Machine_Learning_Guide_to_Authorship_identification.ipynb

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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/VincentK1991/Authorship_attribution/blob/master/Machine_Learning_Guide_to_Authorship_identification.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-_83zDSBxxQC",
        "colab_type": "text"
      },
      "source": [
        "#0. Introduction\n",
        "\n",
        "we're going to explore NLP problem of authorship attribution. The task is to identify who the author is from the text. We are going to treat this as a text classification problem, where we train a classification model on a training set (i.e. the set of texts you know for sure who wrote them), then apply the model on a set where you don't know.\n",
        "\n",
        "We will use a dataset from this [kaggle](https://www.kaggle.com/c/spooky-author-identification). The dataset contains texts from books written by one of the 3 horror writers, H.P. Lovecraft, Edgar Allen Poe, or Mary Shelley. So our task is to classify who wrote which text. A nice thing about this dataset is that besides it being multi-class classification is that since all three of them write in horror genre, the content may be relatively of the same moods and tones. So one might say, it's harder to differentiate, than let's say a horror writer from a romance writer.\n",
        "\n",
        "We will explore 4 ML approaches\n",
        "\n",
        "1. TF-IDF and regularized regression\n",
        "\n",
        "2. Naive Bayes classifier\n",
        "\n",
        "3. recurrent neural network\n",
        "\n",
        "4. BERT classification"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "epGj5Os7X4VJ",
        "colab_type": "text"
      },
      "source": [
        "# 1. Data exploration"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "O6DuznurXqsH",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 70
        },
        "outputId": "ee0bd8f0-af2f-4ba5-e7db-7a3aa8cd02ea"
      },
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import seaborn as sns\n",
        "from matplotlib import pyplot as plt\n",
        "from sklearn.model_selection import train_test_split"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/statsmodels/tools/_testing.py:19: FutureWarning: pandas.util.testing is deprecated. Use the functions in the public API at pandas.testing instead.\n",
            "  import pandas.util.testing as tm\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5wGCdGBhYXWx",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "train_df = pd.read_csv('spooky-author-identification/train.csv')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "S8GIhevv165I",
        "colab_type": "text"
      },
      "source": [
        "One thing we need to check is whether the classes are balanced. Training on imbalanced data can lead to model bias. So we will do sub-sampling so each class has equal data."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lJnvP0K8Yf-w",
        "colab_type": "code",
        "outputId": "da089e69-7dba-45d2-e417-8f9759838291",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "train_df['author'].value_counts()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "EAP    7900\n",
              "MWS    6044\n",
              "HPL    5635\n",
              "Name: author, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5o0QmkyNZFro",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "g = train_df.groupby('author')\n",
        "train_resampled = g.apply(lambda x: x.sample(g.size().min()).reset_index(drop=True))"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GLUn2YqWYg1i",
        "colab_type": "code",
        "outputId": "43e6935f-c7d0-472d-d97f-3bc374690974",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "train_resampled.author.value_counts()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "MWS    5635\n",
              "HPL    5635\n",
              "EAP    5635\n",
              "Name: author, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Ox4A88Zq5FA5",
        "colab_type": "text"
      },
      "source": [
        "We will change the author label to numeric"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ufuMEmPha4Ik",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "train_resampled['author'] = np.where(train_resampled['author'] == 'EAP',0,\n",
        "                                     np.where(train_resampled['author'] == 'HPL',1,2))"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_5GRpH17bOSZ",
        "colab_type": "code",
        "outputId": "3515678f-cc49-4bc2-9947-ae6b4bf7e472",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "train_resampled.tail()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "      <th></th>\n",
              "      <th>id</th>\n",
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              "      <th>author</th>\n",
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              "      <th></th>\n",
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              "      <th rowspan=\"5\" valign=\"top\">MWS</th>\n",
              "      <th>5630</th>\n",
              "      <td>id11811</td>\n",
              "      <td>Unable to endure the aspect of the being I had...</td>\n",
              "      <td>2</td>\n",
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              "    <tr>\n",
              "      <th>5631</th>\n",
              "      <td>id12409</td>\n",
              "      <td>I thought with a sensation of madness on my pr...</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5632</th>\n",
              "      <td>id10423</td>\n",
              "      <td>But if his countenance was more sorrowful, his...</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5633</th>\n",
              "      <td>id27947</td>\n",
              "      <td>Adrian was now fifteen years of age.</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5634</th>\n",
              "      <td>id14473</td>\n",
              "      <td>My thoughts were sad and solemn, yet not of un...</td>\n",
              "      <td>2</td>\n",
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              "                  id                                               text  author\n",
              "author                                                                         \n",
              "MWS    5630  id11811  Unable to endure the aspect of the being I had...       2\n",
              "       5631  id12409  I thought with a sensation of madness on my pr...       2\n",
              "       5632  id10423  But if his countenance was more sorrowful, his...       2\n",
              "       5633  id27947               Adrian was now fifteen years of age.       2\n",
              "       5634  id14473  My thoughts were sad and solemn, yet not of un...       2"
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    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "y3gRuNjjaXDa",
        "colab_type": "text"
      },
      "source": [
        "# 2. TFIDF-regression\n",
        "\n",
        "\n",
        "The first approach we will consider is a more traditional ML model, namely, TF-IDF and regression. I have written elsewhere about how TF-IDF works. But to explain briefly, TFIDF weigh the frequency of words in the corpus against number of documents in the corpus that contain the word. That idea being words that appear often equally across documents are not useful for the purpose of identification. Words that are useful for identification are words that appear often in one document but rarely elsewhere. This could be thought of as a word that one author like to use in particular. So this word carry information to identify the author.\n",
        "\n",
        "Then we will simply take these word weights as features representing the text. And use the ridge regression to classify these features.\n",
        "\n",
        "Note the **advantages and disadvantages** of this method. One nice thing about this is that we can interpret how the model works pretty easily because we know the regression coefficients implies the relative importance of TFIDF words. In the end, we'll see what words are preferred by which authors.\n",
        "\n",
        "But the big disadvantage is that we lose the sentence structure information. This is because TFIDF only look at word level. Words are taken out of sentences. So, let's say that sometimes authors use the same words to describe something, but one author has a particular way to arranging sentence or phraes that is unique to him or her. This information will not be incorporated in the model."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "uidOoCnQ-CqL",
        "colab_type": "text"
      },
      "source": [
        "## 2.1 Vecterization\n",
        "\n",
        "First before vecterization, we will need to clean up the text a bit. This includes getting characterrs to lower cases, removing punctuations, and stem the prefix and suffix. \n",
        "\n",
        "The last part eliminate the part of speeches of words. In general, this helps consolidate different word forms to one root word so that might help keeping the TFIDF dimensions not too high. But it might, in some case, eliminate the information.\n",
        "\n",
        "It may be a good idea to keep the TFIDF features not too high, because later on when we do regression, having too many parameters lead to over-fitting. So we will try with 5000 TFIDF words."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Ii0lDothaZmM",
        "colab_type": "code",
        "outputId": "68cf2d87-edc4-4009-ad1d-78f326484fe5",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "import nltk\n",
        "from nltk.stem.snowball import SnowballStemmer\n",
        "from sklearn.feature_extraction import text\n",
        "import re\n",
        "punc = ['.', ',', '\"', \"'\", '?', '!', ':', ';', '(', ')', '[', ']', '{', '}',\"%\"]\n",
        "stop_words = text.ENGLISH_STOP_WORDS.union(punc)\n",
        "nltk.download('wordnet')\n",
        "lemma = nltk.wordnet.WordNetLemmatizer()\n",
        "stemmer = SnowballStemmer(\"english\")"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[nltk_data] Downloading package wordnet to /root/nltk_data...\n",
            "[nltk_data]   Package wordnet is already up-to-date!\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JK2aUQUnZyXz",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def split_stem_stop(df1,stopword):\n",
        "    df = df1.copy(deep=True)\n",
        "    df['list_sentence'] = df['text'].str.split(' ')\n",
        "    df['list_sentence'] = df['list_sentence'].apply(lambda x: [y.lower() for y in x])\n",
        "    df['list_sentence'] = df['list_sentence'].apply(lambda x: [lemma.lemmatize(y) for y in x if y not in stopword])\n",
        "    df['list_sentence'] = df['list_sentence'].apply(lambda x: [re.sub(r'[^\\w\\s]',' ',i) for i in x])\n",
        "    df['list_sentence'] = df['list_sentence'].apply(lambda x: [stemmer.stem(y) for y in x])\n",
        "    return df[['author','text','list_sentence']]"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "l1-rCy6AamR0",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "train_resampled = split_stem_stop(train_resampled,stop_words)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "klyr_1Mrama0",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "train_resampled['stemmed_text'] = train_resampled['list_sentence'].apply(' '.join)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "w-0q0wIFcPuY",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "train_resampled.to_csv('TFIDF_intermediate_Apr11_2020.csv')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xo14yuQu-Y0f",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "train_resampled = pd.read_csv('TFIDF_intermediate_Apr11_2020.csv')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "oKJMGV24p8wZ",
        "colab_type": "code",
        "outputId": "f023b28a-c252-46ac-c65d-3c9facb13b53",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 296
        }
      },
      "source": [
        "train_resampled.tail()"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "      <th>Unnamed: 1</th>\n",
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              "      <th>16900</th>\n",
              "      <td>MWS</td>\n",
              "      <td>5630</td>\n",
              "      <td>2</td>\n",
              "      <td>\"Alas, for us,\" I cried, \"who have lost this l...</td>\n",
              "      <td>[' alas ', 'us  ', 'cried ', ' who', 'lost', '...</td>\n",
              "      <td>alas  us   cried   who lost latest honour wor...</td>\n",
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              "    <tr>\n",
              "      <th>16901</th>\n",
              "      <td>MWS</td>\n",
              "      <td>5631</td>\n",
              "      <td>2</td>\n",
              "      <td>You well know that on the whole earth there is...</td>\n",
              "      <td>['know', 'earth', 'sacrifis', 'make ', 'labour...</td>\n",
              "      <td>know earth sacrifis make  labour undergo mere ...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16902</th>\n",
              "      <td>MWS</td>\n",
              "      <td>5632</td>\n",
              "      <td>2</td>\n",
              "      <td>Adrian observed a change during a visit that h...</td>\n",
              "      <td>['adrian', 'observ', 'chang', 'visit', 'afterw...</td>\n",
              "      <td>adrian observ chang visit afterward paid them ...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16903</th>\n",
              "      <td>MWS</td>\n",
              "      <td>5633</td>\n",
              "      <td>2</td>\n",
              "      <td>We met baggage waggons, and many females of hi...</td>\n",
              "      <td>['met', 'baggag', 'waggons ', 'femal', 'high',...</td>\n",
              "      <td>met baggag waggons  femal high low rank return...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16904</th>\n",
              "      <td>MWS</td>\n",
              "      <td>5634</td>\n",
              "      <td>2</td>\n",
              "      <td>But in the detail which he gave you of them he...</td>\n",
              "      <td>['gave', 'sum', 'hour', 'month', 'miseri', 'en...</td>\n",
              "      <td>gave sum hour month miseri endur wast impot pa...</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
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              "      author  ...                                       stemmed_text\n",
              "16900    MWS  ...   alas  us   cried   who lost latest honour wor...\n",
              "16901    MWS  ...  know earth sacrifis make  labour undergo mere ...\n",
              "16902    MWS  ...  adrian observ chang visit afterward paid them ...\n",
              "16903    MWS  ...  met baggag waggons  femal high low rank return...\n",
              "16904    MWS  ...  gave sum hour month miseri endur wast impot pa...\n",
              "\n",
              "[5 rows x 6 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8F4UnRobcdCv",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from sklearn.feature_extraction.text import TfidfVectorizer"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zAYH5j99c4LL",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "tfidf1 = TfidfVectorizer(max_features=5000)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Bie_xwZZdfxq",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "train_TFIDF = tfidf1.fit_transform(train_resampled['stemmed_text'])"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MIxvVFLvdosv",
        "colab_type": "code",
        "outputId": "0c2f4cd4-9700-4dd2-a727-67a14a1529fe",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "train_TFIDF"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<16905x5000 sparse matrix of type '<class 'numpy.float64'>'\n",
              "\twith 171289 stored elements in Compressed Sparse Row format>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 65
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tofmtBS7-sfL",
        "colab_type": "code",
        "outputId": "fa412de1-190b-495d-f88b-c53366b63030",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "# to visualize the sparsity \n",
        "plt.figure(figsize=(10,8))\n",
        "plt.spy(train_TFIDF,markersize=0.05)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.lines.Line2D at 0x7f1b62a4b390>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 66
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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EM0XShQqnFrJUazITyzlEUSCdrxAvVJHlGsfnM0zFsuhFgcl4EZtWpcVrQxBgbaONIxNRtnd6EDQiqXwRk07LoYkYvQ0W0sUaB8aTXNhiwWrU8sRAjESuRKVSZT5dwaiT2N3h5NH+MC1uMzVFZt9wDIde4NuH5ri618cqv4XRaJ4ev5X+uSQuqwGvRUskUyGar6LUqoiSxGQsj4zI+oCBrz8/yQVtLtYGrTzSH8Vr0SKrMBnLMZ/MMxUrUKpUeXkiRv9ilb6AkYlEkaDDxLqQneFIgU3NNmaTZU7OJpEEFb0kMh3Lcnohy0S0wPhz/7b8N5//3P9+PW68ZWbqXxVus44Gh5mzkTzXrnETzVf47FXdbG62k1e1/O21XUwkSkRzdX/20HiccLbKrg43iiwzGi1Sq1a56/AsWkniAxc2cUmPnw6/Db1ez19e3cW+sRQbmuxc2u2h2WXBY6mHyRRZ5tsHpzg+k6DJoWdDyEGLx8rODjfPj8SRZZnxeJmPX9KCz25mVdDBhc0W7jo8w5HJZeRajS6viZNzSUDlpnV+gg4DKrDabyNZrHFsOolWq+UDFzaxsclJh9/G2gYLB6czTEfTvDIdR67VWMpVOTYV590bvRyfTeOxaJmIF9k3vMzx6Tjdbi33HZln/3iSj+wMsa3Dw3yyiMeiZS6e5a5D0zw7uMT+0WXaPWYuX+0j5DLT4q4/z8szKXxWAxPRHMdmkjQ5DKiqiiRJvH+zm+MLRfxWHZ+4ootPXtrCNw/MIteqhDMVNBoN1/W6WUwXmYhmaXOb8NiMXNzl5vnROJFslaAVBI2AIGho91qQJInd3T46PGZa3SZ6A2b2jydQFQWtTsf7todotOt/4Uz9lvCpfx0IgoAoiqxrMHJsLodO0vJE/xJjyznev6OF+XSVbq+Ji1f5kSSJroBAt8/M3jNh0sUaqwMWIpkyV6zx0+61MB0vIADv2BhkLJJFr9PS5jExEc0xGssTcpuI5qqMRzLsG4nR6tDiNmt5pD+Cx1z/QWg0GhxmCa1Wy8XdHta2+lnT7KVcLvN3T07yns1enlnMkSrUaPdaSBZkKuUKL01pqdaqzKVKbGxy0Oy1MJ8s8r7tIe49ssBSqohGI6DVSvzBpkZ2dbr4xgvTTMULdHuMDCxm+cgP+pFrKhd1uhEFAQQN49ECL4zF6yHEFivfPDDHhkYTkRxc0GTlxGKBnoANSZII2vVMx4u0ekzcf2wWVVU5OB5nR5sDSZKQZZlWr5mxcIpHzyxzVbeDTz46wyf3NKLRaLj70AyqqrI+aCZVNrOj3cW3D0wgCBqcZh2CUF83rGuwEM5UuHJNgLHFOI8PxtCIGjY2O1ndYGNwKUO318QH7znDtnYX8UKVPd0eRFGkxWPhiaE4HrsRpZiJvxE3ztmZGlWl0WFEr9fx4d3tvG9bkIFIAVlWViIORU7MZXl+KMx4JIOqqj/zk/f0+Ag6TWg0Ap0+KwIwn8izkCpSq9V4rD9Mk0PPmqCda/sC9PitdPqseC1arl/fyCXdHvqXCqDCDX0+YvkqByfiVCoVJFFElmWGlzJUazUOjEb5/vEI993SR7IicEm3G49Vj0Yj8v4LQ+RlgW0tNoI2PVuaHYQzZZodemRZ5tMPDlCtVtnc4qDNY+bEbJJwOs8j/WFuWOfnxHSCTFlmfchBo02LqtHw4MlFRpbz7Fnt5/07mtDr9ezudJIoqTQ59SRLKj0BPUdnswTtRnZ3e/jy29YgihLzyTyjiykS+QqpQpVqpcIXnhrj3v3DfPXZMfr8ZvaNxGhy6Olr8XLdOh8j8SpnFrN0+czs6XYzES+yudXFmqANrU7P6oCNC9o8/PFFbYDAqfk04UyRVreJ2XSND+9sosFuQiPAN58f5ct7B/jmvjEQYFenm41NDrRaLe1eC+1eC1taXLxjY+gXUuOcJXWhqgAqLW4TT/Qv8aOTETxmLR6rgZvWBRiNV7mu10uyJPPKdIJqtcqj/WHKlTInZxL0z6dRFRVFUTk0kUAQNGxvdTCfrrC11Ymiqnxz3xh37J9kLFqgUq1yYj7H4FIGg17Hn+1pA1SeGIqxIWSnwarje0eXuHGtn0MTCSK5GgdGlgGVnW12/mX/PKoqIEoiPquBqViO2VSZDreRQxMJ/u3oPJFMibMLKe48OM0rs2k6PSZEUaTNW487qyosZGok8hWaHQaGloukijLX93kYT1S4bo2LjU0ONKKIChxfKHDLtkamEmVkRSZgN+K16nGa9Ciywlwix9eeG+NTD5xlLpGjWq3xwngcp0nL6qCdy9eG+MPNDQxGq4QcOpYyJbJlmVSphiAIbG1z0+Sx8pkru0gWaixlyoiSlit73Nx7ZIEv37yai3t8tHvNjEUL1JOFAoJGQ7fPhN0o8Vh/hGi2zJWrvbS4rdhNOiStRLvXwlyqjCiKhOw6xiJZHj4VJmTXcWgijlotFd+IG2+JNPmvgzVrN6hf/O5jNDv0zCSKVKpVABaSRS7rDdLm1HHvkUUuaLFyz+FZ/nh3O7VajQdPLZIu1LhlWyMvT6fYszqIIFDPrE0kCDkMtHnMPHhyEa9Fx0XdXmrVKgtZmVaXgb6Qk2q1yh0HJhkN5/jizauZSdeo1WogCPQ1Oth7ZomgVeLgeJxatUq6Ajes9dEXcvLMUJS5RIEml4lKpcq+kSh7ut1IklQn4kySt60PcHAizsXdPg5PJdEIGj60q5U7D06xvdXBRDRH/0KOK7usPHY2xju3NvFPTw2joqLV6tjabOey3iDdPjNPDSxzdDKK36ZnOVumL2Dm4dNLuCwmLuvx0uIyMhXLodVqmUsUUWQZBGhymXnblhZUVeXBY9OEc1VQFVChwa4nnK0StOu5pi/AM8Mx5mI5Lur2Uq3WeGU+h88sspgpE0mXCDiM7Or00OO3cOfBaWq1GmPRIm1OLc8ORrisx0tB1XLz+gDjkSzdDXYmlnO0uo0A3P7CBF1+My1uC6fn0yxnCnznk78fryYXPa/HjXN2ps5ValzW5eTgeIImhwFBEDg9l2EsWqBQKPKZhwbxW0QW0hVWBayowGyyhCRJ3LItxOODMYaWsqiqQrfPzEuTSVAVRFGkJ2BlfaONi1f5QIV7jizQ6jLS2+jgsdML/P1jg6iyysVdLv75uSnK5TIPnFhgJl5kaClDq9uEIAgMLqY4OJ3FIil88ZkJPnn/GUJ2Hc0eC9f0BTi9kAEVjs8mafdaaHObSBWrHJpMcmQywaHxONuabTQ6jQyHszQ6jAiChtMLGSqVMn/75AQHx+NMRLK4bUYKFZWPXdRENF/hheEI//jkCLPxHH6rAb9VR7qscmo+iyRpubjTyUKqxHdemuW+owuE7PVcxkIqx3KmxHNDy5yajjK4mGY5XyXkMHBRt4+Lun2Es1VkWea5oWX+5uF+5uI5gg4jU7E8j55Z5oJmK51+Gy1uC7+3OUSj3cD+0Sh3HpxmZ4eLkNPIB3Y2c/EqP7mqSqIoc0OfjzVBO6fnM/QErAgCPHhikalYnky5xkg4g4rKzRuC9Da6UBW59kbcOGdDev/rG/96W67tElL5EulSjXShyuqgnd2dLh45E+HCdjeCINDuszCfLiHLKnOJPFf3BVjIVPBbdei1Is0uI4+cDhO069nQ7GRzyMoXnxpjMJzhD7eESJZk7AaRi7p9DIezRNMF5lN5bAYdToueCztcvDSVojtg5dq1AY7PpYmkcjw/EsOi1/FHm72YrVY+fUUnzS4Tj/ZH2N7mIOAwMxrJ0uMz8sJYChSZxUwFj9XAha0Ojs6m2dho5f8cmOH6Xi+Hp1KYtQL9cwmsBi3dARtBp4megIVCVeGGPh/94QKKKjAUzvEHWxox67VYDBJjyxnG42VuuSCIzWxglc/EYKRIl9/Mpau8xDIl0hWFWrXKKzNpPFYdRq2Gw5NJHEYt6UIFq15iYCmDqoLdpMdqlEBRODiR5N2bA5SRaHEZiebKHJtKYDHpubYvQMBhYngpw/qQnflUiUpN5uHTYRRFoc1rIVesEHSamE+VMEgCh8ZjXNUXoFqTSZVk3nVBM20uA/2LeZK5EhajjvUhO9/+P99I/fVffuZ/vR43ztmZGgRu6POj1+m4dVuIeL7GFT1ujsykEQSBNreJM4s5UFU+uKOFNk/dr6vXhgi0uk20eKxMxwuoqkKTQ894JMtHvt9PX4OZy3p83H14lvFwhrl4nrPzSUbDGRZSZdLFGgG7DlWFvpCT3d3eekFPtMCVPR7OLubxWvWs9hv42A9HKJVKjEYLTMULKKgcGIsxuJhme6sDg17Pnm43Go2GoMPARZ0ejsyk2dJkYzlfT5rMJIo02HTIsswLo3G2NtsYWMrzoZ0txPM1XpqM8djZZT5+SRvNbhNNTgPKSvVbu8eCVtLynq0NHJnOMBPLsZguc9N6P5FslclYjkS+zI42J6IksbbRhlaS8Nn09AYdtLgMhNNF5uJ5IukSgiBw/fog7R4zyWKNdQ0mbj+8SNAqMR0voEFAlDRcvcbH3rNh+mfj7BuJMh0vsrPDxQ3rG3nHpkaa3RbGwmm0Oi23XNCIIMCLo8soqspjp+Z56NQSO9pd9M8lODKT4eJOB+JKFGY6UULOJSJvxIxzltRaUeDoTIbrej1856U5FEXmn56bZEe7i62tLkRRw43rfByaiPOFvSMoikynx8jes2Fq1RoPnlzksm4nGlHEYxK598gCqqpw/Vofep2OmzY28aFdraiqwlA4ywvDEUSNwO5uL90+K/0LGY5PJxhczCDLMrKiMBZOMxTOcvOGBrSSSJvbRMChXwmPxVhKl+gLmAi5TIwupbn78AzT0Sy1Wo3lbIm5eI5arUo4XSScKiIr9bLZfaMxUFUiuRqrA1b2DiXo8Rl4on8JVVVwGnW4jBKHJuNMR7P0zyX52rNjVCoVZpMl1gRM3Hd0CUWReWk8ykgkx1S8nj39yXCUhUyZqVieWK7C71/QwoYmO6IosqPdwcHxBNOxPEGnEVmu0eaxIMsyB8djyLUakYLKRy9q4uXpFLOJPBe2O9nSWv8lnYlmmYoXuKTLTZNDx4GxGHcdmqHda+HyVW6eGIjT6ZS45Z6TuPSglbS4LXoURcFnrWclv7B3hGKxQP9ilt6AiYVUienlDJKjofWNuHHOkrqmqATsOo7MZohkity8oYG3b2pEb6gvXn78yjzTiSI7Oz1sCNk4PJkkmq3wylScmVgWVVH45P39NJhFXprO8d6tDWg0Is+PxgCoVqvcfXiOhXQZl0mLJIlc0xegL+QgXqjQ6zezqdWJosjceWCaF4bCHJ+O88DxeQQB3EYN/3pglks7bSznauxsd+O3ann8TD35IkkiHW4TsUKVp4fjuMxaRiK5evlrtcJgpMBypoxGI3J5j58bNoS4qMtLyG3mU5e18Wh/hHtfmiZRqNDuNjARLzGyVPeH7SY9bR5zvRhLUTg9l+bWC0Ps7HDhthnp9BgIZ8rMpcqs8plZG7TR5jETdJhY5a8vxsLJAocmEiyl87R5LbQ4DYzGStRqVb6wd5jFZJGA08TmJhuL2RqyLBPNlHnkdD0cenYhxehyDrlW4+R8irtfmkVRZHa0O5mM5fny0+P88fYAh2dy+K1aDk5leP+FTWxqdnJmqUCj04gkSfQF7Wh1daI/cTZGOFNEkiSUcj79Rtw4Z33q++6567YP/fGfcNkqD/vHYsTzFTr9dnr8Fmq1GvcdmeWa3gCiqCGRL7Ou0YZRJ4GgIZqrsMqjZzBSQNBI/M9LW3hiIM4av5HFbI33XhDky8+M8/HLOtjS6mI0kmNXl4ejU0k6PCYePr3EwFIOs15Di9uMzSDhthrY0mRDpxFwGCUePh3BqYNjsxlqisqWNjfZkoxFL5KvqvzBlhA/GUnw6Ss6KFZVPrqnm5DTSCZXIpKX2dpSL7ZymvW0eUycmM8Sz5VIF6pE8zW6vUZiuQqtDh1nIyW++vbVqIKARpTQayVuXOujpGhotGm579giQq3C/uksF7U5OL2U56YNQVxmPa/MJEjmK8xnq1zUZuWJgRiFcg2NKHJpt4ujM2la3GaWslXeu62Zdc1unEaJVLFKo9PMkck4HT4LDrOBoMOEz6ZnOJKnJsu0uIwsZSpc1evHY8V4/LUAACAASURBVDFgN+lwmfVYdRrOzqd4diROr9+E325mQ5OdmWSFhXSRq3v9uMw6EsUaXT4THT4rPQErDQ4jH764C6NW4M5vf7vy15/71D+/HjfO2Zk6XawysZzl6cEoAjAwn6LDbeBbL4zzD08MsbXViSSJTCzneGE0zoGxKOFchfdtD+Gz6nl4IMmmJis7O5x85dlJzs7HueeleW5c5+c7L83js+oRRZHhcA5FVVEUhecGIzw9uMw/vb2XO967CUWWOTQR5+xSFr9J4ItPT/DtQ7M8cGKena1W5jMVNKKGizvdCIAgaGh2WxBFEVEUWRs0872jiyAInJ1P8cJwhOPzGTq9JkJOE5JUT/i+NJXiqtUeNIKAoqjMxXNE8jKpfJlH+mMEbTo+9/Aw+4aX+dCuVtYGLXzjhRkCZg0Gg4FbL2hgLqfwxRtXES3IdHnNiBoNqqqwtc3LFb0NfOKSFv5m7zjbW+28Y3OIz1/bw5ODCS7p9rC724skSawJ2nn89CIvTycJOU1c3u0knK3Q6jbT3WDnhg2N6PU6trfaWUiXkBWFSKbEdCxPu9eMqsLzQ2EePLWIRtSwpdlaj65ki4iixNW9Plrc9Zj8/rEYY0tp7jwwxdBCgm/vn2Q+kQfg5ek0KLXqG3HjnCW13ailzWPm2rUB1jZaiWQrDC6mqVWr+GwGdnd76Qs5uX59kK+8vZdWr40dbU4+/eAAu7u93HPLejKVes3BxiY7fruZPT1e1jTY0GgEPrSrFY1Gw2Q0y+hyDkEQ6AlaabRp+dQDZzk9E+fJgSi3bgtx3RovTw4luffWDTz8kW1safNwaDqLRafhkk4XL4zH67UVXjMdPgsf3NnC2YUUi+kyOzpcBGw67nlplki6QINFJJ6rcGouQ4vLSKffRqPDwHA4i6IoRHNlAN6/vV7HIYqwtc3FzjYbqgoDCymeGQgTsIp85+U5isUiBydTtDp0jERyhFMFEkWZsXCauw/PcvkqF5Fshe8dC7O5ycZMssih8QQA60M2wpkK0/ECH9zZwt6z4frmgXYXtZrMR//9DB6jwGyySLevXsV3Ta+fQ+MJZFlm33CUmlxPfgHMJfIspQpsbHLwjk2NKAr0L9WzwC0uI08PRrlqTT20J2o0XN3rQ9BomI3nSeTLLGcrDC1lec+WAIKkN74RN85ZUs8lCvzg5Ske61/k5GySnoCFqViB0WgBWZaZiuU5PRPjtkfOcPsLEwCsbrAhaSXGl7Pc8/ICXrOOmUQ90/WX1/awnK/yj3uHkWWZpwaXURSFdq+FPd31OuMmp4nDU0lUYCZRwG3R8Xj/Et94YYqaXOWfn5vi7sOzoKrcckGQ+WSxTkK1/suzym/hxZEod+yf5I79Eywm88iyzMBini/cuIp0SeHYXI5isYjDIDC+nKUnYCVk13FwPI4KeMxaxqJ5PvPQEKgqbV4rl69y873jEZK5MgfG4gwsZJlPlelcyVbWqjVGowW++uwoqqry6Ss66ArUw5/fOxZmS5OFHe0Ogk4TqqJSqVT4wt4Rmp31dP3xmQSKonBiOkloJerTv5hlU5OVaKF+/7/+ZIS/e2yQSqXCeLxIg1VXX4uIEumSTJe3nh09u5BmLp5DBRLFGhd32EkU6gvamlzjzoMznJ1PEnIaGY8VuaTTyfHZVL1upNHKaDjF5x8dRc4n3zD6cc761N+/967bfFuvZXeHk2dGEgjA5av92AwS04kCsqIyHc8xuJRFJ2nY2ekhU4F3rPMynSiTLpTZ3ePDbpB48MQSV69tYH3Qykgkx65ON5lSjZpS361yai7NaCRLb8BCh8+OwyBxaiGDXS/R5DKxusHG7lV+blof4ORcmvFonqVkjhMLOTYGzTQ4zVzS4ydeqDGfLLCr043DqCWcKqLXalBUBafVjN0gsSFoZv9kmhNzaZbSJeRqlfteWWJjyAqqSkkR+PSVXVzV6+e50ThtLj1D0TI72mx4bCY+uLOFAxMJ3r4xyHSihFGrYSpR4uo1XorlGgGnmWeHY0SzZYpVhfde0MCf33+W0XCGtUEbp+bTLGYrbG22MriUJZ6vkizJhOx6DoxFmYxmSRZqdPssrA/ZSRZrRDJlijWF9St+8cVdLnJVleVchc9e1QUakelkhd/b2MCR6RSKCtFMkblkkVJNIZpX+MxVXWhEiYBF5MhMlndsCPDKbIq5eJ5cWcag06ITNbgsBlpcBu7/zjejf/P//9U3X48b52yVXjhTxqbToNPpuHmdn6BVy1SsQCRTYUOTAwEBjUbDrg43Wq22XuVlF/nsQ0M02TVkq/XdyTOJIqsCVh4/vcBzQ1Gq1QqhlU2ogiBw3doGruzx8NTAMve8PM/Xfn893T4TC+kSi4l6zLfZbQYE7n5pjoBVh9+qpX8+w85WKycXcnT6BBRFQSuKbG910Bdy1qsCRZG9p+bY0Oql22dm3+AS0VyZd28JIooSklZiKpLm43vamUuVODGbYrXXwB37p7h0TYC1gXqB0e5OA7GCgqLIPD0Uxaqrx4mj+Xr6vtNrod1j5v6TEf7q+jU8OxyjzWNmJlFkKlllzyo3y9kyGo2GLa1uqrV6HUeiUGNTq4ur1/gYWsrgMutIFWtssGhZypQ4OrnMxiYner2ekF2HJIkIgoCk1XJNn52aLHPvkQV2dboB0Ol0XNEbQJEV9o0ss6fLzdHJGJesdzMRL1OrVXn4VJibNwS469AMAauWeLHGZav9qIrC86MxQk4DkewbutPAOex+AIiiyFg4zb7hGP/zgUGeOjNP0GlAo9EQzpapVis8djZKbWWj6eceHmZbs4mXZ3L0BSzMxItoNCJ/sruNVUEn79seolBTaXEZaPdaUBSFb704wWceGkQQ4IM7WxAEgS/sHabBpoOVnSCtbhOgErBI9C9mOLOYY03AxHy6TFmpb/c/PJmgXC7ztefGGVhI0+61cN1qFycXioTTRc7MJ4nlqziNEnsH4yykS1zW5eSB01FUVeG6dUHetiHIY2cjPD8SpVKpEsnVSbt3MI7foiVeqFGrVhlfzvGRH/Tjt+lp8drY0OTg6FyWd28N8cxQlGNTcQ6M1UOX7S4dkijit+rpX8wQtEmcXcoSzZT47NXddPlt3P7CBF/fN0lvwMTuTidDkQK/v87NyzNZXhxP0GAR+fq+Se7YP8n9x+Yol0rc/uIkZxaz+CxaVFXl4dMRzswl6fLbuHFjiD2rfMwl8jw5lOZf909TKOQ5MB4nUawiCALRbBlFhQ0hBzqtxHwijwCcns+wmMwjWt1v+L9izln341vf+tZtf/TeW9Hr9Xzs0k42N9mYS5XZ2eHGazPRYNWyfzxFX4OFQ1MZdna4Wd9oZSxW4Y8uaMJl1pEu1bi2L8CTZyNc0GLnicEoGlXBatTjNOkQJYkrerw0OU3s6vJyfC5Np9fMgbEY21qd7O1fpNtnZiiSYzZeIF2scs3aBrq9JvYNLSPp9Fy3xsNlfSECFi1z6Qo2g8RVvX6OzSQZDOeRBJk/2tbEK7NZOjxGHCYdnV4zr8wmAQ3b2uzkazAZL7Gjw02Hx0ST00hRFuhrsDCbLHJJt4c2twm9VkSn1YIAqxscbG3zsLXZzgujyyiywjs3N/LvR2eZS5XZFLJSqVT5uydHCVhELl3dgFYj8J3DM+zudBHNyxRrKlubbfz4ZIT3X9DA7YfmURQFh1HkG/vn+MH71tPsMvH4QIw9qzxcttpHPF/m+Gya1UE7t2wL8dCpCL0hBzet8/PKbAq7QSKWq9A/l2A5W8JpgHdtDTGdltnV5WFrq5u1TS5seoHHzkbrGxdWufjaTya4cX0Du7p92EwGfvAvfz9w22233fF63Dh3Z2pB4Gw4T4vbhFarRa/XkSxWOTQRp1yp8PjZKOuCFrSSyGeu7KAnYGUuXaXRUc9U/csLUxybiHJ6Ns6zg0vcvn+SD1zYzLYOP61uE9PxIt1eE98+MMXhyQQDi+mfHbSyudVFrValpAicnE8BAjs7PcRyFWRZZv9YjJFoib+7pp3D0zla7BL/+NQYIZuOJpeJvWcjNDv0iBoNuarCw/1hfFYtiqry3PAyqqKQr6jMJwtIoohGELhilYvHTi/w4MlFFlJFmhx6BEFDrVajWqnyD3tHeXYwzOWr3IRcZi7qctPlNfGZhwY5OLrMSxNxPnn/GaYSRa5a5eDUfJrHB6PsbLXy8Okwo0sp5lNFHCYd0bzChpCVJoeOe15e4L0XNBAuKOxZVa8Fl0QNQbuee16e5++fHKVUKtLmNqPTamly14+ZuHK1h+dGEngsWlb5zDw5sMx8osCLI1HGIxmWs2Wm40WmEiUGIgW2t9oRBIHeRjsDCymOTKf58tvW0OYxcffhWSwGLSAwkygSsusQLS7/G1LjXC097V23Qf3ew8/UfV+Nhmqlwv7RGEGHnnCmyq5OF+ORLCdmk0iSSLJQqy+iRmOkygpX9zg5MBZDr9dTrVZJldWVMJPKVDyPJIpctcbHkwMRWl1GphNFrl7j49nhOLvbLPzpDwe4do0Lk9FIi8tIt8/Ml58Zx22SGIsW2NZkYmi5jMcsoZW0LCZzbOv0EXLoeWkyiUaj4X3bQ9x1aBZVVWjxWJiN5zg6neKmdX5qsowk1Q+gaXSaWEgWUQVYiGXIVlW6fXX36LHTS3T7Lexss3NwKo3PbsRtqp8K1eazc1m3k394YoiA3cByrkq1UmUmUaDNY6LRZcWpU9k7FOOm9Q0MRoqsDdb975emUixny6xvtHF8JoHfVj/bY2Q5x+Wr/Swki8hyjcGlDNlijTavhXdf2E6X18QX9g7jtepocppZypQI2o3MxnNcvMrPTLKIqqo0OfQMLyR4/GyUG/p8LOdrpMpw41ovL02lkGWZi3v8HByLspjMsy5o5dRChnUNFg7P5rj9I9fM17Lxptfjxjk7U5dr9WMNnh8OM7ZUj7mG00VOzqZYzhQZj2T5yUiU+VSZG9cFuKTLjaTVotNpubbXw5GZLE8Px3DqIV1W+cyVnUzG8vzo2AzziTxBq8SXnh6rh8PCaebiee48OEWpVOR//GiAJqeeWFEhZNfx4IlFPvqDM9y4zs+e1QG+dPNqnhlNU61UGFnO894LGtDpdYTseu55eQ6fRcJnkbj3yCJBu55mt5lr1zZwSU+AP9vThkYUuefgFPOJAggCqqoQchkJ2g3kagK3bAuhETVoBA3b2l10+y3cfyqC06xnbYOVWL7KcDhLTa7xzHCMpWyNXZ0ePn/NKrJVBYteQ7qkcOu2Rv7tZBSrTkISRW5eH2ApU+alqRQ72p28c0sT161rQJJEfGaJRKEGKrS5zcTyVWJ5GVVRydcU5tMVxsJpvvj0KNVqlXi+Cqhc2OZkLlng+GwaRVVoc5tArZeVPjEQQ1VVnh0Mc3wuwycvbeHR/uX6AjFfRVVVgnYjoqhlIVXk9EyCvcMJ3rOl4RcekXDORj8AdrQ7OTgep9Nv5crVHu4+PIOiQKPTwGKqzJoGGxe2O3l5KsXYcoEbN4ZY5TNz9+FZdnW58VlE9o8nuaTLxbPDcVRVZSqWx2/T8/J0hk9f0cGdB6c4Np3if1zSxrG5LN1BJ3/ut9HX6ODh47M8djbG2zc30uO3MJkoMxbJoqoqH9vdzHePLlAqFvirx8e5qMOOTqflki43p+bSaDQCn9jTyn3HlkAQ6lvNBIF9w1HetdFDXgaNRsOOdieHJ5Ps6nQjiiKKIvPE2RhrGy3MJwqIooYPX9xBJFMkaDcSyVboC5hZLhhAVblhQ4ir1/j40lNjTMULdLgMHJ+vsr7ZhtFo5KH/bxvD4RyHJ+OIYgkNAgGrlodPhfnLa1fxRP8SvYH62SUXNpuJ2Y3MpctsCNnru93lGn0hJx+9tBtJktBo5nj6zCJfubaHr78wy0KqRKPdQOOGBuaSJRA0dPistLgMvDgaxalTeGo4ydvW+5hKVljXWM+4bmx2IooinX4rbV4zE8tZ7OY0f3ZxKwfH42hM9jc8pumcnalh5XDFfJVarcZXnp2gXC4TzZVQVVAUGY2m7oNpNAK3bg+x90yYpwaWuaDFxkyiRCRbpdtnZjlfv/e6tQ10NdhpdJpochnRarXsWd3An+1pI1xQ+cCOZkSNhqMzGWq1Gk8MxLi+142o0fDU4HI9q4bKj47O8NhAjD/a3MDgcom/ujzE8HJ9V7uoEXnHpkb6AmZuvfcUsqJwQbOVSK6KIsvsbLVw++FFdrY5+fDFHcynqyxnyxwcjyMrCmeXcrhMGk4vZFnOlIjnqjx+eoGRaJlKtUJNlnliKIbPLPLCWJyHT8wxHMmhqDKzsRynlwqEbFp2dXr4u0cHfrbNKuQ00eo2sqvbiyhJ+OwG9p5Z4sfHZ1nOVnj3Zj+HpjI0OY1c2xfgTDjHK9NJBpeyjKxkXIfDOVrdZiL5GncdnuFTV3TQ5DITzlbQCHWqtboMKEo9OTawkOJbB+aRZRlJFNk/GiWcrdDmqWdea9UqY5EMU7E8bW4zbls9gZMsySiF9PIb8eKcJbVe0vDydJqb1weYSZbo9Rs5NJ3FrhfoX8gwEsmxvdXJ/vEkDTY98+kyJ2YTVGs1Hj61RItTTzRXodljZXe3l+vWBdHpdLx9Q5BorkaLq16m2u0z80h/hMu6HNzz8jyrG2x8YEf9hKHr13pZzNbdk+eHowyFs1zd60cURdY3WjGZjHR4zXzi4TE+eVk97b6YLjEVL9AVsLO2ycH2Vjv3vRJhc6OZ/sUMe4cS3NDnRW/QI0kSnX4rf3nNKjQaDasDVtYFrexZHcRv1bOhyY7DKLFcUPiDTX6Oz2W4qMvLjX1+EiWV921v5sxChgdPLLC20UamAh/ZGSJSUJBlmVSpftjlgycX627UyUWmojk6fVY+vLuda9c24LQY2NJk5b5jS9gN9UpFnU7HZ6/soiAL7Gp34LOZePTUPOORNBpRw+ev7mIqXuHJsxFA5aIuDwj1SejFkWUePLnEQqqE3aDBbdWxrd3N9esbaXKb2dXlRSOKjC6l+aenRnj6zCLHJmPMJIt8aGcLgkaDz/qLj0g4Z0kNdfdDlCTm4nnC2Sqfv7qLvFz3Db02Izqdli+/bQ09jS6uWxckYKuTzGfVAQJ+m55OX33TwOBimoGFNNOJIgGb7mfHzMqyTCJf5unBZba32gF4on+JE7P1vYMtbhOSKPGhi9roDdoZieRZ32Tn7FKedpeemzcEafOaeXowigpsa7WzlK5vKF0bqIfD3r3JxysLeT51eQe/t6WJGzc2sb6xfnCkoig8PbiMLMt868UJHjq5RKenvn3txbEE1UqZBms9YqECE9Es/UtZfGYt61vcvH1TiA0hOwadjuv6vDw9kuL6Ph9zqRIXryRF3CaR6XgBj0XH/+XuvaMkO+s778+9lXNOnXP3dE9PkjR5RqMsoSyw37UNEkKA2WW9Zs1isHdtczAGg21svH4JykQbDEqjPIwmz0iTpyd0jpW6cs5V975/3LaW9z0rvOf1+uyR7zl1uvqp6r7ddX/11PN8f98gAy1JYv+lMG9cizPgMvDqZJIhrwmPRctTJ5Z48XyQ2USZGwccTKyWuW/cy0SkQDBV4thMglC+wdc/OEYwVSScrTLsU2wRJElCpRL5w7uG+PjuHmaTNQbdOpLFOtciecX2QhQZ8hi5FM5z/0Y/kiwjCCBLEovJMs9fUMQD/yYJTdVGi2OzSYY8RuKFGolinY3dbjZ12BEEgU0dNubiBb72xhy1eh1BEAjYdCylyjSbLY7NKJ9eI34L8/ECJ+bT9Dm1hDMVlpMlfnJ6GUmSeOrkCsN+Ky1J5uhMUvGyE2BThw2VSmQxUUSSJdYFrFwOZTg+l6LXZWTYreX+b53iufMh7h3zcngmRa1a5anjSqds2GfmyGyGZrPB994J4TYIfOpHl/AbBb740mWevxjh4lKS6UiGlWSJDoeCvfd6TDx9MsiOXju7em0cnM3RajVps2npdui5HCni1Ks4MptiYiXFfLzAxVAOn1Hg5SsJrFpFnHxwKsH5lTRPHF0kkqmwnCgiCiLNRoOpcJqDk3HarQpUurHdxt4hj8I9MajodOiYDGeQZbAatIz4LWzptLNn2EuHw0goXUaSJA6vubG+fjXOjl47oqii2Wzy8kSEmUSZGwdcXIqUmI5kmF3N0ec2IUmSMsMLislNTRLZ2GF7F051m9TMJ0ogqjXvVRvv26Ku1FtMrha4Gs6xqcP2LuS0nCrwzbcWWE7kEQCHQcVz50I8f3aZ88EstVqNqdUCsXyVVktiMppHFEQe2dbOX/5iEa9ZzUy8SKaiNGZuHPLwsR2dRHNVEoUqR9beDAjQ7TQQypS5sJLhpYshXrgUZVu3hRPzaY4vZOl1KjNqtNjiozs6eWc5/67JuCiKDPlN+G1GhrwmepwGRJXIc+fDzMSr3LXOxVwsxwsXVwFoNOocW8hx37iPZrPJUyeW0el1bOu1MeCzKoqXUJYNbWY6nUZuHHJxciHNxZDyP3/z0AIjHh0XQnmSxTo3jXi4vs/DsM9MpqLwO7Z2WXj+QpSXryTod+k5Eyywo9dBNF9jdjXHoekkPzoT5NuHZnn82CLPXQxj1Ql86scTBNMVWs0mPS4DiaKy9Bhvt9PntRDKlJiNFZBkiavhHM+dDzO3mqfHY2ZTm4XVQpO3ppVzLiYVuPHBjQG0Wi2fubkPgGi+hiAKgMDBazEEUfWeIMf7Fv1wmLTsG3RxfC6JICpOm195bRqPWcd/3NfDD09H2DusJ5ytKno9lUi9VudKVMZtMeAyqogXqsxEc6g1aubTNX7/9n6mY0V6XSZ+cCaCJEnMrub5s/0rbOt18MCmAKF8g06blpMLWZCVDZbfqqPPbeLOUS8HZzK02/Xs6HVwcjHD6cU0t424ODSf47Fd3XzltSrdTgPXInmShTobO6wcnE6QKjf5D3s6+YfzcZ78zUH+6NV5BFnmd2/uJZSrcymcZ2+/g2OzCeZTVex6NXeN+Tg0nWQqkuHlKzHklkQoW2FqtcBowMbNo230u3R8/oVrdDsMxAsN7h73kiy3GPLbFI63DPFCjfs3+jm1mOWztw8SytW5Z2M7zWaTA1NJEEClUvPwjm56XUZOLWX51I0OFlMlBJQZtNup52/eWuSBjX42d9oQBJEPXtfBcrpMs9HkyGyBGwecCKLA9j5Fbtdh0zGfqbN70MXWPjfPvB1UjDAliTNLSfxWPY16gx+fDvNHdw8z3unkWiTHdLz4K5Uv79uZWq9RcSVaJF6s0W7T84k9vWzustPpMrKp281Hd3ZxajHLjl47AbuRboeeaLHJA5vb+G93j5AqN2m1lI/IVrO1Jt9a4Wdng7yzkuMr9w3zzKkQkixh0St2uW8v5bhjnYcXL8XY2Wcnmq/R5dDzi2txHj+6wO8/d42ARUV0TfP3id09fOaWfn54NkaHVcNsoozbpOHkQpr5eB4E6HGb8FgMPLgpwGtTGT6ytZ2/v5Bg34CTj+3qJlxocs/Gdv7gzmFEUeBCuMCX7xkkV2vy+rUEfU49L19JMuA2YtJr2NXnYjVfZypWpNVq8v3TER7Z1oFKoyGaq3BkNgWygCxJiKJIj8tArtpkIVl6V7vY77XQarX46usz3DRgZ3e/i3C2zGq+hkqlpsNhZLzTwZVomXC2SihdYjld5cHNAfq9Zu4Y9aJSqVgXsNDnMdPtsXDLiJdBv+JT+NiubgCWMxU+vK0TrUbNkN/Gzl4nN434+cC4X3GENWu4Gitx46CT43NJvrT/KvPxAnv77Yg6k+29auN9y/14/PHHv/iFz3wavUbNSjLPuZUspXoLi07NkbkkjUaT00sZSjXFb7rHY8Fr1lJuCTQlmanVAn67kXvGPBxfzGHUqvm1LW0Uqg3GAmYur1YZ9RlwmvVcDOb48gNj2Ex6XCYNk5Ecd2/sIJQqkK9K6DUqNnc72Dfk4p3lPB6jiu+dWmIuUSZTbvLR7e28fCVBvdkiV67R7VYudrJQI5iuMOA1sWfIx0xMcT+16NQcnIpTa0G+0iCWrzEbKxLJVdGoVcynKjSaLdrsBt5ZSDHmN9JsyRj1Wm7oc5EsVNneY6cqiazzGXlnMUufU4egUiNJMkN+M1OxMm1WLcuZKloVXN/rYUObmePzOQbcel68FEMrSDxxMkiz2SJRrGNQC4RzCm3XatRx77iXc8spZhNlMuUaN3Tb+Nm5ED8+E+bmQQdPnwoy4jeTLtdRq1XsGvDQ4TBwIVjg3HKaWL6Ox6giUW7ywMYAz74T5v6NfuZTVdTIJMotPr23h4uhHMliA1mGaE7But/48XdW38v19H27/IjmqkiS4pr0j6eXGPDbeGCjG41GQ4/LwNHpOOv8VtpsOqL5GkvJEquFOo9tCDCbrPDg5jYE4J2lPA9s9KNSqXjtaoxfTMbIlhv83q0DvHQlwT1jbhxGjQKvuUW++vo00XSRWq3Gi5diXN9tZ1e/A7Vaw0KySMCiQUZZltw77iWYrfPdIwv0eC30uU0cmUnz2zd60Gq1LCRLnF1KMRUrKN3BHjvfODDL/Rt8uMzKjPbGtTi9bhNL6TK7+p0MeozMJSucXEjTYdUSLzYJZVKUay3WtdtZSpaYi+VJFir8+5uGOLWYJZwpEs2p2NRhQxRgwGdlwCtzfC6Nz6xGlmHQY+TNqST3rHfz/MVVtnTZGfC52TvcVJThqTKCIHDbiJuvvj7DzGqOw1MxEsUGD2/vYP/lBN97O8Sgx8gNPS7OBAvcN+5FlmXihQbbeky8MhGl32vmzjEvnXZFbPD40XlcZh1vXEvw+dsHmE/VGPaZqVar9HvNHJhKkSo3eWhLO8vpCpIkE8lXf2VtvG9n6m/+3be+2L7tbhotiboE92/w8/ZSnlq9wUQoy9nlLL9+fTvFuoRJI9DrNrGYKPLa1RiJfIWzS2mMeh2/fl0b50NFrDoRp0mLSSPSkgXMBh2PbO/k5YkYcE/qxQAAIABJREFUy6kStXqT44tZbuyzcylSAEQGPIqY9PlLMQrlKtORPIKowqxVsZAqUa5J5KsNDs+k2NBhZe+wn719Vr53OoJeJeAy6xh0GyjXWtgMGpwmLZfDWXLlJiqVSKHWYiJcYNhvxqZX850js3zr8AJGjYobuqy8Mpni1hE313U7UYsCO/qc3LXexxNHFyg0YNBrYWOHjWSpTq5UpdNtwWbQkC7VsGgFZAQOTscJp/KcD+bwGDVMxsvkylV29rsZbbPy3IUwoUyVX7uunZYM50N5WpKMzaDFrFezmq9gMejZ3mvHZdHjMOm5Z2Mbalq8cClGttLkke0dXAgXCaYKVOoN/vFskJMLWWwGFdNRRdhrM2oRVWoGPUYmVlJ8cf8kNw66cFuNGDRqnGYdVp2KYl1i1Gfi6Weelf74C5/9+v+sNt63a2qNSiSaLaNWqdnQbuHt5SxtNh2XQjkSxTrb+lxo1BqarRYHrsU5PB0nnq8iriUDZCvKLD+XqtLt0PHCpVWmo1mOzWV4YFMbCALzqSo7+50MuA3ES00e3d7B6ZUCgx4jPS4jx+cVGOrR7R2o1Go0GjVtdj2CKOA0aplLltkz4OK2UR9qlYoXL4T41I8ncOsFltJlarUaT51cVjgrwRxHZxIMe82IapGHtrQz5Leyoc2y1hUVWd/hZOeAB79Vy98dXsapFxn0Wbl7QxvpSpM3r0Z5ZSKKIAp8+d5h7tvcwVK6gtSSuBpV1tjdTiMHrkb5wxcnuW3ExajfSqIsIQgCE5EiWzsthLJ1nji6wLcOzTATK1Jv1PnyK5McnozRadPx2zf2M+i3MuizsLnTQavV4uRChm6HnlCmzEsXw3zz0CIuk5rtfQ6eORXitmEngiBwbjGF26zl47u7kVoS84kSU5Ec0VyNer3Odw7PcWwuRZfLxFK6wrqAhTvHvMxEsjxxdJFarc5yugIqte69auN9u/yo1BVbqjtGPXztwBxuo5oPjPsZ8FmYixfocysNp2iuisOocHgR4P4NPs4Ei/Q6daxkqty7SfEYXBew8uTxRUYCFlRqFcgyzWaT+USRc8EcH7rOynS8hN+i4dBMlptGYN+Qi4VkiYlwgfs3+FCp1SDLHJtL4jFrub5XaaDsHVKgs6nVAn0eIyq1im6Hnu8cnmfQa0Sr0bKt28JLV+K4jBpchv+RNRgr1glY9QgC/IebBhWmXKyIqFIxEcrzk9PLXN/rQpYkVvN1ZGRUKhWhfAN9OEckW0GlUmHWicQKDfo8Ek6TjltGfKzkJT510yC7BhTzmVqtxquTGe5e72HAY+FsqMjXHuzlyeOLyJJMvFDjieOLfHKvgEatRpIkFmI50pUmsiRxQpYRBQFkmd+7dZC3FzMIgCS3eObkCtu6bRycSuC3GxjxWzgyncCoEUEQeGRbO4fmFU7M3kEvn9yjLLNemYhycnaV5XSZHpfSlHlocxtyo1Z6r9p4387Ufpue9e2Kd/H9G/y02Q08eXyZYZ+ZUKrEYqrMUrpMu91Iu9OE327Ea9HxznKeh7e2MREp02wp8NErE1EEQWDPoIcbh30gy6ykikpkHQI9bsVR6MRcWvG0dhsBiOZqRHM1XEY1JxcyLCZLLCRLBKx6LoQKBEwiX3p5igMTQf7zTy6ykCzz4KY24oWmwhMRBdQqka1dZl6dTDPqNbCaKXN2JcuhqRiSJLGzz0mfx8yR6TgTK2m+c3iexWSJkTYHv3/7ACuZCq1Wi5uG3NhNOu7Z0MaA16ykjqXLbO2ysmfQzYDPxtYuCy9eWkUGhgI2hn0mJqMFTi5kmI8X+cobc9w2YOHoTJqXrsR4eGsbb0wmmYmX8Vi03DvuYcRvZilZot5o8IsrIZ6/tEqz2SRTadJu1/Pozi6OzKYZbbOys9/FSrbGngE3giig0Wj43M3d6LRanji2iCS3EEUVNr2Krx+Y57ZhF3sGPYiCwJPHlebXrcNOdFotNr2aMZ+RgN2oWMU1qv/2ijpZrOM1q3n18io/Oxui2WyymitzOZjm9FKGTpuWu8aUFIHHdvWwZ1Bxo6/Xa8wmyvzuLX1oNWquRfM01jIQlda4xLG5FIligw6HgW6nwnb71I8u4TGKdDsNzKUqHJmOs6PPwa5+xdNjz6AbWZIQgD63iQ+MuXn5SpxBt5Zorowktbhz1EM432Rnv4NfzKQZ8ZpxG1X88MwqdwzZODSdZC6R5+4xD7FsmcNTMY7NJmk2GySKNf7xXJBrkSztVg0jfgsHZzLoRZmJUJ5zy2m29jiVWVwUWUkVqddq/PmbC0yv5hRJ1cQq6/2K9EySZV6eiDC7mkWWZe7eEGBvv51vHQ+yu9+GQ6fimVNBwpkye/vtJEsN/uy1WZrNFsF0iaMzCWYTFTqdBh7a3A6CQKPZ5PWrMfo9el6/GufoTJwepwHVmjbzrckoX3x1noe3trN3yIsgg8WgwWc1IEkST59cZjaWZ9hnJlmscWhylf/ysyv8l1t66bZpeOrtVX7rOh+XIyXkZv09d4vv26LWqgSuRIrcOuwEZA7PpvBadJyYT+Mzifz9O0u8ejlKvV7j889f49hsEp9Zw6HpJI8fXWA5XeGOUa/SOpBlQF4zH6ywu9/Jh67vpNdt4pVrKTZ2WOnzmLgcLbCcrrCnz44oijx/MczR2TgzScWU/UIwx0qyyM/OBzl4LcZqrsREKMeJhRytlsSbkwm6HDrWdzi4a8xHvVHnJ+ej7Oy1cHqlQChTJl1qcmQ2zWKqRChVoNlqcWoxy4Ob2tjSZV+7+CtMrChihXRNxqKRObeSo9lq8vylKBYNTEULqNVqvvfIJlKlFk6Dimy5STBTZjlZpl6rEUyVOL+SpdFsMrVaYCFdp8th4OhchtNLKXb02ml3GDmxWODBTQFuHvGi0Whos+npcipJDPeNe3l7KQvAW9MJ3ppOIAoit424mE9WaTabzMXyHJ9NIksSWzqtzCWrLKVKJEoNDCqZXF1mU4eVHb0OJkIFBEHgoS0KwUmSWnz36AIvXEnxpbt6OLZcYrztPblMwPu4qHUaFQ6jitevxvFadNw87OWxXd00my3OrWQ5vpgjYFaCPR/Z1s7etYy/39rawZDXjCxJvHJ5lblYgXC2yq5+F0dnk0gtiaOzSURBQKvV8rlb+0hVZO5Z7+GhLR3IwKvXUmzrtuKzGdg76OFjO7s5EyzgMWvpcBrZ2G6l3BIo1ZX17Y3Dbm4b9dPjMnFkOs6LF0JcDWc5HyqwzmcmlK6we8DFneNtbO6wcPOwhy2dVlayVRL5Gm02PaNtChFKBvb02fn24Tn+r6cvcP86O1qNFr/dBLLMhoCZfE0isrbxmk9VqTeUr791nZ/zK3k2tZv5xoE5/FYtmzqsnF9RPKEf29XDv9vWzS3rvKjUaiUrx2fh0Z1d6HQ6NBo1PrOa1UKTO8d8DLpNHLgW41wwz60jHu5c385ffmgDnU4jB6ZS/Ok9A7x0Jc5iPEc0V+ZCqAjIzCcK9HnMbOqwkSi1GA+YieYbrGRqbOq0MhMvgaD0Kj1WA6IoctOQm0RVoMuuI5KrIeqM1veqjfdtUcsyzCUqLMYV2dH55RRfeX2GuWSZnYNe/vKBYVQqFd89usgTx5eYjmb54TtBLobyTEZzLMYL77rmy7LMkNdEqtig26knWawz7DMz7DPzzKkQTp3MH78yy9xqjgGvhU/v6eRcRGn/HptNvmvFsHfIQyhTJVFq8e3f2MD2fjfr26zct967lkfT4kooQzBVZMRvwaJXY1a1ePVqjKMzcVx6gdNBRbHz5uUYdqOWjR0WZd1/WXE4LdUlrsXK3DEW4OEb/Dx7OsrVaJZP7+3k2GIWWVaSy0YDJl6+mmQ6mmU+VcWmU7H/ahyjViCWr64lYgnIwHXdNiajeV64FGUhUUSjVvNr13Uy2qaQwk4upBn0GNnZ52D/5Rhek8gTxxa5Fs0yn27w6T2dDPoVgtd0rEgoWyGYLvH1A/Pcu97LfLLGlm4nn7l1ALVaTaygvL6RbIV2hwFBgDa7jj6Pksg16DEyF1Nm7E2dNtKlBl6LlgvBLPPxAqy15t/reN8Wdb0l8fDWdhbSdQRRxK4T+NCWDj6xt49NnXa+fmCRpXSF+zYGcFt0DPqsfGR7J2qVCqtOxdG5NCupAt1OA7F8hSePL+M2a5TQTeC1KwqRyGMUiBUbfGx7O9Fcjclwhr95a5EPX+cnXWkyHSsyH1MsaxeSJUSV8oL/+ZtzxHIVzgXz/Lf9k9gUtiuFWotYvookSeQrDQ4tFNCrBXxmLVPJOrv7XXzkhgCpGvQ5dExEClwIZgllFGXLB6/r4IFNbdy7qZ2rq2VaCCALnA0W2N1rR6vT0pRadLqsPLgpwJDfzmO7e+lwWxAFFflKk6mEkirb4zRwcCrBdKzAQqKoNGdEFaIocueYj+lYkeVEgWi2zFdfn1nLSjfR6zaRrjS5Y7ydD24KEC1KHJ9P0Wy1OLGQxm/W8uiOTjRqNRs6newbdhMvNNBqNFzX7eCxXT1MxYpMxZSZ+8JKloOTMcrlCn91YJbvHp5fCzGVQZbx2RS3JqdBAet2DziRauX8e9XG+xbSa7QkQrkat67zETCL/OefT3HzaBvRkhJx0eE0MuC1cPd4gJcvhVnOVBnw2ZBkuBjMKIlVlRattayWdrsOUGavBze3Eco1aDQa/Oh0CJ9ZC+024sU6sWKNckPhOH9wSweSJPPOco6tXcrH5IDXwrDPzHePzLOj14FKFHGZNWjUakRBwG7Scl2Pi7lkhR39bjxGkUuhAqmqzOYuO6FMhTMrBbb22OjzK2t3URCRkZkIFxjzGXjyxBIf39WDWq3it3d1otFoiObrnF5M8/HdPXz/5BIf2tKJXq/j+Fya3QNOPr67h1cmIriMKno8FmSZd/nL+4bdCIKAKAp8YNzPy5fCfOXVae5Z7+Z8KM+/392J2WRmyGvEb9NzNljk/g1KF1YUBGRk9g56WEyWeWSrh9/+4UUuhfN8cEsHoihyJVpkPGBi0GPk+FyK168l6HboWRewEUrm2NLt4EIwxw/ORPjMzX1oNBr8Vg1SS1H63DvuxW/VcX45TbLcZCVVQjRYXe9VG+/bonaZtETzDbb32Hh7Mcvv7usmlG9y97ifyWgeWZJZSBT52bkgs7E8uwa8yJLMRDiH36ZwsMd8Bl6+mgBZoZG+OLFKJFdFFJXgII1Gwy2jfmRJosttZs+QEmQ0sZJm/5U1Pna7k0d3dPLZn00gSzK3jfmp1evMxEsErDq8Vh27Bz0EM1VkWWY0YOO+Te1MrRYQRJGLoTyRQpNf3+pgvNPJixeCvHgxzZZOxZwRlOg1nU7HXWM+vvr6NA6DmsVUmTG/iR+8vYLDbGDcryeYrfLGtQQjPjMvXYnxR3ePIkky84kiR2fi1Ot1Dsyk+ZM+B98/HeFCUMuNgy56nQZOryiv2dVIjnCuitOk5tRijo0BE391aJkv3DbAV19XXGHHOxw8dWIFt0EJXtJqNNyzoY3ldIUDUynu2+AjVqwDMoIgsKHNwtmlFBOREg9taWM0YOX1a3F8ZjUvXMhxQ5+bTZ021Go1Wp1iUv+LqQRus5ZNnQrbMVmsI8syAjKdbvO/DqQnCEKnIAiHBEG4JgjCVUEQfndt3CkIwgFBEGbXvjrWxgVBEP5WEIQ5QRAmBEHY8ku/65G1588KgvDI/8r5k8U6fquGk4tp3CYl3CeYKrL/Upjj8ymCmRKCILI+YMZpNqxJtgK0OxTX0eu6HUzGazywMcB13Q4WkyW8Fj3be+x4TWqePqnQIPcN++h0Gul1GXlpIs61SI6VbJX7xr28cHGV+TV93if39HPrqI87R7388EyU7V0movkakUyZ4zMJGs0mz19U1sWyrFzsdpsi2bpv3MdKts6rl6OEM2Xu3eBDo1Gzo9dBLFfh+YsR+l163pxKYdeJXN/rIpwpES812dlt4T/t62E+UaHdplvLSpdx6dW8emWVEwsp2i1qRQXutfHFuwZ5dSpDrV7HY9FyfjnD3xycZ2uXFb9Vq6QGWBVPkja7jl6fjc/f2ocoCowHTLx0ScHPH9vVxYVgloVEkQ3tFqZWi9w67CKcKXMxlKdRb/DU8WUmVlKEs0pH9MFNfqRWi9euxpW/SQC9WkWz2eLobJouu2KuPuw1IQiwsd3GkN9Ku03P528fpNQUuG/ch1at/leD9JrAZ2VZHgW2A58WBGEU+AJwUJblQeDg2vcAdwGDa7dPAt8G5U0A/AmwDdgK/Mk/vRF+5cklmW6HnjarjolQniuRHD6LYqH16PZOltJ12iwqJuMV1vktALx2RQkR+tobc3Q59GzpVnztZOD5C1G8JhVPn1zm0EyCj25vZzpeQpIlLqzkGPFb2NBuYTFV5nK4wLqAlX6/hVuHnXzu51cY9BiIZBUM/It39vLcRAqvWc1SqkSsUCOarTLmMwIC3zk8R7PZQhRFQCZeatJl11KvNzg5lyCUrlCpVHnp8ioeq46WJPHa1Rj1WpVjC1luGXSQLrfY6NXx7NkUM6s57AYN+TosJoskCzWmYkps8o4eO//98DJes5qWJPHqZIr1PgOhfIM2qx6fVU+X08CphSxvTScIpsoIgki7Xc/lSIGVZJEnji3x5ddmkCQJmw4++9MJ6vU6oqBCVIm0JImfnlnmT1++htukJlNpkCw12DfsQhAVT0K3SU2z2eSvDyoC6cePLXBwMqawKAXodxt48vgSTx5fYv+lMJIkES81aTSavDgRV0S9diVfvdOu+5U5iv+/i1qW5agsy+fX7heASaAduB/43trTvgc8sHb/fuD7snK8DdgFQQgAdwAHZFlOy7KcAQ4Ad/5z57cb1Oy/kkCWZTZ12lnfZiOaqyBJEnOpqqIen8rwwMYAn9jTy1K6wrmlNMM+Mx6LlhcuRZEkRfkiCAL9fisDPiuDHhMjfkVrKEsSrWaTZLHKyxMRVvM17h4PcPeYmzcmE4wHzLx+Na44r04maTbrvHUtyn/dP8Nv3KCE/diNetwmDT6LlpevJPBbtcQLCt+622nAbzOwvcfGz89HOB/Mkq60uBpO8+rVOBvarWuxGjp6XEbipRZ7+uz88GwUh1HFuXCZDX4t4UwZn03Po9s70GiV6DiHQY0oioy127m+x0a0UCeSrbK5S5FVfXxXNyqVSKrcwGvRsbPfwa3r/Ozsd6JaiwK5b9xHstTgtjE/339kMyqVivl0lQGPga+9OcdYwMgN3Q4i2QoekxZRFEmUmtw06CRXV3B/URBos+uZSVQ4MZ/mgU0B7tvUjiiq6HPpUatVRPN1kuUGv31jP3/1oXEG/TbUKjUf3d7B6WCB37mxm4VEgWPzWewGkWMziV/p0PS/Bf0QBKEH2Ay8A/hkWY6uPbQK/NPJ24HgL/1YaG3svcb/Z+f5pCAIZwVBOJvM5NnYbiGcrYIAuWqL1VxV8aZOKNyIBzb4WUwqO/dOmxZJbvHkiWV29jnxmJW0q4VEkX6PmQ9uVqiN86kKfqtGWYM2Gjx/MYrTpFN24RYNV0IZ/vrgHKfn4lwIZjk0kyKSq9Dt1JMo1InlKoz4Ldw15uOpEytIrSaNep14uckDmwOK0rzDCihJu6u5CicWMvisWv7gjkHu3dDGWLsDv93ITf02fnAmzHSshCiK7BpwkavDrn4XHXYjggDVlsBLl+K8cSXCkbkMHVYNTqOG5UyFRqPBE8eX6HAY11Q6BjrtOp6/GGEiUiSSreE2KlkzPz8Xptls8vjRBWrVGr/30wmeuxglXapxKZjl6ZMrRPI1Praji263mXvGPLx0Kcb0aoHJaB5EgQ9d18neYS+xYpObh1wcmc0gyzK9bhPr/IqZvbiWKT/sNbGSa9Fl1dDuMOCzKEux6XgJURCx6ATenEwqvieLaWKlJjcOOuh2KZmYv8rM5l9c1IIgmIGfA5+RZfn/BbPI8lqr7n/TIcvy47IsXy/L8vUep40up4FkSfnfRvwKFr9aaCBJEifn0ywkFGL9pg4roVwDj1mHgMB8okCrJfHWdJKVpMITARm1Ws2+IReRbJWrkSyiIBCwGbh/g4/VQp0LwSyLyTLdDgPX9bj4o7tH+cSeHjqcJtZ3OHCbtWRrEjt67TxxbBFRJZKvtjg4n8dnUjPosxJMl5kI5enzmOlwGvHbjMiyzGquyqd+fAm/VYsgKEkJv/m9Cb70gQE+ubefsTYlMWtzp511AStXVkt0ukwMODWs77TjMBuwG5WU3WurJbqdRkRRpN2mo9VsIsuwkipyaj6D1aDmoU0BPrG3l1SlwaDHgEolEsqUKTVkwtkKo21WHtrcxsd39ypQp0pFu03PcrrKueU0L1+Ns6XLitdqwKZXHJ7Wd9gZ8ZnJVCQ+MB5grN3GsM/M/ok4ewZddLrMhLNVJqN5er1WdnSbmUnVOL+UfteU/sR8mn6XkhuznCww6DaQKUt8dFsH2Rr0uJRrLqjU2veqk38R+iEIggaloH8ky/Jza8MxQRACsixH15YX/2Q6EgZ+2fusY20sDOz7/4wf/ufOLcvw9IllBr0mREGg3WEgXWnw0e0d7/rjPX1yBb9Fy90b2pBlmaczZa7vNPO902HklsS+QRc6nZYOq4aTC1k+trOT7xyeQxRFVKKK0TZFMfTUiWU+ur2Tt5cy9LgMnF0WiOQqfOfIPJ1OJa/xjnUeslVlk7OSqXE+VOBvHhomWJQZcOn53jsR7vOZ+emZZTxmLWPtSnHuvxhGECBg1ZGtNIlkK2SqMjcPubhrvZ9n3w4jqkSW0mWkVotQRknVvXvUTY9dzddeK/LNX1tPKFcnmC7T7Tazp9lEq9EopHpZ5vmLq3Q5dahEFeMBEy6TlpOLComp2WyhEjVs6rSiUin57OcjZXb0OnhxIobTIDKfqvHYrgCCAAvJEj6rsqF+6XKcIZeW02EldetyMM2pxRz3rHfz5mSSDoeRuWSVBzYFWN9hR63OIaA0fG4edPDhZxYYC5gJ2I14jCIqlYpdfQ7mkhU2tNuRJJmvvTnHxg4rb1xL4DCILCRLjHj0CFqD5b1q41+CfgjAU8CkLMvf+KWHXgL+CcF4BHjxl8YfXkNBtgO5tWXKG8DtgiA41jaIt6+N/cqj3pJwmhQX0WiuRqvZ4O5RN5/60SX+5PlLfO7nV6jX6xycjPGl/Vf5ymtTuI0iZ4NFHtnawaM7u7kcKdJp1/PsqRBei5qrkRznVnLs6ndy26gfURRRq9V8dEcnarWK3QNultMV0sUaW7usnF/JIQoiYz4DX39znnvHvWjUCgX2rnUufvPZCabCGaVVXVdU7Jlyg1S5xdVwVtksTSle06v5KiN+C6Ioctc6Jy9NxHhzMsFsosRHt7XT5zYTTJeJF2r89MwKv//zS9z77TN02jScXslxPpjhyFwav0nFq5MZZEGg26Vg9Q9uDpCvyYwHTPz9uShus5bt3XaevxAmU2myWqghCAK1Wo0vvjaH26Dwve/f6KPPZ+Pje3pZF7BwfC6NLEkkinVOrxSo1Wr84Nwqv3W9kin+1MkVbug0cXIhqxjWbwiwLmBhJVNlcrWAJMu0Wk2OzcT5y7cW2Tfk5tEd3USyZf7uyBLnF+PMxQssJss8sLmNbE1iY7vSa7iwkmEqWuDgZJyXJt4zGQP4l83Uu4CPAJcFQbi4NvaHwJ8DPxUE4TFgGfj1tcdeBT4AzAFl4FEAWZbTgiD8KXBm7XlfkmU5/c+dXKsSua7HSbdDT7PR4KlTIe4a9XDvBi+JcosP9jhYSpfR6XRKdl+iRChTpt1u4Mnji7jNOhAEVCqRm4bdqFWKocv13Q6l4aESeeVylGajwcVQHkmW+NCWDhAEcpUGp1eK3LfBz13rfTxzagW3Wa1s0gR44vgi9VqNu9Z7uRItIooCR+fS3Djs49Ed3Wi1WsbalU8Br0nNoZkUbrOO9QETZxeSTMVKmPUq+ryKlvEvfrHAg5vbUKtVinuTTseGNjP7LycIWNW02Q20WhJ7BzSk6iJ/8dAoG7tdPHFskTabHrVKzZDXhE6n58YBByqVEiGyY8CDx6Ti6mpJCUkqt3h0WzuXIyWSpTTr2218cm8/k9ECf/76LE6DSJ/XSb/XwpDXxFMnZLwWHeF8g/2X4/zOvm5OLWYRRIFup56pmAJ3djl0jPjM7L8Y4lK4wEOb2xlts/Li+RW+8YtZnGY9ewfdBDM1Lqxk2NLjBMBvUxpa16J53BYtXquBj9zg5/unI/y8Uki9V238S9CP47IsC7Isb5BledPa7VVZllOyLN8iy/KgLMu3/lOBrqEen5ZluV+W5XFZls/+0u96WpblgbXbM/8r589WGtSqNZ6/FCVeavLh67yoNWpEQWRXn5OVbBWNWs3Hd/ewvkOJRovlq/S4jORrLQSVCq9FAfrjpSZ3jHqJFxvIcotTCxnuHPXS7dCzWmhw73oPapWahUSRXpeS67ezz86lkOK2unvAzb4RP+vb7Xx8dy+NeoMD0xke2aow6/q9Fv7ddQEQ4K8PzjEXy/Hq5ShzsQLJcovbRn38wZ1DXFjJMhUvc/OQi1tGfCwlS6hUKrZ02Vnnt+AxafjK63NMRbPodDo+vbeLUkPmUjDHO4tpzi5l2ddv4+RiRrm4oki3U48sy8SyZQJmkYV0jVazyZVokR29dk4uFRhvt7JaqCMjkCy3eGCTH7dJcWt66WKI43Mp7lnvps2u58R8ikOTMb762gxek4oLIcUQ895xL2q1hkSxTsCqYzmjbNr7nTpeuLjKxaUk0XyNje0WZcMoKmv4pUyNfYNO/vjeMe7eEMBvM3DnqJenTyzz6I5OBEHghYtRUsUGLr3MR39widVcBamSz7xXbbxvNYrf+vZ3vqgdv51tPXYMKpnvn15ld6+do/MZSrUmVr2aTruOl68kCaWKbOiwcS2SYyRgIVVu8rlb+ri6WkYSFEf+TLXFbSMeLgRzBKxaplcLTCfK+CxVE35xAAAgAElEQVRajs5l+OzNPbx4OcmQz8RkJI9alMnUZH5nXx8+m4FkqcGp+QTXQhmC+QY399t45u0IqUKFE3NJJsIFtvXYmUlUuHnYg9OsbIYe29W9Flha48R8ik67nsd29/LzCxGuRgvYdCq6HHr2X45RqDbosGm4FCkhteq8OBHn9hE32/uUzBYEkfPBHKcXswy49dy53s8Tx5Yplcv8YjLO5Uieu9b7KdVb3Lk+gFqjYUObmWJdwqJXs7HTznKqxHKqjMWgZUevnRcuxRlvM3NoJkm82OCWYRcHp5Ls7HchSTLRQo1ep5FDM0mKtTpDPqUTOt5m4fhckgvBAjf2W3ltMsNNgw6KDbgazqMRZVqyTKFUpdCQmImXuBDMIcsSmzvtHJiM0+e14LLoeWBTAIsG/uFCjK2dVsw6Nb94/sfV91KTv28JTXqNipuHlaD7a4kqH9vRwXCbnVtHfewZdLOcKvLS5TgfucHP0bkMA249wUyVE/NpxvwmPv/cNXwWLb0uhY/ws3MhJagyXwUEjs5leHRHF3ePBxAEZcMy3m5mZW3z9dMLMf7jrjaePrnCl1+epF6rcW4xzdGFDPeMurkWLzPkURQydoOGbf1utFotj+3qJphrMNpmY1e/k9lEWVlHJko4jRrSpTpPHFvEadSwb8DJuWUFTvOaVASsOjQaDR/Z2sZsvIxRo0RIPPv2CgAf29nFH941wgc3uPmz12a4FsmxsdPKxUgJjVqFVa+m2WhyZjmHLMkcmYzx4uUYy8kiy0kFl5dlmfMhJavlnZU8Nw27GArY8Nv03L/BTyTfZMinkJoOTSe5ZdjDaqHOsN9MqtQEZDqcRkSVCmTY2WfnXLjM793cTaTY4q71Prb1WPnZuRBnFhJky3WypQbI8lrgkcCzp4IMec2sC1iRZZlXJiJ863iQT+/pptNl5MxK7lcaRL5vuR8A0Xydfq8Fl1HNRKRAotxi94BLwZuTFW4a9qw1Yto5OJPh3nGv0rrOVcnXW7RaLUCg02ViZ78OtVqDz6an32vmno1tzMRLHJtNcd+4n3eWs/S5TajVGprNltLU+YdJPry1ne09NiKFFr92QycySq75SqbGeJuVMytZelxGdvU5WEyVkSUJEBRSfrJEj9OA1GqhUqvZ2GHjpcsKg282UQZZQhQFHt7WzpmVAtFMmXi+/D+wXp+FLreJLruOFy/F+Ms3Z7AZNUxH8mzvd7GcqXLfpg667DoWEiUuRfLsv5pgW48dBNBqNXxhTw9feW2aZKFKvFBha5+XR3d08deHllnn1QOKfcKxWZG3l3Ns77ZxfrnFsM9Evtak121CpVIp6QBOEQGZfo+ZAbeBYzPCmurFxl8cWODBzQFemYhwdjGNLEsspSosJBt89rYOzkfKjPgtrGRr3DLo4JlTwTXDTqXD+Z/29RLMVonmqnRY1Qga3b8911O9RkW7XXnRBSBgNyJJLYLZGh8YD/CxXT30e8w8dy7M20tZGs0GE5ECoqhCJaq4d8zN0bk0C4kCXXYdr1xJMuDW0243cHwupVBKE0V29tk5vaIEIb29nKfXoWH/5Thj7TYe3t7JPRvbOR0scvs6RS4GiojgrhEnkWyZTruOLV0OXroSp1atspIqIcsSgx4jwXSJQ5OrHJ5Jccc6D5q1TPFOp5ERv4VksYHHomP/ZcVd32fTo9Xq2DXg5rFd3WRriuf0D85EcZvUdLuMOAxaTDoVAbsRURC4uJTkGwfmCGcrfHBzBz0uA7IEoVydR3d2odFo2NJl56YhN8WGsqn73jth7lnv4WIox/MXwnzp5avs7HexrcvCqcUMLrOWmXgJu1HLiYU0oUyVaL6GLElcCOWo1mr83j9O0Go1abVaSpvcrFZydzJl/HYDD21uo8uuo9etJpSrsZqrKI0iq4avvzlPs9Hg6EycZkMxNz21mCGUrhDNVig1xX+bwttKvcVKqoQky6QrStSDRqOl065n/6UwL1xUHPof3NzGx3Z2o9Vo8FkVe7I2u56rMaXz1+MyEcw1+NxtfTx9MkirJSGvSbsEBNRqNbsHlMSox3Z28cypEJvbTXz1jVkajQbTsRLbu228cS2hkJuAz9zYyU/ORzk8m+ZKOEe3U8/94z6OzGW4Fs1zMahsFJuNBqlyk939dmYSZS4EFRPGw3NpWlILUaViV58Tlajk2tw47GXfkBNRFDi9UmDMZyRVafLw1jbSVRmVKOKz6lCpNXS6TNw24mb/lTiSLDObKNNsNlCr1JxaSHDzgJ0njy/xJy9MsBjPcS1e5iv3DaPRKIm6giDQ5jArFFNRpNVs8t+PLNGSWrTbFOPLm4fd7O530W7XsavfhUajxmPWcWw2id2gosNh4rkLEY5MxwinS7w8ESVVbr6bjjAZL7OSaXJmMYnfZqDDrleE1Bt9ZGqKmv9SuECv20in08iOPkVo/ejOLlr5eOi9auN9u/xIFmtcjWTZO+Rh1Gfg889f47Fd3axkq5xfyuC16pEliX94Z4lun52dvQ6C6TLXokrO4ZYuB/1ey7tNjZl4iWargSwr9gjH51LsGfQw4rew/2KY5XSVLocOBIVhhyy9q8yOl1rs7nchiAL/eDaIKIr8xvVtSJLMkbk0T55Y5o71AW5Z5wUZ7t4QYDpW4sxCkk2ddo7MpnlgSxcPbgwoPBSXjr84uMCGdgunlrLcOeLkzZkc3Q49r1xV+C5buyw8cXyJZqNBMFdnPGDiYijLnkE3sXwVZHj27TBfuGOQx48t0GbT86OzMR4as3J4OsEbkwm29dh4KpQmnmtxyzoP33hrmd+/vZ+Htyk+JrsH3Cwmi3gtetRqNZ+5uR9BEDi5kCacKXNttYBVk6BQlxltr/Hx3T1Mrer57uE5Rtps9LoV5/9Wq8VUrEQkW+Ezezs5ulRiz6CHRlOhEKzklULXaBR33lC+yedu7eMHpyN0uhTRQkuSeHtZ2Ui+eHH1X5/78X/icJu1OE0Kf+PIrAJrr2Rr3D7ixmPRoFaraLZanF4p8OHr/Ix3OpFaLYKZKvdv8DPotzLWbqNnzY53PlEiWahzZC7F9h4HsixzdCbOlVCGy5Eitww5WEiW6LAb6PZYeHBTGzd027kcLlCrVjm5oMCm1/e4uHe9h6MLOX52dgWHSQuyTKdNCzKEsxVevbJKo1FnOVOhy6HHadZyNZzlyWMLHLwW5vPPXcNlULGar9NqNvniq3PcPabYDfzOjT1Mxiscm0niMKiJl1o0m00mIgVmozl+cnqZsys5elwGPCaR2USFZKGOTqvlT+8Z4OkzCT4wosCMKxnFeD2SKxMrNPjAqIs/ffkaf/vWAgevhFlIFt81nj8+l+TwdJzldIUdvQ7aHUb29tm5ECmxb8iN36rlz9+YYzKcRkag02Hk1FKOO8e89HvN3Dbqw2fRcP93z1Epl5hPFnlnIcWB6RR9Lj0nFtIsJhW8vNuh52tvzPGRrW1cjSoyuGi2yo4eO1v7vNy30fevy/34P3XUmhLhbB1RFLllxMtto36Q4fFjC8wlKjy6o5MtvV6+cu8Qz76tkHVmE0oRnVxIM7uaZ/+lMHPxAslig3s2BPivd6/j0R1dBHM1gHfx1M/e0sOzb4c5s5Ckz2Om1Wrx3MUIsUKV9W1mzgcVU0i1SkW3U887yzm6bSrChdYaLivyzbcWCWbKRLKKgvvkfIZOh54fnImw3m9EkiRaksRkJM+Qz4gkybTZdOwd8vCJPT2o1WoOTSVBEPj87YOIapE2h4l9g040ajXrAyZSlRb5uoTbpKHRaLB/Ik6jUUdUqbh12MUPz64y5tXxi9kstWqVCytZovkaFr0yORybS6LWqPnmh9aRq8vKplYQmImXuL7DwoVgnkajyfH5NGeXUgq+P+Ji0G8llq9zz5iLV64mEdYUNDv7HDxxfImfnwvT77Vw/+ZOHtoUQK1WoxJE+n0W3GYdapWKNqueToeeE/NphrxGMuU6B2fSbOpUEJBYocbxuTS3Djv5v4+s/MraeN8WtSAI/O7NfahUKlQqkT6PCVmWQIbd/XaeOr7MC+eW+dvDS7yzlEaWZfYNuRhts9Fm0xHJVWg1W4iCyP/D3XtFyXGe57pPdc45Tk/OgzQAiAyQIECCYAZJ0bItWyRFSpbttbVlK3Fblm3Zsq1g2nLQlkSJFJVlyQxgzgSRA4kwwACYnKdzzqmqzkWNeXxh8ZyLs9dZ1Nx1Ty801vTff/31fe/3PMNtdq5GCnzlhat878gMi8kirU4Tu3o9zCSKPPLGLB/dHKBYl+n1GBlZziM2RQRBuTRajVripSY9buVDqdebnFsqoFcrMMZ9qwPcvbGF6wd8hBxmNrTa+MR1XbTYTdy3JcThqQxPn1cyxFqtFhmBM7Np3plL8fT5MJGskofv85n55zeneWFkmWiuwo5uJ2fmMjSaItF8nW1dLj6xqxuzVuCnZ2PcsdaLWq3Ba9Hy+PEFHFqlquI2aVnKNXCbNLS6TErmWaUiVapz+yov3zy0wJBX6eZ1uozY9GqW8nXuHFYWZMhhIFNuKN3Q8QTj4SzxYg21RsOWLicPbm/jjfEURycSiqvdbngP7dBoNriwpFxJPGYtbovyu5GlHMenFfPAeLzEjasC3Lo2yO3DIY5NpVnbYiFZqjERL3Hrajcqo/XXjnN9YBd1vSlxfDrFZKzAYrrE1aUUZ2ZSxPNVLi0XiORK9KzkHu5eH2Q8VuTN8TjfOTTJQkppqoxGS7Q79agEAUkSWddiRlCpaIoioiQiqFT0+m3ctT7A6+Npdve7eX0sicesQ6PR4DVriObrrG6xs7XDzuMnFpBkmVanAZWgwm83cGo2i9gUUa/YA3wWLSPLBV64GOZyJMfpeUV5/KFr2rhpTZA+r4lre910uk3kKk08Zi0hp4HFbJ1P7u7mng0K528pU6Naq1GoNFhIFri8nEEQVAwGLExnGvzNLd1otToOT8TZ2eOmWi3znWML5CsNnGYdnW4jmapIt0eZ4G5zKleHU3NZhvwGDl6IMJ9UDABXYkXm4jmi+SrdXjM9Xgtem4lz82lUajVvjsVZG7QwnyojiTLfPzrHrg5lk0mUGooaQxDY0mHjwnIRsSkyEc1xYalIp1NHtg4HhoOE7EaWsxWOT6dodxl46VJUOY9HFNaez2Lg7bEYyZJIM7089evWxgd2Ues0KpKlJt0e5cP47pFZxmJF7hwOrDj4FETtyFKeWKHOYMBKv9dMfCW8Ey8pNyMnZrMspss8eXaRi5ESTqOGRKHOwfNhpmMFBgNWRFHi7Fyabo9lBU/m4a7hANPpGpvbLLTY9fzkTJjtnXY63Gb6AspZ3Wc3sbnNysELYeYSBeVIcCnO7as9K1cYDdu6HHx8ZweL2RoXlwu8cSXK0+fDpIo1bhj0kSorXOY2h46XRqMrJUkVd6z18/OzMWr1Bqdn0+QrIjIyk8kKGwMG/vGtOURRZCJWZCZZYi7bZFevmz+5QbnZGwxYuabDhSCoWExX6PKY0GiUevO5+QwqtYqQU/EyrvKZmM00aDZFRe+hVvP5G7uYyTb5rWEfpYZMl8eCJMlIksiVaIF7Hj3DeLzMRze38NzFOE+eXWYhXVGYg2o1Op1O4Vsv5lntM7GYqyFJIhqNlh1dTk7OZFjOVrgSzuEw6QhYtTy4s52ZVBW3SYXGEez8dWvjA1v9sBu13LnWj1an45O7exiLFrh/a4hIUdEfD4es9HmNCMC2TgdXI3kSxRp+m5FdvcqH+eZkhjanomE+OavwoedSZcLZKtd0OhEEGF3K8r0j04q1SmwCiq3Vb9fT6zHxd69Mcv/2TvYMeFfY2MqfNFtt8uCOTp4+v4RRA6myApvc2G7j1HyO3f0+GvUG33xzml6vlS/dPsRNgx7+5BclZcJdpeLWtUFixSbnF7NEclUi+Qr3bmzl5GyWkMPA397ex83fSrHOoYBmVCqBty6HeWW8wJ3DOvav8hLJ1+j12djTY+en70Z54uQikVyNv3t5nL+8fRVXIwVanUpA/+71IaYTBcajBTpdRm5Z7ePVKwn+01x7cCTKW1fCaLV63pZlPn9DJz8+E6HdaeDYdIpsReQze7vI1GQe3tfDZLLC6+NpDgwHkJFZzFQJ56qsC1np8VoYD2eI5Su8MRZj36oAo5Eyf37rwHtVkKGVAV0EmZ+eXkQQVOwZ8NJsNpCllQ/jv/n5wO7UuUqDv3tlgkZduQkeCCjieFEUaXPoeHMsySd/eoFEvsIz5yMcGouhVqvxmgSeOR/mybOLtNl1ShUgW+ehnZ0KIlaW2dHtJJyrr+jSlI7ZqqCN5y8luHm1n40dTq7r9/EH13Ux3OYgUqhxyxo/04kix6bS9LgNFOvKrnVpuUBFFPjs3k6OTKZJFGrEcpUVKWmDDoee4TYbL4yEefVKnGJDolavESvUeflynId2dhBymtnZ6yZflZhOFNnV60YQBN6czLCry0FFUlOoydy+RtFSdLk1bGx38vVXp/BbdbwxusSN/3aaer2B06hhXasdv1XP8yPLPHl2kSvhAmPhDKfmMkTzdfYO+fBYdHzuqVHOLyoWrsWsIjd9Zy7Lti474WyJgxdjXNttJ18V2dnt5MBwgEfenGONX8lRHxlP8OZVBdTzzPkwIZuOU7MZDk2mla5rqoRJpwwFjyzm8Vg0vHw5xvMXlnnktQm+f3SWTpeReze20eoy0xRFOt1G3p5Mv+80+Qc20PTDx7//5QMf/ghtTiNffWWcSrXCfKqCw6xHFgR6vGb+554uZFS0usw4LQZ+d6Ofv3lpgiG/heVcFadZx64+L2PhHBKKQnl0OctUtIBZr1q5CdSxoc3BTat8BBxGfDYD/356AYNWxeHJFD0eE1PxIqW6yEKqSNCm4+hUmoBVhyRDqlTnpiEPpxeKrA3Z8Ft1xIt1CtUmhZrIUq5Kh8PAlViZ+7a1cWEhQ7Qo0mKS6Qo4mcvUWRuysbbNideo4th0lrs3tNAUJX5xeo7pRIlP7+2mVBd5cyyJ26wjXqiyts2JLKgwaWAxV+ePd7QQKTS4Giuxu8+D06zn7HyG4ZAVSRZIVST+x+5OEsU6HU4DI+EinW4Ln97bw6VIidVBKzqNQLwk8uENAX7xbphatc5cuoLDpOHUbJbN3Z4V2HqESK6CRa/hwHCQpWwFs06NDNSbMls77Lw+GuFypIBRr8Vv0RFwKgi3fFXk1rUB2h1aQEU8V8Zl1mHRCjx6fIkOuxqjTsdrv/pB5jcu0BTNVUkU6hyfSdPt1DIRr+Iw6d/DGYTzdb7w1GXenkzR6TbS5TbxyFuz2Ex62twmbHoV5xcyjC5leGssgSxJ/MVtQ9y/rY2rsSKCSkWL3cBULM+hsQh/+PMRJiI5Li1myFabtDr0xApVYgXF0ShKStIsnK2SKNYRxSZvT6aQZbi4lGc5rVzS3xpLsCZoIVFs8oldnawK2tFqNdy1QcmatLjMrPEbOL5Qoc2u46ZBN7OpEk+9M8u3jsxTq9d47Ngc9bryxciUFYPviakk9UadkENPpSnTtZI3OTyVwaFX8Y9vL3F6No1Vr+ZiuIAoiiyuXI0CDmVk7bFjc7w7l+aZkQgHhoPsHvAxES+zo9vJ0+fDXI6WuH9LC4+8Octtq9zka00l7ouAsCL17PXbcVoM3DUcXJmWVzRxIadSTdnQ7kCtUTOZUvQmtww6ydVEopkyx6ZSLGVKPHN2gS89N8apqRjfOjzDP74+SSRX5e/v6OXIdJZkqf6+iIQP7E79nUcf/fJ9D3yM4VYHZxeyeM1afn9bO36HmblUhQ9f00KXz4rTpMNjMzIUtFGuS+wb8uC26HnpYgSHSceA30ylISELKpqizLMjUXwWHTevDlJsSCykSiSLDX5vU5CxqDLQ2+7QMx6v0Osx87tb25mMF9nQ7sKmV/ORbR24TRreuJrkvi0hitU6+UqN5XwTj1nDydksHoue6wZ8ZCoi9aZIothkb7+L7x+dp9ttoCYJ7BvyUBFV/Pu7SzSbIvMZBfB4ek4x1j5/OYFVp0Kvlmhz6LkcK6NCwGU10pTAZdGTr4p0uow4zHqGW20MtdjpC9hoihJVEe7d0IIkw3SiiFGn4Z25LFu7nERzNQxaNU6zlh8cX2BDmw2DVsXOHg9T8QKiDAvZOnOJIn96Yx+rgjaKlTqvjSUxaFV0uU2Um5At13h3PkOlIeExK8BJSRLp9dvY3ungxHSKE7M5bAZlCn1zp4NrOj3kq00CVj23rA3S6zWxf8jLoekss/ECDVng3mE/P/rxj6W//LPPP/LfrY0P7E6t16iIFpoMBW2AQKJQ4dmLUbpdehZSBR47OocgCHR7zUzG8rw0GqXFqubgSJQjE3HKTRmHXuCJU0tIksStawKIYpN1ISsb2h0sFxrcvMK3FkWJ5y/FODWbYt+gUrlosStcjLFoEZVKTa1e44XROI8enuboZIpcpc4335zkF+9EuRorc/Ogk1ihwd4BD20upWIzGSugUat5YHsbT5xcpCk2WEpXiOVrxEtNbhryErQZ+OTuHta32gkXRQo1iVNzOVb7TXhMGibiVY7N5FjfYkGn1/LQjnb6vArveTlbAWRGI0WaokiyrBD/Y/kqPrOW2VSJ84tZpfEjigwFrAiASgUjS8rg8fV9LhAELi4XaTab/Oz0EiGHgS/s60Gr1TAZyfLXL4xxbqmAx6RBQCBWaLBv0MPOHjfrW+3cvT7IsZk8rQ49z5wPc3giwVK+jtOsp8OhRavV0e4wcGQqy2DAgkqlJuQycfBilLfHUzx3Kc7uHgfFJvR6jDxxavE3E7pu1GnY2mFV+BwyFOoykiTzty+NEc1XaXUpXbrZZInltMJj/vE7YaRmk+v6fYTsBnI1md29TpKlOiPzSb78wlVevRQmnK/TZtcznarx4I52dDota4NW3FYTVyN5zs5nuXVtgI0dTta2OdnaYeOVqxm+/bvr6PLb+cS1nWzr8dDjs/Lha7yEHAb+40JcUXTIcGEpz1PnlljOlJFlmYl4iYBNh9eiZzxexG3SEMtW/wsIXiZaaNLlNvP5/QPs6HZyaDzBZLKM06Lli7f0Ey0qu/L3jszy3EiECwtZgnYDOp2Oz+7t4qXLKX5/U5Bwrs7VaInzSznOzyuzhNmKyNsTKXxWLVdiZb6wr5eAXflihHNVZpMl1oWUOde1IRvRfI3JRIWQQ7lp6/RY+cyNvdy4JkRfwIbbIPCZ/xjh6y9f5fnROAN+C5+4tot1bS66/VZ2dDnodBlBEAiXoM9jpCgKPLijHbVaTbfXjE6rYVOHiwd3dayUP9XcMOhHrVFTqku/mXlqWZb5yovjfGRLiA3tdtQaNQfW+Tk9lyNo03Pzah9PnFzEb9GgUqvRaLR85dZefvpORAHa2E14zNqVDh7MZ6rs6fOQLNYJOY0cnUzwR3v6eOFihDUBM2OJKl+/exWvXI6RyJe5GlEGSZvNJgcvRLnnmhA6nY52h56vvjqJ26jCZzNwaCKltPFtevoCNmYSJa4fsqyE6P9vr0wkV0OtVuEy6UgU66gEgWSxztqQlceOzbG928nRySS7el0cn86gVmvocupQqdSYjEY2dTpRCeC36NDqtNy9oYWN3X4uL2d5fSzJNR02fnR6mVankd/b0kpf0I5qpV794sUwIBDOVVnlN/GN16fxWnSKhkKGhWQRSZJ4cyxOr8dIyKlIhgYCVpKFOnetD7Chy4cgCNTrdf7h1TGCVi03DnWwf8jLP7w+w4FhP5OJMmsDZn58JozdoOaTu3u4spDAZDKh0yua5u8dnWV7l+M9weh3355iXYuVWLHBtX1eur1mxKbIk++jcf7A7tQAAZuOHq+FWL7OuqCZ5VydbV0OwrkKr1yOs7XDTpfHzMd3dSJKIh994hxLqQIPH7zK7asVUeWtawKEnGZ6/TZaXUZCHgv7Bz1MJRSXyrszSeKlBp+7sZupZJXzSzmKdYlms8mFhSzPnlskni8jSzJ//ewoM8kyjVoNUNHqMHLDoI+HdrSTLEtIkswbV2MIgsBQQOFO/+j0MmKzybX9XrZ3OWlxWxSik8PE6oCJi+ECjWaT6XiBK5EsT55dpNVp4Pp+N6lSA4dRQ6PZZDJeolarczlW5qMbfSznlTLuRDTH2xMpPrZNoVOEs1UuhvPMJkvMJktcXs6xnK1y27ogO3vdRAtKibTFrqfba8Fv0RAv1EiWGopgVIAOp54uj5LX9tp0/Oj0MpcW04wuZRiLFfn03h62dCmCqLcmM6wLKTyPXo+RRFnmG/esZmO7k0a9ztdeX2R+JTh1cibDtg4bB0cifOZXI9TrylypIAgErDoGA4qpbDZde1+N8wd2p67URWRgLlUmVqgRzVfxWQ2cnk6gUgls73Ly5NklpuN5Prylg6V0iXVtDjxmLQe6HDx7Mcp8uoIgqNjRrdR9r0QrfO7GLn50Osz1/YqxKldrss6s4fHjC4ScBu7d2MpkNMdirsa6kI1355IUaiJ9XiMajZrru638+OQcgkpFsqSk7B4+eJW9A15mkkWKdZFarc533p6izWXia3cN8cSJRXpEkW8dWeQPtgf49tEkw0ETR6NNHEZlR39rLEG/z6y0tr0Wjk4kSFca5MoNjk0mseoFnhtN8IltQR49Gebe9dBo+IgVGmzvtPGN16ZZ32an22vm6GSSbo+ZI5MJ3plNki7WeWFkmUi+TshhpMVh5Nxcmk63iRdG42xsUySrCuBSZipZodulAHBaXSZ2D/iRJImjk0li+QrrWqyEV4L/W3u83Lzaz9deneRKWImOAlxYyrFo0rCmRcGunQ9X2NHjYk3I8Z6D/JXLcbxWPQupIuNxJePd47PywPY2vvs+a+MDW/34p3/731/uu+4An9rbh8eiRytI2M064oUGXosOGYGATc/55QI3D7q4FC1z2yo3FyNlXrmiLJCQ08TGTjezyRJbOx24zFpGlktY9Gp6fBbcZh31pqhoKUmh98IAACAASURBVGSZckMiZNdzdCaDJMH6NgfJQg2NWmA6WaHTZeCfD83xWxsCXDfgR6uSEdQaNnQ4lYqAKLCn18GPTi8hSTJNUWRzl5tYocauPi/7B1088to0Nw15OTSe4LZ1QXb1epAFFelSDbtJz82rAyAIiDJkinXMGpm6JHDXcJBoscm+1QGW02U2dLg4PJnCb9FybiFNIl/BbNQjyTLnFnMMh6yAgNeqR6cWcJgNmHUqVCpFSHRmPosMbGy1UWpIlJsQtOlo95j5k90dPH5yCY1KYDldpN9n5vlLCfYOuEiXGkwniph0Gi6H8+wf8vLCaByjTo0oyuQrdc4v5ej1GLl3Q5CDF5Y5PJWlxaYhWmzy0sUIPrsy4nY1WiBg0TKTLFNrShi0GhpNkSfPhbnwxjO/eXLQhiQzFLQzuqRckn90Yp7FVJl7NgRpd1sYWS7Q3+LgS7f087N3ozj0At89Eebhm3q5e1h5TYfbzKDfwtm5FB//8VmePrfMvkE33V4zM4kSn/nlCEvpColinTa3ha0ddk7PF7h1lYddvco0tU6n5Zp2hRXS7THR5THR67cxHs7y1niSoE3PDX1Onh+Nc2O/i3cXiwwGlJBUoljj0cMzzMVzfPfwFC+NRlnOVdg34CJTkwiYBBayNWRZQruSjruymOLJdxYRxSYei4bDUzmaosjjx+e5rtvB6bkcEgpvG2RFVZGtUREFdva4uG1dCx/Z6Of0fJ5mU+Ticp5ksU61quDE5pNF3p5IcNtqDzu6XVyKFJBkiR3dTrI1CUmU+NSvLtNq1zHcauPcUoHvHZ7Grpd5Zz5PwKYj6DDx8V0dOC16Ts0p0/k7u51oNAq0MmDVMbJU4KuvTFBpwK5uB7mazEc3BQnajfS6DfzqzAIBmx5BENCo1Txy71o+ubsHtVpNr8fwvmvjA7tT//SHj3/5f3zy4zx9QaHjz6crdLlNqNRaujwmTHotu/q8BJ1m5lIlruv38cD2dsaiBQ5ejNFq1xErNjHp1Jj1Wj69t5vJeJFzizkaTZFb1wSQBQGbQcOuPi82vYoXL6e4vtfOV16aQBabvHE1wRdu6mMmVWFN0MKzF2MEbEYq9To/OLmIRSuTKdf53rF5HtreysVoBaMabEYNhZpEvdEkYNMynSjT6zWznK5QbTS5GCnRbRP4/ukYG1pMJMsi+1f7sepUfPH5CWw6KFQljDoVWhVs73FzbCqJSq2hx2Pk3HwWQa2i3W3BalDzpzf0olEJ5KpNfnJ8mu8eXeSj1/iYzTbYN+Qjli3zwmicQrXOpnY7c+kKo5EikVwFAYFyQ8Jq1LO5XeEXXo2VMGlVXNvvB7FBoSlwTYeHD20I8tjxRVYHzcxn6nR7LAy32RlZyDASLnH/tjZMOjWlppLYS5UbVGsNNrQ7uRorcXAkxnDIwuGJBC+NRvmr2wY4eCnJmoCZY9NZorkKDVHkzEyKkTefbvzlw5/52n+3Nj6wOzXAiZkMAasBQRZY5TfT6jYDsrLAPSauRpTOWYtNCdy8eDHMPx+aYUeHlcuxMtu7HMylK4q3PFPHb9EiSzLvziZ5/Pg8+4e8RAtNJEniuUtxHt7fi16vJ2jTEbTpSZeqvHIlzltjCWZTykDpJ67rwqDX88DWFiVqajXgt+pYzNZos+sYWc6xlCkjyTJXwlmeOrdEsljj/EKWyWSJbrcRl1HDm5NZDqz1otPpaHWaWNvmYkOXj3+9d5D1nQqJaipRIVupE8lVsZv0rPKbiOTqCqSy08n+VV7enkjx+PEFDk0kCFk1aLRadnVa+cX5OAGbntUhO+mayFDATLfPqvC8W+z86Q09bOp0s7HDyYc3tXPTkJcfnV7k8ePzXN/nIui0MB3P89ZUju3tVrp9Fl4fS9Hj1nMpXKDdqafHZ2EwYOXsQp5bh9x87+gsz1yIckOfk1NzeYJWLcWmzGK6zIPb2zgwHCBdkwlYdbS7zWi1Wu7ZEFTy65KIKDYZWcrjtZmQ/k8Qmv7//onkqmzvcnBtv4eJZJm5dIWLi3naVmj0ir8vx9++OMZipozfrEGtVvPJ7S1cjpUZ9OqZTZbocpvee/0Ll5N4jCqKDYHNbcqHtKvXxVS8wLqQVVGweU0U6xKHxhOU6xI3r/Lx0M5O+vw2ruvzolKpmIpkOTqbp1CTkGWJiigwn1Ra716LHrVGQ4fLiEqtwWHU4TJpuabDxe9vCvLWZJZavY4oaNjR7SKcq9HpVurj//r6VR55c57NbRZ+clqxCxSrIj6zBptRw9NnF9ne7eCmQTdnFvO8PBrjhkEvf3BdF1adiidOLeI16yhLAv1eMz0+C+OxIs1Gg4DDRJvbyhdu6kWjUXNiJgsILGUVJPBrYynu29LKqpBCX213m+hvcfLRzQH+43yU6VgBUWySKjex61WcnM1yZDLB1WiBdrdCdorlKqxrMfOj08t4TALRYgO/SeDQeJwnTi4pjaitrVyKlrEbtTx+fJ5ej3JDiiSzmC7ht+q5c63vfdfGB3ZRO4xaFnP/mZM2sbPPx/p2O8enU5yZSfK9o7N0uk3cscYDssyR6SxX5mP81YsThNMFHju2wJNnl6jXFTrqYMDKH+9qZSxR469v6ebHp8O0OXT0eow8fW6Jc/MZvvv2JI8emabVruPGVQGG25y8fDnG0ckEhyfiNEWRg2cXODGfZ1ubmQPr/IDM7au9zKZrDPjMqFQqHtjaymK2xtpWJ/1+MwsZJQfx3GicG/qd6HSKq/zUnDJRIkmSUvmoiOzqtrOYrTEQsIAsk6k0eWs8gVGQmEo1+eWZec7MpvCa1IqKA7i8nGU+XcZt0nHdgJc9/V6S5cZ7jJLLkRLxfIX7trTwtVen8Fm0gESrXUs0W+Gbb0xRKZc4OZehxW7g3dm0Yv6N5riwmMdiUIYO1BoNPquBqXiJ+7a0ELIbmEuVWd/qYDJZVmzAXiub2yxcXM5Tq9U5OZMnaDdw45CXNoee18dTBGwG9q0OsL3LqYA31/iQkXh3IY9LL/Ol58beF+X7gT1Tf+NfvvXlvbffy5GpFKdmUmxstdLrt7KUqVKoNuj2WGh1GvjBiQX8diOfuraVv311ll6Pkf2rAwTtBjq9Vrr9NoZbLJyZy/Avb0zitmh57mIUp0mHXiNwZDLNTDzHP314PRaDjtMzCUYWM9y7qQ2TXqsQVyWRRLGGQaNiNFrmr/d38BcvTnIlnOfUXJ6QTcOmbh+NZpOTU3FeuBjDqNfy4I52Tk8lqMlqdnXZ+fczC4iywNhyhplUBZVKzW9vDPLCaBKXWUs8V2EqWSFXbdDp0HEhUkKNRLEBbpMGnUbAZdYzES8hyBKxYpN6s8lbk2k8BhVanRa9Rs2lSBGx2cSk15Ip1YhkSjRlgdlkiSuRAla9mquRPO/OZ7i2z0elLhIt1LAZtVj1ag5NpvFbNLw1FidRbNDpNmLUqrlzfSsmnZrXRsNciRRpihIWnRqtRk2X20xTkphPlXj1cpS6CKIo0pQkDFo1ewY8/O8ji9w05KHTayFbalCoSzSaTSQgX2miVauQAUmWOf/iT5O/cSk9s07DkekMW9qtOEw6fnJ6ge8enuaaVgvFJmzvdnBqLsfuPje7et38+4UUf7y7g9UhJ/GSQj66ts+DShA4OZul02XEaVXyFDqdjuE2G4enMmxoMbCUa3IlkkdQCahUyo2OLMuoNWoe2tVBpiqxvtVBX8DO7as9/NvRMJvbHXzptgHWtZg4MV/gxn4njx2bYznfoN+vNISev7DMv5+L8ZENXl4YTZKrSPR6DDRQYDS7e10s5RsMt1rRqNW0u81c3+/hzrUBTs6XuH9rKw1Z4HN72pnONHGYdHxoYwiTTs3VeJk71ngx6PV8bHvbigQo8B4w5z+7n0uZComyxH1bW8lWRbZ2OWl3WxhqsVMVBW4a8jDcakWn07Kr18Nd13Tw5zf3kaqIOI1a7ljjw2PW8rMzSgOmz2tCq9MhScrR68i0YhPo8Slt9nihxupWB9+4ZzVqtTLYMJUocWw6zaf3dqHRaplJFIkWanS5TbTYDYSzFVKlBm6TWvHwSOJvZkrv29/97pf33fXbqDVaru1xcGqxiF6QGEvV+eimAAvZGiYNHJ1S0LDtTgNXowW0Wg1b2q28OZ5iTdDMcIeHcLbM7sEgXS49l6JltnU66Q/YsRs1TKWq9Lv1aHU63BY9H7qmFbtBRcBpIVWoIKNiwG9mIlEhaNXw3KUYTr3Acq5Kqdbk0nKePQNeXHYL13Q48Zq1OC16+v1WnrkYY3OblXhFotdnJlasY9BpuXlNkJuGPDx3KYZBq8Fi0LCty8Wh8QTvzGeoNiTWhKxkynWShTrL+QbXddmIlGU2tjs4OpNhZ7uV8XSD/7m3B69FzztzGcZjRTxGNcfn8tw85OL5Swn6/WbcZuWc77fqcZoNdHvNtDoMbOty8o3XpxlZzPHA9nYWsjUkWeDEjLKZnJzNcHQyiYyK/QNOJhIVXr0c59oeJ+OJMh+6po2PbGlDUGsAgVPTCbwrTHFBoyVZqKISZJoSVJuwuUtpgo1FS2zrcrCqxc5TFyL0es34bToWsnX8JoELkTLzx56t/eUXH/7mf7c2PrA7tUGrIAmaTZFwQeRHHx3mwKYuvnrnAC9eSfLM+TDLuRq9XhMCIEoSqVKTB7a1cXQqRaVS4c+fHaNYLHJhIYcoiizlG9w1HEStUTOXrtDntxFymJhOK+L4maQSDLscKdNoNjk0keKtK8vMpsrct6WFM/N5XEYNHV6rouFIVtBqtCQLNaZjSlbk7HyaMzNJXr0S57N7O9m7KoiAQI/Xwj0bQ2zqdIMsraAG1Ny/NcTF5QLfPzaLIKjY3eOk1WVmKV3muYsxZEkmlivxs/MJdvc40Gp1rPKbWSiKrGmxcGkxw2d/dRGPWcf6NjtHpjP0u7T89J0IothAEAS0WjVdbhOxQp1ms8mhsSh//+oUkizT7zWxtceDRqNhOV2h12MgaNNyai6Hy6xnVcDC7j4XT19O4TGpkGSJSL6G26JDlmVeHo0hiiL9PhPJQpXz4SLnF3JIokjQpmMxU2Nrt4vdvS4WMorD8sGdHRyfTnMlnOOeDS30Bx1oNcr43nyuwYPb296X+/GBbZNXGyIBu4HzCxmazSZn59McWBfgJ2ciHFgXYCpeQBBUjEYKjIWzXImVcBhUvHw5xpHJNI1mkxt67fzToXncFi1j0QLNZpO5tIQsSQhqNWtanfT7zLx6OYy0kpYbj5VwmdRoNBq+emCQhw9eJV5Icm4uw53Dfs4tlbh9OIQkydyyxs9jR+eQBVjOlpFlidFwgZBDT9Ci4fcef4drexzkGyqeuRDBa9His+g4PJlmz4CHPr+Vn5+Nc2A4gEatoVar8q+HZjkwHKDDY0WUJGK5Cq0eF3u0EiqVGkmWUKlVDAUsdLlN/ODEAk6T0vTo89uo1+uMJ2pc36dgwro8Znr9ApIsEXIYOTOT5JpOF1/s9TKfqTCZLHN9r0t5r2KNv39pHKdJjVaj4bZVHv7ihTE2d3n4s329nFnI47cZCGdLbOp0c3QySaLU4N25NBvabGi0Wj61TaE/HZtKIUkSDpNiK5tJ1fj4LgeCoFiHI7kyT52v8aVbB1Gr1YqdazIBgopOl+k3E5FQa0oIAqxvtaNWqWk0Gjx7MYrPouHIRILDEylGlrLctsrD1XiFmwdc6LRablsb5KPb2ljf5mShIPHwTb3sHQogSRIXlnKIzSaRfA2VICCKIi9eDCOKEqMRhVDa7zMxES8yGc3x8uU4H9vWik6nY13IystX0ty3NcSlxQw/Pb3IY8dmSZRqqARo91i5bV0Lw+1O9q8JsabVQYvDyFSyyt0bWvjQRuWL8NzFKHaDiiPTGdRqNfdtVUyyCHBiOsM17Yo/vMtjZjZV5dRMmk0hM987ucwPT8wyHSuQLSuT4zqdjhsGfdy9IYRGo6JWq/PDU2EGPHp0Oi0dLiOzyRJj4TQHL0S5ccBNvioiiSIL2SpqtZp+j4lLkSInZtLce00r/+vmPubSdYI2HY+fmMeoUbGULXNyNsOObictdgPzyTIAbS4TG9udbGy3r1BaLZyczXJiWiFqBW067tsSotxUYqeqFUTZ6FIGtUrFbUMunji5yLPnlwjZtJxbyrO718XZ5eJvZp1ar1FmCHv9Vnw2PX6bAZ9VT4fLwEyqyp4BLz6rgRcvp7h9tYfvHFui3qjzxMkFVCoV925qZShoZTpdo99nZjFbw2dR2rK7er3cNOTlxUsRDk2kcJo03Lbaw1y6wtVIAZtBoZv+/MwSU7EC/2t/HyaTiYf39/LEyUWeOrfE3kHFPrCp003IYeK2tUEmkxWCdgO3D7fw+lhKoStptQwFrIqs067HYdazqcvNQzvaaTabfP6py5ybT9Pj0jOZKoMgIAiwptXBRzYFkAQBnV7HPeuD7FsV4LZ1QfwOJWosSYqn/dRsFo9RxQuXE6wPmTkynaHVrufYdIq5RIHnLsZYHTDy5kSG6/rcXIoUUQkCN6/yodXp+NDGVjo8VoYCVt6azPIP967h9uEQiZJI0GEkVRK5Es69t1gRBCIrlKsut5FIvkGPz0qH24xGo6HVaWR7l4PnL8X5yovj77H7jk0mePtqlKcvRFjlN/Gd48v87gYvF5fyzCbLbO5wMtiibAbvd6P4gT1+gIAA7+niplJVrutxcHI2x0O7OgHo9imQ9OEOD5+5SctMvECn26RozXxW1Co1kijy/aNzfHxXB6NWDc9diiOjTJx/8dYB9g24+eTPLvDDk4usaXXQ7tBTqErcvq6FVpuiq1Br1Ny1sR2NRkOLw0A0W+YT13atTM2I/OHPRrhtXRBJFBFFJaXW7tTzxFic+7eGeGMizQ19Tr76SoK9/R6i+TrhTIJUVWR3nwJOfHMiwwNbW1nI1JhPFHnhYpgn31lk0K/Uq2VZJlVu8NpYks/u7eQbr8/gt+n5+K5OXrrU5MJSjlV+I5fCIqp6ndlUmZDdgIyi0dBqtHS6jcwkRNaFbHS6Tbw+liLkMDAUtLGmVc1zF5YQgMlEmX6fmTUtVjxmHX9wXRePvDnLQ7s6uRLO4baZ2NHtVDC/2QrtLjOrVpR5t6wxrLC5y3zq+i7+5dAMyEoXOGgzKFUpt4nFTJVP7+ni5+fiKxHcPB6LnumE8oVTGW2/9vjxga1+/OCx73/5Yw8+hN2goctnw6KBy9EylYbEHWt9PHpklhNTafRaNeFsmV29Xn757hLJQo1otsK9m9qIFxVU17mFLLF8lUMTKdYELdy5PkS5ISHKkK1JbO10sLnLw/7VARKFGvlyDYtRx/OXYshik3xNpNKQ6fNbqNbqZCoiL11SsAyFSoN+n4nDkynmkkVOz6aJ5Kp0eSzcs87LSKRMm13PsyNRyg2RbV0OmqJItlzjplV+JhIVfntTK/1+Kz84uYTTABeWC/zRdV30eEy8M5vk/GKOS8sFHtrRRqZU51fvLuE1a2lzmZlOlVc0ylqOTef4yJY2cuUaNoNyXyA2mwy32inUJexGDaPLeSaieQxaNbetDTIRK/Cdw7O02HQ8fX4ZlSyBoGIskmc5o0Aec1WJPp+Z6WSZtyeT/O6mEAvZGtPxEjajjnUhO6lSg8aKfHR0OUe8WGdV0E6lVmM0VmZzu41CXVrxPwq4LQbWtrkwaASylQZ+qx67UctSpsLvbGnj777yNwt/9Rdf+vZ/tzY+sMePRKHGeDjLMxci1Gs1rsYV+s/dG1r42muTZMoN7t/eyvYuB4fGUzSbTXwWHZIskyrVuBLOI0kSr40pVtVb1wZZ12Ll0HiCVy7HuHVtAASQJYmZZAlBgOfOL/HY8TlOzaaYTZa4ZdCFz2EmYDPS6TZxcSHNEycXeWBbCL/NgAArrGmJZLnBzh4X3V4LQZuet8difOTxdzkzHeftsQjX9rk5MBzgyXNLvDIa4YVLMWbiBWRZ4gcnFDzwwzf1cDlSps9r4ntHZjg6GWcyrlRkfGY1By+EeeZcmPlMlfF4kS6PCUEQaHMoDsWP7Wij32fi1asJnrkQwW9S8ZNTSme10Wgwk1TGylKlOocmUlyN5Gm16/BYlYzIQMBGpipx05AXhWYs0+axIAgykiihEgR8Fj06nY4+v41NnS4e2NbGfLrCobEY03FFevTntwxw78Y25jMVzi5kGfIaOb2Qp9lo8MyFZf7j7CKiKNJoKHSqs3MZFtMllrMVxqJ5Hj08jUpvsv26tfGBXdQyMsvZMret8rwnqTwxk2F1i52gzcjHtrfz/GicX56Zp9dj4PtH5/BZtYrXRJKZiik3hTf0OTl4IcrBcwsspgq4LXranAZEUWQqlqfRaPDLU3PUGw1GIyW+eucgvX4bIauWP392jCGfgXaPGVFscmI6xe5eF2+MpwjalbPx39/Rz7HpHHeu8TOXrrDKZyScqdDqNHFtjx2HXsWLozEkSWYmUWQmXsRhULMuYCKcrfLQzk4e2BZiLFpkIlHGYVAmfsZiBUDApFfT7zOzok3hM/v6+NMberhhSBmHCphVPHpkhhOTUf7jnQW+9tokN/Q52NLl4sxCniG/mWazyXyyxHyigEoQGArauH9LC7Vaja++Ns0tg07GYkVanUbWrPjYRVFkIVtjT7+XLe02Dl6IsG/QQ5vbzGDAyoDfgihJPPz0FRqNxkoTpUqjIfLDk4sMBizcstpPtVZDr9fz4PZ2tFodB9YF8Fn0jEdy3P/YKSYiGSRZIl6osq3Tzp4BLyqV8JtZ0qs1JK6Ec4iiRLPR4BfvpmhxKsOiQbuBhWwVp17Fa3MVNnV5iOQqPH8pQ6tdR6khcWYmydVYmaVMBbdZw5tjCcoNiT/c2coz55d57OgsboueWtXEdLLCgM/MbLDEG+NpNGoNs+kyJoOGFy8n+dMb7Dx1LkzAbqTba+bETIZYvorXouPrr6XodOmYSRZ56t1FoiWRHqeWoF3HsxfjDAUsDAbsDAWtvD0WJVuTaCSKLOaazKVKRPMVlnJNPruvF1mGZ0Zi3LHag8uoI2g3IAsagjYdkiyRLTc5PB5nMqm4ZRqNBr94N4JBLRPJQdBuoNet5sh0nt/bbOVKooLXouet8QRXIkVaHAZa7HrGI3lOTsbx2C381rCPJ04t4jHr8Vh0TCYr9HmNtDqMynEgVeKZ88sU601evhwjkqvx7UOTpEoNfFY9ewe9SJJIOFdhe5eDx5aztIgS3zsyy8YWA1fjIgGLilcuxwGZk3PKMPBSukQNFa12PWcliVSpzr8emqPVoWU4ZEOlN9t/3dr4wC5qt0XHmlY3iWKV9e0OVGo1Dr3Ay6NRzs6ncJu0dPpsfHVIUQg/fmzuPfTvTKLIYqqMWq2mw2Ol22tm94BPUT8fX+C6PidDoTZWhxw0Gg1+fjbMWLTAC5cS/MkN3exf5eWtyQy/t7mVaKHOybkcsiyys8fJYq7Orl7PSgjJxOeeHGUuVabNZea3tnQQtKh5dTxLj8/K2pCDh3a2YzAYmEyUyVQl/vrWPk7OZolkS0RLMj6Lnms63Wh1OgZ8Znb1uCg0VIhiHZ1Wy4M72ogVGmi1WpwmLdPpKiaVSE2lxaDXM9xqx2vVs73LgVqt4cmzi4TseuKlBncPB5mOFwjFSwy12EgUaixlq/R6TUwJAnsG3Ny2NsjbUym8Fh3tHiutLjPRXBWVWs1Q0IZKUOEw6/jMvj4Mej0adQlRbHJdn5e1bU6azSZ///IYkiRzclZJ/rXYdfS3OJmKF1gbVOSiGo2GW9cEGYsqR5SFTJWP7+rkkbdmSZeb3DDkR0BmZLnA0Znc+84ofmCPH6linU9c28kXbxkkUZK4Y42Xk/MF/CYBWZI5u5gHQKVWMR4rsr3bybuLBY5Pp2g2Gsr8YVXk5tW+9wr+siyDAIcmlDM4wHSqyt5+Dzq9nk9d3wHAnx0c4/oeO0cm07Q6jTQbDdRqDfPpCq02LdOJAt89PMnfvXSVXo+Bu4YDjIYLLKbK/ORMmLvWB9FqtUiyxHOXEvR6FHqR26zl+dEEiWKNFqeFb//OWrR6HZFc/b1B3aDDyNqQhVxdZjaeYzlTptVp5DM39rGlx8POTiuXEzW+emCQAxvb+Ys7VuMzq3n6QoT5dAWvRYtFr0KWZGZTZZYzFTRaDdf1eXCbNLw7nyVeqDHgtxLN13n23CJqtYaAXY8sS6gEaHEo/99r+71s63Kwf3UL0ZKk8KzdRs4tZDk2lVBU1Udm2NDm4Eu3DfGJa7sQVMrU+qDfQscKTapeqyNLEs9fWGY6XkSlUrFv0MNbk1nuXKNgESK5KpciJdYETOwd8P5m7tRqlcB4rMRsShH0PD+a4N5hHz8/F6fHbWS41YYAHJmIgySzZ1WQj+/q4uJCmq+8NMa967x4bEZeuhRlZDmHy6jm7GKBW4fcjMZKfP/YHKtmMuzqcREv1KjXavzw5BJ7h3zs6rbx6tUEhbqIJEmcXyry6O+v59UrCZ44sch1fU7ylSZis8lCRrkabGh3KtPp8xn6vSamU1WqTVgbNPO5J0d55N41aNQqPGYt8UKdq5E8r1xJIIkSGo2aWq3GZ391EZVaTavLTK/HyJGpFPlyjQPr1cynijQaDUZjNa7vtvGTdyK0OIxIYpNvvT2L3aDhrnV+XrlU5HIkj1ajps1jIVGss3fAw3y6ykSsiN2gIeSyABCw6jg4EuHWIRdHpjI4jBoElQqfVUeL3cBTZ5e4tJznFx/fjNFoBOD5C0vIsoQoKsef6WSFnT0ufnBigaBNj9OoIVVSqiDNZhNBEHh2JMKB9S0cn83StwLP/+WZeSRJROxwMeAzs73LyYmZNL88F+ev7hj6P7tTC4KgFgThvCAIL6w87hIE4bQgCFOCIPxSEATdyvP6lcdTK7/v/C//xp+tPD8uCML+/zfvq9WoaDYbdLoMpKtKyD9Th49tb0etUfH2ZOY9LsdYvEitXmdsJdC0hi8F1wAAIABJREFUp8/NyYUiB9b66AvYWNdiI+Qw8uk93aSq8Gf7+/HbTezsdnFkMsFMosgzIxEGAla63EaOTit/z8/c2Itep2dzl5KBVlJ7nWjUGro9ZvavCbGpw0FfwIYgQK/fSrvTwBeeuky/z8znbx6k22tGUAm8fDlGwKojXWlSr9dQq9Wcm0+hUqnY2uHgh6eWGWqxc9+WFnq8Vgx6PT+6bwMuo4YdPS7OzGZ5fSzNQ5u9HJvNKjkMSZm439HjxmfVspCpMRS0s7PHzaYuL91ei9JWFwQkWUat0bB3yMf1g340ajV9ARubOxyMJWvsHfQq3cE2O60OI+FslfWtNvo9eh47NsfIfBKAdpeyuGVZ5rlLcf72jn7UGg0tdj23rg3Q6rLwxVsH+aM9fXR6bezudeGyGBhZzLKn34NWq6HTrTBZBJSubrJY5+svX0GWZO7f0c5Q0Pq+MJv/L44fnwau/pfHXwe+KctyL5ABHlp5/iEgs/L8N1dehyAIq4DfAVYDNwPfFgRB/f/0phadmn95axYQ+LP9fSsRy3a0Oi27+/0MBZVpaa1Gy4DPwtPnwxyZiCNKEtcN+tnd5+LUXI5arcbIUo5nL8aYTZVoNuu8PpbiwLoAi9kaAmA36rhnQ4hr+zyoNRru3xYimm8oQkyHDrVKYHQpiyzLrGqxodRmlAD+WEQpHY4sK4EmlVqFw6ThB8cX6HLq+MmZCPdvCRErKOWrtUErU+k629rMCILAtk4HJ+ey/F/cvWd0ZOd95vm7lXPOVchoNBroBjqR7MgcmqmbpGiNrWCKpCjJWu/YY0sWZc/s8cxYooKjvFZmEBVtirGZmmJodk7shG6gkUOhCpVzDvfuhwvz7J5j0mfGO7uHvp+qbhVQQJ3/fe/7/t/n+T0Pbg8RsGr527dmmI7LgqDXLsUZjzdpNVv0eUw8vKODf7yQplRtcjFS5NxSlrOLWcR2i6Vcg4NTKUSxRb7WAknuAi2mq9TrdRKlBmu9JmKFOv0uPV6LhqGAFSSJ0aCVu0YCXIwUaLfbRHI1YvkK4UyVUwtZ3puL8+3Xr3B+IcnRmRRIkKmK3DHk5C/fmucfTy6wlC7L82UkDkykkCQJURS5Ei/R69KzXGjS7TKye8DDUq5OoljHaVATKzYY8hooNhWcCee5dZ2b7x2cRlCpP9B9+28qakEQQsCdwI9XnwvAjcCvV9/yE+Ce1cf7Vp+z+vpNq+/fB/xKkqS6JEnzwAxw9b/22XqNins3yYq6mVQVt1nLj4/M885EjLVeIyGHgVihzq5+J4/s7qHDaWbbKpTw4JUYZxbkefPTp6LsHfHxx7esQSEITKwUWUwVWcpWkSSRnX1OBAEWM1WeObPEockEz52L4jII/OWBSRkNJsLByThL6RI/OjLPYrpEptzg56eWKdRaiJLE3g1elvMNtnQ52NrtwGfV8CfPjbOj20SkKGchHprJ0us2Megx8MyFBMlinefPR1nJlnj9chxREhEUCjlgSKHgQqSA3QBL2So2rYBareYz2zoIOkx8dc8AH7+qC7/NwEqpzd2jfr5x79D7EXDLuSqCAEatgv2XklzTZeXaAQ+XowUefOIUvzgd4excgnemM5yZT3E5kqctilyMlghYtajUGoJ2HQaNkoVMjW6X8X3ozNYeJ3s3uHljMs+Xb+6l3ISQ3YAEBG16Om0aXh1bIZyR4zdSpTpXd9s4Nptl0Cu3+pQqFalKAwm4kqrzywdH2bHGzWuXYpxZzCM1qsUPqo1/60j9t8CfAOLqcyeQkyTpnynvy0Bw9XEQCAOsvp5fff/75/+Fn/l/HIIgfE4QhDOCIJwJxzOIksQ6nxkEgc9sC5GqtIjnK/z46ALL2QoBm45DUyk5vuwqv6zHNWtIFRuMr5TY2efg4Z1dnFqSFX3/vGBUquRbYCRf4/h8FpdJR5dDT6kh8eD2DpJFOVemy2nkhrUurl3rRqVUyr3nfjedThMeq7yguWfUz0tjcQ7PJAlZ1CiUSvo8FnpdRmw6JQenMywm8jx5PMyuXiuHJuOoVSo+dXWIIb8Fj1mLx6xDQiCSqbK1Uw7IDNp0fGxzkC6nkZVCk5fGErxyPsyJxRw2g0aervgtXLfWw7ZeJ7v7XXzzjRnGwlkKtRZIEoKg4NYhPzajhqVsDUEh4DJp2b3Wy1OfHiVelfidLX4KTYn5VJkNfiOjQQt7N3Vw/+Ygd40EsJv07Fzj4Z6NfuaSZd66kmJmJcvJpSJfua0fs9nMN+8bZsBvZT5VJpypcGRGVklu77Gh1qjZGLKxo9dJslTnewenOTCeZN8GL1f1uLl+0Mc6t5YvvTjLA1cHWSk06XLo/9dEzgmCcBeQkCTpvf/Z3/E/ekiS9ENJkrZKkrTVYjHz5uW4PJqMr/DlZy/x5Zt6CdpNPLitg06HUbZ0OfQ4DEq+cWCaxWSBlXydezYGGAnZiBTk4PugTcd8ukyvx8wtQz5297vY0OHg+rVedvW72BA0yUmrO7tQKGTs7L5RP16rllfH0zJfzqZDrVIxFLCgVqn58s29LOSbhDNl9gzYSBYbvHgx/j47bzEja4cHvfJF2Wq3Ob+c58XzURwGOexIECCWr6JQKOiy65hMlplJVehy6InlG5xaKtLrNvH563r5wu5Osg2BB7d1cOuwn4BNy2uX4qtdHInj81k8Zi0qtRqlQokgKJiJ51EoBG5Y40KhULKUqbG128mOPqcc09dskqy0uK7fSSRf5eenIrxwYYUXz4Y5PJXiieNLXN1tY2PIyv6xJOFshZ3dZp69kGCzX8dUskKj0eBbv5ml36Wn3yPHx+3otXFoJsORmQzXdNu5HC/z7Nll1vuNqFUqbl3nYv8lOQS1z6Hl2EKJL17bwdszOfwW2Zr4YYKmf8tIvRPYKwjCAvAr5GnH3wE2QRD+uasSAiKrjyNAB8Dq61Yg/X8//y/8zAceGqUCSYAnjy/RlkQEhcCB8QS1ep2vvTrJYqokh1uKEkqlgo0dVpRKBUGHgaNzGbxmHZIk8cpYDEGQf+dCqsxKoc6hqQQTKwUGfWbemYjxg0NzTITTHJ5O8dTxMDcOuBntcqLVaPnfr+vmiWNLnF8ucOs6N5NxOeDyW2/MsrPbwpG5PF99YZzRoJmv7hkgmqshIHHHBh9Bp5Gd/Q7SlRYhux6FIKBTCwRtWiQABIpN8Fu0KJUKhvxyfJ2gUPDQzk4e3ikTQR8/usCxhSL/YZOPp09F6XEZWMnXabSaPH50ESQIWHV84fp+blrr5r5NQR7a0cmhmSxnl3JciOTpcRm4Y4OPPo+J4/NZxldyAIiiRKLcZFefi1uGffzBDb2o1Cp29juRRAkEBRejJe7Z6CNdaqDT6bhhwMnLl1McmowzsVJg34iXp05G6HNqmUvXUKnVfOOeIdRqFZs67Owb8ZGvtbkULbGjV5bM7h3xEM3X+fKzl1njNaLT6mg1W0RyFeZS5Q8VNP1PF7UkSV+VJCkkSVI38kLvbUmSPgm8A9y/+rYHgBdXH7+0+pzV19+WJElaPf/bq92RHmANcOpf+/xGW+TWYR+3rvdz46CPR3b1cGYhzblICY9JQ7LcpMOmZWxl9YtSywKfPevc5KptFCqlrIlQKrh9vY8+t4k+j5ndA24EhfA+QjdZauA2abkYLRKwaHh4VzedLiPTSTn5dSFTQRTbbO60r2KFRe7Y4ENQCOh0Ov50zxquXetDpVIxnawwnSwTycmj9LYeO/svJRkJmt8P+BQVakJWDYVam+vWevjWvUNcWpFhjiGHgT1DHn50eI6Hf/IeP3h3lo0hK9cP+rh+wEm81ESS5E5QwKpFKSgY9JnZtcbFheUil5ZzPH8uwm/GYzx2YJqv7xvkvs1BFAoFzWZLbiGKMo1pQ4eDfq9lVSpqZClXI2DTsf9SgnC6hCDI0tZksYbHrGV90IbXKlOhNnfZ8dsMq99PjZFOWUr79kyOB7Z1MOS38NqlOPFCjd9MxFjIVBkO2lb/FoFLkRz7LyZ4cLssD/7CdX2s85uJFetIkkwSEP4/pp5+BfgjQRBmkOfMj6+efxxwrp7/I+BRAEmSLgP/BIwDrwP/myRJ7X/tQ6x6NQM+K7cPe1EolQz6zJQa8P1PjHLDkI91Li2nlwqMBEyo1Sru2OCXAyqPLvHlW/rY1mXh6ZNRgmYV+89HODKTptlqsdZjRKmQF1Pj0QIqlZprep1s6rQxFi1zJVYimqvT59Ry8EqMF86vsJKtcPt6LwvpMkdm0kiShMugotupZ7nQ4Leu6mSNz8o6v4W/vH+E3WtcjEcLHJ/Lst5vIl5s0GnXMbZSYlPAxE/PrGDVKlhIV3j9sux8EQSBcLrExEqB6/vlqGilSokothn0mYmuxrN5jCr++jczLCZlJ48cS6HEZZI3hzocOpxmHUgST51Y5uhsGp3U4MnjYa7rMfHs2QiHp5M8vKOL0S4XX7i+n7tG/ChW8V8bAmYEQWAuVSFVbuI2alAoYDJewmNUcSFS4DfjMXxWLUu5Oj1OPe12m8l4mVsHXZxYyPHs6QXemUxy17CLfKXJSqHBQzs6WchUOTydYsBtYENA1phMrOS5HMnx6lgMn0VOIXt4Z9eH1sb/K5svkiQdBA6uPp7jX+heSJJUA37rA37+a8DX/kc+M1mqcyWa5TtvjDMcsvNGq4VeKTKTrnF5McE/HInw1/cNEq9IzCZLMiloIYOgEPjaq1cI5+r83s4QC5kqb11J8MC2EE8cWcRh0rB3g5fD0yl29jm4Z6OPE/OyC+XezQEGfSauRNL86MgCU4kyWzoszGVqiKJIj8v4vrng5Ly8kM1W5ZSCK7EiX39tiiGvjnixyY4+BwGbjnCmjEJQ0Gq1yKyieR0qFW6Llk67jr95c5Z7Nvrodhp45kyO8WgBSZIoVJvcOeziiSNhQlYtqVKTVLFCtNBgY8jERKzImXCea3pctFttJIn3mXwjISsqhZK7NwY5PR3lcwcX+OQWF4+9PoXToOLUQg6lYp7PX9/PRKzIdKyAUiHQYdfxwrkooyELvS4DXXa5R33zOq+cXz6X589v7+HBn11EbLWQVjstf/HyOC6ThlbLhUMrcWQ+T5dNzUuXEph1SnwmNT8+sijvzrZaPH50kZVcmdGgBVGC+VTl/d3eoE1PtNj694nyNWlVRHM1am0Fbr2CX5xJ0mjDxnCK1yaz/PV96zCbTWg0DY7P52i32+SqLR7e1cNCusJt2jY/OB7lziEXVp2ScK7OZ6/tAUliLlXmciTHZLzIgMfENV0WIoUmM/Eiz5xe5PRCjs/t7iFg8bDGb+X6VptvvTGDRSvwrkqJ36Lj6l4niUKVjR12fnhoHqVC4I9v7OKbb8zi0Cs4OpNiR6+DVqtFp03H0yejOM1anAY1AjKAZne/k00hE9F8DZVKxX2bgzQbTS5ECjgMGl6dyBLNtfjJiTCfusrP8bkkbr2CVxfLdFjVfOHqHgY8Bj7/s3N0OvTs7HMwF89zbiGDWqOm123kXKzGg9v9pCstFtIVkPTs3eBBq9XyytgK8/E8M+kqN611c3gqhQREclXipSZuowqHSYNSKXeOrh908YuzSa7ts3EpVmZjSIUoSXitOrZ12zg8neLEbJo/uLGHE4sFxHKRlXyDVrtFqlwnZNfT7TZz25CHx48s0O+1cDFaosuh4/nzMXw2Hdu7bYRz9Q+tjY+sSeC73/vBn/+n3/88v7stxEKmzoagmY9t8jObbuAza7l7Uwdus47vHZxhjceIhIKFbIV7Rv28NRHje0eW+eqtPXhsRo7OpijUWnxsc4j3wnl6nAbqbXh0z1p0KoFHn7+MSiEQcpjY2S/T7O8aCXB4JkOnQ8+GDjtOo4wPzlfqWPVqNnbYuBIr0RZFgnYDxWqTbE3EbVSxkK5SrDa5HClwZCrJ2UiJL+zuZjhoJZyt4jWpeW08QbHaYDpVI2DVsbnLgd2g5tmzUbrdJu4cCXJVl4XZVIWHtwX51bkEIauGXB36nDI7ZMhv4cULK6gUAh6LlpOzSd68ksaoU7Gt20apKWFUK3GaDfiteswGNbWmnGg72mHDrBGIFBqMBm3cvt7HuXCOwYAVq17D+qCFd64k2dxll8NV57L89tYQBq2Ss0t5RoMWljJlLkbL/P71PVxYqbDeb0IE+rwWmq02AauOqVgBlGr2jXhxWfQ4TFqy1RaiBE6TDp1aSaHexmvRMBKw8NLFBA6dwAv/9IsPRPl+ZEdqCYnnzkVxmzSMR/OIoshQ0MqOHjsvXYzz2GtTfOW2fnIVWby01mtkNGDmqRPLKJVKvnhtFwaDvCEw6JM7I5PxEqwqxHb2OVAqlajVGnqcev7zXUOo1Wp5Ppks8trlBC2xzeHpFAtpGUjZ4zYSzsgdlC6HgU0dVi5Ei3QIELTruX29j1cvxbh3cxCFIHDwSgKFQuDeTUG0Wg1TsTzpchNJlNg34qXf5yBk06DTahkO2rgcyeE0a9nV50SpVHB8KoffpOBn78Xod+mYTde5qsdEstziY8Nu2aqlVLCpw8pytkKpKfDFa7sZT1RJlNqENBLRXJVdA24UgkC/14woSQjI29xPHg/T69DS5ZBBPJlKi0d2e3hjIoWAQKHRJmSR9SFuo5rXLycI2TQ4DUpiBdkh/sA1IWYzdfYMuZlKlFdlshIXI0WGvQbUajV3Dbs4uZhHlODaNS753GgQQRAYClgZC2c4Npelz61kNGTm3FLu32fibaMlcvd6D4/s7uGGtS7sBhWSJPHypST3bg5y76YAs5k6Nw/56LLr+Pqrk4QzZT6zLUTQpiNdk0OOkCTUKiWf3dWNIAj0uIyErBoOTSX4i5cn6LQoCOebMkDyQpRGo8HBqQwdVg0KYGefgx6XgblUhbmELKS/qsPM40cW6HGbuHdjAAGBC8vyBpgkipyYz7PWa0KhFPBa9YRXoe6JYotHbxtAlETmMw0CZhX7x+R+LcBQwIrHrOXwdIpGo0E0U+JMpIJFDQGLFptOSTRbpd2WGXqHp1J4DCpWCg0UCgU3DnpQq1Xcu9HP7gEXt6/3kaq2ODyV4t3JBDOJIu9OxDg8k2IhXeEz20JMpyo8ezYCSPzxTT28OZlBkiQEhcCD27t4+mSE9V4jnU4Tt65z8eTRRSaiBR7cIbccT4eLTEYyvDImJ6e9fClFq9nEZZSjmrvsOo7OZXAbVAStWhazNQY8RkRR5HI0z9hylqOzGa7ptrKUq9Hl0HPflhBitfCBbvKP7PTj7/7P7/75tGMbsWyJc+ECtZbEvRsDGLQa7AY1ao3s0u53G9k/lqDbrubgTJbldJHpRJE/vHkASZCv6ZYoMpMsM7acp9Fs885kimK1yUjQzEvjKYa9BsZWykxEs9RaEtf22zmxmCOWr2DUaXCYtFi0Cg7NpKk3mpxbLlKsNXAaNaSKdQRBIGDXs9Zn4fJylg0BEy9dkCEvnS4TSoWCHf0u9GoF8XyVt6dk9/g7M1m299qx6VW8t5Sn26Hl7FKOK7E8FyJFdvS52BgwMJ2qc3AyQbXZplhvkyhUcZq0zKXKRDJlzDo1ILAhYOI7b8+QKNSZTJSoteCBazpIFGuUGiJahbgaXyeDLN1WI8VKHaNWXic8eSLCw9uDzKerjK+UuW3Yy1yyxHyqgt+mR1AoMargbDjHRLyEQatmvd/Em+NxREnCatCzJWTi1FIBUWxzejFLoVIjXW6ykKqwxmel22ng9GKWX55cRCWIMo5Zkghnq9yzwc1/fnGCSrXG2/t//e8vR1EQBB69tY81AScP7exmwGNAUCjocuh59r0IfQ4tL19c4eULUbZ127h3azd/eGMf6XKTo3N5Xjq3zEyiyHyqTLfTSFsU2dHrYCVfY+8GD4JCYDBo59Fb19DrtXJNt5VCTZZTRostdvTIvdgelwlJkljM1nj0tgF6vDY2dlgZCsi7Z8+dXebsUoYup4Hziyl+dHSRZ89GcJuUZKttbh100eMyMrFSZDZR5MljYT691Y9Go+HhHV10Ow28cDFO0KLi669NrmpZ+nhkdw9I4uqOpg67SYfdqOOGAReVFiBJPHrbACvlNld3WUmvUk7li0jgnhEfnXYdb0wkiebrq+7vAn93/zripSZnl3KscevJ1togwXiiwic2e/jqC1cQJdjWbeFyJEe63MBplGexoijS7zWjVstrij63CUEQiBSaiO02fU4tp5aK7OxzcN2gjz6nnqV0hWK9zbqglTtH/Cxla9yy1okA9HnkHnvIpqfdbPLtN+cI2dQcns2B2Gp+UG18ZIsa4JXLaW5e62ApW+XgdJZnziwxkyiSqTR4dSxGu9Xk9EKafzq9xMsXoyznG/zpHYPs3eAiUW7SbrdpiyKziQLvTCaZz1RYyVU4OpchU2kw4DHyxNElljIVTizkuHGti2S5zS2DLhYy8gJOoRBYSFeI5GQi6e3DXhKlFgpB4LYhz/vAnYV0hZVii/925xq8Fi3xYpP1fhOPPj/BdLyAJIl0Ow0MeI0cn8sSL1SZT5fZP5bgnhEvarU8V51Lljk+J08BLq6U6LTrUKvVq4w/JWqNhh6ngZVCA41Gwx/c0MuppQIOvZL9Ywm+dvdaJCSOzWU5Npeh2WwRtOkZDtoYDJj56ekVYvka7XaLqUSZezcG6PZY+JNb+nntSo7r17rodRl44cIK3393lkG3jplUFVEUOTKTRhDkC2bAJyMRNBoNP/jkRhAUfP21Kbxm+QJot9pMrBSIleH3dgbpcBgAeXr2xLElXCYtixlZIiBJInOZOnev93BNv1e2iNUrhQ+qi49sUVv1akY7LDxxLEyv28THN3oI2Iz0uozcuNbN3k0huhx6CrU2KpWSLoceUWzz2GtTHJrO4NAKvDefkY24ksSuHht3rvfhtxsI2Q04DKsaA0lkZ6+DLqeJu0aDBO16Xr4Y5eximuVshbVeE3ds8LO734UgyMaFh3Z0kio1eerEMnoVCAoFfW4Ttw15eGksCcisjTtH/Fy3xkGXXc98usKhqSQ+s5rZTJVhrxEBuG9zkJFOJ2u9RjKVNq3V9ZFSqWRTh41MuYHfqqPdanEpUkAS27jNOnxmNS9fXGEhXSFVbhCw6hElicden2IuUcJv1fLQji7ixQbhbJkD40k+u7Ob3Wtc+G1GBEHBfLqCKMpR1pPxMm6jij63iZFOJ3ev91CstbgYLbDWa2KN14LfouHITJqD0yna7TZrvSYEhQwdShRrOA0qxLbIs2cjPHNmidvWB9jWY+ZX51P85nKM7x+cptOuk0dnh54+t4l1fgtKpYpPbvXx0liCkEVerH/YNvlHtvuRrzZRCLLfbSqW56lTER7Z2c1z5yKoVEpuXVflJycjfPtj6xEEgTen5Ai1pXSZGwbd7L+UQKFQsLGjzW/GYyxkZWG+3yLLdJVKJa9eipEo1Tm5mGNnn5M3JlJ02nU8ey6CTaskWWzwvXem6XKZkIC7RgL86MgCkuRgS7cTr1Fg/8UVljNlVKvYrcVMmU0dVhRKBY+9PkWyUEOlUvLYvkG+fD6MINj5xGYfP38vRrvV5s6RAK+uCpNGQxbeuBzj89f1M7hq7yrW25xbyjK1kqPabNNp161yTCps7XahUinZu8HL8fksowETZ5dylJttotkqU4kyAZtuFaOg4UeHFwg59Oxe4+LYnMCtgy7+6/5L5KstQBbIvDgWZyhg4cWxBF0OPdlKk6t79YCAUqnioR2dPH5siePzWZaydW5f7+UHh+bIlJsoFQoUChgJmnnrSgKVSsktQz6E1bXNWLTIkZkUQZuBWLFOv0eSgfGpIs+cyWLWKfnx0UVcRs2HTj8+skWtVSnpdhpYzFTpdZu4qtvB3k0hNJo4AbOar75whesGHEwlyjx/boWv3NbPE6vIsWsHPHS7LLLbQ6lgpNOJz16nz2PiyEyaTqeRP7tjkNl0jbtG5OwWOUpYzvG2axXMZeo8uL2TY3MZRAliBXlBuKvPgUKpZM+whyeOh/mLuwfR6bQcmpKF8d1O2bBx92gQtUrFTQMOXrsU442JFOUWOHQS3zuyzB1DDlI1uBIrcts6F29MpLi+z8EzZ8K0Wk2+/toUdw45ubrbjiRJXI4qcRoVnFzIs5StYdLIhdJh0/L8+SiiKOExqWm322wI2Qja9QirSq5Ou465VHl1A0THOr8ZhULg9csJriSqDPlkJaBKpXofZbal08ZiqoharaHbaWAuKRtmv/mbWUYDJu7eGOJKrMhUokzQqiVk88vAzCML9LlNXIwUCVm1fOedOfxmNbsG/dy7MUA4V6PbKSecIcCR6RS7+l10ukyELGoWc3WWUyUEjd78QbXxkZ1+6NQKjs3JQG/Z1SzvwjWbLV65nGRnrxUBmE0UMavbHBhP4NIr6HSZUChlBl+f28gar4XPXdvLn90xyFK2TrxQAwRm0jXWek0cGE/yyliMdruNJEk0m02OLuT53asDshVKIRCyqNi9xsXESoHFTA1JFHnl4goPXB3g9HKJtV4TkiTy2qU4jXqdtyYSXI7m6bBp+f7BGQ7P5rhzxM/3fmeEN6eLfHabl0srFbZ1WXn86BIvnVvmlkEnPzw0iyhJnFgocMc6Bz8+ukAsX8Fn1rC7z4bToufatR7+0019DAWsRHIVFjNVXEYNG/wmLi4XmErWcOpVxAoN+l16Ts9n+KfTi8QKdb50Uy8A/33/OO12m72bQvyX2wfwrFJhx6N5Xh2LMbacpdtpRKlSsW/Ew+HpJKdmU3S7jKvJwzUuR3Icnc3QaDaJFZv0uAwolUpEUWQuWWIkaOaFizFCdi0rpSZBi1rWiriMLGZrIMBaj+xXPDyd4pa1Tn5yMoICgZ39zg8FRH5kR+pas82OXgeLmSqXIzkylRY/ODiDy6JnY4eVg1NpWu02Vq3AxZgcNXFoJsvTn9nE2zN5Ws0Gb0+meXB7B1/69Sw2vYJNHTY8Zg23r/eiVCpRKBTcvt7LS+eEOlm+AAAgAElEQVSX2X8+wkq+hs+q5f5RL8fms7wyFmfPOhf/x8vT/PpzW3l7Jsdt69wIgsBfvjHFYqqESqVgIlaUpzYmJccWctwz6ufodIpWW+SdyRQ/e2gLv7mSpsOm4fM7Anz1hcv0uwyEczW+vneA3//Hy3TYNLw+keaWtXZ29No4NpdBQCZV/fzUMhISa90y9+Sli3HseiUqlcyIVggKxlZK3L+1gzsqVZ4+FWGdX81rlxMoBJhNVbhnNMCjz0+ws8dCqlxf7U3D4ek0h2bzgIKuVXfLO+NRBEH2QB68EufsskySmk0UObeUQxTbHJmBHb0OIoUm23vsHJ5JM5cqE82UmEqUuX6NA4CRgBm/1cALF2Ns6XKwzi+Dl2biRQ5Pp3AbVewfS9B+t8Wgz0KXQ8ffvjX7oR7Fj2yf+ht/8/d/vu32++lyGDg2n2XviI+FdAWf1cCWbgcWnYpyvc2WHif/5Y5BtvV7SBfrvD6ewGVUspQqIooSZqOOa/ts5Gtt1vstvHklyfRKlgPjCfKVBvVGi7evJPFbtawPmMmUGrx4McbVXXb8Vh1fvHGAdR4d5yNyvvY/ng5z3Vo3fS49PW4TtUaTchPu3+SX2XuSxNYeN6MdVhrNFvPpCslSk/UBMz98d5ZitU683ESrkNBr1ViNOtxGNV67iR6nnonlLMW6RNCmZ3efneVsHa9Nh10ncHSxiFGloFirYzXo2dFnx2XWE7LrmIqXWeM28LXXphAliWt6HfR7zAx4jZxZKtBstbBoFRxdKDDsNbDWZ6HQkMGN39g7QK4uEi81GfToee58DKndJF+uM5mssG/Uz3DQJgPgiw0EQUCnUhArtrh/k590uUE4U2FDwMJsps7vXBXimm47z70X5vhchmu6rCSKDQJWHRMrefK1FkqFwLDPSLEu0uvUEim2+Y839OI260ASee6nP8z9u0sSqDTazMfzzCaLBK1ahvwWlEoFAZuO+XQFlVLJfZuDRHN1ZtM1xlcK+CxqFAK8OR7n5UtxksU6nTYt+8cS+MwalnI1BjwGzkaKOI0yBfTFizFcRtWqDSlFNFel26FHEOT8khfOLvGdgwucWUjRYdeiUCp4dWyFI7NpJiJZfnhogaBZzauX4jSbLQQEJEnezTy/nMOkUZAs1ZhOFMjXW7wXLvAfNgdIltvE8jXenUxwflmmFt084ODQfIlao8G5cJ6/eXOWTKWB26ih023j45v8CAqBTLlBvV7jyeNh3rwc5ch0iiGfkXC+zvqAmY9tDqFWqTkyk0alVPLF3R3MpKosFlp8YWeI+VyTcKbEniE3+0b9/NXbizLhf0cX0VKbLZ0WkOD8ShmTRkE4XWY5U+XIfI57NwXY2GEjW23hM2v44aE5fn02It8xFAq+fHMfkiTxpWfHWM5W0KkE9l9KkCrVaTZb/PxkmGaz9f6u7qnZBO9Mpti7wcP33pnh4z84wdtXkh9aGx/ZovaYtEyna5xbyjKfKPCDd+eQRImVfI0uu46VgjxipMsNms0mz56NcHA6w75RH+v8Fq7pcVJpinIXQRIRRQmFINDhMHFNj5Pr13pQqdXcuylAp9OIJAEShJxGnEY1bVEkX2szFi2xOWTGa9ETztX509vXcscGv5wJvpjBbdYgSSLnw1kmE3IkcixfBwTE9qoUNGDm/FKWq7ts9HnM9HtMuE0qPBYNAZtezm906fnp6Rh//1vrWBt08uhta3Ca9Vh1SpKlOpIoIooi04kykWyVs5ESn9riI18XWSlUefFCDL9RKdu5lArZeWOTNeH/cGiJm4a8fOveIQ5M5fn0VX4OTmf4i5dlDp5CAbcNeZhKVuiy69CotZRFJQ9t70CpUHF6KYcoiXzrvvWolCrGIkWGvAbuGg2gUirZ3Gnnmi4rv35vmQefPMWvz4Qxa+T1TbfLSL/HzFq3gVRV5FPXdBAr1PnliXmOz6WZTZaotGCt18RMssxNgy5uGvLSLmXiH1QbH9miLjfarPNZ2Dfq59xyiVargdusJWDTcWKhwMM7ZV70aEh2b9y/Och1axyEcw0y1RY+q57t/W6mkxXEtsihuSxdDj2CQqDTYWQpW4fV0UUURbK1Fh1OE112HQenMxyey/HYvkHu3xIiWxPlToIk8drlON8/OM0bl2KMx0p02LQcnc1wz6ifIb8VAYltPTZ6XAaKDYkb13mI5GpMJqpc1SHvTv7y5AJnlyvE8zVUSgVf3bOWAxMpJhMlToeLNBpN3prKsMYtMzbSpQYXwxkOTqf5zLYQG0J2ruqycS5a5dHbBvBZ9GztNPPdI2GGfUaWc1UuR+WcGwGBfaM+9o4GmUnV8Fg0qNVqbhny4dCreOrkMhsCZg6My17Q6Viez+7q4pHdPWjUapwmDXajjvcWZXnv0bkMdr3Ak8eXGY/m2bXGRYdVw09PrzDsNWBfZfJpNWoGPCaWczVGgmbGEvKu5t5NHTLHJFxi73oPg0EH//H6biYTZSw6Jbv6XYxFiyj0FvsH1cZHtqjNOhUdDj0rxTZ///EhjszlkZBQq1Ts6neuRpOJRAt15tMV+pw6XrmcQhTbbOq0o1YpeWhHJ3OpMlu6HTy4rQOlUkU0W6PXbeTOET9KpZJmo8Hh2Tx/cks/ezeFGO1ysanLzmN7B/n2b+aYS5XZ1Gmnw2Ggx2Wk22lAkiQ2dNjxmTXkqi3ihRqzyRICAivFJs+fizKfrjIcsnPzgIO5TJ0v39TNd48skyxUieYbXN1lYmu3A0GhQKVSsW9TiG/fN0zQZmAsWsRnUpKptsmU6+SqDSK5Kha9BgSBcK6GIEls77MzlSiTLNVRqVT8wQ29xIuyEOrQlTjvTGdYzpQ4t5TjpXNhDk0ncOkEjs5meGhHJ/O5Btf12+l1mehxmWg25Zz1H747x0yswJtXkniter593zC9HhM/ORnh6g4zPR4rn79O7qRIksRTJ8Ls6rWh0+tAgC63mXs3BshW29gMWu7eGOKHn9qEWq3miWNhRjud/P71vZhMJu4Z9bH/UpIfHZ7n8kqR+ZQsYf2w4yNb1Dq1Ug6KFNu8M5vn2n5512/PsJcBj4HDUymarRYhm56QRcO335zjrvVu+r0Wel0mms0Wj70+yZsTCcKZCi9ciNNut5AE2SkjSRIDHiP7x5J8cquXJ46FeWVsBYANfhNvTWdJl+v0uowyGEaSODaXYTqWJ1lqsK3bhnOV1ewxa3l3OsvDu7q4ccBFwCG3Ej99lZ8HfnIeq1bBSknkqi4bbosem0nHUMAGyHqK/ecjNJtNHj+6xHK2gk0r8LPTK9w+6KDSErhryEWxpeQ7968jWmjSaZOd5/OpMoenUzj18nRJoRBIFhusZGW/44PbOlCpZG7Q2aUsrWaL58+vMB7NMxkvc/2Ai4vLRZ44KuOQtVotd6738tnd3cRLTRx6FW2xzU9Pr7C128WD2zv4ycll2u02tw97+dHhBVqtNjcOekhV2uwZ8mDXq+lxGljKNdjUYaMtijz2+hQTK0UEQeCR3d3vO/Y77ToixRb3bQry7fuGuXmdD1ESeXcq9aGJtx/Zos5Xm1xYzsskpEaTmXSNhWSBv3hlgj/81Tkq1QovXozRFiWOzWcYDVmIl5q8M7HCwSsxkqUmI0ELj+zq4bpBL1u6bHICV1skkqvxvYMziKLIaMiy6jsU6bTpGAtneOFCnIBJxfoOu+w4n5Rbpjt6Hazk6kiSwONHFyjURT59VYCbhgPcMODkwHiSc8tFHt7ZzTq/mW+9MctIyMq9m4JE81U6nUa29DgZ9JpZyVU4OJ2hVq3x3lJm9YKSMWpKlYpPbPbw9OkoJrVAugbf2reG//LKDO8tZPFYtbgtenpdRvwWLYminJIwEy8w5NVzbrlKyKphIVNBqZLRuAG7kW3dVpooeeCaEMfnc/S5jfjtBn73miAHJpL4zBoUCgW/mUyzo1e+iySLDXb0yhtfWq2WAY+JC5Gi/PcKcGw2Q5/HRMCqZTya53ykxEyyhEKAZquJANh1SqZWsswk5MHkSqxIvVZjKVujw6rhyEyKP35mjHa7xVi0xI4eK4JKq/+g2vjIFjXAvhEfPS4j4/EK1/XZUas13DPqY43bwPlIkUdvXcMar5mQTc/ejSF29Ng5vZhfdYqLXFxlaxyfk7nVTxxdREJiZ59Dnh9fitNp1/LOZApxVfi0mKnR5dTz8niKT2/1M5uusdZnotdtQqGQFz/3jPoAGPAYePp0lD6njsuxCr1uI9v6XEyvCoC8Fi33bPQTzteJF+q0RRFEkXixymSiwie3eHn9SoZhr4G7RgI8uL2Dc5ESK9kKT51YxqZXkas2mUpWePlSknSpxkjIglqlknXcgkCs2GBjp40NAQvhbIULkQLDQRPDQRsXwgU2hSxcWM5zTbeNn55a4fFPrCdRBa9Jjs/wmTW8cHGFk9Nxovk67XabDptsgHUa1GSrLWbiMo5MFEU6nQb2jfhIFJt8dlc31w16UAgCkWwNtVrDf797LYN+GzcNOPjlmRVylQaxfIWnTyzRbjV5+UKUbpuaA1fS+IyKVeGYllJdxki4jGouxUpIrXr1g+riI1vUkiRxdDbDfKrMvlEfSpXyfVBMqtygyyHvYM0mSry3kKXZbHJ8IUeHTYtCIRCwm9jYYUWlUrG9x8bJxTz9Ljk2WBTlhV/3aryE16Jhe4+dsWiZ24bcfHxrBxv8Rr74yzGazSYhu575dJW5ZAlRbLOUrcq3e0FBr13Dm5Np9o54ZFTAzi7mkyW+f3CWeKHG0dkM7VYbqxbenU6zmC6Rq7a5cZ0Hg8HA3Ru8HJ7J8tK5ZR4/usjeDV42d9lwmWVCk0aj5nevDuA2qam1QRCg1W5zbC7DbFIWLl1YLuA2aUgU6uQqTbZ2WjkwnkRC5IZ+GxfCWdrtNulyjW+/NY/fpODQTI41bgORXAUkWC40eeCaICfn0zx3LkKfU8e55QJ9Ti1vXUnw3166xPcPzrCQLHJ4OiHf9Va/R0mSaLYatMU2L40leOtylKdPR/nE1gAuq4F9I95Vk4GbaL7OTLrGxqCZ00tFWi052evqXifhbJWAVQvShycJfGSLulhrIUoikWyFmUSRswtZTi1kGYuW3keIvXopRiRXpS22+dGRBdwGJZlKg6DdyLZuC8lym36XnnC+Id9alQrenkzxt2/NypR+UeTIbIaFbI1nzy5zx5CTJ46FmU2WiJea7NvoZ33Ijkqlpnd1ty1WbNBut0kUm0SyFU4tZKjX6xyZSbP/fISvvzaJKEGn08CWbicBq47z4RxHZgt8+qoA2ZrIoNfE7n5Z3trrNtLvMRLJVREEgXPhrAzEWUUWXNVlZ/+lBDPJGn6zVo62S5QJWLQoBIFelxFJkmi1mkSKTdZ4TSSKdRaTRdrtNr//zGWylSbzyRJ2gwa3Uc2pcJHP7JD5QrPpOns3eLh3Y4C5bAOlUsWmkDwYXNVtR6WQ8RQf2xLikd09JAo1Ti3kcBuV/M1bM0xGs0zHC7wxKQ8sTqOabLXNA1cH0Wq19Dl0/OxMjH63gW+/Oc+OXhsDbgM1ScnVXRZeHkvSarVoNuocmskwnyiSLtc/NBz0I7tNrlMr2d0vc5UXU0WUKgV/fMsahgJWphJl5pJl7h4NUq/X+caBKXwmNe8tZEhVJMLpIu/OpHng6hCPH1vimk4z4ZyCLqeRz+3uYTJRZjhg5cBEks/t7sFrVPHy5SQnF3Ls7JeTXDudplWeRYk9wx4mE2VuG/KgUql45WKUGwZdRLMVam0BSQKFIHD7ei+9biNrvSaUSiUTKwVm4kU2dtrY3OVgOd9kg8/EpViFZ86E2drt4PnzSTKVFp/b3cuAx8BXnrvMVW4znZIMpRQQ8FkNuI1tZv4ZJYDAHRv8HBhPIggKspUmSzmRP7i+h8VMlVPzGYoNiZvXefnqHgffPDBNotzGY9Gzq9+FVqthJl7g+FyOT2zx8g/vLnFVt427Nwa5usfO7et97y+aJ2JFXGYdgiBLTBVKBd1OI/s2dRBbTTi4fb2P29a5mc/JSr0NQVky7DIInFnKs73PSZfLTLdTvgB/cjLCY/sG0el0/IFKxeHpFBdWKuzuk7tWQ37N/1JA5P9vh0mrYi5VpsuhJ1Nts3eDl6OzGR59fpzpeJHz4Rxj4QxfeX4ch15Fotyiw23h5w9t4eb1IR7a3smppQIuvcDTpyIspcu0220OTCSZTRSZTJTZM+ThN5MZtDotP/zUJq4b9IIgMJOqErKqeXsyyWQ0y/4LESYiGR756VmazSbdTgNIcvvuE1sDaLUavKu7itOxAn/4q/PsvxCh3Zb10ZdW5P/jrckkYytF7ljnIJyt0eUwsLHDxi3rvAwFLLw5meHBHZ3cPRpkz7CHQ9NZYvkqHXY91w64GPRZ+PGRRdqiyGS8xEqhwTq/mVuH/ewb8cotNSQcJh03DXrYM+zh6VMr+GwGrl/n49E9Azx1IkyXVcX5cAGvRU200OT2IYccG91uM5Os8rVXJginS4TssttoS5edxUyV8WiBTZ12lAqB8ahsIu6w6fjh4Tm+9OtL1Go1BEG+wFutBm9fSaJSqti9xs2eYS8I8ML5FX736gBPHFvi0nKOhYw8df7DG/vQaTWrxowPL9uPbFEXa01+djLMTDzPsM/A8fksIPHAthB7NwbxWLQolUoe3tmFVqPhc9f2cu2AW8Z/xYssZqp4jCq0Gg2P7VtHp9NEJFuj0Wjw9kRyNfJCyZ4hWaA0nZSnOUuZKrt7bSiVShwmDSJwbilLJFPh7g0efnxkgcmVPNlaiw0+PWq1mg6bjguR1fl2pozLrCVkUXNwMsGbE/H3aUtrvSY2BMzsv5TEZ1JyYiHH+aU8fpOCVy/FqFarPHcuyvffneG1y3F+e4vMoAtaNTx+bAmXUSEvplaTex/cHuLARIpWu8Wh6QxdFiXnlvM4jBo67Vq+/voU7XaLHb12VEolV2Jy9+H3fnERq1ag3WpxcibBM+fjBK1qnjwe5hv3rCPgMBKyG7h1nQunSUOPy8ieYQ+zySJiW8RpVPPs2QguoxqVSsm2bhuJcpPnz0fxWXQsZysIggKNRsMXd4eYT1X4o3+8wEyiiF0vML5SIFmqI4ptkCSmU/L7PUY10XyNgE0PCpX6g2pD+Gen8kftCPQPS3/51LOkKhLjK3nMKgmFQolKrWJrt4PlXJXd/S5ATsW9ayTAd9+eIl6osZItss5nYSZdYzggB8E7tJAotUgUangsOnkzxW1iPlWm3WqjVCk5H5YvoLevpBgOWfEaVShVKlrNJpfiFUb8ZqL5OpOxIp/Y4uFLL06zrUNPp9tKyG5ArdZwdjHD3evdvDKRpl6rs5ipcnW3431uH4jYdUpeuBjnrz42zKmlAvsvxtizzs1MqoxVq+L+rR08fz6CQ6/iUiTLSIeTWK7CeLzCTYNuJFGk02UiUWqzrdfGu1cSpMsNplbyOEw61rgNaLRyDIfLqCJdbvOV2/p56kSE7b026rUaT5+KylvvwOevlbXUM/ECa4MOKpUKpxbyHJ6Mo1Aq6POY2Nhh5/xyjlS+Ks/NzRo0GjW7BzyrI3MLhULBfKrMzEoWtVrNtm4bf/PmNFs6LWi1erxmFe9MpXEZtWwImhgKORFFmQ14dC7Lybk0Fp2Kz13bx1Xr18y1K/m+f6k2PrJzar9VR7YmsGuNg+09Vv727Tn8Rolr+j3cscHPxEqB+ZQMYwyuRtG12y0sGoG3Vso4jfJC6vPX9dNoNPi9X1xk76iXTqeRWwZdPHk8TDRfJ5qtIAhw/5YQtw97eXUshsOoZkePnV+/J+ebKJVKtnQ5Vun4ba4fcHJmucxf3TPAf319luVcksGggz+7YxBJEjmzXOYrt/Tz5PElRgImJpJ1Wq3W+xkoCoWaDrueaFlk9xo3gqAkaNeRqjQJZ2tMx3JMx2VR/myyhlLIolSpuLbPBgKcCRfI1ERGgxYWM1XydZE7h10kijV2dJs5uVzhs7v877NE3ry0zDffmMZpUNGoN/ju4TCjARlcfzlWYX3IzvcPzjKVKHJDv43PvjDLU58eJVFqsD5gos9tIpytce/GAEem08TyFdQaDT6LRh5pE0V6nAYQBIJWDd87mGFrl425ZJFItoIkCHzpFpnjPRyw4jEqOTyTpd9r4ch0CqVKye5+J0GbHOk30uFArBayH1QbH9miBvBZ1IRzdQIm2bL07kyaTruW1y7HV9lrAtt77SgVCvZfiHBoNseAS0/AoqbYkLhpnReArzw/TpfTQKLUYlu3iW8cmGFzp5U7NvgB+MG7M8ynKswly0iSRLbSlDl5lSYukxYEgVvXuXnyRJizkSKi2OZsWHZNP3BVQJ7PO428djmOQhDY2edgPtvgmi4r33lnnke2+UnU4LmzEdKFMqMhG2adkm67nuMLeR7Z3c1sugYSbO5scz6c/7+4e+/ouO7zXPfZe3rvDRhg0AGSANhFsapXiiq2HNuJmyzJTo+TG8dO4puTnPgoduzcJDcrsRNJVOwUO7ElS5Rk9cJOUaxgAdHbYHrvde/7x4aVrHuvfM4693jdJe+1ZgGzOZghwW/2/H7f977Pi8tiYNBtxG4sMuQxcGYpx5wAN6+z8sAmIwM+C0dn0uwdtHLLoIM/f32OQqXBC1eS7BrwsJStoVIVOTKd5KWrKR7d3U0kX+exl2e4cdAFAlyJldnUZVdg9CJY9Wp+7fuXuWHAyUK2yQObO1hMlTlyLY5Gq4zog04Dsiyxq9fOifkcAkXevJag1W6zodNGt8vMY/evZ32Hjd/+/nm0WiUfZ2PIxWqhwV2jfl6+kmDA22Q+UeJavMQtIx4EUURACVBdydZQmZ2+96uLD+yaOl9pcilS4oZeC4+9MstSusjNI16WsnWQocdpoMdp4Efno8zGi4iiyD8/tJWPbOtCr9fjNGkRBLgWLzEadPCHdw+ze8DFqcUcstymy6Hn4IkVZX1XbIAAK+kygkrEadIiCgJWncgf7l/HBr+Rr786y75BL9/62Dg3jgQUoOPZMI8fXyJZaiAAF5YVO5jSt22xnKszHjDyhy/Ocud6L791Ux9OswEJKDVkFtMVOmx6nji2xKDH+J5W2W/V8/l9fXS7jHgsOlKlBpIM/W4jl1YLnFvOrbUVazx9doXP/fMF1nsN3DPmw2kx4DWpFfmsKPLQzi7sepFoUZnK3rLOx75hD4lijfEORWJ66HwYURD50KYAO/oc3DCsEF4XUmVqtRpH5rLs6nOwfywAMkwnShybTROwKm3Sz+zsxmM1EHQoKIrVovKJNOy38MiubraGFG1SJN/gaiTP7evcZCpNVvNVRnxmkGUWUxXOLGa4GM5zy5Dz59NNjgD3jvv57ukoH9noZSnXosdpQCWKdDt0HJ/L0pYkPGY1kXydcEbB4r6zlGN7j53fu62fiTVq0ie3Bzh4YpkTs2l2hKxsCtp5d7nIrj47oiASsCtc6HwD7t7gw2/Vc3QmyeHZLJVKhe++G6XdbtHr0PDlZyd5+lyYsQ4znXYjPqueLd12DmwKsqnLhiTD0+dWOTwVZylZIleX+PUbelCpVLyzlGdbj4NtISd9btN7G9VEsca1WBGfVctEuIC4ZuK9FCtTr9XIVpqU6k2SxTobO5UksOcuJbh3zMd40Eqv28BzF+M8fymORQ3fPjzP21MJZmJ5vvD9cyxlGnTa9Ny2zsev3DTAYrrC+dUyrVaT88tZ3ppJ4bdoOTmfYzZVVdAS7TaLiTw/vprmo1uVXMhrsSKruSo3DXsABSQpSRKnlwts7XFyz8YOIrkaAZOK//biJKcXszx3McbrkwlemIiwLWji/3htliePLzEaMNPlMCLJEpeiZW4bceE1axn1m/ndH176qaXxwS1qYF3AQrrSIFlp8ekdQU4t5umyKxnlXpPIyfkMKlHFo3t7CDoMRAo1doRsJAt1Pv+vF6k36rSaLT79nQs0m02cevju6VUi+ToP7w4xGnSwnKuzZ1AZbf/+XUNotVqCTiN7Bj18dGsHx5ar/OCRbXxl/3r+6MU5Prk9gFql4s3pND6Lln6vwnOejBVZzdeI5mvcv9FP0GFkLlVhLGBBVKl47KVrhNNFEoUa8VIDn81At0OnxBv/p838eNCqxCEfW+TuERcnl4pYdCJWg4ZMqY6MgEMvct+Yj2cvRDkym+G3bwzR6zbS4zKyMWihhUCHTcdtwy6SZYkbB+2o1ar3Mm/UGg1/cvcAk8k69477eGR3Dz0uA91uEw/tDHF8LsNqvk662ubOdU50Wh1tSUKSZVJlRc8hqlQkijVUosgje3oY9Fnf03U8dzHG/ZsC/MJ1IR7YEuSWdV5Wc1VOrxT4ndsGeGRPDxfDBSRZJl1qcs8GDwdPLHNmOc9KpoTDqEHUGa3vVxcf2KKuN5We8k+cFKcWs3iMIosZhRoaLTYIOkzsHfYyGVXC6dstRe/rMmlAltBoNIx3O/mDOwfocho5vljk0zu66HabmE6UuRzOced6D0NeE8+cW2UyWuTyao7zK3meORcmVW7SaVHx1Mkwr02l+bP7R4hVZH7v9gGcJg1vzyjxx+FsFVmSEBHwmTWs5BuoVSo+cV0HVxNVkGU2+IyIgkCu2lLEQgIcm1PYIpu67MjAhZU8r16KcHgqwbuLium4z2PGbzMgSzLZaoOlZIHvvRtlcjWNSq3iuk4jB/7+PNFMiYVUmXiphd+iRmq3eerkCsM+A9Fii3ZbYjld4vkLYZaSRc6ESzgMKv7yjTneuBLhsZdnabXbLOdqIMs8uqcHh17Fj6+mWc6UeXsyBjI8sElx3+/uc64RrEy8cjXJTLzAy1fi3DTk5oHNAVaLLW4ZdDCxWkBA4U7vHXCj0Wq5FlNarpIkIQtwajFHp8PAb97UR7Ym47cZfj6XH5VGm3qtzsGTy1xeydFuSxyeyxLOVIgV66hExd0x5DFy8MQyrXZbMQeshcb3uE08uG+fIkwAACAASURBVDUIwItXM6hUam4e8aDRKBuXZrPF40fn+bs3p/jj5y6xf4MbURQYCzr48h2D+KyKSOzEvJKf2G63mUlWWU6WeOLYAkjgMCjglXRF0aTs6ndybCFPj9NAt8PAv55NcOewg3PLynhfFFU4jQr3DllmT79bCUjSqBEFgQOjHsotmZDbzD2jHp6/kmDUr0erVUiiyWITv0VDt8fI4ZkMYx1msg2RX9gaYKxb8Su2221W8k3ensngNWsot0QOjPuYiCifEiuZCslinV19Tm4c9tLnNpGrSXz3M5u5f0s33XY9s+ka7Xab8+EiW7utPHS9gkCeimSZT5V5fTLBXEoZzowGHdw95l8jLUGsWOe5iQQdZhWffOos05EsS6kySylFejriM7OSa3DvuBJQuqXLQYdNR4/TwLvhIm6Thr2D7p8Ks/nAGm//9lvf/uONt36YgFXHUrbKveN+rut1c9dYgO29bnQqOB8u8Oa1BKOdFoZ8VtYFrJQaMqlCmS6XlR63mUylxX3jPkY6bEiywFvX4rTbMoV6mxsGXDRbEocmYuTKdUw6Df1uI4/9eAqzXo1JK7K9S6EQqUQRu1FDU5J5ZyFDU5LpcugIua380rYAS9kGuwfcdFh1jAYdZGtthl0aDl2Kky432DPk4TM7uzk6m+ZqNE84W8Np0pIot/iFrZ28u5hlMlZGq1KxPWTl0mqBlUyF1yczPLIryLGZJKUm9Hlt/MFtPRxbKtHnMmLRqam2ZNQqNQfG/MQrbQacOn7huhC5SgOjTsOWkBO9WiTgMGHRq5FkgbYMTpOOdLmBUasmUawzmyzx5rUkH9veSa7WZjlZxKBRcXY5i8+koSGrMKgktGoV5Xobu0lHoyWRrjSx6kT2DvvY3GXDbVJ62Nd1WXh3JY9WrWIyXsZv0SELKq4L2Xj6fIRoXnHiXFzJky432NXr5OJqkf0bPHzjb77d/qMv/c7X/t9q4wN7pVacL0Z63EaGfWa+ezrMbDzPE8eWODef4LmLcW4ZctLlMCICc0mFknTXqI9iXeazu7qRZJnZeJ5/ODzPi5eiDPtMpEp1VnPKkiBcaLwXZaxRq2m1FK2H16Ll4d0hYvkaX/jBVd6ZV6SpS9kaA14zo0E7X39gA+lyi/l4jo88cYYTU1G+/fYcjx+d5/Gj89RqNb7x5gKtVhOVqAj1nzoVRpZlPGY9wz4zQwG7ElIvCIDA5/b1ce9GHwdPLPPWtSR7++14zCoW0mUSFYmdPTZ8ZjUfe+oCe/vsiCo1l6MldvU56XKZWMpW8RhE3pjO0Wq1ODSRYFefS+H8FZqIgrK581nURHNVljIVgg4DAZuWw7MZgjYtao3yd52NF1hIlzi9mOHpcxFOzKbY2mnkhctJ4oUasixx+zoPx2ZTzMTyfPvwHOfm4/zXQ1d49mKEqUiWF6+mCNr1/MFdw3x4S3Bt/wEzySqbuuzcv9FPJFvFYVCxminTbDVxm7R8842Fn0/th16j5IkcPL7M7n4n+/qdqFRqPr7ZzVdfmmLIpeHgiWUkWSJaaBDOlN9rp8nA8xdXefrsKm1JIlao0r2G3wrYTewddHNgU5A+t4m5ZAmNRs294z6uxqsMuA3vjc07HCb+8sH16HQKpeiuDT42dNpRCSKvXkuTq7aYihf547sG0em1PLInxHi3i0f29LKl18tTn9jIL+7sZ7TLzv6xALv7nahVipFWo1Ez5DVyNZLnqy9MspwuM7GS4dnzUdxWPWNBG0fmcmQrbeKlFusDFooNmVixgd9mYDjgULh3nVZW8g16nAbOLKR5fTLBHeucaHU67tvkZzRoJ1po8tDOID0uI8ligzenUnTYDe9JQRPFJg/vDqHV6fjy7YO8s6iYM5wWIw9s6uD+cT/pOpxaLPCbN/UiIJMo1nn5SoIOu567R/3IUpvHjy6AAF++Ywi1Wo1DJ3IpnOdPX7iKJMkspKsM+8ysC1gIZyoKxHI2wZXVHJfCeb5yaJLre+08sCnwU8fkH9jhS77aZDVbxWnS8IMzKyxnq2zvcfC9U/P4LTqOLeS4edhLj9PA3aN+nji2wEwsz1uTMfpceg5Pp7lxyMWA18I7M3GOzaZYylTZ3e9kKVtjrEvRbK9mFcJRtCzxxdv6eO1aGhlZ2e1LEqdXith1Ij+6GOXEXJa9wx72DHoAmQ6zir96c46lTAVRVDEZK/LQzi6mEmVmYwUOHp3j49s72Nnj4Ntvz6LVaLh33M+hSzF+aaufJ48vE7Dp2NRlJWjT8p2TYQa9Jh7d26MQk8JppqN5fGYNgt9KqtLkxhE/Mkpn6OUrSrR0j8fKgNvAQqpMsdYEAYKuLNfiVXqcabxmFV/84WWG/GY2dlo4H5YJOfX846kwbqOISlQx6DHynXdWkSUnHqPIgY2dIMucW8kRsBn5swODrBZaCqFKltnc5aDZVHIV2+02okqNLEts7ravra9l4sUq2UqLVLHGcqpIyG3hhYsR9o8HSJWbHBj1cGU1hyCKfPJ6H4lym5V8nXvGO34qdP0DW9Q2g4Yer5Xre1U8fXYFu0GxrLmsRj67q5uVXB2p3eYbr0zxwOZOWq0WK+kW1xJlVKJIr1NPtFBn/7iBs+EC23rd9HsVnnKP08ClcJb5RJFkqYFDLxJyGHhlMkWPU0+j2WAhWSKWV5zoubrEo3t6CefrLKUrhFxG5pMlXr0cJV+p88ZUir94cIxf+dcJetxGtnTbCTkNmPVqfnQ+wvX5Ou8u5fjd24eZjReQJJlPPHWWT+0Mcfeoj1euJng3XOKhXV3MJ0u8fDXBYqLIqfk0tSa8eiVKp1NpHQ55TXzlGWVyKcso9CRkvr6Uec8UnCxUuRwtc++ol795e5Hbh+30uXSkyg32DriYiJRYyTW4rtvCN1+dolBVzMIPbOngWiTL48cWaTSbpGsgSzI7Qjb++q15UrkSm0JOZY8zpuIbb4b50q29fPHpK/Q6tNy8LkC40OCrL0wy1mFCVKkwaxQY54nZBCqV4rYB8Jq1LGWqjPgtqEQRnU6HT1D65iupEiqrp/v9auMDu/yoNSVCTgPPXoywkq+zd8BJsSHwqe0dhPMNBrwWetcE8qvZClPxMkGnic/t6+MzO7uYTpY5OZem3W7jsykOlxG/ZU1PUeIH764QyTcYC5g5PJOh2WoSzVeZS5R4cyrJUrKEJMmo1EpnQqNR0++1vGfrume8g1tGPFj0GqotmE6UsZm0uC063pxKcmI+y5agGZtRSzRXQZZkJlfTHDy5whdu6MKkVRErNHjpSpy7xwI8srsHBEGh8rfbqNUi1/Xa8RghXZNJFCrIax7GuqQk3IbcFn55dyd9Xjv7N3goNGRcJg3L+QajPiObetzcM+rlxStp3ryWZGOHlXC+wXiHmbvH/Gi1WnrdZsY7bXxoSwdajQZx7YJwaCKG1yiSKtVZyta4d9yPy2bCZ9OjF2RevJpmX5+DlXwTi07g8EyG43Np7ljnwWfTESu1aDQa1FvgMGpQq9V02PXsG/IiiiJ+m543p9Ko1Gr8Vh2NRpMXriQZduuVtN1mvfx+tfGBLep6q82gR7Fsbe2yc3KxwG/fFOJbx8OcWcggSTKLmSoqlYqdvXZGAlZWczUWU2WWMzXy5TqtdpuDJ1bY0mnm/EqO5y9GmE0U6XEZCdj1a+wQFWOdViKFJo/s6WXAZ2HIY6LDoafUgs1dNm5b72dDp/29kNKFVJmrkTyCIDDot7MlaGY5V+eWEQ83Dnm4bX2AXX0OLkTK2HQilyMFBFEgVZEIOfR8/dVZBFFkd7/zvfi5fzgyz7GZFJF8nZDTwO5+N81Gi+UC7OwykS0rI/Q/f22OTR1mVGo1S6kiX35uimq1ylMnl+lxaCk1BfqcRi5HSzx/MYIoivzmjSHMOhUDPgt+k8izEzFemIgy5DXhtejwO4wM+hUtTKxQx2XSo9EobcRooc5SMs+A18yOPie/tMXHpWiFDX4jQaeRSLbChzd3YjVo6LAbeG0qw+5+Nz6zhkJNotEGt1nDhzZ3Eis0GPaZuBLJEy82eHh3N3v6XbxyJcJzl+LcNmDln87EeOZ85OcT5Yss8/VXZxnrsKBWKZLTpXybf/jEZl65mgQBBrxWtgSLvHAlhdOoIugwEs5W6HQY2RZycGw+h9+q4fxyiVJToNdl4OR8DmQZWYYXJiJcWMmxscuuCP+B2USJRKnBTKrKJ7d3ECk0EQQl8VVG+dpt13FkJkm73WbUb+CFKymQBURR4GJYyeo+tVRga5eNK6tZulxmbh52o9FoabaaHJ7JsN5vRq1WI8sN5lNl9g55kNpt6vUGh6cTCvZrtciAW0OuLlNqi6zzKMX2/bMFfsGmRcDMAxv9DPqtHJqIMRGR+LuPjfHUyRW6XCaQZV69HGXIZyJdbVGr1fjboyuEnAZCDiVHcTJaYH2HjblkWaG3Atv6XDzoNHBiPsugU8vbM1nihToqtZqPHjyHz6pBq9FyajZOtNTGa9FiN2kZ9FsZ7bTz7Nkl3riWwKZXoVPDTKKCgEC+oehM1GoVfouG4/NZdvbYmU+W2R5yMpWu8+8Pb2E2VeMbP6U0PrBXagSB+8Z9XIkqa1iVSmQ2UUCtVtPrNnJyPkuz1WQuXVXkndEi55ayxHLKdG8+U2PEa2DAa6HUhId2drGpx8Pnbuijz2Om065nOVUknq9ydjHD29fifOvtGSK5Kk69ik9u83NoIs7ZpRyrOQX7u6HDhiRJLGaqdNoNTK7muRDO02XTMp0oMZOq4japef5Kkh0hK50OA8v5Fv0uPUfn8ywlC8ymqgy6dEwn1zotooBKFNei7kq8PJnk3cUcp+ZTdNp02Iwagm4LX7m9lx9PZkmWW+zus/PClRStVov5jFJsD24PsanTzHdPrzKx/JM9ltIJytYktgStREtt7t8UYEu3g1OLeaS2MvoO2HSsZiv86YuTxHJVwtkqK7kGjUadiWiF9QETep2ObSEHO/vd3LrOx63DTmZSdbYELcSLTdYFbIiCgCRJvD4Zp1Bp4LPqUakETBo4Op9jNGDiQjhPs9kEQSReqDKXKOC3m3Gbtcwnyzx5fIl/f3fp5xeRoFKpODDmZTlbx2/REc3XeGEiQrvdZmevneVMFYdR8chVmvLaWk1Rp/XYtUyla0iyTJ9LzzuLyi/zarTA8dk07bbE6YUsiWKTdruF1JZAlrm+x8aLl2M8dXKFTKnO5m4b24ImrsVLCkYhWaLfY2b/WACnScvl1RKSLHPLeh+P7u3j0b19fOm2AV68nFJyYUacGA0G9vRYEUURu17NuXCRHV0mXptKK92bsQCjnYoo6roeB1+4uY9+j4XZWI7lXAOnDr726hzddi07+5xkq23uG/eh0Wi4YdDJaKedXpeB8ytFtnaauBor02q26HWb8NmNfPHWPmLlNs1mE41ajUoU2T3gIuTUkyq36HUZ2TvoZmvIwaaglVSpTtCmRaVSE3Lq+dCmTj6yvfu97MN0pcWXnrnK3n4bXS4T6UqTbZ1GFtewy26LgR63CY1Gg8ukJltuMOI1MuC14LcpSWjIMl6zjkMTcUa9OnL1NiGHntVMiaVM5WeXoygIgl0QhB8KgnBNEIRJQRB2CoLgFAThNUEQZta+OtYeKwiC8H8KgjArCMKEIAhb/tPzfHrt8TOCIHz6f/T1j0wnePLEEo16nUiuyu5+Nz1OAwePLzOfKrOSqdBqNXljJsuHtnTy4NYu9g55aTWb/PhynA+PullKV7iwWqZSrfLEsQVm4wU67HoG/VYe3B5i34CC+U2Vm4qBVhRZ1+ngLz4yzkjAQq1W4zf/7TKTK2kuh7OEM1VkGaYSZTwWhYKaq7a5sJJjKprjo39/km+9NcuGgIlIvs751TIOncz3zkaIZsvYdQLNNvz4SopGvc7RmSSHLoR54WKEq5E8uWqbQxNxWu02qbrAuF8ZiQuCgCTLzCWLLKYrzKfKLGfKvHYlxo/OLnN8LoPVqDh1DFoRURQ4OpPCa9Xz+lSGu9e7uZasc/s6D6u5OvOJIqeXS3xkk5dnL8RYSFVot1rEik0EQeTZiSjhTJG5ZIU/eWGSRqPBofNhBEFkvc+Iy6JjJlnlwnKeYZeW3/z3K3j1MifncxwY9aJWq7iu28JytslyrsHlSIET81ke2RPiK/vXoVKp+PwN/XQ59bx6LYVdJ5KpNJhKVjGoQFBr9D+Togb+GnhZluURYCMwCXwZeEOW5UHgjbX7AHcBg2u3zwHfAhAEwQn8F2AHcB3wX37yRvhph04tsnfQw7DXxFvTKZLFBrIsoVareXhPD/vHAsiyzHJGofz0uIycmM9weCrO2cUslTZ872yE5XSZX90TZCnfZEdI6aH2ecwspitEcjWuRgqoVCo2BpUr6Uq+sQbO0fDuUo7L0RK3bfDRt2b96nTo37uyZ6tt/uAOZbjiMmnp85gQRBWpSoN2q8nuARfbQw4m41V8ZjVus5a35guM+Ex0Oo30eS0KcyNT5s4NSleg36VjY9CGXq/jl3d3sZSuIIgiAasOlUpUcA0eM7FCk+tDdlazVWLFBnsGXIz4FSeMz2qg121S/IMhK5FCnWNzeW4fsjGbquK3aojm63zm+k6OL5VIlxt0WFQcncvTsTYBfGBjgEpbZEvQQrHa5PBUnGcvRqlUKhyaiPLApgCjQScyMhORIiGHhnChidso8E/vRmi3ZZ6diGPUitwz7ufWER8dNj3/cGThvaUiKEkzGhHmUlVGAlb2DTjQ6w3IrWbt/Wrjf7qoBUGwAfuAJwFkWW7IspwD7gO+s/aw7wD3r31/H/BdWTlOAXZBEALAHcBrsixnZFnOAq8Bd/73Xl8GlrI11CoVt67zsX+Di1OLBVrtNvOpEj++FCOar2LUCByZTnLw+CIuPaQrTbaE7Hx8Wyc3D7s5Fy7y7IUogx4Tao0aURBZF7CuQVgkSvU2bouBoYCdkNvMbcMufnAhQbvdZmuXjU6nhd+7rZ9TizlWc1VCDgMHTywzG8/TaDZ56lQYr1HFkdkMi+kqNw+52Ry0cfDkKs+ej+AzK2NnlUpNri7zJ3cPsaHThs2gQaNWs3vAxWxKeXNNrmZ49mKMcLaMXSeSrLSJllq02+332mIHNnbw4S2d3DDs4Z3lPE6riZ09dgY9RtKVJtF8DYdJSZYN2PS8eDXN9SEre/qs/PFLc0yuZojk6iAKzKXrPLSzi2GfmRevpvnTewZIlJo8fmSBp8+v0u/UU26DoFIhSTJ3jjiZiFcpNxSL26N7e/BatOTrEpFiky6bltjadPLBbUEqLYE+t4F9Q16mE2UQIFFUGOGCAFciypIw34CQXUu61GQ+XeeGAcfPLEexF0gCTwmCcF4QhCcEQTABPlmWo2uPiQE/sd10Aiv/6efDa+fe7/z/4xAE4XOCIJwRBOFMNF3gzvVedg+4WEqX+MqhKT62yYVKVHFxpUDQpiVbaSpjY6uOXruWK4kq63xGnr+kNPqzNZl7NrjJ1toEbGvwF4+JqXiJHpeRdquFWqPhy3cMMN7tVAJ/sg2+fHsf04kyZ1cKOHQyv/r9y7j0Arv6nIwG7Xx2dwhJlslVW5TqEvFSmxsGFHSAWqNm/3gHP/qVHYx32Xj1coxms8nNIx6cBhU/nkxzfCZBuakAKrU6Hd98cAy1WoVGrWFXn4NEscn5SBmfRcOQR0+H04woCrjNWiRJ4pnzEQbcBm5eF+CvPjTEjy6s8vHH32HEredz+/pZ32mjz23invEODox6ODSRIFmV+ezOICMdDkJuE4/u6UUUBY5MJZhNldm/3sU3X19gR8hOwK4jX5NIFOs8vKuHz+/pQq1W82/nYjy8s4sbBpzMJUv86MwSmarE1+4boctuIJxvcC1W/Mn/JZ/Y5iear3EtkkVUiXSv0bMmY0VGAyaOz2ZYLbYY9RspNMGqBY/VgEbz05t2/1+KWg1sAb4ly/JmoMx/LDUAkOWfoMr/1xyyLP+DLMvbZFneFnBamIwWODqTJlWs0+vS8xs/mGTAree+jX5OLxe4echFt9NENF/j1eksG7xG0lWJrd02dvY5AJl4qUmh2uTCco5Wu630mdNVWq0W58JFglYNTxxf5NJKhieOLTEdzfH85SRzSQW2fmQuS6dVw+HZDE+fW+WrL04ynyhyMZzHbdbxO7f2s6vfwWxKeU67VuLjT5zmpcsx3p1Lci5c4ZXLWZZTRS6slrhjyEa2JjHuN/DylQRSu41KpQJBoNdtpFBXWmQHRj1cWMlzNV7jrnUucjWJWK7Ki5djxPNlfut756hWq9z37TPU6w3cJhX/cjrMoQth2s0WT59f5e+PzPHcRILxoJVOm55URVrLBZeZTSndl7emU+ztc3ByIU+qVFM02cUGu3ssLCRLPHF0nn99N4Isy5i1IqIgcGQ2y/ffWeSJE8tI7Tb/eCpMpakInEYCFqaief7qdcXhEs20+daRJXZ2mzm9XGSDz8TT58KcW87it2j40KYOVGo1jWaDF6+m2b/BTdCmQ9SbnT+tMP9njzAQlmX5nbX7P0Qp6rggCAFZlqNry4vE2p+vAl3/6eeDa+dWgRv/b+ff/u+9eK7a4odnVhjvtAAmAk4zu3rtvHwlQThTocNh4MJKlpGAjUf2hOiw6xkOKGTTv3x9liuRPG6Tli6ngguLZMscn00hCiJ3rHPz0pU41/U6WU7mOTWX5vP7+vns7m7UKiXqTiWKnF/JgyTT6TSxZ8C1RihSIUsSq/k6rWaT4/NZLq9kcJl1vD4ZZz5dY1u3nT6PmTMLGYIWmMlBo9HknlEv3z+fRKuCMyt5BjpczKfKzCVL9LlNzKfK2PRqZpJl5TUKTfb02tBpFYmAx6yhx6HHYdAwnaoz4rewocPC9j43r12JISAzsapoVTwmLZ/f10+r1eLVySSSJBGw6nji2AKpcosDmzppNptYDVokqU2X04CoElnN1whYtbx2JU6x3qTHY0IQRa7vsXEurJgxxjqtbOyykyg2eXRfL5fDWU7MpRRDtCSTqUn83cfHefrdBd6aK7Kv08RkssZYwMREpIDLqGEm0eDiagGbFsoNCZ/dzIFRA+8s5piMlpAatf/1Kj1ZlmPAiiAIw2unbgGuAoeAn3QwPg08t/b9IeBTa12Q64H82jLlFeB2QRAcaxvE29fO/dQjYNPjtyvtnz0DLjpsOpZzdQWUIsI94wEloNOs5k+eu8T3zkT4/qkFJEni/o1+XCYt1xJVbl/nIWDTE8438Vt0HJlO8srVBCGHnl19zrXkKZkXJiI8c26VqWiOp44vI4gi94/70Oq07OpzcGqxwIn5jGISzVTptOtJV1q0Wm3mUmXGOiys67Czo9fB9l4X6wJWVnI1bhrt5K5hC0v5FtFCnU/t6MRtNWHVa7hnPMD+sQCRnEIbXUmXWc7WuHFQgcqP+nRMJascmUkitVpci5U4PpdBq9XynU9tZDZVRVSpQAZJlgg6zfzuLb28PZ0kmq9yaSXDF394mYV4gXC2SsipJ+gw8uU7BpEkiT9/bZ7NnWZ+cCFJW5KUjEhBYCVdQhQFzHotfqseWZb553ej3L3ORbKiBIlqNRr2DLq4Ginww7NhwukqkawSy7yz18HLVxO8NKkkjM1nm9TrNd6eTiNJMt1uM7es8zIWMHM5UeO3burjvjEfKyUJj1HFgFv/M+1+/AbwL4IgTACbgMeArwG3CYIwA9y6dh/gx8A8MAs8DvwqgCzLGeBPgXfXbv917dxPPRRAdy9bQ0rs3MWwMizwWTQEnSamExW67Tp+dH6VC5Eyv70vyGKmyjdfmaLZbFKoS+wdcHDwxApnl7JsC9k4sKmTkMfMbSNuTsxnWcpWWddh5/5Ngfdes89tYtBnZMSnjL5vGvIQq8g8ureHbpfCyPuJ7qTDYaTbZcRt0nHoUhwkiYDdxHKqxLVYiR63iU67nnhFIl9tEstVOLGQ5e71LiotmWvxEu12mz2DLo7PZel2mQg5jUpCQr7GRLRKs9nk+pANjUZLuSmzq8/BWIeFfzy1yjMXIgy6DUQLda5EKtiNal6ZTIIgIqxRYx1GNQGHnkShxtPnVllKl3n86AJPHl/iS7f3c9tYkN+/o58rMYVQ5bdqmc8omzWv3Uw0V2Us6GTfgINj83m2d1mQ5DYhp4Gjsyl+eG6Fer1OsQXX99oJOowsZ2uspMvcO+bFaRD4wr4OrsYqIIps7nYgisonQqrS5tf3hXj2YowXrqT4xc0+nr+UYDpR/tl0P9YK8sLaGndcluX7ZVnOyrKclmX5FlmWB2VZvvUnBbrW9fg1WZb7ZVkek2X5zH96noOyLA+s3Z76H3xtDp5cIeTU0+3Q47HoWc1VODyTIeQ0ICMznyrT77Py6R0d/PPZGEaVjN2o5a2ZNINuA7lqm+t7bVyNFBARmU4qb4RXJpWIuQGvhX0DDi5HS5xfzrIxaGM2UWQyUuS5C2HeuJqgy67ltmEXWq2W/eMdSnC938JzF2M8tLMbAbDoRBwmLSq1hu1dZt6aTjGXKOI2argYztPtNHBg1Es43+DScobnLsbocRqo12r88r9cZMClJ7g2hMlWm1yKluh2mvm1vV3kazIHTyyzd8DOA5s7WMpUeWsqxfU9VjZ1Wgk6jPgsGvo8JrZ024kXWxg1An9w9wg3DHvZ2utiIlxgvNOCz2Yg6DAQdCpKx5lkhaMzKU4t5rhng5ujcznC6Qq7e+1k6wJ/enc/Z5fzfGpHB5ciZax6kSdPLHN0SplmypKM32pAbzDw8c0u9Ho9KrWKW4ddnF8tMei3sbfPxhPvJKg22zx0fRcatQpZkmm3W/isWk4u5EiXamzwGQkXGlj0Km5Z50Vu1n7+BE3RfI3tQTOHpxI8eXwJv0XDviEvt6zzohJF2m1l4FGv1UiWldbcar7GTUMuZBnihTpOo5pwvsHGLgeS1GbApef4fPY91MJCqswzZ1eVIYog0uM08My5RnCxrwAAIABJREFUCDadQKLQIGgV+f1nr/KpJ05ybiHB5dUck9EikiThtej48eUYPzyzQrHe5qZBN512HQdPrhCw6eh1G8lUW6RKDRZTZa4la9wx4iBdUz4RnAY17ywX+duPjfLatTTRNXyvKAh8aEuQW9Z7OTGfQRSVgdDh6QyRnDL4setFnjoZJpyr8f13FnjxSgqzTpHUdth1xAs1Hj+6wGK6oqAfam2ixYYiV5UkJbH3Uoz5VJnre2xMx8uo1Wr+24EhplNlnrsQoVKr8djLUxQbSnyHJCv/ZpteRb4Kh6cTpCtNHtrZBbLMy1N5roaVCenrUxnuGfUoCLJ0g8/v7sBq0hPON1hIFpCkNkdms6zmagRsOvLVFm9NJVmM51Gr1O/1sN/v+MAWtVGrABTjeWWXfjGcZyFVRhAEpqM55hNFms0WlxJ1dvbauGfUg6zSEnIaUatULGWrTCwrv+Rht5Yzy3keP7qA36L0e/ePd3D7Og8NBHb2ONjW60KJ8pbJ1SU+taNT0W24jfS4TBybSXF8Nk2PXc2Tx5fZ2WtXfIsmZRN3MVIk5FTkCsM+Rax093oXTqOWPo+F9T4D76xUeOoTY2wL2Tgym8Fv0aDX6+nzmNjVv7bZF2AxU2U5XeLYQoFup4GHd4ewGVR0Ok0KEVSA20f9fGZHJ8W2yK/t7QZB4Ks/nsJngGINfGY1IDCVKHPLiJddvQ529Tm4FC0RzlYIZ8t023WcWMjS69RzeCrBN16fZ1+/A0GlYjpWJFpsc0OfnecvJ8hWmgSdRuVNIYDUlljvM/Hk8SXu3xTg3lE3x+bz9Lv0tNotJlaLxEpNtgS0fP3NMPevVxLLji8UuHsswCd2dNHjNtPjNOA06+h3G7gYrfCLW32Ec/Wfz8wXm0HDngEXao0GURABAUmS6bJpeeZchNVcna0hB4/uDhEuNDm1XOauDV7eWcrz8O4QO3qdnAtXubKc4m+PRRgNGHhkTw/xYoshr4nJaIGDx5fZ02fn2YkYIZeRdxbz7OhV5KCvTiYZdBtZF7DSYTfQ5TLxye0B/vy1OXaELKzkG9y+zoNZI9Dvs4KsLIeQ4cxyjslwhi89c5lLqzmazQZH5nKYNDJff3WGN64msOvk9z5xZuIFjs+lef7iKlatinazSaJQ56Ob/fS5TZxYyHJxJauAIR06CjWJ20fc/MvZOLcPudDptNh1ajSCxNnVCjo1JCpKzMWxmRS1eo2vvjzDM+dXuXfMzw3DfkRBUODyhTozyRLxQg2PWYdWq6PHYcBjNfK3Hx1FZ9DhMOu4ecQDCEQKTQIWyNUlXrkU4e3pFN96Y4pzKyVCNjV/9vI0K+kKHrNivH36Qo4HN1j59skozVaLr9w1xHxGsZAFbVq+9fYsFg2kyg3uXOfin96N0uXQI+hM9verjQ9sUUfzVaUDsSlAj9fCeIeFS9Eix2YzhFwGdvU5uBwtK6NjlYrH7h2m22liLl1jKVPlxhEfuwedjPV4+cLNvVxLNnn5agK/Rc21WJF2W0KSJWZTNbLlOiM+M3uGPGg0GvrXNl9TiSLRbEXpMADfeG2eA2NeVnIN2q0WX31xksNzOZx6BRQzsVpg2G+i22FAktp0Os0YNSIqlYphvxlBhkS+SqYscXqlQa1e5/kLq1wMF7i+x44AXFzJ8sqVKOeW0jx/OUEkW8Jv0VJsyNRqNZ6biKORm7x0OYbbIHB+tcj3Ti5wZiVPttbmk9sDdDn1dDlMzCdLtFotjsxl+cWtAbZ0O1ktNlnfoazFVaLI1pCTm4Y9+O0G9g66lMQEUUBG5s9fm8NpUOEwKHbBWKHG/g1u8jX4327uoYGKL97SQ7IiEbCouRCtMN5hRqUSabclpmMFijLoDUbG/CZOL63xWSSJgF3PdKzAxWiFqViO+WSZQxMxJEnijw5sQKrkE+9XGx/YolavXcWevRCl26FnMlHjd2/pQxQV3bKoEvFadYRcRhrNJr/275dZSZf4+gPriZcUgdeGTjujQQcf3t7Dt39pEwc2dhLJ15lLFJlPluh0GLhpyIXdqOXgyWWGPEYCNh1nl/J85vouRvxWLkRKPLw7xP6xABuDFrQaDb1uI6IosiloZdRnoMdrIWA3MtZpJVNusZKtcXyxwD2j3vdCRWVJkYE6TVqcZhVmHYpOXK3iwKiHcKFJt0OPJKoRRRVdThMmjciVeA1JkljnN3N0PsegU8Mb0wVevhRFVKnodiibsz6XgR29Tv7izUVK9Tb1ep2JiCLechl1xEttZFmiy6bAZARBQAb63EpuY6rUYC5RYiaW/w/LlVXP6YUsuUqL8yt5Yrkqb0+nQYTXptIEbVpWi22CTgPLuSrXhexoNBp29jo4t1LgYriAAVjNlonka1zXbeHpcyscmVaGThu63fzm3k4kQcvmTjMOk47K+2rz/uP4wBa1Vi2ylK7SbrdYF7Ay1mHiu6cVOr/PamAlW8Nn0fLchSjIMvdsUNJtQVlPHp5OELDqeGEiyh8fusRjL00zGS0gtZV2lCS1Ob+c5WI4D7LMdV0Wnjq5wlKySLnR5pWrST63r4+7x/xMxUs8eWyRs0s5JElGWOMxv3EtwfmI0nbbO+jm0mqJjUErX7xzhG98eIx4sU62JpEoVInla8wniyxna/gtGj6+rYOhgIM71it2qy6bFqPJxA8f3YbHqmd9h5WxLid7eswcncsx5DGSLddJlpusDxj43A19DPqsOI1qJKlNstLmw1uCbAraKFabXI6VGOtQ3oRbe50EHQZeuxrnB2eWmYnl6XboiReUCOyA3cTv3TbAhXCe5y8l+OKtfWzpdrB3beD0yesC+Cxa7h33M9rl4oZBB4lSk7lEkdVMhZtHvCBDyG0BBFYLDX79hhClFtw+YmHPoId8TUk/y5Sb7Oi2MhEpUalUePZyhkGXlrfmi3zquk7uG/fxxNFFRJ3J9n618YEtap1axZ0bvCymKzx5bJFYsYHHpCZfl9kz4OLODT4SpSY2ncD5cB5REJGBr74wyemFFG9eSwFw+4gLWZIZ9RsY9BiZSSqyzdVcjYDDzFjAQjjf4PRSgU9d18FcpsaNQy5W8zUee+ka8XyVZy/G+OR1HXQ6TSgXXplkWeKzu0Jc36ugDAbcBgRk3riWZC5R4pWrCeKFOps7zWTrADIuixFJlplO1Wg2mxybTTIVL3HfRj/LuQbDXhMvXY6xkq1zaTlDwKbl1JKi444VapRqLQRRhUkr8s5ijlqtxr+dieK16hnvtLGQriBJEvka3DPqJV5s0uexMOS3sX88gMdqQEBgNaf4Drd3W/jrNxdw6QW+ezrCWMDMF27p4+DJZb53RrmAqESRv35rkXcWsvzF69PU6zUi+Saf3N5Brq7gJ6S2RLrS4tZhJ/1eM21J4vlLSSxqmSMzSvhol0PP1USVQa+R5y8n2eAz8C9n4nxkkw+j0cjtIy6+e3qVs0tZ3CYRQa01vV9tfGCLGmQmY0V6PWYe3tPDZ3eFuBKt8Lu39nFqIc9Ll+N4TRqSpSbZcpMuh46RgJXN3XY6HCaG3To6rRq+9soMM7E8P7oQ5YWLEfb2K7yMkNvMp6/reA/o+Onrg8xnGqzzW+lymghYtbQlmU1BGxs7rXznnQi7+pyIKmX9/JmdQf71bIIbB90IgsBTJ5fZv8FNuSFRrVaQZJlwvoFWo+H37xgg5LXxzQfH2DvsI2DRcGQ2Q7stMRMvoFKJCAJK6u2JMHeOOFgptOiwqHGa1OzsNjOTqtNpN7Cr18FSrkHAquDIfBYNXU4Tm7odvHEtyUqmTNChZnXNhjbkVd5I12JFtve6+Mh1IW4Y9tDnNqFWa9gUNHF0PsvmgJ4XriSRZRmVIPKl2/qJltrsH/WwZ8jLb9wQQi2qOTqfI+jQ8dZsnh29LjqcZiL5Knq1wNdfm2M6pnzybeq2kS43yLfBqhP50OYOHtzWTbfThN9uRKVW8+kdHUwmq7iMGtQqNQ6T4jCfWC0iy9LPxiTw/+eRKTd45lwYSZL4+yNzPH5sEbdFw+tTGTodenpcRhLlJrLUZtBj4G8OL9FsNIgV6+zotnJkPsdfvDbNPRs8uK1GjGpYzVV49mKEmXiBRqPB5/7pHN87G2EmmuGXDp6hXq/T6TDwyuUIK5kyS+kKb02nCLkM+C0aRJXIiM/M0Zk0k7EiN6/zMBSwc8uwBxmBQ5eTjPr1/N3xCCGHnt+5dQC3WcPp5RIuvcjBE8t4DCItQcN40E6nw0C80GTEb6HXZUSr1XL3qBeTycRGv4GvvTpHodLimcsZdvdYEEURtVrFoM/CgU2drAtYqbVlBJR8RQFFl6xRa+j3mOmwGfizV2Y4PBnjh2fD3LHei4CiQJtPlnn7WpxCQ6DXqeela1m2ddtYSFWI5qs8cz7MYqrEsxeieM1qJZe8z8HHt3ag0+kY8Fl4cFsXu/scZGsydrOBsYCJWKHBSrpMr8uEVqPFbwCNVsORuRytZpNYscXDu7qJ5uu8s1RAlhW5cKba5IFNAXb2e9jW46JdSC6/X218YItaoxb50OYgXrOOeL5GwKLhkd09DPgs3D0WQKvT8ukdQa4lq/gtWg5scLOQbSiTLlnmxkEXTpNeySXpt5NrCHx2VwiX1Ui/x0ys2GTXoJfH7goRLkrcMOAkXGhy27CLiXAJAYEdfU7WBawcn03x1nSamagCi/QYRZ67GOPmATt/f3iOlUxlLdRSZjDg5G8+OoZOr2c+WebwTBabRskEPzod4/nLSfb12xnvVAzFuwecXIsVOTKjTOlEQaDDrCJeA59Fi82k5cZBB+lyk0K9zUKmil2vYjpZUexrVr0CkpEUBd7XP7SBfQOKyeG+LV387/vX8ei+PiRZ4oWLEZ48tshkOI0ktdFq1Xzp9gHKLRVfvmOQDpuOWLGO26RhYjXPtqCJYq1JJFdDVKnwmzUcncuxqUuZqh6fS/PcxTj3jft5cEsQnVbHzl47U/EyK7k6+wYdGPQatnY72Ntn5x9PhfGZVf8Xd+8ZJtlZnuveq1blnLuquzrnme7JowkajaRBcZQFGDBCICSzj7297UM2xhsbb5ItY7ANNkEgQNgGg7JGGgUUJs9IE7unp3Puyjmntdb+sfronB/GZ1/XOb72JdWvrtTVV/VbX33f+z7P/TCXqhJymfg/ruvjz24bxmfVc8dIC8+NxZHkJmvZKqLN8+/Kk+EdXNRWvcjGNgfpikSmrKKy/sehK0xHsjw/HqXPY+TlyRRDfhOvTqe4FFYPGmvpEp99chJQaEpNZuNFJmIVPn9jL1arlb957yibOjyIosgHNnv446cWuarNhF6vp8ttZCKcR9FAyGXiusEWQi4zHR4r37h7iItrBZ46u8QbsymGfUa+/uIM07E8U9E8nW4T+aoEgNFoZMCnirH2dts5PJVBkprs6fFi0Qk0mw2+9eoixWLxbdlpm0N1d59ezPDI8SVuHXQRcJgZaVO3QxPREp0uI91uE8vpCoN+dRCzrcNOJFfl5QkVbv7gT8/yxIUIV9ZUec1ktMDz41GS+QoHRwPcv6uN58YTyIrC3h43M8kKd4z6eO5SmMfPrRG0GwjaDVQbCsdmUoCaaPuxXW0cncuioNDlUi1YAauODUEzT14I02w2AIWVbI2hoI0Ot5GxcJHRgJlIvsFKuojTKKLRaBjwW1hJq1LdR08u8+LlME9eCJMvFEARuKbf8+5k6UVyNQ5dCnPv1lZuGQ3x0L5u/FYVtiLLMocuRdRPtEakXJdRFDi1kCVeVvj6HX1oBA1lSaBRryHLMo+eXOGpcyv86Pgy42tZAjYd/3A8zC3DTqaSDVqsOubiBX725hpb2mwMBJ1IssyF1RzLqRKvTKXwWfREiw3297kxGg3cu7UVj1mLy6xHWTffRvJqaNGLVxLctsHNpXCJWwddGAwGPEaIl5qkKjJtdpEnxtL0eowMB1RIzh2b29jWZsNpFHlmLI6kyCTzFdayan63vI5CEzRq9+XJs8scm89y344AGlHLgUEvu3pcjLQ50Ol0NJtNZuMFpGaTcK7OD44ssJypsqPTiahRI7C//9oMj59b5fGzq2hFkQ6XiUuRAkGnielEiVK1QSRb5tRijusGPKqFLlvjhgE355ezPHMxxnyiyK/PqlvFLo+ZdreZmWiOuXiBIzM5zs5FOHwlhduiZTVdYSKcYyKc4ftH5rlvRxCH2YDTIHB4qsjJORU98a50kwcdRvpa7Cxn69yy0c9ypsJsssqNQ+rBrNtr5hP7u3n/zg4+c/Mg27vctLlNfOcDI7w0kyNVbtDhNPDaTBqrKLGhzUGvz8LeXhdL6SpdHjMjLRb0Wh3NZoPVTJlooc79O1vR6/X0eU385MQKgx41O6bTY+aaAS/X9Hm5HC2r8RHJMsvpOrV6nb989goH+pwqZCelCqcOTSTxmwSeu5zk5gEHj51N0Ocx4DZqqMkCP75vlJenUnz31Sl+9aaqdnt1KsFkrIxFp0Lkx9ZKtFh1OM06Ti5meX0yTotZw/ffmGM1XaLPreevX5nHIio0JYmTSwWu6XagKAo/ODKvMuw8Zja1OQg69ICAKGrodBsRBDW86M7RAAOtTj55Qx/hQoM2l4VP3dDPcMCGSadhOVMlmi2rvkedjmq1yvPjUTwWHfdsCXLHiJ9sSX1PlzMq2/rQeIJtIRsVGUp1hc0B9RsPFGRZwaYTUCSZ770+S6PRQCOKXN1jxWnRMxnOgEbzW70A71g+9T9+73t/cc8H7yNTrnN0JoVJC51uE8cXsiTzZd5aypIr1XhtJolRq+HCSpYLy2kihQYmrQaPWYfVqCdVrPPyVJobB9wEXDbmE0W6PWYePx9mbCVNvFQnWahiNui5cbiFU0s5Gg2JSlNBFBSeG49jMYgk8hWW01V6/HZ6PUaKdTXi7txqjqBNR7LU4OxqiaYkEbAb2D8YIJkv8fTFCDVZIF9pcPdmPy9OprgSKVKuy+wdaGFvr4cXJ+J4LToSpQaz8SKfuaGHk4t5GpLqci81JCwGHa02HdWmwsuTaqpCVRY4u5zHZxI5s1qgUGnwqff08ItzMexGPXu6Xby5mOb0Up6gTcu51TzdXit2o5bXppI49QqvTadJlxtsa3eyfzDAbLLM5pATQSPwq7NrVOp1htuc+O0GooUGQz4jPzu1TL3R5OxKAZdZ5OxKnvPhKrcNOQi67fjsZna02wgXGniNqp9xKlnFYxJJVRroNXBoIkmrReHpiQy1WlMViTnMWPQ6nhuLs3zyueaXPvfJr/97tfGOXalrTZmnLkTpdBnxmkXemMsQztYI2g3MJiu4TRrOr2RosanS1DtHW0iWJbwmDe1uM7PJCh0eM39woJ/P3tBDqiLT7zMRzTfY2Kaykf0OCzs7XHjsZja12VjOVGlzmlAUiR6vlQMbgvz5HcNs7XCrWScC9HmN/OTkCk1J4rZNKuY2Vpb46N5OPrIzQMBh4vxKjmaziVarZSjo4P6dAfQGAwaDgZ2dLnZ22unwmAAFnU7He7e20eFRxT1Oi5rk2+42MRpyMuDV4zZpkWWZSEnmvdva2NPnYnd/gPdua+PgxhZ29fq4c6OPPb1e1vJNrurxcE2/GvEWcJh5YHc73S0O3Ba9Cs/0WHAZBX55Psr+PicCAuFclUOXIsiyDChsCNqx6gW0Wh13jfrR6/V4zSIX1wqgQKvLyjU9Dv7g+gE+sb+HXrfIPx1fo9lUtTWnFrNMhnNMxSt47Sa2hWwsZut4zDr6WuzcNtJCTWPk4TsHuGNzkJoi0uqy8MXbhrhzS+u7c0wO8Ce39HNsNs2lcJ4H93ZyYEOA20aD3DDsp8Or8uS2Bo18+dkrHJ9L86EdrRydy9LuNOAyqyKv4aCd8ysZLiwlOXQpwt4elc7w7FiCTa02NBoBl1lLvCSxlq1w46CHy5ECM9EsjUaDuUQRRZbY1uGiw2NlMlrEbhJZSRX53JMTpEpNtoZsyLLCXzw/x/aQeoD74dFFZElCliV+ejrMR3a2spYp02g0mEnWkWSJkwvqawiCQDhfZTFdwapV+OnpVXLlBrV6gzOrdeYSap7LloCJ0XY3Qy02FBROzKU5u5rj/EqGN2bSXF7LcnEtT9CuR1wfoWs0Aiu5mhplZzPS5TFxYj5NpipzYNCHoqgtvlaHuh2RJIknz0e4uJyiUJMwaQWeHUsQsKq9dbdZRJZldoQsvDKd4Z9em0EA9ILMtnYV0fDCeIyg3ci1/WpffyRowWDQ87U7B5lPqz7IVoeKgviXczGmYkVuGXSyp9vFj44vqbKCZv23mgTesSw9g1ZFccmypGofgnaeH4vw+Lkwm9psxItNNvhMfPu1BVWAoygMBJy8OpXk6UtRdX86WSdo0/LqdAarXmApmUerFRkJOdkcsrEYzzMRzuOxqorAhWSZl64kMIoCZ5cyPH5ujUSpgduo4Qu3bWRTh4fnx6Nq281h5LqhAJeXEzx6ao1bNgh8dFcrj52J8M33j64L8BMM+K2kCjWOz6d5azGDQVRodRqw6eGju9r40fFl2pwG9vV5mQ5nmEpU+PCudgQFXrkSx6oFrVZEluGJizHSVYV0qUqiWOeeLUGCdgOdbhOruQUG/EYMBj2raVWiG86qeLRur4W5mAqpXEiW2NfvRZEVGo0Gn/zVJXZ1qD3wLo+F43MpbhlycnQ6QY/PythqDrdZw8HRICvpMmvpIpk6PHs5zkevUoNRLy8nGE8p7GtKdPvUXMSjMwnGV7NolQbPXIrT5Tby87ei9HktKAo8O57gj6/vps9r4vRChb9/fZmRQIKqImLRaRAtrndfOGitKfPVFyaZCGfJViRVzbZW4PM39TIYdPDQvi4ux0pU6hIf3BbAYDQwFLCyudPDnaMtXIyU6XLp+fmbET6wLciGNjdtLvN6FLOKGEsU68wnixTqICsKa9kyiqKgNxgA6PXb+PObu9HqdDx5IcrYaobbRoNs63Tx/JU05XKZH51cpc0ucna1oI6y6xIvXI4xnyxRbzQ5NZ9iJd9AkWXMOoGlTI1ul55TSwUeOb7Ix/e2c3A0yMZWBzq9nqBdT7/fzqW1AkNBGy67jj/c38WeXg+bQ07u2RrEadLjseh46mKUVybjLGcqHNzgYTxWRZYl3lzKsRjPMxUrsZZTaUznl7ME7AYurRWYW5e6ruRqXN3j5mK4RL2uwtp9FpGvHZ7l6HyWuzcHGPAbObZQ4PtvzDEZKxJ0mvlv14QIuW2cX84wFSuSqkKnXUP3euycVqvlwau7MGlkwiWZNpuWyUSVHSELep2W2ze3ctdmNfxoPl1jb5+XT93QQ0XWcPeWVjYEbSBqDb+tNt7BK7XI3ZsCHJv9v1fRkYCFlydTCBqB4aCd924LcWUlxVi0zPu3h3jk+BIP7u1Aq9Vy02CcXE1hKGCj3W3h2kEtL0ymcRvrHLoUIZKv88WDQ3zjpVnuHPEjakSSpSb7+rx8/3dbePTkCo1Gg+8cXeGqTiftbrUb8rcfcHPX1nZu2eDnq89fwagTEDUiPW4L7R4r1w62sJKt0WxKRLNlarKGD2wL0Om18eZSlj6/BVErMuw3E7QbOHw5xmKiQHeLk5uHffzbmSVkWULUatjX66FcKvK9E2tYdAJ+h0qhmogWCDgM3L05yOPnVtXErWKNP7peVTHKskKXz8oNI2qRAZxbztIfcHDX1naeH4/SYpXRabXcuzVIvibx+mwKrV6HqNFw3+52utwmFEXhYrjMlw/2Ey40SZTU9t5vJgt86fYNPH1hlYeu7qQ/4CCcVjUqx+dSXNPn5fmxKOPxKld32mh1WTCZZc4sF2hzGjl8Oc65pQzhXJUH9rSznKmqkPrJON0e1ZX0rtxTG3UaTsyptp5Go8FbS2lem0qqohmflalYiX6fmWwdljOqUm58Jc1nfj3Otw6P8cJEAo9FhcPIssRPTq3x6QPddPhs3LzBR8il2o42Ba08fSnKbCzPXZsDnFzI8OMTy7TY1N5ztiIxEc4xHi1z/24VkHjoUoSZRAW/zUjQZiRRkrhztIVLa0WW0lUqlSoXVrLkqzJDfhPpioRG1GAzajFrZE7NpYiVmoyFi7TatCyka9w87EWr1WLWwanFHBtazPzg6Bw/PpflPT2qk+bTB7rY0+XEbjYgilpEUUOuKhG0G5hPlDg6E+eXpxd59lKYs4tpZFnmmUtxZmJ5FEV+2zmEovDGTIZruqz8/M0o1/V7GGqxoREEzi1lOb+U5ZFji/zoxDJuk8hzlxO8OB5mNGAmX5PJl5sMtVjp9VqJFyWOTMc5uZjnqYtRdnc62NDqYLDVyV/eNsBcpsGrU0k2t9lBAGE9sVir0yE1mhydSfLmQpJXL0eYiJb57qvTvHw58u5U6SUKNZpSE0mW+NqLs6SLVa7td/M3ryww4LfQ7zPxk1Or3LWllU8e6OXZ8TgajYYPbfVxYqGA32ZEUWR+enqNxWSBdKnCj44vsZIu87UXpml3GZFlmfMrORL5CpfCBTSCwO4ulR1yaTVPh9fKx3a10RB0jLZaOb2Y4/JaFlBYTJfZP+DDY9FhM2pZzlYZbbXQbDb5yZkIt4/4cJq1XI6ojvEDfU5WUkVenVP9gB/cFuSeLUGiZYWv3z3MVLzE5bUc8WITn0UkmqshNWXMwONjSbRSlTu/9xZfeX6GW4dctDmNIAj0+UwogN2kxsYtZar4bXoUVGrsZ2/s4dJqHqkp0+U2vZ1c0Ocz8Ye/HMduVGGS6UqTTpeR7V1uPBYRjSjy0NWduG0mTNSZSlRZy1axGTRUJfjxiWUQFNrdJj62K8TeXhefvqGfk/MZnruokmnDxSb7uuw4zWqvX5ZlZEliNd/g8zf2otFqObOUIZ4t0+Yy0ubQoxNFHCYtcq2U+2218Y7dfuhENZKi1WHi/vV212quzp2bnEzFiswl1K+7/QN+BEGDgMC+bgffPxnhT27q4fRSnmv6ffx+0Manf3UJh1FAJsyNAAAgAElEQVTHdUMtDAftagBQuookS4DCxjY1+u358SjRfI14vsrmkF0tBlnirs0BBoJO5uMFRFHk4GiQK5E8jUaDcLGJWQvhbIUr4Ty3bA7x1TsH0el05GsylYZEOFXgv/5ijBa3gy/dFuTMcp4j8xmuJGp88eAgL00mObeU4Qu3DHD9oI+Lq3k06wizGwZtGA0GljMVrulz8Zn39PB/Pj6JQVQYyZQ5t1qk2ciyo9NOstDg7i1Bjsyq33DzCVX0L8syy1nVbCBJEhqNhg6PCqkJ2vTEy01GW22cWszhNsB8qobXamBjmxNFWeTpyzmu63eg0QgY9Qau63dxTb+XmUSZoFXk1u+ewamHD+zsJJqrsK/fy3yixMnpKONhFbvwm0mVwJoqN7lns5Ufn1imw21me2s7n3vqCoOBMpUm9NiM5Gvyf9j9eMeu1A1J4RP7e7ljSxt9fhsn5jPqFC9dpt5ovB3aM58oUq/X8NuNHF3IsqXNQqQocSWS55FjC29nvXgsKofupStJDo4G6XAZ1HxsSaLZbPL8eJSjcxl2dToQBA2xQoM7N7WsB4OKoChE8nUGW6xvf6gWUxVuHnBSlgT8Fh1VRcN7+l2Ei/Lb7myvRUe4JHP3liA//Mg2rDYbE5ECLqOWrR0OXpxI0OE0siXkYDJWRAAQ4NYhDwJwcrHAlnY7d20O4rHo+dmZCDcPudDrdHxify+fvqGPT9/Yh0YQWc5W0Qga+n1mQh4rXR4Lb8xkuGtLkF6/lYVUmXOLaS4tp4gU6rQ5jCTKEjtDVnRaLS02PYcmklw/6OPuLUFeXPdpbglZEEWR+UyDmwedzKXrzCdKWHSg0+v51vs2UJOh26V+kFW0mcRypkq21CRWqHNNr4P3bm/nni2tLGWqRHNlOt1qKnC7y4xWq+X+3e3s7PbS5dS9OwVNoPZMD41FmYnliRdUitFqqsQP3pin0WgQyZY4M5/kKy/MsLPdisdiIFuT6XCZ8NgM9HqMHJ5QrUPpcoPjcyk6XAYmInmeHU8w7Dcynyrx1rKa3/LX944garW0OAy0OgwcnUnQbDQJOVTNSbvb/HaksyLLLMRz/N0bK7Q7DaQqMt/5nRH+5KkrtNpEOh1a7v/pBbI1hY/uDBLJ1QCYWssQKzSo1mr0+qxIksTJhbS6502UiGbLJPM1/uqVeTpsIjazjm6vhZcnIjx7cY3Lq2l+eTZKr9fEi1cSvD4V46mLUa5Esmxvt7GaKXN6Mc0DezoYCTl5cF8np5fy3LUpgLDuDzw+l2ZPtwu9XofbCF87PEOz2eSOza3cuy2EVqflqfMRzswleOlyhFxFhQP95a09/PBUlHtHPciKwhszaX50bBFFlhE1Gj7/1BX2dtn40fFlZFlma8hKvg4DHgM/PRNhNpbn+GyaoEVDoQ4bWh08dE0Xu3u8JIsNInmVwHUpWkYqpNZ+W128g4ta4asvTFGplAlna2wO2YkVmuzudqFZ77cupsoEnSY+uK2Fn5xa4+7NAba0OTg+l8KpF3hjJsV1PXZmU1UcRpGQS41B02m1fPK6Do7N57AbtezqdqMV1andT0+tUq/VURSFQ2MxLq5k+MbL80iSxI1DXr5/ZJ4j03EkWSZdkdje4WBLuzpWVr19Av/02gx//fIs1w942Npq5fhikWfHo/zF02M8dTFKn0vL8cUCtVqdWLHB1b0eBEFDt9dMtioxFLAy4DNyaqXI5qCJk0s5Ivk6VqOeA0M+hgMWOj0Wuj1mJiMFNrVayVdloutbjERBFS+poZwysVyFJy6EWU4VCThMKIrMSrbGjUNeJqIVNgbtiKLI4ctx+vxWbh72sbXTiduspaloqCuqsu6xNyO02kQOTaSIFmrcOOznq3cM8PBvltgasuE26xiPlpFliXPLGZZzEjcN2omVFR7Y1YooijSbDb5zZJn7dgZ5cSLBkZkEGlGgKamsP41GYGen6z/MJn/HFrVeK7Kp1crzE0m8JhXC0uYycmI+zd4uO1qtli6PlYDNwFq2SjhX5fhcWmVapAqcXc5h1Ao8/Mo8ezttGAwGenxWBv0W+rwm/vTZaWx6gRa7mTaniZs3+PjZmTBfvrWHC2sq6nd3r5ev3LOJf/74Dja2OXnk2BJBu4FWh5GBgINtHS4GW2y8MZOi2ZA4PBHnun4XAaeFz9/Uj0YjUGjCdf1u7tzUyr1bVbRuWVEVdZGixL4+L8NBO9FCg8EWK26rnqDdwGKyjFErqNw+h5H7drXT7rawlq1yJVZmLVtlwG+hIgm0Ow04zToW0irUqN9nYW+Pi22dLhaSJQIOEz6LjkxV4oE97Wg0Ih1OAy9Ppbhto4etnWqQkyDA5FqaT/z8PLPhNBfWitw+GuTPbh3kuqEW4oUqfrsRq0Gk1WHEY9HxszfDfGBbC9lqkzaXlds2eNg/4KPNbaPXYyJfl5FkmSNzWdqdejQagYMbPCxnqtw07GU+WWWTT8fZ5SxLySIBm4F29291cgHv4KIG6G+x84fXdnHocpKnLkZptWmZCOf44ckw13bbyNdlTs/FuBStsL/PRdCuJ1GoUWoKdLhNOEx6vBYtz08k2RgwI8kSn/n1GFciBT62q41cVcJj1fPyRIwXJ+J8ZGeQx96MYDNqEUURj1nLUqbK8+MxDo1FUBQJjUYgkq8zFLCxmimRKFSx6SFZalCtVrmwmqPZaDAdLzGdqGDXCzx3OYHfIvKj44s8Mxaj26kj6DRxcDSARiPw3MUwe7odvHglQSJf5bXpJMVak0pTIZ5vMB/NMhYu4jRr6fTZGPKbCLnMzCar/MG+dn52eg27QaRcV5iKF2k0JR47E+a6Hjtj4RIP7etCr9ezqdXGw6/MI0lNTi1kCZgFvnRoml+/tcKRqTg3Dnm5HKtw+0Yv43FVdacRNfz4xBI/PzGLqq2WWU2XWEmVeXkiwky8zEq6TCRXw6mX+NQTU0yGM+ztcanhU7EiNqMWFDg+l2Z8Nctjp5b48ZFZpmJF9nbZ+NLhBcyiRKJQ44nzazR/e9wL8A4uaoNWw4mFLHqdjh/ev417t7Xx7FiCh/Z18zf3DvMvZ+Ps67ZzKVLh2/cOotNpubCaw283ct+OIEGnmVJD5sGru+jyWjg6l0GWZR7c14UoaljKqG4OzXqiQJtdx4+PL9PqNDIUtDMXL3J0PstiosCFlQwdTnUFuX1TK+0eC7IscyVSYDZRUE2zPj3PjSeoVutMJdVVdDhoQ6fVEnLokGQZQSNiECTOrxU5vZBhbFU9/L4+k2Q+WebmYR8aQeDaPjd2s55repxotRqOLWQBaNTqLCULzKRqLMRzTEdz/ODIHOlSjYDTyHX9bgQEig2J393ewn/95TgjQQva9QDO2ze3sa3DSZfPzoP7uohVBL58cICrulzEi3Wm4yVGW60Mhzx86oZ+/vzOEVodRur1OieXStwy5GIqWqTWVNjT7UASdHzl9n7S5SaiKJKqKvztvYNMJmosJMskCjU8ZpEWh5l+v5mQy6SKtVxWOrw2hgLqeP62YTfxkkKmIuE06ZBlGY3eaPtttfGOLWoEgQd2h3jiQoTD6+jdZKHCyYUMK7kGiqAK7nu8Zn4zk2E6XiaeLeM1afjzQ9PE8lWsBg0/OxNmc5uNa3pdPH0xxnBAfTO1Wi0f39OBRqOh1IRjc2kEUaDbY8ZvUeOGf/bRrfT47XitBuaTJaL5+tsx0IfGolzX76YqCdh0AhOxCltarVRkDR/a3sp8usb+AT+SLLGaa/C9Yyv8ziY3S9kaOzocdDgNnFrMMhy089C+HgYCdqZiRbLlGscX8zTqDebTNbo9Jr502zB3jvp5bS6PgMAHtwXJ1mSqlQoT0RIdbiOxXJX5dE31KyKwVmjgsqpMv6fPLfPrt1b54dEFOlxGenxW9Ho9Nw66eewt1W3jNWtpNpu8NpVkJpbnxFyaS8tpfvXWMsWGwn/b38G5tRKiTovHoiNcaHLLkIf5dI1sTWZ/nwsQiBRVJWGv38qw38RSVuKWQSeXohUV9Okw8dD+bkZCTi4uJfneGwvMJCoMB8yMdri4abSNgYDjP4dP/b/9oii8vD7xW4jleOJcGK/NhCRJ3DYa4OoeN+1uCyMhFwJw/1WtxEoSF9fyfGx3iBa7gY2tToJ2PWeXM7wyEQMUnr2wxgvjUZqNBqeXc/jMGu7ZEuTAcICHru7iyFScv3ttkdlImp+eXqNeb6ipVfkaH90dYjJaZDZW4OXLUaLFBh/cFkAQRRBgNlHi+kEv8VKThWSJ4YANnVbHdz8wwh0bvfzifIJmU+WGJEs1doasPD8eZWObg6GAjblEkb4WO/ftCBItSnS7Dcwny8wlikSKEu/f4qPb7yCcq2LXCxydz+C26Ag61a5Mj9vA9YM+OlxGev02vnHXMMfmUry5kKTHZ8FrEnj87Cqz8QL1ep2XriT5yI4AXpsa03dsLsVgi5UOp4GmLPHK+CrTsRqtVhGT2QwI7Ot2YjabqdXrvDaT4sh0Aode7apcieRZShXp95k5Mh3nmbEYGgneWlPjNyJFia0hB+F8E/963uTOLieFukyxrlJRD460cGox/5+W+fK/9VJrytyywQcoHFtQtxV3bW5hIVPjmfOrPHpymWajQYtNxyuTCU4v5tjYaiVXlTg2n+XiUopkSaUeLaUqxEtNPndjLzqdjps3+Inm6zi0Ev/w+gLnl9TojKlYkU6fjT++vou+oJuATc+T58OcXcnTaDT58YllpPX9nsemRyMIPH0xSkUSuHtTAJ/TjKgRaXUY6PZaubyWZSFZ4lO/usTR+RypQgWbSUutVmUiUuKJ82HOL2e4vJbjuUthzi6lcRlFVnI1nCYt86kqFoOW88tZZiMZnh1P4DepbJFzyzk8ViM3DPtpd1t4cynLU+ej/GYyzrHZNL84tcBXn59kLaX20zcGzLw+m1HZIJLED47MU6lW+LvXF7i6y0bIZUIraml1GPnmSzOE0yUOjanJwpPxMjcMuIjlq5xaKvDt9S2Gw6wabRdSZV6cUI3DM/ESPzwyT73RwKrXkJKhWqny6mSSkMNAr9+OrMhcChfY3Obg/Tva6fGYyVRkPvOrS3zluSvsbLf8h3aud+xE0aDVMBUrotPp+NOb+xBFLav5Bl+/a5jFbIOQQ8c/n41i0Sq4zSrt/7nLTR7Y3Aoo/MG/jHHLqJmVXJ09vV4ajSZzqSoIKhpXVhSOzmdpcVgYabVyYi6FAgRseqaSde7e7Eaj0fDeHQrRfINr+tfjMTQaRFHD+7a3I0sSY6tZfm9fFyaTiW3tTpqSxJVYlVtHAjx6IsbmNisz8RKDPh1eq45zK3lydehwGXnf9hCbO72IooiiyJyakXh1Ks1oyInNqMVjNeKxGgCFc6uqGOiFyQytDgM3bQzQ47Py7HiCm4d9PHFujUK1Qb/PjE6nY1uni1enkgz6rSxlKlxcyXHDsJ9YscnB0SAvXUmwmCzSqDf517MRbtwQ5BP7u5mMFvA6LPitOq4fCaKXqkxnZF6aTKIoCvt6XXzz1UXu3hxkIVXmzHKRvf0+VhMFQj47fouIXq/n/EqdmzYGyFVWmE/XKNQkjs0m6fRa6fFauLCSYy1TYiVV5Ko+P3duCvDP56LcucnPmeU8SrNW+W218Y5dqQu1JlPhDPVGg2fG4jx+bpWQXcfDL8/TbtPw8KtL/O62FnI1GZ9Nz2quzq1Dbr7x8hzziRJtbhPRYh1RoyFg0/PmUobXp+J0uU1oNBquHfRz80grP/rwCFdiFYIOI1f3ekiWZD5/Ux+iVouiKKymSm8X9P81ndRoNMwniiykKoRzVU4u5pBkNRL5xEKeTx3o5McnVHdMolAjX20yES3y2lQSm1FHptzEahA5MZ9hIpxnMqoyrwsNNU9le6cLl9XAtg4HsiyxvdNDh8vI7h4PX7hlgMK6/SlcaHJwg4dv/maR20d8bG53spRvct2Ah16/DVGjQafTsr3dwVSySo/Xyu/t6+JnZ8LcsrGFfb1umoLIh3aoC8EPjy6ymCrz4NUdmMxmPndjH2cjNf7w2k4OjgRUznW+xnQ4w8n5NOeXM7Q6DVzT52UqWabFquPXF+JITQm/VUes2KRQbTDQYqU/4CBg1yMIAiMhF3922zAAR9aHMc9Pprl+wMt8skSzKb87jbdWvcjrM2nWMupXp0YQGApYGQmaeezNCB/YGuD0UgHHemLUUrLIM2Nx9ve6MBqN6xFwIreOBEiUmpTqEiGnKv6ZiOSRJJmzCyk+9tgl6k3VfbKYKrO728ELl2NMR7JMhTM8NRajWqtxZDrB0ZkEVyIF3jPgZmytSLtDh92g5eN72llIltFptfyP2/t5ZTqNIECrw0ChrrCr28WHdrSh0+rQCALX9Xso1JpIkppz3uM2sFZo8vC9G9FoRHq8FkBgMVHg+HyWxWSey5Ei09ECl9dylOoyY+Ei1/c5eeZSjHqjxqHxBJtDTh7c28Wb86pR4vpBL9cM+DDodXx4ZxsruToziTIP7Gnn8EScpUyV+65qJ1FqEstXCDj0hHM1BEHD1b1u9Ho9HrPIsbkUjxxbYChg46NXtRKryHx4R4BspUkkV2NDq4OATc9wm4sv3jqAKGpoc5rxGAVWczAVKzLsN3J0NkO1WuXQWBRJkmh3W7huwMtjZyJUKxVabVp+cTayjlv47Zd3bFELgqCeqBWFo3MZnEYNX3t+kgurOQI2HfFSk2iuxJV4mT3dThIlNWNc0GioN+pMxdWRM8BHd4doc5ro8phYTldpNpocn0vhsxm4bsBNyG2l22Pm0mqeuUSRVyZinFtMoyjwB9d0ESvJhFwmru71ICsyf/XiLCNBM796awUFeOTYIiupIpVKlc89fplzSxke2tdFr9+2zu+rcHQ+Q9t6S2stWyGWryKKGm4cdPP1w9PcPOwD4EK4SL1ep1SXSZYatLvNtNoNDPmNuCx6HnsrwjW9LjYGzDy67pXcEnLgMOuJF+o0pSavz2YYcGm5HC3RqNeZTVQQBA2dLgMLqTJXInkVm5BVg4cimTKCIBLL19neZuaJc2u8MRXn16fnmIqXiWYrJIsNAjYd33x1kdvWOdIOo4a9vW7GVzPMJSvU63UW0xWeOK+eD86vZLGaBGRJ5oUr6tDs6FyWWq3KQ4+d463FNPsHfOzrdXBiNsO3X5njg9tb6WlxvEu3H1VVb5yqSDywp4N8AzaH7Ozo8nL7ZtWouqnVxnDAynDQjt+qBsRfCee4uJqnUG0io/DnT4/xX35+nly5wfH5NDdv8LGcrRKwG4gXaySLDfb1edjU4eGuzQH6/DYODPnZ3O7g6YsRIvkq7U4DvX4bgkZlYd+ztZV2p4GxWJmRFhOxfFXVQsxl6G+xsK3DhaIoPHJskTMLaa7pcbIh6OCmjQGGgnYAdFoVefvSZBKPWURRFH56eo3/fnMPL80UeOgqPyvZGqN+Az85E+F8uMRo0Mr9O4PMJcr84myEne1WMqU6Y+EifT4zV3XaWcs16PGaydQERgIWnruc5P5dbVxczXFsJsmBPidHZ5KEczXaXGY6PSamkhXu2uQnYNPxnSPLuM0a6vU6z11OcNeoH51Ox2dv7EWr1dKUJC6sFokXqswnShyZivODowvYdQonF7IIwK5uF36bAZPRyPs2+wg4Ldwy6MRiMXPdoI/BoJO7N7Wg0QhIkszzF1YoSdDhNmI06On2vksnijajli8eHHr7MHXvthB6g4F2p57DEwna7FoePb3GpeU0Dz12DlAIOMwMBWxsDjm4qtvFji4vIY+Vu7cE2dXjRtSohKAutwmNAEGHmXS5wZHpJONrWZ48H+HIdJJoXlW7Ocx6BI2Gpy7GUBSV59HnNbGUqXB4KssXb+ji2YkMpVKJNqfKutYI0O7U89Jkkj6vmS6fhdemEyTWBVlSUz2wbm5VhyKdLiPJUpOxlTS7uxwcvpLhQI+ZTz6l8jCOzef4+O42HtrTzqGJBE+cD+NzmDkw6CNcaOK3m/BYdZxZzPDFZ66wv9uKViuyr9+HVqvFZ9MjarW4TCLxYp1vHJ5+W7J755YQWq2Wfd1OTi1k0el03DHqR5YV5lNV2p1GNKJIttzg64enmQlnyFYa3LUlwN2bW/G7rOzucpIr11nJq05+URS5us9LtiYz2mpjLl0nlivxD28ss7/bRjRfV53x+RqKAkdnk8xnm+zqdvDBXd3cOhJgPlH8D/fU71jux9/+/Xf/QjN0gJV0hbNLGUIOAy6zOvXrdBnw2s3o1qE2HR4bbS4TI0ELZ5ezvLmURbee53dwtJVj8xmiuQq3jAZJ5KsspiqsZkrs6XZSqjXRajXcOtqKz2ZAQWFTyMG+AT/hfJ3fu6Ybr0XHSMhFpVrj2fE4H9jexv5+L+lKE51c5+WZHO0OPYVKg1PzKSZjJf7Lvg5enkzQqDeIFxu02TRcXCtxeiFNyKZlKlFFK2pA0NBi1fOz02vs6LQzkyiTqTTY2GLmvVtaODyRRK/Tkiw3VfFVr48d7XZ6Wux4rEYG/BZ6WxxUqzXcZj3PX0nhNYlsDDkpNRTMWoF0scobsxm+fvewOiwyilzd70MQBH51Zokzixn0OpGdHXbemEkSztW5ts9Nqipj02vZFHLQlCRemkxiM2jIlhvMpat88aYeDl1OoBUF7AYNDrs6cDmzVODAgJt0uc5iqozDqKHHZ2MhXUNBYSVTodFoYjfpuLrHjVGEe7aGOL2U5+xShtlEiWNPP1b40hc++zf/Xm28Y1fqbKXBVKzIjpCVRqPBI8eX1NFzmx1BEJhPFBG1IqlSE1mSOLeU4e9fW8BvN3JVlxu3SeTLz13hSiTPrg5VANVsNnnk6CI3DLqJ5Wv86IT6D5UkicloAY2ovl3H59JMxVTNhApcX+LJs0s8emIFn0ngxSsJLi6n+PKzE5xcKrA7ZOLawRbihRqyotDnNfOb6TRus5ZLq1lMGoWnxtLs7rTS6TaymFYJTO0uM/0tdu7a1s7379uC2WzmM+/pRiNoODKf5SsvTJMog9RsMhKwkMiXaXca+JMnx3n83Bq/uRzmB0fmqVaqHJ5I8pkbevBaDczG8zx9McL1fU4uhfOsZSrcuamF+XSdbq+FtxbSfP+NOS4tp0gUG4ScBra2O3l2LI4giPT7VALrHRt9iKJIj09FHHx8Tzs+u4liTaZSqfKH/3aZo9Oqu14URXZ3OnjszQgeo8IT58M8fVF19a9m6+h1WtqcRtpdJgIOA+fX1IiSv35xiqcuxXjk2AJ+q8i+Pg/3bm19d0bOdXks/NXdw3znyBLhTAmXUeSVqTQP7u0EQUAriuzpdqIAyXITQdDwR9d3c2C4ld1dTtLlJhpBQ6/HqOZzt9lYy9VxmrXMpWps63Dye/u6abNrETRqpsvGVge9fhutTiOgMBUr0Ww22d/vJlZs8PGrO9DrDdw05GU5U2W4xcI9o15+M1vhlctrJIs1cnUFWZG4eYMfjSBQljRsaLXjt2o5Np9lPlFmOS0hKwpdHnW7dGgsykuTSeq1Gp/4+XlaHUau7/fQ6bVhEsFj0fP4uTUuhlVfZn+Ljbs2BUiVmxTrMicXMhhE+PrhGQ4Oe4gVZYb9Jh5+eR6HXiBRqhPJ1ZhPFun1GMjVVGPEYqrCpjYbsaJEj8/Ce7eHuGeL+nsXkwW+/doClUqVX7+1yq4uJ8mKRNBuZG+Pg3JT4OAGLx6bGY9ZhwJoNAIf2dlKuibgs+ppdxoo1FW+3mirGqwqaDT0++3cuSnAtUMtDAYc3DbsRhBUqfCrExF+fXb13elRrDYkHn5lnk63iWJDYUuHi1s2+tVVciXPgX4Xz40l0AgCm0N2PGaR58YTXFlN8tXDMzQlifcM+3hlOo3bpGqobx0JcFWPl6GAlXCujiTLHJ3PUatWURQFRVHUwYwkoygKlWqFhw9PcX45Q8CqV0fyAjx7cY21TJnJWJkTSwX2dFs4tVRkJOTiXz+2hYtrZSZjRX7/+gHeu62NVLmJy2bi+gEv3W4jOg0Ua02OTse5vJZDAG7d2IIoithNOjSiiCiK3DDcgtMsoNGAXq/n+n6VUpov19Fqtbx/RweP3L+d/QN+7GY9k/Eyi6kSHW6TehjbEmAhU2NzyEmb00ilXOYLT09y/YCXTq8VQYD+Fhu3j/rRanXMJ0s8Ox7HZ9Wvx4gYOTmfZDxSYCFZAkGDDPzb2Sh9PjOvTyUZarExkyhTqDT51VsrPDsW54E97Wi1qkYkkqvRaDY5MpdFWkdTzMTyvDYVZyFZwmPR8epsDkVWuG9HkNlkhU2ttv+w+/GOnSgCbAk56HSbWM5WWctUmIwW6HKbkGWFR0+ucPuIl5OLWSK5KudW8hzc4CFeanJ1l43ZZIX3dTnR6/W8NBbhuiE/L04k6HSbVVyvLKEVRfb3uphL1ZiO5FhKV7lh0M0//iZBQ2ryxnQKh0VVjV1YzdEfsNPhMvLUhSyfu6mXPd0u/u2tFbZ3utBptXS4Tfzr+QQdLgPz8SIaQUOnx0ospwIjY8UGa/kqZRl0VRlJlpiNF+hvsTOTUPMidVotNw15+JMnY+wIWVCAoN3AXFpNBDs+myZfbdBoNDi5kGU2ZkAQNOzq8SBLEvFik6YksZSu0Ou3c22fB0EQODUbZypR5U9v6WNHb4CJcI6FVJlXJyKcW83T47OqwaRbQ9TrNb76wjRX9ziQNDo+uLWF2ze1Mh0v0Wg0SJUahJxGZhJl2t0mNKKGWrWKyWgitD7cQlFIFusE7XpMRiPvH/VzZilPm9OIqBFRFEBRaPdY2NPrxWlSFyVFUVjL/tZ6Bv4/rtSCIHxSEITLgiCMC4Lwr4IgGAVB6BYE4bQgCLOCIPxSEAT9+mMN69dn1+/v+n/8ni+s3z4lCMLN/yuvXWvKnF1K8+SFsJpQK2qYieVZTFW4achDvFjjxHyGVruRoN3AtnYHPz+9qkadxYvMxIp8+9V5piNZ1ZovCJeVMuwAACAASURBVHS6jcwlChybTRLN13hjKs5Kro6AgqARaLVreWE8hlWvcGElx7V9bh66ugu/zYjHrK5kgiAwErTwk5OrzMbynF3Ocn4ly03DPg5dThGw6fDbTaxk1C1hrVZDK+pIlWrE8zVuHG5he8hEl0dPuiyxkiqiKDI9bj3feHGGHrfKxajVanz/jTkUReHxsRTtVnjxSp4Pb/djM2p5+mKEseUkj58Ps5JWvY2ryTyKAA1ZIOTQMxPLcWE1y0I8x4W1Ih/Y1sLPTq9xYTHJUrrKjYMe5tNV2ux6Ti5kWM1U6feZeHOlyId3tRNymvmdLS28Pp3mk7+8wOtXYjx5IcKmkB2dXo/DoFFDoSSJp8cS+Cxqsf7w6AItdj0T0SLlhszBDR4ePjzJ+FqWC6t5Ot1GvDYjsqJwcbXA3VuCaNb/R90uPelKEzRa3f/vRS0IQhvwR8AORVFGABH4IPBXwLcURekDMsCD6095EMis3/6t9cchCMKG9edtBG4B/lEQBPF/5W/QiBr8NiNLmSoP7OkgmleTAv7qpTlGg1YSxRor6RLPjEWRmk1ErZY9PS68NhODQTudLhOiqGVDq4PFlKp2C2erXN3rZnuXi3a3hd09HjxWI90eMz85ucJiIkem3GQ2muPVqQTHZpIcm8+SKKqc65lYnolYmft2BhiLFNkWsiEg8OjJFT59oJMuj5lYvkq61KTbpeMnp8MM+Q1qnkmmQjRbYSFdZSxcx2EQSBRrvDGd4AdHFhj2G5mMFVhOFZlJlLiSrNPlMvDofZs4vVLBqIdXZ7M4LUZGW60EPXa2h2x0eKw0GnVeninQYtHxoZ1tPD0W58KK6iRfzDZ4YHcIi9nCYMDKT06tEnLoeGU6w3UDXnRaLe0uM3t7nDz88jwtVi1dbjPffWORJy+EkRUZh1Hkqk4bktRkLV0iZNczvpbjW6/OqwOgDhc6nZZooUarw8BAwMFImwOLXsuJ+QxTyQYHBjxsbXeykqsz0mrl4moOr0XLXKLIW8t57AaBk8tFBtx6NHrTf9qeWguYBEHQAmYgAhwAfr1+/0+Bu9d/vmv9Ouv3v0cQBGH99l8oilJTFGUBmAWu+n97YYdJx/u2t9PhMbOUyPPCeJRWhzoE2dpuZ6jVxdYOdY9720Yf+aZAq8PAibk02UqT+3e1ka/LaLUiD+3rWi+2Om1OdaonALFinZDTRMBpZCTk4qFruuny2fHYjPicVlxmPdcOBXjsgW38zs4OtKKWRFHiUwe6+OZrS8SzZdKlBt51p/qn/u0i33pllmy5zmdv7GUh0+C+7X4OXU7isxqwm3ToDXp8Fj39ATVddlunmzaHAVmWGI+UKNVlQm4zv39tL9f3e1gtSOh0Oj68K8RNG/30eC14bepz79zUwoW1EjcOeenyO9jWqker0xIvNdkaUhVw//32jTywp52ZjMQtG/1cPxTgm+8fRafXc3CkBY0g4Lcb6HAZmU+W+OyNPYiilpGQOiDp9loo1CQQ4OsvzfE/uXvP6Lju89z3t2fv6b1hBoNBrwRIgr1ToihRorpk2XHs2FaxncR2zkniOC4pjo9vrOs4LsmxkziyZcl24iqrUFQjJZGUWMUKEiTRywAzA0zvfWafDxvRzb3LOmfddZJ1r7w/ATOYPViDd2/8/+/7PL9nPl7kxGSU49MxTDo19631otdpeWibH41Gw9Z2K+JKp8lr1oEAjUadh7f7GGxVgD0tZoljEwkEQQXILCYKtDv1aNRqPr+vm5OBHI1K8T+e+yHLclAQhK8DAaAIHALOAylZlv/Nb7MI/JuVvQVYWHltTRCENOBcefz0vzv1v3/N/+0QBOF3gd8FsPo6mQyn6HQZeer8IpFMmXWtNuYSBSVB1mNiJppnbCnPhAAP72hnLlHg8NUw9XqDJ08FWOW1cttgE4++NIHTIPF7N3YxFStSXcmF2eRXVG5Og8hLVyPcucaL3GggCAKtdh3PXAwxEU7y9MUgG9rt3D3cQqPR4PBYnD+9uZM//sUIyUyB5UyRTZ1OqlUFSr6jy86Tp4PUalVevbaM2yiSyFfZ02NnIVmk3aEnXWmws9vBYqaC3JARRRUPbPRTqVR47lIYt1nL/eubmYvnefSlccaDSTpdBk7NprBrlXvVQqpMm0PHV14ap1Iucz5UYU1rFlmGc4k8HU4Df/jTCzQ7zfzRDa28fDVCOK0MgQLJMr0uPa9ej2Azqvnu0SlC6TILiQKRdJE3ry8SSJaQRIlOh4H3b26nr8nI1VCabx2eRFSp6HIb3041+IfXxvnk3j6eOLXAh7f4+OarU/itGpKZEgevlLhjSOmL+ywS3z0yxZpWOx/f3cl3j07y2vUIf3HnAP9wLECzRatkxP9n6KkFQbCj3GU7AR9gRFk+/Kcdsiw/JsvyJlmWN1mMeg5ciTAbL3DfcDMycGwqQatVQ7vTwGNvzLB/qIkel46bBtycnEmAIKBSKRkq4UyV8UiW0cUk1WqZiUiO75+YZyyY5PET8xy6usS3Xpum0agTy1XpcOi5Hs7yzEWF0SxJEipBxWvjMWQUutC1UJpD1xX02eGxGHKjQaJUJ5IucH4uwfHZDPVqlVPTSXZ02ZCRabbpSBWrLKXyHLgS4Sdnlji/kCaWzvPU+QXkhqxkMwJTy1mePLXAlVCGQ1fDPHFinli6RK1WI16q8cZUGqdO4MBIiGaTiu4mM26TRrkQVSruWuPGbzfw9KUwlxfTTC2nmY4V2Ndt5o7vnGYhkecjW308N7JEIJ5TZKZmLetaLKgEgZv6nG+nd51dyGLVq7ljyIkkiRwdW+ZvDk1zYiqOxaBhZ7cTj1nPpUCKaq3OG4ESv7oQpNul5+BolFabBpdJzWIO6nV47nJUAWq+OcuFhSy1eo1XrkUZW86RLjeYjeZY32riyESMXpcBlc7keKc6+d9ZftwCzMqyHJVluQo8DewEbCvLEQA/8G98hiDQCrDyvBWI//vHf81r3vFwm5XItnCqhEqlYkuXm5v63ZyeS78tRr80F+XAlSjVapXroTQqQeCWQQ+3DjVz84Cbm/pcPHlmkWShxkd3tvPxXR3EijJfu3+Q372hiwc2+lndbMJt0jDoszDgNXH/Bh/BVAmVIHDvsIehFit/fns/KkFgJprntkE3L1xZYiFRYLDFikMnKlrqdjt/dls38WKd8WhO2VQiYNcr9P/5tJICu7XHysY2K9mqinSphiw36HQZUQkq1Go1Qy02nvzwMDajFp8R5vJglhr4TRJ7+h10uU0k8vCt16aplCtEsmXi+TLjkSKrmnRIkohJr+GLd/bT67Wyymfj5EwSt0XHhzZ5eW0iybDfwo39HgZ9Vtb5bYpOXJJIluq0u0z81uY2/vaB1Qz6LBy4HMFl0jAZLXD3ahfTceUiA4HlbAmVqMJv09NsFFjfZqPTbWGd30KTSUei2MBjBL/LQLvLQDBb46Z+N4MtFiRJTbtDx/41LWxrN/PMpSWuhzI4jBqFTtuo/6c4XwLANkEQDCtr45uBa8AR4L0rP/Mg8NzK1wdWvmfl+ddlBbl5APjtle5IJ9ALvPW/evN0sYogqJiIFlhMFrh9tYc+rwWfTYdaknh4RxtnA1lu7VOmhSpJQm4oBPEroSzhZJ7LwSwPbWvFadYyFy/w4ugyW9rMPHl6kacvhFhM5PnW4RlOTsd45tw8j744Tq1WYyKSp1Kp8MylMFcXU4wupjg/n2Q2kqa+Mr0EaDZrqSKyu8vKaDjPQqqCx6LjkR3tqCU1kiSyxmek2aJlW6cTrUaD06ilvcnGIzv8fGxXF5IkMRsvcmO/h7vWNuM1a/jLF6ao1uo8dzWL3wjxYp1ToSqj8wlm4nk8VpG7Vzdxej5Fo1Hnxm47HrOafzwe5OY+B1s67ASzNSXFd2sL85kaXouWP/jlNSrViiLOEuDgSIgLAQWu89Ed7WxsVy6aEzMJTs+lGVvKIYkqdvU4cZoUKOUHN3qI5mucnImTLtX47L4exmNltnVYWdXioMOpJ5ypcCmcZ9Cto99r5oGNft6zoYVut4mhNhd/sq+XNqeR5y5HaLVqub5cRC/JLGer2HWKCUOuVf/jsWOyLJ9B2fBdAK6snOsx4HPApwVBmEJZMz++8pLHAefK458GPr9ynqvAL1AuiJeBT8myXP9fvX+1LjPos/DgVj8IirxzJqq0rqajSmttIZHlp+fjXJhP8pHNzSxlyiAIOA0Ss7E80VzpbSPqzHKa83MxTs+n2NGldD+2dzowaAVsBg19K8MKjVpNj8tAIFlmrc9MJF/n6PgSjUaD8wsZXrwSxmPR4rPpCafLeIwSy7ka61qtIAjEchXmYnn2DzWxuc3C908EmIkVWeMz8dxlBYLTZBT55cVljk/GaHfoUakE+poMjC1lGV3Os6fXSW+TmT19FrxWHV6LjlVuLdEKhJJFWq0aTgVyeE0a5hNl/uX8Mj0uPb+9yc8PzwRRiSLdbjPbuxyEc4qj/pM39XL/eh9qSUIQFEh9vVFXcF9bfJyZz7CUVdAPcqOB36bn5lUeHBYD9Xodp0HiH14f5zvH5tnebuKjOzsY8Fo4Mp1m/4CDs4EcpVKJ03Npfu/Gbv7ghnZ+eDZEIFHAb5Y4eCXCsfFlOmxqZuNKKNKGNisgr7DCa3jMEucXMpydiSGoxHfcD/5vDV9kWf4r4K/+Hw/P8Gu6F7Isl4D3vcN5vgJ85f/Ne5u0Ii9cCXP46hJWrYr3bGhBr9dTq9UIJJWIBWQY8ulZ22LhR2+F8JsEEASaLRqypSpWg4Y3J2PYtAJHJmLcurqZj+5oZzKq6KxPzaYQRZGP7+5kqMXKRCRPuVwmnMoBMi02HZ/f18kLV2Ns6nTyvs1GVrfYuB7OMhFOcWY2TqpYwW01sKvHyXyiyFq/hacvBAmmyyynizSZtVh0EpFclbuGnHz76Bzr/LCzy4bfoQcEatUaj740zn3Dzdyz2sNMNEssV6ZSqZArN7gUyvE7WxSm4HQ0xytjcf56j491HW56vBbKpRLfPjbPH9zg4eXxJF0WgcYKhalcKnF8MkO724wgQ5tdD4LA9HKW5WyV/f02/uL5ST68pRmNRrvixMnRbNEiqgQ+vKmZLx8co8WuI1eu02JW87PzMTSaSSRRpMUscWouxYYWPS9ci3HfcDOiKBLK1dnQauXUVJxnLoXZ12vlWqTI7zx+Dp0ks6PHze5eN09fDKMSBW7sVfAQ0UKVRLaEIKl/85wvCAJdLiM3dNuJ5co8eWaRTruGEzNJbhtsot2psJK73CauhLK0WVT8YiTGOq+OUKaMKEnEs0X8duUOqJbU+G0GDo3FmIpkVzaVAr1NJur1Bt9/c45KtcrTF4K8NZcklMrzqwtBDlxexmGQ6HabkESRF0eXlLxkZKwGDVa9hlpNMeU+cynMbavcdHst7Op2sL7Vis2gAUFQch7n0nhNIj85u8grl0NcXEhzbHyZcLrEvWu9HJ+K88tz81xazDARyZGrysiNBls6nVwOZphcynA9UmS9z8jB0Sh/9PNLjIeSvBXI0u7Qc/BalL2dBv6P14L868lpmo0qfno+RDxfxWeSGAlmeOZSiB6njqVshc2tJv7+yCxbWg0cvBpjOpplNp7HohEIpcs8fSHI69fDiKKKHrcRu0mHTq/jzjVNqFQqzswmeHZkCbtG5vXxLFaNwIErEUaDKVqtGuRGnUINLs4leezUIuv8Vj64uYWeJiuCSsEFP7Cxhb19biajeY5OxkkXqnS7dIDwjrX7ri1qvVpkJprn4GgEh1HLTb1OHj+hUPZfvrrMyZkkA14zVo2M26Tm5HyOD270cCFUIJqpMNBkIFdXsW/ASaEGf357L/dtbOOO1V76vFZ6PRZ29TiRGw3+z1em2NRqQpZl3rPBj89u4i/vHKTTrYjVo1llWQPK1KvPbSCSr+Ey62mxatBqtTy8o43NHTZ+9FaQNV4TZ+aVNqtKUOLmtnXaiOUrhLI1Nvu05GpwxyoHkiThsykbvB1dNubjRe4bbmZ7lwOHSZm6PbzNz9VQln94Y5bbB5xodVruWeulyazjtWvL1Gt11rVa8Zi1DLe76LRJfOme1ej0er509yCfvbWHA1eW+cL+Pta3Wjk8HmdLm5kz81kElcALV6PcNeSmy2WiUa+Tqcjs7HbS5jTw5nSKAY/C+HtkRxt+h4nP7+9Fq9Zw73Aza5pNLGcryMrHw33DXqqVCt8/MUcgWWK1V8cd61rYtqKUlCSJDR0KTWsmklXgnSWljeq1aNg70MRCVqaeT4beqTbetUUN0Nds5dP7etFq1HS6TMTzNT62s4M71jTz8V0dVGtVfn4pRrVa44YuG3q9nkd2tKFSCaglif2r3MwkyoiiSCBZ5vmREC9cWWLAa6bfY2I+WUZUq3nfOjenZpN849AkfU0Gtve40Gg0eCx66vUGCAKDKxDFDoeBx0/M88iOdta2mDi3kGd/v50zc2m8JjXVao3lXIWt7VYktZoul55Escbp2SR/eks37U49r0xmoQ4/uRjloW1+wpnKCvCyqKyHnVri2TLdDi2JYoOXry6zp8/Jzi4Hi+kKIBDK1fjMLV0UGooK7+xMlGA8x58+PYpNA+ORPCem4tTqdb52eIqpFU61KEr4TBJn5jPs6nHwT7+9ll6vlUCyyBuTEULpMgNeM6KoosmkwW9f0XIAT51bwGUQ+f2fjODQCbw+HuPIeJR6Q6bVJtHuMjMVyfKDUws4DWo2tlqZipSwa2TeWixQrpQZWUwjyzIX5pK8ej3C0xeDrGk20ePSs5St0GbX0uPSI4iS5p3q4l1rEvja333nS1/60/+KXS+SK9dIFau4TWrenE5w8FKITLHMufkUe/scRHNVxiIFvBYtlxaz6DUqroayCNQxaNUM+mwIAogqZYJVqTV45lKI4RYL+WKFo1MpHljvZTlXZiZexGtSMx3NM7GcJZEvY9Lr2LvKS5NFh10vcmkhjU5SKR0aGpwNZCiWSrw1n6FQqWPRiYQzFQySwJmZGD0eCzoJfnouyPYOGyq5Qb4m80d7u8lUlL7wvgEXCCryxSLfem2GxUwFZBmjVqRYqbOYVGDwDrOe3iYjHU4DL15LsL7FxNZ2G89ejpIplNGJIpcWS6xtMTLcZufMbJrdXRbOBjJcX86zqc3Ck6cXWeXRc3kxxROnQ3xgo4dsRSZTqOK367EZNESzRWbjRYKpPP0eEz1uA89cDOExa8kUK+RroKaBXiNh1EospCusbTETzSsA9+1dDmZjOY7P5WjUK7xnbROTiRqDTXp6PRZOz6X46n2rmIoWuR5MMb6cp1ipMRXNM7mUYfzYs8XfOJNApd7g4myU+/7xNDORDBcDKUKpIjs67fjsBhZX4oJ3dDko1FVsarOiEpTRgc+qQyvUGQkVuLSQps2mYSGeZ/+QR0EHiCparEr4k8+uZ0uHnXOLOd63qY1dPS6Wsoq73GPW0ttkYkObEos8Gkzx1wevs6XdwpuTUS4upEkXa9j0ErmKkua1p08RCdVqiiZ5IVPHoVNxcTFHMJHn9bEooZiiEpyJ5Tk+FaPZJPLJn15hcjlDolBnT6+DT+1u59bVXqx6DdORDOlSBYtBzbYOK8FUiVMzSba0mTg2GefMfIr3b/BgN2rY3m6iLsBCUum1e8wSB69G2dJu4wv7+9DpdDyys43lXJVUqcHv72zhO8fmWYjnCafyK4Mmxey8qkmHVadmbDlHIJ6n1oBzgRRL2SqfubmTS+EC8WyJeK7CQJOOfp+d7Z2Ki//ZkWU63RZu71WmrD88G2bApeHJMyFqtRpmrcDhsTigtG/bHHokUaTPbSReqKHSGu3vVBvv2qI2ayUWUiUe2eFHp9Uw3GIhnq8jCCo+tquD3b1OAukq33xtmr++qxet9v/6b3VuJsZyEf5qfzcei45fnlvg2QsLHLysZJEMeM0IgsDR8QhHxmP47Xo+vquT4TYnoijis+m4dZUbWZZ5dSJJrVbnxdFlel16MhWlG/lvaj2HQYPXauCWQS/3rvWg0ykCf7/dwEgwz+f3+jEaDdy71stwq51Ou4YzEZlUvs6lhRRes4ZgtsY/fmCNgkaQ4dhUitfGIhy+tky5XCJRbFApV7DpRJ4dWWIpU8RtkpDUakxqkGWZo+MxwpkKZ0Ml7lntoNNtRqVSDL2NeoNovspLo0s06orkttVu4Av7+7gQLtLpMvLw9lYWkmXmo1k6XAY+vNnHmzNpEjnFr5muws5uG9t7XOwb8qLT6bhztYdvvn8dc4kCoWyDSqXC0xdDtNq0eK06AvE8b8zmMWtU9DQZ0er0fHx3O2q1mrlYgWCqyNZ2C7mqjNukZZXXhEarYXuPi3ouvvhOtfGuLWqdWuTiQpqlTJltHXbCmTJ3rXbxqwuLPPbGDGq1mi/d3oMkSfzgVIAmk+btzVyhruJHH1rN4ckMdk2DsUgRj1VPu13H0xdCXA2maXcYiBeq7Oqyspgs8Mq1KPW6EkR6djbGoy+NIwgCf7W/mx6PmQ6HjqlYia/eN8hiukK1VuXA5SWSxQrhVJ5KtcKjL0/iNaq4ulxApRIZ9pn43IFpLGINQSUQzZaIl+qs92pQC+CzG+hw6lmI53lxdIlnR5ZY12ZlT59ycSWyRcYjFTrtWpZyDa6FczgMEvVqjSvBLJVymZFQga3tVvI1md/Z3MI33rcWj1VJuu1x6bgcyoIss8Zn4vxckolwmqnlDMFUiel4aaVLY+OJUwu0OQ2cmUvy2BvT/PJcgC67mmSpwYNbW9jY5iCUqeG3GxCQ+ZNfXGZ0McnYUhaLXsMndrWwkCoTz5U5NZvkozvbEUUVKhllmNKQEZDpdpvo95jQaNRsbbfw/GgEnapOolhjNJTFbRBx6MT/qfPlXbum/so3vv2lBx98kPOBDJmCot1YSCmJXSathN+m5cCVZWx6iflonly5gUYl0+E202rX8fdHZtFQ5eRCgfcNu3FZjXQ3WYjlSnS4jMiyjFol8NzlMPlKnd/f3cHhsThmrYoTU3G2ddoZD6d5ZmSZt+bieMxa0sUq07EijbrSqvJZdAx6jBydShDPVdjYZiecrXLbkAeXWUerXUcwqaC5lhI5MqUamXKdQq7MdB7+7r4eDk+mCacK+Kw63CY15+cSlOvgs2iYT1eRqNHhNmGQZLZ1uxAEgWtLOW4aaCJfgyGPnlJDJFusEM0rPd4nTwfwGCWuhHJ02LUsJgtEMmVQCSyny9TqMjcONNHnNnAukGYhXmAymucvbutmPFqg2STx+liMeqNOqSaTq8C2LjvhbJndvW7K1Rq7e+w8PbLEfKxALFvk5etxNrbZUItKjEi9ITO1nGEklMVhkCg2VPS69Dx1IchiosR71nkJ5WqUShXOL+Z4aLOH1ycTXA2mSRTrXD/808QX/+xz3/p1tfGuvVMLgsD+IQ8f3dVBolDl1qFmPndrD5oV7fiJ6SRNFiUAqMluxKYTODYRQ0agp8mERa+mKEt85qY2Xh5L0WJVevk+qw5Zlnn20hKdTj0dDj03dNk4PBbntkE3q/12XGYd7XY9mYpMv1vPzh73ynodulwG1BoNH9vZgSAIvDYe5cYeJ0N+G7t7nUSyZSrVKmPBBD84GeDGHgfDfitDrQ463UY6nEZMZh0uDfz3N4NsbjWzqdPJQrLIhfkkV8M5XCYNarXEZ2/pxKzXMhPNMRotE04VieYqqFQqut1mGvU6z19eplwukyrVqFarnJjN4DFIxIs1qrUqwVSJC4vKMGd9q40mi9I+rFWrfPZXV6mUy0CDnR0WPvXzUZwGNbF8jUIVRFGNlgZDzSZOTMeRZZmrgRjfOz7Pc5fCbGm388jOdgaabWxut7F/qInZZJlavc7TF0OogI9sbeH3buzhntVuooUayDK1WpWDVyM06g1aHUY2tpr55pFFNrVasRl1bGy1/GZix5xGNZ/4yWXqtTqpUo19A06+/uos965MrHb1KOoxlUpkvd9KolinxW6k3a5jKpLFZdHx4NYWrkSrbGhV+HfT0RyXg1n6moysa7MyG8sTz5b5+cVlKpUKL12N0Gg0cBokXrgWpdOuIZSrsa3DTihVotFQ4unuWO1lIlJQTAOxEpFsGZ9Fx4npBLFMkb99eYxnR8IUi0VeuBbj9lVORJWA26TFbVSzqc1GV5Oyzj54Nc6+fiepYoP3bvRj1ops77Bxdj7Fd49MEk6VMUiwp8dGtiKzscOB36pW2otOA/VGg1CmhFWrQpLUfHCDW3GOyDIqlZJBc+dqL6Io0unU0+42savHyWKmyo19DoUWlS5xdDKOzaCh1WlkY4eTTe1Wbux1MB5vMLKQZiycRa43ODmfZdhnAEEx156Zy+Aya0kWqjx+cp5HdrTR32xjbYuZ18ajlMplHj8+y7MjS3xoUzMqlYp2t4X1rTZkWebIRIx1fht7V7lQazRs63Iwm6yw8sv/2uNdW9SxXIXNHTYkScRu1HB4PIHLJDHUoiy1NBo1D25p4Uo4h4yM16rDbdFxfDLGv55ZZI3XyI/fCvOhTQqgvCE3uGutj7UtFp44tUibTcuVUI5bhry8f4OXvmYrrVY1By4tEs1XuGeNh7lEGb9FveKDVGCQV0NproXSzMRy3NjfxCd3t+OxaGk0GiRLdR7c5sdtM3Hv2mZa3TaMaoGvHppmbjnFsck4h68tcWo6hs2oQaNRs7bFxA/PBLl3nZdVzRZESSKQLNJkgOlkleUSTERrRFIFkGVu7rVzdiFPrVZlNlYgW6owHs6SKlRZSub58VshBGCd38auHgeTkTw7exzUGw2ePL1Iq1XDXKJIu13HHau9jIUVVMG+QS+3rPIgiRLhVBGXWYdep2O41cCf3dHPQLOFL9zez019LvJVZY18fiFDs1UDMlg0KiajRfo9JmbjRTocBip1ePZSjFq9jlmj4m9fncFp1NLdZKLTZXz7b3t0Mo6oEtnYbmfPgJebWbcd6gAAIABJREFUVzUhV4q/edD1ar3Bx3d3olKp2Nhm59YBxUD6wpUwKpWKLoeWP31qlH6nhqVMGb/dwHs3tnJDv5sbeuy0Ow1Y9SKvTSSxaQQuBlJcC2UQRZEd3fYVepGWmaUk/3J6gYmlNE+dX+TIeIxGrY4oiqzyWYnmSsgNmY3tjrcnkNORLIFojmPjEY5MxDg6GefIRIz9/XZeuBrnozvaGGx18p6Nfn73hi76m3RMJ2vs7rJhN+mJF2qYJIF+j4IN8Fl1zMUK/M2hKTa2WlhMFhlbLnD3kAufEbZ1GhhdqpAsVPjhmSCf2N3GmlYHgkpAo9Yy4LPS5dAxnyrz5Tt7kIFAIr8SAaLi5EySRr3OTX0u5pMl5qMZnhkJc3AkSLpUI5itolIJCAK0O/UkSnXcZi23r/bQqNd54lSAk1NRPvj4WQKJPN0uHT6HiTtWu2nIMlPxAovpMg9tbeHQ9RiBWIZQrsZ/u7MXm0Gg32tCUKnY2O5gY4cDGWX5eOdaH/et9SBJIju7nSxlq8omNlmkUS5k3qk23rVFXak1uBbK8NSFRWajGT7z1BWq1RoqQWB7p40XR5cJJPMcm0pQrdYIporUalV+dmaWV8ZifPHZUQBu6XcQXxHyz8Xzivk2kgNgk9/IT85F8ZrVyI0GmzqdfPW+VWSrSkj99k4bk7EKHrNEKF1kOprj5EwCWZbx2ZX01gGvmQ9t8dPvMfH8aIS5hOKFrFZrHBuP8IOTCyykKnxgUzM9zXY2tlpoMms5PZ3ku0enkBsNejxmejxmvrC/j2hOMeiWazKiKGLWqxmPltjVaWBzu53JaIGLC2kOXFyk02Xkw9va2NllZyScx6oV+PHZMOUaXApmuX3Iw+/d2MNDW1uYiJVod+hQCQIqlYrP39qLpFbz6Vt6uHu1m9tXe7kSzjOfKHLPGi/xfJXnLy0SyZZpMkps7rDR5jSxlCpwYSHDYjzDT95a5GIgxVfu6sOkVSGp1XQ49SQKDW7utXPwaoxwVkZuyGRKNcXU4TETSBQBmWq1yg9PB+l1K6i0LW1mXr0eZSycQTTaPe9UG+/aorYbNUiS9Pbay2FQK/nWsgIiRG6wb9BDf5OJWK5KJFPhu0enyVbgQxs9NNlMuIxqHj8xT5NJi1qtpt2hZ2IpzWvXlplYznAmkGVVswG7SctIMEO9VufViSQ9LiPzCcWmv7vHgVajRVSJ9HosfHRnB50uI8uZCrcP2LkWThNMl1CrNWxos3PfOh/L2Rq/PBdgKVXgoW0tyLLM7HIGWZY5G0hRrVRoqBTAuygqRKTZWIHr4Sxz8SJemwGPzUCLTYffbsCoVvHqZIFgMstNfS6ShSqvjIZ4+vwiM8spnh+N0WyRiJeUC95hVPPHN/cwnShTrVX5yxenGPQYUavVtNq0JIpKasKF+STfe3OGx47P8/iJOT5zcycdDgMnpxM4jWqOTSVQqUReHYug0Wh5ZEcb2YqM36Kh1WVBLwrEc2UefWmc0VAWudEA4N5hDz96K8Qan5kBj/LZy7LM4yfmaDQatDv0bG418/iJeWwGiWaLhqNjyzz2xgwOg5obet7R9AK8i4taK6kQBPBb1MzElPw+QVCoo4/saCNaaOCz6qnWq3hteu5a7cJt0bK7y8bhiQyf2K2YbRKFGju67Oztdynwmukk/V4ToiCgEuDeYS9zsTxuk4blXIU2mwZQBgnfODSBAHS4DHz8hi4EQeD5kSAnZhKUyxW+emiGa6EcckPm4e1+REmi221ke5eNZquemZWo52CiyIFrcYrFIvVqjXhBxq0X0Gg1tFjUPPqSYk4QBPDbNOzqcXD/cDMHR6P0uo0ggEGEkVCefQNOnEYtLrOWNS1m3phOsKbZSKvTwu/vbEGr1VKs1lCpVAqyeCrBhzf5GPTbmAinOXAlwmf39SjQzfU+VvsVU8HWdhtffXmCX7w1x1KmSLNFw9ffu5bN7TYkSc2WVjNziSK9LgMLqRIf2eJDUksMeM0sJEu02vUMeM386kKQuXiBre0WroZzZMs1trRZ3o6dm4nm+PnZAI++PIFN3cBpUHNxIcVSusBiIo/DqKgJG+X8f7zx9v/ro1xrcD2Y4MWrMW4fdNHfbOXEdII2u47JWJGPbPHx3w6McmwqxQPDbh47FqUsq3DoJdY263nyVIBwpsyPHtzA1w5NkyxWWdvm4OHtbajVarqdOv7pyASXFnJECg22dyqw9qNjS5ycjNLpNtOQGww1G3luZAlRpWImludKKMfnbu3hiVMBfntjM2/MpOhwm/mbQ9PUalXemm6QrcCHNzfzytUwDblBh8fCrX02NBotwUwNZEgU5RV3TYp1rVYEAaYiWU7OpEgXa/R7zKhEkVAyR7tDT6KQw2XU8MPTQRpyA69VT5fLiFWv5dZVLj7xkxEEQeBTdh2yDNORHMu5KtdCKRbjaSRRYjJSYE+fi0NjcfYPujkzn6HZoiWSLXNyJk5DBrdZQzRf5cenF5BlmEsUsWhF/vvRWQWUbteSKVf5wakFbEYNewa8iKLElcUEoijSZNZQrdY4OZvCYZDIl+p84/A4kqSm2aZj/5CHcKbMUJOen54P02xWI6nVmDUCfoeBa6EUFVmkUcwk36k23tV3ap1Wx3fevxq1WqIhy2zvtHNiJsGRayG++sokklriD25oRa1Wk6nUGfYZOL+Y5a25BDORLA6dwNcOTTOymKJer/PIjnaCmRr1ep1P/+IyL16N4jJp+MSuNs4u5pWJoiyjEkUEQcAoCbxwNcadg4q1v9tt4oENfrRaLdu7HJycy3Jjj5171rfyhf29uIxqmiw6HtnRhiSpsRq0jIZy3D5g5+WxOCBz7xoXgz4jjTqIKhGvVQ8yiJLE7UMeVvus3DLYTLvbzLoWE6+NZZiIluhyGehrtrGzx0mqWENA4ORsinannr85NIkgCPhtWp44tUgdFZ1uIx/b1cGA10woV8emE3hkRxu9HoXd8fiJeSXRLFdlY7sDQVDhNqnp8ti4f9iHXg2yABatxJDfxh/e1MV6v5lgusaqJiM0ZHpdBiRJZHunlbEV7qDHpOb4TIodnYqIzKCGUkNFPF+lUa/zg5MLPLjVz1ishN2kY+8qDy6TFqdBxCRBIFXhjlUuBLXuN69PDUo88Q9OBXj5SpB/PjrFsfFlvCYNbS4z71nvY0ObnZNzGWq1KuUajC/nuXvIhdWgJZqHmWiZO1e7EFUqinUls6XLrcTU7R1wsa3LSaJQ5XIoy+ZWE89cCoMM27qc3LO2icVcg+FmA8+PRuh2KlCdfo+R60tZRJWKbpeO75+Y56m3Zvjem3McmYgxFsooQqXpOPeu9VKtVjkymaBWrxNI5HlxLEWxUCRdhTa7lo/v7uTqUoG9PTa+dmgKkOl2GwkmCyylCrhtan5rnYeKrMRai6IKu1HNrl4nLTYdTWY9kiSxf5UDv8tCk1XP/cPNLKbK/OBkAJUo8qldrZyYzfHGeFQBrqdKbO+y88q1CJFsmX0DTkRRhSQpcSLHp+Is5WrMLaWZj2WplCscuLL8Nhc8VaxxLpDitesRxsMpTs4kabXrGVvKMrKY5cNbWpAkSUk2QORzN3fS67XQ5TYhy3UOjcXZ1OHin39nHZFclVUePU9finI6kGV9i5njc2lUWoPlnSrjXVzUMiem4yCD06ij26VnIloglC7RZtMpDmhBwGnSIqk1DHgMIMCbM2mGfDY+uLkJu1nHYqbKULOJ/3JjO4fH4tRqNS4HElwOZnnPeh8+m4nJpTTPXV5mrc9EoqiEBL14Lc7DW30ki3Vq1SovX13Gb1Hz/ePz1Gs1Jb6jz43brFuBIvq5ecDDLUNe5qIZRJXIYrLA8dkkyDJdLhNajUa56CwW+lwSbwWyiKLIPWs9PHkmiMukwWHQKCSpRJ4Li1l2txtIV1UMNxt4fSLO5FKa2WieY+MRgokC8UKVO1a5+M4bC7j0KrxWHSpRoNNlYme3Aso5Hyzwgc3NqDUSXouGG/qUCWk4U8YsNfjcs9fxrvguZVlmNlXmrkGXQkvNy7w+FuVP9nbgterxWPUrIB0vNqOWer1OPF/FqldTrzdIFBVNjCzLeK16jGKdfz2nWPKeu7xMk1FNh0NHl9vI2HKOI+NRLswn8Nh03Lumia3dTdzQ7fjPwY79/+HY3eemzWliuMXMdKzI3n4XPpuOZy6FODqu0P3vWeMBWSZfBVEQ6Wsy0OY2kS7DkM/Mxfkk15Zz/PD0Ajf32Tk+FedX5xeI58uoVCLbOm0kyw3uH/ai1em4e3UTU7Eiq71GroRzBFJlBJVAQ5aZS5TwWTWAwHQ0x3yiyPYuJ202LQcvh4kXqkoHZTzJg1t97BnwsLrZgtOkxe8ws63DRiBd5UObvUSLMl6zZgXUmKfVbuCRHW2kyzXqtTrL6SJaEX5+KU2xWOLIeJREpojfqnSF4oUqLXYDa5qN6HRaNrbZuRLO4TFpOD0VYzaeZzqa41Iwg0MH8ZLMtg4bR8bj9Lj0zMYL/M5GL6fms+zqMIMMwWSB50dCfOWuPgLpOu1NNh7a6mNjl5vD44ouJl1qsKfPSa/XSpfbxMVAmuFWKy6TlnOLWR7e1srGDiermi0USyVGE1Bv1FFLEqvcOp67vMSvzi8yuZRmIVXmxl4nxYbEDd3KJPFCIKnMCoqZ+DvVxbu2qMu1BqJKhd+qYSSYIVdp0OUy0ee14jRICKi4EEjy5OkA8XyNPX0uBn0WUmWZFpMSF7fOb6XNbWFXp5Vej5HJaIEdXXaa7Ub29ruZjec5PhVlQ4sZjUbDbavcnAlkMGsEJLWE12rgriEXPruJLpeJbreRHo8FlUqgUa8zspjBbRCZimQ5MLLEoEf/Npz80PUYxydj5Mp1UoUqW9rNnJ5PY9aoePF6gm1tSm96OpJjKVOm1abhidOL2PRqjk7EWcxWGWw2s6FFjShJNJDJV2p8+9g8f7i3iy/evZpgqsjBqzHm4gUe3O5nIVlERiZXqbGv34lKEFjTbOJn58JsaTUTSCo5ko+fmOfcbIyvHZ5md7edo1NJzs3FKRTL/MuZAN9/c45d3UpfurPJwrDfypHxGKKoZo3PxOVgmmcuhVndbGIpV6PdrsNr0/PQtlbOLmSp1+s8fznEm9MpRKCvycidq928NJbgzkEnGzocLOdq3LbKjU6n49F7+kGQseklmlbSHESLu+2dauNdW9RaSaSvyciTpxZoMmvZ3G5jPlFkwGsmXZbZ0WXjvRtb2dvfxH3rvHS6FHH5/n47X3lpgtFghsvBDB/c4OHZyxHGl3K8MRFhIaUwKsLpEpVKhZcvB7kYzDK5lOHgSIhKpcpCqqwYR+UGv7i4zJY2C3OJArPxAquaLfR7TKjValY3mzh0NcxStsodg07emExSb8iE0iWWshW8Vh2tNh31RkNJy63VqdWrFAoFroSyHJ+KI6P4AQPJMu1OI39+xwAfu6GT2weVJcJbwSrVagUB8Nn0bOtSKEqiqIRw/v0DA6hUIgdGllnbbCSSq4Is8OKVJUYWM9y5pplWl4m5uIJr+/oDq7lplZf3bmzFZzews9uJWq3GoRe5HM7z3rVuroZSPH95iRaLmsVEkXC6hNOo5eHtfsLZCk6DhmG/mWixzu/tbOG5K8sEE3leuR5jS5uZUKpEp8PAF+8cYF2zlulEmR+eWWR9i5GTczluH/KwudXM4bE4bqPII0+e5anzYSbCK8Gng17qmWjgnWrjXVvUiskJ+jxG2lxmVILAhYDS5bl7TRMHRhWOxFszUb5+aJxvHp6gUq1yejZNm8vE7m6Fw/H4iQDtThMPb29jz4CX2wabeP5KhLFwlkAsR7Is8NDWFu5Y4yWcKREvVPGZ1Tx5aoFmi5L1fXo+Tbtdj9xoMLqY5PvH51dMBA0m4yU2+vRcixTpcetp1GskSw0e2ubn6lKOqUiGVLGCSS1QqZQ5N5/htckc5VqdbR3Wt1nNKpWKW/qdfOaXV/j+m7NMRPJ4LDr0Kwm9pRo4jFq2tlv4k19cYnQxyUa/kYd+fBmXts5UJMMbkwm2tFkw6UT2DzWxrtXKdKLMQ1v9XJhPUSyWeOLUIn1NSojS9k47L16L8/5hJ5kqbO6wMRrOES/J+CxqfnV+kcVkFpUg4DYrUt9rwRSnZ2IsJPLUa3X+4Y2AYkLIKDySx0/MsRBP873js5ycTeA06+l2aHHolQ3+ri4rBy4u8pfPj7On24Isy+QrDXQaFaW6wKvXo5yfTyCanb+WtwjvYj3133zrO1/Srd6HjMyw30qlWieSq5Ir17gWznJzn5PZaI5gqsQanwWdRk2jXsdq0jPUbGI6pgiAFpIFWuxGTk7H2dBmJ11u0O81YdOrsejU6NQC4VSR04EsXU4d2zudvD6RwGVUwoNu7nMRTpepNRoEkwXqDRhusxHPVfnVhSA2nUgo10BFAxlYzlSVKAhJw+2rHDxxKkCuWKMuQ7EOm9st+O16fHYdZoOODoeeU7Np1rSYOTufJFko0+4wUK5W6XEZOBtI47Fo8Fo0JAtVnGYdxyYS/Nc9Hbw8FieTLzC6VKRUrWOS6lQaAhORAqdnk7Q79KQKFY6MRbg4nyCYreE1K9LSZL5Mulgjk8nx3ZMh1vlM7BnwkCrV+MAmH9eWMswnigr6uMeJ02rAY9bS6TRyfTmHTi3R22TEpBW5dZWb50Zj3NTn5Nxsgu4mCza9SCxXxa6p8dpkGpVcw6DVcmwyjkkr0ttk5FIwy3hYQSd/ZIsfu14kWarz5bsH+NrXvxn7jdNTq0UFSuO36Tk2vsyR8Sj3r2vmzjXNqFQCb07GOBtIo1KJRLIl7lnTxEK6DAIEk3nOL6RxGiQ0Gg1DHh3ySjdlasWWDzKvj0dxm7RcWExz5yoHJ2cziKKK/7Kng63dTbiMar59dJYms5JUW683EEUVAx4Tgz4Le/vdfGJPL3t6newbaiZdajDkNfD85WVevx7iidMLmPRq+rxGtnQqlrtAuk6TWc1iSnGAP3spzK4eB6uaLQSTyga1Vq9xcTHH6+MxOuwaNrTbmYiWmE8U6XIpG+cfnw2xrcOO22yg32tifYuJkWiN0cUkXrOaIZ+FkcUsMuAyadnY6eKfPrCWT+7t465hH50uI602LaOREneucqDRqJmLF4jmqkoxZ6ps7XSwusWKShTZ1+8kmq/T6TZSr9W4a8hNKFXiUjBHIFniy3f2vd1R2dntYHwpS6VS4V8upvGbRWYTJXZ227ln2ItGq2Fnt4Mbel2EMlW2t1u4HMxw8FqcpUyZPzsw/pspaCpWGyuWeR2RlfDN+USJ8eWcMhWUJB7Y6OfRe/oYjxZ57soyHU4jH93RRrLUYHOblZlEmS/f0c2Pzi2TylfY3mWn3a7jBycCNBrySoxDA5/dzPrOJr56/yDHp+J84/AUrXYtLTYDG1qtSCva5YnlPLf0O/nrg9d57kKA18ejPHVugauhFN1uEwNeM5F8jY1tVm4a8LKn38PePjdem4lovsLaNicf2dICgoDfqkYS1Qy3WhnwmnlpdJmldJEnzwRBllnfYiZTqpEtV1nKVNnT6+L2wSYGfRZixTq1WgOVSiBdrrG90861aIVv39+LyWTCYdSwvdPO5i4nAgKiKLDWb+GJUws89sYMo4tJTkwnCKTK3NZvYy5ZxmvR0uU2sb7VRrfHwqZ2G7t7XaSKdc5MLfPHP7/E5lYTs7E8gUyVb706SThd4Nu/NcSFQIp/PjrFm5NRrAYtfU1GSg0FeLnNr2a43UmjAbOxPKFUCadO4NtvBBjwmrlryMXr02lUooq/+621/OzjW/nYro7/aZ/6Xbv8+MH3v/elx/76szw3skymUKZYrfOpPV08fSHE+UASBIFcqcZTl5bZ12NFpRIpNwTylTqNhkxvk4nJ5QzLuQY7Oy3IgoRBpyZdrNLlVqLfbHo1M3FldKwSJWL5CvPRHNdCKUQVnJqMEM2VMeq0tDkMjAQz3Dbk4cR0gu3dTqw6Rb13cTFLj9uAWSdxfj5NLFfmQ9vaOTObJJAqM+Q1Mhkr8Ud72vnGa9Mcn4iSyFW4Y9iHKCp3sU6XgfvX+xjymjg2nSJfquIyqLgeqTDo0RNKZDk+l8Fu1GBWC+j0Wm4b9HBmJk661KDFpidSlLlj0MWvLoWZiub55I2dzMSLZIpVZmIF+rxmbAYNTrOOfavcFIolHj00jU0rkK/IqFUQzpSYi+YYX8pRrkO5WmUulideqCKJKja2O2k3q5BFiYV0FRUyp2cTTMXyDDab2dbl5PR8jvvXuhHkGocnkvQ1mRiL5BFpsJAsspCq8Ls7WkmXZVLFKk6DiN1sQEbgjck48/ECh5/7mfzFz336q7+uNt61d+pyraEgfNtMpEp1BAQOXg4RTuVRCbC7x8nuXje/vc7Nj88vgQx3r3YjiRIek4aRxQyRXBWPSeLEbIZhv5Vut4lwpsot/Q6++eo0gUSe0VCWWrXKVCTLXCzP9i4HFoOWeq3BZDTPUqbCljYza9scfGxXJ5Ik8Z4NftSShEolMNxi4b0bWpTsw6kkPU16XBY9V4OKafhP9nYgiiIOo4Y/+tlFLFoJv0WkrpLocOjp81q5dUDxHv7gZIBgpsLD21txGjWMhAus9xs5NRXl1HwOn1Ggza7j1YkEW1rNTETyKMw/PR/d2U69pjjdO1xm6vU63zs+R6PewG83sM5vpcdtYv+Qh1q9zotXwpyeS/PA+mb2DXppthl5fSxKs0ULgqL13tZhXZHGWrmp10ksV+XQ5UUeOxXknjUe7hv2Iqkl3rvRz5oWK5+4qY9+n50dXTaeG1nix+eWcesEOlwm7lvrJVP5H9y9d5Bk91nv/Tmnc87d0zM9Oe3M7mzOSVlaabWSLGwwDrJkYZtrLtyXF2xkmcsFg40Tl4JbYLAcBTghK6dV3rzaOBsnx57p6Zxz9znn/eOMVS7KMtRLUbfkX9XU9Pz6dE96+vRznuf7fD/gsRno85l4/lqCN64v89q1GNPJCgGrKv/1WzQEHYZfzSEBFIWnzy/yt2/Ns7/Hgcus48h0hky5wZ5eL3OpMj9+e44vvTzNHWu8KAL8zZtz3NTn4OhMBpdRNRHX63V02LXqZLMosqfXxfdOLeGy6OnwWOj3GvnBuQi1mooVXshU6PaYmU1X2dHtJmDV8rXD44zOJ5hNFvnmsTkEQa29np9P860TC/gtWmLFOvt7XWg1Gta32Tk5k1LB8o+f56cXlri528J4ssFiLMV8XmYkYCCcq9PrMfKXL08y4Lewu9dNOF1mMVNDVmR6fRacRi0GoxGrFibSqmXz5pCN2aRKysqUm8wn8/zZM1c4fC3CF1+e4vO3thNwWmk2GixnyygCjC5leeN6hD9//jpPnFtkKVMhaDcQsBs4MV/god3trGt3IYoaGvU6EzH1dz23mGPYbyRXh7UBM0emU6zxmQlna1xYyHBxQYU45atNxqMF5pJl1rTY2Nbj5U/v7CVeUbiwmGM6WWZNwIpG1NDptbE2oHYmbxxw4zRqOD+f5KUrEb59bIbXr0dRpGb93ULjPRvU5YaMoshsbrcxlqgiCAJffd8wt68NAorq+dYAq1FDh8eCRqOhw2Xk1YkUFq3CXKbB9Uieaq3ODy/EmI2p3tIAnT4rD2xvRafVka82Mes1xIsNOt1GVnJVQOHr7x8h6LKQqzSIlSSOz6QQRZGP72pHFFXf7A1tdno8Jo7OZKg3mkSLDaL5GqPh7DuouIqkYBRl/vroMoeG7BQkHX1uPRMJ1WPj8FiCDSE7E7ESC6kyyWKdFovIYkZNG5bzdbZ2OLBYTBxa43qH9vudk2GOTibo9RiZjpe5vJxjXdDG3m4Hd/39Rew6hUsrZRpNiW63Ca9FT7LU5P5NbWzucPHwnk5Gl3I8dznG3m47s+k6+/p9hBw6js1kVbpw0Ibfquf4vGr+eGWliMOkx23VEcnXSBSqNJsNvnNikUK1Sb3RUHF1L07QaDZZzNZwmfUcGvEhajSE3BbWt1qJZKs8eSHCb27y8cZ4knxV7aBGMhUasojDpEXQGS3vFhvv2aA26zVEcjXyDYE/ur2ffE3m5WtxwukS3z65SCRf5+HdHQwEXURyNYJ2PUGnmQ6XkaVcnV9f76Ymiwy2OvnNLQFmM3WOTMQ5Pp3i5n4XX35lllabBq/dhNtiYFe3i3C2RovNQDhbYzxa5Hokh89h5mv3D/PpmwY4MOznOyfD1Ot1Bnxm3phIUKwr7OywkKlIBG16AjYDPquOXd0uTi4Uef+ID71ez3CLhcWcxFCLhUS5ic2opd1t5sCwn4EWB2vbHBxc38raFgtffnUWq15Dr9+OQSOr7v6bAvxkNMMT55cx6eDPDvbT5jIxn23wwI42XDYzpabA3j4vXoeB/3ZTH//PLb3k6grHppLvgE+bzQZXIsV3XpSdbhNvTarvKnPJEt85FWZNwETIY0Gr0bC9281N/W7mMzU2dbg4tL6FqWSNLa1mCtUmLS4zCgomLTx2bJaL4SzrWy2MhnM8ezlOvV7n+asJPGYdy5kyR2ayeE0iqXKTR5+doCk12dPtYClXZ3+/i3s2ttJiNyLoDL96QW3QinR4rfzRbX0YjUb6/WaihToht5nf2tvF/n4v60Iu1rWY0Ygil5fzCMBCSuV+f/6ZKQ4MudGIIuG8xAc3eGl3mwnaDUwlKoyEHMwmS7TYTbgtet6ez9HhMjIQdLKzV6Xizqer7O91cWo2wwuXIzx2bA6vSeC7p5ZoNpvo9HpGAgb+8cQSLpOGaKFBLF9hKlnl9HyWD2328+TVFHa9SJvHittqIOgy0+Gx0ek2IQgi3z6xQFOSuLqU4Vokx4XFNHt77LR67Bwc9nBqscZMNEOsWKffr2UkaObsQpUnzi8RzdfZ1+vkfKTMvesDPLSrk0ihwR1DHh4/E0Gn0/G+ja3s7nWTrkgcWufjn89GuXdDC0MtNq5EigiCgMPpZty/AAAgAElEQVSk4dkrCW4ddDPot7KUb7KYKCADp2dTXFrKs6dbza8TZYn9fW5eGEshCiKtDiMarSrb12l1rG+10dfiYGPIzv+4uZtCHWq1Kvesb0GjEbmh10W6JnBwXYAHd7azmGsQyVfpchu5FqsTcpno9NtRaqXsu8XGezaoa02ZxVSRv3x5gqcvLJIs1NWzaaqMADw9usKfPHWJ75wOs73TwbZuL3v61OHNfp+FXBNeG4szkyjSYpT5zLNTLCRLLGXUIdE/vKWbH56NIMuqS36Lw8hitoaCwu5uF7PpOpvarFyLljk9m6LdqVKj9HodH9/dzmy6zhfv7udSpITXZuS39nTS7jbR6lKZ6u1uC2cWC+ztcZKvy3xsexshj+rc2u83kypUuWPYR8hlQpZknry4QrVa5dR8jql4mT+6vY/zyyV6XRpOzhcIJwtECirn22QAg8GI36bj0lKebSEbz16O8aO35/jJuTDXwhmazSZrAqpfx7eOzeMxa4kUJR7c3c66kIvxaJFNHU48Zg06nY67htx8/bU5UGQODnuYTddosekp1SWSxRpPX1rhn95epFGv0eG1EXRaaHcZiBXqfHizH61Ww0O72hlLVDk6meDp0SiiIGA1aZlK13l6NMJKpszVlSIP7mxjJlXFaDRw9/oWUqUmFUnkb96/BkEU1bGwX1WVXsilwtxBNbeZT1d4ey6FLMt87s5BWl0WHt7VzvmlErcMuDk2nWJ3jwuTycijd/Rw6xo/4XSZw9N5fm9/B5/c302X387tQz5em0jR5jKxkq1wfjFHOFngln4XxyYTPHZslkajTqEu47FocVr0iBoNe/u8qwaTGiajOb722iwIsL/Pzdden0OWVd+6r782R4fLQLxYYzmlOvZ/9slrbO+ws77VyuWFFKOREl947hrtTgOL2SqfubWbeAU+taedDZ1eri3niOWrtLlM7Ol2YDIZ+eDWNsxmMw/s7OTROwe4HiszlyhyeiFH0K4iQQw0GU81WMmqCJG9vR7cVj0KCh1OI6fnsjx1foEjkzEURWYiXsZpFHhxLMWdQy4mEiWOzmTp9Rjp89sIuSwMBCxs63RyQ5+b2UwDqdlgZ6edYlNgMZHng9+9SK5UR6vV8dnb+vjU/h62dqpU3L+8bx0P7upmc6cbBAGvVc9kvIzTrGUwYKXTY2V7r49Wq4bf+fFVnji/xFws91+DnPu/vRqSrBr2CwLRfJ17NrRw51o/DrOKHAaYSVbUP5RF5LNPXuf0TJLZRImAVcelpRzfPrXI3j4Pv723HVGr4XNPj9Nm0/LytRiXlgtqdcRgIGjXMp2q8pVXptnRaSdXbiBJEl6rehZ1m/XMrkpNVTYT9K3i3nb1eNFpNXgtOpXvrRG5Z73qtO82abi4VOD9G30Mtqj+Hj85s0A4U6PHo4r7F9IVms0mX3t1lht77JycK7ClzcwXXhgjli2DIHB8LofPaqDdY+HUXAYFmdcm0zxyez/3bwmxt9fNYqpMJNegI+Diq/f00+51MB4toCgK6WKNRKHOXKrMlpCFN8eTtLssHFgbUFWEVYW71/l48XqaNQEbAz4TE/ESxyYTyLLE6ekkF5YLjEfz7O9x8PTlKN86scC+bjs7ev08tDNEu8fC+HKKT/zTBf7hyDRvTqZ4/VqEJ84tIssy4XQJjSgSdBg4MZMiWazx2NE5ljJl2mzqu4jNqGFj0MSl6H8hm/z/5rIYtHR6zGgEga0hC3/zxizfPDrLmoCNPb2qgeKDu9p5YyzBSrbGDQNutnU6kWSJi+EsXouOgMOMIiv88+kwPzob4c/u6uHMYpFur4XPHRhA1GjY0GbHZDTy1fvX8scHh4gUJSx6kTOLRYZbzFyMlNTxLqDTbWI2UWIuVWYwYEHUiETzVWq1OsliDUWRieaqnJrLcmQyzlS8jFGnguSDdgMf3d6KTq8nUYFNbWa2drm5ayRIn9/GhnY73z65QKVa4e35HLVqHbdFj8uoZchvXjV8N7Gzy0ksX2MhXuDbJxZ4YyxBvV5HFDXs6XPymVt7+cGFOC12PU1JYi5Zwm3RoygqauPxMxFcVh23rfHw+lSW7V1uDo34iZYU7tsUJOQyI4paijUJn0XDcraK3aTj4LCXVBV6fFbabDo8VgOvjsV5YzJBJFcjXazy1nSa7V0ubhjw85Ht7cRyFd6aznB8bImz8xlGWm1E83V297gYDtrZ2++hVqvxf47Ms6Xdjsesp9DUcPda769m+lGsqnN0jWaT568l2dhmQZIkNBqRmXiBbx6dQ5JkMmXVNHI5U2E8VuTly0sk8hXOLuTZEDSh1Wro8FrZ0+PkhxcT7OpWHZ6evxzhqQvLLMRzyLLC908vMRErcceQj5qi4YHtQa5FS9yzPsCvb21Ho1GpVqIo0OU28dylCEGbgVimxJOX49i0CtVaDQF4eE8nNwwGGAhYuHnQS6PR5AdnI5ycSXP3Oh9/eFMblyJlJEnmpatRTsxkuGtdC81Gk6vRMiN+PekaLKTLXInkuLyco1av8/VXp3hzLMpcqgwC7O5xYTcIfO90mEKtydWlPF96aQKp2WQ+nuP4VIKldBmNRosoCBxcH+QT+7q5b30LX3ttllv6VWTH374xx819qr/31ZUibU4D64Nmjs/mqFYbJMsSiZLER3aoEudzC1mGfAaWM2Ue2N5GIl+l0lBnFkNuM6IoEi83CXns7Ot1MJeXsZtUi4tYXs2544Uq07EC58MF1gct6LRaAk4zHrOWcLqMqDfa3i023rNBbTNquWXQR5fPzvo2O5OxEmfnM+zqdhItNNjZZefoZJx4Qc07x6MFHAaR2VSFHrfK+n7uapJGo4HTpGUuU2VnlyrOmU0UiWQrtDkNXFgurqYRLkRRYCZV494NLbw2kWIuVeJKpMBgwEqPz6LClXxW+n1mnrkUpd6oE8lXuW+diuH43R9dZSVT5PD1OLOJEteWs5ycTiAIsL/XyVi0wP95c4Efnl8hUazT6TbR7bWwu8eJIAjkG3Bjv5s358ts63Jy9zoflbqMRa9hJV3EJMokCjU6HKogSKPRMB4vc+tQC7cMqqVDURRpSk3OLxUI2A2E3Bbu29CC16rjW8fnmUuUeGo0ynDAzOuTGe5ZH6DDZeCrr80wkyhyaMTPaDjHaxNpTGKTXBP29bro8tnodBk4PpOiiUg4WSJRVmg2GuRqMgYtZGsKyxk1JdzT46bNaaDVYebRA/0Mt6lWC36bnlSpgddiUC9yOx0cnUqxtd1KJF3i+kqO6WQFpdl41xzkPWuRoACCKNDrtzEdz5OrNOnxquL8VocBQRCYTlUx63V0uIzodTrcFvVC5OSC6nPc6TGzkKlSbAr0ekyEszU63SKSJNPmNJMsNtjSrhqtLGaq3LnOiVarZSKSocdv4/7NIUQEHjs2R4fHCgJ0uU28Op7i4LCHS8s5lnMKHR4LAyEP+3tdnJjPc/uQD1EU+c6xaeaTMk5zHUEQGfRbKNaaHBz28vTlGKfnM4RcFi4uZZlLlbl/c4iZWJ52p5HpeJ7RcA5JgUK1wRuTDZwWAbdVz2ikhP5SlEfvHOTW4QAH1weZjJdJlhp89rZePvUvo2zrciIisJQpsZAsMhYtMhy0E3LoOFyqsZKvsbfXwpMXlxE1IveOBFjMVJlPV9SqiEVHuiLxB7f4WNvuRZIkvvjSFB/Y6GNfr5u+VgcfMhp5aVxt1CTzJe7bEOTMQg5ZkjgyGefyQoqpeImJWJE1rXa6PGZ6fBam4wUi2QobQna6vGZeuRblu6fCTCdKbG1XLzB/me/He/ZMXaw1GQ3n6PeZuHNtAJfVQNBlYTBgJZKrIQgCXzo0gN2k4/tvL/Mnt3dyeqGEz27lhj4Xer2OC+E83R4LH9veSr6mEHLoOTqVYDScRRAE/uCWbsZjRc7MZ6jX6/zlS5OUSiWeuRRlPJJGajTo9loIuc0cXB+k16fCj24f8jKWrOK16NnVa+fIdIoOp4GxRJVej4kvH55WKw/dDrx2DQG7nni+QqLUoNOlAnu0Gg37+v10eUwE7EYEYNBv4WI4y9dfmeAbb83SaDSoN8Fm1HHToJ2BgI3f2NbJoZFW7l0f4PtvL9NsSnz2yevUalVmE0W+fHiKeKEKMoyGs4AAAqQLFSRJ5vRCDq/NCMBsskSqWGfIZyScrbOYLnFhPs1ytkKmKuO16BhdyjMdy7OQqfK5O/pYzlZAgNFwjpVsCa9FR4tNT66usht9Fq2K0cuUKdZlMiWw6kVWMiWevLjEVCyvWsRJEm9OqiNvNpOebreBLe32dyokSrP+rsTb96xK77Fv/uOffvDDH+Wx4wvMJQt4LXo6PFamEyUyhQrnFjL8y9kIDpMWp1HLTy5G+cCmIL93Sz+pchNBlnCYdBSaAq9ei9Nm1yNoNJTqEn1+K0upMj84GyaSqdLvt9LhtfLR7W18/fV5dnRYePbSCoevx1XfCgm2dXvx2Yw0mhKZSpN+rwlEEVGRmYqpjQqrQYdWp0WrNLAYtDxxYZk1ATOFukyv10Ks2CRXrqLV6qg3Gnx4RwePHV/gjuEAmVKdE7NZQlYNdUkhWqhj1mtpSE1kRcRr0TKfqaMRIF6oUmgIPLy7g0SxTptDx9GpNJlyjV6fhVShSlPQsLvXy8Z2J20OPT85HyHgMPH/3jZAwGGm0ZSIZCtUqjWeHI3x+QO9XI4UsJkMtDlVA/h1QSvfPDaPQashmq8iKhI/OBdlY5uNSlPh2HQKjShg0GkYCFhZztV4YzxBplChKYh85VA/j59focdjYClXp9er2qtZ9BpEjYjPZiSWr5LMV7gULpCtNEiWmlRqDa6eeMXyPz/7+1/6RbHxnj1Tx/I1enxWbhpUlXchl5lun0Vllui0fP6uIT6wJcQn9/fxga0d2E16NFotL1xe4cxMgqcuxxBFkd/a08lg0MZ4vEi3x8ynbuhjMOjkhjUBhtuctHss+Kw6wukK3z25yEjQwrVokZ29Xvb3e1AU2NGpXlxeX8kzlyozE1fnCyOZCmfnM0wn6sTyVVpsWvxmDa9NZrm+lKJcl5lNFAk5jZxfyrO320GlCQMeHXPpKv94dIZMRbVbiBclPrK1hZ9cjOGz6vj4zhBDrTY6PVZuHPAyna5j1Kr1+oVUmY9tb2UiVuTMbJIrEdXwsturWjxkazL7e90c2tjGqbksS7kGQy0WNne6mYiVkGWZy5ECkqymXV67ka+8Mk2i1OBjO1pZzlb5wdllZpMlzAYtI60WRoIWnh5d4UuH+rBYTNy/qZWb1gR4aFc7x2ezbO+wodfp+J39HRiMRvb3uTk2X+TAGif3jgSoNBXaXSY8Zg3JUoP9/T4+sa+bLV0eSnWZ7d0unBYjH97WxoH1bb80p/53g1oQhO8IghAXBOHqz+25BUF4VRCEqdXPrtV9QRCEvxUEYVoQhMuCIGz+ucd8bPX4KUEQPvZz+1sEQbiy+pi/FYRVS/5/Z1kMWkba3fQHbOzudXN+Ic31cJJLywWCdiNTiTKKojATz3NiJsVQiw1ZlnlzIkHAaWLQa0S7WrFAlkkXVNdSYdX1U1Zk9vf7+NiONg6Pp9jRaSNRVNONXEXioV3tgMKR6TTfPDrL6IKqn7hrXQt3jajG7w/t7qDHb6PNqUNqNvmXsxFEjcgX7h4gXYVWp4GqBIupEgKQKDcxaRR+fD5Kr9eMTqvjk/u6GWl384n93cxm6qwJmJhJVvnhuQiTsTKSrBBymbh5wM9Ai52AVQeiyFdemeb1a8ss5ercvdZLi9PEsN/E1k4P3W4Ter0OQRDY1++ly2NSBUf1GkcmYpyYTXHfhhY6PVbcNhOfu72XkMfG3Wt9fP7ZSVodBn5jcwtj8Sqb2iwcm8ny6vUYkUKdF8fSbA1ZORsu4DFrOLdUYmeHlb8/tsQHN/l47HSMj24LMrqQ4V/PhTk+meWPX5hhd5eNSK7GxUgJp0G1cPvSixPc0u9EFrUgQLtLZdR0uoy/NDb+I2fq7wEH/s3eI8DriqL0A6+vfg1wJ9C/+vFJ4BurgetGZZjvQOWW/6+fvRBWj/nEzz3u336vd11jK3mOTsSZWM5wZTnHt0+EOTjsocdnYS5Z5vxCmn89v0Q0pzZFREFkMKhiIxIVBZdJ5KWrUZYzJZZzdZazVcZW8vS4Dfz1q9P89EKYJ84v0eUycGaxwEirBUEAl1XPV1+ZRhQ1fHBzCz67iSfOhZmO5nnxapSrS1kUFP7xrRmy5Qb7+90UGwLDLWZiheY7L54b+z24bWa293job3Gyo9PBbKrMcFCFk+4f8DMUtPPS1RgAM7E8kXyTB3aEeGhvN/0+E5lSlR+cWwYBpKbEifkCHS4Dj9zRj9Fo4uBaLz+4EKNQLPLV1xfx6JtcjRSp11UX2GZThTMtZWu8PhYnWWqwp8dNOKeCOBVZ5rmrCVrsek7P5ejxGlGAo1MZDq3zEysrPLirg3UhN7+2KYjfpuep0RW2hqxMJyu4DQoXIyWCNpEfXIzztx8YIpyrM5Wuc2DQiSTCgTUOjAYj6YrEf9/fSb7BKpYDvvbaHA9uD2I2GVlIlri2UuDUXPY/d6GoKMpRIP1vtu8Fvr96+/vAfT+3/7iirtOAUxCEIHAH8KqiKGlFUTLAq8CB1fvsiqKcVtRW3OM/91y/dOm1IgN+Cyu5CpeW86xrtXHXSIC3F/KcnM1wYK2f9a12NKLISNCKIAhE87V3dBtrWyxkawodLiPFBmzudPOp/T0owFSywn0bgxwc9jEaKSEKIgGbjsuREs9civHZW3sJutSa6bHZLIfW+dFoRO4aaaHTZeLEbJodHXYurZTosGs4NpOl36dOaO/otPGj8ys06nVOzOe5c8iDyWDg3vV+nr0cw6gVWcxUOTKdYXIlwz8emaHdqTIdtToddw55+P7by9TrdS4s5Sk3FYaDVnZ2OSlJIvt7nSiywivjyXdMLT+yNcj1eI0Pb/Xxo9EkzaZqoSBLEk9cWEJRFPwOM7cNt/DonYOMtLtRZJmnL0dxGDWkinX1pKARSBZqjIZzCBqRtW0ONofszCQKxAtVtDodAZueK0tZ5pIlGk2JYzNZutwmxqIltoVsPH52hURJ4nsf3cC5lTpDfgPjKYlaraYin8sKI0ErL15Pc/c69R3m0nKRz9zay65+P+ta7ezqcf2XSE8DiqKsrN6OAj8zwG4Dwj933NLq3i/bX/oF+//uKteld6gBAbsRjUaLTqulw6PaaY1HC1xazuE0Gzg6k0EQBPxWLacXctw04GVbj49N7Q60Wi13rGvlr359AzqdjoVUhWajQSRX5cxinj6fhRsGVO+LX9vSxmdu6+Err0yzrcO+OlkiMJ8uA6pX30yiQMhpZDnf5L/tDfH6dA6LXiVSBZ1mtFot60NObhhq4YY+Dy9dT1Kr1zg+nVydBrfw+7f0MtLmQFjFI88mCozHitzU6+ClsRTlSpnnria4a8iLTa9Bakr809kIN/SpLPNzizkWEgXmkkUWk0Wev5ZkU7sdk8FEv89MQ4bbh3wggiLLLGaq/MbmAJcjeR47NscLl1fo9lrYGHIwn6ogCGqn8JP7etjS5abVbeG2oQB/98Ykr12P8c9vh0kWqmxuNTMazhFymbmwmMVnM3Lb2hY2dbho91hZzFTZ2+vhE/u6MZvNDPitZIo1Ht7m56cXY1wMZ7ltUB3cuGdDgEihidckcG4xy1+8MEY0WyZTrnN6PofSqJbeLTb+03VqRVEUQRCU/+zz/EeWIAifRE1r8LV1MdDi4O71rbx0NUqHJNPfYmcoaGcsWmA6qvrVffX+QSbjZebTZS4sZPDbjEQLdWrVGhqdBnG1vfzytTgarToV/Z2TYQI23arpipEXryfYHHIAAj8+Pc/1aBG3SZ2U2d/r4tpKiU3tLgb8Zk7MpNnRaWM+VWGlqLCzx4Msy+j1Otr0OubTVQ6t83N6NovPqqXLYyZZaiKKMjet8RPJVjg5k8JtFIkW6uzt83B6Ps9tdg2PPjvBncMejs/mMGtkjs5kSBQbIAi8f0uIpXSZ376pjwuLGTQaDeF0iYDNQMhlYjGtDhjcPuDguyfUF6CAev2wkCwyESui0YhsCGnpdJt+9vemJgvs63Hy9KUoC+kKGo2GT+3vpdls8vCFMKIosLvLTqYq88jT49gMAh/e1k6qBisZVXJwbDKBw6hhJV9FEGAuVUJRFDYFjfzNEVVnLYrgNmv57ukwfosWjSiymCoTL1To91vxmLWcW8xy7/ogVqv5l5b0/v+eqWOrqQOrn+Or+8tA+88dF1rd+2X7oV+w/wuXoijfVBRlq6IoW40WC7KiMBFTr+xX8lUazSYvXo0yHSsQyVXxWHT8+fPXeepSBFmSCdiNtDlVw5dXrq9weiZFs9Hk5EyaxVSBO4Z8HL6utsqvrBRZThc4NpthpMWMXq8aF7Z6LPzm1la6/U6cRi3RYp0N7Q5EUeCxY/M8sD3Is1fUYQVFgfVtKhb69iEfsqywnK3wvdNhJLnJW5NJ1gWttDjM6j9DEIhkSvzgzAqvj8cJWHTo9Hoe2hXia6/N8uEtLaSr8ODOELmahMus46ZeG4IoUq1W+enoCs9fjtDiMLK3z0M8r9qEdbiMTMWLOAwCL46lMRlUIOcNg342tjsRRRGPVc+X7lnDsekMr11b4sRMmh6vhf19To7OZFjrN61aR6hVnhevRrlnfQtrAhZOzOcJeax8cncbnV4bPzy3wvZ2K5FCk7lkiXihxnyqxI5OJ1eipdU2fZ4Xx5L02CBRktjVaUOn07Kj08GVSIHlbIV9/R42hlT133SyQixf47HjCwTMwn8JHuNZ4GcVjI8Bz/zc/gOrVZCdQG41TTkM3C4Igmv1AvF24PDqfXlBEHauVj0e+Lnn+qVLr1F1tUcm4nSsXg0/cylKo9GgP2DnhkE/u3pcXFjKsy5gIZKrIjUlLi5mmY7lyVUVyg2ZO4Z97OpxM7ZSUC+YMhU0Gi33rQ9iMBgJOY1cCOfxW7Ucn0nTYtNzLVbh9iEvS/kayUIdRZY5sDbAZLzIqxMp7hnxE3KZiGSrvDGRIFWs88UXxhhdyrGz087X3z/C/sEAHoueCwtpYvkyToOIoigkizWagEknIIgCg34LE/ESawNmXhhL0mxKLGbr5CtNri6m+N5ojka9zltTadYFVU8/UVRRe+/b1IrDpGe41cGA38rocpH1bXY2tKl2DQCRbBlRFEmV6jx+Zpkul4FzC1k+ui3Iidk0Y5E8mWKNS0s53pxMEXLomY7meepChIV4npWixJqAmZ1dLp64lORDm1twWPS8vZDnHz60kfu2dLKp04XNqGU5X+ePbuuj26v6FqIINGSIl2AiUSWWrbCUrbMx5CBg1TObKPHaWJyjMxmakkTQaaHba+KZK/H/HHJOEIQfAqeAQUEQlgRBeBj4MnCbIAhTwK2rXwO8CMwC08BjwKcBFEVJA38OnF39+MLqHqvHfGv1MTPAS/+RoDZoRWYTRcajBabjRXb3eHjfxiDRXJ0Bv5k1LTZOzWbZ2GpVA1pqkijWaUoSz15N0O4y8MhtPTz67CQTkQyFmsRYtECrQ48gQDhX49c2h3hwZztanZqKyJLakPBZdUwlKtwzEuCOQSdHp9UO5MN7urhrXQsnZtJEC3V297qw6QRuXuND1IgcGHTx9cMTfPGFceaSJe4e8XNqIY9FlHjmSozZWJa6IvL7N7bhtKjGl8+OLvOv5xa5Eilw70gLoghdbiOb2qzkmiIBPWwO2Qnnm+ztcXBqLoPPouHUbJq5VJlOt5nDYwn8Vi2bO1QiwfVokT/818u8fn2FeKHOJ/Z1s67NRZvTjCgKVJoCr06k8Jm1hPNN3DYj928OcfOgTwU1ofDpfSFOLRT5+FYfV6JVZuJ5fGaRH15M0O7UE8tXeflanGazyVwsSyRXQ5Ik/uHIDCdm0ty8xs/WHi87e9zs6HFw63CAVreVg+uDdHktPHclxlKmzHCrnb09TgwGA3ev82E2m3GbRJRm413Tj383p1YU5Tff5a5bfsGxCvA77/I83wG+8wv2zwHr/r2f498uk16LqNGwv8/FlUiBS+Esm7vcRHIl/vz562zqcDK2kmdiJUdPqU7AaUGj1RIwG9jU4eLp0Qhnl4rs63VwfDbLoXWqHmM5W+OuEQtHJxM06nWevRxjc5uVDo+Fi0s5/FYDXrOGpy4uc3UxwVSqwT98cJiXrkZZSBYRNeIqIUzFPL86nuX9W0KE0ypCIlaoce8GMwKg1+nZ2WnnarzGzk47habI+hYzh8czJEp1Ht7txm63rYI8RYZb7Xz7xDyXw2myZVWIf3kpyxszeW7stvDSWJrt3W6Oz5T4i0P9fPvkApLcpF6r841jYXrdOpCdtDkMfOneNfzzuRVEUUYQVLerHp+FlVyVPz00zFDQxuHrCb507zCKInNuqcjObidn51KEk0Uy5To7O638r8MLfHJ3G5FsjflsnZ88vI6/emNOJRlMrrCQzPPStQQGjcBta7w88vQYw61Oev1WZmJ54oUKB0ZCiIKAXq+qSRdSFe7dGEQjijQliTfGEpg1EmOJCh/dGuSLL038ag4J/Ez/e3WlyMFhL4Io0uuzEnSY2NLpotdnZThoZ1u3C4PBwMaQnc/fOUjIY0Gn09IXsNPqMGE0GHhwZzvJijqBjiIzHi3S5jSi0+m4c9jDZLzEXSNBNoUc7O5188RonD+4pZvegI0Hd7YRKUqE02Um40X6PEbaXKq/83K2gs8MJ2czpEtNrHoRr0U964fTZY5NJSg1oM2uQ6fV4jFrSZYadHrMbGy18nfHFulxqyNRAIfHEuzrdeK1Gqk2wWczUGvCDV0WXpjIE7Tp+OT+Hj60xc/nnp3gzYkkc8ky3V4zB4dd6HU6Li0kWc7V+crhaVodaufvG29O8r0T80zHC6s0MBN/+dIkktRkKprle6eXeWhXO2sCVpZyNQb8lywAACAASURBVBoNtaav1+nY0+MkUWoynSzT6zXzvdNLCKLISJuNC+E8AasOn1VPyG3mlfEkHquBnV12ZuMF5mI5Ts1XODUR4UenZ6nX6zx3KcLFsGpE3+UxE83X6XbrORMu0mg0mE+VkRXlV9MiIVduAAKKAs9fjSNJEgN+CyGXedUjWoMgCmhEkX6/mSPTGZ67tMylpRyKLHPPiJ9LSznanQbmUmX8FnXKRBAFFtJlOt0m2uw6XrseYzZV4Uo4w1uTaYZabOzvczEZL3FhMU+rw8CVSImH93QyFHSqlrySzGKyyPnFHJECTMSK3LcxiF6vR5YlzoVztDoMamvfbSBbbTKfrrCSLRPO1Yhny+SqEl0eNRBanSZ6/VbaHXpeuJbEa9Fx04AHWZIpVuH0YpH7R3wsZav8/RuT/PFzEzy0M8SfHFyD06znsWNzvDqRIWQVmC/Ajg5VzSgIMBiwMhEvEXKb0Wo0PLCjjUeeHsdpFGi16fj+2SgWrcz1SI5vHZvn7rVe4hXewd2BQrxQY8BvZbDFSrois77VjigIdHtMqgy414coiKzkaqxvs/Hs1QRvTiYJuS0EjHBmuczVeJPFVIkut4n1bXbOzKU4Mh7Db1FNIe9b30KuInFmNo4oimgszpZ3i433bFCXGxLdXjOfv2sNm7vc5KoSf/7CdS4uZhkMqDLQoMNAutygzWmi32chmq8z0mrj4mKWx47PsZCuMLmS5elLUV4bU732fvvGfg6sDfDM5Rj/8NYMi7kGtw26GWl3MRAw8403pxmLFjg+k6IvYOXKSokNbTamk1V+a28n7W4zokZEr9fzuzd20ebW88n9PWzs8jEQsOB3ObCbdMiKgiRLjEZKzCar3DMSQK/T0+40UmwoZEo17t/cRn/ATq/fiiRJzKXKbOt0otfpuRJO8/JYgqoEFr0GBLCbdMykqmztVElfGo2GchN8dhO3Dji5EK3zF3d1U1U0fOa2HpYyVQ5fj+M267l5jY9ur4VXJ9Lc0OckWWrw4liSf3lgBJPJyLGpBNdXcugNBt63qZVbh1SKVrJQYyFdYTxeRKvR4rVouRTOcm4+zXhcrd/v7nXhXa3IJEoSn79zkK+8by2yohCvwse3+rmhz06q1ODoVBxZknl7NsV4tMBr1+NkKxJtTgMus463F4v85pYgUikbfbfYeM/qqc16DVOxPMenUjy8t5N6vU403yBo1zMRKzITyxNOlyg3FJbSZXJ1AbdRQZZl/HYjbrOqu9bpdPzejV08dzWOIKp124lYiUfu6Odbx+e5xaDw/LUk3zw6S6vDwFMzKTpcJvb1eVEUaHeZkGWJo5MxjkwI7B/wAnBg2M/hsSQH1wXQ6/VcX8mj1+l4aGcbp+ZyRLI1NKLIp/e18/yVOFqtBp9dj8dgIFmosJBRZzD7fWauR/J889gcGlFgX4+LVruWV64rbGyzkS5WKUsKJ+cLrAmY2dLpJlqoI8sS86ky79sYpMtj4acXwuzqtHE2XGAunuex43Pkagp7el3MWdWp85/RbhMlifs2Bjk5m+F/v7HA/ZtbAQGNRsuBYT8vX4txMZLHZzUgCCLbu+wEHUb0Oi1LmQoKcP+mVvI1maDDyPffXsauFxhuVbGAL16J8tKlJRBga5uOqUyT4aAdjUZLLF9hZ6eDVpeFh3Z3cnpeJaddiajDGsMtVmLFBqLB4ni32HjPnqkVReGVqxEi2RJ/9/o43z0+x3I6z7NXotRqNZazVVKlJr93YzcGvZ7P3NqNqNHw7KUoPrPIyek4T15c5pXrUU7OpNnU7kIUBGRZRlZkZlJVdnY5Sdcg5NDT4bVyaGOIvoANn00tN52dz3BpKc8bkykCVj3CqhH8E+eX+YsXxmh36kkUasiyzGy8QMCq5/tvR5AViWSpjsck8OhT15mOF7i4mGUxnufrry6wmCyzud3ObLLM//jhBY5PJ7ltKMAXDw3y0wthHn1uCrumyeWVElcjDTSihvvWOpEkiVhRnRQ/MhHn9fEEkiwzmyiQKtV5ZSLFcrpIvCCTLNQ5tC7AydkMiUIdSWoyuqS64+7ocvD2fI42h4FUqcbr15Z56mIEn0XDF18cZyGldlJ397iYS+RXcRdRmpKEJDVJl+o8ezWBwyASzde4sd9NptzgeiT3zv9OFEWWMmUWsw2molnCqQI7ulS99PPXEtgNGsK5OgoKCAKNpsS5xRyyoqiirV+y3rNBXa5LrOTrDPmNnJ5NkyxLbGp30uEysZitsbfPQ9BpZi5VIpqv8pVXZtjd42Jrl5Mj0xlSlSZmjcLNA17SlSYdTr3KZhldRpYkqtUq3z21yIhPx1i0SIfTwEyqxiN3DDCTLHMxnKXLa+bRu9Zwy6APjVZD0GlkXcjF+za2kKs2aTabjIbT1Go1RpfydPss7Ot1kKnI3LehhcuRPBqdBodZhyTLLGYq2E1QbUK6WKXTZcRjM9LmNNLpMfFPZ1cQBJEvHOgi1dQx4BJoKtDu0PN3J1Z48VqWD270vsNRvLHfzVJaZZWvCVg5tD5IyGOnx6tnU4eLs0sFPCYRQRTwWgzctyGIIAg8NRpBkWXuGgniNGp4cjTG2hYTVyJF3CYNyUINl0mk06FlIQ9Xl7N0uE2EUyVmUupA8B/e3MVcqsxETFXVxTIFZlNl2t1m+gI2bl0b4CM72hEVSFdkXhvL8dixWVqcZh65YwC/04JGFJGaTSZW8uSqTQyChFaj4YXrSaRSJvZusfGeDWqAtS1WTs7nMej13DLowWAwqKg5UUSj1bKr28lbkynWtpgZabVyajZDyGliuNXBHYMersVryIrCuqCNZy5HCadKLKYKzCSKfPdkmA67ht//6SQmnSoyajTqzKSq9HnMyLJEi93M2EqeKytFwqkSo+E8siyzmKlxY7+X+WSZRL7JW+MxNoTsHJ9M8E+nw4wErWg0GrwWPTu7PXitBjZ3OHFbjfhtevQiJMsyp+bSBB0mujxmnr+S4KPbghRrEm/Mlnjk5hCnIhKiCAGnid/dF6LFIfJXb8wDAm1uE0vpCmcWsrhNGuKFOjs6ncQLNTQaDRqNyEe2tPCTC1GG/CZeGU9yciZNyKFOlrc4jRweSzDSasVmVvPzDSE72ZqMwyDw3OgKH//ny3TbIV1TGPIZmYyX+Oi2IAG7gc8+eY1cuc5gi53Xr0W4npBoMUGHS2XcXF4u8NZkinQNOp16Wl0a1ra52Nvr5pVxlepwy4CLqUSZpiSxr8dBON9gT7edDqfxl+Ix3rM5tcuip8PnYFePqmCdTRTp9dtYydU4OBIE4JtH1arIm5NJtKIGt8WAvOrLodPr6V711lAbKkb29Xs4OZPm4EiQHq+F6ViBXn+end1ubh308L/fWOCutR4uRgqYtAKtLplnLse5d70frVbLVDSPoihE8zU8ZpG3Z+KggYvLRf77rWt48UoTu1Gdhg6nS7w1leJDW1s5MpMlWmgw2GLluiwzmajzZ/d04nCohuuHx5Lcs97PWzM5Qi6T2rSZzfL3H1ijylurCmPLKXo8Zvw2PX6bSJdb7WjajVraPRbOLCzz5VcKDPhM6PV6GpLE428vc+OAB7PJzJfuW8titsqzV2LIskIkU0FRZBLFBrcNuNBqtSxlKxxa5+eZS1F8NgN9ATv1eo3NHU6+eyaCDplnLkfxWXXESxIOs569vR6+faLAtjYdL46lWEgvsavXz69tbqPPa+J3f3AOr91EWSqp6UyyxKWlAmtbzHz6x1dpsYicj1aQpAROg8hLY2m6PKZfauX7ng1qAdjb72M2WVLb5dOqEg9R4Nqyyh7c1e2kXq8TdBjQaLR0e8yqhUKixI/PzGPWqSq1dEVmU7uNoaCdEzNpxqMFRI2Gu0Za+Ndzi1yP5Hjk6TEe3NnOSkHi03s7WCmqJbdGo8HbC3l2dDp45nJMtdfa3cG3js9RlgR+fVMrnT4rr44nCafLCKKGyUSRmwd92Ex6Irk6HosOv0XDlZUqfruJW/rhpbE0bZ4686kKHS4D4WydWwc9vHhpiRPTKV4fizEb0XEy0uC3WmVO5eqYtAJui56JRJVEsc6WLheybKfPb6PXb2V90IbBYKDFKvLyWBqf3cieXjdvz+dod6m8FkWWQRAJp4oIgsjagIkfXYhyw6CfC+EsT41G2dLlQlJk1vpNPHY6Q9Bp5s4hHzOpGvdvamMmUeAj2y0s56qsabFi04tcyjT47RvcJGoiB0eCTCXKvHA5wniswr1+KxerEj+9sESry8Jnbuvh8TMr/P1vrON7p8NsDNl5/MwKUqPBrZ1O+lvdCFrDu2KchZ85Cr3XVt/weuWnL73BbLJEj9dCr8fITEodLpVlmYvhHB6zjqtLGTQaAa/NRKbcZE2Lhd09Hv7k2avkyg1+/441HFrfiiCoU+GyLNPqNKLVaOhwm/jGm1PodTo+tkNtsrRatfzRT6/wyX1dnJ1LMpOu86FtbQyFPEiShFarZW2bk1qtxl+8MIZV00SvN9DhsXA5UmCk1YYkyZiMRmqNOpfCWVxmHS9fjbGt28VtvTb+9JVFep1afvf2YURR5PhUklankXCmzPhKEZteYS5ZJpav0mzCSIeTHreeyUSFA+uCLGUqtHssRLI1IpkCbW47kXSRTKXBjf0enrm0wj0bWhhsdfHkhWUkWeJKOMeGTjdBh4m9fR6euhjBroezC1k63SaCLjMhl5lIVrWSeOZyjIV0hTvXuEmUJQRBwGfVEsvVeOFanFv7nUwny5QbAu0uAzPxAvm6wsO7O9AbjLQ7dDx1McL5+RQf39tNJF8nkikRzlTZ1efFb9GSKEkqqtqmx2WAH1+Iki03+OtfX8++XTtSjUzE+4ti4z2bU5frEk1JQlEUJldy/OETVxiPZOn2WhEEkU3tDnZ22UlUJPq85nf4LzqtDlGjSkZ3dTk4MOTjhcsrvHA5QqvDwO4eFyu5OrKsIAB+u4mvvG+YSFFCliTmUyWsZj2gcD6cZ1eXHVFUxUg/0z9fXcrwylgSm1bmidEEL15aQtRouHudny63mZfG0tw+5KXHYyZgN7Kzw86BIS9Og8j/fGmOB7f6SdVgJlFiLlkiUaoTcuiRJZkb+lzs6FMD8oGtAfR6kRv6XEzEy0TzdeqNBjqdlg6ngaDTQKGu8NFtLQQcBrxWA4upIrV6nadHo6AotDiMvG9jKzcOenn/5hCfWB0fe/SuNej1OpxmPWtbzCRLDfr8KoHg8TMR3rcxyKGRAMmyTFOSVB+VyzEkWWZrh53ZdI1CTeZTe1r/P+7eOziy8zzz/Z3OOedGzsAEYBInz3CYM2coKlmWKAbJsi3ZlmQF6nrXupZlSbZkreW1AkVSVLIycyaH5OQZcgImAIOMRuoG0Dmn0+fsH2dW5esytVt3t+5d6lShCnWABlCF93z9fe/7PM+PoMPEtg43H9nVzmqhwWKqyPePRHAa1agEJebio7vbKdRkNrc6uKrZyr++tYTHqMJt1rKSq/DSeIb+gIVdXS60Oh2yWP39y/0w6dTMpcpIjQbnFtJYtXDw8gqdXjMyMkuZCq+NF3jPkI/D0xlUl5f53GicL93ey9hSiicuxJEbEp/8+TlUKjUeq54NrU6evhjn9rVelnI1JmJZGg2RkSVFCafWaNDp9fzBphCdPgsWvZZ4vkokHUeUJBJFiR0dThZzda7pdvD8+QUGwxYmVwvsbbfywL+O0O8386e7m7gcy/Gl58dps6l45MgcQauasNNIt1tHsizR5zNx67oA08kKTXYd//xGhLooolKp+PT13QTMAn/5mxFsehUL6RLZUg2/Rcsvz63yniEvf/PcONf3+2l36vnTX1wibNMy1GznqfPLLKTrbGw1IQgCfouOJ4djnJxJErLrefzsEh6rlmv6g+zocLGcrfDdI/PctSFEt9fEG2MCPX4LCAKHJhOETPDqZJqv7F/DxGqJZLGOz6rHa9VzaSHNg0+Ps6fLw0gsz8/3dTObrlGtVolly6jUasRGg1cvLbGYrdPu1JEq1nnqwgoBq55otqo4/wUVt671cnm5xHQ8z9TK24K5gHdwRMK3/uU7X7x+//tQqdVUq3XKosxyXkFYOM163relmWaXkdyVEfStAx6KNYlkvsqF5TJ3rfPQ5NATclkRGxLrm6wYDQa2tFg5MZum26PnB8fniKXyPD+yymKmys1r/WzvcPGNV6fx2XTU6nXG4yVuX+fn6HSWNUEzR6fTbAybefjYHFpBZnOTleMzOSQEtrbZEQSBc4t5jDo1dr2KqXSda7vs7On1EkmVGYmVAJlL0SyRRAmjTk2xIfDn13Swf0MTsiSSLYv88vQ8uivxAwvJIoWqiNNi4ON7W5lKKvvr3oCNsws5PrqzhV+dXcKoEtne5eX8Yob3b23FadZxdCbDGr+JZL7MfKZKs0NPuizR4zVzMpJhX7eTakPJWXluJE48X2FtSCGDpQpVXhtLoFarMRt0rAvbkBFYyVYo1SRqDej2