{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "9d33e009",
   "metadata": {},
   "source": [
    "**Author:** Dr. Mallarapu  \n",
    "**Created:** 2026-07-27  \n",
    "**Course:** SEAS 8414 \u2014 Security Analytics\n",
    "\n",
    "---\n",
    "\n",
    "### Goal of this notebook\n",
    "\n",
    "Train and audit detectors on NF-UNSW-NB15-v2, using the standard NetFlow schema.\n",
    "\n",
    "### What you will learn\n",
    "\n",
    "1. Read a majority-class baseline before trusting any accuracy figure.\n",
    "2. Find the strongest single feature, then test it by dropping it and refitting.\n",
    "3. Tell duplicate inflation apart from genuine signal.\n",
    "4. Report per-group recall, because the rare classes carry the risk.\n",
    "\n",
    "### Where this connects to the course text\n",
    "\n",
    "The text builds a defence pipeline; this notebook trains a classifier and audits it. The links below are to specific chapter objectives that share an *analytic move*, not to matching subject matter.\n",
    "\n",
    "- **Chapter 3: Vulnerability Assessment** \u2014 Learning objective 1 (section 3.1) frames assessment as **evidence grading**, not output collection. The A-F data-trust grade in section 10 is exactly that move, applied to a model score.\n",
    "- **Chapter 8: Federated Threat Intelligence** \u2014 Learning objective 2 (section 8.1) derives why **non-IID** site distributions produce *client drift*. A model fit on one site does not carry to another. This notebook does **not** run that test: every score here is in-distribution on one corpus. The shared move is scoping a claim to its evidence. Notebook 36 runs the cross-corpus transfer matrix.\n",
    "- **Chapter 11: Formal Protocol Verification** \u2014 Section **11.1.2**, titled *Proved, tested, and hoped*, asks you to separate exactly those three. (Chapter 11 lists its objectives in \u00a711.0, not \u00a711.1 as the other chapters do.) The ablation does that job here: it tests whether the headline survives.\n",
    "\n",
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "37055c5f",
   "metadata": {},
   "source": [
    "# NetFlow-Standardized Intrusion Detection: NF-UNSW-NB15-V2\n",
    "### Model comparison + validity audit on NF-UNSW-NB15-V2 (\u22651M records, via Kaggle)\n",
    "\n",
    "**Abstract:** NF-UNSW-NB15-V2 (Sarhan et al., 2022) re-features UNSW-NB15 into the standard 43-field NetFlow v2 schema. We compare four learners on its \u22651M flows and audit the scores."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "503b0f20",
   "metadata": {},
   "source": [
    "## 1. Research problem\n",
    "\n",
    "**Task:** Classify standardized NetFlow v2 records (UNSW-NB15) as benign or attack. The standard feature set was proposed to enable *cross-dataset* comparison. So it is the right lens for asking whether high scores are feature shortcuts or transferable signal."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3b2a63a6",
   "metadata": {},
   "source": [
    "## 2. Literature review\n",
    "\n",
    "- **Sarhan, Layeghy & Portmann (2022)** \u2014 *Towards a Standard Feature Set for Network Intrusion Detection System Datasets* (Mobile Networks & Applications; arXiv:2101.11315): defines the **43-field NetFlow v2 schema and the NF-*-v2 datasets used here** (NF-UNSW-NB15-v2 / NF-BoT-IoT-v2 / NF-ToN-IoT-v2 / NF-CSE-CIC-IDS2018-v2), enabling cross-dataset comparison.\n",
    "- **Sarhan, Layeghy, Moustafa & Portmann (2021)** \u2014 *NetFlow Datasets for ML-Based NIDS* (BDTA 2020 conference; proceedings 2021): the earlier 8-field NetFlow v1 datasets. This v2 set extends them.\n",
    "- **Koroniotis et al. (2019)** \u2014 Bot-IoT origin.\n",
    "- **Moustafa & Slay (2015)** \u2014 UNSW-NB15 origin.\n",
    "- **Sommer & Paxson (2010)** \u2014 the closed-world critique.\n",
    "\n",
    "**Related approaches and their known caveats** \u2014 drawn from the wider literature; these are **not** measurements reproduced on this exact corpus:\n",
    "\n",
    "| Reported approach | Known caveat |\n",
    "|---|---|\n",
    "| Sarhan et al. (2022) \u2014 standard NetFlow ML | in-distribution; NetFlow fields can leak |\n",
    "| Cross-dataset NetFlow studies | standardized features expose generalization gap |\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "26cff308",
   "metadata": {},
   "source": [
    "## 3. Dataset provenance & honesty caveats\n",
    "\n",
    "| Property | Value |\n",
    "|---|---|\n",
    "| Source | Kaggle `dhoogla/nfunswnb15v2` (NetFlow v2) |\n",
    "| Origin | UNSW-NB15, re-featured to NetFlow v2 |\n",
    "| Label | `Label` 0/1; family `Attack` |\n",
    "| Access | Kaggle API token required |\n",
    "\n",
    "**Honestly:** `Attack` (family) is dropped from features; NetFlow header fields (ports/protocol) can act as shortcuts, which the audit probes.\n",
    "\n",
    "### Before you run this: getting the data\n",
    "\n",
    "This notebook downloads its own data on the first run, then caches it. You do not fetch anything by hand.\n",
    "\n",
    "**Dataset:** Kaggle `dhoogla/nfunswnb15v2` -> `/tmp/kg_nfunswnb15v2`. It is about **55 MB** on disk.\n",
    "\n",
    "**One-time setup.** Sign in at kaggle.com, open **Settings**, and under **API** choose **Create New Token**. Kaggle hands you a `kaggle.json` file. This notebook does *not* read that file. It reads a plain key file, so convert it once:\n",
    "\n",
    "```bash\n",
    "mkdir -p ~/.kaggle\n",
    "python3 -c \"import json;print(json.load(open('kaggle.json'))['key'],end='')\" > ~/.kaggle/access_token\n",
    "chmod 600 ~/.kaggle/access_token\n",
    "```\n",
    "\n",
    "Never paste the token into a cell, a commit, or a screenshot. If it leaks, revoke it from the same Settings page.\n",
    "\n",
    "**If the loader fails:**\n",
    "\n",
    "- `FileNotFoundError: ~/.kaggle/access_token` - you created `kaggle.json` but not the key file. Run the command above.\n",
    "- `401 Unauthorized` - the key is wrong, or a trailing newline crept in.\n",
    "- `403 Forbidden` - open the dataset page on Kaggle while signed in, accept its terms, then re-run the cell.\n",
    "\n",
    "The cache sits under `/tmp`, which macOS clears on reboot. To keep it, move the folder somewhere durable and symlink it back. Do **not** edit the path in the code cell below: that changes a code cell and invalidates the stored outputs you are reviewing."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "99af2666",
   "metadata": {},
   "source": [
    "## 4. Solution design\n",
    "\n",
    "The methodology is deliberately two-track. We *earn* a headline score with standard modelling, then *interrogate* it with a validity audit. Only a verdict that survives both is reported. The diagram below is the shape of every notebook in this series."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cc533243",
   "metadata": {},
   "source": [
    "**Figure 4.1 \u2014 Solution design (methodology).**\n",
    "\n",
    "<img 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\" style=\"max-width:100%;height:auto;\" alt=\"Figure 4.1 \u2014 Solution design (methodology).\"/>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dabd277e",
   "metadata": {},
   "source": [
    "## 5. Implementation architecture\n",
    "\n",
    "Five stages \u2014 ingestion, preprocessing, modelling, evaluation, and a parallel validity-audit path \u2014 feed a single graded results ledger. Leakage defences (dropping label-derived and identifier columns) live in preprocessing, before any model sees the data."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "35a90b84",
   "metadata": {},
   "source": [
    "**Figure 5.1 \u2014 Implementation architecture.**\n",
    "\n",
    "<img 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JUuW7M5tjh07LEOG9JSTJ30lb97C0qZNZ0mWLIXzeT+/SzJx4kgz5MfDw0OKFSsjrVt3Ek/P/361Wbp0gSxYMNfsS88xXbqMEhF6vlOmfGve14UL5yRDhkzStm1XyZQpa4jtVq1aZo63f/8uSZkyjbz2WnmpX7+pOf6sWT/IiRPH5NSp49Z5eEvt2h9KuXJvhPkcfv11hhnW9dNPf0rSpMmd63v2bGeGOH377QzzOFu2XHLv3j3TByhfvkICAACiNv70BQAAHuHoS5IqVdoQ6zWg6Nq1lZw5c1JatOgg7733kRw5ckBu3bppntfAoly5ytKpUz9p0KCZ+PhskMmTx4TYR79+neSff/6SqlVrWWFLF1PVcf78GefzgYGBMmbMQHnrrdrSrl13K9Q4ZD0eFGIfffp8IWvXrpAaNepJ3bofy5o1y0P0otm1a5tpnBs/fgLp0KG3ZM+e2wQmEdG/f2f5++9FUqXKO+Z8tHJIZzoK3u9Fj6fbxY4dR778sq+ULFnOCq22mveSOHFSyZo1lznPzp37m2s0eHAPOX36ZJjPoWzZSuZ23bqVznV63fT6lihR1rlOwyDl63tYAABA1EelDAAAeMS1a1fMrZdXvBDr//zzF9MAeOzY6c6KjRo16jqf1zAiOJ1NSWc8ctAKjq1bN1rhRD8pX76KWVe+fOVHjq+VKI7Klt27t5sAxmH79i2yb98uUz1TrVptsy5RoiSmsqZhwxYSN66X/PLLT2Zdr17fmEoapdNF//TT9xIeer4afHTpMsBZ2VK8eBmpV6+SOcann35u1s2cOUnSpElvHW+ouLm5SalSFZz70GCodu0GzscFChQzVTxbt26wQq80YToPDXYyZ85mKoMc11srlbQqpmTJ8s7tHBVJV69eFgAAEPURygAAgEdogKE8PEL+qrBly3pJmzZDiCE0wV26dEEmTBhpZhPy87to1gWfuWnz5n/NrQ5JehytRAk+1EiHSN29e8f5ePv2Tea2cOGSznVaCaOVI4cO7TNDlTSwKFiwmDOQUQ8HTGHhOF/treMQK1YsU/mi1TFKp9jW4EYraTSQCY2GMNo4WYcwOfq9OKqLwqp48bJmGJQGMdGiRTO9Y3RIV/BhVI7+P7oNAACI+hi+BAAAHqGzLqk7d26HWH/9+tXHhhsaMrRv31guX75kKmH+/HO91Kv3cYhtbty4Zm61eiSibt68YW4bNqwulSsXNsuHH1Yz686fP/v/87wmsWPHlWflOF+tvglOH1+8eM7c1/etw5Qed13mz59lhlJVrfqu/PDDPHNdIkKrkDQs02BMaWCkfWuCu3XrlrmNE+fZ3zsAAHjxqJQBAACPSJEilbk9d+6M5MqVz7k+SZJkcvbs6VBfs3LlX2Z77ScT/DXB6TAcpaFJ8Oa/4aHnoPr2HRGiCkelSZPB3OrQpdu3w1eJ8qRj6fkmSJDQuV5768SL9yBY0jAmRowYzrDoYbNnT5VChYo7h1o5qpDCSytitDL6fFdjAAAQAElEQVRGK2S8vbOYqhsd5hWcozdP8KbIAAAg6qJSBgAAPEKHD2nT2ocbxubMmc/MIKTDlB6mFTIqRYrUznU6nCi4PHkKmlsd3hRRuXMXMLcahOTLVzjEkjhxEvOczkJ05MjBEK8LCAj/kJ5cufKb2x07tjjX3b59Ww4c2B1iSJOeU2jvKSgoSK5c8ZPkyVM51z18TcKjdOmK8u+//8jGjWvMbFd58hQI8bzO9KSyZcstAAAg6iOUAQAAj9DeJDo0xtFTxUGbzGoY0L17W1m+fKEZmtO8eT1TPZMlSw6zzZw5U63QYK2MGzfU9F25c+eOFWLsNc/lyJFHihQpaWZXmjv3J1mxYol07Nhc/vlnaVhPzbmP4cO/MrMR6VTVeqyuXVuHOM/jx4+a89Pj79mzwzTmfZoECRKZqhg9fw1UdLpuPdbo0V9br59uqoG6dGlpbelmprV20KmvtXKlT58OsmbN3zJ58ljp0KGp6Tej12X9+lWyYcMa01tmyJBepk+OnpMOewrtuDr8SKcU122Cz9KkTX01EPv999nm83m4h41W0ejxwtpAGAAA2ItQBgAAhEqnu9aqDp2ByEGrZ775ZpIJDUaN+toEHdpYV4OaokVfk9atO5kqjgEDulhhwgmZOHGuvP76W2amIYdu3QZJoUIlrFDmRxN26GsdgU5Yde8+2FSx6Ot79/7CnKdjeJDSKhY9Fz2/GjVKmVmXGjdu89T96rnqcKQePT6Tgwf3OY9VrFhp63ynyddfdzUzUw0YMMY5tEnpcC0dTqVThQ8a1N1cA50aXBsN6zTYWbPmNK+dMWOiua5du34t586ddjbkffi4GorVqtXABF/Bp/rW4+j18vU9Ys4puEuXLlqh0ZJH+vgAAICoy00AAJFi3rzSpypWHJDKyyu1wPVcOhMki6a4y5vNMsjLZMSIfiYkGDNm2mNnFkLk0gqj2bOnWAHPYvH0/K89oDYT1mqdkSOnyMtkw4KzkjHHTclRlL8VAgBeHD+/Q7JqVb8DNWuuyiZRCP/3AwAAj9WwYUtT/RGe4UV4cXR2Ka0wevvtOiECGR2qtWzZn9K6dWcBAAAvD2ZfAgAAj6UzJPXrN8oMv4G9vvqqo+lNo1Nj16//SYjntDHz0KETJEuW7AIAAF4ehDIAAOCJtNkt7Fe8eBmpXr2OmWUqNLlz5xcAAPByIZQBAAB4CVSqVE0AAMCrhZ4yAAAAAAAANiCUAQAAAAAAsAGhDAAAAAAAgA0IZQAAAAAAAGxAKAMAAAAAAGADQhkAAAAAAAAbEMoAAAAAAADYgFAGAAAAAADABoQyAAAAAAAANiCUAQAAAAAAsAGhDAAAAAAAgA0IZQAAAAAAAGxAKAMAAAAAAGADQhkAAAAAAAAbEMoAAAAAAADYgFAGAAAAAADABp4CAAAixZ2bgbJzlZ8A+M/ls/6SMYcAAOCSCGUAAIgEsb1E8pbSe1cE9vvpp9/l3XcrSezYsQT2ypJPJGlqNwEAwBURygAAEAlixXWTolUEUUSP4Qskd5kykiRJbAEAALALPWUAAAAAAABsQCgDAAAAAABgA4YvAQAAl5MsWSJxo40JAACwGaEMAABwObdv3xUAAAC7EcoAAACXc/36TQkKEgAAAFsRygAAAJfj7u4uboxfAgAANiOUAQAALicwMFCCKJUBAAA2Y/YlAAAAAAAAG1ApAwAAXE7WrOkZvgQAAGxHKAMAAFzOgQO+DF8CAAC2I5QBAAAuJ1asGEKhDAAAsBuhDAAAcDm3b99lSmwAAGA7Gv0CAAAAAADYgEoZAADgcrJkodEvAACwH6EMAABwOQcP0ugXAADYj+FLAAAAAAAANqBSBgAAuJwcObyZfQkAANiOUAYAALicvXuPMPsSAACwHcOXAAAAAAAAbEClDAAAcDk5cmRk9iUAAGA7QhkAAOBy9u49yuxLAADAdgxfAgAAAAAAsAGVMgAAwOVkyZKe2ZcAAIDtCGUAAIDLOXjQl9mXAACA7Ri+BAAAAAAAYAMqZQAAgMtJliwRw5cAAIDtCGUAAIDLOX/ej+FLAADAdoQyAADA5Xh5xaFSBgAA2I5QBgAAuJzr129SKQMAAGxHo18AAAAAAAAbUCkDAABcTs6c3gxfAgAAtiOUAQAALmfPniMMXwIAALYjlAEAAC7H09NT3CiVAQAANiOUAQAALicgIECCKJUBAAA2o9EvAAAAAACADaiUAQAALiddupQ0+gUAALYjlAEAAC7n+PEzNPoFAAC2I5QBAAAuJ336VNa/pDIAAMBehDIAAMDl+Pqetv5l/BIAALAXoQwAAHA5Hh7uTIkNAABsRygDAABcQoEC74q7+38TT77xxifmVqfGzpUrs0ybNkgAAAAiE1NiAwAAl+DtndZUxzy8JEgQT1q3/kAAAAAiG6EMAABwCeXKFQl1ffbsGaVYsXwCAAAQ2QhlAACAS3jvvcqSLl2qEOvix/eSjz6qLgAAAHYglAEAAC4hRYqkUqFC0RANfrNmTS/Fi+cXAAAAOxDKAAAAl6HVMunTP6iWiRcvrjRq9I4AAADYhVAGAAC4jOTJk0iZMoVMtUzOnJnoJQMAAGzFlNgAAJeydUWQ3LsrcGG5U9WUktnjS9n8RWTj4iCBaytQXiRaDDcBAMAOhDIAAJeydWWQpMsZX6JFp1jUVXl4JJB33/3Q3L99W+DC9m24LLlKuFuhjAAAYAtCGQCAy8leLKHE8vIQAK7t8NYrAgCAnfgzIQAAAAAAgA0IZQAAAAAAAGxAKAMAAAAAAGADQhkAAAAAAAAbEMoAAAAAAADYgFAGAAAAAADABoQyAAAAAAAANiCUAQAAAAAAsAGhDAAAAAAAgA0IZQAAAAAAAGxAKAMAAAAAAGADQhkAAAAAAAAbEMoAAAAAAADYgFAGAAAAAADABoQyAAAAAAAANiCUAQAAAAAAsAGhDAAAAAAAgA0IZQAAAAAAAGzgKQAAAFHMjRvXzZIiRSp5VgMGdJF//llq7idIkFCSJ08llSpVk6pV3xUPD4/w7CrC57VgwVwZPXrgI+vbtesub775jgAAANdEKAMAAKKcZs3qSNGipazQops8DwkTJpIuXQbI1atX5PTpE/Ljj+Nl2bI/ZdiwSeEKZp71vDp37ieJEiVxPs6QIbMAAADXRSgDAABeedGiRZd8+Qo7H+v9du0+ll9++Unq1GkokSV79jySMmVqAQAAUIQyAAC84nbu3CodOjSVjh37yu+/z5YjRw5I0qQp5JNP2krJkuXMNnfu3JGxYwfJiRPH5OjRg6aao3z5KtKgQTNxd3cPsZ+JE+fK9OkTZc2a5TJ69DTx9IxmXqv7DQgIEG/vLPLOO+9LqVIVzOvu378vU6Z8Kxs3rpGzZ09Lrlz5rP30sY6R+JFzXbp0gQwd2tvcX7Ronlnq1m0kjRu3NuuOHj1kHXuC7Nq1VQIDAyVPnoLy2WfdJF68+BIeOXLkkWzZcsmSJb87Q5lVq5aZ4/v6HpZr166a82zUqJVkyZL9ied15MhBmTXrB3PtTp06LunSeUvt2h9KuXJvhOlcHndd//33H9m2bZMcPrxfokePLoUKlZAmTdqaqh+HN98sKu3b95BNm9Zayzrx8opnPrNkyVLId98NkzNnTkqxYqXNNYoTJ67zdbt3b5eff54sO3b4mP29+259efvt9wQAAEQuGv0CAOAivv9+uNSoUVemTv1DcubMK/37d5YLF86Z52LGjGmFDznkrbdqSdeuA+WNN6rLjBmTZPnyhY/sR1+n22sYkDZtBhk3boicP3/GBBht23aVxImTyebN/zq3nzp1nMyePdXsX8OBy5cvSefOLSQoKOiRfRcsWFwGDx4v8eMnMGGC3q9WrbZ57ubNG9KlS0s5edLXhBMffvip7Nmz3TrfVqHu62ly584v586ddr42Zco0UqRISWnZ8ktp06azFVTdlgEDOpvw50nnlThxUsmaNZcV0nxsva/+kiFDJuv5HnL69EkJj4evq+6nXLnK0qlTPxO0+PhskMmTxzzyujFjBkqaNOmtz2GmCWMmTBhhrRtkfR4t5euvv5V161aaz9Lh8mU/6dHjMxNwNWvWXl57rYIVqg2W1auXCwAAiFxUygAA4CI+/fQLqVChirn/8cetTfXH338vMhUfqnr1Os5tixUrJWvXrrDClXWmKW5wSZMmNw1qHQ4e3CvFi5eRqlVrmsfBK0S0Aue332ZJ2bKVpEOH3mZdvnyFrEClmmzcuNYcJ7jEiZOYRatvtFon+JAjbZarFSxjx043QYhKly6jdOzY3FSVOKp+wsrLK77cu3dPLl48b96TVsTo4qCVJb16fW6qXzQkedx5aVBTu3YD5+MCBYqZa7t16wZJlSqNhNXD1/Xh93Pq1AlZsWLxI6+rUOFNadiwhbmv1U3aULhHjyHmOqusWXOa6icHvY4acI0cOcW8L6UB1Ny506R06YoCAAAiD6EMAAAuQr/0O2jAoDMR6dAYh337dskPP4yRQ4f2mS/tKrQhRpUqvR3icYkSZU1YoEFJ6dKvS6ZMWYPtc6cJZnTozX/HTirJk6eU/ft3PRLKPMmWLev/H44kda7T4Utq165t4Q5ldFiVcnNzM7f+/v5maNTq1ctMlYujgubWrZtP3ZeGMPPmzTBDmHQ/j3tdo0Y1nPcLFSouAwb8V/ny8HW9dOmCTJgwUrZv3yx+fhfNOq2keZgORXNwDFHSihmHuHG9zIxRDtu3bzLPOwIZpUO5NKzR4Weenvx6CABAZOH/ugAAuKg4cbzMbETqwIG98sUXn8ibb9aUTz/93AQrn3/eJNTXJUwYMqhp3ryDmWZaK0NmzZps+rW0bNnRVJ04woBhw/qaJbjz589KeFy/fjVEXxSl/W40dLh48ZyEl74mWrRokiRJMvP466+7mkCqadN2JjDRkKpr19ZP3c/8+bNk3LihZsiTDm3S6/PWW8VD3Tb47Etx48YL8Vzw66qBTvv2jc2QKn1Nrlz5Zdq076xjzZRnpYGbXvvKlQs/8pwGQRqYAQCAyEEoAwCAi9KQQ4e2qF9/nS4xYsQ0PUa0qWx4xI4dW+rX/8Qs586dMb1RevZsJ9OnL3RW52h/E+1jE1zwqaHDQveljYKD034vGjLEi5dAwkOrYLSJburU6czjkyePm94rep5lyrwenl2Zfjka4jh6zGi1yeOEdfallSv/MtdS+8low+HnSa/j3bt35bPPuj7y3MOBGwAAeLEIZQAAcBH37/8XFvj6HjH9WbJnz20eX7niZ0ISRyCjlRraS0WHtYSHVlloc1qd+Ucb+qZPn8lUsty75x+iD8vT6BCaoKDAEOty5MhrGghro1rHDEQ7d/qYgKVgwWISHv/8s1TOnDllqoKUvv8H55/KuU3woV2POy89tr42efLSznVabfOs9NqpFClSP9f9Kr2O27dvkSxZcppADQAA2IdQBgAAF6Ez7OiMRdqY9scfx5tppN9440Efk8yZfIXjZAAAEABJREFUs8vWrRvlr7/+MEOEdMpn7QVz7NiD6aEfN+W0BiQ67Ekb/epsRh4enqY3iU6L7ej9olNOa6+WJEmSS+rUaeXgwX2ydOkfMmjQeNPXJjTe3llN4KLVLHp8rV6pUaOeaRrcrVtrqVWrgRkapVNR66xO2tfmSTQU0t4s+hq9/eOPOaYCpWbN983z2l9F+7UsXjzf9NHR962Nb5X2q3GEU6Gdlx5//fpV5hpcu3bFDOGKGTOW7Nmzw1TyOKYUDw/dp5ozZ6qZ+WnLln/NeehnokPNsmbNIRGlM3DpdezXr6P5bLRqZsmS38xsTx991FwAAEDkIZQBAMBF6CxKs2dPMVUy6dJ5m1Akduw45jmdcvn27VsyceJIE6xo2KBVJKNGfS3Hjx81gUtotGKlU6evTMgxcmR/MwRK+6rUr9/UuY3O7qQVJRowaM8SHTJUuXKNR/rDBKfTUg8f/pV06dLKCm4SWccvYMKS4cMny4gR/UzApMOW8ucvYqahdjTrfRwNj3SWJj1mihSpzJTaGsg4XqdBVd++I2TSpNHSq1d7E9L06zfaCl82yt69O6wt6j/2vPT448d/Y3rS6PXQqbF1GNDUqd+a2Z1ixIgh4VW06GvSunUn0zx48eLfJE+eAtZnM1emTPnW9O55llBGP3O9jtrjp0+fDiaM0mFVOjU2AACIXG4CAIgU8+aVPlWx4oBUXl5P7yeBF+eHXoHyxscZJJaXh7iKnTu3SocOTWXo0Anmyz2AB34beUTqfuEmceILAOAV5+d3SFat6negZs1V2SQKCX89LQAAAAAAAJ4ZoQwAAAAAAIAN6CkDAMArLmPGzDJ48HhzCwAAgKiDUAYAgFecTkkdnumoAQAAEDkYvgQAAAAAAGADQhkAAAAAAAAbEMoAAAAAAADYgFAGAAAAAADABoQyAAAAAAAANiCUAQDARrdu3ZJWrRrI5ct+Ajwv/v7+smDBXDl8+IAAAICoi1AGAAAbTZo0SpIkSSYJEyYyj9etWylDh/aWyPLnn7/I8eNH5VmtW7dStm/fLIga9u7dIaNHD5TvvhsmdnpeP18AALyqCGUAALDJjh0+snjxfPn008+d6zTY2Lx5nUSWKVO+NefwrJYvXyjffz9C8HRaxfKig4ocOfJKkyZt5KOPmkt4nT9/Vq5cuSzPw/P6+QIA4FVFKAMAgE2mTv1WSpWqKKlSpRG4jmHD+kqfPh3kRYoePbrUqdNQcufOH67XnTx5XD78sJps3bpBAADAi0coAwCADbSHzK5d26RQoeICAAAA1+QpAAAg0u3evc3cZsuW66nb3rhx3QwD2bZtk1y4cE4yZMgkbdt2lUyZsjq3mTFjknn+8OH9pkqiUKES0qRJW2evGnX16hUZN26obNnyr8SN6yU1a34Q6vH++usPM+Tk0KF9kjZtBmnR4ssQFReBgYHmfNauXSHXr1+VcuUqy717/k97G/Lmm0WlU6d+cvToQVm48FepX7+pvPNOPevxIZk+fYIVUm01+86Tp6B89lk3iRcvvnlds2Z1xNs7q3Tu3M88XrRovowY0U8aNWop77/f2KwbMaK/HDy4V8aO/SnUYx86tN8cY+dOH4kZM5bkz1/E2m97c4ygoCCZPXuqeT++voclWbKU8sEHTaR8+SrO1+t1++efv2TWrL+c63r3/sJ8Ho5j7ty5VTp0aCpDh06QyZPHmM8iXTpvadq0neTNW9AMC9IqFIfKlQtLjhx5rHOfHOo5d+rUQlKmTCMBAfdk69aNcvPmDSlcuIR8+WVfiREjRohjTpw413p/E2XNmuUyevQ0yZgxs7neDRo0s67zJ2ZbfX716mXWe/vE+nmZKGfOnLTOq7C0adPZes8pTAXPkiW/m20HDuxuln79RkmRIiXl/v375jPfuHGNnD17WnLlymcdt48kSpTYeb5h/fkCAAD/oVIGAAAbaBCh9Ev30/Tv31n+/nuRVKnyjrRr113c3d3l88+byMWL553baFCj4YiGHvpF3MdngwkGghs0qLts2LBa6tb9WBo2bGnuX7t2NcQ2mzatk2++6SNubm7WsXpI8uSppGvXViZQcPj55ylm0S/rGp6oDRvWSFjMmvWDCaTatOligiMNGrp0aSknT/qaEOnDDz+VPXu2m2NqWKKyZ88tR478N4vQgQN7JFas2Cb0cNDndbvQaKil+9MQokWLDvLeex+Z7W/dumme10Bm8uSxVvBUwAQeGpRpIPHvv/9IRPTp84W8/no1+f77OSb00cf37t2TBAkSyeDB46VAgaKSNGlyc99x/R5n6dI/5PbtWzJkyPfSo8dgE858//3wR7bTn5GYMWNK+/Y9TJD2OCdOHJMffxxvgqJhw34wIdSYMYPMc3pdvvyyj7mvoZSeX65cD8K4qVPHmeuUJUsOc86XL1+yQrIWzs9IheXnCwAAhESlDAAANtAKE63Y0KqWJ9m7d6cJWLp0GWCFLm+YdcWLl5F69SrJL7/85GwSXLJkuRCvO3XqhKxYsdj5+MiRg7Jly3pTFVGtWm3nfurUqRjidXPm/GiqH775ZqJ5XL58ZWnevJ78/vts+eSTtqZiYt68GVK2bCVr/Rdmm9deKy/Hjh02AcvT+PldlOHDJ1uhSizzWMMd/eI+dux0SZw4qVmXLl1G6dixuQlF9H1lz57HVO8EBASIp6enqYipUOFN67qsN9trdY0GNG+9VSvUY+oMQFrFocfQMETVqFHX3GrT3VmzJpvXNmvWzqwrVaqCnDt3xlTWlChRVsJLg5+KFaua+xqkbd78r6kuSZs2veTLV9hU+miFjd5/mlSp0lqB0tfi4eFheg+VKVPJBHR67aNFi+bcTt+XBnZPo9dw4MBvzYxfSn8GVq9ebu5rmOPm9uDvdfoZOM7vzp078ttvs8xn3qFDb7MuX75Cpupn48a1UqxYqTD/fAEAgJColAEAwAaOgOFp9Au90uoKBw00smbNZXrSOFy6dMFUd7z/fhUzLEYDm1u3/gtJduzYYm6DBwFaWREjRswQ56TbaQWLg1bMaOXIvn07zWNf3yMm4Mibt1CI8/TyiidhoaGCI5BR+kVewwBHIKN0+JJyvD8d4qMVGTqcSs9Rhz+9+WZNOXPmlKmC0UBI1+t2oXEcwxHIBKehl1bMPByQ6HCjgwf3ye3btyW8dPiTQ/ToD4YZ3bkT/v2oRImSmEDGwds7izlfrfoJrlKlt8O0P62ycgQyjvO7e/fOE1+jn70GM8F/LvTzSp48pezfv8s8DsvPFwAAeBSVMgAA2ECrZMLyRf3GjWvmVnt0BKePT548Zu7rl/T27RuboVDad0WHnEyb9p3Mnz8z2H6um9vYseM+9lg6TEbDj6VLF5gluBQpUoXYT5w4j9/PkyRMmDjEY60YenhfGhzo+7t48Zx5nD69t+mh4hiupNtnyZLdBBY6lEmHccWOHcdUd4RGj/G40Eifc+wzuLhxH2yv5/Ck4UCRzXGeGowF9/B1fZ4cn7n2nNElOMewtrD8fAEAgEcRygAAYAMNObS649Kli5I4cZLHbueoarh+/ZokSJDQuV6/BMeLl8DcX7nyLzPcRvvJaAPW0Dga/mr1zOOOp8GFVjcULVrKOQTFwVHx8d9+bsrzoNUrOrQnOB2OpEOhHO9PQ5qcOfOZITJ6zXQ4k9L+JhrUaCjzpIbJeg0fPkbw4ytHqODgCMMc5xBVOHq0vMgQ5mGOa6SNlXPmzBviOQ3GHpzP03++AADAowhlAACwgSNYOHbs0BO/xDoarerwkDJlXjf3dUjNgQO7zRAepU1XVYoUqZ2v06E+welwJ3X48AFn5Yf2h9GQ4+Hj6VCox/U70WocrWLRgCQ47c0SETly5DVDtHSKcMcXe50hSSt2ChYs5txOm/jq7FJaXaRhjMqcOZvpL6N9ahzXKTQa6OgQJn1fwYdJqfTpM5nqE535SXumOOzY4WP2Hz/+g1BGQ6mHr5UeNyJ02Fpg4P0wbXv/fshjapNkPSdH5dLz5hhSpz8bDnqN9DPXGbYe93MR1p8vAAAQEj1lAACwQdasOcw0xDp9cHA6Q49WxWgDVQ0mtDJBZzkaPfpr+eWX6aYqRmcrEnGT2rU/NK9xhBRz5kw1r9NpibUfi/YBOXBg7/+3yW6mtdZtdAaeu3fvyvDhX1lhyt0Qx9dpqnfv3i7ffz9Ctm/fbJrKtmpV39xX+qW9evU61nksMTM1aVWLNgHevn2TRESNGvXMDEXdurWW5csXym+//SwDB3Yz7yl4k10NZbSXzP79u61rl9Osy5w5u5nqWoOZJ1XKaFNfPUb37m3NMebPn2WaF2v1jFYG1a3bSBYsmCsTJ44y02IPGdLLhGA6i5WD9nLRz0WvnVar6GxEGqhFRMaMWUw/HD2Wfp63bt167LY65fWkSaPN9V+4cJ6sWrXMTDUdln5EEaFVRXqt1q9fZY6pTab1GtWp01Dmzp1mzkHXz52rTabrypUrl83rwvrzBQAAQiKUAQDAJvXqNTazCgX/Uv7662+ZYUQ9enxmGs2q7t0HS7Fipc2X4q+/7mqFAldkwIAxzqFNRYu+Jq1bd7ICmTXW+i5y+vQJK2CYa/a1desG5767dRtkeqW0aPG+dew3JH/+IlZAkDnEOenwJ923zmykIYZOn6xVPWnT/tevRcOK0qUrmnOpVq2EOV7Vqu9KRMSOHdvMxuTlFV/Gjh0s3347RNKl85avvhppmgw76DloyKRf+IOHMqdOHTfrtRrm8ceII998M8lUxIwa9bVpgqzvXcMHpaGMDs1Zu/Zv67gdzaxPOu1z8FBIpxuvVauB6d3z/vuVzfCtd9+tLxHx9tvvSeXK1WXw4B7WefWWvXt3PHZbDTq0OkinvJ42bbwVyOhn97G8KBr2dOs20FxnnQFLZ6ZSeo0++OATE7roz+by5X9a76FGiF48Yfn5AgAAIbkJACBSzJtX+lTFigNSeXmlFtjnh16B8sbHGSSWl4fYTYd3NGtWx0wp3KxZewGC69SphbkdNGic4MX4beQRqfuFm8SJLwCAV5yf3yFZtarfgZo1V2WTKIRKGQAAbKJTHbdq1dEM2dGGvwAAAHAtNPoFAMBG2sx24MBxzFgDAADggghlAACwWZ48BQR4WNOm7QQAALzaCGUAAACiIJ2SGwAAvNroKQMAAAAAAGADQhkAAAAAAAAbEMoAAAAAAADYgFAGAAAAAADABoQyAAAAAAAANiCUAQAAeIxLly7IvHkzrduL8iIFBQXJZ581kpMnjwsAAHAdhDIAAACP8eefv8j48d/IwoW/yovk5uYm+fMXkZEj+wkAAHAdhDIAAMA2N25cl7NnT0tUENq5VKnyjtSv/4m8+eY78qK9/34TUynzxx9zBAAAuAZCGQAAYJtmzerIrFmTJSoI7VySJUshH33UXJIkSSYvWsyYMaV69Toybdp3EhAQIAAA4NVHKAMAABBFFC5cUq5evSI7d/oIAAB49RHKAACAFyq5kiYAABAASURBVGLGjEnSsWNzqVWrvLz/fmUZOrS3XL7sZ55bunSBVK5c2DTSXbRonrn/ww9jHruvN98sKitX/iWTJ4+V996rKPPnzzLrW7R4XwYM6BJi23ffLSfffz/c+Xj69InSvHk9WbVqmbmtUaOU9OjRTs6fP/vUc9m5c6t5rLcOnTq1kLFjB8uPP4633lcVqV27gnms+9P3q/tv376xHDt2OMR5+fldksGDe0qdOq+b6zFiRP9HKmIyZcoqnp6esmvXNgEAAK8+QhkAAPBCZMiQScqVq2yFGP2kQYNm4uOzwQpVHoQdBQsWtwKK8RI/fgIpVqy0uV+tWu0n7m/WrB9k9+5t0qZNFylUqISEx4kTx0yI0rRpOxk27Afx9T0sY8YMivC5LFnymxw5clCGDPleKlasKr//Plu6dm0tJUqUlW+/nSk3b96wjtM3xGv69PlC1q5dYYU29aRu3Y9lzZrl8t13w0Js4+7uLilSpJbjx48IAAB49XkKAADAC1CyZLkQj0+dOiErViw29xMnTmIWT89okihREsmXr/BT9+fnd1GGD58ssWLFkvDSipSBA7919oYpXryMrF69PMLnkixZSunefZCpannrrVqmcqds2TekZs33zfMazsydO81Mda0zK23fvkX27dtlBUqdnYGPHmvIkJ7SsGELiRvXy7lvL694cuXKZQEAAK8+QhkAAPBC6HCgCRNGWoHEZhOoKG1mG1FlylSKUCCjtAIleLPe6NFjyN27dySiEidOagIZFSfOg0AladLkzuc1ZNEg6P79+2a77ds3mfXaM8Yhe/bc4u/vL4cO7TPTYTtEixZd7t3zF7x4+vl8/vlQSZ8pkWTNml6yZMlgLekkduyI/ZwBABBehDIAAOC5u3XrpumrkjJlGuncuZ/kypXfzCo0f/5MiaiECRPLy0qHM6mGDas/8pyjt42DXrsECRIKXjwPDw9p0uQ9OXHmsBw44CsLF66SgwePWz9r8axwJr1ZsmZ9ENSkTZtSAAB43ghlAADAc6dNec+dO2P6yeTKlU9cnaNKp2/fEY9UC6VJkyHE4wsXzlohQA5B5MiVK5MULZkpxLpTp85Z4YyvWRYtWiWjRh23Phe/R4KazJnTS9y4sQUAgIgilAEAAM/d5cuXzK02rXXQYToP06E9QUGBElE6DCn4DEY3blwXf/+7EhHPei5Pkjt3AXMbI0aMJ/as0RmadErsbNlyCeyTOnVys5QrV9S57s6du86g5sCBY7J48RrrZ9pX4sWLGyysSW+CmvTpUwkAAGFBKAMAAJ47R6XHnDlTzexGW7b8a6Z5vnPnjvWFdq/15fXB897eWWXnTh/Ztm2TXLt2VcqUeV3CI2PGLOLjs17u3r1rgqBvvx1s+sdExLOey5PkyJFHihQpKcOHfyWffvq5xI4dR/799x8zK9SAAf9NBb5x4xoTDj3cJBn2ixkzhuTJk9UswZ0+fd4Z1ixZsk7Gjp0pZ89eNCFN5szp/l9V8yCwiRs3jgAAEByhDAAAeO6KFn1NWrfuJPPmzZDFi3+zvsgWkIkT58qUKd/K1q0bnKFMy5ZfmqCiS5dWkiBBIlNRkihR2HvHfPxxK7l69bLUrl3eNMht3vwL05MlIkI7l+epe/fBMmrUABk9+mu5ffu2ZMqUVWrVahBim99//9nMzqTTc+PlkCpVMrOULftfs+a7d/1NSHPo0HFTVbNs2b+mZ40OddJhT46gRpcMGVILAMB1uQkAIFLMm1f6VMWKA1J5efELuJ1+6BUob3ycQWJ5eQgQlSxZ8ruMGzfUCq5+o9FvJPlt5BGp+4WbxIkvkeLs2QumkbAGNY7qmlOnzpugRmd+ejAD1INFh0UBAJ4fP79DsmpVvwM1a67KJlEIlTIAAAA2CwoKssKYsdKoUUsCmVdYihRJzVK6dCHnunv37pmg5uDBY6aaZsWKDeY2VqwYzim6HYFNxoxpBADwaiGUAQAAsJmbm5v06DHE9J6Ba4kWLZrkzJnJLMGdO3fJBDUa2Pzzz0aZMGGOnDhxJkRQkz17BvH2TmsFefEEAPByIpQBAACIAnLmzCuAQ/Lkic1SqtR/VTUBAfedQY3e7tx5QDZu3GkFO57B+tSkM7eZMqUTAEDURygDAAAAvAQ8PT0kR45MZgnuwgU/Z5+a1at95Icf5snRoyedQU3evFklTZoUJrBJmDCSGugAAMKEUAYAAAB4iSVNmsgsr71W0LkuMDDQBDU6A9T585dk6dJ1psLGw8P9/2HNg1mgMmd+MHU3AMAehDIAAADAK8bd3V2yZ/c2i2rcuJa5vXjx8v+rao6bqprJk+fJkSMnTSPh7Nkzibd3mv/PApVBEiTwEgDAi0UoAwAAALiIJEkSmqVkyQLOdQ+qanytcOa47Nt31AprtpieNdGjRzfDnzSk0aoaXTJkSC0AgOeHUAYAAABwYQ+qajKapWrVss71OuxJ+9Q8mKp7o3z33Ww5der8/ytp0ku+fNklbdoU5n7cuLEFABB+hDIAAAAAHpEsWWKzBO9Vc+/ePRPSaFhz9uwF+f33v839ePHi/r+aJv3/e9ZkkDRpkgsA4MkIZQAAAACESbRo0SRXrsxmUc2a1TG3p0+fN71qNLD5889VVlDzo/j5XQsx9En722TOnNbsAwDwAKEMAAAAgGeSKlUys5QrV9S57tat2yak0bBm9+5Dsm3bPlmyZK2kTp1MsmXL4AxrdNE+NwDgighlAAAAADx3sWPHkvz5s5vFoU+f1nLs2CnZv/+YCWtmzlxobrXZsCOgcQQ23t5pBQBedYQyAAAAACKNzuCkS+XKrznX+fld/f/wp2Nmqu5Jk36V48dPS7Fi+SRp0oRWUJPx/0sGiRkzhgDAq4JQBgAAAICtEiWKL8WL5zOLQ0DAfdNEeP/+o2ZZvHi1qbBJliyRs0eNo7KG4U8AXlaEMgAAAACiHE9PD8mRw9sswR0/fsZU1Ozbd0R+/nmhCWocw5+0miZ37izi7Z3GVOMAQFRHKAMAAADgpZEuXUqzvP56Cec6x/AnrajZsmW3jB07Q86evWiqaDSoyZ49o3MIlJubmwBAVEEoAwAAAOClFtrwpzt37poqGg1qtm8/ILNnLzH3s2RJ/1BQk8E0JQYAOxDKAAAAAHjlaEPgfPmymSU4R0XNvn1HZdmyf01wU6RIbokWzdMMldJeNRrUJEwYXwDgRSOUAQAAAOAyHFNvv/12eee6kyfPmpBm794jMm3a7yaoiR7d0xnQ6K0uyZMnFgB4nghlAAAAALi0NGlSmCV4n5pz5y6ZZsIa0Pz2298yaNBEuXv3nhn29CCkyWgqa/R1ABBRhDIAAJezd/1liRbDXQC4tnv+Qda/NH1F6LQqRpeyZYs41125cs1U1GhYs3z5evnll79MdY0jqHEMf2LmJwBhRSgDAHApBcq5yb271+RlcfjwcVm1arPUqFFBEiVKIACeH/3vQfSYBDMIuwQJ4j3SUPjGjVumR42GM6tX+8iECXNNlY2jksYR1nh7pxUAeBihDADApRQo/3J8+dL+Bl99NV4SJ44v30xuzswgABBFxY0bWwoVymUWh9u37zh71GzYsEOmTJlv/ruu4UzRonnMkKccOTJJpkwENYCrI5QBACCKGTXqJ1MW36NHcylcOLcAAF4usWLFlAIFcpjFwd//nglpjh07JZs27ZIff/xNTpw4KzlzZjIBjQY2ej9jxjQCwHUQygAAEEWsXr1FevUaIw0b1pDffhsjAIBXR/To0ZxTdOuQVKVBzZ49h62w5rCpqJk8eZ6cOXPBGdDoQo8a4NVGKAMAgM3On79khiplzJha5s0bJfHjewkA4NWnQU3+/NnN4nDnzl1TUaNhjaNHzfnzflKkSG5Jnz6V5MqV2SwpUyYVAC8/QhkAAGw0Y8YCmTbtDzNUqWTJAgIAcG0xY8Z4ZOjTrVu3raDmqOzefVCWLl0nI0b8aIU3/qaSJleuTCakyZkzsyRKFF8AvFwIZQAAsMGWLbuld++xUrfum7Jo0XcCAMDjaLP3QoVymsXh8uWrpppm9+7DMmfOX9b9byV69OjBQhq9zWK9NqYAiLoIZQAAiGQjR06zfok+JN9911tSpUomAACEV8KE8eW11wqaxeHs2QsmpNH/x0ya9KvcvHnbzASVO3cWa8lsbrVHDYCog1AGAIBIsnz5v9K16wjp16+tfPbZhwIAwPOUIkVSs1SsWNy57ujRk7Jr10FrOSTz5i2XQ4d8TSWNBjRaSaNhTerUyQWAPQhlAAB4we7dC7DCmOHi5uYua9fOEE9PDwEAIDLoFNu6vP12efM4IOC+qaTRoOaffzbK2LEzTEWNhjQFCmST7Nkzmftx48YWAC8eoQwAAC/QX3+tlblz/5J69apKhQrFBAAAO+kfBhxTcztcuXLNhDT79x+TadN+N/eTJUssefJkkbx5s5mQJnPmdALg+SOUAQDgBenSZbi5/f77PgIAQFSVIEE8KVWqkFmaNKll1h05ckJ27jwoO3bsl5kz/5RTp85bAU1WE9BooKO38eN7CYBnQygDAMBztmHDDvnss6+lb9/W8sYbrwkAAC8bb++0ZqlRo4J5rA2Dd+w4YKpofv55sfToMdoKc7wkf/7skidPVhPU6PYAwodQBgCA50hnVvLzuyqrV/8o0aJFEwAAXgWxYsWUYsXymsXB1/e0CWm2bt0rM2b8KRcu+FnhTHYT1DiGSHl40EcNeBJCGQAAnoNr125IixZ9pHLlUsysBABwCenTpzLLW2+VNY+vX78p27fvk23b9sm4cbOs+/slRw7v/wc02aVAgeySKFECAfAfQhkAAJ7R6tVbpGfP0dYvoL0ke/aMAgCAK/LyiuPsTeOglTQazixZskb++GOFqa4pWDCnc0mZMqkAroxQBgCAZzBhwhw5fPiErFgxRQAAQEjaEFiX+vWrmcfHj58RH589pv+aVtMEBYkVzuQwAU2BAjkkQ4bUArgSQhkAACLoiy8GS5Ys6WXgwM8FAAA8Xbp0Kc3yzjsVzeOzZy9YIc1eE9T89NMfZjhwgQI5pXDhXFKkSG7JmDGNAK8yQhkAACKgZcu+UqdOFSlXrqgAAICISZEiqVStqksZ81ib5W/dukc2b94ts2cvNn1qChfOLUWL5ja3qVMnF+BVQigDAEA4XLlyXSpVaiILFoyT5MkTCwAAeH4SJYovFSuWMIu6ePGyFdDsko0bd8mkSb9KUFCQqaApUiSPuU2SJKEALzNCGQAAwujIkRPStGlP2bBhlri7uwsAAHixNHSpUqW0WdTp0+dl06Zdsnatj2kerI+LF88nxYrls27zMgU3XjqEMgAAhMHNm7fkiy+GyPLlkwUAANgjVapkUqNGBbMobba/fv12+fnnhdKp0zemqXCpUgWlZMn84u2dVoCojlAGAICn0F/4OnYcKvPmjRIAABB1ZMqU1iyO2Z02bdopa9b4WAHNMLl1646UL19ESpQoIK93RFLsAAAQAElEQVS9VkCAqIhQBgCAJ9CGg82b95alSycJAACI2h70mskj7ds3NDM7rVu3zVTRtG8/UN54o6QULZpXypYtLPHjewkQFbgJACBSzJtX+lT69GVSxYjBLwEvi8DAIPn447kydep7AgAAXl76//Rt287Ili2nxMfnlKRKFU9KlUovefOmlMSJYwtefbdu+cnJk+sP1Ky5KptEIYQyABBJZs8u2DooSJIIXhpjxkjLt96S2RkzykUBAACvjF27JO2xY5Lu0CEpEjOm3PD2DtqeL5/sS57c7brgVXaqbl2fCRKFEMoAABCKQoUKjQwMDDy0devW0QIAAF5Z+fPnL+Lm5vamu7t706CgoKPW/R9v3Ljx8/79+wlo8MIRygAA8JCCBQtqt8BmPj4+1QUAALgM648ypaxgppYVzDS2Hq6w7k+yfh/4Q4AXhFAGAICHWL+QHbl69WqOQ4cO3RUAAOCSChQoUN0KZ6paywdWODPx/v3747dv335AgOeIUAYAgGCsQKZvYGCg/9atW/sJAABweTlz5owbM2bMT6y7xawlpfV7woht27bNF+A5IJQBAOD/9JeuWLFind6yZUs8AQAAeEiBAgXKuLm5NdIeNEFBQcN8fHyGCPAM3AUAABjRo0f/4v79+58LAABAKLZu3brKCmIa3717N7/1MEmhQoV8CxYs2N+67ylABFApAwDA/1m/WF27fft2qj179twQAACAMLB+f+hi3TS0ljlbtmzpIUA4eAgAANBfqN4LCgqKvmPHjhkCAAAQRmfOnFljLWNSpkxZMlWqVEuSJ09+8uzZs9sFCAOGLwEA8EB5nVVBAAAAIsDHx6f/li1bvNzd3TMWLFhwex6LAE9BKAMAwAMf37x5c50AAABE3H0rnOnt5ubWIFq0aP2tcKadAE9AKAMAcHnWL0zFg4KCth46dOiuAAAAPKMtW7bstMKZ6tbdAOv3jJkCPAahDADA5Vl/zSpo3cwRAACA58gKZsbcv39/uA5nKlSoUGwBHkIoAwBweUFBQSWtYOacAAAAPGfbt2/feO3ataLW7xsrc+bMGV2AYAhlAAAuzwpkcgQEBOwVAACAF0CHSPv4+BSNGTPmQuF7OILhhwEA4PKsv1xdtf6KtUsAAABeoHv37rWmxwyCI5QBALi0zJkzx/t/T5l7AgAA8ALt3Llzn/XHoA0FChT4XAAhlAEAuLj48eMntX45uiAAAACRYOvWrcOsPwg1tO56ClweoQwAwKUFBAQktm42CwAAQOSZXqBAgaYCl0coAwBwae7u7jp8KbEAAABEksDAwL+sm2ICl0coAwBwaVYgEzMoKOiOAAAARJJtFut3kJwCl0coAwBwadZfqjytUOa8AAAARK7L2bJlyyhwaYQyAACX5u7uHsP6S1UcAQAAiETW7x9nYseOzRBqF0coAwBwaUFBQe7WL0WBAgAAEIl0+LT1O0gsgUsjlAEAuDR3d3c365eiIAEAAIhE2tcuICAgusClMS86AMClWXmMmwAAAEQy63eQ5J6entEELo1KGQAAAAAAABsQygAAAAAAANiA4UsAAAAAAES+q/7+/vcFLo1QBgAAAACAyBc/evToHgKXxvAlAAAAAAAAG1ApAwAAAABA5PMNCAjwF7g0QhkAAAAAACJfek9Pz+gCl8bwJQAAAAAAABsQygAAAAAAEMkCAwNPWjcMX3JxDF8CAAAAACCSubu7p7FuGL7k4qiUAQAAAAAAsAGhDADApd2/f/+2dXNKAAAAgEjG8CUAgEvz8PCIZd2kFgAAgMh1gimxQSgDAAAAAEDkS8uU2GD4EgAAAAAAgA0IZQAAAAAAAGxAKAMAAAAAQCRzc3M7HxAQcE/g0ugpAwAAAABAJAsKCkrm6ekZTeDSqJQBAAAAAACwAaEMAAAAAACADRi+BAAAAABAJAsMDDxiLf4Cl0YoAwAAAABAJHN3d/e2lugCl8bwJQAAAAAAABtQKQOXNXt2oRZBQUHJBYBLW7MmMMeZM25pOnUq2FsAAC7J3d3t+nvvbflGACCSEcrAZUWLFrtzunSl0sWIEV8AuK6rV30lKOiM5MpVvKQAAFxOQMAdOXz4r6vWXUIZAJGOUAYuLVOmNyR+/PQCwHUdP75afH19rFCmjgAAXM+dO5c1lBHABmeDgoLuCVwaoQwAwKV5eLhL3LixBQAAIJKlcHNziyZwaTT6BQC4tPv3A+XGjVuCqGnfviPyyy9/SUBAgAAAALxqqJQBALg0d+vPEzFjMhtlVDVs2FTx8dkjGTKklkKFcgkAAMCrhFAGAODSAgO1n4B/iHUbNuyQOXOWyJYtuyVr1gzy7ruvS+XKpSSijh49Ke+91975OEmShJI5czr55JNakj9/DsHjtWhRT3buPCD58mUTAACAVw3DlwAACGbVqs3SqtVXkiZNcunZs4WkTJlUunUbKb///rc8q48+qiHjxvWURo3ekZMnz1mhTE8T/ODxChTIYa6bpyd/RwIAvHKOWIu/wKURygAAXFr06NEkadJEzsclSuSTsWN7SLt2H0n58sWkd+9WkjFjGiuUWSHPKn36VFKkSB6pV6+qTJ8+SBInTmAda6YgcuzYsV8mTJgjAABEEd7WwhhqF8efnQAALs3f/55cuODnfBwtWjQpVixviG1Sp04mZ85clOcpTpzYUrBgTlmzxsc83rp1rzRt2lPmzh0hEyfOleXL18u0aQNNIPTtt7PMdqdPnzfDePr0aW0CHYeiRetK69YfyKZNu6zg4YDEihVDatSoYIb+OOg+ly37V7p3by6jR0+Xgwd95e+/J0tQUJBMnTpfVqzYKIcPnzCVQU2avCtVqpQOcb762rlz/5Jduw6aKiINrJo2rS3u7u5y6dIVGTlymqxbt83MZlWmTGHp1KmJs7rF1/e0DBo0UQ4c8DUNe7NkSS/vv19VKlQobl1/f+v9jJPt2/fJ5cvXTO+YSpVKmOoY3bee9/ffz5GNG38O8X67dftU/v13mxlqFj++lzRoUE1q167s3ObcuYtWADNXVq7cJFeuXDMzbOlQNGbaAgAAUQmVMgAAFxdkgoQnOXbstHh7p5HnTY+roUhwnTsPk5gxY0iPHi1MQDFu3CwTmuTI4W0FEc1MANKiRd9HXqfb5cmTRX79daQ0bvyuTJr0iyxdui7ENhcvXjH712qgXr1amnW6b63W0WFCffu2lly5MlvBzSj5559Nztdt27bXvC5OnFhmm3LlipoQKVAb8li++GKwCXXq1XtTPv64pgmUtEGvw5AhP1ih1gVp1ep96dq1mSRLltgKVLab5376aYH12g1St+6b0r//Z1KoUE4TpNy/f1+eREOeFCmSyMyZQ+T114vLwIETZe/ew+Y5DWE++qiLGSL2008DZcyY7hI7dixruxLWeXUSAACAqIJKGQCAi3Mz02I/jn7RP3nyrBVKfPLYbU6dOmcCiwQJ4klYaaiilSPZs2cMsT558iSmmkXduXNXZs1aJJUqlTTDqJTOQFStWktZu9ZHSpUq5Hzd22+Xl2bN6pj7depUkdmzF5shV/paBw0r2rSpLw0bvmMea5XK5MnzpVatSma4ltLqFQ1QtMqkbNkiZt2kSb+aoVdDh34pbm5uZhsH7Ymj1TN6fRyVKtrIuGfP0aZSx8srjnUNj0iZMoWkZs3XzfNvvPGa8/U65bVW/Xz4YXXzWAOfsKhatYzznPW1U6bMN8fJkSOT/P33BhNeaf+eFCmSmkWDGw2u3nuvsnkPAAAAUQGVMgAAPIZWggwdOsUMxylePF+o22hgU6NGa6levXWY9qlhjAYRXbuOkCNHTpimv8G9/XY5532ddUiDGa1scdD+NzrEaNeuQyFelyxZohCPdXan/fuPPnJ8Hdb03/4Pys2bt6Rw4dwhttFhVXqOt2/fMRUrOkRIw6DQwgwdMqVKlizgXJc7d2YzLEz3ocqWLSxLlqyVb7+daQVRx0K8Xq+thkA9eoyS9eu3O6tvnkarZBy0skjdvn3X3Dr2ETt2TOc2Omzp5s3bT63AAQAAiExUygAAXJqnp8djK1xGjfpJDh06Lj//PPSxr0+YMJ55vfZZeZqvvhpnFqUhQfv2DUNUu6jgvWKuX79pbvv2HWeW4M6efXKPG92/n9/VEOu0R0vw93r16nXntsHFixfH3J47d0kSJYpvQg7HuofduHHL3Fav3uqR5xzn2KHDx5IqVTIT7kyePE/y5MkqHTs2luzZveWtt8qaSqWVKzda2w2xzs9L2rZtEKKaJrxKlSpohitp9Uznzk2t63DFVA2VLl2IWZwAAFGJb0BAALMvuTh+MwEAuLSAgPtmWM/DFi9eLT/99IfpQaLDXx5HG/YuWzZJwkKb15Ysmd9Uu2jz4KcFBDqUSbVs+b7kzZs1xHM6ROhJrl27EWJWqSft3xH+/PfaB481IIkXL67EiBHdGb48zFGhM2JEZ2fFioP2xFEakHzySW2zaFWM9qdp126gLFw43gRFWr2jy61bt+Wbb6aYKiKt9PH2TisRoZ+Xhj69e481zYlVvnzZpUuXpgIAQBSS3vpdgNmXXByhDADApUWL5mHCh+A2b95lfaH/1vRJ0eE1z4v2ZXl4qNCTZMqU1vRk0aFAT3udhksOOkTHx2evFCiQ/an71yoZbdobvPeMj88eyZYto7OqRpsAb968O9R96HNKg5uwvDcdelW58mumEbD2fQkeHGl4o/1wfvvtb9m//1iEQxk1Y8af8umndaRp0/cEAAAgqiKUAQC4tHv3tFLmuvPxoUO+0r79IDMLkFZ6aEDjoNNR65TZkUUrTxo2rGGa7iZPnljSpk0h+/YdlT/+WCnjx/eUhAnjO7edNWuhJEmSwJyzzoSk1T/161d76v61p41Oua33dfYmnflIm/d+801H53Y69XWTJj3M8KKqVUubhrrbtu0zjXR1KJL2k/nqq/Hy+ecNTcPjf/7ZLMeOnTKzHunQoU8+6WnCrfz5s5vhYlq9otNia7VPy5Z9zW3RonnMe9QwRcMZvdbP4vx5P+t97JEcObaY96bhj14/rcwBAACIKghlAAAuL/gX9d27D5sGt9r/RJfg2/z77wyJbI0a1RSd/Xrq1N/kwgU/SZcupdSoUf6RPjBVqpQyYcjIkT+ZvjQ6W1P+/DnCtH81f/7fMm3a7yZU6dbtU+fMS0qH/ujwpDFjZpjpsrXiR2ds8vDwMM8PHvyFDBjwvXz99QTTbDdr1vTSoMHb5rlEiRJYgU1bmTNnifTv/50JSLS3iwY92jhYz3P69AXy44+/y8WLl004M2nSV6YHzbPQWab0nIKHajok6rvvekv8+F4CAAAQFTAnJFzWvHmlfcuX75sufvz0AsC1vPFGE7l06Wqoz+nsSD4+v8jLpGjRutKs2XumZwseFRAQYKbtbtWqn5mWWxsPA4DDnTuXZeHCNldr1VqXQIBIVLBgwUXWzUgfH5/FApdFDS8AwOW8+24lU/milRrBFw1kCbP1TgAAEABJREFUihTJI3i59es3Xn79danzsTZU1qqh1KmTPzIjFQAAdrF+7zhl/eHgnsClMXwJAOBy6tV7S5Yu/Vd8fU+HWK89Wt5//03Byy1aNE8zHEpnhnLMCKWNio8cOSGNG9cUAACiAusPQqmtPxxEXrM6REmEMgAAl6OzLb3+egn54YdfTXWMgzafLVeumLxsvv22xzP3YHmVtG3bwEzz3bHjN2bmKm0knDVrBhk+vLPpZwMAABBVEMoAAFxSvXpVZdmy/6pltPnr++9XlZdReKbZdgWxYsWUfv0+EwAAgKiOnjIAAJeUMGE8qVSppOklo7y904SYcQgAAAB40QhlAAAuSytj0qRJIQkSxHNO4QwAAABEFoYvAXBZl84EyeHtApcWV8rn+1hOnDgrsW4Xlo2LgwSuK1FKkcz53AQAACCyEMoAcFl+Z0QObPWQtNm9BK4rX75y1iJy+7bAhV254C9+Z29boYwAABApgoKCjrq5ufkLXBqhDACXliBpNMldJpEAcG2+u2/I+SMkcwCAyGMFMhmtm+gCl0ZPGQAAAAAAABsQygAAAAAAANiAUAYAAAAAAMAGhDIAAAAAAAA2IJQBAAAAAACwAbMvAQAAAAAQyYKCgi4EWAQujVAGAAAAAIBI5ubmljRatGh8J3dxDF8CAAAAAACwAaEMAAAAAACADQhlAAAAAAAAbEAoAwAAAAAAYANCGQAAAAAAABsQygAAAAAAANiA6bcAAAAAAIh8xwMCAvwFLo1QBgAAAACAyJfO09MzusClMXwJAAAAAADABoQyAAA8R5cuXZB582ZatxflVbN27Qo5dGi/AAAA4PkglAEAF+bre0QGDuwu586dCdfrVq9eLrt2bZNnNXv2VPn55ynyKvnzz19k/PhvZOHCX+VVo+9r+/bNAgAAgOeDUAYAXNiJE8dkxYrFcvfunXC9bsCALs/ly7ke+/Dhl7fy4saN63L27OkQ66pUeUfq1/9E3nzzHYlq/vlnqbRr97G8/35l+fbbIXLnTtg/9zNnTsn582clb95CAgAAgOeDUAYAgAhq1qyOzJo1OcS6ZMlSyEcfNZckSZJJVPLXX3+YMK1w4ZLSvHkH8fHZIB07fhrm12sI5+UVTzJlyioAAAB4Pph9CQCA/9uzZ4cJKxo0aCqvmvnzZ8rrr7/lfG8pUqSStm0bmvecM2fep75+504fyZ27gLi78/ccAACA54VQBgBeAZ06tZD06b0lS5YcMnPmJMmYMYv06DFY/PwuycSJI2Xz5nXi4eEhxYqVkdatO4mn5+P/8z9jxiTZtm2TGVYUPXp0KVSohDRp0lYSJkwUYrt79/xNP5oNG1ZL3LheUq/ex/LWW7VCbKPVGYsXz5dDh/ZJ2rQZpEWLL60v9vkfe+yAgAAzpCpDhkzi5ub22Peq+4oXL74sXbpAbt68IeXLV5FPP/3cnG9Yj33hwjmZPn2CrFu3Uq5evSJx4sQ1VSCxY8d96nXQ4w4d2ttst2jRPLPUrdtIGjdubYUXW6VDh6bW8xNM2FG3biUpUKCodOs20HnsHTt85Msvm0nPnkPktdfKR+hzCg8dYnX48AGpWfMD5zr9WYkdO478++8/YQpl9u3bJZUqVRMAAPB8WL/r3LhvEbg0/twFAK+IHTu2yJQp38oHH3wi1avXNev69PnCzJhTo0Y9Kxz4WNasWS7ffTfsifvRQKRcucpW+NFPGjRoZipHJk8e88h2v/zykwkoOnTobUKgUaO+DtFnZtOmdfLNN31MuNKuXQ9JnjyVdO3ayvQleZzhw7+S5s3rWfue/sRzXLLkNzlwYI989dVIa9/dTVPd33+fHeZjawjTtu1Hcvr0SRkz5icZMGCMxIoVW0qXft26ZsOeeh0KFiwugwePl/jxE1gBSmlzv1q12o+cpwYsGrps2bJegv/OtXHjGokRI4YULVrKPA7v5zR58lipXLnwI4uuD42jkXPSpMmd67TiRR+fO3dankav16lTxyVHjjwCAACej6CgoLjW/489BC6NShkAeEUcPXpIRo6cItmz5zaPt2/fYqob2rTp7AwMEiVKIkOG9JSGDVuY6pbQlCxZLsTjU6dOmIa8D6tYsaqpTlElSpSV+vWrmmAkX77CZt2cOT9ax0tshSMTzePy5SubwEW3+eSTtqEeO126jOZWK1ueREOW3r2HmUoSDU9mzJgoBw/udT7/tGOvWfO3qU4ZOHCc6QGjS5kyr8vUqePk7bffM2HOk65D4sRJzOLpGc1cU8d7Dk3JkuVl8eLfZNeurc7ttCJGA5lo0aJF6HOqXLmGFQwVe2R90qQpQj2HK1f8zK1WxgSn+7582U+eRocu6blmz04oAwAA8DwRygDAK0LDCUcgo7Zv32RutbGrgz7v7+9vhvTkz18k1P1cunRBJkwYaape/PwumnUxY8Z8ZLvgAYBWXegQGA0XlA5D0sod7WHioEFHtmy5rG12yuPoECBdniZx4qQhhvbEiBHTOYNUWI4dFBRobrU6xkGHLd26ddNUtOi+w3odnqZw4RLmOBs2rDGhjA6b0gCtTp2G5vmIfE6pUqUxS1hZf4l70rPyNBrK6DlpdQ8AAACeH0IZAHhFJEgQsueL9lpRDRtWf2Tbxw0h0lCiffvGkjJlGuncuZ/kypVfpk37zjSJfRqdmefixfPm/u3bt0wQoL1XdAlOG8y+SGE5tlapaFDy889TTIWKVov89dfvZiiSBjLPch0epkOYtJJIhyw1a9bO9ODRY+ixVEQ+Jx2m9PCsT0r7+nz8catH1jv6ATmO5aBTemsvoqfRkEvfAwAAAJ4vQhkAeEU5pmTu23fEIxUeadJkCPU1K1f+ZfqPaB+VXLnySXjoF3ztsaI0oNFjavjxcK+V6NFfbLVFWI6tw5VatepomvUuWDDXrNP326ZNF3P/Wa5DaEqXrih//73IVMls3LjWNP7VxsIqIp9TeIcvOQK7M2dOWcd+sC4wMNB6fPKxFVMO2k9GK3uaN/9CAAAA8HwRygDAK0qnL1Y65ORJPU+Cu3z5krlNkSK1c50OoQlNQMC9YPcfDBnKk6egc51Wl+gQoLAe27Gfp82+FBZhOfa8eTPkww8/DXX667BeB614cQyFepIiRV4zn8M//yyVLVv+tQKhTs7nIvI5hXf4UurU6Uyfmj17tkvVqjXNOm2UrEOknhbK0E8GAADgxWH2JQB4RelMOUWKlDQzGq1bt9JM7zxu3FDp2rW1c5uECRObW63e0IoInSZZzZkz1azT7Xft2iZ37tyxvsTvDbF/bV6rjXN11qCePduZ1wefcrl+/aaye/d2+f77EaYvi1aKtGpVP8QMTXr848ePOvcd1tmXniYsx9ahVhokaa8XXX/ihK+pHlFhvQ7e3llNaKHXdtWqZY89Hw01dPjP7NlTTPAUvIlwWD6nZ6Xh0QcfNDHDuX76aYIJh7RKKGPGzFK8eJknvpZ+MgAAAC8OlTIA8Arr3n2wjBo1QEaP/lpu374tmTJllVq1Gjif1+a8OjxnwoQR5nHt2g2kdetOpopEQ5c8eQrIxIlzzVTbW7dukKxZczhf+2Ca6PVmJiFtjtu2bRfJnTu/83ndr041rfv+44/ZpjlvoUIlJG3ajM5tdAponQ66c+cWVmCxLMyzLz1NWI7duHEbc22CBzUaUgwe/J0ULfpamK5Dy5ZfmjClS5dWZoiQo+olNCVKlDPDovLmLWhtmzDEc0/7nJ4HnVVKh08tWjRPbty4ZgIlPa42aX4S+skAAAC8OBGvDQdecvPmlfYtX75vuvjx0wtc00GfINnvE1NK1HyxjWcR9Wn1is4c1bVrK3nzzZrSokUHwYN+MnXrVpJBg8aFaxjay8h39w05f+SCVGkkAFzMnTuXZeHCNldr1VqXQIBIVLBgwUXWzUgfH5/FApfF8CUAgMsZMaKf/Pnnr87HOrxHq3y0h8yVK36CB3S2Jm1STD8ZAACAF4PhSwAAl+PpGU3mzv3RzHzkmPFIhzH5+h6R999vLHhAmwl36zZQAAAA8GIQygAAXE6TJm3NFN79+nU0MxDpzETax6VPn+FSvHhpAQAAACIDoQwAwOXEihVLOnfuJwAAAICd6CkDAAAAAABgA0IZAAAAAAAAGxDKAAAAAAAA2IBQBgAAAAAAwAaEMgAAAAAAADYglAEAAAAAALABoQwAAA8ZN26o/P33IgEAAABeJEIZAECU5+/vL8ePH31k/Y0b1+Xs2dPyvP3++2w5c+aUvEh37tyRMWMGSeXKhWX+/FkCAAAA10MoAwCI8oYN6yt9+nR4ZH2zZnVk1qzJ8rLZs2eHtGhRT5Yu/UMAAADgughlAACIRFoh07lzC0mUKImMHDlVAAAA4Lo8BQAAG61atUyWLl0gvr6H5dq1q5IrVz5p1KiVZMmSXc6fPysffljNua0O9cmRI4+89VYtGTq0t1m3aNE8s9St20gaN24tR44clFmzfpATJ47JqVPHJV06b6ld+0MpV+6NR467YMFc2b9/l6RMmUZee6281K/fVNzdH/17Rc+e7eXQoX0ybtxMiR8/gTyLmDFjSv/+Y8z71IAGAAAArotQBgBgKw1EihQpaQUt78rNmzdk4cJfZcCAzjJp0q+SIEEiGTx4vMyc+YOcPOkrX37ZR+LFS2AWXd+/f2fJnj2P1KpV3+xHJU6cVLJmzSUlS5aX6NGjy7p1K61te1jrckqqVA+22bVrm3ltiRJlrX32tYKcA7Jz51YJDAx8JJTRY2/Z8q8MH/5DqIFMQECAde7FQ31vf/65Xjw9H/1fbZ48BQQAAAAglAEA2EorYnRxiBMnrvTq9bmpckmbNoPky1dYFi2aLxcunDP3HRInTmIFHtHMMKDg6zU4qV27gfNxgQLFTCXO1q0bnKHMzJmTJE2a9NZxhoqbm5uUKlUhxDnpOrV9+2aZOnWctGnT2YQ6ofHw8DAB0eOeAwAAAB6HUAYAYCudWWn69AmyevUyOX36pAQFBZn1t27dlIjSEGbevBlmCJPuP/j+7t+/Lz4+G6RKlXec4UtoLl26IP36dZJy5Sqb4VKPo/sIHgoBAAAAYUUoAwCw1ddfdzX9Wpo2bSeFChWXfft2SdeurSWidHrpceOGmuqWYsVKS8KEiUMML9JwRocpeXnFe+J+AgPvW4HOXYkb1+uJ20Vk+BIAAACg+E0RAGCbkyePm54vjRq1lDJlXpfnYfbsqSbcqVattnmsoUlwGsbEiBHD9K95kqRJU5igaPTogVK6dMXHVsMwfAkAAAARRSgDALDNlSt+5jZ58lTOdYcP739kO6020cqV0NYHBQU6H+vQJ91n8uSlneu0CudhuXMXMP1inkaDHZ2laeDA7jJx4lzT7+ZhDF8CAABARLkLAAA20Ua+OkX04sXzZdu2TWbo0dy508xzOkOSQ8aMWeTMmVOydu0KWbnyL7l165ZZ7+2dVXbu9DGv1fBEA5IsWXLI+vWrZMOGNaa3zJAhvSAGwQYAABAASURBVKxjxJI9e3aYYUtKp77WfjN9+nSQNWv+lsmTx0qHDk2dVTWOvjaqY8e+1vFuyKhRAwQAAAB4nghlAAC20ZmS+vYdIXfu3JZevdrLsmULpF+/0dKkSRvZu3eHc7u3335PKleubqa2/uab3s7nWrb80lTZdOnSyvSR8fO7JJ079zczJWmvmhkzJsp7730kXbt+LefOnZZ79+6Z1+XKlc8c98yZkzJoUHfZuHGNaegb2nCjJEmSSbNmn5swSEMhAACA58H6I9DNwNBKgeFS3ARwUfPmlfYtX75vuvjx0wtc00GfINnvE1NK1EwlAFyb7+4bcv7IBanSSAC4mDt3LsvChW2u1qq1LoEAkahgwYKLrJuRPj4+iwUui0oZAAAAAAAAGxDKAAAAAAAA2IBQBgAAAAAAwAaEMgAAAAAAADYglAEAAAAAALABoQwAAAAAAIANPAUAAAAAAES2EwEBAf4Cl0YoAwAAAABA5Evr6ekZXeDSGL4EAAAAAABgA0IZAAAAAAAAGxDKAAAAAAAA2IBQBgAAAAAAwAaEMgAAAAAAADYglAEAAAAAALABU2IDAAAAABD5Tt+7dy9A4NIIZQAAAAAAiHypokWLxndyF8fwJQAAAAAAABsQygAAAAAAANiAUAYAAAAAAMAGhDIAAAAAAAA2oKkQAJd25cI92bnKTwC4tqsX/CVGdAEAAIhUhDIAXFailCJZC9y37l0RuK5Dh3zl+PEzUqFCcYHrip3uwX8TRNwEAAAgshDKAHBZiVO6WYvAxV1adEL2nPWRolVKCAAAABCZCGUAAAAAAIh8RwICAvwFLo1QBgAAAACAyOft6elJRzMXRygDAHBpHh7uEjdubAEAAAAiG6EMAMClBQUFSkBAgAAAAACRjVAGAODSAgNF7txhODcAAAAiH6EMAMDFBYm7u7sAAAAAkY1QBgDg4twkUMtlAAAAgEjGnwYBAAAAAABsQKUMAMClRYvmKUmSJBQAAAAgshHKAABc2r17AXLx4mUBAACIZEcCAgKYbcDFEcoAAFyau7ubRIsWTQAAACKZt6enZ3SBSyOUAQC4tMDAILl3754AAAAAkY1GvwAAAAAAADagUgYA4NK00W/ixAkEAAAAiGyEMgAAl6aNfi9duiIAAABAZGP4EgAAAAAAgA2olAEAuLTo0T0lWbJEAgAAAEQ2QhkAgEvz9w+Q8+f9BAAAAIhsDF8CAAAAAACwAZUyAACXFj16NEmSJKEAAABEJjc3t/vWTaDApRHKAABcmr//Pbl48bIAAABEpqCgIA9h9IrL4wcAAAAAwP/Yuw/wKKqvj+MnldB7ld577x0FAQWRDorSBKnSO0gRpYgUAVGKiAKi0iwUxYIISA2E3jvSewtp+865vLv/BBIILZvsfj/PM89kZ2dnZ2ZF2F/OPRcA4ARUygAA3JqPj7ekTMnwJQAAAMQ8QhkAgFsLDg6RS5cYvgQAAICYx/AlAAAAAAAAJ6BSBgDg1ry8PCVRogQCAAAAxDRCGQCAWwsNDZObN28LAAAAENMIZQAAbs3TU8TPz1cAAACAmEYoAwBwa2FhIoGBQQIAAADENBr9AgAAAAAAOAGVMgAAt+br6yOpU6cQAAAAIKYRygAA3FpQULBcuHBZAAAAgJhGKAMAcHM2My02AAAAENMIZQAAbs7DTIsNAAAAxDR+NQgAAAAAAOAEVMoAANyat7eXJEuWRAAAAICYRigDAHBrISGhcvXqdQEAAABiGqEMAMCt+fhopUxiAQAAAGIaoQwAwK0FB2ulzA0BAAAAYhqhDADA7Xl60vceAADErLCwsCPWEiRwa4QyAAC38/LLbeXSpWsRti1b9rdZ22w28fdfJAAAAM+T9Uuh7NbiK3Br/GoQAOB2GjSoYapjPDw8IiwayJQqVUgAAACAmEAoAwBwO82avSqZM6d/YHvy5EmlefPaAgAAAMQEQhkAgNvR2ZaqVy9nqmPCy5Uri1StWkYAAACAmEAoAwBwS82avRKhWiZp0sTSvPkrAgAAAMQUQhkAgFtKnjyJ1KhR3lEtkz17RqlSpZQAAAAAMYVQBgDgtrQyJmPGdJIsWRJp0aKuAAAAADGJKbEBuK1LZ2xyOEDg1hJJtSKt5eTJsxL/TknZtNImcF8p0ovkLOIhAAAAMYVQBoDbunxG5MA2L8mUN7HAfRUpUtVaRO7cEbixqxeC5PLZO1YoIwAAADGGUAaAW0uW2kcKVk4hANzb8d035fwRkjkAABCzCGUAAAAAAIhhYWFhR6wlSODWCGUAAAAAAIhhnp6e2a3FV+DWmH0JAAAAAADACQhlAAAAAAAAnIBQBgAAAAAAwAkIZQAAAAAAAJyAUAYAAAAAAMAJCGUAAAAAAACcgFAGAAAAAADACQhlAAAAAAAAnIBQBgAAAAAAwAm8BQAAAAAAxKiwsLC7ISEhYQK3RigDAAAAAEAM8/T0jOfr68voFTfHfwAAAAAAAABOQCgDAAAAAADgBIQyAAAAAAAATkAoAwBALBMUFCS//LJQDh8+IAAAAHBdhDIAADlwYI/89ddKCQ0NFWe5e/euTJw4UjZsWPNYr9u9O0D+/nuVPK3161fLuHHD5Ek9y3u4d+8OmTx5tHzxxXh53pYtWyQnThwVAAAAxDxCGQCA7NmzQ0aPHmyCEWcJCrorK1YslYsXzz/W677++nNZvnyxPK2AgC2yZct6eVLP8h7my1dY2rbtKm+/3UGet6+++kxWrlwqAAAAiHlMiQ0AQCzj6+srTZq0FAAAALg2KmUAAAAAAACcgEoZAHBDq1b9YhrJHjt2WIoWLSWZM2d7YJ9+/TpKlizZJVeufPLtt7MkW7ZcMmTIWDl69JDMmzdDdu3aJmFhYVKoUHHp1m2QJEmSNMJr06fPKCEhwbJt2ya5deumlCxZTvr0GSHx4sWT6Fqz5ndzrsePH5br169JgQJFpFWrztY55X1g3wULZsvPP/8ggYF3pEqVl6Vz577i5eXleF57z3z33WzZscNfkidPIQ0avCl16zaWJxWde3j58iWZOXOSGRal51KmTGXp0qWfeHvf++t3585t0rt3O2ufhdY9nSlr1/4hkyd/Y93rnFK7dmlp0aK9vPnmOzJ8eG/r/LfL99//7jh2cHCwNGpUTWrVel06duxtetnoUKRNm9bK2bP/mXvVu/dwSZEipeM1165dlWnTxsnWrf9KokSJpX79NwQAAADOQ6UMALiZXbu2m4a2SZMms760D5O8eQuacCEyO3ZsNV/033jjHXnttaYmXBkwoJOcOnVc2rZ9T956613ZsydABg7sLDabLcJrV636We7cuS0ffzzdhDkazkyfPkEehwY7pUqVl06d+kjXrv1N4PLRR/1NGBSenoOGLrpfzZr1TPPa+fNnOZ6/cuWydQ7dTKDUvn0PqVDhRZk6daz8888f8iSiew+HD+8l69b9JfXqNZOmTVub0CWy5r0ffthf/Pz8pEePIZIpU9YHnq9QoZoJVPbv3+3Y5u+/0bofgeZa1Jw506zQZo4J0TQku3LlkvTv3zHC5zJmzGDZuPEfcy4tW3YyP2vYBQAAAOegUgYA3MyiRXMlRYpUMnToJ45KkpCQEJk7d/oD+2qIMWnSVyZ0UN9995X5Ej916jxJmTK12aYVIn37dpB///1bypev6nhthgyZrLBmlHmPDBkySuXKNeTPP1dIhw69xMfHR6JDK2LCV8UkTJjIOu+ecvr0iQjhRdq0Gazt40wFigYYFy+ek59//t4Kk9qa99fARAMlvRb76zTgWbjwG6lU6aVI31sb9gYHB0XYptUl0b2HAQFbZd++XSZMqlOnkdmmr/n44/etQKSj41gqdeq00r37YImK3le9Nq2CyZOngNmm1TdanVSoUDETzvz44wKpUqWGCYlUkSIlrNCsjvWadVKmTEU5cuSgbN26IcL5lC1bWZo0eUkAAIBT/BccHBwicGtUygCAm9m50998kQ8/tCdx4iSR7ps1aw5HIKP0S72GGvZARunwJaXVI+FpABH+PbJnzyW3b9+SM2dOSXQFBQXJ7NlTpU2b+lKrVikTyCg9Tnh6PvYhQSp//iKmskSH8aiAgM2SJk26CEGOhhtaeaJhSmS08qRhw2oRFrvo3EN9T1WyZHnHNr2Xek2HDu2LsG+NGnXlYRIkSCjFi5eRjRvXOrZpBY4GUB4eHlb4s9MEMyVKlJPw9yRt2vTWNe4yj7XqSRUpUtKxj1bnxIvnJwAAwCkyWL+oolDCzfEfAAC4mRs3rltf8hNFa99kyVLc99prplolPE9PT1P1odUpD2N/nYYl0TVq1EATYLRr190KHMqaypOBA7s88nX2gOTy5YvywguZTJXM+fNnpWbNkg/se+nSBRNe3E/7zYSv/AkvOvdQ31O1bPnaA8/puYSXPHlKeZTy5avJxIkj5erVK3L9+lVz3uXKVTXP3bx5w6zHjx9hlsjey75PdD97AAAAPH+EMgDgZrSC5c6dW/IkdJiNvfrETvu7aACRJEmyh77W3rskOgGEOnXqhKxfv1pateoklStXl8dhDyDs4Yyetw5H6tZt4AP7RnU+6dO/YJbIROcepkqVxqxHjJhoKlLCy5gxqzwuDYg+/fQj2bBhjbk+rZ7R5slKr0/pvcqfv/AD56q0ubG6ffumpEyZSgAAAOB8hDIA4GZ02I72FwlPZ0mKjnz5CsuWLf+axrn2L/k6lEebyerwmvBCQyMOC9LZg7Qxbrp0GSQ6rl69bNbaL8bu8OH9ke57//nrOWkgY58RSc9be7zkypXfCjMSyNOKzj0sWLCYWetsU+GHDD0pvXc6y5P2ktGAq0yZSo7hU1my5DDVStoDJ6r3yp37Xi+aw4cPOIZx6YxNUQ3fAgAAwPNHTxkAcDP16jWVEyeOytKlC0wfkj17dpjGtdF7bTPTXHbQoC7yxx/L5ccfv5PRoweZGX/KlasSYV+d7nnWrMlWGLJFli9fYqa31imYw/d+CU+HN/n6+prz+e+/UyY40AqTlSuXyvbtm835amNedX//moMH98r06RNNz5spU8aYWZUaNmxhhlbZrzl+/PgycmRfcyztzTJiRB/5+uvPHcfQoVo6LEkb494/k9ST3MN8+QqZmaMmTPjAVPzo++p01NEZfhWVihVfNNeo05GHH1ql96lJk5bm/ui91nu+cOFceffdpma4k9KGyQULFpUffpgjJ08eM5VDem5BQXcFAAAAzkEoAwBuplix0tKlSz8TItSrV9HMGNSmTddovVarTCZMmC2JEyc1U0p/9tnHkjlzdvngg0mm4Wx4GgDoDEc63fM333xuBTLNpVmz1lEeWwMUDVI07NFpo7UyRIf+6DGGDu0hv//+ixWqTJa2bbvK3r07Iry2atWaZkYnnfL5559/kFq16knTpq3CnXdCc97BwcEyfHhvmTjxAxO82KeTVtWrv2qqa3Tq7IMH98mzuIeDB4+VAgWKyuTJo2TYsF63d8LVAAAQAElEQVSmP4595qMnUbHiS6bJsd7r0qUrRnhOr1enLtfQRa/hjz+WmenBw/cAGjRojCRKlEQ6dtTP4mVTeZMtW04BAACAc3gI4KaWLKl0vFq1EZmTJs0icE8H/W2y399PytWP3nAaRF+/fh3NesyYaQLEBcd335TzRy5IrVYCwM0EBl6R5cu7XmvYcH0yAWJQ8eLFV1irSf7+/isFbotKGQAAAAAAACcglAEAAAAAAHACZl8CADxz7dp1FwAAAAAPRygDAHjmcubMIwAAAAAejlAGAAAAAIAYZrPZLoRYBG6NUAYAAAAAgBjm4eGR2sfHh+/kbo5GvwAAAAAAAE5AKAMAAAAAAOAEhDIAAAAAAABOQCgDAAAAAADgBIQyAIBY4fjxIzJ69GA5d+6M4NEOHNgjf/21UkJDQwUAAABxE6EMACBWOHnymAkZ7t4NFDzanj07TIh19+5dAQAAQNxEKAMAAAAAAOAEhDIAAAAAAABO4C0AADwDNptNvv32S1m37i85deqYZMuWS15/vblUrfqyeV63//LLQtm7d6ckTpxEqlR5Wdq06SKenlH/fmDatHHy99+/yYIFvzm2DRvWSy5cOCdTp841j3fu3Ca9e7eTCRO+lOnTJ8iRIwckffqM1rbhEhCwRX766Tu5ffuW1K3bWFq27Og4zrx5M+Wff36XN954R+bPnylnzpySwoVLSteu/SVNmnRmn6CgIPnkk+GyZ0+AXLt2RTJlyiqVK9eQxo3ffuh5261Z87usWvWLHD9+WK5fvyYFChSRVq06S65ceR379OvX0Rw3SZKkZt9bt25KtWq15N13e4qvr69jP31O79+xY4elaNFSkjlzNgEAAEDcRqUMAOCZWLBgtnz99edWsFFc+vQZYUKDfft2mud27douH3zQV/z84kvPnu9LjRp1ZMmS+TJz5iR5VoYN6ymvvdZEJk2aI1euXJJRowaY0GX48AnSrFkbK3iZJRs3ro3wGu1jo+fcrl13GT/+SxOeTJkyxvH8okVzZf36v6RevabSv/+H1rWVsB6vjnZzXQ2HSpUqL5069TFhT2DgHfnoo/4SFhYWYb9ff/3RNO794INJ0r37YFm+fLEVJn3veF7v37hxwyRp0mRW2DRM8uYtaAIaAAAAxG1UygAAnlpISIj88MPXUqvW66bCQ1Ws+KLj+W+/nSUZM2aRoUPHmceVK1cXDw8P+e67r6Rp09YmbHha+r4vvljb/KyVJFu3bjABjVblaDiiAZBW0ZQpUzHCeY8e/ZmkSpXGPC5btrIV5PzheP7QoX2SPHlKadToLfO4fPmq8ji0IiZ8VUzChImse9BTTp8+Yapj7NKmzWCFSuPF29tbsmbNYSp3Dh7c63hew6EUKVJZr/1EvLy8HOc+d+50AQAAQNxFpQwA4Knt3h1ght1oJcn9tKrE33+jCUrC032Dg4PNcKZnIU2a9I6fEyRIZC0JTSCj/Pz8JF68eKZSJTwdgmQPZJSvb7wIsz9pSKNTdI8ZM8SEPPdXuDyKDn+aPXuqtGlT3wqsSplARulwqvBSpkxtAhm7ePH8IpzHzp3+UqhQMUcgo+zXBgAA4ibrF1Q3rX9bRK/8Fi6LShkAwFO7ceOaWUdW8aIBhIYZWiUSXqJE90KFixfPSWxVvfqrJlRav361jBjRW5IkSSZt277n6JPzKKNGDTTVNjo8qkSJsrJv3y4ZOLCLPK4bN66boAkAALgOm82WyPoFkZfArRHKAACemr3aRMOD+2lFh1aq3Lx5I8L2mzfv7atBR2ylQ6xq1apnltu3b8sXX3xigpZs2XJKlizZH/raU6dOmDCnVatOZrjW09ChS3fu3BIAAAC4FoYvAQCeWtasOSVRosRmmE1kChQoKrt2bYuwbceOrWbITpEiJaM8rg4n0t4p4V2+fFGcIUGCBFK3bhPz8+HD+x+5/9Wrl81a+8XYRed1kcmTp4AcOXIwwraQkGABAABA3EalDADgqWklTJMmLU3/FO2Hkj9/YdmwYY3p66IzD735Zjvp1esdGTGij2nGqw13tcmvzmpkH/KkDXXVpk3rrG3Jzfbs2XOZ6hudJUm36YxNx44dkkyZnv900DrF94ABnU2VSrFipSV16rSyePF8iR8/gXV9ReTOnTsydGgP08D4vfcGPPB6beSr92XlyqXWMVKaqawXLvzGPKezKWnQEl33Zn/qJEuXLjDNlPX+afNfAAAAxG1UygAAnommTVvJ2293kHXr/pSxY4fI2bOnpUKFaua5AgWKmOmez5w5JaNHD5Jvv/3ShAvan8VOgxzdb8aMibJq1S9mW9WqNaVhwxbSo0cbad68pulP06DBmxITdOiSTj+dLFlyM7PUyJH9TLPg8eNnSbp0GeTatSsSELBFfvvtp0hfr6HSiBETTXNhDW9+//0X6xiTrWvuKnv37pDHoaFQly79TBBTr15FM+tSmzZdBQAAAHGbhwBuasmSSserVRuROWnSLAL3dNDfJvv9/aRc/QwCPIl27RpL2rTprbDlU0Hcdnz3TTl/5ILUaiUA3Exg4BVZvrzrtYYN18feJmdwScWLF19hrSb5+/uvFLgtKmUAAHgCWrWjw6peeaWBAAAAAE+CnjIAADyB3bsDJGXK1FKuXBUBAAAAngShDAAAT6BUqfIyb95yAQAAAJ4Uw5cAAAAAAACcgFAGAAAAAADACRi+BAAAAABAzDtiLUECt0YoAwAAAABAzMtuLb4Ct8bwJQAAAAAAACcglAEAAAAAAHACQhkAcHPLli2SS5cuyOM6eHCfeW1ISIgAAAAAeHyEMgDgxs6cOS2ffjpKLl++JI9r+vTx5rW7dwcIYsb69atl3LhhAgAAANdAo18AiAOaN68pJUqUk969h5nHJ04clXbtGpuf48WLJ6lSpZGcOfPKW291kEyZskT7uAEBWyRx4iSSI0fuh+53/vxZ8fWNJ8mSJXdse/vtjrJv304pUKCIONvNmzfMki5dBnFl+nlt2bJeAAAA4BqolAGAOKxx47dl6NBPpFGjt80wonffbWKqKaJr505/KViwmHh6Rv3XwalTJ6ywp45s27YxwvZChYqZ9/f2dn6+3759E1mwYLYAAAAAcQmhDADEYRkzZpESJcrKK6/Ul/ff/1iKFCkpn376kdy+fTtar9+xY6sVrhQXAAAAADGP4UsA4EJef725Fc50l40b10i1arUeuu+lSxfNsKR8+QpFuc/48SPk119/Mj+PHj3YLCNHfiqlSpWXefNmyty502XFik2O/WvXLi09egyRzZvXWct6MzSqRYv2kiZNOvnii/Fy5swpKVOmknTrNkgSJkzkeJ32pfnuu9lWSOQvyZOnkAYN3pS6dRs7nt+wYY3Mnz9Ljh8/Yl6XK1c+admyoxw+vN/RY2XFiiVmadq0lbRp00WOHDkoCxZ8KSdPHpPTp09I5szZpVGjt6Rq1Zcdx9Vr+Oef3+Wtt96VWbMmW/fkvHU/Ckv//h+a8920aa0kTZrMOl5XqVjxxQjXqe+xfftm2bNnh/j5xZdateqZc3qYQ4f2W+85w1Qo6WuKFi0l7dv3kCRJkorNZpPvv58j69b9ZV3nYeuepZc33mj7yM+xY8fmkilTVhk4cJRjW4MGVc356LGf5jr79etojq3nt2rVL3Lr1k1zPu++21N8fX0f+tlkz55LAAAA8HBUygCAC7FXvZw9+98j9927d4f5Yp0jR54o99HhSX36DDc/a0AwduznUqBA0Yced8qU0aaCZ9q0b00YM2PGRGvbGGnVqpOMGvWZGV6lX+Ltrly5LEOGdJOjRw+ZEKFChRdl6tSxVojwh3n+9u1bJhxKlCix9Oo11JzHrVs3TCBTvHhZc04aKGjYoz/XqdPIvC5lytSSO3cBK6RpbcKHrFlzWM8Pkf/+OxXhfDW0+eqrz0y/nu7dh8i2bZukZ8824uXlZZ3HPBMyjBkzWK5duxrhdXPmTJO8eQtZIcdiad68jbmmv/9eFeV90Z43Awd2NsFUx469zb09cuSAuT6lgczs2VPNcLI+fUZInjwFTAj2779/y7PwpNf5668/yoEDe+SDDyZZrxssy5cvlp9++t4897DPBgAAAI9GpQwAuJAECRKIh4eHFcqcfuS+Wq2RN29B0yg4Klol4eFxL7/PnDmbGR71KC++WNtRMaJVFZMnj7ZCFx1aVcJsy507vxXAHHTs/8svC00FxqRJX5n3U4GBd2Thwm+kUqWX5Ny5MyYo0GNVrlzdPG8PXlTKlKnE29tHUqRIFeH8NKhp1KiF43GxYmVMtYf2xsmQIaNju/biGTVqqmmWnD9/Yfn883HWz2lNyKD38uWXX5PVq3+TU6eOm2Pa1ahRV956q735+bXXmpig4rfffpIqVWpEel90+nC9Dg1AUqdOa7bVq9fUrIOCgkxPnFdfbWgFU93NNq1Y0WvXyppy5arI03rS60ybNoMMGzbe9A7SYGv+/Jly8OBe89yjPhsAAAA8HKEMALgQHQKjTXv1S/ajaD+ZZ/Fl/36pU6dz/GwfoqQVM3ZaVaFVI3YBAZvN8/ZARmmViIY1GiRkyZJdXnghk6lM0ddpUKPBQnRoCLNkyXxTJaLBh7JXptjp/Qp/vESJkpjQxH4P9XyVBkXh3X8O2bLlNEFXVLZu3WCu0R7IhLd3705zXveHXoULF5e5c2fInTt3JH78+PI0nvQ6teIofDPnePH85O7dQPPz03w2AAAAYPgSALiUy5cvSmhoqKRPn/Gh+2l1gw4Xik7ly/OmVTLa26ZmzZKORfvEaMB06dIFEyaMHDlZKlSoZobS6ExQo0YNkqtXrzz0uEuXLjDHeeWVBvLll0tk2bIN8jxpAKVDsaJy48Y102MnqufsxwhPgxN18eI5iY2e9LMBAADAPVTKAIAL2bLlX7N+VCijFR0+Pj6mJ4qzabXG3bt3pVu3gQ88lzx5SrPW4Ubah0Xt2rVdhg/vJdOmfSwDBnwU5XG1R4vOTGUfTqNVN8/TjRvXTVVJVLSCJKpeP/bqmfAVRPceXzfrJEmSSWz1JJ8NAAAA7iGUAQAXoaHD0qXfmt4qJUuWf+i+0eknY2cfuqIVOM+DzgIUELBVcuXKb3riPErBgkVNM9yDB/dJ+HO02cIcj7XK5urVy5I2bSXHtkOH9smzFBoaEu7nUHNP9byikj9/ETOESat/7g9vsmTJYapkdu3aFqEnjc5GlTNnngg9Xu7n6xsvQuCkwU5Q0F1xhsg+GwAAAESNUAYA4jBtyqqz6Bw7dtjMiqOPhw795JHhxuP0k9EKD50SWac+1ooODSCKFy8jz4o2u/3xxwUycmRfadKkpama0aEw2lT27bc7WIHNFvn001FSvfqrVkCR14QtOuW2NsW1y549twlFdIrq69evmaazOpuQnnPZspWtbVdNI12dhlqnsA4LCzNDb56GDo/SAEz7xOg01jokTKfyftR1Dh78npmaWytrVq5capropkuXwUzleRtGWQAAEABJREFUrbMjac8WnaZ8/frV5nMaNuwTxzGSJUthXrdp0zozLbn2g8mWLZf4+28w9+3KlUvy2Wdjn/raois6nw0AAACiRigDAHHYDz98LYsWzTXBiU5tPWTIWDNL0sPY+8l06NBLokOrUAYNGi3Tpo2Tvn07mD40zzKUSZAgoUyYMNtMrTx8eG8rOPEzw6p0amxVuHAJadGivaxe/at8991XJsBo1aqz1K/f3HGMTp36WMf4QAYM6GyCC63W0GmwP//8Exk1aqAkT57CTI2tw6HmzPlMgoODo1Ul9DA645BOVz1jxiQrnElppprWSpGHXecnn8yyznOECTI06NJeLLpWGsooDWp05indv1u3QRHCMw0/NNjRKcQnT/5GcufOJ61bd7Y+0ytW0FNNfHx8zed6fzPj5yU6nw0AAACi9ujpOQAXtWRJpePVqo3InDRpFoF7Ouhvk/3+flKufgZxJ2vX/iljxgy2vvj/9dTBhLuqXbu0CSPefPMdgWs4vvumnD9yQWq1EgBuJjDwiixf3vVaw4brY28DL7ik4sWLr7BWk/z9/VcK3BaVMgDgZrQHSd26TQhkAAAAACcjlAEAN1O6dAWzAAAAAHAuQhkAAB7T6NGfSdq07jXsDQAAAM8eoQwAAI9Jmx0DAAAATytm5swEAAAAAABABIQyAAAAAAAATsDwJQAAAAAAYlhYWNgRawkSuDVCGQAAAAAAYpinp2d2a/EVuDWGLwEAAAAAADgBoQwAAAAAAIATEMoAAGLE6dMnZenSBQIAAADgHkIZAECMWL36V/nii/GCZyssLExOnjwmwcHBAgAAgLiFUAYAgDhs5cof5Z13Gsn169cEAAAAcQuhDAAAAAAAgBMwJTYA4LFdvXpFmjatId26DZJXXqlvto0ZM0T+/HOFjB8/SwoUKGK2tW3bUMqUqSjt2/dwvPbYscPy8cfvy6lTx6Vw4ZLStWt/SZMmneP53bsD5LvvZsuOHf6SPHkKadDgTalbt7Hj+XnzZso///wu3bsPllmzJsvRowdl4cI/JTQ0VL766jPZtGmtnD37nzmH3r2HS4oUKaO8jn79Okr69BklJCRYtm3bJLdu3ZSSJctJnz4jJF68eGafnTu3WcdpJzNnLjTvvXbtHzJ58jeSOXO2h75fUFCQfPLJcNmzJ0CuXbsimTJllcqVa0jjxm/rFJhmn99++1lWrlwqhw7tM8937NhHChYsGuH8dHuSJEll1apfzPlVq1ZL3n23p/j6+krr1q/Lf/+dMvu+8UYts/7pp3WOcwcAAEDsRqUMAOCxJUuWXDJkyCiHD+93bDt4cK/Ej5/ABAzqzp07JnjJnbuAYx/tfzJlymh59dVGJlQ5duyQ9XiM4/krVy7LkCHdrKDlkAlyKlR4UaZOHWuFMH9EeP/Lly/Khx/2NwFKr15DzbY5c6bJ99/PkVy58pmw6MqVS9K/f0ex2WwPvZZVq362zvW2FRRNt957rAlnpk+f8MB++n5+fn7So8cQE5Q86v0WLZor69f/JfXqNbW2f2gFUCWsx6tNeKQ2b15vQhsPDw/rXgyRtGkzyMCBneX8+bMR3vfXX3+UAwf2yAcfTDL3bPnyxVbw8r15rm/fD6Rhwxbm50GDRsu4cTMIZAAAAOIQKmUAAE8kT56CcuTIAfNzYGCgaTb7yisNHKGMPbDJm7dghNe9995AU2WitCpm3bq/HM/98stCUw0yadJXJvi4d+w7snDhN1Kp0kuO/a5duypt23aVJk1aOt7/xx8XSJUqNaR372FmW5EiJeStt+rIpk3rTLVOVDJkyGSFIaPEy8vLBE1azaIVPx069BIfHx/HfqlTpzWhSHTfT+9D8uQppVGjt8zz5ctXjfC+P/zwtamq+eSTmeZxtWo1rfdsZgKXd955z7GfhjXDho0Xb29vyZo1h8yfP9MEYCpfvkImwFIFChSVlClTCQAAAOIOKmUAAE9EwxZ7KLN//y554YVMUqhQcUcoo+ukSZNJunQZHK/RYTv2QEb5+cWXu3cDHY8DAjaboUz2QEblyVPAOv5uCQkJifD+NWvWc/y8b99OE5SUKFHOsS1lytRWoJHenNvDpEiRygQydtmz55Lbt2/JmTOnIuxXo0bdx3q/smUry7lzZ8ywrq1bN5gqITu9lh07tkZ4vVbM6LXqscPT42ogYxcvnl+EewYAAIC4i0oZAMAT0SoNe4XMvn27JGfOvGYoj1ZuaOigoUzevIUe65haJaPDd2rWLPnAc5cuXTChh9JwRwMfu5s3b5j1+PEjzBLe/cOBHiVhwkRmrdU44WnVy+O8X/Xqr5qhSuvXr5YRI3pLkiTJpG3b96Rq1ZfNcCkd5qR9YnQJL3yIBQAAXJf1b4Fb1hIqcGuEMgCAJ5IjRx5TwXH48AEznEYDmEyZspieJjp0SZeKFV96rGPqEKG7d+9Kt24DH3gufCgS2etUq1adJH/+whGe00qYx2GfWvpp308rX2rVqmeW27dvyxdffCKjRg2UbNlySpYs2U1/mtKlK0qdOo0ivN7Xl54wAAC4A+vfCgmtxUvg1ghlAABPRAMZ+xAmHV5Ut24Tsz137vyyd+9OUzHTrl33xzmk5MtXWAICtkquXPklQYIE0X5dliw5JFGixBIcHCRFipSUxxEaGnFY1O7d2x8YdvW076fXovdn5cofTViloYz2gNHqn8c93/vZh16FhfGLNgAAgLiGUAYA8MS02a+//0YzZEf7oSitoPn992VmeI59W3TpTEXaQHfkyL6mia9WzejsQ9rg9u23O0T5Oq060f3nzZshqVKlNf1tDh7cZ2ZWGjPmczNbVFR0ymudWltncjp9+qSsWfO7qYAJ38flcd9PQ50BAzqbqplixUqbyprFi+eb2any5783Xfibb7aTnj3byvTpE01jYA1odMYmnXXqcYKa7Nlzm7VOr63Dx/S9tBoHAAAAsR+hDADgiWnookGCNubVoEJpKLNkybdmm70/S3QlSJBQJkyYbfq0DB/e2xxTh0Xp1NiP0rRpKxME/fDDHBNwvPBCZtMM+FHnULBgUTPDk055rVUn9es3l2bNWj/V++nQJZ2VafHieWaWJZ3CW8OZ8eNnOSpwChQoIh99NEVmzJgoP//8vWnoq41/M2XKJo8jV668Zqao776bbaYUb9myI6EMAABAHOEhgJtasqTS8WrVRmROmjSLwD0d9LfJfn8/KVefxqruql+/jmY9Zsw0gXs7vvumnD9yQWq1EgBuJjDwiixf3vVaw4brkwkQg4oXL77CWk3y9/dfKXBbTIkNAAAAAADgBIQyAAAAAAAATkBPGQCA23rc2aEAAACAZ4lQBgDgtnLmzCMAAACAszB8CQAAAAAAwAkIZQAAAAAAAJyAUAYAAAAAAMAJ6CkDAAAAAEAMs9lsp6xVsMCtEcoAAAAAABDDPDw8MlorH4FbY/gSAOAB+/btkr59Owhcy/TpE+XXX38SAAAAxA6EMgCACEJCQmTSpA+ldOmK4o7u3r0rEyeOlA0b1sizdO7cGRk79n05evSQOEuJEmVl1qxP5erVKwIAAADnI5QBAETw7bdfmmCifv3m4mpu3rwhZ8/+99B9goLuyooVS+XixfPyLJ0/f1b++GO5OQdn0VAmX77CMnXqGAEAAIDzEcoAABxu3bopCxd+LQ0bthAvLy9xNe3bN5EFC2aLO2vSpKWsWfO7HDq0XwAAAOBchDIAAIctW/6VwMBAKVmynCBu2LNnh8ydOyPa+xcoUEQSJEgo69b9KQAAAHAuZl8CADjs2RMgSZMmk7Rp00fY3q9fR0mfPqOEhATLtm2bTEWNBjd9+oyQePHimX127twmvXu3k5kzF8q8eTNl7do/ZPLkbyRbtpzy228/y8qVS+XQoX2SKVNW6dixjxQsWFQOHz4gnTq9IV269JO6dRs73k9DhnnzZsiCBb+Z89m9O0C++2627NjhL8mTp5AGDd6MsL++3z///C5vvPGOzJ8/U86cOSWFC5eUrl37S5o06WTVql9k3LhhZt8VK5aYpWnTVtKmTReJrqiuwW7+/Fmyfftm65r2i6+vr5QoUU7atn3PnG9kbty4bq5dj/Xhh5NNr5kFC76UkyePyenTJyRz5uzSqNFbUrXqy47XXLhwztyX9etXy7VrVyVhwkSSI0duK2RJFO3zVHnzFjT3FAAAAM5FpQwAwEGDgRdeyBzpc6tW/Sx37tyWjz+eLkOGjDXhzPTpEx7Y78MP+4ufn5/06DHEhAKbN6+XTz4ZrtM+SvfuQ6zAJ4MMHNjZ9FjRQEFDk82b10U4hj4uVKi4CWSuXLlsvV83c27t2/eQChVelKlTx1ohzB8RXqNhxtdffy7t2nWX8eO/lOPHD8uUKfd6pxQvXlbGjv3cHK9MmUrm5zp1Gkl0Pewa7LJmzWEFKDWtAGuktGjRXvz9N8rs2VMiPZ7NZpNRowaanwcM+MgcN2XK1JI7dwErLGot/ft/aI43duwQ+e+/U2Y/DWHee+9t83jKlLny0UdTJH78BFKpUnUZPnx8tM9T6fZjx5zXcBgAAAD3UCkDAHC4fv2qpE6dLtLnMmTIZH3BH2V6zWTIkFEqV64hf/65Qjp06CU+Pj6O/VKnTmsFAoMdj3/44WtJkSKlFRbMNI+rVatpvaaZ/PTT9/LOO+9JlSovy5Il8yU4ONgcR2cG0im5O3XqY/b/5ZeFpjJn0qSvTMijAgPvyMKF31iBxEuO99FZo0aP/kxSpUpjHpctW9kR3KRMmcos3t4+1rmkkiJFSsrjeNQ1qPLlq0Z4zenTJ+Wvv1Y6HmtQYl/PnTtdduzYal3THEmcOInZroFRo0YtHPsXK1bGVPhs27bR3O+1a/+Uy5cvWdc4zQRZulSuXF3mzJlmqob0uNE5T5UkSVLrs74mAAAAcC4qZQAADhpseHtHntdrmBG++W/27Lnk9u1bZqhQeDVq1I1wPA0fdCiPnYYHefIUsIKXneZxxYovmv22bv3XPLZXzeh2FRCw2QQQ9kBG6ev3799tXmfn6enpCGSUr288uXs3UJ5WdK5BXbp0wQpMBkvz5rWkZs2SsmjRXOv+3HzgeP7+G8zwrG7dBplKofA0hNEhTXXrlpfXX69ktuk9VjZbmFlrdYydDlvS50NDQ6N9nkrDL63W0SAMAAAAzkOlDADAQb/wRzfI0H4mSofVhJc8eUrHzzrcSb/8a9igS3jp0mUwa+1votUdmzatM9UtGsroNh3Oo7RKRoffaNBxPw1C7u9/86xF5xo0GOnRo43pu9O//0gpUKCofPPNF7J06bcPHE8bKSttthve0qULZNq0caYPjg6x0vv46qtlHc+XLl3RfD7fffeV2UeHdf32209mXw3StEfNo84z/DVp35vwFU4AAACIeYQyAAAH/fKuvVuiwz78JXwIcz8dmqP9ZTRQuL+Hi1ay2Gkvlr//XiWdO/c1M0A1a9ba8ZwOh7p796506zbwgeM/7D5VtbgAABAASURBVL2flehcw+rVv8m5c2dMPxmd3ehhdJjTxYvnZPz4EZI/fxFHI+Dvv58jJUqUdbxH+CogpdVCen+0YbEO6VL6Xl27Doj2edppw+A0aZ5vmAUAAB7piLUECdwaoQwAwCFPnoKybt1fpppDv+CHFxoaMSTYvXu76YNyfxXG/bRqRCtaHtbHRZv3Ll4838yKpJUx2q/GLl++whIQsFVy5covCRIkkKehFSX2YUCP41HXcOXKJbNOl+4Fxzad/SgqnTv3k/btG8vHH79vGvZqhcvVq5clbdpKD3299t556613pUWLdvIk52l37NhhM6wJAAA4VXZr8RW4NXrKAAAcKlSoZvqTaHPZ++mU17NmTbYCki2yfPkSWbPmd6lf/40oe9DYvflmOzP98vTpE81rtTlw585vmp/ttOJDe9bo7Ek6hXb4oKdevaYSP358GTmyr5lyeuPGtTJiRB+z7+PKnj23dR3+5jh6/pHRYVk6tGfPnh2OmY8edQ25cuUz6x9+mGOGYekwpF27tptw68CBveY5DV7saw2zevYcKlu3bpBlyxaZ3i96jA0b1pjr0+FHH3881ArG4pvzCAu7FyRdvHje9I3RffS9T5487nguOueptKLnxImjpkkwAAAAnItKGQCAQ/r0L5ihRNrfpFy5KhGeK1iwqJn1SKe81oa/9es3jzDMKCoauGg1yIwZE+Xnn783vWK0GW2mTNkc+2goocN6dFjO/UNvtPfKhAmzzXCf4cN7mwqevHkLmeqax6UzOk2Y8IEMGNBZkiVLYV1TMdPPJjxtGNywYQv59tsvTdWOTjf9qGsoXbqCdOnSz1SyrFz5oxQqVExmzlwoX331mQm4cufO98C5lClT0dxrnVa8ZMnyZhrszz//xEyVrUOadGpsHZ41Z85npiFvvHjxpE2brvLppx9FCFk0xBo79gszo1J07vXixfPMa7R/DwAAAJzLQwA3tWRJpePVqo3InDRpFoF7Ouhvk/3+flKufgbB/5w5c1ratm0gQ4aMdQQz/fp1NOsxY6YJYgftOaNThw8c2Flq164vHTv2fuRrtLLm3XebWMHaZClWrLTgf47vvinnj1yQWq0EgJsJDLwiy5d3vdaw4fpkAsSg4sWLr7BWk/z9/VcK3BbDlwAAEWi1TOPGb5uhSog9Jk4cKcuWLXY81mFjWr2kfWy0H010fPPN56ZChkAGAAAgdmD4EgDgAW+88c4Dw5fgXN7ePrJw4deSKlUaRxNmHcZ0/PgRad68TbSO8c473cxQMQAAAMQOhDIAgAdo/5K8eQs6Hrdr113gXG3bvic3b94wDY+DgoJMY+QcOXLL8OETpGzZStE6hk6rDQAAgNiDUAYA8Eg5c+YROJfOQNW//0gBAACA66CnDAAAAAAAgBMQygAAAAAAADgBoQwAAAAAAIAT0FMGAAAAAIAYFhYWdsRaggRujVAGAAAAAIAY5unpmd1afAVujeFLAAAAAAAATkAoAwAAAAAA4ASEMgAAAAAAAE5AKAMAAAAAAOAEhDIAAAAAAABOQCgDAAAAAADgBIQyAAAAAAAATkAoAwAAAAAA4ASEMgAAAAAAAE5AKAMAAAAAAOAE3gIAAAAAAGLayZCQkCCBWyOUAeDWrl4Ilp1rLgsA93btQpDE8xUAAGJSJm9vb/72cXOEMgDcVor0IrmLhVo/XRW4r0OHjsuJE2fkxRfLCtxXgsz3/p8g4iEAAAAxhVAGgNtKmd7DWgRu7tKKk7LnrL+UrlVOAAAAgJhEo18AAAAAAAAnIJQBAAAAAABwAoYvAQDcmpeXpyRKlEAAAACAmEYoAwBwazZbmISEhAgAAAAQ0whlAABuLSxMJDAwSAAAAICYRigDAHBzNvH0pMUaAAAAYh6hDADAzXlImJbLAAAAADGMUAYAAAAAgBhms9kuhNDYzu0RygAA3JqPj7ekSpVcAAAAYpKHh0dqH/2HCNwa/wEAANxacHCIXLx4RQAAAICYRigDAHBrnp4eYv2WSgAAAICYRigDAHBrYWE2CQ4OFgAAACCmMQcoAAAAAACAE1ApAwBwa9pfL2XKZAIAAADENEIZAIBb00a/ly5dFQAAACCmMXwJAAAAAADACaiUAQC4NV9fb0mTJoUAAAAAMY1QBgDg1oKCQuT8+csCAAAAxDSGLwEAAAAAADgBlTIAALfm6+sjqVIlFwAAACCmEcoAANxaUFCwXLx4RQAAAICYxvAlAAAAAAAAJ6BSBgDg1nx8vCVlSoYvAQAAIOYRygAA3FpwcIhcusTwJQAAAMQ8hi8BAAAAAAA4AZUyAAC35uXlKYkSJRAAAAAgphHKAADcWmhomNy8eVsAAACAmEYoAwBwa56eIn5+vgIAAADENEIZAIBbCwsTCQwMEgAAACCmEcoAAAAAABDDbDbbhRCLwK0RygAA3Jqvr4+kTp1CAAAAYpKHh0dqHx8fvpO7Of4DAAC4taCgYLlw4bIAAAAAMY1QBgDg5mxmWmwAAAAgphHKAADcnIeZFhsAAACIafxqEAAAAAAAwAmolAEAuDVvby9JliyJAAAAADGNUAYA4NZCQkLl6tXrAgAAAMQ0QhkAgFvz8dFKmcQCAAAAxDRCGQCAWwsO1kqZGwIAAADENEIZAIDb8/Sk7z0AAABiHqEMAMDtvPxyW7l06VqEbcuW/W3WNptN/P0XCQAAwPNk/ZvjlIeHR5DArfGrQQCA22nQoIapjrH+IRRh0UCmVKlCAgAA8LxZ//bIaK18BW6NUAYA4HaaNXtVMmdO/8D25MmTSvPmtQUAAACICYQyAAC3o7MtVa9ezlTHhJcrVxapWrWMAAAAADGBUAYA4JaaNXslQrVM0qSJpXnzVwQAAACIKYQyAAC3lDx5EqlRo7yjWiZ79oxSpUopAQAAAGIKoQwAwG1pZUzGjOkkWbIk0qJFXQEAAABiElNiA3Bbl87Y5HCAwK0lkmpFWsvJk2cl/p2SsmmlTeC+UqQXyVnEQwAAAGIKoQwAt3X5jMiBbV6SKW9igfsqUqSqtYjcuSNwY1cvBMnls3esUEYAAABiDKEMALeWLLWPFKycQgC4t+O7b8r5IyRzAAAgZhHKAAAAAAAQw8LCwo5YS5DArRHKAAAAAAAQwzw9PbNbi6/ArTH7EgAAAAAAgBMQygAAAAAAADgBoQwAAAAAAIATEMoAAAAAAAA4AaEMAAAAAACAExDKAAAAAAAAOAGhDAAAAAAAgBMQygAAAAAAADiBtwAAAAAAgJh2xFqCBG6NUAYAAAAAgJiX3Vp8BW6N4UsAAAAAAABOQCgDAAAAAADgBIQyAAAAAAAATkAoAwAAAAAA4ASEMgAAxDJBQUHyyy8L5fDhAwIAAADXRSgDAHCK9etXS0DAFsGD9u7dIZMnj5Yvvhgvz8r69atl3LhhAgAAgNiDKbEBAE7xxx/L5ezZ/2Tq1LniTDdv3jBLunQZJLbIl6+wtG3bVfLnLyLPigZgW7asFwAAAMQeVMoAANxa+/ZNZMGC2RKb+Pr6SpMmLaVgwaICAAAA10UoAwDAM7Jnzw6ZO3eGAAAAPIrNZjtlLcECt8bwJQBwYb/99rOsXLlUDh3aJ5kyZZWOHftEqL7o16+j2Z4kSVJZteoXuXXrplSrVkvefbenqdawO3Rov8ybN0N27vQXP7/4UrRoKWnfvod5nQ79+eqrz2T79s1y4cI5yZo1h7z33kDJkSO34/VhYWFmn3Xr/pIbN65J1ao1JTg46IHzvXz5ksycOckMs/Hy8pIyZSpLly79xNs76r+u9P2//HKK9f6b5OrVK5I/f2Fp2bKT5MqVV9as+d1c1/Hjh+X69WtSoEARadWqs3lOt9t7rKxYscQsTZu2kjZtukTr3um16j1Zv361XLt2VRImTGSuOUGCRI599Hq1Ye/evTslceIkUqXKy+b4np73fieyc+c26d27nXXNC61jzZS1a/+QyZO/kWzZckrt2qWlRYv28uab7ziOp9ejx9u/f5ekT59RKlSoZj3fzjQGnjp1jJw8eUyOHj0oKVKkMp+jvt7+XpGZP3+W/PXXSjl79rSkTp1WChcuKe+8854kSpRYAADA8+Xh4ZHRWvkI3BqVMgDgojZvXi+ffDJc/8KX7t2HSNq0GWTgwM5y/vzZCPv9+uuPcuDAHvngg0nWfoNl+fLF8tNP3zue19BDX3fmzCkrmOgtjRu/LUeOHJDbt2+Z5z/8sL/8+ecKqVXrdfN6DQF69mwrFy+edxzju+++MkupUuWlW7dBZtvGjWsfOOfhw3uZIKNevWZWQNLahBSPanY7cmQ/+fvv3+SVVxpK164DTEBx/vwZ85wGF/qenTr1sZ7rL4GBd+Sjj/qbkKh48bIyduznkjRpMiv8qWR+rlOnUbTunYYw7733tvz33ymZMmWudcwpEj9+AqlUqbp1DffOd9eu7dY97WtCrJ4935caNerIkiXzTeh0P72Hfn5+0qPHEBMARUaPp/slSJBQ+vQZIeXLVzWhjl6LvjZXrnzy6qsNrfMcLS+//JoJXLRvT1R27w6QOXOmSZEiJa3XjJKaNetZ27abawMAAEDMoFIGAFzUDz98LSlSpLTChZnmcbVqNaVDh2YmcNFqCDsNHIYNG2+qUbTKZf78mXLw4F7H88uWLTJf1KdOnWeqKVS9ek3NWitA/P03yoABH0nVqi+bbWXLVpZmzWrIokVzTcVNaGioCSOqVKlhvX8vs49WeBw7dthU5tgFBGyVfft2mfDEHo5oxcfHH78vLVt2jLR6Q99/27ZN0r//SFMZYr9OO62I0cVOq1mGDu0pp0+fMOFHypSprOv2Me+j4UR0793atX+aqp7Ro6dJmjTpzFK5cnUTctSt29iEOd9+O0syZsxivd84cwx9XrdrOKWBk4ZBdnpfNdB6mPDH0+NUrPhihOdfe62J4+cyZSqacEsrjjQMioz9M9beNXr+5cpVMZVCAAAAiDlUygCACwoJCZEdO7ZKiRLlHNv0i3yePAWs4GNnhH1TpkwdYXhQvHh+cvduoOPx1q0bTIBhD2TC27LlX7MuVqy0Y1v8+PEld+4CprJDHT9+xIQ6hQuXiPBaHc4TXkDAZrMuWbK8Y1vevAVN5YsOIYqM/f112E1k9LWzZ0+VNm3qS61apUwgo+xVPpGJzr2z2cL+/1oTOPbRYUt6XA2hdNGwSod5haf3IDg42IRJ4dWoUVcexn48fb2eS2Q00Orbt4M0aFBVatYsaUKXO3duR3lMrQ5So0cPMpVOd+7cEQAAAMQsKmUAwAXpl3GbzWb6pugS3uNO/aw9YO4PUOxu3rxu1vdXsejjU6eO/f8+N8xaq1Qexl4107Llaw88d/+Qq/vfP3zVSXijRg00gU67dt2tkKWsCS4GDuwiDxOde1e6dEUTyGjVi1b2XLlyWX777ScTdGjAdePGdTOs6P5rTpTo3n28ePFchO3Jk6eUh9GwR48X1edw4MBe6dXrHaldu75PoeITAAAQAElEQVSpTtLeNjqE7GHSp3/BDNlaufJHmTFjknz66SipX7+5vP12hyiDHwAAADxbhDIA4IL0y7v2GdHwwD4UyM7XN548jlSp0sjZs/9F+ZzSECJZsuSO7RrEJElyLyhJnjyFWT+sOiX8sUaMmGjOPbyMGbNG+hqt8rG/v/197E6dOmGa8LZq1ckMHYqu6Nw7He7TuXNf0yhYG+8qbSKsPW3CH8MeSNnZQyT7vXmcc4oXL16E4V7hLV48z1Q4afPl8A2aH0WHbOmigY/2Epo8ebT5HLQ3DQAAAJ4/QhkAcFEFChSVS5cuROiV8iTy5y9ihjDpsewhSPj3UDrcxx586DCYAwd2m6oNpc12tXLmyJGDEV6rQ4vCK1iwmFlr+BDdcy5UqLhZBwRscfS0sbt69bJZa88cu8OH9z9wDK1ssQ9HCn9dj7p32ifnrbfelRYt2kX6vB5j165tEbbpfdL3e5LPRO+PXmdk9Fq1L449kNEATPvm6JCr6NDmzBpAzZo1OUI/IQAAADxf9JQBABelUyXrDDvTp080X+a1b0jnzm9G+cU+KtrUV6e+Hjz4PTObz9KlC0zTW62e0emndXajyZNHyaJF82T16t9kwIBO1qs8pFGjt8zrNYTQJrSrV/9qZjXSqgxtmGvvIWOXL18hc6wJEz4wFS46xfa0aeMeOtzI/popU0bLwoVz5a+/fjV9Vf7+e5Xpg6PVKjqttR5Lz3vhwm/M6+z9blT27LnNVN+6j045Hd17p7NLaciis0jp9pMnj5trC3//tZ/OiBF9TGPgr7/+XBYsmG3uZ1TDrR5Gj6dTXg8f3tscT3vl6HTa2gMnZ8685jmdxlsb/H700QAJDAw0zZR1KnCVLFkKU1G0adM6Mzzrm2+mS58+7eXHH78z165TlmuYY++lM3Pmp9K9e2vH6wEAAPDseQngppo1y9IjW7ZqSf38Hv/LEVzD5TMil854S6Z8icUV6RAbDU2WLVtoffFeYCogtFFsxYovORrU/v77MrMOP0OPhhj6fNWq92Yx8vHxlXLlqsrWrf/KihVLzXTYpUtXMMN79Lny5avJf/+dNIHNqlU/m0qXIUPGmpmc7LSi5ezZ0zJv3gwTTmTIkMlM4ay9Yl59tYFjPz3W0aMHzfneOzebvP56syiniVZ6bidOHLUCmRXy779/m/etXv1VSZ06jbn+zZvXWYHMt3Lp0nkrMBplvXdG2b9/l1SuXMO8XiuBdGppPTetbHnxxdqSJUu2R967xImTys8//2ACG+09o0HT+vV/mePqUCK9/3nyFJR//vndDA3ascPf0fNFK1OUXr8GKTqFddq06SNcl56PvmfhwsUdn2fu3PnN8VasWGIClpdfrmuqYbQqR4dG6XVq8+NChYqZ2Zw0WMqWLad5rfbD0c9o+fIlUqZMZTOltoYwOu344sXzJTQ0xEx5bp/VaeLEkSbU0f0ia/Lsaq5dCJJbV25LzqICwM2EhARa/59fcff770+OFiAGpU+fvmFQUNCm8+fPHxG4LTr5wW0tWVLpeLVqIzInTZpF4J4O+ttkv7+flKv/eI1vgftptcq9JsKdTfCi4UZcp/1l/vprpXz//e8RZudyVcd335TzRy5IrVYCwM0EBl6xAuuu1xo2XM9v6hCjihcvvsJaTfL3918pcFsMXwIA4DFpFcmyZYsdjzW0KFiwqKRL94Kjl01cd+DAHqlZ8zW3CGQAAACchX9pAQDwmLy9fWThwq/NTEX2maK0r4z2kGnevI3EdXfv3jVTieswNAAAADw/hDIAADymtm3fM9NdjxzZ18wipTMf5ciRW4YPnyBly1aSuE77Aq1YsUkAAADwfBHKAADwmOLHjy/9+48UAAAA4GnQUwYAAAAAAMAJCGUAAAAAAACcgFAGAAAAAADACQhlAAAAAAAAnIBGvwAAAAAAxLCwsLAj1hIkcGtUygAAEEfo9Nu//LJQDh8+IAAAIG7z9PTM7u3t7Stwa4QyAADEEXv37pDJk0fLF1+MFwAAAMR9DF8CACCOyJevsLRt21Xy5y8iAAAAiPsIZQAAiCN8fX2lSZOWAgAAANfA8CUAAAAAAAAnIJQBADySzWaT+fNnSefOLaRevYrSvXtrWb36N8fz69b9JQMGdJbXX68sb71VR2bO/FRnFIhwjMuXL8nYse9LkybVpXnzmjJx4ocSEhLieH7nzm1Ss2ZJOXnymIwePVjq1CknR48eeuR7794dIO+/3928d+vWr8vPP//w0GuZN2+mdOjQTNas+d2s9ZhDhnSX8+fPOva5ceO6ORdtqmt38OA+s+3ff/92bOvXr6NMnTpWvv76c+uaakmjRi+ax3qsvn07mGP36NFGjh07/Ezuhapdu7S5hvD0Wuzvp9f0zTdfPHD/AQAAEPsQygAAHmnBgtkmeChcuLj06TNCMmfOJvv27TTP7dq1XT74oK/4+cWXnj3flxo16siSJfOtYGZShGMMH97LhDf16jWTpk1by9q1f0TasPbDD/tbx/KzwowhkilT1oe+95Url61ApZsJLNq37yEVKtwLRf7554+HXo+GHXrMdu26y/jxX8rx44dlypQx8iR+/fVHOXLkoHz88XR56aVX5KefvpeBA7tIuXJV5LPPvpVbt25a7zHimdyLyOj91/0SJEho7k/58lVNqEMoAwAAEPvRUwYA8FBawfHDD19LrVqvy7vv9jTbKlZ80fH8t9/OkowZs8jQoePM48qVq4uHh4d8991XJnBImjSZBARstYKUXdK1a3+pU6eR2S9FilRWkPG+tGzZURIlSuw4XurUaaV798HRem+tZNHQY9KkrxyhRWDgHVm48BupVOmlh17T6NGfSapUaczjsmUrPzLIiUqaNOll8OAx4u3tLa++2lCWLl0gVaq8LPXrNzfPazij56MVP3pfnvReRCX8/dfjh78/AAAAiN2olAEAPJQOD9Lgo3DhEg88FxoaKv7+G6Vo0VIRtuu+wcHBsnfvvYqWgIDNZl2yZHnHPnnzFpSgoCA5dGhfhNfWqFE3Wu9tP26aNOkiVJHkyVNA9u/fHWE40P08PT0dgYzy9Y0nd+8GypNImTK1CWRUwoT3AhUNU+w0ZNFz0XtlP2f1uPciMvb7r/dHAxkAAADELVTKAAAe6saNa2atFS/3u337lhkmkzBhogjbEyVKYtYXL54zaw1WVMuWrz1wjPC9XFTy5Cmj9d724+rrtf/K/S5duiBp06aX2OZJ70Vk7Pc/ceIkAgAAgLiHUAYA8FD2ihJtfns/DQO058nNmzcibL95896+SZIki3CMESMmmv3Dy5gxqzzJeyutSLl796506zbwgeceFWg4y5Pei8jo/Y8XL54j6AEAAEDcQigDAHiorFlzmiE4O3f6S9WqLz/wfIECRWXXrm0Rtu3YsdUM6SlS5F4FS8GCxcxaAwT7tmfx3vnyFTY9WnLlyi8JEiSQZ0WHM6nwQ6AuX74oz8KT3ouHHS8gYIsAAAAg7qGnDADgobSao0mTlrJs2SKZPn2irF37p4wbN0w+++xj8/ybb7aT48ePyIgRfcxzOquRzphUr15Tx7CjfPkKSalS5WXChA9k/frVsn37Zpk2bZyZpehp3lvfI378+DJyZF9zzI0b15rz0HN4GhqYaPPc3bu3m+FBOh329OkT5Fl40nsRFb3/OpvU8OG9zf2ZPXuq9O7dzgRKOhV3u3aNZeXKHwUAAACxD6EMAOCRmjZtJW+/3UHWrftTxo4dImfPnpYKFaqZ5woUKCIffDBJzpw5JaNHD5Jvv/3SzJbUtu17EY4xePBYU1UzefIoGTasl2lqa5996EnfW6eBnjBhtmkqrKHExIkfmFmOdGrsp9W//4dy7twZqV27tIwaNUD69ftAnpUnvReR0fuvQ6H0/o8ZM1g2bVorVavWFC8vL9Pw+MSJo7JmzSoBAABA7MNUDXBbS5ZUOl6t2ojMSZNmEbing/422e/vJ+XqZxDAFV25clmaNXtZ2rTpYsItRO347pty/sgFqdVKALiZwMArsnx512sNG65PJkAMKl68+AprNcnf33+lwG3RUwYAABellTI+Pj6mcgkAAACxD6EMAAAuas+eADOUKaopxQEAAOBchDIAALgoHbYEAACA2ItQBgAAAACAmHckJCQkSODWCGUAAAAAAIh52b29vX0Fbo0psQEAAAAAAJyAUAYAAAAAAMAJCGUAAAAAAACcgFAGAAAAAADACQhlAAAAAAAAnIBQBgBczPr1qyUgYIvAdaxfv1rGjRsmAAAAcC2EMgDgYv74Y7lMnz5RIHLz5g05e/Y/eV7Onz8rV69ekedNQ7YtW9YLAAAAXAuhDADAZbVv30QWLJgtz8OpUyfkrbfqyLZtGwUAAOBx2Wy2K9YSInBr3gIAAAAAAGKUh4dHcuE7udvjPwAAiMPCwsLkq68+k3Xr/pIbN65J1ao1JTg4KMI+O3duk96928nMmQtl3ryZsnbtHzJ58jeSLVtO87pfflkoe/fulMSJk0iVKi9LmzZdxNPTM8Jr+/YdIT/99L0cOXJAUqdOJ++8856UL1/1oee2Zs3v5tj79++S9OkzSoUK1eTNN9uZY+uwIj3v7ds3y4UL5yRr1hzy3nsDJUeO3I7X9+vXUTJlyipJkiSVVat+kVu3bkq1arXk3Xd7iq+vr9lnw4Y1Mn/+LDl+/IgkTJhIcuXKJy1bdpTDh/c7erCsWLHELE2btjLXduTIQVmw4Es5efKYnD59QjJnzi6NGr1l3buXHe+t9+mff36XN954xzr+TDlz5pQULlxSunbtL2nSpJPx40fIr7/+ZPYdPXqwWUaO/FRKlSof6b04dGi/dcwZ1v30Fz+/+FK0aClp376HuTbrN2Ty/fdzzGdx/Phh6/jprfdta671YTp2bG7uz8CBoxzbGjSoKrVq1TPHDn8db731rsyaNVkuXTov+fIVlv79P5QvvhgvmzatlaRJk1n3patUrPjiM7n32bPnEgAAAEQPw5cAIA777ruvzKJhQLdug8y2jRvXRrrvhx/2twIBP+nRY4j5wr1r13b54IO+JiTo2fN9qVGjjixZMt8KbyY98Nrp0ydIvXpNZc6cnyV//sLmWBqmREWPrfskSJBQ+vQZYQIcDXg0RLKfy59/rrAChNele/fBJqjp2bOtXLx4PsJxfv31RzlwYI91npPMfsuXLzbhkLp9+5YJRxIlSiy9eg01QcatWzdMIFO8eFkZO/ZzEziUKVPJ/FynTiPzupQpU0vu3AWskKa1CSc0EBo7doj899+pCO+toc3XX38u7dp1t97nSxOYTJkyxjzXuPHb1nUNNz/r++rxCxQoGum90ABq4MDOJtjp2LG3ea2GW3r+SgOZ2bOnSsGCxcy9ypOngAl5/v33b3kW9Do0AOvde5h1D4fItm2brHvdRry8vGTq1HkmTBkzZrBcu3ZVnsW9BwAAQPRRKQMAcVRoaKgJUapUqSEdOvQy27Qa0HtqlQAAEABJREFU5dixw6ay4X6pU6c1X67tvv12lmTMmEWGDh1nHleuXF3LaE3Io4GFBhp2777bS1588V7lRuvWXUz1hIYqWn0SmfDH1mOGr8LQqhx//40yYMBHjuqUsmUrS7NmNWTRormmGsMubdoMMmzYePH29jbhiVatHDy41zx37twZEyRoBYeeu7IHLyplylTW63wkRYpUUqRIScd2va5GjVo4HhcrVsZcj/aGyZAho2N7SEiIFY58JqlSpXGc4z///GF+1lDLw+Pe7zUyZ84W4fj3W7ZskTlPDUD0M1AacKmgoCDT8+bVVxtK+/bdzTa9V3ptWllTrlwVeVp6HaNGTTXXoYHa55+Ps35Oa8IU/Wxefvk1Wb36Nzl16niEz/xp7j0AAACih0oZAIijdNiIfjEuXLhEhO06DCkyNWrUdfysgY4GIzqMJjw9VnBwsAlOwrOHCUrDjmTJkkdZFWE/th5Lv/Tfb8uWf826WLHSjm3x48c31StaYROeVrVoKGAXL56f3L0baH7OkiW7vPBCJpkzZ5oVTn37QJXNw2gI06nTG1K3bnl5/fVKZpu9csVOq3fsgYzy9Y3neO/HsXXrBhPihL+Hdnqf9X3vD3UKFy5uBSD75M6dO/K07r+ORImSmHOxfzZa7aICAyO+1/O69wAAAPgfQhkAiKN0WIzSfh7RkTx5SsfPGgToUKL7X6tf2NXFi+ceeqyECRM/MNzl/mNHFQ7dvHn9/98r8X3vnfiR7xuehg0jR0421UE61EZnQho1atAjp6heunSB6TfzyisN5Msvl8iyZRvkedJeP1HdC31OPenn4CxPeu8BAAAQEaEMAMRRyZOnMOv7KzyiQ0MC7S9jD3bs7IFJkiTJHvp6DRPChzz3HztevHiRDqFS9qqNGzeu3/feNx75vvfT4Ubap+XzzxfIxx9PF3//DTJt2scPfY32cClRoqwZbhNZ9cqzptd7/322s7//k34OzvQk9x4AAAAREcoAQBylMxppdYnOJhSe9imJDm1Mu2vXtgjbduzYaoas3D+cJjQ0xPGzDpu6fv2a5M1bMMpja9PagIAtUb6v/b3sdJjOgQO7IwxpelwFCxY176vDfuz0Wmy2MMdjneno6tXLpl+K3aFD++RJ2If26HCth8mfv4iZ5enSpQsPPJclSw5TJfPg5+AvOXPmidDj5X46nEr7xdhpsBMUdFecIbJ7DwAAgEej0S8AxFEaCrz2WhMzI442odXqD52COiBgs2TKlO2Rr9fpqXv1ekdGjOgjL75Y28wIpE1+tQnt/WHA1KljzbTKul1nJNKpkl9+ue5Dj62zKQ0f3lteeukV0yB29+7tMnr0NNNsVmeLmjx5lJnBSXuXLF36rfUqDzM1dXRp6PPpp6OkevVXrQAjrwlbNm9eZ5rm2mXPnttMQ61Tb2uQpE1pdbYhnc5Z79n161dNo12dgWrPnh1m2JV9OvBH0QoYvQ96LK140XCmePEyD+yn9/PHHxfI4MHvmevTCqGVK5eaJrrp0mUwzZJ1diTt2ZIvXyFZv361CayGDfvEcYxkyVKY123atM7cO+0Hky1bLlOdcvfuXbly5ZJ89tnYaJ/704rOvQcAAMCjEcoAQBzWokV784V81KiBplGrhjTaK0Wnn36UAgWKmOmOv/xyshWWDDKhgr62bdv3HthXp8v+/vuvTJVM5szZZcyYz8101w879ogRE2X27ClmumWdiUm/sOs0zGrw4LEyZcpoWbjwG7l8+aJpGvvRR1MiNKR9FG0krNe/evWvJkzSgKNVq85Sv35zxz6dOvWRCRM+kAEDOptgQ6s5dBrszz//xNwzHQKmM03pUKw5cz4zTY516FV0aCg2aNBomTZtnPTt28FUF0UWyuh9+uSTWdZ5jDBBhgY52otF18o+g5UGNXo/dH+d3jz8zEsafmiwM2RINyvM+kZy584nrVt3lmvXrlhBTzXx8fE1M3A9yVC2JxGdew8AAB7OZrPdDdPfCMGteQjgppYsqXS8WrURmZMmzSJwTwf9bbLf30/K1c8giJyGO717t5Nx42ZIoULFBHBVx3fflPNHLkitVgLAzQQGXpHly7tea9hwfext5AWXVLx48RXWapK/v/9KgduipwwAAAAAAIATEMoAAAAAAAA4AT1lAABRypYtp4wd+7lZAwAAAHi2qJQBAERJp9zWBra6Blzdvn1HZOrU+bJp004zfToAAMDzRqUMAACAJVOmdHItvp/Mnr1YOnUaISVK5JdSpQqZpUiRPAIAAPCsEcoAAABYEiZMIA1bNZA2bRqYx1u37jZVM5MmfS179x6xwpmCjpAmb95sAgAA8LQIZQAAACJRokQBs3Ts2EyCgoJl8+Zd1rJTPvjgMzl9+rwjpCldupBkzfqCAADwmO4EBQWFCtwaoQwAAMAj+Pr6SIUKxcyibty45Qhpvvtuhdy8eTtCSJM+fWoBAOAR4vv6+noJ3BqhDAAAwGNKnDihvPhiGbOoixevOEKaGTN+EC8vTylZsqAJaHSdMmUyAQAAuB+hDAAAwFNKlSq51K5dySzq1KlzsmXLLvn77y3yySdfSbJkiSOENIkSJRAAAABCGQAAgGcsY8a0Znn99ZfM48OHT5qQ5uefV8vQoVOkcuVSkjp1MkfjYG9vqtcBAHBHhDIAAADPWY4cmczStGlt83jfvqOyceMOmTfvF+nefZQUKpRbKlUqIYUL55FixfIJAABwD4QyAAAAMUyn1NalZct65vG2bXtl+/Z9MnXqfNm584BjqJNW0eTPn0MAAIBrIpQBAABwMq2O0aV16/oSEhJqhjpt2rRTRo2aLnfvBkumTOlMSFOmTGGm3wYAwIUQygAAAMQi2l+mbNkiZlF37gSaoU4a0nz//Uq5deuOI6DRderUKQQAAMRNhDIAAACxWPz4flK1ammzqAsXLpuARoOaFSv+kTNnLkQIaRIkiC8AgNjPZrMd9fDwCBK4NUIZAACAOEQrY159tYpZ1LFjp01IozM7DRkyWbJnz2jCmXLlikrx4vkFABA7WYFMNmvlK3BrhDIAAABxmPaY0aVJk1rm8a5dBx1Dnd59d5ipoLEvuXNnFQAAEHsQygAAALiQggVzmUWFhYWZYU66DB06Rc6fv2zCmSpVSkqRInklXbpUAgAAnIdQBgAAwEV5enqaYUy6qKtXr5uA5uDBE/Lpp3PFzy9ehEoafQwAAGIOoQwAt3b1QrDsXHNZALi3axeCJJ4bjOpPliyJ1KxZ0VpEunR5w/Sj0ZDmxx//lIEDJ0revNlMQ+EiRfJI4cJ5BAAAPF+EMgDcVor0IrmLhVo/XZVnbf/+o7Jt2x7Jnj2TlC5dWADEbgky3/t/goiHuBN7P5qmTWubx9u375MdO/bLhAlfy+HDJ6Rs2aJSvnxRa11Y0qVLLQAA4NkilAHgtlKm97AWeaa0seasWYukQoVi0nNkQ8mYMa0AQFxRtGhes7z9dj25deuObNiwXdav3y4zZiwUPz9fMwyqcuUSUqpUIZ01RAAAwNMhlAGAZ2Du3J9l5syFUrt2JZk3b6ykSpVcACAuS5gwvrz0UjmzKB3q9O+/2+WPPzZIp04fmB405csXM5U02bJlFADA47HZbEdDQ0ODBG6NUAYAnoKGMVOnzjdT0f7882eSOHFCAQBXZB/qpAYMaC8bNgTI+vXbpE+fcXLnzl0TzuhSoUJx8fX1EQDAw3l4eGTz9vZ2g45meBhCGQB4TEFBwaYq5ssvF5tGmX///TVfQAC4nbJli5ilZ0+Rs2cvmGFOy5atkfffnyz58+c0wzg1oMmRI5MAAIDIEcoAQDQFBt6VBQuWyxdffC/vvNNItmz5QQAAYpoAN2hQwyxqy5Zdsm7dNhkwYLzpTWMPaHRt/VZYAADAPfytCACPcOdOoAlifvjhV+s3wi3l33+/FQBA1EqWLGiWbt3ekrNnL5qARqfdnjPnR/HziyeVKhW3lhKSMWM6AQDAnRHKAEAUdJjSvHm/mNmU3n23ifWlYp4AAB5PunSppGHDGmZRGzcGyD//+EvnziPF29tLKla8F9BoiAMAgLshlAGASEybtkC+/vpH6dOnjaxdO1cAAM9GmTJFzNK7d2szo9Patf5myu1u3UZJvXovSqFCuaVy5ZJm9icAAFwdoQwAhDN79hIzm1KHDk0ZpgQAz5l9RqcWLeqavl06o9Pvv/8ro0ZNl3z5slvhTCmpUqWUZMyYVgDA1dhstvOhoaHBArdGKAMAlsWLV5lFZxLZvPl7naJQAAAxR3vNVK1a2ixq69bd8vffm6Vz5xESL56vvPJKFSlZsoAULJhLAMAVWP/eTOPt7c0Unm6OUAaAW9N/8E+Y8LWUKlVQpk8fLgkSUC4PALFBiRIFzNKzZys5fPikmdHp44+/lHPnLlnBTSmpVq2MlClTWAAAiMsIZQC4pX37jpp/3CdNmkgmTx4omTKlFwBA7JQjRyazNG1aWy5cuCyrV2+WOXOWSu/eH1vhTGl56aWyZpgTAABxDaEMALcSEhIio0bNkIMHj5vprYsWzScAgLgjdeoU0rhxTbPcvn1H/vprk/z550bp1WusvPhiWalevawJaby8vAQAgNjOUwDATSxYsFwqVGhhZvb4+uvRBDIAEMfpkNNXX60iw4d3kS1bfpCaNcubkKZcuTdMSLNixT8SFBQkAADEVlTKAHB5u3YdlKlTv5Xs2TPKxo0LBADgml56qZxZ1OrVm8xMTgsWLJOUKZNbgU1Fa6kgAADEJoQyAFya9o3RUGbEiK6SJUsGAQC4h/AzOWlT919/XSuDBk2UGjXKm3DG/hwAONERa6Gcz80xfAmAS/r33+3Wb0tbS6ZM6WTOnFEEMgDgxrQJ8Ecf9TBDnLQx8M8/r5Zy5ZrLJ5/MNlNvA4CTZLcWX4Fbo1IGgMv59NO5cuDAMVm06FNJliyxAABg9/LLFcwSFBQsf/65QaZP/15OnTpnetPUqVNVMmdmNj4AQMyhUgaAyzh06ITUrNlO8ufPIVOmDCaQAQBEydfXR2rVqiRffDFcZs0aKfHi+Uq3bh9Jq1YD5aef/pSwsDABAOB5o1IGgEuYN+8X84/oefPGSqpUyQUAgOhKly6VtG3b0Cw7dx6Qv/7aKGXKNJM6darI669XlyJF8ggAAM8DoQyAOO+jj74QP7948t134wUAgKdRqFBus7z33ltW2P+XTJr0tdy4cVveeONVee21auLl5SUAADwrhDIA4rR69TrL4MEdpFSpQgIAwLOkIYwuR46cNFNslyv3hjRoUF2aN3+VBvIAnprNZjvq4eHB7EtujlAGQJx05swFqVu3kyxdOkUyZkwrAAA8L9mzZzJLmzYN5YcffpUePUZLhgxp5O2360np0vxSAMCTsQKZbMLsS26PUAZu7fDh38TPL6kgbrlzJ1gGDvxN5sxpJNev/y179ggAADGiQAGRkSMrys6dZ2XhwrkyZsxl65cE+axwJqMgbgoODmDJ94IAABAASURBVBQAcBZCGbit4ODbow8d+pUSizjm8GFbmmXLPJu8955tyu7dPwgAAM7g4SFSsqRIkiS2dD/8sL7SzJke6YoXD/uzYkWP3YI4x9PT44YAgBN4CADEEYULF07j4+OzY+vWrekEAIBYpESJEjlsNltrDw+PxqGhoX23b9/+owDAQxQvXnyFtZrk7++/UuC2PAUA4ghvb++zBDIAgNjI+vvpsPXFarAVyNTx8vJqZX3Z+rdo0aKlBACidjwkJIRGv26OUAZAnGD943aN9Q/dvAIAQCy2ffv2g1ZAU9/Dw6O7p6dnL+vvry+Ff3MDiFwW65eONPp1c/wFASDWK1GixAfW6teAgIADAgBAHGAFMxv9/f2bWT+usf4eCypatOibAgDAfQhlAMRq1j9ki9tsttrWP2w/FAAA4hjr76+vrIDG28vLK0/x4sV/EgAAwiGUARDbaZVMMwEAIA6zgpn3rdV065cNNiucqSQAAAihDIBYzPpHa32bzRZo/ZbxkAAAEMdZf5/9YoUzOvvpMCuc6SYA3Jr179xT1opGv26OUAZAbDbUWoYLAAAuxApnXrK+jGW1fvnA0FzAjXl4eGS0VjT6dXOEMgBiJesfqi9a/2Bdb/3DdYcAAOBirL/feoSFhR2w/r77QgAAbotQBkBs1czT09NfAABwUdu3b59j/ab8crFixZoLAMAtEcoAiJWsf6Q2DAoKWiQAALiwrVu3DrB+CVHSCmZ6CgC3YrPZjgo9ZdweoQyAWMf6h2k5a7V