEelYs4onX0dB9fgqJQgLTgq9QfMXqIrWf6CyX1IJYgQ+Pm/Dp6A/2Bv/VhFIoKAxee2+tzZ0ign4ZWy+T0ui4YnNvtcQpzjJHx2v8fqc+fKos0+l4TFs/hcGp441FqnvqXvbF33rInpG3ecSY5TWh3uvPRog9nRqTMrpMKc9KhYrfTrraduWT4OAD7ffPL2lRPWby+94ZrRAHDWslvLTetyVbNQLkH5LD4dH5dyUL4rYLX69FmSaQgg3e5aq08/W3sPB71EW/PhBZbfR5gNDqlC4LB5SJL8/qvj868dbbe5UzKjyACFYojb5eMLODaLSyoX2CwuhVoMAF6P3+X0cjgst8vL43PcLi8AVJxuHTs1w2ywxyQo+rotcpXI5fTaLS67zZ2QqjEb7JpoqcPm4fHZLqdXMURYx3A4PT6X1ycR8CwOt4jHoVeLPA7L6yfsLi+Pw3J6fAIu2+nxSQRcu8sr5HGsTrfT47O5vOmxKgxFiADp8vq4LJbR5iQpyubypkQpjDZntFJicbilQp7R5oySj7Q/w6DBdrTM/JPe0+wj3WyUJ2FpJyhvyBCPyM+GgoCf6MIxTYC0YQg/QDkDpC1AWvjsAoI0YYgwlOIP6Nl4LAAESAdJOTBUQpJOFBUESHOAdAZIi4AzFgACpI0I6Dms1OA5BVSwTQRwAMAxZnAOkiQZ9kzhlKAhQXjR8Li47WoEwQnSysGjBOwMekZJUl4AQBG2h+jkYDFsTGn1lGAojz73BQRsTIkiXAE7wxvoc/kbKSqAIFfsA/Vr4cfOCk/Ab/A6NraVzo7KRBGkzWHc2FaaJY3KlUavbTze7bLE8qVSNj9OIEMRhKbkSqOzpVFa3iC7swBJSUW88pqu1ASVw+XNS4+uqOsWCbn0r0gH9kuIka/7qTQ/M4Y32AENQ1hWfy+KYJ6AQ8rWIoC4AlZPwCHGVWpOkowd1e48S5+z0Ys20x0hju+vYXFwQ5+Nx2dnFcQnpmkMehuKIjs2luAsLD5ZfXRfVVKaNjUret+2st4ey33PLOILOV1thp2bSzRRUoGI67R7BCLurMWFKIJgKNJU21Nd1p6WE+NyenduLpk0KxtFB+hnTjbz+GxDn00s5c+6poA3MouWdQfP0NKqx2hLjpKzcCw5SjE2I27rsSoeh2W2u4U8tsPtE/LYRIDkcVg+fyAzXu1we1EEOVTepBALqtp0EgFv4bjM2g69TMRDEQRFkdoO/b6yxh6jbXxWPBvHL19glZt37NH9CwAwBBfhSoLy9Xma2eiI4joAgNmxgcfOt7kPoqjAH+jFUSkbT0QA0ds+9AU62Xh8kGJ170IQDi2wjI6vMFTEZ48yOb7lsjN57LwAaQ/x1kBdvipaYNHnOKYItsln53uJNhl/JY+dHdqTcAEUTjnvMn0x0gpGKLDu/HjTs4unZ0arokQ3RfSuUguXUxBAAAMAKW9SpKUxCYAmSJ+4qM5dLIL9DC9a9e63j86fNCUj8YpcCEdRm8dTamznYqx4ofxkf2uzvZ+Lsdgo1uUya3nidLHaSfiUXGGcQHayvzVIyZMxY65np2ozktT0Uoi2uspK1TIsfaeOTaWLslO1AEBPrwAgVzLrXEiMAY+QGuuhLMn0oPO2RKoZKsfalUJskurkoVqZQmi3usUSvkwhjI5XlJ9sdto9Yhm/vbmPy2Xz+OyMvNiOFn1iupbHZwNAVVkbl8sm/AG7zU0SpN3m7u+1tjXrU7KiO1r6YxMUfd3m/j4rl8umW1NqJB0t/SqN2G51a6KlMqXI4/YxBNbclz/ttdiLX32YP1jFGSUXK8QCDgtPjVHWdeilQl56rAoAcAy1OT1+ImBzegMkaXN6xQKOzemRi/i5idpug/VUXSeGIaWNXYkamUIs6Lc6W3tN+ck5p+o61TJRa68pTiVNjVFGycWHK5pnFl6u/fDx/m8BIEcya370YzjCBgAv6WQhEQSWgzCeNKyfoLxRiJ9XkiAI20d0IggeIC1sLAbHVBw80eE5ysKjuOxMknQEKTx2vs29V8KbDwAU5Q+QVqf3FILyUETAZxf6iE6HZ8Azzx/o8xKNFEUgCE6fCzijg20CUABAUVc9/WIoLm5J+N+LSxNYtT36LqN1bl4E9SpJUQgy4DAYeh6gSGywP/AIMwb+ZnFBHVZoHodQCoRYt0akH9pRMX1B3uFdldMX5DFs90iSRJCBzKgRW2agTW9e8vfPASBcYJ1vM+wnIClqz+mGeWPS6SPjZxrqh7saP67V3/dR4+0AcG/qWin7AiYCZ807d+vevTPl30pOYij93KRheOftAYrdcwQAAqRVyl8SjCw2mGfgnObk4GlsPGaINn85/Pr5e38hXNJwOlDdzOcwLetoDLUnhQ3hD/xfKq1GgvA8DucDITHyZQ6mz1iUHzwyELpSiNgyAyca2i/Yz4iBWxeMzQAA+ggj++Guxo/rJgZyMotZF94BbHOWRaQjA+Gnh3feHqCIuDOGvsIg/jDOi4tHdmUxILD2VTV9duT0Nw/eSP/78vf7lSLBg3MmHK5teX/fycZew3/uWVmUOLCccXp9b+88eqSu1UcEhFzOtw/dIOZxO4yWV7Ye6DBYMBRZM6XopomRPTPNTvfq99ddNy73+9PVPiLwyPxJS0ZlAcBnR0odHm+n0Xq6tYskqa1P3irlR26zsrP3la0HTA4XESDn5af9fulMAIjYz/J23Utb9jm9vgmp8cEhFd6m3e1d+c7XN08u3FhcZXN7Vo7LfWz+5F6L/U9b9p1t17EwdPOpSgDY9uRtl2B68z/8MjhR33Fhpt8w/OcWVkNldQkiQBEdzsiW+v8/YODpzMxO+dsPB5v6jKkahcdP7Kpo+P7xNQAwPSt5elbysre+DK3z2k9HnF7fD0/dxmXhOotdzOOSFPXE1z+9fN3cnBiNze1Z+c43OTGa/PjItm0dRotMwP/p6dub+4w3vb9uTFJslFQEAOtPVnx698rXblpodrql/CHb/OxI6bVjcm6aWOAjAnrbQMDM8H4GSOqZ73Y8tWjqgvz08nbdmn+vB4CIbSapZN1mm9tH/PT0bf125+I3Pl9alJ2kkn1057W/37A7Tau8Y9pvIkbKr4Xf/tTQ7fOXNHdemO+yQNVaD1dZ9+s9ze6AjY3yBLhczUlKFU9MFU5goUz1f5O9+Kxlp85d7wk4eJgompc1RrE8jj/IsNEbcG7t+quDMNj9Rt+5VC+v1Zx38Ynj592U+Bp9bvZ1nzRs0HuaDd72AEUAwNrmB0JbG6+8frr6TgDqnbrr/ZTniczvg1ldvm59osddp+Ym3578/sDNAPl27bUYynosYyO9+tC562ush7pclWZfD0H5OKhAwYnLFE8rlC0Kl6FftT6uc9c/krGei4nOmH6ssuwz+boAQMxSZ4inTFbdwuDv97aVGDd3OCuchAlHOWpOUq50bq507iWoVge6gqHIirG5m05VPr9kxs6z9WOSYjSSIdM9Hq5t+eiua7ksHABoWdNjtjXoDI988UOQp81gHkpgAcCiwgwASNEoMqNV5e09UdIMABifEkfry2UC3jBtrhyb+/L3+2u79ctGZ48eOklsj9lqcrgW5KcDQGFCVLxCOlSbSSoZANw8uRAAVCJBvELaZ7XTxAvicE3Lw59uA4D3714+LTvJHwhsPlm180x9m97s8HjlQn5hYvTycTmTM5mRnUPx0d7i93YeB4CDL92rFAkAoFFn2Hii4lRjV5/VTlKUUiRIj1ZOzkxcMS53KO9Cndm+pbjqeH17l9Fqc3skfG68UjotO2nlhDz6eQ6PEw3t3xdXl7f1GO0uAZedpJYvKMxYOSGXjWPsIcyOTjZ03PPhZgBIVMt+fP72oVpe8vfP2/RmAPjk/pUT0iOEV6fh8RHby+qKGztquvrMDo/D6+WycJVYkKiSFyRGTctOyhi8l1LT1XeivqOuu7++p7+93xzMRDn+d++FN37nrLFPLL6sIJw7et6qsuwDADbKF+Eqd8Bm9HYYvR319mMPpH0ZKrAoIHd0v1Vt3Q8AbJQnZimdhKXRfrzRfny84vrpmjtDOUmK4GNSPib1ka4+TzMAhAo1Nfe8It8dsFl8OjbKj+JldLmqAUDLSwtVyUtZtOYL0fLS2p3l/d42LTcNAAjK1+dpAoB+TxttLQEARm8nQfliuDlBafVV6+N0OyyUK8QVTsLc5aruclW3OkpXxr8U8ZlY/X07e95usp8EABxhE5TP4G3X+JibD2fNO/fo3qOARBFMiCvcAWunq6rTVVVrO7wy7s9D5coaCudl5/Xj8q575+snF07ddKrqwTnDhaegg7GGUiiKwjF07/N3j3DRFEwASVHn1RyCwdqiodqcnJ7w41O3Haxp/ueuY1qJ8PXVI0oAOaC+jdSm3e0FABF3YMwhgFzCPkRLnyk1SvHY2h/qus+Hauu12HeV1+8qr19UlPnXm+bjF7KK7Lc6lSLBv3ef/HDPydBcsB1eS4fBUtOlv25CBN8DioKP9p78ZN8pX0hAC6PdZbS7zrT2/Gf/6WeWTVsxfkiDdSJAvrh+z4+nz+dZ8TncZkd3WUv3huNn37lzqZg/0p31S8bmk1XvbD9qdg4KGODy+tv7Le39lsM1LV8fObP/T/eEPsBvfy7fVlIT1tJVgd7TUmXZhyGslfF/ThSMol9yF2FpdpxyEVZGoNFj/d9UW/ezUf6i6CfSxJMQQEkqUG/7ebfu3WLjRhFLVSRfQnNyMVFwAtXjrvu69QkACFIYiOZl0UU+0v3PuhUAsCj6SYbSnUYUL6PdWa73tNACS+euD1BEgmBUu/OMzl2XIBgFALQIi+JlBKvkSGZreKkZoskilgoAAhRx2vT94b61zY5Tna5KxtyQxu6ed+3+/mtink4TTWSjfB/p7nRV8gdnZmh3nt2j+xeK4HO0D+dJ59KTtTZn2Y7uN9scZYf6/jNbe/+IfoNzOC+wNBJhQUL0t8fLjQ7XpLThZgRTM5M+PVTyyvVzOTjeZ3XIBLwYuSRBKV17uOSemeMAoF7Xn6SSD/VlBoAfympvmljQ3Ges1/UXxEfeExmqzequvowo1fz89BSNYvX764a6RLRMLBXwdlU0LMhPr+7u6zCah2pzmDsVcTndJuswDEFUtOt+OF3TqDMIuZyi5Gi1WOjw+EpbuvptTgDYUVZHktTrF0qu229zHtx94t+7TwIAj81K0cp5bJbe6ug22YgAOT07KWKtP67bs61kIG6JSiwYlRQt5nHNTndZa7fZ4XZ4vH9av7ff6rxv3viI1V/4bveOsoFoDWqxcHRKDJ/D6rXYS1u6m3qN93y4OUp2EY6yl4C/bTnw3dHzIV+EXHaCSibgsPttzl6L3e3zA8CysdkMcb+oKDMjZmDO5fUT72w/Rp8/sXgqKyxjQm7cZfl7OgkTAAhwWaLgfIxQPi7Nk85jcLoDtlOGTQAwP/rR9HMGnyiCZUlmBChiR8+bR/u/zJXOuXpmcQAQxUsHAL2nhf6Xno6Nkl3T7jzT7aodEFjupiAnjWting5tBEPw8YrrG2xHde6GbldtRIHV7227LflfqnNCk43yUoTM5BpH9GspoKapbyuQnV/qJgqKZmru+bH7H+Xm7VPUaziooNn6VYpkTaPlcz9pFbFT/QELgrAQQBEETRANSo47aHV644T8x7/+6YHZ44Nqi99v2N3Ya+gwWn6/YY9SxH/2mun58drnl0x/Y/uRa17/PEBSUj738/uul/C579227O8/Hp796qdEgExSyf59x/KhUmZyWbjV5Vn0+mdEgPzjtbPoRWU4UASJ2Obmkqp9VU04hgo47D+vnDNMP9+4adGft+x7e+fRCanxUzOShm5zSNw4Mf+pb3bMfvVTKZ+7+THmyjwUeysaAeD6iflPL50W3FYnSeqD3Sc+2lsMALvK6+cXps/JHy5W0dZT1fsqG9US4dNLp83NTwu+og6P71B1c4omgnvNumNnaWmFY+jvV8xcOSEvuL0QIMlvfj7z5o8/kyT13q7jGTGqGTlMo4SDVc1BaXXPnHEPL5gUXHJanJ6XNu7dV9HUY7IN0+fLxLpjZ4PSKi9e+9g1k8emxAX7QFJUeVvPgcrmlWFTy0kZCZMyBj6rdrc3KLBunFwwlFnDJUPDS2OhXJtfv7Pn7cmqm8WsIVM3tzpKCcrHxYSZYmbElWzJjH29H3gCjnbnmTTRSF3nLgH0vEnvGUgj2OWq5mLCVNEEFsrpdg/MSRkzrKEgY8fo3A0+MnIkj1TReFWkKV4Qdn+/zt0AAFmSGYyiOEEeAAQoQueqTxQWcTBlp+MnHq7mgcZP2uXcQou3BomU+m9AYLl9/gBF5SdEbXhstYDN/qmibmxibElb199WzQeAADmQJMPu8Tq9Prffv2Za0bKxOalqhcHhxDFUb3fIhfw3Vi9CEcTscmvEQ+q/AMDjJ+6fPf7+2YM++BEV27FyyXu3LWUQX7x29ovXMvMC0f1kYFRi9LYnbx1Jm9X/OG/RuuHR1cHzJJV8y+PDyalQjEuNe/H6QR1DUeThhZO6TNbtpXUA8J8Dp4YXWHsrGtUS4bonVqvEg7zGhFz24tERolO4vP53dwy8qH9YOXvlhEHrPgxFb50+GkGQ17YeBoBXtxycmpXIyEHyyb5T9Mm8gvRHFw0KziEVcN+49ZpVb33T0GOAqwOHxxcUNNOyk/55xxLW4ADeKIIUJcUUDa2p/GXAxyQLo5/Y1fPPSsueKsveeEF+lmRmpnha+ETJ4G0HAAUnPjySH4rgCk68zl2v97ReVYElxBVCXKH3tAJQFECPq1bLS0MRTMNN7XHV0daefZ4WEUsZanfqJz21tsPtjjNGX6eLsPpId4Dy0dp9CiKrSDTcC0QKDM7yPmgY8iVyBiwAECtcGAzWSp9IOTkR+QcE1rrTFWwMS1ErDHbn4vxMBEE0YmFQu7TlTHVujIZOfdFnd0h5vHi5BEEQDEFqevR7LI1SHo/PZilFgrOdum6L7fkF04VDmC/9H8aa6ZFzCtw3dzwtsKo6+tr7zQnDqvOfWz6DIa2GwY+lNbQCLlkjH0pLdfOUUV8fPtNjtvWYbXsrGhcUnv+odhotlR0DGajuj7RgxFD09hljfv/trhH252KxraTa4fECgIDD/utNCxjS6jeFTPG0OH5eqWlbpWVPu/Nsu/Psgd6PiuRLJipvYoV4z/hINwCwh3DbpAWcN+CIWHoFEcVLb7SfsPr7vAGXl3RG8zIBIIqX2eWqNng7cITtI12JwlFB/m5XzdauvzgJMwKIipsUzc/iokIWyml1nDb5hkxVzbqQ25CXdAEAAoiMPeQnJ+h7FBTxwyeBHxBYDq9PxudpRMLi1s4Wg6mp30gf6fhnbBzrMg8kyYiWiJUiQbxMEkyMkaSQKUWCPqu9pK0rQS5N1yj57MudkxMBEsdQ+niZTf0yQBAYmxrZ4zxJLY9TSDuNFgCoaO8dRmCJedzhp2AMBI2P5hakDWV8gKLInPy0Lw+XAsDR2vZQgXW2bSDEpVYqSouKnF8+uOy6GjjZMND/hUUZUsFVV+1fJgS4bJr69imqNW3OMxWWXY224ycNG9qd5Tcnvhnc+Kc9zIM2CgzQ4oyDXbFQH0NBy0tvtJ8wervsfgMAxPJzACCGl1UC0OOu46ICANByBxRYftL7fdcrLsISL8hfHPNc6LRra9dfhhFYF8S5GShyV+rHV8o5bOBBPzJzIn3y2KxJjCMALCvICpAUhg5yQVhZJEYRJMgDI3NTkAl4oesvBrw+wu72kiT57d6y1XOLvt1bdtfiCUarM14j6zFY1XKRxe5Wy4QA4CMCRqtTIuCyWThJUUar0+31213erESNxe6mxZxc/MvlwlOJhIKhJ5WpWgUtsJr7hov5mx2nviib6ZqugXDGWTFDalUAIDt2oLSifVAQ3qbegc6kRg0ZfEYh4ot5XJv7qviLVXUM9H9sym8ry+wwQBEsWTgmWTim19O4vu13OndDre1IjmQWXariJgGAwdtOUgF0sJM/SRFGbwcADK/3uSKgtekmX5fJ24UAEsPPBgD6qHPXi3AlhCiwulyVLsICANdEPx0qrQDASZgvpxsqbiIAUEDqPc0XXD+OECN1zaHtAIaPkXb5bgrbjlbxOKz8lOjUWJXbS6TGqjAUqevQlzd2Zydqv9p1usdgffKGGQIe+1BZE5uFOdw+EY/j9RNsFsbjsBAEoXmyEjRd/ZZFE7PT4y7g6ODy+PjcK7B6FfOHixwgEw4oO2wu7zBscuHFbR6Z7AN2AMOvIpXnSk2OQV9+q2tADCmEw1WXCq6WwAraMcQqrkBsll8YWm5aDD+7xVFi9Z1P7JwkHM1G+d6As852JFsyM5S/xnqINoNKCFmLXQJov2gAcBFWGGLQRXEzABCzt6vP06TiJtFLVAEuk7A0fZ4mP9sDgETxBjxkfaQHABBAGPYZZl+3zl1/OV2VsLRablqvp7HYsHFp7O8up6kgflsLLtqBXiLkysV8+thvcbb2GNksrNtg1chF0wpS6BAriVp5Y6chO1HTqbfQ53FqaU//AI9SKohTy7z+C0c0feifW65Iz4fJFgEAHNbAh8HlGy6Y7EUpcUiS8hIEo/2I4J1boTs8g65OWwwAAHfY6tyhM19dDtw+f9AcbyiHzd8Izpp3njB8p/e0BNXPJEU02I52uiphsHknBxVMVt0MAHt079XbjlJAAgAFZJ3tyL7eDwBgovImDnpZS0IUwRScOAAoNm4KXXvSCvKBbmACGTva4G3v97TG8s8rN2P42UZvp9nXreDEBhVt5+ZB1BnzT0HOdmf5xo4/UtTlxp6fqb0XRbA625Gful+jdyQAwB2w6dwNR/u/ogNUXBR+W87PK6bn0yvKSbmJAEAfH7h2MpwLvRJcb6bHq1LjlCW1HRNyElJjlalxShRBlk7NvVjXeaXkyigUhheOHv+AaOCzr9ibiaIIl417fMQFrx4UTMLBc0kea0CQBQVfRAQClxXPwzNEIGwOC0cQoM1jh+L5jcDm158wrPtZ/yWOsAW4nALSRVgIygcAmeJpqaJBxkdjFSts/v5S09ZtXX9lozw+LqE33QCgUHbNeOV1l9+fCcobt3e/3uo4/a/6G0UsRYAiXIR1qvq2cYqVQZ4oXnqN9RAARSuwaETzsmqsB3Xuxlzp+e1sOTs2WzKzxnpwf+9Hp41b+fj/a++t49u4svfhMzNiZlu2zMx2mBrmNGmScpsyc7ft7ra73cJ2d0u7hW13ywwpp03TtGFmNMXMIFvM0owG3j/GUWRJliEufH/vPp98nNGlGV3NnLn3wHMUrqDJS9qThAXlCct3979zPpeaIiq+MPkPP/a+eMa564xzF4pwkDDZGrEIHQl+WwILht5RorH2pFML08J7jWFPWpad9OXuyvLsZLZXVvIYqYTtnnibJqt74E0oH1evcbVEzPq1mpyxPWVYmM9GXKqlg5R6oW2s3RMvJ11o5zg2DNUdRRCZUMDWGh2u4tSE8znLz4oSxeIgg/f4au2E0U2aARARJtMJsooUCwpks6OZQBYk3pYtnXLS9n2Pv84VtAgxaZq4vFy5PEMyaVyup0g+n4cKj1s39AdaXEEzDxXpBBnssisEvTD3jHMXnNW4s2DVWABMhAfW8qQHk0VFVfaf7ESvj3IqePqJqosmq9da8S6A8xJYAJAvm50sLDhh29jmPeEk+miGknMT5LzELMnkvChvtWERKbB2GBubXebb8kaRL+f95iMbOqqWJOffmT/86e8/8s01WZMnalLGdq5xx7H6LgDYX93Gfnz5ntVjG8flD9g8PpUktpq/2Tig3s4ZWr09BpSmJbIC60x3fxzzYl23iT2IcPjOPOuJGtK+R8OLE1ZPbGkY2sASsXLcsjDa3aH1XTQKDDrWUHiqtXdR6RhzOvwCkHMTKpQrZ2uvD9AehmE6fdWpopJOX3W2ZJqXdLZ4TmSIy4WYNEB7+KjYT7mFmFTDz7hAd32A8mj4aV7SLufqArSHoP047ZdyYgRXJAnz/1D4Y/zLYIBEzj6wOdLpOdLpcRpPVK2eqFodUZggyIp5FhTBKpQrKpQrIsp1gsyY7a/JeCn+pUZAytXOTbhpLtw0ql4xESmwFuhzF+hHkfwdAK7PnspHOXYitik3DsZwrnHHmCVUNA42dMR072zus/baB5zFS9PGM33bBQUZP55qAICtlU33LJsZc2VJ08z2qmb2eHreoNjjsrMX02NztvbbMhNiPEhHm7uGiqwMLdDMTi9BUjEjsfbVtcW5/mk5qazA2nSi7u5lM8bsoR6u+/PixLh7ulc6tumFOacdW3moIFsyGQFUylUjgNqInlP2LTKupslzlGYoHiog6WCiMMtPuU7Zt+RJpyEIiiJoX6C5zXuahwrcQasQkxUr5o4tNMfj+0bIn42iUgQwAJSiTSgio2gThiUytAdFVRRtw1AVRZswVEfRJg5mAACGISjahKIKBPgADEWbaNpHM04+t4yibSyFForKQ+3D8fuPNl85q4x13N1V29Lab7tp/uQ4VzhebeJgkMD6pOX41x2nZ+gyHipeAACnbT1v1O/HUNQS8CaL5P+csgYBOGLu+PeZ3RiC+qngBxesE3EilTKnbT3vNB56ZdolAHDP4a9uyp1erkp+pW7vvr7mRKHMgntHfq5nq7f3eB3dPocd9z9WvnSe/md5Cde29/sCxMQ8AxGkzkfB/OHuk8sr8qO1729uG8igVZauN6jlUf3GjqUVuS9u2md2eTvM9i8OVV4+IwYH2Sf7T7HiUiMVLykf9HpI1ykLDDp2/fXGtiPProuRu/ijPbG54gAgTavkcTCCpIIUtbOmOdzDiwUeJN/fdSLO9a+dWvza1sN4kLR7/U9+ue3pq5eNzcQs4HGEPC67lGvstYzc83aEwBCug+hHEcxPefyU24x3WvFuM97ppZwsT0OA8ggwiZ/yiDF5kjD3mO17LspX8pLavaelHLUF75Zy1X7KI+fqxFwlQQfGJrAoqs8X2IYiIgzTUpQNQQRCwSycqCLJH1BMSVE2kuoS8i9AEAHDBBBEwBEaAMDr34wgAppxoYiMLUcRISCow/1fkuric0uDVDufWxZqPxTmFWXNKxqGBnq82sTBoOfz6qxJEi6/yWUKldQ7+7csuYuHYlft+aDZZc6Uah4+/t1nc29IEI4iILbNbd1lbPxq3k0MwPJtr43wXOkS1S5j00+L73ASgWv3ffQzSavXvjtY1WIEgIqc5Pte+faNB4fUiTa6tok4Kj/l5CC8DEkMrpK6HtPvP9r82KULQooqNpaQXQQBwC0LI0NDzxNcDHt4zbwHP9gEAE9/s5um4fKZpefYe2nm0/2n/7lxL/vxoVWzo62QNy+Y/OAHPwDA5pP1KRr5nYunhwSuDw/+c+PeY81D5vvkcbAZeWm7a1sB4JkNu9O0ynB3sF6b69HPtrDeZ0NBKRHeumjKK5sPAsDmkw1Wt/++FTNLBrMSWdzeo03dTUbLfSviqQ4qMpIONnQAwMs/7C806JSjdBCJjxLFfIahEWSAO3+Obh0AzNGtC2X2Y8tD3PmTVRey5WW8xaH248KsT9MOBOUF8ENi4YVe/xYeNydINnE52RimRRABj5vP5aR4/VvEwiVe/xaxcCkA8Lg5Xv8WsXCFP7BLKLjA698iFV3ix/dxOEk8bj4AAIJwOSm+wB62PQC8tvXw/vr2RLnEctYPZv2Byu+O1U7NSf3dilkA8NXh6j6H++6lMwDgpR/2p2oUa6cWR7QBgLd2HD3c2EkzTIpa/tfLF0ePAwBvbDvCssXOLcq6fu7Eqg7j2zuOYShi9fj0StkzVy2LeIUNs6AoUCTyUAwA1HyxlyRMAbeCJxq5tGIzx3Z4bXnyBPZBypEN6RgVcS4uik3Tpt19+EsAuC57HB51n58QCSPXgycbu9/6/WV3vPA1B0Pjv95phur1VWZKZ7d7DkYLrAmZyUa7a2tl49661kmZhkSF1EcQx1t6TM4BhfeqSYXDJvUbAxaX5dyycMpb249SNP2Pb3a+ue3IhMwkmUjg8gVOtPaElP3XzJ6wYmJ+rO65i8sat1Y2AcAbW49sOFw7MSuZZUo40drjCeDZenWaRrmjujnm2W9bNG1/fTtJ0Va374oXPq3ITMrQqoIU3WmxV3X0UTRdlJKglYlZoRYTtyyY2my0sjL9SFPnVS91qiSiFI1cyOP68GCvzWVxewEgL0kbX2BdfUEFK7Dqekwrnn5vUqZBIxPjQdLlD/Q7PVfOLF8zNXZs2gjBBuJGSZyzjM+ADK4dVB59PDYopPeG86nzuAUAqFL2x7Am58p53AHtBI9bxOMW+AP7hII5PG4BWyvlXBnBy87nDXiHtZvse860fnrflcDARc9/wBZeObNMKuA1ndV1rpiQf8VLn965eDoDzI6a5i9/ty66DQBsPH7muXXLC5J1Ia6kiDan2npPt/e+d+dlAHDn2xsmZCYDQEOv+fuHr+dxsOv+80VLvzU7cZDadxiBFZk5li+xEz5zwKMVSGCozLEcnhUfyBzb6DQBQIpY2eDsZy+6zT2kfheLCs624r4Hi+ZnSMdHUf37P33xnxcjgzBRFGUnk6aZkFtQTOTLl7IvSTU/xoJWxOe+edvF9767sc1k21/fHlG7alLhX69YdD4XHwf3Lp+pk0te2rTfixMWt5eVPiEIedy7l02/ds6QpKnPrFuGIMiW040AYHJ5QutBAChI1r1846qfTjcMJbCKUxP+fuXSxz7bipMkzTAnWnpOtJyL5ChJTXzpxpUbDtfGEVgIAs+uW56XpH1z+xEfHgQAm8cX4eAKI2A9nV2Ycfviaa9vPQwAbj++q7YlvNaDx/PX/T8FNOrvSGpRoWDOsG3Y/zqtjhy9BkUQQCA7FkEIAAh53CnZKXvOtNIMMz03bShFysvXr3pv9/Eeq/P6eZNmF8QgR2rptxalJLA/bqEhobHXnKvX5CdrWX2oWiLy4ZF+i+fORDH0w8c3trgtniDe63PdUzA7+gQcFP3bhAvvOfwVD8VIhn5t+uVcFH3s1OZGpylIUy0uywPF87Nl2kSh7Ird7+sEEnY9lSXVzErIunz3ewaxIlWsHOG5vCRBM8xjp35AEdRPEs9NXp0uOa/Mz2pVDNXGksl5t/3zyy6T46bnPr94doxsCOGIepGeg9MbSNcpv3zo6q8OVW853dhusntxgmUcvXhacRymzXHBFTPLFpXmfHW4eu+Zti6Lwx3AZUJBqkYxqyD94mnFLIvpUOBi2D+vXbF6StGGIzWVHUab2y8R8jJ0quUT8tZOLeZiWExlfAjLJ+SVpSeu3195pKmz2+oKBIMqsSgrUb18Qt7KSQUYimbE7Q4ACAI3LZh8yfSSjcfPHGnsajSaHd4ATpJCLlcnl2QlqiZnpURo32LirqXTZ+SlfXGw6nR7r9nppRlGIuTpZJLcJE1ZWtKw3X8ZOPw7ux0vcVBZsvxeqWBKzJJfFwaVvMloYZcX7eYhQ3Mun1H27x8PIAD3LBty5ZuikT91+WKnL3DRcx/sfuK26Aa5es32qiZ2xVDT1ccKNSxupsLfbpqv95qO8FDs6qxJAPCvmp2ZUvWatNiJLUaIL745xuNxSooMrJYmI31gc9ptdrT22jKTVAatYrRjhiiS85O1Xz44UiKa/+H/t2gy35kou1HKnxSnZLzg9eLHTrRV13a3d1hMJpfd4cNxkqJoPp8j4HNVKrFGLU1JUWWma/Pz9Bnp2tAy9t+bDxxp6kxWywMEef28iWVp+kc/29rab/MG8KKUhDsWT0/XKQHglje+RgB587a1AEDRdESbVK3iule/4HEwmmGm5aTetmhqdJt0nfLtHUf317czABfkp9+8YEpVh/H93SdeuO5CAHjgg03Xz50YYVj/7Qqsdo/1sZObRRweydBKnvCJiuXiKItkOKw2zxtv766s7nK7A4Zk5WUXT1k4f1BC2ocf+yr84zN/HdCvt/fZuk3OFJ0iLXFEPO7hCAmsvCTtVw/9T2D9D+fQ43zVGdgHAErhQr3slgDZ0e14wRU4KOCkY6gsW/NikLZHlGCozORZb/V+zwAl4081KB4AgAbTTTLBdA9+gqBM+boPsRFE9nR12z785MCefQ3B4JAuchGQy4Wfvn+bKG5I7G8BvzlP9xDSJeoPZ18zwsY0zTz08OftHQNUc03N/X9/9nsOB507+5ymOSShwvHadwcP1rRnJWmaeywzS9LvuOhnZFYbA1octtt3fLft4hvOZxA77r9xyzcbVl09Xlf1fxcn6rsm5p8vM8Sq37+98fmb47dx48c9+PHChM8AoMF0g5Q/WcIvz9a83Gi+NVl+j5hXAgAYKosoCZAdVu93BQnrAZC6/nVeokrMKwUAFOHnaF8b4eV98fXRt97dSw7tzRsTOq3sty+t4DyDn81+7027vhxJyw73oM3w30/saHfbhmocURvRNyaOnWgLSasQvtpwPKKkvtF48nQHzTABfMD9+siZzg/+dOUTNyz+8M9XHq4dPhnnL4/zpxFS8oX/k1YsXt9w8Jc5kT/YLOaVsBmVxbwSX7B+hL0CwY66/mvq+tcBAEUPmJil/JG6WX6y/tBrb+4arbQCgCmTI03Y+y27u3wdm43ffdTx9o/G7/aad9a5aurdtWzJXvPOdm9LzKGGBUWPfV/3S6ywzH7ve/XHn5h8zkz254mRHMfhCK+N7hsT3d0xxF93z6DCdz/cV3OmBwBKi1MeeeyrF5+9EgD0IWITBhJ/HpKTp3fv8ZPkyZ7exTnZPzY2PTZ/7vTU1P8eOXKgo5NhmFSF4pkli08Zja8fPoqhqMXnTZbJXlixPCSnSIb+3e7N9XazRiB6ed6FKoGw3WW/Ycs3uy69CQCO9XW/ePLgp8svA4CtHc0vnzpI0QxJUy/MXV6qSQSADc1n1tdXdrgdR64cSGPX7rI/uOfHDLmyzmamaObleSvylBoAOGXqffroHi8ZlPH4z8xakiZTDDVmzMJfEdUtxvd/OIphqM3l1atlf71lOYLAhj1VW480UDQzMc9w25oZLT2W9384WtfR/7uXvgWAf9130a3PfP72I1f869NdQZJ6+NqFtz7zxZsPX/bO90eO1HYAwOzyzHVLJwHA7176dlJBSmVzr8XhefXBi0NkRM3dluc/2fn4TUuSNDGcgUXcPJvvR5aP2ENUGoQjivIVcrN5nKT8hA8QwBiGDJGax2Q3j8bpqs53P9w3kpbRmDopUmBxEa6VMCt5qmRhSoD2yzgKLV9X764NlaSLR+r/yTKwMwxjdnuTFLJd9S2TMwwUzbBc6oEg6fD5FSKhFycEXI6XIJQiYY/dlaSQRfOtDwisrV2N37XXCjFur9f15JTFOXLNTbu+LFXrOzz2KbqUy7PLHzu6xYH7cZr804T56VLVw4c3e4KEki8EgJ09zVVW4/2lF/T53H8+8tM78y7d0FrzfccZPsaZm5RVqta/Un2g0WF+6OCmhYacpal5b9Ud+bTx9Btz1uYqtG/UHtYJJWsyi3GKvHzrJ98uuy68ts5uCu/b43WqBaLVGcUkTV+x7ZOvlgyzYQwGB7kpVFZ3vfz8VQ88/BmHg4bYnwmSvOLJj9ITVK1Gq0wseOTNzQDw9K0jSh02csxMS81QKl04/ui8ufvaO6anpm6oPfPyhSsKdedcVOrM5u033cDDsCs++7zZYs3RDFiU25z2Nxeuzlao/3li/8unDj45fUhZ/9/KI8/MWlKiSfCTQS464CO6JrtwVnLahd8OSoV7vL/n4cmzJycaPjxz6o2qoy/MWY5T5KMHt69ffrmMx/+hreGxQ9s/WHLJUGPGLPx10dRl/urpG3gc7NZnPmf5iH46XP/6Hy5DELjzn1+daesrzEh84uallX9898X7V7NdFBKh24c7PH4iSHn8uEIqrGzqrWzueeOPlwHAfS99U56bXJypBwA+l/PcXStD5+JwsJpW47ubjjx/9yrZEESpEn6FjD/1TP8VwDAK4VwJPzZ9dgQEnLQEyVX1/esAMAA6T/cOiozCA/aV/26n6RhLF7VaMrEiPTtLJ5cJ+Xyuz4d7PLjbE+g3uRqb+jq7rCIRr7Ag0pA6VT2TZmj0rLssWziTPyfcRXaE+PxYFRfDsnVqs9t7oqMnECS/Ol7TY3f9cfkcCZ/33akzhUk6hy/w5fFqnVSsEAn5XI7TH/ATwVCb0FDnVlg8lPPPGRdW2/r+W3PwxZmrOtz2xyYtTJMqAWBrV6OCJ3hqypIuj+Ovx7ffVjQNAF69YPWh/o63zxyNuDg77v+46eSXi68J+WHeUjjl27baJycP5ES6pWBqg30ged/qzOKHD21ek1m8s6d5YUpORG2BUhfe10kEHj68eXVG8aH+jiUpg+zcKSkxbOdZmYOcVFEUCblchX7Xaxb/ElmdVUKRIxDgczh8DoaTFAC8dtFFbx071uV03TJ54rzMTAAo1Ol4GAYAapHIEzznfpIskWUr1ACwOC37T/u3xjnLtYXl9+z6fnVW4VX5ZTpRPNVsolgyOdEAAAUq7ZaOJgBosFvanPbLNg2kTUs42z3mmBGFnWbHgbr20gw9RdHNfda8ZG1jj9npC+jkEhRBPAEiW68WcDmn242si81n+yqvnTdRJuLvqm65fFbZma7+iJKqdiM7TqfZ4Q0Q2Xp1ecYwfgl5aTrWeUcpFXkDRI/Z32Vy3PWvATOLNxCDhqwoI/FYXaeQz+Nx6eN1XUUZiW291sL0AbeggrSE5i4LK7DKcgadPUAE//betqXT84eSViyS5Hcmye+MKMzVvhm/RCO5WCO5OLwkTzcivoRTpzta28wRhVwudvcdC5YvKeNEpT4LAceDJrMbi8VFjka5yw7l2UNR9NHjA3GjapU4N2fQotsTIJRioU4mOdLalaKSu/x4slIW4lIX8bglhsRPDp8WcDlJCplGKrZ6fFwMk8sF0Xzr5wSWQSwDgAypstvrBAAhh8tKKwBoc9myFRoASJEour3Obo8jU6YCgAzpIDHBLhY6PfYcuWaEQWEJQgkDjNnv/b79TPx9IgDIeQIZV2DH/Tu6m+8uGaQdnzQhIyNd29Z+7gdDELj6ikHh7PPnFNz/h097eh33PPjJyuXlbGF5dnJNW19TtzkrSVOaNZ6RyeGInow0hfzZpUscgcCSd98/cuftcJbTNRqhNybDxLhdvOQ5LoS12UXzU7K+bKxZ8/0nr8y7cIJuyIc8PAKUFeIMAwaJ7Ke110W0jDlmROHp6p4bF0wGgI92n7hm7sT3dhwX8bkSAc9PBF0+/IYFk97dccztw7P16trO/isuKMtN1rCkzEGKAoC8ZG1ESWV7LztOkKJuXTz14z0nhxVYEfdbRpIqUSV99YGLURQhKZqNN0IQBA+SIbq04kz9+u0nF07KDVL0xv0165ZM4nKwnScG3ILOtPXNKhvYJUXEh2Io8smT637/6sYtR+qXTI0RP/CrYMv2mujCPz64fMG8wujycPD53BTDeXk4AkBdfe+fzlrhZ8/Ke/Ivq8Nr714w8CTeu3AGRHGprywvAICrppaHfsPvTp1BAObmZcqE/Ihf9pzA6vA4AKDNbU+RKGCwrjdbrj5l6QWALo/DIJYni+VH+rvYjwAg5HDdBA4ArS4rABjEimanNdwJnotiPnJIjpGL0ou+bq32kcFkcQxdQETfS7NLf+iooxhaIxi0gkBR5PmnL3vj7d2VVV0eTyA7S3fjdbPLSgfZg1YuL59Ykd7eYUlP0yTpFWzhR1tOHK3vLEhL2H6iaVKu4Yblo/Pcm1OYWf3CkBT1Q4FmmKs+/5LHwRiGuaaiPH7jHrez3mbOV2m3dTZPTEgCAI1QZPZ7nXhAzhdsbT/n197v8ySIJLeUTDL7vSf6e+IIrGjkKTUuAj/S1z010cAAWP1ejVA81JgRhdk61Ue7T1RkJmtkkk3H6nQKiS9AoCgKAN4A8e2RWpVEpFfK3P5AeUYShqJWl6+t3wYADT3mum5TgUEXURIaxx7l8j5CGHSKtXPL7vznVyiKMDTz4v1rBDwOiiALJ+de/9SnerXs2btWFmYkHqnp+P3V84NB6rmPd/79thVCPndCnuG2Z78AYGaUZJQM8QJDAMFQ9OnbL7zvpQ1quXjSeZsdxwW1db0RJbk5icNKq/HCiVOjMFjF5K0L/3RRxZCXPeCHtbWr8f364wkiqdnveXLK4iyZetXm9zYuH7CmMwBPHNtqDfhwivzThPkZMtXvD/3gJ4MpEnmz0/qfC9bctudrGY+fIJK2uWxvzb3k8+bKnzobRFzujIT0q3MrCIq6afeXUi7/kqySCk3yy1X79/S2Fih1s5Myrsgu95HBhRvfvL9s1mVZZXbcH1Eb3nd+cjYA3LH3m4szSxYaRh0LffJ0x/GT7cRZcsu7b18AALc8/8WbD13GUl/e8vwXb//hstEO+7OixWF74tAOrUhcZzOrBaJ/z7tQJRACwOtVRz+pq9QIRYvSsvf3dLBK9xu3ftPpcnAxTCcSvzhnBdvyvt0/9Hhcp03GafqUEk3CHyfPHkpnX2Xp++vhXR6CYIC5qXjiZbklQ40ZXcjywcLZl2fo+j/Zc+rK2eVsyVBtRlgyEpxT7nq8CVKJlyBkAsGuhpbJ6QaGAQmfRzEMhiB2nz9JIQOAQJBka10BPEEq4XM4/mDQ7PEaFHKLxysV8M0eb5Jc5g7gIh7XSxA6abyEmwxDIsgIrVgUABphAWYYEhBAxmQHc3sCF13ycoTp7bab515xaex03+OOex74pKZ2IE4+eoUVAYahECRS9ckAjQAyrFX8nMA6Y++/v3R4Br5D1n2FsmIhJg5QfgEm9JJuMUda5TyZKyngoTwEQXEqwJZLuXIP6VZwlQBAMqQr6JByZD7KRzP0TtOWGZrZPtJrEKWxcfAxe0WDYujrdn7+wfzLowMPh8UNt797923zeWfjnkqKDDBYYN36zy/e+v1lAODz4EIRD4lL087C6HO/W3fsnpIZZr83UST1kISMy/eShBDjekhCIxB3e5xKvsDs9yaJZV6SIGn6SH/XRRmFAEBQlDng8ZFBJx4oUiV4ScKO+514oFStt+E+DooCgIwrsOE+KZfvIQkXEXDigUm6eBwg8UFTdH+HWaVXep2+8G8nEPFRDKVJ2ufx84V8e78jOTuxv8OsSlT6PH631eN2eNMKDPZ+hy5V43X6hGKBz+NX64f3s41Davhz4P2DJ7gYlqVVWzzebrtTIRJiKOInSAxFRDyu0x/gYphSJORzOAsKsgDgyxPVfoJcXpJ7uLWLpGkpnz8tM2VXQyuKIHwOhz12+gMiHrff5VGIhCtL88NzIzEMQVImDqahaBcDlM39lkJ8BUU7BbwiYChAMJr2oqiEou0cVEPRNg6WCAA04/f4t4n40wBoFJUgIACgKdoGAF78kEy4jKJdKCKkGS+GqijahqJyirZzMX2ch7m+0XjHPR9GFL74/JXlpT9vTBgLn59YdfHLFDVg44oQWEHaG6R9PExK0j4+pvAGe614XbL4AgAgaR8HFRGUk4fJe7z7EoQTBJiaZgg/ZeOjcgRBaYYiaZ+Qcy4H3ajFuYOwVTpOZYiz9lt2JwtTUkVpta4qgiaO2g5yEI5emHzSfowtP2TdbyXMlxquFmDC0/bjHJTrIGx8TJApzk4SGhyEHUGQfeadcXpFnPrbttqvWqvW5UwYg7QCgDmz8qpru1XKgb0kK7CmFqbd++8NhWkJte19M4rSAcDa7/rolW1T5xVMm1cwrMzyk8F8hRZD0Bpb/4+dDUq+sEKTtL65MkEoUfKFGIqWqhI1AlGNrR+nyPXNlU9NWRxaOGzpauRjmJDDQxFoc9nWN1cuTslFEXjjzJFuj7NEndjhtst4gm6PM0UiV/KFqVLlCERoPGz5cE/uhMxjW3YLJALCT/S29idlJvS29meVpakSFPVHm2Vq6fwrZjafaq852JA7IdPnNv747q4ZqyYiKIphaPOp9lM7awQSgbXHxrYUSodhfP4lpRUAuAOEUiRMkEmOtnWla5RaidjocrsDuEYicvoDbG2mVrWzvoUVWBiKugO4O0C0mm1zcjOqe/pNbm+r2ba4KGdnfUumVtVqtiXIJE5/IEkh00rE/mAwXGC5/T8iCD8YMKKIWMSfyOcWkFQfAGr3fIwgXD4n1+X/QcAtEPBKnN4vCbIrUfE4ikqdvm8Y2u/wrifILhF/KoZK8WADW4sAaveuRxExRVtQREbRVoLsEvLKBLwSLhZvg+/1xojuDuk9YqLZZXES/hJVkh33aQSSHq/juKVrVkKGmMv3BnEBh2sOeNMlqh6vQyuUeIMExdBHzB2rUmPk6z1d2RmSVtFocn7DQyVqQVGz6zsVP1fFLyBpf4d7m4qf1+z6TsHLUvHzen2HKHrgK3R592AIj6R9AkxtxWt5qDxdupR7NmXGgMBanJK7OGX4+FIAUPE1Mo6i2dPAQ3l8jJ8uzuoN9PgoH8MwEo6k198dKjcGepOFKXxMAAAJAn2V86SMI/eSHjFHIuPKtfyEenetn/LH6RWB1RlFqzPGThKy72DjskUlEdaQm1dMrWzubTVaZ5VmlGTqAUCmFCUkK73ugNvllymGed4UfKFGKO73e5qdlkyZWisUHzV1CzFusliuFYpNfk+XxyngcJudFjvuF2LcFqe10WEmmTwOgmbJVNu6m9dmFh3o62girUKMmy5VHOjrSBJJ8xVaBhgEQIBx8hVaT5DQCsVpUsWBvvNybeXxuX3tZoyLuW0efYYOQRBlghxBEIqkq/bVGXL1qgSF1WjvbOgx5Oj72s2WHitfxEvKSjy1q0abrOps6NEkq9w2jy5No0pQBHyBYQVWfHgJwhMkpDw+iiAeggCAPR1tM1PSVEKhze+XC/gOfyBRKh25lL5n/lnl7oJ46TJzEwbe2Gsrithytn1Zij7UNzdBEyofan/K42a7/Vs5qJaiHRiq5GAaHifdi++naReGqXCyAUWEKCoS8srxYBOfm4+iYgBAgEMyLi6WwufmczCtnzjNwZL43Pwg1R8INnCxJIp2MEyQYhxsOc14hLzy+F/c44khsETCeJ7rfX4XCshb9Ye6vY4KdXKJKqnP79re2+gg/Aqe8KK0klq78YS5s0SV5HXbPms99eSEpegQS7wTJ9vjnIhmggTtMgdOcxA+ivA8ZC+KcAAQtoSLCtWCIifR5qcHHL9l3LQe7z4RJ8EUOCXjpgowNcX4uTDwJI4xljDCESP0cahyiCI5G2GvccSLr26dWJGukIvYe49dYYXDaHXp1bKedsvRPfUcLoYgyIVXThvtWSIod1ilSXQ5i9CzhJx1DIt+uoY6HhtoikYxlKGZiMVjdAlN0Qg6cF3htdEtx4z/HDuiEAjEXJ5WLGp3ODqcDqVAIBcIbX5ft8tVokso1iWUJfzKXqnDgY4glgo7jvjNo9vE7AUAtNO3US5aFdVmSGzZVvPMP3+ILPz+Qd7QDLrdXsfB/jYOism4Ag+Jizg8P0loBBJLwKMRSPQi2Xcd1RlStYjD6/O5en3OSzMrNnZU31s0O3p/c/0tb3d0nqONitgSdri3pkoXhvRTDNBIrPkJKx84Di8JYYye7hECJfRxqHKI5cExkl7jCAGfW3smjK0pSmC9uuHA329elpCslMpFGIbScemxhkLEpWMIErOcRbS5ZKiS6OOxAcVQAIiWONElaNhSNLx2vKQVACRLZVqxyOjxHOnuLtYlAIALD7gCAb1EmqfWeIngb15aQRxiqajfPLpNzF4AgMpFq2OVD4kxLDsMYsUlGeWht2PozRrC74rnAgDFMCE3a7YkAhaLO1xaRSNNujj8IzLE/ITLJvY4WlrBbzn4edwx54JBpONv/3AkokFDpwkAnHYvHggmpakrpo9Pcu3/YSiszh9gxYxeRZ7/WnIMMFpdDZ3mpm7zTRdO/eXP/ssj/O2IDfF9hyoPYVQODeePsQgsHA9u3HR65566jk4rRdFqtaS02LBqRUW0dz+Lg4eaf9hSWVdvdLsDcrkwUSefOSN78YJitTqekXjc8dlXRwGApunWNnNGmqYN9a+dM4ixj8vBAEAk5rsdPrtodBlP3e7AydMd9Y3GtjZzv8lld3gDgSBBUHw+RyDgioS8BJ0sMVGRrFfk5ekL8/U/d2R8R6dl/4Gm+kZje6fV6fT5/QSKoiIhT6ORJOmV2Vm6kmJDUUEyl3u+gTU+H370eFtVdVdbu6W3z+FxBwJ4kMvlSMR8vV6Rka6pKEudMilTLI73feOvMYe9gGMn2ptb+lvbzD29dq+P8HnxAB7k87kiIU8o5Go1UoNBlZKsysrUFRUm8/lD3vNquRgnjbPLs0YlrcZlBsYMhgGny2exen6OwYeF14vvP9j4S55xSB1WuGNFXm7i668M+EB399j/9NhXXbGCjS+6sOLeuxaFuwV7vfjfnvn+8NEYUd0iEf/+uxctWjAKJfpXG47/5/UdI29/712L1qyKEcNFkvRTz27Mm5p61cJBtY++/ePfbl5mt3o+/c8OvpB78++HjyikKHrv/obvN1dWVXfFMZREAEGQ7CzdzOk5F8zMzcwYkuQ+Gn/48xfHzgZAAMAVl0297aa5EW2OHW97/+P9Z6LcCKMhEvKmTsm6765FcvlYUja0tpk//fzwvgONxHB5m/l8zuxZeVdfMT0tdTzTMp481fHt9yePHGsd9gJC4HCwwoKkKZMyF80v1OkiY93NDs+eytYUnWJqwYi8AX6xGcBx0mx2ma0eq9VjsbgtNo/F4rZYPBar22rzkOR5JZSfNiXr6adGlI+aYaDXaG9pNbW0mppbTS0tpn6T63xOzeLbL+4d+R04ohVWa5uZomgMQ2127+/+sN5iccds9t2mU0SQ+sMDA9micDz40MOf1zcaYzb2+fB/PLcJw9D5cyNz+fm8uFA4IjeosYHDQa02b4S0AoA/XbMQAERi/rLLptQcb2MYBon7pt29t/6Nt3f39TtHewEMwzQ19zc193/w8f5PP7g9MWGMub/M5kE/hN9PvPyfbVu2xQjRiAmfnzhyrHUMb363O/DfN3du2VY9Qs0JjpPbdtRu33lm9cqKW2+aKxCcb97AXqPj1de2HzoyanoTkqSqqruqqrveeX9vRXnq3bcvCBHPAoBYwHN6/CNJa/gLz8DH6w9+vP7QqLqMI3x+4vW3drW2mlvbzX5/jKjMXxIjEljBINXRac3M0D77z81DSSsWP26pmjEte9aMHAB4+dVtQ0mrEJ574ceiwuSEsHedxez+8O0902blTp+ZM74y67G/fcseOBzeBO25M4byErIbAY/LX3e6MyVLF0daud2BZ/+1+cChpqEajBDlZWljllYAYDKfe785nf4//PmLxqa+UY0wZVJGnLDYmGho7Hv0yW/i3wYxwTDMho0nj51sf/rJiw3nEbxWXdP958e/dnsCYx6BvZjKqi65fJDbig8P6pQSm9sXX4P2q8/ALwyvF//+h9O/9lUMYKQ6rOZWk9nsOnp8yPQnIbz7wb6Z03Oqa7p+3Fo9bGMcD67//PD99wzYERgGUBRJ0Cs87sC4r7CuuGQgTlAk5KWlDnjiROQl/O/9a08dbI6vwzIaHQ8+8rnR6Dj/S1qxdJi0F/FhOrsg9/nwB/64PjpYf1hMmzKI0qjDW08yhJ0wTVQtiGmrPXGq/c+Pf43jI92CRaO723bnfR/985nLIwL6R4jaup6HHvl85HvAOJg5PSfkRcxCKuL7AkEhjxtHWv3qM/D/Nnr9TQTtSxOXkjTBRfkE2eMN1vqJuiT5PazBdMQCq7m/4exyqbw0ddaMHIVC1NZh2fj9qYh3XVu7+Uxdz1vvDeTvRBBk1oycCeVpUqmgucW0cdMp3+BV5badZ+6+YyH7nmcYpupUp0jE6zc6ov19ZkzLlkoETpfP6fQ7XX6n0+d0+V0uv9Ppd7n9MZmAwlGYH8MmEJGXEMPQoglpfh/R121jXZYi2vebXPc88InVFk/HqdPJdFqZXCaUSQVBkvJ4cGOfs6fXFqFrkEoFs2dFZkseFSxWD8MwDANP/n1jtLRSyEUpKSq5TCiVCgmCdDh87Z0Wa5h2FkFg6mCeSSEmdpF4lqQkprQ6U9f758e/ifmscrnYpAnpOdmJKpVYLhO63H673dfc0n/sRFt0e7cn8NAjn7/ywtWh18YIgePBp5/7Iaa00mll5aUpKSlqtVoi4HMQBHG7Ay53oLPL2tZu7ui0RvdauaI8osRodcnFgjbjkFy4v/oM/L+NPaaPu311AJAqKvq888l16f/gYlqGwBXCRSH3jpEKrK82HGMYQBDkgXsXX3iWmwUAli4svvuBj51Of3jjV1/bwW4GRSL+P568OMSasGBe4fy5BXfd/1E4N77Ph1fXdleUpQIAiiLTZ+Xs2FojEvOjV1hJesVQ0QYMAwuXPxdfZj334o8P3b/06LHWZ1/8cc3KCddeNQNi5SVMSFZu3XCCy+NESyu/n3j40S+Hklb5ufrlS0snTkiPeZEkSbe0mk6caj9+sr2yqpOmmUULis7TSEdRtM3m3bylKnzlK5MJL1kzaca07MwMXfRCwWb3HjveduhI88HDzVmZOsVgV34BJm73nkEAUfISIjo6HL7H//YtjkeyboiEvJuun710SUl0kloAwPHg9p1n3nx3j8s16A5xuwOPPvHN669cNyoN2rffn+rpjeTLzslOuP3meRMq0uJ0JAiyqqb7+Im2w0dbWKchvV4xsSI9oplaJp5dllmQFvndWfyKM3D9NbOuuSpetoGtO2r/9dJPEYWbvrl/JDcYGjetFgBoNdIt3z84VO2fH//6+GBP9wtm5j768MohmsdAyLu101tzTcYzn7T/GUU47CvTS1RxUJWXqBLx8ke3wmKf6hXLysKlFQAYDKpbb5z7/Is/hheGVFcP3rckguMlJzth1YUVXw9mW68908MKLAAABEnUK6pOdYzKo3okZuieXjuKIJ99dfSDN2/+y1+/YQVWdF5CFEOXXhKbQvv1t3ZHM8cDgF6vuOOWeRfMjBfbxOGgebmJebmJV10+zW737t7XMHHwM7bbtJ6D8EiGmKu7cvgvE+q1t/7DTw6wxwgCV1427eorp8d8cliolOIli4qXLCp2ewJ9fZHmAj4m9FFuXjBGwM2Lr2yN1trk5+r/+vgarWbITOB8PnfFsrJZM3KfenrjiVPt4VXdPfZ//2fbI3+4MO73G4StUZRPpcWG55++PI4/NwsejzNpQvqkCem33zKvqbl/287aVIMq+p5p7bXiQbLX4kpJUETvCn/FGcAwNCbBXgicWLVcLjbszAwLmmYQBIkzTgwn5Ljt4wBBUJb/jWFoBmgAwFA5TfXJBDNDK6xRKFxRFLn+mhhJExfOL4zpVZSbkxhtAQSAeXMiOc/CifdomrbbPEnJynHXYaEo0tTSr0+Uy6SCkO/F2tklj12/6JF1C566aemFM86x8ET7KNTV936/+VT0sPl5+tdevja+tIqAUiles2pCasogC7eEo5ylvVjBS2BgFF7Lr76+g91p8nicRx9edcuNc+JIq3BIJYKc7MilBE757YSJjwkjruH4iba9+xsiGqelqp/9+6VxntUQ5HLh359cW1SYHFG+dUftqdMjdTt0OHzR297f3btktM9GTnbCnbfOj3jvspCK+AzDTC5IiZZWv4UZGBuCQarP4vIFCJKi2eO2HmtVY08wSJltHpvTZ3MOkI55/YTZ5gngwX6rGydIi8MbwIM7jzTanF6cIGma6el3hPr2WYZxaOjxOrd2Nb5cdYAesRd+kXz2R+2PWInuD9r/UKpYCAAowuOgah9xhoGBPdkofuzCgmS1KoarJ4/HKS9LOXgoMpv54iF8rHKyElAUCd++9fY62AOKog/ta0QxtKsjnrP/2DCxIv2vT2/822NrgyQV2pN+u79m6ZT8UApViqL3/liFokhHs+naewdlvnjn/X3RM5+TnfDic1eev50eABIE6fvNX4s5irFFJt1/96KYr4dRQcSRKnhaP+WNuIb3Ptof0RLD0Cf/skYmG6n7DJ/P/dvja6++4U2fb1CY7tvv7/3PSyNK5maOWt1oNNL0tPHUASkkwi92VQr4nPsviUxFPvIZ8JIBIRbJkwnjMQNjw+7jTXwux+PDJWI+TpB8LkfA56II8vGmY0azKz8zoaffsXRWYU6adsP2SomIzzBMXkbCvhPNQgGPJKkAQR6r6VQrREazqyAzwe7yowjCjpmoiZe0RSuQ4FTvIkP2yL1wK5RL08VlFrxLw09R8vQAwOMYLJ4vUYSHwMDedhQrrKIhHNkBIC8nBjfj5EkZMRvzeJyEwT57Ia0Qw0BpRdq8hUULlpSM/MJGiHVXTP/o7VvSUtVcDvbvfw2kveoyOa566uPn1u9q6bUCAMMwpZMz5ywvm3dheXjf1jZzxHoeALhc7E9/uHBcpBUA+Cg3hnKdQdMY+s6bU7BsyXkZHFkEKG+A8pL0IKtIbV1PtBvqyhXlo/WBVChE66KCyc/U9Z6u6hxJd6czkn1UOE4zH4JIwFszuyRRJYt4M418Bsy489+Nmw5Y6mIuK85zBsaG9CRVc6c5PzOxu8/BHhsSFb1mZ4JaOmtipkYhNiQo8CAJAEGScnkDQgGvMCuRg2EuTwDDUI8Xp2j6VF03l4P1mpxsX3ac+Oe1BrypEkWhKrZCMAJdvjPsPw9pF2ASD2nv8p0BAIYJ8jmGANkGMLDjGcUKK87bLDEx0plIIIhHFK1Uio1hChSXe8DOyOGgBEG2NPX39TqSU1TouO4KGQY6Oi0hGyVrNLxn7aw7V8/YW9n6328P+HHyv79bi3LQ//z1uwhP9x+3VEUPeOVl08bxDZ8nnQwApx07R0tWgSDIzTdErgjGDBUvUcJVhJdEe6IiCLJuMF/+CHHx6kmffHY4grzphx8rR8IzF6126De5AoHgeL0wAMDi9ABAglIS4TM88hlQcMV6gdIT9A+1rDifGRgbctJ0Wana4zWdU0vTs1I0WalaFEEunFscHbBpSFAsmJ7HlqxeUBoe1Dnwl2ZQdKBvVuowERoKvnBnT0ury7Ymc/holuO27wHAHbT5KKeWn2rFu5U8fUpqAUW7g5SZz0kbtZUQYkmlEDRRW8XUFHUcx0upZJBaF8eDoVskUa/Ysuk0l8cZX2kFAH9/7nuT2aVQDLje/PXR1ewBAgiCIMAMWBZierrvOxAZMIVh6KrBq7DzRIP7mJ0wCjHpaLeE06dlxadqGzlohkYANQd6GNlZ5h8G9h+M9I8tKUoeWxwoj8eZMS17247a8ML9B5tIkuJwhrFn6aNuP4IgN/5w6rKLR0fDHwdJavnGA7VcDhZ+741qBky4c6a2oNdvYxNkRTc4nxkYM1AEmVKSFjqGIQI2F83Ij+gV+Rcdsm80nESgytq30JA9kjj2NYY/AsA3XU9fk/40gqAMMN92P88A7caPkLSdol0hmp1RCKyYCiwW0QGlmrg3dLS1lSRpttBu81AUnfAzxEX3mVyvnt0JhvDadwd3nGialJdy5+qZWclqALCZXACgSZQzNINgCAB0d9uiY6amT82KMyFjgJSjtOBdMSk14mPe7NGprn461iAT8ROU0r1VrdcunoiFWbX5qCBAe2mgqh37SxUXAEBbu9lu90aMcD7uY3MuyIt4XAOBYHVNd0V5PL8EAFAqxakp6s6uQcrNt97dq1FLz195x6LTZBfwOBF+WKOaAb1A+UPvcS7KiSmtWIx5Bs4TJEMhgIyErZdi6LGR+obAAGAIapDIXURg5DosV9Aasvc7iX4EMIVwgdX7LcaRjsVKKJMOqWGNttRE+BBHIPplErLKCUV8l9Nvj+uZOTaoFKJw/y8WiSrpR3++6uGr57PSCgASkpWNNd1+HxHywzpTHyOQeOb0UWfBiI++QNtMzVoG6FFZCSGubjEmAkTQ6Q3sqWyZXpTW3jfIrYmgcQlHzkP4JYpZbMmZup7oEYai5RjZ1UZaygCg+myYfXwsWxyp2SRJ6qmnNz7+1LfxKZlGCNYPa/m0QeJvVDPQ5bPyMV6XL4bvSwjnMwOjRZAm+wN2nCLshNtBeHabTgdp0oI72XK2jZ1w2wk32wanCDPu2GuuxCmCbdnh7a9xtnlIf6fP5KNwK+4iaarbZw7S8Xz9aYY50t8p5vK6vM6RWwmzpBPea31gU89L77U+kC4pAwCKdiMIl6SdMXRYXh8hFMYLShCLh7SXR+/+JJJ45LnR7UOkEVazOzFJ0dke7ycfGyiKXnfTmznZCaynHLslXHNBSSiWkAhSAh6np8PKF3C7Ws/pFJtbYyjCw+MqKCaAU04BpsQpJwCY/CcMkrlB2kdQToJ2qfgFOOXkYdIe7z6dcIIAi62r1vFTD1m+k3CVo9oSyuVC/Sj3g9lJmrY+W4Zevb+67dpFg/LICjAxQQe4qCB0DTHDfdLOQ3OnUIjkcmGEp3FTc/9I+q5cUf7VhuPRjrt79zfsO9A4Y1r2JWsnjVkZRNH0geo2BEVae6x3rD7npTmqGVDzJbP4BfnSGCIphPOZgdFiv6Wah3ISBKrNvYfvzb0YRZDPO3f1BWwTVbk8lJMgUALAPnN1r9+yMHHi5t7DGWJ9jtQQoIidplNW3NUXsM3TVSAIgiJok7t7n7lKxhHxMa4r6FtjiJewBkOQBYbsb9tqpWLZyFdYF2ivKpTNthPGabyLNfwUAMBQGU17MUQUqcOyWD3vfrx/xtSsGVNjmyERBBnVHnvMPtxKtXjarNyc/PHPaXp1LC1pRCzhGw9eotJKp80ryClKdjl8LKd7T0+kdzWfzwm3ELW7flDy89tdm7yksVzzOwRB3URni2tDsngugqD19o+8pFEtKCbpePG6BlGeQZTXF2iL0yYaMbelPa0mgYhfc7h5zuoYea1Jmnb7Am5f4Oblg3JA0Qx1xnVEhMlMga5zQ531OAlBq5Gyrl5+MugNEmIub3tXy7TElMN9XdMSU1xEgGbAiQdylRqz35MskbvwgJjL8wSJBNHApaanaSqrusLH7OqOnOGYEIv5f3hw2cOPfhXNicQwzIFDTQcONaWlqlcsLVu0sEgxOLB5WDAMTMgzSIX8vJRB6uQ4MxANAcY7aK434Y5UcTyd9EhmgGKCDFAc5LxY81NEukOWWlPAwcd4nT5Tu7cvWajJkOgTBMpDltqZmhIAUPOlCEC1o5WP8YQcfr4stdNnQgB0AkWGRJ8s0py0N2r58k5ff4pIp+JJ7YSbi2LDiqFer0vE4TU7R7fyUPMNar4BAKx4t5pvIGm7mF8u5pWFGgwILLlMmJgg93jxoa5jtO55YxZY7NJMNK7ESSwK85Pq6ntb2swZ6ZrQsjwilhAAHFZPBKd7NDtaYoI83POYg4pUgiJXsE3Oz/aTZifRilN2DiKQclP6/UdF3AQ5P5ugXEE6Xnz/QcsGAOgPdKwx3D/yLyWJFdhRf6LN7fDJ1RKKpLEoMobWXutVC2LQhDEAGeIiPirQC8/5o1iskdesUg1s9ttc9vUNlYUqnZ8kt3c2+0kSAGos/SqBCEUQDEFqLP37ezpEXF6fz63kC9dkFYq5PABQKiLVBdE+VkNhyqTMR36/4rkXfiTJyN09i45O63/f3Pnmu7unTs5avqR06pTM+D7iIbDO4psP17l9+LVLlaEHIc4MRIOgSC8ZKJANk1p1JDOAANLtPe4KdieLpqj4mTAmZEuSs8RJgAzQjt+QsQwAWGtSlnhgV8uKrXDb9OLESewB23JZ4lQEQdi+ALC17zgA4g76ZNx4ah+tULzQkF2iHmOA9z7z+tWG33NQhcO/MxBsVYvXsOUDYsjjDXi8AfnQfoBDJVIfsv3I7pJfEp9/dfTE6Y68nIQ9+xvKS1OvvnwaDBFLGMHp7ojyAIqI/0qTLgOAdOmFLG1+ier2EMF+BndViE4/Jql+CDM0awDAhI/OJSdmJFpqrh5BEYqkoqUVAIgEvLc3HxHxuRFiC0MwANhsfD9fNknDT2Jv35DHSQhCwcDi4mhft4DDxVDURQQkPJmLCLgJvMlpvVyXfMDYoRdLm5xWvUjqwP0GiUwrlPjIICuwRFFMGD4fPnIz2aIFRelpmmf++UMcdgqSpNkFl1otWbWifNWKCsVwCZAAQMznefwESdNbjzUunTKgVo8zA9HwkP40sa5QPozAGskMUAxB0G6DeKqPtACMUWBBLPULWxJRHlMREbNlSJzFh4jD29nT3Ot1Z8qGJ9I5aPlyhubSnf3vh0qM/iYAIGknAghB9cWwEiYnKuJoyn8+Or0I9Pc5mxv72ppNV18/a3xPevBI80vPXcXmTL3v95+wAis6ltDl8DEMY7d61lw7EIdEREXbDxWwGk2wz5a4CcIc8CYKpWx21R6vI0WijPhuvf7mJGF2kD7noeMjg1wU5aLxHuOYkatBgmw83VE4OfZdvnRyHgDUd8ZQzEV7ukfH+gqFA35P1xVOYBuFu+o8NOECALhMUhI6jrZqC2MHCZMj1znkZCe89d8bNm469eGnB6NNeOGwWj3vfbj/k88OL1tSsu6K6Zq4YTRenJAIeW4fvnjyuUCrODMQDRlXfNBS3+WzLNFXxDnRSGbAhreIOTqTvzZXPjzz7W8QOEW6CbxMMyLdjoafCgDt3sopqlVsSTt2GgAwRELSLoYhbb5NKtEqCAksmmZ4fK6xz/GrkP+HQ6WWEAQ5/YLcn1VEhl4aa2eXTClIae21ZSap2AAdiVTg9xECITd0AUSUbXGE8XohYAhaa+v7yVuv4Ak5KFqi0kd/ty5ffZIwu9N7Ri/IQhG0z+fZ2dXsxAO3lYw6IUJLTZdMJW6q7MwuSYnmnPhidyVJUQ1d5ievXxJRhQCaIEh1EucsblQU/S7/LCFn6JpGm+mHH0u9EBxiizcUUBRZvWrC0sUl328+/eXXx+JvKgmC/O77Uz9uqb7s4snrrpwxFK17zBVWnBmIhpv01bm6Z2kL4z9HI5kBPib1kuYk0cQxeLqcP5o9TR3etixJTro4drzKsPCSBANwwtRdptYPewPnSqcCQKliQbFiHlvS4j0JABgq0ctuD285MHESicDvJwSCeFbCXwYOm1efpMzKGZFH/6gwaUL6H//yZV5OYn2jccqkzNPN58zVMjHf4vRanN6SDP2B7bVypaij+dwCJHo7PFr+tn6/u9llyZSptQKxye/t8jjyFbqIqc6WTthv/lrDN7AuPBIuz0XgJMNsaqtflTk6P6PCyZl7vjuZWZgcLa0AYG55lk4hae+LwfpEMkSA8hlE59IFcXkYDDJnQSAQueIYLWKOwBuT0lMg4F66dvLaiybu2dfw3fcnq2riOQcQBPnx+kO79zU89siq6MBvAMCDpNHqEvA5IWkFo5yBYT3d44wQMQNcVMRDJVLu+FufRoIuX4eYI+nwtaWK0uL4lMWBjCfwkoSIwxu5SJmkOkdcsTLpfvaAoj0YGmUldHsCGIYm65VjuLLxhUwuPHKwqafLOu7hhNdcOaOmtru90zp9alZhftIjb24GALPDY3P7spLUHX12g1bx3O0rSidncrhYZhjbX7TBISJ+dVhkytQPlM4JfaRivX7VvKRZ2ovZY5Kmd3Q1J0tkjXbLaKUVAAhE/ItumltzODIcncWGfdV8HgcnyNtWRppNccrf4a0TYuKQCpbP48Lg59UfOF9W75i84Dze2CNs2MwA8+cWdHZZN/9UtXVHbZx9Yne37Z4HPn7yL2umRm2Z2VjCU009DHOOsGhUM2Al3JmSxBaPMf4KayQzwMdknd6DTqIrW7Y4uvHPjSxJznHbEYModWzSCgDsAV+5OmmEW0IWp+1bi+RzuCgfAFAEAwCC6ut1vqgQLlQIFwzSYR0/1e5y+632cY4mHQO8XpzH49htvnHMMMzC7yeKiwzFRQaGAbcn8PStywHgj69vevOhS1EUoRnm0bd+5HAwBEWO7ql3O/2GdA17ARIJP8L3x+MdncCKwLC53gBgamKqhMsrVOnGMH58K6FaLr5kdun3h85EPFQMMAiCRuiwFApROHM8APj957vC8ketL8Ri/mip5WMiNUV9+y3zbrlxzoFDTd9uPHmqMrYFA8fJJ//+3RuvXhcR7irgcfJStBFuDaOaATVPStDkLG1h/GXFSGYgSPvseCuGCLLhVxBYqaK0VFFal2/sUdmjiiVkYSeM77TelyEun6BaruWnAgAXVfEwA0m7IldYSxcUj/nKxhdidnMapkIaLzzwyOevvXQNACAI/PmJr//9z6sBoN/uCSljeixOiKXDUqslEY7UEelqxh0cFAWAp47uXJiSlSFTjXafHt9KmJOseX/LMZVUFDEswzDtnlo+KnQQptAKS6uRRiS2GEPyhQhEz55GLamx9B/t616Rmfd+zclclcYe8CsFwgqdPl2m3NTakCyR9njctZb+iKqY42MYOntW3uxZeWwarp2766L9tvx+4h/PbnrtlWsHz0AMJshRzQAX5SxMLBuqNoSYMxA1lChPvrLPXxWV8v6XwNa+HwGg2991Y8atYxvBSQQQQPp87pGrxeclXDdHt67JfXRP/4cEg1+V9lSQtsqFs8P9sH5zzgfNDcbUdA2GIsxwHO2jRTgbScgcM6M4/bp/rH/y/a3X/WP9lIJUAHC7/BiG6sMcwaIT27g9AWMUXef4QsUXGiRyFzGkZ1wciKQCpVbWNwSnWFlW0vVLJucaIj0bUQTNl03ionw5TxtaYaWkRNqkbXavO8rSPyp0dEU6ExqSVRRDqwTCo8buArXWSxATE5IBQMzhAcCitKwXTxxclJYVXRUfmRnaRx9e+dZ/r48ZSVPfaDwyOGOmye6ubYtMOzTaGQhQRJAexoAQcwYiSnDKbQ6cIWn/aEO1xgWLE5ctTly2NHHFmEeQcHmuYIBk6E0ddSPvxTIRsKyjAMBBFV6ixurdEGrwm0tVL5EKLWZXxaSMcV9hURRtsbo1aqnN7g0Z/m5dOW3J5Lwuk+PaJZMy9CqIlfk5KyPGvqyxqS+aP2AcYcf9Kr4wQTQ8m2U04m8J41gJuSh/smoQbWFOVgzldFuHpbTYMIYLAwCr1ePxRG6oszJ1ZVp9kTqBXVpSDIMhSJl2wOeQj3E+XHYJAKzKKoioGgmyMnX//tfVTz//w45dZyKqduyumxqWN0guEe6rbuvot4eHE45qBsy484C53k36r06bM9SbZqgZiCjhoSKC8tJAtbp3ZkkXDv39RorwADI+pvCSRgGm9JNWMVdPUG4uKgrSXiFn0GuMZMa+/Zdw+bcXTQMAi99r8XvFXJ43SBzq7VqYloUhqC3gTxRHLir3mD6udx1IE5fOTbhGy08DAIr2+ol6FBWqYbDj6G8HKIo4Hb7q053X3zp3fEe+4ZpZd/3uY7lM6HT5H7pvaag8LVGZlnhuc+Hz4lKFSKk9JynycmM8HsdOtM25IHbIPsUwPQ6nTipxB3Axj4eiCEXTXpxwBXBXAC9I1Dn8fjGPZ/F4DUq53edXioQWjzdJfo7UkKTp/b3tdtwv8o9Fqxh/SxjHSggAOOXjYcLQCivmY1l7pnvMAqvmTIxY4qLCJDi7EYa4Or6RqP9i9MLQhx9a3tzSH7G1j4iPcfkCde39F5Rlhu9iRjUDx63N7qAP4rKvxJmBcHBRcanqqqEGGQPCA8i0wnKccmXJVtvx+n7/UQ4i8lMmPipPky7joCIA2G3aQTJkl6/zhoxbzvO8P7U1dbgda7IL19dXFWsSvm+p7/d5ut3Ov0ybL+ENWibLuNobM1/koueikTBUpBQt8wcbQ/vi35zA+vliCctLU9e/f7vF6lYpJUOpeCmKjs5LmJ+nF4v5EaRrO3fX3XXb/JgegN+cri3WJ3zVXCPicTkoqpGIK3uMCqEwVSlHEeTbqjNFep1Mw6/tM/1U19TjdM3ISOVzOOECi4OiF2ePXasY33G0tq2vkqLnlMWodQVtO/o/y5dNypdNZmWWRiPNSNeGk+4DwN59jVdeFsmcOUJEM6NzOFhZyTCu4ecPDgdbu3rii//eGl5otrjDWc8UEqFeI/P4Bm3DRzUDk9TZb7dsi++HNbIZYPzkQHShkDM+KVfDA8hI2o8iXD9pdgXbRZiOoJ1ijl6AqUnGzwERAJQrJih4yr7AMImQYQgX+XDoRGIEgaN93UIOh2W5SxJL81VaETfyZVyhXNLjbzAF2nX8tGRRPgAwDB0INg2ch/0Wo/7ePzM6Ws0EQfb1OgwG1c+Rh0KnjUdEHTMvIYahUydn7tw9aCvu9xPbdtauWhHDoZmHYd0OJwdDnf6AXCg41tGdoVZqJOJUpfxQW5eYxy1NSmyz2lsttlSlIk+nSZLLdja2LMzLih5qbIjvODqvIru61fi71zb+5961EVUxOd3nzcmPeFzrG43GPucYdsQ4Th6Oyi8/dXJmHFfMcUT0tothGJ+PCMUtoAiSnaTps0dqxEc+A1KOML4f1ghngAHG6D/lI20E7alQXzcuvqPhAWQd7h8RQPiYokR1OwBEB41ZCcth20EpR5YoGGbpEO33H+H2sTg9B6IMBzEF+mHLN+3eykRhdr1rf5q4dIbmEprx0xCkwoJwf3MC6+fTYY0QMfMSLltcEiGwAODt9/bOmpEbHc90UemAqiU8ZoWturh8gG0jQ628b+4MOPvL5SWMZzKF+I6juytbgkHqxTtWRVd5SWeOpNwgGkT1tWRR8fsf7Y/I+fjhJwf++OCoQ0a+/OaYL8oFadnPwN8fEzHdNcMlBREkPX68KD1SaTXyGaAYWsoVeqkhVfIjnAEE0FTJzGbXNh4mHndPdwTQdOmKiJKINj3+7qWJKw5Y9g1L2C2NosmzRpEFQJSZM6ZAb3IfvSbjaQAEgPmw7eHpmrVu/AgCKAJIKJbwN2clFEv4e3acaW0xjbuVcISwWzz1pztFYn74BUyckB6dccDtDvzrpZ+iTeZwVtUSP2YlvGR8Awxwf1ClkzmGsL7rFJJeq2v7yRhupSJM2utvPW3fM6i9VrZwfqQrzZZtNaOlcHI4fOs/PxxRaDCoZkzLjtl+3BHhnQAAavUg5YDbh2foVSWZkWuKkc8AhqBBmnIQkdHyLEY1AzjlUvOzc2W/TiChM+jc0veDjbAOu+NTyCMFVnuHJVoojxYIICzjKIoIeZzksTCO/jKQK0QJeoXHHfi1VlgypSghWekdfAEIglx/zazoxgcPNz/xt++iiUx/XXQ09K6+Zd7qW+bFrG3oMl+3ZBLDMNFUkDjt7wt09AXaI8qvXzczwt2fYZjHn9owVBLsaOB48NEnvom+j6+7emYc7v9wxA9yHhaBQPD7zZURhYX5g1TdcomwrtO0+XAMM/wIZ0CA8a5Iu+DmrEUQhdHOAB+TWfCGZtfW6KpfAKXyMgDQ8YePkMvLjZTvNM1EcECPEBmS8s86nthj+nh9x+NZ0okAQNIOEa9QI7401OY3J7CcDp9cIbJZPb/WCquppqegIo0kqYgLmDs7f9LEGIGge/c3/O736xsaI9/e8eF2B37cUvXtxpPnda1DQCgWrH/pp2/f2hWzNjtJ/fH2E9hZ/q9w8FBBsXy6nKuN8P3R6xXRqdKNfc4//OmLkfijuVz+R5/cUBvFNVxRlrpwfmHMLtG498FPHvjjZzt2nSGI0QVyAgCOB//2zPdGoyOiPIKanbUSigS8aFH+y8+AJ9jPQ8VO4mfMABYHXf7OJYkrRkLYXVpsiJa373+4P+bGMD5maa+Ypb1cxtXO1l01Q3Mp/J/wwxKJeF4PTlH07h1n5i0aqVN/TNrosYk8iVxo6XeWT8uOXuL9/ndLb7vrg2h6rNq6ntvv+eCCmbkL5xdWlKdJh6CHdrsDjc19VdXdVdVd1bXdFEUvnF+4elUMLr3zxNw1kwCguborZi0DsG5hDCZSAGAY2oR3QSzTz1WXTztxsj0ig15rm/mGW9+5bt2MlcsrJJIYlDsEQe7cXffG27ujJ00mE/7xoVH4JdI0c+p0x6nTHSIhr6I8bcrkjMkTM4dV/AeD1J59DR98cqC7O9KNI0Eni3BMiWklDOEXngERRy3mzNTwx57v43yQJDRs6/9Jzh0+ra9MJqwoTzt5qiO80OH03fPgJ395eGVBfgyX3aFA0H6DqNAgCkvA/tv3w8Jx0mH3JhuUIWlFktSuPfVeL+71EV4v7vXiXh/uYQ/OfvT58GiZ9cpr219/e5dYzJeI+SIRXyzmi0U8sZgvFvHFYr5IxJOI+Qk6eUTC19QsXWpW7Ag+nVb2tyfWPvjwZzHZGvYdaNx3oBFBkPQ0jVIpkstEIhEvEAj6/YTD6evusbtc/uhePwc2vb+XDFItNd0PvhwjpXCb0aaUCEUCrloWaS5AEJSiyQAdQwWDosjjf77o7t993NM7iMwXx4NvvrPn3Q/2VZSnFeQlqVRiuUzo8eI2m7et3Xz4aEtMVTeHgz32p1UR+XRHCJ+fYMn5AEAqFWRmaDPStQlamUjME4v4XC4WwEmvFzcaHW0dluqa7mhCKxb33rUoghfX6vRmJ2maus0xbVi/8AyQdKDDu1/GNch5Y09ZSNCknfBJOYIBNl3Cp+CJ3MGAkMPzkriSJ7ITvgRBjGuw4CYBKiDoEcXMXnnZtAiBBQBGo+PO+z4qKTZMKE/TJypEIh5F0ThOejwBl9tvs3mtNk+/ycXhYG+8eh3b5dOOv1yf8c/wQTBUrJVc6caP/nb9sAAgKVmpDAut8nqJfzy3aWxDBYOUw+FzOGIrQQEgNydxqAzVMVFUmPzc3y975LGvhyJsYBimrd3c1j7aKx1PTF9aqk5UdA2hFDdo5dVtRgBYPjWSB8Lob08UptvDYgnDoVCIXnj2ivv/sD56b0WS9LHjbceOj4iQnsPBHv/zRRMr0kfSOD7c7kBlVVeE/+dIcN26mdGqbrVcjJPG2eVZQ9lAfrEZoBmqx3cMp1x+JLZ/7wjxbtPBHp9jiiZdI5B0emyFCv0OY4OIw7PhXimXb8N9PT7HH0uWSDiRa0M7YReG+Q/Hx6QJ6TOmZx88FMOMU13TXR2X8yc8paY0yuOMol0e4pSQmx0yM/7mdFgMw/D5HGOP49fSYQ2L0pKU1/59TWbGMJlvf0UEfIS1z9k6xI3CwVCTw0NSkbx0AMASy2RJSoa6U3U62X9fvqZkrG7uAKCQi/717BWzZoxzkrSRA0GQW2+aE9OEwuNgSybnRbA1ROCXmQEUwWRcg05QRDEEAzF+qREiUSibl5hLMvQxS7uIwytRJnNR1En4gzTlJAJsrQiL4fws5ohphqaYkVqT/vzHldFm9NHCICo8YfvBFGg34x1mvIMB0hnYT9L2IHXOCe43t8L6+dgaxhGpKerXX7nui6+PffrZofOx4HK5P8v8x48lbOm1Xr9k8saDtREbH4qhTth3JAjSaIZW8oY0Dynkopeev2r954c//PTgaPXfc2fn33fXopHQq0cjK1PXG7WuGS0KC5Luu2tReIq2MeCXmYFxYRxdnVoe4Qx4WfokmmF+7KlZlhwjW30IszRzogvjQCTk/ftfV//juU1HjrWO+WrbvZUA0Ow5zn68PPVxjfjiiDa/OYFltbiLy1L7eh00zYx7tvpxBJeLXX3FtFUXlv/wY+VPW6tHlchTJOJPmZSxdFHx5EljTy4QB/FjCcUC3gdbjnsCxGc7Ty+dkqeSDjw8GILN0KwM0gERNkzENYoiV185fenikk8/P7xle413OHYwDgebPjXrqium5UeZwEeOvz62pr7R+NPW6oOHmkeeZefsBaBTJmcuXVQya0Yu+3g6ie4u79EEYRHNUHaiVc3PcRLdQdqn5KdjCK/WsaFUeTkNZL+/Rs3P9gRNEVUqfsbPPQMKXrqClz6qrxkTMZ0BVxhKwmvHBTKZ8OmnLv1pW/WXXx+LCAyIA0EYh8rlqY8P2x6J6ff4K4KmmS2bTnN5nIVLz9cB+qcTDd4Aka1Xy8XCo42d+QZdkKS6LM7MRBUHQz/bW3nt/IlNvRYfTmTp1QIep7LVmGfQUhTNtinNGMXT1d1tO1XZWd9oZPPau90BnCABgM/jCARcsZifmChPSlSkpqiLi5KzsxJ+Vll8ZGt1b7vZ6/KvG40ZDgDcQXuT55Sf9MzQrhyh/gLHyeMn206e7mhuMfUaHW53gCBIPp8jkQj0ifL0NE1ZScqUSZlS6Xnl14tAd4+9vsHY1m7u7rFbrG67w+fxBAKBIEnSCAJCIU8s4otEvKQkRXamLjsrobw0NeICTts+LVddBQBV9i9KlZedtq2nmeAE9bXV9i+LFGtrHd+UKC89Yn5Dxc8IUK4g7Yuo+tVn4DeO2jM9p6u6ztT1dPfY3Z6AxxOgKJrH44jFfJVSrNVKUw3qtFR1Xm5iepom3A3NinfbCaOSp2ezE0bjN7fCQlFk2ap4GUdGjj67+/qFk97bdmxKbqpOLjnV0qORiydmJ6doFRRN5yVrspPUu6qab1k69ZNdJy0uX3aSurazXyEWsG1GfqJ93R2dLkd6ueqaudnb2puvKSrf2FzvIYhclRoBpN5mNvu8c1IyGmyWbI22H8eP1Vely5VSHq/Jbp2QkJQhP8cV8WblsVvLJvd5Pew429qbF6Vnb2lrMkjlJ/p6CjW6SYnxsgqzmLq4BAC2f3F4tKyt0Zmfo7Gzo/XBHT8q+IIPVl6SKpPz+ZyZ03NmTh9PnRTJUBwkHsW7IVlpSD4vOm8FL7XK/kWisESEqRtdW8UcTYBysFUogvlIm4PoVPOzCdqTKCzu81dHVCnCLHc/xwz8X0dRYXJR4fA3agT2mD5u8ZzQ8tPMeHuWZNIc3broNmPfHh/a3/jZxwdH0vLvj2+oGb0d5/zh8RPfHa5VSUWnWnpcvgC7qBHyuQCAoajF5Wvrt4nOhpLlJmu9AaI8IynUZuTodDkqTX2dLkeiWBKkKADocDquKiytMvUdNXZfXVjGxzgn+nuuKCg52NMZaowiiBPHRZwY5wqNg1PUqX5jkKY3tdRLefwq84iiYY5srf72rV0IgoxWD+glnTKu2kM64rgLvl990h7wtznt3zWOgpgtSJOmgA2nCSvu9FM4QQdxmnAQbpwmzLg9SJNWfMD90oI7Pmj7PkARNsJF0EEb4er09Z1xjV0zEhPpklklykt0goJs2YIc2cIc2SJ23cT+naK9RcFLzZYtKFCs1Aryo6uGGtbvCVj7nEQgaOt3djYYzxxtCRKk1egIEqSpa8DeZ+m1v/34116nr6upj22P+wm72cUe0xRtbDeztWy51ejY9fXR8/y+rgDearV5CcLk9jRbrCe7egmK6nd7/EHS6HKz5WwJWzvwdYKkxeujGKbT7mD/sm0Iiupxuti/53lh4WjznL4h84WVyfffkPlim+dUzDZjX2FNn5U7fVbu8O1+PSilwpVTB9i1o5WL96yaiSJIRoIKAK6eNwEAWK1ZUdqoE/aYfd5L84sPdHe0OGy1VlONpV98ljpDIxR93VCLU2SyVPVN45lEsbTdaWcbF6q1LMfmyux8tnGjzVJvNZ+xmPgcDjvOovSsW3769q2lq2mGceGBiQkjcsOTKsWdjUYqlh2Qxbb2ln2d7XdOnBpBoibhKkhfsOAst0x8cEYjDQ9Zq7gox2n34DQh44qVXJmMK/mp72C6WJ8tSdnRf7Q/YLsla40IEwRoIl2ctK3/sBDj+ylciPHVPDky1lQIcRBSZkdrtUdSFRPfvbVLqhRTJC2U8NWJChRFvnp1a3+ntWJOAY/P0aWoACDgI9ILklAMba7qPLDplFQpLpiU8eNH+9WJCqlSjHHQ3PI0hUbaXNUZxIM/frT/rmevjJl9Eob+HaOBoUit0bSlrilEc/T2oeM9DlexPqFIr/u26oxCKLT7/T0O1/LC3NCT0m6zf3GqujBBV5yU8M3p2uKkBLYXS4h0mjTyOZxk+Vic6WJCzjtrn2UYGTe2rXZ4gXX/HR+89Np1/315azBI3ffQsgfu/uiFV6/Z+M3xLZurKiam33zHfACoq+1Z/9FBDEPtNk9Covzhx1YjCHz07r5jR1q0Opn9bLzVJx/sP3GsDQCmz8y59MppMUde/+GBk8fbaJpJSlY++MiF8a5sOCybmBea+qGijgeVjFWvdP+kGQAwOTEZAP41bxkAFGsSAODG0okQJivDhSbbuEiTwAm7F3NVmhfmDwS7suMAwIcrLgGAVdn5LAPESK6nvb730rsXb/3s0FBbwncrTxzq6byisCTiRucg3FJFDHt/OG4omVBt6k8QSy7OHwVjl0GYcMRareEr3KRPzBFWO5sVXCkf5Qoxfq40rcvXny5OEmJ8AJBxxAqulKCD7qBPgPHcQV+JPPu0o3HYU3gDhDdAyMUCtw+XiQVWl1ci4FtdXpphXD48S6+2urx6tcztw4V8rg8ntPJhHvIxQGdQKXUyY7vFbfeWzMg5vbdem6RML0hOSFEd/qlq+vJyAJCpxAqdzNrn7GrsM2QnKHWymsPNfCGP7Wvrd/V1WHkCXldjn9vu5Qt5XU19HfW9MQ2+Q/2O0TC5vS0WW4ZaqT1Lc6SXSXN1Gi9OsGRHWolY4Obk6jRpKsWhtoH90PHOHgGHw+Vg3Q4n+5ftlSyX7WhsWZCbtWNcaZEoOvhW891qvsGCdwow6YbuZwFgjeGP4W2GF1hyhcjjCTgdvmCQ8npwuVwEAKvWThKLBW2t55L3tTb3v7f+Di4Xe+CuDzvazRiGHj7Y9OqbNzAAN697AwBqq7trq7r/9co1APDnhz4rKUuJOfK2n6r/9MTq7NzE8/fDCtm/fl3EEZqcId6cMTFysk27yfXlf7YRgWBMaeUngyf6YpBejhDz0jJP3XjXaHtlSpIzJEkIIKftDeniJHmCBMICgBYkTAm5qsq44omqAmCz+ADC/l2cODxf4Fd7qyRCHknRQj6Xz+XwOJwp+Sl1nSalVIgiCIoidZ2mI/WdQj7X7PDIxcJlU/LFQyedHxvmXzoVACrmAPuqWHLVTARF2OOMwgEtskwlmTS/CACufWSA4ad8dn644pklYrv2kVUhcsFQy3CM6nfMUCvvn3suFpKlOQq9QS8qGXAhZksuLh9YNK2bXM5eFvuyDCdNytNp2L8xT3eovqPb4kzVKYQ8brPRmpes9RPBboszI0FV09G3uCKn3WSfnBPJ2jhVE0nQFo3hBVZ+QdKp4+1CIY/Ho0+fbM+P4nJlkZ2TyAY6KJRiv49wOf0ZWToERRCAtAwtAHS0mXPz9ezvkpOf2NpsijnyE/+45Iv1h/t6HZdeOW3qjF+IeOT/MVwdl6nqaG83Qf0K9BKsPCpXxg6Oi96EsiXhf+MjSFIuLy4T813ewOS81D1VLRl6VVufrTSz6Gh9l04pbeuzJSglLm9Ar5KpZeIAERx3gXXu4lEk5t/YjQe/ikIsZvF5LM7ndxyK1CiiPFTNviyjSZOG8orotjhrOvtQFPHhxDXzJr63/bhOLp6QlZyqVdR29r2z7djDl8SgEkkRDR8JPwKBVZT8zRdH58wvCAapn36ovPTK2O86FBt06fokRdtZTqvuTisAZGTp9u2pZ50oGuqMU6fn4HgweuSkZOVDj1zodvlvWvfGFxvvH/by/ofRYnfniMJH/s/BoFUsnpQbWjjkGDQogty5agYAXDRTFjoeedap3zh+y7+jxe1dPa3oSENXmk656VidTiEBhmENXDTDXDar9IdjdSsmjzpDMIxEYOXl608cbb37d0uCQerVF7b8+ck1FEU//4/vO9utPi9u6nddc+MF0b1S0zUTp2Tee/sH+iS5PlkJAAVFyaXlaQ/e8xEwzORp2YXFyT4vHjEyQzMP3fsxl4vRNLNq7aQxfB8A8AWDn9VV72hvbrRZHYGAlMdLVygXpGVdU1wu48eIp3/20N7XTh3NVKh2XnUjAGxqbvi8rrrOanIGcI1INEVvuK6kYkLiMNrubW3N3zXVn+rvtfh8XAxNkshmGlKvLakId1mIwMyP3uxxux6cMuueSdMAoNJk/Kim8pixu9/r4WOcRLFkcpLhqsLSQk1kJLaHILa3t+zubK0xm4weN06RIi43SSIrT0i8NL9kYqxL/aGl4bix54zFXGc1ufABL8flX3wY3bLhtvv5WORdcfm3nx/pjbTzvrN8zYL0IfUXLx07+NKxg8lS2YFrbgWA7e0tX9RVV5v7LT6fgMPJUCjnpWZcVzpBJYikfwuh3en4sObUwe7ObrfTSxAxFQRXFZX9Y84A+dTSyQNrt/i8iXGklS3g//xM1Z6u9ma71RnAAUDG56fK5KW6xDmpGbNT0sO38P86uv+V44cB4K+zF1xbPKQjzuGeriu++xwAFqZnvb18zfmcEc77d2Qx8nv1r/t3vlt18v7JM+6fPOOdyhNvVx73EMTUJMPjs+anyOR+Mvj0wb2bWhpwkpyWnPLYzHlpcgXb8Y5l0wGgIjMZot4Q6+ZOAIDMxDHG8fzmHEfPE4d6Ou/d9oPZF4PsTcEXPL9g2aKoZ4wVWBwUrb75nod2/PRDS2SOAATg7knTHpwSWxtt9fvu2LLxaG+MwD0Oij40ddbtFVNidmQF1tVFZX+fs+iFowdeOX4o+pd4a9nqRRmD9sVf1dc8vm+nNzhkPNCNZRMfmxm53l782fuNtshceDER80a/afOGXR2tESxRIxFYCEDNLff+Ze/2bxoiU2wBgFYk/vDCSwo0MexBXzfUPrJ7a/wtj0ogvKls0l0Tp8b7PiPGzo7We7dt8hBDTuz1JROeuGB+6GOf1zProzdJms5TabZccf1QvR7c8ePXDbUA8MbSi5ZkDvLVGu0Z4bx/x9Heq6zAWp6VOz059S97t4fKMxTKrVdcf+dPG7e1n+OnN0jlO666YSgpOV74zTmOng8OdHdet+krkqYBYEqSYV5qhkYkduKBQz1dO9tbHHjgth+//feiCy/MjqFGIWn61h+/3dfVkavSLMvMSZLKfMHgwe6Obe0tDMArxw9rhOLrSiJfpC4cv+Sb9W1OOwDkqjRLM3MMUhlOUaf7jd831xMU9cyhvQAwlMwCAJPX8+bpY/8+foiDojMMqfkqLRdDjR73MWNPv9cz05AW0T5HpWalVbZSPS05JVupEnK4vW7Xjo7WGnM/ALxbeaJCp1+Zkx/e68WFy33BAZKTl44dONDdCQD/nL80Leqlyot1w72zfA3FMM6A3xbwv3L88HdNI/XAYgDu2vL97s42MZe7Mie/SJMAAE0261cNNb5g0Ozz3rHlu+1X3hixjjjVb/zDzp8ohpHx+Q9OmTXTkKrgC1udttdPHt3Z0QoAxdqEzy66PCJJ1Pmgx+26a8v3fjKIAMxPz5qsT1YJhCRNd7ldlf3G4309BEWtyRukYUkUSxZnZG9uaWywWY4ZeybrY/hJ+oLBn1obAUAlFEUI9zGcEc7vdxzzvXqq33i4t/vqorI8teaTmsoGm6XNYX9ox0/b2luuKCwp1iR8WV9baTJ2u50/tTZdlDOWjd7IMQqBtbumpaXfdtOCyXHaHGvunpw99kD28xnZheP3bd9E0jSGIM/NX3px3jnyv5vLJh3q6bx587feIPHHXVvKExIN0hjEb/u6Oq4qKvvb7IWhFewNpRM2NTfcu20TzTDPHd67MjtPJRxkeXx07zb2Dri1fPIjM+aEFr7XFJffVjH5sg2fOfDA84f3LUrPzlLGTtZ0oq93V2fbFL3hpUXLkySDXFranY7oVEhlOv1jM+ddkJqeoxy0qL5/yszH9+74sOYUAHxypjJCYBWF7SuVgoGvUKjRRe83hwKGICqhSCUU6cSRLFrxsbuzLVel+XDlJeGm99sqJq/88iNbwN/udOzsaF08eBX535NHKIYBgJcXrpiXNhBuqRGJJq8wrP36k1P9xhpzf6fLMfKLHxYbm+v9ZBAAHr9g/vUlkZSK3iCxr6ujTBcZMn1tccXmlkYA+LjmdEyB9WNrozcYBIA1uQURQnlsZzyf33HM96rR476qsPTvcxYBwIK0rFkfvckAfNdUd01x+VOzFwLAsqzcKe+/RjHM0d7u8xFYJE0jAFhc0/kozOpzi7PiSysAePXHEfm+jwHDjvzJmUqLzwcA1xRXhEsrFtOTUx+ZPhsAvEHitZOx/Ya1IvHjs+ZF6DguzM5bnVsAAN5g8OvB+5pWh+37pnoAqEjQh98BLHJVmgemzgQAimHeqTw+1GXbAv4Uqfy9Cy+OkFYAkH5WIxCBG8smRkgrAEAAHp4+m12QV5tGlx7iZwWKIK8uvjDCUShZKrvt7Js8Wjt2sLsTABLEkpC0YoEAXFYwEGF6uGc8YyecgYE8N+W6GAGkYi5vaWaMyJtpySm5Kg0A/NjaaAvEYGf8sr6GPbg0PzIwdmxnHDPO81698Oz7L1kqKzgrHC8/+1uohaJ0hRIAutwx2KI9BNHv8RAU1e/x+IOk0e0mKKrH5QqQpMnr9RIEW27x+aw+3w8NwzjcjXSF9dn+yu+O1k7NTb3/wlkAUNVhfHv7MQ6KWN0+vVL29LplLX3Wt7cfPdPVf/db3wLAv2++CEWQrw5V/3SygWLoSVmGu5bNAIC73/p2Sk7K6bZes8v7+u1rW/qsEeMgCET0ajbGGDn6CtnfAwCuK42tAb20oPjpQ3u9QeK7prq/zl4Y7da0KD0r5g780vxiVgWzp7PtlvJzpoCNTfWsUufqorKYitzFGTmP7d0BALs724ecWYA7JkwRR62kxgARl5uhUNRbLd4gQdL0qJy8fj5MP/tURyC0fOj3DiL/DpAku+c1SGO4UIeWxhb/kKSMA+N4cZqmURT1uf1imchl8yAoUr2/fsaFEw9vPlV6Qb4y4dwqO0c18AJ48djB/yxeOfLN5rXF5Y/u3U5Q1Bd11RGbqR6360hPFwCUaBPy1ZEzMOYzjg3nea9mKc6tufQS6RmLCUWQfPU55aNaIGoBW0x93KenK6V8vs3v73Y6SxITi3S6Ez29fA5mdHvEXG6fx6MUCsv1+s+rqp5cuGBYA+5IBdYVs8okAl5z3zkSlcYe88Y/Xc/jYNe/8kVrvzVbr/771UtO/6331VtWsw26LI7NJ+rfuetSBIFbXvu6prO/ODUBAPhczgs3rBxqHB4Hi+4VMXI0fMFgvdUMAFqReCjDHB/jlOkSD/Z0egiiwWqOXkUXDLGuLtXpEQAG4IzFFF5+3DjgtleWEJvXIVEs4aAoSdO9HpefDApjhQ0CwAUp6UN9r9FCzh8wulEMzfltsDMOFa2dIBlYc/nJQZRSAg5HyOH6yaA1lkgKmVNiGnzD8cM7u7g8DAAEEkEwEMwuT8+blIkgyM7PDwai2GJXZuf/+/ihDqdjT2fbrI/evKKw9NL84qF28eFYk1f0zOG9HoJYf6bqtoop4U/bVw21rIwIrQrH5Yxjw3neq+owTYiUxwMAhUAY/kbkYigAsOrjCARp2hkI6KXSPI3GQxBl+kQuhu1oadGIRI5AIFkm04rFx7u7hVxui83WaLFQdE6cXeHYle75Bi2PgwGASiry4jEka2u/rdPiuPX1r9mPvrNtKjIG3cER43Rb/TF7xYfZ52VtWMmxXsshGGRy6AEA6PW4owWWcggTu5jLlfD4bgK3B/zhNtoez0Dk56L17w17hY5AQCiJIbBQBEkYLq4iGm1O+8721hpzf4fLYQ/4PQQRIEmcIn8Vj9D4SBriFwn5gkbbqSck6g90d3a6nLUWU9Hgn+nH1oEtw2T9MKpSr8snV0s5PI7b5lXoZHmTMrsajR11PfoMrTcqHwQPwz5eeenvdmw+buxx4IHXTx19/dTREm3CpQXFa3ILpbwhhaOYy70kr/j96pMdTse+zvbZqemhqm8aatmRVw3WJ57nGceG87lXMQSJXq3zsRh0GjFdDlIV8hV5eeGO9YU6bb5WE14yIy2VvRsemDUz/rWNXWBFB2QiCBIIkqGLyExQ6ZXSN25bi6IISdGhSL2IVV/EODF7RYwcjZDpRMCJ942EZ2tjrl25Q8t1AYfjJnAGIECSIUV4HIN0NCgmdigyB42RbisO+ryeR/ds294eme48NFR0iqpfFzHpKOLj9oqpB7s7aYa5/afvHps5b3pyqojL7XG73jx9bFtbMwBMT06N6W4WjmsfHYjzCAVUpuTqr3vs4vCScKTI5F+tuXJnR+s7lccPdncyANXm/mpz/3OH991QOuHOCVOHWiBfW1L+QfVJBuDj2tMhgXXU2N3hdADA4owcOT82E9aYzzgGnM+9GnO9M/KbdmV+fnSXoVzqh8WIBBZF039Zv7W13+YJ4Ea7644l02M2QxFkSUXuVS+uT1LJXrj+whSN4tIZpbe89hWKogzNvHrLagFv+NPF7BUxcnSvkArAH4xHWRuSa9JYKoMANWTfAEkCAAIgDFM2iblcllfx6bmL4wtKAFAJRPEbjAQ2v2/N158YPW4ASJcrLs4rrkjUp0jlSoFQxOVyUHT115+c7jee/4l+XVyQkvbIjDnPHNrb5XLe8uO3AMDuVtjaMp3+P0tWxus/GNGyKU6UzPy0zPlpmV0u59cNtd801Ha6nB6CeOX44W1tzesvujzmGjxToZppSNvf3bGjvaXP62HNC1/XD2QSvbxgmBDxMZxxDPjl79WfCSMSWBiK/uPqpRGFIcERLkEeWTvIZXHV5MJVkwsBgKRoDoYCQIQeqjRNHz1OqFc4IkaOgFYkZu/p7lh2ihBCVgy9JAYLsMkbO7ewhyDcBA4AKqEo/E7XiSXtTgcATEtKyVCcF5/cCPHqySOstFqUkf3fJSu5aOSynIqlRPi/iFvLJ5M0/dzhfRiCcFCMYmi1UFSo0a3KyV+TW/hz2xNSZPL7J8+4b/KM7W3Nfz+4u93pqLda/nZg978WLIvZ/rqSiv3dHRTDfFFXfe+k6QGSZN2P9RJptCfduJxxtPjl79U4CBKkvd8l10h8ngBN0lUHG2etnOCyetRhGXSGwsAP/2z1tidPb169441X6/as3P76YXMbANx+cP17TYfvOfzlFbvf9ZKRS0qbw2dz+DxevKPHRtNMT58jgAdNFjdOkFa7Nxik+swu9thkdb/+8d72bmt1/YDmjy33+gmzzRMMUmabh+0bwIM2h49tyY7s8xMWmydIUl299jgZ4QUcTqkuEQCsfl+rI3ZmpABJVpv6AEDM5eWpY3hX11piewOcNg2sWYq0g/QpU5MG1CiHogzzPxNYez8APDhlVrS0YgB63MMTqoUW5r+treNgfFxz+rnD+xLEkh8uu7bhtvubb3/gxA13frTykkvzi38x6ycCsCgj+/PVV4i5PAD4qbVpqJbz07NY5Snrx7C9vYXdgl2cVzSq/f7Izwij/B1/+Xs1Dg7+cLqlurOnpf/Tf/6g1itQFP3q1a0fPfu9zx0Ytu+5FdYMXUa6RO0OBh4pXXygv3WaNgMA+BjnlWmXxuy550hjT59j6Zyiti5LVV1PflbCscqO3EzdvmPNQgGXx+XweRyT1S0UcIvzkrLStGarJ6TG2rSjWijg2p0+iYjvcPmNJmd+VmJups7p9m/cXjV7Sg6KIiiKNLb27z7cKJcI+XyOyx24ZPmQEVsAsCa38GRfLwC8V3WS9WeLwJf1NawX34XZeTGpWnZ2tHoIItrA/NVZb5q5KRnh5RflFPznxBGaYd4+fXxtbuGwK+3zh+9sRI48lo1sd0drTG+gCISUSn0ed9H4+V6OI/xk8O8H9wDAnROm5sd6tfySSBBLspSqKlNfgAwOpUXFEGRdUdmzh/d1uZyn+o0/tgxYBi4dDWXYqM4Io/wdf/l7NQ5SchIO/1Rl7rWH2L6SMrTpBclC8fCmhnMvKyVPLOLwNAIJH+Pg9IA2Z6I6krMmBI1SYkhU4gTZ3m3lcTGjySkUcgtz9BwMdXkCacmq5g4zeyyXCFVykUGv6O0f2JGx5SRJu70BnUY6c1IW27eyrlvA57ItLXZPe7c1USsz6BUYinI56FC8iywuzS9OlckB4JPaypDDXghHerueObQHAPgY5/YJsQNlXDj+8O6tEabZTc0NLBewmMtbPThUIlupXptXBACtDtvNmzeYYgUw2vy+j2pO93qGWfgEKarNZmcZadl/rKNdRLNUmYI92NURSRlcZzH/ftdP8c/CImQ7/yJqln4jcAQCrBf4jo6WVoctSP/sps8Pqk812WPnPao09bHuLDkqdZzl0uWFpTwMA4Btbc17utoAYEqSIW0I199xOeOofsdxvFfPH5nFKVc+sHzlTXNvfuLilJzEax9ZtfCK6dOWlI6E0fucoI05M3Hm64IpA+EURbl6OMsvDAAXLS5jXwtZ6dqQ5XJqRQYArFgw8MJhXG0yngAAG9ZJREFU2+w80DB/5iB758XLJrAnXLGgGEWQW64ciDf+aXctgiAuj18uHVIHKeBw/rNk1eXffuYLBn+/86cv62rmpWVoRGI3jh/q7dre1syunJ+avWAoR60CjXZTc321ue/C7LxUmQKnyAPdnVvOLssfmT47mlrgqdkLmmyWSlPf/u6OmR++OdOQmqfWCDncAEkaPe4Gm7nBamEAJiZeG+3IHo7v6xqcgcC01JTPKqtzNepOh1Mm4Hc7XX+ePyd8xbc6t2B/dwcAPLl/V6vDPjXJIOHx+ryefV3t3zc18DBsenLqoZ7OOCcCgBVZec8d3kfS9JbWpss2fLYoI1spEPjIoM3vd+KBx2fNj+5CM4w3SLgJ3IUTIU1fm9PeZLdKeXwpjyfi8saRsUUvkU5JMhzt7d7b2T7/03fDq8RcbopMPjsl46ayiWNwBxkKn9RWPr5vR5pcMVlvyFaqVAIhiiAWv+90v3F7ewv7Dru1fMiAUABQCYQrs/O/bqj9or6a3Q9eHsv9ahzPONrfcbzu1XHBqCwh4Ri3lWE4v/BQFF8RxwtnnbN3kiTN4ZyrD29JkvSiCwowbHjNRYk24YvVV9yxZWOXy3nU2H3UOCgqXczl/WPuojixTk9esOA/J47s6Wz7z4kj4eUIwH2TZ6wrLo/uIuRwP1t9+aN7tn/TUBukqd2dbdEsRRiC8GI5rQxqg6JcFDvW3SPkcnQSCYIgAg4nT6OJiCVcm1e0p6v9+6Z6nCLfrjz+dlgUhUooenPpRb0e97ACK1kqe/KCBX/Zu51mmIhZkvL40Tf6rI/e6nE7oxUlfzuw+28HdrPHKIJM1hs+X315/FOPHPdNmnHbT99GG+O9wWC91VJvtXxeV/3xqktLtKMm4I8J9ubtcDpYX4QIcFD0wSkz10aFIkfg2pKKrxtq2fgwMZe7LDNexoPzP+Nof8fxuld/XXAAIEjjdxdM5qFCAacjTVzc4a25t3CKBe9+auKsAGUNUGIPaVdwdQHay0MFOO0XoGKC9tMM1eGtKVaMLkNsTJjN7q+/PrZu3QybzavXK2w2j1Yr6+tzqNUSn4+gKLqysnPBgiIA8PkIHo/DMIzT6ZNKBT4fIRLxXE6/RitjRVyxNmHHlTd+UVf9U2tTvc3sCASkPH6GQjk3NWNdcXkc9iUACATJDy68+NvGui/rq+ssZjdBsHxYN5ROKB/CPxgAhBzuvxYsu6V88pf11Yd7unrcLjeB8zBOglico9JMT0pZnp03LOU2zdAIAqvy8xVCQVhhpP4CRZBXFl24KD37y/rqGrPJTeBiLs8glc1Ny7ipbJJKIIxvJA3h6qKyQo3u/aqTx/t6WN9xOV+QIpNPiJXkwhuMzUU1+PoZ39CMN6OCC8fv2bZpT2cbB0UXZWRnKVSsBhoAgjRl9fuO9nY32a1OPHD/9h+2XXHDuLDxfbzysu+a6g73drXYbSafh/VikfH4mUrV1CTDZfklcTZ3IZTpEst0iZWmPgBYkZ0fHbg+7mcc1e8I43Sv/rpAGIbpC7SetG1NFGYEaYKL8oI0USSf1e6pEnHkCCDJotxG11E/5eGhAhdpFWHSZFHeSdvW5Um31zr3FckHsffVu9q/6NqGIaiDcOsEqofyr0EA+cl4cI/5BM3QJfKcdenLf3/6pefL73+j5WuSpu7KueyPlS/frVpXd6Z31gV5hw41oQjC43GcTn9uXiIAbP7h9L33Ldm188y8+YUAsHVLtVgiaGsz9fU5V6+euPmH0xmZupychPz8EaWTiQmWDwtikU/9D788rt/09e7ONjGX983FV+XFCkJkAK7b9NXeznYA+O6SdWxMIk0zCBJJKUxSNIIME/0/FMbW97INn7Erna/WXDkpFn9D5FloGkZJ7T9CHDDXvdOyQ8IV3Ji5oFyZEbNkJNhnPtPmMV2bMXfcr3BsQAGg03uGi/JRwAKUh/2LUz4z3q3i6R3BfnfQZsa7UQTzU24FV6fkJbHtLXi3KdBJM5EK0TZvzx/yr3u+/H4zbu/09Rn9lt2m4/8ovfvZsvvqXK2N7k4ZV+wl/a6g1xn0eEm/jCuRyURKpdhqdXd2WFNS1a2tJi4P6+tzVld38fnczk5re7uFTVqVm6fv7bHptLLp03PYWqGAez7SKhy/sJm/8mT7zzTyoX0Nn314YMzdqcERFqP17SJpeszuYA02C7tPuaF0QkxpBQAIwPLMAUazTqcDAAIEuf1ko83tNTk83gBBUjRBUiaHx+b2bT3eiAdJi9PrDRAmh8fq8lldPrbWGyDMTk+r0Xq6ZSANH0FSRpsrQJBWl4/ty7Zky4e9+F6P65ixGwAy5MpJ+uQgTbU57QRF9Xk93iDR7/Wwx34yaPS4/WTQ4vdZ/L4QYST7kS1n27PH4eOwfUdyo27uPXl//sp/T7w5JJuiS0aCC7SFvx1pBeyWcIp6BescH56nZF7C1QBQzlsUOg7lNcmUlLLt2fIIZIkNXJQDAAqu1E/ifUFrb8D8aNV/2Fo/FciVpp12NAgwHg/lVDma8qRpcrlw8pRMALjhxtkAkJWlQxAk/J3JlgOA1eqeNCkzI1PLMMxwJP2/dXzwxu4X3rj+5xh5+gV50y+InethWPhJckdLy1SDgWIYlVBo9npPGo0Ls7K6nU4XjhfpdC4cF3G5HoJIlEjY9l6CEHA4rEcIq3U62t09NyPD7PWmK5U9TqdWIvEShN3vd+F4jlpt9nqTZTIXjou5XA9BhKKgAaDROkCnGZOGNATuWYUma57ffLQuQJAb9tf0Wl0V2ckSIb+l19JrdT146RwURb49UCPkc+1uv0TIo2mm2+KUCvm9VleyRiYXCw1aeWhTuet0M4/DSVLLNuyvfvjK+SiKfLj1eK/VNbUglcfh6FXDqKK/aThzNtq5GAA2NtU78IAvGOx2O1NkcqVAaPP7u93OUl1isUZnD/jX11U9dcE58rWfWhs7XI41OYXr66oSxBKlQFih06+vqyrX6UPjsH0j3J7fa9151NoEALO0BVenz+7xWd9o3nra3mbF3RKu4ImSy52EL6JEwhF+2310e18lxdATlJm3ZC8CgIdOvT9RlVXl6LDgrpcn3CTi8L/qOrS558QkddadOcsAoMbZ+WHrbgxFbbg7Uah8ouRyBJBXGzcb/fZev80Z9D2Yv2qm9pcg8AtF9CAAgzKUDHEcT06gg3NepooStXzVUyV3oghKMhSGoAzAxp7dszQVJENu6z+8xhCpHWTlUMwsgRMnZoS3+W1i87cnd22roWmmtCLtulvn1tV0f/bBgYGkjXr5H59Y29Fq+uzD/U31vX95cD0A/PX5KxAUiegFAH95cH35pIzayi6b1f30y+tamvq++vQwl4uZ+pwTpmTGbCMU8TZ+dWzrD5UVkzJuumsBez3r399/8lgrQzN6g/LBP6+KvsLwi/+urs5PBj+vqel2OWempvE5mD8Y3NRQr5dIUQT5oqZGxOVa/T4pj7+6oEDE5bbZ7Z9VVyXLZFIe303gbDmCIBiC1JpMx3t7ShISPXb7Z9VVi7Oy0bPlBzo7RVxuv8ejEApX5+eLz1pC6bPL3AAZL8RqZ/uAVwdr2uegqNuHJ6ll2ckatUxU296foJRmJ2vMDk9Lr5XNlMPm1ElPVCEIwudyspM1vgChlokNWvnR+gFfyvQE1Z6qln67W8DjtPfZWnqtKVpFdrJGr5LtqWqZVx4vAV+AJN+rOgkAfIxzeWEpDBhS0CSJNF+t8RCEViQWYBz2uEynf7/mpJDDbXHYGu0WlgtIJ5IgCHK0r1vI4SZLZFqRmD2OGKdsMH9WlaOjytH+38m3AsADJ98rV2YUyVP+WnrlH09/eH3m/AKZAQAkHGFESY/PutV4+j+Tb0EAuef423XO7gK5AQB4KOfpsnMJ4i9JmS7hCFo9faGSJo/xi5kPclHOHcfeaPOYUkSa/ea6z2Y+6A767zr+1lDSyu/9iMstIfA9FNXF418ACJ+mjAgiBgRDUR1FtnO5JSR5hsMtIwLbQ234gsjoGvgFKJL1Qs1y/cw/Vf8HA4QG5oni23KlqSft9bdnXRJkyNeav/pD/vU/9zX8kujtse/cUv38f69DEPjjPR81nOkFgNamvne/vJvLxR64/f3ONnN6lu73j62urXzlqX9dOVSvvMIkAODxOI89c85xt7fb9sZHtwHAPTe9M39JcUqaJrrNqksmiyWC9pZzTDjbfqz801MXh1I9DnUuFhwUdeG4QSbP02iSZbLtLS0GmQxBIE2hONjVyUFRZyAQpGgXHmBjxY/1dAs5XLaEpBkXHjC63U1WS5FO12yzZiiVXU5nn8ct5HDZERKl0mabNVEidZ6lFvEFgyGBVaodIMn6sPrU8qzc6OhfimHePH2M3UaVaBMyFSoAWDWjiDVQsH9LMvRw1mQRypSz9Xgjm1OHHSfcoHHRzIGlU16KNsegQc6+DcOz7OQYYu9PQ3hi/w6WD+eas7YdiqERBJmflinnC6LPe13xBLbowckDvjss7SoTthyYaUhDAL5urI0eJ4Q2T3+BzMAuJvJlhma3sUg+pO/kuV5eU7fPcu/xd9iPXmqAb6dMkR6/Y640id0/KXliH4VzUWyiKuuRyo8B4PK0IYkWEFTE4ZWTZBPGzccwAxHYiqJahnYgqCJIHMI4WRxeOUk1U1QniiWF2sCoBNZ+02vFipUK3uj4jvNl6X8qvJE9Dh0sSJiyIOGcR8lh8zu3pItb3RumaW/aMOtfzmCvABupEuqw+R0r3q7mp0/T3hT9cWzXPL7obDP3dtsevvcj9qPfh/MF3Kw8PZu0UakU+6LImGL2Yg+KygbdfCmpatZdJT1T29ttZwVWRJtoPPHs5V9+fLCv13HJ1dOnzswZ6lwsLika9PDnazShh+RSWTFb/kNjw4rcgaza15ZXIADfN9SvyM0LlT8wYyYAsH8phkHProfZEdjymD7cGQrl4ozsrW3Nlaa+pZ9/cHlBSb5aK+HycIo0+bz1VvOW1qZOlxMARFzuP+YuDnUc1ocmlFMnZm3M46FGZnGyr/d4X4+CL7AF/Ftam071GwEgSSK7f/JAvtKLcyNpb2EE/ARI1HHMcULIkibu6q9hgAGAOmfXTE0MKptoZIh1CULFyxNvZPc96FkH8ojtUTSiQ0TshOeO7CWp4nhbeIHwYgAQiC4FoAFQDrcAAGWPz/4FgfASAAoAC2sTA0MKrFm6O+Jf+pjR6T12Wfrr7LGPtFXavp6dcM8Y+kZ//PmueeRIzdDqEuVPv7wORRGSpDEUqT/Tg0Vtb1EEwXEyxHMS3SvULLxXR5uZphkEoK3FdMW1s2K2iUZSsvLBR1e5Xf6br/jv55sfHOpc4dcGQ4sAFEFW5p17KthqtiS8PARsxKKBxQsLlt+x5bt9XR0dTsdzh/fFbFOg0T47d8l4OWGNDaf7jf84uCe8RCkQvr189c/NHRqBYnnqBFXGncfeBIDpmtxiRepIeiWL1GsMU+858TYKKAPMPyuuF2DRi1n6bzVftntNXhLv8ztuzooR7uYjcZphnq3bgAIaoIjHSi5LEcVfiqJD/GWBDVF+DhwA2N33kgCT2olOMUcdoFyLkx49Zfui1rFpWfKTan4GADQ4tzW6d3IQXpp4SqFixabuP+kEec5gb5KwtEixfHffyzjtJmlipu42Bc+wt//ffspJ0oGJ6qsThQXhtQxQlbYNrmDfduOzBlG5RpB93PKxFW/bbnw2QzIjSxqR35CJ0zdBmB/+MV++JP41HzK/7Qoag3RguvZmtsHPhKRk5YrVE/9490cohtA087ezm74IICgyZ2Hh3Te+nahX/OXpS6N78QUxvHikMuE//vK1qc85ZUaOIS1GZjeKov/51Hed7RafFzf1O6+5aU5yiuqhOz/k8jCGZlZdMjnmFcY8168FCY/30cpLd3W0ft9cX2XqN3rcATLIwzgSHi9NrijS6BamZ880pP7qyVB1YkmyVGbx+WiG0Uskc1Mz75gwJSYLyKhQ3dFXkhaZeyI+rs2Yx+1S5iRpJmUYem2uJJUMAJ4tvzaiWUTJ8qSJy5Mm/niiYdnEgbXnPyuuD9Wy4zxeEukJ/I+zGi724LOO/ZPV2RenTAeA15q2VDs6hhNY5wuEYZjdfS+WKC86bH7vgoQ79/a/cqHhHwCw3fhsheoyNT8jQDk3df/54rRXQkr3T1qvv9DwdzkvGQBa3ftNgcZp2htdQeO+/v+sMDz1adtNa1JfEGKKWLV/A4Av2m8PLYv6/GcaXNvnJNwbfWXD9o3+ONQ1G/21Ta4dsxPudRDdB0xvrDA8FXGunbvrfD48I11LknSv0Z6aqkYAaWs3Z2clkCTV0may2by5OYkzpmUfPNycmqI+ery1qCAZQWDDxpOXXTyZIKiOTkthfpLBMBDe1dDYx5b4A8HaMz3ZWTqKYtiRHQ7fzOk5+w40FuTpDxxqvmhlRXePnR0QRRG2MZfLiRgQAGoqO7/78tif/3bxef3gvxTOmEwnenoLdTo3judrNfVmi4cgPASeq9EIOBy2iqTpTocjW61Okkm3NbesKy8L9ep1u7wEkaPWqEXCvW3tFUlJGIqwVT7ebglHpeWnclB+t68uUZAZpHE70afiJ6WLyw5bvpmmWdsXaGGrMIR33LZpmmYNSQfZAy1/gO/ljHMvTvu0/DQhJm33ViUKM4WYrMVzwiDM7/LVFchn2fBuCUfFVlE0aSeMGn4KA4wp0O4h7bN1V43XXG04VOP0BXRyyaQcw66qlssvKOs0Ow7UtZem6/1EsNvizEhQIQg0G615yVoAaO2zlqbrnb4AW8LB0I92nbxm3gSKpj/bW3nt/InZ+oGX2U8nGzwBIluvJik6YpwgRTX1WCxub2FKwpzizD01rRkJKvakCAIR4wyFTp/luTMbhBiPYmg5V/T7gtUizjhzpUZgYN3Fx6QclMdHY7wfnEGjip8Rbi7koHxWWgGAg+hW8dMAQMbVu4J9AMi8xAd2Gp/fbnwGp9xRtaPA+fSNuGY70dnrr9nS+9QRy3sqfow1s8nkunB5eVVNl8nsKi1JKSpIrj3Ts2JZ2anTHVU1XatWVPD5nF6jAwB6eu37DjSsvWhiXm5iVqYuK1OXka5FUcTtDgiF5/YCoZJde+rFYn5DY19o5GCQOlPXS1G0RiMNkhQAhAYMNY4ecNzx74OH1ldWneztPWMyfXTq9ImeXgB469hxAOj3eD4+XQkAoartzS0AsLWpObxxfGyqb5Dy+dX9fdNTU+7/YfOMtNRel+uK0tJj3T2hKqPbPSXFUJGkT5BIghQV3qvT4byitLSqr29LU/O1EypKEhNCVQigFcqlvf6mM859AlTc62+2E329gUY7ce4mCVUlCDISBBlaflroINTGGTRXKJd2+Wpx2i/lqrq8tQ2ug5NVK/XCHATgoPmLNHFpqMoVNKeKi5NF+V2+2gmqZVx0PH8agqQkAp6fCOrkA/OwvbLpytnlRakJ/Xb3hKzksgx9ZVvv2unFRxu7UARx+XARnxsqyUvW5hu0ecnavGRtbrImXMoYbe5LZpScbOmJHudkS8+ls0r5XE6XxQEAnWZH6KTR4wyFVJHm1Um3PF9x3QsTbni85PIIaUWSNOs+yYKmh8naHN54KAzvYivjJtrx9nC3ynDhpeSn2fB2AHAFjTJuIgDohcUrDH9PFBbWOjdH10YAQ7gkHZsEZ9i+I79mJS9Vy89ekvSXJUl/ma69Jbq914f/uLVaoRADACsmVCrx1h21Go1UoRBv2VaD46RMKtiyrcbrxVNT1F9+fay+wYhhqM3m7eyyUhQtl4sqq88xDYVKsjK1Pj9RVJQcGnnGtOz3P94/Y1p2Z5e1uaW/qbk/NGCocYwBmUBOsfjhp1YEyD6KwXHKTNE+isFJ2hugTBQTMHp/wikLAFCMn6Q9FOPHKTPFBAJkX4Ay9Xp+CG8DAAIO58qyQeIjdK5o8YFT1CmjMUjT0Y2HQoFO6yWICUlJW5ubb58yZUtTk4cgvqqpVYtEoSoAYONXWmy2MyZTbb8pVBWKa8lSqd45fqLS2Beq4qED8VUJggyc9htEBR7SXqZY5CGtZrzThLf3B1pDVSiCeUi7Fe8OHYSuEKd9lY7tIo6iy1cboDwIgqn5hiPWb3v9jQwwE1XLaxy7QlUAwEUFACDiKKocO4L0oDgkPxX0kLifCvb73d92Vvb73Ww5QVO9Pqc7GGh1W1rc5pPWLoKmQrUhoAiCoiiCIG39toZuc12XKSNB9dGuEzUdfQAg4nMBQCOTbDpWp1NIKJpWSIQnWnpCJaFxMBS1unxt/ef44LwB4tvDtSqJKHoclUS08egZPEjKRYKNR894AnjopNHjjAQeD97ZZfX5CavVQ9NMb6/D4fDu3lPv9xM4TuJ4cM++ervdG2pDklR3t40luWP7bt95xmJx2+1euz02lSaEtoSTNOsOmF6fm/C7bcZ/LNQ/fNTyQaf3mIaflSKeVKRYccaxucW9l4sKDeIJxYqVgzdizJ7+V/ykg2KImbrbBZh0m/EZHioiaN8s3R1KXkp4LWu/C+9OMcFN3X/io5J8+eJ0SQTzMhO/b/jHAOWKf81HLR+w4i9NMrVAHmku/XrD8bWrJ0b4djEMw5ZEH4SoKUIlFEVHhGeHSkKN4yDUJnQQMaCLqO9yfyXl5cp4BU68GkNFCHD4mNqBV/MwBQIoxQT04qV8TNPm/ABFuACAoSKaIWS8AgW/pNf7I0V7Q20A4N8HDyXJZADA52CuAF6SmCDkcN84euzGSRP5HOz1I0evnzCh1W5jq/I0mtu/3fj66lVbm5vZktLE4V8hFMOEa9zfP3ny2ooKVvcUURWnV3hJdBXD0MgQhq1QVcjhOXTA4qh142T1yoiq6AEjesUseb/5EBfFXMFAj9eRKlHJucJVqaVCjLu5u4aPcaZpM3cZG1R8MYogp2xdPV7Hw6VLJMNtnaJvm5BdlaJpNmYo2tIaUfLJ7lNXzikfqk2Mg9B9ODQP11Dw+YlDh5qNfU6ZTIBhaF6uPjtLt3N3HUXRKqXY2OfE8eC8OQWnTnewbfh8rsvtX3vRRAxD2b59JpdMKqBpptfoWLywKCszBskXMswq7f8fcDh8CsVvl8caANpdnwRIo5SXnyy5sNP9BUX7uZjcF+wSczP4mDpA9vkpY6r0Mj6mabL/h4spUIRL0X4epk6WXOgJtvV6Nok4hlAbdsxzD8CYxMdoYfX51KLf0CR7SYeYoxiXoV6p263kiSRcvpQraHGbEYDrs6dzUaze2bfT2LDMUPx9Z9Xa9IrDplYOikm5gnmJub+M0cDm9qmkv9Ccd3Xbtm2vTUlRqZRiq80rFHLTUtXbd5xJSlL09jr0eoXJ5Jo/ryDUxu7weTyBi1ZOQJCBvnwBFwFITVXbbJ7srITCghjeTv8TWP9XEO5RCAzQCKDs3/CSwR2GL/kfxhfhLmzhJUPV/j+M8BUiuxEJbUdYbN1W4w8E583Jl8nOcahEtInG/wTW//A//A//Z/C/9+3/8D/8D/9n8P8Bgo8TkfoQ1GwAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<PIL.Image.Image