Word2Vec_GR news.ipynb

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        "**M909 : Deep Learning for NLP |\n",
        "Winter Semester 2023/24 |\n",
        "Student ID: lt12200010 |\n",
        "Student Name: Kleopatra Karapanagiotou**\n",
        "\n",
        "**Assignment 1 : Code and Report**"
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      "source": [
        "#Word2Vec\n"
      ]
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      "source": [
        "In this notebook we will use of the Word2Vec package from the [Gensim library](https://radimrehurek.com/gensim/index.html) for producing Word2Vec embeddings on a dataset of Greek news aticles. The source of the dataset can be found on Kaggle [News Articles in Greek](https://www.kaggle.com/datasets/kpittos/news-articles). We will explore several parameters on our Word2Vec model."
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      "source": [
        "# A few notes about the dataset.\n",
        "It is in csv form and contains around 72k news headlines and text from the year 2012 to 2022 obtained from tvxs.gr.\n",
        "Inspired by the Kaggle Notebook [\"Topic Modeling in Greek News using ML techniques\"](https://www.kaggle.com/code/andreasmarinis/topic-modeling-in-greek-news-using-ml-techniques), it will be interesting to explore word similarities and encyclopedia relation extraction from terms in the various topics of the dataset: Politics, International, Society, Culture, Natural Disasters, Health, Economy.\n",
        "So let's get into it!\n",
        "\n",
        "\n"
      ]
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        "#Preparation\n",
        "\n",
        "The text column of our dataset contains entire articles, meaning that each row contains a series of multiple sentences. Since we want our Word2Vec model to learn distributed representations of **words** on the sentence level and capture semantic meaning, we wil have to start with the following preprocessing steps:\n",
        "\n",
        "1. Remove NaNs, Duplicate rows\n",
        "2. Lowercasing and stress normalisation (both modern and ancient Greek stressing removed)\n",
        "3. Punctuation and Special character removal\n",
        "4. link- and url removal through html parsing\n",
        "5. Usernames, email addresses\n",
        "6. Greek alphanumerics (1ος, 2ος, 16χρονος..)\n",
        "7. Sentence tokenisation (full-stop based) and word tokenisation for every sentence.\n",
        "8. Removal of left punctuation (full-stop charater) and numerics.\n",
        "9. Stopword removal, from a list of crawled Greek stopwords\n",
        "10. Removal of sentences will Latin characters (threshold of 0.8 for list items with latin characters for a sentence-- remove sentences with more than 80% latin strings)\n",
        "11. Corpus analysis on the distribution of sentence length to decide the lower and upper boundaries for sentence length in the corpus\n",
        "12. Concatenation of all the lists of tokenized words into one large list\n",
        "\n",
        "**Final output:** A list containing a sequence of lists, ready to be the input for the Word2Vecmodel."
      ]
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        "\n",
        "#import os\n",
        "#for dirname, _, filenames in os.walk('/kaggle/input'):\n",
        "#    for filename in filenames:\n",
        "#        print(os.path.join(dirname, filename))"
      ]
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        {
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          "text": [
            "Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n"
          ]
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        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
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            "Requirement already satisfied: beautifulsoup4 in /usr/local/lib/python3.10/dist-packages (4.11.2)\n",
            "Requirement already satisfied: soupsieve>1.2 in /usr/local/lib/python3.10/dist-packages (from beautifulsoup4) (2.5)\n"
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      "source": [
        "!pip install beautifulsoup4"
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          "name": "stderr",
          "text": [
            "[nltk_data] Downloading package punkt to /root/nltk_data...\n",
            "[nltk_data]   Package punkt is already up-to-date!\n"
          ]
        }
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      "source": [
        "# Import necessary libraries\n",
        "import re\n",
        "import pandas as pd\n",
        "import gensim\n",
        "from gensim.models import Word2Vec\n",
        "import random\n",
        "import nltk\n",
        "import numpy as np\n",
        "import nltk\n",
        "nltk.download('punkt')\n",
        "from nltk.tokenize import sent_tokenize, word_tokenize\n",
        "from gensim.models.phrases import Phrases, Phraser\n",
        "import string\n",
        "from bs4 import BeautifulSoup\n",
        "import matplotlib.pyplot as plt\n",
        "%matplotlib inline\n",
        "import seaborn as sns\n",
        "sns.set_style(\"darkgrid\")\n",
        "from sklearn.decomposition import PCA\n",
        "from sklearn.manifold import TSNE\n",
        "\n"
      ]
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      "source": [
        "#Function to clean the dataframe from duplicate rows, and NaN values\n",
        "def clean_dataframe(dataframe):\n",
        "    # Print the shape before cleaning\n",
        "    print('\\nData shape before cleaning:')\n",
        "    print(dataframe.shape)\n",
        "\n",
        "    # Remove duplicate rows, keeping the first occurrence\n",
        "    dataframe.drop_duplicates(keep='first', inplace=True)\n",
        "    print('\\nData shape after removing duplicate rows:')\n",
        "    print(dataframe.shape)\n",
        "\n",
        "    # Remove rows with NaN values\n",
        "    dataframe.dropna(inplace=True)\n",
        "    print('\\nData shape after removing rows with NaN values:')\n",
        "    print(dataframe.shape)\n",
        "\n",
        "    return dataframe\n",
        "\n",
        "def replaceMultiple(main, replacements, new):\n",
        "  for elem in replacements:\n",
        "    if elem in main:\n",
        "      main = main.replace(elem, new)\n",
        "  return main\n",
        "\n",
        "def normalize(x):\n",
        "  x = replaceMultiple(x, ['ά', 'ὰ', 'ἀ','ἁ','ἂ','ἃ','ἄ','ἅ','ἆ','ἇ','ᾰ','ᾱ','ᾲ','ᾳ','ᾴ','ᾶ','ᾷ'], 'α')\n",
        "  x = replaceMultiple(x, ['έ','ὲ','ἐ','ἑ','ἒ','ἓ','ἔ','ἕ'], 'ε')\n",
        "  x = replaceMultiple(x, ['ή','ὴ','ἠ','ἡ','ἢ','ἣ','ἤ','ἥ','ἦ','ἧ','ῂ','ῃ','ῄ','ῆ','ῇ'], 'η')\n",
        "  x = replaceMultiple(x, ['ί','ὶ','ἰ','ἱ','ἲ','ἳ','ἴ','ἵ','ἶ','ἷ','ῐ','ῑ','ῒ','ΐ','ῖ','ῗ'], 'ι')\n",
        "  x = replaceMultiple(x, ['ὸ','ό','ὀ','ὁ','ὂ','ὃ','ὄ','ὅ'], 'ο')\n",
        "  x = replaceMultiple(x, ['ύ','ὺ','ΰ', 'ϋ','ὐ','ὑ','ὒ','ὓ','ὔ','ὕ','ὖ','ὗ','ῠ','ῡ','ῢ','ΰ','ῦ'], 'υ')\n",
        "  x = replaceMultiple(x, ['ώ','ὼ','ῶ','ῲ','ῳ','ῴ','ῶ','ῷ','ὠ','ὡ','ὢ','ὣ','ὤ','ὥ','ὦ','ὧ'], 'ω')\n",
        "  return x\n",
        "\n",
        "def preprocess_text(text, stopwords_file,min_length=7, max_length=20):\n",
        "  # Load stopwords from file\n",
        "    with open(stopwords_file, 'r', encoding='utf-8') as file:\n",
        "        stopwords = set(file.read().splitlines())\n",
        "    # Lowercasing\n",
        "    text = text.lower()\n",
        "    text=normalize(text)\n",
        "    # Define the list of punctuation to remove\n",
        "    puncts = [',', '...', '\"', '«', \"»\", ':', ')', '(', '-', '!', '?', '|', ';', \"'\", '$', '&', '/', '[', ']', '>',\n",
        "              '%', '=', '#', '*', '+', '\\\\', '•', '~', '£', '·', '_', '{', '}', '©', '^', '®', '`', '<', '→',\n",
        "              '°', '€', '™', '›', '♥', '←', '×', '§', '″', '′', 'Â', '█', '½', 'à', '…', '“', '★', '”', '–', '●',\n",
        "              'â', '►', '−', '¢', '²', '¬', '░', '¶', '↑', '±', '¿', '▾', '═', '¦', '║', '―', '¥', '▓', '—', '‹',\n",
        "              '─', '▒', '：', '¼', '⊕', '▼', '▪', '†', '■', '’', '▀', '¨', '▄', '♫', '☆', 'é', '¯', '♦', '¤', '▲',\n",
        "              'è', '¸', '¾', 'Ã', '⋅', '‘', '∞', '∙', '）', '↓', '、', '│', '（', '»', '，', '♪', '╩', '╚', '³', '・',\n",
        "              '╦', '╣', '╔', '╗', '▬', '❤', 'ï', 'Ø', '¹', '≤', '‡', '√']\n",
        "\n",
        "    # Removal of special characters and punctuation\n",
        "    for punct in puncts:\n",
        "        text = text.replace(punct, '')\n",
        "\n",
        "    # Removal of HTML/HTTPS/URLs\n",
        "    soup = BeautifulSoup(text, 'html.parser')\n",
        "    text = soup.get_text()\n",
        "    text = re.sub(r'http[s]?://(?:[a-zA-Z]|[0-9]|[$-_@.&+]|[!*\\\\(\\\\),]|(?:%[0-9a-fA-F][0-9a-fA-F]))+', '', text)\n",
        "    # Remove usernames @\n",
        "    text = re.sub(r'@[^\\s]+', '', text)\n",
        "    # Removal of email addresses\n",
        "    text = re.sub(r'\\S+@\\S+', '', text)\n",
        "    # Remove Greek alphanumerics\n",
        "    text = re.sub(r'\\b\\d+[α-ωΑ-Ω]+\\w*\\b', '', text)\n",
        "    # Tokenization into sentences\n",
        "    sentences = sent_tokenize(text)\n",
        "    # Tokenization of words in each sentence\n",
        "    tokenized_sentences = [word_tokenize(sentence) for sentence in sentences]\n",
        "    # Remove punctuation-only and numeric-only list items\n",
        "    tokenized_sentences = [[word for word in sentence if word.isalpha()] for sentence in tokenized_sentences]\n",
        "    # Remove stopwords\n",
        "    tokenized_sentences = [\n",
        "        [word for word in sentence if word.isalpha() and word.lower() not in stopwords]\n",
        "        for sentence in tokenized_sentences]\n",
        "    # Remove lists with more than 80% English words\n",
        "    tokenized_sentences = [sentence for sentence in tokenized_sentences if\n",
        "                           len(sentence) > 0 and (sum(word.isascii() for word in sentence) / len(sentence)) <= 0.8]\n",
        "    # Remove sentences with length less than min_length or greater than max_length\n",
        "    tokenized_sentences = [sentence for sentence in tokenized_sentences if min_length <= len(sentence) <= max_length]\n",
        "\n",
        "\n",
        "    return tokenized_sentences\n",
        "\n"
      ]
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        "Applications"
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            "\n",
            "Data shape before cleaning:\n",
            "(72105,)\n",
            "\n",
            "Data shape after removing duplicate rows:\n",
            "(72026,)\n",
            "\n",
            "Data shape after removing rows with NaN values:\n",
            "(72026,)\n"
          ]
        },
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          "data": {
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              "755099"
            ]
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          "metadata": {},
          "execution_count": 8
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      ],
      "source": [
        "df=pd.read_excel('/content/drive/MyDrive/DL_NLP/Word2Vec/Assignment_1_Karapanagiotou/news_articles_greek.xlsx')\n",
        "df=df['text']\n",
        "df=clean_dataframe(df)\n",
        "df=df.apply(preprocess_text,stopwords_file='/content/drive/MyDrive/DL_NLP/Word2Vec/Assignment_1_Karapanagiotou/Greek-Stopwords (1).txt')\n",
        "# Concatenate all the lists of tokenized words into one large list\n",
        "all_tokenized_sentences = [sentence for sublist in df for sentence in sublist]\n",
        "len(all_tokenized_sentences)"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Our corpus contains **755099** lists of tokenized sentences"
      ],
      "metadata": {
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      "source": [
        "#Corpus Analysis\n",
        "We now ready to perform some quantitative analysis for removing outlier sentences from our corpus"
      ]
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rspycHEu/fv0sd9xxh835WUhIiGXWrFnWx4MGDbLccccdluPHj1uXnTlzxhIREWHp0aOHdVlu7G3bthVqPB599FFLZGSkJSUlxbps7969loYNG1pGjhxpXZZ7HrFx48YC+zxw4IClSZMmlpCQEMujjz5qmTx5suXrr7+2ZGRk2LQrifdfk8mUZ+xy9e7d29KpUyeb86CcnBxLt27dLPfff791We4x0qdPH+u5iMViscTFxVkaNWpkSU1NtVgsFktqaqolIiLC0rVrV8vFixdtYuVul5OTY7n//vstzz77rE1fFy5csLRt29byzDPPXGcUL8s9F8jvPb4kzs/69+9vadq0qeWvv/6yLjt69Kjl9ttvz3POEh4efs3z+Ny/Q8aMGWOzfPDgwZa77rrL+rgwf58AjsTX94ASVL58eZ0/f/666ytWrChJ+uabb4r9tScvLy917ty50O0fe+wxm7sCPvjggwoKCtJ3331XrPiF9f3338vd3T3P9PJnn31WFoslzzTlqKgom092GjZsKF9fX+tX6fKLExQUpE6dOlmXeXp6qlevXsrIyNDPP/9c5NwNBoMWLlyoF154QRUrVtRnn32miRMn6t5779ULL7xgvaaUxWLRV199pbZt28pisSg5Odn6Ex0drbS0NO3Zs8em786dO9tc/6tFixaSVOB+StIXX3yhevXqqW7dujax7r77bknK8/WAgsY0JydHmzZt0r333mtzXY8rxyE3bvPmzVWxYkWbuFFRUcrOzi7WGF9p69atSk1NVceOHW36d3NzU9OmTa/5tYcnn3zS5nHz5s1tviq3ZcsWeXp62sxadHNzU48ePYqcX4cOHeTv7299fPVz5uPjI09PT/300086d+5ckfsHgLLo9ddfV0JCghISEvTmm2+qZcuWGjdunL766itrm6KcSwwZMkT169fXqFGjFBsbq7vuuktPP/30NWNf+V6QO9PZbDYrMTHxmu2zs7P1448/ql27dqpVq5Z1edWqVdWpUyf9+uuvSk9PL/IYnD17Vnv37lVMTIwqVapkXd6wYUNFRUUV+1ytQYMGWrdunR555BGdOnVKixcv1uDBgxUVFaVVq1ZZ25XE++/1/P3339q2bZseeugh6yy55ORkpaSkKDo6WkePHtWZM2dstsmdiZ6rRYsWys7O1qlTpyRd/srZ+fPn1a9fP3l7e9tsm7vd3r17dfToUT388MNKSUmxxs3IyFBkZKR+/vlnuy9F4Ojzs+zsbCUmJuq+++5TtWrVrO1uvfVWtWrVqsj5de/e3eZxixYt9Pfff1uPWUf8fQIUBV/fA0pQRkZGvl+n69Chg/75z39q3LhxmjFjhiIjI9W+fXs9+OCDcnMrXM24WrVqRbqo+a233mrz2GAw6NZbb7W+oZeUU6dOqWrVqjYFMel/U9ivjn/LLbfk6cPf37/Ai4qfOnVKt956a57xy41z+vTpIucuXS7+DRw4UAMHDtTZs2f1888/a/Hixdq4caM8PDw0ffp0JScnKzU1VR9//LE+/vjja/Zz9bUPrr59c+6JQGEunn7s2DEdPnz4ulPxr754aUFjmpycrPT0dDVo0KDAuPv3779u3Gtd36Eojh49Kknq3bv3NddffQx5e3vnmb7u7+9vUxA6ffq0goKCVK5cOZt2V09pL4yrxzG3QJU7jl5eXhoxYoSmTp2qe+65R02bNtU//vEPPfbYYwoKCipyPAAoC5o0aWLzgUinTp302GOPaeLEifrHP/4hLy+vIp1LeHl5KS4uznrXubi4OJuCRi43NzebwpIk3XbbbXn6u1JycrIuXLhgbXd1Ljk5Ofrzzz8LfD+9Wu45yvX6/eGHH5SRkaHy5csXqd/cPt98801lZ2fr0KFD2rx5sxYsWKDXXntNwcHBioqKKpH33+s5fvy4LBaL3nnnHb3zzjvXbGMymWyKMAWdMx0/flyS8h333H28+uv7V0pLS7P58KmoHH1+ZjKZdPHixTzn8FLe8/rCuN44njt3Tr6+vg75+wQoCopSQAn566+/lJaWlu8fvT4+Plq2bJm2b9+uzZs3a8uWLdqwYYM+/vhjffjhhzbXPMivD2fJzs4uVE6OcL04lkJceLSkVa1aVR07dtT999+vTp066YsvvtCUKVOsnyY98sgjiomJuea2V180+3pv7oXZz5ycHIWEhFivl3G16tWr2zx21Jjm5OTonnvu0XPPPXfN9XXq1ClSf9fLZ9q0adcs4ly9H846JguKd+U49unTR23bttWmTZv0ww8/6J133tEHH3ygjz76SLfffruzUgWAG5abm5tatmypxYsX69ixY0Uu8EjSDz/8IEm6dOmSjh07lqf4VBa5u7tbL4AdHh6up59+WuvXr1dUVJRT339zz5meffbZ6872ufoc2p5zpqvbjhw5Uo0aNbpmm+IU/a7kqvOzwipoHB3x9wlQFBSlgBLyySefSJKio6Pzbefm5qbIyEhFRkZqzJgxmjt3rt5++21t375dUVFR1/xUzx5X3tlNuvwGdOzYMZtiyfVmJJ0+fdrmhK4oueVeZDM9Pd3mk7YjR45Y1ztCzZo1tX//fuXk5Ni86ebGufrTIXt4enoqNDRUR48eVUpKiqpUqaIKFSooJydHUVFRDotzvXGuXbu29u3bp8jISIccJ1WqVJGvr2+eO+xcK25GRoZD9/FKucdYQECAw2LUqFFD27dv14ULF2xmS+V+qnolR/3O1a5dW88++6yeffZZHT16VI899pg+/PBDTZ8+3SH9A8DNLvcubhkZGZKKdi6xb98+zZkzR507d9a+ffs0btw4rV+/Xn5+fjYxcnJydOLECZvZSX/88Uee/q5UpUoVlStXztruSkeOHJGbm5t19ktR3lNyz1Gu12/lypXtLphcKSwsTNLlrw1KJfP+e739z43l6enpsFi5RayDBw9edwZRblxfX98SO49x9PlZQECAvL2985zDS3nP6x2loL9PAEdi/h1QAhITE/Xee+8pODjY5q4xV/v777/zLMv91Cb3lrG5f0AX5utchbFu3Tqb6xx88cUXSkpKUuvWra3LatWqpf/+9782t6399ttv9eeff9r0VZTcWrdurezsbC1btsxm+aJFi2QwGGzi26N169ZKSkrShg0brMuysrK0ZMkSlS9fXnfeeWeR+zx69Og1v/aXmpqqHTt2yN/fX1WqVJG7u7seeOABffnllzpw4ECe9sX9Wlu5cuWUlpaWZ/lDDz2kM2fO2FwPItfFixetJ/GF5ebmpnbt2unbb7/Vb7/9lmd97idoDz30kHbs2KEtW7bkaZOamqqsrKwixb1aq1at5Ovrq3nz5slsNudZX5xxjI6OltlsthmrnJycPMejZP/v3IULF3Tp0iWbZbVr11aFChXy3AoaAHBtZrNZP/74ozw9Pa1fzyvsuYTZbNaYMWNUtWpVvfrqq4qPj5fRaFRcXNw1Y13Zn8Vi0bJly+Tp6Xndr1+5u7vrnnvu0TfffGNz/SSj0ajPPvtMzZs3txbNct9TrvU+frWqVauqUaNGWrdunc170IEDB/Tjjz+qTZs2BfZxLb/88ss1309zr1GVW5Arifff672nBgQE6K677tLHH39sLYrZGys6OloVKlTQvHnz8rwP557DhIWFqXbt2vrwww+ved1Xey9BIDn+/Mzd3V1RUVH65ptvbK6zdezYsWuei5UvX96uvxsK8/cJ4EjMlALs9P333+vIkSPKzs6W0WjU9u3b9eOPP6pGjRp6//3381xo8Upz5szRL7/8ojZt2qhmzZoymUxavny5qlevrubNm0u6/MdsxYoVtXLlSlWoUEHly5dXkyZNij0F3d/fX0899ZQ6d+4sk8mkjz76SLfeeqvNBaC7du2qL7/8Us8995weeughHT9+XOvXr88zjbooubVt21YtW7bU22+/rVOnTik0NFQ//vijvvnmG/Xu3btY1/a5lm7duunjjz/W6NGjtWfPHtWsWVNffvml/vOf/2js2LF5rodQGPv27dOIESPUqlUrtWjRQv7+/jpz5ozWrVuns2fPauzYsdapzC+//LK2b9+uJ554Ql27dlX9+vV17tw57dmzR4mJifrpp5+KHP+OO+7Qhg0bFB8fr8aNG6t8+fJq27atHn30UW3cuFHjx4/X9u3b1axZM2VnZ+vIkSP64osvtGDBgmtesDw/L730kn788Uf16tXLehvjpKQkffHFF1q+fLkqVqyovn376t///rcGDBigmJgY3XHHHbpw4YIOHDigL7/8Ut98802ea0xcLTk5Oc8tkSVZC7kTJkzQyJEj1blzZ3Xo0EFVqlTR6dOn9d1336lZs2Z6/fXXi7Rf7dq1U5MmTTR16lQdP35cdevW1b///W/rdS+u/CTzjjvukHT51tPR0dFyd3dXx44dCx3r6NGj6tOnjx588EHVr19f7u7u2rRpk4xGY5H6AYCyJPd8Srr8HrF+/XodPXpU/fr1s753F/Zc4v3339fevXu1aNEi+fr6qmHDhho8eLBmzpypBx980Ka44+3trS1btmjUqFFq0qSJtmzZos2bN2vAgAH5vpe98MIL2rp1q5566ik99dRTcnd318cff6zMzEy98sor1naNGjWSu7u75s+fr7S0NHl5eenuu+++7jVHR44cqeeff17dunXT448/rosXL2rp0qXy8/PTkCFDijW28+fP1549e9S+fXvrzPjff/9d69atU6VKlazXkPL19XX4+6+Pj4/q16+vjRs3qk6dOqpUqZIaNGigkJAQjR8/Xk899ZQefvhhPfHEE6pVq5aMRqN27typv/76S59++mmRYvn6+mrMmDEaN26cHn/8cXXq1EkVK1bUvn37dPHiRU2dOlVubm6aPHmynn/+eXXq1EmdO3dWtWrVdObMGW3fvl2+vr6aO3dugbESExPzFL6ky+cbJXF+NmTIEP3www968skn9eSTTyonJ0dLly5VgwYNtHfvXpu2d9xxhxITE5WQkKCqVasqODhYTZs2LXSswvx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          },
          "metadata": {}
        }
      ],
      "source": [
        "# Calculate sentence lengths\n",
        "sentence_lengths = [len(sentence) for sentence in all_tokenized_sentences]\n",
        "\n",
        "fig, axs = plt.subplots(1, 2, figsize=(12, 5))\n",
        "# Plot the histogram\n",
        "axs[0].hist(sentence_lengths, bins=30, color='blue', edgecolor='black', alpha=0.7)\n",
        "axs[0].set_title('Distribution of Sentence Lengths')\n",
        "axs[0].set_xlabel('Sentence Length')\n",
        "axs[0].set_ylabel('Frequency')\n",
        "# Plot the boxplot with adjusted y-axis limits for zooming in\n",
        "axs[1].boxplot(sentence_lengths)\n",
        "axs[1].set_title('Boxplot of Sentence Lengths')\n",
        "axs[1].set_ylabel('Sentence Length')\n",
        "\n",
        "# Adjust y-axis limits for the boxplot\n",
        "axs[1].set_ylim(0, max(sentence_lengths) + 10)\n",
        "\n",
        "# Adjust layout to prevent overlapping\n",
        "plt.tight_layout()\n",
        "\n",
        "# Show the plots\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "95987bfc",
      "metadata": {
        "id": "95987bfc",
        "papermill": {
          "duration": 0.030242,
          "end_time": "2023-11-24T22:57:15.550913",
          "exception": false,
          "start_time": "2023-11-24T22:57:15.520671",
          "status": "completed"
        },
        "tags": []
      },
      "source": [
        "This is the distribution of sentence lengths after removing outliers. Outlier values were only reported on the upper boundary only. So we wee that more than 50 % of the sentences have a sentence length between 7 and 15 tokens , after stoword removal. This means that our window size cannot exceed the 10 tokens per each side. We will experiment with smaller window size values."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "id": "f92b1b06",
      "metadata": {
        "execution": {
          "iopub.execute_input": "2023-11-24T22:57:15.612691Z",
          "iopub.status.busy": "2023-11-24T22:57:15.612284Z",
          "iopub.status.idle": "2023-11-24T22:57:15.711834Z",
          "shell.execute_reply": "2023-11-24T22:57:15.710369Z"
        },
        "id": "f92b1b06",
        "outputId": "4359e1fe-eb55-41e3-d7bc-d5c182dc9be5",
        "papermill": {
          "duration": 0.133564,
          "end_time": "2023-11-24T22:57:15.714485",
          "exception": false,
          "start_time": "2023-11-24T22:57:15.580921",
          "status": "completed"
        },
        "tags": [],
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Skewness: 0.32873334451587416\n"
          ]
        }
      ],
      "source": [
        "from scipy.stats import skew\n",
        "skewness = skew(sentence_lengths)\n",
        "print(\"Skewness:\", skewness)"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "4343e868",
      "metadata": {
        "id": "4343e868",
        "papermill": {
          "duration": 0.029524,
          "end_time": "2023-11-24T22:57:15.773973",
          "exception": false,
          "start_time": "2023-11-24T22:57:15.744449",
          "status": "completed"
        },
        "tags": []
      },
      "source": [
        " Our data are moderately skewed, which is an expected behaviour for sentences in journalistic articles."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "id": "f60f81cb",
      "metadata": {
        "execution": {
          "iopub.execute_input": "2023-11-24T22:57:15.836385Z",
          "iopub.status.busy": "2023-11-24T22:57:15.835965Z",
          "iopub.status.idle": "2023-11-24T22:57:15.848862Z",
          "shell.execute_reply": "2023-11-24T22:57:15.847799Z"
        },
        "id": "f60f81cb",
        "outputId": "e2687dfc-f98e-4d7c-b627-6ab3a0313fe7",
        "papermill": {
          "duration": 0.0475,
          "end_time": "2023-11-24T22:57:15.851382",
          "exception": false,
          "start_time": "2023-11-24T22:57:15.803882",
          "status": "completed"
        },
        "tags": [],
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mean Sentence Length: 12.416945327698752\n"
          ]
        }
      ],
      "source": [
        "mean_sentence_length = sum(sentence_lengths) / len(sentence_lengths)\n",
        "\n",
        "print(\"Mean Sentence Length:\", mean_sentence_length)"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "10ac45ad",
      "metadata": {
        "id": "10ac45ad",
        "papermill": {
          "duration": 0.030998,
          "end_time": "2023-11-24T22:57:15.913097",
          "exception": false,
          "start_time": "2023-11-24T22:57:15.882099",
          "status": "completed"
        },
        "tags": []
      },
      "source": [
        "#Corpus lemmatisation\n"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Corpus lemmatization was a step that was tried but skipped, due to the inaccurate lemmatisation results of the Greek spacy. Furthermore, in our notebook we try to capture similarities between bigram named entities like \"κυριακος_μητσοτακης\" -\"αλεξης_τσιπρας\", but after the lemmatisation process, those entities were not recognized in the corpus."
