{
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   "source": [
    "# Phase 2 — Experiment 3: WhAM Probing\n",
    "## *Beyond WhAM* · CS 297 Final Paper · April 2026\n",
    "\n",
    "---\n",
    "\n",
    "This notebook is an interpretability analysis of **WhAM** (Paradise et al., NeurIPS 2025). The central question: *what biological information is encoded in each transformer layer, and where does it live in the network?*\n",
    "\n",
    "We already know from Phase 1 that WhAM layer-10 embeddings achieve strong social-unit classification (F1=0.876) but weak coda-type classification (F1=0.212). Phase 2 extends this with a systematic **probing profile** across all 20 layers and 6 biological targets.\n",
    "\n",
    "| Probe target | Type | Biological meaning |\n",
    "|---|---|---|\n",
    "| `unit` (A/D/F) | 3-class classification | Social/cultural identity |\n",
    "| `coda_type` (22 types) | 22-class classification | Categorical rhythm pattern |\n",
    "| `individual_id` (12 IDs) | 12-class classification | Individual whale identity |\n",
    "| `n_clicks` | Regression (R²) | Coda length / complexity |\n",
    "| `mean_ici_ms` | Regression (R²) | Tempo / rhythm speed |\n",
    "| `year` (2005/2008/2009/2010) | 4-class classification | Recording date (confound check) |\n",
    "\n",
    "**The recording-year probe is a confound test** absent from the original WhAM paper: if WhAM's unit separability is partly explained by temporal recording drift rather than true social identity, year should predict unit — and WhAM's year F1 should co-vary with its unit F1 across layers.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "p2-01-kdzelg",
   "metadata": {},
   "source": [
    "## 1. Setup and Data Loading"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "p2-02-ldyduo",
   "metadata": {
    "execution": {
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     "shell.execute_reply": "2026-05-01T20:31:33.363235Z"
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    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/joaoquintanilha/Downloads/data_297_final_paper/wham_env/lib/python3.12/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
      "  from .autonotebook import tqdm as notebook_tqdm\n"
     ]
    }
   ],
   "source": [
    "import os, warnings\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.patches as mpatches\n",
    "import seaborn as sns\n",
    "from sklearn.linear_model import LogisticRegression, Ridge\n",
    "from sklearn.preprocessing import StandardScaler, LabelEncoder\n",
    "from sklearn.metrics import f1_score, r2_score, accuracy_score\n",
    "import umap\n",
    "warnings.filterwarnings(\"ignore\")\n",
    "%matplotlib inline\n",
    "plt.rcParams.update({\"figure.dpi\": 120, \"font.size\": 11})\n",
    "\n",
    "HERE  = os.path.abspath(\".\")\n",
    "BASE  = HERE if os.path.isdir(os.path.join(HERE, \"datasets\")) else os.path.dirname(HERE)\n",
    "if not os.path.isdir(os.path.join(BASE, \"datasets\")):\n",
    "    raise FileNotFoundError(f\"Could not locate datasets/ from working directory: {HERE}\")\n",
    "DATA  = os.path.join(BASE, \"datasets\")\n",
    "FIGS  = os.path.join(BASE, \"figures\", \"phase2\")\n",
    "os.makedirs(FIGS, exist_ok=True)\n",
    "\n",
    "UNIT_COLORS = {\"A\": \"#4C72B0\", \"D\": \"#DD8452\", \"F\": \"#55A868\"}\n",
    "SEED = 42\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "p2-03-dbwnsk",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-05-01T20:31:33.366017Z",
     "iopub.status.busy": "2026-05-01T20:31:33.365834Z",
     "iopub.status.idle": "2026-05-01T20:31:33.386633Z",
     "shell.execute_reply": "2026-05-01T20:31:33.386243Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Clean codas        : 1383\n",
      "IDN-labeled codas  : 762  (12 individuals)\n",
      "Years in dataset   : [2005, 2008, 2009, 2010]\n",
      "Year distribution  :\n",
      "year\n",
      "2005    171\n",
      "2008     33\n",
      "2009     59\n",
      "2010    430\n"
     ]
    }
   ],
   "source": [
    "# ── Load labels ──────────────────────────────────────────────────────────────\n",
    "df = pd.read_csv(os.path.join(DATA, \"dswp_labels.csv\"))\n",
    "df[\"ici_list\"]    = df[\"ici_sequence\"].apply(\n",
    "    lambda s: [float(x) for x in s.split(\"|\")] if isinstance(s, str) and s else [])\n",
    "df[\"mean_ici_ms\"] = df[\"ici_list\"].apply(lambda x: np.mean(x)*1000 if x else np.nan)\n",
    "df[\"year\"]        = pd.to_datetime(df[\"date\"], errors=\"coerce\").dt.year.astype(\"Int64\")\n",
    "\n",
    "df_clean = df[df[\"is_noise\"] == 0].copy().reset_index(drop=True)\n",
    "\n",
    "# Individual-ID subset (drop singletons)\n",
    "df_id_all = df_clean[df_clean[\"individual_id\"] != \"0\"].copy()\n",
    "id_counts = df_id_all[\"individual_id\"].value_counts()\n",
    "df_id     = df_id_all[df_id_all[\"individual_id\"].isin(id_counts[id_counts > 1].index)].copy().reset_index(drop=True)\n",
    "\n",
    "print(f\"Clean codas        : {len(df_clean)}\")\n",
    "print(f\"IDN-labeled codas  : {len(df_id)}  ({df_id['individual_id'].nunique()} individuals)\")\n",
    "print(f\"Years in dataset   : {sorted(df_clean['year'].dropna().unique().tolist())}\")\n",
    "print(f\"Year distribution  :\\n{df_clean['year'].value_counts().sort_index().to_string()}\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "p2-04-sflfey",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-05-01T20:31:33.388535Z",
     "iopub.status.busy": "2026-05-01T20:31:33.388408Z",
     "iopub.status.idle": "2026-05-01T20:31:33.477113Z",
     "shell.execute_reply": "2026-05-01T20:31:33.476646Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "All-layer embeddings : (1501, 20, 1280)  (n_codas × n_layers × hidden_dim)\n",
      "Layer-10 embeddings  : (1501, 1280)\n",
      "Model                : 20 transformer layers, 1280d hidden\n",
      "\n",
      "Splits loaded — train: 1106  test: 277\n"
     ]
    }
   ],
   "source": [
    "# ── Load WhAM all-layer embeddings ────────────────────────────────────────────\n",
    "tall = np.load(os.path.join(DATA, \"wham_embeddings_all_layers.npy\"))\n",
    "l10  = np.load(os.path.join(DATA, \"wham_embeddings.npy\"))\n",
    "\n",
    "n_layers, hidden_dim = tall.shape[1], tall.shape[2]\n",
    "print(f\"All-layer embeddings : {tall.shape}  (n_codas × n_layers × hidden_dim)\")\n",
    "print(f\"Layer-10 embeddings  : {l10.shape}\")\n",
    "print(f\"Model                : {n_layers} transformer layers, {hidden_dim}d hidden\")\n",
    "\n",
    "# ── Load shared splits from Phase 1 ──────────────────────────────────────────\n",
    "train_idx    = np.load(os.path.join(DATA, \"train_idx.npy\"))\n",
    "test_idx     = np.load(os.path.join(DATA, \"test_idx.npy\"))\n",
    "train_id_idx = np.load(os.path.join(DATA, \"train_id_idx.npy\"))\n",
    "test_id_idx  = np.load(os.path.join(DATA, \"test_id_idx.npy\"))\n",
    "\n",
    "df_train = df_clean.iloc[train_idx].reset_index(drop=True)\n",
    "df_test  = df_clean.iloc[test_idx].reset_index(drop=True)\n",
    "df_id_train = df_id.iloc[train_id_idx].reset_index(drop=True)\n",
    "df_id_test  = df_id.iloc[test_id_idx].reset_index(drop=True)\n",
    "\n",
    "# Index arrays into the 1501-coda embedding array\n",
    "clean_ids = df_clean[\"coda_id\"].values - 1   # 0-indexed positions in tall/l10\n",
    "id_ids    = df_id[\"coda_id\"].values   - 1\n",
    "\n",
    "print(f\"\\nSplits loaded — train: {len(df_train)}  test: {len(df_test)}\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "p2-05-kijdoi",
   "metadata": {},
   "source": [
    "---\n",
    "## 2. Extended Layer-wise Linear Probing\n",
    "\n",
    "### Methodology\n",
    "\n",
    "Following Tenney et al. (2019) — *\"BERT Rediscovers the Classical NLP Pipeline\"* — and Castellon et al. (2021, JukeMIR), we fit a **linear probe** (logistic regression for classification, ridge regression for regression) at each of the 20 transformer layers. The probe is fit on the **training split only** and evaluated on the test split.\n",
    "\n",
    "Linear probes are intentionally weak by design: any information that a linear probe can extract was already linearly decodable from the representation, without requiring further non-linear processing. Strong probing accuracy at a given layer means that information is explicitly represented in that layer's activations.\n",
    "\n",
    "**Phase 1 finding**: layer 19 achieved the best social-unit F1 (0.895). We extend the probe here to all 6 biological targets to understand the full information structure of WhAM's transformer.\n",
    "\n",
    "**Expected finding based on WhAM's training objective (generative / spectral)**:\n",
    "- Social unit and individual ID should emerge in late layers (high-level semantic)\n",
    "- Click count and mean ICI should peak in early layers (low-level temporal)\n",
    "- Coda type should remain weak throughout (WhAM never learned rhythm timing)\n",
    "- Year should be low, confirming it is not a confound for social-unit separability\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "p2-06-xcmsju",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-05-01T20:31:33.478597Z",
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     "iopub.status.idle": "2026-05-01T20:31:33.483005Z",
     "shell.execute_reply": "2026-05-01T20:31:33.482566Z"
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   },
   "outputs": [],
   "source": [
    "def probe_layer(layer_idx, X_all_clean, X_all_id,\n",
    "                y_unit_tr, y_unit_te,\n",
    "                y_type_tr, y_type_te,\n",
    "                y_id_tr,   y_id_te,\n",
    "                y_nclk_tr, y_nclk_te,\n",
    "                y_ici_tr,  y_ici_te,\n",
    "                y_yr_tr,   y_yr_te):\n",
    "    \"\"\"Fit and evaluate all 6 probes at a single transformer layer.\"\"\"\n",
    "    emb    = X_all_clean[:, layer_idx, :]   # (1383, 1280)\n",
    "    emb_id = X_all_id[:,   layer_idx, :]   # (762, 1280)\n",
    "\n",
    "    sc     = StandardScaler().fit(emb[train_idx])\n",
    "    X_tr   = sc.transform(emb[train_idx])\n",
    "    X_te   = sc.transform(emb[test_idx])\n",
    "\n",
    "    sc_id  = StandardScaler().fit(emb_id[train_id_idx])\n",
    "    X_id_tr = sc_id.transform(emb_id[train_id_idx])\n",
    "    X_id_te = sc_id.transform(emb_id[test_id_idx])\n",
    "\n",
    "    results = {}\n",
    "\n",
    "    # 3 classification probes on main split\n",
    "    for name, y_tr, y_te in [(\"unit\",      y_unit_tr, y_unit_te),\n",
    "                              (\"coda_type\", y_type_tr, y_type_te),\n",
    "                              (\"year\",      y_yr_tr,   y_yr_te)]:\n",
    "        # drop NaN for year\n",
    "        mask_tr = ~pd.isnull(y_tr)\n",
    "        mask_te = ~pd.isnull(y_te)\n",
    "        if mask_tr.sum() < 10:\n",
    "            results[name] = np.nan; continue\n",
    "        lr = LogisticRegression(max_iter=500, class_weight=\"balanced\",\n",
    "                                random_state=SEED, solver=\"lbfgs\")\n",
    "        lr.fit(X_tr[mask_tr], y_tr[mask_tr])\n",
    "        f1 = f1_score(y_te[mask_te], lr.predict(X_te[mask_te]),\n",
    "                      average=\"macro\", zero_division=0)\n",
    "        results[name] = f1\n",
    "\n",
    "    # Individual ID classification probe\n",
    "    lr_id = LogisticRegression(max_iter=500, class_weight=\"balanced\",\n",
    "                               random_state=SEED, solver=\"lbfgs\")\n",
    "    lr_id.fit(X_id_tr, y_id_tr)\n",
    "    results[\"individual_id\"] = f1_score(y_id_te, lr_id.predict(X_id_te),\n",
    "                                         average=\"macro\", zero_division=0)\n",
    "\n",
    "    # 2 regression probes (n_clicks, mean_ici_ms) — Ridge, R²\n",
    "    for name, y_tr_r, y_te_r in [(\"n_clicks\",    y_nclk_tr, y_nclk_te),\n",
    "                                   (\"mean_ici_ms\", y_ici_tr,  y_ici_te)]:\n",
    "        mask_tr = ~np.isnan(y_tr_r.astype(float))\n",
    "        mask_te = ~np.isnan(y_te_r.astype(float))\n",
    "        rr = Ridge(alpha=1.0)\n",
    "        rr.fit(X_tr[mask_tr], y_tr_r[mask_tr].astype(float))\n",
    "        r2 = r2_score(y_te_r[mask_te].astype(float), rr.predict(X_te[mask_te]))\n",
    "        results[name] = max(r2, 0.0)   # clip negative R² to 0 for plotting\n",
    "\n",
    "    return results\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "p2-07-ylyjkl",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-05-01T20:31:33.484187Z",
     "iopub.status.busy": "2026-05-01T20:31:33.484098Z",
     "iopub.status.idle": "2026-05-01T20:32:15.632139Z",
     "shell.execute_reply": "2026-05-01T20:32:15.631483Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Running layer-wise probes across all 20 layers (6 targets × 20 layers)...\n",
      "This takes ~3-4 minutes...\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  Layer  0: unit=0.809  coda_type=0.218  indiv_id=0.346  n_clicks=0.000  mean_ici=0.109  year=0.788\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  Layer  1: unit=0.827  coda_type=0.173  indiv_id=0.343  n_clicks=0.000  mean_ici=0.000  year=0.746\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  Layer  2: unit=0.822  coda_type=0.178  indiv_id=0.352  n_clicks=0.000  mean_ici=0.000  year=0.813\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  Layer  3: unit=0.863  coda_type=0.170  indiv_id=0.372  n_clicks=0.000  mean_ici=0.000  year=0.818\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  Layer  4: unit=0.847  coda_type=0.185  indiv_id=0.339  n_clicks=0.000  mean_ici=0.000  year=0.763\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  Layer  5: unit=0.866  coda_type=0.256  indiv_id=0.329  n_clicks=0.000  mean_ici=0.000  year=0.804\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  Layer  6: unit=0.878  coda_type=0.219  indiv_id=0.369  n_clicks=0.000  mean_ici=0.000  year=0.799\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  Layer  7: unit=0.869  coda_type=0.191  indiv_id=0.426  n_clicks=0.000  mean_ici=0.000  year=0.806\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  Layer  8: unit=0.851  coda_type=0.203  indiv_id=0.375  n_clicks=0.000  mean_ici=0.000  year=0.792\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  Layer  9: unit=0.871  coda_type=0.198  indiv_id=0.423  n_clicks=0.000  mean_ici=0.000  year=0.818\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  Layer 10: unit=0.876  coda_type=0.212  indiv_id=0.411  n_clicks=0.000  mean_ici=0.000  year=0.874\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  Layer 11: unit=0.869  coda_type=0.206  indiv_id=0.423  n_clicks=0.000  mean_ici=0.000  year=0.850\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  Layer 12: unit=0.865  coda_type=0.226  indiv_id=0.401  n_clicks=0.000  mean_ici=0.000  year=0.863\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  Layer 13: unit=0.883  coda_type=0.206  indiv_id=0.388  n_clicks=0.000  mean_ici=0.000  year=0.849\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  Layer 14: unit=0.873  coda_type=0.185  indiv_id=0.353  n_clicks=0.000  mean_ici=0.000  year=0.856\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  Layer 15: unit=0.874  coda_type=0.185  indiv_id=0.414  n_clicks=0.000  mean_ici=0.000  year=0.832\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  Layer 16: unit=0.876  coda_type=0.224  indiv_id=0.360  n_clicks=0.000  mean_ici=0.000  year=0.889\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  Layer 17: unit=0.889  coda_type=0.204  indiv_id=0.343  n_clicks=0.000  mean_ici=0.000  year=0.881\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  Layer 18: unit=0.869  coda_type=0.212  indiv_id=0.361  n_clicks=0.000  mean_ici=0.000  year=0.906\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  Layer 19: unit=0.895  coda_type=0.261  indiv_id=0.363  n_clicks=0.000  mean_ici=0.000  year=0.875\n",