image mode=RGB size=400x200 at 0x7FEE2AFF9B38>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 47
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "cfE_benSuJgI",
        "colab_type": "text"
      },
      "source": [
        "Just by visualising the data from Alphabet vs Microsoft, we can see that Microsoft seems to talk more about their services and products, while Alphabet seems to be more concerned about macroeconomic factors."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "UGnHNKE60oRq",
        "colab_type": "text"
      },
      "source": [
        "## 3. Topic Modelling"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mShB4RJr2XWz",
        "colab_type": "text"
      },
      "source": [
        "We will be using LDA for the Topic Model. LDA stands for Latent Dirichlet Allocation. If we break down that term a little, we notice the word \"latent\", which means unobserved; Dirichlet is named after the German mathematician and \"allocation\" because of the nature of the problem of allocating latent topics to chunks of text.\n",
        "\n",
        "LDA is actually an unsupervised technique, meaning we do not need labelled data, which is a big benefit when you have many 10-k filings as we do. The mathematics behind it is very deep, because it uses Bayesian methods, and so we won't cover it here. If you would like to get a better idea of the mathematics, then I recommend you start here: https://en.wikipedia.org/wiki/Latent_Dirichlet_allocation or view the original paper (with Andrew Ng) here: https://web.archive.org/web/20120501152722/http://jmlr.csail.mit.edu/papers/v3/blei03a.html\n",
        "\n",
        "An intuitive way to understand how topic modelling works is that the model imagines each document contains a fixed number of topics. For each topic, there are certain words that are associated with that topic. Then a document can be modeled as some topics that are generating some words associated with the topics. For example, a document discussing Covid-19 and unemployment impact can be modelled as containing the topics: “Covid-19”, “economics”, “health” and “unemployment”. Each one of these topics has a specific vocabulary associated with it, which appears in the document. The model knows the document isn’t discussing the topic “trade” because words associated with “trade” do not appear in the document.\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "00sqwPxC0rUq",
        "colab_type": "text"
      },
      "source": [
        "### 3.1. Text Preprocessing"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Uufsjyxg167s",
        "colab_type": "text"
      },
      "source": [
        "NTLK, Gensim and SpaCy are the primary packages we will be using to clean the text, preprocess it and then build the model. There libraries are very common in the NLP space nowadays and should become familar to you overtime.\n",
        "\n",
        "We also use a special plotting tool called pyLDAvis. As the name suggests this enables you to visualise the Topic Modelling output by using a number of techniques, such as dimensionality reduction.\n",
        "\n",
        "To prepare the text for the model we need to do a few things. The first is to remove stopwords, which are words that are not going to add much meaning to the text and hence just add noise into the model. The NTLK package has a lot of these words listed which we can make use of right away. Have a look at some of them below, stored in the list variable <code>stop_words</code>.\n",
        "\n",
        "Note: If you find any other words that could be considered meaningless which remain after filtering out the stopwords, then you can just append them to the list."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "l5uBpBpc0v6C",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "%%capture\n",
        "import nltk; nltk.download('stopwords')\n",
        "!python3 -m spacy download en"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "eXRQ2eTX06f1",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "  %%capture\n",
        "from pprint import pprint\n",
        "\n",
        "import warnings\n",
        "warnings.filterwarnings(\"ignore\",category=DeprecationWarning)\n",
        "\n",
        "# Gensim is a great package that supports topic modelling and other NLP tools\n",
        "import gensim\n",
        "import gensim.corpora as corpora\n",
        "from gensim.models import CoherenceModel\n",
        "from gensim.utils import simple_preprocess\n",
        "\n",
        "# spacy for lemmatization\n",
        "import spacy\n",
        "\n",
        "# Plotting tools\n",
        "!pip install pyLDAvis\n",
        "import pyLDAvis\n",
        "import pyLDAvis.gensim"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ruscAQZE6cBV",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from nltk.corpus import stopwords\n",
        "stop_words = stopwords.words('english')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zYJQ4P5o6lHc",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def sent_to_words(sentences):\n",
        "    for sentence in sentences:\n",
        "        yield(gensim.utils.simple_preprocess(str(sentence), deacc=True))\n",
        "        \n",
        "def remove_stopwords(texts):\n",
        "  \"\"\"\n",
        "  This function simply removes all of the stopwords we have specified in the list stop_words.\n",
        "  \"\"\"\n",
        "  return [[word for word in simple_preprocess(str(doc)) if word not in stop_words] for doc in texts]\n",
        "\n",
        "def bigrams(words, bi_min=3):\n",
        "  \"\"\"\n",
        "  https://radimrehurek.com/gensim/models/phrases.html\n",
        "  \"\"\"\n",
        "  bigram = gensim.models.Phrases(words, min_count = bi_min)\n",
        "  bigram_mod = gensim.models.phrases.Phraser(bigram)\n",
        "  return bigram_mod\n",
        "\n",
        "def get_corpus(df):  \n",
        "    words = list(sent_to_words(df.Report))\n",
        "    words = remove_stopwords(words)\n",
        "    bigram = bigrams(words)\n",
        "    bigram = [bigram[report] for report in words]\n",
        "    id2word = gensim.corpora.Dictionary(bigram)\n",
        "    id2word.filter_extremes(no_below=3, no_above=0.35)\n",
        "    id2word.compactify()\n",
        "    corpus = [id2word.doc2bow(text) for text in bigram]\n",
        "    return corpus, id2word, bigram"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zQ6vPtXr6ooT",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "train_corpus, train_id2word, bigram_train = get_corpus(dataframe)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5Gbhv3o99v4j",
        "colab_type": "text"
      },
      "source": [
        "We need to choose the number of topics we are looking for. This is a hyperparameter and cannot be directly optimised."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xUa5ELo294FP",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "NUM_TOPICS = 10"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0TJb1Krn9_YW",
        "colab_type": "text"
      },
      "source": [
        "### 3.2. Model"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "769nusa4-Bdm",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "with warnings.catch_warnings():\n",
        "    warnings.simplefilter('ignore')\n",
        "    lda_train = gensim.models.ldamulticore.LdaMulticore(\n",
        "                           corpus=train_corpus,\n",
        "                           num_topics=NUM_TOPICS,\n",
        "                           id2word=train_id2word,\n",
        "                           chunksize=50,\n",
        "                           workers=4,\n",
        "                           passes=100,\n",
        "                           eval_every = 1,\n",
        "                           per_word_topics=True)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jHp7Kb-COO1J",
        "colab_type": "code",
        "outputId": "c8729ea3-5ae6-4314-b8cb-76e9b8285b67",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "coherencemodel = CoherenceModel(lda_train, texts=bigram_train, dictionary=train_id2word)\n",
        "print (coherencemodel.get_coherence())"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "0.527596795083962\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-fwx3bTo-GY5",
        "colab_type": "code",
        "outputId": "6e263ae4-dd4f-4194-9d52-8d5978b6045f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 861
        }
      },
      "source": [
        "# Visualize the topics using pyLDAvis\n",
        "pyLDAvis.enable_notebook()\n",
        "vis = pyLDAvis.gensim.prepare(lda_train, train_corpus, train_id2word)\n",
        "vis"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "\n",
              "<link rel=\"stylesheet\" type=\"text/css\" href=\"https://cdn.rawgit.com/bmabey/pyLDAvis/files/ldavis.v1.0.0.css\">\n",
              "\n",
              "\n",
              "<div id=\"ldavis_el1241406609040660485268258553\"></div>\n",
              "<script type=\"text/javascript\">\n",
              "\n",
              "var ldavis_el1241406609040660485268258553_data = {\"mdsDat\": {\"x\": [0.10364241279160429, 0.12961313016282205, 0.18593659223096295, -0.1365603136370043, -0.03677817819305517, -0.06194988516294965, 0.11470779146345297, -0.2646013086244952, 0.15627331195917865, -0.19028355299051605], \"y\": [0.13132632654420537, 0.10782562676509225, 0.1521790495557017, 0.042467800173552984, -0.1331029688403822, 0.1253066074430016, -0.20567305848689302, -0.00019734223199588795, -0.18918340709645237, -0.03094863382583055], \"topics\": [1, 2, 3, 4, 5, 6, 7, 8, 9, 10], \"cluster\": [1, 1, 1, 1, 1, 1, 1, 1, 1, 1], \"Freq\": [19.1246337890625, 17.262773513793945, 11.212565422058105, 9.500261306762695, 9.168935775756836, 8.482839584350586, 8.05540943145752, 7.146316051483154, 5.796934127807617, 4.24933385848999]}, \"tinfo\": {\"Term\": [\"mr\", \"vice_president\", \"sec\", \"microsoft\", \"president\", \"clients\", \"us_gaap\", \"nvidia\", \"forward_looking\", \"item\", \"shall\", 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\"contained_registration\", \"content\", \"content\", \"content\", \"content\", \"content\", \"content\", \"contents_part\", \"continue\", \"continue\", \"continue\", \"continue\", \"continue\", \"continue\", \"continue_invest\", \"continue_lower\", \"controls\", \"controls_procedures\", \"copyrighted\", \"corel\", \"corporation\", \"corporation\", \"corporation\", \"corporation\", \"corporation\", \"corporation\", \"corporation\", \"could\", \"could\", \"could\", \"could\", \"could\", \"cover\", \"creatorswe_serve\", \"crm\", \"customer_centric\", \"customer_sets\", \"customers_lowest\", \"dated_march\", \"dated_march\", \"dated_march\", \"day\", \"day\", \"day\", \"december\", \"december\", \"december\", \"december\", \"december\", \"deliver\", \"deliver\", \"deliver\", \"deliver\", \"deliver\", \"deliver\", \"deliver_improved\", \"delivers\", \"delivers\", \"delivery\", \"delivery\", \"delivery\", \"delivery\", \"derived\", \"derived\", \"derived\", \"derived\", \"designers\", \"determination\", \"determination\", \"developers_enterprises\", \"development_sales\", \"device\", \"device\", \"device\", \"device\", \"devices\", \"devices\", \"devices\", \"devices\", \"devices\", \"devices\", \"direct_publishing\", \"director\", \"director\", \"disclosures\", \"discussed\", \"discussed\", \"discussed\", \"distribute\", \"dividend\", \"dividend\", \"dividends\", \"dividends\", \"dividends\", \"division\", \"division\", \"division\", \"division\", \"document\", \"drawn\", \"earn_fixed\", \"earnings\", \"earnings\", \"earnings\", \"earnings\", \"edition\", \"election_directors\", \"embedded\", \"employees\", \"employees\", \"employees\", \"employees\", \"employees\", \"enable_sellers\", \"enables\", \"enables\", \"ended_december\", \"ended_december\", \"ended_december\", \"ended_january\", \"ending\", \"enhanced\", \"enhanced\", \"enterprise\", \"enterprise\", \"enterprise\", \"enterprise\", \"enterpriseswe_serve\", \"entertainment\", \"entertainment\", \"entertainment\", \"entertainment\", \"entertainment\", \"entitled\", \"entitled\", \"entitled\", \"entitled\", \"environments\", \"environments\", \"episodes\", \"establish\", \"estimates\", \"estimates\", \"estimates\", \"event\", \"event\", \"executive\", \"executive\", \"executive_committee\", \"executive_committee\", \"exhibit\", \"exhibit\", \"exhibit\", \"exhibit_form\", \"exhibit_form\", \"expectation\", \"expectations\", \"expectations\", \"expects_intends\", \"expedia\", \"expense\", \"expense\", \"expense\", \"expense\", \"expense\", \"expenses\", \"expenses\", \"expenses\", \"expenses\", \"expertise\", \"expertise\", \"expertise\", \"external_clients\", \"facility\", \"factors_contributing\", \"familiar\", \"fast_reliable\", \"february\", \"february\", \"february\", \"february\", \"february\", \"fees_services\", \"fees_services\", \"fermi\", \"filed\", \"filed\", \"filed\", \"filed\", \"filed\", \"financing\", \"financing\", \"financing\", \"finland\", \"fire\", \"fiscal\", \"fiscal\", \"fiscal\", \"fixed_charges\", \"flexibility\", \"flexibility\", \"flexibility\", \"fluctuations\", \"fluctuations\", \"fluctuations\", \"focus\", \"focus\", \"focus\", \"focus\", \"focus\", \"focus\", \"focus_selection\", \"following\", \"following\", \"following\", \"following\", \"following\", \"following\", \"following\", \"following\", \"foreign_currency\", \"foreseeable\", \"foreseeable\", \"form_contains\", \"form_year\", \"form_year\", \"forward_looking\", \"forward_looking\", \"forward_looking\", \"frames_achievements\", \"frames_achievements\", \"fulfill_customer\", \"fulfill_orders\", \"fulfillment_timely\", \"full\", \"full\", \"full\", \"fund\", \"fund\", \"gamers\", \"gaming_entertainment\", \"gates\", \"gbs\", \"geforce\", \"geforce\", \"geforce\", \"geforce\", \"general_counsel\", \"given\", \"given\", \"global\", \"global\", \"global\", \"global\", \"global\", \"global\", \"global_financing\", \"global_select\", \"globally_integrated\", \"globally_supplemented\", \"go\", \"google\", \"google\", \"gpu\", \"gpu\", \"gpu\", \"gpu\", \"gpus\", \"graphics\", \"graphics\", \"graphics\", \"graphics\", \"graphics\", \"graphics_processors\", \"graphics_processors\", \"graphics_processors\", \"growth\", \"growth\", \"growth\", \"growth\", \"growth\", \"guidelines\", \"harbor_created\", \"hardware\", \"hardware\", \"hardware\", \"hardware\", \"hardware\", \"hardware\", \"harm\", \"held\", \"held\", \"held\", \"held\", \"held\", \"hereby_incorporated\", \"hereby_incorporated\", \"hereby_incorporated\", \"high_performance\", \"high_performance\", \"holders\", \"holders\", \"holders\", \"home_entertainment\", \"i_item\", \"ibm_definitive\", \"ibm_definitive\", \"ibm_definitive\", \"ibm_hewlett\", \"identify_forward\", \"identify_forward\", \"identify_forward\", \"ii_financial\", \"immersive\", \"impact\", \"impact\", \"impact\", \"inc\", \"inc\", \"inc\", \"inc\", \"inc\", \"inc\", \"inc\", \"include_limited\", \"includes_unlimited\", \"income\", \"income\", \"income\", \"income\", \"income_loss\", \"income_loss\", \"income_taxes\", \"incorporation\", \"increase\", \"increase\", \"increase\", \"increase\", \"increase\", \"increase\", \"indicates_otherwise\", \"industry_specific\", \"information_post\", \"infotainment\", \"infrastructure\", \"infrastructure\", \"infrastructure\", \"infrastructure\", \"infrastructure\", \"innovation\", \"innovation\", \"innovation\", \"inspectors_election