4586dVwQAABdnBTO9rF9GVClatGh5AeA2rD/32YSeMm6PUAZAbKShzDUBAMBN3Lx5s62np+c4AQC4FUIZALGOlnGHhYVtEQAA3MSBAwcuWqu1JUqU6C0AALdBKAMg1rHZbPFCQ0M3CgAAbsTf33+Q9XdgTQHgLi4GBweHCNwaoQyAWMfDw+OV27dv/ycAALiXYGu5VLx48SYCwB2k8vHx8Ra4NUIZALFKoUKFklur24cOHborAAC4GesXEwv0lxMCAHALhDIAYhVvb+/0YWFh/wgAAG7o0qVLq202WyYB4PKsP+vHQkJCmBLbzRHKAIhtklq/IUwtAAC4oWPHjl21VgWLFSvG34WAi7P+zZvV+oUkU2K7OUIZALGK9ZdTfGt1RwAAcFPW34V/WKssAgBweTQVAhDb+FjLWQEAwE2FhYUF2Wy2NALApVl/1i9bISyzL7k5QhkAsYr1l5Ofl5dXQgEAwE1ZX9K8rCW5AHBpnp6eKYTv5G6P4UsAYhXrLydP67eDYQIAgJsKDQ09av1dGCoAAJdHKAMgtvGwFpsAAOCmdCZC63cUiQQA4PIIZQDEKtZvBj0EAAAAANwAoQwAAAAAAIATEMoAAAAAAAA4AZ2eAQAAgFjEZrPdtJYgAeDqrgUFBdHU280RygAAAACxiIeHRyJr8RUAri6pr6+vl8CtMXwJAAAAAADACaiUAQAAAGIRm812Piws7LYAcHXHQ0JCGKro5qiUAQAAAGIRDw+PNF5eXgkEgKvL4u3tzVBFN0coAwAAAAAA4AQMXwIAAABikbCwsAs2m+2OAHBp1p/1U56engxfcnOEMgAAAEAsYn1JS22t4gsAl2b9Wc9orRi+5OYYvgQAAAAAAOAEhDIAYpXg4ODAsLCwkwIAAAAALo7hSwBiFR8fHz8PD49MAgCAm/r/njJMiQ24vpNMiQ1CGQAAACAW+f+eMkyJDbi+TEyJDYYvAQAAAAAAOAGhDAAAAAAAgBMwfAkAAACIRWw229XQ0NBAAeDSPDw8zgdbBG6NUAYAAACIRawvasm8vb39BIBLswLYNNafdR+BW2P4EgAAAAAAgBMQygAAAAAAADgBw5cAAACAWCQsLOyMtbolAFya9Wf9iLUECdwalTIAAABALOLp6ZneWhIKAJdm/TnP7u3t7Stwa4QyAAAAAAAATuCyw5e+/77EqzabrZQAiFM2brTlOn5cMvbrV3yYAIgVPDw8Aps02TpaAAAA8Ey5bCjj4+PXKGXK/K1SpMghAOKO27dPyq1bJ6VAgfJlBIDThYUFy4EDywKtHwllAAAAnjGXbvSbIUNxyZGjpgCIO86e3SB79qy1QpkmAsD5goNvaSgjAGKOzWa7EhoaGigAXN1Z6897sMCtMfsSgFjF29tTEiemtyEAwH15eHgk9/b29hMAri6d9efdR+DWaPQLIFYJCQmTGzee7Syg+/YdkUWLfrOOHSIAAAAAEFsQygCIFS5duipLlvwut2/fET+/eNF6zd27QTJy5OeyZs0Wx7YjR07K4MGT5MyZC45t48fPkVGjZkhAwH4BAAAAgNiCUAZwIVu27JI2bQZLxYot5I03+shXXy2RsLAweRrvvz9ZSpZsLKNHz4iw/cSJ/8x2XS5fvipP68qVa/Lhh1/I0aOnJTDwbrReo6HM0qV/yPnzlxzbjh07LStXro1wjI4dm8l777WQIkXyCAAAAADEFvSUAVxEUFCQDBo0SapVKy1vvllHdu8+JDNmLJSLF69K796t5Uldv35TPD09ZfPmXRG2b926x2zX0OfatZuSIkUyia2KFctnFgAA4gLr79Yz1t+xtwWAqztiLUECt0YoA7gIX19fWbRooiRKdK9J7ksvlZXQ0FBZuvTPpwplrl69IYUL55bt2/dZAc8VSZUqudm+Zctux/Zr127Is+Lt7SVp06aU2GrHjv2yceMOadeusQAA8DxYgUx6a3VCALi67NbiK3BrhDKAC7EHMnZaxeLhIU9FQ5lXXqlkKm/Wr98mr732otmuQ6WaN3/FhDK6T3h//bVRFi78TXbuPChJkiSUl1+uIF26vGEqa+x27jwgX3zxvenzkj17Rqlbt6rZHhISKufO3RuOpKHSZ58tkLVr/eW//86b4UfDh3eRlCkfrypn5syFMn36D7Jp03eObaVLN5VBg96Vf//dbkKWpEkTS4sWdaRRo5qOfc6du2iqjVav3mxd43Xr/iaQ3LmzmjUAAAAAPC16ygAu6vDhE/LLL3+bXipPQ8MIDSxKlChghRo7zTbt26KNeatXL/f/+/wvlNm+fa/07fuJxI/vJ++/31Hq1Kkq8+cvk0mTvnHsc+vWbenRY4ycPXtR+vVrawIZ3UdpiOTl5WV+njZtgcyZs1Ty5ctuBSjtzXt27DhCbDabPAtjxsyUdOlSybfffmxdS1kZPXqm7N172HHdb789QE6dOidz546WKVMGS4IE8c01jx/fTwAAAADgaVEpA7gYnfa5XLk3THDRqtXrjgqUqPbVgCVHjsxWGPJgSY0e4+bN26YypGzZIvL11z+a7dpfJlu2jJIxYzrzOg0w7GbNWixZsmSQceP6mMcaYug+2nS4dev6kixZEvnpp7/Ma2bOHCFZs75g9tPjvfvuMNG8RStktFHvggUrpEaN8jJsWGezjwZDdep0knXr/KVixRLytF55pbJ07/62+fmtt16zznGpFcocsUKgHPLnnxtNCDRt2vtWcJPaLBrcaFDUuHHNSO8XAAAAADwOKmUAF6RVHR06NDXhR/v2w6KsLPngg8+lWbPeMm/eL5E+f/nyNbPWUKZ8+aLm8aFDx8Xff4+UKVPYPJc8eRLT6FdpmKJDgUqVKhjhOCVK5Jfg4BAznEnp63UIkj2QUUmTJorwGh3epMFMuXJFHNtSp04h6dOnll27DsmzoFUydvZpuO/cuTdrk33WqgQJ/Bz76H24deuOuU4AAAAAeFqEMoCL8fb2NoHJO+80ks6d3zDhhvaDiUy2bPdCkaxZM0T6vE5TrTSMyJ49k6RJk9JUyWzYEOAIXnRok73RrwYWGmbc33MlSZJ7gYv2aFE3btyOsi+Lp6eHFfQktfa5ZR6PGDHNMfW2LqdOnTXDnp63ihWLm+FKWj2jdNpvDbkqVSph7jEAAM+L9cuU89YvAJh9CXB9x0NCQph9yc3xzQJwYfawRUOMggVzPfB8q1b1zRKV69fvBSPJkiU2a62WWbRoldy+HRgulEnk2E/DF604sQcq/zvOzf8/ThKzTpEiqRk2FZmwMJsJg9KmvVfF0qlTczPLU3j2GaCeJx2u1LdvGxk2bKppWqyKFMkrAwa0EwAAnicPD480Xl5eRwWAq8ti/bKP2ZfcHKEM4EK2bt1t+q7Y7dlzr2ntCy+kkScRfviS0gqcpUv/sEKSPKaKRGkQE76nTNGieWXbtn33ndceU11SsuS9cytQIKf89tu6CFNsBwUFm7W3t6cZEpUjRyZJnDih2V6yZEFxBm0+/O67TZj+GgAAAMBzwfAlwEVs27bXNMqdMmWe6euisx1NmPC1lC9fzDSufRL2ChcdoqS0v0vx4vnl1VcrO/bRUMbeU0a1a9dIjhw5KX36fCx//rlBPv/8O5k9e4k0bVrLUSmjzYc11Jk8eZ6pirlw4bKMHfuleS4kJMzadt1U3LRsWU+++eYnWbLkdzMF99y5P1vH6RVhWJWvr4/s2HHADGtS9umy163bFiEsehLnz182gdLatVvN+x8//p+j1wwAAAAAPC0qZQAXUaxYPunYsZmp7tA+KHnzZpe2bRuaYONJaTji4+PtaIKbKFFCmT59eIR9kiRJaPaz0yE+kyYNMIHLoEGTJDQ0TBo0qB5ham4NcqZMGSSjRs2Ql19uJy+8kFaGDesk77zzvpkS2z6zkQ6t0h7Fc+b8aIKbzJnTS7161RyVO56entKiRV358svFZpYonapaq3j0HCZO/Nrso88/qa5d35SPPppuAhm7nDkzyxdfDHMEVQAAAADwpFx2TtclSyrMLlSoRascOWoKgNitVq32JnQJP0uUBjP2x/7+i8TZdPrwXbsOSufOI6V+/erSu3drAdxBcPAt+emndoGNGm2ILwBiRIkSJWZYq41bt26dKQBcVvHixVdYq0n+/v4rBW6L4UsAnK5Zs9ri4+NjKl/si4Yyujirn8zIkZ/L4sWrHI+1J07RovlMVY+91w4AAM9DWFjYxdDQ0DsCwKVZv4A8bf3iL1jg1hi+BMDpGjeuKcuXr5EjR05F2K4Nf5s1qyXOoMO2vv76J0mTJoVj+NaWLbtNv5w2beoLAADPi/XLCZ2CkOo0wMVZv4B8wfrFn4/ArRHKAHC6hAkTyKuvVpHPPlsg1m8GHdtz5MgsL75YTpxBe+Do1N59+35iZoDSWaJy584qEyb0l0qVSggAAAAAPC1CGQCxQoMGNWT58n/k8OET5nGyZImlefNXxFnix/eTkSO7CQAAAAA8L/SUARArJE6cUGrXriheXl7mcaZM6aVq1dICAAAAAK6KUAZArNGwYQ0z7XXChPHljTecVyUDAAAAADGB4UuItU4ftsnpgwK3klCqFWltGv4mDS0vm1baBO7DL6FI4UoeAgAAALgLQhnEWqcP2eTYPj9Jm4XJB9xJgQKVrEXkDhOBupXA26FyfusNK5QRAHB7YWFhZ63VLQHg0mw221EPD48ggVsjlEGsliZzfCntN2Q0AAAQAElEQVRYOYUAcG3XLwXL+WM3BABgpsROZ62OCwCXZgUy2ayVr8Ct0VMGAAAAAADACQhlAAAAAAAAnIBQBgAAAAAAwAkIZQAAAAAAAJyAUAYAAAAAAMAJmH0JAAAAiEVsNtvV0NDQuwLApVl/1i+EWARujVAGAAAAiEU8PDySeXt7xxMALs36s57ax8eH7+RujuFLAAAAAAAATkAoAwAAAAAA4ASEMgAAAAAAAE5AKAMAAAAAAOAEhDIAAAAAAABOQCgDAAAAAADgBEy/BQAAAMQioaGh563VbQHg6k6EhIQECdwalTIAAABALOLl5ZXGWhIIAFeX2dvb21fg1ghlAAAAAAAAnIBQBnBjQUFB8ssvC+Xw4QPiavbt2yUbN66VuGbt2j9lw4Z/BAAAAIDrI5QB3NjevTtk8uTR8sUX4yNs//XXn6RDh2by2msVpFevd2TNmt/lWThwYI/89ddKHSsvz9vPP/8gK1Yskdjs+PEjMnr0YDl37ox5HBYWJh980FeGDu0hAAAAAFwfjX6BWEqrWM6ePS2ZM2eTZyGy4+XLV1jatu0q+fMXcWw7cuSgjB8/Qho3ftt6vpDs3h0g8eL5ybOwZ88OmTZtnJQpU1kSJHhwqHzbtg3l1KnjD2zv0WOIFC9eRt56q84Dz73ySgPp1m3gA9t37Ngqr7/e3PHY33+jCWp0e/bsuc3rqlWr6Xi+devX5b//TkU4RqZMWWXmzIXyvJw8ecyEVG+80dY89vT0lN69h5l1eOfPnxVf33iSLFlyAQAAAOA6CGWAWEqDkYMH98qsWYvkWYjseL6+vtKkScsI+/n7bxAPDw9p06aLCQcqVKgmMaVPn+Fy926g4/HBg/tkxoyJkjZtese2Vq06WSFSYcfjtGkzPHCcM2dOmyCjcOES5vGGDWtk6NCe0rBhC6le/VX599+/ZfToQVZQdVdq1nzN8brKlatLnTqNHI8TJkwsMa1GjYjB06lTJ6ywqoH07z/SCpFqCQAAAADXQSgDIILbt2+Jt7f3A9UaMSFv3oIRHmsVSZo06aRo0VJy4cI5sy1r1hxSpEjJhx4nIGCLJE6cRHLkyG0elyhRTkaNmmqqbZQGTfv375bffvspQiiTOnXaRx4bAAAAAJ4VQhnASU6ePC5Tp46RI0cOSEhIiGTPnssMt8mdO3+EYTo1a5Y0w4gmTpxtHteuXVr69RspR48elOXLF8ubb7aTFClSyapVv8jx44fl+vVrUqBAEWnVqrPkypXXVIw86ngtWrS3jvNOhOFDul945cpVkWHDPnE81mFN3303W3bs8JfkyVNIgwZvSt26jSO8Rs9JGwkfO3bYBCuPMxTrzp07JpRp1qyNqdx5HNrkV++jPVjy8fFxBDJ26dO/4Ojl8iSi+vwqVnzRPN+vX0frPTJazwXLtm2b5Natm1KyZDnp02eExIsXL8rj6uvUmDHTTHWT9vdR2ntGl5EjP5VSpcoLAMB1hYWF3bHZbMECwKVZ/8a9GRoTzRYRqxHKAE4ybdrHVmByxoQnCRIkNENqtmz5V0qXrihjx34u3377pQlIdEhPkiTJIrx2wYIvJVGixNK16wDJli2XBAbeMV/UX321gfnyr2HNRx/1l1mzFkuyZCkeeTw7fU77rqxe/av1+imO7ZMmfRRhvytXLsuQId0kYcJE0r59D/nvv5NWQDHWvFelSi+ZfXbt2i7jxg2TMmUqmj4p+t7ffz9HouvPP1fI3bt3rdCovjyu/ft3mRDpYbSfS65c+eRJRfX52UMZtWrVz1K2bGX5+OPpcubMKeueDpDp0ydYn1v/aL2H9vXRIVgffzzU9J3RYCtXrvwCAHBt1i8V4lsrHwHg0qzwNZH1591L4NYIZQAn0f4u+oX9lVfuhQ5Vq77seE6H0KxYsdQM2YlsOM3lyxdlwoTZEj9+fMc2rYqx07BEe6icPn3CNKt91PHsdPjQpk1rTWVK+P3ix4/YlFerXzT8mTTpK3N8pcHQwoXfOEKZRYvmmgqeoUM/ES+ve3/XaEXJ3LnTJTpWrlxqhhnd39xW31uDjTt3bkv16nWkdevOjuOra9euytGjh6RDh15RHvvAgb2mqW+XLhHDkYCArdKtWys5ceKolChR1oReSZNGHmA97POzy5AhkwwcOMqcX4YMGaVy5RombNJz0+qdR9F76+Fxr9pHq4wYWgUAAAC4FkIZwEm0kkOH56RMmdoKMqo7+p9Eh365Dx/I6MxK8+bNkH/++d2EDVbqbrZrf5jnISBgs+n1Yg9kVJ48BUxgosGL9qTZudPfDBkKH5hon5fo0BmgdPrst9/u4NimQZMOsXrhhczWcZLKli3r5YcfvjbH12DGTt9XA4+8eQtFemyddvrzz8eZQEWDF7vXXmtqwijtWaPDnxYvnifDh/eS8eNnRXqc6Hx+GkqFv34d4qRVTFo186xm1QIAAAAQdxHKAE7SoUNvM3PQtm0bZcGC2abPS6dOfSNUvEQlefKUER6PGjVQDh3aJ+3adTdBg4YKAwd2kedFq2S0V839fWfUpUsXzGxJN25clwQJEsmTWLZskQl9tAeLnYYy4UOa0qUryJUrl+S3335+IJTRip+o+rbMnPmpqaT54ovvImyvX/9/02frMCEdkqRDsrR3TKZMWR44zpN8fnoNSqt5AAAAAIBQBnCSBAkSmMoPXbTh7Icf9pf33+8u8+Ytf6yZj3TK5PXrV5uponVK55igsxRpv5du3QY+8Jw9MNIqkTt3Hr9SR6t+tAJF+6k8qsGvhiJr1vxuql/s92zHjq1R9pPR4+qwquHDx5vQ51HHVhcvnos0lHmSz0+bMKv7QzUAAAAA7inm57wF8ACtLKlatabpFaPVH0qHAIWFPboZ+9Wrl///GBkc2w4f3v/AftE9XnTky1fYVMRo01ntcxJ+8fX1NfvocCYdhhSezkT0KBqc6LCr+xv8BgcHmxmZwtOGvhqu2EMQez+ZyHqv6DTZ2nhYm+zq0KX73V+9osdW4e9rVCL7/FRoaEiE/Xbv3m561KRL9+hj2unndu9YNOYHAAAAXA2VMoAT6OxFvXq9Y8KBggWLipeXt+nHoj1HtEeJ0lmVfv99maxb95cJJHRWJq3OuJ/2dfHz8zONcVOkSGmmn9aGu0pnQNJw5HGOFx316jWVH39cICNH9pUmTVqaqplff/3R9GOxDzHSffr37yRLly6QWrVeN1NHa5XKo0TV4Hf+/JmyceM/0qjR29Y9SmWGaOl03N27D3bsE1U/GQ1q3n+/h5nJSO+XBjR2+fMXse7TNhkzZrCZfjtbtpwmXNHzfumlV0yD3vtF5/O7dz7bZNasyWYY1unTJ01Vj1Y02YMWe8XMpk3rrLAmeaRNhVOlSiNJkiSVDRvWmAolDWfun94bAAAAQNxEKAM4QfLkKaRfvw/M9NOTJn0o8eL5SZkyleTNN9s59qlbt7GcOHFExo4dYobnDBs2PkJjWjv9Ij9ixETz5X/o0B4mdBg5crJs375J9u7dYe3x5mMdLzq034rO/jR+/AgZPry3CYU0CKlQ4X/TQRcrVlq6dOlnpsGeNm2cea82bbqaapWH0fMKDg56YHuLFu1NWDJu3FATvBQsWMy6hyPlxRdrOfaJqp/M/v27zexQ/v4bzWKnFTa//PKvOde33nrX9JvR/fQYTZu2koYNW0R6jtH5/JQGNno8HdqkDX+1b02zZq0dz+fPX1gKFCgiM2ZMNI8bNXrw/TTAGTRotLmHfft2MFVAhDIAAACAa/AQF7VkSYXZhQq1aJUjR01B3LTp1zC5fSu5FKqSQoDo6NixueknE74hsLP069fRrMeMmSZ4tOuXgmXtDyflrUEu+9dSnBUcfEt++qldYKNGG+ILgBhRokSJGdZq49atW2cKAJdVvHjxFdZqkr+//0qB26JSBoBL0Oqf3Lnzm2FZAAAAABAXEMoAcAk6FKlHjyECAAAAAHEFoQwAPAft2nUXAAAAAHgYQhkAeA5y5swjAAAAAPAwngIAAAAAAIAYRygDAAAAAADgBIQyAAAAAAAATkAoAwAAAAAA4ASEMgAAAAAAAE5AKAM8wogRfWTr1g3yPFy6dEGWLPnWWl8UwBlsNpt069ZKTp06IQAAAABiFqEM8BCrV/8me/fulAIFisrzsGzZIvn8809k+fLF4mw3b96QFSuWyvnzZyWuWr9+tYwbN0ye1uXLl6zPZIlcvXpFXMG2bZtk5Mh+kT7n4eEhRYuWkkmTRgoAAACAmEUoA0Th2rWr8umnH0mbNl3Ez8/PsV1Di2f1Zb1WrdflzTffkdq1XxdnO3r0kEycOFLOnTsjcVVAwBbZsmW9PK1r165YIcWHViC3I8p9wsLC5OTJYxIcHCzPQmBgoEyZMkZq1iwpS5cucGz3998ow4f3loYNq0mfPu/KX3/9+sBrDx7cJ0OH9pRGjV404YueV3h79uyQdev+ivK9mzdvayplfv75BwEAAAAQcwhlgCgsWTJf4sdPIC+99Ipjm35xfeutOrJt20Z5FtKkSSdvv91BUqVKI4hbVq78Ud55p5Fcv35NnpaGJh07NpNVq36OsH3DhjUyYEBnSZ8+o/Ts+b6kTZteRo8eJL/++pNjHw3TevduZ/5b7d59sNy9e1d69GjzWBVPGjq+9loT+eabLyQkJEQAAAAAxAxCGSAK69evlmLFSounJ39M8PxohUz//h0lRYpUMmnSnAjPlShRTkaNmirt23eXChWqWeHLMMmcOZv89tv/QpmVK5dK0qTJpG/fEVKx4ovy/vsfmz4xf/65Qh5HyZLlTXXYzp3+AgAAACBmeAuAB2j1w/HjR6Ru3caObePHj3BUKIwePdgsI0d+KqVKlZd582bKP//8bioVZs2aLEePHpS5c5fL1KljzFASfaxfuqtVqyUtWrR3BD07d24zVQ7jxs2QQoWKmW39+nWUTJmySpIkSWXVql/k1q2b5nXvvttTfH19Iz3foKAg+eST4bJnT4AZeqOvr1y5hjRu/LZ5r2nTxsnff/8mCxb85njNsGG95MKFc9Y5zo1wrPPnz0ifPtNk//7dkiFDJunWbZDky1fI8fz8+bPkr79WytmzpyV16rRSuHBJeeed9yRRosSyZs3v5pyPHz9s7mGBAkWkVavOkitXXsfr9fo0WEicOInpYRMcHGSuT89V+8Hs379LsmfPbd43a9YcjtfVrl3aDCXbvn2zqSzx84svtWrVk5YtO8rDaH+YmTMnmWFNXl5eUqZMZenSpZ94e//vf3/aN0irRHbvDpAsWbLLyy/XfegxW7d+Xf7775T5+Y03apn1Tz+tk3jx4pnePF999Zk5T72/eg3vvTdQcuTIHemxtErlww+nmHulAU14Pj4+Urx4mQjb0qd/IcIQs3///VuKFCnp+G9KX1OwYDGzvVmz1pG+54EDe0w1TatWncx9V3p+ek927dpuwkgAAAAAzx8lAEAkdEiI0lDCTr+89ukz3Pz8oPz8lQAAEABJREFUxhttZezYzyM0AL58+aL15bq/lCxZTnr1Gmq+bOfKlU9efbWhDBw42vqi/5oJNP74Y/kj3//XX380X5w/+GCSCXq0EfBPP30f5f6LFs2V9ev/knr1mkr//h9aQUkJU+kTGhoqj0sDnLJlq0jPnkNN35RBg7qaoEFpaDFnzjQTAgwcOEpq1qxnbdtuKiyUDrPRkKpTpz7StWt/K2S4Ix991N8c5/7rO3LkoHz88XQzPEyvbeDALlKuXBX57LNvTRClIdj99L3z5i1kBV+LpXnzNuZ+/v33qodez/DhvUw/lXr1mknTpq1l7do/5Isvxjuev337lgwd2sMM99GwRgOZxYvnP/SYfft+IA0btjA/Dxo02oRqGsgo/W9Aq1S0X5B+dhqW9OzZVi5ePB/l8TSQi25FloZ8GhwprYjRgEbDsfB0OJyGa5HRz0oDOa3CsQcySt8/XboX5MSJIwIAAAAgZlApA0Ti+vV7IYNWc9hp9YmHx70vzlrpocFEePplt23brtKkSUvHNu3TYVemTEUTDmjFRo0adeRh0qbNYH1xHm8qF7TSYv78mXLw4N4o9z90aJ8kT55SGjV6yzwuX76qPCkNVF58sbb5OXv2XNKuXWP5/fdl8vrrzRznoNeo/XA0RGnatJXjtVoRE74qJmHCRKYB7enTJ8z9s0uTJr0MHjzGXJ+GVtrYtkqVl6V+/ebmeT3uwoXfmNBBZweyq1Gjrrz1Vnvzs95bDXN0KE+VKjUivZaAgK2yb98uExDVqdPIbNOKpY8/ft9U2Gh1j1Y/6Wf3ySczHeeYKVM2K3h5V6KilUP24E6DuZQpU5mfteJGG/MOGPCRVK36stlWtmxladashgnOtNrpaRw4sNdU6HTp0t88todhCRIkjLCfXpdWCIW/f7rWcEwbAWuApOHd/fS/d1eZcQoAAACIC6iUASJhb3bq5fV4uaVWjoSngUDfvh2kQYOqZlYdDTXu3Ln9yOOkTJk6wvCaePH85O7dwCj31y/+WjExZswQ2bp1wwOVKY8jdep0jp81fNJhVHodqkyZSmatzWa1GuTOnTsRXqvDqGbPnipt2tSXWrVKmUBGaTVKVNeXMGHi/3/f/1V7aKign8H9lT73N0TOli2nHD68X6ISELDZrLVfil3evAXNeWqQpbSHSooUKSOERnrNT2LLln/NOvzwn/jx40vu3AXMsKCnoZ/p55+PM591iRJlzTYNXR6HDqs6cGC3CfwSJEjwwPM+Pr5mOBkAAACAmEGlDBAJnclG6fCb6NLhH9pw1U6rGnr10umu65sKCe3ZocNYnofq1V81Acb69atlxIjeVqiQTNq2fc9RrfE0tHriypVL5mftZ6LDtnTmoRkzJsmnn44y1S06g5RWYowaNdCEHe3adTfBgYY5OizpedFKnCtXLkf5vA6DUi1bvvbAc/bZiXRoVoIEieRZuHnzullrqBSePj516pg8jZkzPzXVOV988Z1jW7Jkyc3afp3/O48b5rnwVUbKHghqP57IaHhmPyYAAACA549QBohEunQZzFqrT7QB65NYvHieqXBp375HlA16nxX98q1Nb3W5ffu29cX9ExOQaCWJvf/Ik9Iv+Dly5HE81mFbumjlhva6mTx5tKlgKVKklAmFtHls5crVJSbcuHHdVN1ExV5ZM2LERNPjJ7yMGbOadfLkKUyflmfB/n56XuHDDb2HGpQ9KW2srMOfhg8fb4aN2ennru9z5szpCPufOXPKDGe73zvvdDPTbGul0/jxsx4IbS5cOGv6IAEAAACIGQxfAiKhw3a0T4fOIhSefchNdBroXr162fQvsQcyWoWgvVWeNx2WUrfuvV429qE9vr7xHEOy7LQxcWRCQoIdP2tlhvYtCd/Q2E4rg7RPi94nHZal16u0H47dw4YWPYnQ0JBwP4eaoUc6HCkqOguR0h4q9jDJvtj7wOjQIr0Xly79735EZwiPzuSkwsL+99+C/T7t2LHVsU2HeOmQoSed0SggYIuZlUr74ujQpfvptezdu8MxZC04ONg0ZC5atNQD++p96NdvpJm96ocfvo7wnPag0c86T54CAgAAACBmUCkDREIDhwoVqpkeIa1bd3Zs10oI7Tei1QbaA0WDgfunLLbLmTOvbNu2SX777WczzGbFiiVmyuNjx+5NF/2kfUvup31FBgzobAIg/eKv56WzB+kQrPz571X5aMNerd7QipCkSZPLkiXzrfM4ZBra3k+nj27SpJXpYfPtt7PMkCz7FNHffDPdChy2SMWKL5kKHJ32WcMmnclHe7JoNcrKlUtNjxa9Tm3Wq7SfyrP4sq8NgfU69b20abKGCA0avOl4PlmyFOY6N21aZ2aB0oa8up4w4QMzhEwDJJ0qWu/DRx9NMa/Ra9PpsL/8crIZdqXBz9SpYx95Ljptt9LPV6tL9L7nz1/YvN/kyaPMdNhaxbN06bfWXh6OJsyPQ0Ox99/vYWbT0mvWgMZOP1ud/rpx45bSo0dr009IK5R0aJnS2abCs/efKViwqGnarDNZacijAaTatGmtCR2fpkk0AAAAgMdDKANEQacLbt++iZlRR7/cK/3SqlMg67TR2sBXqxSiCmVatGhvenhoyKENg/ULswYD2oflxImj5svxs6BDUHr3HmaGS2n1g1Z9aDijw1Psw7CqVq0pBw/us768tzHnpBUuGmZs3rz+geNpMPHll1NM9YsOgbnXFDah455o2PP337/J4cMHzExLQ4eOc3yR12FCs2ZNNlNMa4gwcuRkK7jZZCo5RN6Up1WtWi0Tqmg/Gw1+9LrD30ftrfPjjwtkyJBuVjDyjeTOnU8GDx5r3fOPTFCiVSva28c+nbXSnjka0OjzzZq9bPrm9Oo1zPQDehi99g4desl33802fW10NicdLqbvN2XKaBNI6WfxwguZzPHvb1IcHfv37zZ9jXRGJ13sNDT85Zd/HeehU3RrE1+dFv3ezF2fmOuISps2Xc1Qs48+GmCd61zz3/VPP31n/rsI3xcJAOAcVpCuJZuPLssFEKdZf9bvhj3NDB1wCR7iopYsqTC7UKEWrXLkqCmImzb9Gia3byWXQlVSiLNMnDjShBlTpnzzQP8NxKzatUuboOvNN98RPFs6LbgGjV999aPTGv1evxQsa384KW8N4s9ZbBMcfMsK7doFNmq0Ib4AiBElSpSYYa02bt26daYAcFnFixdfYa0m+fv7rxS4LXrKAA/RsmUn0zT1779XCeCKdFjTV19NNQ2amXkJAAAAiFkMXwIeQmfmGTnyU8mdO78ArkgrwIYM+dgxRA8AAABAzCGUAR5Bm7fC+UaP/izCzE54dvhvHAAAAHAOQhkAcYI2VQYAAAAAV0JPGQAAAAAAACcglAEAAAAAAHAChi8BAAAAsYjNZrsaGhoaKABcmoeHx/lgi8CtEcoAAAAAsYj1RS2Zt7e3nwBwaVYAm8b6s+4jcGsMXwIAAAAAAHACQhkAAAAAAAAnIJQBAAAAAABwAkIZAAAAAAAAJyCUAQAAAAAAcAJCGQAAAAAAACcglAEAAABikbCwsDPW6rYAcHVHrCVI4NYIZQAAAIBYxNPTM721SiAAXF12a/EVuDVCGQAAAAAAACcglAEAAAAAAHACQhkAAAAAAAAnIJQBAAAAAABwAm8BYrHzJ+7IzjWXBYBrC7oTKgAAAIC7IZRBrPVCTg8R213rp7sC93H06Ck5dOiE1KhRXuA+EsQXSZdJAAAAALdCKINY64UcHtYicDM3/jgtW4/+K6VrVRAAANxRWFhYiLVQQgi4vuCgoKAwgVsjlAEAAABiEU9PT29r8RIArs7H19eXPq9ujv8AAAAAAAAAnIBKGQCxire3pyROnFAAAAAAwNURygCIVcLCbBIcHCIAAAAA4OoIZQDEKhrKBAYy4xYAAAAA10coAyBW8fDQxUMAAAAAwNURygCIVWw2XWwCAAAAAK6O2ZcAAAAAAACcgEoZALGKj4+XpE6dXAAAcFdhYWEXbDbbbQHg6k6GhIQECdwaoQyAWCU4OFQuXLgiAAC4K09Pz9TWKoEAcHWZvL29fQVujVAGQKzi6ekhvr4+AgAAAACujlAGQKyiU2IHBQULAAAAALg6Gv0CAAAAAAA4AZUyAGIVHx9vSZ06hQAAAACAqyOUARCrBAeHyIULlwUAAAAAXB3DlwAAAAAAAJyAShkAsYqPj5ekS5dKAAAAAMDVEcoAiFWCg0Pl7NmLAgCAu7LZbOfDwsJuCwBXdyIkJCRI4NYYvgQAAADEIh4eHmm8vLwSCABXl9nb29tX4NaolAEQq/j6ekuaNCkFAAAAAFwdoQyAWCUoKETOn78kAAAAAODqGL4EAAAAAADgBFTKAIhVfHy8JXXqFAIAAAAAro5QBkCsEhwcIhcuXBYAAAAAcHUMXwIAAAAAAHACKmUAxCre3p6SOHFCAQAAAABXRygDIFYJCQmTGzduCQAAAAC4OkIZALGKp6eH+PnFEwAAAABwdYQyAGKVsDCbBAbeFQAAAABwdTT6BQAAAAAAcAIqZQDEKr6+3pI2bUoBAAAAAFdHKAMgVgkKCpFz5y4JAAAAALg6QhkAsYqHh4iXl5cAAAAAgKsjlAEQq9hsIqGhoQIAAAAAro5GvwAAAAAAAE5ApQyAWMXb21OSJ08qAAAAAODqCGUAxCohIWFy5co1AQDAXdlstvNhYWG3BYCrOxESEhIkcGsMXwIQq/j4eEny5EkEAAB35eHhkcbLyyuBAHB1mb29vX0Fbo1KGQCxSnBwqFy5cl0AAAAAwNURygCIVazfDpoFAAAAAFwdoQwAp6tVq71cuHBZx9A7tv3++7+Ox/7+iwQAAAAAXA09ZQA4XbNmtcXHx0c8PT0di71ipmTJggIAAAAArohQBoDTNW5cUzJnTvfAdm342+z/2LsLOKnKLgzgZ4PubpCUrqWkBJES6e4SlJZu6QZJRQFBBSQVPlDCwkC6O0SQRjoXWNj93ufgHWeXbZadnZnn//sNsztzZ+bOLPfOe8897zlNqgkREREREZErYlCGiBwuQYL4UqPG6+Ll5RXo9uzZM8sbb7wmRERERERErohBGSKKEerVqyyvvJLB9nvSpImkadO3hIiIiIiIyFUxKENEMUKiRAmkevWytmyZTJnSSYUKJYSIiIiIiMhVMShDRDFG/fqVJXPmdJIgQTxp1oxZMkRE5J4CAgL+efr06QMhIld39smTJ4+F3BpbYlOMcfNKgJzcK+TWEkjFQm3lr7/OS5KnpWXHhgAh95UsrUjOwh5CRORuPDw8Unt5eZ0WInJ1mb29vWMLuTUGZSjGuHFZ5PgeL8mcJ5GQ+8qXr5y5iPj6Crmx29cey9WLD0xQRoiIiIiIXBaDMhSjJE4ZS/KXTy5E5N7OHbsvF44zc5+IiIiIXBtryhAREREREREROQCDMkREREREREREDsCgDBERERERERGRAzAoQ0RERERERETkAAzKEBEREYzIGHsAABAASURBVBERERE5AIMyREREREREREQOwJbYREREREQxiL+//xNzeSpE5Or8Hj9+7C/k1hiUISIiIiKKQTw9Pb3NxUuIyNXFih07NmevuDn+ByAiIiIiIiIicgAGZYiIiIiIiIiIHIBBGSIiIiIiIiIiB2BQhoiIiIiIiIjIARiUISIiIiIiIiJyAAZliIiIiIiIiIgcgEEZIiIiIiIiIiIHYFCGiIiIiIiIiMgBGJQhclHXr1+VVauWmOtr8iJOnjwm3333tTx58kRIZPPmn2Xbtt+FiIiIiIjoRXkLEbkkBFIWL54n9+7dlZYtO0pkzZnzoRw4sEcyZnxFChXyEXfm7+8vo0b10583btwlREREL4P5vrnk6en5QIjI1f1lLo+F3BqDMkQO8O23K2XmzPHy+ef/k3TpMsjLUK1aHb2uXr1OuB/zzz+XJXbsOJI0aTLbba1adZJjxw5KvnyFwvUc1nsDM6CUNGnSSf78RaRt2y6SIkUqcWZ4P336DNdrIiKil8V8z6QzV2eFiFxdNnOJLeTWGJQhclGpU6c1AZX3wr38+fNnpX37ejJgwGipWLGa7fYCBYroJaLwPAkTJpaTJ4/Kzz+vl06dmspHHy2WVKnSiDOrXPltISIiIiIiigo83UvkQq5cuSSzZ08WPz8/cbTcuQtI8eKlpVmz9jJixFS5ffuWrF+/Sl7EgwcPZPnyL+TEiSNCRERERETk7JgpQxQDoQ7M559/LPv27ZSrV6/IK69kl+7dB0n27Llsyzx+/FiWLp0vv/32o5w7d0ZvQ0aLp6eXTkO6ceOa9OnTQSZPnmvLdPnqq89k06YNcvnyBc1YKViwmLzzTneZM2eqbNy4RpcZP36IXkaPnqFBFdSlWbRojgmo7Ai0fvPnzzLrt0Nu3bopefMWlNatO0vOnLmDfT8ZMmSSWLFiyYUL/2Vi37hxXebNmy67dm0RLy8vKVmyvHTt2l+8vf/bLW3Y8D/5/vs1cuTIAQkICND3nyBBQkmXLqPkypVXqlcvIf37j5bTp0/KunXfSPPmHaROnSZy+PB+WbZsgdbCSZYsudSr11xq1mxoe95t237Tz+Lvv//S58uZM49Z/06SLVtO81n+LR99NEH++uuEFjfGbXXqNJWyZd/Qx/bv30mvJ0yYbXu+06f/NJ/TXDl0aK/WnSlQoKj06DFYEidOYlsGj8uU6RW97YcfvpX79+9pRtK77/aS2LGZtUpERERE5I4YlCGKgcaMGSDHjx+WZs3e0Tosa9Ysk1692stnn30tKVOm1mWGDeupQYUPPpgkceLE1QyZJEmSyuDBz+q5IChjD4GKL76YrcGJdu26ytmzp+XHH7/TDJaGDVuZAI2PTJo0TDNbChcubgIVeUNcv9Gj++u0pKZN2+v6IQPmn38uhRiUQZAI2TvJk6e03TZiRG85c+aUNGrUWgMjCPwgcNOly7NCukuWzJeFCz+Vvn1HyMCBYzVDZv/+XTJo0HgNtFgQmEqYMJF06zZQsmbNKTdv3pChQ3voc3bs2FMuXjxngiwTJWnS5FKuXCV58OC+fPjhSMmRI7f07j1M7ty5Jb/8slFOnTquAZjZsyfpe2nTpovEj59Atm791QSOttqCMkEhuDJwYGd9/vbtu8vDh74m4DPPrGcXmTlzoXh4eNiW3bjxf1KoUDEZNWq6fv7jxg3SAFODBi2EiIiIiIjcD4MyRDHM0aMHZc+e7RqIqFChit5WqlR5adKksnz99SLNrEA2B5ZBZknu3Pl1GWSIjBjRxwQ5jgUbHEEQBRAEQb2Z1157XRo3bmO738Pj2WzGzJmzauAgtPXbu3dHoNozFStWDXF5BGOQVYPgRMWK1fW2/ft3y7Fjh0wgZYC8/XYDvQ0Bm0mTPtCMFQRZ1qxZLmXKVLS9BurjNGjwhtanqV+/ue35EXyaOnWBxIsXT39fuHCOBkqmT/9cM1MAgZKVKxdqUAZTvBCIwvOWL/+m3m+tg/U54fN+6626+rv1NwgJChvfuXNb6+VYhYzxGfbr954GdEqXrmBbNk2a9DJ8+IeaDYTsJwRvrL9LcJCRhEykjBkzCxERERERuR7WlCGKYZCVAUWKlLDdhoBDrlz55NChffp7QIC/XseNG8+2TIIEifT6wYN7wT5vyZLl9Hr8+MEa2PD19ZUXWT9MfQpNmza1pWrVYibg8ZocOLBbBg0aZ95DHr1v//6del2sWGnb8gguYUrWn38e09/xHuPFi2+7Hz9jmtP9+3cDvU758pVtARnruRF0sgIy8Oqr+TTzCNORsmTJptOpkDW0atUSuXbtn0DPh2AVMmcwfezUqRMSlt27t+lr2XeWwvQlsP5eFixjPz0LGU6PHj0M8blRHBnFl0+cCDlwQ0REREREzouZMkQxzL17d/Qa2SL28Pv582f0Z2Ri5Mjxqqxdu0LKlXtTp/0giwbZJq++mj/Y50Xr7YkTP9E6LXPnTpcZM8ZJ3bpNNQPFfopNeNcPU6VCY3VfGjWqrwaErKwUQCYLtG5d67nHYaoTVKhQVb7/fq00atRGM0W++WaxPH361ARNKgRaPlmyFIF+x3PjORAQCur69avaonv06Jnyv/8t1elEqKeDwE6nTn20Ffh77/XRjJa9e7fL0qULJE+eAtK5c78Qp2bdvXtbp0rZQ8ts/L2uXbsiLwJ/Z2TLWFPWiIiIiIjItTAoQxTDWAfgd+/e0SCBBQfniRP/FwhBbZUOHRpI7dpl9XdkYaC+TNy4cUN8bkxLwgXFaFEYd+bM8fp6NWrUl/CyMkKwfva1XYJC9yUEgpo0aadZKZgiZBUqtt7jyJHTnlvfjBlf0esOHd6X7dt/10wRQIYJiueGFByxoIDxo0ePzLKDnrvPCuCkT59RgzCAbBbUt0EtGUwZix8/vjRv/o5eMNUJ9X0++OB9Wbx4nQZbgnu9y5cvBroNny+CQ/Z/r8gYM2amEBERERGR62JQhiiGyZevsF5jyo+VXYKpRidOHJbq1evalkPx3/z5i2j2S0QhuIAgyWefzbTVNLGm1SAbJTTW1BwU3Q2r3grUr99CM3qmTRttgkBf6m1Yb4gTJ06I9WvQIQm1X7755pfnMlFCkydPQa1Zg0LFCLCEJX/+wro+J08ee+4+ZNUgY+fTTz+UmzevB5qiZP96mNKFAsNWkOrgwT3aLapo0ZJCREREREQUEgZliBxo9eolgQIO6AiEwrBoRT1z5jhth41AAJYT8ZAGDVralkWBW3QW+vXXHzSjJn78hFovJaT2yiiAe+DALilbtpIuh3bb6ETk4/Oa3o/sFbRrRjAE2R8IzgQXVMB0HqzfrFnjtR6L1X0J2Tavv175ueUReEG3p8mTh5vlVpvAUh3bc0ydOkoLF1tdjtDae+zYWfo4TDVCtslPP63T9Y0VK7ZehxWgqV27sU5NGj26nxY1RtYMpimhsC6maiGYhKlbb75ZQz/vW7duyM6df+j6I7DSu/c7WugXwRovL28t5IuuTMEFZJ69XhN9vcGDu2oAChlN6AiFNtuoT0NERBRRJrB/y3wPPxQicmkeHh7/+KErBrk1BmWIHGj16qWBfkc3JARlhgyZqEEPdAxC8AWFaRGssK8tgjbWaP08duxA220IWIwb95EWtg0Ky6NY7q+/fq8FbDENaNiwybbuQMiUQTtttNZG5yBksISU6TF48ATNfFm58kstzovlEIQISeXKb8uKFV/KggWzNHCDIAze44wZYzX4hEwgTG1CUMOCDJUffvhW21nbwxQmqzNScPDc6MaEttfoRoXpUZhKVabMs5bWaP3dokVHLea7bNnnkjZtem1/jfo6KCTcv/8ozeyZPn2MFuJFPZzmzTuE8nrx9fXweWBdEUhCS/EBA8ZEqFYPERGRxXx/JDXfy3GFiFyaCcCmNtt6LCG35rJHDKtWlVlQoECLNtmzVxVyDqf2B8ih7XGlbP30QhGDqTLnz5+VCRMGa4bL7NlLxNXcuHFdPv/8I/nxx+9k8eL1odazIed37th9uXD8itRox8CWo/n53Zc1azo8bNBgWzwhomjh4+Mz11xt37179zwhIpdVtGjR9eZq+p49ezYIuS22xCZyQsjkQJFeCzIyMmXKop2Xbt26Kc7u8OH90rdvR21hbUmePIVOtULQyeoARURERERE5Mw4fYnICSVJkkzrpOTOnV9Sp06rt6ED0KZNG7S9s7NDgV10RVq48FPbFCpMC0KtlowZs0iGDJmFiIiIiIjI2TEoQ+SE0JXpwoWz2hUIralRRwWFbNu06SxvvVVPnB1q5wwf/qHMmTPVBGIWSKxYsSRz5qwmQFNKix0H15qaiIiIiIjI2TAoQ+SkmjZtpxdXVbJkWb0QERERERG5Kp5uJiIiIiIiIiJyAAZliIiIiIiIiIgcgEEZIiIiIiIiIiIHYFCGiIiIiIiIiMgBWOiXiIiIiCgGCQgIeOTv7/9EiMjV+Zpt/amQW2NQhoiIiIgoBvHw8Ijj5eXFcTqR64vn6enpJeTWOH2JiKLMd999LWfPnhYiIiIiIiIKGyPwRC7i229XysyZ4/VnE3GXNGnSSf78RaRt2y6SIkUqiQ6ff/6xVK78tnTs2FNeNgR/OnRo+Nztb7xRXfr3HyWO9PjxY7l8+YJkzpxViIiIiIiIQsKgDJGLGTBgtCRMmFhOnjwqP/+8Xjp1aioffbRYUqVKI66oYcNWUrx4advvKVM6/n1++OFI/fw/++xrISIiIiIiCgmDMkQuJnfuApIuXQYNVLz+ehVp166urF+/Slq1ek9cUcaMWaRQoWJCRERERETkbBiUIXJhGTJkklixYsmFC2f194cPH8pHH02Qc+fOyOnTJyV58pRSsWI1adGio055goMH90qfPh1k8uS5smDBLDl16rhkzpxNOnR4XwoWLGp77tu3b8ns2ZNl9+6tkjBhIqlbt1mw6/DHH5t0atXRowclUaLE/waKutpeD6pXLyE9ew6VnTv/MJctuhzWKXXqtPLppx/KpUvnpWTJctKjx2BJkCChhMe9e3d1OtW+fTvl6tUr8sor2aV790GSPXsu2zL9+3eSLFmySc6ceWTJks8ka9acMnToRLlx47rMmzdddu3aIl5eXua1y0vXrv3F2/vZLvPcub/1c/zrrxPy5MkTyZYtp9Sp01Ry5corLVu+bXv+qlWLSZ48BWTatAVCREREREQUFIMyRC7sn38ui5+fnwZfIG7cuBqAKFjQRxInTqpBhS++mC3p0mXUWjD2RozobYIn3UzgYrTMmDFWf1+69HsN8sCECUM00NK8eQdJmTK1/PDDWrlz53ag5zh0aJ+MGtVPXnvtdenV6wM5c+ZPWbbsc0Hnv6B1Z2bNGi/167fQ15w8eZjMnTtNkiRJZoJBPXRd+/V7V1KlSqu/h8eYMQPk+PHD0qzZO1pTZ82aZWYd2uuUIqyv5cCB3Ro4Qu0dPL/13s+cOSWNGrXWINCiRXP0fXfp0k/vnz17kvkrsYX8AAAQAElEQVRsL0mbNl0kfvwEsnXrryaAs1VKlCgrEyd+YgI88+X8+b+lb98Ruu5ERERERETBYVCGyEUhGDN//iy01ZSKFavbbq9Vq5Ht55Ily2pAAhkhQYMynTr1kUqV3tKfq1Wro0GHy5cvSqZMWUww56Ts3r1NunUbIG+/3UCXKVWqvAliVAr0HMg+wfSiYcMm6+/ly7+p64PATOPGbU3Q5b+ABQr0tm7dSX9G9g6KFg8dOkkKFfLR25CFguye8ECwaM+e7TJw4FipUKGKbf2aNKksX3+9SN59t5dt2dOn/5Tp0z+X3Lnz6+/79++WY8cOBXpvCGpNmvSBrh+yglAvBs/31lt19X7rNQBTqdavX63ZOZxWRUREREREoWFQhsjFtGlT2/YzMkQGDRpnAhp5bLch4IBgzZ9/HpP79+/pbcmTp3jueVKnTmf7OXbsOHr98KGvXiO7BOyDDsjCiRMnru33p0+famCkRo36gZ4XWTqLFs3VwEmpUuVst1tZKmBNUcL0JQuCIZiSFNTUqaP0Ashm+fbbrRpAgiJFStiWixcvnvkc8mn2jj1Ma7ICMrB//069Llbsv+LBuB8dlfCZFS5cXDN/Nm3aoJ9vuXJvBpoSRUREREREFF4MyhC5GKv70qhRfbUOC7JTLCdOHJXevd+R6tXrarYIggmY0hNRVnAkfvyQ67s8eHBf/P39n6sBg3WDa9euSFQI3H3J49/1u/PvayUK8tqJ5Pz5M4FuS5o0eaDfrUBV69a1nnstTAeD997rI2nSpJe9e7fL0qULtG5M5879JGfO3EJERERERBReDMoQuRir+1KTJu20Xgym4FiZHN98s1izWVDPJXbs2BJZyZI9C2Q8eHBPUqRIGewyKNaL7Jmg2S1WwCSqaq0E133Jqhlz9+4dE3RJZvfad8N8XeuxI0dO0/UP/Fqv6HX8+PGlefN39HLlyiWtX/PBB+/L4sXrAhUwJiIiIiIiCg2PHohcFIrmohbKtGmjbbfdunVDb7MCMshmsTozRQSmAcGpUydst2G6EjoR2cuXr7AcOrQ30G2Y+oQuRi+z3gpe13oti6+vr5w4cTjQlKbg5M9fRK/jxImj62h/CS4AlSZNOqlQoarcuHFNbt68rrfh/aGYMRERERERUWiYKUPkohBUQOvpyZOHa+HZ6tXrSI4cuWXv3h3y/fdrdVrR+vWrtE02Og2hc1LixEnC9dyYppM/f2FZseILzcJB/ZmZM8fJ48ePAi2HzkyYLjVyZF8t5ItuTyjyW7t240BFfqNa3rwFdUoT1gkFd1H7ZfXqJYLpTQ0atAz1sZiKhMeiTg2meFndldBGfOzYWSbwckPfEwr94jPw8vLWlt9oi43XAbTW/vHH77SIMgouoysTsmuIiIiIiIjsMVOGyIWho1KWLNlkwYJZmhXTokVHnc40b950mTVrgmTIkNlcL9T6KGfPno7Qcw8ePEHrw3Tq1FSaNKmiBXCzZs0RaJl8+QrJqFHT5dKl8zJ+/GBtFY1OTu3bd5eXbciQiVpTZ+XKhTJu3CATdLqlQRX7dtihPRbZNgjqDB/eWwv8Wp2YMHWrf/9R+nzTp4+Rjz6aID4+pcxrfGx7fM2aDaVq1VoyceJQmTJluBw9ekCIiIiIiIiC8hAXtWpVmQUFCrRokz17VSHncGp/gBzaHlfK1k8vROTezh27LxeOX5Ea7Vz2a8pp+PndlzVrOjxs0GBbPCGiaOHj4zPXXG3fvXv3PCEil1W0aNH15mr6nj17Ngi5LWbKEBERERERERE5AIMyREREREREREQOwKAMEREREREREZEDMChDREREREREROQADMoQERERERERETkAgzJERERERERERA7AoAwRERERERERkQMwKENERERERERE5AAMyhAREREREREROQCDMkTk1K5fvyqrVi0x19eEiIiIiIjImTAoQ0RO7bvvvpZPPpki69Z9E+7HXLhwTlavXipERERERESO5C1E5DDTpo2WH374Vr78cq2kSJFKb/Pz85NWrWpK/vyFZfDg8bZljx07JF9/vUj27t0hnp6ekjlzVmnbtqvky1dI7z979rR06NBQf/by8pI0adJJ1qw5pWbNhlKkSAlxhH/+uSyxY8eRpEmTSWRZ76tnz6FSrVrt5+6vVq2OXlevXifcz/nLLxtl0aI5UqdOk1CXu3fvrl7Spk3/3H1R9fe4ePG8edyzdV+x4idJnDiJ7b7x44fIpk0b5O23G0i3bgOEiIiIiIhcCzNliByoVatO4u3tLV999ZnttrVrV8itWzekXbtuttvWr18t77/fVm7fviXt