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          "html": "<h1>An Aggregated Multicolumn Dilated Convolution Network for Perspective-Free Counting</h1>",
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          "html": "<p block-type=\"Text\">Diptodip Deb Georgia Institute of Technology diptodipdeb@gatech.edu</p>",
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          "html": "<h1>Abstract</h1>",
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          "html": "<p block-type=\"Text\"><i>We propose the use of dilated filters to construct an ag</i><i>gregation module in a multicolumn convolutional neural</i> <i>network for perspective-free counting. Counting is a com</i><i>mon problem in computer vision (e.g. traffic on the street or</i> <i>pedestrians in a crowd). Modern approaches to the count</i><i>ing problem involve the production of a density map via re</i><i>gression whose integral is equal to the number of objects</i> <i>in the image. However, objects in the image can occur at</i> <i>different scales (e.g. due to perspective effects) which can</i> <i>make it difficult for a learning agent to learn the proper</i> <i>density map. While the use of multiple columns to extract</i> <i>multiscale information from images has been shown be</i><i>fore, our approach aggregates the multiscale information</i> <i>gathered by the multicolumn convolutional neural network</i> <i>to improve performance. Our experiments show that our</i> <i>proposed network outperforms the state-of-the-art on many</i> <i>benchmark datasets, and also that using our aggregation</i> <i>module in combination with a higher number of columns is</i> <i>beneficial for multiscale counting.</i></p>",
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          "html": "<h1>1. Introduction</h1>",
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          "html": "<p block-type=\"Text\">Learning to count the number of objects in an image is a deceptively difficult problem with many interesting applications, such as surveillance <a href=\"#page-8-0\">[20]</a>, traffic monitoring <a href=\"#page-8-1\">[14]</a> and medical image analysis <a href=\"#page-8-2\">[22]</a>. In many of these application areas, the objects to be counted vary widely in appearance, size and shape, and labeled training data is typically sparse. These factors pose a significant computer vision and machine learning challenge.</p>",
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          "html": "<p block-type=\"Text\">Lempitsky et al. <a href=\"#page-8-3\">[15]</a> showed that it is possible to learn to count without learning to explicitly detect and localize individual objects. Instead, they propose learning to predict a density map whose integral over the image equals the number of objects in the image. This approach has been adopted by many later works (Cf. <a href=\"#page-8-4\">[18,</a> <a href=\"#page-9-0\">28]</a>).</p>",
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          "html": "<p block-type=\"Text\">However, in many counting problems, such as those</p>",
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          "html": "<p block-type=\"Text\">Jonathan Ventura University of Colorado Colorado Springs jventura@uccs.edu</p>",
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          "html": "<blockquote><p block-type=\"Text\">counting cells in a microscope image, pedestrians in a crowd, or vehicles in a traffic jam, regressors trained on a single image scale are not reliable <a href=\"#page-8-4\">[18]</a>. This is due to a variety of challenges including overlap of objects and perspective effects which cause significant variance in object shape, size and appearance.</p></blockquote>",
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          "html": "<blockquote><p block-type=\"Text\">The most successful recent approaches address this issue by explicitly incorporating multi-scale information in the network <a href=\"#page-8-4\">[18,</a><a href=\"#page-9-0\">28]</a>. These approaches either combine multiple networks which take input patches of different sizes <a href=\"#page-8-4\">[18]</a> or combine multiple filtering paths (\"columns\") which have different size filters <a href=\"#page-9-0\">[28]</a>.</p></blockquote>",
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          "html": "<blockquote><p block-type=\"Text\">Following on the intuition that multiscale integration is key to achieving good counting performance, we propose to incorporate dilated filters <a href=\"#page-8-5\">[25]</a> into a multicolumn convolutional neural network design <a href=\"#page-9-0\">[28]</a>. Dilated filters exponentially increase the network's receptive field without an exponential increase in parameters, allowing for efficient use of multiscale information. Convolutional neural networks with dilated filters have proven to provide competitive performance in image segmentation where multiscale analysis is also critical <a href=\"#page-8-5\">[25,</a> <a href=\"#page-8-6\">26]</a>. By incorporating dilated filters into the multicolumn network design, we greatly increase the ability of the network to selectively aggregate multiscale information, without the need for explicit perspective maps during training and testing. We propose the \"aggregated multicolumn dilated convolution network\" or AMDCN which uses dilations to aggregate multiscale information. Our extensive experimental evaluation shows that this proposed network outperforms previous methods on many benchmark datasets.</p></blockquote>",
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          "html": "<h1>2. Related Work</h1>",
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          "html": "<p block-type=\"Text\">Counting using a supervised regressor to formulate a density map was first shown by <a href=\"#page-8-3\">[15]</a>. In this paper, Lempitsky et al. show that the minimal annotation of a single dot blurred by a Gaussian kernel produces a sufficient density map to train a network to count. All of the counting methods that we examine as well as the method we use in</p>",
