{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "cell-000",
   "metadata": {},
   "source": [
    "# Seeing Images, Explaining Decisions\n",
    "## CIFAR-10 image classification — Gareth Grant\n",
    "\n",
    "**Python · TensorFlow / Keras · Computer vision · Reproducible evaluation**\n",
    "\n",
    "This project compares simple baselines with convolutional neural networks, explains\n",
    "their errors, and connects the method to decisions in business, astronomy and government.\n",
    "Each numbered step has one main job, its own code cell, and a short explanation.\n",
    "Edit a code comment or insert a Markdown cell beneath any step to add your observations.\n",
    "\n",
    "The code trains **CIFAR-10 classifiers only**. The later domain applications are proposed\n",
    "adaptations requiring different labelled data and independent validation.\n",
    "The distributed executed copy identifies its budget in Step 4. A quick mode is also available.\n",
    "This project demonstrates an understandable workflow, not state-of-the-art performance."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cell-001",
   "metadata": {},
   "source": [
    "## How to use the notebook\n",
    "\n",
    "1. Install the accompanying `requirements.txt` in a Python 3.12 environment.\n",
    "2. Open this file in Jupyter or VS Code and select that environment.\n",
    "3. Read Step 4, restart the kernel, and run all cells in order.\n",
    "4. Keras downloads the official CIFAR-10 archive on the first run (about 163 MiB).\n",
    "5. Read each plot and its limitations before quoting a result.\n",
    "\n",
    "`RUN_MODE = \"full\"` uses all cleaned development rows and a 25-epoch ceiling per CNN.\n",
    "`RUN_MODE = \"quick\"` uses 8,000 training and 2,000 validation images with a 6-epoch ceiling.\n",
    "Both modes keep all 10,000 official test images. Training time depends on the hardware.\n",
    "Changing settings requires restarting and running from the beginning.\n",
    "\n",
    "Outputs are saved under `results/<mode>_seed<seed>/`. The model, class order, manifest,\n",
    "plots, metrics, row predictions and development split record are reusable artifacts."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cell-002",
   "metadata": {},
   "source": [
    "## Review of the supplied notebook\n",
    "\n",
    "| Finding | Why it mattered | Correction |\n",
    "|---|---|---|\n",
    "| Intel six-class data described; CIFAR-10 loaded | The interpretation described the wrong data | One consistent ten-class project |\n",
    "| 587 lines in one code cell | Difficult to annotate, learn from, or debug | Small cells for separate operations |\n",
    "| Saved `SyntaxError` in the main cell | The supplied code could not run | Valid Python and clean sequential execution |\n",
    "| Test data used in early stopping and tuning | Evaluation information influenced development | Separate train and validation data; frozen decisions before test scoring |\n",
    "| `improved_model` built but the existing pipeline fitted again | Scores were not clearly attached to that model | Named Keras models with their own predictions |\n",
    "| Reused tuner names with inconsistent ranges | Search definitions were ambiguous | Two explicit learning-rate trials |\n",
    "| Augmentation object not used by fitting | Its claimed benefit was not implemented | Training-only layers inside the model |\n",
    "| Balanced classes said to imply no bias | Equal counts cannot rule out other bias | Coverage and deployment limitations explained |\n",
    "| One-hot encoding described as essential | Sparse cross-entropy accepts integer labels | Keep integer labels consistently |\n",
    "| Fixed claims without successful saved results | Text could disagree with outputs | Interpretation generated from measured predictions |\n",
    "\n",
    "The supplied file contains an error, not a valid saved trained-model score.\n",
    "Any result below is generated by this replacement workflow."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cell-003",
   "metadata": {},
   "source": [
    "## Problem and evaluation plan\n",
    "\n",
    "**Question:** how much does a compact CNN improve on a simple classifier, and where\n",
    "do its errors suggest additional work is needed?\n",
    "\n",
    "CIFAR-10 contains ten mutually exclusive object labels and low-resolution RGB photographs.\n",
    "The dataset creators describe 60,000 images of 32 × 32 pixels, split into 50,000 development\n",
    "images and 10,000 official test images [1]. This makes a useful teaching benchmark; it\n",
    "does not represent commercial defects, galaxies or multispectral satellite imagery.\n",
    "\n",
    "This is **whole-image classification**. It does not locate an object with a box, label\n",
    "individual pixels, recognise arbitrary new classes, or infer a scientific cause.\n",
    "\n",
    "| Decision fixed before fitting | Rule |\n",
    "|---|---|\n",
    "| Development split | Stratified 80% train / 20% validation after exact-duplicate cleaning |\n",
    "| Duplicate policy | Remove conflicting development hash groups, repeated rows, and development copies of test images using hashes only |\n",
    "| Reference models | Majority class and regularised linear classifier on coarse RGB features |\n",
    "| CNN experiments | Basic CNN; regularised CNN at learning rates 0.001 and 0.0005 |\n",
    "| Checkpoint | Lowest validation loss; early stopping restores its weights |\n",
    "| CNN choice | Highest validation macro-F1; fewer parameters break a tie |\n",
    "| Review threshold | Validation-only accuracy lower-bound target of 90%, with at least 100 accepted cases |\n",
    "| Test comparison | Report every declared model; do not select a new CNN from its test score |\n",
    "| Uncertainty | Paired bootstrap of test-image hash groups, conditional on fitted models |\n",
    "\n",
    "Macro-F1 weights classes equally. Accuracy describes the overall fraction correct.\n",
    "Precision, recall, cross-entropy and the error images describe different failure modes."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cell-004",
   "metadata": {},
   "source": [
    "## 1. Setup and reproducibility\n",
    "\n",
    "Optional first-time installation in a notebook: `%pip install -r requirements.txt`.\n",
    "Restart the kernel afterwards. Record versions and seeds with the result. A fixed seed\n",
    "reduces variation but cannot guarantee identical results on different software or hardware."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cell-005",
   "metadata": {},
   "source": [
    "### 01. Import general utilities\n",
    "\n",
    "These imports support paths, timing, hashes, tables, figures and image loading."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "cell-006",
   "metadata": {},
   "outputs": [],
   "source": [
    "# No training or downloading happens in this cell.\n",
    "import os\n",
    "os.environ.setdefault(\"TF_CPP_MIN_LOG_LEVEL\", \"2\")\n",
    "import json, hashlib, platform, time\n",
    "from pathlib import Path\n",
    "from importlib.metadata import version, PackageNotFoundError\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "from PIL import Image, ImageOps\n",
    "from IPython.display import display, Markdown"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cell-007",
   "metadata": {},
   "source": [
    "### 02. Import splitting, baselines and metrics\n",
    "\n",
    "All model comparisons use the same metric definitions and class order."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "cell-008",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Integer class IDs are retained throughout the project.\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.dummy import DummyClassifier\n",
    "from sklearn.pipeline import make_pipeline\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.metrics import (\n",
    "    accuracy_score, f1_score, log_loss, classification_report,\n",
    "    confusion_matrix, ConfusionMatrixDisplay, roc_auc_score,\n",
    "    roc_curve, top_k_accuracy_score,\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cell-009",
   "metadata": {},
   "source": [
    "### 03. Import TensorFlow and Keras\n",
    "\n",
    "Use the public model API directly instead of assigning a wrapper’s internal attributes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "cell-010",
   "metadata": {},
   "outputs": [],
   "source": [
    "# The CNNs are trained from scratch, not with transfer learning.\n",
    "import tensorflow as tf\n",
    "keras = tf.keras\n",
    "layers = keras.layers"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cell-011",
   "metadata": {},
   "source": [
    "### 04. Choose the experiment budget\n",
    "\n",
    "This is the main editable settings cell. Keep the test set intact in both modes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "cell-012",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "mode                                     full\n",
       "seed                                       42\n",
       "batch_size                                128\n",
       "maximum_epochs_per_CNN                     25\n",
       "test_images                             10000\n",
       "output_folder             results/full_seed42\n",
       "dtype: object"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Use \"quick\" for a smaller first learning run.\n",
    "RUN_MODE = \"full\"\n",
    "SEED = 42\n",
    "BATCH_SIZE = 128\n",
    "CPU_THREADS = 8\n",
    "EPOCHS = 6 if RUN_MODE == \"quick\" else 25\n",
    "QUICK_TRAIN_SIZE, QUICK_VALIDATION_SIZE = 8_000, 2_000\n",
    "TARGET_ACCEPTED_ACCURACY = 0.90\n",
    "BOOTSTRAP_REPEATS = 500\n",
    "LOCAL_DATA = Path(os.environ.get(\"CIFAR10_NPZ\", \"data/cifar10.npz\"))\n",
    "OUTPUT_DIR = Path(\"results\") / f\"{RUN_MODE}_seed{SEED}\"\n",
    "assert RUN_MODE in {\"quick\", \"full\"}\n",
    "OUTPUT_DIR.mkdir(parents=True, exist_ok=True)\n",
    "display(pd.Series({\"mode\": RUN_MODE, \"seed\": SEED, \"batch_size\": BATCH_SIZE,\n",
    "    \"maximum_epochs_per_CNN\": EPOCHS, \"test_images\": 10_000,\n",
    "    \"output_folder\": str(OUTPUT_DIR)}))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cell-013",
   "metadata": {},
   "source": [
    "**Your comment:** Why start with a smaller experiment? What conclusions need a larger budget or repeated seeds?"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cell-014",
   "metadata": {},
   "source": [
    "### 05. Set seeds and CPU limits\n",
    "\n",
    "Run this before model operations. Deterministic operations help reproducibility [2]."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "cell-015",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Devices: ['CPU']\n"
     ]
    }
   ],
   "source": [
    "# Restart the kernel before resetting these after training.\n",
    "keras.utils.set_random_seed(SEED)\n",
    "tf.config.threading.set_intra_op_parallelism_threads(CPU_THREADS)\n",
    "tf.config.threading.set_inter_op_parallelism_threads(2)\n",
    "tf.config.experimental.enable_op_determinism()\n",
    "print(\"Devices:\", [device.device_type for device in tf.config.list_physical_devices()])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cell-016",
   "metadata": {},
   "source": [
    "### 06. Record software versions\n",
    "\n",
    "These values identify the environment actually used, rather than assumed versions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "cell-017",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "python            3.12.14\n",
       "tensorflow         2.20.0\n",
       "tensorflow-cpu     2.20.0\n",
       "keras              3.15.1\n",
       "numpy               2.3.5\n",
       "pandas              2.2.3\n",
       "scikit-learn        1.8.0\n",
       "matplotlib         3.10.8\n",
       "Pillow             12.3.0\n",
       "nbformat           5.10.4\n",
       "nbconvert          7.17.1\n",
       "Name: version, dtype: object"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Save these with the model manifest later.\n",
    "software_versions = {\"python\": platform.python_version(), \"tensorflow\": tf.__version__}\n",
    "for name in [\"tensorflow-cpu\", \"keras\", \"numpy\", \"pandas\", \"scikit-learn\",\n",
    "             \"matplotlib\", \"Pillow\", \"nbformat\", \"nbconvert\"]:\n",
    "    try:\n",
    "        software_versions[name] = version(name)\n",
    "    except PackageNotFoundError:\n",
    "        pass\n",
    "display(pd.Series(software_versions, name=\"version\"))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cell-018",
   "metadata": {},
   "source": [
    "### 07. Choose a consistent plot style\n",
    "\n",
    "Readable navy and scarlet figures fit the portfolio’s palette."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "cell-019",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Style choices do not affect the trained model.\n",
    "NAVY, SCARLET, SLATE = \"#16324F\", \"#FF2400\", \"#667582\"\n",
    "plt.rcParams.update({\"figure.dpi\": 110, \"savefig.dpi\": 170,\n",
    "    \"axes.spines.top\": False, \"axes.spines.right\": False,\n",
    "    \"axes.labelcolor\": NAVY, \"axes.titleweight\": \"bold\", \"font.size\": 10})"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cell-020",
   "metadata": {},
   "source": [
    "### 08. Define a figure-saving helper\n",
    "\n",
    "Display each chart and save a PNG suitable for a website."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "cell-021",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Each chart calls this helper once.\n",
    "def finish_figure(figure, filename):\n",
    "    figure.tight_layout()\n",
    "    figure.savefig(OUTPUT_DIR / filename, bbox_inches=\"tight\")\n",
    "    plt.show()\n",
    "    plt.close(figure)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cell-022",
   "metadata": {},
   "source": [
    "## 2. Load and audit the real images\n",
    "\n",
    "Keras supplies the official data [3]. An offline cache may instead contain `x_train`,\n",
    "`y_train`, `x_test`, `y_test` in `data/cifar10.npz` with the official shapes and labels.\n",
    "The creators publish archive MD5 `c58f30108f718f92721af3b95e74349a` [1].\n",
    "Keep raw images as uint8. Sparse categorical cross-entropy accepts integer labels;\n",
    "one-hot encoding is optional."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cell-023",
   "metadata": {},
   "source": [
    "### 09. Define the class-name mapping\n",
    "\n",
    "The name at each position must match the corresponding official class ID."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "cell-024",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>class_id</th>\n",
       "      <th>class_name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>airplane</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>automobile</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>bird</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>cat</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>deer</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>5</td>\n",
       "      <td>dog</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>6</td>\n",
       "      <td>frog</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>7</td>\n",
       "      <td>horse</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>8</td>\n",
       "      <td>ship</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>9</td>\n",
       "      <td>truck</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   class_id  class_name\n",
       "0         0    airplane\n",
       "1         1  automobile\n",
       "2         2        bird\n",
       "3         3         cat\n",
       "4         4        deer\n",
       "5         5         dog\n",
       "6         6        frog\n",
       "7         7       horse\n",
       "8         8        ship\n",
       "9         9       truck"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Preserve this order in the exported model's manifest.\n",
    "CLASS_NAMES = [\"airplane\", \"automobile\", \"bird\", \"cat\", \"deer\",\n",
    "               \"dog\", \"frog\", \"horse\", \"ship\", \"truck\"]\n",
    "CLASS_IDS = np.arange(10)\n",
    "display(pd.DataFrame({\"class_id\": CLASS_IDS, \"class_name\": CLASS_NAMES}))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cell-025",
   "metadata": {},
   "source": [
    "### 10. Load development and official test arrays\n",
    "\n",
    "Test labels are loaded for later scoring and are not used for fitting or selection."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "cell-026",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Source: local CIFAR-10 NumPy cache\n",
      "Development: (50000, 32, 32, 3) Test: (10000, 32, 32, 3)\n"
     ]
    }
   ],
   "source": [
    "# An environment variable can point to a local offline cache.\n",
    "if LOCAL_DATA.is_file():\n",
    "    with np.load(LOCAL_DATA, allow_pickle=False) as data:\n",
    "        x_development, y_development = data[\"x_train\"], data[\"y_train\"].reshape(-1)\n",
    "        x_test, y_test = data[\"x_test\"], data[\"y_test\"].reshape(-1)\n",
    "    data_route = \"local CIFAR-10 NumPy cache\"\n",
    "else:\n",
    "    (x_development, y_development), (x_test, y_test) = keras.datasets.cifar10.load_data()\n",
    "    y_development, y_test = y_development.reshape(-1), y_test.reshape(-1)\n",
    "    data_route = \"official CIFAR-10 via Keras\"\n",
    "print(\"Source:\", data_route)\n",
    "print(\"Development:\", x_development.shape, \"Test:\", x_test.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cell-027",
   "metadata": {},
   "source": [
    "### 11. Verify the data contract\n",
    "\n",
    "Fail early if dimensions, dtype or development label counts do not match. These checks alone cannot authenticate an arbitrary local file."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "cell-028",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Image and development label contracts verified.\n"
     ]
    }
   ],
   "source": [
    "# Input scaling stays inside the CNN.\n",
    "assert x_development.shape == (50_000, 32, 32, 3)\n",
    "assert x_test.shape == (10_000, 32, 32, 3)\n",
    "assert x_development.dtype == x_test.dtype == np.uint8\n",
    "assert y_development.shape == (50_000,) and y_test.shape == (10_000,)\n",
    "assert np.issubdtype(y_development.dtype, np.integer)\n",
    "assert np.issubdtype(y_test.dtype, np.integer)\n",
    "assert np.array_equal(np.unique(y_development), CLASS_IDS)\n",
    "assert np.array_equal(np.bincount(y_development), np.full(10, 5_000))\n",
    "print(\"Image and development label contracts verified.\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cell-029",
   "metadata": {},
   "source": [
    "### 12. Fingerprint exact images\n",
    "\n",
    "Image hashes identify byte-identical copies, without consulting test labels."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "cell-030",
   "metadata": {},
   "outputs": [],
   "source": [
    "# These hashes also define paired bootstrap groups later.\n",
    "def image_hashes(images):\n",
    "    return np.array([hashlib.sha256(image.tobytes()).hexdigest() for image in images])\n",
    "development_hashes = image_hashes(x_development)\n",
    "test_hashes = image_hashes(x_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cell-031",
   "metadata": {},
   "source": [
    "### 13. Check repeated images and conflicting development labels\n",
    "\n",
    "The same image must not appear in both training and validation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "cell-032",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Repeated development rows: 0\n",
      "Conflicting development hash groups: 0\n"
     ]
    }
   ],
   "source": [
    "# Investigate development labels only at this stage.\n",
    "development_audit = pd.DataFrame({\"hash\": development_hashes, \"label\": y_development})\n",
    "labels_per_hash = development_audit.groupby(\"hash\")[\"label\"].nunique()\n",
    "conflicting_hashes = set(labels_per_hash[labels_per_hash > 1].index)\n",
    "repeated_development = int(development_audit.duplicated(\"hash\").sum())\n",
    "print(\"Repeated development rows:\", repeated_development)\n",
    "print(\"Conflicting development hash groups:\", len(conflicting_hashes))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cell-033",
   "metadata": {},
   "source": [
    "### 14. Apply the declared duplicate policy\n",
    "\n",
    "Remove development copies of test images using identity only. Official test rows stay intact."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "cell-034",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "original_development_rows         50000\n",
       "development_rows_matching_test        0\n",
       "conflicting_label_rows                0\n",
       "repeated_development_rows             0\n",
       "retained_development_rows         50000\n",
       "test_rows_retained                10000\n",
       "dtype: int64"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Exclusion counts may overlap; do not sum them.\n",
    "overlaps_test = development_audit[\"hash\"].isin(set(test_hashes))\n",
    "conflicts = development_audit[\"hash\"].isin(conflicting_hashes)\n",
    "repeated = development_audit.duplicated(\"hash\", keep=\"first\")\n",
    "clean_ids = np.flatnonzero((~(overlaps_test | conflicts | repeated)).to_numpy())\n",
    "cleaning_summary = pd.Series({\"original_development_rows\": len(x_development),\n",
    "    \"development_rows_matching_test\": int(overlaps_test.sum()),\n",
    "    \"conflicting_label_rows\": int(conflicts.sum()),\n",
    "    \"repeated_development_rows\": repeated_development,\n",
    "    \"retained_development_rows\": len(clean_ids), \"test_rows_retained\": len(x_test)})\n",
    "display(cleaning_summary)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cell-035",
   "metadata": {},
   "source": [
    "**Interpretation:** exact hashes do not detect near duplicates or repeated views of one\n",
    "source object. Cleaning can also change comparability with standard benchmark protocols.\n",
    "**Your comment:** Which product IDs, astronomical object IDs or geographic identifiers\n",
    "would you need to prevent source-level leakage in a real application?"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cell-036",
   "metadata": {},
   "source": [
    "### 15. Create stratified train and validation subsets\n",
    "\n",
    "Split cleaned development data, preserving approximate class proportions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "cell-037",
   "metadata": {},
   "outputs": [],
   "source": [
    "# The official test arrays do not enter this operation.\n",
    "train_ids, validation_ids = train_test_split(clean_ids, test_size=0.20,\n",
    "    random_state=SEED, stratify=y_development[clean_ids])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cell-038",
   "metadata": {},
   "source": [
    "### 16. Apply the optional quick-run sample\n",
    "\n",
    "Sample within the already separate development subsets. Full mode retains every cleaned row."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "cell-039",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Quick sampling uses development labels only.\n",
    "if RUN_MODE == \"quick\":\n",
    "    train_ids, _ = train_test_split(train_ids, train_size=QUICK_TRAIN_SIZE,\n",
    "        random_state=SEED, stratify=y_development[train_ids])\n",
    "    validation_ids, _ = train_test_split(validation_ids,\n",
    "        train_size=QUICK_VALIDATION_SIZE, random_state=SEED,\n",
    "        stratify=y_development[validation_ids])\n",
    "x_train, y_train = x_development[train_ids], y_development[train_ids]\n",
    "x_validation, y_validation = x_development[validation_ids], y_development[validation_ids]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cell-040",
   "metadata": {},
   "source": [
    "### 17. Assert that image identities do not cross subsets\n",
    "\n",
    "Make the leakage rule executable, not just a statement in the report."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "cell-041",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>split</th>\n",
       "      <th>images</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>train</td>\n",
       "      <td>40000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>validation</td>\n",
       "      <td>10000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>official test</td>\n",
       "      <td>10000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           split  images\n",
       "0          train   40000\n",
       "1     validation   10000\n",
       "2  official test   10000"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# No selected development image can also be a test image.\n",
    "train_hash_set = set(development_hashes[train_ids])\n",
    "validation_hash_set = set(development_hashes[validation_ids])\n",
    "assert set(train_ids).isdisjoint(validation_ids)\n",
    "assert train_hash_set.isdisjoint(validation_hash_set)\n",
    "assert train_hash_set.isdisjoint(test_hashes)\n",
    "assert validation_hash_set.isdisjoint(test_hashes)\n",
    "display(pd.DataFrame({\"split\": [\"train\", \"validation\", \"official test\"],\n",
    "    \"images\": [len(x_train), len(x_validation), len(x_test)]}))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cell-042",
   "metadata": {},
   "source": [
    "### 18. Save the development split and cleaning record\n",
    "\n",
    "Original row IDs and hashes allow another person to reconstruct the selected examples."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "cell-043",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Saved split identities and cleaning counts.\n"
     ]
    }
   ],
   "source": [
    "# Only the chosen development rows are listed in this record.\n",
    "split_record = pd.concat([\n",
    "    pd.DataFrame({\"original_row\": train_ids, \"split\": \"train\"}),\n",
    "    pd.DataFrame({\"original_row\": validation_ids, \"split\": \"validation\"}),\n",
    "], ignore_index=True)\n",
    "split_record[\"image_sha256\"] = development_hashes[split_record[\"original_row\"]]\n",
    "split_record.to_csv(OUTPUT_DIR / \"development_split.csv\", index=False)\n",
    "cleaning_summary.to_csv(OUTPUT_DIR / \"data_cleaning.csv\", header=[\"value\"])\n",
    "print(\"Saved split identities and cleaning counts.\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cell-044",
   "metadata": {},
   "source": [
    "## 3. Explore training data\n",
    "\n",
    "EDA describes selected training data, not a test set used for development. Equal class\n",
    "counts do not rule out background, viewpoint, source or population bias. A few displayed\n",
    "examples cannot verify every label."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cell-045",
   "metadata": {},
   "source": [
    "### 19. Count training and validation classes\n",
    "\n",
    "Confirm support before fitting. Test support is examined only in the final evaluation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "cell-046",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>class</th>\n",
       "      <th>train</th>\n",
       "      <th>validation</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>airplane</td>\n",
       "      <td>4000</td>\n",
       "      <td>1000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>automobile</td>\n",
       "      <td>4000</td>\n",
       "      <td>1000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>bird</td>\n",
       "      <td>4000</td>\n",
       "      <td>1000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>cat</td>\n",
       "      <td>4000</td>\n",
       "      <td>1000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>deer</td>\n",
       "      <td>4000</td>\n",
       "      <td>1000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>dog</td>\n",
       "      <td>4000</td>\n",
       "      <td>1000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>frog</td>\n",
       "      <td>4000</td>\n",
       "      <td>1000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>horse</td>\n",
       "      <td>4000</td>\n",
       "      <td>1000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>ship</td>\n",
       "      <td>4000</td>\n",
       "      <td>1000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>truck</td>\n",
       "      <td>4000</td>\n",
       "      <td>1000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        class  train  validation\n",
       "0    airplane   4000        1000\n",
       "1  automobile   4000        1000\n",
       "2        bird   4000        1000\n",
       "3         cat   4000        1000\n",
       "4        deer   4000        1000\n",
       "5         dog   4000        1000\n",
       "6        frog   4000        1000\n",
       "7       horse   4000        1000\n",
       "8        ship   4000        1000\n",
       "9       truck   4000        1000"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Counts come from the actual subsets used in this run.\n",
    "class_counts = pd.DataFrame({\"class\": CLASS_NAMES,\n",
    "    \"train\": np.bincount(y_train, minlength=10),\n",
    "    \"validation\": np.bincount(y_validation, minlength=10)})\n",
    "display(class_counts)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cell-047",
   "metadata": {},
   "source": [
    "### 20. Plot training class support\n",
    "\n",
    "A count chart reveals whether sampling changed class balance."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "cell-048",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1100x440 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# One chart describes one property: class support.\n",
    "figure, axis = plt.subplots(figsize=(10, 4))\n",
    "axis.bar(CLASS_NAMES, class_counts[\"train\"], color=NAVY)\n",
    "axis.set(title=\"Training images by class\", ylabel=\"Images\")\n",
    "axis.tick_params(axis=\"x\", rotation=35)\n",
    "finish_figure(figure, \"01_class_distribution.png\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cell-049",
   "metadata": {},
   "source": [
    "**Your comment:** Which sources of bias could remain if every bar had the same height?"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cell-050",
   "metadata": {},
   "source": [
    "### 21. Select seeded examples per class\n",
    "\n",
    "Use random selections rather than describing the first rows as random."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "cell-051",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Two examples per class make a compact balanced grid.\n",
    "sample_rng = np.random.default_rng(SEED)\n",
    "sample_ids = [index for class_id in CLASS_IDS for index in\n",
    "    sample_rng.choice(np.flatnonzero(y_train == class_id), size=2, replace=False)]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cell-052",
   "metadata": {},
   "source": [
    "### 22. Display training examples\n",
    "\n",
    "Look at backgrounds, pose, occlusion and detail. Enlarging a 32-pixel image does not recover missing information."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "cell-053",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1100x880 with 20 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# These are training examples, not model predictions.\n",
    "figure, axes = plt.subplots(4, 5, figsize=(10, 8))\n",
    "for axis, index in zip(axes.flat, sample_ids):\n",
    "    axis.imshow(x_train[index], interpolation=\"nearest\")\n",
    "    axis.set_title(CLASS_NAMES[y_train[index]])\n",
    "    axis.axis(\"off\")\n",
    "figure.suptitle(\"Seeded training examples\", color=NAVY)\n",
    "finish_figure(figure, \"02_training_examples.png\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cell-054",
   "metadata": {},
   "source": [
    "**Your comment:** Which images would also be difficult for a person to label at this resolution?"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cell-055",
   "metadata": {},
   "source": [
    "### 23. Summarise a training pixel sample\n",
    "\n",
    "Avoid allocating a float copy of the entire benchmark just to describe intensities."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "cell-056",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>channel</th>\n",
       "      <th>mean</th>\n",
       "      <th>standard_deviation</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>red</td>\n",
       "      <td>0.491638</td>\n",
       "      <td>0.245968</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>green</td>\n",
       "      <td>0.483317</td>\n",
       "      <td>0.242473</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>blue</td>\n",
       "      <td>0.449165</td>\n",
       "      <td>0.261160</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  channel      mean  standard_deviation\n",
       "0     red  0.491638            0.245968\n",
       "1   green  0.483317            0.242473\n",
       "2    blue  0.449165            0.261160"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# These are descriptive statistics, not fitted preprocessing parameters.\n",
    "statistics_ids = sample_rng.choice(len(x_train), size=min(2_000, len(x_train)), replace=False)\n",
    "sampled_pixels = x_train[statistics_ids].astype(np.float32) / 255.0\n",
    "pixel_statistics = pd.DataFrame({\"channel\": [\"red\", \"green\", \"blue\"],\n",
    "    \"mean\": sampled_pixels.mean(axis=(0, 1, 2)),\n",
    "    \"standard_deviation\": sampled_pixels.std(axis=(0, 1, 2))})\n",
    "display(pixel_statistics)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cell-057",
   "metadata": {},
   "source": [
    "### 24. Plot sampled intensities\n",
    "\n",
    "A histogram describes intensity; it does not prove that labels are separable."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "cell-058",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 880x440 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# The sample is seeded and comes only from training.\n",
    "figure, axis = plt.subplots(figsize=(8, 4))\n",
    "for channel, colour in enumerate([\"#C7353F\", \"#247B73\", NAVY]):\n",
    "    axis.hist(sampled_pixels[..., channel].ravel()[::20], bins=40, alpha=0.4,\n",
    "        density=True, color=colour, label=pixel_statistics.loc[channel, \"channel\"])\n",
    "axis.set(title=\"Sampled training intensities\", xlabel=\"Pixel / 255\", ylabel=\"Density\")\n",
    "axis.legend()\n",
    "finish_figure(figure, \"03_pixel_intensities.png\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cell-059",
   "metadata": {},
   "source": [
    "## 4. Prepare batching and simple baselines\n",
    "\n",
    "CNNs receive raw RGB values in 0–255. Rescaling is inside the model so export and training\n",
    "use the same transformation. The linear model uses block-averaged 8 × 8 RGB features,\n",
    "with a scaler fitted on training data only. Different model inputs are declared clearly."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cell-060",
   "metadata": {},
   "source": [
    "### 25. Define TensorFlow batching\n",
    "\n",
    "Shuffle training only; validation prediction order must remain aligned with its labels."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "cell-061",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Keep the private CPU thread pool bounded.\n",
    "def make_dataset(images, labels, training=False):\n",
    "    dataset = tf.data.Dataset.from_tensor_slices((images, labels))\n",
    "    if training:\n",
    "        dataset = dataset.shuffle(len(images), seed=SEED, reshuffle_each_iteration=True)\n",
    "    options = tf.data.Options()\n",
    "    options.threading.private_threadpool_size = 2\n",
    "    options.threading.max_intra_op_parallelism = CPU_THREADS\n",
    "    options.experimental_deterministic = True\n",
    "    return dataset.batch(BATCH_SIZE).with_options(options).prefetch(1)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cell-062",
   "metadata": {},
   "source": [
    "### 26. Create development batches\n",
    "\n",
    "Test batches are created after the development decision is frozen."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "cell-063",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training batches: 313 Validation batches: 79\n"
     ]
    }
   ],
   "source": [
    "# Validation data is not shuffled or augmented here.\n",
    "train_dataset = make_dataset(x_train, y_train, training=True)\n",
    "validation_dataset = make_dataset(x_validation, y_validation)\n",
    "print(\"Training batches:\", len(train_dataset), \"Validation batches:\", len(validation_dataset))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cell-064",
   "metadata": {},
   "source": [
    "### 27. Define consistent probability-based metrics\n",
    "\n",
    "Check probability shape and class order before calculating quality metrics."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "cell-065",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Accuracy, macro-F1 and weighted F1 answer different questions.\n",
    "def metric_row(name, labels, probabilities):\n",
    "    probabilities = np.asarray(probabilities)\n",
    "    assert probabilities.shape == (len(labels), 10)\n",
    "    assert np.isfinite(probabilities).all() and np.all(probabilities >= 0)\n",
    "    assert np.allclose(probabilities.sum(axis=1), 1, atol=1e-5)\n",
    "    predicted = probabilities.argmax(axis=1)\n",
    "    return {\"model\": name, \"accuracy\": accuracy_score(labels, predicted),\n",
    "        \"macro_f1\": f1_score(labels, predicted, average=\"macro\", zero_division=0),\n",
    "        \"weighted_f1\": f1_score(labels, predicted, average=\"weighted\", zero_division=0),\n",
    "        \"log_loss\": log_loss(labels, probabilities, labels=CLASS_IDS)}"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cell-066",
   "metadata": {},
   "source": [
    "### 28. Initialise named experiment records\n",
    "\n",
    "Each score stays attached to its model, predictions and training history."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "cell-067",
   "metadata": {},
   "outputs": [],
   "source": [
    "# These records start empty on each clean run.\n",
    "validation_rows, validation_probabilities = [], {}\n",
    "cnn_models, cnn_histories, training_seconds = {}, {}, {}"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cell-068",
   "metadata": {},
   "source": [
    "### 29. Fit a majority-class reference\n",
    "\n",
    "Measure what is achieved by always guessing the most frequent training label."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "cell-069",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "model                majority\n",
       "accuracy                  0.1\n",
       "macro_f1             0.018182\n",
       "weighted_f1          0.018182\n",
       "log_loss            32.439288\n",
       "parameters                  0\n",
       "training_seconds          0.0\n",
       "dtype: object"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# The dummy classifier does not learn image content.\n",
    "majority_model = DummyClassifier(strategy=\"most_frequent\")\n",
    "majority_model.fit(np.zeros((len(y_train), 1)), y_train)\n",
    "majority_validation = majority_model.predict_proba(np.zeros((len(y_validation), 1)))\n",
    "assert np.array_equal(majority_model.classes_, CLASS_IDS)\n",
    "row = metric_row(\"majority\", y_validation, majority_validation)\n",
    "row.update(parameters=0, training_seconds=0.0)\n",
    "validation_rows.append(row)\n",
    "validation_probabilities[\"majority\"] = majority_validation\n",
    "display(pd.Series(row))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cell-070",
   "metadata": {},
   "source": [
    "**Interpretation:** on balanced ten-class data, accuracy is about 10%. Macro-F1 is much\n",
    "lower because nine classes are never correctly predicted. Very high log loss reflects\n",
    "the dummy’s zero-probability assignments to the other classes."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cell-071",
   "metadata": {},
   "source": [
    "### 30. Define coarse RGB features\n",
    "\n",
