{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "b4341c3d",
   "metadata": {},
   "source": [
    "# Ch 08 — Unsupervised Learning (notebook)\n",
    "\n",
    "`[← 07 dimensionality-reduction]` · **this notebook** · `[09 neural-nets-from-scratch →]`\n",
    "\n",
    "Runs top-to-bottom in ~3 min on free Colab CPU. Last verified 2026-06-11.\n",
    "\n",
    "**What you'll build**\n",
    "- A working k-means in ~30 lines of NumPy, which you then *break* on purpose with the classic convergence-sentinel bug and fix.\n",
    "- The k-means++ initializer, with a measured before/after on a worst-case dataset.\n",
    "- A k-chooser from inertia and silhouette curves, with the honest caveat about when both lie.\n",
    "- DBSCAN on the two-moons dataset where k-means cannot win, scored against the ground-truth labels.\n",
    "- A from-scratch EM loop for a Gaussian mixture, verified to increase its own log-likelihood every step, then reused as a density-based anomaly detector.\n",
    "\n",
    "**How this notebook works.** Code cells with a `# TODO` are yours to fill in. Run the cell to grade yourself: `[ ok ]` passed, `[FAIL]` shows what went wrong, `[ -- ]` means not attempted yet. Every exercise has a hint ladder (open only as many as you need) and a folded solution below it. The notebook runs top-to-bottom even if you fill in nothing, because the solution cells redefine the functions so the later cells work. See Ch 00 for the full protocol.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "42f90ab3",
   "metadata": {},
   "source": [
    "## Before you start\n",
    "\n",
    "1. You run k-means with `k=5` on data that truly has 2 clusters. How many clusters do you get back? <details><summary>Answer</summary>Five. K-means partitions into exactly the `k` you ask for, structure or no structure. Picking `k` is your job, not the algorithm's, which is why half this notebook is about choosing it.</details>\n",
    "2. K-means minimizes within-cluster squared distance. What shape of cluster does that objective quietly assume? <details><summary>Answer</summary>Roughly spherical, equal-sized blobs. Euclidean distance plus equal weighting is a circle in disguise. On the two-moons dataset it fails badly, which is the whole reason DBSCAN exists.</details>\n",
    "3. Predict before you run: a Gaussian mixture gives every point a probability of belonging to each cluster. What must those probabilities, across clusters, sum to for one point? <details><summary>Answer</summary>One. They are a posterior over which Gaussian generated the point (Bayes' rule), so they are a proper distribution. We assert exactly this later.</details>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c62827b4",
   "metadata": {},
   "source": [
    "## Setup\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "a0e94085",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-06-10T18:44:20.510317Z",
     "iopub.status.busy": "2026-06-10T18:44:20.510235Z",
     "iopub.status.idle": "2026-06-10T18:44:21.132817Z",
     "shell.execute_reply": "2026-06-10T18:44:21.132318Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "numpy 2.2.6 · sklearn 1.7.2 · scipy 1.15.3\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import sklearn, scipy\n",
    "print(f\"numpy {np.__version__} · sklearn {sklearn.__version__} · scipy {scipy.__version__}\")\n",
    "if np.__version__ < \"2.0\":\n",
    "    print(\"WARN: written for NumPy 2.x; older versions may differ slightly (e.g. np.trapezoid)\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "aede9c06",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-06-10T18:44:21.134171Z",
     "iopub.status.busy": "2026-06-10T18:44:21.133982Z",
     "iopub.status.idle": "2026-06-10T18:44:21.138333Z",
     "shell.execute_reply": "2026-06-10T18:44:21.137969Z"
    }
   },
   "outputs": [],
   "source": [
    "import os, random\n",
    "SEED = 0\n",
    "FAST = bool(os.environ.get('NB_FAST'))  # CI smoke mode: ~10x fewer EM/k-means iters, same code paths\n",
    "EM_ITERS = 5 if FAST else 50      # full-run GMM EM iterations; expected log-likelihood logged below\n",
    "KMEANS_ITERS = 10 if FAST else 100  # max Lloyd iterations before we stop; convergence usually hits first\n",
    "rng = np.random.default_rng(SEED)\n",
    "random.seed(SEED)\n",
    "\n",
    "# ── house self-check harness (identical across all chapter notebooks) ──\n",
    "import numpy as _np\n",
    "\n",
    "def check(label, test_fn, required=False):\n",
    "    \"\"\"Run one self-check. test_fn raises AssertionError (with a teaching\n",
    "    message) on failure, NotImplementedError if the stub is unfilled.\n",
    "    required=True is used only in solution cells; it is what CI grades.\"\"\"\n",
    "    try:\n",
    "        test_fn()\n",
    "    except NotImplementedError:\n",
    "        if required:\n",
    "            raise AssertionError(f\"{label}: reference solution incomplete\")\n",
    "        print(f\"[ -- ] {label}: not attempted yet — fill in the TODO above, then re-run.\")\n",
    "        return False\n",
    "    except AssertionError as e:\n",
    "        if required:\n",
    "            raise\n",
    "        print(f\"[FAIL] {label}: {e}\")\n",
    "        return False\n",
    "    print(f\"[ ok ] {label}\")\n",
    "    return True\n",
    "\n",
    "def attempted(*vals):\n",
    "    \"\"\"Treat None placeholders as 'not attempted'.\"\"\"\n",
    "    if any(v is None for v in vals):\n",
    "        raise NotImplementedError\n",
    "\n",
    "def check_shape(x, want):\n",
    "    assert tuple(x.shape) == tuple(want), \\\n",
    "        f\"shape {tuple(x.shape)}, expected {tuple(want)} — check your reshape/transpose order\"\n",
    "\n",
    "def check_close(got, want, atol=1e-5, rtol=1e-4, msg=\"\"):\n",
    "    g, w = _np.asarray(got, dtype=float), _np.asarray(want, dtype=float)\n",
    "    assert g.shape == w.shape, f\"shape {g.shape} vs expected {w.shape}. {msg}\"\n",
    "    bad = ~_np.isclose(g, w, atol=atol, rtol=rtol)\n",
    "    assert not bad.any(), \\\n",
    "        f\"{bad.mean():.2%} of values wrong (max diff {abs(g - w).max():.3g}). {msg}\""
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2f6265ce",
   "metadata": {},
   "source": [
    "> **Note:** seeds make this notebook's printed numbers reproduce on CPU. Library versions and BLAS threading can shift the last digit or two; quoted numbers hold for the pinned environment. If your inertia is 549.31 and the page says 549.29, you did nothing wrong.\n",
    "\n",
    "> **Caveat:** clustering labels are arbitrary names. K-means may call a cluster `0` that the true generator called `2`. Every check below compares *partitions* (which points group together) or recovered geometry (centroid locations), never raw label integers, because the integers carry no meaning.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "192041c1",
   "metadata": {},
   "source": [
    "## The map\n",
    "\n",
    "> **Part 1 — Clustering vs classification.** The unsupervised setup on labeled blobs, where we happen to know the truth and can grade ourselves.\n",
    "> **Part 2 — K-means from scratch.** Lloyd's algorithm in NumPy, the convergence-sentinel bug that ships twice in the wild, and the fix.\n",
    "> **Part 3 — K-means++ and choosing k.** A smarter init measured against random, then inertia and silhouette curves to pick k (and where they lie).\n",
    "> **Part 4 — DBSCAN.** Density clustering on two moons, the dataset k-means cannot solve, scored against ground truth.\n",
    "> **Part 5 — Gaussian mixtures and anomalies.** EM from scratch with a monotone log-likelihood, then density-based anomaly detection.\n",
    "> **Part 6 — Picking a method.** A 2D look decides; we run all the candidates side by side.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c5f6d2af",
   "metadata": {},
   "source": [
    "## Part 1 — Clustering vs classification: the unsupervised setup\n",
    "\n",
    "> **Objectives.** Generate the anchor dataset (four blobs with known ground-truth labels), see why \"no labels\" still leaves recoverable structure, and fix a scoring tool (adjusted Rand index) that compares partitions while ignoring label names.\n",
    "\n",
    "In classification you have pairs $(x_i, y_i)$ and learn to predict $y$ from $x$. In clustering you have only the $x_i$ and learn to group them by similarity. We will cheat for teaching: our synthetic blobs come *with* true labels, so we can measure whether a clustering recovered the real structure. Production data does not hand you that answer.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "3a8d3d5a",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-06-10T18:44:21.139541Z",
     "iopub.status.busy": "2026-06-10T18:44:21.139455Z",
     "iopub.status.idle": "2026-06-10T18:44:21.160464Z",
     "shell.execute_reply": "2026-06-10T18:44:21.160034Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "X_blobs (600, 2) · true clusters 4 · sizes [150 150 150 150]\n"
     ]
    }
   ],
   "source": [
    "from sklearn.datasets import make_blobs\n",
    "# Anchor dataset: 4 well-separated blobs in 2D. We keep y_true ONLY to grade ourselves;\n",
    "# the clustering algorithms never see it.\n",
    "X_blobs, y_blobs = make_blobs(\n",
    "    n_samples=600, centers=4, cluster_std=0.70, random_state=SEED)\n",
    "print(f\"X_blobs {X_blobs.shape} · true clusters {len(np.unique(y_blobs))} · \"\n",
    "      f\"sizes {np.bincount(y_blobs)}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "4a3ddfe6",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-06-10T18:44:21.161352Z",
     "iopub.status.busy": "2026-06-10T18:44:21.161272Z",
     "iopub.status.idle": "2026-06-10T18:44:21.217317Z",
     "shell.execute_reply": "2026-06-10T18:44:21.216922Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1000x400 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# viz: the data as a human sees it (no labels) next to the ground truth\n",
    "fig, ax = plt.subplots(1, 2, figsize=(10, 4))\n",
    "ax[0].scatter(X_blobs[:, 0], X_blobs[:, 1], s=8, c=\"#444\"); ax[0].set_title(\"what clustering sees (no labels)\")\n",
    "ax[1].scatter(X_blobs[:, 0], X_blobs[:, 1], s=8, c=y_blobs, cmap=\"tab10\"); ax[1].set_title(\"ground truth (4 blobs)\")\n",
    "for a in ax: a.set_xticks([]); a.set_yticks([])\n",
    "plt.tight_layout(); plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dff545e5",
   "metadata": {},
   "source": [
    "> **Interpretation.** The left panel is the real problem: four blobs are visually obvious here, but the algorithm gets only the gray dots. The right panel is the answer key we are lucky to have. Our whole job is to recover the right panel from the left.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "aaea82d7",
   "metadata": {},
   "source": [
    "### Exercise 8.1 — A score that ignores label names\n",
    "`Difficulty 2/5 · ~10 min`\n",
    "\n",
    "Cluster labels are arbitrary: a clustering that perfectly recovers the blobs but names them `[3,3,1,1,0,0,2,2]` is *identical* to the truth `[0,0,1,1,2,2,3,3]` up to renaming. We need a score that sees through the permutation. The **adjusted Rand index** does exactly that: it counts pairs of points that agree on \"same cluster / different cluster\" between two labelings, corrected for chance.\n",
    "\n",
    "You will not reimplement ARI (it is fiddly). Instead, fill in `cluster_purity(y_true, y_pred)`: for each predicted cluster, take the count of its most common true label, sum over clusters, divide by the total. A perfect partition scores 1.0 regardless of how the clusters are numbered.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "516b49a5",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-06-10T18:44:21.218613Z",
     "iopub.status.busy": "2026-06-10T18:44:21.218514Z",
     "iopub.status.idle": "2026-06-10T18:44:21.222552Z",
     "shell.execute_reply": "2026-06-10T18:44:21.222275Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ -- ] 8.1 purity (perfect, renamed): not attempted yet — fill in the TODO above, then re-run.\n",
      "[ -- ] 8.1 purity (mixed): not attempted yet — fill in the TODO above, then re-run.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def cluster_purity(y_true, y_pred):\n",
    "    \"\"\"Fraction of points whose predicted cluster's majority true-label they share.\n",
    "    Permutation-invariant: renaming clusters does not change the score.\"\"\"\n",
    "    y_true, y_pred = np.asarray(y_true), np.asarray(y_pred)\n",
    "    total = 0\n",
    "    # TODO 1: for each predicted cluster c, mask the points in it, and add the\n",
    "    #         count of the single most common true label among them to `total`.\n",
    "    #         Hint: np.bincount on y_true[mask] then .max(), but guard empty masks.\n",
    "    total = None\n",
    "    attempted(total)\n",
    "    return total / len(y_true)\n",
    "\n",
    "# self-checks (run this cell)\n",
    "def _purity_perfect():\n",
    "    yt = np.array([0, 0, 1, 1, 2, 2])\n",
    "    yp = np.array([3, 3, 1, 1, 0, 0])  # same partition, different names\n",
    "    assert abs(cluster_purity(yt, yp) - 1.0) < 1e-9, \\\n",
    "        f\"a perfectly-aligned partition must score 1.0, got {cluster_purity(yt, yp)}\"\n",
    "\n",
    "def _purity_mixed():\n",
    "    yt = np.array([0, 0, 0, 1])\n",
    "    yp = np.array([0, 0, 1, 1])  # cluster0={0,0}, cluster1={0,1}: 2 + 1 = 3 of 4\n",
    "    assert abs(cluster_purity(yt, yp) - 0.75) < 1e-9, \\\n",
    "        f\"expected 0.75 (3 of 4 in their cluster majority), got {cluster_purity(yt, yp)}\"\n",
    "\n",
    "check(\"8.1 purity (perfect, renamed)\", _purity_perfect)\n",
    "check(\"8.1 purity (mixed)\", _purity_mixed)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d43c3845",
   "metadata": {},
   "source": [
    "<details><summary>Hint 1 (conceptual)</summary>Loop over `np.unique(y_pred)`. For each cluster id `c`, build the boolean mask `y_pred == c`, look at `y_true[mask]`, and find how many points share that subset's most common label.</details>\n",
    "\n",
    "<details><summary>Hint 2 (pseudocode)</summary>\n",
    "\n",
    "```python\n",
    "total = 0\n",
    "for c in np.unique(y_pred):\n",
    "    mask = y_pred == c\n",
    "    if mask.any():\n",
    "        total += np.bincount(y_true[mask]).max()\n",
    "```\n",
    "</details>\n",
    "\n",
    "<details><summary>Help — \"negative dimensions are not allowed\" from np.bincount</summary>`np.bincount` needs non-negative ints. DBSCAN's noise label is `-1`; filter those out before scoring, or this exercise (which uses clean blobs) is fine as-is.</details>\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "3293eeef",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-06-10T18:44:21.223524Z",
     "iopub.status.busy": "2026-06-10T18:44:21.223442Z",
     "iopub.status.idle": "2026-06-10T18:44:21.226312Z",
     "shell.execute_reply": "2026-06-10T18:44:21.226035Z"
    },
    "collapsed": true,
    "jupyter": {
     "source_hidden": true
    },
    "tags": [
     "hide-input"
    ]
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ ok ] 8.1 purity (perfect, renamed)\n",
      "[ ok ] 8.1 purity (mixed)\n",
      "purity of the trivial 'everything in one cluster' guess: 0.25 (equals the largest true cluster's share)\n"
     ]
    }
   ],
   "source": [
    "#@title Solution { display-mode: \"form\" }\n",
    "# solution: redefines cluster_purity; the checks below re-verify the reference.\n",
    "def cluster_purity(y_true, y_pred):\n",
    "    y_true, y_pred = np.asarray(y_true), np.asarray(y_pred)\n",
    "    total = 0\n",
    "    for c in np.unique(y_pred):\n",
    "        mask = y_pred == c\n",
    "        if mask.any():\n",
    "            total += int(np.bincount(y_true[mask]).max())\n",
    "    return total / len(y_true)\n",
    "\n",
    "check(\"8.1 purity (perfect, renamed)\", _purity_perfect, required=True)\n",
    "check(\"8.1 purity (mixed)\", _purity_mixed, required=True)\n",
    "print(\"purity of the trivial 'everything in one cluster' guess:\",\n",
    "      round(cluster_purity(y_blobs, np.zeros_like(y_blobs)), 3),\n",
    "      \"(equals the largest true cluster's share)\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fd38f482",
   "metadata": {},
   "source": [
    "> **Interpretation.** Lumping everything into one cluster scores the share of the biggest true cluster, around 0.25 for four equal blobs. That is our floor: any real clustering must beat it. Purity has a known flaw (it rises to 1.0 if you make every point its own cluster), so we will lean on the adjusted Rand index from sklearn for the headline numbers; purity is here to make \"permutation-invariant\" concrete with code you wrote.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "03a0accb",
   "metadata": {},
   "source": [
    "> **Key takeaways.**\n",
    "> - Clustering recovers structure from $x$ alone; we kept ground-truth labels only to grade ourselves.\n",
    "> - Cluster integers are arbitrary names. Score *partitions*, not labels: adjusted Rand index and purity are both permutation-invariant.\n",
    "> - Purity inflates as `k` grows (one point per cluster scores 1.0), so it is a sanity tool, not a `k`-chooser.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a538cdec",
   "metadata": {},
   "source": [
    "## Part 2 — K-means from scratch, and the bug that ships twice\n",
    "\n",
    "> **Objectives.** Write Lloyd's algorithm as two alternating steps, give its convergence test an adversarial read, watch a wrong sentinel break the loop before any work happens, and fix it.\n",
    "\n",
    "**K-means** (Lloyd 1957) partitions $m$ points into $k$ groups by alternating:\n",
    "\n",
    "1. **Assignment.** Each point joins its nearest centroid (Euclidean distance).\n",
    "2. **Update.** Each centroid moves to the mean of its assigned points.\n",
    "\n",
    "It is coordinate descent on the **inertia** (within-cluster sum of squares):\n",
    "\n",
    "$$J(\\boldsymbol{\\mu}, \\mathbf{z}) = \\sum_{i=1}^{m} \\lVert \\mathbf{x}_i - \\boldsymbol{\\mu}_{z_i} \\rVert^2$$\n",
    "\n",
    "where $z_i$ is the cluster of point $i$ and $\\boldsymbol{\\mu}_j$ is centroid $j$. Each step holds or lowers $J$, so it converges, though not always to the global minimum (that is NP-hard).\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "43b8b7ae",
   "metadata": {},
   "source": [
    "First a toy you can verify on paper. Four points on a line at 0, 1, 9, 10; two centroids start at 0.5 and 9.5. One assignment step should split them `[0,0,1,1]`; one update step should move the centroids to the group means 0.5 and 9.5 (already there).\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "af655a97",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-06-10T18:44:21.227184Z",
     "iopub.status.busy": "2026-06-10T18:44:21.227112Z",
     "iopub.status.idle": "2026-06-10T18:44:21.229789Z",
     "shell.execute_reply": "2026-06-10T18:44:21.229426Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "labels: [0, 0, 1, 1] (expect [0, 0, 1, 1])\n",
      "centroids after update: [0.5, 9.5] (expect [0.5, 9.5])\n"
     ]
    }
   ],
   "source": [
    "X_toy = np.array([[0.0], [1.0], [9.0], [10.0]])\n",
    "cent_toy = np.array([[0.5], [9.5]])\n",
    "# Assignment: squared distance to each centroid, shape (4 points, 2 centroids)\n",
    "d2 = ((X_toy[:, None, :] - cent_toy[None, :, :]) ** 2).sum(axis=-1)\n",
    "labels_toy = d2.argmin(axis=1)\n",
    "# Update: each centroid = mean of its points\n",
    "new_cent = np.array([X_toy[labels_toy == j].mean(axis=0) for j in range(2)])\n",
    "print(\"labels:\", labels_toy.tolist(), \"(expect [0, 0, 1, 1])\")\n",
    "print(\"centroids after update:\", new_cent.ravel().tolist(), \"(expect [0.5, 9.5])\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "206a53ec",
   "metadata": {},
   "source": [
    "> **Interpretation.** Both match the by-hand answer. The `[:, None, :]` / `[None, :, :]` broadcast builds the full points-by-centroids distance table in one expression; everything else is `argmin` and `mean`. That is the entire algorithm.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dc6d593f",
   "metadata": {},
   "source": [
    "### The convergence sentinel: read it like an adversary\n",
    "\n",
    "Lloyd's loop stops when an assignment step changes no labels. To detect \"no change\" you compare this iteration's labels to last iteration's, so you need a *previous labels* value before the first iteration. The obvious choice, `prev = np.zeros(m)`, is a landmine, and it shipped in this repo's own Ch 07 and Ch 08 labs.\n",
    "\n",
    "> **Stop and think.** The valid labels are `0, 1, ..., k-1`. So `0` is a real label. If the very first assignment happens to put *every* point in cluster 0, then `new_labels == np.zeros(m)` is `True`, the loop breaks at iteration 0, and not one centroid ever moves. The sentinel collided with a legal answer.\n",
    "\n",
    "The fix is a sentinel that can never be a valid label: `prev = np.full(m, -1)`. We will *demonstrate* the bug, not just describe it.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "e2fba436",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-06-10T18:44:21.230526Z",
     "iopub.status.busy": "2026-06-10T18:44:21.230453Z",
     "iopub.status.idle": "2026-06-10T18:44:21.234294Z",
     "shell.execute_reply": "2026-06-10T18:44:21.234034Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "buggy: 0 update iterations ran\n",
      "buggy: centroid 0 still at its init [0.0, 0.0]? True\n"
     ]
    }
   ],
   "source": [
    "# A dataset and init engineered to trigger the bug: one tight blob, two centroids,\n",
    "# one of them placed so far away that on the first pass EVERY point picks centroid 0.\n",
    "X_one, _ = make_blobs(n_samples=200, centers=1, cluster_std=0.30, random_state=SEED)\n",
    "bad_init = np.array([[0.0, 0.0], [100.0, 100.0]])  # centroid 1 is 100 units away\n",
    "\n",
    "def kmeans_buggy(X, init, n_iters=100):\n",
    "    centroids = init.copy().astype(float); m = X.shape[0]\n",
    "    prev = np.zeros(m, dtype=int)            # BUG: 0 is a valid label\n",
    "    iters_run = 0\n",
    "    for _ in range(n_iters):\n",
    "        d2 = ((X[:, None, :] - centroids[None, :, :]) ** 2).sum(axis=-1)\n",
    "        new_labels = d2.argmin(axis=1)\n",
    "        if np.array_equal(new_labels, prev):  # fires immediately if all labels are 0\n",
    "            break\n",
    "        prev = new_labels; iters_run += 1\n",
    "        for j in range(2):\n",
    "            mask = new_labels == j\n",
    "            if mask.any(): centroids[j] = X[mask].mean(axis=0)\n",
    "    return centroids, iters_run\n",
    "\n",
    "cb, iters_b = kmeans_buggy(X_one, bad_init)\n",
    "print(f\"buggy: {iters_b} update iterations ran\")\n",
    "print(f\"buggy: centroid 0 still at its init {bad_init[0].tolist()}? {np.allclose(cb[0], bad_init[0])}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bf069a8f",
   "metadata": {},
   "source": [
    "> **Interpretation.** Zero update iterations. The loop saw \"labels are all 0, same as my `np.zeros` sentinel, nothing changed, done\" and quit before moving a single centroid. The \"result\" is just the initialization. This is a silent failure: no error, no warning, a returned answer that is garbage. Now the one-character class of fix.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "e764fdc2",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-06-10T18:44:21.235120Z",
     "iopub.status.busy": "2026-06-10T18:44:21.235051Z",
     "iopub.status.idle": "2026-06-10T18:44:21.238173Z",
     "shell.execute_reply": "2026-06-10T18:44:21.237916Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "fixed: 1 update iterations ran\n",
      "fixed: centroid 0 moved to the data mean [0.95, 4.31] (near [0.95, 4.31])\n",
      "[ ok ] the -1 sentinel runs real iterations where np.zeros broke immediately\n"
     ]
    }
   ],
   "source": [
    "def kmeans_fixed(X, init, n_iters=100):\n",
    "    centroids = init.copy().astype(float); m = X.shape[0]\n",
    "    prev = np.full(m, -1, dtype=int)         # FIX: -1 can never equal a valid label\n",
    "    iters_run = 0\n",
    "    for _ in range(n_iters):\n",
    "        d2 = ((X[:, None, :] - centroids[None, :, :]) ** 2).sum(axis=-1)\n",
    "        new_labels = d2.argmin(axis=1)\n",
    "        if np.array_equal(new_labels, prev):\n",
    "            break\n",
    "        prev = new_labels; iters_run += 1\n",
    "        for j in range(2):\n",
    "            mask = new_labels == j\n",
    "            if mask.any(): centroids[j] = X[mask].mean(axis=0)\n",
    "    return centroids, iters_run\n",
    "\n",
    "cf, iters_f = kmeans_fixed(X_one, bad_init)\n",
    "print(f\"fixed: {iters_f} update iterations ran\")\n",
    "print(f\"fixed: centroid 0 moved to the data mean {cf[0].round(2).tolist()} \"\n",
    "      f\"(near {X_one.mean(axis=0).round(2).tolist()})\")\n",
    "assert iters_f >= 1, \"with the -1 sentinel the first assignment must count as a change\"\n",
    "assert iters_b == 0, \"the np.zeros sentinel must break before any update (that is the bug)\"\n",
    "print(\"[ ok ] the -1 sentinel runs real iterations where np.zeros broke immediately\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "845f3903",
   "metadata": {},
   "source": [
    "> **Common confusion:** \"but my code worked on my data.\" It usually does, because random data rarely assigns everything to cluster 0 on pass one. The bug is data-dependent and seed-dependent, which is exactly why it survives review and ships. Sentinel values must live outside the value space they guard. We use `-1` from here on.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9f03f2bd",
   "metadata": {},
   "source": [
    "### Exercise 8.2 — K-means, correctly\n",
    "`Difficulty 3/5 · ~20 min`\n",
    "\n",
    "Implement `kmeans(X, k, ...)` returning `(centroids, labels, inertia)`. Use the `-1` sentinel. The check compares your inertia to scikit-learn's on the blobs: a correct vectorized Lloyd, run from a good init, lands within a few percent of sklearn (which runs 10 inits and keeps the best, so allow a margin).\n",
    "\n",
    "**Harder:** make the empty-cluster case explicit. If a centroid wins no points, leave it where it is rather than taking the mean of an empty set (which is `nan`).\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "fde92480",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-06-10T18:44:21.239171Z",
     "iopub.status.busy": "2026-06-10T18:44:21.239097Z",
     "iopub.status.idle": "2026-06-10T18:44:21.300711Z",
     "shell.execute_reply": "2026-06-10T18:44:21.300398Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ -- ] 8.2 kmeans inertia vs sklearn: not attempted yet — fill in the TODO above, then re-run.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def kmeans(X, k, n_iters=100, seed=0, init_centroids=None):\n",
    "    \"\"\"Lloyd's k-means. Returns (centroids (k,d), labels (m,), inertia float).\"\"\"\n",
    "    rng_local = np.random.default_rng(seed)\n",
    "    m, d = X.shape\n",
    "    if init_centroids is None:\n",
    "        centroids = X[rng_local.choice(m, k, replace=False)].copy().astype(float)\n",
    "    else:\n",
    "        centroids = np.asarray(init_centroids, dtype=float).copy()\n",
    "    labels = np.full(m, -1, dtype=int)   # -1 sentinel, never a valid label\n",
    "    for _ in range(n_iters):\n",
    "        # TODO 1: assignment. d2 has shape (m, k); labels_new = argmin over centroids.\n",
    "        d2 = None\n",
    "        labels_new = None\n",
    "        attempted(d2, labels_new)\n",
    "        # TODO 2: convergence. if labels_new equals labels, set labels and break.\n",
    "        # TODO 3: otherwise adopt labels_new, then move each non-empty centroid to its mean.\n",
    "        labels = labels_new\n",
    "        for j in range(k):\n",
    "            mask = labels == j\n",
    "            if mask.any():\n",
    "                centroids[j] = X[mask].mean(axis=0)\n",
    "    # TODO 4: inertia = sum of squared distance from each point to ITS centroid.\n",
    "    inertia = None\n",
    "    attempted(inertia)\n",
    "    return centroids, labels, float(inertia)\n",
    "\n",
    "def _kmeans_vs_sklearn():\n",
    "    from sklearn.cluster import KMeans\n",
    "    # Give our single-init k-means the same k-means++ seeds sklearn would use,\n",
    "    # by handing it a strong init so the comparison is about the loop, not luck.\n",
    "    sk = KMeans(n_clusters=4, n_init=10, random_state=SEED).fit(X_blobs)\n",
    "    _, _, my_inertia = kmeans(X_blobs, 4, n_iters=KMEANS_ITERS, seed=SEED,\n",
    "                              init_centroids=sk.cluster_centers_)\n",
    "    rel = abs(my_inertia - sk.inertia_) / sk.inertia_\n",
    "    assert rel < 0.05, (f\"your inertia {my_inertia:.1f} vs sklearn {sk.inertia_:.1f} \"\n",
    "                        f\"({rel:.1%} off). From sklearn's own centroids you should match \"\n",
    "                        f\"within rounding; check your inertia sum and the assignment argmin.\")\n",
    "\n",
    "check(\"8.2 kmeans inertia vs sklearn\", _kmeans_vs_sklearn)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1d8956f3",
   "metadata": {},
   "source": [
    "<details><summary>Hint 1 (conceptual)</summary>The assignment line is identical to the toy: `((X[:, None, :] - centroids[None, :, :]) ** 2).sum(axis=-1)` is the `(m, k)` distance table. `argmin(axis=1)` picks each point's nearest centroid.</details>\n",
    "\n",
    "<details><summary>Hint 2 (pseudocode)</summary>\n",
    "\n",
    "```python\n",
    "d2 = ((X[:, None, :] - centroids[None, :, :]) ** 2).sum(axis=-1)  # (m, k)\n",
    "labels_new = d2.argmin(axis=1)\n",
    "if np.array_equal(labels_new, labels):\n",
    "    labels = labels_new\n",
    "    break\n",
    "# ... centroid update (already written) ...\n",
    "# inertia: for each point, the squared distance to its OWN centroid\n",
    "inertia = ((X - centroids[labels]) ** 2).sum()\n",
    "```\n",
    "</details>\n",
    "\n",
    "<details><summary>Help — inertia is nan</summary>A centroid won zero points and `X[mask].mean()` returned `nan`, which then poisons the distance sum. The `if mask.any():` guard (already in the stub) prevents the update; make sure you did not remove it. With a strong init this rarely fires on clean blobs.</details>\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "8378bb4e",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-06-10T18:44:21.301748Z",
     "iopub.status.busy": "2026-06-10T18:44:21.301665Z",
     "iopub.status.idle": "2026-06-10T18:44:21.311932Z",
     "shell.execute_reply": "2026-06-10T18:44:21.311557Z"
    },
    "collapsed": true,
    "jupyter": {
     "source_hidden": true
    },
    "tags": [
     "hide-input"
    ]
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ ok ] 8.2 kmeans inertia vs sklearn\n",
      "from-scratch inertia 549.3 · partition purity vs truth 0.995\n"
     ]
    }
   ],
   "source": [
    "#@title Solution { display-mode: \"form\" }\n",
    "# solution: redefines kmeans; the check below re-verifies against sklearn.\n",
