diff --git a/.notebook_test_durations b/.notebook_test_durations
index 1d87378ae..8fd388a2f 100644
--- a/.notebook_test_durations
+++ b/.notebook_test_durations
@@ -1,11 +1,12 @@
 {
-    "notebooks/data_oob.ipynb::": 14.608769827000287,
-    "notebooks/influence_imagenet.ipynb::": 13.570316236000508,
-    "notebooks/influence_sentiment_analysis.ipynb::": 20.546479973001624,
-    "notebooks/influence_synthetic.ipynb::": 5.9324631089984905,
-    "notebooks/influence_wine.ipynb::": 16.114133220999065,
-    "notebooks/least_core_basic.ipynb::": 14.312467472000208,
-    "notebooks/shapley_basic_spotify.ipynb::": 15.608795123000164,
-    "notebooks/shapley_knn_flowers.ipynb::": 3.9430189769991557,
-    "notebooks/shapley_utility_learning.ipynb::": 26.96671833400069
+    "notebooks/data_oob.ipynb::": 14.514983271001256,
+    "notebooks/influence_imagenet.ipynb::": 15.937124550999215,
+    "notebooks/influence_sentiment_analysis.ipynb::": 26.479645616000198,
+    "notebooks/influence_synthetic.ipynb::": 6.61773010700017,
+    "notebooks/influence_wine.ipynb::": 16.312171267998565,
+    "notebooks/least_core_basic.ipynb::": 14.375480750999486,
+    "notebooks/msr_banzhaf_digits.ipynb::": 106.6507187110019,
+    "notebooks/shapley_basic_spotify.ipynb::": 15.657225806997303,
+    "notebooks/shapley_knn_flowers.ipynb::": 3.9943819290019746,
+    "notebooks/shapley_utility_learning.ipynb::": 25.939783253001224
 }
\ No newline at end of file
diff --git a/CHANGELOG.md b/CHANGELOG.md
index 28a6635f3..9fcced76e 100644
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -6,6 +6,9 @@
 
 - New method: `NystroemSketchInfluence`
   [PR #504](https://github.com/aai-institute/pyDVL/pull/504)
+- New method `MSR Banzhaf` with accompanying notebook, and new stopping
+  criterion `RankCorrelation`
+  [PR #520](https://github.com/aai-institute/pyDVL/pull/520)
 - New preconditioned block variant of conjugate gradient
   [PR #507](https://github.com/aai-institute/pyDVL/pull/507)
 - Improvements to documentation: fixes, links, text, example gallery, LFS and
diff --git a/docs/assets/pydvl.bib b/docs/assets/pydvl.bib
index e5b1571e6..724e75f20 100644
--- a/docs/assets/pydvl.bib
+++ b/docs/assets/pydvl.bib
@@ -450,7 +450,7 @@ @book{trefethen_numerical_1997
   langid = {english}
 }
 
-@inproceedings{wang_data_2022,
+@inproceedings{wang_data_2023,
   title = {Data {{Banzhaf}}: {{A Robust Data Valuation Framework}} for {{Machine Learning}}},
   shorttitle = {Data {{Banzhaf}}},
   booktitle = {Proceedings of {{The}} 26th {{International Conference}} on {{Artificial Intelligence}} and {{Statistics}}},
diff --git a/docs/examples/img/msr_banzhaf_digits.png b/docs/examples/img/msr_banzhaf_digits.png
new file mode 100644
index 000000000..a21479b9f
--- /dev/null
+++ b/docs/examples/img/msr_banzhaf_digits.png
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:73bcfab3eadb3935722e78cc6b556a67cde50125cb7f53461ee5f468d6cb5b6d
+size 21291
diff --git a/docs/examples/index.md b/docs/examples/index.md
index 3ab36c0a2..9384ea4d4 100644
--- a/docs/examples/index.md
+++ b/docs/examples/index.md
@@ -53,6 +53,15 @@ alias:
 
     [![](img/data_oob.png)](data_oob/)
 
+-  [__Faster Banzhaf values__](msr_banzhaf_digits/)
+
+    ---
+
+    Using Banzhaf values to estimate the value of data points in MNIST, and
+    evaluating convergence speed of MSR. 
+
+    [![](img/msr_banzhaf_digits.png)](msr_banzhaf_digits/)
+
 </div>
 
 
diff --git a/docs/getting-started/glossary.md b/docs/getting-started/glossary.md
index 56ce7786f..ecb1eaf2c 100644
--- a/docs/getting-started/glossary.md
+++ b/docs/getting-started/glossary.md
@@ -147,6 +147,15 @@ performance when that point is removed from the training set.
  * [Implementation][pydvl.value.loo.loo.compute_loo]
  * [Documentation][leave-one-out-values]
 
+### Maximum Sample Reuse
+
+MSR is a sampling method for data valuation that updates the value of every
+data point in one sample. This method can achieve much faster convergence.
+Introduced by [@wang_data_2023]
+
+* [Implementation][pydvl.value.sampler.MSRSampler]
+
+
 ### Monte Carlo Least Core
 
 MCLC is a variation of the Least Core that uses a reduced amount of
diff --git a/docs/value/semi-values.md b/docs/value/semi-values.md
index 408a90c28..515418601 100644
--- a/docs/value/semi-values.md
+++ b/docs/value/semi-values.md
@@ -21,7 +21,7 @@ the set $D_{-i}^{(k)}$ contains all subsets of $D$ of size $k$ that do not
 include sample $x_i$, $S_{+i}$ is the set $S$ with $x_i$ added, and $u$ is the
 utility function.
 
-Two instances of this are **Banzhaf indices** [@wang_data_2022],
+Two instances of this are **Banzhaf indices** [@wang_data_2023],
 and **Beta Shapley** [@kwon_beta_2022], with better numerical and
 rank stability in certain situations.
 
@@ -84,7 +84,7 @@ any choice of weight function $w$, one can always construct a utility with
 higher variance where $w$ is greater. Therefore, in a worst-case sense, the best
 one can do is to pick a constant weight.
 
-The authors of [@wang_data_2022] show that Banzhaf indices are more robust to
+The authors of [@wang_data_2023] show that Banzhaf indices are more robust to
 variance in the utility function than Shapley and Beta Shapley values. They are
 available in pyDVL through
 [compute_banzhaf_semivalues][pydvl.value.semivalues.compute_banzhaf_semivalues]:
@@ -98,6 +98,37 @@ values = compute_banzhaf_semivalues(
 )
 ```
 
+### Banzhaf semi-values with MSR sampling
+Wang et. al. propose a more sample-efficient method for computing Banzhaf 
+semivalues in their paper *Data Banzhaf: A Robust Data Valuation Framework 
+for Machine Learning* [@wang_data_2023]. This method updates all semivalues
+per evaluation of the utility (i.e. per model trained) based on whether a 
+specific data point was included in the data subset or not. The expression 
+for computing the semivalues is
+
+$$\hat{\phi}_{MSR}(i) = \frac{1}{|\mathbf{S}_{\ni i}|} \sum_{S \in 
+\mathbf{S}_{\ni i}} U(S) - \frac{1}{|\mathbf{S}_{\not{\ni} i}|} 
+\sum_{S \in \mathbf{S}_{\not{\ni} i}} U(S)$$
+
+where $\mathbf{S}_{\ni i}$ are the subsets that contain the index $i$ and 
+$\mathbf{S}_{\not{\ni} i}$ are the subsets not containing the index $i$.
+
+The function implementing this method is
+[compute_msr_banzhaf_semivalues][pydvl.value.semivalues.compute_msr_banzhaf_semivalues].
+
+```python
+from pydvl.value import compute_msr_banzhaf_semivalues, RankCorrelation, Utility
+
+utility = Utility(model, data)
+values = compute_msr_banzhaf_semivalues(
+  u=utility, done=RankCorrelation(rtol=0.001),
+  )
+```
+For further details on how to use this method and a comparison of the sample 
+efficiency, we suggest to take a look at the example notebook 
+[msr_banzhaf_spotify](../../examples/msr_banzhaf_spotify).
+
+
 ## General semi-values
 
 As explained above, both Beta Shapley and Banzhaf indices are special cases of
@@ -130,7 +161,7 @@ values = compute_generic_semivalues(
 Allowing any coefficient can help when experimenting with models which are more
 sensitive to changes in training set size. However, Data Banzhaf indices are
 proven to be the most robust to variance in the utility function, in the sense
-of rank stability, across a range of models and datasets [@wang_data_2022]. 
+of rank stability, across a range of models and datasets [@wang_data_2023]. 
 
 !!! warning "Careful with permutation sampling"
     This generic implementation of semi-values allowing for any combination of
diff --git a/docs_includes/abbreviations.md b/docs_includes/abbreviations.md
index a584b73d8..9465106f3 100644
--- a/docs_includes/abbreviations.md
+++ b/docs_includes/abbreviations.md
@@ -19,6 +19,7 @@
 *[MLRC]: Machine Learning Reproducibility Challenge
 *[ML]: Machine Learning
 *[MSE]: Mean Squared Error
+*[MSR]: Maximum Sample Reuse
 *[NLRA]: Nyström Low-Rank Approximation
 *[OOB]: Out-of-Bag
 *[PCA]: Principal Component Analysis
diff --git a/mkdocs.yml b/mkdocs.yml
index df34dda4e..6c8732760 100644
--- a/mkdocs.yml
+++ b/mkdocs.yml
@@ -34,6 +34,7 @@ nav:
         - Data utility learning: examples/shapley_utility_learning.ipynb
         - Least Core: examples/least_core_basic.ipynb
         - Data OOB: examples/data_oob.ipynb
+        - Banzhaf Semivalues: examples/msr_banzhaf_digits.ipynb
     - Influence Function:
       - For CNNs: examples/influence_imagenet.ipynb
       - For mislabeled data: examples/influence_synthetic.ipynb
diff --git a/notebooks/least_core_basic.ipynb b/notebooks/least_core_basic.ipynb
index 1aded58ff..5967cfe0f 100644
--- a/notebooks/least_core_basic.ipynb
+++ b/notebooks/least_core_basic.ipynb
@@ -293,7 +293,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "4aef7143d90345c3abcbc1cc6a731af9",
+       "model_id": "55d514afeb324f01a622948de437baf9",
        "version_major": 2,
        "version_minor": 0
       },
@@ -341,7 +341,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "43c250efdcf141cb95364b5adc66c72d",
+       "model_id": "d191d4d6ea024602a5c9ff6399bdf099",
        "version_major": 2,
        "version_minor": 0
       },
@@ -372,7 +372,7 @@
     "        )\n",
     "        df = (\n",
     "            values.to_dataframe(column=column_name)\n",
-    "            .drop(columns=[f\"{column_name}_stderr\"])\n",
+    "            .drop(columns=[f\"{column_name}_stderr\", f\"{column_name}_updates\"])\n",
     "            .T\n",
     "        )\n",
     "        df = df[sorted(df.columns)]\n",
@@ -406,7 +406,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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       "text/plain": [
        "<Figure size 2000x800 with 1 Axes>"
       ]
@@ -457,7 +457,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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       "text/plain": [
        "<Figure size 2000x800 with 1 Axes>"
       ]
@@ -547,7 +547,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "d89e8aac1ce4453f9e4d097bee9d8d2e",
+       "model_id": "9395b5d2928a4919b5395fdf05107722",
        "version_major": 2,
        "version_minor": 0
       },
@@ -602,7 +602,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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       "text/plain": [
        "<Figure size 2000x800 with 1 Axes>"
       ]
@@ -669,7 +669,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "241aded4b743402b962d09d78df0705f",
+       "model_id": "fcd1e0c6103a4b2aa4358a2b82218c66",
        "version_major": 2,
        "version_minor": 0
       },
@@ -723,7 +723,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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       "text/plain": [
        "<Figure size 2000x800 with 1 Axes>"
       ]
diff --git a/notebooks/msr_banzhaf_digits.ipynb b/notebooks/msr_banzhaf_digits.ipynb
new file mode 100644
index 000000000..046d318e2
--- /dev/null
+++ b/notebooks/msr_banzhaf_digits.ipynb
@@ -0,0 +1,1532 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Banzhaf Semi-values for data valuation\n",
+    "\n",
+    "This notebook showcases [Data Banzhaf: A Robust Data Valuation Framework for Machine Learning](https://proceedings.mlr.press/v206/wang23e.html) by Wang, and Jia.\n",
+    "\n",
+    "Computing Banzhaf semi-values using pyDVL follows basically the same procedure as all other semi-value-based methods like Shapley values. However, Data-Banzhaf tends to be more robust to stochasticity in the training process than other semi-values. A property that we study here.\n",
+    "\n",
+    "Additionally, we compare two sampling techniques: the standard permutation-based Monte Carlo sampling, and the so-called MSR (Maximum Sample Reuse) principle.\n",
+    "\n",
+    "In order to highlight the strengths of Data-Banzhaf, we require a stochastic model. For this reason, we use a CNN to classify handwritten digits from the [scikit-learn toy datasets](https://scikit-learn.org/stable/datasets/toy_dataset.html#optical-recognition-of-handwritten-digits-dataset)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Setup\n",
+    "\n",
+    "<div class=\"alert alert-info\">\n",
+    "\n",
+    "If you are reading this in the documentation, some boilerplate (including most plotting code) has been omitted for convenience.\n",
+    "\n",
+    "</div>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "tags": [
+     "hide"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "%load_ext autoreload"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "tags": [
+     "hide"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "%matplotlib inline\n",
+    "\n",
+    "import os\n",
+    "import random\n",
+    "\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "from tqdm import tqdm\n",
+    "\n",
+    "plt.ioff()  # Prevent jupyter from automatically plotting\n",
+    "plt.rcParams[\"figure.figsize\"] = (20, 6)\n",
+    "plt.rcParams[\"font.size\"] = 12\n",
+    "plt.rcParams[\"xtick.labelsize\"] = 10\n",
+    "plt.rcParams[\"ytick.labelsize\"] = 10\n",
+    "plt.rcParams[\"axes.facecolor\"] = (1, 1, 1, 0)\n",
+    "plt.rcParams[\"figure.facecolor\"] = (1, 1, 1, 0)\n",
+    "\n",
+    "is_CI = os.environ.get(\"CI\")\n",
+    "random_state = 24\n",
+    "n_jobs = 16\n",
+    "random.seed(random_state)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We will be using the following functions from pyDVL. The main entry point is the function `compute_banzhaf_semivalues()`.\n",
+    "In order to use it we need the classes [Dataset](../../api/pydvl/utils/dataset/#pydvl.utils.dataset.Dataset), [Utility](../../api/pydvl/utils/utility/#pydvl.utils.utility.Utility) and [Scorer](../../api/pydvl/utils/score/#pydvl.utils.score.Scorer)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%autoreload\n",
+    "from pydvl.reporting.plots import plot_shapley\n",
+    "from support.banzhaf import load_digits_dataset\n",
+    "from pydvl.value import *"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Loading the dataset\n",
+    "\n",
+    "We use a support function, `load_digits_dataset()`, which downloads the data and prepares it for usage. It returns four arrays that we then use to construct a [Dataset](../../api/pydvl/utils/dataset/#pydvl.utils.dataset.Dataset). The data consists of grayscale images of shape 8x8 pixels with 16 shades of gray. These images contain handwritten digits from 0 to 9."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "editable": true,
+    "slideshow": {
+     "slide_type": ""
+    },
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "training_data, _, test_data = load_digits_dataset(\n",
+    "    test_size=0.3, random_state=random_state\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "editable": true,
+    "slideshow": {
+     "slide_type": ""
+    },
+    "tags": [
+     "hide"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "# In CI we only use a subset of the training set\n",
+    "training_data = list(training_data)\n",
+    "if is_CI:\n",
+    "    training_data[0] = training_data[0][:10]\n",
+    "    training_data[1] = training_data[1][:10]\n",
+    "    max_checks = 1\n",
+    "else:\n",
+    "    training_data[0] = training_data[0][:200]\n",
+    "    training_data[1] = training_data[1][:200]\n",
+    "    max_checks = 1000"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "tags": [
+     "hide-input",
+     "invertible-output"
+    ]
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 400x400 with 4 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Visualize some of the data\n",
+    "fig, axes = plt.subplots(2, 2, figsize=(4, 4))\n",
+    "for i in range(4):\n",
+    "    ax = axes[i % 2, i // 2]\n",
+    "    ax.imshow(np.reshape(training_data[0][i], (8, 8)), cmap=\"gray\")\n",
+    "    ax.set_xlabel(f\"Label: {training_data[1][i]}\")\n",
+    "plt.suptitle(\"Example images from the dataset\")\n",
+    "plt.tight_layout()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Training and test data are then used to instantiate a [Dataset](../../api/pydvl/utils/dataset/#pydvl.utils.dataset.Dataset) object:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "dataset = Dataset(*training_data, *test_data)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Creating the utility and computing Banzhaf semivalues\n",
+    "\n",
+    "Now we can calculate the contribution of each training sample to the model performance. First we need a model and a [Scorer](../../api/pydvl/utils/score/#pydvl.utils.score.Scorer).\n",
+    "\n",
+    "As a model, we use a simple CNN written torch, and wrapped into an object to convert numpy arrays into tensors (as of v0.9.0 valuation methods in pyDVL work only with numpy arrays). Note that any model that implements the protocol [pydvl.utils.types.SupervisedModel](../../api/pydvl/utils/types/#pydvl.utils.types.SupervisedModel), which is just the standard sklearn interface of `fit()`,`predict()` and `score()` can be used to construct the utility."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import torch\n",
+    "from support.banzhaf import TorchCNNModel\n",
+    "\n",
+    "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
+    "model = TorchCNNModel(lr=0.001, epochs=40, batch_size=32, device=device)\n",
+    "model.fit(x=training_data[0], y=training_data[1])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {
+    "tags": [
+     "hide-input"
+    ]
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Train Accuracy: 0.705\n",
+      "Test Accuracy: 0.630\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(f\"Train Accuracy: {model.score(x=training_data[0], y=training_data[1]):.3f}\")\n",
+    "print(f\"Test Accuracy: {model.score(x=test_data[0], y=test_data[1]):.3f}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The final component is the scoring function. It can be anything like accuracy or $R^2$, and is set with a string from the [standard sklearn scoring methods](https://scikit-learn.org/stable/modules/model_evaluation.html). Please refer to that documentation on information on how to define your own scoring function.\n",
+    "\n",
+    "We group dataset, model and scoring function into an instance of [Utility](../../api/pydvl/utils/utility/#pydvl.utils.utility.Utility) and compute the Banzhaf semi-values. We take all defaults, and choose to stop computation using the [MaxChecks](../../api/pydvl/value/stopping/#pydvl.value.stopping.MaxChecks) stopping criterion, which terminates after a fixed number of calls to it. With the default `batch_size` of 1 this means that we will retrain the model.\n",
+    "\n",
+    "Note how we enable caching using memcached (assuming memcached runs with the default configuration for localhost). This is necessary in the current preliminary implementation of [permutation sampling](../../api/pydvl/value/sampler/#pydvl.value.sampler.PermutationSampler), which is the default for [compute_banzhaf_semivalues](../../api/pydvl/value/semivalues#pydvl.value.semivalues.compute_banzhaf_semivalues)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {
+    "tags": [
+     "hide-output"
+    ]
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "100%|█████████▉| 99.9/100 [01:01<00:00,  1.62%/s]              \n"
+     ]
+    }
+   ],
+   "source": [
+    "from pydvl.utils import MemcachedCacheBackend, MemcachedClientConfig\n",
+    "\n",
+    "# Compute regular Banzhaf semivalue\n",
+    "utility = Utility(\n",
+    "    model=model,\n",
+    "    data=dataset,\n",
+    "    scorer=Scorer(\"accuracy\", default=0.0, range=(0, 1)),\n",
+    "    cache_backend=MemcachedCacheBackend(MemcachedClientConfig()),\n",
+    ")\n",
+    "values = compute_banzhaf_semivalues(\n",
+    "    utility, done=MaxChecks(max_checks), n_jobs=n_jobs, progress=True\n",
+    ")\n",
+    "values.sort(key=\"value\")\n",
+    "df = values.to_dataframe(column=\"banzhaf_value\", use_names=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The returned dataframe contains the mean and variance of the Monte Carlo estimates for the values:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {
+    "tags": [
+     "hide-input"
+    ]
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>banzhaf_value</th>\n",
+       "      <th>banzhaf_value_stderr</th>\n",
+       "      <th>banzhaf_value_updates</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>156</th>\n",
+       "      <td>-1.097920</td>\n",
+       "      <td>6.662418e-02</td>\n",
+       "      <td>5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>21</th>\n",
+       "      <td>-0.925489</td>\n",
+       "      <td>1.230752e-01</td>\n",
+       "      <td>5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>152</th>\n",
+       "      <td>-0.913313</td>\n",
+       "      <td>3.358054e-02</td>\n",
+       "      <td>5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>73</th>\n",
+       "      <td>-0.778884</td>\n",
+       "      <td>3.668419e-05</td>\n",
+       "      <td>5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>85</th>\n",
+       "      <td>-0.644435</td>\n",
+       "      <td>3.454322e-08</td>\n",
+       "      <td>5</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     banzhaf_value  banzhaf_value_stderr  banzhaf_value_updates\n",
+       "156      -1.097920          6.662418e-02                      5\n",
+       "21       -0.925489          1.230752e-01                      5\n",
+       "152      -0.913313          3.358054e-02                      5\n",
+       "73       -0.778884          3.668419e-05                      5\n",
+       "85       -0.644435          3.454322e-08                      5"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Let us plot the results. In the next cell we will take the 30 images with the lowest score and plot their values with 95% Normal confidence intervals. Keep in mind that Permutation Monte Carlo Banzhaf is typically very noisy, and it can take many steps to arrive at a clean estimate."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {
+    "editable": true,
+    "slideshow": {
+     "slide_type": ""
+    },
+    "tags": [
+     "hide-input",
+     "invertible-output"
+    ]
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 2000x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "low_dvl = df.iloc[:30].copy()\n",
+    "low_dvl.index = low_dvl.index.map(str)\n",
+    "plot_shapley(\n",
+    "    low_dvl,\n",
+    "    level=0.05,\n",
+    "    title=\"Images with low values\",\n",
+    "    xlabel=\"Image\",\n",
+    "    ylabel=\"Banzhaf value\",\n",
+    "    prefix=\"banzhaf_value\",\n",
+    ")\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Evaluation on anomalous data\n",
+    "\n",
+    "An interesting use-case for data valuation is finding anomalous data. Maybe some of the data is really noisy or has been mislabeled. To simulate this, we will change some of the labels of our dataset and add noise to some others. Intuitively, these anomalous data points should then have a lower value.\n",
+    "\n",
+    "To evaluate this, let us first check the average value of the first 10 data points, as these will be the ones that we modify. Currently, these are the 10 data points with the highest values:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {
+    "editable": true,
+    "slideshow": {
+     "slide_type": ""
+    },
+    "tags": [
+     "hide-input"
+    ]
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Average value of first 10 data points: 0.650003277874342\n",
+      "Exact values:\n",
+      "39     0.432836\n",
+      "45     0.455392\n",
+      "158    0.533221\n",
+      "144    0.571260\n",
+      "36     0.633091\n",
+      "161    0.697940\n",
+      "77     0.698507\n",
+      "28     0.752367\n",
+      "35     0.838752\n",
+      "175    0.886668\n",
+      "Name: banzhaf_value, dtype: float64\n"
+     ]
+    }
+   ],
+   "source": [
+    "high_dvl = df.iloc[-10:].copy()\n",
+    "print(f\"Average value of first 10 data points: {high_dvl['banzhaf_value'].mean()}\")\n",
+    "print(f\"Exact values:\\n{high_dvl['banzhaf_value']}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "For the first 5 images, we will falsify their label, for images 6-10, we will add some noise."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "x_train_anomalous = training_data[0].copy()\n",
+    "y_train_anomalous = training_data[1].copy()\n",
+    "anomalous_indices = high_dvl.index.map(int).values[:10]\n",
+    "\n",
+    "# Set label of first 5 images to 0\n",
+    "y_train_anomalous[high_dvl.index.map(int).values[:5]] = 0\n",
+    "\n",
+    "# Add noise to images 6-10\n",
+    "indices = high_dvl.index.values[5:10].astype(int)\n",
+    "current_images = x_train_anomalous[indices]\n",
+    "noisy_images = current_images + 0.5 * np.random.randn(*current_images.shape)\n",
+    "noisy_images[noisy_images < 0] = 0.0\n",
+    "noisy_images[noisy_images > 1] = 1.0\n",
+    "x_train_anomalous[indices] = noisy_images"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {
+    "tags": [
+     "hide-input",
+     "invertible-output"
+    ]