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              "html": "<p><span id=\"page-1-0\"></span>Figure 1. Fully convolutional architecture diagram (not to scale). Arrows show separate columns that all take the same input. At the end of the columns, the feature maps are merged (concatenated) together and passed to another series of dilated convolutions: the aggregator, which can aggregate the multiscale information collected by the columns <a href=\"#page-8-5\">[25]</a>. The input image is I with C channels. The output single channel density map is D, and integrating over this map (summing the pixels) results in the final count. Initial filter sizes are labeled with brackets or lines. Convolution operations are shown as flat rectangles, feature maps are shown as prisms. The number below each filter represents the dilation rate (1 means no dilation).</p>",
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          "html": "<p block-type=\"Text\">of the image is truly necessary in order to solve dense prediction problems via convolutional neural networks. Moreover, these approaches seem to saturate in performance at three columns, which means the network is extracting information from fewer scales. The work of <a href=\"#page-8-5\">[25]</a> proposes the use of dilated convolutions as a simpler alternative that does not require sampling of rescaled image patches to provide global, scale-aware information to the network. A fully convolutional approach to multiscale counting has been proposed by <a href=\"#page-9-0\">[28]</a>, in which a multicolumn convolutional network gathers features of different scales by using convolutions of increasing kernel sizes from column to column instead of scaling image patches. Further, DeepLab has used dilated convolutions in multiple columns to extract scale information for segmentation <a href=\"#page-8-13\">[8]</a>. We build on these approaches with our aggregator module as described in Section <a href=\"#page-2-0\">3.1,</a> which should allow for extracting information from more scales.</p>",
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              "html": "<p>Figure 2. UCF sample results. Left: input counting image. Middle: Ground truth density map. Right: AMDCN prediction of density map on test image. The network never saw these images during training. All density maps are one channel only (i.e. grayscale), but are colored here for clarity.</p>",
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          "html": "<p block-type=\"Text\">In this paper, however, we aim to apply the dilated convolution method of <a href=\"#page-8-5\">[25]</a>, which has shown to be able to incorporate multiscale perspective information without using multiple inputs or a complicated network architecture, as well as the multicolumn approach of <a href=\"#page-8-13\">[8,</a> <a href=\"#page-9-0\">28]</a> to aggregate multiscale information for the counting problem.</p>",
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          "html": "<p block-type=\"Text\">We propose the use of dilated convolutions as an attractive alternative to the architecture of the HydraCNN <a href=\"#page-8-4\">[18]</a>, which seems to saturate in performance at 3 or more columns. We refer to our proposed network as the aggregated multicolumn dilated convolution network<a href=\"#page-2-1\">1</a> , henceforth shortened as the AMDCN. The architecture of the AMDCN is inspired by the multicolumn counting network of <a href=\"#page-9-0\">[28]</a>. Extracting features from multiple scales is a good idea when attempting to perform perspective-free counting and increasing the convolution kernel size across columns is an efficient method of doing so. However, the number of parameters increases exponentially as larger kernels are used in these columns to extract features at larger scales. Therefore, we propose using dilated convolutions rather than larger kernels.</p>",
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          "html": "<p block-type=\"Text\">Looking at more scales should allow for more accurate regression of the density map. However, because not all scales will be relevant, we extend the network beyond a simple 1 × 1 convolution after the merged columns. Instead, we construct a second part of the network, the aggregator, which sets our method apart from <a href=\"#page-9-0\">[28]</a>, <a href=\"#page-8-13\">[8]</a>, and other multicolumn networks. This aggregator is another series of dilated convolutions that should appropriately consolidate the multiscale information collected by the columns. This is a capability of dilated convolutions observed by <a href=\"#page-8-5\">[25]</a>. While papers such as <a href=\"#page-9-0\">[28]</a> and <a href=\"#page-8-13\">[8]</a> have shown that multiple columns and dilated columns are useful in extracting multiscale information, we argue in this paper that the simple aggregator module built using dilated convolutions is able to effectively make use multiscale information from multiple columns. We show compelling evidence for these claims in Section <a href=\"#page-5-0\">4.5.</a></p>",
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          "html": "<p block-type=\"Text\">The network as shown in Figure <a href=\"#page-1-0\">1</a> contains 5 columns. Note that dilations allow us to use more columns for counting than <a href=\"#page-9-0\">[28]</a> or <a href=\"#page-8-13\">[8]</a>. Each column looks at a larger scale than the previous (the exact dilations can also be seen in Figure <a href=\"#page-1-0\">1)</a>. There are 32 feature maps for each convolution, and all inputs are zero padded prior to each convolution in order to maintain the same data shape from input to output. That is, an image input to this network will result in a density map of the same dimensions. All activations in the specified network are ReLUs. Our input pixel values are floating point 32 bit values from 0 to 1. We center our inputs at 0 by subtracting the per channel mean from each channel. When</p>",
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          "html": "<p><span id=\"page-2-1\"></span><sup>1</sup> Implementation available on <a href=\"https://github.com/diptodip/counting\">https://github.com/</a> <a href=\"https://github.com/diptodip/counting\">diptodip/counting</a>.</p>",