    "def kmeans(X, k, n_iters=100, seed=0, init_centroids=None):\n",
    "    rng_local = np.random.default_rng(seed)\n",
    "    m, d = X.shape\n",
    "    if init_centroids is None:\n",
    "        centroids = X[rng_local.choice(m, k, replace=False)].copy().astype(float)\n",
    "    else:\n",
    "        centroids = np.asarray(init_centroids, dtype=float).copy()\n",
    "    labels = np.full(m, -1, dtype=int)\n",
    "    for _ in range(n_iters):\n",
    "        d2 = ((X[:, None, :] - centroids[None, :, :]) ** 2).sum(axis=-1)\n",
    "        labels_new = d2.argmin(axis=1)\n",
    "        if np.array_equal(labels_new, labels):\n",
    "            labels = labels_new\n",
    "            break\n",
    "        labels = labels_new\n",
    "        for j in range(k):\n",
    "            mask = labels == j\n",
    "            if mask.any():\n",
    "                centroids[j] = X[mask].mean(axis=0)\n",
    "    inertia = ((X - centroids[labels]) ** 2).sum()\n",
    "    return centroids, labels, float(inertia)\n",
    "\n",
    "check(\"8.2 kmeans inertia vs sklearn\", _kmeans_vs_sklearn, required=True)\n",
    "cents, labs, inert = kmeans(X_blobs, 4, n_iters=KMEANS_ITERS, seed=SEED)\n",
    "print(f\"from-scratch inertia {inert:.1f} · partition purity vs truth \"\n",
    "      f\"{cluster_purity(y_blobs, labs):.3f}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "bbe546e6",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-06-10T18:44:21.313011Z",
     "iopub.status.busy": "2026-06-10T18:44:21.312924Z",
     "iopub.status.idle": "2026-06-10T18:44:21.348401Z",
     "shell.execute_reply": "2026-06-10T18:44:21.347958Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 500x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# viz: our centroids on top of the data, colored by our labels\n",
    "fig, ax = plt.subplots(figsize=(5, 4))\n",
    "ax.scatter(X_blobs[:, 0], X_blobs[:, 1], s=8, c=labs, cmap=\"tab10\")\n",
    "ax.scatter(cents[:, 0], cents[:, 1], marker=\"X\", s=200, c=\"black\", edgecolor=\"white\", label=\"centroids\")\n",
    "ax.set_title(\"from-scratch k-means (k=4)\"); ax.legend(); ax.set_xticks([]); ax.set_yticks([])\n",
    "plt.tight_layout(); plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "81375050",
   "metadata": {},
   "source": [
    "> **Interpretation.** The four centroids sit at the blob centers and purity is near 1.0. From a strong init our single-run inertia matches sklearn's best-of-ten. The remaining gap, when there is one, is entirely about *initialization*, which is Part 3.\n",
    "\n",
    "> **Key takeaways.**\n",
    "> - K-means is two lines of NumPy in a loop: an `argmin` assignment and a per-cluster `mean` update.\n",
    "> - The convergence sentinel must be a value outside the label space. `-1`, never `np.zeros`. The `np.zeros` version fails silently and only on some data, which is why it ships.\n",
    "> - Inertia is the objective and the self-grade: from a fixed init you should reproduce sklearn's value almost exactly.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6a464cab",
   "metadata": {},
   "source": [
    "## Part 3 — K-means++ and choosing k\n",
    "\n",
    "> **Objectives.** Implement the k-means++ initializer and *measure* that it beats random init on a worst case, then read inertia and silhouette curves to pick `k`, and see where both diagnostics lie.\n",
    "\n",
    "Random init can drop all `k` centroids inside one blob; the algorithm then crawls them apart over many iterations and often gets stuck in a bad local minimum. **K-means++** (Arthur and Vassilvitskii 2007) spreads the seeds out:\n",
    "\n",
    "1. First centroid: uniformly at random from the data.\n",
    "2. Each next centroid: a data point chosen with probability proportional to its squared distance from the *nearest already-chosen* centroid.\n",
    "\n",
    "That bias toward far-apart seeds gives an $O(\\log k)$ expected approximation guarantee. sklearn uses it by default; `init=\"random\"` exists mainly to show how much worse the alternative is.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9183a137",
   "metadata": {},
   "source": [
    "### Exercise 8.3 — k-means++ initialization\n",
    "`Difficulty 3/5 · ~15 min`\n",
    "\n",
    "Fill in `kmeans_pp_init(X, k, rng)` returning `(k, d)` initial centroids by the recipe above. The check runs your init plus the (now correct) k-means against random init on an 8-blob dataset across many seeds, and asserts your k-means++ wins on *worst-case* inertia, the metric that matters since you only get one run in production.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "efceb72c",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-06-10T18:44:21.349334Z",
     "iopub.status.busy": "2026-06-10T18:44:21.349252Z",
     "iopub.status.idle": "2026-06-10T18:44:21.359869Z",
     "shell.execute_reply": "2026-06-10T18:44:21.359485Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ -- ] 8.3 k-means++ beats random (worst case): not attempted yet — fill in the TODO above, then re-run.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def kmeans_pp_init(X, k, rng_local):\n",
    "    \"\"\"k-means++ seeding. Returns (k, d) initial centroids.\"\"\"\n",
    "    m, d = X.shape\n",
    "    centroids = np.empty((k, d))\n",
    "    # TODO 1: first centroid: a uniformly random data point.\n",
    "    centroids[0] = None\n",
    "    attempted(centroids[0])\n",
    "    for j in range(1, k):\n",
    "        # TODO 2: d2[i] = squared distance from point i to its NEAREST chosen centroid\n",
    "        #         (the first j centroids). Shape (m,). Use .min over the chosen ones.\n",
    "        d2 = None\n",
    "        # TODO 3: probs proportional to d2 (guard the all-zero case with a uniform fallback).\n",
    "        probs = None\n",
    "        attempted(d2, probs)\n",
    "        centroids[j] = X[rng_local.choice(m, p=probs)]\n",
    "    return centroids\n",
    "\n",
    "def _pp_beats_random():\n",
    "    from sklearn.cluster import KMeans\n",
    "    Xk, _ = make_blobs(n_samples=800, centers=8, cluster_std=0.50, random_state=SEED)\n",
    "    n_trials = 5 if FAST else 15\n",
    "    rand_worst = pp_worst = 0.0\n",
    "    for s in range(n_trials):\n",
    "        r = np.random.default_rng(s)\n",
    "        # random init: k distinct points\n",
    "        ri = Xk[r.choice(Xk.shape[0], 8, replace=False)]\n",
    "        _, _, in_rand = kmeans(Xk, 8, n_iters=KMEANS_ITERS, seed=s, init_centroids=ri)\n",
    "        pp = kmeans_pp_init(Xk, 8, np.random.default_rng(s))\n",
    "        _, _, in_pp = kmeans(Xk, 8, n_iters=KMEANS_ITERS, seed=s, init_centroids=pp)\n",
    "        rand_worst = max(rand_worst, in_rand); pp_worst = max(pp_worst, in_pp)\n",
    "    assert pp_worst < rand_worst, (\n",
    "        f\"k-means++ worst inertia {pp_worst:.0f} should beat random's {rand_worst:.0f}. \"\n",
    "        f\"Check that d2 uses the NEAREST chosen centroid (min over the first j), not all k.\")\n",
    "\n",
    "check(\"8.3 k-means++ beats random (worst case)\", _pp_beats_random)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0c9256ab",
   "metadata": {},
   "source": [
    "<details><summary>Hint 1 (conceptual)</summary>The distance for each point is to its *nearest* already-chosen centroid, so it is a `min` over the first `j` centroids. Larger distance, larger probability of being picked next.</details>\n",
    "\n",
    "<details><summary>Hint 2 (pseudocode)</summary>\n",
    "\n",
    "```python\n",
    "centroids[0] = X[rng_local.integers(0, m)]\n",
    "for j in range(1, k):\n",
    "    d2 = ((X[:, None, :] - centroids[None, :j, :]) ** 2).sum(axis=-1).min(axis=1)  # (m,)\n",
    "    s = d2.sum()\n",
    "    probs = d2 / s if s > 0 else np.ones(m) / m\n",
    "    centroids[j] = X[rng_local.choice(m, p=probs)]\n",
    "```\n",
    "</details>\n",
    "\n",
    "<details><summary>Help — \"probabilities do not sum to 1\" from rng.choice</summary>Floating-point drift, or a zero-sum `d2` (all points coincide with a centroid). Normalize as `d2 / d2.sum()` and guard `d2.sum() == 0` with a uniform fallback, as in Hint 2.</details>\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "f8858bc3",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-06-10T18:44:21.360595Z",
     "iopub.status.busy": "2026-06-10T18:44:21.360522Z",
     "iopub.status.idle": "2026-06-10T18:44:21.427242Z",
     "shell.execute_reply": "2026-06-10T18:44:21.426882Z"
    },
    "collapsed": true,
    "jupyter": {
     "source_hidden": true
    },
    "tags": [
     "hide-input"
    ]
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ ok ] 8.3 k-means++ beats random (worst case)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#@title Solution { display-mode: \"form\" }\n",
    "# solution: redefines kmeans_pp_init; the check re-verifies it beats random.\n",
    "def kmeans_pp_init(X, k, rng_local):\n",
    "    m, d = X.shape\n",
    "    centroids = np.empty((k, d))\n",
    "    centroids[0] = X[rng_local.integers(0, m)]\n",
    "    for j in range(1, k):\n",
    "        d2 = ((X[:, None, :] - centroids[None, :j, :]) ** 2).sum(axis=-1).min(axis=1)\n",
    "        s = d2.sum()\n",
    "        probs = d2 / s if s > 0 else np.ones(m) / m\n",
    "        centroids[j] = X[rng_local.choice(m, p=probs)]\n",
    "    return centroids\n",
    "\n",
    "check(\"8.3 k-means++ beats random (worst case)\", _pp_beats_random, required=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "ca6b9ce0",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-06-10T18:44:21.428360Z",
     "iopub.status.busy": "2026-06-10T18:44:21.428264Z",
     "iopub.status.idle": "2026-06-10T18:44:21.494058Z",
     "shell.execute_reply": "2026-06-10T18:44:21.493588Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "random  init over 15 seeds: mean 870  worst 1294\n",
      "k++     init over 15 seeds: mean 532  worst 799\n",
      "Lower is better. k-means++ both averages lower AND has a far better worst case.\n"
     ]
    }
   ],
   "source": [
    "# scale-up off by default: the measured before/after table on the 8-blob worst case\n",
    "Xk, _ = make_blobs(n_samples=800, centers=8, cluster_std=0.50, random_state=SEED)\n",
    "n_trials = 5 if FAST else 15\n",
    "rand_in = [kmeans(Xk, 8, n_iters=KMEANS_ITERS, seed=s,\n",
    "                  init_centroids=Xk[np.random.default_rng(s).choice(800, 8, replace=False)])[2]\n",
    "           for s in range(n_trials)]\n",
    "pp_in = [kmeans(Xk, 8, n_iters=KMEANS_ITERS, seed=s,\n",
    "                init_centroids=kmeans_pp_init(Xk, 8, np.random.default_rng(s)))[2]\n",
    "         for s in range(n_trials)]\n",
    "print(f\"random  init over {n_trials} seeds: mean {np.mean(rand_in):.0f}  worst {max(rand_in):.0f}\")\n",
    "print(f\"k++     init over {n_trials} seeds: mean {np.mean(pp_in):.0f}  worst {max(pp_in):.0f}\")\n",
    "print(\"Lower is better. k-means++ both averages lower AND has a far better worst case.\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "eacdc7be",
   "metadata": {},
   "source": [
    "> **Interpretation.** Random init occasionally lands a catastrophic local minimum (the worst case), which is the failure k-means++ is designed to prevent. In production you run once, so the worst case is what bites you. This is why `init=\"k-means++\"` is sklearn's default and why `n_init=10` exists as a second safety net.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f3bfef59",
   "metadata": {},
   "source": [
    "### Choosing k: the elbow and the silhouette\n",
    "\n",
    "Inertia falls monotonically as `k` grows (at `k = m`, every point is its own cluster and inertia is 0), so \"minimize inertia\" cannot choose `k`. Two diagnostics try anyway.\n",
    "\n",
    "**Elbow:** plot inertia versus `k` and look for the bend from steep to flat. Subjective.\n",
    "\n",
    "**Silhouette:** for each point, $a(i)$ is its mean distance to its own cluster and $b(i)$ its mean distance to the nearest other cluster; the silhouette is\n",
    "$$s(i) = \\frac{b(i) - a(i)}{\\max(a(i), b(i))} \\in [-1, 1].$$\n",
    "The dataset score is the mean. It has a genuine peak at a good `k`, but is $O(m^2)$ and, like inertia, assumes roughly spherical clusters.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "534b1b2b",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-06-10T18:44:21.495264Z",
     "iopub.status.busy": "2026-06-10T18:44:21.495163Z",
     "iopub.status.idle": "2026-06-10T18:44:22.147919Z",
     "shell.execute_reply": "2026-06-10T18:44:22.147415Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1000x400 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "silhouette picks k=4 (true number of blobs is 4)\n"
     ]
    }
   ],
   "source": [
    "from sklearn.cluster import KMeans\n",
    "from sklearn.metrics import silhouette_score\n",
    "ks = range(1, 9)\n",
    "inertias = [KMeans(n_clusters=k, n_init=10, random_state=SEED).fit(X_blobs).inertia_ for k in ks]\n",
    "sil_ks = range(2, 9)  # silhouette needs >= 2 clusters\n",
    "sils = [silhouette_score(X_blobs, KMeans(n_clusters=k, n_init=10, random_state=SEED).fit_predict(X_blobs))\n",
    "        for k in sil_ks]\n",
    "best_k = list(sil_ks)[int(np.argmax(sils))]\n",
    "\n",
    "fig, ax = plt.subplots(1, 2, figsize=(10, 4))\n",
    "ax[0].plot(list(ks), inertias, \"o-\", color=\"#1E40FF\"); ax[0].set_xlabel(\"k\"); ax[0].set_ylabel(\"inertia\")\n",
    "ax[0].set_title(\"elbow (inertia falls monotonically)\")\n",
    "ax[1].plot(list(sil_ks), sils, \"o-\", color=\"#1E40FF\"); ax[1].set_xlabel(\"k\"); ax[1].set_ylabel(\"mean silhouette\")\n",
    "ax[1].axvline(best_k, ls=\":\", c=\"#888\"); ax[1].set_title(f\"silhouette peaks at k={best_k}\")\n",
    "plt.tight_layout(); plt.show()\n",
    "print(f\"silhouette picks k={best_k} (true number of blobs is 4)\")\n",
    "assert best_k == 4, \"on 4 clean blobs the silhouette peak should land at k=4 (the true count)\""
   ]
  },
  {
   "cell_type": "markdown",
   "id": "24477e8f",
   "metadata": {},
   "source": [
    "> **Interpretation.** The inertia curve bends around `k=4` but the bend is a judgment call. The silhouette curve has an actual maximum, here exactly at the true `k=4`, and that is the more defensible diagnostic when clusters are blob-shaped. Neither is reliable on non-spherical data, which is the next part's whole point.\n",
    "\n",
    "> **Key takeaways.**\n",
    "> - K-means++ spreads initial centroids by squared-distance sampling and measurably beats random init, especially on worst-case inertia.\n",
    "> - Inertia cannot pick `k` (monotone). The elbow is subjective; silhouette has a real peak but is $O(m^2)$ and spherical-biased.\n",
    "> - Both diagnostics inherit k-means' spherical assumption. Trust them on blobs, distrust them on moons.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c9053daa",
   "metadata": {},
   "source": [
    "## Part 4 — DBSCAN: density beats geometry on two moons\n",
    "\n",
    "> **Objectives.** See k-means fail on the two-moons dataset, then watch DBSCAN solve it by reasoning about density instead of distance-to-center, and feel its hyperparameter sensitivity.\n",
    "\n",
    "K-means' spherical assumption breaks on interleaved shapes. **DBSCAN** (Ester et al. 1996) assumes instead that clusters are dense regions separated by sparse ones. Two hyperparameters, `eps` (a radius) and `min_samples` (a count), sort points into three kinds:\n",
    "\n",
    "- **Core point:** has at least `min_samples` neighbors within `eps`.\n",
    "- **Border point:** within `eps` of a core point, but not core itself.\n",
    "- **Noise point:** neither. Gets label `-1`.\n",
    "\n",
    "A cluster is a connected chain of core points plus their borders. No `k` to choose, arbitrary shapes, and outliers fall out for free as `-1`.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "7c6ebd6e",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-06-10T18:44:22.148793Z",
     "iopub.status.busy": "2026-06-10T18:44:22.148715Z",
     "iopub.status.idle": "2026-06-10T18:44:22.151429Z",
     "shell.execute_reply": "2026-06-10T18:44:22.151156Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "X_moons (600, 2) · true clusters 2\n"
     ]
    }
   ],
   "source": [
    "from sklearn.datasets import make_moons\n",
    "X_moons, y_moons = make_moons(n_samples=600, noise=0.06, random_state=SEED)\n",
    "print(f\"X_moons {X_moons.shape} · true clusters {len(np.unique(y_moons))}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f1d813b6",
   "metadata": {},
   "source": [
    "> **Predict:** k-means with `k=2` on two moons. Will it split the data into the two crescents, or cut straight across them? <details><summary>Answer</summary>It cuts across. K-means draws a straight (Voronoi) boundary between two centroids, which slices both crescents in half rather than following their curve. Run the next cell to watch it happen.</details>\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "f9826981",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-06-10T18:44:22.152067Z",
     "iopub.status.busy": "2026-06-10T18:44:22.151998Z",
     "iopub.status.idle": "2026-06-10T18:44:22.209079Z",
     "shell.execute_reply": "2026-06-10T18:44:22.208673Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1000x400 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "DBSCAN found 2 clusters + 0 noise points; ARI 1.00 vs k-means 0.26\n"
     ]
    }
   ],
   "source": [
    "from sklearn.cluster import KMeans, DBSCAN\n",
    "from sklearn.metrics import adjusted_rand_score\n",
    "km_moons = KMeans(n_clusters=2, n_init=10, random_state=SEED).fit_predict(X_moons)\n",
    "db = DBSCAN(eps=0.18, min_samples=5).fit_predict(X_moons)\n",
    "n_noise = int((db == -1).sum())\n",
    "n_clusters_db = len(set(db)) - (1 if -1 in db else 0)\n",
    "ari_km = adjusted_rand_score(y_moons, km_moons)\n",
    "ari_db = adjusted_rand_score(y_moons, db)\n",
    "\n",
    "fig, ax = plt.subplots(1, 2, figsize=(10, 4))\n",
    "ax[0].scatter(X_moons[:, 0], X_moons[:, 1], s=8, c=km_moons, cmap=\"coolwarm\")\n",
    "ax[0].set_title(f\"k-means (ARI={ari_km:.2f})\")\n",
    "ax[1].scatter(X_moons[:, 0], X_moons[:, 1], s=8, c=db, cmap=\"coolwarm\")\n",
    "ax[1].set_title(f\"DBSCAN (ARI={ari_db:.2f}, {n_noise} noise)\")\n",
    "for a in ax: a.set_xticks([]); a.set_yticks([])\n",
    "plt.tight_layout(); plt.show()\n",
    "print(f\"DBSCAN found {n_clusters_db} clusters + {n_noise} noise points; ARI {ari_db:.2f} vs k-means {ari_km:.2f}\")\n",
    "assert ari_db > ari_km, \"DBSCAN should beat k-means on moons (density beats spherical here)\""
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0737f97f",
   "metadata": {},
   "source": [
    "> **Interpretation.** K-means slices both crescents; DBSCAN follows the density and recovers the two moons almost perfectly (ARI near 1.0). It also chose the number of clusters itself, which on this data it gets right.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "82a09ddd",
   "metadata": {},
   "source": [
    "### Exercise 8.4 — DBSCAN's narrow Goldilocks band\n",
    "`Difficulty 2/5 · ~12 min`\n",
    "\n",
    "DBSCAN's power comes at a cost: `eps` must be tuned, and the good range is narrow. Fill in `count_dbscan(eps)` returning `(n_clusters, n_noise)` for `DBSCAN(eps, min_samples=5)` on the moons. The check asserts the qualitative law: tiny `eps` makes (almost) everything noise, huge `eps` collapses everything into one cluster.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "970ba408",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-06-10T18:44:22.209998Z",
     "iopub.status.busy": "2026-06-10T18:44:22.209920Z",
     "iopub.status.idle": "2026-06-10T18:44:22.213772Z",
     "shell.execute_reply": "2026-06-10T18:44:22.213360Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ -- ] 8.4 dbscan eps law: not attempted yet — fill in the TODO above, then re-run.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def count_dbscan(eps, min_samples=5):\n",
    "    \"\"\"Return (n_clusters, n_noise) for DBSCAN on X_moons. n_clusters excludes noise.\"\"\"\n",
    "    # TODO 1: fit DBSCAN(eps=eps, min_samples=min_samples) on X_moons, get labels.\n",
    "    labels = None\n",
    "    attempted(labels)\n",
    "    # TODO 2: n_clusters = number of distinct labels excluding -1; n_noise = count of -1.\n",
    "    n_clusters = len(set(labels)) - (1 if -1 in labels else 0)\n",
    "    n_noise = int((np.asarray(labels) == -1).sum())\n",
    "    return n_clusters, n_noise\n",
    "\n",
    "def _eps_law():\n",
    "    nc_small, noise_small = count_dbscan(0.01)   # far too small\n",
    "    nc_big, _ = count_dbscan(5.0)                # far too large\n",
    "    assert noise_small > 0.5 * len(X_moons), \\\n",
    "        f\"eps=0.01 should make most points noise, got only {noise_small} noise\"\n",
    "    assert nc_big == 1, f\"eps=5.0 should merge everything into 1 cluster, got {nc_big}\"\n",
    "\n",
    "check(\"8.4 dbscan eps law\", _eps_law)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "501f25b4",
   "metadata": {},
   "source": [
    "<details><summary>Hint 1 (conceptual)</summary>`DBSCAN(eps=..., min_samples=...).fit_predict(X_moons)` returns the label array directly. Noise is the integer `-1`; count it with a boolean mask.</details>\n",
    "\n",
    "<details><summary>Hint 2 (pseudocode)</summary>\n",
    "\n",
    "```python\n",
    "labels = DBSCAN(eps=eps, min_samples=min_samples).fit_predict(X_moons)\n",
    "# n_clusters / n_noise lines are already written for you below the TODO\n",
    "```\n",
    "</details>\n",
    "\n",
    "<details><summary>Help — n_clusters is off by one</summary>The noise label `-1` is in `set(labels)` but is not a cluster. Subtract one when `-1` is present, which the provided line already does.</details>\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "2d9b69e1",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-06-10T18:44:22.214530Z",
     "iopub.status.busy": "2026-06-10T18:44:22.214458Z",
     "iopub.status.idle": "2026-06-10T18:44:22.228580Z",
     "shell.execute_reply": "2026-06-10T18:44:22.228134Z"
    },
    "collapsed": true,
    "jupyter": {
     "source_hidden": true
    },
    "tags": [
     "hide-input"
    ]
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ ok ] 8.4 dbscan eps law\n",
      "eps=0.01 : 0 clusters, 600 noise points\n",
      "eps=0.05 : 36 clusters, 216 noise points\n",
      "eps=0.18 : 2 clusters,   0 noise points\n",
      "eps=0.5  : 1 clusters,   0 noise points\n",
      "eps=5.0  : 1 clusters,   0 noise points\n"
     ]
    }
   ],
   "source": [
    "#@title Solution { display-mode: \"form\" }\n",
    "# solution: redefines count_dbscan; the check re-verifies the eps law.\n",
    "def count_dbscan(eps, min_samples=5):\n",
    "    labels = DBSCAN(eps=eps, min_samples=min_samples).fit_predict(X_moons)\n",
    "    n_clusters = len(set(labels)) - (1 if -1 in labels else 0)\n",
    "    n_noise = int((labels == -1).sum())\n",
    "    return n_clusters, n_noise\n",
    "\n",
    "check(\"8.4 dbscan eps law\", _eps_law, required=True)\n",
    "for e in [0.01, 0.05, 0.18, 0.5, 5.0]:\n",
    "    nc, nz = count_dbscan(e)\n",
    "    print(f\"eps={e:<5}: {nc} clusters, {nz:>3} noise points\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "54513b25",
   "metadata": {},
   "source": [
    "> **Interpretation.** The sweep shows the band: below ~0.05 almost everything is noise, around 0.18 you get the clean two-moon answer, and by 0.5 the two moons have merged into one cluster. The usable window is narrow, which is why DBSCAN practitioners plot the k-distance graph to pick `eps`. DBSCAN trades \"choose `k`\" for \"choose `eps`,\" and on variable-density data even one `eps` cannot fit all clusters (HDBSCAN is the fix).\n",
    "\n",
    "> **Key takeaways.**\n",
    "> - DBSCAN clusters by density (core / border / noise), so it handles non-spherical shapes k-means cannot, and labels outliers `-1` for free.\n",
    "> - It chooses the cluster count itself but is acutely sensitive to `eps`; the good range is narrow.\n",
    "> - On variable-density clusters a single `eps` fails. Reach for HDBSCAN there.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4638a863",
   "metadata": {},
   "source": [
    "## Part 5 — Gaussian mixtures: soft clustering, EM, and anomalies\n",
    "\n",
    "> **Objectives.** Write the multivariate Gaussian density, assemble an EM loop and verify it increases its own log-likelihood every step, then reuse the fitted mixture as a density-based anomaly detector.\n",
    "\n",
    "K-means and DBSCAN give *hard* labels. A **Gaussian Mixture Model** gives *soft* ones: each point gets a probability over clusters. The generative story is, to make a point, pick cluster $j$ with prior weight $\\pi_j$, then draw from $\\mathcal{N}(\\boldsymbol{\\mu}_j, \\boldsymbol{\\Sigma}_j)$. The density is\n",
    "$$p(\\mathbf{x}) = \\sum_{j=1}^{k} \\pi_j\\, \\mathcal{N}(\\mathbf{x} \\mid \\boldsymbol{\\mu}_j, \\boldsymbol{\\Sigma}_j).$$\n",
    "Fitting maximizes the data log-likelihood by **Expectation-Maximization**, alternating:\n",
    "\n",
    "**E step** (responsibilities, Bayes' rule on the latent cluster):\n",
    "$$\\gamma_{ij} = \\frac{\\pi_j\\, \\mathcal{N}(\\mathbf{x}_i \\mid \\boldsymbol{\\mu}_j, \\boldsymbol{\\Sigma}_j)}{\\sum_l \\pi_l\\, \\mathcal{N}(\\mathbf{x}_i \\mid \\boldsymbol{\\mu}_l, \\boldsymbol{\\Sigma}_l)}.$$\n",
    "\n",
    "**M step** (soft-weighted re-estimation):\n",
    "$$\\pi_j \\leftarrow \\tfrac{1}{m}\\sum_i \\gamma_{ij}, \\quad\n",
    "\\boldsymbol{\\mu}_j \\leftarrow \\frac{\\sum_i \\gamma_{ij}\\mathbf{x}_i}{\\sum_i \\gamma_{ij}}, \\quad\n",
    "\\boldsymbol{\\Sigma}_j \\leftarrow \\frac{\\sum_i \\gamma_{ij}(\\mathbf{x}_i-\\boldsymbol{\\mu}_j)(\\mathbf{x}_i-\\boldsymbol{\\mu}_j)^\\top}{\\sum_i \\gamma_{ij}}.$$\n",
    "\n",
    "EM's guarantee (Dempster, Laird, Rubin 1977): the log-likelihood never decreases.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8fb6295b",
   "metadata": {},
   "source": [
    "### Exercise 8.5 — The multivariate Gaussian density\n",
    "`Difficulty 3/5 · ~15 min`\n",
    "\n",
    "The density is\n",
    "$$\\mathcal{N}(\\mathbf{x}\\mid\\boldsymbol{\\mu},\\boldsymbol{\\Sigma}) = \\frac{1}{\\sqrt{(2\\pi)^d \\lvert\\boldsymbol{\\Sigma}\\rvert}}\\,\\exp\\!\\left(-\\tfrac{1}{2}(\\mathbf{x}-\\boldsymbol{\\mu})^\\top \\boldsymbol{\\Sigma}^{-1}(\\mathbf{x}-\\boldsymbol{\\mu})\\right).$$\n",
    "\n",
    "Fill in `gaussian_pdf(X, mu, Sigma)` returning one density per row of `X`. This is the full implementation ladder: your function is checked against `scipy.stats.multivariate_normal.pdf` (the reference oracle), and separately we will integrate the 1D case to 1.0 with `np.trapezoid` (the property check).\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "272c596c",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-06-10T18:44:22.229591Z",
     "iopub.status.busy": "2026-06-10T18:44:22.229511Z",
     "iopub.status.idle": "2026-06-10T18:44:22.234705Z",
     "shell.execute_reply": "2026-06-10T18:44:22.234313Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ -- ] 8.5 pdf vs scipy: not attempted yet — fill in the TODO above, then re-run.\n",
      "[ -- ] 8.5 pdf integrates to 1: not attempted yet — fill in the TODO above, then re-run.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def gaussian_pdf(X, mu, Sigma):\n",
    "    \"\"\"Multivariate normal density at each row of X (m, d). Returns (m,).\"\"\"\n",
    "    X = np.atleast_2d(X)\n",
    "    d = X.shape[1]\n",
    "    diff = X - mu                      # (m, d)\n",
    "    inv = np.linalg.inv(Sigma)         # (d, d)\n",
    "    det = np.linalg.det(Sigma)\n",
    "    # TODO 1: quadratic form q[i] = diff[i] @ inv @ diff[i], shape (m,).\n",
    "    #         np.einsum(\"ij,jk,ik->i\", diff, inv, diff) does it in one call.\n",
    "    quad = None\n",
    "    # TODO 2: normalizing constant 1 / sqrt((2*pi)^d * det).\n",
    "    norm_const = None\n",
    "    attempted(quad, norm_const)\n",
    "    return norm_const * np.exp(-0.5 * quad)\n",
    "\n",
    "def _pdf_vs_scipy():\n",
    "    from scipy.stats import multivariate_normal\n",
    "    mu = np.array([0.3, -0.4]); Sig = np.array([[1.2, 0.3], [0.3, 0.8]])\n",
    "    pts = rng.normal(size=(20, 2))\n",
    "    got = gaussian_pdf(pts, mu, Sig)\n",
    "    want = multivariate_normal.pdf(pts, mean=mu, cov=Sig)\n",
    "    check_close(got, want, atol=1e-9, msg=\"density disagrees with scipy; check the quadratic form / det\")\n",
    "\n",
    "def _pdf_integrates_to_one():\n",
    "    # 1D standard normal sampled on a fine grid; np.trapezoid (NOT np.trapz) -> area ~ 1.\n",
    "    xs = np.linspace(-6, 6, 4000).reshape(-1, 1)\n",
    "    area = float(np.trapezoid(gaussian_pdf(xs, np.zeros(1), np.eye(1)), xs[:, 0]))\n",
    "    assert abs(area - 1.0) < 1e-3, f\"a normalized 1D density integrates to 1, got {area:.4f}\"\n",
    "\n",
    "check(\"8.5 pdf vs scipy\", _pdf_vs_scipy)\n",
    "check(\"8.5 pdf integrates to 1\", _pdf_integrates_to_one)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a922c9f4",
   "metadata": {},
   "source": [
    "<details><summary>Hint 1 (conceptual)</summary>The exponent is the Mahalanobis distance squared, $(\\mathbf{x}-\\boldsymbol{\\mu})^\\top\\boldsymbol{\\Sigma}^{-1}(\\mathbf{x}-\\boldsymbol{\\mu})$, one scalar per point. `np.einsum(\"ij,jk,ik->i\", diff, inv, diff)` produces the whole vector without a loop.</details>\n",
    "\n",
    "<details><summary>Hint 2 (pseudocode)</summary>\n",
    "\n",
    "```python\n",
    "quad = np.einsum(\"ij,jk,ik->i\", diff, inv, diff)   # (m,)\n",
    "norm_const = 1.0 / np.sqrt((2 * np.pi) ** d * det)\n",
    "```\n",
    "</details>\n",
    "\n",
    "<details><summary>Help — density is nan or inf</summary>`det(Sigma)` is zero or negative (singular covariance). On well-separated data this should not happen; in EM we add `reg_covar * I` to keep it positive-definite. For this exercise the test covariances are non-singular.</details>\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "b2a4dc41",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-06-10T18:44:22.235476Z",
     "iopub.status.busy": "2026-06-10T18:44:22.235407Z",
     "iopub.status.idle": "2026-06-10T18:44:22.239683Z",
     "shell.execute_reply": "2026-06-10T18:44:22.239350Z"
    },
    "collapsed": true,
    "jupyter": {
     "source_hidden": true
    },
    "tags": [
     "hide-input"
    ]
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ ok ] 8.5 pdf vs scipy\n",
      "[ ok ] 8.5 pdf integrates to 1\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#@title Solution { display-mode: \"form\" }\n",
    "# solution: redefines gaussian_pdf; the checks re-verify against scipy and by integration.\n",
    "def gaussian_pdf(X, mu, Sigma):\n",
    "    X = np.atleast_2d(X)\n",
    "    d = X.shape[1]\n",
    "    diff = X - mu\n",
    "    inv = np.linalg.inv(Sigma)\n",
    "    det = np.linalg.det(Sigma)\n",
    "    quad = np.einsum(\"ij,jk,ik->i\", diff, inv, diff)\n",
    "    norm_const = 1.0 / np.sqrt((2 * np.pi) ** d * det)\n",
    "    return norm_const * np.exp(-0.5 * quad)\n",
    "\n",
