{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "p3-00-jujvfl",
   "metadata": {},
   "source": [
    "# Phase 3 — Experiment 1: DCCE\n",
    "## *Beyond WhAM* · CS 297 Final Paper · April 2026\n",
    "\n",
    "---\n",
    "\n",
    "This notebook trains and evaluates the **Dual-Channel Contrastive Encoder (DCCE)** — the core contribution of this paper. DCCE is purpose-built around the known biological decomposition of sperm whale codas into two independent information channels (Leitão et al., 2023; Beguš et al., 2024):\n",
    "\n",
    "| Channel | Biological signal | DCCE encoder | Input |\n",
    "|---|---|---|---|\n",
    "| **Rhythm** | Coda type / click timing pattern | 2-layer GRU | ICI sequence (length 9) |\n",
    "| **Spectral** | Social / individual identity | Small CNN | Mel-spectrogram (64 × 128) |\n",
    "\n",
    "The two channel embeddings are fused and trained with a **cross-channel contrastive loss**: the rhythm representation of coda A and the spectral representation of a *different* coda B from the **same social unit** form a positive pair. This forces the joint embedding to capture unit identity from orthogonal signal axes — the key novelty over WhAM, which learns representations as an emergent byproduct of a generative objective.\n",
    "\n",
    "### Training objective\n",
    "```\n",
    "L = L_contrastive(z)  +  λ1 · L_type(r_emb)  +  λ2 · L_id(s_emb)\n",
    "```\n",
    "- **L_contrastive**: NT-Xent (SimCLR, Chen et al. 2020) on the fused embedding z; τ=0.07\n",
    "- **L_type**: cross-entropy on r_emb → coda type (22 classes) — rhythm supervision signal\n",
    "- **L_id**: cross-entropy on s_emb → individual ID (762 labelled codas only) — spectral supervision\n",
    "\n",
    "### Comparison targets (from Phases 1–2)\n",
    "| Task | Target | Source |\n",
    "|---|---|---|\n",
    "| Social Unit Macro-F1 | > **0.895** | WhAM L19 (best layer, Phase 2) |\n",
    "| Individual ID Macro-F1 | > **0.454** | WhAM L10 (Phase 1) |\n",
    "| Coda Type Macro-F1 | > **0.931** | Raw ICI baseline (1A) |\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "p3-01-gcawin",
   "metadata": {},
   "source": [
    "## 1. Setup"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "p3-02-rtmfvt",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-05-01T20:32:30.434039Z",
     "iopub.status.busy": "2026-05-01T20:32:30.433772Z",
     "iopub.status.idle": "2026-05-01T20:32:35.004649Z",
     "shell.execute_reply": "2026-05-01T20:32:35.004025Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Device: mps\n",
      "PyTorch: 2.11.0\n"
     ]
    }
   ],
   "source": [
    "import os, warnings, time\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import torch\n",
    "import torch.nn as nn\n",
    "import torch.nn.functional as F\n",
    "from torch.utils.data import Dataset, DataLoader, WeightedRandomSampler\n",
    "import librosa\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.metrics import f1_score, accuracy_score, classification_report\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",
    "AUDIO  = os.path.join(DATA, \"dswp_audio\")\n",
    "FIGS   = os.path.join(BASE, \"figures\", \"phase3\")\n",
    "os.makedirs(FIGS, exist_ok=True)\n",
    "\n",
    "UNIT_COLORS = {\"A\": \"#4C72B0\", \"D\": \"#DD8452\", \"F\": \"#55A868\"}\n",
    "SEED = 42\n",
    "torch.manual_seed(SEED); np.random.seed(SEED)\n",
    "\n",
    "DEVICE = (\"mps\" if torch.backends.mps.is_available()\n",
    "          else \"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
    "print(f\"Device: {DEVICE}\")\n",
    "print(f\"PyTorch: {torch.__version__}\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "p3-03-egfrxo",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-05-01T20:32:35.006346Z",
     "iopub.status.busy": "2026-05-01T20:32:35.006178Z",
     "iopub.status.idle": "2026-05-01T20:32:35.021269Z",
     "shell.execute_reply": "2026-05-01T20:32:35.020856Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Clean codas         : 1383\n",
      "IDN-labeled codas   : 762  (12 individuals)\n",
      "Mel features shape  : (1383, 64)\n",
      "Train/test split    : 1106 / 277\n"
     ]
    }
   ],
   "source": [
    "# ── Load labels and pre-computed features ─────────────────────────────────────\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",
    "\n",
    "df_clean = df[df[\"is_noise\"] == 0].copy().reset_index(drop=True)\n",
    "\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(\n",
    "    id_counts[id_counts > 1].index)].copy().reset_index(drop=True)\n",
    "\n",
    "# Load shared splits (produced in 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",
    "# Load pre-computed mel features (saved in Phase 1)\n",
    "X_mel_all = np.load(os.path.join(DATA, \"X_mel_all.npy\"))   # (1383, 64)\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\"Mel features shape  : {X_mel_all.shape}\")\n",
    "print(f\"Train/test split    : {len(df_train)} / {len(df_test)}\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "p3-04-tpjurv",
   "metadata": {},
   "source": [
    "---\n",
    "## 2. Data Pipeline\n",
    "\n",
    "### ICI sequences and mel-spectrograms\n",
    "\n",
    "The DCCE needs two input representations per coda:\n",
    "1. **ICI vector** (9d) — already in labels; zero-padded, StandardScaler normalised\n",
    "2. **Mel-spectrogram** (64 × 128) — loaded from WAV, fixed time window\n",
    "\n",
    "For the spectral encoder we use the full 2D mel-spectrogram (not mean-pooled), so the CNN can exploit temporal structure across clicks within a coda. Codas shorter than 128 frames are zero-padded on the right; longer ones are truncated.\n",
    "\n",
    "### Cross-channel contrastive pairs\n",
    "\n",
    "During training, positive pairs are constructed as:\n",
    "- (rhythm_A, spectral_B) where A ≠ B but same social unit\n",
    "- This forces the model to learn *unit-level* structure from both channels independently\n",
    "\n",
    "For a batch of N codas we draw N matched-unit partners, giving 2N views for the NT-Xent loss.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "p3-05-hivmpi",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-05-01T20:32:35.022746Z",
     "iopub.status.busy": "2026-05-01T20:32:35.022639Z",
     "iopub.status.idle": "2026-05-01T20:32:35.048493Z",
     "shell.execute_reply": "2026-05-01T20:32:35.047636Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ICI matrix shape    : (1383, 9)  (scaled)\n",
      "Mel full loaded     : (1383, 64, 128)\n"
     ]
    }
   ],
   "source": [
    "# ── ICI feature matrix ────────────────────────────────────────────────────────\n",
    "MAX_ICI = 9\n",
    "\n",
    "def build_ici_matrix(data):\n",
    "    X = np.zeros((len(data), MAX_ICI), dtype=np.float32)\n",
    "    for i, row in enumerate(data.itertuples()):\n",
    "        for j, v in enumerate(row.ici_list[:MAX_ICI]):\n",
    "            X[i, j] = v\n",
    "    return X\n",
    "\n",
    "X_ici_all = build_ici_matrix(df_clean)\n",
    "scaler_ici = StandardScaler()\n",
    "X_ici_all  = scaler_ici.fit_transform(X_ici_all).astype(np.float32)\n",
    "print(f\"ICI matrix shape    : {X_ici_all.shape}  (scaled)\")\n",
    "\n",
    "# ── Mel-spectrograms (load from WAV for full 2D) ──────────────────────────────\n",
    "N_MELS    = 64\n",
    "FMAX      = 8000\n",
    "N_FRAMES  = 128   # fixed time dimension — pad/truncate\n",
    "\n",
    "mel_path = os.path.join(DATA, \"X_mel_full.npy\")\n",
    "if os.path.exists(mel_path):\n",
    "    X_mel_full = np.load(mel_path)\n",
    "    print(f\"Mel full loaded     : {X_mel_full.shape}\")\n",
    "else:\n",
    "    print(\"Computing 2D mel-spectrograms for all clean codas (takes ~4-5 min)...\")\n",
    "    X_mel_full = np.zeros((len(df_clean), N_MELS, N_FRAMES), dtype=np.float32)\n",
    "    for i, row in enumerate(df_clean.itertuples()):\n",
    "        y, sr = librosa.load(os.path.join(AUDIO, f\"{row.coda_id}.wav\"),\n",
    "                             sr=None, mono=True)\n",
    "        mel = librosa.feature.melspectrogram(y=y, sr=sr, n_mels=N_MELS, fmax=FMAX)\n",
    "        mel_db = librosa.power_to_db(mel, ref=np.max).astype(np.float32)\n",
    "        T = mel_db.shape[1]\n",
    "        if T >= N_FRAMES:\n",
    "            X_mel_full[i] = mel_db[:, :N_FRAMES]\n",
    "        else:\n",
    "            X_mel_full[i, :, :T] = mel_db\n",
    "    np.save(mel_path, X_mel_full)\n",
    "    print(f\"Saved: {mel_path}  shape={X_mel_full.shape}\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "p3-06-anlllg",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-05-01T20:32:35.050385Z",
     "iopub.status.busy": "2026-05-01T20:32:35.050224Z",
     "iopub.status.idle": "2026-05-01T20:32:35.164036Z",
     "shell.execute_reply": "2026-05-01T20:32:35.163637Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Unit classes        : ['A', 'D', 'F']  (3)\n",
      "Coda type classes   : 22\n",
      "Individual ID classes: 12\n",
      "\n",
      "Unit sizes (train):\n",
      "  Unit A: 193 train  48 test\n",
      "  Unit D: 257 train  64 test\n",
      "  Unit F: 656 train  165 test\n"
     ]
    }
   ],
   "source": [
    "# ── Label encoders ────────────────────────────────────────────────────────────\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "\n",
    "le_unit = LabelEncoder().fit(df_clean[\"unit\"])\n",
    "le_type = LabelEncoder().fit(df_clean[\"coda_type\"])\n",
    "le_id   = LabelEncoder().fit(df_id[\"individual_id\"])\n",
    "\n",
    "n_units = len(le_unit.classes_)\n",
    "n_types = len(le_type.classes_)\n",
    "n_ids   = len(le_id.classes_)\n",
    "\n",
    "y_unit = le_unit.transform(df_clean[\"unit\"])\n",
    "y_type = le_type.transform(df_clean[\"coda_type\"])\n",
    "\n",
    "# individual ID labels aligned to df_id\n",
    "y_id   = le_id.transform(df_id[\"individual_id\"])\n",
    "\n",
    "# index map: df_id row → df_clean row (for cross-dataset indexing)\n",
    "id_in_clean = df_id[\"coda_id\"].apply(\n",
    "    lambda c: df_clean[df_clean[\"coda_id\"]==c].index[0]).values\n",
    "\n",
    "print(f\"Unit classes        : {list(le_unit.classes_)}  ({n_units})\")\n",
    "print(f\"Coda type classes   : {n_types}\")\n",
    "print(f\"Individual ID classes: {n_ids}\")\n",
    "\n",
    "# Unit index arrays per clean coda — used for positive pair sampling\n",
    "unit_indices = {u: np.where(y_unit == le_unit.transform([u])[0])[0]\n",
    "                for u in le_unit.classes_}\n",
    "print(\"\\nUnit sizes (train):\")\n",
    "for u, idx in unit_indices.items():\n",
    "    in_train = np.isin(idx, train_idx)\n",
    "    print(f\"  Unit {u}: {in_train.sum()} train  {(~in_train).sum()} test\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "p3-07-karnat",
   "metadata": {},
   "source": [
    "---\n",
    "## 3. DCCE Architecture\n",
    "\n",
    "### Rhythm Encoder\n",
    "\n",
    "A 2-layer GRU processes the 9-dimensional ICI sequence as a temporal signal — each ICI value is one time step. The GRU final hidden state (both layers concatenated and projected) produces the 64-dimensional rhythm embedding `r_emb`.\n",
    "\n",
    "GRU is chosen over Transformer for the rhythm encoder because the ICI sequence is very short (≤9 steps) and the ordering is meaningful (click 1 → 2 → 3 is a causal rhythm pattern). GRU captures this sequential structure with far fewer parameters than a self-attention mechanism.\n",
    "\n",
    "### Spectral Encoder\n",
    "\n",
    "A small 3-block CNN processes the 64×128 mel-spectrogram. Each block is: Conv2d → BatchNorm → ReLU → MaxPool2d. The final representation is flattened and projected to 64 dimensions.\n",
    "\n",
    "The CNN is shallow by design — we want the spectral encoder to learn features from the small DSWP dataset (1,106 training codas) without overfitting. Regularisation: Dropout(0.3) before the final projection.\n",
    "\n",
    "### Fusion MLP\n",
    "\n",
    "`concat(r_emb, s_emb)` → LayerNorm → Linear(128→64) → ReLU → Linear(64→64) → `z`\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "p3-08-zfaycl",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-05-01T20:32:35.165626Z",
     "iopub.status.busy": "2026-05-01T20:32:35.165513Z",
     "iopub.status.idle": "2026-05-01T20:32:35.482920Z",
     "shell.execute_reply": "2026-05-01T20:32:35.482350Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "DCCE architecture OK\n",
      "  z shape: torch.Size([4, 64])   r_emb: torch.Size([4, 64])   s_emb: torch.Size([4, 64])\n",
      "  Total parameters: 1,137,602\n"
     ]
    }
   ],
   "source": [
    "class RhythmEncoder(nn.Module):\n",
    "    \"\"\"2-layer GRU on ICI sequence → 64-d embedding.\"\"\"\n",
    "    def __init__(self, input_dim=9, hidden_dim=64, out_dim=64):\n",
    "        super().__init__()\n",
    "        self.gru = nn.GRU(input_size=1, hidden_size=hidden_dim,\n",
    "                          num_layers=2, batch_first=True,\n",
    "                          dropout=0.2, bidirectional=False)\n",
    "        self.proj = nn.Sequential(\n",
    "            nn.Linear(hidden_dim, out_dim),\n",
    "            nn.LayerNorm(out_dim),\n",
    "        )\n",
    "        self.input_dim = input_dim\n",
    "\n",
    "    def forward(self, x):\n",
    "        # x: (B, 9)  →  (B, 9, 1) for GRU\n",
    "        x = x.unsqueeze(-1)\n",
    "        _, h = self.gru(x)          # h: (2, B, hidden)\n",
    "        h = h[-1]                   # last layer: (B, hidden)\n",
    "        return self.proj(h)         # (B, out_dim)\n",
    "\n",
    "\n",
    "class SpectralEncoder(nn.Module):\n",
    "    \"\"\"Small CNN on mel-spectrogram (64 × 128) → 64-d embedding.\"\"\"\n",
    "    def __init__(self, n_mels=64, n_frames=128, out_dim=64):\n",
    "        super().__init__()\n",
    "        self.conv = nn.Sequential(\n",
    "            # Block 1: 1 → 16\n",
    "            nn.Conv2d(1, 16, kernel_size=3, padding=1), nn.BatchNorm2d(16), nn.ReLU(),\n",
    "            nn.MaxPool2d(2, 2),          # → (16, 32, 64)\n",
    "            # Block 2: 16 → 32\n",
    "            nn.Conv2d(16, 32, kernel_size=3, padding=1), nn.BatchNorm2d(32), nn.ReLU(),\n",
    "            nn.MaxPool2d(2, 2),          # → (32, 16, 32)\n",
    "            # Block 3: 32 → 64\n",
    "            nn.Conv2d(32, 64, kernel_size=3, padding=1), nn.BatchNorm2d(64), nn.ReLU(),\n",
    "            nn.MaxPool2d(2, 2),          # → (64, 8, 16)\n",
    "        )\n",
    "        self.drop = nn.Dropout(0.3)\n",
    "        flat_dim  = 64 * (n_mels // 8) * (n_frames // 8)\n",
    "        self.proj = nn.Sequential(\n",
    "            nn.Linear(flat_dim, 128), nn.ReLU(),\n",
    "            nn.Linear(128, out_dim), nn.LayerNorm(out_dim),\n",
    "        )\n",
    "\n",
    "    def forward(self, x):\n",
    "        # x: (B, 64, 128)\n",
    "        x = x.unsqueeze(1)               # (B, 1, 64, 128)\n",
    "        x = self.conv(x)\n",
    "        x = self.drop(x.flatten(1))\n",
    "        return self.proj(x)              # (B, out_dim)\n",
    "\n",
    "\n",
    "class DCCE(nn.Module):\n",
    "    \"\"\"Dual-Channel Contrastive Encoder.\"\"\"\n",
    "    def __init__(self, n_types=22, n_ids=12, emb_dim=64):\n",
    "        super().__init__()\n",
    "        self.rhythm_enc   = RhythmEncoder(out_dim=emb_dim)\n",
    "        self.spectral_enc = SpectralEncoder(out_dim=emb_dim)\n",
    "        self.fusion = nn.Sequential(\n",
    "            nn.LayerNorm(emb_dim * 2),\n",
    "            nn.Linear(emb_dim * 2, emb_dim),\n",
    "            nn.ReLU(),\n",
    "            nn.Linear(emb_dim, emb_dim),\n",
    "        )\n",
    "        # Auxiliary heads\n",
    "        self.head_type = nn.Linear(emb_dim, n_types)\n",
    "        self.head_id   = nn.Linear(emb_dim, n_ids)\n",
    "\n",
    "    def forward(self, ici, mel):\n",
    "        r_emb = self.rhythm_enc(ici)      # (B, 64)\n",
    "        s_emb = self.spectral_enc(mel)    # (B, 64)\n",
    "        z     = self.fusion(torch.cat([r_emb, s_emb], dim=1))   # (B, 64)\n",
    "        return z, r_emb, s_emb\n",
    "\n",
    "    def encode_rhythm(self, ici):\n",
    "        return self.rhythm_enc(ici)\n",
    "\n",
    "    def encode_spectral(self, mel):\n",
    "        return self.spectral_enc(mel)\n",
    "\n",
    "\n",
    "# Sanity check\n",
    "model = DCCE(n_types=n_types, n_ids=n_ids).to(DEVICE)\n",
    "dummy_ici = torch.zeros(4, 9).to(DEVICE)\n",
    "dummy_mel = torch.zeros(4, N_MELS, N_FRAMES).to(DEVICE)\n",
    "z, r, s = model(dummy_ici, dummy_mel)\n",
    "n_params = sum(p.numel() for p in model.parameters() if p.requires_grad)\n",
    "print(f\"DCCE architecture OK\")\n",
    "print(f\"  z shape: {z.shape}   r_emb: {r.shape}   s_emb: {s.shape}\")\n",
    "print(f\"  Total parameters: {n_params:,}\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "p3-09-eebrmt",
   "metadata": {},
   "source": [
    "---\n",
    "## 4. Loss Functions\n",
    "\n",
    "### NT-Xent Contrastive Loss (SimCLR)\n",
    "\n",
    "Given a batch of N codas, we build 2N views using cross-channel pairing: for each coda *i*, we find a unit-matched partner *j* and construct (rhythm_i, spectral_j) as a positive pair. The NT-Xent loss maximises agreement between positive pairs while pushing all other pairs apart.\n",
    "\n",
    "$$\\mathcal{L}_{\\text{NT-Xent}} = -\\frac{1}{2N} \\sum_{i=1}^{2N} \\log \\frac{\\exp(\\text{sim}(z_i, z_{i^+})/\\tau)}{\\sum_{k \\neq i} \\exp(\\text{sim}(z_i, z_k)/\\tau)}$$\n",
    "\n",
    "Temperature τ=0.07 is used following Chen et al. (2020).\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "p3-10-rpmcew",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-05-01T20:32:35.484321Z",
     "iopub.status.busy": "2026-05-01T20:32:35.484213Z",
     "iopub.status.idle": "2026-05-01T20:32:35.526104Z",
     "shell.execute_reply": "2026-05-01T20:32:35.525607Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "NT-Xent loss sanity check: 3.9239  (expected ~log(2B-1) ≈ 2.7081)\n"
     ]
    }
   ],
   "source": [
    "def nt_xent_loss(z1, z2, temperature=0.07):\n",
    "    \"\"\"\n",
    "    NT-Xent contrastive loss between two sets of embeddings.\n",
    "    z1, z2: (B, D) — positive pairs are (z1[i], z2[i]).\n",
    "    \"\"\"\n",
    "    B = z1.shape[0]\n",
    "    z = torch.cat([z1, z2], dim=0)                     # (2B, D)\n",
    "    z = F.normalize(z, dim=1)\n",
    "    sim = torch.mm(z, z.T) / temperature               # (2B, 2B)\n",
    "\n",
    "    # Mask out self-similarity\n",
    "    mask = torch.eye(2*B, dtype=torch.bool, device=z.device)\n",
    "    sim.masked_fill_(mask, float('-inf'))\n",
    "\n",
    "    # Positive indices: i ↔ i+B\n",
    "    labels = torch.cat([torch.arange(B, 2*B), torch.arange(B)]).to(z.device)\n",
    "    return F.cross_entropy(sim, labels)\n",
    "\n",
    "\n",
    "def combined_loss(z, r_emb, s_emb, z_partner, type_labels, id_labels,\n",
    "                  model, lambda1=0.5, lambda2=0.5):\n",
    "    \"\"\"\n",
    "    Full DCCE loss:\n",
    "      L = L_contrastive(z, z_partner) + λ1·L_type(r_emb) + λ2·L_id(s_emb)\n",
    "    id_labels=None when no IDN-labeled codas are in the batch.\n",
    "    \"\"\"\n",
    "    l_contrast = nt_xent_loss(z, z_partner)\n",
    "    l_type     = F.cross_entropy(model.head_type(r_emb), type_labels)\n",
    "    l_total    = l_contrast + lambda1 * l_type\n",
    "    if id_labels is not None:\n",
    "        l_id   = F.cross_entropy(model.head_id(s_emb), id_labels)\n",
    "        l_total = l_total + lambda2 * l_id\n",
    "    return l_total, l_contrast.item()\n",
    "\n",
    "# Quick sanity check\n",
    "model.train()\n",
    "B = 8\n",
    "z1 = torch.randn(B, 64).to(DEVICE)\n",
    "z2 = torch.randn(B, 64).to(DEVICE)\n",
    "loss_test = nt_xent_loss(z1, z2)\n",
    "print(f\"NT-Xent loss sanity check: {loss_test.item():.4f}  (expected ~log(2B-1) ≈ {float(torch.log(torch.tensor(2*B-1.0))):.4f})\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "p3-11-bbdjes",
   "metadata": {},
   "source": [
    "---\n",
    "## 5. Dataset and DataLoader\n",
    "\n",
    "The `CodaDataset` returns (ici, mel, unit_label, type_label, id_label) for each coda. For training, we use **WeightedRandomSampler** to balance unit representation per batch (compensates for Unit F = 59.4%). The sampler assigns sample weights inversely proportional to unit frequency.\n",
    "\n",
    "The cross-channel partner for each coda is sampled at batch construction time in the training loop — this keeps the Dataset simple and the partner sampling flexible.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "p3-12-xwppgp",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-05-01T20:32:35.527619Z",
     "iopub.status.busy": "2026-05-01T20:32:35.527422Z",
     "iopub.status.idle": "2026-05-01T20:32:35.533098Z",
     "shell.execute_reply": "2026-05-01T20:32:35.532603Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train dataset: 1106 codas  |  17 batches of 64\n",
      "Test  dataset: 277 codas   |  5 batches\n"
     ]
    }
   ],
   "source": [
    "class CodaDataset(Dataset):\n",
    "    def __init__(self, indices, X_ici, X_mel, y_unit, y_type,\n",
    "                 y_id=None, id_clean_idx=None):\n",
    "        \"\"\"\n",
    "        indices     : positions into the 1383-coda df_clean arrays\n",
    "        X_ici       : (1383, 9)  all clean codas\n",
    "        X_mel       : (1383, 64, 128) all clean codas\n",
    "        y_unit/type : (1383,) all clean codas\n",
    "        y_id        : (762,) IDN-labeled codas only (or None)\n",
    "        id_clean_idx: mapping from id subset → clean subset index\n",
    "        \"\"\"\n",
    "        self.idx      = indices\n",
    "        self.X_ici    = X_ici\n",
    "        self.X_mel    = X_mel\n",
    "        self.y_unit   = y_unit\n",
    "        self.y_type   = y_type\n",
    "        self.y_id     = y_id\n",
    "        self.id_clean_idx = id_clean_idx\n",
    "\n",
    "    def __len__(self): return len(self.idx)\n",
    "\n",
    "    def __getitem__(self, i):\n",
    "        ci = self.idx[i]                            # index into full clean array\n",
    "        ici = torch.tensor(self.X_ici[ci])\n",
    "        mel = torch.tensor(self.X_mel[ci])\n",
    "        u   = torch.tensor(self.y_unit[ci], dtype=torch.long)\n",
    "        t   = torch.tensor(self.y_type[ci], dtype=torch.long)\n",
    "\n",
    "        id_label = torch.tensor(-1, dtype=torch.long)  # default: no ID label\n",
    "        if self.y_id is not None and self.id_clean_idx is not None:\n",
    "            # check if this clean index has an IDN label\n",
    "            match = np.where(self.id_clean_idx == ci)[0]\n",
    "            if len(match) > 0:\n",
    "                id_label = torch.tensor(self.y_id[match[0]], dtype=torch.long)\n",
    "        return ici, mel, u, t, id_label\n",
    "\n",
    "\n",
    "# ── Build datasets ────────────────────────────────────────────────────────────\n",
    "ds_train = CodaDataset(train_idx, X_ici_all, X_mel_full, y_unit, y_type,\n",
    "                       y_id, id_in_clean)\n",
    "ds_test  = CodaDataset(test_idx,  X_ici_all, X_mel_full, y_unit, y_type)\n",
    "\n",
    "# Weighted sampler: inverse class frequency per unit\n",
    "unit_counts = np.bincount(y_unit[train_idx], minlength=n_units).astype(float)\n",
    "sample_weights = (1.0 / unit_counts)[y_unit[train_idx]]\n",
    "sampler = WeightedRandomSampler(weights=sample_weights,\n",
    "                                 num_samples=len(ds_train), replacement=True)\n",
    "\n",
    "BATCH_SIZE = 64\n",
    "train_loader = DataLoader(ds_train, batch_size=BATCH_SIZE, sampler=sampler,\n",
    "                          num_workers=0, drop_last=True)\n",
    "test_loader  = DataLoader(ds_test,  batch_size=BATCH_SIZE, shuffle=False,\n",
    "                          num_workers=0)\n",
    "\n",
    "print(f\"Train dataset: {len(ds_train)} codas  |  {len(train_loader)} batches of {BATCH_SIZE}\")\n",
    "print(f\"Test  dataset: {len(ds_test)} codas   |  {len(test_loader)} batches\")\n",
    "\n",
    "# Pre-build unit → train_indices lookup for partner sampling\n",
    "unit_train_map = {}\n",
    "for u_idx in range(n_units):\n",
    "    mask = y_unit[train_idx] == u_idx\n",
    "    unit_train_map[u_idx] = np.where(mask)[0]   # positions within train_idx\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "p3-13-mfqphh",
   "metadata": {},
   "source": [
    "---\n",
    "## 6. Training\n",
    "\n",
    "We train 4 model variants for the ablation study:\n",
    "\n",
    "| Variant | Encoders active | Cross-channel aug |\n",
    "|---|---|---|\n",
    "| `rhythm_only` | GRU only — z = r_emb | N/A |\n",
    "| `spectral_only` | CNN only — z = s_emb | N/A |\n",
    "| `late_fusion` | GRU + CNN, standard pair (same coda both channels) | No |\n",
    "| `full` | GRU + CNN, cross-channel partner from same unit | **Yes** |\n",
    "\n",
    "All variants share the same architecture, loss, and hyperparameters. The difference is only in how positive pairs are constructed and which encoders contribute to z.\n",
    "\n",
    "Training runs for **50 epochs** on CPU/MPS. With 17 batches/epoch this is fast (~5-8 min per model).\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "p3-14-sgmwcb",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-05-01T20:32:35.534373Z",
     "iopub.status.busy": "2026-05-01T20:32:35.534276Z",
     "iopub.status.idle": "2026-05-01T20:32:35.540857Z",
     "shell.execute_reply": "2026-05-01T20:32:35.540391Z"
    }
   },
   "outputs": [],
   "source": [
    "def sample_partner_indices(unit_labels_batch, unit_train_map, train_idx, rng):\n",
    "    \"\"\"\n",
    "    For each item in the batch, sample a different coda from the same unit.\n",
    "    Returns global clean-array indices for the partners.\n",
    "    \"\"\"\n",
    "    partners = []\n",
    "    for u in unit_labels_batch.cpu().numpy():\n",
    "        candidates = unit_train_map[u]\n",
    "        # sample until we get a different position (avoid self-pairing in same batch)\n",
    "        p = rng.choice(candidates)\n",
    "        partners.append(train_idx[p])\n",
    "    return np.array(partners)\n",
    "\n",
    "\n",
    "def train_dcce(variant=\"full\", n_epochs=50, lr=1e-3, lambda1=0.5, lambda2=0.5):\n",
    "    \"\"\"\n",
    "    Train one DCCE variant.\n",
    "    variant: 'full' | 'late_fusion' | 'rhythm_only' | 'spectral_only'\n",
    "    Returns trained model and loss history.\n",
    "    \"\"\"\n",
    "    # Fix seed per variant so each model's initialisation is independent\n",
    "    # of training order — ensures reproducibility across reruns.\n",
    "    torch.manual_seed(SEED)\n",
    "    np.random.seed(SEED)\n",
    "\n",
    "    model   = DCCE(n_types=n_types, n_ids=n_ids).to(DEVICE)\n",
    "    opt     = torch.optim.AdamW(model.parameters(), lr=lr, weight_decay=1e-4)\n",
    "    sched   = torch.optim.lr_scheduler.CosineAnnealingLR(opt, T_max=n_epochs)\n",
    "    rng     = np.random.default_rng(SEED)\n",
    "    history = []\n",
    "\n",
    "    model.train()\n",
    "    t0 = time.time()\n",
    "\n",
    "    for epoch in range(n_epochs):\n",
    "        epoch_loss = 0.0; n_batches = 0\n",
    "        for ici, mel, u_labels, t_labels, id_labels in train_loader:\n",
    "            ici = ici.to(DEVICE); mel = mel.to(DEVICE)\n",
    "            u_labels = u_labels.to(DEVICE)\n",
    "            t_labels = t_labels.to(DEVICE)\n",
    "\n",
    "            # ── Forward pass (variant-specific) ───────────────────────────\n",
    "            if variant == \"rhythm_only\":\n",
    "                r_emb = model.encode_rhythm(ici)\n",
    "                z     = r_emb\n",
    "                z_partner = r_emb   # no spectral; self-pairing cancels via NT-Xent mask\n",
    "                s_emb = r_emb       # proxy for combined_loss signature\n",
    "            elif variant == \"spectral_only\":\n",
    "                s_emb = model.encode_spectral(mel)\n",
    "                z     = s_emb\n",
    "                z_partner = s_emb\n",
    "                r_emb = s_emb\n",
    "            elif variant == \"late_fusion\":\n",
    "                # Same-coda pair: rhythm and spectral of the same audio\n",
    "                z, r_emb, s_emb = model(ici, mel)\n",
    "                z_partner = z.detach().clone()   # treat coda as its own partner\n",
    "            else:  # \"full\" — cross-channel\n",
    "                z, r_emb, s_emb = model(ici, mel)\n",
    "                # Sample partners from same unit\n",
    "                p_idx = sample_partner_indices(u_labels, unit_train_map, train_idx, rng)\n",
    "                p_ici = torch.tensor(X_ici_all[p_idx]).to(DEVICE)\n",
    "                p_mel = torch.tensor(X_mel_full[p_idx]).to(DEVICE)\n",
    "                z_partner, _, _ = model(p_ici, p_mel)\n",
    "\n",
    "            # ── Loss ───────────────────────────────────────────────────────\n",
    "            valid_id = id_labels >= 0\n",
    "            id_for_loss = id_labels[valid_id].to(DEVICE) if valid_id.any() else None\n",
    "\n",
    "            if variant in (\"rhythm_only\", \"spectral_only\"):\n",
    "                # No contrastive loss — only auxiliary heads\n",
    "                l_type = F.cross_entropy(model.head_type(r_emb), t_labels)\n",
    "                loss   = l_type\n",
    "                if id_for_loss is not None:\n",
    "                    loss = loss + lambda2 * F.cross_entropy(\n",
    "                        model.head_id(s_emb[valid_id.to(DEVICE)]), id_for_loss)\n",
    "            else:\n",
    "                # NT-Xent + auxiliary heads\n",
    "                l_type = F.cross_entropy(model.head_type(r_emb), t_labels)\n",
    "                loss   = nt_xent_loss(z, z_partner) + lambda1 * l_type\n",
    "                if id_for_loss is not None:\n",
    "                    loss = loss + lambda2 * F.cross_entropy(\n",
    "                        model.head_id(s_emb[valid_id.to(DEVICE)]), id_for_loss)\n",
    "\n",
    "            opt.zero_grad(); loss.backward(); opt.step()\n",
    "            epoch_loss += loss.item(); n_batches += 1\n",
    "\n",
    "        sched.step()\n",
    "        avg_loss = epoch_loss / n_batches\n",
    "        history.append(avg_loss)\n",
    "        if (epoch + 1) % 10 == 0:\n",
    "            elapsed = time.time() - t0\n",
    "            print(f\"  [{variant}] Epoch {epoch+1:3d}/{n_epochs}  loss={avg_loss:.4f}  \"\n",
    "                  f\"({elapsed:.0f}s elapsed)\")\n",
    "\n",
    "    print(f\"  [{variant}] Training complete in {time.time()-t0:.0f}s\")\n",
    "    return model, history\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "p3-15-aqrdjs",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-05-01T20:32:35.541987Z",
     "iopub.status.busy": "2026-05-01T20:32:35.541902Z",
     "iopub.status.idle": "2026-05-01T20:34:29.293593Z",
     "shell.execute_reply": "2026-05-01T20:34:29.292781Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "=======================================================\n",
      "Training: DCCE-rhythm_only\n",
      "=======================================================\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  [rhythm_only] Epoch  10/50  loss=1.1114  (4s elapsed)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  [rhythm_only] Epoch  20/50  loss=0.6665  (8s elapsed)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  [rhythm_only] Epoch  30/50  loss=0.5446  (11s elapsed)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  [rhythm_only] Epoch  40/50  loss=0.5261  (15s elapsed)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  [rhythm_only] Epoch  50/50  loss=0.4721  (18s elapsed)\n",
      "  [rhythm_only] Training complete in 18s\n",
      "\n",
      "=======================================================\n",
      "Training: DCCE-spectral_only\n",
      "=======================================================\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  [spectral_only] Epoch  10/50  loss=2.0470  (4s elapsed)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  [spectral_only] Epoch  20/50  loss=0.8616  (9s elapsed)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  [spectral_only] Epoch  30/50  loss=0.3703  (13s elapsed)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  [spectral_only] Epoch  40/50  loss=0.2767  (17s elapsed)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  [spectral_only] Epoch  50/50  loss=0.2394  (21s elapsed)\n",
      "  [spectral_only] Training complete in 21s\n",
      "\n",
      "=======================================================\n",
      "Training: DCCE-late_fusion\n",
      "=======================================================\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  [late_fusion] Epoch  10/50  loss=1.0347  (6s elapsed)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  [late_fusion] Epoch  20/50  loss=0.4913  (11s elapsed)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  [late_fusion] Epoch  30/50  loss=0.2249  (17s elapsed)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  [late_fusion] Epoch  40/50  loss=0.1466  (22s elapsed)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  [late_fusion] Epoch  50/50  loss=0.1228  (28s elapsed)\n",
      "  [late_fusion] Training complete in 28s\n",
      "\n",
      "=======================================================\n",
      "Training: DCCE-full\n",
      "=======================================================\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  [full] Epoch  10/50  loss=5.0468  (9s elapsed)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  [full] Epoch  20/50  loss=4.2752  (19s elapsed)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  [full] Epoch  30/50  loss=3.9968  (28s elapsed)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  [full] Epoch  40/50  loss=3.9082  (37s elapsed)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  [full] Epoch  50/50  loss=3.8686  (46s elapsed)\n",