    "A linear reference tests whether coarse colour and position already predict useful information."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "cell-072",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Each 4 x 4 RGB block becomes one average colour.\n",
    "def coarse_features(images):\n",
    "    blocks = images.reshape(-1, 8, 4, 8, 4, 3)\n",
    "    return (blocks.mean(axis=(2, 4)) / 255.0).reshape(len(images), -1)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cell-073",
   "metadata": {},
   "source": [
    "### 31. Fit a regularised linear model\n",
    "\n",
    "Fit preprocessing with the classifier to keep validation statistics out of the scaler."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "cell-074",
   "metadata": {},
   "outputs": [],
   "source": [
    "# C=1 is fixed in advance; there is no hidden linear-model search.\n",
    "linear_model = make_pipeline(StandardScaler(),\n",
    "    LogisticRegression(C=1.0, solver=\"lbfgs\", max_iter=1_000, random_state=SEED))\n",
    "linear_start = time.perf_counter()\n",
    "linear_model.fit(coarse_features(x_train), y_train)\n",
    "linear_seconds = time.perf_counter() - linear_start"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cell-075",
   "metadata": {},
   "source": [
    "### 32. Record linear validation results\n",
    "\n",
    "These are validation scores, not final test scores."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "cell-076",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "model               linear_RGB_8x8\n",
       "accuracy                    0.4154\n",
       "macro_f1                  0.412006\n",
       "weighted_f1               0.412006\n",
       "log_loss                  1.719292\n",
       "parameters                    1930\n",
       "training_seconds          10.40988\n",
       "dtype: object"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Save the actual probabilities used in the comparison.\n",
    "linear_validation = linear_model.predict_proba(coarse_features(x_validation))\n",
    "assert np.array_equal(linear_model.classes_, CLASS_IDS)\n",
    "row = metric_row(\"linear_RGB_8x8\", y_validation, linear_validation)\n",
    "linear_estimator = linear_model.named_steps[\"logisticregression\"]\n",
    "row.update(parameters=linear_estimator.coef_.size + linear_estimator.intercept_.size,\n",
    "           training_seconds=linear_seconds)\n",
    "validation_rows.append(row)\n",
    "validation_probabilities[\"linear_RGB_8x8\"] = linear_validation\n",
    "display(pd.Series(row))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cell-077",
   "metadata": {},
   "source": [
    "## 5. Train the basic CNN\n",
    "\n",
    "Convolution learns filters shared over image positions. Pooling reduces spatial size.\n",
    "Dense layers combine learned features into ten scores, and softmax gives a distribution\n",
    "over the known labels. This compact CNN follows the general teaching approach in the\n",
    "TensorFlow tutorial [3]. It is trained from scratch."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cell-078",
   "metadata": {},
   "source": [
    "### 33. Build the basic architecture\n",
    "\n",
    "Keep input scaling inside the model. This reference CNN uses no augmentation or dropout."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "cell-079",
   "metadata": {},
   "outputs": [],
   "source": [
    "# One constructor defines one architecture.\n",
    "def build_basic_cnn():\n",
    "    return keras.Sequential([\n",
    "        keras.Input(shape=(32, 32, 3), name=\"raw_rgb_0_to_255\"),\n",
    "        layers.Rescaling(1.0 / 255.0),\n",
    "        layers.Conv2D(32, 3, activation=\"relu\"),\n",
    "        layers.MaxPooling2D(),\n",
    "        layers.Conv2D(64, 3, activation=\"relu\"),\n",
    "        layers.MaxPooling2D(),\n",
    "        layers.Conv2D(64, 3, activation=\"relu\"),\n",
    "        layers.Flatten(), layers.Dense(64, activation=\"relu\"),\n",
    "        layers.Dense(10, activation=\"softmax\"),\n",
    "    ], name=\"basic_cnn\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cell-080",
   "metadata": {},
   "source": [
    "### 34. Define the common compilation rule\n",
    "\n",
    "All CNNs use Adam and sparse categorical cross-entropy with integer labels."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "cell-081",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Only the declared learning rate changes across regularised trials.\n",
    "def compile_cnn(model, learning_rate):\n",
    "    model.compile(optimizer=keras.optimizers.Adam(learning_rate=learning_rate),\n",
    "        loss=keras.losses.SparseCategoricalCrossentropy(),\n",
    "        metrics=[keras.metrics.SparseCategoricalAccuracy(name=\"accuracy\")])\n",
    "    return model"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cell-082",
   "metadata": {},
   "source": [
    "### 35. Define a fresh early-stopping callback\n",
    "\n",
    "Monitor validation loss and restore its best weights [4]. Create a new callback for every fit."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "cell-083",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Never give test data to this callback.\n",
    "def early_stopping():\n",
    "    return keras.callbacks.EarlyStopping(monitor=\"val_loss\", patience=3,\n",
    "        min_delta=1e-4, restore_best_weights=True, verbose=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cell-084",
   "metadata": {},
   "source": [
    "### 36. Define one timed training operation\n",
    "\n",
    "Keep the budget and validation source the same across CNNs."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "cell-085",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Training is already shuffled by the tf.data pipeline.\n",
    "def train_cnn(model):\n",
    "    start = time.perf_counter()\n",
    "    history = model.fit(train_dataset, validation_data=validation_dataset,\n",
    "        epochs=EPOCHS, callbacks=[early_stopping()], shuffle=False, verbose=2)\n",
    "    return history.history, time.perf_counter() - start"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cell-086",
   "metadata": {},
   "source": [
    "### 37. Create and inspect the basic model\n",
    "\n",
    "Inspect shapes and parameter count before training."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "cell-087",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"basic_cnn\"\n",
      "┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n",
      "┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃\n",
      "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
      "│ rescaling (Rescaling)           │ (None, 32, 32, 3)      │             0 │\n",
      "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
      "│ conv2d (Conv2D)                 │ (None, 30, 30, 32)     │           896 │\n",
      "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
      "│ max_pooling2d (MaxPooling2D)    │ (None, 15, 15, 32)     │             0 │\n",
      "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
      "│ conv2d_1 (Conv2D)               │ (None, 13, 13, 64)     │        18,496 │\n",
      "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
      "│ max_pooling2d_1 (MaxPooling2D)  │ (None, 6, 6, 64)       │             0 │\n",
      "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
      "│ conv2d_2 (Conv2D)               │ (None, 4, 4, 64)       │        36,928 │\n",
      "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
      "│ flatten (Flatten)               │ (None, 1024)           │             0 │\n",
      "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
      "│ dense (Dense)                   │ (None, 64)             │        65,600 │\n",
      "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
      "│ dense_1 (Dense)                 │ (None, 10)             │           650 │\n",
      "└─────────────────────────────────┴────────────────────────┴───────────────┘\n",
      " Total params: 122,570 (478.79 KB)\n",
      " Trainable params: 122,570 (478.79 KB)\n",
      " Non-trainable params: 0 (0.00 B)\n"
     ]
    }
   ],
   "source": [
    "# Reset the seed before this model's initialisation.\n",
    "keras.utils.set_random_seed(SEED)\n",
    "basic_model = compile_cnn(build_basic_cnn(), learning_rate=1e-3)\n",
    "basic_model.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cell-088",
   "metadata": {},
   "source": [
    "### 38. Train the basic CNN\n",
    "\n",
    "Fit only on training images; validation supports checkpointing and model development."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "cell-089",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/25\n",
      "313/313 - 6s - 20ms/step - accuracy: 0.3938 - loss: 1.6537 - val_accuracy: 0.4931 - val_loss: 1.3889\n",
      "Epoch 2/25\n",
      "313/313 - 5s - 17ms/step - accuracy: 0.5281 - loss: 1.3157 - val_accuracy: 0.5429 - val_loss: 1.2856\n",
      "Epoch 3/25\n",
      "313/313 - 5s - 17ms/step - accuracy: 0.5839 - loss: 1.1737 - val_accuracy: 0.5986 - val_loss: 1.1343\n",
      "Epoch 4/25\n",
      "313/313 - 5s - 18ms/step - accuracy: 0.6275 - loss: 1.0636 - val_accuracy: 0.6304 - val_loss: 1.0606\n",
      "Epoch 5/25\n",
      "313/313 - 6s - 18ms/step - accuracy: 0.6526 - loss: 0.9940 - val_accuracy: 0.6266 - val_loss: 1.0723\n",
      "Epoch 6/25\n",
      "313/313 - 5s - 17ms/step - accuracy: 0.6777 - loss: 0.9269 - val_accuracy: 0.6382 - val_loss: 1.0570\n",
      "Epoch 7/25\n",
      "313/313 - 6s - 18ms/step - accuracy: 0.6928 - loss: 0.8820 - val_accuracy: 0.6705 - val_loss: 0.9696\n",
      "Epoch 8/25\n",
      "313/313 - 5s - 17ms/step - accuracy: 0.7081 - loss: 0.8402 - val_accuracy: 0.6778 - val_loss: 0.9235\n",
      "Epoch 9/25\n",
      "313/313 - 5s - 18ms/step - accuracy: 0.7240 - loss: 0.7920 - val_accuracy: 0.6876 - val_loss: 0.9025\n",
      "Epoch 10/25\n",
      "313/313 - 5s - 17ms/step - accuracy: 0.7343 - loss: 0.7625 - val_accuracy: 0.6801 - val_loss: 0.9440\n",
      "Epoch 11/25\n",
      "313/313 - 5s - 17ms/step - accuracy: 0.7504 - loss: 0.7207 - val_accuracy: 0.6872 - val_loss: 0.9225\n",
      "Epoch 12/25\n",
      "313/313 - 5s - 17ms/step - accuracy: 0.7576 - loss: 0.6980 - val_accuracy: 0.6854 - val_loss: 0.9211\n",
      "Epoch 12: early stopping\n",
      "Restoring model weights from the end of the best epoch: 9.\n"
     ]
    }
   ],
   "source": [
    "# This is the first CNN fit in the experiment.\n",
    "basic_history, basic_seconds = train_cnn(basic_model)\n",
    "cnn_models[\"basic_CNN\"] = basic_model\n",
    "cnn_histories[\"basic_CNN\"] = basic_history\n",
    "training_seconds[\"basic_CNN\"] = basic_seconds"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cell-090",
   "metadata": {},
   "source": [
    "### 39. Record basic-CNN validation results\n",
    "\n",
    "The trained model object directly generates these probabilities."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "cell-091",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "model               basic_CNN\n",
       "accuracy               0.6876\n",
       "macro_f1             0.685669\n",
       "weighted_f1          0.685669\n",
       "log_loss             0.902453\n",
       "parameters             122570\n",
       "training_seconds    65.907264\n",
       "dtype: object"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Validation batches preserve the original label order.\n",
    "basic_validation = basic_model.predict(validation_dataset, verbose=0)\n",
    "row = metric_row(\"basic_CNN\", y_validation, basic_validation)\n",
    "row.update(parameters=basic_model.count_params(), training_seconds=basic_seconds)\n",
    "validation_rows.append(row)\n",
    "validation_probabilities[\"basic_CNN\"] = basic_validation\n",
    "display(pd.Series(row))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cell-092",
   "metadata": {},
   "source": [
    "### 40. Plot basic-CNN accuracy\n",
    "\n",
    "Look for validation improvement and its distance from the training trend."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "cell-093",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 880x440 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Epoch numbering starts at 1.\n",
    "figure, axis = plt.subplots(figsize=(8, 4))\n",
    "epochs_completed = np.arange(1, len(basic_history[\"accuracy\"]) + 1)\n",
    "axis.plot(epochs_completed, basic_history[\"accuracy\"], color=NAVY, label=\"train\")\n",
    "axis.plot(epochs_completed, basic_history[\"val_accuracy\"], color=SCARLET, label=\"validation\")\n",
    "axis.set(title=\"Basic CNN: accuracy\", xlabel=\"Epoch\", ylabel=\"Accuracy\", ylim=(0, 1))\n",
    "axis.legend()\n",
    "finish_figure(figure, \"04_basic_accuracy.png\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cell-094",
   "metadata": {},
   "source": [
    "### 41. Plot basic-CNN loss\n",
    "\n",
    "Rising validation loss alongside falling training loss suggests investigating overfitting."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "cell-095",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 880x440 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# The restored weights need not correspond to the last plotted epoch.\n",
    "figure, axis = plt.subplots(figsize=(8, 4))\n",
    "axis.plot(epochs_completed, basic_history[\"loss\"], color=NAVY, label=\"train\")\n",
    "axis.plot(epochs_completed, basic_history[\"val_loss\"], color=SCARLET, label=\"validation\")\n",
    "axis.set(title=\"Basic CNN: cross-entropy\", xlabel=\"Epoch\", ylabel=\"Loss\")\n",
    "axis.legend()\n",
    "finish_figure(figure, \"05_basic_loss.png\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cell-096",
   "metadata": {},
   "source": [
    "**Your comment:** Are the curves still improving? A short budget may leave a model undertrained; a gap alone does not establish overfitting."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cell-097",
   "metadata": {},
   "source": [
    "## 6. Compare regularisation and learning rates\n",
    "\n",
    "Horizontal flips and small translations can preserve these object labels; vertical flips\n",
    "can create implausible upright scenes. Keras augmentation layers are active during\n",
    "training, not inference [5]. Augmentation does not create new independent labelled observations.\n",
    "\n",
    "The regularised architecture also changes normalisation, pooling, dropout and weight\n",
    "penalties. This combined comparison cannot isolate which individual change caused a difference.\n",
    "Batch normalisation uses batch statistics in training and moving statistics at inference [11].\n",
    "`momentum=0.90` lets moving values adapt faster than the default 0.99; short validation\n",
    "runs can expose a training/inference mismatch. The setting is fixed for both trials."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cell-098",
   "metadata": {},
   "source": [
    "### 42. Define training-only augmentation\n",
    "\n",
    "Put augmentation inside the model so it cannot be accidentally omitted during fitting."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "cell-099",
   "metadata": {},
   "outputs": [],
   "source": [
    "# These transformations still need domain-specific judgement.\n",
    "def make_augmentation():\n",
    "    return keras.Sequential([\n",
    "        layers.RandomFlip(\"horizontal\", seed=SEED),\n",
    "        layers.RandomTranslation(0.10, 0.10, fill_mode=\"reflect\", seed=SEED + 1),\n",
    "    ], name=\"training_augmentation\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cell-100",
   "metadata": {},
   "source": [
    "### 43. Preview augmented training images\n",
    "\n",
    "Check that transformations preserve the intended label before using them."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "cell-101",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 880x440 with 8 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# A separate object previews raw 0-255 pixels.\n",
    "preview_images = make_augmentation()(x_train[sample_ids[:8]], training=True).numpy()\n",
    "figure, axes = plt.subplots(2, 4, figsize=(8, 4))\n",
    "for axis, image, index in zip(axes.flat, preview_images, sample_ids[:8]):\n",
    "    axis.imshow(np.clip(image, 0, 255).astype(np.uint8))\n",
    "    axis.set_title(CLASS_NAMES[y_train[index]])\n",
    "    axis.axis(\"off\")\n",
    "figure.suptitle(\"Training-only flips and translations\", color=NAVY)\n",
    "finish_figure(figure, \"06_augmentation_preview.png\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cell-102",
   "metadata": {},
   "source": [
    "**Your comment:** Would these transformations preserve traffic-sign text, detector defects or calibrated astronomical measurements?"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cell-103",
   "metadata": {},
   "source": [
    "### 44. Build the regularised CNN\n",
    "\n",
    "Global average pooling reduces the dense input size; dropout and L2 penalties aim to reduce memorisation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "cell-104",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Both learning-rate trials use this exact constructor.\n",
    "def build_regularised_cnn():\n",
    "    model = keras.Sequential([\n",
    "        keras.Input(shape=(32, 32, 3), name=\"raw_rgb_0_to_255\"),\n",
    "        layers.Rescaling(1.0 / 255.0), make_augmentation(),\n",
    "    ], name=\"regularised_cnn\")\n",
    "    for filters in (32, 64, 128):\n",
    "        model.add(layers.Conv2D(filters, 3, padding=\"same\", use_bias=False,\n",
    "            kernel_regularizer=keras.regularizers.l2(1e-4)))\n",
    "        model.add(layers.BatchNormalization(momentum=0.90))\n",
    "        model.add(layers.Activation(\"relu\"))\n",
    "        model.add(layers.MaxPooling2D())\n",
    "    model.add(layers.GlobalAveragePooling2D())\n",
    "    model.add(layers.Dense(128, activation=\"relu\"))\n",
    "    model.add(layers.Dropout(0.30, seed=SEED + 2))\n",
    "    model.add(layers.Dense(10, activation=\"softmax\"))\n",
    "    return model"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cell-105",
   "metadata": {},
   "source": [
    "### 45. Declare the learning-rate trials\n",
    "\n",
    "The search is small and explicit rather than an expensive ambiguous tuner."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "cell-106",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>model</th>\n",
       "      <th>learning_rate</th>\n",
       "      <th>maximum_epochs</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>regularised_CNN_lr0.001</td>\n",
       "      <td>0.0010</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>regularised_CNN_lr0.0005</td>\n",
       "      <td>0.0005</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                      model  learning_rate  maximum_epochs\n",
       "0   regularised_CNN_lr0.001         0.0010              25\n",
       "1  regularised_CNN_lr0.0005         0.0005              25"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# No trial settings are chosen from the test results.\n",
    "trial_plan = pd.DataFrame([\n",
    "    {\"model\": \"regularised_CNN_lr0.001\", \"learning_rate\": 1e-3},\n",
    "    {\"model\": \"regularised_CNN_lr0.0005\", \"learning_rate\": 5e-4},\n",
    "])\n",
    "display(trial_plan.assign(maximum_epochs=EPOCHS))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cell-107",
   "metadata": {},
   "source": [
    "### 46. Train and record the two trials\n",
    "\n",
    "Keep each fitted model, history and validation probabilities under its own name."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "cell-108",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/25\n",
      "313/313 - 16s - 50ms/step - accuracy: 0.4110 - loss: 1.6144 - val_accuracy: 0.4181 - val_loss: 1.6146\n",
      "Epoch 2/25\n",
      "313/313 - 15s - 47ms/step - accuracy: 0.5253 - loss: 1.3267 - val_accuracy: 0.4287 - val_loss: 1.6679\n",
      "Epoch 3/25\n",
      "313/313 - 14s - 45ms/step - accuracy: 0.5734 - loss: 1.2137 - val_accuracy: 0.5234 - val_loss: 1.3305\n",
      "Epoch 4/25\n",
      "313/313 - 13s - 43ms/step - accuracy: 0.5949 - loss: 1.1554 - val_accuracy: 0.4877 - val_loss: 1.5364\n",
      "Epoch 5/25\n",
      "313/313 - 14s - 44ms/step - accuracy: 0.6201 - loss: 1.0901 - val_accuracy: 0.5284 - val_loss: 1.4521\n",
      "Epoch 6/25\n",
      "313/313 - 14s - 46ms/step - accuracy: 0.6334 - loss: 1.0494 - val_accuracy: 0.5354 - val_loss: 1.4042\n",
      "Epoch 6: early stopping\n",
      "Restoring model weights from the end of the best epoch: 3.\n",
      "regularised_CNN_lr0.001 validation macro-F1: 0.5106\n",
      "Epoch 1/25\n",
      "313/313 - 15s - 47ms/step - accuracy: 0.3977 - loss: 1.6569 - val_accuracy: 0.5225 - val_loss: 1.3432\n",
      "Epoch 2/25\n",
      "313/313 - 13s - 43ms/step - accuracy: 0.5122 - loss: 1.3650 - val_accuracy: 0.5201 - val_loss: 1.3516\n",
      "Epoch 3/25\n",
      "313/313 - 15s - 47ms/step - accuracy: 0.5539 - loss: 1.2566 - val_accuracy: 0.5148 - val_loss: 1.5107\n",
      "Epoch 4/25\n",
      "313/313 - 14s - 46ms/step - accuracy: 0.5813 - loss: 1.1874 - val_accuracy: 0.4337 - val_loss: 1.7202\n",
      "Epoch 4: early stopping\n",
      "Restoring model weights from the end of the best epoch: 1.\n",
      "regularised_CNN_lr0.0005 validation macro-F1: 0.5102\n"
     ]
    }
   ],
   "source": [
    "# Identical seeds support a controlled learning-rate comparison.\n",
    "for trial in trial_plan.to_dict(\"records\"):\n",
    "    name = trial[\"model\"]\n",
    "    keras.utils.set_random_seed(SEED)\n",
    "    model = compile_cnn(build_regularised_cnn(), trial[\"learning_rate\"])\n",
    "    history, seconds = train_cnn(model)\n",
    "    probabilities = model.predict(validation_dataset, verbose=0)\n",
    "    row = metric_row(name, y_validation, probabilities)\n",
    "    row.update(parameters=model.count_params(), training_seconds=seconds)\n",
    "    validation_rows.append(row)\n",
    "    validation_probabilities[name] = probabilities\n",
    "    cnn_models[name], cnn_histories[name] = model, history\n",
    "    training_seconds[name] = seconds\n",
    "    print(name, \"validation macro-F1:\", round(row[\"macro_f1\"], 4))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cell-109",
   "metadata": {},
   "source": [
    "## 7. Freeze development decisions\n",
    "\n",
    "Select the CNN using validation macro-F1. Baselines remain informative references; if\n",
    "the linear model does better, report it. Do not assume a CNN is necessary. Do not widen\n",
    "the search after inspecting test scores while continuing to claim a fresh test estimate."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cell-110",
   "metadata": {},
   "source": [
    "### 47. Compare validation quality, size and training cost\n",
    "\n",
    "Show more than accuracy. Timing is a local measurement that includes first-fit overhead."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "cell-111",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>model</th>\n",
       "      <th>accuracy</th>\n",
       "      <th>macro_f1</th>\n",
       "      <th>weighted_f1</th>\n",
       "      <th>log_loss</th>\n",
       "      <th>parameters</th>\n",
       "      <th>training_seconds</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>basic_CNN</td>\n",
       "      <td>0.6876</td>\n",
       "      <td>0.6857</td>\n",
       "      <td>0.6857</td>\n",
       "      <td>0.9025</td>\n",
       "      <td>122570</td>\n",
       "      <td>65.9073</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>regularised_CNN_lr0.001</td>\n",
       "      <td>0.5234</td>\n",
       "      <td>0.5106</td>\n",
       "      <td>0.5106</td>\n",
       "      <td>1.3111</td>\n",
       "      <td>111722</td>\n",
       "      <td>86.3608</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>regularised_CNN_lr0.0005</td>\n",
       "      <td>0.5225</td>\n",
       "      <td>0.5102</td>\n",
       "      <td>0.5102</td>\n",
       "      <td>1.3292</td>\n",
       "      <td>111722</td>\n",
       "      <td>57.4835</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>linear_RGB_8x8</td>\n",
       "      <td>0.4154</td>\n",
       "      <td>0.4120</td>\n",
       "      <td>0.4120</td>\n",
       "      <td>1.7193</td>\n",
       "      <td>1930</td>\n",
       "      <td>10.4099</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>majority</td>\n",
       "      <td>0.1000</td>\n",
       "      <td>0.0182</td>\n",
       "      <td>0.0182</td>\n",
       "      <td>32.4393</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                      model  accuracy  ...  parameters  training_seconds\n",
       "0                 basic_CNN    0.6876  ...      122570           65.9073\n",
       "1   regularised_CNN_lr0.001    0.5234  ...      111722           86.3608\n",
       "2  regularised_CNN_lr0.0005    0.5225  ...      111722           57.4835\n",
       "3            linear_RGB_8x8    0.4154  ...        1930           10.4099\n",
       "4                  majority    0.1000  ...           0            0.0000\n",
       "\n",
       "[5 rows x 7 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Sort by the declared selection metric and tie-breaker.\n",
    "validation_table = pd.DataFrame(validation_rows).sort_values(\n",
    "    [\"macro_f1\", \"parameters\"], ascending=[False, True]).reset_index(drop=True)\n",
    "validation_table.to_csv(OUTPUT_DIR / \"validation_metrics.csv\", index=False)\n",
    "display(validation_table.round(4))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cell-112",
   "metadata": {},
   "source": [
    "### 48. Select the CNN before test prediction\n",
    "\n",
    "This selects the best CNN in this declared experiment, not the best possible model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "cell-113",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Validation-selected CNN: basic_CNN\n",
      "Best overall validation reference: basic_CNN\n"
     ]
    }
   ],
   "source": [
    "# The name remains fixed in the test evaluation.\n",
    "cnn_ranking = validation_table[validation_table[\"model\"].isin(cnn_models)]\n",
    "champion_name = cnn_ranking.iloc[0][\"model\"]\n",
    "champion_model = cnn_models[champion_name]\n",
    "champion_validation = validation_probabilities[champion_name]\n",
    "champion_history = cnn_histories[champion_name]\n",
    "print(\"Validation-selected CNN:\", champion_name)\n",
    "print(\"Best overall validation reference:\", validation_table.iloc[0][\"model\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cell-114",
   "metadata": {},
   "source": [
    "### 49. Plot the selected model’s own history\n",
    "\n",
    "Training-only augmentation and dropout can make training accuracy lower than evaluation accuracy."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "cell-115",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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FXrrlssFgoFOnTubjjz/+mG+//RZvb2/+/PNPpk2bRqdOnciePbs5ydCyZUvSp08PwJMnT6hZsyZTp05l06ZN/P777zRu3Jjt27eb28yYMaP5/Y4dO1i7di07d+7k/Pnz0e7lq6++YtiwYaxfv57169czY8YMevXqRa5cucydn1KlSkXpGHXq1ImFCxfyxx9/mBffjIvy5cvTq1cv8/GaNWuoWLEiv/zyC9u2bWPNmjWMGDGCggULRvllHpsBAwaQIUOGOMcBpo5H+/btX1ourlstv/XWW1GOBw8ezKZNmxg0aJB50dX/6tatm/n9mTNnqFatGvPmzcPb25uffvqJ0qVLx1jWx8eH+vXrs2TJEjZs2MCkSZPw9PQ0X39+2sLly5fp0KEDU6dOpXr16gQFBb30XmKSNWtW82gogNGjR7Np0ya++eYbxowZE2OdTp06YWNjA5gSX5UrV2bWrFls2rSJWbNmUa1aNa5cuRKlzvPTBbdu3Wp+r2mCIiIpk/po6qM9lVB9tBd5/u9//vz5DBkyhI0bNzJkyJAo9/u03M2bN8mTJw/9+vVj7ty5bNy4kU2bNpkX+Ad4/PhxnMuKJCsJs3SWiDy/IGNsLxcXF+Px48ej1Bs8ePBL6z2/8GVsC3A+fvzYWLNmzZe29fwWxOvWrTM6OTnFWvb5RS43bdoUY5nOnTsbjUbTwtdt2rR56ec/3+aWLVvMi2w+/8qaNasxffr0cVr402g0LVzev39/83bJsb2++uorc53/Lvz5vO++++6F8ce08OdTV69eNTo4OESpGx9bLT+/iOfzr6cLjsKz7a+fGjFixAv/TJ6KjIw0du7cOdZyz7cbERFhLFKkSLQy9vb2UbZljm1x9v/G+FRsPw81atSIMWaj0WicNWtWjIu4x/Rv/qlatWpFKVOhQoVX+vMXEZHkR3009dGel1B9tNgWZ4+IiDC2aNHihffdsmVLY0REhNFoNEZbdD6m15QpU+JcViQ50YgrkQTSrVs3Zs6cSevWrSlevDhZs2bFxsaGNGnSUKRIEfr06cPRo0cpWrRolHpjxoxh69atNGvWjBw5cmBra0v69OkpVKgQLVq04Pfff+eDDz546ec7ODjg7e3N7NmzqVGjBhkzZsTGxobMmTNTokQJevTowbp16/Dw8DDX8fLy4tixY/Tt25fChQuTJk0aHBwcyJ07Nx9++CF58uQxl61VqxZTpkyhQIEC5hEuz7OysuL333/njz/+oH79+ub7z5AhA0WLFqV9+/YsW7aMihUrmuvUqFGDLVu2ULlyZRwdHXFxcaF58+bs3r0bFxeXOP8dWFtbM2HCBI4ePUrv3r0pXrw4zs7O2NnZ4e7uTpUqVRg/fjzdu3d/pfb69OmDu7t7nOMA05bEz3+7GF9mzpzJ559/Tq5cuXBwcKBkyZIsXrzYPP0tJl999RXbt2+nVatW5MyZEzs7O9KmTUuRIkX4+OOPzeUMBgO//PILf/75J40aNcLNzc3877FUqVJ07NjRXNbKyorVq1dTv3590qZNi5OTE7Vr12bHjh1R1guJq5EjR/LNN9+QL18+7O3tKVSoEFOnTmXYsGGx1unUqROHDh2ic+fO5M2bFwcHB9KkSUP+/Pnp0KFDjIvI9unTJ1obIiKSMqmPpj7a8xKqjxYbKysrFi9ezPz586lVqxYZMmTA2tqaDBkyULNmTebNm8eiRYvMUz5dXFwYPXo09erVI3fu3KRNmxZra2syZ85MrVq1WLRoEX379o1zWZHkxGA0vsGqtiIiIilAWFgYzs7OhISEkCZNGvz9/XF2drZ0WCIiIiIiqV70FLyIiEgq8fjxYx4/fsyMGTMICQkBTLszKmklIiIiIpI0aMSViIikWtWqVeOvv/4yHzs7O3Ps2DFy5sxpwahEREREROQprXElIiKpXtq0aalevTqbN29W0kpEREREJAnRiCsREREREREREUmSNOJKRERERERERESSJCWuREREREREREQkSVLiSkREREREREREkiQlruIoPDwcPz8/wsPDLR2KiIiISLKg/pOIiIi8LiWu4ujGjRt4eHhw48YNS4ciIiIikiyo/yQiIiKvS4krERERERERERFJkpS4EhERERERERGRJEmJKxERERERERERSZKUuBIRERERERERkSRJiSsREREREREREUmSlLgSEREREREREZEkycbSAaQ0RqORO3fuEBISQkREhKXDkURmbW2Ng4MDmTJlwmAwWDocERERERGRRKfn4tQrIZ6JlbiKR0ajkWvXrvHgwQPs7Oywtra2dEiSyJ48ecLDhw8JDQ0lR44cSl6JiIiIiEiqoufi1C0hnomVuIpHd+7c4cGDB2TJkoWMGTNaOhyxkLt373Lr1i3u3LlD5syZLR2OiIiIiIhIotFzscT3M7HWuIpHISEh2NnZ6YczlcuYMSN2dnaEhIRYOhQREREREZFEpediie9nYiWu4lFERISGQQpgmterudwiIiIiIpLa6LlYIH6fiZW4EhERERERERGRJEmJKxERERERERERSZKUuJJYrVy5kmnTpsVrm7lz56Z3797x2qaIiMjzIiMj8fO/zY59//Db8o18PWUuHT//hgWrNls6NIlnDx89Zuqvy6nSvA+37t63dDgiIpLC6Jk4adCughKrlStXcuDAAXr27Blvba5YsQJXV9d4a09ERFKn8PAI/G7c5rKvP5d8/bnkd4NLvv5c9vXnyrWbhD4Ji1YnvbMTHzWqZYFoJaEYjUYmzFxC8OMQFv+5lT4dPrB0SCIikoLomThpSDKJq9OnT9OnTx927dpFunTpaNeuHaNHj8bOzi7WOj4+PlSvXj3GawULFuT06dMvLNeyZUsWLVoUPzeQShmNRp48eYK9vf0rlS9VqlQCRyQiIilFWFg4vv63TIkpX38u+d7gsq8/F339uXrtJuGvsOBnRhdncnu4kccjG++UKpoIUUtiSpc2DY1rV2LBqs3MX7mJXu0aY2WlCQUiIpJ49Eyc8JJE4iogIIAaNWqQP39+li9fzrVr1+jfvz/BwcFMnTo11nqlS5dm9+7dUc4FBQXh5eWFl5dXtPJz5syhUKFC5uNMmTLF302kMB06dODXX38FwGAwANC+fXsADhw4wHfffcegQYM4deoUCxYswMvLiy+//JJNmzbh6+tLlixZqFu3Lt9++y3p06c3t5s7d24aNGhg/nvt0KEDBw4cYOrUqfTr14+zZ89StGhRfvrpJ8qUKZPIdy0iIoktJPQJV6/fjJKYepqo8rtxm4iIyJe2kSWjK3k8spHHw+3fJJUpUZXbPRvp06VNhLsQS2r7QW0WrNrMJV9//j5wnMrli1s6JBERSQH0TJx0JInE1fTp0wkKCmLFihVkyJABgPDwcHr27MngwYPJnj17jPWcnZ2pWLFilHNz584lMjKSjz76KFr5t99+m7Jly8b/DaRAw4YN4/bt25w+fZr58+cDkDlzZkaNGsX169fp27cvQ4cOJWfOnOTMmZPg4GAiIiIYM2YMmTNnxtfXlzFjxtC4cWO2bdv2ws+6ceMGffv2ZeDAgaRPn55BgwbRpEkTLly4gK2tbWLcroiIJKDgx6Fc9vPnsu8NLvk9S0xd9r3BtZt3MBqNL20je9aM5HZ/mpRye5aocs+GUxrHRLgLSapKFc1Pkfy5OXnuMvNWblLiSkRE4oWeiZOOJJG4Wr9+PbVq1TInrQBatGhBjx498Pb2pkOHDq/c1oIFC8ifPz/lypVLgEjj7klYGH7+ty0dBu5umbGLwz/4vHnzkjlzZq5cuRItORgQEMD69eupUKFClPM//fST+X14eDh58uShUqVKnD17lgIFCsT6Wffu3eOvv/6iaFHTFA4nJyeqV6/O3r17qVSp0ivHLCIilvPw0WMu+5lGTV3y9eei73VTosrXnxu37720vsFgwN0tsykp5f7viKl/k1S5cmTF0eHVht9L6mMwGGj7QW0GfTuDdVt3czcgiIyuzpYOS0REnpMcn4v1TJx0JInE1enTp+nUqVOUcy4uLri5uZnXqXoVN2/eZOvWrQwdOjTG6/Xq1ePu3bu4ubnRqlUrvv76axwdE/ZbWj//27zTJP4Wcntdu1dM462cMY9ci6uMGTNG+wEF+P3335k4cSLnzp3j0aNH5vMv+yHNnj27+QcUoEiRIgD4+fnFS7wiIhK/gh48YvPfB/lr71EuXrnOJT9/br/Cjm7W1lbkzJ713yl92f5NUJleHtmzYG+nbxTl9TT1qsLXk+fyOPQJS9duo0ebRpYOSUREnpPSnov1TJy4kkTiKiAgABcXl2jnXV1duXfv5d/SPrV48WIiIiKiTRNMnz49AwYMoEqVKjg6OrJ161bGjx/PqVOnWLNmzQvbDAoKIigoyHzs7+//yvGkVFmzZo12bsWKFbRr145u3boxZswYMmbMiL+/P02aNCEkJOSF7f337/7pgvwvqyciIonn1t37bPhrL+u37WXHvn8ICw+PsZytjQ25cmQlt4cbb+V0izKCKke2zNjaJomuh6Qw6dOlpWGt91iydhu/L/eme+v3zeuRiIiIxDc9EyeuFNV7nD9/PmXKlImWySxVqlSUlftr1KiBm5sbvXv3Zt++fZQvXz7WNidOnMjIkSNfOyZ3t8zsXjHttevHF3e3zPHWVkwdwaVLl1KyZEl+/vln87m//vor3j5TREQS35VrN1m/bQ/rtu1h39HTUdaisra2omKpohQv9NZzi6JnI0fWTFhbW1swakmt2n5QmyVrt3H+yjX2HjlJRe0iKSKSZKS052I9EyeuJJG4cnV1JTAwMNr5gICAKOtevciFCxfYt28fEydOfKXyLVq0oHfv3hw8ePCFiav+/fvTpUsX87G/v/8Ly/+Xna1tvE3RS2x2dnavnOF9/PixOSv81NMF7EREJHkwGo2cOn+Fdf8mq06cvRzluoO9HVUrlqR+9Yp4Vi5LBhetIyRJR7kShSiQx52zl/yYt2KTElciIklIcn0u1jNx0pAkEleFChWKtpZVYGAg/v7+FCpU6JXaWLBgAVZWVnz44YfxGpuzszPOzqmzY164cGFmz57NwoULyZ8/P5kyZYq1rKenJ7169WLUqFG88847rFu3ji1btiRitCIi8joiIyM5ePws67aaklWX/W5EuZ7OKQ21q5SjXvUKVH+3NE6ODhaKVOTFDAYDbT6ozfAJs/lz8y5Gf94FF+e0lg5LRESSMT0TJw1JInHl5eXF2LFjuX//vnlu59KlS7GysqJ27dqv1MbChQupVq0abm5ur1R+0aJFAElm98GkqHPnzuzbt48+ffpw9+5d2rdvH2vZ7t27c/HiRX744Qe+//576tSpw4IFC6LtviAiIpb3JCyMXQeOs27bXjb8tZebdwKiXM+c0QWvahWoV60i75V7O0670opYUvN61Rj9v98ICX3CsnU+dPmwgaVDEhGRZEzPxEmDwfj8ghUWEhAQQNGiRSlQoACDBw/m2rVr9O/fn9atWzN16lRzuZo1a3LlyhXOnz8fpf7hw4cpXbo0v/zyC507d47Wfps2bciXLx+lS5fGwcGBrVu3MmnSJOrVq8eKFSviFKufnx8eHh74+vri7u4e5drly5cByJ07d5zalJRH/xZEJKl59DgEn92HWbdtD97b9xP0MDjK9Vw5slKvekXqVa9ImWIFtE6VxKsX9Z/i28dDJrJ8w3YK5c2Jz+IpWqRdRCSR6VlIIH7/HSSJEVeurq5s2bKFPn360LhxY9KlS0eXLl0YM2ZMlHIRERGEx7CL0YIFC7C3t6dp06Yxtl+0aFHmz5/PhAkTCA0NJU+ePAwePJhBgwYlyP2IiIgkBQGBD9i04wBrt+7mrz1HeBz6JMr1IvlzU696BerVeIci+XLpAT+FOn/+POPHj2fPnj0cP36cQoUKcfz48Veqe+3aNQYPHsy6det4+PAhuXPnZujQobRu3TqBo359bZp4snzDdk5fuMrB42cpW6ygpUMSERGRN5AkEldgmju6efPmF5bx8fGJ8fz333/P999/H2u9QYMGKUklIiKpgv+tu6z32cu6bXvYdfA4ERGR5msGg4GyxQtSv3pFvKpVILfHq02vl+TtxIkTrF27lgoVKhAZGUlkZOTLK2HakOadd96hYMGCzJgxA2dnZ06cOEFoaGgCR/xm3i3zNm/lzM7Fq9f5fbm3ElciIiLJXJJJXImIiMjruXDlGuu2mZJVh46fjXLNxtqayuWLU696RepWLU+WTK4WilIspWHDhjRq1AiADh06cODAgVeqN2DAADw8PNiwYYN56mjNmjUTLM74YjAYaNPEk6+n/Moq752M/qwz6dKmsXRYIiIi8pqUuBIREUlmjEYjx05fZN22Pazz2cuZC1ejXHd0sKfme6XxqlYRz8plSJ9OO6ulZlZWVnGuExQUxJIlS5g9e3ayXO+sRYPqjPtxPo9DQlm+YTvtm9W1dEgiIiLympS4EhERSQYiIiLYd/Q067btYf22Pfj6345y3TV9OmpXLotX9YpUq1gSRwd7C0UqKcGhQ4d48uQJtra2VK1alV27dpExY0bat2/P6NGjsU3iO01mzuCCV7XyrN68i9+XeytxJSIikowpcSUiIpJEhT4JY8e+o6zduoeN2/dzNyAwynW3LBnxqlaBetUr8k7potjYJL+RMZI03bhxA4AuXbrQtWtXRowYwb59+xg+fDhWVlaMGzfuhfWDgoIICgoyH/v7+ydovDFp06Q2qzfv4tiZixw9dZ4ShfMlegwiIiLy5pS4EhERSUIePnrM5r8Psm7bHrb8fZCHjx5HuZ43V3bqVX+HetUrULJIvteaBibyMk8XcK9VqxYTJkwAoHr16jx48IDx48czfPhwHB0dY60/ceJERo4cmSixxqZy+eLkzJGVq9du8vtyb0oMUeJKREQkOVLiSkRExEKehIVx5oIvR06e5+ip8/xz6gInz10hLDw8SrkShfPiVb0i9apXpEAedwwGg4UiltTC1dW0iH+NGjWinK9ZsyZjxozh/PnzFCtWLNb6/fv3p0uXLuZjf39/ypcvnzDBxsLKyorWjWsx7sf5LN+wnZH9OuKUJvZkm4iIiCRNSlyJiIgkgrCwcM5e8uXoqQscPXmeI6cucOrcZUKfhEUra2VlRcVShfGqVhGv6hXwcMtigYglNStSpMgLr4eEhLzwurOzM87OzvEZ0mtp1bAm301fyKPgEFZs3EmbJp6WDklERETiSIkrERGReBYeHsG5y34cPXmeo6cvcPTkBU6cvURI6JMYy6dzSkPxwnkpUTgvJYrko1K5YmRyTZ/IUYs8kytXLooVK8bmzZvp3bu3+fymTZtwdHR8aWIrqciaOQO1K5djvc9e5q3wVuJKREQkGVLiShKUj48P1atXZ//+/ZQtWxYAg8HA999/z+effx5rvSNHjlCqVCm2bdtGtWrVXvnzVq5cyfXr1+nZs2eU8x06dODAgQMcP378te5DRCQ2ERERXLh63TSK6uQFjp46z/Ezl3gcEhpjeac0DhQv9CxJVaJwXvJ4uGmtKkkwwcHBrFu3DoArV64QFBTEsmXLAKhatSqZM2emZs2aXLlyhfPnz5vrjRkzhkaNGvHpp59Sv3599u/fz/jx4xkwYABOTk4WuZfX0aaJJ+t99nL4xDlOnL1E0QJ5LB2SiIikInomfnNKXEmi2717N7ly5UqQtleuXMmBAwei/ZAOGzaMR48eJchnikjqERkZycWr/hw5eZ5/Tpum/P1z+iLBj2OeNuXoYE/xQm9RvHBeShbJR4nC+cibK7uSVJKobt26RfPmzaOce3r8tDMcERFB+H/WVmvYsCELFy5k1KhR/PTTT7i5uTFy5EgGDhyYaLHHh+rvlCJH1kxcu3mHeSs2Me7LbpYOSUREUjk9E8eNEleS6CpWrJjon5k3b95E/0wRSd6MRiOX/W6YFk4/eZ6jpy7wz+kL0Xb5e8rB3o63C+ahROF8lCySj+KF85I/dw6sra0TOXKRqHLnzo3RaHxhGR8fnxjPt2zZkpYtWyZAVInH2tqajxrX4vufF7FsnQ/D+rYnjaO9pcMSEZFUTM/EcaOvfCVGc+fOxcbGhps3b0Y5f+/ePezs7Pj555/ZvXs377//PtmzZ8fJyYmSJUvy+++/v7Rtg8HA+PHjo5wbPXo02bJlI23atHzwwQfcunUrWr0JEyZQrlw50qdPT5YsWWjQoAFnz541X+/QoQO//vorJ06cwGAwYDAY6NChg/na22+/HaW9Y8eOUadOHZycnEifPj3NmjXj6tWr0WL97rvvGDFiBFmzZiVTpkx07Ngx2WaqRSRmT5NUqzf9zaj//UrTHsMoWL0NFRt/TI/BE/hp3ip2HTxuTlrZ29lS+u0CdGzuxeSv+rBt0WQubF/I2jnfMnZAV1o0qE6hvDmVtBJJIlq9XxMrKyuCHgbz5+a/LR2OiIgkA3omfharpZ+JNeIqoT15AtevWDoKyJ4L7OxeuXiTJk3o0aMHS5cujbIo6x9//AGYphh4e3vz3nvv0aNHDxwcHPj777/p3LkzkZGRtG/f/pU/a+rUqQwbNozPP/+cWrVqsWnTJjp37hytnJ+fH7179yZXrlwEBQUxffp03n33Xc6ePUuGDBkYNmwYt2/f5vTp08yfPx+AzJkzx/iZvr6+VKlShbx58zJv3jxCQkIYMmQIVatW5Z9//iFdunRR4qtcuTK//vorZ8+e5YsvviBr1qx88803r3yPIpJ0GI1G/G7cNo+ievrf+0EPYyxva2ND0QK5KVE4379T/vJS8K2c2NrqV6hIcpEjW2ZqvFuazTsPMG/FJlo2rGHpkEREUpdk+FysZ+Kk80ysXndCu34FPAtYOgrYdBZy53/l4unTp6devXosXLgwyg/pwoULqV27NhkyZODDDz80nzcajVSpUgU/Pz9+/vnnV/4hjYiIYNy4cbRt25bvv/8egDp16nDr1q1omepJkyZFqefp6UmWLFlYtmwZ3bp1I2/evGTOnJkrV668dOjlpEmTCAsLw9vbmwwZMgBQqlQpihQpwty5c+nTp4+5rJubm/mHvm7duhw6dIhly5YpcSWSTDwKfsyO/cc4fOKcaU2qUxe4ez8oxrI21tYUzpeLEkWeLZxeKG8u7O1sEzlqEYlvbZp4snnnAfYdPcXpC1cplDenpUMSEUk9kuFzsZ6Jk84zsaYKSqxatWrF7t27zUMF/f39+euvv2jVqhUAAQEB9O3bl1y5cmFra4utrS0zZsyIMlTxZfz8/Lh+/TpNmjSJcr5Zs2bRyu7ZswdPT08yZsyIjY0NadKk4eHDh3H6vKd27NhBjRo1zD+gAIUKFaJEiRLs3LkzSllPz6hbZxcpUgQ/P784f6aIJJ5bd+8zb8UmWn8ymsI129G+/1gmz1rKtt2HzUkra2srihbITatGNfl2YHc2/PY9F3YsZPOCiUwY2ot2H9ShROF8SlqJpBCelcqSNZMrAPNXbrJwNCIikhzomdjE0s/EGnGV0LLnMmV1LS173HcsaNCgAU5OTixatIgBAwawZMkSHBwcaNy4MWCaI7tr1y6GDx9O0aJFcXZ25qeffmLx4sWv/Bn+/v4AZMmSJcr5rFmzRjm+evUqtWvXpmzZsvz8889kz54dOzs76tevT0hIzLt5vUhAQAAlS5aMdj5r1qzcu3cvyjkXF5cox3Z2doSGxrzNvYhYzsWr11nvs5f1Pns58M+ZKItRGwwGCr7lYV40vUSRfBTNnxtHBy3QLJJa2NhY06pRLSbPWsrStT4M6d0WB/tXX0ZBRETeQDJ9LtYzsYmln4mVuEpodnZxmqKXlDg6OtK4cWPzD+miRYto2LAhTk5OhISEsGbNGiZOnBhlCGFkZGScPsPNzQ0g2sJz/10Ab8OGDTx8+JDly5ebf2jCw8Oj/UC9qgwZMsS42N3NmzcpUCAJDGEVkZeKjIzkyMnzrPfZywafvZy9FPVbH3s7W6pUKIFXtQp4Vi5HlowulglURJKMjxrVYsrsZQQEPmDt1t009apq6ZBERFKHZPpcrGfipEFTBeWFWrVqxeHDh9m4cSN79uwxD4kMDQ0lMjISu+cWtnvw4AGrV6+OU/vu7u64ubmxYsWKKOeXLVsW5fjx48cYDAZsbZ9N2VmyZAnh4eFRytnZ2b1StrlSpUps2bKFgIAA87kzZ87wzz//UKlSpTjdg4gknidhYWzbdZgBY6dTql4XvNoP4H9z/jAnrVyc09K8fjVmf/8lp7b8xrzJQ2nd2FNJKxEBIFeOrFStUAKAeSs0XVBERF5Oz8SWpxFX8kJP58926tQJFxcXvLy8ANNCdeXKleObb74hc+bM2NjY8M0335A+ffoYs7axsba2ZuDAgXzyySdkzZoVT09PvL292bZtW5RyNWqYdv/p2LEj3bt358SJE0yYMCHakMXChQsze/ZsFi5cSP78+cmUKRO5c+eO9rn9+vVjzpw51K5dmyFDhhASEsLQoUPJmTOnebtQEUkagh48YsuuQ6z32cvWvw/x4FFwlOvu2TJTt1oF6lYtT8VSRbTbn4i8UNsPauOz5wi7Dh7nwpVr5M2Vw9IhiYhIEqZnYsvTiCt5IVtbW5o1a8b169dp2rRplGzyggULyJcvH+3bt6dv3740a9aMdu3axfkz+vTpw8iRI/ntt99o0qQJ586d45dffolSplixYsydO5eDBw/SoEEDFi5cyLJly0ifPn2Ucp07d6Z58+b06dOHcuXKMWLEiBg/08PDg7/++gtXV1dat25Nt27dKFGiBD4+PlG2/RQRy/C/dZe5S9fzYe+RFKnVnh6DJ7DKe6c5aVW0QG4+69qSzfMncmDNDMZ80YXK5YsraSUiL1W7SjkyZTD1H7RIu4iIvIyeiS3PYHx+9Vp5KT8/Pzw8PPD19cXd3T3KtcuXLwPEmM2U1EX/FkTixmg0cvaSHxv+XVz98IlzUa5bWVlRsVQR6lYrT92qFciVI2ssLYlIUvSi/pMljPrfb0z9dTkZXdNzZP0v2Nlq91ARkfiiZyGB+P13oK+mRUTEIiIiIjh47Cwb/trLep99XLx6Pcp1R3s7qr1TCq9qFahVqSwZXZ0tFKmIpDRtmngy9dfl3A0IZIPPPt73fM/SIYmIiEgslLgSEZFEExL6hB37/mG9z142bt/HnXuBUa5ndHHGs0o5vKpVoEr5EqRxtLdQpCKSkuXxcOO9ssX4+8Ax5q3wVuJKREQkCVPiSkREEtT9oIds3nnAtLj6rsMEP466y0muHFnxqlYBr+oVKFe8ENbW1haKVERSk7Yf1ObvA8f4a+9RLvvdILd7NkuHJCIiIjFQ4kpEROKdn//tf6cA7mX3oRNERERGuV6icF7qVquAV7UKFMqbE4PBYKFIRSS1qle9IhnSp+Ne4AMWrNrM4F5tLB2SiIiIxECJKxEReWNGo5GT56+wYZspWXXszMUo122srXm37Nt4VatAnSrlyJEts4UiFRExsbezpUWD6kyfv5qFq7bwRbcPtTOpiIhIEqTfzvHI2tqaJ0+eWDoMSQIiIiKibJMqkhKFh0ew7+gpNvy1j/U+e7l67WaU605pHKj5bmnqVK1ArUplcHFOa6FIRURi1rqJJ9Pnr+bW3QA27TxAveoVLR2SiEiyp+digfh9JlbiKh45ODjw8OFD7t69S8aMGS0djljI3bt3efLkCc7O2gFNUp4HD4PZtvsw3jv2s2XnQe4FPohyPXNGF+pWLU/dquWpVK44DvZK4IpI0lUgjwcVSxVhz+GTzFvhrcSViEg80HOxxPczsRJX8ShTpkyEhoZy69Yt7t+/rwWGU6GIiAiePHlCunTpyJQpk6XDEYkXl/1u4L19P9479rPn0EnCwsOjXM+bKzte1SpQt1oFyrxdACsrKwtFKiISd22aeLLn8Em27jqMn/9t3N00lVlE5E3ouTh1S4hn4iTzdHH69Gk8PT1xcnIiW7ZsDBgw4KXDC318fDAYDDG+ChUqFKXs9evXadq0KenSpSNDhgx06dKFoKCgeL0Hg8FAjhw5yJQpk6aJpVJ2dnZkypSJHDlyaLFpSbYiIiLYd+QUo/73G1Wa96FCox4MmzCLHfv+ISw8HCsrKyqULMzQPu3YsewHdi2fxrC+7SlXvJCSViKS7DSo+S7p0zlhNBpZuHqzpcMREUn29FycuiXEM3GSGHEVEBBAjRo1yJ8/P8uXL+fatWv079+f4OBgpk6dGmu90qVLs3v37ijngoKC8PLywsvLy3wuLCyMOnXqALBgwQKCg4P5/PPP+eijj1izZk283ovBYCBzZn1TJyLJy8umAKZ1cqTGO6WoXaUcNd4tQ0ZXTYUVkZTB0cGeZvWqMWvxWhas2kz/Li00OkBE5A3puVjiU5JIXE2fPp2goCBWrFhBhgwZAAgPD6dnz54MHjyY7Nmzx1jP2dmZihWjrkUwd+5cIiMj+eijj8znli1bxokTJzh16hQFCxYEwNXVlTp16rBv3z7Kly+fQHcmIpJ0Xbl20zwFcPfBE9GmAObKkZXaVcpRu0o5KpYqgp2trYUiFRFJWG2aeDJr8Vqu37zL1l2H8axc1tIhiYiIyL+SROJq/fr11KpVy5y0AmjRogU9evTA29ubDh06vHJbCxYsIH/+/JQrVy5K+8WLFzcnrQA8PT3JkCED69atU+JKRFKFiIgIDh47y8bt+9m08wBnLlyNct3KyoqyxQqak1UF8rhryquIpApF8uemTLGCHDx2ht9XeCtxJSIikoQkicTV6dOn6dSpU5RzLi4uuLm5cfr06Vdu5+bNm2zdupWhQ4dGa/+/a149XQcrLu2LiCQ3Dx4G47PnCN7b97Pl74PcvR91bT9NARQRMWnTxJODx86weecB/G/dxS2LdsISERFJCpJE4iogIAAXF5do511dXbl3794rt7N48WIiIiKiTBN80/aDgoKiLOLu7+//yvGIiFjCy6YA5syRldqVTaOq3imtKYAiIgCNaldi2IRZPHz0mIWrt9C/SwtLhyQiIiIkkcRVfJk/fz5lypShQIEC8dbmxIkTGTlyZLy1JyIS3yIiIjh0/Bwb/01WaQqgiEjcOTk60LRuVX79YwMLVm3m007NtFOqiIhIEpAkEleurq4EBgZGOx8QEBBl3asXuXDhAvv27WPixIlxat/Dw+OF7fbv358uXbqYj/39/bUmlohY3KtMAaxe0TQFsOZ7mgIoIvIq2jTx5Nc/NuB7/Rbb9x6l2julLB2SiIhIqpckElcxrTUVGBiIv79/tLWpYrNgwQKsrKz48MMPY2z/2LFjUc4ZjUbOnDmDp6fnC9t1dnbG2VkPfCJieVev/zsFcPt+dmkKoIhIvCteOC8lCufl6KkL/L7CW4krERGRJCBJJK68vLwYO3Ys9+/fN69FtXTpUqysrKhdu/YrtbFw4UKqVauGm5tbjO3PmzePc+fOkT9/fgC2bNnC3bt3qVevXrzdh4hIfHrVKYCelctSu0o5Cr7loSmAIiJvqE2T2hw99RMbfPZx6+59smR0sXRIIiIiqZrBaDQaLR1EQEAARYsWpUCBAgwePJhr167Rv39/WrduzdSpU83latasyZUrVzh//nyU+ocPH6