\", \"integrated\", \"integrated\", \"integrated\", \"integrated\", \"integrated\", \"integrated\", \"integrated\", \"integration\", \"integration\", \"integration\", \"integration\", \"integrity\", \"interest\", \"interest\", \"interest\", \"interest\", \"interest\", \"interest\", \"internal_financing\", \"internal_processes\", \"international_business\", \"international_business\", \"international_business\", \"international_business\", \"international_business\", \"internet\", \"internet\", \"internet\", \"internet\", \"internet\", \"internet\", \"introduced\", \"inventory\", \"inventory\", \"inventory\", \"inventory\", \"investor_relations\", \"investor_relations\", \"investors\", \"investors\", \"issue\", \"issued\", \"issued\", \"issuer\", \"item\", \"item\", \"item\", \"item\", \"item\", \"item\", \"item\", \"item_certain\", \"item_certain\", \"item_part\", \"item_risk\", \"item_security\", \"item_security\", \"january\", \"january\", \"january\", \"january\", \"january_january\", \"january_january\", \"jassy\", \"joined\", \"july\", \"july\", \"july\", \"july\", \"july\", \"june\", \"june\", \"june\", \"june\", \"june\", \"kepler\", \"kessel\", \"key\", \"key\", \"key\", \"key\", \"key\", \"key\", \"kindle\", \"large_installed\", \"laws\", \"laws\", \"laws\", \"leading_edge\", \"legal_holiday\", \"lets_independent\", \"liabilities\", \"light\", \"limit\", \"limited_number\", \"listed_nasdaq\", \"litigation\", \"litigation\", \"loan\", \"loan\", \"long_term\", \"long_term\", \"long_term\", \"long_term\", \"long_term\", \"looking_statements\", \"looking_statements\", \"looking_statements\", \"looking_statementsthis\", \"low_everyday\", \"ltd\", \"ltd\", \"ltd\", \"mailed_address\", \"management_discussion\", \"management_discussion\", \"management_discussion\", \"management_discussion\", \"management_related\", \"management_related\", \"many\", \"many\", \"many\", \"many\", \"many\", \"many\", \"many_companies\", \"mappoint\", \"margin\", \"marketing\", \"marketing\", \"marketing\", \"marketing\", \"marketing\", \"material\", \"material\", \"material\", \"materials\", \"maximummember\", \"meaning_section\", \"media\", \"media\", \"media\", \"media_center\", \"meeting\", \"meeting\", \"meeting\", \"meeting_shall\", \"meeting_stockholders\", \"meetings_stockholders\", \"merchandise\", \"mexico\", \"mexico\", \"microsoft\", \"microsoft\", \"microsoft\", \"microsoft\", \"microsoft_certified\", \"microsoft_dynamics\", \"microsoft_office\", \"microsoft_office\", \"millions_unique\", \"mobile\", \"mobile\", \"mobile\", \"mobile\", \"mobile\", \"model\", \"model\", \"month\", \"month\", \"movies_tv\", \"mr\", \"mr_bezos\", \"mr_piacentini\", \"mr_valentine\", \"ms_reynolds\", \"msn\", \"msn\", \"name_age\", \"named\", \"named\", \"narrowband_internet\", \"nasdaq_national\", \"nasdaq_national\", \"netherlands\", \"network\", \"network\", \"network\", \"network\", \"network\", \"network\", \"normally_associated\", \"notes_thereto\", \"notes_thereto\", \"notice\", \"notice\", \"notice\", \"notice_meeting\", \"notice_meetings\", \"notice_thereof\", \"novell\", \"number_ways\", \"nvda\", \"nvidia\", \"nvidia\", \"nvidia\", \"nvidia_corporation\", \"nvidia_corporation\", \"nvidia_corporation\", \"nvidia_corporation\", \"nvidia_quadro\", \"obligation\", \"october\", \"october\", \"october\", \"october\", \"october\", \"oems\", \"oems\", \"oems\", \"offer\", \"offer\", \"offer\", \"offer_amazon\", \"offer_programs\", \"offering\", \"offering\", \"offering\", \"offerings\", \"offerings\", \"offerings\", \"offerings\", \"offerings\", \"offers_improve\", \"officer\", \"officer\", \"officer_since\", \"oion_make\", \"one\", \"one\", \"one\", \"one\", \"one\", \"one\", \"one\", \"online\", \"online\", \"online\", \"online\", \"online\", \"online_advertising\", \"online_advertising\", \"open_standards\", \"operate_co\", \"operate_customer\", \"operating_efficiencies\", \"operating_system\", \"operating_system\", \"operating_system\", \"operating_system\", \"operating_systems\", \"operating_systems\", \"operating_systems\", \"operating_systems\", \"oracle\", \"organizations\", \"organizations\", \"organizations\", \"organizations\", \"others\", \"others\", \"otherwise_provided\", \"outcomes\", \"outlook\", \"outsourcing\", \"overview\", \"ownership_certain\", \"ownership_certain\", \"packard_sun\", \"page\", \"page\", \"page\", \"page\", \"page\", \"page\", \"page\", \"page_table\", \"paid\", \"paid\", \"paid\", \"paid\", \"paid\", \"parallel\", \"part_iii\", \"particularly\", \"particularly\", \"payment\", \"payment\", \"payment\", \"payment\", \"payment\", \"pc\", \"pc\", \"pc\", \"pc\", \"pc\", \"pc\", \"pc_based\", \"pcs\", \"pcs\", \"pcs\", \"pcs\", \"people\", \"people\", \"people\", \"people\", \"people\", \"per_share\", \"per_share\", \"per_share\", \"per_unit\", \"performance\", \"performance\", \"performance\", \"performance\", \"performance\", \"performance\", \"performance\", \"performance_plan\", \"performance_plan\", \"performance_time\", \"performance_time\", \"periods\", \"periods\", \"periods\", \"periods\", \"periods\", \"personal\", \"personal\", \"personal\", \"personal\", \"personalized\", \"piacentini\", \"place_meetings\", \"plan_exhibit\", \"plan_exhibit\", \"plans\", \"plans\", \"plans\", \"plans\", \"plans\", \"platforms\", \"platforms\", \"platforms\", \"platforms\", \"pocket_pc\", \"portfolio\", \"portfolio\", \"positions\", \"positions\", \"positions\", \"power\", \"power\", \"power\", \"power\", \"preferred_stock\", \"preferred_stock\", \"preinstall\", \"premium\", \"president\", \"president\", \"president\", \"president_chief\", \"president_chief\", \"president_worldwide\", \"presidents\", \"press_releases\", \"press_releases\", \"press_releases\", \"price\", \"price\", \"price\", \"price\", \"price\", \"price\", \"price_convenience\", \"prices_possible\", \"pricing_shipping\", \"primarily\", \"primarily\", \"primarily\", \"primarily\", \"prime\", \"prior_joining\", \"procedures\", \"procedures\", \"process_services\", \"processing\", \"processing\", \"processing\", \"processor\", \"processor\", \"processor\", \"processor\", \"processors\", \"processors\", \"processors\", \"production\", \"production\", \"productivity\", \"productivity\", \"productivity\", \"products_compete\", \"products_compete\", \"professional\", \"professional\", \"professional\", \"professional\", \"professional\", \"program\", \"program\", \"program\", \"program\", \"program\", \"properties_sellerswe\", \"provide_easy\", \"provides\", \"provides\", \"provides\", \"provides\", \"provides\", \"providing\", \"providing\", \"providing\", \"providing\", \"provisions\", \"provisions\", \"public_conference\", \"publications\", \"publicly\", \"publishers\", \"quadro\", \"quadro\", \"quarter\", \"quarter\", \"quarter\", \"quarters\", \"ratio_earnings\", \"rational\", \"received\", \"recognition\", \"recognition\", \"record\", \"record\", \"reference\", \"reference\", \"reference\", \"reference\", \"reference\", \"reference\", \"reference\", \"reference\", \"reference_ibm\", \"reference_ibm\", \"registrant\", \"registrant\", \"registrant\", \"regulation\", \"regulation\", \"related\", \"related\", \"related\", \"related\", \"related\", \"related\", \"relationships_related\", \"relationships_related\", \"reliability\", \"rental_expense\", \"report\", \"report\", \"report\", \"report\", \"reports_form\", \"representative\", \"require\", \"require\", \"researchers\", \"resellers_oems\", \"respect\", \"respect\", \"respect\", \"respectively\", \"respectively\", \"result\", \"result\", \"result\", \"result\", \"result\", \"result\", \"results_differ\", \"retail\", \"retail\", \"retail\", \"retail\", \"retail\", \"retail\", \"retailers\", \"retailers\", \"retailers\", \"retailers\", \"retirement_plan\", \"revenues\", \"revenues\", \"revenues\", \"reynolds\", \"rich\", \"rights\", \"rights\", \"rights\", \"search\", \"search\", \"search\", \"sec\", \"sec\", \"sec_filings\", \"secretary\", \"secretary\", \"section\", \"section\", \"section\", \"section_securities\", \"section_securities\", \"sections_forward\", \"securities_exchange\", \"securities_exchange\", \"securities_exchange\", \"securities_exchange\", \"seek_earth\", \"seember\", \"seember\", \"segments\", \"segments\", \"segments\", \"segments\", \"segments_north\", \"seller_record\", \"senior_vice\", \"senior_vice\", \"senior_vice\", \"series\", \"series\", \"series\", \"series\", \"serve_primary\", \"served\", \"served\", \"served_senior\", \"served_vice\", \"server\", \"server\", \"server\", \"server\", \"server_tools\", \"service_centers\", \"services_delivers\", \"several\", \"several\", \"several\", \"several\", \"sgi\", \"sgi\", \"shall\", \"shall\", \"shall_held\", \"shall_mean\", \"shares\", \"shares\", \"shares\", \"shares\", \"shares\", \"significant\", \"significant\", \"significant\", \"significant\", \"significant\", \"similar_expressions\", \"similar_terms\", \"since\", \"since\", \"since\", \"since_april\", \"since_joining\", \"sites\", \"smartphones\", \"social\", \"social_media\", \"social_media\", \"software_developers\", \"solution_providers\", \"sourced_outsourced\", \"special_meeting\", \"special_meetings\", \"special_meetings\", \"square_foot\", \"srt\", \"standardized\", \"statement_form\", \"statement_form\", \"statements_supplementary\", \"stock\", \"stock\", \"stock\", \"stock\", \"stock_dividends\", \"stock_dividends\", \"stockholder\", \"stockholder\", \"stockholder\", \"stockholder_matters\", \"stockholder_matters\", \"stockholders\", \"stockholders\", \"stockholders\", \"stockholders\", \"stockholders\", \"stockholders_corporation\", \"storage\", \"storage\", \"storage\", \"storage\", \"storage\", \"strategy\", \"strategy\", \"strategy\", \"strategy\", \"strategy\", \"strategy\", \"streaming_thousands\", \"strive_offer\", \"strong\", \"strong\", \"subject_risks\", \"subject_safe\", \"suffer\", \"sufficiency\", \"suite\", \"suite\", \"sun_microsystems\", \"sun_microsystems\", \"supercomputers\", \"supplemental_information\", \"switzerland\", \"system\", \"system\", \"system\", \"system\", \"system\", \"system\", \"szkutak\", \"table_contents_part\", \"table_contentscontent\", \"talent\", \"tax\", \"tax\", \"tax\", \"taxes\", \"taxes\", \"technical\", \"technical\", \"technical\", \"tegra\", \"tegra\", \"telephone\", \"terms_conditions\", \"terms_conditions\", \"terms_conditions\", \"tesla\", \"tesla\", \"tesla\", \"tesla_tegra\", \"thereat\", \"thereof\", \"thereof\", \"tnt\", \"tnt\", \"tools\", \"tools\", \"tools\", \"tools\", \"trade\", \"trademarks_service\", \"transformation\", \"transformation\", \"trend\", \"trends\", \"trends\", \"trends\", \"unable\", \"unable\", \"uncertainties_could\", \"uncertainties_factors\", \"uncertainties_factors\", \"united_kingdom\", \"university\", \"university\", \"unless_context\", \"upon\", \"upon\", \"upon\", \"upon\", \"upon\", \"us_gaap\", \"use_functionality\", \"user\", \"user\", \"user\", \"users\", \"users\", \"users\", \"users\", \"users\", \"versions\", \"vice\", \"vice\", \"vice_president\", \"vice_president\", \"virtual\", \"visual\", \"visual\", \"visual_computing\", \"visual_computing\", \"voting\", \"voting\", \"watson\", \"ways\", \"web\", \"web\", \"web\", \"web\", \"website\", \"website\", \"website\", \"websites\", \"websites\", \"websites\", \"whether\", \"whether\", \"whether\", \"whether\", \"whether\", \"wilke\", \"wilson\", \"windows\", \"windows\", \"windows\", \"windows\", \"windows_division\", \"windows_operating\", \"windows_operating\", \"windows_operating\", \"within\", \"within\", \"within\", \"within\", \"within\", \"within\", \"words_anticipates\", \"workstation\", \"workstations\", \"world\", \"world\", \"world\", \"world\", \"world\", \"world\", \"world_trade\", \"xbox\", \"xbox\", \"xbox\", \"year\", \"year\", \"year\", \"year\", \"year\", \"year\", \"year_ended\", \"year_ended\"]}, \"R\": 30, \"lambda.step\": 0.01, \"plot.opts\": {\"xlab\": \"PC1\", \"ylab\": \"PC2\"}, \"topic.order\": [2, 9, 10, 5, 8, 3, 4, 6, 1, 7]};\n",
              "\n",
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            "text/plain": [
              "PreparedData(topic_coordinates=              x         y  topics  cluster       Freq\n",
              "topic                                                \n",
              "1      0.103642  0.131326       1        1  19.124634\n",
              "8      0.129613  0.107826       2        1  17.262774\n",
              "9      0.185937  0.152179       3        1  11.212565\n",
              "4     -0.136560  0.042468       4        1   9.500261\n",
              "7     -0.036778 -0.133103       5        1   9.168936\n",
              "2     -0.061950  0.125307       6        1   8.482840\n",
              "3      0.114708 -0.205673       7        1   8.055409\n",
              "5     -0.264601 -0.000197       8        1   7.146316\n",
              "0      0.156273 -0.189183       9        1   5.796934\n",
              "6     -0.190284 -0.030949      10        1   4.249334, topic_info=                Term        Freq       Total Category  logprob  loglift\n",
              "955               mr  101.000000  101.000000  Default  30.0000  30.0000\n",
              "1027  vice_president  109.000000  109.000000  Default  29.0000  29.0000\n",
              "182              sec   94.000000   94.000000  Default  28.0000  28.0000\n",
              "555        microsoft  252.000000  252.000000  Default  27.0000  27.0000\n",
              "979        president  113.000000  113.000000  Default  26.0000  26.0000\n",
              "...              ...         ...         ...      ...      ...      ...\n",
              "2271            full    5.916402   15.215563  Topic10  -5.4847   2.2138\n",
              "2269        entitled    5.916403   16.676447  Topic10  -5.4847   2.1221\n",
              "582              one    6.942564   53.356182  Topic10  -5.3248   1.1191\n",
              "82         following    6.694802   50.540009  Topic10  -5.3611   1.1370\n",
              "1505        graphics    6.522927   61.322559  Topic10  -5.3871   0.9176\n",
              "\n",
              "[677 rows x 6 columns], token_table=      Topic      Freq          Term\n",
              "term                               \n",
              "2156      4  0.953224      acceable\n",
              "1265      3  0.878192   accessories\n",
              "2688      4  0.908284  accompanying\n",
              "356       1  0.023555        across\n",
              "356       2  0.683085        across\n",
              "...     ...       ...           ...\n",
              "1035      6  0.055909          year\n",
              "1035      8  0.223635          year\n",
              "1035     10  0.027954          year\n",
              "2391      4  0.084318    year_ended\n",
              "2391     10  0.843184    year_ended\n",
              "\n",
              "[1363 rows x 3 columns], R=30, lambda_step=0.01, plot_opts={'xlab': 'PC1', 'ylab': 'PC2'}, topic_order=[2, 9, 10, 5, 8, 3, 4, 6, 1, 7])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "SKdmy3X5KNoc",
        "colab_type": "text"
      },
      "source": [
        "## 4. Improving The Model"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6CbHwGhZKQE9",
        "colab_type": "text"
      },
      "source": [
        "As the results show, the model is decent at finding topics, but we can do better. We will look at two ways to improve the model: finding the optimal number of topics, and using Mallet. "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-skI4JvgKs76",
        "colab_type": "text"
      },
      "source": [
        "### 4.1. Mallet"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Oqgpt-hMK005",
        "colab_type": "text"
      },
      "source": [
        "Mallet (Machine Learning for Language Toolkit), is a topic modelling package written in Java. Gensim has a wrapper to interact with the package, which we will take advantage of.\n",
        "\n",
        "The difference between the LDA model we have been using and Mallet is that the original LDA using variational Bayes sampling, while Mallet uses collapsed Gibbs sampling. The latter is more precise, but is slower. In most cases Mallet performs much better than original LDA, so we will test it on our data. Also, as we will see, Mallet will dramatically increase our coherence score, demonstrating that it is better suited for this task as compared with the original LDA model.\n",
        "\n",
        "We need to go through some additional steps to properly install Mallet and the wrapper from Gensim. Here is an excellent guide to using Mallet with Google Colab: https://github.com/polsci/colab-gensim-mallet"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xF-QNr-5L8tU",
        "colab_type": "code",
        "outputId": "535e71f8-3a7b-4692-9c27-33a9d0d0788c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 68
        }
      },
      "source": [
        "def install_java():\n",
        "  !apt-get install -y openjdk-8-jdk-headless -qq > /dev/null      #install openjdk\n",
        "  os.environ[\"JAVA_HOME\"] = \"/usr/lib/jvm/java-8-openjdk-amd64\"     #set environment variable\n",
        "  !java -version       #check java version\n",
        "install_java()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "openjdk version \"11.0.7\" 2020-04-14\n",
            "OpenJDK Runtime Environment (build 11.0.7+10-post-Ubuntu-2ubuntu218.04)\n",
            "OpenJDK 64-Bit Server VM (build 11.0.7+10-post-Ubuntu-2ubuntu218.04, mixed mode, sharing)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Yh0aAHIeMRzc",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "%%capture\n",
        "!wget http://mallet.cs.umass.edu/dist/mallet-2.0.8.zip\n",
        "!unzip mallet-2.0.8.zip"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xH3oyk5nMapA",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "os.environ['MALLET_HOME'] = '/content/mallet-2.0.8'\n",
        "mallet_path = '/content/mallet-2.0.8/bin/mallet'"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YLRZuXsKMheu",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from gensim.models.wrappers import LdaMallet"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "im2Bec8NMkq8",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "with warnings.catch_warnings():\n",