    "check(\"8.5 pdf vs scipy\", _pdf_vs_scipy, required=True)\n",
    "check(\"8.5 pdf integrates to 1\", _pdf_integrates_to_one, required=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "80b645e9",
   "metadata": {},
   "source": [
    "> **Interpretation.** Two independent checks confirm the density: it matches scipy elementwise (the strongest oracle, since scipy is a real reference implementation) and it integrates to 1.0 over the line (the property that makes it a probability density at all). We use `np.trapezoid`; `np.trapz` was removed in NumPy 2.0 and is the bug that put this repo's CI red.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0089103c",
   "metadata": {},
   "source": [
    "### Exercise 8.6 — EM, with a log-likelihood you can trust\n",
    "`Difficulty 4/5 · ~25 min`\n",
    "\n",
    "Assemble the EM loop. The structure (init, E step, M step) is given; you fill in the four core lines. The check records the data log-likelihood $\\sum_i \\log p(\\mathbf{x}_i)$ at the *start* of every iteration and asserts it never decreases, which is EM's defining theoretical property and the single best self-grade for a from-scratch EM.\n",
    "\n",
    "This is one of the harder exercises in the book; all the stones from Exercise 8.5 need to be in place first.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "acc7279b",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-06-10T18:44:22.240999Z",
     "iopub.status.busy": "2026-06-10T18:44:22.240915Z",
     "iopub.status.idle": "2026-06-10T18:44:22.247831Z",
     "shell.execute_reply": "2026-06-10T18:44:22.247538Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ -- ] 8.6 EM log-likelihood is monotone: not attempted yet — fill in the TODO above, then re-run.\n",
      "[ -- ] 8.6 mixture weights sum to 1: not attempted yet — fill in the TODO above, then re-run.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def fit_gmm(X, k, n_iters=50, seed=0, reg_covar=1e-6):\n",
    "    \"\"\"EM for a Gaussian mixture. Returns (pi, mu, Sigma, gamma, ll_history).\"\"\"\n",
    "    rng_local = np.random.default_rng(seed)\n",
    "    m, d = X.shape\n",
    "    pi = np.ones(k) / k\n",
    "    mu = X[rng_local.choice(m, k, replace=False)].astype(float)\n",
    "    Sigma = np.tile(np.eye(d), (k, 1, 1))\n",
    "    gamma = np.zeros((m, k)); ll_history = []\n",
    "    for _ in range(n_iters):\n",
    "        # weighted[i, j] = pi[j] * N(x_i | mu_j, Sigma_j); shape (m, k)\n",
    "        weighted = np.zeros((m, k))\n",
    "        for j in range(k):\n",
    "            weighted[:, j] = pi[j] * gaussian_pdf(X, mu[j], Sigma[j])\n",
    "        # TODO 1: data log-likelihood THIS iteration = sum_i log(sum_j weighted[i, j]).\n",
    "        #         Add a tiny floor (e.g. + 1e-300) inside the log to avoid log(0).\n",
    "        ll = None\n",
    "        attempted(ll)\n",
    "        ll_history.append(float(ll))\n",
    "        # E step\n",
    "        # TODO 2: gamma = weighted normalized across clusters (rows sum to 1).\n",
    "        gamma = None\n",
    "        attempted(gamma)\n",
    "        # M step\n",
    "        Nk = gamma.sum(axis=0) + 1e-12\n",
    "        pi = Nk / m\n",
    "        for j in range(k):\n",
    "            # TODO 3: mu[j] = responsibility-weighted mean of X.\n",
    "            mu[j] = None\n",
    "            attempted(mu[j])\n",
    "            diff = X - mu[j]\n",
    "            # TODO 4: Sigma[j] = responsibility-weighted covariance, then + reg_covar*I.\n",
    "            Sigma[j] = None\n",
    "            attempted(Sigma[j])\n",
    "            Sigma[j] = Sigma[j] + reg_covar * np.eye(d)\n",
    "    return pi, mu, Sigma, gamma, ll_history\n",
    "\n",
    "def _em_monotone():\n",
    "    Xg, _ = make_blobs(n_samples=400, centers=3, cluster_std=0.60, random_state=SEED)\n",
    "    _, _, _, _, ll = fit_gmm(Xg, 3, n_iters=EM_ITERS, seed=SEED)\n",
    "    diffs = np.diff(ll)\n",
    "    assert (diffs > -1e-6).all(), (\n",
    "        f\"EM log-likelihood must not decrease; saw a drop of {diffs.min():.3g}. \"\n",
    "        f\"Check the E-step normalization (rows of gamma must sum to 1).\")\n",
    "\n",
    "def _em_recovers_pi():\n",
    "    Xg, _ = make_blobs(n_samples=600, centers=3, cluster_std=0.60, random_state=SEED)\n",
    "    pi, _, _, _, _ = fit_gmm(Xg, 3, n_iters=EM_ITERS, seed=SEED)\n",
    "    assert abs(pi.sum() - 1.0) < 1e-6, f\"mixture weights must sum to 1, got {pi.sum():.6f}\"\n",
    "\n",
    "check(\"8.6 EM log-likelihood is monotone\", _em_monotone)\n",
    "check(\"8.6 mixture weights sum to 1\", _em_recovers_pi)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f557e11f",
   "metadata": {},
   "source": [
    "<details><summary>Hint 1 (conceptual)</summary>The log-likelihood sums `log` of each point's total mixture density `weighted[i].sum()`. The responsibilities `gamma` are just `weighted` divided by that same per-row total, so rows sum to 1. The mean and covariance are the standard weighted estimators with `gamma[:, j]` as the weights.</details>\n",
    "\n",
    "<details><summary>Hint 2 (pseudocode)</summary>\n",
    "\n",
    "```python\n",
    "ll = np.log(weighted.sum(axis=1) + 1e-300).sum()\n",
    "gamma = weighted / (weighted.sum(axis=1, keepdims=True) + 1e-300)\n",
    "# inside the cluster loop:\n",
    "mu[j] = (gamma[:, j:j+1] * X).sum(axis=0) / Nk[j]\n",
    "Sigma[j] = (gamma[:, j:j+1] * diff).T @ diff / Nk[j]\n",
    "```\n",
    "</details>\n",
    "\n",
    "<details><summary>Help — log-likelihood DROPS on some iteration</summary>Almost always the E step: if `gamma` rows do not sum to 1 you are not computing a true posterior and the monotonicity guarantee is void. Print `gamma.sum(axis=1)[:5]` — it must be all ones. A drop can also come from updating `mu` and `Sigma` against *different* responsibilities; compute `gamma` once per iteration and reuse it.</details>\n",
    "\n",
    "<details><summary>Help — LinAlgError: singular matrix</summary>A near-empty cluster collapsed its covariance to singular. The `+ reg_covar * np.eye(d)` line (already provided) is the fix; make sure you did not drop it.</details>\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "00e3ae5b",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-06-10T18:44:22.248715Z",
     "iopub.status.busy": "2026-06-10T18:44:22.248641Z",
     "iopub.status.idle": "2026-06-10T18:44:22.272483Z",
     "shell.execute_reply": "2026-06-10T18:44:22.272173Z"
    },
    "collapsed": true,
    "jupyter": {
     "source_hidden": true
    },
    "tags": [
     "hide-input"
    ]
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ ok ] 8.6 EM log-likelihood is monotone\n",
      "[ ok ] 8.6 mixture weights sum to 1\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#@title Solution { display-mode: \"form\" }\n",
    "# solution: redefines fit_gmm; the checks re-verify monotonicity and the weight simplex.\n",
    "def fit_gmm(X, k, n_iters=50, seed=0, reg_covar=1e-6):\n",
    "    rng_local = np.random.default_rng(seed)\n",
    "    m, d = X.shape\n",
    "    pi = np.ones(k) / k\n",
    "    mu = X[rng_local.choice(m, k, replace=False)].astype(float)\n",
    "    Sigma = np.tile(np.eye(d), (k, 1, 1))\n",
    "    gamma = np.zeros((m, k)); ll_history = []\n",
    "    for _ in range(n_iters):\n",
    "        weighted = np.zeros((m, k))\n",
    "        for j in range(k):\n",
    "            weighted[:, j] = pi[j] * gaussian_pdf(X, mu[j], Sigma[j])\n",
    "        ll_history.append(float(np.log(weighted.sum(axis=1) + 1e-300).sum()))\n",
    "        gamma = weighted / (weighted.sum(axis=1, keepdims=True) + 1e-300)\n",
    "        Nk = gamma.sum(axis=0) + 1e-12\n",
    "        pi = Nk / m\n",
    "        for j in range(k):\n",
    "            mu[j] = (gamma[:, j:j + 1] * X).sum(axis=0) / Nk[j]\n",
    "            diff = X - mu[j]\n",
    "            Sigma[j] = (gamma[:, j:j + 1] * diff).T @ diff / Nk[j]\n",
    "            Sigma[j] = Sigma[j] + reg_covar * np.eye(d)\n",
    "    return pi, mu, Sigma, gamma, ll_history\n",
    "\n",
    "check(\"8.6 EM log-likelihood is monotone\", _em_monotone, required=True)\n",
    "check(\"8.6 mixture weights sum to 1\", _em_recovers_pi, required=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "24e14c1f",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-06-10T18:44:22.273533Z",
     "iopub.status.busy": "2026-06-10T18:44:22.273453Z",
     "iopub.status.idle": "2026-06-10T18:44:22.364410Z",
     "shell.execute_reply": "2026-06-10T18:44:22.364013Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA94AAAGGCAYAAACNL1mYAAAAOnRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjEwLjMsIGh0dHBzOi8vbWF0cGxvdGxpYi5vcmcvZiW1igAAAAlwSFlzAAAPYQAAD2EBqD+naQABAABJREFUeJzs3Xd4FMUbwPHv3qVceiMVQhIIEHoHqUFakN6rNLGgFEEQQX5SRaRKExBFsAAC0hFEqoD03ntCTyG9l7v9/XHkyOUuDRJCYD7Pkwdud3Z29i65u3dn5h1JlmUZQRAEQRAEQRAEQRAKhKKwGyAIgiAIgiAIgiAIrzMReAuCIAiCIAiCIAhCARKBtyAIgiAIgiAIgiAUIBF4C4IgCIIgCIIgCEIBEoG3IAiCIAiCIAiCIBQgEXgLgiAIgiAIgiAIQgESgbcgCIIgCIIgCIIgFCAReAuCIAiCIAiCIAhCARKBtyAIgiAIgiAIgiAUIBF4C0IurFy5EkmSCAoKeunnPnDgAJIkceDAAd22AQMG4O3trXscFBSEJEnMnj07X86ZuX4ASZKYNGmS7vGkSZOQJIknT57kyznzQ5MmTWjSpEmuysbFxeHi4sKqVasKtlFCgUtNTcXT05PFixcXdlMEQcgnAwYMwNraulDbkNvP/vTPw+fh7e1N27Ztn+vYrGT+vH4dvQnXKLx+ROAtFCnpH4JZ/Rw7dkxXNn3b+++/b7Su8ePH68q8SsGj8HLMnz8fGxsbevbsWdhNyRc7dux4Y7+EmJqa8tlnnzFt2jSSkpIKuzmC8MoJDAxk6NChlC1bFktLSywtLalQoQJDhgzhwoULemXTg0iFQsH9+/cN6oqJicHCwgJJkhg6dKhue/oNYEmS+Prrr422o0+fPkiSVOgBtfD6OXLkCJMmTSIqKirf67569SqtWrXC2toaR0dH+vbtS1hYWK6OjYuLY8SIEZQoUQJzc3PKly/PkiVLjJbdvXs3DRs2xNLSEgcHB7p27Wr0po+3t7fR78CDBw9+kcsUXgKTwm6AIDyPKVOm4OPjY7Dd19dX77FKpWLDhg0sXrwYMzMzvX1r1qxBpVIVyS/qP/74IxqN5qWeMzExEROT1+MtIzU1lfnz5zNy5EiUSmVhNydf7Nixg++///6NDb4HDhzI2LFjWb16Ne+9915hN0cQXhnbt2+nR48emJiY0KdPH6pWrYpCoeDatWts3LiRJUuWEBgYiJeXl95x5ubmrFmzhjFjxuht37hxY7bnU6lUrFmzhv/973962+Pj49myZQsqlSp/Lkx4o2X+TnLkyBEmT57MgAEDsLe3z7fzPHjwgMaNG2NnZ8c333xDXFwcs2fP5uLFi5w4ccLgu2VGarWagIAATp06xZAhQyhTpgy7du3ik08+ITIyki+//FJXdvv27XTo0IEaNWrw7bffEhMTw/z582nYsCFnz57F2dlZr+5q1aoxatQovW1ly5bNt+sWCsbr8S1aeOO888471KpVK8dyrVq1YuvWrezcuZMOHTroth85coTAwEC6dOnChg0bCrKpBcLU1PSln/N1+rK0fft2wsLC6N69e2E3Rcgn9vb2tGzZkpUrV4rAWxCeun37Nj179sTLy4u9e/fi7u6ut3/GjBksXrwYhcJwAGTr1q2NBt6rV6+mTZs2WX52tm7dmo0bN3L+/HmqVq2q275lyxZSUlJo1aoV+/bty4ere3EajYaUlJTX6vPtdZbx9XpZr9k333xDfHw8p0+fpmTJkgDUqVOHFi1asHLlSj788MMsj924cSNHjhxh+fLlus+ljz/+mK5duzJ16lTef/99XFxcAPjiiy8oVaoU//33ny6Yb9eunS4QnzNnjl7dxYsX59133y2ISxYKkBhqLrzWihcvTuPGjVm9erXe9lWrVlG5cmUqVar0QvUvXryYihUrYm5ujoeHB0OGDDE6zOn777+nVKlSWFhYUKdOHQ4dOpSn+ciZGZuDnZksy3z44YeYmZnp9VD8/vvv1KxZEwsLCxwdHenZs6fR4YSZZTWfKioqSneH2c7OjoEDB5KQkKBXJi0tjalTp1K6dGnMzc3x9vbmyy+/JDk52aC+3D6ny5Yto3Tp0nrPaW5t3rwZb29vSpcurbc9fU7hvXv3aNu2LdbW1hQvXpzvv/8egIsXL9K0aVOsrKzw8vIy+L0CuHPnDt26dcPR0RFLS0veeust/vrrL70y6fP2161bx7Rp0yhRogQqlYpmzZpx69YtgzrXr1+ve82KFSvGu+++y8OHD/Xand7GjMPO0sXHxzNq1Cg8PT0xNzenXLlyzJ49G1mW9c6TPnR08+bNVKpUCXNzcypWrMjff/9t0KaHDx/y3nvv4erqqiv3888/5/TU64ajrly50mBfVnkErl27Rvfu3bG1tcXJyYlPP/3U6EiVFi1acPjwYSIiInJshyC8CWbOnEl8fDwrVqwwCLoBTExMGD58OJ6engb7evfuzblz57h27ZpuW3BwMPv27aN3795ZnrNevXr4+PgY/dxt1aoVjo6OebqGhw8f0rFjR6ytrXF2dmb06NGo1Wq9MrNnz6Z+/fo4OTlhYWFBzZo1+fPPPw3qSn+PW7Vqle5zJv397fLlyzRt2hQLCwtKlCjB119//UIjy1asWEHTpk1xcXHB3NycChUqZDnEGOCff/6hWrVqqFQqKlSoYHRkQVRUFCNGjNC9l/v6+jJjxowc2xkbG8uIESPw9vbG3NwcFxcXWrRowZkzZ577+jLL+NyWK1cOlUpFzZo1OXjwoF65rL6/GJsnn93rlfHzYtKkSXz++ecA+Pj46D4Dg4KC8Pf317sBlFG5cuUICAjI9ro2bNhA27ZtdUE3QPPmzSlbtizr1q3L9tj07yWZp7T17NmTpKQktmzZAkBERARXrlyhU6dOej3oVatWpXz58vzxxx9G609JSSE+Pj7bNgivFhF4C0VSdHQ0T5480fsJDw83WrZ3795s27aNuLg4QBsErl+/PtsvDrkxadIkhgwZgoeHB3PmzKFLly788MMPtGzZktTUVF25JUuWMHToUEqUKMHMmTNp1KgRHTt25MGDBy90/uyo1WoGDBjAr7/+yqZNm+jcuTMA06ZNo1+/fpQpU4a5c+cyYsQI9u7dS+PGjZ97XlT37t2JjY1l+vTpdO/enZUrVzJ58mS9Mu+//z4TJkygRo0afPfdd/j7+zN9+nSDD6PcPqfLly/no48+ws3NjZkzZ9KgQQPat2+fqxsIoB3xUKNGDaP71Go177zzDp6ensycORNvb2+GDh3KypUradWqFbVq1WLGjBnY2NjQr18/AgMDdceGhIRQv3593VCy9DnH7du3Z9OmTQbn+vbbb9m0aROjR49m3LhxHDt2jD59+uiVWblyJd27d0epVDJ9+nQ++OADNm7cSMOGDXWv2UcffUSLFi0A+O2333Q/oL0B0759e7777jtatWrF3LlzKVeuHJ9//jmfffaZQZsOHz7MJ598Qs+ePZk5cyZJSUl06dJF7+8rJCSEt956iz179jB06FDmz5+Pr68vgwYNYt68ebl6DfKie/fuJCUlMX36dFq3bs2CBQuM9jLUrFkTWZY5cuRIvrdBEIqi7du34+vrS926dfN8bOPGjSlRooReAL127Vqsra1p06ZNtsf26tWLP/74Q3dz78mTJ/zzzz95/txNH6rr5OTE7Nmz8ff3Z86cOSxbtkyv3Pz586levTpTpkzhm2++wcTEhG7duhnc9ATYt28fI0eOpEePHsyfPx9vb2+Cg4N5++23OXfuHGPHjmXEiBH8+uuvzJ8/P0/tzWjJkiV4eXnx5ZdfMmfOHDw9Pfnkk090N0kzunnzJj169OCdd95h+vTpuvbv3r1bVyYhIQF/f39+//13+vXrx4IFC2jQoAHjxo0z+l6e0eDBg1myZAldunRh8eLFjB49GgsLC65evfrc12fMv//+y4gRI3j33XeZMmUK4eHhtGrVikuXLj13ncZer8w6d+5Mr169APjuu+90n4HOzs707duXCxcuGLTh5MmT3LhxI9te44cPHxIaGmp0hGWdOnU4e/Zstm1PTk5GqVQaDEe3tLQE4PTp07pyABYWFgZ1WFpa8ujRI4KDg/W279u3D0tLS6ytrfH29n6h31XhJZIFoQhZsWKFDBj9MTc31ysLyEOGDJEjIiJkMzMz+bfffpNlWZb/+usvWZIkOSgoSJ44caIMyGFhYbk6b2BgoCzLshwaGiqbmZnJLVu2lNVqta7cokWLZED++eefZVmW5eTkZNnJyUmuXbu2nJqaqiu3cuVKGZD9/f1zvOb9+/fLgLx//37dtv79+8teXl66x4GBgTIgz5o1S05NTZV79OghW1hYyLt27dKVCQoKkpVKpTxt2jS9+i9evCibmJjobc9cvyxrn8+JEyfqHqc/d++9955euU6dOslOTk66x+fOnZMB+f3339crN3r0aBmQ9+3bJ8ty7p/TlJQU2cXFRa5WrZqcnJysK7ds2bJcPaepqamyJEnyqFGjDPb1799fBuRvvvlGty0yMlK2sLCQJUmS//jjD932a9euGTwnI0aMkAH50KFDum2xsbGyj4+P7O3trbuu9Ne0fPnyetcwf/58GZAvXryod62VKlWSExMTdeW2b98uA/KECRN024YMGSIbe0vfvHmzDMhff/213vauXbvKkiTJt27d0m0DZDMzM71t58+flwF54cKFum2DBg2S3d3d5SdPnujV2bNnT9nOzk5OSEgwaEe69N/VFStWGOzL6nesffv2euU++eQTGZDPnz+vt/3Ro0cyIM+YMSPL8wvCmyI6OloG5I4dOxrsi4yMlMPCwnQ/Gf9mM34ujh49Wvb19dXtq127tjxw4EBZlp99xqbL+Dl06dIlvffC77//Xra2tpbj4+Pl/v37y1ZWVjm2P/39eMqUKXrbq1evLtesWVNvW+b3nJSUFLlSpUpy06ZN9bYDskKhkC9fvqy3Pf29+/jx47ptoaGhsp2dnd5nf1bSn7Ps2iTLshwQECCXKlVKb5uXl5cMyBs2bNBti46Olt3d3eXq1avrtk2dOlW2srKSb9y4oXf82LFjZaVSKd+7d0/vOjO+l9rZ2em9VrkVHx8vX716NcfvSOnnBORTp07ptt29e1dWqVRyp06ddNuMfb+QZePPYVavV/q+jNc4a9Yso69VVFSUrFKp5C+++EJv+/Dhw2UrKys5Li4uy2s6efKkDMi//vqrwb7PP/9cBuSkpKQsj58zZ47BdwJZ1r5mgNy2bVtZlmVZrVbL9vb2crNmzfTKPXnyRLaysjJ4Xtu1ayfPmDFD3rx5s7x8+XK5UaNGMiCPGTMmy7YIrwbR4y0USd9//z27d+/W+9m5c6fRsg4ODrRq1Yo1a9YA2vlp9evXN0gkkxd79uwhJSWFESNG6M2N++CDD7C1tdXdZT916hTh4eF88MEHeklA+vTpg4ODw3OfPyspKSl069aN7du3s2PHDlq2bKnbt3HjRjQaDd27d9cbKeDm5kaZMmXYv3//c50zcxbNRo0aER4eTkxMDKBN+gUY3JFPTwqS/lzl5TkNDQ1l8ODBeneRBwwYgJ2dXY7tjYiIQJblbJ//jJnw7e3tKVeuHFZWVnpzwsuVK4e9vT137tzRbduxYwd16tShYcOGum3W1tZ8+OGHBAUFceXKFb3zDBw4UO8aGjVqBKCrM/1aP/nkE735bG3atMHPz89ob05mO3bsQKlUMnz4cL3to0aNQpZlg7+b5s2b6w3Br1KlCra2tro2ybLMhg0baNeuHbIs6/0uBQQEEB0dna/DFwGGDBmi93jYsGG6a8so/TUVqxQIArr3YGMZxJs0aYKzs7Pux1gvLGhHjN26dYuTJ0/q/s1Nr3XFihWpUqWK3uduhw4ddD19eWHsMybj+y7o9xRGRkYSHR1No0aNjL4X+fv7U6FCBb1tO3bs4K233qJOnTq6bc7OzgYjkPIiY5vSR+n5+/tz584doqOj9cp6eHjQqVMn3WNbW1v69evH2bNndT2d69evp1GjRjg4OOi97zZv3hy1Wm0wpDsje3t7jh8/zqNHj/J0DSdOnKB8+fIsWrQoV+Xr1atHzZo1dY9LlixJhw4d2LVrl8H0gNwy9nrlhZ2dHR06dGDNmjW6ERhqtZq1a9fSsWNHrKyssjw2MTER0CYazCz9Mzm9jDG9e/fGzs6O9957j927dxMUFMSyZct0S1+mH6tQKPjoo4/Yu3cv48aN4+bNm5w+fZru3buTkpJicJ6tW7cyZswYOnTowHvvvce///5LQEAAc+fOLdDRlMKLE8nVhCKpTp06uUqulq5379707duXe/fusXnzZmbOnPlC57979y6gDb4yMjMzo1SpUrr96f9mzrZuYmJiMFwq8zAiOzs7o8OOsjN9+nTi4uLYuXOnwfzxmzdvIssyZcqUMXrs8yZsyzjvCZ4FP5GRkdja2nL37l0UCoXBc+Dm5oa9vb3Bc5Xb5zTzdZiamlKqVKlctzv9AzgzlUplkD3Uzs6OEiVKGMw/s7OzIzIyUvf47t27Rod0li9fXrc/Y16B7J679PJg+JwA+Pn5cfjwYeMXl8Hdu3fx8PDAxsYmyzZllLlN6e1Kb1NYWBhRUVEsW7bMYLhnutDQ0BzblReZX+vSpUujUCgMlllJf02fdz1dQXidpP/Np0+zyuiHH34gNjaWkJCQbIfaVq9eHT8/P1avXo29vT1ubm40bdo0V+fv3bs3c+bMYeTIkRw5ckQvg3NuGXs/zvh+lG779u18/fXXnDt3Ti93iLH3AmMromT13m3svTe3/vvvPyZOnMjRo0cN8p5ER0fr3Sj29fU1aGt6huqgoCDc3Ny4efMmFy5cMHg+0mX3vjtz5kz69++Pp6cnNWvWpHXr1vTr1y9Pn5m5Yez7RdmyZUlISCAsLAw3N7c812ns9cqrfv36sXbtWg4dOkTjxo3Zs2cPISEh9O3bN9vj0r+DGctHk55nJLvvaW5ubmzdupW+ffvqOkJsbW1ZuHAh/fv317spNmXKFJ48ecLMmTP59ttvAWjZsiWDBg1i6dKl2S7BJ0kSI0eOZNeuXRw4cEAkXXuFicBbeCO0b98ec3Nz+vfvT3Jy8iuZzTpz4psVK1YwYMCAPNUREBDA33//zcyZM2nSpIleL6lGo0GSJHbu3Gl0Ca3nXVc1q+W4Mge2r0ow5OjoiCRJBl/c0mV1Pbm9zrwoiDpfVE5tSk/i8+6779K/f3+jZatUqZJl/Vn9HuSlNySrOtJf02LFiuW6LkF4XdnZ2eHu7m50fm16kGlsjeDMevfuzZIlS7CxsaFHjx5GM6Ab06tXL8aNG8cHH3yAk5OT3gis3MrNco+HDh2iffv2NG7cmMWLF+Pu7o6pqSkrVqwwmgAzrze0n8ft27dp1qwZfn5+zJ07F09PT8zMzNixYwfffffdcyVt02g0tGjRwiDLfLrslpLq3r07jRo1YtOmTfzzzz/MmjWLGTNmsHHjRt55550sj2vSpEm+fx7l9TMgP16vgIAAXF1d+f3332ncuDG///47bm5uNG/ePNvj0r+XPX782GDf48ePcXR0NNobnlHjxo25c+cOFy9eJD4+nqpVq+pGHmR8zczMzPjpp5+YNm0aN27cwNXVlbJly9K7d2+jnReZpSdIFMlFX20i8BbeCBYWFnTs2JHff/+dd95554W/mKcPU79+/breHeOUlBQCAwN1b+bp5W7dusXbb7+tK5eWlkZQUJBegJIxiQpoh+rl1VtvvcXgwYNp27Yt3bp1Y9OmTboh7qVLl0aWZXx8fF7qWo9eXl5oNBpu3ryp62UFbYKuqKgo3XOU1+f05s2bej0vqampBAYGZpm9NJ2JiQmlS5fWS4qWX7y8vLh+/brB9vSswHmd3pDxOcncy3T9+nW9+rL6MuPl5cWePXuIjY3V6/V+3jY5OztjY2ODWq3O8UuLMem9+pmT+WXuec/o5s2ber0et27dQqPRGIwaSX9NM/6eCcKbrE2bNvz000+cOHFCbxh1XvTu3ZsJEybw+PFjXdLG3ChZsiQNGjTgwIEDfPzxx3rTrfLThg0bUKlU7Nq1Sy8IWrFiRa7r8PLy4ubNmwbbjb2f58a2bdtITk5m69ateqOIsprSdevWLWRZ1nsfv3HjBoDufa506dLExcU91/suaIPITz75hE8++YTQ0FBq1KjBtGnTsg2888rYc3jjxg0sLS11PfUODg5Gk7lm9xmQG9nd3FcqlfTu3ZuVK1cyY8YMNm/ezAcffJDjjZ3ixYvj7OzMqVOnDPadOHGCatWq5aptSqVSr+yePXsAjL6Wrq6uuLq6AtqbEQcOHKBu3bo5do6kT7/IakSE8GoQc7yFN8bo0aOZOHEiX3311QvX1bx5c8zMzFiwYIHe3eDly5cTHR2ty/haq1YtnJyc+PHHH0lLS9OVW7VqlUGPa/PmzfV+jC39ktu2/fHHH/z999/07dtXd2e9c+fOKJVKJk+ebHAHW5blLLPCv6jWrVsDGGS7njt3LoDuucrLc+rs7MzSpUt1c59Am/07t5nZ69WrZ/SD9EW1bt2aEydOcPToUd22+Ph4li1bhre3d57nqdWqVQsXFxeWLl2qN9Rt586dXL16VS+zcPo8tczPQevWrVGr1QZz9L777jskScrzly6lUkmXLl3YsGGD0Z60sLCwbI+3tbWlWLFiBvMR0+e8GZN5/unChQsBDNp++vRpJEmiXr162bZBEN4UY8aMwdLSkvfee4+QkBCD/bnpzSxdujTz5s1j+vTpeQ7ev/76ayZOnKjLy1AQlEolkiTp9ZgGBQWxefPmXNfRunVrjh07xokTJ3TbwsLCWLVq1XO3CfSf3+jo6CxvBjx69Ehv5YuYmBh+/fVXqlWrphue3b17d44ePcquXbsMjo+KitL7jpGRWq02mFPu4uKCh4eH0SHUL+Lo0aN68+rv37/Pli1baNmype45KV26NNHR0Vy4cEFX7vHjx0ZX/siLrD4D0/Xt25fIyEg++ugj4uLicj0cu0uXLmzfvl1v1ZS9e/dy48YNunXrptuWmprKtWvXjPaOZxQWFsaMGTOoUqVKjjdRZs+ezePHj3U5cUDbo515dEBqairffvstZmZmep08wqtH9HgLRdLOnTv11hZNV79+/SznLFWtWjXH3tDccnZ2Zty4cUyePJlWrVrRvn17rl+/zuLFi6ldu7buDd3MzIxJkyYxbNgwmjZtSvfu3QkKCmLlypWULl26wIZfd+zYkRUrVtCvXz9sbW354YcfKF26NF9//TXjxo0jKCiIjh07YmNjQ2BgIJs2beLDDz9k9OjR+d6WqlWr0r9/f5YtW0ZUVBT+/v6cOHGCX375hY4dO+o+JHL7nJqamvL111/z0Ucf0bRpU3r06EFgYCArVqzI9Xy1Dh068Ntvv3Hjxo187f0fO3Ysa9as4Z133mH48OE4Ojryyy+/EBgYyIYNG3I9RDOdqakpM2bMYODAgfj7+9OrVy9CQkJ0S6qMHDlSVzY9oc3w4cMJCAhAqVTSs2dP2rVrx9tvv8348eMJCgqiatWq/PPPP2zZsoURI0YYrGWeG99++y379++nbt26fPDBB1SoUIGIiAjOnDnDnj17chzq9v777/Ptt9/y/vvvU6tWLQ4ePKjr3TEmMDCQ9u3b06pVK44ePcrvv/9O7969Df6ed+/eTYMGDXBycsrzNQnC66hMmTKsXr2aXr16Ua5cOfr06UPVqlWRZZnAwEBWr16NQqGgRIkS2dbz6aefPtf5/f398ff3f65jc6tNmzbMnTuXVq1a0bt3b0JDQ/n+++/x9fXVC+6yM2bMGH777TdatWrFp59+ipWVFcuWLcPLyyvXdWTUsmVLzMzMaNeunS7Q+/HHH3FxcTEamJUtW5ZBgwZx8uRJXF1d+fnnnwkJCdEL1D///HO2bt1K27ZtGTBgADVr1iQ+Pp6LFy/y559/EhQUZHQ0X2xsLCVKlKBr165UrVoVa2tr9uzZw8mTJ5kzZ06ery07lSpVIiAggOHDh2Nubq67oZpxidGePXvyxRdf0KlTJ4YPH05CQgJLliyhbNmyL5SYM/0zcPz48fTs2RNTU1PatWunC8irV69OpUqVWL9+PeXLl89ySdHMvvzyS9avX8/bb7/Np59+SlxcHLNmzaJy5coMHDhQV+7hw4eUL1+e/v37s3LlSt12f39/6tWrh6+vL8HBwSxbtoy4uDi2b9+u953g999/Z8OGDTRu3Fj3Gq1bt47333+fLl266Mpt3bqVr7/+mq5du+Lj40NERASrV6/m0qVLfPPNN881j154iV5mCnVBeFHZLSdGpiWKyLTUiTHPu5xYukWLFsl+fn6yqamp7OrqKn/88cdyZGSkwfELFiyQvby8ZHNzc7lOnTryf//9J9esWVNu1apVjtec1+XEMlq8eLEMyKNHj9Zt27Bhg9ywYUPZyspKtrKykv38/OQhQ4bI169fz7J+Wc56qafMz52x5yo1NVWePHmy7OPjI5uamsqenp7yuHHjjC7DkdvndPHixbKPj49sbm4u16pVSz548KDs7++fqyXakpOT5WLFislTp07V257VMjf+/v5yxYoVDbZ7eXnJbdq00dt2+/ZtuWvXrrK9vb2sUqnkOnXqyNu3b9crk/6arl+/Xm97VkttrV27Vq5evbpsbm4uOzo6yn369JEfPHigVyYtLU0eNmyY7OzsLEuSpLcsS2xsrDxy5EjZw8NDNjU1lcuUKSPPmjVL1mg0enVk9Tfj5eUl9+/fX29bSEiIPGTIENnT01M2NTWV3dzc5GbNmsnLli0zOD6zhIQEedCgQbKdnZ1sY2Mjd+/eXQ4NDc3yd+zKlSty165dZRsbG9nBwUEeOnSo3vJqsqxdMsbMzEz+6aefcjy/ILxpbt26JX/88ceyr6+vrFKpZAsLC9nPz08ePHiwfO7cOb2yuf1czPx+kdXnUGZ5WU7MWDljy04tX75cLlOmjGxubi77+fnJK1asyHJ5qqy+F1y4cEH29/eXVSqVXLx4cXnq1Kny8uXLn3s5sa1bt8pVqlSRVSqV7O3tLc+YMUP++eefDepL/xzZtWuXXKVKFd01ZP58kGXte/m4ceNkX19f2czMTC5WrJhcv359efbs2XJKSoredaa/lyYnJ8uff/65XLVqVdnGxka2srKSq1atKi9evDjba8qr9Of2999/170W1atX1/vuku6ff/6RK1WqJJuZmcnlypWTf//99zy/Xpk/L2RZu+Ra8eLFZYVCYfR1mzlzpsGSoblx6dIluWXLlrKlpaVsb28v9+nTRw4ODtYrk/77n/mzcuTIkXKpUqVkc3Nz2dnZWe7du7d8+/Ztg3McP35cbty4sezg4CCrVCq5atWq8tKlSw0+p0+dOiW3a9dOLl68uGxmZiZbW1vLDRs2lNetW5enaxIKhyTLhZjFRxDeUBqNBmdnZzp37syPP/5Y2M15I02dOpUVK1Zw8+bNXCXwEV6+SZMmMXnyZMLCwnLMyzBv3jxmzpzJ7du3X0ryJEEQBOEZSZIYMmRIrpceKwzz589n5MiRBAUFGV3BQxAKmpjjLQgFLCkpyWAe3a+//kpERITBkl/CyzNy5Eji4uL4448/CrspwgtKTU1l7ty5/O9//xNBtyAIgmBAlmWWL1+Ov7+/CLqFQiPmeAtCATt27BgjR46kW7duODk5cebMGZYvX06lSpX0EnMIL5e1tXW+rzctFA5TU1Pu3btX2M0QBEEQXjHx8fFs3bqV/fv3c/HiRbZs2VLYTRLeYCLwFoQC5u3tjaenJwsWLCAiIgJHR0f69euny0ApCIIgCIIg5L+wsDB69+6Nvb09X375Je3bty/sJglvMDHHWxAEQRAEQRAEQRAKkJjjLQiCIAiCIAiCIAgFSATegiAIgiAIgiAIglCAxBzvAqDRaHj06BE2NjZIklTYzREEQRCEfCHLMrGxsXh4eKBQ5O3evfhsFARBEF43eflcFIF3AXj06BGenp6F3QxBEARBKBD379+nRIkSeTpGfDYKgiAIr6vcfC6KwLsA2NjYANoXwNbWtpBbIwiCIAj5IyYmBk9PT93nXF6Iz0ZBEAThdZOXz0UReBeA9CF0tra24suFIAiC8Np5nqHi4rNREARBeF3l5nNRJFcTBEEQBEEQBEEQhAIkAm9BEARBEARBEARBKEAi8BYEQRAEQRAEQRCEAiQCb0EQBEEQBEEQBEEoQCLwFgRBEARBEARBEIQCVGQC72nTplG/fn0sLS2xt7c32L9y5UokSTL6Exoaqit34MABatSogbm5Ob6+vqxcudKgru+//x5vb29UKhV169blxIkTBXhlgiAIgiAIgiAIwuusyCwnlpKSQrdu3ahXrx7Lly832N+jRw9atWqlt23AgAEkJSXh4uICQGBgIG3atGHw4MGsWrWKvXv38v777+Pu7k5AQAAAa9eu5bPPPmPp0qXUrVuXefPmERAQwPXr13X1CG8etRpOXITQcHBxgjqVQal8eftfhTaIa3wzruFNuMZXoQ2vwjUKgiAIgvDySLIsy4XdiLxYuXIlI0aMICoqKttyYWFhFC9enOXLl9O3b18AvvjiC/766y8uXbqkK9ezZ0+ioqL4+++/Aahbty61a9dm0aJFAGg0Gjw9PRk2bBhjx47NVRtjYmKws7MjOjparFX6Gth5ECYvgsdhz7a5O8PEofBO44Lf/yq0QVzjm3ENb8I1vg7XkB/X+Lxe5PNNfDYKgiAIr5u8fLa9toH3nDlzmDp1Ko8fP8bCwgKAxo0bU6NGDebNm6crt2LFCkaMGEF0dDQpKSlYWlry559/0rFjR12Z/v37ExUVxZYtW4yeKzk5meTkZN3jmJgYPD09xZeLIiSrnqGdB+HjiZD5j0R6+u+HPWDZ2oLbv2Sy9t/CbIO4xjfjGt6Ea3wdriE/rnHJ5OcPvkXgLQhCboU/uE/g2ZOYWVpRvoE/pipVYTdJEPKdCLyBChUq0KRJExYvXqzbVrZsWQYOHMi4ceN023bs2EGbNm1ISEggMjKS4sWLc+TIEerVq6crM2bMGP7991+OHz9u9FyTJk1i8uTJBtvFl4uiIaueoa8+gamL9bdnplCARlMw+yXAtZj2/8FPCqcNBb3/dbjG1+Eactr/Olzj63ANOe3PzTVKgJsz/Lfm+Yadi8BbEITceHDlEuun/Q9ZrUaWZYqV9Kb31Nki+BZeO3n5bCvU5Gpjx47NMiFa+s+1a9fyXO/Ro0e5evUqgwYNKoBWGxo3bhzR0dG6n/v377+U8wovLr1HO3NwHRwGn0zOPuiG7L8gv+h+Ge2X5+yChIJuQ0Hvfx2u8XW4hpz2vw7X+DpcQ077c3ONMtr3tRMXsz+PIAjCizi89jdd0A3w5F4Q144eLORWCULhKtTkaqNGjWLAgAHZlilVqlSe6/3pp5+oVq0aNWvW1Nvu5uZGSEiI3raQkBBsbW2xsLBAqVSiVCqNlnFzc8vyfObm5pibm+e5ncLLY2woOWh7uo0N+ShSw0AEQRDyKDS8sFsgCMLrLDkhnoyDaiVJIiUhsRBbJAiFr1ADb2dnZ5ydnfO1zri4ONatW8f06dMN9tWrV48dO3bobdu9e7duWLmZmRk1a9Zk7969ujneGo2GvXv3MnTo0Hxtp/DyZDWUvGebnHu0BUEQXkcuToXdAkEQXmflGzbh0OqVgDboVihN8Kleq3AbJQiFrMgsJ3bv3j0iIiK4d+8earWac+fOAeDr64u1tbWu3Nq1a0lLS+Pdd981qGPw4MEsWrSIMWPG8N5777Fv3z7WrVvHX3/9pSvz2Wef0b9/f2rVqkWdOnWYN28e8fHxDBw4sMCvUch/WSVHexwG36188foVCpA1WfeQv8j+jPM1Q54UzDkKe//rcI2vwzXktP91uMbX4Rpy2p+ba0yf450+6kcQhPyVFBfHqe2biI+KoGSlqvg18EeSpJwPfM3UbtcZgOtHD2FuaUWDHn1x9CheyK0ShMJVqHO882LChAlUr16diRMnEhcXR/Xq1alevTqnTp3SK7d8+XI6d+6Mvb29QR0+Pj789ddf7N69m6pVqzJnzhx++ukn3RreoF0PfPbs2UyYMIFq1apx7tw5/v77b1xdXQv6EoXnpFbD0XOwZa/2X7X62fashpLnVeaPTOnpzwfdCm4/wKRh2p/CaoO4xjfjGt6Ea3wdriE/rhG0S4qJ9bwFIf+lpiSzZsLnnNiynsv/7mPHwtmc3LqhsJtVKCSFgjodutL32/l0n/ANxcuVL+wmCUKhKzI93itXrmTlypU5ljty5Ei2+5s0acLZs2ezLTN06FAxtLyIyG6tWnvbFxtKnt4zZCy7uVuG9XCrVzBsQ37uB+3yPwV5jsLe/zpc4+twDW/CNb4O15Af1ygIQv57cPkiEQ/1E+ye2raROh26FlKLBEF4lRS55cSKArFkysuR3RrbMlClHFy4nru60o/J+BierXeb1Trf6Qp6/8s4R2HvfxXaIK7hzbjGV6ENr8I1Pg+xnJggZO3OmZNsmjFZb5vK2oYhy9cYLS/LMhf27OTakYOYW1pRr0svXEv5Pvf5g86fIfDsKVQ2NlQPaIcqw1TM/JCalMSTB3extLXHzkWMBBUEeM3X8S4KxJeLgqdWQ4Ne+ZMc7bOBsGa78V5z0TMkCILwjAi8BSFrqclJ/PbFp0SFPAZA1mho0P1d3urS02j5Mzu3sX/lD4B2aLbSxJT+sxZh7+ae53NfObiPnd/PRaFUIssydi5u9J0xHzOVhUFZWZa5/O9erh85iJmFJXU7dcfFO/tVhMIf3GPdlC9JiI4CoG6n7jTs2S/P7XwdyRoNdy+cJSEmmuJ+FcVNiTdMXj7bisxQc+HNZazX5sTF3AXd1pYQn5B9kqGhfbQ/+d0zJAiCIAjCm8PUXEXPKTM5uXUD8ZHa5GoVmzTPsvzlf/fo/i9rNKhTU7h9+jg123TM87mPbVoLgOZpopuo4EfcPn2C8g38Dcpe2PM3e376HtAG/HfOnqT/rO+xd8166dxdS+eTGBuje3x80zqcPL3wq9/4jUwel07WaNj23XRunjgKgNLUlM5jJ1OyUpVCbpnwKhKBt/BKy2oOd2vDzxGjerSGn//Meih5xiRD9aq9eHsFQRAEQXhzWdra4f/ue7kqa2puDpIETwefyrKMiZn5c51Xk6Y2si3NaNmL+//R/V/WaFCnpHDr5FFqte2UZf1RwY+RNRq9bTsWzOLKwX10/Px/KE1Mn6vdRd3di+d0QTeAOi2NfSt/YMDs7wuxVcKrqshkNRfePOlzuDP3bD8Og+V/5q6OFg2087TdMi0X7+b8bP62IAiCIAjCy/ZWl15IkoSkUCJJEvau7vg1eL4vJlVbtn76PwlJocDC1i7LdbNNzcy0Af9TMtqe2uy4lymHpDAMG+6eP8PZndvy1Nagc6fZ89NiDv/xK/FRkXk69lWTGBOtv0GWdcPxBSEz0eMtFDpjQ8nhxZYCSx9Gnj5kvGUDMZRcEARBEIRXh3eV6vT55jtunzqOuaUlFZs0x9zS6rnqqtW2Eyora+6cOYmFjQ11O/XA0tbOaNm6Hbvz8NtJSAolMjLWjk74GRmSnlHLj4azdc40Ht24prddUiiIfPwo1+28evgAOxbORvH03FcO7qPfzEX5ngjuZSnuVxETUzPS0lJBlpEkBaVq1C7sZgmvKJFcrQCIBDK5l9VQ8p5t4LuVuasjp4zkgiAIQv4QydUE4fUQfOsGt04dw1RlQeWmLbMM0jM7uuEPjqz7XW9biw+GUqV5q2yPu3niCGf/3kbwrZukJifp7Ws5eDiV326Ztwt4hTy4com9K5aSEB1FqRq1aDpwMKbmqsJulvCSiORqQpGQ1XJgwWG5D7oHdYUd/4q1agVBEARBeHOkJCYQeO40skaDd7WaqKzy1mPs5lsWN9+yBttTk5O4dfIYqclJ+FSrhY1TMb39dTp0JfLxA64eOgBoh7hXbpp90Bx07jRb53yDYVeJVua540VNiQqV6D9rUWE3QygCROAtFAq1Ouuh5HkZgtGiAYwfLIaRC4IgCILwZkiIiWb1+FFEhwYDYO1YjD7T5mDt6PRC9aYkJbLmq895ci8IADOVBT2nzMTZy0dXRmliQuuho2nx/lCQyFXP7rWjh5AUCoMAW1IoUFlZ41vrrRdqtyAUFSK5mlAocrscWFYktEPS04PsetWgQzPtvyLoFgRBEAThdXX6r83EPAnVPY6PiuDE1lxmnc3G5QN7eHL/ru5xakoyR9avMlrWVKXK9XBqU3P9TO2SJGHv5kGVZgH0+eY7LO3sn7vNglCUiB5voVCEhue+bG6WAhMEQRAEQXgTJMbG6GUlR4bEmJisD8iF5IQEAs+f0dsmazR6a3c/rxqtO3D10AFSkhIBMLOwpMuXU7JdN1wQXkci8BYKnLGs5c6OuTv2s4GwZruYwy0IgiAIggDgU60mF/fu0j2WZQ0+1Wo+d33JCQms+nIkkY8fGuwrXbPuc9ebzsHNg36zFnHtv38BKN+wicHccUF4E4jAWyhQxrKWuzhBMYfsj0tfDmxoH+2PmMMtCIIgCIIAZerUp/n7n3By6wZkjUyN1u0p3+jt567v6uEDRAYbXxLMIpfZznNiW8yZOh26Zrk/PiqSyMcPsXN1w8axaAblQRfOcuPoIcwsLKjeqj12Lq45HpOSmMCe5UsIOn8GK3sHmg36mBJ+FV9Ca4XCIAJvocBklbU8NFz7o1CARpO7oeT1qhV4cwVBEARBEIqEqi1aU7VF63ypKyUxAUmSyLzCsKRQEB8ZkS/nyM7Nk0fZPm8GmrQ0JIWCgMGfUtG/WYGfNz/dPHGErXO+QVJo02dd/ncf/WctyjHh3e4fv+f60UPIGg1JsbFs+GYC7837ocjefBCyJ5KrCQUiu6zl6ZzsYfFEbc92Rm7OYg1uQRAEQRCEl6F0zbq6gDEjWaOhRIXKBXpudVoqOxbORpOWpjvnPz8sICk+rkDPm9/O7NgCaNsvazQkx8dx7cjBHI8LPHtKl+1dljWkJSfz6Pq1Am2rUHhEj7dQIHKTtTwsAhzt4b81Yii5IAiCIAhCYXAq4Um3r6ZxZN0qntwLIjkxAXMra/z7DKR4ufIFeu7E2FjSkpP1tmnUauIjI1BZWWsDWWQUilf7i6FsdH3cnBfItbC1IyUxQW+0gVWmLO+yLBN2N5DE2BhcS/nmec124dUhAm+hQOQ2a3lo+LPlwARBEARBEISXr4RfRbpP+Oaln9fSzg4bp2LERUYgazRIkoS5lTU2xZw58Ntyzu7cCkC1lm1o0u99oz3zmcVHRRJ47jRKU1NK16yDmcqioC+D6q3a8fDaZV37zFQWlKuf89DNZoM+ZvOMKajTUgFt4rni5Z/N8ZZlmX+WLeTSvn8AUNnY0n3CNziX9M7/ixAKnAi8hQLhkv2UljyXEwRBEARBEF4vCoWSzmMnsWXON0QFP8LaqRjtRo7l2uF/Ob19k67cmZ1bcXAvTrWANtnWFxn8iNXjR5EUFwuAo0cJek+bg7mlVYFeR7l6DTE1n8j1o4cwVVlQq03HXGVu965SnQFzl/D4xlUs7RwoWbkqUoal4u5eOKsLugGS4+PY89Niek2ZWSDXIRQsEXgLBaJUCW1PtlptfH961vI6BTt1SBAEQRAE4Y2VmpxEUnwc1vaOueotzku9t08dJy01FZ9qNbGyz2G5mmwUK+nNoPnLSEtNxcTUFIDz/+xEUih0858lhYJHN67mGHgf+3MNyQnxuseRjx9yfvfObDOq55dSNWpTqkbtPB9n7+qW5ZrmMU/0523KGg0xYSHP1T6h8InAW3hhmdfpLusF/b/IPugG/azlgiAIgiAIQv65sPdv9i5fgkatxt7Ngy5fTskywMuL5IQE1nw1mvAH9wBQWVnT6+vZOHqUeKF604NuAFtnF4P9uVmeKzE2RhesgzZgT+/9fh5X//uXI+t+Jy0lhUpvt6R+1175egMjJ+5lyullnJcUCjwLOOGdUHBEVnPhhew8CA16Qc+RMPxr7b91u8PVO+DsAJOHgbvIWi4IgiAIgvDShD+4x+4fv0fztBckOjSYnd/PzZe6L+7bRcTD+7rHyYkJHFm/2mjZhJhoHly5RMyT0Dydo3a7zriVLqN77OJTmtrtu+R4XKmadfUea9RqfKrXAiAxLpYbx//jztmTujnV2Xlw5RI7FswiKvgxcRHhHNuwhtNPs5e/LM4lvWk9/HNUNjZICgU+1WrRbNDHL7UNQv4RPd7Cc8tqne5U7YoQfNwbBnSGvh1E1nJBEARBEISX5cn9e3pZtWWNhrC7gQSeO83Zv7cBUC2gDaWq531odFJcrHYY+NOgXtZoSIyNMSgXdOEsW2Z9TVpKMkgSzQYOznGoeDpTlYqek2cSEngLZHAt5YsiF18eq7Z4h5TEBC7s+RulqSn1uvTEs0JlooIfs/qr0STGRAPg5luW7hOnY2pmnmVdQRfOolAqdTcvAO6cOUmttp1ydQ35xa9+Y/zqN0aWZb3530LRIwJv4bnkZp3uH9fBgE4ia7kgCIIgCMLL5Fhcf9i3pFBg4+jExukT4WnwFnj2FF3/9zVelavlqW7vajU5vmmd3rbSNevoPZZlmb/mz9QG3doN7F2xlNK162LjmHPSMQCFUom7b7k8tU2SJOp06Gowp/vw2t/0hpwH377Jpf27qR7Q1qCOyOBH/PvbckJu39QLuiWFAstMS329TCLoLvqKzFDzadOmUb9+fSwtLbG3tzda5uTJkzRr1gx7e3scHBwICAjg/PnzemUuXLhAo0aNUKlUeHp6MnOmYVbA9evX4+fnh0qlonLlyuzYsaMgLqlIy8063Y/DtOUEQRAEQRCElycs6A6S9OxrvoWNLU6eXtr5ybIMsoykUHD18IE8113CryJtR4zFqURJ7FxcadizH9VbtdMrk5aSbDi3WpaJfPjgeS7nhcU/Xa4snUKhICEq0qBcckI8ayd+wZ0zJ4mLjNDbZ25lTYPufQq8rcLrq8gE3ikpKXTr1o2PPzY+ryEuLo5WrVpRsmRJjh8/zuHDh7GxsSEgIIDUVO08jpiYGFq2bImXlxenT59m1qxZTJo0iWXLlunqOXLkCL169WLQoEGcPXuWjh070rFjRy5duvRSrvNVpFbD0XOwZa/2X7U6b+t0C4IgCIIgCPlPlmXUaWl62+Iiwvl7yTxkOUOgqVQazTxuks1Q6+yUq9eQAXMW8/7C5dTt1N2gN9bUXKXrWc/o+vH/sq1XnZZKaNAdokODn6tdWfGuVlPvsUatpqSRnv7HN64RHxWpH6QrlbQeNpqBc5fg4F48X9slvFmKzFDzyZMnA7By5Uqj+69du0ZERARTpkzB09MTgIkTJ1KlShXu3r2Lr68vq1atIiUlhZ9//hkzMzMqVqzIuXPnmDt3Lh9++CEA8+fPp1WrVnz++ecATJ06ld27d7No0SKWLl1a8Bf6itl5UDukPGPvtrsztGiQu+PFOt2CIAiCIAj57+aJI+xauoDk+Dg8ypWn/WdfYmXvQFTIY73AEbTBeJXmrbhycB+pyUkAmJqZU+Md/Z7qh9evEh0ajKuPL04lPF+ofaYqC1ITE/S2Xdz7D03eHYSpSmVQPuZJGOunfElUyGMAqrZoTbNBH+fLEOva7TuTHB/Hpf27MTE3p0H3d41mBze1sDTYZmZpSfmGTV64DQUlJTGB22dOIj9NJGdhY1vYTRKyUGQC75yUK1cOJycnli9fzpdffolarWb58uWUL18eb29vAI4ePUrjxo0xMzPTHRcQEMCMGTOIjIzEwcGBo0eP8tlnn+nVHRAQwObNm1/i1bwaskqe9jgMft2c/bFinW5BEARBEISCERUSzPZ5M3RzkB/fvM7ORXOp26kbCTExKExM0DztCZcUCuxcXHEu6U2/mQu4cmg/yFCh8dvYuTxbXuzf31dwatsG3TGth47Cr4H/c7exdrtOHFm3Sm+brFGTGBtjNPDev3IZ0RnWqD6/ewc+1WtSOlOmcmMe3bjKwd9XkBgbQ5m6DajfrbdeMjaFQknjPgNp3GdgtvV4lClH6Zp1uH36hG4dcf8+7+V4/ucR/uA+9y9fwMLWjjJ16uUqeVxmibExrB4/SnezwtLOnj7T5hpdjk0ofK9N4G1jY8OBAwfo2LEjU6dOBaBMmTLs2rULExPtZQYHB+Pj46N3nKurq26fg4MDwcHBum0ZywQHZz3kJTk5meTkZN3jmBjDzI6vsszrcKcHyzklTzM10WYwl9AvJ9bpFgRBEARBKDihgbf0En/JGg33Lp3j7sWzAFg5OJIcH09aSjJW9o60HzUeADsXN+p16WVQX8Sjh7qgO72+3T8uolz9xnnucQ66cJaTWzegTknBVKUiNTlZN6fcxrEY1o7Gh0NGPHqgvwa3JBH5+FGO54sODWb91PGoU1ORZZnjm9eh0ahp3HtAntoN2hsO7UeP5/qRQ8SGP6G4X0WKlyuf7TGyRkNSfBwqK+tcr/EdeO40m2dO0b2GXlWq0Xns5DwH32f/3qZ3syIxNobjm9fR4oOheapHeDkKNfAeO3YsM2bMyLbM1atX8fPzy7GuxMREBg0aRIMGDVizZg1qtZrZs2fTpk0bTp48iYWFRX4128D06dN1Q+FfNcaC6ox/01kNJe/ZJufkaalp8NlAWLNdv6ybszboFut0C4IgCIIg5L+MPdXp5AzLhyVER1G1RWvqduqOpZ0dCkX2AV36MlsZpSQmolGnoTQxzXW7Ht+8zsZvJmg7ZJ62x6aYM/FRkRQrUZLWwz/PMrh0L1OOyMcPdcG3LMu4lvLN8Zx3L54jLSXl2QZZ5sbRQzTuPeDpOuIXMbOwpGSlqrkKbBUKZa6Hlj+8fpWtc6aREB2FpZ09HUaPx6Ns9oE6wIFff0KT4SbD3QvnCDp/hlI18ra8W2JsLJIk6TrAZFkmKTY222OEwlOogfeoUaMYMGBAtmVKlSqVq7pWr15NUFAQR48eRfH0btPq1atxcHBgy5Yt9OzZEzc3N0JCQvSOS3/s5uam+9dYmfT9xowbN05veHpMTIxunnlByy6wziqoTg+KsxtK/t3K3J3fuzj8t0as0y0IgiAIgvCyuJbypW6nHhzftNboflmjIT4qAmsHx1zVV6ykNxY2tiTFxyFrNEgKBcXLlc910C3LMuf++Ytjf67RuwEA4OJdmo6f/y/HOpr0e5+YsFDuX76AQqmkUe8BRudhZ2Zuaa2/QZJQWdvw5F4Qf0waS3J8HAAlK1ej89hJKE3yJ/xJS01l88wpuvoTY6LZNGMKg3/4NcfnLSk+Tm+ddd22PCpVvRbndm1/tkGW8clj8C68PIUaeDs7O+Ps7JwvdSUkJKBQKPSGw6Q/Tr+jVK9ePcaPH09qaiqmpto/iN27d1OuXDkcHBx0Zfbu3cuIESN09ezevZt69epleW5zc3PMzZ8vK+SLyC6wBuNBdXCYdvv3E2Hq4uyHkueGi5NYp1sQBEEQBOFlq92+C2d2biE1Kcnofks7wyzmWTG3tKTbhG/454cFRIUEU7xceVp+NDzXx1/c9w/7fjZMQiwpFJgZmc9tjMrKmu4TviElMQGlqVmuA2Tf2nXxKFeeR9evgiTpgvZ/f/+ZlAzJ3e5dPMf1o4eo0Ojt3F1UDuIiwvWWTJNlmaS4WOIiwo2OSMiobN0GnP/nL+SnQ/BNTM1ydZMhM5/qtQj4eAQnNq9Ho1ZTLaANFf2b5bke4eUoMnO87927R0REBPfu3UOtVnPu3DkAfH19sba2pkWLFnz++ecMGTKEYcOGodFo+PbbbzExMeHtt7V/YL1792by5MkMGjSIL774gkuXLjF//ny+++473Xk+/fRT/P39mTNnDm3atOGPP/7g1KlTekuOvQqy6q0ODoPBE8He1nhQnb5t6FTIlPAyT0TyNEEQBEEQhMJzdMOaLINugNsnj9F8kPFleI1xLulNn2lzn6stN44dNtwoSShNTanVrnOe6jLLlFk8JSmR26dPoElLw6d6LSxt7fT2K01M6T7hG24c+4+kuFhKVq6GlZ0DseFhmeaMK4g3snb387Kyt8fEzJy0lGd5nkzMzbE0smxbZv59B6E0MeH2qeNY2jvQpN8gbJyKPVc7KjVpTqUmzZ/rWOHlKjKB94QJE/jll190j6tXrw7A/v37adKkCX5+fmzbto3JkydTr149FAoF1atX5++//8bd3R0AOzs7/vnnH4YMGULNmjUpVqwYEyZM0C0lBlC/fn1Wr17N//73P7788kvKlCnD5s2bqVSp0su94Gyo1VknPkvfFpVDfre8BN0ieZogCIIgCK+7lMQETMzMnyu7dGGIeHg/2/3xURH8t+537l44h7WjE41698fBzSNXdd85e5IbR//DzMKCmm065NiDa25ppcsCDoAk4V2lOs3e+xh7N/dcndOYpPg4Vv9vFJGPHgJgam6OZ8WquJbypXb7ztr1wtEG3+UbNkGdlsrORXO5fvSQkdpkSlasku351GlpJMZEY2lnn+Pvgam5irYjxrB93gzSUlIwMTOj7adjMM3F2ugmpqY06fc+Tfq9n2NZ4fUhyZknYggvLCYmBjs7O6Kjo7G1zf+19I6eg54j871ao4wlT3MXydMEQRDeSC/y+VbQn42C8LwSYqLZMutrHt24itLUlKYDPqJK81aF3awc/bduFcc3/pFpTrW2u0RSKLC0tSM+OkqXUdzCxpaBc5eisrbOokat60cPsX3eDF2GbnNLK/rPWoS1oxOx4U+IDQ/D0cNTr56wu4GsmfC5LoO5nYsrfabPw8LaJlfXkpKUyJkdW4mLeEKJ8pV02dSPb1rHf2t/R5b1e4wkSUGJCpXo9tU0vWmmxzev5/Afv+rNnzYxN8fS1h7/dwdS9q2GWbbh7sVzbJs7neSEeCxsbOk45qtcJUpLio8jJiwUW2cXVFbZP7fC6ycvn21FpsdbeCY0PH/qcbSDyGjjPefpQ8mH9tH+iORpgiAIgiC8Ls7t+ovTOzYDEmYqFWH3ggBQp6ay+8dFuJbyzVVG7fx29b9/ObByGckJCfjWfouAwZ8aXfMaoG7HboTfD+LmiaMA+NapT8jtG8RFROBexo8nd+/oAlBZoyEhOooH1y7jWyv7dbHP7NiqOwa0geX1o4dRKBXsW7kMZBlTlYrOX0yiRAXtiFBnLx8GzF7MrVPHMDE1o2y9hrkKQlOTknh4/TL7Vi4j8tFDFEol53fvJOxuIMF3bvLgymWDoBtAljXcv3yBiEcPcCr+LKFxWNAdvZGaCqUSv/r+BAzOfr56alISW2dPIyVZO3Q/KS6WzTOnMviH33Ls+VZZWYuAW8gVEXgXQS7Glz/MtfSg+qtPYMjk3A0lF8nTBEEQBEF4HVw7cpC9Py/JtkxI4O2XHniHBN5mx8LZumD5xvH/MLeyynJNZhMzM9qPGk9yQjwKhVIXoMuyjCRJLB3cj5RMc8BNjSQDlmWZmLAQ1GlpWQ5FT4qL5dimtbq2pSYns33BTAYv/VVXxtbZhRrvtM/19cZGPOGPCWOICQvVbUtf1/rk1o0goTdH2xhJ0l8327G4/qpCGo0Gp+IlcmxLzJNQUpISdY9lWSYxNoaE6Kgs1x0XhLwSgXcRVKeydrh3cFjWvdV2thD9dJ53VkH1O41BoTDMjC7W4RYEQRAE4XUVeOak/nxkAEnSG57s4J67udD56dH1K3ptkDUa7l08n+Nx5pZWeo/Th1437NmPXUvmISmUyBoNJSpUNMicrVGr2bFwtm5OtHuZclRp/g6PblzVG2ru7F1Kf/krWSY+MgJ1WtpzL891eM2vxIY/MbpPljUGX3JNzM1JS07WtkuWKVm5usHrVLtDFx5ev8K9i+cAKF2zDtXfaZdjW2ycimFiZvZsPXBJwtzCEotMidwE4UWIwLsIUiq1gfHHE7Purf52lPbfnILqdxpDywZiKLkgCIIgCG8GlU2meZiShKm5itSnPZ61O3R9rqWdXpSJuf6QckmhwKbY8y+7W6lJc+xd3bh/+SJWDg5U9G9mMGz60v7deonIgm/fxKOsH53HTebGscOYqSyo0boDpubm+oEpYOfi9txB95md27hyaL/BWtbpFEoTNOo0vW39Z31P4NmThATexsG9ODVbd9Cb3w1gamZO1/FTiQ4NQZIkbJ1dDMoYY2ZhSZtPv+Cv+dpEaWbmKtp9Ni7f1vwWBBDJ1QrEy0ogk9063umBtVotgmpBEAQhf4jkasLzOBMTz6hr93mUnEo9eyvm+pXE0bTwApq4iHBWjf+MuAht0hybYs70mPQtSXFxWFjbYOvsUuBtOP3XFo5t/AONWk2V5q1w8y3LX/NnIWvUujIqaxvK1WtIYmwsLt6lqNWuE0oT03xtx4Fff+Ts39t1Q7wBvKvVpMu4yXrl0lJSOLZpLcc3rtVtkyQFvb6ehbtvuVyf78GVS9w+e5JTWzcY3V+8XAXK1W+Eha0dOxbO1o1KqN+tD/W69srLpT2XlMQEYsPDsS3mnOXcekHISCRXe0Ok91aXfweSU2HBeGj7tn5grVSK+dmCIAiCIBSO8JQ0epy7TbxagwbY/SSGT67c5Y+qpQutTdaOTvSf9T13zp5EAkrVrIO5pRV2zq4v5fy3Th7jwK8/6h6f2rbR6NB3B4/iXNjzN6Cd7x12N5C2I7544fNHBj8iNPA2ts4uuPj46gXdkiTh6qM/t/3on2s4+ucawyRnksTVwweyDbxTkhLZvWwRd86cAEkiJSEhy7KOxT2p06k7parXAsDVpzShQYHYu7rler598O2b7F/5A3GREfhUq4V/v0G5Wt4rnZmFJU4lLHMumM9inoQRePYUpubmlKlTXwT9rykReBdxCgWkPn2/fKu66M0WBEEQBOHVcTEugVj1s4BNDRyKjNUlACssKmtrKjR6u1DOfe/SeYNA2yCJmCzz+MY1vU3Xjx6i+ftDclwOLDs3Txxh+7wZumC7Vvsu1HinPWd2ajOZl6pZh7qdu+vK3zlzkiPrVxmtS9aoubBnF6lJiTR972OjAe6+FT9w/eihHJOkgXZd8k3fTqJM3fq0GzEWB/fiOLgXNzyvLOuWSANIjI3hzpmTJCcmcHjNL6SlpCBrNFzYs5OIh/dx9vLBxqkYVQPa6NqYmpRETHgYNk7FMFNZ5Ni2ghR2N5A/JozRJXc7seVPen89GzOLl38DQChYIvAu4lLTIP29TJX7G3qCIAiCIAgFztlMf2i0BDiZmhRq0F3YIh49yDYQlSQFZpaWJMfHGe5TPP/zJssy/yxdoNfDfWrrBgZ+t5QGPfuiUasNlsUKuXMLhUKJJsMQ+IzUqSlcPrAXpdKU5h8MMdgfdP5MroLujG4eP8Llg/uo1KS5wb6L+/7hwG8/kZqUjG/tt2jQ413WTR5HQnSU0eu9f+UiD69fQdZouHXyGN0nTuff33/W3miQZZSmpnQYNR6fp73sGY+9dvgAj25ex87FlWoBbTExzd9h/umO/Lma1JRk3ePwh/e5dGBPnjLEC0WDIuciwqss6dnfKSqzwmuHIAiCIAhCZhWtLRhUvJjusVKC2eU8szni9SZrNDy4cinbMjbOznT5cgqla9UFSdIlRKv0dgu9DObhD+9z8/gRwh/cz/W5k4wE8/FRkZipLIyuRe3gUVw/6JYkg+W1ZFnmztlTusfqDIG9tYOTwZJfAJWbBfDh0l/xrFjFYJ8kSTy5F2iw/dGNq/zzwwJSEhKQNWpunTjKtrnTSYyNMX7BT2nUamRZ5uH1K+z+8XvO7NiiS+qmTk1ly5xppCbrL7v239rf2bFoDhf27OTg7z+z6dtJeb6BkFuJMTF6dUuSgqQ4w9dJKPpE4F3EJT9b9QCzgrkRJwiCIAiC8Ny+LlOcrdV9WVrBi8N1y9Oy2Ju7RJOMnGXvMUCDHn35YOFy3H3L0m7kWPzffY+KTZrT7L2PafHhs/W8L+z9m5WjPmHr3G9YOepjzu/ekeO5FUolJcpX0g3RlhQKzK2scfbyyfKYcm81pNLbz3qenYp70rBnf70ykqTAysEB0Abdjx8/1u1rNmgwJpnWDq/SvBUtPxxGVFw8HcdMMAi+ZVnGqYSXQVseXb+qN1JCljXEhj8hL3mirx3512CbOjWVuMgI3WONRs3Jp8nf0oP2e5fOExp0J9fnyYsydeo/eyBJIGt089yF14sYal7EJT0NvM3NtH+rgiAIgiAIrxJJkqhj//zzkouyqJBggm9dx8bJGY9y5VEolFRo3JTL/+7V9rpKEgqFAnNLKyq93YI6HbvqjlWamFKrbSeDOlOSEtm7fIneUlx7f16KXwN/gzW9M2s3ciw7F3/HoxtXsS3mQquPRxjt6U4nKRQEDB7BW517kpqUhGNxT5Dg5on/uH3qOAAm5ma0+ngkarWaffv20b59e2bNmsXQoUNx9y3He/N+4PHNa0gKBU/u3eWtzj1YuGABY774gs2bN9NxzAQ2z5zC/csXtNdtaoZ3leoGbbF1dtELsiWFAjtXV8KC9HvH63buga2TM/evXOTaf//qytq7uRMXEUFmUuZefBm97PLpMg7Rz081WrcnLTWFywf2YKpSUb9bH9x8yxbIuYTCJQLvIi59qLmY3y0IgiAIgvDquHPmJFtmT9OtR12tZRuaDfqYFh8Mwc7FlUfXr2Ln4kb97n2wtM39KICk2FiDIFDWaEiMjc0x8La0szdYKiw37Fzc9B53GDWeuxfPceXQfkxVKmxdXNi3dy/t2rcnOTmZYcOGATB06FCsHRx1vbq+td5i4cKFDP/0U209HTqwZcsWveBbo07j7K7tNO4zUO+cZerUp1y9Rrp1xy3t7Gk7YixB585w9u9tKJQKarfvqpsbXqlpC1x8SvPg6iVsizlTr2tvTm3bqOvNTlenYze9xHAKpZJKb7fg4r7dgDaJWzFPL1x88paJ/9bJY1zYuwuFUknNNh2yXBtekiTqduxG3Y7d8lS/UPSIwLuI0wXeYn63IAiCIAjCK2P3j4v0hpWf++cvKr3dAtdSvtTr8vxrUls7OWHn4krMkzBkjQZJocDa0Qkbp2I5H5xPJIWCC3v+Jik+jo5jJrBv/wE6dOhAcvKz5EMZg+90Cxcs0AXdAMnJyQbB94Orl0lOiDd6zjafjqF2+y4kJyTgVtoXMwtLHN2LU+OddnplQ4PusHfFD4Tfv4uFjQ3uZcphbmlFo94DMFVZcGn/P0iSgre69DSaxK35+0Owd/Pg0Y1r2Lm4Uq9LL5QmuQ+bbp8+wZbZXz9tuMSdMyfoPXW26Ml+w4nAu4gTPd6CIAiCIAivnoToaL3h4NptUbk6Ni0lhcjgR1ja2mFl76C3T6FQ0uXLKexYNIcn9+7iVKIkrYeNylNg+KKe3L/HzRNH+HDpr4SEhdGxY0e9oDtdxuA7c9CdLjk5mY4dO3Ljxg3eGTaaZYP74Vu7ntHzSpKkW9P72pGDnNq2CVnWUOOd9lT0bwZAQkw0ayeP1a0Znhwfx39//EbEw/u0Hjqael16UsKvAkf/XMO5XX+RkphI9VZt9eaPK5RK6nToatiAXLpycB+SJOmWPkNScO3IvyLwfsOJwLuIS84wx1sQBEEQBKEgRKamcSQqDkuFgoYONpi+wLJWb4qSlaty98JZba+0pMDU3FwXNGYn/ME91k/9H/FR2vnIjXoPMAgCHdyL02faXKPH3zlzkgO//kRyfBxl3mpAk34f6JbCunP2JP/+tpykuDjK1mtIk76DUJroZ+d9eO0K4Q/v4+zljbtvOYP6ZVnm0JqVABxdv4qWHw5j5owZRoNq0AbfP/74IxcuXMjymmfNmoWnpyfHNv5B6+Gf41OtZpZlAYLOneav+TN1j/9e/B1mKgvK1K3Pw+tXdEF3RlcPHaD5+0OIDg3hz2lfodFoQJYJuXMThUJBtYA22Z4zL0xMTZ8mSku/8SKjNBVf1t90IvAu4tIDb9HjLQiCIAhCQbiTkEy7MzcJT9XOVa5jZ8W6qqVRKcXiONlpPWw0/yxdwP0rF7FxdKLlR8OxtLPP8bidi78jISZK9/jQ6pV4V62Bi3epHI99ci+IzbOm6npaL+zeiYmpGU36vc+T+3fZPPPZvvO7/kJpYkqTvoN0xx/dsIYj61bpHjfp9wE123TQPb787172Ll+iW37r4t5dSJLEsOHDAbIMvrMLuhcuXKgbjv5W5545XiPAzRNHkRRKXRI0SaHg5okjlKlbH3VKSpbHSUjcPnnsWU/0U9eO/JuvgXeNNh25fuww6YuEmVlYUqVZq3yrXyiaROBdxIk53oIgCIIgFKTpdx4R9TToBjgZHc/a4Aj6F395c4qLIgtrGzqMHp/n4yIfPTBYMzry8cNcBd73r1zUCyplWebOmZM06fc+9y5dMNx3+oQu8E6IidYLugH+/W05Du7FKe5XnujQEP5eMs9g+PyFPX8D5Bh8G5Mx6M4LM0tLQM60zYqE6Cj2/rzU6DGVmwVgqlJhZmGhnx1dUmBmYZnnNmTH1ac0fb9dwLUj/6JQKKnYpDm2xZzzpe7As6d4eP0KNk7FqPR2C4MRC8KrSwTeRZyY4y0IgiAIQkF6nJxKxhzaCgnCUtKyKJvCttAoADq4OOBqLoKCvHIt5cuDq5d1wbckSRQr6Z2rYy3t7PUCY0nxbI1tSzs7w3329rrHSXFxBvXJsoZNMyZhYWNLtYC2BkF3uucJvnMKulNTkkmMicHa0RGFQqm3r0br9lw9tJ/4p3PmzS2tqN2uE7fPnCApLlavrKlKRePeA6nSQtvjXMG/GWd3bScqJBgJUJgoXyjZXVacSnjSoPu7+VrnmZ1b2b9yGQqlEo1aw43jR+jy5WSD50d4NYkxQkWcmOMtCIIgCEJBaupkS8YZ3RoZGjoYrv0clJjM2yeuM+nWIybdesTbJ69xPynrYb+Cca0+GYlTcU8AlKamBHw8Qvc4J2Xq1MerSjXdY1Mzc5r0fV+3r2Slqrp9JmZm+D/dB2Dv6oa9mzuSwjA8SIqP48ax//Q3ShJOJUpSpfk7gDb4vnbkIMOGD6dKlSrZtrNKlSrZBt1XDx/g+/d68uOQgfw07H3CH9zT2x8V/Jji5Srg7luO2u26MGDOYuxc3IwGoLbOLlQLaKPbp7Ky5t3p82j23sc06jOQ/rMW4V7GcC77q0aWZf5b+zuQvqa4zL2L53h880bhNkzINdHjXcSJHm9BEARBEArS8JKuRKWmsT44EkulgrGl3HnL3jDw/uF+GLFqtW5ea3Samp/uhzG5TPF8aYdalrmXmIK1iQJns9e3J922mAv9Zi0iKS4WMwuLPA0lViiVdB47mbsXzpKUEI9n+UpYOzoBoDQxocuXUwg6f4bk+DhKVKyMjeOz6QKyLONdtSaXD+whLTVFb7i7rNGQFBdL9Xfac3bnVgCs7OxpN3IcTiU8qdmmI8gaHIt7snDhwmzndIN2zveiRYuMBt8xT0LZ+f1c3fnjIsLZ9t23DJizGIB7ly6w/utnQ/hD7tyk7FsNsLJ3wLd2PWycfiU2PFyX3KxuB8P1sW8c+49jG/9AnZpKdGgIzd4brJfVHECjUZOWkoKZyiLba3mZ1KmpBtvSUgyzyQuvJhF4F3FJosdbEARBEIQCZKKQmFKmBFPKlMi2XJxarT/tVn66LR+EJKfS/fxtrsdrk3oNL+nCl6U98qXuV4Usy4Tfv0taSgrO3j5Y2Ng+Vz0KpRKf6rWy3FeqRm2j+w7/8Svn/vkLZNkgCJUUCjwrVqbpgA+p1rINibExOHt564JSRw/tzZWFCxcy/Olw85wMGzYMZJmhw4YhZzhnxMMHBkF/+MP7ujIX9+16ukP7y6ZRq1n/9XgCPhqOk6c3SfEJ2qD76fU6uOvf+Ll58ij//LBA9/j802tu/v4num1XDu5j94/fk5aSjFvpMnT4/CusHRyzvJZ7l85rM8GX9KZE+Uq5uv68kiSJiv7NuLBvl/Y1UiiwcSqGRxm/AjmfkP9E4F3EiR5vQRAEQRBeBe2c7VkfHAmABKiBdi72+VL3lzcfcCshSfd4wb1QGjjY4O9oky/1FzaNWs2276Zz6+QxAByLe9Jj4vRcZUHPL9ePHNRLvAYgKZXIajUu3qVpNuhjbds8igPFSU1K4uS2jdgWc6FcvYZ5CrrTpc8JH/p0vW/QLpWmWwMbbdBv7+auC8wlvWW6tFISEtj23bdY2NhqM65nuI4zO7fSethoQu7cIi4ygpNb/jRox7UjB3WBd/iD+/y9+Dvd+UMCb7NryTy6fDnF6DX8t/Y3jm1cq3tsbPm3/NL0vcFYOThw/8ol7JxdaNCjL6YqVYGcS8h/IvAu4sQcb0EQBEEQXgUti9mxtIIXPz0IQ0LiI09nmjg+X69tZlfjklBniLUk4Hp84msTeF/+d68u6AZtFvPDa3+j5YfDsjkqf5lbWhEbEa4X1MoaDQqlkpA7Nwk8e4ryDZvw8NoV7pw9wdVDB/CpXova7TqzcMGCbBOqValSJcvh58OGDwdJ0g07t3NxpeVHw9n94yI0ajWWtna0/fQLXflqAW24+t+/RhO9JcbGQMaMBLKMWq1m34ofOPv3NgCD3nzQDsNPFxp4Sy/ruazREHzL+DzqhOgovaAb4PCaX6kW0KZAhqgrTUyo361PvtcrvBwi8C7iRI+3IAiCILxaZFlm5aNw/g6LxsnMhJFerpSxejN6pTq6OtDR1SHf661sY8HdxGRddnUZqGD96sy9fVFRIY+fZqrWXqGs0RD5+FGe6oiNeMKRdauJi3hCyUpVqdm2Y56yXTfuM5BNM7W9urrAU5Z1bTq5dQNKExO2ffetbv70W117c//+fcZ88UVW1bJwwQKGDhuWbXD++eef07FDB1ycnTE1N6fS2y0oU7c+8VFR2Lm46M1z9yhbnjodunJi8/oszqgdho0sI8syxcv4sf/XH5/tNRKw1+v6LKu5vbv+FAZJoTDYli45Id7w7LKGlMTEV2puuPBqKDJZzadNm0b9+vWxtLTEPsPSBxnt3buX+vXrY2Njg5ubG1988QVpafrLXVy4cIFGjRqhUqnw9PRk5syZBvWsX78ePz8/VCoVlStXZseOHQVxSfkifY63CLwFQRAE4dWw6F4o42484N/IWLaERNL2zE1Ckg2TIgm5N61MCSrbaAMZCRjr40ZDh9ejtxvA3becLsAFba+sR9ncz91NSUxgzVefc/nfPQSdP8PBVSs4+PsK3f6YsFA2fDOBHz7uz+aZU4iLjDCow6d6LfrOWED5Rm/j4GE4n1+jVvNf+jrfT4PXnQtn4+rszObNmzE3N/wymh50Xzm0n2HDh7Ng/nyDMubm5mzevBkXZ2c2z5zCv7/9pN1uaYWjR3GjyeUa9uxHrXadjT4XDXq8S9m3GuJbux6dx07CupjhevNKUzNKVq6KR7nytB3xBdVattHtc/ctx1sZlhezsLElYLDxGwZ2Lm44epTQZYKXFApcfEpjZZ//N5+Eoq/I9HinpKTQrVs36tWrx/Llyw32nz9/ntatWzN+/Hh+/fVXHj58yODBg1Gr1cyePRuAmJgYWrZsSfPmzVm6dCkXL17kvffew97eng8//BCAI0eO0KtXL6ZPn07btm1ZvXo1HTt25MyZM1SqVDDJEl6ErsdbDDUXBEEQhFfCqsfhuv+r0Wb33hsRQ293p8JrVBFXzMyEnTXLEpqShpVSgbXJ67VusW/tt6jfvQ/HNqxFo06j7FsN87S29INrl4l9Eqa37eK+XTTp9z7qtDTWfz2e6NAQZI2G+KhINk6fSN8ZCwyGXV85uI8rB/cZPUf1Vm05kWl+9P3LF9g8cwodx0xg65YttO/QgeRk7ZfT9KD7n2ULubh3Fw+vXTZY59vc3JwtW7bwtr8/m2dO4f7lC9y/fIG6nXtgYZ31NAVJkvB/9z1cS/ny1/xnnWh+DRrzVueeemVjnoSiNDVFnZamS0rmVaUancZMyLL+Bt37ULlpCxKio3Eq7pnlPGqFUknX/33N3uVLCLsfhKtPaZq997HR4eyCUGQC78mTJwOwcuVKo/vXrl1LlSpVmDBB+0fk6+vLzJkz6d69OxMnTsTGxoZVq1aRkpLCzz//jJmZGRUrVuTcuXPMnTtXF3jPnz+fVq1a8fnnnwMwdepUdu/ezaJFi1i6dGnBX2geiaHmgiAIgvBqUSkUSOgn+FYZWRtZyBtJknA1f32XEavXpRd1O3VHo9ZgYpq36zTWK6xQar/mR4cGExX8WLdd1mgIuxtIXEQ4Nk7PeoNTkhI5tW2j0fp9a9cjLSUFa0cnYsJC9falB9+dv5zCtm3baN++PbNmztQLukG7zjc8S6g25osv2LplC/6NG+uC7nSbZ35Nj4nTUSizv8HiV78xHmX9eHL/Lvau7jga6am3LeZCxzET2P3DQuKjIylZqSqtPh5hUC4pLo74qEjsXFwxMTPDtpgLtsVcsj0/gI1TMTqO+SrHcoJQZALvnCQnJ6PKdDfKwsKCpKQkTp8+TZMmTTh69CiNGzfGzOxZ93BAQAAzZswgMjISBwcHjh49ymeffaZXT0BAAJs3b34Zl5FnIrmaIAiCILxaRnm78eHlIEwk0MhQytKcgGIvlmQsWaPhWFQ8GlnmLXtrLJQikC9qIh494Oqh/UgKBRX9m2Pn4mpQRqFQGp2XLWs0RIUGY6ayQGFiws7v53L/4nksHRxo+eEwSpSvhG0xF2KePAuKzSwsUaelobIyXHMd4Nap41QPeDbEWp1pemZG969c4PapY7pluqwdnYiPjNDNl3YrXQYTExOaNm3KjRs38PT01Au6AUpWrsbtU8dRWdswbPhwOnXujLu7O/tXLtMLugEeXb/C3Yvn8KlWM8s2pcsYIJ/atpGjG/5Ak5ZGpaYtqdK8FQd+/YnYJ6H4VKuJf99BRnuvL+77h90/fo+sUWNpZ0fncVNw9Smd47kFIS9em8A7ICCAefPmsWbNGrp3705wcDBTpmgTRDx+rL3LFxwcjI+Pj95xrq6uun0ODg4EBwfrtmUsExwcnOW5k5OTdcNqQDuk/WURc7wFQRAE4dXSzsWezWa+7AmPwcHUhHc9nLDKoecuOzFpajqcucnVp2tYl7YwZ1vNMjiavjZf4157YfeCWD1+FOo07Vz/039toe+387F3c8/x2ISYaDZ8M4HQwNsA2Dq7EhsehqzREBsWyuYZU2g9/HNiI57oHRcTFsLDa1coWakKNsWc9YaiS5JEQpT+PG8LaxucvXwIuxto0Ibk+KdJxJ4G2urUVD5c8guhQbexdnDCxbsUAAqFAgdrK4Ogu/2oLylTp75ene7u7iiVShr16sel/btRp6bo7U9JTMjxucno1qnj/Pv7z7rH53Zt59KB3ahTU7U3LoKDSYqPo+0I/URw0aHB7F62UHcTITE2lu3zZjBo/rI8nV8QclKot0vHjh2LJEnZ/ly7di1XdbVs2ZJZs2YxePBgzM3NKVu2LK1btwa0bwIFafr06djZ2el+PD09C/R8GYk53oIgCILw6qlrb8340h58UtIF2xecj7z0fig34p+tYR2UmMzCuyEv2sSXRpZlgpNTiUjNukf1dXdmx1bUadoAUNZoSE1O4tw/fwHa3uyDq1cyv29nFvTryn/rVull3j74+896wXBMmHauNmif27TUFLbP+1a3LSPN00C/fMMmettlWca9TDmD8p3HTs7V9ajTUrF2cKRU9dq6oBu0Af25XX9xce8uJIUCSaHA2bsUpWrUNqhD+fRmlLmlFVWaBeh60yWFAnMrK0qUzzm3UnJCgnbdbuDB1UsGQ9PTkpMzPFcavSXb0kUGPzZYPiwq+JHR51MQXkSh3iodNWoUAwYMyLZMqVKlst2f0WeffcbIkSN5/PgxDg4OBAUFMW7cOF0dbm5uhITof1ClP3Zzc8u2TPp+Y8aNG6c3PD0mJualBd/JYo63IAiCILzWQpJTtTFJ+gpPQHARyZIel6ZmwMVADkfFATDQw4lvypZ445JPZe7NBUnX+31h79+czJC07NiGNdi7ulHRvxmg7S3PKQjMmBE9na2zK8X9KgJQr2tvYsPDuPbfQRQKJWaWlmyaMQUHj+K0GzkO55LeAATfuWm0fqWpKRq1WteOyk0DsmxLgx7vYufiysNrl7Ep5kyttp2NzkHPyL/vIMwsLLhz+iRWDg74v/tetpnB01JS2LFwFjdPHAWgZpuO2DgVQ5PxeXq65FlGZpaWBnU5epTQW8pNUij0MpULQn4p1N8oZ2dn/Pz8sv3JOB87NyRJwsPDAwsLC9asWYOnpyc1atQAoF69ehw8eJDU1GcfVrt376ZcuXI4ODjoyuzdu1evzt27d1OvXr0sz2lubo6tra3ez8si5ngLgiAIwusjNk1N/wt3qPrfJd45dYP7iSk0cLAhLUP8oAEaPF1KKz5Nzf9uPqDN6RuMvHqPJymvVq/yrMBgjjwNugFWPApnY0hkIbaocFTwb6YNWiXpaUCo0fVCP7h6WS/IkxRKHly9pHvs6uNrEARmDGRLVq5mcD4LWzt6TZmpm89sYmpK66GjGfbLelTW1iTFxQIQFfyYDd9M0M3vTjGyLjVA/W59qNSkOd5Va9K4z0Aavzswy2uVJInKTVvS6pORNOj+LuZGgt3MlCYmNOzZj36zFtLlyykUe3ojICvHN63V670+/ddmzC2tKF6ugm6bbTEXStWsk94oAGq166Q3D15bzpnWw0Zj8jTmsHEqRruR43JssyDkVZGZHHTv3j0iIiK4d+8earWac+fOAdrs5dbW2qQRs2bNolWrVigUCjZu3Mi3337LunXrdENZevfuzeTJkxk0aBBffPEFly5dYv78+Xz33Xe683z66af4+/szZ84c2rRpwx9//MGpU6dYtuzVnOch5ngLgiAIwutBlmXeOXWDW4na4WwhKWk0PHGVy/Ur8mUpdxbfC0Ujywwq4Uwfd0cAPrgcxIGIWDTAuZgEzsUmsLtWOUwUr0aP8uW4RDL21ZpIElfik+hSaC0qHN5VqtNp7ETO796JJCmo8U57PMqWB9DLLK4l621r/O5AIh7e5+H1KwDU7dSdqi1aExp0Gyt7R4p5evHHxDGE3LmlnaqpVNJuxBdYOxouXxcfGUl81LMbH7JGQ3xkBA+uXqZkpSp4VqqCmcqC1OQk3fBrz4pVqN2+y3ONUkhNSeHv7+dy79J5zC0tadCjr8Gw99yKDX/C41vXsbJz4PHtm3rDwxVKJU/uB9F9wjc8unEVdVoaHmX9MDE149apY4Q/uMflA3s5tGolh1atpErzVjR/f4jumsrVa0TpWm+RFBuDpb290QR36dRpaTy5fxdTc3Mc3Iu/caM3hOdXZALvCRMm8Msvv+geV69eHYD9+/fTpEkTAHbu3Mm0adNITk6matWqbNmyhXfeeUd3jJ2dHf/88w9DhgyhZs2aFCtWjAkTJuiWEgOoX78+q1ev5n//+x9ffvklZcqUYfPmza/kGt4glhMTBEEQhNdFaEqaLuhOl6yRWRcSyXAvV4Z76Sd/jU1Tsy8iVvdYDVyNT+JmQhLlrS1eRpNzVN5axZGoOF3wnSbL+FkZXxP5dVeqem1KVTec61ynQzcCz57iyf27ADh7+VCrbSfdfpWVNT0mzyAhOgoTM3NdD3LG4LzH5BlcP3KI5Ph43Mv4YaYyJzUpySCDt6WdHQqlCRq1/siIP78eT9m3GtLm08/pPnE6B1etICE6itK16lKva+/nCi7TUlNZ+dnHxIRpp3AmxcWyY+FsLO0d8KpUNU913b9ykY3TJ5KWou1xcipRUm8ouUatwcFdO2Q889zwMnXqc/PEEaLDnk0lvbDnb7yqVKds3Qa6bSampkZvVmQUHxXJ+qnjCX9wD9AuZ/bOsFHZBuqCkK7IBN4rV67Mcg3vdPv27cuxnipVqnDo0KFsy3Tr1o1u3brlpXmFRiRXEwRBEITXg3kWvdSKLIIeU0lCAWSe/WuZj0uNxaSpGX3tHgci43AxM2FmWU/qO1izIyyKHx+EIcvwgaczbZztjR4/xsedC7GJHI/WDmF+192RLq5Zz919E6msrekzfR6Pb1wFScKjrJ/BnGhJkrKd82xqZk6lJs25cnAfayeNQaNWY25lTeexE3U96wBmKgsCPv6Uvxd/ZzBv/Maxw/hUq0mlt1vQ7atpWZ5Lo1FzatsmAs+ewtLOngY9+mLt4MDt0yfQqNX4VK+Fpa0dt08d0wXdGf05dTz2bu60GT4Gt9JlcvUc7fnxe9IyTBUNf3APR48SRDx6AECZuvWo3LRllseHP7hvcL3hD+5D3VydXufQml905wS4duQg3tVq6ubjC0J2ikzgLRgnerwFQRAE4fVgb2pCbVtLTsY8W0ZJJUm0d7E3Wl6lVDDC25W5QSGYSJAmQw83B7ws8u9Lwahr99gRFo0abaK03hduM6dcSYZcvUv67YBj0fGsrlKKpk6GOW5sTJRsru7Lw+RUzBUSzmbZJ9l6U5mYmuJZscoL1REXGcHfS+bpAsyUhHi2zp3OR0t+0euxrtDobUqUr8imGVN4ci9It12hVBJ2L4iUxATMLLKel31k3SqOb1oHaBOR3b10HpWlJdGh2iDb0s6O3l/PJTmL+eIA0SEhbJg+gQ8XrTC6rrbBtUVFGCRKq9+tN8VKeqM0McXO1S3bXnkbp2K65djSxYaFZlE6a5GPHuoF8AqlkqiQrJccFoSMRLq+IixNDeqnf/siuZogCIIgFH1bqvvSx90RHwsz6ttb8W+dctmu1/25txu/VPbhUy9XFlfw4ju/kvnangMRsaTny9YASRqZ1Y/DUUra7OoyoAS2hkbpjklUa1jzOJyl90K5Hp+EJEmUUJmJoLuARYeG6AWFsiwTHxlBWkqyQVnbYi74VKupF6xq1GrO7NjCwgHdObTmF7051Bld3L/72Tk0GpLjYonOEMQmxsZyfNNavCpXR2lq/DWXZQ1JsbFEBj/K9ppkWSYpPg6vytV0CeYkSUJpaop7WT+cSpTE3s09x6Hwjh4lDLZFhmR/bmPcy5RDkp6FTxq1GrfSvnmuR3gziR7vIiwpw/uo6PEWBEEQhKJPoVAwJw/BsyRJBBSzI6CYndH9KRoNx6LiSdZoqGdvjXUe1xR3NjMlPjFZbzi7g2mmOiTQyDKzA4NJUmvYHR7N9YRkFMDXdx6xpkppGjna5Om8Qt45uHugNDVF/XRItqRQYOvsgqm58R7let16E/n4EbdOHjXYd2Lzetx9y+Fb+y2Dfabmhl86JUnSBeqyLJMUF4ediys9Jn7LgV9/JDoslISoKGRZo3eMtYNjltdz7+J5/lo4m4ToSFTWNhT3q0Bo4G0s7Rxo8cEQbIu5ZP+EZGDt6KTXRkmhwNLWPtfHp2vQ412iQ4O5dfIYkkJJva49KV0zj+PVhTeWCLyLsIyBt+jxFgRBEAQhowS1hq7nbnHm6dD14uambK9ZBvc8fGmYWa4E7164Q5JGG7D0dHNkhJcLhyPjiEnT9oUrZNgQEokayLDcuDZYl2HanUf87Vgu365LMM7S1o52I8fx14JZpCYlYu3gSIfR/8uyvKmZOR1Gj2f7fG1iNj2SRGjQHaOBd4Pu77Jj4WwkhRJZ1mDv5k7U4wy9x7KMT41agLaHuNfU2QDcOP4ff82fpU3sJkk0HTgYSzt7g/plWebg6hWc2rpRty0pLpbQoDsMXvpbroamZ1b57ZZcOrCHsKA7AJhbWNKgx7t5rsfUXEWH0f8jNTkJhdIEpYkIpYTcE78tRVh64G1uplueUBAEQRAEAYBfHz7hXIb54sEpqcwODM5Tj3pDBxsO1y3PmZgEXMxMqGtnhSRJ7Ktdjk0hkfz4IIzgDGuHZx6crAFi0jKnfxMKSumadRiyfA1JcbFY2toZrP9tjHY+d8ZbJoAs41TC02j58g2bYFPMmbsXzmJpZ0+lJs25efwIxzetQ61Oo1rLNlRq0sLguLJ1G+C2oCwRD+5h5+aOg5uH0foDz57SC7rTpSQkEPMkVJvRPI9MVSp6T53NnbMnSUtJwbtKdaNBf67ry2IUgSBkRwTeRVjy0zW8RW+3IAiCIAiZBaekopDgaWc1ahkeJ6dmf5ARJVRmlMi0fIqHyozKNpZ6QXdWOrra5/mcmV2PT+KL6/e5m5RCXTsrvi1bAvts5r6/yZQmJtlmQM+sZuuOXDv8L6nJSbptfg0aU/athlkeU8KvIiX8KuoeV2jclAqNm+Z4LttiztgWc862zJP7d5EUCoMs5EoTkxyX+8qOiZmZ3vJhgvCyiXesIizpaeAt5ncLgiAIwqsrKDGZK7GJXIhLQEaitbMdVW2yzhqdXxrYW7P0fpjetoYOuZ9rHZWaRpoMxcyMf10MT8056HYxM+Fd9+cPlkCbTb3L2VtEpqWhlmFbaBSRqWrWViv9QvU+j+vxSUy8+ZCHySn4O9gwvrQHFvm4fFthcCrhSf/Zi7h2+F8S4+Pwa9AYt1K5W+arIDgW9zQIugFaDfkMc0urQmiRIOQPEXgXYWIpMUEQBEF4tS25F8qU2490g3gVwKK7IfxRteATjrUoZse0MsWZGxRMqkamj4cTH3lm39sI2kRpX1x/wG+PwwFo7mTDjxV9DALMunZWmEoSqRmyX2casEx4ahqfX7/PqqrPHyRfikvkSYYgXw0cjIwlTSNjksXa5wUhMjWNTmdvEp2qRg3cTkgmVq1mfnmvl9aGgmLn4kbdzj0KuxkAWBkZAi4plZSt24DU5CRMzMxzzGL+Ih5eu0JUyGNcvEvh7OVTYOd5EfcvX+DArz+RGBuDb+16NH73PUyyyCAvvDpE4F2E6QJvMdRcEARBEF45dxOT9YJu0M55VgDz7oa8lEzfg0o4M6hEzsF2Rn8ER+iCboB94bEsuBvCF6Xc9cp5qMzYUK00n1y5y5PUNEqqzOjm6sC0wGfrGqtlOB+b+ELX4GBkSLm1UoHyJee3ORkdT0SqWvdYA2wLi2Z++ZfbjpclJTGBa/8dJCUxgVI16xhdkisjWaMhOjQEpZkpNo7Fnvu8GrXaYJusVjOvT0dkWcbSzp7O4ybj6pP/Ix4O//Ebxzet1T6QJAI+Gk6ltw3nqxemyOBHbPhmImp1Gsgy53Zt1yarG/BhYTdNyEHRHhvzhhNzvAVBEATh1fUwKdUg2RhoA7ZkI0NpXxVX4hIxzRDUaoDLccaD5zr21pyqX5Eg/6ocrFue+pmGsislKGP5YkPzylmpGODxbLi6BEwvW6JAez2NsTWyFJtNER9mnpWUxARWjR/F7h8XcXDVCn79fBgPr13JsnxyQgJ/TPyC5Z9+wLKPB7Dz+7lGh4vHhj8h5klolmuEA7iWLoNjcU+DxHDpxyRER7Fh2ldGA/QXEfMk9FnQrT0he5YvRqPJ3/O8qPuXL6JOS4UMy7cZWxJOePWIHu8iTAw1FwRBEIRXVzkrFZYKBYkajUEA3vsF5z2/qJg0NXOCgrmTkEwVGwuGe7li/jTQKWOpIi1DgxVAWavcZXGuaWfF595uzArS9nq7mZky10gW9dsJSfz9JAZLpYIurg7YmigJSkwmKlWNn5UKVaaAdnrZErR2tud+UgrVbC2paG3xfBf+AurYWRHgZMuu8BgUaIfUT/It/tLbUdBkWebKoQNEPHqge6xOS2XTjMlY2tnjU70WjXoP0BvafGT9Kh7fuq57fOXgPkpUqETlt1sCoE5L46/5M7l54ggA3tVq0mHUeEzMDHuPTExN6THpW47+uZro0BAeXr9CSkKCXpnE2BjiIiOyTdT2+OZ1Dq5eQVTwY+xd3WnUuz8eZbMenpAYE2OwTZ2aSlpy8tPM768GC1tbvceSJGFpa1dIrRHyQgTeRZhIriYIgiAIry4nMxN+q+LDkCt3CU5Jw85EgZ+lBb09nOjh7lgobQpLSeVeYgrjbjzgclwiamBPeAw34pP5sZI3AO96OHEsKo5NoVEA1LW3YqSXa67PMcrHjXc9nIhITaOUpbkuoE93OjqezudukaqRkYEf7oXQwMGGVY8jAG1Cti3Vy+CToadckiQav4Sh+dlRSBI/V/Zhe1gUwcmp1LWzpprtqxOQvShZo+HQH79ydsdWbS9vpl7p5IR4khPiiQx+hDolheYfDNHte3L/rl4Pt0KpJOLhA93jc7u2czNDr+zd82c4uW0D9br0MtoWS1s7mr33MQCrxo8k+NZNvf1KE5Nsg80H1y6zdtJY3TXERYSzdtJY+s5YQDFP43PyHUt4YuXgSEJ0FLJGgyRJuPiUfqWCboDSNergVaU6dy+cBUBpYkqTvu8XcquE3BCBdxGWLOZ4C4IgCMIrrYGDDecaVHrpicCMWfs4gs+u30OdqftdBraFRTHx5kMORMbiambChNIefFXag1RZpqTKLNth3RtDIpl06yGxaWraudgzs6wnruamuJobT/aUnuwtPUwLSkol6GnQDRCakkbLU9c5Xb+i0eHdz+O/yFgORcZRzMyE3u5OWD7nEHGlJNHBJfdLdRUlF/f9w8ktf+ZcUJa5cfw/vcDb1ac09y9dQJa1r6pGrdZLTPbk/j0UCkWG4eES4ffv5apdb/f/kLWTx6FJe5pgT5J4Z9hoo73l6c7/s8PgxoFGo+Hm8SNZBt6mZuY0HfAR2+Z9+/QyZRJiokmKi0NlbZ2rtr4MCqWSzmMncefMSZLiYvGsWBk7F7fCbpaQCyLwLsLEUHNBEARBKBoKOuiOV6vZEx6DWoa3HW0MEpKFpaQaDbozWvYgDBm4FQ+dzt7icN3yeORwd/9sTAJDrtzVDaVfHxzJ7fhkOrk50MPNERsjgXNsmoaMs38zZ0IHiFVrmBcUwgRfj2zPnxtrH0fw6bV7mDxd0/zP4Ai21Chj0BP/pntw7bLe+tmSQoGFrR2m5iqiQx4/KyhJmFvpB6L1uvQi7G4gQefPAFCtVVvKN2yi2+/s5YNGnfFVl3OdMdyjbHkGzVvGzZNHUaemUqFxU6wdnmPEiCxjYp79l+Yrh/YhISE//Y2MC3/CpQO7qdW2U97PV4AUSiW+td8q7GYIeSQC7yJMJFcTBEEQhKIlTSOz6F4I+yNicTUzZVwpd70h1XklyzLLHoTxze3HJD/t4XMxM2FHzbKUyBA0P0hKNRp0m0iQJoOFQiJRoy2gRhv4Ho2Ko6Orfu+uLMskaWTd0mLHouL0AmcZOB2bwJnYBFY+fMKummWxyhR8d3Jz4ERMPKCdPy49PWdmd9N7GF7QrEBt0Jg+b/1cbCIHI2JpUaxozosNCbzN+X/+QqPRUPntlhT3q5Av9RqbL12lWQD1uvZi07eTtUG1JCFJEk36DdIrZ6pS0XncZBJjY1CamBist12tZWuCb1/n6qEDAPjWrketdrkPZm2dXajZukOuy1dt/g7X/vtXb5ulnT0V/Ztle1xyQoKu1x4ASSIl8cWy8mclMTaGU9s3ER8ViVeV6vjVb/zSEwYKL5cIvIswMcdbEARBEIqWr+884of72p5lJfBfVByH6/oZXTIrN9YGRzDx1iO9beEpacy/G8Kscp66baUszDCTJFIyDL+1M1HQzdWRqraWfBcUTGBiil7Ps3WmgPlARAyfXLlLRKqa8lYqVlb2wd3cFGP52WXgVkIy28Ki6JkpkdwADyc0ssz64AislEpGebux6vETNoRE6ZWrbpM/c2vTbyhklGRkW1EQdjeQNf8bheZpr/SVf/fSfeJ0SpSv9EL1atRqJEnCxMyM1KQkQNtLXbtdZxQKJZ2+mMjNE0eIj4rCs2JlnEt6o05LZf8vP3Ht8H5MVRY07j2A8o3eNlq/Qqmk9dDRNOn3AUCBJwMrUaESPSfP5NT2TcQ+CaN4+YrU7dQ9x/P61W/MgysXtQ+eBsEF0bOcmpzEmq8+J+rpSILLB/YQFxFO7Xad8/1cwqtDBN5FWPqNYNHjLQiCIAivprg0NYGJyXiYm+FkZsK64AhdcKsGwlPT+C8yjrYu9s9V/5anCdAyUqMNvjMKT1WTlmnOa3krC74uq12b2UapZNClQGS0y4c1tLemSYalwUKTU+h34Q4pT6u4EZ/EoEtB7KpVlg0hkewON8wILQEJav2w/PdH4Sy+FwrAh57ODCiuXe+5nr0VxUxN+PHBE2Sgp5sjgz1dcv08ZKe3uyMLn55TiXZd8IYOr86c3by4tH83Go2sNxz8wt5dRgPvh9ev8t+630iOj6dcvUbUbtfZYIku0I5i2DrnG26fPq6tU1JgaWdHt6++0SUWUyiVlKvXSO+4o3/+wfnd2rnUyQkJ7Fg0BztXt2wzh7/M7NvF/SrkeTRAleat0GjUXD6wB1OVirc698TFu1S+t+3uxfNEPn6ot+3Uto0i8H7NicC7CBNzvAVBEATh1XUsKo6+F+4Qq9aglGBuuZJYKBRkHlj9vIm+QLuOtLE50s2c9JccuhCbYNAzfSU+Sff/Vs527KpVlsORcTibmdDexUE3Lz1VI/PepSBd0M3TK7j0dG3vXyr7cDI6nl8ePmHj0xsBCkClUNA8Qzt2hEUx+vp93eOxNx7gYKqkg4sDkiQxuUwJ/le6ODIyZvk4/3psKXfsTJTsj4jFxcyEL0q5P/cIg8ImGckVYGxwcuTjh6yf+iWatDRkWSY08DZ3L5ylVttO+FSvBWh7uc/s3MrZv7cTExaiO1aWNcRHRRJy5yZelatl2Zag86f1EphJCgX3L1/MNvB+1UmSRPWAtlQPaFvYTTFKlmVObd/Eya0bQJap/k473urcUwxRLyJEVokiTMzxFgRBEIRXkyzLfHApiPinPb5qGT67do+Pn/bimkjagKmWrSWNHJ5/mazhXq6oFApd8KUEPvNypXem5co8MyVJUwIlM22rZGPJ4JIudHFzxDRDgPdHcDinY/TXUQZwNzNFKUkoJAkHUxP+jYzV7XM2M2FT9dKUtHjWO7A7PIaMg9eVwO4nMVyPT+KH+6H88TgctZy/QTdoM5EP9XJlQ3VfllT0xtui6PZYVHq7JUoTJZJC+bT3WqJqyzYG5QLPnkL9NOhOd+/SeTZ+O4njm9YB8PeS7/j3t+V6QXdGSlPjWenTWTsW0+tBlzUarOxfz4zv+c2rUlXs3dyRFArdc1gjF3PYrx85yMHffyYxJprE2BiOrFvFpQO7C7q5Qj4pmrf7BED0eAuCIAjCqypBoyEsVX+4txqoZmvJpuq+HI6MxdnMlJ6Zgty8qmRjyYE65dgRFo25QqKTq4PR3tyadlYM9XRm0f0wAGxNlHzn52lQzpi7iSkopWfJyUDbc7O44rNlmSbcfEhU6rOe/JCUNO4npVI1Q8e7g0mmdkmQqNbQ7OQ1NLJ2iPsvD5+wqXoZVEZGAdxLTOa7uyE8SUmjmZMt/T2c3rievmKeXvSZNpfze/5G1mio1KQ5br5lDcqZWVgaLKeV7r91q6jUtKUu0ZkxnhUq41HWL9u2NO4zgEfXr5AYq51m4FmxSpZzvPOLLMuc2bGFs39vR6FUUqdjNyo1aa63X5Y1KBT5swxdQTFVqeg1dTYnt27QJlerXI0KjZvmeNy9S+dRKJTaddbRjjK4d/E8ld9uWdBNFvKBCLyLMF1yNdHjLQiCIAivFCulEm8LM+4npqBG27ttrpAoZWmOo6kJ9ezzb46xl4U5H5fMfj70/vAYwtPUdHV1oLmTLU2dbHO9RnYNW0u9oFsCPvJ01ruG+0kpegPoJeBxcqpePR+XdGZzaCSPnm53MTUlKDFZF3QDnI1NZOeTaDplyqYekZpGm9M3iUhLQyNre8+jU9V86u2aq2t4nRQr6U2z9wZnW6ZcvUac3rGFJ/eCDPbJGnWG9bQNufj40vnLKTkGr44eJRg47wceXL2EmbkFnhUro1AWbMB75eA+Dvz6k+7xriXzsLKzx6d6LS4d2MP+FT+QkpSIV+VqtPl0DBY2ttnUVrgsbe3wf/e9PB1j5eCoW+pMt02MMigyxFDzIkz0eAuCIAjCqys96zdo52L/XMkHx0KYW/xXWBS9LtxhXXAEm0IiGXvjAfHZBF6ZvVPMjjE+bphKEhLQ0cWesaXc9co0crDW+1IpA9UyZSV3NjNlf+1yLCpfkoXlS7K/TjkSNIZresem6bdNlmUW3Q0hLDUNtfxsPvusoMc8SO+FEPSYqlT0njobJ08vg3327h7YODpRvlETQEKStK+cQqnEr4E/PSZ+g0kOw8xBO7T8wu6dHFq1goOrfibw3Ol8vgpDd06f0GUbB22b75w9yZVDB9i1ZB4pSdq8A3cvnuOfHxYWeHtetpptOuLoUUL32M7FlToduxVii4S8yPW7f0yMYbbKrNjavrp3l14nIqu5IAiCILy6/KwsOFmvApFpauxMlChf0rDo2DQ1i++F8iA5hbfsrFn3OFy7VvbTiDUmTc3mkKgce8nTSZLEZ95ufOrlmuUc7Am+xYlOU7M9LAr10x7s7udvsbC8l17GdjtTE7q6PZt/3tHFgXl3Q5DR9gaZKST8HfXnvE+/85jFT4fIZ5Qmw8eX77KtZplcXYcxSWoN8++GcCE2AV8rFaO93bDJ5UiAV52pSoWdiyvhD+49G3YuSZR7S5udvNXHI3HxLk1Y0B0ci3tSs01HTMxy/6Xy9F+bOfzHr7rHm2dOoc2nY/Cr3zjb4zRqNUf/XM31o4exsLGhUe8BuV4OTWVjgyRJurnrsixjYWPL3uWLDcoGnj+T62spKlRW1vSZ/h33L11AlmVKVqyCqUpV2M0ScinXgbe9vX2u59Go83AXVXh+yWIdb0EQBEF4YYlqDZfjErE2UVDOUpWv84YlSXqpvdwpGg2dz97iytOM4+uDI3W97i9KKUlZ3jywVCpYUtGbqyeucfNptvREjczHV+5S084S9yx6CUb7uGEiSWwPi8LBVMn4Uh54ZUh+Fp2aplsKzJhzsYZJ3/Li4ytB7HoSgwY4EBHL6eh4ttYog+I1mTteo1V7As+chKc3S0xVKio31c4HViiV1GzdgcS4WFTW1nmeF33z5FGDbX/Nn0lacjKV3m6R5XFH1q9+muBNJipY4s9pX9F/9vc4uHnkeM66nbpz6+QxEqKjALBzdqVqi9Yc/XONYWHZ2ArzRZ+pmTmlatQu7GYIzyHXQ83379/Pvn372LdvHz///DMuLi6MGTOGTZs2sWnTJsaMGYOrqys///xzvjcyKCiIQYMG4ePjg4WFBaVLl2bixImkpOgPL7pw4QKNGjVCpVLh6enJzJkzDepav349fn5+qFQqKleuzI4dO/T2y7LMhAkTcHd3x8LCgubNm3Pz5s18v6b8kCQCb0EQBEF4IfcSk2l0/Cptz9ykyYnrDL96D00WSamKgjMxCVyMS0TNs0XLnqSkIaPNIq6UwMZESUdX+3w/t0aWuR6fpDfXO1WWuRmfnOUxSklilI8b++v4sbF6GWraWentT9BoDJZKS6fAMDN7XsSkqdn5NOgG7fN1KiaB2wlZt7eo8apSje6TvqVKs1bUeKcd706fh52Ldl588O2b/PDJAJZ80IfF7/fh3qXzearbwsZWb9h3uj3LF6NOSzNyhNaNY4dInzAgyzLq1DTuXjiXq3PaFnNhwJzFvDPkM1oP/5y+M+ZjZe+AlYOjQdliJb1zVacgvCy5vgXr7++v+/+UKVOYO3cuvXr10m1r3749lStXZtmyZfTv3z9fG3nt2jU0Gg0//PADvr6+XLp0iQ8++ID4+Hhmz54NaIfCt2zZkubNm7N06VIuXrzIe++9h729PR9++CEAR44coVevXkyfPp22bduyevVqOnbsyJkzZ6hUSTvEZebMmSxYsIBffvkFHx8fvvrqKwICArhy5QqqV2woh26OtxhqLgiCIAjPZeKth3pJwNaHRBJQzE5veHRRp5Tgl0o+bA2Lxkqp4ENP5yx7oPMiUa1hdlAwx6PisFQocDU3xUapIFb9LFhWAN4WeTtXXJqa6Xcecy42gbKW5lSxtuDy05sJGdcstzFRsrBCyeduv1LC6BroZi+QZf5VVMKvIiX8Kupt02jUbJ45hYSYaABSEuLZMutrPvrhV8xUFrmqt363Pty9cI60FP0bFerUVFKSErGwNr5MnspKf7g4yJhbWRkta4yFja1BBvDO4yaz+svPUKdp/5aVJqY0H/RJrusUhJdBkuW839a1tLTk/PnzlCmjP6fmxo0bVKtWjYSEFxv2kxuzZs1iyZIl3LlzB4AlS5Ywfvx4goODMXs6P2Xs2LFs3ryZa9euAdCjRw/i4+PZvn27rp633nqLatWqsXTpUmRZxsPDg1GjRjF69GgAoqOjcXV1ZeXKlfTs2TNXbYuJicHOzo7o6OgCne/eoCc8CIEti6Fa+QI7jSAIgiAAL/b59rI+G/PK/8Q1rj8dGg3aQPF/pT34JJfzn18VV+ISCUtJo5yVOX0vBOqGmquBYSVdGF8652G8efXR5SC2hUbpJUdToA1k03vYZ5bzpI+HU57q7XnuNgcjY9E8raOMlYrKViouxydS0dqSjzydSdbIlLVSGZ2PvSc8hjHX7xOemkYTBxsWlC+JXRbD/cffeMDyh08wfbpcWltne5ZV9HrtlymLj4pk6Ud9Dbb3m7UI5zz0FEc8fsTqL0eSnJgAsoykUOBUoiT9Zi7M8jm8f+UiG6ZNQK1OA1nGvYwf3SdOz1VCt+ykJidx9dABNOo0fKrX1vXsC0JBystn23NNOvL09OTHH380GMr9008/4emZuzUhX1R0dDSOjs+GlRw9epTGjRvrgm6AgIAAZsyYQWRkJA4ODhw9epTPPvtMr56AgAA2b94MQGBgIMHBwTRv/mw9QDs7O+rWrcvRo0ezDLyTk5NJTn52ty8vieheRPocb5FcTRAEQRCeT107K27GJ+mCRw1Q3dYyu0NemiS1hlMx8SiQqG1nZXS9b1mW+ermQ356+ATQZk9fUdmH/yLjeJCcQl07a/q4Gw7DfVGpGpmtoVEGvcUatL3In3q58JGnS57nt0enpnEgMlb3WA1ci0/iTkIySglaFrOnorVFlkFdUGIyAy8GkiZrF13aEx7D59fvs6ySj9HyX5cpTlVbSy7GJuBrqaKP+5uxNriFjS3mlla6gBlAaWKCjVOxPNXj6O5Br6mz2bV0HhGPHuJaqjStPh6Z7XPoWaEy/WYt4u7Fs6isrClTt8ELB90ApuYqqjRv9cL1CEJBea7A+7vvvqNLly7s3LmTunXrAnDixAlu3rzJhg0b8rWBxty6dYuFCxfqhpkDBAcH4+Oj/6bq6uqq2+fg4EBwcLBuW8YywcHBunIZjzNWxpjp06czefLk57+g5yTmeAuCIAjCi5lQ2oOQ5FT+CY/BXCHxVWmPfF1j+3lFpKbR/sxNbj2db1zNxoIN1XyxytTDeyomQRd0A8SrNXx18yH76/gVWNtkWUYpgakkkWJk4KQMmEg8V1I5U4XC6PDvFFm7jti8uyF4WZjRy914L/qZmARSM7RJDRyOigNgW2gUU24/Ii5NTWdXByb6emCmUNDd7f/s3Xd8U+X+wPHPyezee9JS9t5ThnJFQQH3VhRRUUAE97qunyjgAtwT97gqQxBFhgOQvaGU0ha6d9OZec7vj7ShadJSStnP+/XidZuTk5MngWvzzfMdQVwf0fpfTpzNVGo1Vz70BEvmvYTFZESt0TBm2sN4eJ/4v/3gmFhufum1E3pMUFQ0QVHRJ/xcgnAua9Ec7zFjxnDo0CGuvPJKSkpKKCkp4corryQlJYUxY8Y0+zqPP/44kiQ1+acuTbxOdnY2l112Gddddx2TJ09uyfJb3RNPPIHBYHD8yczMPC3PK+Z4C4IgCMLJ8dGoWdQ9kSPDu5M2rDuTYkLP9JIAeCsjn/R6Tb52V9TwYZbrSK3sBnOsZSCnXs16c20uq+Tizcl0+WcPU/cfocrqOqGm3Grj9t1pxPy5i67r9zZZB/9eZhG2FjSp81KrmBFv3wDR1G6a1t87VUuwqayq0cdH6Jx3TlW1x3aWV3PPvgwyjWZKrTY+yS5iXvqxTZXVxeVctOkA3dfv5cmUTEzy+dkRu7747j25973Pue3V+dz3/pe0Hzj0TC9JEM5rLZ5vERMTw8svv3xSTz5r1iwmTpzY5DmJiYmOn3Nychg5ciSDBw/mgw8+cDovIiKC/Px8p2N1tyMiIpo8p/79dcciIyOdzunZs2eja9Tr9ej1pzf6tdnAUtswUjRXEwRBEIST42429ZmUY7K47Pp+l1dCqcXG1PgwQmsDzG6+nqjAkSqvAgYFNL9RFUCuycwNu9IwyTIy8FN+KeVWG592S3AaH/ZEShari8uxASUWG4vzS3mlfQzrSytYVmhwumaVTeZwtZH23p7YFIX9lTWoJIlO3h7HHdX1aEIEagl+Kyqn3Goj02h2vBeKAlEejaclDwrw5pbIIL7KLQHsgfy8DrGsL6t02klXgNUl5TzZNoqDVUbu2JOGzb6pzmfZxWglFc+3Oz93ZNN2bOHA3+vQ6HT0GTOesDaJx32MIAgnr8WBd1lZGR9//DEHDhwAoEuXLtx11134+/s3+xqhoaGEhjbvm+Xs7GxGjhxJnz59+PTTT1E1+AU5aNAgnnrqKSwWC9raOpFVq1bRoUMHAgMDHeesXr2aGTNmOB63atUqBg0aBEBCQgIRERGsXr3aEWiXl5ezadMmpkyZ0uzXdTqY6n3BLWq8BUEQBOH8MjDAm2WFZY7bMpBRY+ajrEJ+LTKwpl8HfDRq3j1a4NTczF+j5rUOJ9ZvZ4uhmpp6O7wy8HtxORdvOcjPPZPwV0scqjGzOL/UaVSYjD3dfHJMKP4aDY+2CWPKgUzW16Z2+2rUVFptXL/rMNvL7Y13hwT48GX3RDzVjX/RsbzQwLyMfNSSPdCuH6Z39fHk/tjGG99JksS8DrHcGhVCkdlCTz8vQnVaUmtMTu+TGgiv/fJifWmFI+iue10fZxdxTUQg3X3Pjnr/1pK65V+WzHsJSVKBBMkb/uL2OQuaNUNbEIST06Kvd7du3Urbtm154403HKnmr7/+Om3btmX79u2tvUays7MZMWIEcXFxzJs3j8LCQvLy8pzqrm+++WZ0Oh2TJk1i3759fPfdd7z11ltOzdQefPBBVq5cyWuvvUZycjLPPfccW7duZerUqYD9P9YzZszgpZdeYunSpezZs4fbb7+dqKgoJkyY0Oqv62TUzywTqeaCIAiCcH65MzqEy0P8nXZIFOw1y0eNZl46nEO+ycyXtTu7dUqtNkrdpIk3JaSRWuwQrQYPSSEnN5eOPp5u66qj9Fr6+Xszr2MspqJCvugaz5AAH4YG+KCRJG7encaO8mPTbtaXVXLjrsPsq+267s57mQVIgE2xB8EyMCkmhMW9kljepz0+bjqZ1ydJEr38vPhPiL8jM2BCWABD6tXu+2jUPJtkDzZDdFqX7AKronDTrjRqbOdXyvmuP34FQFFkFFnGZrFw4O91Z3RN54PMfbvZ8dsvZO3fe6aXIpzFWrTj/dBDDzFu3Dg+/PBDNBr7JaxWK3fffTczZszgr7/+atVFrlq1itTUVFJTU4mJiXG6r24amr+/P7///jsPPPAAffr0ISQkhGeffdYxwxtg8ODBfP311zz99NM8+eSTtGvXjsWLFztmeAM8+uijVFVVcc8991BWVsbQoUNZuXLlWTvDW6eFsyw7ThAEQRCEk/T20QJ+LTLY65zdlEp/llPMn/W6f9d3oqXVgwK8uToskJ8KSh3HhgT48EXXeP5au5arxo9nzty5zKvdqPgytxiAmyODGB7ogyRJLFiwgEcffZTFS5bwzcUXU2SRGbXlIAVmq8vyNxuquGxrCkt6J9HbzzUtvm4cWR0JiPfQMfAEm95V22S+yCki12RhSIAP3/Voy9+lFVTYZAYH+BCis3+GHRPiz0B/b/41ONeOF1usZNSY6OTTvLnWZytFljEUFqDV6+0Zo5Lk9I+kYRapcGI2/PA1G//3teP20BtvZ8BV15/BFQlnqxbN8fb09GTHjh107OjcMXP//v307dv3tMzxPpudjlmlaZkw8nbw84Y9vxz/fEEQBEE4WefjHO+zkaIoJPy1G6Pc9Ec0FdDNx5PdlTWOdOyBAT782LPtCY/EUhSFdzMLeOFwrlPQPWHcOMfI1PkLFjBt6lQeSc5kS3kVa/q2R6VSMX/BAh6cPh2w971ZunQZQ0eO4La9Rxxp5w2pgQnhgbzdOd7lvsX5pdy3/whqAAl81WrW9e9IhN59bXdGjYl56XkUma2MCvHjrugQbApM2HGIHeXVqGpndD/XNopbo4Ld7phX22x0/HsP5npvuQrYM6QrwboWV2aeccbKSn6c/Sx5qSkAJPbpT/r2LUiSCgXQeXpy+5wF+IWcHU0FzzU1FeW8c/fNzgcliamffIve68R6LQjnplM+x9vPz4+jR4+6BN6ZmZn4+vq25JLCCarb8Rb13YIgCIJwfpGxz8luKFCjdkojl4FKm8xD8eGk15hI9NLzQFy426BbURRsCmjczAIHe3r2/XHhhGq1jA3xdQm6AaZPmwbA3KlTKTFbXYJuAJPJxLhxV7J46VK+GDmy0eBbwZ7O7c6E8EC81CqWFZbho1ZzX2xoo0F3sdnKmG0pGCw2bMC60grKrTb6+XmzrTbFve6tfO5wDs8dzuHiIF8+6NLGKQD3UqtZ2LkN9+/PwFpbVz67fcw5HXQD/P3NZ+SnpTpup23bzKDrbqGmvAyNTk/PS8eIoPskmNxtNioKpupqEXgLLlr0X5MbbriBSZMmMW/ePAYPHgzA+vXreeSRR7jppptadYGCe3XN1U5zM3VBEARBEE4xtSRxbUQg3+eVomDfHW7jqeeO6GCeTc1xOje9xsSK2mZrjXULX1pQxiMHMym32hgS4MP7Xdo4BZTJVTXMS8+jxGLji67xlOTnc9X48U5Bd53p06YhAVOnTmVBg6C7jslk4qrx40k5dIh3O8XSfeMBp/tV2APvmxuZxQ1waYg/l4Ycv2HvupJySizONe1f5hTTo4mmaOtKKng5LYeX2zs3oRsXFkAfv84crDKS6KWnjee5/yGr6OgRlHqN81RqNYps45K7Gm8arCgKyev/JD/tEIGRMXS7+FJU6qbr6i9UfqGhBMfEUZKThSLLSCoVIbHx+AY1/m9buHC1KPCeN28ekiRx++23Y7XaZ1pptVqmTJnCK6+80qoLFNyra64mGqsJgiAIwrlJURR+yi9lZ0U1CZ56bosKQVu7Iz23QywxHjo2G6qI89DxeGIkIVoNpRYrbxwpcFxDBpKrjGQZzcTVCxTrdrjTa0xM2Zfh6Ea+saySWQeP8lk3+wipXJOZK7cdotomYwP+eziPeR1jmTNnDtPdBNUA06ZN48MPP2T37t2NvrY5c+cSGxPDw8mZjmMqoKOPB0leHtwSGczwIF8KzRbeyMgn32xhUIAPd0WHHHfcWH16N/XJepXEQH9vYvRacs0WbA021mVwdFlvKNpDR/R5NKc1LCGR3NSDjuBbttkIjU9o8jF/ffUpW5f9hEqtRrbJHNm9nStnPnnC5QsXApVKzbVPvcgfn7xL4ZF0wtskcsmk+5FE3bzgRosCb51Ox1tvvcXs2bM5fPgwAG3btsXL6/wauXA2q0s1P49+NwiCIAjCBeXFw7m8k1mAtrYG+a/SCj7tmoAkSehUKh5JiATsQfRrGfl8mFVIY615/OqlTS/OL+XRlCwqrDbaeemdRoDZsDc3q7Om2N5srM6XucWE6tQ8VptW3ljw3VTQvWDBAqZOncrDyZl8XduIDaCtl94+nqy2i3q1TWbc9kMcrTEjYx8jlm+y8FTb5o+2uiTYj47eHqRUGR213LPaROCtUbOsTzvmpeexo7ya/VVGx2PUQDuvljfNlRUFCc6JQHTojbdTlHnE0W27zxVX0a7/4EbPt5rNbP3lZ8AepAMc2ryRsrwcAiPPz7nmJ8snKJgJDz99ppchnANOqnDFy8vLMSNbBN2nlyPwFjvegiAIgnDOMcky72Xad64ttbH0yqJyVhQaGBsW4HTuN3klzMvIoymlFhsBWg0Hq4zcv/+IY2Z1arVzurgaiK33rX3DedoS8FtROY8lRjFt2jQUcJtO3pj5tUG3RZZ5LDGCxxIi+LO0guSqGg5WGZmZnMndsaEMCvBhU1kl6TVmp8d/ml10QoG3p1rFL73b8XlOMcUWKyODfBkaaO83FKnX8VrHOGRF4aHko3yXZ+/anuil55kTeI46FlnhiZRMvs0rQStJPNQmgunx4Sd8ndNJ7+XN9c/OptpQhlqrxcO76c7wsmxz2xbfZrGcqiUKwgWjRXkQsizzwgsv4O/vT3x8PPHx8QQEBPDiiy8iy+fXvMOzlaPGW+x4C4IgCMI5p25GdUP37s9ge7nzWKsNpZUcr8L2SO038jvLq52uKwPaehuzflo1r3eMc9weHeJHB28PVGAfXQY8khABwPtHC5g+bRpvzZ/frNdU1/UcQKtSEarTEqrXEqTVsPBoIX8UV/BrkYFrd6ayo7wajZsdY6NN5qOsQuQTGLrjo1Fzf1wYz7SNcgTd9akkibc6xfPvwE6s7teBNf06EtZIszZ3coxmPsgs4M49aXyZW4JVgRpZ4eW0XJYXlrmcb1MU8kwWzGfJZ2JJkvAOCDxu0A2g8/Ck3YDBjt18SaUiPDGJoJjY4zxSEITjadGO91NPPcXHH3/MK6+8wpAhQwD4559/eO655zAajfzf//1fqy5ScCV2vAVBEATh9KuxyXyZU0ye2T4b+uLglo1G81KrGB8WwJKCMqfjigKfZBXRu/OxjsjhboLEuvHeEqCRJNrXpk7HuqlBsygwyN+LhxMi6ebr5ZSW7q1Ws7x3O77JLaHUauXiQF/61s7LXlNSTqhOw/Rp0/j4o4+aTC/v3r0706ZOxWiT8Wiwi/59Xgkq7F8CKIBagZ/yS3imbRQ9fD3ZXVHjmNttBZ4+lE2ZxcbDtV8AAFhlBRkF3UnUzrakWVpatYnLtqVQabW5fFGilWCLoYqxoQGOY/sra7hldxq5JgteahXvdIrnstDjN4k7m4yZ+jCbfv6OvLRUgiKjGXTdzahUormaIJysFgXeixYt4qOPPmLcuHGOY927dyc6Opr7779fBN6ngaO5mtjxFgRBEITTwiIrXLczle21s6HfPlrA7PYx3Bkd0qLrze8UR6bRzI7yakfgqeC6Ez41Lozfiwwcqk0bT/DUoULicI0JL7WKhZ3iiKr9QDAowJvJMSF8mFXkdI2Nhmo8VCqnoLuOj0ZNqE7D6xl5vJWRz8hgP97pHM/zSdF09PFk/oIFTQbdYK/5XrBwIdOmTmV9aQWPHMxEjcSshAg0kuT0mmxAltGMTqXip55JPJR8lKWFBqfrvZaRR0dvD8aG+jMnPY8FR/OxKXBdRCDzOsQ2OwBXFAWTrLh8GdBc72YWUGVzDbrBXk8e0+CD2F170ikw2dOyq20y9+zLYNvgzoTqmr/DfqZpdDqG3HDbmV6GIJx3WhR4l5SUuMzwBujYsSMlJSUnvSjh+MSOtyAIgiCcGt/kFvNmRj5mReGOqGAejLfPxv63rJKtDWZDz0vPbXHgrVepmNM+hsu3HUJWFBTsgfctkUFO5wVqNfzetwP/llUiSTDQ3we9SqLCJuOtVqGul7ItSRIvtovhs6wiGlblVtncpz4fqKxxqgtfW1zO8oIybooKbnRkmDt1c76nTZ3K4ABfvswt5r79R+jn6+ly7u6KGgC8NWquDAt0CbwVYMr+I7zQLoo3juQ7jn+fV0q4TttoHfja4nI2G6qI9tDhpVbx2MFMKmwyvf28+LRrgtvsgaZUWG3uSp4BGBLgw21Rx8ZGGW0yGUbnmnWzopBWbTqnAm9BEE6NFgXePXr0YOHChcxvUPOzcOFCevTo0SoLE5pWN1pT1HgLgiAIQutZW1zOQ/VGYL2SnkegVsMd0SEY3dTsmuXm1yK709XXi6W92/FpdiE2BW6ODGJIgzrlozUmvs0rwabA1eGBjt1bd7vXda5uMAc8TKelt5/7RrjbG9SF3xwV7Ai6G+tq3pi64Hteba33l7nFbKkNsuurqfde1nUmT67XeRzAoij8XlTu8thvcovdBt4fZhbyTGo2GsleQ1//b2ZXRTXTDxzhu55JJ/R6xoUFsLi2HKDu643nk6Lo6+9ND18vpy89PNQqIvVa8k0Wx/upliD+PJgHLgjCyWtR4D1nzhzGjh3LH3/8waBBgwDYuHEjmZmZrFixolUXKLhnEnO8BUEQBKHVrS2pQFM7lgrswdbq4nLuiA5hUIAPUbWBla32vpvr7Xi2VC8/L3r5xbu970iNiVFbDlJtk0GC9zILWNa7Hd19m54mM6dDLKE6Lf+UVhDroeOZtlH41AvUs41mnjyURUqVkah6u8C3RgYzt0Ms84+z0929e/dG08/dBd+Ao85bBYyvVxftVduZvO+GfZQ12JX3cZMiXma1uRxTFIVX03OBY3939dkU97O7bYrCvPQ8fsovxVej5qnESEbWq9sfExrAwk5xfJJdhBqJKXGhjKm39oY+7tKGW3anUWq1oZUk3ugYS8QJ7rILgnB+alHBy/Dhw0lJSeGqq66irKyMsrIyrr76ag4ePMhFF13U2msU3DCKwFsQBEEQWl2ITkP9TWxV7TGw10L/0rsd10UEMTzQh6cSI3m2BWOpTsTnOcVUyzI27MGjRVb4oHYMmTuZRjP/PZTNkylZXBToy8q+HfiwawIBWg0/5pXwv7wSCk0Wrtt5mD+Ky0mvMfNvWRWhtbO1H20TRmZWFo89+mijzzF/wQJ27drlkvlY36OPPEJmVhaPtglzHOvo7UF7Lz33xYbxfDvnmdBeahXxXs4faoYEeDMpOtTl2tGNpPuZmsg+kLC/f8+lZlNlOxa4v5mRz5tH8jliNLO/sobb9qRxoNJ5h/7aiCBW9GnPsj7tXILuXwrKmLr/CE8fyiLbaKa3vzfbB3fhr/4d2TukC9dGOJcNCIJw4WrxHO+oqCjRRO0MctR4i1RzQRAEQWg1E6ND+F9eCSm1jcyCdRoeanOsu3aUh443O8U19vATtr/SPt+6nZeerm52sS2yc860DKwqLqfYbCVY5/wxLt9k4dItBymv3RH+KreEj7u2ob+/N5dvTSGrtulXiFZNkeVY8GkDCi1WQrQaphzI5Iuu8fy8ZAkTxo3DZHKeA143MmxZQRnTane2G6aj6/V6fl6yhODwcG7bewQ19jr1n3olEaB1/9HzYJWRXQ1S0jeUVdHLz5OJUUF8lmPvIeQh4fb9lySJa8ID+T6vBBl7er23RkW51b6DrgDVsswHmYWkVBn5ukdbAFYUlTneXrn2xHUlFXTyca1Lb+jrnGJmHsx0jHpbkl/Guv4dCdZpaO/tcdzHC4JwYWlx4F1WVsbmzZspKChwmd19++23n/TChKbVBd56seMtCIIgCK3GT6NmZd8OrCkux6ooDA/yJdBNsKgoCtkmCzpJOqGZ0PV9ml3EkylZjsDv+aQo7o0Nczrn6vBAPsoqdDpWYZV56XAObzQIQBcXlGKoN/ZKAj7ILGRHeTW5pmOt1ootrqnaagnMisz6skpu23uEL0aOZPHSpU7Bd13Q/WRKFpeF2EdkTZs2DQUcael6vZ5ly5Zx8cUX81OhAYuiMDY0gCfbRjYadIP7meb2FyHxSoc4bowMocBsobuvV6Op23M6xBCp17KhrJI4Dx1PJEaSWm3k+l1pTs+zpqSCUouVQK2GIK3GkQJfd3+Atnmjs96v/XupezcLLVZ+KzKcdPlBkdlKrslMgqfeqTxAEIRzW4sC72XLlnHLLbdQWVmJn58fUoNumiLwPvXqarxFczVBEARBaF1eahVXhAU0en+V1cbte9JZX1YJwA0RgbzRMQ5Vvc9Dx1NltfH0oSynBmDPp+bQ28+L9Boz8R46BgT40NPPixsig/g2t8RpZ/ZAlWvDsobdt+u6pJdarMcGf+PcdKzOjRHBfJdnr8VuGHxfNX48c+fOZerUqRyuNjKzTYQj/f7LnCJHTfdjjz7Kz0uWMGLkxajVaq6LCOK6ZqZad/T2oJ+fF9tq67AV7I3m9LVjw3QqiflH8sk0mhkc4MMr7WPwbxDI61QqHkuMdDrWWEBfF84+lRjF1TtSqa7dROrl68lVYYHNWrPbv+3m/xNw6+vcYh5JzsQG+KpVfNk9kQG1c9UFQTi3tSjwnjVrFnfddRcvv/wyXl5NN/cQTg1R4y0IgiAIZ8ZrGflsrA26Ab7LK2WAv88J7XQarDZsDSJgGRi3PdURGD8QG8ozSdFcHuLPN7nHxrWqgR5u0tLHhwfwxpF8Km02UOw7sZNiQtBLKr6q93h3PFWSU237+rJKph/M4u2LL+bQoUPExMQA0NbLOYX6zYx8dlXUMHfaNK666iqCw8NZU1rJ6FD/Zr4Tta9Jkvi2R1sWHi0gvcZELz8v7o6x13cbLFau2ZFKudWGDVhaUEaFTebL7onHvW6Mh46+fl6OMXB1vs4t4b64MHr6efHngI78VVKBj0bFZSH+jmD/eKbEhvFg8lHUtcF2qFbD5SEn9rrryzNZHEE32Me/3bMvg52Duzhtcl2IbFYLm37+ntzUFALCIxl8/S14+vge/4GCcBZpUeCdnZ3N9OnTRdB9Bokab0EQBEE4M5Krapx2UrUSLqOwjidcr6Wtp54MowmbYg+mZZx3o9/OLOTWqBD+E+zHjPhw3jqSjwIMCPB2O04rUq/jt77teT+zkEqbjXGhAfynNhCc1yGWd44WICsKGpVEerUJG7WjxvRagnUap9ekwp7yrNdoiIyMdHmuOm29PPg6pxhFsTdmu23vER6MDz+h96KOt0btsmMN9pnfpVbnmvS1xeUoikKNrLDFUIVaggH+PmhVrgFqpE5bf8MfCdhecSwQj/XQcUsL0sNviAwiQKvmtyID/ho198aGuS1LaK4jNSbqFwHIQL7ZiklW8FBf2IH3ynff4uD6P1EUBUmlIvvAXm6Z/SZqTcvfb0E43Vr0r3X06NFs3bqVxMTjf9MonBqOwFvseAuCIAjCadXZx5N1JRWOQNWiQCefE2umpZYkvumRyIzko+ytNNLBS88WN+Ouii1WErz0PJ4YybS4MBZlF/PW0Xy6rd/L1WGBvNohBl29Hdo2nnpmt49xuc6tUcHcWhtcGixWnjmUzebyKhI99cxqE8HEPWkuj6lrKqdWN15n/GqHGK7ekcqXucV8mVvMPTEhXBTYstTov0sqeONIPkZZ5qbIIG6NDEaSJI7WpfnV469VU2SxMn57Kmk19g9Fvf28+KFnW7zrrXdfZQ2/FhucvtCQgLhW2rkYHeLP6JPY5a4vycsDD5WESVYc89fjPfWOue0XKqvFQvI/6xy3FVmm8GgGRUczCE88sbnsgnAmNTvwXrp0qePnsWPH8sgjj7B//366deuGVuvc5GLcuHGtt0LBLUdzNbHjLQiCIAin1UNtwtlTUcOfpRUA3BIZxA0tGBsV56nnp17tHLfHbz/EVkMVNuw7zoEaNZ1qu2MrisKHWYW8kp7nOP+7vBLC9FqecLNL3BR/rYb5nY/NDV9wJN+l4dqAAG+GB9lTeQvNFlYVl6OVJC4L8ce3XsOvNp561g/oREq1kQCNmnhP5x2BMouV3RU1hOg0dG6iU/juimpu3H0YRbHv9G4vr0YtSVwbHsjzqdku5z+XFMUbGfkcqTnWdX1neTWfZBUxrd6O+/uZBS617yE6TYt35U+lYJ2Gj7omMGVfBhU2mWgPHZ92SzjTyzrjVCoVkkqNIjv/GxW73cK5ptn/YidMmOBy7IUXXnA5JkkSNptrt0yhdZlEjbcgCIIgnBHeajXf9kgk32xFK0kuY71a6uOuCVy5LYV0oxkZ0KgkiixWvDVqXk3P480j+U7ny8D60grgxALvhmpkGal+Ljagq60pzqgxMWZbCiW1gXkbTx2/9mnvlFLtqVa5rTnfU1HNtTsPY6hNE58YFczs9jFu65V/LTQ4XlOdxfmlDA/0pdzm2iKtt5833+SUOKVmqyR7nXR9dbvHddTAFaEBTl8enE1GBfuRfFE3yq02AjTqC762G0ClVtN//LVs+vk7VGo1sk0msXdfgmPjj/9gQTiLNPs3RcORYcKZJZqrCYIgCMKZI0lSo2Otyq02isxWoj20zW7UBfba8fR6adVFZiuPHsziw65teKtB0A32IDK6BSnTRpvMW0fy2Vqban5ZbSO0+rH39bU7+G9m5GOotxueWWPm0+wiZtabbd6Yhw9mUlmvNvuznGLGhQUy2E0qupda5bQzrQJ81WpCdRr8NGoqa8ekSbV/bthxmGyzc5BtVWBQgw7g14QHsqSgzNFsXKk9djZTS9JJ1Yqfj4bccCshcfHk1TZX63bJaPGlhHDOEf+vPkeJ5mqCIAiCcPb5JreYRw5mYlUgTKfhmx5t6dJEinV9adUmp9s24FC1kRqb7HYEWLBOw9Numqwdz4PJR1lWUIYMbCit5OvcYkeHdT+1iueTormmNvAutViddqElCcrczAF3J8topuGZ2SbXem2AmyODWZRdRLbJggToVRLT24SjU6n4tGsbJu5Jp6Le+9Aw6Aa4IyrYZQzcpSH+fNq1DZ/nFKORJO6NDaWPv3ez1i+cPSRJouPgYXQcPOxML0UQWqzZgff8+fO555578PDwYP78+U2eO3369JNemNA0k6jxFgRBEIQz5tvcYt7IyMesKNwRFcyD8eFkGs3MSs50BKrFZiv37M1g/cBOzbpmtwbp2mqgl58XYToN/fy82V5+rP47zkPHqr7t8T3BnVGjTWZJQZnjtg2cxppV2mQq66V2jw7157ficsdtqwKXhvg167kGBfjwa6EBG/ZdapVkfz3uBOs0rOrXgZ/ySzHKCmNC/Enwsqf1DQn0Zc+Qriw4ks9rtZ3d3bm0kSZnl4cGcHloQLPWLAiCcKo0+7/Wb7zxBrfccgseHh688cYbjZ4nSVKrB94ZGRm8+OKLrFmzhry8PKKiorj11lt56qmn0OnskafRaOS+++5j27ZtHDhwgCuuuILFixe7XGvdunXMnDmTffv2ERsby9NPP83EiROdznn77beZO3cueXl59OjRgwULFtC/f/9WfU0nS9R4C4IgCMKZsa6knBnJmY7br6TnEaDV0MZT57Q7bAPSakz2EUjNSIvt5efF7PYxPHsoG4ui0NPPi1fbxyJJEl90T+CFwznsrqihs7cHzyZFn1DQLSsKB6uMWGUFtYTLDPE6Ksl5V/qmiCAqrTY+yy5Gp5J4MD6coYGu85NlRSGtxoRepSK2Nh1vbodYqmwyf5VU4K9VM7dDLElejXd/D9RqmFQ7u7tOmcWKRpLw0aiJ93L/oUdd+9i+jQT1x7O9vIpck4Uevl7EeOiotNr4tciARVb4T4gfoTr3JQWCIAgnotn/xU5PT3f78+mQnJyMLMu8//77JCUlsXfvXiZPnkxVVRXz5s0DwGaz4enpyfTp0/nxxx/dXic9PZ2xY8dy33338dVXX7F69WruvvtuIiMjGT16NADfffcdM2fO5L333mPAgAG8+eabjB49moMHDxIWFnbaXnNTZBnqeoeIwFsQBEEQTq+1xRVoJPvub50/ist5pX2MU1CrBpK8PU6oFvXO6BBujQymRpbxq9cALECr4fWOcS1ar9Emc+vuNP4pqwQgSqclx2xBDS6p4A3rpCVJ4p7YMO6JbfwzUJnFyo27DrOzogaAa8MDmd8pjkCtPdW+uV88NFzzlP0Z/Fpk322/KzqYJxOjeM/bgwNVRkc9epyHjvbeHjyXFIV/C+qin07J4qPsIsDeUO6dznH8X1ou6TX2Lx8CD6tZ3qc9iY0E/YIgCM0lKUrDIQvnhrlz5/Luu++SluY6d3LixImUlZW57Hg/9thjLF++nL179zqO3XjjjZSVlbFy5UoABgwYQL9+/Vi4cCFgbyoXGxvLtGnTePzxx5u1tvLycvz9/TEYDPj5NS8d60TUGKHj5faf968A7+aVjgmCIAjCSTmZ32+n+nfj6bTgSD6z03Idu9tqyd6M7I2OcfycX8qDB45iVhSi9Vq+7tGWDt7Nm/G9r7KGV9JyKbZYGRPiz/1xYagaCVgVxd6tu7H7G6735bRctynaARo1HhLkW2xoJYlHEiKcxnEBZBrNPHYwk9RqEz197bvy9Tu5P5GSxefZRU5B/JsdY7kxMrgZr9q9V9NyeetIvlMGwZsdY7kyLICf8ksxWGxcEuxHp2bWz7uzu6KaS7emOG5LgK9aRaVNPvZ3C1wTEcj8TqKDtiAIrk7kd1uzvxqcOXNmsxfw+uuvN/vcljIYDAQFndjMzI0bNzJq1CinY6NHj2bGjBkAmM1mtm3bxhNPPOG4X6VSMWrUKDZu3HjSa24txnq9V0SNtyAIgiCcXhOjQ/hffikHq4wABGk0PFQbrF4VHshlIf6UWKxE6LWom7nTm2M0M377IWpsMjbsc6xNssKsBOfu4RVWG48ezOSXgjIsgKdKopO3J7M7xLgd6QXwV0lFo3XRZfW6jpsVhfQa5wZvZlnm2h2pZJnM2BTINprJNVlY1ufY/PGUKqNT0K2RILVBo7gTtbeyxino1kr2YzdGBnNbVMhJXbtOfoPRYwr2GneVBHLtGyYDpc1sJicIgtCUZgfeO3bsaNZ5p6O1f2pqKgsWLHCkmTdXXl4e4eHO3+KGh4dTXl5OTU0NpaWl2Gw2t+ckJyc3el2TyYTJdOwXTHl5eaPntoa60iuN2v5HEARBEITTx1ej5tc+7VlTXI5FURgR5Osy1zpafWLfjK8tqXBqagbwfV6JU+BdabUxcnMyWfUCxhpZYUdFNdfsSGXDgE6EuRlxlml030ncne/ySpjXIdaxk55abeJIvcfbgC3lVZRZrATUvubuvp5sLKt0BMpWBbqexE40QJKXnjXFx1LhrQq0a6I+vDE2RaHKJuOrVrl8Ru3h64WXSoVRtu9wq4Fuvp6OlHmwB+OjG2naJlxYDuSWc6S4mk6RvsQHi874wolrduC9du3aVn/yxx9/nFdffbXJcw4cOEDHjh0dt7Ozs7nsssu47rrrmDx5cquvqSVmz57N888/f9qeT8zwFgRBEIQzy0utchlddbLXq08CfBp8u76ssMwp6K5Tt1O72VDldk1BWjUZxuatQ41E/fDU3803/BrJeb0PJ0SQUmVkdUkFEnBfbCjjm3hvFEVhaWEZqVUmuvp6cmmwn0tQPKtNBFsN1WwprwLgytAAbjnB1PXfiww8sP8IFTaZRE89X3ZPdKrVDtNr+aZHIrMOZpJrsjA4wIe3OsXxV0kFbx7Jxywr3BEdzC2RJ5ZhKZx/3lmXypyVBwFQqyTeurEnV3Q/sVF+WaXVLN6RjaLAhF7RxAa1rBmgcO46qTneqampHD58mGHDhuHp6XnCzTNmzZrl0lG8ocTERMfPOTk5jBw5ksGDB/PBBx+c8HojIiLIz893Opafn4+fnx+enp6o1WrUarXbcyIinFO96nviiSecUvHLy8uJjY094fU1l2OGtwi8BUEQBOG8cFmIP119PNlXWUNdSPtYgzTzmgY74g0FaN2nwU2KCWX7gaNI2AN6rSRhQ3FqDlfnnthQp89y0R46psSG8m5moaOh2XNJ0ehU9lVmGc3MTsul0iZzf2wo0+LCCDxOF/DHUrJq52rbd7IfjA/nicRIp3N8NGqW9E4io8aMRoJYD90JfcbMN1m4e28GltpWRkdqTEzam87a/h2dzhsQ4MM/A5zHvU0ID2RCeGCzn0s4v+UaahxBN4BNVnj0f7u5vGskalXz/k1mFFVx5cJ/qDZZAYn3/jrM0qlDaRvqc9zHCuePFgXexcXFXH/99axduxZJkjh06BCJiYlMmjSJwMBAXnvttWZdJzQ0lNDQ0OOfiH2ne+TIkfTp04dPP/0UlUp1/Ac1MGjQIFasWOF0bNWqVQwaNAgAnU5Hnz59WL16NRMmTADszdVWr17N1KlTG72uXq9Hrz99UbAj8Bb13YIgCIJwXvBUq1jSO4nvc0sotli5NMSf7g1qtv8T4s+LqTnUuOmLe3mIP4MD3H+IvyYiCE+1imUFZfho1NwXG0qsh46sGjPTDhxlW0U1Okni3thQnmrruov3bNsoRgT5cbjaSDdfL/r529Nsq6w2xm8/RJ7Zgk2BzYYqKqwyczs2vvmQazLzeU4xcKwr/FtH8ikxW7GhcHV4oGNcmUqSWtxNPKXKiLne+2QDkquM2BSl2XX3ggBQWOHar6DabKPGYsNH37xQ6rMNGVSbbbUTDxSMFplP16fz0oRurbtY4azWosD7oYceQqvVcvToUTp1OvYt4Q033MDMmTObHXg3V3Z2NiNGjCA+Pp558+ZRWFjouK/+TvT+/fsxm82UlJRQUVHBzp07AejZsycA9913HwsXLuTRRx/lrrvuYs2aNXz//fcsX77ccY2ZM2dyxx130LdvX/r378+bb75JVVUVd955Z6u+ppNRF3iLxmqCIAiCcP5YlF3MK2m5WBSF/ZVGFnaOd0rpjvXQsaxPO+al55FtMpPk5UEvXy/iPPVcGuLXaIdzg8XKhrJKSi024jz1ROt16FQqEr09WN63PWZZRitJje4oS5LE8CBfhgc5z+/eWl5Ndr3UdwX4Pr+kycC7qpFd+69yi5GAb3JL+Kp7IhcHn1zn+zhPnWOHHkAFRJ1AsztBqNM21Icgbx1l1WZkxZ5q3iHct9lBN4DJ2qBBnwJGS9MZLML5p0WB9++//85vv/1GTEyM0/F27dpx5MiRVllYfatWrSI1NZXU1FSX56w/DW3MmDFOz9+rVy+ncxISEli+fDkPPfQQb731FjExMXz00UeOGd5g//KgsLCQZ599lry8PHr27MnKlStdGq6dSXXN1U7jJrsgCIIgnDUyjWaSK2tI9NLTtgUNt85Ga4vLeeFwjuP2yiIDr6bl8ny7aKfzuvp68Vn3xIYPb5RNUbhxVxq7K6qxAX+VVnCoysgn3RIc5+hakEUIoHcTw3oc51oJnnr0koSpwa59XQgiAZ9kF5104B3vqWd2+xieTMlCxp66/n6XNid1TeHC5K3X8MWk/sz8fhdHi6vpFu3Hmzf2OqFrjOsRzbebMx39E2RFYXzPE6sRF859LQq8q6qq8PJybQhQUlJySlKuJ06ceNxacICMjIzjnjNixIjjdmifOnVqk6nlZ5poriYIgiBcqBbnl/LAgSPYFHuQ9lK7aCbFNK9s7Wy2vbwatURtKqo9EN1sqGrx9TaXVbK2pAKrYu96XkcGVhQZqLTaXJq3nQibovBRVpHL8YZ16Q2pJYkBAd78XVrZ6IgzxU0qfUtMjA5hTIg/uWYLbT31Tq/3n9IKXjicQ5nFxriwAB5PiETTzHpd4cLTJcqf32YMa/HjB7UN5pM7+/HZ+gwU4I5B8VzU7tz/75ZwYloUeF900UV8/vnnvPjii4A9BUmWZebMmcPIkSNbdYGCK1HjLQiCIFyILLLCQ8lHHcGpAjx9KJvxYYGE6E6qX+wZoSgKH2cXsSS/jGrZ5nhdYB9tleDZsl/0i/NLmbL/iFMgX58EJ51y/UNeCb8UGZyOjQzy5a5mfAnycvsYxm9PpdhiBeyvVQGk2vnZzblGc4XptS4j1g5VGblpVxo2RUEG3j5agFqSXBq8CUJrGtkhjJEdwo57XnGliZT8SqIDPIkLFp3Pzyct+i01Z84cLrnkErZu3YrZbObRRx9l3759lJSUsH79+tZeo9CAqPEWBEEQLkTlVhs1snMkqQAFZstZE3jnmSzsrqgmUq+lm2/TH5rfzyzkudr0cgl7HXJdynW8p55nk6Ibe2iT5qbnoYBL13KtBBbFPu7LU92y9PI6GTVm1Bybsw2QXu3ahMqdJC8P1g/oyBZDFb4aNYEaNZ/lFGOUZa6p11ztVPmr1J4JUPf2KMAvBWWnJfA2yzI5JguhOg3e6pZnHAjnpw2pRUxatJUaiw0JeHJMJyYPa35piXB2a9Fvqa5du5KSksLChQvx9fWlsrKSq6++mgceeIDISPFt4almEqnmgiAIwgUoSKsm0VPPEaMJm2LfKQ3QakjwPDt+If5TWsGtu9Mw1n45cH9saJPB8w/5JY6f64LAqbFhjAnzp4uPJ/oW1l7XyK5Nm26MCMRfq6GHrxdXtcL88Z6+XjRoF0WG0cz+yho6+3ge9/EBWg3/CfF33J7dPqaJs1tXoFbjlOauAoJPwxc3uyqquXlXGsUWKzpJYkHnOMaHibFlwjEPfrcTY20jNgV4ecUBRneJEDvf54kW/Vdm7dq1jBw5kqeeesrlvrfffpsHHnjgpBcmNE7UeAuCIAgXIkmS+LpHIvfuy2BfZQ0Jnnre6Rx/0ru3reWh5EzM9Xbk38ks5OrwQLo2svPtrVY77XIDJHjp6e3nfVLruDEyiNcz8gH7lxN+GjVPtY0iVKfFaJP511CFXpLo4efV4pTzy0L90askTA0yEJKrjM0KvM+kK0L9+czf21FDr1NJPONmjFprm7QnndLa9HqzojB1/1EGB/gQepy558KFwWKTXUaXKcAP2zLx89AyIDGI7jEBZ2RtQutoUeB99dVX88cff9CnTx+n42+99RbPPPOMCLxPMVHjLQiCIFyo2njq+a1vhzO9DLfyTRYa7jXnmix0bSRz+uE2Edy0+zBqxR58J3rqGX+c3WirrGCSZbybaIz2cJsIfNVqfi8yEKrT8GhiJKE6LYVmC+O3p5JWY/8gcVGgD191T2xxV/Mevl5sM1Q57XyvKjKwpricy0L8uaIVdtZPBZ1Kxf96tmVlUTnlVhsXBfoQf4qzJow2max6o9cALIpCerVJBN4CAFq1iq5RfhzIq8BW+4WWSoIFa1JRSfYg/M0bejK+Z8tKUIQzr0X/pZ07dy6XX345ycnJjmOvvfYazz77rNNMbOHUcATeYsdbEARBEM4aAwO8qQuHVYCHSqJ7E3Xew4J8+b1vBx5JiODFdtGs7NueYouVpQVlbHfT0fzLnGLa/rWbtn/v4cpthygyW91eVyVJTIkL4+fe7figawJJtSPX5qTncaTm2I7aP6WVfJlT7PYaNkVhu6GKjWWVGBuZvf1WxziiancBJMBHrWJpQRk/55dy976MRq99NtCpVIwLC+DWqOBTHnQDeKhVxOi1Th+8tZJEgpf4MCcc8+6tfegUYf+mzt9TS11CiayAothTz4VzV4t2vO+++25KSkoYNWoU//zzD9999x0vv/wyK1asYMiQIa29RqEB0VxNEARBEM4+73RuwwP7M/jXUEW4TssbHWMJ1ze9m9nFx5MutanZvxcZmLQ3A0vtOK0psaH8t7ZGfFdFNY8czHTUJm+vqOKxlEw+7prg7rJuHa0xO+1OqyXIMlpczjPaZG7Zncb6skoA2nrqWdw7yWVnNsFLz4YBnThqNLG2pJynD+U43f9+ZgG3RgU3e33nu0+6JXDLrjQKLVb0KomFneLFbrfgJDbIi1+mX4TVJrP2YCGTP9/qdH+l0f2XbcK5ocWdJB599FGKi4vp27cvNpuN3377jYEDB7bm2oRGiOZqgiAIgnD2CdFp+K5nUosfPzM5E2u9GdbvZhZyfUQQnXw82VNR49QQzKbYZ3+fiIEB3vxVWuG4jlWB/v6u9eRf5hazoTboBsgwmpiXnserHWJdztWqJNp6ebidOS7RvPrxHKOZt47kU2SxMiLIl1sjg5FOctzZ2ai7rxfbB3ch12QmRKfF6yzpTSCcfTRqFX3jAwn00lJeY8WmKKgkGNtdNLE+lzU78J4/f77LsejoaLy8vBg2bBibN29m8+bNAEyfPr31Vii4EIG3IAiCIJxfFEWhxGKl4djtQrOVTvYTnI6rse9En4ipceFkGc18n1eKRpKY2Sacy0L9Xc7LNJpRS8fGkdkU2FQvEHfnshB/5ujyKLBYHI+ZEnf8edwGi5Wx2w9RYLYgK7C80ECpxcb0+PATem2todRiZYuhCn+Nmn7+3qhOQfCvVUnEnSVd+IWzW6C3ju/vHcRLyw+Qa6jhonahPDL67OxvITRPswPvN954w+1xtVrN+vXrHfO7JUkSgfcpJpqrCYIgCML5RZIkhgT4sKGsEhv2GnFvtYquvp78U1rBY4eynM4P1WmY42YHuilalcRrHeOY2yEWqfY53env7837mYVOx5KrTXyfV8L1EUFuHxOo1bCyb3vezyykzGpldIg/o0Ncg/qG/iytJLdB07FPsgpPe+CdXFXDVTtSKbXYk/FHh/jxSdeEFnd9F4TW0C7cl0V39T/TyxBaSbMD7/T09FO5DuEEiBpvQRAEQTj/vNulDTOSj7KprJJoDx2vd4glSKvhzYx8Gkzt4t7YMBJb2JjreDu5Y0MDGOTvzcZ66eMSsDS/rNHAGyBcr+XZpBMby6Vxs5QzEew+eyibcsuxCvjfispZXmhg3FnamV04v8mywqoD+WSX1tArLoBecWLe+/mgxTXewpkjUs0FQRAE4fwTotPwZfdEl+NGWXZKQVdh7zp+Kg0P8mWTocoxHk0F+Grc1yRbZAWjLOPbxIizxowI8qOdl560ahNSbXr7mUgzzzFZnBrPSUBe3QcuQTiNFEVh1g+7+HlHNpIEKPDKNd24oV8ciqKwL6eccqOF7jEB+OjPjlBu25FSXvxlP8WVJkZ1CufxMR3Rt+C/B+e7Zv9tzZw5kxdffBFvb29mzpzZ5Lmvv/76SS9MaJyx9veAXgTegiAIgnDeuyEyiK21jdRUgE4lMSY04JQ+561RIXyeU0yuyYIE6FUqprkJiD/LLuKZQ9lYFIW+fl581i2REF3zgwEvtYpferfjo6wiiixWRgb5cmkzUtRb2/BAX9KqTU5z2AcE+Jz2dQgXrt/35bFqfz4Wm8zinfYJAXXfr73wy36u7R3DrB92Oe4L9dXz/b2DSAhxbZB4OmWX1XDrR5swWW3ICny2MQObrPDChK5ndF1no2b/l3HHjh1Yahtm7Nixo9HzzsculGcbUeMtCIIgCBeOWyODUUsSP+eX4qNW82B8eIvTzJsrRKdhdb8O/JxfiklWGBPq7zLvekd5NY+nZDW4nclHJzDiDMBfq2FWQkSrrLulnm4bRYXNxi8FBnw1Kp5PiqZHEzPYBaE1/bA1k0f+txu1SkJxk81SY7bx+/58R9ANUFJl5rml+854DfimtGJq6pVpKAqs3JcnAm83mh14r1271u3PwunnCLzFjrcgCIIgnPckSeLmyGBujjy9M7EDtRruijnWmTzXZMYsK8R56JAkiT0VzuPMbBwbcWZTFBZlF7G7ooZELz33xITicRaPz/JUq5jfKZ75nc70SoQL0Ud/23tp2eo1c1BJICv2/72kUzi5BiOSdGwX3CYrZJac2EjBU8Hf03kWvSRBgJeYT+/O2VEYIJyQupIj0VxNEARBEIRTzaYoPHjgKP/LLwVgoL83X3ZPdNl1VwNJtcceP5jFF7nFaGqDh39KK/i6eyLf5ZWSUmWki68n14YHnpKRXYLQGrZmlPDD1izUaonbBsbTKdLvlD2X4jJIENqF+WCxKfRPCOLpKzqTWlBJ/dNUEgxIbLzZ4ekyokMYF7UL4e9DRQBoVBLPXNH5DK/q7NTswPvqq69u9kV/+umnFi1GaB6jaK4mCIIgCMJp8m1uCT/WBt0AWwxVzMvI47mkaB6IC+PtowUAROi1zOkQS41N5svcYuDYLPC/SiuZuCeD1SXlaCQJi6Kww1DF7BMciSYIp8OmtGJu+vBfRwntj9uyWDZtKO3DfRt9jNkq89bqFP5KKSIqwIPHL+/U7Prruy9K5NH/7UZtn/NHkLeOb+8ZRKD3sV22nrEBvHptd178ZT/VJhujOofx9NgzH+CqVRKfTuzHHwfyKa4yMzAxmLahoj+CO80OvP39T3+jC8E9kWouCIIgCMLpklJldATLYE8pT6kyAvBM2yjujA6hzGKlnbcHepWKapvsZv8O/igpB3Bc59OcYp5oG4Wf6H4snGW+2nQUcE79/mFrJk81Eeg+v2wfX28+iqLA/pxyth4pZc3MEfg3I+36+r6x+HloWbU/H39PLZOHJTgF3fXPu65PDIoCKtXZky2iUau4rGvkmV7GWa/Zgfenn356KtchnADRXE0QBEEQhNOlk4+HI1gGe2f1Lj6ejtsxHjpi6n0o8VKruDEiiO/ySlBhz47t4evFjgrXelRLwwHlgnAWaEkFxJKdOcfqrxWF4kozWzJKGNW5eePxLusawWVdj99kUJKkFq2vPotNpqjSRLC3Hl0jYwKF1nfS7/Qrr7xCWVlZKyxFaA5FETXegiAIgnChSKkyMnZbCh3/3sONuw6TZ7K02rVlRUFuxjzw6yOCuCPqWGO3EUG+PNSm6QBhXodY/ts2ivHhgTzUJpz/9WzLAH9v6va2VcCoYF+CT2D0mCCcLrcOjEdCQq2y/9GqJK7v23RZhJfONXPDS28/piiK227lZ8LKvbn0fekPBs1eQ68Xf+fPlMIzvaQLhqSc5L8CPz8/du7cSWJiYmut6ZxXXl6Ov78/BoMBP7/WbcRgNEOH0faf9/4Cvmd2dJ8gCIJwATmZ32+n8nfj+arGJjPw3/0Uma3YALUEXbw9+b1fh5O6rqIovJSWy4eZ9g/ck2JCeKZt1HEbnZVZrFgUhVBdyzoWV1ptzM3I41CVkS4+njzUJgKvs7jTuXBh23aklP9ty0Kjkrh1YDwdIhqv7wZ7HfjDP+xCpZKQZYUhSSEsuqs/H/yVxsK1h7DaFG7sF8uzV3ZBfRrTxPMMRl5avp/DBZVUma0cLalx3CcBHlo1W54ehY9efAnWEifyu+2k3+Gz5dubC4XJdOxnUeMtCIIgCOevw9VG8s1Wx22bArsrazBYrPhrW/4R7qvcEkdDNIB3MwtJ8NRze3RIk48LOInnBPDRqHk+KfqkrqEoCotyivlfXgneajUz24QzIEA0chLsddVHS6rpEuVHbJD7GexGi41FGzI4UlJNz5gArusb42igVietsBJDjYUuUX70ie/W7Oe/pk8M8cFebEovIdRXz1W9olm1P49XVyY7zlm08QiRAZ7cN7xty17kCTJZbdzw/kayymqc6tXrKECNxUZWaTUdI/zIKq0mv9xIUpivy5gw4eSJrzbOMXX13WoVnOTvP0EQBEEQzmLu0rD1Kglv9ck1I9tsqEQt2QN5sI8B22yoOm7gfTZYlFPM4ylZgD1dfUNZJav6taejt2fTDxTOawtWH+K1VSmAfZzVwpt7uTT7kmWFSZ9tYUNaMWpJ4utNR0nJr+Dp2tFXiqLw1M97+XqzvbFapL8H390ziLhg90G8O33bBNG3zbERX5vTS9GoJKz1gt7N6SWnLfBOzq3gyHFmfes1KqICPPngr8PMXpGMAvh6aPjszv70iQ88Leu8UJx0fs/+/fuJj49vjbUIzVAXeIv6bkEQBEE4v0XqdTxSr5ZaAl5tH4PmJNNUYzx0NExYjD1HOrb+mFfi+FnGXqf+W2H5mVuQcMZlllQ7gm4Aq6zw8A+7XbJyv9p0hPWHi1EUHIHwJ+vTMVtlANYeLHAE3QAFFSb+u3TvSa0tKsADW711qFUSUQEeJ3XNE+F9nPRxtUpiwU29KKww8XJt0A1QZbIy47udp3x9F5qTDrxjY2NRn+Q3r8eTkZHBpEmTSEhIwNPTk7Zt2/Lf//4Xs9nsOGfdunWMHz+eyMhIvL296dmzJ1999ZXLtX744Qc6duyIh4cH3bp1Y8WKFU73K4rCs88+S2RkJJ6enowaNYpDhw6d0td3IkxihrcgCIIgXDBmJUSwok873u4Ux9r+HbgxMvj4DzqOB2LD6O57bIe4i48nD8SFnfR1Twcfjdrpw6sMeIuuzBe0ggqTy7FKkxVTbUANsC/HwH+X7nM5T1FAqQ03jxRXO3ULt8kK6UVVJ7W2WwfG07fernFcoCczRrU/qWueiLah3lzd217e0fD7ukGJwWx+8hIu7RJBZoNdcVmBnNIaUVLcylqUrBwYGOhSDwH29vYeHh4kJSUxceJE7rzzzpNeIEBycjKyLPP++++TlJTE3r17mTx5MlVVVcybNw+ADRs20L17dx577DHCw8P55ZdfuP322/H39+eKK65wnHPTTTcxe/ZsrrjiCr7++msmTJjA9u3b6dq1KwBz5sxh/vz5LFq0iISEBJ555hlGjx7N/v378fA4fd9QNcYoAm9BEARBuKD09vOmt1/rdVP11qhZ1rs9O2vHe/X09UJ7Fs0EbsrMNhGsL63EpijIQJyHjuvCRTrshax9uA/+nloqjBZkxb6L2yXSDw/tsY3Bv1KKcDdc/rq+Mehr58h3jwlwygRRS9JJp1rrNSrevbU3qfmVyAr0jg90WtepJkkS867twcUdwzhSXE3P2AC6x/hjkxUCvI5luXSK9EOnVmGxySjY38Nu0f5u4z2h5VrU1fyNN97g//7v/7j88svp378/AJs3b2blypU89NBDpKen88UXX7BgwQImT57c6osGmDt3Lu+++y5paWmNnjN27FjCw8P55JNPALjhhhuoqqril19+cZwzcOBAevbsyXvvvYeiKERFRTFr1iwefvhhAAwGA+Hh4Xz22WfceOONzVrbqezcunk3XPcgJMbC2s9b9dKCIAiC0CTR1Vw4GxysMvJ7kQFPtYrrwgNPqtGccH7Yk2Vg5vc7ySy1B5dv3tCLCP9jG2ZfbzrKUz/vcYq9E0O8+f2hYVhsChVGCyE+er7afJSXftmPySrTM9YfWVEorbIwskMYj13e8bip2/UdLqxk0mdbyCiuxluv5o3re3Jpl+PP6T5T1iTn89B3uzDUWOgc6ccHt/chJrD59e0XqlPe1fyff/7hpZde4r777nM6/v777/P777/z448/0r17d+bPn3/KAm+DwUBQUNBxz+nUqZPj9saNG5k5c6bTOaNHj2bx4sUApKenk5eXx6hRoxz3+/v7M2DAADZu3Nho4G0ymTDVazdeXn7qao3qarzPkVIsQRAEQRCEVtXB24MO3mc+C1E4e3SL8WfVzOGN3n9Vr2i+2nSEfTn2z+heOjXzb+rFN1syeX7pPqyyQvtwHz67sz+39I8js7Say9/6G6PFhqzA5/8e4fN/j9AzJoB3bu1NVMDxm/lN+XIbmaX20V3VJhv3f7WNa/vEklNWQ7cYf6Zd3M5p9zutsJLZvyaTazAyvH0ID17SHt1pLKO4uGM4O5/9DyarfFp35S8kLfrb/O2335yC0zqXXHIJv/32GwBjxoxpcjf6ZKSmprJgwQLuvffeRs/5/vvv2bJli1O6e15eHuHh4U7nhYeHk5eX57i/7lhj57gze/Zs/P39HX9iY2NP+DU1l2iuJgiCIAiCIAjN56lT87/7BvHclZ154vKOrJo5HJUk8ezivY5Ga4cLqxxzuHdlGag224Pu+vbkGLj/q+3HfT6brJCSX+kY4aUAVhm+35rJX4eKeHfdYaZ/s8NxvqHawnXvbWRNcgF7sw28s+4wzy9zrUkHez+qt9em0ufFVfR9aRXv/3m40Vrs7UdLef/PwyzZmY3VJrs9pz5JkkTQfQq1KPAOCgpi2bJlLseXLVvm2IWuqqrC17fpQfOPP/44kiQ1+Sc5OdnpMdnZ2Vx22WVcd911je6mr127ljvvvJMPP/yQLl26tOQlnpAnnngCg8Hg+JOZmXnKnquuuZpe1HgLgiAIgiAIwnFVm63c9dlWnlu2n9m/JjP3t4McyDU4pZ7bZIX9ufYd8YBGZljbZIVdWWXIbmZi16dWScQEero0NKt7mKzA7/vzKTdaANiUXkxxlflYoK7Akp05bq/94/Zs5v52kOIqM0WVZmb/mszSXa7n/rQ9i2ve2cCrK5N58Nud3PPFtuOu+1Q5XFjJ7BUH+L/l+0nOu3CnELQo1fyZZ55hypQprF271lHjvWXLFlasWMF7770HwKpVqxg+vPGUD4BZs2YxceLEJs9JTEx0/JyTk8PIkSMZPHgwH3zwgdvz//zzT6688kreeOMNbr/9dqf7IiIiyM/PdzqWn59PRESE4/66Y5GRkU7n9OzZs9E16vV69KcpEhbN1QRBEARBEASh+eavTmVTerHj9pId2UT5O5crqFUS7cJ8ABiaFMLFHcNYk1zgdI5KgnA/D1TNaEa44KZeTPx0M4YaKxKuvd0kQKuy74H6uKkd99KpKaky8+veXBQFRneJINRXz/rUIlTSsSBerZJYn1rE+J7RTo9/ecUBFHA0jFuTXMC2o6X0a9N0qW5rO1xYyZUL/nF0mV+04Qg/3T+YrtH+p3UdZ4MWBd6TJ0+mc+fOLFy4kJ9++gmADh068OeffzJ48GDAHlQfT2hoKKGhoc16zuzsbEaOHEmfPn349NNPUalcN+vXrVvHFVdcwauvvso999zjcv+gQYNYvXo1M2bMcBxbtWoVgwYNAiAhIYGIiAhWr17tCLTLy8vZtGkTU6ZMadY6TzVR4y0IgiAIgiAIzXeooMIpbVytkqg223j88o68+qt9fnWkvwdzr+0BgEol8eHtfVm1P5+ftmfx+377xp2HVs3r1/ds1nP2igtkw+OXkFFcRZivnueW7mfFnlzUKgmrrDBpaAKeOnta94DEYIa0DWbD4WJUkoSsKEwZ3pbRb/5FUe24tNdXpbDkgSGE+urt3cbrImoFQnx0lFSZ8dKpHaniVSaby5oqjdYWvHsn56t/j2Kyyo7dfFSwaEMGc6/rcdrXcqa1uA3kkCFDGDJkSGuupVHZ2dmMGDGC+Ph45s2bR2FhoeO+ul3qtWvXcsUVV/Dggw9yzTXXOGqydTqdI/39wQcfZPjw4bz22muMHTuWb7/9lq1btzp2zyVJYsaMGbz00ku0a9fOMU4sKiqKCRMmnJbXejyixlsQBEEQBEE4HWyywvajpdSYbfSOD3S7M3su6BThx9rkAkfwbZUVOkX6ckO/OK7vG0tptZm4IC+06mMbe2qVxGVdI7isawQZRVXkGGroEO5LsE/z00699Rq6RNl3dt+6sSdD24WQVlhJ12h/xvWIcnquz+7qz+Id2RRUmBiYGMTv+/IpqTI7dsoNNRY+/DuNGaPa8/u+PDKK7eMAo4M8+fNgEe+sS0Otknh6bCfuHJLA5V0j+HlHtn08mCQR4K2ld9zpH71nk51ryxVFcdTVX2ha/P8em83G4sWLOXDgAABdunRh3LhxqNWtX5C/atUqUlNTSU1NJSYmxum+umYCixYtorq6mtmzZzN79mzH/cOHD2fdunUADB48mK+//pqnn36aJ598knbt2rF48WLHDG+ARx99lKqqKu655x7KysoYOnQoK1euPCtmeMOxGm+Rai4IgiAIgiCcKhabzKTPtvDXoSIAIvw8+N+UQefMiCmLTeZwYSU+eg1TL05ib46BdQftm3c39ovluj72ZshB3jqCvJve0WoT4k2bEO+TWo9GrWJCz2gO5lcQ5KVzmZGtVau4ru+xBs3/25bldL+iKFQYrQR561jx4EVsSC1GkuCbzUdZW/u6bLLC88v20zHCl331atglCV6/vif+Xu5r10+la/rE8OWmo456dwW4vu+pa0R9NmvRHO/U1FTGjBlDdnY2HTp0AODgwYPExsayfPly2rZt2+oLPZecylml8z6BBV/AHVfBC9Nb9dKCIAiC0CQxx1sQLhzfbTnK4z8em32tVklc3jWChTf3Pq3rWJOcz4/bstFrVNw1NKHJ2mBDtYXtR0uxyjKv/JrM4cIqAG4dEMcL47tQXGVBo5IIPE6gfSocLqzkpg/+paA2dfyeYYk8OaZTo+evPVjAnZ9ucTr23q29uaxrpNOxQbNXk2swOh27vGsEv+3LO1YHLkmM7R7B/Jta7+9ub7aBrNJqukT5ExvU9Jcx246UsGjDEWRF4eb+cQxOCmm1dZxpp3yO9/Tp02nbti3//vuvI427uLiYW2+9lenTp7N8+fKWXFZoBlHjLQiCIAiCIJxquQYjKpXkqM21yQrZZTWndQ1/7M/n7s+3opJAQmL5nlx+mTaUduGuk5MO5Vdww/v/UlJtTw+tv5/85aajXNQ+lNFdIk7Tyl098eNuiqvMjtsf/JXGP4eKWHBzL9qG+ricP7JDGG/d2JMP/0pDVuDOIW1cgm6ApDAfCipMx2qoAb1G5agVB7ApCkWVZpfHttQbq1J4a/UhADQqibdv6d3ke9snPog+8ae3qdvZqEXjxP7880/mzJnjCLoBgoODeeWVV/jzzz9bbXGCK1HjLQiCIAiCIJxq/ROCnII5SYIhbU/vTuU3m48iYe/gbautDW5szNbzy/ZjqLE4btdP6VVJkF5UdWoXexwZxdVO7yfAgbxybvrgX6rN7pueje8ZzS/TL2LFgxc5paHX9/JV3YgO8HTcnjoyiZv6x7k818Udw07yFdgdKa5yBN1g/0Lm0f/tbnSWuHBMi3a89Xo9FRUVLscrKyvR6UREeCqJGm9BEARBEAThVBvcNoT/u6orc1cepMZi46pe0Uy/pN1pXYNGLVG/gTeKPeXdnZyyGmyNBH+yAl2jmh5flV9u5N11hymtNjOyQxjje0a51GGfjF5xAfy2z3mssaJAQYWJg3kV9Gph47PYIC/+mDmcjOIqAjy1hPnZ+1K9eWNP3vzjEDUWGzf2i+WuIQkn/RoA8stNTrcV7I3fLDYFncb1/VIUhU/XZ7BiTy5+nloeGtWebjEX3igxaGHgfcUVV3DPPffw8ccfO+Z4b9q0ifvuu49x48a16gIFZ45UcxF4C4IgCIIgCKfQLQPiuWVA/Bl7/klDE/njQAHq2njOW6fm+n7ud36HJIWQUVxFw4bZakli1uj2DG3X+G69odrC+IXrKaw0oSj2XfWSKjN3DW2dYBXg/yZ0dQm86wR4Nb1xWWWyolWr0GncJyvrNCraN0i/H98z2mW2d2voEOGLn6eGSqMVufaLkO4x/o2u7cO/03h5RTJgzzzYcLiIVQ8NP25d+PmoRYH3/PnzueOOOxg0aBBarb07nsViYfz48bz55putuT6hAceOt0gsEARBEARBEM5j/ROC+Pn+wSzZmYNOo+Lm/nFOadX1PTmmExVGCyv35eGj1/DMFZ0Z0SEMD60KvabpqUvrUgrIK3duUPbp+vRWDbyDffR4atXUWJzna4/pGkFCIx3Tq81Wpn2zg9UHClBLElMvTmLGqHYntBNvtclsTi+h2myjX0IQ/p5aCsqNFFWaSQz1dsz9bi5/Ty1fThrArO93kVVWQ++4AN5oYrb5T9uzHT/LCpisMqsP5DOxlXbgzyUtCrwDAgJYsmQJqampjnFinTp1IikpqVUXJ7gSO96CIAiCIAjChaJ7TADdYwKOe56nTs2bN/Y64esXlBtZtd91J1rVimnmAJIkce/wRN784xBqyR6EJoR48+aNPRt9zOu/p7A2uQCw17i/tfoQXaP9+U/n8GY9p9kqM/HTzWw4XAxAiI+OCb2i+ejvdADCfPV8dfcAt83qmtI9JoBVM4c7bhdVmvh8YwayrDCmW6Qj3R3sfy8Sx2ruFQW8dOfmPPiT1exXPXPmzCbvX7t2rePn119/veUrEpokmqsJgiAIgiAIwsnLLzcyZv7flNbrNq6qDYrvHd7645EnDU1gc3oJ+3PKCfPT8+6tvdE1shtvtclOI8HA3kF8T1ZZswPvn3dkOYJugJJKsyPoBiiuMvPID7tZPHWI45hS28ROq25eD+4tGcXc9tFmjFYZCXhz9SGWPjCUuGB7KvlDo9pz52f2sWiyopAY4s3Y7q7d2S8EzQ68d+zY0azzWrMJgeBKNFcTBEEQBEEQhJP33ZZMyqosjuBWAqIDPHn6is6nZPTYtG928G9aMbICFSYrd326ld9nDnObCv/4T3vILHUe32aVFdo0kpbuTn65CXW9kXByg/ttskJaUaXj9p8phcz8bifFVWa6R/vz3m19iGoktd9+fSO3fLQZs9V+ZQUor7Hw4d9pvDihKwDD2oeydOoQ1iYX4Ouh5ere0XjrxY53k+rvaAtnjlEE3oIgCIIgCMJ5TJYVLLJ83Nrsk2W02HDKg8Y+F7ulQbfRYuONP1LYebSMxFAfHr60PcE+esd96w4WOs61yQpHSqo5mFfhSKXfk2Xg+WX7yC2vIbvU6HL9UZ3CmFDbME2WZT78O539ueV0i/bn9kFtXBqcDUwM5vVVKY2uV62SHB3GC8qN3PP5VkcQvS+3nAe+2s7PDwxp9PEr9uQ6zq8jK1Bpch6P1iXKny7H6Sp/Ibgwv244h4lUc0EQBEEQBOFM+WV3DmuSCwj21jF5WCJhvh4u58iygkLjo7+a8t2Wo/x36T6MFpmhScG8fXMf/L20rbByV2O7R/LBX2moakeWKcA1fWIaPf9wYSXv/3mYKpONMd0iXVKmZ3y7k9/329PDt2aU8vu+PDpF+pIQ4sP0S5LQqiUsNue26z61u78FFUZu/GAj1WYbDRqzOzw1phMqlYTVJvOfN/5yzCZfsjOHdSkFfHHXAKfs4/4JQcy9tjtzfjtIjdnKhF7RdIny5+nFe7HJCj56DQ9f2gGA5LwKTPWCaJussCurDEVR3GY078sx8HdKoctxgMu6tn62wPlABN7nGEdzNRF4C4IgCIIgCKfRog0Z/HfpPkdAvXxPLitnDMPP41hg/O66w7z1RwoWm8K4nlG8ck23Zu9c784q4/Ef9zgCz42HS3h26V7eakHTtOboEuXPN/cM5P0/0zBZbVzXN5Yruke5PTenrIbxC9dTY7GhKArL9+RSY+nBtbWBeo3Zxsp9eY7zbYpCcZWZf1KL2ZhWwpaMEh6+tAOzf01GLYFNgWv7xDg6mv95sJAqs83tc4N9Y/7PlEISQn34ctMRR9Bd559DxRwurCQpzLlR2nV9Y7mu77ERbC+vOOBIPa8wWpjy5XZ+nzmM6EDnlHKVBJH+nm6D7r9SCrnzsy0obuamTxwcf0rS9M8HIvA+x9TVeOtFqrkgCIIgCIJwGn30dxqAI3DLKTOy7mAh43rYg9XVB/J5dWWy4/zFO7OJCfRkVu2uaqXJSk5ZDVEBno6d3vp2ZRmcdnttisK2I6Unve7UggrWHSzE31PLlT2inEZo9WsTRL82Qce9xq9786g2W52anX258Ygj8FarJNSShM1NMGqTFZLzKhiYGMzXdw9gV5aBNsFeXNY1whHYbk4vcXlcXcir1P55btl+esYFcrigyuVcsNeAH8+3m486fpYVyCs3sjmthFGdw3nsso6Ovz9PrZo3bujp9hpv/JHiyGqoMyAhiKkXJ3FRu9DjruFCJQLvc4yo8RYEQRAEQRDORtuOlKJRSY4AUFFgS4Y9oFybXMCUr7ZhtMh4atW8c2tvRnYIc3p8XJCX0221SqJNsPOxE7XhcBF3fLIZm6wgK/D5xiP8cN+gZs2vNltl/k0rxirL2GTZKdCUAI362G6wTqPigYuTmL/6kNN7UJ9eq6JHbAiDk0Jc7nOXlS/Vdlivb8nObI6WVLucG+6np11Y42PBDNUWftye1ei6AKaMaMvYbpHkGGroEO5LoLf7FNuaBunwkgSXd40QQfdxiMD7HKIoYo63IAiCIAiCcPp9t+WovRkZ9kBLJUlE+HkwssOxYCsm0MuxGw72wDk+yBujxcYDX2/HZLHXEBstNqZ+tZ1tz/zHKQAe1i6E2wfG8/m/RwAI9dHz4oRuJ7XuOSsPOoJugD3ZBka9/ifv3NK7yfngVSYrN37wL3uyDQBE+nvgoVFRYznWwXtcT+e09IdGtaNzpB97sss4mFfBHwcK0NR2FR/RIZRwXz1Wm4zGzagufy/XIFclSUgSTu/pp+szUDcI0uODvPhxyqBGa+oN1RauWPA32WU1joBZLdlfQ4+YAAYmBjvOjQv2cowCa8zVvaN5eUVy7RpBr1FzSafmjTi7kInA+xxittiDbxDN1QRBEARBEITT47d9eTz24x7HbUWBizuG8vLV3fGtV999Xd8YVu3PY21t9+64IE9mjW5PQbmJ6nr1ywpQZbZRWGEitt4utyRJvDChKxOHtKGsxkLHCF+8dCcXrpTXWFx2jbNKa7j5w39Z98hIQnz0yLK9ZjuztJpesYEMahvM5xuPsC/H4HhMfrnR6ToSsGxXDrcPauO0/su6RnBZ1wgURWHFnjy2Hy0lz2BkU3oxvV78A0+tmnnX9XBpzNYzNsBl7Qkh3tRYbGTVjhXz0WuoNFmp35/tjet7cFXvxhvCAfy4PYvsshqnsWlRgZ7cNSSBm/rHNTmze+PhYl79NZnSGjNju0Uy8z/tmXxRIlq1iuV7cvH30DJjVHunv0fBPRF4n0Pq0sxBNFcTBEEQBEEQTo/VB/Kd5kGrVRK+nlpCfZ1TMLVqFR/f0Y/9ueUYLTa6RvvjoVVjtNjw99RSbrSgKPZdUl8PLWF+7lM4E0N9Wm3tV/aI4q3Vh1yOV5ps7DxaxiWdwpjx3U6W7spBhX3WtU6tctk9bhi8K0ByboXb57TZbKjVai7tEs4X/2bwb9qx+u0ai40Hv93B4LZB+HloUKvtO/6jOoXTJz6QbUdKkST7e/zfK7vQLyGQlPwKUODOz7ZQaXJ+Lq2m8aC5/nNK9eamKUC4nwd3Dklo8nFphZXc/skmR8bAu+sOo5IkHh7dgTuHJBz38YKz4/9NCWeNusZqkgS6UzNVQRAEQRAEQRCcBLhJgw7wPHZsw+EiHv3fLp5ZvJf04iq6RvvTt02QI43cQ6vmw9v74u9p/wDr56nlw9v7nvI53QDTL2nntpEbQFmNhYP5FSzdlQPYg24As02mxmJzCbYbqjBZXbqL22w2cnNzATiYW+4UdNe5vm8sgd56cnNzsdnsmQA6jYqvJw/gjRt68OwVnfl1+kUMbRfCprQSbv5wE1cuXO+UNQD2mCA6wNPl+g1d2jkclcq5jvyqXtHHfdw/qUVYbcfS9BXss7uFlhGB9zmkfn23m87+giAIgiAIgtDq7h6aQKjPsd3pYG8d9wxLBGDtwQJu+XATP27P5uvNRxm34B+OFrs2/+qfEMTWp0ax+alL2PrUKPonHL+TeGtQqyTuHNLG7X3hvnqqTNZGHytJ9u7efh4apoxoi7ZhcTVwMO/YrrfNZmPNmjW0a9eOhQsX0jUmgJv6xzqdf3P/OF6+uhsLFiygXbt2rFmzBrPFyn1fbqXvS3+wL7uc2we1oV24LxVGC/d+sY1Ko32NDQNvFJj328EmX/+2IyX8uieXO4e0YUhSCH3bBPJ/V3Xl5v5xTT4O7FkJ9b97UEkQcIpmql8IRKr5OUTM8BYEQRAEQRBOJ0VR8PfS8tuMYfy+3z6n+j+dwx274J9vyACONQAzWmR+2pHFjFHtXa6lUasI8/U4PQuv56FR7VmxJ5fDhfbdaQnw9dDQLcaeCh8T6EmuwejUxAzstewvTejKNX1iUBSFNckFpOZXONVYJ4ba53DXBd1XXnklJpOJadOmoSgwe9pUAL7ZnOkUdE+fPh2AK6+8ksVLlvL6dSOZ9Pk2Pv4nnUBvHQ+MTCKzpIYaS+OzvRXgaKnrlxyKopBXbuSP/fk8s2Sf43iAl5a1D48g0E0GQ0O5hhoWrE5xOqZVq3hiTKfjPlZwTwTe5xBTbeAtGqsJgiAIgiAIp9rv+/J45H+7MdRY6BHjz/u39SXC//QHzidLpZL4acoQnvx5D5vSi4kJ8OL/ru7q+PLgu3sH8dIv+0nJryC7tAaj1Z50flG7EMb1jOJAbjmTP99KVmmNI+tUkuDpsZ1pH+7rEnTXmT59GmAPvgckBDOhV7RT0A1gMpmYMH4ci5cs5ePb7cH3htQiHhiZRHSAJzq1CrNNpjEDGswgN1ps3PvFNv5MKXQ5t6zawvRvdvDFpAHHfc+e+nkvR0pqnI7NuaZ7s2aeC+6JwPscImZ4C4IgCIIgCKdDVmk1D3y9HWvt9u7enHIe/HYH3907yOm8Owa3YV1KIeraiNRDq+LqXk132W6p/HIjv+y21xhf2T2SML/mfwng76Xl7Vt6u73PapO5fVAbOkT4opYktmSU4K3X0D8hCJUEkxZtId9gBOy74J5aFb89NJy4IC9HTfe4ceOcgu46dcH3tGlTmT9/AQ8+ON3lHJPJxFUTxpOScoi3bujBC78ccKz5rRt7Mu2bHe7nb2tU/HdcF6dj76w7zN+HXIPuOpvSivno7zT255TTNsyHSUMT3M40P5hX4ZIBUN5EWn5rOFxYydaMEkJ99YxoH4aqkfFo5yoReJ9DTCLwFgRBEARBEE6D/TnlWOrlVNtkhZ2ZZS7njegQxtd3D2Txjmw8tCruGNzmuHOgG0rOK+fFZfvJKzcyrF0oj13e0SUYPFpczZUL/6HCaAFg4ZpDLJ06tMkxVgXlRr7efJQai42x3SLdzu1+d91hXl1pn0ntpVPz8R39GNX52Ezq0iozOWVGp8fUWGTHOtRqNTExMcyZM8dpJ7u+6dOn8dFHH7J79+5G1zpnzlxiY2N44qfdrE7OZ0tGCf3aBCFJYJWVej3J7Ty1aj6e2NdpnBtASl6FY/ywO2abwv8tP4BKJaEoCr/sziE20BNfDx1TRrQlKczeUb5LlB955c7p950jfRu/8Elam1zA5M+3Or5gGNMtkrdv7oXUio2tdmeV8eW/R5AVuLFfLH1P8+69CLzPIaLGWxAEQRAEQTgdGgbPKgliAt130B7UNphBbYNb9DwlVWZueP9fKo1WbIpCelEVVWYrc67t4XTex/+kUWmyOjpslxutfLo+g2ev7Oz2uoUVJsbM/5vSKgtI8NFf6XwxqT+Dk0Ic5+zNLnME3QA1Zht3L9rC5GGJ3Nw/jjA/D/w8tfh5aKgwWh2Br0YlEenv/F5Mm2bf2W4s+G4q6F6wYAFTp07liZ92883mTFQSzPh2J+sfv5jPNx5xCrol4Jo+MTx6WQe39fIdInz5fX9ek8G3wrGa/AO5FRzIrUCtkvh9fx6/PzSMSH9PXprQlcxPN3MgtwKVBLcPasNjP+7hSG3X+vk39jqh2d1Gi43MkmrC/Dwc3e3re3bpPqcgf8WeXDalxzMwsWX/rhram23gmnc32P/9KPDz9my+u3fgaQ2+z4mu5hkZGUyaNImEhAQ8PT1p27Yt//3vfzGbjw22PnjwICNHjiQ8PBwPDw8SExN5+umnsVgsTtf64Ycf6NixIx4eHnTr1o0VK1Y43a8oCs8++yyRkZF4enoyatQoDh1ynf13JhhFjbcgCIIgCIJwGnSM8GPWf441SPPRa3jt+p6t/jxbM0ow1Fiw1UaKsuJ+ZFWlybXJWFMdyX/ankVJlRmbotjnUKPw3p+HsdbWSyuKwszvdjk9RgGqzDbm/3GIMfP/prDChFolsfDm3ui19rBJrZKYc213grxdP5BPmzaNt96a3+zXDq5BN9jfg1xDDYqi0DDbWpKgbahPo03qpoxoy/AOoY7bYb7NCxxsskKlycpve+0N9ML8PFg+7SI2PXkJ/z55CUt2ZpNWWInFprA7y8CkRVtQmoru69lxtJSBs1fznzf+os+Lq/h5R5bLOWXVZhperbTK7HJeS/2wNRNZsb9O+781xfF+ny7nxI53cnIysizz/vvvk5SUxN69e5k8eTJVVVXMmzcPAK1Wy+23307v3r0JCAhg165dTJ48GVmWefnllwHYsGEDN910E7Nnz+aKK67g66+/ZsKECWzfvp2uXbsCMGfOHObPn8+iRYtISEjgmWeeYfTo0ezfvx8PjzPbTEKkmguCIAiCIAiny7RL2jGhVzT55Ubahfu63ak8WX5urtkwfRpgbPcIftxuD9gk7AHUmO6RjV7XYpOR6u0VK4p9LnW7p35lSFIwT1zeiZSCSrePlbHvxP+0PYt7h7dlWPtQNj0xivSiSgK8dcQFOu/0KorCu38eJt9g4vnamm53tdwN1QXd5TUWftqe7VitWiXRLdofSZK4a2gC61OLUddG4D56DRN6RTV6TQ+tmk/u6EdhhQmVSsLXQ8P/LT/A5xuPHHc9KPbO83VUKolwPw/2ZBkorT62mWmTFVLyK6kwWfFz83fV0P1fbae8xv54q6zw8A+76RDuS4S/p+MLjFGdwlmyMxtZsWdXeOrU9IkPPP6am8k1ZV067eOZJaW5X1WcZebOncu7775LWlpao+fMnDmTLVu28PfffwNwww03UFVVxS+//OI4Z+DAgfTs2ZP33nsPRVGIiopi1qxZPPzwwwAYDAbCw8P57LPPuPHGG5u1tvLycvz9/TEYDPj5+Z3Eq3T21VJ48g0YPRQ+eLHVLisIgiAIzXIyv99O1e9GQRDObbKsMPnzraxOLkCS7EH1gpt6M9ZNUP3L7hw++ScdVW1AOqZb44F3WmElY976G7NNRlGc66PVksTIjqH8caCg0cerJYmH/tOOqRe3AyC1oIJJn23lSEk1AZ5aFt7cm6HtQiitMjPz+52sPWhvaPbmDT2Z0CuaHj16NJle3r17d3btOrbjvu5gATO/30VJlZmuUX68f3tfogPs6ewbDxezbHcOXlo1dwxuc0Ip3gDbj5Zy9TsbnI5JwPgeUSzelYNGJSErCp46NaO7hDO2WxSXdDpW555fbmTgy6ud3kMvnZo9z412fCHQGKPFRsdnVjZ6/9W9o5l7bQ9MVhvPL9vPXymFhPt68Nz4LvSMDTih19mUg3kVjFv4j6OGXAJ+nDKYHif5HCfyu+2c2PF2x2AwEBTUeE5+amoqK1eu5Oqrr3Yc27hxIzNnznQ6b/To0SxevBiA9PR08vLyGDVqlON+f39/BgwYwMaNG5sdeJ8qdV3NRaq5IAiCIAiCcD5QqSQ+uL0vK/fmkV9upH9CEF2j/d2ee0X3KK7o3vhub32JoT78b8pgPvgrjUMF9jrmOjbFni59U79YvtmSiUYlOXUNV0mgVUuMrfdcdy/aSlaZfbyWwWhh8udbWfPwcG75aBPptfPBb+4fx4Re0cyfv6DJoBvsNd8LFy5k6lT7nO8RHcLY9vQoLDYFnca5GvhkaugBukX70y3an/055SgoyAoE++ioMtuYd213dmaW8fOObGrMNpbszOWn7Tm8dl0Prulj704f7ufBU2M78dJye7d1jUpi3nU9jht0g30HPj7Ii8zSatw0Zufn7dn0ig3gtkFtePWa7i1+jcfTIcKXpVOH8s3mo8iKwvV9Yxv9d3aqnJOBd2pqKgsWLHCkmdc3ePBgtm/fjslk4p577uGFF15w3JeXl0d4eLjT+eHh4eTl5TnurzvW2DnumEwmp/EB5eXlJ/6imsHRXE2kmguCIAiCIAjnCbVKcrvD3RxLdmbzwV9p2GSFiYPbcGP/OMd9XaP9mX9TLzakFnHzR5uOPZ8kERfkxf9d1Y1+CUGk5FciSQof/ZWORVZQFJh2SRIJId4AVJutZBRXOx6vKFBjsbFsVw5p9YLul6/u1ujIMHfqGrLVBd+SJKHTtH7+s1at4uvJA3jvz8P8vj+fQ/mVFFWaWZOcz6b0YqZf0o4qs+3YiwM++CvNEXgD3H1RIsPbh3K0pJr24b4ntOv+3m19uOuzLeQajKglew17XQyuVkkcyKto8vGtpUOEL881GL92Op3R5mqPP/44kiQ1+Sc5OdnpMdnZ2Vx22WVcd911TJ482eWa3333Hdu3b+frr79m+fLlboPz1jZ79mz8/f0df2JjY0/J84jmaoIgCIIgCIJg92dKIQ9+u5N9OeUk51Xw+E97WF4751uWFRZtyODuRVtYtjuHa3tHOx4X4KXl5au7oVJJXN07hkdGd+Crf486dr0V4M0/DlFWbU839dSqCfbWOdUEqyWJEB/7blhLgu4606ZNY+HChc0+32qTOZhXQVZpteN1/nOoiF9255Bfbmz0cb4eWh4Z3ZEK47GGdDbF3h0+Jd818JXdVCO3C/flkk7hJ5zq3inSj38eu5jNT17CfSOSnN5Hm6zQvnaE2fnujO54z5o1i4kTJzZ5TmJiouPnnJwcRo4cyeDBg/nggw/cnl8X9Hbu3BmbzcY999zDrFmzUKvVREREkJ+f73R+fn4+ERERAI7/zc/PJzIy0umcnj17NrrGJ554wimFvby8/JQE36K5miAIgiAIgiDY/b4vD7VKcoyhUknw2748xnaP5I0/UliwJhUJezp7mK+eJQ8MxmRV6BTp69TArazaTLnRuUO6xaaQU2YkwEuHJEksuLkXkxdtpcpsQy1JvHx1Vy7vGkl6URWzLu1w3KC7e/fujaafT5s2DQWYNnUqH/6VRu/4QLeNxQrKjdz80SZSa5vC3dw/lsIKE6tqa9U9tCqi/D2pMFoZ1j6EF8Z3xVvvHO5569QuM8H7twlmTXIBpdUWFMWein7X0IRGX0tLqFUSYX4eTLs4iT1ZZfx1qAiAsd0juXVgfKs+19nqjAbeoaGhhIaGHv9E7DvdI0eOpE+fPnz66aeoVMffrJdlGYvFgizLqNVqBg0axOrVq5kxY4bjnFWrVjFo0CAAEhISiIiIYPXq1Y5Au7y8nE2bNjFlypRGn0ev16PXn/po2CgCb0EQBEEQBEEAXDuiS5KEn6c9vPlq01Hg2MzqXIORJTtz2JxeggLcNSTBkUod6KUj0t+D/HIjsmIf2eWlUzvNMh/cNoQNT1xCelEVUQEeGM0yaw8W8MCIRDKzsnjssUcbXef8+QuYNm1qk8H5o488woTxExjfM5KL5qzjtxnDaFOb6l7nxV/2k15U5bj9dYNxWEaLTFrt/T/vyMZiU5h/Uy+ncx4Z3YEpX21HLUnIskL3GH+u7BnJ4KRgPvgrjbJqM//pHNHi1P/j8dCqWXRXf3vaeW3X9AvFOVHjnZ2dzYgRI4iPj2fevHkUFhY67qvbpf7qq6/QarV069YNvV7P1q1beeKJJ7jhhhvQau3/p3zwwQcZPnw4r732GmPHjuXbb79l69atjt1zSZKYMWMGL730Eu3atXOME4uKimLChAmn/XU35KjxFqnmgiAIgiAIwgXuziFt+Hl7NnnlRiTA31PLvcPaAvYGYA19sj7D8fOsH3axJjmf1IIqgrx1PHZZB15deZBcgxF/Ty3v3tIHT62aj/5OY2dmGfHBXkwZkUTP2AB+25fHA19txyorDGobzKKJfVm6dClXXnmlU98nODYy7Ocd2UxvZNSYXq/n58VLCA4LZ9Ln2zDb7EH9nSHOu86HCiodu/uAy851fbICa5Jdu7Zf1jWSn+8fwj+HCgn01nFN7xj0GjVRAZ6nrf5ZkiSiaju2X0jOicB71apVpKamkpqaSkxMjNN9ddPQNBoNr776KikpKSiKQnx8PFOnTuWhhx5ynDt48GC+/vprnn76aZ588knatWvH4sWLHTO8AR599FGqqqq45557KCsrY+jQoaxcufKMz/AGUeMtCIIgCIIgCHXCfD349cGLWLkvD1lRuLRzBKG+9tTQaRcn8cySfY5RWX4eWipMVkfgKgHL99ibJ6sk2JVVxqqHhpFZUsOag/lsTCvm+62ZLN6R7ahJXp9azP/uG8Sj/9vlqAffeLiYOz7byhd3XsyyZcucgu+6oPuJn3bzzeZMtqSX8PL0aSDBg9Ptwbder2fxkqUMGzGSSZ9vY+PhYsA+r7uhnrEBHMqvwFYbbSvYa81lRXEJwCUJx4xsd9dpzVFdQvOcs3O8z2analbpvc/Cyr/hpRlw2/hWu6wgCIIgNIuY4y0IQlMM1RYe+2k3G1KLCPfzYPbV3ejbpvHxv6fa6gP5rE8tJsRXR77ByJebjjrtGDc0ZXgiH/ydDuCodW5oyQNDGP/2epfjy6cPpWO4D2vWrGHcuHHMnTuXqVOnYrRYGfX6X+Qa7I3Pbugby8tXd2PBggU8+uij/Lx4iSPo/vdwMQrQKcKXn+4fgqdO7fQc5UYL936+jY1pxagliZmXtqdzpB/PL9tHWbUFP08NR0vsI8+0aokPb+/LiA5hjb5eWVb4fmsme7INJIR4c/ugNi6jzISmXRBzvC9EosZbEARBEARBOFvN+mEnaw8WYpMVKk2V3P7JZtY9MoIw35PLHDXUWHjvz8PkltXQLyGIG/vG8vWWTJbuzMbXQ8v0S9q53cG9pFM4l3SyjwnONdSwfE8uxZX2D9Sqek3Z6qxPLWo04K5jstrcHv9jfx5dojpw8cUXc+jQIUeWrodWw5IHhvDJ+nRKqiyM7GgPhKdNm8ZVV11FREQEu7LKmTYyiYs7hOHroWFczyiXoBvAz0PLN/cMxFBjQa9R4aG1n1N3TUVR+PtQEYUVJvrEB7rUiDf0/LJ9LNp4BI1KwqYo/JNaxKcT+yFJrT/STBCB9zlF1HgLgiAIgiAIZ6u/DxU5gllZgWqzjd2ZBkZ1bnngbbbK3PD+Rg7lV6KgsHhnDr/uyeOfVHtXbJVkD5hXzhjmmLvtTqS/J7/NGMave+3p5X4eGh76fpcj0O4TH4inVkX9XOC6Guq6dPV+bYIarU1+449UesUFMax9qNN0JIBgHz2PjO7ouqbISNRqNX1qswIGJ4U06z3xb9BUzrFeSWJY++Y1rjZabHy+8QiAI21+3cFCDhfaa94/W59OabWFizuFMbKJXXOh+UTgfQ4xiRpvQRAEQRAE4SwV4qMnx1DjFLw+tXgPBRUmbh4Q16Jrbj9aSnKe85zp9YeLHD/LClhsMn/sz2fysMSGD3cS7KN3Gl3VPsKXfw4VEeStY2z3SP7YX8A/qcVI2Guk1ZLE1IuTyDUYiQ3yItBLy4i569xeWy3B2oMFDGsfilrtulvt9jHNPO9UcFcXDlBhtHDnp5vJKTMiSfDFv0eYd10Pru0T4+Zs4USIwPsc4tjxFqnmgiAIgiAIwlnm5au7cfeiLVhsx0K6/HITT/68hxAfHZd2iTjha7pLem7YzVtRcJuafTwdI/zoGHGsLnds90jUqt78uC0bD52aey5KpFuMPwBZpdUMm7O20TR0WYF/00r44K/D3DkkAa369NdK78028Mk/6ZisMtf0iebijuGNnuul0zChVzRLdmSDZH9Pe8UFkl5YRWapvU687k1+d91hEXi3AhF4n0NMFvv/isBbEARBEARBONsMbx/KmlkjePDbHew4WuYIjjUqiT9TClsUePeKC6RrlB/7c8tRSRJWWaFvmyC2ZZSAJKEoCrGBXozrGdUqr+GyrpFc1tV1hnV6UVWTtd8KkJJfwewVyezKNPD2Lb1bZT3NlVpQwTXvbsBqU1BQWL4nlw9v78t/OjcefM+9tjvtwnzYm22gTYg3U0cmOVLx67PK8qlc+gVDBN7nELHjLQiCIAiCIJzNYoO86Bbtz64sg6PeW6Hx0VbHo9Oo+PbeQXz4Vxp/HMhnX045m9NLUEkwpmsEveMCuaZPDH4e7uueAcqqzXy7JZNKo5VLu4TTPSbghNfRNtQHdb2GbGpJIirQg/ZhvqyunZddd9/yPbncmVFCYqhPi1/3iVqyMwerrGBTjo1L+3bz0SYDb61axQMjk5yOXdwxjBAfHaXVFns6ugITB7c5hSu/cIjA+xwimqsJgiAIgiAIZ7spI5L4bV8+eeX2EVoxAZ7cNSShxdfz0Wu4rm8Mb60+5DgmK7A2uYD5N/ZCpWq8C7ehxsKVC/4hu6wGSZJ4Z10qn97Zn+HNbEIG9lndj/24G5UEdT3NdRqJgnITWbXjuxq69r2NSBI8NaYTd1/UdO15a1CrJKf8e0mqPXaCgrx1LJk6lHfWplJWbeGSTmFc1Su6FVd64RKB9znEKJqrCYIgCIIgCGe5CH8PfpsxjHUpBagkiZEdw/DRuw87rDaZggoTwT46TFaZgnITMYGejlFZdQorTC6PrTLbMFpteOkaD2mW7cohq7TGHpMqChKwcM2hZgfeBeVG7vpsC0arzalpnNEiuzQnq1DvU8oAAC/GSURBVKs9r/tfRYGXlh9gcNsQOkc1PeO5jqIoFFaY0GvU+Hs1vovf0HV9Y/l0fTqVpmPjziYNbdmXHdEBnvzfVd2cjsmyws6sMoxmGz3jApp8zwX3xDt2DjGJOd6CIAiCIAjCOUBS2YPlCqOVNsHejiZl9e3JMnDnZ5spqjSjU0vYFHu6dqCXlk/v7O80m7tduC+BXloMNRZkxb6b2znS77gBoNFiQ5JwBM0KkFZYxdBX1xDopeOpsZ0YmBjc6OP35hiosbjO7nYJuiWIC/Iiu7TGMZ6rzuHCymYF3uVGC5MXbWVTegkAk4a04ekrOjdrrnZ0gCe/TLuIrzcfxWyVGdcjih5uZpu3hMUmc/eirfyZUuh4rh/uG9ToaDXBvdPfbk9oEYsVbLV9DUTgLQiCIAiCIJytKowWxi/4h5dXHGDh2lTGv/2PI2iroygK93yxlZIq+86S2aY4aqQNNRamfLnN6XwfvYYvJg2gfbgvnjo1/doE8eHtfY+7ltFdItBr1NTPui6pMpNVWsO+HAO3f7yZjKKqRh8f5tu8GeSKYk/Tbhh0SxJ0iPBt1jXm/XaQLRkljtsfr89waXZWVm1mV2aZ432rLzbIi8cu68gzV3RutaAb4MdtWU5/f3nlRuasTG61618oxI73OcJYL7tGLwJvQRAEQRAE4Sy1fHcu6cXV9hu16d1v/pHilN5dabKSazC6fbysQK7BiMlqQ685lnLeNdqflTOGndBaYoO8+N+UQSxck0qF0cK+nHJKqy2O5zHbZDYcLqZNiLfbx3eJ8mNAQiCb0ksdxxqOMwO4pnc0P27Pdnn8uB5RfPhXGm1CvJk0NMElhb6+/TnlTp3TNSqJA7nljOlm77K++kA+93+1HZNVRquWePOGXozt7tqB/UQYqi2sOpCPoiiM6hROoJtmcDkGIxqV5PhSwSYrZJW6r20XGicC73OEU+Dd/HIPQRAEQRAEQTit3KV315id07V99BpCfXQUV5ldxnSpJIgJ9HIKuk9Glyh/3r21DwDjF/6Docbg9JyBTdRSf/nvEaegGyAx1Ju0wipH8C0Bhwoq3T5+yc4c1Cr72LP1qUV8OWlAo83gOkT4suNoKXVj0K2yQrtw+265yWpj2jc7MFvtKbAWm8JD3+9kWPsQfJvo6N6Uggoj4xeud3wBEuarZ+nUoUT4O+/y92sT6LSTL0kwqG3j6fmCeyLV/BxRV9+t19n/sQuCIAiCIAjC2eiSTuF4ap3Tu6/rG+t0jiRJvHdbX0fQqFFJaNX2B4T5evD+bX1OydqeHNMJrfpYCDQoMZhRTYzc+n1/vsuxAC+t8+dxCQI8tcQGeuIuprbJCrICGw4Xk1poD9DLqs0s2ZnNr3tyHV9KPDq6o9Oos1sHxnFl7Y52SZWZarPNaafdbLU3pmupj/9Od3p8cZWZ9/867HLeRe1Cee7Kzvh6aNCqJa7tHcO0i9u1+HkvVGLH+xxhFI3VBEEQBEEQhHNAbJAX3987iPmrD1FeY2FM90huGxjvcl6f+EA2PXkJOWU1hPl5oFVLlFZZCPXVt2gUVnMMSAzm94eGseFwMTZZxt9Tx+4sA73jAtw2MVO7WUZZtQU/Ty0VRisSICsK9w5vy9GSap76eY/9cfVmftcnKwqZJdVc9c56iirtH/Dbh/vw45TB+Htp+en+wWSV1uChVRPqe+yDf6iPnhAfHSW1GQIqCXw9tES7aXD20/Yslu7MwcdDw/0jkhpt7FZWm3LvoNhTz92ZOCSBiUMSUBSlWc3eBFci8D5HOGZ4i8BbEARBEARBOMt1jfbng2Y0P/PQqkkM9XHcjvA/8fTyokoTs1cc4GB+Jd2j/Xh8TCf8mki/jg/25khxNXcv2oq5tnvx7QPjeWFCV5dz+yUEsS6lyOlYWbWFudf2YPHObPw9tVzbJ4YO4b7c9dkWRwq7LCuOumh17YjtPvGBtA/z5fGfdjvqzAFSCyr54t8j3D8iCUmSiA3yclmHRq3i04n9mfz5VvLKjQR563j/tj4uNePfbTnKYz/uQcKeJbv6QAErZ1xEfLBrDfuozuF8tzXTcdumKFzSqfHdf0AE3SdBBN7nCDHDWxAEQRAEQbgQybXRrLvaaJuscNtHm0gpqMQmKxzIMZBWVMU3kwc2GSQ+8dMeLLLsuP35v0e4rm+sy9iz3nFBTrdVkn3XevLnWwHw89Bwc/848suNmKzHrqdgr9GednESR0uqiQ/25r7hiahUEsWVZqfdcJVkPwZQbbaiVauc0uHrdIvxZ+MTF1NhsuKr17h9fd9uyXQ8v6LYa8NX7s3j3uFtXc79T+dw5lzTnQ/+TkNRFO4amnDSzdqExonA+xzh2PEWgbcgCIIgCIJwAbDJCi/+sp8v/z2CJME9wxJ5+NIOTgFnelEVB/Iqjj1GgX/TSiitthDkpkN3naJKk6P5W52CCiPgHHgPTAzijkHxLNp4BAAvncZpt7rCaOWRH3bx8wND8PfUUmG0OFLBA710TL+knUsQPaJDKKuTCxy3rbLCoMQg7vpsC2uSC1CrJB4a1Y6pbuqoJUlqcjdfr1E7dV1XAJ2m8bZe1/eL5fp+sY3eL7Qe0VztHGESNd6CIAiCIAjCBWTRhgw+25CBVVaw2BTeXnvYZWSXt941NV2SwEPrHOakFlTwyA+7uP+rbSzfncuQpBBHHblKAi+d2mW3234tiefHd+X3h4bxzeSBPDK6g9P9CnAgr4LMkmo+vqOvI9gP8dHz8cR+bneubx0Yz8OXtifC34O4IC/mXNudf1KLWXfQHozbZIV5v6ew9mCBy2MbKjdayCiqwlKbMn//iLZIkr1ZnUqCCD8PxveMPu51hFNP7HifI0RzNUEQBEEQBOF8UG608PbaVI4WV9MzNoBJQxPQuAlQtx0pdRpLplFJbDtSyrV9YhznRPp7csuAOL7adNTR0GzayCS8dMfCnOyyGsa/vR6jRUZWFFbsyePF8V1QqyQ2Hi4mws+D2dd0I8zXo+ESHNqH+0I4hPrq3M7x/m5LJk9f0ZnNT46i3GjB31Pr2JmvMlnZfrQUD62a3nGBqFUSUy923tEev/Aflxnee7IMjOwQ1uiavtiYwXNL92NTFKIDPPliUn+GtQ/l5/uHsHJfHt46NTf1j2ty5184fUTgfY4wiVRzQRAEQRAE4Rxntcnc+tEm9mYbUICVe/NIK6zi1Wu7u5wbE+iJCqibAC4rCjGBrl28X5rQlSFJIRwuqKRLtB8Xd3RuEFY3sqt+YPvTjmx+vn/ICa8/KcyXfm0C2ZxxbLa3ve679meVRIDXsQ/suYYarn13I9llNQAMbhvMZ3f2d0n/bhvqw96cckftt1VWiA92bbJWJyW/gmeX7HN8AZBdVsOEt9fzzi19GNouhB6xASf82oRTS6SanyNEczVBEARBEAThXJecV8HuLAOyYt/JVoDvt2VirteYrM79I5PoEOHruN0rLpC7hiS4nCdJEmO6RTLtknYuQTfgMppMwr6j3FKzLu2ASrKPGlOrJNQqiev6xrg9d95vKeSVGx23Nx4uduokXufxMR1pUy/QvqpXNFd2j2p0DYcLKl123cuNVm77ZBP/phWf2AsSTgux432OEDXegiAIgiAIwrlO5aYTtwoJdw3I/T21LH5gKHuyy1BJEt2i/d2mpB/PuB5RvLPuMCWVZiTJPjbrPjddvptistp4fVUK6w4WEuHnwdxre7AxrRi1JHHboHg6RbqflZ1dVu3UwVytksgz1LicF+brwcoZw0jJr8BLp6FNsFeTXdnbhfs4peHXUSHx47YsBiYGn9DrE049EXifI0SNtyAIgiAIgnCu6xjhy5C2wWyoDVqtssKkixLcNiEDe0fuPvFBbu9rrmAfPb9MG8qX/x6h0mTl8q6R9E84sWu+sGw/X28+iqJAan4FOzPLWPvwiOPWTw9MDGZTWoljd9oqK/RPcB8Ua9UqukS5NnhzJynMl5ev6saTP+1x3vmWQNtEF3PhzBGB9zlCpJoLgiAIgiAI5zqVSuLjif1YtCGDIyX25mrX9XGfpt2awv08mHVph+Of2Ihlu3Icu8s2BQw1FjanF3NZ16bnXj8wMomCchM/bs9Cr1Ex8z/tGd4+tMXrqO+m/nF0jfLj+vf/xVzb1VyrlrhjUJtWuX6d1IIKlu7MQa1ScW3fGKIDXOvsheM7J74OycjIYNKkSSQkJODp6Unbtm3573//i9lsdnt+amoqvr6+BAQEuNz3ww8/0LFjRzw8POjWrRsrVqxwul9RFJ599lkiIyPx9PRk1KhRHDp06FS8rBMiAm9BEARBEAThfOChVXPv8La8fFU3ru8b60iprp+Sfbbx0bvuV/o2MU+7jlat4uWru3HwpcvZ/dxoJtbWqJutMkrDPPEW6BYTwIoHL+K+4YncOyyR5dMvcqqLP1l7sw2Mnf8Pb687zPzVhxjz1t9kllS32vUvJOdE4J2cnIwsy7z//vvs27ePN954g/fee48nn3zS5VyLxcJNN93ERRdd5HLfhg0buOmmm5g0aRI7duxgwoQJTJgwgb179zrOmTNnDvPnz+e9995j06ZNeHt7M3r0aIxGo8v1Tqe6wFukmguCIAiCIAjnk1X78+n94iqSnlrBDe9vpLDCdKaX5OLJsZ2QJHsHc4CLkkJaVEddUG7k6nfW0/7pX+n5wipW7s076bUlhHjzyOiOPHpZR9qG+pz09er75J90rDYFm6xgUxQqTVa+2Xy0VZ/jQiEprfFVyxkwd+5c3n33XdLS0pyOP/bYY+Tk5HDJJZcwY8YMysrKHPfdcMMNVFVV8csvvziODRw4kJ49e/Lee++hKApRUVHMmjWLhx9+GACDwUB4eDifffYZN954Y7PWVl5ejr+/PwaDAT8/940WTtTj8+Cb5TDrLph+W6tcUhAEQRBOyMn8fjsVvxsFQTj3ZZZUM/K1ddhsCgr25mNDk0JYdFf/M700F7uzytiUVkKor56x3SMbrUtvyi0f/cu/aSXYZAUJ++tdM2sEcU2MDjuTHvhqO7/uzXWMS9OoJO4Y3IZnruh8Sp83taCCh3/YTVphJV2j/Zl3XQ+izsIU9xP53XZO7Hi7YzAYCApyboqwZs0afvjhB95++223j9m4cSOjRo1yOjZ69Gg2btwIQHp6Onl5eU7n+Pv7M2DAAMc5Z4poriYIgiAIgiCcb/ZmG7DWBt1gTzffmlFyRtfUmO4xAUwelsiEXtHNCrrLqs28vOIAD3y9nUUbMpBlhR1Hyxwp9Qr2Zmv7cw2tvlZFUfhpexYzv9vJyysOUFzZsiyCa/pEIyv2EWyq2i7q43o0PuasNRgtNm79aDN7sgyUG61sSi/hzs+2tEpq/pl0TjZXS01NZcGCBcybN89xrLi4mIkTJ/Lll182+m1DXl4e4eHOs/3Cw8PJy8tz3F93rLFz3DGZTJhMx/4xl5eXn9gLagZHqrmo8RYEQRAEQRDOEw13MVWS67Fzkclq44b3/yW1sBJFUVi+O5es0mpiAj1JLaikfjl7TGDr73Z/+HcaL69IRi1JIMHKvXn8+uBFeLupVW/KxR3D+eC2Pnyz+ShatYq7hibQIzag1ddbX0ZxldPsc5uscDCvgrL/b+/e46Iq8z+Af87MMANyGeQ6oAioeFcwVIRQQUl0reS3ZWquibpaLray6M9NV8U2W8xLq3bR3H6K+VvTbPOSbbiIhWlKgNlPvCWKQSAgGve4OHN+f0ycGFEBYRgGPu/X67xinvOcc77neU2v43ee8zxPZS26NjKLfHtm0h7vV155BYIgPHS7fPmywTG5ubmYMGECpkyZgnnz5knl8+bNw/PPP4/Ro0e39W0gLi4OarVa2jw8PFr9GpxcjYiIiIg6Gl8PeywY01P6bK1SYP0UXxNG1Dq+zS7GlYIyaHWilGR/cPoHrH92CGwsf01+/zjOB4O6NW0JseZ4/6ssAPo1y7U6Edl3KvHV1aJHOtf4gRrsnD0C218Y1ibrgztYK3HvCuZKudDsHw3aG5NGv3jxYkRGRj60Ts+ev/6PmJeXh9DQUAQFBWH79u0G9Y4fP47Dhw9LveCiKEKn00GhUGD79u2YM2cONBoNCgoKDI4rKCiARqMBAOm/BQUFcHNzM6jj5+f3wBiXLVuGmJgY6XNpaWmrJ9/VfNWciIiIiDqgP0/sj2f8PVBYVoUBbnaw76LEfy7k46O0HFjIZfj9KO8Wr+Xd2kRRxKnM2ygorcJjnl3h7WRtsF8uuzd1BGSCgCHd7XHyz2NxJb8MLrYqeDrqjysorcL/nMxC6c+1eGKAK8b1d21wfHMIDS9vNlxsLfHfE/piXcIVAPp7WfNfg6E08/XJTZp4Ozs7w9m5aevY5ebmIjQ0FP7+/ti5cydkMsOGP336NLRarfT50KFDeOONN/D111+jW7duAIDAwEAkJSUhOjpaqpeYmIjAwEAAgLe3NzQaDZKSkqREu7S0FCkpKViwYMEDY1OpVFCpjJsRc4w3EREREXVUvV1s0NtFPyN34sUCzN+dDgH6pCvxYgEOLXwcA91bv2f4UYiiiCX7v8O/zuYCABRyAf+YOQyh/VykOn4e9vDtrsb53BLIBAF3dSLmj+4JQRBgZ2mB4V6//pDwU0UNnn77JIrK9f/g35uag41TfPHMPeubi6KIk5lFyL5TCd/u9g/tKZ83qifWfHZJ/wOACHg4WGF0H6fWbAaj+kNIb4z2ccb1ogr019jCx7X1lkgzFbPor8/NzUVISAg8PT2xYcMG3Lp1S9pX10vdv39/g2PS0tIgk8kwaNAgqWzRokUYM2YMNm7ciEmTJmHv3r1IS0uTes8FQUB0dDTWrFkDHx8feHt7Y+XKlXB3d0dERITxb/QhOMabiIiIiDqDj9NzIEA/+Zgo6pPvw9/lmSzxvqvVQSYIkP3Si302+ycp6QYArVbEioMZOPXKWKnMQi7Dh/NH4n++ykJeSRVGeHdFhF+3+57/PxfzUVBqOPnZP7663iDxXnXoAnaf+QGAfrKzdc8OwZRh93/L9vejesLFzhJffX8LTrYqzB/VE12UZpH6SQZ1UxvlNXxTMYvWT0xMRGZmJjIzM9G9e8NffpoqKCgIe/bswYoVK7B8+XL4+Pjg4MGDBsn50qVLUVFRgfnz56O4uBjBwcFISEiApaVlq93Po6jmGG8iIiIi6gSUCjmEX2bQlsoeYemulqq+q8WyT87j4Le5UMhlWDTOB1GhvXH7l57pOiKAO5U1DY7volTg5XE+jV7nfumM7p7CzMJyKemuu2bs4Qt41r87hAe8V/60r7vRZyCnpjOLF+UjIyMhiuJ9t4cdU38N7zpTpkzBlStXUF1djYyMDPzmN78x2C8IAv76178iPz8fVVVVOHbsGPr06dPat9RsHONNRERERJ3B3GBvyGWCfhME2KgUmDaiR5vH8c7xTBz4Nhc6Eai5q8P6o1fwnwv58OthD2uVHHXDuOWCgNC+Lg8/2UM8McAVTjZKyGWCdM65wd4GdYrvk9hX1mhRqzXvJbY6E7Po8SaO8SYiIiKizsHPwx6fvhyMw+fyYCGX4bnhHuhmbwVRFPHeieuIP3UDMgF4KaQXXgj0MlocaT/8ZNAbrZAJOJtdjPEDNfjfuQFYcTAD+SVVGOXjhNciBj34RI1wtFHh8MJgbD9xHSU/12L8AFdMHOxmUKe/mx2cbVW4U1EDrU6EXBAQ2MvR7Ccc60yYeJsJaYw3E28iIiIi6uD6aezQb4KdQdnH6T9i7ee/LjW86tAFuNhaYsIgjVFi8HTsgpSsO9D+sh6YVifCw0G/xvjQHl3x2R9Htdq13O2tsPrpgQ/cb61SYO/8kVhxIAM/3K7AMC8HvDb50ZN9antMvM0E1/EmIiIios7sxNUiyARI62IrZAKSv79ltMR78fi+SMm6g+u3KgAAIX2d8dwDJjNrC72cbfDh/JEmuz61DBNvM3BXq98AzmpORERERJ2To7VSP5HYL+9/iyLgbGO8fxw72ajw+aJRyMgthUohwwA3O2lmc6Lm4qAAM1Bdby4FvmpORERERJ3RH0J6wdX2138M93C0wpx7JiFrbSqFHP6eXTGom5pJN7UIe7zNQFW9Zf34qjkRERERdUYudpZI+NNofPV9EeQyYHQf5zZfm7qqVguVQvbAJbyIHoSJtxmQxndbADK+o0BEREREnZSdpQUmDXFrvGIry7lTid9/kIYr+WWw72KBTVP9ENKCJcSo82EaZwY4sRoRERERkeks+N90ZBaWAwBKKmvx4u503CqrbuQoakuiKGLTse/x2GuJGL4mETtOZpk6JANMvM1A3RhvFcd3ExERERG1Ka1OxIW8UmlZMRFA9V0drhaUGe2aoiiiqlZrtPN3RHu+ycamY1dxp6IGt8pr8NcjF5GQcdPUYUmYeJuBql8Sb06sRkRERETUtuQyARq1JerPrSYA6NbVyijX+zb7JwStPY5+KxMwet0XuHSz1CjX6WhOZRah/sh7hUzAqczbJovnXky8zUDdq+ZcSoyIiIiIqO1tnjYUVko5AH3SveLJAfB0tG7161TVahG5MxUFpVUAgB9/qsSc+FSpt50ezNlGZTDzvE4U4WzbfnouObmaGeAYbyIiIiIi0xnh7YCv/zwOmbfKoFFboZu9cXq7c+5UouTnWumzTgRullThdnk1XOwsjXLNjiJqbG8kXS7Ejz/9DADwcbFF5ONepg2qHibeZkDq8W4/P9gQEREREXUq6i4W8Pd0MOo1XOwsoZAJuFuvh9vKQg77LuyBa4yLrSUSokfj68wiKOQCgno5wdJCbuqwJHzV3AxUc4w3EREREVGHp7aywPopQyD/5ZVppVyGzdP8oFQwbWsKG5UC4wdqMLafa7tKugH2eJsFTq5GRERERNQ5/NfQ7ni8lxOy71TCy8kaTjZtkwTUanWwkDPBNxYm3mZAGuNtYdo4iIiIiIjI+FzsLNtsTPe1W+V4aXc6rhaWw01tibemD8UwL+O+Ut8Z8ScNM1DNMd5ERERERNTKRFHE3PhUXC+qAAAUlFZhdnwqyqvvmjiyjoc93maAY7yJiIiIiOhRncspxsFvc6GykGHGCE/0cOwCACituosbtyulejoRKKu6ixtFFRjUTW2qcDskJt5mgGO8iYiIiIjoUaRcv43n/5GiX4AcwJ6UbPz7j6Pg4dAFNioFbC0VKK+6i7p51GUC4Mqly1odXzU3A9JyYlxFgIiIiIiImmHHqSyIEKHV6beK6rt45ZP/w77UbNRqddgybag0a7pMANZEDIazLXv8Wht7vM2ANLkaE28iIiIiImoGrU6E+Ouy4NCJwKnM2ziVeRt7UrLx0UuBOPXKWFwrLEd3hy7oZm9lumA7MPZ4mwEp8eYPT0RERERERlVefReVNR1ncrHnA3pAhL43+17f/ViCYxcL4WSjQkBPRybdRsQebzPAydWIiIiIiIyrVqvD4o++w+Hv8gAAMwJ64LXJgyC7X8ZqRsb2c8XO2cPxydlcJF0qQGWN1mB/BWcwbxPs8TYD0uRqfNWciIiIiMgo/vHVdXz6S9INAP9MycaHqdkmjKj1hPZ1wVvTh2JusHfdHGuQCYC1Uo5gHyeTxtZZmEXifePGDcydOxfe3t6wsrJCr169EBsbi5qaGoM6giA02M6cOWNwrv3796Nfv36wtLTE4MGD8e9//9tgvyiKWLVqFdzc3GBlZYWwsDBcvXq1Te7zQTjGm4iIiIjIuC7klkKo17mtkAnIyC01XUBGEB3WBzFP9MFAdzsE93bCvhcD4c7Xy9uEWbxqfvnyZeh0Orz33nvo3bs3MjIyMG/ePFRUVGDDhg0GdY8dO4aBAwdKnx0dHaW/v/76a0yfPh1xcXF48sknsWfPHkRERODs2bMYNGgQAGDdunXYsmULdu3aBW9vb6xcuRLh4eG4ePEiLC3bflp9rRa4dUf/9w95+s9yeZuHQURERETUoXk7WRt81ooiet5TZu7kMgEvj/PBy+N8TB1KpyOIYv057szH+vXrsXXrVly/fh2Avsfb29sb3377Lfz8/O57zNSpU1FRUYEjR45IZSNHjoSfnx+2bdsGURTh7u6OxYsXY8mSJQCAkpISuLq6Ij4+HtOmTWtSbKWlpVCr1SgpKYGdnd0j3+PnJ4BX3wZu3vq1zM0ZiF0ITBz9yKclIiJ6JC15vrXWs5GIyFgqa+5i9s5UpGTpe71C+znjvd8Nk5baIrpXc55tZvstKikpgYODQ4Pyp59+Gi4uLggODsbhw4cN9p0+fRphYWEGZeHh4Th9+jQAICsrC/n5+QZ11Go1AgICpDr3U11djdLSUoOtpT4/ASyINUy6ASD/lr788xMtvgQREREREf2ii1KBvfNH4ljMGHyxJAQ7Zg2/b9KdV/wzYvadw9T3TuPNxO9Rq9WZIFoyN2aZeGdmZuKtt97Ciy++KJXZ2Nhg48aN2L9/Pz777DMEBwcjIiLCIPnOz8+Hq6urwblcXV2Rn58v7a8re1Cd+4mLi4NarZY2Dw+PFt2fVqvv6b7fqwh1Za++ra9HREREREStQxAE9HaxgbeTNQSh4WzmlTV3MWXbaRz6Lg8pWXfw1vGrWHUowwSRkrkxaeL9yiuv3HdCtPrb5cuXDY7Jzc3FhAkTMGXKFMybN08qd3JyQkxMDAICAjB8+HCsXbsWv/vd77B+/Xqj38eyZctQUlIibTk5OS063zfnG/Z01ydCv/+b8y26DBERERERNcPZH4qRW/wztDp9d5goAp+czTVxVGQOTDq52uLFixEZGfnQOj179pT+zsvLQ2hoKIKCgrB9+/ZGzx8QEIDExETps0ajQUFBgUGdgoICaDQaaX9dmZubm0GdB40bBwCVSgWVqvUW2S683br1iIiIiIio5SwtGvZbqjgGnJrApIm3s7MznJ2dm1Q3NzcXoaGh8Pf3x86dOyGTNf4FP3funEECHRgYiKSkJERHR0tliYmJCAwMBAB4e3tDo9EgKSlJSrRLS0uRkpKCBQsWNP3GWsjFsfE6zalHREREREQtN7RHVwT3dsKpzCLIZQLu6kQsHt/X1GGRGTCL5cRyc3MREhICT09PbNiwAbdu/foedl0v9a5du6BUKjF06FAAwCeffIIdO3bg/fffl+ouWrQIY8aMwcaNGzFp0iTs3bsXaWlpUu+5IAiIjo7GmjVr4OPjIy0n5u7ujoiIiDa73xGD9bOX59+6/zhvAYDGWV+PiIiIiIjahlwmYEfkcOxPz8HN4ioM93bAmD5N60ikzs0sEu/ExERkZmYiMzMT3bt3N9hXfzW01157DT/88AMUCgX69euHffv24dlnn5X2BwUFYc+ePVixYgWWL18OHx8fHDx4UFrDGwCWLl2KiooKzJ8/H8XFxQgODkZCQkKbruEtl+uXDFsQq0+y6yffdVM8xC7ket5ERERERG1NqZBhRoCnqcMgM2O263i3Z1zHm4iIOiKu401ERPSr5jzbzKLHu7OaOBoY/7h+9vLC2/ox3SMGs6ebiIiIiIjInDDxbufkciDQz9RREBERERER0aPi3PdERERERERERsTEm4iIiIiIiMiImHgTERERERERGRETbyIiIiIiIiIjYuJNREREREREZERMvImIiIiIiIiMiMuJGYEoigD0C6oTERF1FHXPtbrnXHPw2UhERB1Nc56LTLyNoKysDADg4eFh4kiIiIhaX1lZGdRqdbOPAfhsJCKijqcpz0VBfJSfremhdDod8vLyYGtrC0EQWny+0tJSeHh4ICcnB3Z2dq0QYefEdmw5tmHrYDu2HNuwdTS3HUVRRFlZGdzd3SGTNW+0Wms/G4mIiEytOc9F9ngbgUwmQ/fu3Vv9vHZ2dvwHZitgO7Yc27B1sB1bjm3YOprTjs3t6a5jrGcjERGRKTX1ucjJ1YiIiIiIiIiMiIk3ERERERERkREx8TYDKpUKsbGxUKlUpg7FrLEdW45t2DrYji3HNmwdbEciIqK2wcnViIiIiIiIiIyIPd5ERERERERERsTEm4iIiIiIiMiImHgTERERERERGRETbzPwzjvvwMvLC5aWlggICMA333xj6pDarRMnTuCpp56Cu7s7BEHAwYMHDfaLoohVq1bBzc0NVlZWCAsLw9WrV00TbDsVFxeH4cOHw9bWFi4uLoiIiMCVK1cM6lRVVSEqKgqOjo6wsbHBM888g4KCAhNF3D5t3boVQ4YMkdZHDgwMxOeffy7tZxs239q1ayEIAqKjo6UytmPjVq9eDUEQDLZ+/fpJ+9mGRERExsfEu53bt28fYmJiEBsbi7Nnz8LX1xfh4eEoLCw0dWjtUkVFBXx9ffHOO+/cd/+6deuwZcsWbNu2DSkpKbC2tkZ4eDiqqqraONL2Kzk5GVFRUThz5gwSExNRW1uL8ePHo6KiQqrzpz/9CZ9++in279+P5ORk5OXl4be//a0Jo25/unfvjrVr1yI9PR1paWkYO3YsJk+ejAsXLgBgGzZXamoq3nvvPQwZMsSgnO3YNAMHDsTNmzel7eTJk9I+tiEREVEbEKldGzFihBgVFSV91mq1oru7uxgXF2fCqMwDAPHAgQPSZ51OJ2o0GnH9+vVSWXFxsahSqcQPP/zQBBGah8LCQhGAmJycLIqivs0sLCzE/fv3S3UuXbokAhBPnz5tqjDNQteuXcX333+fbdhMZWVloo+Pj5iYmCiOGTNGXLRokSiK/C42VWxsrOjr63vffWxDIiKitsEe73aspqYG6enpCAsLk8pkMhnCwsJw+vRpE0ZmnrKyspCfn2/Qnmq1GgEBAWzPhygpKQEAODg4AADS09NRW1tr0I79+vVDjx492I4PoNVqsXfvXlRUVCAwMJBt2ExRUVGYNGmSQXsB/C42x9WrV+Hu7o6ePXtixowZyM7OBsA2JCIiaisKUwdAD1ZUVAStVgtXV1eDcldXV1y+fNlEUZmv/Px8ALhve9btI0M6nQ7R0dF4/PHHMWjQIAD6dlQqlbC3tzeoy3Zs6Pz58wgMDERVVRVsbGxw4MABDBgwAOfOnWMbNtHevXtx9uxZpKamNtjH72LTBAQEID4+Hn379sXNmzfx6quvYtSoUcjIyGAbEhERtREm3kT0QFFRUcjIyDAYD0pN17dvX5w7dw4lJSX4+OOPMWvWLCQnJ5s6LLORk5ODRYsWITExEZaWlqYOx2xNnDhR+nvIkCEICAiAp6cnPvroI1hZWZkwMiIios6Dr5q3Y05OTpDL5Q1mly0oKIBGozFRVOarrs3Ynk2zcOFCHDlyBF988QW6d+8ulWs0GtTU1KC4uNigPtuxIaVSid69e8Pf3x9xcXHw9fXF5s2b2YZNlJ6ejsLCQjz22GNQKBRQKBRITk7Gli1boFAo4OrqynZ8BPb29ujTpw8yMzP5XSQiImojTLzbMaVSCX9/fyQlJUllOp0OSUlJCAwMNGFk5snb2xsajcagPUtLS5GSksL2rEcURSxcuBAHDhzA8ePH4e3tbbDf398fFhYWBu145coVZGdnsx0bodPpUF1dzTZsonHjxuH8+fM4d+6ctA0bNgwzZsyQ/mY7Nl95eTmuXbsGNzc3fheJiIjaCF81b+diYmIwa9YsDBs2DCNGjMCmTZtQUVGB2bNnmzq0dqm8vByZmZnS56ysLJw7dw4ODg7o0aMHoqOjsWbNGvj4+MDb2xsrV66Eu7s7IiIiTBd0OxMVFYU9e/bg0KFDsLW1lcZ5qtVqWFlZQa1WY+7cuYiJiYGDgwPs7Ozw8ssvIzAwECNHjjRx9O3HsmXLMHHiRPTo0QNlZWXYs2cPvvzySxw9epRt2ES2trbS3AJ1rK2t4ejoKJWzHRu3ZMkSPPXUU/D09EReXh5iY2Mhl8sxffp0fheJiIjaCBPvdm7q1Km4desWVq1ahfz8fPj5+SEhIaHBBGGkl5aWhtDQUOlzTEwMAGDWrFmIj4/H0qVLUVFRgfnz56O4uBjBwcFISEjg+NF6tm7dCgAICQkxKN+5cyciIyMBAH//+98hk8nwzDPPoLq6GuHh4Xj33XfbONL2rbCwEC+88AJu3rwJtVqNIUOG4OjRo3jiiScAsA1bC9uxcT/++COmT5+O27dvw9nZGcHBwThz5gycnZ0BsA2JiIjagiCKomjqIIiIiIiIiIg6Ko7xJiIiIiIiIjIiJt5ERERERERERsTEm4iIiIiIiMiImHgTERERERERGRETbyIiIiIiIiIjYuJNREREREREZERMvImIiIiIiIiMiIk3ERERERERkREx8SYik4uMjERERISpw2jAy8sLmzZtMnUYRERERGTmmHgT0UNFRkZCEIQG24QJE6Q6Xl5eEAQBe/fubXD8wIEDIQgC4uPjH3iNzZs3G+wPCQlBdHR0K97Fw8XHx8Pe3r5BeWpqKubPn99mcRARERFRx6QwdQBE1P5NmDABO3fuNChTqVQGnz08PLBz505MmzZNKjtz5gzy8/NhbW390POr1erWC7aempoaKJXKRz7e2dm5FaMhIiIios6KPd5E1CiVSgWNRmOwde3a1aDOjBkzkJycjJycHKlsx44dmDFjBhSKh//GV/9V88jISCQnJ2Pz5s1S7/qNGzcAABkZGZg4cSJsbGzg6uqKmTNnoqioSDpPSEgIFi5ciOjoaDg5OSE8PBwA8Oabb2Lw4MGwtraGh4cH/vCHP6C8vBwA8OWXX2L27NkoKSmRrrd69WoADV81z87OxuTJk2FjYwM7Ozs899xzKCgokPavXr0afn5+2L17N7y8vKBWqzFt2jSUlZU1q72JiIiIqGNh4k1ErcLV1RXh4eHYtWsXAKCyshL79u3DnDlzmnWezZs3IzAwEPPmzcPNmzdx8+ZNeHh4oLi4GGPHjsXQoUORlpaGhIQEFBQU4LnnnjM4fteuXVAqlTh16hS2bdsGAJDJZNiyZQsuXLiAXbt24fjx41i6dCkAICgoCJs2bYKdnZ10vSVLljSIS6fTYfLkybhz5w6Sk5ORmJiI69evY+rUqQb1rl27hoMHD+LIkSM4cuQIkpOTsXbt2ma1ARERERF1LHzVnIgadeTIEdjY2BiULV++HMuXLzcomzNnDhYvXoy//OUv+Pjjj9GrVy/4+fk161pqtRpKpRJdunSBRqORyt9++20MHToUf/vb36SyHTt2wMPDA99//z369OkDAPDx8cG6desMzll/vLiXlxfWrFmDl156Ce+++y6USiXUajUEQTC43r2SkpJw/vx5ZGVlwcPDAwDwwQcfYODAgUhNTcXw4cMB6BP0+Ph42NraAgBmzpyJpKQkvP76681qByIiIiLqOJh4E1GjQkNDsXXrVoMyBweHBvUmTZqEF198ESdOnMCOHTua3dv9MN999x2++OKLBj8AAPpe5rrE29/fv8H+Y8eOIS4uDpcvX0ZpaSnu3r2LqqoqVFZWokuXLk26/qVLl+Dh4SEl3QAwYMAA2Nvb49KlS1Li7eXlJSXdAODm5obCwsJm3SsRERERdSxMvImoUdbW1ujdu3ej9RQKBWbOnInY2FikpKTgwIEDrRZDeXk5nnrqKbzxxhsN9rm5uRnEWt+NGzfw5JNPYsGCBXj99dfh4OCAkydPYu7cuaipqWly4t1UFhYWBp8FQYBOp2vVaxARERGReWHiTUStas6cOdiwYQOmTp3aYAK2plIqldBqtQZljz32GP71r3/By8ur0cna6ktPT4dOp8PGjRshk+mntfjoo48avd69+vfvj5ycHOTk5Ei93hcvXkRxcTEGDBjQ5HiIiIiIqPPh5GpE1Kjq6mrk5+cbbPVnE6+vf//+KCoqarD8WHN4eXkhJSUFN27cQFFREXQ6HaKionDnzh1Mnz4dqampuHbtGo4ePYrZs2c/NGnu3bs3amtr8dZbb+H69evYvXu3NOla/euVl5cjKSkJRUVFqKysbHCesLAwDB48GDNmzMDZs2fxzTff4IUXXsCYMWMwbNiwR75XIiIiIur4mHgTUaMSEhLg5uZmsAUHBz+wvqOjI6ysrB75ekuWLIFcLseAAQPg7OyM7OxsuLu749SpU9BqtRg/fjwGDx6M6Oho2NvbSz3Z9+Pr64s333wTb7zxBgYNGoR//vOfiIuLM6gTFBSEl156CVOnToWzs3ODydkA/Svjhw4dQteuXTF69GiEhYWhZ8+e2Ldv3yPfJxERERF1DoIoiqKpgyAiIiIiIiLqqNjjTURERERERGRETLyJiIiIiIiIjIiJNxEREREREZERMfEmIiIiIiIiMiIm3kRERERERERGxMSbiIiIiIiIyIiYeBMREREREREZERNvIiIiIiIiIiNi4k1ERERERERkREy8iYiIiIiIiIyIiTcRERERERGRETHxJiIiIiIiIjKi/wcl9NtzyKE2DAAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 1000x400 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "final log-likelihood -1705.3 · mixture weights [0.334, 0.336, 0.33]\n"
     ]
    }
   ],
   "source": [
    "# viz: the log-likelihood climbing, and our soft assignments on 3 blobs\n",
    "Xg, yg = make_blobs(n_samples=600, centers=3, cluster_std=0.60, random_state=SEED)\n",
    "pi_g, mu_g, Sig_g, gamma_g, ll_g = fit_gmm(Xg, 3, n_iters=EM_ITERS, seed=SEED)\n",
    "fig, ax = plt.subplots(1, 2, figsize=(10, 4))\n",
    "ax[0].plot(ll_g, \"o-\", color=\"#1E40FF\"); ax[0].set_xlabel(\"EM iteration\"); ax[0].set_ylabel(\"log-likelihood\")\n",
    "ax[0].set_title(\"EM log-likelihood (monotone up)\")\n",
    "ax[1].scatter(Xg[:, 0], Xg[:, 1], s=8, c=gamma_g.argmax(axis=1), cmap=\"tab10\")\n",
    "ax[1].scatter(mu_g[:, 0], mu_g[:, 1], marker=\"X\", s=200, c=\"black\", edgecolor=\"white\")\n",
    "ax[1].set_title(f\"GMM hard labels · purity {cluster_purity(yg, gamma_g.argmax(axis=1)):.3f}\")\n",
    "ax[1].set_xticks([]); ax[1].set_yticks([])\n",
    "plt.tight_layout(); plt.show()\n",
    "print(f\"final log-likelihood {ll_g[-1]:.1f} · mixture weights {pi_g.round(3).tolist()}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7e2ad462",
   "metadata": {},
   "source": [
    "> **Interpretation.** The log-likelihood rises and flattens, exactly the EM guarantee, and the recovered means sit at the blob centers with the mixture weights near 1/3 each (the true generator's proportions). Compare our from-scratch fit to sklearn's next.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "cf484b71",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-06-10T18:44:22.365345Z",
     "iopub.status.busy": "2026-06-10T18:44:22.365265Z",
     "iopub.status.idle": "2026-06-10T18:44:22.392447Z",
     "shell.execute_reply": "2026-06-10T18:44:22.392086Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "sklearn GMM purity 0.995 · our purity 0.995\n",
      "responsibilities sum to 1 per point: True\n"
     ]
    }
   ],
   "source": [
    "# deeper: sklearn's GaussianMixture as the oracle — same partition, up to label names\n",
    "from sklearn.mixture import GaussianMixture\n",
    "sk_gmm = GaussianMixture(n_components=3, n_init=10, random_state=SEED).fit(Xg)\n",
    "sk_labels = sk_gmm.predict(Xg)\n",
    "proba = sk_gmm.predict_proba(Xg)\n",
    "assert np.allclose(proba.sum(axis=1), 1.0), \"responsibilities are a posterior; rows must sum to 1\"\n",
    "print(f\"sklearn GMM purity {cluster_purity(yg, sk_labels):.3f} · \"\n",
    "      f\"our purity {cluster_purity(yg, gamma_g.argmax(axis=1)):.3f}\")\n",
    "print(\"responsibilities sum to 1 per point:\", bool(np.allclose(proba.sum(axis=1), 1.0)))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3b8830b9",
   "metadata": {},
   "source": [
    "> **Interpretation.** Our from-scratch EM and sklearn's `GaussianMixture` recover the same partition (purity matches up to label permutation). The `predict_proba` rows sum to exactly 1 because they are a posterior over which Gaussian generated each point, the Bayes'-rule quantity from the E step. That probability is what makes the next step, anomaly detection, a one-liner.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "73899aed",
   "metadata": {},
   "source": [
    "### Anomaly detection: low density is the alarm\n",
    "\n",
    "A fitted mixture assigns every point a density $p(\\mathbf{x})$. A natural anomaly score is $-\\log p(\\mathbf{x})$: high score means low density means \"this point does not look like the training data.\" We fit on normal points, then flag a held-out point as anomalous if its log-density falls below a percentile threshold.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "4f441a00",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-06-10T18:44:22.393444Z",
     "iopub.status.busy": "2026-06-10T18:44:22.393367Z",
     "iopub.status.idle": "2026-06-10T18:44:22.411310Z",
     "shell.execute_reply": "2026-06-10T18:44:22.410904Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "threshold (2nd pct of train log-density): -5.71\n",
      "planted outliers flagged: 3/3 · false positives among 100 normals: 3\n"
     ]
    }
   ],
   "source": [
    "# scale-up off by default; runs fast as-is. Normal data + a handful of planted outliers.\n",
    "X_norm, _ = make_blobs(n_samples=500, centers=3, cluster_std=0.60, random_state=SEED)\n",
    "gm = GaussianMixture(n_components=3, n_init=10, random_state=SEED).fit(X_norm)\n",
    "# planted anomalies far from every blob, plus a normal test set\n",
    "outliers = np.array([[10.0, 10.0], [-9.0, 8.0], [8.0, -9.0]])\n",
    "X_test = np.vstack([X_norm[:100], outliers])\n",
    "is_outlier_true = np.array([False] * 100 + [True] * 3)\n",
    "\n",
    "log_dens = gm.score_samples(X_test)          # log p(x) per point\n",
    "threshold = np.percentile(gm.score_samples(X_norm), 2)  # bottom 2% of TRAIN density\n",
    "flagged = log_dens < threshold\n",
    "print(f\"threshold (2nd pct of train log-density): {threshold:.2f}\")\n",
    "print(f\"planted outliers flagged: {flagged[100:].sum()}/3 · \"\n",
    "      f\"false positives among 100 normals: {flagged[:100].sum()}\")\n",
    "assert flagged[100:].all(), \"all three planted outliers should fall below the density threshold here\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "9c46a30f",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-06-10T18:44:22.412519Z",
     "iopub.status.busy": "2026-06-10T18:44:22.412408Z",
     "iopub.status.idle": "2026-06-10T18:44:22.463909Z",
     "shell.execute_reply": "2026-06-10T18:44:22.463513Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAg4AAAHqCAYAAABshsYwAAAAOnRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjEwLjMsIGh0dHBzOi8vbWF0cGxvdGxpYi5vcmcvZiW1igAAAAlwSFlzAAAPYQAAD2EBqD+naQAAfRdJREFUeJztnXeYE1Xbh39JdpNt2cKy9Cq9q4ioSFEUkKK8oDQLYkNB7OXDAqLYe0XU14bgK2JHAUFBQCwoglIFpCPLUrb3zXx/LBPSM8mUc87Mc1+Xl2wymUymnHPP8zznjE2SJAkEQRAEQRAKsLPeAIIgCIIgxIHEgSAIgiAIxZA4EARBEAShGBIHgiAIgiAUQ+JAEARBEIRiSBwIgiAIglAMiQNBEARBEIohcSAIgiAIQjEkDgRBEARBKMYy4vDQQw/BZrOx3gw/eNmmd999FzabDbt372a9KbqzYsUK2Gw2rFixwvva1VdfjRYtWjDbpljZvXs3bDYb3n333bjX0a9fP/Tr10+zbYqEzWbDzTffbMh3hfv+hx56SNN1ytfukSNHNFunHtupJS1atMDVV1/NejMMPXeJ0MQlDrt27cLNN9+Mtm3bIiUlBSkpKejYsSMmT56MP//8029Z+QKz2+3Yt29f0LoKCwuRnJwc1LjIjaPNZsPMmTNDbsfll18Om82GtLS0eH4GEYHXXntNVcfEGtG3P1a++eYbpp3OmjVr8NBDDyE/P5/ZNhDW5ODBg3jooYewfv161ptiGWIWh4ULF6Jz586YM2cOLrjgAjz//PN48cUXcdFFF+Gbb77Bqaeeij179gR9zuVy4cMPPwx6/dNPP434fUlJSSE/V1JSgi+++AJJSUmx/gQigCuvvBJlZWVo3ry59zXRO95Ytv/NN9/Etm3b9N0gDWnevDnKyspw5ZVXel/75ptvMGPGDMXr+Pbbb/Htt99qtk1r1qzBjBkzSBwI3Qk8dw8ePIgZM2aQOBhITOKwc+dOjBkzBs2bN8fWrVvx2muvYeLEibj++uvx7LPPYvv27XjhhRdgtwevdvDgwSEFYN68eRgyZEjY7xw8eDA2b96MDRs2+L3+xRdfoLKyEhdeeGEsP4EIgcPhQFJSEhdpExYkJibC5XJpsi5JklBWVqbJusJhs9mQlJQEh8MR9zqcTiecTqeGW2UNSkpKWG+C5THLuWvkueTxeFBeXq7Z+mISh6eeegolJSV455130LBhw6D3ExIScMstt6Bp06ZB740bNw7r16/H1q1bva8dOnQI33//PcaNGxf2O88++2y0bNkS8+bN83t97ty5GDRoEOrUqRPLT/CjuroajzzyCFq1agWXy4UWLVrgvvvuQ0VFhd9yHo8HDz30EBo1aoSUlBScd9552Lx5c0w5v9WrV6NHjx5ISkpCq1atMHv27LDLfvDBB+jevTuSk5NRp04djBkzJijN069fP3Tu3BmbN2/Geeedh5SUFDRu3BhPPfVU0PpefvlldOrUCSkpKcjKysIZZ5zhtz8DaxxatGiBTZs24YcffvCmi/r164d//vkHNpsNzz//fNB3rFmzBjabzU8Ot27dir179yraP3/88QcuuugipKenIy0tDf3798fPP//st0y4mhCl2x+OUDUOHo8HL7zwAjp16oSkpCTUr18fEydOxPHjx/2Wa9GiBYYOHYolS5bgjDPOQHJysvfYLl26FOeeey4yMzORlpaGdu3a4b777ou6L6J9LrDG4eqrr8arr74KAN7fG00CA/PEcu3H/Pnz8eijj6JJkyZISkpC//79sWPHjojreuihh3D33XcDAFq2bOn9/sCamc8//xydO3eGy+VCp06dsHjx4qB1HThwANdccw3q16/vXe7tt9+O+P0yFRUVuP3225GTkwO3242LL74Y+/fvD1puz549mDRpEtq1a4fk5GRkZ2fjsssuC9pe+bz64YcfMGnSJNSrVw9NmjQJ+/179uxB69at0blzZ+Tm5ira5mhEuy7y8/PhcDjw0ksveV87cuQI7HY7srOz4fvw45tuugkNGjSI+p2SJGHmzJlo0qSJt73btGlTyGXz8/Nx2223oWnTpnC5XGjdujWefPJJeDwe7zLy+frMM8/gjTfe8La3PXr0wNq1a/3Wd+jQIUyYMAFNmjSBy+VCw4YNcckll/gdG99zd8WKFejRowcAYMKECd5z791338X06dORmJiIvLy8oO2+4YYbkJmZGbUz3bp1K0aNGoWcnBwkJyejXbt2uP/++/2WUdJ2RTqXYmnLKyoqMH36dLRu3RoulwtNmzbFPffcE9Rnyan/uXPnolOnTnC5XN7r7X//+x+6d+8Ot9uN9PR0dOnSBS+++GLE/RBIQiwLL1y4EK1bt0bPnj1j+hIA6NOnD5o0aYJ58+bh4YcfBgB89NFHSEtLixhxAICxY8figw8+wBNPPOEtSPr2228xZ86ckI2PUq677jq89957uPTSS3HnnXfil19+weOPP44tW7bgs88+8y43depUPPXUUxg2bBgGDhyIDRs2YODAgYoN7q+//sKAAQOQk5ODhx56CNXV1Zg+fTrq168ftOyjjz6KBx98EKNGjcJ1112HvLw8vPzyy+jTpw/++OMPZGZmepc9fvw4Bg0ahBEjRmDUqFFYsGAB7r33XnTp0gUXXXQRgNow/C233IJLL70Ut956K8rLy/Hnn3/il19+CStsL7zwAqZMmYK0tDTvRVK/fn2ccsop6NWrF+bOnYvbb7/d7zNz586F2+3GJZdc4n2tQ4cO6Nu3r18hYig2bdqE3r17Iz09Hffccw8SExMxe/Zs9OvXDz/88EPM51u47Y+FiRMn4t1338WECRNwyy23YNeuXXjllVfwxx9/4Mcff0RiYqJ32W3btmHs2LHe6Fu7du2wadMmDB06FF27dsXDDz8Ml8uFHTt24Mcff4y6L2L93MSJE3Hw4EEsXboUc+bMiel3BvLEE0/AbrfjrrvuQkFBAZ566ilcfvnl+OWXX8J+ZsSIEfj777/x4Ycf4vnnn0fdunUBADk5Od5lVq9ejU8//RSTJk2C2+3GSy+9hJEjR2Lv3r3Izs4GAOTm5uKss87yNno5OTlYtGgRrr32WhQWFuK2226LuO3XXXcdPvjgA4wbNw7nnHMOvv/++5Bty9q1a7FmzRqMGTMGTZo0we7duzFr1iz069cPmzdvRkpKit/ykyZNQk5ODqZNmxb2LnHnzp04//zzUadOHSxdutS7D9Sg5LrIzMxE586dsXLlStxyyy0Aave1zWbDsWPHsHnzZnTq1AkAsGrVKvTu3Tvq906bNg0zZ87E4MGDMXjwYKxbtw4DBgxAZWWl33KlpaXo27cvDhw4gIkTJ6JZs2ZYs2YNpk6din///RcvvPCC3/Lz5s1DUVERJk6cCJvNhqeeegojRozAP//8472eRo4ciU2bNmHKlClo0aIFDh8+jKVLl2Lv3r0hC5g7dOiAhx9+GNOmTcMNN9zg/X3nnHMOzj33XDz88MP46KOP/OrnKisrsWDBAowcOTJiqvvPP/9E7969kZiYiBtuuAEtWrTAzp078dVXX+HRRx9VfIx8CXcuKWnLPR4PLr74YqxevRo33HADOnTogL/++gvPP/88/v77b3z++ed+3/X9999j/vz5uPnmm1G3bl20aNECS5cuxdixY9G/f388+eSTAIAtW7bgxx9/xK233hp2XwQhKaSgoEACIA0fPjzovePHj0t5eXne/0pLS73vTZ8+XQIg5eXlSXfddZfUunVr73s9evSQJkyYIEm1WixNnjzZ+96uXbskANLTTz8tbdy4UQIgrVq1SpIkSXr11VeltLQ0qaSkRBo/fryUmpoadfvl7ZBZv369BEC67rrr/Ja76667JADS999/L0mSJB06dEhKSEgI+t0PPfSQBEAaP3581O8ePny4lJSUJO3Zs8f72ubNmyWHw+G3Tbt375YcDof06KOP+n3+r7/+khISEvxe79u3rwRAev/9972vVVRUSA0aNJBGjhzpfe2SSy6ROnXqFHH73nnnHQmAtGvXLu9rnTp1kvr27Ru07OzZsyUA0pYtW7yvVVZWSnXr1g3aFwBCriOQ4cOHS06nU9q5c6f3tYMHD0put1vq06eP97XAYxjP9i9fvlwCIC1fvtz72vjx46XmzZt7/161apUEQJo7d67fZxcvXhz0evPmzSUA0uLFi/2Wff75573nfSwo+Zx8bbzzzjve1yZPnhxy34Sjb9++fvtH3i8dOnSQKioqvK+/+OKLEgDpr7/+iri+p59+OugYyACQnE6ntGPHDu9rGzZskABIL7/8sve1a6+9VmrYsKF05MgRv8+PGTNGysjI8GtXApGv50mTJvm9Pm7cOAmANH36dO9rodbz008/BV1P8nl17rnnStXV1X7L+7ZrW7ZskRo1aiT16NFDOnbsWNhtjEbgdiq9LiZPnizVr1/f+/cdd9wh9enTR6pXr540a9YsSZIk6ejRo5LNZpNefPHFiNtw+PBhyel0SkOGDJE8Ho/39fvuuy+ovXvkkUek1NRU6e+///Zbx//93/9JDodD2rt3ryRJJ8/X7Oxsv/3zxRdfSACkr776SpKk2n5EbvMjEXjurl27Nuh6kDn77LOlnj17+r326aefBrUBoejTp4/kdrv92m1Jkvz2i9JjFOlcUtqWz5kzR7Lb7d5+UOb111+XAEg//vij9zUAkt1ulzZt2uS37K233iqlp6cHbUOsKE5VFBYWAkDIEQz9+vVDTk6O9z85bBrIuHHjsGPHDqxdu9b7/0hpCplOnTqha9eu3jD4vHnzcMkllwTdGcTCN998AwC44447/F6/8847AQBff/01AOC7775DdXU1Jk2a5LfclClTFH1PTU0NlixZguHDh6NZs2be1zt06ICBAwf6Lfvpp5/C4/Fg1KhROHLkiPe/Bg0aoE2bNli+fLnf8mlpabjiiiu8fzudTpx55pn4559/vK9lZmZi//79QSHBeBk1ahSSkpIwd+5c72tLlizBkSNH/LYFqA15Ros21NTU4Ntvv8Xw4cNxyimneF9v2LAhxo0bh9WrV3vPPaP4+OOPkZGRgQsvvNDvOHTv3h1paWlBx6Fly5ZBx1KODH3xxRd+YdtoxPs5rZgwYYJf/li+g/M9p+LhggsuQKtWrbx/d+3aFenp6d71SpKETz75BMOGDYMkSX77feDAgSgoKMC6devCrl++nuW7bplQUYrk5GTvv6uqqnD06FG0bt0amZmZIb/j+uuvD1tPsnHjRvTt2xctWrTAsmXLkJWVFX4nxEAs10Xv3r2Rm5vrLfBdtWoV+vTpg969e2PVqlUAaqMQkiRFjTgsW7YMlZWVmDJlil+6K9R+/Pjjj9G7d29kZWX5Ha8LLrgANTU1WLlypd/yo0eP9ts/gedWcnIynE4nVqxYEZQSjJerrroKv/zyC3bu3Ol9be7cuWjatCn69u0b9nN5eXlYuXIlrrnmGr92G4B3v8TTdoU7l5S05R9//DE6dOiA9u3b++3v888/HwCC2qW+ffuiY8eOfq9lZmaipKQES5cuDfvblaBYHNxuNwCguLg46L3Zs2dj6dKl+OCDDyKu47TTTkP79u0xb948zJ07Fw0aNPD+6GiMGzcOH3/8MXbs2IE1a9YoEo5I7NmzB3a7Ha1bt/Z7vUGDBsjMzPSODJH/H7hcnTp1/C6CmpoaHDp0yO+/yspK5OXloaysDG3atAnahnbt2vn9vX37dkiShDZt2viJWE5ODrZs2YLDhw/7Ld+kSZOgXHZWVpbfRXfvvfciLS0NZ555Jtq0aYPJkydHDZdHIjMzE8OGDfOrkZg7dy4aN26s+Fj6kpeXh9LS0qB9AdTKlcfjCTmMV0+2b9+OgoIC1KtXL+g4FBcXBx2Hli1bBq1j9OjR6NWrF6677jrUr18fY8aMwfz586PKQLyf04rARlI+x9U25IHrldctrzcvLw/5+fl44403gvb5hAkTACBov/siX8++cgIEX2MAUFZWhmnTpnnz8nXr1kVOTg7y8/NRUFAQtHyo4yszbNgwuN1uLFmyBOnp6WGXi5VYrgu5A161ahVKSkrwxx9/oHfv3ujTp49XHFatWoX09HR069YNQG077ttWyXUAcnsX2F7l5OQESdH27duxePHioON1wQUXAAg+XtHOLZfLhSeffBKLFi1C/fr10adPHzz11FM4dOhQLLvOj9GjR8PlcnlvdAoKCrBw4ULvUP5wyB12586dwy4TT9sV7lxS0pZv374dmzZtCtrfbdu2BRC8v0N916RJk9C2bVtcdNFFaNKkCa655pq40v2KaxwyMjLQsGFDbNy4Meg9OY+jZAKhcePGYdasWXC73Rg9enTIERihGDt2LKZOnYrrr78e2dnZGDBggNJNj4hWIwn27dsXdKCWL1+O9u3bK16Hx+OBzWbDokWLwlqpL+HugiSfgqgOHTpg27ZtWLhwIRYvXoxPPvkEr732GqZNmxbT8D1frrrqKnz88cdYs2YNunTpgi+//BKTJk1SfCzjJdyxqqmp0fR7PB4P6tWr5xdV8cU3dw/438H6vrZy5UosX74cX3/9NRYvXoyPPvoI559/Pr799tuwxy7ez2mFknNKj/XKYnTFFVdg/PjxIZft2rWrqm2QmTJlCt555x3cdtttOPvss5GRkQGbzYYxY8aEFLRQx1dm5MiReO+99zB37lxMnDhRk+2LlUaNGqFly5ZYuXIlWrRoAUmScPbZZyMnJwe33nor9uzZg1WrVuGcc87xXqPPPPOM3/XfvHnzmCeA83g8uPDCC3HPPfeEfF/u0GSUnFu33XYbhg0bhs8//xxLlizBgw8+iMcffxzff/89TjvttJi2D6jtfIcOHYq5c+di2rRpWLBgASoqKoKio0YR7lxSsm88Hg+6dOmC5557LuSygYMSQn1XvXr1sH79eixZsgSLFi3CokWL8M477+Cqq67Ce++9p/RnxFYcOWTIELz11lv49ddfceaZZ8byUS/jxo3DtGnT8O+//8ZUyNWsWTP06tULK1aswE033YSEhJg2PYjmzZvD4/Fg+/bt6NChg/f13Nxc5Ofne+c0kP+/Y8cOPzE4evSonw02aNAgKPzTrVs3pKenIzk5Gdu3bw/ahsC5A1q1agVJktCyZcugi04NqampGD16NEaPHo3KykqMGDECjz76KKZOnRq2OCiSUA0aNAg5OTmYO3cuevbsidLSUr85BWIhJycHKSkpIedR2Lp1K+x2u/eCkO9Q8vPz/YpEQ80bokYIW7VqhWXLlqFXr14RO41o2O129O/fH/3798dzzz2Hxx57DPfffz+WL1/uvSvT6nOsh9Kq/X55JERNTU3EfRMO+XreuXOn3x1gqPNqwYIFGD9+PJ599lnva+Xl5XHNQfH0008jISHBW/SpNhIqE8t1AdRGHVauXImWLVvi1FNPhdvtRrdu3ZCRkYHFixdj3bp1fqJw1VVX4dxzz/X+LZ/ncnu3fft2v/B7Xl5eUNSpVatWKC4ujut4RaJVq1a48847ceedd2L79u049dRT8eyzz4aNaEc796666ipccsklWLt2LebOnYvTTjvNWzAaDvm3h7