Z06dL88ssvdO7cOVr7YWFhlC5dGoPBwNixYwkODubzzz+nePHirFmzJk6x+vn54eHhga+vL+7u7q93wyIisXj46DE+ew7jvX0/m3dqCqCIpAzJqf/04GEwxet2IvhxCEP7tKNPhw8sHZKIiEiqliRGXLm6urJlyxb69OlD48aNSZcuHV26dGHMmDFRykVERBD+n6kxYJomaG9vT9OmTWNs39bWlg0bNtC3b19atWqFjY0NH3zwAZMmTUqQ+xERiYvLvv5s2nmQTTteMgWwclneKVNUUwBFRBJQurRpaFy7EgtWbWb+yk30atdYi7SLiIhYUJIYcZWcJKdvDEUkaQoLC2f/P6fZtOMAm3Yc4NxlvyjXDQYDZYsXNK9XpSmAIpLcJbf+06HjZ/FqPwCAZT99TeXyxS0ckYiISOqVJEZciYikdPfuB7F11yE27TzItl2HCHzwKMr1dE5pqP5uKTwrlaXGe6XJ5JreQpGKiEipovkpkj83J89dZt7KTUpciYiIWJASVyIiCcBoNHL6wlXzqKoDx84QGRkZpUzeXNnxrFQWz8rlqFCyMLa2+l+yiEhSYDAYaNPEk8HfzWTd1t3cDQjSmoIiIiIWoqckEZF4EhL6hL8PHDMnq/xu3I5y3cbamnfKFMWzclk8K5XlrZzZLRRpKmA0gt9lOHnY9Dp3AlwyQO4CkDs/5CkAOfOCvYOlI01+IiPh5nW4eh78fcHaBhwc/32lefbe3jHqezs70JRXSUaa1avK11N+JST0CUvXbqNHm0aWDklERCRVUuJKROQN+N+6y+adB9m08wDb9x7lcUholOsZXdNT673SeFYuR9UKJXBO52ShSFOw8HC4dOZZkurEITh1BILuv7iewQDZc5qSWXkKQK5/E1q5C0COXGCTin9FhofD9atw5bwpQXX1gun9lfPgexFCQ+LepsEQe1IrLu9fdu1p8szeAbSgtryB9OnS8n6t91iydhvzVmyie+v3td6giIiIBaTiXrmISNxFRkZy5OR586iqY2cuRivzdsE85imApYrm025U8Sk0BM4ce5akOnkYzvwDIY9jLm9tDXkLQ4Fi8OA+XD4HfpcgIsI0KuvaFdPr701R69nagsdbz0ZoPZ/cypYjZYwcCg0B30vPklLPJ6iuXTYlr17GKR1gNP35R0S8uKzRCI+DTa/EYmcfNcFVvyV8NjbxPl+SvbYf1GbJ2m2cu+zH3iMnqViqqKVDEhERSXWUuBIReYmHjx7z194jeO84wOadB7hzLzDKdUd7OyqVL07tyuWoVakM2bNmslCkKcyDQDh5xJScOvVvkur8ydgTJPYOULA4FCn17FWwmClp8bwnT0zJq8vn4PJZuHQWrpwz/ffGvzs8hoXBxTOm1385pjElsHI/N0LraVLLNWPSSmo9evhcYuqCKTn19L2/rymZ9DKZs5mmVebMB7nymd4//a9LhmflwsJMCazQx6b//vd9SPBz719QLtb6/zkOe/Ly2J+Eml5PR9/dv/daf4ySepUrUYgCedw5e8mPeSs2KXElIiJiAUpciYjE4LKvP947DrBp5wF2HzxB2H9Gn+TImsm0VlXlsrxXthiODvYWijSFuHPz2TS/pyOprl6IvXy69FETVEVKwVuFXm16n50dvFXQ9Pqvx8GmxM7zyazLZ01Jrnu3n5U5fdT0+q/0rtFHaeXOb0pqpU33an8WcXX/XtSpfM8nqO7cfHl9gwHcPJ4lo55PUOXMC05pXy0OW1vTK10iLWAdEWEaNfayBNfz7/Mr6SBxYzAYaPNBbYZPmM2fm3cx+vMuuDi/4s+EiIiIxAslrkREgLCwcPYdPWVar2rHAc5d9oty3WAwULZ4QTwrlaVW5bIUyZdLa528jv8umn7ikGk01S3/2OtkcYPC/0lSeeRJmJFNjmmgUHHT678CA/4dpRXDSK1HD56VObrX9IrpPp5fR+tpcitnXrB/QeLTaDQloJ5PTF05D77//jcw4OX3ZWMDOXKbElK58oFH3mfv3XMnz0Xqra0hjZPpJZKAmterxuj//UZI6BOWrfOhy4cNLB2SiIhIqqLElYikWncDgti2+xDeOw6wbdchgh5GXXsnnVMaqr9bitqVy1L93dJkck1voUiTqddZNN3jLShaOmqSKnO2xIr4xdK7QonyptfzniaWnk9oPX1/5bxpqhqYknO3/GH/9qj1rayiLhKfIzfcvRV13angRy+Pz97h2SipXPlMI6eevs+eM3UvNi/yBjK4OFO/xjus2LiDeSs20bllfX1xISIikojUixWRVMNoNHLq/BU27TjA5p0HOXDsDJGRkVHK5MuVwzwFsHyJwtja6n+Tr+R1F00vWvq50VQlTVMAkxuDwZRcy5wNylWOei0iwrRu1vNTDp8mt65dNl2PjDSNQvO7DDu9X/xZTumejZR6fmpfzryQNbt20RNJIG0/qM2KjTs4df4KB4+fpWyxGKYai4iISILQE5mIpGhGo5Ed+/9h3dY9bNpxAL8bt6Nct7WxoWLpIqZkVaWyvJUzu4UiTSb+u7D56aOvvmj68yOpCrwdfdH0lMjaGnLkMr0qeUa9Ftsi8X6XIWOWZ6Olnk9QZciUtBZ/F0kl3i3zNm/lzM7Fq9eZt8JbiSsREZFEpMSViKRIRqORjdv3M3HmYo6eirrId0bX9HhWKoNn5bJUrVCSdGnTWCjKJOppQuXKeVNS5cq5Z++vXzGNEIrN00XTn09S5SmoaWoxedEi8SKSpBgMBto08eTrKb+ycuNORvXvrN8dIiIiiURPEiKSokRGRrJu2x4m/rKEE2cvm88XzpeLulXL41m5HKWK5sMqtU+pCgt7NtrnynlTcurp+2uXX5ycAtOon+w5IV/RqEkq99waESQiKVKLBtUZ9+N8HoeEsnzDdto3q2vpkERERFIFJa5EJEWIiIhg9eZdTJq1lDMXrprPVyxVhM+6taRyueKpbzHdp8mpp6Olrp5/tive9SuxT+17ymAANw/T7ne58pumrD1975Enee5EJyLymjJncMGrWnlWb97F78u9lbgSERFJJEpciUiyFh4ewUrvHUz6ZSnnr1wzn69UrhifdW3Ju2XetmB0iSAszDRCKraRU6+anMqVz5SQyp3/2fucbyk5JSLynDZNarN68y6OnbnI0VPnKVE4n6VDEhERSfGUuBKRZCksLJxl6/9i8qylXPa7YT5f/Z1S9O/SgvIlC1swungWFmYaIXX5XPQE1askpyB6cuppgsrjrdSxSLqISDyoXL44OXNk5eq1m8xbsUmJKxERkUSgxJWIJCtPwsJY9OdW/jfnD3yv3zKf96xclv5dWlD67QIWjC4eHNoFxw9GXRT92mUID3953Wzu0af15c6v5JSISDyxsrKideNajPtxPn+s/4sRn3bAKY3+/yoiIpKQlLgSkWQhJPQJC1ZtZurc5Vy7ecd83qtaBfp1aZ78v/X294NRfWDTyheXy+Yeda2pp+9z5lVySkQkEbRqWJPvpi/kUXAIK7130rqxp6VDEhERSdGUuBKRJC34cSjzVngz9dfl3LwTAJi2JW9Y8x0+7dKCovlzWzbANxURAQunw/hB8OiB6VzmbJCnYMzJKUdtvy4iYklZM2egduVyrPfZy+/LvZW4EhERSWBKXIlIkvQo+DG/LtvIj7+v4M69QMA0RaNx7Up82rk5Bd/ysHCE8eDMMRjaDY7sMR2nSw9ffg/NO4OVlWVjExGRWLVp4sl6n70cPnGOE2cvUbRAHkuHJCIikmLpyUhEkpSHjx7zvzl/UK5hd0ZOmcude4FYW1vRon51diz9gZ/G9E/+SavQEJg4FBqXfpa0qtcCNpyCll2VtBKReHX+/Hl69OhByZIlsbGx4e23477b6uTJkzEYDDRo0CABIkx+qr9TihxZMwEwb8UmC0cjIiKSsmnElYgkCYEPHjJr0Vp+XvAn94MeAmBjbU2LBtX5pGNTcnu4WTjCeLJnGwzrblp8HUy7/X31I9RsaNm4RCTFOnHiBGvXrqVChQpERkYSGRkZp/o3btxg5MiRZMmSJYEiTH6sra35qHEtvv95EcvW+TCsb3vSONpbOiwREZEUSV/ri4hFBQQ+4NufFlCmfje+nb6Q+0EPsbWxoX3TuuxeOY1Jw3unjKRVwF0Y2Ana1jAlrQwGaP8JrDuhpJWIJKiGDRvi6+vLsmXLKF26dJzrDxgwgPfff5/ChQsnQHTJV6v3a2JlZUXQw2DWbNll6XBERERSLI24EhGLuBMQyPR5q5m9ZC2PgkMAsLezpXUTT3q3a0KObJktHGE8MRphzSIY/Qncu206V6g4jJ4JJcpbNjYRSRWs3mD68c6dO1m5ciVnzpyhVatW8RhV8pcjW2ZqvFuazTsP8Ptyb1o0qG7pkERERFIkJa5EJFHdunufn35fyZyl63kcEgqAo70d7ZrVpWfbxmTLnMHCEcYjv8vw1cewfYPp2N4B+oyATv3B1taSkYmIvFRERAS9e/dmyJAhuLmlgJGvCaBNE0827zzAvqOnOHPRN/mvwSgiIpIEKXElIonixu17TP11Ob8v9yYk9AkAaRwd6Ni8Lj3aNCZLRhfLBhifwsPh1ykwZTg8Djade7cWfD0dcuW1bGwiIq9o2rRpPHr0iH79+sW5blBQEEFBQeZjf3//+AwtyfCsVJasmVy5eSeAeSu8GfVZZ0uHJCIikuIocSUiCcrP/zZTf13OglWbCX0SBkBaJ0c6t6xP94/eJ6Ors4UjjGcnDsGQrqb/ArhmhMGToFEb07pWIiLJwK1btxg+fDi//fYbdnZ2ca4/ceJERo4cmQCRJS02Nta0alSLybOWsnStD0N6t8XBPu5/XiIiIhI7Ja5EJEFcuXaTH+b+waLVWwkLDwfAOW0aun3UkC4fNsA1fToLRxjPgh/B/76COZPg6Y5djdvCwAmQMYWs1yUiqcbw4cMpXrw4lStX5v79+wCEh4cTHh7O/fv3SZs2LTY2sXcj+/fvT5cuXczH/v7+lC+fQOv6hYVBeBg4pkmY9l/io0a1mDJ7GQGBD1i7dTdNvapaJA4REZGUSokrEYlXl3z9mTx7GUvXbiMiwpTAcXFOS/fW79OlZX2c0zlZOMIEsH0DDO8B166Yjj3eMk0LrORp2bhERF7T6dOn2b59O66urtGuubq6sn79eurWrRtrfWdnZ5ydE2lE7YRBsGMjTP0D8hRInM98Tq4cWalaoQQ+e44wb8UmJa5ERETi2etvMxPPTp8+jaenJ05OTmTLlo0BAwbw5MmTV6p77do12rdvT+bMmXF0dKRw4cLMnz/ffP3y5csYDIZor4oVKybU7YikOucu+dFr2CTe/aAXi1ZvISIikowuzgzt046Da2bSv0uLlJe0unsL+reGzl6mpJW1NXT7EtYeU9JKRJK1yZMns23btiivEiVKULFiRbZt25Zwo6fi6uIZ00jXs8ehSVnYuNwiYbT9oDYAuw4e58KVaxaJQUREJKVKEiOuAgICqFGjBvnz52f58uVcu3aN/v37ExwczNSpU19Y19/fn3feeYeCBQsyY8YMnJ2dOXHiBKGhodHKjh07lurVn21VnC5dCpuqJGIBp85fYfLsZazy3onRaAQgc0YXerVtTLtmdXFydLBwhAnAaIQ/5sI3n0FggOlcsbIweiYUKWnJyEREogkODmbdunUAXLlyhaCgIJYtWwZA1apVyZw5MzVr1uTKlSucP38egJIlS0Zrx8XFhbRp01KtWrXECv3l3ioIv6yDz1pDwF3o3RQ6fwafjUvU3VtrVylHpgzpuXMvkPkrNzH8kw6J9tkiIiIpXZJIXE2fPp2goCBWrFhBhgwZANM6Cj179mTw4MFkz5491roDBgzAw8ODDRs2YG1tDUDNmjVjLJs/f36NshKJJyfOXmLiL0tYs2W3+Vy2zBno3f4D2jTxxNHB3oLRJaDL52BYd9izzXScxgn6jYG2vU0jrkREkphbt27RvHnzKOeeHm/bto1q1aoRERFB+L/rESY7levAykPQpzn8sw9mTYCj+2DKYsjiligh2Nna8mHDGkz9dQWL/tzGwJ6tsUvExJmIiEhKliSmCq5fv55atWqZk1YALVq0IDIyEm9v71jrBQUFsWTJEnr27GlOWolIwjp66jzt+4+lRqt+5qRVjqyZ+HZgd/aumk7XVg1SZtLqyRP4aSzUL/YsaVWtPqw/CR0+UdJKRJKs3LlzYzQaY3w9HT3l4+PD5cuXX9iOj48Pa9asSfiAX0f2nLBgO7TpZTo+sAMalYK9fyVaCK0bm6aI3w0IZIPPvkT7XBERkZQuSSSuTp8+TaFChaKcc3Fxwc3NjdOnT8da79ChQzx58gRbW1uqVq2Kra0t2bJl48svvyQsLCxa+Y8//hhra2uyZMlC165duXfvXrzfi0hKdfjEOdp8OprabT5nw1+mDrlH9iyMH9KTPat+okNzr5S7BfjhPdCkDEwcAk9CIVNW0zf5M/40PSyJiIjl2dvDV1NhwnzTDoN3bkL7mjDjO9MU7wT2Vs7svFe2GADzVsT+xauIiIjETZKYKhgQEICLi0u0866uri9MLt24cQOALl260LVrV0aMGMG+ffsYPnw4VlZWjBs3DgB7e3s+/vhj6tSpg4uLC3v37mXMmDEcOHCAffv2YfuCodxBQUEEBQWZj/39/V/zLkWSpwPHzjBx5mK2/H3IfC63ezY+7dycZl5VsbVNEv8bSRgPgmDiYJg/7dlDT8uu8MW3kD76TlsiIpIEvP8RFC5hWu/q4hn4/ks4vAu+nQvOLgn60W0/qM3fB47x196jXPa7QW73bAn6eSIiIqlBsn7ijIyMBKBWrVpMmDABgOrVq/PgwQPGjx/P8OHDcXR0xM3NjWnTppnrVa1alaJFi9KgQQNWrFhBixYtYv2MiRMnMnLkyIS9EZEkaN+RU0yYuRifPUfM597KmZ1+nZvzQd0q2Nik8Klxm1fBiF5w89/dod4qCKNmQPkqlo1LREReLn9R+GM/DO4M65ea/p/epCxM/cOU1Eog9apXJEP6dNwLfMCCVZsZ3KtNgn2WiIhIapEkpgq6uroSGBgY7XxAQECUda9iqgdQo0aNKOdr1qxJaGioeWecmNSrVw8nJycOHjz4wtj69++Pr6+v+bVvn9YskJRt96ETNO0xjIadB5mTVvlzuzNtdD92LvuBFg2qp+yk1c3r0KspfNzYlLSytYXew2H1ESWtRESSk7TpTNO6B08CGxu4egGaVzTtCptA7O1sadHAtIP1otVbCAtLpgvei4iIJCFJYsRVoUKFoq1lFRgYiL+/f7S1r55XpEiRF7YbEhLyxrE5Ozvj7Oz8xu2IJGVGo5G/DxxnwsxF7Dp4wny+YN6c9O/SgoY130n5GyBERsLCn2H8QHj47/TgMu+ZRlnlf/H/a0REJIkyGKDjp1C8HHzSwvTlxMCOcOhvGP4D2DvE+0e2buLJ9PmruXkngE07D1Cvuna0FhEReRNJYsSVl5cXmzdv5v79++ZzS5cuxcrKitq1a8daL1euXBQrVozNmzdHOb9p0yYcHR1fmNhas2YNjx49oly5cm8cv0hyZTQa+WvvURp3HULTHsPMSavC+XLxy7cD8Fk0mca1K6X8pNW5E9CqMozoaUpapXWGkT+ZdqhS0kpEJPkr8x6sOgwVTaOhWPILtHwPfC/F+0cVyONBhZKFAZi/YlO8ty8iIpLaJIkRVz169OCHH36gcePGDB48mGvXrvHFF1/Qo0cPsmfPbi5Xs2ZNrly5EmUK4JgxY2jUqBGffvop9evXZ//+/YwfP54BAwbg5OQEwGeffYaVlRUVK1bExcWFffv2MW7cOMqWLUvjxo0T+3ZFLM5oNOKz+wgTflnM/qPPRju+XTAPn3VpSd1q5bGyShJ57YQVGgI/jYUZ38DTnUjrNIVh/4Os2V9cV0REkpeMWWCON0wZDtPHwYlD0Lg0jJ8H1evH60e1/aA2e4+cYsuuQ/j538bdLXO8ti8iIpKaGIzGRNgf+BWcOnWKPn36sGvXLtKlS0e7du0YM2YMdnZ25jLVqlXj8uXLXL58OUrdxYsXM2rUKM6dO4ebmxvdu3dn4MCBGAwGAGbNmsW0adM4f/48wcHB5MiRgyZNmjBy5Mg4TwP08/PDw8MDX19f3N3d3/i+RRKT0Whky98HmTBzCYeOnzWfL1E4L/27tqROlXLmn5sUb+9fMLy7accpgKw5YMSPUKuRZeMSEUmBklz/acuf8EVbePDvGqsfD4ZPvoZ4GmH8OCSUEnU7EfjgEZ93a8kX3VvFS7siIiKpUZJJXCUXSa7jJfIKjEYj3jv2M3HmEo6cfDZisVTR/HzWtSW1KpVJPQmrwAD4boBpmgiY1j9p0wv6jYF0Ws9ORCQhJMn+09WL0KcZnDxsOn63JkxcYBqZFQ8GfzeTWYvXkj1rRg78OSPlT7sXERFJIKlgLpBI6hUZGcm6rXvwbP0Z7fqNNSetyhYvyKIfvmL9r9/hWbls6khaGY2wdjHULfwsaVXgbVi8y7RAr5JWIiKpS863YPHf0Lyz6XjXFmhUGg7tipfm2zTxBOD6zbts3XU4XtoUERFJjZLEGlciEr8iIyNZu3UPE39Zwslzl83nK5QszGfdPqRK+eKpI1n11LUrMKIX+Kw1HdvZQ5+voPPnYGtr2dhERMRyHBxh7C9Q+j3TBh03r0HrqvDleGjf1zQq9zUVyZ+bMsUKcvDYGeat8Mazctl4DFxERCT1UOJKJAWJiIjgzy27mfjLEs5cuGo+/26ZonzW9UPeK/t2yk9YGY1w5bzpG/NDf5v+e/6k6TzAOzXg6+mQO79l4xQRkaSjWUcoUgp6NwXfizDmUzi8C8b8AmnTvXazbZp4cvDYGTbtPMCN2/fIljlD/MUsIiKSSihxJZICREREsNJ7J5NnLeXsJT/z+UrlivFZ15a8W+ZtC0aXwEIew/GDURNVAXeil3PJAAMnwAft3+gbdBERSaGKlISVB2FAe9iyGtYtgdNHYepyyF/ktZpsVLsSwybM4uGjxyxcvYV+nZvHb8wiIiKpgBJXIslYeHgEKzZuZ9KspVy4ct18vlrFkvTv0oIKpV6vo52k3fI3JacO74KDf8PJQxAWFr2cvQMULw9l3oNS70K5KlrHSkREXszZBaatgJnfw8TBpp1nm5WH0TOhYdx3BnRydKBp3ar8+scG5q/cxCcdm2JlpSVmRURE4kKJK5FkKCwsnD/W/8Xk2cu45OtvPl/zvdL069KCcsULWTC6eBQRAWeOmZJUh/59+V2KuWzW7KY1Skq/B6XfhUIlwM4uceMVEZHkz8oKun8JJcrDpx/C3VvQ/yPT76KBE+L8u6VNE09+/WMDvtdvsX3vUaq9UyqBAhcREUmZlLgSSUbCwsJZsnYbU2Yv48q1m+bznpXL8lnXlpQqmszXbXoQCEf2PEtSHd0Djx5GL2dtbUpMlX73WaLKzUNTAEVEJP5UrA6rDsMnLUwjfH+fCv/shx+Wmn7nvKLihfNSonBejp66wO8rvJW4EhERiSMlrkSSgSdhYSz6cyv/m70MX//b5vN1q5bns64tKV44rwWje01GI1y98CxJdXgXnD3+bBH15zm7QKl3TFP+yrwHxcqBU9pED1lERFKZrNnh920wfiDMnghH90KjUjBhAVSu/crNtGlSm6OnfmKDzz5u3b1PlowuCReziIhICqPElUhSERkJaxaZdsBzyQAuGQlLmx7vYxeZuX43p+8+4r7BFgwGGtR8h36dm/N2wbcsHfWrCw15toj606l/d2/FXDZPgWdJqlLvQt5CpqkbIiIiic3WFgZNgJLvwKBOEHAXOteFPiOg19BX+v3UpE5lvpo0h+DHISxZs5Xe7T9I+LhFRERSCIPRGNPwBomNn58fHh4e+Pr64u7ubulwJKU4/Q8M626aJvcCkRiITJcem4yZwSUjuGb6978ZTf99+nL9z3t7h0S6kefcvhE1SXX8IIQ9iV7O3sE0gurptL+SFSFj5sSPV0REEkyK6T9dPAO9m8K5E6bjql7w/e+m37Uv0e/rqSxYtZk8Hm7sXjENg6a3i4iIvBKNuBKxpOBHMPVrmD3BtBA5cC9LLh7cu4dzeAiuRN0tzwojVg/uw4P7wLlX/xzHNLEktV6Q+EqX/tXXjIqIME3zez5R5Xsx5rJZ3J6tS1X6PShcUouoi4hI8vBWQVi2F4b3gFXz4K/10Lg0/LAMipd7YdW2H9RmwarNXPL1Z+bCNXT7qGEiBS0iIpK8KXElYik+62BkL/C7DEBYlhwMsC3KggeOkAasrKxoWvs9+jX3JK+zPdy/a5qecP8uBNwx/ff5c88f/3dk0+Ng08vf99Xjs7aG9BlePKLr5jVTkurIHnj0IHobVlbPFlF/OvUve04toi4iIslXGif4/jfT77Uxn8L1q/BhJRj2P/iwW6y/40oVzU/VCiX4a+9Rhk2YRfp0TrRsWCNxYxcREUmGNFUwjlLMUHexnJvXTR3d9UtNx9bWXPX8iAaHH3PzcRjW1lY086rKp52b81bO7HFv32g0jeR6msi6dyd6Yium44dBb35v6dI/W0S99LtQooIWURcRkZTbf/pnP/RpZkpeATRuC19PN410jsGDh8E07TGMo6cuYGVlxS/ffkH9Gu8kYsAiIiLJjxJXcZRiO16S8CIiYOF0mDD4WZKoeHlWV21Hj3lbiIiIJKNren6dOIhyxQslfnxhYRB4zzSaK+AFia7nz6VL/yxJVfpdyFdEi6iLiEg0Kbr/FHAXPm8D2zeYjgu8DVP/MG00EoO7AUE06TaEMxd9sbWx4fdJQ6j+bqlEDFhERCR5UeIqjlJ0x0sSzskjpsXX/9lnOk7rTGS/0Yy6nYZpC/4EIH9ud+ZNGUpu92yWi1NERCQBpPj+U2Qk/DgafhhhGvnslA6+nQt1Yt498Mbte7zfeRBXrt3E0d6OxT+OoEKpIokasoiISHKhoREiCenRQ/jmc/ig7LOklVdzglceptPRh+akVaVyxVg79xslrURERJIjKyvoMxxmrQeXDKZ1H3s3NfUBwsKiFc+WOQNLf/qabJkz8Dj0Ca0/Gc0/py5YIHAREZGkT4krkYSydQ3UKwqz/t0xMEcumLGGm8Om03jIj6z32QtAq0Y1WfjDcNKn01pQIiIiyVrlOrDqMBQvbzqeNQHa1TCtb/kfuXJkZcm0kWRIn44Hj4L5sPdIzl6KwyYqIiIiqYQSVyLx7cY16NUUujc0LdZqbQ1dB8C6E5xwL4pX+wEc/fdb1SG92zJpWG/sbG0tHLSIiIjEi+w5YcF2aN3TdHxgJzQuDXv/ila04FseLPrxK9I5peHu/SBa9BzBlWs3EzlgERGRpE2JK5H4EhEBv/0AdQuD93LTuRIVYOUhGPAtW4+c4f3Og7h28w4O9nbM/OYL+nZsiiGWbbNFREQkmbK3hxE/woT5ph0G79yE9jVhxnemNbCeU6JwPuZPGYqjvR3+t+7S/OPh3Lh9z0KBi4iIJD1KXInEhxOHoXlFGNXXtK5FuvQw8idYsgsKFWfOknW0/mQ0Dx89JlOG9Pzx8yje93zP0lGLiIhIQnr/I/hjn2mHwYgI+P5L6PUBhDyOUqxCqSLMGT8IWxsbrly7SYteI7h3P8hCQYuIiCQtSlyJvIlHD2Fsf9Pi68cOmM7VbwkbTsFHPYgwGhk2YRYDv51BZGQkBfK4s27ud5QtVtCycYuIiEjiyF8U/tgPdZuZjjethP6tTYms51R/txTTx/bHysqKMxeu0qrP1zx4GJz48YqIiCQxSlyJvK7Nq8CrCMyZZNoG2z2PaTehyYsgixuPgh/T8YtvmfHvzoFVKpRgzZxvyJUjq4UDFxERkUSVzhn+twR6DjUdb1oBYz6NNm2wQc13mTy8NwBHTp6nbb8xBD8OTeRgRUREkhYlrkTiyt8XejaBjxub3tvYQPeBsO44VKkLwI3b92jUZQgb/9oHQJsmniyYMkw7B4qIiKRWBgN8+jW06GI6/n0q/DI+WrGWDWsw5gtTmd2HTtDly295EhaWmJGKiIgkKa+VuPp12QYNXZbUJzwc5kw2jbLatNJ0rvS7pm2vPx9nWnwVOHH2El7tv+DYmYsADP+kPeOH9MTW1sYycYuIiEjSYDCY1sCsWs90/N0AWL0gWrEuHzZgYM/WAGz5+xC9hk4i4j9TC0VERFKL10pcfTVxNsXqdKT38MnsOng8vmMSSXqOH4RmFWBsP9O6Vs4uMOpnWLgDCrxtLrZ55wEadh7E9Zt3cbC3Y9Z3A+jVrol2DhQRERETGxuYshiKlTUdD+wAe7ZFK/Zpp2b0atcYgNWbd/HZmGlERkYmXpwiIiJJxGslro5unM3wT9pz9qIvH3QfRoVGPZg8eyn+t+7Gd3wilvXwAYz+FJqWhxOHTOcafgQbT8OH3cDq2Y/QrMVradtvLI+CQ8ic0YUVM0bToOa7lolbREREki6ntDBjDXi8BWFhpuUHzhyLUsRgMDCsb3vaNa0DwMJVW/hq0hyM/1kXS0REJKUzGN/wt9+Jc5dZuGozyzfsIDDoIdUqlqRVo5rUqVI+RU6N8vPzw8PDA19fX9zd3S0djiQUo9E0HfDrPnDzmumcx1um4f2Va0cpGhERwfCJc/hl0RoACubNyfwpQ/Fwy5LIQYuIiCRN6j/F4tJZaPkuBNyFrDlg6R5wi/rnExERQe/hU1i+YTsAn3dryRfdW1kiWhEREYt448TVUzdu3+PjIRPYfegkABlcnOnQrC59OnyAo4N9fHxEkqCOVypw/aopYbVltenY1ha6DICeQ8DBMUrRR8GP6T54Apt2HACgWsWSzPzmC5zTOSV21CIiIkmW+k8vcHg3tK0BoSGm5QcW7jAtSfCcsLBwOg34Fu/t+wEY2a8jPdo0skCwIiIiie+NdhU0Go1s+fsgnQd8S/n3u3Pu8jV6tWvMmjnf0L5pHWYtXkuvYZPiK1aRhBUeDrMnmhZff5q0KlvJtPh6/9HRklbXb97h/S6DzUmrdk3rMG/yUCWtRERE5NWVegcmLzItP3D2uGnn4tDQKEVsbW2Y+c0XVCpXDICvJs1h3opNlohWREQk0b1W4uqyrz9jf5xH6fpdaPPpGB4FhzBtdD+OrJvFsL7tKVusIF9+/BFTRvRl69+HXqnN06dP4+npiZOTE9myZWPAgAE8efLklepeu3aN9u3bkzlzZhwdHSlcuDDz58+PUiYwMJDOnTuTIUMG0qVLR7NmzfD394/zvUsK9c9++KAcjPsMgh9BelcY+wvM/wvyF41W/Njpi3i1H8DxM5cwGAyM+LQD3w3qkSKnx4qIiEgCq9UIhv9ger/XBwZ2hP8sxO5gb8dvEwdT+u0CAHw+ZhorvXcmcqAiIiKJ77Wesis26Ylblgx82LAmHzWuFetaPvlz56DUv79cXyQgIIAaNWqQP39+li9fzrVr1+jfvz/BwcFMnTr1hXX9/f155513KFiwIDNmzMDZ2ZkTJ04Q+p9vqlq2bMmJEyeYPn06Dg4ODBkyBC8vLw4cOICNjZINqdaDIJg0BOb9aFrXCqBRGxg0ATLG/O964/Z99Bg8keDHITja2zFtdH/q1aiYiEGLiIhIitO6J/j7ws/fwJqF4OYBA76NUsQpjSML/jeMD7oP4+S5y/QaOgknRwc8K5e1UNAiIiIJ77XWuPLevp9alcpgZfVGMw3Nxo0bx5gxY7h69SoZMmQAYMaMGfTs2ZOrV6+SPXv2WOu2bduWixcvsn37dqytrWMss3v3bt599102btxI7dqmhbXPnDlD4cKFWbRoES1atHjlWLVGQwphNMLG5TC6L9y8bjqXK59p8fX3asVabebCNQyfOJvIyEiyZHTl98lDKFkkXyIFLSIikjyp//SKIiPhi3aw+t+ZA8N/gLa9oxW7dfc+jboM5uLV6zjY27Hgf8N4r2yxRA5WREQkcbxW5ql2lXLxlrQCWL9+PbVq1TInrQBatGhBZGQk3t7esdYLCgpiyZIl9OzZM9ak1dP2XVxc8PT0NJ8rWLAgJUuWZN26dfFzE5J8+F2Gbg2hTzNT0srWFnoNg7XHYk1ahYdHMOi7GQwd/wuRkZEUzpeL9b9+p6SViIiIxB8rKxg3G96taToe1Re8V0QrliWjC0unjSRH1kyEhD6hbb8xHDp+NpGDFRERSRyvlX36ZOQPdB34fYzXug0az2ejf4xTe6dPn6ZQoUJRzrm4uODm5sbp06djrXfo0CGePHmCra0tVatWxdbWlmzZsvHll18SFhYWpf2CBQtiMBii1C9cuPAL25cUJiwMfhkP9YqCz1rTuXJVYPVR+PRrsHeIsdrDR49p338ssxebkpw13i3Nn7PG4e6WObEiFxERkdTCzg6m/gGFiptGiPf/CA7tilbM3S0zS38aSaYM6XkUHEKrPl9z6vwVCwQsIiKSsF4rcbV97xHq13gnxmsNaryDz+4jcWovICAAFxeXaOddXV25d+9erPVu3LgBQJcuXShbtize3t7069ePyZMnM3z48DduH0yjuvz8/MwvLeieTB3Za1p8/dsv4HEwuGQwfaM53wfyFY612rUbt2nYeRCb/z4IQIdmdfl90hDSpU2TSIGLiIhIqpMuPcxcB9ncITQEujeEi2eiFcubKwdLpo0kfTon7gc9pEXPEVzyVV9VRERSltdKXN0NCCKji3OM11zTp+P2vftvEtMri/x3t5VatWoxYcIEqlevzpdffskXX3zBpEmTePz48Rt/xsSJE/Hw8DC/ypcv/8ZtSiIyGuHbAdDiHTh91HSuSXvYcBqadYT/jMJ73tFT5/FqP4CT5y5jMBgY2b8T3wzsjo1N7NNSRUREROJFthwwe4MpiXX/HnSuC7dvRCtWNH9uFvwwnDSODty6G0Dzj4dz7cZtCwQsIiKSMF4rcZUtc4ZY59EfOn6WLJlc49Seq6srgYGB0c4HBAREWfcqpnoANWrUiHK+Zs2ahIaGcv78+TdqH6B///74+vqaX/v27Xvp/UgSMn4Q/PK9KYGVpwD8vhW+mwsZXzzNb73PXhp1GcLNOwE4OtgzZ/xAerR+P9p0UxEREZEEk78o/LQKbO1Ma3R2rQ+PHkYrVrZYQX6dOAh7O1t8/W/ToueIRPsiWUREJKG9VuKqSd0qTJ69jFXeO6OcX73pb6bMWcYHdavEqb1ChQpFW2sqMDAQf3//aGtfPa9IkSIvbDckJMTc/pkzZ/jvBooxra31X87Ozri7u5tfbm5uLywvScisCTDj322k67eEP49CxeovrGI0Gpk+fzUdP/+GxyGhZM3kyqpfxuBVrUIiBCwiIiLyHxWqwve/md6fOAR9m5vW7fyPKuVLMOObL7C2tuL8lWt82HskgQ+iJ7lERESSm9dKXH3WtQXvlnmb7oMn8FblD3mnSU/eqvwh3QdP4J3Sb/N5t5Zxas/Ly4vNmzdz//5987mlS5diZWVF7dq1Y62XK1cuihUrxubNm6Oc37RpE46OjubElpeXFwEBAWzZssVc5uzZsxw+fJh69erFKVZJJlb8Bt98bnpfqTZ891usi68/FR4ewcBvfuaribMxGo0UyZ+b9b9+R4nC2jlQRERELKh+Sxg43vR++wYY3sM0mvw/6lYtzw8jP8FgMHD8zCVafzKaR49DEjlYERGR+GUw/ncYUhz8tecIO/YfIyAwCNf0zlSpUJwq5UvEuZ2AgACKFi1KgQIFGDx4MNeuXaN///60bt2aqVOnmsvVrFmTK1eumKcAAvz55580atSIvn37Ur9+ffbv38+IESMYMGAAo0ePNperW7cuJ0+eZMKECTg4ODBkyBCsrKw4cOAANjY2rxyrn58fHh4e+Pr64u7uHud7lUSwdQ30bAwREVC8PPy2BZzSvrDKg4fBdBs0nq27DgFQ670y/Dzuc9I6OSZCwCIiIimb+k/xwGiEMf3g1ymm4z5fQd8RMRb97Y+NfDH2JwCqVijB75OHYm9nm0iBioiIxK83SlzFp1OnTtGnTx927dpFunTpaNeuHWPGjMHOzs5cplq1aly+fJnLly9Hqbt48WJGjRrFuXPncHNzo3v37gwcODDKekSBgYH079+f5cuXEx4eTu3atfnhhx/Inj17nOJUxyuJO7ATOniaduB5qxAs3AEZMr2wip//bdp8Otq8hXSnlvUY1b+zFmEXERGJJ+o/xZOICPikJWz8w3Q89hdo3jnGoj/+toKvp/wKQL3qFZn5zRfq24iISLL0xomr4MehhD55Eu28a/p0b9JskqWOVxJ25hh8VAWC7pu2j178N2TP+cIqR06ep+2nY7h1NwCDwcCozzrTtVWDxIlXREQklVD/KR6Fhpi+pDuwE6ytYfpqqBbz0hfjps1n8qylADSvX43/jeiLldVrrRQiIiJiMa8+R+45RqORSb8s4bfl3ty8ExBjGf/9y98oMJE48b0EneqYklYuGWCO90uTVuu27qHn0Ik8Dn1CGkcHpo/tT50q5RMnXhERkRc4euo8gQ8emZdguB/0kK+n/Mq5S35UqVCcz7q2fOUExPnz5xk/fjx79uzh+PHjFCpUiOPHj7+wjr+/P5MmTcLb25sLFy6QPn16qlSpwrhx48iVK9cb35+8AXsH006DLd+Di6dNi7XP/wuKlY1WdODHH/HgYTCzFq9l6Vof0qZxZNyX3bRLsoiIJCuv9ZXLz/NXm3Zea+6F0Wjk007N+KxrC/LmzI6HW2YmDO0Z33GKxO7uLehYG275g2MamLkO8hWOtbjRaGTabyvpNOBbHoc+IVvmDKz+ZaySViIikmQMnzCbfUdOmY+HjZ/F6k1/kyWTC9N+X2UeRfMqTpw4wdq1a8mXL99Ld2R+6uDBgyxfvpwWLVqwatUqJk6cyLFjxyhfvjy3b9+O8/1IPHPJALPWQ+Zs8DgYutaHqxejFTMYDIz+vDMtG5h2VZ6zdD1jf5yX2NGKiIi8kddKXC1YtZkvureid/smAHhVq8Dn3T5k+9L/kT+PB5d8/eM1SJFYPQiCzl5w5TzY2MCPy6FkhViLh4WFM2DcdEZOmYvRaOTtgnlY/+t3FCv0ViIGLSIi8mJnL/pSqmh+AB6HhPLnll2M+rwzs777kmF927F03V+v3FbDhg3x9fVl2bJllC5d+pXqVKpUidOnTzN48GBq1KhBixYtWL9+Pbdv3+a33357rXuSeOae2/RlnVNa05d4nevCvTvRillZWTFxWG8a1HwHgP/N+YMf5mpmhIiIJB+vlbi6ev0WbxfMg7W1NbY21gQ+eGRqzMqKjs3rsvjPrfEapEiMQkNMuweeOAQGA3z3G1SuE2vxoAePaPPpaH77YyMAtSqVZfUvY8me9cWLt4uIiCS2xyGhODrYA7Dv6GmehIVRt6ppZHCR/Lnwvxk9QRGb11nTyMXFJdquy+7u7mTOnJnr16/HuT1JIEVLwQ9/mL68u3wOerxvGoH1HzY21kwb3Z9qFUsCMPqH35izZF0iBysiIvJ6XitxlSF9Oh4FPwYgR7bMHDv9bGjyvfsPeBwSGj/RicQmIgL6t4Y920zHQ6dAw1axFr96/SYNOg/CZ88RALp82IDfJg7CKY1jIgQrIiISNznds7Hl70MALF//F8UL5TVvfHPnXiBp06ZJ9JjOnj3LrVu3KFw49un4YgGVa8Pomab3h3fDZ61N/aT/sLezZfb4gVQoafr7G/jtDJat80nEQEVERF7Pay3OXq5kYY6cPE+tSmX5oG4Vvp+xiFt3A7CxsWH+Cm8qly8e33GKPGM0wlc9wfvfYe69hkG7PrEWP3T8LO36j+X23ftYWVkx6rNOdPlQOweKiEjS1aP1+/Qf9SMLV20mIOghU7/+xHzt74PHKZIvcRdINxqN9O3bl+zZs9OqVexfFD0VFBREUFCQ+djfX8tIJKimHeCGH0weBptWwuhPYPgPphHpz3FydGDe5KE07TGMf05fpO+I/5HG0YF61StaJGwREZFX8VqJqy+6fYj/rbsAfNKpGYEPHrFi4w5CQp9QpUIJxg7oGq9BikQxaRgsnmF6/2F3+GRkrEWPnjpPs4+H8yg4BKc0Dvw89nM8K0ffdUdERCQp+ahRLfJ4uHHkxDmKFcpLpXLFzNcypE9HjVYNEzWeESNGsGXLFjZs2ICTk9NLy0+cOJGRI2P//SwJoOcQ8L8Ki2fCvB/BLSd0GxCtmHM6JxZO/YrGXYZw7rIf3QeNZ96UYVStUMICQYuIiLycwWg0GuNSwWg0cj/oIWkcHbC3s02ouJIsPz8/PDw88PX1xd3d3dLhpD5zp8CYT03v6zaDyYvA2jrGoheuXKNh58HcDQgkU4b0LJ76FW8X1CLsIiKSenXo0IEDBw5w/PjxV64zc+ZMunXrxqxZs+jUqdMr1YlpxFX58uXVf0po4eHwcWPwWWs6njAf3v8oxqL+t+7yfpfBXL12E0cHe5ZOG0m5EoUSL1YREZFXFOc1rsLCwynq2Z7te48mRDwisVs1/1nS6p0aMH5erEkr/1t3adlrBHcDAknnlIZFPyhpJSIiycfRU+fZvu9ZX+t+0EP6j/qRhp0G8f3PC4mMjEyUOFasWMHHH3/M119//cpJKwBnZ2fc3d3NLzc3twSMUsxsbGDKYiheznQ8sAPs2hJjUbcsGVk6bSRZM7nyOCSUj/p+zfEzF2MsKyIiYklxTlzZ2dqSPUtGIhKpwyQCwPYNps4XwNtlYNpKsLePsWhA4ANa9h6Jr/9t7O1s+W3SYIoVUtJKRESSj+ETZrPvyCnz8bDxs1i96W+yZHJh2u+rmDxraYLH4OPjQ6tWrejatSvDhg1L8M+TeJLGCWasAY+3ICwMen0Ap/+JsWhu92wsmTaSDOnTEfQwmBa9RnL+8rVEDlhEROTFXmtXwY4t6vHz/FWEhD6J73hEoju8G3o3NQ1/z1MAflkHadPFWDT4cSht+43hzIWrWFlZMX3sZ7xb5u1EDlhEROTNnL3oS6mi+QF4HBLKn1t2Merzzsz67kuG9W3H0nV/vXJbwcHBLFu2jGXLlnHlyhWCgoLMx7dv3wagZs2a5MuXz1zn1KlTNG7cmPz589O2bVv27Nljfl24cCF+b1biX8YsMHsDuGaCh0HQpR74+8ZYtFDenCyc+hVpnRy5GxBI857D8fW/lcgBi4iIxO61Fmf3u3GbC1euU7p+V94rU5RMGV0w8GzXEoPBwJgvusRbkJKKnTsBXevD42DImh1mbzR1xmIQFhZO14Hfs//oaQDGD/lYu+SIiEiy9DgkFEcH08jifUdP8yQsjLpVywNQJH8u/G/eeeW2bt26RfPmzaOce3q8bds2qlWrRkREBOHh4ebre/fuJTAwkMDAQN57770oddu3b8/cuXNf57YkMeXObxp51bY63LwGnb1g0U5wdolWtGSRfMybPJQPe4/k+s27NP/4K1b/MpYsmVwTP24REZH/eK0RV5t2HMDOzpY0jvYcPnmeTTsO4L1jf5SXyBu7fhU61oHAAFMna/ZGcM8dY9HIyEj6jZrK5p0HABjSuy2tG3smXqwiIiLxKKd7Nrb8fQiA5ev/onihvLimN402vnMvkLRp07xyW7lz58ZoNMb4qlatGmCaFnj58mVznQ4dOsRaR0mrZKRkBdNGNlZWpi8DP24MoaExFn2ndFFmf/8ltjY2XPL1p3nPrwgIfJC48YqIiMTgtUZcHfhzRnzHIRLV3dvQsbbpG0IHR9M3hgVinvJnNBoZOXkuS9f6ANC99fv06fBBIgYrIiISv3q0fp/+o35k4arNBAQ9ZOrXn5iv/X3wOEXy5bJgdJKs1HwfvvoRvvoY9v0FX3aAifNNyaz/Fn2vDNNG96P74AmcvnCVD/t8zYIpw8jo6pz4cYuIiPzrtRJXIgnq4QPoWg8unjHtGvi/pVDmvViLT/11BdPnrwagWb2qjPi0AwaDIdbyIiIiSd1HjWqRx8ONIyfOUaxQXiqVK2a+liF9Omq0amjB6CTZ+agH+F+F6eNg7SLI5g4Dv4+x6Pue7/Ew+DH9vp7KkRPnqNPuc36bOJgi+XMnbswiIiL/MhiNRmNcKy1Zs+2lZVo0qP5aASV1fn5+eHh44Ovri7u7u6XDSXlCQ6FbA9i12XT8/W/QuG2sxeev3ET/UT8CUOu9MsydMAhbW+VjRUREkhL1n5IAoxEGtIeVv5uOh06B9n1jLb74z618PmYaT8LCcUrjwI+j+uFVrUIiBSsiIvLMayWuspVtEnNjz41y8d+//PWjSsLU8UpAERHwWWtYu9h0PHgidOwXa/F12/bQecB3REZGUq5EIZb8OJI0jvaJFKyIiEjCevQ4hMV/bmXvkVPcD3yAS/p0VCxZmBYNa+Dk6GDp8OJE/ack4skT06Y3uzaDwWAa1V63aazF9x89TYfPx3HnXiAAA3u25tNOzTSyXUREEtVrJa6CHjyKdu7+g4f47D7CrMVr+WlM/xQ7nFgdrwRiNMLI3jB/mum4+0D4fFysxXcfOkHLXiMIfRJGwbw5WTVzjHnRWhERkeTu2o3bNOk2FF//2xTNn5vMGV24ffc+J89fxsMtC8t/HkWObJktHeYrU/8pCXkQBB9VhtP/gJ09/LblhUsy+Pnfpv1nYzl+5hIAjWpXYvLwPvqyUEREEs1r7SronM4p2itn9qy0a1qH1o09GfW/3+I7Tknppn79LGnVvDN8NjbWoifOXqLtp2MIfRKGe7bMLJ76lZJWIiKSonw1cQ4AO5b+wOYFE1n4w3A2L5jI9iU/YDAYGDFpjoUjlGQrnTP8sh7cPOBJKHRvCBdOx1rc3S0zq2eN4/1a7wKwynsnjbsO5vrNO4kVsYiIpHKvlbh6kYJ5c7Ln8Mn4blZSsvnT4H8jTO89G8PX003D12Nw2deflr1H8uBRMBldnFn84wjcsmRMrEhFREQSxV97jzC4d1vy5c4R5Xy+3Dn48uOP8NlzxDKBScqQNTvMWg/OLhAYAJ3rwu0bsRZ3cnRgxjdf8GWPVgAcPXWB2m0/58CxM4kUsIiIpGbxmrgKfhzKvBXeuGXJEJ/NSkq2bolpiiBA+aowaSHYxLy4+q07AbToNYLbd+/jlMaBBT8Mj9ahFxERSQnCIyJxsLeL8ZqDvR0RkZGJHJGkOPmLwrSVYGsH165Al3qmnZ1jYTAY6N+1JbO//xJHB3tu371Pk65DXmnTJhERkTfxWtuvVW3RN9qijGFh4Vy/dZeQ0Cf8MPKTeAlOUridm+DzNqb1rQqXhOmrwD7mxWaDHjziwz4juXLtJna2NswZP4iSRfIlbrwiIiKJpHyJQkyatZR3SxfFOZ2T+XzQg0dMnr2M8iUKWzA6STEqVIXxv8MnLeHkYejbHH7+E2xtY61Sv8Y75HbPRvv+Y/H1v02fr6Zw6vwVhvZpi7W1dSIGLyIiqcVrLc7e96sp0RJX9nZ2uGXNSP0aFSmQxyPeAkxqtLhoPPlnP7StDsGPIGdeWPw3ZMoaY9GQ0Cd82Hskuw+dwGAwMGPc57zvGfsioiIiIsndqfNXaNx1COEREVQqW4zMGV24cy+QHfv/wcbampUzx1A4Xy5Lh/nK1H9K4mZPhHGfmd5/0AG+mR3rsg1P3QkIpPMX35qXCKnxbml+HvtZlESriIhIfHitxFVqpo5XPLhwGlpVgoC7kDkbLPobcr4VY9Hw8Ai6fPkd6332AvDtwO50aO6VmNGKiIhYxPWbd5g+fzV7j5wiMOghLs5pqVCyMF1bNWTvkZM09apq6RBfmfpPycCYfjB3sul97+HwyciXVnkSFsagb2cwb8UmAPLlysHvk4fwVs7sCRioiIikNq+VuHr46DGPgh+TNXP0taxu3r5HWidHnNI4xkuASY06Xm/I3w9avgv+vpAuPcz/CwqXiLGo0Wik/6gfWbBqMwADurfis24tEzNaERGRJGfNll10HTge//3LLR3KK1P/KRmIjDRNGdywzHQ8ega07PrSakajkVmL1zJ84mwiIiJJn86Jmd98QdWKJRM2XhERSTVea3H2fqOm8s1PC2K89t3PC/ls9LQ3CkpSqIC70LG2KWll72BaQyGWpBXAmKnzzEmrTi3q0b9ri8SKVERERCR1sbIyrXdVtpLp+KuPYdval1YzGAx0+bABC3/4ChfntAQ+eESrvl8zc+EaNLFDRETiw2slrvYcOolnpbIxXqv5Xhl2HzrxRkFJChT8CLo1gAunwNoapiyGcpVjLT593ip+mPsHAI1qV2LMF12irasmIiIiIvHI3gF+WgV5C0NEBPRuChMGw4PAl1atWqEEG377ngJ53ImIiGTo+F/oP+pHQp+EJULgIiKSkr1W4irwwUOcnGKeCujk6EBAYOxb6Uoq9OQJ9GkGR/aYjkfPhJrvx1p86dptfDVpDmDqBE39+hOsrF7rn6qIiIiIxIVLBpi1HrLmgCehMH0c1MwHv0819eleII+HG2vnfEut98oAsGDVZpr1GM7te/cTIXAREUmpXisbkCtHVrbvPRrjte37/sHDLcsbBSUpSGQkDOwI2zeYjr/4Fpp1jLX4ph0H+GTkDwCULJqfOeMHYveCLZlFREREJJ7lyAVrj0Hnz8DWDgLuwNd9oF5R2PAHvGAKoHM6J36bNJje7ZsAsO/oKeq2/YLjZy4mVvQiIpLCvFbiqnVjT36ev5qpvy7nbkAQAHcDgvjxtxXMWLCaNk0849zm6dOn8fT0xMnJiWzZsjFgwACevORbHYDcuXNjMBiivUJCQsxlfHx8Yizz4YcfxjlOiQOj0bRDzZ//rofW+XPoNiDW4vuPnqbrl98RERFJvlw5WDBlWIpd5F9EROS/3qr8IXmrtHrpq/ewyZYOVVKD9K4wcDxsPA0NPzKdu3LeNIr+w0pwaFesVa2trRnWtz0/jvoUeztb/G7cpkGnQazZEnsdERGR2Ni8TqXurd/nst8Nxkydx5ip87CxtiI8IhKA9k3r8HHbxnFqLyAggBo1apA/f36WL1/OtWvX6N+/P8HBwUydOvWl9Zs1a8Znn30W5Zy9vX20cnPmzKFQoULm40yZMsUpTomj6ePgt/+Z3jdpDwO+jbXoqfNXaP3JKB6HPsEtS0YW/ziCjK7OiRSoiIiI5X3cppHWc5SkxyMPTJwPHfvBt1/AXh9T0qrle1CnKXw+DnLnj7Fqs3rVyOORnY6fj+PmnQA6D/iOz7u15LOuLbUMhIiIvDKD8Q22+7h49To79x/jXmAQGdI7U6lcMd7KmT3O7YwbN44xY8Zw9epVMmTIAMCMGTPo2bMnV69eJXv22NvMnTs3DRo0eGGCy8fHh+rVq7N//37Klo15UflXpe2cX9GiGTCsu+l99Qbw43KIZcrf1es3adhpEDdu38PFOS2rZ42j4FseiRisiIiIJCT1n1IIo9G00+B3A0wb7gDY2ECrj6HXMMiYOcZq/rfu0uGzcRw5eR6ABjXf4X8jP8HJ0SGxIhcRkWTsjb7qeCtndto1rcOnnZrTrmmd10paAaxfv55atWqZk1YALVq0IDIyEm9v7zcJUSxhwx+mLZTBtKXylMWxJq1u37tPy14juXH7Ho4O9sybMlRJKxEREZGkyGCAGg1gzT8wegZkzgbh4fD7D1Arn2m0fcjjaNXcsmRk5cwxfFC3CgBrtuymYaeB+PrfSuw7EBGRZOi1ElcrvXfy428rYrw27beVrN70d5zaO336dJQpfAAuLi64ublx+vTpl9afP38+9vb2pE2blnr16nHs2LEYy9WrVw9ra2vc3d354osvePw4+i9WeUN7tkH/j0yLshcsBtNXg2OaGIs+fPSY1n1HcfHqdWysrZn13QDKFS8UY1kRERERSSJsbKBlV9h0DvqOgDRO8DAIJgwGzwKw/FeIiIhSxdHBnmmj+zGkd1sMBgMnzl6mTtsv2Hv4pGXuQUREko3XSlz9MPePWHd6c3Cw44e5f8SpvYCAAFxcXKKdd3V15d69ey+s+/777zN16lQ2b97Mjz/+yPnz56lUqRIXLz7buSR9+vQMGDCAOXPmsGnTJjp06MAPP/xA8+bNXxpbUFAQfn5+5pe/v3+c7i1VOXEIejSCsCfgnhtmbTAt7BmD0CdhdPh8HEdPXQBgyoi+1Px362QRERERSQac0kKfr0wJrJbdwMoKbvjBlx2gcRnYuSlKcYPBQN+OTfl14iCc0jhwNyCQpj2GM3/lppjbFxER4TUXZ79w5TqF8uaM8VqBPB5cuHr9jYKKi//973/m95UrV6Z27doUKlSI8ePHM23aNABKlSpFqVKlzOVq1KiBm5sbvXv3Zt++fZQvXz7W9idOnMjIkSMT7gZSisvnoFNdePQAMmaBOd6QNeapoxEREfQaOokd+/4BYPTnXWhWr2piRisiIiIi8SWLG4z+Gdp/AuMHwtY/4fRR6FgbKtWGL7+HQsXNxetUKc+6ud/Rrt8Yrly7Sf9RP3Lq/BVGfNoRGxtrC96IiIgkRa814srB3o7b9+7HeO3mnXvYWMftF46rqyuBgYHRzgcEBERZ9+pVuLm5UalSJQ4ePPjCci1atAB4abn+/fvj6+trfu3bty9O8aQKN6+bOib3boNTOpi1PtbdZYxGI4O+ncmf/26H/Gnn5nRt1SAxoxURERGRhJC/CPy8GuZtg2L/boi00xveLwlfdgR/P3PRQnlzsuG373mvbDEAZi5cw0d9v+Z+0EMLBC4iIknZayWu3ildlB/mLufR45Ao5x89DuHH31bwbpm349ReoUKFoq1lFRgYiL+/f7S1rxKbs7Mz7u7u5pebm5tF40lyAgOgUx3wuwy2djB9FRQtHWvx739exK9/bACgTRNPBn78USIFKiIiIiKJokI1WLYXJi4wLR9hNMLyuVC7AEwcAg+CAMjg4sziqV/RsbkXAH/tPUrddl9w7pJfrE2LiEjq81qJq8G92nDtxm0qNOrBoG9nMHn2UgZ9O4MKjXrg53+bIb3bxqk9Ly8vNm/ezP37983nli5dipWVFbVr145TW9evX2fnzp2UK1fuheUWLVoE8NJy8gKPg6F7Qzh73LSmwaSFULF6rMVnLV7LhJmLAahfoyLfDeqBwWBIrGhFREREJLFYWUHDVrDhNAwcD84uph0HfxoLNfPC71MhLAxbWxu+Gdid7wb1wMbamku+/ni1H8CWv188K0JERFIPg9FoNL5OxUu+/nw3fSE79x8jIPABrunTUaVCcb7o9iEGKyty5cj6ym0FBARQtGhRChQowODBg7l27Rr9+/endevWTJ061VyuZs2aXLlyhfPnzwOwcOFC1qxZQ7169ciePTsXL15k3Lhx3Lt3j4MHD5InTx4A2rRpQ758+ShdujQODg5s3bqVSZMmUa9ePVasiHl3xNj4+fnh4eGBr68v7u7ucaqbooSFQc8m4LPWdDx6hml3mVis2LiDj4dMxGg08m6Zt1n4w3Ac7O0SKVgRERGxJPWfhPv3YPpY+O0H00Y+YFpa4vNvoHYTMBj4+8Axugz4jnuBD7CysmJ433b0aNNIX3SKiKRyr524+q87AYGs3vQ3yzds5+Cxs/jvXx6n+qdOnaJPnz7s2rWLdOnS0a5dO8aMGYOd3bPkRrVq1bh8+TKXL18GYM+ePQwaNIjjx49z//59XFxcqFGjBl9//TUFCxY01xs3bhzz58/nypUrhIaGkidPHj766CMGDRoUpf1XoY4Xpu2Nv+wAq+aZjvuPgY8Hx1rcZ/dh2nw6hrDwcIoVfIsVM0aTLm2axIlVRERELE79JzHzvWSaLrhm4bNzZd4zLeBe6h0u+92gff+xnL5wFYCWDarz3eCP9YWniEgq9kaJq+DHoazbtocVG7azfd9RwiMiebtAblo0qJFiF9xO9R2v8HAY0B7+XGA6bv8JDJkEsXwTduj4WZr2GE7w4xDyeLixetY4smR0Sbx4RURExOJSff9JovtnP3z7Bez769m5Ok3hi294mCkHvYZNYsNfpk2RyhQryJzvvyRr5rht2iQiIilDnBNXERERbN11mOUbtrNx+z6CH4eSNZMrt+7eZ/qY/jSqXSmhYk0SUnXHKywMPmsN65eajlt2g69/Mq1hEIOzl3xp1Hkw9wIfkCWjK2vmfBOnKaQiIiKSMqTq/pPEzmiEbWvhuwFw4ZTpnI0NtPqYyJ5D+XbJJibPMvU73bJk5NeJgyhROJ8FAxYREUt45cXZ9x05xcBvfqZYnU607TeGv/YcoZlXNVbOHM1fS/6H0WgkSybXhIxVLOnJE/ikxbOkVdveMGp6rEmrazdu82GvkdwLfIBz2jQs+vErJa1ERERE5BmDAWo0gDX/wKifIVNW0+j+33/AyjM/g6x9mTGiNw72dvjfukujzoNZ6b3T0lGLiEgis3nVgu93GYzBYOC9sm/To/X7VKtYChsbawCCHjxKsAAlCQgNgd7Nni3E3rEfDJoQ6/TAe/eD+LD3SK7dvPP/9u47rsq6/+P46zBFBAEnCu6BC/fWHLhARStzZMO61cyyYd1W9rszK7W6zazMtLuynaVpjsSFe6eVuXAPUBwhQ2XD+f1xKUpOCrjOObyfj8d5xLnW+VxXhR8/5/v9fCnm7sZXU1+mXs0qhReviIiIiNgPFxcYOBx63w+fToZP/gsXk2DyS/QpH0Cj+0fRd3EUp86d57GXJrPv0HFeGDEIp5t8gSoiIo7ljn/b16lRGavVyuYde/jfd4uZt3QdFy+lFGRsYgtSU+DxvleLVsNfuGXR6lJKKoOffoMDR2Nwdnbi4zefp1XjeoUXr4iIiIjYJ88S8NSrsPKQsVq1kxOcjqHyey+wzekX/hVgNGif+ukcHnn+Tfv5u0h2NsTHwaF9sHUt/LYZTp80FjwSEZHbylOPq/1Hopm7ZA0/Ld9A9KmzFHN3o2u7pnRt35ynx3/AvJmv07qJYxcpilSPhuRLMCIcNq8y3j/xH3h6/E2LVukZGTz07ERWb/4NgPfGjWJgeEhhRSsiIiI2qkjlT5J/Du6B/74IqxfnbNpbPognLpRnr3NJgqpX4st3XzanHUVaGpw/C3Fn4c8zxj/jzkLcNT//ecY45vw5YwrkX7m4QNkKUKESlA8E/0DjZ/9A432FSuDjd9PcW0SkqPjbqwpu+30f85auY1HkJuLik7BYLIR2bMGwQb0dunhVZBKvixdgeC/4ZZ3x/unX4Mn/3PTw7OxsRv7fu8xfth6AcU8PYeRDfQshUBEREbF1RSZ/koKxZbWxAuHuHQBYsTDbLZA33YJI9y3Lp/99gTZN6/+zz7Ba4ULi1SLUlaLTtYWp89f8fCExH27sDhTzuHVhyz8QinsWTiwiIib524WrK7Kysli9+TfmL1vP0rXGKoMB/mX4ZeHM/IrRphSJxOtCEgwNhV83Ge+ffxMee+Gmh1utVl7+7yd8+r0xnfCJh/ryytNDCiFQERERsQdFIn+SgpWdDYtnw5SxcPI4ACk4M9OtGh8VD+Lfzw3nkftCcXZ2vnpORsbV0U/n72BkVEbG34+vpC+UKgelyhpN5kuVBb9rfr6y3a8MpKfBqRMQG335dc3Pp07AuVjjfvPy2f6Xi1hXilvXFrbKVQRX179/byIiJvvHhatrpaSmEbFmK/OXreerd1/Or8vaFIdPvJIS4JHu8Mc24/3YKUYz9lt499M5vDn9GwAG9u7M1HGjsGhIs4iIiFzm8PmTFJ60VPhqGnw0wchbgT8tbixx8SewuAvBZb3wy0rFEncGEuP//ue4ul5feLpZYcq3NLi55c/9gVFAO3sqdzHr9F9+jo+78+tZLFDWP/eorb+O4CpV9qarhYuImC1fC1dFgUMnXgnnYUhX2POr8X7cNHjgiVue8uW8Zfx7wkcAdG3fjM8nv5Sz2qSIiIgIOHj+JOZIOA8fTcD61TQsGel3do5XyWtGPpW9+eio0uWMY235i9iU5JsXtmKjjffJeVj53dUNygdcPx2xTQhUqVlw9yEicgdUuMojh0284s4ZRauoncb712cayxLfwuLITQx7cTLZ2dm0bFSH2dNepbiHeyEEKyIiIvbEYfMnMV/0UZg5icSoPfx2Ool98Sn8aXHnnMUdv+o1uHvwvTRs18aYoudezOxoC4/Vaow4u9l0xNPRcDrmxk3jr+XqCiPGGq/8HFUmIpIHKlzlkUMmXn+egYdCjJVbLBaY+Cn0e+SWp2z4ZReDRo0nPSOTOjUqs+CTCZT0KlFIAYuIiIg9ccj8SWzS+m1/MGn6N+zYtT9nW4eWDXlx5GCa1K9lYmQ2KCvL+HvAzQpbRw9cbUJfqz5M+gyCm5sbs4gUSSpc5ZHDJV5nThlFqyNRxrz2t7+APg/c8pSMjEya9BzG2bh4AiuU5efP3qRcGb9CClhERETsjcPlT2LTrFYrK9ZvZ9L0b9h78FjO9h4dWvDC4/dTt2YV02KzKxeSYPKL8K3RFgQnJ3hkNDzzmrHaoYhIIVEHvqIsNhoGdzCKVs7OMOXb2xatAFZs2M7ZOKPZ5ZdTxqpoJSIiIiI2w2Kx0O2u5kR+O4WZk56neuUKACxdu43Og55lxNh3OHLilMlR2gEvbxg/Hb5eA5VrGCsdfjoZegXDtnVmRyciRYgKV0VVzDG4vwMcP2TMXX/vB+g54I5Onb1oFQAtGtbRN1YiIiIiYpOcnJzo260d6374gKnjRhHoXwar1cr8Zetp1+9Jnn1tGjGx58wO0/a17ACLdsK/njdGXR0/ZHz5/eoTcPGC2dGJSBGgwlVRdOKI8YdNzFFjBZEPfoTu99zRqWfjEli5YTsAg/qEFGSUIiIiIiL/mIuLM4PCQ9g4bzqTXhhO2VK+ZGVl8+2ClbS++3HGvv0/zv4Zb3aYts2jOLz4X/hhM9SsZ2z7Zjr0rA/rl5kbm4g4PBWuippjB+H+u4ymi27uMGMBhPS+49N/XLKGrKxsPIq5E96lbQEGKiIiIiKSf9zdXHm0fxhbF8zglacfxrekF+kZmXz6/c+06DOC19//kvhEjSC6pYYtYP4OePIVcHEx/k7xaA8YMwQSzpsdnYg4KBWuipJD+4yRVmdOGg0VP14Md/W449OtVivfXZ4mGN6lDSU81ZRRREREROxLcQ93nnjobn5ZOJN/PzaQEp4epKSmMe2LeTTv/RjvfPw9Fy4mmx2m7XJ3h6fHGwWs+k2NbfO/gNC6sGyeubGJiENS4aqoOLAbHugIZ2OhuCd8EgFtu+TpEr/vPcT+wycAGBiuaYIiIiIiYr+8ShTn+eED+WXhTJ58+G483N24cCmZt2d+R4vwx5j+5U+kpKaZHabtCgqGOVvg328ZMzn+PANP3guj7jN+FhHJJypcFQV7fzeKVnFnwdMLPltmNFnMo+8WRAJQuWI5Wjepl78xioiIiIiYwM/Hm/889TBbF8zg0QFhuLq4cD7xAuPf+5yWfUYw64clpGdkmB2mbXJxgeFjYPEf0KydsW3pXGP01YKvwWo1Nz4RcQgqXDm63Tvgoc4QHwdeJWHWcmia995UKalpzF9mLHs7sHdnLBZLfkcqIiIiImKacmX8mDRmOJt/ms6gPiE4Oztx5s94XnzrY9rc8wSzF0aSmZlldpi2qWot+GYtjJtmzO5IOA/PPwjDekFstNnRiYidU+HKkf22BR4KgcR4KOkLX6yExq3+1qUi1mwl6WIyFouF/r0653OgIiIiIiK2IdC/LFNfGcX6OR/Qt3t7AKJPneXp8R/Qof9TLFi+gezsbJOjtEFOTvDAE7BkD7TrZmxbuwRC68F3M0HPTET+JhWuHNX2DfBIN7iQCL6l4MtV0KDZ377c7IXGNMH2LYIJ8C+TX1GKiIiIiNik6pUrMnPic6yaPZXuHVoAcOj4SYa/NJmuDzzH8nW/YNVUuOtVrAyfLYVJn4G3D1y6AK+MML5QP37Y7OhExA6pcOWItq6Bf/Uw/pAoVRa+XgN1G/3ty8XEnmPdtj8AGKSm7CIiInbl0KFDjBgxgkaNGuHi4kL9+vXv6Dyr1cqbb75JpUqV8PDwoHXr1mzZsqWAoxWxPfVqVuHLKWNZ8vnb3NWyIQC79x/lwWcn0OvRF1l/OU+Wa1gs0O8RiNgLXfsa27augV4N4LMpkKUplyJy51S4cjQbV8LQMEi+BGXKG0WrWneWoN7MDz+vxmq14l2iOKEdW+ZPnCIiIlIo9uzZw88//0yNGjWoW7fuHZ/31ltvMW7cOJ599lkWL16Mv78/3bp148iRIwUYrYjtatqgFnOmj2fezNdp3jAIgO1/7Kff469w74j/sH3XfpMjtEFl/eHDefDe9+BXBlJTYNJzMKAtHNxjdnQiYicsVo1vzZOYmBgCAwOJjo4mICDA7HByW7cUHu8L6WlQriJ8tcpolPgPWK1WWvV9nGMxp3n43h68PXZE/sQqIiIihSI7OxsnJ+O7yiFDhrB9+3Z27959y3NSU1MpV64cTzzxBBMnTgQgPT2dWrVqERYWxvTp0/MUg03nTyJ/g9VqJXLjDt6c/i279l8t5nZt34yXRg6mXq2qJkZno87/CROegYXfGO9d3eCJ/8DwF8DV1dTQRMS2acSVo4hcBCP6GEWrCpWMVT3+YdEKYMtvezkWcxqAgeFqyi4iImJvrhSt8mLTpk0kJSXRv3//nG1ubm7cc889LFmyJD/DE7FLFouFLu2asfzryXzy1hhqVTUKsivWb6fzoGcZ9uJ/OXTspMlR2hi/0vDO1/DxYuNL9ox0mPofuKc57PnV7OhExIapcOUIls2DJ+8xfvkHVDWKVpWr58ulrzRlr10tkMb1aubLNUVERMS2RUVFARAUFJRre506dThx4gQpKSlmhCVic5ycnOjdpQ1rvn+PD8Y/TaWK5QBYuGIj7e8bxdOvvs+JU2dMjtLGdOoJEXtgwHDjfdROuLcFTH4J0lLNjU1EbJIKV/bu5+/h6f6QmQmVa8C3ayGgSr5c+uKlFBas2AjAwN4hWCyWfLmuiIiI2Lb4+Hjc3d0pVqxYru2+vr5YrVbi4+NveX5SUhIxMTE5r9jY2IIMV8R0zs7O9O/ViY0/TuPtl0ZQvowf2dnZzF60ijZ3P8FLb33MmXPnzQ7TdniVhDdmwpeRxhfvWVkw800IbwQ7NpodnYjYGBWu7NmCr2H0/cYv+mq1jZFW/oH5dvmFKzeSkpqGs7MT/cI65Nt1RURExLFNmTKFwMDAnFeLFi3MDkmkULi5uvJwvx5s+ekjxj/7CKV8vMnIzOSzH5bQos8Ixk/9nLj4JLPDtB2tO8PPu2DIM8ZKhEf2w6D28NpTcOmi2dGJiI2wmcJVVFQUXbt2xdPTk/LlyzNmzBjS09Nve16VKlWwWCzXvVJTcw8zPXXqFPfeey9eXl74+fkxdOhQkpLs+A+NHz+Hfz8E2dlQs55RtCpXIV8/4so0wZC2TSlb2jdfry0iIiK2y9fXl7S0tOvyqfj4eCwWC76+t84LRo8eTXR0dM5r27ZtBRmuiM3xKObOiAf6sG3hTF54/H68SxQnNS2d6V/9RPPw4Uz88GvOJ9jx30XyU3FPePld+H4jVK8DVit89QH0amCsmC4iRZ5NFK7i4+Pp3Lkz6enpzJs3j4kTJ/Lxxx8zevToOzq/X79+bN68OdfL3d09Z39GRgbdu3fnwIEDfPvtt3z00UcsW7aM+++/v6BuqWDN/hhefMT4pR4UDF+thtLl8vUjjpw4xdbf9wEwqHdIvl5bREREbNuV3lb79+/PtT0qKopKlSrh4eFxy/O9vb0JCAjIefn7+xdYrCK2rISnB6OH9mfbwpk89ci9eBRz51JyKu99Npfm4Y8xafo3xCdeMDtM29C4NSz4FR5/GZydIeYYDOkKY4dCUoLZ0YmIiVzMDgBgxowZJCUlMX/+fPz8/ADIzMxk5MiRjB07lgoVbj2SqFy5crRq1eqm++fOncuePXvYt28ftWvXBoxvErt37862bdvsa/j61x/C+CeNn+s1gVnLwbdUvn/M7EWrACjlW5Iu7Zvm+/VFRETEdrVp0wZvb2/mzJlDw4YNAeOLwHnz5hEWFmZydCL2x7ekFy8/+SDD7+/N9C9/YtYPS7h4KYWpn87hk9mLGTqwFyMGh+Nb0svsUM3lXgxGvwE9+sFLj8Le32DOp7AuAsZ/BCHhZkcoIiawiRFXERERdOnSJadoBdC/f3+ys7NZvnx5vlw/ODg4p2gF0LVrV/z8/OxrSedZ714tWgW3MJoZFkDRKisrix8WrwagX1gH3Fxd8/0zREREpHAkJyczd+5c5s6dy/Hjx0lKSsp5f+7cOQBCQkKoUaNGzjnFihXjpZdeYvLkybz33nusWrWKQYMGERcXx/PPP2/WrYjYvTJ+Pox7ZgjbFn3MyAf74uHullPAatZ7OG9qBJahbiOYuxWemwiubnDmFIzoA88MgrhzZkcnIoXMJgpXUVFR1y237OPjg7+/f85yzLfyzTff4O7uTokSJQgLC2PXrl23vb7FYiEoKOiOrm8TPn4bJl6eOtmkDXy+HLx9CuSj1m39g9izcQAM7N25QD5DRERECsfZs2e57777uO+++1izZg3R0dE57/fs2QMYX1plZmbmOu+FF15g3LhxTJ48mbCwMGJiYli2bBnVqlUz4zZEHErZUlcLWI8/0CengPXup3No3vsx3vroWxKSinhzcldXGPESLPzdmEYI8PNsCK0Li74z2qaISJFgE4Wr+Ph4fHx8rtvu6+vL+fO3XjY2PDycadOmsXLlSj788EMOHTpEu3btOHLkSL5c3yaWc/7wDfjvC8bPze+CT5caS8gWkO8WGU3ZG9apTt2aVQrsc0RERKTgValSBavVesNXx44dAVizZg3Hjh3LdZ7FYuGll14iOjqa1NRUtmzZQuvWrQv/BkQcWNlSPrz67CNsW/QxIwaH4+HuxoVLyUz55Aea9RquAhZAjTrw3Xp4eSp4FIf4P42V1Uf0gdMnzY5ORAqBTRSu/on333+fwYMH0759ex5++GHWrl0LwOTJk/Pl+qYu52y1wtRXYOp/jPetO8MnS6BEwc19T0i6yNI1WwEYGK6m7CIiIiIiBa1sKR/Gj36UbQtnMmJwOMX+UsB6e8Z3RbuA5ewMQ56GxbuMvxMBrFoEYfXgh080+krEwdlE4crX15fExMTrtsfHx+fqe3Un/P39adeuHTt27MiX65u2nLPVCu+MhQ9fN9636wYfLzaWiy1A85euIy09AzdXF+7u3r5AP0tERERERK4qW9qX8aMf5ZeFM3nsmgLWO//7nua9h/Pfmd+ReKEIF7AqVYMvVsKE/0EJb7iQCC8PM1YfPLjX7OhEpIDYROHqRr2mEhMTiY2Nva43VX5d32q1sn///tte35TlnK1WePN5mPmm8b5jT5ixAIrdeunp/PDdQmOaYI+OLbWqiYiIiIiICcqW9uW1ywWs4ff3ppi7G0kXk5n88fc061XEC1gWC/QfChF7oXNvY9umSGP0VXhjmPkWxBwzNUQRyV82UbgKDQ1l5cqVJCQk5GybM2cOTk5OdOvWLU/XOnXqFBs2bKB58+a5rr9z504OHjyYsy0yMpK4uDjbW9LZaoXXn4LPphjvu/aFD+cZS8MWsL0Hj7Fz32EABmmaoIiIiIiIqcqW9uX15/7FtgUzbljAmvzx7KJbwCpf0fhyf8q3UKa8sW3f7zD5RehUFe5rDZ+/Z6xIKCJ2zWK1mj8hOD4+nnr16lGrVi3Gjh3LyZMnGT16NIMHD2batGk5x4WEhHD8+HEOHToEwHfffcfixYsJCwujQoUKHDlyhEmTJnH+/Hl27NhB1apVAcjIyKBJkyZYLBYmTpxIcnIyzz//PMHBwSxevDhPscbExBAYGEh0dDQBAQH59xAAsrNh3EiYPdN4H3ofvPONsaJGIXhlymfM/GYh/mVLsWPxxzg7OxfK54qIiIhjK9D8SaQIOXPuPB98MY8vf1xGWnoGACW9PHns/nCGDeqFt1fBthWxWZmZsHmVserg8nnGFMIrLBZo2RF6DoRu94BfadPCFJG/xyYKVwD79u1j1KhRbNq0CS8vLx566CEmTJiAm5tbzjEdO3bk2LFjOavebNmyhZdeeondu3eTkJCAj48PnTt35rXXXqN27dq5rn/y5Emeeuopli9fjouLC/fccw/vvvsu3t7eeYqzwBKvrCxjfvaPs4z3ve+Ht78AF5f8+4xbyMjIpGHov4iLT+TpR/sx9okHCuVzRURExPGpcCWSv06fO88Hn8/jq3kqYF0nLQ02LIPFsyFyAaQkX93n4gJtuxpFrC59CnSldhHJPzZTuLIXBZZ4nT4J9zaHs7Fw90Mw6TNj9YxCsmT1Fh553uiptXn+dKpVqlBony0iIiKOTYUrkYJxswLWiMHhDB1YxAtYAMmXYM3PRhFr7RJIT7u6z80dOoRBr4HQqRd4FDcvThG5JRWu8qhAE6/DUTD3M/j3m+BUuO3HHho9kWVrt9GyUR0WfjqpUD9bREREHJsKVyIFK/ZsHB98Po+v5y/PKWD5eJfgscHhDBvYC68SKspwIRFWLjCmE25Ybsx4uaK4J3QON4pY7bqDu7t5cYrIdVS4yiNHTLzOxiXQKPRRsrKyefeVJ7m/TxezQxIREREH4oj5k4gtUgHrDp3/0+iFtfg72LbWWCDrCq+SRi+sngOhdedCa90iIjenwlUeOWLi9dFXP/Hq1M/xKObO7uWfU8LTw+yQRERExIE4Yv4kYstiz8bx/uc/8vW85aRnZAJGAWvEA30YOqCnCljXOnMKIuYYI7F+35J7n18Z6NHPKGI1a1fos2LkDlmtRjGypK8KjQ5Khas8crTEy2q10qH/U+w/Es2AXp14f/zTZockIiIiDsbR8icRe3HqzJ85I7BUwLoD0UdhyQ9GEWvf77n3lasIYf2NIlZwc2O1QjFPVhb8uglW/mRMAT1xGFzdoEpNqF4n96tabSimwRn2TIWrPHK0xOvX3QcIfXgMAPM/foM2TeubHJGIiIg4GkfLn0TszY0KWL4lvRjxQDhDB/TSjIsbORwFS743phMe2Z97X2A1ox9W2ACo3UBFrMKSkgwbVxiFqlWLIP7POzvPYoGKVYwiVo2/FLVK+hZoyJI/VLjKI0dLvMZMnMEXPy6lcsVybF0wA4t+6YqIiEg+c7T8ScRenTx9jg8+n8c3P63IVcB6/IE+/GtATxWwbsRqhag/jFFYP8+GmGO591evA70GQc8BULWWKSE6tLhzsHoxRC4wmuqnpuTeX7kGdOkL7bpBUjwc3nf1dWR/7pUkb6R0uetHaFWvA+UqqCBpQ1S4yiNHSrxSUtMI7v4ISReTeeHx+xk9tL/ZIYmIiIgDcqT8ScQRnDx9jvdn/cg3P60kI1MFrDtmtcLObUYBa8n3cDY29/56TYyphGH9oWJlc2J0BMcPGaOqVi6AXzdCdnbu/cEtoGtfCOljjKC6WYEpK8soNF5bzLryupB46xg8vXIXsq6M1Aqoqj5aJlDhKo8cKfGat3Qdj788BYvFwo7FH1OxfBmzQxIREREH5Ej5k4gjiYk9xwef5y5g+ZX04vEH+/Jo/zAVsG4lKwu2bzCKWEvnQHxc7v1N2hhFrND7oEx5c2K0F9nZsHvH1X5VB/fk3u/qZqzw2KUvdO5tjIb6J6xWOHf6xgWtvxYj/0p9tEyhwlUeOVLi1X/kONZu3UmHlg35Yfp4s8MRERERB+VI+ZOII4qJNUZgfbsgdwFryH2h9OnWjqDqlUyO0MZlZMCWVbDoO1gxHy4mXd3n5AQtOxpFrG73gG8p08K0KenpsHW1UaiKXGCs7ngtr5LQsacxsqpdd/DyLpy4khKM/mZ/LWjFHL1+5Ne11EerQKlwlUeOknjFxJ6jWe/hWK1WPpowmnt63GV2SCIiIuKgHCV/EnF00bFneX/Wj3y3IDKngAVQu1og4V3bEt61LbWqBpoYoR1IS4V1S42RWJELc/dkcnGBtt2gRQeoHgTVgoxG70Vl6tmFRFizxChUrY3IXeAD8A+ELn2MkVXN7wJXV1PCvKG0VDh64PqC1tEDee+jVbUWuHsYRU1nZ3ByNv5pufz+yjYnp6v7rt1/Zdu1+68cf2W7g1HhKo8cJfGa8skPvPXRt3iXKM4fy2bhUczd7JBERETEQTlK/iRSVETHnmXG1wtZsGID5+IScu0Lql6JPl3b0rtLW2pW1f/Pt3TpotFY/OfZRqEmI/36Y1xdoXJNo4hVvc7VglbV2lDCq/Bjzm+xMUahauUC2LbGGJ12raCGl4tVfaBuY/triJ6VZYzGOnSDaYd/LcwVplsWtvJYCOv7EDw0yrx7QYWrPHOExMtqtdKq7+MciznNw/f24O2xI8wOSURERByYI+RPIkVRVlYWW37by8IVG1m8ajN/ns/d0LpOjcqEd21Ln65tqV65oklR2omkBFjxk9HH6cDu2089AygfcLmgdbmoVe1yUausv+0WeKxW4/5WLjDudfeO3PudnaFZe2NUVUg4BFY1I8qCZ7Ua/bJu1Efr3Gmzo8ubYWNgzFumhqDCVR45QuK1+dc99B32MgARX7xNk/patlVEREQKjiPkTyJFXVZWFpt/vVrEiovPXcSqV6uKMZ2wS1uqVfqHzbOLgrRUOHYQjkQZo3WORF19XTu98EZKeN+4oFWpujnT6zIzYcfGqyOroo/k3l/c0+hT1bUvdAhTn6+0VGPkmTXbGLGVlQXZWUYhMzvr6rYr+69su3b/lePzsv/vfl6TttCuq6mPTIWrPHKExOupce/x/eLV1K4WyNof3sdiq9V6ERERcQiOkD+JyFWZmVls+nU3C1ds5OfIzZxPvJBrf/3aVQnvYvTEqhrob1KUdio7G2KjjQLWlSbhVwpaf5659bkuLlCpxvUFrWpB+d/cPPkSbFxhjCJbs/j6VRVLlTVGVHXpC21CwL1Y/n6+FCkqXOWRvSdeFy+lUL/bEFJS0xj39BBGPtTX7JBERETEwdl7/iQiN5eZmcXGHbtZuGIDS1Ztua6IFRxUjfCu7ejdpQ1VAsqbFKWDSDgPR/dfX9A6cfj20w7LVbjaR+vaglb5inc+7TDuLKxabEwB3LjCGDl0raq1jEJV177QsKVDNgkXc6hwlUf2nnh9u2Alz742DWdnJ36P+IyypXzMDklEREQcnL3nTyJyZzIyMtm4fRcLVmxkyeotJCRdzLW/YZ3qhF9u7F65YjmTonRAaWlw4pBR0Dryl6JW8qVbn+tZ4moR69qiVuUa4OZmTGdcucCYBrhjo9G76QqLBRq1gpDLzdWrBxXsfUqRpcJVHtl74hX+r5fY+vs+undowZdTxpodjoiIiBQB9p4/iUjeZWRksv6XP1i4ciMRq7deV8RqVK8mfbq0pXfXNgT6lzUpSgdntcLpmBsXtM7G3vpcZ2djut9fj3Nzh9Yhxqiqzr2hjEbRScFT4SqP7DnxOnLiFK3vHgnArMkvEtaplckRiYiISFFgz/mTiPxz6RkZrN/2Bwsvj8RKupica3/jejXpc3kkVoB/GZOiLGIuJF4taF1b1Dp+yGjMfa2SvtCxpzGyqn13KOFlTsxSZKlwlUf2nHhN/PBr3vtsLqV8S7Iz4lNcXV3MDklERESKAHvOn0Qkf6VnZLB2y04WrtjI0rVbrytiNW1Q25hOGNKaiuVVxCp06enGqoCH98HJ4xAUDM3am7NaochlKlzlkb0mXllZWTTtNZzYs3E8Njic10Y/anZIIiIiUkTYa/4kIgUrLT2DtVt+Z+HKjSxds40Ll3IXsZoFXylitaFCudImRSkiZtOQmyJi3dY/iD1rLFE6sHdnk6MREREREZGizt3NlW53NafbXc1JTUtnzZbfc0ZiXUpOZfsf+9n+x35eeeczWjSsQ3jXtvQKaY1/2VJmhy4ihUiFqyLiu0WRgLGSR92aVcwNRkRERERE5BrF3N3o0aEFPTq0IDUtndWbf2Phio0sW7eNS8mpbNu5j2079/Gfdz6lZaM69O7Shl4hbShfxs/s0EWkgKlwVQTEJ14gYvUWAAaGh5gcjYiIiIiIyM0Vc3cjtGNLQju2JCU1jdWbfmPByo0sX/cLySmpbPltL1t+28v/Tf6UVo3rEt61LT07taKcilgiDkmFqyJg/rL1pGdk4ubqwt3d25sdjoiIiIiIyB3xKOZOWOdWhHVuRXJKGqs3/8qCFUYRKyU1jc2/7mHzr3t46a2Padqgdk7Bq0aVimaHLiL5RIWrImD2QmOaYGinVviW1NKlIiIiIiJif4p7uNOzc2t6dm5NckoakRt3sHDFBlas305KWjo7du1nx679vPHBl9SsEkCPy0WsxvVq4OTkZHb4IvI3qXDl4PYePMbOfYcBGNRb0wRFRERERMT+Ffdwp3eXNvTu0obklDTWbv2dpWu2snzdL5xPvMDBYzEc/DyGDz7/kXKlfenRwShitW1eHzdXV7PDF5E8UOHKwc1etAqACuVKcVfLYJOjERERERERyV/FPdxzpghmZmaxbec+lqzewtI1W4mOPceZP+P54selfPHjUrw8ixPSrimhHVsS0qYJXiWKmx2+iNyGClcOLCMjk7lL1gJwX89OODs7mxyRiIiIiIhIwXFxcaZN0/q0aVqf15/7F3sOHCVizVYi1mxlz4FjXLiUzE/L1vPTsvW4urjQvkUwoR1b0qNDC8qW9jU7fBG5AYvVarWaHYQ9iYmJITAwkOjoaAICAswO55aWrN7CI8+/CcDm+dOpVqmCyRGJiIhIUWRP+ZOIOK7jJ8+wdO1Wlq7Zypbf9pGdnZ2zz2Kx0LRBrZyRW9Urq7m7iK1Q4SqP7Cnxemj0RJat3UbLRnVY+Okks8MRERGRIsqe8icRKRri4pNYsf4XItZsZc2W30lNS8+1v1bVq83dG9VVc3cRM9nM/31RUVF07doVT09Pypcvz5gxY0hPT7/9ideYOnUqFouFXr165dq+Zs0aLBbLda+BAwfm5y3YlLN/xrNyw3YABoarKbuIiIiIiMgVpXy9GRgewhdTxrI38ktmTX6RAb065azCfuBoDO/P+pHQh8fQOGwoL0yawZrNv5GekWFy5CJFj030uIqPj6dz587UrFmTefPmcfLkSUaPHk1ycjLTpk27o2ucPn2a8ePHU7Zs2ZseM2vWLIKCgnLely5d+h/HbqvmLllLVlY2HsXcCe/S1uxwREREREREbJKnRzHCOrUirFMrMjOz2Pr7XiLWbM1p7n763Hk+n7uUz+cazd27XG7u3lnN3UUKhU0UrmbMmEFSUhLz58/Hz88PgMzMTEaOHMnYsWOpUOH2vZnGjBlDeHg4x48fv+kx9evXp1mzZvkWt62yWq3MXhQJQHiXNpTw9DA5IhEREREREdvn4uJM22YNaNuswU2bu89ftp75y9bj5mo0d+/RsSU97lJzd5GCYhNTBSMiIujSpUtO0Qqgf//+ZGdns3z58tuev2HDBn766SfefPPNggzTbvy25yD7j0QDMKhPF5OjERERERERsT8Wi4X6tavx78cGseq7qWxbOJPXnnuUNk3r4eTkRHpGJpEbf+XfEz4iuMej9Hr0RT78cj5HTpwyO3QRh2IThauoqKhcU/gAfHx88Pf3Jyoq6pbnZmVl8eSTT/Lyyy/j7+9/y2PDwsJwdnYmICCAf//736SkpPzj2G3R7IWrAKgSUJ5WjeuaHI2IiIiIiIj9q1yxHI/dH878jyewe/nnTB03iu4dWlDM3Q2r1covO6N47b0vaH33SO66bxQTP/ya3/YczLV6oYjknU1MFYyPj8fHx+e67b6+vpw/f/6W506fPp1Lly7x7LPP3vSYkiVLMmbMGO666y48PDxYtWoVkydPZt++fSxevPiW109KSiIpKSnnfWxs7K1vxmQpqWnMX7YOgAG9O2OxWEyOSERERERExLGU8vVmUHgIg8JDuJSSytrNvxOxZivL1/9CQtJF9h+JZv+RaN77bC7+ZUvRo0MLQju2pHXTeri5upodvohdsYnC1d919uxZXnnlFb788kvc3Nxuelzjxo1p3LhxzvvOnTvj7+/Pk08+ybZt22jRosVNz50yZQrjx4/P17gLUsSarSRdTMZisTCgVyezwxEREREREXFonh7FCOvcirDORnP3Lb9dbe4ec/ocsWfjmDUngllzIvAuUZzQji25N6wj7ZrVx9nZ2ezwRWyeTUwV9PX1JTEx8brt8fHxufpe/dUrr7xCcHAw7du3JyEhgYSEBDIzM8nMzMz5+Wb69+8PwI4dO24Z2+jRo4mOjs55bdu27Q7vyhyzFxpN2e9qEUzF8mVMjkZERERERKTocHFxpl3zBkz491C2L/6Yld9M4blhA6hbswoASReT+X7xavqPHEeTnsMYP/Vz9hw4itVqNTdwERtmEyOugoKCrutllZiYSGxs7HW9r64VFRXFunXr8PW9fvUGX19fIiIi6NGjxz+KzdvbG29v7390jcISE3uOddv+AGBgeIjJ0YiIiIiIiBRdFouFBkHVaBBUjTEjBnEs5jRLVm3hx6Vr2b3/KKfPnWf6Vz8x/aufCKpeiX5hHbmnR3sNQBD5C5soXIWGhjJx4kQSEhJyel3NmTMHJycnunXrdtPzpk6dSkJCQq5tzzzzDB4eHkyaNIng4OCbnjt79mwAmjdv/o/jtxU//Lwaq9WaM/xUREREREREbEOVgPKMfKgvIx/qy75Dx5m7ZC3zlq7l1Jk4og6f4I0PvmTCtK9o3aQe/cI60CukNSW9SpgdtojpLFYbGJMYHx9PvXr1qFWrFmPHjuXkyZOMHj2awYMHM23atJzjQkJCOH78OIcOHbrptTp27EiJEiVyNV1/4IEHqFGjBk2aNKFYsWKsWrWKd999l7CwMObPn5+nWGNiYggMDCQ6OpqAgIC832wByc7OplXfxzl+8gwP39uDt8eOMDskEREREcB28ycREbNlZ2ez+dc9zF2ylkUrN3HhUnLOPnc3V7q1b06/nh3p3KaxmrpLkWUTI658fX2JjIxk1KhR9O3bFy8vL4YOHcqECRNyHZeVlXXLvlU3U69ePb755hveeecd0tLSqFq1KmPHjuWll17Kr1sw3Zbf9nL85BkABvXRNEERERERERFb5+TkRNtmDWjbrAETxwxj5YYdzF2yhsiNv5KWnsGiyE0sityEb0kv+nRtS7+wjjQLrq3V46VIsYkRV/bEVr8xfGrce3y/eDW1q1di7ffv6ReZiIiI2AxbzZ9ERGzV+YQkFq7YyNyItfyyM3c/6MoVy3FvaAf6hXWgeuWKJkUoUnhUuMojW0y8Ll5KoX63IaSkpvHqM0N4/MG+ZockIiIiksMW8ycREXtxLDqWeUvXMWfJWo6cOJVrX+N6NekX1oE+3dpRxs/HnABFCpiT2QHIP7dw5UZSUtNwdnbi3rCOZocjIiIiNiYqKoquXbvi6elJ+fLlGTNmDOnp6bc9Ly4ujhEjRlCpUiU8PT2pX78+M2bMKISIRUTkiiqB/oweNoBN8z4k4ou3+deAnpTyLQnAb3sO8vJ/P6Fhj0cZ/NTrzFu6juSUNJMjFslfNtHjSv6Z2QsjAejSrhllS/mYG4yIiIjYlPj4eDp37kzNmjWZN29eziI4ycnJuRbBuZH77ruPqKgoJk6cSKVKlViyZAmPP/44zs7ODBs2rJDuQEREACwWC03q16JJ/VqMf/YR1m79nblL1rJ0zVZS0tJZuXEHKzfuwLN4MXp2ak2/sA60a94AZ2dns0MX+Uc0VTCPbG2o+5ETp2h990gAZk1+kbBOrUyOSERERGzJpEmTmDBhAidOnMDPzw+Ajz/+mJEjR3LixAkqVKhww/NOnz6Nv78/s2bNYsiQITnbO3TogIuLC5GRkXccg63lTyIijuTipRSWrN7C3CVrWP/LLrKzs3P2lSvtyz097qJfWAfq1aqqXshilzRV0M7NXrQKgFK+JenarpnJ0YiIiIitiYiIoEuXLjlFK4D+/fuTnZ3N8uXLb3peRkYGACVLlsy1vWTJkuh7TxER21HC04P+vTrxw/Tx/LbkE159Zgj1a1cF4Myf8Xz09QJC7h9NhwFP8/6sH4mJPWdyxCJ5o8KVHcvKyuKHxasB6BfWAVdXzfwUERGR3KKioggKCsq1zcfHB39/f6Kiom5yFgQGBtKtWzcmTpzI3r17uXDhAj/88APLly/niSeeKOiwRUTkbyhfxo/HH+xL5Lfvsub79xg15F4qlisNwP7DJ5gw7Sua9hrG3cNf5uv5K0i8cNHkiEVuT5UOO7Zu6x/Eno0DYGDvziZHIyIiIrYoPj4eHx+f67b7+vpy/vz5W547b948BgwYQL169QBwdnbmgw8+4N57773leUlJSSQlJeW8j42NzXvgIiLyj9SpUZn/G/UgY58YzOZf9zB3yVoWrdzEhUvJbNqxh0079jD27Y/p1r4594Z1IKRtE9xcXc0OW+Q6KlzZsW8XrgSgUd0a1K1ZxdxgRERExKFYrVYeeeQRDh48yLfffou/vz8rVqzgmWeewdfXl4EDB9703ClTpjB+/PhCjFZERG7GycmJts0a0LZZAya9MJwV67czd8kaIjf+Slp6BosiN7EochO+Jb0I79KWfmEdaN4wSP2wxGaocGWn4hMvsHTNVkCjrUREROTmfH19SUxMvG57fHx8rr5Xf/Xzzz8zZ84c/vjjDxo0aABAx44dOXv2LM8999wtC1ejR49m6NChOe9jY2Np0aLFP7gLERHJD8Xc3ejdpQ29u7ThfEISC1dsZG7EWn7ZGUV84gW++HEpX/y4lEoVy3Fvj7vo3KYJwXWqU8zdzezQpQhT4cpOzV+2nvSMTNzdXLm7x11mhyMiIiI2Kigo6LpeVomJicTGxl7X++pae/fuxdnZmfr16+fa3rhxYz755BOSk5MpXrz4Dc/19vbG29v7nwcvIiIFxs/HmyH3hTLkvlCOxZxmXsRa5kas5fDxU5w4eYZ3P53Du5/OwdXFhXq1q9K0fi2aBdemaYPaVKpQViOypNCocGWnZi80lqDu0bElPt4lTI5GREREbFVoaCgTJ04kISEhp9fVnDlzcHJyolu3bjc9r3LlymRlZfHHH3/QsGHDnO07duygbNmyNy1aiYiI/akSUJ7Rwwbw7ND+/L73EHOXrGVx5CZOnztPRmYmv+85yO97DvLp9z8DUKaUD03r16Jpg9o0bVCLRvVq4ulRzOS7EEdlsWo94zyJiYkhMDCQ6OhoAgICTIlh78FjdBr4DACzPxhHpzaNTYlDREREbF98fDz16tWjVq1ajB07lpMnTzJ69GgGDx7MtGnTco4LCQnh+PHjHDp0CIALFy5Qv3593NzcGDduHP7+/ixfvpzJkyczfvx4/u///u+OY7CF/ElERPLGarVy8vSfbN+1nx279rP9j/3sijpCRmbmdcc6OztRp0ZlmtavfXlUVi2qVaqgUVmSLzTiyg7NXrQKgArlSnFXy2CToxERERFb5uvrS2RkJKNGjaJv3754eXkxdOhQJkyYkOu4rKwsMq/5y4iXlxeRkZG8/PLLvPDCCyQkJFC1alWmTJnCk08+Wdi3ISIihcxisRDgX4YA/zL07dYOgNS0dHYfOMr2P4xi1q+7DhBz+hxZWdns3n+U3fuP8sWPSwHwLel1eVRWLZoGB9GkXk28Smi0ruSdRlzlkdnfGGZkZNIw9F/ExSfy9KP9GPvEA4Ueg4iIiEhemJ0/iYhIwTl97jw7rhmVtXPfYVLT0q87zmKxUKtaIM0uTy9sFhxEzSoVcXJyMiFqsScacWVnVmzYTly8sTKQVhMUERERERERM5Uv40fPzq3p2bk1YAy22HPw2OVi1gG2/xHF8ZNnsFqt7D98gv2HT/DNTysA8PIsTpP6NWkWHETTBrVoUr8WviW9zLwdsUEqXNmZK9MEWzaqQ7VKFUyORkREREREROQqV1cXGtWtQaO6NfjXgJ4AnDufwK+7D1yeYniA3/YcJDkllQuXklm7dSdrt+7MOb9G5YrG9MIGRr+s2tUq4eLibNbtiA1Q4cqOnP0znpUbtgMwqE8Xk6MRERERERERub0yfj50v6sF3e9qAUBmZhb7j5zIGZG1Y9cBDh0/CcCh4yc5dPwk3y9eDUBxj2I0qlvjctN3Y5phGT8fs25FTKDClR2Zu2QtWVnZFPcoRniXNmaHIyIiIiIiIpJnLi7O1KtVlXq1qvLQvd0BiE+8wK+7D7Bj1wGj8fvuAyRdTCY5JZVNO3azacfunPMrVyyXMyKraYPa1KtZBVdXlTcclf7N2gmr1crsRZEAhHdpg2dxD5MjEhEREREREckfviW9CGnblJC2TQHIzs7m4LGTOSOyduzaz/4j0VitVo6fPMPxk2eYt3QdAMXc3WjXPJjQji3pdldzypbyMfFOJL+pcGUnfttzkP1HogEYGB5icjQiIiIiIiIiBcfJyYna1QKpXS2QwX27ApB04RK/7T1kNH7/Yz87dh8gPvECqWnprNywnZUbtmOxWGgeHERox5aEdmpJ1UB/k+9E/ikVruzE7IVGU/YqAeVp1biuydGIiIiIiIiIFC5vL086tGxIh5YNAWNm0tHoWDZu383StdtYv20naekZbNu5j2079zH+vc+pXb2SUcTq2JKGdapjsVhMvgvJKxWu7EBKahrzlxlDIAf07qz/0URERERERKTIs1gsVKtUgWqVKvDgPd24eCmF1Zt/I2LNVlas/4Wki8nsP3yC/YdPMPXTOVQoV4oeHVrSo2NL2jSpp75YdkL/luxAxJqtJF1MxmKxMKBXJ7PDEREREREREbE5JTw96N2lDb27tCEjI5NNv+5h6ZqtLF27lVNn4jh1Jo7PfljCZz8soaSXJ13aNSO0Y0s6t2msPtI2TIUrOzB7odGU/a4WwVQsX8bkaERERERERERsm6urS860woljhvFH1GEiVm8lYs1Wog6fIPHCJX6MWMuPEWtxd3OlfYuGau5uo1S4snExsedYt+0PAAb16WJyNCIiIiIiIiL2xWKx0LBODRrWqcGLIwdzNDqWpWuMIta2nVGkpWeoubsNs1itVqvZQdiTmJgYAgMDiY6OJiAgoMA/b8r/vuetGd9R0suTP5bNopi7W4F/poiIiEh+Kuz8SURE5E6djUtgxfpfiFizlXVbjebu11Jzd/NpxJUNy87OZvYiYzXBu7u3V9FKREREREREJB+VLeXD4L5dGdy3K5eSr23uvp3EC5fU3N0G6CnbsC2/7eX4yTMADAwPMTkaEREREREREcflWdyDXiFt6BViNHff/NseItZsZekaNXc3kwpXNuxKU/ba1SvRqG4Nk6MRERERERERKRpcXV24q0VD7mrRkIn/VnN3MzmZHcAVUVFRdO3aFU9PT8qXL8+YMWNIT0/P0zWmTp2KxWKhV69e1+07deoU9957L15eXvj5+TF06FCSkpLyK/x8d/FSCgtXbgJgUO/OmkcrIiIiIiIiYoIrzd1fHDmYtT+8z5afPuLVZ4bQslEdLBZLTnP35974kODuj9D70ZeY/uVPHI2ONTt0h2ATI67i4+Pp3LkzNWvWZN68eZw8eZLRo0eTnJzMtGnT7ugap0+fZvz48ZQtW/a6fRkZGXTv3h2Ab7/9luTkZJ5//nnuv/9+Fi9enK/3kl8WrtxISmoazs5O3BvW0exwRERERERERASoGujP4w/25fEH+3LufALL1+Vu7r5t5z627dzH+Pc+V3P3fGAThasZM2aQlJTE/Pnz8fPzAyAzM5ORI0cyduxYKlSocNtrjBkzhvDwcI4fP37dvrlz57Jnzx727dtH7dq1AfD19aV79+5s27aNFi1a5O8N5YMr0wS7tGumYYYiIiIiIiIiNqiMX96au3dr35xG9WoSVL0StaoGqDfWHbCJwlVERARdunTJKVoB9O/fnxEjRrB8+XKGDBlyy/M3bNjATz/9xP79+xk0aNANrx8cHJxTtALo2rUrfn5+LFmyxOYKV0dOnGLr7/sAGNi7s8nRiIiIiIiIiMjt3Elz98/nLoW5S3POCaxQlqDqlahdrRJB1QMJql6ZGlUq4lHM3cQ7sS02UbiKiori0UcfzbXNx8cHf39/oqKibnluVlYWTz75JC+//DL+/v43vX5QUFCubRaLhaCgoNte3wyzF60CoLRfSbq2a2ZyNCIiIiIiIiKSFzdr7r5u2x/sP3KCi5dSAIg+dZboU2dZsX57zrlOTk5UrljOKGhVr0TtaoHUrl6JGpUr4u7matYtmcYmClfx8fH4+Phct93X15fz58/f8tzp06dz6dIlnn322QK5flJSUq4m7rGxBdtcLSsrix8WrwagX2gHXF1t4l+RiIiIiIiIiPwNV5q7X2nwbrVaOXn6T/YfOUHU5amE+49Es/9INCmpaWRnZ3M0Opaj0bFErNmacx1nZyeqBVagdvXAnFFatatXolqgv0PXDuz6zs6ePcsrr7zCl19+iZubW4F8xpQpUxg/fnyBXPtG1m7dSezZOAAGhocU2ueKiIiIiIiISMGzWCwE+JchwL8MIW2b5mzPzs7mxKmzRkHr0JVi1gkOHo0hLT2DrKxsDh6L4eCxGBZHbs45z9XFheqVK1C7eiWCqlciqFolalcPpEpAeZydnc24xXxlE4UrX19fEhMTr9seHx+fq+/VX73yyisEBwfTvn17EhISAKOpe2ZmJgkJCZQoUQIXF5dbXj8wMPCWsY0ePZqhQ4fmvI+NjS3QnljfXW7K3qhuDerUqFxgnyMiIiIiIiIitsPJyYkqAeWpElCe7nddrTtkZmZx/ORp9h+OJurwcaKORLP/8AkOHz9FRmYmGZmZRB02Rm8tuOZ6xdzdqFGl4uX+WUYxq3a1SlSqUBYnJ6fCv8G/ySYKVzfqNZWYmEhsbOx1vamuFRUVxbp16/D19b1un6+vLxEREfTo0YOgoCB27dqVa7/VamX//v107dr1lrF5e3vj7e2dh7v5++ITL7D08jBANWUXERERERERERcXZ6pXrkj1yhUJ69wqZ3tGRiZHomMvTzW8Mu0wmiPRp8jKyiY1LZ3d+4+ye//RXNfzKOZu9M2qZjSDv1LQqli+NBaLpbBv77ZsonAVGhrKxIkTSUhIyOlFNWfOHJycnOjWrdtNz5s6dWrOSKsrnnnmGTw8PJg0aRLBwcE51//66685ePAgNWvWBCAyMpK4uDjCwsIK5J7+jvnL1pOekYm7myt397jL7HBERERERERExEa5urrkFKCgbc72tPQMDh0/mdM7K+rQcfYfieZYzGmsVispqWn8vvcQv+89lOt6JTw9jL5Zl5vB16leiTo1q1C2lE/h3thfWKxWq9XUCDCm7NWrV49atWoxduxYTp48yejRoxk8eDDTpk3LOS4kJITjx49z6NChm16rY8eOlChRgsWLF+dsy8jIoEmTJlgsFiZOnEhycjLPP/88wcHBuY67EzExMQQGBhIdHU1AQEDeb/YWuj3wHDv3HaZPt3Z8POn5fL22iIiIiFkKMn8SERGRO5OSmsahYyeN6YaHo3NGaUWfOnvTcwaGh/DeuFGFGOX1bGLEla+vL5GRkYwaNYq+ffvi5eXF0KFDmTBhQq7jsrKyyMzMzPP1XV1dWbp0KU899RSDBg3CxcWFe+65h3fffTe/buEfu3AxmSs1xEG91ZRdRERERERERPKPRzF3GgRVo0FQtVzbLyWncOBoTK4VDqMOH+fUmTiCqt26L3hhsIkRV/akoL8x3HPwGEHVAh2i87+IiIgIaMSViIiIPUq6cAkAby9PU+OwiRFXclW9mlXMDkFEREREREREijizC1ZX2M/6hyIiIiIiIiIiUqSocCUiIiIiIiIiIjZJhSsREREREREREbFJKlyJiIiIiIiIiIhNUuFKRERERERERERskgpXIiIiIiIiIiJik1S4EhERERERERERm6TClYiIiIiIiIiI2CQVrkRERERERERExCapcCUiIiIiIiIiIjZJhSsREREREREREbFJLmYHYG8yMzMBiI2NNTkSERERsSXly5fHxUWp1Y0ofxIREZEbuZP8SdlVHp07dw6AFi1amByJiIiI2JLo6GgCAgLMDsMmKX8SERGRG7mT/MlitVqthRSPQ0hNTWXXrl2UKVOmSH+rGhsbS4sWLdi2bRv+/v5mh+Mw9FwLhp5rwdBzLRh6rvmvsJ6pRlzdnPIng/7/Lhh6rgVDz7Vg6LkWDD3X/GdL+VPRzRz+pmLFitG8eXOzw7AZ/v7++na5AOi5Fgw914Kh51ow9Fzzn56peZQ/5ab/FguGnmvB0HMtGHquBUPPNf/ZwjNVc3YREREREREREbFJKlyJiIiIiIiIiIhNUuFK/hZvb2/GjRuHt7e32aE4FD3XgqHnWjD0XAuGnmv+0zMVW6H/FguGnmvB0HMtGHquBUPPNf/Z0jNVc3YREREREREREbFJGnElIiIiIiIiIiI2SYUrERERERERERGxSSpciYiIiIiIiIiITVLhSkREREREREREbJIKV5Inc+bMoU+fPgQEBODp6UmjRo347LPPUI///HPx4kUCAgKwWCxs377d7HDs3hdffEHjxo0pVqwYpUuXJjQ0lJSUFLPDsmsLFy6kZcuWeHl54e/vT//+/Tly5IjZYdmVQ4cOMWLECBo1aoSLiwv169e/4XGffvoptWrVolixYjRs2JDFixcXcqT25XbPNSkpiVdffZUWLVrg4+NDuXLl6N27N7t27TIpYikKlDsVDuVP+Uv5U/5T/vTPKX/Kf/aSO6lwJXkyZcoUihcvzjvvvMOiRYsIDQ1l2LBhvPbaa2aH5jBef/11MjMzzQ7DIUyYMIFRo0YxYMAAli1bxsyZM6latSpZWVlmh2a31qxZw913303dunWZP38+U6dOZefOnXTr1k0JbR7s2bOHn3/+mRo1alC3bt0bHjN79myGDRvGgAEDiIiIoHXr1tx9991s2bKlkKO1H7d7ridOnGDmzJl069aNH374gf/9738kJibSqlUr9u3bZ0LEUhQodyocyp/yj/Kn/Kf8KX8of8p/dpM7WUXy4Ny5c9dtGzZsmNXb29ualZVlQkSOZd++fVZPT0/rjBkzrID1l19+MTskuxUVFWV1cXGxLlmyxOxQHMpjjz1mrVq1qjU7Oztn26pVq6yAdd26dSZGZl+u/X358MMPW+vVq3fdMbVq1bIOGjQo17bWrVtbQ0NDCzw+e3W753rx4kXrpUuXcm27cOGC1c/Pz/rkk08WSoxS9Ch3KnjKn/KP8qeCofwpfyh/yn/2kjtpxJXkSenSpa/b1rhxY5KSkrh06ZIJETmWUaNGMWLECGrXrm12KHZv1qxZVK1aldDQULNDcSgZGRl4eXlhsVhytpUsWRJA017ywMnp1n/8HjlyhAMHDtC/f/9c2wcOHEhkZCRpaWkFGZ7dut1z9fT0pHjx4rm2lShRgho1anDq1KmCDE2KMOVOBU/5U/5R/lQwlD/lD+VP+c9ecicVruQf27BhAxUrVsTLy8vsUOza3Llz2bVrF6+88orZoTiELVu20KBBA9544w3Kli2Lm5sbbdu2ZevWrWaHZteGDBnC3r17mT59OomJiRw5coSxY8fSuHFj2rZta3Z4DiMqKgqAoKCgXNvr1KlDeno6R48eNSMsh5SQkMDu3bupU6eO2aFIEaLcKf8of8pfyp8KhvKnwqH8qXCYkTupcCX/yIYNG5g9ezbPP/+82aHYteTkZEaPHs3EiRPx9vY2OxyHcPr0aZYvX86XX37J9OnT+emnn7BYLHTr1o2zZ8+aHZ7dat++PfPnz+fFF1/Ex8eH6tWrc+bMGSIiInB2djY7PIcRHx8PgI+PT67tvr6+AJw/f76wQ3JYY8aMwWKxMGLECLNDkSJCuVP+Uf6U/5Q/FQzlT4VD+VPhMCN3UuFK/raYmBgGDBhAp06deOqpp8wOx6698cYblCtXjkceecTsUBxGdnY2Fy9eZO7cufTr14+wsDAWLlyI1Wpl2rRpZodntzZt2sSDDz7IsGHDWLVqFXPmzCE7O5uePXuquajYnVmzZvG///2PDz/8kICAALPDkSJAuVP+Uv6U/5Q/FQzlT+IozMqdXArtk8ShJCQkEBoaSqlSpfjxxx9vOzdWbu748eO88847zJ8/n8TERMBY0vnKPy9evEiJEiXMDNEu+fr6UqpUKYKDg3O2+fn50bhxY/bs2WNiZPbtqaeeonPnzrzzzjs521q1akWlSpX46quvGD58uInROY4r3wwmJiZSvnz5nO1Xvkn08/MzJS5HEhERwfDhw/nPf/7Dww8/bHY4UgQod8pfyp8KhvKngqH8qXAofypYZuZO+hNT8iwlJYVevXqRmJhIRERETmNB+XuOHj1Keno6PXv2xNfXF19fX3r37g1Ap06d6NKli8kR2qd69erddF9qamohRuJY9u7dS6NGjXJtCwgIoHTp0hw+fNicoBzQld4MV3o1XBEVFYWbmxvVqlUzIyyHsWXLFvr168fDDz/Ma6+9ZnY4UgQod8p/yp8KhvKngqH8qXAofyo4ZudOKlxJnmRmZtK/f3/27dvH0qVLqVixotkh2b1GjRqxevXqXK93330XgBkzZjB9+nSTI7RPvXr1Ii4ujt9//z1nW1xcHL/++itNmzY1LzA7V7lyZX799ddc244fP86ff/5JlSpVzAnKAVWrVo1atWoxZ86cXNu///57QkJCcHNzMyky+7d371569uxJ586dmTFjhtnhSBGg3KlgKH8qGMqfCobyp8Kh/Klg2ELupKmCkicjR45k8eLFvPPOOyQlJbFly5acfY0bN8bd3d3E6OyTj48PHTt2vOG+pk2b0qRJk8INyEH07duX5s2b069fPyZMmICHhweTJk3C3d2dkSNHmh2e3RoxYgTPPPMMTz/9NL179yYuLi5n5aG/Lj0sN5ecnMySJUsAI3FNSkpi7ty5AHTo0IEyZcrw6quvMnjwYKpXr06nTp34/vvv2bp1K+vWrTMzdJt2u+dqtVrp3r07Hh4ePPvss2zfvj3nXG9vb+rWrWtK3OLYlDsVDOVPBUP5U8FQ/pQ/lD/lP7vJnawieVC5cmUrcMPX0aNHzQ7PYaxevdoKWH/55RezQ7Fr586dsz7wwAPWkiVLWj08PKzdunWz7tmzx+yw7Fp2drb1o48+sgYHB1s9PT2t5cuXt959993Wffv2mR2aXTl69OhNf5euXr0657hPPvnEWqNGDaubm5u1QYMG1kWLFpkXtB243XO98rv1Rq8OHTqYHb44KOVOhUf5U/5Q/pT/lD/lD+VP+c9ecieL1Wq15ncxTERERERERERE5J9SjysREREREREREbFJKlyJiIiIiIiIiIhNUuFKRERERERERERskgpXIiIiIiIiIiJik1S4EhERERERERERm6TClYiIiIiIiIiI2CQVrkRERERERERExCapcCUiIiIiIiIiIjZJhSsRcXj/nfkd5Zr2veHr/Vk/Fno8sxdGUq5pX+Likwr9s0VERETuhPInEbEVLmYHICJSGDzc3Zg78/XrtgeUL2NCNCIiIiK2T/mTiNgCFa5EpEiwODnRrEFts8MQERERsRvKn0TEFqhwJSIClGval5effJCEpAvMXriK1PR0enVuzcQxwynh6ZFzXHTsWV59dxZrt+wkMyuLlo3qMO6ZIdStWSXX9X5YvJqZ3y7k4NEYPD2K0bh+Td56aQSB/mVzjjl15k+efGUqm3/dQ/kyfowe2p/+vToV1i2LiIiI/CPKn0SkMKjHlYgUGZmZWde9rvXp9z9z4GgM749/iv8b9RCLV21m9Bsf5uy/eCmFu4f/H7uijvD22BFMf/1Z4hMv0HfYy5w8fS7nuA+/nM+oce8RHFSdz/77AlNeeZKqgRWu68kw8v+m0KFVI7545yUa1K7KU6++z4Gj0QX7EERERETyQPmTiJhNI65EpEhITkmlYst7r9u+8JOJtGxcFwA3N1e+eOclnJ2dASjm7sbo1z/k38MHUrNqAN8tjCQm9hzr5rxPraqBALRuWo8mPYfx8beLGD/6UZIuXOK/M2fz4D3dmPzyyJzPCe3Y8rrPfrR/GI/0DwOgWcMgVmzYweLIzYweGpjv9y8iIiKSV8qfRMQWqHAlIkWCh7sbP30y8brtNatUzPm5W/vmOUkXQO+QNjz72jR+23OQmlUD2PrbXoKqV8pJugB8S3rRoWVDtv6+D4Dtu/aTkprG/X263Damjq0b5/zs6VGMQP8yxJ6N+1v3JyIiIpLflD+JiC1Q4UpEigSLkxON6ta45TGl/Urmeu9VojjF3N0482c8AAkXLlKmlM9155Up5UPU4RMAnE+4AED5Mn63jcm7hGeu966uLqSlpd/2PBEREZHCoPxJRGyBelyJiFz25/nEXO8vXEwmNS2dcqV9AfD1LnHdMQDn4hLw8S4BgJ+PFwCnz50v4GhFREREzKf8SUQKmgpXIiKXLV//C1lZVxuOLorchMVioVE945vGFo3qsu/QcQ4dO5lzTELSRdZt20nLRnUAaNagNh7F3Jm9cFXhBi8iIiJiAuVPIlLQNFVQRIoEa3Y223ftv257ad+SVAkoD0B6egYPPzeJR+4L5cTJM7z+wZf0Dmmd05NhUHgIM79dyOCnX+fFkYNxd3Plvc/m4uLszPD7ewPg7eXJ88MH8Pr7X5JtzaZHhxZkZ1vZuH0Xd/e467bD7UVERERshfInEbEFKlyJSJGQkpZOzyEvXLf9/j5dePeVJwH414CexMUn8sR/ppKekUFYp1ZMGjM859gSnh7M//gNxk35jOcnTCcrK5sWDYP46X8TqFi+TM5xTz58D6V8SzLzm4V8v2gVJYp70DS4NqV9S173+SIiIiK2SvmTiNgCi9VqtZodhIiI2co17cu4p4cw8qG+ZociIiIiYheUP4lIYVCPKxERERERERERsUkqXImIiIiIiIiIiE3SVEEREREREREREbFJGnElIiIiIiIiIiI2SYUrERERERERERGxSSpciYiIiIiIiIiITVLhSkREREREREREbJIKVyIiIiIiIiIiYpNUuBIREREREREREZukwpWIiIiIiIiIiNgkFa5ERERERERERMQmqXAlIiIiIiIiIiI2SYUrERERERERERGxSf8Phth8+lakM9kAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 1210x440 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Accuracy and loss panels describe the same selected model.\n",
    "figure, axes = plt.subplots(1, 2, figsize=(11, 4))\n",
    "selected_epochs = np.arange(1, len(champion_history[\"loss\"]) + 1)\n",
    "for axis, metric in zip(axes, [\"accuracy\", \"loss\"]):\n",
    "    axis.plot(selected_epochs, champion_history[metric], color=NAVY, label=\"train\")\n",
    "    axis.plot(selected_epochs, champion_history[\"val_\" + metric], color=SCARLET, label=\"validation\")\n",
    "    axis.set(title=f\"Selected CNN: {metric}\", xlabel=\"Epoch\", ylabel=metric.title())\n",
    "    axis.legend()\n",
    "finish_figure(figure, \"07_selected_learning_curves.png\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cell-116",
   "metadata": {},
   "source": [
    "### 50. Define a Wilson proportion interval\n",
    "\n",
    "A lower bound is more cautious than a small accepted set’s accuracy point estimate."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "cell-117",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Standard 95% two-sided Wilson interval, conditional on the cases.\n",
    "def wilson_interval(correct, total, z=1.96):\n",
    "    if total == 0:\n",
    "        return np.nan, np.nan\n",
    "    p = correct / total\n",
    "    denominator = 1 + z * z / total\n",
    "    centre = (p + z * z / (2 * total)) / denominator\n",
    "    radius = z * np.sqrt(p * (1 - p) / total + z * z / (4 * total * total)) / denominator\n",
    "    return float(centre - radius), float(centre + radius)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cell-118",
   "metadata": {},
   "source": [
    "### 51. Define threshold coverage and accuracy\n",
    "\n",
    "Coverage is the fraction automatically accepted; accepted accuracy excludes reviewed cases."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "cell-119",
   "metadata": {},
   "outputs": [],
   "source": [
    "# A maximum softmax score is not guaranteed calibrated confidence.\n",
    "def threshold_table(labels, probabilities, thresholds):\n",
    "    predicted, scores = probabilities.argmax(axis=1), probabilities.max(axis=1)\n",
    "    rows = []\n",
    "    for threshold in thresholds:\n",
    "        accepted = scores >= threshold\n",
    "        total = int(accepted.sum())\n",
    "        correct = int((predicted[accepted] == labels[accepted]).sum())\n",
    "        low, high = wilson_interval(correct, total)\n",
    "        rows.append({\"threshold\": float(threshold), \"accepted\": total,\n",
    "            \"coverage\": total / len(labels),\n",
    "            \"accepted_accuracy\": correct / total if total else np.nan,\n",
    "            \"wilson_low\": low, \"wilson_high\": high})\n",
    "    return pd.DataFrame(rows)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cell-120",
   "metadata": {},
   "source": [
    "### 52. Select the review threshold using validation only\n",
    "\n",
    "Require at least 100 accepted cases and a 90% accuracy lower bound; otherwise review everything."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "cell-121",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Frozen review threshold: 0.8499999999999999 (None means review all)\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>threshold</th>\n",
       "      <th>accepted</th>\n",
       "      <th>coverage</th>\n",
       "      <th>accepted_accuracy</th>\n",
       "      <th>wilson_low</th>\n",
       "      <th>wilson_high</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.40</td>\n",
       "      <td>8991</td>\n",
       "      <td>0.8991</td>\n",
       "      <td>0.7296</td>\n",
       "      <td>0.7203</td>\n",
       "      <td>0.7387</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.45</td>\n",
       "      <td>8404</td>\n",
       "      <td>0.8404</td>\n",
       "      <td>0.7543</td>\n",
       "      <td>0.7450</td>\n",
       "      <td>0.7634</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.50</td>\n",
       "      <td>7789</td>\n",
       "      <td>0.7789</td>\n",
       "      <td>0.7793</td>\n",
       "      <td>0.7700</td>\n",
       "      <td>0.7884</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.55</td>\n",
       "      <td>7166</td>\n",
       "      <td>0.7166</td>\n",
       "      <td>0.8034</td>\n",
       "      <td>0.7940</td>\n",
       "      <td>0.8124</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.60</td>\n",
       "      <td>6602</td>\n",
       "      <td>0.6602</td>\n",
       "      <td>0.8244</td>\n",
       "      <td>0.8151</td>\n",
       "      <td>0.8334</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.65</td>\n",
       "      <td>5980</td>\n",
       "      <td>0.5980</td>\n",
       "      <td>0.8505</td>\n",
       "      <td>0.8412</td>\n",
       "      <td>0.8593</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.70</td>\n",
       "      <td>5447</td>\n",
       "      <td>0.5447</td>\n",
       "      <td>0.8715</td>\n",
       "      <td>0.8623</td>\n",
       "      <td>0.8801</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0.75</td>\n",
       "      <td>4894</td>\n",
       "      <td>0.4894</td>\n",
       "      <td>0.8901</td>\n",
       "      <td>0.8810</td>\n",
       "      <td>0.8985</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0.80</td>\n",
       "      <td>4350</td>\n",
       "      <td>0.4350</td>\n",
       "      <td>0.9087</td>\n",
       "      <td>0.8998</td>\n",
       "      <td>0.9169</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0.85</td>\n",
       "      <td>3731</td>\n",
       "      <td>0.3731</td>\n",
       "      <td>0.9317</td>\n",
       "      <td>0.9231</td>\n",
       "      <td>0.9393</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>0.90</td>\n",
       "      <td>3045</td>\n",
       "      <td>0.3045</td>\n",
       "      <td>0.9488</td>\n",
       "      <td>0.9404</td>\n",
       "      <td>0.9560</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>0.95</td>\n",
       "      <td>2121</td>\n",
       "      <td>0.2121</td>\n",
       "      <td>0.9722</td>\n",
       "      <td>0.9643</td>\n",
       "      <td>0.9784</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    threshold  accepted  coverage  accepted_accuracy  wilson_low  wilson_high\n",
       "0        0.40      8991    0.8991             0.7296      0.7203       0.7387\n",
       "1        0.45      8404    0.8404             0.7543      0.7450       0.7634\n",
       "2        0.50      7789    0.7789             0.7793      0.7700       0.7884\n",
       "3        0.55      7166    0.7166             0.8034      0.7940       0.8124\n",
       "4        0.60      6602    0.6602             0.8244      0.8151       0.8334\n",
       "5        0.65      5980    0.5980             0.8505      0.8412       0.8593\n",
       "6        0.70      5447    0.5447             0.8715      0.8623       0.8801\n",
       "7        0.75      4894    0.4894             0.8901      0.8810       0.8985\n",
       "8        0.80      4350    0.4350             0.9087      0.8998       0.9169\n",
       "9        0.85      3731    0.3731             0.9317      0.9231       0.9393\n",
       "10       0.90      3045    0.3045             0.9488      0.9404       0.9560\n",
       "11       0.95      2121    0.2121             0.9722      0.9643       0.9784"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Threshold grid and target are declared before test scoring.\n",
    "confidence_thresholds = np.arange(0.40, 1.00, 0.05)\n",
    "validation_policy = threshold_table(y_validation, champion_validation, confidence_thresholds)\n",
    "eligible = validation_policy[(validation_policy[\"accepted\"] >= 100) &\n",
    "    (validation_policy[\"wilson_low\"] >= TARGET_ACCEPTED_ACCURACY)]\n",
    "review_threshold = None if eligible.empty else float(\n",
    "    eligible.sort_values(\"coverage\", ascending=False).iloc[0][\"threshold\"])\n",
    "validation_policy.to_csv(OUTPUT_DIR / \"validation_review_policy.csv\", index=False)\n",
    "print(\"Frozen review threshold:\", review_threshold, \"(None means review all)\")\n",
    "display(validation_policy.round(4))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cell-122",
   "metadata": {},
   "source": [
    "**Limit:** Wilson bounds are pointwise, not simultaneous guarantees across a threshold\n",
    "search. A separate policy-validation cohort is needed before deployment. A high score\n",
    "on an unknown image is still possible: this policy does not detect unknown classes."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cell-123",
   "metadata": {},
   "source": [
    "### 53. Save the frozen decision\n",
    "\n",
    "Record the choice before using test labels to score performance."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "cell-124",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'cnn': 'basic_CNN',\n",
       " 'selection_metric': 'validation_macro_f1',\n",
       " 'review_threshold': 0.8499999999999999,\n",
       " 'target_lower_bound': 0.9,\n",
       " 'training_mode': 'full',\n",
       " 'seed': 42,\n",
       " 'test_used_for_model_selection': False}"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# This is an experiment decision, not production approval.\n",
    "frozen_decision = {\"cnn\": champion_name, \"selection_metric\": \"validation_macro_f1\",\n",
    "    \"review_threshold\": review_threshold, \"target_lower_bound\": TARGET_ACCEPTED_ACCURACY,\n",
    "    \"training_mode\": RUN_MODE, \"seed\": SEED, \"test_used_for_model_selection\": False}\n",
    "(OUTPUT_DIR / \"frozen_decision.json\").write_text(\n",
    "    json.dumps(frozen_decision, indent=2), encoding=\"utf-8\")\n",
    "display(frozen_decision)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cell-125",
   "metadata": {},
   "source": [
    "## 8. Evaluate the frozen models on the official test set\n",
    "\n",
    "Test labels are now used for model-performance evaluation. Every declared comparison\n",
    "is reported; the selected CNN remains the one chosen on validation.\n",
    "CIFAR-10 is public and repeatedly studied. A smaller authoring run was checked earlier.\n",
    "Treat this as a descriptive benchmark result, not a newly blind test or field validation."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cell-126",
   "metadata": {},
   "source": [
    "### 54. Verify official test support\n",
    "\n",
    "Check the final scoring labels against the official data contract."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "cell-127",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>class</th>\n",
       "      <th>test_support</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>airplane</td>\n",
       "      <td>1000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>automobile</td>\n",
       "      <td>1000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>bird</td>\n",
       "      <td>1000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>cat</td>\n",
       "      <td>1000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>deer</td>\n",
       "      <td>1000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>dog</td>\n",
       "      <td>1000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>frog</td>\n",
       "      <td>1000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>horse</td>\n",
       "      <td>1000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>ship</td>\n",
       "      <td>1000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>truck</td>\n",
       "      <td>1000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        class  test_support\n",
       "0    airplane          1000\n",
       "1  automobile          1000\n",
       "2        bird          1000\n",
       "3         cat          1000\n",
       "4        deer          1000\n",
       "5         dog          1000\n",
       "6        frog          1000\n",
       "7       horse          1000\n",
       "8        ship          1000\n",
       "9       truck          1000"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# No fitting occurs in this section.\n",
    "assert np.array_equal(np.unique(y_test), CLASS_IDS)\n",
    "assert np.array_equal(np.bincount(y_test), np.full(10, 1_000))\n",
    "test_dataset = make_dataset(x_test, y_test)\n",
    "display(pd.DataFrame({\"class\": CLASS_NAMES, \"test_support\": np.bincount(y_test)}))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cell-128",
   "metadata": {},
   "source": [
    "### 55. Predict with every declared model\n",
    "\n",
    "Store the probability matrices once and reuse them for all metrics and figures."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "cell-129",
   "metadata": {},
   "outputs": [],
   "source": [
    "# The validation-selected name is not changed after this operation.\n",
    "test_probabilities = {\n",
    "    \"majority\": majority_model.predict_proba(np.zeros((len(y_test), 1))),\n",
    "    \"linear_RGB_8x8\": linear_model.predict_proba(coarse_features(x_test)),\n",
    "}\n",
    "for name, model in cnn_models.items():\n",
    "    test_probabilities[name] = model.predict(test_dataset, verbose=0)\n",
    "champion_probabilities = test_probabilities[champion_name]\n",
    "champion_predictions = champion_probabilities.argmax(axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cell-130",
   "metadata": {},
   "source": [
    "### 56. Create the measured test comparison\n",
    "\n",
    "Report quality and development cost for the actual run budget."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "cell-131",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>model</th>\n",
       "      <th>accuracy</th>\n",
       "      <th>macro_f1</th>\n",
       "      <th>weighted_f1</th>\n",
       "      <th>log_loss</th>\n",
       "      <th>parameters</th>\n",
       "      <th>training_seconds</th>\n",
       "      <th>selected_on_validation</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>majority</td>\n",
       "      <td>0.1000</td>\n",
       "      <td>0.0182</td>\n",
       "      <td>0.0182</td>\n",
       "      <td>32.4393</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>linear_RGB_8x8</td>\n",
       "      <td>0.4141</td>\n",
       "      <td>0.4105</td>\n",
       "      <td>0.4105</td>\n",
       "      <td>1.7069</td>\n",
       "      <td>1930</td>\n",
       "      <td>10.4099</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>basic_CNN</td>\n",
       "      <td>0.6922</td>\n",
       "      <td>0.6907</td>\n",
       "      <td>0.6907</td>\n",
       "      <td>0.9012</td>\n",
       "      <td>122570</td>\n",
       "      <td>65.9073</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>regularised_CNN_lr0.001</td>\n",
       "      <td>0.5233</td>\n",
       "      <td>0.5085</td>\n",
       "      <td>0.5085</td>\n",
       "      <td>1.3227</td>\n",
       "      <td>111722</td>\n",
       "      <td>86.3608</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>regularised_CNN_lr0.0005</td>\n",
       "      <td>0.5158</td>\n",
       "      <td>0.5035</td>\n",
       "      <td>0.5035</td>\n",
       "      <td>1.3348</td>\n",
       "      <td>111722</td>\n",
       "      <td>57.4835</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                      model  accuracy  ...  training_seconds  selected_on_validation\n",
       "0                  majority    0.1000  ...            0.0000                   False\n",
       "1            linear_RGB_8x8    0.4141  ...           10.4099                   False\n",
       "2                 basic_CNN    0.6922  ...           65.9073                    True\n",
       "3   regularised_CNN_lr0.001    0.5233  ...           86.3608                   False\n",
       "4  regularised_CNN_lr0.0005    0.5158  ...           57.4835                   False\n",
       "\n",
       "[5 rows x 8 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Highlight the existing selection rather than choosing a test winner.\n",
    "cost_lookup = validation_table.set_index(\"model\")[[\"parameters\", \"training_seconds\"]]\n",
    "test_table = pd.DataFrame([metric_row(name, y_test, probabilities)\n",
    "    for name, probabilities in test_probabilities.items()]).join(cost_lookup, on=\"model\")\n",
    "test_table[\"selected_on_validation\"] = test_table[\"model\"] == champion_name\n",
    "test_table.to_csv(OUTPUT_DIR / \"test_metrics.csv\", index=False)\n",
    "display(test_table.round(4))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cell-132",
   "metadata": {},
   "source": [
    "### 57. Plot the fixed test comparison\n",
    "\n",
    "Macro-F1 weights classes equally. Highlighting the selected CNN is descriptive."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "cell-133",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 990x440 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Exact values remain available in the table and CSV.\n",
    "figure, axis = plt.subplots(figsize=(9, 4))\n",
    "plot_table = test_table.sort_values(\"macro_f1\")\n",
    "colours = [SCARLET if name == champion_name else NAVY for name in plot_table[\"model\"]]\n",
    "axis.barh(plot_table[\"model\"], plot_table[\"macro_f1\"], color=colours)\n",
    "axis.set(title=\"Official test macro-F1; selected CNN highlighted\", xlabel=\"Macro-F1\", xlim=(0, 1))\n",
    "finish_figure(figure, \"08_test_model_comparison.png\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cell-134",
   "metadata": {},
   "source": [
    "### 58. Verify agreement with Keras evaluation\n",
    "\n",
    "Catch mismatches between the model being evaluated and the predictions being reported."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "cell-135",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Keras and saved-prediction accuracy agree: 0.6922\n",
      "Keras total loss: 0.9012\n"
     ]
    }
   ],
   "source": [
    "# Keras total loss includes any L2 penalty; sklearn log loss does not.\n",
    "keras_evaluation = champion_model.evaluate(test_dataset, verbose=0, return_dict=True)\n",
    "computed_accuracy = accuracy_score(y_test, champion_predictions)\n",
    "assert np.isclose(keras_evaluation[\"accuracy\"], computed_accuracy, atol=1e-6)\n",
    "print(\"Keras and saved-prediction accuracy agree:\", round(computed_accuracy, 4))\n",
    "print(\"Keras total loss:\", round(keras_evaluation[\"loss\"], 4))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cell-136",
   "metadata": {},
   "source": [
    "### 59. Bootstrap test accuracy and the paired difference\n",
    "\n",
    "Resample image-hash groups together to retain exact-repeat dependence and use the same cases in the comparison."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "cell-137",
   "metadata": {},
   "outputs": [],
   "source": [
    "# This measures test sampling uncertainty, not retraining uncertainty.\n",
    "_, test_group_ids = np.unique(test_hashes, return_inverse=True)\n",
    "group_sizes = np.bincount(test_group_ids)\n",
    "chosen_correct = (champion_predictions == y_test).astype(float)\n",
    "basic_correct = (test_probabilities[\"basic_CNN\"].argmax(axis=1) == y_test).astype(float)\n",
    "group_correct = np.bincount(test_group_ids, weights=chosen_correct)\n",
    "group_differences = np.bincount(test_group_ids, weights=chosen_correct - basic_correct)\n",
    "bootstrap_rng = np.random.default_rng(SEED + 100)\n",
    "bootstrap_accuracy, bootstrap_delta = [], []\n",
    "for _ in range(BOOTSTRAP_REPEATS):\n",
    "    sampled = bootstrap_rng.integers(0, len(group_sizes), size=len(group_sizes))\n",
    "    denominator = group_sizes[sampled].sum()\n",
    "    bootstrap_accuracy.append(group_correct[sampled].sum() / denominator)\n",
    "    bootstrap_delta.append(group_differences[sampled].sum() / denominator)\n",
    "accuracy_interval = np.quantile(bootstrap_accuracy, [0.025, 0.975])\n",
    "delta_interval = np.quantile(bootstrap_delta, [0.025, 0.975])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cell-138",
   "metadata": {},
   "source": [
    "### 60. Report uncertainty beside the estimate\n",
    "\n",
    "Differences use percentage points. An interval containing zero does not establish a clear accuracy difference."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "cell-139",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test image groups: 10000 Bootstrap repetitions: 500\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>quantity</th>\n",
       "      <th>estimate</th>\n",
       "      <th>low_95</th>\n",
       "      <th>high_95</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>selected_CNN_accuracy</td>\n",
       "      <td>0.6922</td>\n",
       "      <td>0.6834</td>\n",
       "      <td>0.7017</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>accuracy_difference_vs_basic_CNN</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                           quantity  estimate  low_95  high_95\n",
       "0             selected_CNN_accuracy    0.6922  0.6834   0.7017\n",
       "1  accuracy_difference_vs_basic_CNN    0.0000  0.0000   0.0000"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Estimates are conditional on these fitted model objects.\n",
    "accuracy_delta = float(np.mean(chosen_correct - basic_correct))\n",
    "uncertainty_table = pd.DataFrame([\n",
    "    {\"quantity\": \"selected_CNN_accuracy\", \"estimate\": computed_accuracy,\n",
    "     \"low_95\": accuracy_interval[0], \"high_95\": accuracy_interval[1]},\n",
    "    {\"quantity\": \"accuracy_difference_vs_basic_CNN\", \"estimate\": accuracy_delta,\n",
    "     \"low_95\": delta_interval[0], \"high_95\": delta_interval[1]},\n",
    "])\n",
    "uncertainty_table.to_csv(OUTPUT_DIR / \"accuracy_uncertainty.csv\", index=False)\n",
    "display(uncertainty_table.round(4))\n",
    "print(\"Test image groups:\", len(group_sizes), \"Bootstrap repetitions:\", BOOTSTRAP_REPEATS)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cell-140",
   "metadata": {},
   "source": [
    "**Interpretation:** this interval excludes uncertainty from retraining, model selection,\n",
    "domain shift or unknown source dependence. Hash groups address exact copies only.\n",
    "**Your comment:** What would repeated seeds, source-grouped validation and an independent\n",
    "new dataset each add to the evidence?"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cell-141",
   "metadata": {},
   "source": [
    "## 9. Explain class and image errors\n",
    "\n",
    "Rows of a confusion matrix are true labels; columns are predicted labels. Recall asks\n",
    "how many real members of a class were found. Precision asks how many predictions of\n",
    "that class were correct. Read these values with their support counts."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cell-142",
   "metadata": {},
   "source": [
    "### 61. Generate per-class test metrics\n",
    "\n",
    "Use explicit class order and the same saved prediction vector."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "cell-143",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>precision</th>\n",
       "      <th>recall</th>\n",
       "      <th>f1-score</th>\n",
       "      <th>support</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>airplane</th>\n",
       "      <td>0.7828</td>\n",
       "      <td>0.692</td>\n",
       "      <td>0.7346</td>\n",
       "      <td>1000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>automobile</th>\n",
       "      <td>0.8810</td>\n",
       "      <td>0.696</td>\n",
       "      <td>0.7777</td>\n",
       "      <td>1000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>bird</th>\n",
       "      <td>0.6601</td>\n",
       "      <td>0.534</td>\n",
       "      <td>0.5904</td>\n",
       "      <td>1000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cat</th>\n",
       "      <td>0.5206</td>\n",
       "      <td>0.505</td>\n",
       "      <td>0.5127</td>\n",
       "      <td>1000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>deer</th>\n",
       "      <td>0.6578</td>\n",
       "      <td>0.619</td>\n",
       "      <td>0.6378</td>\n",
       "      <td>1000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>dog</th>\n",
       "      <td>0.5701</td>\n",
       "      <td>0.626</td>\n",
       "      <td>0.5968</td>\n",
       "      <td>1000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>frog</th>\n",
       "      <td>0.6880</td>\n",
       "      <td>0.816</td>\n",
       "      <td>0.7466</td>\n",
       "      <td>1000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>horse</th>\n",
       "      <td>0.7093</td>\n",
       "      <td>0.749</td>\n",
       "      <td>0.7286</td>\n",
       "      <td>1000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ship</th>\n",
       "      <td>0.7994</td>\n",
       "      <td>0.837</td>\n",
       "      <td>0.8178</td>\n",
       "      <td>1000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>truck</th>\n",
       "      <td>0.6957</td>\n",
       "      <td>0.848</td>\n",
       "      <td>0.7643</td>\n",
       "      <td>1000.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            precision  recall  f1-score  support\n",
       "airplane       0.7828   0.692    0.7346   1000.0\n",
       "automobile     0.8810   0.696    0.7777   1000.0\n",
       "bird           0.6601   0.534    0.5904   1000.0\n",
       "cat            0.5206   0.505    0.5127   1000.0\n",
       "deer           0.6578   0.619    0.6378   1000.0\n",
       "dog            0.5701   0.626    0.5968   1000.0\n",
       "frog           0.6880   0.816    0.7466   1000.0\n",
       "horse          0.7093   0.749    0.7286   1000.0\n",
       "ship           0.7994   0.837    0.8178   1000.0\n",
       "truck          0.6957   0.848    0.7643   1000.0"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Include classes even if their predictions are poor.\n",
    "report = classification_report(y_test, champion_predictions, labels=CLASS_IDS,\n",
    "    target_names=CLASS_NAMES, output_dict=True, zero_division=0)\n",
    "per_class_report = pd.DataFrame(report).T.loc[CLASS_NAMES]\n",
    "per_class_report.to_csv(OUTPUT_DIR / \"per_class_test_metrics.csv\", index_label=\"class\")\n",
    "display(per_class_report.round(4))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cell-144",
   "metadata": {},
   "source": [
    "### 62. Plot confusion counts\n",
    "\n",
    "These are actual case counts rather than proportions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "cell-145",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 990x880 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Rows: actual; columns: predicted.\n",
    "confusion_counts = confusion_matrix(y_test, champion_predictions, labels=CLASS_IDS)\n",
    "figure, axis = plt.subplots(figsize=(9, 8))\n",
    "ConfusionMatrixDisplay(confusion_counts, display_labels=CLASS_NAMES).plot(\n",
    "    ax=axis, cmap=\"Blues\", colorbar=False, xticks_rotation=45, values_format=\"d\")\n",
    "axis.set_title(\"Selected CNN: test confusion counts\")\n",
    "finish_figure(figure, \"09_confusion_counts.png\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cell-146",
   "metadata": {},
   "source": [
    "### 63. Plot row-normalised confusion\n",
    "\n",