      "  [full] Training complete in 46s\n",
      "\n",
      "All variants trained.\n"
     ]
    }
   ],
   "source": [
    "# ── Train all 4 variants ──────────────────────────────────────────────────────\n",
    "# Each takes ~5-8 min on MPS / ~15-20 min on CPU\n",
    "trained_models = {}\n",
    "loss_histories = {}\n",
    "\n",
    "for variant in [\"rhythm_only\", \"spectral_only\", \"late_fusion\", \"full\"]:\n",
    "    print(f\"\\n{'='*55}\")\n",
    "    print(f\"Training: DCCE-{variant}\")\n",
    "    print(f\"{'='*55}\")\n",
    "    m, h = train_dcce(variant=variant, n_epochs=50)\n",
    "    trained_models[variant] = m\n",
    "    loss_histories[variant] = h\n",
    "\n",
    "print(\"\\nAll variants trained.\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "p3-16-dslcdl",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-05-01T20:34:29.302680Z",
     "iopub.status.busy": "2026-05-01T20:34:29.301961Z",
     "iopub.status.idle": "2026-05-01T20:34:29.465726Z",
     "shell.execute_reply": "2026-05-01T20:34:29.464922Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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tQ02N66XpsIRjst0Snsn7JP2PpB/X/fffr5oky5AvnhLOSDN1CRJtAyapbNu+fbtqaC5DKj2kmbz0gbrmmmvUY+vzOZdqNy0Ma06nej8kVJAASgInLYCS7ZQTDoiqzc+lh1NtAZQtCQMkcKrvvq9NTc3YG/q7w7ZKrjbSIFveQwnXpGLLlgQschIC+bxIM/+bbroJTUHCIDkTqQSHVRt0ny4JBKUvkvRWk9+nWuN8+VxIkCSfe9sTGUjAJNWOEohpAVB9fr815PeEBFwSiFZtGu7oNR0FUI39u1N6XUmoJX3otF50EkpJPywJperbF5GIqK1iAEVERHbkC4b8C7Gwrb6Rf4GXKVoyVae+tH+9l+k8EhLItCZXU1v4JPtEznom1SMvv/yymjoi0zIkyJB/JZdqAwk5avvy5khTVKA15Dlr+pf+5qyQq2+1gUzBkeBEwgSp9JAhQYsMqV6Qyirty6h8edy0aZOqcJCG5HK2R2lYLGfuki/nEi7aNmSuiYQTsk+kKbcEFFIN1Ji0ipOGfEa1SjaZpipVetpxuGzZMlUNIsdo37597daXcEpoZy+rje0xXZ99X5vGPNuchCB1DaC06XU5OTmqOX1VWpAu6zVVACVVPxKuyH6tOpVMC0+KioqsZ0SUz6mjba2JfJ4l3Pn000/x9NNPq8+tFu5X/axLU3uZCisVb6+99poKEyXolSmJQm7LCQxq+v12qs+lI9LEXrZPprbJSRgkEJbPoDYlT7ZFpgfX9JqN/bupV69e6h9gpCm6nJhBfkfIlGr5fSHTmmU6YNX3iYiIqmMARUREduRsS1IJI/+hb3u6d6mYeOONN9R/gEs/jPpMq5GzaMm/qMs0CvnCI1MYWhP58iFfhGbNmqUqP6qSqSmuSL7kab23HJH3s6m1a9dOffGTahw5e5ajbZBgRs5GZVuxpwVL//73v9UQEgpJRZOcDU6+1D7//PN2X1il8kOGkOk+0mtGeh9JFY9M/TxVaCKVGBICSHAl4Ul9AyiZIqRVfzhS0/tQV1K1IWcBkzPeyRQzCYq00MFRtVGHDh2wb98+Fa7WZ+poffd9Teob2DZW5ZGEclrQVFvV5po1a9T+kSqlpiKhvQxH5HOvTVGt7Yxwjsj7Kb/DtWl3cvwcOnRIfUaqTpnTzuApQayjqW1N8ftNe02pMqsajMqxmZycjOYmfxPl59f2gfydlPBJAjoJIrVqTSIiqplzNfcgIqIWJf9BrYVDMq3Atvm1nEZ71KhRqrpDvpCeqhpj8+bN6l/oteoV+RdtIf/aXtMXKo18AdeqsFyBTNfRvrA7mpok/0ruisaOHasu5fTrjt5vCTKampx6XUhw6YjWNFmCn1M1/papU3fddZc1EDlVvzHpvSOVbFJxIkFDXUi1m3j33XexYcOGWteVqU0yJc42bBMSuDmi9WQ6HVpvHwnIpMJHqpQk+JL+V1VpTdm1MOB01GfftyQJ5LRpaBKA1TSkKXdTNiOXQLCm19Z+f8pnVFsmwWJ9yLEigaB2DGnH0RVXXFGtYqm232/yu01rvt+YantNCU8bO5zUwt+qDfpPFThLxauE11JF2BT7gYiotWEARURE6guXTHcYNmyY+tdsaXIt/2FdlUyhkSqTRYsWqS88jipg5IvDY489pnqnSDNf27M5Sf8Xac4tYcHixYurPVam/Mhzy3Qg7V/2XYHWFFxCEtvqFTkrlExnkZ/ZFck0KqmSkEDkueees/vSJ1PU5OxnTU0+N3JGLJnG9dZbb9ndJ5VG0ghaSL8cjUyRkbCm6pdJ+XxpIY5tXxqZmiWVIFXJtDyZ5iNfMB19EXZkypQpapqbnE1Qzv4ln4mq4Z3sR9lGmbr01VdfWZfL8Sc/a3x8fLVwT3rQyFkJT5dUkcnPIgHx448/rsI1mTJatXpMyJnLZF0Jq6QPkaP+ZhKGaD2kGrLvnYm8L1pFmBbO1ES7X/oBaVMVXY021U5CSC1kdDTVVPv9JqGq7WdZKqa0CrfGpr2mfO6rHpPaP2Y0Ji383bNnT7X75HMvUw8dBUxSMSz7RKoj5Sx5RERUO07BIyJqY2T6i/YlS758yn9Ub9myxRqSSLAkUy0cNXaVZsIy7USmaEiAJP/xLT1dZLl8SZcpQvIFQb6QSdNorUeIVgUl0ylkmpo8v0zpkyleEjbJl27pSSMVI1KFJY9z1HRappmcqjGxfGFp7tO8S1WJTE+U/Sj7QgI2+TIrAYn8y7p8qWuu07Y3JqmwkC/YElBIWCGhiLzfMv1FfjZ5L6XhdENPDy+NjWub1iZhZHR0tNoG6V8kFTTSCFmm5EjFgfQkky9/jz76qF3/lR07dqhKPplOJNPg5DnkS6SEZhIoyfQwqW7SSP+cBx54QPV5kSGfPwmk5LMuzy/T8OrTZFi+rMqXUXleOXufNKeXhsVyTEnVkYQ/sg+lGbft51k+t7KfZVukAbMEfPK6O3fuVEGPnEb+dM86KMephCcyBU4L9Go6puQMb3KMy5QjmWokYZ9Mp5XjVsJV+bIugfWIESNw9dVXN2jfOxPpASZTPaUCSM7eVxv5vEnTefmZJFiTY8TVyGddOzOikONKQtCqJPCRs9/J723peyS9zuQfGuQfCaQqVt5TOVYakxwHEoBLRaH83ZBtlWNegujLL79c9WA63SmptuQfR+Tvohx7UtWlVf/KbQnhJeCW40/6IkpzejmOJICTv3fyt03OxNnQ34NERG2KmYiI2oSOHTtK+Yp1uLm5mf38/Mzt27c3T5o0yfzwww+bd+7cWafnMhqN5s8//9x84YUXmjt06GD28vIye3p6qte46KKLzAsWLDCXlpbW+Pht27aZ77jjDnPfvn3NgYGBZr1ebw4NDTWPGTPG/Oyzz5qPHz9ut/7y5cvttr22kZWV1aD9M27cOPX4jz76yG75kSNH1HL52WqTkpJivuWWW8ydO3c2e3h4qP36r3/9y5yYmGh+4okn1HPIpS15LVl+3XXXOfx5ZZscqWmbattWbf+c6vMhz1HV5s2bzeedd545KCjI7O3tbR44cKD5/fffNx87dkw9Jjo6utZ9U9NrnWrYbot8Nq+++mpzTEyM2WAwmENCQsxTp041//LLL9We/+DBg2pfT5gwQX0+5bMpn69BgwaZn3rqKXNqaqrd+vJZlvegT58+5uDgYPV5lvfx/PPPNy9dutTcUIcOHTI/8MAD5sGDB6vn1el0ah8OHz7c/N///te8b98+h4+bP3++ecCAAWq7Zf1p06aZ169fX+Pn4lSfl6r2799v3cdRUVHmsrKyWtfPzs42P//88+YRI0aYAwIC1Oe7Xbt25pEjR5ofe+wx8/bt2xu8752JfL5kn1xxxRV1Wv/OO+9U68+cOfOUx5F8vhz9fmkI7Tj39fU97eeaN2+e9bPw2muv1fo7+9xzzzVHRkaq46NXr17q/SwuLrb+7pTPoa2altuq6XejkMfJ50iOdflZ5ffOW2+9ZTaZTDXu59p+j51qm9555x113MnvOG2fyHpyfLz77rvmyy67zNyjRw91DMg6Xbt2NV955ZXmdevW1fjzERGRPTf5n5YOwYiIiMj1SGWS9AqTChlHUyqJiIiIiDTsAUVEREQ1krOAOZrqItN2ZHqKONW0SCIiIiIi9oAiIiKiGklPH2mmLf1hpC+X9LSSPjnaWQqln9Cp+uUQEREREXEKHhEREdXoxIkTqmG1NByWhsvSfFoah0szcql8kubT0oSXiIiIiKg2DKCIiIiIiIiIiKhJsQcUERERERERERE1KQZQRERERERERETUpBhAERERERERERFRk2IARURERERERERETUqPNiQ7O1udxadDhw7w9PRs6c0hIiIiIiIiInI5JSUlOH78OMaNG4egoKA6PaZNBVASPs2cObOlN4OIiIiIiIiIyOX98MMPOP/88+u0bpsKoKTySdtBXbt2hSszGo3Iy8uDv78/9Po29TYSNRiPGyIeN0TNhX9ziHjcELXmvzcHDx5UBT5azlIXbSq50KbdSfjUp08fuPqHTKYUSqkbAygiHjdE/HtD5Fz432pEPG6I2sLfG896tDdiE3IiIiIiIiIiImpSDKCIiIiIiIiIiKhJMYAiIiIiIiIiIqIm1aZ6QBERERERERGJ8vJypKSkqNPJy3UiV1ReXo6ysjLVB8rdvXFqjOR5pLdTZGRkoz2nYABFREREREREbe5L+7Fjx1BUVASdTqeGm5tbS28WUb3J59ZgMDTa59dsNqO0tFQdGxLOxsbGNloIxQCKiIiIiIiI2hSpfJIv2CEhIYiIiGD4RC7LbDarM+HJGfAaM4RKTU1FZmamOlaio6Mb5XnZA4qIiIiIiIjaFKnskKonhk9E1UmQJceGHCNyrDQWBlBERERERETU5qbgcdodUe0hlBwjjdkfjQEUERERERERtTns+UTUvMcIAygiIiIiIiIiImpSDKBc1Mnc5JbeBCIiIiIiIiKiOmEA5YK2JO7Ef5fNwY8H/1Dd6YmIiIiIiKhtevLJJ9VUKRnu7u4IDg7GsGHDMHv2bCQnVy9ckLOa3X333ejSpQs8PT3V+meffTYWLlxYbd3vvvsOEydORFBQkFq3e/fuuPfee5GYmGhdR3vtqqNr164N+nkSEhLU43/++Wecrm+++QYff/xxteXjx4/HxRdfDFeXkJCg3vNffvkFrkDf0htA9bMrZS9eXT0PJnM5lh5dCejdcMOQS+HuxiyRiIiIiIioLQoMDMSSJUvU9ZycHGzZsgXvvvsu5s2bp5YPGTJE3bdv3z5MmDABvr6+uP/++9G7d2/k5ubi119/xVVXXYVu3bphwIABat377rsPb7zxBm644Qbcc889CAgIwO7du/Hee+/hyJEjWLRokfX1Zd2qgY6XlxdamgRQ6enpuP7661t6U8iZAyhJcZ966qla1zlx4gTatWuHtkTqnWzDpqWH/kZJeSn+PfRqlXwSERERERFR26LX6zFy5Ejr7SlTpuC2227D2LFjcfnll2Pv3r3qjGYSMoWEhGDNmjUqUNKcd955an2pdBKLFy/Ga6+9hvnz5+PGG2+0rjdu3Djccsst+P333+1ev1OnTnav31BFRUWn/RzkvJw2sbjwwgvx2WefVRvPPvusun/w4MFtLnwS/SJ7Yva4WfDWV6bJK46sxVvrPoSx3NSi20ZERERERETOQcKkl156CQcPHsQff/yBlStXYvPmzZgzZ45d+KTp378/YmNj1fXXX39dfee2DZ80EmRNmzbttLdPm2r3xRdf4Nprr1XbK0GYprCwELfeequq7mrfvj2eeOIJlJeXq/ukEkseu2LFCrvnzM/Ph5+fH958801V9SRTCP/++2/rtEApdLG1YMECNVVQ9of8TFLkUnX7vvrqK1UFJuvIdnz++efqftm3MTExCA8Px0MPPWTdtrooLCzErFmzEBUVpSrFZMpk1VBPmyZY2zZW9eCDDyIuLq5aqx6Zhujh4YG0tDS0JKetgJIPv4yqHnvsMXUpqWtb1TO8Cx4dOwvPr5qLgrJCtWzN8c0oMZXinjNuhofO0NKbSERERERE5FJKy0xIyiho6c1AdKgvPAy6RnkuCTGkOmrdunUqOJIxefLkWh9TVlamKqRkWl1dSfhiNBrtlskMnbrM0pGpgFKA8u2336rtsw1TLrroItWb6s8//8TTTz+NPn364NJLL1VTB6XiSoIV+Rk18hyy/VdffTWmT5+OY8eOITs7G++88466XwIkzfr161Uvq1dffVVVXt11110qZ5DpiLYkXJLKMQmzPvzwQ1x33XXYunUrjh49qm5LqPfoo49i0KBBqtqsLm6++Wb89NNPeP7551W49MEHH+Dcc8/F8uXLceaZZ9Z7GzUSGL788ssqdLPdLx999JEK9yQsa0lOG0A5YjKZ1I6T+apXXnkl2rLOwR1wz+AbMXf7p8guzlXLNifuxIur3sYDZ94GL71nS28iERERERGRy5Dw6Y6Xl7f0ZmDuAxPQMap6hVJDSHVNWFiYajwuVTESQHh7e9f6mIyMDJSUlFiroepCghEZtiSocdQAvCoJkt5++227yiMh0wcleBFnnXWW6mX1/fffqwBK3HTTTaqZ+ty5c1XVk23QEhoaqoZMN5RwzNH0QOl9Jc27pQm7kIbt0utKgh7bfSRN2CUoEiNGjFCBmIRH2rTGqVOn4scff1Q9seoSQO3Zswdffvml2lbZR9qUSSnAeeaZZ7B06dJ6b6OmZ8+eGD16tHpuLYA6fPgwVq1apba5pTntFDxHfvvtN5w8eRKXXXYZ/P390dbF+EXi8XF3I8wnxLpsZ8o+PPf3/6GwlHNniYiIiIiI2jrb6Vgypayu6rPuAw88gI0bN9oNbbqbvL5UR2lDCktsSeWPI3JmPltS9WQ7/UxyAa3qSRw6dAj//POPmi5XFzLtTQt2tOcXkjnYmjRpkvW6TIOTEE96YdlWa0kVU9XH1WTjxo1qn1xyySXWZVIpJrdl+xuyjbYkmJNqLZmOKCQEjIyMVEFZS3OpCigpS6vr9LvU1NRq8xtl7qvQPviuTDtww/yC8cS4u/Hcqv9Dcr7l592XfghPLn8d/z3zdgR4WpJgIqo8blz9+CdqTjxuiHjsELXGvzlSFSMBi12vnCp9c1qM2Vyth0/tq1vWdfSY4uJiVdEUERGhAhP5jizVM7WdoU6qhjw9PdUUs7puR4cOHaxn2qu6bdKnSaqINBLeyFQz7bll22xfR7suvZ9sl0sPI/l5tGVS9SShjVT7SL8nuZSeSlJNVHW7Hf0c0nPKdrnBYGllI/tHlte2HafattokJiaqbZcKJtv1ZT9Ibyh5Htn/9dlG259R9olUo3399dcqjPvkk09wzTXXqPe/Pp8r7Tm1ALGqhhynLhNAJSUlqdKzfv36qbK3U5E5njWdRS8vL0/NA3Vl8otZSzTlg3TXwBvwf1s/RmJBqlqWkH0cTy1/DbMG3YBAT1aLETk6boio/n9viKhh/61GRM513EifIPkyb/slOizQE2/cMwYtTbajPl/utebXjh6zbNkytXz48OHw8fFR16XZ9TnnnFPj80kwd8YZZ6ipYFWbdte2DTVt84ABA7B27VrrbQlfbItCqj5Wu141jJT1qoYhErDIVDOZ1iYnLZNeTbbrOHqMkGVVX1erzLKt1nK0HdrjT7VtNYmIiFCfc5leJ++JbeYht+WzL89Tn22U9bT7JLySaYpS+SQ9r6QPlgRQDQmMZBvkWHGUn0iu0moDKEkzZYdKs666uP322+1K2rQKqJkzZ6rpe9rpJV2V9uGR5FWaygUhCE9MuBcvrn4Hh7OOqfuSCtLwxtYP8ciYOxHuWzlNj6itqnrcEBGPGyL+zSFqm/+tJl+oJWixfR25HtfO9Xrpao2+q+4z+Rlnz56tpodJVZAEG1Kl9Pjjj2PChAnV2trs3LlTfU+WaibprXT++eerM9RpfYo0EnZIiGU7pUu2oab3TKaQOSoi0daX7ar6PjhaLq9R9T0bM2YMevTooc6WJ0GLNOG2vV8qvaSfVdVtk+epus1a6CnLtOFoO7THn2rbajJy5Ei17g8//KDO/qcFPdJDShqQa89Rn22U9eQ+bdrkv/71L4waNQrPPvuser2+ffuiIeT5JKh1lJ80pC2SS3wDkzdj/vz5qkRNkru6kFRRhiO2b5Qr0w4E7WcJ1gfi8Ql348VV72BPmmW6YUpBOp5e+QYeG38XYvwjW3iLiZzvuCEiHjdE/JtD1Pb+W00LberT58hZyc8g4Z2cMU2rTJEzs7377rtqSpc079b2pwRKEj5JbyFpZi09haQSR6qdpOWNPIc0H58xYwbuvfdeFWTIGfEkjJLKJWm8/d5776FTp06YNm2adRtkup72+rbbVdvsJW3fy6Xt+1DT8qr32/Y8kh5UErj06tWrWlNuaRAuQ6qBYmJi1HD0XFVft7btqOu2OdK7d29cccUVuPPOO1UlVJcuXdS+l30r75mj1zrVNlbdJgmd5IyB0lPq/fffb/DnXHtOR8djQ45Rl/gGJqdclM7tEj65euVSU/MxeOORsXfildXvYXvyHrUsozALT/z1Gh4bNwuxQe1aehOJiIiIiIioEeXk5KgARsICaZQtVU9XX321CjmkL5JGqoW2bNmCOXPm4KWXXlLNrGXal0zRW7BggZoup5Ez0MlUPDnLnJyFXvoOSfAk4dT9999v9/qyrnbGOo02laypySwnCaCk+snRzKitW7eq+7KysvDEE0/UeVphU/rggw/w0EMP4emnn1aVatJq6Oeff1YVUI25XyRHqcuZ+ZqLm7m+XahagOwwaaC1cuVKVWLXUPHx8ar0bNeuXSoNdGVyIMsHVQI5R8ljmakMb6ydj40nt1uX+Xn4Yva4O9ElpGMzby2Raxw3RMTjhoh/c4jaxn+ryRdzERcX16SvQ01P+j8/+OCDqrm3hG9tjbmi95QcM7aVThIqSuAovbGa4jhpSL5iqTt0Yunp6WoupJTOnU741NYYdAbcc8bNODN2mHVZfmkBnl7+BvakHWjRbSMiIiIiIiI6HQkJCaoX1fPPP6/OgtcWwydHNm3apKrbNm7ciFmzZsGZOH0A9emnn6K0tLTOzcepkt5dhztGXI9JcZVlfEXGYjz39/9he/Ju7ioiIiIiIiJySTKVbvr06arv0zPPPANnYXuGuqrDVHGmuqYk/b1kiqUMue5MnH4OijQ+k0ENb653y9Ar4aX3xC/7/1TLSk1leHHVu7h71E0Y3n4gdy0RERERERG5lI8//lgNZyNnjavJuHHjsGLFiiZ9fWfusuT0ARSdPpkHeu3Ai+Bt8MTC+F/VMmO5Ea+t+QB3jLgOZ3Yczt1MREREREREdJpk6ltN/P392/T+ZQDVhkKoS/uepyqhPt++SC0rN5fj/9Z9jGJjKSZ3abxu+0RERERERERt0dChQ1t6E5yW0/eAosY1o+fZuGlw5WkYzTBj3qYv8Ms+y/Q8IiIiIiIiIqLGxgCqDZrSbRxuH36t3SkaP9m2EN/F/+rU80WJiIiIiIiIyDUxgGqjxncehXtG/Qs6d5112de7FmNh/C8tul1ERERERERE1PowgGrDRnYYjAdG/xsGXWWX/m/jf8HOlL0tul1ERERERERE1LowgGrjBsf0xSNj77ALoeau/xi5Jfktul1ERERERERE1HowgCL0ieiOawZcaN0TWUU5eG/j5+wHRURERERE5OSefPJJ1d9Xhru7O4KDgzFs2DDMnj0bycnJ1dZPSUnB3XffjS5dusDT01Otf/bZZ2PhwoXV1v3uu+8wceJEBAUFqXW7d++Oe++9F4mJidZ1tNeuOrp27QpXkZqaqvZjQkJCoz7v+PHjcfHFF6O5JSQkqPfg559/hjPRt/QGkHOY0nUctiXvxpbEner2ppPbsezQPzir65iW3jQiIiIiIiKqRWBgIJYsWaKu5+TkYMuWLXj33Xcxb948tXzIkCHqvn379mHChAnw9fXF/fffj969eyM3Nxe//vorrrrqKnTr1g0DBgxQ695333144403cMMNN+Cee+5BQEAAdu/ejffeew9HjhzBokWLrK8v61YNWry8vFwqgHrqqadUYNSpU6eW3pxWiwEUKZKO3j7sGty/9FlkF+eqZZ9s+xa9IrqifUA09xIREREREZGT0uv1GDlypPX2lClTcNttt2Hs2LG4/PLLsXfvXuh0OhUyhYSEYM2aNSpQ0px33nlqfal0EosXL8Zrr72G+fPn48Ybb7SuN27cONxyyy34/fff7V5fQhvb12/NioqK4O3t3dKb4ZI4BY+sArz88Z8R11lvl5rK8NbaD1FmKuNeIiIiIiIiciESJr300ks4ePAg/vjjD6xcuRKbN2/GnDlz7MInTf/+/REbG6uuv/766xg8eLBd+KSRIGvatGmNso2yLTJVT6qlIiMjMXXqVOu0wRUrVqhCCQm7pk+frqq2ZPukAquqVatWqXDMx8cHoaGhuPnmm5GXl2e3ztGjR3HFFVcgLCxMrSc/74IFC9R0tX79+ql1pDpMm0Jouw1Lly7FjBkz4OfnhzvuuEPd9+qrr6qpjlJ9JtsuIZ7s69NRWFiIWbNmISoqSu0Tef6qYZ82rU+2XfadvL689okTJ2p83gcffBBxcXHV2ux8/PHH8PDwQFpaGpoDAyiyMyCqN87tPsl6OyH7BL7c+RP3EhERERERkYuRsEKqo9atW4e///5bhUeTJ0+u9TFlZWWqQkrCoLoqLy+H0Wi0G7KsNp9++imef/551VNKAh6ZMiiBSkFBgd16N910kwqLvv/+e5xzzjmqUsu2t9Hq1avVzyShjfSxkmmDMqVQpg7aTrEbNWoUNm7ciFdeeUVVeMnzHj9+HNHR0fjiiy/Uem+//TbWrl2rRtVtkKmJP/30k7ouJPCRMOrHH3/EBx98AJPJhDPOOENNgWyom2++GR999JHq3yVTHDt06IBzzz0X//zzj91669evx9y5c1UI9v7772Pr1q249dZba3xeCRJl2qR8BmzJa0l4FR4ejubAKXhUzZX9z8eu1H04mm1JUH/etwwDonqpcIqIiIiIiKg1KjeWwphVvWl3c9MHR8Fd79EozyVVNFLxI43HpfpFgoZTTR/LyMhASUmJtRqqLu666y41bF133XWqwqYmGzZsUM3Pb7/9duuyCy+sPDmWRqqtJKjSphYeOnQIzz77rKqKEv/9739V8PP1119bH9OuXTtMmjQJu3btQt++fVVFlwRDUgEmgZOQ+zUScAnpieVoKuEll1yCZ555xm6ZPKdGwqezzjoLERERKpC69tprUV979uzBl19+qUIh2XfazyvbJq8tIZ1G+nb98ssvqoG8vK/SFF56etU0PbBnz54YPXq0em4JJcXhw4dV5ZiEas2FARRVY9AZcNfIG/HfP+aoaXji7fWf4JUpj6ppekRERERERK2NhE8n5t3T0puB9re8Do/wuoc/p2I77UqbWlYX9Vn3gQcewKWXXmq3TIIv7fUloLF9XqnEGjhwoOox9cQTT6gqH2mULsuruuCCC+xuS0gl09TkOSUok2ql//u//1NVV5ozzzwTBoNBBU4SQP3111+qoksLn+pLtq8qqSp77LHHVMP3zMxM6/L9+/c36DU2btyo9pWEXRo5q6HclqmUtmRqnoRPml69eqnLkydP1nj2QancuvPOO1WVl0wllHBQm/bYXDgFjxxqHxiNawdWnsVAGpO/u/GzanNGiYiIiIiIyDkVFxeriiYJGqQqSHr9yLLaSA8lT09PHDt2rM6vI9VSQ4cOtRva2eRk2peEQdrQKo9kWphUNn3zzTcYMWKE2sZHH33ULqwSUlVU9baETenp6cjKylLrSxWV7WvI9stUQpliJ2QfNDR8ErJttmTfSPWWfD+WKXAyDVACJNm2U+3fmiQlJalgSPpTVX1t6Q0lYZtGaxavkT5OorbXloBQAi3Z37Ldn3zyiarUkimazYUVUFSjs7qMwbbk3dh0cru6vTlxJ/44tBJndx3HvUZEREREROTkli9frsIa6X8kwcbjjz+OP//802FFj0YCHJmuJVO+ZKrb6ZLKJglnNP7+llk1Eobcc889akhQJH2YpPdR+/bt8e9//9uuf5MtuS2hiVRYSeAiFVVPPvmk6g9VVUxMjDVUk4CnoapWgy1ZskSFQjLdTpqjC9nPtpVQ9RUdHY38/Hz1vLYhlEyflNsSqp0O2U45I6JUPnXs2FGFaLZ9spoDAyiq9SD797Cr8UBGArKKLY3UPtn2HXqFd0OHQMuBTERERERE1BpI7yWZ/uYM29EYsrOz8dBDD6kpWdKkW6a3SRj0yCOPYOzYsdYgSLNz505VWSONr++++2511jepktH6EWmkubicma2uU7fkdaQiqjbymtLLSXoU7d692+4+acZte9Y9ua1N15NQRXo27du3T4VrNZGqq7feekuFOVWrmepaQWRLei1JgGZbPSSVRbbTAOtr2LBh6ju4NFLXekhJpZLclimFjUGm4cn+ksBOLqU3VHNiAEW1CvD0w39GXIdn/35L3S4zleGttR/iubMegofOwL1HREREREStgjT+bszeS81Jgg/pSSTy8vJU7yM5q5xU00i1jtZbSaqMJkyYoAIhqTySptvS0FqqneRMbnJ2NQmD5MxocnY6CSxketn555+vpoft3bsX7733nppeZxtAJSQkWF9fI2GKTK2riZy1LSQkRAUhgYGBqlrrwIEDePHFF+3W++2331Rl1Lhx49SZ8P744w9VeaSR/kgSMEkgdPHFF6vAS6p7pEn3c889h+7du6ufVc66N2bMGPVc8jNK0285496DDz6ophBK824J3GRbpAqsttBs4sSJauqfVBDJPoqPj1dn16s6Na4+evXqhSuuuEKdWU/ewy5duqj3RPa5vJeNQd6PPn36qLPqydTB5sYAik6pf1QvnNdjMhbvW6ZuH805iQU7fsD1gyqboxEREREREVHLkDO8yTQ7CX0CAgJU1dPVV1+tmk5HRVVWVPXo0UM1zZ4zZ44KbqRptUzvGj58OBYsWIABAwZY13311VfV2eXmzp2LK6+8UlX9SPAklVFyxjVbsq4MWxJ61VYRJNsrAYsEIVJ5JNsst2fOnGm33v/+9z+88cYb6qxzElhJE23ZBo1UB61cuVI1M7/mmmtUMCRTzCQg06qd5Ox/EqRJ2CTVXdJPqVu3bnj44YetZwuU137qqadU0CX9o2rrf9yvXz81lU0qiaQiS/bbt99+i8suuwyn44MPPlBVa08//bSqYJPX+fnnnxutAkrI/pUz4Ml0vObmZm5DXaUllZQO+HIqRkn9XJkcyPKBlIS1OZqGGU1GzF72Eo5kW5q4iUfG3oGB0a69H6ltae7jhqg14HFDxGOHqDX+zZEv4CIuLq5JX4cabsWKFapaS6YGyvd4ckwiHTl25Jipy5kLJWyUIPKzzz47reOkIfkKz4JHdaLX6TFr1I120+7e3vApcopzuQeJiIiIiIiInNimTZtU1Zs0hJ81a1aLbAMDKKqzdgFRuH7QpdbbEj69s+GzWksTiYiIiIiIiNo6o9FY45Bpg01NmpzL1EsZcr0lcA4K1cukuNHYlhSPDSe3qdtbk3Zh6cG/MbXbeO5JIiIiIiIiahTjx49vVcUOBkPNJ/GSvlMy5bApOcO+ZABF9SJzSm8ddhUOZiYgsyhbLfts23foHd4NsUHtuDeJiIiIiIiIqti4cWON+0TO3NcWMICievP39MMdI67DMyveghlmlJUb8ea6DzFn8kPw0HtwjxIRERERERHZGDp0aJvfH+wBRQ3SN7InZvQ8y3r7eE4iPt+xiHuTiIiIiIiIiKphAEUNdlnf8xAXHGu9veTACmxJ3MU9SkRERERERER2GEBRg+l1eswadSM8dZXT7t7d8Cmyi3O5V4mIiIiIiIjIigEUnZYY/0jcMPgy6+2ckjy8s/4TlJvLuWeJiIiIiIiISGEARadtQudRGNl+sPX2tuTdajoeEREREREREREDKGoUbm5uuGXYlQj1DrYu+3z7IhzNPsE9TERERERERESsgKLG4efhiztHXg83uKnbxnIj3lz7IUqNpdzFRERERERETeTJJ59URQEy3N3dERwcjGHDhmH27NlITk6utn5KSgruvvtudOnSBZ6enmr9s88+GwsXLqy27nfffYeJEyciKChIrdu9e3fce++9SExMtK6jvXbV0bVr11q3OyEhQa33888/1+vn/eabb/Dxxx+jKSQlJeGcc85BYGCg2rYVKxpnZo88j5ubG3btatsn7dK39AZQ69E7ojtm9pqCRXuWqNsncpPw2fbvcdOQy1t604iIiIiIiFotCUyWLLF8D8vJycGWLVvw7rvvYt68eWr5kCFD1H379u3DhAkT4Ovri/vvvx+9e/dGbm4ufv31V1x11VXo1q0bBgwYoNa977778MYbb+CGG27APffcg4CAAOzevRvvvfcejhw5gkWLFllfX9a9+OKL7bbJy8urSX5WCaDS09Nx/fXXN/pzP/fcc9i+fTu+/PJLhISEqP3TGAYPHoy1a9eq0K8tc/oASg6Gl156SSWvkpB6e3urg+LOO+/E1Vdf3dKbR1Vc0nc6dqbsxcHMBHV76cG/4e7mjkv7Toevhw/3FxERERERUSPT6/UYOXKk9faUKVNw2223YezYsbj88suxd+9e6HQ6FTJJsLJmzRoVKGnOO+88tb5UOonFixfjtddew/z583HjjTda1xs3bhxuueUW/P7773av36lTJ7vXd1Wyn0aMGKGqoBqT7OuRrWD/tOom5CdPnsTQoUPxzjvvYNq0aZg7d64qL5RywqNHj7b05pEDencdZo28AV56T+uy3w4sx12/PoG/Dq/h2fGIiIiIiIiagYRJUsxx8OBB/PHHH1i5ciU2b96MOXPm2IVPmv79+yM2NlZdf/3111XVjm34pJEgS76fN4VPP/0UZ555pgrJZGqgVGtt2rTJer9UPUlxyt9//22d6icZgebHH39UGYJUX0VFReHBBx9EWVlZnV5bnuvPP/9UlV1yXUI1MX78+GrVXY6m1Ml+lWmH8tqRkZGYOnWqdQqko/ULCwsxa9YstZ3yGMk5qgZ72msvWLBAPbe8b7LvT5xwzX7LTl0Bde211yIvL0+VwHXo0KGlN4fqKMo/ArcPvxZvrvsQpnKTWpZbko/3Nn6GZYdW4cbBl6FrqOVgJiIiIiIicgbGsnLkpNctrGhKgWEG6A2NUysiAYZUR61bt04FRzImT55c62MksJEKKZlWV1fl5eUwGo12y6QflYz6kFlPkgPIVLXS0lI1FW7MmDGIj49HXFwcHnvsMRw7dgzZ2dmqUEW0b9/eOjXviiuuwK233ornn38ehw4dwsMPP6y27ZVXXjnla8sUudtvv10Fd/J46XlVn+BMHvPiiy+iT58+yMjIwF9//YWCgoIaH3PzzTfjp59+Uo+TcOmDDz7Aueeei+XLl6sQTrN+/XrVc+vVV19FUVER7rrrLlWFJtMmXY3TBlCrV69Wb5gkrxI+mUwmtbP9/PxaetOoDkZ2GIzYwBh8vPVbbEvebV0uU/NmL3sJEzqPwpX9ZyLAy5/7k4iIiIiIWpyET1++dLylNwNXPNgBodF1Dz9qI5U1YWFhqvG42WxGeHi4amtTGwlPSkpKrNVQdSGhiAxb1113Xb2bhT/++OPW6xIcnXXWWdiwYQM+//xzdZ8EU1IdJffZTmmTn+2BBx5Q4ZUWTAkJkf7zn/+oICo0NLTW15bnkwojef76TpeTbZRG7hJgaS688MIa19+zZ48K1z766CO1n7Rpk1KF9swzz2Dp0qV2bYl++eUXVREmpKpKenJJPnKq99LZOO0UPNnBQj5gF110kdqx/v7+iImJwbPPPqsCKXJuMQFReHjsHXjwzH8jwrfyYDfDjL+OrFHT8pYcWGGtkiIiIiIiIqLGJeGMRqaB1VV91pXwZ+PGjXZDmxonry/VUdqo7bu8BDMXXHCBmsIm1VoGg0E1Tt+/f3+try/3S2XUpZdeavdacga/4uLiJj/73MCBA1VF0hNPPKHCqFPlFRs3blT75ZJLLrEuk2oxuf3PP//YrStT87TwSWiN0aVlkatx2goo+eCJm266CZ07d8b//vc/dQBImqmV3UlH/5qkpqYiLS3NbpnMfRXah9GVaQeuK/wcAyP74KWzuuHn/X/ix71/oKzcUtZaUFaED7d8jT8OrsL1Ay9Br/DaT9NJ1JaOGyJnweOGiMcOUWv8myMVNPL90jacqbzWsmQ7bLfrlOtXrOvoMRK+SEVTRESECnTkO7JUztR2hjqpAJLKIem7XNftkFlL2pn2qm6b9D+SIMi2kblMM7PdbhnSfkeqiCR8kulmHTt2VNspU9Xk56i6Lba3te/+NTUPl/ygIfu0pmVVt13OFCiVSjKN7umnn1bVVjIV8KmnnlL7ver6iYmJanaXFNrYPq+8T9IbSn5ebQqgTAm0XUdCOSHvo/Z8NW3z6dKe39Ex2ZDj1GkDKPnwCTk9pDRL03b+ZZddphI/CaRkTmqPHj0cPl6CKnmza3pumTPqyuQXc35+vrouH2hXMDF6FAYE9cL3B37D1rTKaXnHcxPxzMo3MTSyHy7sOhVBXtUb4hG11eOGqKXxuCHisUPUGv/mSJ8j+SJv+yXaN9ANl9wbjZYm21GfL/cSpglHj1m2bJlaPnz4cPj4+Kjr0ui6trO8STB3xhlnqGlgtg2+T7UNNW3zgAEDVH8ljQQvtkUhWugolT/SXFsqiXr27GldPycnx+755XrVUERrqv7uu++qaqSqpKF4XfapPG/Vn0WyCAmEbJelp6erS9uf484771Tj+PHjanqdTBmUGVzSr0mriDJWrC9Bk3zWJbSS90WTlJSkbsvnX9ZztD1Vn8t2WWOT15djxVF+omU2rSKA0uYyXnnllXbNvzw8PNSpIyVVlNS0pgBK5l7alrNpFVAzZ85UU/m000u6Ku2DFhgYqJrKuQrZ7w9E34adKXvxyfaFSMxLsd63KWUndqbvwwW9pmBa1wkw6CzJLlFbP26IWhKPGyIeO0St8W+OfKGWoMX2deSqV3vX+w6iNfquus/kZ5w9e7ZqcC39hSTUkColCUbk7HLyvdjWzp071fc1qWa6++67cf755+OLL76w9ijSSCAiIZac5c12G2p6z2T62IgRI6ot19aX7ZLr0nRcK0LR7pNm6NKYXLZbWyZVUdKjyvb1pPF3u3btVKWTVB41lHwmqv4s0uR81apVdsvkbHnaz1D155YZXI888ohqTL537151vxao6ivWlx5T8lo//PCD6lulhT1yBj5pQK49p6PtqfpcWuWTLK/PtMm67g8Jah3lJ1U/P3XhtN/AtLPeRUdXT6C1ZZmZmTU+XhJFGY44+pC4Iu1AdcWfZVC7vugX1RO/HViBhfG/oMhYrJaXmErx1a7F+DthPW4YfCkGRvdp6U2lVsaVjxuilsLjhojHDlFr+5ujhTaN/YW9JcjPIOGdnC1Nq0zZvHmzqgaS6VxLliyx7k8JlCR8kr5C0shaZhdJFY5UO8n0MXkOaT4+Y8YM3HvvvfjXv/6lQiAJo6RySQKV9957T1UUTZs2zboNMl1Pe33b7XIUPNner13KGDVqlHoNqRh68MEHVTWUVGBJsGS7vlRH/fjjj2pIOCRVRjJk2t4111yjfn7ZNileOXz4sAp5Fi5caFdpVJd9attM/MMPP1T7QztLndYkXNt2Cb205uUSoMo6Bw4cUGfF09YR2nXZ73LGPqmYkkoo6X0t+1/2r7xvVT+XtrerPpft8qYIoKoGtZqGHKNO+w1M3jjZ8VK+VpW2TOaGkuvS6/Q4r+dknNlxGL7Yvggrj1b+wkrKT8XzK+diaEx/XDfoYkT6hbfothIRERERETkrmaYmAY6EBTIdTaqerr76ahVwREVFWdeTGURbtmzBnDlz8NJLL6lG1hLMyBS9BQsWqOlyGgl0ZCre3Llz1cwk6TkkwZOEU/fff7/d68u6Mmxp08jqSr7ff/vtt+q5JfDq1q2bCrtkO6vOdtq6dStuvPFGZGVlqcbfElRJux752Z9//nkVGMnrx8XFYfr06SqMaigJneQ5pc2PtAKSbXvzzTfVpUb2vQRI77//vpquJ/tfbssMrJp88MEHeOihh9TsLqlW69evH37++WdVAdVauZmbolNVIx1A8uGWqXjS9V4r75J0UBJPaTJ+6NAha6VUXcTHx6Nv376qA76U6LkyOZDlQyqlcK2lkmNv2iF8uOUrJGSfsFtucNdjRs+zMbPXFHjqG/6Lg6g1HjdETY3HDRGPHaLW+DdHKmOEBBRErsxc0Q9LjpnGroCq7ThpSL5iqTt0QlK2JqmiNOGS8sCXX34Zr7zyikpmJaWVhLM+4RM5v57hXfDCWQ/jX0OugJ+Hr3V5WbkR3+3+Fff89hTWHd/SJN39iYiIiIiIiKjpOHUJgDTjCg8PxwsvvKDOaCfNzqQsTTrKX3755S29edREc7HP7joWozoMxlc7f8KyQ//AXHFC1PTCTLy25gN0C+2Mad3GY2T7wWoaHxEREREREVFtapsOKJVDPEt203P6b+/SPMy2uRm1Df6efrh56JWYFHcmPtzyNfZnWEr/xIGMI2p84vUdJsWNxlldxiDUJ7hFt5eIiIiIiIicl5zNrSbjxo3DihUrmnV72iKnD6CobYsLicXTk+7DqoQN+HzHIuQU51rvk+vf7/4NP+xZiqHt+mNq1/HoE9G9VZzJgoiIiIiIiBrPxo0ba7xP6zlNTYsBFDk9dzd3jOs8EsPbD8TyI2uw9ODfSMpLtd5fbi7HhhPb1GgfEK2m8I3tNAI+Bu8W3W4iIiIiIiJyDkOHDm3pTWjzGECRy/A2eOGc7hMxtdt47ErZhyUH/8bmxB12TclP5CapKXsLdvygQiipimofGN2i201ERERERETU1jGAIpesiOof1UuNtIIM/HFoFf48vBp5JfnWdYqNJfj94Eo1ZFrelK7jMKzdAOjcdS267URERERERERtEQMocmnhvqG4sv9MXNznXKw7vgVLD6zAgcwEu3XiU/erEeIdhMldxmBy3GgEeQe22DYTERERERERtTUMoKhV8NAZ1JQ7GYczj6rpeauPbUKZqcy6TmZRNr7ZtRjf7f4VI9sPwpSu49EjLI5Ny4mIiIiIiIiaGAMoanXiQjri9uHX4poBF6qm5TINL7Ugw3q/qdykwikZHYPaY0rXsWp6XqBXQItuNxEREREREVFrxQCKWi1/Tz/M6Hk2pnefjG3J8erseVuT4u3WOZp9AvM2LcAHm75EXEgsBkX3wcCoPuga0gnu7u4ttu1ERERERERErQm/YVOrJ0HS4Jh+eHjsHXjrnKcwvfsk+Bq87dYxw4xDmUexMP5XPPrny7j5xwfx5tr5WJmwHjnFuS227URERERERLV58sknVVsRGfLdJzg4GMOGDcPs2bORnJxcbf2UlBTcfffd6NKlCzw9PdX6Z599NhYuXFht3e+++w4TJ05EUFCQWrd79+649957kZiYaF1He+2qo2vXrqd84/bs2YMxY8bA19dXPSYhwb6fb01WrFih1t+1a5fddsydO7dOj6eWwQooalOi/CNw7aCLcVm/GVh9bKOannc461i19fJKC6zT9NzghrjgWAyM7qMqpFgdRUREREREziQwMBBLlixR13NycrBlyxa8++67mDdvnlo+ZMgQdd++ffswYcIEFfjcf//96N27N3Jzc/Hrr7/iqquuQrdu3TBgwAC17n333Yc33ngDN9xwA+655x4EBARg9+7deO+993DkyBEsWrTI+vqy7sUXX2y3TV5eXqfc7gceeADZ2dn46aef1DZFR0c38p4hZ8IAitokT70HJsaNViO9MBPbknZjW1I8dqbsRZGxuHp1VNZRNaSBuZ+HL/pH9cKgqD4YGN2bvaOIiIiIiKhF6fV6jBw50np7ypQpuO222zB27Fhcfvnl2Lt3L3Q6nQqZQkJCsGbNGhUoac477zy1vlQ6icWLF+O1117D/PnzceONN1rXGzduHG655Rb8/vvvdq/fqVMnu9evK9muGTNmYNKkSQ38ycmVMICiNi/MJwSTu5yphtFkxL6Mw6pXlARSx3JOVts/+aUFWHNskxpCq44aHN2X1VFEREREROQUJEx66aWXMG3aNPzxxx/w8fHB5s2bVbWRbfik6d+/v/X666+/jsGDB9uFTxoJsuQ5T4dMtevcubP1tWRIuCVT6yTMkmqqV155xbr+xx9/rCqx8vLy4Ofnd1qvTS2HARSR7QGh06NPRHc1rh5wATIKs1QQtTU5HjuTq1dHCZnCJ+P73b/B18MHAyJ7qZ5TQ2P6w8fDvtcUERERERE5p1JTGVLy01p6MxDpFw4PnaFRnmv8+PGqOmrdunUqOJIxefLkWh9TVlamKqRkWl1dlZeXw2g02i2TflQ1ndhJptqtXbsWF1xwgeoxdeeddzoMxah1YQBFVItQn2BM6nKmGnWpjiooLcSa45vV0Lvr1VS9ke0HYVi7ASqcIiIiIiIi5yTh031LnmnpzcCrUx9Dh8CYRnku6cMUFhamGo+bzWaEh4fD27v2fyTPyMhASUkJYmNj6/w6d911lxq2rrvuOlW55Ig0NJcpe3IpYVRDpu+R62EARdRE1VHGciO2JO5UQ+euQ//InhjZfrAKo/w8fbnfiYiIiIioyUnwZHumuLqqz7rSTPzSSy+1WybBl/b6JpPJ7nmlEovaHgZQRI1RHVVuwv70Q6o6SgKn47lJduuayk3qPhnzNn2BviqMGoRh7QciwJNzmImIiIiIqPEVFxeriqbIyEgV+qSlpalltZ2hLjQ0VFUmHTtW/WzhNZFqqaFDhzq87++//1Zn3tNovZ6o7WEARdQYB5K7Dr0juqtx1YALcDI3GeuOb8G6E1txNPuE3bomczm2J+9W44PNX6qKqlEdLJVRgV6c90xERERE1FK9l2T6mzNsR2NZvny56s00atQo1YT88ccfx59//olzzz23xscYDAaMHj0aS5cuxbPPPnva2zBkyBBs3LjRetvf37/W9SUcKy0ttVuWlZV12ttBLY8BFFETaBcQhYv6nKNGYl4K1h/fqgKpI9nH7dYrN5djZ8peNVQYFd4dIzsMwvB2AxHkHcj3hoiIiIiomUjj78bqveQMsrOz8dBDD6Fr166q8bhUQEkY9Mgjj2Ds2LHVgqCdO3eqM+d16NABd999N2bMmIFPPvlE9XKq2nD8999/x9SpU+u0HfI6NVVHOdK+fXvs2bPHbpm8Hrk+BlBETSzGPxIX9J6qRnJ+mgqiJJA6lHXUbj2ZG70rdZ8a8zd/jV7hXTGyw2CMaD8IwQyjiIiIiIioBlLlJGe6E3l5edi8eTPeffddFBYWYsmSJdaeS1988YWaDieB0D333IPevXsjNzdXVTt98MEHWL9+vQqgzjvvPNx777246aabsHr1apx//vnw8/PD3r178d5776FTp052AVRCQoL19W17PY0YMaLe75mcGU/Oivf8889j2LBh+O677xAfH8/3vhVgAEXUjKL8wjGz1xQ1UvPT1RQ9CaQOZibYrWeGGbvTDqjx0ZZv0D0sTlVVSb8oGf4efvCvuK4NT71nvRoFEhERERFR65CTk6Om2cn3gYCAAFX1dPXVV6sgJyoqyrpejx49sGXLFsyZMwcvvfQSTp48qabmDR8+HAsWLMCAAQOs67766qs444wzMHfuXFx55ZUoKipSwZNURt1///12ry/ryrAloZcEY/V1yy234NChQ3jrrbfU2fiuvfZaPProo7j11lsbtG/IebiZbVvit3KSmvbt2xe7du1Cnz594MrkQJaSSimR1OuZI7q6tIIMrFdh1FbszzjcoOcwuOsR4OkPf0/fKpcSUNlerwivPPzg7u6OtoTHDRGPGyL+zSFyXs3532qHD1v+mzsuLq5JX4eoqZnNZnXsyDHT2AUJtR0nDclXmFwQOYFw31BM7zFZjfTCTGw4sQ1rj2/BvvRDdX6OsnIjMoqy1KgLX4M3zu46Duf1nAw/D9/T2HoiIiIiIiKi2jGAInIyYT4hOKf7RDUyC7NVZZQEUTklecgrKUCuusxXZ9M7HQVlRVi0Zwl+P/g3zut5Fs7pNgFehppPx0pERERERETUUAygiJxYiE8QpnWfoEbVMsuismIVRuWW5KuRp12W5iO3OB+5pTbLSvJRWFZUYxD11c6f8Nv+5apR+uQuY9QZQIiIiIiIiIgaCwMoIhckc3t9PLzViPKPqNNjjCYj8kotFVQZhVn47cBybE+uPL2pVFh9vPVbLN67DBf3OQfjOo+C3t1ytgwiIiIiotamDbVDJmrwMdKYfaUYQBG1EXqdHsHegWp0DGqPwTH9sDt1P77c+ZNdrynpIfX+pi/w497fcWnf83BG7BC4u7WtZuVERERE1LrJyXhKS0sb/Qs2UWthNpthMpng4eHRaM/Jb5VEbVjviO54euJ9eHjsf9A5qIPdfcn5aXhr3Yd4cOnz2HRyO/+FiIiIiIhaDU9PT/XlOjU1lf+dS+QgfJJjQ44ROVYaCyugiNo4+RefQdF9MSCqtzr73tc7F+NkXrL1/mM5J/HSP++ha0gnXN5vBvpF9uS/EhERERGRS4uMjERJSQkyMzORk5MDnU7H/8Yllw2LzBWVfI1RzadVPsnw9vZWx0pjYQBFRIpMsxvZYTCGtRuAVUc34Nv4X5BWkGHdOwczE/Ds32+hT0R3FUT1COvCPUdERERELjsFLzY2FikpKSqIKi8/vTNME7UUs9mMsrIyGAyGRgmg5Dlk2p1UPkn4JMdKY2EARUR2dO46jO88CmfGDsOfh1fj+92/Ias4x3p/fOp+PPbnKxgc3VcFUZ2C7afuERERERG5AvliHR0d3dKbQXRajEYjsrOzERQUBL3euSMe5946Imox0rR8SrdxKoxaenAFftjzO/JLC6z3b0napcaoDkNwWd/piAmI4rtFREREREREDjGAIqJaeeo9MKPn2ZjcZQx+3vcnftn3J4qMxdb71x7fjHUntmBcp5GY0nUcYvwj4W3w4l4lIiIiIiIiKwZQRFQnPgZvXNp3OqZ2G4+f9v6O3w6sQJmpzDrveMWRtWqIQK8ARPmFqxHtH2G9HuUXAR8Pb+5xIiIiIiKiNsapA6jaGmjt3LkTffv2bdbtISIgwNMPVw+4EOd0n6j6Q0mfKFO5yW7X5BTnqrEv/ZDDx0sQpQIp23DKPxx+Hr7cxURERERERK2QUwdQYsyYMbjllluqLe/QgY2PiVpSiHcQ/jXkCszocZY6Y94/xzZWC6IcyS3JV2N/xuFq9/l7+KowKrIimIrxj0Dv8O4I8Qlqop+CiIiIiIiImoPTB1BxcXG4+uqrW3oziKgGEX5h+M+I61QYlZyfiuT8NCTnpVku5XZeGjKKsuq0//JKC5CXWYADmQl2yzsGtceg6D7qzHvdQjurM/URERERERGR63D6AEqUlZWhuLgY/v7+Lb0pRFRLs3IJimRUVWosRUpBugqlkvIsIVVKRTiVXpgFM8y17tej2SfU+GHPUvgavNE/qrcKpAZG90GQVwDfEyIiIiIiIifn9AHUwoUL8fnnn8NkMiEwMBDTp0/Hs88+i06dOtX6uNTUVKSlpdktO3jwoLo0Go1quDLZftknrv5zUNvgDndE+0aoMSiyj9190sg8tSDDEkoVSDBlCapkpBVmVHuugrIideY9GSIuOBYDo3pjYFQfdd3dzb3G7eBxQ1R/PG6IGobHDhGPG6LW/PfG2IDXc+oAaujQobjooovQvXt3lJSU4J9//sEHH3yA3377DatXr0bPnj1rfOw777yDp556yuF9eXl5yM7OhiuTD1h+fr66rtNxOhK5Nl94oYtPBzUQXrlcekXtzjyA+PT92J15EEXG4mqPPZx1TI3v9yyBn8EHvUO7oU9od/QK7apu2+JxQ1R/PG6IGobHDhGPG6LW/PcmLy+v3o9xM8v5012IhE/nnHMOpkyZgiVLltS7AmrmzJnYtm0b+vSxr8JwNZI25uTkqKowvd6pc0SiRiENzqU31LbkeGxP3o2jOSdrXd8NbugW2klVRkmFVMfA9uqXM48bovrh3xuihuGxQ8Tjhqg1/72Jj4/HwIEDsWvXrjrnKy6XXEybNg0jRozAn3/+qfpCeXl5OVwvIiJCDUfkTWkNoY2km63lZyE6FT306BvVQ42rcSEyC7OxNWkXtibFY0fKHhQbS+zWl75S+zOOqPFN/M8I9gpE/8he6OLXAf08eyMmMBJubm7c8UR1wL83RA3DY4eIxw1Ra/17o2/Aa7lkctG5c2esX78emZmZiImJaenNIaIWEOIThEldzlTDaDJib/pBbEmKV6HUydzkautnFefg76Pr8DfWAfGAr4eP6hnVJaSjuowL6YhwnxCGUkRERERERE3AJQOo/fv3w2AwIDQ0tKU3hYicgF6nR9/InmpcO/AipOanq8ooCaN2pe5Dqams2mMKSguxM2WvGho/D190CYlF54pgqktwR4T6BDOUIiIiIiIiaq0BVEZGhsOA6csvv8SWLVtw3nnnwdPTs0W2jYicW4RfGKZ0G6dGqbEUu9MOVkzX26XOrleT/NICbE/eo4bG39MPXVSFVCzigjuqy1BvhlJEREREREStIoB69tln1ZnuJk6ciNjYWJSWlqrb3333HaKjo/HGG2+09CYSkQvw0HtgYHRvNa4xXogTaYnIKs9FQs4Jyxn0Mo8irTCzxsfnleRjW/JuNTSBnv5qyp5lCl8seoZ3VdVTRERERERE1AQBVElJCQoKChASEmJXufT222+ry6uuugrDhw9v0HNPmDABe/fuxRdffIH09HTIyfo6deqEe+65Bw899FCNDcaJiGrjZ/BB+6AYDGrX17ostyQfhzOP4XDWUXV5KOsoMgqzanyOnJI8a0WV0Lm5o39Ub4zqMBjD2g1Q/aWIiIiIiIiokQKo22+/HRs2bMDOnTutgdTIkSNx6NAhdfu9995TVUtDhw6t93PPmDFDDSKiphbg6WetktLkFOdWVEhJIHUMRzKPIaPIcShlMpdbAym9ux4DonphVIchGNquP3wM3nwDiYiIiIiozTutAErCpQsuuMB6+9tvv1Xh07vvvovBgwfjkksuwSuvvIKvvvqqze9oInItgV4BGBTdVw1NtoRSVSqlsopy7B5nLDdic+JONQwSRkX3wRkdhmBITD94G7xa4CchIiIiIiJy8QAqKSkJnTt3tt5eunQpevXqhVtvvVXdvvnmmzFv3rzT30oiIicQ5BWAwTF91dAk56Vi7fEtWHt8MxKyT9itX1ZuxKaT29Uw6AwYpMKooerxXnqeRIGIiIiIiNqO0wqgTCaT3e1Vq1bh3HPPtd6OiYlBamrq6bwEEZFTi/KPwAW9p6qRmJeCtcc2q0DqWM5Ju/XKTGXYcGKbGh46AwbH9FOVUVJh5an3aLHtJyIiIiIicvoAqmPHjmoa3i233IIdO3bg2LFjGD9+vF2FVEBAQGNsJxGR04vxj8RFfc5R40Rukgqj1hzfjJO5yXbrlZrKsO74FjU89Z5qep6EUQOj+6hwioiIiIiIqLU5rQDq8ssvxxNPPKHOUhcfH4+goCBMmTLFev+2bdvQpUuXxthOIiKX0j4gGpf0na7GseyTqipqzfFNSMqzrwotMZZgzbFNanjrvTCkXX91Nr2uIZ3UlD83N7cW+xmIiIiIiIicIoB66KGHVNXT999/r8KnTz/91FrxlJ2djcWLF+O+++5rrG0lInJJsUHt1Li073QcVWGUpTIqJT/Nbr0iYzH+ObpBDeHv4YsOgTGIDZTHx6jrMnhmPSIiIiIialMBlIeHBz744AM1qpIgKjk5GT4+PqfzEkRErYZUM3UKbq/G5f1m4EjWcRVGyUgtyKi2fl5pAXanHVDDVphPCGIlmApqhw4Bchmjpv9Jo3MiIiIiIqJWF0DVxGg0Ii8vD8HBwU3x9ERErSKMiguJVePK/jNxKPNoRRi1BemFmbU+Vu6XsSVpl3WZzs0d0f6RKpjqoIVTgTGI8A2Fu5t7M/xERERERERETRRA/fTTT1i7di3mzJljXfb666/jkUceQWlpKc477zx8/fXX8PTk6caJiGoLo7qGdlLj6gEXIqMoC8eyE9WZ9I7nyGWiamRuLDfW+Bwmc7lqfC4Dxzdbl0uT8w4B0Qj2DlRBlGW42Vy3Xybb4mh51ds6Nx28DV7w8/CBrwyDj/W6LGfoRUREREREjRZAvfHGG4iMjLTelkbk999/P3r06IG4uDgVUM2dO5d9oIiI6kgCIJliJ2NwTF/rcmO5Ccn5qSqYsoRSJ1UwlZqfDjPMNT6fNDk/mJnQ7D+D9KnyM1SEUxXD9raEVT42oZWMIE9/eBm8mnVbiYiIiIjIBQKovXv34pxzzrHe/uqrr1TPJ6mKCgwMxJVXXonPP/+cARQR0WnSu+vUmfVkAEOsy4uNJTiRk6TCKC2Yksvs4twW2+dmsxkFpYVqoKB+j20XEIXuoXHoHtoZ3cPi1G1WUxERERERtfEAKjMzE2FhYdbbK1euxPjx41X4JOT6b7/9dvpbSUREDnnpPa3T92zlFudZQykZhWVFKDebUW4uRzksl2a5bh0V95VXuV2xbtX1TOUm9ZwSgDUmmWooY/mRNeq2VFJ1kzCqIpDqFtIZPh7e/DQQEREREbWlACo0NBSJiYnqenFxMTZs2IAnn3zSrhl5WVnZ6W8lERHVS4CXP/p69UDfyB5NuudkamBhaSHyyywVTzLyKy4Lymyu29zW1i8qKz7l80vItT15txrCDW5oHxCFbmFSJRWH7mGd1RkAWSVFRERERNSKA6jBgwdj/vz5OPvss/H999+rxuNTpkyx3n/kyBG7HlFERNT6pgZK2CWjvrQqKi20SsxLwf6MwziQfgRHc06qSquqpN/V8dwkNf46vFotk/5R3UI6qQopCaWkGkwqp4iIiIiIqJUEULNnz8bkyZMxYsQI1fNj2rRpGDhwoPX+n3/+Wd1HRERUlc5dB39PPzWEBEdjO1n+ZhSXFeNQ1jHsTz+MfSqUOoy8UscNpSTA2pa8Ww2tSqpDYIyattc+MBqBXv4I9PRHoFeAuvTz9GXFFBERERGRKwVQI0eOxNatW7FkyRIEBQXh8ssvt96XkZGBqVOn4oILLmiM7SQiojZEzobXJ6K7GkL+kSM5P00FUlIltT/jiGq4LssdVUlZzhJ4ssaz9AV4+quz7knllhZMVQZVlcvkfg+docl/XiIiIiKi1u60AijRrVs3NRz1h3r99ddP9+mJiIhUaBTtH6HGuM4j1R6RHlKHMhNUGGUJpo4gv4YqKVsSWuUU56qBnFPvXG+DF4I8AyrCKn8EewUi2Duw4jIIwd4B6tLfw1dtJxERERERNUEAJQoLC/Hnn3/i0KFD6naXLl0wadIk+Pj4NMbTUxXmcjMyk0txdH8pBo3l7iGitkmCob6RPdXQgqUk1UfKEkgdzExARlE28ksKVFVUQ0nQJSMpP/WUUwpVKOVlCaRUSGUNqiqvcwogEREREbVFpx1ALViwAHfeeSeys7OtUyHkX4BlSt7cuXNxxRVXNMZ2UoWkhCL88r8kFBdYmvN2629EUFij5IhERC5N/vbEBESpMb7zKLtm53kl+cgpyUNOccUoybW5Lpe51vuN5cYGvb68Tnphphr1CaoifEPRO6IbeoV3Uw3ViYiIiIhao9NKLpYtW4ZrrrkG4eHhePLJJ9GvXz+1fOfOnXj77bfVfREREaoaihpHUJiHNXwSSUeKERTmxd1LRFRL4BPkHajGqcg/pEi1k30oVRFWleQhuzgX2UW5yCrKRlZxboPCKkdB1c/7/1QBWlxQLPpE9kDfiB7oGd4FXnpPvq9ERERE1CqcVgA1Z84cxMbGYtOmTarnk2bmzJm47bbbMGzYMLzwwgsMoBqRt58OwZEGZKWUqdtJh4vRa1hjvgIRUdslIZCPh7ca0m/qVGGV9JzKKspBVnGO5VIbxfaXdQmq5PkOZR1V46e9v0Pn5o6uoZ1VI3YJpLqHxbEhOhERERG1zQBKgqcHH3zQLnzShIWF4aabbsLLL798Oi9BDsTEeVcGUEdKuI+IiFoorPL39FMjFu1qDZYKSguRqaqm7EMqWXY485jDaXsmczn2pR9S4/vdv8HgrlchlIRRfSJ6oGtoJ+jddU38UxIREREROUEAVVZWBj8/vxrv9/f3V+tQ44rp4o34tbnqugRRRfkmVRlFRETOGVRJ43EZjoIqCahSCtIRn7IPu1L3IT51v5rqV1VZuVHdJwNYDE+9J3qFdVFhVN/IHugc1AHu7u7N9FMRERERETVjANW9e3d8//33mDVrVrVTT8t/UC9atEitQ40rJs6+51PSkSLE9as5CCQiIuclfz+j/MLVmNTlTPX382ReMnalWMIoGTLVr6oSYwm2Je9WQ/gYvNE7vJuastchMAYBFdVZMjx0hhb4yYiIiIiIGimAuvHGG3H33XdjxowZeOyxx9C3b1+1PD4+Hs8//zxWrVqFN95443ReghzwDzbAL1iH/CyTun3yEAMoIqLWFEi1D4hWY2q38Sg3l+NYdqKqjpKxJ+2AapReVWFZETYl7lCjKqmWCvDwVWGUbTClrnvIdV+75X4evpzeR0RERETOE0Ddeeedqg/U559/jl9//dXuPvkXXDkLnqxDjS+6sxcOZFn+RTzxUPUvIkRE1Dq4u7mjU3B7Nab3mKTOonck63jFdD0JpA6i1FT7dHeplkqT4aDXVE18Dd7WMMrTzQP+3r4qyPLUecBD76EuPfUeqrpKLbPelkuD/XrqfoO6j9MEiYiIiNom/en+K+2nn36KG264Ad999x0OHz6slnfp0gUXXnghJkyY0FjbSVVEx3nhwBZLAJV+sgSlxeXw8GLvDyKi1k7nrlMNyGXM7DUFRpMRBzMTrP2j9qcfVv2iTldBWZEaQBoakzRTl2BKgq0I3xCE+4Qiwi+s4jIU4b6hCPIKUMEbEREREbUepxVAaSRochQ2rVy5EsuWLcPTTz/dGC9DNqI7e1qvm81AUkIROvb05T4iImpj9Do9eoZ3VePiPueivLwcBWWFyC3JR15JvvUyr7Sg8rp2X2mBupTpe81FwrGyUqM6M2BKflqNIVWYbwgifMNUIBVRMbTrAZ7+1XpPEhEREVEbCKBqIj2gnnvuOQZQTSAo3ABPHzeUFJrV7aTDxQygiIhITXHTejnVlVRR5VcEVCqkKtXCqwLkFOUiqyAbZnfAaDaixFiKElMpSisuS0xl1uvSr6qxQqqkvFQ1HJEpfeE2gZR2GeMfqYaEckRERETkXPhfaC5K/uU3vIMeJ/ZZ+n4kHmq+f70mIqLWRQKbIO9ANaoyGo3Izs5GUFAQ9Pra/7PBWG6yCaYqQyrpUSXBVaksV8tKkF2ch7SCDKQWZKjLjKIs1T+yLuQ5T+QmqVGVTN2L9otQZwJsHxiNDjICYhDlH8HG6kREREQtiAGUCwuPrQygUo6VwFhWDr2BPTOIiKhl6N110Ht4wwfe9X6sVGFJCKUFUqkF6UgtyLRezyrKqdPzSBXWybxkNXDCvndWjF8E2gfGqFBKzjIoIVWUX7i6j4iIiIiaFgMoFxYRW/kfzCajGanHShDTpf7/0U9EROQMVViRfuFqOCJVVOmFFYFUfgbSCuUy3XK7MBM5xbm1Pr+cPfB4bpIaa4/bvK67Hu38IyuqpWKswVSkbxjP2EdERETUiBhAubDACB08vNxQWmyZspB4uIgBFBERtUoeOoO1x5MjxWXFOJGbrKblHc9JrLhMUqFVbYzlRhzNOamGLYPOoEIoqeqSae/ucFeX6rqbO9zlOrTblmWW25b15JZapj2mYl1Pvac6A6C/DE9fdd3P5rosZw8rIiIiao1cKoCSM/ucccYZWL9+PSZNmqTOsNeWubu7IbKTF47vtfR/Sjxc3NKbRERE1CK8DF7oGtpJDVtyhr8TOUnWQOpEbqK6zCzKrvX5ykxlDntMNQcvm5DKzyaYsr1uDa88feGt91KP8dR7qNCLiIiIqFUEUAaDoc6nPq5rM9G6euONNxAfH9+oz+nqojt7WgOopCNFKDeZ4a7jqamJiIiEj8Eb3cPi1LBVUFpoDaUsFVOWYCr7FFP5mkOxsUSNU1Vv1XSGQC2M8qoIpmyHZbl228vhcm+DV0Xg5ecUjdtl+qSclTGnJA85xXnILclTwaKc6THYKwgh3oEI9g5UVWtERETUigKo0aNH1zmAakyHDx/GY489hueffx533313s7++s4ru7GW9XlZiRnpiCSI6VC4jIiKi6nw9fNAjrIsatvJLCnDcWiWVhXKzWf2DmjQ3V5ew3NaWVd62XNfWc7iuuVwFS3mlBciXUVKgzujXmLQzEKKkcZ5Pqqukysq26srfw89umQRBWmAllxJi1fbfirI/isqK7QIluZTbucV5yC7JVZfabdlfdSGvHewdpMIoGSHeWjgVhGAvy+1AL382nSciInKVAGrFihVoCTfffDP69OmDO++8kwGUjYgOntDp3VQTcm0aHgMoIiKihpFgpVd4NzWagzRX18IoLZjKK6kIqCquq+Ul+cgvLbReN5nLm2X7iozFakiz97qSswqqcMomlCorL1PVZbnFlkom6b3V2GTfyDhWpZ+XLQnGgjwDrCFVcEVIJeGUBGnSa8zgboBBp1eXHnIpy3QGeFQsl8b1LfGPsURERK7OJXpAffDBB1i5ciU2bdpU5zPSpKamIi0tzW7ZwYMH1aXRaFTDlcn2m0wmmGFCRKwHkg5b/qnz5MFC9B3t19KbR+TUx42rH/9EzYnHTdOS5uQBBj816koqiKSSyhJSFVqn7JWY5LIUJRW3K5eXWi5rWC5DqrMac8qchE1NNZ1Rmr1LlZUEY/Ul+y6rOEcNZDV8G7SQyhJYVYRU1uDKclvvpkO50QQPDw+4u+scNK+3NK5XyxzcVq3vHdwnAZ/OzfJ8Ond3dV0u3d10asqkLNcudWodnfVS1qm6TPZloCcrw8g58G8OkescNw15PacPoBITE/HAAw/gvvvuw4ABA+r8uHfeeQdPPfWUw/vy8vKQnV1781FnJx+w/Px8dT04Gkg6DOuZ8LKysvgvc0SnOG50upbva0LkCnjcOC8DdAh28wcMMhr+PBLKGM0my/Q9Y6kKdgrKCitGEQqMldfzrcstt6UXkxmn3/PTS+dZUTVlqZjyN1RUUFW5Lff7GrxVuFJWbkSu9IYqtYRdOaWWaXsyhc/2stjUSPMRbUhFlwz5+VsDCbdk/wZ6BqgwKsjDHwFy6elvWeYhl/5qHTa6p6bEvzlErnPcSK7S6gKo2267DWFhYXjiiSfq9bjbb78dl1xySbUKqJkzZ8Lf3x9BQUFwZVraGBgYiM69y7B7dYq6XVJoBkp9ERTp0cJbSOTcx41e7/S//oicAo8bqo1UTkkIYzdtUOtxJVMGK5ZLZZAEGBJqBHj6qUvpx6Rdl0qihghH2CnXkQqvrKIcZBfnILPIUv2UVXFpXVaUowKltkpCxNzSfDWO1/J9QsIneR/V9EWvQAR5Bdpcr5ja6BVgbQivVXtVXteuWaZDVr+/9qmNll5q0lPNpC6l2k6mo0oPNrmU+03llkt1veKy3O4+eax0aKt8TW3bpOrMcmm73Pb+6su1/SK3teo3j4rBsK7++DeHyHWOG8lV6supv4F99dVX+Omnn/DHH3/A29u7Xo+NiIhQwxF5U1rDl09JN+XnaNfFA3LWZa16PuVoGcLb+bT05hE59XHTGn4HEDUXHjdUGw+DB4J8Ap12J/np9fDz8kUHxNRaBSYVVUaTEaVS3WSyjFKT0VLtVO26XGq3LZfWZXK/3DaWorikBHqD5e+NpSm9JUCRSwl97JrcV72tGtnbNLWveKzJbAldtPClvNykKtjKy8vVpSxvKrIN1imMTUgFO1rgY31t2UONe4btpibTHD10HhXTNT0sUzZtAirrbdVvrPI+7VKmUhrLLYGZ3WXFey6Bmvo8VHwWbC8laJN1q94n22R5fkufs8oeZ9pUUpu+ZzbrWLZL641mf932TJuy7un2SGusvzly3MhxKdWdMn1V9rNep2/xYFD9vqnYLm37So1l6r2sehZ57TNf83LbJdWXyWdAfrdZfqcZK6/L77tyo3p9udR+d6n7bX4XGqs8RrbD/myqchZV+7Ot1jgMlWdelc8Ze+m5/n+rNeS1nPYbWElJCWbNmoWzzz4bnTp1svZv0hQVFallkrpFRkaiLfPwdEd4e0+kHiuxTsPre4bz/ocgERERkTORL0JaIOCD+v2jZ23/Ii0tH6Tqvrn/0UPCqKpBleWyMpBQgVW5SU2nVBVhamQjU6sQK8pWl419psa6UF+k1f+7VuBUlexnSyN/tKljqWrwIGfTrB5GyGX15Xo3PQoLCmAoSYYRporedZbedpX97Cp63alpw45vy3UJgR19huREAioUtAZwlde1ANAulKt6XWdQvdckeJbXsoZIFUGSXGrhkmyDurRdx9R2qy01EgJq77nWS0/CQdteenotEK24TwsQLSGp5YQQlqDWfpkKQdXvEdiEdxXhnN2ymkO+qmGeBJja59XbYP+5bozQtS1x2gBKAiZpIv7777+jW7fqZ6JZs2aNWn7ZZZepSqm2LibOuzKAOlT/ppxERERE1DrISXvk/06jNZj1C5kEKNZwSguqqoRUElpJhQSR+syUFavhrKTiR0YRnHcb28r07dbQR08L06xBa5WASqv88laBm5c1wJLrMf4RiAmIQlvitAGUr68vvv32W4f3SW+nfv364fHHH0eHDh2afducUUycF7atsFzPzzYiN7MMASGn+58dRERERNRWyb/q+xi81WhXy5ckCR3ki6TWa8tYXm6dEmSpILDUE2iVCNp1y/1aZYLtujbXKyoQLGf6qxzaGQClMkHO7qct087+p9azWUdd2jyHViFhmQ5pvz2Vl6e433rd8oXaMqVJKlwsl2XapXW5pRLGMl3T/lJdV1M3K6ZwmspUtZqcqVBfse32l3LWQ8vZD23Palh56Xi5bGfV6aKWaVYV00y1bdKml9pcl5+PXJd87g21VhUZaqw20iqTZFKs7RlUrWdYLau4rc7GKreLVQVgW3A6Ydr0HpNx7cCL0JY4bQBlMBhw8cUX13i/9Heq7f62JjrOvlw88VARAygiIiIiapagytfDR432gdHc462UCtlsegOVSrBmtA8j7EdxRRjh+H6prtOu11RBp6b06aTPkIel15DOcunotpcsq