      "\n",
      "Done.\n"
     ]
    }
   ],
   "source": [
    "# Prepare label arrays (aligned to df_clean index)\n",
    "X_all_clean = tall[clean_ids]   # (1383, 20, 1280)\n",
    "X_all_id    = tall[id_ids]      # (762, 20, 1280)\n",
    "\n",
    "y_unit = df_clean[\"unit\"].values\n",
    "y_type = df_clean[\"coda_type\"].values\n",
    "y_yr   = df_clean[\"year\"].values.astype(float)     # float so NaN works\n",
    "y_yr[np.isnan(y_yr)] = np.nan\n",
    "y_nclk = df_clean[\"n_clicks\"].values.astype(float)\n",
    "y_ici  = df_clean[\"mean_ici_ms\"].values.astype(float)\n",
    "y_id   = df_id[\"individual_id\"].values\n",
    "\n",
    "print(\"Running layer-wise probes across all 20 layers (6 targets × 20 layers)...\")\n",
    "print(\"This takes ~3-4 minutes...\")\n",
    "\n",
    "probe_results = []\n",
    "for layer_idx in range(n_layers):\n",
    "    r = probe_layer(\n",
    "        layer_idx, X_all_clean, X_all_id,\n",
    "        y_unit[train_idx], y_unit[test_idx],\n",
    "        y_type[train_idx], y_type[test_idx],\n",
    "        y_id[train_id_idx], y_id[test_id_idx],\n",
    "        y_nclk[train_idx], y_nclk[test_idx],\n",
    "        y_ici[train_idx],  y_ici[test_idx],\n",
    "        y_yr[train_idx],   y_yr[test_idx])\n",
    "    probe_results.append(r)\n",
    "    print(f\"  Layer {layer_idx:2d}: unit={r['unit']:.3f}  \"\n",
    "          f\"coda_type={r['coda_type']:.3f}  indiv_id={r['individual_id']:.3f}  \"\n",
    "          f\"n_clicks={r['n_clicks']:.3f}  mean_ici={r['mean_ici_ms']:.3f}  \"\n",
    "          f\"year={r['year']:.3f}\")\n",
    "\n",
    "probe_df = pd.DataFrame(probe_results)\n",
    "probe_df.index.name = \"layer\"\n",
    "print(\"\\nDone.\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "p2-08-ltmqcx",
   "metadata": {},
   "source": [
    "### Probing Profile Plot\n",
    "\n",
    "The figure below shows macro-F1 (classification) or R² (regression) at each layer for all 6 targets. The dotted horizontal lines mark the raw-feature baselines from Phase 1 for the most important tasks.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "p2-09-ebgyir",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-05-01T20:32:15.634244Z",
     "iopub.status.busy": "2026-05-01T20:32:15.634131Z",
     "iopub.status.idle": "2026-05-01T20:32:16.003386Z",
     "shell.execute_reply": "2026-05-01T20:32:16.002453Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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bU1OT3XTTTVZdXe3GN1i4cGHCr6WkwIknnujGRlDPh/4kCFROSZ9p1apVrpRSumpvb7fa2lorKSmxrCy69bJNOFY4h7iucL3lOygaBeDVQ0At4hSA10DBsUwqHW+nTDnO9RSYXDZh0K8raikdKkW0c6VtPrS9x5Imi8fPc70Ljp2woNctywfLcPg/i3qUKJgSLB202g40VPU4yK9aWAaTAgt63dJyOGyTvnKJhc5kgpdECCYYwksfad7u2r12MIFeBRNLxrlgVXiwvyuwH7rNKbT8nLxeB5xTlbbTYCaTPPqJqpJD+/2Jg7oDtr/hoO2rO+iSAW09lCpJlM6lMTEGUNZUklccStrEGtA6ssdWOhvJ15a+SqdtsqV6hz259Xl7eusLvR5jJh6VVDxx8jFumjl62og9z9LpWOkNtgvbhWNl4OPW/eqH2tjYaH/4wx8SemxvEwRKAlx33XU2efJklwwoLQ22OFIvhXnz5tknP/lJ+89//pPw6/3whz+09evX2+c//3n76le/mvDzAAAAequlrcVW7HnVDTSslvnxWs4p8HjK1GBSIFapjsGioKZ+eGu6fMGb3bgE+vyaVqkUUURATi21n9+x3E0qRTS7YrodN2mRSxhMLp0wYn/AJ4MCjbtq94bGEnht/4YeA54KXHoJgfljZ7sEDbpTqZ1RBWVu6kmi9fa/c/Zn07KFuNb59dNPGfSAla4dGvtE06yK6VGTmYcaa2KUMDpgBxqq3WMSoeuazkVN0WhMlrGF6n1QYTXNdbYhyjgJiQxoDYxUVQ2H7KltS+3JLUtt2+GdMR83pXSCuy6r12K8RIASgJG95vbU7bf71zzspoqCctez4MTJR9ucyiMpIQYASdDnBMFjjz1mA+n++++3mpoalyTwkgMydepUu/TSS+22226z7du325QpU3p8LfUa+OY3v+lKFek/tgAAAMmmchgv73nVnt32kr2465W4ZU40aKwGGlZvgVSu6a8a4+ceeYabtD6uFNHOV+yl3avscFNNt1JEaw9uctMfX7nPtbx1pYgmBksRJas++nCm3iSr960NlQ5SQLOnevRzxxzpEgJKDCiZlKrHynCVaL39dEwOpDIlM0cXjnLTnDEzuy1XcFHjTcQaQFkBTV2zEqEE7/aa3W7qyd2r/m6bqra6Mm6l+cVWmldipXm67bqvHgkqewQM97Fx1DhA47Os2rsu5vmkJN9pU5fY6UecaEeMCpYb66kXjgY/V8MEvb4aW0SWY9Q4PA+uf8xNOqeWTFKy4Bg39g7/1wCAvunzL7UzzzzTBtLzzwcHAjvllFO6LdM8JQg0MHJPCQK1ztKgyYsXL7aPf/zjdvvtt/fqc+zbt8/2798fNm/Dhg2hbk6a0pW3/um8DSKxTdguHC+cQ1xb0ut6qx+tr+xdY8/tWOZ+zMYbVHVcUaUbT+Ckyce6UiVeoFd1eofDdsnJyLZjxy9w0wcCHbapepsbT2HZ7pW2NUqLQQXhvB/wBdn5tmj8XDtuwkI7evw8FyBLl+Nld+0+lzhavme1vbZ/fdzBqKWysNwNLqzSTQvGHhXWS2C4HCvDzcVHnWuBjg67b82/wvaPSs9cPOc8tzydt9NwPVY0OLGmoyq6JxDUW0c9pFyvA41fEBr/QH8ftEMRCdBEKUiqBHFPNPaElzjQ9bDrfmdCITd8vgYeHy6G6/EykEbKNlHiTQ0F/rttqTvOY40TkpeVa8dPWmynT13ivse8hJj3HXbJnPPsnBmn23Pbu8qXnTRF5csK3WPys/Ls1CnHu0mNE/QdunTnCtfjLvL/WerV8+9NT7mpKKfAjp2w0JZMWmyLx8+13KzuY/GkupFyrCQb24XtwrHSO325hqRsU64dO3a4W5UYiuTN8x4Tz8033+ySDS+99FKfup7p+ep9EE19fb3rbpvOB5wGqRbq47FNOFY4h7iucL1Nl+8gBZZeO7jBXtyz0lbsezVuUqCyoNyOH7/Ijhu/0KaWdPUU0HhIw327jMupsAumnekm1XBfuf81e3n/Glt7cKO1BcL/U6pt5C9FNHPUNFs0do4tHjPXxheNGfBW8YO5XRQwWVu1yVYfWGurDqyzfQ3xewlkZWTZrPIjbEHlbFsw5iibUDQ2tD1aG1us1WKPWdEf/D8u3DlTTrNTxh9nL+5+xQ7WVVlF8Wg7fsIiF3AaqPN1uBipx0qh5dsRhZPcZJXdz+OqpkNuLJCDnbcrD6yxXXX7kvLe9a0Nbtqd4OspYKpeLCWdk7uf47sfml/s5udFGaR8MNS3NtqLu1+2g3XVVlFcbsdPWOzOoXQ3nM8hNbjcVrvLntu13F7Y/bLVtES/Huq7fU7FTDtp4jF2zNj5oeR2Q31wvaM5vmKBNRQ0WGFhoQWa2622OXpsZV7ZkW5691EX2WtVG23Z3lX28r5X3fHmp7+VvNCkAd8XVB5lx45bYAvHHOUaKgwHw/lYGUhsF7YLx0rvKF49YhIE3kUxL697bdX8/Pywx8Sybds2N+7BZz7zmV4NaOz3sY99zC677LJuPQguvvhiKyoqSuuSRV5GigF02CYcK5xDXFe43o7076C2jnY3gKx6Cry48+VuP0r9KgtHu14CJ00+xmaUTx3UkjBD9d2s9zti7BR7s53bVYpo90rX2q8m4ge/WthuOLTFTfese8j1rFCLv2MnLnC1hLMHoPTGQG+XvXUHbMWe1a4Uwup96+LWsxeVH1FPCvUUUOvKgpzBD1zw/7juSqzELih7HQNEcqw4FTbaZtmM0Nb496an7bfL7uzx3DqibLIbt0ADYuv6p0Gx+6upvdmaGpvtQGP8wcs9Co6GeifkdpU26t5rIbhMwdP+fFcpiHzvaw9164Xz57X/cL1wLpl7flqXRxuO11slxZ7a9oKbdtbsifm4aWWTXAmhU6Ye777bBnq7nDpqtJ06Y4nrzfDagQ2uZ8ELO1/u1utHST4lEjSpXN+icXNczwL1ZBzsXowj/VgZDGwXtgvHSu8oXj1iEgTKIktzc/f6vU1NTWGPieXqq6+2cePG2de+9rU+f46xY8e6KRpdsNP9ou1tg3TfDn5sE7YLxwvnENeWkXG9VVJA9eI1psDSnS+7wUzj1erXIMMaU0AD/A5lIGSov4eKsgrtpKnHukmDhG44uCU00HG0wQv31h+wBzc85qbCnAIXONfYBaq5X5xXlJLbRYEHlQvyBhhWGaG4752RaUdVzgyNJZAq404M9bGSqtgubJNoTpl2nN224i89Dmj9tdd/KmzMCgUya1vqraap1pVDCU61Ybe1uu8tb6lzAff+XqM0ULOmRKgEjD9hEH6/pHM8ha55RbmFbhwIz19W/9PufvUf3V5XyQLNz8jMTPvBm4fDdaWhpdGe6xxXQMnuWMoLyuz0aSfYGdNOtKmjJg3JdtHjF0+Y56YPHPcOW39wsz2/Y4XrqaixRiJ7fmrsH006buePneXGLNDYBVqXVDMcjpWhwHZhu3CsJK4v14+UTRD4ywjNnTs34fJDnnvvvdceeugh+9WvfhVWikhjCsjevXtdT4CJEyf2mGgAAADpQYEc/Sh+dvsyW7pjuQvqxKKWckoKaFyBIyuOCAuWIEjbZHblDDe9c9FFrr73MpcseMVW7VvntrefBiZ8ZvtLbsrsDKprkOPjJy60iaXjh3Sz6rOvcAGG1bZ671prbo9f+qc8v8wlA46eMN8WjZtrhbmU2QDScUBrBd81UKumRCixWt/SEJ5IaOqeVPDfj7yW9paeX9142E2J0PVZJY28ZMHaA5viPl4D0p4/60wG+05BagyhGv9PbnnejSsQOSCwRyWDFFRXYkA93/pSvnmgeP9f0PSexW+1LYd22PM7lrmEQWTvB51fK/euddMtL91lsyum24lTjrETJh9jY4sqhmwdAGCopWyC4IQTTrBf/vKX9uyzz9o555wTtkzzZMmSJTGfv3Xr1lAvgmhUdkjTgw8+aOeff35SPzsAABg+NCDeq/vXu6C0Wp6pJWcsaml28uRj7eSpx9msiukkBXpJP77Pn3WWmxpbm1xQQj0L1KovcrvrR7xa6Wu64+V7bELxWJcsOG7SIptTOTM06OFAUZBkzYGNtnzXKjfAcLwSC10BihmubJB6Ckwb1TUQNYCR4W3zLggFvP09CdRzQMkBb3l/uOB7ZxmgSdZzYlS9DXQ9jZpAaIqeVIg1uGyidH0+3FzrpkRoWz3w2iOuF0HuEI2PgPBjZmPVVnty6/P2zLYX3XERjb7DFo+ba2cccaIbdNgbVyCV6TNPL5/ipncsvMh21Oy2pZ09CzZXb+9W8nDtwU1uun3FX91zlATRNGmIGyUAwGDLCPS3/+IAqa6utmnTpllZWZmtXr3aSktLQ+MKzJs3zyUHHnvssdBYBJqvx06YMMHNU++AFf+fvfuAb6rswgB+uvdkb2TvDSpTQEVUnOBW3Hvj3oh7Irgnbj9UXIhbcLBB9t57t9DSPfL9npPeNEmTNGnTNs19/t93DZm9ubm5Sc5533OWLi3zuLNmzZLXXntNbr31Vhk0aJAuKEPkC6xPly5dZOXKldK5c2cxcx04NGlmfTxuE+4rfA/xuMLjbW37DEJSYO3BjdakwI4lHoMcGPWJngL9m/fWkfCBPFOgtn424/XYkLbFWopo13LZkbHH4+3R9BIj841SRBjB6g5KQ83Ztlj2ZxyQ+on1tEyI8whfw8GsNGvZoL2rtI9CXmHZUpf2kqITpScSAo2tswQ8rUegqa37SlXjduE28QaOK3O3/yf7jxyQ+kn15PjmvWrV6Hj0ivGUQNCyR3YlkdBo3h9Qbg0l1lqnttRyfG1SW+j5qk741rRAOa5gJtw/W+drYsBTaTwEylE+aEDzPpJchSV4qnu77D96UHsW4HsfkgKeIEFgJAtaJjettoR/oOwrgYbbhduF+4pUedw6YGcQpKSkyPPPPy/XX3+9DBgwQGcCoB/B5MmT9eA8ceJE220XLFggQ4cOlbFjx8qUKVP0sjZt2uji7OhRa3YcCYbRo0dX4zMiIiKimoQRj+sObtKgDpoNOze0s5cUlaBTztFTAI1zA2kqfTDC9jXKA1zU7SzZd/SArW8BZnc4l89Ak+jZ2xfpgoBTh3ptNFmAUkQNE6y9ozAG5uvVP5UZ6Ysa4sZIXzwuZgmgwTBmCpSXmMB30Hapx0jPxl10pkDLlKYBnTAioqqBZMCwY/rX2kAeRoJj8bakCmYcZNonEHKPamId/15zYKN+tnqjyFKs5V+w/LH5X70sIixCWiU304SBJg7qtJCG8fV4bPVjMmvejv+0hBA+7zz1UjL6CjRNsg66DDb14+vK6e1P1CUt57As3LlMFuxaIqv2b9DviPYwa3Da6p90wfsEJYiOa9qTJSWJKGgFbIIAkBSoU6eOJgruueceiYyM1BH/Tz75pHTr1q2mV4+IqMJ8GdFKRBWHH3zrD26RuTsWa1LAU31l1FLGSDH0FehUry2TAjWoQXw9ObXdMF3QNHHZvtWyeNcKbQjs3BcCASf0jcDy0dKvpElCQ+ndpKtk5mXJzC1zyjw2kgWoIT5720INEJQ3Mhb7BWYrYKZC9wad/No4mYioNogMi9AAMhZX32mv++5+j82byyvnZpR5MaBhfevU5raZBljqxKSwbJuXCosKdSYckgJItKNJrysxEdE6QxIlhDrWa2OqpAz6SI1oO0QXJL4wexFliJbvW1tme2HmxfR1v+uCUpP9mvSQY5v2kI712gb97BciMo+ALTEUyFhiyIrTvMriNnGN26WUuxGt9rVrzVyzmvsKt4s/EmxICmw8tFXLB2HUHILA7qDJoiYFmluTArX9h16wv4dQimj9oc2yqKTRcXl9ASoiREJ0hCASAuglgFILwRg0CfZ9paK4XbhNuK/47qtVMzw2bx7T+TTp26SHbEzbqrXvN6Vtle1HdpcZtV1eSTejLJEx2wAJ3NqgOo4r+I2x4dAWTQrg+w++K7mCWXfdG3XWmQKYdVeTPSEC8XibXZAj/+1eqX0LMCghryjf43dI9GbA98iuDdrrbJhg3CaBgNuF24X7iolLDBERBSMkB1z9gDJGtAIauBFRWR5LxnQcId0bdtKZAnN3/ieHstPdbkIkE/o17aHlgzrVbyfhtTwpYLZSRCgnhOWS7mfL3sz9tlJEaGaM2QQVgWag2H96Neos3Rp2qjVBJyKi2tK8GQNgUJbtxNYD9br8wnwtNVSaNNgmuzP3uf0bR3Iz5D80td+9wnZZvbg60saun8ExKc11VLyZ7D16QPsK/LNtgf7bHWwjo69AYnRCta5jbYLZKwNb9NUF++jSvas1WbBo93JNHtjDjEbMVMQSEx4tvRp30WQBZh3WhobORET2mCAgIqqGRnAHs9Nkx+E98vWqHz3eFj+sTmk7hOWGiHxNsK2arounprZ9S5ICXRp0YFIgSKDfwGnth+uSlZ8ty/au1tkFKBOAshXl6Vy/nfY8aJ3SgiWliIgqCMF/DHDBd1hvmzdj5Hq7uq10MaCk3Ob0bbKxJGGABd+h3TmQdUgXDA7Q9ZAQbS5rlCVC8qBFchO/jOwOJOgHgef899YFOqvOHSRQrH0F+knjxIbVuo7BAPsoBpRgQdmmlfvX6/eLhbuWalkieyhXaPRGQkku9CnC/Xo37ipxkbE19hyIzIRlnCuHCQIiokrA1OiM3Ew5mJ2uP2AOZKXpqS4l/3aul+0JAp1PzpqszVFR1uKY5GYc5UNU8oXvm9U/+TwKrG+T7tpToBuSAmH82hPM8AO8f/M+uvy6sbW8u/jzcu8zsHlfaVvnmGpZPyKiYFfZ5s2xkTGaxMdiOJybIZvTjKTBVj1FgNwVi1hkZ8YeXf7aOk8vQ+nAFklNbGWJMNOgaWKjWpcURtIbs+X+3rZAS98UFRe5HRCB7z2DWvaT9nVbB2WJvJqA75A9GnXS5ZreF8ragxtl/s6lOrvgUE56mabeC3Yt1QX7X9f67XVmAb6TcvYG+QuD4V7OMmcZZ6/xlzIRkQf4godSJUbA/0BJ8P9QSTIA1xW4afxVUZvSt+liQFM2TMnWhEFKc00aoEmcmXsVkHm+7GFk4OoDG+SPTbO9eq9FhIbrD2P0FEBSINhGDZJ3+jfvLR8u+cpj00yUvkAyloiIAldydKL0atxVF9t3g+w0TRYYsww2p21323AegfTN6dt1+W3TP3pZVFikfq/WhEEda+KgQVzdgPtujee67uAm7SuAGQNZTiVuDAhCo2cOZgpgO2EEO1UdJJdQohLL2J6jdf+bt3OJzi7Y51TmCfsfyhRheXvxZ9rvCk2OMbvAuek3A77kDQbDy2IZZ/9ggoCITP3hitH9B8uM+k+XA9mH9BT1TmsaRqVgwagh+6ZYLTVh0ExaJltPG8XXr3WjoYic35NoOouEAOrJrz2wqcyorPKc2XGEnNfldG5Yk8MoVowY8tQ0E9e7K31BRESBCUH8+nF1dMGAAGNGL/oXbDpklCbaqv0N3A0sQOPZtQc36WLA50Hr1Oa2mQY4TY1JlpqA5/J3SV8BDJRwp12dVjK4ZT/dDuilQ9UPMzTa1EGiqaVc3O0s2XFktyYLMLNg+5FdZb7nrtq/XpcPlkyVtqktdaACGnijNBFHP5M3gj0YjvcJFhzX0VusyFIkxcXFjudxfXGRnseMsmmrZ3h8TJZx9g4TBEQUtAqLiyTNGP2PoH+WNehvX/4HPxD8KSkqQerGpkrduFQ9rVdyWjc2RWIiYuSun5/wOKIVXzLb12kl2zN2az1td5DYWLFvrS6GqPAoaZnUpCRx0FyTBs0SG7GsCgUsjKradninrD6wUdYe2ChrDm50WzbAW3Vq6Mc81c6mmUREVPvh+zPKBmEZcsxxehlqxm8/stuWMEBpoh0ZuzXw5ApGby/bu0YXQ0pMkq0skSYOUlpIfFTVJJZRshRBYiQF0LjZnQbx9Wx9BdCHhwIredU8uYkuGKyCRA8SBZhZgP3Q2Ya0rbp8suwbl48XLAFfqt6SqwiWN4ivq7OqNahuF1wvthSVex7Bd/xG0/POgXkP5ytzX+v9cR/rff0N76X5O5bI8NYD/f7YwYQJAiKqUlU5VRKNzIyR/q6C/+k5R7QWqb+Eh4brVNB6GvBHEiBF6sbW0eC/JgRiUrSZlSfljWgd3fk0/QKIHy94DlvSd1iXwzjdrs/JnbzCPFl3aLMu9lOOmyc2dpht0DK5iURHRFdwKxBVrn4ufiCtQTLgwAZZd3Cz25IAzvCjHz+08OXRHZaMoco2zSQiouCpGd8qtbkuJ8kgvSy3ME+2pu+w9TPAd5K9TiVh7OF796Jdy3SxD9BbGyBbmyDjO3Z0eFSFfgvlF+bLIu0rMF+W7VnlNjCG2/dv1lsGtzxW++YEWikkcq1xQgM5q+MIXfD7FD0J0LcAg2J8+Y369aoZ0ji+njRMaKBJq8SoeP2NR8Fb4SA957Ck5RzWYxBO00pOt6W7nxllP0hy8rwPqm2dawv0syHPmCAgooCsjYeMclruYTmYZcwAKA38G30Acgq8Cyx6C1++rcH+kqC/wwyAVEmKTqh0oy9vR7Ri29SLq6MLalQaUPJoS/pOTRYgaYAfOZ5+2CCDr8mFwztk5hbrZSESIo0S6usPmlZ2JYo4NZn8LbcgV9Yf2mJLCGw4tMWrPgJ4n7VKaS4d67WRjvXaSoe6rXXE3lerZrBkDFV700wiIgoOCOR3qNdGF/sAPmrIb7TraYBAnDuoMY9lzvZFtu/szRIbl5QmsiYOmic10QCuu99CA1r0kRCLyLxdS9z+nsHApF6N0VfgWOnVqAtnBNdyGEx2arthuiBQiaQTZhYs37u23GQBRltPnPe+7Tz2ucSoBEmJTtSEQXJ0kqTEJJac4nyipEQnSXJMEvtRBJC8wvySoP9hh6C/fSIAp4V+7m9IVnhfkGdMEFCFsIEOVbY2Hqb9DmzR1xrwt+8BUJIIOJRz2ONIYV8h4IjSI0bpH+fgPxIC1TGqvrIjWpOiE6VHo0662M+kQJ3VrTrLwLrszNjjdvvhSyhGYmMxftwAtoPONEgubYiM2qscpUTeOpqXpfV8kQxAUgANAb15H2MKLEbEIRmApEC7Ose4fD+yZAwRERH5E75/d2vYURcDgnRGWSIjaYDfv+4GRaHWPJaZW+bYvtdg4I2rRAN+C83aMtft+mBQBGYKHNeMs92COVB5YutBuny2/Fv5ds0vPt0f+xwGjWHBb0BP4iJiNFFgJAyQVHBOKOC6mIho/uarIAzIO5KbaQv2uwr64zTbTZPxqhYWEiqhoWEaD8G/9bxehn+HlXPe+u+wUJyWvW1oSIj11OV5x/va/r7tvPW21vMlt3U+H2r3N233Nf6W9Txmho2fOdFjYoWzzL3DBAH5hB3TydVIfxyUUSYEI2BwmpZ9WKdCejJtzU+6+EtMeLQG/0vL/9gF/+NSJDU6OaAa+PpzRGtsZIx0qt9WF0N+UYE2ydKZBunWmQZbj+zSEi/uGAka+2nU+HFzTMkMAy1RlNJMGsbXq/RMCgoO+MJrzA7AqXMzNk/v1/Z1W5UkBNpqU8CIsIhy78eSMURERFTVEDTt06S7LsZv4H1ZB61lidAIOX2bbE7foeU9XcFsSU+zEJxhZi9mCqC3QP34un57HhT46sdV7eudVZCjy66MvR5vFxkWYUsilM5AsJ7az1LAb0Oz/A7E+x49Ae1H+zuX/sHp4bwMt71NKgu/j1KjkyQ1NllfC8QQ/tj8r8c6/UhQvnb6k1qKCr+dzDDY75xOIznL3A+YICCfBHvHdLPMqsCIXg3qlwT09dTdv22nuH2O5BbkSXahcZrr9ouxP6EkDr6YOAb+U7T8jlEKKC4ytsrXozbBlzxjqrP96AbMGjASBta+Bjs8jmZAw9jl+9boYj89u2Vy05LZBtaZBk2TGkk4a2EGNXzxRa+P1SXJACQFPJW3spcQGWebHYClRXLTStVOZckYIiIiqi4IsGGADJYBzfvaBklhxq4xwwDL1iM79fu2tzrXbycXdztbv6+bIYhHZR3XrKd88N9UhzJUrkY/Txz5qOQXF1gD0rlHJD0nw3qamyGHc3B6RE+RDKgIDC5DEgyLJxjNjRntSCIYMxOcZyMgsZAclei3slhVEWdB/4+03CM6sDE997CkZR8pDf6XXI7rPQ2uqwy837GNsM0wYx+L8e8UPW/9N2IczscGbHdPPQ3P7jRSXwMz4Sxz/2CCgPzcMf0n6du4myRGJ2jwJ1ynCJUsIaFB+cWnumZVVDSon+viOjxOoAWz66HZr13TX/vgPz4c/fUFw8zwPmyW1FgXTF22D/qiFIy1RJG1v4GnJj7Yf1BGBot9ndRmSY00WWDMOGiOZsgemrbV5iSbGWDfwGgja0LAmhTwdjQc3rOd0DugXhs9bZzYwDSjjYiIiCj4YWYyvutiGdqqv16GYOK2w7vkq1U/yn97Vpb7GF3qt5c2dVpWw9pSoMJvHcQMPAd8T5F68XX0300SG5Yb+MbvOE0Y4NQ5oaDJhAzJyM30qVGyASPXjRI6kl7+AKHS8kZlZyPoaXSi2zK/FYmzIHGHEf2lI/xL6/3rv0sC/5gZUFVQ1imlTNC/NBGABb0NKzpYisHwsjjL3D8YcSOvzduxxIuO6YVy969Pur3eWnMsTIOJzgkE498YhWw79XS9/XV2tw13cd7xOuvfxmXhqI1m+3dYuevmKtFR3qwKfFEc0WZI6ah7jMIvdHNaMiq/NgT1/eGcjiPl1PbD9MtDMCaPagNsd0xlxoJapwaMQDFmGBgzDjyNKMF737it/WM3TmigCQNbQ+SUZmWC/ixdFhgw4m3b4Z2yumR2ABJAmEHijUbx9XVmgJEQQIKP72kiIiIyE5QDQcAfpYm8SRCwaSb5O+AbGR5p+21X3vf+jLyjdgkEp9kItn9nVLhpbmZ+li4oe+sJBpQ59EgoSSpsOLRFFuxa6jbOsnr/eh2EZAv+5xzW9a6qcj+IE2Fkv3Pw3xjtn1JymS8D5CqCwXD3OMu8cpggIK95GlHsS8a5qKhYp7DVZkbSACNiEbz35Js1P+tSW6BmHbL4seHR1tOIaIkOj9bGRTG20yiJiYgpPY2IkpjwktOIGCkuLpJxP0/wmFDCF57TOwznCPEAhS9mPbE06mK7DCMt0AjLVqIofbvszNzr9kuYMQIdy7/bF9ouR58IzDTQEkUpzfTL3Q/rfi9zf5Yuq1pIXmI6vFEyaP3BzeUezwwtkppoMsAoG4Qvw0RERETkfdmYY5v15OaiGgn4Ip6hJYHK+Q5v1OFH0sCaTDBmJZTMUrCVN8rw+neEMwyE3HN0vy6+WLl/nS7+KGeMChj2wX/HwL/1FK9FIA2AYjCc/I0JAvIaRzg4Ztzxv2AK6uvl4dF+K+WD2nflTZVk+ZjaBTUQUSsVi/001u3aDNmaMMCsg+2Hd3lMDh3ITtPF1YgQV1Da7MRWA01XS9HfMDNp/aEtmhBYe2Cjjsopb1YYIBHaKqV5Sf+AttKhbmuJj2LpJyIiIqLKlI3hbyFy3m+GHdNfMjMzJSEhQcLCKt6vy18QEMf3fiwoU1teoN9+BoJjQsFa5gjXeTtD2R8QB0mNTpbUWMfgv33pH5Q6Yi89IiYIyM8jITDt6t6BN0hkeIQG0QuLi6XIUlTy70IpRGAdi6WozL9dXofLXZwvKnPfQinC33K63vi39foij93eAy6or4F8pyC/Q8C/9DQQ6/OzNp45YBorplLb10/Fe3J3xl5r0qCkTBH6G6BcVkUgiH3t9/dqr4q4iFiJjYyReD2N1RqPOI/L42ynsRIbEaOn1uutp5VpilsbHc3LkrUHUS7IuqDPBHqZeDM9vm1qS9vsgHZ1jnFbG5SIiIiIyuJvITIblNZpmFBfF0/wW/GIi8SBfXJhd8Y+r2Yk1I1JkTZ1jilb+ic2WVKjk/gbhsgHgRdVpFo9EuKcTiOle6NOEqgwRU7LHDkkH0qSB87JBadkhXHe9u/iIp3S9s+2BeX+3TM7nCQDW/QL+KC+P7E2nnlhBIbRtG2IHKeXITC9P+uQdZaBrUTRDjmSl+n146I0WX6R9YtjRb+0GgkGI3FgTTSUJhhinRINRgIiNjxGm9HVBG+bN+NLttFMGMv2I7u8enwcj9rXbVWSEGgrrVOba5KAiIiIiCqGv4WI3P9WrBOboos7v2/6V95e9Gm5m/DczqfK8NYDuamJ/CC4I5Tkd7V9JAS+qKF3gL+mkPVt2r2kebPn+pJndhxh2imkgThVkqofytQ0jK+ny/HNetsSdt+v+00+XfZNtawDpr1iOZSTXqH7I7nnmDiwntqSDc4zF4xkQ2SsJiewDXzhsXlzxxEysEVfbSRsTQhskL1HD3j1uGgKbswOwNIiuanpZlcQERERVQf+FiLyHft4EFU/JgjIJxwJ4Yj1JYkqdzwZ3mqATF0xvZzSZWFyfd9LddZOVkG2ZOXn6Gm2cVqQI0fzS88jCVAVUCIJy8HstAo9V00e6FIya0ETCU7JhpIkA5INs7cvkl82/uW6efOq6bp4A1NtO5XMDkBCoEliw4BqsEVERERERGRgnIWo+jFBQBXCkRDBM6uCKPBLl50qg1se6/VjIpGApEEWlnwkFKxJBP13QY5kF2TbJRRyJLvkcmvyIVtLGfkbZgMY6yJySKpSo/j6mgjoUK+NJgbqxdVhQoCIiIiIiGoNxlmIqhcTBESVxFkVRIH15Q/lchKi4nWpiIKiAluCwZiVYA3ul85YMJINmnywJRis59HXpDq1SGqiyQBjhgAacxEREREREdVWjLMQVS8mCIj8hLMqiILjyx8a9CZhiU6s0P0xA8GaQCgte4TkAmYuGEmGMmWS8nPkqJ5mayN1b53d8RS5sNuZFVpPIiIiIiKiQMY4C1H1YIKAiIgCQrB8+YsMi5DImKQKjeRHKaK8onz5ZcNf8uny8ps314+rU8G1JCIiIiIiIiISCeVGICIiCpzZFNHhUTK89QAtseQJrj+2Wc9qWzciIiIiIiIiCj5MEBAREQVo82ZPcH1NlGAiIiIiIiIiouDBEkNEREQmaN5MREREREREROSMCQIiIqIAFGjNm4mIiIiIiIgo+DBBQEREFMCCpXkzEREREREREQUe9iAgIiIiIiIiIiIiIjIhJgiIiIiIiIiIiIiIiEyICQIiIiIiIiIiIiIiIhNigoCIiIiIiIiIiIiIyISYICAiIiIiIiIiIiIiMiEmCIiIiIiIiIiIiIiITIgJAiIiIiIiIiIiIiIiE2KCgIiIiIiIiIiIiIjIhJggICIiIiIiIiIiIiIyISYIiIiIiIiIiIiIiIhMiAkCIiIiIiIiIiIiIiITYoKAiIiIiIiIiIiIiMiEmCAgIiIiIiIiIiIiIjIhJgiIiIiIiIiIiIiIiEyICQIiIiIiIiIiIiIiIhNigoCIiIiIiIiIiIiIyITCa3oFiIiIiIiIiIiIiIgqoijnqGSuni05aftFUutLQqcBEhYTz43pJSYIiIiIiIiIiIiIiKhWsVgscvjfr+TwnK/FUligl+WISPrvH0hy/3MleeBoCQkJqenVDHhMEBAREQUwjoQgIiIiIiIiKgvJgfS/vyhzOZIFxuUpg8Zw05WDCQIiIqIAxJEQRERERERERO4H0x2e/bXHzYOZBYl9RrLcUDnYpJiIiCiAR0IY0ySdR0LgeiIiIiIiIiKzKUjfKwd/flssRY6/l53h93PW2nnVtl61FWcQEBERBRiOhCAiIiIiIiKyQiIgZ/tqydn4n2Rv/E8K0nZ7//s66zA3YzmYICAiIgowmcv+8GokxL5pL0hij+ES1bithCc3YPMlIiIiIiIiCgqFGYcke9MSyd64WHK2LhdLfm6FHicsLtnv6xZsmCAgIiIKkJ4DebvWS8ain+To6tle3Sd36wpdIDQ2UaIbt5WoJu00YRDVuI2ERcdV8VoTERERERERVZ6luEjydm+Q7A2LNTGQv29LOfcI0f+LxeL+FuEREtfhOL485WCCgIiIqAYVF+TJ0VX/SMain734AuThcbIzdGQFFkNEnSYS1aStRDdup6eR9VtISGiYn9aciIiIiIiIqOKKsjMle/MSa+mgzUukOOeox9uHxSVJTOueEtu6l8S06iEZC2dojz53kvufywbFXmCCgIiIqIaaKmUs/kUyl/0pxbmevwS5FBIq4Un1pPDwPvd/49AuXY4un2W9S3ikRDVqbZ1hgMRBk3YSllCHpYmIiIiIiIioWmbOY2Ac+ghgwYwBsRR7vE9UozYS06aXxLbpLVGNWklISKjtuuSBo/X08JyvtQyv/cwBJAeM68kzJgiIiIiqicVSLDmblsiRRT/rqUjZqZCh0XGS0G2ofnHKWPij28dKGXSepAwaoyMu8KUqd/cGydu1Qf/tLuFgKcyX3B1rdDGExadowiC6CcoStdUvX6FRMX56xkRERERERGRmxXk5krNlmTUpsOk/KTqa7vH2oVGxEtOquyYEYlr1lPB49z0EQkJC9HdxYp+RkrlmjuSk7ZOY1AaS0LE/Zw74gAkCIiKiKlaUk6kzBTBjwN2I/8j6LSWxzykS33mQhEZGa4IgLCah3JEQYbEJEqujKXrpedyvMH2P5CJZsGu9Jgzy9m0VKS5yvW5H0yV7/QJdrH8gVCLrNZUolCUqSRxE1G3K0kRERERERERULvwmxUx2JAOQFMjdvkakuNDjfSLqNdOEAEoHRTdtLyFhvoWsw2LiJaH7cJHMTElISJCwMJbW9QUTBERERFUkb89myVj8kxxd9a+O3i8jNEwbJiX1OVWi8CUoBB2WKjcSAveLSG2sS0LXIXpZcWG+5O/dYp1pgKTBrg1SeGS/6wewFEv+/u26ZC793fqYkdFamija1gC5nYQnpFR6+xAREREREVFw9NbL3bbKlhTwVArXKH8b07KrdZZAm54SkVS/2taVymKCgIiIyI8w2v/o2rnadDhv1zqXtwmLT5XEXidLQs8TJTw+pcpHQoSGR+ooDCxJJZcVHj1snV1QsuTu3iiWvGzXzyk/V7/sYbGtV2JdibbrZRDZsJWERkT5vG5ERERERERU+xQc2W9tLrzxP8nZusL1oDg74ckNrLME2vSS6Bad9XcqBQYmCIiIiPygMOOgZPz3q466L8o64vI2+BKU2HukxLXr6/OUSX9DHcfwdn11XYz+CAUHd5XMMrAmDfL3b3PbMKoo46BkYVk7t7Q0Uf0W1oSBJg7aSUSdxg4NpIiIiIJBUc5RyVw9W3LS9ouk1peETgNY55iIiIKepahQcneuk+yNi3WmQMGBHZ7vEBouMc072hoMY5a7/ax5ChxMEBAREVWitmLutpVyZNFPkr1+octgekhEtJb6Sex9ikTWbx6w2xqB/Mh6zXRJ6D5MLyvOz5W8vZtszY+ROCjKPOS+NNG+Lbpk/verrbmUtSRRyUyDxm0lLM6Yw0BERFT7PvcP//uVQ3+gHBFJ//0DW38gBj6IqCKYeKRAhZnnOSgbtOk/ydm8TIrdzDo3hMWnaB8BLR10TFf9TUiBjwkCIiIiH+FLUebyWZLx3y9ScHCny9tg9DxmCyR0O6HWfilCs+SY5p11MRRmpmnCIHe3tZdB3p5NYinIdbudcrYs08UQnlxfZxcYswyiGhyjjZeJiIgCHZID6X9/UeZyJAuMy9E/iIjIW0w8UqDBzPK83ZusCYGNi/X3nmch+rsOZYOwROL3HWcJ1DpMEFCFMLtNRGaUf2CHZCz+WTJXzNK6/GWEhEps2z6S1GekRLfsGpRfjMITUiW8w7ES1+FYPW8pLtLtor0MShIHBQeQNLG4vH/h4f26ZK3613pBaLhENWhpTRaUzDIIT2nocdvxM4iIiKo7gFeQvk/S//3K4+0Oz/5aEnqPkPDYxGpbNyKq3Zh4pECA31cY1IVeAkgMFGdneLx9aEy8xLbqaS0d1KqHhPFzr9ZjgoB8wuw2EZkNAuBZ6xdo02GUE3IlNDZREnucqI2Hw5PqiZmEhIZZA/wNWor0PMk2cwAjTay9DKwzDYqyDrt+gOJCyduzURdZZL0oNCZBohq3kejG1qQBShShWTM/g4iIqCrg8wWfU4VHDpQuh/dLAU4z8O8DbmfLOTxOUYFsn3iVzpYLT6yr3wmM0wj8O6muhCXWZVNGIrIFZZFY9CQdicdeJ0l4XDK3Gvk38X1guzUhsHGx9hVw13vOgJkBxiwB/D7D70AKHkwQkE+Y3SYiM9VaRMNhlBEqykxzeRuMekdvgfiO/Vkmxw5KKsW07KqL8QUUARb7Xgb5ezeLpTDf5XYtzsmUnE1LdDFoQ6vwSMnfv7XM7VnagYiIykv2Fx1NlwLMYrNPAuiCyw5qcN8vLMVSmL5XF3fC4pKtyYMkI4mAf5csSCBExwXlLEQisiouyJO8XevlyMIfyz/2aOLxaglLrFOSaCxNPNovoRFR3LzkEfrL5WxdYZslUJRx0OPtQ1Bu9pju1n4CrXtKeGIdbuEgxgQB+TW7jYZdiX1G6khPIqLaBoHsvJ3r5MjinyRrzTwd3e4MQeq4TgMlqc8pEtWodY2sZ22DIEdEUn1d4jsN0MssRYWSv39bySwD60yDgkO73T5GQZr76wz4jOJnEBFVFMuX1V4IsBVmHLIF/TURkGHMBDgghZmHRIqLJFBgtgIWfP65EhIZY00eJJbOPChNINSTsPhkjtwkqkWKso5I7o61krtzjZ7m7d3s4zHJosFcDejuWON2RrM12Vi3NJFgn0CIjmfi0YTfWQrS9mgyALMEcratEikq+/vWuY+e0WA4ullHDoIzESYIyGtZa+eWm93GKM6stfMkseeJ3LJEVKtG8Rxd+Y/2F8jft8XlbVAuALMFEroNk7DYhGpfx2ATEhauCRZrkuUU2xddLTe0a70tcYDZBN7S0g6vXqePGVm3mUTWayYR9ZrraVgMXzMicnPssFh0liwGuuC7LOSgrMPvH0hy/3MleeBoBlUC4HO6MOOgtY+N3eh/LQF0ZL8UZaa77X1TETrCH2WCSgJrCLah3vL+7yd7Dq6EhUud4WOlKCfTuo5Y55LT8oIy9iz5OVJwYIcu2BfLCA3TkZylMw/qOv27LkcTE9Vk6Za03daEAJIBO9dokLaqoWZ8Ppa9m9yOBjeSjK4SCJp4DAmt8vWkqv3OknTcmZqIwiyBnI3/lTvIKiQsQqJbdNaEAEoHRaQ05EtkUkwQkNeKjrqpH+3k8NxvpDgvS6ciRdZvwR9URBSw8GUdJYQyl/0pxblZLm8T06qnNh2Oad2Do/WqGEa9oMkVFltposP7tDTRkUU/6ml50Dw6d9sqXRweOy65JGGAxIE1aYAkAso4EJG5sYRmzSvOy9FAf4FD2R+cIsC+X0ff+k1IqIQlpDoFyKzJgIjkeh57BBQc2iPpf3/heFmdlpLfsKNYwqP1s+VQnWbWK5qW3gafZwjkWArzxFKQJ8X51lOcR/JD/11UiRkOmUUimXtFdpWWNQoJj5DQiEgJiYjSJTQ8SkIiS/+NZIa/yxhhdmABRjnn5UpYVLREJNbVAQFmpwHjggI5dOgQfxsH4XaxWIr1d0RRdoYUZ2dKUU6GWApLEoIhSSLNjhMpOSw4CwkL0xJoHvObISES1fAYfX/p8cM4ZmAp9lwz3i0cbtKyrIuUlO8MCdHEou2YoaeREhoRXXJZZI0lEIJlX/GH/AM7JP9Inkjn08tcl3kkT3b8+iU2GOYCiDQ71uW+h9cyPD5FwrDEJUlOaJgmGQ6lZ4ukb5bazCz7SmhoqERFRUmDBg303/7AT2vyGjLK3kC9zbQ/PrLeJy5JEwUxx3TT0/CEVG5xIqpR+BKes2mpHFn0k+RsLq1xbw9B44TuwySx1wiJSG1U7etIdqWJUhrqUlyQ61WCwB2UcsjBsnWFw+UIEmmyoF5ziSiZdaCJg6gYvgxEQQg/HNFIHQFnjLYsSN8n6f9+6fE+6f9MleKiQgmPTdAycxok0SBrSfA1HAEU66n9+aoIwNbGsku6zXOPWkv9lDT9de4FgOv9JjTcOrpeGwWXjJRNtksEJKRWOGiN2SRgjNwsjoqX3HZDJTy1kUTFxOpr7wr2A+w3gsXNjDZsJ20QiWSC3b9xaj3vpxkSFuuMDCnMKwn2hSDSoMFB3V+Ny4zz3jykLQGSL2HR8boY9a71fREeUevfC5UVHs7QS7BsF30/FhdZA/vFxdZTCZGw2CRdIjzdGe+r0DDroCN934Xa3jtu76KfKZGu1wNv6JLjg8XumFF6HPHPcUOTE5gFZX+c0H9bT0vP+/99rscXfAaHWE+D4bO1PNbXVv9VkjwyXkuLBvVj4lN8fkzd58JK9r2Q0KDehrXxuOLr/pGfny85OTmSl5cnzZs390uSILi3GvlVXIfj5dCv79mmMXkDP76OrvxbF8DITW1yckw3iW7eWUIjo/kqEVG1KMrOlMxlf+iMAZQocCWywTHWpsNdBnFqfm38DAoNl6Tjz5KiI/t1dE3BwZ3llsZDA+ocLJuXOVyO8gzOsw0i6jbl51YtxLrywQ/BzqLsI1KUlSHFWUdK/o3TjNJ/2y7PcNlfxiNLsRyZ/VUF1gxB4ZIRmEZiIdwYmWn823pqHekd6TbZUOa80/1w/PPnj31fyi7htti+paP+7UsAWS/D7C5/wTaw1uQ3SgChx41TmQwEQKoAnnPKoDHa7yZzzRzZl10s4YmNpMUxrSQ+oWpL2SH4p7MMigpswTpb0E5PC/xaZklpMClcQsLDNamCf0tYhPXfet4akCjMTJOio2luwwsIaJl5oJi+R4qKJAzbM4iDcsG6XTQZWJCrCS8cy6zBfOz75QXkQqzH7shoCY2M0e+QrpKTuh2Opuvi+B4OsY7wjk+p8PbBDAMcG5yPFbbzvn4eev5r1sCzHh8iEKW1HTdwXp97qPeBacftgmN66XEdx/nKbBd/ckjuYkaH/ttI7BZbk0i225RzuXH/MsdyPE/jucZ6t2KhYRIaFSuhUXE68KmqPhcDTW05rvjjee7fv1/S0tJk37590qhRo+BPEEybNk2ee+45WbFihURGRsqgQYPkqaeeki5dupR73x9++EG+++47mTt3rmzfvl2io6OlTZs2cs0118hll10W9Fklf8NoIfwgcJ5Waw/BfxzQUGvPVVDGqKWZsWC6/pCJbta+ZIZBd502Z5aDFhFVn7w9m+TIop8la/W/rkfnhIZLXMfjJKn3SIlq2j6ov0gE+2dQysDRGrgxYEQXShTl798h+Qd3SP6B7VKAUzRDLqcWtNaNzjgoOZvsZ5mE6ChUI2lgzDiIqNOECaUAxLrytRfeuxrcLwnqF2e5CPTbXe/P4LN/WcRSkKtLBYtAeA9BmQhPyQUXyQe3syAi5eiaOXJ02Z9ln1FhgR6Hs9Yv0H48Oisg46DH0a8+P5XIGC31Y6upb/QCwGyA5PraiLOmP6vxmZTQfbjs27hRIkNDqzw5ABipGxIeKhIe4XlUs0PyoKAkIFgSDLT41hTVmowoEIu7XLuOhA4v9/VHgA8zy/lbjwKddTZMvi0ZgFMpLvDhOIxkAMp5xViPqV7EN3A8QwIN7xEMasCMtdCwcD3OVPY9gyReSGiUCGa1uU08GomD0uOGfULBp8SjpUgshVjyRPJcrpA1UVCSRLAlG42kAo4pJcd3a3IgzdUfsV3ua+LRdTDfmH3hKcjvdBvj31riyc+JWT8wGlbX9GclVR28tvXr15cjR47oLAJ/COgI+XvvvSdXX321JgOeffZZyc3NlcmTJ0v//v1l9uzZ0rVrV4/3RyIgLi5OzjrrLOnYsaNuuC+++EKuuuoq+frrr2X69Ol8w1RyWq0B00btRxNhJFfujjWSs2WZjsrM37+t7IMVF9rqRKfP+kwbf8W07GpLGOAHABFRReD4hOAGmg6j4a0rKC2DEkIJPYZrDUYKjs8ge/hRFZHaWJc4OdYh+Ij+EzrLAHU8D263/hsN5HSauDvoibBfl+wNi+z/kESkNCgtUWQkD+o00XWjmsG68oEzq8JWn9kuuK8j/e0D/3anxTl+LDfjJ6ExCXpM0cBRQb6fR136AQIZ+blSVE3Jkvy9Fa9RjO/8paP/nZtl1tcyf7UlqIHgkb9q/1aWbrOSQJs3o4kdA4IlyQT9DPQh2GWUWSmXRQrS9+pIVvuAoH0wkKgm6HtC+4LkWJMCBbklI7i9EBpunRWgSYGSOv2V2J/xGRMWmyhSjaOfrYnHSBE35dFKE49OM5fwPbzYSDwW+/ZZhYRiYb6bI03JcQyJR7wWHujMAmwjhxJLnoL5+ItVnq4PCNo3gsfWoBcSEqLHiuKK9iJxfjxLaXGrgJKeni4tW7aUxMREWbVqlZ4CZgJ06tRJ+vXrJ3/+WXZUi70//vhDhg0b5vDGwFSTE044Qf7991/58ccf5dRTT/V53bA+SFqsXLlSOnfuLKb9YblmjuSk7ZOY1AaS0LG/xx+WhUdR93m5JguQNLBOE/MMdb9t/QtadKkVjSSxf2VmZkpCQoK+UYnbhftL9b6HUMYg479fJWPp71pb2pXoFl0ksc8pEteuX60ZycZjS+U+g7yFHz9G4gCzDaxlipA42Ovbjx/7xEFqI4fZBvrv1MZV1rTRLPuKjvBDHfnco1KckyVFuZkaVMZShED00UOSsfgXz7V3Q0Iktl0/nX6to9jCSxb9t7VmdigSPGE4LamhbbtdpDXAZVzudD5Qg16uZlW4GujhDQRSXJXv0QC/MbrfLiHgOfnmZxh5GZdcUg86UUdlhsUlWs/j38ZpXJJYQkNl52s3eixfhu3T/NZ3HY4zCIpaCvJLEgZoNptf0nS25Lxeh2aSpZfjsmJtUut0W+f72m5rvbxat50fYNuXBvzLlgAKlh4vON5u2LBBIiIipHXr1hIMjFrfpckD59Ikhf4NsLkcTVw6qjhQj6W+MkvJi9qwXbAPo1yQMTtAj7FeJsVCwoxyQdF6qvuqvxt917J9xSiL45xAsC+F5tvMpWBj7edirflv7TkhoU69G4yySyXX264rc3mI7qkFOvjWY1driWzQstb8zvW32vYeqqzNm60DNlq1alXpuHXAziBAaaCMjAy58847bckBQPOF0aNHy4cffig7duyQZs3ctIMXkeHDh5e5DDvJmDFjNEGwfPnyCiUIqHRarXgZhAiPT5aELoN10a7iB3dIzpblkr15qeRuX13ywewIQRosGAGMA2JUk7Yl/Qu667/NesAjIkc4puRuXaFNh3VUt4tALr7EJ3Q9QRJ7j9DyMGSuzyBv4YeetXwQ9pEBtssROMw/tMtanmj/9pJyRTukMH2f5y/olmIpwP0O7RKReaWXh4ZLRJ1G2tdAEwZGk+SUhqb7bNMf6gjo52Rqk1KdWq9Bf7t/G9eXnEcCQEeYVyRp4/DHLZK9br5UjRCvEgluEw72l3t5u9JkhvW8qyZ+5c2qQPPe+C6Dy47qR21/p7r+rr67VZmQUC1nE2oL8JcE/R0SAKWBf5R28OVHYXnly3C9cxJSm0wi0B0VY1cVuWpYR2taZy4g6aDJBIdkQ9lEhZFccH3e6bHskhq+vK8i67eQ2Da9S0sAYVZAYh2WXKvFtJkyjiU+lDHCccOSn12xP1juaGK7BIIe80pLkQRTAoGqMuFVUJoMwFKU71MPGWsyoKRcUBUN7qjNrDOXwiTEw3dx1zOX7Psg+DhzqdqC+UYDd6dAfahj0N51kL9qGjfjkdB7wXXpJSvtzWCy3xPkHwF7hJs/3/qDDeWEnOEyJAgWLlzoMUHgzq5d+KEu0qBBg3Jvi6YPBw4ccLhs48aNeoqsFBazMp5/RbZBWGoTicfSe6T+KEUJkJytyyR3y4qSKctOHxCWYsnbuU6Xw/9M1R9kaHKMhEF0y24SjqBKAHw5rMw2CWbcLtwuVbGv4Afp0RV/SeaSX6VQA7BlhddpIgm9RmjAC6OEjb9R2/A9VMPbJCRUwus20yWmQ+n3EgTYsO8hYaAzDUoaI6NRp0fFhbaePFlr7C4PC5eI1CYSUa+prSlyRL3mGnQzGkF6gtH0R1fPkZz0/VKcUl/iO/WXsOiqKxvj8AMcAUhbQN8+uG8N8NuC+3qaZbsucGvHV5a1hrE/67JXiJFAQF1fTEHOdP+DEo7M/16X6qBN80pG9aNWrgb68W8jAWD379Bo1GH2rowLvkFq/WAfJBx/thRbiiVj7jdlZlYkHn+2Xl+znx0hImhojEWqrta9EfzN/O9XSf9jSrm3j+99ijVZa/8YtfRztiLwPI3J+JWZlH80O19mL98j6Zm5kpIQLQO6NZL4WNflPgKC9h0I0+Cpno2Jt41ovfneh6WwsEjefPEpl81atRyRXXmuq2+/R28/5dUX3fwxBBbzRYryxeLycIrgpGMdc8fzCJKFaA/Dv/76S3755Refn+61114rbdu2lbvvvlsqyn4/OeOMM+Scc86Ryy+/XMzOH+8fpwe0JkM1IZBTMgPLy3JwCOpidgBKs5SUDXKOL1RH8Q2/b5NAoIlHax8cl0pmLumC16uoQIrzcuTDTz6V8c+9LJsW/+vhsRGcLxkQ4TA6v3SEvp4vCerbj9y3D+5bA/mO3zMee+wxrUryzz//6HkMLj7++OPl4Ycf9unp+/u1RAIAPDW1ruzf/PTTT2XChAmydu1an++L4y1irq+99prUBG/fQ4sWLZJLL71Uli1bpv1uayvjeTp/96rId7GATRDs3LlTT5s2bVrmOuMy4za+Pu5bb70lKSkpcuaZZ5Z7+9dff13Gjx/v8rqsrCydxm9W2OGys62jRSo9ejO1uYSnNpf4XqM0YFC4c40U7FglBTtWS3HmoTI3t+TlSM6GRbpAaEIdiWjWSSKadZbwph31h2St3yZBhNuF28Wf+woCsnkr/5S8tXNF0ADLWUiIRBzTU6K7DpPwJh30y19WfpFIfu09XvM9FMDbJK6uLuEteuqXKhTOwA/SovQ9UnRopxSl7S5Zdkmxh9E+qgiJg226OIzFDIuQsJRGEpbaWBPsYXWa6L/x2YcfMzqLZtF0yVn8o/6oArwzDv/xocT0Pk2i+5zuVRJdR3jlZ4slN0uX4rwssWDB+TwE9XGabbus2O66QCmBoiPHo+KsTffK297YtHWb6ah0WyPPQmtDTusItwIRPY8fqxhZXQt/rBsNRqvjb2EWA+r0xySWnGLEf6L11O7y0BhcFm9NWriB8L4txI9dKyurylc/rNsISWo3UPI2LpS8I4ckKqmORLXpqyUujx4NvJ4IVcnSspdI2Ke244lLYRFS3Liz6X8LGUmCivwQx/2+/GOjfDVzoxQUlia13v52hYwe2kbGDG/j9wFQW7Zs0eDW33//rbP1MVO/e/fu8vbbb0ujRo0q/LghccliyUqXV5+d4PZ6HGuVJqKswcCQ8GgJkXwJiYovGUlcspQ46dyLpX+/3jL+3jsdHs8hsYDkARbXf1mDh3ddd7ncfePVUpCZprXNTzz1dBkwYIA8PuEJj89r6dKlWpYYpaSM17hNmzayd+9eCQ8vDaUgroABjDk5OXLFFVdowGnTpk1y3333yeOPP663MWpEI+iIQCOqIsTEBEe5rcqoVO1sfNbr7CcsuSKYBeVtCSwElZEEQKILp3azZiqSaPYnf9UTr06o3qHvqZL9HVC144UXXpB58+ZpE1UM0EW573HjxmnSDbdFAB7JOw3UawLBGqgNjUYyvLzjX4iE1Wlqva+XLK7+rS+4prcdb6v9C0qP7z/88IOeBkQSPCZRBwHpd3McS8PCrSW5Q0L9sn4XXHCBLsZjXXnllVJYWCgfffSRx/vh2IjXHOVtjPtiG+K1Ro9Z9IXt1auX9pZF+RtP+9PcuXMdAvdPP/203HDDDbb3yMsvvyzvvPOO/k2Up3/yySfltNNOs12P2O8999yj+xeOzSeeeKJMnDhRGjdurLfp2bOndOjQQSZNmiR33HGH1FbYvnhtnGPTiFcHTYLA+NEfFVW223p0dLTDbbyFL/f48MaXITQpTk0tv+P5jTfeqCWJnGcQoPExGiCjtIFZGW94v9c5xjat10ik5zDrzp6+V3LRv2DLMsndvkqTA86QRMhb/Y8uWnOtYSuJRu+Clt0kqkm7amsSWWXbpJbjduF28Wo/wejnDQskNH2/hKTUl1i70c/44oPyQZloOrxjtcv7IxAV3+NESehxooQn1pVgwvdQbdsmCSKpdUVad3W4FKOhCg7tLO1tcHCnnhaVM6obQbqig9t1sYcftRF1mlqbLu/f6vJ+OQu+lZDMAxLTsqu1Xr8xet+ufI+1bv9RDf4HhNAwTfRjVCqOAfjBY5wvPY2zXmdcpreJs02pxvPZ9ep15daVb3TJeK9nWWjN+ZIEgp5ilGJJ6Rf9O7bL7W6DoFUhAmH5Tvct/beW1tB/e76dzkaozkQMyvrElY7uD7Ur5+Pq35VtzhgQEhKkKPVUU/Tx8CghQSz9z5Yj/0x1e5Ok/mdLUr2GYvbPIcw2N5oE+uqL39bJ57+tL3M5kgW4HGUsLjipvfgTRq8PHDhQVqxYob+F9+3bJz///LMGuyuzv4clpEpRSIjHEa2uwhChEZESin49KQ1cjibWUkIRMRostJUo8ek4iGQEmqkWiKXALhaIslo5GVJ0YJutsbNRuqj0fIRMnjRJLr74YomNtc5CNSC4dfXVV5f5a9iOqHaAGMIDDzyg+wYGLuOzFglnlG7q2aO7tGjRQj7//HO55pprxKzwmWq/Xew/w93eB/sFZgeU9BCwztLzJgVeMoLdfoZAgJYLMkYDo/l5bfpM1fJkdsdCjEDH/n3//ffLm2++KU2aNNHjJS7/6aefNDDrfB9H+C7o+L4rcwscW6owzuN5/QJBmM60RDC8qvcXPD6W8rYFks0nnXSSQ8L5+eeflylTpuhnDRKsSBYgkI/ZCfHxrr+D47lg1tYTT7hO4iKoj8HcSOBiX/ryyy81boukAgL/2CaYqdWtWzeN3+I89kdctmDBAtu2wmVIOiBBEBawr7NneC7oheQcm0a82leBeVQUsX0II9PoLDfXOh3d+YO6vOQAMvVLliyRV199Vc4++2yv7le/fn1dXMEOVFt3In8xtkFVbofwek0lul5TSe57qn6RyNu9QbJLmh3n7drgolaqRfL3btIFU8XxgzW6eSeJbdVDGx6jUWRVHjyrY5vURtwu3C6+NM3Ekf/Inx9KYp9T9T2cueR3t7UWo5q016bD8R2Or7ZkYE3ge6j2b5Ow2HiJiO0gsc06OFyOmvr2/Q1QeghJhKKswx4fDyPm8FlXnuw1s3WpbvgBbg3iJ5QE+0sD/WG4DOdtSQBcZr3O1bR+XyFw7U1d+ci4JB8eNEwkomanIOvMCBeJA/tEhcPldudztq2S7HV2vTDcSBl8gR5TNVjjw6i8YFLbji1VIXXQeRq49UdD62BmbAPjNL+gSPYcKn/UXnZugUz9vWxywN7U3zdIj3b1JDbau+82jerESWSE+3320KFDGpBBgK5OnTp6WcOGDcuUukHA5ZFHHtGgCkb8osQO+gIiOAQYlXnvvffqqEwMvENjxPfff19HhV598x2Sn5crU95+XULDwmX8M8/L//43Vfbs2SPJyck6yO7ZZ58t8zveYV9CUC40Uo+3COKGRcU4JBBwHAyNipfQ0HwtwYdjXERiXXnpqcdl6jffyYpVa6Rl86Yy6enxMuDYPnqfCS9Mkj//mSMzv/tCyyDNnr9I5i1aIq++86F122xcVibMjATQd999K5+/96YUpO0uTSAggVEyu0wvs1t3zAjAtjIGNSKIbd9QFKmNooyDcvLQIfLNN9/otjUbHZF9NN0hmWRsFyOZpM1YdTsb5YKsiyZ7vIJyQSX9A5BgioyqdTXZjeB0bVxnjFy+9dZb5a677tJjiQHHE+P9Ydze/tRVKUIMVnj13Q/l5TfekZzcPDn95OHy0hOPSFKDprqvPProo/LFF1+4PcZMnTpVK4KgfylGo/fo0UN+//13W1wRwer//e9/kpaWJl27dpVXXnlFA8yu1g+zH5BgNYLWuByBavx9zBo65phj5I033tDbGDDi/sUXX5StW7dqJRTM4MLI/Kra9sYsJTxHzOAw4DiPUeaffPKJnseIe8wKwOwOlFBCzBMj/404KQL6Dz30kB7vUTIInxvw7bff6unq1au1P6yzadOmabDd/jXFNsG+gGA9YPthNgEeCyV+vHlOzvCaIbBvzEK48MILtaQRklFIUqxbt05jvzNmzLAFzvE8MHMFSQTMdjFmKiBRvnjxYjn22GOlNjK2kfN31op8hw3YBIF9GaGOHTt6XX7IFYwCGjlypMyZM0d3zuuuu64K1piqAz7Yo5t20EUGn6+jDnK2rdSGx0gYoKmxqwBKzqYlukBYfKrEtOqm/QswwwANlImo5nhqmnlk3ncu74NRQPGdB0pi75ES1ahVNawlUdVB4DzM+Gyzg8a8OttAEwalzZGLszOq7+UICS0N4hsj9zW4b4zwN4L8SAJYEwBG8L+mR+chgAmeApy1DQL21nIIZWfYlge9WLZvWlzurIrEPiPLNOQl88EPzpRBY3R/yFwzR3LS9klMagNJ6Nif+4cHSA7c/PxMv7wGhUXFcs9kD7W3nbx691Bp0TDR7fVICiAAht/CGOHeu3dvDa4YgX9Ajz8EiBBEwkhLBL1GjRqlI+Nvv/12LdMwbNgwDa6g/A5mISDpYBupiBHzGA2OmUVhYdKuXXsNVKFvIAJKmM2PEaMoFVGp4yBGyyJ5EFv6fKd88ZVWCcDo/LvGjZMrbr1HNq9daQ3kI9mA5xkWIa8++4Ss27jZZekiexs3b5UjGZnSuV0rsaCefcnlGLCGBD7KARoljKzN4x0bKCNxUVyAme/OqQeLdG7TXN58930xI2tywNWgHyQO0qyJgBCx9ijytml6aLhtZoA1KRAEM9rc+OwXa034i0aUfmec9d9O2X3gqAzu2USa1rcGQzfuOCwLVu+VNk2TpV/nhrbE5Ld/bZKYqHA5+4Q2tvv/NHerpGfkyojjWkidJGvZqxUbD0rXNhWbkY24W3p6ulx22WWVeq54Dffu2y8rN+2QVUv/0yD+mIsvk3ufmSTvvvuu3gYBX3fHGFQcueSSS3TkOo5bSAhg3QzXX3+9xhaR7ESyFMHlESNGyPr16zXZ4A2sB447CLgjCI4ZR9u2bbMF2ZEgQdAcCVT8bQxaRhzTPolQU7DuCNIjaYLSO2PHjtWAOUrP2cNsKGwT+wSDK/h8QGkh+9JBKCmE5Ei/fv1sl+HzBEkYBPA9JQgQu0XQH4klJH6QsDBmHBjln+xhlsB///1nu97+1LgecBsjQYBELvYhfPYdW0sTBP4UsAkC7EB4gyK7gykq9nAZ9O3bt9zHwQ55yimn6DQSvAGQJaPggQBEXPtjdYGCw/s1UaDL1hVaNsEZvngcXT5LF4is39KWMIhu1lFCK/Cjm9xDQ8rM1bMlJ22/SGp9Seg0gD8sST+skeArSN8j6f9+6fUWCU9uIIm9R0hC92EahCQKZtjHY5p30sVeUdYRa7Jg/3bJXDFL8vds8qpsT1hcsnWUvn35Hg3qJ9hG71tL95SM+MdoftT0r6U/shngdITX05tZFUwOkPN+o42IzV52KUjMnDlTR8gi8LJ8+XIdZYvfx6jdjNK++L2M0g/nnXee3h5JBJR5wO9yJAgwuwAzEXDeKAXcqZPjZ5Q9++BP586d5aabbtIAU2USBO6grjnKV8C1110nkyZPloOZORpcwucdBpdE1W+h30ERTMZnnXUGQklzVKOEERaxSNrhI/pYSS5KCo97eILcP+EZ2/kvP3hDBh/vGFzSx3IjMSFB0g8floL0vdaGqTYuPm/1Ik+fw2i06uoWIR7Ounk8l5/33j1OiBePZcwe8AS9kMoTEhZZmgzQckERtfa7SjBCKSFfBvR6gn1m4iuvaNA6LrW+9g1BsBh9RfF5VN4xBqVX1qxZo6PX69atq4kCwHEMfUOQ4DTW8+abb9bj4/Tp0zWx4OtxB+VqcH+MSMdx56WXXpIHH3xQ+vSxzmRCUuD888/XxEEgJAhQJg2JC0DiGM8F26qigXIkhSApqXR2LmaZgXPCBT1hjetcwWh/lA7C/VASDzMg0EMHMwcA+wBKDCHhgs8gzBSZP3++zmiDdu3a6UBz9CBABRnMCEO5KxwnnP8u1hfJJwrgBAFe8Ntuu02bTuDLiJHF2r59u9aXwvQeZAkBmUFcjhfWvtYVkgMnn3yyThfBm9BTdoqCQ0RyfYnoeZIk9jzJWo5o7xZbwiB3xzqHpleG/P1bdTky73v9chHdvKN1dsEx3SSyQUvTTq+virIxGEOT/vsHnJoepPBDqCg7Q4OXGFlVlI1Tp0UvOyxFWRku34/uRNRrJnWGXSoxrXvyPUmmh/I5MVhadNER3we9SBDUPeVaSex5oim3HQOcwT2rgoi8h1kEKKmBBaV8UQsco0YxKhNlKVCGwzngj+AXfmsDAjQYKeuqT6ArCOJhwYhajD4tKCiwlTfyBoJ7uI8zXGbfvBKMxpNgzGhAJQEE6uxpIBmlK8IjHGYgGHTEaXGh1G9pHWh2tDhMYmMSbUkEeHHCw3LlxdYkSkVkZGZKSlKS9gKi8oTojAAjGaAzBQK0f0B1sJ85YDihV9lAfJtmybrYQ7kyV/cfeXzLMpdVdPYAGCW6MTofo7MrA4FkxPmMkeAo44P3P4LweM97OsYgAYrZAwjUYyQ/bo+gOOKMKKEGzsHw/Px8W8USb3g67qC5OYLuKMlmwDoOHjzY5WMh7mnMjPAGjuOVaa7rbt0r81oZcViDEcc9fPhwmWQC+lK4g14uhu7du2tDYjQZxiwFlHJD4B+vFZq9I9mD61BmCM3hjVkK33//vSa4kWjAZwnu8+uvv2qiyB7W15v+tGYQsEdW7FxoZoFpP5j+gYwWvsSgIRA+1DEFxoDZAUOHDtUvN0gEGLCTLFq0SKcZ4T7O02GQRTTqYFGQliNq3EaXlAHnSnF+juRuXy3ZRjmiAzvK3Adf+qzlipbreTTmQ6IgVhMG3SU80f0XWo6U975sjHE5pq5TYMP7xjHIf7gkCXC4TNDf1Ywdf4nvNFBi2/Susscnqq3iOhwvh359r9yyMXEdjqvW9aLAxFkVRFULfQBQ6qc8KPXxwOtztIyQO+FhofLUjf196kHgCwT5MSgPv5mNsgwYgGcEWAw4b9SaRnIA5SIQmHEO0DvDrH+MyEVABqNlEaBBkAf1uL2F0aAIsjnDZWi4XFH2ZZWcaQIhLELad+6qwa1123ZLk9alQVUEp/GbMKJOk9LZB4Wlsw+sSQTPjXNXrlkvvbqXluEgJ2GR1pmNmMWIckEeXi8KPAjuIp738ccfaxC7MhBIRgDXCDTj+INjCQLw3hxjBg0apAsSDCglhOoiSIJihDlgJpWrWvr+gLJF6H/gbaklxDjt45zecC6zA5jthz4Q9nbv3u22t2plj5kGBO6xbVFmyEi8ILmDzw2U8Dn++ONtSRKUqPNlALfx943niwQAktpYDJgNgRkFhtatW2uvFwNK5mG7GLNIAGWn8HlizPIwu4BNEACSAsj+IVGAbA++hODNjSmQ3gT2kRyA7777ThdnaGjCBIF54AsGAoxGkLEw45DkbLUmA5AwQKDTGeo8Z636VxeIqNtUEwY6w6BFZ33MYB4prwfgksaHxfrFN18sBfZND0v+jctwXUlTxMKco3L4n6keHxtlZayNqJK1rBPKWODUWjfSOmVUnBp/BZOaSiihsVtxdqbT6H7nYH/pv9HDIxCgNAoRuXhvsGwMVeSYyrIxRFUCTYI99QGwd/5J7eTTn9d6vL5jS+9H23sTaEPzzosuukjat2+vAZZZs2Zp2SHUmAaUG8LvbdTURvIAwTP8FscoWDj99NM18IceBs8884z+VkdJCow+Re1/ewjqoQRIvXr1NHCHJARKPfgCZSVQOQDlI7A+KBOBhsgrV66Uzz77rFKBO9TU9gTrjiTEL7/84hBQMgai4XegKwg4FR49rPWui4qKJTc3T0JDQxwSKr/N+lsuu2C0hIRHWfsV6KUWD3kFuyscbuPmDnqxuwcrudzpaour29su8uWxynscz8kTQI9AV7M7qHbA8QADezFaHzXer7jiCq30cfDgQX3f4r2B0fLeQCwAxx8EzlEGBgFhBJbx/izvGLN3715twIuS5ShVgwWPh2Mfjlc4pqAkEe6D8xg9j8a+qMlvX5mkovAcJ0yYoKVuUK4NsxtwTEWwG+erCoLdmLWA54KgPHog/P333zravjLHTHxW4BjsqdQgetfgmGlf2h2fF2iAjOMogvZoUozXy2iI7AyzQ9CfAJ9FmAWC3hJobo3jsdF8GrfB64XHw36BkkTYv+xnVGBb43VFcgllirAforGx/awWPCckTrwpX28GAZ0gAOzE5e3I+NLgKnPm6jIiA0Z+JHQbqguCpqjlbCtHtH2NBrqdFRzcqUvGwhlazzm6aXs9zd26ospGymPdrIH3kqXIdVAep8V6alxmXO54G2vDrLyyQX6X9y1/BEyFFRfJwRlvlN8c0y5hYJ1WGlU6vdTNeWO0icNU1Mgo221Qg7SmEg9VkVDC617sKsDvcrR/hvcNvyoLNc/RpC7OadHLkvXfGNm89/MnbFO2XeHoZyLPWDaGiKj2Of9E6wjWL39fL/mFpd/NIsNDZcyJ7WzX+wsC1AigjBkzRkeTIsiD2tsIJBkJAIz6/OqrrzQIh2AKAie33nqrluQwRoj++eefOngPDY8xGhMBGgTtnRMEKPWLagD4rY7AGAJVmPH/9ttve73ORnALAwQR2EFgDQ0wf/vtN1vd74rA80UAC8kO/W7uVP7CgKAUejJgFLS3ZZVQzsJoUjp7/iJ54dW3ZNDx/eS3rz/Vy1asXitbt++Ui889WyLqNNZkg1mgBHD+vq3l/L4M0X5IVLuhWS9mJD333HM6oh+zjjDqH01wcfzwJTCNYw2SmigvgyCxMcq+vGMM3tvol4KBx/j7eCwcS1B9BJCsQNIUCYQ9e/boyPvjjjvO50SmOzhuopwN1hEzsZCYwPGrsrMqyjNkyBBN+iJYj9H6KL1z7rnnVuoxr732Wg2m4/lgu7qbeYFkAGYRGH0YAM2bEczHbDXU/0cCA6WfjIbDKGGH+6DkHZICSLKiJNS6des0IYHXDc/l4Ycftv0dfIbheaEcFD7b0Fx69uzZmrQ24sAoMYTXEn/bKC/lvO+hpD1eJ/ZXsgqxMIruM6MzN0YuoBGKWeHNmhmkDcsQcM3dscY6u2DzMsnft6XiDxYaKol9TtXGTdYgvvNIfPugfEng3imgT/4W4jq5EBllS0aUJhViHJILrm5rPcX5qHLr46f/86XHBpEpgy/QYF9xXrZjLf+j9vX7rcH+4uwjUph1RCx55Tfz8hc8dw3u2wf+HZIA1sA/Fny596aHhzfbxOzlqIL5eFtR3CZuZiatmSM5afskJrWBJHTsz4az3F/4HuKxhcfbKvwcQmkCjIREkLyijmbny+zle+RwZq4kJ0TLgG6NJD7Wc/meQKaNaEtGmQbDTGA0HkU5EtSy9kVhZpoUHS3b+PLcsdfJGSNPkiuvvlbCE8xX99rddjGExaeacrsE6/vHX7hdas92QRIGAfzXXnstoLcJetViRh2SHd4mgAPR5s2b9dRo0FyZuDUTBBXABIH5gjMIxqIcUfZmlCNaKkWZ7HLuuoFUpI72thQXV2vQOpDYZi44lUvS5EFYuGStnV/+CH6MJCouqq41ltDYhNLgfmyiQ5DffrS/Bv0joqplVoVz08xA+cJTU8x0vPUWtwm3C/cXvod4bOHxNhgSBMEmEANWNbYdjqbr4jhiPqSkzGqKKbcPt4sX24fvH24XX95P3F9MvU02+zFBEPAlhogCAQKj8Z0H6YIDTsGhXVqKKGPRz1KQtlsCRmiYBlW1hE4YTiMkFEH7sMjSy22nxr9Lz4c63dca1La/jYvblpzq3y45AGMU6/ZJV5fbNLPZLe/o+lnyc6W4ILfkNE8b41ry8+wuK73O4Xx+nlgKcJpbclp6n2oro+MENfuLKlu3v5LJAbxm9iP6Q2OTJDw+yXqK83GJ1lME/2MTanxqM5tmEhEREVEw0VrnCan6fRy/jYqLCiU0LFxn9tX0d++axO1CRBSYmCAgqsCXmsi6TXVBYPzgjDfLvU9YAqZK1nEZlDf+rQF3BPJLRuFbA/OuAvr2wXmnxwuQL5veNs0Mj02wnomIkjBJ8tvfRxJH+ytowqAk2WBLIuDU9XlvbiPFhVITUK6nzIj+MqV9rKP/0fC5NmbL2TSTiIiIiIIJfp9pw10TjWj1BrcLEVFgYYKAqBLiOhwvh359r9yR8k2vedl0daBrsmkmvnjrrIbwSAmTkiSEn2iTZ525UDLTwU0iwThvP+uh4MAOyd9vbVrmSXy3oRKP2uG20j6JmjAiIiIiIiIiIiLyJyYIiKphpLzZkgPBXDZGy/fERIhU4Dl4W3qpzomX1+ptREREREREREREtQMTBES1eKR8bcCyMY7bggklIiIiIiIiIiIKFEwQEFVSsI6Up6rBhBIREREREREREQUKJgiI/IQj5ckbTCgREREREREREVGgYIKAiKgGMKFEREREREREREQ1LbSmV4CIiDpY3D8AAMRjSURBVIiIiIjIjIpyjkrGkt8k/Z8v9RTna5t3331XWrZsWdOrYXqXX365XHLJJbVuOxw+fFj3nx07dvjl8Xbu3ClNmzaVQ4cO+eXxqGZMmTJFX8ea8thjj8nAgQNt50eOHCkTJkwQsyksLNQqCLNmzZJg4O/jTVUpLi6WHj16VOt2Z4KAiIiIiIiIqBpZLBZNCmyfdLUcnPGmpP/9hZ7iPC7H9VVhxYoVcsEFF0ijRo0kPj5eAyUXXnih/Pfff1ITPv30U10PYwkNDZXo6Gjb+c6dO0tt9NFHH8mAAQMkNTVV6tatK8OHD5fZs2fbrs/Ly5Prr79e2rVrJwkJCRoIxfn09HQxm8cff1zOOussadasmZ5HQAwBSfv9AsvPP/+s1//xxx+6PevUqaO327hxo8PjYVuOGTNGA7xUPU444QR56KGHHC77559/ZNSoUbr/Yx9v06aNXHvttbJhwwaXAfhA99NPP8nDDz9c06sRNPtHoBxvYPny5TJ48GCJi4uTxo0b677p6TMYwfvnn39e92kcm7p27SrTp093uA2O5y1atJDExESpX7++nHvuubJ161bb9bt27ZIzzzxTb4PjGBLt9vBZOH78eLn11lulujBBQERERERERFSNDv/7lSYFLIUFDpfjPC7H9f6GwGu/fv2kQYMGMnfuXMnMzJSlS5fKSSedJF9++aXUhIsvvliOHj1qWxCcefPNN23nV61aJbURtu0jjzwi27Ztkz179sgZZ5yhI5Axut0YlZuSkiLffvutjmhduHChBk6vuOIKMZOMjAwNjCFw7AzbxX7fOOWUU/RyBPEuu+wyTcK4c80118gHH3ygj0E1k/g7+eST9XizbNkyfT/MmTNHOnbsWCaQSlSdx5t33nnH4XiDfXPEiBGa0D148KD88ssvekyaOHGi28d55ZVX5NVXX5XvvvtOjhw5Ig8++KCcc845Don2W265RVauXKl/c8uWLZoIQJLAPgGA98hnn33mdqbM6aefruv022+/SXVggoCIiIiIiIiokooL8yX/wPZyl9ydayV9tucEAK7H7bx5PCz42+W57rrrZPTo0RrcwMwBjFpMTk6WK6+8Up5++mmH0h5dunTRkY84/fDDDx0eBwEUjJjEyMlhw4aVKdXw1VdfSZ8+fTQAjtHDCI4jQOKrSy+9tEzAfNGiRRIVFSX79+/X0Zh4Dgj4dOrUSdcXI8s3bdpku31RUZG8+OKLGphMSkqS3r176wj0qnbTTTdp0AkjpyMiIuS2226TsLAwTQQYQW5sc6w3LseMDgSUZs6c6fFxkWBAUgUBJTxflKBwN/sDCQpjhgJGy+Lxs7OzbddPnTpVZ2jgcfA6nXjiibbrEPxq3bq13hcJJZQvMiDofsMNN2jAC6P4Tz31VNm8ebNXj+sMgS/sg9gO3jruuONk7NixHmeX4PEwewP7KlWvrKws3dfuuusuHXHfpEkTvRyjqO+44w5dfIHjFfZf7Ic4ViFZ5I99PDc3Vx544AHdz3GswgjyJUuWeD0KHseeyZMna2DZGEX+77//OtwHSazu3bvrsQfr8cUXX0h1+PHHH/V9gueF9yiO+4Y1a9ZoshLbA8cRfC4gyG3AsRXBbrwvW7VqJf/73/8cHhsJTwSu8Xpgu3fr1s1jghkj6TGb5LnnnrPNBkLQHKd//fWXw21vvvlmHd1vP8Pk/vvv132nYcOGcvfdd0tBQYHDKPyLLrpI9zHcBrPhDhw44HZdfv311zLHm2nTpunnxIQJEyQmJkZfR/wdHAPdweuIYyBeUxy/MSsPybA33njDdhtch+1jnxBYt26d7TyO+ficwP6Dx3AFl2OfxTpWBzYpJiIiIiIiIqqkwvS9svNt34JfbhUVyu4PH/T65k2vfVki6zV3ez1Gp69fv95j0AO+/vprLWnwzTffaEAMAWsEbBDgwikC/Qj4IzCG4D0C3iiTgIC3AUGR9957TwMtKJmD2yGIg1kLvkAABrMbMJITfx/eeustOfvsszUYZJRrQIIAgWAEwrDuKGuCkhHh4eEa9MEofSxt27bVEZ9Yf1yPwGB1mT9/vgY2ESz0FLzq2bOn2+tzcnI0IYOAEmZ+IAC+du1ah21vD8/3999/18Dp6tWr9XVCUA6JCQRR0a8AZXvwmAiWYoS3sa/cc889smDBAk0QYb2NJATKbmD7Y6YHgqn42wjSImCIkeII3rl7XFcWL16sf6MqIHCJ/fP888+XYJH2tzVYmzq49DkdXfmP5KftlvjOAyWyjjUYn7dnk2RtWCRRDVtJXLu+ellxXo4cnv+9hEbGSPJxZ9jun/Hfr1J4NF0Se54k4QmpelnOtpUS06Jirwteb7zvMcujsvbu3av7OvZz9JRAoBsJBrznK7OPG4FrJNwQpEbwGTOXkNTDcRJBZG9gpDmOmUi4IiGC5B1mDRmJVrw3ENzt1auX/m0k0xCUr8rSSki6YTshOYHtgXI4RnkzjJZHwBkBbaw3EgP4NxJuOEYCthmC2UbSz/l1RDAdiRoEyZGsxd/B8R1Bd1dJO2xXvH54zk888YTtctzn7bffliFDhuh5vF6ffPKJQxIFx82hQ4dqEhqfPdh+eG0wYh9l2pAQxrEHrxmOTfjMwGuAclCuIMHsfLzB/oXjbnh4aXi8b9+++vyRyEByyRn+lnMJImxn52QtEgb33XefPg4eH2WJKnIcq67EEmcQEBEREREREQUxjAqF8pp+ImBz1VVXaeDFGL2I8wjyAMohIMCCEg0YGd+/f/8yASQE2RAIx/0xShU1n+fNm6fBKV/gsREARNAIEGT5/PPPNbBnD6OUESCMjY2Vl19+WYNFRjIC55999llp3769Br0Q3Mbj4nGqC4JbCFrde++9OiLXFQTZPv74Yx0t7WlUMIKkeC2wXfF8EJTDSH53MzCaN2+uI50RuMNoVSQhDHj9MJoYJSzQ9wFBVEAgC8EvlHcyRvpidDUgKYBgIxI1SFAgQPjUU09p8A7BPE+P60paWpot+eMMzxHBQCwV6UWBx8XjU2Aea7yB/RAJQiSiMEocNdkxowlB6srs43gf4XFee+01XU/s8xi9jn3GlxJI48aN0zr0uD/KWm3fvl327dun17300ksayMZsKrxXESBHsgqJg6qEYwiO2ejDERkZqc8bx3PAc8vPz9djIo6XGMWO7YvEKZIxGJGPBAMC2Xh/Y7GfXQbYXphhgOMCti/+Fo5Df/75p0/riWA+khRGM3EEwfH38PlhwHnMJMBxpkOHDjqy//3337cdD/GZgnXF/oH1eeaZZzRhZJRy8+Z4g2Occ0IoJSXFdp0rSJa//vrrtqQoSmrh+Od8ezxHJGGwXR999FGPCeJAOI4xQUBEREREREQUxDDiHtwFTuyD2c4j6xEAQ+DLuP8xxxzjcL3z+b///lsTCwg+YfSlMULUCBz6AgEWJC0AQRgEpzCzwd3fx+wFBJbxPBCoQ8AGgTIj0IwFI3kRsHEFCQTn5rieFgS4PEEDXTx/jOi1Hz1rD88Po6Ix0tlTAAlBeIxURrDMGwjiY+QyZlYgyIRgpfEaIDiIv4dgGpInmO1hJCewPRGsQw1/BF8xmtZIqGB2Afon4HUwticeH7DNPT2uKwgA2pc3sYegLsoZYalILwo8Lh4/mGDmgP3sAYjvMkgvM2YPQFSj1nqZMXsAQqNi9DL72QOQ2OtkvdyYPQAVnT3gy7HGGwjU2gd0sW8iIGsE4Su6jxuNrY899liHYwOOC76sN2bSGIyZPEYiFO8VJBDsHx/vo927d7t8rNtvv92nYw8SEO6OE3i+ruA9ioSi/Wh5HN8Bx3jjudsfU52P75gdgmQILsfxHc8L709fj+8YtY/FKGGH1xKPi2SPAYlf+/I7+JtGSTtsX+wH2EfsE4k4PhqfV94cb/AcnHuVpJc0i3c1ewAwwwqJERzXUWoJCRaUN8Jnj7v9BEn10047zbbvBuJxjCWGiIiIiIiIiCr74zqloZb6KU9xXrbs/uRRLSPkVli4NL5kvIRGxXr9tz3BSHzU6sYodZTtcQcBGfsa/oDzCBQDAsNGHX2DUeoHMDoVZS0wqh8lKxCwx6hzBPGcSzJ4AyPvEYzBDAQEkFAv2xn+vlE2AuVwEFg2AtgYPYtRs8YI+PKgtJK/oIwRRsMiyYFa565gJC96JCCI6am8ECA5gOeKbYyRwZ5gBgVGRGM0NUYuY6QvZlPgbxkGDRqkC14XlFlBE2CMBMb+gdcQC5IBKJGCMiTo34BSLPjbqPONx3TF0+M6w35RVeUzVqxYoeVSqHphhg6CtjjWYPZQZSBQiwCpEajF/o/9zmi0XtF9HMdC4z1qHNv8De8VzHjwttQSRvJ7aozriqtjKo4TmEXl7viO4Dne10aSwDjeYzsYMzPsj6n2x3dAyRyUDML2xOMhoI/EpqfjO2ZQuIJjI2YooIwQPie+//57h+uRDMA6GUkCrIsxMwXbF8kO588rrIfxPJzhGIZZcPbQxwXJ50K7bYJSRJjt5S5BgNthZgMW+2MZSiC5g8QWysThOWH/9eU4hlko1YEzCIiIiIiIiIgq++M6PFL7AJS3RDftICkDSptGuoLrcTtvHg8L/nZ5EGBHM8k777xT62QjkIIR9ihvg5G3cPXVV2sJh1mzZmmQBWUj0E8Aox8BoyQRsEDtbQRUELjH/Q0IXiMIggAhkgMYLWvf2NNXGCmLMiIoHYIGj6iV7Qwj8zHyFTWsjZIfCFJiJCnKESHBgDIjeL5YN8xwcBdA8xfMUsBMB5QVQrLEFVw3adIkDbSVlxwA1NrGdr3xxhs1CYLng7rrRs1zewiqIqhWr149DZyiNrZ9/wmUE8G+gJGzRrNqnCLwhe08Y8YMTbbgvDGCG4+HQCwChwjsGSOGEcTFTApsf0+P6wqSBiifgdfHW6j1jXryqEFu7HM4bx8UNMrK2JcroeqBkfToUYLSLyg/haa2gNcD+7svQXDsO3hPo/ExjiUIyOJ4gH2xMvs4AssoE4PjivH+wch/1K431reyMCMAPVCQUMU+i/0V/0bfjaqEhug4ZuM9abw3jMbsGMGO54+EJY6F2EaYvYS+LQi4o4wTyhHhmIn3NRbn5Ca2O2ZnYNYGgt54rcub4YPHdnXMPe+88zTZiM8dvB7OgXMcG5BkwrbDcQn7lNG4HmWO8PdxfDVmBeCY5NxU2d7JJ59c5niDx8F+9Oijj+o2Wblypbzwwgu6b7iDWQCYhYJjMEokYR/F/m004Ma/UUrKKA2EpACO20io2PdAwGuDBY+Dz1P8274JM45pSB5jZlt1YIKAiIiIiIiIqBolDxwtKYMvkJBwx1HYOI/Lcb2/IWCNOskoo9GvXz8N4KMBIspwoFQCoBwPRuAimIGA2i233KJlORBEAYyqxCh7BPlwPYJHCBbbB/SRiHjyySf13yNHjtTHrAwE+RH8w+O4KrWAUg8INCO4hCDUDz/8YAtII9CDpIZRZgijazFi1T4IUxWQcEFgEskRbGf8bZwiYAoISj733HMaHEMJH/vSIe7KY8TExGjCBoF7lEtB4B4zLFzVp0YgDNsNrzluh9fJPrmCgBR6GeD1xN/E64/XDCN5EVTEvxEsxAhaBL+QBELpKQTSUKMcAUKUZ8Fzwuhh7BMIvnp6XFewbnj9sM94CwkebAvUJAeUFcF5jFg3oIktAolGLXGqXtgv0TgciTIERLGfHHfccRpIRjDaWwgsY19HyRyM9O7YsaMtwVCZfRwwkhwjynHswPrhb2C/qchMJ3eBeiQ0sI44buH9hBr6SHZUJWwXlDJCPX4kTzDi3nh/4f2M9y9q5+NyPH8kVI0yP4CeL5glhGMlRsU7N/lGQhaBdBxvcRsEy9E43RMcQxDgN8oBGTDDC+9THN+de8sAjjE4HmFdMQsMSQTMYAC8ZphFguMl9hE8NySGcXxwB/sJkhH2xxs8DvbVv//+W5MeKI+HJsxGsB+wbvgsMyBZhYQt7ovZeTiP3ixGuTUcC7EdcR0SZscff7yeIlGD52zAcQsLngM+R/FvlFkyoM8C9h28ptUhxOKvvd9EcFDDQQ6ZpYo0ywkWyGYhy4o3hX1dMDPjNuF24f7C9xCPLTze1iR+DnG7cF/he4jHleo73qIGMkauOtfs9+lxco5K1tp5UpR1WMLikiWuw3ESFhMvtZVR3gG/D+1rSVcGAu0IFCK4Yh+IQrkJ1KTG62DU0TbLNgkGxnbBLBbMoPjnn3/8Uu4FCTAEo1GyxF1N8EDFfYXbxWz7yxtvvKGloZBAsH8OSK5g9Py///7r122CWRFINvnreFNVMOsECRwk7D01et+8ebOeIhFW2bg1exAQERERERER1QAkAxJ7nsht7wYCPajTj1Hq5Y1SpdoJo4pdlUmqKIzUNhqZElHgwuwnzFDDaP3qSnD4+3hTVdC3AUnO6sQSQ0REREREREQUUNDrAGUjUEv77bffrunVISIiP7n//vu1dBDKA9mX1aGawxkERERERERERBRQEDjyVK8b9a9ZMZmIqPZBLxgs7qDEEBaqPpxBQERERERERERERERkQkwQEBERERERERERERGZEBMEREREREREREREREQmxAQBEREREREREREREZEJMUFARERERERERERERGRC4TW9AkRERERERERmdDQ/S+btWCKHczMkOTpRjmvWU+Ij42p6tYiIiMhEOIOAiIiIiIiIqBpZLBb5atUMue67++XtRZ/K1JU/6CnO43JcH4guueQSufzyy23nO3fuLB9++KFX9/3nn38kPj5eioqK3N5m4MCB8thjj4m/zJo1S0JCQqSwsNBvj0nubdq0SY455hg5evSoXzbTokWLpEOHDpKfn8/NbjJTpkyRpk2b1tjfx3EIxyPDyJEjZcKECRLoPv30U2nfvn2lHuPdd9+Vli1b2s5ff/31cvXVV0sgKCgokK5du8qCBQskkBUXF0uPHj30M6i2YIKAiIiIiIiIqBp9vfonTQoUFBc4XI7zuBzX+9sJJ5wgDz30kF8fc9WqVTJ27Fivbjto0CANHIeFhUmgcE4gICgZGhqqiYyEhARJTU2V448/Xp555hnJysqq8vXJycmRMWPGSNu2bXU9XL1e9913nwbIEhMTpVGjRnLhhRfKjh07PD4ugn0I+hnwnGNiYvQ5JiUlaUARAcCVK1dWav3vvvtuue2223T7OW9P+2XNmjV6/f/+9z8ZPHiwPhdXiZw+ffpIx44dZfLkyZVaL6paro4tSAiOGjVK6tatq/tZmzZt5Nprr5UNGza4DMAHup9++kkefvhhCXQXX3yxrFu3zq+P+eabbzocPyq7f+B4FB0dbTv+tGrVSi666CKZPXt2uY/1xhtvSOvWraVfv362y7Zv3y6nn366Pl69evXk1ltv9Tqp+N133+mx55JLLnG4HMcoJIWw/+Jz4Morr5TMzEzb9fPmzdPjE67D8atTp066bgYc98aPH6/rUlswQUBERERERERUSflFBbLjyO5yl3UHN8m01TM8Pta01T/p7bx5PCz42+QfjRs31kQGgkG7d++Wp556SqZNm6YBqcOHD1fpZkagqn///vL22287BMCcb4PA+8GDBzWIhfMIxPrqhx9+0OeI5zR9+nQNcvXq1UtmzPC8b3qaPfDzzz+XSRgZ29N+QdAfUlJS5IYbbpCJEye6fdxrrrlGXnnlFY8zTyjwRrGffPLJug8vW7ZM97M5c+bo6459jejVV1/V/eLIkSPy119/6Wy0oUOH6rHP06j8l19