Wdg/GGRiJY+g1rC9w8VT55fJlapc2+Pil2dXWcyU+dz1HfzzoTmu6XWTKIls73BxbilHi9OIWa9lPlXltrU+4kWFzf7K4z8t/+cHP/P1/6g23rHbD71GhUql5sZ+L6myiNeqY1u7i2aXIrh//uIy06t5FlJFuj1G/v7VCAN+IxOrRf5gk59js1meH0vhs2jIlOv89NQS+7ocHJtKspTK863XlRjebFXmhn4Pn7q+m8VcHZVKxZ2DARZTJRazde5e76O/ycN9O1vpDthZEzBxci5HLJUjkizxo1OLiDJUazWOzWYJ2A04DSraPGa0Oh2DQSOzqQoXFvP4rEbWhRV6b6fHjIwSY6ZMJ+NcXEzzizMxJqIpLkSL2AxaNjZZKIky1QaIdZFnLsVZTOTQaDRIsoTLrKMvYKFQqfPYuSyzK1nsZiMtTgNvzuf59DVtXIjmyFdEnEYt8UKNz1zXQSSpMGOevRRnfZONoMPEh7c1sa/Xw4WlLEtpJUHJZtRx84CXoE3HhaUcUkPJPRFUAt1es7IHPjjFaKzA6ZkEL46sELDqyJRF5tIVgjYNnR4jE6sl1jYpCVgbmh10B+1c0+vFb9ESsutwmtT84Pg8/V4Dy7kygkb7tpbyd+xK/ejD3//intvew+RqkcVUiVKtgVGrYnO7F7tBzaVolomVPKMxJaPj5gEPR6fTrORrrOaq7OjysKXVwf6NLRybXEWjApVaw1vzaVqdZlo9Vu4a9DOyXKBQEdnW7cdh1PDkuSWS+RKrhZoS4Hg5SbfXxFi8yEw8x0K2xjXdTg5PpzGoJKoSeC0aGiot92xr4uJihpfGkrS7jdiMWp48G0UWVFj1KoZanLQ69VyMFijXRHoDVjr8ds7MZXhrNkm20uCqFkU7HbKoWclXmFgtYtVr8Fo0aNRqTHotl1eKXNPtZHSlRL0ucmoux4DPyGq2xLX9fmr1OnPZBh/b1cxjJxfJl2vMJYusb3IwtlpkJFbg0lIWl0mLSacikigxnywSSZW5tJTFpNPQ5VWCHidX8lQaEEmWUaM8XBpBYnOrm5lEkXaPBZ1WQ6Va5Y/39bCnL4AkQ7pQYnixgEauUxIFLq8UeO+mIIcnU8goxIdkrsQPT8yTyldIFWtkynWGF/MIcoPzL/0y+3u3UmfLdSLxPG0uAzISkiQxvFRAFOucjGTxX+GrbG610+Ex86OTSwgIHNgQJlWus5SucDGa58kz84qRU5Zp95i4psfLNWtD7O72cGaphFENywURZBlBEKiJdU7PZ/Fb9fSH7Ny9KYRGo2FXpxudVsfnb+y50gM3kaoJ9HgM7Oz0cE2vl/l0hWxd4KYBLz0BB7esDdDkNrMhbGE0VuA3Zxf4/tE5Go0GmWKNVEmZDN68NsDmNhe7uz3o9Tq8Zi2jy3lUKoHVrEyLQ89itsFKUeSz13cy2Ky4yO9cHyRTERnwG0mVG5h0al4bT3A0kqPfZ+DvX5lW8G1Ah8/Ovv4An7mxj7s3NXPdQACdTossw9qQBZfFgCRJuK2Ks/218QTnFjK4rQb2dDnZ1eng7EKWbpeG4zMZarUaS7ka29rsuIwq5jMiU1c6U41Gg6lEhTaHhrlsDYtehVqt+q0B4Z6tTcjIpMoNBkI2CvWGEsLpMbMmbMNrM/3OlfodW9R2o5azC3lEsUG20sBvM/Ln+zpYzNW5Z2uYSzElEDFRrCMIAg6TBp9Vz00DXnx2M4Ig47Xo6Q052NmhqNomV/JcjObp8ZrQarVsb3egVqsJOQz0+i3IQKvbilWvIeQwkChU0ajVyMhoNBp2dipeukiyTNBp4tN7mzgyU+DETJJmh47hxRy39DnRatQcGldUgGpBYDpZxmrUsqXVScBpxqJTs1KAWwY8CAKMLGV/22ERRZGgTY/dpMOkVVFXgdem55a1Xnq8Rr7xWoTBJqUjIwiQLSteyES+gsNswG3V8Y8H+hhocrOxxUGXz4ZWo+Webc1EUmUFP3EFZ3ffjhbejKR46vwyAz4DS7k6XouOwSY7a8IO9q8PYNXCz8/EODSRxK4XeHE8Q9ihR6fTcvs6P4+dWGQqXsZv0RBNlfivB8c5NBG/whmXEGToD1jZ2+0m7DCSKYk8+ORlVrJlHryph+sH/MRzVeKFKulSnQ1NNja1OkGSxLerjXfs9uOhhx764r333sNkooxVKzOTruI265W2W6rK/bva8Fh05KoyIZuWk5Es1Vqdn59Z5t7tzUgI5KsiMgKr+RpXtTow6HXUxQYXogVCNh1TKzleHl2hx2dhMlFmPlni7o1BHh+OYTNoKFQa3LQ2wJHpLHes93NuMY/dqGFHpwKW/825GDa9QFVSEc9VSObLPDcSp9VlJFkUKVbrtHttrA1aqEsCNUlxZp+MZAk5dGSqoFPBockkW1qUwdAT56KIkkQsV2NTs421fj2j8ToqWeb4bJpNrXaKNSXvOVtWfkfQqiVblVkbtBDL1HjhcoJmpzJN3drmYDSaYXgxx6WlLHaDmkePzTEay9PiMjIazaEVZCqSir3dbsp1mE2VMagEzixkuBjNcXWPB5/VQCQjclWbjVpDwHClHZirNbiq1c5cusrmVgfD0QI+q55un4VYtoJJp2FqtUAsW6HcUPG+LWHev6WJI9Mpjs9kaIgSC5kydp0AGh3pQpXVosiRn3878tf/6a++/R/Vxjt2pc6WFTTatjYncxmRD29tpsVpYHhBUY0JgsDx2Qz3bA3z5nyB+7Y30+S2sq3TTX9Qgc0jQ7vbxFCzg0vRPLesC3J1n5/7d7YylyqzmC5jMeq5a2OYB3a2IggC08kKW9qc7O72MhtXnCL7hwK8cGmFJruOSFJpn+7p8WI3ajAYDLx/U5CQw8hA2I7NpCPsMHDXxjCb291sbbUyulrC7zAw4DPw3EgCl9XI/sEAd20M0+W3sz5kZT6jeAmv7fexuc3N3YNeXhyN8/J4im8e6EGn19HutfKRXe2kyg06PBZAxm9T2JEqlYBOq2UgbCds1yPLMq0uA3/3/DjJssRQs51r+330BJ189qY+7t3egkajYSDspD/kYFO7h+6And09PvYPBkmWRTxmHTajordp89r4wo1dGPV6un1mdnW7AZl3bQhj0CvvLD0hBx/d3cEf7e2kO2BDrVaxu8OBWq3BY9Hz2es7OTmb4dHj84DMgaEALR4LTQ4TZ6JlPrAxQMhlpSE1fieb/B3bpwbwWfWsa3ayayVLJFXm9Ym4YjlKF7mwkEKs13nk+BxBq465lBK+ftOAj4ePzrE2aGJ4IaPYgxoNFrI1arUax6bTgEBDklCp1fT7jZyIZDk1lyPsMNLjM/PLN2vIskSy3KBSqaLT6zgzl2IpU2Jnp5vvH4mwvcOB125ml0vDyEqRbEViT5eLA04zhyYTaDSKBuSRI8tc2+dje5uTb746SYvTwJ3r/fzXw/Mky0pOXjRbQZZlxpYVIdW5hQwXF3MMhS28tVjgwSfHuLbfR8Bu5LWpDGv8RvoCFo5Np7h/RwvJYp3b13qvOG8kEsU6b0wm6fRZuGO9H41GQ0NqcHw6TU9Apsdn5vNPXOa+nS1sbbXy1IUVPtrrUmSzkkjIYcRlUKPRatjYZGEspuD8Ts/VcepVnF3KI6gETk4leM/WNm5bH+LMXJr5VJk2t4nnLsYQxQbL6SJji2lUamVtfXksgSQ12NXl4Ven538bpHPvzhZOzKSJFhrs7HTyxHD5d9q53rFFrdcoksnzcwkePbHE/Tua6A/auH9HK19+/jJPnotSFxtoNCrmVjIU6jIus56zc2kShSrzOoHFbI0DQ1pORkp880AP//DqDAeGgvQHrcwmCiQLNVQqFWG7gbYroezPXYyRqYogCGxpc/HNg1N0+234bAbCTiWBqclpYCCoLCTfOrTI+zYHOTAU5rnRJH95bRsr+RpdPiXTT6PRIjYaHBpfwWbWkchX+MGJRWwGDWGX4tEL2o2cX8hSr9cZWy5w/642vBal/Xb7GiPnl0s8dSHGn+/rwGAw8MWnp1nO1ZAFgQefGmN7i4X/cnCaFqcBg8nIZ67r4OBEmuPTKV4eibG708n5qGKlmrqS2HrvjmZ6fGbu/s55TFoVf/fCBO/e3MSx6RSyJHF8Ks4nr+vkb19YwG4ysJItk61KJLIiDpOOrS02XhtTrFeSJOGz6Giy6zg6mWApXcRr1SNLMiv5Gt0+Ez6bnjaXkXPzSmb2zGpBCR7KlLkUyxG0G7gcy7Em7MBnNSCoNbq3q4137PbDoFXT5DByai7HvdvDdPpthB1KH1qlUrN/KESzx8Kn9rVxeqmERavCa9Fzx3o//QEr+aqEy6zn2HSKY5Or/OnPL7ImYGI2WeLFkVVuWRfkC7f0kSmJzCfyPHl+mVqthiAI3LutmaVcnbuGAnQFbGxscytvqX4bAyE77R4lq+PujWE2tdhIlUQiqRJ3rPPx5efGFCOCJBHNVjmw1snkapGXxhLsbrMr36ehbAAAIABJREFU9zNlrun1cGBjC3+0txOdTstQi0NhDWarTK/kSJYaeC16Ipk6t6/1IcsyX3hyFFGss77ZwZ5eH398dRe7Oh2MJSqYdSrmM1U+ubeFP/3FJdo8CkR1fZODVreZq9qcHNgQpsNjVkBQGg0TqyVa3GbMOvDbDawJO9jb66NWqzOfrfPw0QhGjYrb1yvxBlf3uBHUavZ2Ojk5l2NN0M51PS4+/esLXFxM84MTCwRsOgRBIF6o0ZAksiWoyGrqtTqRVBm/VU+v30xFUg66uWqDPq+JgZCdbLmOLMtsabYg6Ixvy3x5xx4Uv/Xt737x/vsfoCaKXFjKc2QijkGjpiY2kGVo81kRrwQU3nNViMlkidFYjvPzKUIuK7et9bG5zYUkC1gNGgw6DYKgxmHWc/NaPy+MrFATG5yOpEgWa2zvcPL6eBKDRs1CtsJqKs/5pQIbW5U44bfm0qRLIg1JZjpeYCSaI2jTEUmWyJRFplfzrA9Zefx8jFangcVMlVcvL3MikmNbqx2XWc9yrsrocgGHUUtNgqv7/KjVanr8VvqCNnLlOg6jikiqysZWJyaNQLZSZ2enh9FYgXK9gU6rpTdow201sJwu8r2jCzywo5mKKJOr1Gn1WFjO1TDpNUwnSogNibfms7iMGoaX8lyO5TFoVbgtBgSVgFkLpyJZOrxmEsUGm5ut/PDNGJ/a18yFpTwT8TIhm5ZESaRWq5Mpi0yuZJlJKDi8hUwFSRZ4/5YmvHYTtw+GKdYlRLHBxHKWcl3iBx8Y4BtvLDAUtuAwG2jI0OI08r6rWmhIMq9cXmFnh4vJeBm1IHAykmH4lV9X//PnPvXV/6g23rHbD4NGxWyyiFqtZqjJxnxSRdCh59Bkkqu73czEC8gynFlQ9Bs+q3I4OruQ4+ub/Nz3k4uKtlivxWvWcWAoiMFgoD94RaAkycynFL/dvh7lkKTRKizEQr7Al15aZmOzjQ6vmYGQneMzGT68LYxWqzBiWhwGnjgfRazX6XQbmVwt8sylFfb1eGhymegNOrl3ezNPDy8RL9bxaQFkBsN1NCo1yWKVp84tEC802NPrpT+oHKxWchU0ai2tTgP/eHCJSrnMMYsBm17FurCNFrcJsSEytZLj4lKBh/9gDbOZOvFCFbkh8fT5ZTxmRYY7myyxp0fJOwGZA/0eBAQWslUGQnZEUeT10Rj7ejzcv6OV2XSNR4/P84HNysMWdFqw69VcWqmgo06koWLAq+PUgsiaoMKRDFp1NDlMPH1hhU2tTkaWMnR4zDTZdLxwYZFMDb55aIFmm4blgogg1zk4vopdr+ZCNM9tazyoVGqWMmUObAgxvJDlI7s7eKScT75dbbxjtx+JQlXR/soykixzei6LAOzr8dIVsBPLVun0Wfjndw+QKjeI56vcNRTitnVBFgoye7qcpEpVbh3wIEkSjxybp92p+22SfqfPwnW9blTAE8NRZuLKnrNWrfH11+f52b1DvGtjE6ciitvl3u1NfOWFCZ4eXmJ6NU8kVWKNz8TxSI6LCym8NiMbWhQfpLI/rvGVF8b52VtLLCRynJ5XDKbHZnNcjpewqGViuSr37WxhIGRHpVLy+ObTVXx2ZQByc6+DhXQdlxHOx4rkKhJhm5ZnLq6ALLMmaOLjvxjh8TOLuM1a8jWJdSELK7kar19e5tx8GkFQsafHS9CmZy5VZj5TocOj4PseOTrHSq7MqdkUH/vX83S49ETTRb752gzfPxrhM9d3UmzA7Wt9BJxWarUar04XoK5s084u5PjXt6KUy2VSpTqNhsQ3Xpnk4EiUI5NxTFo1ImDTKEzznR0uru4PcP+ONja3KYlbb87n6febOT2XpcmuI5Evo1IJv3NP/Y5dqc06NZ0+G1MrWaKZCg6LgQ6vhVNzObpQcikOj6/y3VieTree6WSdZy7F8dsMdHuM/Guuwmyiwo9OLeI0aqnVa3z28REe2NXG8xfzlMtlvvtGmk6PiRMzJcSGyBPDMeq1Old3O3l9Osu5+Syf2tfC3z0/zv6hAF6rllvW+nlxZIXlbJUPbgnym3NLiCoN60IWOn1WlnNVxEaDw5NxUKkIOQwICGxucbC310ur16r4I08usaXDwwuXVujwmhFUKmZW8zjMera22PjaC5ep1apkajC6XOLerWG6AnaWciKbWxzcNhhmYrXIVdkqOzucHJtO0uo0XrGbKa6WoMNEJFkkYFFzcCzONb1eekMOev0WXhqN8+HtTVxeznNobIVmt5npZIWg08RQs51LsSLjK0XyVQmVSmBTmxufzYBTK/G944sMNdsJOExsa7Xxrddn2dTmoNNv5Y71MrFsleVsmd29PjaEi5hMJtY16ZhezdETdDKXLhPNVuj0WdjZ6WY+nkUWBB46PKPgR+IFGoXU759HcSVXpVarEs0pifMf3d3OUJuX+3e2olKp6PBaaHKbMWskhmNF9vV6OTAUIuww8N03JslVJW5d5+cf3zPEu7e04jLruLrHg1qj4fo+N4cmU9yztQmNVs1cukarU+kd6/Q6dnV5aHebKJdL3PPD8wz4jb/FaoytKPG129ud/PDUIh0eExubLByZyTITL/Chq8LkqxJqQdk29frNXIzmuLycVQLQJZk3JhKYdGouRgtE4koMhADcOOBjX7ebZ0eSmHUCYwkZmxbcZi0/fSvKY8cXCFvVLGeVw26Xx4haJXB0Ooksw3y2yhPnomxsthF2KT7JcrnCl5+foF4XuRDNM7akRBg02bV89eUp3ri8zNhKHgFoNBo0O03s39jC/sEgC5kKg0ETw4s5RLEOssyL42laXCYkSSJZrLGUF5FkmctLCoz09GzityaMlXyVZ8YyjMayrPEbeeTEIr94cxZkpZU5mywhinUWszU2t9opVBt4zDrOzCZRW1z/21NP/3+/VCqBSLKMz6QheYUVfmE+xXden+L1sWUajQYtTgPnFvPc0udiJV/n+HSSRkPijckU7S4dGq2WyXiJuljnVET5x0wtZ7m0mMZmUKHVari6189gyMSpuazSVRkM8OszS0wuZxmOFtjb7SKaKQMCu7o8TMSU3vfEcoZqtQYypEoSezqdLCSL/F9PT7C3203TFff1aq5KAzU9XgtyQ+KlS1Gm4iXetymI26xBo9Fw/65W+gJWvvTMKBeiOTa02Lmuz8e6oA63WaDJZSLsMPChrWGOz6a5FM3j0Ut85YUJfBYtq9kKOztd3DUUxGlVJom7utxsbbVxLJKj12dkXbOTz1zXwXMjSZrsStjOgzd2kyqLSKJysPvl6XnmkkWePR/lifNRTkyucHQ2y2q2xBsTyhb3qjYnW9qcPHtxFY9ZT4fHzJZWB/OZGu8d9HByLke728Tnb+rhvVtauabXg8esx2A08sDOFprcNm5dH6LLbyNs1fCDk4u0e8x8ZGcblQZ0eMyMxzKoDBbX29bG/1dF+L/7MmqV3IgzcynM6gaPnljg9ctRxpZzLCUL/ObMErIkYTHoiWZrNEQRBOj0Wbi2z8dMsorfqmV6Nc+bc1lu6Xeymq/z0qUoX31ulIVMldHFFO1OHSOrFTY3mTk6pTwUI9EsHV4Lt60NsqfHy5tzGWYSBdqdOn5ycoFdrWZ+cmqBV0dXyFREisUCKwURtVrNPdvCKOwkibOLeZxGNVtaHbR6zDiNaqbjRbwWLU+dX+bYpMIQf2FkhUajQbYqMtTsJGTR8MO3ltkQspKvyUQzygj+2RGFcR6w6fmnN2ZxGAVi2QpTKzkePxdFkmUGglbF0zkR58dvRXnvoIelrJL78dpUhk9c3cob46v87XNjAKwNmFkpiixlysSzZQRBoMNrZv9gEI1Wy64OBwNhBwMhO01OI1qNllRJAT4FbFrm0xU6/A6Gwhb++WiU3V3KGeZPfnaB8WiGWqXGfKYKskxPQPkZY8t5avU6j51YZF+3iw0tTh49Ps9gsxOx0UBEg1SrvK3x9h1b1DqNGpC5tJTl0ESKD24O0OqxcnW3i2heZH3YikajZV+fmzcjScbjJcJOEwuZGru7PQyE7AwvZJABn1XH6zN5/DYdLrMenV7Lx3aEeOzkIl97eYqdLQqLBGRmEgXCLjOCIJCpSqjVapwWPTf0efjay5PoVBI/fjNK2GHkmn4f7S4Dr0wW8RiV8JcjUwl+fiZGm9vEpmYbF5cVEdHwYpYTcwUcRhUqtYawVcNMSiRs07KcrTKxWuS6/gDtbiM/OrXEDb1u5gsNWj0WAjY9VUnF+rAiTnIY1HT6rKgEFSGHie6gg0SuzBPnYvgtSsBOk9PI3i43P3orikWvVmBPVxBzGo2afb1eAN6YSrG3y8muLjdVWU3AqmUuXWE+rTxIM8kKq7kKQZuOeLHBjk4nG1tcyAi8MZGkVqtx0xofsbzIgaEQ79oY5vnLKb6xv5snh2McnMoRsmpocRqJpCpIkszhiTjTq3mcZu1vOzPno0VuW+tlMlFlZ7sdQaX+f59P/X/qZTdqafXYWN/swKRT89TFVeaTBVbydf7s6jZ0Oi2HJ1bZ3uagKqm456oQdww1KZPBrCKJXExXuXmNnzuHmvjyHX30h11sanOzrcNLoqri1rV+7ljn48xinhankgUXy1bociuJqHcOBlnb5OC6Pj8Tq0WqlSoeu5ndPV6u6fNSEFXs6fbQ6TPQ4bPwxHCMRKHOIx9Yx2Crh2SxzsevbuM9W1r5mwNDfP/9A6TLEiGrhnhVxR9sDhIvC9y7o4V1zS5uGwxxYjaDy6xFrVGxr8fDzX1ORpYLeIwCt60P0uzQcXwqxVq/EZVakXS+a2MTfoeFVreB4cUM8XyFWK7K9X1uyiLY9CoWMzWu73MTy1ZpcZnZ3e1hbLlAplgjXqixmKvzib2txIsNbhrwcdOAD5/DzN5uF5mKMmHd3uHiyeFlBJWAz6bHbtDwm7NLXFpME3boUKvVzGeq3Drg5j89N8VgyMTVHRYWczUeORYhbNOgUqkI2Q2s5GsMNSsqyd6gg2+9ewCdTofVqHgW/5dC1/9Pvto9Ju7a2IQoqHEZ1SDDSr7CybksIZuWU7MpfnN2iV6vgW+9PstwJMEb46tUKlWeHUmwpc3JM8NLfPKXwzw5HGM8liFerLOjw8mNAz5UaiX6Kl1Q9BATy1lGY3nOzOeoVqs8MRzle4emkKQGRybjnJxXAJ9rwg7iRYlbBzw8fzmFz6Thzbk8Nq1EpVLlE78e5a3JGOfmUnzrjVkOji5RKBT44A/OkSxALC+yocmKwaDn+j43jxyb+61IK2jT4TCoWMlW+PXpBb708gJLiRxvzpX5zuuT/Or0AstlePXyKrKsIDfWNjlYH7YQclpwm7TEcjV8JjVfeGaCG7pt6HU62twmXhlLIskSrS4l5UoQBNrdJjJlkWqlyjMjCSqVCg8dnuYrL03iMWpZKYhYtQLnF7P0+BRtSrNdhwC4jCrylTq/Or3A+GoFURRZSBU5OpUkmSvz6mSG89ECoijS6THy9Hml09MdsNHkMqPRqBHFBoVCgQPfOcVrozGanQYuLxd+Z+j6O7aoI8kiDx1Wwr2v7nGTKtYJu0z4LHqCVj0nr0zBMuW6EuurVnFkMs5KtsThqSQHhoLct6OFS8tFerxmhpodXFzK895BD//0+gz/91MKC7XZrketE7i4kFIGFxY9H9/byslIljvW+X7793x0Tycfv7qDY7NZnr0QI2DT8sylOOuCZpYLNa5qsXB8rsBkosx1HVZeuJxErVZzc5+bVy4t84FHT1MXRTRa2BC20OwyEcsUee5iDFmWGY1mefLMHGfn0hyZTgOKRUyFoljs82tJFuvcvamJPq8ev8PMjg4nsizz9PASTw7HCFg0nI+VGGx20Bty0u7U8ejJJepiHZdJw01rfKhVao5OKyz1gZCNkEsZsByN5Lh1wK0YgZ0mHryxmyaXESSZXFWi0ZB46MgskVSJr70wyoWFJCdnk4QdJnQ6HZ+/vp1YQdGCJ0siO3t8fOm2HnoCNq5qU3Togy3KgjAQUgBVbW4Tr42v8pWXpjBqlVhmtUqFRqf5nduPd2yf2m8z8IebA5yYTSNJEulyg1i2giAIxHIV9vb46fYa+dvnLoMg8L0/6OfHb0XxyBJDTQ56fGb+8lcX2dfnYbVQ5+a1fg6Ox/mTn1/kzsEAoysldnS6ODmb4c92BTkXq3DnhhDNNh3//MYsrS4jR8QGKpWKg2MJ1GoNvUEHF5bydHqV4YVKEKhWq9iMOrRaLX99Wz9tDi0f+dkoj/7hOr7y4gSJioTLaiJk1wIyJqGu5JckqxRFmc1tsKvLTY/PzPcPz7Kr086BjWGOTiaYiRfwGStsarEzmqjTkCTWt7hZE7Tgs+l5cjiG26SlxWPGZtLS6bPx8IdauBzLo1KraHWblclsosRbcxk6/Hbu2dbEl5+7zLGZFGq1mh0dLn6dLvChjUEWc3WcZi03rw3w/KUVXh1TxP5dHiOtXhtajZrtbXa+/OIkgqCiVJVwBbTcsd7PcyMJHryph6lEGY1aQ7Va5Senl8mW6ySLNZwGNYIsc2kxzZHJBAGbnt6gnQd2d9AQRY7PpNnd4wVZxj2q5uXfURvv2JUaGT7zxGXKlTLTiQoDQRsP7Gon7FSCexoNkVfHk7hNGpAlZjN1Prytmelkld+cXeSvn7xAplIHWelEvHBphev6fWzr8KDVaFkXtKASBFwG+OYbUTaGjAiyzI/eWiJs1zHYbCdRrLK11cr9O1vo8lt/u0UYi6b5LwenKZVKHJpMo5HrHJqIM5so8tMzK3z2ujZMJoWvohZU3DHoJ2DVc3m5yC8uFBiL5bmmz8tVbQ66/HYiqTJTiQpf2d/HSKykBO7YDWxusaPWCBycSBO2aMhVJer1OuOrZVavyFVTZcUg4jBoeOjQNBcXUvzizTmmlrPIgFanxaITCDrMBMxqHj0+z3y6woe3NiHLMkenk5yLJPnhqSUWkgXUag1jy3lkScJl0rIupMSinZ5JIMkyJ+dyvH9TELdJi92kI12q89ylOC6jmoevWNVuWuPj8mqFa/u8WPVqGhJE0mWWMhWmVws0JIk3xuNUq1WOT6c4GcnSkCSOTMSZWM5yaubfs2r/n9c7tqiNOjXrm+wYdAbu39lK2Gng0RMLDC9mEesij59bImBWMZVQTvWyJHFwIs2HtzXT4bUwtlpmS6sdrVaJhX1tbJWrO2wkilUi8Rw/fXORX55eQKVS0+LSM59WBj17OpzMpStEMxUSuSp/9fQ4Dx2eoc2h5dETC9i0MrFcjaGwst1otqk4t1RldClDoyFRr4v88FQUUVRafH6rljcmkmRr8KHtrdy71UdfwIogCCSLIt1eIypBRZfHwKsTafp8ev72+QkeH44yk6rS7jKwuc3JQqZCt9fMwYk0O9qsBJ1m3r25Wcms81nJlEVyVZGHDs9wcibBYrpMl8/G+zYGSNdUfGJvG198YYqrWqxc1eHm5cvKO4HXpEJ3hZp7dV+AB2/sViJ2ryR7XYoWuGnAS7JURZZkYqkCT19cJV+Hdq+FNU1OHry5lxa3GWSZX741z8uX43zuxi5uGwxTqMtE83U2t9jZ3e2hO2BjX1+AvT1ujk2n8Vk0+CwaJleLNBoNnjq/QqZUBbVG/3a18Y4t6v/O9m52m5hLVVjJ17lvezPv3tyCDKRKIsdnMvQFLOTrMr1+CzcOeHlzPs/+oSAb2tzs7fFx22CIPT0+Gg2J2759ilpNJFOVee/GICGniW6/FY9JAXUKKCub1Ghw/85W/vS6Xj6yuw2fzcSrEyn2dDo4MpPlciyHVqPhQ1vDnI+VGAgptLAuv5WVbJkur1FJEQ3bGF0t0+Ux8q5NTbS7TQwvFcjXJF68FKUuivzd8+M0O3Q8fCSCJDVIlCT2dbv5zA093L+zlXipQSJXQaVWo1IJ7Go18fiFONVqhZm4cijrC1hZ1+zirg1hblgbpC/k4IFdbRyfTfHa5Tjv2xRkY4ePmwd8LOVEQlegRiBwZCrDrjYbPzq1xE9PzPDQ4Rlu6POg1WrwWrQIavUVxk0FUayj0+lo91i4d3szV3V4QJaZjJcVTEa7A7VaRcCs4u9fnuZyTOGo7x8MEsnUmYkXUKlU9PrNnJ3LsJwp8tKFJS4s5tjb5UKn03HHeh+tHgs0xOrb1cY7tqhlWeZnp6PUaooLxWNS8fVXZ+lw6UlXJa7r87JvIMCODhcfuirMQ4cVPHGxVOTZi6vc2q+Eqjx1dgEZ6A/bePemJpo9NvYPBhhezOIxqBBFkfOxMltb7ezt9XNjv5eVYoPPPj5Cl8eIwaBn/1CA2wfD9ATsrAkqw52g3cB8ukK338aWFjvnFvOIoshcusT4SoGLCyl0Oh3Xd1o5HskhNho8fDRCk0NxwMeyFaqVKn6bnjVhB80eM7euC9LkNJGuNJi5Eklm1AgU6g2CZhXVSoVPPjHBnk4H2ZrAXCJPNF3kpdE49+1oIZar0em14LUamIyXcOshkq3y+NlFvndomql4EbEh8tpYAhm4od9Df8jOXK7BjnYrJ2azxDJlRmM5WlzKg7nWbyJeFOn0mNBotdy2zofXouWJ4Ri1ep1Li2m6vUZ2dLg4Np3EZVLST29b60UU61yIlujyWfjKHX2sFBp0ug08fzFGplQjWayxXBSVbk+hxtZWG4IgEM1Wfz9jxwD29rg5t5BFQECtVvHgzb18/eAsHpOGW9eHGIlm+cKTl3EYBHqDDpy6Bq9OpBgMmXluJIGEzPBihqVMBbVaQ6vbSLNDz3gszaHpNCdnUvzJvk6u63MTzddRlRQ828f3tiPJEj88tYTbKPDosRjfCDt4Y3yV2WSJgMPIWDSH06RlQ4uTxUyZLa1O5lIV2r0WBsOKufZ9Qx7e/9gkH94aYl2TE5VKxen5LPdtt1KsC7wxneO929pQXSGPTawW6Q7YaHbo+P7hWSRZolgVKVQbzMVreB0Vfnb/Fh47uYjPrGFktUy2LNLqMvDM8CJvRdKEnUbWh63MJIpcXq1wy4CPseU8YYeRnZ0ujs+kcJl11Gt1Pvf4KP9w91ouLqQ5OZdh/3ofYaeJEzMp6qJINFXgrYjIx3aGFWqB2OCfDs5g1glkKyKr2TJjqyW+f3gGr1XL5GqJfX0e9rhNzKfLnJ3PcM9VQWaTJX54cok9XU6+f2RWsXDtaOHEbIb+oMTYSh7kMiPRLPmyiN+s/f0ckysuCIl0qQaCrGT7An95bTtqtYYXR1ZQCQIP7GzBZTVwc7+LVybz3L+9mRaPnXdtauKvbunnwFAItVrFAztbaXUZefhohCMzWT6+q5kevzJSXsrWkGVFd1Cr1VjKVjgyleEPNweIF0SseoHLsRyJfA2HScd921voDVpJlZW+7IW5BLV6lYuxPOuDFpJlGY9R4C8eH+fDW0MkShIA1/R6cRpUrBbqfPXOXrZ3uXkzkuXZ81E63QYiqTI9PjPHZtLYr4Qr7u12E3QYsRnAolMzsVrixHSCZ0fiHBgMcG2/n26viafOr/DHu5sQkDm/qOgv7t7cTIvbTLEuU6/XOBHJIEmKCfdkJENvwALAD47PIzUaxAuKE/2DW0K0eawE7CZuXePlxfEs798cQhDgro0h1oQcdLjNBF0WvnH3Gra1O/nlmWXu2RYmmqlwYjZNtVbDbdJwaCrNYqrIPdvC3LmxmZBdT9Bm4LnLScIOAy1uM9cPBLiu34/fbuLje1sJuCy/p2NytUCmItMfsIOsUAXu+u4pPvObS/itOhqSxPHpFGqNGrVKzY9PLyt5dJsUgpYky3z5uTEePTFPo9Hge4dnOD6TwmlUMxC0kSxLrBYVx3qL00C7x4zYaPD1lyYYX87T7tbz2MlFtrTYWMxWqVar+B2K4fXrB2eRGhI+q4FYpsTppTKn53N8al8bxyMF7t3ezECzh7+4poM1zR6WMyW+88YUkUSO0VWRV0ZXeO5ygnu3t9DiUchcX3t5mut73YxGc6xmy6gEAVQqZpIVmh16SiIYNbCQqaLWaNg/GESWZc4v5Xj4SASjRubbRxaYSxQAZRzeF7Aiy7C51cGPT87jM2lIFEU6fBY0ajUP7GpjfLWI06xld7eXkNPEJ/a285PTy1zbo/SWhxeyrA+ZiWbK/Ox0DEmSaHKbUGvV3LHOx+nFAqfmsuztdilZJIDfouVnp5fwmTXIksxqvsZTF1a5uJDitbGEYghe60et1nBhKY9KpUKjUTPYZOdfDs/TkKTfzzCbTFlk/2CAZo8ZjVaL0aDn8Y9t5RvvXo9Go6bLZ2Vrm9Ld+Or+fq4f8KPRanjk2BzzqRKRRBG/3cC925rZ0+tDo1azs9NNk8fGx67uYm+fwiQ5sCFMriZxdCrB1HIWp0XPjlYrmYpELFNkMVdnMGTm20cW8Bhgb48yWp9N1fjsDV0E7Ua+cGMnn725n4W8xM52G5OJMrVajVNzebq9JhAE4vkqIZueNX4tzS4LTr2KU/PK3rU7YONzN3ReQd0lcJt1qNUaruvz4rboCbss6DUCZpOJW9cHuXOdosr8p9cj3NzvAkEm4LTyZ1e3ka3K3LUhTLvHzLPnl/jxyTncBgFR0NDmNhFwGBGAnV1uXhhZYXI5R61WAwRGl3LMpUpsb3fwozejbG+xkBdBkmUiGZEffHA9oysV2lxGpIbE0xdW8Jk17LnSX/6n12aQgdsGw6wJ2tDrdQhqgU2tLj53QyeRVIWBsOLTfGI4RrvHxJ1DCtVBpVZz67ogG1vsaDVqGvnk0tvVxju2qDUqAY1GA7LMgaEgHV4Lr4wn+cHxec5E0tTrdR45Nk+lUuWPfnKOM5EkfX4LOzqctLiUQvJZNDx9aZWZ1Twhu57+oJWQQ1kAnhyOcWgyxVyqxNXdbnwWDRdiBXa22/jV+VUGw1Z8VgNNNg0zySof29nEL84u84u3IhyfTtPm0vHSaJynLsU5M5vk5GyacrnCj08tMDIX59Hj82xrs/HshRjX21+pAAAgAElEQVROg4oNzQ6eHUkQz9dptqk4OJVhU8jIU+eXmVrJ8fDRCL8+vUDQpker1RB0muj0WUkUKuzsdNMbtLG3240kSfzwRISldImhkInnRhIgw7qgmaV8g/VhG3NppYfd4jJiMWh5ZTLD39zarUz8rDqeOBej22vi3HyWhljnyEyWSqVMriry8ugKfQErPrOGF8YUIVmyLPOVO/tYLMBgk5VIqky6LOK26LiwlKfHpwjAPnldF3t7fUzES6jVam7o9yLLMtVala++PMliqsi925uZS1dIFiqIYoMnh6O8PrZCQxT5/pEZjkynsf1uONf/uKgFQXhUEIRVQRAu/Zt7/yAIwpggCBcEQXhCEATHv/nag4IgTAmCMC4Iwo3/5v5NV+5NCYLw+X9zv10QhFNX7v9CEIS3ten820unUdHjM/PMpVUOjStoNWTlgLGh2c58ukJ/0IpapcJq0IIMz1+McWwqqcB0xAbPjya5fY2XpWyFs/Npvvz8GGciKUajWQ5sCHHPtiai2Spn5zI8c2EFl15FsiQTtOu5aY2fqUSZR08s0GhI6HQ61obsBGwmAjYtJyI5bl7r52t39jG6nMdjVDGXKqOhzuuTSe7f2UqPz8xvTi/w8niKGwe87B8KUq7DkUiJD2zwEStKDDXZuXV9iN09XoIOE51eC3+8r5ur+/x0uPSMxAocnU6SyJc5NJnkmeFFclWJgE1PqiziMmoQVCpen0giSdIVxqLE4fFV3pzP8RfXdPDJ67oxGo1Es1VanIrhdmw5z8YWO31hN5++rguTyUSHVxlIKfgQFU12HW8tFnFopSukX0XYpBIE+gNWdnY4kZF56twCr43HkSSZ6biCM5FkiYcOz7KYLPHk+WWcBjUAj55YoM1lxGXWEUkW8Zj1JEt12twmdnV52d3h5Mh0BkGj+1/afjwG3PTv7r0CrJVleT0wATx4pUAHgPcBa6685tuCIKgFQVAD/wLcDAwA77/yvQBfA74py3IXkAbu/58paodJh0aj4TvvH0Sr1XB0KsF8Ms/DRyOcjqRoNBrs7HTR7jGh0WoYarbT4bMiA0emEqwU6vzLe9cSLUg0O83cvamZuzc3s7HVyZHJBKMLSX5wfAFRrOM0CGxtd7NvIEizW+nfvjS6ytfvXsdHdrWhVquYS5dZ32QlXqjSE3Tw+Ru6eOHSCvPpCsv5BpF4AbEhspJvkMpX6PGZ+epLU2xqsXPf9iZen87S6jSg0cBf3djGs5dWmU8UAHhxZBVkmZ2dbiV3Q5apVqt84leXaXIYCVq1iLKKfT1ueoIOdrY7FEKZSoNGo2Jnp4v+oIUOj4nt7Q5anUYQBD6w0c+/HF7gyPgqUys5trbaePZSglsHPBydTtHmNiPL0m+hqRuaHfQFrSAIIMsU6jIf3BzgsTejjCymkQGVSkWjIZEo1njq/ApiXWS1INLvNzMTz3NuPsX0ah63SUurx4zDrMdtUXjuu3u8NLuUnr5GoyaarbGnx8NQ2M7RqQRHp+LECxWCdj0qo9X9drXxPyxqWZYPA6l/d+9lWZb/e0DfSaDpyud3Aj+XZbkqy/IsMAVcdeVjSpblGVmWa8DPgTsFQRCAa4BfX3n9D4H9/xM1jSzLfPpXF7gcyxFyGLh/RytNLjNhpxFJljg4FudXZxZ5/FyUna1WLi2X6A9Y+cjudlJFEb9Vx8GJNK0uZWVSqZUDooKAkHjsVJROl0LPfWJ4BZ9FzVy6wvV9blT/jbv3jK7jPO99fzO7916w0TtBEASb2It6l6gSO+4qLnGJk3vixI7j5MSOa2znOMVxYlnFvclqlEyJ6mIVOwmAAAGil917rzPnw+D6ZN1l+eTm3A9Xnk9YLzawNjaeeed5/8+/rGYNToRzLKWreG1Gbl7r5chMiniuhCRJPH5mkacurLCUKmLVC1xYySkJuU4DLoteIcJXypxbySmGNWYV3z08i14j8tT5EMuZMgGbortsc+g4PpuiITWQZYmnzi7y3Ficf3nHAF1eM9FCg6v7PWh1OiRJZjSYYySY5e6NAdrdVq5EclwOF/jV2WW+e3iGvz84znIiz7deW+Dje1oIZ0u8OB5mPlFkXUB5+gHMxvP8/OQcT18IUa3WWIjniOdrLCSKdHnMrG22M5+u8eFdbdw4pMCSN/S7GA3m+cyNvTQ5DGg1GnZ2OcnV4exCCpdJeRCfW8xw01ov92xu4ev3DHJsJsNcLE+rTcv331zm7o3N7O1T2pPlVJFovkaTVU+qpDig/q50rv8veuoHgedXv24Glv7D95ZX195q3QWk/8MN8n+v/9ZLEISPCIJwRhCEMwuRFF+4pYunL4Q4t5DiK89PsZws0u40oNNqeWBHGwG7EYdBxcGJBA69wK9HQkxFCww2mYjmqrQ79SwkSyDLTAVTfO/IPAB7+tz80b5Orh0M0Oc14zRpEASBpUSBv39xhj6vme3dHoZaHPT6rXz25j7mUrVV/zkN0ioMuL7JyNX9Xnq9ZrZ1uYjka3R6zLjMOmZieYL5Bnes8xIvVDk6nWQmVaXNpiFSlGmxG7h1yM9iusx8Uhn+vDmXxmfWMhbKc9ugmx+fiZAqKHBjPF/llkEfgiggNersH25Cq9NywxoXR2fT9HoMbOl0cf+2Fgq1Bm6TmjuGvJxcyBLLlBEQAIFIrsZnb+5jb6+bW9f5ydcEbh1wMhvLcXg6yTq/kdvWB9BoFejyyFSMUEZxABtZTPDpJy9Ra9Q4NB5jV7eLe7e0otNpuX9bC2qVCrVK5Ka1Xjo9Zr588DKvjEd54VKE+3e0oFKpeHM+w45OO4MBG0dnEjxxfoVQusBQk4nbhwPkKw1AAFH9lp31/1FRC4LwOaAO/OT/5Pf8Zy9Zlh+SZXmLLMtbbBYTn31mEodBxWdu7OEdV7UiSQ0WkyWGmy0E83V2dTtJlxsMB0y0Ok2ATKVa5fXJBD6rFlEUaXfoabZqeG0ywY5OG/VGg8eOL9LpMjIbL3AlVsSsUxMtNNjd4+LeTQF0Oi07Ou0Iq7HP331jhiuRLIIoMtiiENvX+Q28eDnJ+HKCbKlGk01Pq9OIShTwWvX0+W38X9d2odNqWB+w8JE9HXztjn4MRiO3rXXiMOl+M5KWJZnlTBWnHtRqFRtb7SymK9y3LYBKpWJHl53JlSQji0ka9TrRvMTRK3Eq5TKPHFvk2n4Pao2aNoeex95cQrUKBU5HcoQzZZZyde7f3kKX1/ybA9mTF4JcDufY023n4HiC5VSJNT4Tl+MVGo0G9VqNb798mYlIDr9Ny2I8z2wsr7g+Bay8NB7m9csRroTSXAlneepCCL/diMsg8rVDV7hjyAvIdDi0/OrsEvPJEjet9bKzy8FiusLYcpoP7WpnfcDCfLLMMxfDyuHWoSeSLYFUr71VnfyXi1oQhPuB24H3yv8rt24FaP0PL2tZXXur9QRgFwRB/f9Y/99etYaEWSMTzVX4xM/H6LRrOL2QoVxWcNpGXcl6cZm0HJ5OspxSAjnfnMtg1QkE02UmVpL89MQs//DyND0eg8Iym8twdZ+L+YSSdnVNnxubScv2DhuLKSUtdlPAyP94aZoD55coFou8PhUnYFbx9IUQ921tZiaW58RCjlarmsfPR8iUlGg5QVDYcPValblEkcNTMV4cC/HYsTk++IMz/PPrc0j1Ok9fjPwm1PTSSppQpsK1vXa+/2YQSVLcSs8tJvn6i9MYVRJHrsS4GKnxjRcmkCQJp0WL36LhyfNBZCRuHHAjCgLz8QIqlZq/uX2Aj+zrZixSYFObnb+7vY9TSzlemwgTTBdJFBvcMehhPlEkXmxw26AblUpEp9fxZ9d08L0j87wyHuKX50LsbLMQTJWZihZ5aTxCt0vPc2MRarU6sXyVpy+EqNVriCqRLS0mDl6KU6vXOTwVZTFZ5tRCilIN2h16vvjcOEeuxAiYVfzDi1M8dHiWcLZKh9PA9i4X48EMc/ECy+kqUqX4lmlG/6WiFgThZuDTwJ2yLBf/w7cOAO8SBEEnCEIn0AucAk4DvatIhxblMHlg9WZ4DfiD1Z+/D3jmP/s+ig0Rv83Av75rHS9dTmDVqTkyl8GrlxkJ5hEFgZ2dDlocBj60u5N2j5X7tzev9p4Nzs4nOb+cY1OLwoo7cDHCAzta6PaYmY8qB6dgpkK6UOHodAJJUjLL//bXUzRblZ3vR6fD7OywcHpJEdF+7cVpuj1mJa/EbuIPNzdhM+qQZIlbBn1s77DxypUM1/U6mAhlsehVdHqs3L7OQ7fHQo/XhEaroc9rYkOrE5VKRbPTgF6v56NXd9HjU0bcG5pt3LbOw2ikxNZWK0YB2pwG1re52NRsYSJWwWvR0qjX+cunxqlWlY1tjd9CMFtTYDmzjpV0iQMjYaqVGmcX0oiiyJ1DXpYyiln99g4HB8eTtDhN3LnOy9+/NI0sN9jT4+KGtV7iRYlmh4Fr17hZG7Dz8mSCLe12XBYtG9vs3LmhCZWowm3Q8PzlNLeuddNkMzATL3HHkBe/w8q7rwow2Gxf1SOCSqVic5uV3T0udve4yFUktrXbODGXxqwVabFrUZkc/3WLBEEQfgacAPoFQVgWBOGDwLcBC/CSIAgXBEH4dwBZli8BvwTGgReAT8iy3Fjtmf8YOARMAL9cfS3AZ4A/EwRhGqXHfuQ/U9BmvYYHdrTRbDfwyLEFLqxk2N1l4wNXNfHNVxe5dcDFjQMe/vm1Oep1iUeOL1Cv1fn6S7PsX++j2WFEq9Fw84AbjUajtAzNZl4Yj/KL0wv85PQKjxybx2UQWMkoblANSWIxVebmQS9arXLged9VfuZTNbZ12Gh3mRCAfp8yXs6UGrwxnVKGE4ki//7GDAupMg/saEGtVrOSLiEisLvLzqVwiXq9xmyiRItNy0yizJ9d245Go6HDaWAinOPWdX5qtTpPnVvh1EyU5UyV+64KcGa5QF1WXKtGFpOMR3L8+XWdtLksdHpt7O1xMJ1Q/LYDVi3z0RxffWESn1mNLMtEMkUShSp/em2XEkY6nUBelXWp1SpuW+fmlkEfR6cTxLJldnQ6ODadwG3SIQhwfjmNLEns7XMjCAIBq47ldJXZUJqRpSwXltOoNSo+fUMXR+eyCIJAj8eETqfjPZs8vDGd5LmLK2xqs7Ozy8l8osSpuSTHZhSkxGbU8MjxBZw6mXhJwm8zIkuNt0wS+N8SmmRZfvdvWX7LwpNl+cvAl3/L+kHg4G9Zn0VBR/5fXfmKwpmWZBmNSkW/R8ePzoS5aa2HXZ0Wnr0UYzlTZXOblalYAWSBbq8ZURRYSJUZDeYZbrURTJe4Eivyxdt6+fmFOJub9WxqtaNWqZUc7pEwPrN61U+6QLJQZyhgRiUKPHxsHkEGi07kzfk0u7qcLCUVAk8sX2Ffn4tOt5EvPT/JZ67p5BcXEwQTebQ6jSIoQEBG4tlLUTa1WDkyk2NLk5YSamLRFH/4yDme/Og2fnQqQrla5eeFGolchY0tFp4fj7Gx3cETZ5fY1uXGaYBsuc6RKzGKVYlHTywiIPCRvV28dDnONX1uQpkqI6E8bqMGt1HDSqZMLF8hnKuiVWuYT5TwmHXE81UEocBKqkS1UuW5sTBn55KkSjUypRpXIllemYzTbNNx345WHj22wEu5Mj0+G9VajUi+Tqtdx/lgni3tdq4d8COKIvPpGteu8YAs8fLlGADfGYtg14ucX0xz14YAz43FuX2dm94mOzu7HBwYiTDoM3IwWSJRhvdc1UylUv39HJObdWo2tNlJF6vcMeRjOtXgC7f18fFresnWRDa2WAllK2jUaq5f40OjUaFSqehwGRGA/ev9aDUa9q3xs7fHyQM/GmHYp+VLByc5v5Rh/3o/wVyNRqNOvlJna7uVdFlm0G8inK/R5rZyw1o/D+xqR6vV4DNrmEsUEUSBSLaMy6Tl52eCHLuSwGfW8uPTIf7uli6iJRmXUaMgCS02VKKKVruO6wcD7Gw38cvRNFaNTEUSuX9bgPl0nZ3dTjo8FppsBm4Y9PHxa/vob7LRYtUwFasxH0mi1ojUJJHdPW56vWa8RjVnljJ86dcTNBoSKlGkxaHnszf10e6x8OG9XaRKirTt/VtbuXaNl0i+SqtTQUlaXSZ2dTtJlhtYDFqGW61E8nXsRt3qZ+qh12fmkWML1Op1SnWBWq3KMxfD+CwaNrU7sBk0nJ5PcWwmydGZJPV6A41GYVCu8ZtpdRi4f1sAv9OK26zhzYU0+4f96PVK0oJGq2Wt38ip5QL7h7zs7nGxlMjzr0eWfmdtvG2LulqXCKZKqFRq5hIFblvr5rlLMURR5J6NzfT4rOzscrC900Ffk42P7O2m32fmmYthzi4kOTodp9GQUKtU9PmVR/SphSyDzRbcJi1PXlhZ9aMWiRVlFtMV7hr2M7KSQ2pI3DLopdtjZiFZYrjFxmgwz3KywM5OBwGn8g/7zA1daLQa0oUK2XKDn56P8q/vGqLbb+e6AR/7h3ykyg3yVeh0aFnKSbxjaDU8yaZlLFSg32dCFEV6fVYe3NmGRq1hKlrg6l4nz47FcJlFqmjZ0mbj/Tva0K6KGI4v5PjYzmaa7EbanXoWEwXOLaZ59PgCy8kCU9EiQ01mzi2mV594ClNQFEVuHfKjVqlYTFX43K1r2N1lx2Q08te39CHLEl0eCy1OE4IA0XwNjVrDn1zdQTDfoN2hJ5QpE83XuabPjdNiYHunnY/s6WRmFYd+4VKUeK7Gq5fjHJxI8OU7+mh3WxBkWBuwMtBkZSaaR2o0uByt8KXb+xgNFZiN5cnWRO4acoMgqt6qNt62RW0zaNBoNLxvi59nLoZ5/Mwi9Xqd8VCW2ViO1ycj/PsbM3z6VyO8PLbCpZUMB0fD3DHk5a7hAPLqyLjHbWA2ludyOEe8WMdr0SOKInOxAueXMtyzqZkPbGtmTcBBv89MoljhcijLdw/P8OpEiFcn48iSwj+J5Ws8dTHM1jYrh0ZDfOPVed4x5GAlW+WP97byR3u7eeFSlBabElH85eensOlE9vU6+cQvRnGpqjw9lubZi2Ea9QaZimLOfuRKnEqlwqefuITfJPDE2RVWUiVyVYnNrTbWNFmYTxQJpstMx/KkS1Xeu8XPvx5dolarcWQ6wVS0wO2DbnZ0Omiy6pBliXMLSawamVRZZjlZIGA3sBDPMbqcotWuYzlZ4Klzi/zqQoRKpcpMLMdUXFG4hLIV2lxm3re1FY9V8UG5ea0XlVqNKKrwW7TEig067Vp+eDLI+fkYB0ZCvDoeosOlKH3+4Z3raTKr+f6by8zHskjAQ4fnKJfLq0kQdW4bdPHyZIJUoYpKpeLPr+tEp9OBLDXeqjbetiKBWK6C2yjyo9Mhmswqgrk6LTaBXreBY9NJWhxGpIaE3aBiPJihzW1mMZ7nhdECDqOaVLHOfdvsPHx0XtmRRZG7hv2cXMiyvdNOJFtiMVni6FSMS8EMe3q9fO3FFRwGDaIg4DGoSJQFruv3MBrMc+fGFg5PRfFZtJyYT6NSq7ir28KDPxlDlOA7R5dZ3+biu0fm2NJm5R1b2gkl8yRLdW5e6+WVy1Femc0SMEFdEHFZ9LzzKuVA6TNrOD6XosetJCe4zRoi2RL3bW/li89N4DQIzCcq7EZGbjQIZmu8OZ+m1aZDEAR2dzuRZYm/f3keu6aBwaAk3k5EimRLFR7c3kKqqsBqPzg2z/HpOLt6lXSCN65keccGH9FCnalIno/taWM5WyNg17OUyNNkU1rb1y9HmI7k2NTuIGBWcWoxh9so8sJihW/d3cc/HV7mH+9Zwx//8hLt7hyRfI3LQYEXL6d5l0HFaLjEvZtbUatVyo1v1/LkuWUWUxU2tZh5/9YAK9k6H/nReawG9e+n74dWLRLJ1+jzmmnz2LhjyItKpeaxE0vcv70ZlSjS5jKhEkTmkmVabVpmU1XsRjVbOly4TFq+emgap57VUHcBlUrF7h4XyDK5qsTHdrcwGckzFszz5Pkgtw24aLIbcZu0/OpiFK9Jzf5NrdyzKQDA6fk0z45GCVgVPvehyRQGNdhMavav96HVatk/3EzAYWKo1cGmDieNRp1vvDzLumYHX7mjl3hN+ZfMRHM8dmKJiwtxxkJ5vEYVrW4TfouGHV0OMuUGgwEbkiRzbC6HJMO2dhtanY4dHTaarDpSxRqXllOcmE+zr8/Hw+9eS6Issa/HycZOL//99jV8dF8XyTIIgriKAFnZ2eNRaLhOE2qVSKYq8+E9nTyws51ovkYoo3yek9ECZ+cTeMwaLoeynF9KEzCr+etnJymXyxyby/Ctu/v41utL2PWK6r3dZaDHr7RS0UKdAb+BsVgFi15NKFuh021GEAU2dzhRq9XcPujh7GKWrxyaoVarISEzELAilbJvabr+tt2pbQYNHW4L5UqFYKaCSlSxucNFo17j0ROLyJLM7h4X5xbT3DroIVyU+ea963jhUoRbh5rocBlxmbWMR8sMN1uJFOrMxQsAvDIZp8Ou5d+OrdDm0LO+1cZQs5kfn1EomR1eCzu6HLw4laHRaLCQKiOKIvduaUWWZW5d5yeYUZ4kz1wM49XJCILI947O84FtAV6fVeYGF5ezyDKsD5i5Y0MLDx9d4BO7WghlyhxbyPG+q5pYztb58+u7+MunJmi3qzm3lKXVocdhVHgpDouBW9Y4+fnZMEvpKl0eE2eXFNhMlmRiJZltbVaeOLdEMl8jYFXaq4lQlrlEkUiuRsCuo8trYS5ewG1UAwJPnl/hqk4XD+7sYCaa4/mxMEvJApPRIvdtVWDEPV0O3phOssmmp16vo9HoWEyX2dJm45qBJk7MpfnGS7Oo1SqQBS4sZ/CZ9UxHshyZinFVixm3QSSUqdLmNqESVQgCNOoNVCoV+9f7Wc5UuWtjQIm1cxpxmpMgy4gG63+d0PT/1ytTqrGvy8prl2OEUgVOz8Xx6iVGQwVceoEmmw5JkrDrFDSiY9UzeT6W44nT83z3jVkEYMhvQhQFtrfbCGertDsNfPn2XtIVmRabls/duganUUO8UOdLt/eyr99HrVbjhydXuHmNg+fHIuzrtDDQZKXLrdgAfPfwLCvJAheXs2iFBkcXy5QrZSqlEu955DR+o4AgCLjNWhaiOc4upHj24gpzoTgPH1/kzFKWTqvIc2NRha+drPCBbc3MJBTjmoDDxDq/idl4gSaLCkEUMWjg5HRE+Zu1CkGr32/mDzd6FVadIPCBbQEGAjbOLSQVC7BEgXw+x+m5BIcno5yciTIZybOSyOKz6qnXG7w2HuKxo7Ocnk/SkGS2txr54akQ77+qiYsrGbRyhTPzSV4YjxFM5lhKFBAFgSNXYsoTUy2yf0MTrU4Tw602avUqwXSFkaUk/+3xMf7x0CxvzsQUM/hEgclQmhcvhZgOJvjV2WUqlSoXl7PU63VOLmTY1W5hKZ77nWPyt+1OXajUee8jZ/GaVcSqEMpVOTyT5s61dp4dy2EQZXQ6LePBFA1Z5K7hPO9+epRyRdlVLXoNl4INJoIC2aqEw6hhb7eDb744RaFSp9etZyZV5/mxCBPhArIs895Hz/DerS0cGAkjyfDo8QXsBg1fODDGX9zQxVhIeZ3HpGEiksegFliOV5GAZy6G6PJaEQWB77+5hE6nx6UXiJVgOprDZVRzYj4LkvIUuhAu4LNIvDYRQq/XE7AZcBg1tLrMBNMlnr4YRpYlZsJFxlayJPMSE6EKMvNMxupsbKnzxnSWQqWBxxxBLzT4l1iem/odvBzMMRxMc2ImwUS4QJtLh9OgYjlTY51Px6tXMvzBllaeGYnwxmQMq07EaRC5HMpycSnDdYM+LkfyjK1kmYtVaLWX2dxmR6NWE8tVcJo0vDIRIZ6vkSxWmYnmODydotul59DlBF/ZP0iTVUutVuPofJF2QeT0bAyNWsullRSpQpWng1nW+M0cnk5iUEn8/EyWh961ltv+7QyFMohG21tOFN+2Re0wafnAjjaeOLfMzm43n+h1U6/XCRUk9IYMgiBydb+Xer3ObLyAVqfj2jUeItkKc4kCdw/7CedqhNJFHlzv49hsisMzGe4Y8hHJVujwWnmPw4BWq+G9KKH2oaPLtNl1mHUKTqtSqel0G/Fb9Qw0O7kUKpAqN/jr2wbo8FqZj+WYNOWpzae5Z1MLg61uKpUqK9kavR4D/+PlDFd3mzEaDLS5zDRZ1BTrMg/ubOPR44s4TFrmUjUe3NnEYLOdlUyJSE4xkrlnU4Batcq3k/N8+vpOvvD8DHv77Gxqc3BqKc/JpTyfu6Wf00s5rmq18Omnx7ltwMqPzkRY7zcQL0l86oZeZqI5ev02TswluWvYxutXktyyzofeYGBzu5Ob++0cmsyg0WhxmSWuWaPlHZtb0Wg0vHtzE8+OxRhuMtLT5KTba+EXJ+dI5KsMBiz8xQ1d/ODkCjqtjusHfNw86EWtVjPU6uDEXJqPXd3DlcdHabaqCeYaXNWuRRZk1vitVKsV+ls9XN/n5Bsvz7K3x8jPLsT506s7qNVrfPSRt0Y/3rbth4BAj9+Kw6wjYFc8KJ4djXHjGjcdHivxfJU1fgvL2TrLySL//Oocf7Svmy/cNcRnbl7D2lb3ajKVwIGRCFdiBfq8RsaCOaZiRW5Z52cpU1lVYof58ZkwH9rm4+uvzJMuVAhlK6ykS4rnnFqFKIqsb7bQ41ZSZ29Z5/+NPtJn1zEWKtDjNvCDUys0Gg1emkzyrbv7SVRFkqU6sixTFzUUKg1OzKVJl2po1Gq+un8NJxeyHBwNs6fXw65uJ1fiRRbjeUZDBZwmLT85HaZQg/UtVtI1gU9e3cV1a32cWc7T7DCykq2ys8NOp9fKljYbBqOBHV0Onr4Y5shsWvlAZbhzYysf2dfNNWt8SA0F8vvii/Os9RtosevZ0ulEFASGWp30eU0cnEiyscXMMyMxLixnkKQGyzmFSBbPVfjYz0aIZsvctNZDj8/Ci/JCuiUAACAASURBVBMxJFniwPklpuMFBpvtrPGZMJmM3L2xmT19XjQqNdFcmQPjyqHzmy/PMdxiRaPRUq1U+MmpJQ5NppCqpcxb1cbbtqhBZiaSw23Ws5IuUqsrAZwPHZmj3aFHEOD5sTABs0iq3GD/sB+1Ws1EKMd8okS/z4woimxud9LkMHFNr5s2l4ktnS5cZiWe7vyCYhnQ6THx3s0+fn4+yt5uB9u6XLS5lYzsFrsRlVpFtVbjwEiEs4sZru6y8t03ZhgPZfj+yRU8RhUes45DEzF6XDpUKhW1apW/fu4Kf7y3nQG/hVixzp9f04bDYmBTwEiiUGVDi02xEetyoFKrkCQJlUrNGp+Z6XgZn1XPuiYLQ20Oulx63lzMs7PLSYfbSDyvmPyEMmVenogyFa9wYTm7+je7GAzYCDgMdDl1HJ1JMBZM8+sRxWH1ibPLnJhL8bnbBrh10MfRuQznFtO02XUsZao0Gsom2eE2sq/fh89p4o51HkJ5ie+9byN/flM/w+0e2p0G7HqRR48rM4RGQyKWrfDqZJz3bvZzaCLGfKrC/vV+RAFOLmS4e1Mzf3f3MP/0jkEMBv1qOJQfv0VDpgoBu4FGQ/qdlfG2LWpZljk6l+G2QcUc/PhMkruGlUkYCGxosbMYz/MPL17BooF2l4HnRoK8djnEiekwz15Y4fBUjFa7joBNx0gwSyRf48YBRQt4cDSEQy/w8NE56vUGjxxfJFOus6fXzfmlHK02HReWC9y01sOeHjfBbI39G5podxr43pF5kqU6V/e4CKUKLKar1GpVyqUyU9ECrXYdoazifffsWBRBEFhJFvj6y/P0OLR8/eVZ1Mi02hU2oSiK3LzWy9MXwiwkizy4sx2PVc+ubheiSiRgN+CyGrimz02f18RfPT1ONFNid4+HnV0OPn5tH5+/cy33bmqm2WGiza7lseNLbGu3cXQ2g1MnoxJEVtJF+jxGMuUG929vYTZZoctnZfeqPEyWZQa9Op4bCfHQ4RkatTon5zO8e1MTp5fytDl0vDAe5dh0km3tVpLFGudX8gz7tJyYTSGKAoN+E45Vl6YWq4btXW4WUmUuLmfZ1mblxKxyaH52TJGYRbMVvnBglFcm49yxzovLrGwKv5dJAoWqxJfv6OPEnBJx0ZCk34zGnzi7hCQ1aEgSDVni4GiYR48t0GrXIUswESpQrVVxG0W+emgGSZIQRZEPbG3mr56ZZGublfOr0RkgEE0XUKlE9g8pxJwOj5HBZhu3r/fyyLFFJFlWmGdeCxqNhpl4kb+4vptYsYEANJlFzi5l+PGpJarVGtORjOIFuEbxt97aZkEURQb8ZpYyFURRRbkm85UXpnjxkqImB9jS4eD6fhdff2kWuxaeOLfC5ZUUy8kiXU495xZSTEbydLsVfstUKM03X5xCkiQ67Bq+fXiJjQEDPzwZZGu7FY1Gwzs2ePjBqRC7Oq00WfUcmojR7TZwaDzKTCzPtb0OHr8QxqJu8DfPjPLTMxEq5RJ+q45EoYqMzEgwRyRbpsdt4NxCivFQhoePzeM0aLi5387fHJzFbVJxfb+Ln50N4bPquXWtmx+dCtHvUrOcLGDXi0qMs1lDKFPBa9HR47Ny14YAK7k6ZpXMsZkE6WKN9X7D76yNt22M84+//8jnm7bexsm5BF1OHaPhEl6zhniuzKFLYZxGLcgy0UKDfb1O7tncikoUqTUaWLVKmKZBq+F921rZ0aWYiZsNWuwGNU6zlolQFqtBx7Z2K09ejLCl1cxKts4bU1H0KpGjs0mW4wWC2QpDATMus57lTI33bvEjrw581jWZeersIpdCNT64swVRVBHJ1bgcyrKjx8OOLidPnFvi8JUkdoOSb2JWy9y41qMoYlqVvPBspcGR6RT3bPDxw1Mh2mxqXhqP0OnS8cpUHpVcZWQlQ6Ko8JzPLiq86HSpxlKqSLNNy5/+coxPXd3MLy8m2NlpIVdp8MpElPFQDqsWljM1MsUqPouWuUQJSZYZClh58lwQnSAxELCRLkk023QUawrCMxrMcVW7nWa7gVq9wdmFFCatgi8Xq3VC2SqFGnxsdwtXDwR4fixMPF9lfYuVw1dSbAjo+eqhRSq1KvlKjXxFooFMwK5nU7vCJRcE2Nxi4eWpJKl8iWy5zrmVPEuv/Sz8t5/77D//ttp42+7UsVyFxUSOTLHKXLLMt985SJvDwIWlLHazHpBJVSRuG3SRKDWYieT41Zllzs4lODafZWe7lWa7csd//GejbG2zIgAdLgOHp2Ks9SnKa41WS7fbyFJO4vZ1XnxWI5Is4TPraHaa6HLq+KdXZ5kKZ/AZ4UM/PEelWuH8QopGo4HLYsCqg0i+zsY2O1varHS4TXR7zByaiOG2GHCadGxsteHQC7xyRUnSSuSrNNsVk8kuj5kHd7Xx4kSc+7YF6HAZCGZr7Ovz0maGRFlGFAT2dttZSBbJlet8cFe7IjCwG7h9uJmAXcdPz0dpsYhMxMoEsxWGm63Y9GrKsppr+txIsoSoUjPUZKbFYaTRaDAZznF6IU0kX+f6AR8f2duFz2YgkqthN2q5uJyhXq8zFStyaTnN5XCOpy+ESBbq7O60IckS3zm6xJd+Pc7LEzGu7XejWnVqWkoUqAMDLjVqjZZPXdeJKAiMBhVNbbVW46nzIRaTJba0WPDZTdw17OeeDc2oTHb/W9XG2xbSA0gUG9j0alxmPY8cX+SNKwnesyXAlk4nAhCo1jg2l6FSKTMSVLOx3U4wXWbQb+LXl2J0uI10uo188up2TsynQFamWW/OJZmL5en0mNjSZmOwxcHOTgehQoOhZjOvTsbZ5jDS7jLw8NE57ljnZWQlx6nZGLFciVcnogy2OHj6omLDlayAJDW4sJRhNl4gU6zw+VWDF1lSyPjH59Jk6yLX9FgJFxpky8rBaiSYoiFJyLLM6bkYb04rQa9DLXbmEiWm8vC+Dg1TKRXT8RIdHuvqQU7AZzWQKioKFo/NRL1WYzRS5qH3bUStVvPdN2bIVhrsH1KyxleydQaLBV6cynDHOg+/Hs+xr9cJAmzvtHNyIcvZhQRbOt0ANNl1rKRKjATz9LgMdHg8rKTLSJJykHt2LEqX28RD79uERqPh1yNBhQarUrG+xUo4W+b+LXByOc/uThMf/9ko925pZU+vh3q9zom5NHcMeXny/AqJfIVOh5anL4b57ns38JnfRy89i17NvZtbGAjY2NrlYVeXk+sHfOzf1EafX7GuOrGQ4yt39tPssXPPxibu3NDCzi4HBycSWA0afBYdvzq3wpvzGZosWlqdRvb2e9je5eTejX7anAYi2TL1Wo0zy3mu63UwsqxkkXe4DIrmT1Sh1Wq4e0MTOo2ODqeBda1OPrS7g4DDhNusp80GnV4r925ppctrwW7ScymYYT656kYrCPzR3i5uX+simK3yx3vbQJBZSZfxmHWMLOdoNBp4zHoEwG/T8417B7lrUyub/FrOhCq0Og00pAZtDh2LyTJPnF+h2aFnoMnKK1dSfPXOfm4eauZPru5gOqFoCv1WLW6zjnCuQrtTz8d2t3BiIc8dQ14GWlx86tpOdDotNr2a58biuA0ip+YV35FguszoSo5orqIYdZYbdHst7O330uoy0eYy0e01I4giUzFF8dfhMhLMlGlz6rkULuAzaxkNF3nHsBed3gAi9HjNSJLMt16eIZIpsTZgxWfV4zIpLeFyusSf/uzc76yNt+1OrdeokCWJ+XSND+6y84lfjPEnV3dyaDxGi01JoHpgRyuLWYm/vLGXFy8nWBwJspgssq3TSZvLzK1Dfh49vsiWFrOiXOk2M58oEs2VSZWUnXI5WWApU+WRD/Tw/TdXFCcoSebolTjRfIX7d7Si1Sj98IZWK9OxHPVGna+9cIW7NzZRqdSI5qHNoUen03HvxgCPn13i5HyaB3e28903ZvjFqQX+7Ppe/ua5SdK5Gk+cD1Kv1piM5PjIvm5UosixmRQtTgORbIlGQ+LTT13CrtfQ6VZM56VGnUiuxkKyjF4jIKIYwe7t8yBJEvf94AK7u2w8fibLrl4PH97TxS9OZXEYVYRSRf7uYAy7QUezTZG21Wo1Pvazyww3GUmX69w/7GM+WcJj1tPtMdPnt1Or1Tg2E6fVZUYURARBYDqaI5KrsrPLRbVaQ6PR8MZklKfOr2DXq0iVGqhEkTuHfMzGcqQKZV68kuGT+zoRBVgbsPH8pQj7NzTR4zXz0mSSVqeRJpseGchVJRYy1d9P9CNTqnFgNMZX7uzn9dkcm1qsnF7K0mLT8MxImFheiUybjig86hvXuEAQaLHrEVUiHS4DU9EC29qt/PDUCqNLSR4/s8hCPE+Tw8Q37x3io/u6uXFdgF6fFZVKxfYOxfRbVKtosulJFescn0nyxmSUer2OKKro9pgRBRXJUlWhffY4kSUUA3a3gdcuRxAFFU1WHc+NBAmvpsiqVCI39jtxmZV883RVJlOuc2Qqjkqtps1lpN2hx2vVo1KruLbfw3CzmcVUBYMaLgQL7O6yc/twgIDDhNdqYDlVZD5RRFSp2NvjpM1twWZUzGVEUaQhSUSzFTwWPffv7ODf3rOed23rIJytKo5MLeZVZX6NLx2c4ienlpFRUhxmYnmePL9MOFOm02Xk9uEAk8E0o8Ecm5tNVKsVHntzBUmSaHUYuGtDgC6vlbuGm2g0JJ68EGQ5XUavN/DRnc0sZ6tIksx4KIssSURyiuL+pgE3vX4boijgtxnp8VrY3Gr7nbXxti1qnVrkrmEfU7EiN/Q7FS+OLidqjYa7NzTz2Zv7FV1cLMdLl8I8fGweZGh16FlJFnj89CKPn11SxKvbmvHajKSLNWL5Cn6LlocOz3FiLsW+fi+3D7r58sFJnr4QIlWsc3WPkzXNDj68p/M3u9qx6QSCAHcN+wGZfT2KRdhMLE9DhmarhqfOLnBoIoYsS9w86OO1qTjIMts6HMjAm7NJZEnm/ds7+PKda9nW6USlEhnwW7hhjZvvn1wmklUybLo9ZiL5BrVajWi+RptNy5nFNBOhLO0uA7t7nMQLdWq1GtVKlTMLKUBmMKDYql1aSSMKIkvpKqcXkrTZdXz9xRkkWabZrmc2lidZrCPV6xRq8LlbehludXBVuw1RFOl0GYjlKsSyJY5OK/mKPzsT5NpOM1/89WWePLfCfVubUKlEgpkKoiDS4TJy5EqMpy4E8Vm07O318Jc3dPKdoyvMR7NMRQvMRnO0O434LVpWUiUePjJPj9vASqrEjk4bqVKdbLmOoNa8Ja73toX0vvUv3/n80NV38O3XZhhdSnHDgJvFdJVoKseFxRSvXkmCVGcmVmBnj4stHR62ddj56sHLrCTzqDUa3retje1dTp4bjdLp1GE2GehyGQmmy8wkSwz6LWTKDb5/fJE93Q729HvJliq8MZ1k62oIks+k4tmxGJvb7auk+gQnZxO4TBpElYp0ocxiIk+hLhMv1Fjj1rO+zcVAs4PFRBGDRiRRrKMWZJ4bjSPIoNWIHJ9LIcsy5QZkilWOzCS5c8iHXqPIrHRqkTU+A0+cD9HjMZEq1MhWZTpdRo5fiSGo1HQ6dLw2FV9NW5AwG3Q8sKOFJy9GGW6xsq7ZwZZWC6FsGZNeSzhTxqBV0+40kK/JeMw6cuU6uXIdr82EyyDywuUkfrOaXFViOqxMKN1mHReWs7TYtbwxnSJbquEwqJhN12h3GrAZNGTLdRwmLUupIn1eC2ubzBwYiXBxMUGP14her+fWATf5qsToSobZRJEb13pZShYJZcu8PBHBb9WTLdWxGPUc/um/XPnbv/nr7/y22njb7tQ2g4aJaIkBv5lCTeaR4wvUqlV+cirI2eUsN/c7eH1KAeuD6RJr/GYuh3MIgoDXYeHv716LKIo8dHiWeqPBpXCJ+7YGuBTOo1Gr+dr+AcVCYa2HTo+JaL7GYLOdNqeJNrue7x2d56WxZQ6OJ/jEvnb6m+wIwN0bAty3qxO1WsX2DhsgUK7D5jY7a31GXpjMUKlUmYoW2N3jUiiZzVa6PRb6fEasJg0bWmzodDq8Zi13DfuRZIkWhwGVWs1YsMCfX99FOFdjKa1ELt+3vRW7Scut63x0eUyUJIGATUc4V2XAb6XXY1RM2oEfn4nw1bsGWExXqTfq/Psb0ywmywRTJe5c72MpUeTASIQb1rjRqFWo1WrcZh3LqSJH5zP8wbCHY7NZblzjRqVS4zLpaHEYEEWBdpeJ6wa8XDfgpSiJ7Omyo1apkGWZhiQx0GSlzWWm3annR6eC9Lu1vDqZ5vRCmqtaLPz4TJh6vYEsy2xotbG+zUXAYUCSJDa32ej2Wgg4jdw64Pz95FMLgsDdG5rY3uPjq3cN0O8109tk43O39HDP6hRqTZOV+3e0Es6UGF1KcnQ6yYM72/jgrnYeObbAaxMhJkJZdnTaWN9i4ftvLuM0qLlvewuPHl9kPpblsROLDAWUU3ytVmNkJc+GVhuNRgONWsMtA06eH0/S6dByYCTK0SsxbhrwEEqX+N7h2dXHPNywxs3p5TK/fGCIyXiVbpeeK+EMr05GeXUyxrGZOLev8xBYlUd96bYe0lWZw1fiPDsS5cYBD2ubrGxss/Hj02G2tlmQJRlJavDYsQXOLRZosmhZztRY6zNxy6CPaK6CJMu0upSbckennfu3NyumMbLEaxNhLq4U2NRqxW9TkCC/Vcv6ZisvTSZotev44K52vBYdf7Svmy6HjtFgnt3dNmYSZex6FX67gXangSarjuV0iXC2SrPdQK/byEgwS6VS5cJylnMLScaWU7TYtCxnquzpstPrt2E2arhrvR+DQc99q+8tVqgSXIUGl5MlDo2FmImXef1yFJde4NNPXPqdtfG2bT/+6dvf+bzvqtvQCnUePrHMhcUkSBKvXkmiUwvkynWQZE4vpDi7lEWNBMi8cjmOXqxzYCRMt8eCJMtMRhUedL3R4HIkz5mFFJcjBTa1WDDpVByeTrHWZ+TAaIx+t5aLK1kiuQrv3NTMc+NxhlssLKZrfHxPCwdHgpyYT5Ms/K9EtFCmSpPLxEd2tfK5A1Ns7bAxFcnx1IUQt67z0+40MB3N8crlOAvxEmeWMhy+EmdPtxO3zciNa1ycXcpy+EqCd25pRq8RePpiiNHlNOF0EbtRSzxfZU+/h063icfPKrjuSqaCShTZ3ukgnC5wJVbmsWPzHLgY5LpeB+F8HZEGgqCiIUmcmk8Rz1W5pt+NVafiH1+d49xcjGPTMdb4TPzgxCKz8QI6rZZej4GXLsdZ7zfy0PElLBqI5Wv0ek28cjmmGPjEClQluGdjM8lCjVNzCU7MJmmx63j1cgy3Vc96n47JRA2NCEdmUkiyQLfbgN2oRRBVeI0CR6eTfHhnCxdXclxYTBLM1oieeq7y3//yU1//bbXxtoX0AOr1Gq9fydHrNdHnNdJiN6DWaNjT62YxWWI5VWS4xcamNgfBbBmVqGJPt42fnFrBolOzq9uJSq3mxGyKrW0WnhmJsMZn4eoBH/VajRNzaSLZCtf2KxTVWwfNPHZ8EYtWQK1SMRPNMRww0+O1IgjwyLEFzixl+drd61CpVbwxEaFSq3FhMc/pmSgiAvFckZVUCY1aTYfLRIfLyONnltjS4WQmmscf0OIy6/FaFHfTDa0qTswXlWGSTc/BsTCdbhP71zdRrVX53DMTvGezj4vLeTrdZgab7biNKtpdZq4bDNDt1PHI8QWW0hXu2exlqNlMMF3mO0eW+cTeVuWm0Iu0u81cJwhMhLM89uYia3xmvnV3Lx//xRjFmsRMNMe6ZhuLyRJ3bfATzNbY0GxiPFrkXZuaiOTrZJI5OlajLNRqNbcOuklWQFSJ7OxycHJBZKNJQzhXpSE1KBVL/PvRZT6+rwOdVsunb+hGFEUeOTqP36aj06Hlw8/NY1QLnFrM4bXqcRhUyJLMyO8IB31bF7UgiNj0GqKZMj6bgVC2qmDXiSK3DPoYD2b52wOXeN/WZtQqtSLxakh4zWrWBqwcGIuyscXGji4HJ2ZTeMyKkbvUaPDUhTA+m54tHU6azCLngiWqtRrdbgOtDiN3WFR88dA8W9ptaLVaBc24rKLdoUMUBWRZZjycpVarka3DYqrCxmqVQrWBIIo8sLONR47NMx3NcXw2zd0bmrAbtdRqdRqNOpGshEqtIpKv02zT0+210mjUefpiiIVYlvFQjgG/BbtO4EdnwlTqCoEJZM4uFrimt4gsmfnoTy9iVCvJXZ1OI48eW+Dqfhf7+n2cmEtRLlc4MF3gk24zrS4TLQ4j7U4DB0YifPLxcfwmkWxR5NlLcba02ZBBETU79Pz0eBqLXk2jyczePqV1WslU+cieLkqlEn/x5DhDLTYa9QYLmRrvv6oZkOl0Gzk+rbRddoOK8UiRzwwH+PjPRmmxa0EQuBTMIEsyW9rtq5F4emr1Oq9dTlFBjVyrFN6qLt62PbVeo0KWJVRqkbFQlkShRpNNRzhT5Mxcgj/75UWuRLOYtCKjwTw7Oh10uoycX0qxlKmRKsvcMeglnK2xmCyxq9tJu8fC/dtbODGvkPSbbXqu73fy3x4fZdin49XJGG9cSXBoLMhPz8f48v4BPr9/iFvW+ZmMKJ9xKF3m4aNzvD4RIZQp0+3SYdTA5lYLo6E8TquRRr3OYyeWCKaU7JM9fe7fePNNRIqcXshwZDpFvd7gQ7vaUWs0SLLEqYUsw81WAnYDhapMvVan0lBc/fV6kdem4vR5TQRsan51MUatXmf/sJ+1zTbCuSrTkSx2o5qDl+LIq8Shiqhl/zoP0UKDpXiB167EObmQYUObjU0tVi6uFDBpVdy9oYlIpsxcLM8T55ZpNBpYDRqWkgUeOjLHZChNJF9jOVWi0ajz4zNh9vS6uabPRYfXyns3+3nqYohPPXGJNyajNNt0lGUV65rtOA0q/uHVeb7z7iHeeVUb79zSytV9LkZDOfb0uPBa9QQsan5+NojHZuST+9p/P23HStUGU9Ei+4f8DAVsbO5w0WbXkS41cBlVXNPvJpKtoFarcRhEjs0mODabZFObkw/uauOzN/dxcCLB/TtaFEFuoki9pkwCt7fbeP/WABeWMowHM6hUKk7MJVnbZOOvbuqlIqt4YHsrGzs9TISy/NtrV2g06jy4439y997RcVzn3f9nZnvvBdhFrwQJsPcqSpRkyVSx7CRukm3JjmM7dhLXlDdxfokdl8R2qrtkO47lN7F672InRbGCIAmiA4vF7mJ7bzPz/jE4Pj75WXJ+v7z/KPecPQe8c+dgiX32znOf51va0Gg1fGBbG36rBotBS5fPweZ2K9etauG3t3byqX1qbfvuLa0s5uospErcMRJAlmX6A6prwH07wnhsehYylZWasJlIts4928J0eS0s5aocXOMj5DZhNWr53b09/N0dA6wOOXnxWhqTFra1m3nk3CJn5zLs6fOxptXO4ak0m7o8/PPvDPPaXJ4en5W/vWsNZosJv0WDoBG4YTDAvTs7EBBIl5v4bQaMRiMdLiNj0TxOsx6/1cgrV+PIioLTYqTXZ0Wv0/Ph3V3s6HZzZDLFvn4Pm7s9XIjkOTeX4YETC/zJzf18810jtHmtlOoKf7C/G1luMp2ps8pvZDxeZC6jynmMRgt4LXpEjQaPWccDxxe4ftDPp/d38tSVFHKl8D9PIqFYa+I06zg1l2Mx36DFKvIPr8zS5jIwHi8hihra3BbuXKfiDVAEQk4jsizzzOUUsUKdNS0Wnhpd4tC1FPsGvCxmK7hMIt87PEOsUKPVYeTYVJodXU6WSw3Wt1mIlSRuGwnw2nwBnV7HkfEEV6I5opkSfpsBi17D4YllfnEuzlDAxGOjMRL5Gg+fi7Kx0825hRwX5zMIosita3y8fHWZE1MpSvUmelEmU5G4FK+suHDpfgkOCjt0/M1z14hnSuSrDYyiQrmpkClW+eGxOSYTJXb0+djWqWEu0yReTDMYNFNpCkiSSjTo81no9lp4YTxFi13PXFq18ghY9bxwJa52TTucfPnpcTwWHYWGwrs2t6nWyQWJkMuIw6iKvP/joTlqtSbbuj0kCjW6/VauxorqF2k2xfWrAuwbDHBxMc/bV/t44GSEB05E2NHt5FK0iMOkQZYkzi+WuHdHG09dWubJsWU+fUMfg0EVX76jx83RiWWaTYlkVWZT2Ma7HzhPq018U8+Xt2z145+//Z0vfvGzv0+b08imdjunI0VaHUYGgjamEgUUVBTfjl4vIOAw6+nx28jVJD65r4tMRWYhXWYmWWZ7t4dev53ZZIHJVI0bVvm5Y10Il0W3UjmRiOQbxPN1ujwWlnI19vR7mU2qj2atBqL5Bh6LgWi2wrs3tzPgN2PWa9GIkC7UePu6MLetD1OuywwFzXjtZtWfZanIxjYrNpOeS1FVO6/bayVbk7mu38OxmSzTiSKiRsvWLhdVCda32bmSqBCy60iVJA4OB8hVGrx3WzsGg5GlXJnP39RHXRLQ60SyVZm5VBFFEDBoRS5G8mzv8WDVC9x/fIF2lx6jXsvBNQH+5cg8dqOGoZCDj+3tZjpVIZIuYdGLZMoNjs9kGWyxc8uaIMvlBvt6XJxfqvDeLW0EHCZcJg3X4gWWCzW29nhZHVJr1Q6jlvUdLo5NZ/i9XW08NbbM2YUcXR4TIZeZz9w0gAYV23E1mkNWQFLg9FyGgMPEezaFMFvM7Gi3ciVR48xT/5b78z/+7N/+uth4y6YfAFPxAl9/fpxvvjRFvVZn74AfjUaD12akxWnmj982gFajodtnpTdgY6jVQTRT5cVraW4Y8CA3m4yEHfQF7YCCwWDgA9vC9AXsiKJAX0BtXOSqEtu6XNy3q5Mur5l0uUmPx8hitko0V0Wr0fLhXV3sG/DR5jTw2MUYo0tFxhMlsqUG0ZzMgUEPE8kKbU49R6dy3DjoZbnU5JN7Oyg0RW5b46fDbWLAb0Wj0dDrNtLttbCrJN8gwgAAIABJREFUx0OX38Ytw0HWtnvY2+9jIOhkc4eTFqeFaq3BuYUsl2NFHj4bQZKaWHWwOuRgOl3lznUhwi4TfX4rrU4L8XydtWEVO/HYaII9PU7ihQbJYg2DQc933ruOd21qo8dvYzJZIZIqcXouy2i0xEirleFWFQcz3OZiuMXK11+a5d3rfVyKZHniwiLzmRpDIQeJYgNFURjwW5hLV4gV6vT7LSDL/OR0lJ3dDvp9ZjIViW+/OsWZqTiHJtM0G03OLWQIOY10e8xcP+BnR4+bEzNZJuN5fvLaIru77W+q+/GW3an/9u//+Ys33/UeFlIlvGYNibLMezaHmYgXSBTrDARsSIrAsckUtUYDh0mH32bEoIFUocZ3j8xwYjqNiEJ/wMKJ6Qwf2Bbm/hMLxDIlHjw5i89uYGIpR6xQZ3OHkyuxEvOZMj0eM987PMvBtUGakkSx1qTLb+fweILXF/Ls73cjCBrWtFqRpQbjyQqyAmGXkWOTSepNiYDdyHg0RazYRC8qvDieoinD6HwGi1HDC2MJTs/nuG7Qz/ZuD9cSZZaLNaYSRV66Gsei1xDJlDgdqeIxCzgNAk6bBVGW+N7xJeK5Iu0rdtUK8OjZRf769iFMOpUR47Ob6fOaGI+X2NLhIF9TaMoyk4kio9EcCgJOsw5EkU/s6cBj0fHtw3OEXEba3BauxQucncvS7zPw8rU0hyaSDAas9AXt3LjKj05QyFWaPHR2ibu3hYlmyhydzuAzi5i08ODrS1gMGvQahXSpzqn5AgeHA/QGbByfTLG7x81jF2LUGw1Oz6bJV2rcvSXEg6/NE0mXGD30pPLnn/+jr/y62HjL5tRWg4Yujxmv1cBsqsS2bgffOzxNuiL9Uq5qernA+pCJB07MI1yO4zZr0ep0jLRYuW9XF1PLBRYzFX54dBaf3cizYwmGW62cnk6SqkhMLia5HC9j1MC5SA6NICKIAkGrDgSYWS7y0pVl3FYDq4I2pKaEJEkkCg0UBV4eXyZfabK32/ZL8fPFdAkFePFSlAdfj/HpGzo5OlXiwKoA1VqNH54oU6/VkQX4nY0tSLLM94/MsqvXzVCrA1mSiGTKbG6zcnkph0cPclPitcUGB1br0eq0BMxQqMkEbToeOb/E2wZdVJoS3zk0yQtjcTx2M394g5GTMxkiyQKnZtJ8an8385kaZ2bTIAjs6vGokAITfOSn5+jx23jX+gBHpzN0uM0cnUwiSxLTySZus459fTZ6/FZkReEvn7jEaCTH+g43a0N2nrkU48Wrce7ZEuIfD83hMGlZ3WLBbzMyFs1yw6oA2ZrCrSMtPH81hcOk5SenozgNIleWijhNOl6fz/KH/36BWLFJqcabHhTfsulHolDn8DVVj/qOdSHu3dnBRKK8IpYu0Wg0eW40yl89NU6mqLZltVodB1f7ODqTR5Yl9CuNGr/TzOcO9LKUrxHL19nc5aHHZ+HlyTy7ux0IArQ4zPgdJtaGVMGXPT0uorkag602RFHgUiTDw+eiTCyXaTabJEt1VrU4uHXIy6uTBb7z6gT1RpOpRIG5dIVsTeIzB7q4kqgiyTL1el0VnbTqiJUk2twmNBqRB44vELRpOTyxzPePzCDJMkGHgR+fWmRbu5WaDE25icMkMBZTzZcEEXZ3u0gUm1gNGi7Fy/T6LEwsl9FqNVj0IscmU0SzZQoNuGXIw2y6gqIouM0a/DYDx6ezjC0k+YunJ7HqBDxmLUemshTrCpPxHAiQq0m4rQYkSeKpy0levhylVq2SLtYYaXNx+0gLOp3qmlCoNDk1l2dju4N7d3YRdttoseuZz6pSDneua+Grz03S7tSzrdfPfTs7+JNbBvHZzeh0OrZ2uWn12PjYrjbWtjkRdEbLG8XGWzao/XYDH97dxfp2F4cmUvzg2CwOkxZRFLh5tZ9YsY7XYWZ1q4ohHk/WWBe2Ey1IfOlgP09cSlKt1pjLVBlusfKjExGCdj0hp5H+Fhcf3dvDQNCOTqunJots6bCjEQW0Oi1Ok5bRWAlREGlzWVjf5mQhV+euDa30+Sxk6wrr2p20OowcnkqjE9VGkSxL1GWRDqeRuzaEyTc1jLSqDPRnxjO8a12ATo+FO9a2sKNX9ZW5d1cHA60u9vb72drpYCFbA1nGZdZzYraAXgetbgfvWhdga4+fD2wL01QEdDqtangatFOoydw0HOKbv7WOdW0uWl1Wwi4TrU4LvV4TR6ZzPH8pxkwix7nFEoIgcO/OdjJVgS/e0stIuxetVsuBIT+3jwQ4MpVjd6+PoVYHt48EKDUFVZ10ocC3XplGEESCdqNqaCrJxItNDg77VXx7ssxCrsbWTieXExWu67YxmVS/2LKicHw6w01Dfo5Npbn/xDxBh5mA3cjmLh93rm3lynKVdOkN7V6At3BO/Y1/+OcvnjWup89vxqDV0uowEC/UuRzNE3CakGSF929u5dkrKVYFbXxqfw+IGsaW8iwXm7iNAk+MJrDqNeSrDeL5KpKskKvUUQSBnb1eJAXWtjlYTOY5u1jBZdLwWxtDTKUq9LiNrGt3kas0SRQb/PbmNpaLdV4eT/JbG1op1CTOL+Qp1Zsgq2i62UyNoRYrVQmG29wcGPQxnaqwt8/D9i4XhydSnJjLowWMWvA4LPjsZla12Innyvzk5CL37QhzZCLNzWsC2PQCsYIqTzyfLqEXRTJ1hYVUidvWhWlzmRBEkS6Phb6gnUylSabSVDWmCxLDrTbOLuTZ2unAqNOSqjS4bsDH+nYXhQb4zQJPjSXpchtxWowspEsUaxJ3bAixJuzk1NQy5xYKdHqtvG97J1qNwB/u70FBYLjVRiRboVBtsKPbzWMXE9w2EqQuC7xrfSs/OrmIXQcPn4uj0ypoNTr6A1aK9SYWgw5JlnGaDdy1Lsi5hSxmncBUukaX28hsusKZR74//z8OeioIAh1uE4cm0iwXayxma3gsOvwOA1KzyXyywFdfmGJ3jwtBEPjyM9eYXi6yvdNJm0PHWKzMt98zgqhRIZkuk0rHH4+XmE3k+f6RGRazZY5Mpnh9scyuLhvjsQKfeegSlUqVJy4t89DZCIIosL3bhSiKzKUrXDfgZanYRJYV1rXZ6XBomUxDPFchXaqzvdNJrtJkIpbjX16+xrMXo3zv8DRzmRobOtx8fE8nfoeR564kCdt1DAasXI7mmM/W+PLtAzx3ZZkrMZXBfXQmR4tVg99m4vx8mReuxLiux4HTrOfaUo4fHJ1BVlCxFpLEVKKAz6Lj4//7EmG7Dq1Wi8esI1Fq0uoy0uK00OW1MJ9VSa5PjiWxaiRenUyjyDLj8QKvzWaYSZZ49Ow8r81kCDpMtDoNPHFhkYuLeX54bI4XLsf4xouTNJoSsVwFBAG7UUSr0+E3a3nhaoq3r/Gxu89Lownv3NDGngE/3T4rIZeZ2ZTKaWxzGvjcI5c5NpnkidFl7t4SQpbkX8Jo32i8ZQ+Keo1Ai8vCBruBbr+Vw9eWSZeaeMxafnEmstJwEFguq4e3gF2PgsAvzkTIlBu4TFqmUuruHMuWuRwv8adv60cUtYDMtk4HwopuXNCqQ2/Qs29Ay+hSgXwdPrGng1hJotNr4cj4MqKoQWo26fHb6POZ+erzk9TrdS7HymxpM7K528erE2l+eiZGv89CJFPm9fkcJo3qYqvIEhciefq9OrJVieGgiYVsjYUVf/M9fV6eu7xMJFPBYdQykyqzq9vJ3780xcfanPQGjFRWBHCW0nleHldY1eKgy2Oi12/l6GSKpWwZr0XLp67rJpJvcGDAzj/F8tyzNczzEzluHfLyxGiCO9YGeeBkhFuHPHzrpSnWhdSSoiAINJpNGvU6R2cLbOl286Gd7TxwYoGrsQJ/c/sgL46n1FRLkhA0AplKk8lYnki2TpdTxxcfW+Tdm1t54MQSIatAUQJZlpEkiUcvxLht2I9Or+foRJIun5Xr+j2Ioo9Gs8mVpTwPvr5Iv8+EaDDb3yg23rI7dbbSAFQh74lYjnS5wdqwnTa3WRXlFkRanFa2tKnkgD0DAW5ZE0Cv0/HXb+9Dp9ehAEuZIus73KxptRMrSuzu8xKw6vjGS1P85ZNXuDif4pFzUaSmxOV4hS/c2IfHrOFnZ2O0OdWnwsRymS6XjhevJHjlSoy/eXaCz97QzW9tbuez+zvJ1AUOrgvx0et6+dD2dvauCtDld3DXhhA+u5GBFbkEk1Dnq8/P0+vUcClRpVgsoqCQyKmHuEi6RKvDiEYUObeg6vwZtHBoMo3TYmBPr4v1bQ6mMzL5agNFkXn47CK9XhO7ej3cua6FqWSV2XSZkE3H05diXIsV+NbL09w86OL0QpEv3NTHcJubvf0+1nZ46fJaeOlqjC8/fZX5dJFHzy/y1OUUXz7YjwD84OgsO7pd7O528sCJBRRFYU+/l66AnT19Pm4cUg+Lb1/j5+XJLNcNeOkPOrmu30u+IbAlrGd0qcixKdU//ienokiSRKvDAApcWioRsml5ajTB0ak0fV71iaJIzfobxcZbNqidJh07e934rAaOTGUZbrGxmCkjCCIeiw5BFNne5eAHR2e5Fi/Q5zXx/JUkQwETH/nfVxhusdLjNnB8JociS7S4zNy8OoBWqyFZUfiD67o5MBRgVYudbLmKIAqsDdt44MQC6arCezcGOTGb5YdH53jPRj8/PrmI06SlKTXxmEW++twED56c4SsvzOAxiYzHSzQbDb5zaJJXr8S4ZU2QW4ZbEASBs4tFqrUa55dKDAb0WCwWbhzw8q9nEoRsOrQ6HU1JIlWR6PFZ2NDhRpYk1SlMhl6fCadRw5HpLG8faaXHJfK9923gd/f2MJEo8NGfXeDFsUWml0u8f2uIpVydB47P0+EycetwkKoEp2ZzBO1qoD91cYl+v4XJZJXb1gbR6fTcvraFfQNBtvf6+cPre5jJNjg9m2FsMcO1pRyPXVwika+udGlLtNq0ahrjNaOgcGQys5LyaJhJlen0mJCaTS4n6tTrdbZ3uTAaTXzlzlXodDoWsyoGZChg5senotw2EiDkMDCXrnBlKfc/s/qRLNZ5+GyEsMvE/kEfoihydqFAtVphMllGQGEmVcZtNWA1qjZtCgq3b2jnz97Wr9pRHJllV4+bgVYXAvD8lSS9XlXm9vUFte0+tpglVpC5cdBLf1BNSW4ZdPHkpSQf2BrGazfx+MUE27tdeM1azi4UUBQYDtkw6PWsCpgpVCVeGlvkoTMRJmJ5XrmW4rGz83zluUl8Fj23Dnk5Op0FBZW9Lct0eC18cGuIWFnh8zf2ciZS4s51LSioYpe5mowkNZEFkZNTScajWYZDDur1OpeSMv/00lW+d3gat9XIP7xzFelSk5+cmOUXry+ws8fFh3a2c2ouT7vXyu5uJ3v6fXS6TVyMFOhwG3l6NEa3W8/p+QJrWu3MZ6vMZyqAgl6vZ1XQxsFhP6KoIZKp0OW18o4NYcIuE7PLeb707AS1alW1DhEEdnbZeWosydYOtdb+g2NzAPR4jSxkqxyZSPLB7W28OJ6m32dGliQm43nOL2RxmbV0eS10ea2kKxKNegOlUf2fBz31WvUE7Co3DkWhL2jn43s6ODZTYF+/l6BLxWhoNRpWt1hBEJAlmW+/MsGJmSwPvz7L4xcWEQRR5c55bRwY9PDZX1zi0NU427ocRLIVFrJ1+v1GJpNVBoM2ms0mX352kkSuxHOXl/nsDV1odRpm02UEjWr0GfZY0Ov13DYSRKtVybdHpjJ8+vouVrd7ed8W1fjycwe6VaOkeJnrBvxs7/XRahH5j9Pz/PzULA+djRC265hMVrl3ZweDQRvffGGSoxNJ+v1W9Ho9921tYTGnMJORWR0wc3i2yIhfi8loQqvVMuC38G9n4niseq4bDDCXrnBkMslCtsaWdivn5jI8PRbj6mKaEzM5Dg776POZWUgV+f6RGd6/uQVRFBEFgZuG/MSzVfp8Zr57aIonx5L0+sxMp6usC9sRNSLxYpNWp4n3bQmzXFb92BVF4chUhjUtFo5MJFnMVHAaNYQ8Vsw66PZaORspcCmSYTFb5skLi1yNFYjmKmQrTVxGkfuPzzGdLLIhbGVyufKmsmP/FW/y+wVBSAiCcOnXXPu0IAiKIAjelX8LgiD8gyAIk4IgXBQEYcOvrL1HEISJldc9vzK/URCE0ZV7/kEQfsPRdmXUJQVFUQ8YL11ZZnwpy6Pnl3AYNei0Wj6yu4t2j5W1IRtanZ6pRAEEyFZl3rcpyKGJNHqDnoPDPl4cT9PpVoOgP2BZgaJWmIiXuWHATarc4OhUitEFVY52TciO125iIV3ia89PMRJy0Ok2kSrWSeTKRDNl2p0GnhxLsjpoIVOFO1a7+NarC1zX76G/Ra2WjMdLaPVafBY9fQEbQZuO58eLLGTrJEsNMhWZmWSRV6/EeHp0ifF4kdtG/CrRVVDlaibSTfYPenAaQaPVcGDAQ7Ym0+a2sKvXy3WrgmzvcpIs1tjd68Zp0a8QAcw8eTmJyyziteh5dTLD5nYrD5xc4EM/Ok2j2SSaKfG5hy+xnCujKAqjkQzHJuIoisK+wSB3rm9l/1CLWvHJ1ZlNlnj/5hYev5ig02Mh7DL90vXMYzNy05CfMws5JLmJVqtlc9jK5USNO9eH+IPre3htvsj7NrUgajTsG/CxdyDA9UMBVSXKoFkRzBGxmbUoivKGItX/lZ36R8DNvyag24AbgflfmX4b0Lfy+gjw7ZW1buAvgK2oPuR/IQiCa+WebwMf/pX7/l+/641GqtwkmqvT6zNxfj5LutLAZ9MRzdW4GisynywQyVTwmrXE8nVuWRPkHRtC/PT1ONcP+vjrg4OcX6pxYNDDdLLExfkULXYDU6kKYYeO6wa8TKWqDLQ42d7p4MRMFlHQELAbuGtDiA6vFY9FC4rC0ak0q/wmxhIVJFlhLl3l4BovS/k6PovIc9dyDLeqWOjZdJmbh/xEcnXu3tyKTqfmmYosE/YaeOf6Fn53dxcf29/LLcMtiBoRWVa4tpRV5Q4EEVAIOowcHPbR6rEgK/Di5TjPjiWwGPUcGPRwfDrN9HKRo5NJLkZyTC8X0awI7gy3ufjcDT2cWyjQ47chAFqNhq/cvoq+Fge7ej1s7HDhs5lQBIE2p4FXLy9RbIpIksTqkANREDg0Huf8guoisJAu88CJeRxmHdPJIs1mk6dGY7xtTZD1bU5eGE+xoc1Bp8/BujYnS0WZTe1OQEEAvGaBj//8ErOJHIcmUgwErOi0WnZ0uyk21BKfIEDQpn9Tc9DfGNSKohwG0r/m0jeBz8GKjLM6bgd+oqjjJOAUBKEFuAl4QVGUtKIoGeAF4OaVa3ZFUU4qiqIAPwHu+K8EtEGr4XMHetFoBARRwGEUcZu0oKj/6V6vkfFEiVPTKeL5Gi0OFdsxGLSxtcPOaLTEYr7Jtk47V+NFZhN5vvbsVWRFps9v5YETCyxmKxyeWKbLqePkXJ4Pbg+zZ8BHLFtlNlWh02Mmnq9wdl61QbuWrvNH+3vo9tup1+scmUxydCqDxaBlU7sDSZaJ52s06g0uLWapVCt86+VpNoVVZvjTl1P82YFOQk4DPz+zRMim2kPs7PHQ4TZxPpInW2ly4yovqVKDWq3GXz99jU0hC5kKuEw6un0WglYdX39xGr9FQ6fXQsCqJ2g30Bd0sK/Pw1SiyJMXFnniQpSm1ORzB3pwW3T8x+kFfnQywnCLdaWSAXesa0FA4T/ORJjONPjU3jCz2QaSJBHJVLgaK2LXK7Q6TYScJtrcFr58sJ9Xxpc5M5dmOp5lbDFLJFNmfrlAutKk02smmquwmK3Q4TLwhYfH+LsXrnEhkuNT+7v43b29DAZtjMeLVKtVTs5kec/GAPcfnyeWKREr1BE0b8x8+f9VpxYE4XZgUVGUC/8pWwgBv+qGHlmZe7P5yK+Z/y8MhT9+bJyd3XYuRsqcnE6xsc3ByZk015bLXIwWuHtriJ+8FqXFoTI3Irka3zs0TaJYY01INaE8MpGi1WkgUajhsRmJZqvsHVAf8Shwdj7N6bkcB/rhq89PcuuQhyuJEgGHEUVRyFUlBlscfHhXJ4IgMLaYRaPR8I0Xomxqd/D5A93846E5rsYLnI0UCDt0vDy+zEtXE0hSExRVPP23Nrdh1ov85ZPjlJsqou+1+QJfuLkPURT57qFpRkJ2NKLIs2MJ3BYdo/MpKk31KazXwPp2B4Igki3XGWrVcH4hTyRT4bXZDIWa2vB5aTyJx6xjQ6ebxy4ssZhr8IVHLuM068iU69y1Mcxivk5/wMporEiH20ixruC2ivT7DTw1lmJNu59nxkrs6HZzKZLm+atp+tN1rhvwEcmUefFKAllWGGm18+TYMoIAKAoarQ6vSfU/V2QFSZKZTJbY3Onkro1hovkG08kSj16I4bPqGY9m+LfTiwwFLYxG0sgKJMsyPU4tL/93dur/PARBMAN/Avz5/9d7/7tDEISPCILwuiAIrxdzGT64vY10ReELN/XRH7SzocOJx27ivl2d3LmuhTOLZT60vR2/Vcv9x+bocJvYM+DjHetDJAoNbh7yIwiqgueGdif37uxEq1VZ4ouZCroVCbO7NwXQ6XSsabFyaj7P7i4nHT6VnbF/wM+ObhdfeW6CP/2Ps3z+4Uv0+cx8571rCblU22WbUcvv7evnzvWt6PV6en1m7tvVSdBl4xN727kQLaIoCjadQFUR2dmt8imDNpUIfDVepMWuZzSaBxQuLOaQmg3SNYHr+xw8MbqMyypyMVqkVqsyEa8hyzK3jfhJlRu8Y30r79nUSqzQwGkQyVSadHstfObGfn5/XyeDLTaKTSjUFY5Pp7h5dYAbVrdy+3CQJy4tc+uQl1aXmR3dLlBkBAE6XAYazQYBp5UP7+rgAzvaiRXrTKaq3L0lRK/XjCiKfGJPOzqtlni+RtCqI12VkJoS0WyJZFl1GpvP1Dg+lUaSZS5Ecqxvc7Kr14Moatjf7+WdG9oYCjn5yO4uQi4TZ5dqyP+XpXx7gC7ggiAIs0AYOCsIQhBYBNp+ZW14Ze7N5sO/Zv7XDkVRvqcoyiZFUTbpLA4WstUVf/Iodr1Asqww4Lcyn6mykK2xo8vJ0ckkD74eo9lUdwGAoVY7iUKNK0sF1doCgaUVW4qgzcCFxQLLxTp7u21899Akf/fSPK/NZlAUGZde4chMlgMDHjQakVihxvHpNLeschMry3S4DPzg6Bw/PDbHmdmUilCrNhlqtTPY6mKk1ca5iPp5rG9z8MyVNPftbEcUNUxn6nxkRwiNVsN0sswj5xdpNpsIQLfPQqvLyi3DLWxod6HV6vjc9R2cXSiQrTbp8phZG7JzbCqNqANJkvjxa1FQQJYVnrgYp9FosJCr4TBpOTqZ4sRMliPTOURRw1fvGOKdm9SP6JmxOH0+M3PpClKzyeOjcWrVKsem0qSrMs1mk5fHlvj8I1c50GsnUWzyr68t0mLTc/2AjyOTSR65sMQPj0zwrVdmuHtLiEiuhkarYXXQwmy6zGK2ztuHfOQrTTZ3ONBqtYgrVtI3Dfl46Owi5+YzLBfrzGUqJItqr+XAUJBdPa7/uyU9RVFGFUXxK4rSqShKJ2rKsEFRlBjwOHD3ShVkG5BTFGUJeA64URAE18oB8UbguZVreUEQtq1UPe4GHvuvvhefUeHwRJrNbXbyNYmdPW7CLjMziTxBq3r4CrlM7B/0sDrspFarcWQixWgkg8+qU2lfssy2LgeCKDCdLBIr1LhjbQsjIRuf+PcxnCYtLXYtn9zXSazQ4D/OJVgVUFF9N68OsLPXSzxb4fVIkVuHPASdFnb3ewnaVH/yRKGOUSPz9OgSTUmi2WyyIWzn1FyeG1f5SJfq9AbsaLUadnU7uRgpkCxU0WgEwk5V2kKSZeYzNe7ZGuJ7R2aQJIkrS6rjQDRfp89nwSJKPDW2TJvbzG+vD2A2mfj6O1Zz20iAWKGuShtoNGwIOwjYjLR7LHx4dxd7et0s5Sr8zTPXEAQBSZKZiqb57qEp5lMFhlttZMs1plM1FODWNSraTqPV8NFdbfzsXAKfWcO+Pi96vYEev5VMVeKOkQAOswGbXg3UbT1ebuh388TFhKqSOuzn2FSS6USVbZ0O2txm3rYmwJ3rQrxwNYkiy/jtRja0u3jb6gBuk4ZvvDjJQqbEWKz432u+CILwIHACGBAEISIIwr1vsvxpYBqYBL4PfAxAUZQ08FfA6ZXX/7Myx8qaH6zcMwU885veE0C9KfH5R8ex6AUEAbKlOqBweibJw+ei/NVTV5lN5Dg7l0aWFQJWPd89GmFdi4H7j80RsOo5NRXn+OQyD5+L4rdo6fXbUBSFmWSRWL7ObSOqwr5GqyGSq7NvIMCDH97CO9a3kCjUuLSY5Vo0u8IOD/Hs1Qxeix5Zkji3kGUmWaZWr3FmrszpmSTPX1jgay/OkCjW8Fu03H98HgWYTBS5vJDkzEKO5y9naTQaGDWQLjV4/HyEqUQBSWryg6OznJpKIssyHouBXE3GooXNbRaem6ywNmRBEESeH09RrdX57qEp/uTRy3hNAt0+K0GbjvPRIqdm1CfIpUiGR85HmU9XkOQm7Q4D44kSr0ykqFarTCxXODOboi7BezcFyVQkZEnmB0dnieWq9AZsWLXw87MxFtJFbl7tpylJeExqmrGxzcFCushXnrtGwKrjTx4fx2oQ6fOZefT8EpeXChSa8MCJBWYSOf7o3y8ymVAtmxGg1WFgOpHjS09dRavVYdYJtNgNXNfveVN96t94UFQU5d2/4Xrnr/ysAB9/g3X3A/f/mvnXgTW/6X385+GzGfnGu4Y5NpXmlasxKhL0ek2EPVZ+v81BNFul3WvjaizKfKZGu8fKJ/Z1kaio6xZzFcaXq5h1qhTvy+NJDq4Lc/iayPlIDo9JiygYaXVbyJTrLOaq9AUlnhmL85Pjs+zt9/LQmQVOzaT56J5uptM1Do6LfchjAAAgAElEQVQEkCSZR84v4bVomQTSpTqSDG6TDpPJxBdu6kWn0yEKAiGnCUVWmEvkODqb41O7Qnw6OsFcpobZoEWrlYmky7S5zfQE7MiyTPdKLr82bKfZNPPqRI6Hzy7iMMJHdnczl5PY3J5hJl3FZhTZ0GbjcqKKLEskSk0ODgc4NKF+0U/MZHGadOztdXN0Jsd8psKg34rTqFH5mttbePhsBKNG4MnLSbwWPVqdjlUtDpLFOscmU5Qk+O0NAa4u13jkzBw/PRXBrBPocBmZKsoqMWM4oHJFvRaOT2d4/moSBbhxTQtrQlU6vFbmkwXcFh2CIBDJVlFkhcVsjUfOL/GJvZ3cOtLKC5djvHQlgSAKyLXS/zxz0FylwVymQjxf4UK0zM4OG3/74jSKAkv5OlPpGu0uI/0BK7etDdIXdNAbsNHpMZOpSrTYDbx/azt/sL+HYkNBVhSuLZfZ0+9jbdjBRLLCUr7KB7aGyVclREHDTLJEt9fC6lY7H93bwzs3tnPbujBvWxPkyESKpVyVR1aEXppNiS6fhXylQROIZUts67TTF3AgoEJnZVlSg61YZ2engwfPJVgbNrOl08V7N4d4/7YO9vT7GV0qqVYcGg0bOlxEMhUefD3K9f0uDBq4Y10rw0EzL11L0+83AzJ3bwsjCiJarY6RViuyJJMsNenxWVjVaidZlvnAtjAOo5bLiQr7B330+O2EXEayVYmgTUe00FSNO0URl0HDhcUiu3o9fGx/H//r4BBanZbr+n0Y9HrevtrLWKxEyGnk4Bo/i4Um1/V7ua7fy9GJJF9/bpyjU2m2dqhQA4/ViKAoaDQC+/ucnI0UGAnZ6fXb0GpEQh4bH97dyUibi1cn0nzthUlWBa10u42IouZNY+MtSxL4+3/6ly+61t+I16JlU7uTmiLS6Tbhtpko12XWhGy8fDXOoYkUGkFVdPrFuSgOowavScPjownsRg3nF4uEXeofaqjVzqoWO4evJRkO2bEZNDx0bolsuc6f3zrA8Zk8LXYdx2cyzCSKCKKAVa/hoXMx+nxm1rRYGYsVMWgEzkVLuE0aWjx2Op0CiTJcjGQ5NJGmWK2xtdPFY5eWWcoUmU6VsJl0FMo18tUmqWKNk3MFBAHWtru5a0OYwRYHkqzgtuhpSjKDPhNPXklj1ytE8nWOTeXRixL5SoOfvraIWSuzXFZY32rm4lKRS9EC924P89TlFN0eI7+9Ocx3Dk1ydDrLh7e3Um6q9KxopkKj2SRZbrIpbOP+EwvYTSqoak+fG6dJTWH6/FZOTS1j0cELV+JcjBZ596YwlYbMucUiPqsOUdQwHi+iAE1JYk2rlQfPJjgw4MZt0fPS1WVen07y87NLrApa0Wq1dHit3DDow242kKlI2HRgNujp9VuJpoucmC9g1cLFlx+tv5FEwlsWT92QFGaWS6RNWqbTNXq9Jp4cTfD9uzcgiiJXlvKIgogoiNyxvhWDXo8gCpydz4CssLHdQchlJuxWKx/r22zMJEvMJsvE8lU2t9s5s1hkJGwnWajy9KUY27ocHJtK0ec1kSo36JAVlgo1XEYRUSNybCqN0yDS7nPyjvWtPH0lxa1DXh46u8jBERcajYZIpsxkskok3+Qrtw/yw6NzGBIl1oad/GyphFaR8dot9Jm16DTqYVej1SJLMjOpEmG7jrNzGWbTFTaEbcRFEY9Z/RhbXGb6gnZ6fWYieZnf2eDni09NctMqD4Igcmq+wFDQzBMX4yjAVLLGgQE3/3Rkgb6AnT++uZ+xRR0/Olmi32XksdEEgwErrU4jfQE7RyaS/Oy1Rf7XrYNcmE/x8ngSn1mgIolsDJpVCbU+L+lyE4dBJOQykyhUWRuyc3haIZZv8I/vWsW/nl7CZ9bQ57NQqEls6nCwt1+Vt5heLvL9Q9N8cEc708sFXp1Is3/QB4qCwWjgni12jkxlEfVG2xvFxls2/fDZDOzo9XJgdStfv2uYdo+V29YGeHYswZeeucrD56LcvNqP26zl8VHVpu3tI628a2M7a9ucpEoNzkdyiKKoakz4LIiCQJtTj8uo4e9fnmJ9iwkRAVmS+NnpKM1mEwW1g+mz6un2W9nZ42E6XeXGQS+pcpPx5Sp3b1Fr5KuDFn5xJsKhaynOzKZZytXo8Fj52p1DHFzbyr+ejhF2m/jbdw1jNpn47P52khXY0+MkYDeyocPFrcMtrAramE2rLPPHR+OsbXOwqd3Onn4fU8kSiXwVi0mg1WlCo9HQ5bPyno0BfnY2QY/PzHWrgrQ6jezp86LX6bhtJMhyscl9uzrQ67Rs7XTx+Rt7uf/4PD86EeErd6ziY/v7uWt9iOVinRcvJ5hKFGlzm3nftnamloucnMmyr9/NmrCHzR1ONKLAw+cWeexigk/uCXN6xY5DEAT0ej33bAmRrjT44bE5AlYdr0wkSZXrbGpXA/qxi3Gml0t0eszs6XVxZCLBKxNpejxGREEkmqtzyyov11JNPrSjHamUjb1RbLxlg7raUL0C44UaT12M8vxYnEtLJdpdRu5c28Ida1t4dizOK9dS2LUKtVqN7x+ZRZIlotkKPquegN3I21YHiOZrvHJ5CUmWefTCEpPJMma9wFdfnOH0bBKNTsfvbGzhtfkiO7tdJAo1lgs1jk6oni2DLSp59J0bw9y9vZ1XJrPU63Ui6TKS1MSghVa3lRaHgblkkc89fBlFUbhnaysXIgUuR/PU6jW+cSjCLatcXFjM88zlZaq1Gk9finEpkqHNoWe53GRtyAGCwORyBUmWcegFDAY9PU7V/lhRFMYX0/zT4Xl6PAZ8dhOCIFJuKMiyQiRdRhBAoxEZCFi5Gi+xXKrzwtUUO7pd7B3w8ONTUcYWc8ymVSBTtlLjQiSHLMu0O/Ucncqyuc1KqtQg7DajKCpp9vaRIAeHfdzz4/OUGzLtTgNBh4mwQ89jF2KMtNq4ElUtqff1eWhxmBldSDObquC1aFFQODaZZDRaIFGos6/XhU6nQ1EUatUqX3txmi6bwo9PRd40Nt6y6Ue1IaHVanjfJpVa77EZ+Mz1XXz9xRl8Vj2iAH6rljaPhfF4gYuxCgfX+Hj1Slw16FFkAg4jz11O4Ddr+OH5NL/Y002v30av18TEsqreKYgih8cTJMsSu3q9akCIGrwWLUGbjpMzWXZ0uZjLVOhwm+jx21CApy8ukq80qNZqKDLcs6WVfzsTJ1Vp0uczM7qQ4cGT07S6LXzv8BRDrU7eOeLj6HSW8Viee7a0cmKuwM5OkYdm0/htenZ0u1kdcvKlZ67S7TbyytgSs3mF9aE6l5N1DMYc92wNMZ9r8u5+O1qNlrVhI/OZCrcNBzg5lyNRqIEAH9rezv3H5vFYDdw+7CdaaDKdLBEv1NnZ7WE2VQJFIeAwkirWGQ7ZOL+Q4+FzUf7w+h6OT2fIVmUUWeLkdIpqE1LFBuvaHKxtc7I2bOeJS0lcRoHHLsTJVJtIskyu2mAhXWQyWeW9GwM8cHyO392rRaOxISvyL41BT87m0Op0gGrjcSlR5bYhJz99PU631/ymsfGW3akLNYmNIQufe2gMWZERgB+fiuAyCrR5zLQ4DCQrCvfu6ODm4RCf2NtBrFBnKlVmJGRjY4eL3b0+JElCEAQ+tCPMT1+PMR7N8NXnp5CkJjOpMigKCgpBm57ZVImpRAG/TU+b10asUCdoVy0pWiwi0wnVHQBF4cBQgFuHvFQbCtkK/OkT1wja9dy5tgVBUBhfyvL8lTQ+kwaP1QCKzKGpNJ1uPW6zlmvpOu/ZEOBaqsEXbuoj5DTx6IUYl6M5hnxGJpJlkuUG37yjB7vNwWqfagT609djbGizE3aaWS7UOTuXXkl9qty3s4O7NoQBlYisILMmaOZHJyLU6nWWclX8Zg0ajYiiKAiiSNhpQqMV6fZaODjsp3tFFq3NbeEDW8MIokhvwMH1q/yMhOwspEuIWi09fhvr2uycXcjzjg2tfO3OIZZLqvxv2G1lV5cDs9nM/j4npyMqxmQpV6PRaPDkpST37eqk22tFUSBRbPKtuwZ5bbFKn99Gj8f0pgKRb9mg9lp0PHFpmVqjjt+q5x0bQrQ6TEylqty0yodWp2Nnj5uFbJXnLi3x5KUErQ4jH9rZyeiSCkk9Mpnk2dEoj16MIwoCH9zexqWlEqsDJg5PJHnozCKHriUYW8xyLpJjJpHnfCRHi13VedOs6PTt7bbxN89PIUkyB3rtHJ5YJpIu89jFGEMtFkJOtTkTL9Q5MpHkaqzA20daWLMi9Li+3UnQYWApV+P0TJZcA+x6gZ+eifOZG7qYztTp9tnwWrQcmUjy6kQaqw5encjyxacmGI2kuRyvEs9V2N6lYrVjhTo+qx5BEPHZDFxbLvP4+QizqTJBqxaNRmRLu4MnLsbp8RoZjRbxWbQ8diFOrV5HEEW6vVZEUUO328ADJxb48rMTrPabOTKZ4sxcikcvLPHKeJIN7S529/k4F8lyNlJkyGfkkfNLdLiM9ARUfuzXX5whYNPjs6lSCz96LUqpVGI0XsVtgJmU6sD75KUkB0fUruXRySTLxTqSJPPV5ydZFbTzvs1B5nJ1lHrlDbEfb9n0oy4prA6YOXRtGY+lzHxaNdns81l4ZiyOIssMtQZVGbBig3t3ejkXrXLbehcnZzK0Oky0OfWcmc/yeztaef5ajluGJfwr6Ltksc6n9nexkK1xOZpnXchOrFBjTdDE+YiqULS108krV2N8Y6HI52/oYiKW45PPLrC+1czqkGNFgkBPtipxbDpFp89BNFtGEDVMp2v80fW9zGXURsNSoca+fg/JYoPVARNXkzV63IYVzeYaXR4zYbeFLo+Z5WIDu17h3ZtN+MwaxuIVRLEEgsBcpoLTpENRZERRi99mZE+/jx3dqtB7r1et068N2Xnk/BJuq0l1EJBl4oUG7R4TxyZS7Ful+kFGMhWmlit87a41PHAygt6gp8OsIewy0Wg00GhUAaHp5RI6rY6P7fLz9JU0U8sFjk7puH0kqBJvV3s4MZ1lY6ebHp+N3b1eFnI13r/Rz1NjSbZ0Otk32EKv38ZkvMDxyTQ7elzMpAyE7Xq+9Gycg2v8/NkTV9kQfsPCB/AW3qkBlssSH93VRqmp0OFWvSK1GpHXJhP89FSEx89HmEtXGAxYeG2+wI5eN7Iss6ffT2/AhtFo5JP7uvj5+QSVapnPP3KZLW12Xp1M4TKp/ogCoNNqiWQr1BtNFrNlUsUqL11NcHQiSarU5Pf3dbCQrfPaYoVv3amysVPlJh/f3cZiUcZrNRB2mtBptezq9XLfrk5kWeHUbB4UhelElhaHgWvxIovJHE+OJXHoRU5MLvPQmQhSs8HUcoFopoJGo+HT+zs4Np0llq/wnWMLrAoYsZn1rA3Z6fKYGYvmVTKDTcvVeIGjkykeubCEx6rn2nKRkZANjUaD0WTgy7cP/B/u3jNIrvs89/ydczrn3D3dk/NgAAwCiZwYQAJgAElliRIpUba89pWvdSVLMrXr9ZXtpWxZ9vq6JK1kiqRoSZYokQRzJkAkgkSeweScejrnnM79cHBVrluXqq3dT9R8m1M9VVPdb//D+z7P8yOYLvH8cBivWY3PomM6lmculmM2ksVnVpGvwWtjUeq1GvVajTs2NIEs89Oz84pOOp7n8rICe/rl1Rh3rXfT4TQQy1V5ZzLC+GqCf3prllCmCDLMRrK8u5Di3HSIpy+t0GLXAQIL8Ty1mpJ6upZSjnqXF5OcnonRZNUTzFYxatVkyo3fTzc5KO6EU3Mp7AYNjUaDULbC53e1kq+LfOtwD70+Ky12HUuJIk0WDdVqlU89ep5KtapM9GQlvvemfg9tHjv7e5xoNGpcJh0dbjObAhZ6m2z8zZ09xLJljk9GCWfK9Pss3DrgY1+vG69Fx0q6QiRX5dNbfDx1NUFDblCr1fn+qWX+65EuhlrtRHJ1Wu1anr60yutXF3nmyio7Om34jBK/urBGrVqjVqtzcaWCSSMyupogX22w3q+cYZFBEAUmgkm+eWycfd12trbaaXUYOLeQ5dCAi5G1LP0+Mxa9mu3tVk7OJLAb1OzpdhKwG/nm7b24TFrC2Sp3bGzi3iE///b+GgIC27uchDMVWhwGvrinkzaHnqsrGYKZMl/a2QQCvDkW4vvHp6lWFehnpgzbWxXCmSzLJMsyj9zdz9m5FE69GrdJw1yiyGqqxPYOBzd0OGk0GlxZSeHSC4RTBSZjDQZc2uvk4BrPDYdxGTW4zRpC2Rouk5qpaIF+rxm1WsOntvpwm3UIkkrzQXXxoS7qHW0WBpqsuIwqnrm8is+sYSZeos9rRK3RUK/XOTUdA0FAEEWWkyX2dztQq5RYq9mIIj3tdBr4/M4WYoUGgwEbH93aQqtdx7VQnkw2y70/fI+6LLO/20GyWMNrUiE36sxGld5tp9vE7m4nl9eK3H9jE8micnE1a0X+9DfjWDXgtSgE3Gq1yi8vRbGoZOZjeZ58f5V2hxZJEnGZtbTYRNqsErOxMmadinCuSrvTgEql4sEdLYys5a4XkcCpuTQ3tFqw6CS8JhUXltKMBtOYdCqW02V63EZUkjJSrtcb1xHS4LVoeG0siiAIeC3q/3DpE65HE8g8eznIkXUuVuNZvvL0OJVyGbNBi0Et8epoBEmSWO838/5ylvPzCTa32vning7GQ1nemojwzkwcv03H3907iF6rZW+Pi8ODClnArpd4fjjMUJub7a16phJVvBYNkXyNvzjUi0ajQRQlvrinjYDdgPv6Eepgv5Onh+MYVQ0Ejf73b/hi0an4zhtzlMtlJJWapWSJdqeeakXZleajOU5ORYnnK7TadXQ4FdG6Wq1Clhu/td+Pr8T53utT/PnTo+xoVy41E8EEj51d4lCfjR+cXkGUROxagblE+XqS0BrPXQ3zxjUl0GY+XmSd38qOdiuvjMcZCpj52I1tuC0GNgeMnJiK8dzVEF0OLT6bgYP9Djq9NpBhX5cdtVrDaqrEXDSPTINj19LUgM0BC802PUupMq12LU+cW+XoBi9eqx5BkNnbZePaSpILSyl+fn6VI4MeREEkX2kgCALRXJlBr4HZaI7JcJZ6XentXwvmuLXPwVwsx+WlNGcmg9z/2EWqtRotDiPToQz1ep0XR6Oo1Bo2BSz0+Kzcu6kJu9lAm0NHQ5bx2AzsaLfhsBiQGw3GgmmeGw5xoMfBzf0e1tIlXhuP8bntAZ69ssajpxeQgWabAadFz84OK9F8jY1+M+FMmWa7XnHI72kH4NXRCG+NReh3a/n1xSXemEzwXw60cH61+PsZ5SsIAvu7HaTLMl/a18m9mwJMhzL81xfGKFeqtNqV6dqd6z1kyg2OXVnj8HrlyCADwWSRQ312ooU6d230csuAkh3y4tVVjl1ZQ240WM1U+X8+tZGDvTYW01X2ddvZ0OxgZ5eHr9zaxfoWB6em4yzFsrxwZYW5aB6bXkIUJRaTRTY0GYnmqhi0Etva7Tx6aoHzC0mylQYtNi1XVzMMr2Zps+vY1WlHUqnY0Gzj09sD3DfkY3hNybI+POhlLpbHZ1axmChx9wYvk+E8b09EmYiU2eQ3oVKp2dlhJ5itcmOblcODXuL5Ks+PhFhJFrDpVXS5Tfjteu7d1MRjZ5ZosWmxaWVWszXWNZloc5o42O/k5GwCh17i3k1+PnZDC3ajkli1HC/Q6TIyF81x7GqIAbeel0bjfGaLh1NzaWajOTb6LcwnK3jMaiK5ChcX4rw2FsVtUvrs9272o1areGBbMy9eiwIitXoNURQ5vN7HyyMhxoIKba3ZqsZh1PDkuSXOzcbZ32HiF5cifHyT93cePz603Y90oargiFEKfCWZJ5Qp4TaqOL+QRKtRs73NyuNnl9nTaSVZUdwglWqVhXiBK4sxnrsa5NZeGyNpic0tVhaTJQ72u1iK53HpRS6vpFmIZvnFhRifudHL6bk03S4DgqiklqrUak5NRa6jiis49Com1jK0OgwEUyWuraSo1+tEsxW+uKedmXiJ6xI9zi2muGOdk7PzKc4vJJmN5rAbNcyGMzRZlRiyNqeBtbRizbowF6Mhy+QqMvt7ndj0KjwWBdGxrdNFqhjipbE437y9hxcvLfLScBCPWUev28DebhfPXlnjzGyCgE3pZz9zOcjTF1eYj+UJWNV4LXqW4gWeOLdCh13Lmfk07e4s7U4Di4kCbQ49s/ECN/W6WctU2OAzcPy6KvAHp1b4k/2tGPTKZV1u1Hl+JMKf3tTB2dk4bQ4dC9E0v76w9NvPbNmqx2XSYNc2+On7azy4rYlGo6G8Nl5gg9/MuYU0DpMKrUaDVSXwb+8HMYg1Li7Efz+PH4VqnR3tVkRRZHg5wfhaBpdJi1arw6pXbPXvL2epN+r8+8U1nAaJP/jZZb79wji1ag2NVsttfQ6W0vXr/VwBv0nFN49NUL1uYRoKWJQcZ6tIh8fMTb1OorkyFxbTHLu0hNxosLPDwZVgnq8f7GZfrxtREul0m9jV6aDPa6LXa0StVjMeyrIYV8bmrQ4jyPAvJxZotutpdxqYiJbZHjAwn6xzbi7G8akYoijit2oZDaZZTpVJler83X2DSKJItqJ8P1ocBmRgIVnGrpcYX8swES1ycTHJnevdzCcrnJ5N4DYpd4zLKxleHYtw32Y/TTYDFp2KeLZMKJVHkgR2dznp9Fp58sEtBNMlnr0SJFeu47UYsGpVjIaU0XkwXeZAr4P9fR5uaLfy5LkVjk+EmQ5nCWUrbG02gyxzYSnDqekYk+E8S6kym1tt+O3G37YEY1UVe1r1/PpqlAcePcfEWop2pwFRFGiy6tCq1Xx2Ryt7erxsa7NyZj7FlbUCCB+sP/3QrtQ2vZoXRqNYNTJnZuMIokSL3cC+bie/ubiCLMOX9nXxzHmJ49NxPnJDO/duaeWFK6vXMQ4NYsUGDw36WYgrN/SVRJG/vauHN6cSzCUUHYViyWqwEM2i1+tx6lVsDLg5PhXj0mKSj9/YxuZmM29NJahWa5g1IuVymZfHk4yvxmi367m5y8pqpkrArOb7xxP8TY+bIxs6aXYYuK1fERnd1G3j/scuYDeI3NhmI5Itc3Sjl1BBRiVJfPW2XuZjBTQaDYIosr/HQatNw0QogygIaCWBFrueoTYX/9veVkaCWc4tpHlwRzOSpOKdyRCiKLK52USH08BgwIYoipQrFZ4bzlGoytzY5abPa+TYlTX8FjWxfBWXUUWzw8hDu1v51vOTPHyknZlYkX89vcDoSopIrkbAqieYyCMKAsFUkXq9wehahul4kS/vb2dzh5tXroVpcyi7hCgq79HzwxEe/VgP3z0Z5pNbnIQyFYZXshwZ9PLU+TRuk4Zmh15pDxZSaLUa/vPNXTQaMqd+R218aFdqvUbCbdRyeTXP7k4HfR4jI8EMJycjXFzO8u0Xx6lUKoys5bDplC/1RCjL25NRfvX+Aj99fxWnVmApWWIlWcBv1TK2luZbL0xzeNDL5270cy2UZzGaxaRXkSzVcegE3phKEEyV6HYZSJcbzEWzzCdKNJkklpMFLq1keOT1Ob5yoAWVpOLqSopnhqP4DCJruRq39zs4djXEo6cXWIxmuP+xixRLJd6YUNiDmwNG1lIF3plJ8ejpRW7psbOYLLG+2Y4oibxwZZXFaJZnLi7z7ZcmWUmVKBSLxHM1FuM5hpcTDK9mQRZYSxU4djXETCTDyHISEJDlBqdnErw0HAQEUqUGRzd42dntRkDhq/d7tHz/5BKDXgO7u5zI9Tr/8OYcpXKZP/73Yc7OJuhx6dFoNexot7GSLBDM1rihxUTApkMQRDRqNXvaLTzx7jKjwTRHNviQgT/75RX+5c0JvvHsOA9t9/DRJ8b41q0tqDUaGjJ4zBq+++YcToMKAZmFSIaz80m2tVo5PRPn7YkYsZJMo5COfFBtfGiLOl2ssq/XxbZ2B8vpCm0uE/duCrCQqvHrL27hgV1tTEeLyA2ZWLbIsxcXOTOXoNOpw2838u8PbubkTJJKtUY0V+HQoJf1zXb+9q4eXh4J8c9vz9Ln0DAaKfOl3c0c3ejj3YUsf3mok3ihiiRK3NLvocdn5XPbmnl5LE6LXc8zf7iNB3e1s5CqsfV6GlGXVeD8coaD/S5iuSrIsLvLwU3r/OxsMzISzLEUz7K5Sc+J6Swr6QqtNiUh6rGzS7RYlaNDh9PAWqZMk0WL1aBhQ8BCj0PDLy8EqQOnJ8OcmoowGUyyMWDGY9ayudVGi1XD6FoOr0liLVPlszf6EASRXo8Bu15iMpJDEBSeerVWRa1S86cHOrgWyvPPx+fJVWXuXO/GazNyx6CL3T0uupvsfHabnzOzMWS5weEBB995fZbl6/azm/vcXF3Nki5WOD0V49jFJU5ORhAEaHUY6HRoMBoM7O4w82dPT/L+TIQrq1m+sKuVhw/30e5WVI2nZpN4DBJn5hJ4zWoktYRDK/9+pp5qVSKNRgOVSqJSLiMIIuubbezrtvPzS2FWkkVmwhlcJg2pQpVTsyluCBi5GswTTpd4ezpJtlLnynIKWZb5yZkFqtUq33h2XIklEEWiBUXA/uZEhL99ZRqLVuD9pRypYh2QUUkiE8EkZ2fjrPebuLqa4fGzy7Q5dLw9FmQ2XsCoqvN+RKZQKDKynGA+nqdWrzMfy1OpVHhtIoFZJXNiKsE/H59DLcFtvVYyVYFarcrYWoa3x0P8wZMXqdVq7Gi3cnI2Qa/XjCSKXFzOUazBXf1mEhWJT232MJeocn4+ysWlNI16nRevRdFKAg0ZKpUy9z9+gWKxwLdfuMbbExEyhQqxQo2FaIaLSxmW4jk0GjUf2RLgI1ua+bObu7i4WqDRaPDEe2ucmYnjM4r8y4kF3pqIcnYmxshajqEWm5LzJ4q02rUEM1Xu3Rxgd7eDkTWlpXhzn5vb13l4fSLO5FqKUK6O36LCZzPw5QPt/N1rM7w0vMZiPEePz8r9O+2O2B4AACAASURBVFoV0Gu+ikqlosOm4anL4d9ZGx/aos6WqvzkzBJb/Hp+cnaFWq3K85dXGAnm2NFmJZavMhzMsKPdQq4u8td3dBMqyBwd8nFjp4s7hwLcM9SE327gG7d1kyjW8dt0rPMrGo+vHuzh5nVNtDhMRPN1vGY1921tZn+/h1vXeWlzmQhYNDx3JcS7cwk63SaGAlai+QrHJ8KcnEmwvcWEXm9gg1NArVbz2NklQCBTVnLnTk1F8Vr1GAx6vnW4hz6/Hb0a/v1KnPUeDS+PJ+j3mrh5wMdXbu3h7HyKhXiRXo8ZjVrNxmYLJp2EWafm+HyWL+3yEypJHBxw0OKycHSTX8kHua0bo07NtWAWlVqFx6RRppQIbGpz0tNk4+u3djKfqPClnU2kK1Ct1VhKllmIZnhuOMSDO5q5scPJb/7wBvb1eXhhNMa9mwP8H0f60Gg0bGl18PDhftRqNcuJHM+PRDi8zkWf34ZKpWKo2cpsoszV1TRvTMa5fcDF0c0tbA6YEASYiZdYSlVwmRTdyvn5JPVGA7khc3I6ydGNXtrsOuZTVbodmt/pJv/QFjWCwO5OC+eXc9yx0UuLXccboyE2BJSI249ubeGOdW4urhb5Pw918YtLESrlMuFMlXq9wUvDa1xeUuhRf//GLIf7nYwEldTO8WAWEFjfbOfIBh8Bm8IJPzkZ4elLK7TaddRqdY4Nhzg65MVu0jDot4KggI/UKhU72y08dWkNs1pmKSWzq9POoQ0BvnJLNwNNFna021hIVfizmzpotiuJq/dtDtDtNbE5YESlUtFq17G3x6VM75pt7O12spYusbvLQcCmI5gsIkoSj356kGoFgukytXqdlVSFWq3OWqbMfCTFa2NRVCqJ+7YE2NluQ6vVUq83aHaa+dbhPra02Xl9PEatVuO7by1yZMDBewsZmq1qLi4rpIb/+9VRnrm0yndem6bHpafJqqPLrUxub2i1EUwVeWU0QrvTgNugUMd+fTlMsVRiNpKj22PmHz+2kb841EebXcf5hSQ/PD7Fq+NRUoU6D24PcHjQg1qtpt1lpNOjdOyurmbo8RiYWktxaiZOh13NdLwMkkr7QaXxoTXe/uuPf/RX2bYD+Mxqmu16nrm8xlqmgsugpibL2PQq/vrlKVqsKt6aSrC+ycjbk3HuHvLx9qTCK5yIFhn0GYjkqoytpUkVa7w2GmY1VSBZquM0qHluOESlWqPFrmcqlEWFzOhqiulYiYBVg8tiRC0KbG6x8o9vzaEVajQ7jLw/F6NYF8gWSlQaMia9ho9u8fOPb89TrdWYjOS5ocXMdKzMRDDFa+MR7Fp4czxKslgnUWygAgqVOguxPJeXMwSsGi4sJRkP5SiWy8xEC8Qyea4ECwxHy5jVAltarYyHFLOrWoDTcynu2uAlllMum0+8u8KDOwKcXcyyt93E88NhZiI5PCY12SoMNpmQVCo2t9rp95lZTeRZiue5slZgX5eVch2mYwWarDpGg1nmo1lGV1Mki3V0ahGvUeLbr8wy5NMx1GLl1GyC5XiOaKbEWrrId16ZpFKtspYp8/GtLaiEBslineVkkeeurnHXBiUN1mNU4bLoiWSKVKs1Hj+7hMukTF6/tDPAvz35RPGDiLcf2pU6V6mzpcXETKxIh8vIwXU+bu5zsafHxfBqjn6fmc/cGKDZafpt9JakkqjXayzE8oiixP5uO7Fig6GAme1dHv7+vkHWN9t5YFcHH9kcYEOLnZ3tVhYTRWq1Ktlyg6VUifFwgc/e2IRGraHdqZhw35xM8N8+OsDVYIF6rUapIfGJzT5KDRGXSXGA//T9IA9sb0aSRDb6zbw4EmVnhw2tRsPdG3wKzFOv5cFdbXx2RwuDzXYkSSReqLKr08ZSqszdG7xsCli4spLjk1s8yJKGT2/14tVBn9fEmdkEFo3AxoCFhWSJT2xpYilVZilRotFo0Os1MtTm4u4NXv72lWnem09QrVUZDubZ0mKl2WEglKnQ7dLzty9PEM9X6fVZ+eN9bWSrIhuaTASsOtqdBposGkAmV2lwc7+bvT1uQnmZvz/azc8uRbi4mKRRV4IgL61kabEqGpeZWJGtrTaeHwlzYTGNw6BiQ0Bhx787n6LZoub4VJxqpUosVyWWr9DuNrKpxUoqX+V7xxd/p576Q7tS/+yJn/xV+957aLbrWE4UMWoEZqJ50qU6HU49J2finF9IokImXqhi1CmFc+zqGo2GzL5uOy9ci9Dn0hIrNrDo1ZyYjCFJItlSlYV4HpNOOXKsJAtECg3uXO9mqMWO06RFklRkyjUOb/BTqDY4NOjlzckEehXYTXrMOhX5ch2NSiKeK1Os1Hn4SD8XFpPIiBSqdfw2PZ0eM/dsamIuXiRbqmHUSDRZ9UQLCgDzQL+PgSZlyHR1KckbExG0KgmXUeRXlyMMehWSbbVWoyqL2HQSb09E0ahVtLuMLCRKbG+3M7KaolBuEM4UyVdkdnU5mYrk2d/rQhAlLHoVG5qtTEWVGLCLS2l8Fi1uoxoECbtRQ7VW5/hUDKNG5LXRCIlcCb1KxKDTsvs69jmeLVKoCTj1IlPRIj0eEw6Lnvs2+biwkuO+IS/bOhVJgsuo+CoNOjVjwQwPH+nnykqaugyJbIlMVRGb+Sw6GoLI53e1cWYhxZZmC28++4viB0UkfGhXakEQ+ItDfezr9QBwfCpOPFfGa1I0BwGrjn6vmUSxxm2DPlocRlQqCUlSYTNcZwSqBV6ZSOKzaDk86EWtVvEHezsI2A3MRAv0uJSz7tdu62NHh4PRUP63gecNGeSGzFgwTbvTwGQ4D4LyfwmCQLPdQDSvePZiuQqSWk21WuX54QgP7W5lf58Pv1XD05dW+MFbU0qv3KZDAK6spNnV6UAQBNb5rUgqpc/ebDfQ4zKwt8dFm9PExmYbXouW5VSJRKmBWSvQ4jRSroHHrOXzO1uRVBJnZmNkSjUi2RKJQo3leJaXr4UYCljodBqJ5ytsb7NxfinHgzsCnJlLcGEhTq1W453pOOVKiYVYlhankVaHjj3dLtb5LSwmiszGSwz6DJycivL05VXeGAuzFM+jUkkcHfLR6bWyt9vFufk0PrOan51XwjplGcLZCja9xF2DLtayZWq1GqIg0mrTolKreHB7M8OrGY5PRDm6sYm3plPc0u+hzWX6nbXxoS1qWZZ59PQCp6ai+Mxq1vlt9HtNDAez+G0G2p16Wl0K1P32AWV8rVarefhwLwGHgXuGfLQ4zfzwUxtRq9S8MRHDZ9Eo3jzgpl4X09ECAZsOWW7wxT3t5KsKRGg6kmcplkGWZZ6+tMJ0OIMoChxZr2BIBAFkZERRQBRFer1m9nfZ+d7bCxxe52AqkgdkRtby2HQSF5ZSIAt0OA2UyxVSxRr1ev23SZ+NRoO5WJ6GLHNhKc3IYoRfXgrxX25qYyZa4MZ2B60WFcFsnXanAZNBTaVa4X9/YZo/v7WLDo+VZquWDU0mbDoVXpOalUSBtycjPHZ2kUqlyvMjYT57o++6GaBxvV2qpsdj5MXhEK+NxygWiqykq5xbSLGn20WX18yDO1p4ZyZJOFviGwe7Wee3Ktr2TInLiynanAoiI56vcOdQgM9u8/Pk+0FWknlubDFxfjHFyakoJo2Krz8zituo4oVrUWw6idfHo3jMOrj+frY7FNxetVr9/cz9KFUV6Gej3uDkTIoHtvtJV8Fj0tFq0/DEe6u02HSMBlM89ORF5EaDbpee18ejRHMV3p1P0pDhlWthujwmDva7CKaKfPnnF3hrPEKtXmM6nKFYKvPlp67x0vAa376jG5Uk0e0yMpessrvbidOoYSVeoM9rQpZlpsJ52p1GwtkqQ81WlmI5EjlFozzUbOHKcpofvzNHv8/MvZuaEAWBdreJL+xq4Z2pKG9MptjVZuLMbByXSQ3AfCyPLMt0OPV0uIw8eT7Eng4rT74XZDmpeA6n4xW2tpjZ1Oai12smX1ext8vKm1NJbuq2cW4hxQvXwoytZTgxk8Rv1fHQ7g7W+a3IsoxDJ/LEuWUuLSWQaSCKIocGPXR4LOzudvPg9gBvzKT5T/vb2NvjYTWjeENXM1W6nXoyZZnXxmN4jBKXVrJUylXm43kmVhLIKCz5iVCWcwspHtzZgs+s4YWRCAaNxPBagSPrnLjMOnp8Fr5+sIvZaJG1TJkv7GpBpVLx6Kk5JtfSFEslTs4maZRy/yu6BfAhLupyrQHIiJJIl1vPo6cXuGu9hxankdMzCT6/oxlRFHAaNFh0Erf2OfnJmUVkWeabt/eyt8eN16zi2JU12qwqnji3wvY2G+WGyP03NnFyJsn5uRgatZqv3NROm0PHx/71Ik+dXyKSLfLI3X1IkkQ4XWAslGF4OcGLV1ex6pX22+4uBysJJcV/JlLhnemEAhkq1ujzKV+A0zMJ1lJFEtkSj7+7RL1WpdejZXgtz3vzSdxGFf+D1CCgDH4ePtzH0U0B/uhANwhgNWhZTZfQShCwKWbg29f7+YtDvahUKpbjWd6YjHPfZj/3bW7mL+9cx8F1CjVsJa3wC1fSFRKlBg/tbuejW1tpcymL4E/OLHKgy0quJtDjtXDneg/nl7IsJYvcNuBGklSUK2WWs0ralIDMaKjAbb02wvkGOrHBT95dZnotzd5eD7VajalIjka9zjszSrF/6oYAXz7QwWsTKTb4zczHCzz+7goHehVe+5uTCT63zY/DqGU1meffz69i0Uq/0871ob0o/uhHP/qruz9+Pxa9mtlonvcXkkRzJba1WXluOIxDJ/LWeIhksUYiVyJRaOC3aZGBpy6uEE4VuLScxm1Sc3omzn/a385IuMStfQ7eX8zQ59ERK9Rpsum5vJRGr1HhNWn4432t/OjUIhOhDPFchVypRk0WsGhFrq5miefK3LbOw4WlFOdmo3xuq5v3l9Pcu8nHYqrCXCzHBr+ZuVgRvRquraaQJBVH1jn56bvLaDUqHAYNRhVcDua4Y70XUVKxu8fNpmYzr41HSRfKXF5OU6tVEYF0vsRcrEKLQ0eiWKdYLHBiMkquXKdarZEr14lkSkRyVZLFGnqVTE+TjVShSrpUxaRRcds6N+myYsuyG9RoVSLjqwl+fHqJO9d7eG4kglqUqdfrbGyxcX4hQSJX4vx8gkeODnBiLsNCNINGlDk9l+b2PhvhAmwNGPDYzdzQYuEnZ5bZ3m4jW67T7tCzkizwymiUdLFMj8eEy6IE42TLddKFCu/ORqk3ZN6ajNFq19PsMGLRChQrdc699Mv8Xz78jX/6X9XGh3qlHlnL0ukyct9mPw/sbEcSRF4ajfEn+1o5PZ9mMlrmc9v8lOrgMUl0e610uU24DGpEUWR/t5PNzVZGg1keeXWKZosiwh8Npjg5m8Rt0tDuMBBO57m0lGAqmufrz4yi14jEcyVqdYWU++COFjRqNfdsasJhUPPoqXkCZjXzkQxffXaeYllmeDVLtVbDrldzZSXNUjzP1eU0kiTR5zXy/EgEj1k5bngsWhLFKltaLPz0vVV6PYrM4dVRpRPjNStakEvLWWbDGUaDWeoyRLJlXNoGPzi1zKvDq7iNEleDObwmhXd+13o3tWqNx99doVwq0+bQKUlS8QzPXFnjrdEgv76wxDuTEVbiedRqDS1OE3cO+bmpx0k4U+bEVJwT4yHemoxRrpQZWc3wD2/O4zMoCD21So3fquGViSSdDi2n5zOUyxW+9ptr1Oo1Dg16GA7mqNXqaDVaPjHkZClZRhAE2p3KxXx3lwNBgLFglmq1wv4eB4nr2dgvjYQIpQuIBqv3g2rjQys9teoVHstUKMWpmRR/d986ujxm5mM5TEYj//jxISZCWWRZxqLXMBoq0OevgSAQL9Zw6iWOT8VwGLXcv735ululxGqqSJ/XjN+mYyxcoFqtMBktcXO/B51WS7tTzw+PT6NWK2aE7R0OTs/G0Go0tDsNCJJIMFVgLqZCr9OxySoTzdWUDsF0/Hp8gcwX97Tx+Nklhlps/A8W1OmpiCK4smo5Nlzi4ICaj+1oYypSoNGo0+Ey0GLT8NjZJfZ2Wtne1srDz43jNmoZ8KlI5iu8MpHkrvVOrqzm6Wuy0e0x8/5SBodRw0/OLLKh1cnndzbz4liUQr7AWChHNF/lP+9vR6NR8qFlQJTE3+IyfnJmkXqtTrJQ5eObfZjNJvb2qPnh8SmG2uzctcHNuYUUA01WmqwawtkizVYNoiiywW+h22tGlESurmR4/N1ljgw4+em5FT691cOfPDXG5iYleGchUaTDaWQhnsdv1bO+2co700k2Fhvcs6mJlUwVt1nH+oCVRiH9gQKQD+1KHc2VqddrDK9m2dNp5fXxKLPR7HXEQ53ZeFnRDEsSXz3Yw0e2NnNmNslivMDWVjsHBpo4uM6n5GNIEpFclTa7jkajTjxfJZSpcHSjj4ureW4I6AlnK4RzVfq8JiqoeOToAH1+O7G8MiD4/E5FeLOl1UGT3choqMBX9jej0+rQq0XOzGe4qc/NplYbyWKN18ejuE0qrqykWE0VabLqkCSRsVCJer3O0Q0+Xh5T7kL9PhOz0ZzSkVCr+dw2P8PBHOfm0+hVIhubDJxbzCOIAkc3+uj02NjWbqfeaPDyWIKHdrdz32Y/Gq2Gne1W9Ho9Rzf4KKPiU1v93NLvIVasc2I6oZzhZRlRlFCpVAz6rYyHskTzFSw6iaeuhDnY72I+nmc5U2VPu4Wz8yki2TJ7uh1EczWGAhbGw3lGV1NUa1XOzCYIZyvcM+SjyaLhvcUUvR4jer2eW3sdNDnNuM1abh9wsxAvsJIsEs5V+cjmZjY0W5GReezMErf02ClUGlxczvx+RiRoJJFT01EcehXhXIVarUYwVaJcrnBmNkGXU8sLV4OcmAjz3NUQtXodv1VLt9fC4fVKkHiHy0CuCvVajZ2ddubjBZIlmcEmxU6lUqvZ1mrh/Eqe84tJ7r/Bx5Pvr3G4384bk3EePtzHJ7a1IQgCL42s8eMTs9SqFb60r5P1PiNff36WLqeObLnGQJNZCRQXRTxWg+JWX8uRLNSQGzLdHhMPbPOj1Yq8Mp5AQOaeTQpCWZZlkGXOziWpVascGw5j0wrU5Qb5Sp2RcAmNBD0es5KnMRMmnC7yzKUgdwy6mIkVOTkVZV+Xcl9os+vQ6XU8cnSAF0djDPnNNNuN7O928PmdLajVavb0uBjwmanX6/R7TRzd6EUSJfo9So7dtWCOz2718cp4knCmhMeiY32znb29bnqbbPjMGmLFBqlinYBNh8+s4djVMFdXM8iyshNMhzIEMxXuXO9mMpzlb14ap15X2D27Ou2cW0jjd5jY6DeTrdR5dTSC3agjYNX8Tunph/b4oZFEJkKKAWBTq416vU690eCdmSS39Lt55VqIlUQBv1VHuVTi2OUgsgCHBj088uoUToMKSSXx13d08+WnRtmaraKSVHztlg6+8cwYTouOPo+RiVCDPd0OnAa1klMXMPDXL01SrCgTv3u2tOK1RGnUG1RrNf7tvWV6fFZUKonPbG/hM1u9/PpCCK9Jw0KiiCCKfPO2bmbiJba0Ori5x8ZfPDfB+Noc84ki37m7D7Vaw+PvLqHWaHju0jKiJNHu0HPHRj9jQUVOemEpj0kr0us1cXOfi5+fD5IsVLm728GxS8t0uY2IApxbSPK5bQFeuhbiyHVO+EKiyOFBC6OrKcKZIq+PhfBY9IyH8vzm4jIf2drMXDTH8bE1TkzF8Jo1jAfT1Ot1VtMlfnh8hkajwbtLWW7ssLGvx81sJMsr18K02LUsxAoIgsAntzSh1+s4NOjlJ6cX2Npm48iGJiRJ4spijGcvr6JqVHluOIxVJyGJEouxLMF0mXC6yA0dTg72O3nklSm2tljQarX86U0d/LcT89CoVT+oNj60K7UgQI/bQKMh8+ZYiGevhhAFgQM9Du4c8gPgs2hAbvDicIjBJiOSKPLGRIyjGxVX+QPbAvzb+RB3bfAgiQK7uxy8OZWg12vk7vUeXr4WYi6aI1NStrwtfj3vL6YZ8Ojp9VoIZ6uK5ateZzVdRKNR0+PUKslCy2maLBr+6BdXyVchmC7RqDdYjuf5v16ZYiacZSGa5rtvzHHLgJfb1zdxaMDNxWCRY1dDWDQCXrOGUFY5Zj13NUyj0aDeaKBSqdjSbKbPYyJbVn5f5zPSZFf8kN1uI7et83HzgJeATc+T7wfpdpsI2PScnE2Sz+V45NUpJoNJBEHAZzOhkiQePtRNBYkTkzHq9TqtThP3b2/lD/d1MuC3sa7ZjtWgpc1lZEubg5v6POzv9TAXy/P8SIRSucSxK2tUqxVWUyVOTMcpVyo8dnaRtbSSCvvS8BojK0keP7OEVSvy/koZh07CZ9Nz1wY36YrMzg4rTXYDB/udPHp6gVq9Sqos02LTsJars8GrB1Gl/qDa+NAWdalaZzKcI54vYTdouGfIx7ZWC+/MJHhpJMR8RIkfaLHpUKlVRHJVvnVkgFa7jvl4nncmI3zlV1cJpfKsZSqIoogkiawkCoSzyodzcSHBHRv9fOeeAURR5NhwmNV4jqWUknu3u9vJ8HKC8wtJREHpb19aK7MUz+HQSwTTZba0mLEaYE+3E5VKosmiwXedwDsTK+EwSNy5sYkOl4lEsUYhn8dlVjMeznN1JcUXdrUxEHDwtVs7eGU0zJmZOEcGnIqtLJlHFAUqlSrXVtPs6LCxmCgSzxQ4NOjh9EyKm3vs7Ol2ctv6Jo5uaWFfp42Xx+Ic6rORrAjcscHLllYbW9rs3NDdxF/dOcCBfjdr6TKr6RIrqQKnZ+O0uozs7XGxrcOOKEq0OfScnE0wtZZiLVXizvVuhRMZsBDMVGi26bAb1JyYVjgvXquOmUiOZy8HadTr9HqN7O91M9Ssp9VpYGIty/PDYTb6LQSzNZosGh55dYqz05Hf7qDvzqcpFAv88mIYQa39/Tt+VOsydr2Kel1kNKh0OZ4frrGt3c7tAy6+ORFhb7ed5euRV61OZaDw+LsrdNgVueqkWoXLpKHVaeSOjX4kSaLRaHByGuq1Kggi11ZT/PidWToceja12Hh9LIQoSYwEc0TyNWL5KgGrlj3dLpZSJf7mSCcGg55Hzyxg06mYjeRw6wVOzyQ40O/hmcsxPrK1GY1azd4uGyem4jx/eYUry2myhTyvzOf42i2d9HsMeC0GpqIF+jxG/vw319jdaSFg03NxNcehATeToSxNRpHnR8LE83XmIjlabFrGI1XGgmlaLCIf+fF57livoPNeHlljNJjGZ1Tx4miUrx/s5hvPjhFKFdHpdfT4MjRkmReHwxzd1ATAlaUC0WyJXR12nr8aolKpIElKaureLjvvTMXpdGg4NZunx2thNVkgli3T32QhmlOSVxEEBATU1/UgS8kS4UyRc4sC1VqNYKaCIAhsbFY6JYMBG41GA0EUkRsNNjZb+d7xBSyqBiOJKgGbhtFyPvVBtfGhXanVkkCyVEdSSbS6jIiSyD2bmtjb4+LNyQR7umysJotcWkrjNKjodJuYiZV45GgfyVKDbq+Fh3a3XU9sUnrAxy4tMRcrKJYkQWJyLUutViOZK7G5zUGXx4TXauDoRh8fvaGZA/1evnFbN6uZCtPhDBfm43zt2CRvjKww4LNy32Y/N7TauBaXGQ2mmIvlWUqWmIvm6HbpGQ4q6snVZAGnUcVsvMpHN7oRRYGxUI56o8ZMOMNkOMe+bhtj4SKHBj20OE00OwykSlVWszVaHXq8Rjg5m2Q5Xeb6XY50BbY0W9jTrSjomkwSV4N5ZiJZYtkKT7y7jEkFS6kyTr2KLqeWM9Nxbmy3I4kSwVTpt3DQd+dTDPqMZGsCfouGQ4MKRq7HY2QmVuKBHc1IKhXRXAWXScmZ8Vn13L3Ry7n5FIIo0ObQo1araHPoEUWJbS1mwjmFZNbnMzO8muOdqQjPX17m0dOLHB704ncY6fGaObrBS6JQp1GXWR+w/H7mfgCsa7Lwye3tfH5nK/mqgCgInJ1X4JonphPE8lUa9TqXVrLUalVqtSqvT8RwmTTMRnLMRXN4TUo/9UCXhRNTceq1Kp0uE4IoYDOoODsbJ12s0mg0ODMbx6KG56+uMRvJMb4SZyKUo82hR6tRK92QrX4W0lV8FjXPXlljd5cTm1phHPZ4LXzllm5Wk0VeuhYiXazxxT3tiCoJURTxmTVcWslwcSGBXi0gX8dZzF/HzQ0FLLw+EePmbhtXVtJ0Ok3c0GpVKFgJ+MSQk3qtzmIe3hoNYtOJrKTLnJqOEcooaOvNzWaydZEv7GohYNeTrSqdldVEjq/+ZpjRYJLA9SySWF4R80sqNY16A4AHtgUI52ocu7TMleUUAasOGa6ny5rwmLWE0kVFDlup8OzlVba1mtnVoXQzqtUas9E89UaDlXSF3Z12hoM59nQ5rgfhuwhlq+zssDITK+Izazk5FWUumsdl1pIoVLCo5d9ZFx/a40exqijJyuUyL40l+PxORfjS7RUQRQEE6HAamYvlcceV6FhZlvFbdHiNKt6fi7GcKmHRqripz8Uf/fwKR4eauLKSpdurbPOCIODSC+SqDer1GsFknqVkGa6vel99doaNfhNOo5pqTZnUtbnNJEsN2h16fvbeMiZVA5NWkaQeuxLirg1uRleS7Oq0c6DXhVqtwW/REUyXcJq0TKxl0asgdh3hIUkiB/tdvDWVpMWuZSaU5g9/fgVZltnabObicpo/2dvKuzNRfvreKn+4rxO3Fs4vpfHZTQw1GWh3WxCiWYLpErcOeBgJ5tFoNLQ7Jfp9FiJZDfVajQGfkUhWaf/ORjJs9JsQJYmdnXaOXVnjhZEgOzpdbAyYeWc6TrtNTbtTSWW6uJBAEpU7w3hIptulJ5ItMxEpcPWFcQ4O+tjf5+Hpy6u4DCrSxToryQI+qx6/w8R8LK/4G9MVdnc5mI3maXfIBDNKmHw0V8Zt0hDLFrmykkWuVUsfnOkFLQAAIABJREFUVBsf2qK26VVMRnK8PRXjQJfSY1YotyYOD3p5+uIqwVSZYDKHKArc2+NGpVJxYjLMuzMx2hw6rHo1B3qcXF3NYjfp6PaauRbO8/bYGnq9np0divNkV3uKkbUcTTYjm9scqEQJk9nEV2/tosNtZD5W4MRkjM/vUnq87Q498/ECfW4DZ+ZSlGvKFn90yMtMOMO1tRy/em+RoixRr9UYDRe5Z8jHz0JJKg1IlBq4DGqimQofH1CYNg8f6ePVsQhrmQo7Oxw02fTU6zXenoxydi5JoQzhfA1Zlmm2q9FqtOxut/LU5RD7+728PpanXqsxqVJxc5+L2WiON8fCDPgsrPMaOLeUY1eng8VEiZVEgXdmkvS4NEiixEVBQAB6fRaObvTyykSC3e0Wvn9ygclwDo/NiNespd6oE83XuaXfzWqyyM4eG5/YpmMumuPqSoZ1fiunpqPs7fHQ6iogyw1eurzILQNefnFhlc/taOPwoJextQwX5qNcWhDZ0uag2Wagw2WgUqlwbi7Bnx7w84Xv/R629Cp1mVv7vdzc60KUJK4sZwili78V7D98pI+9PS7SpTr1eoN/eWeR2Vieeq1OqtxgS6uDdX4b0UKdre12vrCr9XrivZvFdI1Pb/HwxLllTkyEmY5kcRmUDlIkW0UQlambIMC7swmW43l6PUbOzCV4ZyrKj0/Nc34ujigKbAhYWe83cWidm8VkSWGhtNrRG/R8bpufF0Yi2LQClWqVS8ECrXY1D9/eg9NiYDJWQkDAbVa0Kt0eM7u7HLQ4TXS5TVxaSFCsNginixh1sO+65WstowS+JysCfruBlXSFLqeW9c0ODvQqTEVJkuj1mnAbRZ66FOKTm9y8cC3KaqrITQM+fvq5IZqdZlQqFXadxOY2OwM+MxdWctwz1MRHtnWwp8tJvgYbmowEbDpCmQoP7Wmn6/pudWRDE+ub7fT4rIjXOTJT4TyCoHxBbu11cGmlwPm5GLf0ebhjo/96VkuDdKmB12rgll47nW5lx31uJIzNqGEiWv7/N1EUBOExQRAigiBc+5+ef1kQhAlBEEYFQfj7//D8LwRBmBEEYVIQhNv/w/ND15/NCILwzf/wvEMQhPeuP/+VIAgfmGb5H3+sejVqtSLNlCSJDX4TAYeJWq3Gry8sM7amCPcHmiwMBcz86DOb6PGY6fTZ+OKuVuLFBru77OzqdLCWLvHMpSDHx8P89Nwq99/g47tvznOg10mTRcN8soosN/Db9PitWgRgNpLjxHSCWr3OZCRHs0MPsozHqGLAZ0EUBSXGIGDmWjDHnz89wnI8z2KixHc/sp6HD/cRLsgM+Y1cXM7w4rUoj9zdi0ql4JMPDnh5YFcbG1rsSJKKl0dCdDq0PH0pyGIsw2xUCb1xGpWLbkOQaHOb6XKbaLZp+fmlCA9sD3BwnZeVeJ7Ts2maLGremUpweTnFLT12Evkqw8EcPosyWHIYJARB8XP+w1vztDiMfO2WDl6firOaLNLiMOIzK92MF66uIgjQ4TLywrUol5aUnvdEKMfZuQR3rXczvpbhq78eodFoMBSwIEkSLoseWZapVKu8MRFjo0/LWq7GtjYrr4/H6HbpUKnVPLS7jRuaDRz5wXleG15Wjlutdh7a1cag14CoNVr/Pxc18ARw6H8q6JuAo8CQLMuDwD9cf74O+CQweP1vfiAIgiQIggR8HzgMrAM+df21AH8H/JMsy91AEnjo/01RA7TadYQyRZosWsbDRR7c0cwLIxFq1SqzkSxz0RzlcoXvvTnPyHKSk1NRluN5ZBkqlQpPX1rl2StBmixafDY9e7od9HgMzMcLRDMFur0W7hwKcLDPgUql4epqmqV4jlqt9t/be88oy+7qzPt3zs051c2Vc3dXV+ekTgoIJRSQgBEYFBDIBBuPGQyYdzxrlscYDLyeGXuNX16BEAiwBSi1QktCrdCtDupYXV3dlXO6Oed0znw4JYblZeFZnvGakVY/a9WXf517zzm3dv3vPnvv53lQqUS+c/cGDvQqcl23DPiIFeo8NxzCY1bhtxv5ygc6ubSaw2lU8+CeVvZ1u5iNZvjyL4f5y5fG8ZlEhlcLivqRTY8oimTLNZbTZRYTisTvVKzEg3taaHPqeezUIslilWiuSiRX49b1LlQqNQajgT3tNlRrmonFmsyeFiNff1YRwww6jVzX76bTbebTe9sAme+8OsNg0ILPbqDPa+W6PhetThOyLCPLEpubrXQ2mXl9Os1t6z20ukxK9WQlx8RKimeHVpmLF/jGzb3cs7WZra1OHtrbxvqAFVmWODalCLxf2+ME4MxMHEmS+PpNPRyfTvDfjozz4sUlJuMVqtU6P357iYBFxQ/fmqdWrfLkhWXOrRT467v76fLZCWUUutfPTkzzD+dWkSqFzLvFxT8b1LIsHwP+Mcvg88C3ZVmurB3zjq7ZncATsixXZFmeA6aBnWs/07Isz8qyXAWeAO4UlAn464En117/E+CufzaagViuwhtjYcZCBUSVyIcG3MwkFLa1SqNClhUzzHi+QrPTsGZ0KRFKFXh2eJWJSIHEmsVwp9tMs0PJgycjOV69HGI1U6FcKvHZnw4xvJgk6NBzx4CHqXiJV0fDVGs1Hjk2xzMXQ2RLNVQqFR/e5KPFoef4bJqleJY/PTRONFumWG1wci7FVDjD0ckE3U16PBYdJ2dTBKwaLi1nkSSFnlaXBHa1Wjg0tMIP3pqjVCryrZcmOTGb5ME9Sndvd7eHT2xVDIPsBg1fuaGDxVSZk5Mh5uIFdKLE4bEkRrWsCFnOJ6lVK3z35QlOzCTx24zcsdGjPPDa9FxYynBkNEKrw8BEtMjTQyusZivU6zUWE4rcwkK8wB/+4jKdVoFwrsrmoAkZWRmHTeY5dCnMX7w4xqELS7iNKo5NJ7m8GGUpmWd8JcWR8Rh/9vRFnhtaRpIkosUGgiDiMmtpsurpXUsx3GYVkiwznyiys8VCKN9AQMBn1XJmNsbz4zk67OK/iuVcL7B/LW04KgjCjrX1ILD0W8ctr62927oLSMuyXP9H6/8kBEF4WBCEc4IgnCsXC2SqMr+3q5lWu47HTi7yxliYhVSJJqOG1XQJSZZQa7R8fEeQ/oCDg31eNFotO9ocXN/fhNOiZylV4JmLIer1BqvpCtet5eiDzTYOjUQIWtXk6wL1Wo0XRxN8arufTFkx9NFoVAwGrezpdjMezvHkhRVEUcV9O5sJ5Rs8sLuZwRYnBp0KqaFw/vZ32VnMSTy4p5XVVJ7JeIVUqa6IrqtUPLAryHKmiiAKdDcZWcnW2Npm56Fr2vjx2yvMxQpc22Xj3/xoiIBFQ6PR4Nu/niaeLXJqPoPfrCJdkdjWYqEkiVzb40RUqTi/lMWsV9KL+3YF+fHbyxwaDtOo1bltwE2pDqIo8OlrWtne7uLB3S0cGg4TSimOuPt7XPR4Tbw4miCSKTEdL1Ouy1xYStNoSHz5Az3cvTXIpdUsI+ECX72hkyfOhzgzn0KtUbOz04nXauD5kQjXdDr4s1v6uG1zC4PNdq7vc5Mo1jg/n+TQxTBSQ8KqU/P8SJT5WA5ZVpzDPFYDfouKfE34ndWPf2lQqwEnsBv4E+CXwju8o39FyLL8iCzL22VZ3u522vnGLf2IgsDTF1fp9RgRBJl2l5FYvoosrzU1jCJjkRKSJDHQ7OCerUFUahUjoTy1SgVRULQsLi6nCTr0RAsN+rxm7tkaJOgw47MbCdr09AUcDPhMvHAljigIvDmZ5IHdLUTzdfb3NCEDKlEkmS8rHoUdDnQ6Pdd0OamW60TzVdqceqK5Cp/aHmAiUmA5XeVPP9jJjet9aNfsio+MRTi3kMKs1+Gzarh1o5/bBgNMxorsbDUzvJLhJ2dWeWhPCyqVyEq6hFmsk6sLrPOZ0Op0OA1qDvZ5+czedlbSZba02NjeYsNnNxGw6/nOq7N85+4N3LnJx7PDq4QyZba3OZiO5nnuUphGo8GjJxdwGFTIkkJE3tji5J5tLfQFbNy12c/NG4P8x9sHGAyYGQ4VkGUJjVrNXZsD7Gh3sbm9iRankT++oYc7t7Tw0N4OPndtF7cPejk5m+LxMyvcvMEDCBybUahcGo2af3tDF4vpMut8VgaDVoUcIMs8uKcFo0HPF/a3sqmt6XfGyb80qJeBp2UFZwAJaAJWgJbfOq55be3d1hOAXRAE9T9a/+chwOXlFK+ORkgVqgTX7DDeqUrs72kilq8Sy9e4ZZ2Tt6bijCwlmYnlaW+ycku/izPLBUJpJS1J5Kt0uS1c0+ng/FKGhVSZ+3cFSRSqFKoNAKKFBltbHezpbKLfb2EqXgIk5uJFkGFrqx2HWcdPTq/gNWmYjmQ4MrLCXAHq9Rq1Wp1XxpM8cXqOQ8MhLAYNJ2aSXFhMUq8rf7xwrgqSRKtZ5v97a5Fnzy/wwvAqvzqzwMnZNHdtDrCvy4kgCnisesq1BicXcty1wcWmliZmojkWEhWePLdMuVzm6YshCsUix2ZSbGm10+EykipWFBEgl4liHcZDOSSpQSRbwWFQ09FkIllq4LfqGI+VMKsavDwaRWpIDPjM/PRMiBa7FlEUODWf5/N7Wzg1l2YilObEVJwWu44XR8LYjVpOL2T4/htTfPvwFR7+2RBnZ2OIosiBbgfjkTyS3KDXbUKr1bC11YlKreLCfAoEiBbr3LHRyxsTcR45NqtIUkRK7O12IdfKhXcLjX9pUD8LXAcgCEIvoAXiwHPAvYIg6ARB6AB6gDPAWaBnrdKhRXmYfE6WZRl4A/jI2vveDxz6n7kAvVrkkbfmALiuz0O318r+HjdLaUUsfZ3fis9qIFWs8ddHpllJ5nl9NMQb43EOdlp4ZSLN5/e14rHqWE1XUKvVrPMr/oI2o2JI+fVnxhSLZQH6fRYePtBBj89Ki8uEICq126DdACjC7zdv8KLXavmLD/VwOZznzFycYzNJLCJ4zDp0Oi1fu7EDg8HAN27t5//9yEam4iUmQ2lmolm0ag1aQUKW4fRyhW6nDkEUabFr8TuM7Ot2gQxHJ6L8/ZllhQVu1BC0GzgxFafeqNNq11GsAoLAj99ept9j5NSCIit283oPp2ZT9HktyLLM2/NpvnpDO9lKg0Spwc42K3PJMpIs86c39bKu2cn+TjsvXYnTpG3w8zNLvDEZ51M7/fz47RVmY3k+vtXD4bEElUqFhXiO0VCWY5MxhhaS+GwGHtrbht+mw2Uxcus6JyvZGjtbLYys5njy3CLj4RLRfJlmu44uj5m5WB4ZiYBdz95OFzqdjh63kXhBsRNsSA2OTyf+16R8BUH4B+AU0CcIwrIgCA8BPwI618p8TwD3r+3aV4BfAqPAy8AXZVlurOXMfwC8AowBv1w7FuBrwJcFQZhGybEf/Z8J6lK1DrKMTa9mJVnk+FScfp+FWwZ8pIo1XroS5UCfhwNdDoQ1NaBEscG+LjtfeGIEp1FFKFvhwnIeYS2XnI6XqNVqrPdbWc5WsRlU7O92gSzzyFtzvDC8ymysQItdx2SkQK1eRxAEWh2KNsiRiQTrfUb++vUFXAY1dwx4sRp1bGnWc3wuzetjIS6HiiQLVUZXs/zo5CJdTj2JYgNBFLl3SxPLOYkmi4Z9nRaMRgNLyQLPX47xye1+FlMlnr6wjCAIfHxHMz6LhtVsDZ9BYjQNz51fYT5Rwm4W+ci2IJ/Z145areb+XUGqqPnB8TnC2RJ7u5zIMoTSRb5/chWbQcOA18CZxTwf3+LlsZOLPH9ReS+/TUemAqcXslzb10SvW2Gs3L8ryHw8x18dmaWvScvQSp7paJF9nTbFOtqqR5YkXhheYWgpg8OoJlaS6PRYeOFyjAGfiV1dHr5xcxdeq57/+toM1WqV0FpHc2gxzZsTYTocmjWBIJFXRqNMhbNUymUEtUb/Lw5qWZY/LsuyX5ZljSzLzbIsPyrLclWW5U/Ksjwgy/JWWZZf/63jvynLcpcsy32yLL/0W+uHZVnuXfvdN39rfVaW5Z2yLHfLsvzRdyoq/xzSpRoNSSJXlYgVqkSyZX7w1hyNRgOPzUSrQxHFPDGv2Kep1Go8Fi09XgsdbhN7u5xotBr+6NoO1GoVs/EClUqFb/96lt3tNtpdRrRaDbIkUZNFdrVaWU4VqddrnJhNYtbC0YkIc7EsTw2t4Lfp+UCfi0iuzqZmC6upAt99fZ4Wm5ZSQ+D6fi9tLjPpco2D3Q4EASaieZLFGr1eK/Vane+9sUSrTU0kU2YkVKTLZeC6fh+3rW/iD381SrNNh9uixWfVcmE+yfMjUTxGgUOjRcwCyCrlYW8gYGOg2cHbc2nyVYlQvsHf3bsRQRBw6EVOzqY4PhNne7uTP76hi+t6m3hpLEW9XuWn50I4jBpEUaDZpkGr0bDOZ+Jgnxe/Rcfx+QwdDi3PjUSYiZe5Z7CJTE3kS9e2c12fmxNzWer1Oo2GIsjz6qji+76tzUm728rXbuxmc4uNy+ESrQ49L15JsKfDQcCq4eRsks/u6+Aj21txm7UcGYvw8M+HmIoV+eqNXbS7jCwmK5yZTyGIqvffPLVRq+bG9T6u7W1ia6uDb9zaj9+q47FTy/zxta385O1l+rxmPn1NG01WA0GbHrVazUJSqY48t1bPPjWXxGfR8uZkgn6fha/d2MFKtsbp+QxfvbGbC6slru+28dJ4gnq9wVKyRDRbQWo0ODOXplFvsJRSHLS+9fIksizR4TZjMBq4d6uPPT0+bEYNe7uc9Pod9DSZOD6XQVSp+MzeDj66o5X1AStjsTL3bHStKSOp6XDqSRSqNBoNzq8U+W//ZkAhxiIqXEJRIGjVkKrIbG8z8Xs73WxscdHlsSAjc3gkRLPTyO0bPWvjpKucnIoxFS3htWgI2gx0eSzMxwu8NJbkbz66juvW+fnrjw5yz7ZmJFnmRycXOT+fpMtrYTZe4HI4z8e3+ZlLVdnSYuNAl51XJlK49AIvXomjUqvo81qI5spMhPOcnk8BcMcmRWekxablcz8fZiVdUeZKRBGnScVzI1GurOaYjBZpNBqcnEnht+nY0uri///EZh7a28Z3j8xRq9XRakRsRu37U8xGqxJ4YyLG6+NRnh5a4fBImFC2zO52K997bZ6uJoVloVarcJl0hHMVPn1NK6JKpeg9+0yoRDVjoRwdTSZ63Sb+4sUxfnJ6hUqlwkN725hL19gWNPLylSi39DsRRYFEscqAz4iwRiq4fn2Av7t3I+uCTjY1W/Fbtei0Wr5yQycX16YDE9kSP3lbmePWatUc7HGxzmdhLl7g2FSMZoeRAb+JF0fjXI5LyrhrsUqiUOXthQyf2Orm8bMhavU6+3pcfGZvGwCLyTImrQq3Rc9wqMg37+hjKVMlW65xYSlN0KrhmaFVzs0mePzUApIs0+02sLA23NTjNhLO1fhgr5XvvTbHD47NcvhymLcm4wgIHOxxEnCY2NrmQK1SUa83COeUiUVBELm4nGFbiw2NRsPGoMLB3NNhI1eV6fWa2NXuQK1Wc3ImxXwsy1y8yM5OJ/u6nYyE80xHc4iCyGDQwgcHAvzVh9crsmeNOq+NRRgMWHj8zCpT4QwOPazm6ty9JYhKFN+fzJd0qUaf18I6v5VdHU46moxKcDQ7cBpE/FYtr41FqdXrbGm1Ey/WOTwSptmqpVqtMbKao8ko0GRVOnktLiNbWx3cvyvIP5wLMbKUolZVSLzJEjw9HEIQBTYGrLwxkSBTarCz3UGtXuNzfz9Mh0PLxcW0YiBvEfmTpy5zei7B6xNxFpM19rSa+dNnx6lWqgwvZ3nx0ipnZqOcnU9RqVQIZ6rctr6JboeAx6Ijlq9hN6i5b0eAL/7iCk0GkacuLPPU+WWmY3lShSqIAlJDYlu7E7Fe4a9enaHVrsNvEvFZDZyYSZKv1LlzkxeLXkObQ8/l5RSHRqK4jSKPHJ2lXCnz+Nkwtw94uH6dh3anAWSJ1UyFkZU8AYfS/m9z6kkXlQqOJCnmpuPRErValeV0mQvzKSZCaX5yepV2u2bNXVjDwR4nPquGqViRocUUPrMaURTZFLAgIFCr1bi4lAZZ4tdjcZZTJfZ02JlNlBFVIjtazDxyfIHRlQyNRp193U4a9fr7U0tPLYoc7HMTsOmI5sqcmlNGSy8vpzg6neTiUpoer5FnLqwgSzJ3bvSynCrwyFuzvDQawaRu8IsLETb6zGv603WGV7JoNBpuWOfhrekYx6cTtDaZ2dqsx281sL+7CVmWaLLocFn0BO16nhtRcsbJaIGNATOlGjx6cgGXRc+3PryBA90OtFoBrVbDfbsCzKQqDPjN3LTeg1qtZluLjV8NR1lOZnns7RXKdZlYvkKhDiqViql4iR3tNm7fFMBrVTqRq8kSao2GzX4j2aqMzwinV+usJrJMhjK8PJZjW9CIIIgE7Xo0Gg0IkC1LxAoNPrzJR7fXSrJcR6PWcMsGD8vZKqupEsemYgiiyN5uJ3dt9oEMr45FOT4Vx27UcGQ0wjNDqyymy3xsi5fzS1ncBmVA6tJKjk9u9/Lr8QTRXJWATc+x6RTHppI8sKeVAb+Z50aiHJ2IspqpsJDIcXYhRSJfRRBVyLLEng47K9maslG5jJxezHLLejcNSeLCYpqfn5phPl1BlhrvOqX3nh09RYAT0wmurGZxGNU8tLeNyahie3zfnjY6mkzMRHNMRgoMLaYJZSvIMvR7zPQ0GcjWhLWA0yDJigKp16I8XH5mXzs/PqVIZFWrFUajFTzWItORHD89tcSmFhv3bAmg0+mUHFaWkWW4HC7yiR0BRdnIIXF6PstoKIPXpEKSZM4s5bimzcJT55cJZ6s0mbR8/roerl+fVW5JnCOSLiCqVHxk0E1fi4uFRBGVKHL4suKDuKvdyvOXoxzocnBkXBFY1On1PLDNRUnQcutGH3/75pwilZst0agrZF2bXsM6nxW3TU9rk5m35zN8/aZeRpaSnF3KIzWUga2LKxk2N5s4PpUglC7gMmlw6FXs7XbxzNAqrW4zX/1gN7PJKq9cSpMt1XhzOsnvH+hiKV3GaDSyrdXG1hY7d2xpptNjZjaaZz5R5K3ZNJuDJg72eZCBp84v0eE24zJq2N1u5e35NENLGTb6zXhtBt6ajuMzaxhJS0TyNZymGueWi3S79P8qHcX/43CbdezvdXOwx0G2opi9H52IMhPJ0uU2K2Ug4NPXtCELMgG7noBNz7mlDGq1ijs2esnVRdqcBro9Fu7eEuSzBzp5ZjjMoycW2dWuzP6eWcqzv8NGs8uCKIo0O/V4rHqWszV63EaeuxThmYshnh5axqEXiBYaikCOKAAyPU1GVrJ1LiwkcWgkjk4mCKVLSFKdiUie0VB2rUZuxW3ScDlcYjWe5YXRBJVyGZVKxWf2dRDOVtndZueZiyHWe5XKzPqADbdFx1Pnlzk8nsCqlnhtMoXDqOa7r81RqVQYjeT51dlFchWJoNPA1jYnkVwNn1XHcxdX+MazY2wNGDg2naLLY+auQR+dTWY8JpFUscZrYxEShSqzsQIeqx6nUcvjZ1apVCq8Ph7Fbjaw3q9MJXY0mWhIEgupMkfGIjw/vMrroyGGV7Msp0rs7bBxYjaDJEkK9zBow2VQc3Yuyd8eXQAENgUtXA4X8JrUTEYKHJ1J0d+k4/d2NKPT6fjwJj8Gvf53lvTeszt1uljj15cWGYlUuKXfyfGZJMjKLIIMLCcV5dA7B30sp6tsaZGQZZl2pxEBgaV0mVvXOTkxk8JnURPJN1jnt/DhTX5kWWY2nieWrzLgM/FyJM+uLjfdXguLCcWxtcWmZSpW5M5NPhAE5uJFhpfSrLOrWEmXCNqNCAJ4LWo0QKpY44nzYXrcenR6Dc0OE/FCjbl4Aakh8fqVEFPRAkGzgN9ppdTIcnE5xyf2KKr7kiwxnygwE80xnyxy95ZmWhxGLmuSbG93MpcoMpeu8cUb3AwtpfjKDZ1878gsQVsZv8OEWl1BBsKZCtuazYQLDSL5Gu0OLWeXcjywp1lh75xcxKKF5XSVP7y2necuRajWlBHVwYAJlUrF/buC/HosRtCu56aBAO0uA0+dX8Fj0xG0GWh16NGoVEhSg+GVPJ/f18Jjby+xqlexv8sBwHdfmcCqV1EsltjV6WZPp5PtXT7Gwzk63WaeH4ly/+5m5uJF/ua1KZodej66vfU3km2Pfund7THeszt1oVLjucsJfn+Pn+OzGQI2PXu7XWxtcSAKAs0OEzvbHcTLEn9woI1wtsqhkTB2vUisUOPcfJLvvjJBQ2rw2niMSrXMNw+P8eZ4mBMzcZYSBdxmHaIoUKw1WM0o33bJYp1qtcqT55aZDCturHPxAnduaeYrH+jk+ZEo4Ux5zQJa5uenlyk04FO7gty3p5WypOKz17TS7bWg0WoJmFVcWEjhtWjIF4os52RqtQqRgkSyUGY6muXFSyEa9Tr1RoNur5W7Nvnp8pipVCsMLRVoNBq4zHqsWoFfj8Wo1eq8MZNhg89ETVYRtOuVeZWJOMVSia8dGqchSVzT4aAkidRqDU7OpXn0+AKdTh3ZisyWFiun5zO4LFpW8w0Gg2bCuSpuo8h3j8xSrdVIlKBSKfP8lRgOg8L8rtVrrGaqDAatdLnNfGjAw5nFLKvpEkvpKucWM7TbNTjMeva0Wji9XOXSYpL/eHiK8zNhZuMFNFott2/08PxIDFEQ2NhiJ2jT0mhIfP/YHJ949Oz7k3irVSvdvnBRxqZXHjKeGVpFht8QBzQaDZ/c5uPwWJK9XU52d7iYT1e5a5Mfh15Ep9PxwK4gTpMWjUbLBq8yBO9by639Vg2CKKJXq2h2muj3WdjUYuP8UoZUsUqHy8Bn9rYzvJSlWq3y3SNz7Gi1sa3NgSgKtDiNtHusfHSzi1cm06jUavZ3O4jm6yykynxovZu/eXOe+USRs3MJJpJ1HDrQ6vT8/t4WypLiddjm1DMeyfPilRgbA2ai+Ro4mE4cAAAOxklEQVRHxyO8cDmGWoTldIlUvoJarVEkEsJ5AmYVx2cz3LvdT5/fgSRL3L+rmVS5wef2NnPX1la0Wg072+2o1Sr2tNtY57eg1eq4YZ0HtUpFrFBFq9KwvdXK7ZuChNJFLiyluXPQS3/AwVdv7ODJ4Ti39DmYTyuVkbdmUtw24ObVK2F+eWaBo1MJREHkA+u8PPLJzXS4jHz6Z5e5ttuB2Wxia4uJmweD/NkHO/nbo4s06g36PCYWUorxkigKdLmUbudqtsLv72/n+l737zQyeu8GtUpga5sdqSGRrSiuAk1mLZKkWETcNuhnT4eDL/1qjAGfgZVsnYcPdLIhaGc2nufiaoGbeu1877V5RFGF2yDw5mScH55cZC6aYTyc5c2pJLVqDUlQXL1+cGwWqdFgV6eLXp+FhWQJQRBwW7S8PBpjS6tiONTuMhLJ1xAFgU6nnivRKsgyzVYNhy6GlJ1cklhIldjR7uCPru9gd7eHe7f5qMswE8lyfDbNl67rQCUq/6APH+jiS9d2Es032NlqRaNRc8dGDy6TCp9Fx0KyzO0DTag1Gvw2HYvpCv1+C1dCBYqlIkPLOQ4NhxheSvPClTi1Wo3ZeAG/zcBKIsczwyGCdj3NDgO9PsUL8p6tzeztdjERzjGylGImmmc8UqJarXFsKs655Tz7Om2cWy7wzdt70Wo1XNfbRDhbIZyrEs2V2N/lYG+3k3ihxpGJBEGXmZ8+uAW1WkO318rODidDiykeP7NKh8tAu8vAK6MxZEkCQUaSZdJliX2dDtrdFhqSzNBKDkGtM7xbbLxng7rakBlZyfHmVIL7d7fw3HCYcKbE6+MxfnlukRdHQoiiwB9/oIsen412l4HpeJkHdjczsppnc8DEkeksd2x0c8dGN8dm0lzb18Sndzej1+u5YZ2XG/o9pKrwiS1NaDUalpN5Xp+IK7IKAiynihweCeE1q+n2mLlto59Eqc6puTQP7G6ho8nM0EoOv1nFPVubWc5UkSQJt1kxwBxaTHFlOcljp5ZYShUIOozKFF25gU2vYiVTZWgpw3Q0x4npBE9fXGE1lec/vz7L7nYHoVyNSE5JjaoNeHY4zEwkp+TO2Sr37wpSq9f5/oll/vz2fu7Z1kKb28q/vaGLl65EWUwUuLCQZDScx6ZRSBWhTJnuJgNeqyIdIUkNJiM5jk1GcZr1fGyLl8OjCfZ1OfGalObVQjTFt16eZDxSoKPJhEqlxqmHUl3ghStRpsIZIukCgiCwu93Oa1MpXh0NMxXJEk4XWMrUWOe3srlV4VjetF5h6YiCyGq6DLJMsEnhND51Kcbtg97/NebL/62w6NV8/eZeru9zsz5gpctn5aPbmun3WfHZDLTadTx9YZVqtcovziwyFckytpzg269MUa/XyFThv35kHS+OJnjk6CyCKLCaLnN0OonPqiVWkLh1o49Br47vvLaCWy+TKTcwaeCNiSiiqOKaTieNRoNDw+G1r0qROwd9BOx6RdO50aDFrmNoRfmmbLHrUKlURHMVkGXcZh06nR6zBmrVGj8/uwKCjNdqIOiycOtGP3dt8nH7piCtLhOiILKp2Ua318o6vwWfWY3ToiZTlri+18ZiqkybU68MFJlUfPlXl0iVatw56Ecliixnq/R5TWi1WpaSeZrtBrw2A+v8Zo5OpXh9Ika1WuUrT43w5NlFjk1GmU8U8Vj0xPJV1vmtxAo1trTaATi/kGI8WmA5o5Qnb+hzs5iu4LNomIxX+OL+Fv7ouk7ems0wmygStGp47lKUoEWN06SYRi1natw56GVft5NoXulWvnhplelojm1tDlqcJjw2Ax6jyM/OhfnywRY6m0z/KsyX/+Oo1CX+0wujPHl+iQcfO8N6j6JCr9GoCFiVnLghS/zw+DxXVjMsxvNcWs2x3mtAFJSpvZ+eDXFLvxOfU5kR2dftIl+VEBB4cE8z33p5iu+fWKHZJvD42VX2ddpZSpUp1AUe2N3MUqZGu8tIW5PC7fu71yZ56sIyUkOiWq3y9NAK2YpEt8eCJEk8fmaVOwcVAfzjMwnamiw8sKeFTKnOxZU8XpMar8PMni7FUu7QhSX+y2szXFpKctN6D367kbFokds3NPHNwxP8YijC1qDiw5gu1XGYlFFVSZIUTcBsDadRS7NNw5+/MI7fJJLI16jX6szEytzY71ozstdwXV8T6/w2DvZ7eeiaNrr9dvZ0OFGpFUWldLnB/buCzCaqfHBdE0vpClqthi8eaOfDmwOkK8omIACiKOKxGXj8bIjTCznu2xng9sEAoXyDgYCJt+czuC06qpUKrXYdyPCzc2H2dDhodxq4sJji1SvhNcllgUi6yPOXo2zy6fj6oQl+dHLx/Vmnthk0+O0Gbu5zsJwq8+ZkAoBP7QhwcSnDs8Nh7t7sp89r4dreJq5fH+DOQS/HppQ5mIBNR7Va5T+/NoVDL7KQULh5mwMmlpIFpuMl/v1t/YolnUaLTaciWqjjshj4wr5mHj+zSqNe4+RsCodOoMulR5IbxHNVarUaY6EsLqOKgz3O3+xgD+5pQRRF4oUKAZuBjiYjgiAQKdT50IAHj91IrVJlf6+bmzd4iBbqbG2xcOhiiB+8NcdDe9vY3Gzj7FKef3d9Gxt8/4M9PxEusL/TxjqfhVCmjCDAgR4HgiByYlZpxc8nS0iyzBtjIT61M8Bjp5ZwmdRsCpiJF2qAzPHpBMuZKluarZxeyNBs1ZAq1hBkhay7u9XEkckUN/Q6uW19E6OREsvpMvV6XUlp4nm6vVb+/S299PvM7Gg18+PTK8QLNa7rtnN5tcDudhtLsQz/5c0FRhbihLIV/vzWLjYE7Tw/EuOWfieFuowkKZsDgsi2FhuRgsS37uhlf5cdQVS9azn6PVunjuUqLMWyDIdLdLt02A1qarUaX3hiilaHjjsHvYiiyBuTUQaDdvq8Jn54PIbTpIxvXlzKUK3VKDdEYvkqfmuV0UgRDXV0Wh1tbitziSLVahWjRlQ065oEOpxa/v58hD6vmS6PlVqtxt+eifDmZIL1PhPVaoWhpTThfA0plOPsfIZKtUJfwEGj7uCNyQSrmQpSo85b03Fq1RqDQRsqlYhNI/PjoQx++ypPnZcZDJjR6gwcdBo5OZPg1fE4sgy1Wo17HjnD5oCZF8ZztFkhU4YjoxHUag1+i5bxcB5RFOnzKoGfqkKj0cCmlXnifFhpm1cE6o0a46EcmZJiZJooyXxwnZvpeAkZZXPIlut8aIObS6t5Do/G+YODJj7+wzPEc2Xu2Ojm/GKeDpeJFpuGN8aLeCxpLi1nOT8XZSKcp8+ndF2/9swY/T6lm5mvCwwGzBybzdMTSfHh76/yhzf08qEBN89eDLGjzc4jx2Z4YSTCpoCRel3g7FyCk1NRZACVWvdusfGe3am1KoFmt5VPbfOQa6hZSldYTFf4o+s7+Mi2Zk7NZ+j3WQjYDCAIXFlJ4zWr2dbu4ECvG1GlQq/T8fFtPra0ORiNFvm9HUE+trOdP7mpjw8N+rl5veKm6zDr+Hcf6OEz+zsYXilyoMvOgV436/wWDAYjf3l7N81OIxuDViZiZe7eEuBgl4MNARu3bWhiPlXHY9Eiy3Cw28mmFgddHgt+i5bpeIn1XiNHxqK8PJagxQrRXBm7ThF8lGWZ9QErB/u93LRe2b19Vi072pzodDqcerhpY4A+r5ZCHRqSQjgQRJGDPS4mIjmuXx/g3m0BYvk66bJMj9dEh9vK3VuDfGxHO9f2NtHlsXJ0Msm2oIlvvzxFvd4gVlDoVBadyC8vRlhKl9nWasNoNPEfbutnU7Mdg97Anq4mBpstnFnMcd/uZi6v5mjIEnazUqDY1+UkU5G4vs9Fs8PIwwc6eXh/J9+7ZwCTCDs7m1gfdHLTeg8ruRpbWm08vL+TdreVh65pYSqh1Lc9Fj1+u5F2x7s2E4H38E6NIFCtVpnKC/zZbf30eozMJqucnE3iNauRpQaT0QI+u4lSqcyjJ+ZJFSpKFSJb456tQaajOYaW0oiCiCTJdDSZePFyjA6Xkb98aRKbVuE7Zks15pMl1GoV7S4jl1bzRPJ1Tkwn2Nlu409+NcXuTheJkkyby4goirw1k8ZjUhGzGfDbNHhMat6YjAECPW4TzwyFcFu09HrMxIoNmmwG8jWZaqXCTDTHXDzPx3a00eY08JeHJ7hnW5BXxxN8aoefL/zDCAGbhru3BJhPlvjcwS5mEmW6XDrUKhUui55UoYqAwquUlb0NQRTZ3GLD7zTT47MxnyhRrlR4ajjGI/eu46dnQ/zsfJiD3Yrc2n07A3z7lWk8ViMDzQb2dbuYDKcBmY0tTgZasuxut7EhaOfbL09xx6CHxXQFp0mNVmNia6ud4aUMK9kaG/2KmdHr4zHq9TovXokx4NWj1SrX9b2PDPDSlQhtDj3Hp7M8cmyGVpeZ/oCT/6fJxPNXYnx2fzuNRoOzC6nfHRoKTfC9B0FUFdTOoCQVM1G5Xi2rLK6gVMolpFI2JRqsDkGl1jbyyQiA2hnsbuQSK6LOaJUb9apUyqYEjd4gGqwupHpNqhSzos5oBZAb9eo7r1XZvP1ytRQRdSZbo5CKCGrtb7YIuV4tizqjtZFPRjRNrevqmeg8gKgzWqVKMSuotXpBozfJtXJBNFhccr1WlmvlgqBSawHeOafcqFeRZavcqEXlWrmkdga71+5P884g/DvXDCAarA6plE2prJ5mqZRNqEwObz0dmldZPc3Ab2wj3rkPqVLMAqis7lapkC4Lak1ZqhSzcq1ceuczaGSjy2q7v12qFDK//fkhqjVyrVwQNHqTVMomBLVWrzK7mqVSJvrOPahMDu87n41cr5YBRKOtrZGJjIsGq+Od14oGq+sdsuw71yWotXpRZ7LJ9UqpkU9G1HZ/u1yvlAAErcEilXKJ33AR1+7rnXWpnHfKUsPxT8bGezaoBeGcLMvb3+vnuHqe//14z+bUV3EV74arQX0V7zu8l4P6kffJOa6e538z3rM59VVcxbvhvbxTX8VV/JO4GtRX8b7D1aC+ivcdrgb1VbzvcDWor+J9h/8OAO0N4izveOUAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 