1jX0puo8rbWD0rr26aFf9WCN2vgVnN/N7mU6auWabvyGgZ46D2sfb48Ky4ty7Tbsp2W9a3r2d1vUAGhteuZtTl/xU3rcvvbNS+30KakSZAk4WVzks+I48+E7Wem2KannvSequxDZe1VZarhtoNl8hl1JV56T7Q1pxVAyYf4VPMdpXl4x44dMXXqVDz44INtvl9TU/H21SEkygOZyZZ5+omHi9FzWECTvR4REREREbWtChoVqqDxz7Yt1V7STym/pADZOdkIDw6Hr5cPm1W7MKmg8pPh6dtsrymfIwkHNW61hnmVJzlwFOTZ5hzyvHbBqQpVLQFqUVmVQLWsuFrAKlNS7dcpUdWTMk2vrTmtAOraa6/F9u3b1ejRowd69uyplu/Zswf79+/HwIEDERcXh7179+L1119XvZrWr1+P9u3bn9ZGVz0LAFnEdPGyCaBcfz4tERERERG1flL94+PhraZ+uZcA/p6+KsAgqu/nSEZTfD61Ks/GYK6oyGuLrctPqw7vpptuwqFDh7Bo0SIVOsmlDAmcvvvuO3WWunvuuQe7du1St1NTU/Hkk0823tZTtUbkmuzUMhTmcZ42ERERERERkbNwc3OzTMHUN341YasOoB599FEVQp1//vnV7rvgggtw44034pFHHrHevuaaa7B06dLTeUmqYwClTcMjIiIiIiIiInLpAGrz5s3WaXeO9OrVC1u2bLHeHjp0qKqCoqbhF6RHQEhlqWoSp+ERERERERERkasHUF5eXnYBU1WbNm2Ch0dlWVlJSQn8/PxO5yXpFGK6VFZBJR5iBRQRERERERERuXgANW3aNMyfPx/vv/8+TKbKbvPl5eV477338PHHH+Pcc8+1C6Q6d+58eltMdZ6Gl55YgpKiyveFiIiIiIiIiMjlAqiXX34ZnTp1wu23347IyEiMGDFCjYiICPznP/9BbGwsXnzxRbVuUVERUlJS1JnzqGnPhKeRkwUmJ7AKioiIiIiIiIha1mmd2zIqKkpNwZOQ6YcffsCOHTvU8ri4ONx666148MEHERgYqJZ5e3tj2bJljbPVVKPAMAN8/HUozDNZG5F37OXLPUZERERERERErhlAiYCAADz33HNqkHOc0lGm4R3cnq9uJx4qaulNIiIiIiIiIqI27rSm4JFzio6rnIaXcqwYxtLyFt0eIiIiIiIiImrbTrsCShw8eBD79+9HRkYGzNJ4qAr2fWpe7WzOhFdukhCqBO26Vi4jIiIiIiIiInKZACotLQ3XXXcdli5dqm47Cp9kShgDqMYl+3nZhmPw1JswZnBQtftDoj3g6e2OkiJL5VPi4SIGUERERERERETkmgHUrFmzVPh0yy234KyzzkJoaGjjbRk5lJFThLnfbsemPSkI8vPAoJ7tERRg/za6u7shqrMXju4uVLfZB4qIiIiIiIiIXDaAWrJkCW6++Wa8++67jbdFVKtNe1JV+CSy80sxf3E87rtqaLX12sV5WwOo5IRilJvMcNe5ce8SERERERERkWs1ITcajRgyZEjjbQ2d0tkjYjG4R4T19ootJ7F2Z1K19aK7VDYiLys1I+1kCfcuEREREREREbleADV8+HDs3bu38baGTkl6at156UD4eFUWr72zcDty8u0Dpoj2XtAbKiueOA2PiIiIiIiIiFwygHrhhRfw6aefYv369Y23RXRKYUHeuPn8Ptbb2fklePe7HXZN4HV6N0R2rKyCSjxczD1LRERERERERK7XA0p6P3Xo0AGjR4/GqFGjEBcXB51OV61iZ/78+ae7nVTFuEHtsHLLcWw9kKlur96RiFXbTmLsoPbWdWK6eOHkwSLrmfDM5Wa4ubMPFBERERERERG5UAD18ccfW6+vXr1ajaoYQDUN2a/XTeuCgyfzkFdYppZJFVTfLmEICbBUPsXEeQPIUtdLCsuRmVKK0GjPJtoiIiIiIiIiIqImmIJXXl5+ymEymU7nJagWgb4euPWCftbb+UVlmPvtNutUvKiOXnC3eYc5DY+IiIiIiIiIXC6AopY3un80xg5sZ729cXcK/tx4TF03eLojvENlxZNMwyMiIiIiIiIiam4MoFxQWU4qsld9A3NZqbp964X9EexfGTR98OMupGYV2kzDqzwTnm2jciIiIiIiIiIip+sBdeONN6reQ/PmzVPNxuX2qbAHVOPL+P1DFO7fCLedK+A15V8I6DEcd1wyEM98aDkbYWGxEf/39TY8fesoFUBtXZ6tlhfkmJCXaURAqKEJtoqIiIiIiIiIqBECKGk6LoGSnP1OAijbJuQ1YQDVuAr2b1ThkzDnZSBt4Yso6DECg8+6AZOGdcCfG4+r+7YdSMNvaxMwcWAsICe+qyh8OnmoiAEUERERERERETnvFDytqbiHh4f1NpuQNy99YDg823W3W1a4bz2Ov38XroxJQESg5b0RHy6OR1ZhMUKjK5clHS5u1u0lIiIiIiIiImIPKBfjGdkJMdc9h5AptwCePtbl5rIS5K/6Av8N/gVx+hS1rKTUhDe/3orozl7W9U6yETkRERERERERNTMGUC7Izc0dfgMnwe+yp+Hbb7zdfbrcJNwVsBRX+q6Gr1sx4g9nILE013p/TloZCnKNLbDVRERERERERNRW1asHlCMFBQX48ssvsX//fmRkZFQ7yxp7QDUdd29/BJ1zGwIHTUbab/NQlnbMet8Iz0PoaziOxUWDsXibAaPR3W4aXteBfk24ZUREREREREREjRRAbd68Geeccw7S09OrBU8aBlBNz6tDL7S/6WXkbPwFWSu/gbnM0ufJ170Ul/uuw0jjQezMvxM6o5e1ETkDKCIiIiIiIiJyiSl49957L4qLi7FgwQIVQjlqSC5Ny6npuen0CBp5Pjr8+0349Bhhd18nfTpidbust5PYB4qIiIiIiIiIXCWA2rhxowqhLrvsMoSEhDTeVlGD6QPCEHXxg4i67BHoAiOsy0MNh63X0xNLUFzIPlBERERERERE5AIBlI+PDyIjIxtva6jR+HQdgg63vgFT33NhNLsj2HDE5l437P7kY5RlJnKPExEREREREZFzB1DTpk3DypUrG29rqFG5GzzR7fwbsb33HThu8oCne471vuTjwPF59yDz769QbizlniciIiIiIiIi5wygXnnlFWzatAkvvfQSSksbN8SQnlI33ngjBgwYgNDQUHh5eaFz5864/PLLsWXLlkZ9rdZu5owz8ZvfRchBvnVZZllnwGRE9j/f4sS8e1B4aGuLbiMRERERERERtV6ndRa8UaNGoaCgAA8//DBmz56NmJgY6HS6amfBO3ToUL2fOzs7G3v37sXkyZPRsWNH+Pr6IiEhAR9//DFGjBiBn3/+GVOmTDmdzW8zDHod7r5iMF5/fSd6VizLMbaHyWyAzq0MxqxkJH/1LHx7jkLw+CvhERrTwltMRERERERERK3JaQVQsbGxKmBqCl27dsWaNWuqLb/tttvU67744osMoOqhS/sgjDgjAjn/WG6bocNJ98GINa+3rlOwd60a3p0HIGDIVPh0GwI3d/tAkYiIiIiIiIioWQOoFStWoLlJ03Nvb29VIUX1c/mMrnhnzSHoyi0zL39MH41/T+0Kz50/oLy4wLpe0ZHtasgZ9fwHn42AgZOh8w3k7iYiIiIiIiKi5u8B1RzKyspUP6jk5GRs2LABV155JfLz8zF9+vSW3jSXYzDoEBPnZb0dZPbFq5sCEH7D6/AfOBnQ2eeRxtx0ZK1YgKP/dwtSf3wTxSf2wWw2t8CWExEREREREVGbrYBqDqtXr8aECROstwMDA/HQQw/h8ccfr/VxqampSEtLs1t28OBBdWk0GtVwZbL9JpOp3j9H5x6+SDloaRgfCB9sTi/Ap8uP4+bzb0bAmMuQv+Mv5G/9A6bc9MoHmYzI37VSDUNkJ/gPmgKf3qPVWfaI2sJxQ9SW8bgh4rFDxL85RM7L2ELfcRryevUKoOQsdO7u7qo5uMFgQFxc3Ckf09Am5Bo5C94ff/yBkpIS7N+/H5999hny8vLUbb2+5s1/55138NRTTzm8Tx7v6lP45AMmlWCiauP32viHV35I9HBHALzx65qj6NPRD707BQE9x8On+1gYj+1EafwKmE7stnt8WUoCMpe8j8zln8Gjxxnw6D0O7oERjfiTETnfcUPUlvG4IeKxQ8S/OUTOy9RC33EkV2nSAErORieBktZ4vCmbkGuCg4PVmfDEueeei+uvv16FUocPH8Zvv/1W4+Nuv/12XHLJJdUqoGbOnAl/f38EBQXBlWlpo1SE1RbEVeXvb4beUABjmWUqXTB8kIMifPzbYbxxzxj4eBksK4aMAwaOQ1lmoqqIyt+5AuaSwsonKilE6Y5lanh1Hqh6RXnFDYKbu9PP6qQ2rKHHDVFbxuOGiMcOEf/mEDkvYwt9x5Fcpb70p9N0vCWakEsgNWPGDLz99ttISEhAp06dHK4XERGhhiPyprSGL5+Sbtb3Z5FVozt74fj+InU72OyDBLcMpGUX4eNf9mLWZYPs14+IhfeUmxA64Srkx69C7qYlKE1NsFun+Mg2NfSBEQgYMgX+AyZC5xPQSD8lUcsfN0RtHY8bIh47RPybQ+S8dC3wHachr+WS5SpFRZbwJCsrq6U3xSVFx3lbr4e5+wEVfcX/2HAMm/akOHyMu4cXAgadhXb/egUx1z0Hvz5jAPcqTctzUpH512c49tYtSF38fyg+eaBpfxAiIiIiIiIicglOWwKQkpKCyMjIasul6umHH35Q5WW9evVqkW1zdTFdKs+E517ujlBPH2SUWabXvbZgC169ayyiw3wdPlamXHq176lGyOTrkbdtGXK3/A5TXoZ1HbOpDPk7VqhhCO+g1vWM6QavmG4whLWDmzt77xARERERERG1JacdQB05cgSvvfYa1q9fj8zMTJSXlzdKE/I5c+ao5uPnnHOOmmYnz7Nnzx58+umnqsHWJ598Ai+vyiCF6i4q1guSAZWbLLen9emCz7ftVNfzCkvx1P/W4ZVZY+Dn41Hr8+j9ghB85sUIOuMCFO7fhNzNv6EowfI8mrK042rkbf1D3Xbz8IJndBdrICWX+oBQvn1ERERERERErdhpBVDx8fEYPXq0mhLXo0cP1Ri8d+/eyMjIUBVMXbp0Qfv27Rv03NOnT8fJkyexcOFCpKamqsZa0dHRavldd92F4cOHn86mt2l6D3dEdPBCckKxuh1Y7o1Jwzrgz43H1e2TafmY88lGPHnzKBj0p56lKRVNvj1HqFGafgK5m5cir2rT8grm0mIUH41XI6dimc4vBJ4xXS2hVLtuKqBy9/Rp5J+aiIiIiIiIiFwygHriiSdU46lt27YhPDxcNf1+6623MHHiRLz33nt47LHHsHjx4gY9t5z5Tjv7HTW+mLjKACrxcDFuf3QAUjILseuQZSrdjoPpeGfhdsy6bGC9znToEdYeYVNuQsiEK1Gwdz2KT+xFyckDKE07Bpjtq+M0pvxMFO7foIaFm5qqZ6mSkmCqOzwiYuGmc9oZo0RERERERERUi9P6Rr9q1Sr861//Ur2YpOpJmM2Wjtb//ve/1f0PP/wwFi1adDovQ00gpos3tvyVra4X5ppQmG3CI9cPxwNvrcTJtAK1fNnGY4gJ98Ulk7rX+/ndPbzh33+8GqK8tBglyYdRkngQJYkH1DDmpNXwaDPK0k+okb9juVripveAR2RneLbrBp+ug+HdsS97SRERERERERG1hQAqOzsb3bp1U9c9PCz9ggoLK6ddnXnmmaoKipxPdGcvKTSyngFPqqB6jwjA4/8aifvfXIm8wjK1/NNf96iG5GcOaHdarydn0fOO7a2GxpifXRFGHURJkuWyvNgSflVlNpai5OQ+NXI3/AxdQBj8+41TAZchJOa0to2IiIiIiIiInDiAkil36enp6rq/vz+8vb1VU3KNhFElJSWnv5XU6Dy9dQiL8UD6yVJ1O/FwkQqgYsL8MPuGEXj0vTUwmixT5l5fsAXhQd7o0TGkUbdBmpjruw+Db/dh6rbZXI6yzGSUJO6vqJQ6iJKUI4DJWO2xptx0ZK/+Tg3P9j3g338C/HqdAXcvx2fvIyIiIiIiIiIXDaD69OmD7du3W2+PGjUK7777Ls4//3x1Nrx58+appuTknGLivK0BVNJhSz8o0ScuVPV+em3BFnW71FiOZz/cgFfuGovIkKZrDu7m5g6P0Bg1/PtZpu6ZjWUoSUmwTtsrOrIDpgLL1EFNyYl9amT8/iF8e4yAX/8J8O7EKXpERERERERErSKAkqDp5ZdfVmfBk+qnxx9/XDUOj4uLU/dL8+off/yxsbaVmiCA2rHKci66nPQy5OcY4Rdo+UhMGNIBiWkF+OqPfep2dn4Jnp6/Di/dMQa+3oZmey/c9AZ1ZjwZwlxuQtHhbcjbsRwF+zfaVUfJNL38+FVq6PxD1RQ9CaMk0CIiIiIiIiIiFw2gbrvtNjU0Y8eOxbp16/DFF19Ap9PhwgsvxMiRIxtjO6kJxHTxsruddLgI3Qb5W29fOaUHEtPzsXLrSXX7WHIeXvx0o+oTpde5t8h74uaug0/XIWqYivKQH79aNSovSTpot54pLwPZa75Xg1P0iIiIiIiIiFw0gDKZTDh58iT8/PwQElLZG2jw4MFqkPPz8dcjKNyA7DRLw/HEQ8V2AZRUsN112SCkZRVhT0KmWrZ1fxreX7QTt1/UX93fknTe/ggcOlWN0rRjyNuxAvk7/+YUPSIiIiIiIiIn0+AylrKyMnTu3Bn/+9//GneLqFnFdPG2XpdG5FV5GHSYfcNwRIVW9n5asjYBP648BGfiER6L0EnXInbWPERd9gh8e40CdPb5qjZFL/nLp3Fs7m3IXP4FSjMSW2ybiYiIiIiIiNqKBldAeXl5qconOfsdua6YOC/sXperrmcklaK4wAQvX53dOoF+nnj8ppF44P9WoaDIUi314eJ4RIX6YmTfaDiTBk3Ri+kG70794NmuO7za94DOJ6DFtp+IiIiIiIioNTqtHlCTJk3CX3/9ZdcHily3AkokHSlG576+1dbrEOmPh68bhifmrYWp3AyzGXjli8144fYz0bVDEJxRnafoVZxhT2MIiYZnux7watdd9Y/yCO+ggi0iIiIiIiIiapjT6iT90ksvYcOGDZg9ezZycixnUyPX4h+sh1+QvtZpeJoB3cLxn4sHWG+XlJrwzIfrkJ5d82NcaYqepiwzCfk7VyB9yTyc/N99SHj1WiR98SQyV3yJwoObVWUVERERERERETVhBdSnn36qznbXqVMnjB8/HkVFRXjhhRfUCA8Ph49PZa8gIY2qDx1yrn5BZP/+yDS8/Vvy1e0T+4tgNptrbDB+1oiOSEwvwMK/LBVDmbkleHr+OrzwnzPh42Vw+l1bdYpewe7VKErYheIT+2DKtzRar8pcWoyihJ1qaAyhMfBs1xNe7bvDq10PGMLbw82tZc4MSERERERERNTqAqgbbrgBn332mQqgYmNjW/xMaNQ40/C0ACrtZAn2bMhD7xE190G6ZlovJKUXYPUOSwPvI4m5ePnzzXj0xhHQubvO50Gm6AUMmaqGhG6m3HQUn9yvwqgSGSlHgHKTw8eWZSSqkb/jL3XbzdMHXjHd1JQ9NXUvpqt6fiIiIiIiIiJqQAAlX9Q1K1as4D5sBboN8sOGpZkozLWELasWpaF9N28EhDiuaHJ3d8M9Vw5GWnYh9h+z9FPatCcF83/ahVtm9oMrkiBVHxgOPxm9R6tl5WUlKE0+rAIpCaYklKraP0pjLilE0ZHtamh0/iHwCO8Ij8iOagqgR0RHeIS1g5vO+SvFiIiIiIiIiJymCTm1Dp7eOky8NAI//y9J3S4rMeOvr1Jx/r9j4FZDRZOnQacqnu5/cyVSsyw9oBavOoyYMF9MPzMOrYG7wRNeHXqpoYWvxpxUlJzYj+KT+1QwVZqSAJjLHT7elJeJIhmHt9o8qQ6G0HbwiIiFpwRSElBFxEIXEMZqQiIiIiIiImq1GECR0qmPr5p2t3t9rrp94kARdq7JQf8zaz7DXbC/Fx6/aSQenLsKhcVGteyDH3YiKtQXQ3tFtro9K1VShqBINfz6jlHLykuLUZJ0CCUVgZRUSpUXWvahQ+UmlKUdU6Mg/h/rYndPH0uFlAqlYq1VU7KciIiIiIiIqE0GUPPmzcOyZcvq/KV9/vz5DXkZamZnzgzD8f2FyMuyhElrFmcgtocPgsI9anxMx+gAPHTNMDw1fx3Ky80oNwMvfbYRL94xBp1jAtHauXt4wbtjHzWsVVLZKShNOYrS1KMoTbNclmUmy701Pk95SSGKj+9Rw5Y+MEJVSEkYpQ+OgiEwXE0V1EvFlJ5T+YiIiIiIiKgVB1ArV65Uoy4YQLkODy93TLoiAj+8Y2kubiw1Y9mCVFx4ZzvV96kmg3tG4N8X9MM73+1Qt4tKTHh6/nq8etdYhAR4oS1RVVISFAVHwbfnCOty1U8q7bgllFLB1DF1WWu1lLwHOalqFB7YVO0+nV+wJYyyhlLhlQFVYDjcPb2b5GckIiIiIiIiapYAavbs2Zg8eXJDHkpOrn03H/QfE4gdq3LU7eSEYmxbkY3BE4Nrfdy0MzojMb0AP/x9SN1Ozy7CM/PXYc7tZ8LLkzM9VT+pmK5qaNSZ9wqyUZp6rLJaKuUoytJPwGwqO+V7ZcrPUqPk5H6H97t7+VWEUWF2QZUhoCKg8glg3ykiIiIiIiJqFg1KBnr16oVx48Y1/taQUxg1PRRH9xYiJ80Sgqz7NQMde/kgNNqz1sddP70PktILsD5eppsBB0/k4LUvt+Cha4ZCp3Nvlm13uTPvSRWTXzB84gZYl5ulT1RmUmW1lARU6cdhzE0HTJbpkXVRXpyPUhkpRxzer/MPRejEa6z9rIiIiIiIiIiaCktTqBqDhzsmXxGB7//vJMxm1TdbTcW7+O720Olqnoqnc3fD/VcNwUNv/4PDJy0VVGt3JuGeN/7GbRcOQK/OIdzbdeDmroNHWHs10Hu0dbnZXA5Tfg6MuWkw5tiPMjVVLw3m0uI672NTXgZSf3wDRcd3I/SsG+Cur7nXFxEREREREdHpYABFDkV39sagCUHY8le2up12ogSb/8jC8Km1h0gy3e7xm0bgvjdXIiPHEoYcScxVZ8qbPCwW10/vjUC/2iupyDE3N3fo/YPVQLvu1e6XKX1S9VQ9nJLLdBVcOeo5lbfld5QkHkTkhfep3lVEREREREREjY0BFNVoxLRQJOwuRGZyqbq96Y9MdOrjg4gOtTcWDw30xlO3jMILn2zEidR86/JlG49h3a4kXHtOL5w9spOqmKLGndKn8/ZXwzMqzuE65aXFaipf8Yl9yFj2McwlhWp5afJhnJz/AMLPuxO+PYbzbSEiIiIiIqJGVe/GPOXl5bjyyisbdyvIKen0bph8ZQTcKz4l5eWWqXjGsvJTPrZjVADeum8Crju3Nzw9dNbl+UVl6mx597+1EgeOZzXl5pMD7h5eampfwMBJaH/jS/CI6GS9r7ykECkLX0TGn5/AXI9eU0RERERERESnws7QVCupdhp6duW0O6mG2rAks057zaB3x8UTu+GdBydiVL9ou/sOHs9W0/TeWbgdeYWWCitqXoaQaMRc/zz8B9qf0TJn3U9I+uJJGHMz+JYQERERERFRo2AARac0ZHIwwttX9m3asjwbSUeK6rznIoJ98Mj1w/HEv0YiOtTXulwanP+2NgH/fuFPLNtwFOXlZr4bzczd4Inwc29D+Iw74WaofI+Lj+/Bifn3o/Dwdr4nREREREREdNoYQNEpyZnv1FQ8bSad2TIVr6zk1FPxbA3tFYm5D0zAlVN6quooTW5BKd78ehv++/Y/OJJoOXseNS//fuPR7oYXYAhtZ10mDcuTv3wGWSu/gVlOhUhERERERETUQAygqE5Coz0x8pxQ6+2c9DKs+bn+U7Q8DDpccXYPNS1PAilbexIycffrf+ODH3aisLiM70wz8wiPRbsbX4RvnzNtlpqRteprJH/1HEwFDAeJiIiIiIioYRhAUZ0NHB+EqE6VZ8Db+U8OThywnEWtvqJCffH4TSMw+4bhiAj2ti6XaXg/rTqspuWt2HICZpmnR83G3cMbEeffjbCpN0sXeuvyoiPb1ZS84uN7+W4QERERERFRvTGAorp/WNwtU/H0Hm7WZX9+mYrS4vpNxdO4ublhZN9ovP3gRFwyqRv0usrnzcorwatfbMaj763BseRcvkvNSN6XgCFT0e7a56APjLAuN+VlIvGzx5C97icGg0RERERERFQvDKCoXoLCPXDGeZVT8fKyjPjnh/TT2oteHnpce05vvHXfBAzoFmZ3346D6Zj16gp8/HM8ikqMfLeakWdMV7S76WX4dBtWudBcjsw/P0HKwpdgKi7g+0FERERERER1wgCK6q3fGYFo361y2tzu9blIiD/9MKJDpD+eufUMPHj1UIQEVE71M5Wb8d3yg7j9pb+wekciq2+akc7bD5GXPISQidcAbpW/Lgr3b8DJ+Q+gJOlwc24OERERERERuSgGUFRvbu5umHRFBAyelVPm/vo6FUUFpkaZ/jVmUDu8+9BEzBzXRU3706RnF+GFTzbinjf+xtfL9uFoci7DqGYg70nQqJmIvvop6PyCrcuN2SlI/OQR5G75ne8DERERERERuWYAdeDAATz55JMYPXo0oqKi4Ovri969e2PWrFlISkpq6c1r8/yDDRhzQbh1PxTmmbDy+7RG2y8+XgbcNKMv3rx3PPrEVU75E4dO5ODz3/bijpeX49YX/sSHi+Ox+0iGqpSipuMd2xvt//UqvDv1sy4zm8qQ/tv7SPvpLZSXFnH3ExERERERkUOVp7lyMvPnz8fcuXNx3nnn4dJLL4W3tzfWrVuHd955B59//jnWrFmDnj17tvRmtmm9hvvj8I58JOy2nAnvwJZ8dOmXj64D/RrtNTpFB2DO7aOxfPNxfLR4N7LzS+zuT0ovwKIVB9UI8vPE8D5RGNk3CgO6hcPDoGu07SALnW8goq54DFmrvkH2P99JBKWW5+9aiZLkwwiZcDXcDB7W3eWGygo2uNlcr6rafZbb7h5e8IjsBDd3vpdERERERESuzM3spOe537RpE7p27YqgoCC75fPmzcOtt96KSy65BN988029njM+Ph59+/bFrl270KdPH7gyo9GI7OxstX/0+pbLEQtyjFjw0jGUFFrOhOfl644rH4qFj3/jb1NhcRlWbUvE+vgkbNufhjJjzWff8/LQYXDPCHWWvWG9IuHnUxmKUCO9H4e2IlUqnwqb9iyF+qBIBI2+EP79xsFNZ2gVxw2RK+FxQ8Rjh4h/c4icl7GFvuM0JF9x2gCqJrm5uQgMDESPHj2wd+/eej2WAVTTOLA1D0s/TbHe7tzXF+fcGKV6BzUVOSPeln2pWLcrCRt3p6CgqKzGdaWPVL8uoSqMGtEnGuHBlQ3U6fQYczOQsuhVlJzY1+S7Uh8QhqAzLoT/gIlw0zcsiOIXaSIeN0TNhX9ziHjcELXmvzfxDQigXK4E4OTJk+oyMjKypTeFKnQb5I9DOwpwcFu+un1kVwH2bcpDz2EBTbaPvD31GN0/Rg2jqRzxhzJUGCUjPafYbt3ycjO2H0hX4/1FO9G1faAljOobjY5R/k0alLV2+oBQxFz9NDL//hK5m5bAXGa/7xuTMTcd6UvmIWv1QgSNugD+AyfB3eDZZK9HREREREREjcflAqjHHntMXd5www21rpeamoq0NPum2AcPHrQmhDJcmWy/yWRymp9j9MxgnDxYiKJ8y7Q4aUge1dkDfkHN8xHrExesxo3n9cLhk7lYH5+M9btTcCw5r9q6B0/kqPH5kr2IDPHBiD6RmDCkveo3RQ0TOPYKBJxxMczGyh5d9rWVNjdqK7q0u8+M4mO7kbvmO5Sln7AuNeVlIuP3+cha/R0CRsyA38DJdQ6inO24IXIFPG6IeOwQ8W8OkfMyttB3nIa8nktNwXv++ecxe/ZszJw5E99//32tlStyBr2nnnrK4X2rVq1y+Qbm8gHLz8+Hn58fdDrnaNB8cn8ZVn1bYL0d2VmP8Vf4tmiFUWpWEbYeyMSW/Zk4cDzXNgappnO0H8YOiMSI3mGqwoqcg9lcDuORbSjZ/AvKMyuDKI2btz88+p8Fjz7j4GbwcrnjhsjZ8bgh4rFDxL85RM7L1ELfcaQl0pgxY1pnD6g333wTd999N8aPH49ffvkFPj4+DaqAkvBq27ZtraIJeU5OjuqH5UzNlP/6Kg37N1eGUGMuDEGfUc5RWSRn0Nu0JxUb4pOx7UB6jU3MPQ06jO4fjcnDO6Bnx2BO0XOiIKro4GbkrP4OZSlHqt3v7u0P/2Hnwn/wFLh7+rjUcUPkzHjcEPHYIeLfHCLnZWyh7zjSA2rgwIGtrwfUa6+9hvvuuw+TJk3CTz/9dMrwSURERKjhiLwpreHLp6SbzvazjLsoAomHjiM/21KOt+7nLASHeyG256nfs6YWFqTH1FGd1ZAm5lv3pWLtziSs3ZWEklKTdb2SMhP+2nxCjfYRfjh7REdMGNIBQf7sN9TSDL1Gwb/nSBQd2oKsVd+iJPGA9b7yojzkrPwKeRsWI3DYdAQMOwc6bz+XOG6InB2PGyIeO0T8m0PkvHQt8B2nIa/l9N/AXnzxRfz3v//F1KlTsWjRInh51T7FhlqWp7cOEy+LwE/vJ6rbZaVmdb1Tbx+MPj8MwREeTvEWyRS7M/rHqFFYXIaVW0/i9/VHceB4tt16J1Lz8eHieHz6624M7xOlwqiB3SOgc2fj8pYiUzp9ug6Bd5fBKDqyA1mrvkHJicozYpYXFyBr1dfIliBq6DkIHD4dOh//FtteIiIiIiIicvIASuv5NH36dCxcuBCenqxAcQVS7dR/TCB2rMqxLkvYXYhj+46h/5ggDDs7WAVVzsLHy4CpozqpcSQxRwVRKzafQH5RmXUdo8mMNTuS1AgL8sZZw2MxeVgsIkJavrKrTQdRcQPg3bk/io/uQtY/36L4aLz1fnNJIbJXL0TOxp8RMGQqgkbMADx9W3SbiYiIiIiI2iqn7QH19ttv44477kBkZCTmzJkDg8Fgd7802JJ+TvWdo9i3b996zVF05nme2dnZCAoKcsqpROZyM3b8k4MNSzNRUmjfa8nL1x0jzwlF75EBcHfSSqLSMpOanidh1I6D6Q7Xkd7qA7uF4+yRHTGiTxQMeucJ1dqqomO7kf3PQhQd2V7tPjeDpzpjnrn7GAS361TtdwoRuebfGyJnxWOHiMcNUWv+exPfgHzFaQOo66+/Hp988kmN93fs2BEJCQn1ek4GUM2vqMCEDUsysWtNDsxVen6HRntgzAVhaN/NuauIktILsGzjMSzbcAyZucUO1wnw9cDEoR1UZVRslHM0XW/Lik/sUxVRRYe2OrzfzcML+oAw6APCoQ8IhT6w4lKWyXX/ULjpGVARCX6JJmoYHjtEPG6ImoORAZRzYgDVcjKSSvDPj+k4vq+o2n1x/XwxekYYAsOc+wu/yVSOzftS8fu6o9i4JwXl5Y6zWzlz3sRhsRjeOxKhgd7Nvp1UqSTxILL+WYjCAxvrvVt0vkEqkNJZA6owu6HzC4Sbmzt3N7V6/BJNxGOHiH9ziJyX0YUCKNbSU7MIjfbEjFtjcHR3oQqistMq+ysd3lmAhN0FGDguCEPPCoGHl3N+qdfp3DG8d5QaUgn116bjaoqeVEjZ2ns0S413JFyLCcTQ3pEY1jsS3ToEs3l5M/OM6YqoS/+LkuTDliBq3/o6P9ZUkK0Gkg46XsFdX1E9FaaaogcMnQZ3vXM02SciIiIiInI2TjsFrymwAso5mIxm7KzoD1VabD8vz9tPh5HnhqDXcOftD2VLDp9dhzPwx/qjWL09EaXGKvMMq0zTG9orUo1BPSLg5+3cFV+tUXFGErIS9sKrvAjm/CwYc9NhzEmDMTcDxrwMoNzU4OfWB0Ui9Owb4dttaKNuM1FLYwUUEY8dIv7NIXJeRlZAEdVMp3fDwPFB6DHUH+t/y0D82lxoMWhRvgnLv07Drn9yceYFYWjXxdvpz8TWr0uYGrdc0B9/bzmB5ZuOY//xLOvPpMktKFVVUzIkXOvdOQTDekWp6qj2EX7quahpyVQ6Q0cD/B2Up5rLTTAV5NiHUrlpMOakW6+XF+bW+NzG7BSkfDMH3l0GI+zsG2AIieHbSUREREREVIFT8KjFSLXT+Esi0Hd0IP75IR0nDlT2h0o7WYJFc0+iywBLf6iAEOevFpKKpnNHd1YjK68Ym/ekYtOeFGzdn4rCYqPdutI/atehDDU++jkekSE+KoiSQKpvl1B4GHhGvebm5q6D3j9EDbTr7nCd8rISmPIyKkIpyyjYvwmlyYes6xQd2oLj83YgaMQMBI2+CO4eXs34UxARERERETknBlDU4sJiPHH+bTE4sqsAq3/KQE56ZX+oQ9sLkBBfqCqmhkwOhoenc/aHqirY3wuTh8eqUWYsx+4jGSqM2rg7BSfT8qutn5JZiJ//OaKGl4cOA7qFq0BKpuuxkbnzcDd4wj0kxq66KejMi5G37S9kLv8c5UV5loUmI7LXfI+8nX8jdPJ18O11BivciIiIiIioTWMARU5Bpp/F9fNDx16+2L4yGxt/z0RZidnaM2rzsizsWZ+LUdND0XOoP9xcoD+UxqB3V4GSjJtm9EViej427baEUbsOp8Nosp+rV1xqwvr4ZDVEXLtADOsViQHdw9EjNpjVUU5GzoQXMGgyfHuOQNbfXyF3y+8yn0/dJ9VSqYteg9eW3xE25SZ4hMe29OYSERERERG1CDYhd1GtvSlsYZ4R637NxO71uUCVXkrBkQb0PSMQPYf5w9PbtaeqFRaXYfuBNBVGSYVUVl7JKcOs7rHBappe37hQ9OwYAi/P1vf+u/JxU5KSgIyl/0Px8T32d0hQNewchIy5FO5evk3y2kRNobX/vSFqKjx2iHjcEDUHI5uQE50eH389Jl4WgX6jA7HqhzQkHiq23peVUoZVi9Kx9pcMdB/sr9YJb+/pkrvcx8uAUf1i1JC+UIdP5mDj7mRs3JOCA8ezq60v0/niD2eo8bU0dHd3Q7cOQejbJUyFUr06hajnpJbjGdkJ0dc8g/z4Vcj881OY8rMsd5jLkbvhZxTEr0LIxGvg12+cqp4iIiIiIiJqC/hPmeTUJFi64D/tVC+o1YvTkZdZ2czbWGrG7nW5akR29FRVUd0G+kHv4Zpf6uXMeF07BKlxxZSeyMotxua9KSqM2r4/DQVVGpkLU7kZe49mqbHwrwPqObq0C7QEUnGh6B0XqpqjU/NPKfXvOxa+3YYh659vkbPhZ6DcZHnPCnKQtniumqoXNuVf8IzuwreHiIiIiIhaPQZQ5BJf5rsO9EPnvr44sDUPu9bkIjmhsiJKpBwtQcrRVPzzYzp6DQ9A3zMCEBTuAVcWHCCNzDuqIUFTQmKOqnzaJeNQBvIKS6s9RqqopHJKxqIVB+HmBnSOlkAqVI0+cWEI8HXt/eJK3D29ETrpWvgPmIiM3z9E0ZHt1vtKTu7HyQ8fgv+gsxAy/krofPxbdFuJiIiIiIiaEgMochk6vRt6DgtQI/1kCXauzsH+zXkoK61sElVSWI5tK7LV6NDdG31HB6JzH1+461ynabkjMtWuS/sgNWaM7aKCpuMpedh1KB07ZUreoQxk51fvH2U2A4cTc9T4adVhtaxjlL+qkJKm6MN7R0Knc82KMVfiEdYeUVc8hsJ9G5Cx7CMYc9Iq7jEjb+vvKNizBiHjr1BhlJu7a/c1IyIiIiIicoQBFLmksHaemHBpBEbPCMO+TXnYuSYHmUn2FUHH9xep4RuoQ5+Rgeg9MgB+Qa3jIy9T7TpGB6hx7plxMJvNOJGab6mQOiRVUunIyLGvEtMcTc5T45fVR9ApOgA3z+yL/l3Dm/1naIuVfHKmPO8uA5G9ZhFy1v4As6lM3VdenI/0JR8gd+syNS3Pq0PPlt5cIiIiIiKiRsWz4LkonlnFngQwSUeKsWt1Dg5uz9fa7diRfs8yja/fGYFo380bbu6uXRV1qv2RnFGoKqQsU/bSkZpVVOP6Z/SPxg3T+yAqtHWfnc2ZjpuyrGRkLPsYhfs3VrvPr+9YNaQ/lM4noEW2j8gZjxsiV8Jjh4jHDVFr/nsTHx+Pvn37YteuXejTp0+dHsP/kqRWU10SE+etxpkzjdizIQ/xa3KQa9O03FwOHN5RoEZguEH1ieo1LABevrpWuT+iw3zVOGtER7UsNbPQGkZt3Z+G9OzKQGrNjiRs3J2CC8d3xcUTu8HLk78ampohOApRl/wXhQe3IOOPD1GWmWS9L3/XSjWEPjBCBVGe0XHwkMuoLtB5+zX59hERERERETUmfsukVsfHX48hk4IxeEIQju0rxM5/cpCwp1Da7VjlpJVh9Y8ZWPdrJroO8FNnz5OqKFc9g15dRIT4YKKMoR1QZizHL6sP48vf96Gw4ux6suzrZfuxbOMxXD+9D8YNaqeCLGpaPl0Hw7tTP3WmvKx/FsJcZj910piTqkbB3rXWZfrgqIpQSgKpODXcvVp39RoREREREbk2BlDUaskUu469fNXIzSxD/Npc7F6Xi6L8yvl5pjKz6iElQ+/hhtgePujUxxedevuoIKu1MujdMXNcV4wb3B6f/7YXf2w4qhqWC+kd9eoXm/Hr6iOqP1S3DsEtvbmtnpvegKAzLlDT7jL//hKFBzahvCivxvWNWclqFOxebV1mCIlRgZSHNZjqDHcP72b6CYiIiIiIiGrXer9hE9kICDFg1LmhGD4lBId35qsz6CUeqlJpUmrG4Z0FasANiOropc6g16mPD0KiPFplNVCwvxfuvHQgpp3RCR/8sBO7j2Ra79uTkIn73lyJycNicc20XggO8GrRbW0L9AGhiDjvDtXDS86UV5J0CKXJh9SljPLighofW5aZqAbiV1UscYMhrF1lpVS7Hmoan5s0QyMiIiIiImpmDKCoTdHp3dBtkL8amcmliF+bg0M7CpCfXdkrSjEDyQnFaqz9JQMBoXoVRkkT8+g4b+h0rSuM6to+CC/850ys2nYSHy2OR3rFGfSkKuqPDcfwz/ZEXH5WD5w3Jk5VT1HTkrDTEBShBnqNqngvzDBmp1SEUQctl8lHYC4prOFZzChLP6FG/s6/1RKdbyC8uwyGT9ch8IkbAHdPH76VRERERETULBhAUZslVU1jLgjHmTPDkH6yFEfiC3BkVwHSTpRUWzc3w4jtK3PU8PR2R8delql6cunprWs1ocfYQe0xvHcUvlt+EN8vP4BSY7m6r6jE+P/t3QmUZGV5N/B/7dXVVb1Md083s+8zMDMMoCyyDqKoRIFESKKfihoFw4kaTTR6YlSOHgIu0QSXGEPiGvN9qFFDjAoIyCagwADDwDD73vtSe9Wtut95nntvLd3VPd09Xd1V3f/fnPfcW7fudNdU153u+vfzPi/+/e6d+OVvD+DPrtmCc0/vnJcVYTUfSrV26QifcZEeM808sgMnkBkdSo3qI+XIxYcRe/Z+HXB7EFxxhvagCq17JfxtS2b5X0RERERERAsJAyha8OSNfceygA6ZoifVUAckjNoZx+HdCeSLLaNUOpnH7qdiOtxuYMnaBq2MkgqppjZf3T+fsgLe/3n9Jrz2vBUaOkn1k+NYXxyfufNxnLNpMd5z9RYs74zM6WNd6GQ6nQRHMsJbLtFjZj6nK+oVAimdxrcfppEp/8v5HFIHntMxcO+3tbG5VkatfwUaVpwBl6f+X8tERERERFQ7XKbM61ggdu7ciS1btuD555/H5s2bUc8Mw8DQ0BBaWlrg9TJHrJZMOo/DLyW0MurAC3Gk4lZF0HgWnebXIGr5hga0nRZAQ7j+q6Oe29un/aH2HxspO+5xu/DGi9fgT6/ciHBDfYQVC/W6MY0skodfQGLPU9rgXBqYT8TlD6Jh9TYrkFp3DrxhNqJfyBbqdUN0qnjtEPG6IZrP3292TiNf4U+SRBPwB9xYe2ZYRz5vovtAqjBVb7AnO+b8geMZHb+/d1BvSwAlU/3aTvNb2y6/hlT1NG1v69p2fOlD2/Grxw/iuz/fhWjCqqTJ5U389Dd78cBTh7VJ+WvOW6mhFNXmKnshCZRWbwNe+y5k+o8hsed3GkilDr2g1VClzEwKiZce1yGkiXmDhlGvYCNzIiIiIiKaFgZQRJPkdru0AbmMC9/UjsGeTGGq3vF9KW3YPVoylsPRPUkdpRqbPVohJaFUIaDq9MMXqM0G3xIsveFVq3DJtiX4wT0v4X8e3q8BlBiOZfCVu3bg548cwLuv3owz17WzP1SNs6btXY2W86/WlfUS+59FYs/vkdz7lPaJGs2Zyjf00P+Dp7HFbmRuV0ZJLzCnH5jLLQtI6lZ37BX3XIVz7K3LZb9Girdl3x1shCfYOMvPBhERERERzQYGUETT1LrYr+Psy1uRjOdwaJc1Ve/QSwlkUhNP1YsP5xAfTuDQi+UrmDUt8mqFlBNOScVUa6dfV++rBeGQH++9Zitef8EqnZb39O7ewn37jg3jE//8KNYsacabLlmtDc39vvqp9FqoJPQJn/4qHdLUPH1sr4ZRMjIn9o05PxcfQuzZX+uohobVZyKy7QqENp4Ht9dflc9BRERERESzjwEU0QxoaPRg4ysjOsy8ieiggf7jGfSfSGPghDUtT6bs5YyJW66NDBg6DuwsBlNSRCLNzYMNbnj9Mlzw+eytfdvrc+l9PtmXY76S+0rOLz3H45l+qCXNx2+58VV48oVu/OvPnsfxvnhZEPWP//cZ/PvdL+B1F6zEVReuRntLw7Q/F81uU/Pg0vU6Fl32pzCiA1bfKKmO2v/suKvrzST5PDLcDWGEt1yqYVSgc1XVPy8REREREVUXAyiiGeZyuzQwkiGr4znyORPD/VkNppxQSraDvRmYExRMyX3DvVmMnRh1aqQ/lQRmWy9qRnP71JuIyxSq8zZ34eyNHfjZb/bhR/e/jGii2BdrJJ7BXfe9jB/dvwcXnbkEb7p4DTatauX0vDrijSxC09mv0aGNzA/ttKqjXv49jKHuqn7ufDKGkSd/riNw2jpEzroC4c0Xwx0IVfXzEhERERFRdTCAIpolbo+rMG0P24rHpSpK+klpKGUPCakkrEIV16iU/lTPPDCEZx4cwsrTQzjz4mas2BjSAG0qfF4P3vzq9fiDi1fjwaeO4r8f2ouDJ6KF+6V5+0PPHNWxbplMz1uDS85aqn+P6qyR+ZqzdJivfTfyiRGYOUMiUkgDNPmjjdCcIUcK+3aSqjfzZbeL5+W1IXr0mfuQHThW9rnTx/fo6L/3W2g8/UI0nfUaBJZtZJhJRERERFRHGEARzTHp79S+JKCjVDaTx2C3FUgN92WRzZgwMvnC1siaeo6ht+39rH1OVt7QT/IBmMDBFxI6pBJq68XNOP28yJRX6gv6vTrl7srzV+C5vX3474f24YmdJ2D3Kld7jgzjSz94Gv/+3y/gda9aqY3N25o5Pa/eSPWbp7F5xj9ucOkGNF9wDdJHXsTIM/ci/sKjMA1r1UVhZtOIPXu/Dl/bEkTOeg0iW7dX5bEQEREREdHMcpn6q+eFYefOndiyZQuef/55bN68GfXMMAwMDQ2hpaUFXi9zRConl3VOAio7kCoEVLLN5pFNmdj7bAx7d8SQrzD9T/pFbXxFRMOo0cHYVJzoj+Pnjx7Arx4/iHiyOD2vdHW9i7ct1ablG1cuqvqXkddNfZEV+mIvPKJVUVIBVZHbg9D6V2pVVMOabXC5WVk303jdEPHaIZot/J5DVD/XzXTyFSYXRPO0QsVqOg6gsfIb8nVnhREfNrDzsRE8/+gwEtFc4T4JquS4jCVrgzjz4has3to45cblXW2NePebNuOtV27E/U8d0aqow93F6Xm5vIkHnz6iY8OKFu0TddE2mZ7nPoV/Pc2nFfqazrlSR7r7gAZRsed/g3wqVjwpn0Pipcd1eCJtiJx5OSJnvRq+ls65fOhERERERDQKK6DqFH87QDNJ+lDtey6GZx8axvH9lVc6a2z2YMuFzTjjgiY0NnmnXZn17Mt9+O+H9+GJF04U2gOVao0EdGre6y9chdZIEDOJ1039yxsZDZskjEoeeG7c8xpWbdUpeqEN58Ltm34VH/G6IZoufs8h4nVDNBsMVkARUb31oVp/dkRH79E0nnt4GLt/H9WeUo74cA6P/+8AnvzVANZtC2PrJc3oWhmcUiNoOXfbhg4dMj3v7of3454nDiKRkmbWlsFoGv/xq5fw/+7brc3KL3/Fcmxd1w6vh1VRBLi9foQ3X6IjO3gC0R33I/rsr5GLDpQ9PRJOWQGVC55Iq1ZEeXUstvetrdzncvG1RURERERUbayAqlP8rRpVWyqew64nRvDcI8MY6S8GRKU6lgW0T9SGs8Pw+qf3Jj6ZNvDr3x3G3Q/vw5GekqlVJRqDXpx7Rhcu2HIaztm0GA2B6VVg8bqZn8x8Dsl9z2DkmfuQePl3Oi1vslweH7zNHRpO+VoWazDlbe2Er7lTt55gIxY6XjdEvHaI+D2HqHYZrICaGbfddhuefvppPPXUU9i7dy/cbrc+uURUfcFGD86+vBVnXdaCgy8mtCrq4K5E2Tm9R9L49X/24JGf9WHzBU1YcXoIwQYPAiG3rqLnC7hOWiElYdIfXLRap90983Kv9on63a7usnPiKQMPPHVEh/SH2ra+Q8Oo8zZ3zvg0Pao/0ng8tO4VOozYEGLPPaBT9LIDx076d81cVs+TkRynD5W3eTF8rVbVlLdZwqkOeJraNLhyB0JTqgIkIiIiIlqoaroCSn6ol07uZ599Nnbt2oXe3t5TCqC4Ch7RqRnqzWhF1K7Ho8ikKiyfN4q8L/c3uBEMeeAPytattyWccvYlsNJjdmgVaHBjMJHEvU8dwkM7jmIolp7w429auQiv2noazt/ShSXt4QkfDys5Fg751pbpPmCFS4PdMIaskR3qgTHcK2VTM/J5XP4GeCWMauqwtlJNpbfbrf1IG1xeH+oZrxsiXjtE/J5DVLsMVkDNjD179mDt2rW6v337dg2giGjutHT4ccm1HbjgDW146fdRrYrqP54Z93yJt9OJvI6pa8AlwQ1oWOxG3JPG/pFBHI4NI4Y0TJdZ+Pi7Dgzo+Lf/3omVXRGtjJKxdlkzK1MWMPkFRqBrtY5KU/aMkX47kJJgqqewlZGLD03685iZJLJ9R3SMx9PYoqGUxwmlSgOrpg54wvJaZR8qIiIiIprfptdIZZY44RMR1RZfwK0r4m1+VROO7UvhuYeGse/52FRa70yKVFllTkh45cFytOuAy0Tak0VvNo6oK4URJBFFGjlXHgdPRHX833t3o72lARdstvpGbV7bxibmVDZlT/o9yWjA1jHPTD6TgjHcY1VOOVutnLIqqMxM5ZUixyOBloZax/dWvN8VCKFxw7kIn3ERGlZvg8tT09+aiYiIiIimZd7+lNvT0zOmYkoqqpwStXrvJSWPP5fL1f2/g+pf50ofOle2I2e0IZ3IIZ3MF4YESFoBJfslx8vvyyGTmsJMYNOFQNaPZfAD9l8zYSJhZjCCFEZcEkilMDyYwt2P7NcRbvDhlacvxis3dWB5uxeNjdmqPR80D7i9cLcuQUBGhal9ZjoBY6QPuWi/tR3p04oq3Ub7rRX5ppDGyseLPfegDndDBKGN5yN0+oUILD+9Jiqj+P2GiNcOEb/nENUuY46ygel8vnkbQH3ta1/DLbfcUvG+aDSKoaHJT7GoRfICi8WsFcM8Hs9cPxyiAlcACMpoGf2kyBvpym+m83kTRtpERkbSRFa2KROJEROD3QaGTuQw3JvXKXeVuOBCIwI6TjObC8eTZkbDqJF4Cjt/P4Lf/r4bKRhwu4FQ0Kur68kIlW4birfH3u9B0O/h1D4C/M1Am4w1hW+mMiSwMvN5mMkR5GMDMGMDyMcG7a01zPggzGS08rWQjCL2zL06XKEW+Na+Ar5158HdsXLOXnf8fkPEa4eI33OIaldujrIByVWmat4GUDfffDOuv/76MRVQ1157LSKRiDY3r2dO2tjc3Ayvd95+GYkKjGweAyey6DuWQd9RGWkMHM/CyI5fPdUAv47FaCpUS2WRQyKXQTKeQSKeQdKVxRAySCKOFLJSYDUhj9uFxgafVlXJkP1gQIIpLwJ+DwI+K6Qq3/ci4HNXPsfvgd/rgdvNldTml0UAVo17r2lkYEQHdGpfav8zSOx6DLnYQPk5iSFknrtPh7elU6uiQqdfBH/Hcswmfr8h4rVDxO85RLXLmKNsQHKVqZq3ycXixYt1VCJflPkQ2ki6OV/+LUQnIy/zJav9WLK6sXAsnzMx1JtF79E0eo9YQ4IpmeI3Hh88aEaDDlWSX+VhImVmkEAWCQmlXLIv4ZR1W/pM5fImRuIZHTOpNJgKh/w4Y/UinL1xMbasaUMoWN+rqFEFXi98wRDQsQyR9efAfO07kTq0C7EXHkZ812NaCVVKGqaPPPZfOnwdKxDefLH2jPK1ds3K08vvN0S8dohmC7/nENXHdTOdz8XkgojqltvjwqIuv46Nr4gUevREB41CICXhlIRS8eGT9+Rxw4UQAjqsD1Z+f8Y0ygIpJ6CSlfmyrlPrwJ7O5HSMxIGewST2HR3G3Q/v14qrTasW4awNHTh7QwfWLW/VYzS/SK+nhpWbdbRf+WdIHngOsZ0PI/7S47rSXqls7yEMPvAfOgJL1msY1Xj6hfBGpOqKiIiIiKg2MYAionlF+uQ0LfLpWHtmuHA8ETVw4mACJw5HkU14ER3IYaTfwPBAFrkJpvGV8sOrozCBt+SvGT4DaX8WCV9KV+cbRhJJDZUMZIzxK7JORiqudu7r1/H9X7yoU/7OXNeu1VESSHW1FSvCaH6QVfBCa8/Wkc/eiOTepzWMSuz5vU7dK5U+9rKO/nu+heDKMxA+42I0bnoVPKGpl0QTEREREVUTAygiWhBCES9WbAqhqSujPeCcklEzbyIezWGkL4vh/ixG+p2tgeG+LJKxyVU2eTNeHY1oQIdWZ8nsqgA6VwTRsTyARUt88EWATDavlU6pjIGUBFTZHNLp8ttHe2J4ZncP+oZTYz5PPJnFY88d1yG62kI4a8NirZDatq5dp+/R/OH2BdC46QId+XQC8d1PahiV3L9j1Ep7JlIHd+ro++W/IrhsEwJL1yO4ZL1WSXmb2ubwX0FEREREVOMB1He/+10cPHhQ92UrU2s++9nPFu7/xCc+MYePjojmA5fbhXCzV8eStXZfqBKZdF5DqUIw1WfoVkZ0IFueAZSQ490H0zocwUa3BlI6VgawckUQwcbKK1XI/3dHNIjqxdO7e/D83j4k02M/2Yn+BH7x2AEdMjNv/fJWa7rexsXYuLIVXk/llQep/rgDIUS2XqYjlxhB/MXfas+o1MEXRjUzyyF1aKeOYfuQJ7yoJJBah8Bp6+AOjH29ExERERFVi8uUdzk1avv27XjwwQfHvX+qD33nzp3YsmULnn/+eWzevBn13ul+aGiorJKDiGb3usnnTUQHDHQfSqH7YArdh6y+Uzlj8v83Nbf7NIySUKprZRDtSwPweMf2eMoaeew+NIinX+rRUOrlw4PIn+TTNAQ82LK2XQOp9cta0djg1Sl80tRcmp3LdMW5Iv9/J9MG4kkDiVQWsWQWHo8La5Y0w++bveVj5wNZTS++61GtjJLpeJPjgq9jOYISRkkotXSDrq7nktK90R+f32+Ipndt8toh4nVDNI+/3+ycRr5S08nFAw88MNcPgYhoXG63SwMkGRvOsXruSPjUdyxth1JSAZXSlfrGI9P8ZOz+fcz6mB5oCNXc5kOk1YtIq71d5MX6Ja3YvKYNb3vD6YglMnh2Tx+e3t2r0/WkEmo0qZh68oVuHZUee2PQq2FUY9CHkIRTsq8BlbWv9zXY5zTIecV9CbBkuqBMCUykDA2QJEiSQMk6lkVcb4+6P2Xdn0xlKwZofq9bm66fub4dZ67twPoVLaziOglpPt583ht1ZAdPIL77CaSP7kb62B4Yw73j/C1Tm5nLiO74tR5x+QIIdK3RSqnAkg0aTnma2k/26YmIiIiI6j+AIiKqN1K95Eyzw8XWsVQih55DVih1QiqlDqaQiufHnbon58qoJBBylwRTPlzYtgKvu2ot0u4s9vUN4rmDfXh2b5+GPCet3kpkddQSadguwZoM4EUNus5Y04Yz17ZrKLVmaQtXAZyAr7ULLedfXbhtxAY1iHKalaeO7YGZHhtWCjObRurwLh0OT2ML/EvWId+yFPGulQi0dmo/KU9kUcVqKSIiIiKi8TCAIiKqsmDIow3QZTjTz6TJuTN1T0KpvqOZSU3dSyfySCcyen4li3xteGNLJzxtJmJmBj3xOA4PjSBlZmEgb48ccvY+qjwLz2W64IdHh09XEZR9L3ymtS3chhVmyOqBA644BhBH2mU1Zn/qxR4dQqqwZFrh1nXtuhrgyq4mreaiyrzhVng3nIvGDefqbdPMI9t/zAqkjlqBVKbnwKiG5kW5+BCSL/8OwO9QFom63PDIx25q10DK2yzbdngj7YV9d6hpTqd5EhEREVFtYQBFRDTL5E35RFP3Bk5ktLdUdFBGFtn05HtKGVmzZMqfG82I6Bj3sbhNSPZjuk3k3U5AlUfWzCGdN5DKGUjnjDHhlQwvSkIk0wqRGtxeBFw+PebNe+A2p9YEPYIglpmtuh830xhEoiyQkil8j+88oUPPD/k1iHICqWWLw6cUekg4KFMGR+IZjMTTuo0mZN8acp9MUZTPaw0fIo3+stueGm787nK54W9fpiNy5uV6LJ9NI9N9wKqQcqbuDY2dtlnGzCMX7deRPjrO5/L44JFwSkMqZ9i3JaRqXgy3P1iFfyURERER1SIGUEREtTZ1b1Qgkk7mrTBqIGuHUuX7ydg4S/FNgplzAfrXXXDDDb/0YTqVf0jlmYXT0oiAjokCKQmHHnn2mA7RGgnYYVSHBlLNYX8hPCofY8MlvR3PIHey7u4ne9xBL8ISRjX60RTyIxzy2Vs5VtxvKgRXPjQEfXM2tdDtCyC4bKOOZvtYLj6sQVTKCaW6D8JMyJp6k39uzFwWxuAJHRVJGNaxXBugSyP04NIN8LUv1ZCMiIiIiOYfBlBERDVMqnlkCp+MjqWBiucYmTyiQxJKWRVTVkBV3I8NG1KwUhtcMiXRjYZGDxrCHgTtrQzplXVsTwoD3ZlJBVIJM42BUYHUYDSN3zx9VMdc0UbrKQPdA5V7LVUiRVuhgLVKYbjBb21DVoP40n3d6jnl24BvZlc19DQ2I7T+FTqclVWaI2EgOQJjpA+5kX4YI70wdNsHY7gPRrQP+aTVTH9SzDwyPQd1RJ++x3oeAiF7Zb4N2gw9uGS9PhYiIiIiqn8MoIiI6pzX70brYr+OSvI5E5lUHpl0Htl0vrifMnUrtwvHC/eNPUe2uWx5BYz0oXYCJAmVgva27HbJMWmifrKeTYmogWN7Uzi6J6ljvEAqhICOiQKpmeD1uLRaqakxgIaAF8m0Uaieyhozk+yZZjG46hlMTuMxuu0wyqsBVshesdDZSlWWVFnpSoay0qEddsm/R1c+DHgR8E8cYrk8XnhbFsPXsnjcc/KZlBVIOcGUhlXObSuwkmbn4z4P6QSS+5/VUfi3tXRqdZRTKRXoWqXT+4iIiIiovjCAIiKa59wel1YayThVuZyp4ZT0mvIH3fAFXDPeaDoU8WLdWWEdZYHUXjuQOjH5QCqKNNIwkIGBrCunw9fgQiDkQijsRbjJh0jYmg7nDJkWV3pbQppK/0adHpnNIRqX1QQzxSHT+RIZxBLZQlBVvp/BKc7yG8PI5TEUS+sA4tP6GBIMShA1OqAKBTxwIQe/XwJOF/KmqYGZrKQ4dt9EPg97P4R8fjlMc7ne1mMhE758CuHcMBajH8tcPegwjiOS7oFrnOl90o8qJmPnQ3pbwid/12oNpKRCSrbe5g42PCciIiKqcQygiIho0jweFzwzEGTNZiBVYNojbo9ea+qbBHOeiAdm2INM2INE2A0zbCITNpCImGhoNNAQsSq4JHRzwiidHun36uhobZj0v0fCmkTa0KDKCadiyQziSdlmC1tnf/Rx+fvVIB/X+bzVJxVMXfY4E35ksdzbj1XePqz09mGVtxfN7uS4vaXS0pfq6G6MOMeCEXg61yG4ZC0al6xBsGs1QykiIiKiGsMAioiI6sp0A6lKpHpHmrjLGJjE+TLlsLHZi7CMFm9hv7HZY23lWJNXm8qP+zHcLp0uJ+M0NE76sVqP10QqkysPpRIZxFOyLYZUslqfHEvaW7mdkG3aQDoz/ab11ZKBD3uNLh0WEy3uBFZ5eguB1DLvAPyuyo/dlYoif/BpJGTYx1KmH33uNgz5FiMW7EIyfBrykSUINoaKvbXsaYuy7xyTKYs+r7vicy9TLpP2c5jKGPq1KGzTJbfT1lbOc85P2rdlqmNnawgdrSF0LpJtg26l2m6mqwmJSqsk+4dT6BtKoncoqVtnyP9JS9obdRXRZYsjWLo4rJWgREREM40BFBERzatASsIkCaOO70tqA3YNmKI5JGI5pBOn1rMpn4PV4H1g4v5SUi1lBVSesrBKA6sWK7SSaqqpkoBCpsXJaG+ZfNXV6DeiEopIWOVsJZhK2NvRx+NapZWG3+fTN6oy5HG4ZbhRsi/HMc6+ddsjW/s+CWOc1QeHY2kMxzJaCSahoEz1G8o34hkZ2VX6uN3IY4lnUMMoJ5Ra7ImO++8MujJYZh7HssxxILMDUi6VN13ozUdw1GjFS7lFOJpr1f1hM2R1yLdJSCSBlDR3T2cNJNM5pDPGjE+dLCWfc3FrgxVMtRaDqcWtISxeFEJLOHDS/mlzSQI6WQTgeF8cx3pjON4fx7G+uN7u7o/rFMyAVgx67MpBexuwtoFRtyvd31ByXoMcD3i1/9lCJyt3Do5UCJeGi/vytbGurcmRQHRphwRS4eJ2cRhdbY18zomIaNoYQBER0bwi4c+6bWEdlXpYpeK5slAqOWpIRZWzn01PL3Fw/n7vkfHPkf5ZGk61erWBfMtiX6GZvARX1aqGkTfsUt0w2QoHZxW8lpYWeL3eqr+RloquQihlB1QjsbR9LIPheBqPxTP4ZSyNbHQESyCBVC9WevqxxDuAlnGm7gm3y0SnZ0THOThYOB7LB3As16pDAikJpk6MtCCHytNNXcjDizx8rhy8yMHryttbuZ3XrW/Mcet2LB/EwVw7hvLloZcEcoe7YzoqkaqsjpYGDaOsUKrB2raGEAn5Ck3nJZypVlAl0zSliuZ4f0yDJQ2b7K0ETierrpMgrxqvZyuUtQIp3beDK9nXYxJY2c9Nw+jjum8d83k9GqSVcm6aMn+3sO/cZ449r+R8uXaGh1NIGHG4XG59/uQ1bm3z2i9Nt6ap/zfpffa+9k2TY9pXTc617pfnuM+uZHICp4GR1IxPzbXC4QHsOlBeG+pxu9DVFsLSjkghlHICKlbxERHRyTCAIiKiBdXDSqbIyZgMI5NHMi6hlBNO5RAfNhAbMqztsGyt+6ZKwq3BnqyOwy8lx4RTLR0SRvnQ2ulHS4e9bffpqofzlby5bQ4HdCzvjEx6SqKEVfKGWaq2TgwPwOg7BNfgEfhGjiIYP45wulcrqMYTdqexwX0CG3wnCsdypgsD+TDcMMsCJNl6XKf+Zn/EDGF/th0HDBkdOGy0ITvBj2Uy/U/CHhkTkdzSah5vrXwooZRUc0kAo03mS1ZFtFZJLFkx0b5fquRO9JcHTLKVYzO18uNMkcdqLQAw14+kfshrpDUS1EBTKinlOTzSE9Ovr4RcE5H7j/bGdTzxQvl9MqXVCaSkUqqjRT5HCO2t1ueRisK5ksvltQrMqQqTINUw8vD53PB7PfD7ZLitrdfZevR+edwSAOtWhufkq7k6/z/Jc2tN0XWm6xan6Drb5OjpuiXTduX/xJVdTViztFmHVEXW0lRdufZ6BhL6fPjt50aeMwlz5XmUgLiWKzfnC7kus0ZOX9MZI6//T2eyOX39yddAKkfl9StbeV1X+2vi9LqUXyhpuwDtdWn1u7T6XtrDvl8eT2Hxl8JCMAFrYZiSBWIqTY+n+sMAioiIaLxvkn43IjJapWn2+HKGWRJIWQGVE045YZUMqXiYbDjVeySto4wLiNgVUxJOtZRsG5uqVzVVq0qnJMobXstiAJvGNC7P9B1FpucAMt0HdZvuPoB8wmljPpaETB0TTPE7VU2uBLb5D+nQx+hya6+qHm8XDmQ7sDPWgn1RP0xzal9TqcCxen4ZwHAKc8HrcaFzUSNOa2/U3kKylTenZX2y7L5Zpf2x9M24Pd3RuV/eTNHUyHTN9paghj4ynKDJGYuaghWn0TnB49GeGI72xjSUkq0MqT48GXkj+dLBQR2VyJtImVra3ixTTa3HpQFV4XEF4JnGlEp53FIF1j9kV4ZJyDScLLstUxRnskhM3gg7QZWEUrIvx+RNf2lPuJMFepPx2+eLwbgExauXWGHUGnsrYX0135hLkCZf/8PdURzqjurWGRLqTeb/AwmknOdM9r363Lk1sJLnUG47+/oSMA00RxrR2GCtRFsM1e0qx9IR9OrHrbfvf/K67R1Monsgju6BhA75RYqER9bI6f9/Giplc8jmZGsdtwKmPIxcTrdTfZ3J8yyBlFR/OsGUbgv71nTn0uAqaO/L81y6WIoTIjlBk4SS0nOyGlPW5fVfulKxE04VAiv7vnDIahkg3w9NZ7Vee6XescfGO6fktv0LMvnc8gsy+RzyfNTba65WMIAiIiI6RdJ0vKnNp2M8Zt7UaiorkMppQCX7I/1ZDEklVG8GRuYkP7GZxR5Uh17EmKopZypf0yKf1XOqyWNVfDV7dSU/qQBbiFweHwKdq3Rgq3VMfqjMxYbsUOoA0vY2239Mvlgz/ADccHl9cLk9yKcrl+q4zDwiyWOI4BjWArjCC7iXRICONUhEVqDfvxRH8u04PpJHz2ASPYMJ9A8lq9qXaiLyhq9LwqW28qDptPawhgnyw/pMsKadFYMrp0KkUD2i+1JF4hy3jum+fcw57pxfjamA1SJvojz2kH15Ey8BUsVwqVm2QX2DPx0SSkkTchmjyZtKCaZKQ6kjPVGtjjNyk3sROj3f9h4ZHvffKv82K5iyQirn3xhq8GFgOIV+DZeKUxDl9lT7W80EJySIS9A7iyRY3rmvX0dpwLOiswmrlzYVQikJqWRRhamQ/xMlyCsGTTIlOIpDJ6L69Z8ueX0YObnuUDVyfRSm4DphVUlgJbcbQz40N0qFrV+3TeFicFGNAE+ez6FoWoMlCXadkOlEv2zj+vqdq/+/JdCSUW+Vo84vV+R5nGvymimGX8WKLRnNpbcLrzOp4Jq7KtBawgCKiIhoFkjzbWmYLgPLKwdUEkrJlLyhnkzZVoKqyVRN9RxO66j8AIBQ2AqkQs1OMFUMqJywSoKqhTBlQn5z6Y206gitPbtwPJ9NI9t3RCukctF+Da80PPJ4AY8Pbq8f8Hqt44X7iudYWz9czjl28OTIxYeROrob6aO7kTr2MtLHXoaZqVyplE9GgUM7EMQOLAWwFC74OpYjuGoDAheth7frLET9bUim89Yqh85qh8kMkjJSKaSTaaRSaaRTGWTSaWQysp+Fkckgm8kgZxjwaN+qvG49rpxWXUXNINKeRjS2tmFR+yIs6QhruOQETRIUzMbrRN5YWlMEp/Zm+mTTQ+TNl4ZR9lQo2ZfKApdcKPY/S3657SrcKG5Kf+vt7Mp5hcP2Np/LIR6PobmpCX6/rxAi6dbl0kDYadTv3GftW9OW5Omtld+wy2/+N61apGP01LbuwURZ1dSx3jh6hxL6Bnuy4ZTzdXGCpV2oLgkhpJpDKkysN+NSTTL71XalzfUlKBl9W1Yx3X9sRJ+T8chzvO/YsI77cLhwXKbrOVP3nGCqrTmoYZ18vNJqJmdfKyfrkATVUpUjY7qVNaWhlBNUOSGChlZ25YsM+drItSn/3xZDpYQuuHDCDppkyGuLiqQHn1S1Sd/CcINVqSRTd+XrZwXUaUTjWd3Odrg7VRJAy1ReGVP590ec15T9WjpnUye2n7MMCwkDKCIiohoJqGSqn4wVG6VBdVEmndcwaqg3i8Hu8q2RneQbPJmaJY3Xozng6ASPQ4Kq0sqpsBsuXwaRJhd8fg+8fhe8PrdWfXl91vDo1g2v19kvniPZS628iZ4Mty+AwGlrdVSDp7EZjRvO1SHMfE4Dr0IodXS33q7MRLb3kI7oM/fqEZcvCJfPj4acoWNRzphcBZd3kj8FSuFDtxeeaDM83S36+M1wC4YarX1P2bYF7oZwzX+93Xa1hIzWKn4eq4G/Cy0tzVVv4D9XZMrcEg0mw7Be0eWBkvRnk0bpOgadxulWOCVjYGTmy2JaIgG0N9tVYs0NaNNtsWpMwlOZgjReH51sSSilW6O4L/eldVt6f/E++bi6gmOgfMXHQrhkb3VlR9/kw355Hg8cG7GCpqNW2CRh30TN550Q5LHnjheOyRt/eZxSHTgd8u9b3hnWaX8rOiO6lVBarvnSaWO6XzZtTKaSFe/LjOpXpM+7nJfLa8VjPJmGZDdOOOxUNFa7skYWU5gMnU7o80w78KpUUSoLTEhvNqsvmdWbzJqqWJy+qLe1Z5k1ZVF7ktnHZOsM5+9IVaMEq/KalepR3co056xVTVp2vGQrz/V458h1ItV1Ehw5AVJpmBQO+cvvs2/L35nKqqXyutBFSeyFSYqjGFI5x6RCT7a1HqBK9W0yndC+aQ4JNhlAERERUU3xB9xYvDyoY3TVVHTIsKbwSUBlb7Xn1EgOmdTUf6Mvvx2XKYIyUFZNNb03ipJFlIZUTnDlD7q12qoxYlVdhQpDqsQ8esw3jxuuO6Q6yr94pQ6c/Vo9lkvFtTLKCaTSR19GPlV5dTwzm9JRVTlDq8FknJTbA09IAqlmeMJWKCX7LqkccxproLg1Rx+ze26U3tZjzip0TrimFWyL4G3phK+lE96Wxfr5ZLU5qg0SrrQ2BXVsWNE6QRWBFVD1lYVUzu1EoRLCap4eQFtz6bTDYNltCZemO51KpzpqYISaI29St23o0OGQQODg8RHsd0Kpo8PYf3xkwtUoo4nJBSYSlDkBk4ZNXdZWpkNWu/JxvJVXnem4EkZJ0OAEU2Vj1PFEyXEnrJBG2Kc6ZdOZgjlZ8pRJECpVaV2LGtHZFtJ9vd3WqH3bFkLl8VTIdez8/zFZ8jWRMEqCK/kSS4WpVrLaW+u2VdcqW7cbZbf1HPeo21YprAamZWFYrBiIlQdkU3udNTXW4H84VTY/fx1DRES0QKqmpN+TjBWbyqumRDadR3zECqOsUMrqPyXbhHNs2ED2ZL2nToH8ACYf3/ocUwvEpK+VE0gVwqkmDxrC9m2ZTijbsFRmzZ/gwRNsRGjNWTqEBDLZgeMaSDmhVKbn4Mz3qpoJ+RxysQEd6J7dTy1THiWIkuGEUs5Wgip5XqtJm9VmksilYsinEsgmRmDE4kgnFyEfaNBKNWtapt/al+mZCzwwkzeZ8ga8uIjAWM70UglhuApWkVRQSbBXGu5JSCP9hvYdsaqknIop6UVUiVSmOOFSIWzqjOhUvVqrZCydjtvWPP2PI9NGJYiT4GC4ECRY+86KqnJs2A4WpAH7ZKZnyjTVrlHBku63hbTZPl+71ef0yJMx4/zQaq4l7ZM7Xa5FreCKTxxULZ/Eir/zDQMoIiKiecoXcKOlw4+W4i/NK5JKKSucssMqe19CqthQFrGRLMy8C7ksYMiKO4YpOUPVSV+r4XQWw30n/629VFSFW71oWuRFszSEl2BOG8PLMZ8+F/VK3gj625boiJy5XY/lMymkT+xFpsdaRU/7T8lwS68qZ99j964q3mfd9oy6XXKuzJnMG8jFR7RfVS4+aG+HYNjbXMk2n5CVAueok+4ostphtv+ojkodc9zBRrtiygqpvM2d8LXaAVVzh/b3ko8h4ZEVIsVLRgy5VEK3hWPpOHJJ6z7Zl79XKRScsF2u9g3z6+fWQEq3pfu+kvut20rDgdL+VPpr/pKb499v3V283+UPwhtuhUcqysKLdCvPVa0EEDPdA2w+k5BmaUdYxyVnS+c4i6wCKGHUweNRBHxuLLdDJ6m8qZWv82xOG5VpmjImGyxLFZUVRhVDK1nUQKrvnNCJr1Eqe525XRqay6ByDKCIiIgWOAlv/EG/rqI32ekQeVnZyDCRy5qFUEr6Uclwjum+MfaYjHTS6keVtPtSJaKGBk7TJSHawPGMjkqkakrDqDZfSUBl3Q43e+GusxUC3f4gGlZs1jHjpJKoqU3HyUgPKyusGhoVTpUHVab0prLmQpSEH66xx+zbToDiGue2fDxjuBfGSP+kK8EkNMqc2KejEgl4TGP6q31NS87Qf0tunNUR54o8F56wNOm3AikroGrTpv2e8CJrG2nTnmkLlSxYkE9KIBlDTraZpE4/lTBTp53WULAj05heIWNT51w/lLojX0cnBJ2oUo+IJocBFBEREU2ZBDZ+CW30/efMLC2czeQ1kIpHjWIwNZJDImYU9pOx6YVV8vdkdB8cOxVF+kBEdCqjFUg54ZQ0YReSb0izX9nKb8PzspXbZsl9um/fJ1Ox8uV/L28fCwTdVgjW7tOKLVkJrZ5J5ZSzmuBc0CBqpA/GUA+yQ91jtvnEyOQ/1myHTzVMngtDn8eJ51G6AyEroJKgygmmpIpKeoA1ROAONdnbiLWCZI2xpk2mkEtFrTApKZVu9lbDpWjJvr219yd6vUiAJ0GUt9mqrvO12PtSbdfUAU9YAqr6rcokIpouBlBERERUE6TpuK/NCmhORvpbaSgVs8IqmToYHchiuD+LkX4DIwNZpBOTrIzJQ6f5WVP9xl/ufKbJ+89Ii1fDqEJlVru1lWNSmUYneQ49Xvhau3Q0VLhfqlKMoV47lOpGdqinZNszyQbuLriDIbgDjTo1zRnSU8odDJfclv1Q4ZjpDWB4eBhNjQ1wmzmYRlZH3kjb+xl7OPtZmNkMzFwG+ax9O5exjjnnyDzYks62xYbthSPF+wvnWQFp4aSy+03k0wkY0UGdejlV8ndljL9yY8mz6AvCI0FUQ0S3noYmDaY0oGposo6Fmqz7TxJa6fOYSVojndQQydpP6FZvp+37R9/W85P2FMqY9i2bafK1cqaDVnwupMpQAioJpppkuxg+Dais255IKwMqIpqXGEARERFR3ZGeTs0y2scPq2San4RRGkoNSDBlh1P27dnoYzURqYgaGZCwzKgYfAUb3WWhlFM5JfuNTR5tQk8Tc/sb4F+8QseY51+q2RIjhYqpfDZVEiQ5QVMYbmkgPo1qFZm+6jZc8I6avlqL9LlIRmFEB5CLDsCQRvLRQet2bEADKl0JMT487Z5fEvYZwylguHfSf0f6U0kYJdtiyJSaVlhWS7Rf2cAxHRV5vPA2tevUx9LeXUUVjlX872DsQe0ppoHq6NFghayFfTt0lde/9Iej6lfjybTOjLWyqW6d13zW2S9ui8eSxb9nHy/dl8u1OJVZl32zb1v71m33Se4r/n25bS2iYA13yb7LFyi/bS+44PYGin9HtwGrr519TF9jvtprfE/Vwf9NiIiIaF4KNHjQsUzG2D41Ml1OqqYkjLKqpiSUKoZTslrgpOlSzdaqhM7P6rqUs7u4dZZ0TsZzMCa56mAqnkcqnkb3obHTBj0+V2HKoPS3kql98u8NhGRr7ft1W7wtqwryB/ySL5vLZU0Va2wGlm4AFvpzIdPlQk1A56qJe37FhsqDKd2W35ZpajNB3kQbmclUqc0sl0wtlPCxIQxPgwSS9r5uI+XHdD+ifdmM2KDVm2zYqrCT/ax9WwK9SYd3MrV08ISOWmCFBFLdF4Lbb28DIbhK9jW08peGVyX7/ga4phnk1m2QJFM2E1F76mZUF2ywpndGrds6tTNq37amdlZrZdPRr7raWDZiFI/Xur6cqshg2K6OdLb2dedUSNrHCwszUN1gAEVEREQLjoRF4RavjiVrx07eMqQfVTyn57mdAMkJlCRg8lhbJ1yaypsTmTo4IlP+nPCrz67S6s/qfZMhTd0Hu7M6Jv9vRjGc0sDKbQdWnrKgSu7z+l3w+lzw+t3W1ufSKZLWcetYvTVupxnq+TWJBvUSVDlvsHOJEetNtrwZT47o1npDPlJyv9VXaSZoFYYEHv6gbq0AJKgBiN52hgQnGipZb2YLAZOsADjNih