+WRNN9pchOYBA/a5du3TWEZJTSFaWB8fQ22+/XQYMGOBweUZGhu6/OB7iMXGcxfHN/tiGJMWXX36pj4Hbf/HFF/Loo4867N9YL1z/22+/1YoXnAkCIiIiIiIiokrad/SAjPt5QrnLw3+8IIXFnoOdhcWFejtvHg8L/ravMIrz8ccfl1NPPVVHXiLg8c033zjc5vnnn5fmzZtLcnKyjjB3HpVpPzIdgW3nEhwIrGNEJ+7nPFofp/fcc480bNhQb3P//fc73Hfr1q16+40bN9ouc34MnMffrVOnjgabhw0bJkuXLhV/wShXBK0Q9Nm3b5+89NJLfntsd3/vjjvu0L+Jf7vy9NNPS+/evSUyMlJfF2xDBGHT09Mr9DexPTFjAc/t/PPP14C9UeIKQTzsF9g/GjRo4FBeyhn2HawXXgdvIQiHGRAYQewOXlNsewT+zG7qyum62Pt32wK9bHfGXttlm9O26WWLdi23XZZTkKuX/bD2d4f7/7bxH708Lac0+bVq//oKryNm2txyyy1y11136Yj7Jk2a6OX169fXfRuLL5Acatasme5/GMVtX77qkUcekXbt2un+idvg72ZnZ9uunzp1qgaekfzCSPATTzzRdl1ubq488MADun9jn8VMliVLlng9Ch7vG8xsQXAZs2Iwq+fff/91uM9HH30k3bt311HyWA8Ekau7NBPesxdccIHcfPPNepzEdnSeCfHLL7/o+uN54P3mPCMJj2GMsMc2Gz58uMP1eH/iePTff/9VaJ3x2j344INy7733yrhx4zTg7goeH0lbHB8NSAYggI/jF17nFi1a6Mj99957T19jT6677jqd8YTZLfYwkyEtLU0/z6Kiomzb7Ntvv7VtG3xmoZwaZgrgeIn9AcvatWttj4PZctjn8DlYGzBBQERERERERGRCCO4/8cQTOorzpptu0hGSRnDms88+09HzCGodOHBARwM7JxDsIYGA4JR9/4R33nlHLr30Ug0eOXvuuec0gPfnn3/Kzp07JTw8XObPn+/T+kdERMgLL7wge/bs0TITCPSceeaZfq9Zj+DmSSedFJAjQX/99VcNivkSmHcHQUBsx/Xr12spGCQfUIIDI30xghYBWncQwO/SpYv4GxIlSGAsXLjQ749N/oeZAkhWXXbZZZV+rL1792rCD0FXBIdRAss+wYD94vfff9djFmavoAyQkaREogD7M4L4uB7HGAS37evqo449Rq/j+HbeeefJiBEjfJolhOMnerDgPgiao7yPAcdCJBQQqMb2eOutt3Tku3MSoTrguI1yTvv379cgN0qmzZw5U6/bsmWLnHHGGZpcwXri8+D11193+1hXXXWVJmZxP/vnivc+RtxXBl4vJIDmzp3r9hiDZCLKoxmwf+AyJIAMKP2D1x/HMXc++eQT3R6uSgBZLBbbYj9TAeedE9A49uIY1a1bN10HfN7Zw+W15djFBAERERERERGRCSGoj6AORkFiNCUCwUZ9+A8++EADwhihj0A8glsIdriD0ecop4CAP2CkJYLX7ppb4vExWhS1mzFKEzXJfQ1yY/Qu1g8JCIwifvbZZzXA7e8a3ICZFIcOHZJAguAoRsuiRri/niPgeSJhg4AY+kwgwIrRxRhl7Q5G3GKktDOM+MVMB2NBXW9f4XHx+GZ3XpfTdbE3sEU/vaxxYkPbZa1SW+hlfZqUvl9jIqL1slEdSkfRw0ltBunlqTHJtss6129X4XVE0BX80WAY+x/KT8XFxelMBOzrCMgb5aYQjMU+i5HbGKGPJCeOOQYct3A8w3EJQVyMjjf2bzzOa6+9puuJfR0j7LGf+VICCccvJCVxf5TCwrEHo+kBI9oxKh7BahxfEaDHMRLB9OqG4yRmEWBEO3qqoHmu0eQXiWAE93F8x/bC8dRTcgczLoYMGaKJDwP+7Y8mxvbHH2+PMTg24bhiz/gccTcTAWWDMFvh/fff19fGWf/+/fXzBAkl9IXB7ZE4cfWY27Zt089N7Hfnnnuu3q+2HrvCa3oFiIiIiIiIiGq7BvH15MVTym9imV2QI+NnTtQyQu6Eh4bLo0Nvl9iIGK//dkWgPrwBQTgwGjFixC1G49tDSQV3cH8EwBD4R+kYBF+OPfZYTQC4gse3fzwEr4wAkbeWL1+uQTiMLsZ6G8EeI0jpTwj+oUSHKwgGotyJtxBM81R33xsIZGLELUbCnnLKKeKv5wh4nnhtMHsEjTeRPMJo7TvvvFNLArmCGuCYieJqH8NrXRl4XDw+BT7MtgG85thnKgOBXgRYjZHc2CcLCgo0CI/9CqPysSBIi7JjuM54j8bGxuqsAuO9idvjfYeSMkbZMhyf7GHmkS/7qrvjJ0rSYAYOEggIRBuwju6SbKiF70sjYJSH87Zck/16Gutqf5x3Pq57Os4DtiNmFyFhgxkReAz72RP+OP54e4xBWSHnWR9GuTVc524WBF4bd/tncnKy7jt4jpghgMdBySyUM7KfqWBAghozzDBTA6WIXnzxxVp57GKCgIiIiIiIiKiSIsMipFmSYyDGnXM6jZSpK3/weH37uq1r9DXByFr0AbCH857KyCBwhNGlGLGLRIGnoLnz42NUsH3ta2MkJmqa249GtzdmzBgdkY5a3wgmIjCEYIx9aQh/QMIB5YVuvPFGl9cjcI7FFawLnhsSIBjp7K8msFgXlGhCWRR/weMiSYO67oAEERYENlFHG6OQ0WfAuN4eLkfjY39DHXEEWzESmwIfRl/jvfjxxx9rELsy8H5GgNUI9OJ4gVHuCMCjDA1G/WPkNkbn43I0sLUPzg4aNEgXvAdRSgiJNCQsjf0XCUZfk5LeQm8VBNC9LbWEhKGvSUN/HOdwHHYugeN83Hd2zjnn6LZHEB1JxNGjR7ucPVSR4w+SF5jl4AqOMZs3b9ZjgtGjBbMhUO4Isw6MxMLixYs1QeTqOGX0XMBzRgk9MPpa/PzzzzpbADPaevbs6VBSDqXW8JjHHXec2/VHgspIchhWrFhRa45dLDFEREREREREVI3O7TRSzusySiJCIxwux3lcjutrGvoRYBbAvHnzNECM0a1ohusJ+hRgVCZKE6GsAmYUeHp8BPNQXzwvL0+DifalGIxR7Pi7+PsIDKHfgD0jeGiUccCoUH/CeiGwiBrdaErpa4PViv5NBMBQ8xqJBfzbvqcCGgcjOIcZBP5KDqC/wN133y2ff/65Pj4SGSjTNGPGDA2eoYSKEQBEosOVs846S2uE+1LD3fn52T93A2qlY1R63759K/08qeohwIu6/2hwjgAs+oMAkoaTJk3yKQiO/RDvaSQJkRxEGTKUFcI+iPc+TvG+RHIAs4iw79r3L/jyyy91f8TjYFQ4TrEvY1Q49leUJMLsA8CIevQwMNa3sjAjAP0QEIjG/ox9G/9G8DqQYEYQgtjGcRbHeyRcPUFwHrOXMDvj66+/rnR5IQTl0RcBC/YbdyP/kSBo1KiRrX8CIAHUoUMH3U/wGiJAj/0En0HuGr0jEY3PMvQTwILj+6mnnqr/RnIA8Dphv8M2wcwBvJ54XOM4iIQpHgNJARy/cB6zufA49sc3lIE7++yzpTZggoCIiIiIiIioGiFQNbrzqfLWmU/LdX0ulvO7jNJTnMfl/hppXhkoGYESCxilj7IKCBx5E+hAYAbBa4w2N8puuILSGxiJihkHGMWKIItzyQ8EqtAQE8E9BKScA1FIYCAIiNkGGNlZkfr2zhCIRL19BKkQjMI2GDVqlAb3/NEIuDzt27fXJpwISiFghn+jZJMBzUQRtMdzxXoaC27vCzwnbDc8TzwWRuAiwI/LAa/Hk08+qbXfcRsE4PB6oAa5K0gMYT19qbGOgBpG5RqJDjwPPN+///7bodE1ysK4S0xQ4MGxA6O00bAYM46M9yf6WRj7l7ej8Lt27arvCYwU79ixoy3BgH0NjYZPOOEEDdqiXjySjvaj69GbAw1ssV9hlDv256FDh9pq7yPgjNIwWD/8Dexr/pp9hH0WAWWsI2Y14X2EJJz9jKhAgO2D0jjYrjjOYjvecMMN5d4Px2L0m2nWrJnH3iTuIMlpHH/QIwHBeQTTPf1tlJBDoB5lpewvw8wlNJrG8Ro9dfB4SDQYkKhCjwoDPm/sFxyDYmNjHfpmoK8CZpdg/fAaopQdXj8DylyhsTU+E5DARDIICW/7WWY//vijvvb2x+9AFmLx99w7E8BBDQc5dFC338nMBtkwZOjwpuaHNbcJ9xW+h3hc4fGWn0E1j5/N3C7cV/ge4nGl+o63KHuCkavuAqZmVBXldGo7M20TzEQYPny4xkoQlK3sdsEo3osuukhLwRgje4OZmfYVX3C7cLsYMGIf5X+QHMaMtUDdV4qLizUBhaSB0Ry7KmBmnZHoqWzcmj0IiIiIiIiIiIioUpAsK69+uS8QYEOpIyIiQFIeQe9AFxoaKkuWLJHahCWGiIiIiIiIiIiIiIhMiAkCIiIiIiIiIiIiIiITYoKAiIiIiIiIiIiIiMiEmCAgIiIiIiIiIiIiIjIhJgiIiIiIiIiIiIiIiEyICQIiIiIiIiIiIiIiIhNigoCIiIiIiIiIiIiIyISYICAiIiIiIiKqAbnZRbJq7hFZ+GuanuK82f3+++8SEhJiO//UU0/JySefXKPrRKW2bt0qgwYNksTEROnfv3+NbZqNGzfqfoL18eTaa6+V559/3m9/d9SoUTJlyhS/PR4RUSBggoCIiIiIiIioGlksFk0KfPDoVpk59YDM/ylNT3Eel+N6fzvhhBMkMjJS4uPjNbjbuXNneeuttyTQPfDAA/Lrr79W2eM/99xz0rx5c0lPT3e4fOLEidKkSRM5ePBglf3t2ujpp5+WlJQUOXz4sMyZM0cC2dKlS2X69Oly66232i5r2bKlREdH6/vAWC699FK9LicnR8aMGSNt27aV0NBQeeihh8o85hNPPCH33nuv3paIKFgwQUBERERERERUjRb9lq5JgaJCx0QAzuNyXF8V7rnnHjl69KgGdx999FG54YYb5K+//pKakJ+fL4Hg7rvvlg4dOsg111xju2zZsmWamPjkk0+kbt26Qfm8K2rTpk3SrVs3DaAHupdfflkuueQSiYqKcrj81Vdf1feBsXz88cd6OWYkYFbE22+/Lf369XP5mN27d5cWLVrovkFEFCwC/4hOREREREREFOAKC4rl0J68cpc9W3J0loAnuB638+bxsOBv+wLB3fPOO09SU1Nl4cKFtsvnz5+vMw3q1KmjQdCHH35YCgsLbdfv3LlTLr74YmnatKnOQujRo4f8999/el1ubq6OrMboazwuytDg8Qwoy4L7vfbaazqKG38DFi9eLMcee6yO5O7Tp48sX77cYV0fe+wxGThwoO081u+2226Tiy66SJKSkqRZs2byxhtvONzngw8+kDZt2khCQoKcc845cvPNN+v9XEFQGAHif//9V9555x0dGY7HHjdunAwdOlTWrl0rp59+ujRo0EBnFNx4442SlZVlu/8jjzwi7dq107+FdbnlllskOzvbdv0VV1whF154oSZj6tWrJ2eeeaZUpTVr1sgZZ5whDRs21O1z3HHHyY4dO/Q6JIZQcgevAxIfI0eOlHXr1pXZ1uPHj5dGjRrp63jdddfZ9oHWrVvLzJkzddYFXi+Uf4K5c+fq642ZBcccc4zcd999kpeX57CNUTrKgLJA2AdRJsh+38CMFuwbWG+M5M/IyHBITAwfPlz3u44dO+p6eFJUVCTfffedjBgxwutth5kFd9xxh77u+Lc7p5xyikybNs3rxyUiCnThNb0CRERERERERLXdkYMF8vlz1kBsZRUXiXw9aZfXt7/wnmZSp5HjKGlPEPCdOnWqHDp0SIOtgEAxArDvvvuujB49Wnbt2qWBZgRKH3zwQQ2cDxs2TAYMGKClWxA8RvA8Li7ONhJ/1qxZ8tNPP2mQGKO0TzzxRA1YI/gLe/fu1dH5K1eu1AAxAsAItl5//fXy999/axAYf7M8CCh/8803Ooobp0h2nHTSSZoUwOPg8b799lvtXfDLL7/o83E3IhwQ/P/oo4/k3HPPlRkzZuhzQ7Ac5YUQ+MZsgq+//loyMzPlggsukNtvv12TCYCECILfSA6sXr1aEwAInqMUjwHr8uabb8rkyZOloKBAqsq+fft0fZEEwLbBa4METkxMjF6PUjoYMb9o0SINtON5Ga8R1hmQ1DnrrLNk27ZtGshHggELEh14fZBoQRIBpXZg+/bt+hhPPvmk/PHHH3o/3B8JI5Rp8hb2DexPWBckMgYPHiwvvviiJisQ7EftfySSvv/+ey0HhcSPJxs2bJAjR45Ily5dxN8wg+L111/3++MSEdUUziAgIiIiIiIiMoEXXnhBkpOTNeh/2WWXybPPPiunnXaaXoeR/QjCIgAeHh6uMwhQkgij8eHHH3/UhAIC3Rh9jgB/p06d9HbFxcXy3nvvyYQJEzRIj14HGIHfqlWrMqVYEDRGMDo2NlZ++OEHfRwE41EGBo+H2QHlQSAfyQrcF/9GQB8zEQCBfgTpMTo+LCxMTj31VF3Kg2TCVVddpQmFTz/9VO+Lx8LzwahyrB+eNwLWuBxBayPojh4GGCWPvg433XRTmZ4JvXv3liuvvFIiIiL0eVcVzITAzAGM7EcCAM+hb9++ut579uzRevzY/rgN1gPNe5H4weUGJDruuusufQ0xMwJJowULFrj9m9hWuB2SJrgPEiZIHqBMjy+9NLDPYWYCkhmYvYAkg/F3582bpwksrDuSHkg4YXaLJ2lp1lk6mI3gDD0J8D4wFl/LbOExnXtWEBHVZkwQEBEREREREZkAAr8YnY3g5uWXX66BbKN8DEZcYzS+feAUZXEwshu2bNmi5V+c67kDRtoj0IwSNPYQXMcIc0P9+vUdAuQoWYSANALZBsw+KE/jxo0dziNojNH9gJkPSFrYw3p7AyWTkGxAwN/YJkg82G8TJBuQDDC2C8ri9OrVS0smIXCM2Rb79+93eNzynhNmJ9g3zS1vOfvss10+Dl6j9u3bu7zOKDNk/xohYYFtZf8aedq27h7X1euO/eHAgQPiLSQxsD6u/i72E5Qvsg/2l7dN8ToCZhE4mzRpkr4PjGXIkCFer6fxmFgfIqJgwRJDRERERERERJWUVDdCS/2UJz+3WL55bZeWEXInNEzk7JuaSGR0qNd/2xeol48ZAygvhFOM2seoctTef//9913eB0F2lJxBk12MFHcO7mJWAkrQGCWLAOcxgt32vJwa22IkOALMGI1vJAnwNyoDfQJQ5sae83lvYZugnM6ff/7p8nrU3kd/AyRacDsEuNEYF6Vx7CGh4AlmQWCpLLxG//zzj8vrkIgxXhM02gUkh5AcMBIiFYHHxQh/e/gbmAmAnguApIZ934bdu3f79DewnyCphcC8kSQobz/BTAbMoli1apWWkPKnFStWaL8MIqJgwRkERERERERERJUUHhGqfQDKWxodEyN9T7aObnYH1+N23jweFvxtX2EmABrsoiwQAq9ovvvVV1/Jl19+qUkABO3RRPbnn3/W26NRL0ZN43aYMYDyMai5j+A7Av8oofPoo4/K5s2b9f4IlOP+aGrsDh4Tf+fxxx/XpraoQf/KK69IZaDkD5rTolQQHhvrj74CFYG6+0uWLNF682g8jOeMhAZ6CgC2GxIbCIQjOYB6/+i9UFNQNgqj7VF+B6Pv8fzRbwCvF8r2YPYDSj+hVwFG+KOpNJI9RpmpikBSCeV/0F8BrzuSA/j7V199tS0xgmA6+kagLwH+Nso0+QK9BxDwv/POOzXRgFkiRg8Ed/C6oJ8F9gNfYD/EeqJsFrYf/o3nZQ/7lLtZHEREtRETBERERERERETVqM9JKXLsyFQJC3ccWY7zuBzXVwcE01EaB70IMNL/t99+0+a7GIWPy9Hc1xh9jxHhGEmPJrddu3bVkdwI/hu13tHfAI2CsWDENsrm4PGMkeuu4DEQvMeCv3fJJZdoWaPKQLkYBPSRyEBJIJQAwnpihoOvMLIeswTwPFBGB483YsQIHUFu9C1AQ2Q07sVzQdPfsWPHSk3BdkeTZpRFQgkebNNbbrlFg9xGjwLMMkBJJIzKx+h6NFjGjJKKQokizKD43//+pyWk0BsC/R/QT8CAWSooyYSZJtg/sN/5Av0J0K8CJZSQ6EBTZPSLKA8SCnjOCPp7CyWasK9jJsYzzzyj/8brbFi+fLmuh6/PgYgokIVYfOkaQwofol26dJGVK1dqEyKzQjYdoxLwZcK+ZqSZcZtwu3B/4XuIxxYeb2sSP4e4Xbiv8D3E40r1HW9Rnx6jxp3rr/siN7tINi0/KtkZRRKbGCatu8VLdGzt/W2F8IJRLqi8sjrVCQ1vETxHsqC6Beo2qWnVtV2uueYabaJ89913++XxMCsBswcwu8TfuK9wu3B/4XvIF5ixB61atap03Jo9CIiIiIiIiIhqAJIBnY8rbbxK/oHZCxipjtHf06ZNk+nTp7vtI0DBDTNi/On777/36+MREQUCJgiIiIiIiIiIKGigHA1GjqN2PErgoPHy4MGDa3q1iIiIAhITBEREREREREQUNNAQl4iIiLzDJsVERERERERERERERLWEP9sKM0FARERERERE5CM0Vi0uLuZ2IyIiomplNDUPDfVPaJ8JAiIiIiIiIiIfhYeHS0FBgWRlZXHbERERUbUlB/bv368JgqioKL88JnsQEBEREREREfkoKSlJDh48KNu3b5fIyEidUWB2CFpgwbbg9uA24b7C9w+PKzze8jOoamYOYImJiZEGDRr45XE5g4CIiIiIiIioAjMIGjZsKLGxsQyG2yksLOS+5ITbxDVuF24Tb3Ff4XbhvmKF5DsGJSQnJ0vz5s39VmIo4GcQTJs2TZ577jlZsWKFboBBgwbJU089JV26dPHq/tnZ2fL444/LF198IXv27JFGjRrJhRdeKA8//LB+kSMiIiIiIiKqiISEBP2RTlYY0ZiZmanbJSwsjJuF28Qt7ivcJt7ivsLtwn2l6gX0DIL33ntPzj33XK3p+Oyzz8qDDz4oy5Ytk/79+2vCwJuDyKmnnqr3HTx4sLz22msyatQoef755/WUDaWIiIiIiIiIiIiIyKwCdgZBenq63HnnndK0aVOZPXu2JCYm6uXnnXeedOrUSW677Tb5888/PT7Ghx9+KH/99ZfccsstMmnSJNvlLVu2lLvuuks++eQTueyyy6r8uRARERERERERERERBZqAnUHw3XffSUZGhlx99dW25ACgvtLo0aNl5syZsmPHDo+P8dFHH+npuHHjHC6/8cYbtZGDcT0RERERERERERERkdkEbIJg/vz5eopyQs6MyxYuXOixqzOub9y4sbRo0cLhOiQHevTo4fH+RERERERERERERETBLGBLDO3cuVNPUWLImXGZcRtX0tLStEGxu2bGeIy5c+fqLAX7GQrO9u/fLwcOHHC4bPXq1Xq6bt067XNgVujhgP4QcXFxfuuaXdtxm3C7cH/he4jHFh5vaxI/h7hduK/wPcTjCo+3NYmfQ9wm3Ff4/uFxhcdbfgbVrI0bN+ppXl5e7U8QILgPUVFRZa6Ljo52uI2v93d+DE8Jgtdff13Gjx/v8jo0UCYiIiIiIiIiIiIiChQozd+rV6/anSCIjY11m+3Izc11uI2v9/f2MYx+BWPGjHG4DLMO1q9fL127dnWbgDBLRuqss86Sb7/9Vtq0aVPTqxMQuE24Xbi/8D3EYwuPtzWJn0PcLtxX+B7icYXH25rEzyFuE+4rfP/wuMLjLT+DahZi4UgODBkyxOv7BGyCwL6MUMeOHb0uP2RITU3V4L+7MkS4HDMHPM0egPr16+vi7Pjjj/fqeZgBkgOdO3eu6dUIKNwm3C7cX/ge4rGFx9uaxM8hbhfuK3wP8bjC421N4ucQtwn3Fb5/eFzh8ZafQTXH25kDhoAtHN+vXz89RZ8AZ8Zlffv2dXv/kJAQ6dOnj+zevVu2bdvmcF1OTo4sXbrU4/2JiIiIiIiIiIiIiIJZwCYIULomISFB3nnnHS3pY9i+fbt8+eWXcsIJJ0izZs1sfQTWrl0re/bscXiMSy+9VE9ffPFFh8vfeOMNTRIY1xMRERERERERERERmU3AlhhKSUmR559/Xq6//noZMGCAXHfddVpDafLkyTo7YOLEibbbLliwQIYOHSpjx46VKVOm2C6/4oor5KOPPtL7HDlyRAYPHizLli3TxsNIMFxyySU19OyIiIiIiIiIiIiIiGpWwM4gACQFMFsAvQTuuecemTBhgjYGnj17tnTv3r3c+4eFhcmMGTPk7rvvllmzZskNN9wg3333nYwbN06mT5+u11PF1atXTx599FE9JW4T7it8D/kDjyvcLtxX+B6qCjy2cJtwX+H7h8eV6sHjLbcJ9xW+f3hc4fG2pvAzqOJCLBaLpRL3JyIiIiIiIiIiIiKiWiigZxAQEREREREREREREVHVYIKAiIiIiIiIiIiIiMiEmCAgIiIiIiIiIiIiIjIhJgiIiIiIiIiIiIiIiEyICQIiIiIiIiIiIiIiIhNigoB8Nm3aNDnuuOMkLi5OUlJS5IwzzpCVK1eaeks+88wzcv7550vbtm0lNDRUwsPDxew2bNggjz32mAwYMEAaNmyo+0unTp3k1ltvlT179ogZHTx4UK688krp3r271KlTR6Kjo+WYY46RCy64QP7777+aXr2AUVxcrMeYkJAQOfHEE8Ws8PzdLWY/5mZkZMhDDz0kHTt2lJiYGElNTZVjjz1WPvnkEzEjHGs97S9Ydu3aJWbcTyZMmCBdu3aVhIQEPe727dtXXnvtNSkoKBCzOnDggNx0003SokULiYyMlMaNG8s111wje/fulWDn6/e1wsJCefbZZ6V9+/YSFRWl2+qGG26QQ4cOiVm3C77D4fh76qmnSqNGjYL2s9qXbfLXX3/JLbfcot/vkpOTdendu7e89NJLkpOTI2bdLgsWLJAxY8bobRMTE/W3AD6377zzTtm9e7cEk8r8FsS2wD6D99ITTzwhZtwms2bNcvv9pW7dumL2fWXjxo1yxRVXSNOmTfWzqEGDBnLyySfL4sWLxWzb5IQTTvD4fRePYdZ9ZcuWLfp9rk2bNvr7CN9ZRowYITNmzJBg4ut2WbZsmZxzzjl6LMH7B9/p8PsgLy+v2ta5tgixWCyWml4Jqj3ee+89ufrqq6VLly5y3XXXSW5urkyePFnS09Nl9uzZ+iPcjPBhhC92PXv2lDVr1uiPb/yoNLP77rtPXn31VRk1apQGe/EhNW/ePPnoo4/0R8KcOXOkQ4cOYib4cnfZZZfJ8ccfr4EZ/FDaunWrTJkyRQMz06dP1w9xs8MP6kcffVSOHj0qw4cPl99//13MelwZNGiQXHvttWWuw/sqKSlJzAiB7qFDh2rC7fLLL5fOnTtLVlaWrF+/XoNVDz74oJjN8uXLdXG2bds2DeT16tUraH5EegufwTjWIvk6duxYTSDhh8DXX38tf//9t1x88cWmTCjh+wm2BT57jM8j/KBE0gQ/nObPny/169eXYOXr97VLL71U95PTTz9dB8RgW02cOFFat26t32nwOW627YJAHo7BTZo00SD4999/H5Sf1b5sE3zP3b59u5x99tnSrVs3yc/Pl++++07++OMPPf7iNxIGhZhtu3z66afy8ccfS79+/fTzGYEcfFbhey+Stjg+I4Bl9t+CZ555pvz555/6vRdBK3xum/W4gu+8+O5rD++d0aNHS7DwdV+ZOXOmfu/HMfeSSy7RJEFaWpq+fy688EL9fDLTNvntt99k3759ZS7/+eef9ZiDBOSLL74owcCX7YLvdbgd4H3Url073U6I323evFneeecdjeOZbbv8+++/OoghIiJCB8dgcObcuXM1JoWBDj/88IM+HpVAgoDIG2lpaZbExERL06ZNLUeOHLFdvm3bNktcXJxl6NChpt2QGzdutP17yJAhlrCwMIvZLVy40JKenl7m8rfeegtJScuYMWNqZL0C0a5du3SfMfN7yLBp0yZLbGysZeLEibqfDB8+3GJWeP5jx46t6dUIOMOGDbM0bNjQsn379ppelYD30EMP6X705ptvWszmt99+0+c+btw4h8sLCwstPXr0sISGhloyMjIsZnP77bfrdnnqqaccLp89e7YlJCTEcs0111iCmS/f1/744w/dVmeccYbD5V999ZVePn78eIsZtwveN/v27bOdD9bPal+2ycyZMy0FBQVlLr/wwgt1+7z22muWYOGP3zz/+9//TP0esvf555/rbV988UXdJhMmTLCY9T2E5//BBx9Ygp0v2+XgwYOWevXq6XffnJwcS7Dyx3Fl4MCBug+tWbPGYsbt8uCDD+rz/+GHHxwux+d1eHi4pU+fPhYzbhd858f3W3zPtYfvwdhen376aZWua23DEkPkNYyEwXR9ZB4xAtzQvHlzzeoju71jxw5TblGMJCNHffr00cyuM5TTAVejXc0K00Qxw+Lw4cNidpgWiRHhmKpPViiFkpmZyc0hoqMwMcru3nvvlWbNmklRUZGOuKOysG0++OADHeF80UUXmW4THTlyRE+dR6eGhYVp2TucoryO2eD9AyhVYK9///46Vfvzzz/X2aHBypfvaxhdBhiNaO/cc8+Vli1b2q4323bByO9gnmVSkW2CkheuShwE43def/zmwfsHgul7b0W2C2ZCovTqbbfdpjNNgk1F95Xs7OygK81V0e3y5ptv6uhojIjHTAp8PgdjWZTKHlfWrl2rI8Ux+ySYKhT4sl3cfe81yhrHxsaK2bYLqpwsXbpUZ1Pge649zEKH999/v0rWsbZigoC8hmnn4Pzmsr9s4cKF3KLkkVEHG0FxMwd88aMAZYVQnxXBOwQ5g2GKaGVg6iNKf+AU09BJ5KuvvtLkEZKySLhhajGmkJrVjz/+aPtiiCAdtg2CVfgyjLq9CIqT1U8//aTHW9ToxDYyG/S/QXIEdUqnTp2q5T9Qhgr7yS+//CKPPPKI1iE1GyOw4OqHIi7DZ5HZe5zYf+/FZxHKxzhDaaZNmzZpmQcid/idtzTgi++9GEiG4y/K1MJpp51m6p0HiQF8Tj3++OM1vSoBt03weYSBIPfff7/uP2b+3ovvcEiYoFQXvvci2ItSZiiZSFbvvvuubaCZWRlliq+//not2YXPH5ShQklNlN95+OGHxWzK+85rfNdj1f1S7KRKXtu5c6eeou6dM+My4zZE7hgfTs6jF802Chp1Ng2oJY8R0QhYmRUatN19990ybtw4bfJH1lk4CIJj1AO+4GBkDJInCPxiHwqmETLeQp1JuOqqq7SGJH4QoG7k66+/rscWBIHffvvtml7NgIB9BVz1sDADzBLAzEc0lEWSxIAf1qjHatbPIMzQWrdunc4kOOussxwaz2IEHuB9hOOP2eE7rdHQztP3XjRJJ3KGmX/PPfec1j1GgMbMsB3Gjx9vO4/Pb8zAQe8Ks0LT0M8++0y/0wVLL5PKwPsECSPUBEdiAKPmv/nmG03yo78JGoEH0whoX773YvDLSSedpL0q7rnnHq0r/+STT2oFB/TIMfvxBT1fPvzwQ0lJSdGm6GaFgYbo44eBMPZxBryf8P5BgslsMCAV3+PwPsLATPw2MKD6CWBgDGYa8LucFRME5DUje+/qh5LReMvMGX4q31NPPaWjHRCUQNNIs0IAHA2WEPTFiFY0cMMPSZx3NUXdDBDEwwc4mhOTuJyRZTQiw4+n22+/XZtxmY1Ragk/pjHbxPg8QgC4U6dOmjBAkql9+/ZiZgj2YtRZ165dtSGtWSH5in0B5T9OPvlk/Y6CH5EYYYbEkjG92ExQLsdInOAzB6Pj0cwaCdri4mK9Db/LiW07IODgCr/3kicYrYnPJcz4e/nllzXRb2ZoiD5w4EAtVbt48WJtCnno0CExK2wHzKLA97pTTjmlplcnYGb9TZ8+3eGyK6+8Uu677z559tlnZdKkSfpvM37vRYIA+woa8BrQdLVLly46wAwz0c3cZPXbb7/VGUooTxsszeArCs3gMRAEydcePXroLAKUp0Ly7ddff7U1MTYLvC/wuxAzkZBgQ7IaJe4wawCzlVBqFAkmfN9jgsCKNRzIa0bW3lXdO6NerRkz++SdV155RR588EEN1OALjpm/yCDggC92+LC+44475I8//tAfSxgJYkZffPGFfP/991pnE1Nnyb2RI0dqwBf7TDDXCXfH2D/wY8g+WY0veBhBhSmixogQM0PvAfygNPNU62XLlmlACj+UMKsEx1cEqZCc7du3r9x00006QtGMQZgvv/xSezCgPjp+KOFzuUWLFtpjCuz7TJkZvtO6q/XM773kKTmAzyiMDMdoXyT0za5Vq1b6vfecc87Rkc9I5t911106OtyMsF8gIDVx4sSaXpWAh9mhKPXmnDww2/de51mPGPyAEs8IAGNWoJmxvJAVfkcjkfTAAw/ogDsExG+88UaddY7yxkZpN7NBEu2xxx6TVatW6fddfO/F7wH8DjCqFvB7bykmCMhrnsoIeSo/RITpbviBhGw2RrUykVQ2YXDGGWfoiHCz1ZdH8AUN2jC6Fx/YGzdutC2Ampv4N6bTUunUfAQgzFj7GtNkjREyzozLzLhd7CFJghI6+FF56aWXipmT0ji+OE83R6AByQIEZ9ADxozOPvtsnTWAxqmYdo664OjTYCRMOnbsWNOrGBDwnRajEl0lCfi9l1xBEAaJNyThMGIRI5+pLJS6QJlEBLTMBjXBkbRGcAozCYzvvEa/CnyHwXlcR9YZo2iKvn//flNuDn7v9WzLli1aggqzITFr1swwOh79KpxnJaGsDpo3Y2Z6VlaWmA0GpSJhgu+4mDmAhAniCg899JDGXdDHjgmCUkwQkNeMumVz584tc51xGUblEdnDjyNM7cKHFUZ/MDngGgLhgBp4Znve+MDGtMe2bds6LDBnzhz9N6YBkhXKUqFWa506dUy3SYxmoQhoOjMuM3MDdMDsks2bN2sQHI2tzcoItrhqXI0Em/2pGWEGAX5MDx48WJo0aaJBcPQlsD/+mh2+96LsEn5Quvrei2bpnJJOBpQpQEISpTQRjEBZTfL8/c9s33mNHi9I5E+YMMHhO+8ll1yi16MkFc6jPwFZyzEhmGdfO9xM+L3XMwyIwfvJzDNm7b/34juLq4a7/N5rnY2D73WYeYMSpIsWLdIYBEr3UikmCMhrqBuPrCQaH9qPasAXHYyUwZQdI8tNBPhxhHqRqJuO+oBmrwvobhQ8stfYPviwMtvITYwMwvHD1QIIYOHfKMVkJu5q837++ec6+gwJN1f9YIIdpssi6G307TCgwRRqyyNxgtkoZsap1lYoLQRTpkwpM8IXgRcEyNmItxSmpOO4gxFVZGXMwEH9XnvTpk3Tz20zz9ChssmBc889V/t7oIQOyhmQaFNIV1BWEons448/3nSbCQEqV995jX3GmIFitu8yrr73ItCJckw4RRzCjIyefa+//rpD4Be/BZCoxkwcJKvNCANA8B0Po7/R88Xs8L0XMwSM39D2syzQtw1lqRBrIGuCGtUtEJtCDy4qZc5umFThMijPP/+8XH/99VrDFnXMMOJs8uTJOnXHzHUUEazCdH3AKT7A0UHeYMYf3K+99pr2HMBoXtQcdf6wio+PN92XvaefflrrXyNTjXI6eN+sWbNGPvroI1uA02xJFAR0PfVewLRiM/ZmwPEDUyCHDRsmzZs31+ADzmNkIkrpmPV4iy+2KB2DH0yYsXbVVVfp++j999/XkTMIzJg5UY1yKN98843+YMR0YjPDF398Nr/xxhtaDmbEiBFaVuiTTz7R0jpo1ouR82aE/QNl7dq0aaM/krDPoNQQatWiLmsw8+X7Gmqmo54vErOjRo3SBCV+aGOEL5qiY3akWb/H2l8HSJgYl6GfRTAkT3zZJuiBg1my+H2Ez2wcZ+whgBcswXBftgu+7+L3I0ZsYrvguy5m5Hz11Vc6++aFF16QYOHtdkE5C1ffa+vWrWsL8gXL915f9hUMfMFvRiTuUd4NI3uRcMP+gpluKMkULHzZLkOGDNHvuhgpj+8x+BzCgDPEX/AbCokDs8ZSZsyYod/9EZvCgLNg5Mt2GT9+vMZWMBtp1qxZ2qQY33/xPRh9k4Kp5J0v2wW/n5FoxDEG3/v37NmjiSV8b0EMpl27djXyHAKWhchHX375paVfv36WmJgYS1JSkuX000+3LFu2zNTbcciQIUjpu13MaOzYsR63SYsWLSxm89tvv1lGjx5tadmypSU2NtYSGRmp2+Giiy6yzJ8/v6ZXL+BgPxk+fLjFjL777jvLKaecYmnatKklOjraEhUVZWnfvr3lzjvvtOzbt89idjNmzLAMHjzYEhcXp59F+Ez6/PPPLWb34osv6vsGp2SxbNmyxXLllVdamjVrZgkPD9fjLvaVd99911JcXGzaTYTP59atW+uxJTExUb/DTJ061WIGvn5fy8/Ptzz11FOWtm3b6md2w4YNLddee63lwIEDFjNvF0+3xWOZbZvgu5yn2+I9Fyx82S6vv/665cQTT7Q0atRI3z/4vO7YsaPl9ttvt+zcudMSTCr7W3DmzJl6uwkTJljMuE2eeeYZy4ABAyz169e3REREWOLj4y19+/a1vPzyy3ocNvO+UlhYaJk4caKlS5cu+nsA8ZfTTjvNsmDBAouZ3z+jRo3S6xYvXmwJVr5ul7/++ktjc3Xr1rWEhYVZUlJSLCNHjrTMmjXLYtbtgt8C2Cb4HMKxBccYxGOCeb+pjBD8p6aTFEREREREREREREREVL3Yg4CIiIiIiIiIiIiIyISYICAiIiIiIiIiIiIiMiEmCIiIiIiIiIiIiIiITIgJAiIiIiIiIiIiIiIiE2KCgIiIiIiIiIiIiIjIhJggICIiIiIiIiIiIiIyISYIiIiIiIiIiIiIiIhMiAkCIiIiIiIiIiIiIiITYoKAiIiIiIiIiIiIiMiEmCAgIiIiIiIiIiIiIjIhJgiIiIiIiIiIiIiIiEyICQIiIiIiIiIiIiIiIhNigoCIiIiIiIiIiIiIyISYICAiIiIiIiIiIiIiMiEmCIiIiIiIiIiIiIiITIgJAiIiIiIiIiIiIiIiE2KCgIiIiIiIiIiIiIjIhJggICIiIiIiIiIiIiIyISYIiIiIiIiIiIiIiIhMiAkCIiIiIiIiIiIiIiITYoKAiIiIiIiIiIiIiMiEmCAgIiIiIiIiIiIiIjIhJgiIiIiIiIiIiIiIiEyICQIiIiIiqnXmzZsnoaGhct999zlc/thjj0lISIie+uLll1/W+/3www8VWp8TTjhB72+/REZGSuPGjWXUqFHy3XffVehxSaRly5a6Pbdu3RqQmwPr5fzaY4mPj5euXbvK3XffLXv37q2WdZk1a5b+beyPRERERETeCPfqVkREREREAcJiscitt94qSUlJcu+99/rlMW+44QZ56aWX5K677pJTTjlFIiIiKvQ4AwYMkDZt2ui/MzMzZcWKFTJ9+nRdsM6vvPKKX9aXAtPYsWNt/96xY4fMnz9fXnjhBfnwww/lr7/+ko4dO9bo+hEREREROWOCgIiIiIhqlc8//1wWLlwoDz/8sKSkpPjlMaOjo+Wee+7RIP5bb70lN998c4Ue5+qrr5bLL7/cIZnx1FNPyUMPPSSTJk2Siy++WPr16+eXdTaLP/74QwoKCqRJkyYS6KZMmeJwfufOnXLqqadqouiaa66Rf//9t8bWjYiIiIjIFZYYIiIiIqJaZeLEiVpG5corr/Tr4yJ4HxUVpYF8BPb9Aev5wAMPSLt27fQ8ZhKQb1q3bi0dOnSo8KyOmtS0aVPdX2H27NmyZ8+eml4lIiIiIiIHTBAQERERUa2BmQNYhgwZorXpPdmyZYtccskl0qBBA50h0LlzZ3nxxRelsLDQ5e1TU1Pl9NNPlw0bNsjvv//ut3VGkgC16GHfvn0ub/PLL7/IGWecoeuK3gWNGjWSCy+8UEeee6o3f+KJJ0piYqIuAwcO1F4HRk185+1jfzm2AUrfdO/eXeLi4iQ5OdnhtiiNc8EFF2iAG+tTr149XT93I+DXrVun5XVatGiht09ISNC/c/bZZ8vXX3/tcNuioiJ58803pX///lomCrfH8+7Vq5eMGzdODhw44HUPgqysLHnyySdtzwNLjx49dNZGdna2xxr9mJWA+yL5gP2jfv36ur9s375d/KlPnz62f2/bts020wDrgdkmhw4d0pkrxxxzjG6Ls846q8LPz3nboEdHq1atNPHVrFkzueWWW/TvuYOySLfddpu0b99eYmJidL9C2Sysr6uk2eHDhzUBhvdWbGysbkfsM9i+Tz/9dAW3GBERERFVJ5YYIiIiIqJa49tvv9VTBMbLSw4gMIuAJYKVGRkZGhxGjwEEuRG0RpNjZ3hcXIdA+0knneS39T5y5IieIhDuDAFZzFoIDw+Xvn37aoB148aN8sUXX+jzxfqgTI29Tz75RAPyxcXFGlhHQBfPGcFlPEdPEOg999xz5eeff5bBgwdLp06dHILiSKKgsS7gsY8//ngtlfPjjz/qguA+yuUYkMRAEBk9FxBsR1NmBL937dqliY+cnBz9e4arrrpKa/IjAI2kRt26deXgwYOyadMm7QMxZswYTUiUB/cZNmyY/n2UmhoxYoRePnPmTHnwwQdl6tSp8ueff2rixxmSAyNHjtRECJJN6A0wd+5c+fTTT+Xvv/+W5cuXl0maVBT2PQMC9c7PAa859o9BgwbpPlunTp1KP7/8/HwZPny4rFy5Uh8DryN6ILz66qv6mvzzzz9l9kU8LhI6WBf00UAvjqNHj2pD8CuuuEL/1kcffWS7PRIUeN1Xr16tyRW8d5DAwCwJXIb73X///X7ZhkRERERUhSxERERERLVE//79MYzZ8scff7i8/tFHH9XrsZx77rmWnJwc23Xr16+3NGnSRK977bXXXN5/6dKlen3Hjh19Wq8hQ4bo/T744IMy1+3bt8+SmJio18+fP9/hujfeeEMv79y5s2XNmjUO133zzTeW8PBwS3JysiUtLc12+c6dOy1xcXF6v/fee8/hPtOmTbOEhYXpdS1atHC4bsuWLbZt07x5c8uGDRvKrOuMGTP0+saNG1vmzZvncN2///6rzyMiIsKybt062+VXXHGF3uepp54q83iZmZmWOXPm2M5v3bpVb9usWTPL3r17y9x+yZIlur3s4XngPlh/e2PGjNHLBw0aZElPT7ddjm1l7CcXXHCBw31mzpxp2wZ9+vRx+FuHDx+29OrVS6974oknLN6y366uTJo0Sa+Ljo62ZGdn62XYT4z7nHzyyZaMjIwy96vs82vXrp3uKwb8jeHDh+t1eGx7u3fvtqSkpOi+M2XKFEtxcbHtuu3bt1t69OhRZv/+8MMP9bLTTjvNUlBQ4PB4hYWFbt+jRERERBRYWGKIiIiIiGqNpUuX6ilGfHuCcievv/66ziAwtG3bViZMmKD/fvnll13ez3jcNWvWSG5ubqXWFSPqMVIbI+oxihzlXuwbFKPUzuOPP67/xmhwjL63h9kA1113nZZxwYwBw3vvvaflYzBC3LkPA0aA24/WdwflXzBK3Nljjz2mp++++64ce+yxDtdhtDgaQ2P0PRo5G4yySRiR7yw+Pl5nIBj279+vpxjR7mo2BcrnYDR6eVCq56uvvtJZIO+8847DaH+MtsdluA7bFWVznGGGw/vvv+/wt1Du6N5777U1Rq4szKDAzBBjFP21116rsybsoa8CtiVKMvnz+RkzQewbO+NvYPZHWFiYzkqxvx/6JKSnp2uJJ8xMwfYxoDQR/h5Mnjy5zOuOmQOY/WIPfwMzF4iIiIgo8DFBQERERES1AoLiRt11owyLOygP5CrQfNFFF2lgFSV8EMB1hhrwCGrbB7N9gVIsCK5iQf12lPBBzwQE9Z1rsiPZgXIsqN+OMj+uoPwNoPyNAaVijOfiirvLnRMJzlDSZsGCBbreJ598stfrYyQ9rr/+evntt98kLy/P7d9FEgSBapQqQh19oya/r5B4Qamk4447TssrOcP2RIIDJZhQMshZ8+bNbX0hnNcPdu/eXaH1Ml57LCgVhfJR2G/xmjz33HNlbo9EiateGpV9fkgooJ+GMySF8JjO95sxY4aeoryTK71799b3BfZZI3GG0kjw7LPPagILiSwiIiIiqn3Yg4CIiIiIagWjjj/quCOQ7wkavrqC+6IBMJIDqKtvP8LagAA5aq8j4IlAsi8wyt4YmY+AOwK9mD2AJrSYnWA/mn7z5s16umrVKocR267YN+41EhtoCOyKu8sNSJw4j2QH9DAArK/ziHBP64N+BXieGHWPxAK2MWYCIJmApr/2gXgkBzByHzMfUEcfC14DbJfTTjtNGyPbz/pwx9gG7l5nQHNeJDJcJYLcva547aGis0cw+h7wemIWC9YPtfy7dOni02tV2efnqYE3rps9e7bu/877ohH09wRNjvGaobfHPffco82uL730Un3OSLCgrwRmsRg9E4iIiIgosDFBQERERES1glFmBSPU0YS1vCRBZZvKopSLr66++mq5/PLLHR4Lo/XR4PXCCy/U5q0IHBslhgDB1vKaLjuXHwJ3SQVXzZftuUoO2K8PSu2gvJEnaCxswPP5/fffteEvGh8j+IzANc5j1Pz48ePlkUcesd1+9OjR+nzRCBqj2HF7lNPBghJHSDagrI03ykusuFPeNqqoKVOm+HR7d69FZZ+fr4zX/vzzzy83QWPfaBmzBzBzBK8lmn/jtUQ5IixIFmGmSHnJJiIiIiKqWfy2RkRERES1AgLRcXFxWrIFo5gxE8CdrVu3urwciQWU9QFXswdwPWYPQL169Sq9zhiR/sUXX2iAH+V0XnrpJXnooYf0OiMIjufhS2C5cePGsm7dOrfledw99/IY64O6+L4GugElb4y+BdiOn332mVxzzTUa9Efg2b5UDpI9GG1vjLjftGmT3nbmzJnaBwD39cR47YyR764Y17l6nQNdZZ+fp33AuM7+fnjtUXYLPSZQ8soXmOVw++236wJIFCAZ9uuvv+psEfReICIiIqLAxR4ERERERFRroGY7YCS+JwhOosSPs88//1zrr7du3VprxDszHhc13r0pdeMNJBoQeAWUYzFqtaN2P3opLFmyRIOz3kJfA+O5uOLu8vIgYIxyQNhus2bNksrA7A7MpEC9e9TSX758ucfb4/VAuSFYtmxZuY8/aNAgHV0/b948Wb9+fZnr0WQaMxgwU8DYXrVJZZ8f9jGjr4A9JGLwmHhs+/sZDaa//PLLSq87SgwZs2i8eS2JiIiIqGYxQUBEREREtcbQoUPLNMl1Bc2Mb7rpJoeGuQiOGoF6NI91xXhc4+/4yw033KD15tFH4eWXX7aN1Mf6oLwLSvqgQbAzjMT//vvvZe3atbbLrrrqKi1Ng4bAH374ocPtcdvKBHknTJigp+gdgCSLM6wryiUhyGx4/fXXdUaDqxHu6K9gX2sfyZD//e9/kpOTU+b2P/zwg8NtPcFtUOceyZ7rrrvO1p/CCI7jMlx33nnneV2uKJD44/mNGzfONlsGMDPmxhtv1NcQZa/s+zCgjwRmu6Bx9GuvvSaFhYVlHg+v5bRp02znv/nmGy0RhfWwh9cWJaeM50FEREREgY0lhoiIiIio1kAg/fHHH9cApFGqxxU0TUX9c4xMR+PgzMxMLV+D5rOjRo3S5IErRmDzzDPP9Ot6o2471hsldV555RUtx4IeB0hUoFQQkgYoz9OtWzddZ4zAR/NZBNRRUumnn36y9SFAQPiNN96QK664QkdqT548Wcv3oMkwEhx33HGHPl5FejTgeb/44ovafBZNZtu1a6ePHR8fL3v37tX1QYAafx+zA+Dtt9/W7YmmuWjGa9wWpWaQ4EDjYcyWADxXnEe5KMwGwXPBbfC4SCigiTG2kzewDkicYLYD/jaa5gJe5/T0dOnevbsGu2uryjw/NH1GIgCv37Bhw3Rf+Ouvv7S5NPYv5/vhdfj222+1P8TNN98sTz75pJYaQkNrvN4rVqyQHTt2aKmoc845R++Dx8O+jBkyPXv21FMkMubMmSNpaWm6vyKRQURERESBjTMIiIiIiKjWQCASgWmMXPZUZx0B1YULF2q5EwRU//jjD2nZsqU2zf36669dNqlFUHP69OnStm3bcpsGVwRG5aOED4Ko6EVgwL8RbEXgHIFfJDbQ7Belfk4//XT59NNPteSMPSQakMxA8Bej9zFzANDo1wjg2jcS9sWdd94pixcv1pkKCDJjpgJG9+/cuVPL0qABLUauG5544gkNBGMEOoLDWIcNGzbIkCFDZOrUqbr+Brx2Tz/9tD4OHg9BaTwPJAww4h2B6D59+ni1nnh+SIhg1gPKIyGJggXBbgS40TA3NTVVaqvKPD8kBDDTA68Lyjth/8BlSORg9kfDhg3L3AezZjBL4IEHHtDEAG6H9wouw/sJrxv+rgHJKfSLQBJi5cqVOnMFs2DatGmjCSr8Gw2viYiIiCiwhVhQFJSIiIiIqJZA0180QUV5Hm9Hm3tj0qRJOqIfI/Ixirq2QkD5kUce0eeA50JEREREROQOEwREREREVKtgfAtGoqN5K8rSoFRPZaH0EEY+x8XF6Who9AcIZNu3b9eyRQ0aNHC4HI1pUSYGzwcjuL0djU9ERERERObEEkNEREREVKuEhIToaH+U6nn22Wf9Vu8dNf9feOGFgE8OABoIN27cWHr37q0lhdB0tlOnTnLaaadpk9gHH3yQyQEiIiIiIioXZxAQEREREdUyq1ev1mQGGgHv27dPsrOztR49ZgzccMMN2ruAiIiIiIioPEwQEBERERERERERERGZEEsMERERERERERERERGZEBMEREREREREREREREQmxAQBEREREREREREREZEJhdf0CtRGhw8flr/++kuaNWsmUVFRNb06RERERERERERERGRyeXl5smPHDhkyZIgkJyd7dR8mCCoAyYGzzjqrInclIiIiIiIiIiIiIqoy3377rZx55ple3ZYJggrAzAFjQ7dp00bMqqioSLKysiQuLk7CwsJqenUCArcJtwv3F76HeGzh8bYm8XOI24X7Ct9DPK7weFuT+DnEbcJ9he8fHld4vOVnUM3auHGjDmw34tfeYIKgAoyyQkgOdO7cWcz85S8zM1MSEhKYIOA24b7C9xCPKzze8jMoAPCzmduF+wrfQzyu8Hhbk/g5xG3CfYXvHx5XeLzlZ1Bg8KUsfkA3KX7mmWfk/PPPl7Zt20poaKiEh1csn5GdnS333XeftGzZUjcOTu+//369nIiIiIiIiIiIiIjIjAJ6BgGC+Gim0LNnTzl69KgcOHCgQiMYTj31VO0bcOmll8rgwYNl2bJl8vzzz8uCBQvkt99+0+QDEREREREREREREZGZhAd6zaTWrVvrv0844YQKJQg+/PBDTQ7ccsstMmnSJNvlmEVw1113ySeffCKXXXaZX9ebiIiIiIiIiIiIiCjQBfTQeSM5UBkfffSRno4bN87h8htvvFFiYmJs1xMRERERERERERERmUlAzyCoLIvFIgsXLpTGjRtLixYtHK5DcqBHjx56vb8VFxfLvn37JC8vT/8dzNu3oKBADh06JCEhITW9OgGB26TmtgtKhaHHSIMGDVg2jIiIiIiIiIiIyOwJgrS0NG1E3KVLF5fXN23aVObOnSsZGRmSmJjo8jb79+8vU9oIpY+M/gZY7CEhsHPnTsnJydGmymFhYRLMjMbRCAATt0lN7iv5+fn6fs/NzdX3dm3oLWIcQ5yPI2bGbcLtwn2F7yEeW3i8rUn8HOI24b7C9xCPKzze8jMosPCzmduF+4pvKhJjCuoEAYKFgFHFrkRHR9tu5y5B8Prrr8v48eNdXpeVlSWZmZkOl6Wnp+vjpaamSt26dYN+ZD0SIrUhEFuduE1qZrsg8XDw4EFNDCJJl5KSIrXhoG0cp4I9megtbhNuF+4rfA/x2MLjbU3i5xC3CfcVvod4XOHxlp9BgYWfzdwu3Fd8g3i1r4I6QRAbG6unKPXjCkYa29/OFfQqGDNmTJkZBGeddZbExcVJQkJCmQRBRESEljkJ9uSAMRIcQd9gf67e4jap2e2C952RtHN+bwZyVhfrygQBtwn3Fb5/eFzh8ZafQTWPn83cJtxX+B7icYXHW34GBRZ+NnO7cF/xDeLVvgrqBAFG8SP4j9HEruByzBxwN3sA6tevr4srCOg5B/UQCMVlZhpVj4AvEwTcJoGwr+Cx8f4z3oe1gXEcqS3rWx24TbhduK/wPcRjC4+3NYmfQ9wm3Ff4HuJxhcdbfgYFFn42c7twX/FeReJLQR3FRrCwT58+snv3btm2bZvDdegRsHTpUunbt2+V/F0iqhl8/xEREREREREREZksQYA63mvXrpU9e/Y4XH7ppZfq6Ysvvuhw+RtvvKFJAuN6IiIiIiIiIiIiIiIzCegSQx9//LFt5D9OUTbkiSeesF3/0EMP2f69YMECGTp0qIwdO1amTJliu/yKK66Qjz76SCZPnixHjhyRwYMHy7Jly7T58AknnCCXXHJJNT8rIiIiIiIiIiIiIqKaF9AzCN577z15+OGHddm6dasUFxfbzmPxtu7SjBkz5O6775ZZs2bJDTfcIN99952MGzdOpk+fzrrf5fj6669l2LBhkpycLFFRUdKuXTu58847tWwT4HVBvwVsS0PLli3lrrvu8vp1RkIHZWGOHj0qtdn69evlsccek8OHD3t1eyS8evToIR9++KHtMiStjDr94eHhui2vu+46OXDggFeP+cILL8jw4cMr/ByIiIiIiIiIiIjIPAJ6BgEC+t5CYBUBV1fi4+Plueee06U2WP3E05K7d6/H20Q3bCidHrq/StcDSZSJEyfqLIw77rhDmzmvXr1a3nzzTdmyZYtMmzbN5f2++eYbqVOnjpgNEgSPP/64lq3y5vlPnTpV0tLS5KKLLnK4HDNhnnrqKSksLJT//vtPZ8ps2rRJfv/9d71++/bt+pogYbZ//35Net188816HZIJTz75pL538J4gIiIiIiIiIiIiqpUJArNCciBnx84aXYcffvhBXnrpJZ3FceWVV9ouHzJkiFx77bXy66+/ur1vz549q2kta7dJkyZpMiEiIsLh8tTUVDnuuOP03wMHDtT+Gvfff7/O2mjcuLHUrVtXX5/Y2FhN1LRu3Vouu+wyTeAkJCTIueeeqyW1mCAgIiIiIiIiIiKiWltiiGrOyy+/LL169XJIDtiXbRo5cqTb+7oqMfT333/ryHjM5khKStLg9ZIlS9w+xvPPPy/R0dHy/fffe1zPd955R7p27aq3bdCggYwePVp7TdiP0sf1KI/UrFkzefDBB3VkvgElgRBwd4YSP6+++mqZ54Tt0rRpU0lJSZELLrjAVk4II/ZHjRql/27btq2WXcJ93Nm4caPMmTNH17c83bt319MdO3boKRIDWOCvv/7ShAwSAwYkCFDyCbMTiIiIiIiIiIiIiNzhDIJqUJyfX27JIHuWggKvbpO9fbtP64GyRKGRkeXerqCgQIPXKDHkDwien3TSSZogQL39uLg4mT17tuzatcvlbAOU6XnmmWc0OXDyySe7fVw0rH7kkUfkxhtv1IQCRtr/+OOP2ssASQjMcjj//PN1dD2uX758ufauOHTokJZJ8hWSDd26dZO3335bdu7cqb0YHnjgAW14jWQK6v8jifDll19KkyZNNGnhzh9//KHbwQj+e4KSQkg4tGjRwuFyPFeUE0LpISQ0DMcff7y+hv/884+ceeaZPj9PIiIiIiIiIiIiMgcmCKoBkgNLbrnDz4+5z+fH7Dn5ZYlt3rzc2yGAnpeXJ829uK03UB4HgfBffvnFFsg+5ZRTXN4WAXeUx/npp5+0nJE7GLmPOv233367lkIynHPOObZ/I3mAmQpGE2Djb2J9UNcfMwF8gVJA3377rTYPBvRj+OKLLzRBgPI+7du318vReBhlf+yD9s4WL14sHTt21MC/M/TSwCyHoqIivd3TTz+tZZ0aNmxou83HH3+s/SFmzpxZ5nmgoTReuwULFjBBQERERERERERERG6xxBC55SnA7a2srCyZP3++jB07ttzHw4h8BNsx8t9TcgDmzp0rOTk52qzXFQTX0eB3zJgxDpdjRgGa++L+vsIMCCM5AJ06ddImwRit76u9e/e6LG0EaP6MZARmIAwYMEATA+hXYEBpJsyKwPa85JJLNAmCXgT28Nj4G0RERERERERERETucAYBlVGnTh2t2Y/SNpWVnp6uI+IbNWpU7m2//vpr6d27t/Tt29erWQ7g7nEPHjyogXv0JbBnnK9IfX6MzLcXGRmpzw2zLZwbDZcnNzfX1kfA2bBhw+TZZ5/Vx0UzYvwbMx5wCijLhL/rCV4//A0iIiIiIiIiIiIid5ggqAao/Y/yPt5a8+QzWkLI82M2kI4P3ufzengDwW6MXEdJINT5rww080UZnT179pR7WzTWPf3003V0/CeffOKy/I59EgPwuK5G4uMyPA+M8Le3b591u6ampuopRunn5+eXSWpUNfx9dyP8sc369Omj/8brcODAAS0ndPPNN2ujZW+gBJPxHImIiIiIiIiIiIhcYYmhaoDGwKj97+0S4sVodNzGl8fE4k2DYgNq+y9atMhWv98eSvT8/PPPXj0OGvEee+yx8tFHH5U76r1r167aewCJguuvv97jbdGINyYmxuX6QVhYmM5GQMNg50bDSDzg/oD6/ZmZmdow2YASRxWBGQXgzch99CtwLgvkzvjx4/X05Ze9SzLh9cHsj3bt2nl1eyIiIiIiIiIiIjInziAIQN6M9Pd2NkBFjRo1SnsCXHXVVTJ79mxtdhsfHy9r166VN998U1q2bCkjRozw6rGeeeYZOfHEE2XkyJHabBdJA/QAwCh5zBiw169fPy2rg9ui8e8LL7zgttzPww8/LA8++KDOADj11FO1JM+PP/4ojz76qDRp0kQD61hH9Cm44IILZMWKFXqfa665xtbYF42LkWi48sorZdy4cRq0x/OrCKNJ8TvvvCMXXnihPk8kPVzBzIDHH39cZwfUq1fP4+NiXdHDAY+LxsvOpY6crVu3To4ePap/g4iIiIiIiIiIiMgdJggCUKeH7pdA8OKLL0r//v3l1VdflYsuukibAiMxcMYZZ8hdd93l9eMMHjxYfvvtNw3Oo6kuRtqjjv5ZZ53l8vZoUIxGvUhKJCQkaMDflfvvv1/L6Lzyyivy1ltvaWke/C3cB04++WT54osvtEzSp59+KvXr19ckgDEi3yhFhN4HeD5YH8w6+Oyzz7QBsa9atGghzz//vEyePFlee+01Dexv3brV5W3RWBjrjpkYl156abmPfd9998kHH3wgb7zxhj5vT/CYrVq10m1MRERERERERERE5E6Ipby6L1TGqlWrpEuXLrJy5Urp3Lmzw3WbN2/WUwRogx12naKiIi3nExISUtOrU+u2yW233SYbN27UWQ/+hPJJp512mjY2NuO+Upveg9gmKHGFpBa2DXGbcF/h+4fHFR5v+RlUs/jZzG3CfYXvIR5XeLzlZ1Bg4Wcztwv3Ff/Frd3hDAKiGnL33Xdrn4D169f7rV/A/PnztQwUejkQERERERERERERecImxUQ1BCWI3n//fdmzZ4/fHjMtLU0bN5fXp4CIiIiIiIiIiIiIMwiIahCaJ/sTmjsTEREREREREREReYMzCIiIiIiIiIiIiIiITIgJAiIiIiIiIiIiIiIiE2KCgIiIiIiIiIiIiIjIhJggICIiIiIiIiIiIiIyISYIiIiIiIiIiIiIiIhMiAkCIiIiIiIiIiIiIiITYoKAXHrsscckJCRE2rZt6/J6XB4aGiqPP/54QG3Byy+/XPr06VPm8qVLl8r5558vDRs2lMjISGncuLFcfPHFsnDhQtttTjjhBBk9enS1rWt+fr5uZ6xbsLFYLNKjRw/58MMPbZcNHz5c9xnsV+Hh4dKyZUu57rrr5MCBA1495gsvvKCPQURERERERERERP4R7qfHIT/65ptv5MiRIx5vk5SUJGeffXaVbvfo6GjZsmWLLFq0yCHojqD61q1b9fraYNq0aXLBBRfI4MGD5eWXX5YmTZrIrl275NNPP5WTTz5Z0tPTa2S9kCAYP368BsoRTA8mU6dOlbS0NLnoooscLh86dKg89dRTUlhYKP/995889NBDsmnTJvn999/1+u3bt8sVV1whxcXFsn//frnhhhvk5ptv1uuQTHjyySdl1qxZmswhIiIiIiIiIiKiymGCIAAhOXDo0KGaXg2Ji4uTXr16yRdffOGQIMD5YcOGyeLFiyXQ7d69W8aOHSsXXnihTJkyRUevG3DZ9OnTa3T9gtWk/7d3H2BSVefjgA9FQRAUewHUxIaKXeyCsUSNYgNjLMEaW4zGghJjjLHFGKO/YI0NuwlGgy2WRNSIBWPsURE1gopiR0VQYP7Pd/Kfze6yLLu0Gfa+7/OMI3fK3jlz7rl3znfOd37/+7T//vunBRZYoM72xRZbLG2yySb5/7fYYos0ceLENGjQoPw9xayOJZZYIt15552pQ4cOOTj17W9/O/3whz9MnTt3Tp06dUp77rlnGjx4sAABAAAAAMwBUgzRqBh5H6PBI2VMiPv4d2xvyD/+8Y/Uu3fv3MG7+OKLp0MPPTR9/vnnNY+PGzcuHXTQQelb3/pWWmihhdKqq66aR5HHaPqymJ0QHfnxd2LUeMyW6Nq1azrttNPyyPLmuPLKK/N7n3/++XWCA2U777xzxWpAdHiHGDEf+xa3+Oxh0qRJaeDAgalbt26pXbt2aZ111kn33HNPndfHzIMTTjgh/frXv07LLrtsLqfjjz8+f0fx3DXXXDP/jd12263OLIkYgR9/6/7778+fPwJB3bt3T5dddtl0+xjfQc+ePfM+xL6ccsopefR/Y0aPHp0ee+yxJqVris8Vxo4dm++j3sQtPPzww2m99darKacQAYII6sTsBAAAAABg9phBMA9Eh+qnn37arOc35Tkffvhhs/Zj0UUXzbnfm2OPPfbIaV4effTRtOWWW+YAQOSMj+0nnnhineeOGDEibbvttrlD+tZbb82zIE4++eTcOR3/DrHPMYr8d7/7XerSpUsaNWpUzsMf73n55ZfXeb/oII8O4Xjt3//+97zeQXR677XXXk3e/+hkjtkPMTK92jz44IN5JkYESL73ve/lbdHRH6JzfeTIkTkFUYyij476vn375nRPtdMRxWyOXr16pWuuuSbP6Ij3iiDKI488ks4444z01Vdf5RQ9MUq/fgDg4IMPzqP8jz766JzWKr7nCMSUgyYRQIh1G2IE/3nnnZeef/75dOqpp+bvtaFgQll8VxF0KHf+NyZSCsW6BCussEKd7XfffXdOJxSph2oHdjbddNP0zTff5Hq46667NrmsAQAAAIDpCRDMAxEcqL1Y65xKQ9Tc94xUO83tKI+gwg477JA7oiNAEPfx7xitXl8EAzbbbLP0xz/+sWZb5PuPhWVffPHFtNZaa+XR6LHYbNnmm2+eO5NjVkGkjokFhMtizYAY+R+22267dO+99+b1BJoTIIi1BmIUejXaaKON8n0EAMppd8od7NFBHiP9YzZGiLUSIpgSneZDhw6teW6sAxH/btOmTf5ehg0blsvxtddeSyuttFJ+znPPPZfrSv1O/R133DGvBxC++93v5rUAzjzzzJoAwS9+8Yucyqdcz+L9QwQbIhARwYSGRKCiR48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",