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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bUc/DJnn84V3zDaQ61fcY4JiIeAT5CPMi8tG0eUxyEY+ZrnMmRMSe5EbuW8DzOk9tjXzV1jd0LTLI/m1iO7wnpXTEdOqqmaMJP+P+z+WuM35E3Er+W96cfObAInIOmvJL8lH8R0fErM5tW9mluv85klrDz8xJ7bKE/J/pC6tTAyfzt+TPyv2M3qf29eNK8ueknlydNtZt75rjr8+25Cbh+z0aua24//TFOh5LPmq1tMdj+zQwPuQLj0w23t50HY0cgovJ+/M/7fHYn1br73WFx0mllK5KKX2M/Jxup3fTO3Ek7XDyUTmA/+lnPZUl1f0hETEfeDL5wjfdTeH/VfdTNS8DqT6/+cWU0kvIR3MfQ25QJ5v/NvIL4B0jYpces+xb3fe17aeo8drqs2XPJV9wZ+/qM17rc3F1v3f0/hqIxuscosdW92f1aDb2Ijc696lOVf4V+Xc0r8d4vfZvF5KP3A+Us35z1GHic6brzFuderwt+W9xkCP9U6o+D/h98tVYez33ia9y+U6PxySNiM2c1CIppV8Bx5FPKzqr14Ucqs9JnERu+l6THnjJ7kHWeTf5tKW55KNYnevanXzRiWH5PfmUyj06L+hRfSbmJPKLl7qWk49aPblzYkT8HeteKGFQS6r7f++8kEd1qe/jG1rH+ny8uj8+IrboWP8W5K9SgHxlwElFxKMi4tE9HnoQ+bS1dS6MQj5t9efkI2kvAX6W+vwS+8oXyUdQ/ob7jzQs6THfyeSjFkdHxF7dD0b+rr0F011p5O+H++Me0zchH2mEnM/1+Tj5Yhjvqo6YT4yxLfkqihPzDCQitouI3Xo8tCX5NL81TPG1F9UFgL5JPoq3uGv8p5NP37yF3qf3tc3y6n5B58TqaNgHJlnmE+TXO8dXX8Exscwj6NoecN/Fa04F9oz8vYvrvBkTEY+pvu6hqRxBzsmdwD90/i1W639X9ePnejSxTfpQdf+2av81UcPTyKf73kj+ChlJLeFpllL7HEN+oXYEcElEfB24nNzgPYv8vWV3AS8b8IVzL28kf3fQG6oXd98nn3r1EvJnNRbS40p6daWU1kbEe6v1XxoRZ5K/k21f8ougc7j/qMGgTiQ3bedVFyZYSb7Qyt7k72+r/QW4KaXzI+J95AsgXFZ9lmnie+ZuocbFW6a5/k9HxIHk39flEXEG93/m8FHkUx+7rxTYbXfyhRZ+CPyU/FnM7cjPYRPu/wxd53pTRPw3cEI16cMD1n9XRHyOfGn5vydf5v0rPeb7Q0S8mNx0/F9EfJv8t5HInyF9JvnzTt0XuJnM5uRcXEW+AMc11bL7kT+7elZK6adTjPFu8hGLA8l/r18lH9n4a/Ipc+9MKfW6yMZ07QhcHBGXAj8hXyBla/J3kD0MeO8kFy7q9mryKdnviog/J1+MZOJ75tYCh05znFH7Ifl5/FVEfJ98avr25N/Bz+j9GeJ3kv8WXgo8PiK+QX7z6iXk79VcyLr7t38kn1b4FuAVEXEe+fN1O5Cz8TTyVYCvppkcTZxy/PfkNy2WRcSXyF/ZsID8+d6fs+5ppOsVEe/m/jfFJo5C/mtE/E317zO6LpjyGfJVTV9Mzt2XyX9TB5GP8B82yYWZJI1KasH3I3jz5m3dG/mUoVPILxbuIp9ecxn5xeM635tWLbOIHt/x1jVPz++OIr9oPIX8zutd5CsbHsL93ym2uGv+pUz+PXPHTLLu5az7HVSzyI3rFdV6rydfrnxn7v9epHkd88+jj++Zq5Z5AfkUvdvIR3a+wf2fO1pne/Wqs+OxY6plFnRND/ILwJ+SL8ZwHflIwdz1jdfP76h6bJ1tUk3fiNwIXUR+d/9O8gvLfwA2mmod5CsGHkd+oTxxQYnfkL8o+HnrqfVB5KPEdzHN7zybZJy9uf87td43xbzzgPcDvwBWkY/qXVnlZuF0tlf12CbkF8dfI194ZxU5//9Hbn42nU4uyC/cjyL/fd5V5ew88hsuvWqfNL90/V2RT0N+M/nUtt9Wv5ffVfO9jI7vTZvGNt6RfOTlGvLRvJvIF8t4Wr85XM861tlGrGe/xHr2GZNtK/IbPR+s1rWKfKrrceQmerLf0TbAe8l/l6urvLyevJ9NwIk9ltmU/Df9ffKbQKurnHybfETvIYPkaBrbcAH5u+xuqX5PV5Eb0m0G+LtazgO/r6771mu7zyJ/dvbSKs+3kN/Ue1a/6/fmzdvwb5FSP9dJkLShiYi3k1+o7p/y92BJ96lOazwH+FRK6RXrn1tql4g4jHxE+dUppUE+7ylJI2UzJwmAiNghpXRd17TdyO9K3w3smPIH5KX7VKcVPg94RkrpglHXI/Uyyf7tkeQjqA8Hdu5+XJJK4GfmJE24qPrMx2XAHeTPizyffPre4TZymlA1+S8A9iA3cmfbyKnlvlBdkORH5FOt55EzvAX5OxRt5CQVySNzkgCIiKPJFwKYB8whv+D5P+Ddqfdl/bWBiohF5Is03Ap8Hfj7lNJQLpcuNaG6sMgryG9SzSV/Bvli4P0ppS+OsjZJqsNmTpIkSZIK5PfMSZIkSVKBbOYkSZIkqUA2c5IkSZJUIJs5SZIkSSqQzZwkSZIkFchmTpIkSZIKZDMnSZIkSQWymZMkSZKkAtnMSZIkSVKBbOYkSZIkqUA2c5IkSZJUIJs5SZIkSSqQzZwkSZIkFchmTpIkSZIKZDMnSZIkSQWymZMkSZKkAtnMSZIkSVKBbOYkSZIkqUA2c5IkSZJUIJs5SZIkSSqQzZwkSZIkFchmTpIkSZIKZDMnSZIkSQWymZMkSZKkAtnMSZIkSVKBbOYkSZIkqUA2c5IkSZJUIJs5SZIkSSqQzZwkSZIkFchmTpIkSZIKZDMnSZIkSQWymZMkSZKkAtnMSZIkSVKBbOYkSZIkqUA2c5IkSZJUIJs5SZIkSSqQzZwkSZIkFchmTpIkSZIKZDMnSZIkSQWymZMkSZKkAtnMSZIkSVKBbOYkSZIkqUA2c5IkSZJUIJs5SZIkSSqQzZwkSZIkFchmTpIkSZIKZDMnSZIkSQWymZMkSZKkAtnMSZIkSVKBbOYkSZIkqUA2c5IkSZJUIJs5SZIkSSqQzZwkSZIkFWjWTK8wIgLYAbhtptetIs0BrksppVEXAuZXA2lNhs2vBtCa/IIZ1kBak2HzqwFMmd8Zb+bIIf7NCNarcu0E/HbURVTMrwbRlgybXw2iLfkFM6zBtCXD5leDWG9+R9HMjd27EZ/+9KcbGef5z39+7TGaquU1r3lNI+M0pE2ZaVMtjfye3vGOdzRQCaxcubL2GLvttlsDlTRTS8Pakpu21NGYD33oQ42MM3fu3NpjHHzwwQ1U0kptyk0jtey9995NDNPI/7nnnXdeA5U08/9BC/edTWlLhhup45GPfGQTwzSSvV//+tcNVNLMa+ANNb+jaObGzhZbbNHIOFtvvXXtMTbffPMGKlEpZs+eXXuMJnIH0MQZLPkMFGn6mtrnNbUfVxlmzWrm5U8T+8+msuf+c8Ox0UbNXPKiifxutdVWDVRifuvwAiiSJEmSVKCBmrmI+IeIWB4RqyLigojYq+nCpGExvyqdGVbJzK9KZ4bVJn03cxFxEHACcCzwVOAS4OsR8dCGa5MaZ35VOjOskplflc4Mq20GOTJ3BPCRlNLJKaUrgFcDdwJ/22hl0nCYX5XODKtk5lelM8Nqlb6auYjYFNgD+NbEtJTS2urnZzZbmtQs86vSmWGVzPyqdGZYbdTv5Zy2BTYGbuiafgPwhF4LRMRsoPOSe3P6XKfUFPOr0vWVYfOrlnEfrNK5D1brzMTVLI8EVnbc/LJElcT8qmTmV6UzwyqZ+dXQ9dvM3QTcC2zfNX174PpJljkemNtx26nPdUpNMb8qXb8ZNr9qE/fBKp37YLVOX81cSulu4EfAsyemRcRG1c8/mGSZ1SmlWyduTPEt5tKwmF+Vrt8Mm1+1iftglc59sNqo38/MQb4c6ykRcRFwIbAY2BI4ucG6pGExvyqdGVbJzK9KZ4bVKn03cyml0yNiO+AtwMOAZcD+KaXuD4NKrWN+VTozrJKZX5XODKttBjkyR0rp/cD7G65FmhHmV6UzwyqZ+VXpzLDaZCauZilJkiRJapjNnCRJkiQVaKDTLMfJiSeeWHuMAw88sH4hwCmnnFJ7jGXLltUvREPXVGbe85731B7jkksuaaAS2H333WuPsXjx4vqFAMccc0wj42i4mtj/HnLIIfULAQ499NBGxlEZFixY0Mg4c+fOrT1GU/8fzJ8/v/YYS5curT2Ghu+MM85oZJwmXjPOmzev9hgAixYtqj1GE/+nlMgjc5IkSZJUIJs5SZIkSSqQzZwkSZIkFchmTpIkSZIKZDMnSZIkSQWymZMkSZKkAtnMSZIkSVKBbOYkSZIkqUA2c5IkSZJUIJs5SZIkSSqQzZwkSZIkFchmTpIkSZIKZDMnSZIkSQWymZMkSZKkAtnMSZIkSVKBbOYkSZIkqUA2c5IkSZJUoEgpzewKI7YGVtYdZ/78+fWLAS6++OLaYxx77LENVALHHHNMI+OMobkppVtHXQQ0l98TTzyxfjHAvHnzao+xcOHC2mMALFiwoPYY55xzTv1CgH333bf2GEuXLq1fyP1akeGm8tuUFStW1B5j+fLltceA5v5PGVOtyC80l+Em9p3QzH5v8eLFtccAOOOMM2qPMcavQ1qR4aby29Tvqan9ZxOa+DsY4/34evPrkTlJkiRJKpDNnCRJkiQVyGZOkiRJkgrUVzMXEUdGxA8j4raI+H1EnBERjx9WcVLTzLBKZn5VMvOr0plhtVG/R+b2AT4APAPYD9gE+EZEbNl0YdKQmGGVzPyqZOZXpTPDap1Z/cycUtq/8+eIWAT8HtgD+G5zZUnDYYZVMvOrkplflc4Mq43qfmZubnV/c91CpBExwyqZ+VXJzK9KZ4Y1cn0dmesUERsBJwLnp5QuW898s4HZHZPmDLpOqUnTybD5VVuZX5XM1xAqnftgtUWdI3MfAJ4EvHSK+Y4kf0HixO03NdYpNWk6GTa/aivzq5L5GkKlcx+sVhiomYuI9wMvAPZNKU0VzOPJh6EnbjsNsk6pSX1k2PyqdcyvSuZrCJXOfbDapK/TLCMigPcBLwQWpJSunmqZlNJqYHXHGP3WKDWm3wybX7WJ+VXJfA2h0rkPVhv1+5m5DwAHAwcCt0XEw6rpK1NKdzVamTQcZlglM78qmflV6cywWqff0yxfQz5MvBT4XcftoGbLkobGDKtk5lclM78qnRlW6/T7PXMeH1bRzLBKZn5VMvOr0plhtVHd75mTJEmSJI2AzZwkSZIkFWjgLw0ftUWLFo26hPssXLiwkXGWLl3aijE0fPPnz29knDb9vpuo5cwzz6xfCLB48eLaY7Rp246rFStW1B5j2bJltcfQhmf58uWNjLNkyZLaY8ybN6/2GAAXX3xxI+Oo/Y455phRl3CfAw88sJFxmvo72BB5ZE6SJEmSCmQzJ0mSJEkFspmTJEmSpALZzEmSJElSgWzmJEmSJKlANnOSJEmSVCCbOUmSJEkqkM2cJEmSJBXIZk6SJEmSCmQzJ0mSJEkFspmTJEmSpALZzEmSJElSgWzmJEmSJKlANnOSJEmSVCCbOUmSJEkqkM2cJEmSJBXIZk6SJEmSCjRr1AUMapttthl1Cfc544wzGhnnnHPOqT3G6173ugYqgRNPPLGRcdTbihUrRl1CKy1btqyRcRYsWNDIOOqtqf1vE+PMmzev9hgAxxxzTO0xlixZUnsMgOXLlzcyjsowf/78RsZZunRpI+NI/Tj00EMbGaep19IbIo/MSZIkSVKBbOYkSZIkqUA2c5IkSZJUoFrNXES8MSJSRJzYUD3SjDG/Kp0ZVsnMr0pmftUWAzdzEfE04HDgJ82VI80M86vSmWGVzPyqZOZXbTJQMxcRWwGnAocBtzRakTRk5lelM8MqmflVycyv2mbQI3MfAL6SUvpWk8VIM8T8qnRmWCUzvyqZ+VWr9P09cxHxUuCpwNOmOf9sYHbHpDn9rlNqivlV6frJsPlV27gPVsnMr9qoryNzEfEI4CTg5SmlVdNc7EhgZcftN31VKDXE/Kp0A2TY/Ko13AerZOZXbdXvaZZ7AA8FfhwRayJiDbAP8E/Vzxv3WOZ4YG7Hbac6BUs1mF+Vrt8Mm1+1iftglcz8qpX6Pc3y28BuXdNOBq4E/jOldG/3Aiml1cDqiZ8jot8apaaYX5WurwybX7WM+2CVzPyqlfpq5lJKtwGXdU6LiDuAP6SULuu9lNQO5lelM8MqmflVycyv2qrWl4ZLkiRJkkaj76tZdkspLWigDmkkzK9KZ4ZVMvOrkplftYFH5iRJkiSpQDZzkiRJklSgSCnN7AojtiZ/10YtCxYsqF8McM4559Qe4ylPeUoDlTTznN7znvfULwR41KMeVXuM5cuX1y8km5tSurWpwepoKr/HHHNM/WKAhQsX1h5j/vz5tcdoytKlSxsZp4nsLVq0qPYYHVqR4aby25Qmft/77LNP/UJa5pRTTqk9xuLFi2uPkVJi5cqV0JL8QvsyfOCBB9Ye44wzzqhfCF4pcQqtyHDb8jtv3rzaY1x99dX1C6GZ1zNnnnlm/ULaab359cicJEmSJBXIZk6SJEmSCmQzJ0mSJEkFspmTJEmSpALZzEmSJElSgWzmJEmSJKlANnOSJEmSVCCbOUmSJEkqkM2cJEmSJBXIZk6SJEmSCmQzJ0mSJEkFspmTJEmSpALZzEmSJElSgWzmJEmSJKlANnOSJEmSVCCbOUmSJEkqkM2cJEmSJBUoUkozu8KIrYGVM7rS9Vi2bNmoS7jPGWecUXuMo48+un4hwMKFC2uPceaZZ9YvJJubUrq1qcHqaCq/8+bNq18McPXVV9ce46STTmqgEpg/f34rxgBYsGBB7TEa3je0IsNt2/8uXry49hjvec976hcCnHvuuY2M04R99tmn9hj77rtv7THWrFnDeeedBy3JL7RvH9zE/9u77757/UKASy65pPYYK1asqF8IsGTJklaM0aEVGW7bPriJ/B544IH1C6GZ/G6zzTb1CwGWL19ee4wmXod0WG9+PTInSZIkSQWymZMkSZKkAtnMSZIkSVKB+m7mImLHiPhURPwhIu6KiEsjYs9hFCc1zfyqdGZYJTO/Kp0ZVtvM6mfmiHgQcD5wDvA84EZgF+CW5kuTmmV+VTozrJKZX5XODKuN+mrmgH8Drk0pHdoxrf7l9KSZYX5VOjOskplflc4Mq3X6Pc3yAOCiiPhcRPw+Ii6OiMOGUZg0BOZXpTPDKpn5VenMsFqn32bu0cBrgF8AzwU+BLw3Ig6ZbIGImB0RW0/cgDkDVyvVY35Vur4ybH7VMu6DVTr3wWqdfk+z3Ai4KKV0VPXzxRHxJODVwCmTLHMk0Mw3WUv1mF+Vrt8Mm1+1iftglc59sFqn3yNzvwOu6Jr2U+CR61nmeGBux22nPtcpNcX8qnT9Ztj8qk3cB6t07oPVOv0emTsfeHzXtMcB10y2QEppNbB64ueI6HOVUmPMr0rXV4bNr1rGfbBK5z5YrdPvkbn3AM+IiKMi4rERcTDwKuADzZcmNc78qnRmWCUzvyqdGVbr9NXMpZR+CLwQeBlwGfAfwOKU0qlDqE1qlPlV6cywSmZ+VTozrDbq9zRLUkpnA2cPoRZp6MyvSmeGVTLzq9KZYbVNv6dZSpIkSZJawGZOkiRJkgrU92mW42bBggW1x1iyZEntMQCOPro9X0Vy7rnnjrqEsbZ8+fJGxjn22GNrj9FU7i655JLaYxxyyKTfHdyXZcuWNTKOhuvEE0+sPca8efNqjwGwaNGi2mM0lbuFCxfWHmPp0qW1xxhnu+++e2vGWblyZQOVNFNLU//3uw8ervnz5zcyzoEHHlh7jGuumfRitH0544wzGhmnCStWrBh1CX3xyJwkSZIkFchmTpIkSZIKZDMnSZIkSQWymZMkSZKkAtnMSZIkSVKBbOYkSZIkqUA2c5IkSZJUIJs5SZIkSSqQzZwkSZIkFchmTpIkSZIKZDMnSZIkSQWymZMkSZKkAtnMSZIkSVKBbOYkSZIkqUA2c5IkSZJUIJs5SZIkSSrQrFEXMGoppdpj3HnnnQ1UArfeemsj4zShie2i4Vu1alXtMZrK3e233157jHvuuaeBSrQhaeJvAJr5O7jjjjsaqMS/g5nQ1DZuIjdN7YMjovYYTWX43nvvbWQc9dbU9m0ie7fddlsDlTS3L2/C6tWrR11CX2KmX7RHxI7Ab2Z0pSrdTiml3466CDC/GlgrMmx+NaBW5BfMsAbWigybXw1ovfkdRTMXwA7AZK38HHLQd1rPPBpcadt3DnBdasmhwmnkF8rbxiUpcdu2JsPmtxVK276tyS+Y4RYocdu2JsPmd+RK3LZT5nfGT7Osipm8u7z/NIHbUkrtOe9wTBS4fVtV41T5hSK3cTEK3batqdP8jl6B27dVNZrh0Sp027amTvM7WoVu2ynr9AIokiRJklQgmzlJkiRJKlAbm7nVwLHVvZrn9h0+t/HwuG2Hz208XG7f4XMbD4/bdvjcxsMzltt2xi+AIkmSJEmqr41H5iRJkiRJU7CZkyRJkqQC2cxJkiRJUoFs5iRJkiSpQK1r5iLiHyJieUSsiogLImKvUddUuog4JiJS1+3KUdc1jszvcJjhmWOGm2d+Z475HQ4zPDPM73CMe35b1cxFxEHACeTLhj4VuAT4ekQ8dKSFjYfLgYd33PYebTnjx/wOnRkeMjM8VOZ3yMzv0JnhITK/Qze2+W1VMwccAXwkpXRySukK4NXAncDfjrassbAmpXR9x+2mURc0hszvcJnh4TPDw2N+h8/8DpcZHi7zO1xjm9/WNHMRsSmwB/CtiWkppbXVz88cVV1jZJeIuC4ifhURp0bEI0dd0DgxvzPCDA+RGR468ztE5ndGmOEhMb8zYmzz25pmDtgW2Bi4oWv6DcDDZr6csXIBsAjYH3gN8CjgexExZ5RFjRnzO1xmePjM8PCY3+Ezv8NlhofL/A7XWOd31qgL0PCllL7W8eNPIuIC4BrgJcDHRlOVNH1mWCUzvyqdGVbJxj2/bToydxNwL7B91/TtgetnvpzxlVJaAfwceOyISxkn5ncGmeGhMMMzxPwOhfmdQWa4ceZ3Bo1bflvTzKWU7gZ+BDx7YlpEbFT9/INR1TWOImIr4DHA70Zdy7gwvzPLDDfPDM8c89s88zuzzHCzzO/MGrf8tu00yxOAUyLiIuBCYDGwJXDyKIsqXUS8G/gy+ZDyDuTL3t4LnDbKusaQ+R0SMzxjzPAQmN8ZY36HxAzPCPM7JOOe31Y1cyml0yNiO+At5A98LgP2Tyl1fyBU/dmJHNiHADcC5wHPSCndONKqxoz5HSozPAPM8NCY3xlgfofKDA+Z+R2qsc5vpJRGXYMkSZIkqU+t+cycJEmSJGn6bOYkSZIkqUA2c5IkSZJUIJs5SZIkSSqQzZwkSZIkFchmTpIkSZIKZDMnSZIkSQWymetTRBwTEQN9OV9ELIqIFBHzGi6rcx3zqnUsGtY6VDYzrJKZX5XM/Kp0Zrh9NphmLiKeGBGfiojfRsTqiLguIk6NiCeOurY2iojnRMR3ImJlRNwWET+KiINGXdeGzAxPX0RsExEfjogbI+KOiDgnIp466ro2ZOa3P+6D28X8Tk9EPDsiPh4RP4+IOyPiVxHx0Yh4+Khr29CZ4ekpMcOR0kDNdVEi4q+A04CbgY8BVwPzgL8DHgK8NKX0pWmONQuYlVJaNUAdGwObAKvTkDZ89W7H1cChKaUlA45xKHk7fRM4C7gXeDzw25TSu5upVP0ww30tvxHwPWB34F3ATcDfA48A9kgp/aKxYjUt5rfvMdwHt4j57Wv5i4AHA58DfgE8GvhH4E5gfkrp+saK1bSZ4b6WLy/DKaWxvgGPAe4Afgps1/XYttX024FHTzHOlqN+LtN8vvOABCyqsfydwEmjfi7e7vudmOH+ln9JtfyLO6ZtB9wCfHrUz29Du5nfgZZ3H9ySm/nte/k/BTbqMS0Bbxv189sQb2a47+WLy/CGcJrlvwJbAK9KKd3Y+UBK6SbgcGBL4A0T0yfOB46IP4qIT0fELcB5nY91jhMRm0fEeyPipup0mLMiYsdqjGM65lvnXOGIWB4RZ0fE3hFxYUSsqg7pvrJrHQ+OiHdHxKURcXtE3BoRX4uI3afaABGxSUQ8YZqHiF8NbAy8uVp2q4iIaSyn4THD/WX4xcANwBc7ttONwGeBAyNi9jTGUHPMr/vgkpnfPvKbUvpuSmlt9zTyEaFdp1peQ2GGxzzDG0Iz95fA8pTS93o9WP2ClgPP7/Hw58h/AEcBH1nPOpYArwW+CvwbcBfwlT5qfCzwefIpNa8nH0FYEg88j/nRwELgbOAI8uljuwHnRsQOU4y/I/mdl+OnUctzgCuBv4iI3wC3AX+IiLdGPn1NM88M95fhpwA/7t4ZAxeSt8XjpjGGmmN+3QeXzPz2l991RMRWwFbkU94188zwmGd41qgLGKaImAvsAJw5xaw/AQ6IiDkppds6pl+SUjp4inU8lXxa14kppddVkz8YESeTP7MzHY8H/nTiDy0iPgtcCxwK/Es1z6XA4zpfoEbEJ8n/6f8d8NZprmsqu5A/n3Ey8E7gEuCvgDeR83JkQ+vRNJjhgTwc+G6P6b+r7neoatGQmd+BuA9uCfPbmMXApsDpQ1yHejDDjVlMizM81s0cMKe6v229c93/+NZd8/73NNaxf3X/wa7p7wMWTWN5gCs63zFJKd0YET8jvwsxMW31xL8jf4B0G/I5zj8D1nuVvpTScmC6p+lsRT5i+8aU0n9W074QEQ8G/jkijuv6Q9dwmWH6zvDmwOoe01d1PK6ZYX5xH1ww80vf+X2AiPhT4Gjgsyml7wwyhmoxw4x/hsf9lI2JQM5Z71yTh/3qaaxjZ2Btj3mvmsayE37dY9otwIMmfoiIjSLidRHxC/IL1ZuAG4EnA3P7WNdU7qruT+uafhr5RfBTGlyXpmaG+3cX0OtzcZt1PK6ZYX775z64PcxvDRHxBOBLwGXA/xvGOjQlM1xDKRke62YupbSSfGrVk6eY9cnkSz7f2jV9pl703TvJ9M53EY4CTiCfPvY3wHOB/YDLafb3eF11f0PX9N9X9w9CM8YMD+R35FMtu01Mu67HYxoC8zsQ98EtYX4HFxGPAL4BrAT+wqPJo2GGB1dShse6maucDTwqIvbu9WBE/An5MqZnDzj+NeTt+Kiu6Y8dcLzJvBg4J6X0dymlz6SUvpFS+hb5MHOTflTd79g1feLDpTeimWaG+7MMeGqPi0U8nXzJ9583vD6tn/ntj/vgdjG/fYqIh5BfBM8GnptS+t0Ui2i4zHCfSsvwhtDMvYv8zsL/VL+c+1SfQfhv8gu8dw04/ter+7/vmv7aAcebzL10ne8bEX/Nuv/hr6OfS7Jy/4c7/65j+Y3IH0K9mftfaGjmmOH+Mvx5YHvyRSMmlt8W+Gvgy53n3WtGmF/3wSUzv33kNyK2JF/RcEfy0YxfDFivmmOGxzzD434BFFJKv4iIQ4BTgUsj4mPk83rnkf+z3BZ4WUrplwOO/6OI+AKwuPoj+T9gH+6//HlT33B/NvDm6upA3ydfjvXlwK+msezEJVlPYeoPo54JfBs4snoBfAn5UrB7A4f7QnjmmWGgvwx/nvwcTo6IPyKfV//35O/uOnqgyjUw8wu4Dy6W+QX6y++pwF7Ax4FdI6Lze7luTymd0V/ZqssMA2Oe4bFv5gBSSp+LiCvJl3SeCO4fgHOA41JKl9VcxSuB64GXAS8EvgUcRL7Czqr1LNeP48hf6nhwNfaPyd8J8o6GxgcgpZQiYiHwtmo9i8jP429SSqc2uS5NnxmevpTSvRHxF+R3Gf+JfNGIHwKLUko/a3Jdmh7zO33ug9vH/PZlfnX/t9Wt0zXAGQ2vT9Nghvsyv7ovJsORUlMNszpFxHzgYvwPWIUywyqZ+VXJzK9KZ4Znzobwmbmhi4he31u1mHyp1l5fXiy1ihlWycyvSmZ+VTozPFobxGmWM+ANEbEH+XD1GuB51e3DKaVrR1qZND1mWCUzvyqZ+VXpzPAIeZplAyJiP/KFFf4I2Ir85YefBN6eUlozytqk6TDDKpn5VcnMr0pnhkfLZk6SJEmSCuRn5iRJkiSpQDZzkiRJklQgmzlJkiRJKpDNnCRJkiQVyGZOkiRJkgpkMydJkiRJBbKZkyRJkqQC2cxJkiRJUoFs5iRJkiSpQDZzkiRJklQgmzlJkiRJKpDNnCRJkiQVyGZOkiRJkgpkMydJkiRJBbKZkyRJkqQC2cxJkiRJUoFs5iRJkiSpQDZzkiRJklQgmzlJkiRJKpDNnCRJkiQVaNZMrzAiAtgBuG2m160izQGuSymlURcC5lcDaU2Gza8G0Jr8ghnWQFqTYfOrAUyZ3xlv5sgh/s0I1qty7QT8dtRFVMyvBtGWDJtfDaIt+QUzrMG0JcPmV4NYb35H0cw18m7E6aef3sQwrFq1qvYY+++/fwOVwGabbVZ7jOOPP76BSuAd73hHI+M0pE3vYDVSy8EHH9zEMFx++eW1x9h3330bqASOPfbY2mPMnTu3gUqasc8++9QeY82aNZx//vnQngzfBnDQQQex6aabDjzIvHnzGinmrW99a+0xHvSgBzVQCZx77rm1x3jyk5/cQCWw66671h7jpz/9aQOV3Kct+YWqlmuvvZatt9564EEOO+ywRorZZJNNao/xwQ9+sIFK4IUvfGHtMbbddtsGKoG1a9fWHmO33XarPcaqVasmXhe1JcO3AXznO99hq622GniQa6+9tpFinvOc5zQyThPa9P9/Ew4//PDaY9x9992cfPLJMEV+R9HMNWKLLbZoZJyNNqr/scE6/6F0aqKZa2IMDV+dF9KdNt5449pjzJ49u4FKmvs7aItZs4rdPU5p0003rZXBNu1n8llL9c2ZM6eRcZrQxN/1uNt6661r7XOa2gc30cw1te9sYp/V1Ha59957a4/Rpv1M07baaqtazdyWW27ZSB3j9v92mzT12mo6vACKJEmSJBVooGYuIv4hIpZHxKqIuCAi9mq6MGlYzK9KZ4ZVMvOr0plhtUnfzVxEHAScABwLPBW4BPh6RDy04dqkxplflc4Mq2TmV6Uzw2qbQY7MHQF8JKV0ckrpCuDVwJ3A3zZamTQc5lelM8MqmflV6cywWqWvZi4iNgX2AL41MS2ltLb6+ZnNliY1y/yqdGZYJTO/Kp0ZVhv1e+mjbYGNgRu6pt8APKHXAhExG+i8pEt7LhmmDY35Ven6yrD5Vcu4D1bp3AerdWbiapZHAis7bn5ZokpiflUy86vSmWGVzPxq6Ppt5m4C7gW275q+PXD9JMscD8ztuO3U5zqlpphfla7fDJtftYn7YJXOfbBap69mLqV0N/Aj4NkT0yJio+rnH0yyzOqU0q0TN6b4FnNpWMyvStdvhs2v2sR9sErnPlht1O9n5iBfjvWUiLgIuBBYDGwJnNxgXdKwmF+VzgyrZOZXpTPDapW+m7mU0ukRsR3wFuBhwDJg/5RS94dBpdYxvyqdGVbJzK9KZ4bVNoMcmSOl9H7g/Q3XIs0I86vSmWGVzPyqdGZYbTITV7OUJEmSJDXMZk6SJEmSCjTQaZZtsNVWWzUyzpIlS2qPcdVVV9UvBLjuuutqj/G+972vgUo0lR/96Ee1MtjE7xpgwYIFjYzTFieddFIj4+y0U/2rP7/oRS9qoJJ2Ovvss4mIgZe/+eabG6njM5/5TO0xDjrooAYqgY9+9KONjNOEyy67bNQltN7cuXNrLf+YxzymkTqa+v+/CRdffHHtMR784Ac3UAncdlv9izaedtppDVTSTq997WuZNWvwl+DnnXdeI3Vsv333Nyz074Yb/KhgL7Nnz556poZ4ZE6SJEmSCmQzJ0mSJEkFspmTJEmSpALZzEmSJElSgWzmJEmSJKlANnOSJEmSVCCbOUmSJEkqkM2cJEmSJBXIZk6SJEmSCmQzJ0mSJEkFspmTJEmSpALZzEmSJElSgWzmJEmSJKlANnOSJEmSVCCbOUmSJEkqkM2cJEmSJBXIZk6SJEmSCjRr1AUM6sMf/nAj43z+85+vPcYVV1zRQCVw0EEH1R7j8MMPb6AS+J//+Z9GxhlX2267LVtvvfXAyz/ucY9rpI7NN9+89hgR0UAlcPfdd9ceY82aNQ1UAkcccUTtMebMmVN7jJQSt99+e+1xmnbppZfWym+dZTu99KUvrT3Gaaed1kAl8IQnPKGRcZqw11571R7jta99be0x7rrrLl71qlfVHmcY9txzT2bNGvwlzD//8z83VkddF110UQOVwIknnlh7jC9+8Yv1CwG+8IUvNDLOuPrZz35W6//exYsXN1LH61//+tpj7Lfffg1Ukv+/rGuXXXZpoBI44YQTao9xyimn1B7jnnvumdZ8HpmTJEmSpALZzEmSJElSgWzmJEmSJKlAfTVzEXFkRPwwIm6LiN9HxBkR8fhhFSc1zQyrZOZXJTO/Kp0ZVhv1e2RuH+ADwDOA/YBNgG9ExJZNFyYNiRlWycyvSmZ+VTozrNbp61JQKaX9O3+OiEXA74E9gO82V5Y0HGZYJTO/Kpn5VenMsNqo7mfm5lb3N9ctRBoRM6ySmV+VzPyqdGZYIzfwl7RExEbAicD5KaXL1jPfbGB2x6T6X94kNWA6GTa/aivzq5L5GkKlcx+stqhzZO4DwJOAqb719UhgZcftNzXWKTVpOhk2v2or86uS+RpCpXMfrFYYqJmLiPcDLwD2TSlNFczjyYehJ247DbJOqUl9ZNj8qnXMr0rmawiVzn2w2qSv0ywjIoD3AS8EFqSUrp5qmZTSamB1xxj91ig1pt8Mm1+1iflVyXwNodK5D1Yb9fuZuQ8ABwMHArdFxMOq6StTSnc1Wpk0HGZYJTO/Kpn5VenMsFqn39MsX0M+TLwU+F3H7aBmy5KGxgyrZOZXJTO/Kp0ZVuv0+z1zHh9W0cywSmZ+VTLzq9KZYbVR3e+ZkyRJkiSNgM2cJEmSJBVo4C8NH7Xrr79+1CXc58ADD2xknKuuuqr2GJddNul3r6pBRxxxBJtsssnAy8+a1cyf3itf+craY1x++eUNVALnn39+7TG22267BiqBf/mXf6k9xuGHH157jNWrV/O+972v9jhN22mnelfHnjOnme+9vfTSS2uPsfPOOzdQSbuuMnfhhRfWHuPee+9txRjD8id/8ifMnj176hkn8bKXvazBaur5r//6r0bGaWK/p5mxYsWKWssfddRRjdSx/fbb1x7jz//8zxuoBL7//e/XHmO//fZroBI44IADao8xb9682mOsXbt2WvN5ZE6SJEmSCmQzJ0mSJEkFspmTJEmSpALZzEmSJElSgWzmJEmSJKlANnOSJEmSVCCbOUmSJEkqkM2cJEmSJBXIZk6SJEmSCmQzJ0mSJEkFspmTJEmSpALZzEmSJElSgWzmJEmSJKlANnOSJEmSVCCbOUmSJEkqkM2cJEmSJBXIZk6SJEmSCjRr1AUMarfddmtknNtvv732GFdddVUDlcCb3/zm2mMccMABDVQCe+65Z+0xjjvuuFrLr1q1ire85S216xiGvfbai80333zg5VeuXNlIHQcddFDtMR73uMc1UAmN/K7OOuusBippZv/w/Oc/v4FK2umd73xnrfxecMEFjdSx88471x7jqKOOaqCSZsybN6+RcTbbbLPaYyxatKh+IS223377seWWWw68/NOf/vRG6nj7299ee4zXv/71DVQCa9asqT3GG9/4xgYq0bBtv/32jYzzhje8ofYYH/3oRxuoBE488cTaYyxcuLD2GAAf+tCHao+xfPny+oVMk0fmJEmSJKlANnOSJEmSVCCbOUmSJEkqUK1mLiLeGBEpIk5sqB5pxphflc4Mq2TmVyUzv2qLgZu5iHgacDjwk+bKkWaG+VXpzLBKZn5VMvOrNhmomYuIrYBTgcOAWxqtSBoy86vSmWGVzPyqZOZXbTPokbkPAF9JKX2ryWKkGWJ+VTozrJKZX5XM/KpV+v6euYh4KfBU4GnTnH82MLtj0px+1yk1xfyqdP1k2PyqbdwHq2TmV23U15G5iHgEcBLw8pTSqmkudiSwsuP2m74qlBpiflW6ATJsftUa7oNVMvOrtur3NMs9gIcCP46INRGxBtgH+Kfq5417LHM8MLfjtlOdgqUazK9K12+Gza/axH2wSmZ+1Ur9nmb5bWC3rmknA1cC/5lSurd7gZTSamD1xM8R0W+NUlPMr0rXV4bNr1rGfbBKZn7VSn01cyml24DLOqdFxB3AH1JKl/VeSmoH86vSmWGVzPyqZOZXbVXrS8MlSZIkSaPR99Usu6WUFjRQhzQS5lelM8MqmflVycyv2sAjc5IkSZJUIJs5SZIkSSpQ7dMsB7XVVlvVuqrPM5/5zMbqqGvPPfdsoBI49thja4/xqU99qoFKmnHUUUeNuoShOfvss5k1a/A/n4ULFzZSx5ZbbtnIOE1o4m9ps802a6AS+OpXv9rIOOPqDW94Q63l99lnn0bqOProo2uPcdxxxzVQCZxwwgm1x7j66qsbqAR++ctf1h7j5ptvrj3G7bffzp/92Z/VHmcYfvazn7H55psPvPw111zTSB1ve9vbao+x227dF0gczGWXtecaHE3837TddtvVHmPt2rX8+te/rj1O01760pey6aabDrz8Jz7xiUbqeNSjHlV7jN13372BSuCzn/1s7THadLXQJv5vWrVqFW95y1umnM8jc5IkSZJUIJs5SZIkSSqQzZwkSZIkFchmTpIkSZIKZDMnSZIkSQWymZMkSZKkAtnMSZIkSVKBbOYkSZIkqUA2c5IkSZJUIJs5SZIkSSqQzZwkSZIkFchmTpIkSZIKZDMnSZIkSQWymZMkSZKkAtnMSZIkSVKBbOYkSZIkqUA2c5IkSZJUoEgpzewKI7YGVs7oStfjk5/8ZO0xnv70pzdQCdxwww21x3jGM57RQCVwwAEH1B7jkY98ZK3l7777bk4++WSAuSmlW2sX1ICJ/EYEETHwOB/72McaqeeZz3xm7TEe//jHN1AJtbbHhKb2R6985Strj9HEvqFDKzLc1P53iy22aKAaePazn117jLPOOquBSsZPE3+PHVqRX7g/wytXrmTrrbceeJwnPvGJjdRz+eWXNzJOEz7zmc/UHuOxj31sA5U087qoiX3wnXfeyWGHHQYtyfBEft/whjcwe/bsgcd561vf2kg9M90DDNs3v/nNRsbZb7/9ao8xk/tgj8xJkiRJUoFs5iRJkiSpQDZzkiRJklSgvpu5iNgxIj4VEX+IiLsi4tKI2HMYxUlNM78qnRlWycyvSmeG1Taz+pk5Ih4EnA+cAzwPuBHYBbil+dKkZplflc4Mq2TmV6Uzw2qjvpo54N+Aa1NKh3ZMu7rBeqRhMr8qnRlWycyvSmeG1Tr9nmZ5AHBRRHwuIn4fERdHxGHDKEwaAvOr0plhlcz8qnRmWK3TbzP3aOA1wC+A5wIfAt4bEYdMtkBEzI6IrSduwJyBq5XqMb8qXV8ZNr9qGffBKp37YLVOv6dZbgRclFI6qvr54oh4EvBq4JRJljkSOHrA+qQmmV+Vrt8Mm1+1iftglc59sFqn3yNzvwOu6Jr2U+CR61nmeGBux22nPtcpNcX8qnT9Ztj8qk3cB6t07oPVOv0emTsfeHzXtMcB10y2QEppNbB64ueI6HOVUmPMr0rXV4bNr1rGfbBK5z5YrdPvkbn3AM+IiKMi4rERcTDwKuADzZcmNc78qnRmWCUzvyqdGVbr9NXMpZR+CLwQeBlwGfAfwOKU0qlDqE1qlPlV6cywSmZ+VTozrDbq9zRLUkpnA2cPoRZp6MyvSmeGVTLzq9KZYbVNv6dZSpIkSZJawGZOkiRJkgoUKaWZXWH+0sSVL37xi9lkk00GHue0005rpJ6nP/3ptce44IILGqgEmvhdvOpVr2qgElixYkXtMT73uc/VLySbm1K6tanB6pjI75w5c2pdlerWW5t5Oq973etqj3HCCSc0UAk86UlPqj3Gc57znAYqgZNOOqmRcRrUigxP5HeXXXZh4403HnicK6+8srmiatpvv/0aGecb3/hG7TF+8pOfNFAJ7L///rXH2HLLLWuPsXbtWn71q19BS/IL92e47jhf+tKXGqgGFi5cWHuMb37zm/ULobm/hSbMmtX3p3jW8b3vfa/2GHfcccfEdmlFhpvK7xVXdH87wmB23XXX2mPceOONDVQCD33oQ2uP0dRriFtuuaX2GLNnz649xpo1a7jwwgthivx6ZE6SJEmSCmQzJ0mSJEkFspmTJEmSpALZzEmSJElSgWzmJEmSJKlANnOSJEmSVCCbOUmSJEkqkM2cJEmSJBXIZk6SJEmSCmQzJ0mSJEkFspmTJEmSpALZzEmSJElSgWzmJEmSJKlANnOSJEmSVCCbOUmSJEkqkM2cJEmSJBVo1qhWfM8994xq1Q+wZs2aUZdwn1tvvbX2GHfffXcDlbTn99NWKaVRlwDA6tWra4/RRO4A7r333tpjNPF8NLUmfldt0dQ+vIm/g9tvv72BSmDt2rVjM0Zb3XnnnY2M00Ru7rjjjgYqaW5f3oQm/o9sYrs0tW3bpql9TROZue222xqopBlN/X/QxP+RTdQy3Tpipl+URsSOwG9mdKUq3U4ppd+OuggwvxpYKzJsfjWgVuQXzLAG1ooMm18NaL35HUUzF8AOwGSt/Bxy0HdazzwaXGnbdw5wXWrJobBp5BfK28YlKXHbtibD5rcVStu+rckvmOEWKHHbtibD5nfkSty2U+Z3xk+zrIqZvLuMmPjnbSml9pwzMCYK3L6tqnGq/EKR27gYhW7b1tRpfkevwO3bqhrN8GgVum1bU6f5Ha1Ct+2UdXoBFEmSJEkqkM2cJEmSJBWojc3cauDY6l7Nc/sOn9t4eNy2w+c2Hi637/C5jYfHbTt8buPhGcttO+MXQJEkSZIk1dfGI3OSJEmSpCnYzEmSJElSgWzmJEmSJKlANnOSJEmSVKDWNXMR8Q8RsTwiVkXEBRGx16hrKl1EHBMRqet25ajrGkfmdzjM8Mwxw80zvzPH/A6HGZ4Z5nc4xj2/rWrmIuIg4ATyZUOfClwCfD0iHjrSwsbD5cDDO257j7ac8WN+h84MD5kZHirzO2Tmd+jM8BCZ36Eb2/y2qpkDjgA+klI6OaV0BfBq4E7gb0db1lhYk1K6vuN206gLGkPmd7jM8PCZ4eExv8NnfofLDA+X+R2usc1va5q5iNgU2AP41sS0lNLa6udnjqquMbJLRFwXEb+KiFMj4pGjLmicmN8ZYYaHyAwPnfkdIvM7I8zwkJjfGTG2+W1NMwdsC2wM3NA1/QbgYTNfzli5AFgE7A+8BngU8L2ImDPKosaM+R0uMzx8Znh4zO/wmd/hMsPDZX6Ha6zzO2vUBWj4Ukpf6/jxJxFxAXAN8BLgY6OpSpo+M6ySmV+VzgyrZOOe3zYdmbsJuBfYvmv69sD1M1/O+EoprQB+Djx2xKWME/M7g8zwUJjhGWJ+h8L8ziAz3DjzO4PGLb+taeZSSncDPwKePTEtIjaqfv7BqOoaRxGxFfAY4HejrmVcmN+ZZYabZ4ZnjvltnvmdWWa4WeZ3Zo1bftt2muUJwCkRcRFwIbAY2BI4eZRFlS4i3g18mXxIeQfyZW/vBU4bZV1jyPwOiRmeMWZ4CMzvjDG/Q2KGZ4T5HZJxz2+rmrmU0ukRsR3wFvIHPpcB+6eUuj8Qqv7sRA7sQ4AbgfOAZ6SUbhxpVWPG/A6VGZ4BZnhozO8MML9DZYaHzPwO1VjnN1JKo65BkiRJktSn1nxmTpIkSZI0fTZzkiRJklQgmzlJkiRJKpDNnCRJkiQVyGZOkiRJkgpkMydJkiRJBbKZkyRJkqQC2czNgIhYGhFLR12HNCgzrJKZX5XODKtk5ne4bOYqEbEoIlJErIqIHXs8vjQiLhtFbU2JiI0i4jURsSwi7oqIP0TEdyJi91HXpvo2kAzvGhH/GxG3R8TNEfHJiNhu1HWpvg0kv+6Dx9g4Z7jK7qKIOCsiro2IOyLisoh4U0RsNur6VJ/5LZfN3LpmA29seMw/r26j9nHgvcCPgNcCbwF+DTx0lEWpcWOZ4YjYCfgu8FjgKODdwPOBb0bEpqOsTY0ay/xW3AdvGMYxw1sAJwPbAf8NLAYuBI4FvhYRMbrS1DDzW5hZoy6ghZYBh0XE8Sml65oYMKV0dxPj1BERLwEOAf4qpfSlUdejoVrGGGaY3MBtCeyRUvo1QERcCHwTWAR8eHSlqUHLGMP8ug/eoCxj/DJ8N/DHKaXvd0z7SEQsJ78gfjbwrVEUpsYtw/wWxSNz6zoO2JhpvCsREbMi4j8i4pcRsToilkfEcRExu2u+dc4VjojXRsTlEXFnRNwSERdFxMHVY/tWh7pf2GOdB1ePPTMi5kbEEyJi7jSe1xHAhSmlL1WHm7ecxjIq07hm+EXA2RONHEBK6VvAz4GXTGN5lWFc8+s+eMMxdhlOKd3d9UJ4wsQbE7tO9VxVDPNbGJu5dV0NfIL8rsQOU8z7UfJpMj8GXgecCxwJfGZ9C0XEYeRTba4gH+o9mvxOyNOrWZYC1wIv77H4y4FfppR+ALwQ+Gl1v771bQ3sBfwwIo4DVgK3R8SvIr9brPEyjhnekXwq2kU9Hr4QeMr6lldRxjG/7oM3LGOX4fV4WHV/04DLq33Mb2lSSt5SgnyaVgL2BB4N3AOc1PH4UuCyjp93r+b/SNc476qm79u17NKOn8/oHGuSeo4DVgFzO6ZtV9V1TFfNi6YY6ynVfDcB1wOvAQ4GLgDWAvuPevt7q38b8wzvWc33ih6PvbN6bPaofwfeBr+NeX7dB28At3HO8HrW8U3ymxPbjHr7e6t3M7+j/x0MevPIXA8ppV8BnwReFREPn2S2v6juT+ia/l/V/fPXs4oVwE4R8bT1zPMJ8odQX9wx7SDy5xw/VdW5JKUUKaUl6xkHYKvq/iHAgSmlD6WUPk0+R/gPwJumWF6FGcMMb17dr+7x2KqueVS4Mcyv++ANzBhmeB0RcRTwHOCNKaUV/S6v9jK/ZbGZm9zbyIGZ7JzhncnvqF7VOTGldD05pDuvZ+z/BG4HLoyIX0TEByLij7vGuRL4IQ88xPxy4P9SSg9Y5zTcVd1fnVK6oGMdtwNfBvaKCC+GM37GMcOzezy2Wdc8Gg/jmF/3wRuWccrwA0TEQeTn97GU0ofqjKXWMr+FsJmbRPWuxKdY/7sSkA/v9jv2T4HHAy8FziNf2OG8iDi2a9ZPAPtExE4R8RjgGVVN/Zq4GtENPR77PbAJ+SqBGiNjluHfVfe9nsfDgZtTSr2O2qlQY5Zf98EboDHL8H0iYr9q3K8Ar64zltrL/JbDZm79Jt6V+Lcej11D3n67dE6MiO2BbarHJ5VSuiOldHpK6VDgkeRQ/Xs88MsLPwPcC7yM/G7EPcDp/T6JlC8tez2wzpdAAjuQT1O7rd9xVYRxyfBvgRvJ5/J324v8wWmNn3HJr/vgDddYZLijtqeTrwB4EfCSlNKaQcdSEcxvAWzm1iOl9EvyOwCHc/8VbyZ8tbpf3DX9iOr+K5ONGxEP6VrP3eQr+gT5HdqJ6TcBXwP+hhzi/62mTYzTz2WxTwceUb0jMbH8tsCBwHdSSmunMYYKM2YZ/gLwgoh4RMfyzwYeB3xuGsurMGOWX/fBG6BxynBE7FrVtBx4QUrJU9vHnPktg+foT+3twCvIh4Mvn5iYUrokIk4hH37ehnw51r3IXwp7RkrpnPWM+Y2IuB44n3zaza7APwJfSSl1vzv7CeDz1b//o+uxF5K/0f5QYMkUz+N48ndxfSEiTiBfvefV5D+ao6ZYVmUblwwfB/w1cE5EnES+qMS/ApdWY2g8jUt+3QdvuIrPcETMAb4OPIh8tcLnR0TnLBOXitf4Mb9t1+sSlxvijY5LsvZ4bEn12GVd02cBbwZ+Rf52+V+TX3DO7ppvKQ+8JOuryKG/iXx6zVXky6tv3WPdmwI3kz9MutkkNS+a5nN8NPBF8ouIO4FvA08b9bb31sxtA8nwE8k75DuAW8jvGG4/6m3vrf5tA8mv++Axvo1zhoF51XyT3ZaMevt7q3czv6P/HQx6i+pJqqWqK5xdB3w5pfR3o65H6pcZVsnMr0pnhlUy8zs1PzPXfgvJX5L4iRHXIQ1qIWZY5VqI+VXZFmKGVa6FmN/18shcS1VX3Hky+fzgm1JKTx1xSVJfzLBKZn5VOjOskpnf6fPIXHu9BvgQ+TuIXjniWqRBmGGVzPyqdGZYJTO/0+SROUmSJEkqkEfmJEmSJKlANnOSJEmSVCCbOUmSJEkqkM2cJEmSJBXIZk6SJEmSCmQzJ0mSJEkFspmTJEmSpALZzEmSJElSgWzmJEmSJKlA/x9fyZEJyvsLqwAAAABJRU5ErkJggg==",
+      "text/plain": [
+       "<Figure size 900x500 with 10 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, axes = plt.subplots(2, 5, figsize=(9, 5))\n",
+    "for i in range(5):\n",
+    "    axes[0, i].imshow(np.reshape(current_images[i], (8, 8)), cmap=\"gray\")\n",
+    "    axes[1, i].imshow(np.reshape(noisy_images[i], (8, 8)), cmap=\"gray\")\n",
+    "    axes[0, i].set_xlabel(f\"Original: {training_data[1][indices[i]]}\")\n",
+    "    axes[1, i].set_xlabel(f\"Noisy: {training_data[1][indices[i]]}\")\n",
+    "plt.suptitle(\"Original and noisy versions of images 6-10\")\n",
+    "plt.tight_layout()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {
+    "tags": [
+     "hide-output"
+    ]
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "100%|█████████▉| 99.9/100 [00:59<00:00,  1.69%/s]              \n"
+     ]
+    }
+   ],
+   "source": [
+    "anomalous_dataset = Dataset(\n",
+    "    x_train=x_train_anomalous,\n",
+    "    y_train=y_train_anomalous,\n",
+    "    x_test=test_data[0],\n",
+    "    y_test=test_data[1],\n",
+    ")\n",
+    "\n",
+    "anomalous_utility = Utility(\n",
+    "    model=TorchCNNModel(),\n",
+    "    data=anomalous_dataset,\n",
+    "    scorer=Scorer(\"accuracy\", default=0.0, range=(0, 1)),\n",
+    "    cache_backend=MemcachedCacheBackend(MemcachedClientConfig()),\n",
+    ")\n",
+    "anomalous_values = compute_banzhaf_semivalues(\n",
+    "    anomalous_utility, done=MaxChecks(max_checks), n_jobs=n_jobs, progress=True\n",
+    ")\n",
+    "anomalous_values.sort(key=\"value\")\n",
+    "anomalous_df = anomalous_values.to_dataframe(column=\"banzhaf_value\", use_names=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Let us now take a look at the low-value images and check how many of our anomalous images are part of it."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {
+    "editable": true,
+    "slideshow": {
+     "slide_type": ""
+    },
+    "tags": [
+     "hide-input",
+     "invertible-output"
+    ]
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 800x400 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from scipy.stats import norm\n",
+    "\n",
+    "\n",
+    "plot_data = anomalous_df.loc[anomalous_indices].copy()\n",
+    "plot_data[\"original_banzhaf_value\"] = df.loc[anomalous_indices][\"banzhaf_value\"]\n",
+    "plot_data[\"original_banzhaf_value_stderr\"] = df.loc[anomalous_indices][\n",
+    "    \"banzhaf_value_stderr\"\n",
+    "]\n",
+    "plot_data.index = plot_data.index.map(str)\n",
+    "\n",
+    "fig, ax = plt.subplots(1, 1, figsize=(8, 4))\n",
+    "yerr = norm.ppf(1 - 0.05 / 2) * plot_data[\"banzhaf_value_stderr\"]\n",
+    "original_yerr = norm.ppf(1 - 0.05 / 2) * plot_data[\"original_banzhaf_value_stderr\"]\n",
+    "ax.errorbar(\n",
+    "    x=plot_data.index,\n",
+    "    y=plot_data[\"banzhaf_value\"],\n",
+    "    yerr=yerr,\n",
+    "    fmt=\"o\",\n",
+    "    capsize=6,\n",
+    "    color=\"red\",\n",
+    "    label=\"Anomalous\",\n",
+    ")\n",
+    "ax.errorbar(\n",
+    "    x=plot_data.index,\n",
+    "    y=plot_data[\"original_banzhaf_value\"],\n",
+    "    yerr=original_yerr,\n",
+    "    fmt=\"o\",\n",
+    "    capsize=6,\n",
+    "    color=\"green\",\n",
+    "    label=\"Original\",\n",
+    ")\n",
+    "ax.set_xlabel(\"Image\")\n",
+    "ax.set_ylabel(\"Banzhaf Value\")\n",
+    "ax.set_title(\"Data valuation scores of anomalous images\")\n",
+    "plt.legend()\n",
+    "plt.xticks(rotation=60)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "As can be seen in this figure, the valuation of the data points has decreased significantly by adding noise or falsifying their labels. This shows the potential of using Banzhaf values or other data valuation methods to detect mislabeled data points or noisy input data."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {
+    "tags": [
+     "hide-input"
+    ]
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Average value of original data points: 0.650003277874342\n",
+      "Average value of modified, anomalous data points: -0.02501543656281746\n",
+      "For reference, these are the average data values of all data points used for training (anomalous):\n",
+      "banzhaf_value            0.006044\n",
+      "banzhaf_value_stderr     0.103098\n",
+      "banzhaf_value_updates    5.000000\n",
+      "dtype: float64\n",
+      "These are the average data values of all points (original data):\n",
+      "banzhaf_value            0.005047\n",
+      "banzhaf_value_stderr     0.115262\n",
+      "banzhaf_value_updates    5.000000\n",
+      "dtype: float64\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(\n",
+    "    f\"Average value of original data points: {plot_data['original_banzhaf_value'].mean()}\"\n",
+    ")\n",
+    "print(\n",
+    "    f\"Average value of modified, anomalous data points: {plot_data['banzhaf_value'].mean()}\"\n",
+    ")\n",
+    "print(\n",
+    "    \"For reference, these are the average data values of all data points used for training (anomalous):\"\n",
+    ")\n",
+    "print(anomalous_df.mean())\n",
+    "print(\"These are the average data values of all points (original data):\")\n",
+    "print(df.mean())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Maximum Sample Reuse Banzhaf\n",
+    "\n",
+    "Despite the previous results already being useful, we had to retrain the model a number of times and yet the variance of the value estimates was high. This has consequences for the stability of the top-k ranking of points, which decreases the applicability of the method. We now introduce a different sampling method called Maximum Sample Reuse (MSR) which reuses every sample for updating the Banzhaf values. The method was introduced by the authors of Data-Banzhaf and is much more sample-efficient, as we will show.\n",
+    "\n",
+    "We next construct a new utility. Note how this time we don't use a cache: the chance of hitting twice the same subset of the training set is low enough that one can dispense with it (nevertheless it can still be useful, e.g. when running many experiments)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "utility = Utility(\n",
+    "    model=TorchCNNModel(),\n",
+    "    data=dataset,\n",
+    "    scorer=Scorer(\"accuracy\", default=0.0, range=(0, 1)),\n",