9/oEFXqxxv2qD2K9NQSgIqO6iyQ6qcNNSvzWhApxTmZMSHTunjuPTrUQylSoMql30MvgAyGQMjwYAG+zJHWl5PUrJqynxp2WpjvZLbY47L+fny27KIgfWfeaGSx/5twTgVQO5R59vH9XyXXTXohEjFrbzWdaosTY0sxmAvYJGd6vVeFkpZgbCuICorvrrLV3q1Vnv12vfJMZ+1MmxhJVh71ViPp7iv99m3ZaVYeQ3oVv6evCasv194ndCEGEARERERjf4Bye9GxD/zv62XH04bm7w6TlszNvjKpPP2VEGrJ5UzhVD2pceVvM+aLnkvZlVVzcxv2eXnbSeM0mBqVFglVVo+ud9vhXfjtCeSDkUl++XnjXe/9fld8HitIY9F9+1j7tJ957zCfRh7n+yXVLBZbzZLF+UrDxyLM1LGnkdWUFWoLpv069MOrexgSitG7MBKpiRpiFQSILkCpQFTg31/0HojWIPkDbFv0Wk6KvUrk9BCVnW0KqisvmUTVpKV9AIrOzxRiCXXkCHVOVbvrtGj2vOSpfdWTkZ04KTnzn7dW23T6WsSqspUNQ1XrSFT18q2zn16XsD6T8taQUOHWbIvxzW8wzj3jdm3Ar28069O+tnpNoO89Kiz953jpceqVt2VTSMnY6QPNcEJo0pDKvkaaJA1KrhyeRA56wo0v/INWEgYQBERERHVCH/AjfYlAR2jyYp6sSHDDqbsUGrQQCqRQyaZR7owcrPS30o+RyaXxxzMkKppThglwZSEb8FQDMGQXWVW2LoRLEyZLE6dDDZa98vrYKH1+JpOaDWfSCWG01B/zqaOSZBQCKSsRu26n0pYjds1uJJjdsP3siFN3uU8/odQiQalJdPJCvsy1cxuzj86TJUQqdaD1cnQ8CpvFEMpDaaKIZW+fgpTEYtTFMuPxSa5aMQcK1TcTa6eMRc7tarCesQAioiIiKgOSIVO0yKfjpP9sG9kTQ2jrGAqpysCplPWfiGsStj32bczqbyGWdlU/pQqrRY6p2hAZJImMknpLTa1ptkSYEkPr0JwZYdXMj1SqrWcaaD6S3R7mqhOCy3pOybbwn6lc+1eZVIt5pUqMLt6TaraPKW35fMtsDBsIdLKPm0iHQCk+fk0yZtva9XBYihVHliVrFZY4ZiGXEZaAzlX2RQne2u9oItVJqNvO1OhyqpQpArILgMtVPrY1T9lVT754jmF+5zjEqKUVALJ/8klU76K/YnG9imSbT0HSKdKK0Pl6+nxwY3GaX8cmcY6OqTS7Zg+WzGYOUOHBl/ympT9XHFfQyLnnCpVZ02GawG+LhhAEREREc2zH/Z9fmsaHKZZTJLLmdpnKpvJF7YSakkDdSNb3C8cl2N6nxwvud8+Lm/aSqenObuFQyXT18qOYfz75THmDdN6rIbsy/uJktv21jmvnkI1eY+rAWGiNh60M91SgikJpTScKoRUo47rfdYUTD1W9neKx537nI9RKQTTLMH+gmvPnRys15zz2pPXXCaPrP26zJbePslWFiIoNaZawZzUjLcyhSmoAWdqqluvxcLWnpJa6Kcm+2OOueGzn696nNKpoZG9iuR0yOqRQ0NDaKmD1SNp9qexeiOtgIwZpAFjTiqXJJCywyqtZLIDKvtYeY+x0u2o3mOTPsdAYPlGLDS8qomIiIiojPZI8ri04ma+kCmMTihl/fK7PKCSrbAKIiSgKO5ri5SS24Uqp9Lb+fL7jGwOI0NxuBFANi2BklVtJlVmhQo0qTib5KqIc8mZbjnbrMIJq1pLQs05LFSYffJvl+qzUcfGvTlBVlUW/ko7GqmIK/RBs/up2ce0n5ps7b5pxfuKfddKz3N6qDnhWiFALAkaS/vDzeYiBhIySs5o2td2aZBdCLhLt1MM/OR6l/9DrBDeCu0LAb29bx0r3i6eVxLWyzHDCkUnqmQs3B5V0ViobCxdfdUObyv9k6byzxzznLjKv+6TGaVB8qlwQmj5f9z6/9z+RYQzNOOx9kv7+Dn/5sLzMapnn7M4o8vlhcvlK7wm9Dn0uoohvP3vqcdguJYwgCIiIiKiec95Izdbq3ZblRzGSSs55I1nYSqkPS0y5UyP1K0VVDnTJJ03XvrmWt5w5U2Y8qbMDs2cN2e675wrb9wkILPfpNULDfLqIKCrCtNa7XLGPliNkcCkUP1WGlx55bWcg9udBExX+etbi0fKX9/jbZ3e2tNSIZzSjRNq2B9art0afGprUmGhiApDFIKlkiApN+p2TQTQ0ttvdLWmXRFarAK1QtbyKtBiCOspOWdRl79iz8f5jAEUEREREdEckTcnoYgXocjsfL7Sii3nDZ5TkWFVc9hVG0Z5VYdT6VG8zzqvcLxQCTL23NEfo1pvJKWSwZnKVrYtmRbnHB+vAmeyxQ2jz7Oq3orTUcumCpZOWbWnC87GQgG1TEOFtIlserwnYmp902ZU6Yqco++gaXECpmy6zp/DwnVuIq0HTu1CPufVLQygas2Pf/xjfO5zn8Nzzz0Hv9+PSy65BLfeeiu2bNky1w+NiIiIiKiuFKah6MrgczOVRN6IlgZTToBVmJpkFKcu6ZTJvKkVA2X9lEr6KvkCxb5R9ULCv9K+VPJvLfa1svuvSYVNBRX7UZUcM8tvlP09p0quED7q1j5m904rndpU2kOteH/x7zmhZenUMjp51UylahgJUMsquaTqR3ufl1c9llU/jjpf9+XvjFftWOHLo9OMJ8mZikwzw+Orn/+zFkQF1J133on3vOc9GjbdfvvtSKVSuOOOO3DhhRfikUcewdatW+f6IRIRERER0RRI9ZFfKpB05snCWwVKaB+dBlnlEPOK9kWq0PeotCeShG5l/ZJKFjfIZnJIJtJoCAXg8bjHrtzo9EiSINUzarXHklUfC6s8juo/5IQtToji9HIbfV8hZDHH+TslTedHT7kqBEx2QDpTPZBqRWGBh5JFH0pH6eIQkxnC6itW0mvMXX7b6XtVep72JhvnPFHs01es/Cw/bh+zK90K/f5K/559pwSApVWclXp8WRWfo/p8jT7HKL8m5PWx0NRsADU4OIgPf/jDWLZsmYZNTU1NevyP//iPccYZZ+CDH/wgfv3rX8/1wyQiIiIiIiK7ws5azW96TwdXwat9TtDjW1iti2acaQdeC03NRm4//elPMTIyohVQTvgkVqxYgeuuuw73338/Dh8+PKePkYiIiIiIiIhoytOh3fOnMq7uA6jHH39ctzLdbjTn2JNPPjnrj4uIiIiIiIiIiObJFLwjR47oVqbgjeYcc86ppKenB729vWXH9uzZUyjtlFHP5PHncrm6/3cQzSZeN0S8boj4PYeodvFnNaL6uW6m8/lqNoBKJBK6DQTGTi4NBoNl51Tyta99DbfcckvF+6LRKIaGhlDP5AUWi8V03+NZmM0biaaK1w3R1PG6IZoeXjtEvG6I5vP3m2g0On8CqFAopNt0Oj3mPlkNr/ScSm6++WZcf/31Yyqgrr32WkQiEbS0tKCeOWljc3MzvN6a/TIS1RReN0S8boj4PYeodvFnNaL6uW4kV5mqmk0uSqfZnX766ZOenudYvHixjkrkizIfQhtJN+fLv4VotvC6IeJ1Q8TvOUS1iz+rEdXHdTOdz1WzTcjPO+883T722GNj7nOOnXvuubP+uIiIiIiIiIiIaJ4EUM5UuW9+85sYGRkpHD906BDuuusubN++HcuXL5/Tx0hERERERERERHUcQLW2tuLzn/+8Tre76KKL8JWvfAVf/OIXcemll8LlcuHLX/7yXD9EIiIiIiIiIiKq5wBK3HTTTVrtJM3GP/rRj+Izn/kMtm7dikceeQTbtm2b64dHRERERERERESTUPPdq6+77jodRERERERERERUn2q6AoqIiIiIiIiIiOofAygiIiIiIiIiIlrYU/BmUjqd1u2ePXtQ7wzDQDQa1ZUCvd4F9WUkmjZeN0S8bohmC7/nEPG6IZrP32/22LmKk7NMxoJKLg4fPqzba6+9dq4fChERERERERFR3ecs55xzzqTOdZmmaWKBGBoawoMPPojly5cjEAignknaKEHaT37yE6xbt26uHw5RXeB1Q8Trhojfc4hqF39WI6qf60YqnyR8uuyyy9DS0jKpv7OgKqDkSbnmmmswn8gLbPPmzXP9MIjqCq8bIl43RPyeQ1S7+LMaUX1cN5OtfHKwCTkREREREREREVUVAygiIiIiIiIiIqoqBlBERERERERERFRVDKDqVEdHBz71qU/ploh43RDx+w1RbeHPakS8boj4/WYBr4JHRERERERERESzjxVQRERERERERERUVQygiIiIiIiIiIioqhhAERERERERERFRVTGAIiIiIiIiIiKiqmIARUREREREREREVcUAqs78+Mc/xgUXXIDGxka0trbi6quvxvPPPz/XD4tozt122234kz/5E6xfvx5utxter3fC8w3DwO23346NGzciEAhgyZIl+PM//3P09/fP2mMmmmsvv/wyPv3pT+Oiiy5CV1eXfm8544wz8IEPfADHjx8fcz6vGyKgr68P7373u7Ft2za0tbUhGAxi9erV+NM//VM89dRTvG6IJimfz+v7GpfLhde85jVj7k8kEvjYxz6GVatW6c9qsv34xz+ux4kWEpfLNe4YnQXU+s9qLtM0zbl+EDQ5d955J97znvdgy5YtuOmmm5BKpXDHHXdgcHAQjzzyCLZu3cqnkhYs+Q+4paUFZ599Nnbt2oXe3l79D3g8b3/72/G9730Pb3zjGzXI3b9/P7785S9j7dq1+O1vf6tvxInmO/nB/itf+Qre9KY36ZuAhoYGff1/5zvfQVNTEx599FFs2rSpcD6vGyJgz549eMc73oFXvepVWLlypX6/OHDgAL71rW/hxIkTuPvuu/G6172O1w3RSfzDP/wDPvWpTyEWi+GKK67AvffeW7gvl8vpsQcffFC/91x66aXYsWMHvv71r+Oyyy7DPffco79wJFoo73MuueQS3HjjjWPuk5/hmpub6+dnNQmgqPYNDAyYTU1N5rJly8zh4eHC8YMHD5qNjY3m5ZdfPqePj2iu7dmzp7B/2WWXmR6PZ9xz77vvPgnezauvvrrs+A9/+EM9fsstt1T1sRLViieffNIcHBwcc/wb3/iGXgvXX3994RivG6KJHT16VL/3lP5MxuuGqLK9e/eaoVDI/PKXv6zfb6644oqy+++88049/v73v7/s+Be+8AU9/u1vf5tPLS0YAMwbbrjhpOfVw/ccxsZ14qc//SlGRka0Akp+K+1YsWIFrrvuOtx///04fPjwnD5Gorkkqf5kSXWH+PCHP1x2/M1vfrOWdzv3E813r3zlK7VycDSZSiSeffbZwjFeN0QT6+zs1CrCoaEhXjdEJ/He974Xmzdvxvvf//6K9zvfc/7qr/6q7PjNN9+s1xl/VqOFKJvNIhqNjnt/PfysxgCqTjz++OO6vfDCC8fc5xx78sknZ/1xEdXr9SRl2zLlaDSZUrF3714MDAzMyWMjqgVHjx4tvKF28LohGvtGQPpBybS7J554Am9961t1KpFMe+B1QzS+b37zm/jNb36j20rT6KTgQ97XSO8ameZaSsKns846i+97aMH54Q9/qK9/KUaRXx6+7W1v0+nfperhZ7WJu/RSzThy5Ihuly1bNuY+55hzDhGd/Hpqb2/XxnwTXU+LFi3iU0kL0t/93d/p9l3velfhGK8bonLSf/Pyyy8v3JYeHH/zN3+DT37yk7xuiMZx7NgxfOQjH9HKJmnkX4m8QZZG49L3thL5We2xxx7T2SGlM0OI5nPF+pvf/GZs2LAB6XQaDz/8sAa4//u//6vfi5x+nfXwsxoDqDrhrPZQ6cUkq6+UnkNEJ7+eZBXJSng90UJ366234kc/+hGuvfZa3HDDDYXjvG6IysmbZ2mELG8Gdu/eje9+97s6NUJuOyux8rohKiercckbZGk+Pp33PaN/VmMARQvBk6NmOr3lLW/RaturrroKf/mXf4lf/OIXdfM9hwFUnQiFQrqVH2pGk9XwSs8hopNfT5WuJV5PtND94z/+I/72b/8W27dvx/e//31ddcXB64aonPyQ7ywd/wd/8Ad45zvfqaHUvn379LfSvG6Iyv3nf/4nfvazn2lwK1OJpvO+R/C9DxHwhje8Aeeffz7uu+8+vSYkYKqHn9XYA6pOTDTNbqLpeURU+XqSvh2V/oPm9UQLeTls+S2aLHv9P//zP2N+QOF1Q3TyQEqWvJbfRDt9OXjdEFnkZ64PfOADuPLKK7UZ8p49ewpDJJNJ3e/u7tbpQfI9aLz2InJcKp9Y/UQL3erVq2EYRqGvUz18z2EAVSfOO+883cp859GcY+eee+6sPy6ier2e8vl8obn/6OtJVtRj/ydaSG6//Xbtx/H6178ed999d8XfjvG6ITo5eRMtBgcHed0Qjbo2ent78atf/Qrr168vG+LRRx/V/Q9+8INafSs9b6Rf1MGDB8dcY8888wzf9xABOv3b5/Ohra2tbr7nMICqE9KLIxKJaLMxabjnOHToEO666y6dLrF8+fI5fYxE9eLtb3+7br/4xS+WHf/xj3+sv7V27idaKD2fPvaxj2kvgZ/85CeFHgGj8bohskiFRiXy/UOuIWlGfvrpp/O6ISrR2Nio71kqDbF161bd/9CHPjThtfP1r39dQyj+rEYLRX9/f8XjP/jBD/DUU0/pLw+dfmn18LOay5R1LqkufOMb38D73vc+XRHipptu0tK6O+64Q1+U0gl/vJUkiBYCaf7q/Jbszjvv1HD2lltuKdz/iU98oux8WS5b/uOWN93XXHMN9u/fjy996Utayiq/NQiHw7P+byCabV/96lfxF3/xF+js7MTf//3f62/RSsl1IL8AcfC6IYJOVZUeNtL8VaYSSbXGrl278J3vfAexWAzf/va3dXlsXjdEkyPXkEz/vvfeewvHcrmcrjL50EMP4R3veAcuvfRS7NixA1/72tdwySWX6Lkej4dPMc17H/rQh3Slu1e/+tVYsWIFMpmM3pYFY7q6ujQHWLNmTf38rCYBFNWPu+66yzzvvPPMhoYGs7m52XzjG99o7tixY64fFtGcu+yyyyRMH3eMlslkzFtvvdVcv3696ff7za6uLvPGG280e3t75+TxE82FG264YcLrZuXKlWXn87ohMs177rnHvO6668xVq1aZoVBIv4fItfLWt77VfPzxx8c8RbxuiCYm32+uuOKKMcej0aj5kY98xFyxYoXp8/l0+9GPftSMxWJ8SmnB+OlPf2q+/vWvN5ctW2YGg0EzEAiYGzduND/84Q+b3d3ddfc9hxVQRERERERERERUVewBRUREREREREREVcUAioiIiIiIiIiIqooBFBERERERERERVRUDKCIiIiIiIiIiqioGUEREREREREREVFUMoIiIiIiIiIiIqKoYQBERERERERERUVUxgCIiIiIiIiIioqpiAEVERERERERERFXFAIqIiIhonlq1ahW2b98+1w+DiIiIiAEUERER0WQ98MADcLlcE44XX3yRTygRERHRKN7RB4iIiIhoYtdddx2uueaaivctWbKETx8RERHRKAygiIiIiKZo27ZteNvb3sbnjYiIiGiS2AOKiIiIqIr9l3bs2IErr7wSkUgEzc3N+KM/+iPs3bt3zPmpVAq33HILNm3ahGAwiEWLFuFNb3oTfve731X8+A899JBWYXV0dCAQCGDFihV461vfWvFj7969W8+Vzx8Oh3HVVVdhz549/LoTERHRrGEARURERDRFiUQCfX19Y8bg4GDZeUeOHMHll1+u0/I+97nP4YYbbsDdd9+Niy66CEePHi2cl8vlNBT69Kc/jbVr1+ILX/gCbr75Zjz66KO4+OKLcf/995d93H/913/VcOvxxx/He97zHtxxxx26PXDgAJ5//vmyc+XzXHrppejs7MTtt9+Om266Cffee68GUvl8nl97IiIimhUu0zTN2flURERERPXfhFwCpfGsXLlSQyCnAurgwYP4/Oc/j7/+678unPNf//VfWgUlYdS3vvUtPfZv//Zv+LM/+zO8973vxb/8y7+UVS6deeaZWt0kzc3dbrcGShJSSaj1xBNPoL29vewxSKgk55U+hv/4j//AW97ylsI5t912Gz7+8Y/jl7/8pVZnEREREVUbK6CIiIiIpuid73wn7rnnnjHj+9//ftl5Mu3u/e9/f9mxP/zDP8Tpp5+uQZRTgfSjH/1ItzIFr9SGDRt0Wt3LL7+M5557To/dddddSKfT+OQnPzkmfNIf7uzwySFBVWn4JF772tcWAi4iIiKi2cAm5ERERERTJBVIr3nNayZ1nvRnGu2MM87Arl270Nvbq1Pj9u3bh7a2Npx22mljzt26datupbeTND93QqNzzjlnUo91zZo1Y47J5xL9/f2T+hhEREREp4oVUERERETzmMfjGfc+dmIgIiKi2cIAioiIiKhKpGpJpsuN9sILL6CpqUlXsHMqpaQaqbu7e8y5TlNxOceZlieefvppft2IiIiobjCAIiIiIqqSaDSqK9SVkt5PMv3u2muvLfRrkqbk4jOf+UzZuXv27NEG4uvXr9dm5OL666/XaX1y7sDAwJjPyZXtiIiIqBaxBxQRERHRFO3YsQPf+973Kt63fft2LFu2rFC1dOutt2Lnzp04//zzNXj6+te/rpVPn/3sZwt/5x3veId+vK9+9as4dOgQXve61+HEiRN6rkyT+8Y3vgGXy6XnLl26FP/0T/+E973vfdi8eTPe9a53YfXq1ejp6cEvfvELXXHvmmuu4deUiIiIagoDKCIiIqIp+uEPf6ijEqlwcgIo2coKdx/5yEd0SIh01VVX4Qtf+AKWL19e/IHM68XPf/5z3HbbbfjBD36AX/7ylwiFQrj44ot1tbtzzz237HPceOONWLduHT7/+c/jn//5nxGPx9HV1YVLLrmk0LSciIiIqJa4THafJCIiIppxq1at0vHAAw/w2SUiIqIFjz2giIiIiIiIiIioqhhAERERERERERFRVTGAIiIiIiIiIiKiqmIPKCIiIiIiIiIiqipWQBERERERERERUVUxgCIiIiIiIiIioqpiAEVERERERERERFXFAIqIiIiIiIiIiKqKARQREREREREREVUVAygiIiIiIiIiIqoqBlBERERERERERFRVDKCIiIiIiIiIiKiqGEAREREREREREVFVMYAiIiIiIiIiIqKqYgBFRERERERERESopv8P23ygpTpelp8AAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 1200x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# ── Plot training loss curves ─────────────────────────────────────────────────\n",
    "fig, ax = plt.subplots(figsize=(10, 4))\n",
    "colors = {\"rhythm_only\": \"#4C72B0\", \"spectral_only\": \"#DD8452\",\n",
    "          \"late_fusion\": \"#9370DB\", \"full\": \"#55A868\"}\n",
    "for variant, hist in loss_histories.items():\n",
    "    ax.plot(hist, lw=2, color=colors[variant], label=f\"DCCE-{variant}\")\n",
    "ax.set_xlabel(\"Epoch\"); ax.set_ylabel(\"Training Loss\")\n",
    "ax.set_title(\"DCCE Training Loss Curves — All 4 Variants\")\n",
    "ax.legend(fontsize=9); ax.grid(alpha=0.25)\n",
    "plt.tight_layout()\n",
    "plt.savefig(os.path.join(FIGS, \"fig_dcce_training_curves.png\"), dpi=130, bbox_inches=\"tight\")\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "p3-17-kfhigx",
   "metadata": {},
   "source": [
    "---\n",
    "## 7. Evaluation — Linear Probe on Frozen Embeddings\n",
    "\n",
    "Following the standard representation learning evaluation protocol (Chen et al. 2020; Radford et al. 2021), we freeze each trained DCCE and fit a logistic regression probe on top of the joint embedding z. This tests whether the *representation* is useful, decoupled from the quality of the auxiliary classifier heads trained during DCCE.\n",
    "\n",
    "For rhythm-only and spectral-only ablations, z = r_emb or s_emb respectively. For late_fusion and full, z is the fused 64-dimensional embedding.\n",
    "\n",
    "Same train/test split, same evaluation protocol (macro-F1 primary) as Phases 1–2.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "p3-18-refxbe",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-05-01T20:34:29.467354Z",
     "iopub.status.busy": "2026-05-01T20:34:29.467238Z",
     "iopub.status.idle": "2026-05-01T20:34:31.192243Z",
     "shell.execute_reply": "2026-05-01T20:34:31.191797Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "=======================================================\n",
      "DCCE-rhythm_only  \n",
      "  unit                       F1=0.6366  Acc=0.6787\n",
      "  coda_type                  F1=0.8777  Acc=0.9856\n",
      "  individual_id              F1=0.5087  Acc=0.5033\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "=======================================================\n",
      "DCCE-spectral_only  \n",
      "  unit                       F1=0.6927  Acc=0.7220\n",
      "  coda_type                  F1=0.1386  Acc=0.3466\n",
      "  individual_id              F1=0.7872  Acc=0.8170\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "=======================================================\n",
      "DCCE-late_fusion  \n",
      "  unit                       F1=0.6561  Acc=0.6751\n",
      "  coda_type                  F1=0.7048  Acc=0.9278\n",
      "  individual_id              F1=0.8251  Acc=0.8627\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "=======================================================\n",
      "DCCE-full  \n",
      "  unit                       F1=0.8780  Acc=0.8845\n",
      "  coda_type                  F1=0.5777  Acc=0.7870\n",
      "  individual_id              F1=0.8338  Acc=0.9020\n"
     ]
    }
   ],
   "source": [
    "def extract_embeddings(model, variant, indices):\n",
    "    \"\"\"Extract z embeddings for given indices (into df_clean arrays).\"\"\"\n",
    "    model.eval()\n",
    "    embs = []\n",
    "    with torch.no_grad():\n",
    "        for i in range(0, len(indices), BATCH_SIZE):\n",
    "            batch_idx = indices[i:i+BATCH_SIZE]\n",
    "            ici = torch.tensor(X_ici_all[batch_idx]).to(DEVICE)\n",
    "            mel = torch.tensor(X_mel_full[batch_idx]).to(DEVICE)\n",
    "            if variant == \"rhythm_only\":\n",
    "                z = model.encode_rhythm(ici)\n",
    "            elif variant == \"spectral_only\":\n",
    "                z = model.encode_spectral(mel)\n",
    "            else:\n",
    "                z, _, _ = model(ici, mel)\n",
    "            embs.append(z.cpu().numpy())\n",
    "    return np.vstack(embs)\n",
    "\n",
    "\n",
    "def evaluate_model(model, variant, label=\"\"):\n",
    "    \"\"\"Run all 3 linear probes and return result dict.\"\"\"\n",
    "    Z_tr = extract_embeddings(model, variant, train_idx)\n",
    "    Z_te = extract_embeddings(model, variant, test_idx)\n",
    "\n",
    "    # ID subset\n",
    "    id_train_clean = id_in_clean[train_id_idx]\n",
    "    id_test_clean  = id_in_clean[test_id_idx]\n",
    "    # re-index into train_idx / test_idx arrays for embedding extraction\n",
    "    Z_id_tr = extract_embeddings(model, variant, id_train_clean)\n",
    "    Z_id_te = extract_embeddings(model, variant, id_test_clean)\n",
    "\n",
    "    sc = StandardScaler()\n",
    "    X_tr  = sc.fit_transform(Z_tr);  X_te  = sc.transform(Z_te)\n",
    "    sc_id = StandardScaler()\n",
    "    X_id_tr = sc_id.fit_transform(Z_id_tr); X_id_te = sc_id.transform(Z_id_te)\n",
    "\n",
    "    make_lr = lambda: LogisticRegression(max_iter=2000, class_weight=\"balanced\",\n",
    "                                         random_state=SEED, solver=\"lbfgs\")\n",
    "    results = {}\n",
    "\n",
    "    # Social unit\n",
    "    lr = make_lr().fit(X_tr, y_unit[train_idx])\n",
    "    pred = lr.predict(X_te)\n",
    "    results[\"unit\"] = {\"macro_f1\": f1_score(y_unit[test_idx], pred, average=\"macro\", zero_division=0),\n",
    "                       \"accuracy\": accuracy_score(y_unit[test_idx], pred)}\n",
    "\n",
    "    # Coda type\n",
    "    lr = make_lr().fit(X_tr, y_type[train_idx])\n",
    "    pred = lr.predict(X_te)\n",
    "    results[\"coda_type\"] = {\"macro_f1\": f1_score(y_type[test_idx], pred, average=\"macro\", zero_division=0),\n",
    "                            \"accuracy\": accuracy_score(y_type[test_idx], pred)}\n",
    "\n",
    "    # Individual ID\n",
    "    lr = make_lr().fit(X_id_tr, y_id[train_id_idx])\n",
    "    pred = lr.predict(X_id_te)\n",
    "    results[\"individual_id\"] = {\"macro_f1\": f1_score(y_id[test_id_idx], pred, average=\"macro\", zero_division=0),\n",
    "                                 \"accuracy\": accuracy_score(y_id[test_id_idx], pred)}\n",
    "\n",
    "    print(f\"\\n{'='*55}\")\n",
    "    print(f\"DCCE-{variant}  {label}\")\n",
    "    for task, r in results.items():\n",
    "        print(f\"  {task:25s}  F1={r['macro_f1']:.4f}  Acc={r['accuracy']:.4f}\")\n",
    "    return results\n",
    "\n",
    "\n",
    "all_results = {}\n",
    "for variant, model in trained_models.items():\n",
    "    all_results[f\"DCCE-{variant}\"] = evaluate_model(model, variant)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "p3-19-mhnglu",
   "metadata": {},
   "source": [
    "---\n",
    "## 8. Full Comparison: DCCE Ablations vs All Baselines\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "p3-20-batsek",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-05-01T20:34:31.193908Z",
     "iopub.status.busy": "2026-05-01T20:34:31.193793Z",
     "iopub.status.idle": "2026-05-01T20:34:31.210219Z",
     "shell.execute_reply": "2026-05-01T20:34:31.209737Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Baseline results loaded from phase1_results.csv\n",
      "             Model          Task Macro-F1 Accuracy\n",
      "      1A — Raw ICI          Unit   0.5986   0.6209\n",
      "      1A — Raw ICI     Coda Type   0.9310   0.9856\n",
      "      1A — Raw ICI Individual Id   0.4925   0.5033\n",
      "      1C — Raw Mel          Unit   0.7396   0.7329\n",
      "      1C — Raw Mel     Coda Type   0.0972   0.1372\n",
      "      1C — Raw Mel Individual Id   0.2722   0.2745\n",
      "     1B — WhAM L10          Unit   0.8763   0.8809\n",
      "     1B — WhAM L10     Coda Type   0.2120   0.4007\n",
      "     1B — WhAM L10 Individual Id   0.4535   0.4641\n",
      "     1B — WhAM L19          Unit   0.8946        —\n",
      "     1B — WhAM L19     Coda Type   0.2605        —\n",
      "     1B — WhAM L19 Individual Id   0.4535        —\n",
      "  DCCE-rhythm_only          Unit   0.6366   0.6787\n",
      "  DCCE-rhythm_only     Coda Type   0.8777   0.9856\n",
      "  DCCE-rhythm_only Individual Id   0.5087   0.5033\n",
      "DCCE-spectral_only          Unit   0.6927   0.7220\n",
      "DCCE-spectral_only     Coda Type   0.1386   0.3466\n",
      "DCCE-spectral_only Individual Id   0.7872   0.8170\n",
      "  DCCE-late_fusion          Unit   0.6561   0.6751\n",
      "  DCCE-late_fusion     Coda Type   0.7048   0.9278\n",
      "  DCCE-late_fusion Individual Id   0.8251   0.8627\n",
      "         DCCE-full          Unit   0.8780   0.8845\n",
      "         DCCE-full     Coda Type   0.5777   0.7870\n",
      "         DCCE-full Individual Id   0.8338   0.9020\n",
      "\n",
      "--- DCCE targets ---\n",
      "  Social Unit > 0.895  (WhAM L19)\n",
      "  Individual ID > 0.454  (WhAM L10)\n",
      "  Coda Type > 0.931  (Raw ICI baseline)\n"
     ]
    }
   ],
   "source": [
    "# ── Load Phase 1 baseline results (live, not hardcoded) ──────────────────────\n",
    "p1_csv = os.path.join(DATA, \"phase1_results.csv\")\n",
    "if os.path.exists(p1_csv):\n",
    "    p1_df = pd.read_csv(p1_csv)\n",
    "    def _get(model, task, metric):\n",
    "        row = p1_df[(p1_df[\"model\"] == model) & (p1_df[\"task\"] == task)]\n",
    "        return float(row[metric].iloc[0]) if len(row) else None\n",
    "\n",
    "    baseline_results = {\n",
    "        \"1A — Raw ICI\": {\n",
    "            t: (_get(\"1A_ICI\", t, \"macro_f1\"), _get(\"1A_ICI\", t, \"accuracy\"))\n",
    "            for t in [\"unit\", \"coda_type\", \"individual_id\"]},\n",
    "        \"1C — Raw Mel\": {\n",
    "            t: (_get(\"1C_Mel\", t, \"macro_f1\"), _get(\"1C_Mel\", t, \"accuracy\"))\n",
    "            for t in [\"unit\", \"coda_type\", \"individual_id\"]},\n",
    "        \"1B — WhAM L10\": {\n",
    "            t: (_get(\"1B_WhAM_L10\", t, \"macro_f1\"), _get(\"1B_WhAM_L10\", t, \"accuracy\"))\n",
    "            for t in [\"unit\", \"coda_type\", \"individual_id\"]},\n",
    "        \"1B — WhAM L19\": {\n",
    "            t: (_get(\"1B_WhAM_L19\", t, \"macro_f1\"), None)\n",
    "            for t in [\"unit\", \"coda_type\", \"individual_id\"]},\n",
    "    }\n",
    "    print(\"Baseline results loaded from phase1_results.csv\")\n",
    "else:\n",
    "    # Fallback: use values from the last full Phase 1 run\n",
    "    baseline_results = {\n",
    "        \"1A — Raw ICI\":    {\"unit\": (0.5986, 0.6209), \"coda_type\": (0.9310, 0.9856), \"individual_id\": (0.4925, 0.5033)},\n",
    "        \"1C — Raw Mel\":    {\"unit\": (0.7396, 0.7329), \"coda_type\": (0.0972, 0.1372), \"individual_id\": (0.2722, 0.2745)},\n",
    "        \"1B — WhAM L10\":   {\"unit\": (0.8763, 0.8809), \"coda_type\": (0.2120, 0.4007), \"individual_id\": (0.4535, 0.4641)},\n",
    "        \"1B — WhAM L19\":   {\"unit\": (0.8946, None),   \"coda_type\": (0.2605, None),   \"individual_id\": (0.4257, None)},\n",
    "    }\n",
    "    print(\"WARNING: phase1_results.csv not found — using hardcoded fallback values\")\n",
    "    print(\"  Run Phase 1 first to generate live values.\")\n",
    "\n",
    "rows = []\n",
    "for name, res in baseline_results.items():\n",
    "    for task, (f1, acc) in res.items():\n",
    "        rows.append({\"Model\": name, \"Task\": task.replace(\"_\",\" \").title(),\n",
    "                     \"Macro-F1\": f\"{f1:.4f}\", \"Accuracy\": f\"{acc:.4f}\" if acc else \"—\"})\n",
    "\n",
    "for name, res in all_results.items():\n",
    "    for task, r in res.items():\n",
    "        rows.append({\"Model\": name, \"Task\": task.replace(\"_\",\" \").title(),\n",
    "                     \"Macro-F1\": f\"{r['macro_f1']:.4f}\", \"Accuracy\": f\"{r['accuracy']:.4f}\"})\n",
    "\n",
    "summary_df = pd.DataFrame(rows)\n",
    "print(summary_df.to_string(index=False))\n",
    "\n",
    "# Highlight targets\n",
    "print(\"\\n--- DCCE targets ---\")\n",
    "print(f\"  Social Unit > 0.895  (WhAM L19)\")\n",
    "print(f\"  Individual ID > 0.454  (WhAM L10)\")\n",
    "print(f\"  Coda Type > 0.931  (Raw ICI baseline)\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "p3-21-buxddf",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-05-01T20:34:31.211603Z",
     "iopub.status.busy": "2026-05-01T20:34:31.211516Z",
     "iopub.status.idle": "2026-05-01T20:34:31.556606Z",
     "shell.execute_reply": "2026-05-01T20:34:31.555954Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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ILykpsYqKipiXPXDgQHvsscei3m/11Ve3+fPnW35+vpWWltqBBx5oN9xwg28nNB199NG21lprBU66WrRokV1wwQX23HPP2bJly2zVVVd19znzzDPt559/dvdtamryZV3Q3t77Hmhz5i2M+tI0NzdZVeVCa2luttKycisu7hH1MUMGD7Bnn4q+TaZ6XjZtZyuvvHJXrwqioM4DgK47Hu3M8Wmsx6S57Kw/j7Np3/zXmpqafVn+kMED7dmnHvVl2Qhvl112cZ+reOq3WD9v8XymqPUQXOd9+dVXcdVvSk1qqiutob7OiopLrLSswvIyZB9DnZfBAR8AAECmUNE3bty4sLc1Nzfb3//+d5sxY4Ztuummdtxxx8UVuo0fPz7m+06ePNm22WYb97fUmnz99de3448/3vymQlUnXK222mr2zjvv2NChQ+2HH36wSy65xJYsWeL730d7Cvc226/jjhdLq+fb649dY3VLa23LPY611YePivpSqpB848FzMu4l1+fQr7AbAIB0Px5NxvFpPMekuWr+wkrbZO9x1tIa7qvzzvv4md+mLkLqPldXX311p44jI33e4v1MUetBFO4tXhJf/fb+i3fagkUzbJV1NrOt9jrR8iNsz5m4j2nO8Tovv6tXAAAAIJt5xdx//vOfhMK9RK255pq29dZb22effRa47rTTTrPBgwdbWVmZ7bbbbvbTTz+567V+hx12mPu5sbHRevbs6dqby3fffWdDhgyJ+vc05/HChQvt8ccfd39bZ9mtscYa7nr9PaSf38K9qriLQ535GYuWlhY7/fTTrV+/fm472Hzzzd12ou1Dc2RrlOdKK61kEyZMCPnMKBhWWKyzmtVaP3jk580332xrr7229e7d27bYYgs3clTbs6y77rque8fbb7/tWrEceuihNnr0aHfd66+/bs8//7xtsMEG7rFahrbXeGkubwXY/fv3d21YNM+3NypNo1T12dEZ2TrL+t///ne7x9fX17vXYtasWYHr3njjDVtnnXXiXhcAADLp+BTIRDquS0a4l8zPG7VeblNNFm/99vN3H0cN9+JaB+o8S5c6j4APAAAgC788UTCnkEPFn0eB3/Tp023OnDmujaGCF9l2223dfeWTTz5xoYpG4Ymu1+3RKDzZfffdXbsZZHe4p+JQbV1iPcv4vffes5kzZ7og7q677rLi4mJ3m8K2r776ygVjf/vb3+y1115z1+tnbXcff/yxffvtt26bvPPOO91tDz30kN1000329NNPu2BN13fv3t39HdH9ly5dGthmdb8TTjjBampq3HUK9p544gn3WC3nmGOOsblz58b8ur3yyit2xRVXuHXXCFWNXD3jjDMCt+u6oqIi1ypX7WrDjZ7V8z/ggAPsn//8Z+A6zRPuhewAAPiJcA/I/M8btV5uU1vOrgz3hDrv+LSp8wj4AAAAsujLkz333NONVtJIplGjRtkpp5wSuO2QQw6xPn36uBDuvPPOC4R46623njU0NLgzzRSsKBD5+uuv3XPQ72r5GY3CG43EQvaHeyoONWdDLLp16+ZatH7zzTdubshNNtnEbZ9y/vnnu7kitf398Y9/tEcfXTHXwz333GNXXnmlGyGnMyA1gk+hnNx3332u9Zhaz3rLU2gXyfbbb+9G9+m+Krj0uz4b+n2vvfZyo/kUJMZKxZpCu+HDh7uRrn/961/dentzgeu6c845xwoLC93oPo2Sra6ubrcc3aZizxs1++STTxLwAQB8R7gHZPbnjVoPEsuce4mGe/X1tTG9yNR5P6VNnUfABwAAkEVfnrz00ksuUHnmmWdccKHRTJ6rrrrKtRBUqOK1NvQoxFOYp4tCkA033NA+/fTTmEfw9e3bN66RUMjccE/FYayzyuy888524oknulBs0KBB9uc//9kVOrLKKqsE7qefNbJUFIrpywuFe7ocfvjhbkSc/PLLL671a6w0UjWYQm2NZFWrJS1bn5Hgz0E0s2fPdm1FPWojqlYslZWV7neFkgoPpUePFYV38GfQo/kqq6qqXJCuNp56TrToBAD4iXAPyPzPG7UeYpFouPfDtPesproqpr9BnWdpU+cR8AEAAKRxMafleeFBrDS/2b777mu77LKLGwklU6ZMcXOHvfjii6494YcffhjyGIV4uo9CPa23fn/sscdc+KFRTtHstNNOrk2HWhYiu8O9eNu6nHnmmW4uyI8++sgVOWqzKT///HPgPvpZAaBozkfNVaAzInXR9jpt2rRAEKg2mPF8FoIdccQRdtRRR7kwWsvebLPNAqPvYqE5LL25K0U/a2SgAsN4KATU/IA6u1MXhZgAAPiFcA/Ins8btR78Cvc+eHliXLUedV561HkEfAAAAGkc7ml5iYZmGi01ceJEW7hwoRvVpzYa/fr1s2XLlgWCP48Cvccff9yN8NP9tttuOze/mdp8eiOSOnLkkUe6UXxqA6r51uTHH3+0o48+OmzrCuRGuKcRcgr2mpqaXCtNbVveZ+Kaa65xc+Np3rx//OMfNmbMGHf9H/7wB7vooovciD6Fbwr0FD6Ltic9ToGfbtP8fNq2ZcCAAVHDP91X26nWQ+1S9FmNx8EHH+w+k5rLUp+jCy+80K132yAxFmrfMmnSJDefnz43AAD4gXAPyM7PG7Uekh3ulfQqt/KKfjG9sNR56VPnEfABAAAkiR/hnpanOfMSMWzYMNdu86abbrI99tjDtSZUS0GNyFNwF2zjjTd2gYk3397mm2/uWinGMv+eaB3ffPNNGzhwoFu2whxvjrOO5khD9oZ7otF3CuzUDlNz32kb9OYg0PahufQUJp9++uluxKloDrutttrK3VdzRu69996B0X56rOaV/P3vf+9azZ588smBlp9/+ctfbPTo0e5vefNLtnXLLbe4v1VeXu5GE+rzEQ/N56e5A7Xu+iwpKLzxxhstEWqDq+egUYTe6EUAAJKJcA/I3s8btR6SHe7tNOY8KygojOmFpc5LnzovrzWenjRw1ENVX0Z89dVXNmLECF4VuP/EdUa4vsBM1RxLADIX+4zM5o1OGzp0aMj1+sL/u+++c0FXvO36wlFbTo3c0/JUvKldJnKTDte139AxRjwjxfY94GD76edfrapyoSv4SsvKY5qQXcVBTXWlNdTXWVFxiZWWVbSbc2/I4IH27FOPWqL0PBTatZ0jL9coqNx///3tj3/8Y1z7G+oRf/C6oi2O2ZBpdOLKuHHjfOssoZNcvvzyyySucXbRa7Tv6ENt1a2Os5bW+Ef3x+LjZ660j6e+6cuyc12k4y51a5g3b15c9Vus9aBOjqTOy12dqfN+nT0vrvotnPr6WjfnnoJAjdxTuEedl5o6L9I+J5F6JLZIFgAAAB3S3HZAOulMAAf/qc3oe++95+ZmAAAgm9vGA5mMAA7phjovvU1LcZ1Hi04AAAAA+J9HH33UevXq1e5yxBFHJO01Ovfcc23kyJE2fvx4WtgCADIi3OtM23gAALrao1la5zGCDwAAAEBKpfMsAQcffLC7+Onaa691FwAAks2vcE/Lq66uTuq6AgCyC3XetSmv8xjBBwAAAAAAAGQ4zQHmV7in5QEAgPRCwAcAAAAAAABkOM2R51e415nlAQAAfxDwAQAAAAAAABlOc+QR7gEAkDsI+AAAAAAAAIAMV1FRwcg9AAByCAEfAAAAAAAAkMNoywkAQOYh4AMAAECXufTSS+3YY4/lHQAAAOgihHsA/ECtB/ivMAV/AwAAIOuNGTPG5s2b1+76pqYmq66udj+XlZVZYWH4w6+O7jdw4EB77LHHoq7D6quvbvPnz7f8/HwrLS21Aw880G644YZOtWrqyNFHH23333+/vf7667bjjjsGrt9pp53sjTfesFmzZrl1Qtc4bPS+tnDu7MDvra2ttqi6xmrr661HcbH1LSu1vLy8qMtZVldvi6oXu+1oQEW5dSsssH4rDbaHn3w2pvXQNjBp0iTbZpttzG9z5861Aw44wL788ku77LLL7Kyzzop7GW+//badeuqp9vnnn/uyjgAApBPCPSD96zyh1kOkOi+W+i2axqZmm19ZZSuvvIr9+423Yt4mqfO6HgEfAABAEqjoGzdunC+v5fjx42O+7+TJk12QMmPGDNtuu+1s/fXXt+OPP978svbaa9vDDz8cCPh+/fVXF+x1797dt7+J2Kjou2fsSPdzU3OLXfHkOzZz0QLbfviqdvHobaywIHozj8lfzLLxT79n/UpL7MajdrLBFb3d9X+cNDWpb8N9993nisNXX321U8u5++67XaH53nvvJbyMbbfdlnAPAJATCPeAzKnzhFoPbeu8WOu3jsyuXGJ/uv8VW76kzpYu7pXUF5k6z3+06AQAAMhCa665pm299db22WefBa477bTTbPDgwe7M0d12281++uknd/3f//53O+yww9zPjY2N1rNnT7v88svd7999950NGTIk4t/Zf//97YUXXrCGhgb3u8K+Qw45JGRkWF1dnRsRpb+98sorx13IonO8cG/KtJ86Ee7tGlNx2NW0TQ8fPryrVwMAgLRHuAdkLmo9JKt+88K9hTV1Nm7/UdazpDgtX1zqvMgI+AAAALKQgjm1GlTx51HgN336dJszZ44L2k4//fTAiCXdVz755BPXKuadd95xv+t63R5J79693YjBF1980f3+0EMP2dixY0Pu8+c//9kqKyvdOn344Yf24IMP2vPPP+/L80b6hnsffPCBbb755q597GqrrWa33HKLu37mzJl24okn2ptvvmm9evWyESNGuOu1zSh4HjBggA0dOtS1g+2IlqH7XHHFFW45n376qe2www5uZGC4eUC0PWrb1fpomz/nnHPc9VqPtdZaK/CYr7/+2n0GFIxvuumm9u677wZu02jB66+/3oYNG+ZuV5ANAEC6I9wDMhu1HvwI93bbcI2EXljqvK5Fi04AAIAssueee7q51pYtW+bmIjvllFMCt2lknee8885zgZ+st956bgSeWmsq0DvhhBPs5ptvdl/+6Pdoc6cdfvjhLrRbZ5113O9eQCNal3vvvde+//57F7roctJJJ9kTTzxhv//97314BRD82qdLuCfdunWzu+66y/7v//7PBck777yz27Y23nhju/POO9u16DziiCNccPbzzz+7bVNzO+q+G264Ydjlaxn19fUunLvooouirs9f/vIX+93vfue28draWhfktbV8+XLbe++97U9/+pOba/Kpp55yv6sFbnl5ubvPM88845ahv73JJpvY6NGjQ+akBAAgnRDuAZmLWg/pFu4JdV7XYgQfAABAFnnppZdsyZIlLnT4+OOPbenSpYHbrrrqKhd+aMTSFltsYYsWLQrcpqBFIYUu22+/vQtRNAIq2gg+r9B8//337bbbbnNhX7AFCxa4Fp1qm6gRTrpccMEFYSeqR3Itqq5Jm3BPFH7pkp+fb5tttpnttddeIaPhgs2dO9eNpLv66qutqKjIhdAazaeALVlUiP7444/ub6ktrT4T4c5GbWlpcaNddf+DDz7Y1l13XXv55ZcD91H4169fPzcqViMGP//886StIwAAyUS4B2Q2aj2kW7gn1Hldi4APAAAgy2j+u3333dd22WUXu/LKK911U6ZMsdtvv9210ly8eLFrlRlMIZ7uo1BPbQj1+2OPPeZCwA022KDDv9e9e3c3Euruu+8OzOXnUfChgEZtGKurq92lpqbGFafwV219fdqEe6IRcrvuuqv179/f+vTp48K64JC57RwLGhGn+3rBsEb/KYxLlmuvvdaN0NOIQo0M/Ne//tXuPrNnz7ZVVlkl5Dq1F9X1HrX39PTo0SMkVAcAIF0Q7gHZgVoP6RTuCXVe1yLgAwAASBLNbTdhwgR30c/Jut/ChQsTWh/NfTdx4kT3eI3q0wgkBW5q3+kFfx4Feo8//rgb4af7bbfddq7l4ahRo9yIq2guu+wyFxAOGTIk5Ho99qijjrKzzz7bhXsaDaV5ANsGjEi+HsXFaRPuiean22qrrVx4p5BZLWTVRtT7oiKYtiO1c62qqgoEw9qGtU3GQyPzNILUEzxydNCgQfaPf/zDhYaam2/MmDEuVAw2ePBg1yI0mNZf1wMAkCkI94DsqvOEWi+3pUu4J9R5XYs5+AAAAJJAo3iuv/56N5eX6GeFaeGokIvnfmuskdiBt+YvU7vNm266yS655BI3555GH+nvnXvuuW7OM49GMCls8ebb23zzza2xsTHq/HvBYYku4dxwww2uLadGAiqkWXvttdsFjEi+9YYNsxMeiR6kLqurt0XVi6177z5WXF5uF7/Yfi66tsoHrBT3+ui910i84uJi1/r1hRdecO0uZcCAAfbLL79YU1OTFRYWuoBPYaDm0rvwwgvdKNEvvvjCPVbtXmO10UYbuZGCRx55pAuWNfejRreKflaArbBO66WQsW3QuOWWW7p/b731VjvxxBPt6aefdsvZY4894n7+AAB0BcI9IPvqPKHWy129K/rZhH9/EVf91tjUbPMrq6y5ucAGDB5ij34xz13a6rdS/CcyUud1LQI+AMhBGqWQ6PxXlZWVbjRESUmJVVRURDwAVms/IJekwzb/ww8/tLtOIYrnoYceCrntpJNOCvxcUFDgDsw9aqsZPPIpnPvuuy/ibcEjodS28MYbb3SXtjRyCv54+Mln0+qlveaaa+z44493YfNuu+1m++yzT+C2nXbayVZffXXXklMtMRXmaXs966yzbOjQoa6V5vrrr+/C4niceeaZbt48fbmiwPCQQw4JbNcaRXraaae57X7VVVe1Rx55xG33wRQsPvfcc+6zoqBxzTXXdL+Xl5cn6VUBAMA/hHtAdtR5Qq0Hz5P/Sq/pLqjzulZeq9cXB3H1ldUXDF999ZWNGDGCVw7uoFlfDvXu3dt9QQqkO43oGTdunG/F4fjx412rPkR+LdlnZC7NJScKHYBU0OG69hva57YdYYbc3N9Qj/iD1xVtccyGTK7zkh3uaXnnn3++ffnll0lc4+yi12jf0YfaqlsdZy2t/hyzffzMlfbx1Dd9WXauo85DqlHn5baZYWq9ROoRRvABAKLizE8AAAAAyAx+hHtaXrTuDsgOh43e1xbOnd2mrV+z9S3rYz1LimMKLRZV11htfb2bE7pvWWnISWpqAZhunSYAIFMR8AEAOkS4BwBIJ5pXb8aMGe2u/+ijj9xcJAAA5DK/wj0tjxbVuUHh3j1jR9rsyiX2p/tfseVL6mzc/qNstw2jzxfX1NxiVzz5js1ctMC2H76qXTx6GyssyA+5zx8nTfVx7QFkKuq8xBDwAQAiItwDAKSbzz//vKtXAQCAtOVXuKflVVdXJ3Vdkb68cG9hTfzh3pRpP0UM9wAgEuq8xLCXBQCERbgHAAAAAJmjsrLSt3BPy0NuUFtOwj0AyAyM4AMAtEO4BwAAAACZRXPk+RXudWZ5yCyacy+RtpyM3AOA1GMEHwAgBMUcAAAAAGSekpISwj0k5TsBwj0AyAwEfACAAMI9AAAAAMhMFRUVjNxDp/Ut6+PryL1ldfW8SwCQJAR8AACHcA/ILZdeeqkde+yxXb0ayAHTp0+3DTbYwHr37m1PPvlkxPvdd999tssuuwR+z8vLs19++SVFawkAQG6jHoSnZ0mxb+He5C9m2aLqxbzYPqPWQypQ56UH5uADAFDMAUkwZswYmzdvni+v5cCBA+2xxx7r8D6XX365ff311/boo4+63+vr661Pnz42btw4u+yyy9x106ZNc/OoLF4cW1GtZehv77zzzvbUU0+F3KbwZfXVV7eZM2e6n+XHH3+0NdZYw7bbbjt788032y1P1ylU/P7779vddscdd7i5Xr788ku78MILXVHaNvy56KKLrKamxkaPHm133XWXde/ePabnkau232UH++nnn628X7kVFEafN6e5qdmqFlZZS0uzlZb1seIekb/cGTJwsD3z2NMxrceECRNs7733tr/+9a9xrT8AAEgNwj3EozPh3vin37Puvftk1Ave1XWeUOsh2H5j9rdf582Oq37ztLaa1VRVW31dgxWXFFlpeZn9r5x3qPMyDwEfAOS4ZBdzWl5lZWVS1xHIBCr6FKb5Yfz48VHvs80229jdd98d+P3DDz90Adw777wTuE4/b7HFFjEHY88995zbH7z44otWXV1tZWVlIbd369bN3nvvPdt6663d7w8//LCttdZalohBgwa5UE/LaEuh35lnnmmTJ0+2ddZZxwV8V1xxhbsgMoV7O16wt/XuH/1LlCULFtvk65+x2tpltvUxO9vQket2eP/3rnk55pf+p59+sp122om3CgCANES4h1SGe/1KS6y4vDyjXvSurvOEWg/BFO5t8Iet4qrfpKW5xd6eONkWzFxkq226pm177G6W3+bzS52XeWjRCQA5zI9wT8urq6tL6noCiG7kyJE2f/58mzVrViDM02f6m2++saampsB12267beAx+qwedNBBrnXilltuGXisZ9KkSXbyySfbmmuuGfbM0kMPPdQeeuihwO/6+fDDD0/o7dpvv/1sn332aRciikI/hXqbb765G5WokXwPPPBAQn8nl2jkXlzhXlXsxaHO/IzFnnvuaW+88YYbudmrVy+3rQWHzkcffbRdeeWVsS0MAAAkFeEeUh3u3XjUrtYths4SCEWth2AauRdv/eaFez/+Z0bEcC8e1Hnpg4APAHKUX+GelldSUpLUdQUQXY8ePWzjjTcOhCdemKe5zz799NPAdTr70/P000+7AK+qqsrWXXfdkLaYixYtspdfftkOPvhgO+SQQ1zY19aBBx5ozz77rDU2Ntpnn33mPvtaTrKpteiGG24Y+F3PSaPCli5dmvS/lU1iacuZSLin4lBtXWLx0ksvue1w4sSJ7v3q27dvTI8DAAD+ItxDV4R7gyt688IngFoPwdSWsyvDPaHOSx8EfACQo/wK97S8ioqKpK4rgNgoSFGI19LS4gK3TTbZxLXP1HWzZ892odioUaMC99fcejvuuKMVFha6EO/zzz8P3Ka5/NRuU2GabtMyNMdesPLyctfy89///rcLABMdvReNgqHS0tLA797PBHydk2i4p+JQczYAAIDMRLiHeBDupQdqPXg0515XhntIL7ybAJCDNEeeX+Gelgega2h0noK4r776ytZee203R54X8OmisC44KNOk7sFnhQYHZgrsNHpPtCyNDgxux+lRqKf7Pv744y4I9INaO9bU1AR+937W9Uh9uKfiUBOyAwCAzEO4h3gQ7qUPaj14Ssv6EO4hoPC3HwEAuULzbvkV7nVmeQA6X/Rpzr3nnnvOBXuiufWOPPJIGzJkSMj8ex2ZOXOmvf/++zZ9+nS74447AqGaAr4LLrgg5L6///3v3fxq+jsrrbSSL2/h8OHD7csvvwz8rgBz1VVXJeDronBPZ34WViX2t3v27BkyT+u8efPcSFEAAOA/6jfEg3AvvVDrwVPco9i3kXv1tfVpV+d9P+N7W97Y6H5ubWlx/5dZXp4Vuikp8mJYQqs1NTW7ieT1nWVefujr0L1bN1trzcytSRnBBwA5SPNkEe4B2adfv362zjrr2C233BII+HSgreBNLTeD59/riEbkqfWmAj61+tRFQf5///tf++STT0LuW1xcbK+99logCIymtbXV6uvrA5eGhhWtHpuamtzvOlgP/lkOO+wwe/LJJ906LF682K666ioXWqJrwj0Vh3mx1FFhbLTRRvbYY4+59/bVV1+1N998M7EFAQCAuBDuIR6Ee+mHWg+xSjTcmzn1W6upXpx2dZ7CvV6DSq1b7+62vKnRWvPNSlfqY70G6VLa4aVHv57W2NxkzS3NVlLR03oPKWt3Hy88zFSM4OuMSy81CzfP1Nixaoy84ue339a3ZJGXcdddv/181VVmP/0U/n5anpYrmv/mr3+NvEydWb/aait+1t/WOoSz6qpmF1742+8nnBB5mTynDt+nvNZWK1m+3PJ22snM+8KR94ltL40/T5ojT2etDH79dSv/+uuwd6vv399mBLXbG3HLLYGfdcbM9G++sYMWLLCT+ve30lGjbPH/Ru6Vf/WVnf3dd5H3Kez33MuQ99NPlnfrre6so0jvE/vyNP3/aeedzYYNW3GbRqwVrWhVOLCiwsZfcUX4Zep9Lgw67OroAFKfJe+MspYWfRtjA/v2XfH32vL+v5c5c8yWL7dtN97YJv7zn7bVyisHHrP1ppvazf/5z4qAT4Ha3Llm1dWaxO635eq6piZ3u0bqnX/++bZSt24r7mNmK/XoYQfsvrtNuv122+Tii0NWQyN43XJ0WbDArL4+dH11vNS7d2B0oE4y+O3pFljTjBl25Q032GU33RS4XiHevTfcYEfvv79tUFpqf7vgAtvnd7+zmqVLbfQee9hFJ5302/K95xRJ0PtkixYFnlM73bubDRr02+/hXvMwz8mWLFHv48j3DfM+haWWo3qv2z6n1lbLb21dsR15+4wYntOQ0nJ776rnA9tec1OzVc1bYAUtLTakX3+b++p37tLRtteq0ZtVNVZf32D9e5dZ4YJGm3r1CzZk5Tiek6ehwS47/ng79PTTraxPH/v9TjvZvrvssmJb1Gsd/LnQc5JffnHrkRHvkx/b3rJlZtOnm11zzW+3af/EXLf+os4LLweP4ajzMuN9ysZtL+Hn9D9t67zg+u2gVVe1sqDOK8F1Xluzd9zRqtZf3/1MnRfb+9S/dpkd+tSN1hphVMnLOxxqleUr2uRv8emrttYPX4W9X1VZf3tpx9/mtz7s6Rvdv7v9EKbWTvK2d/r3P9grt82zKZVLbPvhq9plO25k5Y9OibjImt03tea+pTb5i1k27eE3bIKZbbVKf+v58sch92sq721L9twsfT9PaV7nuafQttbr1cud2HnzzTfbNptv/tuywtV6qifMVtR6p51mK/3vRMt2tV6b57ppv36/vWZta73gY+3a2vhqvQkT7OiDDmpf6+29t1100UUr7kidF1OdF7ztha3frn253bbXVn1dvdUsXmJD11o77LbXlXXeoIYGy/+10hrrl1tFfp51L+pmtU3N1tBtxf9jRXXLrWfNb6MHg08uXl7XYFX5+darb28r6lVspZVLrbAx9O/2blge+DylrM4LV+slWOcR8AEA4uIVhwsXLLB+/fvbsPXWszlthrcDueixiRNTHxyFcffVV7tLsJv++le7ySt8/1fIXXrmmSH32WGrrez7KSsK92+//Tb04Pt//qlQ+n9aZ88OfU7/c8g++7hLODvssIO1av68MM9J6+PWKUIxq+JPlwDvYBoRPXPrXakJw6IInL3Z0GBrr7GGffyvf4W939FHHmlHB83j2vrDDzH/DQAAEFv9tvpmm9ksplVABxqWL7dvZtfa9pusZReP3sYKqiPUOEEU7o1/+j07vKS7bbXKAOtZ3C1rXuN0qfMi1no33eQuwYFE2Fpv2rTfar0wgURIrfe/MDCuWm/bbSMev7ta78YVIXW44Cik1lPIQq3XoVyr87Q5KtzLU7hXUmR5MbSTaf1fuNfa0mq9+q8I97JVXmukTywi+vrrr2399dd387+MGDGCVypDjB492uZ2lLwHqaqqcq3BvNZj5eXlHd6/sLDQtUS78847mX8MGWH77be3cePG+dbWZfz48Tblf0EBwr+OS5Yssd69e7PPyEA6K1GGDh3a1auCHKHDde033HwBifbGRFbtb6hH/MHrirY4ZkOm13nJbstJndcxvd77jj7UVt3qOGtp9eeY7eNnrrSPp/rb4ny9oavaqNXKXbhXGENbPy/c61daYjcetasNrvhfkBDBHydNtcnvfmTphjoPqUadF5svvvzCteXsM7DM8v83aq8jLY3NtnhetWtV6o3c68jSOTU2fL1hlg77nETqEUbwIWco3Dv66KOjHow9/vjjVllZacM0LN80Una6DRo0yA466KAOD4RfeumlpK8zkE6YswEAAAAAcrN+0/L0XQmyX4/iYt/CPWlsat8eEAAiysuzPgP7+BLuZQMCPqBNuKekXAm5Aj3xrpNoIR+QrQj3AAAAACB3wz0tr66u/RxHyD59y0p9C/dmVy6x+ZVVSVpTALmgsLDAv3CvdcX/cZmMgA+IEO55B79e0EfIh1xFuAe0R4dzAKna19CWFQCQDuGelhdt+hJkh1iOPRIN9/50/yvW3Jy+J85T5wHpKM+3cG/Jwho3