      "text/plain": [
       "<Figure size 1560x1080 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Saved: figures/phase2/fig_wham_probe_profile.png\n"
     ]
    }
   ],
   "source": [
    "fig, axes = plt.subplots(2, 1, figsize=(13, 9))\n",
    "fig.suptitle(\"WhAM Layer-wise Linear Probe: All 6 Biological Targets\",\n",
    "             fontsize=13, fontweight=\"bold\")\n",
    "\n",
    "layers = list(range(n_layers))\n",
    "\n",
    "# ── Top panel: classification (F1) ────────────────────────────────────────────\n",
    "ax = axes[0]\n",
    "clf_targets = [\n",
    "    (\"unit\",         \"#4C72B0\", \"Social Unit (F1)\"),\n",
    "    (\"coda_type\",    \"#DD8452\", \"Coda Type — 22 classes (F1)\"),\n",
    "    (\"individual_id\",\"#55A868\", \"Individual ID — 12 IDs (F1)\"),\n",
    "    (\"year\",         \"#9370DB\", \"Recording Year — confound (F1)\"),\n",
    "]\n",
    "for col, color, label in clf_targets:\n",
    "    ax.plot(layers, probe_df[col], marker=\"o\", lw=2, color=color, label=label, ms=5)\n",
    "\n",
    "# Raw baseline dotted lines\n",
    "ax.axhline(0.5986, color=\"#4C72B0\", ls=\":\", lw=1.2, alpha=0.5, label=\"ICI baseline — unit (0.599)\")\n",
    "ax.axhline(0.9310, color=\"#DD8452\", ls=\":\", lw=1.2, alpha=0.5, label=\"ICI baseline — coda type (0.931)\")\n",
    "ax.axhline(0.4925, color=\"#55A868\", ls=\":\", lw=1.2, alpha=0.5, label=\"ICI baseline — indiv ID (0.493)\")\n",
    "\n",
    "ax.set_ylabel(\"Macro-F1\"); ax.set_ylim(0, 1.02)\n",
    "ax.set_title(\"(a) Classification Probes\")\n",
    "ax.legend(fontsize=8, ncol=2, loc=\"lower right\")\n",
    "ax.set_xticks(layers); ax.grid(alpha=0.2)\n",
    "\n",
    "# ── Bottom panel: regression (R²) ─────────────────────────────────────────────\n",
    "ax = axes[1]\n",
    "reg_targets = [\n",
    "    (\"n_clicks\",    \"#C44E52\", \"Click count (R²)\"),\n",
    "    (\"mean_ici_ms\", \"#8C8C8C\", \"Mean ICI — tempo (R²)\"),\n",
    "]\n",
    "for col, color, label in reg_targets:\n",
    "    ax.plot(layers, probe_df[col], marker=\"s\", lw=2, color=color, label=label, ms=5)\n",
    "\n",
    "ax.set_xlabel(\"Transformer Layer (0 = earliest, 19 = last)\")\n",
    "ax.set_ylabel(\"R²\"); ax.set_ylim(0, 1.02)\n",
    "ax.set_title(\"(b) Regression Probes\")\n",
    "ax.legend(fontsize=9, loc=\"upper left\")\n",
    "ax.set_xticks(layers); ax.grid(alpha=0.2)\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.savefig(os.path.join(FIGS, \"fig_wham_probe_profile.png\"), dpi=130, bbox_inches=\"tight\")\n",
    "plt.show()\n",
    "print(\"Saved: figures/phase2/fig_wham_probe_profile.png\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "p2-10-nemvww",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-05-01T20:32:16.004882Z",
     "iopub.status.busy": "2026-05-01T20:32:16.004760Z",
     "iopub.status.idle": "2026-05-01T20:32:16.010950Z",
     "shell.execute_reply": "2026-05-01T20:32:16.010436Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "=== Best layer per probe target ===\n",
      "  unit                  best layer=19  F1=0.8946  (layer-10: 0.8763)\n",
      "  coda_type             best layer=19  F1=0.2605  (layer-10: 0.2120)\n",
      "  individual_id         best layer= 7  F1=0.4257  (layer-10: 0.4114)\n",
      "  year                  best layer=18  F1=0.9062  (layer-10: 0.8744)\n",
      "  n_clicks              best layer= 0  R²=0.0000  (layer-10: 0.0000)\n",
      "  mean_ici_ms           best layer= 0  R²=0.1085  (layer-10: 0.0000)\n",
      "\n",
      "=== Confound check: year vs unit correlation ===\n",
      "  Spearman rho(year, unit): 0.0111  p=0.7701\n",
      "  Interpretation: if |rho| < 0.3 and p > 0.05, year is NOT a significant confound.\n"
     ]
    }
   ],
   "source": [
    "# Best layer per probe target\n",
    "print(\"=== Best layer per probe target ===\")\n",
    "for col in [\"unit\", \"coda_type\", \"individual_id\", \"year\", \"n_clicks\", \"mean_ici_ms\"]:\n",
    "    best_layer = int(probe_df[col].idxmax())\n",
    "    best_val   = probe_df[col].max()\n",
    "    l10_val    = probe_df[col][10]\n",
    "    metric     = \"F1\" if col not in (\"n_clicks\",\"mean_ici_ms\") else \"R²\"\n",
    "    print(f\"  {col:20s}  best layer={best_layer:2d}  {metric}={best_val:.4f}  \"\n",
    "          f\"(layer-10: {l10_val:.4f})\")\n",
    "\n",
    "print(\"\\n=== Confound check: year vs unit correlation ===\")\n",
    "yr_arr  = df_clean[\"year\"].astype(float).values\n",
    "unit_num = pd.Categorical(df_clean[\"unit\"]).codes\n",
    "mask = ~np.isnan(yr_arr)\n",
    "from scipy.stats import spearmanr\n",
    "rho, pval = spearmanr(yr_arr[mask], unit_num[mask])\n",
    "print(f\"  Spearman rho(year, unit): {rho:.4f}  p={pval:.4f}\")\n",
    "print(\"  Interpretation: if |rho| < 0.3 and p > 0.05, year is NOT a significant confound.\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "p2-11-opcsaq",
   "metadata": {},
   "source": [
    "---\n",
    "## 3. UMAP of WhAM Embeddings\n",
    "\n",
    "### Why UMAP over t-SNE?\n",
    "\n",
    "t-SNE (used in Phase 0 and Phase 1) preserves local neighbourhood structure but distorts global distances — cluster separations in t-SNE are not directly comparable. **UMAP** (McInnes et al., 2018) preserves both local and global structure, making the inter-cluster distances more meaningful. For interpretability analysis, UMAP is the standard in the NLP probing literature.\n",
    "\n",
    "We use the **best-performing layer** identified by the probing profile above. We show four colourings of the same 2D projection:\n",
    "\n",
    "1. Social unit (A / D / F)\n",
    "2. Coda type (top 6 + other)\n",
    "3. Individual whale ID\n",
    "4. Recording year\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "p2-12-fpqysi",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-05-01T20:32:16.012798Z",
     "iopub.status.busy": "2026-05-01T20:32:16.012679Z",
     "iopub.status.idle": "2026-05-01T20:32:23.094243Z",
     "shell.execute_reply": "2026-05-01T20:32:23.093765Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Using layer 19 (best for social unit, F1=0.8946)\n",
      "Running UMAP (n_neighbors=30, min_dist=0.1)...\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "UMAP projection shape: (1383, 2)\n"
     ]
    }
   ],
   "source": [
    "# Use best layer for social unit\n",
    "best_unit_layer = int(probe_df[\"unit\"].idxmax())\n",
    "print(f\"Using layer {best_unit_layer} (best for social unit, F1={probe_df['unit'].max():.4f})\")\n",
    "\n",
    "emb_best = tall[clean_ids, best_unit_layer, :]   # (1383, 1280)\n",
    "\n",
    "print(\"Running UMAP (n_neighbors=30, min_dist=0.1)...\")\n",
    "reducer = umap.UMAP(n_components=2, n_neighbors=30, min_dist=0.1,\n",
    "                    metric=\"cosine\", random_state=SEED)\n",
    "proj_umap = reducer.fit_transform(emb_best)\n",
    "print(f\"UMAP projection shape: {proj_umap.shape}\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "p2-13-monauo",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-05-01T20:32:23.096573Z",
     "iopub.status.busy": "2026-05-01T20:32:23.096438Z",
     "iopub.status.idle": "2026-05-01T20:32:23.636918Z",
     "shell.execute_reply": "2026-05-01T20:32:23.636262Z"
    }
   },
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<Figure size 1680x1320 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Saved: figures/phase2/fig_wham_umap.png\n"
     ]
    }
   ],
   "source": [
    "fig, axes = plt.subplots(2, 2, figsize=(14, 11))\n",
    "fig.suptitle(f\"UMAP of WhAM Layer-{best_unit_layer} Embeddings  (n=1,383 clean codas)\",\n",
    "             fontsize=13, fontweight=\"bold\")\n",
    "\n",
    "# ── (a) Social unit ────────────────────────────────────────────────────────────\n",
    "ax = axes[0, 0]\n",
    "for unit, color in UNIT_COLORS.items():\n",
    "    mask = df_clean[\"unit\"] == unit\n",
    "    ax.scatter(proj_umap[mask, 0], proj_umap[mask, 1], c=color,\n",
    "               s=12, alpha=0.6, label=f\"Unit {unit} (n={mask.sum()})\", edgecolors=\"none\")\n",
    "ax.set_title(f\"(a) Social Unit  [F1={probe_df['unit'][best_unit_layer]:.3f}]\", fontsize=11)\n",
    "ax.legend(fontsize=9, markerscale=2); ax.set_xlabel(\"UMAP 1\"); ax.set_ylabel(\"UMAP 2\")\n",
    "\n",
    "# ── (b) Coda type (top 6 + other) ─────────────────────────────────────────────\n",
    "ax = axes[0, 1]\n",
    "top6 = df_clean[\"coda_type\"].value_counts().head(6).index.tolist()\n",
    "palette = plt.cm.tab10(np.linspace(0, 0.9, 6))\n",
    "for ct, color in zip(top6, palette):\n",
    "    mask = df_clean[\"coda_type\"] == ct\n",
    "    ax.scatter(proj_umap[mask, 0], proj_umap[mask, 1], c=[color],\n",
    "               s=12, alpha=0.7, label=ct, edgecolors=\"none\")\n",
    "other = ~df_clean[\"coda_type\"].isin(top6)\n",
    "ax.scatter(proj_umap[other, 0], proj_umap[other, 1], c=\"lightgrey\",\n",
    "           s=9, alpha=0.4, label=\"other\", edgecolors=\"none\")\n",
    "ax.set_title(f\"(b) Coda Type (top 6)  [F1={probe_df['coda_type'][best_unit_layer]:.3f}]\", fontsize=11)\n",
    "ax.legend(fontsize=7, markerscale=2, ncol=2); ax.set_xlabel(\"UMAP 1\"); ax.set_ylabel(\"UMAP 2\")\n",
    "\n",
    "# ── (c) Individual ID (known IDs only) ────────────────────────────────────────\n",
    "ax = axes[1, 0]\n",
    "id_mask = df_clean[\"individual_id\"] != \"0\"\n",
    "id_labels = df_clean.loc[id_mask, \"individual_id\"]\n",
    "id_palette = plt.cm.Set3(np.linspace(0, 1, id_labels.nunique()))\n",
    "for (idn, color) in zip(sorted(id_labels.unique()), id_palette):\n",
    "    mask = df_clean[\"individual_id\"] == idn\n",
    "    ax.scatter(proj_umap[mask, 0], proj_umap[mask, 1], c=[color],\n",
    "               s=13, alpha=0.8, label=str(idn), edgecolors=\"none\")\n",
    "unknown = ~id_mask\n",
    "ax.scatter(proj_umap[unknown, 0], proj_umap[unknown, 1], c=\"lightgrey\",\n",
    "           s=8, alpha=0.25, label=\"unknown (IDN=0)\", edgecolors=\"none\")\n",
    "ax.set_title(f\"(c) Individual ID  [F1={probe_df['individual_id'][best_unit_layer]:.3f}]\", fontsize=11)\n",
    "ax.legend(fontsize=6, markerscale=1.5, ncol=3, loc=\"upper right\"); ax.set_xlabel(\"UMAP 1\"); ax.set_ylabel(\"UMAP 2\")\n",
    "\n",
    "# ── (d) Recording year ────────────────────────────────────────────────────────\n",
    "ax = axes[1, 1]\n",
    "year_vals = df_clean[\"year\"].astype(float)\n",
    "year_known = ~year_vals.isna()\n",
    "years_unique = sorted(year_vals.dropna().unique())\n",
    "yr_colors = plt.cm.plasma(np.linspace(0.15, 0.85, len(years_unique)))\n",
    "for yr, color in zip(years_unique, yr_colors):\n",
    "    mask = year_vals == yr\n",
    "    ax.scatter(proj_umap[mask, 0], proj_umap[mask, 1], c=[color],\n",
    "               s=12, alpha=0.65, label=str(int(yr)), edgecolors=\"none\")\n",
    "unknown_yr = ~year_known\n",
    "if unknown_yr.sum() > 0:\n",
    "    ax.scatter(proj_umap[unknown_yr, 0], proj_umap[unknown_yr, 1],\n",
    "               c=\"lightgrey\", s=8, alpha=0.3, edgecolors=\"none\", label=\"unknown year\")\n",
    "ax.set_title(f\"(d) Recording Year (confound check)  [F1={probe_df['year'][best_unit_layer]:.3f}]\", fontsize=11)\n",
    "ax.legend(fontsize=9, markerscale=2); ax.set_xlabel(\"UMAP 1\"); ax.set_ylabel(\"UMAP 2\")\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.savefig(os.path.join(FIGS, \"fig_wham_umap.png\"), dpi=130, bbox_inches=\"tight\")\n",
    "plt.show()\n",
    "print(f\"Saved: figures/phase2/fig_wham_umap.png\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "p2-14-tgsdwp",
   "metadata": {},
   "source": [
    "---\n",
    "## 4. Recording-Year Confound Analysis\n",
    "\n",
    "### Motivation\n",
    "\n",
    "The Dominica dataset spans 2005–2010. If recording conditions changed substantially across years (microphone placement, hydrophone depth, signal processing), WhAM's embeddings might cluster by year rather than by biological unit identity. This would be a data artefact, not a genuine representation of social structure.\n",
    "\n",
    "The original WhAM paper (Paradise et al., 2025) did not report a year-confound test. We report it here as a methodological contribution.\n",
    "\n",
    "**Test design:**\n",
    "1. Does recording year predict social unit? (Chi-squared test on unit × year contingency)\n",
    "2. Does WhAM's year F1 co-vary with unit F1 across layers? (Spearman correlation)\n",
    "3. Is year better predicted than unit at any layer? (Direct comparison from probe profile)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "p2-15-mytmsu",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-05-01T20:32:23.638886Z",
     "iopub.status.busy": "2026-05-01T20:32:23.638769Z",
     "iopub.status.idle": "2026-05-01T20:32:23.649932Z",
     "shell.execute_reply": "2026-05-01T20:32:23.649457Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Unit × Year contingency table:\n",
      "year  2005  2008  2009  2010\n",
      "unit                        \n",
      "A       10     0    30     0\n",
      "D        0    23     0    79\n",
      "F      161    10    29   351\n",
      "\n",
      "Chi-squared=361.67  df=6  p=0.0000\n",
      "Cramér's V = 0.5108  (>0.3 = moderate association, >0.5 = strong)\n",
      "\n",
      "⚠  Year is statistically associated with social unit — potential confound.\n",
      "   Examine Cramér's V: if < 0.3 the association is weak despite significance.\n"
     ]
    }
   ],
   "source": [
    "from scipy.stats import chi2_contingency, spearmanr\n",
    "\n",
    "# ── 1. Unit × year contingency ────────────────────────────────────────────────\n",
    "contingency = pd.crosstab(df_clean[\"unit\"], df_clean[\"year\"])\n",
    "print(\"Unit × Year contingency table:\")\n",
    "print(contingency.to_string())\n",
    "chi2, p, dof, expected = chi2_contingency(contingency)\n",
    "print(f\"\\nChi-squared={chi2:.2f}  df={dof}  p={p:.4f}\")\n",
    "cramers_v = np.sqrt(chi2 / (contingency.values.sum() * (min(contingency.shape)-1)))\n",
    "print(f\"Cramér's V = {cramers_v:.4f}  (>0.3 = moderate association, >0.5 = strong)\")\n",
    "print()\n",
    "\n",
    "if p < 0.05:\n",
    "    print(\"⚠  Year is statistically associated with social unit — potential confound.\")\n",
    "    print(\"   Examine Cramér's V: if < 0.3 the association is weak despite significance.\")\n",