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          "html": "<p block-type=\"TextInlineMath\">where γ is the scale factor, <math display=\"inline\">\\hat{y}_i </math>is the prediction, <math display=\"inline\">y_i </math>is the true value, and <math display=\"inline\">n</math> is the number of pixels. We use a scaled mean absolute error because the target values are so small that it is numerically unstable to regress to these values. At testing time, when retrieving the output density map from the network, we scale the pixel values by <math display=\"inline\">γ^{-1} </math>to obtain the correct value. This approach is more numerically stable and avoids having the network learn to output only zeros by weighting the nonzero values highly. For all our datasets, we set <math display=\"inline\">γ = 255. </math></p>",
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          "html": "<h2>3.2. Experiments</h2>",
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          "html": "<p block-type=\"Text\">We evaluated the performance of dilated convolutions against various counting methods on a variety of common counting datasets: UCF50 crowd data, TRANCOS traffic data <a href=\"#page-8-4\">[18]</a>, UCSD crowd data <a href=\"#page-8-17\">[5]</a>, and WorldExpo crowd data <a href=\"#page-8-7\">[27]</a>. For each of these data sets, we used labels given by the corresponding density map for each image. An example of this is shown in Figure <a href=\"#page-2-2\">2.</a> We have performed experiments on the four different splits of the UCSD data as used in <a href=\"#page-8-4\">[18]</a> and the split of the UCSD data as used in <a href=\"#page-9-0\">[28]</a> (which we call the original split). We also evaluated the performance of our network on the TRANCOS traffic dataset <a href=\"#page-8-1\">[14]</a>. We have also experimented with higher density datasets for crowd counting, namely WorldExpo and UCF.</p>",
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          "html": "<p block-type=\"Text\">We have observed that multicolumn dilations produce density maps (and therefore counts) that often have lower loss than those of HydraCNN <a href=\"#page-8-4\">[18]</a> and <a href=\"#page-9-0\">[28]</a>. We measure density map regression loss via a scaled mean absolute error loss during training. We compare accuracy of the counts via mean absolute error for the crowd datasets and the GAME metric in the TRANCOS dataset as explained in Section <a href=\"#page-3-0\">3.2.2.</a> Beyond the comparison to HydraCNN, we will also compare to other recent convolutional counting methods, especially those of <a href=\"#page-8-14\">[21]</a>, <a href=\"#page-8-15\">[24]</a>, and <a href=\"#page-8-16\">[4]</a> where possible.</p>",
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          "html": "<p block-type=\"Text\">For all datasets, we generally use patched input images and ground truth density maps produced by summing a Gaussian of a fixed size (σ) for each object for training. This size varies from dataset to dataset, but remains constant within a dataset with the exception of cases in which a perspective map is used. This is explained per dataset. All experiments were performed using Keras with the Adam optimizer <a href=\"#page-8-18\">[10]</a>. The learning rates used are detailed per dataset. For testing, we also use patches that can either be directly pieced together or overlapped and averaged except in the case of UCF, for which we run our network on the full image.</p>",
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          "html": "<p block-type=\"Text\">Furthermore, we performed a set of experiments in which we varied the number of columns from 1 to 5 (simply by including or not including the columns as specified in Figure <a href=\"#page-1-0\">1,</a> starting with the smallest filter column and adding larger filter columns one by one). Essentially, the network is allowed to extract information at larger and larger scales in addition to the smaller scales as we include each column. We then performed the same set of experiments, varying the number of columns, but with the aggregator module removed. We perform these experiments on the original split of UCSD as specified in Section <a href=\"#page-4-0\">3.2.3</a> and <a href=\"#page-8-17\">[5]</a>, the TRAN-COS dataset, and the WorldExpo dataset because these are relatively large and well defined datasets. We limit the number of epochs to 10 for all of these sets of experiments in order to control for the effect of learning time, and also compare all results using MAE for consistency. These experiments are key to determining the efficacy of the aggregator in effectively combining multiscale information and in providing evidence to support the use of multiple columns to extract multiscale information from images. We report the results of these ablation studies in Section <a href=\"#page-5-0\">4.5.</a></p>",
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          "html": "<h4>3.2.1 UCF50 Crowd Counting</h4>",
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          "html": "<p block-type=\"Text\">UCF is a particularly challenging crowd counting dataset. There are only 50 images in the whole dataset and they are all of varying sizes and from different scenes. The number of people also varies between images from less than 100 to the thousands. The average image has on the order of 1000 people. The difficulty is due to the combination of the very low number of images in the dataset and the fact that the images are all of varying scenes, making high quality generalization crucial. Furthermore, perspective effects are particularly noticeable for many images in this dataset. Despite this, there is no perspective information available for this dataset.</p>",
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          "html": "<p block-type=\"Equation\"><math display=\"block\">GAME(L) = \\frac{1}{N} \\cdot \\sum_{n=1}^{N} \\left( \\sum_{l=1}^{4^L} |e_n^l - t_n^l| \\right) \\tag{4}</math></p>",