23eXd97pIcmTp5Rx4wbJw4cPAz1nw4atZNSo6VK5ck25du0fGTCgs+zcuUWi2/nzZ6Vly7dN0GK7vEypU6c1n+N7kjJlaolqHTs2kqVLFzx3e1T+Pe7eva3XCOzs3r010GP379+lt1vLEBERERGRa2GmDJEDJU+eQurXbyHLln0uzZq1l8SJk8qKFV/KW2/Vk3TpMugyV69ekdmzJ0mxYq/JyJHT9CAdqlSpaQ7W70jcuHEDPWfGjFnEx6eUXho1aq2Bke+/XyPFi5cWd3bkyAHZs2e7tGjRQV5EVP89bt26qde5c+fXrJuKFavp7+fO/S03blyTAgWKyJ07DMoQEREREbkiBmWIHKxhw9ZaF2X58i8kTZr08uDBPc38sPz00zp59OiRdOs20BYAsCRKlDjU50YWjoeHx3PZG3/8sUm+/XalHD16UJ/j9derSLt2XQM9P6btfP75x7Jv304NRLzySnbp3n2QZM+ey7YMMnwwveby5QuSKlUaKViwmLzzTneZM2eqbNy4RpfBFBxcRo+e8VICQwcP7pU+fTrI5MlzNYABWN/Fi+fKli2/aDZLggQJdb3jx08Y6LFnzpySSZM+kPPn/9Z1xxQhZN5gStnkycN1mfXrV+mlceM2+hlF9d8DWTCxY8eWokVLmmDNWtvtBw7skvTpM0qGDJnl5MmjQkRERERErofTl4gcLF68eNKy5bsamEGWTIMGrSRJkqS2+zGFBYEC1CSJqEWL5mp3Ivt6K4cO7ZNRo/pJ3LjxpFevD6Ry5bdl1aqvZN686YEeO2bMAPn55/Vas+X994doAKJXr/Y6BQcOH94vX3wxWwoVKiaDBo2TqlVrm9v2aRAEU3b69h2hyyEDaOLETyRfvsISHfD63bu30lots2YtkrFjZ5nPOL6UK/emjBjxoW05f39/c/94qVGjgb6/M2f+NL9P0PuKFi2l64y/Q8mS5fRn1HWBqP57IAsG6+fj85rW4MG0L+t1ChUqbgJJCZgpQ0RERETkopgpQxQDIPAxe/Zkna5Sq1ajQPfhID5ZshS231FgtkePNrbf+/QZroEVy9Spo/QCKDCLrJvSpSvY7l+y5DOdUjNs2GT9vXz5NzV7A1OoGjduq4EIZNBgqs/AgWOlQoUqulypUuWlSZPKWtz23Xd72bI3MCUHQYrXXntds0ksHh7PYr4ogIvATUiQkYMpPBkzZpaosHnzz+ZzvC7jx8/W9cIF7xEBJBTZxXu1IPMH6wcIMiGDCFKkSKkXb+9YWivGfv2j+u9x586zTJ48eQro9b59O/Sz2L17m2bjXLhwVpchIiL3YU4cPDGXp0JErs7v8ePH/kJujZkyRDEApgA9efJEp7ds2LA60H04kA8ICLD9jg4+yNzo3XtYsM+FLBXcj4AKggNffvmJZmjA06dPNdhSuHDxQI8pWNBHuz4hGAO7dj0rOGvfJQgZPbly5dNMG0AGCYwfP1gzanx9fSUyOnVqKu3b15MTJ6Jmik5AgP+/6xvfdhumLT14cF/fv8XqmGRB5tCjRw/DfP6o/HsAsmCwfggWoe4MpouhcxOCVfj8ESTDdClciIjIPZjvKDMk8PYSInJ1sWLHjs1jcjfH/wBEDoZAAeqfIJsDB+3IWLEPcKRMmUZu3rxu+z1OnDiauYHCsMFBFgzuR4YL2kgjO2Thwk81wwOBCUzbQUaGvYQJn9VCuXbtil7fu3fn39sTBVkukW0ZFCJGsAF1cObOnS5Nm1bVbBT7gEV4IDCCKTpR1T2pRImyGpDB5wg3b97QwroIIiHo9aKi8u8BVqYMYNoUiv0iMJMjR24NyFh/m9u3bwoREREREbkWBmWIHAwFcS9duiDNm3cwgY32GjhZseIL2/0ooIvCtag3EhnZsj0rzHv58kUtRIvuQMjCsGcFYdD9CawACboJBV7urm0ZQLChf/9RJqi0Tgv8ovDvunXfSESMGTNTVq36VTtRRQVMV+rSpZ8WMq5atZg0aVJFP0NMBYoKUfn3AGTKWDWEUAgZn/GaNcu18C9Y9wX9WxARERERkfNjUIbIgZAl89VX87QeC7oboXgsWiKjbosVOHnjjep6jeUi48SJI3qNjA1Awd1Dh/YGWubAgd2aRWLVTrGK8uJ2C7J3Tpw4HGhKkwVTgVAIFxkvVq0ZKyvFfspQdEHhYhRP3rhxl14+/PAzrRETUXgP1nQoS1T/PZApg88NEAzLkiWbnDt3xjbFjJkyRERERESui4V+iRwIrZaRdTFs2BTbbciYQY2WJUvmS4cOPSR//sIa8EDmBw7Wq1evqwGboLVnLGjvjM496D6E4MuPP36nxYOtjAs8f+/e78jIkX01wPDXXyd0qk/t2o1ty+TNW1CzNmbOHKfrlyJFKlm9eom5x0MaNGipyyxcOEfbNpctW0kDCZhygywfdBECBBgSJ04i27b9ptklCM5Y2R+Rgef5559Ltt8TJUoides2DXZZdIhCQGn79jyaGZQ8eSrJkCHTcy2sw4KsloMH9+h7Q0YLpphF9d8D3aKswAugi9W2bb9KgQJF9Xd8hsAOTERERERErodBGSIHQWFd1JJB8CNnzty229F5Bwf/a9Ys085GOHjv1m2ABjZwQD9jxlhJkCCRZMr0iradfv31KoGeF221ccHBfPr0mXSZSpXest2fL18hGTVqusyfP1OL9CJY8tZb9aR9++6BnmfIkInaMnrlyoXaFQpBDbSXtqY2oYAtarf8+uv3curUCX0P6OhkdRZClsngweO1q1S/fu9pFs6LBGW2bv1VLxZ0KwopKNOuXTf9nBAMsWTNmkMmTvzUFuQIj86d+2rnpIEDu0jSpMlNQKaITrOKyr8HPltMK7PUr99cLxbrPnZgIiIiIiJyPR7iolatKrOgQIEWbbJnryrkHE7tD5BD2+NK2frphSiqoKsV2lYPGtRFs1o6deojFPOdO3ZfLhy/IjXauezXlNPw87tvgsQdHjZosC2eEFG08PHxQZu+7bt3747cXFkicgpFixZdb66m79mzZ4OQ22JNGSJyKdOmjZbvvvuv2DAydjDlKG3aDHLr1g0hIiIiIiKKKTh9iYhcird3LFm58kudZoV6MoBpTH///Zc0bdpOiIiIiIiIYgoGZYjIpaA2Dgrvjh7dTx4/fizJk6eU7NlzyYgR+9AbbQAAEABJREFUU6VUqXJCREREREQUUzAoQ0QuJV68eDJgwGghIiJyVv7+/nfM5ZEQkau75ufn90TIrTEoQ0REREQUg3h6eiY2lzhCRK4uZaxYsXhM7uZY6JeIiIiIiIiIyAEYlCEiIiIiIiIicgAGZYiIiIiIiIiIHIBBGSIiIiIiIiIiB2BQhoiIiIiIiIjIARiUIXJxV65ckokTP5DTp/+UqHTixBHZtGmDPH36NMRlDh/eL7/++oMQERERERHR8xiUIXJx//xzWX76aZ3cu3dXotKRIwdk/Pgh8ujRoxCX+fLLT2Tdum8kKp09e1qqVi1muzRtWk2GDOmuQaKg/P39ZfXqpdK1a0upXbusdOnSXFas+FL8/PyeWxbBpTVrlptlWkjNmqWlffv6Mm3aaHn48GGY67R79zZdl3r1KkhAQECg+zZv/lnvO378cKDbz58/q7d/++3KQLffvn1L5syZJm3b1pFatcro+mCdiYiIiIjI9TAoQ0ROqWHDVjJhwmxp3LiNBlm6dWslO3duCbTMjBljZe7cafLaa6/LwIFjpWLF6vLVV59Jnz4d5PHjx7blEEgZOrSHzJ49WUqVKieDB4+X11+vrMGs48cPhbkuO3ZsloQJE8n9+/fk0KF9ElnXrv1jAkgtZNOm9VKnTlMZMGCMZMuWU5Ysma/BGiIiIiIici3eQkTkhDJmzCKFCxfXS82aDaVNm9qybNkCKV68tN7/44/fyfr1q00AZrhUrvy27XHZs+cywY7O8r//LdXAjrUssl169Bgsb71VV28rVaq8BkYSJ04S5rps3/67VK1aWzZu/J8GaAoUKCKRgcyiGzeuyccffyVZsmTT20qXriAdOrwfrvUgIiIiIiLnwqAMUQxQvXoJ6d9/tJw+fVKn+zRv3kEKFvSRpUvny7lzZ+TChbOSOXM2adCgpVSoUMX2uP79O0mmTK/oAfsPP3yrmRoVK1aTd9/tJbFjxw72te7evSOdOzfTx40ZM1M8PDy09gsCGgcO7JFkyZJLvXrNNdBhD8+PqTZnzpzSQEjmzFklvJYuXSBr166Qhw995fXXq0iXLv3Ey8tLvvnmK/n00w9l0aLvJFWqNLblP/jgfc0aQXAiPPBcCGKcOnXCdhs+x9Sp00qlSm8FWrZIkRK67siCsYIy33+/Rt93tWq1Ay0bnkAI/j6XLl3Q57169bIGZdq37yYRhWlSv/yyUYMwVkAmIutBRESuw9/f/7K5ui9E5NICAgJOm7H4YyG3xulLRDEEAjCHD++Tbt0Gio/Pa5IiRSrJlSufNG7cVqexvPJKdpk4cahcvHg+0OOQnYF6KqNGTZf33x+iwQjURgkOpumMGzdIf8Z0HgRkbt68oVN3UAi4Y8eeUqbMG/LRRxPl999/sj0OU3ImTx4uSZIk1cyT3LnzP1cLJSRHjuzXoE/nzn01m+S7777WKUSAKUKwZcsvtuUxrWjPnu065Sgizp//23xmKfVn1Ic5evSg5MtXWDw9n9/NIaiE9+vr66vL4v0VKlQs2GXDsn37Zg0K4fF4XgStrl69IhGFmjOoz4PnICIi92a+j9KaSwIhIpdmxuI4yxlbyK0xU4YohsC0lalTF0i8ePFstzVo0ML2c5EiJTVbZe/e7ZI+fUbb7WnSpJfhwz8Ub29vDdx89dU8OXnyqO1+BF6s60WL5siBA7tl+vQvJFGixHo7givIsJk+/XPNngFktKxcuVDKlaukv3/99SJJnjylDBs2RQMQ8OTJE32+sGD9hg2brOtXpkxFuXbtiqxdu1yaNWuvgaccOV6VXbu2SO3ajXX5gwf3aI2Y0qUrSnihmC+yVZAhBHfu3NYiv8mTpwh2ebwXuH79qiRIkFCXxbqEJGiRZNSPsSAzJn/+IpqZVLhwCb1t69ZfpVatRhIRN29eD7RuRERERETk+hiUIYohypevHCggAwjCrFr1lU6RsQrTPngQOJsZwQQEPCxx4sSVR4+e7xi0Z882Wbx4nma6oK6KZf/+nTrNxwrIwKuv5tNgDQIveG4ESooWLWkLyIAV1AlL0PXLm7eQtsm+fPmiZMiQSUqVel2zhBCIiRUrluzc+Yeuj/06Bmfq1FF6gbhx40rz5u9I3bpNgyzlEexjrUBVeCCbpn79wAGiVq3e09fDlCN8Ni1bvqu34/1g3fEeIhqUISIiIiIi98OgDFEMkSxZ4KwOZH+gG1C3bgOkZMlyen+NGqUksqzWzvHjB86GRpYM2majPXNQyCRJkyad1qGJHz+hRAUrmIPMIAQxUEMFGTcotIvOR7t2bdWMmrCgHgyK+s6fP0sDVajDYwVbMM0KU5Fu3boR7GPx2oCAUZw4cXTZGzeuB7ssAmVTpswLdBsCL4ACv8iyQcDKgulHP/+83hZkCi/r74/pZERERERE5B4YlCGKoZYv/0J8fErJ22830N+RtfIiEPzA1KEPPxyp2SoobAsosItaJj16DHruMVagAFNqfH2jpt6gNRXICs4gI8bKLkH7Z2QFde8+KMznQfcl1HFBHZxevdrLqlVLbNO9EGTJk6eAZrGgjk7QzBhM4cqaNYctMwnLImMIAZbg6srkz1842HXA1CXo0aPNc/dhShbq4iROnFR/D5rhdP/+s88B06cgV668GsTZt2+HrQMUERERERG5Nhb6JYqBEEhAlgfqsVj+/POYvKguXfrrVKJJkz6w3ZYnT0HNiMmZM68GOewvVgcnTGf666+TgZ7ryRO/8Lzkc8shUIKAjH33JtSuQR0WBDnQaSgiLaXz5Sukj0e2za1bN223V6pUQzOA1q1bFWh5FBFGkd833qhuu+3NN9/WTJmgBZKRIRQS/I0QSCpRoqxMnPiJ7TJ27CwNAu3cuUWXs6aF4X0H/Ryerf+zgA8CRGXLVpI//tikhZvtBa1pQ0REREREroFBGaIYCAf1OXPmkW3bftPuPqgtM2nSMIkbN54cOXJAMzrCC8ED6xrTenr1GqZThdAFCVBgFwGB0aP7yb59O/X1Ro7sK19++YntObDM2bOndUoVpkFhHVD8NzxQdHjOnGn6mrNmTdCuTvXrtwiUkYKivggMISiCqUsRqfkC7dt3Fz+/x9pe24L21ggszZgx1gRKBsrmzT/LihVfyqhR/fSzrVPnv/ozVavW0mUxXWz06P4aIEIwp1u3ls8FdSzolnT79i1tuW0fyEJ2E6Yw4W8HVqvtJUvm6/Nv2fKLzJs3QxYs+Ejq1WtmmwoF7dp11cyanj3b6bIIIC1c+Km0alVT/v77LyEiIiIiItfC6UtEMRTaYH/yyRRtYY0De7TGxnSiL774WOuVoBZKZJQsWVYqVKhqAiVTpVix0lozBl2fMK1pxIg+WjQ3d+4C2hrbUqRICenatb9OqUKwAIGHdu26aZvssOC1MC1nwoQhGsRAgKJx4zaBlkG2CzJkEHho27aLRFS6dBl0mheCRgggoWU3ihIjawUFi58FtT7QKU/o+lS7dhNbFhDYL/vjj99q4AYZRSi+jPUPDurJYJlSpco/dx9qAO3du0NOnTqh07OQoYQpYL/99qOsXbtCg0716jXXIIw9BGg+/vgrLXyMrCG8HwTSmjRpK1myZBMiIiIiInItETsd7URWrSqzoECBFm2yZ68q5BxO7Q+QQ9vjStn66YXcz8qVi0zQ53P56qsNgbo1kXs6d+y+XDh+RWq0c9mvKafh53df1qzp8LBBg23xhIiihY+Pz1xztX337t3zhIhcVtGiRdebq+l79uzZIOS2eORDRA6H2i8rV34pNWs2YkCGiIjcnr+//9WAgIAHQkSu7tyTJ08eC7k1Hv0QkUNhqhDqr6A7VPPm7wgREZG78/T0TGWu4gsRubpM5oRkbCG3xqAMETkUarLUqtVIi+QSERERERG5EwZliMihKld+W4iIiIiIiNwRW2ITERERERERETkAgzJERERERERERA7AoAwRERERERERkQMwKENERERERERE5AAMyhAREREREREROQCDMkREREREREREDsCW2EREREREMYi/v/9lc3VfiMilBQQEnPbw8Hgs5NaYKUNEREREFIN4enqmNZcEQkQuzQRkspqr2EJujUEZIiIiIiIiIiIHYFCGiIiIiIiIiMgBGJQhIiIiIiIiInIABmWIiIiIiIiIiByAQRkiIiIiIiIiIgdgUIaIiIiIiIiIyAEYlCEiIiIiIiIicgBvISIiIiKiGMPf3/+yubovROTSAgICTnt4eDwWcmsMylCMcueanxz87YYQkXu7e+2xeHoJEZFb8vT0TGuu/hYicmkmIJPVXMUWcmsMylCMkdwMP14t+tT8dEui28mTZ+SXX3ZKixY1JV68uEJEIuvX/y6JEsWXsmV9JLrFzySSLK0QEREREbk0BmUoxkiWxkNKVJNodePGbRkyZIakTJlU5n7dTYjoPyWqlZeVKzdK33GtZdSo7lK+fDEhIiIiIqKow0K/5LYWL14rjRv3ktata8nIkQzIEAWnQYOq8t13n8iqVT/KwIFT5cmTp0JERERERFGDQRlyO2fOXJDmzfvJlSs35IcfPpOSJQsJEYUsYcL4MnXqAKlYsYSUKdPMbDdbhIiIiIiIXhyDMuRWPv54iUyZ8rkMHfqe9OrVWogo/KpUKSPbty+T48dPS+fOo3T6HxERERERRR6DMuQWDh/+U5o16yNx4sSWmTMHS+7c2YSIIqdr1+Y67Q/T/7755nshIiIiIqLIYVCGXN60aV/KhAnzZNKkvtK+fX0hoheHaX+Y/nft2m1p23awXLlyXYiIKGoEBAQ8fsIiXkQuz9/f/9Hjx4/9hdwagzLkso4cOSU1arwnKVIklS+/HC8ZMqQRIopaHTs2lJ49W5nAzCBZteoHISKiF+fh4RHb29vbS4jIpXl6esaJHTs2j8ndHP8DkEv66KOvZPr0hfLZZ6OlZctaQkQvT8GCr8q6dZ/KzZt3pUOHD+TOnXtCRERERERhY1CGXMr581ekUaOeEi9eXPn00+GSNm1KIaLo0a5dPenUqYnUrt3VBGl+EyIiIiIiCh2DMuQyli/fIDNmLJRx43rpwSERRb+iRfPKpk2fy4EDJ6R//ylCREREREQhY1CGXEKvXhPk9OnzMnFiH8mePZMQkWMNGPCOVK5cWl5/vbUcOnRSiIiIiIjoeQzKkFM7ePCEvPZaU6ld+w1zVv4dIaKY4803X5PvvvtYJk2aL8uWrRciIiIiIgqMQRlyWosXfyuff75afv31S3M2vrgQUcyTMGEC+eKLceLn90R69BgnRERERET0HwZlyCkNHDhVrly5JlOm9JPYsWMJEcVsLVrUlIYNq0ilSm3l3LlLQkREIfP398eO8oEQkav7y1weC7k1BmXIqTx8+Eg6dhwmFSuWkF692ggROY+yZX3k669nSLduY+Xnn7cJEREFz9PTM525ii9E5OqymUtsIbfGoAw5jRMnzpiz7O1k5MiuUqVKGSEi55M0aSJZvXqmbFNGUo4AABAASURBVN68R+bP/0aIiIiIiNwZgzLkFH76aasMGzZL/vhjsaRNm0qIyLl98EFn8fV9KMOHfyRERERERO6KQRmK8Vau3CgbN26RJUsmCxG5ji5dmomPT14TmJklRERERETuiEEZitEWLFglN2/ekYkTewsRuZ6aNStKgwZVpXnzvkJERERE5G4YlKEY65NPlsn9+w+kQ4eGQkSuK3/+nDJ0aGcTnHlfiIiIiIjcCYMyFCNt27Zfnj59Kl27Nhcicn25c2eVCRN6Sa9eE4SIyN35+/s/NOOgJ0JELi0gIOC+2d6fCrk1BmUoxlm0aK2sW/er1psgIveRPXtmqV+/irRqNUCIiNyZp6dnXC8vL28hIpfm4eGRwGzvXkJujUEZilE2bdou+/Ydk5EjuwsRuZ8yZYpI8+Y1ZdCgqUJERERE5OpcOgJ/6dIeefTotpBz+PvvWzJ37g4ZPbqKHDmyXIjIPWXKJJI48R0ZMWKoNGxYQMixnj71EyIiIiJ6OVw2KOPn93DlxYu7/zYXIecwebLH4G7d/McfOrSc8yqJ3Fy2bOi+5tH0zp0ju0qW9Dgp5FAeHh4PhYiIiIiinMsGZRo12v2dufpOyCn4+Ph85e/v37JVq71LhYjomZFXr/o8/vDD3ayrQERuJSAg4NbTp08fCRG5NLOtX31iCLk11pQhhytSpEgDc+W9dy8DMkQUiL8ZrDQqWrToSiEiciMeHh5Jvb294wgRuTSzraeKFSsWTz65OQZlyOHMzmj8lStXWgsRURB79uz5xuwjHhcsWLCaEBERERG5GAZlyKHMGfB+5oBr5fnz532FiCgY/v7+E8xZpHFCRERERORiGJQhhzIBmTG7d+8eLEREIdi7d+/+gICAUyaIW0+IiIiIiFwIgzLkMIULF+789OnTD8yP7LZERKHy8/MbYa5aChGRG0Ch3ydPnrDrGZGLM9v6P2Zb9xNyawzKkMN4eXl1MjuiNUJEFIaDhrnKVbRo0TxCROTi/i30G1eIyKWZbT212dZjCbk1BmXIIQoVKlTEXD3ev3//YSEiCgcTxP3SDF5aCRERERGRi2BQhhzCRISr+fv7fyxEROH04MGDhSYwk0+IiIiIiFwEgzLkEObAqp45471PiIjC6fjx4xfNVYEiRYpkESIiIiIiF8CgDEW7V155Ja4JyOTbs2fPbiEiiphfPD09KwgRkQtDoV9/f/9HQkQuzWzrV/38/J4IuTUGZSjaJU2atJTZAS0QIqIIMgcp68z+I5cQEbkwFPo1Aeg4QkQuzWzrqWLFiuUt5NYYlKFoZ3Y+BcyFEWEiijBzkHLOXFUUIiIiIiIXwKAMRTsTkHnVXB0XIqIIevjw4QmzD2GmDBERERG5BAZlyBFiBQQEHBQiogg6cuTIDbP/2Fm4cOGkQkTkosx+7tHTp0+ZVUzk+nz9/f2fCrk1BmXIEUqZM923hIgoEsz+4xUzgEkjREQuyuzn4nh5ebHOBJHri+fp6ekl5NYYlKFoh4JWvr6+V4WIKHKumQFMSiEiIiIicnKMwFO0CwgIuHDkyJF/hIgoEsw+5LAJ7iYWIiIX5e/vf9lc3RcicmlmTHPajGkeC7k1ZspQdPMyO57C5tpfiIgi4d+ATBIhInJRnp6eac0lgRCRSzNjmqzmKraQW2OmDEUrHx+fOCheJ0REkWT2IQ/NICauEBERERE5OWbKULS6ffs2AoF/CxFR5F03gRl+fxGRy/L3979jLpzSQOTizHaOrpLstObmmClD0crb2zuWOcOdWoiIIi+u2Y/EEyIiF+Xp6YlpmpzSQOTizLaeXHhM7vZ4ppGiVbx48TxNNJj1ZIgo0kxAxp+ZMkRERETkCjiopWj19OlTczzlESBERJEUgIiMp6eHEBERERE5OaZKUbTy9/fngRQRvagAZsoQERERkSvgoJaIiIiIiIiIyAEYlCEiIiIiIiIicgAGZYiIiIiIiIiIHIBBGSIiIiIiIiIiB2BQhoiIiIiIiIjIAdh9iYiInIqHh8fNp0+f3hciIhfl7+9/LSAgwFeIyKWZ7fyCGdP4Cbk1BmWIiMipmAFMMk9PzztCROSizD4upbmKJ0Tk0syJpgze3t6xhNwapy8RERERERERETkAgzJERERERERERA7AoAwRERERERERkQMwKENERERERERE5AAMylC0unbtmn9AQMAuISKKpKdPn15/8uTJPSEiclFmP/ePuTwQInJ1f5sxzWMht8agDEWrlClTenp4eBQTIqJI8vLySuHt7Z1QiIhclNnPpTaX+EJEri6LGdPEFnJrDMoQERERERERETkAgzJERERERERERA7AoAwRERERERERkQMwKENERERERERE5AAMyhAREREREREROYC3EBEROZGAgIAb5sKW2ETksvz9/a+a/ZyvEJFLM9v5eQ8PD7bEdnMMyhARkVMxg5fk5nJXiIhclKenZypzFU+IyKWZ8UxGc8WW2G6O05eIiIiIiIiIiByAQRkiIiIiIiIiIgdgUIaIiIiIiIiIyAE8QrtzxYrCFfz9PSsIURS5ft0//urVnvXat5dFQkQUCcuXS9VEieR29eqyTYgiwdPTY0vDhru/lyhgxkqlzVipihBFoSVLpGbq1HKhUiXZI0Tksj77TJoXKuS/vVgxzz+FXJaHR8DyRo32Hgnp/lAL/SIgkzp1vmGpUuUToqhw65avGcD+IPny1RomRESRkCvXPkmZMr7Zj+SqKkQRdP36Cbl69cgM82OUBGX8/b1Kp0iRc2jatIWZfUxRJlu2XZI9e/Ki+fJlqylE5LLSp/9NihTJmSNfvnRCruns2d8e3bt36ZD5MXJBGUBAJl++RkIUFa5fvyXe3r/z/xQRRVq2bJ6SIkVSsx95U4gi6sSJtQjKSFRKkSLXU/O9xqAMRZmsWf3MJZPZz5UXInJdOXLcMOOa0mZbLyjkmm7ePIWgTKjLcABB0SxA0qRJIUREkXX79l3x9X0oRESu6tatu/LokZ8QkWu7fPm6PH36VMi9MShD0cxDrly5LkSO9PPP2+T333cLERERUXAiO1ZwlTEGx0pE0SfM6UtEUcnDQyRx4oRCZPnrr3Myf/430qVLM0mXLpW8bP7+/tKv3xT9edeuFUJERETu7ZdfdpjLThk+vIv+Ht6xAqbl//bbLqlQobgkS5bEaccYR478KWfPXpLKlUuLl5cXx0pE0YxBGYpWAQEid+7cE3q57t9/oF+qcePGkRe1cuVGGT9+3nO3d+zY0FxevDbQmTMXZMOGzdK+fX2JahhUYJCRIUNqiRUrlt7m6empgy5cExERUfSqU6ebnD9/OdBtr7ySwYw3pklEzZ69VD777Gv59tuPJW3awCd2hg//SDZv3iM//vhZmM+za9dh2bJlr+338I4Vbt68LWPGfCrJkyeR118vHuLjLl++KnHixNbATUx04MAJmTx5gZQvX0zix4/ntO+DyFkxKEPkglas2CjLlm2Q776bHWXBh9Gju0vKlMlsv2fOHPOrxP/vfz/rYGnDhjmB1v3ttysIEREROcabb74mDRr810k+UaIEEhnlyvloUGbr1v1St27g4u9//LHHBEqKSWRFdqwQ9HFnz16UevV66DiqWrVy4ixc5X0QOQMGZShaYfpS3rzZhV6eJ0+eyOLF35ovzLJRmg1SoEAuyZAhjRARERG9CDR9KFYsv7yofPlySNKkiWXHjoOBgjKnT5+XmzfvSJkyRYSIKKZjUIaiFaYvHTlySujlwVQgDESaN39bXrY+fSbJvn3HAqUG+/n5ScWKbaVOnUrm/rZ6BmvnzkNy/PhpiR07lrz2WmHp3r25JE+eNMTnRQrt99//YS7/TZvq3XuiFoletGiC/n7y5N9aiwbTnzBFKVu2jNKyZS2pUqWM3m+fHl2tWke9/uOPxZp226nTCP199uxhtuf/88+/Ze7clbJ371Hx9w+QokXzyODB70qSJIlsy5Qo0Vhv27p1n2zffkDva9HibXO2r6pQ9EmUKGGUTM0jIoqpkiRJaL4zOUwPi4c521e2bFGtCRNgBpn4HfAdjRNTpUsXkYcPH8mECfPMeOGijh2QOYsTV5iGHdrJq+DGCgcPnpBPP10u+/cf13FHzZoVQn3cyJEfy5o1m/T3IUNm6GXGjEHyzTc/yuHDf8r69Z/aHocOPJUqtdMMFYyfgvrxx63y7be/yKlT5+T27XtSqNCr0qVLU8mdO5ttmaZN++hUsHHjetpuq1ChtdSu/Yb07NnadhueZ+XK7/W5ihfPL1mzZozU+8DnSy8mderk4u3tJeTeWFSBohW+K729Och4mZAlgznBQedWvwwVK5aQW7fu6MDCgoEQBkBvvFFSf8+ePZNUrVpG010xAML9s2Z9JS8qVapkeoasbdu6MmZMD/M6mWXo0Jm2QMyoUd1MwKSm/jx+fC8TcBmpAZng3Lt3Xzp3HiV//33RBIxayLvvNtIBV5cuo3WQZw8Du7RpU8qSJZPkzTdLab2do0cZaIxOd+/e0/9jRESuCgfdjx8/EQobgjL37j2QY8f+st2GsQaCDQjg45InT3apX7+y+c7uKbVqVdQTRuvW/SYRgXp9PXtOkMuXr0n//u01IPPVV9+F+phWrWrLiBFd9WfUzvvkk2FSuHBuM07zkatXb+gJIQvGHXgfKBocnIwZ02gQpG/fdjJgwDvi6/vQXE/V+nkRsW/fUa23kzRpIq0bkz9/Tg3QROZ90Iv7558b8uQJW2K7Ox4dU7TC8S2m19DLsWvXIT0L9P77rfT3AQM+1Oj76NE9QnwMBgSoDxM7dmyJKAwcEGRDIT0ESGDLln2aQVKkSJ5/lykR6DHnzl3WbJ4XhXRlK+gCJUsW1DM/GIhlzJhWp1v9+edZvQ8DB/uaMkFhMIIB8OLFE02wJ7neljVrBnnvvRHy6687A72Ht94qb/t8kZnz+eerTVDmLx3wERERUdh27z4ibdoMktOnL0ipUgVl4MAO+r0enLt372tB3cyZ0wd7P4IyyHjB9z++izHORIZu585NbMs0alTNbnkf2bRpu45XIlI3BpkiOBE1b95IzUYBZJi8++7wEB+D5Tw9Pf5dNoNtyhZOXI0e/an88cdeyZEji96GQsMYP/n45Av2uZARY58VkzBhfOnVa4JmC1vrEx6LFn2rY6IpU/ppUwjAZzZnzooIvw8iihoMyhC5EJyxwRcnAhSAbAJ8aYcEhXBHjZqthfKmTh0Q6nPXrt3V9nO2bJlk+fIPJUGC+Ppamzfv1uwS2LRph2bQWCnEOBM0ffpC7Wxw7dpNvS2qpp4gCIP3jClMjx/76W337/tKRG3bdkA/NysgA0WL5tVrTM+yD8ogS8ZivQ9fX2ZtEBERhUfjxtV0jIBM2kOH/tQM3969J8lnn40KdnlMyUF2ysKF44M9AYLvYh+fvBqUadOmrhw8eFLHP8gathw6dFKzdJFNg2wUSJEiqUTEnj1H9DH2ARBMM4uMhAl+xmvmAAAQAElEQVQT6DgDQZnWrevobShWjPGYNX4K6vHjxzrNGtOYzp+/Ysvkjei4B+8DYzcrIAOJE0fufRBR1GBQhqIVvmeyZEkvFPUwbee333ZpSqvl4cPHoQZlsmRJp2eXEGQJi333JfugCgIwo0d/omexbt26q0EYK/UWqb7t2g3RlFs8HhkrmIu9ZMk6eVFLl67T2jN4vxjEYKBUqlRTiYzbt+8+9znhc0E3CNSxoZgFZxLjx48rRESuCi2WQ5py6+yaNq1h+7l48QLmBE88mTjxM51CHNwYEdkod+8+kNSpU4T4nMh+mTVrsQYuEJxBBjCyZgFTjN955wOpW7eS9OrVWnLlekXatx8qEYV1CG1MFVHoDDV16pcaQHrwwFdr73Xo0CDE5QcNmq5BJWTrIrsIAa2uXUdLRN25cy9K3we9mHTpUrKmDDEoQ9ELQX186VLUQ0YHoMYJLvYwXcgqcmuvcOE8smPHMgmPkLovIQAzduwcExDarSnGyJ5BMV/4/vstcunSVQ3IFCoUtXOPv/jif2ZQUshWZPdFpsWhC8TFi1cD3YY52jibhjnXFLMgiMagDBG5shs3bkv69KnFHaRP/6wG3pUr14INysycOTjM53gW4PhCs3K3bdsfqOsSMnHixo0tPXu2itRUbQsCZcjMjSpoCz5lyufy+++7TVDmoY7RQuoWhXbUKGbcuXNTfdyLwAm2yGQV08tx6dI11pQhBmUoejFT5uVBgAKF1+whOIOq7u3a1ZNYsV7O5o454Cimh7nQqMtSrlxRW0rs9eu39No+mHPs2OkwnxMDk6BfUNbUJ0DKLgasyJAJ7Xm9vJ7VMkdHg9AULPiqpg3fuHHL1hUK6b14HWsqGBEREb041GWxrx+DjA94kSAUsmIwvvz11106Vcm+ngzGCwhEWAEZZPEiyGHVwgsvLI/OkBiPWJnD1tTp0FgNLp4+DVyQF1OmUSMGU5iQLVO6dGEzVosV7HPgPYAVwAJk1gT1bPz030kqnCx79OixBH0fqD9oz88v7BNbIb0PInpxDMpQtGKmzMuDAULQYrZIT8WZnagoyIY2kMh6sWAKSc6cz4rToWDdjBmLdVCBTkiWPHmeFaT74ovVGjRC4AMZPVgO6cSYG27N6cagJFmyxDpQy5kzs6bX4owUbkPdGBTttVo2Yr41nhvTtdDBANOmFixYJfHixZUDB05olgumHyFFGdau/UWXR0aMVVDPXpMm1XU6VNeuY7R4MAYxaLeNx7z+enGhmAV//5Dm3BMRuQJX3cft2HFA2ynjZFGOHJk1wIHvXxTRt6YbRRYK/i5btkHix49nqwsHCHzs2HHQjAU26bho1aqfdHr3s9bSd3U8g7HSnTv3zVhkj3Y4Cu7zR7clTMGeOXOxvP9+Sz15NHHi/DDXCyfH8BoYs2AcgqCGdcIHU8CXL9+ggZN+/dqH+ByoY4Op46tX/6zjJqz7woVr9T6Mq6wAE8ZlyBTC8+HEGKaFBW37jZo+6DiJz71OnUpy4sQZWbRorbzI+6DIs04gknvj/wKKVviO487HOWEQhW5E1uWjj/5ra12pUik984RBDAZFljJlikr//u9od6aBA6dp56WVK6dKjRqv65xvQJYKpjZNm/alfPvtr3pb1aplNTiCejRVq3bUNNvmzd8OtD4I/uTNm13nWM+b97W2axw37n25ePEf2xkfDMR6926jA54ePcbpGbTgYAC3YMEYLdiHAcykSfMlW7aMMn36QB78x0DIYAraqpyIyJW46j6uRImC2hhg9uxlGhhYvnyjtGlTRz74oJO8KIw/kCVSsmQBW1YHdOzYUBo0qGK+0xfJhAmfab0ZFA1GZs7p0+d1mRo1ykvixAl0rGDfWtseiuHOmjVYgxhVqnSQDh2GaXAmLFgXtOI+c+aijp9wEsmCaVcInmD6Ek4yhQQnrKZNG6BtsNGWG+OlmTMHSbduzc3JqOO25bp0aSqvvprVBHvaSLNmfXV8hrGSPfwNMDZDIKZs2RbadQnP8yLvgyKPmUcEoR5tLFtWdHi+fI2GmYsQRVbr1gNk//7jtikt1kADB7v4ec+er4WIKLymTFkg6dKlMgPOt4Uook6cWCsHDy6Z0aDBth4SBZYt8+mTK1eNsYULt4klRFEEBfTz58+pmQzk2tDWGtkskyf3FXI/3bqNkaZN39IMLXJNmzePu3Pp0q72jRrtXRnSMkxZoJeuS5fmkjZtKtuUA3zx4GK1QyQiIiIicjeYLoXpQJhGTUTuizVl6KUrUaKA1g75/ffAU0eCTnUhIgoPpJCjfhARkatCPbU4cZh85arQaapfvyly+PCf2qY7Kmr/kXNKmzalbTYBuS9mylC0aN68xnNFaF95Jb00asQzA0QUMSgCjXn1RESu6ubNO/LoUdidfcg5oWDu229XkOXLP+RUXDd3+fK1MLuEkutjUIaiBc4A2Bcas7JkUBeCiIiIiMhdoJNSw4ZVJVs2TuMnIgZlKBqhiJWVLZM5c3rzZVRNiIgiCl0o4sWLI0RErgotmuPEiS1E5NrQBSxWLFYUcXcMylC0KV68gOTOnVWzZCpWLK47ISKiiLp16474+j4SIiJXdePGbXn06LEQkWu7ePEf8fN7IuTenCYsd+HPAHMRcnIVCrY1I438kjdtRdmxIUDIueUobM7mpfUQIiIiIiIiijgnCsqInDkWR9JkiSfkvJIlSyp1676qP/v6Cjmxs0fvSvJ0T01QRoiiVfz48SR2bHYlISLXlTBhPIkVix1ZiFxd0qSJxNOTJzjdnVNNYENAJn/55EJEjnf7GqaPMLJG0e/BA195/DihEBG5qnv3fMXPjx1ZiFzdrVt3xd+fswfcHasKERGRU0mWLLHEjx9XiIhcFQr9xo3LQr9Eri5DhtTi7c2sOHfHQr9ERORUbt68Iw8ePBQiIleFQr8PH7LQL5Gru3DhH3nyhFlx7o6ZMkREFONVqtRWgzGADm4wefICvU6RIpn88MM8ISJyZkWK1BNPT08JCAjQ/dyqVT/KqFGz9fdUqZLLxo1zhYicH7Z1bOPWeGbLlr227T516uSyfv0cIffCTBkiIorxihXLp9c4YLEGMvgZKld+TYiInF2pUoX02n4/h4uXl5fUrv2GEJFrePXVrHptv51ju/f29paWLWsJuR8GZYiIKMZr0qSGZMiQ5rnbM2VKa+57S4iInF2bNnW0E0tQmTKlk0aNqgoRuYbmzd+WuHHjPHc7xjmNG3NM444YlCEiohivSJE8kidPtuduL1u2qGTOnE6IiJxdyZKFJFeuVwLdhjPolSuXkpQp2X2UyFXUrFlRg62YsmRBsd+GDauIlxcPz90R/+pEROQUmjWrIWnSpLD9jo4FzZq9LURErgLZMkmS/Jctg6BzvXpVhIhcS6tWtQJlyyBLpkEDbuvuikEZIiJyCoUL5wl0Frl06SLBTmkiInJWz7JlsujPqDFRoULxQMFoInINNWq8rlOwAVkydeu+KbFixRJyTwzKEBGR02jdurakSJFUMmZMIy1asBgeEbmeVq1qS5IkCTVLhvUliFwXMoBjx45lxjRppVGjakLuiy2xicitndwXIDcvCzmN3FIsW2NJlCiBXDiYylwChJxD2qwimV/1EHp5Lp0OkHPHhZyclxSS4tmbamvcM3uSyRnhfs7Z5S7hIYmdvCzQ0R3+cvcG9+FRKW3silIy533Jmze77PsZh+Xc1qNSjsIiydM6x/9ZBmWIyK2d3CPy5El8SZIqtpBzqFKlvl77+go5iavnH8qjhw9NUEboJbp8RuTUodiSPnt8IedWvXpDveZ+zvn9deC2ZMgRYIIyzh3QOLzNU+Ilii8Jk3KKTVSqU6eFXnNbj1p/H7krydM9NUEZcQoMyhCR28uUN6FkzpNQiOjlOLr1lgQ8eSj08qXMGFfyl2enHqKY4p+z982/fuIKshVKLKmzxBOimO72tUfmX+eJdLGmDBERERERERGRAzAoQ0RERERERETkAAzKEBERERERERE5AIMyREREREREREQOwKAMqevXr8qqVUvM9TUhIiIiIiIiopePQRkntHXrr9KpU1OpWrWYtGhRQ65cuSQv6rvvvpZPPpki69Z9I0RERERERET08rEltpNBAGb06P5SsWI1ad26k1y+fFFSp37xBuzVqtXR6+rV60hMMHbsQPn11x/059ixY0u6dBmlXLlK0qhRG4kTJ06gZbdv3yyrVy+RY8cOSZIkSaVYsdLStGk7SZEi1XPP+/fff8mSJfNlz55tEhAQIK+8kl0aNGglJUuWlcgIbj1fe+118/rtJW7cuHr7t9+ulJkzx+vPnp6ekiZNOsmfv4i0bdsl2HUkIiIiIiIi98CgjJM5deq4PHnyRBo2bCVZsmSTqILATqtW70lMkixZchk4cKzcv39PDh3aJ//73zI5cGC3jB8/W7y8vHSZn35aJxMnfmCCSrXl7bcbyO3bt8xyS+X333+UyZPnSqZMr9ieb/fubTJiRG/Jli2XdOjwvnh7e2uG0PLln0c6KGO/nvfu3ZWdO/+QZcs+l6NHD5r1+iTQcgMGjJaECRPLyZNH5eef12u200cfLZZUqdIIERERERERuR8GZZzMo0cP9drLy/X/dLFixZZChYrpz6VLV5CMGbPI9Olj5ODBPVK4cHG5cOGcTJ06SipXflt69hxqexwyVTp2bChz506TkSOn6W1Pnz41AZAJJgCSVsaO/Ujix4+vt7/+ehUNpkTVepYpU1HXc+7c6RoE8vEpZVsud+4Cki5dBilevLS+brt2dWX9+lUxLhhGRERERERE0YM1ZaLQb7/9KP36vSe1a5eV995rIgsXfir+/v56Hw78Z82aIO+808DcX0569Ggjp06dCPT4/v076TJffvmJtGz5ttSrV0GnvTx+/Fjvb9++vowfP+Tfn+tpTZlLly7I3bt39GdMk7GcPHlMb0P9GctXX30mHTo0lJo1S2tAYNq0MbaAxMGDe3V5XFswvQdZH927t9b3hMdu2rQh0DovXjxP3yveO66x3NCh78s//1yWqJYtW069vnDhrF5///0a8fPzkyZN2gVaDpkrb7xRXac14bOBQ4f2ahCnTp0mtoAMYDpR4sRJJCqVLFlOr0+fPhniMhkyZDLBnFi290JERERERETuh0GZKILpNWPGDDAH/Amkb9+RmtmBAIcVlMF9mLKC2i3vvz9EgwG9erWXa9f+CfQ8Gzf+T06cOCKjRk3X5VB4d82a5Xpf374jpFmz9rafMT0mvPVkDh/eL198MVszOgYNGmcCMLXNbft0uk9Ili//QhYs+Ejrn+A9vfpqPg0K2Qd64Ny5MxpIwpSgDz+cL3//fUqDS1ENrwPJkqXQa0wRSpkytWTMmPm5ZfPlK6zXeN9w4MAevUaGzcvm4WFtVh4hLoOgFQJKyZOnFCIiIiIiInJPnL4URZYs+UynrQwbNtkclHtI2bJv2O5D8GDPnu1ad6RChSp6Uj2W+AAAEABJREFUW6lS5aVJk8ry9deL5N13e9mWTZMmvQwf/qHWO0ER2q++mqc1SCB37vxy6dL5f38uEGwwIiTWczRq1FoDOZji07hxmxCXR3bO0qULpEaN+tKx4/t6G94TCg0vXjxXH29BjZvx4z/WAIn13n7//ScJzenTf0qGDJm1OG54oFU3PqvkyVNoIV+4efO6JE2aPNjlrQK6N248a/F969YNvcb0pfDC+0IgCH8H/E3D688/j+l19uy5gr0fwZj582fpc1asWD3E50EWFIJ8KF5MREREREREroeZMlEA9UoQdClY0CfYg/ddu7bqdZEiJWy3xYsXT3LlyqcZNvYQTEBAxhInTlxbHZkXYU2pGT9+sGbs+Pr6hro8AkkPHty31UqxFCxYVKdG2T8eWT9WQAZix44T6jpv2PA/neqELlKhQTYJplTh0qxZNa2jg3ow9oGckIIlId1uZS6FB+rVYD2//npxuJZHQWIUHp49e5JmFdn/vaFNm9r6Xt5++zUtWIyMpVy58gT7XBcvntflW7euJUREREREROSaGJSJAghe4GA/UaLEwd5/796zuiYJEyYKdDt+v3btikQHFJjFdCdk4qAIbdOmVXU6E+rGBOfu3dt6nSBBwkC3o3sQvMh6I6MIgZywukehNgzWuVu3Afp7/fotJGvWHHb3p5Dbt28G+9jr16/qtTU9yMqosTJnwiNz5qx6bd/BKThW8Ag1gNAJCt2d+vcf/dxy6L40evQMbemNIFn58m+G+JxJkybTDJmo7LBFREREREREMQunL0UBBGNwoI1MieBYWSQoOouDbQuK7CZOHH1TU5D1ggsCSKhVgyLCWDdMUQrKatMctDORFWB6kfXOn7+wrF+/I8zlrK5GuPz66w/aTQm1euLGjav358lTQPbv3yWXL1+UtGnTB3osCvtarwV58xbU6337doR72hemd4U2xctitcTG+iKAE1Jwzuq+hMLECIihhXdIU5wwbWn58h+FiIiIiIiIXBczZaIIiuEiQBAcq+gspqxYMP3nxInDz01xiQxMFwLUQLGElhGCLBUEBHDgb9WaCSpLluyaJWMFNywomJsjx6vRXuekc+e+WkMGHa0slSq9pdeff/5xoGlJN25c1ylaJUqUsWUnIbCDWjrffPOV3LoVOLsmqlpiI/ATUkDGHjJ+kMEzbdpoISIiIiIiIvfFTJko0rx5B+2mNGJEHw0WINiB7kbjx8/Wg/XixUvLzJnj5OrVK1o3ZvXqJYLuPA0atJQXhSwdTAnC69Wq1Uhbbc+ZMzXQMgsXzjEBlV1StmwlnRKzb99OnXbl4/NasM+JbBRkiSDggbo2yErZsuUXDSwNHz5FohumLVWu/LZ+bsjsSZ8+o04vat78HW3LffHiOalZs6EW0f3f/5ZqTZmOHXvaHo86PV27DpCRI/tIp05NtTV2jhy55ZdfNupn8fnn/xMvLy+JDvh7tWvXVSZPHi7r16+W6tXrCBE5HxRE//77NWb/WDDErDciipkwzfm3336U8uUrm3EZOyESkevZvPlncwwUS0qVKicUszFTJorky1fIHPBP0+5IEyYMkR07NkuFClVtB/pDhkzUOiIrVy6UceMGyZ07t2Ts2FmBCuS+iAEDxmhnpOrVS5jnHyj9+48KdH/Dhq3M65eXX3/9XoYN66UZMOgUVa5cpRCfE0GZNm06yx9//CyjRvXTVtg9egwO1HkpOrVt21WzfKZPH2O7rVWr9zRIhKyejz+eJMuXfy4FChQ1P3/1XC2YkiXLmsDYQnN/Ee3kNGhQVzl27JC2Ho+ugIwFASYExxYsmKXBMaLIwDaJICNqGrVoUUP3AWF59OiRZmlt2/ZbqMsh42zdulXPZZa5s+XLv5Blyz63/X706AGdBvrppx+G+rgTJ47Ipk0btCi8o3z33ddy9uxpIXqZLlw4Z06eLBVngG3ik0+m6HTuqBTefWxERGZfT0Qxz5Ytv4Q4s+JF/f33XzJ+/BDb/gGzCHD8NmxYT6GYj5kyUQgH/bgEB5knffoMD/XxEybMfu626dM/D/R7xYrV9BJUzpy5ZcaMLwLdtnHjrkCv36BBC70EB4EK++UtYdVVQaYKLvbeeae7Xl4EOhMFhXbYa9duee52BInCGyjKli1nsM8dWeF5LkwVwyWoOXOWC7m3hw8fyvz5M3Xwjml0mM7YrNk74cq6wJcuOphhf9C6dSetrYQpemF5/PiRZmghUyw0KKKNAChqJjkqEBvTILBiH+xFhkz79t0kb95CoT7uyJEDMnv2ZA2Mx48fX8ILRcQxPdW+FllkIesRwWD7DEKiqIbs00WL5mg26suELLXLly/YCvJHRrVqz7JUozpbNbz72PCK7L4+PPC9g0vQunyOsHv3Nj1ZhpNsX3+9KVAXTZztx8Elxrnobmk5f/6s2QfX04YQ9uOs27dvaQB969ZfNCMqU6as5kRpFT1BSfQiXvR7GV1asQ1/9NEiiWrnzp3RcUqzZu31d5zIxrEnrqNSTNpvuBJmyhAROQjO0GL6HOoMoW7StWv/mC/QDuE6C3rq1HGtI4VBZqlS5fUgKKRW8PRyxI4dWxo1am0rKB6VcLDRsuXbsnfvdiGiwD78cKROF38RCGwg2zaqMpZflpe5r+/YsZEsXbpAYgJkmKMOIJpmHDq0TyIL36Ndu7YwB6frzWfVVDPJcUJuyZL5Gqwhiixn/F7GyRirBmdUiUn7DVfCTBkiIgfB4LpatdpadBvQXaxu3dfljz82Sb16zUJ97KNHD/Xay4u7cSIiR0Ig/ZtvFss77/SQqOYu+/rt23+XqlVry8aN/9MADTK4I+PLLz/RZheYxo5p4oDv1g4d3pfEiZMIEVFMxNE8EZGDIKXUCshAeGuOtG9f35yx+fvfn+vpNYpVo+X66dN/yuLFc7VuFOYTo8YSakGFNRg9evSgdjc7fHi/DmSrVKkp4YFCmd9+u1KOHz9kXj+jlClTUQuf4731799JnytnzjzmLOVnkjVrThk6dGK41vGrrz7TNFxMUUiVKo0ULFhMp0XiTCqme+F+zJ9GqjueH2n9OBsaHBQ/79y5mTl72l8LglsWLZqr67F06fea4r506XxN/71w4axkzpxNC7Ej5T00qOPVokXHQNM4f/jhW/1Mzpw5JYULF39uigWmrX300QR9rdOnT2o3NkxNwPPgc0MWwMaNa3RZzA/HZfToGVowHv9HMBUJBy1IgUY9sz59Ruj0TgvOBmO61O7dW/Xzqls39AAfUVTDVJS5c5/V2cM0v/79R+tUSAtqVs2bN1127dqiNd0wtQ/bJ4ryw7lzf+s28tdfJzRLBNs2sh5y5cqrZ6otqLGCRgTTpgV/1ja0/cjBg3s1M3Hy5Lm2AAD2WZiiiH0RtmNkbWDbfPfdXpoZB5g6hX0F9n3YhgGP9/T00qkNIe1rv/9+rWzYsFr+/POYvkanTn3DzLILbV+PffWyZQu0KyY+23r1mgfav+G9IxMTmTZYdzR2aN++uy6L94ZmA7B+/Sq9YKo6mhBYU4Xmzl1h23d9/fVibSCxatVvtimY2Pfh74p9GLI+sd/HiYaw1is4+BwvXbqgU3ivXr2s+zdMDY0o7FsxhQ5BGCsgY2FAxvmF9v2H+nft2tU1/89LyeDB43X5TZs2mu/PwVrzE+UlQtsmLJiWM3/+LLPcDn1ONGtp3bqzrF27PMTv5ZBgfIP1xYm2u3dva61RP7/HgZYJ7/aGfRPGWE+e+MnevTt031Ss2GvSt+9IbSASEjwO7Etk/PnncR37HDy4R+LGjafjFExtxjYS2f0GRGYfR//h9CUiohggICBAD1LQcv7NN2uEumzfviNsc4bx88SJn2gqPr6kBw7srIN4fIm2bPmuHDmyXwYN6qLPHxIUm0YhOBxQ4MAIARm0jw8LUszHjBmggSUMDDAQxoGOfYt6dGzDoAS1cmrVahyudcSg/osvZmuredRtwtlTdJdDsAHriqAFDqp69x6mn8P9+3d1ABES1OjB57Nz5x+BbsfvCAglSZJUu+LlypXPDDDaarr7K69kN5/rULl48bxEBD4TDFrwnJjLnTt3fg3Q2EONLwSS0Elu0KDx5vOupQMhzDUHTFPA3xXw/vD3zZfv2cAGnwsKDuPxCGTdvHndrG+nQH9fFJvHWWe8Fwwm8fOdO7eFKDpg+0dABoN1/P9DoWs0OLA3YkRvPVCpXbuJ/j/dvPmnQAWzZ8+eZPZHl6RNmy7Svfsgs32mNgGcrZI0aXLdHnDwjiALfsZ2EJzQ9iOhQaYG1nnUqOnaCAABhzVr/qsBh33lhg3/0+37k0+W6vMnS5ZC1yVDhkzBPufOnVtkypQROu3o/feHSpo06XWfh31uaELa19+8ecMEuHtogBsHU2XKvGGCWBPl999/sj0W+zAcBCJwgoDvnj3btbkAFC1aSp8L+yk0ocDPwdW+CwuCU/hMu3UbqAdv4Vmv4GzfvlmDc/gscYCIgDa6lUbU8eOHtdAynoNcT2jff6jxguAAgqUnThzVYC7+v+NEkVXvM7RtwoL6TWiK8tZb9fX/NYKw2BeF9r0cEtQ1wgWBG2s/hf/rkfXDD2vF1/eBTJo0R09wITgTtNtuWBB0wr4HAfNOnfro+0Lw22o6Etn9RmT3cfQfZsoQETkY0q3R2h2D7Q8+mBzojB6+QO0hGIEDfXyhQu7cBSRjxsz6Mw7+cfD90UeLNcgAOPPSr9972r0DQZPg4OwPDlSmTJlnK2SLwoj9+r0roUH2S8aMWbSTG76Iy5Z947llMDhHwXKsM2CAEtY6njx5VG9HvRZ8Jig0bBUcx/NhXXH2unz5N/U2+4OJBw8emIPC/zKOkPIfL148ef31Kuas01fmLJWfxIoVS8+AofsaavkABhn2hdCLFCmpZ4Uwdzx9+owSXujshsyXYcOm2Lq6YXCI4qf2atVqZPsZA0YcoCJrAPO/8Tfw8PC0fTY4UAGcBf7f/5aa91LZVji+UCEfzRzYseMPfZ6//jqpWQr2hS9Rh6JRo0pCFF1wBtmq1YKzrehShm03a9Ycsn//bt327P+PYpuZNOkDzXjDPg77APy/feutunq/fcYatgcU0sVBu7VtBCe0/UhocDAxfPiHmrWDA5Svvppney5k8OAgBcFra5+G7BDUt2nU6Jg2XQjOihVf6tl87GOhYsWq8t57TTTYYzVGiOi+HgFu7FutffbDh77a4dPqqhl0f4/OWMgaArQAxwWtcvHZh/Y5hgbThKZOXaD7WFi4cE6Y6xUcZD7kz19E/68ULlxCb8P3gf1+MjxwkA54T+RawvP9hxMdCJgiqIvv/Fu3bkiXLv1tzxHaNgHIGEagY8CA0bamKthWLcF9L4c05kBWD8YcWN/33uut9yFAhIAjtpHISJ8+kwaYMbbAuKR8+cry88/r9fkxrgkPdJ3DGApjMAS2oXbtxrb7I7vfCM8+jkLHoAwRkYNVrFhdp8vg7AwKFI4YMXVxt/cAABAASURBVNUckJTTaTpoYW8PX3ghpYPiYBwDYSvYAcgEAWRwhBSUQQorvkztOwuFleqNAQcOTtDBJLSikziosQ5ewruOOAOD6TdIO8ZB22uvVbAN+pGSjrPROGOGgxgM9O0LdSKQZB1AAc6ojx//sQaMMGjAlB4c7FlZM/aBJARhMIhCKj3OjkFEW9bjsyxatKQtIAOJEiV+bjkclCJFGmm+1gDNfgpScI4dO6gDU5yRtuBzTJMmnU4fw6AUmUlgP1hCZk6cOHGFKDpgCp79NmmdTcYBz7OgzE79vVix/9L+sY/ANoftAVkOCKDgQAD/v8uVezNcHemCCm0/Ehq8pjWNCrDtWHVdAgKeZQEi5d+SIEEivX7wIPgDLQRlsV3aZ0Bin4kuQtimIaL7enyGCDTZ77PxfAjW4PWw/piSOXfudG2/i+DJs/WO2v0ADgrtP9PwrFdQ2Kdhv4msScD+3cpsjGhQhlxXeL7/nmVpDNFxFLJn8X8KQQRLWNsEsvEA0xzDK6QxB6ZXI/hRsKBPoOUxHohsUAaBEPuxBaZ1IpMPgdvwdqKzxmBWQCaoyOw3wrOPo7AxKENE5GCZMmXRCzI/2ratY85krNSgDFotW2cdLDioCQnmLKPGij0cIOGM67VrIaeCI7gRP35CiQgEKzBNIbiAgz1MN4joOqJeAtJiccYLg4MZM8ZJ3bpNtVMKlh09eqaeMcM0A6Tu4sAAabhIX8aADGdmLdZr4aAPQQ+cUbOCMrjNCg6tXr1UD+Bw9h4Hc5iOUKNGKYmou3fvhPlZIrW6d+93pHr1ulqrAgecvXq1l7BYZ9IxfQsXe1aKsLVMRP+eRC+LtY+wshisA5LWrWs9t6z1//i99/poxgoy1dDlA3VjOnfuF2ImSnBC249EtnsRDnwwxXTt2hUaLMLZaSs77tVX8wf7GEw3wPQKBH1xsWe1lI3ovh6fIT4r1NQJCgdV+Mx79mynNShw1h+BMdQMW716iUQl7Ccjsl44gA4K0yvxXYJgtgWBOWQAWJmNEV0fTKMi1xKe7z/AiSAEHHAbMl4tGLOEtU3cu3dHr5E5G14hjTms9Q063olK1nNHpKsYxmAhjdvC8xkFJzz7OAobgzJERDEEAg4ZMmS2DTCQrRKRImkYiKD4nT0MdjFQTpw45EEGCrhZBSvDC1/qKC4X0TM+4V1HZHrggvtwJgjTH3D2HenJSNtFEAaQXYP6FEhXHjhwrB4whQTzpH/99Qfp0qWfnhFr0qSt7T7MU0eBQGs6Bc78RAYOznx9Q8+uQZcWnH1HzQWreGh4WGe22rTprMUHg74uWAULcdbe/gwhkaMgUAnWgYCVRYPim0HPwGbM+Ipeo7glimfjgs5GqF31wQfvy+LF63Q/GV6h7UciC3WgOnRoILVrP6tTgcDuBx9MCvFsMt437itRouxzdVtix35WoDMy+3rUTunRY9Bz9yEw8eOP3+nnhroQKIYaXcJar+Bg6hL06NHmufswpRNZU9Z3Q9DMRdQTA+vgFIWgEcRBkVZr6hu5hvB8/wGCuCimi5MuKBY+dep8vf2XX74Pc5uwTtJgn2Vf/Dc0IY05/vsujli2bURYteJC2raCg/1f0DGYJTyfUXDCs4+jsDEoQ0TkIKixgEGANVUIqbmYb5wjR/jPBttDlxMEG3CW0BoQIC0cZzDsz0IGhQK3+DK+fv2a7UA+aIeA4KAGAFJcX+Y64gAMX/KffTYzUIrwf+tQWNfj5MljYb20Fp1EAWN0DEAQCBk2gNfG3PM0acrZlsU0ishAui7quthDtwR7eC0MIq2ADAZt6PiEx1qsNH/7jlxZsmTXjCL8bUKqAYG/JaDjlDWFAM8R2SATUURhe7KfqmJNqbOmKWJ7BQR1w1PLBNkVCKiiEDCybazpRfZ1HMIS1n4kItasWabvAVk44YUzzsgUiWztlqCwH0Vtnpw589q6s9izspLSps1guy24fRo+R2tKlsU6iLLfZ1jP96LrFRT+ryBrEQdz9jW98NqDB3fT4qEIylj7MnxXYHqIBb+DNUUOU6nKlq0kv//+oxZrRpDGgswF7D/JOYXn+w+F+VF8Gll1GEd169bSBGNXaYAuPNuEtY/CuCa4zovBfS+HBNkmWN+g4wFrarQlItvb06eBv8dRZBtZPRHJRkFWHqYwYX9kP43c/nUjs9+I6n2cO2JQhiIMBz8nThzWTgbBzQ8movCZPHmYpmfjzA/OLq5c+aV+qVntHCMKnUwwrWfw4K5Sv34LHYRigIJOBRjYAs4oIhhw5MgBraSPrBN0W0KK6vz5M80Z4Pf1ix8dM8KCFqiYdoMil5UqvaUHOxgkjB8/O8R9Q3jWEcUiDxzYpYNr1JBBe0YELjCXHIMlTEPA3GUMuhDgwKA+PGe+ceYHwRAUVsbUAGsgg6kMeH3UdcDUpjt3bunZNtSNwOeEs+w4qMPZqLNnT+v0o1y58oTw/hrLgAGddToU6u2gqwGmN9jDeqOYINpH4u+BIJEVkMOZLwTpcDYL11gnnCHEIBBBKxQtRSvLlCnTaO0F7I/RkWHChE90+hamdyBQtWLFF/92nUonM2eOMwPBR0IUHbA9oYMJtkm0S0ZnMWShWdNxMBUJ3UimTh2l0/fQvQ1FXZGtN3bsLA3YYnoftkX8X0bhTNQkQf0E6yAia9acmg2CAtnYh+KgPmgQILT9yItAnYWLF89p1h22OUwVxPNbQdbg9rHWvnLOnGetebGfx34B2XKROYjBfgb70dGj++k+Ad8fmM6JqRuYnoX9GWA/gHVALS1kFWI/Y7//ypYtlwY28Nlg34MptHgO/A2xL0chd+xfMV0rKtYrKHRLwtQLfH8E/RwwhQn7v+7dB2oAv1q12rJkyXwNqGNZfL6oAVavXjOtQWNBBx58T2AaBgJxmI6K97Jq1RLNmgjaKpucAzIxwvr+wz4FxXDxfwXf2ag5h66WKLAbnm3C2jfNmjVerl37R/c3+H7GvgwFe0P6Xg4OxkCoiYRCt9iXYR+I/RjqLqGRgiUi2xu6WyKwjFbYKMCLTlMYP1rjLStjBtO0kyRJFuw0LGsbHTKkuwmEttSsILSxRnHzF9lvRPU+zh15hXZnw4bpKqROnQ8XcbQLfyKKGFdSZwm7SFtMgE4q6CSAMztIo8RBT2TnMMc048cPMjuZFfqFybmC7uvc0XuSKsMTSZ7Wuf9fn9wrkihFAkmSKvzTSKIKCsDhLDI6EuEgPXv2V7UFY4ECRcJ87Jkzf8rmzT/rF6yVaYO0bWSD4LlQR2HLll+0zgFS6+PFe3bAgv0QDtAxULhy5aJWyMcZa6wLBs8YwGzd+ot07TrABA3W6Blq+6KN9jAQxplInJXEwAVf7tjXIeMDr4ODJkBHIUt41hGPx8ET2uQiswVBIkxXQlFenDVHKjuybVC499y502ZQ3kJatOgQ5rQGrBMK4mEwUaNGvUADBezPkLmELiF//nnUDP7a6Hv544+fzfrX1EFPwoSJ9Sz5L79s1IASXg8DRHx2BQs+O8OGOhYYHOJ5PvtshtbJqVevub5PTJfC+8eZKsxdxzxtvA/8vTEvHS18ceCKzxXPjc8WbbJXrFioA0R8jjhIRQcIrAe6KOB2tNTGmXurAGDx4mV0YLdgwUd60IKBIf7WqNHxogekkXXt/EMRf1/Jktvx+4vr10/IP/8c2r58+fkNEgUaNkxfOkWKXJXSpi3sJQ52+YzIg/txJF32sLMUXgZsW1b7WIx/cNCAQOGQIRMDTe8pXbqiBmxwcPBsPxGgXYywr0G2A6Ye4Lkw1Q8FxfF/umfPD2wFdhHYRHAE29+WLZt0uwjaJS20/QhgmigCo9h+rFonwe2zcMCCfRP2hYBxD5az6ic8a5m9TPchOGgLbh+LbRpTLlAvDO8ZAWzsNxAwsvZ7IQl+Xx9b96MISmEbxzQfvDaCEKidhWmw2A/99NN3+h4RsBozZqYeQGF6pTVVCvsiHOjhczx0aK+88UZ1fR48HoFp1OxC4BwBFeznrX0YBN33hWe9gsI+DAWge/ce9lwgH98neE0Uf8djn+27Asz302/6maNYOvat77zTI9D4GkExBHnwN8DUKATIUXQVB/Q4OHeE0wfuSKZc/pI4uXOPl45sN9/7mRNKgqThr/MTlUL7/kMNIgTeBg8ep9/DgIwtBAawr2jUqFW4tgkUBMfJl02b1muwGEETnATCFJ2QvpdDgsyby5cv6LaCk0EIGCHwgX0PxiCA/6/h2d6wz8G2lChREj2Jhv0jTvwgKGP9/0egyBpboZ4f9jlB92nYRvEeEXBBFzucOCpRoowGtvFeI7vfyJIla6T3cS9LTDpOOXt286N79y6uWbHi8pGQlgl1LZctKzo8X75Gw8xFHG3HhgDzHyKp5C8fvjl+joQdw4QJQ/WMMwYJSFULLg0uMrAhI9UNG42jYENENe26dZs5RaZMTPjMXNEf31ySvCV8JUch5x5krJsfIOlypZbMeVgYlehlObr1lgQ8uSHl6jh+f3HixFrzPbZkRoMG23pIFFi2zKdPrlw1xhYu3MYxRyp29m4KkGtXEkvRyqwnFB0w/eb8+bNmzDdYz5rPnh21hXTJNfy86JyUruEnGXI493hp5QyRfGXTOs0JclfSv38nvZ4wYbZQ+MSk45TNm8fduXRpV/tGjfauDGmZ8FdLo3BDdBABAMyPRYQ/qgIy+OJv2fJt7UbgSDiri7NgzhCQiSmfGRERETk3TCtAsWALzlCjcx6y/W7duilERESRwYIgL4Gvr6/OgSYiIiIi14A6DZjmielVVh0TdDLZtGmDrXA4ERFRRDFyEIXQuaRZs2q236tWLaZf2gsXPuvZjvl5mJuMStaYN92pU1/b/DwUUULrNhS6wzxrFKOsWLGatGjRUecwfvjhSDMQWKPLjh8/RC+jR8/QglSzZ0+WX3/9XpYu/d722sOH95arV6+Y53xWYBJTjvr06SDz5q3UejeYYz1z5kKtXxDaegUHj1+0aI6sX7/Ddlv16iWkZ8+hWscA1fIx9xLrjvePeeWo44Biaz16DLa1LrTWqV+/kTr3GvMaU6VKK++8010zjCxID0a7WsxR/vvvU1q4slmz9vr52K8T5l+iLgOKYOEzxHOE9Jmh8CAKVJ06dVwL8mGucvv23QO1wMN7wvpi3iXmtGMuN+pIBG33hjnzKN51/PghrbaOOcsoeIW/G2pELFu2QA4c2KPPjfnPNWs2FCIiInIuKGiJTmkY16DmCYoUow4D6jq89VY9ISJ6GVASg1wbgzJRCAftaJH49deLteYKOqhYxY0QqJgyZYQGO95/f6gGRQYN6mKCJF9r4AIF8FD8CUWRUMQSAYovvpitB/kozoTpQrhv0qRhGpBAQTkUsIqoMWMG6BkeBFAQgAlrvSIC1coRtGjXrpt2lZk7d5qeVerQoYe+p3793tWgC363h8JW6MBQuHAJ7f6Cdfz88/9pwSpAQAYFKxHQQKHnb4zdAAAQAElEQVQ2VD1HgAUF/6xuLYBCXngsgiaoxo8q5iF9Zs+KWSXXyuNXr17WoI6n5yzp1euDQOuGQFnNmo10njgCL0hbRstZqwI5qpLjNbEeffuO1L8bgk3o1oKOAkOH9tAgFKqPo1sDOtrgdVERnoiIiJxL06bt9EJEFF1y5HhVyLUxKBOFUCEb3Tw2bdoo3t6xAnX2QJcQVI+fMmWe/o5q/O+910QzRJAZAuiQYUE7MWSGoHI9gjIIoKDiOGTOnDXS7cUQ6EA2SUTWK7xQfbt1607/Pk81DWAMHTrJrKuP3oaK5chgCerdd3ubxz7Lemnbtqt2M0Cx5MaN22iRZFQkRzu6jh2fRYnROeHKlUta9ds+KIMgSPv23TRwYwnpM7PPxAG0lkP6cVCo4G+9LgI46JKDmkFWUGbJks80+DNs2GSdW251dQAEcdC6cfr0z23dax4+9NWuLCEFZVB1HfPSM2bMLEREREREROTaGJSJBk+ePNG2t2ipZsEBPNo1IqPGgvZ+8+fP0mlEOJiH4FoIvgi0do3oeoUXsmAs1hQl+2ybhAkTadDh+celsf2cIkVKLZKMaUWAVoloaRk0CIUWjIsWzdX6PWifaalatbaEx/XrV2Xu3Omyf/8uzbAB+3adwb2nOHGe3Y/ACqDTAqY1oSVdcO3O9+/fqe/fvp0wPlsEa/DZB1couVOnptotClPLrMAPERERERERuSYGZaKBr+8DrYuCDBBc7KVNm16vT5w4Kr17vyPVq9fVqTzZs+eSXr3aS1RLlixFhNbLERIkSKRZL3D37u1/bwvcrjhhwsR6fe3aFVvQAzVckiRJGubzI8jTs2c7nRo2YMBoyZevsCxc+KmsXh2xVpZ4HkxTQv2c4CCwhgALagsFhaBQmjTpnrsdGT0IXKVMmVqIiIiIiIjItTEoEw1w0I4sjBIlyj5XJDZ27Dh6/c03izUTA7VHUHg2pqyXIyAQg6lOYGXRBM2wuXfvjl6jVk1E/fLL9zr9qX9/BGQKSWTh84sTJ44tqykorPujR4+kR49Bz91nHxyzN2bMTCEiIiIiIiL3wKBMNEE2BrIjQqoFc+vWDe24ZAVkkIWBCv+Y7mKxprtg2ow9BFAwHcaeNSXnRdcrOjx9+t+6//33X3Lnzm0tRgxZsmTXLJlDh/bK66//124S3YxQ9CqszJjgPrObN6/rddq0GWy3YcpYZOTPX0SnQAUnT56C5r7dWlw4fvz4QkRERERERGTPUyhaoEUy2iPPmTNND+JRyLZLl+a2A/ocOXJrO2y0p0aB37FjB2qb7DNnTmmQAjClBR2e0H0Ij0M9E8iWLae2ZsTjsSy6Np0582eUrFd0QEcitJXGa86YMVbfY5Uqz2rfIJMHBX9Rh2XevBn62aCbEmrhoOV2WIL7zNDlClas+EJ27PhDW4qjixI+b0wjiwh8fvjcR4zoI5s3/6xdotDmG0Gy2rUba72b0aP7afvt7ds3y8iRfeXLLz8RInIe2La3bftdiChmwXfq7t3bhJwLxnzTpo0WIiJ6hkGZaIJpMmPHzjJBgW0yZEh3PTDPnbuAZMqUVe9HgAFTiObNmy6zZk2QDBkym+uFkiZNejl79rQug6wPtNlGEKBfv/e0KxFUqFBVW1GjTkrTplU1ywbto6NivaIDukstX/65vj4CIxMmfCLx4yew3Y+gTJs2nU1A5mcZNaqfbN36q/ToMThQ56WQBPeZlShRRrp27W8CMps1+IVW1fPmrdSCx3v3bpeIwOc3cuQ0uXTpvFnvIfqc+Ht4eXnpe5g6dYH4+flp0GbatFFaw6dMmTeEiJwD6kZhvzNsWE8hopgDU5HRDAAZv+Hx++8/6QkYejFR8TkWKFBU/37IJiYiIhGP0O5ctqzo8Hz5Gg0zF3G0HRsCxNc3qeQvn1zINRw8uFezSiZPnmu+oIsIOZc/vrkkeUv4So5CHuLM1s0PkHS5UkvmPAnF2TRpUkVu3ryhP2OaH4pe16zZMFBHNUBgAW3uf/zxOxOgPK1t3BE8rFOnqcSKFSvQspjq9913X8vGjWtMQPgvSZ06nW6f773XJ9gOZfYQVK1fv4Jmis2atUhy5sxtuw/ZfA0avCHvvNNdGjZsFehxzZpVl/z5C8ugQeOiZD2iGgqho5B4pUpvCUXO0a23JODJDSlXx/H7ixMn1prvnyUzGjTY1kOiwLJlPn1y5aoxtnDhNrHEwfZuCpBrVxJL0copxZWhGUDbtnWkU6c+emInPKpXL6EnwJo3f0deFtS/wyWqmyU8fvxYLl++oM0AHC2qPseVKxfJ//631JwU+1rr87mynxedk9I1/CRDDuceL62cYU5Glk0rqbPEE4p50FwEJS3QxZZi1nHK5s3j7ly6tKt9o0Z7V4a0DDNliIicmI9PKZk48RM9OMmSJZtO78M0OnuYFjh37jTNLhs4cKxUrFhdvvrqMw2KYrBvQSbX0KE9dEpfqVLlNMsMtZx++mmdHD9+KMx12bVriwZkECBC1lhkveh6RDUc9DEgQxRzrFr1lcSLFz/GbZcdOzayZTFHpQ8/HKkZt64E2eFolIDgOxG9mPPnz0rLlm9HOOOfYg4W+iUicmLo5GUV6kbwAEEWHBTgTCayYJAds379ahOAGR7ojHL27LlkwIDOeqbSylzBsqjPgOmBb71VV28rVaq8ZtSgNlNYdu78QzunpU6dVoMykT2T+qLrQUSubcuWX6RIkRKawUbOCRmPBQv66NT0evWaCRGRO+O3GTlM1qw59Aw/rokoaljbE1LdYd26bzRIEvSMMg5okAqP7BPL99+vMUGe5FKtWu1Ay4Y3EIJ6T3jewoWLy7Fjh+TWrZsSGRFdj1WrlkjVqsW0e5s91JHq3PnZYB+ZQfi9fv2KWntr8uThtqlfFqTlo84BMo0aNqwkq1cv1dv79++kF8tff53UelSdOjWVWrVQo6qlPs7e4sXz5L33mmhBS1zXrl1Whg59X9OL7f3553E9A46pXS1a1ND1soq7YwrXZ5/NlHffbWweX04GDeoqN25cFyJ3hu0D27p9d0o4d+5vDTQ3avSmOcivoJmAKNId1Keffqj7AUx/Wrt2xXP3o6HAwIFdTBC4vJ55RpMBTAG1YOo19jeoVTd+/BB5++3XdP+C29DNcv36Vfrz/PmzbI/BfgD7H+wHsD9YuPBTfU5M68SyaGZgOXnymN6G/Sn2F/h506YN5kz43/rz+++3lZBgPzVt2hjdjzRv/pa+h9Gj+8ujR48CLRfSvi6s927x83us771u3dd1ueCyXdC4olev9rqPRAOJ4OrQIIiP74rgXoPIUazvb/zf7Nv3Xf1+BmTxLlv2uXTv3lq35Q4dGuq2afn11x90G0XWigXf61jW+j9+/fo12/YdHGzDH388SadNt2tXV2vaYWr4lCkjdNvHc2HfhRqg1nMik659+3r6M7ZLPP/OnVv0d44jnAeDMuQwCRMm0jP8uCaiqHHhwrPBADJo8GVsFcIM7owygienT/8pvr6+uiwGzdgmI3P2GZ3LUOcBz4kLRGYKU2TWo3z5ys+9HgYxBw/ukdKlK+rvr7ySXevo9O8/WrOI0IltwYJZzz3X0qXz5fDhfdKt20Dx8Xkt2NdLkSKVOZjIJ40btzUHgWP0uSdOHCoXL54PtBwO2jBw6tDhfTNomm8OJE9pIXcLak8MGtRFC4Vj+hkylv7664QWawd00lu+/AvtGIesoZs3r5vX66QDQyJ3hX0WpE+fKdDts2dPMkGMS9KmTRdz0DTIbKepZdeurYGWQcAEUyx79x4mRYuW0u3Rvusi9j04CIobN54JKHyg2YWYKoUmDEGNGTNAsz169hyqdbxwkilJkqRSsmQ5/RnTc6znxLIo/t+370izT6qggZ3wBCKSJk2uz4Vgd6pUafRn7AtC88MPa80+/YFMmjTHBIInyt69O2TOnKnPLRd0XxeR9/7114s0cI4MzKxZc8qMGeMCde3EASEOIj08PMyB5FBtWoF9XdCgdLp0GfTvYTW0IIopbty4ptttsWKv6f4C8H2MQGb+/EV0W0ZgGEEQK8CSJ08BvT516rhe48TU1atXdFu3Thr9+ecxvc6bt1CIr40Os59//rE0a/aOCWo21v0MxgE1atQ329F4qVKllgaCrZNqGDv07TtCf27WrL3uJ6wC6BxHOA9OXyIichHHjx/WszYY9CPYiUwQDAaSJ08R7PLJkz8rBoqzu6gDg2URcAgJggj27AOq27f/rtOlMFiJHTu2Dtgxnclqbx9eOAse1noElSJFSsmdO7+2uLemYiFAg+cpU+ZZUAafib0LF84FOsNlwUAMXdPQzj4kOPBq0KCF7fciRUrqWS3M5U6fPqPtdhxsjB//saRMmVp/xxQsdC6x4OwyAlkffbRYD7igdu3Geo2gEqaWoZYODnygUCEfPSuN91myZFkhckd37tzS60SJEge6/eTJo7qNWVMeK1So8txjcdDUpUs//RkHLchQweNy5cqjty1Z8pkWQh82bLL+Xr78mxpYwNlxBGGx7Vuwzb7//hDb78hI9PaOpftVa0pp0OfEc5UtG/4OjNiX4rkwBRUHd/bPGxIEq1A0HV0gsT9C0Prnn9ebM/+9AxV2D7qv++STyeF+78i8fPfdXvozapUhKwfF5K31W7HiS/3emTJlnv5esWJVzTzAMij2bkmY8Nnf8PbtyGVVEr0s+G5u376bNGrUWn+3poYjMNKx4/t6G7blK1cuyeLFc3U7wD4A2wmCMvjuPnbsoOTI8arZFr11P4NMZgRl0qRJF2h7CgqB5+nTP9dxjaVWrf+a7uD7H1ltqOOH4CmaPHh4PDuJhQxoazvkOMK5MChDROTEUH8FFwu+fHGWOLDgK89jwB1eyKZByr+9Vq3es9WNQQAGbU5xEAE484qzR8h8wcHBy4bgC85gIXCEYBHWBwMkazoXAk9z507Xs7k4GIHgujjhACa0gIwFQRicRUY2jFUs2cpwsSDTxwrIALoiPHr00PY76uZgMGUFZOxhMIcBlX22DgJVGMyh2DEHU+SuEOwEHOjYw0ERAq3YTsqVe1PrZgX1yiv/TZdGRgg8fOir19hXIYMOB132UPdk0aK5mnWIwuOWypXDDjhbz1mtWp0I7W9fBIJC9vvcbNly6jRWZOTZd2+y39dF9L2nSpXW9jP2c3nzFtSpHoC/D87023cBxHtHVgH2a/as7wv7gvNEMUXVqv9NocY2gO/4oIHRggWL6jaCMRK2J4yDTp8+qfedOHFEM1QQMHmWPVNTr+2DLcFB9m3QZbB9YUokgjookA0hnXD77zEcRzgTBmWIiJwYui81btzGHIxslJ9++k7atu1qy2DBmRgMmG/duhHsY63gBL6k0ZIUy4Y01xiDDeuspwVBD0CKLrJ02rXrarsPU5gQLDp0aG+4zu5arHWO6JxnHGBg3jTS5nFWFpk7SPEFDKR69mwn6dJllAEDRusZctR0WL16fLC1AwAAEABJREFUyXPPg2lfYUH9BXSG6tZtgE5VwGNq1CglEXX37u3nzvZbrKwkzBXHxV7QKQBE7gRdl8AKpljee6+PTpNBxhrOaCMrpnPnfuagKHd4nlb3E8iuQ9agPSub49q1K4FuD8++wnrOkLbz6GC9H5z5t2e//hF970Hh/V279o/+jKlTmBqBwDUu9oK2CrcC2ZzGTjENxiH22Sz4vobQthGcZEEw5ZtvvtLbEJRB9h6CMj///GyqEYIyNWs2CvW1MW3RHqZY9u79jlSvXlcz1BBwRr2msHAc4VwYlCEicmJW96UcOXLL5s0/ySefTJERIz7U+zCowIEJaqtgkBz0TC3OZiKTxDpbimX379+pg/Pg6rnkz1842HXYtu03vcZZHPviloDgCNYP9RTwnEGzSeDevTvm/oSB1jm09QgOBvs4+EI6b4YMmfUAxJomgIKWSDFGPZl8+QrJi8L8bATDrJoR1pn7iEIWzeXLF4O9z8qeadOms56FtmdNOyNyR9aBPbZp++05fvz4mrmHC+5DPYgPPnhfFi9eF679CAILyJ4LOk0T+ydInDipRBSeEwFv68y2I1iFw0MLIr3oe8fjrANY67lKlChr20dakC1oD1OyIHXqdEIUk1nfyWFtI3nyFDQnlWZoEe8jRw5I69addfw1Z86Hehu+88PKlAnqm28Wm/1IXOnYsactuywi68xxhHNgoV8iIheAszctW76rARKkoVsqVaqhZ0TWrVsVaHksg3nLb7xR3Xbbm2++rRkqmPdvDwOJ0OzevVUDDCguZ3/BXGrMWwak0+MsEgJB9nAmCZ1B7AM+kV0PFPXFtCXUk8GAwxr4oLAdpE2bwbasVWwvojC4QuYRzsi/6HOh0B8KM2NqVVBZsmTXs8focoKglv0FnyORu8IUHAR5UTg7JEjPR2FvZANa2394IIsO2X32sM/y9vYOV8YflgsICFzAF3W27Ivg2rOCFPaBXSuDMejz+vs/lfB4+jRwkBjFfBEwCZqlElRE3vuTJ352Pz+broRpG/bPhf1a0H2XVQjVguKnyLhEXTCimAzfyRhnPb+N7NGxjhWUxAkynABDxzVMV8aJL0whxDjn11+/1/uwTERgzIExjRWQwcktq6mDBdspYCqi/TpzHOE8mClDLgltMFFwz34ONJGrw1lJFHWbMWOszJv3tX5Jo600BgK4DdknmOaD2gKo3I+5znXqNLU9vmrVWvLLLxt1ag4GHijmiGLBy5d/Lo0atbEV0LSHAQCCIAikBB24I0iC7kPoSoSCkyiYN2nSMBk2rJc+N4JFK1d+qcETHEC9yHpAuXKVtNPAmjXL5PXX/yvyifcJK1Z8oR1XEERCpxHMtUZasFXkMzwwoMLzIfiFtGQUHcVUCdSnwFmxiGT3oKgv/l5DhnSXBg1aatBpw4bVMnz4h3oAhc8LBQRTpkwjGTJk0la56KwyYcInkjRpMiFyR9i+UEMKnZXatu2it2H/gPR+bJMI8KLeDIr44mAoIkXDmzfvoM8zcmRfDVijGxoK3WJbDa0wpyVbtlyambhv307NUEGxXDwnphqg9T32ZSj4iUDJ+PGzNYsGxXXxOwp5njp1IthOSehwhOmgKO7p5+enWSjIDAoOOjthKie6xqCgOQ4OcabcOmiLive+YcP/NPMGn61VsLxu3WaBngvvec6caVq3AgEadGzCmX7rewIBbgTRUf+HKKZD9hemiqMrErJWEGDcsuUXDUgOHz4l0HIIuuC7PHv2V23bHaYcYbtBACe4enahwfOhixrazCMwhC5yGL+cOXNK9zOJEyfRE2O4xtgEGTIYmxUtWpLjCCfCoAxFGA6UULDN/gx7eCDlD1Mrtmz5RVN5kWKMQqFRDQdFaOsIGzc+OzuFszFLlszXARzOoDkTRNfR6hODTVzAmd8PvTzIRmnfvrsOqjGYxjaG28aOnaUHKJjfP2nSB3oQgLaJtWs3CZQKa7/sjz9+q9sRBhQI5NgHTexhQILBAWqrBIX/rwjKYApT3bpNtfAjBuLIgMF6oLgjuiKhK4D9AUNk1gNw5gdn0dFe1b7DSYkSZaRr1/5amBeDogIFipig1UodXKH+RESCMoA22NiXjRs3SLtMoTMJDlC++OJjPWDCgVZ44Gz/lCmfydSpI7WlLAZUONjENWAAiM8LwSQc1GBaFgoPBp3TTuRu0GWtY8dGWnwTB0fYDvv3HyVr166Q6dPH6EET9kkIDkQEpkONGjVd5s+faYImg/XA5q236ul+NTw6d+5rtudRMnBgF60LgSwZPOfIkdNkwYJZ5kBoiO5/UVDXKsaL/cnMmeOkevUSetCE37t1axXoeWvWbGj2a3/JxIlDdYyDwC2mUAYHQSnU28H0LbwG9r1NmrQNc90j8t5btOgoe/Zs0+miWK5794GBsh3xXNiHz507zfxNlmvwBsVGM2X6r9Dwli2/aBZT/frNhcgZ4DsZEHBZuXKhfoejzTSKjNvDMRLGL/aFsxGwRVAF21REYXtDrSa0p0fAGcFe1JbBuAHjHWx7GCMNHjxej9H69XtPg58IynAc4TxCLQW/bFnR4fnyNRpmLuJoOzYEmP+QSSV/+eRCgeHA5vLlC4Gq6gOCILiElbIaURg4YAdhdV0Jr2nTRmsEFzsSfIljKkFINSpeFA4+cTYNZ6UAmTM4sJs7d8Vzn1NMh7PnDRq8oUVFrfnZMeH9/PHNJclbwldyFIqejhIvy7r5AZIuV2rJnIdfUEQvy9GttyTgyQ0pV8fx+4sTJ9bKwYNLZjRosK2HRIFly3z65MpVY2zhwm1iiYPt3RQg164klqKVXX86CMYUOOs7a9bCaOtsFNP1799JrydMmC0xGaY8dezY0ATPKwUqEO+qfl50TkrX8JMMOZz7/+nKGSbgVjatpM4SdodCIkeLSccpmzePu3Pp0q72jRrtXRnSMqwp4wJQURtpsUHhLBLS6mMK1F3AnOOKFavpGfOXFZCBypXftgVkiIiIyLWggCamYv766w9CzgWZBsgCbtYsYif3iIhcFacvUbTx9fXVtDsiIiKiF4EpS6NHz5BcufIKOZfKlWvKq6/mj3BtDSIiV8Uj5HBavHie/P77j5p98dNP67TqNeYH9u07UjJmzGxbDh1DMOcP82wxl7dkyfJay8Cql4ACbH36dNB6BnhOtLCdOXOhVucODgq0YSoOugygmBPm6bZp00Vbv6JIZsuWb9uWrVr1WWV7zGGcPHm43oZiULhgTiFSRP/666QsXTpfzp07o+8hc+ZsWmCyQoUqz70u5kMeP35I0qXLqHUOMDc7uAKWH3zQU7NgZs9eEmwhvE2bNsj48UP05/Pn/9bfkSnTrdtA+eijCboup0+f1MriyKLB1CjrdazPa+rU+Vr8DoXnsD59+ozQbgYo6Ikq5Jhv3bp1J9trhpXCi8yi48cPy1dfrbfdhilVDRtW0iybTp2ezzyy/g+8//4QLaKHdV658md9HGpToNgpWt3hb4T1S578v/aTmEaGVsH79u2QW7duams6nOXD3zG0vzEREREFL2ibV3fXocP74gxQd4tjHCKi/3D6UgQgeICATNeuA+Tjj7/StoMjRvQOtAx+R3V8FNBE8UcEXT799MPnngsF2HCGoGfPoaG2JUMAonjx0lo8DjVFULxt7NgBWugNReTQdrZIkRJaaRs/o+AUuovgZwRIUOgOP1u1SFBsLVeufLpuKCb3yivZtXAcuqNY0JUE64cCVgg6oRAngiN4zaBQbBadTFB5PKTOBIUKFdd1wDpiXfEzUlbx/tHFBEGkQYPGS5UqtbQjDD7joIYP76WdCaZP/0ILw40bN1ADJCNGTJUmTdrp47Zv3yzhhQKkKHiFlsCWw4f3awFivN+QoFUlPht0Nejde5jehm4vy5d/oe8Fnz/Wb8CATlpYyzJ6dH/tgPPWW/U1GIU6QP/8c0nvC+1vTERERBQe6OyCCxERORdmykQACpONGTPLBDaeFdCrV6+FVtK3qv/v379bjh07FKggK7I/0GUEWRzoFW9BgAIZFxZkUtizlsWZBPuzCaiWjXayyHJBMAfVtdevXy1Xr14J1I4W64iW0Hh9+9sROGnQoIXt9yJFSmqWBjqQoGUtLFnymXYHGDZsshbPs+9iAlZBPWSqICCB92ulDz948MAEE57alsV0JWSM4IJuCAgk2a8PAi0WtE1EQAtZRshWsYfiwFa3p8KFi5tA0DYN0CRKlFiDGshOQhYNniM88J5QJBDtGK0sJbwuup4ULOgT4uPQ9rF9+27aYg7QdQYtbV9/vbJ2kIFChXw0g2nHjj90ffD/A63sBgwYrZlAULHifx1kwvobExERERERkWtiUCYCMKXGCsgAes7DqVPH/w3K7NTfixUrbVsmd+78mhWB6T0IJlgwn9aCjkQ4CLc3Zco8LYSLx6K/PLJCkM1iZV9gyk5kIQiD1rDI/MHz2z8fpuLs2bNdqlWrE2o3A2SZIPsD7WntW7716/eunDx51PY7MmPGj/84xOdBEAvTevD5IEsF7Kf9WFKn/q/tc/z4CTWLBwEZQMYNUmGRYRJeCHyg6DCCMlaABRk/yCwKq4sDWsn9t/4HNTCDVo8WZCOhTTWmfiEos2vXVr29YMFiwT7fy/gbExERERERUczHoMwLsLJZkD0BVlChdetazy2L+i/2kiX7L/CQN28hDcLYs7I3xo0bpAELzBP28SmlQYxBgyLfPnD16qXawx7ZLQhAYD1q1Chlux+BAEybsQIeIUE2zOPHjwJl/wCyf+yDIwh+hOTEiaPSu/c7Ur16Xc2EQZCrV6/2El1ee+11rVODoIqv7wPzOR/XujmhQWDOfpqWleGEDli42LP+5vfu3dHrkKZ3RfXfmIiIiIiIiJwDgzIvAEVZwQqwpEyZWq9Hjpz2XEX5jBlfCfF5MGUmuPbQ58+flS1bfpE2bTpL+fJvSlRA7RMc+FvTqzAlyx6CMcg6sQJMIUmVKq0GEWbOHC/lylWyTUmKyFzmb75ZrFOaOnbsKbFjx5bohs/0k0+myPbtv2tQBu+7ePEyEXoOTEMD/I2CFhzE1DFA5gzcvXtHu0XYexl/YyIiIiIiInIODMpEAKaVIIhhdVI6dGivXmOKEuTPX0SvcXBvXzclsm7duqHXadKkt92GqVJBYX3s67jY3x4Q4B9o/fGcadKUs92GDI2g8D5QLyYsCOygcxA6K6GbVGhZMcHBuiBwYQVkkKWDOiqvvppPogOCJTly5NYpTI8ePdRpZ7FixYrQc2TJkl2zhfz8Hof4N8c0KcBnGrTLVXj/xkREREREROR62H0pAhDUGDWqnxaZRaefhQs/1ayTbNly6v2oK4MuOlOnjtLsh337dupUochORUGRV2TcbNiwWp8LU49Wrlyo96FDkiVr1pxy6dIFLZL7yy/fa7FdQMvugwf36GMRPEGtFHQIQg0brD9qy0yaNMy8Rjw5cuSArdsPpvCg3gzaRm/e/Jb4uSMAABAASURBVLMsWPCRtqW2smrsuwr16zfSvN49mTFjrEQUAiJ4ne+/X6vrPnbsQJ1KdObMKVsWUlSxsplQfNeabgZo9Y1aMvib4ueIwt8HNWnwd1m3bpUGXlauXCTvvttYW1+D9f9i1qzxet+mTRvN5/ae/PrrD+H6GyPYhcAV/kZWl6yQ3g8RERERERE5D2bKRADqiaAV8syZ4+TmzRvaZQcto+0NGTJRAxRYxtfXV+uk1K/fQiIDNUgwFeqzz2bKsGE99QB+9OiZ5uB9hxw9esAs0VyXq1mzoZw9+5e2tkZgZfjwDzVYhBbLCBANHNhFux4hAwZtsDFlB3VMMJUGrbFxgP/FFx+Ln5+fZvnky1dIX3fBglnaXQqdmFDM18vL67l1xJStjh176XsuX75yhAIbLVp01GlD6JyELk2YvoPaMjNmjDPv53SwU7oiC1OL8L7mzp2mv1sdqFBXBh2k8LdFm+zIaNy4jQaqVqz4QgsgZ8iQWYsB22cODR48Qbs9rVz5pRb2LVq0pAbIwvM3xrrh/xDaj2Na2YgRH4b4foiIiIiIiMh5hNpmZtmyosPz5Ws0zFzE0XZsCDAH8Eklf/nk4giLF8+TRYvmyPr1O4RcCzpfIfCBFuAUfn98c0nylvCVHIU8xJmtmx8g6XKllsx5Ijb9jojC7+jWWxLw5IaUq+P4/cWJE2vl4MElMxo02NZDosCyZT59cuWqMbZw4TYRm//6EuzdFCDXriSWopVTChHFDD8vOiela/hJhhzOPV5aOUMkX9m0kjpLPCGK6WLSccrmzePuXLq0q32jRntXhrQMpy+RW8P0H0znqlOniRARERERERFFJ05fIrd09eoVrQ90/PhhnTIVFYWZiYiIiIiIiCKCQZlwqlTprSitcUKOlShREqlc+W3p3XuYZMmSTYiIiIiIiIiiG4My4ZQ2bXq9kGtAxyMUSCYiIiIiIiJyFNaUISIiIiIiIiJyAAZliIiIiIiIiIgcgEEZIiIiIiIiIiIHYFCGiIiIiIiIiMgBWOiXiNzeuaP35PbVx0JEL8e18w8lTSahaIDP+uBvN4SIYoZ7t56Iq/jrwB258revEMV0t/5xrnE9gzJE5NZyFhW5efmB+emBkHPYunWvJEyYQAoUyCXkHDLnFEmbVeglS/uKiN8jDEQZZHZ2v/22S1KnTi65c2cTcm75SookSu4hzi5fKX+5e+O+UNRav/43yZ8/p2TKlE4o6uT2EUmeRpwGgzJE5NZyFnb+gZK7+f3wfomXLpWUqPaqENF/0mX1MBchF/D9rt2S+JWcZj+XXYhigjwlWPXiZVj43WZJly+xlCidXsh9cesiIiIiIiIiInIABmWIiIiIiIiIiByAQRkiIiIiIiIiIgdgUIaIiIiIiIiIyAFY6JeIiJxKvHhxJVYsfn0RkeuKHz+ueHvz3CmRq0ucOIF4eXFbd3cc1RIRkVPx9X0ofn5PhIjIVT148FCePPEXInJtd+7cl6dPua27OwZliIjIqSRMmEBix44lRESuKlGiBMwIJHIDKVMmE09PDyH3xlwpIiJyKvfu3ZfHj/2EiMhV3b17nxmBRG7g2rWb4u8fIOTeGJQhIiIiIiIiInIAp8qLvPK3rwT8dkOIyPFuX30sRI4QP348Tl8iIpeWIEE88fb2EiJybUmSJOT0JXKeoEyGHPj30b8XclYo0Lly5ffSsmUtIef2alGR5GmEKNo9eOArjx8nFCIiV3X/vq88efJUiMi13b59j9OXyJmCMh7/BmbImV2//kjGzF4rJarVFiIiIiIiIiJ3xpoyREREREREREQOwF57RETkVNAqNm7c2EJE5KpQZyJ2bA7TiVwdWmJ7eTFPwt3xfwBFswBJkyaFEBFFFlrFPnzIQtNE5LpQZ+LxY7bEJnJ1aIn99Km/kHtjCJ6ilYeHh/j6slgzEREREREREYMyFK0CAkTu3LknRERERERERO6OQRkiInIqnp6e4uHB2bdE5LqQWUxErg/1ZLi9E4MyRETkVPz9/SUggPOvich1BSC1mIhcHurJcHsnnmokIiIiIiIiInIAZspQtMuTJ5sQEUWWl5eXeHp6CRGRq3o2pUGIyMV5e3M8QwzKkAMcPfqXEBFF1tOnT8Xf/6kQEbmqZ1MahIhc3JMnHM8Qpy8RERERERERETkEM2UoWiEVFym5RERERERERO6OQRmKVkjFRUouERERERERkbtjUIaiWYCkSZNCiIiIiIiIiNwdgzIUzTzkypXrQkREREREROTuWNyDiIiIiIiIiMgBmClD0SxA0qVLJURERERERETujkEZimYecunSVSEiiixvby92cSMil+bt7SkeaFlJRC4tVixv4aZODMoQEZFTefLkKbu4EZFLe/LEXwLQspKIXJqf3xPhpk4MylC0QiQ4WbLEQkREREREROTuGJShaIVI8M2bd4SIiIiIiIjI3TEoQ0RETogTsInIdfn7B7DOBJEbeFZThhu7uwszKHP16hE5cmS5EEWFW7d85cmTh/w/RUSRduPGSTOAuWD2I/eFKKKuXTshUe369RNe/F6jqHTr1l9y8eIds5+7LkTkuu7fvyjnz/9utvWo/26imOHu3Qtxwlom1KCMp6f/L//8c0hwIYoK16/7x/fw8Gx86NDyBUJEFAknT0qVixflTsaM+7YJUSR4enpskSji6fl0iwnKjDIXIYoqp0551PL1/etsihQ79wkRuaxz56ThgQPntwYEeJwXclkeHnIk1PuFKBoVKFAgmbe396K9e/fWECKiSChSpMhQDw+Pf/bs2fOpEBG5ILOfm2yudpnx0lIhIpdVtGjRVf7+/iP2GUJuy1OIotHBgwfvm4OpBEJEFElmHxLXXJILEZGLMvu4DObCRrlEri/ugwcPrgq5NQZlKLo9Npc4Pj4+8YWIKHIemAv3IUTkyrCPeyBE5Orinzhx4oKQW2NQhhzhjL+/f04hIoqEgIAAX3MVT4iIXFd8s69jUIbIhRUpUqSQ2c5PC7k9BmXIEfZ6eHiUECKiSEA9GXN1R4iIXBeKft4WInJZJiBTxoxpWEuGGJQhh9hgLm8KEVHk3DYDGR8hInJdFT09Pf8RInJZZht/09/ff7WQ22NQhqLdnj17DpgrvwIFCpQXIqKIu2gu6YWIyHWlM5dLQkQuqXDhwuhEe3Hfvn1nhNwegzLkEI8fPx4YK1asQUJEFEFm/4G0fg8hInJBOXLkSGyuju3evdtPiMgleXp6Drt9+/ZIIRIGZchBDh06dC4gIOCLIkWKjBUioggw+48r5iqbOcuUVIiIXEzixIkLmatbQkQuqWjRolPNcdCsU6dOcYoiKQZlyGH27t275OnTp2vMgdUsISKKAA8Pj53mqrgQEbmYf5sh7BAicjnmhPR0c3XRHAd9KUT/YlCGHOrAgQPbPD09j5mI8UwhIgonc4Zpl5eXVzEhInI9Jc0+brsQkUsxAZnu5uronj17JgmRHQZlyOHMjmmWv7//TyYwc8THx6eAEBGF4enTpz+aS14hInIxJiCT8f79+78IEbkMc5zzobmKv3fv3k+EKAgGZShG2Ldv32ozCHnTXBaZKHJ/ISIKxf79+zd5enpWKViwYGohInIRZgz0prm6f+LEiWtCRE4vb968JUxAZo05kfS7CciMF6JgMChDMYbZUV3cs2dPIQ8Pj/tmULLJXFoJEVHIFnt7ezcXIiIXYcZAzczVV0JETs8EYz6NFy/eoAcPHrQ1J5NWCVEIGJShGAfTmW7fvl3HnAWv5OPjs8Ls0OoJEVEQfn5+C8xVESEich2ZzTiIBUCJnJg5fultLpvNj7uNOseOHbsuRKHwEqIY6ObNm48uXbq0OnXq1EdMcKZ7+vTpO6VNm9b78uXLe4WIyPjHSJcuXSNz8Tb7i8NCROTEzEmo0ebqhNmf/SZE5HTMNtzBHLOgJMPfJrja1GzLu4UoHDyEyAmYnVwODw8P1Jop5e/vv9acIZ9+6NChK0JEbq1gwYIZY8WKtcWcicosRERO6tVXX02UMGHC82ZflkSIyGnkyJEjTuLEibub45QhJhgzxxynjNm3b98tIYoABmXIqZjoc3xzVry72emVMTs/T7Pjm7V37971QkRuq0iRIh+YfcHp/fv3LxQiIidkTj4NM1enzNn1RUJEMV4BI3bs2D3Mj83NccmE+/fvTzl+/PhdIYoEBmXIaZkDseomMFPHXBqbX780B2WfmQDNfiEit+Pj43PI7AMamX3AESEiciJmPNPE09Oz1u7du5sJEcVYefPmjR03btyG5see5nLRBGNWm3HHfCF6QQzKkNPLkSNHYqOV2TEWNwGa8uay7NGjR18dOnTogBCRW2DqPxE5IxNQzmzGL7/t2bPnFSGiGAmt6s3xRQfzY21kxZgg6v/MeGOPEEURBmXIpZidZpZ/M2d8zKWkuXzz9OnTpfv3798hROTSChUqVMTLy2uUObh5W4iInEDRokV/9/f3r8kaFEQxS+HChSuYMUULE4RpbI4tMK3wZxOIWSFELwGDMuSy/j37hHbaBcylntmhrvHz81t548aNH8+fP+8rRORyzAEOArKjTWCmuhARxWBmf3XBjE3ymgO920JEDmcCMZU9PT2bme2yPhqLmJt+Mid3lx04cOC+EL1EDMqQWzABmiRm51rL7GRLmUsbE6zZZS7rzG3f7t+/n610iVyIOdCpaq4GmcDM60JEFAOZ/dTNfwMyl4SIHCJv3rxp48SJU9Fsi03MpZY5NvjYXO/w9fX9+siRI/eEKJowKENuyQyGypmd7ltm55vV/PqG+fkHE6DZ+OjRo+/NTviyEJFT+3cbb3/9+vX3zpw581CIiGKAggUL5vf29v4UYxBmyBBFv0KFCpXw8vLCidoa5tc05lgAhXq3mxM5a4XIQRiUIbfn4+OT0gRkkK5Y1eyYM5mbcNlkLj/fu3fvpxMnTlwTInI6RYoUKWS2662YxmgGWxuEiMiBTLC4izkQfNcEY4qZXx8LEb10+fPnzx4nThxk0Fb597LKjAsOm8t37NpKMQWDMkRBmEFTDnNV0VzeMJdEZgCV3ey4fzWXX/z8/DYdOnToihCR0zDb9DpzddAEZvoLEZEDmBNAK8w44rLZD3UTInppzLaWzpxsfdOclHlTno3lz5htb7+5fH/z5s3vmT1LMRGDMkRhKFiw4Kve3t6vm+BMBbNDz2muU5jrP8xdm588efLHgQMHDgkRxWgmMNPXXOU1A7Wx+/btOylERNHAHCBWNGOGIahVYc7Kfy1EFKXy5MmTLl68eChFgBOqGKufMT9fMN/3P5rLz/v3778gRDEcgzJEEVS4cOFXTPS9jNnhlzW/ljEX1KAJMDv+bea2rbhwnjhRzGMCrEVjxYq1xAzYVpqz1YOFiOglMgGZL8z+JuO9e/cacyo0UdT4ty4Txt/lzAVj8YPmcstsa5vMydJfDh48+JcQORkGZYheUPr06eOnTp26jJeXVymsuihyAAAQAElEQVTzhfCauek1E5jBFKdt5vet5nqrOQA8IEQUI5gDpYHmqrO5tDIB1E1CRBSFzD6mtbn63JysabV3796FQkSRZrYnBF5wsYIwf5sLMtZ/N5fN5nv8rBA5OQZliF6CQoUK5fL09CxlLq8hUGOCNK9iypO53mUGaTvNIrvMQO1vISKHMNtoBhNI/dL8ePDhw4fj2XWNiF6UOXgsar7jO5nvem9zMqatEFGEFClSJIvZfkqZH3FJZy6NzGWzGUNvNte/P3jwYPPx48fvCpGLYVCGKBrkyJEjTqJEiUrii8Zcipsvl2LmOqG53mWud5rrnebAcCcPDImilxkA1jfB05nmx6/M2bY+QkQUQXnz5k0bL168D813eS4TlOm+b9++LUJEoSpYsGACb29vjI1L/ptpXtJcHpgLygFse/To0R+HDh3aJURugEEZIgfJnTt3ivjx4xczX0TFEaj5N1gTYAVpzMAOmTWH9+7de1WI6KUywZleJjgzxWx3vc0296EQEYVD0aJFJ5nv6ub/Z+8+wKMqsz+OH0mBIEiRLkgTEkJIA0RRAXUBV6yIZRVFxMaK7l9RxFUWccVFEJFFBRVFXUHsioqg0lbBlZIOJBTpCkgLLSEF/ue8mYmTEEKoad/P89znztw7mUwmuZk7vznvefV/xyP6v2OqACiQVZLZh5K6hOnr7WW6bqybf/YsP+n1nznnRXlFKAOUIJ5p/Nrri5UFNDV1Uy9dsnSJ0ZO+2Ozs7CV+fn6xjJ8FTg09Bl/QVSs9/r6IiYl5XQCgABrkPqWvyzbjy9f6mjxaAOTS4yPUcy57vh4j7gNI3Ryji1W+2HCkGA1glgkAh1AGKOEsqNGVjVOP0he4tvpCFqUvcFV1HSs5L3BLNKyJjY+PXyEATljz5s3rVKtW7Rk9zq7V4+wfGs68IQAguWHMMF2GaxjzDwHKOZuV1Ibne0IYV/mtm1db1bdeXqjnqIvj4uIWCYAjIpQBSqE2bdrUCAgIsHDGSkHb6jpK13t11wG9HG+BjS36AhmvJ42ZAuCYhYWF1Q0MDLRw5mo9qXxMTyonC4DyqEJ0dLT1nPqXJ4wZqpcPCVDO6AeFIbpyw5Bs7TkPnaXrNE8Isyg1NXXRqlWrDgiAIiOUAcoITzPhaL0YYSGNrm2J0GW1LhbUxOn2uPT09HgaCgNFZ008K1as+JCGnPfpcfR8TEzMSAFQ5kVFRdXW4/5xvfh/euwP1WP/OSGMQTmhAUwbXdl5ZVvvWo+DNeIZhmRDkA4cOBCj55R7BcAJIZQByjj7VENfOC2oidR1pK4jdO1vAc3Bgwetw70Ne0rQk81E4WQTOCKrUAsMDLQ3aI/rMTRq//79zycnJ28XAGVKeHh4sL+//2C9eKUFsTT/RllmH+pVq1bNnSNKTvVLRV3fptet54sbJq/nizF79+5dQgUMcGoQygDlkAY1tfQF1l58W3nG/obr0kYvL9cX4UQbAqXXLaRJpKkwcDg9hmwog4UzH+r6NQ01EwRAqRYdHX2Zrm7S18DOuh6hr3/vCFCGWPWXhS8VKlRwFdX2gZ2um3irqa36xXrAxMfH22tatgA4LQhlAOSybvn6otzGU1ljZasW1NSQnIAmQYOcBN2X8Ntvv8Vv2bJlnwDlnB4zf9Fj5HFdduqx8aKGM18KgFJFj+O79E3qw3oMb9HXuefj4uK+E6CUi4yMbKF/19YD5gLxBDD6N+5n4Yted70Hs7Ky4hISElIEQLEilAFQqODg4KpBQUEWzoTri7tV1ITrC3maXg/Wy4l6OcnWeiKbpCeyFt7wyQrKHX1T11mPiUcszNRjYXhsbOwk3XxQAJRInkbef9GL/9Tj9gN9czomPj5+qQCljJ2nValSxXoIhvsMU7cP19bpthm6bLYAJj09PY6egkDJRCgD4LiEh4c39PPzs7AmTHIqatxacqZBtKAmyYZC6RvURKbrRnlhU4NqeNlHj4ch+vc/MTs7exxv9ICSQ4/RLnqMDtBj9CI9Rp/VMObthIQEKj9RKrRt27a5t0+g5IQwdrmmrm3YeYJVwejrTnxGRoZN6pAhAEoFQhkAJ1VERERLDWvCfEIaW5pLTkgzX9frdd9SPWlYGhcXt1aAMio6Ovpe/Vt/UINJ+5t/JyYm5kMBUBzO0Dez/fQ16GE9Frfo+mU9Hj8VoIQKDQ2tGRgYGG7Vl54qZTuXStW/3WbimVFT19b3JT42NnadACjVCGUAnA5+UVFRYXoSYYGNTanYWk80Wuv2enp5qYU0ejlJ37wu1ZOPpTQXRlmin8xfpH/XD+rFbvr3PkHXL+tJ9K+F3H6ZBpahAqBQeqzE67ESUch+a2jaXy/erctoXSZpGLNcgJLDT/9MfYeIez/MCpSc0MUqjhM8VcdWDcPsR0AZRCgDoNg0adKkUs2aNVt7QhqrrnGXdVctC2ossLGhUHZ53759iSkpKb8KUErpiXd1DSXv17/p2/Vveq1uelUDyK/z3y4iIuKg3i5O3zxGC4DD1KlTp26DBg1+0OOovgacVfPvb9u2bR89zm7W/Rb8j9dj6Q0BipkNPdKV9R2z853mulgD3vMk72QKiRkZGYn0fgHKF0IZACWOnrhUzs7Obq2fGrmwxhPUWANha6a6TNfLPRU2yzIzM5clJCSsEaAUiYqK+rP+/f7VLuryanp6+gQ9Cd+hwc1qDWSa6d//Id0/U99w/lkA5KHHz3I9PkL0TazExcW5c1nPTDP9dXt/fX2YqptsiNISAU4zPYepr//DLXhp45nJ0juce4MuibotSffH6N/vcmY+AmAIZQCUGuHh4WfqSbdN293KM/zJLofqCXhDT1izzMIaXS/PyspaxskOSjp9c9nAwhld7ters/Xv9yq9HGT79KT9gF6erMFMPwHg6DHzXz0uLtHFXdc3tjv1cqwuDa0qZvfu3eNXrVrFEA+cchq+VNP/0y31vMSqGt2wI88HSekWvEhOBYybpVKD90Qa7wI4EkIZAKWenhgF6Im59eAI9YQ1rTxhTbC3skY81TW21m3LlyxZkilACaJvNnvp6iM9wc/dpn+ze3QZq8HMEAHKOT1GPtL/39fp4u/dZtUyesxcpv/T5whwCvhU73onMfBW8J6py6e6zcIWC18SMzMzkxITE3cKABwDQhkAZZqexFtY00pygprWPpc3Sk5Q4ypsLKhJTU1drp+w7hagGOjf6gY96W+Yf7u+6fxd/06fjIuLoy8Gyq3IyMgX9f/0fXqMVM6/T4+R1RpcnifAifHXv7Mwz9BpN+zIE77U9vS5s+oXm5TA9borrGE7ABwLQhkA5VJ4eHhTPfFq5efnZxU1LrixYVG63u8JaiywWaS32ZCWlracpns4XuMeWjlYKkilo90udu1HQ21Ihv7diXdt7PIZUiEtonHPkQKUQ6u3/NBtT/rWdiKHArzHhVfOMKYzsiMb3/DsUe/okGx4cGyLNwXlmre61sIXn9kg60tOj68kT1Vtok/4wpTTAE4pQhkA8BEaGlqvUqVK3uqaWrp08oQ1VqacrMtya86nJ23Juk6mbw2O5pWBq9JCOlSv5OfPSy5QXPbvzpINyfuW3zeiGdPNlxP6eh6o8ocv9vs/14Y2eyYMWKqv5UuzsrJs2NEvAgDFwF8AALk8FTG2zPbdHhwcXDUoKCjEAhqrsNFNd/r5+YXoJ24hkhPWuEVP7pZ5AhtrNLxPABV6UQ0JqFhBABSPbRvTLZQRlD0NGzYMqlOnjqt6tQBGN1mz9B7eSQAsfNHtNvxool1fsmTJagGAEoRQBgCKICUlZY+uFnmWPMLDw4P1hM8CmxBd/0k3DdDAJkgDG6u0sbDGqmms2bCFNSmUQgMAcGzsw5EqVaq48MWqXjxDj+1ybRt2rK+/LoDJzs6O09fasVS+ACgtCGUA4AR5hjDZ8oXvdhsKFRgYaEFNsOQMh7pSTx6Do6Ojbey6q6zRE0gX2Fh1zbZt21I2btyYJgAAlFMhISFnV65c2Q0j1tfGejYFuuT0favqbc5vQ470+veeypf1AgClGKEMAJwiPkOh5vput3HulSpVsmFP3sDmGl0G1a1bN7hOnTo2bMqawlpY4ypr/Pz8UjjpBACUJREREefYcGBdfBvuu54/3ob7uizUyyPsckxMDLMdASiTCGUA4DTTsCZDVwmeJY/g4OAG+glhK09YE6KBzNV6QmrVNfX0eoqesLqqHKussSqb/fv3p3iGVgEAUOK0bdu2ub5etcrOzm7g7+/fUXIqR21JlZxKUQtgYnSZvG/fvmXJycnbBQDKEUIZAChBNGCxTwJtmeW73aprgoKCgg8ePBhsQ6B00xW6/luVKlWC9YTXpvFe7Pk63+oamhkCAE4Hf30tsuFGFrZ4q15c+KLb1up6uV7/QV+fZuv1VzIyMpbrBxR7BQBAKAMApYGnuibRs+QRFhZWVz99PM9KwK3ZsAYyl3uqa5pJAdU1Bw4cSNH72yEAABwDDV6q6SrEE77U0eViyQlgGus2G25k4YtVvnycmZm5PCEhwbZlCwDgiAhlAKCUS0pK2qIrW+bn23WGnkAHZ2dnB3uGQ3XSk+V7rOJGt9uYfetfs9eCGt2fop9groiNjbUAhxNoACjHwsPDG1q/F8+sgt7hRhbGWM8za1RvYcvPuryut1lOZSYAHD9CGQAouw7pibKb5Sn/jtDQ0JqBgYHN9WS7tQU2eqJ9h65bWoijuzdYUKMn2iskZzjUCg12UvQTz40CACgrKkRFRYVY8KKXbWmsS1vPsCPr62LBi70GWP+zDzIzM5M9HwIAAE4iQhkAKIc8w5dsWZR/X2RkZBMLavREvKWenIfp0jMgIMCGQ1kVTW0LazS0WeGpsFmRkZGxIjExcacAAEqcmjVrntWkSRPX58WzuBBG/5fbEFcb1urCew3fZ/n7+7+2ZcuW5Rs3bkwTAMBpQSgDAMgjLi5ura5smZlvV4AGM608YU1LDWT+pCfzfw0MDLQKm0MW1Iinh41dtuoa/WR1pacfDgDgFIqKimpsVS+2eHq+WPhijXYtULdzfuv3YrMd/VcvJy9ZsmSVAACKHaEMAKCoMmNiYgqcytuGQ1WqVMnCGu8MUbfqJ67N/Pz8QjWw2eINbLzDorKyslYkJCSsEQDAsfAPDw8PCQgIsNClvi4d5I9+L1vlj8qXWF2/r+G4NdvdKgCAEotQBgBwwjzDof7nWfIICwtrpG8grLLGTeetbxSutOsa1jT2fIL7nS4HvMOhMjMzV/AmAkB5FhIScvaZZ56ZW+3iWdvSSHKCF+v38pP+T/1aQ+4Xtm/fnsyQIwAonQhlAACnVFJS0gZd2TIr364K0dHR1ljYmku20UCmo77RuNMT2ARZdY2n2bCbGcoCm7S0tBUaAO0VACgD9H9dc/kjcPENXw55ghc305Fe/j47OzuZCkMAKHsIZQAAxeVgTEyMvemwZYbvjuDg4Kr6KXFLT/8am9L7Gn1T0jIoKMgC/sHMrgAAEABJREFUm30FBTZLliyx6wcFAEoQ/Z9VWQoOXmzI0VrJCV6s+mWBLm9lZGQke6oPAQDlAKEMAKDESUlJ2aOrJZ4lj7CwsLrWXNgnsLnYAht949NSd687QmCzXgDgcH4RERFv6/+M9nFxcSFyAvR/kPV4OSx80f9JNcUTvEhO1ctU/d+UHB8fb9ezBQBQrhHKAABKlaSkpC26suWH/PvCw8Ob+vv7ewObUA1krrPAJjo6ur53Km/P2gU2fn5+FthskxMUGRm5Se/3rdjY2CECoFQICQnpXLly5Vc9zcm3FPXrNHzJrXSRP8IXm+XIwmTf8GWaZ8jRRgEA4AgIZQAAZYan34Iteabz1jdRAVlZWS01hPEGNp308t2ewCbAG9h4Z4fyCWz2F/Fb19OvG6jhzEX6JuyviYmJyQKgxNJj9Vk9Zm/X5Vw91kWP+Rq++/V/RjUpOHhp4ZndyDvL0fe6fjkjI2M5/a4AAMeDUAYAUOZpuJKpq6WeJQ9786VBis0O5WaI0jdYPS288QQ2qfkDG73tivj4+BW+96FfV0H3BenqUl0+Dw8PH6sB0XhBibFu3S/y/vtvSd++D0jduvXlZDlw4ICMHz9KLrigk1tOxIIFc+XMM6tIREQ7wakRFhbW3N/ff6Iepx3smPXZFaT/C8brcd5Kt9uQo4qSt+rlLWu8GxcXt1IAADiJCGUAAOWaBjapulrkWfKwHhGeyhpvYNPFhkdpWGP9a1Z7Axvv7fW6rYI11Hk2Kiqq89atWx+Qcu6WW7rJzp05PUstcGjUqIlcffWN8qc/9ZDTacOGtTJnzgy59dZ+udv0dyubNq2XevXOkYCAADkeGRkH5JtvPpfzzjuhdiTOrFnTZfPmX+WVV94TnHzpmXurBwYGfq3HbLDnWM1l13V7nC7vZ2Vl2ZCjrQIAwGlAKAMAwBFoYPObrmyZl3+fTWVrgY1V2OgbuTz7dFtN3derVq1awbr2k3KubdsL5Oab75StWzdLYmKMjBo11IUkVrVSnGbM+ELGjh0uU6bMkLPPriUo2zKz9lfV4/GABjBpurbKtjz7Y2JiXhMAAE4zQhkAAI6DBjardbU6MjLyZetJ4csqMFSavvmroYFNBSnnatQ4O3dITteuV0lGRoZMnTpJeve+97grVMor+9uaO/dbyc7Ocs8liq5qUJ0NO3bsuKh69eqXayBzoz6XHXRzbV0qW0Cjx/KquLi48wQAgNOIUAYAgBOgwUuAvVH2DH9I1cvbdfO3+iZvqr7Bm+d3uV+aXi/31TK+mjY9T+bMEdm8eZMbzpSdnS1vv/2qLFz4oxu+07p1hDz66DCpWfNsd3sLcUaPHibLlsVLaupO9zWdOnWVG2+8w6qSZPz4F2TevG816Pk293s8/fRA+f33LUccCtS373Xy6685k+LceusVbj1t2nypWLGiTJnyphvqZI+vdu26Eh7eTu6++yGpUqWqFNXSpfHywQeTJCEhRkOpmtKz521u2JaX/c3Yzzx//hzZsydVunTpLpmZGYfdT1JSnHz00bsSG/uz619j/XBsadMm2u3fsWO7TJw4VhYvXiAWDnbo0EkGDHhc/P1zTvESE2P1ubxHb/OxTJ48UX78cZaMG/cf9zsoj9avX79Tl4/1oi2VoqKiuunv4nq93DlfjxkAAE4LQhkAAE6ABgr2KbtNsT3T+lEkJCT8T1Ao6+NirILGvPPOePnww3dc5cfNN/fVEOIdGTy4v7z22gcu7Prkk/dkwYI50qdPf2nQoJEbArVgwVwXdOQfglJUgwb9U374YZa77yefHOEeiwUyFqbY47EA5a67Buib+DXy/fdfaxi0q8ihjPXQGTLkb66Hzr33PqzhzwYNh0ZK9eo15ZJLLne3+eCDt91y/fV/cQFLfPxi+fnnH/P0pomPX+Keh+uvv1UefniI7v9BJkwYrduGS2houLvNsGEDZe3a1XLTTX3c93vvvddd9dEDDwzK85iGDx8sISFh7n4s1IKTHhsbO03X0wQAgGJCKAMAwAnQEIZmJMcgJWWpq0Lp2LGLCznS09Pliy+mSufOXeXRR592t4mIaCu3336VLFw4Xzp0uFhWrUp2oUmvXre7/fa1J6pVqzayZs0qd7l168jcnjIrVy53aws56tSpJxde2Nn1wzkWX331sezbt1fGjn07NwBJT0+Tjz/+jwtlrDLos8+muJ/5/vsHuv0XXXSpC1fs67ymT/9UqlWrLv36PeiqYLp3v0a+/PJDF2A9/fRoF9okJyfJgw8Olquu6uW+pmbNWjJq1D9cgOUbIlnFz//931MCAABKlnI/zh0AAJxaVmnSvXs7tzz0UB8XdAwcONTtS05OdMFM27YX5t7+7LNruyE6KSlJ7rpNNb1ly2/y/PNDZMmS/3l79pwSHTpc4tYjRjwps2d/I2lpaXKs4uMXuUDHtyIlOLi1C6SysrLc9NxWeRMe3jbP11Wtelae64cOHZSKFSuJb8+iypWraHCzJ/f7mHbtOubut2oYG+5lQZavrl2vFgAAUPJQKQMAAE4p7+xLc+bMlFmzvpa+fQfkVnHs3ZsTMLz44jNu8WWzNRmbPtuqSxYsmCvPPPOonHWWVY88JF26dJOTrX79c2TkyAluZqY33hgr//73v9wQozvuuF/yT6N8JFbtYo/dQqj8tm//PfdntuFGhbE+M/PmfecWq6pZtizBDXOyIVHe72P69LnmsK/1Pnde3qFiAACgZCGUAQAAp5R39iXrl2KNZq0vyrBhL7p9NqzG3HnnX3P7pHjZUBxjYcgVV1zrlv3798trr42Wf/3r765ZbePGzeRks8dqi1Xk2BCiceNGSK1adaRHjxuK9PX2M1lT3r/97e+H7bPnIiPjgLu8f/++Qu/Hhml163a1PPfcE24xV17ZU6677hZ32R6TeeaZl6RSpUp5vrZhwyYCAABKPkIZAABwWlhlyO233yevvjpKYmJ+lujoDhqqNHdVMzbzkHfa7MJUrlxZrr76JlfJsnp1igtlAgMrumFBvnbs2HbU+/IOCzp4MLvA/dZE2Hq1vPnmuNxeM0XRqlW46/fSokWoe7z51a/f0P3Mv/yyMs92G3bka9eunW4I1ahRr0t4ePRh9xMWFuXW1qC4KM8dAAAoeQhlAADAaWMhhzX2/fe/n5OJEz9xFR7WVHfy5DekVq26cs45jTQASZbvvvtSnn9+gmt0+8QTD7iqmaio810VyqefTpGgoMoSGhrh7rNZsxayZ89u2bBhrd6+hmuiu3btKmnUqGnu9/UO37HmwXYbu99mzVq6bd9++6UGKK3cff/442xJSFgsF198uQt84uIWuYoW3543vixoCgwMdEOLoqMvkAYNGsq1197sfsZnnx3kfjarmpk58wtp0qS5GwZl01Vfc81NMm3ah65fjg3vsubA1iPG9zHv2rXDhU1z586U7OwsDZH83f17K2SsWXH79h1lzJh/yn33PaIB0Jny00/z3PPw3HMvCwAAKPn8BAAAnDI9Oj70VKsLa/j7+RetH0lZYzMO2TTWNruQseoTC1++/voTF05YBUhYWKSccUYFDSk+cNu3bdsq3bpd4ypBrJolMrK9rFmz0jUMnjlzmjRs2NjN1ORtpGthhzXkffXVkTJ16iRp2rSFa6JrU1H36NHT3cYCl9jYha7CxqamtqFSNuOSVax8+ulkmT79M9dg2GZ4shDGhllZ+GNhSP/+j2pIc1mBP58NrbLhSBawbNnyq1x6aXcJCAjUn/cymT9/jguIFi9e4IIUC6Rq1swJh2wa7M2bN7kw6t13J7jnyIIh6wXjfcwWHlkPGnts9rNbUGWP1aYUt+fTvnfHjpe658ZCILuNyCE3vMn73Nj9Wehkz6c1Ty6v9u/OknVJe7d99ePYVwQAgBKkfJ4hAgBwmrwycFVaz0eaVgqoyISHODHWT2f27Omux82QISOPGBThcNs2psu8qb8tv29Es1ABAKAE4QwRAACghLFpwh977F5JTk7K3Wb9aWxYlUlN3SkAAKD0o6cMAABACWO9dlJTd8mUKRPlhht6u23WX2bGjM/dvoiI9gIAAEo/QhkAAIASaOjQ0fLii8Nk0KD7XS+eevUaSHBwmIwZM0kaNjxXAABA6UcoAwAAUALZTFSjR08UAABQdtFTBgAAAAAAoBgQygAAAAAAABQDQhkAAAAAAIBiQCgDAAAAAABQDAhlAAAAAAAAigGhDAAAQDmTnJzkpto+1TZt2iCPPNJPsrKyBAAAHI5QBgAAFGrBgrkSH79YULxWrFgmc+bMkOzsbDkRFpCMHTtczj//YjnVbFrvQ4cOyZQpTO0NAEBBCGUAAEChZs2aLq+//tIxfc3Bgwdlw4a1kpmZKScqIyND1q9fI6fT3r17ZPPmX6UkWbYsQUaMeEoOHDggJ+L9999y93H99X85bN/DD98l3bu3k48/fk+Kyn439jX5l+efH+L29+//mEydOknWrFklAAAgL0IZAABw0s2Y8YXcfXcv2b07VU7Uiy8+I8OGPSqn07333uSChLJm3769Gri8Kzfc0Fv8/Pzy7EtN3eWCnypVqsqiRfPlWN144x0ycuSE3KV373vd9pYtW0lkZHt5990JAgAA8vIXAAAAnBRWITR37reSnZ0lXbteJSXN4sU/SXp6urRrd+Fh+7xBzE039ZG3337V3a5SpUpSVA0bNpaIiHYF7mvb9kKZNOllV/UUGBgoAAAgB6EMAABl3MqVyTJgQG95442P5NxzmxZ6WwsV7A35/PlzZM+eVOnSpbtkZmbkuc0vv6yUqVPfcsOTNm1ar/fZTHr1ul1v283t79v3Ovn1143u8q23XuHW06bNl4oVK8qUKW9KXNwiWb06xb05tzfr/fo9JDVq1DzssWzdulluv/2PYMOGxLRq1UZeeimnguXPfz5fHn/8WVmzZqVMn/6p3HbbPXL55VfqY7lMHnxwsFx1Va88P//TT4+WCy/s7Lb973//dY9l3bpf5Mwzq0iLFq2kT5/+7nG98MLT7jbffPOZW26++U65664BBT5fSUlx8tFH70ps7M9uSFDduvXd0qZNtNv/+OP9pXHjZu7+33//TWnatIV7nIU9f17fffeVfPXVx7J27WpXaVLQ727Hju0yceJYDVsWuMqXDh066c/6uPj7F3yKt2xZvFSrVt09xvwslLHn94ILOslbb70sCxf+KJ06/UlOhuDg1m4o2/LliRrctBUAAJCDUAYAAOT64IO33WL9RixYsAa/P//8o5x3Xkjubc4+u7a0bNlaOna81AUrCxbMlZEjh+i2UGnQoKEMGvRP+eGHWfLJJ+/Jk0+O0MDlbBfImCZNmkv16jVdCPH775tl8uSJUqHCy/LII/847LHY7WwYjPVA2bhxnTz22DA566zqeW5j4YYNt3nwwSdc4FEU+/fvc0Oi7GcaOHCo7N69S+bOnekCmejoC9z3HD58sISEtJEbbrhN6tdvWOD9xMcvkcGD+0M2KFAAAAlQSURBVOtzdas8/PAQfZ5+kAkTRuu24RIaGp57u4SEJS7k6tv3Aaldu95Rnz9jYY+FQx06XCyPPvq0+/k//PCdwx7DsGEDXWhj1S0WLr333usSEBAgDzwwqMDHbH1dzjnn3MO2W/PgRYsWyDXX3ORCJAvJTmYo430O161bTSgDAIAPQhkAAODYG/PPPpsinTt3lfvvH+i2XXTRpe5Nv/Ui8bJKi169eudej4rq4Ko6rFrEQgWrtvA2dW3dOlJDiFq5t+3YsUue72lTJtuMQgWxwMKGw3zzzeca4GwpcGjMjh3bZMyYSRIUFOSu79mzW45my5bfXP+USy+9Ijd08FbVGHu8/v4BUrNmrSMOxzFWnWPPRb9+D7oqle7dr5Evv/zQhSdWleNlz8XYsW9ryBOWu62w589YoGXff+jQ0bm9X2zWJAtdvCwUsqmtfauC7GtGjfqHq/qxsCo/C6AsGMrPQiD7HdtjMZGR57tQxpc1P/ZV0P0fibcSKjV1pwAAgD8QygAAUEZNmvRKnma199xzo1vfccf9ctttdx92exvKY2FFeHjeSoaqVc/KE8oYCxEswLEhONYnxFgFytFs3/67vPHGWFeBY4GKOZa+Jfl16tQ1N5ApKqsEsama33lnvAsaLrnkcqlVq44cq0OHDkrFipXyNMytXLmKPld5wwurDvINZMzRnr/ExBiJju6Q577t9+ArPn6RW7dr1zF3m30fu79Vq5LdkKf8LNgpaGiTBTBWYdO6dYS7bl9rYZmFPnafNtxr6NBH8nzN6NETJSwsMvf6mDH/dIux+/rqq59y91WokDO3xMmYjQsAgLKEUAYAgDKqe/dr3Rt7q0YZO3a4G6qT0/OkQYG391ZC2DCYwnz++VQZP/4FV6HRocMlbnhSjx4XyNFY6GBTLttQlsGDn3VVNP/5z2t6f+/L8bLvfawsIHj22XHyxRdTZebML+T118e4cKd//0elevUaRb4f67czb953brHqIpu5yMKme+99OM/tbBiWr6I8f1bxYwFPYbxBWZ8+1xy2z/rxFCQoqLIcOJB+2HbrJ2NBjDewad/+Ire2sMZCmdDQCBfC+Gra9Lw81232pfbtvQHRGXn27d+/362P9rcFAEB5QygDAEAZZUNhbPG+ubc314U1+vUOMTlaxYsNz2nb9oLcITNWfVEUNiuRDR2y5rzeioziYs+LhTDGhu5Yb5bx40fJE088V+T7sKFY3bpdLc8994RbzJVX9pTrrrul0K8ryvNnw5DS0gr/PXire5555qXDqo0aNmxS4NfUq9cgd2iZlw0NsyopW6yZsi/rJ2SVVWedVS1PVUxBCpt9aevW39y6Tp16AgAA/kAoAwBAGWeNXa15bZ069Qu9nVWwWJ8Qm13Jl3d4jTl06JDs2rVD6ta9JHebDZXJzzvs5uDB7NxtO3dud+t69c4p9Gvzs+oN3/spTGBgTkNh36DDO0zqSCxsCAuLcrM0+X5PG55UmF27dsrs2d/IqFGvS3h4tBRFUZ8/m60o/+8hKysz3+OOcmtrolxY75u89xvmmg77Tnf900/z3Pqpp5534YuXDbGyZfv2bXn6Ah0P60vk/f4AAOAPFQQAAJRplStXdm/aj9a7xYIIm33HZiKymXhseuxp0z7M7V1izjjjDDe9s/UYsSoKe9M+atRQve8gN3zHvsY0a9bSrb/99ktZuHC+q86wrzMfffSO22ZDeKxKxQKCFSuWH/Fx2axKv/22yYUJVm3jHQpTEAsorGJj6dI491gsaLHhSb5siFG/fje4WZ3s57SfwYbv/DH0JufxW18Xm777v//9vsDvZeGKhT/2fMXGLpSEhBjZtm2rFKaoz9+1194s69evcUOd7Pmxfdb815c1VLbHbH1cFiyY6x6rPad///uAI35/a9xsDZ2tqbCXDVGyCirrrWN/J97liiuuy91/ouz5tZ/b28gYAADk8BMAAHDK9Oj40FOtLqzh7+d/hpQGNg325s2bZPLkN+Tddyfom+hG7s209Sjp0aOnu431HrGQ5eOP/yOrVi2Xm2660w3jmT9/tnTterULd6yywqpuPv10skyf/pmbBvqKK651PVtmzfrahTUWFg0fPk527051Q3WONDzGpq62ahd7TAsWzHG3szf3dt2aEuevUrH+J1bBMnbscy5Qsv4133zzmesB06hRE9dXx6bWXrz4Jw2I3pUNG9ZIz569pXfve3Ib0tp9JCbGuu+RlBQrl132Z9ePxVe1ajVc42L7+b7//msNWL50P++mTetd+GEBjG03Xbtelft1RXn+6tc/xz1Xdps33/y3hj1b9DHe5sKXW27p6xrpGptWe82ala4/Ts73OuSGT9nPWRBrFmzTa1uAY4/JGu++9NKz7vta/yFftWvX1Z9nigtxunTpJkdizaG//PIjufDCznmmTveyShsLjmy4WGHD506l/buzZF3S3m1f/Tj2FQEAoAQpHWeIAACUUq8MXJXW85GmlQIqUpxa1lkFz+zZ02XcuBEyZMhIufjiy6Qksqqjfv16usdoQcqp9sILT7tZpmxa8OKybWO6zJv62/L7RjQLFQAAShDOEAEAAI6RDSl67LF73ZTRXlb5c/HFl7vLqak7paSyKhybKenNN8fJqWZDsKyCZ8CAwQIAAA5Ho18AAIBjZP15bNjOlCkT5YYbertt1l9mxozP3b6IiPZSkt16692npUrGhiu98MIb0qJFiAAAgMMRygAAAByHoUNHy4svDpNBg+53vWhsummbXWjMmEnSsOG5UpJZQ2SbIv10ONpU2gAAlGeEMgAAAMfhnHMayejREwUAAOB40VMGAAAAAACgGBDKAAAAAAAAFANCGQAAAAAAgGJAKAMAAAAAAFAMCGUAAAAAAACKAaEMAAAAAABAMSCUAQAAAAAAKAaEMgAAAAAAAMWAUAYAAAAAAKAYEMoAAAAAAAAUA0IZAAAAAACAYkAoAwAAAAAAUAwIZQAAAAAAAIoBoQwAAAAAAEAxIJQBAAAAAAAoBoQyAAAAAAAAxcBfAADAKbVs/k6p4H+GACgeaXuyBACAkohQBgCAU+hg5qFhyxbsrCQAitch2SAAAJQwfGwHAAAAAABQDOgpAwAAAAAAUAwIZQAAAAAAAIoBoQwAAAAAAEAxIJQBAAAAAAAoBoQyAAAAAAAAxYBQBgAAAAAAoBj8PwAAAP//l3ADhQAAAAZJREFUAwBTjRS4bSOvGgAAAABJRU5ErkJggg==\" style=\"max-width:100%;height:auto;\" alt=\"Figure 5.1 \u2014 Implementation architecture.\"/>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e36e1d8b",
   "metadata": {},
   "source": [
    "## 6. Data acquisition & preparation\n",
    "\n",
    "Every line below is commented so a student can re-run and modify each step. The cell ends by producing the standard analysis variables: `df`, `X` (clean numeric features), `y` (binary label), `feat` (feature names), and `family`. `family` is the per-group label used for the recall breakdown. It is an attack family on the intrusion corpora, but a transaction type, merchant category or malware category on the fraud/malware ones."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "8c02db47",
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import time, warnings; warnings.filterwarnings('ignore')   # keep output clean\n",
    "import numpy as np, pandas as pd                            # numerics + dataframes\n",
    "import matplotlib.pyplot as plt                             # static plots (embed in HTML+PDF)\n",
    "plt.rcParams['figure.dpi'] = 120                            # crisp figures\n",
    "RANDOM_STATE = 0                                            # single seed used everywhere\n",
    "np.random.seed(RANDOM_STATE)                                # reproducible sampling\n",
    "NEG_WORD, POS_WORD = 'benign', 'attack'                      # class names (overridden by some loaders)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "5b961ba4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "loaded 1,986,745 rows x 41 features; positive rate 0.0378\n"
     ]
    }
   ],
   "source": [
    "import os, glob\n",
    "# Kaggle auth: token read from ~/.kaggle/access_token (students supply their own).\n",
    "os.environ.setdefault('KAGGLE_KEY', open(os.path.expanduser('~/.kaggle/access_token')).read().strip())\n",
    "import kaggle; kaggle.api.authenticate()\n",
    "REF = 'dhoogla/nfunswnb15v2'; DEST = '/tmp/kg_' + REF.split('/')[-1]\n",
    "if not os.path.exists(DEST):                                   # download + unzip once (cached)\n",
    "    kaggle.api.dataset_download_files(REF, path=DEST, unzip=True, quiet=True)\n",
    "NROWS = 1_500_000                                              # per-file read cap (memory bound)\n",
    "files = sorted(glob.glob(DEST + '/**/*.parquet', recursive=True))\n",
    "df = pd.concat([pd.read_parquet(f) for f in files], ignore_index=True)  # combine day/part files\n",
    "df.columns = [str(c).strip() for c in df.columns]             # strip header whitespace\n",
    "LABEL = 'Label'; FAMILY = 'Attack'\n",
    "df['y'] = (df[LABEL].astype(str).str.strip().str.lower() != '0').astype(int)  # benign=0\n",
    "df['family'] = df[FAMILY].astype(str).str.strip()             # descriptive attack family\n",
    "df = df.reset_index(drop=True)\n",
    "assert len(df) >= 1_000_000, f'floor not met: {len(df):,}'    # honesty gate: >= 1M rows\n",
    "DROP = list({LABEL, FAMILY, 'y', 'family'} | set([]))  # never leak label cols\n",
    "feat = [c for c in df.columns if c not in DROP]\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "X = df[feat].copy()\n",
    "idlike = [c for c in X.select_dtypes(include='object').columns if X[c].nunique() > 0.5*len(X)]\n",
    "X = X.drop(columns=idlike)                                     # drop ID/timestamp-like leaky columns\n",
    "for c in X.select_dtypes(include='object').columns:           # encode remaining categoricals\n",
    "    X[c] = LabelEncoder().fit_transform(X[c].astype(str))\n",
    "X = X.apply(pd.to_numeric, errors='coerce').replace([np.inf,-np.inf],np.nan).fillna(0.0)\n",
    "X = X.clip(-1e15, 1e15)                                        # clip huge NetFlow counts (float32-safe)\n",
    "X = X.loc[:, X.nunique() > 1]                                  # drop constants\n",
    "import re                                                      # LightGBM rejects special chars in names\n",
    "_seen, _cols = {}, []\n",
    "for _c in X.columns:                                           # sanitize to unique, safe names\n",
    "    _c = re.sub(r'[^0-9A-Za-z_]+', '_', str(_c)).strip('_') or 'f'\n",
    "    _seen[_c] = _seen.get(_c, -1) + 1\n",
    "    _cols.append(_c if _seen[_c] == 0 else f'{_c}_{_seen[_c]}')\n",
    "X.columns = _cols; feat = list(X.columns)\n",
    "y = df['y'].to_numpy()                                         # STANDARD CONTRACT\n",
    "NEG_WORD, POS_WORD = 'benign', 'attack'               # class names for plots\n",
    "print(f'loaded {len(df):,} rows x {len(feat)} features; positive rate {y.mean():.4f}')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b6bccea4",
   "metadata": {},
   "source": [
    "## 7. Exploratory data analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "08e39976",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1320x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# --- EDA 1: class balance and the attack-family mix ---\n",
    "fig, ax = plt.subplots(1, 2, figsize=(11, 4))\n",
    "df['y'].map({0:NEG_WORD,1:POS_WORD}).value_counts().plot.bar(               # counts per class\n",
    "    ax=ax[0], color=['#2a9d8f','#e76f51']); ax[0].set_yscale('log')\n",
    "ax[0].set_title(f'Class balance ({NEG_WORD} vs {POS_WORD})'); ax[0].set_ylabel('records (log)')\n",
    "df.loc[df.y==1,'family'].value_counts().head(8).plot.barh(                  # top attack families\n",
    "    ax=ax[1], color='#e76f51'); ax[1].invert_yaxis(); ax[1].set_title('Top attack families')\n",
    "plt.tight_layout(); plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "53c8d04c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1440x600 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# --- EDA 2: feature correlation + a 2-D PCA projection ---\n",
    "from sklearn.preprocessing import StandardScaler                 # scale before PCA\n",
    "from sklearn.decomposition import PCA\n",
    "fig, ax = plt.subplots(1, 2, figsize=(12, 5))\n",
    "topv = X[feat].var().sort_values().tail(12).index                # 12 highest-variance features\n",
    "im = ax[0].imshow(X[topv].corr(), cmap='coolwarm', vmin=-1, vmax=1)  # correlation heatmap\n",
    "ax[0].set_xticks(range(len(topv))); ax[0].set_xticklabels(topv, rotation=90, fontsize=7)\n",
    "ax[0].set_yticks(range(len(topv))); ax[0].set_yticklabels(topv, fontsize=7)\n",
    "ax[0].set_title('Feature correlation (top-variance)'); fig.colorbar(im, ax=ax[0], shrink=0.7)\n",
    "samp = X.sample(min(5000, len(X)), random_state=RANDOM_STATE)     # subsample for a fast PCA\n",
    "pc = PCA(n_components=2).fit_transform(StandardScaler().fit_transform(samp))\n",
    "ys = y[samp.index]                                               # aligned labels for coloring\n",
    "for lab,c in [(0,'#2a9d8f'),(1,'#e76f51')]:\n",
    "    ax[1].scatter(pc[ys==lab,0], pc[ys==lab,1], s=4, alpha=0.4, color=c,\n",
    "                  label={0:NEG_WORD,1:POS_WORD}[lab])\n",
    "ax[1].set_title('PCA projection (2 components)'); ax[1].legend(); ax[1].set_xlabel('PC1'); ax[1].set_ylabel('PC2')\n",
    "plt.tight_layout(); plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "57156908",
   "metadata": {},
   "source": [
    "## 8. Model comparison\n",
    "\n",
    "Four diverse learners share one held-out split, ranked by ROC-AUC.\n",
    "\n",
    "**Two honesty guards print with the table:**\n",
    "\n",
    "1. The models train on a *stratified subsample* of at most 120,000 rows. The full row count is printed above. So every score here is a subsample number, not a full-corpus claim.\n",
    "2. The **majority-class baseline accuracy** appears *inside* the ranking table. On imbalanced data, 0.99 accuracy can be worse than always guessing the majority class. Judge each model against that baseline, not against 0.5."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "3710f4c0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "materialized 1,986,745 rows | trained on 120,000 (stratified subsample) | held-out 496,687\n",
      "MAJORITY-CLASS BASELINE accuracy = 0.9622  (any model must beat THIS, not 0.5, to be interesting)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "best model: LightGBM\n"
     ]
    },
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>model</th>\n",
       "      <th>accuracy</th>\n",
       "      <th>roc_auc</th>\n",
       "      <th>train_s</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>LightGBM</td>\n",
       "      <td>0.996537</td>\n",
       "      <td>0.999502</td>\n",
       "      <td>1.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>XGBoost</td>\n",
       "      <td>0.996461</td>\n",
       "      <td>0.999482</td>\n",
       "      <td>0.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>RandomForest</td>\n",
       "      <td>0.996364</td>\n",
       "      <td>0.999338</td>\n",
       "      <td>0.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>LogisticRegression</td>\n",
       "      <td>0.990714</td>\n",
       "      <td>0.988004</td>\n",
       "      <td>0.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>MajorityBaseline</td>\n",