    "Rows summing to one make class recall and error destinations easy to compare."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "cell-147",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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rGt8/p548eYKUlBTF315eXmrnTV9f7t+/j9q1ayud8VIlfZwZ/2cA0L59+xz372vi+CXq1q0LW1vbr15OxjELgoC2bduqnffevXsaHzaTcVlWVlZo2LDhZ/dHEAQ0b94c586d0zhfxph6eXnhr7/+QkpKCsaMGYMxY8bAyMgIZcuWRaNGjTBs2DDY2dl99rxEeYGXFBKRaOzs7DBixAjs27cPISEhigOldH5+forfM99crk52B1k5XU5OlvWlcmssX6patWqK3588efLZlwNmNG3aNEilUqSmpmLKlCmf1bZGjRqKA72lS5cqfefXt1akSJEs016/fq1UbHXr1g0nTpzAxYsXMW7cOMX0gvh9UpaWlkp/Z3xSpJDpawLy0pesr8uWLVP8bmtri40bN+L8+fO4ePGi0qVjufF/+9ZxVJWngHKcMhaoAFQ+wCM/bAcz8vPzUyq2Ro4ciVOnTuHixYvo2bOnYnrG/1m9evXw77//YuzYsXB3d4e9vT1iY2Nx48YNzJs3D//73/8Ul4F/zrxEeYEFFxHlumvXriEkJCTLdDc3N3Ts2FHxd8ada5kyZRS/Dxw4EIIgqPzJ7ulghQoVUtzzBcif8qZqOYmJiYqzaGXLllXMf+/ePZX3ZmQ8uEq/hybzGFSNZe7cuSrfPzU1Vem+lYzvk35vFwA8fvz4s85uAVB6yp8gCBgyZEiWgzQASE5OxpMnTzQuq3Dhwhg5ciQAKM5qfI7Zs2dDW1sbcXFxuHPnzme3zy2qDkAzF6Jr1qzBjz/+iHr16qk9M5gxV44ePYrExESl1zMfhGeXK+pkzKHLly8rvZbxvqCM8+UWa2trpcJFzPd3dXWFtra24m8/Pz+V60tMTAxWr14NQPn/1r17d/Tp0wf169eHo6Ojyv9bxv8ZAMVZ9YzELEIz5l52OaCuUMp41jrjttXHxyfL/bCA8v9FW1sbISEhauM6fvx4jX3KGL8PHz7g/PnzWebJLn4Z/2dWVlZYsmQJmjRpgnr16ild+ZB5mW5ubpg/fz7OnTuHkJAQfPz4EbVq1QIgv98tfZv0OfMS5QVeUkhEue7EiROYPXs2GjVqhCZNmqBMmTIwMDCAv7+/0qVlderUUfzer18/7Nu3DwCwbt066OnpoWnTptDT00NwcDAePnyII0eOYNKkSRofGy6RSNCnTx/8/vvvAIBBgwYhKCgIlStXRmJiIoKDg3Hz5k0cPnwYN2/ehLOzMzp16oTJkycjMjISSUlJaNSoEcaPH49SpUrh7du32LdvH0aNGoUGDRoAkB8wpLt48SKOHTsGMzMzFC5cGC4uLujXr5/iwRXTp09HbGysYqzBwcG4c+cODh8+DG9vb7i7u6Ny5cpwdXVVPP66T58+mDVrFvT09BQPd/gcNWrUwJAhQ7By5UoA8sKgVq1a8PLyQokSJRAbG4ubN29i27Zt6NmzZ7aP7R43bhzWrFnzRZdQlSlTBp6enti0aZPG+Xr16qX0ZdKZH28thuLFiyv9PWnSJLRs2RI+Pj6Kh55kNmDAAMVDOZ48eQJ3d3cMGTIENjY2ePbsGdatW6copAF5rrx48QIAsGfPHjg7O0NPTw+lSpVS+mAgsz59+uCff/4BIP9eOwcHB9SvXx8XLlzArl27lObLbemPJ09/WMiCBQtgbGyM8uXL4+DBg0oF2Ne+v7m5Odq3b694QEj79u0xfvx4lClTBjExMXj58iX8/Pxw7NgxREVFAVD+v+3duxe1a9eGTCbDzJkzVR74u7m5oW7duop+Dxo0CP7+/qhbty5iY2Nx4cIF2NvbZ1t4fCkrKyvFB0Vbt26FlpYWdHR0UKFCBZiamuZoGRkvc961axeKFSsGAwMDLFiwQOX85cqVQ82aNXHt2jWkpqaiSZMmGD16NIoXL46PHz8iKCgIFy9exOPHj/H48WON792kSRMUK1ZMkcdt27bF+PHjUblyZXz48AEnT55E48aN0b17d7XLyPg/+/DhA2bPno1q1aph7969ar8mY+TIkXj69CmaNm0KR0dHWFlZITg4WPEgEACIj4//7HmJ8oSI94cR0X9Udo//BSCYm5sL9+/fV2o3adKkbNtlvAlb3cMr4uPjhUaNGmW7rIyPSz9+/LhgZGSkdt6MN7ufPn1a5Tx9+/YVBEF+c3337t2zff+Myzx79qziARUZf2xtbQUzMzOND15QJSUlRRg9erTGx5cD6h+HPXDgQKXlzZ8/X2P/VT00I93Lly+zPNjgax8Ln05dfwRB/QMpMsr4AIqMP+kP/ACyPgBixowZ2T4WPt3kyZNVzuPt7a2xj6mpqULHjh01/u80PRY+p48OVyc2NvazHgsvCF/20AxBEIT3798LFSpUyHZ9SXf//n2VD+Rxc3NTemBOxnEGBgYKxYsX/+z14GvjKAjKD3TI+HPx4kVBEHKW+4GBgYK+vn6WZRQtWlQwNzdX2aeAgAChaNGiGmOa0//TrVu3lB6kkvknu8fCp6amCvXq1cvSTkdHRynPMq4DAwcO1Nh3R0dHITo6+rPnJcoLvKSQiHLdgAEDsH79enTr1g0VKlSAra0tdHR0IJVKUbZsWQwbNgx37tyBm5ubUrvZs2fDx8cH7du3h729PXR1dWFmZobSpUujY8eO8Pb2xs8//5zt+xsYGODUqVPYtGkTPDw8YGVlBR0dHVhbW6NixYrw8vLC8ePHFV9+C8gf737v3j0MHz4cZcqUgVQqhYGBAZydndG5c2cUK1ZMMW/jxo2xdOlSlCxZUumejnRaWlrw9vbGvn37FF98rKOjA0tLS7i5ucHT0xN79+5VXO4CyG/OP3v2LP73v//B0NAQ5ubm6NChA/z8/GBubv7Z/wNtbW0sWrQId+7cwdChQxWfpuvp6SnOlixcuBADBw7M0fKGDRsGBweHz+4HABQtWlTxhcD5zfr16zFmzBg4OTnBwMAAlSpVwl9//aV0X0lm6Y9M79KlCxwdHaGnpwdjY2OULVtW6dHhgPzR4IMHD0bhwoWVLi/MjpaWFv766y/s2LEDjRs3hqWlJbS1tWFpaan4ct8///zzs5b5OaRSKc6ePYtVq1ahbt26MDMzg46ODmxsbNCiRQscP35c7dmVz2VlZYVr165hyZIlqFu3LszNzRVfsly1alWMHj0avr6+ivnd3Nzg4+ODunXrwtjYGIUKFULv3r1x7tw5xdcuZObk5IR///0Xc+fORc2aNRVflly4cGH88MMPGh8a8bX++OMPdO3aFYUKFfqse6sycnJywuHDh1GtWjXo6ekpxnzt2jWYmZmpbFOiRAncvXsXM2fORNWqVWFiYgI9PT3Y29ujVq1amDJlisrLK1WpUqUKHjx4gEmTJqFSpUowNjZWbEtat26NSpUqaWyvpaWFQ4cOoV+/fihSpAikUinq1KmDkydPqn0IR9euXTFw4EBUqlQJ1tbW0NHRgaGhIUqXLo1hw4bBz88PxsbGnz0vUV6QCEI+unuWiIiIiIjoO8IzXERERERERCJhwUVERERERCQSFlxEREREREQiYcFFREREREQkEhZcREREREREImHBRUREREREJBIWXP9xKSkpCAkJQUpKSl53hYiIiIjou5P1GzvpP+Xt27coWrQozDusgLaRVV53J18KXNMxr7uQryUmy/K6C/mang4/19JExq+C1EhHm/mjSaqM+aOJttaXfdHyf8mHmKS87kK+ZqjLbZAm1ia6OZqPUSQiIiIiIhIJCy4iIiIiIiKRsOAiIiIiIiISCQsuIiIiIiIikbDgIiIiIiIiEgkLLiIiIiIiIpGw4CIiIiIiIhIJCy4iIiIiIiKRsOAiIiIiIiISCQsuIiIiIiIikbDgIiIiIiIiEgkLLiIiIiIiIpGw4CIiIiIiIhIJCy4iIiIiIiKRsOAiIiIiIiISCQsuIiIiIiIikbDgIiIiIiIiEgkLLiIiIiIiIpGw4CIiIiIiIhIJCy4iIiIiIiKRsOAiIiIiIiISCQsuIiIiIiIikbDgIiIiIiIiEgkLLiIiIiIiIpGw4CIiIiIiIhIJCy4iIiIiIiKRsOAiIiIiIiISCQsuIiIiIiIikbDgIiIiIiIiEolOXneAvk8lCpvgty5VUa1EIcQkJGOfXyDmH7yH5FRZtm0LmxtifNsK8ChfBEb6Ogj+EItlxx7iwLUgxTwuhU0xrWMl1Cppg6QUGc7efY0Zu/9BeEySmMPKNf6BoZi4aC9u3HsBI6k+OjWrgYlezaGnq3mVFAQBy7zPYPO+S/gQEYNyrvb4bWRbVCtXTDHP+etPsP2wH27dD8T78GgULWKJLi1qYWBnd+jqaIs9tFwREBSKqX/sw837gTCW6qPdD9Uxrv9POYrPyh1nse3AJXyIiIWbqx2mD2uLqm7Oinmu3PZHx+Ers7Rt5VEZq2Z65vZQROEfGIqJi/fiZlr+dGxWAxMH5ix/lnufweb9l/AhPAZuJe3x24is+bPjiHL+dG5esPLHPzAUk5fsxY178vzp0Kw6JgzIWXxWeJ/BlgOX8CE8Fm6u9vh1RFtULeesmOf2wyDMXXsUj569QWR0HKwtTVC/eimM798cha3NRB5Z7vAPfIsJi/bixt0XMJbqo+NPNTDJq0XOtj/bTmNThu3PrJE/o1r5T/nzPjwaizedxM0Hgbj/9BV0dbQQ5LtI7CHlOv/AUExaLN9GG6etYxM+Yx3bkmEd+1XNOvZPpnVsQIFax5hDmjx7GYpZyw/g9sMgGBnqo3WTqhjZp1mO4rP+Tx/sPHwFHyNiUKaEPSYObo1KZZ2U5rv9IBAL1h3F/afBMJYaoJl7RYzp3wKGBnpiDivXBASFYtrS/biVto//+YdqGNsvZ/v4VTvOwvvgZcU+ftrQNqiSYR+f7qzfA6zYdgYPn72Gro423Fzt8cfkbihiYy7OoLLBM1xpfH19IZFIcPPmzVxbprOzM4YOHZpryysozKS6+OsXD+hpa6H/6kv4/cBddK1fAtM7Vsq2rY2ZAQ5NaAxbc0OM974Bz+UX4e0bAD2dT6lqbKCDv35pCEsTfQxd74fJO2+ihqs1tg1rAIlExIHlkoioOLQduhxJKSnYPK8vJg9qgW2HrmDq0gPZtl3mfQbz1x+HV2d37Fg4ALZWpugwfBUCX71XzLP14GXExCVg/ICfsGuxFzr+VAPz1x/H6Ll/ijmsXBMRHYdOI1YiKTkV62f1xvgBzbHzyBXMXHEw27Yrd5zF4k0n0L+jO7b+3h82VmboNno1gl6/zzLv4oldcGjNSMXP2P4/iTCa3BcRFYefhy1HcnIKNs3ti8leLeB96Aqm5SB/lnufwfwNxzGwkzu2p+VPxxHK+bMtLX/G9f8JOxd7oWOzGliw4Th+mVdA8icqDu2Hr0BScio2zemDiV4tsP2QH6YvO5ht2xXeZ7Bg4wkM6OQO74X9YVvIFJ1GrkJQhvhERsfDxckWv438GbuWDMIvfZvh4k1/dBm9GolJKSKOLHdERMWhzZDlSEpOxZbf+2HyoJbYdvAKpv6xP9u2y7adxu/rT8Crizt2LhoI20KmaJ9p+/MmLBIHTv+DQhYmqFSmqJhDEU1EVBzapa1jm+f2xaS0dWx6DtexBRuOp+WQfB3rlGkd8z54GbFp69iOxV7okLaOjSlA6xhzSL3I6Dh4jlmD5JRULJ/hiVF9m2H3sauYt/pwtm3X/+mDZVtPole7+lg7uy+srUzRZ/xaBL/+oJjnVehH9Bq7BoaGelg+oxdG9WmGo2dvY/y8XWIOK9dERMeh88hVSE5OxbpZvTGuf3PsOuKH31Ycyrbtqh1nsWTz3+jXsQE2/94PNpam6P7Lmiz7+P2nbmLglM2oVdkFm+f1w+JJXVGhVFEkJiWLNaxs8QxXmipVqsDPzw9lypTJ664UeN0buMDEQAf9Vl1CRJz8jJOOlhZmd6uK5ccfIjQyQW3bye0q4XV4HLr/cR4yQQAAXH4cqjSPp7srTA118cOvF/A+OhEA8CI0Gsen/IAfKtnj79uvRBpZ7thy4BKiYxOwdV4/WJgZAQBSU2UYt2APRnk2VfspeUJiMpZuPY1BXT3g1aUhAKBWpRKo1WEWVu3wwfxxHQEAC8Z1hJW5saJd3aqukMkEzF17DDOGtVZ6LT/afvAyouMSsGFOH1iYyuOTkirD5MV7MaxHExQupD4+K7efwYBODdG/kzsAoEbF4qjfdTbW7jqHOb90UJq/VPEiqFjaUdSxiGFrWv5syZA/KakyjF+4ByOzy59tpzGoi3L+1O44C6t2+mD+WHn+zM+cP1XS8mfdMUwfmv/zZ9vBy4iOTcDmeX0V+ZOaIsOERXswomcTjfFZ5n0GXl0aYmBneXxqViyBOp1mY9VOH/yeFh/3mqXhXrO0ol3dKq6wtzFHp5GrcfdJMKpn+KQ+P9qy/xJiYhOw7Xfl/Bm3YDdG9voBRTTE54+tpzG4a0MM6uIBQJ4/NTv8hpU7zmLBuE4AADcXOzz6ew4A4Pf1x/HAP39vj1VJX8c2Z1rHJizcgxHZrGPLtp2GV6Z1rE7HWVidIYd+V7OOzVt3DNMKwDrGHNLszyN+iI1NwIqZvWFuKgUg38fPXLofA7s2gq2afVhiUjLW7vJBn/bu6NW+AQCgavni+NFzHjbu8cWMEe0AAGt3+sDU2BCrf+0DPT35YbypiRTDZ27FQ/8QlHV1+Aaj/HLbD11BTFwC1s/uDXPFPj4VU5bsw5AejTXu41ftOIv+nRqiX0d3AECNCsXh3m0O1v3pi9mj2wMAIqJiMXXJPkwf1hY92tRVtG9ar5y4A8sGz3ClMTU1Ra1atWBkZKR2nvj4+G/Yo4KrYbkiuPgoVFFsAcCRmy+hJZGgvlsRte2MDXTQolpRbPMNUBRbqrg5WuBhSISi2AKAu0Hh+BidiMYV7HNnECI66/cIDaqXUuyoAKB1o8qQyQScu/ZYbbsb914gOjYBrRtVVkzT09VBc/cKOHPloWKaqp11+ZIOEAQBoe+jcmkU4jl37RH+V7Wk4mAZAFo2rASZTMCF6+rjc+u+PD4tPSoppunp6qBZ/QrwufpQbbuC5uzVR6ivJn98NcQnPX9aZcqfn9wr4Gx2+VOq4OSPj98j1K9WSil/WjWqlG18bqbHxyPT+tWgAs76PdL4nun/i6Tk/H+G64zfwyz506ZxWv5cUz/O6/eey7c/jasopsm3PxWVtj9aWgX/sMLnK9exzNvo720dYw5pduH6Y9Su4qootgCgmXslyAQBl28+UdvunweBiIlNQDP3ioppero6aFKvPC5kiOujgFeoXqG4otgCgHrVSwEAfPzy/77O9+oj1KtaUlFsAUCLhvL8uXhDfXxu3Q9EdGwCWjSspJimp6uDH+tXwLkM+/gj5/5FqkyGTs1ritL/L1Wws/oz+Pn5oVWrVrCzs4ORkREqVaoEb29vxeuqLimUSCSYN28exo8fj8KFC8PGxgYA0KtXL5QrVw4nTpxAuXLlYGBggKpVq+Lq1atf1YeM/Th9+jS6du0KExMTODk5Yf78+SqX5+HhASMjI5iZmaFr16549+7d14QpV7gUNsWzt8o7jaj4ZLyLjIdLYRO17co7WkJfVxvJqTLsHeOBF6s74p+FrTGpXUXoaH+6VlBfVwtJKVnvBUtKSYVrEdPcG4hIAoJC4eJkqzTNzEQK20Km8A8KVdNKfk8BALhmalvSuTBCQsMRn6D+/rVrd55DX08HjnZWX9HzbyMg6B1KqIiPjZUpAl6qz+/010o4Krd1cSqMV6ERiE9Ujk/Psevg2GAUqv08Hb+tPJTl9fwqICg0Sw6YmUhha5VN/gT9N/LHPygULk42StPS4xOQg/hkXjddnW3xKjQ8S36kpsqQlJwC/8BQ/LbyMCqUckDNCsVzaRTiCQgKhavz529/AtRuf2wR8lZz/hQ0/uq20dmsYwFqcyjn65hTAVjHmEOaPQ9+h+KOytsgU2NDWFua4Hmw+n3Y87R9WOa2JZxs8fpdBBIS5ZfDJSYlZ7nXSVdbGxKJBM9fqo9/fvHs5TuUcMy8jTaU7+M15M+ztLFlbuviZItXoRFISNtG334QhBKONtj79w3U7vArijX8BT/2WYBzVzV/cCa2/8wlhUFBQahbty68vLxgYGCAy5cvo2/fvpDJZPD0VH+j/NKlS1GrVi1s3LgRKSmfPr188+YNBg8ejBkzZsDCwgLz5s3DDz/8AH9/f0Vh9jV98PLyQo8ePXDgwAEcPHgQ48ePR4UKFfDjjz8CkBdb7u7u+Omnn/DXX38hNjYWU6ZMQevWreHn56d2PFFRUYiK+lQMvXnzJkfx+xxmUj1ExWW9TjYiLgnmRvpq21mbGQAAFvSsgZ0Xn2HxkfuoVMwSv7QqL7/c4sBdAMCL0Bh0qlsMBrraSEhOBQDYWUphY2aI2MT8/wlzRFQczEwMs0w3N5EiIipOfbvoOOjr6cBAX1dpupmpIQRBQGR0vMobZp+9fId1u33h2bYujKXq459fREbHwdRYVXwMNcYnUl18TDLER18PpkaGGNTVA7UqloCBvi4u/+OPtX+eQ0BQKLbOH5Dr48ltEVFxMFMVH1PN+aMuPuYmmvPneXBa/rQpOPmjKj5m2eZPvJr8kcrjExUPQ+tP8Wk7ZBmu330BAKhYuih2LBoInQLwwAO1+WMiRXikpu2P6viYp8UnQk3+FESRamJkls06pm4bnZN1bP1uX/RsUxdGBWAdYw5pFqVmH2ZmIkVktPorpaJi4qGnqwN9PeX4mBqn508cDPTN4GxvjXtPgiEIAiRpN67fffxSMU9+p24fb2ZiiAgN/Ve/jTZU5E9hfT2EfYzG85dhWLTxBCZ6tYSNlSm2HbiEvhM34MSmMShVTP2VVmL6zxRcnTt3VvwuCALq16+PkJAQrF27VmPBZWlpif379yuSOt3Hjx+xZ88eeHjIr0Nu0KABihYtiiVLlmDu3Llf3Yd27dphxowZAIBGjRrh2LFj2Lt3r6LgmjBhAqpVq6bUt/Lly6NcuXI4fvw4fvpJ9QMAFi9ejJkzZ6odb17SShvHpUdv8duefwEAV568g5GBLgY2LY0/jj5AQnIqdl58hr6NSmJej2qYu+8ODPV08HvP6pAJAjRcififFB0bj94TN8LJzgqTvFrkdXfyhXIlHVCu5Kdr3OtWLQkbK1NMWbIPtx8GoXKmp0H9l0XHxqP3hI1wKmKFicwfJYsndkFUTDxehLzHiu1n0GH4KhxZOxImRgZ53TUqQNLXMUeuY5RDXVrXQa8xa7BowzH06eCOdx+iMHPZfmhraaFAPDlMZDKZDLHxiVg6tbvivq3alV3g3nUOVu/0wR+Tu+VJv/4zlxSGh4dj+PDhcHJygq6uLnR1dbFu3To8ffpUY7tmzZplKbYAwMzMTFFspf/duHFjXLt2LVf60LRpU8XvEokEZcqUQUhICAAgLi4Oly9fRocOHZCamoqUlBSkpKSgZMmSKFq0KG7cuKG2D6NHj0ZwcLDi5/r16xrH/yUi45JgYqibZbq5VA8RsYkqWnxqBwCXHyufcr/8KBQGutpwtpFf9/48NBpjtl1H4wr2uLWwDS7NaYHI2CT43HuDd5H5/z47c1MpomKy9jMiOk7pmu8s7UykSExKUVxWkC4yKh4SiSTLWbOk5BR4jt+IiKg47FrsBSPD/P/JKSD/FDBaZXziNcbHTF18olXHJ6OWDeX3XNx7EvyFvf521OZPlOb8URefCDXxSUpOQa/xGxERHYedBSx/omKzPpgnMtv8MVSTP3Hy+Jgqx8fFyRZV3JzR7odq2L10MF6EhMH70JXcGYSI5PmTNT4R0XGwMNO0/VEdn4i0+JhrWL8KGjM161hkNuuYum10dutYZAFbx5hDmpmaSBGtchuk+uoWRTtjQyQlp2R5kl5UTHr+yGNbu7IrxvRvDu8Dl1C73XS09VqMauWLobSLHWws8/9tFWZq4xMPc5Mv2UbHK+VPepzqVnFVzKOro40aFYvj6Yu3uTGEL/KfKbh69eqFXbt2YcyYMTh16hRu3LiBPn36ICFB/RPzAMDW1lbldGtra5XzarpE73P6YG5urvS3np6eYr7w8HCkpqZi1KhRisIt/efly5cIDlZ/0GhqagoHBwfFT5EiuX9qNeBtFFwy3UtlYqgLGzNDBLyNVtvu6etIjcvV1/10uc4+v0BU/uUgGk0/gWpjD2HAmstwsjHGP88/aFhC/uDiZIuAIOWiMiomHqHvo7Jc255R+jXzme9j8g8KhYOthdKlGDKZDF7Tt+HO42D8uWQQ7G0tcnEE4nJxsskyxqiYeLz7EAUXR9WX6wJQvJb5GvlnL0Nhb2sOQ/2Cf6kKIM8ff1X58yGb/HH6vPwZNH0b7jwJxq7FBSt/XJ1ss9wHkB6fzPfWZG4HyC/Bzcg/KBT2thYa88fa0hRFrM0RGBL2FT3/NuT5oyI+2Wx/XNK3P5na+geFwqGwxXdxKVg6V3Xb6GzWMRc1ORSgZh0bPH0b7j4Jxs4Cto4xhzQrXtRGcT9WuuiYeIR9jEbxour3Yen3br0IVt6OPH/5DnY25kqX0vXv7AG/fTNxeP0YXN4zA5OHtsHLV+9RsUz+v0KjhKNNlnVEsY/XkD/p92dn2ccHvYO9rTkM0rbRJYsVVruMvHws/H+i4EpISMDRo0cxZcoUDBs2DB4eHqhWrRpksuy/hFfV2S0ACAvLumMNDQ1VW8B8TR8yMzc3h0QiweTJk3Hjxo0sP1OmTPnsZeamc/ffoF4ZW5hmOMvVompRyAQBFx6oL0hffYzDo5AI1CujvML9r2xhxCemZCnIklNlePI6Em8j4lGntA2K25hg95UXuTsYETSqXQbnbzxRutb60Nnb0NKSoGGGx01nVr18MZgYGeDw2duKackpqTjmeweN65RVmnf8gj04dek+ts3vh7Iudrk/CBE1rFkGl24+VYrP0XP/QktLgvo11Menajl5fI6e+1cxLTklFScu3IVHrbJq2wHAobP/AAAqlsn/j4lvVKsMLtxUzp/DPvL8cdcQn/T8OeKjnD/Hfe+gUeb8WbgHpy7fx7bfC17+eNQugwuZ8ueIz7/ZxqeaIj7/KqYlp6Ti+Pm7aFRb89eFvAoNR8jbj3C0K/TV/Rdb49pl1W5/3GuqH2eN8sXl259M8Tl2Luv2p6Dz+Mp17LBP1m105nVsQto6trUArmPMIc3q1yiNK//4K50l/fv8HWhJJKhbrZTadlXcnGFsZIAT5+8opiWnpOLUpXuoryKuUkN9lCpeBJbmxjh46iYEQOkJh/mVe630ffyn+BzzvQMtLQn+V119fKqWc4aJkQGOnVOOz98X7qJhhn18o9ry3y/d+nT1WFJyCq7deYbypfLue93+E/dwJSYmQiaTQU/v06cn0dHROHw4+y+hUycyMhI+Pj6KywojIyNx5swZDBkyRPQ+GBkZoXbt2nj06BFmzZr1ZQMQ0fbzAejtURIbh/wPy48/RGFzQ0xuXwnbzwcofQfXn6MbwsFKinqTjymm/X7gLjYN+R9mdKqMs3dfo2IxKwxsWgqrTz5GfJL8ARmGetr4pVU5XH0ahsTkVFQpXghDm5XB4iP38TxU/Rm0/KJX23rYsOcCeo7bgJG9muBNWCRmrjgEz7Z1lb7f5eehKxD89iNu7J0GADDQ18UIzyZYsOEErCyMUbaEHTbtu4iPkbEY3O3T5a1LtpzClgOXMbRbI+jr6eDm/U9FaKlihWFilL8v2+jepi4277uIvpM2YliPJngbFonZqw6je+s6St/P0WnESoSEhuPyn/IPGAz0dTGke2Ms2fw3LM2NUaZ4EWw9cBnhkbEYmPadOAAw7FdvODsUQvmSRaGvp4PL//hjw25f/PC/8gXie7k829bDhr0X4Dl+A0Z4yuMzc8UheLZRzp92aflzPWP+9GyCBRtPwMrcGGVK2GHz/rT86fopf/7YcgpbD1zGkG6NoFcA86dnm7rYuPcCek3YiBE9m+BNWAR+XXkIPTPFp/2wFQh5G46re6YCkMdneI/GWLjpb1iZG6XF5xLCM8Vn3Py/YGlmjIqli8LE2BDPXoZiza5zsLY0QdeWtb75eD9Xr5/rYf2e8+gxbj1GeTbFm7BIzFh+EL3a1lX6/qS2Q5Yj5O1H3Ng3HYA8PiM9m2D+hrT8cSmCzXsv4WNkHIZ0a6T0HukfCj198RapMkHxd+WyTihaxPIbjfTLebatJ8+htHXsTVgkflWzjoW8/YhrGdax4T2bYGGGdWzL/osIj4zFoO9oHWMOada5ZW14H7yEIdM2Y2DXRgh9H4n5646ic8vaSt/B5TlmNV6HhuO09yQAgL6eLgZ28cDyradgaWaEksWLYNehK4iIikXfDu6KdsFvPuDgqZuokLa/uvpvALbtu4A5YzsrLqfLz7q3roMt+y6i/+SNGNq9Md6+T9vHt1Lex3ceuQqvQsNxcddkAPL8GdytEf7YchKW5kYoXdwO2w5eQnhULAZ0dle0K1+qKJo1qIAJC3YjIioONlam2HrgEsI+xsCrc8PM3flm/hMFl5mZGapXr4558+bB2toaOjo6mDdvHszMzL74MeqWlpbo27cvZs6cCXNzc8ybNw+CIGDkyJHfpA8LFiyAh4cHOnXqhM6dO8PCwgIhISE4ffo0evfuDXd39y8aV26IjEtG50Xn8FuXKtg4uB5iElPw56Vn+P3APaX5tLUk8ps8Mzhz9zWGrL+CES3c0KOBC95FJmDR4ftYeeLT4zwFAShtb46OdYpDaqCDZ2+jMGnnLewpAGe3APn17/uXD8XERXvhOW4DjKUG6N6qdpaHWqSmypCa6fH3w3s0hiAIWLXDBx8iYlDO1R67lw6Gs/2nT9Z9077La8WOs1ix46xS+4Mrh6FuVVfkZ+YmUvz5x2BM/WM/+k7aCGOpPjq3qIXxA5orzZcqkyE1NVVp2pBujQBBwNo/z+FjRAzKuthjxyIvOGU481CqWGEcOH0L6/70RVJyCooWscTQHk0wtHvjbzK+r2VuKsW+tPzpNX4DjKQG6NZSRf7IZEhNVc6fYen5s/NT/vz1R6b8SfueoZU7zmJlpvw5sHKY0nXx+ZG5qRR7lg3B5MX70HvCBhhJ9dG1ZW1MHJg5fwSkZIrP0B6NIQBYvescPkTEwM3VHn8uGQSnDPGpVNYJ2w9dweb9F5GUlAJ7Wws0ql0WIzybwDLD9xLlV+amUhxYMQwTFu1Bz3Hr07Y/dTB5UNb8yRyf4T2bQBDkuSHPHwfsWaacPwDQZ9ImlX8vn9oNXVrk/6LU3FSKvcuHYlKmdSzzQy1kGtax1WnrmJurPf7MtI6d17CO7S8g6xhzSD0zEym2LvDCbysOYMi0zTAy1Ef7n2piVJ9mSvPJZEKW/Onf2QOCAGza44uPEbEo42KHjfMGoGiGrwvQ1dHG9TvPsHXfBSSnpKJ0cTusmNkbDWsXjLOE5iZS7FoyGNOW7kO/yZtgLNVHlxa1MLa/8sPeZCr28YO7NYIAYN2fvvgYKd/Hey9U3scDwJJJXTFv3THMW3sUMXEJKFfSATuXeKF0ibw7mywRhP/Gc90CAgIwcOBAXL16FVZWVhg+fDhiYmKwcOFCxMTEwNfXFw0bNsSNGzdQrVo1APLLCRcsWIAxY8YoLatXr164efMmfv/9d4wdOxbPnj2Dm5sbVqxYgTp16ijmc3Z2RosWLbBixYoc9QGAyn4AQJs2bRAREQFfX1/FtJs3b2L69Om4fPkykpKS4ODggEaNGmHy5MlwcMjZN42HhISgaNGiMO+wAtpG+f/7P/JC4JqOed2FfC0x+fMvi/0v0dP5T1y5/cU0fck5ATrazB9NUmXMH020tfjUuux8iPk+vv9LLIa63AZpYm2S9SFxqvxnCq7clF5w3b9/P6+78tVYcGWPBZdmLLg0Y8GlGQsuzVhwacaCSzMWXNljwaUZCy7NclpwMYpEREREREQiYcFFREREREQkkv/EQzNy25YtW/K6C0REREREVADwDBcREREREZFIWHARERERERGJhAUXERERERGRSFhwERERERERiYQFFxERERERkUhYcBEREREREYmEBRcREREREZFIWHARERERERGJhAUXERERERGRSFhwERERERERiYQFFxERERERkUhYcBEREREREYmEBRcREREREZFIWHARERERERGJhAUXERERERGRSFhwERERERERiYQFFxERERERkUhYcBEREREREYmEBRcREREREZFIWHARERERERGJhAUXERERERGRSFhwERERERERiYQFFxERERERkUhYcBEREREREYmEBRcREREREZFIdPK6A5Q/XJ7dAvYODnndjXzJacBfed2FfC1kQ+e87kK+Jgh53YP8TUciyesu5Gsy5g99BS2uXtkyMeChsCaBYbF53YV8zdpEN0fz8QwXERERERGRSFhwERERERERiYQFFxERERERkUhYcBEREREREYmEBRcREREREZFIWHARERERERGJhAUXERERERGRSFhwERERERERiYQFFxERERERkUhYcBEREREREYmEBRcREREREZFIWHARERERERGJhAUXERERERGRSFhwERERERERiYQFFxERERERkUhYcBEREREREYmEBRcREREREZFIWHARERERERGJhAUXERERERGRSFhwERERERERiYQFFxERERERkUhYcBEREREREYmEBRcREREREZFIWHARERERERGJhAUXERERERGRSFhwERERERERiYQFFxERERERkUhYcBEREREREYmEBRcREREREZFIdPK6A/R98g8MxeQle3HjXiCMpfro0Kw6JgxoDj1dzSknCAJWeJ/BlgOX8CE8Fm6u9vh1RFtULeesmOf2wyDMXXsUj569QWR0HKwtTVC/eimM798cha3NRB5Z7ihR2ASzulZDNZdCiElIxt4rgZh/4C6SU2Vq29QuZYO94xqpfC3gTRQaTDmm+NvW3BC/da2KBm6FkZwqw4lbIZj51z+ISUjJ9bGI4WngW4xfuBc37r6AsVQfnX6qgcmDWuQof5ZuO42Ney/hQ0QMyrnaY/aon1G9fDHFPO/Do7Fw00ncvB+I+09fQVdHC8HnF4k9pFz1NPAtJixSjs8kr5zHZ9O+T/GZNTJrfBZtOombDz7F56VvwYuPWPkDAG/CIjF+4R74XnsMHR1ttHCviFkj28LU2FDMYeUa/0z50/Ez8meZivyplil/FmfKn6AClj/+gaGYtHgvbtxLi0+zGpgwMGf7r+XeZ7Bl/yV8CI+BW0n5/qtauU/xuf0wCHPWHMWjZ6/T9l+mqF+9FCYMKDj7L4Db6Oz4B4ViyuK9uHlffgzU/sfqGJ/TY6DtZ7D1wCV8TDsGmjFc+RgoI5lMhmb9FuHekxCsm9UbLRpWyv3BiCAw5B2WrD+Ke4+DIDXURzP3yhjQrQl0s4nPvuNX4ffPEzx8GoyIqDjMGtcFHnXKK80za+leHD/3j8r2Xj1+QM92DXJtHJ/juzvDdfDgQaxatSqvu6GWRCLBwoULNc7j6+sLiUSCmzdvfla7/CIiKg7th69AUnIqNs3pg4leLbD9kB+mLzuYbdsV3mewYOMJDOjkDu+F/WFbyBSdRq5C0Kv3inkio+Ph4mSL30b+jF1LBuGXvs1w8aY/uoxejcSk/F9QmEl1sXusB3R1tNBv5UXM238X3RqUwPROlTW2uxf0ES1nn1L66bL4HFJlMpy791oxn462BDtGuaO4rQmGrruCSd434V6uMFYOqCPyyHJHRFQc2gxejuTkVGz9vR+mDG6JrQevYMof+7Ntu3TbacxbdwKDurhj16KBKFzIFO2Gr0Jghvx5ExaJA6f/gbWFCSqVKSrmUEQRERWHNkMyxGfQ58Xn9/Xy+OxcNBC2hUzR/nuMj4j5k5ySivbDV+LZy3dY95snFo7vCJ+rjzBg6lYxh5Vr0vMnKTkVW37vh8mDWmLbwSuYmoP4LEvLH68c5E+hApw/7YYtR3JyCjbP7YtJXi3gfegKpi89kG3b5d5nsGDD8bT91wDYWpmi0wjl+ERGx8HFyRazRrXDn38Mxpi+P+LSzafoPGo1EpOSxRxaruE2WrOIqDh0HLYCySmp2DinDyYMbIEdh/0wIyfHQNvPYNHGExjQ0R1bF/SHjZUpuoxSPgbKyPvQFbwNi8zlEYgrKiYew6duRHJKCuZM6Aav7k1x6NQNLNt8PNu2f/veRmRUHGpXLaV2nl4dG2Ld715KP51a1gUA1K5SMtfG8bm+uzNcBw8exM2bNzF48OC87soXq1KlCvz8/FCmTJm87soX2XbwMqJjE7B5Xl9YmBoBAFJTZJiwaA9G9Gyi9lO8hMRkLPM+A68uDTGwc0MAQM2KJVCn02ys2umD38d2BAC41ywN95qlFe3qVnGFvY05Oo1cjbtPgrN8Gp3f9HB3hYmBLvqtvIiI2CQAgI6WBHO6V8Py4w8RGhGvsl1MQgr+ef5BaVrHusWgraWFA9eCFNOaV3VEKTszuE85hmeh0QCAiLgk7BrdEJWKWeLfFx9FGlnu2Lz/EqJjE+A9vx8szOT5k5Iqw9j5uzGq1w8ooiF/lmw5jSHdGmJwVw8AQO3KJVC9/W9Ysf0sFo7vBABwc7HDk7/nAADmrTuOB/6vvsGocs/m/ZcQE5uAbb9nis+C7OPzx9bTGNy1IQZ1SYtPpRKo0eE3rNhxFgvHfYrP47T4/L6+YMZHzPw5fPY2Hj9/i6u7J8PVyRYAYG4iRfvhq3DrQSCqujmLP8ivsEVN/oxbsBsjPzN/alUqgZodfsPKHWexIEP+PCrA+bP1wKW0/ZdyfCYs3IMRnk0177+2nYZXFw94dZHvv2pVKoE6HWdhtdL+qwzca37at9et4gp7Wwt0HLEKdx8Ho3qF4iKP8OtxG62Z98HLiI5LwMa5GY6BUmWYuGgPhmdzDLTC+wwGdm6IARmOgep1no01u3wwd0xHpfk/RMRg/rpjmDq0NUbP2SXuoHLRwb+vITY+EfMmdIepiRSAPH8WrT2Mnu3dYW1pqrbt2nkDoaWlhTeh4Thx7rbKeRyKWMGhiJXStNXbTqJYURu4FiuSewP5TN/dGa7vgampKWrVqgUjI6O87soX8fF7hPrVSik2NADQqlElyGQCfK8/Vtvu5r0XiI5NQCuPT2d69HR10LxBBZz1e6TxPdM3+knJ+f8MV8PyRXDxUaii2AKAIzdeQksiQQO3wp+1rDY1nfD8bRTuBH4qojzKF8GjkAhFsQUAFx68RXhMIjzK2339AER25spDNKheSvE/BYC2jStDJhNw7pr6PLh+9zmiYxPQpnEVxTQ9XR20cK+I01ceKqZpaRXszd5Zv4eonyk+bXISn3vq43PmO4qP2Plz5spDuLnYKYotAGhYszQsTKU4ffkh8rszGvLHNwf50zpTfJp/Z/njc/VRlvi0blQ52/3XjbT9V+tGyvuvn9wr4OwVzXmh2H+lpH5l778NbqM187n6CP/LdAzU0kN+DHRe0zHQfXkOtcx0DPSTmmOguWuOok4VV9St4pq7AxCZ3z9PUa1CCUWxBQCN6paHTBBw/ba/xrZfkhthHyJx51Egmtav9Nltc1O+yWo/Pz+0atUKdnZ2MDIyQqVKleDt7a14fcuWLZBIJHj/Xvm0aqVKldCrVy8AQK9evbB161Y8ePAAEokEEolE8RoA7N+/H5UqVYKBgQHs7OwwevRoJCQkKF5Pv5Tv5MmT6NixI4yNjeHo6IidO3cCAJYtWwZHR0dYWlqiX79+SExMVOrLvXv38MMPP8DIyAhmZmZo3749Xr58mWWsKSkpGDduHKytrWFiYoJevXohOvrTwbGqSwpVOXbsGGrWrAlDQ0NYW1tj0KBBiI2N1Rzob8A/KBQuTjZK08xMpLC1MkVAUKjGdgDgkuFABgBcnW3xKjQc8YlJStNTU2VISk6Bf2Aoflt5GBVKOaBmAfh00KWwKQLeRClNi4pPRmhkPEoUVv/JTmaFTA1Qt7St0tktAChR2BQBb6OyzB/wJgouRXK+/LziHxQKV2flHDAzkaJwIVP4B2afP66Z8qdkMVuEvA1HfEKSqmYFjrr42BYyVcRAZbtANfFx/m/EJ7fyR9XyJRIJXJ1tNcY/vwj4wvwJ+A/lT+Z9UPr+S2N81O6/CiMkNGt8Mu6/fl1xqMDsvwBuo7MToOkY6OWX5FDWY6DbD4Nw4PQtTBvaOhd7/m0EvQqDk4O10jQTY0NYWZgg6FVYrr/f6Yt3IZMJaFK/Qq4v+3Pkm0sKg4KCULduXXh5ecHAwACXL19G3759IZPJ4OnpmaNlTJ06FWFhYXj8+DF27NgBALC2lv9TDx8+jPbt26Nz586YN28eHj9+jEmTJuHly5fYu3ev0nIGDRqEXr16oX///li/fj169OiBO3fu4P79+1izZg2eP3+O0aNHo3jx4pg0aRIAIDg4GPXr10eJEiWwfft2JCQkYPLkyWjQoAHu3r0LExMTxfKXL1+OKlWqYOvWrXjx4gUmTJiAhIQE/PnnnzmO1969e9GpUyf07t0bM2fOxJs3bzBhwgSEh4drXE5UVBSioj4djL958ybH75lTkdFxMFNx87iZiSEiouI0tIuHvp4ODPR1M7WTQhAEREbFw9BaTzG97ZBluH73BQCgYumi2LFoIHR0tHNpFOIxk+ohKi7rjiUyLgnmRnoqWqjWqrojdLS1cDBTwWVupIf7L79++XklIioOZiaq8keKcA35ExGlOn/M0/InIjoehgb5f/zZiYhSvX6Zm0gREakhPmrWr+8yPiLmT0R0PCqoib+m5ecXmvInnPmDSDXxMTOVatx/RUTHqYmPoXz/lSk+bQYvw/W7zwEAlco4YsdirwKx/wK4jc5OZHScygfo5NYxkEwmw6RFezCwc0MULWKF4Dcf1Cwxf4qOiYexUdb4mBgbICpa9S0VX+PUhTsoV8oRdraWub7sz5FvCq7OnTsrfhcEAfXr10dISAjWrl2b44KrRIkSsLa2RlBQEGrVqqX02owZM1CrVi3F2aoff/wRUqkUAwcOxL1791C+/KennHTo0AHTpk0DANSoUQP79+/Hrl278OzZM+jqylcEX19f7NmzR1FwLVmyBMnJyTh16hQsLeX/1MqVK6Ns2bLYsmULhg0bpli+vr4+Dh48CG1t+cbV0NAQ/fr1w4wZM1C69Kd7k9QRBAFjxoxBp06dsGHDBsX0IkWK4KeffsLUqVPh5uamsu3ixYsxc+bMbN+jIFg8sQuiYuLxIuQ9Vmw/gw7DV+HI2pEwMTLI6659E21rOeFO4Ac8z3DpIBER5X9LJnVBZEw8XoSEYYX3WXQYthJH142EiYoDUaKMdh65incfozG0R+O87kq+FxjyDk+fv8bo/i3zuiv555LC8PBwDB8+HE5OTtDV1YWuri7WrVuHp0+ffvWyY2Ji8O+//6J9+/ZK0zt1kt+geenSJaXpTZo0UfxuZmYGGxsb1K9fX1FsAUDJkiURHBys+PvixYvw8PBQFFsAULp0aVSsWDHL8lu2bKkotgCgffv2EAQB169fz9F4nj59iqCgIHTs2BEpKSmKnwYNGkBLS0vjpYijR49GcHCw4ien7/k5zEykiIpNyDI9Mjoe5qZSFS3S2xkiMSkFCYnKT2qKjI6DRCKBmanyjsjFyRZV3JzR7odq2L10MF6EhMH70JXcGYSIIuOSYGKY9VM8M6me0n1dmjhZG6NK8UI4cDUoy2sRsV+//LxkbipFVIyq/ImDhYb8MTdVnT8RafljruIT2YJIXXwiouNgbqYhPmrWr/9KfHIrf8xNDBEVm/VT2Ihslp9faMofC+YPzEyliIrJ+v+NjIrTuP8yN5GqiU+8fP9lknX/VdXNGe1/qI7dy9L2Xwfz//4L4DY6O2YmUkSrjM/XHwPFxiVi7tqjGOnZFMnJKYiMjkN02vFWfEKS4vf8zMTYELFxWfsZHZMA01zOgVPn70BbWwuN6pXPfmaR5ZuCq1evXti1axfGjBmDU6dO4caNG+jTp4/SPVZfKiIiAoIgwNY20zXHZmbQ19fHx4/KT20zNzdX+ltPT0/ltIx9Cw8Pz7J8ALC1tc2yfBsb5Wt7TU1NYWBgkOPL+9LvY2vbtq2iONXV1YVUKkVqaqpSIZiZqakpHBwcFD9FiuT+E1tcnWyz3KsVFROP0A9RWa5NztwOAJ69fKc03T8oFPa2FjDUV3+pgbWlKYpYmyMwJPev/81tAW+j4FLERGmaiaEubM0M8UzFvVeqtK3phFSZDIeuZy24nr1Vfa9WCRX3juVHrk62We4DiIqJx9v3UVnuG8jcDkCW3PMPDIVDYYvv4lIVIC0+qtav91FZ7o1QauesJj5B32F8RMwf+fKVt1GCICAg6J3G+OcXLl+YPy7/ofwJCFL+/6bvvzTGR83+KyAoFA62muNjk7b/ehGi+tHf+Q230Zq5ONlmuVdLcQzk+GU5lH4M9DEyBuGRsRi/YDfK/DgRZX6ciMae8wEAI2btQL3Os3J5NLnPyd4aQZmO1WJiE/AhPBpO9tZqWn2Z0xfvoHpFF1iYGefqcr9Evii4EhIScPToUUyZMgXDhg2Dh4cHqlWrBpns05fAGhjILxNLSlL+hD48PDzb5Zubm0MikeDdO+UkjoyMRGJiotJZqS9laWmZZfkAEBoammX5meeLiopCQkJCjouf9OWtWLECN27cyPLTp0+fLxxF7vCoXQYXbj5FZPSna5WP+PwLLS0J3Guov2SyWvliMDEywBGffxXTklNScfz8XTSqrfkR+a9CwxHy9iMc7Qp9df/Fdu7eG/yvbGGYGn46Y9qiWlHIBAHnH7zN0TJa13SC35N3eBeZ9QMJn3tvUNbBHMVsPm1g/lfGFpYm+vDJ8H1d+VXjOmVx/sYTpfw5eOY2tLQkaFhTfR7UqFAcJkYGOHT2X8W05JRUHD13B03qlBWzy99Uo9pZ43PobA7iUz4tPpnWr6Pn7qDxdxQfsfOncZ2yuO//Sumg6PyNJ/gYGYsmdfN/HBtryB/3HOTP4Uz5c+w7yx+PWmVw4aZyfA773M52/1U9bf912OfTo6qTU1JxzPcOGmUTn/T9l5O9lcb58gtuozXzqFUGF2+oPgZqoOkYqJw8h46e+1cxLfMxkLWlKfYuH6r0s2qm/LabX/r+iA1z+oozqFxUu0pJ3LwTgOgMZ5J9rtyDlkSCGpVz74mLD54G49Xbj2jyv4q5tsyvkS/u4UpMTIRMJoOe3qdPN6Kjo3H48GHF3w4ODgCAR48ewc7OTvF75rM5mc88AYCxsTEqVaqEvXv3YtSoUYrpu3fvBgDUq1fvq8dQr149rFu3DuHh4bCwsAAAPHnyBHfv3s1SAB05cgSLFy9WXFa4d+9eSCQSVK9ePUfvVbp0aTg4OOD58+cYMmTIV/c9t/VsUxcb915ArwkbMaJnE7wJi8CvKw+hZ5u6St8/0X7YCoS8DcfVPVMBAAb6uhjeozEWbvobVuZGKFPCDpv3X0J4ZKziOzsAYNz8v2BpZoyKpYvCxNgQz16GYs2uc7C2NEHXlrWy9Ce/8fb1R+9Grtg49H9YfvwhCpsbYkqHyvD2DVD6Dq6/xjSEvaUR6k06qtTezdECJe3MsPak6sfLHrv1EsOal8W6wf/D7/vvwFBPG1M7VsaZO6/y/XdwAUDvn+th/e7z6D52PUb1aoo3YZGYvvwgev9cV+n7XdoMXo7gtx9xa/90APL8GdWrCX5ffwJWFsYoW6IINu67hI9RcRjavZHSexw6Kz8oevLiLVJlguLvKmWdULRI3t5Ym53eP9fD+j3n0WPceozybIrXafHp1TZTfIYsR8jbj7i571N8Rno2wfwNJ1DI3BhlXIpg095L+BgZh6HdlONzOFN80v+uXFDiI2L+tGpUGYu3nILnhI2YOqgl4hKSMG3ZQTSt65bvv4MLAHplyp83YZGYoSJ/2qblzw0V+WOVlj+b0/JniJr8eVoA88ezbT35/mv8BozwbII3YZH4dcUheGbaf7UbugIhbz/i2l75/d4G+roY3rMJFm5Mi08JO2zZfxHhkbEYlGH/Nfb3v2BpboSKpR1hamyAZ0HvsFqx/6r9zcf7JbiN1qxHm7rYtPcC+kzciGE9muDt+wjMWnUIPVor51DH4fJjoCu7Px0DDe3RGIvTjoFKl7DD1rRjIK+0774z0NdFnUyPgU9/aEapYkXy/feQAkCbH2ti7zE/TJi7HT3buyPsYxRWbjmBNj/WUPoOrmFTN+BtWAT2rBmjmPYoIARv3oUjIlL+RO4HT+Q1gIWpESqXU37K56kL/0JfTxcNauWPYj5fFFxmZmaoXr065s2bB2tra+jo6GDevHkwMzNTnA2qWbMmihYtilGjRmHu3LmIiorCvHnzYGWl/IlQmTJlsGnTJuzatQuurq4oVKgQnJ2dMWPGDLRp0wbdu3dH9+7d8eTJE0yaNAnt2rVTemDGlxo1ahQ2b96Mpk2bYvLkyUhISMCUKVPg6Oio9Gh6QF5gtmnTBoMHD8aLFy8wfvx4tG/fPsdfdCyRSLB48WJ07doVsbGxaN68OYyMjBAUFIRjx45hzpw5KFky775N29xUij3LhmDy4n3oPWEDjKT66NqyNiYObK40X6pMQEqqTGna0B6NIQBYvescPkTEwM3VHn8uGQQn+09nriqVdcL2Q1ewef9FJCWlwN7WAo1ql8UIzyawNMv/310WGZeMTgvPYVbXqtg05H+ISUjBrovP8Pv+u0rzaUkk0NGWZGnftqYTEpJTcfyW6ktHU1IFdF/ii1+7VsXKgXWQkirgxD/BmPHnP6KMJ7eZm0pxYOUwTFi4Bz3GroexkQF6tK6DKYNaKM2XKpNlyZ8RPZtAEIAV28/iQ0QMypV0wN6lg+Fsr3zms/fETSr/XjGtG7q2yN9Fu7mpFAdWDMOERXvQY9x6GEsN0KNVHUzOFB+ZpvjsSIuPqwP2LlMRn0mbVP69fGoBiY+I+aOro409SwdjwqK96D91C7S1tdDCvSJmj/r5m4zva2XMn55p+dNdRf6ois/wtPiszJA/e1TkT59M+dMnQ/50KQD5s3f5UExatBe9xm+AkdQA3VrWxkSvrOtXaqb4DOvRGIIgYPVOn0/7rz+U41O5rCO8D13B5n0Z9l91Cs7+C+A2OjvmplLsXjYEk5fsQ5+JG2As1UeXFrUxISfHQN0bA4L8GOhjRAzcXOyxM9MxUEFnamyIZb/2xeL1RzBh7nZIDfXQskl1DOzWRGk+mUzIso7tO3YVx899OpbZdUj+jITKbsWwcvangis1VQafy/dRr3ppSA31RRxNzkkEQRDyuhMAEBAQgIEDB+Lq1auwsrLC8OHDERMTg4ULFyImJgYAcOvWLQwaNAj379+Hi4sLlixZgl9++QWVKlXCli1bAMgvzxs4cCBOnz6NDx8+wNPTU/Havn378Ouvv+Lx48ewtLRE586dMXfuXMXlir6+vmjYsCFu3LiBatWqKfrm7OyMFi1aYMWKFYppM2bMUOobANy9exdjxozB5cuXoa2tjSZNmmDx4sVwcnJSzCORSDB37lyEhYVhy5YtSEpKQtu2bbFixQqYmpqq7YdEIsGCBQswZsynSv/06dOYPXu24iEZzs7O+PHHHzF16lSYman+JvPMQkJCULRoUTx4Ggj7tLOIpKy41+687kK+FrKhc/Yz/Yfljy1s/iXJ+pkCZSBj/miUygBppKviQztSlpAsy36m/7DAsLz/ftf8rLJTzr7fNN8UXJQ3WHBljwWXZiy4NOMWVjMWXJqxntCMBZdmLLiyx4JLMxZcmuW04MoXD80gIiIiIiL6HrHgIiIiIiIiEgkLLiIiIiIiIpGw4CIiIiIiIhIJCy4iIiIiIiKRsOAiIiIiIiISCQsuIiIiIiIikbDgIiIiIiIiEgkLLiIiIiIiIpGw4CIiIiIiIhIJCy4iIiIiIiKRsOAiIiIiIiISCQsuIiIiIiIikbDgIiIiIiIiEgkLLiIiIiIiIpGw4CIiIiIiIhIJCy4iIiIiIiKRsOAiIiIiIiISCQsuIiIiIiIikbDgIiIiIiIiEgkLLiIiIiIiIpGw4CIiIiIiIhIJCy4iIiIiIiKRsOAiIiIiIiISCQsuIiIiIiIikbDgIiIiIiIiEgkLLiIiIiIiIpGw4CIiIiIiIhKJTl53gPKHpFQZElNked2NfOnVxi553YV8rdnKK3ndhXxtV69qed2FfI3bHc20tSR53YV8TSbkdQ/yNzNDHuZlR1eb65gmroWN87oL3wWe4SIiIiIiIhIJCy4iIiIiIiKRsOAiIiIiIiISCQsuIiIiIiIikbDgIiIiIiIiEgkLLiIiIiIiIpGw4CIiIiIiIhIJCy4iIiIiIiKRsOAiIiIiIiISCQsuIiIiIiIikbDgIiIiIiIiEgkLLiIiIiIiIpGw4CIiIiIiIhIJCy4iIiIiIiKRsOAiIiIiIiISCQsuIiIiIiIikbDgIiIiIiIiEgkLLiIiIiIiIpGw4CIiIiIiIhIJCy4iIiIiIiKRsOAiIiIiIiISCQsuIiIiIiIikbDgIiIiIiIiEgkLLiIiIiIiIpGw4CIiIiIiIhIJCy4iIiIiIiKRsOAiIiIiIiISCQsuIiIiIiIikbDgIiIiIiIiEolOXneAvk8BQaGYvnQ/bt0PhLFUHz//UA1j+v0EPV3NKScIAlbvOAvvg5fxISIWZV3tMG1oG1Rxc84y71m/B1ix7QwePXsNXR1tlHW1xx+Tu6GIjbk4g8pFTwPfYvyCPbh+9wWMpfro1LwmpgxqkaP4/LH1NDbuvYgPETEoX9Ies0e1Q/XyxZTmexMWgfEL9uDctcfQ0dFGy4YVMWvkzzA1NhRzWLnGwdwQg+oXQ9nCJohLTsXZx2HYdu0lUmSCxnZbelSBralBlumt1vghOVXetqSNMTxrOcLZSgoTfR2ExyXjdnAEtl17iY9xyaKMJ7c9CwrF9GUH8M+DQBgZytevX/o2y1H+rNnpg+2HLuNDRAzKuthj6pDWqJxh/Rozdxf2nbyhsv24/s0xqFuj3ByKKJ6/fIdZKw7g9sNAGBkaoHWTqhjR+8ds47Pz0GWcv/4Ydx+/RHhkLP6Y1gM/1q+YZb7Q95GYteIgLt96Cl0dLTSpVx4TvFrB2Chr7uVHz16G4tflB3D7QRCMDPXRpmlVjOqTs/xZt8sHOw5fwceIGJRxscekwa1RuayT0nw37z3HH5v/xqOA19DWkqB8aUf80u8nlHWxF3NYuebZy1DMWn4Atx/K49O6SVWMzGF81v/pg53p8Slhj4mDW6NSpvjcfhCIBeuO4v7TYBhLDdDMvSLG9G8BQwM9MYeVq/wDQzF5yV7cuCffx3doVh0TBjTPUYxWeJ/BlgOX8CE8Fm6u9vh1RFtULeesmOf2wyDMXXsUj569QWR0HKwtTVC/eimM798cha3NRB5Z7vAPDMXERXtx494LGEn10alZDUz0yll8lnmfweZ9l/AhIgblXO3x28i2qFbu0z7+/PUn2H7YD7fuB+J9eDSKFrFElxa1MLCzO3R1tMUeWq74L8aHZ7i+kV69eqFcuXIa53F2dsbQoUO/+D3Mzc0xY8aML26fWyKj49Bl5CokJ6di7azeGNu/OXYd8cNvKw5l23b1jrNYsvlv9O3YAJt+7wcbS1P0+GUNXr5+rzTf/lM34TVlM2pXdsGmef2weFJXVChVFIlJ+f+AOSIqDq0HLUNSciq2ze+HqUNaYeuBy5i8ZH+2bf/Yehrz1h3H4K4N8ediL9hamaHdsJUIDPkUn+SUVLQbthIBL99h3W+9sGh8J5z1e4T+U7eIOKrcY6yvjXlt3KCrJcFvJ55g69WXaOZmi/51nXPU/mLAe4zae1fpJ73YSl9+SHg81l58gcmHH2LHjWBUcjDDrJZloaslEWlUuScyOg5dR69GckoKVv/aC2P7/4RdR/wwa2X269eanT74Y8vf6NOhATbO7QcbK1P0HLsWL19/UMwzrGcT7F85XOmnT/v6AIAGNUuLNq7cEhkdB8+xa5CUkopl03thVJ9m2H3sKuatOZxt20OnbyEiMhb1a6gfZ3JKKvpNWI/AkDAsnNQV04e3w6WbT/DLnB25OQzRREbHoecva5CcnIqVMz0xul8z/HX0Kuauyj4+63b5YOnWk+jdrj7WzekLa0tT9B6nnD/PX75D73HrYGighyVTumPO2E6IiIqD55g1CPsYJebQckVktLyvySmpWD7DE6P6puXP6uzjs/5PHyzbehK92tXH2tl9YW1lij7j1yI4Q3xehX5Er7FrYGioh+Uz5Pl59OxtjJ+3S8xh5aqIqDi0H74CScmp2DSnDyZ6tcD2Q36Yvuxgtm1XeJ/Bgo0nMKCTO7wX9odtIVN0GrkKQa8+7cMio+Ph4mSL30b+jF1LBuGXvs1w8aY/uoxejcSkFBFHljsiouLQduhyJKWkYPO8vpg8qAW2HbqCqUsPZNt2mfcZzF9/HF6d3bFj4QDYWpmiw/BVCMwQn60HLyMmLgHjB/yEXYu90PGnGpi//jhGz/1TzGHlmv9qfHiGKx85cOAALCws8robX237oSuIiUvAutm9YW5qBABITU3FlCX7MLRHY9gWUv0JVUJiMlbtOIv+nRqiX0d3AECNCsXRsNscrP3TF7NHtwcARETFYtqSfZg2rC16tKmraN+knuaCNr/YtO8iomMTsH1Bf1iYyeOTkpKKMfN3Y3Tvpihiba6yXUJiMpZsOYUh3TwwuKsHAKB25RKo1u5XLN9+FosmdAIAHDp7G4+fv8W13VPg6mwLADA3laLdsJW49SAQVVWcLcxPfnIrDKmeNn498QQxifKdq7ZEgiENiuOvWyHZnoUKj0vG49AYta//ExyJf4IjFX/fex2FsOhEzGntBhcbYzx6G507AxHJjsNXEBObgLW/fVq/UlJlmLZkH4Z0V79+JSYmY/XOs+jX0R19OzQAAFSvUBwe3edi/V/n8Nso+frlZF8ITvaFlNr+vu4YXJ1tC8QZij+P+CE2NgErZvSCuakUgDw+vy7bj4FdGqmNDwDsWjYUWlpaCHn7EYdO31I5z8kLdxEQFIpjm8aieFEbAICpiSH6TViPu49fokJpx9wfVC7adcQPMbEJWPVrb0V8UlNlmPHHfnh1Ux+fxKRkrNnlg74d3NE7LX+qlS+Opj3nYeNuX8wc2Q4AcPrSPQiCgOXTPWGgrwsAKFW8CDy6zcHlm0/Rpmm1bzDKL6fIn5nK8Zm5dD8GdtUcn7W7fNCnvTt6tZfHp2r54vjRcx427vHFjBHy+Kzd6QNTY0Os/rUP9PTkh2CmJlIMn7kVD/1DUNbV4RuM8utsO3gZ0bEJ2DyvLyzS9/EpMkxYtAcjejZRexYqITEZy7zPwKtLQwzs3BAAULNiCdTpNBurdvrg97EdAQDuNUvDPcOHO3WruMLexhydRq7G3SfBWa7oyG+2HLiE6NgEbJ3XT7GPT02VYdyCPRjl2VRjfJZuPY1BXT3g1UUen1qVSqBWh1lYtcMH88fJ47NgXEdYmRsr2tWt6gqZTMDctccwY1hrpdfyo/9qfHiGKx+pXLkynJ2d1b4uCAISExO/XYe+kO/VR6hXtaTiYBAAmjesDJlMwIUbT9S2u3U/ENGxCWjesJJimp6uDn6oXwG+Vx8qph099y9SZTJ0al5TlP6L7YzfQzSoUVqxoQGAtk2qQCYTcO7qY7Xtrt99jujYBLRtXEUxTU9XBy0bVsTpKw8+Lf/KQ7i52CmKLQBoWLM0LMykOH35AfK7ak7muB0coSi2AOBCwHtIJEAVR3NR3jMqQf5eOgXgDJfvtceom2n9auFeCTJBwEVN69eDtPXLvZJimp6uDn6sXwHnrj5S2+5tWARu3HuO1o2r5kr/xXbxxmPUruKqOFgGgGbuFSETBFy+9VRjWy2t7HeJF68/RqniRRTFFgDUrVoSZiZSnL+mPo75xflrj1GnqnJ8fkrLn0s31efPP/cDERObgGbuny6x1NPVQdP/lVcad3KqDHq6OtDX+/R5romR/FJmzRcE5w8XrqvKH3l8LmuKzwPV8WlSrzwuZIjPo4BXqF6huKLYAoB61UsBAHz8Pu3n8jMfv0eoX62UotgCgFaNKkEmE+B7Xf0+7Oa9F4iOTUArj8qKaXq6OmjeoALO+mled9L3l0nJ+f8M11m/R2hQvZTSPr51I/kx0Llr6uNzIy0+rRtlio97BZy58ik3VBUM5Us6QBAEhL7P/2eR/6vxYcH1jZ04cQLlypWDgYEBqlatiqtXrypey3xJYfpliMePH0fFihWhr6+PI0eOAAAOHTqE0qVLw8DAADVq1MCNG6rvucgLz16+QwlHG6VpZiaGsLEyxbOgUA3t5K9lbuvqZItXoRFISEwCANx+EIQSjjbY9/cN1OnwK4o3/AXN+izQeNCYn/gHhsLVyVZpmpmJFIULmeJpoPr4pL+WsZACgJLOhRHyNhzxCUmflp9pHolEAlenwhqXn18UNZciJDxeaVpsUio+xiahqHn296A1LGmNw161sH9ATfzaogycLaUq59OSyAssB3ND9K3jBP93MXjwJv/vrJ6rWL9MTQxhY2WCZy/fqW2X/lrmtiWcbPD63af1K7PDZ29DJhPQKsNOLj97HvxOqRgCAFNjQ1hbmuCFhvh8zfIlEgmKO9rgRXDYVy9fbOriY2Npguca4vM8WE3+ONqm5Y/8zHOLhpWQmirDkk0nEB4Zi9D3kZiz6hCK2JijcR23XB5N7nse/A7FM69fafmTHgOV7dJil7ltCSfl+CQmJWe5T0VXWxsSiQTPX+b/7TMA+AeFwsUp8z5eClsrUwRo2Mf7p73mkmn/5+psi1eh4YjPtA1KTZUhKTkF/oGh+G3lYVQo5YCaFYrn0ijEExAUmmWMZiZS2BYyVcRAFf/0fbyTin186Kd9vCrX7jyHvp4OHO2svqLn38Z/NT68pPAbevPmDQYPHowZM2bAwsIC8+bNww8//AB/f3/Y2NiobPP69WsMHz4cU6ZMgaOjIxwdHfHvv/+iXbt2aNasGRYvXowXL16gY8eOOTr7FRUVhaioTweVb968ybXxpYuMjlP5cAYzE0NERMdpaBcPfT0dxWUo6UxNDCEIAiKj42Ggr4ewj9F4/jIMizaewESvlrCxMsW2A5fQb+IGnNg0BiWLFcn1MeWmiKg4mJmoio8UEVGx6ttFx6mMj7mpFIIgICI6DoYGeoiIjkMFk6yXpZibGiIiSn388wtjfW3EJKVmmR6TmAoTA82brKuB4XgSGo130YkoYmqATtUcsPDnchi6+w7eRimvH/PblIObnSkA4GloDKYdfYRsnsmRL6hdv4yl2axfcfIzD5nyx8xYqrR+ZXb47D+o4uaMokXy/44cAKKi42HyBdufHC8/Jh4mxlkfjmFqnDvLF1uUmvwxNZEiMjpeRQu5yOj4tDNX6rbPcTDQN4OzgzW2LvLCoCmbsXrHWQCAQ2FLbF3gpfL/kt+oi49ZNvGJilETH+NM8bG3xr0nwRAEARKJ/Iz63ccvFfMUBJHRcTBTt45p2Meo28ebmaRtg6LiYWj9aRvUdsgyXL/7AgBQsXRR7Fg0EDoF4KEQ6vbx5iZSjfFRt483M/10DKTqwSrPXr7Dut2+8GxbF8ZS/a8fgMj+q/FhwfUNffz4EXv27IGHh/z+mwYNGqBo0aJYsmQJ5s6dq7JNeHg4Tpw4gZo1P10+17lzZzg6OuLgwYPQ1pZvfAwNDdG3b99s+7B48WLMnDkzF0aTd2QyGWLjE7F0anfFfVu1KrvAvescrN7pgyWTu+VxDymvrLn4QvH7gzfRuBUcgfVdK6NdJXusvPBcad4l5wJgrK8DOzMDdKhsjzmty2LMvvuIS85a7P1XPQsKxQP/V5gxvG1ed4UKiBfBYRg6fSvqVSuJNk2rITEpBRt3+6LvhPXYvXwYClma5HUX81SX1nXQa8waLNpwDH06uOPdhyjMXLYf2lpagCT/X9L8LS2e2AVRMfF4EfIeK7afQYfhq3Bk7UiYFJCngX4L0bHx6D1xI5zsrDDJq0VedyffyU/x4SWF35CZmZmi2Er/u3Hjxrh27ZraNlZWVkrFFgBcu3YNLVu2VBRbANC+ffsc9WH06NEIDg5W/Fy/fv0zR5E9MxMpomMTskyPjI6HuYnqy7vk7QyRmJSiuPQiXVR0PCQSieITEbO0ZdSp4qqYR1dHGzUrFsfTF29zYwiiMjeVIiom6yelkdFxSvflZGlnIlUZn4ioOEgkEkVszU2kiIrJGv+IqHil+xLyq5jEVBjpZf0U01hfG9EJn3f9fnhcMh68iYaLTda4vopIwJPQGJx7+h6TDj+EvZkhmrnZqlhK/iJfv1TkT0xcNuuXFEnJKUjMlD+RMXFK61dGB8/8Ax1tLbTwKBiXEwLyMy5fsv3J8fKNVS8/KiZ3li82UzXb56ho1Z86pzMzMZTnT5K67bN87Is2Hoe1pQkWTOyKulVLwqN2Wayb0xdRMXHYuv9i7g5GBOriE5lNfEyN1cQnRjk+tSu7Ykz/5vA+cAm1201HW6/FqFa+GEq72MHG0jR3ByMSMxMpotStYxr2Mer28ZHRadsgU+X4ujjZooqbM9r9UA27lw7Gi5AweB+6kjuDEJG6fXxEdJzG+Kjbx0dGxavcRiclp8Bz/EZERMVh12IvGBnm/7NbwH83Piy4viFra+ss02xtbTVe1mdrm/UA8M2bN1kuQTQ1NYWBQfaf+piamsLBwUHxU6RI7l9+V8LRJsu9JFEx8Xj3IQolnNQf0JZwlL+W+Tr5gKB3sLc1V1zuVLJYYbXLKAiPhXd1ts1ynXJkTDzevo9CSWf18Ul/LXNb/8BQOBS2UJxKd3W2VVzrnE4QBAQEhWpcfn4RHBGHohbKG06pnjYsjfQQHKH+kp6vERGfjPexiShilv8/OS2udv2KznJ/TUbprz3LtH49e/kOdjbmKi8nPHL2H9StWjLfP/Uqo+JFbfAi0xijY+IR9jEaxTTE57OWnyn+giDgRfA7FCuadRuf3xQvapPlXq3omHi8+xid5f6jzO0A4Hmm+9SeB6fnj/wyn4CgUJQuYac0j5GhPpzsCmX5eo/8SF18wj5GZ7n3TaldWuwy38f3/KVyfACgf2cP+O2bicPrx+DynhmYPLQNXr56j4pllL+vK79ydbLNcq9WVEw8Qj9EZbk3J3M7AFm2X/5BobC3tYChim1QOmtLUxSxNkdgSP6/T9LFyRYBQVm30aHvo7Lcf5RR+r3XASri42BroXS5nEwmg9f0bbjzOBh/LhkEe9uC84Tr/2p8WHB9Q2FhWTcUoaGhGoseiYpLDIoUKYJ37zIla1QUEhKyfuKUF9xrlcGlm0+Vrnc/5nsHWloS1E97GpMqVcs5w8TIAMfO3VFMS05Jxd8X7sK9VlnFNI/a8t8vZXjiWFJyCq7deYbypYrm5lBE0bh2WZy//kTpev1DZ25DS0uChrXUf/9PjQrFYWJkgENnbyumJaek4ojvHTTJcDN64zplcd//ldJO7fz1J/gYGYsmdfP/Tes3gyJQycFc6SzX/0pYQRCAf15GfNayLKW6cCtigqcaHhMPAIWM9WBjrI+3UfljHdLEvWZpXL7lj6gM69dx3zvQkkjwP03rl5t8/Truq7x+nbxwDw1rlcky/+2HQQh6/QGtMjwVsyD4X/XSuPKPv9InqH9fuAstiQR1q5b8+uXXKI3Hz98oHfj5/eOPiKg4NKiZNY75TYOaWeNz4rw8f+pVU58/Vco5w9jIACcy5c+pi/eUxm1va4GH/q8gCJ9uiIyOTUDgq/ewL2yZy6PJffVrqMiftPjU1RQft7T4nM8Un0v3UF9FXkgN9VGqeBFYmhvj4KmbEAClJxzmZx61y+DCzadK+7AjPv9CS0sCdw3fYVetfDGYGBngiM+/imnJKak4fv4uGtXWvO68Cg1HyNuPcLQrpHG+/KBR7TI4fyPTPv5s2j5ew3cZVk+Lz+FM+/hjvnfQuE5ZpXnHL9iDU5fuY9v8fijrYpd5UfnafzU+vIfrG4qMjISPj4/issLIyEicOXMGQ4YM+azl1KhRA0eOHMHixYsVlxXu3bs31/v7pbq3roMt+y5iwOSNGNK9Md6+j8ScVYfRrVUdpe8w6TJyFV6FhuPCrskAAAN9XQzu1gh/bDkJK3MjlCpuB++DlxARFYuBnd0V7cqXKopmDSpg4oLdiIiKUzw0I+xjjOK7PfKzPu3+h/W7z6PbmHUY3fsHvHkXgWnLDqD3z/WUvoOr9aBlCH77Ef8cmAFAHp9RvZri9/XHUcjcGGVd7LBx70WER8ZiWPdGn9o1qozFm0+i5/gNmDq4JeITkjF16QE0reeW77+DCwCOP3iLVhWKYNpPpfHXrRBYGemjbx1nHLv/Vuk7uOa2LgsbE3303S7f+DZwLYSazha4ERSOD7FJKGxqgE5V7SETBOz/97Wi3dAGxRGVkAz/d7GITUqBg7khfq5kh4j4ZJx8+PVPsRNbt1Z1sHX/JQyYsgmDuzdG6PtIzF1zBF1b1VZav7qNXo1Xbz/Cd6d8/dLX18WgrunrlzFKFS8C74OXEREVi/6dsq43h8/+AwN9XfxQr/w3G1tu6NyyNrYfuowh0zZjYNdGCH0fiQXrjqJTy1pK8ek1dg1eh4bj1LaJimn3ngTjVehHhEfIH15z59FLAIClmTFqVCwBAPihfgWs3XkWw2duw6i+zZCQkIT5a4+iQc0y+f47uACgS8va8D5wCYOmboZXWnx+X3sUXVoq50/PX1bjVWg4zm6fBADQ19OFVxcPLNt6CpbmRihVrAh2HL6C8KhY9E373sT05Q+auhmjZ+9A26bVkJiUjE17ziMpOQUdf8r/X+XRuWVteB+8pJQ/89cdRedM8fEcsxqvQ8Nx2vtTfAZ28cDyradgaWaEksWLYNehK4iIikXfDu6KdsFvPuDgqZuKXLn6bwC27buAOWM7Ky47zO96tqmLjXsvoNeEjRjRswnehEXg15WH0LNNXaXvUGo/bAVC3obj6p6pAOT7sOE9GmPhpr9hZW6EMiXssHn/JYRHxiq+WxIAxs3/C5ZmxqhYuihMjA3x7GUo1uw6B2tLE3RtWeubj/dz9WpbDxv2XEDPcRswslcTvAmLxMwVh+DZVjk+Pw9dgeC3H3Fj7zQA8viM8GyCBRtOwMrCGGVL2GHTvov4GBmLwd0+xWfJllPYcuAyhnZrBH09Hdy8/+ne5VLFCiu+hiG/+q/GhwXXN2RpaYm+ffti5syZMDc3x7x58yAIAkaOHPlZy5kwYQKqV6+ONm3aYPDgwXj+/DkWLlyYo0sKvwUzEyl2LhmM6Uv3of/kTTCW6qNzi1oY2/8npflkMhlSU5UfUDCoWyMIANb96YuPkTEo62KPbQu9snyqtXhSV/y+7hh+X3sUMXEJKFfSATuWeGW5lCU/MjeV4uCq4Ri/YA+6j1kHYyMD9GhdB1MHt1SaL1UmQ0qqTGnaSM8m8i8V3X4WHyJiUL6kA/YuGwxnh0/x0dXRxt5lQzBh4R70n7IF2tpaaNmwEmaP+vmbjO9rxSSmYuKhBxj0v2KY2qw04pNTcfJRKLZefak0n5ZEAu0MZ4BDoxJgKdXDwHrFYKSnjdikVPwbEgnv6y8RGv3pCYVP38WgWVlbtChXBLraEoTFJOJGUDj+uvUK0Yn5/ztezEyk2LF4EGYs3Y+BUzbBSKqPTs1rYkw/5fUrNTVr/nh19YAAAev/8sWHiBiUdbHD1gUDszwqNzVVhmPn/kWjOm4wKgBPvcrIzESKLfMHYtaKgxg6fQuMDPXRvlkNjOzTTGk+VfHZcegyDp66qfh7857zAORfEO29eDAA+fq1YV5/zFpxEL/M3gEdbS00qVceEwe1EnlkucPMRIqtC73w2/IDGDxtM4yk+ujwU02M7pspPjIBqZniM6CLBwQAG3f74mNELMq42GHz7wOU8qdx3XJYNq0nNvx1DiN+84aujjbKutjDe/EgODvk/0suzUyk2LrAC7+tOIAh0zbL8+enmhiVKX9kKuLTv7MHBAHYtOdTfDbOG4CiGeKjq6ON63eeYeu+C0hOSUXp4nZYMbM3GtZW/oQ+PzM3lWLPsiGYvHgfek/YACOpPrq2rI2JA5srzZcqE7KsY0N7NIYAYPWuc/gQEQM3V3v8uWSQ0petVyrrhO2HrmDz/otISkqBva0FGtUuixGeTWBppv4+5/zC3FSK/cuHYuKivfActwHGUgN0b1U7y0MbUlNlSE1Rjs/wHo0hCAJW7fDBh4gYlHO1x+6lg+GcIT6+ad9VtWLHWaxIexJouoMrh6FuVVfkZ//V+EiEjOf9STS9evXCzZs38fvvv2Ps2LF49uwZ3NzcsGLFCtSpUweA/Hu4WrRogRUrVii1uX//fpblHThwABMmTEBQUBDKlSuHlStX4ocffsDIkSMxY8aMHPcrJCQERYsWxe1Hz2Fnn/+/4T4vmBrqZj/Tf1izlfn/Jua8tKtXtbzuQr6WmGmHSsq0C8CXceelgvBVDnnJzJCfq2eH6xh9DWP9nN2dxYLrP44FV/ZYcGnGgkszFlyaseDSjAeDmrHg0owFV/a4jtHXyGnBxYdmEBERERERiYQFFxERERERkUhYcBEREREREYmEBRcREREREZFIWHARERERERGJhAUXERERERGRSFhwERERERERiYQFFxERERERkUhYcBEREREREYmEBRcREREREZFIWHARERERERGJhAUXERERERGRSFhwERERERERiYQFFxERERERkUhYcBEREREREYmEBRcREREREZFIWHARERERERGJhAUXERERERGRSFhwERERERERiYQFFxERERERkUhYcBEREREREYmEBRcREREREZFIWHARERERERGJhAUXERERERGRSFhwERERERERiYQFFxERERERkUhYcBEREREREYmEBRcREREREZFIdPK6A5Q/GOpqQ6qnndfdyJcEQcjrLuRre/vWyOsu5GueO/7J6y7kaxu7VM7rLuRrrz7G53UX8jVrU/287kK+lpzK/Vd2tLUked2FfE3GFMoVPMNFREREREQkEhZcREREREREImHBRUREREREJBIWXERERERERCJhwUVERERERCQSFlxEREREREQiYcFFREREREQkEhZcREREREREImHBRUREREREJBIWXERERERERCJhwUVERERERCQSFlxEREREREQiYcFFREREREQkEhZcREREREREImHBRUREREREJBIWXERERERERCJhwUVERERERCQSFlxEREREREQiYcFFREREREQkEhZcREREREREImHBRUREREREJBIWXERERERERCJhwUVERERERCQSFlxEREREREQiYcFFREREREQkEhZcREREREREImHBRUREREREJBIWXERERERERCJhwUVERERERCQSnbzuAH2f/ANDMXHRXty49wJGUn10alYDE72aQ09Xc8oJgoBl3mewed8lfIiIQTlXe/w2si2qlSummOd9eDQWbz6FW/cDcd8/BDo62gg6t1DsIeWqp4FvMX7hXty4+wLGUn10+qkGJg9qkaP4LN12Ghv3forP7FE/o3p55fgs3HQSN+8H4v7TV9DV0ULw+UViDylXBQSFYuof+3DzfiCMpfpo90N1jOv/U47is3LHWWw7cAkfImLh5mqH6cPaoqqbs2KeK7f90XH4yixtW3lUxqqZnrk9FFHYmRmgb20nlLIxRnxyKs4HfMCft0KQIhM0tlvVsSJsTPSzTO+y5QaSU+VtTQ100K6SHUpaG8PZSooUmYAe226JMg6xBASFYsbS/bj1IBBGUn20a1oNv/TLWf6s3nkW2w9exoeIWJR1scPUoW1QJUP+pPPxe4AV3mfw6Nlr6Opoo6yLPZZM7oYiNubiDCoXBYW8w+INR3H/cRCkhvr40b0yBnRtAt1s4rP/xFX43XqCh/7BiIiKw6yxXdCwTvks8915GIj1u04j4MUbaGlpoYyLAwb2aIqSxezEGlKuevYyFL8tP4DbD4NgZKiPNk2qYmSfZjnKn3V/+mDn4Sv4GBGDMiXsMWlwa1Qq66Q03+0HgZi/7ijuPw2GsdQAzdwrYmz/FjA00BNzWLkqICgU0zJto8fmcBu9SsU2OuM65qdmG92yAG2jxTwGOn/9CbYf9sOt+4F4Hx6NokUs0aVFLQzs7A5dHW2xh5Yr/ANDMWmxPD7GUn10bFYDEwbmLD7Lvc9gy/5L+BAeA7eS9vh1hHJ8bj8Mwpw1R/Ho2WtERsfB2tIU9auXwoQBzVHY2kzsoanFgus7sGXLFujp6aFr16553RUAQERUHNoOXY7iRa2xeV5fvAmLwLSlBxGXmITfx3TQ2HaZ9xnMX38cUwe3QlkXO2zaexEdhq/COe/xcLYvBAB4ExaJg6f/QeWyjqhY2hEPAl59i2HlmoioOLQZvBzFi9pg6+/98CYsAlP+OID4xCTMH9tRY9ul205j3roTmDakJdxc7LFx7wW0G74KF7Yrx+fA6X9QpawTKpUpigf+BSw+0XHoNGIlnB2ssX5Wb7x9H4lfVxxEfGISZo9qr7Htyh1nsXjTCUwc2BJlSthhy4FL6DZ6NU5uHgsnu0JK8y6e2AUlnGwVf1uaGYkyntxmpKeNGc1K401UAhacDYCVVBeeNR2hr6OFjX5B2bb3e/ERh++9UZqWXmwBgKVUD3WLWyEgLAbP3sfCyVKa62MQU2R0HLqOWgVnB2us+a03QsMi8duqg4hPTMavI9tpbLt651n8sflvjBvQAmVK2GHbgUvoOWYNjm8cA8cM+XPg1E2Mn/8n+nVsiDH9fkJsfCJu3H2OxKRksYf31aJi4jFs2kYULWKFOeO7IexDFJZvPo6ExGT8MqCVxrYnfG8DAGpVKYW/037PLOhVGEbN3Iyq5YtjxujOSE5OwbZ9vhgxbSO2LxsJKwuTXB9TboqMjoPnmDVwsi+EFTM8Efo+EnNXH0Z8YjKmD/9ZY9t1f/pg2daTGNOvOUoVL4Idh66g9/i1OLT2FzjaWQEAXoV+hOfYNaheoTiWz+iFd+8jsXD9MYR9iMbyGQWjmEjfRhdzsMa6tG30b2nb6FnZbKNXpW2jJ6Rto7embaP/VrGNXjSxC1wybKMtCsg2WuxjoK0HLyM+IQnjB/wEB1sL3HwQiPnrj+PJi7dYPrXbtxjiV4mIikO7YctR3MEam+fK4zN92UHEJyRhbjbxWe59Bgs2HMfkQfL4bN53EZ1GrMLZbZ/iExkdBxcnW3RrVRvWliYIevUeizedROdHL3Fy0y/Q19P9FsPMggXXd2DLli0wNjbONwXXlgOXEB2bgK3z+ik2kKmpMoxbsAejPJuq/YQhITEZS7eexqCuHvDq0hAAUKtSCdTqMAurdvhg/jh5MeLmYoeHJ2YDAOavP17gCq7N++Xx8Z7/KT4pqTKMnb8bo3r9gCIa4rNky2kM6dYQg7t6AABqVy6B6u1/w4rtZ7FwfCcA8vg8+XsOAGDeuuMFruDafvAyouMSsGFOH1iYforP5MV7MaxHExQupD4+K7efwYBODdG/kzsAoEbF4qjfdTbW7jqHOb8ob8hLFS+CiqUdRR2LGJqWtoGhnjYWnPFHTFIqAEBLS4L+dZyx/85rhMdpPuiPiE+Gf1is2teDPsah3075wXTHyvYFruDafugKYuISsG5Wb5gr8icVU//YhyHdG8NWQ/6s3nEW/To2RL+O7gCA6hWKo2H3OVj3py9mjZYfSEZExWLaH/swdWhb9GhTV9G+Sd1y4g4slxw8eQ1x8YmYO6E7TE3k/9tUmQyL1h5Gz/busLY0Vdt27dyB0NLSwpt34WoLrgtXH0KAgFlju0JfX35gU8K5MDp4LcSNOwH40b1y7g8qF+064oeY2ASsnNkb5qby+KSkyjBz6X54dW2kNn8Sk5KxdpcP+rR3R+/2DQAA1coXxw+e87Bpjy9mjJAX+2t3+sDU2BCrf+0DPT35IZiZiRTDZm7FQ/8QlHV1+Aaj/DrbD15GTFwC1mfYRqembaOH5mAb3T/TNrrBd7aNFvsYaMG4jrAyN1a0q1vVFTKZgLlrj2HGsNZKr+VHW9Pis3me8jHQhIV7MCKb+CzbdhpeXZTjU6fjLKze6YPf0z6wdq9ZBu41yyja1a3iCntbC3QcsQp3HwejeoXiIo9QNd7DRbnurN8jNKheSunTqNaNKkMmE3Du2mO17W7ce4Ho2AS0bvRph6ynq4Pm7hVw5spDxTQtrYKdtmeuPMwSn7aN0+PzSG2763efIzo2AW0aV1FM09PVQQv3ijj9HcXn3LVH+F/VkoodOQC0bFgJMpmAC9fV58+t+/L8aelRSTFNT1cHzepXgM/Vh2rbFTSVi5rh3usoRbEFAFdefIREAlS0//rLJTRflJj/nb/2CHWrllQUWwDQvKF8/bpw44nadv88CER0bAKaN6ykmKanq4Mf/1cB5659yp+j5/5FqkyGTs1ritJ/sV395ymqVSihKLYAwKNuecgEAdf/9dfYNifblpTUVOjp6CiKCQAwlhoAkF8OlN9duP4Ytau4KootAPjJvRJkgoDLNzXnT0xsApq5V1RM09PVQZN65XE+w3b9YcArVK9QXCk+9aqXAgD4+BWM7dS5a49QL9M2usVXbKN/rF8B576jbbTYx0CqCqryJR0gCAJC30fl0ijE43P1EeqriY+vhvxRF5+f3Cvg7BXN+ZP+XkkpqRrnE1PBPjL7Tvj5+aFp06YwNTWFiYkJatasidOnTwMAJkyYgPLly8PY2Bj29vbo0qUL3rz5dDmQu7s7zp8/j2PHjkEikUAikWDGjBl5NBK5gKBQpcsAAPkneLaFTOEfFKq2nX+g/DXXTG1LOhdGSGg44hOScr+zecA/KBSuzlnjU7iQqSIG6toBKuJTzBYhb7+f+AQEvVO61A+Qx8fGyhQBL9+pb5f2WglH5bYuToXxKjQC8YnK8ek5dh0cG4xCtZ+n47eVh7K8nl/ZmxniVUS80rS4pFSExyXD3swg2/b/K2GFXb2qwbtnVUxqWhKOFoZidTVPPHv5DiUcbZSmmZkYwsbKFM9eql+/nqWtX5nbujjZ4nVoBBLS8uP2wyCUcLTBvr9voG7HX1HC4xc067sA566q/7AkPwkKCYOTvbXSNBMjQ1hZmOBlSNhXL79xvQpIlcmwbsdpREbFIexjFJZuOgbbQmb4X42yX718sT0PfofimXLA1NgQ1pYmeBasfvvzXLH9UW5bwskWr99FICFRfuY5MSk5y30qutrakEgkGvMzP3kW9E7lPl6+jn3+NtpVzTbac+w6ODUYheo/T8esArSNzotjoGt3nkNfT0dx6Wp+5q8uPlaa4xOQ9lrmtq5q4pOaKkNScgr8A0Px64pDqFDKATXz6OwWwEsK89zly5fh4eGBWrVqYcOGDTA3N8fNmzfx8uVLAMC7d+8wadIk2NnZISwsDIsWLUKDBg3w8OFD6OjoYNWqVejevTukUikWLpQ/OMLBQf0lCVFRUYiK+vQJSMbiLbdERMXBzCTrQZy5iRQRUXHq20XHQV9PBwb6ytfXmpkaQhAEREbHF6ibitVRFx8zEynCNcUnKl5lfMxNpBAEARHfSXwio+Ngaqwqfww15k+kuvwxyZA/+nowNTLEoK4eqFWxBAz0dXH5H3+s/fMcAoJCsXX+gFwfT24z0tdGbFLWT+lik1JgrK95k37zZTj8w2LxPiYRNiYGaFfJDr+1KIOxBx/gXXSiWF3+ptTlj5mJISI15U9MPPSyyR8DfT2EfYzG85dhWJR2r6CNlSm2HbyE/pM24PjGMShZrEiujyk3RcfGw9goa3xMjAwQFROvosXnKWpXCEtn9sWEud7Yts8XAFDExgJ/zOgLY6PsPxDIa1Fq80eKyGj18YmKiYeerk6W+0PMjNPzJw4G+mZwtrfGvSfBEAQBEokEAHDn8UvFPAWBpnUsN7bRJmnb6JoZttHr0rbRWwrANvpbHwM9e/kO63b7wrNtXRhLsz4UKb+JjIqDmar8Mf2y+JibqI5Pm8HLcP3ucwBApTKO2LHYCzp5+FARFlx5bNy4cXBxcYGPjw+0teWJ0LRpU8XrmzZtUvyempqK2rVrw8HBAT4+PmjatCnKli0LU1NTGBsbo1atWtm+3+LFizFz5szcHwhRAVGupAPKlfz0oUTdqiVhY2WKKUv24fbDIFTO9ESx78mmqy8Vvz8KjcGdV5FY2r48WpUvjA1Xsn/gBgGCTIbY+ET8MbW74r6tWpVd0LDbHKzZ6YPFk/P/TetievnqPSbP34EalVzxo3tlJCWlYNehi/jlty1YO28gLM3z90MzxNa1dR14jlmDhRuOoW8Hd4R+iMKvy/ZDW0tLUYD916nbRk/9D2yjP1d0bDx6T9wIJzsrTPJqkdfdyVeWTOqCyJh4vAgJwwrvs+gwbCWOrhsJExUfOH0LvKQwD8XFxeHq1avw9PRUFFuZnThxAnXq1IGZmRl0dHQUZ6+ePn36Re85evRoBAcHK36uX7/+xf1Xx9xUqvKT0ojoOKXr4rO0M5EiMSlFcelFusioeEgkEpWfGBVE8vgkZJkeGR0HC03xMTVUGZ+I6DhIJBKYfyfxMTORIlpl/sRrzB8zdfkTnX3+tGwovyb83pPgL+z1txObmAqpXtbthZGeDmISUz5rWRHxyXgcGoMSVgXj6V85YWYiRXSsqvUrHmaa8sfYEEk5yJ/0e5/qVHZVzKOro40aFYvjaeDb3BiCqEyMDBETlzU+0bEJKs9afK61O07C0twEU0d0QPWKLqhbvTTmT+6J6Nh47D565auXLzZTtfmj+qyFop2xIZKSU7I8qTIyJj1/5HlTu7IrxvZvDu8Dl1Cr3XS09VqMquWLoYyLncYHluQn6rbRkdxGA/h2x0BJySnwHL8REVFx2LXYC0aG+f/sFiA/k6UqPpFRXxafCDX54+Jki6puzmj/Q3XsXjYYL0LC4H0w77ZBLLjyUHh4OGQyGezsVH83yY0bN9CqVSvY2dnB29sbfn5+uHr1KgAgISHrDiEnTE1N4eDgoPgpUiT3L39xcbJFQJDyddxRMfEIfR+V5drkjNLva8p8n45/UCgcbC2+i8vlAPn12Znv1YqKicfb91FZ7u3K3A74dB1zOv/AUDgU/n7i4+JkkyUHomLi8e5DFFwy3R+h1C7tteeZ7rN49jIU9rbmMNT/PuLzKjI+y71aUl1tWEh18Sryy7YL35MSjjZ4pmL78+5DVJZ7R5Tapa1fWfPnHexszWGQlj8lnQurXUZBeCy8k4M1Xr5SvlcrJjYBH8Kj4ehgraZVzgUGv4NrphhJDfXhUNgKr95+/Orli614URvF/VjpomPiEfYxGiWKqt/+FFdsf5Rj+/zlO9jZmCtdBtW/sweu7puJI+vH4MqeGZgytA2CXr1HpTIF48xNCQ3b6Mz3sGWkbhsd8J1to7/FMZBMJoPX9G248zgYfy4ZBHtbi1wcgbhc1cXng+b4pN+7lfk+wYAcHCPaWJqiiLU5XoS8/4qefx0WXHnI3NwcWlpaeP36tcrXDxw4ADMzM+zevRutWrVCrVq1ULiw+p19ftGodhmcv/FE6Xr0Q2dvQ0tLgoY1S6ttV718MZgYGeDw2U+PG05OScUx3ztoXCf/32ydU43rlM0Sn4Nn0uNTRm27GhWKw8TIAIfO/quYlpySiqPn7qDJdxSfhjXL4NLNp0rxOXruX2hpSVC/hvr8qVpOnj9Hz/2rmJackooTF+7Co5bm+Bw6+w8AoGKZ/P8I4tvBkahgZ6Z0lqt2MUsIAnDnVeRnLctCqovStsYIeK/+MfEFTYOaZXD51lOl+22O+96R50/a0+BUqeLmDBMjAxz3vaOYlpySir8v3EXDmp/yp1Haunbp1qerDJKSU3DtzjOUL1k0N4ciilpVSuLGnQBEx36Kj8+Ve9CSSFCjkquGljlT2NoCT1+8UXoiYWxcAkLevEcRm/x/UFi/Rmn4/eOv9An8ifN3oCWRoG41zfljbGSAv88r58/pS/fQQMV2XWqoj1LFi8DS3BgHT92EACg94TA/U7WNPvYV2+i/L9xFw2y20YcL0Db6WxwDjV+wB6cu3ce2+f1Q1qVgfKF4Oo9aZXDhpnJ8DvvI4+OuIX8U8fHJGp9G2RwDvQoNR8jbj3Cyz7uHivAerjxkZGSE2rVrY9u2bfjll1+yXFYYHx8PXV1dpeu6d+zYkWU5enp6X3zGSwy92tbDhj0X0HPcBozs1QRvwiIxc8UheLatq/T9Cj8PXYHgtx9xY+80AICBvi5GeDbBgg0nYGVh/H/27jusqeuNA/iXDQECioqAA2W490atoqCte9S9cItb6x5V66oLt9aJKGqrKG5bFUfdu9ZVxQGKCqIQNgJJfn8EAgESqM2V4O/7eR6eR27uCee8nnPPeZM7UNnJHtsPXERUTAJG9mmh8jcyBtyTkHDIZHLl77UqlUVpu6JfqKWfZ2CXJtiy7wL6Tt6CCV6t8C4yBnPWHsLALo1VnsHVaeRavA6Pwu2DcwAo4jPByxNLtmTExw7bDlxCVGwiRvdtqfI3DqcfsJ+8DIdUJlf+Xruy7senb6fG8D1wEYNnbMOYfp4Ij4zBwg1H0Lejm8rzXXqMW4+wiGhc/nUWAEV8RvX1wErf31HU2gKVytvBL/AyomMSMDz9mR0AMOanXXAsVQzVXEvDxNgQl+8EY+u+82jdtFqheObLqX/e47vKtpjq4YKD996iqMgY/eqXxql/3qs8g2vOdxVQzMIEY/b/DQBoXL4o6pa2xp2wGEQlpsDW0gSda9hDJgeOZnsQckNHxcK4lLUp9PX0lL8/+5CAD/G6faewvh3d4HfwIobN2oZRfT0QHhmDRRuPoE8HN5VnKPWesAFvIqJxYc9MAIr+492nJVbt+ANFrc1Rsbw9dh26BElsAob1bK4sV9W1NL5rVh0zlu9DTGwiituIsevQJXyIisewnu7Zq6NzOrVugIDjVzF9sT/6fd8cHz7GYoPfSXRsXV/llLaxP25FeKQE+zZOUm57/CwM4e+jIYlVJOgPnypO77IWm6NW1fLp718f0372x7yVv+Hb5rWRkqq4hislVYr2HnW/YEs/T6/2jeB/6BJG/uiLEb1bIuJDDJZuPoae7Rup9J/+kzbibUQ0zuyaAQAwMTbC8F4tsNbvFIpamcO1vB32HL6C6NgEDOrWXFnu9buPCDx1S3msufbXM/gd+BOLJ/dUnnao6zKO0UNmbMNoDcfonunH6Eu5HKNtrC1QsbwdduZyjB6bfoyumn6MvlLIjtFCr4FW7jiFHYGXMbpPS5gYG+LWg5fK1yqUK1lg1yjl14DOTbAt4E94Td2KcQMU8flp3WEM6KQan66j1yEsPArXs8RnbH9PLN92EjbWFqjkZI8dBy8iOiYB3r0z4zN5yW8oam2OGhXLQGxhiueh77Fx7zkUL2qJ3u0bffH2ZmDCVcB+/vlntGjRAh4eHhg5ciSKFCmCO3fuoFixYvD09MSqVaswZswYdO7cGVevXsWuXbtyvEelSpXg5+eHo0ePws7ODvb29mpPU/wSrMUiHFw7GtNXBGDAlK2wEJmib4dGOS7olEplkKbJVLaN7ecBuVyODbvP4qMkHlVdHLBv9UjlE8QzDJ7hm+vva2b1Qa92uv18HGuxCIHrx2Da8v3oN3kLLMxN0a+jG2Z5Z4uPTIY0qWp8xvX3hFwOrPMPUsTHtRQCconPwOnbc/193Y990Ltd3jdXKUjWliL8umokZq86iMEztsFCZIKe7Rpi6rC2KvtJZTJIpap36xvVpyUgl2PTr+cQJYlHZWcH7F4xAmXtM+NToVxJBJ6+jc2/nkdKahpK2xXF6H6eGN3X44u0779KSJFi3sl/MLhRWUzxcEFSqgxBTyKx93aYyn76enowyPJhzfu4TygiMoZXgzKKOx1+kuLBu1j8ducN3mdLoia1dMn193V/vsD54II7JSM/rCxF2OMzEnPWHMCwmduV/WfSkDYq+ynGl2r/8e7dEnI5sOXX8/gYo+g/fstGoIy96vhaMb03lm4+jiWbjyEuMRnVXEtht88IVHTS/U+axRZmWDNvMFZuPYrpi/0hMjNGO496GN7HU2U/mUwOabbjz4ET13Dy3B3l73sPXwIA1KpSDusWKBKupg0qY/6kXthz6CJ+XLEXRoYGcClnj7Xzh6B0tjjqIitLEXYsG4H56wIx8kdfmJuZoFubBpgw6DuV/XKLz7CeLSCXA9v2n0eUJAGVnO2x/edhKrfqNjI0wI17z+F34E+kpklRsbw91s8bCPdGhecshYxj9I+rDmJIlmP0lHwco0f2aQl5tmO0f7ZjtKuaY/SoQnKMFnoNdD79WV7rdgdh3e4glfKH1o9B4zr//ZtqIVmLRQhYOxozVgTAa+pWmItM0ad9I0zPFh+ZTJZjjI1Jj8/GPYr4VHFxwK+rVONTq3IZ7Dp8Bb4HLiIlJQ0OtkXQ0q0yxg3wRFGrgrteWU9eGJ5E+JW7cuUKZs2ahevXr8PAwABVqlTBggUL0LJlSyxduhRr165FVFQUGjdujPXr18PV1RXLli3DpEmKTx7fvHmDYcOG4cqVK5BIJJgzZ06+n8UVFhaG0qVL459noXDQcDv5/2cG+rxzlCaJnwruQYKFwYDdd/Le6f/Ytl618t7p/9ibqP9+q/avWXFx4bhRQEExy+UGO6TK1IhX12giY5agkdg0f/2HCdf/OSZceWPCpRkTLs2YcGnGhEszJlyaMeHSjAlX3phwacaES7P8JlzsZURERERERAJhwkVERERERCQQJlxEREREREQCYcJFREREREQkECZcREREREREAmHCRUREREREJBAmXERERERERAJhwkVERERERCQQJlxEREREREQCYcJFREREREQkECZcREREREREAmHCRUREREREJBAmXERERERERAJhwkVERERERCQQJlxEREREREQCYcJFREREREQkECZcREREREREAmHCRUREREREJBAmXERERERERAJhwkVERERERCQQJlxEREREREQCYcJFREREREQkECZcREREREREAmHCRUREREREJBAmXERERERERAJhwkVERERERCQQJlxEREREREQCYcJFREREREQkEMOCrgDpBj09Pejp6RV0NXQS46KZTC4v6CrotK09axZ0FXTaNwuDCroKOu3SrJYFXQWdlirl8UeTxBRpQVdB55ka8bsHTQz1uQbSBvYyIiIiIiIigTDhIiIiIiIiEggTLiIiIiIiIoEw4SIiIiIiIhIIEy4iIiIiIiKBMOEiIiIiIiISCBMuIiIiIiIigTDhIiIiIiIiEggTLiIiIiIiIoEw4SIiIiIiIhIIEy4iIiIiIiKBMOEiIiIiIiISCBMuIiIiIiIigTDhIiIiIiIiEggTLiIiIiIiIoEw4SIiIiIiIhIIEy4iIiIiIiKBMOEiIiIiIiISCBMuIiIiIiIigTDhIiIiIiIiEggTLiIiIiIiIoEw4SIiIiIiIhIIEy4iIiIiIiKBMOEiIiIiIiISCBMuIiIiIiIigTDhIiIiIiIiEggTLiIiIiIiIoEw4SIiIiIiIhIIEy4iIiIiIiKBGBZ0BejrFBwSjmkrAnDz75ewEJmge5v6mDGiHYyNNHc5uVyONTtPY/uBS/goiUdVFwcsGN8FdauVU+7zIToOPtv/wK2HIXjw9A2MDPURen6F0E3Sqqch4Zi6bD9upMenR9sGmOWdv/is8juNbQEX8VESj2quDlg4oSvqZYkPALyLlGDqsv04d/0fGBoaoL17DSwY3wViCzMhm6U1z0Ij8OPqg7j9IAQWIhN0aV0Xk4e0yVd8NuwOwq5Dl/FRkoAqLvb4cXQn1K7imGPfoKsPsW7nGTx6/hZGhgao4uKAVTP7wK6EtTCN0qLnoRGYsyYQdx6GwNxMEZ8fBn+Xr/j8sucs/A9fxkdJPCo7O2D2qI6olSU+kxbvxYE/buZafsrQtvDu01KbTRGEYzFzTGtfCTXLWCPhkxRH777BujPBSJPK8yxbQmyCMZ6uaFKhOETGBngbnYQt55/jxL13AIAqDmJ0b1AGtR2LoLilKd7HJuPMwwhsOfccSalSoZumFRxfeXv+KgLzsoyxzq3qYmI+x9imvYoxFpU+xmaO6ohalR2V+0z+eS8Oqhljk4e2xYjeuj/Gnr+KwIK1gbj7KBTmZibo6FkH4wflLz5bfj2LPUeuIEoSj0pODpg+siNqVi6rst/dhyFYtvkYHjx9DQuRKb5rXgOThraDmamxkM3SmuCQCExfEYCb91/CXGSCHt/Vx/QRbfO3Btp1Br5Z1kDzx3dG3arZ1kC+p3D7QQgeBIfB0NAAoeeWC90krXqabY3Y41+sEVfnskasl22NuCLbGvGVDqwR+Q3XFyKRSKCnp4cdO3YUdFUEJ4lNRKdRa5GSKsWOJUMw07s9dh66gtmrDuZZds3O01iy5SRG9GqOPSuGw7aYGN+P3YCQNx+U+7yLjEHg6TsoVsQSNSuVFrIpgpDEJqKj9xqkpEqxc+kQzB7VAX6BlzFzZd7xWeV3Gj9vPoGRvd3xq88I2NpYoeuY9QgJy4xPapoUXcesx7NX77F5vhdWTO2BoKuPMXT2DgFbpT2SuET0HL8BqalSbF4wEFOGtsXeo1cxf93hPMtu2B2Elb6/Y0j3ZvBdMgQliorR94dfEPr2g8p+B0/dwvBZvmhYyxm+Pw+Bz4zeqF6hND6lpArVLK2JiUtE74kbkZqWho0/eWHy0DbYe/QqFqzPOz6/7DmLVTt+x6BuzbBt8RCUsBGj/+RNePX2o3KfMf09cXD9WJWfQd9/AwBo1qCiYO3SFktTQ2wZXA9GBvqYuPsvrD39FF3rlcbkNnnXvZilCXYOb4jiYhPMP/QAY3bexv4br2FsmDlVtq5mhzI2Iuy4+BKjd97G7iuh6FqvFNb0qy1ks7SG4ytvMXGJ6DtxI1JT07BhnhcmDWmDX49dxaINecdo096zWL3jdwz6vhm2LBqC4jZieGUbY6P7eSJg/ViVn4EZY6y+7o+xmLhEDJj0C1LTpFg7dwAmDP4O+45fw88bj+RZdsuvZ7HG7w94df0GmxYORnEbMQZN3YTXWeLzJiIKXpN/gZmZMdbO9cKEQd/hWNBdTP15r5DN0hpJbCI6j16LlLQ0+P48GDO922Hn4SuYvTowz7Jrdp3B0i0nMKJnc+xePgy2NmJ0y2UNdOj0HRQrYoEaFcsI2RRBZKwRU1Ol8FsyBLO828Pv0BXMyscacXX6GtE7H2vE4jq2RuQ3XKR1Ow5eQnxCMnYuGYIiVuYAgDSpDFOW7cN4r9awK26Va7nkT6lY5XcaI3u7w7tXCwBAw5pOaNBtPtbvDsKyKT0AAFWc7fH490UAgCVbTuBh8Jsv0Crt2X7gIuISkuG/bGhmfNKkmLR0HyYObAW74ta5lkv+lIqVO05hVJ8WGNlbEZ9GtZxQt+tPWOsfhBXTFPE5HHQX/7wIx/V9s+DiaAsAsBaL0HXMetx+GII6uXwarUv8D19BfGIytiwcCGtxRv+RYtbKAxjVzwMli6nvPxt2B2FoD3cM6d4cAFC/enk077MIm389j4UTvwcASGITMHvlAcwZ0xn9OjVWlm/VpKqwDdOS3UeuID4hGZvmZ42PDD+uPIBRfT1gqyY+nz6lYuOeIAzp3hyDuzUDANSrXh4t+i7Glt/OYf4ERXzKOhRDWYdiKmWXbD4OF0dbVHZ2ELBl2tGtfhlYmBhiwu67iE1SLPAN9PUwo31lbD3/ApFxn9SWndDaFeExyRi54xZk6V+G3XgRpbKP758vEJ2YmTjcehmF2KRU/NyjBirZi/H4baz2G6VFHF9525M+xjZmG2NzVh2Adx8NYyxFMcYGd2uOQVnGmEe/xdj62zn8pGGMLdt8HC5lbVGpEIyxX49eRUJCMtbNGwhrsQgAIJXKMG/1QQzv3VJjfDbtPYtB3zeH1/eK+NSpVh7fDvgZ2/afx9xxXQEAm/achdjCDBt/GgRjY8UyVWwpwth5fngUHIbKLqW+QCs/347AS4hLSIbfz5lrIKlUhinL9mPCgFYoqWENtNrvNLx7t8CIXu4AFGught0WYMPus1g6pTsAxRro0cmFAIClW07g4bPCtQbyVbNGnLxsHyb8yzVio5pOqN9tPtbtDsLyLGvEf3RwjchvuL5Cnz59gkwmK7C/f+bqI3xTr4JyIAFAJ49akMnkOH/9sdpyN+6/QFxCMjp6ZH5SbGxkiLbNa+DMlUfKbfr6hbvbnrn6CM3qV1SJT2fP2pDJ5Dh37R+15W78rYhP52zxae9eA6evPMx8/yuPUMXZXplsAYB7g4ooYiXC6csPoevOX3uMJnVclQsdAGjnrug/F28+UVvu9oMQxCUko517TeU2YyNDfPtNdZy7ltl/jp77C1KZDD3aNhCk/kI7f/0fNM4en+Y1IZPnEZ+Hivi0bV5TuS0zPurHZXikBDfvv0BHjzpaqb/QmrgWw7XnH5XJFgCcuh8OfT09NHIupracuYkBWlWzw77rr5TJVm6yJlsZ/nmnSLJKiE0+v+JfCMdX3i7c+Adu2WLU1j19jN3SHKP4hGS0yRaj1k2ra5z7MsZYh0Iyxv688Q8a1XZRJlsA8F36MeiyhvjceaiIz3fNayi3GRsZwrNJNfyZJT6Pn71BverllckWADSpVwEAcPZqZl/TVUFXH6NZtjVQx5aKMXbuuvo5/ub9l4o1UMtaym2KNVD1r2oNFKRhjXguH2vETtnWQO0KyRpRN2v1FdiyZQscHR0hEonQsmVLPHv2LMc+O3bsQPXq1WFqagoHBwfMnDkTUqnqNQBhYWHo27cvihUrBjMzM3zzzTe4ffu2yj6Ojo4YPXo0li5dirJly8LMzAxRUaqfyn5Jz0IjVBb7AGBlKYJtMTGCQyPUlwtRvOZSVrWsq6MtwsKjkZScov3KFoDgkIgcbbSyFKFkMTGehqiPT8Zr2WPr6lhSJT7BITnjr6enB5eyJTW+v654/uo9nMqUUNlmZWmGEjZiPNPQf56/UryWvaxzWVu8iZAg+ZMiPncfhsKpTAkE/H4Tjbr9hHLuP+DbQcs0Jh265EUu8RFbmqGEjSWev3qvtlzGa9nLOpUtgbfvM+OT3ZGgu5DJ5OiQZRGgy8oVN0dIZILKtrjkNHyI/4Ryxc3VlAIq2VvB2FAfaVI5tg2pj1s/tULQNHeMb+0KQ309jX+zVtkiAICX2f6uLuL4yltuMRJbKMbYCw1j7MVnjrGjZxVjrH0hGWMvXr9H+VziU7yoJV68zjs+2cs6lbVNj4/iw4xPKak5ruUxMjCAnp4eXrzS/TnsWWgEnHOZ4/NaAwWrXQOVRFjEV7QG+sw1ovr4FI41IhMuARw7dgzDhg2Du7s7AgMD0bJlS3Tr1k1lHx8fHwwZMgStW7fG0aNHMXXqVKxZswYzZ85U7hMdHY0mTZrgr7/+wtq1a3HgwAGYm5ujRYsWeP9e9aB24MABHDt2DKtXr8bhw4dhbp77wiI2NhZhYWHKn3fv3mm9/ZLYRFjlcnMGa0sRomMS1ZeLS4KJsSFMTYxylJPL5ZDEJWm9rgVBEpsIK8uc8bGyFEESq37BJolLzD0+4oz4JCr3y+39rcVmkMSqj7+uiIlLzPXmHlaWZso25l4u9/5jZWmm0n8io+Lw4lUkVmw7iR8Gfwe/pcNQqmRRDJ6+FU9ean88aJva+FiI8ohPIoyNDGGSPT4Wiv4To2Z8HQm6g9pVHFHazua/VfwLsTQzQlxyzm+hYpNSIRYZ5VJCoZiF4mL8OZ2r4tGbGHj73oL/5RD0cXPESA8XteWsRUbwbumMs48i8Oojx1dhH18AEKsmRmILkcZjaEx8+hgzzh4jzWPsaNAd1CpEY0xdfKwsRWrbCACx8Um5xkdsYZYeH0VsHR2K4/6T15DLM79q/vufVyr76DJ1c7y1peb+o26OtxKbaew/hY2mNaLkK14j8houASxYsABNmzaFr68vAKB169ZITk7G/PnzAQBxcXGYM2cOpkyZgkWLFOeZenp6wtjYGBMnTsTkyZNhY2ODVatWQSKR4MaNGyhRQvGJUMuWLeHq6orly5dj6dKlyr+ZmpqKkydPqk20Mvj4+GDevHlCNJuoUJDJZEhI+oTVs/sqrytpVMsZzXsvwsY9Z7FqZp8CrqHueB4agYfBbzB3bOeCrorg9PQU32Jdf/4BK04qTou6+TIKIhNDDGjiiE1nn+FTmuqp2ob6eljSQ3F61MIjun+q05fA8fXvPH+lGGNz/g/GWH716ugGr0m/YMXW4xjUrTnef4zFvDUHYaCvD+hp/raZSFfxGy4tk0qluH37Njp3Vj14fv/998p/X7lyBfHx8ejWrRvS0tKUPx4eHkhKSsKDBw8AAKdOnYK7uzuKFi2q3MfAwADNmjXDzZuqt5Rt3rx5nskWAEycOBGvX79W/ty4cUMLrVZlLRYhNj45x3ZJXCKKWIlyKZFeztIMn1LSlKcVZC2np6cH61w+MSqMFPHJ+UlMTFyiyjUDOcpZinKPT2xGfETK/XKNf2ySyjn3usrKUoS4hJz1j4lLUrYx93K595+YuCSV/mOV/h6Na2d+a2FkaID6Ncrj6ctwbTRBUIr45NJ/4hPziI8IKalp+JQ9PvGK/pPbJ7KHztyBoYE+2rUoHKc6AUBcUiosTHN+kyU2M0JsLtdfZYhN/1Ys+00ybjz/CBMjA5S2yRnbeV2qomopa4zyu40PGm7GoUs4vvImVjPGYuMTNR5DrSzSx1hK9hipH2OH08dYW/fCM8bEavtQ7t/sKMtZmOUan9j4pPT4KGLbqJYLJg1ti12Bl9Co6xx0HuGDutXKoaKzPUoUFWu3MQJQN8dL4jT3H3VzfExsktr+UxhpWiNaf8VrxHx/w/X34+f/6o2rV3L615X5GkRGRiItLU35jVQGW9vMc04/fFDcvrJ27dxvI/z69WvlfteuXYORUc7Fg5OTanyzvr8mYrEYYrGwByznsrY5zsONjU9CxIfYHOfeqpRLP6f3WWgEqrpm3oUoODQCpUoWKTTP38iLi2PO+MTEJyH8QyxcHdXHJ+O14NAIVMsanxDV+Lg42uLRs7cqZeVyOZ6FRsC9QQVtNUMwTmVK5LgWKTY+Ce8/xuY4L161nOK1F6/fq9xN73noezjYWsPURBEf13IlgXO5v0dhuG11ebXxictx7UhWGa89zx6fV+9hXyIzPlkdDbqDxnVcYWNtoaXaC+9lZEKOa7UsTAxRzMJE4zVWL97Ha3xfE0PVzyd/+K4CWlWzwyi/W3gaHvf5Ff7COL7ylluM4tLHWPbrj7LKeO3Fq/cqdxt8kccYcytkY6x86RI5rmWLi09CZFQcypfOOz4vX0eiopO9cntmfDLXOkN7tkCfjo3x+l0Uihe1hNjSDA07/4jubRpquTXa51zWFs9Cc46xvNZAGdc1PXv1HlVdMvtPcGgEStl+RWugz1wjuhTyNWK+E65W/SYpT7nQRC6XQ09PD+9u5n0//a9R8eLFYWhomOMaq4iIzM5VtGhRAMDBgwdRunTOZwSUK1dOud+3336rPBUxKxMT1bth5ef/5kvxaFQZK/1OpX/apfi04nDQXejr66F5g0pqy9WvVh6W5qY4cvYv5WBKTZPi+Ll78HCr/EXq/iV4NKqMlTuyxeeMIj7uDdU/g6V+dUV8DgfdVSZcqWlSHD1/D55uVTLf360y9p28qXLh94UbTxAVkwDPxlVyfW9d0rxhJazfdQYxcUnKT/SOn78HfX09NK2nPmGsU9URluamOH7unnJBmJomxe9//g33hpn9p2WjyvDZ/jsu3X6K1k2rAQBSUtNw/d5z1K+h+x8UNW9QERv8gxAblwRxenxOnL8Hfb084lNFEZ8T51Xj88ef9+HeMOe4vPsoFKFvP2KsV2thGiKQS08/YEjz8rA0NURcchoAwLNaScjkclx99kFtuXeSZDwNj0MDJxv8eu2VcntDZxskpUjx4n1msjbom3Lo6+aI6fv/zvGNmK7j+Mpbs/oVsXF3EGLjk5TXKp24kD7G6mqOkYW5KU5cuKdMuFLTpPjj4v1c576/HoXi1duPGDugcI2xb+pXxC97VOPze3p8GmuIT+0qivicvHBPmXClpklx6tJ9fJNLfERmJqhQ3g4AEHDyOuSAyh0OdVXLRpWwyu90rmsgdw3PMqxXrZxiDRR0V5lwpaZJcfz817UGaqlhjeiejzXi4WxrxGOFZI2Y74Tr4Kaci37KycDAALVr10ZgYCAmTJig3B4QEKD8d6NGjSASiRAWFpbj1MOsPDw84O/vj0qVKuXrdEFd4dWlCbbsv4B+U7ZgwoBWeBcZg7lrD8Grc2OV5yt0HrUWYeFRuHlgDgDA1MQI4wd4YunWk7CxtkAlZzv4BlxCVEwiRvVpqfI3jgTdBQA8fRkOqUyu/L1W5bIobVf0C7X08wzq2hRb9l1An0mbMXFga7x7L8GPawIxsEsTlWdwdfReg9fhUbgTOBeAIj4TvFphyZYTKGZtgcrO9tgWcBHRMQkY0zczPh1b1oKP7x/oP3UrZo9sj6TkVMxeHYhWTaro/DO4AKBvRzfsOHARQ2duw+i+Hgj/EIOFG46gbwc3lWcE9Ry/AW8ionFxr+JGM6YmRhjZpyVW7fgDRa3NUbG8PXYeuoTo2AQM69lcWa5ahdL4rll1TFu2D5LYRJSwEcMv8BIio+Ixoqf7l27uv9angxv8Dl7CsFnbMbKvByI+xGDxL0fRu0Mjleff9Jm4EW/Co3B+jyI+JiZG8O6tiI+NtQUqlLfDrkOXIYlNwNAeOdt9JOgOTE2M0LpJtS/WNm3Yf+MVejUqg5V9amPbhecoITbFxG8rIODma5VncG0eVA921qZo73NRuW3d6adY1ac2JrepiItPIlGllBUGNCkH34svkZSquIPsd9XtMK51BRy7+wZvohNRrXRmzMM+JuZ623hdwvGVt94d3LAz8BJGzNoO7z6KMfZzLmOs78SNeBMRhXO708eYsWKMrd7xB4paKcbY7sOKMTZEwxhr1bRwjbGe7Rth16FLGPWjL4b3bomIDzFYuvkYerZXjc+ASRvxNiIap3fNAKCIz/BeLbDW7xSKWpnDtbwd9h6+AklsAgZ3a64s9/rdRxw6dQvV0x/qe+2vZ9h54E8smtxTuUDXZV6dm2Dr/j/Rf8pWjPfyxLvIGMxbdxgDOjdWeQZXl9Hr8Do8CjcDfgSgGGPjBnhi2daTsCligcpO9th+4CKiYhIwsk8Llb9x5KxizfMkJBwymVz5e61Kur8GGphtjfg2MgZzclkjdkpfI97KZY1YLH2NuD19jThazRrxiQ6tEfOdcLnVKTwPLSxoM2fORMeOHTFw4ED07NkTt2/fxq5du5SvW1tb46effsKUKVMQFhaG5s2bw8DAAC9evMDhw4dx4MABiEQiTJw4Ebt370azZs0wbtw4lClTBpGRkbh+/Trs7e1VEjpdYi0WIXDdGExbsR/9p2yBhcgUfTu4YaZ3O5X9pDIZ0qSqF6GP7e8JuRxYvzsIHyXxqOpSCvvXjIRjtodEDpqxPdff187ug17tdPuUA2uxCIc2jMXUZfvRd9JmWJibol9HN8we2V5lv9ziM36AJ+RyOdb6K+JTzbUUAtaMhGOpzPgYGRogYM0oTFu+H0Nn7YCBgT7au9fEwgldvkj7/itrSxH2rhyJH1cfwJCZ22EhMkGvdg0xeWgblf1kMlmOxyiM7NMScgCbfz2PqJh4VHZ2wK7lI1DWXrX/rJzRGz9vPo6fNx1DfGIyqrqWwp6VI1ROc9FVVpYi7PbxxtzVBzF81naYi0zQo20DTBqiGh+pNGf/GdG7BeSQY8tv5/FREo/KzvbwWzYcZextcpQ9fu4vtHSrAnOR7j9bKqu45DQM234T09pVwso+tZGYkoaDt8Kw9vRTlf309fQUF+FnceGfSEzbdw/D3J3QvUEZRMZ9woagZ9j+5wvlPo1cFH2pXS0HtKul+pDa2QH3ceSubjxkUx2Or7xZWYqwa4U35q05iBGzFWOse9sG+GFwbjFSHWPDe7WAXC7H1n3nEZU+xnyX5j7GTpz/Cy0aVYG5WeEaY1aWIvgtG4H56wIx6kdfmJuZ4Ps2DTBh0Hcq+8lk8hzxGdqzBeRyYPv+84iSJKCSsz22/TwMpbPEx8jQADfuPYffgT+RmiZFxfL2WDdvINwb6f63GIBijj+4djSmrwjAgClb09dAjTBjRLY1kFQGabYb8Yzt5wG5XI4Nu8+mr4EcsG91zjXQ4Bm+uf6+ZlYf9Gqn28/Ay7pG7Je+RuyXyxpRlssaaFz6GnFdljViQC5rxIHZ1ogDs6wRexfQGlFPnvW+m//S05evce/RM7yJ+IDeHTxQolgRvHz9DsWLWsPCXLcvXhPapk2bsHDhQkRGRqJBgwZYunQpGjRoAF9fX3h5eQEAfv31V/j4+ODBgwcwMjKCk5MT2rVrhx9//BGGhopcODw8HLNmzcKJEyfw8eNHlChRAg0bNsSECRPg5uYGQPEcrnbt2mHdunX/up5hYWEoXbo0njx/BYdSuv309oJikMczeP7fxSXp9if6BU2q6Sm6hGaLzhZ0FXTapVkt897p/1iqlONLkzQef/JU1Fz94yJI8eEUqScyzl98PivhSkz6hInz1+Hw6cvQ19ODTC7HHzuXoXolJwyZshRlHErgx3Fe//ZtqQAw4cobEy7NmHBpxoRLMyZcmjHh0owJl2ZMuPLGhEszJlya5Tfh+qzbws9b5YtLt+5j75rZePbnHpWH07VsUgdnr9z9nLclIiIiIiL6qnzWg4+PBl3FnHED0LxRrRzneJe2K4HX796rKUlERERERPT/47O+4UpMTIJtsSK5v5aU82FmRERERERE/48+K+Gq5OKIY2ev5vramUu3UaOS83+qFBERERER0dfgs04pnDikOwb8sAhJyZ/Q3qMx9PT0cPdhMAL/uIi9R85g95rZ2q4nERERERFRofNZCZdn07rYtGgS5q3egQMn/wQATP15E+xL2GDDgon4pr7uPwmciIiIiIhIaJ+VcAFAew83tPdww/PQN4iSxMFabAGXcrytOBERERERUYbPTrgyOJV1gFNZbVSFiIiIiIjo6/LZCdc/z19h9fYA3HnwFBEfomFbrAjqVKuAsQO7oqJTGW3WkYiIiIiIqFD6rLsUnr54C559JuL2/Sf4tll9TBrWA982q49bf/8Dzz4TcfriLW3Xk4iIiIiIqND5rG+4flq9A+5utbFj+TTo62fmbHPGe6H/xEX4afUOeDatq7VKEhERERERFUaf9Q3Xq7fv4fX9tyrJFgDo6+tjUPc2ePX2vVYqR0REREREVJh93oOPncvi1ZuIXF979SaC13ARERERERHhMxOun6cNx0b/w/jt6FnExiUAAGLjEvDrkSBs9D+Mn6cN12oliYiIiIiICiM9uVwuz8+O5Zv2hJ6envL31NQ0pKZJAQBGhgYq/zYyMsTzP/cKUF3StrCwMJQuXRpPnr+CQyk+Ry03Bvp6ee/0fywuKbWgq6DTpLJ8HWL/bzVbdLagq6DTLs1qWdBV0GmpUo4vTdJ4/MlTUXOjgq6CTtPX4xpIE5Fx/uKT75tmePftqJJwERERERERkWb5TrgmD+8lZD2IiIiIiIi+Op91DRcRERERERHl7bOewwUAL1+/w69Hz+JF6Bskp+S8hmPXypn/qWJERERERESF3WclXHcfBqPz0JkoZVcCz1+9RWWXsoiNT8Trt+9hX8IGjqXttF1PIiIiIiKiQuezTin8abUfOng2xoV9qyGXy7Fy9mjcPLIJR7cthp6eHsYM6KLtehIRERERERU6n5VwPQoOQefW30BfX1H8U/ophfVqVMSk4T2xYN1O7dWQiIiIiIiokPqshEtPTw9GRobQ09NDsaJWeP3uvfI1+xI2eB76VmsVJCIiIiIiKqw+K+FyLVcKIWHhAIC61SrgF//DePwsFM9C3mDNjgNwLFVSq5UkIiIiIiIqjD7rphn9urTC63eRAIAZo/uix6i5aNFrAgBAZGaCbUumaK+GREREREREhdRnJVzd2ror/+1arjQuBazDrb+fIOlTCupUc0Xxotbaqh8REREREVGh9dnP4crKXGSGZg1rauOtiIiIiIiIvhr5TriOn736r964bYtG/7oyREREREREX5N8J1yDpyzN95vq6enh3c2Dn1UhKhh66T9E/5ahwWfde+f/xjtJYkFXQaddn+NZ0FXQafXmnCroKui0G/NaFXQVdFpiirSgq6DzOIdpliaVFXQVdFz+Vs/5TrhuHt302VUhIiIiIiL6f5TvhKu0XQkh60FERERERPTV4feoREREREREAmHCRUREREREJBAmXERERERERAJhwkVERERERCQQJlxEREREREQCyfddCrNLTU3DnsNncPdRMN6Gf8DP04ajfBl7HDp1CZVdysK1XGlt1pOIiIiIiKjQ+axvuELCwuHWdRR+WuOHkNfhuHjzPuITkgAA1+48xHq/QK1WkoiIiIiIqDD6rIRr5rKtsLEW48bhTQjY+BPkcrnytUa1q+DqnYdaqyAREREREVFh9VkJ15XbDzBhSDfYFBFDT0/1tRLFiuD9h2ht1I2IiIiIiKhQ+6yEy9BAH1m+1FIR+VECkcjsv9SJiIiIiIjoq/BZCVejOlXxi/9hpKamKbfp6elBLpdjV+ApNK1fXWsVJCIiIiIiKqw+6y6Fs8f0R7tB09C02xi0/qYe9PT0sH3fCfzz/BVevn6H3/2WarueREREREREhc5nfcPlUq4UTvmvQL3qFRH4x0UY6Ovj9KVbKFfaDr/7LYVjaTtt15OIiIiIiKjQ+ezncJV1sMXan8Zpsy5ERERERERflc/6houIiIiIiIjy9lnfcHUZPjvPfQ5umv85b01ERERERPTV+KyEy9LcDHrZHsAVExePv/95ASsLc9So7KyVyhERERERERVmn5Vw+fnMyHX7x+hY9J+4EJ1aNflPlSIiIiIiIvoaaPUaLpsiYozq3xk/b9yjzbclIiIiIiIqlLR+0wyZTIb3H6O1/bZERERERESFzmedUvj34+c5tqWkpSH4ZRhWbP4Vtaq4/OeKERERERERFXaflXC16jcpx00z5HI5AKB2VVesmDXqv9eMiIiIiIiokPushCu3W76bGBvD3tYGdiVs/nOliIiIiIiIvgb/OuFK/pSCvx8/R7OGNVHJuawQdSIiIiIiIvoq/OubZpiaGOPnDbsRHRMnRH2IiIiIiIi+Gp91l8IqFcrh6YvX2q4LERERERHRV+WzEq4Fk4Zg056jOHrmChKTPmm7TkRERERERF+FfF/Dte/YOXg0qYOi1mJ0HTEbqalpGDZ9OQDAzNRY5a6FetDDsz/58OP/Z09DwjFtRQBu/v0SFiIT9GhTHzNGtIOxkeYuJ5fLsXrnaWw/cAkfJfGo6uKABeO7oF61csp9PkTHYcX2P3DrYQgePH0DI0N9vDq/QugmadXTkHBMXbYfNzLi07YBZnnnLz6r/E5jW8BFfJTEo5qrAxZO6KoSHwB4FynB1GX7ce76PzA0NEB79xpYML4LxBZmQjZLa4JDIzDLJwC3HoTAQmSC77+th6nD2uYrPuv8z8Av8BKiohNQxcUBc8d2Rp2qjrnuL5PJ8N2QFbj/JAybFwxEO/ea2m+MAELC3mPFpqP4+59QiMxM0Ma9Nkb09YRRHvEJOHEVV249wcOnYZDEJmDR1N5o2bharvteuvkPduw/h+CX72BkaACX8vaYO6E7bItZCdEkrcroPzfT+0+3f9l/dmTpP/Py6D/fpvefLYWo/5Qrbo7p7SujRllrJH6S4sidN1h7+inSpPI8y5YQm2Bsa1c0dS0BkYkB3kYnYfO55zj+11sAQKkiZpjWoTIq2olhLTJCTFIq/gqNxppTTxH6IVHopmkNj0GaPX8VgZ/WBOLOwxCYm5mgc6u6mDD4u3zFZ9Pes9h9+DKiJPGo5OyAmaM6olZlR5X9bt1/gZXbT+Lxs7cw0NdHtYqlMWloW1R2dhCwVdrDOV6z4JAITF8RgJv3X8JcZIIe39XH9BH5G19rdp2Bb5Y14vzxnVG3quoa0cf3FG4/CMGD4DAYGhog9NxyoZuUp3x/wzVu3lqEhIUDALz7dsS4Qd/jh6Hd8cPQ7hjZrxO8+3ZU/ozo20GwCn9tJBIJ9PT0sGPHjoKuitZIYhPRadRapKZK4bdkCGZ5t4ffoSuYtepgnmVX7zyNJVtOwrtXc+xZMRy2xcT4fuwGhLz5oNznXWQMAk/fQfEilqhZqbSQTRGEJDYRHb3XICVVip1Lh2D2qA7wC7yMmSvzjs8qv9P4efMJjOztjl99RsDWxgpdx6xHSFhmfFLTpOg6Zj2evXqPzfO9sGJqDwRdfYyhs3cI2CrtkcQmovuYdUhNk2LbokGYNrwddh+5irlrDuVZdp3/GazYdhLDujeH37KhKGEjRq8JGxCapf9ktevwFYRHxmi5BcKKjU/CqFlbkZomxZLpfeHdrzUOnbqBVduO51n2xNm7kMQmwq2Oq8b9Tp67i2mL/VG7anmsmD0AP47vhkrODkhJSdVWMwQjiU1EtzHrkJLef6YPbwf/f9F/lqf3n53p/afnV9Z/xKaG2DqkPowM9DHB/y5W//EU39cvjSltK+VZtpilCfy9G6GEpSnmBT7AaL/b+O36KxgZZC4lRCaG+BD3Cav+eIIRvrew/MQ/cCxmjm1DGsBaZCRk07SGxyDNYuIS0W/iRqSkpmH9PC/8MKQNfj12FYs2HM6z7Ka9Z7F6x+8Y+H0zbFk0BCVsxPCavAmv3n5U7vPi1Xt4Td4EkakJVs7ui0WTeyAmNhH9f9iIyKhYIZumFZzjNZPEJqLz6LVISUuD78+DMdO7HXYevoLZqwPzLLtm1xks3XICI3o2x+7lw2BrI0a3XNaIh07fQbEiFqhRsYyQTflX8v0NV8ZztgBg8vBeglSGvg6+By8hPiEZO5cMQRErcwBAmlSGycv2YYJXa9gVz/0T8uRPqVjldxoje7vDu1cLAECjmk6o320+1u0OwvIpPQAAVZzt8c/viwAAS7acwMPgN1+gVdqz/cBFxCUkw3/Z0Mz4pEkxaek+TBzYCnbFrXMtl/wpFSt3nMKoPi0wsnd6fGo5oW7Xn7DWPwgrpiniczjoLv55EY7r+2bBxdEWAGAtFqHrmPW4/TAEdao4Ct7G/2LXocuIS0zGtsWDUUSsiI9UKsP0Ffsxtr8nSmroP+t2ncHwnu4Y1tMdANCghhOa9FyIX/aexeJJ3VX2/yiJx9LNxzF7dEdMXLRX2EZp0cGT15GQ+AlLZvSFlaUIgCI+y345DK9u7ihuI1ZbduvSEdDX18fbiGicOHc3131i4hKxbNNhTBjSDl3bNFRu/6ZBZe02RCA70/vP9iz9Jy2f/Wdtev8ZnqX/NO65EBv3nsXPufSfJZuP48fRHTGhEPWfbg3KwMLEEOP87yA2SZFAG+rrYWbHythy7jki49RfJjDxuwoIlyRjhO9NyNKXBNeff1TZ52l4HOYefKCy7WFYDI5PagY3l2I4ce+ddhskAB6DNNtz5AriE5Kxcf5AWGeJz5xVB+Ddx0Ptt+CfUlLxy54gDO7WHIO6NQMA1K1eHp79FmPrb+fw04TvAQCnLt2HXC7H2rn9YWpiDACo6GQH994LcenWU3RuVfcLtPLzcY7XbEfgJcQlJMPv58w1olQqw5Rl+zFhQCuN42u132l4926BEb0U46thTSc07LYAG3afxdIpivFVxdkej04uBAAs3XICD5/pxhrxs67hItIk6OojfFOvgnIgAUAnj1qQyeQ4d/2x2nI37r9AXEIyOnnUVm4zNjJEu+Y1cObKI+U2ff3C3W3PXH2EZvUrqsSns2dtRXyu/aO23I2/FfHpnC0+7d1r4PSVh5nvf+URqjjbKw/EAODeoCKKWIlw+vJD6Lqz1x6jad0KyoUOALRvURMymRwXbqiPz60HLxGXkIz2LWoptxkbGaJNs+oIupqz3y3+5RjcarugcW0X7TZAYFdvP0G9Gs7KZAsAPJpUg0wux/W/gjWWzc/YCbp0HzKZHB08dXtRo05u/afDv+g/HfLZfxYV0v7TtEJxXHv2UZlsAcAf999BX08Pbi7F1JYzNzFE62p2+O36K2WylV+SRMXfyvpNmC7jMUizP2/8A7c6rspkCwDauNeETC7HpVtP1Ja78yAE8QnJaJPltEljI0O0alodF7KsDdLSpDA2MoSJceY3opbmpop/yP9l5ysAnOM1C7r6GM2yrRE7tsxYI6qPz837ivHVsaXq+GrbvHqhWCP+q1oF/nERv/gfzvtn9xGh6lvobdmyBY6OjhCJRGjZsiWePXum8rpMJsOCBQvg6OgIExMTVKxYEZs2bcrxPoGBgahQoQJMTU3RsGFD3LlzB9bW1pg7d+4Xaol6waERKgcCALCyFMG2mBjBoRHqy4UoXnMpq1rW1dEWYeHRSEpO0X5lC0BwSESONlpZilCymBhPQ9THJ+O17LF1dSypEp/gkJzx19PTg0vZkhrfX1c8C42Ac9kSKtusLEWwtRHj2Sv19X+W3recs8XWxdEWbyKikfQps//cfRSKwNO38ePojlqs+ZcR+iYSjqWKq2yztDBDsSKWCA2L/M/v/+DJK5QtVRzHz95Bx8FL4NZpJvqOW4Mrt9UvpHTJs9AIuHyh/jOnEPYfx+LmeBkZr7ItLjkNkXGfUK64uZpSQCV7MYwN9ZEmlcF3aAPcWdAa52a0wIRvK8BQXy/H/np6im/O7K3NMLNDZbyTJCHooe4ffwAeg/Ly/NV7lC+jGh+xhRlK2Fji+av3GssBgFO2sk5lS+DtewmS0+PTrkUtSKUy+Gw7ieiYBER8iMHC9YdhV8IaHo2rark12sc5XjPF+NLmGrEkwiJ0f434rx58vGXvsXztp6enhxF9eB1XdseOHcOwYcPg5eWFnj174vbt2+jWrZvKPpMnT8bq1asxa9YsuLm54dixYxgxYgRSU1MxevRoAMDdu3fRrVs3tG/fHitXrkRoaCh69OiBT5/yvmNkbGwsYmMzz4F+9077p3dIYhNhlcuFm9aWIkhi1F80LYlLgomxIUxNVM/zt7YUQS6XQxKXBDNTY63X90uTxCbCyjJnfKwsRZDEJqgvF5eYe3zEGfFJhJmpMSRxiahuWSpHeWuxGSSxun/RekxcYq4X/lpZaq5/jJr+Y5Xef2Jik2BW3BgymQwzVuzH8J7uKG1ng9fvPqp5R90UG58Ei4xPe7OwtDBDTNx////9GB2HV28isWn3aYzx+g42RSwRcOIaJi3YCf/VY1G+jG3eb1KANPWfaA39J6/jT9b+M33FfowopP1HbGaEuOS0HNtjk1JhpeEaq2KWJgCAuV2q4cDN19gQFIxqpawxytMFMrkcq/94qrL/om7V0a6W4gYHrz4mYOi2G4j/lPPv6iIegzSLVRMfsYUIMRriExufmOObKyBLfOKSYGpiDMdSxbFzhTdGzNqOjbvPAABKlSyKnctHwLIQ3BSCc7xm6uJjbSnSWH918bESmyn7jy6vEf9VwnVixxLUrqr5YmtSb8GCBWjatCl8fX0BAK1bt0ZycjLmz58PAPjw4QPWrl2LyZMnK7+patWqFT58+ICffvoJ3t7eMDAwwOLFi1GuXDkcOHBA+dWppaUl+vXrl2cdfHx8MG/ePGEaSFQI7Dl6De+j4jC6n0dBV0UnyeRyJCalYN7EHsrrtupUK4/vR6zAzgMXMHdC9zze4eu2++g1RP4f9p+ML7GuPfuA5ScUp/3cfBEFcxMDDGhaDr8EPcOnNJly/3Wng+F/JRR2Vqbo29gRWwbXR/9friE8Jrkgqq9TeAzS7OXr9xg1Zwea1K2Azq3r4lNKKrb+dh6Dpm3B/rVjUayoZUFXkehf080THb9CUqkUt2/fRufOnVW2f//998p/X79+HampqTm+9erRowciIyPx9KniE8SbN2+iXbt2KuepduyYv9MSJk6ciNevXyt/bty48blNUstaLEJsfM5JVRKXCGsrUS4l0stZmuFTShqSP6neCU0Slwg9PT1Y5/KJSGGkiE9Sju0xcYkq58TnKGcpyj0+sRnxESn3yzX+sUmwFquPv66wshQhLpf6x8Rprr+Vmv4Tk95/rMRmSEj8hMWbjmH8gFZITU1DTFwi4hIUfyspOUX5b10mtjBDfC71jItPUrmu67+8PwDUre6k3GZoaIBaVRzxQsPpVLrCSk3/j4lLQhEN/Sev40/W/jMul/6TWEj6T2xSKixMc37WKjYzQkyi+rtQZlzzdeOF6rcx155/hImRAcrYqMb2TXQSHobF4MzDCIzwvQl9PT0MalZeCy0QHo9BmoktRYhLyDmHxcYnwkpDfMQWIqSkpuFTipr4pM/xy7eeQPGillg+ozca13FFi0ZVsGXxEMTGJWLHgT+12xgBcI7XTF18JHGJGuuvLj4xsUkq/UdX/atvuOjzRUZGIi0tDSVKqJ67bGubeXpOdHR0jm1Zf4+KigKgOA2wePFs13BYWsLUNOdpRtmJxWKIxervYqYNLmVtc5yHGxufhIgPsTnOvVUpl35O8rPQCFR1zfy6PDg0AqVKFtHpr4r/DRfHnPGJiU9C+IdYuDqqj0/Ga8GhEaiWNT4hqvFxcbTFo2dvVcrK5XI8C42Ae4MK2mqGYJzL2ua4TiI2PgkRH2PhrOF0toxzwp+/eo8qLpnPankWGgEH2yIwMzHG63cfER2TgKnL9mHqsn0q5cct2I3iRY/g3tEFWmyN9pV1KI7QN6rXasUnJONDdBzKZru263OUK2ML4H6ur6Wk6P4pYQXZfxYUPYK/dbz/hEQm5LhWy8LEEMUtTfAyUv3pTs/fx6t9DQCMjQzUvpacKsPLyHiUttH9xSDAY1BenMqUwIts12rFxSfh/ce4HNdnZS8HKG77XinL87RevHoP+xLWyjsSPguNQK3KZVXKmpuZoKxDMZXbx+sqzvGaOZe1xbNQ1f7zr9aIr96japbxFRwagVK2ur9G5DdcX0jx4sVhaGiI9+9VO1lEROagLFq0KACo3SfjdTs7O0RGqi644uLikJysG5+MtWxUGRduPlG5nuRw0F3o6+vBvYH6Z73Ur1YeluamOHz2L+W21DQpjp27Bw+3wnFL6vzwaFQZF25ki8+Z9Pg0rKi2XP3q6fEJyrydd2qaFEfP34OnW5XM93erjAfBb1QuXr5w4wmiYhLg2bgKdF2LhpVw8eZTlfgcPfsX9PX10Ky++vjUrVoOluamOHbuL+W21DQpTlz4Gy0bKfpd8aJiBKwdrfKzYd4AAMAPg7/F1kWDhWmUFjWqUwE37z1DXJZPCIMu34e+nh4a1PzvdztrUk8R45v3Mm/ok5qahrsPX6JiIXjo6H/tP0fz6D8H1o5W+dmY3n8mDf4W2wpB/7n4JBINnYvBMsu3XK2qlYRMLseV4NyfFQUA7yTJePouFg2dVO9k2Mi5GJJSpHgRoT4hMzcxhEtJS4RF6f71JQCPQXn5pn5FXLkdrPItxYkL96Cvp4cmddUv+GtXdYSFuSlOXrin3JaaJsWpi/fRLMvawMG2CB49e6PyOKK4hGSEhH1AqZJFtNwa7eMcr1nLRpU0rBHVx6deNcX4OpItPsfPF441Yr6/4Qq/lfcDyUg9AwMD1K5dG4GBgZgwYYJye0BAgPLf9evXh5GREfbv349atTJve7lv3z6UKFECrq6K6+fq1auHY8eOYcWKFcrTCg8dOvRlGpIPA7s0wZb9F9BvyhZMGNAKbyNjMGftIXh1bqzyDK5Oo9YiLDwKtw7MAQCYmhhh/ABPLN16EsWsLVDJ2Q7bAy4hKiYRo/u0VPkbGQPuyctwSGVy5e+1KpdFabuiX6iln2dQ16bYsu8C+kzajIkDW+Pdewl+XBOIgV2aqDyfo6P3GrwOj8KdwLkAFPGZ4NUKS7acQDFrC1R2tse2gIuIjknAmL6Z8enYshZ8fP9A/6lbMXtkeyQlp2L26kC0alJF55/PAQD9OjXG9oA/MWj6Nozp54nwDxIs2HAY/To2Vnk+R/ex6xAWHo0r+2YDUMRndD8P+Gz/HTbW5qjoZA+/g5cQHZOAEenPdTM1MYJbtlswZ1ywXqGcHepVKwdd1+W7Bth//AqmLNqFAd2aI/JjLNb6nkDnb+urPINr1KytCH8fjQObJyu3PQ4Ow9v30ZDEKL7JePDkNQCgiJU5aldVnO5V0ckB7m5VsXh9IGLiklCsqCUCjl9FlCQefTt/8wVb+nn6p/efgdO3YWx6/5m/4TD6Z+s/3dL7z9Us/WdMPw+sSO8/lZzssSO9/3h/Rf1n//VX6O1WFqv71caW8y9gKzbBD20qYv+N1yrP4NoyuB7si5ih7fLMU7jWnArGmn61MaVdJVz85z2qlLKCV9Ny8P3zBZJSpQAA75bOsDQ1xN1QCaITUmBfxAx93MrC2FAf/pdDvnRzPwuPQZr17uCGXYGXMGLWdnj38UDEhxgs+eUoenVopPIMrn4TN+JNRBTO7p4JADAxNsKI3i2xZscfKGplgQrl7eB/+DKiYxMwpIe7slyv9m7wnr0dExf4o1PrukhJScO2feeRkpqG7m0b5qiPruEcr5lX5ybYuv9P9J+yFeO9PPEuMgbz1h3GgM6q46vL6HV4HR6FmwE/AlDEZ9wATyzbehI2RSxQ2cke2w9cRFRMAkb2aaHyN46cTV8jhoRDJpMrf69VqeDWiDyl8AuaOXMmOnbsiIEDByrvUrhr1y7l68WKFcOYMWOwbNky5e3eT5w4gT179mDt2rUwMFCcsjF9+nTUq1cPXbt2xbBhwxAaGorly5fD1NRUJ54/YC0WIXDdGExbsR/9pmyBhcgU/Tq4YaZ3O5X9ZDIZ0qQylW3j+ntCLgfW7Q7CR0k8qrqUQsCakXB0UP1UdeCM7bn+vnZ2H/Rup9sHZGuxCIc2jMXUZfvRd9JmWJibol9HN8we2V5lP2ku8Rk/wFPxQEh/RXyquabHp1RmfIwMDRCwZhSmLd+PobN2wMBAH+3da2LhhC5fpH3/lbVYhH1rRmHmygMYNH0rLEQm6NWuEaYNb6uyn1QmzxGf0X09ADmwce85REniUcXZAXtWeqOsg/rnCxU2YgszrJs/BCs2H8GUhf4QmZmgQ6t68O7bSmU/mUwGqUw1PvuPX8Xxs3eUv+85dBEAULtqOWxcNEy5fc74btiw83ds2Pk7EhI/oaKTA9b+NBjOjiUFbJl2ZPSfWSsPYGB6/+n9L/qPXA78svccPqb3n71fWf+JTU7DkK03MKNDZazuWxuJn9Jw4OZrrDmlepdBA309GGS73fuFf95jyq9/YXgLZ/RoUAaRcclYfyYY2y68UO7z+G0s+jdxRLtaDhAZG+B97CfcfhmFH3bfRVh0zus2dBGPQZpZWYqwc4U3flpzEN6zt8NcZILubRtg4uA2KvtJZTJIs8VneK8WkMvl2LrvPKIk8ajkbA/fpcNRxt5GuY9nk6pYM6c/tv52DuN+2gUjQwNUdnGAv8/IHI/E0EWc4zWzFotwcO1oTF8RgAFTtsJCZIq+HRphxgjVNaJUKoM0TTU+Y/t5QC6XY8Pus+lrRAfsW51zjTh4hm+uv6+Z1Qe92jUQoFV505PLC8FT5L4imzZtwsKFCxEZGYkGDRpg6dKlaNCgAXx9feHl5QWZTIaFCxdi69atePfuHRwdHfHDDz9g+PDhKu9z8OBBTJ8+HaGhoahatSpWr16N5s2bY/ny5Rg3bly+6xMWFobSpUvj6fNXcCiV8zajBOjn8owZypSUIi3oKui01x8Lx2lUBSX7zRZIVb05pwq6CjrtxrxWee/0fyyRx+c82Vjo9rU/BS17UkiqLEzy90UHE66vRFBQEDw8PHD+/Hk0a9Ys3+WYcOWNCZdmTLg0Y8KlGRMuzZhwacaESzMmXHljwqUZEy7N8ptw8ZTCQmrkyJFo2bIlbGxs8PDhQ8yfPx+1atVC06ZNC7pqRERERESUjglXIRUdHY0xY8bgw4cPsLKywrfffovly5frxDVcRERERESkwISrkNq7d29BV4GIiIiIiPLAr0OIiIiIiIgEwoSLiIiIiIhIIEy4iIiIiIiIBMKEi4iIiIiISCBMuIiIiIiIiATChIuIiIiIiEggTLiIiIiIiIgEwoSLiIiIiIhIIEy4iIiIiIiIBMKEi4iIiIiISCBMuIiIiIiIiATChIuIiIiIiEggTLiIiIiIiIgEwoSLiIiIiIhIIEy4iIiIiIiIBMKEi4iIiIiISCBMuIiIiIiIiATChIuIiIiIiEggTLiIiIiIiIgEwoSLiIiIiIhIIEy4iIiIiIiIBMKEi4iIiIiISCBMuIiIiIiIiATChIuIiIiIiEggTLiIiIiIiIgEYljQFSDdoKevB319vYKuBhVCcrm8oKug02ytTAu6CjrNgMcdjW7Ma1XQVdBp9h6zCroKOu3D+UUFXQWdxzlMMx6jtYPfcBEREREREQmECRcREREREZFAmHAREREREREJhAkXERERERGRQJhwERERERERCYQJFxERERERkUCYcBEREREREQmECRcREREREZFAmHAREREREREJhAkXERERERGRQJhwERERERERCYQJFxERERERkUCYcBEREREREQmECRcREREREZFAmHAREREREREJhAkXERERERGRQJhwERERERERCYQJFxERERERkUCYcBEREREREQmECRcREREREZFAmHAREREREREJhAkXERERERGRQJhwERERERERCYQJFxERERERkUCYcBEREREREQmECRcREREREZFAmHAREREREREJhAkXERERERGRQJhwERERERERCcSwoCtAX6enIeGYumw/bvz9EhYiE/Ro2wCzvNvB2Ehzl5PL5VjldxrbAi7ioyQe1VwdsHBCV9SrVk5lv3eREkxdth/nrv8DQ0MDtHevgQXju0BsYSZks7SG8dHsWWgEZq08gFsPQmAhMsH339bDlKFt8hWf9f5B8Au8hChJAiq72GPumM6oU9Ux1/1lMhnaDPHB/adh2DTfC+3ca2q/MQJ4FhqBuasP4vbDEJiLTNC1VV38MCR/8dm4Jwj+hy7joyQBlZ3tMXt0J9Su4phj37NXH2LdrjN4/PwtjAwNUNnZAStn9oFdCWthGqVFwSERmO4TgFv3X8JcZILu39XH9OFt8xWftbvOwPfgJXyMjkcVVwfMH9cZdatmjq8LN55g99GruP0gBB+i41Darih6tm2I4T2bw8jQQOimaUVwaARm+QSojK+pw/IXn3X+ZxTjKzoBVVwcMHes5vH13ZAVuP8kDJsXDCw04wsAXMoUx5Jx7VG/alnEJ37Cb3/cwYKtp5GaJtVYrohYhNlDW8GzYQUUEYvw6l0Uthy8Ct8jN5T7NKvjhP7t6qFu5TIoVsQcr8KjsefEbWzcfxlpUpnQTdOK4JBwTFsRgJvpc1j3NvUxY0T+5rA1O09j+4FL+CiJR1UXBywY3wV1s8xhH6Lj4LP9D9x6GIIHT9/AyFAfoedXCN0krXoaEo6pyzPj06NNfczM5xy/eudpbAvIjM/CCV1ymeNjMHX5fpxPn+PbNa+BBeM7F5o5Xsj4fIiOw/Ltf+DWg8z+8/pCwfcffsP1haxcuRJlypSBgYEBOnXqVNDVEZQkNhEdvdcgJVWKnUuHYPaoDvALvIyZKw/mWXaV32n8vPkERvZ2x68+I2BrY4WuY9YjJOyDcp/UNCm6jlmPZ6/eY/N8L6yY2gNBVx9j6OwdArZKexgfzSSxieg+dj1S06TYunAgpg1ri91HrmDe2kN5ll3vH4QV209iaI/m2LF0KGxtrNB74kaEvvmQ6/67Dl9B+IcYLbdAWDFxieg9YQNS0qT4Zf5ATBnSFnuOXcWC9YfzLLtxTxBW+f6OQd2aYfvPQ1DCRoz+k37Bq7eq8Qk8dQsjZvuiYU1nbFs8BCtm9Eb1iqXxKSVVqGZpjSQ2EV3GrEVqahq2Lx6MmSPaYdfhK/hxdWCeZdfuOoOlW09geI/m8F8+DLY2YnQftwEhWfrPzkOXEZ+YjClD22CPzwh0/64+lm09gR9+/lXIZmmNJDYR3cesQ2qaFNsWDcK04e2w+8hVzF1zKM+y6/zPYMW2kxjWvTn8lg1FCRsxek3YoHl8RRau8QUAVhamOLxqCIyNDNB/lj/mb/kDA9rXx8LRbfMsu+On3vi2cSUs2nYavab74cyNp/CZ1Bn929VT7uPVoQEsRCZYtP00uk/Zgd/+uItpgzywanJnIZulNZLYRHQatRYpqVLsWDIEM73bY+ehK5i9Ku85bM3O01iy5SRG9GqOPSuGw7aYGN+PVR1j7yJjEHj6DooVsUTNSqWFbIogJLGJ6DRyLVJTpfBbMgSzRraH36ErmJWP+KzeeRo/bz4J717NsXfFcJQsJkbXbPFJTZPi+7Hr8fzVe2yePwDLp3bH2WuPMWy2n5DN0hqh45PRf4rrWP/hN1xfQHBwMH744QdMnToV7du3R7FixQq6SoLafuAi4hKS4b9sKIpYmQMA0tKkmLR0HyYObAW74ta5lkv+lIqVO05hVJ8WGNm7BQCgUS0n1O36E9b6B2HFtB4AgMNBd/HPi3Bc3zcLLo62AABrsQhdx6zH7YchqJPLp/W6hPHRbNfhy4hLTMbWRYNQRJweH6kMM3wCMKa/J0oWs8q1XPKnVKzzP4NhPd0xrEdzAECDGuXRtNdC/LL3HBZP6qayf5QkHku3nMCPozpi4uK9grZJm/wPX0F8YjI2LxgIa2V8pJi96gBG9fWArYb4bNwdhCHd3TGke3MAQL3q5eHedxE2/3oeCyZ+DwCQxCbgx1UHMHt0Z/Tr1FhZ3rNxVWEbpiV+gZcQl5CMHT8PyRxfUhmmLt+P8QNaoWRx9fFZvfM0vHu1wIhe7gCAhjWd0Kj7AmzYcxZLJ3cHACyd0h021hbKco1ru0Amk2Px5uOYM7qjymu6aNchxfjatniwcnxJpTJMX7EfY/t7aozPul1nMLynO4b1VMSnQQ0nNOm5EL/sPYvFk7qr7P9REo+lm49j9uiOmLio8IwvABjUsQEsRSboO9MfkrgkAIChgT6WT+gIn13nEP4xLtdyJYpa4JvaThi5aD/2/n4HAHDxzgvUrlgKXVpWx85jNwEAP/gcQlRMorLc5b9eQl9PDzOHeOLHjSdVXtNFOw5eQnxCMnYuUR1jU5btw3iv1rDT0IdW+Z3GyN7u8O6lmMMa1nRCg27zsX53EJZNUcxhVZzt8fj3RQCAJVtO4GHwmy/QKu3xPag4Bu1aqhqfyUv3YUIe8Vm54zRG9XFXmePrfT8f6/yDsHyqIj5H0uf4a/tmwqVs+hxvKcL3YzcUijle6PhUcbbHk/T+8/Nm3ek//IbrC3jy5AnkcjmGDh0KNzc3uLq65tgnKSmpAGomjDNXH6FZ/YrKgQQAnT1rQyaT49y1f9SWu/H3C8QlJKOzR23lNmMjQ7R3r4HTVx5mvv+VR6jibK9MJgDAvUFFFLES4fTlh9B1jI9m5649RtO6rsrFIAC0b1ETMpkcF26oj8+tBy8Rl5CM9i1qKrcZGxniu2bVcfbaoxz7L950DG61XeBW21mr9RfaheuP0biOqzLZAoC27rUgk8nx580nasvdeRiCuIRktM1yWpexkSG+bVod565nxufYub8glcnQo20DQeovtKBrj/FNvQoq46tjS0V8zmvoPzfvK/pPh5a1lNuMjQzRpnl1BF3JjE9uCVW1CqUgl8sR8SFWS60Qztlrj9G0boX/ML6yxadZdQRdfZxj/8W/KMZX49ou2m3AF+DRsAIu3H6mTLYAIPDcfejr68G9nvr2ZJxSGpuQrLI9NiEZenp6yt9zS6j+Dn4LfX19lLSx/K/VF9yZq49yjLFOHulj7HrOvpDhxn3FHNYx2xzWtnkNnMkyxvT1C/fS9MyVR2iWLT6d0+NzTlN80uf4Ttni0655DZzOEh/lHF822xwvFuH05Zxzna4ROj662n90s1ZfES8vL7Rv3x4A4OTkBD09PezYsQN6eno4fvw4vv/+e4jFYnTrpvj0PTQ0FN9//z2srKxgbm6O1q1b4/79+yrvmZKSgrFjx6Jo0aKwtrbG8OHDsWfPHujp6SEkJORLNzGH4JAIlQMBAFhZilCymBhPQyLUlst4LWuiAACujiURFh6NpOSUzPfPto+enh5cypbU+P66gvHR7FnoeziXyRkfWxsxnoe+11gOQI6yLmVL4k2EBEmfUpTb7j4KReDpO/hxVAct1vzLeP7qPZzKlFDZZmVphhI2Yjx/pf7/93mo4rXsZZ3L2uJthATJ6fG5+ygUTmVK4MDvN9G4+09wavEDvhu8DOeuqZ8Idcmz0NzHl62NGMGh6uOT8Vr2sq6OJREWkTm+cnP93guYGBuijL3Nf6j5l/EsNALOZbP3H0V8nmnoP8/S4+OcLT4ujrZ4ExGdy/i6jR9Hd9Rizb8clzLFEfwqUmVbbHwywj/GwbVscbXl3ryPQdCNp5jY1x0VypaAhZkxOrlXg3tdF2wNvKrxbzas5ojkT6kIfRetlTYI6VlozjnGylIE22Kax9izEHVjzFZlDivsgtXEp2QxMYI1zMFqj0HlVOOT2/vr6enBxdFWY/x1hdDx0VVMuAQ2e/ZsLFmyBABw8OBBXL16Febmiqx+2LBhcHJyQmBgICZNmoS4uDg0b94cd+/exS+//AJ/f398/PgR33zzDV6/fq18z2nTpmHTpk2YOnUqfvvtN8hkMkybNi1f9YmNjUVYWJjy5927d1pvsyQ2EVaWOS/ctLIUQRKboL5cXCJMjA1hamKkst1aLIJcLockLlG5X27vby02gyRWt0/FABifvMTEJeZ64a+Vpeb6x6iJj5WlGeRyOWLSP62WyWSY6ROA4T2bo7Sd7i+Qs9MUnxhN8YlPgnE+4hMZFYcXryKxYvtJTBz0HXYsGYZSJYti6IytePpS+8cLbZPEJsIql/hYi0Wf1X+ss8Unuxev32PzvvMY0KkxLEQm/63yX8Dnj68kNeNLcfyJic0cXzNW7Mfwnu6FcnwBiv/zmPjkHNtj4pJgbSnSWLb/LH9ERsfj2q4JeP3HPGyZ3QMz1x3H0Qvqzy4oX8oGw793g++RG0hI0u1FI6BhjFmKEK3hdEiJmj5kbZkxh30dZ/pomuOjNYwxSWz+4iOJS1If/0IwxwsdH13Fa7gE5uTkpDyFsFatWnB0dERysuJA3qFDB2UyBgBr1qxBaGgoHj58iEqVKgEAmjVrhjJlymDVqlVYsWIFoqKisHHjRsyaNQtTp04FALRu3RoeHh4qSZk6Pj4+mDdvnrabSVRo7Dl6De+j4jC6r0dBV0UnyWUyJCR9wqrZfZXXbTWs5Qz3Povwy56z8JnZp4BrqDviEpIwcNo2lLWzwfQR7Qq6OjpBOb76/X+Or3XTvkf5UjYYMu9XhH+MhXtdFywa0w6SuCQcPPt3jv0tRSbYtaAvXoVHY8GWPwqgxkT0JfAbrgLUtq3qHY8uXryIqlWrKpMtAChatCg8PT1x6dIlAMD9+/eRnJyMDh1UT4Xq2DF/p25MnDgRr1+/Vv7cuHEj70L/krVYhNj4nJ80xMQlqlx3kqOcpQifUtKQ/En1TmiS2ETo6ekpP1m0thQhNpdPHyWxSbAWa/70URcwPppZWYoQl5BbfDTX30pNfGLikqCnpwcrSzMkJH7Cz5uPY1x/T6SkpiEmLhFxCZ8AAEnJKYhLyBk3XaOIT+6fvltpio+FGVLyiA8AiNP7kVutzGtVjAwNUL9GeTwNCddGEwSlbnxJYhM/q/9IssUnQ0pqGrymboMkLhF7fEbA3Ez3v90C0vuPum9vNMbHTM34Uhx/rMSK8bV40zGMH9AKqcrxpfhbhWV8AYr/c7G5aY7tVpZmyjMJctO6UUV0blEdXj/uxoGge7j810ss2HoKv/5xB/NHtcmxv5GhAXYt7AsrCzN0n7IDicm6fxdQIGOM5TLHxCWiiJX6PmStpg9J4jLmsMJxS/O8qItPTFwiimgYY9bi/MXH2tIMsbnMkZI83l9XCB0fXcWEqwDZ2qqehxodHZ1jW8Z+UVFRAKA8BbB4cdXzyEuUKJGjXG7EYjFKlSql/LGzs/ucqmuU23nEMfFJCP8QC1fHnO3LkPFa9rLBIREoVbIIzEyNM98/23m+crkcz0IjNL6/rmB8NHMuW0J5PVaG2PgkRHyMhVNZ9f0847qU569Uyz4LjYCDrTXMTIwRFROP6JgETFu+H5W/m4HK382Ap9dSAMD4hXvQtNdCLbdG+5zKlMhxLVtsfBLef4yFUxn1/79O6ee9v3itWvb5q/ewt7WGqYmi/7g6llT7HoXhtvDOZW0RrKb/ZD/3P6uM155l6z/BoREoZZs5vgDFaXPec3bi3pPX2OvjDQfbIlpsgbCcy9rmuFYrIz7Zr3/MXg5QN76KqIyvqcv2odK301Hp2+nwGKAYX+MW7EaTngu03BphBL+KhEsZ1TlWbG6CkjaWeBoaqaYUUMGxBNLSpHj0QjW+94Pfwb64FcyynAqlp6eHzbN7oKarA7pP8cWb94Xn9vmKMZZLH/qgeYw5p88/z7LPYaGqc1hh51I25xwcmz7HZ792KXs5IJf4ZJ/jy9oiOER1HCrm+Pca468rhI6PrmLCVYCy3rUIUHyb9f59zpsCREREoGjRogCgTJAiI1UP+rmVKygejSrjwo0niMnySeDhM3cVd3hqWFFtufrVy8PS3BSHg+4qt6WmSXH0/D14ulXJfH+3yngQ/EZl4r9w4wmiYhLg2bgKdB3jo5l7w0q4eOupSnyOnfsL+vp6aFZffXzqVi0HS3NTHDv3l3JbapoUJ//8Gy0aVgYAFC8qxv41o1R+NsztDwD4YdC32LJwkDCN0qJmDSrh8u2nKtcUnTh/D/r6evimXgW15WpXcYSluSlOnL+n3JaaJsXvf/4N9waVldtauin+fen2U+W2lNQ0XL/3HNVcdeeZJuq0bFgJf95SHV9HzirGV3MN/adeNUX/OXpWdXydOH9PGZMMU5fvx6nLD7BzyRBUdrbXfiME1KJhJVy8qTq+jp79/PF14sLfaNlIcVZG8aJiBKwdrfKzYd4AAMAPg7/F1kWDhWmUlp259gTN6jpDbJH5LVfH5tUUd1G7Gay23OuIaBgaGqCqk+qHFjUq2ON9VBySsnwyv3xCB3zrVhG9Z+zKkaDpOo9GlXHhZrY5LCh9jDWopLZc/WqKOezI2b+U21LTpDh+7h48so2xwszDLWd8DmXM8Zrio5zj/1JuS02T4ti5e/DMEp9c5/ibGXO87sdR6PjoKl7DpUOaNGmCgIAAPHnyBBUqKBZO0dHROHPmDIYNGwYAqFq1KkxNTXH48GHUqFFDWfbQoUMFUeVcDeraFFv2XUCfSZsxcWBrvHsvwY9rAjGwSxOVZ0x19F6D1+FRuBM4FwBgamKECV6tsGTLCRSztkBlZ3tsC7iI6JgEjOnbMrNcy1rw8f0D/aduxeyR7ZGUnIrZqwPRqkkVnX/+BMD45KVfx8bwDbiIwdO3YUx/T4RHxmDB+iPo19FN5Rlc3cetx5vwaFz+bRYARXxG9/WAj+/vsLG2QEUnO/gFXkZ0TILyuUqmJkZwy3ab6tfvPgIAXMuVVHlava7q29ENfgcvYtisbRjV1wPhkTFYtPEI+nRwU3kGV+8JG/AmIhoX9swEoGi7d5+WWLXjDxS1NkfF8vbYdegSJLEJGNazubJcVdfS+K5ZdcxYvg8xsYkobiPGrkOX8CEqXvn8JV02oHMTbA34EwOmbsW4AYr+M2/dYQzo1FjlGVNdR6/D6/Ao3Aj4EYAiPuP6e2LZtpOwsbZAJSd7+B68iKiYBOUzXwBg1Y5T8Au8jFF9WsLY2BC3HrxUvlahXElYmuv2aS39OjXG9oA/MWj6Nozp54nwDxIs2HAY/Tqqxqf72HUIC4/GlX2zAaSPr34e8Nn+O2yszVHRyR5+By+lj68Wyn3Uja8K5ewKxfgCgO2Hr2NoVzfsXtgPPv7nYVdMjJ9GtoHvkRsqz+A6tHIwStsWQZ3eywEAp68+wevwaOyY3wdLfYMQ/jEOLeq7oPe3dbDY94yy3MS+zTGoU0Os2XMBKalpqFs584OMJyHvEZf46cs19jN4dWmCLfsvoN+ULZgwoBXeRcZg7tpD8OrcWOUZSp1HrUVYeBRuHpgDQNE/xg/wxNKt6WPM2Q6+AZcQFZOIUX1aqvyNI+kfLD59GQ6pTK78vVblsihtV/QLtfTzDOzSBFv2XUDfyVswwUsRnzlrD2FgF9X4dBq5Fq/Do3D7YGZ8Jnh5YsmWk7ApYoHKTnbYduASomITMTrLHN+hZS347DiFAdO2YbZ3eyQmp+DHNYfQqnHhmOOFjg8A5QfTT9L7T8bvtQuw/zDh0iEDBw7EypUr0bZtWyxYsACmpqZYuHAhDA0NMX78eACAjY0NvL29sXDhQpiamqJmzZrYv38/nj5VfBqtC88fsBaLcGjDWExdth99J22Ghbkp+nV0w+yR7VX2k8pkSJPKVLaNH+AJuVyOtf5B+CiJRzXXUghYMxKOpTIfFm1kaICANaMwbfl+DJ21AwYG+mjvXhMLJ3T5Iu37rxgfzazFIvy2eiRmrTyIQdO3wUJkgl7tG2LqMNVrHmVSGdKkUpVto/q2hBxy/PLrOURJ4lHZ2QG7fUagrMPX87BxK0sR9viMxJw1BzBs5nZYiEzQs11DTBqieo2Iov+oxse7d0vI5cCWX8/jY4wiPn7LRqCMvWp8VkzvjaWbj2PJ5mOIS0xGNddS2O0zAhWddP/bHGuxCAfWjsb0FQHwmroV5iJT9GnfCDOy3dRCKpNBmm18jennAblcjg17zuKjJB5VXRzw26qRcMzSfzKe5bV+dxDW7w5SKR+4fozOP3fKWizCvjWjMHPlAQyavlUxvto1wrThquNLKpPnOP6M7usByIGNexXjq4qzA/as9P6qxhcAxMQno9OErVgyrgP8F/ZFfOIn7Dp2E/O3nFLZz0BfH4YGmXNufFIKOk7YhllDW2HOiG9hZWGGV++iMHP9cWw5mHlb+IxneY3t3QxjezdTec92Yzfj8l8vocusxSIErhuDaSv2o/+ULbAQmaJvBzfM9M45xrL3obH9PSGXK8aPYoyVwv41qmMMAAbN2J7r72tn90Gvdg0FaJX2WItFCFw/BtOW70e/yVuUc/ysfMRnXHp81qXP8VVdSyFgtWp8jAwNsH/1SExbEYChsxVzfLvmNQrVHC9kfABg4PTtuf6+7sc+6F1A/UdPLpfLC+Qv/x85dOgQOnfujJcvX8LR0RHnz5+Hu7s7bt68ibp166rsGxoaiokTJ+L06dOQSqVo3LgxVqxYgWrVqin3SUlJwQ8//AB/f3/IZDJ07twZ9erVw+jRoyGRSGBllftTunMTFhaG0qVLI/jla5QqVUprbab/H4mf0gq6CjotVcpDrCYiY4OCroJOS5Ox/2hi7zGroKug0z6cX1TQVdB5+np570OkjplR/joQE66vRL9+/XDp0iW8fPnvPhljwkX/FRMuzZhwacaESzMmXJox4dKMCVfemHDRf5HfhIunFBZCFy5cwOXLl1GnTh3IZDIcO3YMu3fvho+PT0FXjYiIiIiIsmDCVQhZWFjg2LFjWLJkCZKSklCuXDn4+Pgor/MiIiIiIiLdwISrEKpTpw6uXLlS0NUgIiIiIqI8FPwt7YiIiIiIiL5STLiIiIiIiIgEwoSLiIiIiIhIIEy4iIiIiIiIBMKEi4iIiIiISCBMuIiIiIiIiATChIuIiIiIiEggTLiIiIiIiIgEwoSLiIiIiIhIIEy4iIiIiIiIBMKEi4iIiIiISCBMuIiIiIiIiATChIuIiIiIiEggTLiIiIiIiIgEwoSLiIiIiIhIIEy4iIiIiIiIBMKEi4iIiIiISCBMuIiIiIiIiATChIuIiIiIiEggTLiIiIiIiIgEwoSLiIiIiIhIIEy4iIiIiIiIBMKEi4iIiIiISCBMuIiIiIiIiATChIuIiIiIiEggTLiIiIiIiIgEYljQFSDdIJfJIZPJC7oaOklfX6+gq6DTjAz4uY0mcsgKugo6LTFFWtBV0GlSHpc1+nB+UUFXQacV67apoKug86IPjCjoKui0lDTOYZqYGeVvjciVEhERERERkUCYcBEREREREQmECRcREREREZFAmHAREREREREJhAkXERERERGRQJhwERERERERCYQJFxERERERkUCYcBEREREREQmECRcREREREZFAmHAREREREREJhAkXERERERGRQJhwERERERERCYQJFxERERERkUCYcBEREREREQmECRcREREREZFAmHAREREREREJhAkXERERERGRQJhwERERERERCYQJFxERERERkUCYcBEREREREQmECRcREREREZFAmHAREREREREJhAkXERERERGRQJhwERERERERCYQJFxERERERkUCYcBEREREREQmECRcREREREZFAmHAREREREREJxLCgK0Bfp6ch4Zi2IgA3/34JC5EJerSpjxkj2sHYSHOXk8vlWL3zNLYfuISPknhUdXHAgvFdUK9aOeU+H6LjsGL7H7j1MAQPnr6BkaE+Xp1fIXSTtOppSDimLtuPGxnxadsAs7zzF59VfqexLeAiPkriUc3VAQsndFWJDwC8i5Rg6rL9OHf9HxgaGqC9ew0sGN8FYgszIZulNcEhEZjuE4Bb91/CXGSC7t/Vx/ThbfMVn7W7zsD34CV8jI5HFVcHzB/XGXWrZsbnwo0n2H30Km4/CMGH6DiUtiuKnm0bYnjP5jAyNBC6aVrxLDQCs1cewK0HIbAQmaDrt/UwZWibfMVnvX8QdgZewkdJAqq42GPOmM6oU9Ux1/1lMhnaDvHB/adh+GW+F9q519R+YwTwLDQCc1cfxO2HITAXmaBrq7r4YUj+4rNxTxD8D13GR0kCKjvbY/boTqhdxTHHvmevPsS6XWfw+PlbGBkaoLKzA1bO7AO7EtbCNEqLnr+KwLw1gbjzMATmZibo3KouJg7+Ll/x2bT3LPwPX0aUJB6VnR0wc1RH1KrsqNxn8s97cfCPm7mWnzy0LUb0bqnNpggmONsc1v1fzGFrcpnD6mabw3yyzWGhhWwOc3GwxpLBTVC/gi3ik1Lx24WnWLD3BlLTZGrLNK5ij2M/dcj1tadvotFg7G8a9zt46RkGrzyjnQYIjHO8ZsEhEZjhE4Cb99PH13f1Me1fzPE7sszxP2Wb4+8+CsWiX47h8fO3iIlLRPGiYnxTrwKmDWuLksWthG6aWky4PoOXlxdu3bqFBw8eFHRVdJIkNhGdRq2FU+kS8FsyBO/eSzBrdSASk1OwdHJ3jWVX7zyNJVtO4sdR7VHZ2QHbAv7E92M34IL/VDg6FAMAvIuMQeDpO6hdpSxqViqNh8FvvkSztEYSm4iO3mtQvnQJ7Fw6BO8iYzBz5UEkJadg2RTN8Vnldxo/bz6BOaM7oIqzA7bu/xNdx6zHn/7T4FhKEZ/UNCm6jlkPANg83wtJySmYvToQER934LeV3oK377+SxCaiy5i1KF+qOLYvHozwSAl+XHMISckp+HlSN41l1+46g6VbT2CWdwdUdrbH9gMX0X3cBpzdmdl/dh66jKRPKZgytA1KlSyC2w9CsGzrCTwNCceaWX2+RBP/E0lsInqMXQ/H0sWxZeFAhEfG4Kd1ivgsnPi9xrLr/YPgs/0kpo9oj0pO9thx8BL6TNyIP3wno2x6fLLyP3wF4R9ihGqKIGLiEtF7wgY4liqOX+YPRERkDOZvOISkT6n4aXxXjWU37gnCKt/fMWVYO1RyssfOwEvoP+kXnNg2CWXsM+MTeOoWpi79FUO6u2PSkDZISPqEm3+/wKeUVKGb95/FxCWi78SNcHQohg3zvBDxIQYLNxxG8qcUzB2nOT6b9p7F6h2/Y/LQdqhQ3g7+hy/Da/ImHN0yCWXsbQAAo/t5oneHRirljp/7C74Bf6JZ/YqCtUubMuaw8qVLYEf6HDZ7dSCSklOwJI85bE36HDZ7VHtUyTKHnc9lDqtVSOcwK3NjHJ7bHi/exaD/sj9gV9QcC73cYGZiiClbL6kt9/eLSHhOO6iyzVJkjP0z2+DMndc59h+57hyCw6KVv3+MS9ZeIwTEOV4zSWwiuqbP8b6LB+NdpARz0uf4xfmY45dtPYGZ6XO874GL6DFuA4KyzPExcYlwLmuLPh0aoXhRS4S++QCf7X+g5+NX+GP7DzAxNvoSzcyBCRdpne/BS4hPSMbOJUNQxMocAJAmlWHysn2Y4NUadmo+YUj+lIpVfqcxsrc7vHu1AAA0qumE+t3mY93uICyf0gMAUMXZHv/8vggAsGTLiUI3WW0/cBFxCcnwXzY0Mz5pUkxaug8TB7aCXXHrXMslf0rFyh2nMKpPC4zsnR6fWk6o2/UnrPUPwoppivgcDrqLf16E4/q+WXBxtAUAWItF6DpmPW4/DEGdXD6t1yV+gZcQl5CMHT+r9p+py/dj/IBWaj+hSv6UitU7T8O7VwuM6OUOAGhY0wmNui/Ahj1nlcn+0indYWNtoSzXuLYLZDI5Fm8+jjmjO6q8pov8D19GXGIyti4ahCLizPjM9AnAmP6eKFlMfXzW+5/BsJ7uGNqjOQCgfo3y+KbXQmzaew6Lsk10UZJ4LN1yArNHdcQPi/cK2iZt8j98BfGJydi8YCCslfGRYvaqAxjV1wO2GuKzcXcQhnR3x5DuzQEA9aqXh3vfRdj863ksSE9mJbEJ+HHVAcwe3Rn9OjVWlvdsXFXYhmnJniNXEJ+QjI3zs8ZHhjmrDsC7j/r4fEpJxcY9QRjcrTkGdWsGQBEfj36LsfW3c/hpgiI+ZR2K5Ujel20+Dpeytqjk7CBgy7Rnh5o5bMqyfRj/L+ewhjWd0KDbfKzfHYRlWeawx4V4DhvUqgoszYzRd+kfkMR/AgAYGuhj+dCm8DlwB+HRibmWi0tKxa3g9yrberlXgIGBPgIuBufY//GrKPz1PFL7DRAY53jNMuZ432xz/LTl+zEujzl+zc7TGJFtjnfrvgAb95xVfhjSvEElNG9QSVmucW0XONgWQfdxG/D3P69Rr3p5gVuYO17DpUPkcjk+ffpU0NX4z4KuPsI39SooBxIAdPKoBZlMjnPXH6std+P+C8QlJKOTR23lNmMjQ7RrXgNnrjxSbtPXL9zd9szVR2hWv6JKfDp71lbE59o/asvd+FsRn87Z4tPevQZOX3mY+f5XHqGKs73yQAwA7g0qooiVCKcvP4SuC7r2OEf/6dhS0X/O31Afn5v3XyIuIRkdWtZSbjM2MkSb5tURlKX/5JZQVatQCnK5HBEfYrXUCuGcu/YYTeu6KpMtAGjfoiZkMjn+1BCf2w8U8WnfoqZym7GRIb5rVh1nrz3Ksf/iTcfgVtsFbrWdtVp/oV24/hiN67gqkwkAaOuu6D9/3nyittydhyGIS0hG2yynTRobGeLbptVx7npmfI6d+wtSmQw92jYQpP5Cu3DjH7jliE9NyORyXLylPj63H4QgPiEZbbLFp3XT6jiv4bgeHinBzfsv0MGjjlbq/yWc0TCHaWprxhzWMdsxuu1XNod51C6NC/fDlMkWAARefg59PT241yz9r97r+ybOePZWgruFMLFSh3O8Zmf/4xzfMY85PjcZfyslTfofa//5CveoL2Dnz59HrVq1YG5ujvr16+P27dvK15KTkzFx4kTY29vD1NQUNWvWRGBgoEp5Ly8vVK1aFSdOnECNGjVgYmKCo0ePIjU1FZMnT0aZMmVgYmICOzs7tG/fHjExmaf2SCQSjBw5EnZ2djAxMUGdOnVw6tSpL9Z2TYJDI1QOBABgZSmCbTExgkMj1JcLUbzmUla1rKujLcLCo5GUnKL9yhaA4JCIHG20shShZDExnoaoj0/Ga9lj6+pYUiU+wSE546+npweXsiU1vr+ueBaae3xsbfLoP6Hq+k9JhEVo7j/X772AibGh8rQoXfYs9D2cyuSMTwkbMZ6FvldTCsrXspd1LlsSbyIkSPqUGZ+7j0Jx6PQdzB6V+/UWuuz5q/dwKlNCZZuVpRlK2Ijx/JX6/vM8vf9kL+tc1hZvIyRITo/P3UehcCpTAgd+v4nG3X+CU4sf8N3gZTh3Tf1CXJfkFh+xhRlK2FjixSv1/SfjtexlncqWwNv3mfHJ7ujZu5DJ5GifZZGk65595hz27P9kDnNxKILgNxKVbbGJKQiPToSrg3W+36e4lRm+qeaAgIvPcn1938w2+LBvGB5s7ouf+jeEqXHhuMaWc7xmwaERcP6MOf5Z+mvZy7qomeOlUhlSUtMQHBKBn9YdRvUKpdCggL7dAnhK4WcLDw/H2LFjMW3aNFhZWWH69Ono3Lkznj9/DiMjI/Tp0we///47Fi5ciIoVK2Lnzp3o2rUrDh06hA4dMhcxb9++xdixYzFr1iyUKVMGZcqUweLFi/HLL79gyZIlqFKlCj58+IBTp04pv/1KSUmBp6cnIiIisHDhQjg4OMDf3x9t27bFnTt3UK1aNbX1jo2NRWxs5qf4796903psJLGJsMrlwk1rSxEkMbmfagAAkrgkmBgbwtRE9fxaa0sR5HI5JHFJMDM11np9vzRJbCKsLHPGx8pSBElsgvpycYm5x0ecEZ9EmJkaQxKXiOqWpXKUtxabQRKrPv66Qm3/EYs01j9GXXwszSCXyxGjpv+8eP0em/edx4BOjWEhMvnvDRBYTFxirhdGW1tq/v9VFx+rrPExMYZMJsMsnwAM69kcpe1s8PrdR623QUjq4mNlaYYYTfGJT4JxHvExNTFGZFQcXryKxIrtJzF9eHuUsBFj56FLGDpjK05smwTXcnZab5M2xaqJj9gij/EVnwhjI8Mc1z9YpR+fM+KT3dGgO6hVxRGl7XT/w4wMmuawaM5hsDY3RkxCzrNxYhI+wdoi/8fQzo2dYJjL6YSxiZ+wOvAurjx6h6SUNHxTzQGjO9SAq0MR9Fx88j/XX2ic4zWLUTO+rPKY49XGR80c32nkGtz4+wUAoGalMtjtMwKGBXhjLCZcnykqKgoXLlxAlSpVAADm5uZwd3fH9evXIRaLcfDgQfzyyy8YPnw4AODbb79FSEgI5s2bp5JwRUdH4+TJk2jQIPP0lBs3bqBVq1YYOXKkclvXrpkXM+/evRt//fUX7t27h8qVKwMAWrdujeDgYMyfPx/79u1TW28fHx/MmzdPO0EgKuTiEpIwcNo2lLWzwfQR7Qq6Ojphz9FreB8Vh9F9PQq6KjpJLpMhIekTVs3uq7xuq2EtZ7j3WYRf9pyFz0zdv/HKl/L8VQQeBr/BnLGdC7oqpIO6NXXB3Wfv8fyd6o157r/8iPsvMz/oufjgLSKiE7FsaFPUdi6BO8/UfxNLlGHljF6IiU/Cy7BIrNsVhG5j1uPY5vGwNC+YOznylMLPZG9vr0y2ACgTn7CwMFy8eBEA0K2b6kXoPXr0wN27d5GQkPkJh42NjUqyBQC1a9fGiRMnMHfuXNy8eRMymeptVk+dOoVq1arB1dUVaWlpyh9PT0/cvJn77XgzTJw4Ea9fv1b+3Lhx4983Pg/WYhFi43PeTUgSlwhrK5H6cpZm+JSShuRPqnf6ksQlQk9PD9a5fGJUGCnik5Rje0xcosp1FTnKWYpyj09sRnxEyv1yjX9sEqzF6uOvK9TFRxKbqLH+VuriE5cEPT29HJ84pqSmwWvqNkjiErHHZwTMzXT/2y1A0c64hFziE6f5/1ddfGKyxCch8ROWbD6Ocf09kZKahpi4RMSlf5KdnJyCuATdv0uYIj456xkTlwQrTfGxMENKHvEBAHH6OHOr5aLcx8jQAPVrlMfTkHBtNEFQYjX9JzY+j/FlIUJKalqOOzHGpB+fc/tE//CZOzA00Edb98JzOiGgeQ4rwjkMkoQUiEU5v6mzMjdRua5LE0dbMeq62mK/mtMJswu88hwAUNMp591UdQ3neM2s1MUnjzlebXzUzPHOZW1Rp4ojvm9dD/vWjMTLsEjsOnRFO434DEy4PpO1tbXK78bGioNPcnIyoqOjYWRkhKJFi6rsY2trq/haWCJR2ZbdzJkzMXXqVPj5+aF+/fooWbIk5s2bB7lcDgD48OED7t69CyMjI5WfBQsW4PXrnLdWzUosFqNUqVLKHzs77Z/+4lLWNsd5uLHxSYj4EJvjvGaVcunnJD/LVjY4NAKlShb5Kk7FABTtzB6fmPgkhH+Ihauj+vhkvJa9bHCIanxcHG2V18NlkMvleBYaofH9dYVzWVsEZ7sWKTY+CREf8+g/6a89y3YdSnBoBErZqvYfmUwG7zk7ce/Ja+z18YaDbREttkBYzmVL5LhWKzY+Ce8/xsK5bAk1paB8Lft1Os9DI+Bgaw0zE2NExcQjOiYB05bvR5XvZqDKdzPQymspAGD8wj34ptdCLbdG+5zKlMBzNfHJfv2aSrn0/vPidbb4vHoPe1tr5elyro4l1b5HYbgtvFOZEnierQ/ExSfh/cc4lC+jvv9kvJa9/7x49R72JazVnk7oVsdV5+/8mZ3zZ85hzv8nc1jwm2i4OKgeM8UiY5QsIsLTbNd2qfN9U2dIpTIcvJS/hKsw4RyvmUtZ21znsLzm+Ixrt7Ifv57lMsdnV6KoGHbFrfEy7MN/qPl/w/p8jm0AAHgNSURBVIRLAEWLFkVqaiqio6NVtkdERCg+pciSrOnp6eUob2Jigrlz5+Lly5cIDg7GkCFDMHfuXPj7+yvfv3r16rh582aOn2vXrgnatvxo2agyLtx8gpi4zHNxDwfdhb6+Htyz3Kozu/rVysPS3BSHz/6l3JaaJsWxc/fg4VZZyCp/UR6NKuPCjWzxOZMen4bqn1NTv3p6fILuKrelpklx9Pw9eLplftvq4VYZD4LfqByULtx4gqiYBHg2rgJd17JhJfx5SzU+R84q4tNcw3N86lUrB0tzUxw9qxqfE+fvoWW2/jN1+X6cuvwAO5cMQWVne+03QkDuDSvh0q2nKvE5du4v6Ovr4RsN8alTVRGfY+f+Um5LTZPi5J9/o0VDRXyKFxVj35pRKj/r5/YHAEwc9C22LBwkTKO0qFmDSrh8+yli4jI/QT1x/p4iPvUqqC1Xu4ojLM1NceL8PeW21DQpfv/zb7g3yOw/GX3p0u2nym0pqWm4fu85qrn+uzu0FYRm9Sviyu1glU+YT1y4B309PTStqz4+dao6wsLcFCcuqMbnj4v3VW7BnOGvR6F49fYjOrSsneM1XeehYQ7Lra0ZMuawI9nmsONf2Rx25s5rNKvuoPItV8dG5SGTy3HuL80f+mbo2sQZlx6+RYQkf9ccdWmsuFvqnWe6fzdDzvGatfiPc/yRbHP88Vzm+OzeREQjLDwKZR0K7lpSXsMlgCZNmgAA9u/fj2HDhim379+/X3lXw/xydnbGokWLsGnTJjx+rLgLloeHB06cOAF7e3vY2+veYnFglybYsv8C+k3ZggkDWuFtZAzmrD0Er86NVZ5f0mnUWoSFR+HWgTkAAFMTI4wf4ImlW0+imLUFKjnbYXvAJUTFJGJ0n5Yqf+NI+gHpyctwSGVy5e+1KpdFaTvVbxZ1zaCuTbFl3wX0mbQZEwe2xrv3Evy4JhADuzRReT5HR+81eB0ehTuBcwEo4jPBqxWWbDmBYtYWqOxsj20BFxEdk4AxfTPj07FlLfj4/oH+U7di9sj2SEpOxezVgWjVpIrOP58DAAZ0boKtAX9iwNStGDfAE+GRMZi37jAGdGqs8nyOrqPX4XV4FG4E/AhAEZ9x/T2xbNtJ2FhboJKTPXwPXkRUTILymSYAsGrHKfgFXsaoPi1hbGyIWw9eKl+rUK5kgZ3fnV99OzaGb8BFDJ6+DWP6K+KzcP0R9O3opvIMrh7j1iMsPBqXf5sFQBGfUX09sNL3dxS1tkAlJzv4BV5GdEwChqc/08TUxAhutV1U/l7GTTNcy5VE3WrlvlArP1/fjm7wO3gRw2Ztw6i+HgiPjMGijUfQp4ObyjOmek/YgDcR0biwZyYARdu9+7TEqh1/oKi1OSqWt8euQ5cgiU3AsJ7NleWqupbGd82qY8byfYiJTURxGzF2HbqED1HxGNbT/Us391/r3cENOwMvYcSs7fDu44GIDzH4+Zej6N2hkUp8+k7ciDcRUTi3WxEfE2MjePduidU7/kBRKwtUKG+H3YcvQxKbgCE9crb7SNAdmJoYoVVT9Tdx0lVe2eawd5ExmJvLHNY5fQ67mcscZpM+h/mmz2Gj1MxhTwvhHLb91EMMbVMVu6d+C5+Dd2BX1Bw/9W8E31OPVJ7BdWhOO5Qubok6o1Wf41etnA0qli6K9Uf/zvX9N41tgRfhsfj7RSSSU6X4ppoDvNtVx7HrLwvFc7k4x2s2oHMTbAv4E17pc/y7yBj8pGaODwuPwvUsc/zY/p5YnmWO33FQER/vLHP85CW/oai1OWpULAOxhSmeh77Hxr3nULyoJXq3b5SjPl8KEy4BVK9eHV26dMHEiRORlJSEChUqwN/fH1euXMHhw4fzLN+pUyfUqVNHmZwdPXoU0dHRaNFC0aH69++PTZs2oXnz5pg0aRJcXV0hkUhw9+5dpKSkYPHixUI3USNrsQiB68Zg2or96DdlCyxEpujXwQ0zvVVvSiCTyZAmVb0+bVx/T8jlwLrdQfgoiUdVl1IIWDNS+QTxDANnbM/197Wz+6B3u4YCtEp7rMUiHNowFlOX7UffSZthYW6Kfh3dMHtke5X9pLnEZ/wAT8jlcqz1V8Snmmt6fEplxsfI0AABa0Zh2vL9GDprBwwM9NHevSYWTujyRdr3X1mLRTiwdjSmrwiA19StMBeZok/7RpiR7aYWUpkM0mzxGdPPA3K5HBv2nE3vPw74bZVq/8l4zsf63UFYvztIpXzg+jFonC3h0DXWYhF+XT0Ss1cexODp22AhMkHP9g0xdVhblf2kUhmkUtVnjozq2xKAHJt+PYcoSTwqOztgt8+IHA+qLcysLEXY4zMSc9YcwLCZ2xXxadcQk4a0UdlPMb5U4+PduyXkcmDLr+fxMUYRH79lI1DGXjU+K6b3xtLNx7Fk8zHEJSajmmsp7PYZgYpOuvcBWHZWliLsWuGNeWsOYsTs7TAXmaB72wb4YbBqfGS5jK/hvVpALpdj677z6f3HHr5Lh+d4nIJUKsOJ83+hRaMqhebayKyyzmH90+ewvrnMYbkdo8emz2Hrs8xh+3OZwwZlm8MGZZnDeun4HBaTkIJOc49iyZAm8J/aGvFJqdgV9Bjz96heE26grw9Dg5wnUnVr6oLklDQcufoi1/f/53U0un3jglEdqsPE0ACh7+Pgc+AOVgbezXV/XcM5XjNrsQgBa0djRrY5PvuNq3I7BmXM8RvT5/gqLg74NdscX6tyGew6fAW+By4iJSUNDrZF0NKtMsYN8ERRq/x/4aFtevKMC4Mo37y8vHDr1i08ePBAuU0ikaBIkSLw9fWFl5cXkpKSMGPGDPz666+IiopCxYoVMWfOHHTp0kXj+wDAsmXLsG/fPgQHByMtLQ0VKlTApEmT0KtXL+U+sbGxmDt3Lg4cOIB3796hWLFiqFWrFkaOHIm2bVUXXpqEhYWhdOnSePr8FRxK5bzNKAH6+jlP+6RMqWmyvHf6P5YiZXw0SZNyCtJEKmN8NLESGeW90/+xYt02FXQVdF70gREFXQWdlsI5XiOxaf6uzmLC9X+OCVfemHBpxoRLMyZcmjHh0owJl2ZMuDRjwpU3JlyaMeHSLL8JF2+aQUREREREJBAmXERERERERAJhwkVERERERCQQJlxEREREREQCYcJFREREREQkECZcREREREREAmHCRUREREREJBAmXERERERERAJhwkVERERERCQQJlxEREREREQCYcJFREREREQkECZcREREREREAmHCRUREREREJBAmXERERERERAJhwkVERERERCQQJlxEREREREQCYcJFREREREQkECZcREREREREAmHCRUREREREJBAmXERERERERAJhwkVERERERCQQJlxEREREREQCYcJFREREREQkECZcREREREREAmHCRUREREREJBAmXERERERERAJhwkVERERERCQQJlxEREREREQCYcJFREREREQkEMOCrgDphuQ0KZJSpQVdDZ1kYsjPJTTR19cr6CroNBM99h9NzIzYf+jz8fij2cf9wwu6CjqvSMPxBV0FnRZ2YXlBV0HH5W+O50qAiIiIiIhIIEy4iIiIiIiIBMKEi4iIiIiISCBMuIiIiIiIiATChIuIiIiIiEggTLiIiIiIiIgEwoSLiIiIiIhIIEy4iIiIiIiIBMKEi4iIiIiISCBMuIiIiIiIiATChIuIiIiIiEggTLiIiIiIiIgEwoSLiIiIiIhIIEy4iIiIiIiIBMKEi4iIiIiISCBMuIiIiIiIiATChIuIiIiIiEggTLiIiIiIiIgEwoSLiIiIiIhIIEy4iIiIiIiIBMKEi4iIiIiISCBMuIiIiIiIiATChIuIiIiIiEggTLiIiIiIiIgEwoSLiIiIiIhIIEy4iIiIiIiIBMKEi4iIiIiISCBMuIiIiIiIiARiWNAVoK/Ts9AIzF55ALcehMBCZIKu39bDlKFtYGykucvJ5XKs9w/CzsBL+ChJQBUXe8wZ0xl1qjrmur9MJkPbIT64/zQMv8z3Qjv3mtpvjACCQyIwfUUAbt5/CXORCXp8Vx/TR7TNV3zW7DoD3wOX8FESj6ouDpg/vjPqVi2n3OfCjSfwP3IVtx+E4EN0HErbFUWvdg0xvGdzGBkaCN00rQgOCce0FQG4+fdLWIhM0L1NfcwY0S5/8dl5GtuzxGfB+C6oWy0zPh+i4+Cz/Q/cehiCB0/fwMhQH6HnVwjdJK1i/9Hsabb+0+Nf9J/VufSfetn6z4ps/edVIes/jE/enoaEY+qy/biREaO2DTDLO38xWuV3GtsCLuKjJB7VXB2wcEJXlRgBwLtICaYu249z1/+BoaEB2rvXwILxXSC2MBOyWVrDPqSZS9kSWPJDV9Sv5oj4xE/47eRNLPjlBFLTpBrLFRGLMHtEW3i6VUYRKxFevY3CloCL8A28kuv+enp6OOs7ETUrlsaA6b44cu6eEM3Ruv/HNSK/4SpgXl5eqFq1ap776enpYfny5V+gRv+dJDYRPcauR0qaFFsWDsTUYW2x58gVzFt7KM+y6/2D4LP9JIb2aA6/pUNRwsYKfSZuROibD7nu73/4CsI/xGi5BcKSxCai8+i1SElLg+/PgzHTux12Hr6C2asD8yy7ZtcZLN1yAiN6Nsfu5cNgayNGt7EbEJIlPn6HLiM+MRlTh7XBXp8R6N6mPpZuOYGJi38VsllaI4lNRKdRa5GSKsWOJUMw07s9dh66gtmrDuZZds3O01iy5SRG9GqOPSuGw7aYGN9ni8+7yBgEnr6DYkUsUbNSaSGbIgj2H80y+k9qqhR+S4Zglnd7+B26gln56D+r0/uPdz76T/FC3H8YH80ksYno6L0GKalS7Fw6BLNHdYBf4GXMXJl3jFb5ncbPm09gZG93/OozArY2Vug6Zj1CwjJjlJomRdcx6/Hs1Xtsnu+FFVN7IOjqYwydvUPAVmkP+5BmVpZmOLxuFIwNDdB/mi/mbzyOAR3dsHBcpzzL7ljkhW+bVsWiLSfRa9JWnLn2GD5Tu6N/x4a57j+wsxvsiltpuQXC+n9dI/IbrkLi6tWrKFu2bEFXI1/8D19GXGIyti4ahCJicwBAmlSGmT4BGNPfEyWL5X5wSP6UivX+ZzCspzuG9mgOAKhfozy+6bUQm/aew6JJ3VT2j5LEY+mWE5g9qiN+WLxX0DZp047AS4hLSIbfz0NQxEoRH6lUhinL9mPCgFYoqebgmfwpFav9TsO7dwuM6OUOAGhY0wkNuy3Aht1nsXRKdwDAsindYWNtoSzXuI4LZDI5Fm86jrljOqq8pot2HLyE+IRk7FySGZ80qQxTlu3DeK/WaieX5E+pWOV3GiN7u8O7VwsAivg06DYf63cHYdmUHgCAKs72ePz7IgDAki0n8DD4zRdolfaw/2jmq6b/TF62DxP+Zf9pVNMJ9bvNx7rdQViepf/8U4j7D+OTt+0HLiIuIRn+y4ZmxihNiklL92HiwFawK26da7nkT6lYueMURvVpgZG902NUywl1u/6Etf5BWDFNEaPDQXfxz4twXN83Cy6OtgAAa7EIXcesx+2HIahTxVHwNv4X7EOaDercGJbmpug7bTsksYkAAEMDfSyf/D18/E4j/ENsruVKFLXEN3VdMXL+Huw9fgMAcPF2MGpXKoMuHrWx8/A1lf2LWplj5vA2mL3mMNbP7i1so7To/3WNyG+4ComGDRvCzs6uoKuRL+euPUbTuq7KgQQA7VvUhEwmx583/lFb7vaDl4hLSEb7FjWV24yNDPFds+o4e+1Rjv0Xbzr2v/buOyyKqwsD+LuA9CooigUVS6xgr7GBGmPBjsaGvXdjiRqjJvby2WJiRY0lir0l9t5jLMTeUCxgoXfY8/2BjCxNTVyW8v6exydhdmY5c7hzZ87MnRnUqlQCtSoV/6zxa9uRc7dQr2opZUcFAO6uFaFWC45dSDs/l24k5MfdtaIyzTCXAZrVr4DDZ9/nJ7UD4vIlC0JE4J9GR5+ZHD53E3WT5aeVW0J+jl+4leZyF288TMiPWyVlWkJ+nDXyo6eXtbs9tp/0HUmn/Rz7iPbTKln7aZ7t2g/z8yGHz91EvWpfaOSodaNKCTk6n/Y2dvF6Qo5aJ8tRiwbOOHT2n/fff/YmyhZ3UIotAGhQ/QvYWJni0Jl/kNmxDaXPrWZpnLh0Vym2AGDHkavQ01OhQfUv0lwucch2SFikxvSQ8CioVCnn/35gc5z66x5OXbn/eQLPIDn1GDFrt+os4p9//sHXX38NW1tbmJqaolSpUpg9e7bGPMePH0fFihVhZmaGatWq4a+//tL4PPmQwvr166N58+ZYt24dnJycYGJigvr16+POnTsZsk7pue8bAKfC9hrTrCxMkdfWEvd9A9JdDkCKZYs75sMz/yBERsco0/6+6Yudh65g0qCWnzHyjHHf1x/FHVPmx97OEvd8/dNc7t7jhM9KJFu2ZJF88PMPRGRUTGqLAQAuXHsII0MDFHaw/Q+RZ4z7vv4aByLAx+Xnfpr5sYffy/Tzk5Ww/aTv3r9sP2nnJ3u1H+bnw+499k+xnlYWpshnZ4m7j9POUeJnyfNbskg+jRzde5zyb6BSqVDCMV+6359ZsA2lr4Rj3hR5CAmLxMvXISjpmDfN5Z4FBOHI+dsY2b0RShWxh7mpEVq5uqBBtVJYue20xryVyhRGu8aVMGnxbq2sgzbl1GNEDinMAC1atIC9vT1WrVoFKysr3L9/H35+fsrnL1++xNChQzFu3DhYWVlh/PjxaN26NR48eIBcuXKl+b1XrlzBgwcPMHPmTADAxIkT0aRJE9y5cwdGRkapLhMSEoKQkPdnqV+8ePGZ1vK94NCIVG/8tbYw0Tjjk9pyRoYGMDbSXGcrCxOICIJDI2FiZAi1Wo2J873Rt2N9FMpvi6cv3nz2ddCmoJAIWFmklh/TdPMTlFZ+LJPkx9gwxXIPngRg+Zbj6N66NsxNU28XmUlQSASsUm0/pggMTi8/kanmx9rCFCKCoDTyk9Ww/aQvvfYTxPbD/HyEtLYxKwtTBIWEp71cGtuYtWVijiJgYmyIoNAIVLAomGJ5a8v095GZBdtQ+qwtTREcGplienBoJKwtTdNdttu41Vj9Y3ec3zweQMJQ1rHzt2PPsevKPCqVCnNGt8PSjcfx9MVbFMqf+/OugJbl1GNEFlxa9vr1azx69AgLFy5EixYtAAANGjTQmOft27c4ceIEypYtCwAwMzNDgwYNcOHCBdSpUyfN7/b398eJEydQokQJAEDFihVRqlQpeHl5oV+/fqkuM3/+fEyZMuVzrJrObNxzHgFvQzG4i5uuQ8n0QsMj0WP8Kjg62OK7/s11HQ5lMWw/REQZZ8nETihWKA96T1qHl29C0KBaSUwf3hpBIRHYfvhvAEC3ljVgb2uB/607rONoM6fMeozIIYVaZmtrC0dHR4wfPx5r167VuLKVyMHBQSm2AKBMmTIAkOq8SZUrV04ptgCgePHicHZ2xoULF9JcZuTIkXj69Kny7+LFi5+6Sh9kZWGK0PCUZ3eCPnB2x8rCFNExcYiKjtWYHhwaCZVKBSsLE4RHRGPW8n0Y1q0RYmLjEBwagdDwaABAVFQMQsOjPu/KaIG1pWmKMdpAwtnR9PJjnVZ+Qt7nJ6mY2Dh0H7sKQSER2DS/P8xMMv/VCSAxPyn/jkGhEbCxSi8/JqnmJyg0AiqVCtapnLHOith+0pde+7Fm+2F+PkJa21hwaASsk9x3kmK5NLaxoJDEHJkq86X6Nwj58BWQzIBtKH1BIalfwbH6wBWcJrXLoLVbRXh+twbbDl3BmSv38eMv+7H5wCVMG+oOADAzMcSkAc0wd80h5MplAEtzE1iaJfTNJsaGsMgCoxBy6jEiCy4tU6lUOHjwIEqXLo1BgwahUKFCqFKlCk6ePKnMY21trbGMoWHCJfWoqPQbRt68KccC29vbpztM0NLSEgULFlT+aeNBHMUd86YYhxsSFomANyEons745cTPHj7RXPaBrz8K2FvDxMgQb4PDEBgcjnFzt6Js0+9Qtul3aOyZcD/c8J82om6nnz7z2nx+xR3tU82P/+uQFGPbk0ocM38/WX7u+fqjoL2NxlAMtVqN/pPX4drtp9i8YAAK2Nt8xjXQruKO9qmOf/9Qfoon5ifZsvd8/VEwn022GKoCsP18SIl/2X5K5JD2w/x8WIkiKXMUnHgPTpG0c5T4WfJl7z3WzFGJIvbK/UyJRAT3ff3T/f7Mgm0offd8A1Ai2bGOpZlxwj2A6dyjVKpoPsTFxePmA81juBt3/eCQ1xomRrmQ29octtbmWDCuA3wPz4Dv4Rk4/dtYAMAvkzvj0tYJn3+FPrOceozIgisDlCxZElu3bkVgYCCOHz8OIyMjtGjRAmFhYf/pewMCUm64/v7+On+aYYMapXH68l0Eh74/k7P3WMITeupWS/sJPZXLFYWFmTH2HruqTIuNi8eBk9fRsEbCVb88uS2xZdEgjX9Lf+gGABjZ8yus+KmndlbqM3KtWRonLt3RyM+uI39/8AlGVcsn5Gf3kb+VabFx8dh3/BrcapXRmHfsnK04eNoH62b3RpniDp9/JbTIrWaZNPNTv3rpNJerVr5YQn6OXlWmxcbFY9+xlPnJyth+0ueaTvtp8BHtZ1ey9rM327Uf5udD3GqWwYmLyXJ0+F2OaqS9jVWr8C5HybaxPcevoVGt96NY3GqVgc+9Z3iQ5MDxxMU7eBscjka1yyKzYxtK3+Fzt1CvakmNq1zuri4ffJLs05eBMDDQR7lkfa5zqUIIeBuKyOhYBLwJQfOBSzT+9Zq4FgAwY8UBdBu3Wjsr9Rnl1GNE3sOVgXLlyoV69eph3LhxaNmyJZ4/f/6fvs/Hxwf3799H8eIJj7y8f/8+rl27lub9Wxmli3ttrPE+hV7jV2FIt0Z4+SoYPy3djS7utTTer+AxbCn8XgbizO8TAQDGRrkwqIsbFqz5A7mtzVHaKT/W7jiDwOBw9Hv33iBjo1yoVamExu9LvCGyZNF8qJLkbfWZlWfrOli59SS6jVmJ4Z6N8OJVMKYs2YXurWtrvEOpzeAlePryLS55fw8gYd2HdW+EOSsPwNbGHGWcHLB62ym8DQ7HwM4NleUWeB2E144zGNzZFUaGBrjs80j5rFTRfLAwy9zDNjzb1MGKrSfQdcwKjOjeGC9eBeOHxTvh2bq2xvtdWg9aDL+Xb3Fp22QACfkZ3r0RZq88AFtrc5Qunh9rvE/jbXAEBnV21fgdiUXH3UcvEa8W5eeKZRwz/Q3IbD/p65Gs/Tx/FYzJqbSfVu/az+VU2o/du/az+l37GZxG+7mTBdsP8/NhPdt+iRVbTqDz6OUY2aMJXgQE4ftFO9CjTR2Nd3C5D1iEpy/f4sqOHwAk5GiEZ2PMWrEfdtbmKFPcAau8TyEwOBxDurzPkbtrRcxf8ye6jV2JSQNbIDIqFpMW7kDjOmUz/Tu4ALahD1m94wz6tP8SG2b3wnyvQ8ifxwpTB7fEmh1nNd7BtXPxQBTKZ4PK7ROuuhw6exNPX7yF14wemL3qD7x8HYKG1b/AN82qYcbKAwCA6Jg4nEn2GPjEfNx++BIXbzzOmJX8D3LqMSILLi27fv06Ro0aBQ8PDzg5OSE4OBgzZsxAkSJF4OTk9J++297eHi1atMDUqVMBAJMmTUKBAgXg6en5GSL/96wtTbF54UBMWrAdvcavgrmpETq2qIGxfZtpzBcfr0Z8fLzGtEFdXAEIft18DG+DwlCmeAFsmN8fjgXsMnANtMva0hTbFw/G+Hne6D5mJcxNjdGlZc0UDyWIj1cjPk6tMW1oVzeICH7ecBRvgsJQrkQBbFk4EEWS5Of4uzNoSzYcwZINRzSW37l0CGpX1uyMMhtrS1PsWDIE4+ZtRbcxK97lpxYmDEiWH7UacfHJ8tOtEUSApRuOvMtPQWxdpJkfAOj53epUf148qTM6Na+hhbX6fNh+0pe0/XR91366ptJ+1Km0n2Hv2s+SJO3HO5X20yNZ++mRpP18kwXaD/OTPmtLU+z8eSjGztmKLqOXw9zMGF3da2HSwBYa86XWBw3v3ggigsW/JeSofMl3OSr4Pke5DPThvWgQxs3dij4TvaCvr4cWDVzw04g2GbJ+/xXbUPqCQyPRasjPmDWqDX6b3QthEdFYv/s8pv2yT2M+fX09GLx79xYAhEVEw33Iz5jYvxkmD2wBKwsTPHn+FhMW7sQK71MZvRpak1OPEVUiIroOIjsLCAjAqFGjcPbsWTx79gxWVlb48ssvMWPGDJQoUQKenp64fPkyfHx8lGWCgoJgY2ODNWvWKMWTSqXCnDlzMHr0aAAJ7+EyNzdH27ZtMXXqVLx48QLVq1fHL7/8gtKl076kn5yfnx8KFSqE63cewaFAysfUEmBkwJG36VGl9kZGUrCLTZ8e2w/9B3p6bD/pUavZ/3yIba0Rug4hU/M7MffDM+VgtmYfd+2KV7i0LG/evFi/fn2an3t5eaWYZm1tneIgLa2Dth49eqBHjx7/KUYiIiIiItIOnronIiIiIiLSEhZcREREREREWsIhhVnU8ePHdR0CERERERF9AK9wERERERERaQkLLiIiIiIiIi1hwUVERERERKQlLLiIiIiIiIi0hAUXERERERGRlrDgIiIiIiIi0hIWXERERERERFrCgouIiIiIiEhLWHARERERERFpCQsuIiIiIiIiLWHBRUREREREpCUsuIiIiIiIiLSEBRcREREREZGWsOAiIiIiIiLSEhZcREREREREWsKCi4iIiIiISEtYcBEREREREWkJCy4iIiIiIiItYcFFRERERESkJSy4iIiIiIiItIQFFxERERERkZaw4CIiIiIiItISFlxERERERERawoKLiIiIiIhIS1hwERERERERaQkLLiIiIiIiIi0x0HUAlDkY6uvByID1d2qi49S6DiFTMzXU13UImVq86DqCzE2l0nUEmZuw/aQrXs0EpUfN/HyQ/+n5ug4hU7OvOVTXIWRqkX8v+aj5eIRNRERERESkJSy4iIiIiIiItIQFFxERERERkZaw4CIiIiIiItISFlxERERERERawoKLiIiIiIhIS1hwERERERERaQkLLiIiIiIiIi1hwUVERERERKQlLLiIiIiIiIi0hAUXERERERGRlrDgIiIiIiIi0hIWXERERERERFrCgouIiIiIiEhLWHARERERERFpCQsuIiIiIiIiLWHBRUREREREpCUsuIiIiIiIiLSEBRcREREREZGWsOAiIiIiIiLSEhZcREREREREWsKCi4iIiIiISEtYcBEREREREWkJCy4iIiIiIiItYcFFRERERESkJSy4iIiIiIiItIQFFxERERERkZaw4CIiIiIiItISA10HQNnTvcf+GD/PG5duPIKZqRE8mlbD+P7NYJgr/SYnIli0/jDWbDuNN0FhKFeiAKYNb40q5Yoq85y4eAe/7T6Hv3we43VgKArlz41OzWugX8f6yGWgr+1V+yzu+/pj0oJtuOzzGOamRmj7VVWM6fP1R+Vn6W9HsG7HabwJCkfZEg6YPKQ1Kpcrkur8arUazXrPx427fvhlmieaN3D5/CujBXcfv8TYud64dP0RzE2N4PF1NUwY0Pyj8rNw3SGs8n7ffn4a0QZVy79vP68DQzF39Z+47PMYPnefIZeBHp6emKftVfqstLl9vQ4Mxfw1B/GXz2P43PODgYE+fI/N1fYqfVZsP+m7+/glxs3TzM93/T8+P6uTtJ8fh6fMz7zVf+LyP+/z8+R41soPANxLlqMOn5CjRankqEqyHM1PliPfLJaje4/9MX6+Ny6/64M6NK2G8f0+rg9avP4w1mw/jTeBYShbsgCmDUu5j9+wR3Mf37FZ1trH33vsj+/mJ/TR5u/yM+4T8uOVJD9T08jPlWT56ZuF8lPC0R6zvm2PahWKIiwiGr/vu4Afl+1FbFx8usvZWJlh0oAWaFS7DGyszPDk+Rus2HISa7afVuapXakE9v46LMWy2w/+hV4T1nz2dflYvML1H+3cuRM///xzhv7Ox48fQ6VSwdvbO0N/78cKColA68GLERMXhzUze2HCgOZYt+ssJi3c8cFlF60/jNkr9qN/x/rYMLcv7G0t0X7oz3j87LUyz9qdZxAWEYWxfb/Gpvn90eHrapi9Yj9GztiszdX6bIJCIuAxdCli4uKx4qceGNu3GTbuPospi3d+cNmlvx3B/NUH0MejPtbO7oO8tlboPHIZfJPkJ6nfdp3Fy9fBn3kNtCsoJAKtBi5GbGw81s7qjYkDW2DtzrOY+L/tH1x24bpDmLn8AAZ0qo9N8/ohn50l2iZrPy9eBWPHoSvIY2MBl9KFtLkqWqHt7evFq2DsPHQFdjbmcP6isDZXRSvYftIXFBKBVoOS5GfAp+Vn1oqE/Gyc1w/2dpZol83yA7zPUUxsPLxm9caEAS2wbudZTPqIHC16l6P+H5Ejuyyao6CQCLQZshixsXFYPaMXJvRvjvW7zuL7j+iDFq8/jNkr96OfR3389q4P6jBMMz/r3u3jx/T5Ghvn90eHptUwZ+V+jJqZdfbxbd/lZ82MXvjuXX4mf2R+5qzcj74e9bH+XX48kuVn/c4zCH+Xnw3z+6P9u/yMziL5sbIwwa5lQ2GYSx/dxqzEtKW70b11bfw0os0Hl/Wa0RNf1S2P6b/uQ6eRv+LwuVuYP74jurWqlWLegVPWo1GPucq/H5ft0cbqfDRe4fqPdu7cicuXL2PgwIG6DiXT8NpxGqHhUVg7szdsrMwAAPHxaoyZsxUjujdGvjxWqS4XFR2LhWsPYcA3DdG/UwMAQA0XJ9Ro/yN+3nAUs8d0AADMGdMBttbmynK1K5eAWi2Y8es+/DDEXeOzzOi3XWcQGhGFldN7wsYyIT9x8WpMmO+NId0aIZ9d2vlZ+tth9O3YAH086gMAqjkXQ91OP+HXTccwfXR7jfnfBoVh9or9mDTIHaNmbNLqOn1Oa7YntJ/1s9+3n7h4Nb6dvQUjPJsgfzrtZ4HXIQzq3AADv2kIAKhZ0QlV203Dkt+OYO5YDwBA2eIOuPPHdADAzOX78c+9ZxmwVp+PtrevssUdcPPATwCA2Sv245/7WSs/bD/pW7P9NMLCo7BuVrL8zPlwfv639hAGftMAAzq9y4+LE6q1n4YlG45g7pj3+bn9Lj+zVmS9/ACAVxo5GjNnC4Z/Yo5quDihevtpWLrhCOYkydGtLJyjte/6IK+ZmvkZO3crhn+oD1p3CAM6afZBNTv8iJ83HsXsbxP6oNnJ9/GV3u3jl+/D5MGZfx+fmJ81yfIzbu5WDPtAfhatO4T+yfJTq8OPWLbxKGa9y8+sNPIzc/k+fJ8F8tOz7ZewMDNGl29XICgkAgBgYKCPuWM6YP6ag2meJM5ra4G6VUth4JT12LT3AgDg1OW7qFSmMNo0rox1O89qzH/rwQtcvfVEuyvzCXiFKwOICKKjo3UdRoY5cu4W6lUtpXQ0AODuWhFqteDYhdtpLnfpxiOEhkfB3bWiMs0wlwGa1a+Aw2dvKtNS60zKlywIEYH/65DPtBbac+z8LXxZpaRSbAFAi4YuUKsFJy+mnZ+/fBLy06KhizLNMJcBmtargKPnb6aYf8ave1GrUgnUqlT8s8avbYfP3kzRflq7JbafW2kud/H6Q4SGR6GVWyVlmmEuAzSv74xDSdqPnl7W7va0vX1l9fyw/aTvyLmbqJssP60+Jj830s5Pdmo/AHA4nRwd/4gcuSfLUbNslqMj52+lyE9iH3Q8nX1YYh/UMlkf9HX9CjjyoX18qayzjz/6H/OTvI/Obvlxq1kGJy7eVootANhx6Ar09FRoUOOLNJdLHC4ZEhapMT0kLAoq7YT6WWXtrV7HPD09sXbtWvzzzz9QqVRQqVTw9PSEp6cnypUrh/3798PZ2RlGRkbYs2cPvLy8oFKp8Pq15vAvFxcXeHp6akw7d+4cGjduDEtLS1hYWKB69eo4dOhQmrFcuXIFefLkQc+ePaFWq7Wxuh/tvq8/ijvaa0yzsjCFvZ0l7vn6p7ncvccJn5VItmzJIvng5x+IyKiYNJe9cO0hjAwNUNjB9j9EnjHu+wbAqXDK/OS1tcR934B0lwOQYtnijvnwzD8IkdHv8/P3TV/sPHQFkwa1/IyRZ4x7vv4oUSRlfvLZWSptJK3lgFTaT1F7+L1Mv/1kJbrYvrIStp/0pZWff99+sld+gIRt7N/k6H4OydF9X/8U62hlYQp72w+0obS2sWy2j7+XVh/9gfzcf/dZ8mVLfEJ+HLNAfkoUsU+Rh5CwSLx8HYKSyba7pJ75B+HIuVsY2aMJShXNB3NTI7Ryq4gG1b/Ayq2nUsy/5X8D8Pr8IvjsnYapQ1vB2CjXZ1+XT8Ehhf/BpEmT8OrVK9y+fRsbNmwAAOTJkwfTpk3D8+fPMXToUEycOBGFCxdG4cKFcfr06Q98Y4IzZ86gYcOGqFGjBlauXAlra2tcvnwZT56kfmn0zJkzaNasGbp164aFCxdCpUq71g8JCUFIyPszIC9evPiENf44QSERsLIwSTHd2sJU44xGiuVCI2BkaJBio7CyNIGIIDg0EibGhimWe/AkAMu3HEf31rVhbmr031dAy4JDI2Bpnlp+TNLNT3Ba+bFIkh8jQ6jVakyc742+HeujUH5bPH3x5rOvgzal1X6sLEwRmF77CYlMNT/WFqYQEQSl0X6ymozevrIatp/0BYVEwCrV/scUQcHptZ+ckR8g/RwFMkdp58cy/T4orX2YtUX6fdDDp+/28a2yyD4+jfxYfSA/afXRH5OfFVuOo1ur2jDLAvmxtjRFcGhkiunBoRGwTjLyJzXdxqzA6uk9cX7LRABAXFw8xs7dij3HrirzhIRFYuHaQzj7931ERseibpWSGNzFFSWL5EPHkb981nX5FCy4/gMnJyfkyZMHvr6+qFGjhsZngYGBOHDgAKpXr65M+9iCa8yYMShevDiOHj0Kff2ES6iNGzdOdd7Dhw+jVatWGDp0KKZPn/7B754/fz6mTJnyUXFkBaHhkegxfhUcHWzxXf/mug4nU9i45zwC3oZicBc3XYdCRET0r4WGR6LHuFVwzG+L8dzHp5CYn8I5JD9Lvu+CYoXzoPeENXj5OgQNqn+B6SPbIigkEtsP/QUAuHHXDzfu+inLnLp8F/5vQjBnTAdUKuOIKzd9dRI7hxRqia2trUax9bEiIiJw/vx5dO/eXSm20rJ37140b94cEyZM+KhiCwBGjhyJp0+fKv8uXrz4yTF+iLWlaYoxtkDC2RtrS9O0l7MwRXRMHKKiYzWmB4dEQqVSpThrHRMbh+5jVyEoJAKb5veHmUnmP7MDJJxpDw1PLT+R6ebHKq38hL7PT3hENGYt34dh3RohJjYOwaERCA1PuH8wKioGoeFRn3dltCCh/aSMMzg0AjbptR9Lk1TzExQaAZVKBetUrnpkRRm1fWVVbD/pSys/QaERsLZKr/3kjPwA6efIhjlKuw8KSb8PSmsfFhSa9j7ec+wqBIVGYGNW2senkZ/gD+QnrT76Q/kJzmL5CQpJfZSPlYUpgkLC01yuSZ1yaN2oEjzHrsK2g3/hzJV7+HHZHmzefxHThrdO93fuOHQFAHT6VFAWXFpib5/2ONT0BAYGQq1Ww8HB4YPz7tmzB6ampujYseNHf7+lpSUKFiyo/MufP/+/ijM9xR3tU9yLFBIWCf/XISnGbieVOGb+/hPNZe/5+qOgvY3GpXS1Wo3+k9fh2u2n2LxgAArY23zGNdCu4o55U81PwJsQFHfMm+5yAPAwWX4e+PqjgL01TIwM8TY4DIHB4Rg3dyvKNv0OZZt+h8aeswEAw3/aiLqdfvrMa/P5lXC0T3GvTeL47uT3VSRfDng/Dj7Rvcf+KJjPJlsM5QEyZvvKyth+0lfCMfX7Jz66/STPj2/2yg+QsI39mxwVzyE5SshPKn3Qmw+0IcdP28cPmLwO1+48xab5WWsfXyKtPvoD+Um8d+tBsvzcTyM/Ayevw/U7T7Exi+Xn3uOU9wBamhkjn50l7qZzn22povkQFxePmw+ea0y/cccPDnmtYaLje7Q+hAWXlqR2H5WxsTEAICZG88bHwMBA5f+tra2hp6eH5881G1Rq5s+fjzJlysDV1RV+fn4fnD+juNYsjROX7iA49P1Y5V1H/k54Ak31tJ9AU7V8UViYGWP3kb+VabFx8dh3/BrcapXRmHfsnK04eNoH62b3RpniHy5OM5MGNUrj9OW7GvnZe+wq9PRUqFst7fxULpeQn71JxirHxsXjwMnraFgjIT95cltiy6JBGv+W/tANADCy51dY8VNP7azUZ+RWq0yK9rPzcGL7KZ3mctUqFIOFmTF2HbmqTIuNi8feY9fQKFn7ycoyYvvKyth+0udaM2V+3refdPJT/l1+jl5VpiXmJzu1H+DdU9TSyFH9j8jR7mQ52pfNcuRaozROXtbMz+6j7/KTzj4ssQ/ac1SzD9p//Bpck+/j527FwTM+WDcr6+3jG/7H/Ow+mrKPTp6fce/yszYL5ufwuZuoV62UxlUu98QnpZ5P+ymOT1++hYGBPsqVKKAx3fmLQgh4E4LIZFcGk2rTuDIA4MpN3T0mnvdw/UeGhoaIivq4YVoFCxYEANy6dUu5gnXr1i08ffpUmcfMzAw1a9bEunXrMGrUqHSHFZqZmWH//v1wdXWFq6srTp48+a+vrH1Onq3rYOXWk+g2ZiWGezbCi1fBmLJkF7q3rq3x/ok2g5fg6cu3uOT9PQDA2CgXhnVvhDkrD8DWxhxlnBywetspvA0Ox8DODZXlFngdhNeOMxjc2RVGhga47PNI+axU0XywMMvcwza6uNfGGu9T6DV+FYZ0a4SXr4Lx09Ld6OJeS+MdXB7DlsLvZSDO/J5wc6ixUS4M6uKGBWv+QG5rc5R2yo+1O84gMDgc/d69s8PYKBdqVSqh8fsSH5pRsmg+VClfFJldjzZ1sGLLCXT5dgVGeDbGi1fBmLx4J3q0qa3x/ptWAxfj6cu3+Gv7ZAAJ6z7CsxFmrUhsP/mxattpvA2JwOAurhq/Y9e7ouPOo5eIV4vyc6UyjiiUP3cGrem/o+3tC4Cyw7/z+CXUalF+rlg68+eH7Sd9PdrUwYqtJ9B1zAqM6N4Yz9/lx7N1svwMWgy/l29xedv7/Azv3gizVx6AnbU5ShfPj9Xep/E2OAKDO2vmZ3ey/CT+XDEL5AcAPJPl6MWrYPyQSo5av8vRpVRyZPsuR2ve5WhQGjm6mwVz1L11Haz0PonuY1diWPeEfdiUJbvQvZVmH9T2XR90MWkf1K0R5qx6lx8nB6zZ/q4P+uZ9H/Q/r4NYu+MMBnV2hWEW3Md3b10Hq7xPwvNdfl68CsbUNPLj9/ItLiTJz9BujTA3SX68tp9CYHA4BmSj/Kzedgp9OtTDhrl9MX/Nn8if1xpTh7bGmu2nNd7BtfPnISiULzcqt0l47sChM//g6Yu38JrZC7NXHsDL18FoWL00vmleAzOW71OW+3VqNzx8+hrX7zxFVHQs6lYtiQGdGmDvsWs6fS8XC67/qHTp0li9ejU2bdqEEiVKwM7OLs15q1evjkKFCmHEiBGYMWMGQkJCMHPmTNjaaj7Gc+bMmWjYsCHc3NwwcOBA2NjY4MqVK7Czs0PPnppXKCwtLXHw4EE0aNAAbm5uOH78eIrvy2jWlqbYvngwxs/zRvcxK2FuaowuLWumeKhFfLwa8XGaj7Af2tUNIoKfNxzFm6AwlCtRAFsWDkSRAu/zevzdu4aWbDiCJRuOaCy/c+kQ1K6sWXBkNtaWpti8cCAmLdiOXuNXwdzUCB1b1MDYvs005ouPVyM+Pl5j2qAurgAEv24+hrdBYShTvAA2zO8PxwJpt7usxtrSFDuWDsG4uVvR9dsVMDczRlf3Wpg4IFn7UasRF6/ZfoZ1awQRYMlvRxLaT8mC8E7WfgCgx/jVqf685PvO+Ka55gNwMhttb18A0Ou7Nan+vGhiZ3Rq/un3pmYktp/0WVuaYseSIRg3byu6jlkBc1NjdG1ZCxOS5UedXn42vMtPiYLwXpRKfr5bnerPiydl/vwAmjnq9i5HXVLJUWptaOi7HC1NkqOtqeSoZ7Ic9UySo06ZPEfWlqbY9q4P8hy7EmamxujcIpU+SK1GfLL8DEnsgza+74N+/1+yffy7d1Ut3XAES5Pt43csHYLalTL/Pt578WB8lyw/yR9qoU4nP8ve5adsiQLYnCw/J9LJz/YskJ/g0Ei0GrgIs75tj9/m9kVYeBTW7zqLaT/v0ZhPX08PBvrvB+KFRUTDfeAiTBzYApMHu8PKwgRPnr/BhP9tx4otJ5T5bj98ifZfVcGgzg1hZGgA3+dvMH/NQSzwOphh65galYiITiPI4kJCQtCvXz8cOnQIb968Qffu3QEAly9fho+PT4r5//rrLwwYMAA+Pj4oXrw4FixYgFGjRsHFxQVeXl7KfGfPnsXEiRNx4cIF6Ovro2zZsvjxxx/h6uqKx48fo2jRoti6dSvatWsHAHj16hXq1asHExMTHD16FFZWqb/JPDk/Pz8UKlQIt+/7osC7K3CkKTpOt+81y+xMDdN/uEtOF69mF5sefb2s8MpK3eEeOn1MT/rU7H8+iBlKn33NoboOIVOL/HvJR83HgiuHY8H1YSy40seCK30suNLHgit93EOnj+lJHwuuD2OG0seCK30fW3DxoRlERERERERawoKLiIiIiIhIS1hwERERERERaQkLLiIiIiIiIi1hwUVERERERKQlLLiIiIiIiIi0hAUXERERERGRlrDgIiIiIiIi0hIWXERERERERFrCgouIiIiIiEhLWHARERERERFpCQsuIiIiIiIiLWHBRUREREREpCUsuIiIiIiIiLSEBRcREREREZGWsOAiIiIiIiLSEhZcREREREREWsKCi4iIiIiISEtYcBEREREREWkJCy4iIiIiIiItYcFFRERERESkJSy4iIiIiIiItIQFFxERERERkZaw4CIiIiIiItISFlxERERERERawoKLiIiIiIhIS1hwERERERERaQkLLiIiIiIiIi0x0HUApFtxcXEAgJcvXug4kswrJl6t6xAyNeNc+roOIVNTq0XXIWRqenoqXYeQubH5pIvpSR/7nw9jhtInMWG6DiFT8/PzQ758+WBgkH5JpRIRtrUc7NKlS6hWrZquwyAiIiIiynKePn2KggULpjsPC64cLioqCjdu3ECePHk+WJ1nhBcvXqBatWq4ePEi8ufPr+twMh3mJ33MT/qYn/QxP+ljftLH/KSP+Ukf8/NhmTVHH3OFS/dH2KRTxsbGqFq1qq7DSCF//vwfPFuQkzE/6WN+0sf8pI/5SR/zkz7mJ33MT/qYnw/LijniQzOIiIiIiIi0hAUXERERERGRlrDgokzF0tISkydPhqWlpa5DyZSYn/QxP+ljftLH/KSP+Ukf85M+5id9zM+HZeUc8aEZREREREREWsIrXERERERERFrCgouIiIiIiEhLWHARERERERFpCQsuIiIiIiIiLWHBRUREREREpCUsuIgo2+PDWImIiEhXWHARUbalVqsBACqVikVXKpiTT8N8ERHRv8GCi4iypYiICPTr1w9//vknABZdqVGpVACA6OhoHUeS+cXHxyv5ogTx8fEA3p/YIE2JeUnMExFpT2bfzlhwUbaR2Te2rCA7HTg9ePAAq1atwpw5c3D8+HEALLoA4Pjx41i5cqXy88CBA/H777/n+LykR0Sgr68PAKhXrx5GjhyZrbaVf0tfX185sbF//35dh5OpqNVq6OnpITY2FjVr1sTWrVt1HVKWwH7ovbi4OF2HkKUk9tFz587Fs2fPMl0fbaDrAIg+h7i4OBgYGCAiIgKbNm3C27dvUbBgQbi6uiJv3ry6Di9LiI+PVzqsCxcuQK1Ww8TEBC4uLroN7F8QEZQvXx7nzp1Dy5Yt8cMPP+D7779Hw4YNlaIrJ16tiI6Oxv79+7FlyxYEBgbi9OnTOHv2LAYMGJAj8/Exkm4X27dvx6tXr9CoUaMcfWCY2N+q1Wrcvn0bq1atwps3b2Bqaor69evrOjydS8xPTEwMrl+/jlevXqF///4wNzdH06ZNdR1eppV0W0ssWBMl/zm7i4+Ph4GBAcLDwzFlyhQEBATA3t4eLVu2RNWqVWFoaKjrEHVOrVZDrVbDwOB9KbN+/XrMmTMHo0eP1mFkqVNJTt5rULaQ2BGHhoaievXqiImJgUqlwpMnT1C/fn1069YNnTt31nWYmVrSnVnnzp1x+vRphISEIDY2Fl27dkX//v3h7Oys4yg/XmxsLHLlygUgoXhs2rQp6tati6FDh6Jhw4YAkGOLrrdv32LAgAHYv38/DA0NcejQIVSqVCnH5uNjbdy4EUeOHEGuXLmwaNGiHHvAk7S/bdOmDb744gts2LABoaGhqFmzJqZNm4Z69erpOkydSZqfFi1awNjYGEFBQbh8+TKMjIywceNGuLu76zrMTCdpsTV58mTcvn0bQUFBaNiwITp16oTChQvnmKIrsS8ODw+Hi4sL4uPjkTdvXjx8+BBWVlZo3749Jk6cCFNTU12HqjNRUVFo0aIF+vbtC3d3d6U/3rBhA6ZPnw4fHx8AyFT7tOzfcinb09PTQ1xcHDp06IC8efNi7969uHHjBgICAnDt2jXMmzcPN2/e1HWYmVriTqx///44deoUli1bhn379mH//v1YtWoVRo0ahdu3b+s4yo+jVquVYmvgwIHYtWsXYmNjsXfvXkyZMgUnTpwAkDOHF8bHxyN37twwMzODiMDMzEwZCqZSqTgsNw0HDhxAly5dsGfPHuTLl0/Zuee09gMk9BXR0dGoX78+YmNj4eHhgZMnT2L9+vW4fPkyJk2apGxjOVHiMEJ3d3fExsZi2rRpOHXqFPbt2wc3Nzd07NgRu3bt0nWYmU5isdWuXTssX74cFhYWUKlU2L59O9zc3HD79u1sX2yJiHKvqFqtxqFDh+Dk5IRjx47h/PnzePToEerWrQtvb2/89NNPiI2N1XXIOhMbG4uXL19i5MiROHjwoHIfskqlUtpOZhtSCCHKBvz9/aVcuXKyZs0aiYuLExGRP/74Q1QqlSxYsEBERJlOImq1OsX/v3jxQqpUqSI///yzREVFiYhIaGioGBkZSf/+/SUyMlInsf5b3bt3l0KFCsnmzZtl79698ssvv4iRkZF8+eWXcvz4cWW+pLnIKfbt2yfHjh2TNm3aSIkSJWTq1KnKZ/Hx8TqMLPNavHix5MqVSxwdHeXy5cu6Dkenrly5Ivny5RNvb2+N6WfPnhUTExOpU6eOHDlyREfR6d7jx4/FwcFBFi5cqDH97t270qJFCzE1NZX9+/eLSM7sf9Li5eUlRYsWlTNnzij76xUrVohKpZKxY8fqODrtCQ4O1vg5IiJCevfuLfXr15euXbuKWq1W+uWIiAjp0aOHFC9eXM6fP6+LcDONwMBAqV+/vuTLl0927dolcXFxsmjRInF2dlbmSXrcp+t9Gwsuyhb++ecfUalUsnv3bhER2bhxo6hUKpk+fbqIJHRoK1eulJcvX+oyzEwhLCxMxowZI1evXtWYfvPmTdHX15etW7eKiMidO3fExsZG2rdvL+Hh4SIicvToUYmJicnwmD+Vr6+vFCtWTGbNmqXRyZ49e1bMzMykdu3acvToUWV6dj7oSe9Ew7Nnz6R169ZSvHhxjaIrIiJCTpw4ofzdc5L08rVw4UKxtraWzp07yz///JOBUWUuly5dEpVKJX/++aeIJOQsMW8bNmwQfX19cXd3lxMnTugyTJ158uSJFCpUSObMmSMimm1q//79olKpxNLSUsmfrg8EM4vJkyeLi4uLBAQEiIjIo0ePxMbGRrp37y5hYWEikrI4yerOnDkjNWrUkHv37inTjh49KoULFxZbW1sZOHCgMj1x3xsaGiq5c+eW8ePHZ3i8mUHS/XVgYKDUrVtX7O3t5c8//5SZM2dKpUqVJDY2ViIjIzVOFOv6+I8FF2U5qR0cv3z5UsqWLSuTJ0+WlStXKsVW4rxbt26VevXqyd9//53B0WY+f/75p6hUKunUqZP4+Pgo0x89eiSOjo6ycuVKuXnzptjY2EiHDh0kJCRERBKuGH755Zdy/fp1XYWeptjYWOX/IyIi5MmTJ6KnpycrVqwQkYQDmsR5vL29RV9fX1q1aiX79u3TSbwZJemB3q+//irff/+9DBgwQG7fvq0cwDx9+lTatGkjJUuWlIkTJ4qvr6/07dtXXFxc5NWrV7oKXSeS5mvjxo0yb948Wbt2rVy6dEmZPmvWLLGzs5MuXbrkuKIrsT8NCAiQihUrSqdOneT58+ci8j53f/31l1haWoqpqam0atVK3r59q7N4M0Jq+yO1Wi316tUTFxcXpUBIzE94eLhUrVpVKlWqJGZmZnLz5s0MjTezSJq3xIKzd+/eUr58eRF5X2x16NBB6atWrlwp06dPzxIn/T7W7du3ZfTo0SKiWXjv379fypQpI8bGxspJUJGEvMXGxkqlSpWkT58+GR6vriXtoxNPCAYFBUndunUlf/784u7uLiqVSipWrCh58uQRBwcHcXJykkKFCkm1atWU4xldYMFFWUriQXNcXFyKs+/Dhw8XlUolKpVKfvjhBxFJ6Jzu3r0rdevWFQ8PD55JfGfr1q2iUqmkQ4cOGkVXhw4dxMbGRiwsLMTDw0Pp3F6/fi29evWShg0bKmcfM6Pu3bvLqlWr5M2bN1K/fn1p2rSp+Pn5icj7jtrX11fy5s0rKpVKWrRokW2v4iRt6x07dpRixYqJm5ubVKhQQYoVKyYrVqxQDob9/PykY8eOkjt3bnFwcBAHB4ccN2wu6QFg27ZtJW/evPLFF1+ImZmZVK1aVSZMmKB8Pm/ePLGzsxNPT0+5du2aLsLNEInbTGJukh7oTp48WXLnzi0zZ85Uii4RkePHj8uAAQNk3759oqenJ5s2bcrYoDNQ4v4oPj5eoqOjJSQkRMnV9evXxc7OTlq0aCFBQUHKMmfPnpV69erJnj175IsvvpDRo0dLfHx8tr7KnlzSg+akB8D79+8XW1tb+e6778TW1lY6duyo5O7hw4fSunVrmTRpkkRHR2d4zNqQvHjo3LmzMtRUROTAgQPi7OwsLi4usmXLFmWZe/fuScGCBTX6pJwgab5+/PFHmTVrltL/vn37Vho3biwqlUq6desmixcvlvXr18uqVatk4cKFMnfuXJ2f3GDBRVlG4gFkaGiodO/eXWrVqiX9+/eXnTt3KvP07t1bVCqVTJgwQf78809ZsWKFVK1aVVxcXDR2jjlNSEiI9OvXT96+favs2H///Xel6EocXhgQECBff/21qFQq2bhxo7x48UIuXLggXbt2ldy5c2sUZ5lB0g546dKlYmtrK2fPnhURkdmzZ0uePHlkxowZ8uLFC2U+Hx8f6dKli/zxxx9y586dDI85ow0ZMkSKFSsm586dExGRuXPnikqlkjx58siyZcskMDBQRBL+9tu2bZNffvlFHj58qMOIdWvy5MlSpEgROXXqlEREREh0dLR07NhRVCqVxj1L8+fPF5VKJf369cs2B4BJJW5boaGhMnz4cGncuLG0bNlSfvzxR2UeT09PyZ07t3To0EH27NkjGzdulKpVqyonMhwdHWXEiBG6WgWtSpofDw8PqVKlitjb20v//v2V4cqbNm0SOzs7qVChgvz4448yZ84cqVixojRr1kxERCpVqiQeHh46WwddSNpnjxw5Ujw8POTx48ciknC13d3dXUxMTKRy5crKfC9fvpSePXuKk5OT3L9/P8NjzgiPHj0SlUolNWrU0Lj/cc+ePeLs7CyGhobSp08f6dixo9SvX1/Kli2rMbIju0t6QqJ9+/bi6OgoM2bMkGfPninT37x5I40aNRInJyfZu3evLsJMFwsuyhISN7aIiAhxcXGRChUqSPv27aVAgQJStmxZ+d///qfMO2zYMKlYsaLkypVLqlevLh4eHkrHlJM6qKQOHDggTZo0SXE1Z/PmzSmudD169EiaNm0qtra2Ym1tLeXKlZNy5cqluOdL15J2wJs2bZJJkybJ7NmzNXbo3bt3F2tra/H09JTz58/LyZMnxdPTU8qUKZNtr2wlzcuVK1ekRo0asmfPHhERmTFjhuTKlUu2bdsmjRo1EhsbG/n555+z/bCvT9GiRQvp3bu3MozpxYsXYmlpKT179pTw8HCN/C5dujRbFu2J6xgaGiqlSpUSFxcXad26tbi6uoqBgYE0btxYGW76ww8/SJUqVUSlUom9vb18+eWXEhcXJy9evBBHR0eNvjm7CQsLk1KlSkmNGjVkzJgxMnToUClWrJgUK1ZMtm/fLiIJJ3gaNmwojo6OUqBAAWnevLlERkZKUFCQuLi4yJgxY3LMFa7kV5FLliwpkydPlkePHinTL126JI0aNZKCBQuKp6enDB06VL766iuxtbXNdPug/yK1IZW3b98WGxsbqVKlikbRdeDAASlTpozkzp1bmjdvLvv371eWyWnHNJMmTZKCBQvK2bNnJSIiQkRS3tNVr149KVCggGzdujVTDT9lwUWZXmLHEh8fL+fPn5fmzZsrBzm3b9+WVq1aScmSJWXu3LnKMi9fvpQ7d+5IcHCwsjHmtI4pqaT3MC1dulTjik9i0dW+fXuNe1KOHj0q3t7ecunSJfH398/wmFMTGhoqCxYskNDQUGXa+vXrxdzcXMzMzGT16tUiIkpHLCIyceJE+eKLL0SlUknu3Lklf/782WrHnVTSYjPx3pEFCxaIv7+/bNu2TaysrGTNmjUiIvLgwQNxcHCQMmXKyPz58zVymhOp1WoJDQ2VQoUKKTejJx4AdejQQSnQ16xZI4cPH9ZlqFrx+vVrpchMfCra0KFDpXLlykp/Gx4eLnv37pW8efNKkyZNlGXfvHkjV65ckbt374pIQn/TpUsXcXBwyJZXSxP3KT/++KOUKlVKYx2PHDkiX3/9tTg5OcmxY8eU6c+fP1f60cDAQPH09BRbW1slZznJxIkTxdHRUc6ePas81CBpn/3kyRNZtGiR1KpVS1xdXWXYsGHZ5sRG0n1xfHx8iof03Lx5U6ytrVMUXXv37pXKlStL3bp1las3OaVQTxQbGytNmzaVnj17Kk9STpQ0D0FBQVKhQgUpVapUptqvseCiLCE6OlqqVKkirq6u0qlTJ43Pbt68qRRd8+bNS3X5nNQppeevv/4SlUolbdu21SiikhZdmbkYGTp0qLRq1UpjCNejR49k4MCBYmZmJi1atFCmJ+2Qnzx5IkeOHJHDhw9rDEHITpLuuEePHi0DBw6U06dPK9MGDBggTZs2VYYQhoaGSqVKlcTQ0FAKFCigTM8pUns1gkjCMLkaNWrIwYMHxdbWVjp06KAUr3/99Zc0b95ctm7dmq36lAcPHoidnZ0sW7ZMKbpERBo1aiTu7u4p5t+9e7cYGBjIjBkzUny2Zs0aadKkidjb22e7hxQlbmOJJwF79OghlStXlvj4eI2h6ocPH5YiRYrI4MGDU3zHiRMnpHHjxlKgQIFsl5+0JM9b06ZNpXfv3srnd+7ckc6dO0ujRo1kwoQJyn23ibLDtpZ8vxMaGiqdOnUSV1dXadeundy4cUMpOtMqunbt2iUVK1aUWrVqybZt2zI0fl1I/ncPDAyU/Pnzy6hRo9JcJnHIaXBwsDJUNbNgwUVZQkhIiPTt21dUKpXUrVtXXr9+rbEx3rx5U9q0aSOlS5eWadOm6TDSzC0mJka2bdsmNjY20qZNm1SLrm+++SZTF12JO6WdO3cqD/B49uyZDB48WMzNzWXo0KHKvNnx3poPadeunRQpUkQWLFigHLjEx8dLkyZNpFKlSsp89+/fl06dOklAQEC2LULTkvysctK+ZN26dVKgQAExNjZW7rURSbiK07NnT3FxcZGnT59mWKwZpUmTJmJjYyOrV6+WoKAgUavV4ubmJl999ZVER0drFBTh4eFSr149adu2rcTGxmrk76+//pLevXvL7du3dbEaWpG0oHr79q2MGjVKIiIiZOTIkWJvb6/0SUmHLw0YMEAKFCigUcCKJFw1XbZsWY65spW03ezYsUOeP38uHh4e4urqKr///rtMnz5dTExMpGrVquLm5iampqYyf/58jXdPZfWC6/Hjx+Lk5CQjR45UppUuXVpcXFykVatWUqxYMSlUqJDs2LFDuSKT+KTg6tWrazxIY//+/VKkSBFxc3NL0bayk9TutY+IiJCvvvpKatWqlWoxdeDAAZk6daq8efMmI0L8ZCy4KMsICAiQcePGKS8zTr5B3rx5U+rWrSsdO3bM8h3055DW+4RiYmLE29tbLCwsUhRdW7ZsEZVKJT169Mh0xUrSv/evv/4qKpVKJk+erHSufn5+MmjQIClYsKAMGzZMmTcnDSWdP3++ODg4yOnTp5WDv8S8rVq1SqytrWXo0KGyd+9e6dq1qxQpUiRTP3VSG5JuFz/88IO0aNFCWrZsKRMmTFBy9eOPP4qVlZU0b95cjh07JuvWrZNvvvlGrK2ts91TCZPfjG5qaqoMzf3ll1803m+YdBts0aKFxhXlpN+Vme6b+C/u37+v8d6nqKgoqVatmjRq1Eju378vhw4dkvz580vv3r2V/jIxR8OGDZNatWql2g/nlAc3JV1Pd3d3KV26tDx+/Fi8vLykcuXKYmlpKaVLl5bp06cr8zZs2FBatWqlq5C14tGjR+Lh4SGOjo4yadIkuX//vrRt21b8/PwkJiZGgoODpX79+pInTx7ZunWrRtGlUqmkV69eGrn8888/s+VQ3dRMmDBBZs+erfy8fPlyUalUMmXKFI2no75580Y6deokzZs31+mj39PDgosyneQ7qKQ/BwQEyLBhw0RfX1+WLFmSYsf1+PHjbHNW7N9IbUf+66+/yvTp02X69Ony5MkTZXpaRdf27dsz3fuFUluvjh07ioODg0yePFlev34tIglPuRo4cKAUKFAg3WEH2VWPHj2kSZMmqR7wvnz5Ujkjb2NjI05OTpn6SqY2JC8uChUqJN26dZNu3bpJ3rx5pXr16spTLv/3v/9Jo0aNxMjISL744gv56quv5MaNG7oKXWuSnpAICAiQ4sWLS9GiRWX16tXy7Nkz6dChg5iZmcnu3buVosLHx0dKly4tY8eO1VXYWnfy5EnJly+f8g6kmJgYuXHjhjRp0kQZChgTEyODBg2SIkWKSK9evSQiIkJiYmLk7t27UrFiRenevbvuVkDHkm5rz549kzZt2igvehZJeGz+tWvXNPY1AQEB4ubmpjxIJDtIzMPDhw+lZ8+eUrBgQaldu7Y0bdpUY9uLjY2VBg0aSN68eTWKrkePHmm8DicnefPmjVSoUEHKlSsnS5YsUaaPGTNGuQVi48aN4uXlJW3btpXcuXNn6j6aBRdlKokdSlhYmEycOFHc3d2lf//+8ttvvynzvHr1Sim6li5dmmrHnF06608RFhYmXbt2lYMHDyrT3N3dlXcrJR5oL1u2TLkq5O3tLZaWltKhQweNB2lkJkmvtD1//lxjqFLv3r0lb968KYquoUOHiomJifLwg5wgPj5e6tevL66urso0tVqtceBz9OhRuXv3rpw6dSrT/r0/t7i4OI0b8kVENmzYII6OjnL06FHlYObAgQOiUqk0nqoXGRkp9+7dk9DQ0Ex18/XnkthPhoSESOXKlcXd3V2cnZ3Fzs5ObG1tZc2aNXL27Fnlsfhubm7SrFkzKV++vDg7Oyu5y24nt549eyZFixaVHj16iEjCwXD16tWlcOHCUrNmTeVBDyIJuRsyZIg4ODhInjx5xNnZWZycnLJ1fj7Ft99+K87OzlKyZMl0h+L6+PhInz59JG/evNlqOGrSourZs2fSq1cvyZcvnzRs2FCZnriPi42NlYYNG0r+/Pll3bp1Gv1WThipkdqTG588eSKurq5StmxZWbx4sfL5vHnzpGTJkqKnpycFCxaUmjVryvXr1zM85k/BgosyjaTv2SpTpoxUr15devbsqQxzSfoUwlevXsnw4cPFyMhIZs2alaN3aInu3bsnKpVKatWqJSdPnpRdu3aJi4uLnD59Wp4/fy4BAQHSrVs3MTY2lrlz50psbKzExcXJ9u3blZcFZpZCNTw8XE6ePKkxzdPTU4oUKSIqlUpq1qwpy5cvFxGR/v37S548eTSKLl9fXxk9enSOuU8i0dChQ6VAgQJy/vz5FJ8dPXpUhg8fnqPu14qKipJatWrJkiVLNO53mDBhgjg7OytDT27fvi158uQRDw+PFMVZZtkmtCU2Nlbc3NykevXqcu3aNXn9+rVcu3ZNvv76a7G2tpY1a9bIixcvZMWKFdK0aVNp1qyZfPvtt9n6VRsXLlyQfPnyydmzZyUqKkqGDx8uP/30k+TNm1fy5csnV65c0bjHKDIyUo4dOyYjR46UIUOGyJw5c7J1fj5WWFiY9O7dW4oXLy5FihRRtq3kOZk0aZJ89dVXUqxYsWx11T1xPcPCwmTx4sVy69YtCQgIkF69eom+vr7GFeKkRVf58uU17h/NCVI7hkvM35MnT6Rhw4ZStmxZjStdfn5+cuvWLXn69KnG0N/MigUXZQqJO66oqCj56quvxM3NTTkD37RpUzEzMxM9PT2NB2K8evVKPD095csvv8zxBVdi/nx8fMTKykoaNWok3377rbRp0ybF8LK+ffuKubm5smOLjo6W3bt3Z5qzimq1Wnr37i2Wlpayb98+EUl4eW+hQoVk5syZsmHDBnF1dZWiRYvKoEGDRESkT58+Ym9vL1OmTFHeD5TThl+IJPz9TU1NpVWrVhr3Gr1+/Vq6d+8uNWrUUIrSnMLV1VUpHBJ3ymPHjhUXFxcRSXhCmo2NjXh4eChF2eTJk2XhwoU6izkjPX36VJycnDTukxBJ6FO+/vpr5UpXarJrMRESEiLOzs5SsWJFKVKkiFSvXl0ePnwoXl5eYmZmJl26dFHaUnr7npzWB6WWi1evXsmYMWNEX19f2rVrp0xPmpuVK1fKpEmT5MGDBxkSZ0ZIXL+QkBBxcXGRevXqyfz580UkYZhgr169xMHBQSZOnKgsk1h0xcXF5bi2k8jT01N++ukn5efEPsbX11dq164tjo6OsmzZMl2F95+w4CKd+eeff2Tz5s3Kz4nv2Wrbtq2cOXNGRBJejliwYEHlJn+VSqV0WiIJ71vIyfdsJZXYQV+/fl0sLS1FpVJJy5Ytlc+T3tRdunTpTH1/we3bt8XV1VWKFy8uO3fulBEjRsjvv/+u/I1fv34tP/zwgxQpUkS58tmtWzcxMjKSGTNm5Lj3kyS1b98+MTY2lvLly8uoUaNk2rRp0qRJE7G2ts70Qy4+p6R//3bt2ikPg4iJiZGdO3eKSqWSJUuWiJ2dnbRr104ZMvj06VNp27atjBo1KtM9OEYbAgMDpWjRojJ8+HBlWmJfcuXKFbG1tZUSJUrIkiVLNIZVZtftK3G99u/fLyqVSqytrZV7tqKjo2XVqlViZGQkvXv31jirnt2vhH5I0gIhMjJSIiIilKGXr169ktGjR4uDg4P06dNHmS/pycDs8qCVpCIiIqRSpUrSqFEj+fvvv1O8qqRXr16SP39+jaIr6UmMnFB0JV3HBw8eSK9evUSlUmkMH0zMycOHD8XGxkacnZ1TnCDKClhwUYZTq9USFRUlVapUkSJFisimTZuUzx4/fize3t6iVqtl1qxZ4ujoqAwtu3DhgqhUKlGpVPLtt99qfGdO39klSuyYEodIqVQq2bFjh/J54jCYhg0bSvPmzXUU5ce5f/++NGjQQJycnMTMzEy54Tpxx/zmzRvp2LGjlChRQvn79+7dO8cNI0zN5cuXpUmTJlK4cGEpWbKkNG/eXHx8fHQdVoZKeuASEREh1apVUx4GERoaKt27dxeVSiVffvmlUlj5+flJjx49pGjRonLv3j1dhZ6hIiMjpXXr1uLs7JxiONfLly+lQoUKkjdvXmnYsGG2LbJS07FjR6lataoULlxYKlWqpDxYKCoqSqPoyqxPRMtISQ+ax48fL02bNpXy5ctLly5d5MKFCyKSUHSNGjVKChYsqPEOrux6lVREZO3ateLk5KTR9165ckW8vb3l6NGj4ufnJ71795aCBQtqvM4kJ/rmm2/k119/FR8fHxk+fLioVCpZtGiRiGie3KlTp47Y2dlJnTp15O3bt7oK919hwUU6c/PmTalYsaI4OztrPBQj8YD666+/ls6dOys/R0dHi7OzszRo0EBcXV1z1M4/LemdAfvnn3/EyspKKlWqJAcOHFCmv337VmrXri2dO3dO8Q6dzObevXvSqFEj5WpEosQD5H/++UdUKpUy9JDei4yMlKCgIAkODta4yT8nSNqmu3btKi1btpTy5cuLiYmJ2NnZyZo1a+TUqVPSo0cP0dPTkxEjRkiPHj3k66+/Fjs7u2x1H8nHSDoUNenLeC9fviwtWrSQgICAHDeS4NmzZ+Lv7y/Hjh2TYsWKSaVKlZRh7olFl6mpqbRt21bCw8N1HG3m0L59e3FwcJC+ffuKh4eHlCpVSgwNDcXb21tERPz9/WXUqFFSrFgx8fDw0HG02jdnzhwpWLCgPHnyRHx9fWXmzJliZmamnAz94YcfJDg4WNq0aSPu7u45ZtsS0Tx28fLyEjs7O2Vkk5+fnwwZMkRUKpXG0O4XL15It27d5MSJExpPXM4qWHCRTiRubHfu3FGeeJW06AoMDJRq1apJu3btlDNgFy5ckMaNG8uZM2eUjikndVDJJe2wfv31V/nuu++kf//+cujQIXn58qWIiFy7dk2srKzE1tZWBg8eLNOmTZNOnTqJmZlZpnv0e1oePHggNWrUEHNzc9m1a5fGZ8eOHRMbGxs5deqUjqKjzGzo0KFib28v+/fvlzt37sjp06eV4ZVr1qwRX19fWbhwoXz99ddSv359GTt2bKa5lzGjHThwQMzMzKR8+fIyaNAgmTZtmlSoUEFq1aqlFFs5cSRBfHy8HD16NNWia9GiRVK/fv0cmZfktm7dKoUKFZJjx44p065evSodO3aUXLlyyYkTJ0Qk4UpX//79pVy5chrvUcqOrl27JsbGxlKyZEkpU6aMmJuby5IlS+Tq1asye/Zs0dfXl1evXsmzZ89y1AmNpOu4YsUKmTBhgvzwww8axzRPnjxRrnQNGTJEFi9eLN27dxdHR8csd2UrEQsu0pnUiq4NGzYon8+ePVtUKpUMHjxYZsyYITVr1hQ3NzdlY+VOLkGbNm0kf/78UqZMGSlQoIDY2NhI165dlaF1Pj4+yhm1xo0by/fff5/lDiofPHggdevWlTx58sjatWvl9evXcuPGDenRo4fkz59f/Pz8dB0iZTKBgYFSq1YtGThwoMb0+Ph4ad68ueTOnVt5wS/7lAQ+Pj7Srl07cXJyklKlSkmzZs1SvEA7J4qLi1OudFWuXFk5oRUTE8O2807i/ZCPHj3SmH7r1i2pUaOG1KxZUwIDA0UkYZRF0nc/Zmfnzp2T3r17y9y5c+XKlSvK9F9++UWcnZ018pCd21BqhaSXl5c4ODiImZmZcs9W0vvcAgICZMGCBWJhYSH29vZSsmTJLD36gAUXZai0hsDdunVLypUrJ87OzrJu3Tpl+sSJEyV37txSuHBhadSoEXf+ycycOVPy588v586dk6CgIBFJGENfsmRJad++vfLekzt37ohKpZLWrVtn2eEvDx48kJo1a4pKpRIHBwdp1aqVODs7awyBIkoUHR0tLi4uGkOXkp7kKViwoJQsWVJ+/vln3oeTRFRUlAQHB8uzZ8+Ug6TsfJ/Nx0osukqUKCEFCxZU3mUokjOuSiSVdP+b+P8zZ84UKysrpeBK+sCZGTNmiJ2dXZYcBva5xcTEyK1bt+TLL7+Udu3a5Yi2o1arZfv27TJz5kxlWv/+/WXMmDEyevRosba2ltq1ayufJe9vXrx4IY8ePcryT9hlwUUZJnEjioiIkMOHD8uWLVvk2bNnyuXhf/75J9Wi6/79+/L8+XOlY+fO/z1PT09p0qSJREVFaXTcU6dOlbx588qqVauUaTdv3pRbt27pIszP5uHDh9K4cWOxsLAQLy+vLFs8kvbFxsZKnz59pFSpUnLp0iWNz6KioqRGjRpiZGQkBQoUUE5WUEo8ufVeXFyc/PHHH9K+ffsc8QS51CQtpF6+fCm+vr4iknDPW/78+aV169YplvHy8pJChQrJ48ePMyzOzOj169cyY8YMqVOnjri4uCjHMtl9G4uKipI5c+ZI3rx5Zfjw4eLu7i52dnZy//59CQ4Olu+++06sra2la9euKU7yZKeClAUXZaiQkBApX7682NjYiEqlEltbW+nWrZvcuXNHRN4XXS4uLrJ+/foUy2f3julTubq6Ss2aNZWfk+4Ma9euLa6uriKSvR4ve/v2bWnZsqXcv39f16FQJnfz5k0xNzeXVq1aaTwS/8mTJ9K2bVu5c+eOck8O0cdIug/KTv1qelJ7EX3Pnj3FyclJzM3NZeDAgXL79m1ZtGiRmJubS8eOHSU8PFxiY2PF399f2rdvL9WrV88SL6fVpoMHD0qtWrWka9euOe7F2IGBgTJq1CgxNjYWa2trjaGBQUFBMnbsWHF0dNR4XU122770QKRl8fHxyv+PGjUKefLkgbe3N/z9/dGvXz/cuHEDnTt3xt27d1GmTBls3boVADB27FgcOnRI47v09HJmk1Wr1alOd3d3x507d7B+/XoAgKGhIeLi4gAATk5OiIyMBADo6+tnTKAZoFSpUti6dSucnJx0HQplcqVLl4a3tzcOHjyInj174scff8T69esxatQonD9/Hubm5siXL5+uw6QsJOk+KDv1q2kREQwbNgzNmzfH/v37AQAjRozAsWPH0K1bNwwZMgTr1q3DmDFjUKpUKUyePBmHDh1C6dKlUadOHbRu3RqHDx/G8uXLYWlpqeO10S03Nzds2rQJa9euhYGBAeLj42FgYKDrsDKEtbU1VCoVjI2NYWhoiJUrVyqfWVlZYdy4cejYsSOOHTuG3r17A8h+25dKRETXQVD2FxERAS8vL1y4cAHNmjVDhw4dlM9+++03zJ8/H87OzliwYAGsra1x8+ZNzJw5E2vWrMl2G92nio+PV3Lw8OFD5M6dGyYmJjAyMoKPjw+6desGQ0NDjBo1Cu3btwcABAUFoW3btnBwcICXlxf09PSgUql0uRpEOnPjxg0MHz4ct27dQmxsLOzs7LB582Y4OzvrOjSiTO/OnTsYNGgQfH19sWDBApw/fx6VK1dG69atAQDnz5/HV199hdq1a2P8+PGwsrLCsmXL8Pr1axQuXBh9+/ZFyZIldbwWmYtarc5xJ5B9fX3x/Plz/P7779i2bRvc3d2xZMkS5fOgoCDMnj0by5cvR7t27fDLL7/oMNrPjwUXaU3SQuH777/H3Llzoaenh3379qFevXqIioqCsbExAOC7777D8uXL4ePjk+KMc9Lvycl69+6Nw4cPw8jICDVr1sTs2bORN29enDhxAgMHDkR4eDgaNmyIIkWK4K+//sLx48dx7tw5lClTRtehE+lcWFgYQkJCEBQUhHz58iF37ty6Dokoy3jw4AF69+6Np0+fIiQkBPv370eVKlUQGxuLXLly4fLly3Bzc0OlSpWwbNkylCpVCkDCFTKe7KOkXr16hR9//BHbt29PUXR5eXnBx8cHAwcORLFixXQY5eeXs8pryjBqtRr6+voICAjA4cOHMWTIEHTq1AkRERFYt24dAMDY2BhRUVEAgMGDB+Pt27c4d+5ciu9isQVMmzYNx48fx4gRI1CnTh2cPXsWrq6ueP78OerVq4d169ahVatWOHbsGDZu3IiYmBicOXOGxRbRO+bm5nBwcECZMmVYbBF9IicnJ6xYsQLFihXD69evcfPmTQBArly5EBcXhypVquDIkSPw8fFBhw4dcPXqVd0GTJlWnjx58N1336FNmzbYtWsX+vbtqxT0CxcuxPDhw7NdsQXwChdpQeIZrejoaJQvXx7m5ua4cOEC/Pz8MGXKFGzduhUjR47EtGnTlGX++OMPdOzYETt37kT9+vV1F3wmkfyq3siRI1GkSBEMHToUALBp0yZMnToVarUax48fR/78+REbGwsRQWxsLPT19ZWrh0RERJ/Dw4cP0aVLFzx69AgrV65Es2bNAABxcXEwMDDAhQsX0LFjRxw/fhyOjo46jpYys4CAAMyZMwcrV66EkZERcuXKhZ07d6Jy5cq6Dk0rWHDRZ5VYKMTHx2Pnzp1YtWoV5s6di1KlSkFfXx8PHz7E1KlTsWXLFnh4eKBTp054/fo1lixZgri4OJw7dy7HX9FKWmydPXsWMTEx2LdvH1q3bo1atWoBSLiCuG3bNkyaNAkigpMnT8Le3p7DL4mISKsePnyIXr164dmzZ1iwYEGKois6OhpGRkY6jpKyguDgYNy4cQOPHj1C3bp1s3WRzoKL/rNXr14hT548ys9RUVFo3rw5TExMYGxsrDx1MLEYSCy61q9fD0NDQ3zzzTcwNTXF7NmzYWJiwqLhnfbt2+PIkSMICgoCAPTs2RNLly5VdmQigm3btmHKlCkICAiAj4+Pxt+BiIhIGxKHgPn5+WHRokVo2rSprkMiytR4Dxf9J3fv3kWRIkVw6dIlZZqxsTEiIyOxb98+3L9/H2FhYQAAlUoFEUGxYsXw/fffo2vXrihQoAAKFCiAxYsXw8TEBFFRUTm22Er6+PxffvkFN2/exNKlS3HkyBHUqFEDf/75J1asWKE89l2lUqFt27YYN24cHB0dERISoqvQiYgoB3FycsLKlStRpEgRdOnSBQcPHtR1SESZGq9w0X/y6tUr/Pnnn+jSpQvi4+MhIsp7JTp27IgtW7Zg7ty56NOnDywsLKBWq6FSqaBSqXDv3j389NNPOHz4MPr374+JEyfqeG0yB29vbzx+/BhRUVFKTgIDA9GmTRv4+flh2LBh6N+/v5JnEUFYWBgsLCx0GTYREeUwd+/exejRozF//nwUL15c1+EQZVosuOiziImJQd26ddGrVy94enoiV65cAIBmzZrhxIkTmDdvHjp37gxzc3ONouv+/fuYOXMmNmzYgClTpmDMmDE6XhPdOn36NOrWrQsAmDJlCiZNmqQMsQwKCkLr1q3x9OlTjBw5En369FHyTEREpAuJj4YnorRxSCF9FmFhYbC3t8eIESOwZcsWxMbGAgD27duHunXrYtSoUdiwYQPCwsI0XvZXvHhxfPvtt+jZs6fyEsWcrEqVKvjtt99ga2uLY8eO4eXLl9DX14darYa1tTV27NiBokWLYtKkSfDy8tJ1uERElMOx2CL6MF7hon8ltQdbBAcHY8iQIdiyZQtWrlwJDw8PjStdZ86cwffff48BAwbAxMREY9mceIYsrYeDREZGYsuWLejfvz86deqE//3vf7C0tFTeTP/27Vt0794dCxYs4BAOIiIiokyOBRd9ssRHv0ZFReHYsWN4+vQpihQpgsaNGyMmJgY9e/aEt7d3iqKrevXqsLCwwKFDh3L8m+eTFlvbt29HQEAAIiMj0blzZ+TNmxcAsHr1agwcOBDffPONUnQlLpf4rjMiIiIiytwMdB0AZS3x8fEwMDBAaGgo6tevD7VajRcvXiBv3rwoU6YMNm/erAx16927N1QqFTp06IBcuXLhwoULyv1bOblgEBGl2PLw8MC5c+cQFhaG+Ph4zJkzB5MnT0anTp3Qs2dPqFQqDBgwAAYGBpgzZw6srKwAIMfmjoiIiCirYcFFn0RfXx8RERFo3LgxrKyssGrVKtjZ2aFu3brYsmUL3rx5g0OHDmHNmjVQqVTo168fIiIi0KNHDxgYGEBPT08ZGpdTJRZL48aNw+nTp/Hbb7/B0dERenp6GD58OEaNGoWoqCgMHDgQXbp0gb6+Pjw9PWFkZIRFixax2CIiIiLKQjikkD7Z0qVL4e3tjdWrV6No0aLw8PDAmTNn0K1bNyxduhR16tTBvn37EBsbi1atWiEiIgLHjh3TddiZSnh4ONq2bYtixYrh559/1visQ4cOOHz4MM6ePYsvvvgCUVFR2LFjBypWrIgvvvhCRxETERER0b/Bgos+iVqtxv79+xEYGIiuXbtiyJAh2LFjBw4cOICiRYuiX79+2LRpE5o3b47du3cjPj4eKpUqR1/RAlI+FCQyMhKVK1dG5cqVsX79egAJj9Y3NDREeHg4SpcujebNm6coxoiIiIgoa8nZR8H0yfT09NCgQQN4eHjA19cXhw4dwg8//IAyZcrA3Nwc/fr1Q+HChbF3714MHjwY+vr6yjDCnEatVmPfvn24ceOGUmz98MMPOH/+PAwNDeHs7Iy//voL165dAwAYGhoq/7W3t0dwcLDOYiciIiKiz4MFF30yMzMzGBoa4s2bN3j58iWMjIyUh0BcvXoVlSpVwu7du7Fw4UJlmZx4hcvPzw9btmyBh4cHbt26hdatW2Pt2rXInTs39PX1MWzYMNy7dw+zZ8/G7du3leUCAwNhYGCAAgUKAEh4yAYRERERZU18aAb9a/b29jA1NcXOnTtRqFAhmJqaYtu2bahVqxaaN28OIO13TeUEhQsXhru7O27fvo1atWrBxMQEp0+fRtGiRaFWq1GjRg1s3LgR33zzDR4/foxWrVohX7582LNnD+7cuYO1a9cC4BMJiYiIiLIy3sNF/8mJEyfQokULxMXFwdjYGE5OTjh37hwMDAxy9KPfkz6J0c3NDUePHkXx4sWxYcMGVK1aVePetuPHj2Ps2LG4d+8ezMzMkD9/fqxcuRIVKlTQ8VoQERER0X/Fgov+s7t37+LChQswNDREu3btoK+vr7wcOSdKelUv8d1aenp68Pb2RnR0NFasWIFq1aohPj4eQMKj9oODgxEcHIzY2FjY2dkp79siIiIioqyNBRd9djl5GGFS06dPR+XKldGkSRMAwObNmzFv3jyl6KpevTrUajViYmLg5+eH4sWL6zhiIiIiIvrcWHARacHNmzdRt25dlC1bFqNHj0aLFi0AAJs2bcKCBQsQHR2N5cuXo3LlyhgxYgR8fX2xadMmmJqa5thhmERERETZEQsuIi05fPgwRowYgdy5c2PUqFFo2bIlgISia+HChbh+/ToqVaqEq1ev4tSpU6hYsaKOIyYiIiKiz40FF9F/lPQBGYDmkMrDhw9j2LBhsLOz07jSdeTIEZw6dQr+/v4YPnw4SpUqpZPYiYiIiEi7WHARfSYDBgxAu3bt4OrqmqLoGjBgAGxtbTF58mQ0bdpUWYb3uxERERFlbznvbbREWvDkyRNcvHgRrVu3xunTp6Gvr688hdDNzQ3z58/HxYsXMW/ePHh7eyvLsdgiIiIiyt54hYvoX0g+jBAA/vnnHwwfPhznz5/HgQMHUKdOHcTGxiJXrlwAgLJly+L58+eoU6cONm/eDDMzM12ETkREREQZiAUX0SdKOgzwypUr8Pf3h729PSpVqoTHjx+jZ8+euHTpEvbt24e6desCSHhX2dSpU+Hu7o7KlSujWLFiulwFIiIiIsogLLiIPkHSK1vdunXD33//jQcPHqBkyZIoX7481q9fjydPnqBnz544f/485s2bhzx58uCPP/7AxYsXcfbsWZiamup4LYiIiIgoo7DgIvoXevbsiaNHj+KXX35BtWrV0KNHD+zZswdNmjTBgQMH8OrVK4wePRrr16+HjY0NzMzMsHv3bri4uOg6dCIiIiLKQHxoBtEn2rFjB65evYpVq1bhq6++wvr163HgwAH06tUL58+fR7NmzZAnTx6sXbsWp0+fxsGDB3HhwgUWW0REREQ5kIGuAyDKSkQElpaW+Oabb+Dq6opVq1Zh3Lhx+O2339C0aVPkypULv/zyC1q2bIldu3ahVq1aug6ZiIiIiHSIQwqJPlFYWBgiIyNhYmICNzc3uLq6YuLEiTAxMcGDBw9Qt25dvHjxAk2bNsW+fft0HS4RERER6RCvcBF9InNzc5ibm+P58+d4+vQpLCwsYGJiAgC4du0aihYtiqlTp6J+/fq6DZSIiIiIdI4FF9G/ZGhoCFNTU5w9exZXr16FlZUVDh48iKJFi6JTp058GiERERERcUgh0X9x6tQpNGnSBMbGxjA3N0dkZCSOHDmCChUq6Do0IiIiIsoEWHAR/Uc+Pj7YuXMnTExM4O7ujuLFi+s6JCIiIiLKJFhwERERERERaQnfw0VERERERKQlLLiIiIiIiIi0hAUXERERERGRlrDgIiIiIiIi0hIWXERERERERFrCgouIiIiIiEhLWHARERERERFpCQsuIiIiIiIiLWHBRUREWdacXzfBvnIr5V8Z125o028Szv/9j1Z/78S5K1GleR/l5827j8C+ciu8CQz56O/Yf+w81mzZr9W4UjPn100oWqfjJ393leZ9MH7W8n8bmoahkxeiboehn+W7iIgyOxZcRESUpZkYGWKf1yzs85qFWeP7IzA4FO36T8at+74ZFoPbl1Wwz2sWrCzMPnqZP45fwBrvP7QYFRERZQYGug6AiIjov1Dp6aFK+VIJP5QHKpYtgSot+mLdtj8xY2zfFPOLCGJi42BkmOuzxWBnYwU7G6vP9n1ERJR9sOAiIqJspWD+PLC1scSTZ/4AEoavXb31AN8P7YaflvyGe4/8sOynkWjhVguXrt/GjKUbcMXnLgz09eFWpzKmje6FPLmtle97+eotvp2+DKcuXIOVpTn6dGqe4ndu3n0Ew6Ysxs3D62BrYwkAiI6JxfyVW7D9j5N4GfAGtjZWqFutAhZNGYahkxfi973HAAD2lVsBADyaN8CiKcMA4LPF9THCI6Pw46J1OHHhKp6/fA273FZoUKsSJg3pBstUrtgtXbcDyzfuQXBIGOrWcMGc8f1hnye38nl0TCzmLt+M7QdOIuBNIBwL5MOI3u3Rtmm9fxUfEVFWx4KLiIiyldCwCAQGh2oUAf6v3mLCnJUY0as9CuTLg4L58+DS9dto03ciXGtXxvIZoxERFY2ZP29A95EzsN9rlrJs95HT8dz/DWaN7w9LCzMs9tqO5/6vYaCf/qj8nt/OwplL1zG0ZztULl8KbwKDsf/oeQDAyN4d8CYwBPce++Hnn0YCAOysEwo1bceVXGRUNOLj1Rg/sAtsbSzx3P81/rfKG91HzcCO5T9qzLv/2HkUzJ8Hs8b3R3BIGKYtXoce387SiKvPuDm4ePUWRvXxQMmiBXH4zF8YNOl/sLY0h2vtyp8UGxFRdsCCi4iIsry4uHgAwPOA1/hhwRrEx6vRwrWW8nlQSBg2LvoelcuXVKaNmLoEzmWKY83ccVCpVACA0sUdUa/DUBw+fRludarg6NkruHrzPryXTcWX1SoAAGpXLoeKzXrDxtI8zXhOnL+Kw6cvY9lPI9Hmq7rK9MT/L1IoP2xtLPH0pdH74ZDv/LR4vdbiSo2djRVmf9dfI5eFHezRotd4PPB9BifHAspnYRGR2LToe+XKl4O9HdoN+B7Hzv6NBrUq4vSlG/jzxEX8vmQy6tesCACoV8MF/q8DMfvXzSy4iChH4kMziIgoS4uIjEKB6m1RoHpbVG3RD2cu+2DG2L5oUKuiMk9uKwuNYisiMhoXr91CC7daiI9XIy4uHnFx8XAq7IAC9na4evM+AOCKz11YmpsqRQ0AWFqYoW4153RjOnnxOkyMjdC6yZefuC7ajSstW/cdg+s3I1C0TkcUqN4WLXqNBwA88H2uMV/tKuU1hhl+Wa0CbKwscMXnLgDg+PmrsLGyQJ2qFZTY4+LiUa+6M3xuP0R8fPy/io+IKCvjFS4iIsrSTIwMsXPldKhUQG5rSxSwt4Oenub5xDy21ho/B4eGIT5eje/nrcb381an+M5nL18DAPxfB8I2lYdhJL2XKjWBwaGwt7NRrlB9LG3HlZr9R89j8PcL0bVNY4wf2Bk2Vpbwf/0WPUbPRHRMrMa8qT0YxM7GCv6vAwEAb4NCEBgcigLV26b6u/xfB8LB3u6TYyQiyspYcBERUZam0tODS5niH5hJs/CxtDCDSqXCsJ7t0LR+9RSz57a2AADY29ngTWBwis9fvQ1K99fZWFnA/3UgROSTii5tx5Wa3YfPoFypopg7YaAy7exfPqnO+zqV3/k6MBj2djYAABsrc9jaWGHjokmpLm+Xm09yJKKchwUXERHlOGYmxqhSoRTuPXqK8QM7pzlfxbIlEBIWgVMXryvD90JCw3Hy4rV075WqW90ZS9Zux65DZ9CqcZ1U58mVywDR0TEZGldqoqJjkMtA83Bg24ETqc575vINhISGK8MKT128jsDgUFQqlzBcs241ZyxZuwO5chmgbIkinxQHEVF2xYKLiIhypMnDPNG2/yT0GTcHrRt/CStLM7wIeIMT56+hY8uGqF2lPBrWqoQKXxTDwIkLMGlowmPSF63ZBgsz03S/u151Z7jVrozhUxbjsd8LVCpXEkHBYdhz5CxWzPwWAFCiaCFs2n0E2/84iWKFHZDb2gKFHey1GldasY6btRzzV/yOyhW+wJEzf+HUxeupzmtuaoJOQ6diiGdbhISGY9qidahYtoRyv1y9Gi5oXLcqOg2egkHdW6NM8SKIiIrCnQdP8ejpCyz4fvAnx0dElNWx4CIiohypqvMX2L1qBub8sgnDpixCbGwc8tvb4cuqFVC0UH4AgEqlwtr53+Hb6b/g2+nLYGVhhl4ezfDqbRD+OH4h3e9fNWcs5i7/Heu3HcTcX39HHlsr1Kvhonze2d0Nf/9zDxNmr8Db4FDlPVzajiu5bm2bwPeZP1b+vh9L1+9EgxoVseynUfjac0yKeb9uUAP589pizPRlCA4NR93qzpjz3QDN9Z49BovXbIfX1j/g9yIAFuZm+MKpMDq1bPhJcRERZRcqERFdB0FERERERJQd8bHwREREREREWsKCi4iIiIiISEtYcBEREREREWkJCy4iIiIiIiItYcFFRERERESkJSy4iIiIiIiItIQFFxERERERkZaw4CIiIiIiItISFlxERERERERawoKLiIiIiIhIS1hwERERERERaQkLLiIiIiIiIi35P7cUfGGrhgfrAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 990x880 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Row normalisation does not produce a precision matrix.\n",