        "    warnings.simplefilter('ignore')\n",
        "    lda_mallet = LdaMallet(\n",
        "        mallet_path,\n",
        "        corpus=train_corpus,\n",
        "        num_topics=NUM_TOPICS,\n",
        "        id2word=train_id2word,\n",
        "    )"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "elIVXeZ6NZ_H",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "gensimmodelMallet = gensim.models.wrappers.ldamallet.malletmodel2ldamodel(lda_mallet)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "a59dELOEPNIM",
        "colab_type": "code",
        "outputId": "9b17c68a-db09-4998-c6c4-b829d2650aaf",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "coherencemodel = CoherenceModel(gensimmodelMallet, texts=bigram_train, dictionary=train_id2word)\n",
        "print(coherencemodel.get_coherence())"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "0.7143588181690383\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "brAfrv94PQgl",
        "colab_type": "text"
      },
      "source": [
        "As you can see we get a huge boost in coherence!"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "oACQXzFYNJck",
        "colab_type": "code",
        "outputId": "22bd7a10-98e1-490e-be54-d694e4923619",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 861
        }
      },
      "source": [
        "# Visualize the topics using pyLDAvis\n",
        "pyLDAvis.enable_notebook()\n",
        "vis = pyLDAvis.gensim.prepare(gensimmodelMallet, train_corpus, train_id2word)\n",
        "vis"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "\n",
              "<link rel=\"stylesheet\" type=\"text/css\" href=\"https://cdn.rawgit.com/bmabey/pyLDAvis/files/ldavis.v1.0.0.css\">\n",
              "\n",
              "\n",
              "<div id=\"ldavis_el1241406609040656889169475742\"></div>\n",
              "<script type=\"text/javascript\">\n",
              "\n",
              "var ldavis_el1241406609040656889169475742_data = {\"mdsDat\": {\"x\": [-0.00036009976255528574, -0.0004684744168904773, 0.0007252545170450865, 0.000551977949961471, 0.0004291377441777555, -0.0008511964703090856, -9.088528039444167e-05, -0.0004680933994748654, 0.00041848488410629544, 0.00011389423433354851], \"y\": [0.00022432496527072742, 0.0002958391636726999, 0.00028717931195115017, -0.00024674528623412157, 0.0005515155963843187, 9.813871587095208e-05, -0.0012838609809410334, 0.00011494202608466412, -0.00021018810892788805, 0.00016885459686853072], \"topics\": [1, 2, 3, 4, 5, 6, 7, 8, 9, 10], \"cluster\": [1, 1, 1, 1, 1, 1, 1, 1, 1, 1], \"Freq\": [13.321327482630013, 12.93358178758577, 12.756409198350433, 11.026036927358462, 9.672990203158143, 9.245948350983536, 9.202207283791369, 8.422534177052873, 7.993819296874816, 5.425145292214594]}, \"tinfo\": {\"Term\": [\"owners_management\", \"enables\", \"tasks\", \"similar_expressions\", \"forms\", \"features\", 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\"supply_chain\", \"supply_chain\", \"supply_chain\", \"supply_chain\", \"supporting\", \"supporting\", \"supporting\", \"supporting\", \"supporting\", \"supporting\", \"supporting\", \"supporting\", \"supporting\", \"supporting\", \"switzerland\", \"switzerland\", \"switzerland\", \"switzerland\", \"switzerland\", \"switzerland\", \"switzerland\", \"switzerland\", \"switzerland\", \"switzerland\", \"system_builders\", \"system_builders\", \"system_builders\", \"system_builders\", \"system_builders\", \"system_builders\", \"system_builders\", \"system_builders\", \"system_builders\", \"system_builders\", \"tablets\", \"tablets\", \"tablets\", \"tablets\", \"tablets\", \"tablets\", \"tablets\", \"tablets\", \"tablets\", \"tablets\", \"tailored\", \"tailored\", \"tailored\", \"tailored\", \"tailored\", \"tailored\", \"tailored\", \"tailored\", \"tailored\", \"tailored\", \"take_advantage\", \"take_advantage\", \"take_advantage\", \"take_advantage\", \"take_advantage\", \"take_advantage\", \"take_advantage\", \"take_advantage\", \"take_advantage\", \"take_advantage\", \"tasks\", \"tasks\", \"tasks\", \"tasks\", \"tasks\", \"tasks\", \"tasks\", \"tasks\", \"tasks\", \"tasks\", \"team\", \"team\", \"team\", \"team\", \"team\", \"team\", \"team\", \"team\", \"team\", \"team\", \"technical\", \"technical\", \"technical\", \"technical\", \"technical\", \"technical\", \"technical\", \"technical\", \"technical\", \"technical\", \"temporary\", \"temporary\", \"temporary\", \"temporary\", \"temporary\", \"temporary\", \"temporary\", \"temporary\", \"temporary\", \"temporary\", \"ten\", \"ten\", \"ten\", \"ten\", \"ten\", \"ten\", \"ten\", \"ten\", \"ten\", \"ten\", \"third_parties\", \"third_parties\", \"third_parties\", \"third_parties\", \"third_parties\", \"third_parties\", \"third_parties\", \"third_parties\", \"third_parties\", \"third_parties\", \"toll\", \"toll\", \"toll\", \"toll\", \"toll\", \"toll\", \"toll\", \"toll\", \"toll\", \"toll\", \"touche\", \"touche\", \"touche\", \"touche\", \"touche\", \"touche\", \"touche\", \"touche\", \"touche\", \"touche\", \"trademarks_registered\", \"trademarks_registered\", \"trademarks_registered\", \"trademarks_registered\", \"trademarks_registered\", \"trademarks_registered\", \"trademarks_registered\", \"trademarks_registered\", \"trademarks_registered\", \"trademarks_registered\", \"training\", \"training\", \"training\", \"training\", \"training\", \"training\", \"training\", \"training\", \"training\", \"training\", \"transaction\", \"transaction\", \"transaction\", \"transaction\", \"transaction\", \"transaction\", \"transaction\", \"transaction\", \"transaction\", \"transaction\", \"transaction_processing\", \"transaction_processing\", \"transaction_processing\", \"transaction_processing\", \"transaction_processing\", \"transaction_processing\", \"transaction_processing\", \"transaction_processing\", \"transaction_processing\", \"transaction_processing\", \"transactions\", \"transactions\", \"transactions\", \"transactions\", \"transactions\", \"transactions\", \"transactions\", \"transactions\", \"transactions\", \"transactions\", \"transistors\", \"transistors\", \"transistors\", \"transistors\", \"transistors\", \"transistors\", \"transistors\", \"transistors\", \"transistors\", \"transistors\", \"twitter\", \"twitter\", \"twitter\", \"twitter\", \"twitter\", \"twitter\", \"twitter\", \"twitter\", \"twitter\", \"twitter\", \"types\", \"types\", \"types\", \"types\", \"types\", \"types\", \"types\", \"types\", \"types\", \"types\", \"unaudited\", \"unaudited\", \"unaudited\", \"unaudited\", \"unaudited\", \"unaudited\", \"unaudited\", \"unaudited\", \"unaudited\", \"unaudited\", \"uncertainties_factors\", \"uncertainties_factors\", \"uncertainties_factors\", \"uncertainties_factors\", \"uncertainties_factors\", \"uncertainties_factors\", \"uncertainties_factors\", \"uncertainties_factors\", \"uncertainties_factors\", \"uncertainties_factors\", \"unified\", \"unified\", \"unified\", \"unified\", \"unified\", \"unified\", \"unified\", \"unified\", \"unified\", \"unified\", \"unique\", \"unique\", \"unique\", \"unique\", \"unique\", \"unique\", \"unique\", \"unique\", \"unique\", \"unique\", \"united\", \"united\", \"united\", \"united\", \"united\", \"united\", \"united\", \"united\", \"united\", \"united\", \"united_kingdom\", \"united_kingdom\", \"united_kingdom\", \"united_kingdom\", \"united_kingdom\", \"united_kingdom\", \"united_kingdom\", \"united_kingdom\", \"united_kingdom\", \"united_kingdom\", \"unlock\", \"unlock\", \"unlock\", \"unlock\", \"unlock\", \"unlock\", \"unlock\", \"unlock\", \"unlock\", \"unlock\", \"valid\", \"valid\", \"valid\", \"valid\", \"valid\", \"valid\", \"valid\", \"valid\", \"valid\", \"valid\", \"venture\", \"venture\", \"venture\", \"venture\", \"venture\", \"venture\", \"venture\", \"venture\", \"venture\", \"venture\", \"ventures\", \"ventures\", \"ventures\", \"ventures\", \"ventures\", \"ventures\", \"ventures\", \"ventures\", \"ventures\", \"ventures\", \"videos\", \"videos\", \"videos\", \"videos\", \"videos\", \"videos\", \"videos\", \"videos\", \"videos\", \"videos\", \"virtual\", \"virtual\", \"virtual\", \"virtual\", \"virtual\", \"virtual\", \"virtual\", \"virtual\", \"virtual\", \"washington\", \"washington\", \"washington\", \"washington\", \"washington\", \"washington\", \"washington\", \"washington\", \"washington\", \"washington\", \"way\", \"way\", \"way\", \"way\", \"way\", \"way\", \"way\", \"way\", \"way\", \"way\", \"web_based\", \"web_based\", \"web_based\", \"web_based\", \"web_based\", \"web_based\", \"web_based\", \"web_based\", \"web_based\", \"web_based\", \"website\", \"website\", \"website\", \"website\", \"website\", \"website\", \"website\", \"website\", \"website\", \"website\", \"wide_variety\", \"wide_variety\", \"wide_variety\", \"wide_variety\", \"wide_variety\", \"wide_variety\", \"wide_variety\", \"wide_variety\", \"wide_variety\", \"wide_variety\", \"windows_xp\", \"windows_xp\", \"windows_xp\", \"windows_xp\", \"windows_xp\", \"windows_xp\", \"windows_xp\", \"windows_xp\", \"windows_xp\", \"windows_xp\", \"work\", \"work\", \"work\", \"work\", \"work\", \"work\", \"work\", \"work\", \"work\", \"work\", \"workplace\", \"workplace\", \"workplace\", \"workplace\", \"workplace\", \"workplace\", \"workplace\", \"workplace\", \"workplace\", \"workplace\", \"worldwide_operations\", \"worldwide_operations\", \"worldwide_operations\", \"worldwide_operations\", \"worldwide_operations\", \"worldwide_operations\", \"worldwide_operations\", \"worldwide_operations\", \"worldwide_operations\", \"worldwide_operations\"]}, \"R\": 30, \"lambda.step\": 0.01, \"plot.opts\": {\"xlab\": \"PC1\", \"ylab\": \"PC2\"}, \"topic.order\": [7, 4, 6, 1, 3, 10, 5, 8, 9, 2]};\n",
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            "text/plain": [
              "PreparedData(topic_coordinates=              x         y  topics  cluster       Freq\n",
              "topic                                                \n",
              "6     -0.000360  0.000224       1        1  13.321327\n",
              "3     -0.000468  0.000296       2        1  12.933582\n",
              "5      0.000725  0.000287       3        1  12.756409\n",
              "0      0.000552 -0.000247       4        1  11.026037\n",
              "2      0.000429  0.000552       5        1   9.672990\n",
              "9     -0.000851  0.000098       6        1   9.245948\n",
              "4     -0.000091 -0.001284       7        1   9.202207\n",
              "7     -0.000468  0.000115       8        1   8.422534\n",
              "8      0.000418 -0.000210       9        1   7.993819\n",
              "1      0.000114  0.000169      10        1   5.425145, topic_info=                     Term       Freq      Total Category  logprob  loglift\n",
              "233     owners_management  11.000000  11.000000  Default  30.0000  30.0000\n",
              "1427              enables  11.000000  11.000000  Default  29.0000  29.0000\n",
              "1474                tasks  12.000000  12.000000  Default  28.0000  28.0000\n",
              "186   similar_expressions  11.000000  11.000000  Default  27.0000  27.0000\n",
              "1434                forms  11.000000  11.000000  Default  26.0000  26.0000\n",
              "...                   ...        ...        ...      ...      ...      ...\n",
              "1252                suite   0.794960  12.209497  Topic10  -7.7362   0.1824\n",
              "2174          governments   0.771161  11.730112  Topic10  -7.7666   0.1921\n",
              "2104          smithsonian   0.772784  11.885209  Topic10  -7.7645   0.1811\n",
              "186   similar_expressions   0.770173  11.686620  Topic10  -7.7679   0.1945\n",
              "2030          represented   0.770439  11.962074  Topic10  -7.7675   0.1716\n",
              "\n",
              "[456 rows x 6 columns], token_table=      Topic      Freq                  Term\n",
              "term                                       \n",
              "716       1  0.165279                access\n",
              "716       2  0.165279                access\n",
              "716       3  0.082639                access\n",
              "716       4  0.082639                access\n",
              "716       5  0.082639                access\n",
              "...     ...       ...                   ...\n",
              "2112      6  0.083963  worldwide_operations\n",
              "2112      7  0.083963  worldwide_operations\n",
              "2112      8  0.083963  worldwide_operations\n",
              "2112      9  0.083963  worldwide_operations\n",
              "2112     10  0.083963  worldwide_operations\n",
              "\n",
              "[4165 rows x 3 columns], R=30, lambda_step=0.01, plot_opts={'xlab': 'PC1', 'ylab': 'PC2'}, topic_order=[7, 4, 6, 1, 3, 10, 5, 8, 9, 2])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 32
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "K2XnGjaiN28N",
        "colab_type": "text"
      },
      "source": [
        "### 4.2. Finding Optimal Number of Topics"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "yjrsO95yN9vM",
        "colab_type": "text"
      },
      "source": [
        "There is no easy way to obtain the optimal number of topics you should use. This is a hyperparameter and must be tuned manually. One way, albeit still not great, is to create a number of models, each with different topic numbers, and calculate the coherence scores for each model. Then we plot the coherence vs. number of topics, and find the elbow - the point at which the curve tapers off."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3EmV43_PODPB",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def plot_coherence(dictionary, corpus, texts, maximum=30, minimum=3, step=4):\n",
        "\n",
        "  coherence_values = []\n",
        "  model_list = []\n",
        "\n",
        "  for num_topics in tqdm(range(minimum, maximum, step)):\n",
        "    model = LdaMallet(\n",
        "        mallet_path,\n",
        "        corpus=train_corpus,\n",
        "        num_topics=num_topics,\n",
        "        id2word=train_id2word,\n",
        "    )\n",
        "    gensimMallet = gensim.models.wrappers.ldamallet.malletmodel2ldamodel(model)\n",
        "    model_list.append(gensimMallet)\n",
        "    coherencemodel = CoherenceModel(model=gensimMallet, texts=texts, dictionary=dictionary, coherence='c_v')\n",
        "    coherence_values.append(coherencemodel.get_coherence())\n",
        "\n",
        "  return model_list, coherence_values"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0FimJvoGQecc",
        "colab_type": "code",
        "outputId": "6767acbf-3e57-4cdc-c113-667941653631",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 120,
          "referenced_widgets": [
            "2955e2cb95804d02bf607854cfaa3359",
            "f33beaf3ac8045e0a2be90b634540f5b",
            "f90469b5db794357a7a55c702252bc7a",
            "faca31e689b84e8fa2439395d2fd9b4b",
            "cb0cd58990e4400d985aa95df3a3888b",
            "748ace4b12104e24b0c318321a8cfbf4",
            "52606119ff5741d98a2c6606f58e6e31",
            "e3c604ad0bb94fd6b475886faa6315cb"
          ]
        }
      },
      "source": [
        "models, coherences = plot_coherence(train_id2word, train_corpus, bigram_train)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "2955e2cb95804d02bf607854cfaa3359",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(FloatProgress(value=0.0, max=7.0), HTML(value='')))"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/smart_open/smart_open_lib.py:253: UserWarning: This function is deprecated, use smart_open.open instead. See the migration notes for details: https://github.com/RaRe-Technologies/smart_open/blob/master/README.rst#migrating-to-the-new-open-function\n",
            "  'See the migration notes for details: %s' % _MIGRATION_NOTES_URL\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5TWUO2juW6Vg",
        "colab_type": "code",
        "outputId": "109d9c33-719d-4ef2-f7a4-206361f21ac8",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 606
        }
      },
      "source": [
        "x = range(3, 30, 4)\n",
        "plt.figure(figsize=(15, 10))\n",
        "plt.plot(x, coherences)\n",
        "plt.xlabel(\"Num Topics\")\n",
        "plt.ylabel(\"Coherence score\")\n",
        "# plt.legend((\"coherence_values\"), loc='best')\n",
        "plt.show()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1080x720 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "dikt8fqLc4pS",
        "colab_type": "text"
      },
      "source": [
        "Unfortunately it looks like this is quite messy, but the scale on the y-axis is quite narrow, so it doesn't make too much difference which value we choose. Also, if you run this again, the line can look much different. Therefore, we should choose a number of topics that makes sense in this content. The fewer topics we choose, the more general and broad the topics would be and vice versa."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "SNoYlUPlKnlQ",
        "colab_type": "text"
      },
      "source": [
        "## 5. Using The Model"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "cO76dE9oct7W",
        "colab_type": "text"
      },
      "source": [
        "Using the knowledge from the optimal topics we can select a \"best model\" to analyse the data with."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "J8RWGRtYkDOx",
        "colab_type": "text"
      },
      "source": [
        "### 5.1. Visualisation"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "szjLAJmicT6D",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "bestModelMallet = models[1]"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "b0B692yaXlBC",
        "colab_type": "code",
        "outputId": "5934dfe1-64b1-4eed-b11e-fa3ebc9477e5",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 861
        }
      },
      "source": [
        "# Visualize the topics using pyLDAvis\n",
        "pyLDAvis.enable_notebook()\n",
        "vis = pyLDAvis.gensim.prepare(bestModelMallet, train_corpus, train_id2word)\n",