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          "html": "<p block-type=\"Text\">The UCSD crowd counting dataset consists of frames of video of a sidewalk. There are relatively few people in view at any given time (approximately 25 on average). Furthermore, because the dataset comes from a video, there are many nearly identical images in the dataset. For this dataset, there have been two different ways to split the data into train and test sets. Therefore, we report results using both methods of splitting the data. The first method consists of four different splits: maximal, downscale, upscale, and minimal. Minimal is particularly challenging as the train set contains only 10 images. Moreover, upscale appears to be the easiest for the majority of methods <a href=\"#page-8-4\">[18]</a>. The second method of splitting this data is much more succinct, leaving 1200 images in the testing set and 800 images in the training set <a href=\"#page-9-0\">[28]</a>. This split comes from the original paper, so we call it the original split <a href=\"#page-8-17\">[5]</a>.</p>",
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          "html": "<p block-type=\"Text\" class=\"has-continuation\">First, we split the dataset into four separate groups of training and testing sets as used in <a href=\"#page-8-4\">[18]</a> and originally defined by <a href=\"#page-8-0\">[20]</a>. These groups are \"upscale,\" \"maximal,\"</p>",
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          "html": "<p block-type=\"Text\">\"minimal,\" and \"downscale.\" We see in Table <a href=\"#page-6-0\">3</a> that the \"upscale\" split and \"downscale\" split give us state of the art results on counting for this dataset. For this experiment, we sampled 1600 random patches of size 119 × 79 pixels (width and height respectively) for the training set and split the test set images into 119 × 79 quadrants that could be reconstructed by piecing them together without overlap. We also added left-right flips of each image to our training data.</p>",
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          "html": "<p block-type=\"TextInlineMath\">We then evaluate the original split. For this experiment, we similarly sampled 1600 random patches of size 119 × 79 pixels (width and height respectively) for the training set and split the test set images into 119 × 79 quadrants that could be reconstructed by piecing them together without overlap.</p>",
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          "html": "<h4>3.2.4 WorldExpo '10 Crowd Counting</h4>",
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          "html": "<p block-type=\"Text\">The WorldExpo dataset <a href=\"#page-8-7\">[27]</a> contains a larger number of people (approximately 50 on average, which is double that of UCSD) and contains images from multiple locations. Perspective effects are also much more noticeable in this dataset as compared to UCSD. These qualities of the dataset serve to increase the difficulty of counting. Like UCSD, the WorldExpo dataset was constructed from frames of video recordings of crowds. This means that, unlike UCF, this dataset contains a relatively large number of training and testing images. We experiment on this dataset with and without perspective information.</p>",
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KEET38zgjuM8V0Fcx8Pzv8IW8o6SSykfQOV/pXT06ytUkuxtRozo01TqK0lv69fxCiiisjUKpJ/yF3/3D/wCy1dqkn/IXf/cP/stAF2iiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAqCb/X23++f/QTU9QTf6+2/wB8/wDoJoAnooooAgt/9Zcf9dP/AGUVPUFv/rLj/rp/7KKnoAKKKKACiiigAooooAa/+rb6GmW3/HrD/uL/ACp7/wCrb6GmW3/HrD/uL/KgCWiiigAooooAKKKKACoLn7sf/XRf51PUFz92P/rov86AJ6KKKACiiigAooooAKKKKAMP/me/+4Z/7VrcrD/5nv8A7hn/ALVrcq59PQ5sN9v/ABMKo6rbRSWF5L5SGb7NJGJNo3BSOmeuM9qvUyaMSwyRno6lfzFSnZ3N5puLS6nhX2u5uYYkuJmk8rdHEGPRQxwB+FHjHw1fab5gO2VbZBI7p0CMdoP50+XRr+48WWPh1o1t5mlaUSuCAyqG3duQdhx9a9Xlijk8amOVFeOXTsFWGQcSen417M63sJxnHzdv6+Z5eAwLwNWhmVTWc5Xt2Turf1sZmiKYvHVxGRgnR7ckH2JFdjXLwkD4oXOOjaQh/wDIrV1FeZiHeSfkj38U7yi/JfkFQW3Wb/rqf5Cp6gtus3/XU/yFYHMT0UUUAFFFFABRRRQAUyb/AFEn+6f5U+mTf6iT/dP8qACH/UR/7o/lT6ZD/qI/90fyp9ABRRRQAUUUUAFFFFAEFz1h/wCuo/rU9QXPWH/rqP61PQAVBB/rrn/roP8A0FanqCD/AF1z/wBdB/6CtAE9FFFABWZf+I9E0q5W21DV7G1nbkRzTqjY9cE1p157ZvJpN/rcV14N1DUDPeyyrdpBE/noxyAdzAgAfKPYDgdKAPQEdZEV0YMjDKspyCPUVlan4itNMvFsjDd3V00fnGG0gaVljzjccdBnIHc4OKw/At3e21jHpNz4f1OxCvNIrSqnkxKzsyxqQxJABAHHbtUvifxPaaXqaaXaXOn2ur3MIZ7q8kREt4gSAzEkFzknag9ycCgDSl8W6WtrZT2xnvTeo0lvFawtI7Iv3mK9gMgHOOTjrWlp2oWuq6fDfWcnmW8wyrYIPXBBB5BBBBB6EVxWjrpOg+J9MEOoQyabJo7w2920ylZZFm3SfN03HOTj0PpWn4KurePR2d54401DUruWyV2CmVGldhtB65GW47c0AX4/F2ly6gtqpuNjzm2S6MDCB5hkFA/QnII9CRgHNa15/wAeU/8AuH+VebW9xC3w90nRFmT+111OGAwBh5iypdBnJXqMKrNn05r0m8/48p/9w/yoAnoqDyp/+fj/AMcFHlT/APPx/wCOCgAb/j+T/rm38xU9UjHN9sQefz5bc7B6ipvKn/5+P/HBQBPRUHlT/wDPx/44KPKn/wCfj/xwUAT0VB5U/wDz8f8Ajgo8qf8A5+P/ABwUAT0VB5U//Px/44KPKn/5+P8AxwUAT0VB5U//AD8f+OCjyp/+fj/xwUAT1B/y/D/rmf50eVP/AM/H/jgqHy5vtgHn8+X12D1oAu0VB5U//Px/44KPKn/5+P8AxwUAT0VB5U//AD8f+OCjyp/+fj/xwUAT0VB5U/8Az8f+OCjyp/8An4/8cFAE9FQeVP8A8/H/AI4KPKn/AOfj/wAcFAE9FQeVP/z8f+OCjyp/+fj/AMcFAAP+P5/+uS/zNT1SEc32xh5/PljnYPU1N5U//Px/44KAJ6Kg8qf/AJ+P/HBR5U//AD8f+OCgCeioPKn/AOfj/wAcFHlT/wDPx/44KAJ6Kg8qf/n4/wDHBR5U/wDz8f8AjgoAnoqDyp/+fj/xwUeVP/z8f+OCgCeoE/4/pf8Armn82o8qf/n4/wDHBUKxzfbJR5/PlpzsHq1AF2ioPKn/AOfj/wAcFHlT/wDPx/44KAJ6Kg8qf/n4/wDHBR5U/wDz8f8AjgoAnoqDyp/+fj/xwUeVP/z8f+OCgCeioPKn/wCfj/xwUeVP/wA/H/jgoAnoqDyp/wDn4/8AHBR5U/8Az8f+OCgAj/4+5/ov9anqlHHN9pmHn84XJ2D3qbyp/wDn4/8AHBQBPRUHlT/8/H/jgqlq9+NF0m41G6uG8mBdzBYwSecAD3JIFNJydkOMXJqK3ZLrOpQ6TpVxdzSBNqkJnu2OBXBaHd65qWn22maVpzaVIU3yarJGGDQ5JQrkcliTx2H14sWmm6x4m1cwa/cAW6Bbp7IIMQ54WMkdSQAT+I713iwzKoVZwFAwAIxgV1XjQXLo3+C/z/Iik4RryqNKTj7q7La789dO2nW5Q8MaRJoXhuy0yaRJJIFIZkzgksTxn61r1B5U/wDz8f8Ajgo8qf8A5+P/ABwVzSk5ScnuzSc3OTlLdk9FQeVP/wA/H/jgo8qf/n4/8cFSST1ST/kLv/uH/wBlqbyp/wDn4/8AHBVaFXXVn3vuOw84x/doA0KKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigCtfahZ6ZbNc393BawKcGSaQIufqaZp2radrEBm02+truNThmglDgH0OOlc54uimTXtDvxolxrFvbidZIIkVvLLBMSYYgFhggezHkY5yo9Surfxf/AGrbeC9Zt7f7G0EgihhDTsWUgsN+PlAODkn5j0oA9EopsbF41YqylgDtbqPY06gAqCb/AF9t/vn/ANBNT1BN/r7b/fP/AKCaAJ6KKKAILf8A1lx/10/9lFT1Bb/6y4/66f8AsoqegAooooAKKKKACiiigBr/AOrb6GmW3/HrD/uL/Knv/q2+hplt/wAesP8AuL/KgCWiiigAooooAKKKKACoLn7sf/XRf51PUFz92P8A66L/ADoAnooooAKKKKACiiigAooooAw/+Z7/AO4Z/wC1a3Kw/wDmez/2DP8A2rW5Vz6ehzYb7f8AiYUUUVB0mFqOgyXfi7RtZjeMJZJMkqtncwZcLj6EmqniGf7FrlvcEHbJY3Cbh2Kru/pXUVx3j5/KgspP+uyf99JiuilJzlGL6K35s4s2rS+q8z+za3/gV/1ZQ8KvnxZYkn72ggD/AIDORXoFeWeHLp4vFGjxO+BDFLDJ7D962PzxXa+GNbOtR3zNIGaK4YIAuMRn7v8AI/lV4qDvzeX6sjB5jTrUaMHo7cv/AICtTeqC26zf9dT/ACFT1BbdZv8Arqf5CuQ9AnooooAKKKKACiiigApk3+ok/wB0/wAqfTJv9RJ/un+VABD/AKiP/dH8qfTIf9RH/uj+VPoAKKKKACiiigAooooAguesP/XUf1qeoLnrD/11H9anoAKgg/11z/10H/oK1PUEH+uuf+ug/wDQVoAnooooAK8m1VIpdWubvV9X1SEQaw0F6iXckMdvauriBgAQAuQh3epOa9ZrgZbzUtR1LVIbrxbZ6OIbl4VsXtoWPlD7jEyHJ3A59OaANfwA6P4UjEMsk8CXE6RXMjszXCCVgsmTzyMe3pgVvzafZXMnmT2lvK+MbnjDH8yKr6GrJpEKtqUepEZH2mNERWGTgAJ8ox049K0KAK0mnWU1sttLZ2726nKxNEpUH6YxUxhiJjJiT939z5R8vGOPTin0UAQCytVuzdi2hFyw2mYRjeR6butF5/x5T/7h/lU9QXn/AB5T/wC4f5UAT0UUUAQN/wAfyf8AXNv5ip6gb/j+T/rm38xU9ABRRRQAUUUUAFFFFABRRRQAVB/y/D/rmf51PUH/AC/D/rmf50AT0UUUAFFFFABRRRQAUUUUAFFFFAEA/wCP5/8Arkv8zU9QD/j+f/rkv8zU9ABRRRQAUUUUAFFFFABRRRQAVAn/AB/S/wDXNP5tU9QJ/wAf0v8A1zT+bUAT0UUUAFFFFABRRRQAUVUTUbd9Tl08E+fHGJWGOME4/wA/WrSsrqGUhgehBzTaa3JjOMtmLRRVTUtQt9NsnuLiaOLghN7AbmwSFGep4PFCTbsht2VzI1bxDHYW0t1ZmK5dpFiChuOMls+mADWXqXxK0uCyk+wpLPfFR5UUkTKrPkZXd04Byawry106PwRNqFtKJb2SIW6hJchpZcqwI/vAMfyrZ/4R+6DReHWtzJp6TLcLdMPuJj5lB7HP4813RpUoq8lfV+Wxx06uIpU1iK0L82iiujsmm9L2bbT/ADLml+OVuJ3j1bTZtJDQefAZz/rVHDY46g9B3BzVfxrqVvqfhmzjspklW8u4lwDnpl8EduVHHvXMeL2e2srKz1K3lWWyhuYIrjHySZTKEH/gIq5o3h+CLWLC3ay3X6SRXL3XPMOwHB7fe4zitPZU42rLT/gGlXGT5FXhBwUlyre/P8+i7912PQdJ0v8As6OV5JmuLqdt88zDG49gB2A7CtCiivNbbd2bU6cacVGOwUUUUiwooooAKpJ/yF3/ANw/+y1dqkn/ACF3/wBw/wDstAF2iiigAooooAKKKKACiiigAooooAKKKKACiiigAooooA4/xnE13qui2M99eWdhcmaNntpWj3XBC+UrMvODlzjoSBXP+AZbT/hILAW99eXV5JpbjUYLi5kl+xzq8YIwx43HdweflGOCc9R4sv7+2vdLtbfVIdJtblpPNvpYlcK6gFIxu+UFvmOT/dpfDsUqalM7+K7bVt8ZLQxQQo2cj5yU5OOnPrQBtarHqE2myx6XNFBdvtCyyruCDI3HHcgZwDxnGa4j/hKr2xtfES2mpNqkdkkCW9xdRqrpPK2zaQqruUEoc4HUjJ7dtq8GoXGnOml3aW14GV43kTcjYIJVh6EAgkcjORXMXvhTU9fm1G81R7SzuJrJbW3jtXaVUKyCQO7Mq7vmA4xwM880AXrGbUdJ8VW+kXupy6jBeWck8ck8aK8ckbIGA2KBtIcHBGRjrW9cusctuzsFUOcknH8JrG07TNWuPEMesa0tlE9vatbQQ2kjSAl2Uu5LKuM7FAGDjnk1tzf6+2/3z/6CaAD7Zbf8/Ef/AH0KPtlt/wA/Ef8A30KnooApQXduHnzPGMyZHzDngVN9stv+fiP/AL6FFv8A6y4/66f+yip6AIPtlt/z8R/99Cj7Zbf8/Ef/AH0KnooAg+2W3/PxH/30KPtlt/z8R/8AfQqeigCD7Zbf8/Ef/fQo+2W3/PxH/wB9Cp6KAK73lsUYefH0P8Qplvd2620QM8YIQAjcPSrL/wCrb6GmW3/HrD/uL/KgBv2y2/5+I/8AvoUfbLb/AJ+I/wDvoVPRQBB9stv+fiP/AL6FH2y2/wCfiP8A76FT0UAQfbLb/n4j/wC+hR9stv8An4j/AO+hU9FAEH2y2/5+I/8AvoVDcXduwjxPGcSKfvD1q7UFz92P/rov86AD7Zbf8/Ef/fQo+2W3/PxH/wB9Cp6KAIPtlt/z8R/99Cj7Zbf8/Ef/AH0KnooAg+2W3/PxH/30KPtlt/z8R/8AfQqeigCD7Zbf8/Ef/fQo+2W3/PxH/wB9Cp6KAOdF1B/wnBfzk2/2bjO7jPmVt/bLb/n4j/76FZI/5Hs/9gwf+ja3aufT0ObDfb/xMg+2W3/PxH/30KPtlt/z8R/99Cp6Kg6SD7Zbf8/Ef/fQrmfGBt7waVH5iOhvUV8N2PWutprxpJt3orbTuGRnB9aqEuWVzDE0Pb0nTfU8n8T6e9lr8strtENwxMRRwc7h8w68dSPxroPA6Lpl7qlrcSxq4EWfmGM4bOD361peKiYdZ8M3B/1f9oeSw9S6HH6is/WNb/4R7xbcSJAjC4jiMmfQEg9O+P5V2uUqlNR7r8meRiMvpZfVhjXL3W38r81/xt8jsftlt/z8R/8AfQqGC7t1M2Z4xmQkfMPajSL46npNtelAhlXJUHIBziprbrN/11P8hXC1Z2Z7kJqcVOOz1D7Zbf8APxH/AN9Cj7Zbf8/Ef/fQqeikUQfbLb/n4j/76FH2y2/5+I/++hU9FAEH2y2/5+I/++hR9stv+fiP/voVPRQBB9stv+fiP/voUyW7tjC4E8ZJU/xCrVMm/wBRJ/un+VAEEV3bCFAZ4wQo/iFP+2W3/PxH/wB9CpIf9RH/ALo/lT6AIPtlt/z8R/8AfQo+2W3/AD8R/wDfQqeigCD7Zbf8/Ef/AH0KPtlt/wA/Ef8A30KnooAg+2W3/PxH/wB9Cj7Zbf8APxH/AN9Cp6KAKU93bsYsTxnEgJ+Ye9TfbLb/AJ+I/wDvoUXPWH/rqP61PQBB9stv+fiP/voU21dZHuGRgymQYIP+ytWagg/11z/10H/oK0AT0UUUAFecDTJbPV9W+0+ARq4mvZJo71/szM6tyB87ZAHQe2OBXo9c7d+LI9KvJYdX02+srdXIjvfL82B17EsmSn/AgPrQBo6EqrpEIXSBpIy3+hgIPL5P9z5eevHrWjVeyv7PUrZbmxuobmBukkLh1P4irFABRRRQAVBef8eU/wDuH+VT1Bef8eU/+4f5UAT0UUUAQN/x/J/1zb+YqeoG/wCP5P8Arm38xU9ABRRRQAUUUUAFFFFABRRRQAVB/wAvw/65n+dT1B/y/D/rmf50AT0UUUAFFFFABRRRQAUUUUAFFFFAEA/4/n/65L/M1PUA/wCP5/8Arkv8zU9ABRRRQAUUUUAFFFFABRRRQAVAn/H9L/1zT+bVPUCf8f0v/XNP5tQBPRRRQAUUVna9etp+iXVyhIkCbY8ddx4H6mmld2RFSapwc5bJXNGiszw9dPe+H7K4lcvI0eGY9SRwf5VcvLuKws5bqdsRRKWb/D603Fp8pMKsZU1V2TV/1OIvrC+1Pxddtal1t3kW1ndP4UCqzc++DW9pkf8AY2uy6THn7HPH59spOfLIOGXn86seHbeeOzuLm5j8uS8uGuPLJyUVsYB98Cq+sXEMPijQ/wB6gmLSIY9w3FWXAOPTIFbuTk+Tol+R5dLDKnH6yr8zlf0TdmvSzv6+h0FcP8R4Wu49Fs16yXuR9QpA/VhXcVxurXJuviRotgwDRQo8pUjo21jn8wlThrqfMuib/A9StKXK4Q3kmvvTu/krstWPgjQrPV47qG1YSWoRlG87WfBG9l6Fvf1rqKgj/wCPuf6L/Wp6znUlPWTudE6k525nex598XbU3HhyxKj5hfKn/fSsP54rv0jWNVCgDChR9BVDWtGt9ctIra5Z1SOdJwUxnKnOOex6Vo1pOqpUow7X/GxrUqqVCFP+Vv8AGwUUUVgc4UUUUAFFFFABVJP+Qu/+4f8A2WrtUk/5C7/7h/8AZaALtFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQByHjPT57nU9GvE0D+3IbczLLas0YRdwXD4c4LDbgcdz04qfw3HGmoyFPBS6IfKP+khbcbuR8n7sk89fT5a1dY1W40oQyx6Td38DE+abTazxdMHYSCwPPTJ46UzSvE2j6y5isr6Nrhfv28gMcy/VGww/KgDWooooAKgm/19t/vn/wBBNT1BN/r7b/fP/oJoAnooooAgt/8AWXH/AF0/9lFT1Bb/AOsuP+un/soqegAooooAKKKKACiiigBr/wCrb6GmW3/HrD/uL/Knv/q2+hplt/x6w/7i/wAqAJaKKKACiiigAooooAKgufux/wDXRf51PUFz92P/AK6L/OgCeiiigAooooAKKKKACiiigDCH/I9t/wBg0f8Aoyt2sIf8j23/AGDR/wCjK3aufT0ObDfb/wATCiiioOkKKKKAOW8f7ofD0WoqAW068gu9vrtcDH/j1YfiVbLX/EVrFaSuJRO1ncMV4UrtOQO/Dmut8WWZv/CWq26jLNbOVHqQMj9RXIfDSaDWJNWvJY1eRbpJkJ6ozJg/yrvo6UHU/lv+NjTF4GONy6UZfZa/OP6XOj8GXkMmjCwD5ns2ZJFPuxwa3bbrN/11P8hXJaLp1xpHjOcS4CXizNGAc/KGBBrrbbrN/wBdT/IVy1kue66nmZbKbw6hNWcdPu2/CxPRRRWR3hRRRQAUUUUAFMm/1En+6f5U+mTf6iT/AHT/ACoAIf8AUR/7o/lT6ZD/AKiP/dH8qfQAUUUUAFFFFABRRRQBBc9Yf+uo/rU9QXPWH/rqP61PQAVBB/rrn/roP/QVqeoIP9dc/wDXQf8AoK0AT0UUUAQXt5b6fZy3d1J5cEK7pHwTtHrxXmh8TeG9eu7uXV/F959jWd0isYla3jKA4G4oNzg+7D6CvSb6G6ntSlndLazblIlaISAAEEjGR1GR7ZriNUv59Nn1Oa18XWlpbw3QWS2h0cSsssnITKtl3PfAz60AX/DGj6JdSWWv6DaNpluomgMUcfli6TdtVmHcZXcCeea7Gsrw3dtfaDbXLakmol9x+0rB5O75iMFMnaR0IPcVq0AFFFFABUF5/wAeU/8AuH+VT1Bef8eU/wDuH+VAE9FFFAEDf8fyf9c2/mKnqBv+P5P+ubfzFT0AFFFFABRRRQAUUUUAFFFFABUH/L8P+uZ/nU9Qf8vw/wCuZ/nQBPRRRQAUUUUAFFFFABRRRQAUUUmQCBkZPQUAQj/j+f8A65L/ADNT1k6xqLaWktxHGJZSscccZONzMxAFWNI1H+1NPS5MRifcyPGTnawOCM1XK+Xm6GXtoe19lf3rX+ReopGIVSxOABkmuVX4ieHvlaS5eNJJAsbMh+YED5z6KM9TThTnP4Vc1V3JQWre33X/ACR1dFIrK6hlIZSMgg5BpagAooooAKKKKACoE/4/pf8Armn82qeoE/4/pf8Armn82oAnooooA4zxB4sv9K8Z2GmQxQNYyCH7Q7qd6+ZIyZBzgYwO1aF7LLrHiCLTUtz9lsZ0muJSwwx27lXH1Nc14lsZ9U1rxQtpG0l1bWdoYlQZO4OZMAevBrpPCN4mqDU9TjVlS4uRtDjBACLwR+Nd1SEY01KK1S/Oz/VlZnSi4UYR0u/e8/d5vxf4IseFfk0ye2/59ruaLH/As/1qDxldRW2m2rTgmAXSvMoGcxoCzcd+lZniK9ufD15f/ZdyjUUWSJ1H3JFID/mvNV/HV39p0KzkzxLZTzH8YsD9WqKdPmqRl0Z49GpGcfqT3TjF+jlyr8LP5ktjq/jLX/KSGyttKglT7Qt6y+aDEwBRQueX65/kO+fc+GZU8eaWdV1W4vri9gmBmVRDs8sKVCgdOpNd5o8fk6JYRf3LaNfyUVieIf3fjHwpN2E1xH/31F/9arhW99xgklZ7ej67n0vPGrKdHlSi1JWtvo+r1/Efa67/AGZp17BqUwe7sG8tcnDTgjKHHqa5fQrufU/iq8l0qJcQW0nmxochCAi4/WvRJNOspbtLuS1ha4T7spQbh+Ncz4HsraVtW1nyEN1cajchZsfN5e8YXPplamnOChOVtbW+88zB4aUL1KsrqCtFeqau79bO349TqI/+Puf6L/Wp6gj/AOPuf6L/AFqeuM0CiiigAooooAKKKKACiiigAqkn/IXf/cP/ALLV2qSf8hd/9w/+y0AXaKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigDg/FXi/TF1e30ceJZtK2+Z9raC3y4xtwu5gducnBAOfUd6Njp3grxFcT6XpqzX97LavK+rSl5ZYGBAU+Y/IbLZAXA+U1v6zBfQz6ereI7S11Ca4ljtpH01ZHZWwRGo3ZGAoy3fjOKr+Edbl1LV7mB/FEeqLHG2IRpptt2GCmRHJO9QQV44yfpQB19vG0NtFE0jSsiBTI3ViB1PuakoooAKgm/19t/vn/0E1PUE3+vtv98/+gmgCeiiigCC3/1lx/10/wDZRU9QW/8ArLj/AK6f+yip6ACiiigAooooAKKKKAGv/q2+hplt/wAesP8AuL/Knv8A6tvoaZbf8esP+4v8qAJaKKKACiiigAooooAKgufux/8AXRf51PUFz92P/rov86AJ6KKKACiiigAooooAKKKKAMIf8j2//YNH/ow1u1hL/wAj2/8A2DR/6MNbtXPp6HNhvt/4mFFFFQdIUUUUAMlQSRPGejKR+deOfCLzYPEdzEXdVktpN8eeNyugBx6jcfzr2auNNrZ6X8TLIQQx24u7GckKMCSTeGJ+uBn8K7MNUtTqU7br8rnfhK1qVWlb4lf7rmtqHy+LdGP9+OdfyANX7K7tpp7qKK4ikljlYOiuCV6dR2qhq3y+JNBftvmX80/+tXlOj6sdH+IY1q4JW1v55FlC/wAAkORnPYHB+i0UsP7aOj1S+/VnlYH2U8RVoylabbcV3tGLaPcqKKK4zYKKKKACiiigApk3+ok/3T/Kn0yb/USf7p/lQAQ/6iP/AHR/Kn0yH/UR/wC6P5U+gAooooAKKKKACiiigCC56w/9dR/Wp6guesP/AF1H9anoAKgg/wBdc/8AXQf+grU9QQf665/66D/0FaAJ6KKKACvMr9bHSvE5jvvEmixWtreTalDazSbZTcSKdok54VSxIPXGOK9Nrg7ddY0TUtVij8IC+hub2S4iuvtcCNIG5wQxzxzj2xwKAN3wbHGvh5Zo7+2vmuZ5riWe1P7oyPIzME9gTj8Kn8ReJtM8MWH2rUZsFuI4UwZJT/sj+Z6CrelSzz6dHJc6cdPlJObYur7OT3Xjnr+NYnxBgjbwVqs3lIZlg2q+0bgCy5APpwKANLVtbl0qZETRdTvlZNxktFjKr7Hc680/QNbi8Q6PFqdvbXNvDKTsW4VQxAON3ykjB7HPNZ3jWSSXS7TSonZG1a8jsndTgrGctJj/AIArD8a2WuLLTms7EukJm/dW0QGAdq52j6AfpQBl23ilLzUJLa10jVJoo7lrZ7pY08oMrbWOS+cA5zx2rYvP+PKf/cP8q8zVtIto7Y+Hr7UU8QnUgDY3F0xk+abMwkhB2hNpdt2PQ5zXpl5/x5T/AO4f5UAT0VB9nf8A5+Zv/Hf8KPs7/wDPzN/47/hQAN/x/J/1zb+YqeqZgb7Yg+0S/wCrbn5fUe1S/Z3/AOfmb/x3/CgCeioPs7/8/M3/AI7/AIUfZ3/5+Zv/AB3/AAoAnoqD7O//AD8zf+O/4UfZ3/5+Zv8Ax3/CgCeioPs7/wDPzN/47/hR9nf/AJ+Zv/Hf8KAJ6Kg+zv8A8/M3/jv+FH2d/wDn5m/8d/woAnqD/l+H/XM/zo+zv/z8zf8Ajv8AhUXkN9sA+0S/6vr8vr9KALlFQfZ3/wCfmb/x3/Cj7O//AD8zf+O/4UAT0VB9nf8A5+Zv/Hf8KPs7/wDPzN/47/hQBPRUH2d/+fmb/wAd/wAKPs7/APPzN/47/hQBPRUH2d/+fmb/AMd/wpGiKKWa6lCgZJO3gflQBYrjvEF5Nb+KLe7jP7nT4kaf/dkbaf0waXStT1C41aKW6u5EsbyOV7ddqgqqMME8emTS6dFbaml095e+VNrG4QRMVDPEgwCBjkjOeK6IQcHd/wBf1qeTVqvGwUKN073/APAVdP0u4/Ivamhu/Fml23WONTcuP93IU/mak0b/AETWtXsDwDKLqP3DjnH0Io03QxYXrO17cz3HkKhlcj7uTwBjgcVneKo7zTri21CyllaaVWs2PGfm5Xt65pK0nyL+uo6nPRj9amtU7tb6W5bfqdBqtzHDpd8xkUNHAzEA8jg4rzlbKTSLezkhtxLZ3NnFNdbk3Boy3zLz2IK5H0rV8UeGxpXhy8v7S8lSRLdvtBc7vOPBH/j2K6a20rzvDUFi88u1rRYiDjA+QD0rSElTgpJ3TZdbC4nEQ9rL3HH4bO+u77dLL7zH0i1i8O+NG0m0aRNMvbL7RBC0hZUkVsMFyeMgg12NeeiWSW+8HXUksgljmntJOmVOwrjp/siu7+zv/wA/M3/jv+FRiL3jJ7tfk2j151fb06df+aN/zRPRUH2d/wDn5m/8d/wo+zv/AM/M3/jv+Fc5kT0VB9nf/n5m/wDHf8KPs7/8/M3/AI7/AIUAT1An/H9L/wBc0/m1H2d/+fmb/wAd/wAKhWBvtko+0S/6tOfl9W9qALtFQfZ3/wCfmb/x3/CuZ1Lxjp2ka1Ppl7Lfq8aqyOkYcSEjJAwOMAg8+9XCnKbtFXKjFydkUl0yXUfFniSeC4ngmiEYiaJ9uXEYAB9RkHj3rQ8ASCTQZv74unD/AFwp/qKl8MQz3K3+ov58C3dxvizty6ADBPH1qt4VgaLVvEtqJ5FEeoeZgY/jRT6e1dE25RlHtb/I4sLhfayeLb6N27ptKL+UTpruzgvYik0SOQDtLDO0kEZH515brFw914WuwwIaw09bZlPVZGmC4/75Fep/Z3/5+Zv/AB3/AArjfGenWkGi3Yt5WM95eWyTruByd6kZGOPWlhJ2qJPuvzN6eFdTG0ai2Ulfz1TX3NI7eJBHEiDoqgVzHjJlgu/DdyzBVTVY0LE4ADKwrovs7/8APzN/47/hVXUdEtNWthbX+64hDBwkgUjI79PesaU1Gakzpo1FCopS2M678c+HrO/hszqEUjy7svE4ZI8AnDsDgE4wBTPAETR+CNOaQEPKHmbPfc7N/Iip7jwfo1zpz2DWiJbOVLJEiR5KnjlQD/8ArNayWnloqJPKqKAFUBQAPTpWk50/Z8kE9+vlf/M1qVKXsuSmnq9b+V/8/wABY/8Aj7n+i/1qeqccDfaZh9ol4C8/Lz19ql+zv/z8zf8Ajv8AhXOcpPRUH2d/+fmb/wAd/wAKPs7/APPzN/47/hQBPRUH2d/+fmb/AMd/wo+zv/z8zf8Ajv8AhQBPRUH2d/8An5m/8d/wo+zv/wA/M3/jv+FAE9FQfZ3/AOfmb/x3/Cj7O/8Az8zf+O/4UAT1ST/kLv8A7h/9lqb7O/8Az8zf+O/4VWhQpqzguz/IeWx/s+lAGhRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAch44ja3+x6pDrGnabPDHNbK9+2EIlC5KkHO8bAR+NUfCa6VLrljBp2uabeW+k6abe2htn3SvuMfmSSdgMqMAf3q0/Fdpf/2vo2p2OjDVvsnnJJC00cYRXC/MC5+8NvHsT0q3od9qF1dsLrwydMi8ssJ/tMMgY5Hy4Qk8jnPTigDfooooAKgm/wBfbf75/wDQTU9QTf6+2/3z/wCgmgCeiiigCC3/ANZcf9dP/ZRU9QW/+suP+un/ALKKnoAKKKKACiiigAooooAa/wDq2+hplt/x6w/7i/yp7/6tvoaZbf8AHrD/ALi/yoAlqrJfwR6jDYsT58qM6jHGB1q1XEzTy/8ACxY5nZjBHILZR2UtFn+ZrSnDmv5I5MXiHQULfakl9+521QpdwSXUtskgM0QUundQelTVx/h3UItS8ZatcQMTFJEm0kY+7gUow5k32HXxCpVKcOsnb5Wb/wAjsKKKKg6gqC5+7H/10X+dT1Bc/dj/AOui/wA6AJ6KKKACiiigAooooAKKKKAMNf8Aken/AOwaP/RhrcrDX/ken/7Bo/8ARhrcq59PQ5sN9v8AxMKKKKg6QooooAK5XxYWtda8Mahtykd/5DHPTzVKg11Vc942s3u/Cl48LbJ7QC7ibGfmjO7+hH41th2lUV+un36HRhWlVSez0+/Qx9X8U6bf3kTadOJpNOmJkbaduTG+AD3+6a5fQrBtYttR06IRlry2kijZ/urIpB9P85rjNGlne4uC4IinO88cFlPY+279a7HTQIY9PmRhtGr3GGB4wHjr1Z4dULqL7f5/meNm2FWDzCpiIN/uXTa/7esnf7mei+DdQl1HwtZyXJJuYgYJ89d6Eqc/ln8a3q5fwRxba0n9zWLof+PCuoryq6SqysexiUlWkl3CiiisTAKKKKACmTf6iT/dP8qfTJv9RJ/un+VABD/qI/8AdH8qfTIf9RH/ALo/lT6ACiiigAooooAKKKKAILnrD/11H9anqC56w/8AXUf1qegAqCD/AF1z/wBdB/6CtT1BB/rrn/roP/QVoAnooooAK8i1i50rT/GENx4gWZL/APtWbdLLHIytZNBIqCPAI28qCBzuzmvU9RtZr2wkt7e9mspXxieEKXTBB43AjnGOneuG0e01XUZtWWfxvfwmzupLdYtkAdAvR3ynRuo4HBHNAHQ+CIp4fDEKypcRw+bKbWO4z5iW5kbyg2ec7cdeQMV0Vc74JvLm98NpJdXr30i3E0QuyFAnVZGUOuABtIHH8z1roqACiiigBNq7t2Bu6ZxzUN5/x5T/AO4f5VPUF5/x5T/7h/lQBPRRRQBA3/H8n/XNv5ip6gb/AI/k/wCubfzFT0AFFFFABRRRQAUUUUAFFFFABUH/AC/D/rmf51PUH/L8P+uZ/nQBPRRRQAUUUUAFFFFABWL4pdxo3kIxU3M0cGR1wzYP6ZrarmPGeo2+mQaZPdsVgjvUlcgZOFz2/EVpSTc0kc+LTlRlGO70+92/Usa7oEmoRWENk626wExsf7sTLtYD8OKwNd0KLWPGVlpUUslt9i0ozW8sZ5ifzAqn36V2/wButPMjj+1Q+ZIAUXzBlgfQd65vS5Y734i6zPFIsi29lBBuU5HJZiP0rejOcby7J/jp+p24KgqFSpXgrO3+SKx8OeJZLoLJ4smzIg+0lLdUwuTxHj7p4xn3qWbQvFqYgtvEsMluh8xJLm1DS5HRSRwRnqetdQP+P5/+uS/zNT1n9Zn2X3L/ACNPrU+qX/gK/wAjidW0/wAY63p76PfRaQltclVlurZ3zGgIJ+VupOMV2oAVQB0AwKWionVc0layXYzqVnNKNkku3n/wx53qP+j3VqOhg8TRn6LID/jXoleceOGbS7i4nljYQ3F1a3EUgIwGjIDZ9ODmvQZ7hILOW5JBjSMyZB4IAzW1ZXhB+v6HPQklhIp/Zcl9zuvwaZKzBVLMQABkk9qpaXq9rrEU0toWZIpTEWIxuIwcj25rB0vw/Lf6VazXmo3jR3K+bPb+Z8r5OQPUDBGR3q9osSWmvazaRIqRKYZEVRgAFMcflWThFJ63aOKGJrTnTbjyxl83s2vTb+uu9RRTZJEhiaWV1REGWZjgAVkeg3bVjqgT/j+l/wCuafzauV1LxLqUlqZLOyKWl0Tb205JDlzwGx2HXH0rU0W4vI9SudO1F1kuIYkKTL/y1TLYJHY84NaOlJK7OKnj6VSpyRv6208vvS06GjqOqWmlQpLeS+WjuEU4zzgn+leY+H4dS13xDeearNaTTqblu23Akwfr0FdvrdpFrHiCw0ycFoUhlnlUHHX5VP51W8FwpHdeIvLGI01JoVHoERQBXRSahSk+tv1F9W+uSqup8MLJd7txb/C8fmzqwAqhVAAAwAO1cxdeHdYg1y/1TRtZht/tpjMttNah1JVdud2c/lXUUVzQqSg3br8z0qdWVO/L18r/AJnHT+Gte15Wi8QarFFbIhWOLTdyeYx6O5Pp129KpaT8O547uC+1bVpri4MomuokJ2TOh/dnJweB2