      ],
      "metadata": {
        "id": "pz0vfNJXKyYP"
      },
      "id": "pz0vfNJXKyYP"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "1339f5d8",
      "metadata": {
        "execution": {
          "iopub.execute_input": "2023-11-24T22:57:15.976692Z",
          "iopub.status.busy": "2023-11-24T22:57:15.976027Z",
          "iopub.status.idle": "2023-11-25T00:43:57.495145Z",
          "shell.execute_reply": "2023-11-25T00:43:57.493908Z"
        },
        "id": "1339f5d8",
        "papermill": {
          "duration": 6401.589446,
          "end_time": "2023-11-25T00:43:57.532727",
          "exception": false,
          "start_time": "2023-11-24T22:57:15.943281",
          "status": "completed"
        },
        "tags": []
      },
      "outputs": [],
      "source": [
        "#def lemmatize_corpus(corpus):\n",
        "    # Load the spaCy language model\n",
        "#    nlp = spacy.load(\"el_core_news_sm\")\n",
        "\n",
        "    # Initialize a list to store the lemmatized corpus\n",
        "#    lemmatized_corpus = []\n",
        "\n",
        "    # Iterate through each list of tokenized texts in the corpus\n",
        "#    for text_list in corpus:\n",
        "        # Join the tokens in the list to create a single string\n",
        "#        text_str = ' '.join(text_list)\n",
        "\n",
        "        # Process the text with spaCy\n",
        "#        doc = nlp(text_str)\n",
        "\n",
        "        # Initialize a list to store lemmatized tokens for the current text\n",
        "#        lemmas = []\n",
        "\n",
        "        # Iterate through the tokens in the processed text and append their lemmas to the list\n",
        "#        for token in doc:\n",
        "#            lemmas.append(token.lemma_)\n",
        "\n",
        "        # Append the list of lemmatized tokens to the lemmatized_corpus\n",
        " #       lemmatized_corpus.append(lemmas)\n",
        "\n",
        " #   return lemmatized_corpus\n",
        "\n",
        "\n",
        "\n",
        "#final_corpus = lemmatize_corpus(all_tokenized_sentences)\n",
        "#print(final_corpus[1])\n",
        "#len(final_corpus)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "605294e7",
      "metadata": {
        "execution": {
          "iopub.execute_input": "2023-11-25T00:43:57.599692Z",
          "iopub.status.busy": "2023-11-25T00:43:57.599251Z",
          "iopub.status.idle": "2023-11-25T00:44:07.353190Z",
          "shell.execute_reply": "2023-11-25T00:44:07.351912Z"
        },
        "id": "605294e7",
        "papermill": {
          "duration": 9.79126,
          "end_time": "2023-11-25T00:44:07.356149",
          "exception": false,
          "start_time": "2023-11-25T00:43:57.564889",
          "status": "completed"
        },
        "tags": []
      },
      "outputs": [],
      "source": [
        "#corvert corpus into a dataframe and save int .csv to avoid reusing spacy lemmatization process\n",
        "\n",
        "#df = pd.DataFrame({'Text': final_corpus})\n",
        "#df.to_csv('corpus.csv', index=False)"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "1f16e60f",
      "metadata": {
        "id": "1f16e60f",
        "papermill": {
          "duration": 0.032584,
          "end_time": "2023-11-25T00:44:07.421793",
          "exception": false,
          "start_time": "2023-11-25T00:44:07.389209",
          "status": "completed"
        },
        "tags": []
      },
      "source": [
        "#Bigrams:\n",
        "We are using Gensim Phrases package to automatically detect common phrases (bigrams) from a list of sentences. https://radimrehurek.com/gensim/models/phrases.html\n",
        "\n",
        "The main reason we do this is to catch Named entities like \"κυριακος_μητσοτακης\" or \"αλεξης_τσιπρας\" and limti the corpus's vocabulary size"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "id": "ca42b64a",
      "metadata": {
        "execution": {
          "iopub.execute_input": "2023-11-25T00:44:07.484250Z",
          "iopub.status.busy": "2023-11-25T00:44:07.483851Z",
          "iopub.status.idle": "2023-11-25T00:44:57.157698Z",
          "shell.execute_reply": "2023-11-25T00:44:57.156578Z"
        },
        "id": "ca42b64a",
        "papermill": {
          "duration": 49.708544,
          "end_time": "2023-11-25T00:44:57.160627",
          "exception": false,
          "start_time": "2023-11-25T00:44:07.452083",
          "status": "completed"
        },
        "tags": []
      },
      "outputs": [],
      "source": [
        "phrases = Phrases(all_tokenized_sentences, min_count=10, progress_per=10000)  #Phrases() takes a list of list of words as input and creates the relevant phrases from the list of sentences\n",
        "bigram = Phraser(phrases)\n",
        "sentences = bigram[all_tokenized_sentences]   #Transform the corpus based on the bigrams detected"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "2ca17586",
      "metadata": {
        "id": "2ca17586",
        "papermill": {
          "duration": 0.029779,
          "end_time": "2023-11-25T00:44:57.220973",
          "exception": false,
          "start_time": "2023-11-25T00:44:57.191194",
          "status": "completed"
        },
        "tags": []
      },
      "source": [
        "\n",
        "#Train a Word2Vec model (gensim implementation)"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "070da719",
      "metadata": {
        "id": "070da719",
        "papermill": {
          "duration": 0.029701,
          "end_time": "2023-11-25T00:44:57.280967",
          "exception": false,
          "start_time": "2023-11-25T00:44:57.251266",
          "status": "completed"
        },
        "tags": []
      },
      "source": [
        "With our preprocessing, we are now ready to start training our Word2Vec model. Let's keep in mind that this dataset contains around 72k articles of a Greek news website, where each article belongs to a different topic and in each article different incidents and different named entities are mentioned.\n",
        "With this dataset we can give our Word2Vec model development 2 directions:\n",
        "\n",
        "A. Named entity recognition: We look for similar words which are **interchangeable**, i.e. one word can substitute another in the similar context. (like names of countries, political parties, sport teams etc., e.g. Mitsotakis-Tsipras, ARIS-PAOK)\n",
        "\n",
        "B. Topic extraction: word similarity equals **topical relation** between two words. These words talk about the same topic or are terms of the same domain.(like events and their participants or their cause, an illness and its medicine, entities and their role e.g Mitsotakis- νεα δημοκρατια, covid-vaccine)\n",
        "\n",
        "Based on our min and max sentence length and the recommendations of [Lev Konstantinovskiy(2017)](https://www.youtube.com/watch?v=tAxrlAVw-Tk&t=648s):\n",
        "- For the first direction (interchangeable) we will train Word2Vec with **skipgram** and experiment with **small window sizes** (2 and 5).\n",
        "- For the second direction (topical relation) we will run W2V with **skipgram** again and experiment with ** a bigger window size** (8).\n"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "d9efb1f8",
      "metadata": {
        "id": "d9efb1f8",
        "papermill": {
          "duration": 0.029828,
          "end_time": "2023-11-25T00:44:57.340941",
          "exception": false,
          "start_time": "2023-11-25T00:44:57.311113",
          "status": "completed"
        },
        "tags": []
      },
      "source": [
        "**WORD2VEC MODELs**\n",
        "\n",
        "**Set model parameters**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "id": "a4053d1c",
      "metadata": {
        "execution": {
          "iopub.execute_input": "2023-11-25T00:44:57.403153Z",
          "iopub.status.busy": "2023-11-25T00:44:57.402756Z",
          "iopub.status.idle": "2023-11-25T00:44:57.412671Z",
          "shell.execute_reply": "2023-11-25T00:44:57.411868Z"
        },
        "id": "a4053d1c",
        "papermill": {
          "duration": 0.04368,
          "end_time": "2023-11-25T00:44:57.414694",
          "exception": false,
          "start_time": "2023-11-25T00:44:57.371014",
          "status": "completed"
        },
        "tags": []
      },
      "outputs": [],
      "source": [
        "model1 =Word2Vec(sg = 1,min_count=5,window=2,vector_size=100, alpha=0.1, hs=0,negative=5,workers=3,sample=0.1)\n",
        "model2=Word2Vec(sg = 1,min_count=5,window=5,vector_size=100, alpha=0.1,hs=0,negative=5,workers=3,sample= 0.1)\n",
        "model3=Word2Vec(sg = 1,min_count=5,window=8,vector_size=100, alpha=0.1,hs=0,negative=5,workers=3,sample= 0)\n",
        "\n",
        "\n",
        "# Size of dimensionality of word vectors: 100\n",
        "# Window, the maximum distance between the target word and its neighboring word :2/5/8\n",
        "# Minimum count, ignoring all words with total frequency lower than this, words must appear this many times to be in vocab: 5\n",
        "# Sg, training algorithm: Skip-Gram\n",
        "# The number of worker threads used to train the model: 3\n",
        "# 0: Negative sampling\n",
        "# Number of negative samples: 5\n",
        "#alpha: the learing rate\n",
        "#sample :Words that are more frequent than this threshold will be randomly downsampled.The smaller the sample the more aggressive the downsampling useful range is (0, 1e-5)"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "d8fe1e37",
      "metadata": {
        "id": "d8fe1e37",
        "papermill": {
          "duration": 0.029929,
          "end_time": "2023-11-25T00:44:57.475171",
          "exception": false,
          "start_time": "2023-11-25T00:44:57.445242",
          "status": "completed"
        },
        "tags": []
      },
      "source": [
        "**Build the vocab**\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "id": "9cc1c1c2",
      "metadata": {
        "execution": {
          "iopub.execute_input": "2023-11-25T00:44:57.537937Z",
          "iopub.status.busy": "2023-11-25T00:44:57.536998Z",
          "iopub.status.idle": "2023-11-25T00:45:20.394193Z",
          "shell.execute_reply": "2023-11-25T00:45:20.392944Z"
        },
        "id": "9cc1c1c2",
        "papermill": {
          "duration": 22.891666,
          "end_time": "2023-11-25T00:45:20.397016",
          "exception": false,
          "start_time": "2023-11-25T00:44:57.505350",
          "status": "completed"
        },
        "tags": [],
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "421db349-8864-4e04-ec43-a76bfdf266ee"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Time to build vocab: 0.87 mins\n"
          ]
        }
      ],
      "source": [
        "from time import time\n",
        "t = time()\n",
        "model1.build_vocab(sentences )\n",
        "model2.build_vocab(sentences )\n",
        "model3.build_vocab(sentences )\n",
        "\n",
        "\n",
        "print('Time to build vocab: {} mins'.format(round((time() - t) / 60, 2)))"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "8a26a2dc",
      "metadata": {
        "id": "8a26a2dc",
        "papermill": {
          "duration": 0.031551,
          "end_time": "2023-11-25T00:45:20.460358",
          "exception": false,
          "start_time": "2023-11-25T00:45:20.428807",
          "status": "completed"
        },
        "tags": []
      },
      "source": [
        "**Train the Word2Vec model**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "id": "426a9b0b",
      "metadata": {
        "execution": {
          "iopub.execute_input": "2023-11-25T00:45:20.524655Z",
          "iopub.status.busy": "2023-11-25T00:45:20.523858Z",
          "iopub.status.idle": "2023-11-25T01:03:09.095816Z",
          "shell.execute_reply": "2023-11-25T01:03:09.094602Z"
        },
        "id": "426a9b0b",
        "papermill": {
          "duration": 1068.638941,
          "end_time": "2023-11-25T01:03:09.130155",
          "exception": false,
          "start_time": "2023-11-25T00:45:20.491214",
          "status": "completed"
        },
        "tags": [],
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "b12cbd50-a0c0-4001-9369-92f29ddf2f93"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(79737380, 82913990)"
            ]
          },
          "metadata": {},
          "execution_count": 15
        }
      ],
      "source": [
        "model1.train(sentences, total_examples=len(sentences), epochs=10)\n",
        "model2.train(sentences , total_examples=len(sentences), epochs=10)\n",
        "model3.train(sentences , total_examples=len(sentences), epochs=10)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "id": "f1185ea5",
      "metadata": {
        "execution": {
          "iopub.execute_input": "2023-11-25T01:03:09.195675Z",
          "iopub.status.busy": "2023-11-25T01:03:09.195217Z",
          "iopub.status.idle": "2023-11-25T01:03:09.220574Z",
          "shell.execute_reply": "2023-11-25T01:03:09.218106Z"
        },
        "id": "f1185ea5",
        "papermill": {
          "duration": 0.061548,
          "end_time": "2023-11-25T01:03:09.222928",
          "exception": false,
          "start_time": "2023-11-25T01:03:09.161380",
          "status": "completed"
        },
        "tags": [],
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "5d5a68e2-30b4-46d3-80c1-cd371c597609"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accessing word vectors for model1:\n",
            "The vocabulary includes 126298 unique words.\n",
            "The length of the word vector for \"συριζα\": 100\n",
            "Numpy vector of the word \"συριζα\":\n",
            " [ 0.17366193 -0.03214309 -0.13077275  0.20077015 -0.14254698 -0.12942344\n",
            "  0.39593163 -0.11420102 -0.1873117  -0.5829744   0.23360558 -0.73815954\n",
            " -0.40248132 -0.3628786  -0.40283564 -0.38855067  0.23514554 -0.06594463\n",
            " -0.14734583 -0.458289    0.04377849  0.65312684 -0.02027591 -0.01557787\n",
            "  0.09225133 -0.2513552  -0.38776946 -0.03537207 -0.40884674 -0.26646847\n",
            "  0.18642575  0.27552956 -0.17687961 -0.60617995  0.0108176  -0.3630864\n",
            "  0.7999938  -0.46470279  0.20259935  0.26120633  0.03387893  0.50368524\n",
            " -0.00822095 -0.29190952  0.38410416  0.09891783 -0.53562564  0.39920938\n",
            "  0.42076787  0.39499846 -0.11455019 -0.31675938  0.0605185  -0.05237727\n",
            " -0.20784308 -0.05130203  0.31436935 -0.43071917  0.06741287  0.26339087\n",
            "  0.47459465 -0.5356562  -0.12148676 -0.55819374 -0.36246163  0.25523603\n",
            "  0.04197605 -0.6163737   0.03923107  0.17772837 -0.07434203 -0.0604846\n",
            " -0.7238933  -0.3985091   0.5038313  -0.1528327  -0.14631203 -0.12814376\n",
            "  0.20153134  0.01798995 -0.471419   -0.59691346  0.19738528  0.0149001\n",
            " -0.47683176 -0.30305645 -0.10858805 -0.01748404 -0.16018374 -0.14790109\n",
            "  0.04672011  0.32740733  0.4642271   0.324421    0.5712852   0.1560952\n",
            " -0.43164352  0.00812873  0.20561123  0.14272034]\n",
            "\n",
            "\n",
            "Accessing word vectors for model2:\n",
            "The vocabulary includes 126298 unique words.\n",
            "The length of the word vector for \"συριζα\": 100\n",
            "Numpy vector of the word \"συριζα\":\n",
            " [-0.58717495  0.3460758  -0.18466571 -0.04053934 -0.29589993 -0.28215992\n",
            "  0.28955966  0.03723802 -0.18608522 -0.21167788  0.11711269  0.30989876\n",
            "  0.17016119  0.04230622 -0.02889966 -0.3636484  -0.79598796 -0.3813403\n",
            " -0.06096775 -0.3815072   0.02210373  0.09621135  0.10656284  0.10749176\n",
            "  0.31741768 -0.14320536 -0.4095176   0.16193593 -0.13598125  0.03524401\n",
            "  0.14714985  0.09324379  0.4199711  -0.7506059   0.11202881  0.05897781\n",
            "  0.6265636   0.04796536  0.1887006  -0.4487917  -0.1562624  -0.06550926\n",
            "  0.33225545  0.03087334  0.2175804   0.21210204 -0.34112078  0.3386827\n",
            "  0.36858287 -0.17949167  0.00173229 -0.18424793  0.00530896  0.25641572\n",
            " -0.31625754 -0.41985017 -0.03342593 -0.60358846  0.21053073 -0.12931687\n",
            "  0.09866029  0.16536395 -0.02040484  0.14870921 -0.1440967   0.19148183\n",
            "  0.01841859 -0.03050453  0.2922961   0.50815797 -0.19489835 -0.08992071\n",
            " -0.43975976 -0.37183154  0.04992762 -0.08829108  0.27980247 -0.1279661\n",
            " -0.22601937 -0.8254171   0.18042783  0.03319382 -0.00845767  0.17269164\n",
            "  0.03553085 -0.24114008 -0.1429126  -0.13635875  0.1779859   0.49874327\n",
            " -0.1390613   0.47071445 -0.15325643 -0.17388363  0.18925855  0.26437116\n",
            " -0.36733407  0.41920978 -0.00788841  0.25823468]\n",
            "\n",
            "\n",
            "Accessing word vectors for model3:\n",
            "The vocabulary includes 126298 unique words.\n",
            "The length of the word vector for \"συριζα\": 100\n",
            "Numpy vector of the word \"συριζα\":\n",
            " [ 0.09606268  0.36965835  0.3812009   0.15192553  0.01329936 -0.272967\n",
            "  0.0628019   0.25653845  0.02906775 -0.19396304  0.3281387  -0.278341\n",
            " -0.04489523  0.10226624  0.14438292 -0.02512326  0.08900554 -0.34611058\n",
            " -0.06700701 -0.5568259   0.12280089 -0.07912282 -0.1324072  -0.1023889\n",
            "  0.34812635 -0.24133411 -0.32860172 -0.06506924 -0.21898699 -0.00672453\n",
            "  0.17658417  0.04833092  0.26204103 -0.2570522   0.26904842 -0.0517192\n",
            " -0.2190515   0.1132502  -0.26013422 -0.13784353  0.534233   -0.46977425\n",
            "  0.29473636  0.20064187 -0.25996777  0.243549    0.3340382   0.39770386\n",
            " -0.08445417  0.4138738  -0.35616502  0.27194196 -0.00879094  0.146224\n",
            "  0.15147515 -0.3695855   0.17360526 -0.1379069  -0.11084989 -0.12227737\n",
            "  0.5656192  -0.23564208 -0.26699156 -0.19182308 -0.18328784  0.52999765\n",
            " -0.0354594  -0.47042292  0.08557816  0.07357532  0.18411125  0.43887997\n",
            "  0.01709626 -0.19450794 -0.40371242 -0.35069206  0.1971103   0.49540398\n",
            "  0.16670543 -0.32353887 -0.00490805  0.24179062 -0.07500957  0.378143\n",
            "  0.31330696 -0.22471659 -0.15503559 -0.5219206   0.23813643  0.321133\n",
            "  0.20375498 -0.15079069  0.00830794  0.28017214  0.11117683  0.45520452\n",
            " -0.2951503   0.23200883  0.30508643  0.00879953]\n",
            "\n",
            "\n"
          ]
        }
      ],
      "source": [
        "# Access the word vectors\n",
        "############################################################################################\n",
        "models = [model1, model2, model3]\n",
        "\n",
        "for idx, model in enumerate(models, start=1):\n",
        "    print(f\"Accessing word vectors for model{idx}:\")\n",
        "    word_vectors = model.wv\n",
        "    print('The vocabulary includes {} unique words.'.format(len(word_vectors.key_to_index)))\n",
        "\n",
        "    # Access the word vector for a specific word (e.g., 'συριζα')\n",
        "    word = 'συριζα'\n",
        "    if word in word_vectors:\n",
        "        vector = word_vectors[word]\n",
        "        print(f'The length of the word vector for \"{word}\":', len(vector))\n",
        "        print(f'Numpy vector of the word \"{word}\":\\n', vector)\n",
        "    else:\n",
        "        print(f'The word \"{word}\" is not in the vocabulary of model{idx}.\\n')\n",
        "\n",
        "    print('\\n')"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "10f08819",
      "metadata": {
        "id": "10f08819",
        "papermill": {
          "duration": 0.031376,
          "end_time": "2023-11-25T01:03:09.285476",
          "exception": false,
          "start_time": "2023-11-25T01:03:09.254100",
          "status": "completed"
        },
        "tags": []
      },
      "source": [
        "#Model Exploration"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 17,
      "id": "cddb4a02",
      "metadata": {
        "execution": {
          "iopub.execute_input": "2023-11-25T01:03:09.350418Z",
          "iopub.status.busy": "2023-11-25T01:03:09.349993Z",
          "iopub.status.idle": "2023-11-25T01:03:09.531793Z",
          "shell.execute_reply": "2023-11-25T01:03:09.530573Z"
        },
        "id": "cddb4a02",
        "papermill": {
          "duration": 0.217726,
          "end_time": "2023-11-25T01:03:09.534458",
          "exception": false,
          "start_time": "2023-11-25T01:03:09.316732",
          "status": "completed"
        },
        "tags": [],
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "f76c8f1d-dde7-4bc7-be78-f7443ab9a8ab"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Random words from model1:\n",
            "                  τοξικο_κλιμα                        αλατιου\n",
            "                        σαγκαη                     απεπλευσαν\n",
            "               μελων_επιτροπης                       μεριμνας\n",
            "                       μπασμπι                   ηλικιας_ετων\n",
            "                διαχειριστικης                      αγγλικους\n",
            "                      θυμωμενο                     λειτουργια\n",
            "                        εσπασα               συνολικη_αποδοση\n",
            "                  δολοφονημενα                   ιβαν_σαββιδη\n",
            "      επιστημονικης_κοινοτητας               μυστικη_υπηρεσια\n",
            "                      αρσενιου                      συντροφοι\n",
            "Random words from model2:\n",
            "                        mosaic         φαρμακευτικος_συλλογος\n",
            "                 απροετοιμαστη                       κορινθος\n",
            "       περιστατικα_αστυνομικης                  κρυπτονομισμα\n",
            "                           εντ                   βραδυ_τριτης\n",
            "                 νεων_φαρμακων             αγνωστη_κατευθυνση\n",
            "                     φωτισμενα                      γραμμενος\n",
            "                         σπλιτ                     υπερβολικα\n",
            "                         τοπος                     αναρριχηση\n",
            "                       ταπητος                     τροχονομων\n",
            "             ιστορικα_γεγονοτα                αντρες_γυναικες\n",
            "Random words from model3:\n",
            "            ποσοστο_χαμηλοτερο                    καζαντζακης\n",
            "                 london_school                       υποφερτη\n",
            "            ηνωμενων_πολιτειων                         αλμυρη\n",
            "                πληροφορημενοι                      eurοferry\n",
            "      ασυνοδευτα_προσφυγοπουλα              ξενοδοχουπαλληλων\n",
            "                εθνικες_καλπες                       χαινεμαν\n",
            "                 χειροκροτητες                      χρεος_αεπ\n",
            "                          τρακ                    μεταμορφωσε\n",
            "                  συντονισμενο                 αντιµετωπισουν\n",
            "                    εννοουσαμε                       κληρικου\n",
            "                          Word    Included\n",
            "                          ====    ========\n",
            "Checking vocabulary for model1:\n",
            "           κυριακος_μητσοτακης    True\n",
            "                νεα_δημοκρατια    True\n",
            "                αλεξης_τσιπρας    True\n",
            "          υπουργειο_εξωτερικων    True\n",
            "         υπουργειο_οικονομικων    True\n",
            "               γιωργος_σεφερης    True\n",
            "                  τζο_μπαιντεν    True\n",
            "             υποκειμενο_νοσημα    True\n",
            "       διασωληνωμενοι_ασθενεις    True\n",
            "\n",
            "\n",
            "Checking vocabulary for model2:\n",
            "           κυριακος_μητσοτακης    True\n",
            "                νεα_δημοκρατια    True\n",
            "                αλεξης_τσιπρας    True\n",
            "          υπουργειο_εξωτερικων    True\n",
            "         υπουργειο_οικονομικων    True\n",
            "               γιωργος_σεφερης    True\n",
            "                  τζο_μπαιντεν    True\n",
            "             υποκειμενο_νοσημα    True\n",
            "       διασωληνωμενοι_ασθενεις    True\n",
            "\n",
            "\n",
            "Checking vocabulary for model3:\n",
            "           κυριακος_μητσοτακης    True\n",
            "                νεα_δημοκρατια    True\n",
            "                αλεξης_τσιπρας    True\n",
            "          υπουργειο_εξωτερικων    True\n",
            "         υπουργειο_οικονομικων    True\n",
            "               γιωργος_σεφερης    True\n",
            "                  τζο_μπαιντεν    True\n",
            "             υποκειμενο_νοσημα    True\n",
            "       διασωληνωμενοι_ασθενεις    True\n",
            "\n",
            "\n"
          ]
        }
      ],
      "source": [
        "def get_random_words(model):\n",
        "    vocab = list(model.wv.index_to_key)\n",
        "    random_words = [(random.choice(vocab), random.choice(vocab)) for _ in range(10)]\n",
        "    return random_words, vocab\n",
        "\n",
        "def check_vocab(model_vocabs, try_words):\n",
        "    print(\"%30s    %s\" % ('Word', 'Included'))\n",
        "    print(\"%30s    %s\" % ('====', '========'))\n",
        "\n",
        "    for model_name, vocab in model_vocabs.items():\n",
        "        print(f\"Checking vocabulary for {model_name}:\")\n",
        "        for word in try_words:\n",
        "            print(\"%30s    %s\" % (word, str(word in vocab)))\n",
        "            # If the word isn't already lower case, try lower case as well.\n",
        "            if not word.lower() == word:\n",
        "                print(\"%30s    %s\" % (word.lower(), str(word.lower() in vocab)))\n",
        "        print(\"\\n\")\n",
        "\n",
        "# Initialize a dictionary to store vocabularies for each model\n",
        "model_vocabs = {}\n",
        "\n",
        "# Print 20 random words and populate model_vocabs\n",
        "for idx, model in enumerate(models, start=1):\n",
        "    print(f\"Random words from model{idx}:\")\n",
        "    random_words, vocab = get_random_words(model)\n",
        "\n",
        "    for word_pair in random_words:\n",
        "        print('%30s %30s' % word_pair)\n",
        "\n",
        "    model_vocabs[f\"model{idx}\"] = vocab\n",
        "\n",
        "\n",
        "# List of words to check\n",
        "try_words = ['κυριακος_μητσοτακης',\n",
        "                    'νεα_δημοκρατια',\n",
        "                    'αλεξης_τσιπρας',\n",
        "                    'υπουργειο_εξωτερικων',\n",
        "                    'υπουργειο_οικονομικων',\n",
        "                    'γιωργος_σεφερης',\n",
        "                    'τζο_μπαιντεν',\n",
        "                    'υποκειμενο_νοσημα',\n",
        "                    'διασωληνωμενοι_ασθενεις']\n",
        "\n",
        "# Call the check_vocab function with the model vocabularies and try_words\n",
        "check_vocab(model_vocabs, try_words)\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "d1b3e0d2",
      "metadata": {
        "id": "d1b3e0d2",
        "papermill": {
          "duration": 0.032303,
          "end_time": "2023-11-25T01:03:09.599045",
          "exception": false,
          "start_time": "2023-11-25T01:03:09.566742",
          "status": "completed"
        },
        "tags": []
      },
      "source": [
        "**Find the most similar named_entities**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 18,
      "id": "646f40c3",
      "metadata": {
        "execution": {
          "iopub.execute_input": "2023-11-25T01:03:09.665868Z",
          "iopub.status.busy": "2023-11-25T01:03:09.665474Z",
          "iopub.status.idle": "2023-11-25T01:03:10.709461Z",
          "shell.execute_reply": "2023-11-25T01:03:10.707624Z"
        },
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          "duration": 1.079488,
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          "exception": true,
          "start_time": "2023-11-25T01:03:09.631881",
          "status": "failed"
        },
        "tags": [],
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Most similar words for named entities in model1:\n",
            "\n",
            "Most similar words for \"ελλαδα\":\n",
            " [('χωρα', 0.8036606311798096), ('ευρωπη', 0.7219130396842957), ('ουραγους', 0.6944064497947693), ('ιταλια', 0.652439534664154), ('τουρκια', 0.6506747603416443), ('χειροτερη_θεση', 0.6380224227905273), ('ευρωπαϊκη_χωρα', 0.6375015377998352), ('σλοβακια_σλοβενια', 0.6209450960159302), ('πορτογαλια_ισπανια', 0.6208418607711792), ('θλιβερη_πρωτια', 0.6205682158470154)]\n",
            "\n",
            "\n",
            "Most similar words for \"κυριακος_μητσοτακης\":\n",
            " [('μητσοτακης', 0.8690891861915588), ('αλεξης_τσιπρας', 0.8272742033004761), ('πρωθυπουργος', 0.8200730681419373), ('τσιπρας', 0.8038445711135864), ('προεδρος_νδ', 0.7942981719970703), ('πρωθυπουργος_κυριακος', 0.7594904899597168), ('προεδρος_συριζα', 0.7515526413917542), ('ιδιος_κυριακος', 0.7166814804077148), ('απευθυνομενος_κυριακο', 0.715607762336731), ('μητσοτακης_διαγγελμα', 0.7010779976844788)]\n",
            "\n",
            "\n",
            "Most similar words for \"ολυμπιακος\":\n",
            " [('παοκ', 0.8500607013702393), ('παναθηναϊκος', 0.8368938565254211), ('αεκ', 0.8158099055290222), ('ιωνικος', 0.7994675040245056), ('βολος', 0.7972644567489624), ('παναθηναϊκο', 0.793934166431427), ('ολυμπιακο', 0.7852041125297546), ('μπαρτσελονα', 0.7726141214370728), ('ματς', 0.7635631561279297), ('οφη', 0.761829674243927)]\n",
            "\n",
            "\n",
            "Most similar words for named entities in model2:\n",
            "\n",
            "Most similar words for \"ελλαδα\":\n",
            " [('χωρα', 0.8100768327713013), ('ευρωπη', 0.7287224531173706), ('θανατους_εκατομμυριο', 0.6720889806747437), ('ανερχονται_σημερινα', 0.6615619659423828), ('ουραγους', 0.6596614122390747), ('χειροτερη_θεση', 0.641739010810852), ('τουρκια', 0.6232765913009644), ('ευρωπαϊκες_χωρες', 0.6223753094673157), ('χωρες', 0.616368293762207), ('ακριβοτερη_χωρα', 0.6124633550643921)]\n",
            "\n",
            "\n",
            "Most similar words for \"κυριακος_μητσοτακης\":\n",
            " [('μητσοτακης', 0.86124187707901), ('τσιπρας', 0.8449526429176331), ('πρωθυπουργος', 0.8295382857322693), ('αλεξης_τσιπρας', 0.8238332867622375), ('κυριακο_μητσοτακη', 0.8021169304847717), ('πρωθυπουργος_κυριακος', 0.7589097619056702), ('προεδρος_συριζα', 0.7456313967704773), ('ιδιος_πρωθυπουργος', 0.7331551909446716), ('βημα_βουλης', 0.7320737242698669), ('κυριακου_μητσοτακη', 0.7302076816558838)]\n",
            "\n",
            "\n",
            "Most similar words for \"ολυμπιακος\":\n",
            " [('παοκ', 0.8453704118728638), ('παναθηναϊκος', 0.841935932636261), ('αεκ', 0.8273478150367737), ('ολυμπιακο', 0.8264310956001282), ('παναθηναϊκο', 0.823947012424469), ('ματς', 0.8224079608917236), ('αελ', 0.7876371741294861), ('ερυθρολευκοι', 0.7874976396560669), ('τριφυλλι', 0.7870306968688965), ('σλοβαν', 0.7827320694923401)]\n",
            "\n",
            "\n",
            "Most similar words for named entities in model3:\n",
            "\n",
            "Most similar words for \"ελλαδα\":\n",
            " [('χωρα', 0.7946726679801941), ('ευρωπη', 0.7219750285148621), ('ευρωπαϊκη_χωρα', 0.6378759145736694), ('θανατους_εκατομμυριο', 0.6178833246231079), ('ανερχονται_σημερινα', 0.6115953326225281), ('τουρκια', 0.6111419796943665), ('ουραγους', 0.6022518873214722), ('αναλογικα_πληθυσμο', 0.597266435623169), ('χωρες_ευρωπης', 0.5969671010971069), ('χωρες', 0.5949955582618713)]\n",
            "\n",
            "\n",
            "Most similar words for \"κυριακος_μητσοτακης\":\n",
            " [('μητσοτακης', 0.8688746690750122), ('πρωθυπουργος', 0.8192901015281677), ('αλεξης_τσιπρας', 0.7930730581283569), ('τσιπρας', 0.7922583222389221), ('κυριακο_μητσοτακη', 0.7804480195045471), ('κυριακου_μητσοτακη', 0.7687729001045227), ('ιδιος_κυριακος', 0.7503688335418701), ('βημα_βουλης', 0.7438319325447083), ('πρωθυπουργος_κυριακος', 0.7384399175643921), ('προεδρος_συριζα', 0.7324647903442383)]\n",
            "\n",
            "\n",
            "Most similar words for \"ολυμπιακος\":\n",
            " [('παναθηναϊκος', 0.8843861222267151), ('ματς', 0.8432577848434448), ('αεκ', 0.8339738845825195), ('παναθηναϊκο', 0.8272392153739929), ('ολυμπιακο', 0.8267912864685059), ('παοκ', 0.8176252245903015), ('μακαμπι', 0.8110042810440063), ('ερυθρολευκοι', 0.8082222938537598), ('χιμκι', 0.807280421257019), ('ζενιτ', 0.8039939999580383)]\n",
            "\n",
            "\n"
          ]
        }
      ],
      "source": [
        "named_entities = ['ελλαδα', 'κυριακος_μητσοτακης', 'ολυμπιακος']\n",
        "\n",
        "for idx, model in enumerate(models, start=1):\n",
        "    print(f'Most similar words for named entities in model{idx}:\\n')\n",
        "    for entity in named_entities:\n",
        "        similar_words = model.wv.most_similar(entity)\n",
        "        print(f'Most similar words for \"{entity}\":\\n', similar_words)\n",
        "        print('\\n')"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "In the \"most similar to\" test, we can make the following observations from our models:\n",
        "\n",
        "**Model 1:** It seems that the first model captured the semantic similarities very well. Almost all similar words of all 3 terms are of the same semantic category.\n",
        "For the country \"ελλαδα\" most of the similar words were also countries.\n",
        "For \"κυριακος_μητσοτακης\": Most terms were either other named entities with the same property (prim minister, like \"αλεξης_τσιπρας\", or they are coreferential terms for the entity, like \"προεδρος_κυριακος\", \"ίδιος_πρωθυπουργός\".\n",
        "For \"ολυμπιακος\":all similar words were other sport teams.\n",
        "\n",
        "\n",
        "**Model 2:** We observe quite different results for the 1st term \"ελλάδα\". Apart from the two first similar terms which are common for all 3 models, the remaining words are topically related to the word 'ελλαδα', like: \"θανατους_εκατομυριο\", \"ανερχονται_σημερινα\", \"χειροτερη θέση\", \"ακριβοτερη_χωρα\". This is probably referring to the covid mortality report for Greece. For the rest 2 tested terms, the results were similar to the first model.\n",
        "\n",
        "**Model 3**: The results were quite similar to those of model 2."
      ],
      "metadata": {
        "id": "mBYmcuLlTUjE"
      },
      "id": "mBYmcuLlTUjE"
    },
    {
      "cell_type": "markdown",
      "id": "3e42e3a4",
      "metadata": {
        "id": "3e42e3a4",
        "papermill": {
          "duration": null,
          "end_time": null,
          "exception": null,
          "start_time": null,
          "status": "pending"
        },
        "tags": []
      },
      "source": [
        "**Compute similarity between named entities who belong to different topics**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 19,
      "id": "f9176708",
      "metadata": {
        "id": "f9176708",
        "outputId": "3f7d271e-89bc-4b40-84dd-77a352d42abb",
        "papermill": {
          "duration": null,
          "end_time": null,
          "exception": null,
          "start_time": null,
          "status": "pending"
        },
        "tags": [],
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Similarity between named entity pairs in model1:\n",
            "\n",
            "Cosine similarity between \"κυριακος_μητσοτακης\" and \"γιωργος_σεφερης\" is: 0.15008542\n",
            "Cosine similarity between \"ολυμπιακος\" and \"κορονοιος\" is: 0.0\n",
            "Cosine similarity between \"ουκρανια\" and \"ισπανια\" is: 0.38975897\n",
            "\n",
            "\n",
            "Similarity between named entity pairs in model2:\n",
            "\n",
            "Cosine similarity between \"κυριακος_μητσοτακης\" and \"γιωργος_σεφερης\" is: 0.15068278\n",
            "Cosine similarity between \"ολυμπιακος\" and \"κορονοιος\" is: 0.0\n",
            "Cosine similarity between \"ουκρανια\" and \"ισπανια\" is: 0.29676044\n",
            "\n",
            "\n",
            "Similarity between named entity pairs in model3:\n",
            "\n",
            "Cosine similarity between \"κυριακος_μητσοτακης\" and \"γιωργος_σεφερης\" is: 0.13648142\n",
            "Cosine similarity between \"ολυμπιακος\" and \"κορονοιος\" is: 0.0\n",
            "Cosine similarity between \"ουκρανια\" and \"ισπανια\" is: 0.22373444\n",
            "\n",
            "\n"
          ]
        }
      ],
      "source": [
        "entity_pairs = [\n",
        "    ('κυριακος_μητσοτακης', 'γιωργος_σεφερης'),\n",
        "    ('ολυμπιακος', 'κορονοιος'),\n",
        "    ('ουκρανια', 'ισπανια')\n",
        "]\n",
        "\n",
        "for idx, model in enumerate(models, start=1):\n",
        "    print(f'Similarity between named entity pairs in model{idx}:\\n')\n",
        "    for entity_pair in entity_pairs:\n",
        "        similarity_score = model.wv.n_similarity([entity_pair[0]], [entity_pair[1]])\n",
        "        print(f'Cosine similarity between \"{entity_pair[0]}\" and \"{entity_pair[1]}\" is:', similarity_score)\n",
        "    print('\\n')"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Similariy between to words: With this test we wanted to test the similarity between two terms that are semantically related, but topically unrelated. The have the same Part of speech but are not probable to co-occur in the same text.\n",
        "\n",
        "\n",
        "For the first two tested pairs it seems that all models did very well in capturing the non relatedness of the terms. The interesting is the last pair, in which the 1st model gave around double higher score, meaning that the low window size help the model understand that these two words are similar because they are both countries.\n",
        "Model 3 seems to be more efficient for capturing topical relations between words and it seems to understand that the two countries are less likely to have co-occured in the same context."
      ],
      "metadata": {
        "id": "qtTwxFrQZNX3"
      },
      "id": "qtTwxFrQZNX3"
    },
    {
      "cell_type": "markdown",
      "id": "bb01fcf6",
      "metadata": {
        "id": "bb01fcf6",
        "papermill": {
          "duration": null,
          "end_time": null,
          "exception": null,
          "start_time": null,
          "status": "pending"
        },
        "tags": []
      },
      "source": [
        "**Odd-one out**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 20,
      "id": "43fc49cd",
      "metadata": {
        "id": "43fc49cd",
        "outputId": "b94e4759-14bb-4051-b0dd-7818c6ea8d19",
        "papermill": {
          "duration": null,
          "end_time": null,
          "exception": null,
          "start_time": null,
          "status": "pending"
        },
        "tags": [],
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:gensim.models.keyedvectors:vectors for words {'καραισκακη'} are not present in the model, ignoring these words\n",
            "WARNING:gensim.models.keyedvectors:vectors for words {'καραισκακη'} are not present in the model, ignoring these words\n",
            "WARNING:gensim.models.keyedvectors:vectors for words {'καραισκακη'} are not present in the model, ignoring these words\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Word that does not match with the rest in model1:\n",
            "\n",
            "Word that does not match in ('παοκ', 'ολυμπιακος', 'καραισκακη'): ολυμπιακος\n",
            "Word that does not match in ('κυριακος_μητσοτακης', 'αλεξης_τσιπρας', 'νεα_δημοκρατια'): νεα_δημοκρατια\n",
            "\n",
            "\n",
            "Word that does not match with the rest in model2:\n",
            "\n",
            "Word that does not match in ('παοκ', 'ολυμπιακος', 'καραισκακη'): ολυμπιακος\n",
            "Word that does not match in ('κυριακος_μητσοτακης', 'αλεξης_τσιπρας', 'νεα_δημοκρατια'): νεα_δημοκρατια\n",
            "\n",
            "\n",
            "Word that does not match with the rest in model3:\n",
            "\n",
            "Word that does not match in ('παοκ', 'ολυμπιακος', 'καραισκακη'): παοκ\n",
            "Word that does not match in ('κυριακος_μητσοτακης', 'αλεξης_τσιπρας', 'νεα_δημοκρατια'): νεα_δημοκρατια\n",
            "\n",
            "\n"
          ]
        }
      ],
      "source": [
        "triples_list = [\n",
        "    ('παοκ', 'ολυμπιακος', 'καραισκακη'),\n",
        "    ('κυριακος_μητσοτακης', 'αλεξης_τσιπρας', 'νεα_δημοκρατια')\n",
        "]\n",
        "\n",
        "for idx, model in enumerate(models, start=1):\n",
        "    print(f'Word that does not match with the rest in model{idx}:\\n')\n",
        "    for triple in triples_list:\n",
        "        not_matching_word = model.wv.doesnt_match(list(triple))\n",
        "        print(f'Word that does not match in {triple}: {not_matching_word}')\n",
        "    print('\\n')"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Even though the model did not catch the vector for the word \"καραισκακη\", it seems that in the last model, it managed to give less probability to \"παοκ\" and capture the similarity between \"καραισκακη and ολυμπιακος\". For the second pair, it can be infered that there is a strong cooccurence of the named entity \"αλεξης_τσιπρας\" Next to \"κυριακος_μητσοτακης\", so that no other topical related term can be easily dropped out. Also \"αλεξης_τσιπρας\" Is both a named entity and a topically related term, which implies that maybe our **model** 3 is capturing both the semantic and the topical relation."
      ],
      "metadata": {
        "id": "RkgRmm8FdCya"
      },
      "id": "RkgRmm8FdCya"
    },
    {
      "cell_type": "markdown",
      "id": "36a4e039",
      "metadata": {
        "id": "36a4e039",
        "papermill": {
          "duration": null,
          "end_time": null,
          "exception": null,
          "start_time": null,
          "status": "pending"
        },
        "tags": []
      },
      "source": [
        "**Analogy difference**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 21,
      "id": "2970fd00",
      "metadata": {
        "id": "2970fd00",
        "papermill": {
          "duration": null,
          "end_time": null,
          "exception": null,
          "start_time": null,
          "status": "pending"
        },
        "tags": [],
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "5239108d-5958-43a0-e8a8-30d35eb3a7ce"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Analogy difference for model1:\n",
            "\n",
            "Most similar words based on analogy difference: [('αεκ', 0.7181831002235413), ('παοκ', 0.7127392292022705), ('αελ', 0.6921119689941406)]\n",
            "\n",
            "Analogy difference for model2:\n",
            "\n",
            "Most similar words based on analogy difference: [('ματς', 0.7287254333496094), ('παναθηναϊκος', 0.7239900231361389), ('αεκ', 0.7216362953186035)]\n",
            "\n",
            "Analogy difference for model3:\n",
            "\n",
            "Most similar words based on analogy difference: [('παναθηναϊκος', 0.7370814681053162), ('μακαμπι', 0.7092284560203552), ('αεκ', 0.7079706788063049)]\n",
            "\n"
          ]
        }
      ],
      "source": [
        "# For model1, model2, model3\n",
        "models_1 = [model1, model2, model3]\n",
        "\n",
        "analogy_difference_1 = [\"ολυμπιακος\", \"μητσοτακης\", \"τσιπρας\"]\n",
        "for idx, model in enumerate(models_1, start=1):\n",
        "    print(f'Analogy difference for model{idx}:\\n')\n",
        "    analogy_result = model.wv.most_similar(positive=[analogy_difference_1[0], analogy_difference_1[1]],\n",
        "                                           negative=[analogy_difference_1[2]], topn=3)\n",
        "    print(f'Most similar words based on analogy difference: {analogy_result}\\n')"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Considering our encyclopedic knowledge of Greek sports teams, we can say that model 1 and 3 captured teams that are connected the same way that the named entities \"μητσοτακης\" and \"τσιπρας\" are connected. Again, **model** 3 is the one that managed to capture the deeper connection between the two temas which is the same connection between the two named entities. Both pairs are also opponents in Greece, the first pair in the polilical field the other pair in the sport field."