720x576 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Yb8pQSJ8dqIf",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "train_label = np.zeros((16905,3))"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "46h8SlgHd9Mp",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "for count,i in enumerate(train_resampled['author.1']):\n",
        "  train_label[count,i] = 1"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mTMW8d5AeWxy",
        "colab_type": "code",
        "outputId": "7583054f-68b4-4512-e827-c929c161e82c",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "train_label"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[1., 0., 0.],\n",
              "       [1., 0., 0.],\n",
              "       [1., 0., 0.],\n",
              "       ...,\n",
              "       [0., 0., 1.],\n",
              "       [0., 0., 1.],\n",
              "       [0., 0., 1.]])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 69
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nBKUdGP3eamq",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "X_train, X_val, y_train, y_val = train_test_split( train_TFIDF, train_label, test_size=0.1, random_state=42)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tqjvfUyFc4D9",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from sklearn.linear_model import Ridge\n",
        "regrtest = Ridge()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "UWcDBrjNiQxI",
        "colab_type": "text"
      },
      "source": [
        "## 2.2 parameter search\n",
        "\n",
        "We wil take the TFIDF weight matrix (5000 dimensions) to train the multi-class classification regression.\n",
        "\n",
        "The hyper-parameter to adjust is the alpha, which is the regularization strength of the model.\n",
        "\n",
        "The objective function is shown here:\n",
        "\n",
        "$ ||y - Xw||^2_2 + alpha * ||w||^2_2 $"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tkNqNcd6e2XC",
        "colab_type": "code",
        "outputId": "a3abb8da-8390-411b-ffc0-8cbe08be807b",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "regrtest.fit(X_train,y_train)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Ridge(alpha=1.0, copy_X=True, fit_intercept=True, max_iter=None,\n",
              "      normalize=False, random_state=None, solver='auto', tol=0.001)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 57
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zkyF7_8Te8BI",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "predict_ridge = regrtest.predict(X_train)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mccyuG_LamZg",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def accuracy_report(pred,target):\n",
        "  pred = np.argmax(pred,axis=1)\n",
        "  target = np.argmax(target,axis=1)\n",
        "  return np.sum(np.where(pred == target,1,0))/len(target)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mFZ_EhkFflLF",
        "colab_type": "code",
        "outputId": "27e585e8-6c70-4cc2-c4f1-0790bb1fb174",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "accuracy_report(predict_ridge,y_train)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.9152754042329433"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 63
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bQBbMcm6ftmE",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "predict_ridge2 = regrtest.predict(X_val)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "owaqetOWfpSs",
        "colab_type": "code",
        "outputId": "54a8cd24-53be-4478-875e-3ca5652ea988",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "accuracy_report(predict_ridge2,y_val)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.7930218805440568"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 66
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "k5vrh188f0VT",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "list_alpha = [i for i in range(20)]\n",
        "list_accuracy = []"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "raz8ucqAfLp1",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "for i in list_alpha:\n",
        "  regrtest = Ridge(alpha=i)\n",
        "  regrtest.fit(X_train,y_train)\n",
        "  predict_ridge2 = regrtest.predict(X_val)\n",
        "  list_accuracy.append(accuracy_report(predict_ridge2,y_val))"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8eJmJA4VfJX1",
        "colab_type": "code",
        "outputId": "9f0f758a-abc9-4732-af69-14d71d32a2ec",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "plt.plot(list_alpha,list_accuracy)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x7f55506c6b38>]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 82
        },
        {
          "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": "nyh9yjhCiVH_",
        "colab_type": "text"
      },
      "source": [
        "## 2.3 final model and explanation\n",
        "\n",
        "We'll use alpha = 2 to train the model."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ePRIFcjOg856",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "regrtest = Ridge(alpha=2)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bk-v7yONiLwH",
        "colab_type": "code",
        "outputId": "0448c1b8-2789-4c4f-faf7-2dbf8896c179",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "regrtest.fit(X_train,y_train)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Ridge(alpha=2, copy_X=True, fit_intercept=True, max_iter=None, normalize=False,\n",
              "      random_state=None, solver='auto', tol=0.001)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 33
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "O7S2biZTiO6G",
        "colab_type": "code",
        "outputId": "2da2ab26-6572-46df-d315-c415587477ff",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "predict_ridge = regrtest.predict(X_train)\n",
        "accuracy_report(predict_ridge,y_train)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.9016037859865913"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 36
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8AJM97eMihhR",
        "colab_type": "code",
        "outputId": "c6af94e5-a41f-4de1-b99b-bafa4dd333c5",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "predict_ridge = regrtest.predict(X_val)\n",
        "accuracy_report(predict_ridge,y_val)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.7995269071555293"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 37
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kgDXYjX4oKia",
        "colab_type": "text"
      },
      "source": [
        "## 2.4 feature importance\n",
        "\n",
        "The TFIDF accuracy is about 80% so that is a pretty good start. Now as promised we can take a look at what words contribute most. "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fwq_BCXAmuCa",
        "colab_type": "code",
        "outputId": "bc044e7d-a960-4df8-844e-baa9c5603a5d",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "dict_author = {1:'HP Lovecraft',2:'Mary Shelley',0:'Edgar Allen Poe'}\n",
        "for author in range(3):\n",
        "  print('author = {}'.format(dict_author[author]))\n",
        "  print('----------')\n",
        "  print('top 5 most unlikely words')\n",
        "  for i in list(np.argsort(regrtest.coef_[author])[:5]):\n",
        "    print('{}'.format(tfidf1.get_feature_names()[i]),'  {:.3f}'.format(regrtest.coef_[author,i]))\n",
        "  print(' ')\n",
        "  print('top 5 most likely words')\n",
        "  for j in list(np.argsort(regrtest.coef_[author])[4995:]):\n",
        "    print('{}'.format(tfidf1.get_feature_names()[j]),'  {:.3f}'.format(regrtest.coef_[author,j]))\n",
        "  print('----------')"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "author = Edgar Allen Poe\n",
            "----------\n",
            "top 5 most unlikely words\n",
            "raymond   -0.681\n",
            "despit   -0.565\n",
            "shew   -0.542\n",
            "west   -0.536\n",
            "endeavour   -0.535\n",
            " \n",
            "top 5 most likely words\n",
            "drawer   0.667\n",
            "however   0.671\n",
            "minutes   0.687\n",
            "ordinari   0.769\n",
            "dupin   0.773\n",
            "----------\n",
            "author = HP Lovecraft\n",
            "----------\n",
            "top 5 most unlikely words\n",
            "and   -0.511\n",
            "portion   -0.489\n",
            "sun   -0.487\n",
            "heart   -0.449\n",
            "which   -0.435\n",
            " \n",
            "top 5 most likely words\n",
            "birch   0.771\n",
            "gilman   0.795\n",
            "despit   0.883\n",
            "west   0.918\n",
            "later   0.958\n",
            "----------\n",
            "author = Mary Shelley\n",
            "----------\n",
            "top 5 most unlikely words\n",
            "later   -0.511\n",
            "however   -0.481\n",
            "posit   -0.442\n",
            "thing   -0.426\n",
            "german   -0.410\n",
            " \n",
            "top 5 most likely words\n",
            "her   0.720\n",
            "adrian   0.720\n",
            "perdita   0.749\n",
            "endeavour   0.796\n",
            "raymond   1.046\n",
            "----------\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "RiL2jLNfYIQF",
        "colab_type": "text"
      },
      "source": [
        "It turns out that a lot of these are names of characters. So they are pretty specific to the novel. For example, Dupin is a character in \"The Murders in the Rue Morgue\" by Peo. This name has highest regression coefficient meaning associated for Poe.\n",
        "\n",
        "Similarly, Gilman is a character in Lovecraft \"The Dreams in the Witch-House\".\n",
        "\n",
        "Adrian and Perdita are characters from Mary Shelley's novel \"The Last Man\"."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hiu9MpxTZZcJ",
        "colab_type": "text"
      },
      "source": [
        "# 3. Naive-Bayes Classification\n",
        "\n",
        "Another ML approach we will look at is Naive Bayes classifier. Given a word vector \n",
        "$x_1,...,x_n$ representing a sentence. The value of the vector can be frequency of words in the document belonging to a class or a binary values of whether the word is presence or absence in the document. We can ask what is the conditional probability that this vector belong to a class $C_k$, i.e.\n",
        "\n",
        "$p(C_k | x_1,...,x_n)$\n",
        "\n",
        "The Bayes' theorem states that \n",
        "\n",
        "$p(C_k | x) = \\frac{p(C_k)p(x|C_k)}{p(x)}$\n",
        "\n",
        "And we can write \n",
        "\n",
        "$P(C_k,x_1,...,x_n) = p(x_1|x_2,...,x_n,C_k)p(x_2|x_3,...,x_n,C_k)...p(x_{n-1}|x_n,C_k)p(x_{n}|C_k)p(C_k)$\n",
        "\n",
        "The problem is that some pairs of $x_i$ are not independent. Certain words tend to go together, and certain words are not likely to go together. So that makes the actual Bayes' theorem calculation will have more terms representing the probability of one word depending on another. This is the part that is difficult to calculate.\n",
        "\n",
        "Now the reason Naive Bayes is \"naive\" is because make an assumption to simplify this problem. The assumption is independence among features. That is to say,\n",
        "\n",
        "$P(x_1,x_2) = P(x_1)P(x_2)$\n",
        "\n",
        "So the model above is simplified to \n",
        "\n",
        "$p(C_k|x_1,...,x_n) = p(C_k)p(x_1|C_k)p(x_2|C_k)p(x_3|C_k)...$\n",
        "\n",
        "$ = p(C_k)\\prod_{i=1}^np(x_i|C_k)$\n",
        "\n",
        "So with this assumption, even though it is unlikely to be corect, but it will work pretty well in practice. And now we should have everything we need to do the work pretty easily.\n",
        "\n",
        "One more thing to think about is how to model a probability distribution of words in classes, i.e. whether \n",
        "$p(x_n|C_k)$ is normally distributed or distributed according to Bernoullidistribution, etc.\n",
        "\n",
        "In our case, we will look at Bernoulli Naive Bayes since word occurances are binary, i.e. either presence or absence. "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IUuxGCw8lPIL",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from sklearn.feature_extraction.text import CountVectorizer"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "G-Lyl9totGMh",
        "colab_type": "text"
      },
      "source": [
        "## 3.1 Bernoulli Naive Bayes"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4v5x5nFYnMsN",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "count_vector1 = CountVectorizer(max_features=5000,binary=True)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kLX3l5J7odgO",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "train_count = count_vector1.fit_transform(train_resampled['stemmed_text'])"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MZfSkUqAbYqw",
        "colab_type": "code",
        "outputId": "fcdfaa72-c8df-42c9-d541-6d834617b7d7",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 50
        }
      },
      "source": [
        "train_count"
      ],
      "execution_count": 39,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<16905x5000 sparse matrix of type '<class 'numpy.int64'>'\n",
              "\twith 171345 stored elements in Compressed Sparse Row format>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 39
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jWKxceStpLCQ",
        "colab_type": "code",
        "outputId": "9c74ab11-0ed1-4ae1-8cbf-a61ac63e30c6",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 503
        }
      },
      "source": [
        "# to visualize the sparsity \n",
        "plt.figure(figsize=(10,8))\n",
        "plt.spy(train_count,markersize=0.09)"
      ],
      "execution_count": 40,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.lines.Line2D at 0x7ff16da5bc18>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 40
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x576 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6rWkIjsmrFTc",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "train_label = list(train_resampled['author.1'].values)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "AMJy2BhCqxTx",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "X_train, X_val, y_train, y_val = train_test_split( train_count.toarray(), train_label, test_size=0.1, random_state=42)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "laZV8PqOq96d",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from sklearn.naive_bayes import BernoulliNB\n",
        "gnb = BernoulliNB()\n",
        "y_pred = gnb.fit(X_train, y_train).predict(X_val)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "G50z1eVlsErd",
        "colab_type": "code",
        "outputId": "0e7d03bd-8244-451d-9a68-b6830c7be691",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 50
        }
      },
      "source": [
        "print('overall accuracy')\n",
        "print(np.sum(y_pred == y_val)/len(y_pred))"
      ],
      "execution_count": 45,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "overall accuracy\n",
            "0.7983441750443524\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tO0Pp-6trSFG",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from sklearn.metrics import confusion_matrix\n",
        "array_confusion = confusion_matrix(y_val,y_pred,normalize='true')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DCer64Sgr3ut",
        "colab_type": "code",
        "outputId": "dfcaaae6-e608-448c-b12d-44b1a3846694",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        }
      },
      "source": [
        "sns.heatmap(array_confusion, cmap=\"YlGnBu\")"
      ],
      "execution_count": 49,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7ff16dafa940>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 49
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rCAL213VtLQY",
        "colab_type": "text"
      },
      "source": [
        "## 3.2 Multinomial Naive Bayes\n",
        "\n",
        "The Bernoulli Naive Bayes only look at binary count, i.e. presence or absence. If we want to a word distribution model that takes into account th number of counts, we need another probability distribution. In this case, we will use Multinomial distribution. So the model is Multinomial Naive Bayes. "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VrH5k9wWtOV4",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "count_vector1 = CountVectorizer(max_features=5000)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ASy-UcJUtW0T",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "train_count = count_vector1.fit_transform(train_resampled['stemmed_text'])"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "062sh3S3tZIE",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "X_train, X_val, y_train, y_val = train_test_split( train_count.toarray(), train_label, test_size=0.1, random_state=42)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "u3Bv7fEnteB6",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from sklearn.naive_bayes import MultinomialNB\n",
        "gnb = MultinomialNB()\n",
        "y_pred = gnb.fit(X_train, y_train).predict(X_val)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TqrBHgrYtmGe",
        "colab_type": "code",
        "outputId": "6bd0a98b-6b36-4d50-fcd8-95072a5244e5",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 50
        }
      },
      "source": [
        "print('overall accuracy')\n",
        "print(np.sum(y_pred == y_val)/len(y_pred))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "overall accuracy\n",
            "0.8036664695446482\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "AUQToMyntuQa",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from sklearn.metrics import confusion_matrix\n",
        "array_confusion = confusion_matrix(y_val,y_pred,normalize='true')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "t54ZbyfztyuC",
        "colab_type": "code",
        "outputId": "5f23708b-8ed8-47f9-eef3-be136bf89362",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        }
      },
      "source": [
        "sns.heatmap(array_confusion, cmap=\"YlGnBu\")"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f1b5bd99400>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 118
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tRIOk_uHoPaN",
        "colab_type": "text"
      },
      "source": [
        "# 4 recurrent neural network\n",
        "\n",
        "The models we have so far only look at the word level information. And even though they are performing pretty well, one might want to look at higher level information, such as phrases or sentences. So we need a model that perserves the word order somehow.\n",
        "\n",
        "First we will use a recurrent neural network to model the sequences of words in sentences and finally classify them to one of 3 authors.\n",
        "\n",
        "The diagram for recurrent neural net is shown below.\n",
        "\n",
        "![alt text](http://www.easy-tensorflow.com/images/NN/06.png)\n",
        "\n",
        "Look at the unfolded diagram of the network. Our strategy is to feed words (in the form of numerical tokens), one token as a time to the model. these tokens is embedded in the form a vector. The model receives input from this embedded vector and a hidden state from previous token (for first token, the hidden state is generated and feed into the model directly). The hidden state carries information from previous words in the sequence to the next word. Until it reaches the last token. The hidden state on the last token is then sent to a fully connected classification layer that classify to the author label.\n",
        "\n",
        "Now, one problem that we will encounter is that if our text is long, by the time we train the model to end, the information from the beginning of the text is lost. The hidden states that is carried from the beginning get changed successively too many times, such that the back-propagated gradients can vanish.\n",
        "\n",
        "\n",
        "![alt text](https://upload.wikimedia.org/wikipedia/commons/thumb/5/5f/Gated_Recurrent_Unit.svg/1920px-Gated_Recurrent_Unit.svg.png)\n",
        "\n",
        "To ameliorate this delay problem, we will be using gated recurrent units (GRU) as a neuron in the recurrent network. Roughly speaking, this kind of unit provides a gaing mechanism that control the flow of information. This full understanding of the math is quite complicated and won't be pursued here. "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "apGay58kqvhL",
        "colab_type": "text"
      },
      "source": [
        "## 4.1 Packages and libraries"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "l7govH3IwEKq",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "import torch\n",
        "import torch.nn as nn\n",
        "from torch.utils.data import TensorDataset, DataLoader\n",
        "import timeit\n",
        "import torch.nn.functional as F"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ag8cJuLIrN8M",
        "colab_type": "code",
        "outputId": "fba2aed1-bb66-465c-8e3a-655d6f29c9e9",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 50
        }
      },
      "source": [
        "import nltk\n",
        "nltk.download('punkt')\n",
        "from nltk.tokenize import word_tokenize"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[nltk_data] Downloading package punkt to /root/nltk_data...\n",
            "[nltk_data]   Unzipping tokenizers/punkt.zip.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tAIcvuRSv6Mo",
        "colab_type": "code",
        "outputId": "46abcdd9-e9df-439f-df9d-c4fbd54ac29c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 50
        }
      },
      "source": [
        "if torch.cuda.is_available():    \n",
        "\n",
        "    # Tell PyTorch to use the GPU.    \n",
        "    device = torch.device(\"cuda\")\n",
        "    torch.backends.cudnn.benchmark = True\n",
        "    torch.backends.cudnn.enabled = True\n",
        "    print('There are %d GPU(s) available.' % torch.cuda.device_count())\n",
        "\n",
        "    print('We will use the GPU:', torch.cuda.get_device_name(0)) #hopefully we get Tesla P100 \n",
        "\n",
        "# If not...\n",
        "else:\n",
        "    print('No GPU available, using the CPU instead.')\n",
        "    device = torch.device(\"cpu\")"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "There are 1 GPU(s) available.\n",
            "We will use the GPU: Tesla P100-PCIE-16GB\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fYODxFCTrGX5",
        "colab_type": "text"
      },
      "source": [
        "## 4.2 tokenization\n",
        "\n",
        "We will implement our own tokenization steps here."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "k6Ejf_xZio3M",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "train_resampled = train_resampled[['author','text']]"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0BLHSijDt73e",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "punc = ['.', ',', '\"', \"'\", '?', '!', ':', ';', '(', ')', '[', ']', '{', '}',\"%\"]"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "eyuAwxx3rVaF",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def tokenize_frame(frame1):\n",
        "    frame = frame1.copy(deep=True)\n",
        "    frame['tokens'] = frame['text'].apply(lambda x: word_tokenize(x))\n",
        "    return frame"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JPSvZUnTrXv5",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "train_resampled = tokenize_frame(train_resampled)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3MhaDRofuT69",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "train_resampled['tokens'] = train_resampled['tokens'].apply(lambda x: [y for y in x if y not in punc])"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "phSw5N6treYD",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "list_vocab = []\n",
        "for sentence in train_resampled['tokens']:\n",
        "  for word in np.unique(sentence):\n",
        "    list_vocab.append(word)\n",
        "list_vocab = np.unique(list_vocab)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "b4RIEBIfnGUv",
        "colab_type": "text"
      },
      "source": [
        "We will create a dictionary mapping between word token and numerical token. \n",
        "The goal is to create a tensor of numeric tokens of the same lengths. Therefore, if text are longer than the allowed length, we will trim off the extra words. And if the text are shorter than the prescribed length, we will pad the tensor with [0].\n",
        "\n",
        "Note that the padding is on the left side, because the sequence goes from left to right. You want the last tokens in the sequence to be your words, so the hidden states contain the information about those words. If we padd the right side, the information (carried in the hidden state till the fully connected layer in the end) can get lost when encountering a long sequences of padding. "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tTMAa5XMr5mr",
        "colab_type": "code",
        "outputId": "1db3bc87-be85-44ab-8bbe-33e4e08cdcb5",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "len(list_vocab)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "26077"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sAMtoSTer9sY",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def create_vocab_dict(vocab):\n",
        "    dict_str2int = {}\n",
        "    dict_int2str = {}\n",
        "    for i,j in enumerate(vocab,1):\n",
        "        dict_str2int[j] = int(i)\n",
        "        dict_int2str[i] = j\n",
        "    return dict_str2int, dict_int2str"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Vvyn3ynysOT_",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "dict_str2int, dict_int2str = create_vocab_dict(list_vocab)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9lavmQt1sSSS",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def apply_dict(list1):\n",
        "    result = []\n",
        "    try:\n",
        "      for i in list1:\n",
        "        result.append(dict_str2int[i])\n",
        "    except:\n",
        "      for i in list1:\n",
        "        if i not in dict_str2int.keys():\n",
        "          print(i,' not in dict')\n",
        "          dict_str2int[i] = len(dict_str2int.keys())\n",
        "          result.append(dict_str2int[i])\n",
        "        else:\n",
        "          result.append(dict_str2int[i])\n",
        "    return result"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NSsCp4jpr76e",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def word2token(frame1):\n",
        "    frame = frame1.copy(deep=True)\n",
        "    frame['numeric_tokens'] = frame['tokens'].apply(lambda x: apply_dict(x))\n",
        "    return frame"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Jiz8uAZTrEQJ",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "train_resampled = word2token(train_resampled)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "i8QyxcOesqq9",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "list_len = []\n",
        "for i in train_resampled['numeric_tokens']:\n",
        "  list_len.append(len(i))"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TUAfWM8ItPTY",
        "colab_type": "code",
        "outputId": "4bf94c17-cf51-45f3-dfae-8268665c3907",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "np.quantile(list_len, 0.99)  # most text is shorter than 85 tokens."
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "85.0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 28
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jYSRH8yotXhk",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def pad_truncate_array(frame,max_len=160,truncate='Post'):\n",
        "    result = []\n",
        "    for item in frame['numeric_tokens']:\n",
        "        if len(item) > max_len:\n",
        "            if truncate == 'Post':\n",
        "                result.append(item[:max_len])\n",
        "            else:\n",
        "                result.append(item[len(item) - max_len:])\n",
        "        else:\n",
        "            result.append([0]*(max_len - len(item)) + item)\n",
        "    return np.array(result)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IOHG1qrXtP3U",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "train_array = pad_truncate_array(train_resampled,max_len = 100)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vLE_49FztI5c",
        "colab_type": "code",
        "outputId": "bfa2067f-9acc-4ee7-8aab-8a6a2a573c9d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 134
        }
      },
      "source": [
        "train_array"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[    0,     0,     0, ...,  7200, 17247,  5430],\n",
              "       [    0,     0,     0, ..., 25862,  4810,  2999],\n",
              "       [    0,     0,     0, ..., 14317, 21911,  7358],\n",
              "       ...,\n",
              "       [    0,     0,     0, ..., 23509, 17494, 16103],\n",
              "       [    0,     0,     0, ..., 26005, 17459,  5246],\n",
              "       [    0,     0,     0, ..., 17459, 24649, 17859]])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 31
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "o-3VdVDpth9_",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "X_train, X_val, y_train, y_val = train_test_split( train_array, train_resampled['author'], \n",
        "                                                  test_size=0.1, random_state=42)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jcu15NJgwI22",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "batch_size = 96\n",
        "train_dataset = TensorDataset(torch.tensor(X_train,dtype=torch.long), torch.tensor(y_train,dtype=torch.long))\n",
        "train_loader = DataLoader(train_dataset, shuffle=True, batch_size=batch_size)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "veqlt2VvwOhI",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "val_dataset = TensorDataset(torch.tensor(X_val,dtype=torch.long), torch.tensor(y_val,dtype=torch.long))\n",
        "val_loader = DataLoader(val_dataset, shuffle=True, batch_size=batch_size)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "j2VErmc7wRcJ",
        "colab_type": "code",
        "outputId": "029247ba-8aed-4979-97e2-6f80f9625761",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 286
        }
      },
      "source": [
        "dataiter = iter(train_loader)\n",
        "sample_x, sample_y = dataiter.next()\n",
        "\n",
        "print('Sample input size: ', sample_x.size()) # batch_size, seq_length\n",
        "print('Sample input: \\n', sample_x)\n",
        "print()\n",
        "print('Sample label size: ', sample_y.size()) # batch_size\n",
        "print('Sample label: \\n', sample_y)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Sample input size:  torch.Size([96, 100])\n",
            "Sample input: \n",
            " tensor([[    0,     0,     0,  ..., 25573,  6578, 12445],\n",
            "        [    0,     0,     0,  ...,  5620, 16746, 13111],\n",
            "        [    0,     0,     0,  ..., 17459, 23509, 13914],\n",
            "        ...,\n",
            "        [    0,     0,     0,  ..., 26030, 20989, 13749],\n",
            "        [    0,     0,     0,  ..., 23768, 11114, 23519],\n",
            "        [    0,     0,     0,  ...,  5513, 13768, 22075]])\n",
            "\n",
            "Sample label size:  torch.Size([96])\n",
            "Sample label: \n",
            " tensor([1, 0, 1, 1, 0, 0, 0, 2, 2, 2, 0, 1, 0, 0, 0, 0, 2, 2, 2, 2, 1, 2, 1, 1,\n",
            "        2, 1, 0, 0, 1, 1, 1, 2, 0, 1, 1, 2, 1, 1, 1, 2, 2, 1, 1, 1, 1, 1, 1, 0,\n",
            "        0, 2, 1, 2, 2, 1, 1, 0, 2, 1, 2, 1, 0, 1, 1, 0, 1, 0, 1, 1, 1, 1, 2, 1,\n",
            "        1, 0, 2, 2, 2, 0, 1, 1, 2, 0, 2, 0, 1, 1, 1, 2, 0, 1, 0, 0, 0, 2, 2, 1])\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Z8CEBwAmp6Ba",
        "colab_type": "text"
      },
      "source": [
        "A nice thing about Pytorch is that, unlike in Keras, in pytorch you get to see the inside of the model and how it trains. This helps with learning about the how the model works.\n",
        "\n",
        "We will define a recurrent net with has word embedding layer, number of GRU units, size of hidden dimensions, and size of output layer."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mtOwW5dyrr7q",
        "colab_type": "text"
      },
      "source": [
        "## 4.3 Model building and training"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QpbDGyGxwVv_",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "class GRUNet(nn.Module):\n",
        "    def __init__(self, vocab_size,embedding_dim, hidden_dim, output_dim, n_layers =1, drop_prob=0.3):\n",
        "        super(GRUNet, self).__init__()\n",
        "        self.hidden_dim = hidden_dim\n",
        "        self.n_layers = n_layers\n",
        "        \n",
        "        self.embedding = nn.Embedding(vocab_size,embedding_dim)\n",
        "        self.gru = nn.GRU(embedding_dim, hidden_dim, n_layers, batch_first=True, dropout=drop_prob)\n",
        "        self.fc = nn.Linear(hidden_dim, output_dim)\n",
        "        \n",
        "        self.sig = nn.Softmax()\n",
        "        \n",
        "    def forward(self, x, hidden):\n",
        "        embeds = self.embedding(x)\n",
        "        out, hidden = self.gru(embeds, hidden)\n",
        "        out = self.fc(out[:,-1])\n",
        "        out = self.sig(out)\n",
        "        return out, hidden\n",
        "    \n",
        "    def init_hidden(self, batch_size):\n",
        "        weight = next(self.parameters()).data\n",