        "vis"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "\n",
              "<link rel=\"stylesheet\" type=\"text/css\" href=\"https://cdn.rawgit.com/bmabey/pyLDAvis/files/ldavis.v1.0.0.css\">\n",
              "\n",
              "\n",
              "<div id=\"ldavis_el1241406608346227044291152238\"></div>\n",
              "<script type=\"text/javascript\">\n",
              "\n",
              "var ldavis_el1241406608346227044291152238_data = {\"mdsDat\": {\"x\": [0.000125777115167806, -0.0006271437295807589, -8.990123408807613e-05, 0.0004501837473672254, 0.00016929813383106756, 0.0008201435361108669, -0.0008483575688081299], \"y\": [5.585331469919465e-05, -0.00022198271589499166, -0.000975123398174184, 0.00022901700124830369, -0.0003036322339650495, 0.0004470422885629524, 0.000768825743523774], \"topics\": [1, 2, 3, 4, 5, 6, 7], \"cluster\": [1, 1, 1, 1, 1, 1, 1], \"Freq\": [16.683084122234252, 16.008020929926147, 15.432293359600887, 13.996191368014873, 13.99534536648047, 12.665464638774385, 11.219600214968981]}, \"tinfo\": {\"Term\": [\"appraised\", \"graphics_processors\", \"exce\", \"number_trademarks\", \"execute\", \"bring\", \"called_held\", \"performance_time\", \"ada_changing\", \"compete\", \"operating_system\", \"centric_company\", \"public\", \"hosting\", \"fulfillment_timely\", \"graphics_processor\", \"trading\", \"competition\", \"employee\", 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\"technical_staff\", \"technical_staff\", \"technical_staff\", \"telephone\", \"telephone\", \"telephone\", \"telephone\", \"telephone\", \"telephone\", \"telephone\", \"term_thinking\", \"term_thinking\", \"term_thinking\", \"term_thinking\", \"term_thinking\", \"term_thinking\", \"term_thinking\", \"thereat\", \"thereat\", \"thereat\", \"thereat\", \"thereat\", \"thereat\", \"thereat\", \"throughout_world\", \"throughout_world\", \"throughout_world\", \"throughout_world\", \"throughout_world\", \"throughout_world\", \"throughout_world\", \"time_time\", \"time_time\", \"time_time\", \"time_time\", \"time_time\", \"time_time\", \"time_time\", \"trade_secrets\", \"trade_secrets\", \"trade_secrets\", \"trade_secrets\", \"trade_secrets\", \"trade_secrets\", \"trade_secrets\", \"trademarks_registered\", \"trademarks_registered\", \"trademarks_registered\", \"trademarks_registered\", \"trademarks_registered\", \"trademarks_registered\", \"trademarks_registered\", \"trading\", \"trading\", \"trading\", \"trading\", \"trading\", \"trading\", \"trading\", \"transaction_processing\", \"transaction_processing\", \"transaction_processing\", \"transaction_processing\", \"transaction_processing\", \"transaction_processing\", \"transaction_processing\", \"transformation\", \"transformation\", \"transformation\", \"transformation\", \"transformation\", \"transformation\", \"transformation\", \"ulis\", \"ulis\", \"ulis\", \"ulis\", \"ulis\", \"ulis\", \"ulis\", \"unaudited\", \"unaudited\", \"unaudited\", \"unaudited\", \"unaudited\", \"unaudited\", \"unaudited\", \"unlimited_free\", \"unlimited_free\", \"unlimited_free\", \"unlimited_free\", \"unlimited_free\", \"unlimited_free\", \"unlimited_free\", \"use_following\", \"use_following\", \"use_following\", \"use_following\", \"use_following\", \"use_following\", \"use_following\", \"users\", \"users\", \"users\", \"users\", \"users\", \"users\", \"users\", \"vice_chairman\", \"vice_chairman\", \"vice_chairman\", \"vice_chairman\", \"vice_chairman\", \"vice_chairman\", \"vice_chairman\", \"voyager\", \"voyager\", \"voyager\", \"voyager\", \"voyager\", \"voyager\", \"voyager\", \"waive\", \"waive\", \"waive\", \"waive\", \"waive\", \"waive\", \"waive\", \"want\", \"want\", \"want\", \"want\", \"want\", \"want\", \"want\", \"web_search\", \"web_search\", \"web_search\", \"web_search\", \"web_search\", \"web_search\", \"web_search\", \"website\", \"website\", \"website\", \"website\", \"website\", \"website\", \"website\", \"wide_web\", \"wide_web\", \"wide_web\", \"wide_web\", \"wide_web\", \"wide_web\", \"wide_web\", \"wilke\", \"wilke\", \"wilke\", \"wilke\", \"wilke\", \"wilke\", \"wilke\", \"workloads\", \"workloads\", \"workloads\", \"workloads\", \"workloads\", \"workloads\", \"workloads\", \"worldwide_operations\", \"worldwide_operations\", \"worldwide_operations\", \"worldwide_operations\", \"worldwide_operations\", \"worldwide_operations\", \"worldwide_operations\", \"xbox_xbox\", \"xbox_xbox\", \"xbox_xbox\", \"xbox_xbox\", \"xbox_xbox\", \"xbox_xbox\", \"xbox_xbox\"]}, \"R\": 30, \"lambda.step\": 0.01, \"plot.opts\": {\"xlab\": \"PC1\", \"ylab\": \"PC2\"}, \"topic.order\": [1, 4, 5, 3, 6, 2, 7]};\n",
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            "text/plain": [
              "PreparedData(topic_coordinates=              x         y  topics  cluster       Freq\n",
              "topic                                                \n",
              "0      0.000126  0.000056       1        1  16.683084\n",
              "3     -0.000627 -0.000222       2        1  16.008021\n",
              "4     -0.000090 -0.000975       3        1  15.432293\n",
              "2      0.000450  0.000229       4        1  13.996191\n",
              "5      0.000169 -0.000304       5        1  13.995345\n",
              "1      0.000820  0.000447       6        1  12.665465\n",
              "6     -0.000848  0.000769       7        1  11.219600, topic_info=                     Term      Freq      Total Category  logprob  loglift\n",
              "2924            appraised  9.000000   9.000000  Default  30.0000  30.0000\n",
              "3259  graphics_processors  8.000000   8.000000  Default  29.0000  29.0000\n",
              "253                  exce  9.000000   9.000000  Default  28.0000  28.0000\n",
              "2254    number_trademarks  8.000000   8.000000  Default  27.0000  27.0000\n",
              "2509              execute  9.000000   9.000000  Default  26.0000  26.0000\n",
              "...                   ...       ...        ...      ...      ...      ...\n",
              "1623        joint_venture  1.246374   9.433985   Topic7  -7.9295   0.1634\n",
              "36               continue  1.257906   9.751053   Topic7  -7.9203   0.1396\n",
              "1163             possible  1.270242  10.262561   Topic7  -7.9105   0.0982\n",
              "576                oracle  1.246827   9.544186   Topic7  -7.9291   0.1522\n",
              "2087             wide_web  1.242973   9.660450   Topic7  -7.9322   0.1370\n",
              "\n",
              "[340 rows x 6 columns], token_table=      Topic      Freq              Term\n",
              "term                                   \n",
              "1927      1  0.109326  access_unlimited\n",
              "1927      2  0.218651  access_unlimited\n",
              "1927      3  0.109326  access_unlimited\n",
              "1927      4  0.109326  access_unlimited\n",
              "1927      5  0.109326  access_unlimited\n",
              "...     ...       ...               ...\n",
              "1587      3  0.103334         xbox_xbox\n",
              "1587      4  0.103334         xbox_xbox\n",
              "1587      5  0.206668         xbox_xbox\n",
              "1587      6  0.103334         xbox_xbox\n",
              "1587      7  0.103334         xbox_xbox\n",
              "\n",
              "[2184 rows x 3 columns], R=30, lambda_step=0.01, plot_opts={'xlab': 'PC1', 'ylab': 'PC2'}, topic_order=[1, 4, 5, 3, 6, 2, 7])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 142
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3QX0Aj4kFQNh",
        "colab_type": "text"
      },
      "source": [
        "### 5.2. Trends"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "cU8KOvLctZWa",
        "colab_type": "text"
      },
      "source": [
        "One great thing we can now do is look at how certain mentions of certain topics change overtime within each company. Inspecting topic 3 reveals the terms: “improved_advertising”, “cloud_platforms”, “phones_intelligent”, “information_investors”, “global_compute” and so on. This topic can be interpreted as integrating technology with current business practices. Let’s simply call with topic “Tech” for short."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xSV_WndVnW7j",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def get_df_topics(row, topic_id):\n",
        "  words = list(sent_to_words([row]))\n",
        "  words = remove_stopwords(words)\n",
        "  bigram = bigrams(words)\n",
        "  bigram = [bigram[report] for report in words]\n",
        "  tokens = train_id2word.doc2bow(bigram[0])\n",
        "  topics = bestModelMallet.get_document_topics(tokens)\n",
        "  if topics is not None:\n",
        "    for index, score in topics:\n",
        "      if index == topic_id:\n",
        "        return score"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rxSVmZUWkqSF",
        "colab_type": "code",
        "outputId": "6a166982-6fbe-4392-f4c7-b4639c8f0510",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        }
      },
      "source": [
        "MSFT_df = dataframe[dataframe.Company == \"MSFT\"]\n",
        "MSFT_df = MSFT_df.sort_values(\"Year\")\n",
        "MSFT_df.head()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>Company</th>\n",
              "      <th>Year</th>\n",
              "      <th>Report</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>30</th>\n",
              "      <td>MSFT</td>\n",
              "      <td>1994</td>\n",
              "      <td>anufacturing and distribution operation consis...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>MSFT</td>\n",
              "      <td>1995</td>\n",
              "      <td>mpete with unix-based operating systems from a...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>MSFT</td>\n",
              "      <td>1996</td>\n",
              "      <td>operating systems from a wide range of compani...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18</th>\n",
              "      <td>MSFT</td>\n",
              "      <td>1997</td>\n",
              "      <td>distribute microsoft's copyrighted software pr...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>24</th>\n",
              "      <td>MSFT</td>\n",
              "      <td>1998</td>\n",
              "      <td>providing these technical services. notes to f...</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   Company  Year                                             Report\n",
              "30    MSFT  1994  anufacturing and distribution operation consis...\n",
              "8     MSFT  1995  mpete with unix-based operating systems from a...\n",
              "13    MSFT  1996  operating systems from a wide range of compani...\n",
              "18    MSFT  1997  distribute microsoft's copyrighted software pr...\n",
              "24    MSFT  1998  providing these technical services. notes to f..."
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 194
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6bWZsxebrrov",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "score_list = []\n",
        "year_list = []\n",
        "for index, row in MSFT_df.iterrows():\n",
        "  score_list.append(get_df_topics(row.Report, 3))\n",
        "  year_list.append(row.Year)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wz7-NXK6rDKj",
        "colab_type": "code",
        "outputId": "0e8ccf30-8365-41b0-f356-2bc8ab65210e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 281
        }
      },
      "source": [
        "plt.plot(year_list, score_list)\n",
        "plt.title(\"MSFT Tech Mentions\")\n",
        "plt.show()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Uw7JTYQ-sDmc",
        "colab_type": "text"
      },
      "source": [
        "As we can see, mentions of topic 3, which seems to represent technology has been fluctuating since 1995, and more recently is at a low-point for Microsoft. Let's compare with IBM."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NuZ9G6SGs-sU",
        "colab_type": "code",
        "outputId": "e48b43bd-39a9-4a92-d0bb-8f2a265b35ba",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        }
      },
      "source": [
        "IBM_df = dataframe[dataframe.Company == \"IBM\"]\n",
        "IBM_df = IBM_df.sort_values(\"Year\")\n",
        "IBM_df.head()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>Company</th>\n",
              "      <th>Year</th>\n",
              "      <th>Report</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>59</th>\n",
              "      <td>IBM</td>\n",
              "      <td>1994</td>\n",
              "      <td>tatement no. 33-6889 on form s-3 filed on july...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>61</th>\n",
              "      <td>IBM</td>\n",
              "      <td>1995</td>\n",
              "      <td>erland)................. switzerland 100 ibm u...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>68</th>\n",
              "      <td>IBM</td>\n",
              "      <td>1996</td>\n",
              "      <td>world trade corporation..........................</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>78</th>\n",
              "      <td>IBM</td>\n",
              "      <td>1997</td>\n",
              "      <td>with any and all amendments and subsequent res...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>71</th>\n",
              "      <td>IBM</td>\n",
              "      <td>1998</td>\n",
              "      <td>the ibm supplemental executive retirement plan...</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   Company  Year                                             Report\n",
              "59     IBM  1994  tatement no. 33-6889 on form s-3 filed on july...\n",
              "61     IBM  1995  erland)................. switzerland 100 ibm u...\n",
              "68     IBM  1996  world trade corporation..........................\n",
              "78     IBM  1997  with any and all amendments and subsequent res...\n",
              "71     IBM  1998  the ibm supplemental executive retirement plan..."
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 197
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XqCLr47mtGa1",
        "colab_type": "code",
        "outputId": "92b8aec8-c8ed-46b3-b1b3-389c8aa057af",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 281
        }
      },
      "source": [
        "score_list = []\n",
        "year_list = []\n",
        "for index, row in IBM_df.iterrows():\n",
        "  score_list.append(get_df_topics(row.Report, 3))\n",
        "  year_list.append(row.Year)\n",
        "\n",
        "plt.plot(year_list, score_list)\n",
        "plt.title(\"IBM Tech Mentions\")\n",
        "plt.show()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HE2Vf3lTtRef",
        "colab_type": "text"
      },
      "source": [
        "Interesting! IBM seems to have been on an uptrend since 1995, but in the last two years has dramatically fallen to 2004 levels. Now what about Amazon?"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9MKFXDQ5u4Yq",
        "colab_type": "code",
        "outputId": "4d35fb38-b158-4cc8-f1f9-9d7fd52b9cea",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        }
      },
      "source": [
        "AMZN_df = dataframe[dataframe.Company == \"AMZN\"]\n",
        "AMZN_df = AMZN_df.sort_values(\"Year\")\n",
        "AMZN_df.head()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>Company</th>\n",
              "      <th>Year</th>\n",
              "      <th>Report</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>51</th>\n",
              "      <td>AMZN</td>\n",
              "      <td>1998</td>\n",
              "      <td>prospects, financial condition and results of ...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>43</th>\n",
              "      <td>AMZN</td>\n",
              "      <td>1999</td>\n",
              "      <td>ecutive officers and directors of the company ...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50</th>\n",
              "      <td>AMZN</td>\n",
              "      <td>2000</td>\n",
              "      <td>losses from a major interruion. computer virus...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>44</th>\n",
              "      <td>AMZN</td>\n",
              "      <td>2001</td>\n",
              "      <td>iminish the value of our trademarks and other ...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>47</th>\n",
              "      <td>AMZN</td>\n",
              "      <td>2002</td>\n",
              "      <td>ional retail seasonality. traditional retail s...</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   Company  Year                                             Report\n",
              "51    AMZN  1998  prospects, financial condition and results of ...\n",
              "43    AMZN  1999  ecutive officers and directors of the company ...\n",
              "50    AMZN  2000  losses from a major interruion. computer virus...\n",
              "44    AMZN  2001  iminish the value of our trademarks and other ...\n",
              "47    AMZN  2002  ional retail seasonality. traditional retail s..."
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 199
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "aA3bcYwhu8Kz",
        "colab_type": "code",
        "outputId": "5cdc7e1b-280f-4edd-b05e-acc9087d118b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 281
        }
      },
      "source": [
        "score_list = []\n",
        "year_list = []\n",
        "for index, row in AMZN_df.iterrows():\n",
        "  score_list.append(get_df_topics(row.Report, 3))\n",
        "  year_list.append(row.Year)\n",
        "\n",
        "plt.plot(year_list, score_list)\n",
        "plt.title(\"Amazon Tech Mentions\")\n",