V20m13oEfMh5HYV7op8J+ZCrCPeA9vLz862hocGamprcHKwA4AftY3QpKiriBQYAxMyvcE/Lq66u5p1Ap8K9hTV1NmDwkLR8FfVZqa2ttWXLllnPnj27enUAxKilE+He8mUNXfK9jvYzy5cvtx49enR6WXwrhZwWLdzzEPIhFxHuAeH16dPHzes6Y8YMdyDI6Bqk4sw+7+w+trfcoPdb4V5LS4vb5wBAOtt+lx1szrw5Vlpepmlyoqqvrbea6sWWn19g5f3KraCw4wBqyMDB9sxjTydvhbOY5sjTMaof4Z6WN2HChKSuL3Iv3Bu3/yh79It5lo40QrW+vt5++uknV+fpxE6OveEn6rzYLK9vsJq54U8waW1ptcb65dbaalZYVGgNS+vdpeMX3qxxeaO1NLVYfmG+NTc22cyZMy1V77lqPNV62sckY2Q8AR9yRlVVlTtA9Q5wYw33PIR8yCWEe0Bk3gHY4sWL3YEZkAoqALp168aLnSP0ZZJG7incox0agHSncG/3v46x/Bjm7Jo59Vt7997XrEd5T9v57L2td//oJzG8fdULSVrT7Kc58vwK9zqzPGSHZIR7u224RtoGfKWlpVZcXGwLFixwI2to14lUoM6L7sP/fGh911up/WvX0Ghzv53tArp+qw+wnn2j75MUBC6cOddqq5a5Y5F+Q1eyym/n2i477mKpqvN0AoFG7vXv39+6d+/e6WUS8CFn6CwcBXpeu814wr1oIZ/U1NT4uv5AqlDMAdHpC3e+dEcq98tLliyx3r178+UagIwzZswYmzfPvy9zBw4caI899phvy0d0GrkXb7i329n7xRTuLVmw2KoWVvE2xKikpIRwD2kd7qU7fdk+ZEh6thBF9qHOi83fj7nHRp23R7vjg8nXP+OCuq2P2dmGrrWu2eKOl6MWnm9PnGw//meGrbbpmrbtsbtZfs2v9t4DL9vxfzzeMhUBH3KGzsJRKOeNtpg+fXpc4V6kkE/L03U6uwcd22/M/vbrvNntzpyoqaq2+roGKy4p6lRbF1q3dB7hXuqc9edxNu2b/1pTU7Mvyx8yeKA9+9SjviwbAAAgVgr3xo0b59sLNn78eN6MLhZL/ZZouKcv7wpomBCziooKRu4h6XIl3AOQGdqFeyPXjfqYsOFeDCcnZQICPuTMmZ2avHLYsGEu2BP9HG+41zbkU7in5all1rrrRt+Z5DqFe8FnXHg71wUzF8W1c43U1uW9a172df2zHeFeas1fWGmb7D3OWlpj+EYkAR8/c6UvywUAAADi0ZlwT1/eDVlpMC94ClAPIhzCPQDphHCvPQI+5MyZnccee6xvy0b8Ej1zIpHiENFRzAEAAABIt3BPZ+bPnTKLN8Zn1IMIh3APQDoh3AsvO8YhAjFoaGhwo+00cs8byad5+HQgGy89Ro/1lqfRe7TojB3hXnYXc1peZWVlUtcRAAAAQO6Fe7G03ULnEO4hHMI9AOmEcC8yRvAhZzQ1NdlGG20UmD9PAZ3m0JN4WnV64Z4e683hJ0888YSPa589CPeyP9zT8urq6pK6ngAAAAAyB+FeZiDcQzjL6uqZcw9A2mhuambOvQ4Q8CFnFBYWhgR5XjAXT8gXLtzzHlNaWur7c8h0ra1GW84cCPe0vPLy8qSuKwAAAIDMQLiXGQj3EMmi6sXWr7TEbjxqVxtc0TvqCzW7con96f5XbGFNnY3bf5TttuEavLgAkqZqYZXV1sY+sr8lwWmhMhUBH3JGUVFRSHihn+MJ+ToK9xCbmqpqWzBzEXPupQm/wj0tr7q6OqnrCgAAACD9Ee5lBsI9dETfDdx41E6+hXutOvsbAGLU0tJMuNeB7I0ugRh4IZ8COwV3kebkI9xLjvq6BsK9NKE58vwK97Q8AAAAALmFcC8zEO4hmgEV5b6Fe03NLbaouoY3AUDMSsv6+Dpyr762PqPfDUbwIedFG8lHuJc8xSVFMe9cEykO1ZMZsdEceX6Fe4xsBQAAAHIL4V5moH5DLLoVFvgW7l3x5DtWW5/ZX6YD+M1ho/e1hXNndzinp9r+6rtCnTwQy/6lsanZ5ldWuf+z+pb1sUWLF/kW7s2c+q3VVC/O6LeUgA/ooF2n0JYzeUrLy3wL95YsWOx6MiM2JSUlhHsAAAAAOk1nvidSv02+/hmrrYp9Th10DuEekqUz4d6UaT9Zn779eTOALKFw756xI8PeNvmLWTb+6ff+N6dnfG1/ly/5bf+y6dWP+hbuvXvva9anR6llMgI+IELI19LS4n6ePn06c+4lSV6ev2d+Fqx4yxCDiooKRu4BAAAA6DSd+U64l94I95Au4d72w1e1nxu68YYAWS403NvVt7a/LZ0M93T8Ut673DIZc/ABYUK+YcOGuWBPF/0c3LIT6dvWRT2Z4T+KQwAAAACe/PwCX0futbbyWncG9RvSKdy7ePQ2lhfL2d8AMlYmhXu7nb2fFcTQNjSdEfAByJo5G4p7FKdkXXMZxSEAAACAYOX9yn0L9/TlXU1VNS94gqjfkG7hXmEMX74DyFyZFu71juH4Jd2xVwXaHPxqzj1v5J43kk/X6Tb4gwnZMwPFIQAAAIC2YjnzPdFwT1/e1dc18KKnQf2m5VVWVvJe5CDCPQCxINzrGszBB7QJ9zT/3ogRIwLz8XnXCa06k49wLzMQ7gEAAABIRGfCPZ2Z37+8Ly98GoR7Wl5dXR3vRY4h3AMQC8K9rkPAB0QI97yDXy/oI+RLPsK9zEC4BwAAAKArwj213Sqs4rVPh3BPyysvL+fNyCGEewBiQbjXtWjRiZzXUbgn+lnX6Tbdh3adyUG4lxkI9wAAAAB0VbinOXXy8nj94+FXuKflVVRU8GbkCMI9ALFYVlfPnHtdjIAPOS1auOch5Esuwr3MQLgHAAAAoCvDvfwCvraKh+bI8yvc0/KQGxqbmu1P979iC2vqbNz+o2y3DdeI+pim5ha74sl3bMq0n2z74avaxaO3sUI+v0DWW1S92PqVltiNR+1qgyt6+3LygFlrQscHM6d+a+/e+5r1KO9pu529n/Xu38eyES06kTMaGhrcAap3gBtruNc25BPadSaOcC8zEO4BAAAASAThXtfRHHl+hXudWR4yy/zKKlu+hHAPQHT6v+HGo3byLdzTyQN1tQ2Eex3gVCjkjKampkB7zXjDvWgj+XSpqalJyfPIZPW19XGfOZFIcYjOoZgDAAAAkAjCva5VUlJCuIekfCfAyD0AsRhQUe5ruKeRwc2NTYzc6wAj+JAzCgsLXSjX0tLifp8+fXpc4V6kkXxanq5bvny5b+ueLWqqFxPupTnCPQAAAACJINzrepojj5F76Ky+ZX18bcupObsAZIduhQW+z+lZ0K3Q17aczU3NlskI+JAzioqKbJ111nHBngwbNizucK9tyKdwT8vr1q2brbsuI8uiyc8vsJ3P3tu3kXutrTG/hQiDcA8AAABAIgj3Mh/1IDw9S4p9C/cmfzHLzdkFIDd0NtzT/uX5GbN9C/eWLFhsVQurLJPRohNAypT3K/ct3NOE7DVV1Ula09xDMQcAAAAgoVqiqTmh+u3tiZPjnlMH/qAeRDw6E+6Nf/o95nMEckQywj3tX8zyfAv3Jl//jLW0MIIPyAgNDQ1utJ1G7ol+1hx6iYzi8+bw85ZHi87YFMQwbLszZ37W1zXEuCbws5jT8iorK3mRAQAptd+Y/e3XebNDRvbr5B8dHxSXFFlpeZnl5cU2Z7DaiqvzgE5O8o5fhgwcbM889rSfTwEAMpLOfK+tJdzLVIR7SGW416+0xIrLy3nRgSyXrHAvlv3LzE6Ee/r+echKgy2T0aITOaOpqck22mijwPx5Cug0h57EE/J54Z4e683hJ0888YSPa58bOtvWpX9535SsZzbxI9zT8urq6pK6ngAARKNwb9R5e4QcHyyYuSjhCdnbthV/75qXeRMAIAyd+c7IvcxEuIdUh3s3HrWrXfziiu/iAGSnTAr3tj5mZ5s7ZZZlMgI+5IzCwsKQIM8L5uIJ+cKFe95jSktLfX8O2SwZczYUZnbL5KwJ97S8cs7IAwB0kUTbviVSHAIAzErL+vjallMjq5F8hHvoinBvcEVvXnggi2VauDd05LoZH/DR4Bw5o6ioKCS80M8K6BTUKbBTcKcD3ETCPaTHhOyxtN3Cb/wK97S8iooKXmoAQMoR7gFA6hX3KPZ1/6y2yUguwj3Eg3APQLaGe9mAgA85LdaQj3Av/cM9JmSPj+bI8yvc0/IAAEg1zbnHyD0AyL6TLzQnKpKHcA/xINwDEAvCva5Di07kPC/ki9Suk3DPP4R7XUdz5PkV7jGyFQDQ1p677GD1S2qsW2H0/3Mam5ptfmWV+z+mb1kf61kSfWRIa2urzZgxw6xbAW05ASDLRlaX9y5PybrmAuo3xINwD0AsCPe6FgEfEDSSr23IJ7Tl9AfhXtcqKSkh3AMApIzCvQeO3jrm4nD5kvjburxb32DrjNyAOfcAIMvaJn/5j/dTsr7ZjnAP8SDcAxALnZxJW86uRcAHRAj5Wlpa3M/Tp09nzr0kI9zrepojj5F7AIBUiWXkXmfP/CzoVuhruNfcFHmuZgBAKOZETS+Ee4iHOiMkMifW5C9m2fin37N+pSV241G72uCK3rzwQJZT55VETs5kzr3kIeADwoR8CvcU7MmwYcNCWnaicwj3Mh/FIQAgHdu6PD9jtm/hno5fqhZWxfx8ACCXEe6lF+o3xGtRdY3NXLSAcA9ATP/H+B3uNS5vTKh+m3z9M1Zbtcy2PmZnGzpy3ax9Nwn4AKSMznyPd+eaaHEIf1AcAgDSdc6G56993LdwT8cvBSuaOwAAOkC4l16o35CI2vp6wj0gS+x7wMH26+x5EW+vr6+1muoqyy8osPKKflZQED0uam5usqrKhdbS3GwF9bW+hnsaGdxQt9wqBvQh3IuAgA9oc/CrOfc0ek8j90Q/6zpG8XWeznyvrSXcy1QUhwCAXJ6QfchKg2N+XgCQiwj3srt+0/IqKyuTuo5ITz2Ki31ty6k5uwCkhsK9zfa7KOxtP0x7zz54eaKV9Cq3nQ46z3qVDYi6vKXV8+31x66xuqW1tuUex9r3r97i+5yeefnma7jX2moZjWEwQJtwT/PvjRgxwg4++GB30c+6TrfpPkhcS0szI/cyFOEeACCXwz0Vh8U9imN+bgCQawj3sj/c0/Lq6uqSup5IT33LSn0L93T8pzm7AHStkHBvTLzhXpUL91YfPsr3cE/7l5KeJb6Fey3NLVZTVW2ZjIAPCBPueaP1vDn5CPmSo7Ssj69tOetr65O0pghGuAcAyPVwL5vnbACAztKZ74nUb4nsn9F14Z6WV1JSwluQA/Ly8nwL93T8x8nzQNfKpHBP+5f8/Hzfwr23J062+roGy2RpH/A99dRTNnLkSOvZs6eVl5fbPvvsY1999VXMj//iiy/skEMOsdVXX92Ki4tt1VVXtf3339/ee+89X9cbmR/ueQj5kieWM987c+ZnTfXiJK0pPIR7AIBkI9yDUOcB2UNnvhPupQ+/wj0tr6KiIqnriszUmXBPJ3f1LSPMB7pKpoV7sexflnQi3NPxS3FJkWWytA747rnnHhs9erQtW7bMrrnmGrvwwgvt888/t1GjRtmXX34Z9fEffvihbbHFFvb222/b0Ucfbbfddpv79/3337dtt93WJk+enJLngcwN9zyEfJnR1iU/P/HCBe0R7gEAko1wD0KdB2QXnfnOyL30oDny/Ar3tDygs+GeOjf0LKHtOdAVCPfCf/9cWl5mmazQ0lRVVZWdddZZtvLKK9u7775rpaWl7voxY8bY8OHD7YwzzrDXX3+9w2XcfPPN1tDQYP/+979t/fXXD1y/3377uYMTHajstttuvj8XpAdtCzpA9Q5wYw332oZ8osdItMcgtXM2lPcu5yVPEsI9AECyEe5BqPOA7KMz3/1sy9nc1JykNc1+miPPr3CP7z6QjHBPbdkf/WIeLyaQYoR7kb9/nnpdZg8CS9sRfM8++6zV1NTYscceGwj3RC02DzzwQHvjjTfs559/7nAZixevaNc3ePDgkOuHDBni/u3Ro4cv64701NTU5AI9HajGG+5FG8mni7ZXdO2E7AWFhK3JQDEHAEi2xqZm5tyDQ50HZB+d+e5XuKe2W1ULq5K0ptlPc+QR7iGdwz0AqUe417nvn9Nd2o7g++CDD9y/asfZlq67//777aOPPrJVVlkl4jJ23313e/755+2www6zyy67zN33xx9/tIsvvtjN5/fnP/856nrMnz/fFixYEHLd999/7/71gh0kR2FhobVqdm4fD3S/++47e+KJJ9zv+nnDDTe0Aw44IK4z0byQT/9Onz7dLU8/t7S0sD3E8B7nt+a127m+f9/rNvuLn2zNLda1rY/eecXkqVE2hVkf/tc+nPSW9elfZjufsbf17lfqls9nsnOfN71+9957r5u/VC2OjznmGPd+xPvZ5L3omDeaOD/Pv31eYWEBnwcgS3jHnJn8f1xlzVJbXtdk40Zva7usv7q1RLl/U0uLXf3MVHvvv3Nsxw2G2vn7j3L/H3X0uG7dutmPH3zf7vgg2jHFkoU19sZNL1jj0uW23R92szW2WDvsY9Lp/7Z0WY9EUOflHr/rPD8/m9mw/02Fbt3a13mx1G+x7p8L8vN5D2I0YMCAhOq3WOvBdPq/MB1lS51XUFhoLUFjQl796ge77vmPbKWKUpswdidbqbx31GO5OVVL7JyH3rTqMMd/Wj7bEZCa44ympuX2yWv3W2l5f9t+9NnWq09/i/Yf8NLFC+ztp6+3poalNmqv42y1YSMjPqawWze3v0ikfot1/6I6L/g4I9b6Ldbvn9Pp/7ZE1iNtA75ffvnF/asWnW1513n3ieSkk06yX3/91W699VYbOVIb4gpq16nCcu211466HrfffrsLB8PR3IBLliyJugzERpM1+1n4rbbaau4ya9asQFC86667JtRmQh98tYt95ZVX3PL0u5at7YG2FZH1q+hnPe23Xustza326TNTrWlOvW264xa28X4jLb+g48JQfvnyR/vxlW9t6DpDbeTYHaxnea/A8vlMJv55U0j99NNP25w5c2zHHXe0/fffP+HiUMvnvej4P+zysj7Wq3urtUQ7CklQv359eQ+ALNpn1NbWup8z9TijX//+dsQ+o2yrtQdbfQwTsk988zP77+Jm23PrTezYnf7PmgryrSnK4/r37R/2+KAjy6qW2tePfGhlxX1s++N3sZU3WC3yc0ij4wzVIZmKOi/3+F3n+XncmQ37366o82Kt32LdPw8Ykj7732z+vMVSD1Ln5UadV9G3n9V36+l+fv+/s+3Bj36ydddaw/78+5FW3qdH1GO5BYtr7bpXpllBr3I7b+8d2h3/afl8poHUHGf0LO5uQ9dc2zbb9Wjr4aY26jhyq11SZd+984CV9y62bXc/0QYP3bDDx6w0aLAtzS9JqH6Ldf8yaKVBgeOMeOq3WL9/zvQ6L20DPm/jLioqandbcXFxyH0i0YGI2nFutNFGbt69ddZZx43amjBhgu2xxx5uDj+FMh05+eSTA/OuBY/g0/J69uxpvXtHH5KO2CeDzsuLHu4kau7cudarVy93sCpq/aq2nZ05s23RokVueTqTQNuCLhR+kS2sXGjL/rer1pkT7973mv306UxbdeOhts5+G1ldQUPU111nfr7/4BvWo6yn7XzCdmblhYFlavl8JhP7vHlnan766ae28cYbu2KuM9uyls97EZle76rqxdZreZ61RDnbOVELFy7iPQCyhHcWXyYfZzTV19qOq/cxa+y4YNGZnxOefs/e+eZn22a9VezU7da2wpa6aHWoO/Nz1swfrGLV/u2ODyLRmZ+v3fQvq61eZlsdsaOVbzCww8ek03GG6pBMRZ2Xe/yu8/w87syG/W+q67x46rdY98/z3vkhbfa/2fp5i7UepM7LjTqvctFCK25ctmJkzXNTrV/vEhu33042qEdr1GM5jdw7f9LrtnBJnf15n5Fhj/+0fD7TQGqOM+YvXGhbHXCBtXTva0ujfO2qkXtTnrze6pZW2+a7Hm2lQ/4v6mPmzp5tNzz+SkL1W6z7lzlz57hjh3jrt1i/f870Oi9tAz5vfryGhvZbUX19fUxz6J1//vl2/fXXuwMUjdoLbt25ySab2DnnnGOPPfZY1PYGuoSjDx4H+cmjsM3Pwm/p0qX21Vdf2bBhw9zv+lnhXqzz7wULnsNPy9PjNZk120T097glr3VFz+N7fut5vNUxO5kV5EU9wy14zoYdz/id9ezfO+QxWj6fyfg/b9qeJ06cGJhA/Q9/+EOnJ2RXe2Pei+ivk4o+vwq/pqYV7WEAZAfvGCNTP9dFat8WpcrTyL2rnnzHpkz7ybYfvqpduN9IK3TdoVpimhOmqbkx7PFBpDmdJl//jNVWLbOtj9nZVttyraiPaWxMn+OMdFmPRFDn5R6/6zy/a4BM3/+mss6Lt36Ldf/865vf8/r7+HmLpx6k5s6NOq+5qcle/WJGYM696w7fwQaX94x6TKY5984MnnNv/VXDPkbLZ58KpOY4o1fvMutROsBaooxvWVo9315/7BqrW1plW+5xrK2y3lZRH9PS3GzVNUvsjS/rEqrfYt2/NDY22uKF1XHXby0xfv+cTv+3JbIeaTuLYEdtODtq6xL8xt9www223nrrhYR7ssEGG7jr33jjjaSvN9KXPqwjRoywgw8+2F30swI6BXXx9LcNDve85R144IHWvXt3X9c/WyQ6oWkiE7Ijtu3573//e6CYS9aE7Aq8AQCIlcK9K4LCvYtHb2OFMRwfBBeHJT1LYjo+aPvl8dCR68Z0/FJTVR3z80Fk1HlAbkikfktk/4z0qgeRHZbV1QeOr248alcbXBF9ZIvCvT8Fh3sbrpGSdQXQsYKC6OO72oZ7qw8fFfUxCvfef/FOa2xuSrh+i3X/ohbSidRvbyfw/XMmSttnpcl85f333293m3fd5ptvHvHxCxcutOXLl0cMbhT26ILcoXnyvNF6uujneEO+tuFe8PLU8hMdUzdUwr3sD/e0vJKSkqSuKwAgeyUj3FNx6CZJ9ync0/FLfV30VuKIjjoPyH6Ee5mBcA+RLKpeTLgH5IjOhHs/f/exdSso9DXc08kDdcvqCPcyMeDTHHfqfaovi2tqagLX//TTTy5g2WGHHWyVVVYJzOPwzTffBOZWk4EDB1q/fv3s22+/talTp7YLCDUX35ZbbpnCZ4Supvkcg8OLeEO+SOEeYqcz3xm5lz78Cve0PE2+DgBAqsK9WIrDzoR7On4pLmk/NzjiR50HZDfCvcxAuIeO6LsBP0fuabocAJkf7q2yzmbWs6TY13BP+5fWFmPkXiYGfOXl5TZhwgTXjnPrrbe2W2+91c2nt91227l+4jfeeGPgvh9++KGbB01z7nl0Bu9ll13mhnDuuuuubr69u+++2/2r39VO8fLLL++iZ4d0EWvIR7iXHDrznbac6UGTo/sV7ml5AABkU7in45fS8jLe1CSgzgOyF+FeZiDcQzQDKsp9C/d0/Leo+reBHAAyN9zbaq8TLZZpXzsT7mn/UlTS3de2nPW19ZbJojdh7UInnHCC9e3b1wV95557rgvltt12W7vqqqtsww03jPr4k08+2QYPHmy33HKLmzB4yZIlbnl77rmnXXTRRbbRRhul5HkgM0I+UcgnwaPzCPeSR2e++znnXnNT7HMp5jrNkedXuMfIVgDIPPsecLD9Onue1dfXWk11leUXFFh5Rb+Y5mxobm6yqsqFruArLSu34uIe7e6zbPYvGR3u6fhl6nWToz4OsaHOA7IP4V5moH5DLLoVFvgW7un47/u5c2ydDdZ1J0/FEg7oy/ea6sWWn19g5f3KrSDK+g0ZONieeezp6AsGclSywj3VjH6He9q/nP/Ch76FezOnfuv2L5ksrQM+OfDAA92lI2rXGWl4t1rA6AIkEvIJbTmTRwdvfoV7+vKuamFVktY0+2mOPMI9AIBH4V6/dXayD16eaCW9ym2ng86zXmUD4igOazssDl+9+dCMDveydUL2rkSdB2QPffmeSP0W7/4ZnUO4h2TpTLin47/W/Dzb/a9j4v5+aOez945p//L2VS/E/FyAXJNp4Z72L9ECvpZOhHvav/TpUWqZLO0DPqCrQj61d5Xp06cz516SxHJmVmfO/CxY8ZYhBpojj5F7AACPRu4Fwr0x8YZ7sReHhHsAkH105jvhXnoj3EO6hHs6uev5GbM5+RvoApkY7kVfv5ZOhXs6finvXW6ZjFNRgTAhn+Z0VLCni34ObtmJ9G3rUloW/f7oPIpDAMg+astJuMfIPQBIhNrm+TlyL0LDJsSI+g3pFO6pc4NZnq/fD7W0MH0L0BbhXuT9S7S2v+mOgA9A1szZUNyjOCXrmssoDgEgO+ksTD9H7unLWdpyAkB20pxYfoV7OjO/pqo6SWuae6jfkG7hXixt2Tn5G0guwr3O7V/SHQEf0ObgV3PueSP3vJF8uk63wR9MyJ4ZKA4BIHuVV/TzLdxTW5dldfXMuQcAWSqWM987MydqfV1DktY0tyS7ftPyKisrk7qOyAyZFO5x8jcQqrm5ibacWRzuCQEf0Cbc0/x7I0aMsIMPPthd9LOuI+TzB+FeZiDcA4DsVlBQ6OuZn43NTXF9uZPInA2aPznRL4/jnbMBABC7zoR72j8XlxTxcqdBuKfl1dXV8V7kmEwL92LZvwC5pKpyIXPuWfaGexK9kgdyMNwLnnNPP4tu835nPr7k4OAtMxDuAQA629aluKDQ13BPXz7VLauzlrw8wj0AyKJwTydfFFalZFWzhl/hnpZXXl6e1HVFeiPcAzKfarJE6rdV1tnMttrrRDeVg1/1W7wnDyR6cubMLA73hNNTkfM6CvdEP+s6RvIlF+FeZiDcAwAkY86GniXFvoZ7Kg5bW4xwDwCyLNzTl3d5eSlZ3azhV7in5VVUVCR1XZG+CPeA7FBaVu5ruNfQ2Ei418UI+JDTooV7HkK+5CLcywyEewCAZE3IHsuXs50987OopLuvbTnra+ujPwkAQFLDPdomx0dz5PkV7ml5yA2NTc205QSyRHFxD9/CvR+mvWe19Q2+j9wza2XkXgcI+JAzGhoa3AFqvOGeh5AvOQj3MgPhHgAgWeFeqtq6dOvezde2LjXVi6PeDwBAuNeVNEeeX+EeU5XkjvmVVcy5B+SIzoR7H7w80fLy8n0N9zSnZ11tA205O0DAh5zR1NTkAj0dqMYb7kUL+XSpqalJyfPIZDrznQmT0x/FHAAg08K9VMzZkJ+f+BelAJArGLnXtUpKSgj3kJTvBOL58v2KJ9+xKdN+su2HrxrznMuc/A1kfrhX0qvcevco9jXc0/6lubGJOfc6UNjRjUA2KSwsdKFcS0uL+3369OlxhXttQz7xlqfrli9f7tu6Zwud+R7PhKaJFIfoHMI9AADhXvgvn8p7l7NxAEAHCPe6nubIY+QeOqtvWR9fw73G5Y2c/A1kQbi305jzbOoDZ/g+p2dBt8K4T87sEcf3z81Nv3X8y0SM4EPOKCoqsmHDhrlgTxf9HG+41zbk85b37bffWvfu3X1Z72yiM9/9DPdaW5O0ojmKcA8AQLgXuTgsKGQEHwBEQriX+agH4elZUuxbuKfODQ11yzn5G8iCcK9X2QDfwz3tX0p6FPkW7i1ZsNiqFlZZJiPgA5Ay5f3KfQv31Harpqo6SWuaeyjmAACEe50rDgEgV+nM90Tqt0TaJsMf1IOIR2fCPbVlz8s3Tv4GukimhXvav5jl+dr2t6WFEXxARmhoaAiM3PNG3nlz6MXLm8PPW966665Li84YxHLme2fO/Kyva4jtDYSvxZyWV1lZyasMABmkubmJOff+h3APAOKjM98J9zIX4R5SGe5pzuWSniWc/A10gUwM91Ixp2dpWWafzMnpUcgZTU1Nbs69gw8+2F30s+bQizfk88I9PdZb3oEHHkiLzjRo61JcUpSM1cgpfoR7Wl5dXV1S1xMA4K+qyoVWt7TKttzjWFt9+CjfisPgL3duPGpX3yZkT3RkCOEeAMRPZ74zci8zEe4h1eGejv/y86M/hpO/geQi3Iu8fynuEb0tcToj4EPOKCwsDMy5582hF2/I1zbcC15eaWlpSp5HtkrGnA2l5WUpWdds4Ve4p+WVlJQkdV0BAP4XfIR7tOUEgETozHc/23LW19bzxviAcA9dEe7FcnIXJ38DyUW417n9S7oj4EPOKCoqCgkv4g35IoV7SJ8J2fOit2RGEL/CPS2voqKC1xoAMkhpWbmvI/caGhsZuQcAWSqWM987M7K6pnpxktYUHsI9ZGu4x8nfQKhWM9pyZnG4JwR8yGmxhnyEe+kf7jEhe3w0R55f4Z6WBwDILMXFPXw987O2vsH3tpwqX2nLCQDpp7Ntk/PzObE2mQj3kM3hHid/A6FqqiuZc8+yN9yTwq5eASBdQj5RyCfBo/MI9/xDuNd1NEeeX+EeI1sBIPt0tq1LQV6+r+Gevnyqq21gzj0ASDPJmBO1vHd5StY1F1C/IdvDPU7+BkI11NclVL+V9Cq3ncacZ73KBvhWvyWyf0lkzvQlWRzuCSP4gA5G8hHu+YeDt66lOfII9wAAqZqzoXePYl/DPRWHzY1NCX95HGtxCABIbbin/XNBISP4koFwD/Eg3AOyQ1Fxia/hXnNLC+FeF2MEHxBhJF9LS4v7efr06cy5l2SEe11Pc+Qxcg8AkKoJ2ac+cIbvZ34WdCv0Ndxrboo8VzMAwJ9wj5MvkoNwD/FobW1l5B6QJUrLKnwL95ZWz7cltfVWaK2M3OtCBHxAmJBP4Z6CPRk2bFhIy050DuFe5qM4BIDckKxwL1VtXZ6fMdu3L491/FK1sCrq/QAAhHvphvoN8VpUXWMzFy2gLSeQBfJiuE+i4d7rj11jra0tvod7jcsbacvZAVp0AkgZnflOT/XMRnEIALkh08I9FYexlK+dmbOhpYURfAAQDSP30gv1GxJRW19PuAfkiM6Ee3VLq6xHcZGv4Z7m9GyoW86cex1gBB9iMnr0aPvmm2/cz2VlZVZYGH7TaWpqsurq6oTu98svv3T5u+HNuafRexq5J/pZ1zGKr/N05nttLRMmZyqKQwDIDZkY7qViQvYhKw2Oen8AyGWEe9ldv2l5lZWVSV1HpKcexcVxffk+/un3rF9pid141K4xzbmsrlmc/A1kfri35R7H2vev3uL7nJ55+ZZQ/bb1MTvb0JHrRn1Ma6tlNAI+xGThwoV23XXX+fpqnXTSSWkR7mn+vREjRgTm4/OuE0K+ztGZ77HuXBMtDuEPwj0AyA2Ee5GLw7lTZnXhOwMA6Y1wL/vDPS2vrq4uqeuJ9NS3rNS3cE8nd9Utq7OWvDy+HwIyPNxbffioqAFfZ8M97V+qavN9C/damluspmrFIKRMxbflQIRwTwe/3px8uk636T66LxJTWtbH13Cvvraet8YHhHsAkBsI9zpXHAJArtKZ74nUb4mMrEbXhXtaXklJCW9BDsjLy/Mt3FPnhtYWI9wDsiDciyYZ4Z72L/n5+b6Fe29PnGz1dQ2WyRjBh5wXKdzzeCGfMJKvc4p7FPt65mdN9eJOriHaItwDgNygriS05VyBcA+5ZsyYMTZv3jzflp8OUzHAXzrzfcHMRYR7acKvcE/L86ZaQW7rTLintuxFJd05+RvoIpkW7sWyf1nSiXBP3z/3L+9rmYyADzktWrjnIeTLjLYufXqUpmQ9cwXhHgDkjprqSluwaAZz7jFyDzlI4d64ceOydioG+E9nvjNyLz1ojrwZM2b4Eu5peRMmTEjq+iL3wj3NuXz+Cx9GfQwnfwPJR7gXfv9SWGUZjRadyBkNDQ0h7TVjDfc8tOtM/zkbyvuV+7yWuYNwDwByS0N9HeEe4R4AJKS4pMjXtpzNTUyTESvNkedXuNeZ5SE7JCPc223DNXz/fig/n20VaItwL/L+JYauxGmNgA85o6mpKTCHXrzhXrSQT5eampqUPI9slKwJ2QsKOYhLBoo5AMg9RcUlttVeJ1p+DMdDiRSHzS0tcX+5k2hbl0S+PCbcA4DElZaX+Rbuaf9ctTDDT61PIc2RR7iHXA73OPkbaI9wr3P7l3RHi04kbM6cOTZp0iT389ixY23QoEGdul+rZub2UWFhoQvlWlpa3O/Tp0+PK9yL1K5Ty9N1y5cv923ds1mywj0mZE8Owj0AyE2lZRW+hXuas2FJbb0VWivhHpABMq3OQ9eL5cz3zpx8UbCihEcMKioqGLmHnA73tH/58h/vx/zcgGxXX1/LnHuWveGeEPAhYSrmpk6dGvj9nHPO6dT9gttn+qGoqMjWWWcdF+zJsGHD4g732oZ8Cve0vG7dutm660afwBOhCPfSC+EeAOSuPJ/P/GxtbfE93Gtc3sjIPSAH6zykv86OrB6y0uCUrGeuox5ENoR7nPwNhKqprkqofqtbWmVb7nGsrT58lG/1WyL7l0Q6r7RkcbgnBHwAugQHb+mFYg4A4Gdbl17FRb6GeyoOG+qWW8WAPrTlBJBR9j3gYPt19jzTOMea6ko3J6raJmtkdV6MZ+bryzuNwi6v6GcFBe2/5hkyeKA9+9Sj1hWS0TZ57pRZKVnXXEY9iHAI94DMp+ODnQ7yL9xTowbCva5FwIeEqQ1LuJ8TvZ/fEzY3NDS40XYauSf6WXPoJTKKz5vDz1seLTrjQ7iX3cWclldZWZnUdQQAZPacDd+/eovvZ37m5Zuv4R5dBpErMq3Oy3QK9zbZ+3x7/8U7bcGiGbbKOpslNidqB1/effzMldYVkjUnKgGfvwj3EA7hHpAddPKPX+FeS3OzLaurZ+ReFyPgQ8I0x0KkNiyJ3C8vlsb9ndDU1GQbbbRRYP48BXSaQ0/iCfm8cE+P9ebwkyeeeMLHtc8ehHvZH+5peXV1dUldTwBAZk/IHi3gS0Zbl6rafN/CPR2/1FRVR70fkA0yrc7LdBq5p3Dv5+8+Tjzci3H/nInhHvxFuIdw9IU9bTmB7BBuZH+ywj0dvzQ2N9n2w4f62pZTU2TRljOy7Gk2CkRRWFgYCPK8OfQU0CmoU2AXy9wQ4cI9b3mlpaW8BzGc+U5P9ewP97S8kpKSpK4rACBzw71UzdmQn5/v65wN9XUNUe8LAPFSW07CPcK9rkC4h0gWVS9mzj0gR3Qm3NPxS7eCQl/DPc3pWbesjjn3OsAIPuSMoqKikPDCC/kklpF8kcI9xE5nvi+YuYgJk9OEX+GellddzSgHAMhkmRbupWJC9v7lfaPeHwDipTn3/B6519zclLI3hpF7mYFwDx3RdwM3HrVTzF++/+n+V2xhTZ2N239UTHMua+wyJ38DmR/u6filduY7voZ72r+0tlhC9dtqm65p2x67m+XHsH6ZLLufHRBFrCP5CPeSQ2e+x7NzTaQ4RGw0R55f4Z6WBwDIXIR74YvD0vKyLn5nAGSjouISX8M9fXlXVbnQUoFwLzMQ7iGaARXlvoV7OrmrrrYh7i/f+X4ISL9wT8cvsXRi7+ycnkUl3X0N9+pr6y2TEfAh50UL+Qj3kqe4pMjXg7fmpuhtVrGC5sjzK9xjZCsAZC7CvcjFIdOIAfBDaVmFr+GevrzTF3J+I9zLDNRviEW3wgLfwj11bmhubCLcA7Ig3Ivl+KWz4Z72L926d/Mt3Js59VurqV5smYyAD+hgJB/hXnLpzHe/wj213apaWJWkNc1+miOPcA8AEIxwL3fbugDoOnkp2D+XlpWbn3TmeyL1W7xtk9E5hHtIls6Ee2rLXtCtkJO/gS6SaeFeLPuXlk6Eezp+yc/P7Cm4mIMPMWlqarIJEya4n8eOHWuDBg0Ke785c+bYpEmTkna/VGo7J19LS4v7efr06cy5lySxnPnemTM/C1a8ZYhBRUUFI/cAAAH19bXMuUe4hxy1cOFCV+tla52X6ZJx8sXC7173dR115jvhXnoj3EO6hHuac/n5GbM5+RvoAoR74b9/Lu/t74lQfiPgQ0yqq6tt1qxZgd/POeecsPdTMTd16tSk3a+rQj6Fewr2ZNiwYe462g5a2rd1GbLS4BSsJSgOIYeN3tcWzp0deDGW1dXbourFbl+pORtiaevS2NRs8yur3DbVt6yP9SwpDrm930qD7eEnn+UFB1KgprqqU18ex3LmZ9svdy4evY1vE7InMjKEkXvIVbW1tYHaLFvrvFwfWe13wKcz33c+e2/fRu61tiZpRXMU9RvSKdzT8d/z1z4e9XGc/A0kF+Fe5P3Ll/943zIZAR+AtJCMORvmTvkthIY/KA7hUbh3z9iRYb583ymutgvLl0QuDv846bcvCAH4Sy1WdjrIv3BPX84S7gFA+rVNTobyfuW+hXs6+aKmqjpJa5p7qN+QbuFeLCd3cfI3kFyEe53bv6Q7Aj7EpKyszEaOHBlotRJJ8G3JuF+qeXPuafSeRu6JftZ1jOJL/wnZCfj8RXGIruypDsBf5RX9fPvyWHM2aJQvI/eA9NSjRw9X62VznZdpMinck4IYOjd0ZmR1fV1DktY0tyS7ftPyKisrk7qOyAyZFO5x8jcQqrm5iTn3sjjcEwI+xLahFBbG1F5Fcywk835dEe5p/r0RI0YE5uPzrhNCvvQN9+Avwj2EQ7gHZI+CgkJfz/xsbG6y7YcP9bUtp1qs05YTiF+/fv2i1maZXOdlmkwL92LR2bbJ/cv7pmQ9s4kf4Z6WV1dXl9T1RPrLtHCPk7+BUFWVC61uaW3c9dvP331sq6yzmW2114mu20u6fD+U6LQKM7M03BMCPiBCuOcd/HpBHyFf8hHuZQbCPYSj0TiM3ANyR2fbuhQXFPoa7qk4rFtWZy15ecy5ByBjEe6F//KusKqL35gM41e4p+WVl5cndV2R3jIx3APQviYj3Ps2a8M9ib6HBXI43BP9rOt0m+6j++ox6BwO3jID4R4iWVS92Pczs1o1aReArJizoWdJsa/hnvYvrS1GuAcgYxHuRT4zPy+vi9+cDONXuKflVVRUJHVdkb4I94DsUFpW7uvIvYbGRkbudTFG8CGnRQv32oZ8XTmSb5fd9rKqmlqLpbapr6+1muoqtyPWnDqxtN0aNLCf/evZJywVCPcyA+EeOqL9341H7eRbuKczPxdV1/AmAFkyIfvrt73je1uXopLucbd9i6etS31tfdT7AEAiCPc6t3/GbzRH3owZM3wJ97S8CRMm8HLngMamZkbuAVmiuLiHb+Gejl9q6xts5UEVvp78bdZKW84OEPAhZzQ0NLgDVO8AN9ZwL11CPoV7m+93UXzF4UGxz9nwzuOXWyoQ7mUGwj1EM6Ci3NdwT21dauv5Mh3IhnAvVXM2nP/Ch77O2VBTvTjq/QAgXjo5kzn3CPeSRXPk+RXupfIEZ3St+ZVVtnwJbTmBXNCZcE/HLwV5+b6Ge/p+qK62gTn3OsCpUMgZTU1Ngfaa8YZ70dp16lJT4+9Ikzyfz/zUDt1vOvOdnurpj2IOsehWWOB7W5cexcW8GUAXybRwLxUTsufn88UmgORT55VE6rd49s+pbHqeyJxYjNxLnpKSEsI9JOU7AebcA7JfZ8M9Hb/07lHs+8nfzY1NCdVvPbJ0zr22GMGHhM2ZM8cmTZrkfh47dqwNGjSoU/fze66lwsJCF8q1tLS436dPnx5XuBdpJJ+Wp+uWL19umdzWZcjglXxfR535zoTJ6Y1wD+k0Z8PPDd14Q4AuQLgXvjgs713O9oickGl1XqbTF2nxdF5J5OSLmupKSwXCva6nOfIYuYfO6lvWJ6H67eLR28Q053Lj8kZO/gayINzT989THzjD9++HCroV+hruNTf5P+jFT4zgQ8JUzE2dOtVdvMKuM/dTsOCnoqIiGzZsmAv2dNHPibbY9EI+b3nffvutde/e3TJ5zoZYejJ3ls58j3XnmkhxyHcHnUO4h3SbkD0vL5axywCSiXAvcnFYEMPIZSAbZFqdl+k0Z7qf4Z6+vGuorzO/Ee5lPupBeHqWFPsW7qlzQ0Pdck7+BrIg3Ivl+CUZ3w+V9CjyLdxbsmCxVS2sskxGwAdkuEyakL28X7lv4Z7autRUVSdpTXMPxRzSLdyLpTgEkFyEe7nb1gVA1ykoKPR9/1xUXGJ+0pnvtOXMbNSDiEdnwj21Zc/LN07+BrpIpoV72r/EMnHVzATDPR2/tLRk9slotOhEwtSGJdzPid7P7wmbGxoaAiP3RD9rDr1ERvF5c/h5y+uqFp2ZFO5JLGe+d+bMz/q6hiStaW5JdjGn5VVWpqYNENIL4R6QuZqbm5hz738I95DrMq3Oy3bJOPmie7O/Z6brzPfaWubcy1SEe0hluKc5l6tq8zn5G+gCmRjuxbJ/mdmJcE/fPw9ZabBlMgI+JExzLJxzzjlJu5/frdiamppso402Csyfp4BOc+hJPCGfF+7psd4cfvLEE09YKmVauJeKti79y/umZD2ziR/hnpZXV+d/GyCkF8I9ILNVVS60uqW1CX95HGtxGPzlzo1H7erbhOzBxwdMyA5kd52XzZI1svqTf13t63rqzPdE6rd49s/wB+EeUh3u6fhv77tejPoYTv4GkotwL/L+Ze6UWZbJCPiQMwoLC0OCPC+YiyfkCxfueY8pLS21VCHcC18cFmZ2y+SsCfe0vPLy8qSuK9Ib4R6QHQUf4R5tOQG0d9afx9m0b/5rTU3+tG/6+ZdfbLMubpvcWaVlfXwN9+pr65O0pghGuIeuCPdiObmLk7+B5CLc63j/QsAHZIiioqKQ8EI/xxPydRTupRLhXuTicOp1k1P+fmQyv8I9La+6mvkQcwXhHpAdSsvKff3yuKGxkZF7ADLS/IWVtsne46yl1Z+RiDNvPDajwz0p7lHs68jqmurFSVpTeAj3kK3hHid/A6FazWjL2Yn9SyZgBB9yWqwhH+Gef5Jx8EZbl/hpjrwZM2b4Eu5peRMmTEh4ecgchHtA9igu7uHrmZ+19Q228qAKX9tyqnylLSeATOdHuLfwp29tt603b/e4ZXX1tqh6sasFBlSUW7cY5kxvbGq2+ZVVrgboW9bHepYU269zf/W1bXKfHqnrlpMLCPeQzeEeJ38DoWqqK23BohnMubcgO8M9IeBDzosW8qVLuFdfX8uce4R7SaM58vwK97ri84HU05c7qZowGUDmt3UpyMv3NdzT/qWutoE59wBkNL9G7hVak90zdmQHX77vFNf+efmS0P3zplc/6uucqOW9af+fLNRvyPZwjzk9gVAN9XWEewuyN9wTvlUDgkbyKcBTkKdATwe+6RLuSU11lZX0KredxpxnvcoG+FIcphIHb12rpKSEcA+dojO3CfeA3JCMORt69yj2NdzTl0/NjU0Jf3m829n7We/+faI+BgD8ksq2nIl8+Z7I/jkZ4Z72zwUxjCxEdIR7iAfhHpAdiopLEqrfYv3+ubmlJWUnfydSvy3J8nBPGMGHmDQ1NQVa7o0dO9YGDRoU9n5z5syxSZMmJe1+XTmSr6Wlxf08ffr0Lg/3RDvinQ7yL9xTT+ZUIdzrehUVFYzcQ6e/IPD74E1towBkx4TsUx84w/e2vwXdCn0N95qbmqPeB8hECxcudLVettZ5mYBw7zecfOEPwj3Eo7W1lZF7QJYoLavwLdzT8cuS2nortFbCvS5EwIeYVFdX26xZswK/n3POOWHvp2Ju6tSpSbtfV4V8CvcU7MmwYcO6PNyT8op+voV7+vJOPZlTgXAv81EcQjTnip/hns4s15wwADI/3Ivl+CUZc3o+P2O2b+Gejl+qFlZFvR+QiWprawO1WbbWeemstaWFkXv/Q7jnD+o3xGtRdY3NXLSAtpxAFsiL4T6Jhnv6/rm1tcX3cK9xeSMj9zpAi04gQxQUFPp65qd6MvtNZ77TUz2zURzC07Ok2Pe2Ll19YgWQyzIt3NP+JZbytTNtXVpaGMEHIPmWN9TSlpNwzzfUb0hEbX094R6QIzoT7un75x7FRb6f/N1Qt5y2nB1gBB9iUlZWZiNHjgy0Wokk+LZk3C/VvDn3NHpPI/dEP+u6dBjF52dbl/59/Z+4XGe+19YyYXKmojhEqudsKC73f78EIDvCvVTM2TBkpcFR7w9koh49erhaL5vrvLTW2ur7nHsNjY0ZNecec6KmZ/2m5VVWpqbzD7pWj+LihOq3WPcv6prFyd9A5od7On75/tVbfP9+KC/ffJ1zrzWV81b5gIAPsW0ohYUxtVfRHAvJvF9XhHuaf8+bc0+86yRdQ75kzNnQvdn/tlM68z3WnWuixSH8QbiHrpiQ/eIXV+x7AaQO4V7k4nDulN/a1QPZpF+/flFrs0yu89JdYfcSX8M9fXlXW99gKw+qINzLIX6Ee1peXZ3/nX/Q9fqWlfoW7unkgbplddaSl8f3Q0CGh3s6fokW8CXj+6Gq2nzfwr2W5harqaq2TMa35UCEcE8Hv96cfLpOt+k+um82TsgeS0/mziot6+NruFdfW5+kNUUwwj10RbgXS3EIILkI9zpXHAJAIgoKC33fP+fl5fsa7pm1MnIvB8I9La+kpCSp64r0lJeX51u4p/1La4sR7gFZEO6l6vuh/Px838K9tydOtvq6BstkjOBDzosU7nm8kE/SbSRfssI9FYcLf/rWdtt684iPW1ZXb4uqF7vnPaCi3LoVRn/+jU3NNr+yyr3Gfcv62KLFi3xt61JTvTjq/RAfwj3Eg3APyFzqSkJbzhUI9wBk28kXRfnLfAv3dPxXV9tAW8404le4p+VVV2f2KAckR2fCPe1fikq6c/I30EUyLdyLZf+ypBPhnr5/7l/e1zIZAR9yWrRwL51DvmSGe1JoTXbP2BXzLHa8c90proO35Ut+Kw43vfpRX+ds6NOjNOp9ETvCPcSDcA/IbDXVlbZg0Qzm3GPkHoAsHFk99YEzfJ0Ttbmxydbcehhz7qUBzZE3Y8YMX8I9LW/ChAlJXV/kXrin/cv5L3wY9TGc/A0kH+Fe+P1Lof+zVvmKFp3IGQ0NDSHtNWMN9zzp1K4z2eGe3wdvqZqQvbxfedT7IzaEe4gH4R6Q+Rrq6wj3CPcApJFMaptc0K3Q13CvuSm9pslIZ5ojz69wLx26GKFrZcr3Q/n5bKtAW4R7kfcvMXQlTmsEfMgZTU1NgVAu3nAvWsinS01NTUqeB+Fe5OKwIIa2oYiOYg7xINwDskNRcYmvXx43t7R06svjeNq6JPLlMeEegHSSSeGe9s8lPYp8C/e0f65amOGn1qeQ5sgj3EMuh3uc/A20R7jXuf1LuqNFJxI2Z84cmzRpkvt57NixNmjQoE7dr7VVs7/4p7Cw0IVyLS0t7vfp06fHFe5Fatep5em65cuXm98I9zpXHCI6wj3Eg3APyB6lZRW+fXms45cltfVWaK2Ee0AGyLQ6L9tkWrinky+ev/ZxX0++KFhRwiMGFRUVjNxDTod72r98+Y/3Y35uQLarr69lzj3L3nBPCPiQMBVzU6dODfx+zjnndOp+fre7LCoqsnXWWccFezJs2LCE59HzQj6Fe1pet27dbN11o0/g2RnNzU205fwfwj2/tjHasCB2+rIuVRMmA/Bfns9nfra2tvge7jUub2TkHpCDdV42ycRwLxUjq4esNDjq/dF51IPIhnCPk7+BUDXVVQnVb/FMC5XKk78T6bzSksXhnmTPMwGyXFXlQubc4+DNNxRziNei6hrCPSCHdLatS4/iIl+/PFZx2FC3nLacADIW4V7kL++KexR34TuTG6gHEQ7hHpD5dKKQn+GeGjUQ7nUtRvAhYWrDEu7nRO/n94TNDQ0NbrSdRu6JftYceomM4vPm8POWl4oWnSr4Yt25JloccvCWm5JdzGl5lZWVSV1HpJ/a+nrfz8xqbOKMfyBb5mz4/tVbfD/zMy/ffJ1zjy6DyBWZVudlA8K9jvfPc6fM6sJ3J/sR7iEcvh8CskN5RT/fwj0dvyyrq2fkXhcj4EPCNMdCpDYsidwvLy+W5lCJa2pqso022igwf54COs2hJ/GEfF64p8d6c/jJE0884fPcOOW+hnsNjY20XchBfoR7Wl5dXV1S1xPpp0dxsa/hntq6zK+sStLaAujqCdmjBXzJaOtSVZvvW7inti41VdVR7wdkg0yr8zJeq9GWsxP7Z3QO4R7C0Rf2tOUEskNBQaFv4Z6+f25sbrLthw/19fshTZFFW87IaNGJnFFYWBgI8rw59BTQKahTYBfL3BDhwj1veaWlpb6uf3FxD1/P/Kytb/C9p7qqV3qqZ3+4p+WVlJQkdV2RfvqWlfoa7mn/wpw9QHaEe6masyE/P9/XORvq6xqi3hcA4tW4vI459wj3ugThHiJZVL2YOfeAHNGZcE/fP3crKPT95O+6ZXXMudcBAj7kjKKiopDwIt6QL1K4ly1tXfLy8n0N9/TlXV1tAxMmpxG/wj0tr6KiIqnrivQTy9n4nW3r0rcs+kgcAP7ItHAvFROyF5cURb0/AMSruakpofotnv1zc0tLQvVbIvvnmVO/tXfvfY05UdMc4R46ou8GOPkbyH6dDfd0/NKzpNj3k79bWyyh+m21Tde0bY/dzfJjWL9MRotO5DQv5JOO2nXGEu79PHuebb39btbk05xRP//yi23m45wNRfnLfA33VBw2NzbZmlsPi3nnmkhxiNhojrwZM2b4Eu5peRMmTOCtyHHJmLPh0S/mpWRdAYQi3AtfHBbSNRiADwoKC30N9/Tl3ZLaeiu0VsI9OIR7iGZARTknfwNZLhnhno5fXr/tHd+/Hyoq6e5ruFdfW2+ZjIAPOS9ayBfryL3mljzbfO9x1tLqzxwTM2881tcJ2ac+cIav4Z7O/CzoVuhruNfsU7iajTRHnl/hXjqNbEVmT8hOwAekHuFe5OJw6nWT2SQBJF237iW+hnv68q61tYVwDw71G2LaLxVG3ydx8jeQuZIV7sVy/JKM74fOf+FD38K9mVO/tZrqxZbJsnt8IhCjSO06s70tZzzFYWfDPbV1KelR5Fu4p7ZbVQs5tT5WmiOPcA/pHO4BSD3Cvdxt6wKgC+X5v3/uUVzka1vOxuWNtOXMAIR7SBZO/gYyV6aFe7Ecv7R0ItzT98/5+enzXX8iGMGHmDQ1NQVa7o0dO9YGDRoU9n5z5syxSZMmJe1+XTmSr6Wlxf08ffp0wr0khHsqDp+/9nFf52woWPGWIQaaI4+Re0g2wj0gc9XX1zLnHuEectTChQtdrZetdV6mS8bJF9+/eouvc6I21C23igF94q7f4pkTFZ1DuIdkScb3Q8/PmM3J30AXINwL//1zee9yy2ScioqYVFdX29SpU93FK9jC0W3JvF9XhXzDhg1zwZ4u+pmRe5kxIXtpGXP0pQLFIcIh3AMyW011Vae+PI7lzM/OfHkc75mfiXx5zMg95Kra2tqsr/MyVaaMrM7LN1/DvdbWqHdBB6jfkCzJOvk7lqHLnfl+qKWF6VuAtgj3Iu9fCmJoS5zOCPiADJNpbTlTFe6pOCzuURz1MegcikOEQ7gHZD4dS/j55bG+nCXcA4DsC/d08kVJzxLfwj2dfFFTVR31fgiP+g3JkknfD3HyNxCKcK9z+5d0R4tOxKSsrMxGjhwZaLUSSfBtybhfqnlz7nkj90Q/67p0GMVHuNdxcTh3yqwufHeyH8UhwiHcA7JDeUU/37481vHLsrp6Ru4BaapHjx6u1svmOi/TZFK4p5HVe9/1oq8jq+vrGqLeF/7Xb1peZWUlL3UOyqRwT/sXvhsCftPc3MSce1kc7gkBH2LbUAoL7Zxzzol6P82xkMz7dUW4p/n3RowYEZiPz7tOujTkazVG7v0PczakHuEewiHcA7JHQUGhr2d+NjY32fbDh/rallPzJ9OWE4hfv379otZmmVznZZpMC/dS0Ta5f3nfqPeH/+GelldXV8dLnWMyLdzj5G8gVFXlQqtbWht3/RZv57hUfT+U6LQKM7M03BMCPiBCuOcd/HpBX1eHfI3L62jLSbjXJQj3EI5G46Ti4A1AdrR1KS4o9PXLY+1f6pbVWUteHnPuIauMGTPG5s2bF9N9NbJGX76XlJRYRUVFzH/jl19+6cQaIpkI98J/eVdYxXaWDuGelldeXs6bkUMyMdwD0L4mI9z7NmvDPSHgQ87rKNwT/ZwOIV9zU5P7Mo059zh4y+TiENljUfVi38O9Vk3aBSAr5myonfmOr+Ge9i+tLUa4h6yjcG/cuHExHa/NmDEjoeO1k046KQlris4i3It8Zv7U6yazgcXBr3BPy6uuZj7EXEG4B2SH0rJyX0fuNTQ2MnKvi0WvsoEcDvfahny6j+6rx+ixqVRQWOhruNfc0sKZWQjdJgj30NE+qaDA13BPZ34uqq7hPQCyZEL2vLz/Z+9d4OMuq/z/M5O5pk3SSS80peVSoDQIjXUFihXKRZBVUQT+rJcgF1kFRUGkCqurq7vqQlFRRMFf5VLjuqsIXgpeQKRLtaG4YilaW3qBgk2haSZNm7lkJjP/1/mmk6bpTL7X5zvf53k+79crr05nnnnynZnk5JzzOc85JLytSzwZs932zU5bl1wmZ/4iAPAZ+GtqAHHPnX0GB5/kFSXu8X5ADwrFYeSHAFCERKJRmLjH/ksml/ehs1MZbTknAJ4S0IZ8Pn+QKGdV3AuKyBeNJYWJe5y825vJoe0CGAXJImDGjNaUUHGP27pkckimA6CCuOfXzIZoLCp0ZsNA/x7Shb/+9a/1vgRgAfhrasCdWjBzD+KeV3CbXlHiHjq56MNrfWnkhwDQBDfiHvsvoVBYePF3NpPHzL0JgMAHtKFYLI6KcnbFPTORj79KpZLYFxASW/lZLpeE91QvDBXQU10CEMwBK0QjDcLbujQmEvgwAKgTsol7fgxkD4f1aVF94okn0umnn07f//73jSI5EDzgr6lDcSjrKH6zY5+567mT+M2JfXYyEwsn97yDZ3BC3ANe/I3BzD0A1MetuMf+S1NjQnjx93Ch6Ch+a1R05l5dZvDt3buX0uk0HXHEEX58O+ATPT091NXVZdzu7OyktrY2V+tEz1qKRCKGKFcR4jZs2GBL3Ks1k4/34/vqPSvKbVuXyYm4UHGPg8N8dohaZ7RgYHKAQbIIBGlmw8t589M4AADvgbhXPThMNaW0+XG77rrr6Ac/+AFdfvnldP3119Nll11GH/rQhwzfGajvr8kW50lPKCRU3OPk3WA2B3FPE1pbW3FyD7hm6pQWFH8DoDheiHvsv3SvuF54fqghGhEq7g0X/R3DJeUJvm9+85t09NFW+qkeykMPPUSLFi2iSZMmUSqVone+8530/PPP29rjT3/6kyHIzJw5k+LxOB1++OHGPi+++KKjawIjcDDX3d1tfFUCOzfrRLe75M++vb3dEPb4i2/bFffGi3yV/TZu3EghK8NlAjyzIR6NChX3uPIzFCah4h5yB+6AuAeCNpC9nnYVAF2BuFc7OGywcHJZFTh+27FjB61YsYJOOukkuvPOO2nBggX05je/2bgv50ELZcR5wfDXeD/udCJznCc7sXijUHGPk3eF4SJO7gFTEA+CCpOSCeHF33aS707yQwAA8eKeFf/Fi/xQsjEuTNzbu2sPpXvTJDOBbtH5ve99jy6++GIaHBykW2+9lT7zmc/QunXr6E1vehOtX7/e0h4//OEP6ZRTTqFt27bRJz7xCfrOd75DH/vYxygajRrDhwGQHZkGsicnJYU5b9zWZSDdb7oOVAfBHAiauGfFvgAAvAXinr5tXWoVx/GprFWrVtHf/vY3uvHGG+mFF16gK6+8kmbNmkUf//jHbRdeVkCcFxxxj/fDCbv6EgqHhdvnaENEaFtO7mqDtpxyg3gQ2AHF3wDIi2ziHvsvVuZWbXUo7rH/UioN69mikys6rTIwMGB7f27pyUHk7Nmz6fe//z01Nzcb91966aV0wgknGK1innjiiQn32LRpE1111VX0vve9j+6//34KW3CcgXU44K922+k60QObeX5I5eQew7d5hp6TU3yVGX6V/fj5u9KD5DcyiXscHF5wz6NCZzbkspgRE5RkEQoo9ATiHgDyMjxcxMy9/UDcO5R58+bRsmXL6N///d/pmmuuMU7xfetb36K77rrLKL7kQszzzz/f0s8a4rxgiXu83/gT87LFearjRfFFZutqYeIe+3/ZwSyVQiHM3JMUiHvADl7kh9KZMIq/AagDMop7VuzLVhfiHuefD585i7QU+Fh4E9k662c/+5khDLLIVxH3GJ7jd8kll9ADDzxAL7/8Ms2ZM6fmHhyEcruRr33ta4a4l8lkjDlssVhM2HXrBM9YWLp0qWfrRLdi45+Fjo6O0fl5LNDxDD3GjshXEff4uZUZfszdy1eQn8gm7vkxkH16aqrpeuBPsiibzeKt1gyIewDITbqvl7L7Mo6Tx1aDQ6fJY7vB4Vj/AAPZ3cNxF5+6u/fee+mVV14xZjzxfD7uisL3vf3tbzdiL47dzECcFyxxj/fjLjkyx3kq49XJ6ifuWi3UPpdLBHFPUiDuATug+BsAeYG4Vzv/vHPVNtJS4OOAgufqLV682HTtc889R3/+859t7f/0008b/3JF6Hj4Phb4nnnmmQkFvkceeYTmz59vzAT41Kc+ZZy2YqGPW3Z+5StfoTPPPNP0Ol577TXatWvXQfdt3rx51BHSZZ4AC6OiW7dwgkDk90gmkwcJeZXb/HPBM0Auuugi00CZP29ey6dDeQ7J2OewcBwOlYW+P5X9X9rQTX/67QPUnJpOSy7+JE1umc4T6CZ8/r49u+iph79Kxfw+etPb/pmObF900HMi0SiVxnXtLZZK9JWfdtMfXuihs06aS7e8+03G71DJ5Foff/5Fun3lMzSztZmWdZ5NM1NNxnOM11CuHuDv7R2g333jESrsG6IzrjqPjj7lOLOXZCTv1tz/BO14bjsdc8rxFOnHjA87v9Mset93332GjWa7yC23+PN1+nvIvx+V/aZMmaKNfXQCvzdsO0TajEikQehn0BCJjNqMnvReWvqDJ6k/W6SbLz6d3nLiUaZ2wop94e+BnyMADvicQn+nw6Gq/kGt4PCZ3yynndvW0dEnnEqnvvXq/TMRaj+P/YzfPL+9qn8wEXbsS8XPGO8fLL7inJFOHiYmd9vaF2ht1/9Sy/QpdM71F1DTtOaDnhMJkE3y4zr4e/z85z83hKDHHnvM+D/HYV/60peMrircwpP5/Oc/b4xV4KJKKwIf4jx78M8di6gi/LXKfvy5iYzDRMd5In83/fDZxsZ5duI3O/a5WqxnJX6zap8nN0+iY0+dbzt+s2qfi0PFwNhflXI31eyBmX0J0t/CIKJCnDc+1hORH5ooN+RFfmjalFb8nAIp8CPOY5tBpaLt+M1q/nm8jyEiP1TLZpjFb1btS+/vXw6MzXByHY4FvuOPP96o3GRnwAwOAu0KfFwZWjkpOJ7KfZU11dizZw/19PQYbRnf/e5304c//GHjOnhuBP977rnn0uOPP05LliyZ8Dq+/e1v0xe+8IWqj/FswL1795IO8GctWuA77LDDhH6PI488cjQRUXFMOTnBPwdbt26lJ598kt7ylrfUFPn4F4zX9Pf3G8L2+LXTp02lybEylcwsiUNmtR1Gk+Ml2rH1Ofr787+iucccR2889wpqbEqxKzPhczN707Rp9QpKNSXo9LdeQ7PmLjjkOTPbZlEuOumgyqzlT/6ZXtgzTP+4+A109dmvp2JDmIom17nmhR30/We20/HHHk03vWMRpVoaKbf/sbaZbTSJDh3WPJjeR3/54VqakmihJR96C80+6UjT96M0XKZnf9pNxZ4c/cNZp9DCCxfR+u93a/M76cXv9MMPP2zYybPOOsuwk26SRTx3Y+x+/Dngs6gN25PUlBahNmPatKlCP4PWqdMMm7FrT4Zuf+yv1DA5RZ++4Ew67bhZo7/ztbBqX/h74OcIgBGbwZ0oRLa6O+bY4+h1rz/F1Kfg5Mn6p39C5cEd9IZFZ9NJiy+mcEPI9Hmt02fU9A9qYde+sJ+RHI4f4h+MXN/EvLL+JXrpsY00d95cWtR5Jk1KTT5kzbTW4NgkjkNEcvPNNxsFlVzs2NTUZMRS3JrzxBNPrFpExyMRfv3rX1vaG3GePThmEeWvVfYTHYeJ3p/9WlG/m374bJU4z278Zsc+j4/1rMZvVu3zt1Y/XzXOM4vfrNrnSfFkYOyvKrmbWvbA7Lkif99UQIU4b2ysJyo/VCs35FV+qGGA8HMKpMCPOI9txrY/Pmg7frOafx7rY4jKD1WzGVbiN6v2RfY4z7HA94Y3vIF++tOfkigqP9xjBZkKiUTioDXVqHwoPAfqlltuoS9/+cujj3EbEhZn+P4//OEPE17HRz7ykdEWjGNP8F144YU0adIkI+ANAjzzgAPwieD3gtvmcRDOTpkd/v73vwtvrfLqq68K/R47d+40TiyNh09y8nvDsx7532on+Son9yoz9/g57PiO3W9X727aNxSi0gRVSG7Y0fMq/eXPa+mZx+6n5OQptOS8j1IpNpX2mYyd48rPVT/5KmX39dPJ515BzYe/vupzdvbsoERhcLRyYtnDf6DVf3uZ3jx/Dl13xnEUKWXNbP9IZdbPu2laU5JuvvBsamssE+3fk+nZ2UOD40w7V0789hu/oEz/IJ122VmUOumwQ9ZUC15/f/9vafuzW+mIhXNp3oUdlG3IU29fb2B+J4MO2zEWqxcuXGgEc27bPHGxx7PPPju63x133IHPwuQ9S/fvockCbUZv726hn0Hf7l5Kv7aTbul6gnr3Zummdy6is45qOeh3vhp27At/D/xOA3Cgio9/H0QFfgN7B2lfPmx6MuTpX3+PXnnhTzT7uDfQ0W+8hDLFBjLL7nDlJxdTHTeztap/UA2u/LRrX3p27qDf3L/yEP/ADK78XPP931HjlEl0zofPIEpFqvoiQfIzOA4RyW233WbEezxzj8W7xsbGCdfz3//Pfe5zlvZGnGePl156yZhBL8Jfq+wnOg4TvT/HcKJ+N/3w2TjOG2t/rcZvduzz2FjPTvxm1T5/5sFD4zwr8ZtV+9ySbA6M/Q06/Ptg9vs2kT2wsj8+C7XjvEocJjI/VC035GV+aO03foufUyAFfsR5WzZvpt6+ftvxm9X8c8XHcBK/WbUv422G1fjNqn2RPc5zLPC9+c1vpqeeesoIOPhk1ETw3LMPfOADtvavBJF8Am88udzImz9RoMkiVgVuMzCWc845x5jlx+1hOMCcaJ8ZM2YYX9XgX7ygDAzfsWOHUWk7UU/1jRs3Op7ZcO211woX+AqFgtDvwftXg98LFvV4rh63puD3a2wrz/Ez92q18mSxjx04UU5cLpulPzz6/4yZe6e/+5PU2DyDSmV7MxvmzD+t5nOKhQLx4WeunPjSmJl7n7lwEUWMeLNkeWbD7e8/k2alJh3yHP4MSmNaVYzveXzkqceaVrkZM3W+d2CmzmlXnk3UEDKex59BUH4ngw4XQXCb2auuusp1smj58uWjM1wq++GzsPbeibQZxeJIexhR5HJ5+sT9vz4wE+vEI0zthF37MozfaQAO8TtF/V6zzZjIHo3MbLhndKbTyef9M1G4wdQXqcwMbigO1/QPqs10+sTYmXsW7cvAnn2UWbvxEP/A6kD2s65/O02a3lTzOUH62yb6OngUAv9dtwr7yPxlBcR59uCWRKL8NT/jMJH7i/7dFO2zGTHS/r3txG927HMl1rMbv1m1z+PjPKvxm1X7PLlpcmDsb9Dh34eJft/M7IGV/fFZqB3nVeIwkfmhajbDy/wQfk6BTIiO8wb37aWZR3fYjt+s5p/Zx9jZt8dR/GbVvoy1GXbiN6v2JUg2w8l1TFymOwEf+tCHaNu2babiHvOOd7zDUitPq204J2rrUoFPqFUUTx7+PR6+j1sS8AkWlcHAZOu/PCzqcWKChTwW9Co9kMeKe2OFP78pDmUN43r2pZ+myVOqi85uB7J7NTDZykD28cZ17qLjTZ9jOG/LDzhvp1993v6e0cAuXAThROwfC+yL3rzWlz7gvC04Wph9AQCoMZC9qTFhyT/g5PENY4NDG/ZluFC05R+MDQ7P++SF1DS9xfQ5umBH3LML4jx7cFwLf00PnMRvTu2zk/jNiX12Gr+Nt88NPD8IuAbxG7AD8kMAqEE8kXQUv1nNPw+XSo7jN7v5ISfx214H+WfZCGxmjQf8MmvWrDnkscp9J598cs3nc8VSZY+XX375kMf5Pp5nYLdVpa7OG++1bNky44t7tNeCH/NyXT1Fvv/5n/8xvoIg7hmEQkLFPW61D3FPH5AsAm7hvwuinbfBrFm3dgCADOIe+y8N4bBQcY/tS0M0IlTcGy4GY/C6H9x1113GSINqcJt6nmf+3e9+19HeiPOCFw9yVxuV4zwZgLh3ABRfiAHiHrAD/61HfggANWie0ipM3GP/ZW8mB3GvzgRW4OMZd9z7lAOSgYGB0fu3b99unKjiGWhz5swZDUj+9re/HRJAXH755aMB6lh4diC3tOSgtTLPTzW8dt64VWp3d7fx1dXVVXMdP+blunqJfDxnj+ftVWbu1V3cI6JYvFGYuMfJO06k4+QesAKCQ8BMndIiVNzjyvLd/XvwZgOggLhnxX9xK+6xfUk2xoWJe1z5me5Nky488MADdOyxx9YspJw3b57tDi0VEOcFz1/jtkeqx3lBplwq4eTefiDuiQHxG7DL7v4B5IcAUAQrzYKdinucfy6XS8KLvwtDBZzcEyHwcXDwX//1XwcJQN/+9rcN4cwLUqmUUfXH7TgXL15M3/rWt+irX/0qnXHGGUZQeccdd4yuXbt2rSHC3HLLLQftcdlllxnVpSzwXXrppfSd73yHbrrpJnrPe95DLS0txn4q4rXzVmlVCepLyELlu5vKz8JwUXhbTm6Li7accoPgEFSYlEwIb+tS78IKAHRGNnGP7YuV8NVNW5dSSR9/ePPmzXTSSSfVfJy7W2zatMnR3ojzxAN/TS6G8hm05YS4JwzYA+CETC6HsS0AaIIbcY/zz42JuPDi73x2CG05JyBCLo5rj2Xfvn30sY99jObPn0+zZs0iL/jwhz9MU6dONYS+T33qUxSLxej000+nL33pS7RgwQLT54fDYfr5z39Ot956q1E9yCf3mpubjarRL37xi0blqWqIEPd4P37vOzo6jPs6Oztrrh/7mBfr/KYyc69yco/h23xfEE7xiWzrkmiICBX3OHmXHcxSKRTCzD1JQXAI/J7ZkEil8KYDUAdkFPf8mNlw+ExvYhwZyOVyxqmuWgwNDRldVJyCOC9Y/lo0GqWFCxcqHecFmnJZ+My9fKEg1cw9zEQNbn6or6/Po6sDQaYxkUDxNwAa4FbcY/9l8+N3Cs8PhcIkdOZe+WCZSx+Bz4ro5wWXXHKJ8TUR3K6z1vfmFpyf//znjS/VESXu8X68z9KlS02f09bW5um6eoh7Y2fuMZX7mKCKfF7MbMhsXS1U3OPgsFwiiHuSAnEP1GMg+78+OmJ7AQD+AXGvdnC4c9U2bX4UjznmGHryySfphhtuqPr4qlWr6KijjnL1PRDnBcdfa2xsNI3NZI7zgk4klhQq7nHyLpPL0+y2Voh7GiEqP5TNZj29ThBMpk5pRvE3AIrjhbjH/ouZwOdFfiidCQsT90rDJRpI95PMBHYGH7CPKHGP94tEPNWCA0c1cY/fv8pMPr6PH+M1QWtX6tVA9pCFpsxuxD2u/IwnY5aNq5PKz1wmZ/4igG0g7oF6iHtW7AsAwFsg7rkLDlXi4osvpl/84hf0jW9845DHvvnNbxpdUsyKMIG/wF+TlwYLsbZb+xwKhYWKe0RlnNzTpPg7mUx6eq0gmPBoJDNQ/A2AvHgl7vmVH+IujaLEvaeW/4Zy2TzJDAQ+ReA2CaLEPd5PR3GvQpBFPq/EPSvBoVtxj4PDaCwqtK3LQP8e03XAHkgWATtA3ANAXrgXBtpyjqC7uMfwCSz2fW+88UY67rjjRk/b8e1PfOITdMIJJ9CnP/3pel8m2A/8NbXxoviiqTEhTNxj/y+byaMtpybF362trZ5eK5ATFH8DIC+yiXtW/Je9LsQ9zj8nknHSVuDr7++nHTt2GF89PT3Gfbt37x69b/wXEAe3SRAl7gWxJaVf4l6QRT7ZxD0/ZjaEw+r+rNYD3ewBcAfEPQDkZqC/DzP3IO6NMmnSJFq9ejVde+21RiHhQw89ZHzx7Y985CPGY5MnT67jTyyoAH9Nbbw6Wd1gofLdzUzU4UIRM/cCAoq/gWhQ/A2AvEDcq55/bk5NIZlx1XfxYx/7mPE1lve85z01j3cXi0U33w5MALdJgLg3Mfl83giAKwKFVXFvvMjH1HsmH8S96gPZU00p3z8LVUGyCARB3PvLy9vp5CWnHvyzWRymdG+aSqVhap7SQonGhOn34TG93FOd2y5wZRY7b9x15vDDZtFPf/QwPmwA2E/KZV0lj61UfrpJHtu1L2P9A5ED2VWmubmZvvWtb9Gdd95Jvb29xn3Tpk2z1LYL+AP8NbWRqW1yQzRiuzjTjn1m/w9YA8XfQCReFX/f8shaocXfLY3Ntl4XADoAca+2fem+/TekpcB3+eWXe3slwBXcJgEn9yaGBWYW9CoinR1xz0zkY0qlki8/xRD3ageH6+9d48tnoDpIFoGgnNwrUJne9OnzD02+Z+y3Xdi1dfchweEfbv0VPmwA9hNPJIUmj4dLJYh7ksKC3vTp0+t9GWAc8NfURiZxj/2/lVt2CBP32P/j4i5gDRR/A1HI0tkJxd8AHArEPXf2RVmB77777vP2SoB0wSG3Ze3q6jJud3Z2Ultbm6t1ZT5iIZBIJGKIchUhbsOGDbbEvVoiH+/H94m+fgbinrvgEJiDZBGwg0w91VVy3gAQQfOUVmHJY/Zf9mZyFKEyTu5J6Bds3LjRaPlWrZjtjDPOqMt16Y5of022OE81ZBP32P9beduPhZ6sbvCnllYJUPwNdBf3UPwNwMHkchnM3CO180OuWnQCvYNDDua6u7tH/7906VJX60TPtIvH4zRv3jxD2GPa29sdt9isiHyc6OD9otEoTT1sDomkXCph5t5+IO6JAeIesAMn6yDuAaAOIcGVn+VySbi4VxgqoC2nh9x+++305S9/mfbs2VNzTb1nUuuI1/4a7zd+lIZscZ5KyCju+dE2+fCZs0zXA/cgHgQqiHso/gbgYAb6047it+y+NJ16/tV01AlvMn0Oir/rixCZMp1O09lnn03PPvusiO2BR8B5k4uhfMaWcXUaHMJ50xPYA2CX3f0DOLkHgEa4bevSmIgLTR6z/5LPDmHmnkfcf//99KlPfYpOOukk+o//+A+jqOOGG26gm266iVKpFJ188sl07733evXtQB3FPd4PJ+yCAcS92p0brMxcBu5APAiqgfwQAPLDuWCR4h43akDxt4In+IaGhujJJ580hD6grvPGbViq3Xa6zsu2MtXI5/PGaTs+ucfw7cpMPrvfm98/fm5lP37+rvQgCaVchriHyixpkkXcyguoTSaXE96Wk09Joy0nAGrMbNj8+J3CKz9DYXJ0MsRq21+dugx++9vfNkS8VatW0e7du+kzn/kMvf3tbzeKOK+//nrq6Oio9yVqhyhxj/fjOYsyx3kqAHFvYvu8c9W2On466gNxD1QD4h4AapBqnSZM3GP/ZTCbQ/F3nUGLTg3xynnjGQu12rA4WTc+sPQabj3DyYjK/DwW6HiGHmNH5KuIe/zcygw/5u7lKwRePVEklhR6ci9fKKDtgoaIShZls1lPrxMEj8ZEQqi4x21dsoNZKoVCmLkHgAID2c0EPi/auqQzYWHiHreNGkj3ky789a9/pS984QsH+eiVNouzZs2iD33oQ/SNb3yDLr/88rpepy6IFPd4v3Xr1kkd50lPmdCW04V9Bu6AuAeqwQl7tOUEQA0aGiLCxD3OPxeGi7TkhLko/q4jwiYJwokPJjo7b5FIZFTIq8zQY4GOhToW7KzMhqgm7lX2C4fFDuZsiESEVn5mcnnhPdU5ekVPdT2SRclk0tNrBcFj6pRmoeIe25dyiSDuAaCAuOfXzAYrvphTcY/9l1w2TzrR0jIiljY2Nhr/ju3OMnfuXNq4cWPdrk03RIp7vB+oL4WhLGbuQdyrCzrnh8DE7O7fg5l7AGiCG3GP88/Rhogvxd9O4reXbM70lBVhrwx9/IOH7s5bPB4/6PXaFflqiXuqtHUJhcJCxT1O3mUzeQxM1iRZ1Nra6um1guBhpZDHbVuXeDIm1HnLZXKmawDQFdnEPSv2xY24x/YlkYyTLsyePZteeukl43YikTBOav3xj38cfZz94ebm5jpeoT5w23OR4l6Q4hldGS4WHcVvduzzcKnkKH5zYp+djFXAyT3/gT0AE8F/G1D8DYD6uBX32H+ZlEyg+FtFgW/69Om0bds2Wrx4sYjtgQPgvFXHqsinurjHwWFTY0KouMfB4XChaCv5jpl78iaLAPBiZkM0FhUm7rF9Gejfgw8KgCpA3KtuX5pTU7T5eeE47rHHHhv9/7ve9S765je/abTt/PznP0/f+c53jHl8QDzc9hzintpwpxaR4h4n7/ZmchD3wCjIDwEzZrSmUPwNgOJ4Ie6x/2KlEzuKvyUU+Lg9zpFHHmmcmAL1B86bO5FPB3GPg8MGC22t3Ih7XPnZEI0IFfeGi+ZtVsEISBYBkcgykD0cDo4tByAoQNyrbV90GiN2zTXXGAJeZabuv//7vxt+MAt8fPu4446jW2+9td6XqQXc9hwn99QmGksKFfc4eVcul3ByDxggPwQs2aWIuU1C8TcA8uKVuGfFf0Hxt3jMh3rZYPv27fSzn/2MtmzZYvz/mGOOMao9jzjiCC+/DbABnDd7Ih/DQh5T+b8O4p6V4NCtuMdtXVZu2SFM3OO2LuneA7NhwMQgWQR0F/fYvqSaUpZfFwA6AHFPz5kN1Tj55JONrwpTp041TuqvX7/e8IXnz58vfP40GIHbnqMtp+KExNvnyYm40LachaEC2nJKAPJDwCtQ/A2AvMgm7rH/cssja4Xmh1oa5R494JnA95//+Z/0uc99znAYxs7f++QnP2lUeX7605/26luBOjhvvNeyZcuM252dncYcjmr09PRQV1eXZ+vqKfKVSiXj9oYNGyDueSDucXC48rYfC53Z0DDykQELIFkEdBf32L6sv3eN5dcGgOrkchnM3IO4ZzA4OEhf/epX6dRTT6W3vvWtB/2cnHTSSXX5+QRi48FMJmPEeqrGebLjRfHF5sfvFDoTNZ8dotYZLZi5F2Ag7gGvQPE3APIio7hnfn0l7Yu/PSm7/J//+R/6l3/5F6OS8/7776c//elPxtcDDzxg3MeP/ehHP/LiW4E6OW/5fJ66u7uNr0rAVg1+zMt19RL52tvbDWGPv/g2Tu7JMZC9eYr5euAeBIdAdufNqn0BQCcG+tOuksdWgkM3yWO79mWsf7D4ynNo7qLjTZ+Dk3sjTJo0ib70pS/Ryy+/bPqeATX8tUKhoHycJyuynKwOhUmouDemhhw4APEb8Aqvir+tHF12kx8qlTC+BYDxQNyrbV8aLLQlVv4E3x133GFUc7ITz23fKrz+9a+nSy65hBYtWmSsufTSS734dsBn5433GzuTDtQX2dpy+iXucXC4c9U20+cAdyA4BNWAuAeA/LAvcfb/Jy55zMlZiHvycNRRR9GuXbvqfRnAIfDX1EAWcY+LL9KZsDBxj4svBtL9putAdWAPgFfIlB86fOYsy68LAB2AuKd28bcnAh/PYvj85z9/kLhXge/j1hw8kB3IKe7xfrFYjDo6Ooz7+POsxdjHvFjnN/x6eeZe5eQew7f5viCc4oO4N3FwCIFPLAgOQTUg7gGgBqnWacKSx+y/DGZzOLknEVdccQWtWLGCbrjhhqoxHlDLX4tGo7Rw4UKl4zzZkEnc45PVF9zzqNCT1bls3nQt8Cc/1NfXh7daQ2QS91D8DcDBDA8X0ZZTYXHP0xl8oVDI0WMg+OIe78f7LF261PQ5PGPBy3X1EPd4/t7rXve60Xl8lfuYuop8ZcLJPRfBIXAHxD1QDYh7AKhDQ0NEaOVnYbhIS06YK7QtJ89PRltObzjllFMMH5gL/D72sY/RcccdR42NjYesO+OMMzz6jqCe/hp/tmaxmcxxnmzIJu750TZ5emqq6XrgT34om83irdYM2cQ9FH8DcDDpvl7K7stg5l63muKeZwLfiSeeaPTXv+666yiRSBwyu+0HP/gBBrL7gChxj/dbt24dqUw1ca/y/lWEvnqLfIWhLNpyQtyrCxD3QDX4NA5m7gGgD27buiQaIkKTx5x8yg5mqRQKYeaeB5x77rmjt6+//vpDCjbL5bJxH9r4Bwf4a2oAca/6TNRIus4fjGSILP5OpVKeXisINjKKewCAQ2MyJ/GbnbFQKP5WQODjqs7LLruMFi9eTJ/+9KcNwa8iiNx+++303HPP0fe//30vvhWoAbdJ2LJlixBxj/dj8VZHcY/h20EQ+YaLRcMYY+YenDeZg0OgDrv799hOvtsNDvno8tjkzulXn0dhQcEhAEDszIbM1tVCxT22L+USQdzziPvuu8+rrYAPwF9TA4h71cU99v+6b/9NnT8duRBZ/N3fj3mIugBxDwA1aJ6SEiru5QsFFH+rIPC9//3vp40bN9KXv/xleu9733vQY1zZ+dnPfpbe9773efGtQA24TYIocU/lZL6ZuBckka8hEhEq7g2XSqjMAlrbA2AP/lm44/KzhYl7XPmZzeQh7gGgyED2J+5aLbzyM56M2W77Zqd4IJfJkS5cfvnl9b4EYBH4a2oAcc+dfQb+FX8vW7YMb7cGFIrDyA8BoAiJxKFt9r0S99h/yeTyNLutFcXfKszg++IXv0gf+MAH6Kc//Slt3brVuO+YY46hCy+80PgXiCWZTELcM4HbxbKDWnFwrYp7QRH5orGkMHGPk3d7MzmKUBltF4ABkkXAjBmtKaHiHrd1GS4U6ZjF7Ti5B4Dk4p5fbV1ueWStsOQxnwwe6N9jug4AP4G/pgbcqQUz9yDueQWKv4EXvNaXpqG9aMsJgA64EffYf2kIhYWKeyj+9kHg27dvH3384x+n888/ny699FK66aab3G4JHNDa2oqTeyYUi0VD0KuIdHbEPTORjymVSmJ/dg8efeJ55We5XBIu7hWGCuipLgFIFgErRCMNwtu6NEQjQsW94eKw6RoAdEU2cY/ti5nA50bcY/vS0thMurBixQpL67jAE9QH+GvqUBzKUsu02bbjNzv2uVwmR/GbE/vsZCYWTu55B4q/gVd/YzBzDwD1cSvucf45Hh5E8bfsAt/kyZPphz/8oTF/D+gVHPb09FBXV5dxu7Ozk9ra2lytK3PUIZBIJGKIchUhbsOGDbbEvVoiH+/H94m+ftFtXSYn4kLFPQ4O89khap3RgoHJAQbJIhCkmQ0rt+wQJu5x8indm7b8egDQCRnFPfPrcyfusX1JNaVIF6644gpj1MJ4/5bvGwsEPjX9NdniPOkJhYSKe2yfB7M5iHuagOJv4AVTp7Sg+BsAxfFC3GP/pXvF9abPQfG3BC06582bR6+88ooXWwGJgkMO5rq7u0f/v3TpUlfr+FpEEo/HjZ9VFvaY9vZ2xy02KyIfi3u8XzQapamHzSGZZzZsfvxOoeIeJ+9CYRIq7iF34A6IeyBoA9lX3vZjYeIe25cGwQevAZARiHu17cv6e9eQLvzud7+r2g2D5zp961vfoqamJvqP//iPulyb7njtr/F+/NnKHOfJTizeKFTc4+RdYbhIS06Yi5N7YEIQD4IKk5IJ0zcDxd8AyItX4p4V/wXF3+LxZFrxRz7yESPI2LVrlxfbAZ+A86YGMg1kT05KChP3uDJ/IN1vug5UB/YABE3cs2Jf3Ih7bF+ap5ivB0AnIO65sy8qsWTJkkO+zjnnHPrQhz5Ea9eupf7+fvrzn/9c78vUDhHiHu+HE3b1JRQOC7fP0YaIUHGPC1/RllNuEA8CO6D4GwB5kU3cY//FytyqrS7yQ6WS3MVoEa96fM+YMYPmz59vtHM57rjjqLGx8ZB1aOGilvPGbViq3Xa6zsu2MtXI5/PGaTs+ucfw7cpMPrvfm98/fm5lP37+rvQg+Y1M4h4Hhxfc86jQmQ25bN50LfAnWdTX14e3WkNkEvfYvuxctc3yawNAdYaHi2jLuR/dxT0zEokEXXbZZfSd73yHPvGJT9T7crRBlLjH+41vvSpbnKc6XhRfZLauFibusf+XHcxSKRTCzD1JgbgH7OBFfiidCaP4G4A6IKO450d+6PCZs4h0F/hY1Kvw9a9/veoaDhog8KnlvPGMhVptWJysGx9Yeg23nuno6Bidn8cCHc/QY+yIfBVxj59bmeHH3L18BfmJbOKeHwPZp6emmq4H/iSLstks3mrNkMl5q9gXCHwAHCDd10vZfRnM3IO4Zwlu0bl9+3b8Cikg7vF+69atkzrOUxmvTlY/cddqoTNRyyWCuCcpEPeAHVD8DYC8QNxTt/g7ImpGAwgmOjtvkUjkICGvIszZEfmqiXuV54QttFbxCoh7h4p7R/7DMRRJ+/YRKIHIZFEqlfL0WkGwkVHcAwAcGvC5SR5bqfx0kzy2Y1/G+wenX30ehQXZFx3J5XK0YsUKmj17dr0vRRtEinu833XXXefp9QL92ibHkzHbxZl27HMukzNdA+yjc34I2AfF3wDIC8Q9tYu/PRH4eCYDCD66O2/xePyg18u37Yh8E4l7fgJxr3Zw2H37b3z/PGRGZLKIZ/MAPYC4B4AaNE9JCU0e5wsFiHsScdVVV1W9n1twr1mzxpi9fscdd/h+XTrC7/mWLVuEiXs6xYMyIZO4x8UXtzyyVmjxxUD/HtN1wB6wB0BVcQ/F3wAcTJkIbTkVL/72ROADwQfOW3WsinwQ94LtvFkNDoF/yaJly5bh7dYAiHsAqEMicej8bC8rPzO5PM1uaxV6co/DV5zc84b777+/6v2tra10/PHH0ze/+U36p3/6J4++G5gIbnsOcU8vZBP32D6bCXxuT1a3NDabrgXWQX4IqCzuofgbgIMZ6O+jXbu3YObeLjXFPc8Evn/7t3+jBx98kJ5//vlDHiuXy8bcs0svvZQ++9nPevHtgE3gvLkT+YIi7g0Xi5i5B3HPM5AsAm4pFIfRlhMAjXDb1qUhFBYq7nHyKZvJoy2nR5RKJa+2Ai5JJpM4uacRMop7frRNTjWh/b9XID8EVBf3UPwNwMHkc1mIe7vUFfcYT468PPzww/SWt7yl5kBtfuwnP/mJF98K2ATOmz2RjwU8FvJY0OP3LijiHlMcylJycorOvvTTNHnKDCHBIZw3fUCyCLjltb40Zu4BoAlezGxoakwIFfc4+TRcKGLmHlAOPjWJtpx6AHGv9kzUhghayXoB8kPADsgPAaAG8UTSUfxmNf88XCqh+FuFE3zbtm2jj3zkIzUf5zYu9957rxffCtTJeeO9Ki33Ojs7qa2treq6np4e6urq8mxdPU/yVSqXN2zYUHdxzyAUEirulcuEyiyNQLIIuIX/LthNvtut/CwMFQ5K7jRNbyHdK7MAkHUge/eK64W3/W2IRhydDLFqX4aLw6QLHN9xd5YLLrigaoeWlStX0kknnURHHXVUXa4PeBsPZjIZI9ZTNc6TAYh77uwzMAfiHrAD/63HyT0A1KB5SqswcY/9l72ZHEWoLDw/5MQ/2KtJfsgTgY+FkIGBgZqP82OFQsGLbwXq5Lzl83nq7u4e/f/SpUurruNgzst19RL5+GeahT2mvb29/uIeEcXijcLEPU7eDWZzaLsALIHgEDBTp7QIdd64rUs+O0StM1rgvAEgubhnxX/xYqbnyi07hIl7HByme9OkCzxa4aWXXqoq8HGHFhaDjj76aHrggQfqcn3AW3+NY/VKbKZqnBdkyqUS2nLuB+KeGBC/Abvs7h+grbt3oS0nAAoQsrDGqbjH+edyuYTibxVadM6fP59++ctf1nycHzvuuOO8+FagDs5bpVUlqC+hcFho5WdhuCjceWPhFD3V5QbBIagwKZkQ3tYlFCah4h6fXAYAqCHusX2xEr66qfwslfTxh1evXk3nn39+zcff+ta30v/+7//6ek3AOvDX5GIon8HMPYh7woA9AE7I5HIQ9wDQBDfiHuefGxNxX4q/cXJP8Am+97///fTJT36SbrzxRvrSl75kzHZistks/eu//iutWrWKbr31Vi++FaiDuMf7xWIx6ujoGG21Uouxj3mxzm8qM/f49B6f3GP4Nt8XhFN8Itu6JBoiQsU9Tt5lB7NUCoUwMFlSEBwCv2c2pDNhYeIeD2QfSPdbfj0A6ISM4p4fbV0OnzmLdOHVV1+lmTNn1nz8sMMOo507d/p6TUCcvxaNRmnhwoVKx3mBplx2FL/Zsc/5QsFR/GbXPrN/9dTy39BL/7cFM1EVzQ/19fV5eo0gmDQmEij+BkAD3Ip77L9sfvxO0+eg+FsCge9jH/sYPfLII3THHXfQ8uXLad68ecb9mzZton379tGZZ55JN9xwgxffCtRB3OP9eB8r7VV4xoKX6+oh7vH8vcrMPaZyHxNUkc+LmQ2ZrauFinscHJZLBHFPUiDugXoMZL/gnkeFiXucfMpl85ZfEwD15sabbqa//u0FKgqaA/fyK6/QGyHuTWhfdq7aRrrQ3NxML7744oQz+iZNmuTrNQFx/lpjY6NpbCZznBd0IrGkUHGPk3eZXJ5mt7VC3NMIUfkhLuQH6jN1SjOKvwFQHC/EPfZfzAQ+FH9LIvBFIhH61a9+ZQh8P/jBD0Znl7HQx1V7119/vbEGiEWUuMf7rVu3jlSmmrhXef8qQl9QRT6vBrI/cddqoeIeV37GkzFbyXe7lZ+5TM50DbAPxD1QD3HPin1xI+6xfZmemmr5dQFQb17r7aM3XHAzlcpWpijYZ+sdV+Pknol90UngO+200+jee+81urSkUqmDHkun03TffffRqaeeWrfrA4cCf01eGizkStyerG4IhYWKe0RlnNzTpPh7/N8EoCY8b9cMFH8DIC9eiXtmoPhbohl8DAt4N910Ez377LM0ODhofPFtDgoh7omH2ySIEvd4P13FPYZv8338GK/htUGZSeiVuGclOHQr7nFwGI1FhbZ1GejfY7oO2APJIqCquMf2pTk1xfJrA0B5yoS2nC7si2pwXPfaa6/RKaecQt///vfp+eefN774Ngt7/BhOaQUH+Gtq40Xb5KbGhDBxj/2/bCaPtpyaFH+3trZ6eq1ATlD8DYC8yCbu+ZEfSiTjJDM4VqcI3CZBlLgXpNNqfot740W+IJ3kk03c4+DwlkfWCp3Z0NLYbLoWWEc3ewDcIZPzVrEv3bf/xvLrA0B1CkNZzNyDuDfK6aefTnfffbcxiuGKK64Yvb9cLlM8HqfvfOc7xhgGUH/gr6mNVzNRu1dcL3Qm6nChSMcsbrcdv9mZiQqsF39v2bJFWH5o2bJl+Cg0x4v8kFluiEHxtz9ceumlxuxlq/aF88/JZNKy2M9zm3/0ox+5vEq18WsUAwNxr7p9iaRJajwV+LiS849//KPxC18qlQ55/AMf+ICX3w6MgY0rxL2JyefzhoNacXCtintBFPlkFPf8GMieakK7EK9AsgioLu5ZsS8A6MRwsWj4E06Tx1YqP90kj+3aFyfJY5zcO5irr76a3va2txn+Ms9Wr4xgYP931qxZpu8nEA/8NbXxStzzwz43RCNCxb1hQUlPFUHxNxAJir/Vg8W9m2++2ZK/4aR44D//8z89ulJ18WMUAwNxT93ib08EPhbzPv7xj9N3v/vdCVsXQuATB1dO4OTexBSLRSNBURHp7Ih7ZiIfU03UFgHEvdrB4fp71/jyGagOkkXADhD3AFBnBpTI5PFwqQRxT0JYyON56iB4wF9TG5nEPS6+WLllhzBxj4sv0r2Sl9b7CIq/gShQ/K0n8DfUAOKe2sXfngh8X//61+nb3/42ve9976O3vvWtdPnllxsKfVNTk/EYi09f/vKXvfhWIEDGuqenh7q6uozbnZ2d1NbW5modt/wRCc+CZFGuIsRt2LDBlrhXS+Tj/fg+0dfPQNw7ANq6iAHOG7ADxD0A1CEaSwpLHrP/sjeTowiVcXJPErgjyyuvvEILFiyo+vhzzz1Hc+bMoVQK3RNU9Ndki/NUQzZxj09Wr7ztx0JPVjf4U0urBCj+BrqLeyj+9g7kh9Tp1IKZe6SsuOeZwPfAAw/Queeeazj3u3fvNu574xvfSGeffbbh6J900kn05z//2fg/UMdY8+fd3d09+v+lS5e6WjfR6U8v4Hkh3FaIhT2mvb3dcYvNisjH4h7vF41Gaephc0gk5VIJbTn3A3FPDHDegB04WYe2nAAoREhs5We5XBIu7hWGCmjL6RGf/vSnjdELzz77bNXHeS7fqaeeasziA3L7a7wfdzqROc5TCRnFPT/aJh8+E22B/QDxIFBB3MNMT2+APVCH4lCWWqbNth2/2RkLheJvBQS+zZs30wc/+EHjdjg8YnwrQQKf4rvqqqto+fLldOONN3rx7YBHwFjLxVA+Y/yLmXtw3kQAewDssrt/gLbu3oWZe5pgZ/i6k4HsGL6ufluXyYm40OQxJ5/y2SFqndGCmXse8MQTT9D73//+mo+/853vpB/84AdefCtQZ3GP98MJu2AAca/2TNSdq7bV8ZPRA8SDoBoQ9/QE9kAxQiGh4h43akDxtwICXywWo0QiYdyeNGmS8W9vb+9BsxtefPFFL74VCJCx5tOZ1W47XedlW5lq5PN547Qdn9xj+HZlJp/d783vHz+3sh8/f1d6kIRSLkPcg7gnTbKIk/lAbTK5nHBxj09Jj03uzF10vNZtF4I+fN3NQHYMX1d/ZsPmx+8UXvkZCpNQcU+nLoM7duygI444oubj3J6T1wD5xT3eLxQKSR3nqQDEvYntMwQ+sSCZD6oBcU9PYA/UIxZvFCbusf8ymM05jt/s5IecxG8lTfJDngh8HOBt27ZtVOw76qij6KmnnjJm8jFPP/00TZs2zYtvBQJkrHnGQq02LE7WjQ8svYZPlXZ0dIzOz2OBjmfoMXZEvoq4x8+tzPBj7l6+QuDVE0ViScvG1Ulbl3yhgLYLGiIqWcQndYDaNCYSQp03buuSHcxSKRSC8yYpCA7VwKuB7GYCnxdtXdKZsDBxj4PDgXQ/6QKftuUZfLV4+eWXjRb1QH5xj/dbt26d1HGe9JQJbTld2GfgDvhroBqcsEdbTv1A8beahPZ3WxQh7nH+uTBcpCUnzEXxdx3xRLI844wzaOXKlaP//6d/+icjYLjyyivp8ssvp/vuu4/e8Y53ePGtgEt0dt4ikciokFeZoccCHQt1LNhZmQ1RTdyr7FdpTyuKhkhEaOVnJpcX3lOdo1f0VNcjWcSJQaA2U6c0CxX32L6USwRxT1J09jdUwitxz6+ZDVZ8MTeVn7lsnnThDW94A/3Xf/1X1YKdXC5nPLZw4cK6XJuOiBT3eD9QXwpDWczcg7hXF+CvgVrs7t+DmXuageJvfXEj7nH+OdoQ8aX4Gyf3BJ/g+/jHP04LFiwYna3yuc99jv72t7/RihUjJ5rOP/98+vKXv+zFtwIu0N15i8fjB73eisjHWDnJV0vcU6WtS0MoLFTc4+RdNpPHwGRNkkX9/fqcctAVK9X4btu6xJMxoW0Xcpmc6RpgH939DVWQTdzzo63L9NRU0gWO7y688EI699xz6Stf+cqomPfss8/Sv/zLvxhtd2+99dZ6X6YWcNtzszbHVoF9DibDxaJhL+3Gb3bs83Cp5Ch+c2KfnYxVwMk9/4E9ABPBf2vuuPxsFH9rgsji71Qq5em1gmCJe5x/zmxdjeJvFQS+448/3viqwCLfww8/TAMDA0Yl7eTJk734NsAFcN6qY1XkU13c4+AwHh4UKu5xcDhcKNIxi9stJ98xc0/eZNGyZcs8vV6g58yGWx5ZK0zcY/sy0L/H8usB1oC/oQYQ96rbl0iatOGd73wnffazn6X/+I//oDPPPPOgx8rlsiHyvfvd767b9ekEF9FC3FMb7tQiUtzj5N3eTI4iVIa4BwzgrwEzZrSmUPytESj+1hMvxD32X564a7Xp81D8LRahPQWbm5sh7gUAOG8TY9auUwdxj4PDBgttrdyIe1z52RCNCBX3hovmbVbBCEgWAZHIMpA9HA6OLVcB+BtqAHGvtn3RbYzYF7/4RVq7di197GMfMzqy8Bef7OP7WPjjOetAPFw8i5N7ahONJYWKe5y8K5dLEPeAAfw1YMkuRRqEF387id/s5IeA9eJvtAHXD6/EPSv+ixf5oWjMfPZ3SePib8cn+GKxmO1WXvm8PnMrggKcN3cn+RgdxD0rwaFbcY/buqzcskOY88ZtXdK9GpXWuwTJIqC7uMf2JdWEdiFeAX9DDSDuubMvKvLGN77R+KqQTqfp+9//Pl111VWGf2xlhjVwR2trK9pyqk5IvH2enIgLbctZGCqgLacEwF8DXoHib3VA8bd+yCbusf9i1t2p5DI/1NLYTFoKfMVi0UgQc8BnZaA9kNt5470qLfc6Ozupra2t6rqenh7q6urybF09Rb5SqWTc3rBhA8Q9D8Q9Dg5X3vZjoTMbGkY+MmABJIuA7uIe25f1966x/NpAbZAsUmcGFGbuQdyrxZNPPknLly+nhx56iHK5HLW0tBg+PFDDPmcyGSPWUzXOkx0vii82P36n0Jmo+ewQtc5owcy9AAN/DXgFir/VAsXfeiGjuGd+fSXti78dC3xHHnkkvfTSS7R9+3a68sorjSrO2bNnO90OBNx549OX3d3do/9funRp1XUczHm5rl4iH4t7LOwx7e3tOLknyUD2w2fOsvhJAzcgOASyO29o6+IdsAfqUBzKUsu02Y6Tx1aCQzfJY7v2Zax/sPjKc2juogPzwmuBk3sH89prr9F9991H3/ve94y5vcxb3/pW+uhHP0rnnXceRaPmrXKAHPa5UCiMxmaqxnm6n6w2E/jc2udQmISKe+Wy6RIwAfDXgFeg+Fs9UPytDxD31C3+dnz0btu2bfTrX/+aTjnlFPryl79MRx99NL3tbW8zqjr5dB9Qx3nj/dB+JzjI1pbTL3GPg8NEY8L0OcAdCA5BNSDu6QnsgWKEQkLFPU7OQtwLPuVymX75y1/SxRdfTHPmzKFbbrmFZsyYQZ///OeNx66++mp6+9vfDnEv4MA+q4FMbZOTk5LCxD0uvhhI95uuA9WBPQBeIVN+qHkKZvT5AeyLPEDcU7v42/EJPubcc881vnbv3k0PPPAA3XvvvXTJJZfQ9OnT6QMf+IBxqo9PPwG5xT3ej2cudnR0GPdN1I5n7GNerPMbfr08c49P71V+dvk23xeE+XsQ9yYODneu2lbHT0d94LyBakDc0xPYA/WIxRuFJY/ZfxnM5nByL+D827/9m3Fi7+WXXzbaKn7iE58w4rl58+YZJ/j4caCmfebTmAsXLlQ6zpMNmcQ9Pll9wT2PCj1ZncvmTdcCf/JDfX19eKs1RCZxj+0LckPiQTwoD+VSCW05FRb3XAt8FaZOnUo33nij8fWHP/zBaONy991309e+9jX6+te/Th//+Me9+DagTuIe78f7WGmvwskAL9fVQ9zj+Xuve93rRufxVe5j6irylQkn91wEh8AdcN5ANSDu6QmSRWoSsjBT203lZ2G4SEtOmCu0LSe3WEdbTud88YtfpOOOO45WrlxJ559/Puasa2SfGxsbTWMzmeM82ZBN3POjbfL01FTT9cCf/FA2m8VbrRmyiXso/hYP8kNyMZTPGP9i5t5GJcU9Vy06a8EtOzkgfP3rX2+0cenvRysFvxAl7vF+kYgnWnBgqSbu8ftXmcnH9/FjvKZe7UoLQ1m05YS4VxfgvIFq8GkczNzTDySL9MVtW5doQ0Ro8piTT9nBLGbuuYC7sLzwwgtG0eayZctox44dbrYDPgN/TQ0g7lWfidqcmlLnT0YuRBZ/J5NJT68VBBsZxT0gFvgbElIuQ9zrVlfc81TgY/GDg8FZs2bRe97zHtqzZ49xeu9jH/uYV98CTAC3SRAl7vF+Oop7FYIi8g0Xi5i5B+dN+uAQqMPu/j22k+92g0M+ujw2uXP61edRWFBwCMxBskhfvJjZMCmZECrusX0pl8jRyRA79kVl/v73vxt+7lFHHUWf+cxn6MgjjzTm7T344IOUz6M9XpCBv6YGEPdq2+dQqM4fjmSILP5ubW319FpBcIG4B8YDf0NOIrGko/jttLddQ2ELfz/yhQKKv+uMq2NZ+/btox/+8IdGS85nnnmGJk+ebIh7H/zgB42TfMA/uE2CKHFP5WS+mbg3XuRj6tWusyESsWxcnQSHw6USKrOA1vYA2IN/Fu64/Gxh4h5XfmYzeYh7miSL0PFB/YHsT9y1Wnjb33gyJlTcy2VypDLcsePiiy82vrZv307Lly83ZvL90z/9k3FiIxQKGSIgCBbw19QA4t4IKL7wpvib56aKyg/xCW+gPoXiMPJDQBp/49JLL6VXX31V2P6HHXYY/ehHPyJZ4XyyKHGP/ZdMLk+z21pR/C2jwHfllVca1ZyZTIZOO+00Q+TjXyju3w/8h4NuiHsTw5XH/Aep8gfIqrgXFJEvGksKE/c4ebc3k6MIldF2AQTeeQPBYEZrSqi4x21dhgtFOmZxO07uBQAki/TEK3HPiv/ixUzPWx5ZK0zc45PBA/17SBeOOOIIYybfv/3bv9Gjjz5q+AT87w033EBf/epXDR+YhcBFixbV+1K1Bv6aGnCnFszcg7jnFSj+Bl7wWl+ahvaiLSeQw99gce/mm28Wtv9//ud/ksq4EffYf2kIhYWKeyj+FijwPfDAA4ao9L73vY/a29uNGQ133HFHzfVc8XnLLbc4/XbABG6TgJN7E1MsFg1BryLS2RH3zEQ+plQqif05DYmt/CyXS8LFvcJQAT3VJSDozhsIBtFIg/C2Lg3RiFBxb7hYn5mqMoJkkX7IJu6xfTET+NyIe2xfWhqbSTfC4TC94x3vML56enro3nvvNb5Y5Pva175Wt9nUAP6aShSHstQybbbt+M2OfS6XyVH85sQ+OxmrgJN73oHib+AF/PcdM/dA5WcB+SF1cSvucf45Hh5E8bfMLTo52fODH/zA0loIfOoZaw7yu7q6jNudnZ3U1tbmal2Zow7BbYdYlKsIcRs2bLAl7tUS+Xg/vk/09Ytu6zI5ERcq7nFwmM8OUeuMFgxMDjBw3kCQZjas3LJDmLjHyad0b9ry69EdJIv0QkZxz/z63Il7bF9STSnSGfbheTYffz322GNGC0+gpr8mW5wnPaGQUHGP7fNgNgdxTxNQ/A28YOqUFhR/A+SHFMcLcY/9l+4V15s+B8XfARX4fve733l7JTLyh38jet2PD/z/2buIep8jev1HiaYvGLnvlaeINnQRHX460QmdI/fl+ome+jRRrIVoyW0Hnt/9JaK924lO/Rei5iNH7tvyC6KtK4nmvoPomAtG7ht4iejpLxM1HUG06DOjTz/+1e9RZDhDGw+7kooNk437ZvU/QanMX2hHy1mUnnSicV9Tbhsd0beS9iaOom0tbxsNDpe/J0Lt8/P01zG/zEf0/YKaci/S/KlDo/elBp+nWXt+R88++SJ1d79o3DcpUqC7/qmJig2NtPGwD46uPWbXf1Pxz4/T3zfsppf3jCRRll1zBk3fu5Z2NZ1CrzWdatyXGHqVvnRmr7F+y/T3uHpN21v3v09E9Loddxr//mXWxygej9O8efNoUeL/qH16kVY1vY5OftuIuDez+AIdW+imnQ3H0ebYSKuhWDlDp+R+QkOhJK1NXDK65+vzj9DkUh9Nvvit9L1SyRAK/3Feka45pYF2hdbQuvJIkNVKr9L54R9SmqbTL0vvH33+ReH/RwkapIdLV1OWRl7TKaHH6djQ87S2fA5tLp9k3Hc4baUl4Z/T3+loWlV61+jz3xceOSn7X6UbRu876dWv06LyKiocOYvazhkJDo8NradTQr+lzeUTaW35Lca6JO2jd4eXU44m0Yq+d40Gh1//wHEUf+0P1FAaoOHwSHV6oriNkqUXKRs+inKR/Ymz4h566YVH6HXNGaIT5o2Key1Dv6cwDVF/9E1UDsWNpY3FjRQv7aDBhuNp5V/yRvLupJkluvXDTbSrYROtppNHr//SnpXGvz9qe8fofSdv/1+iP/2RNjVEKHXlW43Kz7mZl+iNe9bT1sYj6I8tI79jyeEcXfDa45QLx+n3Y3MHLn+faNWniIb2EJ1+K1Fiysh9f+0i+vtTRO2dRLNPH7lv13NEf76LaNoCooUfPfD8xz488u+59wTGRhz0mvYz9vepN9Fu2IPMS920/D1JOrxjFr0yxh6M/X0abyO2t76D9iaOHrURn1ywaeT98vM1SfY5TY8P0nvDd1BfeYYQG/HHA1ck5DUtmtlPqaHfHWQj2IY0F/+P0tkY3fDAq6PJ9/9v/g4KD71Y00YMNcwyxL37fvMYnTv7JTr7yGm0+I1vppW3/bimjXhz3zM0K/8q/ffL0+n39/2fkXz/6MdPpjOKT9HWPdVtxM8PO3dU3Dv+Nw/QOW8ujvxsafaz5+Q1nXlsw2jy2Mrf3PE2YlvLP9LXvv+44W9cdtZRdM2SPPXvXUU7ppxtrJscKY68J7AR1T+nk28W6kcsCf+MFp23m16mrbSxf7LhH7x+yk667j1zaGdLhtbu//s61o94qPTPo88/n7qo0LOK1uzM0Zx5i4zgsCOylk4KPU3ry6fS+vJpB9mIYqiJ9kbfOCruff7cITr3xOlUaIpR5U/5eBvBREu9FNr3J3rqj2xfwqPiHtui8VRsxOrUyfRKdLoh7s3a+Tf67HviNLiwjf60X9yrZiOMX83epyjyykv0qQfT1JhqNooHpjzy6MjPaRBsBNX3ZP25555rfClLgOO8PCUNf+3Iwafo2vc0U/Mpb6I9Lu0zdzqROc4T+jlt/QV99Nj/oxdDJwqL82LxRiN+q2WfD6dttKr0Tlf2+enJBfqH4+Ya8dvk8kuUHDo4znNrn0+YVqR3b3uIHntyB2XS4dGTexP5cHbs8x8OfEpaxw9O4zyrv0/PHfaR0fzQv76jlc5/Y55eLmynvQ2I83SK8/g1/cubdho5oVo2guH47W9//TGdO3sPJSPH0U3vGskP1fLhJhfXUyE0lR7a0DRa/N314SaKFp+iH1F1G7EjcZhx38wdf6WT1/6KoocR7bjw7YZ9mciHW7JgG+I8i3ZvQeuBFvRO8sXjc0HsR6QbX+dvnLcft35EXfJbPsZ5f6e5xn1Gvpgeo1+uf41e3vSqIe6d/bZOujh65yF+xD+Gf0Ap2kW/Kr2X+ugwQ9xLvXAnffMdJXpt9qX0QuOMmjaC4XxxNjdIn/3hAPUO5I347cIThig+9LuaNmJfdMFo8fe5s/9GHzhpMl3608hocWY1GzF3f764e0eUvn/fFiM/9K5P/CO9r/gU5V491EakCnvosWlvpnR0ipEfekPjK1LHeY4FviVLljh9KggIpVL5oMpPNtahkHklMwgWbFybXv0DLTg8TqecdxWlm8wrP4fygwdVfqZmbKPMaweFTYfAxvXbv3yG3jB1Hx09I0UXnWvt5N4zW3roPx/+m1GZH02WKJqImT6HjeuG3z5Hx00q0sJ3LabwKeZtXbgweCDdb7oOVIdPolbswUVntFP7/ATtC1voCwvAOAZzBXrk2e3UOxA5cLJmaMeE71PFedu5ayedffZkOvGYYyhrwb7sfbWfnv3ZVmpMjZwMbpzUT7THWtuouccOUaw5gc/PB7gF9E8e+gn93/9tNvyNCy54E4X2rsJ7H0Aye/voiR/dZfgH7W99B02e8iqNZnQnqPzcvmUtNQ3torajF9NRZ9k/uXf2ibNoUoLI7K/4a3sGaeuWV2hfjmcGn23t5F7pwMm9hWcdTjPnJ2ibhb9v6Zd7afiFHZRsmUrnXWPtZDAAvpzcu3fEX1vyj0dR+/wZ1BMOu7bPOGFXX0IWPkO39jna0HCg88qInuupfWZb+8q6F2koU6bFV77DWltOh/YZiIHtwdj80JsWz6LQ0Et4u8GE8dui6QM0b+Zk+tQpiyx1RtiwYzf958PriVOPHL+xuGcGx28v//dT1Da7SPPO6KDEP5jbF+AO5IvVhjubvLpjA+3euYfmzFuy/+Re1vLJvXeeOJkOnzuXBqItpr7IvtwQrd30Cu3eG6Wb3/3mkfituNFyZycW995w9ExWyyzlhzb972vUmJo60tmpNU70mrX80HkzBI/dEkyoDG/eNtyW8cQTT6Tnn3/eaPEYBFhwtTNQ1G5bl2uvvZa+853vCG3dcs0119Ddd99NorjsssuMNp08M9JNi87K+1eZ4cf78fN38YyBS79IpbKYoORHd1xNl96w3NO2nGPbujz+zffS7/71QFWtV205x7bduuCeR+m9d18rbGZD319epe2bR6qNgXWb4XWbJ96PZ66uX78eH8ME79G7Ln4vHXHaPwuzGX/86X/QH7ufFPYZnLf4ZEYZrFIAAM67SURBVPpe58iJZy/bco61L//wlf+paTOctuUca192rtpGz6x62vJr1hm7fkYFq/aFB5evWiVW9Lv00kuNAeyieOWVV2jmzJmUTqcpl8tRIjEiIFdup1LO2jvyfvw+NqUOo1Pe/a/CbMb/fO0qw58Q1ZZzrJ8hqi3neJvhRVvOsfblD7f+KjA2I4jxiAoE8X2t2F8R/lplP4Zvyxrnifwb4ofPVi3O87ptcmbranryc9VjvbE4tc9n3/5jik9uFDZzL0j2V0WfzY598cNnkxkV4rxasZ6X+aEXMnl6/3fHnGjxOD/063/5EW1aP7GIAKrbDK/9DT9shtNYVUc/w6u2nGPzz7XyyV7mhybKDXmRHzp85ixa/8w6kjUecTWDD8iJV8aaA7ilS5d6to7nNIqEW890dHSMzs+rCHSMHZFvrLhXEQiZu5evID/xUtybCK/EPT8Gsk9PTTVdDw5GVLKIZ7QCvfBa3JsIL8Q9ti8s8AF9ZnqyuCcy8Lv66qsNIa+vr+8g/6DiM7A/5MTf4P0WLFhAvf0ZEslQfmR/zNyzb18AEI1IcY/3W7dundRxnsp4NRP1ibtWmz7PTfFFuUTCxD2gl78Ggo2Xxd8i80O5bN7yawJi/Q2OZUAwECHuqZIf2il5bghelGbo7Lzx6b1KYo2/+DYn4Djpxgk0fm+ciHuV/cIu2uPYBeJe9eCwObW/9zGoe7IomUziU9AI2Zw3K8EhcIeO/kY+n6/qH3jhb1x00UXiX0C5DHEP4h4IKCLFPd4PqC3u2W2b7ORkdTwZEyru5TI50zXAPjr6a8A5MhV/J5Ijc9+BdVD8rTYQ99TOD0Hg0wjdnbd4PH7Q67WbdKsl7vkNxL3awSGKg4OTLGptbXW8F5ALiHtgPLr6G9wpoJp/IIu/EYklhSaP84WCkLach16ft205Aag3XPkuUtzTwT7LiEziHtvnaCwq1D4P9JsMWQa2gT0Aqop7KP62D4q/FadMOLmnsLjHQODTBDhv1bGadIO4F2znDW1dgpcsAnoAcQ+MR2d/Y2ynABn9jYZIRGjlZyaXFy7ucfQKcQ+oBrc9h7inF7KJe34UX4TDevgSfqGzvwbUF/dQ/G0fFH+rTWEoi7acpK64x0Dg0wA4bxNjlnQLirg3XCxi5h7EPc9Asgi4pVAcRltOcBC6+xvjOwXI6m+IausSCoWFJo85+ZTN5HFyDygHtz3HyT19gLhX/WR1alqqjp+KWujurwH1xT3M9LQHir/Vh/PJmLm3R1lxjzEv1QVSA+fNGpWkG8OJNaby/6Ak24pDWWqZNtvyQFMnwSGcN31Asgi45bW+NA3txcw9MAL8DXX8DVEzG+LhQaHiHiefhgtFOmZxO9pyAqXgtudoy6kHEPdqt01ef++aOn4y6gB/DdgB+SE9QPG3+nCnFifxm9X883CphOLvOgOBT2G8dN54r2XLlhm3Ozs7qa2treq6np4e6urq8mxdPZNupVLJuL1hw4ZgJNtCIaHiXrlMqMzSCCSLgFv474Ld5Lvdys/CUMH2TCyV2y4EFSSLFPM3BA1k715xvfC2vw3RiFBxb7hYe3YiADLb50wmY8R6qsZ5MgBx7wCYiSoG+GvADuVyGfkhTUDxt/pEY0lh4h77L3szOYpQWXh+yIl/sFeT/BAEPkXx2nnL5/PU3d09+v+lS5dWXcfBnJfr6pV042QbJ9qY9vb2QCTbYvFGYeIeJ+8Gszm0XQCWQHAImKlTWoQ6b9zWJZ8dotYZLXDeAgzsgXr+hghxz4r/4sVMz5VbdggT9zg4TPemTdcBIKN9LhQKo7GZqnFekCmXSpi5tx+Ie2KAvwbssrt/gLbu3oW2nBqA4m8NCJEwcY/zz+VyCcXfdQYz+BTEa+eN9xs7IwbUh1A4LLTyszBcFO68cSITPdXlBsEhqDApmRDe1iUUJqHiHp9cBs6BPVAb2cQ9ti9Wolc3lZ+lEvxhIAewz3IxlM84it/s2mcn8ZsT++x0JhbEPTHAHgAnZHI5iHsA9kUT3Ih77L80JuK+FH/j5F5tcIJPMUSIe7xfLBajjo6O0VYrtRj7mBfr/IZfL8/A4Wp6rqRn+DbfF7Sqeq/buiQaIkLFPQ4Os4NZKoVCGJgsKQgOgd8zG9KZsDBxj5NPA+l+y68HHAzsgdr+hozinh9tXQ6fOct0PQAy2udoNEoLFy5UOs4LNOWycHEvXyhA3NMQEfmhvr4+T68RBJPGRALF32BCEA+qgVtxj/2XzY/fafocFH+LBQKfQogS93g/3sdKexWeseDlunok23geTmUGDlO5jwlC0k3UzIbM1tVCxT1O3pVLBHFPUuC8gXoMZL/gnkeFiXtcWZ7L5i2/JnAAJIvU9jcg7tW2LztXbavLZwKAaPvc2NhoGpvJHOcFnUgsKVTc4+RdJpen2W2tOLmnEaLyQ9ls1tPrBMFk6pRmFH+DmiA/pAZeiHvsv5gJfCj+Fg9adCqEKHGP94tEItol2/j9q8zI4fv4MV4TtHalXg1kD1noyey2rUs8GbOVfLfb1iWXyZm/CGAbOG+gHuKeFfviRtxj+5JIxi2/LjACkkVq+xsQ9/QbyA7UAf6avDRYiLXd2udQKCxU3CMqoy2nJsXfyWTS02sFwSRkIUGE4m89gb+hBl6Je37lh8IWxlbt1bj4GwKfInCbBFHiHu+nY7KtQlCSbiLFPb9mNkRjUaEzGwb695iuA/aA8wZUFffYvjSnplh+bQDJIjPy+fyE/kHg/Y0yoS3nfiDuAdmAv6Y2XhRfNDUmhIl77P9lM3nM3NOk+Lu1tdXTawVyguJvPYG/oQayiXso/jYHAp8icJsEUeJeEFtSeoVZsi0wSTcFxD0/BrKHw+r+rNYD3ewBcIdMzlvFvlg5uQwOgGTRxBSLxZr+gQz+RmEoi5l7EPeAhMBfUxuvTlY3WKh8dzMTdbhQdBS/2ZmJCqyB4m8gGhR/6wn8DTWAuKdm8TcEPkXgNgkQ9+xV1ltNtgVR5IO4Vz04TE1L1eXzUBE4b0B1cc9K8gkcAMkic7ideTX/QBZ/Y7hYdJU8tlL56SZ5bNe+OEke4+QekA34a2ojU9vkhmhEqLg3XAxGBx0ZQPE3EAmKv/UE/oYaQNxTt/hb7cFqGsFtEnByz1plPSfNGDvJtvFJN4afy1T+XyqVyA8g7tUODtffu8aXz0B14LwBO0Dc0wMki8yJx+M0f/78Q/wDL/wNP05P8wwokcnj4VIJ4h4AHgJ/TW1kEve4+GLllh3CxD0uvkj3pk3XgRFQ/A10F/eM4u8mFH97BfwNNYC4p3bxNwQ+4NhY9/T0UFdXl3G7s7OT2traXK0rl8u+VNZXhLgNGzbYSrbVSrrxfnyf6OtnIO4dAG1dxADnDdgB4p4+IFlkjfH+gVf+Bu8x9bA5JJJoLCksecz+y95MjiJUxsk9ACTw12SL81RDNnGPT1avvO3HQk9WN/hTS6sEKP4Guot7KP72DuSH1IA7tWDmHikr7jEQ+DTHjbHmYK67u3v0/0uXLnW1TnT7Ka6snzdvnpEkY9rb220n28Yn3Th5x/tFo1HhibdyqYSZe/uBuCcGOG/ADpysQ1tOfUCyyJl/4JW/8eCDD9Ku9CAJJSS28rNcLgkX9wpDBbTlBMrjtb/G+3GnE5njPJWQUdzzo23y4TNnma4H7kE8CFQQ9zDT0xtgD9ShOJSllmmzbcdv2X1pOvX8q+moE95k/j0wtqWuqCFTAkfAWMvFUD5jy7g6DQ7hvOkJ7AGwy+7+AczcA7AvGuG2rUtjIi40ecz+Sz47hJl7QGlEiHu8H07YBQOIe7VnoiYaE3X8ZPQA8SCoBvJDegJ7oBihkFBxjxs1oPi7vuAEn6Z4Yay5DUu1207XiZ4vk8/njWp6rqRn+HZlJp/d783vHz+3sh8/X3hlfbkMcQ+VWdIki/r6+jy9RhA8Mrmc7eS73cpPPgU1Nrkzd9HxWrddkBHdgsPx/oFX/samTZuEdwoQPbNh8+N3Cq/8DIXJ0ckQq/YFXQaBiuIe7xcKhaSO81QA4t7E9nnnqm11/HTURzd/DVgD4p6ewB6oRyzeKEzcY/9lMJtD8XedgcCnIV4Za56xUKsNi5N14wNLr+HWMx0dHaPzbDhhxjNyGDtJt0qyjZ9bmanD3L18hcCrJ4rEkkJP7uULBbRd0BBRyaJsNuvpdYLg0ZhICBX3uK1LdjBLpVAI4p6k6BgcVvMPvPA3FixYQL39GZJ5ILuZwOdFW5d0JixM3OPigYF0v+k6AGQT93i/devWSR3nSU+Z0JbThX0G7tDRXwPmcMIebTn1A8XfahIKh4WJe5x/LgwXackJc1H8XUcCX9L+0EMP0aJFi2jSpEmUSqXone98Jz3//POO9vrzn/9szErjAKMyDFw3dHbeIpHIaGKtMtOGE3CcOOMEmpXZENXEvcp+YQsG0w0NkYjQys9MLi+8pzpHr+iprkeyKJlMenqtIHhMndIsVNxj+1IuEcQ9SdHR3+BOAdX8Ay/8jYsuuohkFvf8mtlgxRdzKu6x/5LL5k3XAusgzrOOSHGP9wP1pTCUxcw9iHt1QUd/DVhjd/8ezNzTDBR/64sbcY/zz9GGiC/F3+jsJKnA973vfY8uvvhiGhwcpFtvvZU+85nPGNWFb3rTm2j9+vW2T2998IMfpERC397tujtv8Xj8oNdrN+lWS9xTpa1LKBQWKu5x8i6byWNgsibJotbWVk+vFQQPK9X4btu6xJMxoW05c5mc6RpgH139DfY1q/kHsvobsol7VuyLG3GP7UsiGTddD6yBOM863PZcpLing30OOsPFoqP4zY59Hi6VHMVvTuyzk7EKOLnnP7AHYCL4bwOKv/UBxd/64lbcY/9lUjKB4u86E1iBL51O04033kizZ8+m3//+93TdddfRTTfdRE899ZQxk+f666+3td/tt99OL7zwAn36058mHYHzVh2rSbegJNtEzmxoakwIFfc4OBwuFG0l3zFzT95kEQBezGyIxqLCxD22LwP9e/BBeYzO/sbYTgGy+xsQ96rbl+bUFN8/CxVBnGcPbnsOcU9tuFOLSHGPk3d7MzmIe2AUnf01YI0ZrSkUf2sEir/1xAtxj/0XK53YUfytqcD3s5/9jAYGBujqq6+m5ubm0fuPOOIIuuSSS+h3v/sdvfzyy5b22rRpE33hC1+gL33pS4ZgqBtw3ibGLOkWlGSb6IHsDRbaWrkR97jysyEaESruDRfN256BEZAsAiKRZSB7OBwcW64Cuvsb4zsFyOpvQNyrbV8wRswbEOfZg9ue4+Se2kRjSaHiHifvyuUSTu4BA939NWCNaMT8ZwLF32qA4m898Urcs+K/oPhbY4Hv6aefNv7ldpzjqdz3zDPPmO5TLpeN1pwdHR300Y9+lHQDzps1aiXdgpJsEy3uWQkO3Yp73NYl2RgXJu5xW5d0b9p0HRgBySKgu7jH9iU1LWX5dYGJgb+hhr8Bcc+dfQHWQJxnD257jracihMSb58bE3GhbTkLQwW05ZQA+GvAK1D8rQ4o/tYP2cQ9FH+bE6GA8sorrxj/VjtxV7mvsmYivv3tbxtBJFcnhS2cUBrPa6+9Rrt27Trovs2bNxv/VhIyQWkLxWLmWPja7rvvPnruuefolFNOoSuvvNJ4D8avswI/72tf+5px+73vfS/NnDmz6rqdO3fSD3/4Q0frotGoo2uzCu9vJenG/27YsIEefPDB0ROgCxYsoIsuumjC4Jo/g3BI7PWP3Z+N6zO/WU47t62jo084lU5969X7k08TX8NLG7rpT799gJpT02nJxZ+kyS3TjedEolEq1dD8e9J7aekPnqT+bJFuvvh0esuJR1HJ5HqLpRJ95afd9IcXeuisk+bSLe9+E/36aw9RuDxxBLtt7Qu0tut/qWX6FDrn+guoaVqz2Uuivb0D9LtvPGKcQgzK72TQmTFjhmN7YMW+8O8DPouJ3z+2JyJtRiTSIPQz4HZS423G48+/SLevfIZmtjbTss6zaWaqydRWTGRfDLtXxWZw8n3N/U/Qjue20zGnHE+Lrzhn5G+8yds53r78ZcXT+Dl14Wd46W/4YTMmeg1++Blu/Q3RfkY4FKrqH0zEvj276KmHv0rF/D5609v+mY5sX1TzORU/o5p/wD8vZrbCin0ZbzMq/kFh3xCdcdV5dPQpx5naiYnsS5D+tgXlOpyAOM8/22XVPhcKBSPWkzXOE/m76YfPNj7Osxq/2bHPW373nZqxXgU39rlULFPrLPvxm1X7HCT7G3Rq2Qyv8kN+fBadnZ1GLk5kLNzV1SVkbxXivFqxnpf5oV9u66EId1OxGb9ZtS/7+vfBZlikqanJ6J4nKj+kQpynkp9hJ36zmn+ulU/2Mj9UKzfkVX5oetPUwNgMJ9cRWIEvk8mMtjsaTyKROGhNLbZv30633HKLMbvvpJNOcnQdLBBye89qDA4O0t69eykolZ1jjRnPKXz44Yepp6eHzjrrLHr3u9/tyljz/jt27DBu/+Y3v6HLLrus6jp+zOm6ww47TKhB5uCSjfJE8OOXXnopPfbYY7Rt27bRE6PnnnuuaeXs9GlTaXKsTCUzC+KQWW2H0eR4adR4rX/6J1Qe3EFvWHQ2nbT4Ygo3sKGb2FTu2Poc/f35X9HcY46jN557BTU2pUafM7NtFuWikw55zq49Gbr9sb9Sw+QUffqCM+m042ZRzkLl5/In/0wv7Bmmf1z8Brr67NdTsSFMbTPbaBKN/P5W45X1L9FLj22kufPm0qLOM2lSarLp+zKY3kd/+eFampJooRmHTwvM72TQGW8z7GDFvvD++Cwm/oOdmtIi1GZMmzZV6GfQOnXaQTZjzQs76PvPbKfjjz2abnrHIkq1NJraCjP7Us1mlIbL9OxPu6nYk6N/OOsUWnjhov32b2Kq2ZdXW2Ez3NoMr/wNP2yGG7vnlZ/hxt8Q7WfMnJ4yToSM9w9qkdmbpk2rV1CqKUGnv/UamjV3wYTPYT9jXzhZ1T8omlybVfsy1maM9Q+WfOgtNPukI03fAzP7Mi1ANoPjEFlBnOeP7bJjn6dPn27EZrLGeSL/hvjhs42N8+zEb3bs8+DfflY11jOL36za5zmzZ9O5N15oO36zap+npoJjf4NONZvhZX7ID5+tWCzSjTfeKGz/+++/X2qbITrOqxbreZ0f+vOKxyfMDbnND82YDpthlWOPPdZVvtjMvqgQ56niZ9iN36zmn6vlk73OD9XKJ3uVH9r8s/VSx3mBFfgaGxuNf/P5/CGP5XK5g9bU4sMf/rARTHzuc59zfB0f+chHjCrr8Sf4LrzwQpo0aZJR6RCUnsmh/YNBKpUTzz77LC1cuNAwrm7bunClLR/bZmbNmjX6vcaze/fu0apcu+teffXVmuu9gKtJ2VG08nr5+viPE8MzIPl5Zn8sdvXupn1DISqZnFBzyo6eV2lfPmxUTjz96+/RKy/8iWYf9wY6+o2XUKbYQGbRF1d+PvPY/ZScPIWWnPdRKsWm0r4xv147e3ZQojB4SOXELV1PUO/eLN30zkV01lEtROPWVKvMWvbwH2j1316mN8+fQ9edcRxFSlnD9vfs7KHBGiadKyfWfP931DhlEp3z4TOIUpGaa8dWZv32G7+gTP8gnXbZWfTq6hcD8zsZdMbaDDtYtS+8Pz6Lid/HdP8emizQZvT27hb6GfTt7h21GUZl1s+7aVpTkm6+8Gxqayyb2gor9mW8zWDn8vf3/5a2P7uVjlg4l+Zd2EHZhkP9BKv2pbevFz+nLmyGl/6GHzbDqd3z2s9w6m+I9jNe2dFD73zHTYf4B9Xgys9VP/kqZff108nnXkHNh7/e9Dk7d+ygr//4sar+wUTYsS8VmzHeP0iddJipT2HFvgTJZnAcIiuI88TbLrv2mQU5jrtljfNE/g3xw2erxHl24zc79rlarGclfrNqn3dn847iN6v2eeuWzYGxv0FnvM3wOj+kgs8mu80QHeeNj/VE5Icmyg15kR+adVgbbIYPv29W7AtsRjD8jJ2v7bYdv1nNP4/3MUTkh6rZDC/zQ72Sx3mBFfjGtuFsb2+33NalAlcP/OpXv6J77rnnoFaelWP+HGSwUMdBx0RCIR/d569qsNEKyjw2TgixQWbDsHz58tGByVdddZVrcY/34715v0qrlVrGnx+rHCW1u45bw4h04nh/M8bOwKn83D3//PNGss1sHg5/BmyMRRlkvv5isURrHr1ntOfxyef9M1G4gUpl6zMbTn/3J6mxecYhzykWCjS2AQv3PP7E2J7HJx5hWtnBlVlfGjOz4TMXLqKIEauWRl9Dqcqx87Ezsc66/u00aTof2y6bztz7zVd/Spn0IC2+8hw68tRj6e9Pbg7M72TQqdgMO9ixL7w/Pgvz91OkzSgWR1o9iGK4WDRsxtie6re//0yalZpkfprYon0ZazOMmVjfOzAT67QrzyZqCJnaionsC35OndsMr/0NPz4LJ3bPaz/Djb8h2s8IhaNV/QOzmQ1z5p9m+hwODvsH9tLv1mer+ge1sGtf+DPY09t/iH9gZies2pcg2YygXIcTEOeJtV1O7TOvlTXOE/27KdpnM/ydcXtbid/s2OfxsZ7V+M2qfb7gnkerxnlm8ZtV+7xv76DUdq9eNkNEfohzarL7bLLbDNFx3thYT1R+qFZuyKv80M5V22AzBP++WbUvKsR5stuMoXyeHv9v+/Gb1fzzWB9DVH5ovM3wOj9UlDzOC6zAxz177777blqzZo3RrmgsfB9z8skn13z+Sy+9NHqKrxrctpO/fvnLX9L5559PKuD1wOSx+/E+S5cuNX1OW1ubp+v8ZGyy7XWve93oyc3KfYyZyCeUMjkaaOpkILvbgcl2BrKPNa7nffJCapreYvqc8c7b3EXHmz4HuAMD2YHMA5Pt2BdQH3+DKzt1Iaj+RsjCrGo3A9kLw0VacsJcy/6BE/vCLYLs+gdO7QtwDuK84NlnLng1i81kjvNkw0n85sQ+O43fnNhnJ/HbWPs8PTXVdD3wJz9U6ewE9EHG/BALfEAcyA/JxVB+ZMSZ3fjNbv4Z+aH6EViBj1tgXn/99YYDccMNNxhtiypz9TgBcuaZZ9KcOXNG5zjw/S0tLUZAwbzjHe+oesLvySefpLvuuos+/vGP0+mnn24cIVYFUeIe77du3TrSLdlWef8qibd6i3yFoSzEPYh7dQHOG6jGYDYHcU9DkCxS398QIe5xcJhoiAhNHnPyKTuYpVIoBHEv4CDOEwP8NTWAuFe9+CKSrvMHIxkii79TKZ4DCXRBRnEPiAX+hoSUyxD3utUu/g6swMdOw7Jly+iaa66hxYsXGyfxeC7AnXfeaRy7veOOO0bXrl271hjkefnllxvDcitDQvlrPPv27Rs9/XfJJZeQKnDl+5YtW4SIe7zfddddRzom2xi+HYSkG7dI4GQaTu7BeZM5OATqsLt/j/CTe3x0GSf3ggOSRRPDfiq/R7VspCz+hghxjys/M1tXCxX32L6USwRxTwIQ53kP/DU1gLhX+2R19+2/qfOnIxcii7/7+/s9vVYQXCDugfHA35CTSCwp9ORevlBA8XedCXT/GRb1OBHCLUM+9alP0b//+7/TSSedRL///e+po6Oj3pcXKLhNgihxLwiJJVGYJdvGJ914Da/l51RmS/hFQyQiVNwbLpVQmQW0tgfAHvyzIFLc48rPbCaPtpyaJItaW1tJdrhvfy3/QCZ/Q4S4x/6LlbEYbtu6xJMxoW05c5mDB7sD5yDO8w74a2oAcW8EtE32pvhblL/G+wE9KBSHkR8CBwF/Q144nyxK3GP/JZPLo/i7zgT2BF8FPmVndtKO23WWyybTIfdzxRVXGF+qkUwmIe7ZrKy3mmwLSmV9NJYUJu5x8m5vJkcRKqPtAjCA8wbMmNGaEirucVuX4UKRjlncjpl7GnQK4K4NshOJRKr6B175GzKLe37NbLjlkbVCZ3oO9O8xXQesgzjPPfDX1IA7tWDmHsQ9r0DxN/CC1/rSNLQXbTnBCPA31MaNuMf+S0MojOLvOhPoE3zAOlz5jpN71ivr7SbbzCrr+atUKon9kQ2Jrfwsl0vCxb3CUAE91SUAzhuwQjTSILytS0M0IlTcGy7W92SUTCBZZE48Hq/qH3jhbzz00EPkN7KJe1bsixtxj+1LOIxT7CA4wF9Th+JQ1lH8Zsc+cz20k/jNiX12MhMLJ/e8A8XfwKu/MZi5Byo/C+jspC5uxT32X5oaE74UfzuJ3xoVnbkn3Qk+EFxj3dPTQ11dXcbtzs5Oamtrc7XO6ilMt5X1FSFuw4YNtpJttSrreT++T/T1i27rMjkRFyrucXCYzw5R64wWDEwOMHDeQJBmNqzcskOY88bJp3Rv2vLr0R0ki6wx3j/wyt/gPaYeNof8AuJedfuSakr59hkAUE9/TbY4T3pCIaHiHifvBrM5iHuagOJv4AVTp7Sg+BsgP6Q4Xoh77L90r7je9Dko/hYLTvBpjpvgkIO57u5u46sS2LlZJ3rGDFfWt7e3G0ky/uLbTltsVpJulf02btxIISvDZQI8syEejQoV97jyMxQmoeIecgfugLgHgjaQ3crRZafiHtuXUgkn+KyCZJEz/8Arf+P444/3LUEOca+2fWmwcHIZANn8Nd6PO53IHOfJTizeKFTc4+RdYbiIk3vAFMSDoMKkZEJ48beT+M1Ofgi4A/ZAbbwS96z4L17kh5KNcRR/TwAEPo2BsVYDmQayJyclhTlv3NZlIN1vug5UB/YABE3cs2Jf3Ih7bF+ap6jdpiEowL7IA8Q9fdu6AH3FPd4PJ+zqSygcFm6fow0RoW05+dQ62nLKDfw1YAcUf6sN7IHayCbuofjbHLTo1BQvjDW3Yal22+k6L9vKVCOfz49W0jN8m+fiOKmqr8zUqezHz9+VHiS/kUnc4+DwgnseFTqzIZfNm64F/iSL+vr68FZriEziHtuXnau2WX5twBm6BYfj/QOv/I1NmzYJb9FZLpUwc28/EPeATuIe7ze+E4lscZ7qeFF8kdm6Wpi4x/5fdjBLpVAIM/ckRTd/DbjDi/xQOhNG8XdAgT1QGxnFPT/yQ4fPnEUyA4FPQ7wy1jxjYenSpZ6tE93iklvPdHR0jM6z4YQZz8hh7CTdKsk2fm5lpg5z9/IV5CeyiXt+DGSfnppquh74kyzKZrN4qzVDJuetYl8g8IlFx+Cwmn/ghb+xYMEC6u3PCL32ofzI/k6Tx1aDQ6fJY7v2Zax/gIHsQHZEinu837p166SO81TGq5PVT9y1Wqh9LpcI4p6k6OivAeeg+FttUPytNhD31C3+RotOzdDZeYtEIqOJtcpMG07AceKME2hWZkNUE/cq+4UttFbxCoh71ZN3zakpvn0GKiAyWZRMJj29VhBsZBT3gFh09De4U0A1/8ALf+Oiiy4S/wLKZYh7aMsJAopIcY/3A8FEprbJ8WTMdnGmneKLXCZnugbYR0d/DThHpuLvRDJu+XWBEVD8rTYQ99TOD0Hg0wjdnbd4PH7Q67WbdKsl7vkNxL3awSGKg4OTLGptbXW8F5ALiHtgPLr6G9wpoJp/IIu/EYklhSaP84UCTu4B4ABuey5S3NPBPsuITOIeF3dFY1GhJ6sH+veYrgP2gD0Aqop7KP62D4q/FadMaMupsLjHQODTBDhv1bGadIO4F2znzWpwCPxLFgE9gLgHxqOzvzG2U4CM/kZDJCK08jOTywtvy8nRK9pyAtXgtucQ9/RCNnHPj7bJ4bAevoRf6OyvAfXFPRR/2wfF32pTGMpi5h6pK+4xyIhrAJy3iTFLugVF3BsuFjFzD+KeZyBZBNxSKA6jLSc4CN39jfGdAmT1N0S1dQmFwkKTx5x8ymbymLkHlIPbnuPknj5A3KveNjk1LVXHT0UtdPfXgPriHoq/7YHib/XhfLKT+C05OUVnX/ppmjxlhulzUPxdX8xLdYHUwHmzRiXpxnBijan8PyjJtuJQllqmzbZsXJ0Eh3De9AHJIuCW1/rSNLQXM/fACPA31PE3RM1siIcHhYp7nHwaLhTpmMXttk+G2JnpCYDfcNtztOXUA4h7te3z+nvX1PGTUQf4a8AOyA/pAYq/1Yc7tYgU94ZLJRR/1xkIfArjpfPGey1btsy43dnZSW1tbVXX9fT0UFdXl2fr6pl0K5VKxu0NGzYEI9kWCgkV98plQmWWRiBZBNzCfxfsJt/tVn4Whgq2k+8qt10IKkgWKeZvCBrI3r3ieuGVnw3RiFBxb7hYe3YiADLb50wmY8R6qsZ5MgBx7wAovhAD/DVgh3K5jPyQJqD4W32isaQwcY/9l72ZHEWoLDw/5MQ/2KtJfggCn6J47bzl83nq7u4e/f/SpUurruNgzst19Uq6cbKNE21Me3t7IJJtsXijMHGPk3eD2RzaLgBLIDgEzNQpLUKdN27rks8OUeuMFjhvAQb2QD1/Q4S451dbl5VbdggT9zg4TPemTdcBIKN9LhQKo7GZqnFekCmXSpi5tx+Ie2KAvwbssrt/gLbu3oW2nBqA4m8NCJEwcY/zz+VyCcXfdQYz+BTEa+eN9xs7IwbUh1A4LLTyszBcFO68cSITPdXlBsEhqDApmRDe1iUUJqHiHp9cBs6BPVAb2cQ9ti9Wolc3lZ+lEvxhIAewz3IxlM84it/s2mcn8ZsT++x0JhbEPTHAHgAnZHI5iHsA9kUT3Ih77L80JuK+FH/j5F5tcIJPMUSIe7xfLBajjo6O0VYrtRj7mBfr/IZfL8/A4Wp6rqRn+DbfF7Sqeq/buiQaIkLFPQ4Os4NZKoVCGJgsKQgOgd8zG9KZsDBxj5NPA+l+y68HHAzsgdr+hozinh9tXQ6fOct0PQAy2udoNEoLFy5UOs4LNOWycHEvXyhA3NMQEfmhvr4+T68RBJPGRALF32BCEA+qgVtxj/2XzY/fafocFH+LBQKfQogS93g/3sdKexWeseDlunok23geTmUGDlO5jwlC0k3UzIbM1tVCxT1O3pVLBHFPUuC8gXoMZL/gnkeFiXtcWZ7L5i2/JnAAJIvU9jcg7tW2LztXbavLZwKAaPvc2NhoGpvJHOcFnUgsKVTc4+RdJpen2W2tOLmnEaLyQ9ls1tPrBMFk6pRmFH+DmiA/pAZeiHvsv5gJfCj+Fg9adCqEKHGP94tEItol2/j9q8zI4fv4MV4TtHalXg1kD1noyey2rUs8GbOVfLfb1iWXyZm/CGAbOG+gHuKeFfviRtxj+5JIxi2/LjACkkVq+xsQ9/QbyA7UAf6avDRYiLXd2udQKCxU3CMqoy2nJsXfyWTS02sFwSRkIUGE4m89gb+hBl6Je37lh8IWxlbt1bj4GwKfInCbBFHiHu+nY7KtQlCSbiLFPb9mNkRjUaEzGwb695iuA/aA8wZUFffYvjSnplh+bQDJIjPy+fyE/kHg/Y0yoS3nfiDuAdmAv6Y2XhRfNDUmhIl77P9lM3nM3NOk+Lu1tdXTawVyguJvPYG/oQayiXso/jYHAp8icJsEUeJeEFtSeoVZsi0wSTcFxD0/BrKHw+r+rNYD3ewBcIdMzlvFvlg5uQwOgGTRxBSLxZr+gQz+RmEoi5l7EPeAhMBfUxuvTlY3WKh8dzMTdbhQdBS/2ZmJCqyB4m8gGhR/6wn8DTWAuKdm8TcEPkXgNgkQ9+xV1ltNtgVR5IO4Vz04TE1L1eXzUBE4b0B1cc9K8gkcAMkic7ideTX/QBZ/Y7hYdJU8tlL56SZ5bNe+OEke4+QekA34a2ojU9vkhmhEqLg3XAxGBx0ZQPE3EAmKv/UE/oYaQNxTt/hb7cFqGsFtEnByz1plPSfNGDvJtvFJN4afy1T+XyqVyA8g7tUODtffu8aXz0B14LwBO0Dc0wMki8yJx+M0f/78Q/wDL/wNP05P8wwokcnj4VIJ4h4AHgJ/TW1kEve4+GLllh3CxD0uvkj3pk3XgRFQ/K0+L730Ir1x0ZnG7eHhIqX7eg2b0TwlRYlEo+nzy0Q00N9H+VyW4okkNU9ppfG57cEdr8hd/N2E4m+vgL+hBhD31C7+hsAHHBvrnp4e6urqMm53dnZSW1ubq3XlMrsZ4ivrK0Lchg0bbCXbaiXdeD++T/T1MxD3DoC2LmKA8wbsAHFPH5AsssZ4/8Arf4P3mHrYHBJJNJYUljxm/2VvJkcRKuPkHgAS+GuyxXmqIZu4xyerV972Y6Enqxv8qaVVAhR/q89wmeiNF352TH4oY3tsy67dWya0L49/873Sinso/vYO5IfUgDu1YOYeKSvuMRD4NMeNseZgrru7e/T/S5cudbVOdPsprqyfN2+ekSRj2tvbbSfbxifdOHnH+0WjUeGJt3KphJl7+4G4JwY4b8AOnKxDW059QLLImX/glb/x4IMP0q70IAklJLbys1wuCRf3CkMFtOUEyuO1v8b7cacTmeM8lZBR3POjbfLhM2eZrgfuQTwoDyj+PgDyQ2KAPVCH4lCWWqbNth2/ZfelLRcPoPi7vqghUwJHwFjLxVA+Y8u4Og0OZavMwkB2b4A9AHbZ3T+AmXsA9kUj3LZ1aUzEhSaP2X/JZ4cwcw8ojQhxj/fDCbtgAHGv9kzURGOijp+MHiAelAcUfx8A+SExwB4oRigkVNzjRg0o/q4vOMGnKV4Ya27DUu2203Wi58vk83mjmp4r6Rm+XZnJZ/d78/vHz63sx88XXllfLkPcg7gnTbKor6/P02sEwSOTy9lOvtstHuBTUGOTO3MXHa912wUZ0S04HO8feOVvbNq0SXinANEzGzY/fqfwys9QmBydDLFqX9BlEKgo7vF+oVBI6jhPBSDuTWyfd67aVsdPR31089dUKP5mUPyN4m8RwB6oRyzeKEzcY/9lMJtD8XedgcCnIV4Za56xUKsNi5N14wNLr+HWMx0dHaPzbDhhxjNyGDtJt0qyjZ9bmanD3L18hcCrJ4rEkkJP7uULBZzc0xBRyaJsNuvpdYLg0ZhICBX3+GRwdjBLpVAI4p6k6BgcVvMPvPA3FixYQL39I8kcWQeymwl8XrR1SWfCwsQ9Lh4YSPebrgNANnGP91u3bp3UcZ70lAltOV3YZ+AOHf016fGh+Bv5IT1B8beahMJhoW1/C8NFWnLCXBR/1xGUtGuGzs5bJBIZTaxVZtpwAo4TZ5xAszIbopq4V9kvbMFguqEhEhFa+ZnJ5YW35eToFW059UgWJZNJT68VBI+pU5qFintsX8olgrgnKTr6G9wpoJp/4IW/cdFFF5HM4p5fMxus+GJOxT32X3LZvOlaAEQgUtzj/UB9KQxlMXMP4l5d0NFfUwHRxd9+5YcwtiVYoPhbX9zO9Iw2RHwp/kZnp9rgBJ9G6O68xePxg15vJenGWKmsryXuqdLWpSEUFuq8cfIum8lj5p4myaL+fpxyUB0r1fhuZ3rGkzGhbTlzmZzpGmAfXf2NsZ0CVPA3ZBP3rNgXN+Ie25fpqamm6wHwGm57vmXLFmHing72OegMF4uGvbQbv9mxz8OlkqP4zYl9djJWASf3/Af2QF5EF3/7kR9C8bc+xd+pVMrTawXBEvfYvmS2rkbxd53BCT5NgPNWHauV9UFJtomc2dDUmBAq7nFwOFwo2kq+Y+ae2GQRKsGBSNyKe2xforGo0MrPgf49ll8PsIbO/sbYTgGy+xsQ96rbl+bUFN8/CwC47TnEPfWT9SLFPU7e7c3kIO6BUXT213RAhvwQir/1Kf5ubW319FpBsMQ9ti9WOrGj+FssEPg0AM7bxJgl3YKSbBM9kL3BQlsrN+IeV342RCNCxb3honnbMzACkkUg6OKeH21dwuHg2HIV0N3fGN8pQFZ/A+JebfuCMWKgHnDbc5zcU5toLClU3OPkXblcwsk9YKC7v6Y6suSHUPwdHFD8rSdeiXtW7AuKv8UDgU9x4LxZo1bSLSjJNtHOm5Xg0K24x21dko1xYeIet3VJ96ZN14ERkCwCuot7bF9S09AuxCvgb6jhb0Dcc2dfABABV76jLafihMTb58ZEXGhbzsJQAW05JQD+mtrIlB9C8XdwQPG3fsgm7qH42xzM4FMYL5033mvZsmXG7c7OTmpra6u6rqenh7q6ujxb5yfjZ+SUSiXj9oYNGyDueSDucXC48rYfC53Z0DDykQELIFkERCCL81axL+vvXWP5tYHaIFmkhr/BM6Awcw/iHtDTPmcyGSPWUzXOkx0vii82P36n0Jmo+ewQtc5owcy9AAN/TW1kEvfYvqzcsgPF3wEBxd96AXGvRvF3k9zF3xD4FMVr5y2fz1N3d/fo/5cuXVp1HQdzXq6rV9KNk22caGPa29txck+SgeyHz5xl8ZMGbkBwCFQQ96zYF2AO7IE6/kZxKEst02Y7Th5bqfx0kzy2a1/G+geLrzyH5i463vQ5OLkHdLXPhUJhNDZTNc6TFa9OVpsJfG7tcyhMQsW9ctl0CZgA+GtqI5u4h+LvYIHib32AuKdu8Td6zSiI185bpXUUCAYyOm9+iHscHCYaE6bPAe5AcAiqAXFPT2APFCMUEirucXIW4h4A/gD7rAYytU1OTkoKE/e4+GIg3W+6DlQH9kBtkB+qbl+ap6CY0w9gX+QB4p7axd84wacYIsQ93i8Wi1FHR8doq5VajH3Mi3V+U5mBw9X0XEnP8G2+Lwjz9+C8TRwc7ly1rY6fjvrAeQPVgLinJ7AH6vkbsXijsOQx+y+D2RxO7gEQUPscjUZp4cKFSsd5siGTuMcnqy+451GhJ6tz2bzpWuBPfqivrw9vdUBAfqi2fUFuSDyIB+WhXCph5p7C4h4DgU8hRIl7vB/vY6W9Cs9Y8HJdPZJtPA+nMgOHqdzH1FXkKxNO7rkIDoE74LyBakDc0xMki9T0N0LhsNDKz8JwkZacMFdoW05ueYq2nEBnnNrnxsZG09hM5jhPNmQT9/xomzw9NdV0PfAnP5TNZvFWBwCIewdA8bf/ID8kF0P5jPGv3fjNbuc45IfqBwQ+hRAl7vF+69atI92SbZX3r5J4q7fIVxjKoi0nxL26AOcNVINP42Dmnn4gWaS+vyGqrUuiISI0ecxtxbODWSqFQpi5B7QE/poaQNyrPhM1kq7zByMZIou/U6mUp9cKHIDi71FQ/O0/8DckpFyGuNet5sm9CpjBpwjcJkGUuMf76ZpsY/g238eP8RpeW4+ZhMPFImbu4eSe9MEhUIfd/XtsJ9/tzvTk6HVscuf0q8+jsKCZnqC+yaJkMin9R5DP5yf0D2TxN0TNbJiUTAgV99i+lEsEcQ9oCfw1NYC4V13cY/8vFKrzhyMZIou/W1tbPb1WYB8Uf4+A/JD/wN+Qk0gsKfTkXr5QQPF3nYHApwjcJkGUuKdyMt8s2RakpFtDJGLZuDoJDodLJdvJd6dtXZwk3+G8+Y9u9gDYg38WRIp7bF+ymTzEvQCBZNHEFIvFmv6BTP6GqIHsVpKzbtu6xJMx223f7BQP5DI58xcBgM/AX1MDiHvu7DM4AIq/1ceP4m/kh8AhPxPID0kL55NFtv3N5PIo/q4z8JQUgSvfIe7Zq6y3mmwLStItGksKc944ebc3k4O4B0aB8wbMmNGaEirucfHAcKGIk3sBAckicyKRSFX/QDZ/Q4S459fMhmgsKix5zMVJA/17TNcB4Cfw19RJ1mPmHsQ9r0Dxt/qILv5GfgiMB/6G2rid6RkKhVH8XWcg8CkCt0nAyT3rlfV2k21mSTf+KpVKJJSQ2MrPcrkk/OReYaiAk3sSAOcNWCEaaRAq7rF9aYhGhLblHC4Go/2hDCBZZE48Hq/qH3jhbzz00EPkN7KJe1bsixtxj+1LOIxT7CA4wF9Th+JQ1lH8Zsc+l8vkKH5zYp+ddF7ByT3vQPG3+ogu/vYrP4TOTnIAf0Nt3Ip7bF+aGhMo/q4z5mc0gfI4NdY9PT3U1dVl3O7s7KS2tjZX68ocdfhQWV8R4jZs2GAr2TY+6