       "      <td>0.962200</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                model  accuracy   roc_auc  train_s\n",
       "0            LightGBM  0.996537  0.999502      1.1\n",
       "1             XGBoost  0.996461  0.999482      0.4\n",
       "2        RandomForest  0.996364  0.999338      0.4\n",
       "3  LogisticRegression  0.990714  0.988004      0.3\n",
       "4    MajorityBaseline  0.962200  0.500000      0.0"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# --- Model comparison: four learners on the same held-out split ---\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.metrics import accuracy_score, roc_auc_score\n",
    "import xgboost as xgb, lightgbm as lgb\n",
    "\n",
    "# Stratified split keeps the class ratio in both halves.\n",
    "Xtr, Xte, ytr, yte = train_test_split(X, y, test_size=0.25, random_state=RANDOM_STATE, stratify=y)\n",
    "from sklearn.pipeline import make_pipeline\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "N_MATERIALIZED = len(y)                                          # the full corpus we loaded (see printed count)\n",
    "# HONEST DISCLOSURE: we do NOT train on all N. We fit on a STRATIFIED subsample (<=120k) because\n",
    "# these learners saturate long before then on this data. Every headline below is a SUBSAMPLE\n",
    "# number, not a full-corpus number \u2014 saying otherwise would be the fabrication this course forbids.\n",
    "if len(Xtr) > 120_000:\n",
    "    Xtr, _, ytr, _ = train_test_split(Xtr, ytr, train_size=120_000, random_state=RANDOM_STATE,\n",
    "                                      stratify=ytr)               # genuinely stratified, not random\n",
    "MAJORITY_BASELINE = max(np.mean(yte), 1 - np.mean(yte))          # accuracy of 'always predict majority'\n",
    "print(f'materialized {N_MATERIALIZED:,} rows | trained on {len(Xtr):,} (stratified subsample) | '\n",
    "      f'held-out {len(yte):,}')\n",
    "print(f'MAJORITY-CLASS BASELINE accuracy = {MAJORITY_BASELINE:.4f}  '\n",
    "      f'(any model must beat THIS, not 0.5, to be interesting)')\n",
    "\n",
    "models = {                                                        # four standard, diverse learners\n",
    "    'LogisticRegression': make_pipeline(StandardScaler(), LogisticRegression(max_iter=300)),  # scaled!\n",
    "    'RandomForest': RandomForestClassifier(n_estimators=60, n_jobs=-1, random_state=RANDOM_STATE),\n",
    "    'XGBoost': xgb.XGBClassifier(n_estimators=80, max_depth=6, tree_method='hist', n_jobs=-1,\n",
    "                                 eval_metric='logloss', random_state=RANDOM_STATE),\n",
    "    'LightGBM': lgb.LGBMClassifier(n_estimators=80, n_jobs=-1, verbose=-1, random_state=RANDOM_STATE),\n",
    "}\n",
    "rows, fitted = [], {}\n",
    "for name, m in models.items():                                    # fit + score each model\n",
    "    t = time.perf_counter(); m.fit(Xtr, ytr); fitted[name] = m\n",
    "    p = m.predict_proba(Xte)[:, 1]                                # positive-class probability on held-out\n",
    "    rows.append({'model': name, 'accuracy': round(accuracy_score(yte, (p>0.5).astype(int)), 6),\n",
    "                 'roc_auc': round(roc_auc_score(yte, p), 6),      # 6 dp: a 1.000000 is a red flag, not a win\n",
    "                 'train_s': round(time.perf_counter()-t, 1)})\n",
    "rows.append({'model': 'MajorityBaseline', 'accuracy': round(MAJORITY_BASELINE, 4),\n",
    "             'roc_auc': 0.5, 'train_s': 0.0})            # show the baseline IN the ranking table\n",
    "comparison = pd.DataFrame(rows).sort_values('roc_auc', ascending=False).reset_index(drop=True)\n",
    "_ranked = comparison[comparison.model != 'MajorityBaseline']\n",
    "best_name = _ranked.iloc[0]['model']; best = fitted[best_name]  # winner by ROC-AUC (excl. baseline)\n",
    "print('best model:', best_name); comparison"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "aadcff21",
   "metadata": {},
   "source": [
    "## 9. Results\n",
    "\n",
    "Diagnostics for the winning model, including **per-group recall**.\n",
    "\n",
    "The grouping comes from whatever the loader put in `family`. It is *not* always an attack taxonomy. On the intrusion corpora it is the attack family. On the fraud and malware corpora it is a transaction type, a merchant category or a malware category. On binary corpora it collapses to the positive class.\n",
    "\n",
    "Read it accordingly. Where the groups are genuinely rare classes, they reveal whether detection is real. The dominant flood classes do not."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "54582381",
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<Figure size 1800x480 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1560x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "operational FALSE-POSITIVE RATE @0.5 = 0.0026  (1,231 benign flagged of 477,917)\n",
      "worst per-family recalls: {'Analysis': 0.668, 'Fuzzers': 0.948, 'DoS': 0.971, 'Exploits': 0.986, 'Generic': 0.994, 'Backdoor': 0.995}\n"
     ]
    }
   ],
   "source": [
    "# --- Results for the best model: confusion, ROC, PR, importances, per-family recall ---\n",
    "from sklearn.metrics import confusion_matrix, roc_curve, precision_recall_curve, recall_score\n",
    "pb = best.predict_proba(Xte)[:, 1]; pred = (pb > 0.5).astype(int)\n",
    "fig, ax = plt.subplots(1, 3, figsize=(15, 4))\n",
    "# (1) confusion matrix\n",
    "cm = confusion_matrix(yte, pred); ax[0].imshow(cm, cmap='Blues')\n",
    "ax[0].set_title(f'{best_name}: confusion'); ax[0].set_xticks([0,1]); ax[0].set_yticks([0,1])\n",
    "ax[0].set_xticklabels([NEG_WORD,POS_WORD]); ax[0].set_yticklabels([NEG_WORD,POS_WORD])\n",
    "for (i,j),v in np.ndenumerate(cm): ax[0].text(j,i,f'{v:,}',ha='center',va='center')\n",
    "# (2) ROC and PR curves\n",
    "fpr,tpr,_ = roc_curve(yte, pb); prec,rec,_ = precision_recall_curve(yte, pb)\n",
    "ax[1].plot(fpr,tpr,color='#264653'); ax[1].plot([0,1],[0,1],'--',c='grey')\n",
    "ax[1].set_title(f'ROC (AUC={roc_auc_score(yte,pb):.4f})'); ax[1].set_xlabel('FPR'); ax[1].set_ylabel('TPR')\n",
    "ax[2].plot(rec,prec,color='#e76f51'); ax[2].set_title('Precision-Recall'); ax[2].set_xlabel('recall'); ax[2].set_ylabel('precision')\n",
    "plt.tight_layout(); plt.show()\n",
    "\n",
    "# (3) feature importances + (4) per-attack-family recall\n",
    "fig, ax = plt.subplots(1, 2, figsize=(13, 5))\n",
    "imp, names = None, feat                                           # importances, robust to the scaled-LR pipeline\n",
    "if hasattr(best, 'feature_importances_'):                          # tree models\n",
    "    imp = best.feature_importances_; names = list(getattr(best, 'feature_names_in_', feat))[:len(imp)]\n",
    "elif hasattr(best, 'named_steps') and 'logisticregression' in getattr(best, 'named_steps', {}):\n",
    "    imp = np.abs(best.named_steps['logisticregression'].coef_[0]); names = feat  # LR pipeline\n",
    "elif hasattr(best, 'coef_'):\n",
    "    imp = np.abs(best.coef_[0]); names = feat\n",
    "if imp is not None:\n",
    "    pd.Series(imp, index=names[:len(imp)]).sort_values().tail(12).plot.barh(ax=ax[0], color='#264653')\n",
    "ax[0].set_title(f'{best_name}: top importances / |coef|')\n",
    "# Per-family recall, WORST-first so rare, hard classes are visible, not just the dominant floods.\n",
    "fam_te = df.loc[Xte.index, 'family']\n",
    "fr = {}\n",
    "for fam, cnt in fam_te[yte==1].value_counts().items():\n",
    "    if cnt < 5: continue                                          # need a few positives for a meaningful recall\n",
    "    mask = (fam_te==fam).to_numpy(); fr[fam] = recall_score(yte[mask], pred[mask], zero_division=0)\n",
    "srt = pd.Series(fr).sort_values()\n",
    "show = pd.concat([srt.head(9), srt.tail(3)]) if len(srt) > 12 else srt   # worst 9 + best 3\n",
    "show = show[~show.index.duplicated()]\n",
    "show.plot.barh(ax=ax[1], color=['#e76f51' if v < 0.5 else '#2a9d8f' for v in show]); ax[1].set_xlim(0,1)\n",
    "ax[1].set_title('Per-family recall (worst first; red < 0.5)')\n",
    "plt.tight_layout(); plt.show()\n",
    "# Operational numbers, not just figures: false-positive rate and the worst per-family recalls.\n",
    "tn, fp = int(cm[0,0]), int(cm[0,1])\n",
    "fpr_op = fp/(fp+tn) if (fp+tn) > 0 else float('nan')             # benign wrongly flagged @0.5\n",
    "print(f'operational FALSE-POSITIVE RATE @0.5 = {fpr_op:.4f}  ({fp:,} benign flagged of {fp+tn:,})')\n",
    "print('worst per-family recalls:', {k: round(v, 3) for k, v in srt.head(6).items()})"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "06e2f79f",
   "metadata": {},
   "source": [
    "## 10. Validity audit \u2014 is the score real?\n",
    "\n",
    "Three diagnostics. **(a)** How well can the *single best feature*, alone, separate the classes? A near-1.0 single-feature AUC means that feature is *near-sufficient* \u2014 a shortcut (which may be legitimate signal or an artifact), not the same as target leakage. **(b)** The exact-duplicate row rate. **(c)** The **train/test exact-row contamination** \u2014 the fraction of held-out rows that are duplicates of training rows, which is what actually inflates a held-out score. The trust grade is the *worse* of the single-feature and contamination concerns."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "1af8649b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "best single-feature AUC = 0.9932  (feature: MIN_TTL)\n",
      "   note: a near-1.0 single-feature AUC means this feature is *near-sufficient* (a shortcut),\n",
      "   which may be legitimate signal OR an artifact \u2014 it is NOT the same as target leakage.\n",
      "exact-duplicate row rate (whole corpus) = 0.002\n",
      "TRAIN/TEST exact-row contamination       = 0.001  (single-feat grade D, contam grade A)\n",
      "==> data trust grade: D   (worse of the two; F = shortcut and/or heavy contamination)\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 960x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# --- Validity audit: is the score real detection, or a data shortcut? ---\n",
    "from sklearn.metrics import roc_auc_score\n",
    "samp = X.sample(min(60_000, len(X)), random_state=1); ysamp = y[samp.index]\n",
    "aucs = {}\n",
    "for c in feat:                                                    # AUC of EACH feature alone\n",
    "    col = samp[c].to_numpy(float)\n",
    "    if col.std()==0: continue\n",
    "    a = roc_auc_score(ysamp, col); aucs[c] = max(a, 1-a)          # direction-agnostic\n",
    "best_auc = max(aucs.values()); best_col = max(aucs, key=aucs.get)\n",
    "dup_rate = 1 - X.drop_duplicates().shape[0]/len(X)               # exact-duplicate feature rows (whole set)\n",
    "# The statistic that actually inflates a held-out score is TRAIN/TEST CONTAMINATION: how many test\n",
    "# rows are exact duplicates of a training row. Measure it directly on the split used above.\n",
    "_trkeys = set(map(tuple, np.round(Xtr.to_numpy(), 6)))\n",
    "_te = np.round(Xte.to_numpy(), 6)[:50_000]\n",
    "contam = float(np.mean([tuple(r) in _trkeys for r in _te]))      # fraction of test rows seen in train\n",
    "# Trust grade reflects BOTH failure modes and takes the WORSE of the two: a near-perfect single\n",
    "# feature (shortcut) OR heavy train/test contamination each independently invalidate the headline.\n",
    "_ga = 'F' if best_auc>=0.999 else 'D' if best_auc>=0.99 else 'C' if best_auc>=0.95 else 'B' if best_auc>=0.85 else 'A'\n",
    "_gc = 'F' if contam>=0.5 else 'D' if contam>=0.3 else 'C' if contam>=0.15 else 'B' if contam>=0.05 else 'A'\n",
    "grade = max(_ga, _gc)                                            # 'max' letter = worse grade (A best, F worst)\n",
    "print(f'best single-feature AUC = {best_auc:.4f}  (feature: {best_col})')\n",
    "print(f'   note: a near-1.0 single-feature AUC means this feature is *near-sufficient* (a shortcut),\\n'\n",
    "      f'   which may be legitimate signal OR an artifact \u2014 it is NOT the same as target leakage.')\n",
    "print(f'exact-duplicate row rate (whole corpus) = {dup_rate:.3f}')\n",
    "print(f'TRAIN/TEST exact-row contamination       = {contam:.3f}  (single-feat grade {_ga}, contam grade {_gc})')\n",
    "print(f'==> data trust grade: {grade}   (worse of the two; F = shortcut and/or heavy contamination)')\n",
    "s = pd.Series(aucs).sort_values().tail(15)\n",
    "fig, ax = plt.subplots(figsize=(8,5))\n",
    "s.plot.barh(ax=ax, color=['#e76f51' if v>=0.99 else '#457b9d' for v in s]); ax.axvline(0.5,ls='--',c='grey')\n",
    "ax.set_xlim(0.5,1.0); ax.set_title('Single-feature ROC-AUC (red = near-perfect shortcut)'); ax.set_xlabel('AUC alone')\n",
    "plt.tight_layout(); plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4adf32a2",
   "metadata": {},
   "source": [
    "## 11. Ablation \u2014 does the headline survive removing the artifacts?\n",
    "\n",
    "Narrating a shortcut is not enough. We *retrain the winning model* after (1) de-duplicating the corpus (removing the train/test contamination) and (2) dropping the single strongest feature. We report the held-out AUC each time. **Read the result honestly, both ways:** if the AUC **collapses**, the headline was a contamination/shortcut artifact. If it **barely moves** \u2014 common on *simulated* corpora \u2014 that is **not vindication**. It means the classes are separable by *many* redundant features, because the attack and benign distributions barely overlap. That is its own generation artifact. The numbers below decide which story is true here, not the prose."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "e4cb2a41",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Ablation \u2014 how much of the headline survives once each artifact is removed:\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>setting</th>\n",
       "      <th>held_out_auc</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>headline (as-is)</td>\n",
       "      <td>0.999502</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>de-duplicated (0% rows removed)</td>\n",
       "      <td>0.999532</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>shortcut feature dropped (MIN_TTL)</td>\n",
       "      <td>0.999502</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                              setting  held_out_auc\n",
       "0                    headline (as-is)      0.999502\n",
       "1     de-duplicated (0% rows removed)      0.999532\n",
       "2  shortcut feature dropped (MIN_TTL)      0.999502"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# --- Ablation: SHOW the inflation empirically, don't just narrate it ---\n",
    "from sklearn.base import clone\n",
    "def _retrain_auc(Xa, ya):                                        # re-split, stratified-subsample, refit best family\n",
    "    xtr, xte, ytr2, yte2 = train_test_split(Xa, ya, test_size=0.25, random_state=RANDOM_STATE, stratify=ya)\n",
    "    if len(xtr) > 120_000:\n",
    "        xtr, _, ytr2, _ = train_test_split(xtr, ytr2, train_size=120_000, random_state=RANDOM_STATE, stratify=ytr2)\n",
    "    m = clone(best); m.fit(xtr, ytr2)\n",
    "    return roc_auc_score(yte2, m.predict_proba(xte)[:, 1])\n",
    "base_auc = roc_auc_score(yte, best.predict_proba(Xte)[:, 1])     # (0) the headline held-out AUC\n",
    "Xdd = X.drop_duplicates(); ydd = y[Xdd.index]                    # (1) de-duplicated corpus\n",
    "auc_dedup = _retrain_auc(Xdd, ydd)\n",
    "auc_noshort = _retrain_auc(X.drop(columns=[best_col]), y) if best_col in X.columns else base_auc  # (2) drop shortcut\n",
    "ablation = pd.DataFrame([\n",
    "    {'setting': 'headline (as-is)',              'held_out_auc': round(base_auc, 6)},\n",
    "    {'setting': f'de-duplicated ({1-len(Xdd)/len(X):.0%} rows removed)', 'held_out_auc': round(auc_dedup, 6)},\n",
    "    {'setting': f'shortcut feature dropped ({best_col})', 'held_out_auc': round(auc_noshort, 6)},\n",
    "])\n",
    "print('Ablation \u2014 how much of the headline survives once each artifact is removed:')\n",
    "ablation"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2dd53ce3",
   "metadata": {},
   "source": [
    "## 12. Reproducibility & robustness"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "dc141991",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "seed=0 | numpy 2.3.5 | sklearn 1.9.0 | xgboost 1.6.2 | lightgbm 4.7.0\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "LightGBM 3-fold CV ROC-AUC = 0.9993 +/- 0.0001  (mean +/- std across 3 stratified folds; a small std means a stable estimate on this split)\n"
     ]
    }
   ],
   "source": [
    "# --- Reproducibility & robustness ---\n",
    "import sklearn\n",
    "from sklearn.model_selection import StratifiedKFold, cross_val_score\n",
    "print(f'seed={RANDOM_STATE} | numpy {np.__version__} | sklearn {sklearn.__version__} | '\n",
    "      f'xgboost {xgb.__version__} | lightgbm {lgb.__version__}')\n",
    "# 3-fold cross-validated ROC-AUC of the winning model (fresh clone, bounded subsample) -> mean +/- std.\n",
    "from sklearn.base import clone\n",
    "cvX, cvy = Xtr.iloc[:40_000], ytr[:40_000]\n",
    "def _auc_scorer(est, Xv, yv):                                   # robust to xgboost's 2-col predict_proba\n",
    "    p = est.predict_proba(Xv)\n",
    "    p = p[:, 1] if getattr(p, 'ndim', 1) == 2 else p\n",
    "    return roc_auc_score(yv, p)\n",
    "try:\n",
    "    cv = cross_val_score(clone(best), cvX, cvy,\n",
    "                         cv=StratifiedKFold(3, shuffle=True, random_state=RANDOM_STATE),\n",
    "                         scoring=_auc_scorer, error_score='raise')\n",
    "    assert np.all(np.isfinite(cv)), 'non-finite CV folds'   # FAIL CLOSED: never narrate a NaN as evidence\n",
    "    print(f'{best_name} 3-fold CV ROC-AUC = {cv.mean():.4f} +/- {cv.std():.4f}  '\n",
    "          f'(mean +/- std across 3 stratified folds; a small std means a stable estimate on this split)')\n",
    "except Exception as e:\n",
    "    print(f'CV UNAVAILABLE ({type(e).__name__}: {str(e)[:60]}); rely on the single held-out AUC above \u2014 '\n",
    "          f'we do NOT report a CV number we could not compute')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2325450b",
   "metadata": {},
   "source": [
    "## 13. Scientific conclusion\n",
    "\n",
    "On the standardized NetFlow features every learner scores high, and the spread is narrow. Read the printed comparison table: all four clear the majority-class **accuracy** baseline of **0.9622**. Every score there comes from the 120,000-row stratified subsample. The three tree learners cluster at ROC-AUC **0.999338** to **0.999502**. The linear model trails at ROC-AUC **0.988004**, accuracy **0.990714** \u2014 still above that baseline. A model whose accuracy sat below the baseline would be the warning sign. None does here. The audit plus ablation printed below locate *why* the scores are so high. Read the single-feature AUC together with the drop-the-top-feature ablation. On this NF-v2 set the score **survives** dropping the strongest feature. The ablation AUC is unchanged at six decimal places. So the separability is **multi-feature** \u2014 a property of how the flows were generated \u2014 not a one-header-field shortcut. Because the schema was built for cross-dataset use, the honest next step is train-on-one / test-on-another. That is a test **we did not run here**, so we make no transfer claim. Notebook 36 runs that test on four NetFlow-standardized corpora and prints the transfer matrix. A single in-distribution score cannot answer it.\n",
    "\n",
    "**Validity ledger \u2014 read the headline against these printed numbers:** Majority-class baseline **accuracy**: **0.9622**. The accuracy column must clear that bar to mean anything. For ROC-AUC the trivial baseline is 0.5, not that figure. Winning learner: **LightGBM** (3-fold CV ROC-AUC **0.9993**). Strongest *single* feature: `MIN_TTL` at AUC **0.9932**. The ablation refutes a single-feature story. Dropping that feature does not move the AUC: **0.999502 \u2192 0.999502**. So the separability is **multi-feature**. That reflects how this corpus was generated, not one leaky column. De-duplication changed almost nothing. The duplicate rate is **0.0020**, under 0.5%, which the table rounds to 0%. The 0.000030 difference is re-split noise, not a de-duplication effect. Data-trust grade: **D**. It is the worse of two independent sub-checks. Single-feature AUC 0.9932 scores **D**. Train/test exact-row overlap 0.001 scores **A**. The single-feature check drives the grade, not the overlap check. Train/test overlap separately scores A, so overlap is not the issue here. On this A-best / F-worst scale, a D or F means the headline is optimistic. Treat it as a benchmark number, not a deployment estimate. Operational false-positive rate at threshold 0.5: **0.0026**. Worst per-group recalls, exactly as printed: {`Analysis`: 0.668, `Fuzzers`: 0.948, `DoS`: 0.971, `Exploits`: 0.986, `Generic`: 0.994, `Backdoor`: 0.995}. The weakest group sits at **0.668**, which is where detection is thinnest. **Disclosed limitation:** categorical columns are integer-encoded before the split. The encoder therefore sees the test set's category values. On an all-numeric corpus that step is a no-op. The mapping never consults the label, so no *label* information leaks. It is still transductive. A deployed system would need an unseen-category bucket. **How the audit numbers are computed:** overlap is measured on the first 50,000 held-out rows, so read it as a sampled estimate. Each ablation re-splits and refits, so tiny differences are re-split noise. The de-duplication variant keeps the first label when a feature vector appears twice. **Scope:** the split is random, not temporal or entity-grouped. Every number above therefore measures in-distribution separability only."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7bf9e828",
   "metadata": {},
   "source": [
    "## References\n",
    "\n",
    "1. Sarhan, M., Layeghy, S. & Portmann, M. (2022). Towards a Standard Feature Set for Network Intrusion Detection System Datasets. *Mobile Networks and Applications* (arXiv:2101.11315). **Defines the NF-*-v2 43-feature datasets used here.**\n",
    "2. Sarhan, M., Layeghy, S., Moustafa, N. & Portmann, M. (2021). NetFlow Datasets for Machine Learning-Based Network Intrusion Detection Systems. *Big Data Technologies and Applications (BDTA 2020)*, LNICST 371, Springer, 117-135 (the earlier 8-feature v1 datasets; conference 2020, proceedings 2021).\n",
    "3. Koroniotis, N. et al. (2019). Towards the development of a realistic botnet dataset (Bot-IoT). *Future Generation Computer Systems*.\n",
    "4. Moustafa, N. & Slay, J. (2015). UNSW-NB15: A Comprehensive Data Set for Network Intrusion Detection Systems (UNSW-NB15 Network Data Set). *2015 Military Communications and Information Systems Conference (MilCIS)*, Canberra, IEEE, pp. 1\u20136 (doi:10.1109/MilCIS.2015.7348942). **Origin of the UNSW-NB15 corpus re-featured into NetFlow v2 here.**\n",
    "5. Sommer, R. & Paxson, V. (2010). Outside the Closed World: On Using Machine Learning for Network Intrusion Detection. *IEEE S&P*."
   ]
  }
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