+    "    cache_backend=MemcachedCacheBackend(MemcachedClientConfig()),\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Computing the values is the same, but we now use a better stopping criterion. Instead of fixing the number of utility evaluations with [MaxChecks](../../api/pydvl/value/stopping/#pydvl.value.stopping.MaxChecks), we use [RankCorrelation](../../api/pydvl/value/stopping/#pydvl.value.stopping.RankCorrelation) to stop when the change in Spearman correlation between the ranking of two successive iterations is below a threshold. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {
+    "tags": [
+     "hide-output"
+    ]
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "  0%|          | 0.0/100 [00:03<?, ?%/s]/home/jakob/Documents/pyDVL/venv/lib/python3.10/site-packages/scipy/stats/_stats_py.py:5445: ConstantInputWarning: An input array is constant; the correlation coefficient is not defined.\n",
+      "  warnings.warn(stats.ConstantInputWarning(warn_msg))\n",
+      " 96%|█████████▌| 95.69618345024082/100 [00:07<00:00, 13.37%/s] \n"
+     ]
+    }
+   ],
+   "source": [
+    "values = compute_msr_banzhaf_semivalues(\n",
+    "    utility,\n",
+    "    done=RankCorrelation(rtol=0.0001, burn_in=10),\n",
+    "    n_jobs=n_jobs,\n",
+    "    progress=True,\n",
+    ")\n",
+    "values.sort(key=\"value\")\n",
+    "msr_df = values.to_dataframe(column=\"banzhaf_value\", use_names=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Inspection of the values reveals (generally) much lower variances. Notice the number of updates to each value as well."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {
+    "tags": [
+     "hide-input"
+    ]
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>banzhaf_value</th>\n",
+       "      <th>banzhaf_value_stderr</th>\n",
+       "      <th>banzhaf_value_updates</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>137</th>\n",
+       "      <td>-0.264918</td>\n",
+       "      <td>0.093597</td>\n",
+       "      <td>11</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>20</th>\n",
+       "      <td>-0.217394</td>\n",
+       "      <td>0.127022</td>\n",
+       "      <td>11</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>19</th>\n",
+       "      <td>-0.210309</td>\n",
+       "      <td>0.087179</td>\n",
+       "      <td>11</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>41</th>\n",
+       "      <td>-0.210119</td>\n",
+       "      <td>0.071534</td>\n",
+       "      <td>11</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>192</th>\n",
+       "      <td>-0.191667</td>\n",
+       "      <td>0.130774</td>\n",
+       "      <td>11</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     banzhaf_value  banzhaf_value_stderr  banzhaf_value_updates\n",
+       "137      -0.264918              0.093597                     11\n",
+       "20       -0.217394              0.127022                     11\n",
+       "19       -0.210309              0.087179                     11\n",
+       "41       -0.210119              0.071534                     11\n",
+       "192      -0.191667              0.130774                     11"
+      ]
+     },
+     "execution_count": 19,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "msr_df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {
+    "tags": [
+     "hide-input",
+     "invertible-output"
+    ]
+   },
+   "outputs": [
+    {
+     "data": {
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",
+      "text/plain": [
+       "<Figure size 2000x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "high_dvl = df.iloc[-30:]\n",
+    "high_dvl.index = high_dvl.index.map(str)\n",
+    "ax = plot_shapley(\n",
+    "    high_dvl,\n",
+    "    title=\"Images with high values\",\n",
+    "    xlabel=\"Image\",\n",
+    "    ylabel=\"Banzhaf Value\",\n",
+    "    prefix=\"banzhaf_value\",\n",
+    ")\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {
+    "tags": [
+     "hide-input",
+     "invertible-output"
+    ]
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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JkiRpyEzISJIkSZIkSZIkDZkJGUmSJEmSJEmSpCEzISNJkiRJkiRJkjRkJmQkSZIkSZIkSZKGzISMJEmSJEmSJEnSkJmQkSRJkiRJkiRJGrItRh2AJEmSJEmStNxNTE6tAlZtxkdsWLd29YbFikeStPhMyEiSJEmSJEmjdyhw1GasfzSwZnFCkSQNgwkZSZIkSZIkafSOB06dZvo2wDn1/X7AVTOsb+0YSWq5yMxRx9ApEbED8Ftgx8y8YtTxSJIkSZIkaXxNTE5tC/y+jm63bu3qK0cZjyRpU4PmDVYsXUiSJEmSJEmSJEnLkwkZSZIkSZIkSZKkITMhI0mSJEmSJEmSNGQmZCRJkiRJkiRJkobMhIwkSZIkSZIkSdKQmZCRJEmSJEmSJEkaMhMykiRJkiRJkiRJQ2ZCRpIkSZIkSZIkachMyEiSJEmSJEmSJA2ZCRlJkiRJkiRJkqQhMyEjSZIkSZIkSZI0ZCZkJEmSJEmSJEmShsyEjCRJkiRJkiRJ0pCZkJEkSZIkSZIkSRoyEzKSJEmSJEmSJElDZkJGkiRJkiRJkiRpyEzISJIkSZIkSZIkDZkJGUmSJEmSJEmSpCEzISNJkiRJkiRJkjRkJmQkSZIkSZIkSZKGzISMJEmSJEmSJEnSkJmQkSRJkiRJkiRJGjITMpIkSZIkSZIkSUNmQkaSJEmSJEmSJGnITMhIkiRJkiRJkiQNmQkZSZIkSZIkSZKkITMhI0mSJEmSJEmSNGSdTshExAMi4n8j4pcRkRHx2DmWP6Au1z/sskQhS5IkSZIkSZKkZajTCRlgW+B84PnzXO+OwKrGcOkixyVJkiRJkiRJknS9LUYdwObIzNOA0wAiYj6rXpqZlw8jJkmSJEmSJEmSpH5dryGzUN+MiA0RcXpE7DfbghGxdUTs0BuA7ZcoRkmSJEmSJEmSNCaWW0JmA/Ac4PF1+DlwZkTsO8s6RwC/bQzrhx2kJEmSJEmSJEkaL51usmy+MvP7wPcbk74YEbcFXgT8/QyrHQO8sTG+PSZlJEmSJEmSJEnSPCyrhMwMvgLcf6aZmXk1cHVvfJ591UiSJEmSJEmSJC27Jsumc1dKU2aSJEmSJEmSJElD0ekaMhGxHXC7xqQ9I+KuwK8z86KIOAbYNTOfWpc/HPgpcAGwDfAPwIOBhy5l3JIkSZIkSZIkaXnpdEIGuAfwucZ4r6+X9wCHAKuAPRrztwLeAOwK/AH4FvBXmdn8DEmSJEmSJEmSpEXV6YRMZp4JzNipS2Ye0jf+WuC1w41KkiRJkiRJkiRpY/YhI0mSJEmSJEmSNGQmZCRJkiRJkiRJkobMhIwkSZIkSZIkSdKQmZCRJEmSJEmSJEkaMhMykiRJkiRJkiRJQ2ZCRpIkSZIkSZIkachMyEiSJEmSJEmSJA2ZCRlJkiRJkiRJkqQhMyEjSZIkSZIkSZI0ZCZkJEmSJEmSJEmShsyEjCRJkiRJkiRJ0pCZkJEkSZIkSZIkSRoyEzKSJEmSJEmSJElDZkJGkiRJkiRJkiRpyEzISJIkSZIkSZIkDZkJGUmSJEmSJEmSpCEzISNJkiRJkiRJkjRkJmQkSZIkSZIkSZKGzISMJEmSJEmSJEnSkJmQkSRJkiRJkiRJGjITMpIkSZIkSZIkSUNmQkaSJEmSJEmSJGnITMhIkiRJkiRJkiQNmQkZSZIkSZIkSZKkITMhI0mSJEmSJEmSNGQmZCRJkiRJkiRJkobMhIwkSZIkSZIkSdKQmZCRJEmSJEmSJEkaMhMykiRJkiRJkiRJQ2ZCRpIkSZIkSZIkachMyEiSJEmSJEmSJA2ZCRlJkiRJkiRJkqQhMyEjSZIkSZIkSZI0ZCZkJEmSJEmSJEmShsyEjCRJkiRJkiRJ0pCZkJEkSZIkSZIkSRoyEzKSJEmSJEmSJElDZkJGkiRJkiRJkiRpyEzISJIkSZIkSZIkDZkJGUmSJEmSJEmSpCEzISNJkiRJkiRJkjRkJmQkSZIkSZIkSZKGzISMJEmSJEmSJEnSkJmQkSRJkiRJkiRJGrJOJ2Qi4gER8b8R8cuIyIh47ADrHBAR34iIqyPiRxFxyPAjlSRJkiRJkiRJy1mnEzLAtsD5wPMHWTgi9gSmgM8BdwWOBf4rIh42pPgkSZIkSZIkSZLYYtQBbI7MPA04DSAiBlnlOcBPM/Of6viFEXF/4EXAp4YSpCRJkiRJkiRJWva6XkNmvu4LfKZv2qfq9GlFxNYRsUNvALYfZoCSJEmSJEmSJGn8LLeEzC7AJX3TLgF2iIgbzbDOEcBvG8P64YUnSZIkSZIkSZLG0XJLyCzEMcCOjWG30YYjSZIkSZIkSZK6ptN9yCzAxcDOfdN2Bq7IzD9Ot0JmXg1c3RsfsK8aSZIkSZIkSZKk6y23GjJfAg7sm/aQOl2SJEmSJEmSJGkoOp2QiYjtIuKuEXHXOmnPOr5HnX9MRLy3scp/AreJiNdGxJ0i4nnA3wD/vrSRS5IkSZIkSZKk5aTTCRngHsB5dQB4Y33/ijq+Ctijt3Bm/hRYTakVcz7wT8A/ZOanlipgSZIkSZIkSZK0/HS6D5nMPBOYsVOXzDxkhnXuNrSgJEmSJEmSJEmS+nS9howkSZIkSZIkSVLrmZCRJEmSJEmSJEkaMhMykiRJkiRJkiRJQ2ZCRpIkSZIkSZIkachMyEiSJEmSJEmSJA2ZCRlJkiRJkiRJkqQhMyEjSZIkSZIkSZI0ZCZkJEmSJEmSJEmShsyEjCRJkiRJkiRJ0pCZkJEkSZIkSZIkSRoyEzKSJEmSJEmSJElDZkJGkiRJkiRJkiRpyEzISJIkSZIkSZIkDZkJGUmSJEmSJEmSpCEzISNJkiRJkiRJkjRkJmQkSZIkSZIkSZKGzISMJEmSJEmSJEnSkJmQkSRJkiRJkiRJGjITMpIkSZIkSZIkSUNmQkaSJEmSJEmSJGnITMhIkiRJkiRJkiQNmQkZSZIkSZIkSZKkITMhI0mSJEmSJEmSNGQmZCRJkiRJkiRJkobMhIwkSZIkSZIkSdKQmZCRJEmSJEmSJEkaMhMykiRJkiRJkiRJQ2ZCRpIkSZIkSZIkachMyEiSJEmSJEmSJA2ZCRlJkiRJkiRJkqQhMyEjSZIkSZIkSZI0ZCZkJEmSJEmSJEmShsyEjCRJkiRJkiRJ0pCZkJEkSZIkSZIkSRoyEzKSJEmSJEmSJElDZkJGkiRJkiRJkiRpyEzISJIkSZIkSZIkDZkJGUmSJEmSJEmSpCEzISNJkiRJkiRJkjRkJmQkSZIkSZIkSZKGbItRByBJkjQKE5NTq4BVm/ERG9atXb1hseKRJEmSJEnjzYSMJElarg4FjtqM9Y8G1ixOKJIkSZIkadyZkJEkScvV8cCp00zfBjinvt8PuGqG9a0dI0mSJEmSBtb5hExEPB94KbALcD7wwsz8ygzLHgK8u2/y1Zm5zVCDlCRJrVObG9skqTIxObVtY/T8dWtXX7l0UUmSJEmSpHG1YtQBbI6IeCLwRkqTIftSEjKfiohbzrLaFZT24nvDrYcdpyRJkiRJkiRJWt46nZABXgy8IzPfnZnfBZ4D/AF4xizrZGZe3BguWZJIJUmSJEmSJEnSstXZhExEbAXcHfhMb1pmXlfH7zvLqttFxM8i4ucRcUpE7D3H39k6InboDcD2ixG/JEmSJEmSJElaPjqbkAFuDqwE+mu4XELpT2Y636fUnnkM8HeU7f9iROw2y985AvhtY1i/GTFLkiRJkiRJkqRlqMsJmXnLzC9l5nsz85uZeRZwEHAZcOgsqx0D7NgYZkveSJIkSZIkSZIkbWKLUQewGX4FXAvs3Dd9Z+DiQT4gM/8cEecBt5tlmauBq3vjETH/SCVJkiRJkiRJ0rLW2YRMZv4pIr4OHAh8DCAiVtTxNw/yGRGxEvhL4BNDClOSJEmSJElaFiYmp1YBqzbjIzasW7t6w2LFI0lt09mETPVG4D0R8TXgK8DhwLbAuwEi4r3ALzLziDr+r8CXgR8BNwFeCtwa+K+lDlySJEmSJEkaM4cCR23G+kcDaxYnFElqn04nZDLzQxFxC+AVwC7AN4GHZ+YldZE9gOsaq+wEvKMu+xvg68D9MvO7Sxa0JEmSJEmSNJ6OB06dZvo2wDn1/X7AVTOsb+0YSWOt0wkZgMx8MzM0UZaZB/SNvwh40RKEJUmSJEmSJC0rtbmxTZIqE5NT2zZGz1+3dvWVSxeVJLXHilEHIEmSJEmSJEmSNO5MyEiSJEmSJEmSJA2ZCRlJkiRJkiRJkqQhW3BCJiL2iIj/jIjvR8SvI+IBdfrNI+JNEXG3xQtTkiRJkiRJkiSpu7ZYyEoRcWfgbEpC51zgdr3PysxfRcT9gW2BZy5SnJIkSZIkSZIkSZ21oIQM8FrgcuA+QAKX9s2fAp648LAkSZIkSZIkSZLGx0KbLHsA8LbMvIySkOl3EbDrgqOSJEmSJEmSJEkaIwtNyKwA/jDL/FsAVy/wsyVJkiRJkiRJksbKQhMy3wBWTzcjIrYA/hb48kKDkiRJkiRJkiRJGicLTcgcAzw8It4G/EWdtnNE/BXwaWAvYO0ixCdJkiRJkiRJktR5Wyxkpcw8LSIOAY4Dnl0nvw8I4ArgqZn5+UWJUJIkSZIkSZIkqeMWlJAByMz/joiTgYcCt6PUtvkx8KnM/N0ixSdJkiRJkiRJktR5C07IAGTmlcBHFykWSZIkSZIkSZKksbSghExE7DHIcpl50UI+X5IkSZIkSZIkaZwstIbMOiAHWG7lAj9fkiRJkiRJkiRpbCw0IfMMNk3IrAQmgKcClwJvWXhYkiRJkiRJkiRJ42NBCZnMPGGmeRHxb8C5wI4LjEmSJEmSJEmSJGmsrFjsD8zMK4F3Ay9a7M+WJEmSJEmSJEnqokVPyDQ+d5chfbYkSZIkSZIkSVKnLLQPmWlFxA7AA4CXAuct5mdLkiRJkiRJkiR11YISMhFxHZAzzQYuAp630KAkSZIkSZIkSZLGyUJryLyCTRMyCfwG+DHw6cy8ZnMCkyRJkiRJkiRJGhcLSshk5ppFjkOSJEmSJEmSJGlsrRh1AJIkSZIkSZIkSeNuoBoyEfGuBXx2ZuYzF7CeJEmSJEmSJEnSWBm0ybIHs2mfMXOZ7/KSJEmSJEmSJEljaaCETGZODDkOSZIkSZIkSZKksTVoDRlJkiRJktRCE5NTq4BVm/ERG9atXb1hseKRJEnS9EzISJIkSZLUbYcCR23G+kcDaxYnFEmSJM1kwQmZiHgE8GJgX2BHIPqXycyVCw9NkiRJkiQN4Hjg1GmmbwOcU9/vB1w1w/rWjpEkSVoCC0rIRMTjgf8BLgA+CDwXOJGSlHkM8EPgY4sToiRJkiRJmkltbmyTpMrE5NS2jdHz161dfeXSRSVJkqR+C60hcwTwFeD+wE6UhMy7MvOMiJgAvgz8dFEilCRJkiRpSOx/RZIkSUtloQmZOwNHZOa1EXFNnbYlQGaui4i3Ai8D3rsIMUqSJEmSNCz2vyJJkqQlsdCEzB+APwFk5uURcTUbP1F0CbDnZsYmSZIkSdKw2f+KJEmSlsRCEzLfp9SS6fkm8PcR8b76mU8GLtq80CRJkiRJGi77X5EkSdJSWbHA9T4KPCYitq7jrwYOAC4HLgP2B9ZubnCSJEmSJEmSJEnjYEE1ZDLz9cDrG+Mfj4gDgIOAa4GpzPzcYgQoSZIkSZIkSZLUdQttsmwTmXk2cPZifZ4kSZIkSZIkSdK4WFCTZRHxPxHxuEaTZZIkSZIkSZIkSZrBQvuQ2Q/4CHBpRPx3RDwqIrZcxLgkSZIkSZIkSZLGxkITMrsBBwDvAx4CnApcEhHvjIiHRsTKRYpPkiRJkiRJkiSp8xaUkMni85n5fOBWlKTMh4FHA58ELo6I/1y8MCVJkiRJkiRJkrproTVkrpeZ12XmZzPzUGAVcCiwFfCszf1sSZIkSZIkSZKkcbDFYnxIRKwCngA8EbhPnfzFxfhsSZIkSZIkSZKkrltwDZmIuGVEPC8izgJ+DhwLrAReAuyRmfsvTohzxvH8iFgXEVdFxLkRca85ln9CRHyvLv/tiHjkUsQpSZIkSZIkSZKWrwUlZCLis8AvgDcD2wP/AtwmM++Tmf+emb9YxBhni+OJwBuBo4F9gfOBT0XELWdY/n7AB4B3AncDPgZ8LCL+YinilSRJkiRJkiRJy9NCa8jckpIEuWNm7puZ/5aZ6xYvrIG9GHhHZr47M78LPAf4A/CMGZY/DPhkZr4uMy/MzJcD3wBesDThSpIkSZIkSZKk5WhBfchk5l8udiDzFRFbAXcHjulNy8zrIuIzwH1nWO2+lBo1TZ8CHjvL39ka2LoxafuFxCtJkiRJkiRJkpavBfch0wI3p/RZc0nf9EuAXWZYZ5d5Lg9wBPDbxrB+3pFKkiRJkiRJkqRlbUE1ZJaZY9i4Vs32mJSRJEla9iYmp1YBqzbjIzasW7t6w2LFI0mSJElqty4nZH4FXAvs3Dd9Z+DiGda5eJ7Lk5lXA1f3xiNi3oFKkiRpLB0KHLUZ6x8NrFmcUCRJkqTxNTE5tRLYn/JA1Abg7HVrV1872qjmp+vb0PX426KzCZnM/FNEfB04EPgYQESsqONvnmG1L9X5xzamPaROlyRJkubjeODUaaZvA5xT3+8HXDXD+taOkSRJ0pLocmH6xOTUQcBxwG6NyesnJqcOW7d29ckjCmteur4NXY+/TQZKyETEG4H/zszz6vgewGWZ+cdhBjeANwLviYivAV8BDge2Bd4NEBHvBX6RmUfU5Y8DzoqIfwKmgL8F7gE8e4njliRJUsfV5sY2SapMTE5t2xg9f93a1VcuXVSSJElabF1OZkC3C9Nr7CdNM2tX4KSJyamD3Ybh6nr8bTNoDZnDga8B59XxnwJ/D5w4hJgGlpkfiohbAK8AdgG+CTw8My+pi+wBXNdY/osR8WTgVcBrgB8Cj83M7yxp4JIkSZIkSZJar2vJjGn6OXwQ8LppFu0Vpr8U+Fxj+kj7OeyLfwXw1vq+vx+JABJ468Tk1EXcUAbcqn4aazLvuDo60zYcOzE5dUobk3xdj7+NBk3IXALcpjHemo5UMvPNzNBEWWYeMM20DwMfHnJYkiRJkiRJkooVjff7T0xOnd6FwtuO1gwYtJ/DXvnu6/umj7qfw/n00xiU/sG/2pg26viZmJzaB9i7ju7Fxsm8fgHsDqyZmJy6sE67YN3a1ecPMcRZdT3+ths0ITMF/GtEPBS4vE77p4j421nWycx8zOYEJ0mSJEmSJC1U15uaqjqZzOipSY03NSadRotrmPR0uGZAs5/DuwNvH2CdZwNfr+9HXbvko8AP6vv7Ai8YYJ03c0Mf4RcMI6h5Og544DzXObLx/izggEWLZv66Hn+rDZqQOQy4lFLFbW/KCWd34KazrJObF5okSZIkSZK0MF1ramo6XU1m9HSthklfc1l3Z7CaAc+YmJy6Ppkx6uaymv0cTkxO3XHA1X6/bu3qbwwvqnl5HIPXkOl5ATckbo4GRl074zA2rmFy5CzL9rwKuL6GyTCCmoeux99qAyVkMvNK4P/1xiPiOuDwzBxpHzKSJEmSJEkanq7WMOlaImA6Xd+GjtYw+ac6zEezBsobgJcsXjjz15dU2m7A1babmJzat74fdVKpWcNnBfBx4JZM34VGUioRPIpGHzLDDnAutbmu8+H64+AQynE70zasB9a05Tjoevxtt2LuRab1IOD0xQxEkiRJkiRJ7VETAusoHX6fWF/X1emtNUAiAEoiYOXSRTU/47ANlETebszcF3Wvhsn+SxbR8nAopfmxrzNYc2XU5XrrHDqkuAaybu3qDevWrv5GHb4GPK/O6m+NqTf+vHVrV3+tsc7IEzJNNUlxWB2daRsOb2syo+vxt9GCEjKZeVZmXrbYwUiSJEmSJGn0GrUzdu2b1aud0aqkzMTk1D4Tk1NPnpicejKlQ+9BEgFreuvUTqzbZBySGavmXmReyy2FN1CaKrs7cE/gEmbuliHr/Hs21nnDEsQ4l+O5IZ67U2rsJNMXpmed31z++CWLdAC1FtjBwC/6Zq0HWl1LrKfr29D1+Ntm0D5kNhERdwFeCOwL7MimyZ3MzNtuRmySJEmSJElaYh1tamrcOqHuYjKj36A1FVpTo6HZ/wrAxOTU8yiJyWTjY2Gj2hlLF+Hc+rcB+MbE5NRPmaY/JUrNhtYXpq9bu/rkicmpU+hg84k9Xd+GrsffJgtKyETEAcAngd8AXwPuBpwBbAPcl9Jxz9dnWF2SJEmSJEktUmuINDtxHqQz8zUTk1PXd+Jc+x0YlXHrhLpzyYxpnE0p9J+r74mzlzKo+aiF0AfT4WQGjEdheo31zFHHsTm6vg1dj78tFlpD5hXAT4D7AFtROk96TWaeERH3Bk4DXrY4IUqSJEmSJGnIOl3DZAw7oR6HZMa1E5NThzF7DZPW9z0xDskMsDBdaouFJmT2BY7KzCsiYqc6bSVAZp4bEccDr6QkZiRJkiRJktRuY1PDZBwSAeOwDbBRDZM3sXF/RF2rYWIyQ9KiWGhC5hrgd/X95cCfgVs25v8EuPPCw5IkSZIkSeq2WlOjE0/Vj1sNk3FoamqMkhknT0xOnQ5cUSc9Aji9rfuOJA3TigWu9yPg9gCZmcD3gMc15q8GLt680CRJkiRJkrppYnLqIGAd8DngxPq6rk5vtVpQflgdzb7ZnaqdAUwADwKeXF/37EoiA67fhr0akx5Bx7ahuq7xvrWJSUkatoXWkPkE8IyIOCIzrwHeCLw7In5Y598WOGIxApQkSZIktdfE5NQqytP/C7Vh3drVbe6UWpq3mnQ5aZpZuwInTUxOHdz2AvVxqGECY9PUlMkMSRoTC03IvJLyg3wtQGa+JyKuBR5fp706M09YlAglSZIkSW12KHDUZqx/NLBmcULRuOlSk189Nebj6mh/c19BqWFy7MTk1Clt35Zx6cxckqS2WFBCJjP/DPxf37T3Ae9bjKAkSZIkSZ1xPHDqNNO3Ac6p7/cDrpphfWvHDE+zmfL9JyanOtVnQ61lskntjInJqcPaVjtjYnJqH2DvOroXG8fcL4DdgTUTk1MX1mkX1D5cWmdMaphIktQKC60hI0mSJEkStbmxTZIqE5NT2zZGz1+3dvWVSxeVajLjTY1Jp9HSZAZM2/Tdg4DXTbNor8mvl1L6ZOkZddN3xwEPnOc6RzbenwUcsGjRSJKkVlpwQiYiHgY8E7gNsBObVsPNzLztZsQmSZIkSZLmqaP9lwza9F2v7OH1fdNH3fTdYWxcQ+bIWZbteRVwfQ2ZYQQlSZLaZUEJmYh4KbAWuAT4CvDtxQxKkiRJkiTNX4f7L2k2fXd34O0DrPNs4Ov1/UibvqvNjZ0P138Hh1ASYP3fAZTvYD2wpmXfgSRJGrKF1pA5DDgDeGTtT0aSJC0j0zQrMl+jblZEkqRxtT+D9V+yPy3qF6TZ9N3E5NQdB1zt9+vWrv7G8KJamHVrV187MTl1GKWWUrJxUibr6+EmYyRJWn4WmpDZCTjJZIwkScvWoM2KzGTUzYpIkjSuBn1gYnMerBi2QR/aaO3DHevWrj55YnLqYEptpWaCbD0lGdO2JuMkSdISWGhC5ivAoE+sSJKk8dNsVqRpG+Cc+n4/4KoZ1m9tAYokSdWKxvv9JyanTu9IjYZOJjP6at/+ntJE+i2ZucmvS4HfT0xO7Vunta72bU3KnEKpjbSK8j8/uyP7kSRJGoKFJmSeB5wWEV/LzBMXMyBJktR+zWZFmiYmp7ZtjJ6/bu3qK5cuKkmSFsfE5NRBwJsak04D1k9MTh3WgZoNZ1NqYczVf8nZSxnUAOZT+zaAnYGvNqa1svZtTb6cOeo4JElSOwyUkImIb82w7n9HxNsoF3P9T3hkZu6zmfFJkiRJ0rJTOwXv+lP1naxhUpMxJ00za1fgpInJqYPbnJTpcP8l09W+fRDwUkrypecS4HXA5/qWbVXtGEmSpOkMWkPm19xw4dbzf8APFzccSZIkSVreakJgk34nOlI7A+huDZOaCDuujvbXLgnKffGxE5NTp7QwoXG9Rv8lb6Ikknpa23/JDLVvvzExOXUs3U9OSpIkAQMmZDLzgCHHIUmSJEnLXtdrZ0Dnt2F/Nk6E9Qtg97rcmUsR0ELVpMzpwBV10iOATtRSarLJL0mSNE5WzL2IJEmSJGnYBqidAaV2xsqli2p+xmAbVs29yLyWG7XrGu+tWSJJkjRigzZZtpGIuI7SbusTM/Pz08x/CvDezGzrRbYkSZKkMdaVPlgmJqf2Afauo3sxWO2MNROTUxfWaResW7v6/CGGOF9dr2EyaD8k9lciSZKkeVtQQqbaBvhMRLw0M4+bc2lJkiRJWmQTk1Or2LS2wrQdgU9MTk3bEXjtu2JUjgMeOM91jmy8Pws4YNGi2Xxdr2FyNqWflV3ZtIYPlD5k1tflJEmSpHnZnITM4cC9gH+PiHsAz8rMqxYlKkmSJI29GQrS52PUBeljqyu1S6pDgaMGWG5n4PXTTD8aWLOYAc3TYWxcQ+bIWZbteRVwfQ2ZYQS1GTpdw2Td2tXXTkxOHUbpAyfZOCmT9fXwFh8PkiRJarHNScj8OTOfHxHnAm8D7hwRj8vMixYpNkmSJI23QQvSZzLqgvTOG4PaJQDHA6fW9yuAjwO3ZObaDZcCj+KGvjVGGn9tbux8uD4Rdghz185Y0+KEQOdrmKxbu/rkicmpg4E3UbajZz0lGXPyaCKTJElS121OQgaAzHxvRHwL+Ajw9Yj4280PS5IkSctAsyC9aRvgnPp+P2CmWtijTgSMg67XLqEmhDYATExOHcDGiaR+Uedvt27t6jOHHtw8jUPtjHHYBrg+KXM6cEWd9Ajg9LbHLUmSpHbb7IQMQGZ+MyLuDpwIfJIWP+0kSZI0zrrU1FSzIL1pYnJq+8boDsC5bd2GGaxovN9/YnKqzYW4na5dMo2u91/SrJ1xHLBbY1ZnameMUQ2T6xrvW3sulSRJUnesmHuRwWTm5cBq4DXMv1NKSZIkbaaJyamDgHWUZqVOrK/r6vROqLFe2Jh0Gh3ahq7Fv27t6g3r1q7+xrq1q78BbEepPTJdMgY2rl3yjTq0LSHT6f5LemrCYoLSfNyT6+ueHUpk9LZhr8akR9CxbZAkSZIW20JryOwJXNY/MTMTOCoiPgzcbHMCkyRJ0uBqgf9J08zaFThpYnLq4LYXhHZ9G7oePx2tXdLXD87vgUuYu5bP7ycmp/at09rQD84mam2MM0cdx2ayhokkSZLUsKAaMpn5s8z8wyzzv5OZZy08LEmSpJHpb25q5cgiGVCN8bg62l8I3Rs/ts3b0vVt6Hr8VVdrlxwKfL0OX2WwWj5fbaxz6BLEKEmSJEkL70MmInYCngTcBtiJTW96MjOfuRmxSZKk7ulS3xmbqDUc3tSYdBqwfmJy6rC21WyYmJzaB9i7ju7Fxn1N9Atgd2DNxORUrzmtC9atXX3+EEOcr/0ZbBv2p521BroeP5R+INdTavTMVLtkPe3rL7LZD07Pg4CXUpIvPZcAr6M05dfUtgSTJEmSpDG1oIRMRDyM0hzDtsAVwG+mWSw3Iy5JktQxXUpmTKeDzU0dx/z77Tuy8f4s4IBFi2bzdbK5rIZOxt/X3BfAsZSkRbJxUqZ3bX8csM/E5FRv+sib+6p/vz+Gb0xMTh1LSYCtqvNtMkuSJEnSSC20hswbgIuBgzLz24sYjyRJ6qAOJjM2MkBzU0lpbuqUFhXoHsbGNWSOnGXZnldxQ4fzFwwjqM3Q1eayeroa/6HAUQMs1zsuXt83/WhgzWIGtFjGpA8WSZIkSWNkoQmZ2wEvNRkjSdLiqUmBzj3N3dFkRr/ONTdVmxs7H67/Dg5h7qam1rT4O+hqc1k9XY1/uua+oDQ/eDfg5sCvgPPYuIP2nrYlmCRJkiSptRaakPkhsP1iBiJJ0nJWa5gcx8ZJga4099W5ZMY0OtncVM+6tauvnZicOoxSS2mmpqYOb3EypvPb0NX4Z2juq+drSxmLJEmSJI27hSZkjgTeEhEnZua6RYxHkqRlp+vNfdHxZEbV1eamrrdu7eqTJyanDmaaxB4lEdDmfQjYaBveRNn/ezqxDV2PX1qoafoi6tmm8X6ficmpq2b4iJH3RSRJkiQthRULXO9A4DLgwog4NSLeEhFv6huOm+tDJEla7gZo7gtKc18rly6qeet8MoMbmpvKGeYn8HPa19zURmqB/wTwIODJ9XXPLiUCaqx7NSY9gg5tQ9fjlxboUODr0wznNJY5Z4Zlvl7XlyRJksbeQmvIvKDx/lEzLJOUzmYlSVLDxOTUPmzcGfsgzX2tmZicur4z9tp/SFt0te+M63W1uanpjElH5s2+SjrRl1KfrscvzddMfRENqs0Je0mSJGnRLCghk5kLrVkjSZJKjZgHznOdIxvvzwIOWLRoNtO4JDNsbmrp2cyRNB7m6ItIkiRJUtXZxEpE3DQi3h8RV0TE5RHxzojYbo51zoyI7Bv+c6liliSpOgx4Sh1eNeA6r2qs07oaqDVZcTDwy75Z64G294FzPZubWnI2cyRJkiRJWjYW2mRZG7yf8kTlQ4AtgXcDb6e0lz6bdwD/2hj/w1CikyRpBrW5sfPh+j5kDmHu5r7WdKSGyenAFXXSI4DT2x73NGxuaunYzJEkSZIkadlYcEImIh4BvBjYF9iRaQqRMnMoHRBHxF7Aw4F7ZubX6rQXAp+IiJdkZv/TuU1/yMyLhxGXJGlpzNLM0QrgbsDNgV8B57Fx4XpPa5o5GpfmvhpMZmhgNnMkSZIkSVpOFpSQiYjHA/8DXAB8EHgucCKlEOkxwA+Bjy1OiNO6L3B5LxlTfYZSCHRv4KOzrPuUiPg74GLgf4FXZuaMtWQiYmtg68ak7RcctSRpsRwKHLUZ6x8NrFmcUDZfo++S44DdGrPsu0SSJEmSJGlMLLSGzBHAV4D7AztREjLvyswzImIC+DLw00WJcHq7AJc2J2TmNRHx6zpvJicCP6O0b38X4N+AOwIHzbLOEWxeoZ8kafH1N3P0IOB19f10NUxeCnyuMb11T+TXpMwpwP6U2j8bsIaJJEmSJEnS2FhoQubOwBGZeW1EXFOnbQmQmesi4q3Ay4D3zudDI2JtXW82e80xf0aZ+fbG6LcjYgPw2Yi4bWb+eIbVjgHe2BjfnvLEsiSNldqXSSeSAc1mjmrcp9RZ/c1nBiUpcxhwbFu3p6fGd+ao45AkSZIkSdLiW7HA9f4A/AkgMy8HrmbjtvwvAfZcwOe+gZJwmW34CaW5sVs2V4yILYCb1nmDOre+3m6mBTLz6sy8ojcAv5vH50tSJ0xMTh0ErKPUIjmxvq6r09tuf0ozX5v0ZVYFsHtdTpIkSZIkSRqJhSZkvk+pJdPzTeDvI2KLiNgGeDJw0Xw/NDMvy8zvzTH8CfgScJOIuHtj9QfX7Tl32g+f3l3ra+uarpGkpVKTLicBu/bN2hU4qQNJmVVzLzKv5SRJkiRJkqRFt9CEzEeBx9QO7wFeDRwAXA5cRnkKee3mBjeTzLwQ+CTwjoi4V0TsB7wZ+GBm/hIgInaNiO9FxL3q+G0j4uURcfeImIiIv6Y0qfb5zPzWsGKVpDarzX0dV0ena+4L4Ni6XFsNmlQ3+S5JkiRJkqSRWVAfMpn5euD1jfGPR8QBwEHAtcBUZn5u+rUXzVMoSZjPAtcBHwH+sTF/S+COwI3r+J+AvwIOB7YFfl7XedWQ45SkVpmYnNoH2LuO7kVp7msmvea+1kxMTl1Yp12wbu3q84cY4nydTenba1emb7Ys6/yzlzIoSZIkSZIkqWlBCZnpZObZLGFhV2b+mtI02kzz19EomMvMnwMPHH5kktR6xzH/8+GRjfdnUWpFjszE5NQqNm6C7FjgdZTkSzMpk/X1OGCficmp3vQN69autsaMJEmSJEmSlsyiJGQiYgvg9sB2wIWZ+fvF+FxJ0lAcxsY1ZI6cZdmeVwHX15AZRlDzdChw1ADL9ZIzr++bfjSwZjEDkiRJkiRJkmYzr4RMRDwSeBJwDfDfmXlGRDyW0nRY70nlP0XEGzJzkAI+SdISq82NnQ/X9yFzCHM397Vm3drV1y5VjAM4Hjh1mukrgLsBNwd+BZxHadayn7VjJEmSJEmStKQGTshExMOBjwN/Bv4I/F1EPAN4J/Bd4MP18x4GHBERP8vMdyx+yJKkxbJu7eprJyanDgNOYubmvg5vWTKG2tzYTEmVry1lLJIkSZIkSdIgVsxj2X8GvgPsnJk3oSRijgdOB+6WmS/KzBcCd6Y8kfycRY5VkjQE69auPhk4GPhF36z1wMF1viRJkiRJkqTNMJ8my/YG/i0zL6/jbwKeDbwvM3tPUZOZ10TE+yn9DUjSslGb/9qf0oTjBuDsttUsmcm6tatPnpicOoWOxi9JkiRJkiS13XwSMrcALmmMX1pfL5lm2UuBbRYalCR1zcTk1EHAccBujcnrJyanDutKDZOafDlz1HGoGyYmp1ZxQ/9xTc3f/30mJqeumuEjNtSm5yRJkiRJkpaF+SRk4Ib+BPrfS9KyVZMxJ00za1fgpInJKZv90jg6FDhqjmXOmWXe0cCaRYtGkiRJkiSp5eabkJmIiH3r+x3r6+0j4vK+5fbcrKgkqSNqM2XH1dHomx2U5PWxE5NTp9j8l8bM8cCpm7G+tWMkSVok1lyVJEnqhvkmZF5Zh6a3TrNcrxBSksbOxOTUPpR+tQD2YuNmyvoFsDuwZmJy6sI67YJ1a1efP8QQpaGrhTYW3EiS1A7WXJUkSeqA+SRknj60KCSpW44DHjjPdY5svD8LOGDRopEkSdJyZ81VSZKkDhg4IZOZ7xlmIJLUIYexcQ2ZI2dZtudVwPU1ZIYRlCRJkpYna65KkiR1w3ybLJOkZa82N3Y+XN+HzCHArmzahwyU5hvXA2vsQ0aSJEmSNM7s00qSZmdCRsvKLBcGg/LCQBtZt3b1tROTU4cBJ1GSL82kTK8vrcNNxkiSJEmSlgH7tJKkWZiQ0XIzyIXBbLww0CbWrV198sTk1MGUvmV2a8xaT0nGnDyayCRJkiRJWlL2aSVJszAho+VmpguDbbjhCY39gBmrzg4jKHVfTcqcAuxPqYW1ATjbmjGSJEmSpOXCPq0kaXYmZLSszHRhMDE5tW1j9Px1a1dfuXRRaVzU5MuZo45DkiRJg7O/A0mSJC0VEzKSJEmSpOXM/g4kSZK0JEzISJIkSZKWM/s7kCRJ0pIwISOpFSYmp1Zi/yuSJElaYvZ3IEmSpKViQkbSyE1MTh0EHAfs1pi8fmJy6rB1a1efPKKwJEmalf1OSJIkSZLmw4SMpJGqyZiTppm1K3DSxOTUwSZlJEktZb8TkiRJkqSBmZCRNDK1mbLj6mj0zQ4ggWMnJqdOsfkySVIL2e+EJEmSJGlgJmQkLamJyal9gL3r6F5s3ExZvwB2B9ZMTE5dWKddsG7t6vOHGKIkSQOx3wlJkiRJ0nyYkJG01I4DHjjPdY5svD8LOGDRopEkSZIkSZKkJWBCRtJSO4yNa8gcOcuyPa8Crq8hM4ygJEmSJEmSJGmYTMhIWlK1ubHz4fo+ZA4BdmXTPmSg9CGzHlhjHzKSJEmSJEmSumzFqAOQtHzVJMthdTT7ZvfGDzcZI0mSJEmSJKnrTMhIGql1a1efDBwM/KJv1nrg4DpfkiRJkiRJkjrNhIykkatJlwngQcCT6+ueJmMkSZIkSZIkjQv7kJHUCrVZsjNHHYckSZIkSZIkDYMJGaljJianVgGrNuMjNqxbu3rDYsUjSZIkSZIkSZqbCRmpew4FjtqM9Y8G1ixOKJIkSZIkSZKkQZiQkbrneODUaaZvA5xT3+8HXDXD+taOkSRJkiRJkqQlZkJG6pja3NgmSZWJyantG6M7AOfWflkkSZIkSZIkSSO2YtQBSC3RPBb2n5icWjmySBZgYnLqIODCxqTTgHV1uiRJkiRJkiRpxEzIaNnrejKjxnkScKu+WbsCJ3VlOyRJkiRJkiRpnJmQ0bLW9WRGrclzXB2Nvtm98WO7VuNHkiRJkiRJksaNCRktW2OSzNgf2I1N4+8JYPe6nCRJkiRJkiRpREzIaDkbh2TGqkVeTpIkSZIkSZI0BCZktJyNQzJjwyIvJ0mSJEmSJEkaAhMyWs7GIZlxNrAeyBnmJ/DzupwkSZIkSZIkaURMyGg563wyY93a1dcCh9XR/u3ojR9el5MkSZIkSZIkjYgJGS1b45LMWLd29cnAwcAv+2atBw6u8yVJkiRJkiRJI7TFqANQd0xMTq1i8/pT2bBu7epWNf+1bu3qkycmpw4G3gTs2pi1npKM6UQyo27H6cAVddIjgNPbnkySJEmSJEmSpOWiswmZiPgXYDVwV+BPmXmTAdYJ4GjgWcBNgHOA52bmD4cW6Hg5FDhqM9Y/GlizOKEsnq4lM2ZJjG3TeH8FsM/E5NR0H9G6xJgkSZIkSZIkjbvOJmSArYAPA18CnjngOv8M/CPwNOCnwCuBT0XEnTPzqqFEOV6OB06dZvo2lOQWwH7ATP/LNicBrmu8P7utyZhqkMTYObPMa2ViTJIkSZIkSZLGWWcTMpl5FEBEHDLI8rV2zOHAqzLzlDrtqcAlwGOBD86w3tbA1o1J2y805q6rtSo2SapMTE5t2xg9f93a1VcuXVTL0kyJsUG1OTEmSZIkSZIkSWOpswmZBdgT2AX4TG9CZv42Is4F7ssMCRngCDavmS5pUc2UGJMkSZIkSZIktdeKUQewhHapr5f0Tb+kMW86xwA7NobdFj80SZIkSZIkSZI0zlqVkImItRGRcwx3WsqYMvPqzLyiNwC/W8q/L0mSJEmSJEmSuq9tTZa9AThhjmV+ssDPvri+7szGzT3tDHxzgZ8pSZIkSZIkSZI0p1YlZDLzMuCyIX38TylJmQOpCZiI2AG4N/C2If1NSZIkSZIkSZKkdjVZNh8RsUdE3BXYA1gZEXetw3aNZb4XEY8DyMwEjgWOjIi/joi/BN4L/BL42FLHL0mSJEmSJEmSlo/OJmSAVwDnAUcD29X35wH3aCxzR2DHxvhrgf8A3g58ta738My8aikCHmPN/Wj/icmplSOLRJIkSZIkSZKkFupsQiYzD8nMmGY4s7FMZOYJjfHMzH/NzF0yc5vM/KvM/MEo4h8XE5NTBwEXNiadBqyr0yVJkiRJkiRJEh1OyGj0atLlJOBWfbN2BU4yKSNJkiRJkiRJUmFCRgtSmyU7ro5G3+ze+LE2XyZJkiRJkiRJkgkZLdz+wG5smozpCWD3upwkSZIkSZIkScuaCRkt1KpFXk6SJEmSJEmSpLFlQkYLtWGRl5MkSZIkSZIkaWyZkNFCnQ2sB3KG+Qn8vC4nSZIkSZIkSdKyZkJGC7Ju7eprgcPqaH9Spjd+eF1OkiRJkiRJkqRlzYSMFmzd2tUnAwcDv+ybtR44uM6XJEmSJEmSJGnZMyGjzVKTLns1Jj0C2NNkjCRJkiRJkiRJNzAho8VwXeP92TZTJkmSJEmSJEnSxrYYdQDSUpqYnFoFrJpm1jaN9/tMTE5dNcNHbFi3dvWGxY9MkiRJkiRJkjTOTMhouTkUOGqOZc6ZZd7RwJpFi0aSJEmSJEmStCyYkNFyczxw6masb+0YSZIkSZIkSdK8mZDRslKbGzOpIkmSJEmSJElaUitGHYAkSZIkSZIkSdK4MyEjSZIkSZIkSZI0ZDZZpoFNTE6tAlZNM2ubxvt9JianrprhIzbUJsMkSZIkSZIkSVpWTMhoPg4FjppjmXNmmXc0sGbRopEkSZIkSZIkqSNMyGg+jgdO3Yz1rR0jSZIkSZIkSVqWTMhoYLW5MZMqkiRJkiRJkiTN04pRByBJkiRJkiRJkjTuTMhIkiRJkiRJkiQNmQkZSZIkSZIkSZKkITMhI0mSJEmSJEmSNGQmZCRJkiRJkiRJkobMhIwkSZIkSZIkSdKQmZCRJEmSJEmSJEkaMhMykiRJkiRJkiRJQ2ZCRpIkSZIkSZIkachMyEiSJEmSJEmSJA2ZCRlJkiRJkiRJkqQhMyEjSZIkSZIkSZI0ZCZkJEmSJEmSJEmShsyEjCRJkiRJkiRJ0pBtMeoAJEmSRmFicmoVsGqaWds03u8zMTl11QwfsWHd2tUbFj8ySZIkSZI0jkzISJKk5epQ4Kg5ljlnlnlHA2sWLRpJkiRJkjTWTMhIkqTl6njg1M1Y39oxkiRJkiRpYCZkJEnSslSbGzOpIkmSJEmSlsSKUQcgSZIkSZIkSZI07kzISJIkSZIkSZIkDZkJGUmSJEmSJEmSpCEzISNJkiRJkiRJkjRkJmQkSZIkSZIkSZKGrLMJmYj4l4j4YkT8ISIuH3CdEyIi+4ZPDjlUSZIkSZIkSZK0zG0x6gA2w1bAh4EvAc+cx3qfBJ7eGL96MYOSJEmSJEmSJEnq19mETGYeBRARh8xz1asz8+LFj0iSJEmSJEmSJGl6nW2ybDMcEBGXRsT3I+JtEXGz2RaOiK0jYofeAGy/RHFKkiRJkiRJkqQxsdwSMp8EngocCLwMeCBwWkSsnGWdI4DfNob1ww5SkiRJkiRJkiSNl1YlZCJibUTkHMOdFvr5mfnBzDw1M7+dmR8DHgXcEzhgltWOAXZsDLst9O9LkiRJkiRJkqTlqW19yLwBOGGOZX6yWH8sM38SEb8Cbgd8doZlrgau7o1HxGL9eUmSJEmSJEmStEy0KiGTmZcBly3V34uI3YCbARuW6m9KkiRJkiRJkqTlp1VNls1HROwREXcF9gBWRsRd67BdY5nvRcTj6vvtIuJ1EXGfiJiIiAOBU4AfAZ8axTZIkiRJkiRJkqTloVU1ZObpFcDTGuPn1dcHAWfW93ek9PsCcC1wl7rOTYBfAp8GXl6bJZMkSZIkSZIkSRqKziZkMvMQ4JA5lonG+z8CDxtuVJIkSZIkSZIkSZvqbJNlkiRJkiRJkiRJXWFCRpIkSZIkSZIkachMyEiSJEmSJEmSJA2ZCRlJkiRJkiRJkqQhMyEjSZIkSZIkSZI0ZCZkJEmSJEmSJEmShsyEjCRJkiRJkiRJ0pCZkJEkSZIkSZIkSRoyEzKSJEmSJEmSJElDZkJGkiRJkiRJkiRpyEzISJIkSZIkSZIkDZkJGUmSJEmSJEmSpCEzISNJkiRJkiRJkjRkJmQkSZIkSZIkSZKGzISMJEmSJEmSJEnSkJmQkSRJkiRJkiRJGjITMpIkSZIkSZIkSUNmQkaSJEmSJEmSJGnITMhIkiRJkiRJkiQNmQkZSZIkSZIkSZKkITMhI0mSJEmSJEmSNGQmZCRJkiRJkiRJkobMhIwkSZIkSZIkSdKQmZCRJEmSJEmSJEkaMhMykiRJkiRJkiRJQ2ZCRpIkSZIkSZIkachMyEiSJEmSJEmSJA2ZCRlJkiRJkiRJkqQhMyEjSZIkSZIkSZI0ZCZkJEmSJEmSJEmShsyEjCRJkiRJkiRJ0pCZkJEkSZIkSZIkSRoyEzKSJEmSJEmSJElDZkJGkiRJkiRJkiRpyEzISJIkSZIkSZIkDZkJGUmSJEmSJEmSpCEzISNJkiRJkiRJkjRkJmQkSZIkSZIkSZKGzISMJEmSJEmSJEnSkJmQkSRJkiRJkiRJGjITMpIkSZIkSZIkSUNmQkaSJEmSJEmSJGnITMhIkiRJkiRJkiQNmQkZSZIkSZIkSZKkIetkQiYiJiLinRHx04j4Y0T8OCKOjoit5lhvm4h4S0T8X0T8PiI+EhE7L1XckiRJkiRJkiRpeepkQga4EyX2Q4G9gRcBzwFeM8d6/w48GngC8EDgVsDJwwtTkiRJkiRJkiQJthh1AAuRmZ8EPtmY9JOIuCPwXOAl060TETsCzwSenJln1GlPBy6MiPtk5pdnWG9rYOvGpO0XYRMkSZIkSZIkSdIy0tUaMtPZEfj1LPPvDmwJfKY3ITO/B1wE3HeW9Y4AftsY1m92pJIkSZIkSZIkaVkZi4RMRNwOeCFw/CyL7QL8KTMv75t+SZ03k2MoyZ7esNvCI5UkSZIkSZIkSctRqxIyEbE2InKO4U596+xKab7sw5n5jsWOKTOvzswregPwu8X+G5IkSZIkSZIkaby1rQ+ZNwAnzLHMT3pvIuJWwOeALwLPnmO9i4GtIuImfbVkdq7zJEmSJEmSJEmShqJVCZnMvAy4bJBla82YzwFfB56emdfNscrXgT8DBwIfqZ9xR2AP4EsLjVmSJEmSJEnaXBOTU6uAVdPM2qbxfp+JyamrZviIDevWrt6w+JFJkhZLqxIyg6rJmDOBnwEvAW4REQBk5sWNZT4LPDUzv5KZv42IdwJvjIhfA1cA/wF8KTO/vPRbIUmSJEmSJF3vUOCoOZY5Z5Z5RwNrFi0aSdKi62RCBngIcLs6rO+bF/V1S+COwI0b814EXEepIbM18CngeUONVJIkSZIkSZrb8cCpm7G+tWMkqeUiM0cdQ6dExA7Ab4EdM/OKUccjSZIkSZIkSZJGZ9C8wYqlC0mSJEmSJEmSJGl5MiEjSZIkSZIkSZI0ZCZkJEmSJEmSJEmShsyEjCRJkiRJkiRJ0pCZkJEkSZIkSZIkSRoyEzKSJEmSJEmSJElDZkJGkiRJkiRJkiRpyEzISJIkSZIkSZIkDZkJGUmSJEmSJEmSpCEzISNJkiRJkiRJkjRkJmQkSZIkSZIkSZKGzISMJEmSJEmSJEnSkJmQkSRJkiRJkiRJGjITMpIkSZIkSZIkSUNmQkaSJEmSJEmSJGnITMhIkiRJkiRJkiQNmQkZSZIkSZIkSZKkIdti1AF02PYRMeoYJEmSJEmSJEnSaG0/yEImZOav949dP9IoJEmSJEmSJElSm2wPXDHTzMjMJYyl+6JUi7kV8LtRx9Iy21OSVLvR3f+N2zB6XY8fur8NXY8fur8NXY8f3IY26Hr80P1t6Hr84Da0Qdfjh+5vQ9fjB7ehDboeP3R/G7oeP7gNbdD1+KH729D1+KH729D1+Idte+CXOUvSxRoy81T/mb8YdRxt02i+7XeZOWMGsM3chtHrevzQ/W3oevzQ/W3oevzgNrRB1+OH7m9D1+MHt6ENuh4/dH8buh4/uA1t0PX4ofvb0PX4wW1og67HD93fhq7HD93fhq7HvwTm/J+sWIooJEmSJEmSJEmSljMTMpIkSZIkSZIkSUNmQkaL5Wrg6PraVW7D6HU9fuj+NnQ9fuj+NnQ9fnAb2qDr8UP3t6Hr8YPb0AZdjx+6vw1djx/chjboevzQ/W3oevzgNrRB1+OH7m9D1+OH7m9D1+MfuZilfxlJkiRJkiRJkiQtAmvISJIkSZIkSZIkDZkJGUmSJEmSJEmSpCEzISNJkiRJkiRJkjRkJmQkSZIkSZIkSZKGzISMJEmSJEmSJEnSkJmQkaRFFBEx6hgkSZIkSd0XEVvU15WjjkWStDhMyGjRRYT7VUt07bvoWrxNEXGjiNgjM3PUsSyWrieXurw/jYuu70PQzW1w35fUBhGxTUTcadRxLLYu/i5I2lQXjuWI+FvgfRHxLeDdEXHQuCRmuvD/n07vOrur8UOJPSJuNOo4FqrG39n//3S8f1t+/MK1aCJiZUTcNDOvG3UsCzEOJ8CIuGlE3CciDo6IlV36LprxdvS7eDfwglEHsRjqsbxjV5NLXT4XRcRWEbEqIm7WxYvMiLhfRBwTER+KiHv09qGObksnj4OIeAjw1oj4XEQ8vjG9M99B148DaTGMSYHb24A3RMRtRh3IYmr+LnThHBURT4iIvXpP2Temtz72pojYPSIeFhHPj4g7RMTNRx3TctTR+7Rptf1YjogHUc6jvwE+DmwLvAT4QkTce5SxbY6I2CIidu/aNXZP7x6zy/c5wCuBg/onduH4jojIqsvXSh0vu7tJR/f7VomOngPVQhHxOmB34GWZ+bNp5kfbfnQj4i+BJwCvycyr6rTWxTmIiLgtcBxwL+By4I/AMzLz623epojYnfIdPAM4Bzis9110RUTclxL7bTJzXZ12R+Ay4LrMvHx00c1fRBwL/Dwz39A3vbX7UVMXz0Vw/T7zEuBJwNeBf8rMr402qsFFxL7Ah4B1wFbAfYA79o6JrunicRAR9wA+AnyR8h08DrhLZn5npIHNQ9ePg371N+4vgDsDZwD/l5kXjTaqwXU9/qZ6s3vtqOMYRL0+fQHw75n5vVHHsxARcS/KPrN/Zp4XETsADwGuAP6YmV8YaYDzFBF3AO4N3AE4G/hFZl4w2qjmVr+HTwLPAk7JzGumWaa1v2s9UWpanQjcEvgzcGvgNOC/gTMy89IRhjdvXTofNUXEocCVwKcy87LG9BWZeV1H9qU7APcD9ga+DPwUOK+NcUfE2cAXMvOIOr4d8ADgscBdKcfEcW2MfTYR8R/AnsALM/On08xv3X4UEbsCDwWeQrnHPxP4amZ+Y5RxLURE7AOcB+ydmRfWadtm5pWjjWx2NQm/P/Ao4EeZecyIQ1qwrpbdAUTEXSgJvbcBX8zMK0YcUmeZkNGiiIi/AM4FVmfmmXXavsCvANp68x4R5wL3BDYA/5qZ76zTAzZ+aqbtIuILwPeA91JuVP4FuCgzn9e3XKtO8BFxOqXg8BvA44HTM/OZdd6WmfnnUcY3iIi4ADg1M4+oN77PBR4DbA2cArw9M89s2/9+OvUH9ptsfIF2s8z8v5EGNqCunovg+vPRz4CTgWcDATyCcnzcpM2xA0TEN4DTgVcDV1G24/3AwylP9p2emVMeB8MTEV8HPg28PDOviYgTgS8Bqyk3j18F/isz/zDCMGfV9eOgqRYg/jcl9hWUwp8zKEmzqbZvS9fjB4iICWC/zHx/HQ/K/U+rn0KMiC9Sktq/Af4DeHNm/mq0Uc1PRLwP+FNmPiMiHg5MUq65LwMuAj4DvC4z/zjCMAcSEXcGPgZcW4fdga8BnwU+kpnfH110s4uIrwJnZ+aLa2HWI4C7UH6nP5mZ54w0wAHVY+JbwLHADymFWK+inJc+DExm5pVtvsbo6vmoJyJ2oxy7fwL+l1KY+K3MvCIits/M3400wAHUY/kjlPPQFpTz7PeBU4EPZOY3Rxfdxmry5YOUQv+j++btSknaPwt4UmaePoIQF6Rxr/bwzDw7IraiFLL/jvKb8c1RxjeTiDirvj0X2Bd4MOV34FTghMxcP6rY5isivgacm5nPj4g9KeUWjwRuCrw1M99Vl2vV+TQiPgLcFvg/4I6UZOoTgQ1tinMQXS27A4iIdwJPB35LuTZ6M3B+vff8y8z89ijj65TMdHDY7AH4H+A99f19gJMoWd7LKBfJB9d5MepYGzHfH/gO8GTgdcAfKE/j7te33CrKj25rYp9mWx4K/BzYuTHtcXXabRvTWrUNwNOA9cAt6/hBlAvPYyiFiG8E7jTqOOfYhoOB64Dd6viX6/HwZMrFzXn1e7j9qGMdcHu+Rin4gXKhczTwbeBHwBrgVm3clxrxd+5cVON5POWicts6vopa0AP8mJLoeCqwYtSxzhD/YygFJDdvTPs25Sb3/fX9BuDeo451wO3p3HFASbr8ENi5FxdwPqUA682U5PBFwKNHHess29Dp42Ca7fkCpTB99zr+vPp78VPgP4EtRx3jOMdfYz6rxnwOcP/G9JVt3Y/qb9f5lILzf6QUPPyoXlds01huD+DRLd6OtcDa+v7blGT9rsCdgNfXbXrYqOMccFvOpjwJuqqO3wV4F+U+4kRK8p62fRc1zguAO9TxMynX1+fX8+l5wD+MOs4BtuM29fft/nU8GvOeQinI/SSw/ahjnWM7Onc+asQYwPaUmtCvr/vRH+v7Xeu+9bhRxzngd3AcsGMdf3Tdf86r1xq3HnWMffG+ph6vezPNNWfd798+6jjnuU3vAd5f398f+ARwNeVhnDOAl1IeamzTNfbf1GufHRrT3g/8hHKdfcyoY5zHtjyuHrs7NfahL1ASA2+nJOtPAbYbdax9cffKjW5NabpvX+AHwN/U+a3ZXwbYlk6W3TXiekC9Bvrrelz8inKP8Brg83Tg/qAtQ+vbB1T71XYbf0N5mhjgLcCvKU8NPJVy8faqiLhT1iO4JW5Badblq5n5UuAAyoXA2VH6P1hVl3sv8Lcti73f/pREwO8bbTl+gvIEU7N92XdHxNOWOrhZvIByAdNrZmBbyo/RrtxQ8PbG+uRMW92VcpO4NiJOBbYEDs3MEzPzlMy8W13u4FEFOKiI+GvKxU3vKay3APsBU5Tj4EXAhyPiJm08Hjp8LgK4G6XAqteUyP0px/XngJcDF1NuUG49kujmti/lack/AETpu+QOlIvkp2TmX1KSYk8cXYiD6fBxMEEpKLkyMzMiDqI0M/W3mfmCzHwMpTm5TdqLbpGuHwfXq81BrALelJk/j9J3w8mUm9z3Uc5JJ0TEliMMc0Zdjx8gIu5HuZ74f8AlwCci4gMRsUtmXpuleZ0VEbFjRNxstNFuZBtKodS6zHwT5Tj+FKUQ6xNR+uraEngn8Mhs79P1lwD7R8QBlN/it2TmLzLze5n5EuBC4O9GGN9Aaq2AHYGPZ+aG+rTqtzLzGcDLgHsA74l29l13EbVWT0Q8CdiB8ru8D+Vceg7wgojYZYQxDmIdcCnwQCgtGETE1vX9+ym1Zf6C0iReK3X4fASU/3mWGjCfB26amfelNC/6DMrv9r6UgrnWioi9KA+tvBO4ou5Dn6YkaU6l7EfvjIgbjy7KTZxISdi9FbhnbNq/xxeA20df/1AtdzHlIRsoybEfUpLHT6DUGHg6cM+WXWPvB5yVpTbYTeq0z1J+q48HXhYRL4BO9MHyj5Sky4Mi4tWUhzuemplPzcxnU1o2uD+lbKxNng38W2b+LDOvzNJU3CcoiZrr1dpjbdfVsrtezc5vU35zr8nMPSkPUf87pSb0d4E2nUNbre0nC3VAlvZvL6fcdN2VcuF/RGZ+KTNPy8yHU9qaffboopzWx4F3ZOYPATLzK8DfUwqrbg/8qFaLPJBysdBK9aR4FvCN+uOUAJl5NaU5iIfV5e5JKUA5d1SxNkXp4PXHlKd+eo6htEf5D5n5r5TC23tQ2otuq1dSnpjfivLD9M7M/A1c32HhlpSL5S50Tv03lJvEZ0TEmymFcU/LzMnMfAXwl5QnWx8/y2eMTIfPRVCerror8LCIWE1JAvxLZr45M08EjqIklO4yuhBn9RpKzaReU1irgKdn5vn1OAjK05M37sCNSlePg3dQmtv4fR2/jpKM+W6j0PxzwFYtvnE/n24fB02/ojyBuDNAln4bVlKa6Hg5JVF8X+B2I4twdl2PH8pv8lcpNSWfAxxGedL+uxFxNFzfMe8JdX5bfAE4PjMvjNIvwyWZ+XxKc1/XUI7jT1OuT181wjjn8hFgJ+BwYBfKsd30CWDnlj90Q5ZmaH5BqQnanwyYonwvt6DUYGqbP1Jqpz6X8tv1icz8OUCWZoFeC2wH7DWqAOdSj4HrKMfFv0TEk6Hc59Tri5WUp9S/TTkntVVXz0f93gncOiLunJlvodyjbUPZj46N0jn1tiONcGa/pBRE37YmmK6mNNH0UMpDOPek1Iq+28wfsbSy9AH4aErzamdSHlS8T0Tctja/9neUPpQ26RuqxS4HDoyI/SkF0K/NzO9n5leyNNe0gfYdA71mEskb+oZ9IfCrehy8jlJboHcct9mTKeehN1MeMntdZv6kMf87lHPq7iOIbVpR+n26mlKbjUaZyieAvWtiOyPiQZRz6nYjCnVQZ1H6repM2V1PPXf+BngFpeyUzHwN5aGty4B/oDy4eMCoYuyStheKqOUaJ8OPUjLpz6DcLN6mzl9Z538M2K1NT1Jm5p8z86tww5MM9aT4McqTAa+k1NY4MjN/MbJA51BP4l+kFFz1P5XxcUpCA0rW+u3Zgs5h65NHv6bcIF5Qp92W0jTK64E/133nUkrVzd1GFOqcMvPqzPwA5YLmFZQq771511COhz2B9S170mc6h1Oa5DgY+FvgP3r7fj3Wf03p62fnUQU4gE6diyJi24jYg1LA9hNKDYxnUG5WesfGSsrTZL+l1CJrlfq08NWZ2Uyuvgv4AJTjoO7796R0wNj2G5XD6eBxkJl/yo07mP445eKYvKEvrocC327xjXuvPeX30bHjYBq/pewrb4uIR0XEfSi1rD5c5/8SSNqb0PgN3Y4fyjnovZn5oyw1cd9HefL2jcBTI+IHEfFaSkH7h2f5nCUTpe+8ayjNclCfml8ZpQPwb2bmQ4FnUmoKtPb6tP4uXER5AOJ2lAed/iEi7h0RN42IW1K246uZ+adRxjqbxjX1J4FDIuJw2DgZkJm/pdQa2KdtDxzUAp7/ojxA8Fjg4RFxi8YiF1MKuFr1e9bUu2bIzJdR7xMi4oMRsXe9vri2budNKA/dtFXnzkf9anLsKsoT0L1k8HGUhyn2ojwI9T+UBE0b/YFyrf2OiHhqlJrEnwU+WPezayi/e3cYYYybyMyfZuZ+lJYlDqIkuz8NnAZ8PzNfOcr4FuCDlOP1mcCN2DQh/GHgpr3Ed0t8Btg2Ir4TEUdHxMmUB7ZeXedfQHnorM0PkfZ+mzdk5rMovwvvpVzPXT+fcu39J8o1Xitk5g8oZVn9fZ6dTYlz7zr+BuB9jYfT2upcyu9Zs4wCWlp2BxuVWVAf7PsicId6XXd7yn3zAyjx78XGNX00k2xBu2kO4zEAh1CeaLiOcpF2C8rTlFtQssCvHXWMjVjnbBMT+FfgN6OOdY4YHwZsPcO8FZQb4O9SLpavBFaOOuYa21uBdzfGe/0dbNW33LOA74063hm24daUphH+hlJ1vzd9ZeP9jSlJp0tHHe8A27Oi8f4vKcmlB0/zHX0eOGzU8c6xLV06F72l71i4R43zvykd1Pb2o2cCvxx1vDNsw3Z949H3uhXlSbeuHQd3pSTmW30cNM5FB1PbRO/fnsa56OJRxzvDNmxFSVxvUcf3rcfB+yi13Fp/HDS2ZQtuaJv+LpRCuF9TnrA/re87+QqlmYiRxz3DtuxNKTjpZPx929I8tm8M3I9Sq+w6WtL2OyUB/CFK4eZJlOvQW/ZvA3AE8NtRxzvLdmzXF++elEKryykJgK9T+hc7e9SxznO7DqckL74M3Ktv3pfash/NEPuTKc2XXUep4fCQer1xZFt/F2rcu/d+F+r4qvpb9llKTdaPUmo2nEZ5gKuV/bD0rh0a481tauX5aI7tuTWl4PAxlKfWH9GYd7tRxzdH7DejFIReTknOfIAbru22qMf3c0Yd5xzb8ChKrZl70nfd1/ah8bvwqMY56X2Ua+5tKbWgvwD8+6hjrXE+hxv6WrkHJcH9M0ozZfdtLPePlE7NRx7zHNvT66Ox2Q9Xf/nL31H6rmvF+bR+Bzv0x8MN9wwfo1wv/XWbr40aca9ovvbe0+KyuxrjWygtwTSnHQm8m3Jd9/7+3zqHAf6vow7AobsDJRmwVfOkQqlZcjLwZ8oN5Yfqhc2FbTip15PcmvpD+mngfjMstyOlTdBDRh3zLNtyX0qh8y3mWO7cerHzjFHHXOM5sMZzHXD0LMvdgXJz1brvgJLx/wLlCeivUZ5S7V9mS0qfB+uBJ4465gG3a2XfBdqWjfcr6gXar9twLPfF3X/DvkU9P53U1nNRjbN5LLy6b94z6vT/reeqH7VtPxrkfErpBPYFbYx/hm1aWV+b+/6NGu9bdRzM41x0BKXjyyeMOuZp4rtLvZi/mtL/04GNeU+tx8En2nocTLM9RwGHN8Z3oxRI703tcJpy7fRUSi2Uke9H02zDto33uzfi36Ej8ff/JsQM719ej52RbwPwIEozcS+n9O3xibrv/wh4cfO7oTw9fMioY55mG2b8Tai/BbegJDVeRimMu9WoY55je3agNF+0T93nV1Lucz5JeZL+C/XcdU79nka+H9W4d6LRUXBj+i6Up4d/R7kW+hOlv8ZHjTrmGbZjH8o9zHNpFPLX3+G/oCTop+pv36tpFI62bWD6Argt2no+GnCb1tZz1Pvq+Eb3EG0cuCHxciNKIuzW1Acb67RnUhI1nfgOujbQd41NqT35Nkozcj+hPOz0bUot9JF/B8Bf1fPkzfumN4/jlZTro0va+LvciHNPSvL6F5T+kvaYZpnePc5FwLNHHXPjO/hz/3fQt8wLKff6v6M0ez/yuGeI89aUfrfOpyS0/4XGPU9juVaV3dWYpi2/ozRZ+VVK/2G3rdO2GEWMXR16P0rSvETEfSmJl7tk5mV9825OecL+KZQbxy9S2ja9YJMPWmIRcQ7lZH02cB9Kkw+PzMwv9C23BXD3zGxVm41NEfFd4PTMPKzGuwtwW8qP6K+zNKFARDyd0uZ7KzrTjoifUJ4u+QXlxv2FmfnxWoU26zJ3o9TQyMz865EFO4OIuIDyQ3oC5emwJ9ThdpQfoU/U5XYH7pOZrWx6ACAitsq+5kJqExzX9k17HqWjtldk5n8tZYyzidLx9Nsp38WnM/PHdXpQnoK7C+XJ0Fadi2DaY+EfM/N/G/MfQSmI+D/gpCxt1bfGIOfT2jfAXwB7Zel4t5Ui4taUqvvPpHQefAHw2cz8VGOZoHwfrTkO5nEu2pXym3bqiEKdUUScR7kB/yBwKHBzSqHnlpQC0VtTknq/p4XHQVNE3IuS+E1KH0rvnWG5v6fsR8dm5juWMMRZRcSelBqG/0ApEHle1v4mGsusoFzftS5+mPk3oW+ZoDSXcibwX5n5H0sY4rTq+fS0zHxVHd+d0izNzynXdu/OzONq0xa3zo3be2+FGX4THpWZn+9b7vrrvbaqfTO8AlhNaYr2DZn5kTpvJ0otvqdRri2+BHwmS58sIxcRZ1CSpW+gtFH/x775NwPuXpf5WZbms1qn9qvycsrTwt+n1GQ4KzMvi4h9sjaTGhHbZGlGq3XqtcUTKO3s/5yyr3w5Mz/bWGYF5UHAM2nJ+ahnhnuEFVmaUtyL8vDQ29p4PuqZbhtmWO5gSvPT78zMdw0/suWj7xr7p5Rj+tOZ+Zk6/7aUfWkF5fr7i23YpyLix5Tm7P4lInakPCy6L+VY/lFm/iAiJij9Qd0sM586umhnFxFnUfoU+wylqcQfA4+tx/JOmfmbiNiecv19h8x89Swft2T6voPtKU1h7UNpZu2H9Tu4JyWJ8dXMbG0zWRHxJUpC4xxggvLAVlJiPz4zv1+Xa1XZHcxafreS8oDNHzLz9BGG2F2jzgg5dHOgVqWr77egPI14AKXgZLvGcq3JkFLasf5xX3ynUToyg75mdto8UC5oNjTG30DpbO5PlOYgXg/sUuftSK1qO+qBcqH7c8qTkjvX//8GSkFhc7mbUNqgXDXqmKfZhoMoN4bNp+a/Ramq+T3KD9WJzFFzqS0Dpa3no5n9yZOtKQUrLxp1vNPEdjTl4uY7lMKrg3vb0tyvaNR4aMMw6LFQlx35U2LTxDTQ+bQrA6WQ5BOUJ9PfRakmvoFy8XnnuszWwP40aj+MOObOn4sozRD8ELhxHb8l5anzT1Gesj+H+rQbLaq2P8v2XEBpCuXfKE+M9fad/mYWbg48ftTxThP/WZSn/19St+V/ueGJ1mbTnLdoY/w1tul+E25a5927sdyNaUltK8pN+ReBx9Tx3tPDp9Xv4g31fHSnUcc6yzbM9ZuwSQ2BNg+UWhfvohROvYfSPN9NKfc5d2ost9WoYpwh7qdQkmI/piSx/w24E333Y134HihNGH2C0p/nGZSC3FfX3+WfckNzNa293qBcW5wDvJZyvf3FOv5G4I6N5VpzPuqLf9Z7BBrNZrf1e5hrGxrL3RT4q1HHO44D019jX0zjGrsu15p9iFKb86e9uCgJ4Z9RalD9rF5f3LPOXwXcZNQxz7ItT6Xcc/auhe5OeZjrHZTaGh+g1tSg1AZtRfndgN/Bver8FzXPqW0bKE0M/pJGmRwlsfTa+rvwXmqtJVpUdtf43w5UZuGwgP/vqANw6N7A7MmAS+p4q5ohqCeQ06gFyo2L+KdRqsg2q54+jmmqcbZpqCfFD9b3L6AU/LyQksg4jHIz9t+jjrMv5i0oVZIPbkzbhlLw9ilg9953NepY59iOp1OeHL5VY/wKSkHt7SiFPz8HHjbqWAfYlsdTCq5+S2lu48nM0CdRWwfmvmHfqi7XmsKHeRwLrUoiNWKdz/n0oA6cTw+qcTcLEl9HSW6cVS+Sdxx1nNPE3elzUd2PzqDUDOtNeyblRv3ZlILQd9fj+DajjneA7XkmcCml+Yq9KMmyr9PCBwtmiH+QG/bWF1bN8pvwjnqctzKxV8+pb22M34vSLFavP5bvAc8fdZwzxD7f34TdRx3zHNvzmLrf9P73W1ESxZ+h1KD8FqW9+hvTsmtWSr8Gb6zvn0UpuPoppXZn77diJXAM8IBRxzvHtqykNEnWS8o/m9L05p8ohex3atv/vy/+QQrgWnssMOA9Ai26vt6MbWjl78I4DMx+jf15Sp+ZNx1VfDPEvKJeD322jr+Ukkh9dB1fTanZfTaNJl7bOtTfrxc1xp9Uz6Ovo5QffY2SNLvJqGNdwHfwBUpzg60uv6Dco51HqUlF87erHiMbgA+NOs5p4p6rzOLWdZrn0AUOK5Dmbw2lkIqIeAGlJsObKE+2voZywfxvdX6MJsRN3JhyMbY1QGZeU6d/hnKjdX+AiHgkpa+JX48gxoFExJ0oBT23ioiTKJ1OH5OZ/5GZl2fmcZQbrwMjYodRxtrn1sApmXkSXN8s1lWUp8TuSul8naxn9Rb7MmV/ekVE/Bvw78CrMvPszPwR5cfpR8DdRhjjoP4aeCsl1tMoT4GeFhH36y0QESsj4pm1aYI2+jaluu/NMvPBlEKGJ1AKF78K3KY2j3LdCGPsN+ix8OeRRTi7+ZxPex2Ct9k+lMTGHyNiuzrty5Snr95GKdh9GZTvaiQRTq/r56IbUzpkbjYN+ipKPzhvz8xPUq4pktIkZ9sdC7w0M6/NzAspN4tbAkfWZkV7TdO01bMpBbm94/UOwEMpBbr/Rekf5BURcZORRDe4mX4TnkYpdLh9i65Nm9fJHwWeExGfrtd2HwdemZm/j4itKYVXu48qzjnM9zfhNyOIcT5uT0li9L6bx1ASZO+kJDk+QrnGuGObrlnreebjlGsfsjQneNM67S3AhyLi4ZRj/Z8ofXa1Vpamc9cAj67XcW+nHMOXUvpPOx44pE3Hc5+bUx5UXAHXN9V3fmb+M6Ulg4fU17ZdW/QMco+wAnh6i+8RBt2GQyJi79GEOPZmu8Z+K/BYyvmoTddI21F+q7aOiO9Q+gZ8TdZmpbM0nfuPlOukNpW1bKI2qfZVNr7Wfj2lSc6XZWki8bmUplFvteQBzmzQ7+D2lETS1SOLdDBfpjSl/jAo5V29e4PMPJlyTXHf2sR0m8xVZvEKuP73Wgsx6oyQQ7cGytNIn6E80XAS5abqoL5l/o7yRND2o463L66VTJ+V/jQ3PNV3IeVkP/J459iWVZSaMJ8HTgF2a24XsB/lRusuo451wO15aN1njqbFT7v1/seUC/wpygXNJ9i4w90tKU8Tt7ZTuRrnjpSnYp7emHbXejxcQ3maeA/KU/ZX9Y6dNg7APetx0Nv/TwTWUy72z6I8ldLq/aqxLZ04FsbsfPokSuFbs0mmb1D6DKAeJ18fdZzTxN35c1Hdhl7zTFsB9+OGJrJW1t+6bwCPGHWsc2zH3wAX9G3XCuB59fz5/0Yd4xzxT1ASes1O2H8BHMkNTU3dk1IQuteo4x1gezr5m0ApNDyZUmj47Mb0LShNsL1g1DHOEvs4/SYcQKkJc3jdV37VPIbreek7bTy39p9TG9NvQ7l/u5pSY+BfRx3rANuyBeXJ5y9Qkqp3oHTufGfgjvVYfuGo45wl/r0o/Xo+ublNjfcPqfN3HXWs08Te+XuEcdiGcRjo9jX2XSkPpLwP2LMxbwWlf8xvU/qKHXm8c2zLTtRyOUqTs0+jPEAR9bf7NpRk94NGHes4fgc11hvVc85vgUOnWWaPepwcOOp457FdnSizaPsw8gAcujcwJsmARryvotysPxO4dNRxzXMb9qY8WXKjvumHAt8ddXyDfAd12IbyJPQfuvJDxA1NcryEUoCyJ+Um8V+AdaOOb8Bt2IHaFBObNinyQ0rh2++pzV+0caDjN+yN7ejssdCLv7527nwK7Ep5+vwX9WL5jPr+RnX+o+uNyq1HHesM8Xf+XDTLtr0I+P6o4xgw1t7+En3TX0Qp3H3SdPPbMtDRG/ZptqPzvwk0mn6gtNd9DHDRqOOa5zZ08jeh7u87Ufo5+B6lEOhrwFMay2xJKVB85qjjHWB7VrBxEuBDwIWjjmue+9BTKDXIvlW/j1aeQ6f5v3e6AI7xuEfo/DZ0faD719hbUWr5bNU3/XAaD+J0aaCveSlKzc8fjjqu5fAdAC+nJIC/TKktc0vKfds/ApeNOr4Bt6HTZRZtG3oXOtK81aq9twc+lZl/bEw/FDgsM+88suDmISLuT+m4dkfgb7JWyWubiNiJ8kN0ySzLBKXQ4VOUZl9OWKLwFkVEnAz0mqrphIi4OSU5uSWwLeVJgSMy8/SRBraZImJLSufOt83MiRGHM6PaBERGxFMoT7ndlnLh//fZ4R+4Lh4L0J3zab96HL8QeBSlWYuTM/Mbdd7zKAW4bW2SAxivc1E9/9yH0lzBP2bmR0Yc0px656LG+IrMvC4idqY0y3E/YP8szcm1Xm2W4NrG+LOAf87M248wrDmN229CRDyA8oTxSZn52VHHM19d/U0AiIjbZ+YPI+KNlFonL6zn2YOBNZnZhaYUgeubAro9pZbSEzPzwyMOaRMz3efUZo5OpTSd8rDM/FH/+anNIuLllAckvklpduc8ym/0o4GXZ+YtRhfdwnTlHmE247ANXTEO19g9db+5HyW5/fy2Xp9Odz6d5jq1V250GqXW5AlLHugCdPk7qNPvA/wzpTnUCyhNxa2nNDvdmWuknq6WWbSFCRkNZJyTAbWflYsoGfb9Rh3PTCLiDEoTcW8AzmsmwRrL3IPSjv0VmfnIpY1w4RoFVztk5hWjjme+6r6/P+Vm8SuZ+f0RhzSjQY7lutwE8GPgGZn5nqWIbRDjesPeMwbHQlfOp4P+pu1LuVH55y78pnXpXDSbiLgV8GbKE/XPGXU8M5nH+XQLyhP1R2bmqUsS3ADG4YZ9GfwmBHCLzLx01LEsxDj8JkTEoyj70lcoT+reiFKQ3qmCk4i4C/B3WfowaZ3Z7nMi4p6Umj5fquMrsl19AwLdL4Dr+j0CjMc2dN24XmMD1D4+/gu4JDMPGXE4Mxqw3GhfSr/Pv8/Mxy1xiAvW0e/gm5n5h775twYeQakx9oOu3bN1vcyiLUzIaCDjnAwAiIitgB0y81ejjmU69UnP/6RUrd6Z0jnnu4Ef5Q2dp/YubiaAP8x1IarlaZBjuS73cEq7y09cyvjmMg437OOu7edTmPsiuS6zO6XZpt3anBQYV1E6Od5qpnNUGwx4bbQyM6+NiDtl5veWPMhZjMMNu78J7dfB34RNjoWIuBOl4+lfUVoGOHPJg1wEbT0G5nGfs1Vm/mk0Uc6t6wVwXb9HgPHYhq4b92vs+pDN1pl55ahjmc48y412A67KzMtGEetCdfg7+HFm/nmUsaldTMhoTiYDRi8ijgeuzMwXR2k25HWUC53XAqdk5i9rcwSvoVQD/soIw1VLzeNY3jIz/xwRN57uInpUxuWGXaM16H5Ul92CkhRozXGgduj6+Wgcbti7/h2oHebzm6DhmOM+538zc309F70G+ERmnj3CcKfV9QK4rt8jwHhsQ9d5jT168yw3+mhmnjvCcMfSgN/BFsArgU9m5lkjDFcjtGLUAagTHgC8IzNvS+mc9lBK9dJn1WZFqCf1YyjNKpiMWUT1ZP1x4KsAmfkO4KZ12luAD9WnfJ4DvBho1dNWapVBjuUAXhER923hBfJs8e8G18d/dETsP7ow1XID/aZFxFrgni08DtQOg55Pj47SD0jbzOfa7lZtS8ZU/iZoMQxyLGwREWtbeix32gD3OR9o3Of8E3D+iEKdy0z70T/07UfHRMQDRxjnTLp+jwDjsQ1d5zX2CC2g3OjCEYU6tubxHTyL8pt23ohCVQtYQ0azqieURwDbZeYH6rQVwHHA84FzgFcDt6E0V3aLzPztaKIdX/XicYv6NM/1T3pGxG2At1P6DNiS0snoK0YYqlpqHsfynnXazdvUHmjX41c7uB9pMXR9P1rAtV2r4ofufwdqB/ejduj6fU7X96Ouxw/jsQ1d53fQDl0/n44DvwMNyoSM5uQJpX3qxc2KXtXfiPgQcJfM3Gu0kanNun4sdz1+tYP7kRZD1/ejrscP47ENGj33o/bp4n1O1/ejrscP47ENXed30D5dPJ+Om//f3t2FylbWYQB/nnOEDCUt7aYiMToSgRURRQVREn0QRRfJKRMSNIISjCgDoSj6JsG+LpLCysKwKEgOHKXyo4sgqBTESMjI6lT0qXYsu/C8XezZOA4qR91zZtbs3+9mZt5515r/u993LfbwsNaYAx6OQIZHzQllfczmYl+2LjfdP8b47opLYkKmfixPvX7Wg3XETpj6Opp6/clmjIHVs47Wx5S/50x9HU29/mQzxjB15mB9TPl8uinMAYuOW3UBTM8Y40iSI3MnlLOT7F9tVbvTGONI2yckudQJnUdr6sfy1OtnPVhH7ISpr6Op159sxhhYPetofUz5e87U19HU6082YwxTZw7Wx5TPp5vCHLDIFTI8Lm2fl+TcMcbFq65lN2u7Z/YPDzwmUz+Wp14/68E6YidMfR1Nvf5kM8bA6llH62Hq33Omvo6mXn+yGWOYOnOwHqZ+Pt0E5oBtAhkeNycU2AxTP5anXj/rwTpiJ0x9HU29/mQzxsDqWUfshKmvo6nXn2zGGKbOHAA8QCADAAAAAACwZHtWXQAAAAAAAMCmE8gAAAAAAAAsmUAGAAAAAABgyQQyAAAAAAAASyaQAQAAAAAAWDKBDAAAAAAAwJIJZAAAgI3V9ry2o+2LVl0LAACwuwlkAAAAAAAAlkwgAwAAAAAAsGQCGQAAYNdo+/W2h9s+s+2B2fNDbd8ze//Mtte3vbftnW3PWdj+KW0vbXvrbNt72h5s+/yH+KzT2l4z29df217W9rWzW6i9cqHvS9pe2/butv9pe1Pbly/zbwEAABxbAhkAAGC32ZvkYJI/JLk4ye+SfKnteUmuTfLzJB9M8u8kV7Y9fW7bZyV5c5IDSd6X5LNJzkxyU9unbXdqe0KS65O8OskXknwiycuSfGaxmLZnJflJkicl+WiSS5KcnOT6ti/ekREDAAArd9yqCwAAADjGjk/yrTHGp5Kk7VVJ/pTkiiRvG2NcPWv/YZJfJ3lHko/Mtr01yRljjCPbO2v7zVm/85N8bNb8rszCmzHGD2b9Lk9y83whbZvky0luSPL6McaY63tbko8nec0Ojh0AAFgRV8gAAAC70Ve3n4wx7kpye5J7k3xnrv32JHdlK1jZbvvfdhjTdm/bU5Icnm3/wrn9vy7JoSTXzG17X5KvLNTxgiT7klyV5JS2p7Y9NckJSX6c5BVtfW8DAIAN4AoZAABgt7lvjPG3hba7k/xx+wqVhfYnb7+YhSMXJXl3ktOzdfuzbf+Ye35akjseYn+/WXi9b/b4jUeo96Qk/3qE9wEAgAkQyAAAALvN/Y+yvXPPL8nWbcmuSPKhJP9MciTJ5/LY7kCwvc0HktzyMH0OP4b9AgAAa0YgAwAAcPTekuSGMcb5841tT07y97mmO5M8t20XrpJ59sL+7pg93jPG+NFOFwsAAKwP9yIGAAA4evfnwVfMpO3ZSZ6+0O+6Wdub5vodn+SdC/1+ka1Q5v1tT1z8sLZP3YGaAQCANeAKGQAAgKN3IMmH234tyU+TnJnk7Ul+u9Dv8iQXJvl2288n+fOs332z90eSjDGOtL0gycEkt832eyhbYc6rktyT5I1LHREAAHBMCGQAAACO3ieTnJDknCT7k/wyyRuSfHq+0xjjcNuzknwxyUXZ+h2YK7MV4nwvDwQzGWPc2Pal2fpNmguTnJjkL0l+lq1gBwAA2AB98O2MAQAAWJa2701yWZJnjDEOrbgcAADgGBLIAAAALEHbJ44x/jv3+vgkNyfZO8Y4Y3WVAQAAq+CWZQAAAMvx/ba/T3JLkpOSnJvkOdn6LRkAAGCXEcgAAAAsx3VJLshWALM3ya+SvHWMcfVKqwIAAFbCLcsAAAAAAACWbM+qCwAAAAAAANh0AhkAAAAAAIAlE8gAAAAAAAAsmUAGAAAAAABgyQQyAAAAAAAASyaQAQAAAAAAWDKBDAAAAAAAwJIJZAAAAAAAAJbs/yxe0RuE7owpAAAAAElFTkSuQmCC",
+      "text/plain": [
+       "<Figure size 2000x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "low_dvl = df.iloc[:30]\n",
+    "low_dvl.index = low_dvl.index.map(str)\n",
+    "ax = plot_shapley(\n",
+    "    low_dvl,\n",
+    "    title=\"Images with low values\",\n",
+    "    xlabel=\"Image\",\n",
+    "    ylabel=\"Banzhaf value\",\n",
+    "    prefix=\"banzhaf_value\",\n",
+    ")\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Compare convergence speed of Banzhaf and MSR Banzhaf Values\n",
+    "\n",
+    "Conventional margin-based samplers produce require evaluating the utility twice to do one update of the value, and permutation samplers do instead $n+1$ evaluations for $n$ updates. Maximum Sample Reuse (MSR) updates instead all indices in every sample that the utility evaluates. We compare the convergence rates of these methods.\n",
+    "\n",
+    "In order to do so, we will compute the semi-values using different samplers and use a high number of iterations to make sure that the values have converged."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {
+    "tags": [
+     "hide"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "if is_CI:\n",
+    "    max_checks = 1\n",
+    "    moving_avg = 1\n",
+    "else:\n",
+    "    max_checks = 10000\n",
+    "    moving_avg = 200"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.linear_model import SGDClassifier\n",
+    "\n",
+    "if is_CI:\n",
+    "    utility = Utility(\n",
+    "        model=SGDClassifier(max_iter=2),\n",
+    "        data=dataset,\n",
+    "        scorer=Scorer(\"accuracy\", default=0.0, range=(0, 1)),\n",
+    "    )\n",
+    "else:\n",
+    "    utility = Utility(\n",
+    "        model=TorchCNNModel(),\n",
+    "        data=dataset,\n",
+    "        scorer=Scorer(\"accuracy\", default=0.0, range=(0, 1)),\n",
+    "    )"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def get_semivalues_and_history(\n",
+    "    sampler_t, max_checks=max_checks, n_jobs=n_jobs, progress=True\n",
+    "):\n",
+    "    _history = HistoryDeviation(n_steps=max_checks, rtol=1e-9)\n",
+    "    if sampler_t == MSRSampler:\n",
+    "        semivalue_function = compute_msr_banzhaf_semivalues\n",
+    "    else:\n",
+    "        semivalue_function = compute_banzhaf_semivalues\n",
+    "    _values = semivalue_function(\n",
+    "        utility,\n",
+    "        sampler_t=sampler_t,\n",
+    "        done=MaxChecks(max_checks + 2) | _history,\n",
+    "        n_jobs=n_jobs,\n",
+    "        progress=progress,\n",
+    "    )\n",
+    "    return _history, _values"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {
+    "tags": [
+     "hide-output"
+    ]
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "PermutationSampler requires caching to be enabled or computation will be doubled wrt. a 'direct' implementation of permutation MC\n",
+      "100%|█████████▉| 99.99000199960008/100 [17:48<00:00, 10.69s/%]       \n"
+     ]
+    }
+   ],
+   "source": [
+    "# Monte Carlo Permutation Sampling Banzhaf semivalues\n",
+    "history_permutation, permutation_values = get_semivalues_and_history(PermutationSampler)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {
+    "tags": [
+     "hide-output"
+    ]
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "100%|█████████▉| 99.98000399920016/100 [09:38<00:00,  5.79s/%]       /home/jakob/Documents/pyDVL/venv/lib/python3.10/site-packages/numpy/core/_methods.py:118: RuntimeWarning: invalid value encountered in reduce\n",
+      "  ret = umr_sum(arr, axis, dtype, out, keepdims, where=where)\n",
+      "100%|█████████▉| 99.99000199960008/100 [09:40<00:00,  5.81s/%]\n"
+     ]
+    }
+   ],
+   "source": [
+    "# MSR Banzhaf values\n",
+    "history_msr, msr_values = get_semivalues_and_history(MSRSampler)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {
+    "tags": [
+     "hide-output"
+    ]
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "100%|█████████▉| 99.99000199960008/100 [15:59<00:00,  9.59s/%]       \n"
+     ]
+    }
+   ],
+   "source": [
+    "# UniformSampler\n",
+    "history_uniform, uniform_values = get_semivalues_and_history(UniformSampler)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {
+    "tags": [
+     "hide-output"
+    ]
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "100%|█████████▉| 99.99000199960008/100 [15:46<00:00,  9.47s/%]       \n"
+     ]
+    }
+   ],
+   "source": [
+    "# AntitheticSampler\n",
+    "history_antithetic, antithetic_values = get_semivalues_and_history(AntitheticSampler)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {
+    "tags": [
+     "hide-output"
+    ]
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "100%|█████████▉| 99.99000199960008/100 [15:41<00:00,  9.41s/%]       \n"
+     ]
+    }
+   ],
+   "source": [
+    "# RandomHierarchicalSampler\n",
+    "history_random, random_values = get_semivalues_and_history(RandomHierarchicalSampler)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {
+    "tags": [
+     "hide-input",
+     "invertible-output"
+    ]
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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Bycf0olyvlNrrpl8p5T/341VMz9j9Vpr9wLIRSqmTqjuhUipGKXVckO1RmPl1YAKEQGP8g3Vl1lS7x69tPiG9blYiie+B861U88HanGH1yPm8inlfPqoqrqPVkT1JWGrK9/4fo5SK96srHpPZEmr5/rd60N7EZNEc7b/P+j2tLClMXaqT949PmD+3cPiGD7ev6QFKqeZKqYwguxphhteVYTICgslACAGBr1LqcEzW1X3FRuXv5zLgjaoOro+fh1JqgNXbG8jXKxfs74QQBzUZXiiEAEBr/Yj1T/J+4Del1E+Yyd5bMP8oB2DWS5rhd8xPSqnHMGtCzbbmPGzFfIrZCzNssdo1YmrYvh+VUgsww9iigI+tjIeB5b5SSjkxaaTnW3ONFmJumDpgPoH+gSCLPFdy3k1KqRxMFsWfrOQRvnW6emMSJpyIX6YzrXWxdfPyCvC3UuozYJ7V7vaYT5TXYJIkhEVrvVaZxU/fBb5WSn2KSQTSGLMmWTvM0Ce01uuUUhdghmlOtxKT/I0ZGtQOM5E+BZOhrCpxwA/Wz2EmsMg65jTMnMCP/LOuWVZg5rXMVkp9ZL0GF2CGnj6ntf7O75rCed0uwfSyvayUuhmzXtJGTC/rYZj3YX9MlkuAxzHrNw0GfldKTWPPukbfYdZLqhGt9USl1DnWsX8rpT7AvKbnYl77t61ektq6CzP091Yr0PKt0zUUs/RCjdscjjp8//gL9ecWjq8w8wNftBJibAY2aq2freKYNsAspVQh5vdpCeZ3ahBmuN54q8cUzByukZihticD8zF/Iwdh1jQbyr7xF2b43kyl1OfseT8nY7J0Bhv6Haiufx7jMT3KP2KC051AX8ww9kWYNfuEOLToBpC3Xh7ykEfDeWBunp/BLAy6CfPPcgWmh+u/+K235HfMRZgbwc2YLIB/A3cDsUHKeqh8naDRmBu5kyrZfw971qUZXM11HI8ZkrjcuoY1mHTxTwBH1rRNfmXOxGTh24ZZs+hDzM3/VKs9yUGOycD00C3CpKBeb72u/wdkBpT9hiBrJFn7rrTOcWWQfT0xN3/LrOtchQkErw1S1o7pTZpv/Zw2YYavvQ6cW4P3RhQmwP4UszZbqfW6TgeygehgrytmIn+u1cYdQBHmU3hVyXlq/LpZ5RMxgclMzIcE2zGBthu4lr3XXmtsvQ+WWddQjJnw34kQ1umy6orALPY6w3pvbLPakYPfukl+5ScQ4tpR1nGtMMHoGuv6/rDeFydRz+t0hfP+qcl1hvJzo4rfAWu/Br4Jsn249X7bYZWp7vc8GbgPE4D43q8rrNfm4sD3LGZx5o8wwchW61qutl6rvd5LVb0uVPH3r7Lr9103Zuj1G1Y7SjHDWi8JUk/Q90s9/DyGYBZrn2/VtQnzO/ww0DyU97485HGwPJTWlc6BFEIIUQVrGOW/mGCjdXXlDzVKKQ+A1tq+f1sixMFJKaWBb7XWJ+3vtgghqiZzuoQQohpKqWT/OTvWNoXpeWuPGXYlhBBCCBHUITmny1o74nGsydRa65f2c5OEEA3bMcDb1nwJD2Z+2DGYzIVLCEhwIIQQQgjh75ALuqxEAU8AJ2Myks1USr2vtV5X9ZFCiEPYXMzcreOAszB/O5diJos/ooMk9BBCCCGE8Dnk5nQppY4FRmqtz7OePwX8orWetF8bJoQQQgghhDgoHXBzuqy1Hz5WSi1XSmml1LlByuQopTxKqVKl1C8B64qkYjIS+SzDpIgVQgghhBBCiDp3wAVdmAUK/8Sk492LUmooZvjgA8ARVtlpdbDIohBCCCGEEEKE7ICb06W1/hSzRgwmedhehgMvaq1ftcpkAw7gKsCFWbPHv2erDfBrZedTSsVgFvb01xSzbowQQgghhBDi0JYILNdVzNs64IKuqiilojErno/xbdNae5VSX2JWUgcTYPVSSrXBJNI4E3ioimpHAffXT4uFEEIIIYQQB4G2VJzCVMFBFXQBzQAbsCpg+yogDUBrXaaUuh34GjO88rFqMheOwQxX9EnEZC1rC2yuo3YLIYQQQgghDjy+2KDKuOBgC7pqRGv9EfBRDcvuAHb4nvsNadystd5U960TQgghhBBCHAgqme60lwMxkUZV1gLlQMuA7S2BlbWp2MqIOIcq5n8JIYQQQgghRKCDKujSWu8EZgKn+LYppSKs5z/Xsu5crXUPoF+1hYUQQgghhBDCcsANL1RKJQBd/DZ1VEr1AdZrrRdj5l/lK6VmYHqlbsWkmX91HzdVCCGEEEIIIQ68oAs4EpMEw8eX5CIfuFJr/bZSqjnwINAK+AMYqLUOTK4hhBBCCCGEEPVOVZFOXvhRSuVgFmSOALoDSZJIQwghhBD1aebMmYlAaw6yKSFCNHBeYEXfvn2rzVSulGqMWYaqythAgq4Q1fSFFUIIIYQI18yZMyOAu2w22xVKqSigZinShBB1QWutd5WXl78GPNK3b19vZQVrGhsciMMLhRBCCCEOdndFRUVd36pVq52NGjXappSST8mF2Ee01mrr1q3xK1euvH7Xrl0A/6ttndLTFSLp6RJCCCFEfZo5c2Zjm802IzU1NapFixbr9nd7hDhUrV69OmX58uW7ysvL+1Y21LCmsYGMDxZCCCGEaFhaKaWiGjVqtG1/N0SIQ5nVyxyFmVdZK3U2vFCZ5ZhPBmKAH7TW1U48O5AEJNIQQgghhKgvEZhbKxmOJMR+ZP0OKurg/j+sCpRSDyulvvZ7roDPgS8AN1ColOpc28Y1JLI4shBCCCGEECIc4UZtgzELD/tcAJwC3AMMAmzA6Fq1TAghhBBCCCEOAuEGXW2ABX7PzwfmaK3HaK0/AZ4HTqpl24QQQgghhDhkzZ07N1op1Xf8+PEp+7stonbCDbrKMHO3fEMLTwE+89u/CmhWu6YJIYQQQoiDzfjx41OUUn19j5iYmCPsdnuvK664ov2SJUsOuOWMZs6cGTt8+PDUuXPnRodbR15eXtMHH3ywRV22SzQs4b6xZwOXKaXeBM4DUjBzuXw6AGtr2bYGRRJpCCGEEELUnREjRizv2LHjjtLS0ogff/wx4c0332xeUFCQVFRU9HdiYmKli9E2NH/99Vfck08+2TozM3Nz9+7dd4ZTx9tvv9103rx5cffdd99q/+1du3bduXXr1t+jo6MlqcoBLtwA4kGgDyawehH4UWv9td9+B/Bb7ZrWsEgiDSGEEEKIunP22WeX3HDDDeuHDx++dsqUKZ6rrrpq1bJly6InTpyYXJt6N2/efNB8QB4REUF8fLyOjDzgOgBFgLDelFrrL4AjgOHAVcDpvn1KqSbAd8AzddFAIYQQQghx8DvllFM2AyxcuDAa4Lnnnmvas2fP9NjY2COSkpL6DBo0qNOCBQui/I/p169f965du/b8/vvv44888sjucXFxh99yyy1tfHOh7rvvvpZjxoxp3rZt24y4uLjDjzvuuK4LFiyI8nq9jBw5snXLli0Pi42NPeKUU07pvGrVKpt/3UqpvsOHD08NbGebNm0yBg8ebAczVPKqq67qBHD22Wd38w2ZnDp1aiLAG2+8kXzSSSd1adGixWHR0dFHtGvXrtfIkSNbl5WVVbiGb775Jmn58uXRvuPbtGmTAZXP6froo48S+/bt2z0uLu7wxMTEPqecckrn33//Pda/zPDhw1OVUn1nz54dM3jwYHtiYmKfxMTEPhdccIH9YApMDxRhh81a6znAnCDbNwC31aZRQgghhBDi0LJgwYIYgJSUlLI777yz1dixY9ucddZZG6644oq1a9asiXz55ZdbnHjiiWl//vnnnGbNmpX7jtu4caPtnHPO6fqf//xn/UUXXbSuVatWuyOad955J2XXrl3qmmuuWbV+/frI559/vtUFF1zQ+YQTTtj8448/Jt58880rFyxYEJOfn9/ixhtvbPfOO+94QmnzqaeeuvnKK69cPWHChBY33njjyvT09O0AvXv33g6Qn5+f0qhRI292dvaqhIQE7zfffJM4bty41E2bNtn+7//+bynAqFGjVtx1111tV65cGfXwww8vAahqeOUHH3yQeMEFF3Rt27btzhEjRizfvn17xMsvv9zi5JNPTvv111/nBA5xHDJkSKd27drtvOeee5bNmjUr/u23327WvHnzXc8///yyUK5V1E6t+yqVUglAE8zCYRVorRfXtn4hhBBCiEOdV2u27ShrUL0T8TGR3gi11+1fjW3YsMG2YsWKyG3btqmCgoKExx9/vHVsbKz3nHPO2dSjR4+MO+64Y5nL5VrpKz906NAN/fv37zFu3Ljm/tvXrl0b9dhjjy0aOXLk7nwCvqQWq1atipo/f/7slJSUcoDy8nKVm5vbqrS0NKKwsHBOVFSUr47IDz/8sOn27dsXxcXF1Xj+VI8ePXYOGDBgy4QJE1qcccYZmwYNGrTZf//777//b0JCwu767rjjjjWXXHJJ+9dee635U089tSwuLk6fd955m8aPH79z06ZNthtuuGF9deccNWpUu6SkpPJffvmlqGXLluUAF1544cZjjz22x6hRo1Lfe+89j3/5Xr16bZs8efIi3/P169dHvvXWW80k6Nq3wgq6lFKxwP3AfzFJNCpjq2KfEEIIIYSogW07yiJ6jf788P3dDn+zR58+KyE2KuyEF+eee243/+epqak7X3rppYWTJk1K9nq9XHbZZRtWrFix+161bdu2ZR06dNjx3XffJQK7g67o6Gh90003rQt2DofDscEXcAEcc8wxW3NzcxkyZMg6X8AF0K9fv61Tp05tunDhwqgePXqElQwjGP+Aa8OGDRGlpaURJ5xwwpZJkyY1/+OPP2L79++/PZT6Fi1aFFVcXByXnZ290hdwARx99NHbjz322E1ff/11UuAxOTk5a/yfH3fccZu/+OKL5PXr10c0bdr0gElYcqALt6frOSAL+AD4HthQVw1qqCR7oRBCCCFE3RkzZszitLS00sjISFJTU3f17t271Gaz8f777ydrrcnIyOgV7LjIyMgKPVEtWrTYGRsbG7R3qn379hUCqOTk5HKAdu3aVdielJRUDrBu3bpIoM6CrhkzZsSOGjWqzfTp0xO3bNlSoTNi48aNIXdOLFiwIBqge/fupYH7unXrVvrDDz803rRpU0Tjxo13B1OdOnWqcD1NmjQpB9O717Rp0zq7VlG1cIOu84GXtNbX1WVjGjKtdS6Qq5RqDJTs7/YIIYQQ4tARHxPpnT369Fn7ux3+4mMia9VLcuyxx24dMGDAtsDtXq8XpRTvvPPOfJvNtlcwFTjfqbKACyDY8QCVZQPUuvqRheXl5TUaU7l27Vrbaaed1r1Ro0beO+64Y3mXLl12xMXFeWfMmBH/8MMPt61pPbVVm2sVdSfcoEsDv9dlQ4QQQgghRHARSlGboXwHks6dO+/QWtO1a9cdhx122I791Y7GjRuXB/ZGlZaWqrVr11bIoKiUChq9fPrpp4kbN26MnDhx4twzzzxzi2/7P//8ExNYVtVwblyXLl12AsydOzc2cN/8+fNjk5OTy/x7uUTDEe5QuQ+BU+uyIUIIIYQQQlx00UUbbTYb99xzT6rXWzF+8Hq9rFy5cp/kDGjXrt2O6dOnJ/hve+KJJ5qVl5dXKJeQkOAFkxjEf7uvl83/GkpLS9VLL73UIvBc8fHx3s2bN1d7XR06dNiVlpa2/Z133mm2du3a3eV/++232B9//LHxySefLKOxGqhwe7oeAiYrpV4A/g9YDJQHFtJaV5uBRQghhBBCCJ+ePXvuuOOOO5aNGTOmzZFHHhnjcDg2JCYmehcuXBj96aefNrniiivWPPjgg6vqux1ZWVlr7rjjjg5nnHFG51NOOWXTn3/+Gfftt98mJScnl/mX69ev3zabzcbjjz/eauPGjbaYmBivw+HYnJmZuaVx48bl1157bcdrrrlmtVJKv/322ynBhvUdfvjhW91ud5Orr7667VFHHbUtMTGx/JJLLgkaQI0ZM2bJBRdc0LVfv35pl1122VpfyviEhITyRx55ZHk9vRyilsINuuZbXw/HZDCsjGQvFEIIIYQQIXnkkUdWdu/evXT8+PEtH3/88VSAVq1a7RwwYMCmCy64YOO+aMPw4cPXLly4MGbSpEnNvvvuu8ZHHnnklmnTps07/fTTK2RdbN++fdljjz226Mknn2x122232cvLy/n444/nDRo0aPO77747f+TIke0effTR1MTExPLBgwevO+200zYPHjy4q38dI0eOXPPnn3/GT548udnLL79sS01N3XnJJZcUBmvXueeeu3nKlCnzH3zwwdSxY8emRkZG6qOOOmrLuHHjlqalpUlijAZKhTOJTik1GjOvq0pa6wfCaFOD5pdII0lrvWl/t0cIIYQQB5eZM2emRUZGfta1a9ct8fHxe2WpE0LsG9u2bYudP39+QllZ2cC+ffsWBytT09ggrJ4urfXocI47kEnKeCGEEEIIIUQ46iSAUErFKaXi6qKuhkprnau17gH0299tEUIIIYQQQhw4wg66lFLtlVKvKqVWAVuALUqpVUqpV5RSHequiUIIIYQQQghx4ApreKFSKg34AUgGvgCKrF1pwBXA2Uqp47XWc+uikUIIIYQQQghxoAo3e6EL8AKHa60rZFZRSvUCvrLKnFe75gkhhBBCCCHEgS3c4YUnAuMDAy4ArfVs4FngpFq0SwghhBBCCCEOCuEGXVHA9ir2b7PKCCGEEEIIIcQhLdygaxZwtVIqKXCHlav+v8DvtWmYEEIIIYQQQhwMwp3TdT/wGVCslHoVmGdt7w5kASmYNa2EEEIIIYQQ4pAW7uLIBUqps4CxgDNg9x/A5Vrrr2vZNiGEEEIIIYQ44IXb04XW+kvgcKVUK8C3LtcirfXKOmlZA6OUysH03tXJgtJCCCGEEEKIQ0PYQZePFWQdlIGWP611LpBrzVkr2d/tEUIIIYQQQhwYahR0KaWusL59XWut/Z5XSWv9WtgtE0IIIYQQQoiDQE2Hyk0AXmVPGvgJNXi8WgftE0IIIYQQB5Hx48enKKX6KqX6Tps2LSFwv9frpVWrVocppfqefPLJXXzbS0pKIm677bbUrl279oyLizs8OTm5T1paWo9hw4a183g8u5cqGj58eKqvfqVU38jIyCPatGmTceWVV7Zbu3atbV9dpxD+ajq8sCOA1nqn/3MhhBBCCCHCERMTo19//fWmZ5xxxhb/7Z988kniqlWroqKjo7Vv244dO1T//v27L1y4MPb8889fd+21127bsmWLbc6cOXEffPBB08GDB2+02+27/Ot59NFHFyckJJRv3bo14uuvv26cn5/forCwMH7mzJlz99U1CuFTo6BLa72oqudCCCGEEEKE4sQTTyxxu91Ndu3atTgqandHFW+++WbTnj17btuwYUOk37bkoqKi+Oeff35hdnb2ev96tm3bpkpLS1Vg/ZdffvmG1q1blwGMHDly7aBBgzq53e4mX3/9dfzJJ5+8rR4vTYi9hJWJTyn1r1LqP1XsH6SU+jf8ZgkhhBBCiIPZRRddtL6kpCTy/fffb+zbVlpaqj755JMmF1xwQYXAasGCBTEAmZmZWwLriY+P102bNvVWd77jjjtuM8C8efNia996IUITbvpzO7DXGFw/CexJIy+EEEIIIWpDe2HH5ogG9dDVxjlV6ty5844+ffpsnTRpUlPftnfffTdpy5YttiuvvLJC0NWhQ4edAC+++GKK1xveeT0eTwxASkpKWS2aLURYapMyXlex7yhgYy3qFkIIIYQQPju3RjCm7eH7uxkVjFo6i5jEWkVeF1544bqHH3647ZYtWxYlJCToiRMnNj3qqKM2B87PuvTSSze4XK7W48aNS504cWKz/v37bz7++OM3X3jhhSVt2rQJGkStWbPGBrB58+aITz/9NPG1115r3qRJk7LAOWRC7As17ulSSt1iDSv8FxNwPeV7HvBYB9wKfFJPbRZCCCGEEAeBrKysDaWlpWry5MnJGzZsiPj666+ThgwZsj6wXEJCgp4xY0bxddddtwpgypQpKbfddpu9ffv2vbOystpt3759rzldvXv37pWamtq7e/fuGbfeequ9Xbt2Oz788MP5iYm1CxSFCEcoPV2rgb+t7+3AMuvhTwNbgZnAc7VtnBBCCCGEAKIbeRm1dNb+bkYF0Y1qHbykpqaWHXvssZsnTZrUdOvWrRHl5eXqiiuu2BCsbEpKSnleXt7SvLy8pfPmzYv+5JNPEp955plWr732WoukpKTy8ePHL/cvP2HChH+SkpLKV61aFfX888+3WLp0aUx8fLwEXGK/qHHQpbWeBEwCUEp9DfxPa/1VfTVMCCGEEEJYVAS1HcrXUA0dOnTd8OHD7WvWrIkaMGBASbNmzcqrO6Zbt247u3Xrtu7SSy/d2Llz54wpU6akBAZdp59++hZf9sKLLrpoY48ePXpedtllnWbPnj3HZpPlusS+FVYiDa31yRJwCSGEEEKI2rrssss2RkRE6D///LPRRRddtNfQwqo0b968vH379jvWrl0bVVW5pKQk75133rm8uLg47uWXX25SuxYLEbpwsxcCoJSKUkplKKWOV0oNCHzUVSOFEEIIIcTBKSkpyfvYY48tHj58+PKLLrpoY7AyP//8c9yKFSv2GqE1b9686H/++SfWbreXVnee6667bn3Lli13Pfnkk63roNlChCSs7IVKqQhgDHADEF9F0YOm71YplQPkUMtAVQghhBBCVHTTTTetq2r/p59+2njs2LGpp5566sZ+/fptTUhI8P77778xb731VsrOnTsj7r777uVVHQ8QExOjr7322lUPPfRQ23fffbfxBRdcsKnurkCIqoWbMv4uYCTwf8APwOvAnZg08TdgEmrcUQftazC01rlArlKqMVCyv9sjhBBCCHGouOSSSzZs3rzZ9vXXXzf+6aefGpeUlNgaN25c3rt3763Dhw9fdfbZZ2+uST233nrrmieffLL1Y4891lqCLrEvKa2rWm6rkoOUWgDM0FpfpJRKAdYAp2qtC5RS0cDPwDSt9V1129z9zy/oStJayy+rEEIIIerUzJkz0yIjIz/r2rXrlvj4+GqHzQkh6se2bdti58+fn1BWVjawb9++xcHK1DQ2CHeoXFugwPp+h/U1FkBrvRN4A7g8zLqFEEIIIYQQ4qARbtC1DkgA0FpvATYBnQLKSGYYIYQQQgghxCEv3Dlds4Cj/J5/DdyqlJqFCeRuBv6sZduEEEIIIYQQ4oAXbk/Xi0CMUirGen43kAx8B3wLNAZur3XrhBBCCCGEEOIAF1ZPl9b6Q+BDv+dzlFKdgZOAcuAnrXVIi9sJIYQQQgghxMEo5KBLKRUHPAx8rbX+2Ldda12CXyAmhBBCCCGEECKMoEtrvV0pdR0wpx7aI+pIRn5GeyAd+LIwq7C8NnXZne4YwJeydoLH5RhW2/YJIYQQQghxqAh3TtdMoFddNkTUnYz8jDigGPgMKPPfZ3e6VU3rsTvdJ9md7l8A/6GiV9qd7tg6aagQQgghhBCHgHCzF94KfKKUmg1M0FqXVVNe7FvXAXG+Jxn5GdduLnLlA48BN9ud7p+A4z0uR6UrY9ud7mMwWSmDaQKsqMP2CiGEEEIIcdAKt6drAuAF/g/YpJSar5T6K+AhKeP3g4z8jAjgtoDN/5eY7iyNTPj7Zuv5sUCraqp6qIp9cVXsE0IIIYQQQvgJN+haD8zFpIj/BViKWTDZ/yHZC/cxu9OttDfqFKA9QOmqs5b7749r9zpEbPM9PbOa6k60vt4EZAGD/fZdWvvWCiGEEEIIcWgIN2X8SXXcDhEmu9N9N2a4ZyIQXbalm4pq/De7Svqwa/2A1LKSviR029Npldj9QQC2Lbr2QeCVgLqigT7AdMA39+sjj8ux2NrvK9qovq5HCCGEECIcgwcPtk+fPj1x2bJlhb5tJSUlEddee227r776KnndunWRw4YNW/3KK68s2Z/tFIemcHu6RANgd7pbAA8CzYAYFblJRSYWAbBz3YkAv+ryRkdvLnJlaK/tA/9j4zu80CYjP0Nn5GdE+W3+FNNz6Qu4pgD+f5jGW1/vDCUhhxBCCCGEz/Dhw1OVUn1XrFgR9MP/rl279uzXr1/3ujjX3Xff3XrKlCnNsrKyVufm5i4cNmzYurqoV4hQhZtIA6VUY+AG4GSgBXCd1vpXpVRT4ErgI631gjpppahMX0zgvAw4rlHHp+5QynuD1mq6d0frEz0ux87c7AKVk5epwXFeRn7GIODjgDp2ZuRnHFOYVfgLMMBv+xyPy3FBQNl//b6/Cni5ri9ICCGEECIcEydOXOT1eits+/777xN79+695fHHH5cEYGK/CqunSynVFpiF6WVpCxwGJABorddjsufdVEdtrHNKqfeVUhuUUu/u77bUkt36OiMx3blBRW7LAlBK543cGEdudsFcwJubXfAOQGFW4dTNRa63tsy7h/LSCnk0ptud7jeByMNXzyNt/aKPgd5Bzves3/cv2Z3uxnV+RUIIIYQQYYiJidFxcXEVMjOvW7cuqnHjxrVar9Tfrl27KC0tldE+ImThDi8ci5lD1AeTcCHwzfcBcGrYrap/TwNX7O9G1AHf3KpNwOnW89XAm5G7tvwLdLP2X5CbXaBzswv+jNYM1eUJbFt4K95dSS8AHFPk5enpd1/y6ucP88hPL/Dkd8+c/ekHI/YKmj0uR7l1Hp8Su9P9Yr1dnRBCCCEOaVOnTk1USvV96aWXmtx5552tWrZseVhMTMwR/fv37zZ79uwY/7KDBw+2t2nTJsP/uGXLlkV/8803SUqpvkqpvnPnzo0GWLZsWeSQIUM6pKSk9I6JiTmie/fuPZ555pkU//rmzp0brZTqe99997V88MEHW7Rr165XbGxs399//z3WN0Tyr7/+ijnnnHM6JiYm9mnSpEnvW265JdXr9bJgwYKoU045pXNCQsLhzZo1633//fe33HevmmiIwh1eeDrwpNZ6jlIqJcj+f4F24Terfmmtv1FKnbS/21EH4q2v24Ah1vdv3/zNA2fvjEloE6T8YbeUxDEncjtfxsPYFyI3dtzkW2Jth/XY7YmitPRHgbPSi4u+9G30uBxf+CXUALja7nR/6XE53q6TKxJCCCGECPDEE0+0ioiIICcnZ2VJSYntueeea3XJJZd0/Ouvv4qDle/du/f23NzchXfffXe7Vq1a7brppptWArRu3bpsy5YtasCAAd0XL14ck5WVtbpjx44733///SY333yzfePGjbZ77713tX9dEydObLZjxw51xRVXrI2JifE2b958d8/Z0KFDO3fp0mX7Pffcs+yzzz5LGj9+fOumTZuWTZgwoflxxx23+d577106efLkpg8++GDbY445ZuuZZ565pX5fKdFQhRt0xQFrqtifGGa9KKUGACMx85VaA+dprT8IKJNjlWkF/AncpLX+NdxzHsBM0BVRugMYaG17y1a+4x2AiPKdZMx+gT9731jhoB5lcfTYBB02r7mjmvqjgC+K0tKvTS8u8u/RSge+wAwtBXjL7nSv8Lgc39XucoQQQggRjFd72bZrW4NKgBYfFe+NUPumSTt27Ij4+++/58TGxmqAJk2alN97773tfvvtt9ijjjqqNLB8u3btym644Yb1Dz/8cJtWrVrtvOGGG3YvZfTQQw+1+Pfff2Ofe+65hddff/16gBEjRqw55phjurtcrjY33njj2iZNmuyeHLZq1aqouXPnzk5NTS0LPM/hhx++deLEiYsAbr/99jVt27Y97IEHHmg3atSoZQ8//PBKgP/+97/r27Vrd9hLL73UTIKuQ1e4QdccTNKF/6tk/7mYOV/haIQJpF4B3gvcqZQaCjwBZGMy7d0KTFNKdddar7bK/EHwaztda708yPYDVTxAZOLfyZhAd6VrYmYPT8eWqcq7i/6/3E/Mzk1kfpODBr4+KbfCwfO6DqH7vLdQQFFbyDvLxooURfclmofeqDD8+YWitPSX0ouLNIDH5SgG2tmd7rfZ08P2rd3pTvG4HLI+mxBCCFHHtu3aFtF/Uv/D93c7/P188c+zEqITvNWXrL1LLrlkrS/gAjj55JM3A8ybNy8mWNBVlWnTpiU1a9Zs17XXXrv7niUmJkZff/31q6677rpOn332WeLFF19c4ts3cODAjcECLoDrrrtudydEZGQkGRkZW7/88svkG2+8ca1ve7NmzcrtdvuORYsWxQSrQxwawv144ingIqXUnUCSry6lVBel1OtAf+DJcCrWWn+qtb5Ha/1+JUWGAy9qrV/VWs/BBF/bMNn0fHX00Vr3CvIIOeBSSsUopRr7HtSiF68exANERK/x/RLPK49MfBggYcvylX3++kVhrcWlgMxvcjj61wd3H7w89QQ+Oep2zjx3HMOPHOdZ3lR9CDC3neLiO2x9gM5+5+oV5PyBiySfUAfXJIQQQohDnFKqQkKM9u3b7/R/3qxZs3KA9evXh9yBsGzZsugOHTrssNlsFbZnZGSUAng8nmj/7Xa7vcL8C3+dOnWq0K7GjRuXx8TE6NatW1cI0hITE8s3bdpU8YTikBLu4shvKKU6AP8DHrY2f4a5t/cCdwUOCawLSqlozLDDMX5t8SqlvsQEevVhFHB/PdVdWyboilndGODoYu+OrY1atQDYHtfsI6vMdewJSJ868vdvbnv02pf0SRGdAIhr1ImRG+HnmF1n/KlYAJQDlNvUH0NGRSZPHlP2G3AUZhmA2/1P7nE5yuxOdy9gtrXpA7vT/T/gAY/LEfQTISGEEEKELj4q3vvzxT+HO4qoXsRHxYfVyxUbG+sF2Lp1a9AsgNu3b4+IiYmpEHRFRga/ZdVaB91el+Li4iq9zmDtioiICNoorbVkPTyEhb1Ol9b6YatXazDQBdNr9g/wntb63yoPDl8zwAasCti+CkiraSVWkNYbaKSUWgpcqLX+uZLiYzDDGX0SgaU1bnH9SgCIjF94OMAZM72xG9ukAlAW1eh9gPTiojICskv+1rT1O+t27bhw8NY9vdz9d0S99EZW4YCM/Iw2mHW/ADYC4zBBV9AhDR6X42+70z0GE5wC3APcYHe6O3hcDhm3LIQQQtSBCBXBvhrKV986dOiwE2D27NmxXbp02eW/b/PmzRErV66MGjBgQEnwo2uvTZs2O+fOnRtXXl6Of2/X7NmzYwHsdvvOSg8WIkzhrtPVXikVp7VerLV+Umudo7W+Xms9Tmv9r1IqTinVvq4bW1e01qdqrZtrreO11m2rCLjQWu/QWm/yPYDN+7Cp1WmlbJtRtu2t0Fq3W9++286YZNDeHcAPVRzn/jfKy9NJ2/23nZCbXXB8YVbhckzwDMAjQyI2Wd+eVJSW7r948m4el+MuTPDt0xToGs4FCSGEEOLgNmjQoE1RUVH6+eefb1FeXnEJrSeffLJZeXm5OuusszZVcnitnXHGGSVr166Neumll5r6tu3atYu8vLwW8fHx3oEDBzakez1xkAi3p2shcDkwsZL9/7H21fXY1bWY4W+Bax20BFbW8bkqsDIm5hD+PLj6kGRrNB+APv9qz5bEdh2t7T/m5GVW1cuUALBTwdT4nTMGbYs+0tr+LeZn1hWz3lezPzpHPPhNhuakQq2s/UG7xj0ux3t2p7s/4Atgjyf8ZCpCCCGEOEi1adOm7Lbbblv+2GOPtenXr1/3M888c2N8fLz3559/Tpg6dWrT4447btPFF1+8sb7Of9ttt62ZMGFC85tuusk+c+bMeLvdvuODDz5o+vvvvyc8+OCDS/wzFwpRV8INIKobkxqFmdtVp7TWO4GZwCm7G6JUhPW80t6qOjp3rta6B9CvPs8ToqioJBPXXPCDd8vaZr3NVhVRXfr8T33fFEWXn8+eXrGI3OyCLoVZhRpIxSy6zLvH7XmbFKWl77Voso/H5Zju93S83enuane6e9md7oYUqAohhBBiP3v00UdXPvfccwvLy8vVE088kfrAAw+0+/vvv+Nvv/325V988cWCwCQXdSkhIUF/9913c88555z177zzTsro0aPbbdy40fb00097AtfoEqKuqJpOQLQy9yVbTz3ALcCHQYomA48Ah2mtQx5iqJRKwMwRA9NTMhz4GlivtV5spYzPxySI+BWTMn4IkKa1DpzrVees16EESLKGG+43He9+e1lCt/+ltl6neeJFrb898RlfMDwwJy9zWlXH2p3utsA6j8uxPTe7IAIrgYalZU5e5uqM/IwoYCfA4y+W0c4kP90MdEkvLgr6R8nudL8HnBew+RKPyzEp5AsUQgghDkEzZ85Mi4yM/Kxr165b4uPjQ0qHLoSoO9u2bYudP39+QllZ2cC+ffsGXYi7prFBKD0Qt2GGFS4ENCZt/MIgj1nAWUBeCHX7O9Kqwzc07Qnr+wcBtNZvAyOs538AfYCB+yLgamgiYpfGAZz5m965sUmaf+/jN9Ud63E5lnpcju0AOXmZgb2Sq3KzC2IKswp3YRagxjls9ydOifhljwzioiDbhlbXHiGEEEIIIQ5WoQRdnwN3AHdihhe+ZT33f4wEbgD6aa0fCadBWutvtNYqyONKvzLPaq07aK1jtNZHa61/CedcoVBK5Sil5mB61xoEW+yKmOhdmpNmeyPWpuxeRuu5nLzMSteTqEKzgOcXARRmFa4C7t0VqZh8/O63S+/KKvG4HDuB9sB//TafI0MMhRBCCCHEoarGiTSsDH8/AyilGmFSwxfWV8MaGq11LpDr14W430XErIzutkwTu4vIbfEtdwAxwO/h1JWTl7kOULnZBXmYoZsTMMM4wQwXfWh6mmKImf3VtygtPSK9uCjovD2Py7EEeMXudCdiekQBzgXeC6dtQgghhBBCHMjC6n3QWj9wKAVcDVVEzEpb96VQZoulpHEn389yXi2rneL7Jje7oB9AYVahF8hf1aRCuQeqq8jjcjwdrF4hhBBCCCEOJTXq6VJK3YeZx/Ww1tprPa+O1lo/VKvWiUpl5GfERkYp5fjNy/LU4/DaoqOAIuDH2tSbk5f5RW52ge/pL7nZBWfn5GVOBa7dFamy/IreU5SWfl96cVF1mVhWAK1r0yYhhBBCCCEOZDUdXjgaE3Q9islmN7oGx2hAgq56ostjU9ouSSGhdBHFzQ/3bX46SFKMcPwPuMf6/uPc7IKRhXmF4zLyM44YlWX7fUy+SXT4/FkRWePNMMSq3AC8j0l6IoQQQgghxCGnRsMLtdYRWmubtU6W73l1j/pbYGE/aGiJNLbMG702ZcEAtsemsCnR7tv8WV3UnZOXeS9wl9+msbnZBecAf/yTqspWJpuNvf/V/ytKS4+rprql1tcWddE2IYQQQgghDjSSUa6GGuDiyFEdN61gU2IHUArg15y8zEV1VXlOXuYYoIffpg+yf37aG7MrvmnyVtYCHFus2yxNwV1NVSusr6l2p/ugCsSFEEIIIYSoCQm6Dlw7//PPD6t3xCT7nv9b1yfIycssAo7y3zZsxpjnY3bxge958lZOzsjPaFVFNf7rp42s2xYKIYQQQgjR8EnQdYD69IMRyQllpS22JLTxbVpSH+fJycucAaT5bbr06xOf/X1WJzUTIKEUgBUZ+RkqyOF4XI4yv6cP1kcbhRBCCCGEaMgk6DpwlW6La37DqhZH+YKaj+vrRDl5mXOBjrs3KPWcTrx5lO9ppxUaoG8NqvqpzhsnhBBCCCFEAydBVw01tEQa6cVFm6YfPbpIR9gigUXAD/V5vpy8TA8mqyEAG5t0+7zMFguAa0I5wG8Z+RkDKjn8ZuvrpsAddqc71e50a+vRJnC/EEIIIUS4lFJ9hw8fnlrTsldccUX7+m4TwODBg+1t2rTJ2BfnEg2DBF011AATaQB0s77+nZOXWd16WbVmZTW8zff8uxMepzwiCoA2azXAtxn5GdFBDvUl+Ei1O9132Z3uE/z2/eL3/VK70z3C7nTL+1IIIYQ4yLlcruZKqb6HHXZYWvWlK/f2228n1TSw+uKLLxoNHz48de3atfWe3Mvj8UQNHz489aeffqou07M4BMjN7YGti/V1wT4859P+T74d8BQAI6eU+zbtyMjP0Bn5GU6/Yiutr32Bh4Hv7E53pN3pbg60Dah/LFDu1/ul7U53hzq+BiGEEELsZ5MnT05JTU3dWVhY2Gj27Nkx4dbjdruTnnzyydbB9m3duvX3Rx99dLnv+ffff5/w5JNPtl63bl29B12LFy+OevLJJ1vPmDEjPnDfxIkTFy1YsGB2fbdBNBxhB11KqXSl1GNKqXeVUl8ppQoCHl/VZUNFUHbr68J9dUKrRy3Bf9usw24kdT1E7arQ2TYmIz/Dt9ZXsCQfu4DV1vdrMcFWZTzhtVYIIYQQDVFxcXH0rFmzGo0ZM2ZJkyZNyl599dWm9XGe+Ph4HRUVVR9V10pMTIyOi4ur91FKouEIK+hSSl0OFAI3YXpbIgAV8JBetPrn6wGqs/W5aiInL3Mrfj/fDU3TWdw2k6deKA8MtB/OyM/QienO71VkyftVVLkUuBO4te5bK4QQQoiG5pVXXklp3Lhx+ZAhQ0rOOuusDVOmTEnx3z937txopVTf++67r+W4ceOatWvXrld0dPQRvXr1Sv/222939xwNHjzY/vrrrzcHMyfL9/Dt95/TNXz48NSHHnqoLUBaWlqGr+zcuXMrTI14/fXXk7t27dozOjr6iC5duvR89913Gwe2f+HChVEXXnihPSUlpbev3FNPPbX7GqZOnZp44oknpgPccsstdt+5xo8fn+Jrd+CcrvLych566KEW3bp16xETE3NEkyZNep9wwgldv/vuu716ysSBJzLM40YDs4AztdZr6645oqZyswti2bN48T4NusD0eOVmF1wJTABY2uZEjl1asAsTjLWhYu9W54SuYzoDd20ucn0AzAmo7nWPy6ExQxefthZRzsC8xwCwO919PS7HzHq6HCGEEELsQ++++27TgQMHboiNjdWXXXbZ+jfffLP5t99+G3/iiSduCyy3detWW1ZW1hqlFM8++2yroUOHdlm0aFFhTEyMvv7669esXLky6qeffmqcm5tb5cifoUOHbpg/f37M1KlTmz7wwANLmjVrVgbQunXr3cvb/PrrrwmfffZZ8pVXXrkmMTGx/IUXXmhx+eWXdz7++OP/atWqVTnAkiVLIvv375+ulNJXXXXV6ubNm5dNmzat8W233WbftGmT7b777lvdu3fv7SNGjFg+bty41Isvvnjt8ccfvxngpJNO2lJF++xTpkxJGTBgQMnll1++tqysTP34448JP/zwQ6MBAwZsq+w4cWAIN+hKBcYdSgGXUioHyKHh9OD5Aq4NwNz90YCcvMz83OyCBcAPpXHNKDgpd2D2z3iBJTPbTBv5W/tPAocMPpKY7szfXOSKBS4A3rC2P+tfyONylNud7sA/nDPsTvdfwL3AOuAnK1ATQgghDnra68W7bVtDuQcBICI+3qsiQm/S999/H79w4cLYJ554YjHA6aefvqVly5a78vPzUwKDrhUrVkTPmzdvdvPmzcsB0tLSSi+77LIu7733XuOLL7645NRTT9362muv7fjpp5+44YYb1ld13qOPPnr74Ycfvm3q1KlNhw4durF79+47A8v8+++/sbNmzfq7Z8+eO6y2bT7mmGN6vPLKK03vuuuuNQAjRoxo4/V6+euvv+b4ArE77rhjzdlnn91x7NixqcOHD1/Trl27srPPPrtk3Lhxqcccc8yW6tr28ccfJ06ZMiXlyiuvXP3qq6/6f3C9yuv11uBVFQ1duEHXX5jA65Chtc4FcpVSjYGS/d2enLzM33OzC44HmlrD/faX+UG2teu77IyxfZedkZ6Tl1mckZ9xGvB5zE7Ny0+V/xNdPiIW6LQkofnQdlvWvA28VvTBiMvSi4v8F1LejBl26J9o4zDgQ98Tu9PdweNyLK6HaxJCCCEaFO+2bRHzjjzq8P3dDn/dZvw2y5aQEHJEkJ+f3zQlJaVs0KBBmwEiIiL4z3/+s/69995LKSsrWxIZuef2dNCgQRt8AReYAA3gn3/+CTvxRlWOPfbYTb6AC0yglpCQUP7vv//GAHi9Xj799NMmgwYN2qC1VitWrNjd2NNOO23T1KlTm/7000/xp59+ekj3Zu+8804TpRQul2t54L6IMAJb0fCEG3QNB95RSn2qtZYFb/eTnLzMPxtAG1bnZhd8BZwSuM9Wtv27OWnp7skw4rMj1PsDf9fnAbHW7n/bbVnjKzoUGFqUlv4g8HB6cdFOj8vhtTvdnTHv0YGYoYeBmQ4/BXoGa5fd6b4OOA8YBqwBxljfPwV0B54DzgEKPC7H5+FdvRBCCCFCUVZWxkcffdS0f//+m4qLi3cHTsccc8zWF198seVHH33U+Pzzz9+9rmf79u0r9Eb5ArANGzbUS/bBNm3a7NX71bhx4/KNGzdGAqxYsSJy8+bNtkmTJjWbNGlSs2B1rFy5MuTMHR6PJ6Z58+a7WrZsWV59aXEgCjfouhPT2/O9tWDwYiDwTaK11ufUpnHiwJCTl3nqWxc+fXjCluW92i/54rWVLfsxv+sQyiPjmn99Uu6VJ3+Tc+XA32s0EvA+4L6itPTz0ouLPvC4HDuBncB7wHt2p3s48Lhf+V3BKrE73a8CV1pPAz8xesj6epn19U670z0OGOtxOVYjhBBCNEAR8fHebjN+m1V9yX0nIj4+5F6ujz/+uPGaNWuipk6d2nTq1Kl7ZSx84403mvoHXTabLegNhNZahXrumrDZgsdyWptmlJeb291zzjln/ZVXXhl0ms1RRx21vT7aJg5s4QZdhwEaE2wlsGd+kT+Zb3MIueidW2YBs4rSPvq47bLvNixudyo7Ys3f0nldh9B9/uTdZV8/OYLLvzZ/p1/PjOCib72/RJVztF917xelpUcFDDfE43I8ATxhd7qHAG8Dve1Ot/K4HNrudB+L6cGaxJ6Aq6ZGACPsTnc+cL3H5ZA/lkIIIRoUFRFBOEP5Gpo33nijadOmTcvGjRu31/SA9957L/nzzz9vsmXLlpAShCmlanzPqVTtYrXU1NSyRo0aecvLyzn33HM319W57Hb7jh9++KHxqlWrbNLbdXAKK+jSWtvruB3iIJFeXLSxKC392/6/3H/iNyc+A8CyNidiX/QZJXGbTr3xhsg0YMjHx0QM8B3z8dERRydv0Zz+u5cLftz9d/N24NFKTuOfmv5iu9PdE/CtCfZKQNl8wD8b4veY3q/JmKQojfzKZgFZdqfb5nE5Dvh/bEIIIURDsmXLFjVt2rQmZ5111vphw4ZtCNzftm3bnVOnTm06adKk5AEDBtR4TlSjRo28AGvXrrU1a9asyoDFVzbcxZEjIyMZOHDgho8++qjpb7/9tuKoo44q9d+/fPnyyNTU1DKABCtI3rhxY7XnuvDCCze8/vrrzZ1OZ2pAIg28Xq/M6zoIhNvTJURVTo7Q3kt6/v3y73/3/O8cgB+PHbMqJy/zq0ITMOVm5GcoYHdgszFBMXmAjQt+3N255fq5b/qb/WcWLQ2s3ONyrLM73b6nb1bRjh89LseVlexrDGB3uhVwcUA9TTAZEoUQQghRRyZNmpS8devWiP/85z9BE5JlZmZubdKkSdmkSZNSQgm6jjzyyG0A11xzTbvTTz99k81m09dee+1eQR3A0UcfvRXgrrvuanPhhReuj4qK0hdddFFJ48aNa/xh65NPPrn0559/ThwwYED6xRdfvLZHjx7b169fH/nHH3/E//jjj41LSkr+AOjRo8eOxMTE8ldffbV5YmKit1GjRuUDBgzYmpaWtte8sbPPPnvzueeeu27ChAkt/v3335jTTjttk9fr5ccff0w88cQTN/kyJ4oDV9hhs1LKppS6SCn1f0qp95VSGdb2JKXU+UqplnXXzP1PKZVjzV/7dX+3paFLLy7S6cVFb570/ZtFQLa1uWVudoHdV6Ywq1BjMmD+AexOZHH7f/d8GJS8lSVFaem6KC09LshpltWgKbdXV8DjcmiPyzER8F94MKkGdQshhBAiBJMmTUqJiYnR55xzzqZg+202G5mZmSXff/9949WrV9e4Y+CKK67YkJWVtfrbb79NysnJ6Xjdddd1qqzsiSeeuG3kyJHLi4qK4m+++eaO1113XSf/DIQ10a5du7Jff/216MILL1z36aefJo8aNar9iy++2GLjxo2R99133+4Pi2NiYvTzzz+/0GazMXLkyPbXXXddp88//zyxsnrfeecdz7333rt0yZIlMQ8++GDbp556qnVpaakKJQAVDZfyTQwM6SClkoHPgH7AFswQrdO01gVKKRtmsd7XtNZ3VV7LgckvZXyS1jroHw1RUW52ge9N9nlOXuYZlZXLyM+4AcjNe6aMpn5LB+6ycf9hfxc96F/W7nTHAjMw2QsfAr4AfsQvoYvH5Qhp4Lbd6V6KWdj5SFmIWQghxP4yc+bMtMjIyM+6du26JT4+vrT6I4QQ9WHbtm2x8+fPTygrKxvYt2/f4mBlahobhNvT5cLc7J4BdAJ239xqrcuBd4GzwqxbHHx8c7NOz80uqHR9t8KswueAiOybIp99+4SI3VkHo8p5oCgtPcW/rMflKPW4HL08LofyuBz3eVyO7z0uh9cKtI4H0sJop29sY5swjhVCCCGEECKocIOuc4FntNZfEDxL4TzAHmbd4uDzP7/vl+VmF5yRm13QKFjBwqxCXZhVeNPoF/9u88S5Ee/67VpblJZeaZe8P4/L8aPH5ZgbRjs7WF9fALA73XFWj5oQQgghhBBhCzfoSgIWVrE/CknSISw5eZlbgNf8Nn0GbMnNLoiv5BAApqdHXD7hlIjdq8Ival6hjvrU0u50XwFsA7bbnW5td7rb2J3u6GCF7U73ULvTnWt3uiW1kBBCCCGE2Eu4N4n/AEdUsf90YE6YdYuD0/VBtlW5vkVhVmHpV31UX9/zCM25VtbD+jLf7/v8gH1LgR1WAHa3b6OV/fAt4AbgjXpsmxBCCCGEOECFG3S9BFyllBrKnvlcWikVo5R6GBgI/F9dNFAcHHLyMrdh3m/H+22OyM0u0LnZBTGVHTfjmtl//9BD3Q3Qbi1c/E25NyM/o0dGfkZORn7G+XXczL3S01fif1bwpfFLe49ZM+z1Om6TEEIIIYQ4wIUbdD2NGS42CTN/C2AipudiFPCC1vrl2jdPHExy8jJ1Tl7mj5jhp/5Kc7ML2lV23PFz9ryXzvtZM3lM2d8RXv0sMCUjP+PMOmziwIDnKVZijnus5z/WoI7L7E7373an2253uqscPimEEEIIIQ4NYaWM332wUscDFwJdMAHcP8BkrfV3ddO8hkdSxteN3OyCBPYeXtg4Jy8z6JDDorT059mz5hf3XG5jXts9Iw0LswrrZNih3em+FXgSaOZxOfZaINnudHdlzwcNNXU4UAh0xSShGWNtHw+sBx7zuBzbw2yyEEKIg4ykjBeiYajLlPG1CroORRJ01Z3c7ILDgD/9Nj2ck5d5j9/+RMD3GnfP/CbnIuABgNVJcOMNFXK1fAEMKswq3GuV9/pmd7p7AH9jEoQE9pbVSKhrigkhhDh4SdAlRMPQYIIupVQbYADQApiitV5qLY6cBJRYa3YdFJRSOUAOpkevOxJ01QlrPtdCoHUNiid3WDTt9S0Jbc5ut+QrZreZ3/HZ/9gCs2ieUJhV+ANAUVp6S2AY8GZ6cdESa5sDaJdeXJRXh5exm93p7gh8inmPhCISaArs8Lgc8r4SQohDmARdQjQM+z3oUkop4HHgRszNogZO01oXKKWSgCXAfVrrp0KuvIGTnq66l5tdcBTwa6jHDfj+9jMuuaNsE/Bz9C7Nf6ZrhvzgrbS8V/F4hOZ26+nS9OKiSueR1ZaVPr49kAgMB5phgqrRHpfjC6tMZb98bwIPhbnWmBBCiAOcBF1CNAwNIei6AzMv5VHgK8zQrlO11gXW/glAZ631CSFX3sBJ0FU/crMLfgL6B9l1J/Av8E7gjvaLP6fLvx+yI7pxrq18R05k+Y69j67GlGNV23tembMs5APrgN3p3gEEXfvLshTzocYm4AKPy1G4TxomhBBiv5KgS4iGoS6DrnAXML4GeE1rfZdSKiXI/r+AuswqJw5+JwO+fyy3AluAV3PyMr0AudkF/wCd/Q9Y3P50Frc/HcywTwBit6+h/y8PoDAfJnxwjOLc6ZV/sHDUPL00Iz+joDCr8JS6u5Qa60TVaerbWl9bAX/Zne5S4FjgZuBKoKPH5fDUZwOFEEIIIUTthRt0tQN+qmL/VqBxmHWLQ1BOXuYO9qz5FsyxwMfAO8q7q1RHRD0TrFBpXHNclx7FoB9+e779Gj1i1IQ52zLyMxJ6ebybL/3ayxeHR/BthuLp/yunRQm0XwvNSnRmRn7GF8DphVmF+yyzjMflWEbANdud7mggEzMvLFAs8Lvf8xmYYYtCCCGEqIXBgwfbp0+fnrhs2bIGP6pk7ty50WlpaRlPP/205+abb94r03JD169fv+4bNmyInD9//t9Vlavv65w6dWri2Wef3e3jjz+eN2jQoKDZs+tSuOt0rcYEXpXpCywOs24h9pKTl7k6Jy/z6Jy8zHE3vHDGs8D9lZU9elkWazo+e+MxvxdtA8j++ekeR696vOWoYZHRBX0itpbbFDdeb9vdRfzcc+WklOhTgfn1fyVV87gcOz0ux2dADHAK5nc0EpgWpPgf+7BpQgghRJ0YP358ilKqr+8RGRnZt0WLFocNHjzYvnDhwsC1PA96gwcPtsfHxx9e2X6lVN8rrrii/b5sk6h74fZ0vQdkW3O3SqxtGkApdTpm6NNjtW2cEJXJyct8MDe74Btga05e5szc7ALf8ERfD2x5bnbB7vJR3uhN2T8/3SQnLzPBt63IlX4GJs07zz9XzsQTIzpnkOHr6SoE+hdmFW7dB5ezF4/LsRPwXUA5MNBabPluIAtoA+y0O903AO97XI4V+6OdQgghRLhGjBixvGPHjjtKS0sjfvnll0ZTpkxp9ttvvyUUFxf/HR8fL2saVaJr1647t27d+nt0dPRB/RodbNcZbtB1P2YOzh/A95iA606l1EOYZAizgEfqooFCVCYnL/M7v++/BvAPtAI0Bm7DZN30+RxwAw6AS771cuR8L/dcYQOlMgAncG89ND0sHpdjG3C33eleDTyFmTd5JpBrd7rzPC7H9XanewQwFjgfWAtM97gcu/ZXm4UQQojKnH322SUDBgzYZj1de8MNN5Q9//zzrSZOnJh89dVXb9ivjWvAIiIiqMugdNOmTRGNGzeuPP3zPqojUF1f5/4W1vBCrXUJcAymN6sNpofhRCAZs3jtCVrrbZVWIET96VDFvnG52QWn+Z6kFxfp9OKiQcAI37Zuy2Gya/fycvdk5Gc0qZ9m1kqwBaCzrRT0Y63n7wHfYXrDdMCjxT5rqRBCCFFDAwYM2ALwzz//xACUlpaqW2+9NbVnz57piYmJfeLi4g7v27dv948//jjR/7i5c+dGK6X63nfffS3HjRvXrF27dr2io6OP6NWrV/q3334bH3ie119/Pblr1649Y2JijujatWvP1157LTlYezZt2hRxzTXXtG3VqtVh0dHRR9jt9l733XdfS6+3YmzhG/73yiuvNOncuXPP2NjYI/r06ZP266+/xgGMHTu2Wfv27XvFxMQc0a9fv+5z586tKnNxtXzXO378+ArJ7GbNmhU7cODATklJSX1iYmKO6NWrV/qbb76Z5F/GN7TT7XYnXHbZZe2bNm3au23btocBzJs3L/qyyy5rb7fbe8XGxh6RnJzc58wzz+wU2N6q6gCYPHly46OOOqp7o0aNDk9ISDi8V69e6Xl5eU0Dr2PmzJmxRx99dLe4uLjDW7Rocdg999zTsqbXedZZZ3Vq0qRJ79jY2CPsdnuvm266qY1vf02vY18Lt6cLrfV24H/WQ4gGIScvczEBySlyswtOBL6xnn5u9Ya5cvIyRwGkFxc9XpSWXg486Tvm6bwy7sqysTVOrc/IzxhamFU4eZ9cQM2c5ff9Usxi5ImVlA1mld3pTvS4HFvqtllCCCHqi9aaXaXl4c7FrxdRsTavWbq1bixcuDAaoEmTJuUAGzZssE2cOLHZOeecsz4rK2vt5s2bI954441m559/ftdvv/226Nhjj93uf/y7777bdOvWrbasrKw1SimeffbZVkOHDu2yaNGiwpiYGA3w3nvvNb7yyis7d+7ceftdd921dN26dZE5OTn2Vq1aVRgV4vV6OeOMM7r88ssviUOGDFnbu3fvbV988UXSQw891HbZsmXRL7/88hL/8r/88kvCF198kXTVVVetARg/fnyrc889t8tNN9208uWXX25x1VVXrdmwYYPt+eefb5WVlWWfPn36vMDrX7FiRdj35TNmzIg9+eST01q2bLnrpptuWtGoUSPve++91/Tyyy/vUl5e/s8VV1yx0b/8Lbfc0qFp06ZlI0aMWL5161YbwI8//thoxowZCeedd976tm3b7vR4PDGvvfZa88zMzO7FxcV/JyYmequrY/z48Sm33nqrvUuXLttvuummFcnJyeV//PFH/LRp0xpnZ2ev9x1bUlJiGzRoUNczzzxz4/nnn7/hgw8+aPLwww+3Peyww7YPGTKk0rTrv/zyS9ypp57aPTIyUl966aVr7Xb7jn/++Sdm2rRpScCycK5jXwnrh6uUOgP4UWstN22iwcvJy/w2N7vge8B/3ThnbnbBCKBJTl7mlvTioqeK0tKfBrwArTfAq0+VM/IqG4taqrcz8jN6F2YV3r0/2h/Ek8Ag6/t3MD11JwNfWtt6A0djerLzgDL2/l2/1+50P4qZk3kC8JfH5ViPEEKIBmlXaXnEi7d9V2myhf3hmicHzIqOiwz7BnbDhg22FStWRG7btk19//33jcaOHZsaHR2tBw8evBGgefPmZYsXLy6MjY3dPcTslltuWZuWltbrqaeeanHssccu8q9vxYoV0fPmzZvdvHnzcoC0tLTSyy67rMt7773X+OKLLy4BuPvuu9umpKTs+vnnn+empKSUA5x88slbzj///K6pqam7R5JMnDgxefr06Yl33HHHskcffXQlwKhRo9aceeaZnV599dUWw4cPX92zZ8/dC4R6PJ7Yv/76a3b37t13AjRp0qRs5MiRHZ544onW8+bNm92kSRMvQHl5ucrNzW01d+7caF9ZgO3bt0ekpqb2Dve1vPnmm9u3bt16559//lkUFxenAe688841Rx55ZNp9993XNjDoSkpKKvvpp5/mRkbuuT248MILNw4bNqzCsM5zzz1346mnnpr22muvJefk5Kyvqo5169bZ7rrrrvYZGRlbf/7557n+QwMDewfXrFkT9eyzzy701XnLLbesbdeuXcYrr7zSrKqg68Ybb2yvtVa//vrrnK5du+5+/bxe7+41V0O9jn0l3Ij6U6BcKfUnZk7X98D3Wus1ddYyIepQTl7mgNzsgv8CL/ltjgQ2Y/WMpRcX6aK09FPZE7ww9pVy3jwpgg/7R9yVkZ/xRmFWYdE+bXhw/ulsx3pcDi9mkXL/jxv/sr7+n2+D3emegcksCnCH9cBvfz+Py/Fb3TdXCCGE2Nu5557bzf95amrqzpdeemlh586ddwFERkYSGRmpAcrLy1m7dq3N6/WqjIyMrYWFhY0C6xs0aNAGX8AFcPrpp1cYrrho0aKo4uLiuBtuuGGlL+ACOO+88zZ17ty5dPv27bt7Ej/55JMkm82G0+lc7X+OESNGrPrss8+afPjhh4179uy5+763f//+m/yDqOOOO24rwJlnnrnRF3ABHHPMMVtzc3OZO3dujH/5mJgY/dZbbwXNonzeeed1C7bdZ9WqVbbp06cnjhgxYvnGjRttGzdu3L0vMzOz5PHHH09duHBhVMeOHXf35g0bNmytf8AFkJCQsDtI2rFjh9qwYUNEjx49diQmJpb//vvvjYAKwUpgHR999FHjrVu3RowYMWJl4FysiIiKnbTx8fHe66+/fnd9sbGx+rDDDtu6ePHimMquc/ny5ZEzZsxIGDZs2Gr/gCuw/lCvY18JN+g6BhgAHA9cDtwCaKXUPCoGYZ66aKQQdSEnL/Pl3OwCX8/Q7gQZudkFV+XkZb4CkF5c9FVRWnos8ApwCcCl33j5tZtiRYqaQ9Vrie2lKC1dAY3Ti4tKqi1cQx6XY43d6f4vUBpi1sLjge1V7P/V7nS3BNZ7XI4yu9N9CmbO5kfAqcBXHpdDJjYLIcR+EBVr817z5IBZ+7sd/qJibbUapjVmzJjFaWlppRs3brRNmDCh2W+//ZYYExNToc5nnnkm5Zlnnmm5cOHC2LKyst3/g9u0abPX/Ob27dtX2OYLwDZs2GADWLBgQTRA165dSwOP7dSpU+nff/+9e/7XkiVLops3b77TP2AC6NOnTynAokWLKgQHge1p2rRpOUC7du0qbE9OTi4HWL9+vc1/e0REhD733HPDWitqzpw5MVprxo4dmzp27NjUYGWWL18e6R90denSZUdgmS1btqh77rmn9VtvvdVs9erVUVrviZs2bdpkCywfWMeCBQtiAA4//PCq7jUAaNmy5c7AQCw5Obm8uLi40tikuLg4BqBXr15V1h/qdewrYQVdWutfgV+BcQBKqR6YIUonAAOB/2IyGoY9NlWI+pCTl7kJuC83u+B+rKGEwMu52QUvW98Xc1Juj5y8zEuL0tLdwJsAT79QzhCnjYz8jBcLswqvKUpLjwCaAs2GjIpcAHgLswor/GEuSksfhZXFsygt/b704qKH6uo6PC7HK2EcU2p3uo8C/HuzfgDmANdaz1cB2J3uyqqpu8H7QgghakwpRW2G8jVExx577FZf9sLLLrts45FHHpk2bNiwTvPnz5+dlJTkfe6555refPPN9lNPPXXjLbfcsrJVq1ZlNptNP/roo62D9YjYbLagme601vX+v8tmC34vvy/a5PV6FcC111676swzzwz6IW+PHj0qBEjx8fF7vZf++9//tn/33XebXXXVVauOPfbYrcnJyeVKKT1s2LBOgcMDK6ujpip7vepCqNexr9Q6KFJKxQItrEdLoAnmxuyf2tbdkCilcoAcwl9QWjQgOXmZOje74EhgRsCuNOAC4J304qKJRWnp67DW8jr7F82KpvrqorT0q/0PmDymjEcviOA/rl6jPnLOdmXkZ0Rc+2l56qkVl014sCgt/UHgBuCz9OKihfV3dZXzuBwzCBI42Z3uj4Cp1R1vd7qjJAW9EEKIuhYZGcn//ve/ZWeffXa3Rx99tMUjjzyy8r333mvStm3bHdOmTfvHv1fkwQcfbFNFVZXq0qXLToD58+fHBu77999/K2xr167dzp9//rnxhg0bIvx7u/78889YgA4dOuzVU7S/pKWl7QCIiooKu7cM4JNPPmly/vnnr3vxxReX+rZt27ZNbd68uUbxgq/na9asWXG9evWq89fHd52zZ8+Oq6pcba+jvoQVQCilBimlHlVK/YSZiP8lMBgoAq4CWmutqxx/eqDRWudqrXsA/fZ3W0TdyMnLnAncFGTX5NzsAg2QXlw0DVjhVTYu/QbumBL8E5I73/Xy6ITyMXfc2FM33aTLT/1DLwlaEJ4D/i1KS3+mKC09sigtvaoU9/uMx+VwYzIg5gfsuj/guS8F/et2p1vW4hNCCFFnBg0atDkjI2PrCy+80HLbtm3K1xviPzysoKCg0R9//LHXfK6a6NChw660tLTt77zzTsq6det2d7W8//77jf/5558KQddZZ51VUl5ezmOPPVZhmZUnnniipVKKc845p9JkD/tamzZtyvr167f5jTfeaL5o0aKowP3Lly+vUbBhs9kqvNYAY8aMaVFeXl7JERWdc845JY0aNfKOHTu21bZt2yp8wFsXPUypqallRx555JbJkyc3mz9/foX07/711/Y66ku4Ed9HQDkwBZMy/kdr7S4hDig5eZnPAs8C5GYXDMQkicF6bn5jT8rdXb7v7+NI2mQ6qVYnQYuAd/2wL70M+3LP8zdOjqDVBs2pf+w1uuBG60FRWnoJcEJ6cVFhYKF9yUohf6X12M3udH+KGU7s7zJr3yjMUOI84H8el2N5vTdUCCHEQeu2225bddVVV3V69tlnm5111lkbP//88+TTTz+985lnnlmycOHCmNdff715586dS7dt2xZWx8FDDz20dOjQoV379+/f/dJLL127fv36yFdeeaVFly5dKtR58cUXb3z22Wc3P/roo20WLVoUc9hhh2378ssvG3/11VfJV111VYXMhQ3Bc889tzgzMzOtT58+PS655JK1nTp12rFq1aqoX3/9tdGKFSui586dO6e6OjIzMzd+8MEHKVdddVV5jx49tk+fPj3hhx9+aJycnFxWkzY0bdrU+9BDDy0ZPnx4hz59+qQPHjx4fZMmTcr//PPPuO3bt0e89957ntpe5/jx4xefeuqpaf369Uu/9NJL13bs2HGHx+OJ+eKLL5KKi4vn1MV11Jdwh8q5gU3AEOB5IFcpda1SKr3OWibEPpaTl/kZcEVVZWYeMYJ1TXvwyOWn4LqkJ0OcVY9J/uiYCF4408YQp42f0yodvp0E/FWUlq6tR9BJsPtLDTIaKuB6YJnVC3b2PmiWEEKIg9AVV1yxoV27djueffbZljk5OeucTuey4uLi+Lvvvrv9119/3fiFF15YeNhhh20Nt/4LLrhg0yuvvPJPeXm5euSRR9q63e4mubm5nsA6bTYb06ZNW3DVVVet+vLLL5PuvffedgsWLIi79957l7744ouVjWbZb/r27Vv6888/zzn55JNLJk+enOJ0Otvn5+c3j4iIYNSoUTX6QPSFF15Yct555617//33m95///3tVq1aFTVt2rS5oczduu2229a++eabCxISErxPPfVU64ceeqhNYWFh/MCBA+ukc6Z///7bv/3226Kjjjpqy2uvvdb87rvvbj916tTkgQMHbqzL66gPKrD7LaSDlerFngQaJwCpmDSMP2KyFz5eF41sSJRSjTFDKpO01g2ma1nUndzsgg6Ap4bFL8/Jy3wjN7vgdazeH5/ef7hOv/76Fasx6dvbAEsAoso0t37g5aj5lf7u6fTiogY1d9DudPsa+19gIiYJSTZm+OFioE8lh5YAvTwux9JK9tc5u9NtA/C4HPt3HIEQQoRp5syZaZGRkZ917dp1S3x8/F6Z9oQQ+8a2bdti58+fn1BWVjawb9++xcHK1DQ2qFXQFXDCGOBi4E6gO6C11vstLWN9kaDr0JCbXdAEs6ZVc+BvzHpe/4ZRVWpOXubutO4Z+Rm9gNuBnsBRjbZrLvzBy1kzKv4e3n+p7bmi9ur2wqzCBvHP1u50XwEcBdzucTn2StNrd7oTgQnA+UEOH+NxOe6q3xbubocNmAm0xQx7/A7zQdB5wDfAeOB7j8tRN3/4hBCiHkjQJUTD0CCCLqVUAnAcZr2uEzA3ZNFAGeam53ut9Z1hVd6ASdB1aMvNLrgImBTKMTl5mUHHFWbkZwwFhgKzgXtt5ZpJj+3pnPnvLTZ+un62ysjPSMEM6R1XmFX4brhtz8jP6IvJ1ugozCr8JNx6qmJ3uqOAwKDsDo/LMbY+zhfk/KcBn9eg6DTMB0R/1UUAZne62wGHYQL1XGCDtWi1EEKETIIuIRqG/R50KaVmYm4wbMAW4GfMej/fA79oratdFO1AJUGX8MnNLvgJ6G893QSk5ORlluVmF8QA/v8kXwa6YRYVb5yTlxn0H2hGfkbzU/7wrr7u0z336jdfZ2NNEpTbFEprHnqtnG7LwQvrLxoV2awwq1Bbx/YGLh/2eXnvM2fqUwEWN2dW+zVcAKwYMiqyB3unx8fadkNhVmF187ZCZne6x2IWogaIqO/eJbvTHUvViz9XZzwwPNRhiXanO1ggvh2IA34BegMvAlGYD6fuAb7zuBzbatFWIcRBTIIuIRqGugy6ws1e6AFexwRZs7TW8omuOBSdDpwMTM3Jy9wdUOTkZe7IzS6wYTJ8gpkH5bM9N7tgAiZD4LycvMzuvh2FWYVrMsiIPf5vPavnYp0OMP7/TBX/NzCCwzyabtZU2AhoOnlMmbdojMldMzlI49qv4XCs9fIGFHr5LiPoNLEjMZkJ62PhyA1+358HvFcP5/DX3v+Jx+VQdqdbAbEel2M7gN3pvg14opLjbwZmYYZJhiJYz6dvDZGjra/+SxN8arXlfI/L8X6I5xJCCCHEASjcyfq3Af+ntZ4ZLOBSSsUppdoHOU6Ig0ZOXuaWnLzMj/0DLr99Xipf0+1K62u33OyCN/x3FGYV7rjg8zk9Ag+47jMv/YvD7yi6caqXlE0a4Itg+zPyM+pjza0pft+fGurBdqc7ye509wnhkCZ+348D8Lgc2hdwWc+f9LgcCugFXBikjmDbqmqj/3ooQzHB1jnApcBjwDbMaIANex/No6GcSwghhBAHrnB7uhYCl2OymAXzH2vfQZdIQ4iaysnL/C03u+AEYDTwFXAXkBBQ7NLc7IJLg8z7SsD0DKUR0IPze2f18RH/6ErTsv/bkqN/6hExe1kKW+N3wE0fm89FnnixvPCIv4pO95XLyM+wY36XAUZl5Gf8AQwEbgWeBt4vzCr8qCgtPcJqw8r04qJQhrnM8/s+nPS67wCn2Z1u3/NKe4bsTvcJ+AWUHpdjZFUVe1yOvzEJUpTd6XYAU61doUa2bf2+n+pxOUox6xiC+RtYYV6r3elOBroAvwEVFtwUQgg/XmCvBV6FEPuW3+9grUf1hTunywtcprUOGnQppS4DXtVa77Uq9oFO5nSJcOVmFyRi5n6BWVT8Hr/do3PyMh/IzS5ow55f7G05eZklRWnpCZjsiQDx6cVF24vS0tsBa3bZSP2nNa3vuzyyOfBZYLbDjPyMY18YX3Z68lbuD9Kkq4eMinwPk90vKOfk8muP+Ee/UMnux4GR6cVFlf4RsTvdb2B6fe73uBwPVlEuHtB+wwDbUnWg1gpYjZkj9TRmvtxuVm9Wjdid7t7AH2Ee+y4wOJTj7E63728IQFOPyxGsF0wIcQibOXNmis1m+6Vjx45lSUlJW/Z3e4Q4VJWUlCQsXLgwsry8vF/fvn2D3i/V+Zwuq8Jkv00plQwhTAYuAlYE2SfEISsnL3OzNdcrOScvc31udsFCTJINgNG52QUjgUb+x+RmF/zJSbknBPaEpRcX+QKSfw+Df8+r5JyFWYU/kcVPRWnpwYKulyaPKftsl42tUeU0er+/4q0BETTeDiWNFGhNFQEXmNT3txelpTdKLy6qLCnESutr48Addqe7DbA0YFtbj8uxDLPYclVWVrHv+WqODbQ6xPL+Bod6gMfl2OTXe9cTk4RICCH8bdBarywpKekoQZcQ+09JSUmi1nohsLG2dYUyvPA24D7rew08ZT2CUVT8FF8Iwe65Xuut71/JzS5YiUkHDwEBl6U3cI/VS3Y90DknLzOc9cKaA2uCbF8aZaX7OO9nzXk/h7We8NaitPRioFd6cVFgBausr/7D8LA73RHAnGDt8QtIAAo8Lscpdqe7GXAJplerMudj5lOF9LfH43KssDvd4zCZFoO9RkFZ2RJr602gQwjnjAA6Af/IWmNCHLz69u3rnTlz5riNGzc+ExMTk5KQkLBVqfrIdySECEZrzZYtWxpt3LjR6/V6x/bt27fWwwtDCbo+x0wIV5gJ4pOA3wPbCGwFZmqtg6WnFkL4ycnL/CQ3u+B6qu6ducPv+38AlZtd0BFYDFwDdAXuzMnLLKusgvTiorWAKkpLV5h5YhcD99a0nUNGRXL4Au8jd7zrXdWrqGh8UVq6DZiOyX6IVWdZUVr6ROC69OIi3yezvsDqCF9ddqf7aUymwJqYAuBxOdZiUrqPtzvd9wEPWPt/AK7zuBy+84SbDXAMJuhqbne644CWQIrH5ZhptTkKaORxOTb6HWMP81z+2tudbhVCAOULaguAU+rg/EKIhuujXbt29VyxYsXF1tqoQoh9SGu9pby8fBLwcV3UF+6crvuBKVrr2XXRiAOJzOkS9cEadliA6ZE6LScvc1ludkE3YG4I1disnjRyswuiMD1n8Tl5mcsDC1rB162Y9OkbABdBsun92xKcV+312UyXwqzCfwBePL/Hg8fP0cGCt6vTi4tetjvdGcBfVbR5qcflaAdgd7qDpZU/0hf4+LN6mVKsoYi1ZqWW38zevY3/YHr0RwGpAfvuBR6yvr/J43I8G8L5/P/wtvK4HKsqLVyxjf6ftDX2uBybKysvhDg4zJw5szFmHmu4GaeFEKHzAiv79u1b7b1+vS6OHORkScAWrXVYY5MOJBJ0iX0pN7vgZ0ySiAWYrHfVmYTpxfJXkpOXmZybXfAPZmgawH9y8jKr/eQmIz8jCthZXbmzp3u5/OuKPe83ZdtY1URRuvJsVMQudpX0QZcl+xf5GTjJ43JUqN/udOcAvgCms8flCGc4ZcjsTnchJpV8yEJJvmGdy/8P7zEel+OXSsopzEL0gzALbo8LKLIB0xs4Dfja43KsC6UdQgghhKideg+6lFJHYjKwDQCigdO11gVKqWaY5ABPaq2/CavyeqSUaodZ2LkFUAY8pLV+J4TjJegS+0VudkESdTCRM8DvwMk5eZmVvpcz8jMaY4Kgy6ur7MovyteeNUM38z0f6rShg8xD0OVx3Wdf9eu8vXYAdqc7DSiynrbwuBw1nmdVG3an+1xgLCa4nQL8ghlKDSaYXQIcZz383exxOZ4J8VxHAP49eOdgetouB4aF2vYA84FdmKHe5wPLZP6XEEIIUT/qNehSSh2LGQq1DLP+0NXAqVrrAmv/N8AKrXXgJ+77nVKqNdBSa/2HUqoV5sanm9Z6aw2Pl6BLNAi52QUROXmZ3tzsgnjMDXZtNQc2+uaGWUMebTl5mbt7ojLyM8YAzqoqGfptOYN/2vN35eN+isXNFd9mKNg7AOtYmFXo8d9gd7qbsCeNfazH5dgR5vXUmt3pjgSSAnuQ7E53c+BMTG/dgnCCGrvT/TJwVS2a9yWQhEmbXxOjPS7HA9UXq8jqbbscyLc2PY2Z/1YOZgHqUOsUQgghDhb1HXR9g/lnfwyQiEm57B903Q9kaa07VVpJA6GU+hMYpLWu0eKtEnSJhshKrPEj0BrTK3OdlaK+C6bnw+dUIB0IpWfm9Zy8zCv8N2TkZ1wDdCjMKrwnIz9DYdLHj/Xtf/7Zsr9TNtPT/5j3+ysmnRgRLPDyOXfymLITMZlS+T71sIevLXj7HoCitPSI9OKiWmcOakisYGYlVS+SvBi4H7PQcpzH5SixO90PA52BSz0uR7lVVwJwGvAcsB0zFr1zYGWhDoO06r4As1B1dSYC3wOvYhKRLJGATAghxMGuvoOurcAorfV4pVQKJs2yf9B1NTBeax0fRt0DgJFAX8wN5Hla6w8CyuRYZVoBfwI3aa1/DeNcfYF8rXWN53FI0CUORLnZBT2BVTl5mWsDtt9BkAQaQdyRk5c5tqoCGfkZyZje78snjymbBQSbi/XGtTfZGm1MUHstLdZxhebRCVVOC10O9MFkKDwOaJNeXLRXkpADjRV89QXWelwOTx3Wa2PPcMhHrM0dPC7H4hDr+QTTqxeO/3lcjhpnyRRCCCEONPUddG0ERmutn6ok6LofyNFaV/UJbmV1n4m5SZiJyWRWIehSSg0FXgOyMXMubgUuBLprrVdbZf4geDr807XWy60yTTGfyl6jtf4phPZJ0CUOOrnZBf0wv0+VWQJ0zMnLLLfKK8xC6NuBk4DPfZkT/RWlpV+OmSN1X8CuW+enMvLLPhFtvj5MEVUOb44NLw/PdTfaRm9IVCnATUAGpud9A6ALsworTaN/KPFL3DHI43K4qyy897EezFpiAz0uxzS7030SJqPj28A64P+qOj6c3jUhhBDiQFHfQddnQILW+vjAoEsp1Qj4G/hNa31heM3ffR7N3kHXL1bdN1rPIzA3hM9orV01rDcG+AJ4UWv9eg3KxvhtSgSWIkGXOMjkZhdEYG6uhwFf5ORlfp+bXZCMCWB8/osZPrYIaBekmqNy8jL3WqOvKC39JODrmrTj/kttHLHAyzm/1Pxv09eHKZ532Crb3acwq/DPGld2ELI73XOBboDD43J8EuKxa4EUoIfH5Siqpmw8Jsje/XpL0CWEEOJgVt9B19HAt5gkGpMwPU+3Y7JvjcDcjPXXWle1Pk9NzlMh6FJKRQPbgAsCArF8IFlrfU4N6lSYuQdztdaja1B+NGZORSAJusQhITe7INQ/EjYrwYf/OmPPn/Ttzc9H6PIq/yb8ZVf872ITPEXv0jQvgTIbbI6Do+dqui7XtF2rKY1S9FlYsVkPXRRBYcdKl7F5oDCrcHSI13HQsDvd32IyzQ7xuBw1ztZqJRLZZT1t73E5ajT31T8lvgRdQgghDmY1DbrCWmhPa/0LcBbmE83XrM2PAy8ANuCs2gZclWhm1R+4kOgqzPyumjgOGAqcq5T6w3pkVFF+DCZpiO/RNrQmC3HAuzHE8u7c7ILOVFzY+fpvThz/V8FJuScA7YE7gTTgLf8Dxw6OuNT3/c4oxbJmilVNFNtiFV/3juCFM23cd3kkj1xkY8ioSB64OOISX/lRk73r2fMhUuBYxfsz8jOmZuRn6Iz8jJdDvJ6DwfHW1ztDPO58v++3hHDctSGeRwghhDioBZv3VCPW/K3uSqnDMcFXBPAPMFPXxYrL9URr/QMhBJta6x3A7pTVqvLMa0IclHLyMnNzswu2YYYdnuC36xbMUMOtVs/WB5j1pgZiFnMO5rn04qLD2LP+1cXWg9zsgr7DZuIG2BWx43/5R95zT5ltJycvuJTua/r5ju874ci755ZGbdlZmFW4C6BoUvoi4MdIL00nu3bHWjaAknhecg6zXb2usQJwWPuuysjP8KVqHwCswMxNagksCHUemLWAdCdgXmFWYUP92+f7m9c3xOP80/VvDuG4733f2J3umJqm/bc73bHAd8CbHpfj6RDOJ4QQQjRoYS+OvC/Ux/DCWrQlB8jB3Lx0R4YXikOQlUDjCGB1Tl7mXkPNggxFfBu4FLMQuc+TOXmZwwOO6wB4QmjKP0C/nLzM9QBFaek7gajKCv+Urnjq3ErnfAX6EPM7/iowA5gK5GHS7Hsw6dgBiPDqHTG7iLnznXJ6LIF1iSxI2cydwBQNf1420nb7rkg1vTCrsMbrqBWlpSvAjckYuA7zeq9ILy7aVeWBVfAf7gfEeFyOnZUWrnjcicA3ENowQbvT3Zk9gfdNwBuYRC3drG0vAL2AY4HJwEfAucAFvjpkWKIQQogDQb3O6bJOEANcgxlmaLc2e4BPgJe01qVhVVzxHJUl0vhVa32T9TwCs5bNszVNpFHLNkn2QiEqkZtdkMCeHpGhOXmZk63tNioGXgk5eZlbrX0t2HvIcI3k5GUqgKK09I4ET1FfwZBRkVGY4cVv+G9XWqFVaH8Lo8p0jTIulkVAZMW8jrenFxc94XtSlJbeARN0HFZNVV0wmVqnphcXzQ6lrXan23+9trYel2NZDY87Dfgc+MvjcvQO4XyhBtHBnOBxOX4I4ZxJmJ7YaR6Xo8YBqt3pbgQoj8sRyvBJIYQQAqj/RBptMdn/umOG5vg+0eyCWVtrHiab4dIw6k6w6gGYBQzHZD1br7VebKWMzweuA37FpIwfAqRprcO6cQuxfRJ0CRGG3OyCwEV2G7FnIV+fx4AnMX9X/J2E+bvyFWZxZ5/eOXmZu+ePFqWltwI0ZlhcH/bOmHgvkLIrMv6E7497tC8qgojyrV6vrdHuIcc/2N9lR+R2eq08gX+b/smCpl/TYXU5g37VfNVHsbi5IrIcnn4hvBT3ltmY+VJXAneFcfwaoEt6cVGN/wbZne7lmL/PR3hcjlk1POYBrHT/IfZ0RbD3vLpApcBaIA6THTGQ9rgcNRoKbne6GwOFmPmC93pcjv/V8Dj/RCGJEngJIYQIVX0HXR8CpwFXaK3fDdh3ISYo+iKc4X5KqZMInlo6X2t9pVXmRvYsjvwHcLOV3KPeSdAlRPhyswueA67327SSiklwInLyMrU1jPE0TIA1PicvU/vV4Z8VEaBlTl7m6srOaQViK7Y0as1vfUehI2o8zBCARluW0W/GI1QVcWi47ffOatG6xow9fZbufFO2jZs+Kqdb7ZZubo15bSoNkNKLi0IJhP6wNZrXO779K75NP2GGUq4Ffi3MKtyr96w2WQjtTvcPmMRFn2GGKC7yuBxvVVI20eNybLY73c0wAaVPMVAAPORxOVYGOS4SUJj3Q0e/XccA/YBTMR/MeTwuhw44NhboiRlCClDqcTniQrlGIYQQor6Dri3A01rruyvZPwa4SWudEHLlDZTM6RKi9nKzC/x7FgJF+AdX1dRzC2aBXmDPMMPKTLrwmbPWp/QMaVFgfzGlGzh2+j2VBV6p6cVFu3vmMvIzbACFWYXlRWnpKQta0+LhobY5ZTY4c4bmkm/3WkMa4Ob04qJnitLSowCVXly006++yKwvy/Mdv+lLgh14yUhbt7JIlVKYVTjdKp8CvIT58GoupsevJkOvzy7MKpzqv6GWQVcs0AOYFRjwVHNcdWWXY9ZODNY7VhNlmEQrwa5nE+b9uRA40to2G3gR08u6DBNIfiu9YkIIIaD+g64VgEtrHTS7lFLqFsCptW4dcuUNnPR0CVE7QeZ3AaTn5GUWh1BHsOFrZ+fkZU4NUlZRcQgjAFG7tpTuikqIBeg+dyJtVvxIeUQka1MOI6psKytb9mNlq2N2l48o37klbe7Ewparf+tv3a0vSy8uqvESEhn5GRpAeTXDP/CStFWTZgZgd0gvLlocpHxPzA1/RVrjl6URgNuusbGsWXh5J5RXY18NyVs0f3RSI/4aNvtx3z6/AOgnj8txXFgnCJHd6V7InnnCDV0fj8txSC+8LYQQh7r6DroewWTWOk5rvS1gXwLwAzBVa31PyJU3cBJ0CVE3crMLWgHvA0/k5GXWeMFev+MTMT0T/l7AJKTwRUuNMantH/Ir0zUnL3N3Svvc7AKVk5epi9LSDwMq3ED/nX4lq1oeVVUzbAA5eZlBu6/8ZeRnKOAVzDwuf1nAx8B6v21FVJy7VkGj7ZpXn6oYeO2ywdU329geWzH46vOPl0UtFBsSzfbtSy/78sZv//5iap8mdxyx/s+UYT8Enwp74a0tp+woOXbwrg3HAeR7XI7AdtcLu9PdHNPLNM3jcnjtTrcduBpoCuwE+lv7IzDrjh2LmRt4kVXFtZiMiDswwf3RmHmCyzDzj4cCS63HhX6nXoFZN64z8J+atrehZ1m0O93tMCn8/8/jcozZ3+1pSKzhqZEel6PWib+EEIeuOg26lFLnB2yyAfcATTDzt3w3MF2BKzA3D49orSeH3vSGTYIuIRqO3OyCo4HpIRwSnZOXWWlmu6K09HRMuvY/gbHAHwUn5dqBv6uocz0mkFtfRZndMvIzkoENNW2wn2Lg9sKswk8A/u+CHscMmK1/Diw0xGkDMwT87XN+9v730m+qjQer9NBFEdz71u46BmIWSV6bXlw0t/Kj9i27060wqf13AINDHM54GjDX43Ls1dvoVyYSs6ZbErAImOm3OyKU8/m19zTgWWvTb5i5e99j5pgdA3QA3vFlYrQ73Udhkked5XE5Pg3hXG9hAs1Qk6Gcgwlu7/e4HLV7EzVAAUlUWmB6zhtjfr42j8tRFlC+qVWuBSaj5xUel+Ogu8cRQoSuroMuLyYjmO8Ptv/3ldFa69BmrB8AJOgSomGx5omNw/RoVeXCnLzMd6spU9k57Jh1pJ6spMjDOXmZIfXsZ+RnxAPVrd/1EiZTqw628LK1ptcbQOB8r4WYG8gazXua07QDPdYvqknRQKcAM9OLi0qCtC0yvbiorCgtvQ9gSy8umrnX0Qcou9PtwAR5AI08Lse2qsoHOT6UIK0Q02N3uN+204E2mPeHDVgNjMAkRQF4EPN/+hPgft9BIQZdvjbOwIxs2YEZ9vkfzHIA31nt2gLEAms9Loe2gpl84EePy/FcCOfrgslE/LzH5QjnQ4mQ2J3uNpjezrA19F5OIcS+UddB14nhNEJr/W04xzVEkkhDiIbLCrzeBXwZU28GxvsVeSwnL/POOjrXiZgb4GAB2AU5eZlTalpXRn7GxcBE6+mrwH+BVEx21lcKswr/quxYf0Vp6cmE13vG5qi4xkMcD22D8v9GNfn5zphmX0Y9NGlLu7Ra3Y7WyHOYoYIT04uLxtX72eqQ1VPl6/1J9bgcgUscVHVsMhV/VgsxN/8n1FkDK1HTICHg+ur9fNY5pwIOINfjctwYynnsTncicBQmwUmN1nKwO93pwJxQzhNIgi4hBOyDxZEPVQdET9fopHjMHIgdjC75v/3dHCH2ldzsgiaYFPLF1nMb0A0ormlmxBDOdRsmJflZAbsuBObk5GXW6oaumnNHY3oeAJ7Pycu8oSgtvbJFpjthburL0ouLQnoNnryoT3cbO/9w94uIPeUPLwNma145LYLbPqzb0WZvnxDBljg4sdB7/11XRj6OCWp/KswqbLDD2uxO93rMEPt0j8tR4yQwdqf7MuB162msx+XY4bcvEeiF+XmtAk7EvH+fDaynGouBRKt9/sZgeq7er2pIpLVgdF1kZ+wCLAwcnmgFntsDrt3Xni0elyMxlJPYne5vMcM/n/C4HLf7bY/2uBw77U63LTAYszvdF2Lm/gEswfwub8X03qawZ8mGbzBDe4uAjVQMRkMeWiqEOPhI0FVPGnTQNTopChiGGU6Sam1tzuiStZUfJISojdzsgkuAN4PsyszJywy25mBVdSWyZ55Jc0xgtT0nL3NzQLm9/nDn5GWqorR037yT7cDN5RHRr3874MnHgUygR05eZlEo7QHIyM+IAC4HJvhvH/yD1zn0e29NUtGH5On/RPBjz91rIkcEG1bZENidbg9m3tX5Hpfj/RCOCzkNv93pbsWeBcPLMYF0BvBJkPXHlG9bkF41f98A0ZhEJAuB84C+wO+YpCVf+VeLWcy8xONyLLPqbo5JbNIXE7TYgd6YuZCBtmLe1/HWOQE2Y97fz1nbnNb21R6Xo2UlbQ4qYLjmVqutgWZjlhs40ro+fzVeGNvudCdg2g6Q4HE5qhsiLIQ4yEnQVU8abNA1Oqkn8BpwRMCeHowuCflGSwhRc7nZBXHA2cDbQXbPAB7GrJtVCuwKzHZopbVvh5nEH8yxOXmZP+dmF2QAlQ05HAaMwvSMvA9MA/ICyizFDIEMeTH5weOuanXcwvNvm9rj+Ts0XtbHL8cb4b25MKvwGf9yRWnpJwFr0ouL/vbbNqBcseNiZ+RxwONnzPAStxPWNoabP967M2tZU2i9ASLMv6flgD29uKjSBCjBWMHiNEwPxn8LswpfqeaQCorS0iOB9zA/11mY3rdW6cVFqyDs4KnCcgkhDr97FTgf07NWo2W39/UwwTo631qPy9G8poUDEmKEJcSfg/9yFS09LkelC7PXFbvTHQ3YPS7HvPo+1/5id7onY6ZunLQv5vQJUZck6KonDS7oGp2kgOuBJzALhm4AHsBkl2wGHMHokln7r4FCHDpyswtygRtqWNzmC75yswtKMIkvqvInpifBX2v29IDUWHWLSedmF+RgelJuwKSu33u9MKCg8xvYN2SwOHnO/cUtpz9UWa+UtWB04NpsADh+9ZL1VY3u0yNqOjwyIz9jABA4p3gOZumApUBPzBC/+zGZA3cBH2F61sqtBCU/YHqBAmUCJww984EH3v7U5KjwotZEoH2BQhlwbHpx0W+BB9qd7lNBf2Hloeoeyk20FdBE+w/Jq+FxfYAmHpfja2vB6sGY5CvTMD1Cx1dXR5gJOMAM+euE6Q3bhOnZigL+xSyc7cC8h+eyZ5mHUM/nv3TEi5he51aY3iyP9T2Yn+UGzPUvxMyhiwSO97gce2UBreacvmvM9LgcIfVmh8PudE8ELgYcHpfjk/o+375md7pPwCRmATM09kxMcpaOmOG2izDvWRtm2GxrYB57el9rNI9PiPoiQVcda5CJNEYnpWKyRJ1qbfkEuJrRJSsYnbQAs97MsYwuCekfihCidnKzC84APqth8eOAH8M4TU5OXuZzudkFvTAZ7qryPObDGZ8tOXmZiQC52QXdgM05eZkrrOedgH9CbczCJn/xRbcJT/857I9bA/dl5GfchvlgKNBvwPmFWYVLAYrS0l2YtbeCWZpeXNQuIz8jxirzQmFW4cqM/IzowqzCnX7nehi4K9T2g1n/7MovvZtOnK2rC4Br5JeW6Yw+5ipQCmBwXLtXp0Qm7JVpfz5wdmFW4X5NwW/1wnkxgUhTzJp3/wEe8rgc94VQT0g9gL7hkHan2/+Dh2NrGgjZne5mwBrr6VEel2NGDY+LBVJ8wyVD4XeN4z0uR3VZU2stIJCNxwzl7IDpRV8PaL8hpbGYzJU7MR+crAvnGvclu9P9A+bvYLhsB+OyBuLAIUFXPWkwPV1m/tYvmCEvO4G7gccZXaKt/bMxn+aewuiSgv3VTCEOVbnZBV0xGdVKMEN/A+eRVOYR4H85eZnbrXpewQwd9LkyJy8z3+88EZhJ/t2sTXZMT/fVQEZOXubs3OyCGMwizFUl1hkGPB5CO4OafJjrmfWNVvTE9AgFc3JhVuE3VdVRlJbef24bbOMG275/cfzeH2L/38AIitortkfDhkRF16Wah1835V7PjODjfsoX6ADcATwGgNY88EY56X6ZGV88I4I/OilOLNQM+SHofdsHmN6+7lW1ORR3XmljYeu94hHfTecvAIVZhfv903v/+WEhHBPysEvruCFUHJ77LWY47npMT+QpmDT5j2I+ZIjA9GR9hek922fZBP2u8WKPy/HWPjxfdXzDLKP8Nzb0LIt2p3sOey8GvwHTi78Ysz5gVY7wuBwyokfsN/s06FJKJQFbtNb7/Z9EfWtAQVdfzD+lXcDxjC75O2D/b5gJww5Glxx0wxGEOFBZ6e2DzUFZBfTNycvc61Pp3OyCnph5Ybfn5GWG3AvlV49/QoZQvAr81z8DZG52wX3AA2URO/+J9EZ3DnbQ591e5d+UP/w3NS3MKqzxfI2M/IzsDqv082Nfqfxfyw89FMfPqfh/bHYHxUMXRdz417DZuUVp6W3vvdy2o/E2/eHIKd7+NT03wGUjbOyMMverWV+WNzvub50eVc5ZjXawDHh2uy16SbmKeHl66563P37ERYnR3jL+O3sq/1lYfcdlzvU21iRXey/crTCrcH4obd7f7E73/zAfAl7mcTmCJZip7LgjMT2fYduHQdcHmOUprvO4HC/sg/PV9katxj2H+4Pd6X4eyAamYNZqi/BfnNrudEdhhhnuxKxJtwFoixkmCnCGx+X4fJ82Wgg/9R50KaWOBP6HGbMdDZyutS5QSjUDXgae1Fp/E1blDViDCboARifZga6MLvkiyL7vMWP1BzO65L193DIhRBWsnqezMDcPvjkh/XPyMqfvg3MfA9TkBqwF8CFwZ05e5vfVFX741kk/JZe2DBrULE2aS9yuxG0p21Ivtjb9kJOXub66OjPyMxTg7bhC8+iEsD7TG4dZNLjGHrg4omhOB5WuVZX3750wc7d2FWYVrvTfYSXwOLzTCj3DVX2b/5jTjn/idjL4riwb5bYqz9m9MKuwxnPAitLSuwML0ouL9umHodbcszYelyOkld7sTndN35dgPjhoHbhxHwZdr2Eyeo70uBz1vsac39DLIuBj4GGPy7HJyk7ZAuhjfU0BPsX0Ar6L6Rn0qXIInhXYHI6ZC17kcTkWVla2rtmd7peBq4C7PS7HIyEc9xNmrb9QM4jaMPPGfvG4HGuqKy9EdWoaG0SGWfmxQAGwDDO58WrfPq31Wqvn6zpMSlpRX0aXeDDDK4Iptb7G7ZO2CCFqLCcvcwcmwyC52QVtgdY5eZk1motSB+eeDigrPX0/zAT2XOAazIT1fsCOnLzMEoInkggqubTl8YuS//69w8aegck+aFvSHcxclA9923KzC0ZjhtIdi5mD1TsnL7NCj72VmEP1frXP15PPzDmp2fbulEas23XKD49HxewsQQOlMU1Z1OF0lqeewM6IraWnfz0qNmLPoIsKAZfvI0ZlFs9+GXODOgAzf8kLjHvr/r//ycjP6I5Zm6kyw4EbATLyM4YAP2FuinOBkwH+ba2Y1Ulx+L+aq2+2sSkeUCpl8piydX719OmxhD4Akx4r55lBEXyfEUGnFZqj53p568QI/IK/uRn5GW0Lswp394Rm5GfEAjv91zOzsi7u8nv+FnBNenFRXay7VS1rOGI4S2vPxPzfig0hW2ITzPDDfc13U1Unc/+qYgWxCdbTU/wX4va4HBsxa4ftFYzbne4rqJj2v8TudN+HafsZmPu/DpjlZbZhPkjwP/5Bj8txf51dSNV8r2OoH2T70vUHWyKgKpdghnzPZ8+wbCHqXVg9XUqpb4AkTLahREx376la6wJr//1Alta6U6WVHKAaVE9XVUYnfQwMwiTWeHl/N0cIcWjJzS7Iw3z4FoqCnLzMU6zj22Cy29kxqfBrLPObnKDbfzr6wVWlcSn+a0D9Jycv82PrfPGY0QG/AF7f2mhW5sVJmEWvw7WzMKswBsDKjFijSf/TDle8PNAWbNcbmAD57oDtY12vlPXrtIoTgx304dHq14knReS+/HT5+oRSVl13o232hkTVCdMjEgH8UJhVuE+Cs2CsdOw6lHlkfkP99mVPl3+ylrOAz2rSZrvTneRxOUpCPJf/QtWhrCdW6/T9+/D1/Pz/27vv+Laq84/jn8fOJEMJK2zMtgGzN4WCWS2GQltGW0oD/AoYBC1QoIaWcksHLrSU5WLKKGFDKVCKoVAwe4QZMGCzgtmEmQshOz6/P85VIiuyLcmWJdnf9+ull617z733KL6x9eic8zzAHsDkjobaq7M47nb8z76uo6G2p/Wqqcfdif/dUvTr3aQ05HV6oZl9DZzmnLvQzJbDZw5KDrp+ClzonFsmp94XoaLMXtiTIPZP4ADgOIKwsdDdEZGhKapB9kP876MY3SfYSHYdcEgfLnvE2tPvWK7inXt2As512MRHd/jjKQtGjN8pTduL8MFL6u/zn8abarp8YFU9pTqGH1nozTz8m8G3gVfTpdJvq6xaC586vTc3HnZi+bwdX3GTd5/WSfCjcnZ+yTFmHty2gzFyPuz/ZCerfQrbvJbd3/P3l4XfHFrOV8t0ed/549bJrRmvxepONDV0deDdfBe4rqhvPhp4uKOhdkBqUlbUN9+HT+yR6hHgKXy68xr84Go5cCN+OuRRwG87GmqDLK6VWIfpgGHZZOlLWQt2I35KXSu+fMJM/DrSVYAn8ffq2/iMh/dFx5RlGQDvAqze0VB7TZp9BmyLL9zehv8wZQH+3+nyqFm20wQTqfQv6mio/VkWx/0bn5lzQIKuqKD2rcDtHQ21f8viuHH4EfmbOhpq/5Wv/knf5TvomgkEzrnzuwm6zgTizrkVc+l8MSuhka7Fc84JwrzPORcRyVRUB+zi6Onl+Dd622Zw6MH432v7pGz/NkuKT/enPeNNNV3WzFZPqd4QSEyD/B7+zVSy77RObv1PJiePpgJS1d62sK2yak26ny6etUNOLmfDdx0n/6uTkWkrpHV1UH15csbH41snt16c2qZ6SnUFfqpbFXBL6+RWVz2legSwMHmKY/WU6pPwmTCTHQf8DagE3mmd3Pp11HanaN9BwO2tk1u/21tf2yqrJia6zdIFwFetam/LqHh0rirqm/+Ez4qZqzEdDbWzM7zW4vst2wChor75KnzW0jM6Gmp/n+ExyVNrs5pimCbhx0v4oHM8MIHepwEe39FQu9R918P1Lgf+L3q6fEdD7Wc9tU867lYgcZ8NT07akQ8V9c2nEGVQzTKjZ/J9llUALAMrr2u68J+KHACcn+bCY/Cph1MLU8rASrz5GFXQXoiILO1v+E/ZX4g31bwO0FjXMgn4KKXdi8C5+PXBH8abahYBNzfWtVyB/932k2gb0TkOBP7Zy7V3jTfVPNhY1zIav5Yl2ezoWntHz+9trGsBWDveVPMWQOvk1leIqhtHrHpK9bjWya1f9fqqU1S1ty1M+v7tKAhbBT+dLMaS7GxZOfHIchYMt5dfWNuuPPSUsgOAs/ad2rnyoS2dV3Z3zF8uX8SvflLO3JGGOXfRKcdvNOugRzqnjZ2LA6YBTKorZ8bEJS+9eko1AMMXOJ7bpOrbV+xZ9vDD1WXjWTrgAh9kX5x07NHAYfhECAn7V0+pPuDmsxf+G19QeUFVe9uzAG2VVeXAzfhAtyfvt1VWlVe1t+WzblOAz6TXgp/CtyewBj74Xy6p3evAemmO/7qivvk2fEDSgf95b4oPZh8DngcaOxpq5+ODplwdjh/FzSYITZ6++Bt8AfFeVdQ3p3s/uXEW1wU/GpeNtZK+34QlSYm6qKhvHoEfVSvDT6XdNWn3WDIbve6LxcFmRX3z8h0NtZ9meFxyVtgauq7R61ZFffNqwF/xa/1+19FQ298fRkmOch3p2hYfVN2Pn+t+NfAL4Cv8wuXVge2dcy/2X1eLQwmNdF0IHA/8gSD8daG7IyKSica6lnXwGdoujzfVpCuo3Nvxf8cnBUlnlUQR6KjtKviEUBCNxMSbalxjXcuh+L9ryYbHm2q6fCLeWNcyCj9y80JyOv3+csNBF08aPefTj95fdWdWnPEMy858lZHzZjJh5mt8PWZVFpaPZNmZr4HPgnn9nOH8dfLJw8qA6enqfLVVVu0AHHHikeVXz5hA5TlXLvpytc+4IbnNNbuWcegDPccrB9eX48ww57ipoetljjq+nJljl/4wv3yRo/I9x/RJxpxR/ti0GSKd4+aGpbqei+Wq2tuWSrJRPaV6GXwilHtaJ7f2mqK+rbKqrK8BXDS17qf4hC3ZeIykosEDNBVuWSB5xOhU/HrGrYF98QH4+6mjLhX1zSvg1/cnuwVf5242PoD4Lz7I3B4/DXNOR0Pt/Cib4IiOhto5WfY19f/cXHxgNQJ4FT9A8GN8cNuTP+OnPf4Tn7mxX0e+KuqbT8Z/eJR8vSfw/85r4ZMXteCnG9fiA8NnSblfskgwk8jqmHArvtbZMsAdwD340cxXga86Gmq/jI5bGR+gPwrsrJG1zA1Eyvga4BKW/hTnTeCnzrlBOdJVQkHXOcApwHkE4S8K3R0RkYHUWNcyAZ9G+zMgTA2YMjh+FinToeJNNRbtOwxfuyzh1HhTTfKbqj5rrGv5DknZHjOwM/4N3BRg60yzYbZVVpUB/ZpW/qMJ8I89yi457Z+dFzxQbe8v/yUnV7/tlhox+c82xjW7lQNcBdy80ufurgsvzaorpwMXVrW3fQ3QVln1An7EI2FcVXvbrOop1cPwo1D7sWQ6GviRnN9H225tndz6eXSeMcC7wMSktrdUtbcdGO03fOa/dzNNyV9R37wj/s1swqP4xC2Z+mNHQ22XxCltlVUxIFbV3vZOFufpUbT+KNNR2w/wxaovwq91X7ymboACxHwEBQuBZ/B1wG7Fv6ZW/BTPWYB1NNSmq7PYrYr65gbgl/3Qt+/iA6aR+Pt8Ln4E7CD874qVo20/74drHdjRUHtLP5ynR9EHEo3Auvjfqa34GrOj8f8Hn8h02mghDVhxZDPbDB94leEDrmddf1RcLlIlFHQF+GkBTQThMQXujYhIyYmSgLzBknTa3U0XA59YYVvggnhTTUbrdXq47tEsvVYpFyPiTTULonPuhk+PfRk+AGkHdsLPTDmn5sH4TfiRjC6atzIe3KSMU/61iBWzyruXsf3xCSiWemP1zgqwRlIVpZfWtEs3fMdZmePSqva251Lbt1VWLcOSNOJ0wls/PqV894XDLNOC4qsDu9989sJ/9Nqyqz2r2tuWrpeZpKK+eTP8tEGANzsaateNtpd3NNQuSmk7Fp/oYRF+Gu3KwDkdDbWdbZVV4/FJZj7AjyIB3FvV3rZXln3uqa+p7+G+oGsA2pOFHQ21w/urLz1J6ecW+FGib+IDkhWBu/BJeZ7BByeL8KU6HgQ2Sjr2UXyQVZHhpVfoaKj9NMq2uRF+ZG0ksAJ+2c4i/PTp4fhpoxemHD8VP7r1MZDIphripxQnvBAd2x8exH+wsQD/O6AW2BA/8tWdxzoaarP5UCAnFfXNO+BHdLvTif85HpNt7b+BNGBB11BTQkHXz4ALgFsIwr6kOhYRGbIa61q61L3K0H7xppo7crzeuvjgLuFToBk/KnIV8LtczpuB8TUPxnfBTz86rqq9rbF6SvU38HXcDPjOzWcvvBP/hjJ1FGNSVXvbx22VVacBPRW3fZOu61S69UC1VV+yT/lDwLJpdj8E7Nk6uXV+umPbKqtOB/6Quv3sA8t4ft2yHq/789sXsWNb9u+L5g3jR4eeMuw5/HS2i1ont3aZapeSoKLXkaC2yqrNWBKk3YQPSivpPvvn81XtbVtk0tcos6QlJz9JVVHf/DfgGGDljobaj6JtZcB6y8798khg7Oejxq9AmjV2A5hq/hN8MedsE1QYfsSqM2XbCvgPHvYjzQcQ/WRKR0PtYWn6VIafYjk3adsf6b1cxsssCSBb8f8eyYXDT+poqP1rugMr6ptXjNrvjw9uZgCL130O0GjlHXT9t56JH+H6DJ9gKbFG8nPg5x0Ntdfmu0+5yHf2wp17aeLwQ5zvOec+7KVtSSjBlPE/wK+3e4gg3KWwnRERKV2NdS334JMlJNsdv3D/bNJnspsYb6qZmeV1tsF/Cp4QBy7pbb1YY11LM0uSf+SqFT968kS8qea3KedfGZ/k4c2aB+Pz8VPXLwVeq2pfEqFE7V4aPfvjhzd7sXHNYQu/3nz4wjkA9cA9Ve1t06LRqC3wHwqmCxIerWpvW5zaPwr8Hummz+vgR4K6FNUuX+Q477JFrPxF9y/2g4k0nVA3rC5526h5jqvPWzLo9KNTyllYDst9BZc09j6L8JK9y3hg0y5B3Q+B/7VObv2sor55fWzeq1Y+Bxs2izFrXVyWmkq/rbJqZFV727y2yqrFKc1zsP5Bpw0bDsxsndz6AUD1lOrV8GsUP8OP5mwVtY21Tm7t8j4mSliyIn50a0xVe9tnSfu2wI8KjU696J1rbc8+bz3B18NGvjVm4bwD8T/bvwPnVrW39SXTY7cq6pv3xK8TO6mjofb8fFwjus4I/P//jLKS9mLNjobajKeDVtQ3L5PIdBkFZpX4wOQTYLlEQJxyzKn4aZ8A8SzT1C++Jwd6imi660XZO69hye+K84GTU0eHCy3fQVcnPrDKxOvAmc65m7K+UBEqoZGu3fC1Nl4hCDfqrbmIiHSvsa4lhp+61A68EW+q6Yy2J+oopbMW8G5yhsUezp+anezpeFPNNhn2zYBR8aaaOdHz4fhPhsf2cNgC/BuYU9LsOyTeVHN9Y13LGHwShJn4tN/gpyetip+aeFYG3ftBvKmmy9//xrqWPwO/sM6FD+z68M8TmeQurGpvS7sWpXpK9VrAT/BZA5PNoofXePPZvS7jG1nV3jYf4Nt/2fiSk25dVLdaFGL85sflO7avbl/ig1HPueS0+gDs+HInP79jyYDRxfuU8XB1z6NpKQLg8pvPXngefm1ONrbAT2Gb2l2DK/co479b2lL9Thg323HFBb3enqfi/51/k2X/Etaoam97N8dje1RR3zxqoLLzVdQ330TXn9HvgOs6GmpfTWlnRKPBHQ21ndHz0fjEIXmfXlZR37w2flQZ4Cfp6qb1cGxOQVdUxHvljobaNzLuaIbXi4LeP+AT9YEffT+so6E2p+yu+ZDvoGtPfBQ9Ej8/PPGPvB4+Q88c/OLUNYGj8b+kD3bO5X1RXr6VUNC1CX5O8CcE4aCrlyYiUiwa61pWBzrjTTXvN9a1XMvShZ13iTfVLJVcqrGuZTx+PcjyJCUhiJQnArs+9s3woyZt8aaa1xrrWo4A5sWbaq5L2p/uOu/jg6v+sEx0zc7Gupa98KMTi+34+GnLbPbi1F4z10XT4jYEtmNJQd1ulS9yf7zhnEUXVbW3fdRWWbUrPkNcOrvg170kHFXV3nZZ0jWr8W9iK/AFhE8Fzkg0XucDx9lTugYuJx5ZzvvL9/Ce1Tn+cvkiVv8UwvFr8ewWJ7P8py+wyUtLEtYdH6Xo3/L1Tnaf5vjPNsbu0xyXfrvsR/NG2CbAGa2TWxde8MMNV97zeddrWvj7NzXW/cDx6EZl/Hs7Y9wcMgm4+ss1Ve1tPxmoi+VDRX3zJcDi0dGBmkaZi4r65l/iU/Yfnk02xor65j2Ae6OnV+L/X4zDD7SsjP/wZVX8tN/Vo+0vs6Tu2VYdDbXPZnG954HNyKAmXEV982H4ciOj8R8qfWOgCqL3Jt9B13n4dJTfdM7NT9k3Cv9Desg598vo+TPAHOfc1llfrMiUUNCVqHnTCYwlCLNKxSoiIrlprGv5nKUTD7TjpwaBz9L1W9IXQ/5tvKkmyFff0mmsa/kRPuFAX72ITyjySTf7t8K/H0jnD/GmmozLm1RPqU5987Jx9Ngdn0hhz9bJrWlTf7RVViUSGXRneHINtR76sCXwUuvk1nltlVU341OrL3bJ3mW/mzucM6atbcwdAa7MmDDLvXTsnZ0bb/y2Y1gU6rbs0tjlvOu8eRuX1DzC9FWyCIiyTLU/bxhfvrky4zfMfvxpGr6+2qf40c5/40d0t8Mn9piAD4h3xb8xTl2T992q9rbbs75qP6ueUr0zPkHOjfhlI534zJ+JkaztgddbJ7cunl5ZUd98BXBE9PTgjobamweuxwOnor55Ol1roGXj/o6G2t2zuNb9+HWKh3Q01F6fQfsK/H22KXB1R0NtX+rY9Zt8B10zgN875y7qZv/xwK+ccytFz+uBM5xzvVUjL3olFHQZPu3rGGB9gvD1Xo4QEZF+0ljXciZLT4frVSItfaE01rUcQO8Fpj/Bfzo9G7/254x4U83vk84xCjgAvxajO7PpPnvadvgA9bZ4U03av7PVU6r3xSf9eBL4Rrq6ZD1pq6y6HZ8wIdXoqva2paarNda1PIP/ZH8V/JS+xAeZi/A13FxbZdU+ZLHux2E8v+nxzJy4Qdr9ry3/9EYt6137Hv49R0bOuH7RdVXvuiuHdfLEImOjcse99JJ5cPpKUH/4MKzTMWIhzBsOo+bDFm+6B4/7T+fGwzp9sor3l2PZx6ts1m8uf6XHxDLVU6pHAV+Mm+2+vOKCRUvNtHl2XTv/TweW/+Xmsxd+BYytam97f+mzLNFWWbUyvm7VNHyw92988ojf4kdahvVWS616SnWM3Isg/7h1cut1FfXNyWs7x3U01M7q6aBSVVHfPBKfEOaH+A9KFuBHvt/BJ7l4Hz+T7Sj8lOjdUk5xE/AlvjbZCGAlfKHmmcA8fHKMBfg1ho/j/7/v39FQm1GJjIr65t2B/5GUBbTQ8h10zQLOd86l/VTKzP4I/Mw5NzZ6Xgec45wbn659KSmZoAsgiL2Or33wTYLw4UJ3R0RkKInWVp1B0lS0Hvwt3lQTz3OXMhL1+yTg5ugxAb9MoA7/N6U+kYq+l/OcjU+isZR4U41FwdkjLEns0JODgVv6Y8plQltl1RHAFdHTE6ra2y6AxRkr/4hPCHZb9PWVqN07+DpItyWd6kf4aYc7VL/09/kj5828YM6o5fhw5e1Z/d0WlvuijfnDxzJjxa0o61zISjOe5KGdL8imq0c3bf/zm/CjRhZde5OUNvcBh7ZObu2SWKGtsmp1fKDwP/yyj5tJCQx/clL5/nNH2u3dXXz8146vRvuRuiRjWye3Lk7PXz2legP8m+hTSEosM2GWo7rDcfx/uv+xOZj30UTqV/6CRJa9LfFTOc+ia2r3TB2FD/gXVrW3LayeUj0CHyRntdguxUNfvfqbq+lc5orjpt3Ct95+6vZy1/kycEynMfmzcby1wpe8gv/5JNYmXgKUVbW3dSkh0VZZNRI/ZXVaJiOqxa6ivvkofGKdvtizo6G2x7ILSddbC19Ieh6wTHIWykLJd9B1B3448AfOuTtT9u2LH6693zn3nWhbE7CDcy71l0TJKbGg62H88P8PCMJBkchERKTUNNa1LMeSkZ+R+GlXwOLgw3rLUFiqGutajsdPVfoGkFhisHG8qeblpDZrkn6qZTpj4001XzfWtRwCHIovbPwRcDd+ZOySTPsWFYaueXy7sxbMHbXcg/iEFNexdF2lPhn71bvMGrd6b812izfVtDTWtWzGklTxyTaLN9W8kHhSPaV6HHAe8OfWya2vpmnfrbbKqsRIAcDmVe1t06qnVK+ArxuVjUb8zy2jwuDnXLGQimyv0Ecn/bT8n1u+4Q784YOd/PW7ZTyznrGofHHwWA80JDV/EPg+/n69Dp+p2nOObz/jOPy+nN7fXwT8/IlKu/m5dWyVeHPnDkn7zgOCqva2xQWp2yqr7I2VWX3dDzkAnyH0zmht4e5Rn0YC56VmvyyUKKtiXxcH7tjRUPt4htcbgf8gxICVOhpqZ/Tx2n2W76BrDXyq3Ar8MGMiS8o6+AV2bwO7OufejtZ03Qr81znXr7/ICqHEgq5Epp2TCMK0dRpERGRgNda1TMAXQn1nsAZbqaLX/AfgsnhTzbQe2j0K7IhPX79KN83OB07oZt/++Gmdt+FHPP4FnNDdv3NjXctI/Bu4XMwhTfr0LM0Atog31XRJhNFY15Ju9GCLeFNNuoCsX0TTAjfFr82bhP8Afds+nPJskupMDV/oWHMG/PHqnN6fjwT+gk/YligEfSC9T4XtwsH8/zuh/A+zRtuNrZNbX6ueUr0MMDp57Vay6inVZwFnHHPnInYtUIzj4PmDTxv2Cksn6DmydXJrrwll+qJ6SvXu+H/zS3uq67beKbdfvqBs2P9hRkdDrUUZG3fATyl8Bb9+bhE+aJyKH6V6kiUjtpt3NNROy7RfFfXNn+KnKVZ3NNS+lPUL62d5L45sZsvgpxrshZ/bCT7Yuge41Dn3dXfHlrISC7rOA04E/kwQpksLLCIiUpQa61qWiTfVzG6sa3kCv+4jVy342TlHxptqLo/OvQZ+bdBmWZxnPH6tCkTTQRvrWrbCr2FZG59A4nCgPGpzMXBcD+cbHW+q6THga6xrOQU4J2nTmHhTzezu2ve36inV++E/WH+jdXLr3GjEpbfhnhNaJ7deEB0/Cv8+sQK4rHVy62yAlyurJp/3vbKDXl/F9r70Yh+EnXhkOd96ppO9nneLn3+4LHT6aY3/bJ3cmjalfltlVWLoyoCd8YMCPZmJL+rcic8a6qra2z5pq6zaHHgVP6XxTnydvMfw2fsWjyZOnwQfLGeUOXixwpg00zF+Nuz2wpL3029NgrX6cfylh2yYZ7VObk2b9a+tsmo48Gt8qv9HgF0yWPt2OX70GPzU28T3i3+mSee/Dl9Qeo/Etj9ufejb11xzekWvLwioqG9eFj8lFWCjjobaV3pqn3LsO/g1llt3NNR2l5xnwOQ96BqqSizo+gXwZ+AGgvBHhe6OiIhILhrrWpKnxCXcgF/sn40V8Emmegp2HgNq4001aRNYJILBbvaNB75O1GaLygkcAtwbb6p5rrGuZQv8m/2XM1kXF53jLNKvC9wg3lTzWjfHLJWaH/8G+lb8iOMk/HS6e+JNNVkn2qqeUr030Bw9fRIf1H4P+LR1cus9OZwvk8QzV+KTuFwQXe9aYHL0dT3gtdbJra56SrXhXOfxd3Sy0yuL3+Oujk8C0SdHHl9OONbATxHeF/isdXLrq9G6sfVJrusGHHr/IvZ9asn77NN/Us6icuhYEY74XycrhLDFm0u/D39/Wbhgv3LO+ceSUcHgR2W8vaLtdMaNix4ZNR9u2bGMI/7XSbgMzJholVu86V7Dr7/8fKkTev+oam87InVjFEhPZcn037SW+9Lx/cd8+YIMjKhqb+vx/o5Gw6big9pNOhpqM/r/EB37Gv5nvlNHQ+2jmR6XLwq6+pmZxfGfepThh0dLIej6Af6P0sME4TcL3R0REZFcNda1fIl/g5awIX4EogpfoDp1itgrUZtMrBRvqin42pB0Gutaulszc1u8qeZ7adqnC1B7cn28qabL1LXGupbvRN8eBdRG30+K+vEFPiPdaXQdyauNN9XclcV1u6ieUr0cvtbrk8Cj+Cm4t+KLkmfqGOAX+IQvCWWtk1tdW2XVhviaUrk6vaq97eyeGlRPqV4W+Dt+bRjW6Tj44c779nnKPTliEVcfdNqwscBz6Y5dLnR8Pg5cmV2GT0ay4Tdf7Hws3tyveSJmAmcCl961pe2513Pujtt2MJq3LuPrUaQvou0cP7m/k32ezjpeGFfV3tZjhseK+uZygI6G2qzmnFbUN7+C/3+/a0dD7YPZdqy/DcT0wr3wn5qsjU9HmvqTcs65dXI6eRErsZGunfCVu98kCIsiraaIiEguokLOZLoOrrGuZVn8G/j1umlye/S1Jd5Uk7YETrForGsZA6R7A7tHvKnmvqR2pwJ/6sOl/kfSdLEc/AC/FgxgPj5leKq0xcK7Uz2lelXgvRz7s0rr5NYPE0/aKquWATJZ/vIH/Cjq2viRySOq2tumZHPhKDnJqq2TW6elbB+Gn9aY+r5s2dbJrV8kb4imTk7HT8/MynvLwV1bl/H2inbZH65edGQmx8SPKeeTCVYJtFun469/X/TeKl+wWnft793ceLyq7MtX1rS3yhe5H99wzqLW7tpG7qo/rHy/6StbMz6r5pf40a49Rixw37v2z4suBFZjSXH2l4Hqqva2Lv/nK+qbW4nq8nU01N6fyWvLp3wn0jgFn/FlBvAU/lOPpTjnDs/65EWuxIKuCuAtYCG+QPK8wnZIRESkMBrrWpKL20IGa6qKTTQC9R6+bhWwpLZbFGQmj/adHW+qOT3aV4OvqbT4VPhaSSfkucvd+T7+vckz+JGpC+JNNe901zgKVBrwGSsTtb9Owadn90GBM5advRJfjwhZcdaazBw9Y4PHj34o7fRLWJy63QFPA3e27NKYXAZpJH5m05P4NV5j+vteqZ5SPR4/JfOfyen3u+nrJkAie+UC4G/4UauLnl3H9tvyTZcofcA9mxtXfKu8y/HjZjuuuCDjwaTK+cOonDOCw2Kz2T9l37E37Fy274tr2bffXGXpUbFhCx3Xn9vzda6pKeM/23bN3v+tZzo54n/djuj9sKq97cbkDRX1zc/j12N+q6OhNuvprP0t30HXe/gofW/nXMZzMAeDEgu6DP+fcjywCUHY2ycQIiIig1JKpsJr4k01Pylkf/oiZURrWeBIuo5wTQV2SK5rFk1TnBhvqvksaVtiGUI6n+Fny/wSPyqTburgRGBvfIr1vpoYb6qZmW5H1PdngM3v2PDiDUYsHDXjW6/99Et8/bYz8cW0Uz0R9W0nfJK3UcAa8aaal6JzjsKPxN2IL9L7ux76Ng34Nn7N1BfppqI21rVcCzwfb6r5S9K2rEZne9JWWTUGn1CmJXXkp3pK9Q74tW7d1ryLzXJ8Z2onez/tKE86+rVVeH39D7odDU74HXBtVXvb4iC2ekr1CbC4ttpi1um46U89B16NtWWEY+CdFco56OXzAShbNI+Kt+9mzXfuw+jy8tqr2tuqkjdU1Dc/g6/nVtvRUJvzlNb+ku+g62vgJOdcX4uhlZySCroAglgi69NBBGFWqVVFREQGm8a6lkrgzUwTWRSjxrqWcvxIUTp3xptq9s3iXBsCm+PXZ22Eryt1ebyp5q00bYfFm2qWum5jXcvm+MQdiVGoBvwI1Fn49OCfRpkoe5v++Ef8Wq6fx5tqFjXWtXwDn3mvr27B18pL+A8+EUZfTEgkW2msazkQX3gafLbHdMtrRsWbagZkxlH1lOo18KN4Mbom97gVP7oGPpHKXq2TW+cDtFVWJWq7pppU1d7WbYW16inVPwHSTr0cM8fXN7t7K2PMXPjrZYsYHsVjDnhgl8aljnlt/OVM/OIFnlnPOPfKxcHbllXtbYvXwlXUNz+JL2ewX0dD7R3d9W2g5DvoegB43jl3Uu5dLE0lGHT9AzgMOJMgPKvAvREREZF+0FjXchU+e1+yR4GdC1X/Lcre2Blvquk2gUJjXcv5+GmB+dBdwJMv6+HfE2Za9nlEIYL96inVG+GDlKu6q7fVVlk1DL+mbxf8KGEzcFlPAVc31xqFH1lsPfqJ8z8w7A8AC23BXeM+Ordyu7YP1jYcz2z+C76Mrd3teZorLyF++0us/READVXtbYtrvlXUNz+GrwP2vY6G2tuy6V8+5DvoqsJXfz/dOXd9zr0sQSUYdCU+VbqJIPxBobsjIiIifddY17IRkFwYdk68qWaZQvUnW9HUu3Xx0xjTFifOwBTgXuCO5ECvsa5lCpDN9NFmfMr6F+NNNfNT+pmcHr8/nBBvqrmg92ZLa6xrWR5f/+05/CymBxJJYBrrWqxYiq1HP9uNSEmh34NV8YWud0je+PLY4zn+zk4WlPPsJi+3LZ46WVHf/BC+JtuBHQ21t/RTt3OW76DrRfwc4pXx2XTeY+l0ps45t2nWJy9yJRh07QvcAbxEEFYXujsiIiLSfxrrWobj1zVdVKxp73vTWNeyPkkFiNPYDmgHvkpep9bD+Qz4C7721KH4pGJf4tdk3QQciM/SuCH+363HN8PR+SqADnySjTlpmr2CT+5waPRanklMJ2ysa5kOrJXUdvnktXWZaKxr2R9IN6pzTXTNhFvxa/zK8aNWXwP3p05tbKxrWQVYNd5U83T0vAxYLt5U80k2/eqmr9cBGdWH3fap36231XMPvJFuyuxjq50//YxrX08Mh21c1d72MkBFfXMLsCvww46G2hspsHwHXQ8CvR7onNs165MXuRIMuirwv2zmAcsQhP1a8EFERESkvzTWteyInyb5PXyQdP1ArYXKVBSgdODrxk2INq8Wb6p5v5v26+GzD45O2XUhcGJvgWRjXcs4fNCYq+Z4U80+Sec7A7/eDuASfLKUR4DEYMm34001qcW1U/u0CnA08G68qebypO1V+AA04Wm6L7z8/XhTza1Jxy6Dnx76YmLb2JlPsc20Kcwfxu83fantDICK+uZ78UHzjzsaavsjiUufqDhynpRg0DUcn62pDFiZIPyowD0SERERGXIa61pOBM5L2XxBvKnmhF6O+ytd0/s34ke4nszi8u/jsz0+mmH7D4BVsjj/rsADabavE2+qmZ68IZomuUx3ZQIa61q2x2eUBGD7J8/AdX4+Y5n5rFLV3tZZUd98N/At4LCOhtqs6qflQ6axQVl3O2SQCMIF+P84sHQhPhEREREZGBem2fadxDeNdS2rRtNFSdq2CksCrofiTTUWb6o5Lt5UMxVYP6npOHzGyGndXHtVMg+4ILuAC5YOuH4R9XV6asN4U82nPdVlizfVPAHsl3j+ycQKlpnPJCCRwC8xDbE89dhi1qegy8yGm1m1mX3DzHZOffRXJ6XPEvOkNyhoL0RERESGqHhTzSJ8ToQV8IkgANZqrGsJGutaHD5HQiINfSzaljxl8dCU872On7I4Ot5UMyveVPN+vKlmc/xatW9FhbPT1TADGA5UsSQQeynq191ZvKT/9LCv28LUmYg31dwBNAFM28DHlp3G6W2VVSNYkkdiWF+uMdBy6qyZlQFnA8cCPWXKKakIdBB7A9gNvwhURERERAog3lTzBUBjXctjSZvPTPp+dBRspfptvKnm3TTnm5tmWxvQFn3/amNdy6b4NWUA/wIOitaRtZNSm6uxruVg4ETgm8CdwHT86NnZwL/xOR0m4UfdXmysa1kJH+QdjA8Y18CPRPW4JixDtwF1y7CD+3jCXbbizC8n4otUl+RIV64R4unAKcCl+Aj5GvwivJn4QMwBp/ZD/6R/JIZwfw2cUciOiIiIiAx18aaazsa6lpuBgzJofmq8qebcPlzrRcAybPsVS5JsJEubjTDeVPMR8BHQkmv/evA/oLWM8urnK6vZ68nHAGoo0ZGuXKcXHgbc7Jw7hiWR7LPOucvwxdcc/h9l0DCzuJm9AjxV6L7kYEmffWINERERESms5BpTmwJ7p2kzsi8BVymLUvnfCeBG7wFAp7E1JTrSlWvQtRpLItpEGs9RAM65+cC1pMw7LXXOuUbn3IbANoXuSw7ux9dpANiikB0REREREYg31fwTOA34cbyp5sV4U83dUfIJw9cDK0st1jwE/QNghFuBBcPGAGw9ft7XiVG7ITHS9RkwFsA5NwtfO2DtlDYT+9Av6U9B6FiSVnTjQnZFRERERLx4U01DvKlmqVpT8aaa+b0VbR4KomQhLwNMX72aMsewQ9v+u160e0iMdD1P10JnDwAnmNmOZrYT8DOWLNiT4vBy9LW7LDYiIiIiIsXmJoDX1toWgK0/bl8r2j4kRrr+Dow0s5HR81/hK3I/DDwEjAd+0efeSX9qj74q6BIRERGRUnENwJjO9fkiti7Lz545cfSCuTAURrqcc3c4577nnJsXPX8FWAf4Hr7I23rOuWyqZEv+KegSERERkZISb6rpcLiXAN5afXPKcWV7dzwBPrFfyehTceRkzrnQOfdv59ydzrnP++u80m8SQde6BLGRPbYUERERESkShv0W4LPlN8dhVH86HeDmwvYqO32aC2lm44A18Ukzlsr/75x7uC/nl371ET7hyXhge+DBgvZGRERERCQzdy60BQuHudiwz5etYpNP33gPuKjQncpGTiNdZracmd2Az2L4Av4N/ANJj8RzKRY+g2FiBHJQ1VATERERkcEr3lQzNxz9SRtAOL6C0Yvmr3r37Sd/Wuh+ZSPXka7LgH2BC4FHgC/6rUeSTw1AE7A/8JvCdkVEREREJDMLy+a3ANVfxCrAz7DbFHiqkH3KRq5B157AX51zp/ZnZyTvbsEHXdUEsc0IwmkF7o+IiIiISK8mzar4J/DzcEIlDjBYnxIKunJNpDEb6OjHfshACMLPgLeiZxcXsisiIiIiIll4FgArZ86o5QHWKGhvspRr0HUt8N3+7IgMmD9GX3ckiJVUfQMRERERGZriTTVz55XP/gxg3sgJUGJBV0bTC81si5RN/wS+aWb/xRdKfhdYlHqcc+65PvdQ+ts/8GvyADYEWgvYFxERERGRjCwsW/DOyEUs9/z6q8ypefqN/xW6P9nIdE3XM4BL2ZZIEb9HmvYWtddISrEJwkUEscSzFwli2xKEJTMfVkRERESGphGLRj0IbP7y2quNbtp92HOlNHKQadB1eF57IQPtYWDn6PupBLFVCcIPCtkhEREREZGeDO8c+STACrNWAz9j660eDygiGQVdzrkp+e6IDKjdgAVJzw8Bzi1QX0REREREMvEcwLKzV2HYohGVQHOB+5OxXIsjDzOz8T3sH29muaajzyszm2Bmz5jZNDN7ycyOLHSfBlwQLiQIDV+3C6CukN0REREREcnA9IW2YN4wN5yVvlpr20J3Jhu5Zi+8EHi8h/2PAX/J8dz59hWws3NuM2Bb4HQzW66wXSqY86KvaxPE1i9oT0REREREehBvqulcWD7/TYC1P9vsnUL3Jxu5Bl3fwhfa7c4twN45njuvnHOLnHOzo6cj8Uk/rIdDBq8g/CTp2asF64eIiIiISAZGLRzzOsCGH+/wdqH7ko1cg65VgPd72P8BsGouJzaznc3sP2b2gZk5M9s/TZu4mXWY2Vwzm2pm22R5jQlm9gLwHnCuc+7TXPo6SFy7+Lsgdk0B+yEiIiIi0ptE0sLvF7QXWco16PoM2KCH/VXAlzmeewzwAhBPt9PMDsZPi/stsEXU9h4zWzGpTWK9VupjFQDn3Ezn3KbAWsCPzGxSjn0dDH6S9P2PCWJFuRZPRERERAQ4HziHEgu6zLnU8lsZHGR2BXAQfm3U8yn7tsCnJP+nc65PqebNzAHfdc7dnrRtKvC0c+646HkZvjjzRc65hrQn6vkafwNanHNpp0ua2Uj8NMSEcfgRsphzLtfAsrgEsbH4tW4AdQThpYXsjoiIiIhIKYiSC4b0EhvkOtJ1Bn4k6ykz+5eZnRU9bgWmRhc+I8dzd8vMRgBbAvcltjnnOqPn22d4jklmNi76PoavV9XTeqbT8K8n8Xgvp84XsyCcBTwbPWsqZFdERERERAabnIIu59wHwFbA9fiaT7+OHjXAdcDWzrl8BCfLA+XAjJTtM4CVMjzHmsAj0ZquR/AjZD0VtD4biCU9Vsuqx6Xj5MXfBTHV7BIRERER6Sc5r99xzn0ITDYzA1aINn/icpmvOICcc08Bm2XRfh4wL/Hcv9xB6aGk708miAUE4dcF642IiIiIyCCR6/TCxZz3cfTId8D1KbAISE18MQn4KJ8XjjImvgI8lc/rFEwQOrpO0ZxFEBu0EaaIiIiIyEDpc9A1kJxz8/Frj3ZLbIsSaewGPJHnazc65zYEskpPX1KC8EngT0lb2grVFRERERGRwaLogi4zG2tmm5nZZtGmtaLna0TPzwOONLPJZlYFXIJPM/+PAnR3MPp90vcbEMSOKlhPREREREQGgZxSxueTme0CPJBm1xTn3GFRm+OAU/DJM6YBP3POTR2g/mWUFrKkBbFxdK2ztgNBmNeRRBERERGRUpNpbFB0QVexMrM4vmBzGb4w9OANugCC2Ip0zRI5hiCcXajuiIiIiIgUGwVdeTIkRroSgtjqwDvRsw+B1QjCzgL2SERERESkaOS7ODJmNt7M6s3sHjN73sy2ibYva2Ynmdm6uZ5bikQQvgvcEj1bGVhEEBtfwB6JiIiIiJScnIIuM1sNeB44C18seBNgLIBz7nPgaOD4fuqjFNbBdF3fdXuB+iEiIiIiUpJyHek6FxiHLzL8TSC1ntPtwO4590qKRxB2EoQx4NZoy64EsWML2SURERERkVKSa9C1J3Chc+4VIN2isOnA6jn3qggN+uLIvfth0vcnFqwXIiIiIiIlJtegazTwSQ/7x+V43qI1JIoj9yQI5wNbR8/WJYitWcjuiIiIiIiUilyDrleAnXvYvz9+zZcMLs8mfX9VoTohIiIiIlJKcg26zgd+YGa/BGKJc5nZumZ2DbA98Nd+6J8UkyB0wFHRs10IYqlr+UREREREJEXOdbrM7FdAgE+iUQZ0Rt93Ar92zv2pn/pYVIZUna50gthY4Kvo2ctAdRSMiYiIiIgMKQNSHNnM1gC+D6yLD7zeBG51zk3P+aRFysziQBz/OjdgqAZdAEHsAWCX6NnBBOHNBeyNiIiIiEhBDEjQNRQN+ZEugCC2EvBh0pYyjXaJiIiIyFCTaWyQ65ouGcqC8CNgvaQt2xaqKyIiIiIixS6noMvMOs1sUW+P/u6sFJEgfANoj549QRAbXcjuiIiIiIgUq2E5HncWSxdFLgcq8OniXwXuzLlXUireBSqj72cTxDYiCF8pZIdERERERIpNTkGXcy7obp+ZrQw8CbyWY5+kdOwNLEh6/jJBbBmCcE6hOiQiIiIiUmz6fU2Xc+5DoAk4o7/PXUhmFjezV4CnCt2XohGEC4HhQPKiwdmq3yUiIiIiskS+Eml8DayVp3MXhHOu0Tm3IbBNoftSVIJwIUEYS9l6R0H6IiIiIiJShPo96DKzjYGfoemFQ03yvbQPQcwRxDYpWG9ERERERIpETnW6zOwtlk6kATABiAGzgf2dc/f3qXdFSHW6ehDE1gHeSNn6FTAO2I8g1AiYiIiIiAwaeS2ObGZXsXTQ5YAvgDeBG51zn2d94hKgoKsXQWwF4OPu9gLvAFepmLKIiIiIlLq8Bl1DmYKuDAWxS4Gjemm1HEE4KINzERERERn8Mo0N8pVIQ4a6IDwaX7ttfWC3blo9OGD9EREREREpkIxGuszsNzmc2znnfpfDcUXJzOJAHB+oboBGurITxNYFXu9m75oE4TsD2R0RERERkb7q1+mFZtaZQx+cc648h+OKmqYX9lEQKwcMuBfYNWXv8QThxQPfKRERERGR7GlNV54o6OonQWw90pcVmAkEBOEFA9shEREREZHsKOjKEwVd/SyITQAeB6pS9swDRivLoYiIiIgUKyXSkNIQhDMJwg1ZOtnGSEBTDUVERESk5OU80mVmmwDHA1vgCyKnBnDOObdO37pXfDTSlWd+5OuLlK37EoR3FqA3IiIiIiLdyutIl5ntAjwF7AN8AKwNTI++XxOYBTycy7lliAvCmcCIlK3/IYg5gtjwAvRIRERERKRPchrpMrOHgeWB7fBvkD8GdnfOtZjZtsDdwCHOubv7s7PFQCNdA6T7FPNrAssCrQThooHtlIiIiIjIEvle07UFcEV04sQb33IA59xU4FJg0NTokgIIwjcIQmPpe/Rt4HlgIUFsy4HvmIiIiIhIdnINuhYCX0XfzwQWACsm7Z8ObJh7t0QiPnvhxG72PkMQGz+Q3RERERERyVauQdcbwHrgs2UA7cB3k/bXAh/1rWvFxcziZvYKfi2bDCS/zqsMuCXN3pAg1kIQGzawnRIRERERyUyua7rOAo4AKpxzC81sMvAP4M2oyTrAac65P/VbT4uE1nQViSB2CVCXsnVZgjA186GIiIiISF7ktTiymQ0HxgOfRyNdmNmPge/j13jd6Zy7Kod+Fz0FXUUkiB0HXJS05WuCcGyhuiMiIiIiQ0teg66hTEFXkQliBswAVoi27E4Q3l/AHomIiIjIEJHvka5zgBucc8/n3sXSpKCrCPnAqzNl6wrAMkBIEIYD3ykRERERGezyHXTNwdfnmg7cCNzsnGvNsa8lRUFXkQpiW9NzkpPlCMLPB6o7IiIiIjL45btO14rA4cBrwKnANDN72czOMLMNcjynSO6C8Gng2B5afEYQqxmo7oiIiIiIJOQUdDnnvnLOXe2cqwUmAUcB7wFnAK+Y2TQzq+/Hfor0LggvwRfpboi2pE45vJ8gdsbAdkpEREREhrp+TaRhZssBhwK/BcY658r77eRFQtMLS1AQuxA4PmXrM8BPgRcBA4YRhPMHumsiIiIiUroGNHthlEL+28DBwL7AWOBd59yafT55kVHQVaKC2MVAvJdW5wL/Bp5WACYiIiIivcl70GVmw4A98YHWfvi6XR8CtwA3Oecez+nERU5BVwkLYtX4ka1MbA5sG31/LbAKMJEg7ClZh4iIiIgMIfnOXngFsD8wEfgU+Bc+i+HDrj/nKxYhBV2DQBArAw7AF1ZeMYczDCcIF/Zvp0RERESk1OQ76PoMuA24CWhxzi3KtaOlRkHXIBXEJgIPAxtneMQognBeHnskIiIiIkUu30HXMOfckPykX0HXEBDETgTOwyeEeRa4ARjTTevDCcKrBqhnIiIiIlJEBjSRxlBgZnF8IoYyYAMUdA09QWxz4Llu9m5EEL4ykN0RERERkcLq16DLzN7C1zyqdM4tiJ73dqBzzq2TRZ9Lgka6hCB2LNCYZs9EgnDmAPdGRERERAqkv4Ouq/BB1k+dc4uSnvfIOXd4ph0uFQq6ZLEgNhx4HUgtjbAnQfi/AvRIRERERAaQphfmiYIuWUoQ+x3w65StKwOfEISLktqNApYnCN8bwN6JiIiISJ4o6MoTBV2SVhA7BTgnzZ5FwG/whZeTCy6vBHxMEOo/oIiIiEiJGpCgy8zWANbG1+uy1P3OuVtzPnmRUtAlPQpit+OLhWdjM4LwBYKYASOUil5ERESkNOQ7ZfwawJXArolNaZo551x51icvcgq6pEdBbATQ16BpJ4Lw0f7ojoiIiIjkT76DrgeA7YG/AFOjCy3FOfdQ1icvcgq6pFd+7dZY4HNgeeDN6PlkgvBqgth44JfA6RmcbTXgc4JwTnTu0cCqBOEbBLHvAusA5xOEQ7JunoiIiEgh5TvomgOc45w7M/culiYFXdKvgthqwLsZtDwJn6Y+k1G0kQTh/N6biYiIiEhf5Dvoeh1odM6dn3MPS5SCLsmLILYKfm3kycBh/XTWt4A/4EfbbiYI3+qn84qIiIgI+Q+6jgaOA7Z1zs3OuZclSEGXDJggthPwcC+tTgT+muEZJxGEH/etUyIiIiKSkPfshWZ2LPAr4BbgPXxq7GTOOZfpm8GSoaBLBlwQqwT+DawPdALDuk01H8TOxY+WpTMdH6SdAsQJwhf7v7MiIiIiQ0e+R7o2Bu4E1uihmbIXihSKTz9fA5wBfLOHlkcShJcPTKdEREREBpd8B12PA1XAqfScvfDtrE9e5BR0SckJYhOAL3pptQ9wl4o1i4iIiGQu30HXbOBM59y5uXexNCnokpIUxGqA+4GfAAuAG7ppOY4gnDVg/RIREREpYfkOul4GrlLQpaBLSlgQWx74JM2e3wMvAg8QhJ8ObKdERERESke+g64DgD8DOznnMqkxNGgo6JJBJ4gNB7qr67UfQXjHQHZHREREpFTkO+i6ENgZn03tPnxx13TZC3+e9ckHiJktA7QB/3TOdZftLd1xCrpkcApiDwM7dbN3LjAKmEAQpl3DKSIiIjLU5Dvo6sygWVFnLzSzPwDrAu8q6BKJBLE1gDOB7+CLKqezEkE4o4/XKQMqgVcJwtQPbERERERKQt7rdJUyM1sPaAD+A2ysoEskjSC2I/BoN3vXIgg7ktoOwyfoAFidIHxv8fYgXBh9vz9wWzfnGwGsBnwDKMMn/RgNvA1sBnxKEE7P9aWIiIiI5EPJBl1mtjO+eOuWwMrAd51zt6e0iUdtVgJeAI53zj2VxTX+HR2/Awq6RHoWxDYBfg08BaQmzzkG+BHdT0skOjaG/z/XnU58sNWT0QTh3F7aiIiIiAyYTGODYX28yHbArsCKwN+cc69Ha6Uqgdecc7mknh6DD6SuBG5Nc82DgfOAOnyNsBOAe8xsA+fcx1GbaaR/bXsCW0d9e83MdsihfyJDSxC+CBzkv48ZcE7S3ksyOMPvM2jTW8AFMIcglvh+H+AdgrA1g+NERERECirXNV0jgBuB/QADHLCHc67FzEYB7wF/dc79oU+dM3OkjHSZ2VTgaefccdHzMnwij4uccw0ZnPNs4Mf4xB9jgeHAX5xzZ3XTfiQwMmnTOPzr00iXDE1B7BzSj1q9BqxNzx/mTCEID0s61wS6Fm5+Az+adinwNFABrNBLj44D/kUQftRLOxEREZF+le9EGn8CTsS/2XkAeBXY3TnXEu2/BNjSObdNDn1Pvk6XoCsK9mYDB6QEYlOACc65/bI8/2H0Mr3QzAJ8YoFUCrpkaAtiI4BrgXnAEQThgjRtvgE8ArwO1Cxe69W1TRmwB/A0Qfh5mv3J68W68xKwCT75xwKCcGYWr0REREQkJ/kOut4BbnPO/dzMlsMXWE0Oun4G/MY51132s0yvkxp0rQK8D+zgnHsiqd05wDedc9tmef7D6D3o0kiXSDEJYivgE24sNf04xdoE4VsD0CMREREZovK9pmtFoKe1FIuAZXI894Bxzl2VQZt5+E/yATCzfHZJRHoThJ/gsyD6/4xB7CYSa866mk4Quw74OfAVQdhdAWgRERGRvMo16HoXnyyjOzvi12b0t0/xAd2klO2TgLyu54gyJsbJbMG/iAycw/C/FzYD1qPr77VDogdREo5L8VOj7wB2j9pMA36HH0VvJQhnE8RGAusShC/nu/MiIiIy+OU6vfC3wEn4bICv4acX7uace8DMjsRnNKt3zv25T53rPpHGU86546PnZcA7wMWZJNLoK6WMFykBQexS4Kgcj55H1ynFzwK/AT7Hf6B0CUE4u28dTMOvkdsOeCYv5xcREZF+l+81XSPwhYVrgDZgI/x0w2XxBU7vAvZzzi3K4dxjgXWjp8/jg7sHgM+dc+9EKeOnAEfj6wadgJ9aVOmcm5H1i8m+fwq6REpBEFsW+D7w9zyc/WF88pA3cz5DEPs+8DVwD7A6vhB0sm8BC/Efbt1KEE7N+VoiIiKSF3kvjmx+cdMhwAH4KT1lwJvAzcA1LscTm9ku+CAr1RTn3GFRm+NYUhx5GvAz59yAvCFR0CVSonyWxO2Aq4CLgX8DXwFr4T/IWQlYLoczn4LPnng3MAdYkSBMX6PQ9+EtYI0crgNwGEE4JcdjRUREpJ/lPegaalLWdG2Agi6RwSuIGUHoomLQewPrAGviR94z8SN8MPcL/AyA/rYJfh3b62lT9YuIiMiAGPCgKxr52hW/FuJR59xX/XLiIqORLhEhiK3B0tMBc/E2Pph7BGgkCG+KUuJvBtyLXx97Nn7daneexo/gDcdnlp0LzCII5/RD/0RERKQH+V7T9Qd8raxdo+eGf4NQg0/j/A4+sUbu6x2KlIIuEVksiK0FTI+e7Yhfh3VGL0f9CjibIMzul28Q2wofYGVqLYKwI6triIiISFbyHXS1A/92zv0yen4gcBP+zcQL+LTMDzrnDs2h70VNQZeIdBHEVgZmE4Rh9HwffKKha/EfQJ2FTw40giB8vh+utz9+7ezwDFp/E3gGWI4gfDfD84+LjtsKuAbYBmgjCKfl0FsREZFBLd9B11fASc65y6LnNwDVzrmNo+enA8c451bPpfPFTEGXiBSNIDYWnwykr07FJwJZEz+dsTpNm+OAy1RkWkREZIl8B11fAGc45y6OphbOAK52zp0c7f8/fN2s0Tn1vggpkYaIFK1E4o8lzyvwWRLzIcSP4h2f9RRJERGRQSbToKssx/O/BPzYzCYCh+PTLDcn7V8T+DTHcxcl51yjc25D/FQbEZHikRr8+LVcB/bhjHcBywDXAT9L2RfDfwDVSRBbsw/XEBERGTJyHenaA79mIbGm4DHn3M5J+58F3nbOfa9fellENL1QREpSEPse8CRB+EFUL8xFafFHAT/Fp7i/EZgMnEoQzk46dizwY3w2xZ7sSRD+L+m4rYCvCMJX+/W1iIiIFImBKI68IbAHMBO4yTk3N9o+EfgNPtHGgzmdvIgp6BKRISuIDQNWo+epiz8FrgT+B+wGzMNntp1LED5HEBsBdBKEC6NzjgYmAR8RhHP7ub9rAdsCN2kqpIiI5IOKI+eJgi4RGfKCWDm+UPQ5WR75AbBKD/snJGWB3AKoAD4GTgGOwSf4OBB4CLg2GqkbBpwJtBCED0QFre8G9ko592+BfYEtgJ0IwkcJYssDjcAE4F8E4d+zfD0iIjLEKejqZ0qkISLSgyD2S6Chn852HXBIP50rW9sRhFMLdG0RESkxCrryRCNdIiJp+BGmjfCjSbsAnwN1+ILO6/Vy9AIyqzs2kPYDjgI2xieHWocgnN7zISIiMtQo6MoTBV0iIjkIYt8AHgG+RRDeE22rBD4gCL8kiP0YX4w52TP4kiS3A5cB/wWOx9cSOyCp3dPA1knPGwjC05KuPRb4Cz64+xtwBbBdtPdC4H58opDeMj6uSBB+0sNrNOA7wCsE4etp9m8NbApcoTVmIiKDg4KuPFHQJSKSR0FsIn5q4bUE4cwBvvauQEuGrXcGHicIFxHEtgfWoWvQeDlBeGQUiJ0DnJzmHCMIwgV96rOIiBSUgq48UdAlIjLIBbFa4AjgSHz2xRnAmBzOdBNwcC9tDsGvYfsbfhRvFHApcC5gwMcE4Yc5XFtERAaAgq48UdAlIjIEBbF/AZnUnnwDWLefrz4R6AS+jkbWRgHzukxRDGIx/JTJ+xen48+FP8/WwArAv7vUaxMRkaUMRJ2uvYD/A9bG/0GwlCbOObdOTicvQspeKCIiAASxnfFp61NVE4QvEcR83bElriYIJ6ec40/AqTlcfTr+7y7AKIJwXlSE+umkNg340bmbo2LY5UAiELsKv65sc2AkQTg/qU+7AfelXO9ZYGutQRMRSS+vQZeZncKSX+pPAV+ka+ecOzzrkxc5jXSJiMhiQWw4sPpSmQ2D2DbAVOBDgjB9bbIgth6wO34a4mc5XP1u4Nu9tGkDqnppcyx+emN3ngLOB65P2f5d4GmC8P1ezi8iMmjlO+h6D/+LfG/n3JBaBKygS0RE+l0QWxlYRBB+nLJ9LeAeek+7D36Uavd+6M25wEjgZ1kccyBBeAtBbBX81MQXATRCJiKDXb6Drq+Bk5xzl+bexdKkoEtERAoiiA0DVgLex6/xSnYEQfgPglgZfurjulHbZKOB7YE18Cn409VGW54g/Cy6XvK0xL74E/AxcOWAZ6QUEcmzfAddDwDPO+dOyr2LpUlBl4iIFJwfUUpM66skCF9N08bwQdbrwKdpR538FMfXgB8ShDem2V+GHz0bAdwLVAPP46ck7oGvS5apx4Bd+pToQ0SkyOQ76KrCzyU/3TmXOsd7UFPQJSIikmRJ4WuA54AtemjdDlRG3/vRufTntMVBYhBbA3gbeBfYiyBs64deJ19rGeAEYG/gFuBZgvCRHo8REYnkO+h6EVgWWBmYBbwHLEpp5pxzm2Z98iKnoEtERCRFEFsOn9J+bpp9W+Cz/x7RzdE3E4QHE8Qm4LMi/zna/grwDeDzbo7bF1/Men2CcFrKNTfFr4ObShC+m6ZPBhwFNHVz7oMJwpujtuOAy4GDon1+/ZqICPkPuh4Eej3QObdr1icvUkoZLyIikiNfW2xOHq8wDfgRvrj0cyn7XsFneRyFr6OW+iFxLv4APEcQ3toP5xKREqbiyHmikS4REZEcBLFK4GJgCr6O2b1ZHP1r/IeeZ+WhZ+AzNb4A7Az8LovjPgNWSJoKOR7oJAhnZd0DP/r2MH50z+GzRE8A1iUI8xmwikgfKOjKEwVdIiIi/SSIrYMffUr4AtgY+BCfuj4O3EIQHhq1N8CA5fAZEbszCzgbPyLVk/UIwje6bAliLUDyTJ07gHrgHWBN4OWUc5yDr2O2Mr6YNMAZwB8JwtQsk8nXsejYk4H18QlNerIq/rXvClzf47mXXGM0EANmdEmkklgzF8RG4OuuHgLsBowH2gjCL5LaxoCf4APlW4Ct8eUNnkVEBiboMrPh+AWxMfwnUF045x7O+eRFSkGXiIhIPwtiqwF7AVcThAuStltGtb6CWD0+yAI4iCD8Z7R9b6A52v4OPl3+AmBMl+ssfb4VgLkE4Vdp9m2JX592bK/9WmISMCOL9r0LQov6M5IgnLdke2x5/Pqzxj5e4TFgx17afF9TLGWoy/earjL8L7djgWW6a+ecK8/65EVOQZeIiEgRyjRA67/r5Wud2khgJ3ySsu2B9Bkel3YyfnTQ8tCnnnwTP8J3AXARQfjCAF9fpKDyHXT9Gj+v+lLgUeAa4JfATHwg5oBTnXP3ZX3yIqegS0RERAAIYhPpml1xO+Bp/PTCIIMzfADsAryITwCyV9r1YEHsC/z6rnz4GP/erRI/dfK2NG2+jZ/S+Td8UDgyw3OvTxC+nnWPfH24sQSh3mdJ0ct30PUG8Ixz7gdmthzwCbC7c67FzEYATwD3OOdOz637xUtBl4iIiGQsiN0D7Bk9GwvMznpELogNB07Ep7gvw69968kZBOHvs+xp4lpj8OvH1gPu6ravPmlImMEZzwTmRedq7eG6q0fnWxtfgBuglSDcJOO+58KPWM4b0FFSGVTyHXTNBX7mnPu7mY0FvgT2cc7dFe0/ETjJObd6Tr0vYgq6REREJCtBbGxOGQ0zP/9G+Ol9dxGE5+XtOktfdwN8wWvwa+WGZ3DUaIJwbpTN8k18kNXeQ/un8DOotgUOB24Fvso4SPKJQE4HPsUnLgH4E3AaflRvv2jbJsC7BOHMjM6bKZ+sZBtgOkH4QR/PVQ8cCtyJH6H8NTAXWAlf3Hs8PvFMf5RFkAzlO+h6H/iLc+686PlM4NfOuYuj5ycCv3POjc2h70VNQZeIiIhIkiBWvtQb/SB2BvlL8Q9+3dsk4EmC8P2Uax9L7olE9iYI7+5j3yCIxfElEnrzJj6LZncjigZcCRyWfR/CgV7fNyTlO+i6E/jKOffD6PltQDUwGT/sfTXwgXOut6w3JUdBl4iIiEgG/EjY9fhsj9OyOPJI4FqyS1SyLUH4FEFsN6A/cgrcji9d8Bnw+y4ZItMJYs340ab+8i4+qBzRx/OM6rXv0if5Drq+g4+4f+icm2dmG+IL+k3EZ835Aqh1zj2ZQ9+LkpnF8fVCyoANUNAlIiIikrkgdgRwRZo9exCESwdKfr3VyfjsiE/gM2fX5Xj1f0bHzgSSR+XK8LXhDgZ+1cPxVwM3Lv4ahMcTxNbH14Q7gp6Lak8DNsux36mq8WvkFgIdUb21CfgplD8A0i3tOQG4lCCcm/XVgtiv8CUItuqxzMIQNuDFkc0shs/Aswh43Dn3ec9HlCaNdImIiIjkyK9xsj6NvgSxVYEfAhX4D8TTWY0gfD+rUgJBbD/8CFd/+BioIAh7Hq0LYjcDB/ZyrmfxI3mZr9UKYm34jJTJXsFn2/wHfurnz/EB4Yf42nWfRMeOwK+huwq/5i7Z4/jA+Rx8EPo74MGkax0LXEEQzs+4ryVuwIOuoUJBl4iIiEiRCGLl+OUtySNoZxGEZ+Z4vjH4AYRKfJKKe0k/epTOd4F/90smxCC2MvAz4HyCMLfC2kHsNOCPfe5LbrYjCKcW6NoDKu9Bl5mV4yPzXYEVgd8451qjEa/dgMecc/1bfb0IKOgSERERKTI+4cTJwFSC8OF+PncZ8Bt8+vv38Sn1U61AEH7ar9ftD0FsDeDtPp7lCuD/cjhus6FQLDvfa7omAP/Fp8CcBYwB9ojqdJXjf7hXq06XiIiIiAw6fiTqs5KYRucD0jJ8kesFwBrAPfgg8ghgNvA6sGnSUfcCpwAvL57W6IPPg/C5DW7Bp6i/Ap+C/zr86GBqLbYFwDXAKQRhZkuP/HUOwMcYdxd7DbV8B11NwCHA9/AF7D4mKo4c7T8f2MU5t1nWJy9yCrpERERERLoRxLbAr0NL9RGwAUH4ZTQttBmI4deanYFPctIAHJ90zIkE4fl57W8f5Tvo+gi40jl3upktB3xC16DrWOCPzrkJuXS+mCnoEhERERHpQRD7L7BXP5zpQ2D1Yi74nGlsUJbj+WPAWz3sHw4My/HcIiIiIiJSqoLwW/g4oxyoyuEMTcBXwMr4XBElL9eg601gix7274kfKhQRERERkaEmCB1B2EkQthOEhs8Hkez7wGosybB4N37d2XCC8Bj8OjGA06N1aSUt19Goy4E/mdmDwP3RNmdmI/HZXb4FHNX37omIiIiISMkLwm93s+dXpC9M3QgcDXwTqGFJzFGSch3pugBfkfsG4LVo2/X4YcDTgL8759JVHBcREREREelZEL7EkvprpxWyK/2hT8WRzewb+JSO6+EDuDeBm51z/VsfoYgokYaIiIiIyAAIYusAb0TP1iYIe8opURCZxgZ9SnbhnHsUeLQv5xAREREREVlKEL5JEJsOrA3sA1xU4B7lLNfphSIiIiIiIvl2ZfT1OwXtRR9lPL3QzO7I8tzOObdf9l0qTmYWB+L4QHUDNL1QRERERCS/gtj6wKvAAmAFgjAscI+66PfiyGbWCczFV5POJG2jc86tndHJS4jWdImIiIiIDKAg1o4f9DiYILy50N1Jlo81Xe8DqwKf4jMV3uic+6hPvRQREREREenZf/BB175AUQVdmcp4TZdzbnVgV+B54AzgXTO7z8wON7Nx+eqgiIiIiIgMaYllTrUEsT4lAiyUrBJpOOcecs4dDayETxX/GXAx8LGZ3WpmB0QFkkVERERERPrDE8DnwESguyLLRS2n7IXOuQXOuX875w4GJuGrRa8E3ASc2o/9ExERERGRoSwIFwIt0bNdCtiTnPUpZXw0qrUXsB+wOT7RRkffuyUiIiIiIrLYrdHXnQraixxlHXSZWZmZ7WVmVwEzgBuA0cCRwIrOuWv6t4siIiIiIjLEPRV93ZIgVnLLmTJeiGZmOwA/Ag4ElgOeBE4HbnbOfZqf7omIiIiIiDAdmAlMALYGHi1kZ7KVTfaPR4E5wF340a2OaPsaZrZGugOcc8/1qXciIiIiIiJB6AhiU/FLmzZkEAdd4KcRfh/4Xi/tDHBAeS6dEhERERERSfEyPujauNAdyVY2QdfheeuFiIiIiIhIz16Ivm5X0F7kIOOgyzk3JZ8dERERERER6cFj0ddNCWIjCML5Be1NFvqUMl5ERERERGSATMcXSR6BL1dVMhR0iYiIiIhI8QtCx5LU8VsUsivZUtAlIiIiIiKl4sXoa2VBe5ElBV0iIiIiIlIqXsMHXh8XuiPZMOdcofsw4MysA/gS6AS+cM7tmsWx44EQiDnnvsxPD0VEREREpNhlGhtkW6drMNnBOTer0J0QEREREZHBTdMLRURERERE8qjogi4z29nM/mNmH5iZM7P907SJm1mHmc01s6lmtk2Wl3HAQ2b2tJkd0i8dFxERERERSaMYpxeOwVebvhK4NXWnmR0MnAfUAVOBE4B7zGwD59zHUZtppH9tezrnPgC+4Zx738xWBu4zs1bn3Itp2ouIiIiIiPRJUSfSMDMHfNc5d3vStqnA086546LnZcC7wEXOuYYcrnEu8LJz7qpu9o8ERiZtGge8B6wGfJXt9UREREREZNBIxAaDJ5GGmY0AtgTOTmxzznWa2X3A9hmeYwxQ5pz7yszGAjXAzT0cchpwZprt72XccRERERERGczG4bOjp1VSQRewPFAOzEjZPoPMC6RNAm4zM6JzXeace7qH9mfjpzMmWxb4PMPr5ZNG3SRbumckW7pnJFu6ZyRbumckW8V2z4wDPuipQakFXX3mnJsObJpF+3nAvJTNRVGfKwocAb5SzTDJhO4ZyZbuGcmW7hnJlu4ZyVYR3jO99qHoshf24lNgEX60Ktkk4KOB746IiIiIiEjPSirocs7NB54FdktsixJp7AY8Uah+iYiIiIiIdKfophdGyS3WTdq0lpltBnzunHsHv75qipk9AzyFTxk/BvjHAHe1GMwDfsvS0x9FuqN7RrKle0aypXtGsqV7RrJVcvdM0aWMN7NdgAfS7JrinDssanMccAqwEjAN+JlzburA9FBERERERCRzRRd0iYiIiIiIDCYltaZLRERERESk1CjoEhERERERySMFXSIiIiIiInmkoKuEmVnczDrMbK6ZTTWzbQrdJ8kvMzvNzJ42s6/M7GMzu93MNkhpM8rMGs3sMzObZWb/MrNJKW3WMLNmM5sdnedcMxuW0mYXM3vOzOaZ2RtmdtgAvETJMzOrNzNnZucnbdM9I12Y2apmdm10T8wxs1Yz2yppv5nZWWb2YbT/PjNbL+Ucy5rZdWb2pZnNNLMrogzFyW02MbNHor9j75rZqQP1GqX/mFm5mf3OzN6K7oc3zewMS6pgq3tmaDOznc3sP2b2QfQ3aP+U/QN2f5jZgWbWHrVpNbO98/KiUyjoKlFmdjA+ff5vgS2AF4B7zGzFgnZM8u2bQCOwHbAHMBy418zGJLX5K7AvcGDUfhXg1sROMysHmoERwA7AZOAw4KykNmtFbR4ANgPOBy43s73y8qpkQJjZ1sDRwIspu3TPyGJmNhF4DFgAfBvYEPgF8EVSs1OBnwF1wLbA1/i/QaOS2lwHbIT/XbUPsDPw96TrjAfuBd4GtsRnJQ7M7Ki8vDDJp18CxwDHAVXR81OB45Pa6J4Z2sbg36vGu9k/IPeHme0A3ABcAWwO3A7cbmYb9/kV9sY5p0cJPoCpwMVJz8uA94H6QvdNjwG9D1YAHLBz9DwGzAcOSGpTGbXZLnr+bWARMCmpTR0QAiOi538CXkq51o3Afwv9mvXI+V4ZC7wG7A48CJyve0aPbu6VBuCRHvYb8CFwctK2GDAX+EH0vCq6h7ZKavMtoBNYJXp+DPB54h5KunZ7of8N9Mj6nrkTuCJl27+Aa3XP6JHmfnHA/knPB+z+AG4C7kzpz5NAU75ft0a6SpCZjcBH8PcltjnnOqPn2xeqX1IQsejr59HXLfGjX8n3RjvwDkvuje2BVufcjKTz3AOMx3+ClGhzH13dg+6vUtYINDvnUn+uumck1XeAZ8zsn9FU0ufN7Mik/Wvh62Qm3zMh/sPA5HtmpnPumaTj7sO/Qdo2qc3Dzrn5SW3uATaIRtukdDwO7GZm6wOY2abAN4C7o/26Z6QnA3l/FOxvlYKu0rQ8UA7MSNk+A3/TyhBgZmX4KVyPOedeijavBMx3zs1MaZ58b6xE+nuHDNqMN7PRfeu5DDQz+wF+GvJpaXbrnpFUa+M/MX4d2Au4BLjQzCZH+xM/857+Bq0EfJy80zm3EP8BUTb3lZSGBvzIdruZLQCex4+mXxft1z0jPRnI+6O7Nnm/f4b13kREilQjsDH+00SRtMxsdeACYA/n3NxC90dKQhnwjHPu9Oj589F6hzpgSuG6JUXsIOAQ4EfAy0TrOs3sA+ec7hkRNNJVqj4lWl+Rsn0S8NHAd0cGmpldjF9Euqtz7r2kXR8BI8xsQsohyffGR6S/d8igzZfOuTl96LoMvC2BFYHnzGyhmS3EJ8v4WfT9DHTPSFcfAq+kbGsD1oi+T/zMe/ob9BH+vlvMfLbLZcnuvpLScC7Q4Jy70TnX6py7Bp+gJzG6rntGejKQ90d3bfJ+/yjoKkHRXNVngd0S26KpZrsBTxSqX5J/UUrVi4HvAjXOubdSmjyLzziWfG9sgH+zlLg3ngCqUzJd7gF8yZI3Wk8knyOpje6v0nM/UI3/5DnxeAafBSrxve4ZSfYYsEHKtvXxGcEA3sK/QUm+Z8bj11Uk3zMTzGzLpHPU4N93TE1qs7OZDU9qswfwqnMuOVOiFL9l8Gtrki1iyftM3TPSk4G8Pwr3t6rQGUz0yO0BHIzP6jIZn9HlUnw630mF7pseef25/w2YiR+pWCnpMTqpzSX4N0e74kc5HgceT9pfDrTiF45uil+z8THwx6Q2a+HTtZ6Dz2R3LLAQ2KvQ/wZ69Mt99CBR9kLdM3qkuT+2xgfipwPr4qeMfQ0cktTml9HfnO/gg/rbgenAqKQ2dwPPAdsAO+KzZ16ftD+Gf6N1NT4hy8HRdY4q9L+BHlnfM1cB7wG1QAX+g8FPgD/pntEj+tmNZckHfw44Mfp+jYG8P/BlTxbgy2BUAgE+g+/Gef83KPQPQY8+/PB8PYy3gXn4KH/bQvdJj7z/zF03j8OS2ozCr/f6PPplcyuwUsp51gTuAmZHfxj/DAxLabMLfjH0PODN5GvoUdoPlg66dM/okXqP7IMPtOfipxYembLf8HXaPora3Aesn9JmWeB64Ct8eYErgbEpbTYBHonO8R7wy0K/dj1yul/G4RM7vQ3Mif7//56uqbt1zwzhR/T3Id37l6sG+v7A16R8Nfpb9RKw90D8G1h0cREREREREckDrekSERERERHJIwVdIiIiIiIieaSgS0REREREJI8UdImIiIiIiOSRgi4REREREZE8UtAlIiIiIiKSRwq6RERERERE8khBl4iIiIiISB4p6BIREckzM9vFzJyZ7VLovoiIyMBT0CUiIiXHzA6Lgpitoud7m1lQ4G5hZsea2WGF7oeIiBQXBV0iIjIY7A2cWehOAMcCh6XZ/jAwOvoqIiJDjIIuERGRNMwb3R/ncs51OufmOuc6++N8IiJSWhR0iYhISTOzq4B49L1LPJL2l5nZCWb2spnNNbMZZnapmU1MOU+Hmd1pZnuZ2TPAHODoaN/hZtZiZh+b2Twze8XMjkk9HtgI+GZSPx6M9qVd02VmB5rZs2Y2x8w+NbNrzWzV1NdnZrPMbFUzuz36/hMz+7OZlffHv6GIiOTXsEJ3QEREpI8uBVYB9gAO7Wb/YcA/gAuBtYDjgM3NbEfn3IKkthsAN0THXAa8Gm0/BngZuANYCOwL/M3MypxzjVGbE4CLgFnAH6JtM7rrdLT26x/A08BpwCTg58COZra5c25mUvNy4B5gKnAysDvwC+BN4JLuriEiIsXBnHO9txIRESkiSQHL1s65Z8zsYiDunLOUdt8AHgEOcc5dn7R9L+C/ydujkao1gW855+5JOc9o59yclG3/BdZzzq2TtO0l4FPn3C4pbXcBHgB2dc49aGbDgfeAj6PXMDdqVwvcCZzlnDsz2nYVMBn4jXPud0nnfA7odM5tleE/m4iIFIimF4qIyGB2IBAC/zOz5RMP4Fn8iNSuKe3fSg24AJIDLjOLRed4CFjbzGI59GsrYEXgb4mAK7pOM9AO1KY5pinl+SPA2jlcW0REBpimF4qIyGC2HhDDjyils2LK87fSNTKzHYHfAtsDy6TsjuEDu2ysGX19Nc2+duAbKdvmOuc+Sdn2BTAREREpegq6RERkMCvDB1yHdLM/NZCZk9rAzNYB7scHQycB7wLz8WnqT2RgZo0sGoBriIhInijoEhGRwaC7Bcpv4pNOPJa6JisL+wIjge84595JbDSz1KmJPfUj1dvR1w2AlpR9GyTtFxGRQUBrukREZDD4GsDMJqRsvxmf+e+M1APMbFia9ukkRpkWJ+mI1nEd3k0/MjnnM/gRuDozG5l03m8DVUBzBucQEZESoZEuEREZDJ6Nvl5oZvcAi5xzNzrnHjKzS4HTzGwz4F5gAX6t14H4FO239HLue/HTCf8TnWsscCQ+aFo5TT+OMbNfA28AHzvnUkeycM4tMLNf4jMwPmRmN7AkZXwH8NdsXryIiBQ3BV0iIjIY3IqvkfUD4Mf4UakbAZxzdWb2LL7Q8R/xdbY6gGuBx3o7sXPuVTM7APg98GfgI3xtrE+AK1Oan4VPknEqMA6f4XCpoCs671VmNhuoB/6EHyW7DfhlSo0uEREpcarTJSIiIiIikkda0yUiIiIiIpJHCrpERERERETySEGXiIiIiIhIHinoEhERERERySMFXSIiIiIiInmkoEtERERERCSPFHSJiIiIiIjkkYIuERERERGRPFLQJSIiIiIikkcKukRERERERPJIQZeIiIiIiEgeKegSERERERHJo/8H1zkF5GUCe1cAAAAASUVORK5CYII=",
+      "text/plain": [
+       "<Figure size 1000x500 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Compare convergence speed of both methods\n",
+    "names = [\"Permutation\", \"MSR\", \"Uniform\", \"Antithetic\", \"RandomHierarchical\"]\n",
+    "all_values = [\n",
+    "    history_permutation._memory,\n",
+    "    history_msr._memory,\n",
+    "    history_uniform._memory,\n",
+    "    history_antithetic._memory,\n",
+    "    history_random._memory,\n",
+    "]\n",
+    "distances = [[] for _ in names]\n",
+    "moving_avgs = []\n",
+    "\n",
+    "for sampler_id, name in enumerate(names):\n",
+    "    for iteration in range(max_checks):\n",
+    "        abs_dist = np.abs(\n",
+    "            all_values[sampler_id][:, iteration]\n",
+    "            - all_values[sampler_id][:, iteration + 1]\n",
+    "        )\n",
+    "        if abs_dist.max() == 0.0:\n",
+    "            distances[sampler_id].append(0.0)\n",
+    "        else:\n",
+    "            distances[sampler_id].append(np.mean(abs_dist[abs_dist > 0]))\n",
+    "    moving_avgs.append(\n",
+    "        np.convolve(\n",
+    "            distances[sampler_id], np.ones(moving_avg) / moving_avg, mode=\"same\"\n",
+    "        )\n",
+    "    )\n",
+    "\n",
+    "fig, ax = plt.subplots(1, 1, figsize=(10, 5))\n",
+    "for sampler_id, name in enumerate(names):\n",
+    "    ax.plot(list(range(max_checks)), moving_avgs[sampler_id], label=name)\n",
+    "ax.set_xlabel(\"Iteration\")\n",
+    "ax.set_ylabel(\"Mean semivalue change between iterations\")\n",
+    "ax.set_title(\"Convergence speed of different samplers\")\n",
+    "plt.yscale(\"log\")\n",
+    "plt.ylim(1e-5, 1)\n",
+    "plt.legend()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The plot above visualizes the convergence speed of different samplers used for Banzhaf semivalue calculation. /It shows the average magnitude of how much the semivalues are updated in every step of the algorithm. \n",
+    "\n",
+    "As you can see, **MSR Banzhaf** stabilizes much faster. After 1000 iterations (subsets sampled and evaluated with the utility), Permutation Monte Carlo Banzhaf has evaluated the marginal function about 5 times per data point (we are using 200 data points). For MSR, the semivalue of each data point was updated 1000 times. Due to this, the values converge much faster wrt. the number of utility evaluations, which is the key advantage of MSR sampling. \n",
+    "\n",
+    "MSR sampling does come at a cost, however, which is that the updates to the semivalues are more noisy than in other methods.  We will analyze the impact of this tradeoff in the next sections. First, let us look at how similar all the computed semivalues are. They are all Banzhaf values, so in a perfect world, all samplers should result in the exact same semivalues. However, due to randomness in the utility (recall that we use a neural network) and randomness in the samplers, the resulting values are likely never exactly the same. Another quality measure is that a good sampler would lead to very consistent values, a bad one to less consistent values. Let us first examine how similar the results are, then we'll look at consistency."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Similarity of the semivalues computed using different samplers"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "metadata": {
+    "tags": [
+     "hide"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "names = [\"Permutation\", \"MSR\", \"Uniform\", \"Antithetic\", \"RandomHierarchical\"]\n",
+    "values = [\n",
+    "    permutation_values,\n",
+    "    msr_values,\n",
+    "    uniform_values,\n",
+    "    antithetic_values,\n",
+    "    random_values,\n",
+    "]\n",
+    "top_consistency = np.zeros((len(names), len(names)))\n",
+    "low_consistency = np.zeros((len(names), len(names)))\n",
+    "twenty_percent = training_data[0].shape[0] // 5\n",
+    "\n",
+    "for sampler1_id, sampler1_values in enumerate(values):\n",
+    "    for sampler2_id, sampler2_values in enumerate(values):\n",
+    "        sampler1_values.sort(key=\"value\")\n",
+    "        sampler2_values.sort(key=\"value\")\n",
+    "        top_20_1 = set(sampler1_values.indices[-twenty_percent:].tolist())\n",
+    "        lower_20_1 = set(sampler1_values.indices[:twenty_percent].tolist())\n",
+    "        top_20_2 = set(sampler2_values.indices[-twenty_percent:].tolist())\n",
+    "        lower_20_2 = set(sampler2_values.indices[:twenty_percent].tolist())\n",
+    "        top_consistency[sampler1_id, sampler2_id] = len(top_20_1.intersection(top_20_2))\n",
+    "        low_consistency[sampler1_id, sampler2_id] = len(\n",
+    "            lower_20_1.intersection(lower_20_2)\n",
+    "        )"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "metadata": {
+    "tags": [
+     "hide-input",
+     "invertible-output"
+    ]
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x500 with 3 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fix, axes = plt.subplots(1, 2, figsize=(10, 5))\n",
+    "axes[0].matshow(top_consistency, vmin=0, vmax=twenty_percent)\n",
+    "mat2 = axes[1].matshow(low_consistency, vmin=0, vmax=twenty_percent)\n",
+    "axes[0].set_xticks(np.arange(len(names)), names, rotation=90)\n",
+    "axes[1].set_xticks(np.arange(len(names)), names, rotation=90)\n",
+    "for (i, j), z in np.ndenumerate(top_consistency):\n",
+    "    axes[0].text(j, i, f\"{int(z)}\", ha=\"center\", va=\"center\", c=\"white\")\n",
+    "for (i, j), z in np.ndenumerate(low_consistency):\n",
+    "    axes[1].text(j, i, f\"{int(z)}\", ha=\"center\", va=\"center\", c=\"white\")\n",
+    "\n",
+    "axes[0].set_yticks(np.arange(len(names)), names)\n",
+    "axes[1].set_yticks(np.arange(len(names)), names)\n",
+    "axes[0].set_title(\"Top 20% of points\")\n",
+    "axes[1].set_title(\"Low 20% of points\")\n",
+    "fig.colorbar(mat2)\n",
+    "plt.suptitle(\"Overlapping high and low value points between samplers\")\n",
+    "plt.tight_layout()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This plot shows that the samplers lead to quite different Banzhaf semivalues, however, all of them have some points in common. The MSR Sampler does not seem to be significantly worse than any others. \n",
+    "\n",
+    "In an ideal setting without randomness, the overlap of points would be higher, however, the stochastic nature of the CNN model that we use together with the fact that we use only 200 data points for training, might overshadow these results. As a matter of fact we have the rather discouraging following result:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "metadata": {
+    "tags": [
+     "hide-input"
+    ]
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Total number of top 20 points that all samplers have in common: 0\n"
+     ]
+    }
+   ],
+   "source": [
+    "all_in_common = set(permutation_values.indices.tolist())\n",
+    "for sampler_id, sampler_values in enumerate(values):\n",
+    "    sampler_values.sort(key=\"value\")\n",
+    "    top_20 = set(sampler_values.indices[-twenty_percent:].tolist())\n",
+    "    all_in_common = all_in_common.intersection(top_20)\n",
+    "print(\n",
+    "    f\"Total number of top 20 points that all samplers have in common: {len(all_in_common)}\"\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Consistency of the semivalues\n",
+    "\n",
+    "Finally, we want to analyze how consistent the semivalues returned by the different samplers are. In order to do this, we compute semivalues multiple times and check how many of the data points in the top and lowest 20% of valuation of the data overlap."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "metadata": {
+    "tags": [
+     "hide"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "max_checks = [1000, 2000, 3000] if not is_CI else [1]\n",
+    "num_retries = 3 if not is_CI else 1\n",
+    "num_samplers = 5\n",
+    "twenty_percent = training_data[0].shape[0] // 5"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "metadata": {
+    "tags": [
+     "hide"
+    ]
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "  0%|          | 0/45 [00:00<?, ?it/s]PermutationSampler requires caching to be enabled or computation will be doubled wrt. a 'direct' implementation of permutation MC\n",
+      "  2%|▏         | 1/45 [01:33<1:08:51, 93.89s/it]PermutationSampler requires caching to be enabled or computation will be doubled wrt. a 'direct' implementation of permutation MC\n",
+      "  4%|▍         | 2/45 [03:05<1:06:34, 92.90s/it]PermutationSampler requires caching to be enabled or computation will be doubled wrt. a 'direct' implementation of permutation MC\n",
+      "  7%|▋         | 3/45 [04:37<1:04:48, 92.58s/it]PermutationSampler requires caching to be enabled or computation will be doubled wrt. a 'direct' implementation of permutation MC\n",
+      "  9%|▉         | 4/45 [07:35<1:17:51, 113.95s/it]PermutationSampler requires caching to be enabled or computation will be doubled wrt. a 'direct' implementation of permutation MC\n",
+      " 11%|█         | 5/45 [10:33<1:24:31, 126.79s/it]PermutationSampler requires caching to be enabled or computation will be doubled wrt. a 'direct' implementation of permutation MC\n",
+      " 13%|█▎        | 6/45 [13:33<1:28:07, 135.57s/it]PermutationSampler requires caching to be enabled or computation will be doubled wrt. a 'direct' implementation of permutation MC\n",
+      " 16%|█▌        | 7/45 [18:04<1:38:05, 154.88s/it]PermutationSampler requires caching to be enabled or computation will be doubled wrt. a 'direct' implementation of permutation MC\n",
+      " 18%|█▊        | 8/45 [22:35<1:44:27, 169.39s/it]PermutationSampler requires caching to be enabled or computation will be doubled wrt. a 'direct' implementation of permutation MC\n",
+      " 20%|██        | 9/45 [27:06<1:48:25, 180.70s/it]/home/jakob/Documents/pyDVL/venv/lib/python3.10/site-packages/numpy/core/_methods.py:118: RuntimeWarning: invalid value encountered in reduce\n",
+      "  ret = umr_sum(arr, axis, dtype, out, keepdims, where=where)\n",
+      "100%|██████████| 45/45 [2:05:31<00:00, 167.37s/it]"
+     ]
+    }
+   ],
+   "source": [
+    "results = [[[] for i in range(num_samplers)] for _ in max_checks]\n",
+    "pbar = tqdm(total=len(max_checks) * num_retries * 5)\n",
+    "for sampler_index, sampler in enumerate(\n",
+    "    [\n",
+    "        PermutationSampler,\n",
+    "        MSRSampler,\n",
+    "        UniformSampler,\n",
+    "        AntitheticSampler,\n",
+    "        RandomHierarchicalSampler,\n",
+    "    ]\n",
+    "):\n",
+    "    for check_index, max_check in enumerate(max_checks):\n",
+    "        for retry in range(num_retries):\n",
+    "            _, vals = get_semivalues_and_history(\n",
+    "                sampler, max_checks=max_check, progress=False\n",
+    "            )\n",
+    "            results[check_index][sampler_index].append(vals)\n",
+    "            pbar.n = (\n",
+    "                sampler_index * (len(max_checks) * num_retries)\n",
+    "                + check_index * num_retries\n",
+    "                + retry\n",
+    "                + 1\n",
+    "            )\n",
+    "            pbar.refresh()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "metadata": {
+    "tags": [
+     "hide"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "# Extract results from experiments\n",
+    "\n",
+    "plot_results_top = [[] for _ in range(num_samplers)]\n",
+    "plot_results_low = [[] for _ in range(num_samplers)]\n",
+    "for check_index, _ in enumerate(max_checks):\n",
+    "    for sampler_index, sampler in enumerate(\n",
+    "        [\n",
+    "            PermutationSampler,\n",
+    "            MSRSampler,\n",
+    "            UniformSampler,\n",
+    "            AntitheticSampler,\n",
+    "            RandomHierarchicalSampler,\n",
+    "        ]\n",
+    "    ):\n",
+    "        value_list = results[check_index][sampler_index]\n",
+    "        top_20 = None\n",
+    "        lower_20 = None\n",
+    "        for vals in value_list:\n",
+    "            vals.sort(key=\"value\")\n",
+    "            if top_20 is None:\n",
+    "                top_20 = set(vals.indices[-twenty_percent:].tolist())\n",
+    "                lower_20 = set(vals.indices[:twenty_percent].tolist())\n",
+    "            else:\n",
+    "                top_20 = top_20.intersection(\n",
+    "                    set(vals.indices[-twenty_percent:].tolist())\n",
+    "                )\n",
+    "                lower_20 = lower_20.intersection(\n",
+    "                    set(vals.indices[:twenty_percent].tolist())\n",
+    "                )\n",
+    "        plot_results_top[sampler_index].append(len(top_20))\n",
+    "        plot_results_low[sampler_index].append(len(lower_20))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "metadata": {
+    "tags": [
+     "hide-input",
+     "invertible-output"
+    ]
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 800x800 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Plot results\n",
+    "\n",
+    "fig, axes = plt.subplots(2, 1, figsize=(8, 8))\n",
+    "for sampler_index, name in enumerate(names):\n",
+    "    axes[0].plot(max_checks, plot_results_top[sampler_index], label=name)\n",
+    "    axes[1].plot(max_checks, plot_results_low[sampler_index], label=name)\n",
+    "axes[0].legend()\n",
+    "axes[1].legend()\n",
+    "axes[0].set_xlabel(\"Number of iterations\")\n",
+    "axes[1].set_xlabel(\"Number of iterations\")\n",
+    "axes[0].set_ylabel(f\"Number of common points \\nin top 20% in {num_retries} runs\")\n",
+    "axes[1].set_ylabel(f\"Number of common points \\nin lower 20% in {num_retries} runs\")\n",
+    "fig.suptitle(f\"Evaluation of consistency of samplers (Max value: {twenty_percent})\")\n",
+    "fig.tight_layout()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Conclusion\n",
+    "\n",
+    "MSR sampling updates the semivalue estimates for every index in the sample, much more frequently than any other sampler available, which leads to much **faster convergence**. Additionally, the sampler is more consistent with its value estimates than the other samplers, which might be caused by the higher number of value updates.  \n",
+    "\n",
+    "There is alas no general recommendation. It is best to try different samplers when computing semivalues and test which one is best suited for your use case. Nevertheless, the MSR sampler seems like a more efficient sampler which may bring fast results and is well-suited for stochastic models."