      ],
      "metadata": {
        "id": "PIzZFdEzfISy"
      },
      "id": "PIzZFdEzfISy"
    },
    {
      "cell_type": "markdown",
      "id": "3799b058",
      "metadata": {
        "id": "3799b058",
        "papermill": {
          "duration": null,
          "end_time": null,
          "exception": null,
          "start_time": null,
          "status": "pending"
        },
        "tags": []
      },
      "source": [
        "#t-SNE visualisations\n",
        "t-SNE is a non-linear dimensionality reduction algorithm that attempts to represent high-dimensional data and the underlying relationships between vectors in a lower-dimensional space.\n",
        "We will plot our 300 dimensions vectors into 2 dimensional graphs, and see if we can spot interesting patterns.\n",
        "\n",
        "To make the visualizations more relevant, we will look at the relationships between a query word (in **red**), its most similar words in the model (in **blue**), and other words from the vocabulary (in **green**)."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 33,
      "id": "6a873d4e",
      "metadata": {
        "id": "6a873d4e",
        "papermill": {
          "duration": null,
          "end_time": null,
          "exception": null,
          "start_time": null,
          "status": "pending"
        },
        "tags": []
      },
      "outputs": [],
      "source": [
        "def tsnescatterplot(model, word, list_names, title=''):\n",
        "    \"\"\" Plot in seaborn the results from the t-SNE dimensionality reduction algorithm of the vectors of a query word,\n",
        "    its list of most similar words, and a list of words.\n",
        "    \"\"\"\n",
        "    arrays = np.empty((0, 100), dtype='f')\n",
        "    word_labels = [word]\n",
        "    color_list  = ['red']\n",
        "\n",
        "    # adds the vector of the query word\n",
        "    arrays = np.append(arrays, model.wv.__getitem__([word]), axis=0)\n",
        "\n",
        "    # gets list of most similar words\n",
        "    close_words = model.wv.most_similar([word])\n",
        "\n",
        "    # adds the vector for each of the closest words to the array\n",
        "    for wrd_score in close_words:\n",
        "        wrd_vector = model.wv.__getitem__([wrd_score[0]])\n",
        "        word_labels.append(wrd_score[0])\n",
        "        color_list.append('blue')\n",
        "        arrays = np.append(arrays, wrd_vector, axis=0)\n",
        "\n",
        "    # adds the vector for each of the words from list_names to the array\n",
        "    for wrd in list_names:\n",
        "        wrd_vector = model.wv.__getitem__([wrd])\n",
        "        word_labels.append(wrd)\n",
        "        color_list.append('green')\n",
        "        arrays = np.append(arrays, wrd_vector, axis=0)\n",
        "\n",
        "    # Reduces the dimensionality from 300 to 50 dimensions with PCA\n",
        "    reduc = PCA(n_components=10).fit_transform(arrays)\n",
        "\n",
        "    # Finds t-SNE coordinates for 2 dimensions\n",
        "    np.set_printoptions(suppress=True)\n",
        "\n",
        "    Y = TSNE(n_components=2, random_state=0, perplexity=15).fit_transform(reduc)\n",
        "\n",
        "    # Sets everything up to plot\n",
        "    df = pd.DataFrame({'x': [x for x in Y[:, 0]],\n",
        "                       'y': [y for y in Y[:, 1]],\n",
        "                       'words': word_labels,\n",
        "                       'color': color_list})\n",
        "\n",
        "    fig, _ = plt.subplots()\n",
        "    fig.set_size_inches(9, 9)\n",
        "\n",
        "    # Basic plot\n",
        "    p1 = sns.regplot(data=df,\n",
        "                     x=\"x\",\n",
        "                     y=\"y\",\n",
        "                     fit_reg=False,\n",
        "                     marker=\"o\",\n",
        "                     scatter_kws={'s': 40,\n",
        "                                  'facecolors': df['color']\n",
        "                                 }\n",
        "                    )\n",
        "\n",
        "    # Adds annotations one by one with a loop\n",
        "    for line in range(0, df.shape[0]):\n",
        "         p1.text(df[\"x\"][line],\n",
        "                 df['y'][line],\n",
        "                 '  ' + df[\"words\"][line].title(),\n",
        "                 horizontalalignment='left',\n",
        "                 verticalalignment='bottom', size='medium',\n",
        "                 color=df['color'][line],\n",
        "                 weight='normal'\n",
        "                ).set_size(15)\n",
        "\n",
        "\n",
        "    plt.xlim(Y[:, 0].min()-50, Y[:, 0].max()+50)\n",
        "    plt.ylim(Y[:, 1].min()-50, Y[:, 1].max()+50)\n",
        "\n",
        "    plt.title(f't-SNE visualization for {word.title()} - {title}')\n",
        "    plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "3df65b05",
      "metadata": {
        "id": "3df65b05",
        "papermill": {
          "duration": null,
          "end_time": null,
          "exception": null,
          "start_time": null,
          "status": "pending"
        },
        "tags": []
      },
      "source": [
        "**10 Most similar words vs. 8 Random words**\n",
        "Let's compare where the vector representation of the country Greece (Ελλαδα), its 10 most similar words from each of the 2 chosen models, as well as 8 words with different syntactic role (e.g. verbs) and 8 words from a different domain field (e.g. sports)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 34,
      "id": "780a3d0f",
      "metadata": {
        "id": "780a3d0f",
        "papermill": {
          "duration": null,
          "end_time": null,
          "exception": null,
          "start_time": null,
          "status": "pending"
        },
        "tags": [],
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "5dbab97f-46cf-4195-bf1c-c73c7495c8d8"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 900x900 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 900x900 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 900x900 with 1 Axes>"
            ],
            "image/png": 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99RaqVq0KrVYLk8mEt99+O1fjWwpCduHwWR3/cRX8vLh+/Tr27duHESNGQC6Xo2PHjggPD8fWrVvRpUsXy3r79u3D9evXLeOvOnbsiE8//RRHjx5FnTp1LOXLeg85OTlBqVSiXr16Vst1Op3dFlPzvWP+MOJJ9mlPaGgooqKiLN8PHz4cYWFhj90OAGrWrAk/Pz988803uHXrls1Mjk+TyWSCIAj4+++/IZfLbV5/3Mx9ueXn54dq1aph9erVDD1ERRBDDxFZya4y/8knn1h9Su/l5ZXjfooVK4ayZcvi0qVLOa4XFhaGAQMG5DgrU3506NABy5cvx759+3DlyhWIopjjNMNmMpkMjRo1QqNGjfDpp59i+vTp+Pnnn3HgwAE0btzY0nKRteUkc6URAC5evIjr169j0qRJVl3Q9uzZk+dzmjBhAs6dO4f58+fbPKMlMTER+/btQ1hYGIYPH25Zfv36dZv9PMlzeHx9fXHt2jWb5VevXrW8/rSY953d8UuUKFFgFduchIeHQ6FQoHfv3gCk0O7p6Yl169ZZhZ4FCxagZMmSlvusdevWGDduHNatW2cJPT4+PjZdAnft2gW9Xm/1QF/zuvbO3Tzbmbe39xPv057JkydbPVOnbNmyj90ms06dOuGPP/5AxYoVLS1MWeX2vVSr1dBoNHZn/Mu6rZ+fH0RRRJkyZQrs2V3ZSUtLy7bLLhE93zimh4ismB+2mLUyX716dTRu3NjyValSJQDS9Mj2Zr2KiorClStXHlsJqV+/vqW152k8xLBx48YoXrw4IiIisG7dOtSoUeOxlbn79+/bLDNX4swVHvMsVYcOHbKsYzQasWjRIqvtzC0emVs4RFHEnDlznvxkIM0ItnDhQowbN87us1/sfdINAP/++6/Nsuzea3uaN2+OkydP4tixY5ZlKSkpWLRoEUqXLm25H54GLy8vVK1aFStWrLAK3hcvXsSePXvQvHnzp3ZsM51Oh+XLl6Ndu3aWoCmTydC+fXvs3r3bcg3v3r2LrVu3olevXpYxTeapm83jyQApMN26dQuHDx+2HGPhwoUAgBMnTlgdu3nz5jhz5oxVS+vDhw+xaNEilCpVyjLd+pPs0546depY/Yw/aejp1asXhg8fbjNTXWa5fS/lcjmaNGmCzZs3W7rEAVLQ2717t9U+27ZtC7lcjmnTptm0JIqiaDVVe24YDAabbqgAcPLkSVy8eBHVq1d/ov0R0fOBLT1EZOWll14CAPz888/o2LEjlEolWrZsme0n6Xv27MHUqVMREhKCmjVrQqvV4tatW1i6dCnS09Nz1T1m+PDhGDhwYLavX79+HStXrrRZ7uHhgeDg4Bz3rVQq0aZNG6xduxapqak5VsjMfvvtNxw+fBjNmzdH6dKlER8fj//++w/e3t6WT+orV66MWrVq4aeffkJiYiKKFSuGiIgIGAwGq335+/vDz88PkyZNwt27d+Hi4oINGzbkaWxLQkICvvzyS1SqVAkqlcrmmrRp0wYuLi6oV68e/vnnH+j1epQqVQp79uyx+6ylJ3mv33nnHaxduxaDBw/GgAEDUKxYMaxYsQK3bt3C1KlT8zTW6UmMHj0agwcPRp8+fRAaGmqZ5tjV1dWqRSuvDh8+bDd0BwYGokqVKoiIiMD9+/dtnvnToUMHzJ07F1u2bEG3bt2wePFiiKJo012zY8eO2LZtG44cOYJ69eqhd+/e+OuvvxAWFoY333wT0dHR2LlzJ9q1a4cNGzbgt99+Q5cuXVC2bFm88847iIiIwLvvvmuZLnzZsmWIjo7GTz/9ZAm6T7LPp6F06dK5+nnP7XsZFhaGXbt2oV+/fnj11VdhNBoxb948VKpUCRcuXLCs5+fnhw8++AA//vgjoqKi0Lp1azg7O+PWrVvYvHkzevfubZloJTdSUlLQokULdOjQAZUrV4aTkxMuXryIZcuWwdXVFe+9996TXRgiei4w9BCRlRo1auD9999HeHg4du3aBZPJhC1btmQbetq2bYvk5GTs2bMH+/fvR2JiItzc3FCjRg0MGjTIMhA4Jw0aNED9+vXtPkgUkIKVve5g9evXf2zoATImJhAEIVdd20JCQhAVFYWlS5fi3r17KFGiBOrXr4+wsDDL5A0A8MMPP2DcuHH466+/4ObmhtDQUDRo0ACDBg2yrKNUKjF9+nRMmDABf/75J9RqNdq0aYN+/fqha9eujy1LZikpKdDpdLh8+TJGjx5t87r5ffrxxx/x9ddf47///oMoiggODsbff/+Npk2bWq3/JO+1h4cHwsPDMXnyZMybNw86nQ6BgYGYPn36U3v+S2aNGzfGP//8gylTpmDKlClQKBSoV68ePv744wKpxM+dO9fu8uHDh6NKlSpYsGABqlSpYgm9ZuZZ1datW4dXXnkFixYtQvPmzW26+4WEhECj0WDdunWoV68e3NzcMHv2bEycOBHTpk2DXC7H4MGDMXLkSIwbNw7Tp0+Hj48PypYtCy8vL4SHh+OHH37A/PnzodPpEBAQgGnTpqF169aWYzzJPgtTbt/LKlWqYMaMGZg4cSKmTJkCb29vhIWFITY21ir0AFIoL1++PGbPnm15no63tzeCg4MREhLyROXTaDQIDQ3FgQMHsGHDBuh0Onh5eaFTp04YOnToE0+SQUTPB0F8VqNKiYiIiIiICgHH9BARERERkUNj6CEiIiIiIofG0ENERERERA6NoYeIiIiIiBwaQw8RERERETk0hh4iIiIiInJoDD1EREREROTQGHqIiIiIiMihKQq7AEVVfHwSivJjXQUBcHd3LfLnQYWD9w/lB+8fyg/eP5QfvH9yZr4+joihJ49EEQ7xw+Io50GFg/cP5QfvH8oP3j+UH7x/Xjzs3kZERERERA6NoYeIiIiIiBwaQw8RERERETk0hh4iIiIiInJoDD1EREREROTQGHqIiIiIiMihMfQQEREREZFDY+ghIiIiIiKHxtBDREREREQOjaGHiIiIiIgcGkMPERERERE5NIYeIiIiIiJyaAw9RERERETk0Bh6iIiIiIjIoTH0EBERERGRQ2PoISIiIiIih8bQQ0REREREDo2hh4iIiIiIHBpDDxEREREROTSGHiIiIiIicmgMPURERERE5NAYeoiIiIiIyKEx9BARERERkUNj6CEiIiIiIofG0ENERERERA6NoYeIiIiIiBwaQw8RERERETk0hh4iIiIiInJoDD1EREREROTQGHqIiIiIiMihMfQQEREREZFDY+ghIiIiIiKHxtBDREREREQOjaGHiIiIiIgcGkMPERERERE5NIYeIiIiIiJyaAw9RERERETk0Bh6iIiIiIjIoTH0EBERERGRQ2PoISIiIiIih8bQQ0REREREDo2hh4iIiIiIHBpDDxEREREROTSGHiIiIiIicmgMPURERERE5NAYeoiIiIiIyKEx9BARERERkUNj6CEiIiIiIofG0ENERERERA6NoYeIiIiIiBwaQw8RERERETk0hh4iIiIiInJoDD1EREREROTQGHqIiIiIiMihMfQQEREREZFDY+ghIiIiIiKHxtBDREREREQOjaGHiIiIiIgcGkMPERERERE5NIYeIiIiIiJyaAw9RERERETk0Bh6iIiIiIjIoTH0EBERERGRQ2PoISIiIiIih8bQQ0REREREDo2hh4iIiIiIHBpDDxERERERObQiFXoOHTqEd999F02aNEFgYCA2b95s9booivj111/RpEkT1KhRA2+88QauX79utc79+/fx4Ycf4uWXX0bdunXxv//9D8nJyc/wLIiIiIiI6FkqUqEnJSUFgYGBGD9+vN3X//77b8ydOxdffPEFFi1aBCcnJ7z11lvQ6XSWdT766CNcvnwZs2bNwvTp03H48GGMGzfuWZ0CERERERE9Y0Uq9DRv3hwjR45EmzZtbF4TRRFz5szB0KFD0bp1a1SpUgXff/89YmJiLC1CV65cwa5duzBhwgTUrFkTdevWxdixY7F27VrcvXv3WZ8OERERERE9A4rCLkBBuXXrFmJjY9G4cWPLMldXV9SsWRPHjh1Dp06dcOzYMbi5uSEoKMiyTuPGjSGTyXDy5Em7YSo7glCgxX/mzOUv6udBhYP3D+UH7x/KD94/lB+8f3LmyNfFYUJPbGwsAMDd3d1qubu7O+Li4gAAcXFxKFmypNXrCoUCxYoVs2yfW+7urvko7fPDUc6DCgfvH8oP3j+UH7x/KD94/7x4HCb0PGvx8UkQxcIuRd4JgvQDX9TPgwoH7x/KD94/lB+8fyg/eP/kzHx9HJHDhB5PT08AQHx8PLy8vCzL4+PjUaVKFQCAh4cHEhISrLYzGAxITEy0bJ9bogiH+GFxlPOgwsH7h/KD9w/lB+8fyg/ePy+eIjWRQU7KlCkDT09P7Nu3z7Ls4cOHOHHiBGrXrg0AqF27Nh48eIDTp09b1tm/fz9MJhNq1KjxzMtMRERERERPX5Fq6UlOTkZkZKTl+1u3buHcuXMoVqwYfH19MXDgQPzxxx8oV64cypQpg19//RVeXl5o3bo1AKBixYpo2rQpPv/8c3z55ZfQ6/X4+uuv0alTJ5QqVaqwTouIiIiIiJ6iIhV6Tp8+jYEDB1q+nzhxIgCge/fu+O677zB48GCkpqZi3LhxePDgAerUqYN//vkHarXass0PP/yAr7/+Gq+//jpkMhnatm2LsWPHPvNzISIiIiKiZ0MQRfZozIu4uKI9AE4QAA8P1yJ/HlQ4eP9QfvD+ofzg/UP5wfsnZ+br44gcZkwPERERERGRPQw9RERERETk0Bh6iIiIiIjIoTH0EBERERGRQ2PoISIiIiIih8bQQ0REREREDo2hh4iIiIiIHBpDDxEREREROTSGHiIiIiIicmgMPURERERE5NAYeoiIiIiIyKEx9BARERERkUNj6CEiIiIiIofG0ENERERERA6NoYeIiIiIiBwaQw8RERERETk0hh4iIiIiInJoDD1EREREROTQGHqIiIiIiMihMfQQEREREZFDY+ghIiIiIiKHxtBDREREREQOjaGHiIiIiIgcGkMPERERERE5NIYeIiIiIiJyaAw9RERERETk0Bh6iIiIiIjIoTH0EBERERGRQ2PoISIiIiIih8bQQ0REREREDo2hh4iIiIiIHBpDDxEREREROTSGHiIiIiIicmgMPURERERE5NAYeoiIiIiIyKEx9BARERERkUNj6CEiIiIiIofG0ENERERERA6NoYeIiIiIiBwaQw8RERERETk0hh4iIiIiInJoDD1EREREROTQGHqIiIiIiMihMfQQEREREZFDY+ghIiIiIiKHpijsAhAREQFAtxUdsTd6d47rlHX1w5EBp59RiSR3k+/gu4MTsDVyM2JTY+CsdMFL7tXxZvXB6FKpe4EdZ0/ULnRf2QluqmJoUroZJjX7EaWcvQts/0RELzKGHiIieq60LNsKXtpSdl8rqXF/pmVJSn+Azsvb4saD6/Bx9kW78h2Rok/Gnqhd2Bu9Gz+nJ6FftYEFciwvbSn0DnwVh+8cRMS11XiQnohlXdcUyL6JiF50DD1ERPRcGfHyKASXblrYxQAAhJ+fjxsPrqORbzAWdFoKrVILADh69zDaLw3BtOO/FFjoqVwiANNa/YmH6UkIXlAPu6N24kLCeQSWrFIg+yciepFxTA8REVE2DtzeDwAIq/2BJfAAwMul6sJd447IBzcK/JguKlf0r/Y6AGDhhf8KfP9ERC8itvQQEVGRVGduddxMiszVuof7n4KfWzkAwL20BPx58ndEXF2NW0m3YBKNcHfyQJWSVfFW0DsI8Wtj2e5eWgIAoLybv9X+7qfdQ0JaAjycPO0eL0WfginHfsKyi4txOzka3s4+6Fm5Fz6o8zE0Cg3qzqsBN5Ubtva2P4apT+Br+OHQd1h6cRHGNvwCMsH+Z5QXEy7g9+NTsCd6F+4m34FKrkYpbSm85FEd/2swHuWLVcjV9SEicnQMPUREVCR19u+KPdG7cDL2OF5yD0J1jyDLa5FJN7Aveg/Ku1VAA59GcFa6AABSDanovKwtLt2/CC9tKTQt0xwauRoJaQk4EXsch+8csgo9RtEIAFDJVZZlSekPMGr7CIgQEeLX2qZcyfpk9FzZGUdjjqBS8cpoX74TDt89iJ+OTMbt5Nt4s/pgRD64jtH1/pftud1LS4AIEbeTo7Hj5ja09Gtls875hHNov6QlUgwpCCxRBe3Kd4QJJsSmxGDzjU14/aW3GHqIiB5h6CEioiLpy+BvMOXoTzgZexwdKnTC6PoZIWL5pSXYF70HDXwaYWqr6ZblO29tx6X7F9G0TAss6LTEKswAgM6oy/Z4Sy4uxIJz83A89hiS0h8goEQgxjb60ma97w9+i6MxR9Dary3mdAyHQqZAsj4ZHZaGYOGF/yxBqqP/K9ke67fjv1r+v/DCf3ZDz3/n5iLFkIJRdUdjTP2xVq+ZRBOMJmO2+ycietEw9BAR0XOl+8pO2b72To2hmNBkUp73LX/UTUyAYLfLmFquznbbU7EnsStqh+V7V5Ubrty7hFKZZpoTRRHh5+cBAEbW/RgKmfRn1lnpjME1huLD7SOw6MIClHergGruL9k9zs2kSKy+shIlNSWRok/B+mtr8TA9CS4q1yznIrf6NzOZIINMzmG7RERmDD1ERPRcyWnK6tpedfK17+ZlQvCyVx3svLUNdeZWR51S9eCsdEYDn0aWyQOy80XjCfiw7mhcuX8ZEdfWYPqJaQhd3QUz2s1FhwpSULv24Cru6e7B08kL9bwbWG0f7NvE8v+cWnn+OvE7jKIRbwUNweV7F7H88lKsurICr1UdYLXewJcGIfz8PEw+NBFrr65GYIlAqORq9Kv2Ohr6NHrSS0NE5NAYeoiI6LnyNKesVsqVWNJ1NUZsGYo1V1dizdWVltceF3oEQYCbuhhql6qD2qXqoE6pehi4ri++3DvWEnriU+MAAGVcy9hsX6FYRbgoXfFQn4SOFeyHnge6RMw/NxdahRZvBb2DEzHHsfzyUiy6sMAm9FQo5o/V3Tfi7Y2v42z8aZyNlx7aGly6KUMPEVEWDD1ERPTCSNYno/eqbrh0/yL+ajMLzcq2yPMDT9tX6AgPJ09cTbyCRN19FFMXt7ymUTjZrC8IAjycPCATZKjrXc/uPmefmYmH+iS8HTQEJTXuaF62JXydS2Nf9B5EPrhhmYEOkCYy6LWqKzycPLGy2zoEedaEy6MJG4iIyBo7/BIR0QtjzplZOHz3ID5t8Dm6Ve6Z58BjZh4XpDcZAMAyhfWd5Ns2615IOI/rD67hoT7J7oQJeqMeM079CYVMgXdrDrfsv1dgX4gQsejCAqv1v9n/Be6m3MH0NjPQyDeYgYeIKAcMPURE5HAECHaXX7l/GQDgofF4ov3dTbljsyw2JRaxKTFwUbqipKYkAKC8WwUUVxfH9cRrNs8QMocWk2jChYRzNvtbemkRbidHo0vFblYtOn2rvAYAWHwx3O65uDs92bkQEb2IGHqIiMjhqBUaABkPFzWrVKISAODXoz8hNiXW6jWTaMKB2/vt7i9sy7u4mnjF8r3eqMcXez+DCBGty7WxtPgIgoAelXtBhIjPdo2G3qgHANx4cB3/npkJlUyaInvNlVU2x/jj+DQAwLDaH1gtr1i8Mup5N8C1xKtW5atUvDIAYMK+8TYtR8n6ZJyMPW73XIiIXkQc00NERPmmN5pwOS4ZOr0J3m5qeLtp8ryvKUd/Qvj5+dm+PqnZT9AqtTnuw79YRQDAlshNeGNdP7z+0pto6dcK/au+jnln/8WpuBOoM/cl1PNuAE+tJ+JT43Hh3nkoZUocGXDaZn9XE6+g6YL6aOzbBK4qN5yMPY7IpBtw17jjs4ZfWK37Yd0x2Hh9PdZfj0CT8HoI8qiJ3VE78CA9ET80/xU/HP4OU4/9jKuJV/BOzffQ0KcRtkZuxrmEM2hRNgRBHjVsjt+3Sj8cunMAiy4sQAOfhgCAj+t9it1Ru/Df+bnYeGMdanm+DBeVC+6m3MWp2JPo5P+K1TOKiIheZAw9RESUZyZRxJrTd7Hi9HXcTkqESTRBrVCjbhlvvF6vPMqVzDmc2LPt5pYcX5/Q5DsAOe83sGQVDKk5DOHn5yPi2mo0LdMMLdEKLipXrOu5BX+e+B3rr0fgaMwR6IxpKK4ugUrFK6Nd+Y529ze+0QSsuboCh+4chN6UDm9nHwyo9gZG1vkYZVzLWq3rqfVERM/NmHTwG2yJ3IS1V1ehmLoYxtQfi4EvDUJtr5cxavsIrL26Cm3Lt0dDn0b44/hUAMDwLK08Zt0q9cDY3Z9g1ZXl+KbJJGgUGgR51sTW3rvx2/Ep2HVrO3ZH7YQIEe4aDwSXboJO/l1yvEZERC8SQRRFsbALURTFxSWhKF85QQA8PFyL/HlQ4eD9Q4D0IM5/9t/AklNnkCLfC6XTOQiyVBjTfWBKbYCy2iB807EWyrtbB5SidP90W9ERe6N343D/U1bjbKjwFKX7h54/vH9yZr4+johjeoiIKE8uxiRj5ZnL0KlXQeO2G3JlPGTyFCidrkBVfDFupZzEjIPXCruYREREDD1ERJQ3Wy7F4qExEgqnCzavCTI9BO1+HI+6g1v3UwuhdERERBkYeoiIKE+uJSTCKL8GQbDfR0ShikKaMQ1RiWnPuGRERETWOJEBERHliUahgCiqs31dNGkgF2RQyYvu52srukUUdhGIiKgAFN2/REREVKjq+3lAY6oOk8n+9NT61EC4O7mhaimXZ1wyIiIiaww9RESUJ80rucPXpRT0iR0gmjJafEQRMKSVh1rfDJ2rlYNGKS/EUhIREbF7GxER5ZGbRonP2gRhwibg9v1ySJWdhCBLhcxYDs5CRbQJrIDetXwLu5hEREQMPURElHeBXi6Y2qMudlyOw97r/khJ16NcCTe0CiiFmqXdIBOEwi4iERERQw8REeVPcSclugb5oGuQT2EXhYiIyC6O6SEiIiIiIofG0ENERERERA6NoYeIiIiIiBwaQw8RERERETk0hh4iIiIiInJoDD1EREREROTQGHqIiIiIiMihMfQQEREREZFDY+ghIiIiIiKHxtBDREREREQOjaGHiIiIiIgcGkMPERERERE5NIYeIiIiIiJyaAw9RERERETk0Bh6iIiIiIjIoTH0EBERERGRQ2PoISIiIiIih8bQQ0REREREDk1R2AUgIiKi55NOB1Sv7oLERAFNmhiwbFlqYReJnqFu3Zywd2/OVcWyZU04ciT5GZWIKO8YeoiIiMiujRsVSEwUAAB798oRHS3A11cs5FLRs+LmJr3XHh4mtGpltLtOyZK8H6hoYOghIiIiuxYvlqoJpUqZcPeuDEuWKDFiRHohl4qelYkTdTh6VI6YGBlCQnTo3t1Q2EUiyjOO6SEiIiIb9+4BW7Yo4OQk4qef0gAAS5bws9IXSenSIv79NxUajYj339fgyBFWG6no4t1LRERENlasUEKvF9CunQGtWxvh52fC+fNynDplW3UYMUIDLy9XzJqltFoeFycgMNAFDRo4Iy3NehujEZgzR4kOHbSoWtUZZcu6ICjIGd26OeHPP633Y5aYCISFaVCqFODt7YLKlV3w+usaXLpkXaawMA3KlXNB69ZarFhhG9T275fDy8sVVas6IynJ+rXNm+UIDXVC5couKFvWBY0aOePrr1VITLReb/JkFby8XPHVVyqr5WlpQIMGzggIcEFsrGBVJi8v18d+1anjbFPe8HAFWrfWws9PukbDhmkQHS3t+/33NfDxcUFCAmyOZW6py486dUz45Zc0pKUJGDjQCbduCTmu362bE7y8XBEZKSAiQoEOHbQoX94FAQEuePttjd3tM2+TVWSkAC8vV3Tr5mTz2sWLMnzwgRr16jnDz88FlSq5IDhYi3fe0eD6dfvljIhQoGZNwM/PBaVLS+tPn66EyWS7rpeXK3x8XGyWd+8ulXfLFrlNecqUccErrzhBtNPr7+BBGXr3doK/v3Q9evZ0wqFD0r27cKECXl6uWLeOHyw8LQw9REREZGPxYil4hIbqIQhAjx56q+WZtW4tdXvassW6wubhIeLDD3W4dk2GOXOst3v/fQ0++kiDc+dkqFnThE6dDKhRw4Q7d2TYsMG24hcVJSAkxBnh4UqUKwd06WKAt7cJ69ZJwen06YwqTYMGRjRsaMTZszK8844T9u+3rpx++aUaADBqVDpcXTOW//qrCq+9psXevXLUqGFEhw4GpKYCU6eq0b69M2JiMirS2Z2zRgN88YUO9+8LmDIlIxA1aGBEnz56y1f9+tL2/v4mq+WdO1t3IfvkEzVGjHBCVJQUQL28RCxerESfPk5ISgLWrVOgUSMjSpa0uWQFpkcPA0aN0iE2Vob+/Z3w8OHjt5k1S4W33tJAoxHRqpUBLi4iVq1Sols3ba62f5zz52Vo21aL//5TQaMR0a6dAc2bG+DuLmLzZgWiomyruD/+qMLrrzshMhIICTGgZUsjbt2SYdw4Dd5/X5PrY7dqJb1Hmzdbv/cBASYMGqTHgQMKrF9v/dry5Qp07arFvn1yBAcbUauWEbt3y9GrlxYXL8qwerUSWq2IFi3YhfBpEUTRXhalx4mLS7Kb4osKQQA8PFyL/HlQ4eD9Q/nB++f5d/WqgIYNXeDubsKpU8lQKKRPsZs0cYaXlwknTiRDnilHPHgAVKniApUKOH/+ITSZ6o8JCcBLL7nA39+EPXtSLOtXruwCd3cR27aloFQp6xtBpwPUausydenihP37FfjppzSMHKlBXFwSTCZgwgQVpk5Vo3p1I7ZuTbHaZs0aBd580wmdO+sxc6bU1LR6tQJvveWEcuVM2LMnGapHueTYMRk6dNDCyQlYsiQFdeqYLGUZNkyDVauUVvsRRaB6dWfExspw9OhDlCmTcQ4GAxAU5Ay9XsDZsw8tx8gsPFyBESOc0K9fOn7+WWf3fdi4UY7+/bXw8TFhy5YUeHiIEEXggw80WLBAiVdf1WPBAiW+/TYNb7+tt2wXFqbBwoVK/PZbKnr1KphKtCgCgwdL16FdOwP+/TcVMjsfnZtnfNNqRSxenIJ69aTrmJoK9O7thAMHFPj++zS88YbeZpvDhx/Cz8/6XoiMFFC3rgsaNzZgxYqM2QPHjVNj+nQVRo3SYcwY63FmJpPUkqjMlLN37pQjNFSLl14yYvt2OQDp98+1awK6dtXizh0ZZs9ORceOGdfLy8sVcrmI27etU9qFCzI0beqMcuVMOHTIeua606dlCAlxRkiIAeHhUnljYgTUry+1dq5cmYoGDaRJIcz3QI8eeqxZo0CbNgbMmpWlSfQZM/9+dkRs6SEieop0Rh0qz/CD1+9u6LGyc2EXhyhXliyRaotduxqgePSBdUCACTVqGBETI8OOHdYtJ25uQP36RqSkCNi71/q1kiWl1oxLl+SIipJaSsyVZUGA3Ypz1sBz4oQM+/crUK+eEQMGZFSWBQEYMyYdvr4mnD4tt2nR6dzZgCpVjNi4UYGEBCmMfPuttPP//U9nFUZmzFDBZBLw9tvplsBjLsvEiTo4OYmIiFBYzkEQgJAQqfKa9RN/hQKoWdOEBw8EHD1qXaYnMX++9D68+aYeHh6i5bjvvy+FpAULpNc7dHj6rQOCAEyapINKJWLDBgU2bsz5vIYMSbcEHgBwcgLee0967w4cyPs1MTOHbrmdXclk1oEHAP76S3qzv/hCBw+PjOUVKogYNUoKTTNm2O9WmVVgoAl+fibcuCGz6VpZrZoJWq2I3bvlMDx6W1asUCAlRUCzZkZL4AGAvn0N8PExYdkyJdLTBavARQWPoYeI6CnaeH09EnX3AQB7o3cj+mFU4RaIKBeWLpUqf7166a2Wm7+318XNPKXxpk22XdNcHg2LMHcPc3EB3nlHj9hYGerXd0a/fk4YPlyD8ePVNtsCwO7dUs22e3e9zWtKJfDKK1Jl0V5l+tVX9UhPF7BsmRJz5ypx5YoMtWoZ0a2bdQXTHJh69rSteHp6imje3ACTScDBgxnHMHdxyxp6pHMUrc45L44fl46VNdT4+4vw9pYCRa1aRpQu/WyaTL/7ToX0dOmZTe3a2Z/C2qxFC9vX/f2lMmce65RXAwemw93dhMmT1WjRQoshQzQIC9PYBF+zPXvkKFFCtFsuc9fNw4fldsf22BMSIr0nmzZZH08mA7RaEenpAu7fl87z2DH77yMANGoklUepFNG2LUPP08TQQ0T0FC2+GA4AKKX1hkk0YcnFRYVcIqKcHTokw7VrMvj7m6xaPACge3cD5HIR69YpkJzleZRt2tgPAHo9cPmyVN3I/Iyfr7/W4ZNPdNDppKC0aJESa9bYH8R9+7a0fZky9mukZctKy+/csa1Mh4YaoFSKmDtXiR9/lD7tHzdOByHLqnfvClb7ysrc7er27YwNW7QwQKGQPtXPOlHD+fNSmX18clmLtiM+XjqWvfMOCpKWPavWgY0b5fj3XxWKFxcxbVqazfXLyt55OzlJ19BQAEWuUEHE6tUpqFbNiLNn5Vi+XImFC5V2JzBITASSk4Vs7x83N6BYMRGpqQLu38/d8bO732NiBMTFyaDViihRQjpf8/to794KCpJCT+PGRhQrlrtjU94w9BARPSX30hKw5cZGOCmc8FOLKQCAJY9CENHzytyK8+AB0Lmzk9XXm29qoFQCKSkC1q61ruxVqWJC2bK2XX5WrFAgKUlAtWpGq7E7n32mxvffq/Dhh+k4fvwh7txJwpEjWZLUIw8e5L1lwNNTROvWBpw7Jz1vplUrA5o0ybmVwh57lfxixYB69Wy79e3fL8fFi3K4u5tQq1beQ4+Zxs4Yey8vab/mFoenKSZGwAcfSIX4/vu0XD2g1l63xYJ0/rwM3btrIQjAypUpuHo1CTExSejb1/Z65Of+yU6TJkZoNCIOHJBbzQA4b57089OsmcGm653991G6ls/ifXzRcV48IqKnZMXlZdCb9Ojk/wpal2sHP9dyOJ9wDqdiTyDIs6bVut1WdMTe6N252u/yrmsRXLqp5fsLCefx85HJ2B21E/fSEuDu5IGmpZtjZJ2PUalEZct6kQ9uoO68oFwdY0rIH+hbpZ/VsuiHUfh018fYF70bD9IfoIS6BFqVa4vPGoyHj4uvzT6O3j2M349PxbGYI7ibfAdapRY+zqUR5BmEP7v9AUCO8PPzMWLr0FyVqU/ga5jaarrl+22RW/D3yT9wJv404lPj4KpyRWnXsgjyqIGfW06zrPf9wW/xw+HvMKb+WIyqOzpXx3pR6fXAqlVS1SAuToa4uOxrrosXK9G7t3VFLSTEgH//VWHTJjkqVzYhJQWYNEnqsvbBBxmDzU+elOHvv1Xo0kWPDz98/MNOzRNe3LolA2AbWG7elMrp7W2/Mt6rlwHr1ikhk4n4/HP7kwaUKiUiMlLArVsyBAbaBhXzdMo+PtbHaNXKiH37FNi0SYGQECNMpozZ4YYO1duMLTF7XEsJIM1+Fx0t4O5dweq4JlNGN8K4ONsd5WbfT+L99zWIi5OhVy+9TbfAgqTTCQCsr29Cgv2T+eYb9aMH5ibbfb8yM98/5vFYWSUlAYmJApycxFy3tjg5Sa0zW7cqsH27Aq+8YsDduwJ+/10FQRARFpZxX5vHY9lriTTP8mZuDaKnhy09RERPyeILUqtOaEAfCIKAHpV7ScsvLrRZN8SvNRr5BgMAyrtVQJ/A1yxfrf3aAgA8nbzQJ/A1eGlLWbbbeWs72i5pjmWXFqOU1hud/bvAw8kTiy+Go82S5tgfvdeyrrPSxWq/fQJfg1YhPROkS8XuVsvLF/O3Kt/puFNoubAx1l9bi6ruL6Frxe5wVrpg0YUF6LC0lc1Ype03t6LTsjZYdWU5Smrc0cn/FTT2bQqtUovVV1biftp96VyL+aNP4GuQCdKfo6zlc1W5AQA6+3dFA59Glv0vODcPfdZ0x9abm1HW1Q+dK3ZBPZ+GAIBFFxY8ydtEmWzerEBCgjTmJSYmye7X9etJ0GpF7Nolt3QJMzN3+TFP4/zdd2pERsrQqZN1ZfnKFen9dnd/srEoK1faflZrMMDS6pR5kHhm5lYYNzegcmX7FeSGDaVtly+3PUZcnIDt2xUQBBH161sfI2s3p5kzlThyRI46dYx4773sA515nFNaWvaV3Ro1pGPt3GndZLB9u9RqBQDnztlW5bRa6bqmp+e/Ij1jhhJbtijg52fCd9893ZnFrl61Le+pU/bH6Fy5Iq37JPdQQoLM5loCwPLlUjKtW9do1Tqj1YowGgUYs2kYzLjfpY3GjFHjwQMBQ4borSZxqFlT2sGuXdb31r17GffNuXP5n9yBcsaWHiKip+Bq4hUcvnsQ7hp3hPi1ASCFn1+O/oBllxZjfKOvIZdl/JEb8fIolHX1w77oPWjg08iqRePwnYPYHLkRlUsEWC1P1idj6Ka3kWpIxcSmP+CtoHcsr00/MQ3j9vwP7256C/v7HYNGoYG7k7vV9oA0uUJKUjK+Cv4Wvi6l7Z6LwWTAG+v7IUmfhPmdFqF1uXYAAL1RjxFbh2LppUUYs+sjzOmQETb+PTMTRtGIX1r+hteqDrDan1E0wNPNDfcSUtDQpxEa+jTCistLoTPqbMp35L86SEp/gC8aT4CfWznL8hmn/wIALH5lJZqWaW61jc5o/5N8ejzzwyztTRhgptUC7dsbsGyZEkuXKiwzcgEZXX7275dj/nwlpk9XoWJFE6ZMsa4sV6okVQiXLlWib189ate2DiKnTslQoYLJEgzMDhxQYP58Bd5/P2PZ99+rcOuWDNWqGS3BJbPr1wXLM4Lu3xewfr3CMvFBZm++mY4lSxT4+28V2rc3WLqlpacD//ufGqmpAjp31ttMGlC1qgllykjd+ubPV+KLL9Tw8DDhn39SLTPf2VOlilTWs2dlMJnsdwfr1cuA9euVmDxZjZYtjfDyEqHTZbSeKZUiIiKUGDpUb7W9+fqeOZO/z7YvXJDhq6/UkMmkcTyuT3km419/VSM4OMXyvt+6JeDXX6VxWCkp1oGoUiUTLl+WY8IEFSZN0lnN+JecLAXrGjVsA+748Wo0z/Qr48YNwTLWa9Ag6/u+UiUTTp6U48wZ+/vK/LyeadNMWLtWiQYNDBg3zvp3UJcuBnz9tYglSxR49VW55T6dPFm6r1Qq6WcmLk6wtApRwWNLDxHRU7DkgtSa07VSDyhkUs0noGQganjWQkzKXey4tS3fx1h1eTliU2NQt1R9q8ADAO/WHI6anrURnRyFNVdX5us4666tReSD6+hSsbsl8ACAUq7EN00nQavQYsO1CEQl3bK8JhekQGduwclMIVNYBb68kD/ar/k4manl9mcAexGk6Y2ISkxF3EMdnvQxfImJUpcpmUx8bBcmcyjKOoubVivNRqXXCxg5UoPixUXMnZtiU1kOCjIhNFSPBw8EtGvnjPbttXjnHQ3693dCkyZatGrlbLdb0xtvpGPkSA0aNACGDNGgWTMtfvlFDVdXEVOn2m+FmDhRjfR0wTIzVni4/f5mL79swpgx6UhKEtCxoxahoU4YMkSDBg2csWKFEv7+Jnz3nf1AbR6PMXKkBoIAzJyZ9tgZ1SpWFFGnjhHnzskREqLFsGEanD1r/fPSubMBrVoZEBkpPRfm7bc1aNHCGceOyREaqkenTgYcOiRHt25OlrEk5u20WhEzZigRGuqEsLDcP3gzs6+/lirlHh4i5s9XIixMY/eroNy5I6BBA2e8/roGvXo5oWlTZ6jVIpydRZw8KUO/fk44dEi6Rh9/nA4XFxH//adC7drOeO01J7zzjgZduzohKMgFf/9t+3CkOnWMkMmASpWAN97QYMAAJzRv7ozbt2UIDbV9MKx5tsLevZ3w9tsaq2sMAOXLi6hUSZrG/auvNPDzM2HmzDSbsOvjI2L06HTo9QJ69nRCv35OeOUVJ/zzjwr+/iaMHatDUpKAzp212c5gSPnH0ENE9BQsvSTN0tYrsK/V8l4BfQBkdH3Lj/23pa5rPQN623099NHy/dH78nWcPVE7AQDdK4favFZS447mZUMgQsSBOxnHeafGe9AqtHh/63vosDQE7256C2Fb3sWlexfzVRaz4bVHQiFToOeqV9B1RQe8t3kwwra8i/jU+ALZf1ETn5yOv/fewOuzT2HIv+fx5uyz+GTleey/npDrfaxcqYROJ6BhQ6PNuJWsWrY0onhxEWfOyG0q6uYuP1qtiDlzUlGpkv19TZuWhl9/TUWTJgZcuybD6tUKHDwoh0oFDB6cjpIlbbfr2tWAefNSIZdLYyGio2Vo316PiIgUy2xmmZ08KcOKFQq4uor49dc01KljxNattt3yzD74IB3z56egUSMjjh+XY+1aBVQqYPhwHdavT7YMOs/KfM5yuYjffkuz2+Jkz6xZqejcWY9bt2RYvFhpM+ZDEKR1Pv5Yh5IlRaxfr8Dt2wK6dNHjp5/SMHGiDh076nHsmNxqum5vbxHz5qWidm0TDhyQY+HC3D1/JquHj57JGRMjw8KFymy/Csr06amoV8+IXbsUOHJEjmbNDFi8WDp/jUZqlTHfF0FBJmzdmozXX0+Hm5s0rfm6dQrcuCFDcLARnTrZtlaq1SKWL0/Ba68BR47IsW2bHL6+JnzxRZrd0Dx4sB4ffSQde80ahd0p0Vu3lt5rDw8TwsNT4Olp/x4ZMSIdv/+eiqAgE/bskePoUTlq1TIiPDwFgwfrMXy4DvHxQrYzGFL+8coSERWwQ3cO4FriVfgXq4g6pepZvda9ci98sXcs1l1bi2R9MpyVznk+zp3k2wAAP1c/u6+XdS33aL3oPB8DAKIfbV/Gpazd183Hv5N8x7Ksvk8DLOmyCu9uegtH7h7GkbuHAQB9q/RDQMmAfJUHAF6p2BUq+Xx8sPU97IveY1n+cb1P4e7knu/9FyWxD3X438qLuHxZBuM1P8gfuEFU6rHfJwZnIm/gnZY6dA3yeex+Bg7UY+DA7Lu1ZaZSARcvPrT72ttv6/H224/fj0wGvPqqAa+++mQD49u2NeK114C4uId4XGPWV1+pIYoChg/Xwd1dRN++ehw5osHChUqMGGF/vE2bNka0aZP6RGVq104aA/WkvL1FzJyZ8zgZjUZq1fj4Y9vyajQiZs+2v32TJkasX5/yxGXKbMWKJ7sOed3GzNvb/vm8957eqhulWfnyIiZP1gHIfZdWNzfgt9+AL79Mfuz9I5MBo0enY/To7MdmffWVDl99lbvjh4YaEBpq/34fNy4d48Y9flIPyjuGHiKiAmZuxXmQnojOy9ravK6UKZFiSMbaq6vQO/DVp1YOoYCmcErSPXjibfZE7cLAda+ipmctzGg3B4Elq0KjKLhuMMsvLUHYlnfRvkInfFzvU1Qo5g+V3LY7y4tg5r6buHReAfmRICj0ma5BgjuSEm9ipjwStcsUh18Jp8IrZCHYtk2OnTsVKFXKhCFDpMpk9+56fP65GjNmKPHaa3qOnyB6gbB7GxFRAdIb9Vh1ZTkAIC41Dgfv7Lf5SjNKn2Tmt4ubt7P06f3Nhzftvn7zwY1H69lOJ20m2Blzk5X4aArZW9kdJ+nmo+N4W5b9b9dopBpSMLv9fNT0ql2ggcckmjB65yiU0JTE9DYzEFiySo6Bp6DC3/PobpIOey8mAVfKQqa3vQaqqNJIilNhy8XYQihd4RFFaTwKAHz0UTq0Wmm5mxvQp48et2/L0LixM/r3d7I8RJSIHBtbeoiICtDmyI1ISEtALc/a2Nhrh911UvQpqDarInZF7cDd5DsolSks2JNdpb2hT2MsOD8Pyy8twZvVB9u8vuTR1NgNfRvZvGbmopSmSTIHsZysvLwM7cp3sFp2Ly0B229uhQAB9b0bWpZfTbwMjdwJLqr8TfckwPbcY1Njkai7D+8SVSyTROTEPC13ugPO6nY9IQUpKYAioaTd1wVRBv3tErh4+8lb64qypUsVOH1ajooVTejXz7pb1FdfSWM0Vq1SYMsWOYYOddxQnFlEhALr1uWu2tevnz7X45KIigp+vEFElEWa3ohUvfGJZ78CMlpvuj96Jo89WqUW7St0gEk0YemlxY/dp1outZLcS7MelN6lUnd4OnnhwO19mHNmltVrf5/8A8djj8HH2Red/btmu+8qJasAAE7HnnxsOVZeXoatkZst3xtMBny+51OkGJLRtnx7lHHNGPNTsXhlPNQn4ftD38Josq483UtLwJmYM489HgCoFbbn7qHxQAl1CVy4dx5zz8622eb2w2hcT7xm+d78gNYz8adzdcyiRCZIg91FWfYPZxRlJsjlRbtiP3VqGmJikhAcnLuKeGioATExSdi3L9lmJi0nJ+Drr3U4cSIZt28/zPU+i7rTp3OejCDz1/XrT+d+8fMTEROTZDPuZ8WKVMTEJMHP7+l0N8zuuPRiYUsPEREAkyhi5+V4rD0XhUtx9wAA/iWLo3O10mhR2QOyXHSRStTdx6Yb6yETZOhWqUeO63av3AvLLi3B4gvheK9WWI7rlnMrB7kgx7mEs+i7pgdCA/ogNKAPnJXO+KPNPxgQ0Qcf7Xgfc8/ORsXiFXHp3iWcijsBZ6UL/mwzM8euZb0DX8OyS0sePW9nMWp41sSHdT+xu+6Aam/g1TU90cg3GKW0pXAk5ggiH1yHt7MPJjb9wWrdcY2+xICIvvjp8PdYeP4/BHnUgEquxp3k2zgZexyfNPkEw6t/mON5A4B/sYo4HXcSQza9iSalm+OHFr9ALpPj80Zf4cPtI/Dh9hH47divqFKyGgRBwO2HUTgRexw/t5yG8sUqAACalWmBUlpvbLi+Dp2WtUGFYv74svG3DjHhQYCnC9xcBcR6xEF127YboygzQl0mATXLehRC6eh58rgB+USOji09RPTCM4kipu+5hu+2HcL+u+txXzEP9xVzcTh2Hb7ffghTdlyBKRetPisvL4fOqENDn8bwccl+HA0AtCzbCsXVxXEm/hTOxufc6uGqcsPnjb6Cu8YdWyM341SmVplmZVpgQ+h29KgciuiHUVh9ZSViUu4iNKAPNoXuQEPfxjnuO8SvNX5uMQ0+Lr7YeH0dNt/YkO2679UagV9DfseD9AdYd20tHqY/QK+Avljfc6tVK4+03zZYH7oNvQL6QibIsCVyEzbdWI87ybfRtnwHtPZvnWO5zEbW+RhVS1bD9QfXsObqCsvy/tVex7Kua9DZvytSDCnYdGM9tkVuwT3dPfSo3Au1vF62rOukcMKCzkvRpHQznI47iUUXFiBZb3/msaKmmJMSbaoXhyLgJozO1uckCiboKl2Bh6cJLSsz9BDRi00Q89J/gxAXl/TYqQ6fZ4IAeHi4FvnzoMLhaPfPjsvxmLj1INI0i6F0umL1miGtPFSpffBR83poE+hZSCUsXN1WdMTe6N043P8U/NzK5Xt/jnb/FLaUdCO+2XAJh86nIfWGO2SJ0pTV8rIxcPdNxycdyqGeX4nCLmaB4f1D+cH7J2fm6+OI2L2NiF54EWdvIUU4BXWWwAMACs11pKQdQ8Q5H7QO8HDomcCoaNKq5BjXIQA7Ksdhw5l4RMbHQaOUo1lgMbStUh7lSmoLu4hERIWOoYeIXmg6gwkX4+5Dpr6U7Tpy9RVcjb+P5HQjXNT8tUnPH7VChrZVvNC2ildhF4WI6LnEMT1EREREROTQ+JElEb3Q1AoZKnsUx767laB0umh3HaOuIip4FIezSv6MS/d8WNEtorCLQERElC9s6SGiF16naqXhLNaAPs3f5jVDWjloxdroVK0Mx/MQEREVUWzpIaIXXtOK7jhzJxCrzvZGctpJyNWXAIgw6SpBK9ZE+4AAtArglL9ERERFFUMPEb3wZIKAd4MroGopN0Sc88WluAYAAH/P4uhUrTRa5vLhpERERPR8YughIoIUfFpW9kDLyh5I1RshioCTUsYubURERA6AoYeIKAsn5Ys5YQEREZGj4kQGRERERETk0Bh6iIiIiIjIoTH0EBERERGRQ2PoISIiIiIih8bQQ0REREREDo2hh4iIiIiIHBpDDxEREREROTSGHiIiIiIicmgMPURERERE5NAYeoiIiIiIyKEx9BARERERkUNj6CEiIiIiIofG0ENERERERA7NoULP1KlTERgYaPXVvn17y+s6nQ5ffvklGjRogNq1ayMsLAxxcXGFWGIiIiIiInraFIVdgIJWuXJlzJo1y/K9XC63/P/bb7/Fjh078Msvv8DV1RVff/01hg8fjvDw8MIoKhERERERPQMOF3rkcjk8PT1tliclJWHp0qX44Ycf0KhRIwBSCOrYsSOOHz+OWrVqPeOSEhERERHRs+BwoefGjRto0qQJ1Go1atWqhQ8//BC+vr44ffo09Ho9GjdubFm3YsWK8PX1zVPoEYQCLvgzZi5/UT8PKhy8fyg/eP9QfvD+ofzg/ZMzR74uDhV6atSogYkTJ6JChQqIjY3Fb7/9hn79+mH16tWIi4uDUqmEm5ub1Tbu7u6IjY194mO5u7sWVLELlaOcBxUO3j+UH7x/KD94/1B+8P558ThU6GnevLnl/1WqVEHNmjXRsmVLrFu3DhqNpkCPFR+fBFEs0F0+U4Ig/cAX9fOgwsH7h/KD9w/lB+8fyg/ePzkzXx9H5FChJys3NzeUL18ekZGRaNy4MfR6PR48eGDV2hMfH293DNDjiCIc4ofFUc6DCgfvH8oP3j+UH7x/KD94/7x4HGrK6qySk5Nx8+ZNeHp6onr16lAqldi3b5/l9atXryI6OpqTGBAREREROTCHaumZNGkSWrZsCV9fX8TExGDq1KmQyWTo3LkzXF1d0bNnT3z33XcoVqwYXFxcMGHCBNSuXZuhh4iIiIjIgTlU6Llz5w5GjRqF+/fvo2TJkqhTpw4WLVqEkiVLAgD+97//QSaTYcSIEUhPT0eTJk0wfvz4Qi41ERERERE9TYIoskdjXsTFFe0BcIIAeHi4FvnzoMLB+4fyg/cP5QfvH8oP3j85M18fR+TQY3qIiIiIiIgYeoiIiIiIyKEx9BARERERkUNj6CEiIiIiIofG0ENERERERA6NoYeIiIiIiBwaQw8RERERETk0hh4iIiIiInJoDD1EREREROTQGHqIiIiIiMihMfQQEREREZFDY+ghIiIiKiA6HVC5sgu8vFzRo4dTYRcHABAWpoG3twteftkZv/6qgigWdomKnm7dnODl5Zqrrzp1nO1u7+vrgoYNnTFvnvKplnXePCVCQrQoV84F1ao54733NLh5U3iqxywKFIVdACIiIiJHsXGjAomJUgVz7145oqMF+PoWbspo0MCItDSpbN98o4a3twl9+hgKtUxFVcuWBnh52X8/k5OBNWvsB5qQECPc3ERs3qzAqFEaVKxoQqNGxgIv35gxasycqYKPjwlt2xoQEyNg6VIFtmxRYO3aZFSq9OImXoYeIiIiogKyeLFUtSpVyoS7d2VYskSJESPSC7VM/fvr0b+/HmvXKjBokBNmzlQx9OTRiBHpCA62H1YiI4VsQ4/5Hpg+XYlx4zSYMUNZ4KFn82Y5Zs5UoXVrA2bMSIXTo4bGDRvkeOMNJ7z3nhM2bkwp0GMWJezeRkRERFQA7t0DtmxRwMlJxE8/pQEAlix5fj5f7tjRgHLlTDh2TI6LF1kFLAwDBujh7CxiwwYFEhMLdt8//6yGQiHi55/TLIEHANq1M6JvXz2OH5dj+3Z5wR60COEdT0RERFQAVqxQQq8X0K6dAa1bG+HnZ8L583KcOmVb3RoxQgMvL1fMmmXdMhAXJyAw0AUNGjgjLc32GOfPyzBokAYBAS7w93dBx45abN4sVWT37ZPDy8sVf/1lv7VBEIDevfUAgEWLcg5jCQnAF1+o0aCBM8qWdUFAgAv69HHCtm3WleaxY9UoX94Fb7yhwf379ve1erUCXl6uePNNjWWZeYzM/v22lfDwcGn9kSPVVsvv3BHQoIEzfH1dsHq1bfk3bpSjc2cnlC8vjWUZOFBjCXc//KCCl5crTp8u3KqvszPwyisG6HQCVqzIfmxPnTrO8PJyBSCN0WneXAs/P+m8Ro5U49496/Xv3hVw+LAM9eoZUaqUbRe2jh2llr21azOuW3bX+eRJQKUCOnSwX7Zdu4AuXYBSpQCFQrqvsn7VqpWxfvny0jJ7tm+XXnvjDdvXDAZg6lSgTh3AxUX6ql8f+OMPwJiHRjKGHiIiIqICsHixVIkNDdVDEIAePfRWyzNr3VqqhG7ZYl159/AQ8eGHOly7JsOcOdbb7dkjR/v2Wqxbp0DNmkYEBxtx8qQMAwY4YfduOVatkvbVoUP2Xdfi46Xa55IlSphM9te5fVtAu3bO+P13FfR6aX9BQUbs3ClHnz5aTJ+eUa7atY2oVMmEiAglPv1UY3d/c+dK6w8YoM+2XI9z966A7t21uHlTwJ9/puGVV6zPcdo0Jfr31+LsWTlatjSgYkUT1q9XokcPJ8TECFi9WgE/PxOqV8/mpJ8h83uwcOHjJzT48ks1xoxRo1QpESEh0jnPn6/Ca69prd6/kydlEEUBNWrYPz/z8pMnH9/SU6MG8N57wPr1UijJbMUKoGVLYPVqwMMD6NULaNo0+1CTV0Yj0LUrMGIEcPky0KYN0Lo1cP68VLZevZDt/Zsdhh4iIiKifLp6VcDhw3K4u5sQEiJ9DB0aKlVSly1T2Hwy3aKFAQqFiN275TYtOqGhesjlIv79N6NSnJ4ODB2qQUqKgN9/T8PixamYOzcV4eGpMJmAqVNVWLNGgRo1jChb1v5g9Xv3gPBwaZ/R0TLs3Gm/AvzxxxrcuCFDjx567N+fjL/+SsPSpalYvjwVWq2IL79UW1qvevY0YN26FAQGGrF6tcKmtScyUsCOHXL4+ZnQokXexrBIgccJN24ImD7dNvCcPi3DhAlqODuL2LQpGbNmpWH16lR8/LEOMTEyfPONGufOyS2tHYXp4kWZpWXu8GE5rl7NOS0sXqzA9u3JWLQoFbNnp2H37mRUrGjCkSPWXdUiI6X3Y98+OcLCNDZfEyaoH62Xu3QyaJD078yZGct0OmDIECmQfP45cOYMsGABsHMnsGYNIJcDrq7AzZvAwYO5vSL2/fILEBEBvPQScPEisHy5FLguXAACA6Xvf//9yfbJ0ENERESUT0uWSGGia1cDFI8abwICTKhRw4iYGBl27LAOGG5uQP36RqSkCNi71/q1kiUBf38TLl2SIypKqqRu2aLAnTsyVKpkRI8eGZX3Jk2MqFvXhG3bFLh7V5ZjxX7WLBVSUgT4+kofkS9aZNvScP26gI0bFXB2FjFxYhpUqozXGjY0YuBAPYxGwapbnlIJ9OunR3q6gNWrrfc5f74Soiigf399nloDYmMF9OzphGvXZPjjjzR06WJ7fgsXKmEyCejRQ4+KFTMC39Ch6VCrRSxYIJXpeQg9f/whXY+c3oPMxoxJt5pxrWRJYNAgaVKEAwcy7pukJOninjwpx8KFSrtfmdd7nKAgQKMB9uzJWLZuHRATA1SoAIwfb71+x45Az55AUhLw33+wum/yYsoU6d+ffpK60Zn5+ACTJ0v///XXJ9snQw8RERFRPi1dKlUqe/Wy7sJl/t5eF7dWraSWj02bbMenuLhI/8bESJXU48elKlv79rYV9+DgjGXZVex1OmDGDCXkchFz5qRCoxEREaHAw4fW65kr0iEhBpQoYbsf8/lkHYvTrZsBMpmI5cszzsVoBBYsUEKhEPHqq0/etS0+Xgo8Fy/KMXx4Orp2tX9ux45J1ybrubu4ZHTr8vAwoX79gp8i+knExAhYskQJNzcRs2alApDui5yem9Sihe05+/tL5xQbaxtgRo3SISYmyeYrOjrpicoqkwHFiwO3b2csO3RI+rdXL6lVJ6vXXpP+3br1iQ5lIzJS+vL0BNq2tX29c2epbJcvA3fu5H6/DD1ERERE+XDokAzXrsng729CnTrWAw26dzdALhexbp0CycnW27VpI1VoN2+2Dj16PXD5slRFMz/jJy5OquCWKWNbQw4Kko7p729ClSr2BzosWaJEbKwMnTsbUKOGCR07GpCSImDNGutj370rHTe7LnJ+ftL+b9+2rkJ6e4to3NiIvXvluHNHKuvGjVLrVNu2BruD6x9n/XolLl+WQSYTMX++0nINsoqPl8pi/9pIQadDBwNkhVzrnTlTCZ1OwMCB6ahd24S6dY24eVNm09KXmb1nPJlnZtPrM66Hq6u0XkqK/WtkvvfM6z2OKAIPHliPm4mLk/4tX97+Nv7+0r+3buXqENmKjpb+LVfO/uuCkPFaVFTu98vQQ0RERJQP5lacBw+Azp2drL7efFMDpVKqjGaeOQsAqlQxoWxZE27ckOHSpYwq2YoVCiQlCahWzXYmLicn20qrp6e0zDzQPStRhGXygbAwqWtUnz5Sy0tuBtNnllMXte7dDTCZBKxYIZ3nvHnSvgcOzNsEBkqliL/+SsOwYemIi5Nh1Ch1jutrNLbXxvwg0ZYtC7eVJyUFmD1bCbVaxJAh0vXo2/fx70Fug1q5clI6MbcMZhUdLe3Izy93oScyUiqzr2/GMnPrjiGbXoJ6vfV6T1Neukoy9BARERHlkV4Py6xpcXEyHDyosPlKS5NqaPa6uJmDyqZNUk0xJQWYNEmq3H/wQcZDTc3B5s4d26rb+vXS8c2zgmW1ebMcFy7I0bSpwdLdq3lzI3x9Tdi7V46bNzO2K1VKev3WLfv7Mg+Y9/GxbVHq3FkPpVLE8uVKREUJ2Lo1+wkMclNp7dHDgM6dDfjkk3RUrWrE+vVKzJ9vew09PKSy5OXaPCsLFiiRkCBDaKjeEmS7ddPDyUnE6tUKpOTzmaFBQSYIgogzZ+xX7U+ckJbXqJHxXpjHnplMttdm7Vrp34YNM5aVLSv9e/26/TKYl5cubf91e1Owm1uPMjMHrRs37O8n82vZHcsehh4iIiKiPNq8WYGEBBlq1TLaHUsRE5OE69eToNWK2LVLjrt3rSuY5i5u5qmrv/tOjchIGTp10qNbt4yP1M1hZdcu64/RDQZYxtGcO2e/Wvf779Ko8uHDM0KUTCY9s0cUBasw1qCBVCneutX+wzPNEzY0bGgbZEqUkFpUjh2T45tv1DAaBfTrp7fbWqHVSv+mp9u+ZqZQSOFApQKmTUuDUini88/VuHHD+hrWrGn/2ly8KMPx49Ky7K7Ns2AyAX/+qYIgiBg2LOOE3dykbnfJybatgE+qVCkR9eoZceGC3G7wMb/HnTpl3FPm7oBZW4cMhoyZ0V5/PWN58+bmfdlv7Zk713q9rC5dsl127JjtMj8/6Ss2Ftiyxfb1tWulmQgrVQK8ve0fyx6GHiIiIqI8WrxYqqx27559Fy6tVpqAwGQSsHSpdeW2SRMjNBoR+/fLMX++EtOnq1CxoglTplh/LN6ypQHu7ibs3q3AypUZ+/j7byVu35ZBpRJx4YLM8jBOsxMnZNizR4Hq1Y02XbzM3asyzyBWvryINm0MePhQwGefaSxdlgBp7NLs2dJkCIMG2T9f83VYskSawOC11+yvV7GiFFSya5nIKijIhFGj0vHwoYBhwzRWY0169pSO8ddfKly+LFXgTSZgwgQp7KlUIjZvVthtaXgW1q5V4Pp1Gdq3N1jNxAbkrotbbplbBj/4QGMVWOfMUWLXLunZTplb3apVM8LFRcSePXJcvy5dN1EERo+WpqNu0wZo1y5jP40bA3XrSlNSjx0LqwkYli4FVq0CihXLmO46qy++sA65p05lhKus493CwqR/R42Swo/ZnTvAxx9L/3///cddEWsMPURERM+I+Sn0T/KV2+dq0LOXmCjNvCaTiVatMvaYw0DWLm5aLdCokRF6vYCRIzUoXlzE3LkpcHW13t7JCfjmGx0EQcQ772jQs6cT+vRxwvjxGpQsacIPP6RBFAWEhjohLCzjIaH2WnnM/P2l1oGrV2U4dCijSvjDD2nw8zNh0SIlGjZ0xpAh0vG6dNEiJUXAuHE6y+QJWbVvb4BWK9WG27TJfgID6QGuIr7+Wo3+/Z0wfnzO43UA4P3301G7thEHDyowdWrGnMi1a5vw+uvpuH9fQOvWznjjDQ3atNFi/XolGjUy4J130h+1nmnx66/5nEs5D/74I/v3oFkzI0qXNmH3bjlu387fz3rr1ka8+WY6TpyQo2FDZ7z9tgYdO2rx0UcalCgh4o8/Uq3Wd3MDhg1LR1qadN0GDHBC8+Za/Pyz9Cyc//6zPca8edIU0pMmAdWqSTO2BQcDoaHSWJ7ZswEvL/vlO3wYCAiQ1m3ZEqhXT2qtAaTn/HTsKAUqABg5EujQATh5EqhcGejRA+jeXdr+3DmgWzfpIaVPgqGHiIjoGQkJMaJPH73VV/36UmXZ09Nk81qfPno4OxdyoR3YQ50BEWfvYsL6Sxi35gJmHYjE9YTcD65YuVKajathQyN8fHIeIN6ypRHFi4s4c0aOs2etq1/mLm5arTSddNbWALMePQxYsCAVjRtLXch27ZIjIMCI8PBU9O1rwJdfpsFgyGg1uHVLwOrVCvj5mbKd7tk8lXTmlgYfHxEbNyZj6NB0yOVSS8XJk3I0bWrEwoUpGDo0+1YtZ2egeXPpWL17Zx8Ea9Qw4e+/01C5sgnbtsltZpGzR6GQurlpNCImT1ZZHpAKAJMm6fDNN2koX96ErVsVuHRJhqZNDZg1KxUffZSO115Lx5UrMmzd+gxG2Wdy8KAMhw/L0aCBAfXq2QZFczdDk0mwO+brSX33nQ4//ZQGb28RGzcqcP26gNBQPTZvTrZ7X40alY6xY3UoXlzEtm1ypKQIGD1amp7aw8N2/4GBwNGjwLvvSuPPliyRuq316AHs2yeFkexs2CCFnHXrgBMngFdfBTZtklpsBEF6BpD5951cLrUc/fqrNCvchg3Axo3S8X/7TTruk87GJ4hiTrODU3bi4pJynFf9eScIgIeHa5E/DyocvH8oP3j/WAsPV2DECCc0bmzAihWpj9/gBVdQ98+Fuw/x7bqriLorQnerBESDHMpSiXD10OHVxl7oW9sXQl6miCKEhWmwcKESy5enIDi4cGdNy8oRfv/s2SNH9+5a9Omjx9SpBdtnz3x9ClL58tLEA4V9vdnSQ0QvrNx0NapThx+zZyc8XAEvL1d06+Zk9/vnUc+e0nueUxeX0aPV8PJyxbBhmmzXoaItISUdX0dcwbWTLhB31IHmfBU4Xa4M+Z46SDxYAXN2xGDzRTvTShFRkcXQQ0QvvJYtDXa7FfXpo0fnzjn303+RlS8vok8fPUJCjHa/fx5Nnix1jfnxRxWuXrX9FP/AATn+/VcJd3cTvvpKVwglzNmFCzK8+64G1as7o3RpF9So4YxhwzSWwduZhYVp4OXlahlobzZ7thJeXq6YMiX74BcRoUCLFlqUK+eC0qVdEBysxfTpSqvB42bmDw/277fuNrRsmQI+PtK2mWcsy65cZgaD9EyZpk21KFvWBS+/7IxPP1Xj/n3p9e7dneDpCRjzcZttuRiH27cFqM5WgUyfcR0ECFDd9sXDi15YdvQuTIX90TQRFZj8zY9HROQARoxIf+66YBQFDRsaraatzfr988jfX8RHH6VjwgQ1Pv5Yg6VLM7qTpacDH32khigK+OqrNLi7P18V3p075RgwwAmpqQKCgowIDjbi0iUZFi9WIiJCgQULUnN1/V9/XY+5c5X45RcVBg1Ktxkw/+OPKkyapEaxYiJCQgzQ6wXs2iXHuHEanDkjz1V3mpUrFRg2TIMKFUxYtiw128HsWZlMwKBBTtiwQYHSpU1o396AM2dkmDFDhYsXZfjjjzTs2yfHwIFSn/+8ZpI9l+4j/YYnNEb71SDF3VKIjInBtfgUVPRga6+jWbECCA/PXUtuv3765/73GuUOQw8REb1Q3nsvHcuWKbBrlwLh4Qr07Su15v3yiwoXLsjRooUBvXo9Xy18ycnA0KEapKYKmDgxDW+9lTGQfPp0JcaN0+DddzXYvz8ZmsfU5QQB6NNHj7FjNVi9WoHXXss415075Zg0SY1q1YxYujTVEvyuXRPQtasWCxcq0aGDAR07Zn99Vq9WYOhQKfAsX577wAMAs2YpsWGDAkFBRqxenQKtVmr56dvXCTt3KizPfunePde7tCtFZ4Kgz37QuEyvhNEEpBnsz1BGOZs6Na3Ax5oUpOPHcz9FdHCwweoBnc+D4GDpmVBFRXYPM33W2L2NiCiXIiMFeHm5on59609+U1KAevWcUb26Mx48sL9tYiIwYoQG1ao5w8fHBZUru+D11zW4dMn+r+FvvlHBy8sV336b8/Sqo0ZJ40/mzs34A24uZ26+so5ZelzXoh49nFC1qrNV16KPPpLKEB5u+zmaeZzPyJHW09FGRwP16zvD19cFq1dnbGcue7duTkhNBb7+WoWXX3ZGmTIuqF/fGb/8orLbxepJKBTAzz+nQSYTMX68BrGxAi5elGHKFBW0WhGTJ9uvrM2ZI3ULe/fdnFPFzz8//r27cEGGnj2dULGiC95/X9rf1asyuw+DBIAFCxSIjZVBqRTx5ZdqBAdr8ccfSoii1HIjk4mIjpblagYsAKhVS3oDDx607pL2119Smb/4QmfV0lWhgohRo6TpdmfMyL6yuGaNAkOGaFCunPjEgQcA5s+X9j1sWLrl4ZUKRcZUvwsWKKHVimjT5ol2a6OsuxpCiWx+WAEYXZOgVgGlXJ799Mb09H3xBRAba/9Bslm/zB+KUNHH0ENElE9aLfDllzrExMgwfbptJSkqSkBIiDPCw5UoW1ZEly4GeHubsG6dEh06aHH6tPWvYr0+o/LXr1/2U8MCQJ8+0h/kWbMyKqLOztIn+d7eUjrIOmbJ/FDA+vUNVmOWzF2Lxo3T4OFDAe3bG6DRiJgxQ4W33nJCTIyAffvkaNvWCHk+Zn29c0dAy5bAzZsC/vwzDa+8YlupSE8X0Lu3E+bNU6FmTakr1927Ar79Vo2vvnr88zwep3ZtEwYP1uPePQFjx6oxapQa6ekCRo/WoVw5+xV18zXu3/9x74n0/JF585RWD3Y027pVjrZttdi/X46XXzZaAsidOzJ07apFUpYPcKXzloKRp6f04Mh79wSMH6/Bjz+qsHmzAiaTNGYm67ia7JQsKT7at/W9t2ePHCVKiFYPMDTr0UM6mcOH5XaD5/r1UuAxGoG5c1OeOPDodMC5czIIgoj27a3viQYNjJDLpf21amV4bGvW47Su6g6n0okwuNmmTFFmhFguCo0qu8LDJf/3GhE9Hxh6iIgKQLt20tPSFyyw/RR86FANbt6U4ccf07BhQwr+/DMNO3emICxMhwcPBIwYYV2D27VLjrg4GapXN2ZbATerX98IDw8TTp+W48oVqeLr7i5i6tQ0VK0q1UxHjEi3dDeZOjUNwcFShbJ/fz2+/DJjsH7mrkV79iTj77+lcjZrZsCuXRldizp2zLnSn5O7dwV07+6Eq1eB6dPtBx5AqljL5cDhww8xa1YaFi5MxZo1KVAqRcyYocy2Re1JjBmjQ9myJixfrsTBgwrUqGHEkCH2z+3yZQFnzsjh7m5Co0Y59+/39RVRs6YJcXEy7NplHULu3wcGD3aCXA5ERKRg8eJUy5PtixUTcfas1L0ss08/VePhQ+m9nTw5DTNmpGH79hR4eprw558qLFkiPRwTkIJTbiQmZjy1PmMZkJwsoEwZ+01pbm5SGVNTBUvLX2a//66CTAaIooApU548LNy/L8BoFODuLlpaecycnIDKlaVydeqU/0/e6/mVQNOXXCCrew7pvlEwKdMhCiYYSiQgvdZplKmchlfr+ub7OET0/GDoIaIXXvfu2my7f40dm7vKm1wOVK9uQlSUDFFRGTNVnTghw/79CtSrZ8SAARkVakEAxoxJh6+vFFgyf0K/bZvURalZs8cPnhUEoHFjab0tW/I3TDO3XYuaN8/boN7YWAE9ezrh2jUZ5s9Htg9LBACZTMQPP6RZDbIPCjKhdWsDdDoBx47l/wGDzs7AwIEZ78mECbpsW7C2bpWubePGuWvlMk+MYX4vzf77T4mkJAFvv52OmjWtw0WFCiYIgoj585VIe9TD7t49YN06BQDr8OvlJaJfPz0SEwWsW6dElSpP1ufP3K3S2ztjvw8e5O+ZNIGBRuzcmYzy5U0ID1di7dq83Y/ZteJ4ekplDQnJf+hRyAR81Koi+rUsjlLBNyAPOQS03geXpufQNNiIb7pWRrmS2sfviIiKDE5kQEQvvJYtDfDyst+iUrt27iv4GV2GBJQuLf1/926phty9u20LglIJvPKKAX/+qcKBA3LLDEEnT0oV0pdesj52bKyAK1dkqFjRZKkAAkC1aiasWgWcPCkHkLdWmNx0LTIahTx3LYqPlwLPxYtyvP++Dr17qxGXw2NQypQR7T493N9fWhYbm/+HRt6/D/z5Z0bL3KpVimxnaZKuLfDSS9bh4sED4OxZOcqUMaFMmczvifV7abZnj/Rnt1s324q7VivipZekEHzqlAz16plw8qT8UeuHCfHxAm7elAGQ9t24sRG//CJt6+9vwtmzckuXRgBQKqXy2OuKtnmzVI46dTLO1zwTWubQnllSktRC5OQkolgx29e/+06HChVETJmShm7dnPDRR2rUq2e0+dnK7nmfxYuLkMtFxMYKMBphFS7v35emEweke6lyZfv7eBJqhQxvNvRDr1q+OHsnCQaTiNLFNSjPsEPkkNjSQ0QvvKzdvzJ/9eyZ+0+V7XUZun1b+jWbXZehsmWl5XfuZNQEY2KkbTIHG0AaM9GlixYbNlh/XuXhIT7aLu9BILddi3KatSsn69crcfmyDDKZiP/+UyI2Nuf1fX3tXy8nJ+lcDQUwtviLL9SIi5OhRw89ihcXMXOmEseO2f+zaL62Wd+To0fl6NJFazWRBJD5PbHeX3S0tB/z+55Vxv0gbRcfb73+8uUZ731QkLRMEERcuyatnzm0mUNY1jJERgrYsEEBrVbEK6/YhuSEBBl27rRtzlq+XDrHunXtt3aZlzVsaMS77+oRHy/DyJG2CVmrlcqVnm59v6rVQJUqJuh0gs0ECytWKC3rnz2b/1a+zFw1CjQoXwLB/iUZeIgcGEMPEVEBuXy5YLoMCYL9VqeHD6V/k5Ozri/9KyuA3+hPq2uRUinir7/SMGxYOmJjZRg8OOf1C+JccrJ7txz//aeCj48Jkyen4fPPdTCZBHz4ocZuoMqudcI81iY52XqFjPfE+r1MSspbMPX3F+HpacKBAwrMmSOFDzc3ERqNCC8vEWfOyOHjY7KamKJ+fSkALV+uQEqKubzAsGEa6HQCPvggHSVK2D/eF1+okZCQ8f2NGwJ+/FGapMM8Biknn36qQ2CgEZs2KfDvv9aBsFIlKaydOWP7JoeG6i3HN9/v9+8DU6eqLC1XuZ2hjogoM4YeIqJcUj6qu9nrLnThggyRkTJ4e1t3czJ3Gbp1y/6vW6m7knVQMneTM3/KbxYdLa1rbj0yM69XokTeH6aZtWtRZlm7FmWVXSDIrEcPaaa4Tz5JR9WqRqxcCcybl7vnZBS0tDTgo4+kdPfttzq4ukqTOjRoYMDp03KrLm9mmd8TURQRnZiGy3HJuHpDuhlu37a+CNm9J+b7wfy+Z2W+T8zd1MwtRnFxAv74Iw1OTiI++kiDNm20CA11QlqagLt3ZXB2FvHnn2lWobVJEyOaNpXOqUkTZ7z+ugaNGjnjwAEFunTRY8SIdLtlqFPHCJkMaNDABYMGaTBggBOaN3fG7dsyhIbqrYJVdtRqYNq0NCgUIsaPV+Pq1Yzr07mzAVqtNCFFaKgTwsIyCv3WW3pUr27EsWNyNG7sjMGDNWjRwhk3b8oQFpaOOnWMWL5ciY4dgYgIhh8iyj2GHiKiXCpVSoRKJSIuTrB5Evzff0sV5d697X8KvnKlbQXNYIBlsHeDBhlJwzwo/cQJ6248x4/LHi23/tVt/j63g9nthZTcdi06d862a5G5O5xOl336USikC6ZSSZVhpRL4/HM1btzI/9icJ/XjjypcvSpDhw56y0xgggD8+KMOKpWIyZPVuHnTulzmmfC27TPg4+XnMWTOOYyYdxF/LpeaI44czfqeSNcpMND+e7Jqle39kJoq4PRpKcBUry5tV726ETKZiCNH5KhTx4gNG1LQo4ce0dGC1eQXixen2B2PNHNmKgYMSEdqKrB9uwKeniImTUrD33+nZduaplaLWLZMOs6RI3Js2yaHr68JX3zxZA+crFnThJEj05GSImDYMCdLmPb2FjFvXipq1zbhwAG51UMiNRpg+fIUDBmSDqVS+vlIShLw5pvpGD06HdOnp6JJEwM2bYLNVO9ERDnhbwwiolySyaRPwVNSBKuxFWvWKDBvnhKeniYMG2b/0/MDBxT47z/riu7336tw65YM1aoZrSqsHTpIFfHFixWWAfunTslw6JAcbdsasH+/HBcvSr++r14VLLO2tWuXu65n5taAhATrin1euxaZuyudOpW7Pyk1apjw+edS17BhwzT5ftjokzhzRobfflPB1VXEd9/prF4LCDBh+HCpkv7JJ9b9/Nq1M0AQROzf6YSdq4vj4c5qSNpQG/FnPOFS9j6io+RYsEq6fvHxApYska6T+b3M6p9/VJbr1bevAdevJ8HVVYQoCnj1VT2cnKT1SpYEWrUyIjlZwFdfqREYaML06WmYOzcVcjmgUomPzsv+OJdixaQwd+5cMm7ceIitW1MwaJD+sa1zbm7ApEk6nDyZjFu3HmLv3hS8957e7lieFStSEROTZDd0ffxxOmJikrBuXYrVtk2aGLF+fQpu3nxo82T5YsWAr7/W4ciRZERHP8SVKw/x3Xc6yGSwPPRUrwdGj7b/s0ZEZA/bhomoyHqoM+ChzgBXjQLOqrz/OpsyRYXw8Oy7hk2alGZpzfjkk3T06CHHsGEazJ9vhCgCu3cr4OIiYtas1GzHSLzxRjpGjtRgzhwTypc34exZGc6fl8PVVbT59LxVKyOaNTNg504FQkK0aNjQiJ07FXB3F/HVV2k4cMAZ3bo5IThYWp6WJqB3b32uW3oqVJDW++UXNU6elOPzz3UoXVrEW2/psXSp0tK1qEEDIw4dkiM6WoZRo3TYsUOB5cuVePBAwIABesukBh06GDB+vIg5c1S4dk0GX1/R6prZ8+mnwPLlRhw8qMDUqSq8//7Tr8CaTHg0ZkfA//6XBh8f2/d85Mh0rFypxObNCqxcqbBMq12ydCq8aj/A3aM+uLm+MlxcRKSkCBBNgK+LFtdcdfhoeDFsWWXC4cPSc5YaNzagVSv7s8GFhurRvr0WTZoY4eYmYt8+OWJiZKhSxYgxY6zD2Bdf6HDwoByzZqlw8KAcFSqYsHWrAkYj8NtvaRg5UoOxY9XYt0+ODz9Mt0w6QUREGdjSQ0RFztX4ZPyw5QoGzjqFwf+ew8CZp/Dztqu4npCSp/1t26bAwoXKbL/0mXqsNW5sxL//puKll0w4eFCO8+el2b82b05G/frZVza7djVg7txUyGTSLGzR0TK0b69HRESKZRauzGbPTsXgwVIQ2LhRAX9/E+bOTYW/v4g5c1Lh6ytiwwYFnJxEfPCBDj/+mPtuR6GheoSEGJCWBixbprRMtpDbrkU7dsituhZ5eIj4779U1K1rxMGDcptrZo9CIVXYNRoRkyerct1KlB///KPE0aNSN7HsBuOr1dIDQAHgs8/USEyUlm+7FA/Pxtfh5S2Nd3nwQIBcDpQvb4KTSg6/gFSoPR9i4yY50tOlkDtnTmq2LSrffqvDZ5/pcOuWgPXrFRAE4M0307FqVQrc3KzXrVzZhHXrktG1qx537wpYt04BV1cRP/+chu7dDfj331SULWvCsmXKfM3gR0TkyARRzNoznXIjLi7Jpk9/USIIgIeHa5E/DyochXn/nIx+gK/XXEXMdS0Q6Q1ZqhNM2hTA7w58yqdifJeKqFrK9fE7ekbCwjRYuFCJ5ctTLA+sfNHl5/75/nsVfvhBjSlTUtG3bwHMW53b4265jFXLVXC6UMXu6yJEiK3249Puvmgd6JntfurUkQblZ+3S9TyIjBRQt64LGjc2YMWK1MIuTrb494vyg/dPzszXxxGxexsRFRnpBhN+3nwdd8+WgOpsIATxUevAg2IQ75ZCdOo5/Ky9jt9frQ6FjJ94U8FRyAQIihxCq8wEQRAhL8L3nZ+f+FyGMSKigsDubURUZBy4cQ9RMUYor5TPCDyPCKIMiivlEXnXgKM37xdOAclh1fB1g6Z0Ikwqnd3X9R5xcHEVUd3HMT8hJSIq6tjSQ0RFxtX4FOjvO0OR5mT3dXmKM9ITNbgSn4L65bKZUYAcRny8gC++UOdq3cqVTdk+lyY3gv1Lokyp27hS5RJUZ6pAMGb8+TRqkyGvcgPNqhSDp0vuykNERM8WQw8RFRkyARCE7CcLECECMvG5asKeOvXJnm1CORs9Ot0yVXFkpGD1jJecNG5swIgReT+uk1KOT9v74yvDFdwucQS6SE/IdGqIbknQ+CXg5cpqvBPs99j9HDmSnPdCEBFRnjH0EFGRUaWUC1Ql4pDu/BDyZBeb100uD6EppkNVb3YxehE86zEoAV4u+KVPVWy+EIudF+LxMM0I7+JKtK1WGk0rukOteJ7iNhERZcbQQ0RFxstlisPfV4kzgVchO1HNqouRSaGHscoVVC6t5rgKemo8nFXo+3Jp9H25dGEXhYiIngBDDxEVGXKZgNFt/DFedxk3XY7CcMMLwqMpq5XlYlCpHPBxm0qQPe5x80RERPRCYeghoiKlvLsWP/YMxIbzsdh05g4epBpRXKtAm+ol0a6KJ0pqVYVdRMqiWzcn7N1r789NRotc2bImjnchIqKnhqGHiIocDxc1+tUtg351y8AkimzZKSJatjTAy0t6GqBGo0Ramt7yWsmSfEogERE9PQw9RFSkMfAUHSNGpCM42Pjoid9KxMWl8YnoRET0THCqGSIiIiIicmgMPURE9FyJjBTg5eWK+vWdrZanpAD16jmjenVnPHhgf9vERGDECA2qVXOGj48LKld2weuva3Dpkv0/d998o4KXlyu+/TbnsWCjRqnh5eWKuXMzngtkLmduvurUyTiX8HAFvLxc8f33Kty6JeDddzWoWtUZfn4uaN1ai4gIdsIgIipoDD1ERFQkaLXAl1/qEBMjw/TptiElKkpASIgzwsOVKFtWRJcuBnh7m7BunRIdOmhx+rT1nzy9Hpg/Xwox/frpbfaXWZ8+BgDArFkZocfZGejTRw9vb+mBuS1bGtCnj97yVbGitLx+fQM6dzbY7PPmTRnattXi6FE5mjY1IijIhJMn5Rg0SIONG+VPcGWIiOhxGHqIiKjIaNfOAHd3ExYsUNq8NnSoBjdvyvDjj2nYsCEFf/6Zhp07UxAWpsODBwJGjNBYrb9rlxxxcTJUr25EuXI5Dy6qX98IDw8TTp+W48oVaRyZu7uIqVPTULWqFG5GjEjH1Klplq/gYCno9O+vx5df6mz2uXChEr16GbB/fzL++isNa9emYOLENIiigJ9/Vufp+hARkX0MPURE9Ex0766Fl5crPD1dIQiAp2dG96+xY3NXyZfLgerVTYiKkiEqKmMSixMnZNi/X4F69YwYMCCj1UYQgDFj0uHrKwWW/fszWlC2bZO6kTVrZnzscQUBaNxYWm/LloLpfubnZ8Jnn+kgy/SX+I039ChZ0oTjx2VISyuQwxARETh7GxERPSM5TVldu/bjg4eZeXrru3cFlC4t/X/3binMdO9u201NqQReecWAP/9U4cABORo2lI518qSUNl56yfrYsbECrlyRoWJFEzw9M1qAqlUzYdUq4ORJOYCcu8PlRnCwEaosvfTkcqBcORHHjslw754AHx9Ob0dEVBAYeoiI6JkoqCmrExOlFh6TKWPZ7dtSgClTxmRvE5QtKy2/cyejdSgmRtomc7ABgPXrFfjwQw1++ikN/ftnhBsPD/HRdgUzTbqPj/2yOjlJx9HnP1cREdEj7N5GRERFyuXL0p8ub++MsPLgwZMHEUGwn7gePpT+TU7Our70r6yA/nIW1H6IiOjx+CuXiIieK8pHcxSY7DSEXLggQ2SkDN7eJpQpkxFazC1Gt27Z/7N286ZtUDJ3k4uPtw5M0dHSuubWIzPzeiVKsMsZEVFRw9BDRETPlVKlRKhUIuLiBJvub3//LSWi3r3t9/1audK217bBAKxdKy1v0CBj/E6VKlKqOnHCenro48dlj5Zb/4k0f2/e7nGEgukFR0REBYChh4iIcmQwmpCcboApLwNw8kAmA+rUMSIlRcDy5RkhZs0aBebNU8LT04Rhw9LtbnvggAL//WcdfKSHgMpQrZrRMokBAHToIE0pvXixArGxUkI5dUqGQ4fkaNvWgP375bh4UfozefWqYJm1rV0722fu2KN5NEN2QgLTDxFRYeNEBkREZNeVuGSsOX0Xuy4mwmAAimnlaBdUEh2rlUJxJ9vn5DzOlCkqhIebZ28D0tKsn5szaVIatFrp/598ko4ePeQYNkyD+fONEEVg924FXFxEzJqVihIl7B/jjTfSMXKkBnPmmFC+vAlnz8pw/rwcrq7SM3Uya9XKiGbNDNi5U4GQEC0aNjRi504F3N1FfPVVGg4ccEa3bk4IDpaWp6UJ6N1bn+uWngoVpPV++UWNkyfl+PxznWW2OSIierYYeoiIyMahyHv4LuI64m9qId6sACFdhQSXh5hx6y52XryPr1+pDE+XJ3uApvm5OBmsg9OECRmhpHFjI/79NxWTJ6tx8KAUWnr00GP0aB38/bMPDl27GtC6tQG//KLG+vUKKBRA+/Z6fPZZOgIDbcPK7NmpmDhRjVWrFNi4UYFq1Uz45ps0+PuLmDMnFZ9/rsaGDQqULCni9dfT8eGH9luY7AkN1WPjRgX27pVj2TIl3n8/naGHiKiQCKL4jPorOJi4uKQ8TbX6vJCmjHUt8udBhYP3j2NLTNVjyPwziDrmAfWlShDEjJ7QJpUOxpdPo2UjOcZ3DMjT/p/G/RMWpsHChUosX56C4ODcP/PnedStmxP27lXg8OGH8PPjD1hW/P1D+cH7J2fm6+OIOKaHiIis7LwSj7g4AeqrFawCDwDI0tXApXI4cjUZN++lFlIJiYiIngxDDxERWbkQk4y06OIQDPbH7SgSSiI5WcCl2IfPuGRERER5w9BDRER5wp4hRERUVHAiAyIishLg6QyN712IFw0QjHaee1PiHlydRVT2dCmE0tk3dWqazexsRdWKFew2SERU0NjSQ0REVppXcoeHhwid/zWIWdpzTMp0oPIN1KngDL8SToVUQiIioifDlh4iIrJSzEmJD1r74Xv9DcQXTwZuloKQroLJ5SEU5e8isCLwXrPKhV1MIiKiXGPoISIiGw3Ll8SkUBVWn4rBnktXYTAAbloZ2gW5o1M1L5TQqgq7iERERLnG0ENERHZV9nTBqBAXDG9mgs5gglYlh1wmFHaxiIiInhhDDxER5UilkEGl4BBQIiIquvhXjIiIiIiIHBpDDxEREREROTSGHiIiIiIicmgMPURERERE5NAYeoiIiIiIyKEx9BARERERkUNj6CEiIiIiIofG0ENERERERA6NoYeIiIiIiBwaQw8RERERETk0hh4iIiIiInJoDD1EREREROTQGHqIiIiIiMihMfQQEREREZFDY+ghIiIiIiKHxtBDREREREQOjaGHiIiIiIgcGkMPERERERE5NIYeIiKiQnblioCyZV1QrpwLrl8X7K7z4AEQFOQMLy9X7Nwpf8YlJCIq2hh6iIiIClnFiiJGjUpHaqqAjz/W2F3nyy/VuHtXhr599WjWzPiMS0hEVLQx9BARET0Hhg9PR9WqRuzYocDChQqr1/bvl2PePCU8PEz48su0QiohEVHRxdBDRET0HFAqgR9/TINMJuKLL9SIj5e6uel0wKhRaoiigAkTdChRopALSkRUBDH0EBERPSfq1jXhzTf1iI+X4fPP1QCAn39W4fJlOVq1MqBHD4Pd7Tp21MLLyxXbt+c81qdhQ2eUKuWCK1fsjxsCgLlzlWjUyBlly7qgbFkXhIRobVqezFJSgN9+UyIkRIvKlV3g5+eCWrWc0auXE5Yssd1Grwdmz1aic2cnVKokrV+/vjNGjNDgxAlWSYjo6bH/W4yIiIgKxf/+p8O6dQosWaJEUJARU6eqoNWK+P57+93aTp2S4fBhOfz8TGjePOexPn366DFxohqzZ6vw9dc6m9dHjlRj/nwVPD1NaN/egPv3BezZI0dYmBNu3NBh9Oh0q/Vfe80Je/cqUKyYiIYNjXB2FnH/voCLF2XYuVOB0NCMkJacLK2/b58CWq2IBg2MKFZMxM2bMixdqoCbm4iaNW3LRERUEBh6iIiIniMuLsCkSWno31+L8eOlSQ3GjUtD2bKi3fVXrJD+lHfoYICQfQOOZZ2JE9VYuVKBr77SWa3/338KzJ+vQvPmBsyenQpnZ2n5sWMy9OihxU8/qdC+vQE1apgAAGfPyrB3rwJVqhixdm0KXF2tj6XLkl/GjlVj3z4FGjUyYMaMNHh4ZJxPTIyAmzcfU3gionxgWzIREdFzpm1bIypVklptKlQw4e239dmuu22bFHqaNbPf9S2zKlVM8PAw4c4dGc6csa4C/PmnCjKZiIkT0yyBBwBq1zbhzTfTYTIJmDVLaVkuf9STThBgN2yp1Rn/v3NHQHi4Emq1iGnTrAMPAHh5iahTx/TY8hMR5RVDDxER0XNmyRIFLl+WUsWNGwJOnbL/5zo9HTh/XnrtpZesQ8PVqwL275fbtLhUrSqtl3mf8fECzp2To3p1EypVsm1R6t5dClT792d0EAkMNKFjRz3OnZOjbl1nDByoQViYBlOmqGy237NHDqNRQMuWhmxbrIiIniaGHiKiLHRGHSrP8IPX727osbJzYReHXjAJCcC4cWrIZCIGDpRaWD78UAOjneE69+4JMBikZhZPT+sw8f33anTpokVUlHUzjHm9mJiMKkB0tLROmTL2W1v8/KTld+5Y72vGjDQMGpSOe/cErF+vxMKFSmzdajuZQlSUdKzy5Rl4iKhwMPQQEWWx8fp6JOruAwD2Ru9G9MOowi0QvVDGj9cgLk6GQYP0mDxZh/r1DTh1So4//1Q+fuNMkpMFq3+zytwlLSnpycfTmEzAm29qMH++Et99p8OZMw9x924SVqxIfeJ9ERE9bQw9RERZLL4YDgBoWqYFTKIJSy4uKuQS0Ytixw45Fi5UwtfXhM8+kyYa+PFHHZRKEd9/r7YZ7F+8uAiZTGo9MT/Xx8zcepO1dca8XsmSGa0u4qP/3rplv1pw86a03Ns7Y5uICAXWrVNi8GA9Bg3Sw9NTzHYihdKlpZai69c5WQERFQ6GHiKiTO6lJWDLjY0opfXG5GY/AQCWPApBRE9Tairw8cfSbG0TJ+rg4iItDww0ISwsHSkpAsaM0Vhto1YD/v5SoMj8nJuUFODiRen748czupuZTMDp07JH+7XtL3f6tAxXr9oGE/MMcQ0aZEyWcPWqtB9398dPQBAcbIRcLmL7doVNdzsiomeBoYeIKJMVl5dBb9Kja6Xu8C9eCXVK1cX5hHM4FXsi2222RW7Ba2tCUfPfKigz3QNVZ1ZA68XNMHLbcJt1LyZcwAdbh6HevBrw+9MLlf4pi+D/6uKdjW/geuI1y3p7onbB63c39FzV5amcJz1/Jk9W4fp1GTp10qNDB+uZ2D74IB3+/iZs2qTAqlXWT5to315a988/VZZxP/PnK6HTCWjb1oBFi5RIfdTjbMkSBeLjZfDyMuHll23Diskk4NNPNUhJyVh24oQMM2aoIAgi3ngjYxa5ihWl7WfOVOHaNdsgs39/Rtjy9hbRu7cBaWkCwsI0uHcv99eFiKgg8Dk9RESZLL4gtep0rxwq/VspFEfuHsbiiwsR5FnTZv0F5+bh/W3vQSbIULdUfTTybYwUQyqiH0Zh0YUF+LnlNMu65xPOof2SlkgxpCCwRBW0K98RJpgQmxKDzTc24fWX3kL5YhWezYlSgTGJIk5FP8CJqAcwmESUKa5B4wol4aLO/Z/YU6dkmD5dBTc3ERMn2j6gU6MBJk9OQ8+eWnz2mRotWhjg5ia9NnSoHosXK7F7twIhIVqULy+Fo5YtDRg8OB29emnRtq0WFSqYsGWLVKbPPtNZppzOrG1bA86ckaF+fWc0amTEgwcCdu+WQ68XMGqUDrVqZQSl9u0NCA42YM8eBRo3dka9ekb4+IhITBRw5YoMN27IEBOTZFn/m2/ScPmyDLt3K1CnjgsaNTKiTBkT6tY1omdPA2T8GJaIniKGHiKiR64mXsHhuwfh51YedUrVAwB0rdQD4/b+D8suLcb4Rl9DLrOuKc44/RcAYPErK9G0THOr13RG68rrf+fmIsWQglF1R2NM/bFWr5lEE4wmO9Nz0XPtbpIOkzZewbmbOujinWDSK6AscQ8e7tF4t0VZNK/k/th9GI3Ahx9qYDAI+OyzNKtxM5k1bWpEnz56LFyoxNdfqzF5snR/eXqKWLkyBV99pcbu3QpERcnQrp0B332nQ6lSIiZNSsNvv6mwbZsCFSua8N576ejb1/4zfUqUELFunbSv7dvlSEoSEBBgwpAhttvI5UB4eCpmz1Zi5Uolzp6V49AhoFgxEeXLi3jlFev738UFWL48BXPnKrF0qRL798uRlKTArFnAli16TJ+elptLTkSUJww9RESPLLmwEADQvVJPy7JSzt4I9m2KXVE7sOPWNoT4tbbaRi7IHv1r+7G5Wq7Osq4823VlggwyOT/qLkqS0w34cs0lnD2jgOxMDcgeukAOASZlOm753cSPaZFw7iJHXb/iOe5HLgc2bkzJcR2zqVPTMHWqbTjw9xcxe7b90DBokDTRQG55e4v4/ffcBRC1GhgyRI8hQ3K3f5UKeOstPd56S1r/3j0gNFSLZcuU+OwzHZ/hQ0RPDf/CEhE9svSSNEubuWubmfl7c9e3zIbXHgmFTIGeq15B1xUd8N7mwQjb8i7iU+Nt1h340iC4a9wx+dBEtFjYGEM2DkLYlnex//a+p3A29LTtuByPi5H/Z++uw6O4vgaOf2c9G3ckCRDcKVZcC8WLQ720pUJ/pe5K5aVu1KhCKRQKxYq1FHd3gkuAQEKEuKzM+8eQDctucEs4n+fJ0zJzZ+bO7OzunL33nutAv7kW+ix/FLRxLTqbCfP+WNL2BjNx/XFUVR7kixMcDM2aaS2c6emS4EAIcfVI0COEEMC6E2s4mH6AGiE1qRVa221dj9hemHQm5h6cTbYt221dz8p38GuX8QSZg1iVsIIpeyYxafcEsm1ZHseoFBjL333+pVZoHXambGfavr+YtHsCh9IPXNVzE1fH4t2p5MeHosu3eKxTUDAklGXX0TyOnJJuW8V54w0z48YZqVTJSfXq588CJ4QQl0q6twkhBGckMKjS32NdkCWY9jEd+efQXGYfmMnA6ne61k3bO4UnFzxGl0rdeaHJK1QKjMWkN3k9xq7UOAbMvIMwn3Bm9J5L3fD6+Bn9rs4JiasuLduBkusZ8BTS5fpgd0Bmng3wuXYVK0FmzTLQooWD99/Pw3hxc68KIcRFkaBHCHHTszlszNw/DYDeVft5LdOnan/+OTSXybsnuoIep+rkxaXPEmwJ4ftOP2PQnfsj9f3Vb5OYc4IpvWZSPaTGOcsWdpUSN65wfwO7/Yofi+O05mA0QJD1xn+ab9nS4ZZp7VrZtCn7/IWEEOIKuOig56WXXqJ///40adLkatRHCHEOvad3Y2XCcrdlVoMvFQIq0rVSN5645Sn8TQHXqXYl13/x/5Kal8otEQ2pFBjrtcztFbthNVhZdmwJidkniPQtw8nck6Tnn6JMcI3zBjwA+0/tAyDUJ+y8Za1GKwAFDs/0xeLG0L5GKOv2HMNxKAd9rtVtnYqKPSqBuhV8KB8orTxCCHG9XfSYnszMTIYMGULnzp35/vvvSUxMvBr1EkKcQ/vojgyqfheDqt9F4zJNOZxxkM82fEyXKR04lXfzzfqXXWDnREYeWfne0/Cez9lz83jja/Tl9opdcapO/to7GYAwSxjB5mB2p+1i3M4xHtscz0pwm3C0SlBVAN5b9ZZHOutsWzZbT252/btSYCw6Rcf+U3s9yoobQ+vYEOpUMqE23Ik9OBUVLWGB05JLfo09hFdN587G5a5zLYUQQgAo6iWklUlNTWXGjBlMmzaN/fv307x5c/r370/Hjh0x3iSdcpOTMynJCXkUBcLC/Ev8edxsClt6pt0xm5blW7uWH844RL8ZPYnPPMzj9Z9kRMv3r2o9bpT7Jz4tl2lbE1h64BgFDhsGnYFmFcrSt145qoZf2FiZ9PxT1BlTFZvTxub74ijjW7bYsvMOzuG+uYOpHVqXRYNWAPD7zrE8t3g4KiqxgZWpEVILRVE4nnWMLSc383n7rxlc424Atp3cwh3Tu5FlyyTMJ4wG4Q3xM/mRmJPItpNb6R7bk1Edv3cd7765dzLv4GwqB1WhQXhD7q/zEM3KNr+MK3ZjuFHunyshLaeALxYdYsP+bHLTjeDQo/fPo1yYnifaR9MkJvh6V7HUKU33j7j25P45t8LrUxpd0piekJAQhgwZwpAhQ9ixYwdTp07lxRdfxGq10qtXL+666y4qVqx4hasqhChOhYCKvNDkFZ5c+BhzD8666kHPjWBPUhZv/7OFhJzdqOaN6CwpOB2BzNnXgLXxtXitUz0aRgWddz8z9k0j35FPi3KtzhnwAHSIuY0gcxA7UraxM2UHtUJrc0+t+6kUGMvP235gfeJa5h+eh1FnItI3kr5VB9AgoqFr+7rh9Vk4cDnfbP6KZUcXs/zYUlRUQi1htCzfiu6xvdyO92X7b3jN6Md/h//hr71/0ja6fakIekqTYKuJEd2rcSAlm83HMrA7nEQFRdIkJgijzLskhBA3jEtq6SmUlJTE9OnTmTp1KomJiXTu3JnExETWrVvHCy+8wAMPPHAFq3pjKem/EMgvHSVTcS09ADtTdtBuUnNMOhNHH0sG4KO1/8cn6z/g5aav82zjF11lP1r7f4za9DkVAioypM5QHqr7iNu+DqYfoNUfTVBQWHHXenSKjkbj6hBhjWTb/XvQ6RTX/TNy9bt8tuFjPms3intq3e+2nz2pu/l281esSFhGYvYJTHozkdZIaofV4dVb36JiYCUAVhxbRp8Z3Wkd1Y6/es0873Vwqir/m7KB7WkrMAXNRFGKurWpqo6C9M5UsLbjx4FNsRg9JwIV15d8/ojLIfePuBxy/5ybtPScwWazsXDhQqZOncqKFSuoVq0a999/Pz179sTPT+tOMn/+fF599dVSHfQIcaPJKtDmhTHpzectWyesHu2jO7Lk6CJeWfY8geZA+lcb5Fo/cs072Jw2Hq3/BBUCKgJQPbgGu9N2sfXkZhpE3uIq+3SjF/g97jdGrnmXAdUHYz59/F2pcXSZ0p4cew7Vg2twe8VuOHFyMieJ/w7P5/7aD7mCnou1+Vg6B9KSMfgtcwt4ABTFidF/OYnpdVl5KI0OVc+fNEBovCXKOJ/192wjJqDCVaqREEIIcWVcdNDTqlUrVFWle/fuTJ48mZo1a3qUufXWW/H3L51RohA3qn8PzQXwmFjTm26xPegW24NNiRvoNvU2Rm/51hX0bE7ayIx90wgwBfJMo+dd29xW4XZ2p+1i/uF/3IIei8FCnyr9GL31W+YemOVK+Twhbhw59hyebfwiLzd93e34TtWJw+m45HM9kJyDjVT0xpNe1+v0WRQoRziQXFeCnovQIeY2ov1j3JYdTD/A2hOrCfeJoEPMbR7b+Mo8Q0IIIUqAiw56XnnlFbp27YrZXPyvyQEBASxcuPCyKiaEuDAnso8zde8Uvt/yNQAP1Hnogre9JbIRHWJuY/7hf4hL2UnN0Fq8s+pNVFSGN3yGEEuoq+xtFTrzzeYvWRD/Ly80fdltP40itRT2i48sdAU9ekXv9t8z6RQdussY76DTKaDqUVWtKd4bBQN6ncx1czGGN3zWY9nEXeNZe2I1VYOruSVZEEIIIUqSiw56evfufRWqIYS4GH1mdPdYpqDwdMPn3bqpXYg7a9zL/MP/8Meu32kb1Y7lx5ZSzrc8Q+s97lbu1rLN8TcFsClpI8m5yYRR1JrrZ9J+7U/KKUphf1/tIUzc9TsfrxvJ7AN/Uz24Oia9mbtr3X/Zg/FrRfph0QWTU1Aeg/mYx3qHPRiTGkXNSGmFEEIIIcQlZm8TQlxf7aM7EmGNRFEULHoLlQIrc3ulrsQGVr7ofd1esSuhllCm7JnE0qOLAXip6Wv4GNwnVDToDLSL7sDf+6ez4PC/1Ih+1LUuLjUOgLJ+5V3LKgXG8neff3n43/vZmbKdnSnbAWhZvvVlBz3VI/yoFRnJuuMdcBqmoNPnutapThP2zI5UCQ6jcXTQZR1HXJzdqbv4fMPHLD+2lLS8VEJ9wmhdvi3PNHqBKsFVXeVWHFtO72+60TCiEfP6L3Lbx7D/hjJlzyQm95xB2+j2ruVPLniMSbsn8E3HHxhQfbDHsbNt2Xy96QvmHPibwxmHyLHneK3jmC4T6BbbA4BjmUf5bssoFsTP53hWAjpFT5hPGLVC6/B0o+fcMu8JIYQo2SToEaIEGt7wWY/sbZfKqDfSv9ogRm/9luTck9QMqcWgGnd5LXtbTGf+3j+d/w7/yxOttKDHqToZv3MsgNuYj12pcQyYeQdhPuHM6D2XuuH18btC4z8UReG5dtV5c14B+9OCyDNsRGdIRXUEYrDdQkW/CrzUoRYGSRl8zSw9uph75wwi155L3bD6tCzXir2n9jJ5z0TmHJzFH92n0KxcCwBuLduMQHMgm09u0loNfYrGXY1o8X/M3DeNEaveYGH0hSVVsDls9J/Ziw2J67AarLQs3xofg5VVCSs4mZvkdZuknCQ6TWlLcu5JYgIq0iGmEwadnuTcZFYlLKdLpW4S9AghRCkiQY8QpZhS3ICXswyoPpjRW78F4PVmb6NTvAcLHSp0QkFh0ZGFrkQEY3b8zIH0/dQIqUmXit1cZd9f/TaJOSeY0msm1UNqnLueXPzYmzIBFj7q0YB/d5fn3z3RpObkE2Ax0bFuebrUjCDc7/xZ7MSVkW3L5vH5D5Nrz2Vk60/cUqB/v+Vr3lzxKo/Nf4jVd2/CYrBg0BnoXLkzk3dOZsHhf92C7HBrOB0rdGbuwVlsSFznGi92Lr9s/4ENieso7xfFjN5zXdnksm3Z3D/3LpYeXcQTDZ7ipaavYdKbAJi5byrJuScZVP0uvurwncd7pcBRcCUujRBCiBuE/AwqRClmNfgCUODIP2e5Fce0X9R1io4GEY2KLRdpjaReeAPS80+x8shKjmUe5f9Wv4NJZ+KrDt+h1xUlLdh/ah8AoT7nz55mNVovqJ5nC7IaGXhLOX4adCtTHmjNmLuacW+TaAl4rrGZ+6ZxMjeJxpFNPeZ8eqz+/6gffgsJ2ceYdWCGa3m3qlqAvCD+X4/9nZkY40JM3DUBgBeavOKWPtvX6Mt7rT5AQeH3uLEoiuIK6HWn71WdovP640BhcCSunf37FaKj/ahQwY9Dh7z/EJKRAXXq+KIosHSpzMF1pkaNfImI8D/nX+/ePuffkRCllAQ9QpRiheModpweT+NNRn46X278BNC6qk3eM/Gc++xYoRMAU3ZO4YF5d5NRkM77rT/y6ApUJUg79nur3iL/rGAm25bN1pObXf+uFBiLTtGx/9Rej7IXSneBrVriylt9fCUA/aoN9Lq+/+nlqxNWuZZ1rdLV1Wpod7rPteQtMUZx8h35xKXuAKB3lX4e62uE1KR2WF3S80+x7eQW1/K+VfpTIaAif+z6nVvHN2DoPw/w5ILHmHNg1nmPKa6OypVVnn22gNxchRdesHgtM2KEmcREHQ88AG3aXHra+9KsRw8bgwZ5/+vQQa6ZuHlJ9zYhroGjp3KZv/skG44mYXc6qRkRym3VI6gV6XfBXdAuRZuodkRay/DPobl0n9qJSoGxjGjxf4T6FKWi/mrj56TmpdImqj1rj69i4q7fGdbgyWL3eVtMZz5b/xFfrf0KgAdqP8T9tR/0KPdCk1dYfmwZE3aN49/Dc2kQ3hA/kx+JOYlsO7mV7rE9XSmQA81BdK7YlXkHZ9NuUnMahDfk/joPXXbCA3FtnMg+DkDMWXP8FIr2r3C6XIJrWaRfJPUjGrA5aRPrTqyhebmWrnW7UnYCUM63POdzKi8Np+okzCfc1WJ4tgoBFdmevJXjZxw/yBLMrL7zeezfB1mRsIyD6QdO1zXGlehAXHv/+18B06cbWLLEwKRJBgYNKgqIV6/W8/vvRsLDnXz6qQ6n8zpW9Ab29tv5xMSo17saQtxwpKVHiKtsyb4Uhk9by7jNy9lyaio7M/9i2u5FvDRrDb+vP4qqXr0vJx+DD3/0+ItW5duwPXkrf+7+g2xblmv98awEftz2HXpFz/+1+oiulbqzKzWOjYnri91nw8jGhJ6ev6d7bE9Gtv7Ea7m64fVZOHA599d+iABTIMuPLWXuwdkcTj9Ey/Kt6B7by638l+2/oX+1QaTkJvPX3j85dPohVJR8xQX2t8V0BuC/w0Vd3LJsWUzb9xeA18lQz6Y7PQ+U46zWojPZnTa3sgCJ2SfoM70b8ZmHmdB9MnsfiidpWAYvNn31vMcUV4/RCJ9+modOp/L222ZSUrR7Jz8fnn3WjKoqvPdePiEh17miQogSR4IeIa6iAynZfLF0Gycd/6EL/hFLwArM/qsxBI0j0ziVCZt2sGhfygXvb3rvOSQNy7iozG11wuoy9Y5ZHH4kkaRhGW5jHj5a93/k2nO5s8Y9VAupzqAadwPwx67xxe5Pp+jY9dBB1LdUxnQd7zaO52wVAyvxcdvPWX33JuIfTeLIoyfZfH8c47pNokulbm5lgy0hfHvbj+w5/fA5+HRdxI2vjG9ZAI5kHfG6/kjG4dPlyrktv61CYdDzj2vZp+s+JD3/FB1ibqNueH3X8uICpxBLCD4GH9Ly08gqyPRaJj4jHoCyp+sJ8OXGT9l3ai8ftvmU2yrcTqA56FynKK6hxo2dPPigjZQUHW+8oY3P+/xzE/v26enY0U7fvt4D3G7drERE+LN48bnH+jRr5ktkpB/79xffyj5unJHmzX2JjvYjOtqPDh2sTJrkvXNMTg58842RDh2sVK3qR0yMHw0a+DJggA9TpnhuY7PBmDFGevTwoUoVrXzTpr4MH25hy5Zr91i2YoWeiAh/+vVzH+eTmKhQtaofbdtasRfzW0JCgsL991uoVs2PMmX8qFnTl//9z8Lx496v6WOPWYiI8GfcOOM569S/v4/Ha1hYzwv5O3PM0kcfmYiI8GfiRAM7d+q4914fqlbVxoy1aQOrVhV/n2zYoOORRyzUq+dLVJQfder40q+fz3nrL25sEvQIcRXNi0vilO0Q5oBlKEpRXwxFAZN1B1nKBmZsO3JVW3uKsyd1NxN3jcfH4OP6dbtddAfK+pZj8u6JHDidiECI82lWVktFPW3vFK/rp+yZpJUr595d8ZbIRoT5hBGXupNjmUfZenIzo7d+Q5A5iI/bfuFW1mooTHbhnlVNr9PTtEwzAP7aO9nj2NuTtxGXuoMAUyB1wuq5lrsSbVjOn2hDXHuvvppP+fJOpkwx8t13RkaNMmG1qnz0UZ7X8tu26Vi/Xk9MjJO2bc89bmXQIBuqqjBmjPdkFc88Y+a55yxkZECXLnaaNXOwa5eOJ5/04aOPPLe56y4fRoywcOSIjmbNHHTrZqdGDSf79+tYutQ96MnO1h7sX3zRwvbteho1ctCli53QUJW//jIwefL1f6iOjFR5/vl84uL0XoO27dt1tG/vy7x5BmrWdHDHHXZ8feHPP4107WolIcE98Dl5UmHWLANWq0qfPrZzHnvwYG39r78WXYeICJVBg2z4+2vfk2ePWSpTRvtubd/e7nXM0ubNerp1sxIfr9C+vZ3YWCfLlmmvw7Ztno/BP/xgpHt3K9OnG4mMVOneXXs94+J0jBghSXJKsps26Bk/fjwdOnSgbt26DBgwgK1bt17vKolSaNXh4zhM21AU70GNwbKLA6mpJGVd+/S4761+C4fqYGjdx12/1OsUHQ/VfZQcezYd/mzF4Fl9WXFs2TWvm7h0dqfKsfRcjp7Kxea4NoMeelXpQ7hPBGuOr+K3Hb+6rftx63dsPrmJsr7l6BF7h9s6naKjfbTWhW3S7gk8OO9enKqTb2/7keizxgcVJsbYkbLN4/iP1HscgE/Xf8iB9P2u5ZkFGbyy7HlAG3t2Zka2wv19uO59Mgsy3PZX4ChgQ+K6C78A4orz84MPP9QCnLfesmCzKbzySj7R0d4/S6dP1x7Ou3a1c75hkl27as0XM2YYOPv3pgkTDIwfb6JtWztr12bz4495TJ6cy+zZOfj6qnz2mYmtW4senXbu1LFypYEaNRxs3JjFuHG5fP99HhMn5rJxYzYff+wepL3+uplVqww0b25n/fpsJk3K5Ycf8pg7N4dNm7LPGxRcKwMH2tDrVSZMcA/C7HZ44AEfMjNh/PhcZszIZfToPFatyqZfPxsJCTpeftk9MJg920BBgULbtnb8zjNVW6dOdgwGlYULDWSd7oldtaqTUaPyiIzUPs/efjufUaPyXH81a2rLhw8vYPhwz+/SX34x8dpr+SxZksMPP+SxaFEOzz0HBQUKo0a5B7GrVul54w0zVitMmZLD/Pk5jB6dx5QpuWzdms333+d67F+UHDdl0DNnzhxGjhzJE088wbRp06hRowYPPfQQKSkX3s1IiAtRYHeiKMVnI1N0+aio1+zhtNCa46uZd2gOweZghjd8xm3dEw2G82KTVwm3RrD06GKOZMZf07qJS2N3OJmx7TiPT17Ho5NX8NiUFQydtJZJG4+RZ7u6GZt8jb581+knfAw+PL/kKTpNbstj8x+k45+teW35S/ga/Rjd6RcsBs+MXIVd3D5Y+x7xmYd5rdnb3Fbhdo9yPSrfgdVg5edtP9B/5h08ueAx17pOFbvwSL3HOZF9nHYTm3PXrP489M99NP29PmuOr6JZ2RY83+Rlt/09cctTRFrLsDD+Pxr8VotBf/fhsfkP0m9GT+qPrc67q966wldJXKzOnR1UqaLdu5UqOXn44eIDgkWLtKCnTZvix3YVqlHDSViYkxMndOzY4f4YNHq0CZ1OZeTIPHx9i5bfcouTBx8swOlU3Foh9Kd7SCkKXoMt8xnP/ydOKEycaMRsVvn66zzCwtwjrogIlUaNLv+7oHFjv2K7f40efWEtScHBUKGCysaNercubnPnGoiP19Grl53bbiv6XDEa4f3387BaVf75x8CxY0UXY9Ei7SJdSLa9wECoW9dJfr7CsmVXJtdW06Z2hg51v3def13775o17l3cvvrKhKoqPP10gUd9DQbczlmUPDdl9rZff/2VgQMH0q+flt50xIgRLF68mL/++otHHnnkPFsLceFiQwM4fjwG8Px1GsBeEI2/2UKY77WdE+TWss1IGpbhdZ1ep+f5Ji97PCSKG5fdqfLZ4n38t283ufq1GCz7QFFJz63Ij+uasv1ENV7rVBOL8erNa9Imqh3/9F/MFxs+ZtnRpexM2U6IJZT+1QbxbKMXXenTz9Y+uiN6RX+61fExnrzlaa/lyviW5ffuf/LeqrdYc3wl+Y58V/Y/gPdafUjDyMb8uv0nVh1ficNpp1JgZR5vMJxH6w/DrHf/9bmcX3kWDFzOt5u/YmH8fFYfX4ndaSfEEkqdsHrFpt8W186UKQb27dPu2cOHFbZt09GggWdQUFAAu3ZpwUvt2u7rDxxQSErSccstDrcApGZNJ8uW6di2TUedOto2KSkKcXF66tVzUKWKZ4tSnz52Ro0ys3q1AdB+zKpe3Um3bjbmzDHSuLEvTZs6CAzUWifObnVYsUKPw6HQqZOt2BarK6FHD5tbwHamatUuPKgKDlY5cEBHWppCeLhW3xUrtNfDW4tUSAi0bWtn7lwja9boXWOvtm3Ttjn7tTl6VOHoUR21ajkICChaXquWg02b9GzdqqNr1wuubrHatfMMVIKCICzMycmTRcGZ3Q4rV2p1ve8+mZy4NLrpgp6CggJ27NjBo48+6lqm0+lo0aIFmzZtuuD9lPQpQQrrX9LP40bXpUY5NibUoSB/EwZzgts6p90fQ0ETOteKwcdUsibZk/vnxrJ0fzIL9u/D5jsRi/moa7nBdByHbR+rj9zD3LhQ+tYve469eHdnzbu5s+aFJZWoGVqT0Z1/OW+5M++fYJ9gTgxLu6D9t45qwz8DFhW7vl+1AfSrNuCC9gUQ6RvBiJbvMaLlexe8jbg2UlPhzTfN6HQq995rY+xYE889Z2H+/BwMp59cCu+jtDQFu137R0SE6va59PHHZv76y8iaNVnExhYFGoUP8UlJOlf5wkH40dFOr59tFSpoD+0nTihu63/5JY9XXlEZM8bIvHlaS0qLFnaeesp9+4QELTCrVEm9qp+dI0ZcWMrqM9+H3uqTcfp3MVXFyzXyfg6Fx01MLLpGSUmFr437df3tNyNffGFmypQct3FYhS1gJ08qXo9RXH3PXl74/+XKOb0u9/GB5GTF7T7KzVUIDlYJDvbc/82iNH+v33RBT1paGg6Hg9DQULfloaGhHDhw4SlyQ0P9r3TVrovSch43qt7Bvmw8kc7cuMEU2Fdisu4DxYEtNwZdfnMalKvJIx2qEmQtmbO/y/1zY1gwdwd5+m1YfRM4u9eywZBCTt5G5u+L5qH2VdHrbpxvNLl/RHGefx6Sk+F//9O6HO3bp7Uy/P67P889p5UpvH/O7H4VFuaP8YweXAWnf7A3mfwIOyNnheV0T0t/fzNhYVoTkE5XWNZIWJhnN7DC/SqKQliYdmynE/r1gzlz4JtvtP8PDwdFMQDu93dh64uPj4mwsCv/mV9Y/5AQ93MtTmCg9l+j0eA6n0I2Gxw6BCYTVKvm5zr33NNDWoKDfb0ew+d08jRfXwthYdpFLnyIPrtejtNxjl5vdVteeJ2sVvfrVNiV8Oz9mEyF5+O+H6u1cLmP17rqTl+wwnMvrM+Zr68oXW66oOdKSUnJ9BgAWZIoivaFUdLPoyR4skUlylotzN4ZTvKpTEAlwGSlQ81o7m4UhT0nn+Sc4sf93Ijk/rlx2J0qcQkpKIYD2O3eu67ojAc5mpbO/iOphFzjrpTeyP0jzmXJEj1jx1opV87Jc89lk5ICH36oo317K2++CR07ZtOggZ/r/nE4QKfzw+lU2L07izJlim6qQ4esgJ6dO3OIiipqTUhI8AEMmEx5JCdrXbVOndIDVvbvd5CcnONRL238jy+RkU6Sk7MBmDXLwPTpPvzvfwUMGKB9jhc3PDgoyAD4EBdnIznZexa6y+F0+gI6UlOz8PM7/xsrO1s73/x8O8nJRQP075jWjZUJy+E17d+m/ztjo3baX8e/Y4grt91jn3v3WgAj/v65JCdr0WhIiC/Hj+vYuzeb4OCiz6gDB7Syu3cXvQYAR46YARM+PvkkJxd1M3M4tNfy7PMrKNBey/T0HJKTi17jnBwTYCYzs6guUPT543Q6AR2bDuxg5Jp3WXRkIbyWRWpydUb+8zBDG9133mtYGhVen9Lopgt6goOD0ev1HkkLUlJSCLuQn0ZOU1VKxZd1aTmPG5lBp2Nww/L0rluGQ6k5OFSICrQQ6KP9dFaSr7/cP9efgvbLpKoW/3GuYkBBOV3u2tXtfOT+EWfLzYXnn9daCEaOzMfXV7tHqlVz8uSTBXz2mZkXX7Tw779F94/JBLGxTvbt07N5s47bb9cefHNyYM8e7df8zZv1dOqkLXc6caUqrl7d4boHC/+7fbuO/fsVt+5wANOmae+xW2+1u8ru36/tJyTEed57uUULB3q9yqJFBo4eVShf/urc/Bf6voqK0gKQpKRiPhf23U6jGuFUqVwUqCxcqOfkSR3BUSEe26SlaQklFEWlSZOi61qjhpPjx3Vs2aKnSZOifW3apDXdbN6sR1WLgp4tW7Tl1at7v6Znn19hS9LZy4v7fzehe+j4Z2vSC9JpXrYluvgyJPkt49XV/2Nf1hY+aPNpMRuKkuimy95mMpmoXbs2q1atci1zOp2sWrWKW2655TrWTJR2FqOeGpH+1C7j7wp4hLhcOkWhYflwKKhZbBlnXjUqhwYTaLnpfucSJczHH5s4dEhH9+42V2rpQk8/XUBsrJP58w1MPmtKpi5dtLKjR5tc3ZTGjzeSn6/QubOdP/80urpmTZliICVFR0SEk4YNPVtHnU6FV16xkHNGY8+WLTp+/tmEoqg88EDRA3rl0wHBL7+YOHjQs+vo6tVF4zXLlFEZONBOXp7Ck09aSLuwoWxXTdmyKtHRTvbs0bN2bdHjYOE8O9GHX2DmkG8Z1fF711/VHb/A9DEcGv0pCxcWnZvdDm+8YSEnR6FzZwdRUUVRRuFr8+uvRtc1nTdPT0KCjs6d7cyebSA5WTvmqlV6tm7VY7GotGt3/kx8AGazdqzU1EvoutvnXrJsWUzqMY1pvWfzQ6cxMGov+sMd+GX7j/xzaO7F71PcsG7Kb8AhQ4bw0ksvUadOHerVq8fYsWPJzc2lb9++17tqQghx0brWKsOq+BpkZjXA6LvZbSCqLac6VvUWetaOQinNI1RFibdtm47vvzcREKAycqRnl1+LBT7+OI9+/aw89RQsXw7+p3vhPP64jcmTjSxfbqBDBysVK2rBUfv2doYOLWDAACudO1upVMnJggXao89rr+W7xomcqXNnOzt26Gja1JfmzR1kZCgsX67HZlN49tl8twxyXbrYadnSzooVBlq08KVJEwdly6qkpyvs36/j8GEdSUmZrvLvv5/Hvn06li830KiRH82bO4iKctK4sYN+/eyucTmX4+23zcVmb/PxUfnoI+3aKgq89FI+//ufD/37W2nZ0sGpUwqH6uqgIrz8coHb+Kgz3XuvjTvv9KF5cweRkSobNuiJj9dRpoyTkSPdu+7ddZeNX381smuXnnbtfKlb18GCBdrcRq++mk+nTla6dLFSr56DhQu11+aJJwoICbmw8y1skXv9dTOzZxv44os815itc8krNx+i1nJPrYdpG90e0Frj3nrFyIgvfoGnYnns9y/ofqIPlSs7uftuGxER0jRdkt2UQU+3bt1ITU3lq6++4uTJk9SsWZOffvrporq3CSHEjaJhVBBDmtRizDrISKuFaopDUZxQUA1fpRr96lWnXZXQ8+9IiEukqip7krJZeSiVjDw7QT5GWsWGEBtqvaBg2+GA556zYLcrvPZantu4nDO1bu1g0CAbkyYZefdds+sBPjxcZcaMHN55x8zy5QaOHdNx++12Pvggn8hIlQ8/zOObb0wsWmSgcmUnw4YVMHiw95aE4GCVuXO1fS1erCczU6FaNSePPuq5jV4PEyfmMmaMkRkzjOzcqWfdOggMVKlYUaVnT/fgzc8Ppk3LYdw4I3/9ZWT1aj2ZmQZ+/RUWLLDx/feXP9Zn1qziexIEBBQFPQADB9qx23P59lsTy5bpCQ1VibxNJREoV674B/xhwwpo0MDBDz+Y2LhRT8GwShBwmLG37yIqqpxbWbMZvhy7i8cnfMYh3UIO+SZAowJ2Ae0WA69BPNpf9J4UXrzLSlbjEUR8+wFfdfiOeuENGLnmHQ4MWAVKAUNXNmCE4U2alWsBwMMPFzAxbyjHy0xm6smatNv7JIPr9nOrw+qElfSa3oVQSyjr7t1CGP7kVJwGQM/Kvd3KPvGEjUaNwrl32S2kB69mytiTONPL8cMPRgZ88zrf7xrpVq/Vx1dhcxRQL7wBr95aVK8zZduy+XrTF8w58DeHMw6RY/ccMwYwpssEusX2cP174q7x/LRtNHtSdxFoDqJNVDtea/YW5fzK89TCYfy5+w92DNlHiCWUj9eN5ON1I/nfLU/zZvN3XPvIs+fRdlIz0vJSWXHnBsKt4cW+rqWdoqrSo/pSJCeX7AG4iqJlLCnp5yGuD7l/bkzbj2cwLy6RTQlJqCrUjgzh9hplaRQdeEO18sj9U7rk2hx8seggy3dlkpPsgz3DgiEwB9/QfDrWDeSJ1hUxGa5cb/qrdf+sWKGnTx8rgwbZGDXqyicaKE5aGvTvb2XbNj0bNmRd1Tl8LkTv6Voig2l3zKZl+dbu63r7sHKlgfXrs9zSYjcaV4cjmfFsvi+Ocn7l3bY5nHGILlPak5KXQrhPBM3LtSTXnsPyY0vJtee6ld37UDyB5iA+Wvt/fLL+Ax6sM5SJu8ZTIaAS1UOqs+/UPrYnb8WkMzG33wLqhtcH4PedY/l7/3SWHVuC3WlnZp9/aFa2uWu/Xf/qyIbEdbzf6kMeqf84YWH+mP7XBFv4evY9dIQAc6DHdXhu8VOM2/krv3edzLapPfnwQzMtXn2dlab3L7heADaHjV7Tu7AhcR1Wg5WW5VvjY7CyKmEFJ3OT3I55ZtDz0tJn+XX7T4RaQmlVvi0H0vezLXkL1YNrMKfffzQaV4c6YfWYescsADYlbuD2v9pTM6QWSwavdtvv3IOzuX/unTxabxjvtvrgnK9/4furNLopW3qEEKI0qlM2gDplAwDvE4EKcTWMWnKI/9bl4txSE31aMAYUVFSyQlOYnbMXsyGeYa0rXu9q3rCCg6FZMwfbtulJT1eue9Bzpb2x4hVS8lJoH92RX7uMx2rUckkfzjhEz2m3cyL7OL93m0SbqPZYDO790n7Z/iPvt/qQofUedy17a8VrfLdlFKM2fc4PnccAcE+t+7mn1v3M2j+TB/+5hx+2fOsKev7eP4MNieuoEFCR+2s/5NqPw/8gOHW8tvwlr/XekaJlpzuSdZjOne18+KGZ/HwFTBdeL+0cfmBD4jrK+0Uxo/dcYgIqAFrrz/1z72Lp0UU80eApXmr6Gia9ll3z30Nz+XX7T5T1LceCgcsJ8wlDVVWeXvQEf+z6ndeXv8yp/FN0q1TUKtQgoiHhPhHEpe7kaOYRovyjXes6VbidUEsof+wazxvN33Ed52Zz0yUyEEIIIcSVcTAlh2VxGTi2VcaQFoKC1qKooGBMCcO2vRL/bksjMbNkpeW/lt54w8y4cUYqVXJSvbr3tPPXQ58Z3Yn4NsDtb2VnI7yt8OkO74HC2VLzUvj30Fx0io6RrT92BTwAFQIq8kyjFwD4dftPHgEPQNMyzdwCC4BnG2vbrDm+2qN8j8q9qBFSk38PzSU1LwW7087/rRkBwKu3vun2sO80ZoDOyaTdE7z+bU/eCsDG7Vk89phWt8LEFRdTr4m7JgDwQpNXXAEPgK/Rl/dafYCCwu9xY1EUBZ2iPZaPjxsHwIN1hhLmow29UBSFpxo+C8Afu34HoOsZQY+iKHSIuQ2A/w7/61YHg85A/YhbyChIZ2Pieo/rdrOQlh4hhBBCXJI1h9PITjNhSPE+ZsyYFEHmqUOsOZxGrzplrnHtSoZZswy0aOHg/ffzXIkDfv/dyJo1XrIseDF8eAFVq175YKl9dEcirJFuywpTVte+pdEF7WPryS04VScNIxoRG1TFY32/qgN4aemzrEpYgVN1uh76C7WL7uCxTaA5iDCfMI+uYYXurHEvb618lal7JqPXGdh/ah8Nwm+hd5V+XssffywNvc7zWo9c8w6fb/iEw/F6dDr4+utcDsU4IenC65XvyCcudQeA1+PXCKlJ7bC6bE/eyraTW2hcpikAm5M2Au5BDUBsUBXK+JblRPZxGoTfQnn/KLf1t1XozKTdE/jv8D88UOcht3V+Rq3LWlJOotfrcDOQoEcIIYQQlyS7wI4zx4we72PGFFWHmm8ip8Dhdf2NpGVLh1umtWtl06Zsj2Vr1uiZNOnCpjYYPNhG1avQo3V4w2c9xvTQ8eL2kZqnzYkY7V/B6/oAcyDB5mDS8tNIy0sj1Mc9eD57fFAhH4OV5Nxkr+v6VxvEe6vfYtzOMaScPv6bLd71GNcY4utPal4qufYc/EyeY1iybdrr0qebDw+9pCUe+GjtxdXrVF4aTtVJmE+4WyvXmSoEVGR78laOZye4lqWc3seZXdQK1Q2rx4ns43SL7emxrl10Bww6A8uPLSXPnufWerYrdScAZf3KeWx3s5CgRwghhBCXJNRqQh+QjqpzoDg9fy13GmzorPmE+kr2wIsxalTeNU2mcLXoFe2esKvFz7ljVx2ny3qOuDi75edChFvDua3C7cw9qA3w7xjTiVbl23iUqxBQkdS8VBJzTngNehKyEk6X8wzYLrReutPn73Ce4/ydNreyZ/LW5a+w9a2wK9uZAs1BNClzK6sSVrAyYRkdYjoBsPr4Kvak7SbUEkqD8IYXVPfSSMb0CCGEEOKStIoNISDYga3MCa/rbeUSCA5WaV4x+BrXTFxLSjEtfYUtIkcy472uT8tLJbMgA6vB12sGtUs1oNpgQAtO3jgjffOZGkRoE9LvSN7udf2Wk5tQUKgb3uCS6xFiCcHH4ENafhpZBd5bEeMztGtT1resa1mYj5ZWOjHb/X3lVJ3MP/wPQLEtXR1jOgO4yjlVJyNWvg7A4w2GY9TfvJOjS9AjhBBCiEsS5mdmQNNwLPUOkx8dj9NYAIDTlE9exYP41jnK3c0j8TNLx5LSrLDrVr7DPWFF3bD6+Bn92XZyC3vT9nhsN3n3RACalWt+Sa06xVmZsAyAAFMAVYOqeS3TPbYXAFP3TvFYt/r4Ko5kxtO4TFMizxrXdDH0Oj1NyzQD4K+9kz3Wb0/eRlzqDgJMgdQJq+daXi+iAQBLjy52K7/4yALXmJy4lJ1ej9mpwu1AUTKDX7Zp2eMaRTZmWIMnL/lcSgMJeoQQQghxyQY3LM/DHSMo2/wouvbrcbZbg77dBqKbnWBYp7KSwOAmUDlIG1R0dquJxWDhvtpDUFF5eelzZNmyXOv2pu3hi42fAvBIvWFXrC6H0g/y245fATiVf4p5h+Z4Ldc2uj0NIxox5+Df/Ln7D9fy1LwUXlryDKCNa7pcj5zO8vbp+g85kL7ftTyzIINXlj0PwAO1H3LLLDeg2iAAPl7/AUk5WmKEfEc+H659HwCjzsicg3/jVD0TWNQMrUWUXzSHMw4xfudvvL3ydcJ8wvip828YdDf3jw8399kLIYQQ4rLoFIUBDcrRtWYEG46kk5FnI9hqpHF0EBbjhWUgE1dGWk4Bh1JzUYDKYb74Wy79Me+rjZ8xcdf4Ytd/2OYzVwvPgGqDmX1gJv9b8ChT9kyiYWQjV8DwYpNXWXdiDcuOLaHp7/VoUa41ObZslh1bQr4jnycaPOV1fMqlGrnmHQqcBXSu0IV/D89j4q7f6Vn5Dq9lv7ntB7r9dRv/W/Aov+34lUjfMiw/uoS0/DSG1HmY2yt2vez6dKrYhUfqPc4PW7+j3cTmtCrfBh+jlZXHlpGSl0Kzsi14vsnLbtv0iL2DjjGdWBA/n9Z/NKF1VDt2pGxj/6l99K82CLvTxvR9U+k9vRsDq9/JPbXud9u+Q0wnftv5C88s/h9mvZlfbv/dI9PbzUiCHiGEEEJcNj+zgbZVJGHB9XAq18Zva+NZfOAo2QVaprFAix+3VY3mnsbRWE0XH3wuOrLgnOvfa/UBoAU93WJ7MLL1J4ze8g3/HJrDqfw0V9BjNVr5q9fffL/la6buncy/h+Zi0BlpGNmYh+o8Qq8qfS66bsXZenIz0/dNxd8UwJcdvuOeOQNZGP8fidkniPT1bHGsHFSV/wYu4/9Wv8OSowvZenIzVYKr8UbzdzwCicvxXqsPaRjZmF+3/8Sq4ytxOO1UCqzM4w2G82j9YZj1ZrfyiqLwa5fxjNr0OVP3TmbewdkYdEZ6Ve7DZ+1GkW3LpsBhY0H8v1QIqOhR104Vb+e3nb+gV/R80/EHmpVrccXOpSRTVFUtXVP/XiPJyZmU5CunKBAW5l/iz0NcH3L/iMsh94+4HHL/uMvIs/H6nG1sOxmH07wSg+UgoGDPrYK+oCW3lq/JW7fXKlWtbo3G1eFIZjxJwzLclvefeQdLjy7ilaZv8EzjFxi74xdeWPI0rzcbwfCGWpe1q3n/FFevkqTw+pRGMqZHCCGEEKKE+nt7IjtO7kMX8Acm3+3o9Nno9FmY/DaD/0TWHzvIf3tOXu9qXnWL4hew9OgiIq1leLT+EwD0qdIPi97Cz9tGF5vtTNw8JOgRQgghhCiBbA4n83bHYzOuR2fwTImsN6aQp9/MvF3HrkPtrh1VVXl39VsAPN/kZddYowBzIINq3M3x7ARaTGjIPbMHsisl7npWVVxHEvQIIYQQp+3frxAd7UeFCn4cOuR97pGMDKhb15eICH+WLi09XYZEyZOeZyc9Nxe9KaHYMnrjUY6lZ2F3eGb6Ki3+2vsn25O3UjmoCnfXvM9t3Tst/49H6w3Dx2BlQfx8UvKkxedmJYkMhBBCiNMqV1Z59tkCRo4088ILFiZPzvUoM2KEmcREHYMH22jTxnEdaimExqzXoVN0qE5LsWVU1YJBp0en8x7El0Qb7nVPjd2/2iD6n07zfDYfgw/vtvqAd1t9AGhjVq5VvcSNRVp6hBBCiDP8738F1KzpYMkSA5Mmuf82uHq1nt9/NxIW5mTEiLzrVEMhNP4WA3XLhuPMq+11UL6qKij5dWlZsQy6q/m0L0QJIEGPEEIIcQajET79NA+dTuXtt82kpGgPi/n58OyzZlRV4b338gkOvs4VFQLoVac8Abq6FGQ1RlWLAhtV1ZOf0YogYxW61pIJYoWQoEcIIYQ4S+PGTh580EZKio433tDm0Pj8cxP79unp2NFO3752r9t162YlIsKfxYvPPdanWTNfIiP92L+/+F/fx40z0ry5L9HRfkRH+9Ghg9Wj5elMa9fqGDjQh9hYP6pV86NfPx/WrdO+5idNMhAR4c/cudKrvbRpEhPMI83qEKz2wJH2MHnprclLb4sz7RHC9Z14pk1dqkf4Xe9qCnHdyaefEEII4cWrr+Yzd66BKVOM1K3rYNQoE1arykcfee/Wtm2bjvXr9cTEOGnb9txjfQYNsjFypJkxY0y8+26+x/pnnjEzfryJ8HAnXbrYOXVKYcUKPU8+6cPhw/m8+GKBW/lp0wwMG2bBYIB27Rzk58OSJXoGDLDy7785/P23EatVpV0778GaKNl61SlDvXIB/Lf7JDsTa6NTFOqXC+W26uGUDSh+vI8QNxMJeoQQQggv/Pzgww/zuOceK2+9pT04vvlmHtHR3mc0nD5d+0rt2tV+3sHSXbvaGTnSzIwZBt55J9+t/IQJBsaPN9G2rZ0xY3Lx9dWWb9qko29fK599ZqJLFzv16mnZuJKSFJ55xoKqwpQpudx6qxZwTZxoYPhwHz77zMTixXo6dbLj43MZF0Tc0CqGWHm4eQWgwvWuihA3JOneJoQQQhSjc2cHVapoQUSlSk4efthWbNlFi7Sgp02b87em1KjhJCzMyYkTOnbscP8qHj3ahE6nMnJknivgAbjlFicPPliA06nw669G1/Lp0w3k5Ci0aeNwBTwAgwfbKVvWydSpRgoKFLp1k1YeIcTNS4IeIYQQohhTphjYt08bn3P4sMK2bd6/NgsKYNcubV3t2u7zoRw4oLB6tZ78s3qx1ayplTtznykpCnFxeurUcVKlimeLUp8+WuCyenVRR41Nm7T6de3qGdQ0b64FQUajSufOEvQIIW5eEvQIIYQQXqSmwptvmtHpVO67T2thee45Cw4vw3XS0hTsdq2PWni4e7Dy0UdmevWycuyYe5+3wnJJSUVfxQkJWpmoKO8TScbEaMtPnCjaV2F2uehoz23q1tUq26KFg8DA4s9VCCFKOwl6hBBCCC/eestCcrKOIUNsfPxxPk2b2tm2Tc/o0cbzb3yG7GzF7b9nO3M8T2bmpc+lYvEyXj0iQgusOnSQVh4hxM1Ngh4hhBDiLEuW6Jk0yUi5ck5ee01LNPDpp/kYjSoffWTmyBH34CQoSEWn0wKMwpaXQoWtN2e2zpxZLiSkqGWocILJo0e9fz0fOaItL1OmaJuwMNXr/gHmzTN4rZMQQtxsJOgRQgghzpCbCy+8oDWbjByZj9/pKU6qV3fy5JMF5OQovPyye7OK2QyxsVr3si1bir5ac3Jgzx7t35s3F83d43TC9u260/v17C+3fbuOAwc8A5XCDHG33lrUclO/vrb9smXuCVnT0uC//7RlcXHnnjdICCFKOwl6hBBCiDN8/LGJQ4d0dO9u80gO8PTTBcTGOpk/38DMme5BRpcuWtnRo02ucT/jxxvJz1fo3NnOn38ayc3Vlk+ZYiAlRUdEhJOGDT3H4jidCq+8YiEnp2jZli06fv7ZhKKoPPBAURa5Xr3smEwqU6YYWL26KLj5+GMzubkKJpPK6tV6kpOltUcIcfNSVFX1PuGAOKfk5ExK8pVTFAgL8y/x5yGuD7l/xOW4mvdPfFouJ7PysRj1VA/3xaC/uN/2tm3TcfvtVqxWWL48260bWaFly/T062clMtLJihXZBARoy0+eVOjQwUpioo6aNR1UrKgFR61bOxg2rIABA6xUr+6gUiUnCxYYsNkUvvwylzvvLAqsVqzQ06ePlc6d7ezYocNm0zKwZWQoLF+ux2ZTePbZfF5+2X1y0q++MvHee2aMRpV27RxkZMCaNQZiY5088EABb75pITbWye232xkxwnMy1JJEPn/E5ZD759wKr09pJJOTCiGEKPF2J2Xx2+qjbD2cS34+6PVQLsxAv0YRdK0ZgXK+2UIBhwOee86C3a7w2mt5XgMegNatHQwaZGPSJCPvvmvm44+1ICI8XGXGjBzeecfM8uUGjh3Tcfvtdj74IJ/ISJUPP8zjm29MLFpkoHJlJ8OGFTB4sPcEA8HBKnPnavtavFhPZqZCtWpOHn3U+zbDhxdQrpyTn34ysWKFHpsNGjRw8MMPuURHqyQlKYwbZ2LWLEOJD3qEEOJSSEvPJSrpvxDILx3icsj9Iy7Hlb5/4hIzeWvGfk7s8UMfH4Uu0x/VVICtzAl8q5/g3jbh3N046vIPdA0UtvQMGmRj1Ki8612dG5J8/ojLIffPuUlLjxBCCHEDUlWV0cuOcHxXAObttVDU093Z7Eb0ByqTm+vDJNNBWsWGUCHEen0rK4QQ4rqRRAZCCCFKrN1JWew9lo/hcHRRwHMG44kyZKWYWLwv5TrUTgghxI1Cgh4hxE0t35FP1Z9jiPg2gL4zelzv6oiLlJCRT16egj4jwOt6RdVRkBjIkVTpKiaEEDcz6d4mhLip/XtoHun5pwBYmbCchKxjlPMrf30rJS6YWa9Dp1NxGuwodqPXMorZho+pZPzG17Klg6SkzOtdDSGEKHVKxreAEEJcJZP3TAQg0loGp+pkyp4/r3ONxMWoU9afoAAFe0SS1/VOcx4+5dJpGB14jWsmhBDiRiJBjxDippWWl8qCw//iY/Dhs3ZfATDldBAkSoZAHyNd6oVgrBGPPSjNbZ3TWIC99m4qltPTvGLwdaqhEEKIG4EEPUKIm9b0fVOxOW3cXrErt1W4nRj/CuxKjWPbyS0eZYcvfJyIbwP4dftPbsuTc5Op/nMFbh3fgDy757iRXalxDJl3D9V+jiH2x/J0++s2/jv8DwCrElYQ8W0AP2z51mO72Qf+pte0LmxK3MDmpI20n9SSCj9Ecsf0rmw9udnr+exJ3c3TC5+gye/1iBkdQZWfomk5oTGP/PsAh9IPet1mzoFZtJvUggo/RFL++1BaTmjM91u+xqk6Pco6nA5+2/ErXf/qSM1fKhE9Opy6Y6rRe3o3Rm/5xq1s7+ndiPg2gNXHV3k97pV0b5MoOjX2xdJiJwUNt5JX8SB51XejtN1AlTp5vNa1Mhaj/qrXQwghxI1Lgh4hxE1r8m6tVad/tUEoikLfqgO05XsmeZS9LaYzAAsO/+u2PMwnjOcav8TB9AP8tuMXt3Urji2jy5T2zD04i/rht9CyfCu2ntzMvXMGs/zYUmbunwZA11j3BAqfrv+QIfPuZlPSBqxGX55aOIydKdtpUqYZqxNW0mtaFzYmrnfbZldqHJ2ntGXCrnFY9BZur9iNttHtCfUJ47/D8zmWddTjnD5d/yEPzLuLY1lH6RDTifbRHTmadYQ3V7zKUwuHeZR/atEwnl/yFHEpO6gfcQvdY3tRL7w+J7KP88+huee81leTyaDjhdsq8/6ASvTsrNCocyrtbs/huV5l+HJgTUlVLYQQQhIZCCFuTgfS97M+cS2hllA6xHQCtODni42fMHXvZN5q/i56XVHrQLvoDhh0BpYfW0qePQ+LweJa17/6IN5a+Rpjd/zCI/W1YKHAUcDj/z1Mjj2H7zv97Aqolh9bSr8ZPRm18XN2puygXngDov1jXPvalryVj9b+H0adkb96zaK8X3niUndSJ6weU3rN4OtNX/LOqjd4ZtH/WDJ4tWu7CXHjyLHn8GzjF3m56etu5+pUnTicDrdlS48u5sO171MrtA5/9fqbUJ9QAA6mH+CO6V2ZtHsCXSv1oNvpgCwjP53JuycS5hPOooEriPQt47a/fEf+pb0QV4hOUWgYFUTDqKDrWg8hhBA3JmnpEULclKbs1lpz7qjSF4NO+/2nWkh16oU3ICknkSVHF7mVDzAH0rRMM3LsOaxMWOa2LsQSSmxgZfae2sOxTK1FZUH8fE5kH6dKUFVXwAPQqnwbGpdpyqIjC0jMOUG3Su6tPJN2jUdFpV+1gTQteytp+do4lQCTlpL5kXqPE2ktQ1zqTrdubnpF7/bfM+kUHUa9e2azwi51b7d4zxXwAFQKjOXZRi8C8PP2H4r2cToAVFDQeTmGWW/2WCaEEELcKCToEULclP7aq2VpG1B9sNvyAdUGAUVd387UsYLWxW3+6TE5Z/Iz+QGQlJMIwOakDQB0qdTdo2zLcq1d/98ttqfbus1JmwC4o3IfAFRVdVtv0ptoE9UOgK1njD26r/YQQi2hfLxuJO0mteDRf4fw5ILHih1TsyJhOcHmYNpFd/BY17dqfwDWn1jjGtvjZ/TjkXqPczI3iabj63P37AH8b8GjvLXiNa/7F0IIIW4kEvQIIW46606s4WD6AWIDK9Mosonbuj5VB6BX9Mw9OJtsW7bbuk4Vbgfgv7PG9dgcNvad2gfgmuMnOTcFgCj/aI/j1w2vB0BsYGVqhNR0W5eSlwxAtH+FYutf3i9KK5ub7FpWKTCWv/v8S63QOuxM2c60fX8xafcEDqUf8Ng+Pf8U2bYsos7oVnemAHMggeYgcu25nMovyoj2bqsPeKnpa+Q78ph/+B/+3P0Hsw7MKLaeQgghxI1CxvQIIW46ha04GQXp9Jja2WO9UWckx57N7AMzGVj9TtfyGiE1ifaP4XDGIfam7aFqcDUApu/7i8yCDGqF1vEY6+Kj9/HYf7g1EoAOMbd5rNOd/i3KiWf2NFf9TndV0+uKPsJ3pcYxYOYdhPmEM6P3XOqG18fP6Od1+4yCjGL3fS6vLXuRn7aN5sWmr3JnjXso41sWnSK/nQkhhLjxSdAjhLip2Bw2V9a05Nxkks9oLTnb5N0T3YIegA4xnRi742fmH/6HqsHVyLHl8OHa9wF4uuFzrnLhPmEAnMg+7rHfeQdnA+4tNYXK+JU7PTboiEcrUKHCfZb1Leta9v7qt0nMOcGUXjOpHlKj2HOCoi5zx7KOeF2fWZBBev4pfAw+BJqCANh6cjM/bvueXpX78Fzjl865f9DG/gghhBA3CvmJTghxU/kv/l9S81JpEH4LScMyvP4dGnoCq8GXZceWkJh9wm37ThXcU1d/sPY94jMP0z22F72r9nOVqxd+CwDLji1x297utDNt7xQA4lJ3etSvWdnmAMw6MNNr/R1OB0uOLEJB4dbTZQH2n+5eF3o62LoQqXmpLD262GP5tL1/AdA4sqkrg13R/kM9yntjNWppoguuc1Y3IYQQAiToEULcZAq7tvU5I6Pa2axGK10qdcWpOvlr72S3da3Kt8Wit7D6+ErG7/yN77d8TeWgKnzVwX2C0fYxHQm1hLL82FJm7JvqWv7j1u85np2ASWdid+ou9qTudtvurhr34mPwYcqeSWxO2ui2TlVV3l71OvGZh+lSqbvbeKEqQVUBeG/VWx7po7Nt2cVOaPr2ytdJzUtx/ftwxiE+Xf8hAEPqDPXY/197JrMpcYPHfrYlbyWrINP178qny+9I3u71uEIIIcS1pKhnpwYSFyQ5OZOSfOUUBcLC/Ev8eYjr43reP8nZBWyIP0WOzUGEn5nGMUGYDRf2+016/inqjKmKzWlj0707KetXrtiy/xyay71zBlE7tC6LBq1wWzfo7z4sOrIAgCBzEHP6LqBKcFWPfUzdO5nH5z8MQKuothgUPYuOLCDEEsLbLd5n+MLHKeNblrZR7RnV8XvXdn/E/c7Ti57ApDdxa9kWLD26iDCfcCKskexM2U6UXzSz+853q/+2k1u4Y3o3smyZhPmE0SC8IX4mPxJzEtl2civdY3u6jhGfcZjGv9elUWQTbE4bhzMO0ap8G+xOG8uOLiHHnkP/aoP49rYf3c5n2H9DmXJ64taGEY2ICahAji2HQxkH2ZO2m/X3bCMmQEvAsPXkZjpNbotep6dD9G1UDqrKiJZaN0D5/BGXQ+4fcTnk/jm3wutTGsmYHiFEiVBgd/LrmsPM232YTNspVPLQ408ZvxCGNK1K+6rn79Y1Y9808h35tCjX6pwBD0D76I4EmYPYkbKNnSk7qBVa27WuU4XbWXRkAVaDld+6TfIa8AD0rTqAIHMQozZ+wabEDeQ5cqkWXJ2vO46mQURD0vLSGLXpcybtnuAW9NxZ8x4ifcvw+YaPWXdCm4A0OfckoDK4xt28euublDljPA9A3fD6LBy4nG82f8Wyo4tZfmwpKiqhljBalm9F99heHvUz681M6jGV99eMYO7B2aTmphATUIF7aj3Ao/WGeZT/uuNoWpdvy+Q9E9mRvI0tJzfjb/Inyj+GoXUfI8QS4ipbL7wBP3Yew6frP2TRkQXEpe50BT1CCCHEtSYtPZeopP9CIL90iMtxre8fVVX5dNFe5u3dgc38H0af3Sg6G067PwXZjQiiDc+3a0DbKhc23qQkKWyVaVGuFdN7z7lh93kx5PNHXA65f8TlkPvn3EpzS4+M6RFC3PB2J2WxaP9h7NaZmHy3o+hsAOgMmZgDFnNKXc5v6/dhdxSf5lkIIYQQNy8JeoQQN7xl+1PJdh7BYD7osU5RwOi3iWMZqWw9fmnzzwghhBCidJOgRwhxwzuZnYddOY6ieO+LoDekYXMWkJJtu8Y1E0IIIURJIIkMhBA3vECLCT0hxa53OnzRK0b8zfprWKtrIyagAknDrmwL1tXYpxBCCHEjk5YeIcQNr1nFYHyohKMg0ut6W05dwqyBNCgfeI1rdnMJ7N2N8IiAc/6FNKrjto1xxTLCIwIIrRJNwAN3o0s8Uczei4Q0qkNYVBjB7VpgmncVEy2oKpaxvxDUozOhVWMIKxtMWMWyBLdrgXXkO1BQcPWOLYQQ4pqSlh4hxA2vQflAbilXntXHemP3nYXedAxFAVXVY8upjY+9Lf0bx2Ixlr6WnhtRQfuOOCO8B6DOEPcMes6ISPIG3olh/VrMc/5GyUgnY9qsc+4/v8cdGHbtxLRoAQEP3UvqsrU4YytfsfoXsn7yAb4fj0S1WLA1aYYzIgLsdvRH47F+/SW5Dz6KGun9PIUQQpQsEvQIIW54ep3Cyx1r8PFC2JgQRm72MRxKOga1HEGGcAY2rErvumWudzVvGjnDn8XWsvUFlXVUrUbm16NRsjIJbtkE0/Kl6HfvgrAmxW6TPUKbz8f3tRex/vg9PuPGkP3Wu1ek7mcyz5wGQPr4ydhat3VbpyQno/qXzrStQghxM5LubUKIEiHQx8i73erwcY9m3NegI4Nq3sFjTVsxekBz7m4chU5RrncVxTmofv7k3XM/AOZJEy5om9xHn0BVFMx//QkOxxWvkzNCC5TNf09HSUt1W6eGhYHZfMWPKYQQ4vqQoEcIUWIoikLtsgEMuTWGJ1pXYuAt5SgTYLne1RIXKG/QXVoQM+VPcJ5/TiVnTAVsLVqhP3Ec45JF5yxrWLuGwIG9CY0tT2i1GAL79cKwbg2gBVnhEQGY5s522ybr3ZE4ypbDZ8zPhNauQlDntlg/fB993M7iD5STg/WD9whpWp+w6HBCmtTD+sG7kJcHQEjjegR1aHXecxNCCHFtSdAjhBDimtClpaKoKvrjCfDffxe2TarWAmP5s/jWIfO0KQTd0QXjqhXYWrbC3qAhxuVLCBrQG/2e3Zj/no5qtVLQroPbds6wcBwVK2GvWo3cR4ahWn2xfvUZwe2a4/vGy56JDLKzCerbHd/PPkI1GMjv0h3sdnw/+xi/l57FsGUT+vhDFHTrcXEXRgghxFUnY3qEEEJcEz7ffFn0j7FjoWHzc5Y3LpyPIW4HAOa5s8nKzED1D3AroyQl4f/Mk6CqnJryN/Zbm2nlJ44nYPjjWD/7CNPihRR06gI+PkUbFhQQeGc/dKfSSFuwDDUoGADdsaMEPHgP1tHfgtNJ9vsfuTbx/ej/MG7cQP5tncn4bSIYDJCdTXDXDlgmTUA53QUvv1vPS75GQgghrg5p6RFCCHFRgvp0LzZlte/rL3ndRnckHvPfM3CGhKBaLDB9OkpW5jmPY/1mFACOcuVRcnMxz5zuUcYyfQpKTja2Nu1cAQ9A/uC7cZQth2XqZJSCAvLPan3xGfMTxm1byHniKVfAA+AsH0X6hL9w+vnjM+ZnlKQkbYWqYpn4OwA5z7ygBTwAvr7kDn0cxenE8ucfOCpWwlGr9jnPSwghxLUnQY8QQoiLUtC+I3mD7vL6Z7+lkddtfH74FsXhIPehRyno2h1ycjDNmF7sMfTbt2FathhHxUpkfaq1EHlLgGDYtBGA/K6eXcpszVsCoBqNFHTu4rbOPHmS61zOpoaGUtCxE4rNhnH1CgB0Bw+gS0vDGR6Bvcmt7sdpWTSGR1p5hBDixiTd24QQQlyUi0lZDaBkpGMZPw7VaiX3oUcwbt2MedpfWP78g7y77vW6jfXbr7RjPf4kBe1vw1GuPMY1q9AdPoSzQkVXOV1KMgDO6GiPfdjr1oepk7G1aIUaGOS2zrBnl7ZdufJej194DH3CMbfjOKKiPMo6KlXG6eePLitTgh4hhLhBSUuPEEKIq8oy5hd0WZnk3nUvakgotrbtISoKw6oV6OIPe5TXJRzDPGMqzrBw8u68B3Q68gcMRlFVLH/+4fUYqsXHY5kzIgKAgg6dvGygAqAU5HuvdG7O6Z2o5z0OioIaFoYzIBB74+LnHxJCCHH9SNAjhBDi6rHZ8Pl5NKrBQO5j/9OW6XRw773FBjE+P3yHYrOR+/CjYNFSkucNvgvAo7wzLFzb5YnjHvsxz5ujrTvdSuO2XZmy2rr4eK/V1p8Oxpxlypz3OPrdu9AfOqiNUcovJogSQghxXUnQI4QQ4qox//Un+uMJ5PfqjTOmQtGKBx7Q1k+e6FZeycrE8vtYVKsvuUMedi13VK6Krcmt6A8fwrB6lWu5vX4DAIzLlrjvJy0V03//AKA/nQHuTAWnx/tYpkzyWKckJmJavBBVp8N2q5ZhzlmxEs6gIPSHDqI74h4oFQZiitOJYXdcsddCCCHE9SNBjxBCiKvG+t3XAOQ88bT7imrVsDW5FcPBAxjWrHYttvw2Bl1GOrn33o8aHOK2Sd7gu7UyZ8zZk9+rD6rJhGXKJLdgyPrxSJTcXFSTCePqVSjJ7q09eQ89gqrTYRnzM4b1a4tW5Ofj/8rzKPn55Pe4A2f502N4FIX8vgNQVBW/114Emw0A3eFDWMb+gmoyAWCeNfPiL5IQQoirTlFVVT1/MXG25ORMSvKVUxQIC/Mv8echrg+5f0qWPJuDtfGnOJ6eh9Ggo2FUIBVDrBe9n8De3TCtXE5B+444IyKLLZf54WdgtWJc+B9Bg/tS0K4D6X9Od60vvH8yPx+F/7PDyb13iJahzW4npEk9dIknSF27BWeUe3ICJTOD0DpVUY0mUrbvdXV98/nqc/zee0vL0tauA7qMDIxrVmGPrUzeAw/h9+ar2GMrU3B7N7JHvO/an8+3o/B7+zVUvR5bi9Y4Q0Mwrl2DPuEY9ipVOTVjHmp4eNHxT54k+PZ26I8ewV4pFnvd+piWL0GXmkrmJ19i/eQDdIknKOjei5xHhmFvdu55iMSlkc8fcTnk/jm3wutTGknQc4lK+ptF3vTicsj9U3KsOJDKuCW7yUtOJTI7lWyDmczAUGpWLcewdlUIsBgveF+FQc/5JO+NRw0MInDAHZiWLOLUlJnY2rRzrS+8f1IOHiOkdlVUk5mUbXswz55JwOMPk9d/EJnf/uh13/6PP4zlrz/JGP0L+X36u5abp0zC56fvMeyKA5sNe+06ZPwwBmd0DL7vj8AybgxqQACpG7a77c+48D+s343CsHkTSm4OjvJRFPS4g5zhz3hkfANtTI/1w/cxLZiP7mQSamAguY8MI+fZFzFs24Lfs8MxbN1M5pffkn+6ZUpcWfL5Iy6H3D/nJkGP8FDS3yzypheXQ+6fkmF9/ClG/b2J9juWcWf8WiLzM3GgsDakIt/Xvp2AerV5s2cdjPpr29NZ7h9xOeT+EZdD7p9zK81Bj4zpEUKIUkhVVf5cc5DGe9bx1N4FROZnAqBHpXnqQd7cOIUj+4+y9vCp61tRIYQQ4hqQoEcIIUqh/Sk5JCYk0/voJhQv6ytnJ1P/2C6W7Um85nUTQgghrjUJeoQQohRKz7Wh2GxE56YVWyYm8yTpmbnXsFZCCCHE9SFBjxBClEL+ZgOq0cBxS2CxZY75huDna7mGtRJCCCGuDwl6hBCiFKoS7ktYZCh/l6uHt7G6R3yC2Fi+Fi2rRlzzugkhhBDXmuF6V0AIIcSVp1MU+jatxA9JtxKcn82goxsIsOehAjsCyvFV7S5EVCxH80oh592XKH3sTjsT4sYxY/804lJ2kJGfTpAlmPrhDehfbRC9q/RDUbyNBoOsgky+2vg5/xyay+GMQ+Q78vA3+VMjpBZ31riHO2vec43PRgghzk9SVl+ikp7qUFI2issh90/J8e+uJCYt240uNZUKaQlkmH1JDC5DhYpleKpTDUJ9Tde8TnL/XF9HM49w9+wBxKXuxKw307Rsc8J9wkjISmB94lrsTjstyrViTJfxBFmCPbbvM707KxKWEWIJoUmZW/E3BZBjy+FA+j4cTgcr7lp/Vesv94+4HHL/nFtpTlktLT1CCFGKda4RQfOKISw/mMLx9Hwq6BXujw6iVqRfsb/ki9IrIz+d3tO7EZ95mO6xvfik7ZeE+oS61h/NPMITCx5hZcJy7pzdj7/7/ItBV/SokJybzIqEZfgZ/Vlz92YCzUFu+z+WefRanYoQQlwUGdMjhBClnL/FQNeakTzYLIZ7m0RTu4y/BDw3qXdWvUV85mFaR7Xjp85j3QIegCj/aP7o/hfVg2uwIXE9324e5bbez+iHr9GPHHs2M/dPp8BR4La+vH/UVT8HIYS4FBL0CCGEEDeB1LwU/tw9AYD3W32IXqf3Ws5qtPJG8xEA/Lj1OxxOh2udxWDhk7ZfYNKZeG7xcKr/UpEBM+/gp63fk5h9wuv+ek/vRsS3AcRnHGbOgVl0/asDFX8oQ7WfY3j4n/s5mnnEVXZb8lbKfhdM24nNWHJkkdf95TvyCf84nKjvw0nJTQFgxbFlRHwbwJMLHiMtL5UXljxDnTFVifo+jNZ/NOW3Hb8We13+iPudHlM7U+WnaCK/DSTi2wCPv9eXv+Qqn2PL4ZtNX9Hhz1ZU/TmGmNERNBhbkwEz72DKnknFHkcIcX1J0COEEELcBJYfXUqeI4/aoXWpEVLznGU7xnQmyBxEYs4JtidvdVtXMbAS4dYIOsZ0YkC1QZzMPcmry1+k+YRG/BH3e7H7/HX7Tzz0z71Y9D50rNAZP6M/M/dPo/eM7mTZsgAINgfTpVJ39p7aw5B595CWl+qxn9n7Z5Kck0z32J4eLVXpBel0m3ob/xyaQ7OyLbi1bHP2ndrL80ue4udtP3js680Vr/LUomGsO7GGWqG16V2lL9WDa5zz2tw1uz8jVr3Okcx4mpVtTrfYHtQIrcn+U/tYenTxObcVQlw/MqZHCCGEuAnsSNkGQIOIW85bVq/TUyesHsuPLWV78jbqn97mYPoBBv7dh5blWzO2ywRXN8mVx5bz8L/389SiYQSYA+ke29Njn79u/5GZfebRpMytAOTacxn4d2/WHF/FlN2TeKDOQ0T5R/Nrl9/5YsMn/N+ad5i6dwoP1X3EbT/jdo4F4L7aQzyOMe/gbPpU6cdXHb/HrDcD8O+hudwzZxBfbPiEB+sMddV528ktjN7yDQadgd+7/UmHmNsAUFWV/1vzDl9u/JRGkU2Ydsds17imnSk7WJmwnBohNZnddz7+pgC34+c78s97bYUQ14e09AghhBA3gdS8NABCLWEXVD7MRyuXkpfiWvb2ytfJKsjklaZvuI0La1G+Fd901FpSPl430uv+Hq0/zBXwAPgYfBjWYDgAa46vcit7T60HMOgMTN072W35gVP7WHFsGdVCq9GyfGuPY/ibAhjZ5hNXwAPQuWJX6oTVIzHnBAczDriWT9o9ARWVPlX6uwIeAEVReLHJq5TzLc+GxHXsTNnuCnr0itYlUEFBUTwfoc48rhDixiJBjxBCCCHOKz3/FP8emktZ33LUDK3lsb59TEfCfSLYmbKd5Nxkj/Xtojt6LIsNrAzAydyTbsvDfMJoXb4t60+s5UhmvGv573G/oaIytOFQr3WsH96AEEuox3LXcXKKjrMpaSMAfav29yhv1BvpWfkOAJYfW+paXj2kBt0q9SQudSeNx9Xhvrl38uSCx/hq42de6yOEuHFI0COEEELcBEJOz7mTkucZkHhTGLiEng4i9p/ah0N1UNavXLHbxARUAOB41jGPdWV9PbfzMfgAYHfaPNb1rToAFZVpe6cAYHPYmLhrPCadiQcaPOD1+N6OUdxxUk+3YEX7V/C6TYWAigAknHUuP9/+G0PqPExaXhrzDs5m0u4JLIz/z+s+hBA3Dgl6hBBCiJtA7dC6AGxO2nTesk7VyY5kbQxQnTBtOxVtJsez01SfKdee69r+bDov3cHOpVtsDyx6C1NPBz3zDs0mOfck3WJ7EGb13kXvYo5R2FXNrtq9rrc57W7lQDuvB/+5l/E7f+ODNp+y44H9JD6ezvTecy74uEKI60OCHiGEEOIm0CqqDWa9mR0p29iduuucZRfGzyctP41IaxnqhNUDilpRjmbGo3qZyl5VVVf66TK+ZS+7vv6mAG6rcDs7U7azO3WXK+20twQGl6KcX3kAt+5zZzqSeRiAMme0bM05MIu5B2cxtN7jDKnzMOHWcJnzSogSQoIeIYQQ4iYQYgllYPW7AHh9+UteW2NAa615Z9WbAAyt97hrPp9yfuWJCahIWn4aC+L/9dhu7sHZZBSkUykwlkjfMlekzn1Oj7f5fMPHLD26mNjAyrQq3+aK7LtFuVYATN3zp8e6fEc+M/dPP12upWv5gfR9AIT6XFgyCCHEjUOCHiGEEOIm8WbzEcT4V2DJ0UUM/fcB17iWQscyj3LXrP7sSo2jYUQjHq//P7f1Q+s+CsA7q94kKSfJtfxE9nHeXa0FSo/Wf+KK1bdThdvxM/ozde9kVFTuqfXAFWtZubPGPafnCprOP4fmupY7VSfvrXqLpJxEGkU2oVFkE9e6ykFVAfhl2w8cTD/gsc/VCSuvSN2EEFeezNMjhBBC3MDybA6WHUhlwZ4EErNyCPKx0L5yWdpVDSXAYryofQWag5jeew53ze7P3/unM//QPJqWbU6YTygnsk+w9sRq7E47zcu1ZGyXCRj17vsfWu9xViQsZ97B2TSf0JBW5dugorL86FKybJn0iL2DIbUfvmLnbjFY6Bbbgz93/4FJZ2Jwjbuv2L4jfcvwZYdveHT+g9w3ZzBNyzajvF95tpzczP5T+yjjW5ZvbnOf0LRLxW60LNeaFQnLaDGhEU3K3EpZ37KkF6Sz/9Q+DmccImlYxhWroxDiypGgRwghhLhBncq18e4/O9iaeIQ83TYUQzJqVgBbT9Rn5o7yvN2lLlFBPhe1zyj/aBYMWM4fu35nxr6p7EjeSkZBBkHmYNpFdaB/9UH0qdLfa4uKTtHx6+2/M3bnL/wR9ztLjiwEoFpwde6seS/31Rpyxce4dI/txZ+7/6BDzG2uuYOulJ6VexPlF80XGz9l7fFVbExcTxnfsjxYZyjPNHrBo5ueXqdnYs+pjNn+EzP2TWNnyg7WnVhDoDmQigGV6Fm59xWtnxDiypGgRwghhLhBjVq6j42J21ECJmMxnHItdzpWsze9DyP/0/Nl34YYdBcXaBj1Ru6rPeSSkgLodXoerDOUB+t4nyvnbJeb2SzAFABorVTFaVm+9SW3sNwS2YixXSdccHmz3syj9Z+4ot34hBBXn4zpEUIIIW5Ah1NzWHskAdX6H/ozAh4AnT4Xvf889qcmsfHIKa/bCyGEKCJBjxBCCHED2nY8gxzHKQzmw17X642p5HGELQkyhkQIIc5Hgh4hhBDiBmRzqIANRfGeWhpAJQ+703POHCGEEO5kTI8QQghxA4oJ9sGoBFFgC0VvTPFYrzrNGNUYYi4ykcGNJCagwnnH4lzOeJ1Cozp+z6iO31/WPoQQJZu09AghhBA3oAblA6kQFIotqwWq6v51raqQn3krIZYwWlcOuU41FEKIkkOCHiGEEOIGpNcpPNGqOpGmphSk9ceWWxmHPQh7Xgz5p3oSTDsea1HjoufqEUKIm5F0bxNCCCFuUPXKBfB+t4ZM3BjGhmM1seXZMOgM1CgTxsAGMTSJCb7eVRRCiBJBgh4hhBDiBlY9wo+3utQiKbMyaTk2fM16ygdarvgkoEIIUZpJ0COEEEKUABH+ZiL8zde7GkIIUSLJmB4hhBBCCCFEqSZBjxBCCCGEEKJUk6BHCCGEEEIIUapJ0COEEEIIIYQo1SToEUIIIYQQQpRqEvQIIYQQQgghSjUJeoQQQgghhBClmgQ9QgghhBBCiFJNgh4hhBBCCCFEqSZBjxBCCCGEEKJUk6BHCCGEEEIIUapJ0COEEEIIIYQo1SToEUIIIYQQQpRqEvQIIYQQQgghSjUJeoQQQgghhBClmgQ9QgghhBBCiFJNgh4hhBBCCCFEqSZBjxBCCCGEEKJUk6BHCCGEEEIIUapJ0COEEEIIIYQo1SToEUIIIYQQQpRqEvQIIYQQQgghSjUJeoQQQgghhBClmgQ9QgghhBBCiFJNgh4hhBBCCCFEqSZBjxBCCCGEEKJUk6BHCCGEEEIIUapJ0COEEEIIIYQo1SToEUIIIYQQQpRqEvQIIYQQQgghSjUJeoQQQgghhBClmgQ9QgghhBBCiFJNgh4hhBBCCCFEqSZBjxBCCCGEEKJUK1VBT4cOHahevbrb3w8//OBWZteuXdx1113UrVuXtm3b8uOPP16n2gohhLiS7Hb47Tcj/fr5UKuWL1FRftSp48vdd/swbZoBVb3eNRRCCHG9GK53Ba604cOHM3DgQNe/fX19Xf+flZXFQw89RPPmzRkxYgR79uzh1VdfJSAggEGDBl2P6gohhLgCjh5VuPtuH+Li9JjNKk2bOggPV0lIUFi0SM/8+QbGjrUzZkwuQUHXu7ZCCCGutVIX9Pj6+hIeHu513cyZM7HZbPzf//0fJpOJqlWrEhcXx6+//ipBjxBClFAZGdC7t5X4eB3du9v45JN8QkOLmnWOHlV44gkLK1cauPNOK3//nYOh1H37CSGEOJdS97H/448/8t1331G2bFl69OjBAw88gOH0t9vmzZtp3LgxJpPJVb5Vq1b8+OOPpKenExgYeMHHUZQrXvVrqrD+Jf08xPUh94+4HFf6/nn3XTPx8TratLHz88956PXu66OjVSZOzKVzZysbNuj57jsTw4cXXJmDi2tOPn/E5ZD759xK9XVRS5FffvlFXb16tRoXF6dOmDBBbdy4sfp///d/rvVDhgxR33jjDbdt9u7dq1arVk3dt2/fta6uEEKIy5ScrKoWi6qCqm7ffu6yf/+tlStbVlXt9qLlFSpoy48ccS8/aJCqKoqqrlzpfX8Oh6q+/76qVqyoqkajqvr4qGqLFqo6a9a569CypaparaoaHq6qd9yhqjt3autGjNDqsXmz9u+gIFUtX15VP/+8+P316KFt8/ff7st37FDVu+5S1TJltLqVK6eq996rqrt2ee7j/vu1fYwb5778u++05R984P3Y8fGq+uSTqlq1qvYaBAeraqNGqvr226qanl58nYUQ4nq44Vt6Pvnkk/MmG5gzZw6VK1dmyJAhrmU1atTAaDTy1ltv8dxzz7m17lwJKSmZJXpQrKJAaKh/iT8PcX3I/SMux5W8f2bMMJCX50Pt2g4iI3NITi6+bJMmEBTkx/HjCosXZ1O/vhMAp9MX0JGamoXFUlSht95SmDbNl+efdzBjRq7bvmw2uOceHxYuNFChgpMePRycOKGwapWenj3hiy/yuOsuu9s2X39tZMQIC35+Ku3b20lOVpgxw8CqVU4WLcph4kQfKlRQKF8+m+Rk6NnTzKxZRp55RiE6Ooe2bR1u+0tIUJg715fy5VWaNMl2nfvSpXruuceH3FyFunUdNG/uZO9eHePG6Zk6VeWPP3Jp3rxoX3l5FsBIZmYuyclFde7XD7791sp77+kYODALf/+iY69apefee31IT1eIiXHSubODvDyFvXt1vP22jjZtsqlb13kBr+DFk88fcTnk/jm3wutTGt3wQc+DDz5Inz59zlkmOjra6/L69etjt9s5evQosbGxhIWFkXzWN2Lhv8PCwi6qXtrvXxe1yQ2ptJyHuD7k/hGX40rcPzt2aElIGzRwnHdfOh3UqeNg+XID27bpqVfP/aH87PqEhal07Ghn7lwjBw4oVKpUtPKTT0wsXGhg4EAbX3yR5xoj9O+/eu6/34dXXrHQrl02Zctq22zfruPdd834+qrMn59N5cra8o8/NvHxx2bee89MXJyexx4rcNXh00/zGTTIRo8evowda6RNG/egZ/x4Iw6Hwp13FqDTaXXPzobHHrOQm6swcmQeDz1kc5X//nsjb75p4bHHLKxenY3F4n59vL0egwbZeP11CzNnGlxBXFoaPPighfR0hbfeyuPxx23ozsgFu26djshI9ap/Nsjnj7gccv/cfG74lNUhISFUrlz5nH/FteLExcWh0+kIDQ0FoEGDBqxfvx6brehLYOXKlVSqVOmixvMIIYS4MaSmah3Qz0xccC5hYVq5lJQL67h+yy1aYLRuXdFAofx8GDvWiK+vynvv5bklRejc2UHPnnZychT++MPoWj5pkhGnU6FvX5sr4AF4/PECzGbVVbZbN/fWoaZNndSq5WD+fANZWUXLnU6YMMGIXq9y991F32kzZxo4eVJH48YOt4AH4LHHbNSv7yAhQcesWRf2m2eDBlqgtXZt0fn//ruJ5GQdHTrYeeIJ94AHoEkTJ+Hh8jQphLix3PBBz4XatGkTY8aMYdeuXRw5coSZM2cycuRIevXq5QpoevbsidFo5LXXXmPv3r3MmTOH3377za1bnBBCCFEoOFh7eE9MLPq6jIvTkZKio107u9f01336aIHL6tVFgcKmTdr2Zwc1fn64WpzCwpw0beremgPQt6+dvDyF2bOLApVFi/QcPaqjQwcH5csXBRirV2tl+vWzeewHoH9/m0fdziUkxPP8ly7Vtr3vPu/HEEKIG1GpCXpMJhNz5szhnnvuoXv37nz//fc88MADvPvuu64y/v7+/Pzzzxw9epS+ffvywQcfMGzYMElXLYQQJVThQ/mFttwkJ19cy1B6ulb+zG4wCQnaV2dUlPd9REdrQUxiYlGdUlKK36ZuXS3Q6drV7tFqAtCnjw1FUZk6tajl6LfftP+/9173LHQnTmjHjInxPp4mOlo9Xe7Cvv4Lz995xu4SErRlFStenTE7QghxNdzwY3ouVO3atfnzzz/PW65GjRpMmDDhGtRICCHE1Va7tvbgvXnz+VsunE7YsUMrV6eOZ4uKN/v2acFBZGTRA35GxsXWssiZiRIKRURoy9q3916n6GiVxo2dLFumJzlZweGA+fMNlC3rpFOnCzuPQhebjnbvXu38y5SR7mpCiJKt1LT0CCGEuPm0amXHbFbZsUPP7t3n/kpbuFBPWppCZKSTOnWKgpjCMTnOsxouHA5tG4DGjT2Di6NHvUcQhcsjI4sChbAwbefeWljmzdMqcK7Wqr59bdjtCjNnGvjjDyN2u8Kdd9o85iQqDE6OHPF+LY4cUU6XKzpZo1Hb5uzzB/jvP61ujRoVnX+5clr5Q4fkEUIIUXLIJ5YQQogSKyQEBg7Uxpa8/rrZ64M7QG4uvPOOGYChQ92DhagobaOkJPegY+ZMA0lJOho1crglHyi0eLGB9HTPY02frnU9u/XWokChMD32smXuUcqePTpXK1VcXPFfyT172tHrVf76y8j48UZ0OpV77vEcU9OsmTZmaNo07x05pkwxni5XVLfCLndJSe7Hj49X+OcfA1arSs+eRccqzCI3bpwRIYQoKSToEUIIUaK9+WY+MTFOliwxMHSohdRU9/XHjincdZcPu3bpadjQweOPu4+DKUweMH580UP8/v0Kr71mRqdTeeutfK/Hzc5WePNNC/YzchMsWKBnxgwDPj4qd95ZFCgUJhb44QcT+/YVjZN57z0t+6jJpPLffwby8ryfY0SESqtWDtat03P4sJbAwNv4oF697ISHO1mzxuAa91Poxx+NbN6sp2xZJz16FFW68PynTTOQk6Mty8qCJ56wkJ+v8PTTBQQHF+3nnnsKCA11smCBgR9+MEraXyFEiaCoqnxcXYrk5JI9qZWiQFiYf4k/D3F9yP0jLseZ98/B5By2Hs/A7lSJDrJwS1QQBt1FDjxB61JWGNhYLCpNmzoIC1M5cUJh7Vo9drtC8+Z2xo7N9ci4duoUNG/uS0qKjrp1HZQrp7JsmZ7cXHjvvXyGDnVvUZk40cDw4T7062dj/nwDQUEqjRo5SEzUJidVVYXPPsvzaIl54QUzY8easFpV2rWzc+SIjm3b9DRvbqdRIwdff22mbl0Hd9xhZ/hw98AM4I8/DDz1lA8AY8fm0rWr3aMMaNnV7r1Xm5y0fn0HlStrk5Nu26bH11ebnPTMlh6Afv18WLbMQFSUk7p1HWzcqCcxUUevXjZ++CHPI8HCihXaMbKyFCpWdNKggYMKFZz06mW/apOSFpLPH3E55P45t8LrUxpJ0HOJSvqbRd704nLI/SMuh6KAzsfMm1O2sX5/NjkZBnDoMPoVEBNp4Il2MdQvf/Fzp9ls8McfRmbMMLBzp46MDIWgIJX69Z3072+jTx97sQP5d+/W8fbbZlat0qPXa/PTDB9eQNu2nmN5CoOe55/Pp3dvO++9Z2L1agP5+VpihaefzqdzZ8/tnE74+Wete9qBA1oU0aSJgx9/zMVigVdfNTN9upEGDRxMn57rsX1GBlSp4k9wsMrOnVke43nOtGuXji++MLFsmZ5TpxRCQlRat3bw7LP5VKni+aZNT9e6/82ZYyAnR6FyZSf33mvjgQdsxV6z+HiFb74xsWiRgePHFfLzFfR6ld9/z6Vjx4tLsHAx5PNHXA65f85Ngh7hoaS/WeRNLy6H3D/icuTbHbw1dz9rNthR4yphSA1BQcFhzcZe6TARNU/xfu8qVI/0u95V9erMoOfFFz1bZK6miAh/oqOdbNiQfU2PeyGWLdPTv78PLVo4mDbNM2i7UuTzR1wOuX/OrTQHPTKmRwghxDW1dH8Km3bnoWyshTE1FAWtKUGf44tpZw2S9vkxcUPCda6luFgtWjgIDCya20cIIW4kpWaeHiGEuNrO7D4VF+fZfap37+K7T4kiC+JSyYsPwZRr9VinqDp0R8qx4cBukjLzifA3X/HjR0Sc/1fMQYNsjBpVTFYB4WHXLh1vvGHm1CmFhx7yPtZICCGuJwl6hBDiAhw9qnD33T7Exekxm7WB8uHhKgkJCosW6Zk/38DYsXbGjPEcKC/cJabb0GX7Frtel2PFZoO0HNtVCXoKDRrkmfK50JnppsX57dun48ABHcOH5/PMM9e2y58QQlwICXqEEOI8MjKgd28r8fE6una18dln+YSGFnUGP3pU4YknLKxcaeDOO638/XeOa8JL4SnI14DTUnwrimrOx2AAP8s5RupfAZfakjN4sJ3BgzOvcG0uTFLS9Tnu+fToYXdLgy2EEDcaGdMjhBDn8d57ZuLjdbRsaefnn/PcAh7QJnf8449cqld3sGGDnm+/NV2nmpYM7WsEY66QjNPo2SKgomIvf5yaURbKBViuQ+2EEEKURhL0CCHEOaSlwcSJ2iSP77+fX2wLjtUKb7yhTWL5449GHGf1jsrJgQ8+MNG0qS/R0X40aeLLBx+YXJNRNm7sS4cORWNc+vTxISLCnwUL3Fs79uzRERXlR8+ePl4zD61dq2PgQB9iY/2oVs2Pfv18WLdO+6ifNMlARIQ/c+dqJxEfrxAR4X9BfxMnup94aiq8/baZW2/VzqdaNT8GDfJh0SLP1pmJE7XjPvOM1lWtQ9UwqlQ0kBV9kK1bda70zU6DjfzYAwRXSWNQ47Lk5Ch89ZWJTp2sxMb6UbGiHy1aWHnpJTP791/bwVONGvkSEeFPQoL7cUeNMhER4U/Pnj5kZblvk5YGr71mpkEDX2Ji/GjZ0sp332mTeebmQsWKftx/f1Fg17u3zwW9Fr17+3jU788/DfToob3uFSr40batlS+/NHmd7LTwOPHxntew8J44+xgffWTyeh+cfX28bfPZZ+4/AixYoCciwp8XXii+6+KaNXq6drVSsaIf5cr50bix+/vF27HPfm0K5eTAp5+aaNPGSkyMH5Ur+9Grlw/TpklzrBA3E3nHCyHEOSxfbiAvT6FWLQe1ap170sWOHR0EBakkJurYvl1H/fpa+exs6NfPysaNeqpUcdCli4P16/V89pmZ48d1PPhgAfHxOl58Mf+MfdlZscLAf/8Z3OY8qVbNyZAhNkaPNjFvnsFtgspp0wwMG2bBYIB27Rzk58OSJXoGDLDy7785/P230TUxJoCvr+e4lr//1uZp6dXLhs8Zz70VKxZFWMePK/TqZeXwYR1RUU66drWTkqKwdKmeRYusvPNOHo89Vvx4GX+LgU8H1+EFdStZR45xcn0Up8ocJKjOCcqFqDzWLpooUzBdu2qTjQYFqbRs6cBkUjl8WMfYsUZiY51Urlz8Ma6Fb7818u67Zpo2tfPHH7n4nZFhOzFRu0YHD+qoV89Bo0YOVq3S89ZbFrKzFapXd5KTo9CtW9Hr16GDg+joouu8fbuOHTv01K3rfu9Vrep+Hz73nJlx40xYLCqtWjnw8VFZuVLP+++b+ecfA1Om5GD1zBlx3XTs6KBLFxvjxhkZNqyASpXco/c//jDw7LMWTCZo3dqB0aiybJmBzz4zs26dnsmTLzwddlYW9OljZcsWPWFhTjp1spOTo7B8uZ7Vqw2sX1/A++/nn39HQogST4IeIYQ4h+3btVaIBg0c7N2r46uviu+6NmpUHnXqOFi+3MD27XpX0PPRR2Y2btRz2212fvstF4NBC4S6drUyaZLB1Sp05gNwp04O3nkH/vvPwMiR7g9lgwZpQc/YsUZX0JOUpPDMMxZUFaZMyXUNxC+cU+azz0wsXqynUye7K5gJDVU9xrWsXOlLTo7CO+/kU66c90ksXnjBwuHDOvr2tfHVV3mYTl+S1av1DB7sw4gRZlq2dFC3bvFBYkyolS8H1mJKaDZP3gVB+aE80xNax4bibzHQr5+FXbv03HGHjc8/z3MLKOLjFTIzr2+avNGjjbz9toWmTe1MnOge8AC88oqZgwd13HtvAZ9+qr1+SUkK7dtbGT3aRIsWdgwGlc6di17z4cPdu/t99JGJHTv0dO9u59lnvScH+PtvA+PGmShTxsn06TnExmqvWUYG3H23D2vWGPjwQzMjRtxYD/Z33mln3jwjkyYZefnlonPbu1fH889biIxUmT49xxVsJycr9Ovnw7JlBn780cjrr1/Ycd5/38yWLXpatdLee4Wv0969Ou64w4cffzTRtq3d62SyQojSRbq3CSHEOaSmag/XYWEqSUkKkyYZi/0rLAeQkqJtp6pF3eOeeaaoe5yvLwwdasPpVPjzTyMVKzrdfs2vXt1JTIyTw4d17N3r/lFdq5YTq1Vl+XI99tPPzNOnay00bdo43DKPDR5sp2xZJ1OnGikocG9ZuBSHDin8+68BX1+VkSOLAh6AZs0c3HefDYdD4ddfjefdl0GnMKCDHxaLyqmDgXSrFYm/xcDGjTqWLTMQFub0CHgAYmJUatc+d6vbhThXF7I5c4r/TfCnn4y88YaFJk0cXgOetDSYO9eAXq/y3HNFD/QRESp3320jPV1h7lwjzZs7CA6+vHP46SftOj//fIEr4AEICIAPPshHUVR++83otVvY9XTLLdo9um6de3fIX34xYrMpPPdcgVvrYliYyltvaYHbzz9f2Ji57GyYMMGITqfy4Yf5bq9T1apOVyD5ww8yBk+Im4G09AghxAVq2dJx0dmzDh5USEtTCA930qSJ+4N6y5ZFAYi3YKRDBztjxpiYP1/v1qVJpwOrVSU5WcepUwphYSqbNmkPj2d2dyvUvLmDqVN1GI3uLQuXYs0avatu3h7YBwyw8f33JlavvrDMazodBARoAWWhpUu1r6a+fe0eAcWVdK6U1VFR3oOqX3818uWXZqxWlfHjc7zWb+tWPQ6HQsOGDo/WshYtHHzxhfb/lxuA2mywYYN2nfv18zyX2rW1QHrHDj3bt+to3PjyA8UrJThYuy6Jie4tditWaOfTp4/n+XTooHUfPXRIx/HjYDxPXL11q57cXIUGDRweXQJBu1dffdXCunV6nE7tXhRClF4S9AghxDmEhLi33JxPcrJWrjDDW+F2UVGeXcUqVVLx81PJylLo1s3zIa9TJy3o+e8/A8OGFa1PSlJITtZhtaquh8fC40RHez7c1a3rYOpUIy1aOAgMvKDTKFZiou70cbx3fYuJ0Y5//PiFPUGqKmRlKTjPqPaxY9q5VKx4dR/SLyVl9ZdfmjGbVXJyFH780cQLL3h2Oyt6zb29FtoyRVEvO+hJTVUoKFAIDXXiW8y0R9HRWtCjvR43TtCTnq5dI+dZVUpI0BEUpBYb7EZFOTl1Sk9CAlSocO5jnDhR/HsCIDBQC7gzMhROnYKQkIs5AyFESSO/awghxDnUqaM9MG3efP6WC4cDtm/Xn97OfYyAxeIZJCiK1m0nIED1+it8q1YOLBaVNWv0ZJ7RwPT779pP3G3a2NGfVS2LlyzPERHasTt0uPrzqCgXOdTm6FGFnByFyEjvQdSNpnlzO4sXZxMUpPL55yY2bSr+a9TbaxEQoGKxqNSo4aRs2at/zhf7elwr+/Zp161MGfdrkJFxbSt8o14fIcSVJ0GPEEKcQ6tWdiwWlR079Ozefe6PzAUL9Jw6pRAZ6XQFS4VjfE6c8Nx2924dhw7pyMqCfC/jzH18tO5QNpvC4sVaw3xiosK335pQFJUnnyxqZSg6judT3Lx52rYX0lp1vofAyEjtvI4e9V4wPl47z7Jli4K4wnFMTqfnNvPnaysbNy4KEsuX187l0KEb7ytq1Kg8KlfWxjPZ7dqktDk57mXO9VosXqwnL0+5oNfifN2tQkJUTCaVlBQd2dneyxw54vl6FMrP96xD4Ri24uTleV/v7XwKu5+d3ZoDRa97o0aeCQROnVI80n8XOnZMO59y5YqWFXfPFgZUR496v5AZGVqLk4+PSlCQ930IIUqPG+8bRQghbiDBwUVjP15/3ez1AQ60uUDefVebd2ToUJurBaZiRfX0OASFI0fcn87+/FN78HM6lWIDqk6dtNaZwvl6Xn7ZTEaGwqOP2tzGCNWvrz08Llvm3ms5LU3LAAcQF3f+1io/P+1BsbiB74VJEhYuNJCe7rl+yhTtSbdZs6KH2cKufWeO2wGw22HMGK38wIFF3ffatNHOeepUQ7EPv9dLYQDXr5+dnj1t7NunZ8QI9/lm6tRxoNOpbNig9whGJk/WzjcpSXfewKfwtfAWEIMWVBQGDdOnew5wiYvTsWOHDl9f1RWEn+nAAc/jb9t27nukcE6ls/eTk+O5r8LufWe/7pmZ2pxRiqIyYID31scZMzzPZ9EiPWlpChUrOilbtmi51er9OtWrp6Xv3rJF5/VcC1+LJk0cMp5HiJuAvM2FEOI83ngjn5gYJ0uWGBg61EJqqvv6o0cV7rrLh9279TRs6ODxx4taYBQF+va1oaoKr71mxnb62f7wYYWxY02YTNoD26xZ3odYduyoPRT+95+Br782Mnu2kVtvtfPmm+5PeL162TGZVKZMMbglEfj4YzO5uQomk8rq1XrXmKPi1KihPagWdtM7W8WKKp062cnKUnjtNYvrfADWrdMxZowRvV5lyJCiFbVqOfDzU1mxQs+hQ0VZ7UaMMLNrl562be106FAUJDVs6KRVKzvJyVr64rNbUm4UH32UT3i4k19/NbFwYdH1CgnR5qLJzlZ45x2zaxLZjRt1zJplOO9rXqh6de21OFcg8vDD2nX++GOT69qCNj/NK6+YUVWF++6zee1q9+WXZreg8uhRhS+/1DKZeQtiAP74w+gWQOTkwJtvFu38zCCvUSMt+Js7E6xOlAAAGZZJREFU1+B6z9hs8MwzFpKTddxzj41q1bz/ivDJJya3yVNTUhRXcPngg+7jqCpX1vaxY4f7dfL1hTvv1DIkvvSSxa1u+/crfP65dq5Dh3pPBy6EKF0UVfU2p7c4n+TkTK+zoZcU2lgC/xJ/HuL6uBnvnyNHFO6+W5ss02JRadrUQViYyokTCmvX6rHbFZo3tzN2bK5HV5mTJxVuv93K0aM6KlVyUreug+XL9aSm6vjkkzw++cREYqJC9+52HnnE5tZKAtCihZV9+7QHupgYJ3Pn5hAe7nnhv/rKxHvvmTEaVdq1c5CRAWvWGIiNdfLAAwW8+aaF2Fgnt99uL3beloUL9QwebMVqVWnb1k69ek63tMugTU7as6eV+Hgd0dFOGjd2kJyssHKllrVsxIg8Hn/cPTHDp5+a+PBDMwEBKs2bOzh2zMD27VClioO//851JX448xj9+vmwb5+ekBAnt96qTdzZqpWdLl0ub06ViAh/4NzZ28qXd7rNH9OokS9HjujYvDnLLSPbvHl67rvPSpkyTpYsyXZltNu7V0e3blbS0xVq13ZQqZKThQsN5ObC99/n8cwzFhwO6N7dznPPFXjNLlZQAM2ba8dt1MhB5cpO3ngj32P8U+HkpD4+7pOTJidr2/31l/vkpL17+7BypYGoKCf5+VrXwpwchfXr9ZQr5yQhQUdurha4Pf10Pk2aOPnoIxOffGImKspJSorCrbdq4802b9aTkaEQHe1k9249zZrZ6dPH7gp6n37azIQJJsLCtPskLk7P4cM6br3VzuTJuR7BWESEP1FRWta55cv1tG7twGBQWb7cQHq6QqtW2nZlyhR9/syZY+CBB3xcE+82bOh0zXl09uSkLVo4XJOT5uUpDB0qk5PebG7G76+LUXh9SiMJei5RSX+zyJteXI6SdP+cyMhjd1IWqgpVwn2JCvK55H3ZbNq8HzNnGti5U0dGhkJQkEr9+k7697fRp4+92PEFJ04ofPihiQULDJw8qRAYqPLIIzaefbaAbdt0PPusha1bdXz5ZR6DB7t3+XnzTTPff689OM6cmUOVKsVf9ClTDPz0k4ldu3TYbFra4h9+yCU6WuX9902MG2ciIEBlw4ZiBoEA48cb+fprreWgQQMtyDpbaqrWUjB3roGEBAUfH23ulccfL6B9e8+gRFVh1CgTv/1m5MQJhfLlFXr0yOeZZwqKzdSVlQU//mhi1iwD+/frXK0PTz+dz6uvXvqv84VBz7nUru1g0aKi8y4u6AEYPtzCxIlGevWy8dNPRf0C9+1T+PBDMytWaN2ywsJUXnkln7vusrNkiZ5XXjGzb5+eadNyaNnSeyC3Z4+O1183s3atnpwchRUrsr0GSH/+aWDsWBM7d+pwOLTMd3372nn00QLXZLSFCoOeWbOy+fZbkytFeOvWdkaOzGf6dAMffaQFOGPH5lK5suoKej75JI/9+3VMmWIgI0OhTh0nb72Vj8MBQ4daUBRtfqCePbV7OD8fPvjAzF9/GUhLU4iJcTJggJ1hwwrc5ng687WJjnaycmU2n35qYupU7X6JjFTp39/G008XYLV6fv78/LOR0aNNHDmiBWTTp+e69pmdDd99Z2LGDAOHDukwGrXXd8gQG337Xv3kHuLGUpK+v64HCXqEh5L+ZpE3vbgcJeH+ScspYPTyeFbtzSQ7W3vo9vWFRrG+PNa6ApH+5vPvRFwVl3r/HDum0L27leRkhYMHs847T4vwrjDoWb8+i5iYC3sBCoOer77K9QjKr6TCoOdcQXlJ+PwRNy65f86tNAc9Mk+PEKLUycq389asvWzbqaDuroYhJRQFyAhJZdGJeI6m7uHDvtUJscpM7CVJ+fIq9eo5mDfPSG7u+SenFEIIIQpJIgMhRKkzLy6JHfsc6NbXwZgcjqLqUFQdxpQw9BvrsP+AjpnbTlzvaoqLkJMDTz1l4d9/DTRvbicg4HrXSAghREkiLT1CiFJFVVXmbUuh4FAElnzPlFU6m4n8Q5HM336MuxtHYdTLbz8lQV4ezJ+vp2dPO++9VzTw/KuvTOzde2Gv4dtv53skTBBCCHFzkKBHCFGq2BwqKVl29JnF90nWZ/qTmeskM98uXdxKiJAQ2LnTc5zHwoV6Vq68sK+yF17IJzT0Stes5DlzkP+FevHFAl588eqndk5KyrzqxxBC3Jwk6BFClCoGvYLRoKCain9Ac5oK0OvBbJBWnpLuUh7ghRBC3HzkG18IUaroFIW2NQJRohNRFc/UvioqRCXStLI/vib53UcIIYS4GUjQI4QodbrViiQsOo/8antR9UXpdVWdg/zKBwiOyaRX3YjrWEMhhBBCXEvyM6cQotSpFGrl5a4V+UR/iOSoVHKPBYGqYC53isgwB092iKF2WUn/JYQQQtwsJOgRQpRKjWOCGH13bZbsS2HniSxUVaVaZBjtq4ZJ8gIhhBDiJiNBjxCi1Ar0MdKrbhl61b3eNRFCCCHE9SRjeoQQQgghhBClmgQ9QgghhBBCiFJNgh4hhBBCCCFEqSZBjxBCCCGEEKJUk6BHCCGEEEIIUapJ0COEEEIIIYQo1SToEUIIIYQQQpRqEvQIIYQQQgghSjUJeoQQQgghhBClmgQ9QgghhBBCiFJNgh4hhBBCCCFEqSZBjxBCCCGEEKJUk6BHCCGEEEIIUapJ0COEEEIIIYQo1SToEUIIIYQQQpRqEvQIIYQQQgghSjUJeoQQQgghhBClmgQ9QgghhBBCiFJNgh4hhBBCCCFEqSZBjxBCCCGEEKJUk6BHCCGEEEIIUapJ0COEEEIIIYQo1SToEUIIIYQQQpRqEvQIIYQQQgghSjUJeoQQQgghhBClmgQ9QgghhBBCiFJNgh4hhBBCCCFEqSZBjxBCCCGEEKJUk6BHCCGEEEIIUapJ0COEEEIIIYQo1SToEUIIIYQQQpRqEvQIIYQQQgghSjUJeoQQQgghhBClmgQ9QgghhBBCiFJNgh4hhBBCCCFEqSZBjxBCCCGEEKJUk6BHCCGEEEIIUapJ0COEEEIIIYQo1SToEUIIIYQQQpRqEvQIIYQQQgghSjUJeoQQQgghhBClmgQ9QgghhBBCiFJNgh4hhBBCCCFEqSZBj/j/9u4/psry/+P4CxKmiTL5Yc6hRRigoqDSCDzOqZlumonmhmn+XNLULEeZFiVkkx+5hphT02ZimrkpM0vWapU/BqIWaDpckZUoc3Iw5QulItzfP5ynz/lofqqT3OdcPh8bm+e6rxve19n7nOOL6z4HAAAAwGiEHgAAAABGI/QAAAAAMBqhBwAAAIDRCD0AAAAAjEboAQAAAGA0Qg8AAAAAoxF6AAAAABiN0AMAAADAaIQeAAAAAEYj9AAAAAAwGqEHAAAAgNEIPQAAAACMRugBAAAAYDRCDwAAAACjEXoAAAAAGI3QAwAAAMBohB4AAAAARiP0AAAAADAaoQcAAACA0Qg9AAAAAIxG6AEAAABgNEIPAAAAAKMRegAAAAAYjdADAAAAwGiEHgAAAABGI/QAAAAAMBqhBwAAAIDRCD0AAAAAjEboAQAAAGA0Qg8AAAAAoxF6AAAAABiN0AMAAADAaIQeAAAAAEYj9AAAAAAwGqEHAAAAgNEIPQAAAACMRugBAAAAYDRCDwAAAACjEXoAAAAAGI3QAwAAAMBohB4AAAAARiP0AAAAADAaoQcAAACA0Qg9AAAAAIxG6AEAAABgNEIPAAAAAKMRegAAAAAYjdADAAAAwGiEHgAAAABGI/QAAAAAMBqhBwAAAIDRCD0AAAAAjEboAQAAAGA0Qg8AAAAAoxF6AAAAABiN0AMAAADAaIQeAAAAAEbzmdCzZs0apaWlKT4+XomJibecU1tbqzlz5ig+Pl7JycnKy8vTtWvX3OaUl5crNTVVcXFxGjlypHbu3NkW5QMAAACwic+EnubmZo0ePVqTJ0++5fGWlhalp6erublZ27ZtU25uroqLi1VYWOiaU1NTo/T0dCUlJWnXrl2aPn26MjMztX///rZaBgAAAIA21s7uAv6qBQsWSNKf7swcOHBA1dXV2rhxo8LCwtS7d289//zzWrFihebPn6/AwEBt27ZNERERWrx4sSQpKipK33zzjd5//30NGTKkzdYCAAAAoO34TOj5XyorKxUdHa2wsDDXmMPhUFZWlqqrq9WnTx9VVlYqOTnZ7TyHw6Hly5f/7Z/n5+dxyba6Ub+vrwP2oH/gCfoHnqB/4An65/ZMvl+MCT1Op9Mt8Ehy3a6rq7vtnMbGRl2+fFnt27f/yz8vNLSThxV7B1PWAXvQP/AE/QNP0D/wBP1z97E19KxYsULr16+/7Zw9e/YoKiqqjSr66+rr/0+WZXcV/5yf3/UHvK+vA/agf+AJ+geeoH/gCfrn9m7cPyayNfTMmjVLqampt53To0ePv/S9wsLCdOzYMbcxp9MpSQoPD3fNuTH2n3OCgoL+1i6PJFmWjHiwmLIO2IP+gSfoH3iC/oEn6J+7j62hJyQkRCEhIf/K90pISNDatWtVX1+v0NBQSVJpaamCgoLUq1cv15x9+/a5nVdaWqqEhIR/pQYAAAAA3sdnPrK6trZWVVVVqq2tVUtLi6qqqlRVVaWmpiZJ1z+QoFevXlq0aJFOnjyp/fv3q6CgQFOmTFFgYKAkKS0tTTU1NcrPz9ePP/6oLVu2qKSkRDNmzLBxZQAAAADuJD/L8o3NvcWLF6u4uPim8aKiIiUlJUmSzp49q6ysLB06dEgdOnRQamqqMjIy1K7dHxta5eXlysnJUXV1tbp166a5c+dqwoQJf7sep9O3rwX185PCwjr5/DpgD/oHnqB/4An6B56gf27vxv1jIp8JPd7G1x8sPOjhCfoHnqB/4An6B56gf27P5NDjM5e3AQAAAMA/QegBAAAAYDRCDwAAAACjEXoAAAAAGI3QAwAAAMBohB4AAAAARiP0AAAAADAaoQcAAACA0Qg9AAAAAIxG6AEAAABgNEIPAAAAAKMRegAAAAAYjdADAAAAwGiEHgAAAABGI/QAAAAAMBqhBwAAAIDRCD0AAAAAjEboAQAAAGA0Qg8AAAAAoxF6AAAAABiN0AMAAADAaIQeAAAAAEYj9AAAAAAwGqEHAAAAgNEIPQAAAACMRugBAAAAYDRCDwAAAACjtbO7AF/l52d3BZ65Ub+vrwP2oH/gCfoHnqB/4An65/ZMvl/8LMuy7C4CAAAAAO4ULm8DAAAAYDRCDwAAAACjEXoAAAAAGI3QAwAAAMBohB4AAAAARiP0AAAAADAaoQcAAACA0Qg9AAAAAIxG6AEAAABgNEIPAAAAAKMReu4Ca9asUVpamuLj45WYmHjLObW1tZozZ47i4+OVnJysvLw8Xbt2zW1OeXm5UlNTFRcXp5EjR2rnzp1tUT68zPDhwxUTE+P29e6777rNOXnypJ566in169dPQ4cO1fr1622qFt5oy5YtGj58uPr166dJkybp2LFjdpcEL7Nq1aqbnmdGjx7tOn7lyhVlZ2crKSlJAwYM0HPPPSen02ljxbDT4cOH9eyzz8rhcCgmJkZffPGF23HLsrRy5Uo5HA71799fM2bM0M8//+w25+LFi8rIyNDAgQOVmJioV155RU1NTW24CtxphJ67QHNzs0aPHq3Jkyff8nhLS4vS09PV3Nysbdu2KTc3V8XFxSosLHTNqampUXp6upKSkrRr1y5Nnz5dmZmZ2r9/f1stA15kwYIFOnDggOtr6tSprmONjY2aPXu2unfvrp07d2rRokV655139NFHH9lYMbzFnj17lJOTo3nz5qm4uFixsbGaPXu26uvr7S4NXuahhx5ye57ZunWr69jy5cv11VdfqaCgQJs3b9b58+c1f/58G6uFnX777TfFxMRo6dKltzy+fv16bd68WVlZWdq+fbs6dOig2bNn68qVK645L774oqqrq7Vx40atXbtWR44c0euvv95WS0BbsHDX2LFjhzVo0KCbxr/++msrNjbWqqurc41t3brVGjhwoHXlyhXLsiwrPz/fGjNmjNt5L7zwgjVr1qw7WzS8zrBhw6yNGzf+6fEtW7ZYDz/8sKt3LMuy3nrrLWvUqFFtUB283ZNPPmllZ2e7bre0tFgOh8Nat26djVXB2xQWFlrjxo275bGGhgarb9++VklJiWusurraio6OtioqKtqoQnir6Oho6/PPP3fdbm1ttQYPHmxt2LDBNdbQ0GDFxcVZn3zyiWVZf/TPsWPHXHP27t1rxcTEWOfOnWu74nFHsdMDVVZWKjo6WmFhYa4xh8OhxsZGVVdXu+YkJye7nedwOFRZWdmWpcJLrF+/XklJSRo/frw2bNjgdilkZWWlEhMTFRgY6BpzOBz66aefdOnSJTvKhZe4evWqTpw4oZSUFNeYv7+/UlJSVFFRYWNl8Ea//PKLHA6HRowYoYyMDNXW1kqSjh8/rubmZrc+ioqKUvfu3XlNwk3OnDmjuro6t37p1KmT4uPjXc87FRUV6ty5s/r16+eak5KSIn9/fy6/NUg7uwuA/ZxOp1vgkeS6XVdXd9s5jY2Nunz5stq3b982xcJ2Tz/9tPr06aPg4GBVVFTo7bffVl1dnZYsWSLpeq9ERES4nXOjd5xOp4KDg9u8ZniHX3/9VS0tLQoNDXUbDw0N1alTp2yqCt6of//+ysnJUWRkpOrq6rR69WpNmTJFu3fvltPpVEBAgDp37ux2TmhoqOs1C7jhRk/c6nnnxvvAnE6nQkJC3I63a9dOwcHB9JRBCD0+asWKFf/zzeF79uxRVFRUG1UEX/Z3+mnmzJmusdjYWAUEBGjp0qXKyMhw290BgH9q6NChrn/HxsYqPj5ew4YNU0lJCb9kA/CPEHp81KxZs5SamnrbOT169PhL3yssLOym7dsbv/0IDw93zfnvT8ZxOp0KCgriBcgAnvRTfHy8rl27pjNnzujBBx/8016RdNNuIe4uXbp00T333HPThxbU19fTG7itzp0764EHHtDp06eVkpKi5uZmNTQ0uO321NfXu16zgBtu9ER9fb26du3qGq+vr1dsbKyk669NFy5ccDvv2rVrunTpEj1lEEKPjwoJCblpK/afSkhI0Nq1a1VfX+/a/i0tLVVQUJB69erlmrNv3z6380pLS5WQkPCv1AB7edJPVVVV8vf3d/VOQkKCCgoK1NzcrICAAEnXeyUyMpJL2+5ygYGB6tu3r8rKyvToo49KklpbW1VWVub2CYDAf2tqalJNTY3Cw8MVFxengIAAlZWVadSoUZKkU6dOqba2ltck3CQiIkLh4eEqKytT7969JV3/lNGjR4+6PtV2wIABamho0PHjxxUXFydJOnjwoFpbW9W/f3/base/i9BzF6itrdWlS5dUW1urlpYWVVVVSZJ69uypjh07yuFwqFevXlq0aJFeeukl1dXVqaCgQFOmTHFdrpSWlqYtW7YoPz9fEydO1MGDB1VSUqJ169bZuTS0sYqKCh09elSPPPKIOnbsqIqKCuXk5GjcuHGuQPP4449r9erVevXVV/XMM8/ohx9+UFFRkes9P7i7zZw5Uy+//LLi4uLUv39/bdq0Sb///rsmTJhgd2nwInl5eRo2bJi6d++u8+fPa9WqVfL399fYsWPVqVMnTZw4Ubm5uQoODlZQUJDefPNNDRgwgNBzl2pqatLp06ddt8+cOaOqqioFBwere/fumjZtmtasWaP7779fERERWrlypbp27er65UtUVJSGDBmi1157TdnZ2WpubtayZcs0ZswY3XfffXYtC/8yP8uyLLuLwJ21ePFiFRcX3zReVFSkpKQkSdLZs2eVlZWlQ4cOqUOHDkpNTVVGRobatfsjF5eXlysnJ0fV1dXq1q2b5s6dy39U7jInTpxQdna2Tp06patXryoiIkJPPPGEZs6c6fZ+npMnT+qNN97Qd999py5dumjq1KmaM2eOjZXDm3zwwQd67733VFdXp969eyszM1Px8fF2lwUvsnDhQh0+fFgXL15USEiIBg0apIULF6pnz56Srv9x0tzcXH366ae6evWqHA6Hli5dyqVId6ny8nJNmzbtpvHU1FTl5ubKsiwVFhZq+/btamho0KBBg7R06VJFRka65l68eFHLli3Tl19+KX9/fz322GPKzMxUx44d23IpuIMIPQAAAACMxt/pAQAAAGA0Qg8AAAAAoxF6AAAAABiN0AMAAADAaIQeAAAAAEYj9AAAAAAwGqEHAAAAgNEIPQAAAACMRugBAAAAYDRCDwAAAACjEXoAAAAAGI3QAwDwKhcuXNDgwYO1du1a19i3336ruLg4lZWV2VgZAMBX+VmWZdldBAAA/2nv3r2aN2+ePvzwQ0VGRmr8+PEaMWKElixZYndpAAAfROgBAHil7OxslZaWKi4uTt9//7127NihwMBAu8sCAPggQg8AwCtdvnxZY8eO1blz57Rjxw7FxMTYXRIAwEfxnh4AgFc6ffq0zp8/r9bWVp09e9bucgAAPoydHgCA17l69aomTZqk3r17KzIyUps2bdLu3bsVGhpqd2kAAB9E6AEAeJ28vDx99tln+vjjj3Xvvfdq6tSp6tSpk9atW2d3aQAAH8TlbQAAr1JeXq6ioiLl5+crKChI/v7+ys/P15EjR7R161a7ywMA+CB2egAAAAAYjZ0eAAAAAEYj9AAAAAAwGqEHAAAAgNEIPQAAAACMRugBAAAAYDRCDwAAAACjEXoAAAAAGI3QAwAAAMBohB4AAAAARiP0AAAAADAaoQcAAACA0f4fjDiErCn9V0EAAAAASUVORK5CYII=\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 900x900 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 900x900 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 900x900 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "tsnescatterplot(model1, 'ελλαδα', ['ριχνει', 'λεει', 'αναφερει', 'τονισε', 'εισηχθησαν', 'επεβαλε', 'αποτρεπει', 'οδηγησε'], title='Model 1')\n",
        "tsnescatterplot(model2, 'ελλαδα', ['ριχνει', 'λεει', 'αναφερει', 'τονισε', 'εισηχθησαν', 'επεβαλε', 'αποτρεπει', 'οδηγησε'], title='Model 2')\n",
        "tsnescatterplot(model3, 'ελλαδα', ['ριχνει', 'λεει', 'αναφερει', 'τονισε', 'εισηχθησαν', 'επεβαλε', 'αποτρεπει', 'οδηγησε'], title='Model 3')\n",
        "\n",
        "tsnescatterplot(model1, 'ελλαδα', ['ριμπαουντ', 'τριποντα', 'ασιστ', 'αγωνιστικη', 'βολες', 'ομαδα', 'σουτ', 'αεκ'], title='Model 1')\n",
        "tsnescatterplot(model2, 'ελλαδα', ['ριμπαουντ', 'τριποντα', 'ασιστ', 'αγωνιστικη', 'βολες', 'ομαδα', 'σουτ', 'αεκ'], title='Model 2')\n",
        "tsnescatterplot(model3, 'ελλαδα', ['ριμπαουντ', 'τριποντα', 'ασιστ', 'αγωνιστικη', 'βολες', 'ομαδα', 'σουτ', 'αεκ'], title='Model 3')"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "For the first visualisations with 8 words from a different part of Speech it seems that:\n",
        "- model 1 managed to create cluster around the 10 most common words and the remaining 8 random words of different part of speech were scattered around that cluster.\n",
        "- model 3 did not create a clear cluster of the common words, and there is no clear differentiation between the verbs and the nouns (this is a good sign for model 3, because it is mainly meant for topical relation due to its bigger window size).\n",
        "- model 2: was able to create the clearest clusterings not only for the query word and its common words, but also for the 8 verbs. It seems that model2 has developed very good embeddings for the part of speech of verbs.\n",
        "\n",
        "For the second visualisations with 10 common words and 8 random words of a different domain, it seems that:\n",
        "- In model 1 the words from the different domain are far but not very far from the common words for \"ελλαδα\"\n",
        "- In models 2 and 3 there is a clear clustering around the 8 domain specific words and the 10 common words for \"ελλαδα\" and the two clusters have a bigger gap between them."
      ],
      "metadata": {
        "id": "AYcMsW2Bir22"
      },
      "id": "AYcMsW2Bir22"
    },
    {
      "cell_type": "markdown",
      "id": "73d7b000",
      "metadata": {
        "id": "73d7b000",
        "papermill": {
          "duration": null,
          "end_time": null,
          "exception": null,
          "start_time": null,
          "status": "pending"
        },
        "tags": []
      },
      "source": [
        "#Comments/ Overall Thoughts"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "After a lot of experimentation in preprocessing it seems that all our 3 models gave meaningful results, and all three models can be further optimised.\n",
        "- Model 1: The small window size (2) helped to capture the similarity between words of the same semantic category or POS tag pretty well. It could be otpimised for tasks that require Named Entity Recognition.\n",
        "- Model 2: The bigger window size (5) seems to have helped capturing semantic similarity between words of the same POS tag and also showed some signs for capturing topical relation between words. This model could be optimised for POS tagging.\n",
        "- Model 3: The bigger window size and the more aggressive subsampling of highly frequent words helped to capture similarity between words belonging to the same topic and thus this model could be optimised for tasks that require topic modelling or topic extraction.\n",
        "\n",
        "Some optimisation steps we could try in each of the 3 models:\n",
        "1. Increase vector dimensionality to 300\n",
        "2. in negative sampling, increase the number of negative samples\n",
        "3. Adjust the learning rate by adding a min_alpha value to the training process\n",
        "4. Increase the min count to exclude more infrequent words.\n",
        "\n",
        "In our experiments we chose to work with the Skipgram algorithm and Negative sampling as it is considered a good baseline for capturing semantic similarities.\n",
        "Since Greek is a morphologically rich language we could also try CBOW algorithm in order to capture syntactic relationships between words and get vectors that capture the morphological variations of each word.\n",
        "Also another task for further experimentation is to apply Doc2Vec and try to capture semantic similarities between the articles of this dataset.\n",
        "\n",
        "\n",
        "\n",
        "\n"
      ],
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      "source": [
        "- [GENSIM models.word2vec – Word2vec embeddings](https://radimrehurek.com/gensim/models/word2vec.html)\n",
        "- [Gensim Word2Vec Tutorial](https://www.kaggle.com/code/pierremegret/gensim-word2vec-tutorial)\n",
        "\n",
        "- [Deep Learning for NLP: Word2Vec, Doc2Vec, and Top2Vec Demystified](https://medium.com/mlearning-ai/deep-learning-for-nlp-word2vec-doc2vec-and-top2vec-demystified-3842b4fad5c9)\n",
        "\n",
        "- [Text Preprocessing in Python: Steps, Tools, and Examples](https://medium.com/product-ai/text-preprocessing-in-python-steps-tools-and-examples-bf025f872908)\n",
        "\n",
        "- [Topic Modeling in Greek News using ML techniques](https://www.kaggle.com/code/andreasmarinis/topic-modeling-in-greek-news-using-ml-techniques)\n",
        "\n",
        "- [Optimize Computational Efficiency of Skip-Gram with Negative Sampling](https://aegis4048.github.io/optimize_computational_efficiency_of_skip-gram_with_negative_sampling)\n",
        "\n",
        "- [Vector Semantics and\n",
        "Embeddings](https://web.stanford.edu/~jurafsky/slp3/6.pdf)\n",
        "\n",
        "- [A Beginner’s Guide to Word Embedding with Gensim Word2Vec Model](https://towardsdatascience.com/a-beginners-guide-to-word-embedding-with-gensim-word2vec-model-5970fa56cc92)"
      ]
    }
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