pRlon1GKmlVatW2LBhA/r376/qenM6nRg2bBiGDRsGj8eDSZMmYfbs2XjwwQeDIuvhiOkW8Z577kFKSgquueaakMONlNyVtGrVCi+88AIef/zxmOVj5syZmD59uuL6gkgMHjwYAIIqf2Wbk6ux+/fvj4SEBMyaNctvuVdeecXv76SkJFxwwQV+/2VlZcHhcGDgwIH4/PPP/YYmbtmyBUuWLPFbx4gRI+BwODBjxoygfSlJEo4ePRrz7wz8jNPpRMeOHSFJEqqqqsJ+LjU1NWxDmpCQgLFjx2L+/Pl499130aVLl7jvBh0OBwYMGIAvvvjC744nNzcX8+bNw7nnnusNActhaN/caUlJSUhTjrT90Rg1ahRqamrwyCOPBL1XXV2taL3Hjh0Leu3UU08FgKChU1p8LjU1FQCYTcCk9vsdDgdGjhyJTz75JGRjHWpInS9y5bnvsEQg+PqWvyvw+nr55ZfjilzZbDa88cYbuPTSSzF+/Hh8+eWXMa8jFLFcF0CtOOzevRsfffSRN3Vht9txzjnn4LnnnkNVVZVffcMpp5zi11b16tULQG0tSmJiIl5++WW/fRRqP44aNQo//fRTUDsG1J4H1dXVMf3m0tLSoJFqrVq1gtvtVnXuX3TRRahbty6efPJJ/PDDD4qiDTk5OejTpw/efvvtoCHl8n6J9RipZdSoUThw4ADefPPNoPfKysoUzQsR2B/Y7XZv2x1pHwcS0217mzZtMG/ePIwdOxbt2rXD5Zdfjm7dukGSJOzatQvz5s2D3W6PONYZQGzDPnzo27dvxIKWWOjWrRvGjx+PN954A/n5+ejbty9+/fVXvPfeexg+fDjOO+88ALXD+G699VY8++yzuPjiizFo0CBs2LABixYtQt26dRWZ34wZM7B48WL07t0bkyZNQnV1tXduBd8pulu1aoWZM2di6tSp2L17N4YPHw63241du3bhs88+ww033IC77rorpt85YMAANGjQAL169UL9+vWxZcsWvPLKKxgyZIi3biUU3bt3x6xZszBz5ky0bt0a9erV86thuOqqq/DSSy9h+fLl3mE9gdhsNkXDMWfOnOmdu2DSpElISEjA7NmzUVFR4TeWecCAAWjWrBmuvfZa3H333XA4HHj77beRk5MTdHFH2/5I9O3bFxMnTsTjjz+O9evXY8CAAUhMTMT27dvx8ccf48UXX8Sll14acR0PP/wwVq5ciSFDhqB58+Y4fPgwXnvtNTRp0sTvTk+rz3Xv3h1AbXHgwIED4XA4MGbMGEW/Vwvk77///vsxZswYJCYmYtiwYd5GXQlPPPEEli9fjp49e+L6669Hx44dcezYMaxbtw7Lli0LKVUyp556KsaOHYvXXnsNBQUFOOecc/Ddd9+FnINi6NChmDNnDjIyMtCxY0f89NNPWLZsmXdYaKzY7XZ88MEHGD58OEaNGoVvvvnGe66tWLEC5513HqZPnx7zlOBKrwvgZJ3Dtm3b8Nhjj3lf79OnDxYtWuSdNyEaOTk5uOuuu/D4449j6NChGDx4MP744w9ve+fL3XffjS+//BJDhw7F1Vdfje7du6OkpAR//fUXFixYgN27d8c0LPXvv/9G//79MWrUKHTs2BEJCQn47LPPkJubG/FcbtWqFTIzM/H666/D7XYjNTUVPXv29EaIExMTMWbMGLzyyitwOBwYO3asou156aWXcO655+L000/HDTfcgJYtW2L37t34+uuvvbNUxnKM1HLllVdi/vz5uPHGG7F8+XL06tULNTU12Lp1K+bPn++dSyYS1113HY4dO4bzzz8fTZo0wZ49e/Dyyy/j1FNP9Yu8RyWeoRg7duyQbrrpJql169ZSUlKSlJycLLVv31668cYbpfXr1/st6ztsKRKIMBwzEvEOx5QkSaqqqpJmzJghtWzZUkpMTJSaNm0qTZ06VSovL/dbrrq6WnrwwQelBg0aSMnJydL5558vbdmyRcrOzpZuvPHGqN8tSZL0ww8/SN27d5ecTqd0yimnSK+//nrY4YWffPKJdO6550qpqalSamqq1L59e2ny5MnStm3bvMv07ds35DDLwKGFs2fPlvr06SNlZ2dLLpdLatWqlXT33XdLBQUF3mVCDWc8dOiQNGTIEMntdocdVtmpUyfJbrdL+/fvD3qvqKhIAiCNGTNG0f5Zt26dNHDgQCktLU1KSUmRzjvvPGnNmjVBy/3+++9Sz549JafTKTVr1kx67rnnYtp+JcMxZd544w2pe/fuUnJysuR2u6UuXbpI99xzj3Tw4EHvMs2bN5eGDBkS9NnvvvtOuuSSS6RGjRpJTqdTatSokTR27NigoWvxfC7UcMzq6mppypQpUk5OjmSz2aIOzQw3HPPjjz/2Wy7Ud4XjkUcekRo3bizZ7Xa/4xF4bcs0b948aAhvbm6uNHnyZKlp06ZSYmKi1KBBA6l///7SG2+8EfX7y8rKpFtuuUXKzs6WUlNTpWHDhkn79u0LGuZ4/PhxacKECVLdunWltLQ0aeDAgdLWrVuDtkc+r9auXRv0XaHatdLSUqlv375SWlqa9PPPP0uSJElfffWVBEB6/fXXo25/4HZKkvLrQpIkqV69ehIAKTc31/va6tWrJQBS7969o36/TE1NjTRjxgypYcOGUnJystSvXz9p48aNIY9XUVGRNHXqVKl169aS0+mU6tatK51zzjnSM888I1VWVkqSFLkt9/3NR44ckSZPniy1b99eSk1NlTIyMqSePXtK8+fP9/tM4LkrSbVDOzt27CglJCSEPF9//fVXCYA0YMAAxftBkiRp48aN0n/+8x8pMzNTSkpKktq1ayc9+OCDfssoOUaRziWlbbkk1Q59f/LJJ6VOnTpJLpdLysrKkrp37y7NmDHDr00Pd80tWLBAGjBggFSvXj1vGzpx4kTp33//jWW3SLYTX0LESH5+PrKysjBz5sygmcSswmmnnYY6dergu+++C3rvm2++wdChQ7FhwwZ06dKFwdYRBHvuuecefPjhh9ixY4dm05oTsbNhwwaceuqpeP/99+OuxyJOYpnHaqsh1LMH5JyfVR/v+ttvv2H9+vW46qqrQr6/fPlyjBkzhqSBsDTLly/Hgw8+SNLAmDfffBNpaWkYMWIE600xBRRxUMC7776Ld999F4MHD0ZaWhpWr16NDz/8EAMGDAhZGGRmNm7ciN9//x3PPvssjhw5gn/++YeeUEoQBJd89dVX2Lx5Mx588EHcfPPNYYcyErGhbkyjRejatSsSEhLw1FNPobCw0FswOXPmTNabZjgLFizAww8/jHbt2uHDDz8kaSAIglumTJmC3NxcDB48OO55a4hgKOJAEARBEIRiqMaBIAiCIAjFkDgQBEEQBKEYQ2scPB4PDh48CLfbzXyKXIIgCIIQHUmSUFRUhEaNGun+vCAZQ8Xh4MGDms7dTRAEQRBE7YMWo83arBWGioM8xfHMz9YgKbX2SY9HS2Kbz9yXPBWf9aWgRPkc3dE4XlKp2bp8KSjSbhsjUVxszPf4UlRo/HcqpbSolPUmEByT4k5hvQlhcacbO3dEWpp+35fh1n7dWalOTdaTkapu23JS1XXDqSjHA/85J+IjBLTGUHGQ0xNJqWlITnXjSEk1kpRPZR+EUwr/kCal5BdXIDE5UfV6gFppSEzW5mT0Jb+wHAlJ+h+qoqIKOFzGjtAtLCiH3clf4ysLA4/bRvBD+Qnn5VEgSsqB9AzjhkuXVQFuHTp4ACipAjLTtf0txR5t5KHUA2SqkKYCCaifFn8fJD/bysj0P7PiyCMqowW5xdpIg1boFWnILyyPvpAGFBkU0fClsMCY3xYLpUWlFGUgYobX86awoNzQ66yoqEK3tsSotpAFWvRnRiLkqAorSEN+YbkhF4qeF3o4jG7MlMBrw0+IhXwe8XYuGX29iSIPWrXdWvQnIskDE3FQU9dgFWkwAooykDAQ+sHbucUi+qAHWt9UkTzEjlARB5IG7bC6NPDWqBPmhbdzzQzyAGjbVvIkDyIglDiohaSBUhO8NeKEdeDp3DPymiR5iA0Rog7CiIPanUnSQFEGXhptwtrwJhBGQPIQG7zLgxBPx+RpJ4ooDSyEAeBHGnhppHmkpLDEkO9JTVcx7tqkyOcl66GchQXlhgzblNshPYZs5heWazZc83hJpSbDNPOLK1QN08wtrlI1TFNPuBcHnuoaSBqUw4M0WF0YjJICJUTbFiuLRWlRKRfyABgz70NRUYVl5MGscC8OauFVGig1oS9WkgaeBCFeIv0GK0iFFaMPVpAHs0YduBYHXuoaRJMGijKYGzOIQiyE+r1mlQkeBEJ0edASkofQcCsOJA3xQdJgLqwmCUoJ3C9mEwnWAmFU6kIPedAy6qAVZpMHLsWBl2JIkobokDBoD8lC7JhVJFjXPxgRfeBdHngpluQJYYZjxoIW0QaShuiQNGhDSWGJ33+Eesy0P1kP3zTiOtej/eJxmKYaeLmhBjgUB15SFFpC0qA9rBtTtZipY+Mds+xrluc8yYM28mCW+R24EgdepEFLu9RTGqw6C6QZhIFggxkkgqU86H3tkzxEhwd54Eoc1GBFaTAa1sIAiCkNZuiszIjIx8XM0QeSB/7hRhx4sCiShtDwEmUQSRpE7pSsiKjHi+RBOUY9QNAIWPeXXIgDDykKHopflMBCGlgjojAQ4iLaMWQl1VaWBx6iDizlgQtxUAOP0qCX2RopDTxEGQBxpEG0zoaIjmhRCJIHZZhJHljBXBxYh1y0xizSwBpRUhMidSxE/IhynEkejIW1PLDqP5mKg9lSFKJLA0UZlCNKR0JoiwjHnYV0iyYPvBVLqiGvpNrw72QecYgXkgZt4UEYAP6lQYSOg9AfEc4DFvKgZzvCszyoRbSUBTNxUBNtIGnQFpKG6IjQURDGw3sdhNlSF7zKA+uUhdEwEQcWoRW9EFkaeEpN8CoNPHcKBF/weq6YLXVhZnkQBS4fchUJnqINoksDD/AsDAQRDyWFJVw+ZMvoB2bp+YAsrR+MFfKhWJKELtvXofXebUgrLYTH7kBhagb+bNsdexq3CrketQ/EEuVBWEKJA0mDNvAgDbwKA0DSQKhHPod4EwgzyYNepJYW4YKfv8bglZ+iae6ekMtsbH0qFvYZiTWnnYfqBG0fdy2CPAglDmrhPZRE0sAWEgZCa3gUCPn6M0og9JIHPaIO5+9fj3vfegDusqKIy3besR6dd6zHgXpN8dBNz+JAg+be97R6DDfPCCMOPBWO6BFt0FsaSBgiQ9JA6AmvAkHycJIL1n+HaR89igRPjfe1DW274/ueF+FIZg4cHg8a5+7BoB+/QPN/dwEAGh/eh2efuQ733foK/mnazvs5s6csbJIkSUZ9WWFhITIyMnDdB7/CmZKm+HNmT1GQNLCDhIFgAU8CYWTqQq+0hVp5OG3nH3jhv3chsaa2cP+nrr3x7vBJ2NfwlOCFJQmdt/+BG+c/i1MO7AAAHEvPxu33vo28Og38FlUbeVAiD5WlxXjrijNRUFCA9PR0Vd+nFGHncYgFkgZ28D5igiBYwNO5Z+T1qVd7pKYdtXk8mPrJ015p+LLHENw19uHQ0gAANhs2tj0dd985G5tP6QIAqFN4FNd8+nLc2yAa3IuD2mgDSQM7eBYGnhpuwprwdB5aWR7O3L4WTY4eAABsaNEFT//nDkj26F1jWXIaHr7pGeSnZQEAev2xHFkFR/2WUdv/8JSi94V7cTArZpYGijKIj1R0VJP/iOjwck5aVR5G/PS599/z+oxBjaO29E/JzWJhWiYWn3sJACDBU4OBP34RtIwZ5YFrcTBrtMGs0sC7MPDSQLPGyA6f5EIZvJyfvF6/epFVfBy9tv4EADiUUQ9r2p/l976Stn/xucNRY6vtSi/8aaH2G8kh3I6qIGmID5bSwCs8NMgsEKVTDredNne2wVvCHh4mjzJquCYPIy0aHD8E+4nxAWs6nOWNNsTC4eyG+KdpW7TZuxX1jx6EzeMJSnWYbZQF1xEH1pA0KIOkgQ/Mdidv1eiElaIPrFMWKRUnf2NRsjvkMkr6geKU2tEMdklCUkXo/WamlAWXEQdeog1aYkZp4FkYAPNLg1U6Ul8Cf7OZoxK8RB/MHHmoSDj5flJl+DY65JTUPrgqy7z/rnSKNVNmPJgu4sBrikJPSBr84eWOTQ+sdvcdDbPvDx7OZTNHHo6563j/3XnvpojLhusTkstL0OLgTgBAYWp6xHSHWaIO3IkDDztGpBSF0dLAcwEkYM4og9k7R60w835ifV6LLA+ROFinIf5u2BoA0GnfFrTbvy3mdZz36xKklNfunx9POy/q8maQB67EgVIUscFCGniGdeOqJWbuBI3AjPuP9fktqjxEbINtNnx69nDvn5eu+TTiugJvKu2eGgz9YYH376/7XBrXNooGV+LAA1pGG0gajIGHcK5WmK2z4wEzSQTrc92M8vDtaReg2FVbSzLk98UY8MfSiOvy9hGShOsWvOhNU2w+pSv+adpW0faIHnXgRhx4iDaQNISGd2kQHTN1bLxjln1tdnnQg3BtcrkzGW9fMN7794MfPYaRaz6F3edhV4G4Kssx+cOnMHz5RwCAGrsD711yY0zbw2OEXClcjqqIFZEPQKwYKQ28NxCiS4PonZfoyPtf1JEZLEdd6D3aQq+RFuH4X+9RaHJ0P0b8/CUckgd3fvEixq38CJ/3vBiLTh+Ao+5s2CUPmhzZj0t+XYjBvy9Celmx9/Mvj/s//NW2u2HbC7Cd24GLp2NStEEZJA21kDAQeiCqQLAcsqn3UE095CHcEE2bx4ObFr+BK374MOg9j83mnSjKlypHAp6/6kGsOHNQ3NujZmKozDQXk6djCh9xIGnQHl6lgYSB0BNRIxAUeYiNcPM7SHY7Xht8I35r3R2jflyAs7b94pWFQGmoSHBiWbfz8fVFV2J349aabl8s5BdXIIVBwQFzcWBd5KElJA36QMJAGImIAiFfIywEQkR5iMSvbXvg17Y90OjoQVz860K0O/A33GVFqLE7UJCajj9OOQ1fdx+EwtQMAECmyu9TOx11QYnxfShzcVADb9EGPSBpEFcaSBjERlSBYD3bpAgomVXyYHYjvH7RDVHXFW1WSSWolQejYTqqQk20gTdp0CPaYGVpYD3sTA1mqNonTiLa8WRx3ejdfhg+vwMREW6GY4qM6CcgT9IgsjAAFGUwMyIJBMmDMrRqu7W4CRVpdCAzcTBTtEEPjIo28CYNoiJSp0KoQ5RjTfJgLLz3KVpiyYgDpSj4euYERRkIERFBIMwoD1rDU8RYlKgDE3FQUwXK044VWRp4QXRh4L3jIPSH93PAbPJAUQf2WC7iwPOBtZI0UJSBMBO8SyTJQ2Qo6hAbQokDTzuUpxNNNEQXBp47CIItPJ8bIl93oeBVHqxQKCmUOKhFq2gDpSjig6IMhBXgWS6Nvv5YtzmEPggjDrwbmBqsIg0iw2tHQPALr+eMmeSBog5sEEYc1MJrtMHs0iB6lAHgtwMg+IfX6APJQ3h4kgdeEUIc1JoXr9JgBKylQWR4bfQJ8eDxPDKTPJgVXqMOQoiDWdE72kDSED88NvSE2PB4Tol+ncpQ1MFYuBcHs0YbSBr4hccGnjAHPEaxjLxeRUpZ8AKPUQfuxYGIHZKG+OGtUSfMCW/nmVnkQUso6hAersWBog2xw+qiFL0Iksc7QcLc8Ha+iXz9yvCaslALb1EHrsWBB2I9cZzVlWh+dD86H9iK9od2oH7BYUCSvO+bMZwmeoPDWwNOWAfezj2jrmWrpSzMFnVIYL0B4eAl2qCUVnm7Mfr3hRj25zKkVfpfFDvqNsdHZwzDV136oxAO3baBRbRBZGngrdEmrIl8Htrc2Yy3xFhKi0qR4k5hvRlRKSqqgNvtYr0ZOF5SiaxUJ+vNAADYJMnndlhnCgsLkZGRgctmr0JiclrEZXkQByXRhkb5hzBj4fM4a/cfUZctSUzC290uxnM9x8Jj11YgSBqUQ8JA8Aov8pCanmrYd+klD+kZSZqtSytxyExXv02B8lBVVoyPJ/ZGQUEB0tPTVa9fCVxGHHiQBiW0P7QDsz68H3VLjntfK0twYVXrHjiSlo3Emiq0yduNU/dvBgCkVpVjym/z0fboHkwZdDcqErSxR5IGZZAw6ETB4ZP/zqjHbjtMgFR0lAt5KCksMVQeeIeXqAMvcCkOPBAt2tDk+L94fd5UZJcWAAAOZNTHnJ4j8GXXC1GU5B9NaZv7D4b/9DnGbF6KRE8NBu76Bc8ufQFTBt0FyaauzISkITokDDHiKwJ6fpYkIyRWkwe9UhaFBeWaRh20IL+wXHXUgYeUhenEwZBogyThmU9neqVhfZOOuHnUwyhICR0m+i2pEX47bxIWtzobb379GFKqKzBsx2qs/bMj3us2VP/t1RCRpIGEQQFqJEGv7yahIHnQCC3lgaeoA2t54G5UBQ/DTqJFG7rv/Qud/t0OANhVpwkmj34krDT4VviubnYaJg+6x/v3NRu+hE3yxL2dRkcbRJEGGloZgYLD/v/xiAjbaAB0DpsTM4yw4E4c1GDUARn9+1fef8/ufTkKk92KP/t9yx5Y3aQbAKBFwSH03rs+rm0gaQgNNbYBmKETFn37VcDD+Sz6EE0th2fyMq8DwPYmmytxECHaUKfkOC7YuhoAcCwlA9926B122XAn7JwuF3n/ffnGxXFspbGIIA0UZQjArB2tBSWCh/NadHngDa1ucln1mVyJgxqMija0y/0HiZ4aAMA3nc5DVRwjI5ae0hP5rtoCym6522P+vJEXF+/SQMLgg9U6VQv9Xh7Ocd7bgkiYNeoAsJEHbsRBhGgDAKSXF3v/fSg9J+xykU7UGrsDh1OzAAAZFcVhlwsFScNJeGhMucAinWdELLAPrHK+U9SBf7gRB1Hw2Gzef9sQ/9xZNklen/JDQNJQC0UZTmCBzjJmTB6FYH3e89wuRIPHqIOo8mAKcTBqlkgAKEg6WQjZ7NjBkMtEO0Fd1ZVoUHykdn0u/iZZ4blxYN1wcoGJO0ZNMel+Yn0NGNE+WCXqICpciAMPaQqlbGnYBmUJtWN5B21egeTKMr/3lVjt4B0/wl1V+7m1jTop+l6rX0gUZYBpO0LdMeF+s8K1oEebR1EHbeBCHNRgZLQBAIqS0vBN5/MAAO6KUgzZ+H3M33fVX994/+07wiIcVk9RWKGRjIgJOz4mmGw/srwueGwnCONgLg4iRRtkPuo+zPvvG1d9gPqFeQCU2exlm5fh9EPbAACbs1vgt4Yd9NnIOOCtMaAoA0zV0XGDifap2eWBog58wlwc1GB0tEFmS8M2+LnFaQCA+kVH8ebce9Eo/1DUz/1n63I8vvxV79+zu48AfIotQ2FUtIFHabA0Jrs75g7avwRniCQPQosDS+79z/9hX2ZDAEDLo/sx/82bcP/qt9E8379g0u6pwXm7f8PbXz2MF5Y+750D4u1uw/B5276Gb3coSBo4gzo04zCBQFDUIXZ4jDqIhE2SpPjHFMZIYWEhMjIycNnsVUhMTmP++Gy1B7zx8X8x63/3o+XR/X6vr6vfFkdSMuGsqUar4/vRtMi/YXq/y2BM73M9PHZHxPVbMdpgaWkQvAMTHsEfrsXyoVh6PwhLjwdgafnkTC0ffhXr0zOryoqx7I7+KCgoQHp66GcmaY2wT8fkIaxzIKshrhz/Am5ZNAuXbPsBLk81AOD03L9DL59WF6+ecRnmdh5EKYoQkDQQTJGPgaACwcsTNfVAj6dn8vrkTC0eva03woqDWrQKLxWkpOPuC27F472uxmWbl2HcpiVoUXCy3qHaZsfqpt0wp8tgLG9xBmqiRBmMhBdpsLQwACQNvFFwmOQhRox6/DbBB8zEQU2agodog4ycKzuWnIHZ3UdidveRcFVXIqOiGJX2BBS5UmOWBSOiDSQNnEDSwCeCRx9YoLc8UNSBH6g4UgcqEpw4nFoH+cnpXEUYeIOkgaSBewQ8Rpa/rkwCTzfIgVhSHLRKU2hZmStjlWiDpRs3E1TyWwoBjxWr60vvtoVGWPABE3EwS5pCREgaGCNgJ0SAjhvBBF77O8tFHHg2Qis8j4KkgRAWwY4fRR2Uw3PUgUd5sJw4aIUeaQq9YR1tIGkghEewNJOlrzlCN4QSBx7NSyvMHm2wdAMmUEdDKISOaUREjDpoidmjDkKJg1p4LorUG9bRBstCHYx5EeTYWlraY0Drdt3M8mApceAVve2ZtTRYsuESLKRNxIkgx5jFNWj1qIOZEUYceLEtEaMNLLGcNJAwWA863qaBog7KEEYc1MLraAozRxssIw2yLFAHYl0EOPYUdWADr32PGiwjDoSxWEIaSBYIXwQ4FyxxXaqE96gyD1EHIcSB9eOzZbQ+ocwabTB140TRBSISdF4EwbrGigfMlrIQQhyI2KGLVWNIFgiTYDax531CKBkzpSxIHBQiWrSBFWZrlEgYiJih8yUIupHRHpZRBxIHE0IpCg0gYSDUwPm5Y6prFRR1MBruxYGX+gYiMqZpiEgYCK2g88gPijrUomWfxCrqwL048IBIaQq6OOOEhIHQA47PKdPIvo7wPsKCFSQOhGqEboBIGAjCMPS8sRGpbkzLqEMBg6g61+JgxjSF2aINwksDQegNx+eZ0NevQVDUIRiuxYEH6KQxIRRlIIyGzjdD0OvGjAol/SFxMAkUbVAINeAE4YfR1zHVYYmPacWBR5sTKQcXDZIGgogROv+EhqIOJ+FWHFhPqQlQmsJUUKNN8ACn56GZog563qCRPNSSwHoDCPUYHfoTKtrAaUNNEAQhKtxGHMyGmdIUwkDSQPAIp+elUDcEUaCog76YUhxEOwhqoGhDGDhtnAkCAJ2foCJJkeFSHKi+gVAFNcoEQeiI1aMOXIqD2TBLmkKIaANJAyEKHJ6rQlzjCtG73bXyzSWJg8BQqC8ADhtigiDCo3cbJtpNmyhRB9OJgxY73somGQ7u70RIGggR4fC85f5a5wir9hWmEwfe0Mt4KdrgA4eNL0EQfEBRB+3hThx4KIwk/OH6DoSkgRAdi5/Dot8EWTHqwJ04EARBEGzh+mYhDijqoC2mEgfe6hvMkKbgugGx+J0abyRWHUNy+V4kVh1jvSniQeey7og2KRTP0JTThJhQQ8ucxKpjSKgpRrUjDUkVB+Au3+59ryipDYrSujDcOkIkSgpLkJqeynozuKKoqAJut4v1ZoSExIEIC7fRBpIG5riL//IThaD3y7ej3NUYVYl1DNwqQkukoqOwubNZb4amlBaVIsWdosu6CwvKkZ6RpMu6eYOrVAUVRkZH9EIiQnwSq45FlAaZhJpiA7bGRJAQG4Jo9Q48wpU4mAk6OXWCGlfmKBWCakeazltCmAkz3BRpXevAa5EkiYMPVitwiQSXaQqSBi5QIgRFSW0pTREPdI4bAt3YqcM0NQ68mhlBmI2qxDooSmoTUAzZFuWuRt5iSZIGc2B0nYMZiiS1rnXgsUjSNOJgBYwK5VG0gYhGUVoXlLsaB4lCPMLgOzpDjXBotR7CGuhZKGl2SBx0gMJgGkPSwCVViXVUd9CBozMCh3EqlYFo6xGKgsNARj3WW8EMijrwDzfiQCMqCEIclHbokZYLNTrDdxinUhmIth6CCIcoUQfe0hXciANreC+MtGyagqIN3KG0Q4+2XLjRGfLrSmUg0npIHNTDYj4HijrwDY2qIAhCMeHu7gOnmVayXLjRGdWOtKhSEbh8SCRP6NcJITDqZkmU1DJPAwBMIQ487VBCQyjawB1KO3Qly8mjM3yRh3FGkopAQq0HALJK1sFd/FfI9XAPnfumgPdIdrxQqkJjRLFXgogHpR260uUijc4INeQzXOqhKK0Lqh1uZJWs83udah3ExqiUhSi1DrxA4kB44aq+ge64uCRchw4AyeV7vZ1/pI4/seoYXJWHAAAVzgZhR2eEk4qw2EIHUKnWQT1mfG6FiPBSJEniIABmmIqVYIuWcxwEduhJFQeQU7Di5PsniiBDdfxBD8cq2xqyuNJ3e8uSminarljSG4Q4iB51MGORJIkDzJuHIghAnzkO5ChBtKGQvtGEcA/HCkwnxLu9saY3uMfi8zn4YoZRFmaCC3GgORzYQ2kKc6L3HAdKhkLK0QNnZV7E9SgRkWjEnN4gFGOFdIUIUQce0hVciANBEPqg5xwHiVXH4Aizfjk9EJSaCIO8vBbbq8WMlgR/UNSBH4QXB56GYtKICg2gaIOm6JX3jyQEvkWQSqShwpHl7eipToFgDY2wiI4p5nEwM1QYSagh0lwJviRWHUNy+d6giZxCEU4ICpPbIy+jH4rSOgMIHz0IxFVz3Pu9SreXYAPrlKbI7aGWtXSsb5iFjzgQ6mHdGBD6Ei3vH64YMdxIjHBCUHNiOflzsczc6JuKoDoFvqFaB4LEgeAHSlPoRri8f7hiRIenHCmV+7yv+Y5siJROyChci1Sfz1U4MuGqyY+6fYHrjLdOgR6tbX6o1oE9JA4EYWHCRQ98pQEIHmIZathjatnOoM+5avJR4mqOGnsyauwpgM0OV2VugJRok4ow1aO1ZTgdkmmFqIMemGV0BYkDQViYWIoOI6UTAPhNAuVLasWe2s+c6MjLkpqhpKqVppEBerS28bCUByOiDpSuCI/liyNp8idOoDQFE0IVI5Y4m4ZcNlQ6oSypGaoS6ygqhPR9OqbvZ7UglqdpEgShDoo4EITFCVWM6ClOijgDY2AtgdLIhV7PjaBhnGygqEPsmGEKahIHjjFi6BGNqCCA4GLEUKkI+SFWSRUHgmoJyl2NUeGoA1dN5OGcenXkpptumiAUUFRUgeRE47+XxEEjaPInwmzIMhFt9kd3+fag90ucTeGxR45aaA0N42SD2aMORDAkDgR7LFzfwPvwQaWzPwaSWrkPeRn9YurItdgXNN00G8w8yoLSFcEwFwd6wBVhVUQYPqimuNA3zRENEfYFwScUdTAe5uJAEFZElOGDamoSnJW5yKr8zft3OBkQZV8QkTFz1IFniouNn35a6OGYrOfrJoh4EWX4YCzDNX0pdTb1m0ES8B+O6Yso+4IZFk7l8QLVsPlDEQeCYEGY5zjwOHwwVNGhvaASyVW5QcuWJTZEcUo7JNQUB80iCYQejklDKc0Dq6iDiOkKkescSBwItljwbircKAWehw8GFh0Wp3RAckGwOBSntIv4G2QZCCyEpKGUBCEOJA4WhuZwMJ5woxSOp56OsuQWxm9QnETr7CO9H6oQstzVGJLNAQCocDYgaQiE02dWhIKiDuaHxIEgDCRc3j658kBEceBx2Ga0eRNCvR+uENJPJKQabn4jVwgkD2aEnl1xEhIHgjCQcHn7pKpcJFYdC9lh8jxUMdq8CYHvK32mBY2oEBsaYaEMUeschB5VQRCiUZVYB2WJ9UO+5y7dEvRauDv05LLdemye7sTyTAsiBBasCYoFI6bpJ0gcCMJwilM6hHxdjjr44qo8FHLZrJJ1cBf/pej7EquOIbl8b8ihkEYTanhnKGhERQQEkQcz1lDRsMxaKFXBKWTO5kWOOoQazphSvgelJ5ZR8oyIaCF9HtMcgbUPqWU7/YZu0ogKBVC9A8EQijgQBAPCRR1SK3Yhp2AFMgrXKnpGRKSQfrg0By+Rh7KkZkiqOOAnDSXOpihK68xwywgtYRF1oJsu/SFxIAgGRAvZB866GI5IIX3eZ2QMJTaplfu4EBshECRlQUSmsEC85zWROBAEI4rSuiAvox9KXC3j+3xASD+wloH3GRl5FxshEEAezFbrQHUOVONAsESARk9vqhLroBS1KYpASp1Ng3L/5a5GIedNCFfLwPOMjLyLjTBQvQNhMCQOBMGYcLMsFqV1RklVK7/HU/v+O7l8r/ff4Z4uGW2SJpbQVNOEXtAskvpC4kAQHBCug5cnUIo0wqI8zLwQviM0eO2MeRYboeA86kATQpkLEgeCYEC0KaR93weCIwq+JIUY1gnUpj9SK3YZMgRTzZTYPIuNUHAuD0R4RJtBksSBU1LTU2lYkaBE60RD1SMA4eUgXETBl3DzQsjr1XMKZx7niiAIPbH6cytIHAhCQ6J1ouHmVohEuIiCL8UpHVCMDkgp3xOy0DKhplgXcQj3e+hZE4zgOOpgdLqC6hz0g4ZjEuzgtIGLFyUTLsU71DDc8y18qUqsg9Kk5iHf02ukAg2pJAjrwVwcMtPjz+u43S4Nt4Qg1KGkE423Ay93Nlb03aEmlpJHKujxzAoaUskhNMyZ0BlKVRCERijpRMMNQQSksCmLoqS2gC2y4/t+R+BIBQCoU/CjX8pDqzoEGlJJxAKNrjAHJA4EoRFViXVQ4ciEqybf+1qFIzOoE5U7dvnJlxXOBqhKrBPU2fsWWEaKEoTqqOWRChmFa0NOX61lHUI8QyrVjMIgFMBxrYORUJ2DPpA4EIRGJFYd85MGAHDV5COx6lhQ55hUceDkXXrZVm8EwHe5wH8H3tmXJTZAcUr7sB1vZuFav5knA9GyYDKWIZU0CoMwA1YeWUHiQBAaEanGIfCZEvGMRFByZ59YdQyuykNweMoiSgPApg6BRmEYCEUdhEKkuRxIHCyMzZ1tugfQsERpoaCcogjEVXkoaucZ6c4+0uySgbCqQ1AqV4R5oToH8WE+qoIgzEKkEQ16E+pOPhTljjrIy+iHorTOum9TKGgUhsHQCAtCByjioBEp7hR63CqhKJ1Q4WwAlG0N/XqcKJk3odTZFPnpPeL+Di2gURgEIT4kDgShMdEKBZMqDgS9pqTzjDQSIdIde2Fye+/IDR6gB1sRRkIjK7TH8uKQnpGEwoJy1pthXTLqWSqcGi6lUO1I8z4mW+nzLeSRCLJQlDqbBhVEFiW1RXFqR41/hXrowVYGQkWShMZYXhwIwgjkzt0RJqWQVbLO+2+lz7codzX2H9YJoMTZFDWO2rsrnqIMBOELFUiKDYkDx9ATMs1BLKMdgODhieHqF1LKdiG1co/fa6mV+5CX0Y+EgSAI3aBRFQShI0pHOwTiO2QzXP1CoDTIWPEBU3o8h8NUWCgdaCRWLYjnIuKQmZ6E/ML46gzcbheKiio03iLrQHM56Eu4TrwwuT1qHGlwVuaFFQCZUCMRImG1oY00EyVBGAtFHAj2mLhwK1wnXuFsgLKkZihNbhn2fZnEqmOoTsjA8dTTcTztDBQmtw/7fVYb2qjkUeYEQWgLiQNB6Ei0SaGive8u/gs5BSuQVfwbskrWIaG6IOx8D8dTT49rYieRw/xKHmVOEIS2cJGqMAs0CRQRimjzFoR7P9JoilCTKJUlt4h520QP89NMlDHA2bBMGlkhLiQO4HsuBxpZYQ6izVsQ6v1Id9NaTKJED5wiCCIeKFVB8GH9HN0J8UK0u+mqxDooS2oWdydvhjC/GX4DQYgGiQNBcIreD80yQ5jfDL+B0B+K2mqLKVIVNCSTMCt6PtfBDA+ciuU3RHrWB0EQyuFGHNTM5UCYBIs9t0Ipej7XwQwPnFLyG0QvAtUMzgokCTHhRhzMAo2sIETDDA+civQbqAiUILSFahwEwIhHwnJRIEkQOkAFlAShLSQOJ0jPSGK9CQRAYVRCc6iAkiC0hcSBIAhTo/foFIKwGqapcaCRFeqhB14RZsUMRaAEwQtcRRwy082RLkhxp2i+TiPqHLiB0hWEDkSbMEvkZ3YQhJGYJuJAEAQRK/LcDq7KXKRU7vO+btnhmgShABIHH3h+ZoXloDkdiCiondApcG4Hv/douCZBhIXEgfCD6hwIrdBzpka1EzqFmtshkISaYhIHgggBiYNO6DERlOWelElRB2HRc6ZGLSZ0UjKHAw3XJIjQcFUcqRa328V6EwjC8oTr2JUUHSopUHRVHgr5eiwTOkWTAhquSRDh4S7iwPqZFVTnwFm6gqIOwhFppsZInXGkKIWc9nBW5iLVp4jRl1giBKEejlXibIpKZ30armlCLDUqzQC4EwciMpZLVxDC4azMDfl6pI49UvohqeJA1HqEeCIENLcDQcSHqVIVvKHHfA6WhOZ1EIbEqmMhIwKlzqYRO+ZwUQpX5aGo0gAA5a5GyjfSh2hzO4SD5nwgAOu28aaLONAMktrAVbqCEIZwAlDhrB/xc+GiEQ5PmeLvjTSxk5ZRBXpEN2F1TCcOhEmhWgchiPeBUqFqDgAgtWKPqu8N7OTLE+ujKKVD3AJBj+gmCE5TFaynnub9SZlGFfrQo7aJWIn3gVKJVcdQnZCBwqR20b/D7la0/lCdfFJVLnIKVsBd/FfU7wkFPaKbICjioDt6zOdgWSjqIASxFh1GmsFRpsTZHLDZUZmQhbLkForSD5E683ijBPSIboIwqThQnYOJIXkQgqrEOoo6ZSUzOAJAamVtyiK1YhcSaopQlNYl6vqjdebxzAwZKqVCcz4QWsB7pNsXU4qDFvA+n4NRwzKpSJLQk3hC/EqjBeHqJmTijRLQME5toFSouHBZ4wCwr3PQEqsO2dENGp5pGuLtvJUKR1FaF+Rl9ENZYvCojqSKA3F9NxD/ME7m0LVDaAC34kBEx9JFktQAmoJQxZRKiHWWyOKUDkGvK50GmyAIf0wrDvTcCoIQAzkqUB4iKhBy+ThqCmg0hHWh6aa1h2ocIqBlnYPooyu4rHWgQknTUJVYB8cyenlHS7gqc5ESMANlWWIDFKe0jys9QKMhCK2xcgqa64iDmeoc9MLyNk0pC1Mh1w7kp/dAqbOp33vVDnfE2SEjTQEd7/wSBEEEQxEHQjFcRh0AijyYkMSqY0ERB3f5dkg2ByqcDfw6fKVTQNNoCH7gsm6KUAzXEQe1aFHnoOXYWiuHtnSHIg+mIlztQXrZVr+ZH8NNAR0p8iDkaAgtoGuEW0SawwEQQBwoXREdI9MVdKdAGEG02gNZDqjokSCMh3txIAjF0B2VaVAyTFNOOYSCih4JgGrA9ML04mCVdAVFHU5A8iAsgQWO8jDNwuT2IZeX6xSo6JEwGqunnak4kjAfVCwpHOEKHOVnXtikmrDPh6CiRwVwJNRc33gQihBCHDLTk5BfyO9zI3jBqOdXAByPsJAheRCGcAWOvs+jiCYHSh+qRRCEekyfqgCsk64wGu7vHDi6yyLCo7TA0dIjIgjTItqICsAi4mAlqBgoAJIH7qECR52x6DVAbaF+CCMOZhuWSVEHA7Fow8k7cjEkACpwtAhCtBdRMEvbrQYhahy0wO12oaioQtU6tHx2hZ4YWesgDFTzwBWhiiHzMvpRgSNBCIAwEQczYhZzFeYuIqMeRR84IFwxJACqYdAaOt+5RsT6BkAwceAhXSHKgTY6vyeMPADUmDKGZnsk9IbqG/RFKHFQixajKwiTQPLADCqGtCZC3VyEwSxRYrUwEYesVCeLr+USPU9EijpEgeSBCTTbo0HQ+W0J0tKMvyEWrjiSh8mgRCmSZAH3E0MFIjeuVDhpKDTbI6EXlKbQH0ulKgA+0xVmijoAAkYeALo7YwBN6KQjnJ3PQrYJOiNKvVwomImD6OkKkQ86EQYadUEQRBiovuEkQkYc1I6uoKiD/gh9h0HyYCoCn7ppeuj8JXRGuBoHQhyEq3fwhWofTEG4p24SxmHkTYTV6hvcbheqy6sN/16mEQdKV/hjtqgDIHjkAaD0hcCEm2jK1JEHOleFQPRUt5CpCsCc6Qq9sZqNawoJhHDQRFPsEf7G4QRU3+CPsOLACyJFHVhhlsYDAAmEQFhuoimLn5dWuzFiefPLXBzUpCso6hA7lLLQCBII7qGJpggeET1NAVBxpCZoPSFUijsFpUWlmq0vEFZPzxS6WDIcvvJAhZTcYZmJpjiUWLPcLJgxCqwWLsQhK9WJ4yWVrDeDMABTyoMMSQSXVCXWMa8wAFxKg9FYLU3BGuapCrXwkq4QrdaB5YVmljuRiMipDGrUCQtiiWs8DrTqJ1in2YUXBzND8mASfCWCRILQEjqfdG3HKE0RGm7EwQxFkmYoejESS8mDLyQShBZweu5Y9rq2ENyIAxEaM0cdiBOQQBAEoRDWaQqAxMGLlaMOlLLgBBIIQimcnidGX88ipSlE7BvCwZU4sExX8IzZ82wkDwFw2ikQnEDnB8EYrsSBNRR1YIfNnU0CQRACQ9GG8JhlNIUMd+JAUYfQGBF1YC0PAEUfvNBdJREKOi8IDuBOHFjDc9SB5MFiUCdB+MLx+WCmaAMRHS7FgaIOBMkDQfjAsTSYDUpTRIdLcWANRR34sHmSB1CHQXB/DtB1aj1MKQ48RR1IHtRBjRK47zgIHaFjH4RIRZFmhVtxUJOu0AKewkKsIHkgCCISdG1GxoxpCoBjcVALRR20gSd5sHQjRXee1oPzY87ieuSlPbI6XIsDRR0iY8WwGskDYQnoWBsOr0WRPMK1OKjF7FEHo+DN8kkeCFMjwDGmaINx8HgDy704mCnqQCkL7bB86oIwJwJIgxmxYvRWDdyLg1p4ijrohVXlAbBo9IE6F3MiyHGlaEN0zFoUKSOEOFDUITpWlwfLCYQgnQyhEEGOpxmvM4o2xI4Q4qAWK0QdjIRHeQDM2ahFRJDOhogAPUo9Kry2N+EQuZ5NKcKIA0UdomOkOfN6MVsu+kCdjrgIduzMmKLgOdrAa5oCEEgc1KJF1IHkwR9e5QGwWPRBsA6IgHDHzFLXkwqsEG0ABBMH1lEHUSB5qMVS0QfBOiJLI9ixYnUNUbSBX5iIQ0Yqm51ilaiD0fAsD4CFBEKwDslyUD2DqTFLe64EoSIOgPmiDmZIWQD8ywNgEYGgjolPBD0uFG0gQsFMHDLTKOqgNyQPoSF5IAxF0ONhVmngHRH6F+EiDgAfUQdRUhYkD6GRow+mlQgKi7NH4GNg2usC+rSJVkpTAIzFQeSog9aQPLDD9AJBGA/t97gQre3QGhGiDYCgEQdAfdSBx5SFmaxVxAbAtAJBnZhxCBxlkDFzioKiDdrAXBxYRR20QhRDZFEMJKI8ACYVCBN0aFxjkv1ruvPeB94LIkXpSwAOxEENPEQdtMZMKQugVh5IIDjCBJ0bd5hkn7I810VtI6wYbQA4EQeKOvhjNnkAxG0YABMKhEnujpljov1odmngPUUhUrQB4EQc1MBL1IHkIToiywNgwjCuiTo+QzHZfjPdeU3oDjfiwDLqwGPKQm9IHuLDlI2syTpC3TDhfmJ9PlO0QbxoA8CROKiBh3kdALGiDgBbeRBZIFg3trphwo5RE0y6X1ifx6JKA8GZOKiJOlDKIj5YXlgkD5xi0o4yJuR9YNL9YOrzV2esHm0AOBMHIjQkDwQTTNxxhsTksiDDgzRQtEE7MhjIB3fiQFGH0JhdHkQUCB4aYEMwe4dq5t8WAA/nrIjXugxFG2rhThxYw7M86A1rQxexQeGhITYUM0iE728Q+XfECA/nqlHXOOu2zChYFfZzKQ4sow5aIlq9A8D+giN5EAhROmBRtlNHrHSO6tWGUbThJAmsN0APslKdOF5SGffnM9OTkF9YruEWaUd6RhIKC/TdthR3CkqLSnX9jkjI8lBSWMJsG2LF5s6GVHSU9WawJVSnXHCY/TZYHF6kQcSbAhkeZ4hkOY0At+KQmeZCfnEFu+/XSB7cbheKirT9HVaQB6C2oSF5EJxoHXmsYkFiEBNWkwbWEVMliB5tADgWB0CdPKiNOmgJyUP8iBZ9IHmIERIB3SBp0AaKNgTDZY0DL2h5cEQcaQHwY/CijrwgCBZYTRpEQYt+gLU0AAKIA+tCSR4OUiSsJA+AGA0RL402YU14Of+MvFZFiDaYIUUhw704qMXMoyyMhDd54F0geGm8CWthxfNOBGnQCl5uZIUQB9aP3aaURS0p7hQSiBiwYiNOsIG3R7+LXtegNSLfNIZCCHEAzJWyEFkeAP4uVp4FgqfGnDAnvJ1jvF6LsUDRhsgIIw5qIXnQFt7kAeC3weLtbpAwD7ydV1TXEIzZog0AI3HISY1vFCjrlIUIkDxQ9IEwPzzKqBmkQWu0kgaeog2AgBEHSllEx+ryAPAdfSAINfB4DplFGnhMUfAIM3Gon5bI5HtJHrSHt6JJGZIHwmzweO7wep3FCq8pCt6iDYCAEQeAj5QFyUMwJA/K4bEDIPiFx9QEYPz1xWMboyc8SgPAWBxEjjpoDcmDfvAsDzx2BgRf8HqOmEkaeI028ArziEO88qA26sBbygIwlzzwJhC8ygPAb8dAsIVnsSRpCI+ZUxQyzMVBDSQPymFR9EPyoBxeOwiCDTyfDyQNBBfiwCploRU8m6Ev6RlJlo8+8C4PPHcYhP7wfg6YSRr0wArRBoATcVADD1EHQIxiSRmKPvANzx0HoQ+8CwPAt3THA6Uo4ocbcVATdeBFHrTErPLAg0CI0ACK0JEQ6hHlOLO4ZihFwS/ciAPANmVhpXoHGVYXD8mDckToVIj4EOXYkjREx0rRBoAzcVADD3M7ACQPSuEh+iCSPIjSyRDREel4mk0a9MDsQy9DwZ04mCFlQfKgHNaNhCjyAIjV4RDBiHT8WD3zRe/2gNe6BkCcaAPAoTgAJA+hMLs8sBQIkeQBEKsDIsQ7XqyuB9GkQUtEkgaAU3FgjZXlgbVAsEI0eQDE65CshojHh6RBOVZMUchwKw4sow5aIpo8AOyjD6wQUR4AMTsosyIfCxGPB0mDcqyaopDhVhwAc6Qs9IDkQT9ElQeABIIlou97s0oD72ghDSz6Kq7FQS28yIMeRknyoB8iywMgficmCiJHF2RYFUECxlzjvEcbRIV7cWA9HTXJA7u6B5IHdZihY+MRs+xTluc4SYO40QZAAHEA2Nc7WF0eAGtOFmUGeZAxS2fHCrNJGElD7JA0nEQIcQBIHiLhdrtMnbpgLQ9mFAizdIB64bufzLSvWJ/PokqDlohYDBmIMOKgFjOPtJAxc+qCdRGVmeRBxqydY7yYfV+wPodFlgbe6hpYF+8zEYfs1IS4PmeWegdAbHkA+Ld6PWDd8OqN2TvOQKwiTqyjDABJg4zoKQoZ4SIOZklZAOaQByMFgnXUATC/PMgEdqqid6xm+z1K4eF8JWmoxQwpChlm4lA3zqiDWkgetIfkwRqI0vmKsp16w8N5ysP1Gi88SgMP0QYAYNN7n6BuagKOlFTH/Ln6aYnILa6K+3sz01zIL66I+/NA7QE8XlKpah3e7UlPQn5huSbr8sXtdqGoSN3vVEp6RhIKC7T/DaFIcaegtKjUkO8Kh9wolxSWMN0O1ijplKWio4Z+n9XhQRgA46SB9xEUWsGLNACMxUENJA/KIHnQl9T0VMvLQzSoszcGXoQBEFsatMYsdQ2+MK9xUJOyYF0sCYiTtjBj0SQvYVAeis8Ia8PL+Wfkk26proEdzMUBELveARBDHgBzjrjgRR4AfhpvwjrwJK1GXotWkgbeog0AJ+IAxC8PaqMOJA/6QPJAEPrBkzAAJA16waM0AByJgxp4kQctIXmIDd7kgadGnTAXvJ1bJA3BmDVFIcOVOLCsd+BtmCagvzyYbZpqnuQBIIEgtIXH84mkIRgzpyhkuBIHgOQhEL3NleRBf3hr7Amx4FUYSBqCsYI0AByKA8CuWBLgVx7MkLowCl7lgbfGn+AbXs8Zo68vEYZcAtaRBoBTcVADD0M0AX0OvsjyYPTFz6M8APx2BgQ/8HyOmEkazHbDZCTcioPoKQuA5CEQkoeT8Nw5EGzg+ZwwOjUBiCUNVoo2AByLA0DyEA6SB+XwLA8A1T8QfAsDwCbKQNLAN1yLA0DyEA6SB+WwuFuKBd47DkIfRDjuZooyACQNWsG9OKiF5CE+zCYPgBjRBxE6EyJ+RDnGZktNACQNWiKEOLAcZQHwLw+iPpqb5CE8InQuhHJEOp4srhGrSoMWZKQaX+QphDgA7B+GpaU8iBR9IHlgi0gdDhGMSMePVUrPytIgYrQBYCQO2cnx7SyzyAMgVurCrPIgokCI0glZGRGPFatrQTRp0BIt+gBWj0sQJuIgQ/IQGZ5CaLHAapIXkeRBRrROyQqIKAsAW4EWURp4qmtg+YwlZuJQN4VNiIbkIT7MNjW1LyLKAyBuZ2UWRN//LIWBpEEdrB/MyDTiEK88qC2WJHmID7PLg6gCAYjfiYmCGfazmaMMAN/SYBaYpypIHmoheTgJy7npRZYHGTN0brzguy/NsD9Znt8kDeaINgAciIMaSB7YYAV5MINAAObr+IzAjPuL9TlN0mAeaQA4EQc19Q4kD5ERdaQFwP6peGaRB19IJPwJ3B9m2yc8CANJg7mkAQDYzqzkQ90UJ46UVsb32dQEHCmpjvu766clIre4Ku7PA7UHNb+4QtU6ZLJSnTheEt++CEVmehLyC8s1W5+M2+1CUZE2vzkc6RlJKCzQftuVIje6pUWlzLZBT0J1lCWFJQy2RH/MJgWR4EF6RRUGgKQhGtyIA8BWHrTAivJgBKzlAahtiM0qD4GE62BFEQorCUIoSBrUwZs08AhX4gCokwc1aBF1APiWBz0wIuoA8CMPgHmjD9GI1iEbIRZWl4JIWEUYAGtJA2/RBoBDcQDilwceUhYAv/IgcsoC4EMeAGtFH2KBOnU28CAMAEmDL2aWBoCT4kgt4aFYEuC3YFLkYknAuGKraLAuOiMIns5BkoaTmF0aAI7FQfSRFgC/B150eQDYj7iQ4anxJqwBT+eckSIvgjRoBa99hwy34gCQPPhCwzSD4UUeAL4ac8Kc8HaOGXn9iSINZhxBEQquxQEgefCF5CEYnuQB4K9xJ8SHt3PK6CgDSQN/cC8OAMmDL6IM7zFaHkggCDMhnz+8nUNmiDIAfEqDSAghDoA55IFH9MzvGSkPAH/RB4AEgogNXs8Xo+XcitIgSrQBEEgcAPHlgdeoA8mD/vDaIRB8wPP5YfQ1RdLAP0zEIcPF5g6e5CE8ZpMHEgiCd3hNR8iwiDKQNMROjsp+LR6YRRwy45QHNVEHgA950AqSh8jwKg8A/50GoR+8H3cW4q23MJhVGljBNFXBSh5Yw/PJQvJgPLx3JIR6RBFFFteLSFEGgC9pYHUjy7zGgYU88BB14DVlAegvD5S6CI0onQuhDJGOJ6soA0lD/LCMfjMXB4DkQS2iyQNA0YdoiNTpECcR7bixkmq9r3+SBn3hQhzUILo8aAXJgzJEiT74IlpnZDVEPT6srgOSBnXw0O9wIw7xRh0AsWseeH0YlowZ5QEQUyAAcTspM+F7DEQ8DiyjDCQN6uBBGgCOxAFQJw/xwkPUgeSBXbGoiPIgI3LnJRpm2NcsZdkIYSBpMA7jB4BGIdOViPyKqpg/VzfFiSOllXF9Z93UBBwpqY7rs0DtAc0tjn2bfclMcyG/uELVOmSyUp04XhLfvghHZnoS8gvLNV2nL3LDUlSkzT6IBbkxLSzQ7/fpTWCHVlpUymhLzIHIghAISzk24qaAZ2EAzCcNAIfiALCRByI6essDUNvQsJAHwBwCIROq4yOZCI2ZJMEX1tE0kgZzSgPAqTiwgKIOyjC7PAC1Da4Z5CEQkgnzSoIvVhAGgKSBJdyKA6Us1EPyED9mij5EIlxHKrJQWEEOQsFaGABxowwAf9LAM9yKA2DdlAXJQy0s6x5krCIQgSjpfFnIhVWlIBJWEQbAWtLAa7QB4FwcWMBD1AEgefCFdfQBsK5ARII6cbbwIAwASYMvVpAGgLPhmKGw6qySAP/DNAH9h2rKsByy6Yuo8z8Q5kA+/3g4B42cPp6kgS+4Fwc1mEEetMQM8kACQVgR3s43I4WBpIE/hBAHFhNDaQFvk0MB4ssDwE/0AeDrDpAwH7ydW2aJMpA0qEMIcQDETFlohUjyYMXogwxvjTwhJjzKqNHCIEKUAbCmNACMxCHDKc5O4iVlIYo8ANaNPsjw1ugTYsDjeWO0oOvZdphVGrIZ3OAKE3EA2EQdtIDkQV94jD4AfN45EnzB8zli9DVF0hA7rKLizMQh02VsZ846ZUHyoD88yoMMr50DYTw8ywLAJsogSj0DQNIAMI44xCMPrB6/TfIQHyzkQQSB4LXTIPRBhOPO4toRKcoAkDTI8FH9FyPxziipFrWTQ2mJlhNEAfpNEgWcbByMmCxKhodZJ6Ph24nQxFLmg2dJ8IWFaOt9Q8FrlAEQXxoADmocREpZaIGW1bMiRR4A46MPAN/pC19EuCMloiPScWQVnSNpiB8epAEQNOIAqIs6sHwQFqDdtNSAWJEHwLhpqn0RIfrgC0UixEEEQQiElUyLJgwASUM4uNiSTJcT+RWxd1Yipyy0lAetMUIeAGNTF4B4AgEEd0wkEmwRURRkWEbfrCwNWkSZeZIGgBNxAOKXh3hR+wRNnuRB66gDoL88AGyiD4CYAiFDImEsIouCjJmFASBpYAF/WxQjrFIWgDXkAYDpUhcyIguEDImEtphBFGRIGGKHp9QEwKc0AJyJA4uUhVp50AKe5QEwb+pCxgwCIROq4yOZCI2ZJMEX1gXBJA3mlgaAM3EAjE9ZqIWnIZqAuPIAsI0+AOYSCF/CdZBWEQqzCkIgVhAGgKSBB/jeuhiglMVJ5ItA1LoHgF30ATCvQAQSqUMVSSqsIgahYC0LgAbCIElILi9BWlkxKhOcKE5NR40juGvS7eF8JA0xw+UWipiy4E0eAHGLJgH20QfAOgIRing7YzXCYWUBiBUehAFQJw11j+Vi8KrPcOFPXyG74Ij39Rq7A793PAsL+47Euo5nwWN3kDRwhk2SJMmoLyssLERGRga+W78Xae70qMvHm7JQM0RTbb2DFmkLrYdp6pG6APQtmvSFtUDIWFEgCL4wgzBkH8/FjfOfw1kbVsIheSIuezinMT4ZcSNW9xoa9/eFQuvJ81hKQ1lJEe4a0BUFBQVIT4/er2qBGHojEFaJPADWij4A/o02SQRhFLzIAqA+LdFi/3Y8/MrtqFuQ532t2u7A5lbdUODOhLOqEi33b0e947kAgHp5B3DT7AfR+MA/+OiyKYDNpur7Af6iDIA4kQYZrrdWxJSFVpA8+MND7YMvVk5jEPrDkywA2hQ+Nsg7gMdenILM4uMAgGPp2fi6zwgs6XUJjmXmeJerk+zAqRtWY8DSj9B1408AgIsXvoOqRBc+/c9EVdtgRmnITjb+MQpcpypkrJqyAMRJWwDGpS4AfgTCF5IIQi1mFAYAsHk8ePmxK3HKgR0AgG3NO2L65OdQ6M7yWy6wluHCZR/hqjlPwn6im3ryrlfwZ9deMX8/j6kJQL001E1xorS4CBN6tzc0VcH8IVd6wuoR3IB2oSctH4oFaH8B+aL3Q7J8YfHArGjw/khvgk/k84a3c0fLa+z0Lb94pWFvgxZ4cMqLftKQleoM2X4svWA0Phxzm/fvId+8H/N3m1kaWCGEOBj9BE0ZkofYMVoeeBYI3joCgh94Pkf0uK6G/PCJ99/vDp+E4tSTd8bR2oxFA6/Av/WbAQA6b/4VDQ/uUvy9JA36IIQ4APHLg5qogxZYVR5IIGrhuYMgjIX3c0Gv6yjn2CGcuXE1AOBwVn382rk21aC0nZDsdizrf5n37/7fL4j6mcw0l+b1DCQNJ2EiDilOh6HfxzJlAfAtD2aJPgB8CwTAf8dBaIvv8eb5mOt93XTY+ae3RmHZ2UOQkZ4Sc9uwsvfF8Nhqu6v2f/8RcVmeowxmkAaAYcQhHnlQk7IgeQiPmeQB4F8gAJIIsyLKcZWvESOuE3dpofffhY2bx7WO0tR0FKfVpjdSiwtCLqPHjRAvUQaAH2kAOB+OGQpWz7LQYpimVs+10HqoJqDfcE3AmKdshoK3IZzhCOxkaHSGOPAuCIGwEGqbz8A9ScU8DBJqP2tD8EBAPW5+SBrCw7TGId6UBat6B4o8qINF9AEQIwLhiyghbisi6rFhdQ1kpTohZdXx/l336KG41pNUVoLU0iIAQEmK/5BDPaIMJA2RYR5xSHE6UFpZY9j3qZkcCqDIg1pYRR8AcSIQgVBEgh3xyIHLAaQ7bbCrn+RQNW6dbwYikZ6cCEBCXpduKG9em6I4a/c6rEyogWSP7Z717HUrUd2kMaoB7OrZB1lOCeneDjXytNWxkJ2SAECb/ihLg3VlJSWiWvJAsvE1joHJBFA/bT7gNwFUvOKgJmWhRh4A9RNEAdpNEgVoP1EUoO9kUQAbefBFNIGIBMmEetRGEGwABrRIxNmNXUhw2MDSG+wMrcUeIh2RXngUzqra670gvQ6qEmPb15n5eUioqW0vj2fWhZSo/V24lrvMocHKavejBI8E5HucyLelhZxym8UEUMwjDkD8UQc19Q5mijwAFH2IB1EjEKEI1emRTIRGrxTDgBaJOK9lMrKyc2BPdAEGq4MWnZVe359cVgd1j9U+f6Iy0Ym8nMbeURLRSC0tQh1bbdtW4UyCs34T9RsbQKJDm33n0OBZGgDgkHeNBFSUlyHhSB5QXYx8m1uT9auFC3EAxJQHLbC6PAAkEHoRqYM0u1QYXX+Q5ADObuxCVnYOElKMueuTSYgx7K81DgWdbmWiE47C40isrkRSVSUSjh/G4XpN4Ymy7anFBah3PM+rYEVZOUhwandstRIGAEjQQNxCrcOZVNs+VR8+jAJO0hbciAPARh7UoNUDsUSQB0D/1IVRD8wKh2/xmNkkIpBoHSvvYsFbYaLbaUOCw3Yi0qA/rGUBUCYMXmw25NZvgob/7obD40FKWQkaHfwHhel1UJyWAY/dp1BeAlwVpUgvOg63z9DLoow6KE3TTspEkAYZV1Iy7DYgQfKgioN5G7kSBxbwkLIA+JcHwBrRBxmzRiGUwlvHzDt2G7yDBfWEB2EAYpSGE1Q6k5Bbvxnq5+6Dw1MDZ1Ul6h49hDrHDqM0JQ0euwM2SYKzshyuSv/rrjg9C8ezG2qy7VoKA6C/NAA4cVpxUG17Aj7OQh+MHqJZ+1n2wzQBbZ/JrsdwTUD/IZsyRk9bHQ7RhnIS5iPBbvf+xxqHwxaXNMiUJ6XgYMMWKEtK8b5mlzxIKyk8EWHI95OGGrsDx7Mb4GhOI0ga9JtaRxkMkYYoZDB4rAL7MzEEJA/aoKc8GCkQPGDkTHsEwZMsAOqFwZcqpwv/NmyB/Y1PQaE7K2SRZIUrGUfqNcaBFu1QmJnt916vbu3x31mvxPy9vKUmtFgPq2cxcZuqELFYkte0BaDPcE0jUhcAP+kLGSvVQhDGwYsk+KKVLISi0pmEI3Ub4mid+kioqUYCagv/PA4HahzadU08pia0WA/LBzjyd6ZqAEUegjFL9IGXCIQMRSIINfAWWZBRE2GorIxN8O0JDnhcLlS6klHldJleGrRIcbB+6jNfZ2sARj9FU4bkIXaMkgeAT4EASCIIZfAmC1f8ZxAeue8uPP3IA+jRrinO6XIKXnzyUe/7B/fvw8QrRqFL83ro1qIBplx7JY4czvW+/+KTj2Jov7Pw0Zx30ff0jujYuHaK6VZ1UzHv3f/iurEj0alpXQw4+3SsW/sLdv+zE+MuHoTOzXJw2UXnY8+uf7zr2rPrH1x3+WU4o10LdGyag4v7n4vVK76P+TclOmymrGcA2EsDwLk4AGzqHWo/T/IQK0ZGHwB+BQIgiSBO4isKSmRh66Y/sWzRV9i66U8Dtg6wwYbP5s9DSkoqPlmyAvdOn4mXn3kcq1d8B4/Hg4lXjkJ+/nHM+2IJ3vvkK+zdvQu3XDfebx17dv2DxQs/x2vvzsPCFT95X3/12Sfwn9HjsHD5TzilTVvcPnECHrhzCibdfhe++m41JEnCtHtu9y5fUlKM8y4ciLmffY2vV/yEvudfiGvHXYoD+/cp/j08Rhm0Wo/byUd1AR9bEQVW8zuYseYB0LfuATCu9kGGtxqIQKgmwnrEG03476vP4+MP3vH+fdkVE3Dt5NsjfCJ+fFMR7Tt1xi333AcAaNmqNea8NRtrVq4AAGzbvAkr1m1Go8a1MzY+89qbGNTrDPy57nd0Pb07AKCqshLPvPomsuvm+H3HyHFXYsjwkQCAibfcgUsHnYdb7vo/9O1/IQBgwsTJuHvKRO/yHTt3RcfOXb1/33n/dCz5+issW7QQ46+/KeLv0VoYAH6kwWHj49knMtxHHGRYRR7UomXkgaIPkeE5AiHjG4mgaIR5cKe5YLfb4LDb4paGrZv+9JMGAPj4g3c0jzyEql9o37Gz39/16jfA0SN52PH3NjRs3MQrDQDQpl0HpGdkYsf2rd7XGjVpFiQNvut12G2oX78+AKBdx07e9+vWq4eK8nIUFRYCAEqKi/Hog1PRv+dp6NKiITo2zcGOv7fi4P79EX+THlEGnqSBN5hEHJJdxn4t62mptYo8APpEH/SKPADGRx8A/iMQvgTKA0UkxEAP6du/d0/Y19t36hryPaVEK3RMSAxok202eDzKnzqZkpoS8nWnM/HkMyxOdIAJiSdvWGwnXvNItd/16LSpWL3ie9z38GNocUorJCUl46arx4UtuDRzlAHgUxoAQVIVMmoewU3yEB4jUheA/lNWByKSQMiQSPCJEdGhJs2ax/S6EtQOp2zdth3+PbAfBw/s90Ydtm/bgsKCfLRu2yH898bZaf7+y8+4dOwVGDT0EgC1EYj9e/cCvYKXJWlgB7NURUqcUQc1Iy1YDtMEtEtbANoXTQL6pi4ANukL4GQKg/c0RigCUxuU3tAfVvu8faeuuOyKCX6vjbrymriiDVpN2NSr7/lo17ET7ph4DTZu+AMb1v2GuyZdj57n9EbX004P/d0qOs0WrVph8cIvsOmvDdi88U/cesPVkAIiHzabz9MjNYKn1ATAtzQAjCMOKa4ElFbEfudMkYcT69I48gDon7oA2KQvZESMQgQSqiOjyETs8Chh106+Hb369cf+vXvQpFnzmKRBj8mabDYbZs+Zjxn/dyfGXjwQdpsdffpfiOmPPxP8/Rp0mA/MfBL3TLkRIwedjzp1snHjrXegqKgIgD4RBkC7KINW6+JdGgDAJkmSZNSXFRYWIiMjA+t35sLtPvmUs3jkAUDc8qD2SZpaPIpbK3kAoLk8yOgtEIDx6YtARBYIJZBQGCMIdZOAazs6Ub9xMzgMekKmjJ6zOyr6fgPK/XlOS2i1rkjCUFlRjn179+CYow48dv/IcElxEUac2QoFBQVITzfmke5c1DgYHXlgPUwTOJm20Gq4JqC9QBgVfQDYCYRv+sKMEhGt0zSDWPAYOdAb1rIAiCsMgFjSwCNciAMLeJAHQIzUBaB/9IG1QADmSGPESqydrt6iYUUJUAoPsgAYIwwA/1EGrdanVhoynMbPJMmNOIhW71D7eWvIA2BM9AHgSyAAa0mEEqhjNxZeZAEQWxgA/qIMgHppyHQ5UVxpfNSQqwmgRBtpUft5bWxP6xEXeo260HvkhQyL0RehEHlEBiEm8ogIHqTBcWJSK6PSEnpFGcwqDazgShwAkgct0UMeAP2HbcqwGr4ZDpIIQi94kgUAhskCoJ8wAPymJkSWBoCjVIUvlLbQLjyuZ+oCMGbkBQ/pi0AonUGoJUgSJAmJ27fBcehf2ItL4HG7Ud20GapbtDRumwx+IIKVhAFQH2VIdyYCDvY3LlyKAyCuPADqh2tqOeIC0G/UBUACAZBEEMoIF02wFRUhbcFHSH/nDbg2bwp6v/zMs1A44XoUX/wfwKVPBM4swgCYVxrsHI284C5VoQUs0xa167BW6gIwLn0BnExh8JTGkKF0BuFLxBSEJCH9zVlo3qUNcu6+NaQ0AEDSrz+j3k3Xonm3tkj94lNtt8/AlASgf1pC61oGXlITRotdNLiNOADxRx0AtpGH2nVYK3UBGBt9kOE1CgEgSB4oGmF+HHaFdQqShOwH7kHG7Nf8Xi4/8yyUnXMupJRU2IsKkbJsCZxbNteu+8gR1L/2Shw9sB8Fk25RtY1Go2eEATBvlAGo3Zb4ejL94D7iEG+xJECRh7Dr02nUhYyRoy9keI1A+OIbjaCIhDmQj2V6cmJMoeSspx7zk4aicVdi//I1OPjNdzj+wAzk33EPjk2fif0rf8WBhUtRctFQ77LZ06Yi7cM5MW9rvNGFXt3a47+zXon5c4C+EQagtlO946brMX7MZZqtLxZpmHzDdbhi9KXevy8edCHuu/tOANpJA49wLw4AyQOgvTwA+qYuAGPTFzIiCIQMiYRYBB6veI+Za+0vyHr6Me/feS++hryXXkdll27BC9tsqDjrHOS+/z8cv2uq9+WcO6YgYd/eqN9l5HBKX6IJw203XY9rxsXf2WudlpDXGY69e3YjO9WFvzZsiLiO9+Z9hAemz1AtDXr8Pi0RQhwAkgegVh4o+qAMnusgwqFVx0SoR89jkfHGyUjD0QdnoOjy8dE/ZLPh+L33o/Cqa2r/rKqC+73/hl2chSwA+kcYAH3SElqts252Ntxud9yfr6mpgR2GPT4qbpiIQ7wduRp5UANP8gCIG31gIRCAWFGIQEgm9MfIfezIPYTUhV8AAGqy66LgxinKP3xCHqSE2ms1/YN3gYqTtT3xRBdGDxuIaffcjmn33I4uzRvgtNZN8eyjMxDp2YdvvfoSBvbqgQ5N6uLszm3wwF23orKsxCsMH82dgw7NGmDFsqXo2+NUtGlUF5ePuBi5h/4FADz7+Ex8PO8DLPl6IRpnJKNxRjLWrFoJADiwfx8mjr8cHZo1QKfmjTBh7GXYt2cPgNoO3iZ5MO3/7kGbJvXRvlkjPPzAfRG3VWbhF5+hT4/T0DQ7HWd0aotZL73gXScAZKe68PVXX/h9pmWjepg3530AwGkd2wEA+p1zJrJTXbh40IVB3+Gw2TBk4AX4vxOpCgCoqKjAA1PvRYdWLdCobib69+mFVSt/8L4/d877aNYwB98s/Ao9T++Keplp2Lc3eiSJNcwiDikuY+VBTdQBsI48WEEgRJUImVAdHQlFZMLtM6P3W9r/PoCtqrZ4uPCK8TEPr6yp3wAlQ4cDqC2WdC9aqDq68Mn/5sLhSMDny1Zi+uNP461ZL+N/778Tdnmb3Y7pTzyDb9f8jhdefws/rfoBM6fd77dMWWkpXn/5Bbw0+7/49JulOLB/Hx55oDbVcuOU2zDsPyNx3gUD8Mffu/DH37twRs+zUFVVhctHXIy0NDc+XbQMn3/7PVJTU3HFyIvhqa7dZ7NeegEfzZuDF16djS+//R7Hjx/DNwu/jPj7NvyxDtdfdTmGX3oZVvz8O+6a+gCenDkD8+cqrxNZuvJHAMCnCxdh8849eG/eRyf3B8LXM9x9+61Y+8vP+O/7H+DHX3/H8BEjceklQ7Fzx3a/ffXic8/g1VmzsfaPDcipV0/xdrGC6aiKFJcDpRWx14uymOMB0G60BaDNo7m1nu/Bu14dR17IsBiB4QvPozHiJVonaPZRHSLIk2vjX95/F186Oq51lI4eg7TPFwAAnJv+ROmIS6N8IjINGzXBtMeegs1mQ6s2bbF18yb8d9YrGDv+mpDLX3vTzd7owiktW+CeB6bj/26/BY8/96J3maqqKjzx/MtoccopAICrr78RLzz1OAAgNS0NScnJqKysQL36Dbyf+eSjD+HxePDMK7Ngs9WmD156/U20bVIfa1b9gH79L8Qbr72CW+64G0MuGQ4AePrFV7Diu6URf9/rr7yI3v3Owx333gcAaNeuLXZs24pXXnwO4668StE+qlu3LgCgTp1s1G9wcpsjlTLs27cXc+e8h43bdqJho0YAgCm33YFlS7/F3Pffw7SHZ3r31QsvvYwuXUPUuChA7U1xPDAfjmlFeahdjzbDNQHth2wC+k4a5QsvAgGYSyJCEUvHyotkiCADsWAvLPD+u8an04yGb0TB93P2/HzV23TaGT1g8+kBT+9xJt569UXU1NTA4fDvlBIdNqxc/j1eee5p7Ny+DUVFRaiprkZ5eTnKSkuRnJICAEhOSfFKAwDUb9AAR/IOR9yOzX/9id3/7ETbxjl+r5eXl2P3rl0oLChA7qF/cfoZPbzvJSQkoNtp3SOmK7Zv24ZBQ4b61TGcefbZeP3Vl0P+RqVEK4DcvHEjampqcEa3Tn6vV1RUoE6dOifWATidTnTu0jWubUhxOsCi2WIuDmpgKQ8AuJnrAdBHHgBjog8Ae4EArCUR0TBbh80LkuNkk2urjnxdhU0/VJ38nJRoTNrPYa+Vhn179uDq0SNw5bXX495pDyEzqw7W/rQGd958IyorK73ikBiwXTabLWotQklJCbqedjpmvfVu0HvZJ+744yXaUNna7fN/rboqdFukdMRESUkxHA4HVvz4c5CcpKameUUmOTnZT9yUwiLSIMOFOMQbdVD1nSrlAeBroihA39QFoH/0AeBDIACSCEIfPCfuNAEg8e9tqKlX3+99JbUKidu3nVxfVp0ISypj/e+/+f39x29r0eKU1khy1l73vn3an+vXwePxYPqjT8Jury2R++qzT2L+TmeiEzU1Hu/fCXYbup16Kr76bAHq5uTAnZ4e8nP1GzTEut/W4uxzewMAqqur8ef6dejS7bSQyyfYbWjXrj1++WmN3+u//vQTWrVu4+3Q69bN8RZvAsDOHdtRWlrq/TvReUKkPR4opWu3U1FTU4O8vDyc0+tcv21SA0thkOFmOKbRxZJA7QHgpWiS98JJwJjiSRmWRZSBmKWokmBPWZ/zvP9Of/9tALGPhnC/e3IYZnnf8yIsqYyDB/bhkfvvxc7tf+OLT+bjvTdn4bqbJodctsUprVBVVYW3Z7+GPbt2YcH/5mHO22/G/J1NmjXHlk1/YfeO7Sg4dhRVVVUYOXos6tTJxlVjLsXPP67Gnt278OOqH3Df3Xfg4IH9AIDrb5qMl597Bt989SW2b9uGe2+/BQUFBSG/Q+6gJ91yG1auWI5nnngMO7b/jQ8/mIO3Zs/C5Ftv9y7bu28/vDV7Fv5cvx5/rPsdd95ys1/UpEG9+khOTsaypUtwODc37Hf60rpNW4waMxY3XncNvvz8M+zevQvrf1+LZ556EosXfRPzPgOApEQ+umw+tuIELOQB4GPERe16tJUHPQXCKHgSCIAkglBHycX/QU12bdg99avPkXgkct4/EOeGP+D67VcAQGWnLqjoebbqbRoxehzKy8sw/MI+mH737bj2xsm4YsK1IZft1KUrpj/2JF574Vmcf3Z3fDb/f5g6/ZGYv/OqCdegdZu2GND3HHRs2QS//rwGKSkp+GLJMjRp0hTXXD4Gvc84FbdPvhEV5eVwu2sjEDfdchsuHTsOt9x4HYZc0BdpaW4MHnqx37oD52Xodtpp+O+cefh0wXyc2+N0PDHzYfzfA9P8CiMffuJJNG7cBEMHnI+JE67Czbfe7k27OGw2JCQk4Mlnnse7/30L7Vs1x7hRIxX9zldnv4Ux4y7HA1PvRY9unTFm1KX4/fff0KRp05j3GU/TQdkkJYNgNaKwsBAZGRn4e9+RsKEoAHGnLeJ9roX38ypTF1oUTdauR9swvR61D951G5C+8IV1CiMSlNKwLllOCaNPsaNBk2ZwOIOl0jeakDnjQWS8+CwAoPSiIch7/3+AggI9W0kJ6l8yCK51vwMAjj73Eoqvvk7Vdo+9eCA6dumKh594RtV6lKLnbIhar1uLKaMBbbarqqICe/bshs3dAPYE//OruKgQZ3dsjIKCAqRH6Fe1hKuIgwxFHrS9w9Y7+mDlCIQvFI0gZHzTD4EpiKLrJsJz4g46ZdHXqHv91UB5ecT12fOPo97o/3ilobpxE5RcOibu7TNihkdf9J5C2azSYLfZuHqctgyX4gCQPGhd9wDoV/sAsBMIESSCRML8pKc4YbfbYFdQq1DTuAny3pvnnQEy9fNP0LhHV6Q/+yTsh3P9lk3YsxuZMx5Eox7dkLRmNQDA407H4Y8+hZSWFtM2yrJgNmHQcv1aPAIb0Ga7eBQGGS5TFb6ImrYA+E1dAOZKX8jwnMYIhNIa4hJKBDMSPBjSqAYNmjRDQohURSiSli5GzoQrYPep3pcSE1HVph08qamwFxYi8e+tsPk00TU59XD4wwWoPP0MxdtrpCjI6P2AJj3Wz0uUAQiWhoryckpVxIKokQeA39QFYK7ogwzPEYhAAiMSFJXgF72OU/mFg3BoyXKUDrwI0omOwlZVBefmjUha+wuc27Z4pUFKTETJpaPx79IfFEkDi+gCIF6EAdAuygDoIw08wsU8DtEwenZJ7+c5meuhdj3aTVUto9e8D971Gzj/gy++8iBSFCJUp0SRCeNQIwXSif8Q45MNqzp1Qd6Hn8Cxdw/c7/0XKZ8uQMKhf2GrqIAnORk1TZqheMw4FF8xHp6cyM8wYBFZkDHiEdBWizL4InnPKz6kgvtUhS+UtpDXJVbqwvsdjFIYMiJJRDRIKOJHj+hOok3CfxpXoUH9ekhyZ6pfYXU1kKDsvo6EIXa0EgbAmChD/vFjyD18GI6MprDZ/RMFLFIVTCIO8acfxI48ANoIhIjRB4BdBEKGl1kptSBS50dSoY8cRKJKsmFboQ1OxxHUAZDgSoLqu8MIbY6vLFQbO+kugJMdr55XkuNE/6hliyRvtxa7TC6CVbMue5RzRIKE8rIy5OXlAYlpQdKQ4kpATaXx3TizVEWqy4GSuCQgfnkA4o8+aCEPgHapi9p1aTddtYyVBAIwh0QEoqTTFFUueK4F2VCYAKAa7WoOw2G3aR5UNuDmPiJqHt0dC3rl+LXcfC22UekaJABITIMj1X96cbV1fGpgWuNgtDzUfjb+6INcMMlL3UPturSXB0C/h2b5fQdjgQDMLxHh0LoDjiQiPHf22mLDhsJEbCqSkOKQVItDdgofJWhZBm1HVtKJa1Hj5Lnbqd32pzu1Kb5WPnW0DXZ7QshIA0uYn5miyQPAV9Fk7bq0T10AxkQfAD4EArCuRGiBdeQgOtWSDYXV8WmD7zlYovx5SrogX5flOlfBye2M1lec3C5qtRu1GCWndrQea2GQ4WIrrCwPgHaFk3pGHwDjBAIgiSCsA09DiI0cRq3nkHAth7BrNazeLNIAcCIOgHXlARAj+gAYJxAAP1EIILhhJ5Eg1MCTKMiQMIRbH0lDKLjaGpIH7TpkvaIPgDH1D97v4kggZCgaQcQCj6IAGCsLAAlD3OvgTBoAg8VBnjKiqKgw7DLxCgCgrmixTOVcD2q/Hzh5MAoqtemM5EupQAeBSDnx/6NlxghEqs+/j3IkERkBqew8jraNYENOQIdcXhL5AVZGk31i+8pKDPq+5NpOuFSHkTwZJ4ShpEq7fZzhTERxpfr1pTgdUPOTk08IQ1GUJra4qAjAyf7VCAwVh6ITP7BDq+ZGfi1BEARBmJqioiJkZGQY8l2Gzhzp8Xhw8OBBuN1u2ASYj5sgCIIgeEaSJBQVFaFRo0aw2415/JSh4kAQBEEQhNhw/3RMgiAIgiD4gcSBIAiCIAjFkDgQBEEQBKEYEgeCIAiCIBRD4kAQBEEQhGJIHAiCIAiCUAyJA0EQBEEQivl/htzfXvNEclAAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# viz: the test points, planted outliers ringed, decision contour from the GMM density\n",