xXfUVssXVS5Y6L0N1jayi4xdl5Jf1/SCiiiuY5AooooAgj/AOPuf6L/AFqeoI/+Puf6L/Wp6ACiiigAooooAKKKKACiiigAqkn/ACF3/wBw/wDstXapJ/yF3/3D/wCy0AXaKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigDi/Hq25fTW1aOeTQlE32pY1ZlEu0eUZAvJT7/tkrmsj4ezQXl5pE+licxRaLHBqchVliaZRGI1GeC6gSZI7EVe1y11a28TadaL4vvrSDUHmfLxw7U24IiTKdTu4yTwp61b0s32n+OU02TxFc6rBJYyTNDKsWYGV0ALbFH3txx06Hr2AOyoqjq76immyf2THE96zKqecfkQFgCx5GcDJx3xiuVvPEmp6C+vW93d29+bDTPtqTrD5flyHcFjcAkc4BHQ4z9aAO4qCb/X23++f/QTXN+FNZn1O7kjn8Q2V+6QhmtobFoHUkj5gWc7l6jIGDkc10dwwWa3LEAbzyf8AdNAFiimedF/z0T/voUedF/z0T/voUAR2/wDrLj/rp/7KKnqtBLGHnzIvMnr/ALIqbzov+eif99CgB9FM86L/AJ6J/wB9Cjzov+eif99CgB9FM86L/non/fQo86L/AJ6J/wB9CgB9FM86L/non/fQo86L/non/fQoAV/9W30NMtv+PWH/AHF/lQ80Xlt+8Tof4hTLeWMWsQMiZ2Dv7UAWK4dyHne/yMf28i57bVG3P611d3qtnZSW6TzKpnfy0OeM47+lcxbwx3Xw4kXKeYyyTckZ3By354Fb0tFd9bI8vHtVJckdXFSl81a35j9L1by9d8SRyy4RS0i7m6bcg4/T8qxNIn/4RfbeugkefTvNVAcZJkAH6YqjewyT2UesRuqm6uZo2XPzfPn9Mbq6LxdpJnn0oWg3xgC3cIOFUEYz6DrXTaKdn13+R4ilWqQdSK1hrH/t93v8jQ0nxhHqniBrBIwsDJmJ+dzMACQfb735V1FeeR2o8O2+gXkkS+esri4x2D8cn1A/lXRWGuvP4s1HTpJE8iNFaHoMEAbue/Jrnq01vDb/ACPXwOMmlyYl++2vldXX6o6GoLn7sf8A10X+dSedF/z0T/voVDcyxlY8SL/rF7+9c57BZopnnRf89E/76FHnRf8APRP++hQA+iuWi1118dz6c0m+3eNVT5/lQhdxOPfpXTedF/z0T/voVUoONr9TChiIVubl+y2n6ofRTPOi/wCeif8AfQo86L/non/fQqTcfRTPOi/56J/30KPOi/56J/30KAMVf+R7k/7Bo/8ARhrdrAWSP/hOXO9cf2cBnP8A00NbnnRf89E/76FXPp6HNhdp/wCJj6KZ50X/AD0T/voUedF/z0T/AL6FQdI+imedF/z0T/voUedF/wA9E/76FAD6bLGk0TxSKGR1Ksp6EHqKTzov+eif99Cjzov+eif99CgDzHxr4XfSfDmgW+nySSRWdw8BJGWIlOSePQjH4im+G9Nhl+ErzzRBpFkkliY9V+YD+lemytDLE8bSLtdSpww6GuYfTrbRvAl5pkExeOCNlVnIy3fPFd6xblTVN73v+f8AmaY+vGrl9SnNLmerff4nr6N6DfAVxFLFrCoWLPfvcnPTEnT/ANBNdfXAeAWFreXUUjqonhjlQFhyASD+pNd550X/AD0T/voVz4lfvGcGAxFTEYeNSr8T3H0Uzzov+eif99Cjzov+eif99CsDrH0Uzzov+eif99Cjzov+eif99CgB9Mm/1En+6f5UedF/z0T/AL6FMmlj8mT94n3T/EPSgB8P+oj/AN0fyp9QwyxiCP8AeJ90fxD0p/nRf89E/wC+hQA+imedF/z0T/voUedF/wA9E/76FAD6KZ50X/PRP++hR50X/PRP++hQA+imedF/z0T/AL6FHnRf89E/76FAEdz1h/66j+tT1WuJYyYcSL/rB3+tTedF/wA9E/76FAD6gg/11z/10H/oK1J50X/PRP8AvoVFbkNLckEEeYOR/urQBYooooAKx9T8LeH9YulutS0iyubgADzJYgWIHQE9x9a2K8l1jw/p7and6lrej3Ny9vq7NeSvE7h7ORXEZTHUJ8mQvI2mgD1aBIYoEit1jSKMbFSMAKoHGAB0xUlcx4AhS38JwxQWz29os8/2VZIyjtD5jbGYHnJXHXk9e9dPQAUUUUAFQXn/AB5T/wC4f5VPUF5/x5T/AO4f5UAT0UUUAQN/x/J/1zb+YqeoG/4/k/65t/MVPQAUUUUAFFFFABRRRQAUUUUAFQf8vw/65n+dT1B/y/D/AK5n+dAE9FFcnf6pex+Kl8q4ZbOGeC2ljwNrGQMSfw4q4QctjnxGIjQSclu7HWUUUVB0BRRRQAyWVIIXlkOERSzH0A5rzLXtNuNTtNG1C+uHkXU7+KJ7dj8qqz5G322g5+tdt4plZdDkt4ziW7dbdPcscH9M1neJokXUPCligwi6grKPaONq68M+WSa8/wAEZUaca+NipbQ9757r7uX8SRvh74Z+xNbRacIScETI7eahHQhySRWrpGgaXoMbpptoluJAocqSS+0YGc/U/nWlRWMq1SStKTa9TrniKs1yyk2vUgH/AB/P/wBcl/manqAf8fz/APXJf5mp6yMQooooA57xdbwzWVm88avCl0glVuhjbKsP1rn4tLa38St4XttWvRpzW/mNFK/mEKNoMak8gYI/Wu5vrKHUbGa0nGY5Vwfb0I9wea4a002+0v4i6XLfzrNJdxXESuP4lREwT78fpXZQleLjfZN/qcroVKtZ0tfZy1ev8sZb+un4o0PFfiPUfDupaVZaVp/2xZYpN0CqS3G1UwR0AJ59qw7fxu1prUd3fWeyV1W01JFJAtnVyC/f5eV79+tdLN+9+KFqn/PHSXk/76lA/pWZ8QbOFI3uUiRXuLK5jlcDBbbHuXPr0rWl7L3aco6tb/eejiFRjCjGUOsdfWVvyZ3YIIyORXJS21/4j1O523IXSI7gQSQn/loEwSR9W4//AFVpjVFs/CEGoHk/ZI2Qf3mKjA/MirOhWLadotrbP/rQm6T/AHjyf1Nca/d3fXY8zFU1VrKg9lq/ySfk9fuL+xAqrtXauMDHAx0rl9a1GLRfFdrfTh/JktGhcoMn72RXVVSuLaO8e7t5RlJYFQ/Q7hUwkk9djTE0pVKdoO0lZr5f1YzdPmin8T6ndNIgRFitYmLfeOCxA/E1W8D/ADWmsTf89tXuX/8AHgP6VieJfDwttE0DR1unSWfVkL3EYw24q/zD6cflXZ6Lo9voemJY27yOqku0kjZZ2JyWJ9Sa6KnLGlo9/wAkdeFp+ywSc/jm7tdtW9/w+RoUUUVyEBRRRQAUUUUAFFFFAEEf/H3P9F/rU9QR/wDH3P8ARf61PQAUUUUAFFFFABRRRQAUUUUAFUk/5C7/AO4f/Zau1ST/AJC7/wC4f/ZaALtFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQBU1HTbDVrQ2uo2kF1bk5McyBhn157+9QaVouj6GjQaXY2toJPmZYUCl8dz3PWsHxnpEWr6rosGoWk9zpbGaKRY1ZlSZwoidwOw+cZPAJFc78PrCxsvENothpMlreRaY0Grs8DIqTqyBdrMMZbDn5eCAD2oA73xG2sDRJ10GOJtRfCxmVgoQE8tzxkDp74rA0vTtbttEvbGDRbSyuHQyi5urz7UbmbIz5vygnIzz27DjFdpRQBzFnZapqXim11nULBNPSztZIEjE4keVpCpJJXjaNnHck9BW/cKGmtwwBG88Ef7JqxUE3+vtv98/+gmgB/kRf88k/75FHkRf88k/75FSUUAVYIYi8+Y04k/uj+6Km8iL/AJ5J/wB8imW/+suP+un/ALKKnoAj8iL/AJ5J/wB8ijyIv+eSf98ipKKAI/Ii/wCeSf8AfIo8iL/nkn/fIqSigCPyIv8Ankn/AHyKPIi/55J/3yKkooAieGLy2/dJ0P8ACKZbwxG2iJjQnYP4R6VM/wDq2+hplt/x6w/7i/yoA4/x1aNcPYQW4RHCTTHtwig/41paFaLN4JhjESl5LdwOBkk5qrJbzaj48ukbebeC18tj2Tev8zk/lXSafZrp+nwWiMWWFAgY98d66JytTjH5nj4ai6mLq17aO8fusv0Zj+HdHh/4Rmygv7NTIpMhWVOQ24kdfY1u+RF/zyT/AL5FLJLHEAZJFQEhQWOMk9B9afWMpOTuz06NGNGnGEeiS+4wvFcEf/CMXxWNQQgIIX0YVyXgwNLrNvPOTI03nKWfknCof613ur2hv9Hu7VRl5ImVRnHzY4/XFc7ZaaNI1Dw1Bs2SMk/nDOfnKAn+Vb0pr2bj11/I8jHYebxkK32Vy/fzJfqdX5EX/PJP++RVe5W3DxxgRCQup24GcZ649KtO6RxtJIwVFBLMxwAPU1wdnrR1Xx7vXZ5SKYIyv8Sh85/GsYQck32PSxGKjRlCD3k7HdeRF/zyT/vkUeRF/wA8k/75FSUVB1Hn0VvFF4m/thiPKOpvabSPlxsIz+efyrS1zXLVrLS7vT5UEMl4okYDb8o+8re3IqleQCfwLqEgUs4vnkXHr5mP5E1zpgMcP9mSqweJZLhkYYKnyA38x+ld6gpu76aHyNTEVMNF04LSdpX82/8AgM1fFuqXEGuXC2x8uNIlgUoccnDk/XtXoUMcMkEcnlJ8yg/dHcV562g3GqeFG1eW6BmJe4YFP4VUqAP++RXf6Y2/SrNv70CH/wAdFY1lFRSXTQ9PLJVpV6k6l7TSkvR3JvIi/wCeSf8AfIo8iL/nkn/fIqSiuY9swFij/wCE5kXy0x/ZwOMf9NDW35EX/PJP++RWOv8AyPUn/YNH/ow1uVc+noc2F2n/AImR+RF/zyT/AL5FHkRf88k/75FSUVB0kfkRf88k/wC+RR5EX/PJP++RUlFAEfkRf88k/wC+RR5EX/PJP++RUlFAEfkRf88k/wC+RVKTT4L6zvLWRQqylkLKBkAgdK0agtus3/XU/wAhQnbUUoqScXszi57a00fx5o0BZFt1smj3SkDOA5yT0zxXaolvKu6NYnU91AIrlvEug2PiDxDBZX6sY5bNwjIcMjKwII9+T+dZHw/v4tLvZ/D6StLb+YxgdiOoyT+YwfwrsnBVKaknql/nqcdPEYehBUm2pczSVtLb7/NL5noXkRf88k/75FHkRf8APJP++RUlFcZ2kfkRf88k/wC+RSeVBu27I92M4wM4qWuS1PUE0/xuLiUt5cemsWC9ThmOP0q4Q5nZHPicQqEVKWzaX3nU+RF/zyT/AL5FVbiaxjlNqzQLcPGWSM4BI9qh0TXINchmmt0dUjfZ83U8A/1riPEpvV8V3WoxK5gsmiVnzkJlV4/Ek/nV06TlJxehzYvMI0qMa1Ncybt8tbv5WPQ4I4GiQbIywUZGBkcVJ5EX/PJP++RXB+EdWk1DxZdTSgL58GFVeny7cfpmvQKmpTcHZm2CxccVTdSO12vuI/Ii/wCeSf8AfIo8iL/nkn/fIqSiszrI/Ii/55J/3yKPIi/55J/3yKkooAj8iL/nkn/fIo8iL/nkn/fIqSigCrcQxAw4jT/WD+Ee9TeRF/zyT/vkUy56w/8AXUf1qegCPyIv+eSf98io7ZQstyFAA8wcD/dWrFQQf665/wCug/8AQVoAnooooArX1ob21MC3VxbEsreZbsFcYIOMkHg4wfY1wmrSJbazdnVtX8V6RbtKTHPG6NaFc8YZUbYPZsYr0Sucu/DV3qt5M2p67eNYs58uytD9nQJ6Oy/O35ge1AGloaQJpEAttTl1OE5K3csqytICT/EoAOOn4Vo1V07TbPSbGOx0+2jt7aLOyOMYAycn9TVqgAooooAKgvP+PKf/AHD/ACqeoLz/AI8p/wDcP8qAJ6KKKAIG/wCP5P8Arm38xU9QN/x/J/1zb+YqegAooooAKKKKACiiigAooooAKg/5fh/1zP8AOp6gJAvck4AiP86AJ64iVTP4V1PVVGWlvftMZ/2UcAfoDWjceIH1HQYZrD9zLd3QtFLfMVySM/lzXO6vr0nhzU59HaCW50O300RzbEBZJHDFWJHTdjHoM110KU27Lf8AyOJUJZlJwoapRb+ctFbz3+9GvqvipJYdYsVUKVTy7dwf9YThWH1G78q1vC0sp0k2k5zNZStbMfXb0P5EVz/g22hvYLrS9SsVFxp80