    "else:\n",
    "    print(\"✓  Year is NOT significantly associated with social unit (p > 0.05).\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "p2-16-nxklsf",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-05-01T20:32:23.651093Z",
     "iopub.status.busy": "2026-05-01T20:32:23.651007Z",
     "iopub.status.idle": "2026-05-01T20:32:23.655184Z",
     "shell.execute_reply": "2026-05-01T20:32:23.654700Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Spearman rho(year-F1, unit-F1) across layers: rho=0.6301  p=0.0029\n",
      "\n",
      "⚠  Year and unit F1 are correlated across layers — WhAM may be encoding\n",
      "   recording-period drift as a proxy for social unit.\n",
      "\n",
      "Year F1 at best social-unit layer (19): 0.8748\n",
      "Unit F1 at best social-unit layer (19): 0.8946\n",
      "Gap (unit - year): 0.0198\n"
     ]
    }
   ],
   "source": [
    "# ── 2. Correlation: year F1 vs unit F1 across layers ─────────────────────────\n",
    "yr_f1_vals   = probe_df[\"year\"].values\n",
    "unit_f1_vals = probe_df[\"unit\"].values\n",
    "valid = ~(np.isnan(yr_f1_vals) | np.isnan(unit_f1_vals))\n",
    "rho, pval = spearmanr(yr_f1_vals[valid], unit_f1_vals[valid])\n",
    "print(f\"Spearman rho(year-F1, unit-F1) across layers: rho={rho:.4f}  p={pval:.4f}\")\n",
    "print()\n",
    "if abs(rho) > 0.5 and pval < 0.05:\n",
    "    print(\"⚠  Year and unit F1 are correlated across layers — WhAM may be encoding\")\n",
    "    print(\"   recording-period drift as a proxy for social unit.\")\n",
    "else:\n",
    "    print(\"✓  Year and unit F1 are NOT strongly correlated — social unit signal is\")\n",
    "    print(\"   not primarily driven by recording-year artefacts.\")\n",
    "\n",
    "# ── 3. Direct comparison ──────────────────────────────────────────────────────\n",
    "print(f\"\\nYear F1 at best social-unit layer ({best_unit_layer}): {probe_df['year'][best_unit_layer]:.4f}\")\n",
    "print(f\"Unit F1 at best social-unit layer ({best_unit_layer}): {probe_df['unit'][best_unit_layer]:.4f}\")\n",
    "gap = probe_df['unit'][best_unit_layer] - probe_df['year'][best_unit_layer]\n",
    "print(f\"Gap (unit - year): {gap:.4f}\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "p2-17-wbjdrt",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-05-01T20:32:23.656393Z",
     "iopub.status.busy": "2026-05-01T20:32:23.656300Z",
     "iopub.status.idle": "2026-05-01T20:32:23.906244Z",
     "shell.execute_reply": "2026-05-01T20:32:23.905823Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1320x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Saved: figures/phase2/fig_wham_year_confound.png\n"
     ]
    }
   ],
   "source": [
    "# ── Visualise: unit F1 vs year F1 across layers ───────────────────────────────\n",
    "fig, ax = plt.subplots(figsize=(11, 4))\n",
    "\n",
    "ax.plot(layers, probe_df[\"unit\"], marker=\"o\", lw=2.2, color=\"#4C72B0\",\n",
    "        label=\"Social Unit (F1)\")\n",
    "ax.plot(layers, probe_df[\"year\"], marker=\"s\", lw=2.0, color=\"#9370DB\",\n",
    "        ls=\"--\", label=\"Recording Year — confound (F1)\")\n",
    "ax.fill_between(layers, probe_df[\"year\"], probe_df[\"unit\"],\n",
    "                alpha=0.12, color=\"#4C72B0\",\n",
    "                label=\"Unit − Year gap (genuine social signal)\")\n",
    "\n",
    "ax.set_xlabel(\"Transformer Layer\")\n",
    "ax.set_ylabel(\"Macro-F1\")\n",
    "ax.set_title(\"Confound Analysis: Social Unit F1 vs Recording Year F1\\n\"\n",
    "             \"(gap = social signal not explainable by recording-date drift)\")\n",
    "ax.legend(fontsize=9); ax.set_xticks(layers); ax.grid(alpha=0.2); ax.set_ylim(0, 1.02)\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.savefig(os.path.join(FIGS, \"fig_wham_year_confound.png\"), dpi=130, bbox_inches=\"tight\")\n",
    "plt.show()\n",
    "print(\"Saved: figures/phase2/fig_wham_year_confound.png\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "p2-18-oykcmu",
   "metadata": {},
   "source": [
    "---\n",
    "## 5. Updated WhAM Baseline: Best Layer vs Layer 10\n",
    "\n",
    "Phase 1 used layer 10 following the JukeMIR convention. The probing profile above shows that layer 19 is the empirically best layer for social-unit probing on this dataset (F1=0.895 vs 0.876 at layer 10, a 2.2% improvement).\n",
    "\n",
    "We re-run the 1B classification with layer 19 to establish the true WhAM ceiling. This becomes the definitive comparison target for DCCE in Phase 3.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "p2-19-ljymzw",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-05-01T20:32:23.907830Z",
     "iopub.status.busy": "2026-05-01T20:32:23.907727Z",
     "iopub.status.idle": "2026-05-01T20:32:25.660098Z",
     "shell.execute_reply": "2026-05-01T20:32:25.659626Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best layer for social unit: 19  (F1=0.8946)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "=== 1B (layer 19) — Social Unit ===\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           A       0.96      0.98      0.97        48\n",
      "           D       0.80      0.80      0.80        64\n",
      "           F       0.92      0.92      0.92       165\n",
      "\n",
      "    accuracy                           0.90       277\n",
      "   macro avg       0.89      0.90      0.89       277\n",
      "weighted avg       0.90      0.90      0.90       277\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "=== 1B (layer 19) — Coda Type: macro-F1=0.2605 ===\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "=== 1B (layer 19) — Individual ID: macro-F1=0.3631 ===\n"
     ]
    }
   ],
   "source": [
    "best_unit_layer = int(probe_df[\"unit\"].idxmax())\n",
    "print(f\"Best layer for social unit: {best_unit_layer}  (F1={probe_df['unit'].max():.4f})\")\n",
    "\n",
    "emb_best_clean = tall[clean_ids, best_unit_layer, :]\n",
    "emb_best_id    = tall[id_ids,    best_unit_layer, :]\n",
    "\n",
    "sc_best   = StandardScaler().fit(emb_best_clean[train_idx])\n",
    "X_tr_best = sc_best.transform(emb_best_clean[train_idx])\n",
    "X_te_best = sc_best.transform(emb_best_clean[test_idx])\n",
    "\n",
    "sc_id_best   = StandardScaler().fit(emb_best_id[train_id_idx])\n",
    "X_id_tr_best = sc_id_best.transform(emb_best_id[train_id_idx])\n",
    "X_id_te_best = sc_id_best.transform(emb_best_id[test_id_idx])\n",
    "\n",
    "def make_lr():\n",
    "    return LogisticRegression(max_iter=2000, class_weight=\"balanced\",\n",
    "                              random_state=SEED, solver=\"lbfgs\")\n",
    "\n",
    "results_1b_best = {}\n",
    "\n",
    "# Social unit\n",
    "lr = make_lr().fit(X_tr_best, df_train[\"unit\"])\n",
    "pred = lr.predict(X_te_best)\n",
    "results_1b_best[\"unit\"] = {\n",
    "    \"macro_f1\": f1_score(df_test[\"unit\"], pred, average=\"macro\", zero_division=0),\n",
    "    \"accuracy\": accuracy_score(df_test[\"unit\"], pred)}\n",
    "from sklearn.metrics import classification_report\n",
    "print(f\"=== 1B (layer {best_unit_layer}) — Social Unit ===\")\n",
    "print(classification_report(df_test[\"unit\"], pred, target_names=[\"A\",\"D\",\"F\"], zero_division=0))\n",
    "\n",
    "# Coda type\n",
    "lr = make_lr().fit(X_tr_best, df_train[\"coda_type\"])\n",
    "pred = lr.predict(X_te_best)\n",
    "results_1b_best[\"coda_type_all\"] = {\n",
    "    \"macro_f1\": f1_score(df_test[\"coda_type\"], pred, average=\"macro\", zero_division=0),\n",
    "    \"accuracy\": accuracy_score(df_test[\"coda_type\"], pred)}\n",
    "print(f\"=== 1B (layer {best_unit_layer}) — Coda Type: macro-F1={results_1b_best['coda_type_all']['macro_f1']:.4f} ===\")\n",
    "\n",
    "# Individual ID\n",
    "lr = make_lr().fit(X_id_tr_best, df_id_train[\"individual_id\"])\n",
    "pred = lr.predict(X_id_te_best)\n",
    "results_1b_best[\"individual_id\"] = {\n",
    "    \"macro_f1\": f1_score(df_id_test[\"individual_id\"], pred, average=\"macro\", zero_division=0),\n",
    "    \"accuracy\": accuracy_score(df_id_test[\"individual_id\"], pred)}\n",
    "print(f\"=== 1B (layer {best_unit_layer}) — Individual ID: macro-F1={results_1b_best['individual_id']['macro_f1']:.4f} ===\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "p2-20-goxcpx",
   "metadata": {},
   "source": [
    "---\n",
    "## 6. Phase 2 Summary\n",
    "\n",
    "### Complete Baseline Table (updated with best-layer WhAM)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "p2-21-jiamks",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-05-01T20:32:25.661919Z",
     "iopub.status.busy": "2026-05-01T20:32:25.661795Z",
     "iopub.status.idle": "2026-05-01T20:32:25.666945Z",
     "shell.execute_reply": "2026-05-01T20:32:25.666386Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "            Baseline          Task  Macro-F1  Accuracy                    Note\n",
      "        1A — Raw ICI   Social Unit    0.5986    0.6209  ICI vec. len-9, LogReg\n",
      "        1A — Raw ICI     Coda Type    0.9310    0.9856                        \n",
      "        1A — Raw ICI Individual ID    0.4925    0.5033                        \n",
      "        1C — Raw Mel   Social Unit    0.7396    0.7329 mean-pooled mel, LogReg\n",
      "        1C — Raw Mel     Coda Type    0.0972    0.1372                        \n",
      "        1C — Raw Mel Individual ID    0.2722    0.2745                        \n",
      "       1B — WhAM L10   Social Unit    0.8763    0.8809 VampNet layer 10, 1280d\n",
      "       1B — WhAM L10     Coda Type    0.2120    0.4007                        \n",
      "       1B — WhAM L10 Individual ID    0.4535    0.4641                        \n",
      "1B — WhAM L19 (best)   Social Unit    0.8946    0.8989     Best layer for unit\n",
      "1B — WhAM L19 (best)     Coda Type    0.2605    0.4296                        \n",
      "1B — WhAM L19 (best) Individual ID    0.3631    0.4118                        \n",
      "\n",
      "============================================================\n",
      "DCCE Phase 3 targets:\n",
      "  Social unit Macro-F1 > 0.8946\n",
      "  Individual ID Macro-F1 > 0.4535\n"
     ]
    }
   ],
   "source": [
    "# Load Phase 1 results for comparison\n",
    "rows = [\n",
    "    # baseline,               task,            f1,    acc,   note\n",
    "    (\"1A — Raw ICI\",     \"Social Unit\",       0.5986, 0.6209, \"ICI vec. len-9, LogReg\"),\n",
    "    (\"1A — Raw ICI\",     \"Coda Type\",         0.9310, 0.9856, \"\"),\n",
    "    (\"1A — Raw ICI\",     \"Individual ID\",     0.4925, 0.5033, \"\"),\n",
    "    (\"1C — Raw Mel\",     \"Social Unit\",       0.7396, 0.7329, \"mean-pooled mel, LogReg\"),\n",
    "    (\"1C — Raw Mel\",     \"Coda Type\",         0.0972, 0.1372, \"\"),\n",
    "    (\"1C — Raw Mel\",     \"Individual ID\",     0.2722, 0.2745, \"\"),\n",
    "    (\"1B — WhAM L10\",    \"Social Unit\",       0.8763, 0.8809, \"VampNet layer 10, 1280d\"),\n",
    "    (\"1B — WhAM L10\",    \"Coda Type\",         0.2120, 0.4007, \"\"),\n",
    "    (\"1B — WhAM L10\",    \"Individual ID\",     0.4535, 0.4641, \"\"),\n",
    "    (f\"1B — WhAM L{best_unit_layer} (best)\",\n",
    "                         \"Social Unit\",\n",
    "                         results_1b_best[\"unit\"][\"macro_f1\"],\n",
    "                         results_1b_best[\"unit\"][\"accuracy\"], f\"Best layer for unit\"),\n",
    "    (f\"1B — WhAM L{best_unit_layer} (best)\",\n",
    "                         \"Coda Type\",\n",
    "                         results_1b_best[\"coda_type_all\"][\"macro_f1\"],\n",
    "                         results_1b_best[\"coda_type_all\"][\"accuracy\"], \"\"),\n",
    "    (f\"1B — WhAM L{best_unit_layer} (best)\",\n",
    "                         \"Individual ID\",\n",
    "                         results_1b_best[\"individual_id\"][\"macro_f1\"],\n",
    "                         results_1b_best[\"individual_id\"][\"accuracy\"], \"\"),\n",
    "]\n",
    "\n",
    "summary_df = pd.DataFrame(rows, columns=[\"Baseline\",\"Task\",\"Macro-F1\",\"Accuracy\",\"Note\"])\n",
    "print(summary_df.to_string(index=False, float_format=lambda x: f\"{x:.4f}\" if isinstance(x, float) else x))\n",
    "\n",
    "print(f\"\\n{'='*60}\")\n",
    "print(f\"DCCE Phase 3 targets:\")\n",
    "best_unit_f1 = max(results_1b_best['unit']['macro_f1'], 0.8763)\n",
    "best_id_f1   = max(results_1b_best['individual_id']['macro_f1'], 0.4535)\n",
    "print(f\"  Social unit Macro-F1 > {best_unit_f1:.4f}\")\n",
    "print(f\"  Individual ID Macro-F1 > {best_id_f1:.4f}\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "p2-22-oxkljz",
   "metadata": {},
   "source": [
    "### Key findings from Phase 2\n",
    "\n",
    "| Finding | Interpretation |\n",
    "|---|---|\n",
    "| Social-unit F1 rises monotonically through layers, peaking at layer 19 | Social identity is a high-level semantic property encoded progressively deeper in the network |\n",
    "| Coda-type F1 is consistently low (<0.26 at any layer) | WhAM's generative objective never learned to represent rhythm timing; ICI trivially surpasses it (0.931) |\n",
    "| Click count R² peaks in early layers | Low-level temporal structure (how many clicks) is encoded early, before semantic abstraction |\n",
    "| Mean ICI (tempo) R² is moderate across layers | Tempo is a mid-level feature — captured but not the primary training signal |\n",
    "| Recording year F1 is substantially below unit F1 at all layers | Social-unit separability is **not** an artefact of recording-date drift — it reflects genuine biological structure |\n",
    "| Year F1 ≈ coda-type F1 at most layers | What little year signal exists is comparable to the (weak) rhythm-type signal — both are marginal for WhAM |\n",
    "\n",
    "### Implications for DCCE design\n",
    "\n",
    "1. **Use best-layer WhAM as the comparison target** — not layer 10 (the JukeMIR default)\n",
    "2. **The dual-channel hypothesis is strengthened**: coda type (rhythm) and social unit    (identity) are encoded by completely different types of features. A model purpose-built    around this decomposition should outperform an emergent representation from a    generative objective on identity tasks.\n",
    "3. **Individual ID is the key challenge**: even at the best WhAM layer, individual    identity is hard (F1≈0.46). DCCE's cross-channel contrastive objective is the    proposed solution.\n",
    "\n",
    "**Next step**: Phase 3 — build and train DCCE.\n"
   ]
  }
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