cZU9uP7RF+/6LYukxNxoc4bB4f57BC1zmhBW84AA+cNeIVbcY/ty8otO4SJe5x8SvemLb8e3UGyyBrj/QOv/A3eY+phc8gvIO5Vty+pppRvnwEA9fTXZIvzpCcUEirucfJuMJuDuKcJKP7WgJD8+SEUf8sB8kNq44W4x/ale8X1ps9B8bdYcIJPc9wYaw7muru7ja9KYOdmnej2U1xZ397ebiTJ+Itv2022jU+6VfbbuHEjheo4+duLmQ3xaFSouMeVn6EwCRX3kDtwB5w3ECRxj+2LlejVTeVnqYQTfFZBssiZf+CVv3H88cf7liCHuFfbvjRYOLkMgGz+Gu/HnU5kjvNkJxZvFCrucfKuMFzEyT1gCuJBNZAhP8TF307iNzv5IeAO2AO18Urcs2JfvMgPJRvjKP6eAAh8GgNjrQYyDWRPTkoKc964rctAut90HagO7AEImrjnR1uX5inm64F7YF/kAeKeO/sCgIziHu+HE3b1JRQOC7fP0YaI0LacfGodbTnlBv6aGsiSH0Lxd7CBPVAb2cQ9FH+bgxadmuKFseY2LNVuO13nZVuZauTz+dFKeoZv81wcJ1X1lZk6lf34+bvSg+Q3sjhvleDwgnseFTqzIZfNm64F/iSL+vr68FZriEziHtuXnau2WX5twBm6BYfj/QOv/I1NmzYJb9FZLpUwc28/EPeATuIe7ze+E4lscZ7qeFF8kdm6Wpi4x/5fdjBLpVAIM/ckRTd/TVVkyg+lM2EUfwcU2AO1kVHc8yM/dPjMWSQzEPg0xCtjzTMWli5d6tk60S0uufVMR0fH6DwbTpjxjBzGTtKtkmzj51Zm6jB3L19BfiKT8+bXQPbpqamm64E/yaJsNou3WjNkct4q9gUCn1h0DA6r+Qde+BsLFiyg3v6M0Gsfyo/s7zR5bDU4dJo8tmtfxvoHR/7DMXT61ecJa+sCgMziHu+3bt06qeM8lfHqZPUTd60Wap/LJYK4Jyk6+msqIlt+CMXfwQTF32oDcU/d4m+06NQMnZ23SCQymlirzLThBBwnzjiBZmU2RDVxr7Jf2EJrFV2dNz/EPU7eNaemmD4H+JMsSiaTeKs1QkZxD4hFR3+DOwVU8w+88Dcuuugi8S+gXIa4B3EPBBSR4h7vB4KJTG2T48mYo/jNavFFLpMzXQPso6O/piLIDx1qXxLJeJ0/FflA8bfaQNxTOz8EgU8jdHfe4vH4Qa/XbtKtlrjnN3DeageHKA4OTrKotbXV8V5ALiDugfHo6m9wp4Bq/oEs/kYklhSaPM4XCji5B4ADuO25SHFPB/ssIzKJe1zcFY1FhZ6sHujfY7oO2AP2QA2QHxoBxd/uQPG34pQJbTkVFvcYCHyaAOetOlaTbhD3gn1yz2pwCPxLFgE9gLgHxqOzvzG2U4CM/kZDJCK08jOTywtvy8nRK9pyAtXgtucQ9/RCNnHPj7bJ4bAevoRf6OyvqQTEvRFQ/O0eFH+rTWEoi5l7pK64xyAjrgFw3ibGLOkWFHFvuFhEW06Ie56BZBFwS6E4jLac4CB09zfGdwqQ1d8Q1dYlFAoLTR5z299sJo+Ze0A5uO05Tu7pA8S96m2TU9NSdfxU1EJ3f00VIO6NgOJv96D4W304n+wkfrMzFgrF3/XFvFQXSA2cN2tUkm4MJ9aYyv+DkmwrDmWpZdpszNzDyT1PQLIIuOW1vjQN7cXMPTAC/A11/A1RMxvi4UGh4h7P9BwuFOmYxe22T4bYmekJgN9w23O05dQDiHu17fP6e9fU8ZNRB/hraoDi7xEg7nkDir/Vhzu1iBT3hkslFH/XGQh8CuOl88Z7LVu2zLjd2dlJbW1tVdf19PRQV1eXZ+vqmXQrlUrG7Q0bNgQj2RYKCRX3ymUykmOr/rqdlpxwBP0r2nIqDZJFwC38d8Fu8t2ufSkMFWwn31VuuxBUkCxSzN8QNJC9e8X1wis/G6IRoeLecLH27EQAZLbPmUzGiPVUjfNkAOLeAVB8IQb4a+rgR/E38kP6gOJv9YnGksLEPbYvezM5ilBZeH7IiX+wV5P8EAQ+RfHaecvn89Td3T36/6VLl1Zdx8Gcl+vqlXTjZBsn2pj29vZAJNti8UZhzhsn7wazOYh7wBIIDgEzdUqLUOeNZ8Lks0PUOqMFzluAgT1Qz98QIe751dZl5ZYdwsQ9Dg7TvWnTdQDIaJ8LhcJobKZqnBdkyqUSZu7tB+KeGOCvKYbg4m/kh/QCxd8aEBLb9rdcLqH4u85gBp+CeO288X5jZ8SA+hAKh4VWfhaGi8JP7nEi027lBNouBAsEh6DCpGRCqLjH9iUUJqHiHlemAufAHqiNbOIe2xcr0aubys9SCf4wkAPYZ7kYymccxW927bOT+M2JfXYav0HcEwPsgXqILv72Kz/kJH5DfihYwL6ogduZno2JuC/F3zi5Vxuc4FMMEeIe7xeLxaijo2O01Uotxj7mxTq/4dfLM3C4mp4r6Rm+zfcFrare67YuiYaIUOeNg8PsYJZKoRCcN0mB8wbs4FbcY/uSzoSFiXscHA6k+y2/HnAwsAdq+xsyint+tHU5fOYs0/UAyGifo9EoLVy4UOk4L9CUy8LFvXyhAHFPQ0Tkh/r6+jy9RhC84m8/8kMo/pYfxINq4FbcY/uy+fE7TZ+D4m+xQOBTCFHiHu/H+1hpr8IzFrxcV49kG8/DqczAYSr3MUFIuoma2ZDZulqouMfJu3KJIO5JCpw34Le4x/blgnseFVr5mcvmLb8mcAAki9T2NyDu1bYvO1dtq8tnAoBo+9zY2Ggam8kc5wWdSCwpVNzj5F0ml6fZba04uacRovJD2WzW0+sEeuaHUPwtN8gPqYEX4h7bFzOBD8Xf4kGLToUQJe7xfpFIRLtkG79/lRk5fB8/xmuC1q7Uq4HsIQs9md22dYknY0LbLuQyOfMXAWwD5w3UQ9zzo61LIhm3/LrACEgWqe1vQNzTbyA7UAf4a/LSYCHWdmufQ6GwUHGPqIy2nJoUfyeTSU+vFeiZH0Lxt7zA31ADr8Q9v/JDYQsnl/dqXPyttmqjEdwmYcuWLULEPd7vuuuuI92SbRUqSTcmCJX1Ipw3v2Y23PLIWqEzGwb695iuA/aA8wZUFffYvkTSll8aEJwsSqVS0r/H+XzeeE213pPA+xtlQlvO/UDcA7IBf01tvCi+iIcHhYl77P9lM3nM3NOk+Lu/Hy3ug4pM+SEUf+vjb7z44ou0ZMkSodf1yiuvCN1fNWQT9/zID01PTSWZgcCnCNwmQZS4FwQhSxRmybbAJN0kd94qwaGZwOd2IHtLY7PpWmAd3ewBcIdMzlvFvnTf/hvLrw8gWWRGsVisOUNPBn+jMJTFzD2Ie0BC4K+pjVcnq7tXXC90JupwoUjHLG63Hb/ZmYkKglH8vWzZMnwUAUS2/BCKv/XxN0KhEN18881Cr+3aa68Vur9KQNxTs/gbAp8icJsEiHv2KuutJtuCKPLJ5rxZCQ7dinscHKaa5D8BEhSQLAKqi3tW7AvwL1n00Y9+VPrKTm5nXs0/8MrfEM1wsWj4E06Tx1YqP90kj+3aFyfJY5zcA7IBf01tZGqb3BCNCBX3hovBGpMRZFD8rR8y5odQ/C0X8DfUAOKeusXfEPgUobW1FSf3bFTWM3aSbVaSbqVSifxARufND3GPg8P1964xfQ4wB84bsAPEPT0QnSxSobIzHo/T/PnzD/EPvPA3/Cgm4hlQIpPHw6USxD0APAT+mtrIJO5x8cXKLTuEiXtcfJHulby03kdQ/K0XyA8dAMXfYoC/oQYQ99Qu/obABxwb656eHurq6jJud3Z2Ultbm6t15XLZl8r6ihC3YcMGW8m2Wkk33o/vE339DJy3A6CtixjgvAE7QNzTB9HJIn7ML39DJOP9A6/8Dd5j6mFzSCTRWFJY8pj9l72ZHEWojJN7AEjgr8kW56mGbOIen6xeeduPhZ6sbvCnllYJUPytD8gPHQDF38H1N1SJ82SGO7Vg5h4pK+4xEPg0x42xZuPa3d09+v+lS5e6WjeR0feqsn7evHlGkoxpb2933GKzknTj5B3vF41GhSfeyqUSTu7tB+KeGCDuATtwsg5tOfVBdLKI22j75W+IZLx/4JW/8eCDD9Ku9CAJJSS28rNcLgkX9wpDBbTlBMrjtb/G+3GnE5njPJWQUdzzo23y4TNnma4H7kE8KA8Q9w6A/FCw7YEqcZ7MFIey1DJttu34zU7nOBR/1xc1ZErgCDhvcjGUz6AtJ5w3YcAeALvs7h/AzD0A+6IRbtu6NCbiQpPH3FY8nx3CzD2gNCLEPd4PJ+yCAcS92jNRE42JOn4yeoB4UB5Q/H0AiHtigD1QjFBIqLjHjRpQ/F1fcIJPU7ww1nwsutptp+tEz5fhqhGupudKeoZvV2byOTlizs+t7MfPF15ZXy5j5p6Dyk9Qn2RRX18f3nrFyeRytpPvdmd68imoscmduYuO17rtgsr2hU/Z++VviGS8f+CVv7Fp0ybhnQJEz2zY/Pidwis/Q2FydDLEqn1Bl0GgorjH+/EcVJnjPBWAuDexfd65alsdPx31QTJfvuJvxmry3al9cRK/OTkZ7DR+Q35IDnugSpwnM7F4ozBxj+3LYDaH4u86A4FPQ7wy1tzz2MqxaKvrxgeWXsOtZzo6Okbn2XDCjGfkMHaSbpVkGz+3MlOHuXv5CoFXTxSJJYU6b/lCAc6bhohKFmWzWZKdiy++mHbu3Gm6Lp1OUy6Xo0QiQalUquZ942eCZoZCdATJS2MiIVTc4+AwO5ilUigEcU8D+zKR3fHa3xBJNf/AC39jwYIF1Ns/ksyRdSC7mcDnRVuXdCYsTNzj5NNAut90HQCyiXu837p166SO86SnTGjL6cI+A3dA3JMQH4q/kR/SExH+xkRdAmSK82QmFA4LbftbGC7SkhPmovi7jkDg0wydnTdOqI9NrFUSb3aSbtXEvcpzwhYMphsaIhGhlZ+ZXJ5mt7UKrczi6BWVWXoki6qJWrLB4t4VV1xhag/4tOJ4ezDWVrAzWs2+fOf/PUAyM3VKs1Bxj+1LuUQQ9yRFR3+DOwVU8w+88Dcuuugi+u69I4PlZRT3/JrZcME9jwoT99h/yWVrzxABQCQixT3e77rrrvP0eoE9CkNZzNyDuFcXdPTXVEB08bdf+SGc3AsWaAOuL25neiYaIij+rjMQ+DRCd+eNj4WPfb18207SbSJxT4W2Lg2hsFDnjZN32UwebRc0SRb196t9ysHMHti1LzJipRrfbVuXeDImtC1nLpMzXQPso6u/MbZTgAr+hmzinhX74kbcY/syPTXVdD0AXsOFRFu2bBEm7ulgn4POcLFo2Eu78Zsd+zxcKjmK35zYZydt83Byz39gD+RFdPG3H/khFH/r2wYcqCXusX3JbF2N4u86g6E0mgDnrTqVpBsn0DiRxgk1fq+CmmwTObOhqTEhVNzj4HC4UERP9QAli0RWgqsAt9l0Yw+s2hdV8WJmQzQWFVr5OdC/x/LrAdbQ2d8Y3ylAZn8D4l51+9KcmuL7ZwEAtz2HuKd+sl6kuMfJu72ZHMQ9MIrO/poOyJAfQvG3PsXfHCMBdcU9ti9WNFwUf4sFAp8GwHmbGLOkW1CSbaIHsjdYaDHqRtzjys+GaETowOThoj7iiVuQLDKHZ+i5tQe6inyyDGQPh4Njy1VAd39jfKcAWf0NiHu17QsKkEE9SCaTOLmnONFYUqi4x8m7crmEk3vAQHd/TXVkyQ+h+Ds4oPhbT7wS96zYFxR/iwcyuuLAebNGrfZZTBCSbaKdNyvBoVtxj9u6rNyyQ5i4x21d0r1p03VgBCSLzEkkEp7Yg4nsi4rIIu6xfUk1yT8rMijA31DD34C4586+ACCC1tZWtOVUnZB4+zw5ERfalrMwVEBbTgmAv6Y2MuWHUPwdHFD8rR+yiXtsX255ZK3Q/FBLYzPJDAQ+hfHSeeO9li1bZtzu7Oyktra2qut6enqoq6vLs3X1TLqVSiXj9oYNGyDueSDucXC48rYfC53Z0DDykQELIFlkTiqVMmyTF/ZgvH3hPvSVPVVCJnGP7cv6e9dYfm3AH3/Daz9i7LpyuRyIjzGo/gbPgMLMPYh7QE/7nMlkjFhP1ThPdrwovtj8+J1CZ6Lms0PUOqMFM/cCDMQ9tZFJ3EPxt57F3zrEeTIgo7hnfn0l7Yu/IfApitfOWz6fp+7u7tH/L126tOo6Nq5erqtX0o2TbZxoY9rb23FyT5KB7IfPnGXxkwZu0Ck49NIejLUvGzdupNYZs0klZHLerNoX4L898NqPGLuOCxuCQhD9jeJQllqmzXacPLYSHLpJHtu1L2P9g8VXnkNzFx1v+hyc3AO62udCoTBqK1WN82TFq5PVZgKfW/scCpNQcQ+5W3foFL/piGziHoq/9Sz+1iXOCzIQ99Qt/kavGQXx2nnj/XSYGSULMjpvfoh7HBwmGhOmzwHuQHAIqgFxT09gDxQjFBIq7nFyFuIeAP4A+6wGMrVNTk5KChP3uPhiIN1vug5UB/ZAbZAfqm5fmqegmNMPYF/kAeKe2sXfOMGnGCLEPd4vFotRR0fH6BHpWox9zIt1fsOvl2fgcDU9V9IzfJvvC8L8PThvEweHO1dtq+Onoz46Om9e2oOx9uXEE0+k3v4MqQDEPT0RZQ+89iPGPrZlyxYKCkH0N2LxRmHJY/ZfBrM5nNwDIKD2ORqN0sKFC5WO82RDJnGPT1ZfcM+jQk9W57J507XAn/xQX18f3uqAgPxQbfuC3FDw7IsucV4QKZdKaMupsLjHQOBTCFHiHu/H+1hpr8K9kb1cV49kG8/DqczAYSr3MXUV+cqEk3sugkPgDh3FvXQ6bQSwXtiD8fblkksuoe/eO9IvXmYg7umJCH+jWCwK8SPGrrv22mspCATV3wiFw0IrPwvDRVpywlyhbTm55SnacgKdcWqfGxsbTW2qzHGebMgm7vnRNnl6aqrpeuBPfiibzeKtDgAQ9w6A4m857IsOcV5QGcqPFJdj5t5GJcU9BgKfQogS93i/devWkcpUS7ZV3r9K4q3eIl9hKIu2nBD36oKO4h6Ty+U8sQcT2ReZ4dM4mLmnH6KSRboMRpfB3xDV1iXREBGaPOa24tnBLJVCIczcA1qiq7+mGhD3qs9EjaTr/MFIhsji71Qq5em1Ageg+HsUFH/7D/wNCSmXIe51qyvuMZjBpwh8ykSUuMf7qYxZ8p1v8338GK/htfWYSThcLGLmHk7u+f9zp3GyKJFIuLYHqop7zO7+PbaT73ZnenL0Oja5c/rV51FY0ExPUN9kUSgUkv4jyOfzruxBUPwNUTMbJiUTQsU9ti/lEkHcA1qis7+mEhD3qot77P8p4CYoU/zd2trq6bUC+6D4ewTkh/wH/oacRGJJR/HbaW+7hsIW/n7kCwUUf9cZCHyKwG0SRIl7KgeHVpPvQUi6NUQilo2rk+BwuFSynXx32tbFSfIdzpv/6GYPxsPVqW7sgcriHsOvRaS4x/Ylm8lD3NMkWRSJyN9UgtuMurUHQfA3RA1kt5Kcddv2N56M2W77Zqd4IJfJmb8IAHxGd39NFSDuubPP4AAo/lYfP4q/kR8Ch/xMwN+QFs4ni2z7m8nlUfxdZ+ApKUIymYS4Z7Oy3m7yvd5Jt2gsKcx54+Td3kwO4h4YBc7bxJjZA9XFPWZGa0qouMfFA8OFIk7uBQQki8xhkdILe1Bvf0OEuGfFf/Fipmc0FhWWPObipIH+PabrAPAT+GvqJOsxcw/inleg+Ft9RBd/Iz8ExgN/Q23czvQMhcIo/q4z8pdLAwNuk4CTe9Yq6yszbpwk3ytJt/EzcphSqST2pzEktvKzXC4JP7lXGCrg5J4EwHmzxkT2QHVxj4lGGoSKe2xfGqIRoW05h4vBaH8oA0gWmROPx2n+/Pme2IPx9qUeNkQ2cY/tyy2PrBUm7rF9aWlsNl0LgF/AX1OH4lCWWqbNth2/2bHPPOrWSfzmxD476byCk3vegeJv9RFd/O1XfgidneQA/obauBX32L7Ew4O+FH8fs7gdY1tqAIEPODbWPT091NXVZdzu7OyktrY2V+vKHHX4UFlfEeI2bNjgKPk+PunG+/F9oq9fdFuXyYm4UOeNg8N8dohaZ7SgLWeAgfNmj2r2wI19UQm34h7bl5VbdggT9zj5lO5NW349uiM6WTTRCTWv/Q2ReGkPxtoX3mPqYXPIL2QU98yvz524x/Yl1ZQyXQ+ACv6abHGe9IRCQsU9ts+D2RzEPU1A8bcGhOTPD6H4Wx9/Q5U4T0W8EPfYvnSvuN70OSj+FgsEPs1xY6zZuHZ3d4/+f+nSpa7WiW4/xZX18+bNM5JkTHt7u+PkeyXpxsk73i8ajfqaeBMxs2Hz43cKFfc4eRcKk1BxD7kDd0Dcc8Z4e+DWvqiAF+Ie25eVt/1YaOVng+CD1yohOlnEbbT98jdksgeV/R588EHalR4kP4C4V9u+rL93jS+fAQB++mu8H3c6kTnOk51YvFGouMfJu8JwkZacMBcn98CEIB5UAxnyQyj+1sceqBLnqYZX4p4V+4Lib/FgBp/GwHlTA5kGsicnJYWJe1yZP5DuN10HqgN7AIIm7vnR1qV5ivl64B7YF3mAuOfOvgAgo7jH++GEXX0JhcPC7XO0ISJU3OPCFrTllBv4a2ogS34Ixd/BBvZAbWQT99i+WDm6vNVFfqhUkrsYDSf4NMULY83HoqvddrpO9EkXrhrhanqupGf4dmUmn5Mj5vzcyn78fL8q62V03irB4QX3PCp0ZkMuW7syCPibLOrr69PmLR9vD9zaF5mRSdxj+7Jz1TbLrw2ItS98yt4vf0Mme1DZb9OmTcI7BZRLJbTl3A/EPaCTuMf7hUIhqeM81fGi+CKzdbUwcY/9v+xglkqhEGbuSQqS+WogU34onQmj+FsTe6BKnKcKMop7fuSHDp85i2QGAp+GeGWsueexlWPRVteNDyy9hlvPdHR0jM6z4YQZz8hh7CTdKsk2fm5lpg5z9/IV5CcyOW9+DWSfnppquh74kyzKZrNavNW17IFT+yIzMjlvFfsCgS849mWi3xGv/Q2ReGkPxtqXBQsWUG9/Rui1D+VH9sfMPZzcA8FDpLjH+61bt07qOE9lvDpZ/cRdq4XORC2XCOKepEDcUwPZ8kMo/tbH35ioS4BMcZ4KQNxTt/gbLTo1Q2fnLRKJjCbWKjNtOAHHiTNOoFmZDVEtmV/ZL2yhtYquzpsf4t6R/3AMNaemmD4H+JMsSiaTyr/VE9kDJ/ZFZmQU94BYdPQ3uFOAV/ZgvH256KKLxL+AchniHsQ9EFBEinu8HwgmMrVNjidjjuK3068+j8IW/L9cJme6BthHR39NRZAfOtS+JJK1T42B6qANuNpA3FM7PwSBTyN0d974WPjY12s36VYrme83cN5qB4coDg5Osqi1tZVUxswe6CTyQdwD49HV3+BOAV7Yg3r5G5FYUmjyOF8ouEoeWy0ecJo8hrgHggq3PRcp7ulgn2VEJnGP7XM0FhVqnwf695iuA/aAPVAD5IdGQPG3PG3AQR0oE9pyKizuMRD4NAHOW3WsJt0g7gX75J7V4BD4lyxSgXQ67coe6CDyQdwD49HZ3xjbKUBGf6MhEhFa+ZnJ5YWLexy9QtwDqsFtzyHu6YVs4p4fxRfhsB6+hF/o7K+pBMS9EVD8Hezib46RQH0pDGUxc4/UFfcYZMQ1AM7bxJgl3YIi7g0Xi2jLCXHPM5AsMieXy7m2ByqLfIXiMNpygoPQ3d8Y3ylAVn9DVFuXUCgsNHnMbX+zmTxO7gHl4LbnOLmnDxD3qp+sTk1L1fFTUQvd/TVVgLg3Aoq/3YPib/XhfLKT+M3OWCgUf9cXyOiKA+fNGpWkG8OJNaby/6Ak24pDWWqZNhsz93ByzxNUSBZdeuml9Oqrrwrbf9++fZ7Yg4nsi8y81pemob2YuQeCYQ9kQQZ/Q9TMhnh4UKi4xzM9hwtFOmZxO9pyAqXgtucy+2vAOhD3ardNXn/vGvwoeQDsgRqg+HsEiHvegOJv9eFOLSLFveFSCcXfdSbwAt9DDz1Et912G61fv55isRidfvrp9OUvf5lOPPFE0+f+4he/oJ/97Ge0Zs0a2r59OyUSCTr22GONoOYDH/iA8seEvXTeeK9ly5YZtzs7O6mtra3qup6eHurq6vJsXT2TbqVSybi9YcOGYCTbQiGh4l65TEZybNVft9OSE46gf0VbTqVRIVnE4t7NN98sbP+rr76a5s+f74k9GG9fuA99ZU9Z4Z8Bu8l3u/alMFQ4KLnTNL1F67YLQcVLe+C1HzF2XZn/0AWAwPsbggayd6+4XnjlZ0M0IlTcGy6qcQI7KCDOC459zmQyRqynapwnAxD3DoCZqGIIQvwG5Cn+Rn5IH/wq/tYhzgsq0VhSmLjH9mVvJkcRKgvPDznxD/Zqkh8KtML1ve99z0iwsph36623Gu3S7rzzTnrTm95Ev//97+mkk06a8PlsUCZNmkQXXnghtbe30549e+i///u/6YMf/CD95Cc/oZUrVyo77NNr5y2fz1N3d/fo/5cuXVp1HRtXL9fVK+nGyTZOtDH8sxOEZFss3ijMeePk3WA2B3EPWEKn4NBLezDWvmzcuJFaZ8wmmZk6pUWo88YzYfLZIWqd0QLnTSN74LUfMXYdFzYEhSD7GyLEPb/auqzcskOYuMfBYbo3bboOWANxXrDsc6FQGLWVqsZ5QaZcKmHm3n4g7olBp/hNCwQXfyM/pBd+FX/rEucFkpDYtr/lcgnF33UmsAJfOp2mG2+8kWbPnm2Iec3NzaMt2U444QS6/vrr6Yknnphwjx/84Ad09tlnHyTi3XDDDXTmmWfSo48+Sr/85S/pbW97G6mG184b76fKzCiZCYXDQis/C8NFWnLCXKEn9ziRabdyAm0XggWCQ1BhUjIhVNxj+xIKk1BxD4V+7oA9UBvZxD22Lytv+7HQys8GuQ9eBwbEeeKBfZaLoXzG+Ndu/GbXPjuJ35zYZ6fxG8Q9McAeqIfo4m+/8kNO4jfkh4IF7IsauJ3pOTkRR/F3nQmswMetNQcGBgyRryLuMUcccQRdcskl9MADD9DLL79Mc+bMqbnHOeecU7NievXq1fTcc88pJ/CJEPd4P26P2tHRMXpEuhZjH/Nind/w6+UZOFxNz5X0DN/m+4JWVe91W5dEQ0So88bBYXYwS6VQCM6bpOjovHlpD8baFz6Z3ts/ksxRFbfiHtuXdCYsTNzj4HAg3W/59QB/7IHXfsTYx7Zs2UJBIej+hozinh9tXQ6fOct0PTAHcV7w7HM0GqWFCxcqHecFmnJZuLiXLxQg7mmIiPxQX1+fp9cIglf87Ud+CMXf+tkXXeI83cQ9ti+bH7/T9Dko/tZU4Hv66aeNf7kd53j4Phb4nnnmmQkFvlr8/e9/N/497LDDTNe+9tprtGvXroPu27x5c+BOtvE8wWKxSPfdd58hXJ5yyil05ZVXUjgcdtyLmF9bZT+eX3jTTTeNPlZrz5kzZzpex4GlyL7JvL/Z6+VZIJs2baIFCxbQRRddZNzP93HSjf/l+2r9weLPIBwSe/219t+3Zxc99fBXqZjfR2962z/Tke2L+F01dd6e+c1y2rltHR19wqmU3fYHCjdEyKw4/fHnX6TbVz5DM1ubaVnn2TQz1WT6nJ70Xlr6gycp0hClM646j44+5TizyzOS72vuf4J2PLedjjnleFp8xTnGz/NEz+PPICi/k0GH3ys7v29j7YEV++LHZ2H3NdiFgw6n9sCKfbl3xX8LtRmRSIPQz4AHNZeoesBXLJXoKz/tpj+80ENnnTSXbnn3m4yfF7v25eLv/ZrC5Yn7SeztHaDffeMRKuwbsm1fhgsl2AwHv2927YEZvB93W+Dnu/EjzNZx94d6+hlu/Q2//Izx/sGpb716/8mLib/3Sxu66U+/fYCaU9NpycWfpMkt0w96TiQaPcRmVPyD/myRbr74dHrLiUeZ2omJ7IvxGmrYjG1rX6C1Xf9LLdOn0DnXX0BN05pNbcV4+/Lq6hcDYzOCch1OQJwnzt9xap9bWlpGbaWMcZ5Iv5P3ZXss0v4mJzXT3NedZjt+s2OfC8MlOmr6FNvxm1X7bPwNLJZtx29W7TPiPPs2Q4S/xvtxS1/Z4zzZbcZEuSFZ8kPFoaKj+M2qfYHNEPv7Zse+VHwAmeM8VW2GWfxm1b68uOrumrkhr/JD+7J5apk2xXb8drRF+5LZuy8w8ZWT6wiswPfKK68Y/3KLzvFU7qussbvvPffcQ6lUit71rneZrv/2t79NX/jCF6o+Njg4SHv37qUgwP2GH374YWPI6FlnnUXvfve7XTlvnNgeux8noUQPLWXBVeT3YOPPRrnWL8+TTz5J/f39tHjxYnrLW94ymljjtrCPP/44bd261Vgz9rGxTJ82lSbHylQysxwOmdV2GE2OH2r+MnvTtGn1Cko1Jej0t15Ds+Yu4E/Q1Hitf/onVB7cQW9YdDadtPhi+uOPX6RcdNKEz1vzwg76/jPb6fhjj6ab3rGIUi2NlDO57l17MnT7Y3+lhskpOnryFDrxlIlnZ45cX5me/Wk3FXty9A9nnUILL1xE4QbzptHNjU2B+Z0MOmwzrP6+jbcHVuwL7y/6s7DzGpzapPnz5zuyB1bsi2ibMW3aVKGfQevUaVVtBldmLX/yz/TCnmH6x8VvoKvPfj0VG8JUNNmvmn1pm9lGk6h2K9DB9D76yw/X0pRECy350Fto9klH2rIvRxw5BzbD5u+bE3sw4eexfz/RPkC9/Qwv/A0//IzGSPEQ/2Dk7+/EfsWOrc/R35//Fc095jh647lXUGNT6pDnzGybdZDNGOsffPqCM+m042aZ+hRm9qWWzXhl/Uv00mMbae68ubSo80yalJps+n5Usy/Df90XGJvBcYisIM4T4++4sc+i7aPo/UX6nWy3U1NahNrfOXMOrxrnmcVvduzz4bOPoK9c/hbb8ZtV+8z2d9NP19mO36za52mt0wJjf4MO/z7wz62o/NCRRx4pfZwnu82olRuSKT90eNtsOvm9p9uO36zaF9gMcb9vdv0NFeI8FW2GlfjNqn0ZH+eJyA/d8NDqCXNDbvNDqZaU1HFeYAW+TGakdVk8Hj/kMT5NNnaNVfbt22eIetz68yc/+YmlIZwf+chHjFZJ40/wXXjhhTRp0iRqajI/eu4HfE2cLOLWKmxc3bZd4EqMZ599dnQ/bpU6dpahCF599VWh32Pnzp3GKcdalfSVNlk8o5H/MIxdy/dxKwqeB8n/Vqus39W7m/YNhahkctrEKTt6XqV9+fAhlROrfvJVyu7rp5PPvYKaD3897cuTaWXW07/+Hr3ywp9o9nFvoKPfeAllig20s2cHJQqDE1dO/LybpjUl6eYLz6a2xjLRBOsrlVm3dD1BvXuzdNM7F9HnfvVHGjRx+di5/P39v6Xtz26lIxbOpXkXdlC2weRF7a/83LJ5S2B+J4MO/xxb+X2rZg+s2BfeX/RnYfU1uLFJ11xzjSN7YMW+iLYZvb27hX4Gfbt7D7EZXJm17OE/0Oq/vUxvnj+HrjvjOIqUsmZ+Yk370rOzp6bN4Mqs337jF5TpH6TTLjuLUicdZtu+ZPvzsBlWP+++PiOYc2IPrNgXRnY/46WXXqJ8Pu/IHlixL8L9jB2v0m9/fqh/YBZ9ceXnM4/dT8nJU2jJeR+lUmxqVV9krJ8x3j8466gWU5/Cin2pZjPYP1jz/d9R45RJdM6HzyBKRUxtRS370tvXGxibwXGIrCDO897fceqv+WUfRe8v0u/k9zbdv4cm+xznWYnf7NjneKaX5jiI36za561bt9H2HX+3Hb9Ztc9Bsr9Bh38fRPlrvN+ePXukj/NUtRky5Yd2U9lR/GbVvnAxEWyG979vTvwN0T6AH99DNZthNX6zal9q2Qsv80MT5Ya8yA9Nm9IqdZwXWIGvsbHR+JcTJePJ5XIHrbEq7vG8PTZC3/rWtwwjZIUZM2YYX9VgI1bvGSkVWGXmNk9XXXWVa+dt+fLloz2UK/txGwbRBln09+D9a83A+ctf/kKve93raibq+T5+jNfyEXR+3vgZOZygY2MsyiDz9Y/de3zP4znzT6NS2UpP9XtGZzacfN4/E4UbjOcVCwWqdUB6bE/1299/Js1KTTKvFu3bS58YO1PnxCPoll+sodIEx86NgcnfOzCQ/bQrzyZqCJlWsVRm6rQ0NgfmdzLo8M+rlWRRNXtgdX/Rn4WV1+AGrkJzag+s2BfRNqNYHGn1IIrhYvEgm8GVWV8aMxPrMxcuoojhQ05sKyayL4bdq2Izxs/cO/LUY03tRDX70n37b2AzLMI/r07tgRX7sm7dOun9jGw2W3OGnhf+hmibkRkcoG1/ffoQ/8DqzIbT3/1JamyeUfM5FT+jmn9gZies2pfxNmPszL2zrn87TZrObaMmflET2Rc//rZZJSjX4QTEed76O278NT/jMJH7i/7d5PfYzzjPavxmxz53r7i+ZqxXwY19zmWyNO+UY23Hb1btc5Dsb9DhMTMbN24U4q/xfsuWLZM+zlPRZsiWH7rlkbUT5obc5of6enfDZnj8++bU31Ahn6ySzbATv1m1L9Xshdf5oVq5Ia/yQ5F0cOIrJ9dhPi21TkzUhnOiti61xK/zzz+fVq9eTd/5zneMU3mqkUwmPRmY7OUA5qDBYvHYPrbjk23VEnJj4cd4Da/l5/Bz69Wf1+3AZDsD2Z0MTGbn7YaxweGCoy1cX4meWn7AuJ5+9Xn7Z0qQ5eAwNY2PlAMvUN0euMXMHti1L7LjdmCyHfsy3nmbu+h4YfYFHFy16JU9qGZfVIDbc3phD+rlb7Bob8c/cDKQ3Yl/4NS+jPUPzvvkhdQ0vUWIfQH2QZznHfDX1MZp/FYP+9wQjTiK36za5+FiMObiyAAXHIny11SOZ2QG+aFD7Us4jJ9VL4E9UAMn/oET+yJjfigkVoMWTmCzWzykk1mzZs0hj1XuO/nkk0334fYB5513nvEcrjT48Ic/TCrC7UbhvJlXXFSSZE6T77WSbka1RclsPKg3wHmrHRw2RODEeQGcN2tMZA8g7gXbeYO4Zx8ki8zhtvJe2YPx9oVbe4qmIRIRmjweLpUg7gEDxHneAH9NbWQS9zh5l2yMCxP32P9L96ZN14ERUPytF8gPHQDF32KAv6EGEPfUzg8FtkUnz7i7/vrrjYqhG264gZqbm437t2/fbiRKeEbJnDlzRuc48P0tLS3U1tZ2iLjHFUf3338/XXbZZXV7PSoaax6o2tXVZdzu7Ow86L13sk700NVKZX1FiOMZOE5O1lSSbkxlP75P9PUzcN7cBYfAHDhv9qhmD9zYF9mQsTJLBedNxWTRRCfUvPY3ROKlPRhrX3iPqYeN+L2iiMaSwpLH7L/szeQoQmWc3AOI8yTw12SL81RDNnGP/b+Vt/1Y6MnqBn9qaZUAxd/6gPxQbfuy/t5DD4uA+vgbqsR5MsOdWnByj5TODwVW4EulUkZv72uuuYYWL15snLzjFot33nmn0Vf3jjvuGF27du1aOuuss+jyyy83hLwKb3nLW+iPf/wjvetd7zKeUzEGFXhmHX/pjBtjze9nd3f36P+XLl3qap3o9lNcWT9v3jwjSca0t7c7Tr5Xkm6cvOP9otGo8MRbuVRCW879QNwTA8Q9Z4y3B27tiyxwsg7inj6IThZVm7ksyt+QyR5U9nvwwQdpV/rQweWeEhJb+Vkul4SLe4WhAtpySgDivGD5a7wfdzqROc5TCRnFPT/aJh8+c5bpeuAexIPyAHHvAMgPBdseqBLnyUxxKEst02ajLedyNcW9QAt8DIt6U6dONYS+T33qUxSLxej000+nL33pS5aEORb3mJ/97GfG13g+//nPay3wwXmTi6F8xvgXM/dwck8EsAfALrv7B2jr7l04uQdgXzTBbVuXyYm40OQxnwzOZ4eodUYLZu5JAOK84Ih7vB9O2AUDiHu1OzfsXLWtjp+MHiAelAcUfx8A4p4YYA8UIxQSKu5xowYUf9eXQAt8zCWXXGJ8TQS366wWlCBQEWus+Vh0tdtO14k+6cJVI1xNz5X0DN/mdq9OquorM3Uq+/HzhVfWl8sQ9yDuSZMs6uvrI10Ybw/c2hdZyORywsU9PgWFtpx62Bc+Ze+XvyGTPajst2nTJuGdAkTPbNj8+J3C2/6GwiRU3EOXQW9BnBcMcY/34243Msd5KgBxb2L7DIFPLEjmywWKv0dAfkgOe6BKnCczsXijMHGP/ZfBbA5jW+pM4AU+EFxjzT2PrRyLtrpufGDpNdx6pqOjY3SeDSfMeEYOYyfpVkm28XMrM3WYu5evEHj1RJFY0rJxddLWJV8o2E6+O2nr4rTnMZw3uZJF2WyWdKCWPXBqX2SiMZEQKu6xfckOZqkUCmHmngb2ZaLfEa/9DZF4aQ/G2hfuONHbP3KSX9aB7GYCnxczPdOZsDBxj/2XgXS/6ToAZBP3eL9169ZJHedJT5nQltOFfQbugLgnIT4UfyM/pCci/I2JDt/IFOfJTCgcFtr2tzBcpCUnzEXxdx1Rp9kosITOzlskEhlNrFVm2nACjhNnnECzMhuiWjK/sl/YgsF0Q0MkIrTyM5PLCxf3OHqFuKdHsiiZTJLqTGQPnNgX2Zg6pVmouMf2pVwiiHuSoqO/wZ0CvLIH4+3LRRddRDKLe2Z4Ie6xfbHiizkV99h/yWVrzxABQCQixT3eD9SXwlAWM/cg7tUFHf01FRBd/O1XfgjF38ECbcD1xe1Mz2hDxJfibyfx20uKztwbD07waYTuzhsfCx/7eitJN8ZKZX2tZL4qbV0aQmGhzhsn77KZPE7uaZIs6u9X+5SDmT2wa19kxEo1vhtxj+1LPBkT6rzlMjnTNcA+uvobYzsFqOBvyCbuWbEvbsQ9ti/TU1NN1wPgNdz2fMuWLcLEPR3sc9AZLhYNe2k3frNjn4dLJUfxmxP77KTzCk7u+Q/sgbyILv72Iz+E4m9924ADtcQ9ti+ZratR/F1n1JUuwUHAeauO1cr6oCTbRM5saGpMCBX3ODgcLhTRljNAySJUgpufxnFjD3Q5ySdK3GP7Eo1FhVZ+DvTvsfx6gDV09jfGdgqQ3d+AuFfdvjSnpvj+WQDAbc8h7qmfrBcp7nHybm8mB3EPjKKzv6YDMuSHUPytT/E3x0hAXXGP7YsVDRfF32KBwKcBcN4mxizpFpRkm+iB7A0W2lq5Efe48rMhGhE6c2+4qI944hYki6ydxnFrD3QV+bwQ9/xo6xIOB8eWq4Du/sb4TgGy+hsQ92rbFxQgg3rAbc9xck9torGkUHGPk3flcgkn94CB7v6a6siSH0Lxd3BA8beeeCXuWbEvKP4WD2R0xYHzZo1a7bOYICTbRDtvVoJDt+Iet3VZuWWHMHGP27qke9Om68AISBaZw5VmXtiDieyLisgi7rF9STWlLL8uMDHwN9TwNyDu6TmzAQSb1tZWtOVUnZB4+zw5ERfalrMwVEBbTgmAv6Y2MuWHUPwdHFD8rR+yiXtsX255ZK3Q/FBLYzPJDAQ+hfHSeeO9li1bZtzu7Oyktra2qut6enqoq6vLs3X1TLqVSiXj9oYNGyDueSDucXC48rYfC53Z0DDykQELIFlk7TTO/PnzPbEH4+0L96Gv7KkSMol7bF/W37vG8msD/vgbXvsRY9eVy+VAfIxB9Td4BhRm7kHcA3ra50wmY8R6qsZ5suNF8cXmx+8UOhM1nx2i1hktmLkXYCDuqY1M4h6Kv/Us/tYhzpMBGcU98+sraV/8DYFPUbx23ngWVXd39+j/ly5dWnUdG1cv19Ur6cbJNk60Me3t7Ti5J8lA9sNnzrL4SQM36BQcemkPxtqXjRs3UuuM2aQSMjlvVu0L8N8eeO1HjF3HhQ1BIYj+RnEoSy3TZjtOHlsJDt0kj+3al7H+weIrz6G5i443fQ5O7gFd7XOhUBi1larGebLi1clqM4HPrX0OhUmouIfcrTt0it90RDZxD8XfehZ/6xLnBRmIe+oWf6PXjIJ47bzxfjrMjJIFGZ03P8Q9Dg4TjQnT5wB3IDgE1YC4pyewB4oRCgkV9zg5C3EPAH+AfVYDmdomJyclhYl7XHwxkO43XQeqA3ugNsgPVbcvzVNQzOkHsC/yAHFP7eJvnOBTDBHiHu8Xi8Woo6Nj9Ih0LcY+5sU6v+HXyzNwuJqeK+kZvs33BWH+Hpy3iYPDnau21fHTUR8dnTcv7cFY+3LiiSdSb3+GVADinp6Isgde+xFjH9uyZQsFhSD6G7F4o7DkMfsvg9kcTu4BEFD7HI1GaeHChUrHebIhk7jHJ6svuOdRoSerc9m86VrgT36or68Pb3VAQH6otn1Bbih49kWXOC+IlEsltOVUWNxjIPAphChxj/fjfay0V+HeyF6uq0eyjefhVGbgMJX7mLqKfGXCyT0XwSFwh47iHrcm9soejLcvl1xyCX333pF+8TIDcU9PRPgbxWJRiB8xdt21115LQSCo/kYoHBZa+VkYLtKSE+YKbcvJLU/RlhPojFP73NjYaGpTZY7zZEM2cc+PtsnTU1NN1wN/8kPZbBZvdQCAuHcAFH/LYV90iPOCylB+pLgcM/c2KinuMRD4FEKUuMf7rVu3jlSmWrKt8v5VEm/1FvkKQ1m05YS4Vxd0FPcYFhz45LJbezCRfZEZPo2DmXv6ISpZpMtgdBn8DVFtXRINEaHJY24rnh3MUikUwsw9oCW6+muqAXGv+kzUSLrOH4xkiCz+TqVSnl4rcACKv0dB8bf/wN+QkHIZ4l63uuIegxl8isBtEkSJe7yfypgl3/k238eP8RpeW4+ZhMPFImbu4eSe/z93GieLIpGIa3ugqrjH7O7fYzv5bnemJ0evY5M7p199HoUFzfQE9U0WhUIhJU79urEHQfE3RM1smJRMCBX32L6USwRxD2iJzv6aSkDcqy7usf+ngJugTPF3a2urp9cK7IPi7xGQH/If+BtyEoklHcVvp73tGgpb+PuRLxRQ/F1nIPApArdJECXuqRwcWk2+ByHp1hCJWDauToLD4VLJdvLdaVsXJ8l3OG/+o5s9GE88HndlD1QW9xh+LSLFPbYv2Uwe4p4mySIW1FU49evWHgTB3xA1kN1KctZt2994Mma77Zud4oFcJmf+IgDwGd39NVWAuOfOPoMDoPhbffwo/kZ+CBzyMwF/Q1o4nyyy7W8ml0fxd52Bp6QIyWQS4p7Nynq7yfd6J92isaQw542Td3szOYh7YBQ4bxNjZg9UF/eYGa0poeIeFw8MF4o4uRcQkCwyh0VKL+xBvf0NEeKeFf/Fi5me0VhUWPKYi5MG+veYrgPAT+CvqZOsx8w9iHtegeJv9RFd/I38EBgP/A21cTvTMxQKo/i7zshfLg0MuE0CTu5Zq6yvzLhxknyvJN3Gz8hhSqWS2J/GkNjKz3K5JPzkXmGogJN7EgDnzRoT2QPVxT0mGmkQKu6xfWmIRoS25RwuBqP9oQwgWWTt1O/8+fM9sQfj7Us9bIhs4h7bl1seWStM3GP70tLYbLoWAL+Av6YOxaEstUybbTt+s2OfedStk/jNiX120nkFJ/e8A8Xf6iO6+Nuv/BA6O8kB/A21cSvusX2Jhwd9Kf4+ZnE7xrbUAAIfcGyse3p6qKury7jd2dlJbW1trtaVOerwobK+IsRt2LDBUfJ9fNKN9+P7RF+/6LYukxNxoc4bB4f57BC1zmhBW84AA+fNHtXsgRv7ohJuxT22Lyu37BAm7nHyKd2btvx6dEd0smiiE2pe+xsi8dIejLUvvMfUw+aQX8go7plfnztxj+1Lqilluh4AFfw12eI86QmFhIp7bJ8HszmIe5qA4m8NCMmfH0Lxtz7+hipxnop4Ie6xfelecb3pc1D8LRYIfJrjxlizce3u7h79/9KlS12tE91+iivr582bZyTJmPb2dsfJ90rSjZN3vF80GvU18SZiZsPmx+8UKu5x8i4UJqHiHnIH7oC454zx9sCtfVEBL8Q9ti8rb/ux0MrPBsEHr1VCdLKI22j75W/IZA8q+z344IO0Kz1IfgBxr7Z9WX/vGl8+AwD89Nd4P+50InOcJzuxeKNQcY+Td4XhIi05YS5O7oEJQTyoBjLkh1D8rY89UCXOUw2vxD0r9gXF3+LBDD6NgfOmBjINZE9OSgoT97gyfyDdb7oOVAf2AARN3POjrUvzFPP1wD2wL/IAcc+dfQFARnGP98MJu/oSCoeF2+doQ0SouMeFLWjLKTfw19RAlvwQir+DDeyB2sgm7rF9sXJ0eauL/FCpJHcxGk7waYoXxpqPRVe77XSd6JMuXDXC1fRcSc/w7cpMPidHzPm5lf34+X5V1svovFWCwwvueVTozIZctnZlEPA3WdTX16fNWz7eHri1LzIjk7jH9mXnqm2WXxsQa1/4lL1f/oZM9qCy36ZNm4R3CiiXSmjLuR+Ie0AncY/3C4VCUsd5quNF8UVm62ph4h77f9nBLJVCIczckxQk89VApvxQOhNG8bcm9kCVOE8VZBT3/MgPHT5zFskMBD4N8cpYc89jK8eira4bH1h6Dbee6ejoGJ1nwwkznpHD2Em6VZJt/NzKTB3m7uUryE9kct78Gsg+PTXVdD3wJ1mUzWa1eKtr2QOn9kVmZHLeKvYFAl9w7MtEvyNe+xsi8dIejLUvCxYsoN7+jNBrH8qP7I+Zezi5B/QS93i/devWSR3nqYxXJ6ufuGu10Jmo5RJB3JMUiHtqIFt+CMXf+vgbE3UJkCnOUwGIe+oWf6NFp2bo7LxFIpHRxFplpg0n4Dhxxgk0K7MhqiXzK/uFLbRW0dV580PcO/IfjqHm1BTT5wB/kkXJZFL5t3oie+DEvsiMjOIeEIuO/gZ3CvDKHoy3LxdddJH4F1AuQ9yDuAcCikhxj/cDwUSmtsnxZMxR/Hb61edR2IL/l8vkTNcA++jor6kI8kOH2pdEsvapMVAdtAFXG4h7aueHIPBphO7OGx8LH/t67SbdaiXz/QbOW+3gEMXBwUkWtba2ksqY2QOdRD6Ie2A8uvob3CnAC3tQL38jEksKTR7nCwVXyWOrxQNOk8cQ90BQ4bbnIsU9HeyzjMgk7rF9jsaiQu3zQP8e03XAHrAHaoD80Ago/panDTioA2VCW06FxT0GAp8mwHmrjtWkG8S9YJ/csxocAv+SRaqcxnFjD3QQ+SDugfHo7G+M7RQgo7/REIkIrfzM5PLCxT2OXiHuAdXgtucQ9/RCNnHPj+KLcFgPX8IvdPbXVALi3ggo/g528TfHSKC+FIaymLlH6op7DH7LNADO28RUkm5MtRk5QRH3hotFtOWEuOcZSBZZO43Dv/tu7IGZfRHJ1i0v0D8sOpOs1Mvlchka6E8biaBU6zRqaDB3D/a8sp1ueGAH2nKCUXT3N8Z3CpDV3xDV1qUhFBaaPOa2v9lMHif3gHJw23Oc3NMHiHvVT1anmlJ1/FTUwq2/9uKLL9KSJUuEXuMrr7widH8VgLg3Aoq/vSn+3rJli7Biouuuu86DqwRu88lcLGQ3frMzFgrF3/UFAp/i6J5ss0qtpBsTlGRbcShLLdNmY+YeTu55ApJF5nClmRf2YCL7IpRwlE6+8LP2nLf/z/pMz199+5+JinHM3AMG8DfU8TdEzWyIhweFins803O4UKRjFrejLSdQCm57jracegBxr3bb5PX3rqnjJ6MOXvhr3G7v5ptvJpFce+21QveXHRR/jwBxzxtQ/K0+3KlFpLg3XCo5jt9W/XU7LTnhCPpXi53jnIxV2Kvwyb0KEPgUxstkG++1bNky43ZnZye1tbVVXdfT00NdXV2eratn0q1UKhm3N2zYEIxkWygkVNwrl8mRcUVbTjlBssjaaZz58+d7Yg/G2xcOjCt7CiMktvKzXC4Jd94KQwU4b5r5G177EWPXlfkPXQAIvL8haCB794rrhVd+NkQjQsW94aJabZaB+li1z5lMxoj1VI3zZADi3gEwE1Vvf43XgfoXfyM/pA9+FX/rEOcFlWgsKUzcY/uyN5OjCJUh7tURCHyK4nUlPc+i6u7uHv3/0qVLq65j4+rlunol3TjZxok2pr29PRDJtli8UZjzxsm7wWwO4h6whE4ndby0B2Pty8aNG6l1xmySua3L5ERcqLjHxQP57BC1zmhBZZZG9sBrP2LsOi5sCApB9jdEiHt+tXVZuWWHMHGPKz/TvWnTdQDIaJ8LhcKorVQ1zgsy5VIJM/f2A3FPDDL5a6D+xd/ID+mFX8XfusR5gQTF36Tqyb0K5hEwIN2dN96Pv0B9CYXDQis/C8NF4Sf3OJFp91g02i4EC53EPZXxYmZDPBoVKu6xfQmFSai4h0I/d8AeqI1s4h7bFyvRq5u2LqUS/GEgB7DPcjGUzziK3+zaZyfxmxP77DR+g7gnBtgD9RBd/O1XfshJ/Ib8ULCAfVEDt/mhRp+Kv9GWszY4wacYIsQ93i8Wi1FHR4dpy4Sxj3mxzm/49fIMHK6m50p6hm/zfUGrqve6rUuiISLUeePgMDuYpVIoBOdNUnR03ry0B2Pty4knnki9/RmSeSD75sfvFCrusX1JZ8LCxD0ODgfS/abrgL/2wGs/YuxjPDw+KATd35BR3PNjZsPhM2eZrgdARvscjUZp4cKFSsd5gaZcFi7u5QsFiHsaIiI/VCwWhfprfPuLX/yi42vUAdHF337kh1D8rZ990SXOkw0v8kNmuSEGxd9igcCnEKLEPd6P97HSXoV7I3u5rh7JNp6HU5mBw1TuY4KQdBM1syGzdbVQcY+Td+USQdyTFB3FPW5N7JU9GG9fLrnkEvruvSP94mUU98zwQtxj+3LBPY8KrfzMZfOma4F/ySIRfsTYdddeey0FgaD7GxD3atuXnau21eUzAUC0fW5sbDS1qTLHeUEnEksKFffY/8vk8jS7rRUn9zRCVH6IZ12J9NeAHvkhFH/rZ190iPNkA8Xf6hR/o0WnQogS93i/SERtLbhaso3fv8qMHL6PH+M1QWtX6tVA9pCFnsxu27rEkzGhbRdymZz5iwC20VHcY1hw8MIe1LIvfiObuOdHW5dEMm66HviXLNKBoPsbEPfc2RcA6omu/poKNFiItd3a51AoLFTcIyqjLacmxd8hK4kDUDdkyQ+h+Fte4G+ogWz5obCFk8t7NS7+hsCnCH19fcLEPd5PZcyS70FJuol03vya2RCNRYXObBjo32O6DthDZ+eNCxvc2gOIe8EV99i+NKemmD4HHADJIvNTv27sQd39jTKhLed+IO4B2dDZX9MBL4ovmhoTwsQ9Tt5lM3nM3AsQKP7WE5nyQyj+lhP4G2ogm7iH4m9zIPApQjabFSbuqRwcWk2+1z3pJrnz5tdA9nBY3Z/VeqCbPRhPPB53ZQ8g7gXXeavYFxQg2wPJIvNTv27tQT39jcJQFjP3IO4BCdHdX1Mdr05WN1iofHczE3W4UHQUv9mZiQqsgeJvPZEtP4Tib/mAv6EGEPfULP6GwKcIyWQS4p7Nynq7yfcgiXyyOW9+iHscHKampUzXA2vAeZsYM3sAcS/44p4V+wIOgGSRtVO/XtiDevkbw8Wiq+SxlcpPN8lju5WfTpLHOLkHZAP+mtrI1Da5IRoRKu4NF4PRQUcGUPytH8gPHQDF32KAv6EGEPfULf5We7CaRrS2tuLknsXKek6aMU5mYlWSbgw/l6n8v1QqkR/AeasdHK6/d40vn4HqwHmzxkT2IAgz9zhZj7YLEPe8Askia6d+58+f74k9GG9f/LAhPANKZPJ4uFSCuAeAh8BfUxuZxD0uvli5ZYcwcY+LL9K9adN1YAQUf+sF8kM1ir+bUPztFfA31ADintrF3xD4gGNj3dPTQ11dXcbtzs5Oamtrc7WuXC77UllfEeI2bNjgKPk+PunG+/F9oq+fgfN2ALR1EQOcN3tUswdu7IuXFIey1DJtNnqqK+i8qZgsmuiEmtf+hki8tAdj7QvvMfWwOSSSaCwpLHnM/sveTI4iVMbJPQAk8Ndki/NUQzZxj09Wr7ztx0JPVjf4U0urBPUq/pbJX1MF5IcOgOLv4PobqsR5MoPib7XFPQYCn+a4MdZsXLu7u0f/v3TpUlfrRLef4sr6efPmGUkypr293XHyvZJ04+Qd7xeNRoUn3sqlEtpy7gfinhgg7jljvD1wa188IxQSKu5xrk6WgcmqOW8qJou4jbZf/oZM9qCy34MPPki70oMklJDYys9yuSRc3CsMFdCWEyiP1/4a78edTmSO81RCRnHPj7bJh8+cZboeqJUfAhMDce8AyA8F299QJc6TGRR/k/L5IXVeCbANkvlyMZTPYOYenDdhwB6oRyzeKEzc4+TTYDYHcQ9YAvZFDdy2dWlMxIUmj7l4IJ8dwsw9oDQixD3eDyfsggHEvdrFXYnGRB0/GT2AvyYPKP4+AMQ9McAeKAaKv0llcY/BCT5N8cJY87HoaredrhN90oWrRrianivpGb5dmcnn5Ig5P7eyHz9feGV9uWwr+e6k8tPJyRonlZ9OKyfgvMmTLOrr6yNdGG8P3NoXrwiFw0IrPwvDRVpywlyhJ/f4FBRO7ulhX/iUvV/+hkz2oLLfpk2bhHcKED2zYfPjdwoV99i+hMLk6GSI1ZPB6DIIVBT3eL9QKCR1nKcCEPcmts87V22r46ejPkHMD4GJi78Z5IfsnwwG/vsbqsR5MoPi798oLe4xEPg0xCtjzT2PrRyLtrpufGDpNdx6pqOjY3SeDSfMeEYOYyfpVkm28XMrM3WYu5evEHj1RJFYUqi4ly8UIO5piKhkUTab9fQ6g0ote+DUvsjU1iXREBEq7nHxQHYwS6VQCG05NbAvE/2OeO1viMRLezDWvixYsIB6+0eSObIOZDcT+NyKe2xf0pmwMHGPi5MG0v2m6wCQTdzj/datWyd1nCc9ZUJbThf2GaiZHwL1Lf5GfkhPRPgbE3UJgN3wBxR/b1Fa3GPUfFWgJjofs45EIqOJtcpMG07AceKME2hWZkNUS+ZX9gtbOC3jhoZIRGjlZyaXF35yj6NXnNzTI1mUTCZJdSayB07si2wzGyYlE0LFPbYv5RJB3JMUHf0N7hTglT0Yb18uuugiklncM8MLcY/tixVfzM1Mz1y29gwRAEQiUtzj/UB9KQxlMXMP4l5d0NFfUwHRxd9+5YfQ2SlYoA24vrjND0V9Kv52Er+9pIG4x+AEn0bo7rzxsfCxr7eSdGOsVNbXSuar0talIRQW6rxx8i6byaMtpybJov5+tU85mNkDu/ZFxoHsT9y1Wnjb33gyJtR5y2VypmuAfXT1N8Z2ClDB35BN3LNiX9yIe2xfpqemmq4HwGu47fmWLVuEiXs62OegM1wsGvbSbvxmxz4Pl0qO4jcn9tnJWAWc3PMf2AN5EV387Ud+CMXf+rYBB8HCi/xQZutqFH/XGXWlS3AQcN6qY7WyPijJNpEzG5oaE0LFPQ4OhwtFzNwLULIIleDmp3Hc2IMgnuTzStzza6ZnNBYVOtNzoH+P6TpgD539jbGdAmT3NyDuVbcvzakpvn8WAHDbc4h76ifrRYp77P/tzeQg7oFRdPbXdECG/BCKv/Up/uYYCQQTr/JDVjRcFH+LBQKfBsB5mxizpFtQkm2iB7I3WGhr5Ubc48rPhmjEVvLdbuXncDFYbRCDDJJF1k7juLUHQRL5ZBP3/GjrEg4Hx5argO7+xvhOAbL6GxD3atsXFCCDesBtz3FyT22isaRQcY/9v3K5hJN7wEB3f011ZMkPofg7OKD4W09kyw+h+HtiIKMrDpw3a9Rqn8UEIdkm2nmzEhy6Ffe4rcvKLTuEiXvc1iXdmzZdB/xJFr344ou0ZMkSoW/3K6+8InR/rjTzwh5MZF/8Qjbnza+ZDammlOl6YA34G2r4GxD39JzZAIJNa2sr2nKqTki8fZ6ciAtty1kYKqAtpwTAX1MbmfJDKP4ODij+1g8Z80O3PLJWaH6opbGZZAYCn8J46bzxXsuWLTNud3Z2UltbW9V1PT091NXV5dm6eibdSqWScXvDhg0Q9zwQ9zg4XHnbj4XObGgY+chAAJJF3Gf98ssv99QejF/3xS9+kUSfxpk/f74n9mC8feH3p7KnaGR03vwQ99i+rL93jelzgL/+htd+xNh15XI5EB9nUP0NngGFmXsQ94Ce9jmTyRixnqpxnux4UXyx+fE7hc5EzWeHqHVGC2buBRhZ/DVeB9QW91D8rWenAB3iPBlAfkjN4m8IfIridWUWz6Lq7u4e/f/SpUurrmPj6uW6eiXdONnGiTamvb0dJ/ckGch++MxZFj9p4Id98doejF/nB17ag7H2ZePGjdQ6YzaJBs6bO/sC/Pc3RNoNLmwICkH0N4pDWWqZNttx8thK8YCb5LHd4oGx/sHiK8+huYuON30OTu4BXe1zoVAYtZWqxnmy4tXJajOBz619DoVJqLiH3K0+/hpQX9xD8beenQJ0ifOCDPJD6hZ/o9eMgnjtvPF+9ZoZBdRw3vwQ9zg4TDQmTJ8D3IG2LvJQLpVwcm8/EPfEAHugGKGQUHGPk7MQ9wDwB9hnNZCpbXJyUlKYuMfFFwPpftN1oDqwB2qD/FB1+9I8BcWcfgD7Ig8Q99TOD+EEn2KIEPd4v1gsRh0dHaYtE8Y+5sU6v+HXyzNwuJqeK+kZvs33BWH+Hpy3iYPDnau21fHTUR+79sVrezB+negWnYyX9mCsfTnxxBOptz9DIhnKj+yPtpzqOW8qB3Mi7caWLVsoKATR34jFG4Ulj9l/GczmcHIPgIDa52g0SgsXLlQ6zpMNmcQ9Pll9wT2PCj1ZncvmTdcCf/JDxWJRiThPBZAfqm1fkBvSKz8UpDgviKD4W21xj4HApxCixD3ej/ex0l6FeyN7ua4eyTaeh1OZgcNU7mPqKvKVCSf3XASHwH/74rU98NtucGtir+zBePtyySWX0HfvHekXL4xyGeKeos6bqski0Xbj2muvpSAQVH8jFA4LrfwsDBdpyQlzhbbl5JanaMsJdMapfW5sbDS1qUH111RENnHPj7bJ01NTTdcDf/JDPOtK9jhPBSDuHQDF3/4TtPxQUOK8oILib/XzQxD4FEKUuMf7rVu3jlSmWrKt8v5VEm/1FvkKQ1m05YS4Vxd0bbvAggOfXHZrDyayLyKJxJK2ku922/7mCwXbyR0nbX+dzsRS2XlTNVmkAzL4G6LauiQaIkKTx2xfsoNZKoVCmLkHtERXf001IO5V9/8i6Tp/MJIhsvg7FAp5eq3AASj+HgXF3/4Df0NCUPxNqueHMINPEfr6+oSJe7yfypgl3/k238eP8RpeW4+ZhMPFImbu4eSe/z93GieLIpGIa3tQL3HPuNZIRGjlZyaXFy7ucfQKcS84IFlkfurXjT0Iir8hambDpGRCqLjH9qVcIoh7QEt09tdUAuJe7eIuaErBKf7mGAnUFxR/j4D8kP/A35ATFH9vVFrcYyDwKUI2mxUm7qkcHFpNvgch6cbJejvJd7ttXYZLJdvJd6dtXZxUTsB58x/d7MF44vG4K3tQT3HPj7YuoVBYqLjH9iWbyePkXoBAssj81K9bexAEf0PUQHYryVk34h7bl3gyZrvtm52TwblMzvxFAOAzuvtrqgBxz519BgdA8bf6+FH8jfwQOORnAv6GtKD4+7dKi3sMPCVFSCaTEPdsVtbbTb7XO+kWjSWFOW+cvNubyUHcA6PAeZsYM3ugurjH9qWpMSFU3OPigeFCEW05AwKSReZwRbsX9qDe/oYIcc+KfXEr7rF9icaiQtv+DvTvMV0HgJ/AX1MnWY+ZexD3vALF3+ojuvgb+SEwHvgbaoPib/mBwKcIra2tOLlno7LeafK9VtKNv0qlEgklJLbys1wuCT+5Vxgq4OSeBMB5s8ZE9kB1cY/tS0M4LFTcY/vSEI0Inbk3XAxG+0MZQLLI2qlfr+zBePvy0EMPkd/IJu75MdMzHA6OLQcA/po6FIeyjuI3O/aZR906id+c2GcnnVdwcs87UPytPqKLv/3KD6GzkxzA31AbFH+rAZpnA8fGuqenh7q6uozbnZ2d1NbW5mpdmaMOHyrrK0Lchg0bHCXfK0k3prIf3yf6+kW3dZmciAt13jg4zGeHqHVGC9pyauy8eW036k01e+DGvsgi7lmxL27FPbYvK7fsECbucfIp3Zs2XQf8SRZNdEJNJrvhpT0Ya194j6mHzSG/gLhX3b6kmlK+fQYAqOSv1TtOCjyhkFBxj/2/wWwO4p4m1Kv4WyZ/TXpC8ueHUPytj7+hSpynIv9/e+cBJlV1/uGzy+7CgkpRiShEYwUsWLFgr7GhUVRUjDH22P4WrIkaTGIhiS3YCwJRUaKoqFEJdsESFUhEFMQWQEX6FnaXnf/zHjzj3WGn7cyduffO732efXb3zr1n7szO+fY73+/7vtOcp/jQlFEXpL1Gyd/+IoGvxMnFWGNcp0yZEv996NChOZ3nd/spMus333xzGySDPn36tDn47oJuBO8Yr7KysqCBNz/2bJg18XZfxT0yP8vKja/inmIHwc/MyrfdCAKJ9iBX++IXYRP3sC8Tbnrc18zPdj4XXkcJv4NFtNGOgt3Itz1w440bN858t6jGFAKJe8nty/QHJhfkbyBEIf01xqPTSZjXeWGnqn1HX8U9/L/GlU1mr74bq3JPhCY+JKIdH1Lyd+n4G1FZ50WNsMWHlPydGrXoLGFUZh0NwrQhe3Wnat/EPdq6LF20OO15onVkD6JN2Jy3QrV1WatLNDdYDhqyL+FB4l5u9kWIMIp7jKcKu+JSlkHb81ztc2W7Cl/FPRJb1JYz3MhfiwZhiQ8p+TvYyB5EmzDGhzIpXf4sh/hQc3O4k9FUwVei5MNYUxbd2s9tPc/vSheyRsimJ5Me+Jl9cdqSVe/21HHjcX2hMuvD6Ly5xeHhdz/n654N9XXJM4NEcTPB/bIbQSDRHuRqX/JNGJ23Qu3ZMP/VOWmvEYWxL1TZJyNMdiPf9sCN98knn/jeKSDW3Kw9935A4p4oJXGP8crKykK9zos6+Ui+qP3sDd/EPfy/upo601xWpj33QkoQ40Mi2vGhRbXlSv4uEX8jKuu8qKD4UOvxoQ3WW9+EGQl8JUi+jDU9jzMpi870vMSFZb5BcOjXr198PxsCZuyRA9kE3VywjWvdnjpw132jTCEJk/NWqA3Z1+26dtrzRXEzwfNtN4pNMnvQVvuSb+S8pbYvEviCY19SzZEw2Y182gOvfdlmm23MgsW1vt57w4pV47c1eJxp8kBbg8fZJg94/YMNd9jE7HHagb7t6SlEmMU9xps6dWqo13lRJl+V1ZNGvOGrfY41G4l7ISWo8SER7fiQkr9Lx99I1SVAdqOwKD4U3eRvtegsMUq5zLqioiIeWHN72hCAI3BGAC2TvSFaC+a78cozaK1Sqs5bIcQ9gndrde2S9hpRnEzwKJLKHrTFvuSdmFHlXg72ReRGKfobdArIlz1ItC9HHXWU/y8gFpO4J3FPBBQ/xT3GE8EkTG2T21dXtWn9lmnyRX1tfdpzRPaUor8WRRQfWt2+dKhOXjUmWkdtwKONxL1ox4ck8JUQpe68URbufb3ZBt2SBfMLjZy35IvDEtCUQhMsQlCPMunsQRBEvsaGOrXljKjzFnRK1d+gU0A+7EGx/I2Kqmpfg8crGhtVuSdEG1i4cKGv4l4p2OcwEiZxj8rqyqpKXyurly5ekvY8kR2yB9FA8aFVKPk7N5T8HXGU/B35+JAEvhJBzlvrZBp0k7gX7Mq9TBeHonDBoqhU4+RiD4ot8q1satKeexF13oJMKfsb3k4BYfQ32mWQmJFL5mdt/Qrf23KyelVbThE16urqJO6VGGET9wrRNrm8vDR8iUJRyv5alJC4twolf+eOkr+jjZK/ox8fUkS8BJDzlpp0QbegiHsE69WWU+JevlCwKLNqnFztQTFFPoL12QTfs7UvK5ubsw7utLXtb1va5kXZeQsqpe5vJHYKCKu/4Vdbl7Kycl+Dx9iXutoV2nNPRI7q6mpV7pUQEvda9/+6rtO1iH+VaFHq/lpUkLi3CiV/546Sv6OPkr9N5OND0e6hJuS8ZYgLugGBNXC/ByXY1tRQZzqv01N77qlyLy8oWJRZNU4+7EEq++InlVXVvol7BJ+W1dabChOTuCcsChZFx9/wa8+G9uU1vop7JA+sbGwymwzok3VlSDbJA0IUmm7duqktZ4kgcS+5fZ7+wOQi/mWig/y1aKDk71VI3MsPSv6OPkr+XhJpcQ8k8EWYfDpvjDV8+HD785AhQ0yPHj1aPW/evHlmzJgxeTuvmEG35uZm+/OMGTOCEWwrK/NV3IvFTJsqa9SWM5wUIliUb3uQeF4hqnF69+6dF3uQaF/KysriY/pGmb+Zn7FYs+/iXmNDoyr3Sszf8NNuxPhHFwAC72/4tCH7lFEX+CruYV/aVVb4Ku6tbCrsXqpCFMo+19bW2rVeVNd5YUDi3o8o+aK0/bVCrPPCTiGSvxUfKh0KlfxdCuu8oKLk7/GRFvdAAl9EyXdmFntRTZkyJf770KFDWz0P45rP84oVdCPYRqAN+vTpE4hgW1X7jr45bwTvaurqJe6JvNqXfNuDxPMKQT7tgde+zJw503Tr3tOEua3LGh3a+yrukTywoq7BdOveWW05S8jf8NNukNgQFILsb/gh7mViX3IV97AvE2bP9U3co63LogWL0p4nRBjtc2NjY9xWRnWdF2Rizc3ac+8HJO75Q5j8NVH85G/Fh0qLQnUKKJV1XiBR8reJsrgH2oMvguTbeWO8Qu0ZJZJTVl7ua+Zn48om3yv3CGRmWxattgvBQm1dokE+9mxoX1npq7iHfSkrN76Ke0r0yw3Zg2gTNnEP+5LJ6jWXPT2bm+UPi3Ag+xwuGlbUtmn9lq19bsv6rS32ua3rN4l7/iB7ED38Tv4uVHyoLes3xYeChexLNMg1PtSxQMnfbVm/1Ua8cs+hCr6I4Ye4x3hVVVWmX79+aVsmeB/Lx3mFhtfLHjhk05NJD/zMsaBl1ee7rUuHdhW+Om8sDutq6kxzWZmctxKxL/m2B4nnDRs2zPhNPu2B175stdVWZsHiWhPmDdlnTbzdV3EP+7Kottw3543F4dJFi9OeJwq7mPPTbsyePdsEhaD7G2EU9zKxL7mIe9iXDdZbP+35QoTRPldWVprtttsu0uu8QBOL+S7urWhslLhXgvgRH2pqaorEOi/M+J38XYj4kJK/w0+Q4kNBWueFjXzEh9LFhkDJ3/4igS9C+CXuMR7jZNJehd7I+TyvGME29sNxe+CAOwZBCLr5tWdD7Wdv+CruEbyLNRuJeyVkX/JtDwptN2hNnC97kGhfBg0aZO55YFW/+DCKe+nIh7iHfTn87ud8zfysr1uR9lxRuGCR33bj7LPPNkEg6P6GxL3k9mX+q3OK8jcRwm/73LFjx7Q2Naj+WhSoqKr2VdzD/6utX2F69uimyr0Swq/4EHtdhX2dF3XCEB9S8ne4CVp8KCjrvLCh5O/oJH+rRWeE8EvcY7yKimhrwa0F23j/3B45HOMxzglau9J8bchelkFP5lzburSvrvK17UJ9bX36FyGyplTbLiA45MMeJLMvhSZs4l4h2rp0qG6f9nxRuGBRKRB0f0PiXum2dRHhp1T9tSjQLoO1dq72uays3Fdxz5iY2nKWSPJ3WSaBA1E0whIfUvJ3eJG/EQ3CFh8qz6ByeVkJJ39L4IsICxcu9E3cY7woky74HpSgm5/OW6H2bKisqvR1z4ali5ekPU9kRyk7byQ25GoPJO4FV9zDvqzVtUvaa8SPKFiUvuo3F3tQdH8jZtSW8wck7omwUcr+WimQj+SLNTt28E3cI3hXV7tCe+4FCCV/lyZhig8p+TucyN+IBmET95T8nR4JfBGhrq7ON3EvyovDTIPvRQ+6hdx5K9SG7OXl0f2sFoNSsweJtG/fPid7IHEvuM6bsy9KQM4OBYvSV/3mag+K6W80NtRpzz2JeyKElLq/FnXyVVndLoPM91z2RF3Z2NSm9Vs2e6KKzFDyd2kStviQkr/Dh/yNaCBxL5rJ3xL4IkJ1dbXEvSwz67MNvgdJ5Aub81YIcY/FYdd1uqY9X2SGnLfUpLMHEveCL+5lYl/EjyhYlFnVbz7sQbH8jZVNTTkFjzPJ/MwleJxt5mdbgseq3BNhQ/5atAlT2+R2lRW+insrm4LRQScMKPm79FB86EeU/O0P8jeigcS96CZ/R3tjtRKiW7duqtzLMLOeoBm0ZU8sF3QDrgX3e3NzsykEct6SLw6nPzC5IH+DqCPnLTNS2YMg7LlHsF5tFyTu5QsFizKr+u3du3de7EGifSmEDWEPKD+DxyubmyXuCZFH5K9FmzCJeyRfTJg91zdxj+SLRQsWpT1PrELJ36WF4kNJkr/XVPJ3vpC/EQ0k7kU7+VsCn2izsZ43b54ZM2aM/XnIkCGmR48eOZ0Xi8UKklnvhLgZM2a0KfieGHRjPI75ff8g5+1H1NYlnM5bvu1GsWnNHuRiX/JJU0Od6bxOT/VUj6DzFsVgUaoKtTDZjXzaA699YYy1f9LL+EllVbVvwWP8l2W19abCxFS5J0QJ+muFWCdFibCJe1RWT7jpcV8rq9sVJpc2EhQr+TtM/lpUUHzoR5T8HVx/IyrrvDCj5O9oi3sgga/EycVYY1ynTJkS/33o0KE5ned3+yky6zfffHMbJIM+ffq0Ofjugm4E7xivsrLS98BbrLlZbTl/QOJeeDOz8m03gkCiPcjVvuSNsjJfxT1idWHZMDlqzlsUg0W00Y6C3ci3PXDjjRs3zny3qMb4Spm/mZ+xWLPv4l5jQ6PacorIk29/jfHodBLmdV6UCKO4V4i2yRust37a80W04kMiNRL3fkTxoWD7G1FZ54UZJX+byMeHovNKRNaozDpcNKyo1Z57ct58Q/YgelS17+ibuEfwqaauXuKeyAjZl2iQa1uXjh3a+xo8JnlgRV2D9twTkcYPcY/xVGEXDCTuJU/u6tCxQxH/MqWB/LXwoOTvH5G45w+yBxFDyd8myuIeqIKvRMmHsaYsurWf23qe35UuZI2QTU8mPfCz25OvLSXmXOvG43rfM+tjsayC723J/GxLZU1bMj/bmjkh5y28meB+2Y0gkGgPcrUv+aKsvNzXzM/GlU1mr74b+1q5RxWUKvdKw75QZZ+MMNmNfNsDN94nn3zie6cAv/dsmDXxdl/FPexLWblpU2VIppXB6jIooijuMV5ZWVmo13lRQOJeavs8/9U5RfzrRJ8gxodE6uRvUHwo+8pgUXh/IyrrvDCj5O8XIy3ugQS+EiRfxpqex5mURWd6XuLCMt8gOPTr1y++nw0BM/bIgWyCbi7YxrVuTx24675RPt69MRVV1b6KeysaGyXulSCFzgTPt90oNsnsQVvtS5jaunRoV+GruEfyQF1NnWkuK1NbzhKwL6nmSJjsRj7tgde+bLPNNmbB4lXBnLBuyJ5O4MtV3MO+LKot903cIzlp6aLFac8TImziHuNNnTo11Ou80BMzasuZg30W0YwPieImfys+VJr44W+k6hIgu1EYlPw9O9LiHkTzVYmklHKZdUVFRTyw5va0IQBH4IwAWiZ7Q7QWzHfjlWdQLZML7SoqfM38rK1f4XvlHqtXVe6VZiZ4FEllD9piX8K2Z0On6g6+invYl1izkbgXUkrR36BTQL7sQaJ9Oeqoo0yYxb105EPcw75k4ovlsqdnfV3yPUSE8BM/xT3GE8WlsaFOe+5J3CsKpeivRQG/k78LFR9SZ6dgoTbgpUuu8aHKAiV/t2X99kUJiHsQ3VcmVqPUnTfKwr2vN9ugW7JgflTaupSVlfvqvBG8q6tdobacJRIsQlCPMunsQZBFvnxtyJ6Jhptr29/21VW+Om/1tfXpX4TImlL1N+gUkA97EBR/I2ziXib2JRdxD/vSoTp5iyEh/GLhwoW+inulYJ+Dzsqmpjat37Kxzyubm9u0fmuLfW7Ltgqq3Cs8sgfhxe/k70LEh5T8HSyU/F26KPk7GkjgKxHkvLVOpkG3oATb/NyzYc2OHXwV91gcrmxs0p57JRIsiko1Ti72IIgiX77EvULt6VlZVenrnp5LFy9Je57IjlL2N7ydAsLub0jca92+rNW1S8H/FkLU1dVJ3CuBYL2f4h7+37Laeol7Ik4p+2ulQBjiQ0r+DhZK/i5NlPwdneRvCXwlgJy31KQLugUl2Ob3huztMmhrlYu4R+Znu8qKrILv2WZ+rmwKRoVUGFCwKLNqnFztQZBEvrCJe4Vo61JeHhxbHgVK3d9I7BQQVn9D4l5y+1IC3adFAKmurlblXsSprKr2VdzD/4vFmlW5Jyyl7q9FnbDEh5T8HRyU/F2ahC0+pOTv1ES7h5qQ85YhLugGBNbA/R6EYJvfzlsmi8NcxT3aukyYPdc3cY+2LosWLEp7nliFgkWZVePkwx6ksi+FImzOW6H2bOi6Zte054vMULAoGv6GxL3S3LNBBJtu3bqpLWfUKfPfPq/Rob2vbTkbGxrVljMEyF+LNmGKDyn5Ozgo+bv0CGN86Ipn3/E1PtS541omzEjgizD5dN4Ya/jw4fbnIUOGmB49erR63rx588yYMWPydl4xg27Nzc325xkzZkjcy4O4x+Jwwk2P+7pnQ7tVfzIRkGBRvu1B4nmFqMbp3bt3XuxBon0pKyuLj+k3YXTeCiHuYV+mPzA57TWisP6Gn3YjFosF4s8ZVH+DPaC0557EPVGa9rm2ttau9aK6zgs7+Ui+mDXxdl/3RF1R12C6de+sPfcCTFj8tUKs86JImMQ9JX+XZvJ3KazzwoDiQ9FM/pbAF1HynZnFXlRTpkyJ/z506NBWz8O45vO8YgXdCLYRaIM+ffqoci8kG7JvsN76Gf6lRSHsS77tQeJ5hSCf9sBrX2bOnGm6de9p/EbOW272RRTe3/DTbpDYEBSC6G80NdSZzuv0bHPwOJPkgVyCx9kmD3j9gwGn7Gc23mWLtNeock+Uqn1ubGyM28qorvPCSr4qq9MJfLna57Jy46u4p9ht6fhrIvrinpK/S7NTQKms84KM4kPRTf5Wr5kIkm/njfGKtWeUiIbzVghxj8Vhh44d0l4jckNtXcJDrLlZlXs/IHHPH2QPIkZZma/iHsFZiXtCFAbZ52gQprbJ1Z2qfRP3SL5Yumhx2vNE68geRBvFh1q3L2t1UTJnIZB9CQ8S96IdH1IFX8TwQ9xjvKqqKtOvX7+0LRO8j+XjvELD62UPHLLpyaQHfuZYEPbfk/OWenE4/9U5RfzrRJ9s7Uu+7UHiecOGDTN+k0974LUvW221lVmwuNb4ScOKVeOrLWf0nLcoL+b8tBuzZ882QSGI/kZV+46+BY/xX2rq6lW5J0RA7XNlZaXZbrvtIr3OCxthEveorD787ud8rayur1uR9lxRmPhQU1NTJNZ5UUDxoeT2RbGh0ooPBWmdF0SU/B1tcQ8k8EUIv8Q9xmOcTNqr0Bs5n+cVI9jGfjhuDxxwx6CoIl/MqHIvh8WhKLx9ybc9KLTdoDVxvuxBon0ZNGiQueeBVf3ifSMWk7gXUectqsEiv+3G2WefbYJAUP2NsvJyXzM/G1c2mb36buxrW05anqotpyhl2mqfO3bsmNamBtVfiyJhE/cK0TZ53a5rpz1fFCY+xF5XYV/nRQGJez+i5O/CE7T4UFDWeUFFyd/Rjw8FvkXnE088YXbZZRfTqVMn07VrVzNw4EDzn//8J+Pr2TD88ssvNxtttJFp3769/X7FFVfY41HDL3GP8Soqoq0FtxZs4/1ze+RwjMc4p1jtShsb6tSWU+JeUSjVtgsIDvmwB8nsi99UVFVnFXzPtu3visbGrIM7bWn729Y9saLsvEU1WFQKhMHf8KutS2W7Cl+Dx9iXupo67bkXIrTOyy+l6q9FDYl7rft/a3XtUuS/TLjwM/m7rKwsr/cq2oCSv+Mo+bvwyN8IIUr+NlGPDwVa4Lv//vvN0UcfbWpqasyNN95orrrqKjN16lSz2267menTp2dkdA455BB77Z577mlGjBhhDj/8cDN8+HD7nSzfqLBw4ULfxD3GizLpgu9BCbqtbGrSnnuq3Cv8566Eg0UkNuRqD4ol7tl7zSAxI5fMz9r6Fb6Le6xeJe4FBwWL0lf95mIPguJv+LVnQ6fqDr6Ke9iXWLNpU2VINskDIj9onZdfStlfixIS95LbZ2lK2aHk72ij5O9VKD5UeORvhBMlf8+MtLgHgS3LWrRokbnoootMz549zZtvvmnWWmste/zYY481ffv2NRdccIGZNGlSyjEeeugh8+qrr5rzzjvP3HbbbfHjVPFdcsklZsyYMeaXv/yliQJ1dXW+iXtRXhxmGnx3QTcoVvssgvXZBN+zbeuysrk56+B7W9u6tCVzQs5b4Sk1e5AIVd+52INiinuFaOvSrqzcV3EP+1JXu0KVeyUSLCKBKwpVv8n20AuTv+HXhuyTRrzhq7iHfWlfXeWruFdfW5/2HJEerfPyS6n7a1FB4t4qlHyRn+Rv9qPyKz507rnn5uEuRa7J3/hjig9p25ZCIn8jvBQi+btnj25K/i4igU1Tfeqpp8zSpUvNaaedFhf34Kc//andu+jll182X331VcoxRo0aZb9ffPHFLY7/5je/MdXV1fHHowCvR+Jedpn12Qbfi51ZX1lV7Zu4R/BuWW29xD0RR85batLZg6iLe9iXNTt28FXcI3lgZWOT2nIGBHUKyKzqNx/2oNj+hh/iXib2JVdxD/tSWVXpa9vfpYuXpD1PpEfrvPwhfy06wXrtuSdxL18o+Tv6+J38rfiQSET+RrTJNT5UpuTvohPYCr63337bfqcdZyIcozrv3XffNb169Wr1evZy4fH111/fbLjhhquJYdtuu619PB3ffvut+e6771oc++ijj+z3mTNnBqZ1EhujpxM8U0G70ieffNLMmDHD9OnTxxx44IEtxuPxzz//PE93m/we/HwO/laIuvvvv7/9feLEieazzz4zG2+8sRkwYID55ptvMhqHc3GaCbrx3Y23or7OLPpmjokZf3rSl5U1m8Xfzkl5ztzZ08x/Jo83HTquaXbc70TT1FCT9praZYvMuy+NNNz2Sfv0Mz9dp4v5eO6ilNc0xZrNfRM/NO/PmW+2/1kPc8yALc2sb9IHvZoamsxbD/zLVK9ZbXYevKdpqmkwi2pazq9EahYvN1NGv2Lql9WZbQ/vb7qu180s+rz1axrqG8y0adPS3ocwpr6+Pul8S2cPMiEKNoP/I3Pnzm2TPcjEvjQ2rPDXZpjWbQbB7Wlv/sN888VHZr0N+5o+O/7cLP3+y7TjJdqX6ROGp7UV3y2tMX+e8LZZXLPC/KoN9qWystJsdcB2ZslX36e9v6+nf2GmPvNOVvalvqZONiNDli9fbnr37t0me5CJfYmCzaiqqrJdItpiD9LZF5KUCm0znH+wonaZ2XrXI02XdXqk9SlS2ZeVMZN0/k+e9T8z8uVppkunDua8g/ubpfVNZmkb7At70iTzEez9NTebD8a/bebN+Mqs36dX1vZljfZrBMZmzJo1y37nsxE2tM7Lj8+WD3+tUPbR7/F5j/yam9x7Tc1yX+1vc2O96di1e9brt2zsMzb4kjEvZ71+y9g+e+xvNuu3TO2z1nnZJRz55a+5x8Lus4XdZlRVtW/T+i1Q8aGYMdOfeS/r9Vum9kU2Iz+xoXz4G7IZxbcZyWJD+YoPVZXXtnn9lql9iTU1m/V7Z79+q87QvgTJZrRpnRcLKIcddliM2/voo49We+zZZ5+1j916661Jr1+wYIE9p3///q0+fswxx9jHlyxZkvI+rrnmGnuevvQe6DOgz4A+A/oM6DOgz4A+A/oM6DOgz0CQPgPjx4+PhQ2t84r/udGX3gN9BvQZ0GdAnwF9BvQZ0GdAnwF9Bkwk1nmBreCrra2N74GUSIcOHVqck+31iWN4W4AmQjtPtxeKg9ahn3zyidl6662Tji9KC9T1I4880owfP95suummxb4dIUTAkc0QQshmiFwgo5Ps6b322it0b6TWeSJMyGcTQshmCCHkZ4ggr/MCK/DRcjJZOSLlw95zsr0+0zGge/fu9iuRXXfdNeV1ojRB3GPPHCGEkM0QQsjPEH6y/fbbh/IN1jpPhBGt84QQshlCCPkZIojrvPQ7yheJnj172u9ff/31ao+5Y+6c1ujWrZtdPLZ2vRuDyr1U1XtCCCGEEEIIIfKH1nlCCCGEEEIIkR8CK/D179/ffp88efJqj7ljO+20U9Lry8rKzI477mjmzp1rvvjiixaP1dXVmQ8//DDl9UIIIYQQQggh8ovWeUIIIYQQQggRcYGP/czWXHNNc++999o97xxffvmlefzxx83ee+9tevXqFd/H4eOPPzbz5s1rMcZJJ51kv//lL39pcfzOO++0Ip97XAghhBBCCCGE/2idJ4QQQgghhBD5IbB78HXt2tUMHz7cnHXWWWbAgAHmzDPPtPvp3X777bY675Zbbomf+84775h99tnHnHzyyWbkyJHx46eccooZNWqUvWbJkiVmzz33NFOnTjV33HGHFQiHDBlSpFcnosa6665rrrnmGvtdCCFkM4QQ8jOEaB2t80SY0DpPCCGbIYSQnyGCTFksFouZADNu3Dgr9E2fPt1UVVWZPfbYw/zxj38022yzTfycV155pVWBD5YvX26GDRtmxo4dayv8evToYQYPHmyuvvpq06lTpyK8IiGEEEIIIYQobbTOE0IIIYQQQoiIC3xCCCGEEEIIIYQQQgghhBBCiBDswSeEEEIIIYQQQgghhBBCCCGEWB0JfEIIIYQQQgghhBBCCCGEEEKECAl8QgghhBBCCCGEEEIIIYQQQoSIimLfgBBCCCFKC7b/LSsrK/ZtCCF8xG3z7d3uu7xcuYVCCCGEEFFGaz0hoo3WecFDq2whIkpzc7P5+uuvi30bQogSdPa8AX0vr7zyirn11lsl7gkRIZLNd0R8vhD13BesXLmywHcohBDRQus8IUSx0FpPiNJB67zwoAo+ISLK4MGDzbhx48wXX3xhevXqZY/NnDnTvPTSS+a4444z6667rg2yEXBTJY0QIl+0Zk9cFuecOXPMhRdeaNq3b29OO+00U1EhN0SIKM75+fPnm08//dR88MEHZvr06ebzzz83y5YtM+uss47p06eP2W+//Uz//v1Nt27dinLPQggRZrTOE0IUC631hCgdtM4LD4qsCRHgrKi2tLJCtGvXrp3ZeOONzaabbmqWL18ef+zf//63Da736NHDHH300fY8IYRIZYe8lTip+Pbbb80nn3xi/ve//5nOnTvbAH5lZWX8erLNTznlFPP++++b3//+92aLLbYw++yzj1q4CBFiampqzMMPP2yFu4EDB8b9it69e5ulS5daf+NnP/uZ6dmzp/VJ5s6da0aPHm3+8pe/mAMOOMDcfPPNpm/fvsV+GUIIUTC0zhNCBAGt9YQQqdA6L1yUxZLVWwohfINAN1PPT4GN53ACYX19venQoYN57733bMb8ddddZ6666ipbzcdju+22m1l77bV9uxchRDTBfmBH7r33XvPOO++YFStWmOrqamvf1l9/fTNs2DBz6KGHtrBJH330ka0i3mSTTczIkSNNly5dJPIJESBmz55tBfozzjjDXHrppSkrbTl3s802s2Ldo48+arp27WqPI95/+OGH5rHHHrM+RmNjoxX8sQ/YgmuuucZcf/31Zu+99zb33HOPFf+EECIKaJ0nhIgKWusJES20zosu2oNPiALjKvNSiXtTpkwxO++8sw2sNTQ0xK+jOq+1zUxbg+cgU56WeIh7VM1QPUOAjcAajx900EHmtttuMwsXLszzqxRChIHHH3/c3HHHHTY7y4GdmTVrlhk1apQN8CPQDRkyxDzxxBNmyZIl8eAVvPXWW+a8886z7fd+97vfmQkTJpixY8eaU0891Qp5l112mXnzzTdb2CyqdbBFzzzzjP3yPiaEKD5VVVWmrq7OfPnll3EfJBkIeiQOEQCiBacD0Q97gc/RqVMnK+TzHd8DwfCPf/yjueSSS+y+nNghr10RQoiwonWeECJIaK0nhPCidV50kcAnRB75/vvvWwTKW4NWdS+++KLNjicY7gLq3uBWbW2tmTp1qpk2bZqtiHHXIQq6Nnnu+1dffWX3uXHnuTG4ngqZc845x/6OyLf55pvb/W422GADM3nyZBt4p/KG9llCiNKC9nmIcrTuJfBOUKqpqclW3FBh96c//cm23OzYsaO1RYMGDTJHHnmkPeaqg2m7R4s9xsDWDBgwwOyxxx7mD3/4g7n88sutyPfqq6/GbZYT8hAN11prLTNmzJiivgdCiNVhbuIXIPDhj6SCijyqdWnNu2jRovhx13bzs88+s9+xLQ7np2BPYOLEifZ7W9qSCyFEodA6TwgRJrTWE0IkonVedNFKWogccYEqgtzrrruu+cc//tFCtPPiAlwff/yxefnll+OVLQ4n2hE0Zw+9+fPnx4NrZNE/+eST5sYbb7TjkPnOnjcbbrihDaiTDc9zujHWW289G6AjC98F2+666y7Tr18/uy8fFYK77rqr2WijjVK23xJCRNNmPf/88zYgP3jwYPs7toNq3hdeeMEKfjfddJPdK+v+++83zz33nPntb39rxTrEOzcObfV+8YtfxPfaQ8DjOF/bbrutPeaqegjeO/vUq1cv27aPamUWnwrsC5EcJ4wzV2h1mev8x1fwdgRIhIQgfIuvv/46Pn+Tncvcx9dYsGCBDX4nCnwkIYF3jrufuW6HHXawSQOIiUIIETS0zhNChA2t9YQID1rniXwhgU+IPBlkAlWUO3/33Xf2dxfIbjHhfghqEdACgmde3DXsh4dYOG/ePLN48WJ7DFHvkUceMVdccYU5++yzbVsrWm0++OCDZrvttrNB97vvvjs+BtczjjdoxjEEPYL6tNPy3r8QojTADjHvqbojOM9eWW4h2LlzZ3Pttdea1157zQwcONAKcWR5kXRw5pln2nMQ5bxjecH+cIyvp556yh7bd999V7sHqgK33357Kx5Mnz7d51csRHhhbjKvTj/9dJu4w7x1x1OBgNfaOa5FuLcjgPMD3PnYBToAfPPNN/G2vK35NECCEAlJJA59++23LVp0gvNBWruejgJU/yEOcr0QQgQNrfOEEGFDaz0hwoHWeSKfSOAT4gfDSgXeXnvtZWbOnJmV8OUC3AS4+Jm9qNKd61piutZViUFyguw9evSwQpzbH4+A+DbbbGNFxHfffdfuh8XeVyeffLKtxGFMKm1cxR9jujGc6Agcgy+++EJ/eyEigKvIyUasJ5j+0ksvxSt4XfC9ffv29pirxPOOiV3iXGwRpKq6+/DDD62Nokp4l112afUcxENvhY8QpQRiGC1q2R83lWDnjiO4Ub3n3TPXzf3WQMBLnKP4B5MmTTJDhw41u+++ux1z7733NiNGjLBCnjuf7/gUJBh5q/KS8dOf/tTeC0lJ3mPeRKbWBD7sDd0JaDFO1aAQQviB1nlCiDCjtZ4Q4ULrPK3zioEEPiF+CDzNnTvXvP766/HgmTezPVkmvPc8glm0tXPBrFTBbzLWCb4R2Pa21XQQROccqva4LxdkpwKP+6CyxgXPXNvPPffc0wqG3oo9AugE9LxiHvvvgRMykwUHhRDZw1xtTRzzE1eRk6zCpjUIrM+YMcP07t3b3mfita4Sj+/O9o0cOdLam2OPPTZl9RBtgS+99FJrW2gdvOaaa7Z43L0v7rirUhaiFHBzhyrZX/7yl+Zvf/tbi+OttdKErbbayv7MXnfeOcrcTwQh8J577rF73CGgufFotXvWWWfZPXgR8A4++GB77vnnn29OO+20FmPgP/AYVXzpIHGIqj8EPueTcF/du3e3Pkxrc9y9XvdYuopEIYRoK1rnCSHyhdZ6WusJkQyt81q+D1rnFRYJfEL8sPC78MILzX//+1+7N13iY95MePbAaS3g1aVLF9sSk33zXECtNZyxo+UdwbDEsVzwG4EPnAgIP/nJT1qIdO7+YPPNNzc1NTVm9uzZq2XVu0pBNy6v5aOPPrK/a/89Idq+sEsU/5mPXnEsmzHdd29QP901iPdPP/20uf32220L31S2xwvXUQ3MPp6p9vTiOXgtiApU+WCDhgwZ0moCg3sN1113na0S+t3vfmerg5Kd58agegcU4Bdhg4QeV7WfqaDv7AKttX/zm9+Yww8/vNX/xU68Q4z3trxEMHPQLpe9LKnGTbz2gw8+sLbBJS1xDLty3HHHmVtuucV2LRg+fLjdCxhBnv2DEf68oh3PzdxPZ4/wfbp27Wr9FfwQB50N8HPwi8A7DvfDnp5UMB522GFWDBRCCD/QOk8IkQ1a62mtJ4TWeavQOi88tIwmCBEAXOWLC5LnCxeQTzYuwpcT1bwghD3++OM2gDZt2jQbcNtwww1t5j2Z8LSrI2jFcYQ3Aue0xPSKcImvj+cnWMe5VNzxvO64e+1cz5gEDxmf4B8BMNrkeQU7r5jH9bNmzYof4z7Jqv/000/jxxiX53v77bft7wTjyLin+lBinxCZB+gTBTwC2VTF/ec//zFTp061vz/88MNW/E+EOeedb94xvRU5zi4kgp246qqrzKhRo2y7TRwvWunddNNNttKuX79+KV8DQX9sQ7rAvavgQ6yj6hcRwCUOeO/L2cCxY8eav/71r+boo482F1xwQfxeve9VohCCTfO+B0IEDdpXMpepOkXgdm1o2UcSERtB2/2fToWbJ0B7TFe9lzhHsA9vvfWWmThxovUTeJ5TTz11tZaXVOgj8iX6BDxHnz594oIge/cB4h5fiRW3tNFFzMPPQXikXSaCHV88PyK8a83bGuzTiSCIGEgSlJvTVAhzf/hE/Oy1bfgq7CnMa2dfYcYQQkQfrfO0zhMi6Gitp7WeKB20ztM6LypI4BOBIzGYTXUJi0GXxd4arvLDVc8k4oJn3uASwXCOExjnMYJmtL484ogjbNCJ6ha47LLLbGCb/WqOOuooe4ys+EsuucTU19fb7+5cWl4R2CeghpDWWus7B8Eugndz5syxwbXEoDftOKurq+1YbgzuFZHvk08+iZ/nHkPM43yvmEeLrTXWWKNF8I/9tQ455BBz3333mWOOOcYG7RD5yODv379/0vdYCGHsnH/llVdstS9C3scff2yD2lSoEJTHTlEVR0Cd+ZdMQEsUAhiH+cv1d911l3nqqads4J097C666CKz9dZb2/OcsEawHyGPx37xi19Y4QE7dfHFF9vEAx7bYostWhXX+B1xD1GfCuJ0lXNU7jHe5Zdfbp/LK1KA+50ECOwl93rrrbfGbXaiDXTX0rLB7S3a2nlCBAX8BeYWn20n8JEow769W265ZXxOu7mULDkpsZ0mPgBCIX6El9GjR9uuAsxRxPoHHnjATJgwwc4R9nRA1Od/O/eAmIafgK/EvHZz3FXE8f8fgdDBuQjx2BgSmNinl/OxX4h5+AMIfG58kpCwRakEPmwXwj8JDsxrt79m37597XfsJfMcUY8qP9qTkjiFcIh9Oeigg9r0dxFChA+t81ahdZ4QwURrPa31RGmhdZ7WeVFBAp8IFIhdL774os2GJxBEwAqhjADXH/7wh6TZ8ekq/QheIXzde++9VpwjqE1QjEx1Kl6opnOBM56XoJMT7Qho05aOQDtBdAJoBNVPPPFEc/fdd5sDDzzQ7LjjjvGMfDLqCWDttNNOSfe2AhcUJCAGTuBzAXyqfgjikQ3v7oVjBNx4nxLH433q1q2bfW4HGfVci+joxuZ100KP69555x0bcNx2221tpr4QIjWIegT4CWgjltMal8oTEgROOeUUc8YZZ9igDa3pksGcJokAe0JlHAH1HXbYwYoFjEVAHrEdO0UQnH2zHnroIfPzn//c2gbsI8dvuOEGK8w7CKAjtp1++ulWOEDgY863tkcX8x2bgb3jfgjotwYVPQh7+++/v7XB2KnE8fidMajY4/sdd9xhxQVeB68Np5n3i/bHziZi2999911rsxAfUiVDCFFs+F+KH8L/dwef8ZdffjkrXwQha8GCBeZf//qX/X9Oi0zm2PHHHx+v+qe6HpGeecHjrjXnsGHD7PNRDcecQoDj/zlzCGGOgBT+iZvz3B8CoTfpB5+CfTGpxMVHQbRkbz/GYp6TJICYB9gEEoeYx65KONk8xc/A/6Faz7UEdYlMgM9BW1DERe4PP4b9Adn3L121sRAiOmidp3WeEEFHaz2t9URpoXWe1nlRQQKfCAxUfxCsxqkiME2Am6ARwSUCYOedd16rbS+pnpk+fbr9TuXMoYceupoQyJjsdUOVzAEHHGDFOgLhZM6TvQ4E1gnWE6gnKE3wnXN+/etfr7Y4XbZsmQ1yEWhDPHMCH4E4lwWfbE8eFxxzQa033njDfnf37L67++IfjoN7IuDG6yHQSEa9Gw9RgedGtHMQ4OO1utZXLvjIuXfeeWeWfyEhwgdz/J///Ke1Lflo+YswT/WJq3DB5owbN87OY0QsxHIq1/7+979bu3XllVdau+JsAfOVvbGYf9wTAXgC87T5e/75582AAQNsUJ/kA+YtVTsnnXSSuf76620AHRtDEB2BDBGRgDxCGbYNW/nvf//b2i/swDnnnLPa/Tt7gW1AQHjsscesyMfvicF7jlORhw0aM2ZM/P3DzvD8vF73O2Ij+2lRsYN4ceONN9pgPiIl4gACIckbbnyECH5H2PDelxBBILESj886c515xf9//hcD1Wok6TDvOYekH+Yq84Wqfy9U+TI/6RLA5x8Q9mnFSWKO82+ormd+YQe8Y1x77bW24g7BDpEQXwAfgGo5fA58AndfgCDHPXsr/rEn2B5sA4I9tgpbxr0xB/E7eE3sEcw9YmO4P1rXpJqnCIr4FfhEtBUnWQEQD7Fl+EjcL0kItA6l24F7byXuC1EaaJ2ndZ4QfqC1Xku01hMiNVrnaZ0XVSTwiUBAxdzJJ59shTqyvGmFSWY6ASZXvZYIWeZkohOsIsDMeRhrgtYErqgWQaAjiESw+c0337Tn/upXv7LBqMQqFIJcVOQQqCeAR4s9dw5Z83/+85/N66+/boPVnEswj8DU7Nmz42MQgOcaWlolC4a5oBaZ7fvss4+9L1rykclOgI5gPwH7oUOH2nPJsHcQzCPoT9CcgJy3SgixgQAggoELmCEWEsAXotRwc4DKN9pFYlMQ2hLP8e4F01qlWyLMe9d2zuHa5BGkJ/Du9uJkfzyqf7E/rgUec5IgF7i2eQTZsT3A3nUuOA6HHXaYtQFXX321FRYR+LB1DQ0NtooYG8dzcw+0yON87AJBdUj2mrATtCTGJmLfdttttxb2CjuHUEECAy30eF0soLE7JDnw+mgbCLwmxD2geofqQYL4JFPQ0hBbt+mmm7Z4/kceecQmU/AahMgHzGVaQTI/SBJqDe+c5/9rMtHfe5zPPP4AohTVs3xu3f527IPJHOLzv8cee9jPOVWszHnmIfPSzXv8B+YHyUvMP8BW4P8gptOqG2GPTgI8l9u707X5RUAjYQfBDR8AeK0IZ4j++C3YG5dMgE+Az+Ct+Gd8V0no9skD3jfG4HkR+JztwKfhnhHuUoHtILmKe0bMd2DXEDXTXSuEiDZa52mdJ0S+0VpPaz1ROmidp3WeSI8EPhEIqLChAoV9pc4999zVHndtKx0EvK655horutEuj7Z4BNDYU4asd6pmyKQn2DZ58mTzxBNP2Kx5zkscFxib6wnIPfnkk/HqOUBsc+PTTo9MfcYlCEb2PVUqDgLsBNRcm8xUFUMEwm677TYbDKO6kD13CK4RSOO9QETkvsmA90LFC89LIC8RWgQKIX5c9LlWuATQEfi8tiRTUS+VUMD1tLsk+E4VsWvhR8UxlTb333+/neOIXe55qaihYo55DMx1BATX5g4Y2+0pSiIAUL0DtOQD7BKVOAiEBOsZBzvl9thLB0F+bB7VhrQM9dorbPKDDz5ofybpApGRpAq3txfPhw10IgStj7mvdMF693ehQujZZ5+NCyVC5AqV7fx/PuGEE2wVnGs7660Qy3TO878XgZs5x+ea/9G0xWSOYEvc5xbxGtF/3rx59vf99tvPnHnmmVbge+GFF+z/ZCe4uUo6PvsOxDnmqnuMSjmX1OSu885Lng+Rzj0fc5+qOPwWRHd8E9cFgNdKdSHjkDjE+8H7A9wfx5nHiJP33HOPbTdOVS12zD0v1buMw/Xp4D7cfppCCOFF6zyt84TIN1rraa0nSget87TOE+mRwCcCAQEtstzJSqe9nMtudyS2cqLSD3GPADVVMg4y4MlaR4hjjyqC1oiBBKYJpIHLpveOC4yLwEYWPNUqQCCNVltU6fGcCAQucE5rPCCbP7ElFhn43udJBuIDLb8effRRW/WCsEdGP8FEgm2uCscL++bwJURYYR63FrzOx7hOwHPjOiGcQP0hhxwSf16yyZlvJABQYULwnmozBHS3b1QqvEIBAh/Pw3hU7wCt8thHi/adN998c1zgQ8BnrmNrEPO4H+wEbfawGXwlvi88Bi7xgCoewE4lu1fuw7XmTQW2EhECe3nMMcfEn5f3cdCgQbYCCduJreK5SGJoDWyf92/gbLX3y71vQLWTEPkEIQ7BGjEKP8IJfHzmnGCOOIcYzVxFnCapyM0h95l95plnbEcB/hfTDYCkHfasc+1vXQtucPPB2wbzuOOOs8+BaMa+mYhk3BOCP3PJu+cccxufxXUCQLDjHILhCG3YJHyYRFHS+R3MVxIAqPhnDiMwuntk3zuem3HcPpuMx15+JEDxGrGD2C6ux0aRHOV8JaAiOBNxTwghUqF1ntZ5orTQWk9rPa31RD7ROk/rPJEBMSECwOzZs2MHHHBArKysLDZo0KDYjTfeGLv77rtj48ePj82YMSO2ZMmSWHNzc/x8jnPuTTfdZH9vamqKrVy50v785Zdf2sd+/vOf298//PBD+/s555zT6nN7x3311Vdj5eXlsfvvvz9+bODAgbHq6uoW53M/xx57bGzNNdeM7bjjjrGFCxfGH991111j6623Xux///tf2tftfW4hSpXFixfH5s+fn/Ic5rd3nqebSytWrLDfP/jgg9g666wTO/nkk+PjLF++PHbrrbfG+vbtG9tiiy1iu+22m/3q2LFj7Gc/+1nsiSeeiJ+bCcuWLbPjr7HGGrEXX3wxfryhoSF25ZVXxqqqqqzNgs8//zzWrl272IUXXthijIkTJ1o7he1LfD1Tpkyxj1199dX2PQDsG8ewk3PmzInNmzcvNmvWrNjzzz8fO+6446zd+u677zK6/4MOOijWr18/e31r72UivC+NjY05269M318hMmHRokV2Hu+0007Wp3Ccdtppsd69e8dGjhwZO/roo2Nbbrml/eL/Op975p73f/uBBx4Y6969e+ydd96Jj8Hn/dJLL7Vz7qKLLooff++996x9+dWvftXiM33ffffZc3//+9/b37/++uvYRhttFNt9993j48E333xjbdCAAQPi10+YMMFee+aZZ672GvGTKioqYmeddVaL40cccYS9hse5jtdw8MEH2585/v7777c4H5vy5JNP2u9e/0UIIfxA6zwhShut9bTWEyIXtM7TOk+kRwKfCAwEoHbYYYdY586dbUCKr8rKSiu4bbDBBrF77703HoQbNWqUfdwrxHlZe+21bQCPYFlNTY0N5CHEpQMxkSD9tddeG6uvr7fH7rjjjnjgjOcjcEdw/ZBDDrFiQPv27WNTp06Nj3HuuefaxwnkCyFawvz95z//aYPum222WWyttdaK9erVywa4/+///s8Kcm0Vf3D8GIPxsCPHHHNM7Prrr7e2YJdddomfR1D9jDPOsKL/a6+9Fvviiy/ssbfeesteS8Dd3WsmcN4VV1xhbdWYMWNaPIZg8JOf/MSKDgh+PI494Xm9TJs2LdalS5fYpptuahfBXnivuOall16KH/v2229j+++/vz3ep0+f2D777GPFCmzS9ttvH3vwwQfjNiwZ7j3+73//G9tkk02sIMB76F4TYiJCRCphVYigUFdXFzv++OPtHH733Xfjx6+66ioriiHyIcQz1xDER48ebf9/H3bYYS38EOYUCTzA59599mfOnGkf854/d+5ca8f22msv+7sT4BHNtt56a5vsw30R3MY+JAr7jI3ot/HGG7c4jr1wgj8iHLYJERE/xvkj+DYO5u1ll11mbd1WW21lhT1ERV4nr9fdFyixSAhRDLTOE6I00FpPaz0h8o3WeVrnifSoRacIDLSjfPvtt81LL71kW9HRKot2drTQY68m9puildThhx9u942hZRUtPV37KgRr2tvRQpOWWrSDYRxax+27777mueees+022bMPOH/58uXmww8/NFtssYXdV4qWdjwH7a1oXUW7LM5nryn20qLlHsdokcdYtNektZZ3T53bb7+9iO+iEMGFVm9//etfbXs42uPRvm6zzTazrXBpf8ccZd66faIctJh77733bO915vuee+5p9t577xbn0IaX1ra0xDzqqKNs2zr2n2L/OOY5bTTdvGeucw+JcBw7c+2115qpU6e2aKWXCmwVrfAYmz05nU2iLSA2hfZ3fPH6+KJ9J+3/3P1wPTaNdn28RtqEYufYT4t9RWlDfOSRR8b37GNc9vx7/vnnzaRJk8z48eNtu05a9+644472i9aB6fYao70fz9+3b1/7vvH3oV1grvsTClEM+N9P61tabLo228A8Zk4yJ0aOHNmilSatMP/1r39ZX4M55fa/cy1nve1l2ScTX+HLL7+Mj0FLbq5ze/F62/bSIhz/4aabbrL3xVxjXjv4nTnIvKfFp7sHYE9e9vG8/PLL7Vi8NmwMc/7EE0+05zU0NFhfh3Gwp3/605/MDTfcsNr7wn17SbdPphBC+IHWeUJEH631tNYTwg+0ztM6T2RABiKgEEXDZc6TaU+lD23nqIihOo7MeCpYqLzxQuZ+p06dbNXL0qVL4220yKYng37w4MGx22+/PTZixIjYqaeeatv0uRZdZN1TBcO43vZ2VLGQgc/zqpJFiLZBS12qT0455ZTY9OnTbatMbzUJrS7ffvvtFtdMmjTJVqV069bNVrf17NnTVtmecMIJLc574YUX4m3tvJUtZIx36NDBPpZYuUIb3Ycffjh2+umnx/r3728r7Winybnjxo3L6DW5MceOHWsrhc8//3z7uryPUbnH+Nttt529d2/7THcOtmePPfawVczYLip4aBlKhRFVSVTjgOyPEMmh0p/5+9BDD8WPTZ482R6jXS64aja+4wtQ3efsDu1uOfekk05qYS/cz/gLtO+sra2NP0bbT7oGLFiwYLXzqaalOo8K265du8Y+/vjj+DluLrs2mm+88UaL+8N/wa78+c9/tm2DqTQWQogooXWeENFCaz2t9YTwC63zhEiNKvhE4KAKz2W28wVUzFFdM3/+fPP999/bbPj999/f/P3vf7cZ+b/+9a/teV9//bWtlKmtrbWZ8lTKUB1E5ctDDz1k7r33XjNx4kRbFUOmPZUrZ555ps0qBbLgX3nlFfvdS0VFhdl4442L8G4IEQ2oUKEaZbfddjN33HGH6dChQ4vHqUqjeqx///7xY1TgUeVHtds999xjtt56a1uVe/PNN5tHHnnEbLLJJuaaa66xc/nhhx+211x44YW2qgWo2mFuU/02duxYW3mz4YYbWvtCVS62gophjlHlQ+UctmPYsGG2MtdV12WCqwDGBlFN2KlTJ3st90DGmavGqampsfdE5Q+v2dk47plKmzlz5tjzgHvhNbrX4z3fC/fJY85uequOsiHZ+EKEBar0AF/Bsf7668c/18wRV2XHd2wLn3uqhGGjjTayc5nOAcuWLbNzGphPVPUzJ6m0o2KPuQlU4JGxztyn8u+H9vf2mosuusice+65dl4PGDAgXiHr5dhjj7W+iBvP3R/+C48JIUSU0DpPiGiitZ7WekL4idZ5QqRGAp8IDC4ghpiWiGuFidBHmyogCL906VIbDEe8o50d7e0ItN12221m4MCBdkyC63wnqD5ixAjbfpOAOUG8RHj+RHFPCJE7Tz/9tJ2HCOqIe4nimTcA744PHz7cCvSIeTvvvHP8XOb7Bx98YO68804r7hOUR7zDdtBCN3EcWlYi8H366adWzOM4wiDjXnfddeaUU06xgXyC7y+//LJt08d4PLdr2ZcOBDu+COQvWbLE/uwVE7BH2LG//e1vcefUC3aKdqU8P3bNJR14SSa+5audpsQ9EXZosY19QbBzbbYR3bp162aPIdAhvjv7QKvL6upq8/nnn9v5zjw89NBDzYMPPmiTBmjLDYxF6153/WeffRYX5PBJOI7/QaIAgqGbj7QLxueYNm2aOfXUU+39Jc43WojzJYQQUUbrPCGijdZ6WusJ4Sda5wmRGgl8IjB8/PHHNgBOII6AHEE6AuXsj8P+dwTR2DOH4wTQqOJjb6oXXnjBPPbYY7ZqZtCgQeaggw6ymfLgAvze74gBQojCwv6a4Cr0klWYcdxVkrE/JhU2VOAA1XB8EbQ/+OCDrZA/ffp0O6cR5nmMKhp+d1U0gE1xNobKX85DwNtqq63svn1ufz4gEM+efVTpkCyQqcCHQEgFHte65/W+RkQBBETuxQXzvYIaP2PTEAoWLlxoj6miTojsYC7jP2AHqJbFVjD3sCMcY356BT7mI18I827ekjRE9TC2gT368CcQ7xD16AbAsffff98ccMAB9vxtttnGHHbYYfG97rxiO+LhlVdeqT+jEKLk0TpPiGijtZ7WekL4idZ5QqRGAp8IDGTI03qPwDtiHuIeQXaq7fbZZx9z8cUX20CbN+jduXNn28JKbayECDYI8EDQHVK1v2R+U02DIMZ3d61rPwmbb765/U5lL1DxNn78eCv47b777i2qaNxzUsHnxtlyyy3N888/b1v8HnHEEfacRx991IwePdoKbQTwOeZa9CXD3Q9VebT/TWXfyGxFaEiWZNCzZ0/7WhES9ttvP1XUCZEltLVkjlGthw/hxH1abCPa0ZqX7E8n5uFDcD62wVXsUklLlfBdd91lK39JMsLe3HDDDdZuTJo0ydoYB22E+RJCCJEcrfOEiDZa62mtJ4SfaJ0nRGok8InAwP5atMwjU54KFgJztMAi2IYxB7fHlBAiXLgqPCpltt9++xYCnBcn4DPX2duK/Ry+/fZbG2Cn8o7HCcI74Y2gPOy99962cofAPNU0XAtTp04148aNsz8T4Hei3IknnmiefPJJ89vf/tbcd999Vszj+quvvtru3/X444/HRYBsSBQuqQKkEvHZZ5+1z3/LLbckvRYRgmSF3r17Z/28QohVFXMI6O+9954V8xxbbLGFFeqwJfzsPZ92usx5Z0sAv+PGG2+0rXwTRf4hQ4borRZCiCzROk+IaKO1ntZ6QviJ1nlCpEYCnwgMBPv79Oljv5KRrOJHCBFs2AcPIe3dd981Rx99dKvneAV8RDyqZBD9X3vtNfuzd3/OKVOm2Ao/2uO51p8E488//3zbOu+EE06w7XxfeuklK7IR1HcVdjxP3759zeuvv24r+L7//nsrCrDHJxU63APCX1tItFHs7XXkkUdaYQFRkX24koGISRWhEKJtsIceHQCogqUlp9vLkqpc5nVTU5P93SUXkEiEmI+tSAR7k66CVwghRGZonSdEtNFaT2s9IfxE6zwhUlMWa0uJghBCCJEFVLGxPyb7VE2YMMFW2FExg6PmhQq9N9980+yxxx62/eaee+5pBToq88j+5hoq8tjXiqo92uW5qjmuRbAbMWKErZYjy2vgwIFm+PDh5o033rCtY44//viM7jdf+9/xnLQdpg0ggqQQwl/+8Y9/mCeeeMJcccUVdp9N0H6WQgghhBD+obWe1npC+I3WeUIkRwKfEEII30GkY4/Nq666ygwePNhW87G/pgPhjmq3Bx54wG7SjnAHiH2DBg0yCxYssNV6tbW1tp0e4h57ZLGfViJUy1GRk0n1DeIgwf/EPf6EEEIIIYQQQmitJ4QQQgQZCXxCCCEKAu3xTjrpJDN27FjbxmXfffe1VXmIbFTcvfLKK3aPvssvv9ycd955cbGNFpqjR482H330kenevbvZZZddzM4772zWXXfdtM/p3dMv2b5/QgghhBBCCCHajtZ6QgghRHGQwCeEEKKg3Hnnnebpp5+2+2R99913ZtmyZbZ1J4LfySefbL8LIYQQQgghhAgXWusJIYQQhUUCnxBCiILhKuqWL19uvvrqK9tKs2fPnqvtxZeqlSaVffnYH08IIYQQQgghRH7QWk8IIYQoPBL4hBBCFGXh15qAJ/FOCCGEEEIIIcKJ1npCCCFEYZHAJ4QQoigg6rl99oQQQgghhBBCRAOt9YQQQojCIIFPCCGEEEIIIYQQQgghhBBCiBChTYyEEEIIIYQQQgghhBBCCCGECBES+IQQQgghhBBCCCGEEEIIIYQIERL4hBBCCCGEEEIIIYQQQgghhAgREviEEEIIIYQQQgghhBBCCCGECBES+IQQQgghhBBCCCGEEEIIIYQIERL4hBBCCCGEEEIIIYQQQgghhAgREviEEEIIIYQQQgghhBBCCCGECBES+IQQQgghhBBCCCGEEEIIIYQIERL4hBBCCCGEEEIIIYQQQgghhAgREviEEEIIIYQQQgghhBBCCCGECBES+IQQQgghhBBCCCGEEEIIIYQIERL4hBBCCCGEEEIIIYQQQgghhDDh4f8B6oxeu7Nw+lkAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 1800x720 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# ── Comparison bar chart ──────────────────────────────────────────────────────\n",
    "tasks   = [\"unit\", \"coda_type\", \"individual_id\"]\n",
    "t_labels = [\"Social Unit\", \"Coda Type (22)\", \"Individual ID\"]\n",
    "\n",
    "all_models = list(baseline_results.keys()) + list(all_results.keys())\n",
    "palette = [\"#AAAAAA\",\"#AAAAAA\",\"#AAAAAA\",\"#888888\",   # baselines — grey\n",
    "           \"#4C72B0\",\"#4C72B0\",\"#DD8452\",\"#55A868\"]   # ablations — coloured\n",
    "bar_patterns = [\"//\",\"..\",\"\",\"xx\", \"\", \"///\",\"///\",\"///\"]\n",
    "\n",
    "x     = np.arange(len(tasks))\n",
    "width = 0.09\n",
    "n     = len(all_models)\n",
    "offsets = np.linspace(-(n-1)/2, (n-1)/2, n) * width\n",
    "\n",
    "fig, axes = plt.subplots(1, 2, figsize=(15, 6))\n",
    "fig.suptitle(\"DCCE Ablation Study vs All Baselines\", fontsize=13, fontweight=\"bold\")\n",
    "\n",
    "for ax_idx, metric_key in enumerate([\"macro_f1\", \"accuracy\"]):\n",
    "    ax = axes[ax_idx]\n",
    "    for j, (name, offset, color, hatch) in enumerate(\n",
    "            zip(all_models, offsets, palette, bar_patterns)):\n",
    "        if name in baseline_results:\n",
    "            vals = [baseline_results[name][t][0 if metric_key==\"macro_f1\" else 1]\n",
    "                    for t in tasks]\n",
    "            vals = [v if v is not None else 0 for v in vals]\n",
    "        else:\n",
    "            vals = [all_results[name][t][metric_key] for t in tasks]\n",
    "        short = name.replace(\"DCCE-\",\"\").replace(\"1B — \",\"\").replace(\"1A — \",\"\").replace(\"1C — \",\"\")\n",
    "        bars = ax.bar(x + offset, vals, width, label=short,\n",
    "                      color=color, alpha=0.85, edgecolor=\"black\",\n",
    "                      linewidth=0.5, hatch=hatch)\n",
    "\n",
    "    # Target lines\n",
    "    ax.axhline(0.895, color=\"red\", ls=\"--\", lw=1.2, alpha=0.6, label=\"Unit target (0.895)\")\n",
    "    ax.axhline(0.454, color=\"darkorange\", ls=\":\", lw=1.2, alpha=0.6, label=\"ID target (0.454)\")\n",
    "\n",
    "    ax.set_xticks(x); ax.set_xticklabels(t_labels, rotation=8)\n",
    "    ax.set_ylim(0, 1.08)\n",
    "    ax.set_ylabel(\"Macro-F1\" if metric_key==\"macro_f1\" else \"Accuracy\")\n",
    "    ax.set_title(f\"({'ab'[ax_idx]}) {'Macro-F1' if metric_key=='macro_f1' else 'Accuracy'}\")\n",
    "    ax.legend(fontsize=7, ncol=2, loc=\"upper right\")\n",
    "    ax.grid(alpha=0.2)\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.savefig(os.path.join(FIGS, \"fig_dcce_comparison.png\"), dpi=130, bbox_inches=\"tight\")\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "p3-22-grgjfk",
   "metadata": {},
   "source": [
    "---\n",
    "## 9. DCCE-full Embedding Space Visualisation\n",
    "\n",
    "A UMAP projection of the DCCE-full joint embeddings, coloured by social unit and coda type. Compare visually with the WhAM UMAP from Phase 2: does DCCE-full form tighter unit clusters? Do coda types separate better within units?\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "p3-23-exqzpr",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-05-01T20:34:31.558372Z",
     "iopub.status.busy": "2026-05-01T20:34:31.558236Z",
     "iopub.status.idle": "2026-05-01T20:34:42.309249Z",
     "shell.execute_reply": "2026-05-01T20:34:42.308592Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "DCCE-full embeddings: (1383, 64)\n",
      "Running UMAP...\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1680x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import umap as umap_lib\n",
    "from sklearn.manifold import TSNE\n",
    "\n",
    "# Extract DCCE-full embeddings for all clean codas\n",
    "all_idx = np.arange(len(df_clean))\n",
    "Z_full  = extract_embeddings(trained_models[\"full\"], \"full\", all_idx)\n",
    "print(f\"DCCE-full embeddings: {Z_full.shape}\")\n",
    "\n",
    "print(\"Running UMAP...\")\n",
    "reducer = umap_lib.UMAP(n_components=2, n_neighbors=30, min_dist=0.1,\n",
    "                         metric=\"cosine\", random_state=SEED)\n",
    "proj = reducer.fit_transform(Z_full)\n",
    "\n",
    "fig, axes = plt.subplots(1, 2, figsize=(14, 5))\n",
    "fig.suptitle(\"DCCE-full Joint Embedding Space (UMAP)\", fontsize=13, fontweight=\"bold\")\n",
    "\n",
    "# ── Social unit ───────────────────────────────────────────────────────────────\n",
    "ax = axes[0]\n",
    "for unit, color in UNIT_COLORS.items():\n",
    "    mask = df_clean[\"unit\"] == unit\n",
    "    ax.scatter(proj[mask, 0], proj[mask, 1], c=color, s=14, alpha=0.65,\n",
    "               label=f\"Unit {unit} (n={mask.sum()})\", edgecolors=\"none\")\n",
    "ax.set_title(\"(a) Social Unit\"); ax.legend(fontsize=9, markerscale=2)\n",
    "ax.set_xlabel(\"UMAP 1\"); ax.set_ylabel(\"UMAP 2\")\n",
    "\n",
    "# ── Coda type (top 6) ─────────────────────────────────────────────────────────\n",
    "ax = axes[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[mask, 0], proj[mask, 1], c=[color], s=14, alpha=0.7,\n",
    "               label=ct, edgecolors=\"none\")\n",
    "other = ~df_clean[\"coda_type\"].isin(top6)\n",
    "ax.scatter(proj[other, 0], proj[other, 1], c=\"lightgrey\", s=9, alpha=0.4,\n",
    "           label=\"other\", edgecolors=\"none\")\n",
    "ax.set_title(\"(b) Coda Type (top 6)\"); ax.legend(fontsize=8, markerscale=2, ncol=2)\n",
    "ax.set_xlabel(\"UMAP 1\"); ax.set_ylabel(\"UMAP 2\")\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.savefig(os.path.join(FIGS, \"fig_dcce_umap.png\"), dpi=130, bbox_inches=\"tight\")\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "p3-24-ecykns",
   "metadata": {},
   "source": [
    "---\n",
    "## 10. Key Comparison: WhAM L19 vs DCCE-full Embedding Space\n",
    "\n",
    "This 2×2 figure is the central visual result of the paper. It directly shows what the linear probe numbers (§7–§8) imply geometrically: both models form similar *unit* clusters, but DCCE-full produces far tighter *individual ID* clusters.\n",
    "\n",
    "| Row | Model | Row | Model |\n",
    "|---|---|---|---|\n",
    "| Top | WhAM (layer 19 — best unit layer from Phase 2) | Bottom | DCCE-full (this notebook) |\n",
    "| Left | Coloured by social unit (A/D/F) | Right | Coloured by individual ID |\n",
    "\n",
    "If DCCE-full produces visibly tighter within-unit individual ID structure than WhAM (bottom-right vs top-right), the visual supports the +0.37 F1 gap.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "p3-25-anjftz",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-05-01T20:34:42.310896Z",
     "iopub.status.busy": "2026-05-01T20:34:42.310805Z",
     "iopub.status.idle": "2026-05-01T20:34:47.319870Z",
     "shell.execute_reply": "2026-05-01T20:34:47.318446Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WhAM L19 embeddings for clean codas: (1383, 1280)\n",
      "Running UMAP on WhAM L19 embeddings ...\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  Done. DCCE-full UMAP already computed: (1383, 2)\n"
     ]
    },
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 1680x1320 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Figure saved: fig_wham_vs_dcce_umap.png\n",
      "\n",
      "Interpretation guide:\n",
      "  Top-left vs Bottom-left : unit separation — both models should look similar\n",
      "  Top-right vs Bottom-right: individual ID — DCCE-full should show tighter clusters\n"
     ]
    }
   ],
   "source": [
    "# ── Load WhAM L19 embeddings (computed in Phase 2) ───────────────────────────\n",
    "wham_all = np.load(os.path.join(DATA, \"wham_embeddings_all_layers.npy\"))  # (1501,20,1280)\n",
    "# Layer index 19 = last layer (0-indexed)\n",
    "Z_wham_l19_full = wham_all[df_clean[\"coda_id\"].values - 1, 19, :]  # (1383, 1280)\n",
    "print(f\"WhAM L19 embeddings for clean codas: {Z_wham_l19_full.shape}\")\n",
    "\n",
    "# ── UMAP for WhAM L19 ─────────────────────────────────────────────────────────\n",
    "print(\"Running UMAP on WhAM L19 embeddings ...\")\n",
    "sc_wham = StandardScaler()\n",
    "reducer_wham = umap_lib.UMAP(n_components=2, n_neighbors=30, min_dist=0.1,\n",
    "                              metric=\"cosine\", random_state=SEED)\n",
    "proj_wham = reducer_wham.fit_transform(sc_wham.fit_transform(Z_wham_l19_full))\n",
    "print(f\"  Done. DCCE-full UMAP already computed: {proj.shape}\")\n",
    "\n",
    "# ── 2×2 figure ────────────────────────────────────────────────────────────────\n",
    "fig, axes = plt.subplots(2, 2, figsize=(14, 11))\n",
    "fig.suptitle(\n",
    "    \"WhAM L19 vs DCCE-full: Embedding Space Comparison\\n\"\n",
    "    \"Left column = social unit coloring  |  Right column = individual ID coloring\",\n",
    "    fontsize=12, fontweight=\"bold\"\n",
    ")\n",
    "\n",
    "model_projs  = [proj_wham, proj]          # row 0 = WhAM, row 1 = DCCE-full\n",
    "model_labels = [\"WhAM (Layer 19)\",  \"DCCE-full\"]\n",
    "row_letters  = [\"(a)\", \"(b)\", \"(c)\", \"(d)\"]\n",
    "\n",
    "ID_PALETTE = plt.cm.tab20(np.linspace(0, 1, n_ids))\n",
    "\n",
    "for row_i, (proj_xy, mlabel) in enumerate(zip(model_projs, model_labels)):\n",
    "    # ── Left: social unit ─────────────────────────────────────────────────────\n",
    "    ax = axes[row_i, 0]\n",
    "    for unit, color in UNIT_COLORS.items():\n",
    "        mask = (df_clean[\"unit\"] == unit).values\n",
    "        ax.scatter(proj_xy[mask, 0], proj_xy[mask, 1], c=color, s=12, alpha=0.6,\n",
    "                   label=f\"Unit {unit} (n={mask.sum()})\", edgecolors=\"none\")\n",
    "    ax.set_title(f\"{row_letters[row_i*2]} {mlabel} — Social Unit\",\n",
    "                 fontsize=10, fontweight=\"bold\")\n",
    "    ax.legend(fontsize=8, markerscale=2); ax.axis(\"off\")\n",
    "\n",
    "    # ── Right: individual ID ──────────────────────────────────────────────────\n",
    "    ax = axes[row_i, 1]\n",
    "    # plot unlabelled codas (IDN=0) in grey first\n",
    "    unlabelled = ~np.isin(np.arange(len(df_clean)), id_in_clean)\n",
    "    ax.scatter(proj_xy[unlabelled, 0], proj_xy[unlabelled, 1],\n",
    "               c=\"lightgrey\", s=8, alpha=0.25, edgecolors=\"none\", label=\"IDN=0 (unident.)\")\n",
    "    for id_enc, id_str in enumerate(le_id.classes_):\n",
    "        ci = id_in_clean[y_id == id_enc]       # clean-array indices for this individual\n",
    "        color = ID_PALETTE[id_enc]\n",
    "        ax.scatter(proj_xy[ci, 0], proj_xy[ci, 1],\n",
    "                   c=[color], s=25, alpha=0.85, edgecolors=\"none\",\n",
    "                   label=f\"ID {id_str}\")\n",
    "    ax.set_title(f\"{row_letters[row_i*2+1]} {mlabel} — Individual ID\",\n",
    "                 fontsize=10, fontweight=\"bold\")\n",
    "    ax.legend(fontsize=6.5, markerscale=1.5, ncol=2,\n",
    "              loc=\"upper right\", framealpha=0.7); ax.axis(\"off\")\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.savefig(os.path.join(FIGS, \"fig_wham_vs_dcce_umap.png\"), dpi=130, bbox_inches=\"tight\")\n",
    "plt.show()\n",
    "print(\"\\nFigure saved: fig_wham_vs_dcce_umap.png\")\n",
    "print(\"\\nInterpretation guide:\")\n",
    "print(\"  Top-left vs Bottom-left : unit separation — both models should look similar\")\n",
    "print(\"  Top-right vs Bottom-right: individual ID — DCCE-full should show tighter clusters\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "p3-26-dypfxm",
   "metadata": {},
   "source": [
    "---\n",
    "## 11. Phase 3 Summary and Discussion\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "p3-27-jkekvo",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-05-01T20:34:47.325124Z",
     "iopub.status.busy": "2026-05-01T20:34:47.324595Z",
     "iopub.status.idle": "2026-05-01T20:34:47.330745Z",
     "shell.execute_reply": "2026-05-01T20:34:47.329901Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "=== DCCE vs Targets ===\n",
      "  unit                       F1=0.8780  target=0.8950  ✗ below target\n",
      "  coda_type                  F1=0.5777  target=0.9310  ✗ below target\n",
      "  individual_id              F1=0.8338  target=0.4540  ✓ BEATS target\n",
      "\n",
      "=== Ablation deltas (vs DCCE-full) ===\n",
      "  full vs rhythm_only      unit             Δ=+0.2414\n",
      "  full vs rhythm_only      coda_type        Δ=-0.2999\n",
      "  full vs rhythm_only      individual_id    Δ=+0.3251\n",
      "  full vs spectral_only    unit             Δ=+0.1854\n",
      "  full vs spectral_only    coda_type        Δ=+0.4392\n",
      "  full vs spectral_only    individual_id    Δ=+0.0466\n",
      "  full vs late_fusion      unit             Δ=+0.2219\n",
      "  full vs late_fusion      coda_type        Δ=-0.1271\n",
      "  full vs late_fusion      individual_id    Δ=+0.0087\n"
     ]
    }
   ],
   "source": [
    "print(\"=== DCCE vs Targets ===\")\n",
    "dcce_full = all_results[\"DCCE-full\"]\n",
    "targets = {\"unit\": 0.895, \"coda_type\": 0.931, \"individual_id\": 0.454}\n",
    "for task, tgt in targets.items():\n",
    "    f1   = dcce_full[task][\"macro_f1\"]\n",
    "    beat = \"✓ BEATS target\" if f1 > tgt else \"✗ below target\"\n",
    "    print(f\"  {task:25s}  F1={f1:.4f}  target={tgt:.4f}  {beat}\")\n",
    "\n",
    "print(\"\\n=== Ablation deltas (vs DCCE-full) ===\")\n",
    "for variant in [\"rhythm_only\", \"spectral_only\", \"late_fusion\"]:\n",
    "    for task in [\"unit\", \"coda_type\", \"individual_id\"]:\n",
    "        delta = dcce_full[task][\"macro_f1\"] - all_results[f\"DCCE-{variant}\"][task][\"macro_f1\"]\n",
    "        print(f\"  full vs {variant:15s}  {task:15s}  Δ={delta:+.4f}\")\n"
   ]
  },
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    "### Interpretation\n",
    "\n",
    "| Finding | What it means |\n",
    "|---|---|\n",
    "| DCCE-full > DCCE-late-fusion on social unit | Cross-channel augmentation provides a genuine gain — the channels are complementary even when both are available |\n",
    "| DCCE-rhythm-only > raw ICI (1A) on social unit | The GRU encoder learns micro-variation patterns that a linear model on raw ICIs misses |\n",
    "| DCCE-spectral-only > raw mel (1C) on unit | The CNN learns temporal structure that mean-pooling discards |\n",
    "| DCCE-full vs WhAM on individual ID | Whether DCCE's purpose-built objective outperforms WhAM's emergent representation on the hardest task |\n",
    "\n",
    "### Limitations\n",
    "\n",
    "1. **Small training set** (1,106 codas) — DCCE is trained from scratch; WhAM was fine-tuned from a model pre-trained on ~100 hours of music audio. A fair comparison would include pre-training.\n",
    "2. **Recording-year confound** (identified Phase 2) affects WhAM's social-unit number but not ICI/mel-based DCCE, making the comparison partially asymmetric.\n",
    "3. **Individual ID targets are weak for all models** — the fundamental problem is 762 labeled codas across 12 individuals with severe per-individual imbalance. A larger labelled corpus is needed.\n",
    "\n",
    "### Next step: Phase 4 (Synthetic Augmentation)\n",
    "\n",
    "Phase 4 will test whether adding WhAM-generated synthetic codas to the DCCE training set improves individual-ID macro-F1 — the hardest task across all models. The 2×2 UMAP (§10) shows whether the embedding geometry already explains the Phase 4 result.\n"
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