+   ]
+  }
+ ],
+ "metadata": {
+  "celltoolbar": "Edit Metadata",
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.19"
+  },
+  "vscode": {
+   "interpreter": {
+    "hash": "4e000971326892723e7f31ded70802f690c31c3620f59a0f99e594aaee3047ef"
+   }
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/notebooks/support/banzhaf.py b/notebooks/support/banzhaf.py
new file mode 100644
index 000000000..f144494b0
--- /dev/null
+++ b/notebooks/support/banzhaf.py
@@ -0,0 +1,114 @@
+from typing import Optional
+
+import numpy as np
+from numpy.typing import NDArray
+from sklearn.datasets import load_digits
+from sklearn.metrics import accuracy_score
+from sklearn.model_selection import train_test_split
+
+from pydvl.utils.types import SupervisedModel
+
+try:
+    import torch
+    from torch import nn, optim
+    from torch.utils.data import DataLoader, TensorDataset
+except ImportError as e:
+    raise RuntimeError("PyTorch is required to run the Banzhaf MSR notebook") from e
+
+
+def load_digits_dataset(
+    test_size: float, val_size: float = 0.0, random_state: Optional[int] = None
+):
+    """Loads the sklearn handwritten digits dataset. More info can be found at
+    https://scikit-learn.org/stable/datasets/toy_dataset.html#optical-recognition-of-handwritten-digits-dataset.
+
+    :param test_size: fraction of points used for test dataset
+    :param val_size: fraction of points used for training dataset
+    :param random_state: fix random seed. If None, no random seed is set.
+    :return: A tuple of three elements with the first three being input and
+        target values in the form of matrices of shape (N,8,8) the first
+        and (N,) the second.
+    """
+
+    digits_bunch = load_digits(as_frame=True)
+    x, x_test, y, y_test = train_test_split(
+        digits_bunch.data.values / 16.0,
+        digits_bunch.target.values,
+        train_size=1 - test_size,
+        random_state=random_state,
+    )
+    if val_size > 0:
+        x_train, x_val, y_train, y_val = train_test_split(
+            x, y, train_size=(1 - val_size) / (1 - test_size), random_state=random_state
+        )
+    else:
+        x_train, y_train = x, y
+        x_val, y_val = None, None
+
+    return ((x_train, y_train), (x_val, y_val), (x_test, y_test))
+
+
+class TorchCNNModel(SupervisedModel):
+    def __init__(
+        self,
+        lr: float = 0.001,
+        epochs: int = 40,
+        batch_size: int = 32,
+        device: str = "cuda" if torch.cuda.is_available() else "cpu",
+    ):
+        self.lr = lr
+        self.batch_size = batch_size
+        self.epochs = epochs
+        self.device = device
+        self.model = nn.Sequential(
+            nn.Conv2d(
+                out_channels=8, in_channels=1, kernel_size=(3, 3), padding="same"
+            ),
+            nn.Conv2d(
+                out_channels=4, in_channels=8, kernel_size=(3, 3), padding="same"
+            ),
+            nn.MaxPool2d(kernel_size=2),
+            nn.Flatten(),
+            nn.Linear(in_features=64, out_features=32),
+            nn.Linear(in_features=32, out_features=10),
+            nn.Softmax(dim=1),
+        )
+        self.loss = nn.CrossEntropyLoss()
+        self.optimizer = optim.Adam(self.model.parameters(), lr=lr)
+        self.model.to(device)
+
+    def fit(self, x: NDArray, y: NDArray) -> None:
+        torch_dataset = TensorDataset(
+            torch.tensor(
+                np.reshape(x, (x.shape[0], 1, 8, 8)),
+                dtype=torch.float,
+                device=self.device,
+            ),
+            torch.tensor(y, device=self.device),
+        )
+        torch_dataloader = DataLoader(torch_dataset, batch_size=self.batch_size)
+        for epoch in range(self.epochs):
+            for features, labels in torch_dataloader:
+                pred = self.model(features)
+                loss = self.loss(pred, labels)
+                loss.backward()
+                self.optimizer.step()
+
+    def predict(self, x: NDArray) -> NDArray:
+        pred = self.model(
+            torch.tensor(
+                np.reshape(x, (x.shape[0], 1, 8, 8)),
+                dtype=torch.float,
+                device=self.device,
+            )
+        )
+        pred = torch.argmax(pred, dim=1)
+        return pred.cpu().numpy()
+
+    def score(self, x: NDArray, y: NDArray) -> float:
+        pred = self.predict(x)
+        acc = accuracy_score(pred, y)
+        return acc
+
+    def get_params(self, deep: bool = False):
+        return {"lr": self.lr, "epochs": self.epochs}
diff --git a/src/pydvl/reporting/plots.py b/src/pydvl/reporting/plots.py
index 7c0f19b73..8f72ccf27 100644
--- a/src/pydvl/reporting/plots.py
+++ b/src/pydvl/reporting/plots.py
@@ -240,6 +240,7 @@ def plot_shapley(
     title: Optional[str] = None,
     xlabel: Optional[str] = None,
     ylabel: Optional[str] = None,
+    prefix: Optional[str] = "data_value",
 ) -> plt.Axes:
     r"""Plots the shapley values, as returned from
     [compute_shapley_values][pydvl.value.shapley.common.compute_shapley_values],
@@ -260,9 +261,9 @@ def plot_shapley(
     if ax is None:
         _, ax = plt.subplots()
 
-    yerr = norm.ppf(1 - level / 2) * df["data_value_stderr"]
+    yerr = norm.ppf(1 - level / 2) * df[f"{prefix}_stderr"]
 
-    ax.errorbar(x=df.index, y=df["data_value"], yerr=yerr, fmt="o", capsize=6)
+    ax.errorbar(x=df.index, y=df[prefix], yerr=yerr, fmt="o", capsize=6)
     ax.set_xlabel(xlabel)
     ax.set_ylabel(ylabel)
     ax.set_title(title)
diff --git a/src/pydvl/value/result.py b/src/pydvl/value/result.py
index bd6a78d10..76a7ff28b 100644
--- a/src/pydvl/value/result.py
+++ b/src/pydvl/value/result.py
@@ -41,6 +41,7 @@
 samples random values uniformly.
 
 """
+
 from __future__ import annotations
 
 import collections.abc
@@ -676,12 +677,15 @@ def to_dataframe(
         column = column or self._algorithm
         df = pd.DataFrame(
             self._values[self._sort_positions],
-            index=self._names[self._sort_positions]
-            if use_names
-            else self._indices[self._sort_positions],
+            index=(
+                self._names[self._sort_positions]
+                if use_names
+                else self._indices[self._sort_positions]
+            ),
             columns=[column],
         )
         df[column + "_stderr"] = self.stderr[self._sort_positions]
+        df[column + "_updates"] = self.counts[self._sort_positions]
         return df
 
     @classmethod
diff --git a/src/pydvl/value/sampler.py b/src/pydvl/value/sampler.py
index a63519d1d..3b915f5b7 100644
--- a/src/pydvl/value/sampler.py
+++ b/src/pydvl/value/sampler.py
@@ -44,6 +44,9 @@
       Frank, and Geoffrey Holmes. [Sampling Permutations for Shapley Value
       Estimation](http://jmlr.org/papers/v23/21-0439.html). Journal of Machine
       Learning Research 23, no. 43 (2022): 1–46.
+[^2]: <a name="wang_data_2023"></a>Wang, J.T. and Jia, R., 2023.
+    [Data Banzhaf: A Robust Data Valuation Framework for Machine Learning](https://proceedings.mlr.press/v206/wang23e.html).
+    In: Proceedings of The 26th International Conference on Artificial Intelligence and Statistics, pp. 6388-6421.
 
 """
 
@@ -72,13 +75,16 @@
 
 __all__ = [
     "AntitheticSampler",
-    "DeterministicUniformSampler",
     "DeterministicPermutationSampler",
+    "DeterministicUniformSampler",
+    "MSRSampler",
     "PermutationSampler",
     "PowersetSampler",
     "RandomHierarchicalSampler",
-    "UniformSampler",
+    "SampleT",
+    "StochasticSampler",
     "StochasticSamplerMixin",
+    "UniformSampler",
 ]
 
 SampleT = Tuple[IndexT, NDArray[IndexT]]
@@ -312,6 +318,26 @@ def weight(cls, n: int, subset_len: int) -> float:
         return float(2 ** (n - 1)) if n > 0 else 1.0
 
 
+class MSRSampler(StochasticSamplerMixin, PowersetSampler[IndexT]):
+    """An iterator to perform sampling of random subsets.
+
+    This sampler does not return any index, it only returns subsets of the data.
+    This sampler is used in (Wang et. al.)<sup><a href="wang_data_2023">2</a></sup>.
+    """
+
+    def __iter__(self) -> Iterator[SampleT]:
+        if len(self) == 0:
+            return
+        while True:
+            subset = random_subset(self.indices, seed=self._rng)
+            yield None, subset
+            self._n_samples += 1
+
+    @classmethod
+    def weight(cls, n: int, subset_len: int) -> float:
+        return 1.0
+
+
 class AntitheticSampler(StochasticSamplerMixin, PowersetSampler[IndexT]):
     """An iterator to perform uniform random sampling of subsets, and their
     complements.
@@ -450,4 +476,5 @@ def weight(cls, n: int, subset_len: int) -> float:
     PermutationSampler[IndexT],
     AntitheticSampler[IndexT],
     RandomHierarchicalSampler[IndexT],
+    MSRSampler[IndexT],
 ]
diff --git a/src/pydvl/value/semivalues.py b/src/pydvl/value/semivalues.py
index 1407086a8..46cd1f16e 100644
--- a/src/pydvl/value/semivalues.py
+++ b/src/pydvl/value/semivalues.py
@@ -81,44 +81,43 @@
     [Beta Shapley: A Unified and Noise-reduced Data Valuation Framework for Machine Learning](https://arxiv.org/abs/2110.14049).
     In: Proceedings of the 25th International Conference on Artificial Intelligence and Statistics (AISTATS) 2022, Vol. 151. PMLR, Valencia, Spain.
 
-[^3]: <a name="wang_data_2022"></a>Wang, J.T. and Jia, R., 2022.
-    [Data Banzhaf: A Robust Data Valuation Framework for Machine Learning](https://arxiv.org/abs/2205.15466).
-    ArXiv preprint arXiv:2205.15466.
+[^3]: <a name="wang_data_2023"></a>Wang, J.T. and Jia, R., 2023.
+    [Data Banzhaf: A Robust Data Valuation Framework for Machine Learning](https://proceedings.mlr.press/v206/wang23e.html).
+    In: Proceedings of The 26th International Conference on Artificial Intelligence and Statistics, pp. 6388-6421.
 """
 from __future__ import annotations
 
 import logging
 import math
 import warnings
+from abc import ABC, abstractmethod
 from concurrent.futures import FIRST_COMPLETED, Future, wait
 from enum import Enum
 from itertools import islice
-from typing import Iterable, List, Optional, Protocol, Tuple, Type, cast
+from typing import Any, Iterable, List, Optional, Protocol, Tuple, Type, cast
 
 import numpy as np
 import scipy as sp
 from deprecate import deprecated
 from tqdm import tqdm
 
-from pydvl.parallel import (
-    ParallelBackend,
-    _maybe_init_parallel_backend,
-    init_parallel_backend,
-)
+from pydvl.parallel import ParallelBackend, _maybe_init_parallel_backend
 from pydvl.parallel.config import ParallelConfig
 from pydvl.utils import Utility
 from pydvl.utils.types import IndexT, Seed
 from pydvl.value import ValuationResult
 from pydvl.value.sampler import (
+    MSRSampler,
     PermutationSampler,
     PowersetSampler,
     SampleT,
     StochasticSampler,
 )
-from pydvl.value.stopping import MaxUpdates, StoppingCriterion
+from pydvl.value.stopping import StoppingCriterion
 
 __all__ = [
     "compute_banzhaf_semivalues",
+    "compute_msr_banzhaf_semivalues",
     "compute_beta_shapley_semivalues",
     "compute_shapley_semivalues",
     "beta_coefficient",
@@ -149,28 +148,156 @@ def __call__(self, n: int, k: int) -> float:
 MarginalT = Tuple[IndexT, float]
 
 
-def _marginal(
-    u: Utility, coefficient: SVCoefficient, samples: Iterable[SampleT]
-) -> Tuple[MarginalT, ...]:
-    """Computation of marginal utility. This is a helper function for
-    [compute_generic_semivalues][pydvl.value.semivalues.compute_generic_semivalues].
+class MarginalFunction(ABC):
+    @abstractmethod
+    def __call__(
+        self, u: Utility, coefficient: SVCoefficient, samples: Iterable[SampleT]
+    ) -> Tuple[MarginalT, ...]:
+        raise NotImplementedError
 
-    Args:
-        u: Utility object with model, data, and scoring function.
-        coefficient: The semivalue coefficient and sampler weight
-        samples: A collection of samples. Each sample is a tuple of index and subset of
-            indices to compute a marginal utility.
 
-    Returns:
-        A collection of marginals. Each marginal is a tuple with index and its marginal
-        utility.
+class DefaultMarginal(MarginalFunction):
+    def __call__(
+        self, u: Utility, coefficient: SVCoefficient, samples: Iterable[SampleT]
+    ) -> Tuple[MarginalT, ...]:
+        """Computation of marginal utility. This is a helper function for
+        [compute_generic_semivalues][pydvl.value.semivalues.compute_generic_semivalues].
+
+        Args:
+            u: Utility object with model, data, and scoring function.
+            coefficient: The semivalue coefficient and sampler weight
+            samples: A collection of samples. Each sample is a tuple of index and subset of
+                indices to compute a marginal utility.
+
+        Returns:
+            A collection of marginals. Each marginal is a tuple with index and its marginal
+            utility.
+        """
+        n = len(u.data)
+        marginals: List[MarginalT] = []
+        for idx, s in samples:
+            marginal = (u({idx}.union(s)) - u(s)) * coefficient(n, len(s))
+            marginals.append((idx, marginal))
+        return tuple(marginals)
+
+
+class RawUtility(MarginalFunction):
+    def __call__(
+        self, u: Utility, coefficient: SVCoefficient, samples: Iterable[SampleT]
+    ) -> Tuple[MarginalT, ...]:
+        """Computation of raw utility without marginalization. This is a helper function for
+        [compute_generic_semivalues][pydvl.value.semivalues.compute_generic_semivalues].
+
+        Args:
+            u: Utility object with model, data, and scoring function.
+            coefficient: The semivalue coefficient and sampler weight
+            samples: A collection of samples. Each sample is a tuple of index and subset of
+                indices to compute a marginal utility.
+
+        Returns:
+            A collection of marginals. Each marginal is a tuple with index and its raw utility.
+        """
+        marginals: List[MarginalT] = []
+        for idx, s in samples:
+            marginals.append((s, u(s)))
+        return tuple(marginals)
+
+
+class FutureProcessor:
+    """
+    The FutureProcessor class used to process the results of the parallel marginal evaluations.
+
+    The marginals are evaluated in parallel by `n_jobs` threads, but some algorithms require a central
+    method to postprocess the marginal results. This can be achieved through the future processor.
+    This base class does not perform any postprocessing, it is a noop used in most data valuation algorithms.
+    """
+
+    def __call__(self, future_result: Any) -> Any:
+        return future_result
+
+
+class MSRFutureProcessor(FutureProcessor):
+    """
+    This FutureProcessor processes the raw marginals in a way that MSR sampling requires.
+
+    MSR sampling evaluates the utility once, and then updates all data semivalues based on this one evaluation.
+    In order to do this, the RawUtility value needs to be postprocessed through this class.
+    For more details on MSR, please refer to the paper (Wang et. al.)<sup><a href="wang_data_2023">3</a></sup>.
+    This processor keeps track of the current values and computes marginals for all data points, so that
+    the values in the ValuationResult can be updated properly down the line.
     """
-    n = len(u.data)
-    marginals: List[MarginalT] = []
-    for idx, s in samples:
-        marginal = (u({idx}.union(s)) - u(s)) * coefficient(n, len(s))
-        marginals.append((idx, marginal))
-    return tuple(marginals)
+
+    def __init__(self, u: Utility):
+        self.n = len(u.data)
+        self.all_indices = u.data.indices.copy()
+        self.point_in_subset = np.zeros((self.n,))
+        self.positive_sums = np.zeros((self.n,))
+        self.negative_sums = np.zeros((self.n,))
+        self.total_evaluations = 0
+
+    def compute_values(self) -> np.ndarray:
+        points_not_in_subset = self.total_evaluations - self.point_in_subset
+        feasibility_map = np.logical_and(
+            self.point_in_subset > 0, points_not_in_subset > 0
+        )
+        values: np.ndarray = (
+            np.divide(
+                1,
+                self.point_in_subset,
+                out=np.zeros_like(self.point_in_subset),
+                where=feasibility_map,
+            )
+            * self.positive_sums
+            - np.divide(
+                1,
+                points_not_in_subset,
+                out=np.zeros_like(self.point_in_subset),
+                where=feasibility_map,
+            )
+            * self.negative_sums
+        )
+        return values
+
+    def __call__(
+        self, future_result: List[Tuple[List[IndexT], float]]
+    ) -> List[List[MarginalT]]:
+        """Computation of marginal utility using Maximum Sample Reuse.
+
+        This processor requires the Marginal Function to be set to RawUtility.
+        Then, this processor computes marginals based on the utility value and the index set provided.
+
+        The final formula that gives the Banzhaf semivalue using MSR is:
+        $$\hat{\phi}_{MSR}(i) = \frac{1}{|\mathbf{S}_{\ni i}|} \sum_{S \in \mathbf{S}_{\ni i}} U(S)
+        - \frac{1}{|\mathbf{S}_{\not{\ni} i}|} \sum_{S \in \mathbf{S}_{\not{\ni} i}} U(S)$$
+
+        Args:
+            future_result: Result of the parallel computing jobs comprised of
+                a list of indices that were used to evaluate the utility, and the evaluation result (metric).
+
+        Returns:
+            A collection of marginals. Each marginal is a tuple with index and its marginal
+            utility.
+        """
+        marginals: List[List[MarginalT]] = []
+        for batch_id, (s, evaluation) in enumerate(future_result):
+            previous_values = self.compute_values()
+            self.total_evaluations += 1
+            self.point_in_subset[s] += 1
+            self.positive_sums[s] += evaluation
+            not_s = np.setdiff1d(self.all_indices, s)
+            self.negative_sums[not_s] += evaluation
+            new_values = self.compute_values()
+            # Hack to work around the update mechanic that does not work out of the box for MSR
+            marginal_vals = (
+                self.total_evaluations * new_values
+                - (self.total_evaluations - 1) * previous_values
+            )
+            marginals.append([])
+            for data_index in range(self.n):
+                marginals[batch_id].append(
+                    (data_index, float(marginal_vals[data_index]))
+                )
+        return marginals
 
 
 # @deprecated(
@@ -190,6 +317,8 @@ def compute_generic_semivalues(
     coefficient: SVCoefficient,
     done: StoppingCriterion,
     *,
+    marginal: MarginalFunction = DefaultMarginal(),
+    future_processor: FutureProcessor = FutureProcessor(),
     batch_size: int = 1,
     skip_converged: bool = False,
     n_jobs: int = 1,
@@ -204,6 +333,8 @@ def compute_generic_semivalues(
         u: Utility object with model, data, and scoring function.
         coefficient: The semi-value coefficient
         done: Stopping criterion.
+        marginal: Marginal function to be used for computing the semivalues
+        future_processor: Additional postprocessing steps required for some algorithms
         batch_size: Number of marginal evaluations per single parallel job.
         skip_converged: Whether to skip marginal evaluations for indices that
             have already converged. **CAUTION**: This is only entirely safe if
@@ -277,8 +408,16 @@ def compute_generic_semivalues(
 
             completed, pending = wait(pending, timeout=1, return_when=FIRST_COMPLETED)
             for future in completed:
-                for idx, marginal in future.result():
-                    result.update(idx, marginal)
+                processed_future = future_processor(
+                    future.result()
+                )  # List of tuples or
+                for batch_future in processed_future:
+                    if isinstance(batch_future, list):  # Case when batch size is > 1
+                        for idx, marginal_val in batch_future:
+                            result.update(idx, marginal_val)
+                    else:  # Batch size 1
+                        idx, marginal_val = batch_future
+                        result.update(idx, marginal_val)
                     if done(result):
                         return result
 
@@ -300,7 +439,7 @@ def compute_generic_semivalues(
                     if filtered_samples:
                         pending.add(
                             executor.submit(
-                                _marginal,
+                                marginal,
                                 u=u,
                                 coefficient=correction,
                                 samples=filtered_samples,
@@ -311,6 +450,10 @@ def compute_generic_semivalues(
                     return result
 
 
+def always_one_coefficient(n: int, k: int) -> float:
+    return 1.0
+
+
 def shapley_coefficient(n: int, k: int) -> float:
     return float(1 / math.comb(n - 1, k) / n)
 
@@ -350,7 +493,7 @@ def beta_coefficient_w(n: int, k: int) -> float:
 def compute_shapley_semivalues(
     u: Utility,
     *,
-    done: StoppingCriterion = MaxUpdates(100),
+    done: StoppingCriterion,
     sampler_t: Type[StochasticSampler] = PermutationSampler,
     batch_size: int = 1,
     n_jobs: int = 1,
@@ -420,7 +563,7 @@ def compute_shapley_semivalues(
 def compute_banzhaf_semivalues(
     u: Utility,
     *,
-    done: StoppingCriterion = MaxUpdates(100),
+    done: StoppingCriterion,
     sampler_t: Type[StochasticSampler] = PermutationSampler,
     batch_size: int = 1,
     n_jobs: int = 1,
@@ -479,6 +622,73 @@ def compute_banzhaf_semivalues(
     )
 
 
+@deprecated(
+    target=True,
+    args_mapping={"config": "config"},
+    deprecated_in="0.9.0",
+    remove_in="0.10.0",
+)
+def compute_msr_banzhaf_semivalues(
+    u: Utility,
+    *,
+    done: StoppingCriterion,
+    sampler_t: Type[StochasticSampler] = MSRSampler,
+    batch_size: int = 1,
+    n_jobs: int = 1,
+    parallel_backend: Optional[ParallelBackend] = None,
+    config: Optional[ParallelConfig] = None,
+    progress: bool = False,
+    seed: Optional[Seed] = None,
+) -> ValuationResult:
+    """Computes MSR sampled Banzhaf values for a given utility function.
+
+    This is a convenience wrapper for
+    [compute_generic_semivalues][pydvl.value.semivalues.compute_generic_semivalues]
+    with the Banzhaf coefficient and MSR sampling.
+
+    This algorithm works by sampling random subsets and then evaluating the utility
+    on that subset only once. Based on the evaluation and the subset indices,
+    the MSRFutureProcessor then computes the marginal updates like in the paper
+    (Wang et. al.)<sup><a href="wang_data_2023">3</a></sup>.
+    Their approach updates the semivalues for all data points every time a new evaluation
+    is computed. This increases sample efficiency compared to normal Monte Carlo updates.
+
+    Args:
+        u: Utility object with model, data, and scoring function.
+        done: Stopping criterion.
+        sampler_t: The sampler type to use. See the
+            [sampler][pydvl.value.sampler] module for a list.
+        batch_size: Number of marginal evaluations per single parallel job.
+        n_jobs: Number of parallel jobs to use.
+        seed: Either an instance of a numpy random number generator or a seed
+            for it.
+        config: Object configuring parallel computation, with cluster address,
+            number of cpus, etc.
+        progress: Whether to display a progress bar.
+
+    Returns:
+        Object with the results.
+
+    !!! warning "Deprecation notice"
+        Parameter `batch_size` is for experimental use and will be removed in
+        future versions.
+    """
+    # HACK: cannot infer return type because of useless IndexT, NameT
+    return compute_generic_semivalues(  # type: ignore
+        sampler_t(u.data.indices, seed=seed),
+        u,
+        always_one_coefficient,
+        done,
+        marginal=RawUtility(),
+        future_processor=MSRFutureProcessor(u),
+        batch_size=batch_size,
+        n_jobs=n_jobs,
+        parallel_backend=parallel_backend,
+        config=config,
+        progress=progress,
+    )
+
+
 @deprecated(
     target=True,
     args_mapping={"config": "config"},
@@ -490,7 +700,7 @@ def compute_beta_shapley_semivalues(
     *,
     alpha: float = 1,
     beta: float = 1,
-    done: StoppingCriterion = MaxUpdates(100),
+    done: StoppingCriterion,
     sampler_t: Type[StochasticSampler] = PermutationSampler,
     batch_size: int = 1,
     n_jobs: int = 1,
@@ -568,7 +778,7 @@ class SemiValueMode(str, Enum):
 def compute_semivalues(
     u: Utility,
     *,
-    done: StoppingCriterion = MaxUpdates(100),
+    done: StoppingCriterion,
     mode: SemiValueMode = SemiValueMode.Shapley,
     sampler_t: Type[StochasticSampler] = PermutationSampler,
     batch_size: int = 1,
@@ -603,7 +813,7 @@ def compute_semivalues(
       parameters of the Beta distribution (both default to 1).
     - [SemiValueMode.Banzhaf][pydvl.value.semivalues.SemiValueMode]: Implements
       the Banzhaf semi-value as introduced in (Wang and Jia, 2022)<sup><a
-      href="#wang_data_2022">1</a></sup>.
+      href="#wang_data_2023">1</a></sup>.
 
     See [Data valuation][data-valuation] for an overview of valuation.
 
diff --git a/src/pydvl/value/shapley/common.py b/src/pydvl/value/shapley/common.py
index c407b3ba8..c9a767aa7 100644
--- a/src/pydvl/value/shapley/common.py
+++ b/src/pydvl/value/shapley/common.py
@@ -16,7 +16,7 @@
 from pydvl.value.shapley.owen import OwenAlgorithm, owen_sampling_shapley
 from pydvl.value.shapley.truncated import NoTruncation
 from pydvl.value.shapley.types import ShapleyMode
-from pydvl.value.stopping import MaxUpdates, StoppingCriterion
+from pydvl.value.stopping import StoppingCriterion
 
 __all__ = ["compute_shapley_values"]
 
@@ -24,7 +24,7 @@
 def compute_shapley_values(
     u: Utility,
     *,
-    done: StoppingCriterion = MaxUpdates(100),
+    done: StoppingCriterion,
     mode: ShapleyMode = ShapleyMode.TruncatedMontecarlo,
     n_jobs: int = 1,
     seed: Optional[Seed] = None,
diff --git a/src/pydvl/value/stopping.py b/src/pydvl/value/stopping.py
index 56dad2695..e3947788e 100644
--- a/src/pydvl/value/stopping.py
+++ b/src/pydvl/value/stopping.py
@@ -115,6 +115,9 @@
 [^1]: <a name="ghorbani_data_2019"></a>Ghorbani, A., Zou, J., 2019.
     [Data Shapley: Equitable Valuation of Data for Machine Learning](https://proceedings.mlr.press/v97/ghorbani19c.html).
     In: Proceedings of the 36th International Conference on Machine Learning, PMLR, pp. 2242–2251.
+[^2]: <a name="wang_data_2023"></a>Wang, J.T. and Jia, R., 2023.
+    [Data Banzhaf: A Robust Data Valuation Framework for Machine Learning](https://proceedings.mlr.press/v206/wang23e.html).
+    In: Proceedings of The 26th International Conference on Artificial Intelligence and Statistics, pp. 6388-6421.
 """
 
 from __future__ import annotations
@@ -126,6 +129,7 @@
 
 import numpy as np
 from numpy.typing import NDArray
+from scipy.stats import spearmanr
 
 from pydvl.utils import Status
 from pydvl.value import ValuationResult
@@ -139,6 +143,7 @@
     "MinUpdates",
     "MaxTime",
     "HistoryDeviation",
+    "RankCorrelation",
 ]
 
 logger = logging.getLogger(__name__)
@@ -623,3 +628,86 @@ def reset(self):
 
     def __str__(self):
         return f"HistoryDeviation(n_steps={self.n_steps}, rtol={self.rtol})"
+
+
+class RankCorrelation(StoppingCriterion):
+    r"""A check for stability of Spearman correlation between checks.
+
+    When the change in rank correlation between two successive iterations is
+    below a given threshold, the computation is terminated.
+    The criterion computes the Spearman correlation between two successive iterations.
+    The Spearman correlation uses the ordering indices of the given values and
+    correlates them. This means it focuses on the order of the elements instead of their
+    exact values. If the order stops changing (meaning the Banzhaf semivalues estimates
+    converge), the criterion stops the algorithm.
+
+    This criterion is used in (Wang et. al.)<sup><a href="wang_data_2023">2</a></sup>.
+
+    Args:
+        rtol: Relative tolerance for convergence ($\epsilon$ in the formula)
+        modify_result: If `True`, the status of the input
+            [ValuationResult][pydvl.value.result.ValuationResult] is modified in
+            place after the call.
+        burn_in: The minimum number of iterations before checking for
+            convergence. This is required because the first correlation is
+            meaningless.
+
+    !!! tip "Added in 0.9.0"
+    """
+
+    def __init__(
+        self,
+        rtol: float,
+        burn_in: int,
+        modify_result: bool = True,
+    ):
+        super().__init__(modify_result=modify_result)
+        if rtol <= 0 or rtol >= 1:
+            raise ValueError("rtol must be in (0, 1)")
+        self.rtol = rtol
+        self.burn_in = burn_in
+        self._memory: NDArray[np.float_] | None = None
+        self._corr = 0.0
+        self._completion = 0.0
+        self._iterations = 0
+
+    def _check(self, r: ValuationResult) -> Status:
+        self._iterations += 1
+        if self._memory is None:
+            self._memory = r.values.copy()
+            self._converged = np.full(len(r), False)
+            return Status.Pending
+
+        corr = spearmanr(self._memory, r.values)[0]
+        self._memory = r.values.copy()
+        self._update_completion(corr)
+        if (
+            np.isclose(corr, self._corr, rtol=self.rtol)
+            and self._iterations > self.burn_in
+        ):
+            self._converged = np.full(len(r), True)
+            logger.debug(
+                f"RankCorrelation has converged with {corr=} in iteration {self._iterations}"
+            )
+            return Status.Converged
+        self._corr = np.nan_to_num(corr, nan=0.0)
+        return Status.Pending
+
+    def _update_completion(self, corr: float) -> None:
+        if np.isnan(corr):
+            self._completion = 0.0
+        elif not np.isclose(corr, self._corr, rtol=self.rtol):
+            self._completion = corr
+            # self.rtol / np.abs(corr - self._corr) might be another option
+        else:
+            self._completion = 1.0
+
+    def completion(self) -> float:
+        return self._completion
+
+    def reset(self):
+        self._memory = None  # type: ignore
+        self._corr = 0.0
+
+    def __str__(self):
+        return f"RankCorrelation(rtol={self.rtol})"
diff --git a/tests/value/test_sampler.py b/tests/value/test_sampler.py
index 5fbf8f5c0..626078150 100644
--- a/tests/value/test_sampler.py
+++ b/tests/value/test_sampler.py
@@ -10,6 +10,7 @@
     AntitheticSampler,
     DeterministicPermutationSampler,
     DeterministicUniformSampler,
+    MSRSampler,
     PermutationSampler,
     RandomHierarchicalSampler,
     StochasticSampler,
@@ -65,6 +66,7 @@ def test_proper_reproducible(sampler_class, indices, seed):
         PermutationSampler,
         AntitheticSampler,
         RandomHierarchicalSampler,
+        MSRSampler,
     ],
 )
 @pytest.mark.parametrize("indices", [(), (list(range(100)))])
diff --git a/tests/value/test_semivalues.py b/tests/value/test_semivalues.py
index bea03bf27..41ca2a006 100644
--- a/tests/value/test_semivalues.py
+++ b/tests/value/test_semivalues.py
@@ -17,14 +17,16 @@
     UniformSampler,
 )
 from pydvl.value.semivalues import (
+    DefaultMarginal,
+    MSRFutureProcessor,
     SVCoefficient,
-    _marginal,
     banzhaf_coefficient,
     beta_coefficient,
     compute_generic_semivalues,
+    compute_msr_banzhaf_semivalues,
     shapley_coefficient,
 )
-from pydvl.value.stopping import HistoryDeviation, MaxUpdates
+from pydvl.value.stopping import HistoryDeviation, MaxChecks, MaxUpdates
 
 from . import check_values
 from .utils import timed
@@ -52,15 +54,36 @@ def test_marginal_batch_size(test_game, sampler, coefficient, batch_size, seed):
     marginals_single = []
     for sample in samples:
         marginals_single.extend(
-            _marginal(test_game.u, coefficient=coefficient, samples=[sample])
+            DefaultMarginal()(test_game.u, coefficient=coefficient, samples=[sample])
         )
 
-    marginals_batch = _marginal(test_game.u, coefficient=coefficient, samples=samples)
+    marginals_batch = DefaultMarginal()(
+        test_game.u, coefficient=coefficient, samples=samples
+    )
 
     assert len(marginals_single) == len(marginals_batch)
     assert set(marginals_single) == set(marginals_batch)
 
 
+@pytest.mark.parametrize("num_samples", [5])
+def test_msr_banzhaf(
+    num_samples: int, analytic_banzhaf, parallel_backend, n_jobs, seed: Seed
+):
+    u, exact_values = analytic_banzhaf
+    values = compute_msr_banzhaf_semivalues(
+        u=u,
+        done=MaxChecks(200 * num_samples),
+        parallel_backend=parallel_backend,
+        n_jobs=n_jobs,
+        seed=seed,
+    )
+    # Need to use atol because msr banzhaf is quite noisy.
+    check_values(values, exact_values, atol=0.1)
+
+    # Check order
+    assert np.array_equal(np.argsort(exact_values), np.argsort(values))
+
+
 @pytest.mark.parametrize("n", [10, 100])
 @pytest.mark.parametrize(
     "coefficient",
@@ -261,3 +284,31 @@ def test_banzhaf(
         parallel_backend=parallel_backend,
     )
     check_values(values, exact_values, rtol=0.2)
+
+
+@pytest.mark.parametrize("num_samples", [6])
+def test_msr_future_processor(num_samples: int, dummy_utility):
+    proc = MSRFutureProcessor(dummy_utility)
+    assert proc.n == num_samples
+    assert proc.total_evaluations == 0
+    data1 = [1, 3, 5]
+    data2 = [0, 2, 4]
+
+    # Iteration 1
+    marginals1 = proc([(data1, 1.0)])
+    assert marginals1 == [[(i, 0.0) for i in range(6)]]
+
+    # Iteration 2
+    marginals2 = proc([(data2, 0.5)])
+    assert marginals2 == [
+        [(0, -1.0), (1, 1.0), (2, -1.0), (3, 1.0), (4, -1.0), (5, 1.0)]
+    ]
+
+    # Iteration 3
+    # Values are -0.5 for even and 0.5 for uneven indices
+    # New values are supposed to be -1.0 and 1.0=(1.0+2.0)/2-(0.5)/1
+    # Marginals need to be 2 and -2 because (2*0.5 + 1*2)/3=1
+    marginals2 = proc([(data1, 2.0)])
+    assert marginals2 == [
+        [(0, -2.0), (1, 2.0), (2, -2.0), (3, 2.0), (4, -2.0), (5, 2.0)]
+    ]
diff --git a/tests/value/test_stopping.py b/tests/value/test_stopping.py
index efebac9c4..44eee75f9 100644
--- a/tests/value/test_stopping.py
+++ b/tests/value/test_stopping.py
@@ -13,6 +13,7 @@
     MaxTime,
     MaxUpdates,
     MinUpdates,
+    RankCorrelation,
     StoppingCriterion,
     make_criterion,
 )
@@ -196,3 +197,31 @@ def test_max_checks():
     for _ in range(4):
         assert not done(v)
     assert done(v)
+
+
+def test_rank_correlation():
+    """Test the RankCorrelation stopping criterion."""
+    v = ValuationResult.zeros(indices=range(5))
+    arr = np.arange(5)
+
+    done = RankCorrelation(rtol=0.1)
+    for i in range(20):
+        arr = np.roll(arr, 1)
+        for j in range(5):
+            v.update(j, arr[j] + 0.01 * j)
+        assert not done(v)
+    assert not done(v)
+    assert done(v)
+
+    done = RankCorrelation(rtol=0.1, burn_in=3)
+    v = ValuationResult.from_random(size=5)
+    assert not done(v)
+    assert not done(v)
+    assert not done(v)
+    assert done(v)
+
+    done = RankCorrelation(rtol=0.1, burn_in=2)
+    v = ValuationResult.from_random(size=5)
+    assert not done(v)
+    assert not done(v)
+    assert done(v)
diff --git a/tox.ini b/tox.ini
index 499bd1996..600c8dc14 100644
--- a/tox.ini
+++ b/tox.ini
@@ -24,7 +24,7 @@ deps =
     {[testenv]deps}
     -r requirements-notebooks.txt
 commands =
-    pytest --nbmake --nbmake-timeout=60 -n 0 --cov "{envsitepackagesdir}/pydvl" notebooks/ {posargs}
+    pytest --nbmake --nbmake-timeout=90 -n 0 --cov "{envsitepackagesdir}/pydvl" notebooks/ {posargs}
 
 [testenv:linting]
 skip_install = true