    "confusion_rates = confusion_counts / confusion_counts.sum(axis=1, keepdims=True)\n",
    "figure, axis = plt.subplots(figsize=(9, 8))\n",
    "ConfusionMatrixDisplay(confusion_rates, display_labels=CLASS_NAMES).plot(\n",
    "    ax=axis, cmap=\"Blues\", colorbar=False, xticks_rotation=45, values_format=\".2f\")\n",
    "axis.set_title(\"Selected CNN: fraction of each true class\")\n",
    "finish_figure(figure, \"10_confusion_rates.png\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cell-148",
   "metadata": {},
   "source": [
    "### 64. List the largest error routes\n",
    "\n",
    "Rank off-diagonal counts rather than guessing the hardest class pairs."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "cell-149",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>actual</th>\n",
       "      <th>predicted</th>\n",
       "      <th>count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>cat</td>\n",
       "      <td>dog</td>\n",
       "      <td>181</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>automobile</td>\n",
       "      <td>truck</td>\n",
       "      <td>174</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>dog</td>\n",
       "      <td>cat</td>\n",
       "      <td>161</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>bird</td>\n",
       "      <td>deer</td>\n",
       "      <td>98</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>bird</td>\n",
       "      <td>frog</td>\n",
       "      <td>92</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>deer</td>\n",
       "      <td>horse</td>\n",
       "      <td>92</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>airplane</td>\n",
       "      <td>ship</td>\n",
       "      <td>91</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>cat</td>\n",
       "      <td>frog</td>\n",
       "      <td>89</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       actual predicted  count\n",
       "0         cat       dog    181\n",
       "1  automobile     truck    174\n",
       "2         dog       cat    161\n",
       "3        bird      deer     98\n",
       "4        bird      frog     92\n",
       "5        deer     horse     92\n",
       "6    airplane      ship     91\n",
       "7         cat      frog     89"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Exclude the correct-prediction diagonal.\n",
    "error_pairs = pd.DataFrame([\n",
    "    {\"actual\": CLASS_NAMES[actual], \"predicted\": CLASS_NAMES[predicted],\n",
    "     \"count\": int(confusion_counts[actual, predicted])}\n",
    "    for actual in CLASS_IDS for predicted in CLASS_IDS if actual != predicted\n",
    "]).sort_values(\"count\", ascending=False).head(8).reset_index(drop=True)\n",
    "error_pairs.to_csv(OUTPUT_DIR / \"largest_error_pairs.csv\", index=False)\n",
    "display(error_pairs)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cell-150",
   "metadata": {},
   "source": [
    "### 65. Inspect confident mistakes\n",
    "\n",
    "An average accuracy score can conceal predictions that are confidently wrong."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "cell-151",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1100x880 with 12 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Confidence is a score, not proof of correctness.\n",
    "confidence = champion_probabilities.max(axis=1)\n",
    "mistake_ids = np.flatnonzero(champion_predictions != y_test)\n",
    "confident_mistakes = mistake_ids[np.argsort(confidence[mistake_ids])[::-1][:12]]\n",
    "figure, axes = plt.subplots(3, 4, figsize=(10, 8))\n",
    "for axis in axes.flat:\n",
    "    axis.axis(\"off\")\n",
    "for axis, index in zip(axes.flat, confident_mistakes):\n",
    "    axis.imshow(x_test[index], interpolation=\"nearest\")\n",
    "    axis.set_title(f\"True: {CLASS_NAMES[y_test[index]]}\\n\"\n",
    "        f\"Pred: {CLASS_NAMES[champion_predictions[index]]} ({confidence[index]:.0%})\", fontsize=9)\n",
    "figure.suptitle(\"Most confident test mistakes\", color=NAVY)\n",
    "finish_figure(figure, \"11_confident_mistakes.png\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cell-152",
   "metadata": {},
   "source": [
    "**Your comment:** Describe what is visible first. Background reliance or label mistakes are hypotheses to investigate, not facts proved by one thumbnail."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cell-153",
   "metadata": {},
   "source": [
    "### 66. Compute macro one-vs-rest AUC and top-three accuracy\n",
    "\n",
    "AUC is a ranking measure [6]; top-three accuracy changes the decision task."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "cell-154",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Macro one-vs-rest ROC-AUC: 0.9522\n",
      "Top-three accuracy: 0.9192\n"
     ]
    }
   ],
   "source": [
    "# Do not label a flattened micro curve as every class's performance.\n",
    "macro_auc = roc_auc_score(y_test, champion_probabilities,\n",
    "    multi_class=\"ovr\", average=\"macro\", labels=CLASS_IDS)\n",
    "top_three_accuracy = top_k_accuracy_score(y_test, champion_probabilities, k=3, labels=CLASS_IDS)\n",
    "print(f\"Macro one-vs-rest ROC-AUC: {macro_auc:.4f}\")\n",
    "print(f\"Top-three accuracy: {top_three_accuracy:.4f}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cell-155",
   "metadata": {},
   "source": [
    "### 67. Plot class-specific ROC curves\n",
    "\n",
    "Each class is compared with the remaining classes. For rare events, also study precision-recall."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "cell-156",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 880x660 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Curves describe ranking, not the fraction correctly classified.\n",
    "figure, axis = plt.subplots(figsize=(8, 6))\n",
    "for class_id, name in enumerate(CLASS_NAMES):\n",
    "    binary_truth = (y_test == class_id).astype(int)\n",
    "    false_positive, true_positive, _ = roc_curve(binary_truth, champion_probabilities[:, class_id])\n",
    "    class_auc = roc_auc_score(binary_truth, champion_probabilities[:, class_id])\n",
    "    axis.plot(false_positive, true_positive, label=f\"{name}: {class_auc:.2f}\", linewidth=1.3)\n",
    "axis.plot([0, 1], [0, 1], linestyle=\"--\", color=SLATE)\n",
    "axis.set(title=\"Selected CNN: one-vs-rest ROC\", xlabel=\"False positive rate\", ylabel=\"True positive rate\")\n",
    "axis.legend(fontsize=8, ncol=2)\n",
    "finish_figure(figure, \"12_per_class_roc.png\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cell-157",
   "metadata": {},
   "source": [
    "## 10. Diagnose confidence and apply the frozen review policy\n",
    "\n",
    "Softmax sums to one over known labels but can be overconfident, including on unfamiliar\n",
    "images. This section diagnoses confidence, not a calibrated-probability model or an\n",
    "unknown-class detector."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cell-158",
   "metadata": {},
   "source": [
    "### 68. Compute confidence reliability bins\n",
    "\n",
    "Compare average maximum score with observed correctness inside each bin."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "cell-159",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10-bin top-label expected calibration error: 0.0248\n"
     ]
    },
    {
     "data": {
      "text/html": [
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       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>bin</th>\n",
       "      <th>images</th>\n",
       "      <th>mean_confidence</th>\n",
       "      <th>observed_accuracy</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>0.1851</td>\n",
       "      <td>0.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>220</td>\n",
       "      <td>0.2693</td>\n",
       "      <td>0.3182</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>767</td>\n",
       "      <td>0.3572</td>\n",
       "      <td>0.3507</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>1177</td>\n",
       "      <td>0.4514</td>\n",
       "      <td>0.4486</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>1291</td>\n",
       "      <td>0.5480</td>\n",
       "      <td>0.5174</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>6</td>\n",
       "      <td>1158</td>\n",
       "      <td>0.6489</td>\n",
       "      <td>0.6140</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>7</td>\n",
       "      <td>1112</td>\n",
       "      <td>0.7498</td>\n",
       "      <td>0.7320</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>8</td>\n",
       "      <td>1215</td>\n",
       "      <td>0.8532</td>\n",
       "      <td>0.8222</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>9</td>\n",
       "      <td>3054</td>\n",
       "      <td>0.9670</td>\n",
       "      <td>0.9375</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   bin  images  mean_confidence  observed_accuracy\n",
       "0    1       6           0.1851             0.0000\n",
       "1    2     220           0.2693             0.3182\n",
       "2    3     767           0.3572             0.3507\n",
       "3    4    1177           0.4514             0.4486\n",
       "4    5    1291           0.5480             0.5174\n",
       "5    6    1158           0.6489             0.6140\n",
       "6    7    1112           0.7498             0.7320\n",
       "7    8    1215           0.8532             0.8222\n",
       "8    9    3054           0.9670             0.9375"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# This is top-label reliability for multiclass predictions.\n",
    "correct_predictions = champion_predictions == y_test\n",
    "bin_ids = np.minimum((confidence * 10).astype(int), 9)\n",
    "reliability_rows = []\n",
    "for bin_id in range(10):\n",
    "    in_bin = bin_ids == bin_id\n",
    "    if in_bin.any():\n",
    "        reliability_rows.append({\"bin\": bin_id, \"images\": int(in_bin.sum()),\n",
    "            \"mean_confidence\": float(confidence[in_bin].mean()),\n",
    "            \"observed_accuracy\": float(correct_predictions[in_bin].mean())})\n",
    "reliability_table = pd.DataFrame(reliability_rows)\n",
    "calibration_error = np.sum(reliability_table[\"images\"] / len(y_test) *\n",
    "    abs(reliability_table[\"mean_confidence\"] - reliability_table[\"observed_accuracy\"]))\n",
    "reliability_table.to_csv(OUTPUT_DIR / \"confidence_reliability.csv\", index=False)\n",
    "display(reliability_table.round(4))\n",
    "print(f\"10-bin top-label expected calibration error: {calibration_error:.4f}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cell-160",
   "metadata": {},
   "source": [
    "### 69. Plot score-versus-accuracy reliability\n",
    "\n",
    "Points below the diagonal indicate overconfidence in that bin. Small bins remain uncertain."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "cell-161",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 660x550 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# ECE depends on the binning choice; its definition is recorded.\n",
    "figure, axis = plt.subplots(figsize=(6, 5))\n",
    "axis.plot([0, 1], [0, 1], linestyle=\"--\", color=SLATE, label=\"perfect agreement\")\n",
    "axis.scatter(reliability_table[\"mean_confidence\"], reliability_table[\"observed_accuracy\"],\n",
    "    s=30 + 170 * reliability_table[\"images\"] / reliability_table[\"images\"].max(), color=SCARLET)\n",
    "axis.set(title=\"Selected CNN: confidence reliability\", xlabel=\"Mean maximum softmax score\",\n",
    "         ylabel=\"Observed accuracy\", xlim=(0, 1), ylim=(0, 1))\n",
    "axis.legend()\n",
    "finish_figure(figure, \"13_reliability.png\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cell-162",
   "metadata": {},
   "source": [
    "**Your comment:** Which bins look overconfident? Why is calibration a stronger claim than good accuracy?"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cell-163",
   "metadata": {},
   "source": [
    "### 70. Measure the frozen review policy on test cases\n",
    "\n",
    "Apply the validation-selected threshold once. A failed validation rule remains review-all."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "cell-164",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "threshold               0.850000\n",
       "accepted             3714.000000\n",
       "coverage                0.371400\n",
       "accepted_accuracy       0.920032\n",
       "wilson_low              0.910868\n",
       "wilson_high             0.928328\n",
       "review_fraction         0.628600\n",
       "dtype: float64"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# There is no test-time threshold search.\n",
    "if review_threshold is None:\n",
    "    policy_result = {\"threshold\": None, \"coverage\": 0.0,\n",
    "        \"review_fraction\": 1.0, \"accepted_accuracy\": None, \"accepted\": 0}\n",
    "else:\n",
    "    policy_result = threshold_table(y_test, champion_probabilities,\n",
    "        [review_threshold]).iloc[0].to_dict()\n",
    "    policy_result[\"review_fraction\"] = 1.0 - policy_result[\"coverage\"]\n",
    "(OUTPUT_DIR / \"test_review_policy.json\").write_text(json.dumps(policy_result, indent=2), encoding=\"utf-8\")\n",
    "display(pd.Series(policy_result))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cell-165",
   "metadata": {},
   "source": [
    "### 71. Plot validation coverage versus accepted accuracy\n",
    "\n",
    "This describes the development trade-off, not a new policy selected from test results."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "cell-166",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 770x440 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Omit undefined accuracy where no examples were accepted.\n",
    "figure, axis = plt.subplots(figsize=(7, 4))\n",
    "valid_policy_rows = validation_policy.dropna(subset=[\"accepted_accuracy\"])\n",
    "axis.plot(valid_policy_rows[\"coverage\"], valid_policy_rows[\"accepted_accuracy\"],\n",
    "          marker=\"o\", color=NAVY, label=\"validation accepted accuracy\")\n",
    "axis.axhline(TARGET_ACCEPTED_ACCURACY, color=SCARLET, linestyle=\"--\", label=\"target level\")\n",
    "axis.set(title=\"Validation: coverage and accuracy\", xlabel=\"Coverage\",\n",
    "         ylabel=\"Accuracy among accepted images\", xlim=(0, 1), ylim=(0, 1))\n",
    "axis.legend(fontsize=8)\n",
    "finish_figure(figure, \"14_validation_coverage.png\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cell-167",
   "metadata": {},
   "source": [
    "### 72. Generate the interpretation from measured results\n",
    "\n",
    "This narrative changes when predictions change. Keep the scope and limitations beside the score."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "cell-168",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Out[72]: 679\n"
     ]
    },
    {
     "data": {
      "text/markdown": [
       "### Measured interpretation\n",
       "**full mode:** 40,000 train, 10,000 validation,\n",
       "10,000 official test images, at most 25 epochs per CNN.\n",
       "Validation selected **basic_CNN**. Test accuracy is **69.2%**\n",
       "(bootstrap interval **68.3%–70.2%**),\n",
       "with macro-F1 **0.691**.\n",
       "The accuracy difference from the basic CNN is **+0.00 percentage points**;\n",
       "its interval includes zero. The weakest recall is **cat**\n",
       "(50.5%). The largest error route is\n",
       "**cat → dog** (181 images).\n",
       "Macro AUC **0.952** describes ranking; top-three accuracy\n",
       "**91.9%** describes a different task.\n",
       "This demonstrates an evaluated benchmark workflow, not field reliability or a\n",
       "trained astronomy, defect or satellite classifier.\n"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Each number is computed from this run's predictions.\n",
    "selected_row = test_table.set_index(\"model\").loc[champion_name]\n",
    "worst_class = per_class_report[\"recall\"].idxmin()\n",
    "largest_pair = error_pairs.iloc[0]\n",
    "delta_evidence = \"excludes zero\" if delta_interval[0] > 0 or delta_interval[1] < 0 else \"includes zero\"\n",
    "summary_text = f\"\"\"### Measured interpretation\n",
    "**{RUN_MODE} mode:** {len(x_train):,} train, {len(x_validation):,} validation,\n",
    "{len(x_test):,} official test images, at most {EPOCHS} epochs per CNN.\n",
    "Validation selected **{champion_name}**. Test accuracy is **{computed_accuracy:.1%}**\n",
    "(bootstrap interval **{accuracy_interval[0]:.1%}–{accuracy_interval[1]:.1%}**),\n",
    "with macro-F1 **{selected_row['macro_f1']:.3f}**.\n",
    "The accuracy difference from the basic CNN is **{accuracy_delta*100:+.2f} percentage points**;\n",
    "its interval {delta_evidence}. The weakest recall is **{worst_class}**\n",
    "({per_class_report.loc[worst_class, 'recall']:.1%}). The largest error route is\n",
    "**{largest_pair['actual']} → {largest_pair['predicted']}** ({largest_pair['count']} images).\n",
    "Macro AUC **{macro_auc:.3f}** describes ranking; top-three accuracy\n",
    "**{top_three_accuracy:.1%}** describes a different task.\n",
    "This demonstrates an evaluated benchmark workflow, not field reliability or a\n",
    "trained astronomy, defect or satellite classifier.\n",
    "\"\"\"\n",
    "display(Markdown(summary_text))\n",
    "(OUTPUT_DIR / \"measured_interpretation.md\").write_text(summary_text, encoding=\"utf-8\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cell-169",
   "metadata": {},
   "source": [
    "## 11. Export the model and evidence\n",
    "\n",
    "Keep the class mapping, preprocessing, policy and model identity with its weights.\n",
    "The export is a reusable benchmark model, not a deployed service. Save/reload checks\n",
    "confirm the artifact matches the predictions reported above."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cell-170",
   "metadata": {},
   "source": [
    "### 73. Save the selected model\n",
    "\n",
    "Native Keras format retains its architecture and weights. Raw pixels are rescaled internally."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "cell-171",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Saved model: selected_cifar10_cnn.keras\n",
      "Model SHA-256: bb7bd1d88c0599b03f26b83c667ea9ddefc947e88cbd13137da5bb8072064e1e\n"
     ]
    }
   ],
   "source": [
    "# Do not divide by 255 again when using this artifact.\n",
    "model_path = OUTPUT_DIR / \"selected_cifar10_cnn.keras\"\n",
    "champion_model.save(model_path)\n",
    "model_sha256 = hashlib.sha256(model_path.read_bytes()).hexdigest()\n",
    "print(\"Saved model:\", model_path.name)\n",
    "print(\"Model SHA-256:\", model_sha256)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cell-172",
   "metadata": {},
   "source": [
    "### 74. Reload and verify inference\n",
    "\n",
    "A successful save should also preserve its actual predicted probabilities."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "cell-173",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Saved/reloaded probabilities agree within tolerance.\n"
     ]
    }
   ],
   "source": [
    "# Compare one raw-RGB inference batch with saved test probabilities.\n",
    "reloaded_model = keras.models.load_model(model_path, compile=False)\n",
    "roundtrip_probabilities = reloaded_model.predict(x_test[:32], verbose=0)\n",
    "assert np.allclose(roundtrip_probabilities, champion_probabilities[:32], atol=1e-5)\n",
    "print(\"Saved/reloaded probabilities agree within tolerance.\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cell-174",
   "metadata": {},
   "source": [
    "### 75. Save row-level predictions\n",
    "\n",
    "These records make metrics recalculable without fitting the model again."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "cell-175",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Saved 10000 test predictions.\n"
     ]
    }
   ],
   "source": [
    "# Probability columns follow the official class mapping.\n",
    "prediction_record = pd.DataFrame({\"test_row\": np.arange(len(y_test)),\n",
    "    \"image_sha256\": test_hashes, \"actual_id\": y_test,\n",
    "    \"predicted_id\": champion_predictions, \"maximum_score\": confidence})\n",
    "for class_id, class_name in enumerate(CLASS_NAMES):\n",
    "    prediction_record[\"prob_\" + class_name] = champion_probabilities[:, class_id]\n",
    "prediction_record.to_csv(OUTPUT_DIR / \"selected_test_predictions.csv\", index=False)\n",
    "print(\"Saved\", len(prediction_record), \"test predictions.\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cell-176",
   "metadata": {},
   "source": [
    "### 76. Save the reproducibility manifest\n",
    "\n",
    "Record what was measured separately from proposed future work."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "cell-177",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Saved model, manifest, plots, metrics and predictions.\n"
     ]
    }
   ],
   "source": [
    "# Array hashes identify the actual image content for this run.\n",
    "manifest = {\"author\": \"Gareth Grant\", \"dataset\": \"CIFAR-10\",\n",
    "    \"dataset_reference\": \"https://www.cs.toronto.edu/~kriz/cifar.html\",\n",
    "    \"mode\": RUN_MODE, \"seed\": SEED, \"maximum_epochs\": EPOCHS,\n",
    "    \"batch_size\": BATCH_SIZE, \"train_images\": len(x_train),\n",
    "    \"validation_images\": len(x_validation), \"test_images\": len(x_test),\n",
    "    \"development_array_sha256\": hashlib.sha256(x_development.tobytes()).hexdigest(),\n",
    "    \"test_array_sha256\": hashlib.sha256(x_test.tobytes()).hexdigest(),\n",
    "    \"class_names\": CLASS_NAMES, \"input_shape\": [32, 32, 3],\n",
    "    \"input_scale\": \"raw RGB 0-255; model rescales internally\",\n",
    "    \"selection\": frozen_decision, \"model_sha256\": model_sha256,\n",
    "    \"software\": software_versions, \"bootstrap_repeats\": BOOTSTRAP_REPEATS,\n",
    "    \"test_review_policy\": policy_result, \"application_models_trained\": False}\n",
    "(OUTPUT_DIR / \"run_manifest.json\").write_text(json.dumps(manifest, indent=2), encoding=\"utf-8\")\n",
    "print(\"Saved model, manifest, plots, metrics and predictions.\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cell-178",
   "metadata": {},
   "source": [
    "### 77. Define inference for one new image\n",
    "\n",
    "Convert to RGB, respect orientation metadata and resize to the benchmark input."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "cell-179",
   "metadata": {},
   "outputs": [],
   "source": [
    "# File handling is not a domain-shift or unknown-class detector.\n",
    "def predict_image(image_path):\n",
    "    with Image.open(image_path) as source:\n",
    "        image = ImageOps.exif_transpose(source).convert(\"RGB\")\n",
    "        image = image.resize((32, 32), resample=Image.Resampling.BILINEAR)\n",
    "        pixels = np.asarray(image, dtype=np.uint8)\n",
    "    probabilities = reloaded_model.predict(pixels[None, ...], verbose=0)[0]\n",
    "    predicted_id = int(probabilities.argmax())\n",
    "    score = float(probabilities[predicted_id])\n",
    "    review = review_threshold is None or score < review_threshold\n",
    "    return {\"label\": CLASS_NAMES[predicted_id], \"maximum_score\": score,\n",
    "            \"review_required\": review}, probabilities"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cell-180",
   "metadata": {},
   "source": [
    "Example after running the notebook: `decision, scores = predict_image(\"my_image.jpg\")`.\n",
    "Every input is forced into a known class; an unfamiliar galaxy could receive a confident\n",
    "animal label. Resizing removes detail. Validate the actual intended imagery before use."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cell-181",
   "metadata": {},
   "source": [
    "## 12. Business: connect classification to a decision and its cost\n",
    "\n",
    "These are proposed adaptations of the workflow, not capabilities proved by CIFAR-10.\n",
    "\n",
    "| Use | Decision supported | New data and design required | Pilot measures |\n",
    "|---|---|---|---|\n",
    "| Catalogue photos | Route a photo to a product category for review | Actual labelled catalogue images; product-ID split; unknown categories | Macro-F1, corrections per 1,000, review time, coverage |\n",
    "| Manufacturing inspection | Send suspected defects to an inspector | Label defects and normal items; separate batches; detection if location matters | Defect recall, false alarms, missed-defect cost, workload |\n",
    "| Asset-photo triage | Prioritise claims or maintenance photos | Labels tied to operational action; separate assets/sites/cameras; suitable resolution | Error cost, throughput, time to resolution |\n",
    "\n",
    "A business needs to see the action following the prediction, the cost of each error,\n",
    "the remaining manual workload and improvement over its existing process. A benchmark\n",
    "score alone does not provide that evidence. Use a real operational pilot, split by\n",
    "product/batch/site rather than repeated photos of the same object, and compare simple\n",
    "and pretrained approaches with the current human process.\n",
    "\n",
    "**Auditing:** keep input identity, model hash, mapping, score, threshold, review action\n",
    "and reviewer correction. That makes an action traceable; it does not prove the label is\n",
    "true or that a process meets a particular legal standard."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cell-182",
   "metadata": {},
   "source": [
    "### 78. Declare an illustrative business scenario\n",
    "\n",
    "These rand assumptions are fictional. They are not prices, measured savings or estimates from CIFAR-10."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "id": "cell-183",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>scenario</th>\n",
       "      <th>automation_coverage</th>\n",
       "      <th>accepted_accuracy</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>downside</td>\n",
       "      <td>0.3</td>\n",
       "      <td>0.85</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>central</td>\n",
       "      <td>0.6</td>\n",
       "      <td>0.95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>optimistic</td>\n",
       "      <td>0.8</td>\n",
       "      <td>0.98</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     scenario  automation_coverage  accepted_accuracy\n",
       "0    downside                  0.3               0.85\n",
       "1     central                  0.6               0.95\n",
       "2  optimistic                  0.8               0.98"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Replace these inputs with operational pilot measurements.\n",
    "MONTHLY_IMAGES, MANUAL_SECONDS_PER_IMAGE = 50_000, 30\n",
    "HOURLY_REVIEW_COST_R, MODEL_COST_PER_1_000_R = 180, 2\n",
    "FIXED_MONTHLY_COST_R, COST_PER_AUTO_ERROR_R = 2_000, 20\n",
    "business_scenarios = pd.DataFrame([\n",
    "    {\"scenario\": \"downside\", \"automation_coverage\": 0.30, \"accepted_accuracy\": 0.85},\n",
    "    {\"scenario\": \"central\", \"automation_coverage\": 0.60, \"accepted_accuracy\": 0.95},\n",
    "    {\"scenario\": \"optimistic\", \"automation_coverage\": 0.80, \"accepted_accuracy\": 0.98},\n",
    "])\n",
    "display(business_scenarios)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cell-184",
   "metadata": {},
   "source": [
    "### 79. Calculate conditional monthly net value\n",
    "\n",
    "Count manual work that remains, automatic errors and operating costs. Automation can also lose money."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "id": "cell-185",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Assumed all-manual monthly cost: R75,000\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>scenario</th>\n",
       "      <th>automation_coverage</th>\n",
       "      <th>accepted_accuracy</th>\n",
       "      <th>manual_review_cost_R</th>\n",
       "      <th>expected_auto_errors</th>\n",
       "      <th>error_cost_R</th>\n",
       "      <th>operating_cost_R</th>\n",
       "      <th>net_monthly_value_R</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>downside</td>\n",
       "      <td>0.3</td>\n",
       "      <td>0.85</td>\n",
       "      <td>52500.0</td>\n",
       "      <td>2250.0</td>\n",
       "      <td>45000.0</td>\n",
       "      <td>2100.0</td>\n",
       "      <td>-24600.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>central</td>\n",
       "      <td>0.6</td>\n",
       "      <td>0.95</td>\n",
       "      <td>30000.0</td>\n",
       "      <td>1500.0</td>\n",
       "      <td>30000.0</td>\n",
       "      <td>2100.0</td>\n",
       "      <td>12900.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>optimistic</td>\n",
       "      <td>0.8</td>\n",
       "      <td>0.98</td>\n",
       "      <td>15000.0</td>\n",
       "      <td>800.0</td>\n",
       "      <td>16000.0</td>\n",
       "      <td>2100.0</td>\n",
       "      <td>41900.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     scenario  automation_coverage  ...  operating_cost_R  net_monthly_value_R\n",
       "0    downside                  0.3  ...            2100.0             -24600.0\n",
       "1     central                  0.6  ...            2100.0              12900.0\n",
       "2  optimistic                  0.8  ...            2100.0              41900.0\n",
       "\n",
       "[3 rows x 8 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Each exception still gets the full assumed manual review time.\n",
    "manual_cost_r = MONTHLY_IMAGES * MANUAL_SECONDS_PER_IMAGE / 3_600 * HOURLY_REVIEW_COST_R\n",
    "operating_cost_r = FIXED_MONTHLY_COST_R + MONTHLY_IMAGES / 1_000 * MODEL_COST_PER_1_000_R\n",
    "business_scenarios[\"manual_review_cost_R\"] = manual_cost_r * (1 - business_scenarios[\"automation_coverage\"])\n",
    "business_scenarios[\"expected_auto_errors\"] = MONTHLY_IMAGES * business_scenarios[\"automation_coverage\"] * (1 - business_scenarios[\"accepted_accuracy\"])\n",
    "business_scenarios[\"error_cost_R\"] = business_scenarios[\"expected_auto_errors\"] * COST_PER_AUTO_ERROR_R\n",
    "business_scenarios[\"operating_cost_R\"] = operating_cost_r\n",
    "business_scenarios[\"net_monthly_value_R\"] = manual_cost_r - (\n",
    "    business_scenarios[\"manual_review_cost_R\"] + business_scenarios[\"error_cost_R\"] + operating_cost_r)\n",
    "business_scenarios.to_csv(OUTPUT_DIR / \"illustrative_business_scenarios.csv\", index=False)\n",
    "print(f\"Assumed all-manual monthly cost: R{manual_cost_r:,.0f}\")\n",
    "display(business_scenarios.round(2))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cell-186",
   "metadata": {},
   "source": [
    "**Interpretation:** conditional arithmetic is not an ROI forecast. The example excludes\n",
    "implementation, labelling, integration, supervision, training, downtime, tax and revenue\n",
    "changes. Use a class-specific cost matrix when errors have different consequences.\n",
    "**Your comment:** Which measured assumption would change the conclusion most?"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cell-187",
   "metadata": {},
   "source": [
    "### 80. Plot the hypothetical business outcomes\n",
    "\n",
    "Show negative as well as positive outcomes to make the cost assumptions visible."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "id": "cell-188",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 770x440 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# The title explicitly distinguishes assumptions from measured savings.\n",
    "figure, axis = plt.subplots(figsize=(7, 4))\n",
    "values = business_scenarios[\"net_monthly_value_R\"]\n",
    "axis.bar(business_scenarios[\"scenario\"], values,\n",
    "    color=[SCARLET if value < 0 else NAVY for value in values])\n",
    "axis.axhline(0, color=SLATE, linewidth=1)\n",
    "axis.set(title=\"Illustrative monthly value — assumptions, not measured savings\", ylabel=\"Rand\")\n",
    "finish_figure(figure, \"15_illustrative_business_value.png\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cell-189",
   "metadata": {},
   "source": [
    "## 13. Astronomy: survey classification and expert review\n",
    "\n",
    "Related workflows can organise galaxy morphology or rank candidates for inspection.\n",
    "The Euclid Collaboration studied a Galaxy Zoo/Zoobot CNN adapted for emulated Euclid\n",
    "images [7]. Zoobot research also studies reusable galaxy representations for similarity,\n",
    "anomalies and new labels [8]. Those are separate research results, not this notebook’s result.\n",
    "\n",
    "| Proposed use | Value to an astronomer | Changes required | Meaningful evaluation |\n",
    "|---|---|---|---|\n",
    "| Galaxy morphology | Build a catalogue of smooth, disk or featured objects | Galaxy images; expert/citizen labels; domain-trained representations | Per-class recall, label-distribution agreement, brightness/redshift strata |\n",
    "| Candidate triage | Prioritise scientifically useful follow-up | Labels matching the research question and rare-event sampling | Purity, completeness, precision-recall, confirmed discoveries per review hour |\n",
    "| Quality screening | Identify artefacts or poor observations | Instrument-specific labels and appropriate image scale | Missed artefacts, rejected real sources, cross-instrument validation |\n",
    "\n",
    "Preserve FITS calibration metadata, photometric bands, intensity scales and sufficient\n",
    "resolution. Arbitrary RGB conversion or a 32 × 32 resize may destroy useful structure.\n",
    "Choose augmentations with an astronomer. Keep repeated observations of one object in\n",
    "one subset and test new sky regions, times, seeing conditions and instruments.\n",
    "Report performance by magnitude or signal-to-noise; bright familiar sources do not\n",
    "establish success on faint objects. A CIFAR-10 animal/object classifier cannot perform\n",
    "these scientific tasks without new data and training.\n",
    "\n",
    "**Your reflection:** Which scientific signal might preprocessing destroy, and what would\n",
    "count as independent validation for your proposed astronomy project?"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cell-190",
   "metadata": {},
   "source": [
    "## 14. Government: inspection and environmental information\n",
    "\n",
    "ESA describes satellite land-cover classification supporting agriculture, planning,\n",
    "environmental protection and crisis management [9]. NASA describes geospatial foundation\n",
    "models for applications including flood mapping, burn scars and crop classification [10].\n",
    "These examples use different data and models from this RGB benchmark.\n",
    "\n",
    "| Proposed use | Decision supported | Data and design required | Measures |\n",
    "|---|---|---|---|\n",
    "| Land-cover monitoring | Prioritise planning or environmental review | Multispectral imagery, georeferenced labels, separate regions/seasons; usually segmentation | Class recall, map accuracy, area-estimate uncertainty, regional results |\n",
    "| Disaster assessment | Send possible flood/fire impacts to analysts | Event-specific data; validation on new events/sensors/cloud conditions | Missed areas, false alerts, latency, workload |\n",
    "| Maintenance routing | Direct infrastructure photos to the right team | Local labelled assets; site/asset/time separation; detection if location matters | Routing accuracy, missed urgent cases, review time, resolution time |\n",
    "\n",
    "This notebook does not create pixel maps, estimate flooded area, forecast disasters\n",
    "or establish whether an asset is unsafe. A public-sector pilot needs task-specific data,\n",
    "field validation and comparison with the existing process. Record versions, actions and\n",
    "corrections; examine regions and conditions separately. Keep accountable review for\n",
    "consequential actions. Where photographs include personal information, minimise collection\n",
    "and control access in the actual service design.\n",
    "\n",
    "**Your reflection:** What action would a prediction trigger, who checks it, and which\n",
    "missed cases matter most?"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cell-191",
   "metadata": {},
   "source": [
    "## 15. What this portfolio can honestly demonstrate\n",
    "\n",
    "The core achievement is a complete, readable evaluation workflow: contracts and leakage\n",
    "checks, useful baselines, explicit CNN experiments, validation-only decisions, error and\n",
    "confidence analysis, conditional uncertainty, reproducible exports and decision-focused\n",
    "applications. Quote scores with the run mode, training count, test count and metric definition.\n",
    "\n",
    "Do not describe this as an Intel dataset analysis, transfer learning, a production\n",
    "classifier or a trained astronomy/government system.\n",
    "\n",
    "Useful next experiments:\n",
    "\n",
    "1. Repeat seeds and run a larger search while recording compute cost.\n",
    "2. Use ablations to isolate augmentation, regularisation and pooling effects.\n",
    "3. Compare suitable pretrained features with their required preprocessing.\n",
    "4. Build one real domain pilot with source/location/time grouping and independent validation.\n",
    "5. Calibrate on a separate cohort and evaluate unfamiliar inputs.\n",
    "\n",
    "These are future experiments, not implemented features. Add your final reflection:\n",
    "what did the simple baseline teach you, which errors matter most, and what evidence is missing?"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cell-192",
   "metadata": {},
   "source": [
    "## References and attribution\n",
    "\n",
    "Primary sources checked during authoring on 4 October 2026. Application examples are\n",
    "paraphrased context, not results for this experiment. Keep attribution when publishing\n",
    "and check dataset/source terms before redistributing third-party raw assets.\n",
    "\n",
    "1. Krizhevsky, Nair and Hinton. [CIFAR-10 dataset page](https://www.cs.toronto.edu/~kriz/cifar.html).\n",
    "   Dataset report: Krizhevsky (2009), *Learning Multiple Layers of Features from Tiny Images*.\n",
    "2. TensorFlow. [Operation determinism](https://www.tensorflow.org/api_docs/python/tf/config/experimental/enable_op_determinism).\n",
    "3. TensorFlow. [CNN tutorial](https://www.tensorflow.org/tutorials/images/cnn).\n",
    "4. Keras. [EarlyStopping](https://keras.io/api/callbacks/early_stopping/).\n",
    "5. Keras. [RandomFlip](https://keras.io/api/layers/preprocessing_layers/image_augmentation/random_flip/)\n",
    "   and [RandomTranslation](https://keras.io/api/layers/preprocessing_layers/image_augmentation/random_translation/).\n",
    "6. Scikit-learn. [roc_auc_score](https://scikit-learn.org/stable/modules/generated/sklearn.metrics.roc_auc_score.html).\n",
    "7. Euclid Collaboration (2024). [Measuring detailed galaxy morphologies for Euclid with machine learning](https://arxiv.org/abs/2402.10187).\n",
    "8. Walmsley et al. (2021). [Practical Galaxy Morphology Tools from Deep Supervised Representation Learning](https://arxiv.org/abs/2110.12735).\n",
    "9. ESA (2020). [Land-cover maps of Europe from the Cloud](https://www.esa.int/Applications/Observing_the_Earth/Copernicus/Sentinel-2/Land-cover_maps_of_Europe_from_the_Cloud).\n",
    "10. NASA (2024). [Foundation Models for Generalist Geospatial AI](https://www.nas.nasa.gov/SC24/research/project27.php).\n",
    "11. Keras. [BatchNormalization](https://keras.io/api/layers/normalization_layers/batch_normalization/)."
   ]
  }
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