        "        hidden = weight.new(self.n_layers, batch_size, self.hidden_dim).zero_().to(device)\n",
        "        return hidden"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "A0ZYqgJSsUx6",
        "colab_type": "text"
      },
      "source": [
        "We will send the model to GPU. GPU will help shorten the training time tremendously."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "j1ocViRrwZFE",
        "colab_type": "code",
        "outputId": "2d303165-10c2-45e2-9827-2ec0cbe65bc8",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 118
        }
      },
      "source": [
        "vocab_size = len(list_vocab) + 1 #len(dict_str2int + 1 (for padding token = [0]))\n",
        "output_size = 3\n",
        "embedding_dim = 300 \n",
        "hidden_dim = 400\n",
        "n_layers = 2\n",
        "drop_prob = 0.5\n",
        "\n",
        "net = GRUNet(vocab_size,embedding_dim, hidden_dim,output_size, n_layers,drop_prob)\n",
        "\n",
        "net = net.to(device)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "GRUNet(\n",
              "  (embedding): Embedding(26078, 300)\n",
              "  (gru): GRU(300, 400, num_layers=2, batch_first=True, dropout=0.5)\n",
              "  (fc): Linear(in_features=400, out_features=3, bias=True)\n",
              "  (sig): Softmax(dim=None)\n",
              ")"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 40
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "K2GvJgLdsjDz",
        "colab_type": "text"
      },
      "source": [
        "After defining the model, we now will define the optimizer and loss. "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "o7qin8HQV1GX",
        "colab_type": "code",
        "outputId": "2e777c7c-0cfa-4513-9a11-4145ad6946a1",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 118
        }
      },
      "source": [
        "learning_rate = 0.00001\n",
        "weight_decay = 0.001\n",
        "criterion = nn.CrossEntropyLoss() #nn.NLLLoss()\n",
        "#criterion = nn.NLLLoss()\n",
        "optimizer = torch.optim.Adam(net.parameters(), lr=learning_rate,weight_decay=weight_decay)\n",
        "net"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "GRUNet(\n",
              "  (embedding): Embedding(26078, 300)\n",
              "  (gru): GRU(300, 400, num_layers=2, batch_first=True, dropout=0.5)\n",
              "  (fc): Linear(in_features=400, out_features=3, bias=True)\n",
              "  (sig): Softmax(dim=None)\n",
              ")"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 49
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MJmTdMFXwtsl",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def measure_accu(torch_output,torch_label):\n",
        "  pred_array = torch.argmax(torch_output,axis=1).detach().cpu().numpy()\n",
        "  correct_array = torch_label.view(-1).cpu().numpy()\n",
        "  correct = np.where(pred_array == correct_array,1,0)\n",
        "  #print(correct)\n",
        "  return np.sum(correct)/len(correct)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JrLMsTYcVliy",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "iter_val_loss = []\n",
        "iter_train_loss = []\n",
        "epoch_train_accu = []\n",
        "epoch_val_accu = []"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1Swd3MTfstbF",
        "colab_type": "text"
      },
      "source": [
        "We will train the model for 20 epochs. Note that we need to do gradient clipping every iteration. The idea is to re-scale the gradient to prevent it from getting too large.\n",
        "\n",
        "This is to prevent gradient exploding problem. "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9rvYhyDxwpI-",
        "colab_type": "code",
        "outputId": "2e2b5e85-aaa9-4ad2-a98e-c84f82ab6a7f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "epochs = 20\n",
        "clip = 3\n",
        "print_every = 159 # = len(train_loader)\n",
        "count = 0\n",
        "\n",
        "\n",
        "net.train()\n",
        "for ite in range(epochs):\n",
        "    print('start epoch ')\n",
        "    start = timeit.default_timer()\n",
        "    temp_accu = []\n",
        "    for inputs, labels in train_loader:\n",
        "\n",
        "        count += 1\n",
        "        batch_size = inputs.shape[0]\n",
        "        inputs, labels = inputs.to(device), labels.to(device)\n",
        "        h = net.init_hidden(batch_size) #.to(device)\n",
        "        net.zero_grad()\n",
        "        \n",
        "        output,h = net(inputs,h)\n",
        "        temp_accu.append(measure_accu(output,labels))\n",
        "        loss = criterion(output, labels)\n",
        "        loss.backward()\n",
        "        \n",
        "        nn.utils.clip_grad_norm_(net.parameters(),clip)\n",
        "        optimizer.step()\n",
        "        \n",
        "        iter_train_loss.append(loss.item())\n",
        "\n",
        "        if count % print_every == 0:\n",
        "\n",
        "            val_losses = []\n",
        "            val_accu = []\n",
        "            net.eval()\n",
        "            for inputs_val, labels_val in val_loader:\n",
        "                batch_size = inputs_val.shape[0]\n",
        "                inputs_val, labels_val = inputs_val.to(device), labels_val.to(device)\n",
        "                val_h = net.init_hidden(batch_size) #.to(device)\n",
        "                \n",
        "                output_val, val_h = net(inputs_val,val_h)\n",
        "                \n",
        "                val_loss = criterion(output_val, labels_val)\n",
        "                \n",
        "                val_losses.append(val_loss.item())\n",
        "                val_accu.append(measure_accu(output_val,labels_val))\n",
        "                iter_val_loss.append(val_loss.item())\n",
        "            \n",
        "            epoch_val_accu.append(np.mean(val_accu))\n",
        "            stop = timeit.default_timer()\n",
        "            \n",
        "            net.train()\n",
        "            print('Epoch: {}/{}...'.format(ite + 1,epochs),\n",
        "                 'step: {}...'.format(count),\n",
        "                 'loss: {:.6f}...'.format(loss.item()),\n",
        "                 'val loss: {:.6f}'.format(np.mean(val_losses)))\n",
        "            del inputs,labels\n",
        "    epoch_train_accu.append(np.mean(temp_accu))\n",
        "    stop = timeit.default_timer()\n",
        "    print('one epoch takes {:.3f}'.format(stop - start))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "start epoch \n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:17: UserWarning: Implicit dimension choice for softmax has been deprecated. Change the call to include dim=X as an argument.\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Epoch: 1/20... step: 159... loss: 0.639097... val loss: 0.797074\n",
            "one epoch takes 5.536\n",
            "start epoch \n",
            "Epoch: 2/20... step: 318... loss: 0.603204... val loss: 0.802459\n",
            "one epoch takes 5.520\n",
            "start epoch \n",
            "Epoch: 3/20... step: 477... loss: 0.662570... val loss: 0.798582\n",
            "one epoch takes 5.537\n",
            "start epoch \n",
            "Epoch: 4/20... step: 636... loss: 0.618116... val loss: 0.799298\n",
            "one epoch takes 5.486\n",
            "start epoch \n",
            "Epoch: 5/20... step: 795... loss: 0.601601... val loss: 0.798625\n",
            "one epoch takes 5.512\n",
            "start epoch \n",
            "Epoch: 6/20... step: 954... loss: 0.643404... val loss: 0.803875\n",
            "one epoch takes 5.524\n",
            "start epoch \n",
            "Epoch: 7/20... step: 1113... loss: 0.599640... val loss: 0.802888\n",
            "one epoch takes 5.525\n",
            "start epoch \n",
            "Epoch: 8/20... step: 1272... loss: 0.599972... val loss: 0.798667\n",
            "one epoch takes 5.544\n",
            "start epoch \n",
            "Epoch: 9/20... step: 1431... loss: 0.633334... val loss: 0.804388\n",
            "one epoch takes 5.534\n",
            "start epoch \n",
            "Epoch: 10/20... step: 1590... loss: 0.642718... val loss: 0.799941\n",
            "one epoch takes 5.545\n",
            "start epoch \n",
            "Epoch: 11/20... step: 1749... loss: 0.585173... val loss: 0.800599\n",
            "one epoch takes 5.489\n",
            "start epoch \n",
            "Epoch: 12/20... step: 1908... loss: 0.629835... val loss: 0.801930\n",
            "one epoch takes 5.509\n",
            "start epoch \n",
            "Epoch: 13/20... step: 2067... loss: 0.582539... val loss: 0.805265\n",
            "one epoch takes 5.525\n",
            "start epoch \n",
            "Epoch: 14/20... step: 2226... loss: 0.625760... val loss: 0.801004\n",
            "one epoch takes 5.522\n",
            "start epoch \n",
            "Epoch: 15/20... step: 2385... loss: 0.681899... val loss: 0.803423\n",
            "one epoch takes 5.522\n",
            "start epoch \n",
            "Epoch: 16/20... step: 2544... loss: 0.557136... val loss: 0.800993\n",
            "one epoch takes 5.520\n",
            "start epoch \n",
            "Epoch: 17/20... step: 2703... loss: 0.577202... val loss: 0.802166\n",
            "one epoch takes 5.540\n",
            "start epoch \n",
            "Epoch: 18/20... step: 2862... loss: 0.692896... val loss: 0.807039\n",
            "one epoch takes 5.497\n",
            "start epoch \n",
            "Epoch: 19/20... step: 3021... loss: 0.665762... val loss: 0.804176\n",
            "one epoch takes 5.515\n",
            "start epoch \n",
            "Epoch: 20/20... step: 3180... loss: 0.599873... val loss: 0.803388\n",
            "one epoch takes 5.530\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xdoQiYuv6MXZ",
        "colab_type": "code",
        "outputId": "26a0e20f-2246-4853-af18-01669d56d2a2",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        }
      },
      "source": [
        "plt.plot(epoch_train_accu)\n",
        "plt.plot(epoch_val_accu)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x7f2b132acbe0>]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 51
        },
        {
          "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": "code",
      "metadata": {
        "id": "I-nsG48PwBx-",
        "colab_type": "code",
        "outputId": "892362da-02a8-46e0-b047-f374bf27575b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "np.max(epoch_val_accu)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.7504119585687383"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 53
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3HIF3ATLu7EH",
        "colab_type": "text"
      },
      "source": [
        "So it seems that the recurrent neural net is decent but certainly not as good as the first two ML models. We do see overfitting in the training set, while the validation stays at about 0.75 accuracy.\n",
        "\n",
        "Again, the problem that we might be seeing here is the long dependency problem. "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "atlXh4pVUgD5",
        "colab_type": "text"
      },
      "source": [
        "# 5. BERT classification\n",
        "\n",
        "Now finally we get to the last approach which is BERT (Bidirectional Encoder Representations from Transformers). I have written [here](https://github.com/VincentK1991/BERT_summarization_1) as a primer for BERT, so go check that out. In the previous repository, that is just using pre-trained BERT to do the embedding. In this case, we will fine-tune BERT for classification.\n",
        "\n",
        "The advantage of BERT over other models are several. First, BERT looks at sequence information and sentence level information. This is the advantage over TFIDF or Naive-Bayes. Second, BERT is pre-trained, so this is already a big advantage over the recurrent neural net that we made previously. Third is that BERT is bi-directional, meaning the flow of information can be from later sequence to the fron of sequence, unlike the recurrent neural network where information flow only from left to right. \n",
        "And finally, to solve the long-term dependency that is so problematic in the recurrent neural net, BERT has an attention mechanism that weight the importance of particular words to other surrounding words.\n",
        "\n",
        "\n",
        "What we need to do here for fine-tuning is to plug in the final layer, a fully-connected classification layer with the correct output size. This step is even simplified when we use the Hugging Face transformers package. We can simply import \"BertForSequenceClassification\", and the fully-connected layer is already there. We just specify the output size.\n",
        "\n",
        "The training strategy is also very simple. Just calculate the loss and do back-propagation."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YFJk4biFr3ge",
        "colab_type": "text"
      },
      "source": [
        "## 5.1 Packages and Libraries"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FBwW_P9GWpyp",
        "colab_type": "code",
        "outputId": "06e6d56b-4505-42cc-8ee7-541b32fe0fae",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 84
        }
      },
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "import torch\n",
        "import torch.nn as nn\n",
        "from torch.utils.data import TensorDataset, DataLoader\n",
        "import torch.nn.functional as F\n",
        "\n",
        "nltk.download('punkt')\n",
        "from nltk import sent_tokenize\n",
        "%tensorflow_version 1.x\n",
        "from keras.preprocessing.sequence import pad_sequences"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[nltk_data] Downloading package punkt to /root/nltk_data...\n",
            "[nltk_data]   Unzipping tokenizers/punkt.zip.\n",
            "TensorFlow 1.x selected.\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "Using TensorFlow backend.\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rxvdsLBeWp_a",
        "colab_type": "code",
        "outputId": "5fe843be-a2e5-42ca-fb47-112b6fd5ebca",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 675
        }
      },
      "source": [
        "!pip install transformers"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Collecting transformers\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/a3/78/92cedda05552398352ed9784908b834ee32a0bd071a9b32de287327370b7/transformers-2.8.0-py3-none-any.whl (563kB)\n",
            "\u001b[K     |████████████████████████████████| 573kB 2.8MB/s \n",
            "\u001b[?25hRequirement already satisfied: regex!=2019.12.17 in /usr/local/lib/python3.6/dist-packages (from transformers) (2019.12.20)\n",
            "Collecting tokenizers==0.5.2\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/d1/3f/73c881ea4723e43c1e9acf317cf407fab3a278daab3a69c98dcac511c04f/tokenizers-0.5.2-cp36-cp36m-manylinux1_x86_64.whl (3.7MB)\n",
            "\u001b[K     |████████████████████████████████| 3.7MB 14.3MB/s \n",
            "\u001b[?25hRequirement already satisfied: numpy in /usr/local/lib/python3.6/dist-packages (from transformers) (1.18.2)\n",
            "Requirement already satisfied: tqdm>=4.27 in /usr/local/lib/python3.6/dist-packages (from transformers) (4.38.0)\n",
            "Collecting sacremoses\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/99/50/93509f906a40bffd7d175f97fd75ea328ad9bd91f48f59c4bd084c94a25e/sacremoses-0.0.41.tar.gz (883kB)\n",
            "\u001b[K     |████████████████████████████████| 890kB 33.2MB/s \n",
            "\u001b[?25hCollecting sentencepiece\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/74/f4/2d5214cbf13d06e7cb2c20d84115ca25b53ea76fa1f0ade0e3c9749de214/sentencepiece-0.1.85-cp36-cp36m-manylinux1_x86_64.whl (1.0MB)\n",
            "\u001b[K     |████████████████████████████████| 1.0MB 37.6MB/s \n",
            "\u001b[?25hRequirement already satisfied: requests in /usr/local/lib/python3.6/dist-packages (from transformers) (2.21.0)\n",
            "Requirement already satisfied: dataclasses; python_version < \"3.7\" in /usr/local/lib/python3.6/dist-packages (from transformers) (0.7)\n",
            "Requirement already satisfied: boto3 in /usr/local/lib/python3.6/dist-packages (from transformers) (1.12.38)\n",
            "Requirement already satisfied: filelock in /usr/local/lib/python3.6/dist-packages (from transformers) (3.0.12)\n",
            "Requirement already satisfied: six in /usr/local/lib/python3.6/dist-packages (from sacremoses->transformers) (1.12.0)\n",
            "Requirement already satisfied: click in /usr/local/lib/python3.6/dist-packages (from sacremoses->transformers) (7.1.1)\n",
            "Requirement already satisfied: joblib in /usr/local/lib/python3.6/dist-packages (from sacremoses->transformers) (0.14.1)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.6/dist-packages (from requests->transformers) (2020.4.5.1)\n",
            "Requirement already satisfied: chardet<3.1.0,>=3.0.2 in /usr/local/lib/python3.6/dist-packages (from requests->transformers) (3.0.4)\n",
            "Requirement already satisfied: urllib3<1.25,>=1.21.1 in /usr/local/lib/python3.6/dist-packages (from requests->transformers) (1.24.3)\n",
            "Requirement already satisfied: idna<2.9,>=2.5 in /usr/local/lib/python3.6/dist-packages (from requests->transformers) (2.8)\n",
            "Requirement already satisfied: s3transfer<0.4.0,>=0.3.0 in /usr/local/lib/python3.6/dist-packages (from boto3->transformers) (0.3.3)\n",
            "Requirement already satisfied: botocore<1.16.0,>=1.15.38 in /usr/local/lib/python3.6/dist-packages (from boto3->transformers) (1.15.38)\n",
            "Requirement already satisfied: jmespath<1.0.0,>=0.7.1 in /usr/local/lib/python3.6/dist-packages (from boto3->transformers) (0.9.5)\n",
            "Requirement already satisfied: docutils<0.16,>=0.10 in /usr/local/lib/python3.6/dist-packages (from botocore<1.16.0,>=1.15.38->boto3->transformers) (0.15.2)\n",
            "Requirement already satisfied: python-dateutil<3.0.0,>=2.1 in /usr/local/lib/python3.6/dist-packages (from botocore<1.16.0,>=1.15.38->boto3->transformers) (2.8.1)\n",
            "Building wheels for collected packages: sacremoses\n",
            "  Building wheel for sacremoses (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for sacremoses: filename=sacremoses-0.0.41-cp36-none-any.whl size=893334 sha256=711e8a7ce364055ad142e5657dc31b8fe97042ff72feadacf6fe8792db982a74\n",
            "  Stored in directory: /root/.cache/pip/wheels/22/5a/d4/b020a81249de7dc63758a34222feaa668dbe8ebfe9170cc9b1\n",
            "Successfully built sacremoses\n",
            "Installing collected packages: tokenizers, sacremoses, sentencepiece, transformers\n",
            "Successfully installed sacremoses-0.0.41 sentencepiece-0.1.85 tokenizers-0.5.2 transformers-2.8.0\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "FmpCrB322erT",
        "colab_type": "text"
      },
      "source": [
        "Instead of making a new tokenizer ourselves,We will use BERTtokenizer so we don't need to train BERT from the beginning."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "z-77p-BIW4FG",
        "colab_type": "code",
        "outputId": "8338bfec-347f-432c-e149-c66d86d887da",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 82,
          "referenced_widgets": [
            "435077a934514290beab55b75d0fa78b",
            "20ded56132d94ebba778351a4a028fcb",
            "8e7da2059d49492eaa24ff8772f17b7d",
            "883a9003f7474ba493cefe4dd29bf375",
            "1c4da6cab3ea40768e8da23754b25ba3",
            "897ec8088d6e4e7397f962350fad379b",
            "1611a20069824d3496897f18d37462f2",
            "dce497a099cb4ad2bf7f7a064060c10f"
          ]
        }
      },
      "source": [
        "from transformers import BertTokenizer\n",
        "\n",
        "# Load the BERT tokenizer.\n",
        "print('Loading BERT tokenizer...')\n",
        "tokenizer = BertTokenizer.from_pretrained('bert-base-uncased', do_lower_case=True)"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Loading BERT tokenizer...\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "435077a934514290beab55b75d0fa78b",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(IntProgress(value=0, description='Downloading', max=231508, style=ProgressStyle(description_wid…"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DtRNwXJXsoB2",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 50
        },
        "outputId": "87017544-0c7e-4a77-9599-af885bc61918"
      },
      "source": [
        "if torch.cuda.is_available():    \n",
        "\n",
        "    # Tell PyTorch to use the GPU.    \n",
        "    device = torch.device(\"cuda\")\n",
        "    torch.backends.cudnn.benchmark = True\n",
        "    torch.backends.cudnn.enabled = True\n",
        "    print('There are %d GPU(s) available.' % torch.cuda.device_count())\n",
        "\n",
        "    print('We will use the GPU:', torch.cuda.get_device_name(0)) #hopefully we get Tesla P100 \n",
        "\n",
        "# If not...\n",
        "else:\n",
        "    print('No GPU available, using the CPU instead.')\n",
        "    device = torch.device(\"cpu\")"
      ],
      "execution_count": 51,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "There are 1 GPU(s) available.\n",
            "We will use the GPU: Tesla P100-PCIE-16GB\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HC4ysTUBsp-k",
        "colab_type": "text"
      },
      "source": [
        "## 5.2 Tokenization"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_UwwtNQ92-DW",
        "colab_type": "text"
      },
      "source": [
        "This step is for making numeric tokens, with padding and truncating. Also we need the special tokens [CLS] and [SEP] for marking the start and end of a text. We will also have an attention mask which tells the BERT model to ignore the padding [0]."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SqW8Tlq1W4K9",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def split_tokenize_formatting(paragraph,max_len=100):\n",
        "\n",
        "  input_tokens = []\n",
        "  for i in paragraph:\n",
        "    input_tokens.append(tokenizer.encode(i, add_special_tokens=True))\n",
        "  # temp = []\n",
        "  # for i in input_tokens:\n",
        "  #   temp.append(len(i))\n",
        "  # if np.max(temp) > max_len:\n",
        "  #   print('sentence longer than the max_len')\n",
        "  # if np.max(temp) > 512:\n",
        "  #   print('warning: sentence longer than 512')\n",
        "  #   print('suggest to change max_len to 512, the remainder will be truncated')\n",
        "  input_ids = pad_sequences(input_tokens, maxlen=max_len, dtype=\"long\", value=0, truncating=\"post\", padding=\"post\")\n",
        "\n",
        "  attention_masks = []\n",
        "  for sent in input_ids:\n",
        "    att_mask = [int(token_id > 0) for token_id in sent]  # create a list of 0 and 1.\n",
        "    attention_masks.append(att_mask)  # basically attention_masks is a list of list\n",
        "\n",
        "  input_ids = torch.tensor(input_ids)  \n",
        "  attention_mask = torch.tensor(attention_masks)\n",
        "\n",
        "  return input_ids, attention_mask"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BIwfDGwVZn3y",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "list_comment = list(train_resampled['text'].values)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ksYuf_U0Xc_8",
        "colab_type": "code",
        "outputId": "375abf6b-8d9d-4dd5-dac5-eb68ac6ce364",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 70
        }
      },
      "source": [
        "input_ids, attention_mask = split_tokenize_formatting(list_comment,100)"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Token indices sequence length is longer than the specified maximum sequence length for this model (953 > 512). Running this sequence through the model will result in indexing errors\n",
            "Token indices sequence length is longer than the specified maximum sequence length for this model (640 > 512). Running this sequence through the model will result in indexing errors\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fKPvzNJGXdD8",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "all_label = torch.tensor(train_resampled['author.1'].values)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "e6j6i8onXqLE",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "input_train, input_val, label_train, label_val = train_test_split(input_ids, all_label, \n",
        "                                                                  test_size=0.1, random_state=42)\n",
        "mask_train, mask_val, label_train, label_val = train_test_split(attention_mask, all_label, \n",
        "                                                                  test_size=0.1, random_state=42)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hJ4rd0boXqWL",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "batch_size = 100\n",
        "\n",
        "train_data = TensorDataset(input_train, mask_train, label_train)\n",
        "train_sampler = RandomSampler(train_data)\n",
        "train_loader = DataLoader(train_data, sampler=train_sampler, batch_size=batch_size)\n",
        "\n",
        "# Create the DataLoader for our validation set.\n",
        "validation_data = TensorDataset(input_val, mask_val, label_val)\n",
        "validation_sampler = SequentialSampler(validation_data)\n",
        "val_loader = DataLoader(validation_data, sampler=validation_sampler, batch_size=batch_size)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "pQrFimk9suqc",
        "colab_type": "text"
      },
      "source": [
        "## 5.3 Model building and training"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ann4i4OT3iZr",
        "colab_type": "text"
      },
      "source": [
        "Note here that the version of BERT we'll use is \n",
        "**bert-base-uncased**. \n",
        "\n",
        "This model only contain lower-case characters. It has 12 layers, 768 hidden dimensions, and 110M parameters.\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "haiL41ByXqTe",
        "colab_type": "code",
        "outputId": "b9c3dbcf-0544-46c9-fafc-592fa4caf6bb",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 114,
          "referenced_widgets": [
            "7e76b3c8b80e47ef88ce6995b02c64eb",
            "019feba27e704a94b624c1ab61c7c42e",
            "8f6b2b1ea6b14411a656df437b22de51",
            "0fc801e031b445a5a2d5f380564329ee",
            "2c4d0a28136d4565b67c1d80468fad58",
            "e79cfcef863147579fb81e829687fe5e",
            "c3edfb721ddc41ecaeaeda1b488a6dce",
            "bb56c0a2160f4c5faf1e9bce1e3a32c6",
            "d952ae7c9dcb43abbaac2c79a6d3bfcc",
            "9b1c5ca3e949471bad205c82f5a7b50b",
            "063767b2f78d409f9437be83a27e2d9e",
            "faab788fb8d1452aaf469674aca8a10c",
            "80262141e6ec475aa4abeaea8fe8af78",
            "60172a56642b4f8085a649aa1548d7f5",
            "4daf7cd67a3447658ab6027d4cb5e0ec",
            "2b7e4bc5fb6540a08da8ffd675346feb"
          ]
        }
      },
      "source": [
        "from transformers import BertForSequenceClassification, AdamW, BertConfig\n",
        "\n",
        "\n",
        "model = BertForSequenceClassification.from_pretrained(\n",
        "    \"bert-base-uncased\",\n",
        "    num_labels = 3,  \n",
        "    output_attentions = False,\n",
        "    output_hidden_states = False)\n",
        "#model.to(device)"
      ],
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "7e76b3c8b80e47ef88ce6995b02c64eb",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(IntProgress(value=0, description='Downloading', max=361, style=ProgressStyle(description_width=…"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "d952ae7c9dcb43abbaac2c79a6d3bfcc",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(IntProgress(value=0, description='Downloading', max=440473133, style=ProgressStyle(description_…"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tg9_gSVbXy5G",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from transformers import get_linear_schedule_with_warmup"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "X-8lrGJx4oqU",
        "colab_type": "text"
      },
      "source": [
        "### 5.3.1 objected-oriented class to help with training\n",
        "\n",
        "This code is just for keeping track of the parameters we will use for training the model. \n",
        "\n",
        "This will take quite a bit of time even with GPU, each epoch will take a couple of minutes to complete."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vIiZMisKlWr8",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "class train_BERT(object):\n",
        "    def __init__(self,input_model,device,batch_train_dataXY,batch_val_dataXY,\n",
        "                 batch_test_dataXY=None,num_class=10,learning_rate=0.001,L2=0):\n",
        "        super(train_BERT,self).__init__()\n",
        "        \n",
        "        self.model = input_model #().apply(weights_init)\n",
        "        #self.model.apply(weights_init)\n",
        "        self.model.to(device)\n",
        "        self.train_loader = batch_train_dataXY\n",
        "        self.test_loader = batch_test_dataXY\n",
        "        self.val_loader = batch_val_dataXY\n",
        "        self.learning_rate = learning_rate\n",
        "        self.L2 = L2\n",
        "        self.optim = AdamW(self.model.parameters(), lr=self.learning_rate,eps=1e-08, weight_decay=self.L2)\n",
        "        # self.scheduler = get_linear_schedule_with_warmup(self.optim, \n",
        "        #                   num_warmup_steps = 0, # Default value in run_glue.py\n",
        "        #                   num_training_steps = train)\n",
        "\n",
        "        #self.criterion = nn.CrossEntropyLoss()\n",
        "        self.loss_train_list = []\n",
        "        self.loss_val_list = []\n",
        "        self.train_accu_list = []\n",
        "        self.val_accu_list = []\n",
        "        self.test_accu_list = []\n",
        "        self.total_epoch = 0\n",
        "    \n",
        "    def train(self,epochs,learning_rate=None,L2=None,eval_every=2,print_every=2):\n",
        "        if type(learning_rate) == float:\n",
        "          self.learning_rate = learning_rate\n",
        "\n",
        "        if type(L2) == float:\n",
        "          self.L2 = L2\n",
        "        self.set_new_rate()\n",
        "        scheduler = get_linear_schedule_with_warmup(self.optim, \n",
        "                    num_warmup_steps = 0, # Default value in run_glue.py\n",
        "                    num_training_steps = len(self.train_loader)*epochs)\n",
        "        for index_epoch in range(epochs):\n",
        "            temp_list = []\n",
        "            self.model.train()\n",
        "            for step, batch in enumerate(self.train_loader):\n",
        "\n",
        "                batch = tuple(t.to(device) for t in batch)\n",
        "                input_sent, input_masks, input_label = batch\n",
        "\n",
        "                self.optim.zero_grad()\n",
        "                outputs = self.model(input_sent, token_type_ids=None, \n",
        "                                    attention_mask=input_masks, labels=input_label)\n",
        "                loss = outputs[0]\n",
        "                temp_list.append(loss.item())\n",
        "                loss.backward()\n",
        "                torch.nn.utils.clip_grad_norm_(self.model.parameters(), 1.0)\n",
        "                self.optim.step()\n",
        "                scheduler.step()\n",
        "                del input_sent, input_masks, input_label, loss, outputs\n",
        "\n",
        "            self.loss_train_list.append(np.mean(temp_list))\n",
        "\n",
        "            if index_epoch > 0 and index_epoch % eval_every == 0:\n",
        "                self.model.eval()\n",
        "                temp_list = []\n",
        "                for batch in self.val_loader:\n",
        "                  batch = tuple(t.to(device) for t in batch)\n",
        "                  input_sent, input_masks, input_label = batch\n",
        "                  outputs = self.model(input_sent, token_type_ids=None, \n",
        "                                        attention_mask=input_masks, labels=input_label)\n",
        "                  \n",
        "                  loss = outputs[0]\n",
        "                  temp_list.append(loss.item())\n",
        "                  del input_sent, input_masks, input_label, loss, outputs\n",
        "\n",
        "            self.loss_val_list.append(np.mean(temp_list))\n",
        "        \n",
        "            train_accu= self.test_model(which_model='train')\n",
        "            val_accu = self.test_model(which_model='validation')\n",
        "            self.train_accu_list.append(train_accu)\n",
        "            self.val_accu_list.append(val_accu)\n",
        "            self.total_epoch += 1\n",
        "            if index_epoch % print_every == 0:\n",
        "                print('total epoch = ',self.total_epoch, ' train accuracy= ', self.train_accu_list[-1],'; val accuracy = ', \n",
        "                      self.val_accu_list[-1])\n",
        "    \n",
        "\n",
        "    def test_model(self,which_model='test'):\n",
        "        self.model.eval()  # eval mode (batchnorm uses moving mean/variance instead of mini-batch mean/variance)\n",
        "        \n",
        "        if which_model == 'train':\n",
        "          loader = self.train_loader\n",
        "        elif which_model == 'validation':\n",
        "          loader = self.val_loader\n",
        "        elif which_model == 'test':\n",
        "          loader = self.test_loader\n",
        "\n",
        "        with torch.no_grad():\n",
        "            num_correct = 0\n",
        "\n",
        "            for batch in loader:\n",
        "                batch = tuple(t.to(device) for t in batch)\n",
        "                input_sent, input_masks, input_label = batch\n",
        "\n",
        "                outputs = self.model(input_sent, token_type_ids=None, \n",
        "                                     attention_mask=input_masks, labels=input_label)\n",
        "                num_correct += self.count_accuracy(outputs[1],input_label)\n",
        "                del input_sent, input_masks, input_label,outputs\n",
        "        return (num_correct / len(loader))\n",
        "\n",
        "    def count_accuracy(self,preds,target):\n",
        "      preds = preds.detach().cpu().numpy()\n",
        "      target = target.cpu().numpy()\n",
        "      pred_flat = np.argmax(preds, axis=1).flatten()\n",
        "      labels_flat = target.flatten()\n",
        "      return np.sum(pred_flat == labels_flat)\n",
        "\n",
        "    def get_train_loss(self):\n",
        "        return self.loss_train_list\n",
        "    def get_val_loss(self):\n",
        "        return self.loss_val_list\n",
        "    def get_train_accu(self):\n",
        "        return self.train_accu_list\n",
        "    def get_test_accu(self):\n",
        "        return self.test_accu_list\n",
        "    def get_val_accu(self):\n",
        "        return self.val_accu_list\n",
        "    def get_params(self):\n",
        "        print(self.model)\n",
        "    def get_rate(self):\n",
        "        return self.learning_rate, self.L2\n",
        "    def get_model(self):\n",
        "        return self.model\n",
        "    def set_new_rate(self):\n",
        "      self.optim = AdamW(self.model.parameters(), lr=self.learning_rate,weight_decay=self.L2)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "aATRJSwDRXB8",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "trial1 = train_BERT(model,device,train_loader,val_loader,None,3,1e-5,1e-8)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "eMbToxxeR08J",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 101
        },
        "outputId": "d34f1478-979b-4082-a9e6-ca94cd3d3a21"
      },
      "source": [
        "trial1.train(5,eval_every=1,print_every=1)"
      ],
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "total epoch =  1  train accuracy=  79.57516339869281 ; val accuracy =  77.47058823529412\n",
            "total epoch =  2  train accuracy=  85.56862745098039 ; val accuracy =  82.17647058823529\n",
            "total epoch =  3  train accuracy=  89.30065359477125 ; val accuracy =  84.17647058823529\n",
            "total epoch =  4  train accuracy=  91.359477124183 ; val accuracy =  84.6470588235294\n",
            "total epoch =  5  train accuracy=  91.91503267973856 ; val accuracy =  84.94117647058823\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Ux5KPcEjSD2y",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 421
        },
        "outputId": "8bd0601e-3c62-4172-9497-9f7268f9405a"
      },
      "source": [
        "fig,ax = plt.subplots(1,2,figsize=(12,6))\n",
        "ax[0].plot(trial1.get_train_accu(),label='train')\n",
        "ax[0].plot(trial1.get_val_accu(),label='validation')\n",
        "ax[0].set_title('BERT classification accuracy')\n",
        "ax[0].set_xlabel('epoch')\n",
        "ax[0].set_ylabel('accuracy')\n",
        "ax[0].legend()\n",
        "ax[1].plot(trial1.get_train_loss(),label='train')\n",
        "ax[1].plot(trial1.get_val_loss(),label='validation')\n",
        "ax[1].set_title('BERT classification loss')\n",
        "ax[1].set_xlabel('epoch')\n",
        "ax[1].set_ylabel('loss')\n",
        "ax[1].legend()"
      ],
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.legend.Legend at 0x7ff43e2e50b8>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 32
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 864x432 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "WL1MVeTG4xQp",
        "colab_type": "text"
      },
      "source": [
        "Result\n",
        "\n",
        "The validation accuracy is about 0.85. And it might get slightly better if one plays around with hyper-parameters, or simply train longer.  \n",
        "\n",
        "The result is much better than the recurrent neural net, but only marginally better than the Naive Bayes. The disadvantage of BERT is that, even with GPU, it still takes a long time to train."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "icNBHkHh-LkI",
        "colab_type": "text"
      },
      "source": [
        "# 6. Citation\n",
        "\n",
        "[Naive Bayes](https://www.geeksforgeeks.org/naive-bayes-classifiers/)\n",
        "Basic article on Naive Bayes\n",
        "\n",
        "[GRU and LSTM blog](https://towardsdatascience.com/illustrated-guide-to-lstms-and-gru-s-a-step-by-step-explanation-44e9eb85bf21)\n",
        "nice illustration on how LSTM and GRU works. \n",
        "\n",
        "[overview of BERT fine-tuning](http://jalammar.github.io/illustrated-bert/)\n",
        "illustrated overview of language model fine-tuning, BERT, OpenAI GPT-2, ELMo. \n",
        "\n",
        "[BERT fine-tuning with codes](https://mccormickml.com/2019/07/22/BERT-fine-tuning/) Nice blogpost on BERT sequence classification. Very easy to read, has codes to follow, and has a lot of illustrations.\n",
        "\n",
        "[explaining Attention](https://lilianweng.github.io/lil-log/2018/06/24/attention-attention.html) This blog post does a pretty good job explaining various forms of attention mechanisms used in machine learning.\n",
        "\n",
        "[BERT's attention](https://www-nlp.stanford.edu/pubs/clark2019what.pdf)\n",
        "A paper analyzing how BERT's attention works.  "
      ]
    }
  ]
}