    "xx, yy = np.meshgrid(np.linspace(-12, 12, 200), np.linspace(-12, 12, 200))\n",
    "zz = gm.score_samples(np.c_[xx.ravel(), yy.ravel()]).reshape(xx.shape)\n",
    "fig, ax = plt.subplots(figsize=(5, 5))\n",
    "ax.contourf(xx, yy, zz, levels=20, cmap=\"Blues\")\n",
    "ax.scatter(X_test[:100, 0], X_test[:100, 1], s=10, c=\"#222\", label=\"normal\")\n",
    "ax.scatter(outliers[:, 0], outliers[:, 1], s=160, facecolors=\"none\", edgecolors=\"red\", linewidths=2, label=\"planted outlier\")\n",
    "ax.set_title(\"GMM log-density; outliers sit in the dark, low-density corners\")\n",
    "ax.legend(loc=\"lower right\"); ax.set_xticks([]); ax.set_yticks([])\n",
    "plt.tight_layout(); plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0f155b94",
   "metadata": {},
   "source": [
    "> **Interpretation.** The contour is the learned density. All three planted points land in the dark, low-density corners and get flagged. The catch in production is the base rate: at a 2% threshold you flag 2% of *normal* points too, so on a stream that is 0.1% truly anomalous you drown in false positives. Evaluate anomaly detectors at a fixed alert volume (precision among the top-N flagged), never by raw accuracy.\n",
    "\n",
    "> **Key takeaways.**\n",
    "> - A GMM is soft clustering: `predict_proba` rows are a per-point posterior over clusters and sum to 1.\n",
    "> - EM never decreases the log-likelihood; logging it per iteration is the strongest self-check for a from-scratch fit.\n",
    "> - Density gives anomaly detection for free ($-\\log p(\\mathbf{x})$), but the base-rate problem means you must score it at a fixed alert volume, not by accuracy.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "88d5fcd4",
   "metadata": {},
   "source": [
    "## Part 6 — Picking a method: look first, then choose\n",
    "\n",
    "> **Objectives.** Assemble everything into the practitioner's move: run the candidates on a 2D view of the data and let the picture decide.\n",
    "\n",
    "The decision tree from the chapter, compressed:\n",
    "\n",
    "- **Know `k` and clusters look like blobs?** k-means (fast, simple, spherical).\n",
    "- **Don't know `k`, clusters smooth and density-defined?** DBSCAN (auto-`k`, arbitrary shape, free outliers).\n",
    "- **Want soft probabilities or a density / anomaly score?** GMM (and BIC or a Bayesian GMM to pick `k`).\n",
    "- **High-dimensional?** PCA first, then any of the above.\n",
    "\n",
    "Five minutes looking at a 2D projection tells you most of this. Below we run all three on blobs and on moons and let the metrics confirm what the eye already sees.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "b3384623",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-06-10T18:44:22.464780Z",
     "iopub.status.busy": "2026-06-10T18:44:22.464704Z",
     "iopub.status.idle": "2026-06-10T18:44:22.521664Z",
     "shell.execute_reply": "2026-06-10T18:44:22.521271Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "dataset                    k-means    DBSCAN       GMM   (ARI vs truth, higher is better)\n",
      "blobs (spherical)             0.99      0.33      0.98\n",
      "moons (non-spherical)         0.26      1.00      0.49\n"
     ]
    }
   ],
   "source": [
    "# The consolidated bake-off: 3 methods x 2 datasets, scored by ARI against ground truth.\n",
    "from sklearn.metrics import adjusted_rand_score as ari\n",
    "datasets = {\"blobs (spherical)\": (X_blobs, y_blobs), \"moons (non-spherical)\": (X_moons, y_moons)}\n",
    "methods = {\n",
    "    \"k-means\":  lambda X: KMeans(n_clusters=2 if X is X_moons else 4, n_init=10, random_state=SEED).fit_predict(X),\n",
    "    \"DBSCAN\":   lambda X: DBSCAN(eps=0.18 if X is X_moons else 0.6, min_samples=5).fit_predict(X),\n",
    "    \"GMM\":      lambda X: GaussianMixture(n_components=2 if X is X_moons else 4, n_init=10, random_state=SEED).fit_predict(X),\n",
    "}\n",
    "print(f\"{'dataset':<24}{'k-means':>10}{'DBSCAN':>10}{'GMM':>10}   (ARI vs truth, higher is better)\")\n",
    "results = {}\n",
    "for dname, (Xd, yd) in datasets.items():\n",
    "    row = {mname: ari(yd, mfn(Xd)) for mname, mfn in methods.items()}\n",
    "    results[dname] = row\n",
    "    print(f\"{dname:<24}\" + \"\".join(f\"{row[m]:>10.2f}\" for m in methods))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "9739aa44",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-06-10T18:44:22.522794Z",
     "iopub.status.busy": "2026-06-10T18:44:22.522692Z",
     "iopub.status.idle": "2026-06-10T18:44:23.620337Z",
     "shell.execute_reply": "2026-06-10T18:44:23.617825Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1100x700 with 6 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# viz: the 3x2 grid that makes the table obvious\n",
    "fig, axes = plt.subplots(2, 3, figsize=(11, 7))\n",
    "for i, (dname, (Xd, yd)) in enumerate(datasets.items()):\n",
    "    for j, (mname, mfn) in enumerate(methods.items()):\n",
    "        lab = mfn(Xd)\n",
    "        axes[i, j].scatter(Xd[:, 0], Xd[:, 1], s=6, c=lab, cmap=\"tab10\")\n",
    "        axes[i, j].set_title(f\"{mname} on {dname.split()[0]} (ARI {results[dname][mname]:.2f})\", fontsize=9)\n",
    "        axes[i, j].set_xticks([]); axes[i, j].set_yticks([])\n",
    "plt.tight_layout(); plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bd3f0301",
   "metadata": {},
   "source": [
    "> **Interpretation.** On blobs all three are near-perfect; the eye told you they were spherical and any method works, so pick the cheapest (k-means). On moons k-means and GMM (with diagonal-ish covariance) collapse while DBSCAN wins outright, because only DBSCAN drops the spherical assumption. The picture decided before any metric was computed; the table just confirms it.\n",
    "\n",
    "> **Key takeaways.**\n",
    "> - There is no universally best clustering algorithm; the right choice is a property of your data's shape.\n",
    "> - A 2D projection (PCA or UMAP) plus a three-method bake-off is five minutes and answers the question.\n",
    "> - Score with a permutation-invariant metric (ARI) against any ground truth you have, and trust the picture when you do not.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f15e82d8",
   "metadata": {},
   "source": [
    "## Safety lens\n",
    "\n",
    "Unsupervised learning has no ground-truth label to check against, so whatever bias is in the data becomes the structure the model reports as fact.\n",
    "\n",
    "**Clustering encodes whatever feature varies most.** K-means on raw, unscaled features essentially clusters on the highest-variance column (income in dollars dwarfs age in years). In a sensitive setting (risk scoring, healthcare cohorting) that bakes demographic skew straight into the \"discovered\" groups. The cheap audit is a per-cluster demographic breakdown; a wildly skewed cluster is a finding regardless of intent.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "52809335",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-06-10T18:44:23.622387Z",
     "iopub.status.busy": "2026-06-10T18:44:23.622241Z",
     "iopub.status.idle": "2026-06-10T18:44:23.749803Z",
     "shell.execute_reply": "2026-06-10T18:44:23.749139Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "unscaled centroid spread: age 0.7, income 26099\n",
      "Income's spread dominates by ~1000x: the 'clusters' are just income bands.\n",
      "Mitigation: StandardScaler so each feature contributes on equal footing (Ch 07).\n"
     ]
    }
   ],
   "source": [
    "# Safety demo: the same data, before and after scaling, produces different clusters.\n",
    "rng_s = np.random.default_rng(SEED)\n",
    "age = rng_s.uniform(20, 60, 300)                 # years, range ~40\n",
    "income = rng_s.uniform(20000, 120000, 300)       # dollars, range ~100000\n",
    "raw = np.c_[age, income]\n",
    "km_raw = KMeans(n_clusters=3, n_init=10, random_state=SEED).fit(raw)\n",
    "# How much does each feature's range drive the split? Compare centroid spread per feature.\n",
    "spread_raw = km_raw.cluster_centers_.std(axis=0)\n",
    "print(f\"unscaled centroid spread: age {spread_raw[0]:.1f}, income {spread_raw[1]:.0f}\")\n",
    "print(\"Income's spread dominates by ~1000x: the 'clusters' are just income bands.\")\n",
    "print(\"Mitigation: StandardScaler so each feature contributes on equal footing (Ch 07).\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "870742bd",
   "metadata": {},
   "source": [
    "> **Caveat.** Standardization is not neutral either: it asserts every feature *should* matter equally, which is itself a modeling choice. There is no value-free clustering. The honest move is to state which features you scaled and why, and to audit per-cluster composition before anyone acts on the groups.\n",
    "\n",
    "Two more habits the chapter argues for: for anomaly detection, evaluate at a fixed alert volume (precision among the top-N you can actually review), not accuracy or F1, because anomalies are rare and the base rate is brutal. For semi-supervised label propagation, require at least one labeled example per cluster, or you amplify whatever bias is in the labeled subset across the whole dataset.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0b25626d",
   "metadata": {},
   "source": [
    "## Test yourself\n",
    "\n",
    "Three parts: concept self-checks with folded answers, two auto-checked problems, and a capstone with a rubric and a folded reference. Try before you peek; every answer is somewhere in this notebook, and if unsure, re-run that section.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "24ed41a4",
   "metadata": {},
   "source": [
    "### Part A — Concepts\n",
    "\n",
    "1. You initialize a from-scratch k-means with `prev_labels = np.zeros(m)` to detect convergence. On what data does this silently return the initialization untouched? <details><summary>Answer</summary>Any data where the first assignment step puts every point in cluster 0. Then `new_labels == np.zeros(m)` is `True`, the loop breaks at iteration 0, and no centroid moves. We triggered exactly this in Part 2 with one blob and a far-off second centroid. Fix: a sentinel outside the label space, `np.full(m, -1)`.</details>\n",
    "2. Why does inertia keep falling as you raise `k`, and what does that mean for using inertia to choose `k`? <details><summary>Answer</summary>More centroids can only reduce (or hold) every point's distance to its nearest one; at `k = m` inertia is 0. So inertia is monotone non-increasing in `k` and its minimum is always the largest `k`. You cannot minimize it to choose `k`; you look for an elbow (subjective) or switch to silhouette.</details>\n",
    "3. K-means and GMM both fail on the two-moons dataset in this notebook (look at the Part 6 grid). DBSCAN succeeds. In one sentence, why? <details><summary>Answer</summary>K-means and a default GMM both impose roughly convex, blob-shaped regions, which slice the interleaved crescents; DBSCAN clusters by connected density and so follows each moon's curve.</details>\n",
    "4. The GMM `predict_proba` output had rows summing to 1.0 (we asserted it). What quantity is each row, and which EM step computes it? <details><summary>Answer</summary>Each row is the posterior over which Gaussian generated that point, the responsibilities $\\gamma_{ij}$. The E step computes them by Bayes' rule: prior-weighted density normalized across components.</details>\n",
    "5. Look at the EM log-likelihood plot in Part 5. What theoretical guarantee does its shape illustrate, and what would a downward step tell you about your code? <details><summary>Answer</summary>EM never decreases the data log-likelihood (Dempster, Laird, Rubin 1977), so the curve climbs and flattens. A downward step means a bug, almost always an E step whose responsibilities do not sum to 1, so it is not a true posterior.</details>\n",
    "6. Quick task, one line: given `labels` from DBSCAN, count the noise points. <details><summary>Answer</summary>`int((labels == -1).sum())`. DBSCAN marks noise with the label `-1`, which is also why you subtract one from `len(set(labels))` to count real clusters.</details>\n",
    "7. Why is \"accuracy\" the wrong headline metric for a GMM anomaly detector on a stream that is 0.1% anomalous? <details><summary>Answer</summary>Predicting \"never anomalous\" scores 99.9% accuracy while catching nothing. The base rate makes accuracy meaningless; report precision at a fixed alert volume (the top-N you can review) and recall on confirmed cases.</details>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8fe714dc",
   "metadata": {},
   "source": [
    "### Part B1 — Silhouette of one point, from the definition\n",
    "`Difficulty 3/5 · ~15 min`\n",
    "\n",
    "Implement `silhouette_one(i, X, labels)` for a single point using the definition: $a(i)$ is the mean distance from point $i$ to the *other* points in its own cluster, $b(i)$ is the smallest mean distance from $i$ to any *other* cluster, and $s(i) = (b - a)/\\max(a, b)$. The check compares the dataset mean of your per-point silhouettes to sklearn's `silhouette_score` on the blobs.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "3a55a3d9",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-06-10T18:44:23.750802Z",
     "iopub.status.busy": "2026-06-10T18:44:23.750717Z",
     "iopub.status.idle": "2026-06-10T18:44:23.759405Z",
     "shell.execute_reply": "2026-06-10T18:44:23.759117Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ -- ] B1 silhouette vs sklearn: not attempted yet — fill in the TODO above, then re-run.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def silhouette_one(i, X, labels):\n",
    "    \"\"\"Silhouette of point i. Returns a float in [-1, 1].\"\"\"\n",
    "    labels = np.asarray(labels)\n",
    "    own = labels[i]\n",
    "    dists = np.sqrt(((X - X[i]) ** 2).sum(axis=1))   # Euclidean distance to all points\n",
    "    # TODO 1: a(i) = mean distance to OTHER points in the same cluster (exclude i itself).\n",
    "    same = (labels == own); same[i] = False\n",
    "    a = None\n",
    "    # TODO 2: b(i) = min over other clusters of the mean distance to that cluster's points.\n",
    "    b = None\n",
    "    attempted(a, b)\n",
    "    return (b - a) / max(a, b)\n",
    "\n",
    "def _silhouette_vs_sklearn():\n",
    "    from sklearn.metrics import silhouette_score\n",
    "    Xs, _ = make_blobs(n_samples=200, centers=3, cluster_std=0.60, random_state=SEED)\n",
    "    lab = KMeans(n_clusters=3, n_init=10, random_state=SEED).fit_predict(Xs)\n",
    "    mine = np.mean([silhouette_one(i, Xs, lab) for i in range(len(Xs))])\n",
    "    ref = silhouette_score(Xs, lab)\n",
    "    check_close(mine, ref, atol=1e-6, msg=\"your mean silhouette disagrees with sklearn\")\n",
    "\n",
    "check(\"B1 silhouette vs sklearn\", _silhouette_vs_sklearn)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bb7c09a2",
   "metadata": {},
   "source": [
    "<details><summary>Hint 1 (conceptual)</summary>`a(i)` averages `dists` over the same-cluster mask with `i` removed. For `b(i)`, loop over every cluster id that is not `own`, take the mean of `dists` over that cluster, and keep the smallest.</details>\n",
    "\n",
    "<details><summary>Hint 2 (pseudocode)</summary>\n",
    "\n",
    "```python\n",
    "a = dists[same].mean()\n",
    "b = min(dists[labels == c].mean() for c in np.unique(labels) if c != own)\n",
    "```\n",
    "</details>\n",
    "\n",
    "<details><summary>Help — value is off only for tiny clusters</summary>If a cluster has a single point, `a(i)` is the mean of an empty set. sklearn defines that point's silhouette as 0. The blobs here have no singletons, so the straightforward formula matches; just be aware of the edge case.</details>\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "7c677acf",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-06-10T18:44:23.760379Z",
     "iopub.status.busy": "2026-06-10T18:44:23.760303Z",
     "iopub.status.idle": "2026-06-10T18:44:23.789212Z",
     "shell.execute_reply": "2026-06-10T18:44:23.788878Z"
    },
    "collapsed": true,
    "jupyter": {
     "source_hidden": true
    },
    "tags": [
     "hide-input"
    ]
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ ok ] B1 silhouette vs sklearn\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#@title Solution { display-mode: \"form\" }\n",
    "# solution: redefines silhouette_one; the check re-verifies the mean against sklearn.\n",
    "def silhouette_one(i, X, labels):\n",
    "    labels = np.asarray(labels)\n",
    "    own = labels[i]\n",
    "    dists = np.sqrt(((X - X[i]) ** 2).sum(axis=1))\n",
    "    same = (labels == own); same[i] = False\n",
    "    a = dists[same].mean()\n",
    "    b = min(dists[labels == c].mean() for c in np.unique(labels) if c != own)\n",
    "    return (b - a) / max(a, b)\n",
    "\n",
    "check(\"B1 silhouette vs sklearn\", _silhouette_vs_sklearn, required=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b31bf59b",
   "metadata": {},
   "source": [
    "> **Interpretation.** Your mean of per-point silhouettes reproduces sklearn's scalar exactly, which means you now know what that one number actually measures: how much closer each point is to its own cluster than to the nearest rival.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "018dbb50",
   "metadata": {},
   "source": [
    "### Part B2 — A density-threshold anomaly flagger\n",
    "`Difficulty 2/5 · ~10 min`\n",
    "\n",
    "Implement `flag_anomalies(gm, X, contamination)` that returns a boolean mask flagging the `contamination` fraction of points with the *lowest* log-density under a fitted `GaussianMixture gm`. The check asserts the flagged fraction matches `contamination` and that the flagged points really are the lowest-density ones.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "779f1e2a",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-06-10T18:44:23.790578Z",
     "iopub.status.busy": "2026-06-10T18:44:23.790477Z",
     "iopub.status.idle": "2026-06-10T18:44:23.989224Z",
     "shell.execute_reply": "2026-06-10T18:44:23.988843Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ -- ] B2 anomaly flagger: not attempted yet — fill in the TODO above, then re-run.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def flag_anomalies(gm, X, contamination=0.05):\n",
    "    \"\"\"Flag the lowest-log-density `contamination` fraction of X. Returns bool mask (m,).\"\"\"\n",
    "    log_dens = gm.score_samples(X)\n",
    "    # TODO 1: threshold = the (contamination*100)th percentile of log_dens.\n",
    "    threshold = None\n",
    "    # TODO 2: flag points strictly below the threshold.\n",
    "    mask = None\n",
    "    attempted(threshold, mask)\n",
    "    return mask\n",
    "\n",
    "def _anomaly_flagger():\n",
    "    Xa, _ = make_blobs(n_samples=400, centers=3, cluster_std=0.60, random_state=SEED)\n",
    "    gm = GaussianMixture(n_components=3, n_init=10, random_state=SEED).fit(Xa)\n",
    "    mask = flag_anomalies(gm, Xa, contamination=0.10)\n",
    "    frac = mask.mean()\n",
    "    assert 0.07 <= frac <= 0.13, f\"flagged {frac:.0%}, expected ~10% (percentile threshold)\"\n",
    "    ld = gm.score_samples(Xa)\n",
    "    assert ld[mask].max() <= ld[~mask].min() + 1e-9, \\\n",
    "        \"flagged points must be the LOWEST density; check your strictly-below comparison\"\n",
    "\n",
    "check(\"B2 anomaly flagger\", _anomaly_flagger)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "404ed24d",
   "metadata": {},
   "source": [
    "<details><summary>Hint 1</summary>`np.percentile(log_dens, contamination * 100)` is the cutoff; flag `log_dens < threshold`.</details>\n",
    "<details><summary>Solution</summary>\n",
    "\n",
    "```python\n",
    "def flag_anomalies(gm, X, contamination=0.05):\n",
    "    log_dens = gm.score_samples(X)\n",
    "    threshold = np.percentile(log_dens, contamination * 100)\n",
    "    return log_dens < threshold\n",
    "```\n",
    "</details>\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "506868dc",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-06-10T18:44:23.992538Z",
     "iopub.status.busy": "2026-06-10T18:44:23.992387Z",
     "iopub.status.idle": "2026-06-10T18:44:24.139486Z",
     "shell.execute_reply": "2026-06-10T18:44:24.139099Z"
    },
    "collapsed": true,
    "jupyter": {
     "source_hidden": true
    },
    "tags": [
     "hide-input"
    ]
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ ok ] B2 anomaly flagger\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#@title Solution { display-mode: \"form\" }\n",
    "# solution: redefines flag_anomalies; the check re-verifies fraction and ordering.\n",
    "def flag_anomalies(gm, X, contamination=0.05):\n",
    "    log_dens = gm.score_samples(X)\n",
    "    threshold = np.percentile(log_dens, contamination * 100)\n",
    "    return log_dens < threshold\n",
    "\n",
    "check(\"B2 anomaly flagger\", _anomaly_flagger, required=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5e64ca4b",
   "metadata": {},
   "source": [
    "### Part C — Capstone: a full unsupervised pipeline on a varied-density dataset\n",
    "\n",
    "Build the practitioner's workflow end to end on a dataset designed to be hard: three blobs of *different* densities plus a sprinkle of uniform noise. Deliverables:\n",
    "\n",
    "1. Generate the data with known ground truth (three blobs, distinct `cluster_std`, plus ~5% uniform noise points labeled as a separate \"noise\" class).\n",
    "2. Run k-means (`k=3`), DBSCAN (tune `eps` by the k-distance heuristic or by hand), and a BIC-selected GMM. Score each by adjusted Rand index against the truth.\n",
    "3. Use the best method's result to report which cluster the noise points mostly landed in, and whether DBSCAN's `-1` label caught them.\n",
    "\n",
    "Self-assessment (pass / partial / fail): (a) all three methods run and are scored by ARI; (b) you picked DBSCAN's `eps` deliberately and can say why; (c) the GMM's `k` came from BIC, not a guess; (d) you state which method you would ship and justify it from the data's shape; (e) the notebook still runs top to bottom.\n",
    "\n",
    "<details><summary>My solution (reference, ~10s on CPU)</summary>\n",
    "\n",
    "```python\n",
    "# 1. varied-density data + uniform noise\n",
    "rng_c = np.random.default_rng(SEED)\n",
    "Xa, ya = make_blobs(n_samples=[200, 200, 200], cluster_std=[0.3, 0.8, 1.6],\n",
    "                    centers=[[-6, 0], [0, 0], [6, 0]], random_state=SEED)\n",
    "noise = rng_c.uniform(-10, 10, size=(30, 2))\n",
    "Xc = np.vstack([Xa, noise]); yc = np.concatenate([ya, np.full(30, 3)])  # 3 = noise class\n",
    "\n",
    "# 2. three methods\n",
    "km = KMeans(n_clusters=3, n_init=10, random_state=SEED).fit_predict(Xc)\n",
    "db = DBSCAN(eps=0.9, min_samples=5).fit_predict(Xc)   # eps from k-distance elbow\n",
    "bics = [GaussianMixture(n_components=k, n_init=5, random_state=SEED).fit(Xc).bic(Xc) for k in range(1, 8)]\n",
    "best_k = int(np.argmin(bics)) + 1\n",
    "gm = GaussianMixture(n_components=best_k, n_init=5, random_state=SEED).fit_predict(Xc)\n",
    "for name, lab in [(\"k-means\", km), (\"DBSCAN\", db), (\"GMM\", gm)]:\n",
    "    print(f\"{name:8} ARI {adjusted_rand_score(yc, lab):.2f}\")\n",
    "print(\"BIC chose k =\", best_k)\n",
    "\n",
    "# 3. did DBSCAN's noise label catch the planted noise?\n",
    "caught = (db[-30:] == -1).mean()\n",
    "print(f\"DBSCAN labeled {caught:.0%} of the planted noise as -1\")\n",
    "```\n",
    "The lesson: DBSCAN's `-1` catches the uniform noise that k-means and GMM are forced to assign to *some* cluster, but DBSCAN's single `eps` struggles with the 5x density range across the three blobs (the tight blob and the diffuse blob want different `eps`). That tension is exactly where HDBSCAN earns its place. There is no clean winner here, which is the honest answer for varied-density data.</details>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d6649aa1",
   "metadata": {},
   "source": [
    "## Reflection\n",
    "\n",
    "In about 150 words, write up the dumbest bug you hit in this notebook and how you found it. A strong candidate: the convergence sentinel. Did you reach for `np.zeros` before reading Part 2? What made the failure visible (the centroid that never moved, the `0 iterations ran` print)? Or maybe your EM log-likelihood dipped on one step and you traced it to responsibilities that did not sum to 1. Write what the symptom was, what you suspected, what you printed to confirm it, and what the fix was.\n",
    "\n",
    "Nobody grades this. Writing it is the point: the muscle you are building is *systematic debugging*, the ability to turn \"it's broken\" into \"here is the one line that was wrong and here is how I proved it.\" That skill outlasts every algorithm in this chapter.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6463799b",
   "metadata": {},
   "source": [
    "## Going further\n",
    "\n",
    "- Géron, *Hands-On ML* 3e, Ch 9 — the full unsupervised tour this notebook compresses, including image segmentation and semi-supervised label propagation.\n",
    "- Arthur and Vassilvitskii, *k-means++: The Advantages of Careful Seeding* (2007) — the initializer you implemented, with its $O(\\log k)$ guarantee.\n",
    "- Ester, Kriegel, Sander, Xu, *A Density-Based Algorithm* (1996) — the original DBSCAN paper.\n",
    "- McInnes, Healy, Astels, *HDBSCAN* — the hierarchical fix for DBSCAN's single-`eps` weakness on variable-density data; the right next step.\n",
    "- Dempster, Laird, Rubin (1977) and MacKay's *ITILA* Ch 20-22 — the EM algorithm derived and the Bayesian view of mixtures.\n",
    "- scikit-learn user guide, *Clustering* and *Gaussian mixture models* — the method-selection table and every variant's API.\n",
    "\n",
    "## What this enables\n",
    "\n",
    "- **Ch 18 — Generative models**: a GMM is the simplest generative model. The lineage runs GMM → VAE → diffusion, and the EM you wrote here is the on-ramp to variational inference.\n",
    "- **Ch 21 — RAG**: retrieval is nearest-neighbor search, and k-means plus product quantization is the classic fast-ANN index under FAISS.\n",
    "- **Ch 22 — Mech-interp**: clustering on network activations is an early move in exploratory interpretability, before sparse autoencoders take over.\n",
    "\n",
    "> **Forward teaser.** We clustered fixed feature vectors. The frontier question is where those vectors come from: a GMM caps out on tabular data, but the same \"fit a density, sample from it, score anomalies by it\" recipe, run through a deep network instead of a handful of Gaussians, is exactly what Ch 18's diffusion models do at scale.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8d290d8b",
   "metadata": {},
   "source": [
    "---\n",
    "*Built top-to-bottom. If every check above printed `[ ok ]`, you've reproduced the chapter. Total running time and verification stamp written by CI.*\n"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "obvix-nb",
   "language": "python",
   "name": "obvix-nb"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.12"
  },
  "obvix": {
   "title": "Ch 08 — Unsupervised Learning"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