        "plt.show()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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Cc0ea+eqN53HpigWz1OrIZF0dE9Gq6jvJS0tkQXpSUM+zUgjRrbm7n3/51W5KclL4yo3nIhLYRPx4inNS+fSby3noA5eQFB/H3/3oJT758H6659FKZBboTUSrqu8KujcPsCw/nURXHIdOW6CPNsMe5SMP7KHNPci9t6wP2VDLhrIctn/oMt5/2VJ+9beTXHvPczx/rDkk5450FuhNxPJ4lCMN3dMK9AmuOJYvTLdFSKLQvTuO8eejzXz++rWsLcoK6bmTE1x86k3l/PofLiYxPo53//AlPv27/TG/zrAFehOxatvc9A4Os2qaY+xWCiH6PH+smXueOsLbLlzM1k0lUz9hmjYuyWX7By/jfZcu5X9fOsm133qO54/Hbu/eAr2JWJVBlj4YbU1hBs3d/TR19YeyWWaWNHb28cH797AsP51/e+u6GY3LByIl0cVn3lzOg/9wMfFxwrt/8BJ3//5ATPbuLdCbiHUkwFWlJlJuE7JRY2jYwwfvf5me/iG+e8t60pLmLiFw05JcHv3Q5bz3tUv5nxdr2PLt53jxRMucvf5csEBvIlZlQxcluSnT/kdvmTfR49tPH+XFE6188a3rwpIOm5Lo4u63lPPAtouJE2HrfS/yuUcO4h6Ijd69BXoTsarqu1i1KPA7YkfLSUukIDPZAn2Ee6aqkf/80zHetbGEmzaMWxtxzmxemsujH7qM2y9Zwk+fr+aN3/4zNS09YW1TKFigNxGpf2iYV5p7gr4jdrTyIpuQjWRnOnr5yAN7WF2QwedvWBvu5gCQmhjP565fy/3bLqKjd5Ct971IdXN0B3sL9CYiHW/sYdijAS0fOJk1hRkca+qmb3A4RC0zoTI47OFffvkyA0Me7r1lPckJk68HPNcuOiePX77/IvqHPLzrvhc40dQd7iZNmwV6E5GqGrzDLTPt0a8pzGTYoxxrjN5/pLHq649XUVHTxlduPI9l+enhbs641hRm8qv3X8TQsLL1vhej9nNkgd5EpMr6LhJcwtIZVhz0TcjajVOR5alDDXz/uRP83UWlvOX8onA3Z1KrCjL41baL8Kgv2EffUKAFehORjtR3sSw/nYQZVp5ckpdGckKcTchGkK6+QT7+m32sW5zJp99UHu7mBGTlogzu33YRIrD1vhc50hBdwT6gf0UiskVEqkTkmIjcNc7+y0Vkt4gMichN4+zPFJE6EfmvUDTaxL7p1rgZzRUnrCrItEAfQX70l1do7Rngy287N+LG5SezfKE32MeJcPN9L1IZResdTBnoRcQF3AtcB5QDN4vI6D/DJ4HbgV9OcJovAs9Nv5lmPunoHeR0R19IAj1AeWEGh890oaohOZ+ZvtaeAX7451e4bl3BpPXlI9Wy/HTu33YR8S7vnbTRUjQvkB79ZuCYqp5Q1QHgfuAG/wNUtVpV9wFjlmYXkQ3AIuCJELTXzANHG6a32MhE1hRm0tE7yJmOvpCcz0zfd3ccwz0wxMeuWRnupkzbOfnpPLDtYpLi43j3D1/k4OmOcDdpSoEE+sVArd/vdc62KYlIHPAN4M4pjtsmIhUiUtHU1BTIqU0Mq5xh6YPRrBRCZDjT0cvPX6zh7euLWb4wuheDWbIgjfu3XURqgot3/+AlDpyK7GA/25Ox/wRsV9W6yQ5S1ftUdaOqbszPz5/lJplIV1XfRUZSPIuzU0JyvtW+zJso+Zodq77z9FFUlQ+/fkW4mxISZXlp3L/tYtKT4nn3D15kX117uJs0oUAC/SnAv15osbMtEBcDd4hINfB14FYR+UpQLTTzTlVDFysLMkJWvTA9KZ7S3FRbLDyMTjR182BFHbe8pozinNRwNydkSvNSuX/bRWSmJHDLD19iT21kBvtAAv1OYIWILBWRRGAr8EggJ1fVW1S1VFWX4B2++bmqjsnaMcZHVamq7wp5Yas1zoSsCY97njpKUnwc/3zV8nA3JeRKcr3BPjs1gb//4UvsPtkW7iaNMWWgV9Uh4A7gceAw8KCqHhSRL4jI9QAisklE6oB3AN8XkYOz2WgTuxo6++noHQzZRKzPmsJMqlt6YqYaYTQ5eLqDP+w9zXtfu5T8jODW/o0WxTmpPLDtYnLTE7n1R3/juSNNdPQORkymV0D1X1V1O7B91La7/R7vxDukM9k5fgr8NOgWmnnFl5scqtRKnzWFmah6J3rXl+aE9Nxmcl9/vIqslATef/k54W7KrCrKTuH+bRfx7h+8xK0//hsACS4hNy2R3LQkFqQnOo+9i92/+ti7Py89kYyk+FlZcGXuqvsbEwDfHYfTXT5wIv6ZNxbo587O6lZ2VDXxr1tWk5USmkW+I1lhVgq//cdLeOZIIy3dA7T0DNDS3U9rzwDN3QPUtLhp6e6nZ2D8Invnl2Tz+39+bcjbZYHeRJTK+i4WZiSRk5YY0vMW56SQkRRvKZZzSFX52mOV5GckcfslS8LdnDmTk5bI2y6cvK5+3+AwrT0Dzh+D/pHHmSmzE5It0JuIEqrSB6OJCKttQnZOPXOkiZ3VbXzxhrWkJEZPqYO5kJzgoig7haIQpRBPxYqamYgx7FGONnaHfNjGp7zQW/PG44mMCbJY5vEo//FYFSW5KbxrU2m4mzPvWaA3EaO6pYeBIc+s9OjBOyHrHhjmZKt7Vs5vXrX9wBkOnenko29YSWK8hZlws/8DJmJU1ftq3Ex/ndjJ2GLhc2No2MM3nzjCykXpXH9+QNVSzCyzQG8iRlV9FyKwfOHsrDa0qiCDOLFAP9se2lXHieYe7rxmFa640KcKmuBZoDcRo6q+iyV5abM2cZec4GLpgjQO2YTsrOkbHObbTx/lgpJs3lC+KNzNMQ4L9CZiVDV0zdpErM+aQluEZDb94sUaznT08fFrV83KjT9meizQm4jQNzhMdUvPrE3E+qwpzORUey8dvYOz+jrzUXf/EN995jiXLl/AJcsXhLs5xo8FehMRjjZ0oxr60gej+e6QrbRefcj98M8naO0Z4P9duyrcTTGjWKA3EWG2atyMVl7k1Ka3QB9SviUCr127iPNLom+JwFhngd5EhKr6LhLj41iSlzarr7Mww1tMysbpQ+u/n/EuEXjnNdabj0QW6E1EqGroYsXC9FlPxxMRVhdkUNXQPauvM5+c6ejlZy/U8LYLi1kxy5PpZnos0JuIMFs1bsZTlpdGTUvPnLzWfBBrSwTGIgv0JuzaegZo7OoP+WIjE1mSl0q7e9Ayb0LgleYeHqyo492bSynJjZ0lAmONBXoTdlVODfpQLx84kbI8b0A62WI1b2bquzuOkeiK446rrTcfyQIK9CKyRUSqROSYiIxZ81VELheR3SIyJCI3+W2/QEReEJGDIrJPRN4Vysab2HDotHdi1FeLZraV5nonfGtabfhmpvaf6uC1y/NidonAWDFloBcRF3AvcB1QDtwsIuWjDjsJ3A78ctR2N3Crqq4FtgDfEhHLvTJn2VXTRlFWMosyk+fk9Xw9+hrr0c+IqlLb6qY4x4ZsIl0gC49sBo6p6gkAEbkfuAE45DtAVaudfR7/J6rqEb/Hp0WkEcgH2mfcchMTVJWKmlY2L82bs9dMS4pnQXqSTcjOUJt7kJ6BYRubjwKBDN0sBmr9fq9ztgVFRDYDicDxYJ9rYlddWy8Nnf1sWjK367iW5aVaj36Gap26/iU5c7NKkpm+OZmMFZFC4H+A96iqZ5z920SkQkQqmpqa5qJJJkLsqmkDYEPZHAf63FRbgGSGatu81680z3r0kS6QQH8KKPH7vdjZFhARyQT+CHxKVV8c7xhVvU9VN6rqxvz8/EBPbWJARU0r6Unxs7bYyETK8tI409FH3+DwnL5uLKlt7QWgxMboI14ggX4nsEJElopIIrAVeCSQkzvHPwz8XFUfmn4zTayqqG7jwtLsOV+gwjchW2u9+mmrbXOTm5ZIWlIgU30mnKYM9Ko6BNwBPA4cBh5U1YMi8gURuR5ARDaJSB3wDuD7InLQefo7gcuB20Vkj/Nzway8ExN1OnoHqWroYmNZ7py/dqll3sxYbavbxuejREB/ilV1O7B91La7/R7vxDukM/p5vwB+McM2mhj18sk2VGHjHE/EgneMHqDGevTTVtvqZu3irHA3wwTA7ow1YbOrpg1XnHBBGMra5qYlkpEUbymW0zTsUU6199r4fJSwQB8l7vz1Xv7fr/eGuxkhtbO6lTWFGWEZ4xURSi3FctoaOvsYHFZKcm3oJhrYLEoU6B0Y5g97T5OeFI+qxsRanIPDHvbUtrN1U2nY2lCWl8phWyh8Wl7NobcefTSwHn0UePFEC/1DHlp6Bmjo7A93c0Li0OlO+gY9YRmf9ynLS6O21c3Q8JhbO8wUatuc1Eq7KzYqWKCPAjuqGkceHzrTEcaWhE6Fc6NUODJufMpyUxnyKGc6+sLWhmhV2+pGBIqy56Y+kZkZC/QRTlX5U2UjF53jDYi+So/RbldNK4uzUyjICl+gsBTL6attc1OQmUxSvCvcTTEBsEAf4Y439VDX1subzytiSV4qB2Mg0KsqO6vbwjpsA96hG7ByxdNR12oZN9HEAn2Ee8YZtrlyVT5ri7I4FAOLWte29tLU1c/GOa5vM1phZjKJ8XHWo5+G2jY3xZZxEzUs0Ee4HVWNrFyUTnFOKuVFmdS0uOnsi+4l8CpqWgHYuCR84/MAcXFCSU6K5dIHqX9omPrOPuvRRxEL9BGsu3+Iv73SylWrFgJQXuQt/FUZ5SmBFTVtZCTFz9nSgZPxLhRuPfpgnG7vQ9UybqKJBfoI9tdjzQwOK1c6gX6ts9TewdPRnXmzq7qNC8ty5ryQ2XhKnXLFqhrupkQNq0MffSzQT5Oq0tM/NKuv8UxVI+lJ8SOTlgszk1mQnhTVmTcdbl8hs/COz/ssyUvFPTBMU3ds3J8wF3x16K1HHz0s0E/TH/adYdOXnqJ+lnKwVZUdlU1ctmIBCa5X/zeVF2VGdebN7pNO/nyYM258fJk3J234JmC1rb0kuGTO1vg1M2eBfpr21bbjHhjmty/Xzcr5K+u7qO/sGxmf91lblMnRxi4GhqLzbs6KmtawFTIbj+XSB6+2zc3i7JSIGHozgbFAP03VTmB4aFfdrIzv+u6GvWLV2StulRdmMjisHG2MzgnZiuo21hZlkpoYGWWWinNSiBMrVxyMula3DdtEGQv001Td0kNSfBwnmnp4ubY95Od/prKJtUWZY74e+zJvonGcfmDIW8hsrteHnUxSvIvCLEuxDEZtWy/FlloZVSzQT8OwRznZ4ubGDcWkJLh4aFdoh2863IPsOtk2ZtgGYEleGqmJrqgcpz94uoP+IU9Y69uMp8zKFQesp3+I1p4BK08cZSzQT0N9Zx8Dwx7WFmVy3boC/rD3dEgXmf7zsSaGPcpVq8culO6KE1YXZETlHbK7aiJrItanLM+bYmmmNpJxYz36qBJQoBeRLSJSJSLHROSucfZfLiK7RWRIRG4ate82ETnq/NwWqoaHU3Wz92v+0rw0btxQTFffEE8eagjZ+XdUNpGdmsAFJeMHxLVFWRw+3YnHE1253xXVbZTkpkRctkZZXhqtPQNRf8fxXKht9ZYnLrUx+qgyZaAXERdwL3AdUA7cLCLlow47CdwO/HLUc3OBzwKvATYDnxWRyOrOTUO1M55btiCNi8/JoygrOWTDNx6P8uyRRi5fkT9hVkN5USZd/UPUOTXBo4GqUlHTFnHDNvDq+rGWYjm1kZulLNBHlUB69JuBY6p6QlUHgPuBG/wPUNVqVd0HjM75uxZ4UlVbVbUNeBLYEoJ2h1V1cw+J8XEUZiYTFyfcuKGYPx9tCklO/YHTHTR3D4w7bOOztij67pCtaXHT3N0fUROxPpZiGbjaNjdpiS5yUhPC3RQThEAC/WKg1u/3OmdbIGby3IhV3eKmLDeVOKfHfeP6YjwKD798asbn3lHZhAhcvmLiQL9yUQauOImqcfqKCB2fBytXHIza1l5KclNjYjnL+SQiJmNFZJuIVIhIRVNTU7ibM6Walp6R4ACwZEEam5bk8NCu2hnn1O+oauT84mzy0pMmPCY5wcXy/PSoyrzZVdNKRnI8KxeGv5DZaOlJ8SxIT6Sm2Xr0U6ltdVtqZRQKJNCfAkr8fi92tgUioOeq6n2qulFVNyO8374AACAASURBVObnT9yTjQQej1LT4mbpgrM/7DeuL+Z4Uw9766Y/nNLS3c/euvZx0ypHKy/KjKpc+orqNjaU5Yx8C4o0pbmp1qOfgqpS2+a21MooFEig3wmsEJGlIpIIbAUeCfD8jwPXiEiOMwl7jbMtatV39tE/5DmrRw/wxvMKSU6I46FdtRM8c2rPHW1ClUnH533WFmVS39lHSxQU42p3D3C0sTtiCpmNpywvzSZjp9DaM4B7YNhSK6PQlIFeVYeAO/AG6MPAg6p6UES+ICLXA4jIJhGpA94BfF9EDjrPbQW+iPePxU7gC862qDWSWrng7ECfmZzAlrUFPLJn+jn1OyqbWJCeyLqirCmPLXdKFkfDOL0vf35DBGbc+JTlpXKmsy+k90PEmlony8sybqJPQGP0qrpdVVeq6jJV/ZKz7W5VfcR5vFNVi1U1TVXzVHWt33N/rKrLnZ+fzM7bmDu+GjdleWM/7DdtKKGzb4inDgefUz807OHZI01csXJhQMMb0VQKoaKmjfgIKmQ2nrK8VFShrs169RN5NbXShm6iTURMxkaTmpYeEl1xFGaN/bBfvGz6OfV7atvp6B0MaNgGIDs1kcXZKVExIburuo21i7NISXSFuykTKs11Mm9s+GZCdlds9LJAH6RXmnsozUsd92YmV5zwtvWLee5IEw2dweXU76hqxBUnXLY88Mno8qLMiB+66R8aZm9de0SPz8Or39As0E+strWX3LRE0pIio/KoCZwF+iDVtLhZMs6wjY8vp/53QebU76hsYkNpDllB3IhSXpjJiaZuegcid1z5wKlOp5BZZAf6vLRE0pPirebNJOra3LZ8YJSyQB8Ej0epbulhyaiMG3/n5KezoSwnqDr19R19HDrTyZUBDtv4rC3KxKNQWR+5vfpdNd659w0ReKOUPxGhNDd1pLyFGau21U2xTcRGJQv0QWjoclIrF0wc6AFu2lDM0cZu9gWYU//sEe8iI4Hkz/srHymFEPpA/8LxlpCsiVtR3UZZXioLMyKrkNl4yvJSLcVyAsMe5VR7r43PRykL9EGodu6cnGzoBuBN5xWSFB8X8KTsjsomCjKTWV0Q3F2ji7NTyEpJCPk4/fGmbm7+wYt86P6XZ3Snr6qyq6YtIuvbjKc0L5XaNjfDUVYVdC40dPYxOKyWcROlLNAHwfe1frKhG/Dm1F+7toBHAqhTPzDk4S/HmrlqdX7Q9UNEhPLC0C8W/uj+MwA8dbiRnz1fPe3zVLe4aekZiMiKleNZkpfG4LBypiN6qoLOlZHUSuvRRyUL9EGodlIri7Kn7tXctKGYjt5Bnj7cOOlxFTWtdPcPcWWQwzY+5UWZVJ7pZGg4dIuFP3qgnvWl2Vy9eiFf3l457SqZO6u94/ORWMhsPL5yxZZ5M5bdLBXdLNAHobq5h5LclAnrxPt77fIFFGQm85vdkw/fPFPVRIJLeO3yBdNq09qiTPqHPLzSHJpJxJMtbg6e7uSN5xbyHzedR3ZqAv/yq5dxDwQ/Xr+ruo2slASW56eHpG2zzcoVT6y21Y0IFGVH/lyLGcsCfRC8qZWTD9v4uOKEt69fzLNHmmicJKd+R2Ujm5fmkj7N3OSRO2RDNE7/6AHvsM2WdQXkpSfxra0X8EpzD5/9/cGgz1VR0xrRhcxGK8xKIdEVZ8XNxlHb5qYgM5mk+Mi96c1MzAJ9gFS9qZWji5lN5sYNxQx7lN/tGT+nvrbVzdHG7qCzbfwty08nMT4uZOP02w/Uc15x1kgp2kuWLeCOq5bz6111/H6C9zGetp4Bjjf1RM1ELHj/OBfnpli54nHUtVrGTTSzQB+ghs5++gY9Y8oTT2ZZfjoXlmZPmFP/zBFv7f3pjs8DJLjiWLUoIyQ1b06197K3tp3r1hWetf1Dr1vBxrIcPvXwAWoCzDMfWQg8igI9eMfpa+ymqTFq29wUW8ZN1LJAH6CRdWKD6NGDd1L2SEM3+0+NndB8prKR0txUluUHd87R1hZlcvB0x4wXPXnsQD0A160rOGt7vCuOb229gDiBf/nVywwMTT3xu7OmlQSXcH4EFzIbj7dccc+Mr2Us6R8apr6zz3r0UcwCfYB8PdnR5Ymn8ubzikgcJ6e+b3CYvx5v5qpVwadVjlZelEmbe5D6IOvrjPbYgTOsKcxkyTjvsTgnla/ddB776jr4+hNVU55rV3Ub6xZnkZwQXWO6ZXmp9AwM09IzEO6mRIzT7X2oehdnMdHJAn2AXml2k+ASCrOCyzrISnk1p75/6NWc+pdeaaVv0MOVq6c/bOMzslj4qekP3zR29lFR0zamN+9vy7pC/u6iUu577gTPVE2cNto/NMy+Ux1RN2wD/sXNbELW59XyxBboo5UF+gDVtPRQkpNKvCv4S3bThmLa3YP8yS+nfkdlI0nxcVx8Tt6M27a6IBORmWXePH6wHlV447kTB3qAT7+pnNUFGdz56700do3/DeLAqQ4GhjwRvdDIRKxc8Vgj5YltjD5qWaAP0CvNPeMOaQTi0uULWJSZdNbwzTNVjVyyLC8kQxtpSfEszUub9o1NANv317N8YTrLp1i8OznBxX/efCHd/UN89IG9eMYpF7Cz2reiVPT16EtyUxCxQO+vtrWXRFcci6KgXpEZnwX6AKh6FwQfb1WpQLjihLddWMwzR5po7OrjleYeqlvcXBWCYRufNTOoTd/S3c9Lr7TwxkmGbfytWJTBZ9+ylr8ca+Z7zx0fs7+iuo0leankZyRNqz3hlBTvoigrxcoV+6ltdbM4JyVq7ocwYwUU6EVki4hUicgxEblrnP1JIvKAs/8lEVnibE8QkZ+JyH4ROSwinwht8+dGU1c/vYPDQU/E+rtpw2KGPcrvXz7NjkrvEM6VK0MX6NcWZVLb2ktH72DQz33iUAMe9Y7BB2rrphLedG4h33jiCLtPto1sV1V2n2xj45LoG7bxsXLFZ6ttc1Nsdeij2pSBXkRcwL3AdUA5cLOIlI867H1Am6ouB+4BvupsfweQpKrnAhuAf/D9EYgmvvICwaZW+lu+MIMLSrw59TuqGlmWnzZyy30o+BYLPzyNXv2jB+pZkpfKmsLAq2eKCF9++7kUZiXzwV+9PPIH5kRzD609A1E5Eetj5YrPVtvqtonYKBdIj34zcExVT6jqAHA/cMOoY24AfuY8fgh4nXhzBhVIE5F4IAUYAGZtlQyPRxkMYXEvH9947VTliady04Ziqhq6vNUqZ3CT1HjWFmUBwS8W3uEe5PljzWxZVxh0mmdWSgLfuflCznT08cmH93vLEjvj89FSyGw8ZXlptPQM0B2CevzRrrt/iDb3oOXQR7lAAv1ioNbv9zpn27jHqOoQ0AHk4Q36PcAZ4CTwdVVtHf0CIrJNRCpEpKKpqSnoNwHeuzov/OKTPLLn9LSeP5lXWnqIjxMWB1C1cjJvcXLqVQnp+DxAfkYS+RlJQZdCePJwA0MenTLbZiLrS3P42DUr+eO+Mzyws5ad1a1kpyZwzoLoKGQ2HkuxfNWrqZU2dBPNZnsydjMwDBQBS4GPicg5ow9S1ftUdaOqbszPD245PZ/CzGQ8HmWX33hxqNS09FCSO73USn9Zqd6c+ozk+Fnp8ZYXBj8h++j+MyzOTuHcxVnTft0PXL6MS5cv4HN/OMiOqiY2RlEhs/GUWrniEVaHPjYEErlOASV+vxc728Y9xhmmyQJagHcDj6nqoKo2An8FNs600eOJixMuLMthd03oA3118+QLggfj325Yx8P/9NpZqQK4tiiTow1dZ92YNZmuvkH+fLSZLesKZnR3blyc8M13nU96UjzN3f1RmT/vr8zKFY+wOvSxIZBAvxNYISJLRSQR2Ao8MuqYR4DbnMc3AX9Sb7GQk8DVACKSBlwEVIai4ePZUJpDVUMXnX3BZ55MZDpVKyeTlZrA8oWzM6xRXpTJkEc52tAd0PF/qmxkYNgz7WEbfwszkvnGOy8gPSmeK1ZO71tZpMhITiAvLZGTVq6Y2lY3aYkuclITwt0UMwNTBnpnzP0O4HHgMPCgqh4UkS+IyPXOYT8C8kTkGPBRwJeCeS+QLiIH8f7B+Imq7gv1m/DZUJaDKuw52R6yczZ19+MeGA5Zj342BTsh++j+ehZlJnFhSWiGka5Ymc++z14zUiM/mpXmpY6sETyf1bV5M25mWo/JhFdAq12o6nZg+6htd/s97sObSjn6ed3jbZ8t55dkESfeErmXh6hXObIg+Axy6OdKWW4qaYmugMbp3QNDPHOkkXdtLAnpeHo0j837K8tNHbnDdz6rbe21YZsYEFN3xmYkJ7CqIPOsG3hmKtAFwSNBXJywpjAzoFIIz1Q10TfoCeomqfmkNC+N0x29Ac93xCJVpbbNbRk3MSCmAj3AhrJsXj7ZzvA4NVimo8ZJrYyWOwPLizI5fKZr3Bo0/rbvP0NeWiKbl0b3xOlsWZKXiirUOZOR81FrzwDugWHLuIkBMRfo15fm0N0/xJGGrpCcr7rZe/v3TFMr58raoky6+4cmrdXSNzjMjspGrllbENBC5/OR5dJbxk0siY7oFQRfxcRdIUqzDGXGzVwoL3QmZCcZp3/uSBM9A8MhybaJVVau2G6WiiUxF+hLc1NZkJ4Yknx6X9XKmRQzm2srFqUTHyeTjtM/dqCerJQELgpBLfxYtSA9kdRE1/wO9G12s1SsiLlALyKsL80JyYRsc7e33sl0yxOHQ3KCi+UL0ydMsRwY8vDk4QauKV9EQpQMR4WDiHjXj53H5YprW3vJTUskLSmg5DwTwWLyX/qGshyqW9w0d/fP6Dy+8dloSK30V16UOWHNm78eb6arb4jrbNhmSmXzvFxxXZubkihJQjCTi9lAD8x4+MZXnjgaUiv9lRdm0tjVT1PX2D90j+4/Q0ZSPK9dviAMLYsuZXmp1LX2hiyDK9rUtroptonYmBCTgX7d4iwSXDLjAmc1LW5cUZRa6TNyh+yoCdnBYQ9PHGrgdWsWzkqtnVhTlpfGwLCH+s7x18aNZcMe5VR7r43Px4iYDPTJCS7WLc6acY++uqWH4pyUqBvL9i1CMnqc/qUTrbS7B7nuXLtJKhDzOcWyobOPwWEdqeRpolt0RbAgbCjNYW9dBwND01+IJNpSK32yUhMozkkZk3nz6IEzpCa6or7o2FyZz+WKLbUytsRuoC/LYWDIE1A5gPGoKjXNbpZGUcaNv9G16Yc9yuMH67lq9UKSE2zYJhBF2SkkuGR+BnrfzVI2dBMTYjbQr5/hjVMtPQN09Q9FZY8evJk3rzT30OMsh1dR3Upz9wDXrbNsm0C54oSSnNR5Wa64ttWNiPePnYl+MRvoF2UmU5yTMu18+ldTK6OzR7O2KAtVqKz3loJ49EA9SfFxIV+rNtaV5qXOzx59q5vCzGQS42M2RMwrMf1/cUNZDrtq2vCugRKckfLEUdyjB2/mjcejPHagnitW5tvNL0Eqy/UG+ul8hqJZbZulVsaSmA/0DZ39nGoPvgJhdUsPcQLFUTpGWZSVTHZqAodOd/BybTv1nX280bJtglaal0Z3/xCtPQPhbsqcqm211MpYEtOBfn3p9Mfpq1vcFOekRu1XVxHxTsie7uSxA2dIcAlXr7Fhm2D5VharmUelEPqHhmno6rOMmxgSnVEsQKsLMkhNdE0rn766uSeqatyMZ21RJpX1XWzfX89lK/LJTLZ1P4M1H3PpT7X1omoZN7EkoEAvIltEpEpEjonIXePsTxKRB5z9L4nIEr9954nICyJyUET2i0hy6Jo/uXhXHBeUZAd9h6xvQfBoHZ/3KS/KpH/Iw6n2XrZYts20FOekIjK/cumtDn3smTLQi4gL7yLf1wHlwM0iUj7qsPcBbaq6HLgH+Krz3HjgF8AHVHUtcCUwGLLWB2BDWQ6Hz3SNpBkGos09SFffUNQVMxvNVwohPk64pnxRmFsTnZITXBRmJnNyPgV6u1kq5gTSo98MHFPVE6o6ANwP3DDqmBuAnzmPHwJeJ95l468B9qnqXgBVbVHVOV2Ec31ZDsMeZW9de8DPebWYWXT3aM5ZkEZSfBwXL8sjOzUx3M2JWqV5qfNqjL62zU2iK45FGXP25dvMskAC/WKg1u/3OmfbuMeo6hDQAeQBKwEVkcdFZLeIfHy8FxCRbSJSISIVTU1Nwb6HSa0vCb6SZbSWJx4t3hXHt7dewKffNPoLmAlGWW7avBqjr2vtZXFOCnG2zGTMmO2k6njgUmAT4AaeFpFdqvq0/0Gqeh9wH8DGjRtDmrCclZrA8oXpQWXeVLe4ndTK6P/qumWdpVTOVGle6sgiNOnz4D6E2jZ3THz2zasC6dGfAkr8fi92to17jDMunwW04O39P6eqzarqBrYD62fa6GBtKM1h98l2PAHWFa9u7qEoO8VK+Rrg1Zvm5ss4fW2r2yZiY0wggX4nsEJElopIIrAVeGTUMY8AtzmPbwL+pN5bCR8HzhWRVOcPwBXAodA0PXAbynLo6B3kRHN3QMfXtPRE1TqxZnb5UiznQ82b7v4h2tyDlloZY6YM9M6Y+x14g/Zh4EFVPSgiXxCR653DfgTkicgx4KPAXc5z24Bv4v1jsQfYrap/DP3bmFwwBc5UlVdiIIfehE6p81mongc9esu4iU0BDTiq6na8wy7+2+72e9wHvGOC5/4Cb4pl2JyzII3s1AR21bTxrk2lkx7b7h6ks28o6nPoTehkJieQk5owL3LpRwK99ehjSkzfGesTFyesL80JqEfvWwzaAr3xV5aXNi+Gbuxmqdg0LwI9eMfpjzf10DZFcarqKC9PbGZH2TwpV1zb6iYt0UVOqpXLiCXzJtD7Cpy9XDt5r7662bvggvVojL+y3FROt/fOaGnKaFDX5s248d7vaGLFvAn055dk4YqTKYdvqlt6KMqy1EpztrK8NDzqDYSxrLa1N2pLc5uJzZtAn5oYT3lhJrtrJi+FUN3ittRKM0bZPChXrKrUtrkt4yYGzZtAD95x+j217QwNT/z1u6bFUivNWL4Uy5rm2J2Qbe0ZwD0wTKkNW8aceRXo15fl0Ds4PLKO6mjt7gHa3YOWcWPGyE9PIjXRxf5TneFuyqwZybixoZuYM68C/YYpbpzy3RAT7cXMTOiJCDdtKOa3L9fx/LHmcDdnUoGW+hjt1ZulLNDHmnkV6IuykinITJ440MdIeWIzOz5x3RqWLkjjY7/eS4d7TpdVCNh9zx1n3ece59tPHaVvMLiK4CedQG8FzWLPvAr0IsKGsolvnKpu6bHUSjOhlEQX337XhTR19fPJ3+3HW84pcvxx3xm+vL2SBelJ3PPUEa791nPsqGwM+Pl1bW7y0hJJmwcVOuebeRXowTtOf6q9l/qOvjH7alrcFGWlkJxgqZVmfOcWZ/GRN6zkj/vO8PDLo4u4hs/uk2189ME9bCjL4YmPXM4v3vcaXHHCe366k/f/vGJkWGYyta29FFsnJybNu0DvG6ffPc46slbMzATiA1csY/OSXO7+/cGAAuhsq2118/6fVVCQlcx9f7+B5AQXl65YwGMfupx/3bKavxxt5vXffJb/fHry4ZzaNjclNmwTk+ZdoC8vzCQpPm7c4Zualh6biDVTcsUJ33jn+Qjw0Qf3MDzNyc9Q6Ogd5Paf/I0hj/Lj2zeRl540si8xPo5/vHIZT3/sCl6/ZhHfeNIZzqkaO5wz7FFOt/fasGWMmneBPjE+jvOLs8cE+g73IG3uQZuINQEpyU3lC29dy87qNr737PGwtGFgyMM//mIXJ1vdfO/vNrAsP33c44qyU7j3lvX8z/s24xLhPT/ZybZRwzn1nX0MDqulVsaoeRfoAS4sy+bg6Y6zvsZa1UoTrLdesJi3nF/EPU8eYV8Qi8+Hgqry6d/t5/njLXzl7edx8bK8KZ9z2Yp8Hv3wZXx8yyr+fLSZN9zzLP/1p6P0Dw1bHfoYNy8D/YbSHAaHlf2nOka2VcfIguBm7ogI/3bDOvIzkvjw/XtwDwzN2Wv/97PHebCijg9evZwbNxQH/LykeBf/dOVynvrYFVy9eiFff+II197zHA/v9k4sW48+Ns3LQD/eilPVzd4ejd3+bYKRlZrAN955Pq+09PClPx6ek9f8v32n+dpjVVx/fhEfecPKaZ1jcXYK371lAz9/72biRHigohYR7zCPiT3zMmF2QXoSS/JSzwr0NS09FGUlW2qlCdolyxaw7bJz+P5zJ7hq1UJeX75o1l5rV00bH31wLxvLcvjaTefNuJzw5Su9wzk/+Ws13X1DJMbPy75fzAvo/6qIbBGRKhE5JiJ3jbM/SUQecPa/JCJLRu0vFZFuEbkzNM2eufVlOeyuaRu56aW6pYcyG5830/TRa1aypjCTf/3NPpq6+mflNU62uNn28woKs5K579aNIeuUJMW7+MAVy7jz2lUhOZ+JPFMGehFxAfcC1wHlwM0iUj7qsPcBbaq6HLgH+Oqo/d8EHp15c0NnQ1kOLT0DI6sGVbe4bXzeTFtSvItvb72A7v4h/vU3+0J+12yHe5D3/NSbRvmT2zeRm5YY0vOb2BZIj34zcExVT6jqAHA/cMOoY24AfuY8fgh4nTjfKUXkrcArwMHQNDk0/AucdfQO0tozYKmVZkZWLsrgE9et5k+VjfzipZMhO+/AkId//F9vGuX3/34D50yQRmnMRAIJ9IuBWr/f65xt4x6jqkNAB5AnIunAvwKfn+wFRGSbiFSISEVTU1OgbZ+RFQszyEiKZ9fJNmqcjBsbujEzddslS7h8ZT5f+uMhjjV2z/h8qsqnHn41jfKic6ZOozRmtNmeefkccI+qTvqJV9X7VHWjqm7Mz8+f5SZ5ueKEC0qz2V3TNlKe2FaWMjMlInz9pvNISXDx4QdenvEas9995ji/3lXHB1+3Iqg0SmP8BRLoTwElfr8XO9vGPUZE4oEsoAV4DfA1EakGPgx8UkTumGGbQ2ZDWQ5VDV0ccPLpLbXShMLCzGT+/e3nceBUJ9966si0z/OHvaf5j8eruOGCIj7y+hUhbKGZbwJJr9wJrBCRpXgD+lbg3aOOeQS4DXgBuAn4k3pnoy7zHSAinwO6VfW/QtDukNhQloMqPLLnNIVZyaQkWmqlCY0t6wp418YS/vvZ41yxMp/XjDPkMuxRGrv6ON3ey6l273+9P97HRxq62LQkh6/eOPM0SjO/TRnoVXXI6YU/DriAH6vqQRH5AlChqo8APwL+R0SOAa14/xhEvAtKshHx1vm46JzccDfHxJi731LOi6+08NEH9/KBK5dxxi+Qn2rvpb6zb0xBtMzkeIqyUyjKTuHiZXnccdVyu7fDzJhE2uIJGzdu1IqKijl7vS3feo7K+i62birhKzeeN2eva+aHl0+28a7vv8jAsIf4OKEgK5mi7BQWZ6dQlJ08EtQXZ6dQmJVMRnJCuJtsopSI7FLVjePtm5d3xvrbUJZDZX2X5dCbWXFhaQ4vfOJqBoeV/IwkXHE2BGPm3ry/39mXT2859Ga25KUnUZCVbEHehM28D/RvKF/E+y5dyqUr5iat0xhj5tq8H7rJSE7gM28eXdHBGGNix7zv0RtjTKyzQG+MMTHOAr0xxsQ4C/TGGBPjLNAbY0yMs0BvjDExzgK9McbEOAv0xhgT4yKuqJmINAE14W5HgBYAzeFuRASy6zKWXZOx7JqMNZNrUqaq497iH3GBPpqISMVE1eLmM7suY9k1GcuuyVizdU1s6MYYY2KcBXpjjIlxFuhn5r5wNyBC2XUZy67JWHZNxpqVa2Jj9MYYE+OsR2+MMTHOAr0xxsQ4C/SjiEiJiOwQkUMiclBEPuRszxWRJ0XkqPPfHGe7iMh3ROSYiOwTkfV+57rNOf6oiNwWrvc0UyG+JsMissf5eSRc72mmpnFNVovICyLSLyJ3jjrXFhGpcq7XXeF4P6EQ4mtSLSL7nc9JRTjeTyhM45rc4vyb2S8iz4vI+X7nmv7nRFXtx+8HKATWO48zgCNAOfA14C5n+13AV53HbwQeBQS4CHjJ2Z4LnHD+m+M8zgn3+wvnNXH2dYf7/YTpmiwENgFfAu70O48LOA6cAyQCe4HycL+/cF4TZ181sCDc7ykM1+QSX5wArvOLJzP6nFiPfhRVPaOqu53HXcBhYDFwA/Az57CfAW91Ht8A/Fy9XgSyRaQQuBZ4UlVbVbUNeBLYModvJWRCeE1iRrDXRFUbVXUnMDjqVJuBY6p6QlUHgPudc0SdEF6TmDGNa/K8Ey8AXgSKnccz+pxYoJ+EiCwBLgReAhap6hlnVz2wyHm8GKj1e1qds22i7VFthtcEIFlEKkTkRRF5KzEgwGsykfn8OZmMAk+IyC4R2TYrjZxj07gm78P7zRhm+DmZ94uDT0RE0oHfAB9W1U4RGdmnqioi8y4vNUTXpExVT4nIOcCfRGS/qh6fpSbPOvucjBWia3Kp8zlZCDwpIpWq+twsNXnWBXtNROQqvIH+0lC8vvXoxyEiCXj/p/yvqv7W2dzgG35w/tvobD8FlPg9vdjZNtH2qBSia4Kq+v57AngGbw8nKgV5TSYynz8nE/L7nDQCD+MduohKwV4TETkP+CFwg6q2OJtn9DmxQD+KeP/U/gg4rKrf9Nv1CODLnLkN+L3f9ludTJOLgA7nK9njwDUikuPMqF/jbIs6obomzrVIcs65AHgtcGhO3kSITeOaTGQnsEJElopIIrDVOUfUCdU1EZE0EcnwPcb7b+dA6Fs8+4K9JiJSCvwW+HtVPeJ3/Mw+J+GelY60H7xflRTYB+xxft4I5AFPA0eBp4Bc53gB7sU7I74f2Oh3rvcCx5yf94T7vYX7muDNKNiPN2NgP/C+cL+3ObwmBXjHVTuBdudxprPvjXizMY4Dnwr3ewv3NcGbWbLX+Tk4z67JD4E2v2Mr/M417c+JlUAwxpgYZ0M3xhgT4yzQG2NMjLNAb4wxMc4CvTHGxDgL9MYYE+Ms0BtjTIyzQG+MMTHu/wMs8RNkrfut2wAAAABJREFUNa8GHgAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1jH9nnG1440N",
        "colab_type": "text"
      },
      "source": [
        "## 6. Conclusion"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xWrKkWWNuM6c",
        "colab_type": "text"
      },
      "source": [
        "\n",
        "We have learned about topic modelling, and more specifically LDA and Mallet. Topic modelling is a great unsupervised tool for extracting topics from documents, and Mallet is a particular model for performing topic modelling, which improves on the weaknesses of the original LDA model - precision.\n",
        "\n",
        "We learned how to preprocess and clean text for building an LDA/Mallet model. First, cleaning the data with regular expressions, then removing stopwords, and creating a Bag of Words model. We discussed the assumptions of the BoW model, but recognised that it can be a useful simplification in many cases.\n",
        "\n",
        "Then we built the Mallet and LDA models, chose the optimal number of topics, and visualised the topics in pyLDAvis. The visualisation methods of pyLDAvis were covered in detail.\n",
        "\n",
        "After finding the topic representation for each document, we saw how further work can be done, such as looking at how certain topics trend overtime.\n",
        "\n",
        "If you would like to go further, I would recommend looking into Deep LDA or Neural Topic Modelling with Reinforcement Learning. \n"
      ]
    }
  ]
}