VwGJ5zIgcZ9xjBHTirT3mp6f4p1Kz021jn83F0yuSMrtAIHuTROCV6a6a/kcbWJw1WFfFfasrLVrmTl+GiN3TdYXUNT1KzCBTZyBAc/eBzn9Qa1K4nR7TVLEjW4bIzNdCQ3NsW2SDMhIIyOTjtXUWGq2+oWclyokhWJ2SVZhtZGHUEVjUhZ+7sdWCxMqkUquknd+q3X3Iz5B/afi1Yzzb6bGJCOxlfpn6LzWFp19qPi7X9G1VdKa20yykuGE7TKwlyCi4HB6g0kPijStP0LVr6XUbdby7knlii3jzDgEINvUdO/rXR+EbT7D4R0mAjBFqjMPQsNx/Umuhp0oOTWuy+53PRwCdPDSxEo6zejd9mmtPlY2aKKK4iSAf8AH8//AFyX+ZqeoB/x/P8A9cl/manoAKKKKACuX8R/u/FvhSf0uJ4/++oiP6V1Fcr47E1tptlrEMRl/su7S5kjB5aPBVsfmD+Fb4f+Il3uvvVjowutVLvdfemh1r+9+Jt+/wDzx0yKP83Zq0vEWmW2paPcLcIWMUbvGQSCG2n8/pWL4H0bVLb7TrOr3n2i51KGFwpXDRgBjtP/AH0PyrrpUEkTxnoykH8aqs1GolF3tYWNhTnL2T95JJfhr+Jx/h6G41vT9HeWIRaZZRIYgTkzuq4DeyiuyrlPh1ewXPgyxgSaN57ZTHNGrAtGdzYyO2QKt+M9Qu9O8OSNYS+VezSxQQPgHDM4Hf2zRVg5V3T87fiZU8C6VZ0eZuTdrvr0OgqBP+P6X/rmn82rmE1rxJokgXXdM/tCB1GyfSImcqw7Mh5565HH9NfSNb0/WT9qs7hWSVFUKxwwYFsqR1yPSs50ZRV913RpUoTgubdd1sZ3ir5ta8MR+uobvyRq6iuX8R/N4s8Kx+txM35RGuop1Pgh6P8ANlVv4dP0f5sKKKKxOcKKKKACiiigAooooAgj/wCPuf6L/Wp6gj/4+5/ov9anoAKKKKACiiigAooooAKKKKACqSf8hd/9w/8AstXapJ/yF3/3D/7LQBdooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKAOP8SWV1Z21vsuvE00HmyySy6dLG0kYYggFSuWUc42gke9P8JPpk93LLY+KtS1SRYyslpezLui5HzGPYrKeMZPqa2dZ0/UtREMVjq76dFk+e0UKvI44wFZshe/ODUWkeGNM0a5e8hSaa+kTZJeXUrSzOuc4LMeBkDgYHHSgDZooooAKgm/19t/vn/wBBNT1BN/r7b/fP/oJoAnooooAgt/8AWXH/AF0/9lFT1Bb/AOsuP+un/soqegAooooAKKKKACiiigBr/wCrb6GmW3/HrD/uL/KoZr+BL1bAsfPkiaRRjjaOKmtv+PWH/cX+VO1iVJSvZ7GRonz6xrk3rcqn/fKAVuVh+G/m/tZ/72ozfkMD+lbJmiDunmJvRdzLnkD1I/A/lV1PiMMHrRT73f3ts4nxbYTeJvFWn+G5Jmi04W7XtwYyA7YJRcEg9yPzNO8Pazf6Hq0PhXxATLM6k2V6uWEy9cMTzuHI/D6ErpOsWGvfEb7bptws8A0YpuAIw3n8gg8jpVzxvoH9p6euqW00sOo6YjzWzIwAzwTnjn7tdt0uWhUVlb7n3/z8j3rpcuGqqyt809df8/I6qud8S3cWn6jo19OSsMUzqzAZwGTFcl8NfEk+qeItXW5GPt3+kp83AZcBlGfZh+AruvEOkjWtIltRsEuQ0TN0Vh/9bI/GuedF0KvJP+rnkZthKtGEqcdZKzXqrP8ANWMfxbeS3w0/SLN49mo4JkP93IIIqpHosPh7V9ATcrSSPKskoGN542/zxVPwsZdV12zivAyPpluwVcYPBAAP5/pW34v1CwtnsBJMn2y3nS5WMfe8sMAx/UVdnFqkvM+fppYmE8c1qrW8lGzf43OppkriKF5D0VS35VE19apNbwtMokuQTCv98AZOPwqr4gvV0/Qby4ZSwEe0AerfKP51yqLbS7nu1KsYwlO/w7/dcg8KR7PDNmTyzq0hPqWYn+tYyaeLvxrr0RAzJZ7FJHTcqjP86z9U8YTeHNF0mz0+0F1dLaR3FyMEiGHAySAepzXWm+0a3EOrSXFvD9uVEjmd8eYCMqBXS4Thedvivb7zF5bUnhsO2tNH90bfqQrYPYeDJbGQqZEs5Fbb0yVP+NXNDbfoGnN620f/AKCKyvC+vP4q8N3FzLEkcoklgdU+7x0xn2Iq74XfzPDOnt6RbfyJH9KzqRlG6lumVKg8PilRfSLX3Nf5mvRRRWB0mGv/ACPUn/YOX/0Ya3Kwk/5HqX/sHL/6MNbtXPp6HNhdp/4mFFFFQdIUUUUAFFFFABUFt1m/66n+QqeoLbrN/wBdT/IUAc740sbu5jsZLHzPtBkNv8n91xzn0HHX3riNA0+fS7ubUppUD2eqi2mj/ujA+bPp2r2CvMfHVq1h4hMtvIwj1O33XEX8JeIqAw98Gu7C1HJOl/X9WOCvgKbhXr/a5eZesNfxt+B6Vb3EV1bpPBIJInGVZehFSVyXgKa6/s64s7ncv2dxsRlwVDDP/wBf8a62uSpHkk4m2Er/AFihGra1/wA+ojMFUsegGTXkN++o3d1cao0LCO5O1dxyAr5CgH6CvVtQfy9Nun/uwuf0NcVr8cjfDjSrSKTymupLaHeBkrvI5FdGFdpW7uxzYzAvMK0MOpcu7+eiX6lnwak2l6zqejTbfkxIDnr0x+YINF2sd7pXiyVZU8sOH35yMRqGPT6VwMN9c6V4uhOoTPdPp195BkAw0iKjAE++MV2VjFJB8MddnkUg3EU7rnuCmP55roq0eSalfe35kQwUYV6eXyd1ee3SL0X3qRnyaUJNK0CbSXKyXqtukDEDdhePb7pr0HQ71tQ0OzupDmR4xvPqw4J/MVzfw0llTw9Lpc4BewmKK3qjjeD/AOPGqlxdRw/Dg23nKs29lVN2GwJuwrKtFym6fZ7+TuGIwsMpxNRwd48rdv8ADy2+bu/vO/oqK2cyWsTk5LIDn8KlrhPUTurhRRRQMKKKKAILnrD/ANdR/Wp6guesP/XUf1qegAqCD/XXP/XQf+grU9QQf665/wCug/8AQVoAnooooAgvRdmzlFi0K3W390Z1JTP+0AQcfSuEi1bxbp0lxBqes+FIJjO7Kl3KwYIT8uAGGFx0zz616FXnUUKadq2sLeeB7vVDNfSTJei3hkMityB87AgDoPYDpQB1/ho3DaJHJd6nb6lNJJI7XFscx8uTtXk8L938Kr+KPFdl4WsfOnSSe4ZS0VvECWYAgEk/wqMjJP8AOr2iPG+lRNFpcmmIS2LSSNEZOT2Ukc9eves3x3BJceCdVigieWVogFRFLMfmHQCgCxrWuzaZfWVjaac99dXayOsayrHtVNu4ktx/EB+NaNhPc3FnHLd2htJ2zuhMgcrzxyODkc/jXNeKYNCfW7OTxBpEtxb/AGeRI7oo8sSMWUlGRQcE4BBI7etP8Hyy6dotra3UV4kdzdzixjljdnig3M0ayH+H5Rxu9h1oAfF4x82SG4OmyrpE919kivvNXl9xQMU6hCwwDnuOADXQ3n/HlP8A7h/lXnVusr+FNO8IfZrkapBfxJKDA4VI45xIZd+Nu0qoxzyTjrXot5/x5T/7h/lQBPRUH2Vf+ek3/f1v8aPsq/8APSb/AL+t/jQAN/x/J/1zb+YqeqRtl+2IN8v+rY/6w+o96m+yr/z0m/7+t/jQBPRUH2Vf+ek3/f1v8aPsq/8APSb/AL+t/jQBPRUH2Vf+ek3/AH9b/Gj7Kv8Az0m/7+t/jQBPRUH2Vf8AnpN/39b/ABo+yr/z0m/7+t/jQBPRUH2Vf+ek3/f1v8aPsq/89Jv+/rf40AT1yl1c6vquo6jFpht0hhVrYibOWbHzEEdCMjFdJ9lX/npN/wB/W/xrz7SPFFhYtrRuDMZIrolI4nO+dpD8qqO54Nb0YSldxV2jnq0Z4ipGjC+t27b9F+bRd0bTLi28QRaV5bNb2UxuXk/hy0QC/rmtLw+PP8Y+Krjqvm28A/4DHz+rVRlh8ZaxBKbZbbSLeQ7FS4mkedV6F8qdoPoP/wBda/hzwsugW11E1/c3bzzmUyuxDYwAAcHk8da2m0oS5mrtWsteqb8jqwmCp4GjUhzpuXTsrrTtokV9L/dfETxAn/Pa2tZPyDLXSCCITtOI0ErKFL45IHQZrlo4Fj+J00e+XEukLJnzDnIlI6/jXTfZV/56Tf8Af1v8axr7p+S/I1xKTlF+S/KxyuvXuvN4xhstEuYlMOntdPbzLlJv3m3aT1B9D7VneXLrmiX+rxyrBp8qNcXNrz5izxoQyH23DPvxWraW6zfEnUTvlxBp0MefMOfmdm61jKq2vhjxxCXkBhuroqN5xh0BH8zXVG1lFLVcv4/0jWthKOJjCE4/Dyv1vun3Wp0PhDR9Pj8NabdGxtvtVxaxvLMYl3uSoPJxmul6Vl6NYrFoWnx75Rsto1wJCOij3q79lX/npN/39b/GuKrJym2znqu83bYnoqD7Kv8Az0m/7+t/jR9lX/npN/39b/GszMB/x/P/ANcl/manqkLZftjDfL/qwf8AWH1PvU32Vf8AnpN/39b/ABoAnoqD7Kv/AD0m/wC/rf40fZV/56Tf9/W/xoAnoIBGDyKg+yr/AM9Jv+/rf40fZV/56Tf9/W/xoAnoqD7Kv/PSb/v63+NH2Vf+ek3/AH9b/GgDC1Hwda3up/bba9utNMkfl3C2TCPzhnIycdevPWsq58GanHrGkeTq95eaXBcrNLDeShjGUyUKnHPXH5fh2X2Vf+ek3/f1v8aPsq/89Jv+/rf41vHE1I6XOmGMqxVr3J64PWdJsrD4iaJqVvAI57icrKVPDExuM46ZOK7X7Kv/AD0m/wC/rf41yfiyFY9Y0B90n/ISgTJc5+YSDr+FPDSanZdU/wAh4STVSye6a/BlvXefHXhRfT7W3/kID+tdRXJarbqfiB4eTfL8sF03+sORworpvsq/89Jv+/rf41NX4Yen6smt8FP0/wDbpE9FQfZV/wCek3/f1v8AGj7Kv/PSb/v63+NYnOT0VB9lX/npN/39b/Gj7Kv/AD0m/wC/rf40AT0VB9lX/npN/wB/W/xo+yr/AM9Jv+/rf40AT0VB9lX/AJ6Tf9/W/wAaPsq/89Jv+/rf40AEf/H3P9F/rU9Uo7ZTczDfLwF/5aH396m+yr/z0m/7+t/jQBPRUH2Vf+ek3/f1v8aPsq/89Jv+/rf40AT0VB9lX/npN/39b/Gj7Kv/AD0m/wC/rf40AT0VB9lX/npN/wB/W/xo+yr/AM9Jv+/rf40AT0VB9lX/AJ6Tf9/W/wAaPsq/89Jv+/rf40AT1ST/AJC7/wC4f/Zam+yr/wA9Jv8Av63+NVoYxHqzgFj8h+8xP92gDQooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKAOM1248XWWtRXMN/oNtpKiRQ12WQc7dof5uW4bG3jrntU3h++1PUdcMl5r+h3MUduwFnpchbcSy/O2WJ4xjj+9TfGVq7atol6dCm1q3t/OWS2REYLuC4fDEAkYwP948jHNnw9Payag6weELnR28ok3EttDGGGR8uUYn39OKAOmooooAKgm/19t/vn/wBBNT1BN/r7b/fP/oJoAnooooAgt/8AWXH/AF0/9lFT1Bb/AOsuP+un/soqegAooooAKKKKACiimyOI4nkPRVJP4UA3Y46W+M3xKEOABFbmEY7/ACF/6/pXXW3/AB6w/wC4v8q4OzSS2Okazeybpr26cswHQOm1RW14ru5bPwtDLDIySb4sFTg+v9K6akLyjFeh4uExLp0a1afdyt5NJot+Ffm0mWb/AJ63Uz/+Pkf0riPFF1qOm+ONT1eyYmC0tYI7uIDJeJ8jP4HBrqbe4bSvh080Muy5SwluIycZ3YLZx9SK574bRaheX2unWw1y8kVujNMNwkUhmAOevysPwNdFFcntKz1S0t31R9Hk0HSwft5WaUUrd72ubXw68P2WleHLa/hiYXV9CkkrucnpkAeg5zXXSxJNC8UgyjqVYeoPBpURY0VEUKijCqowAPQU6uKrVlUm5vqZ1q0qtR1Huzza+8MR+CdKsNRtpmlFhqfnvIRtZYJMIyn1/h5+vFbieNB/wnsvh2WCNLcKBFcb/vuUVgvp3P6V0t/ZRajp9xZTruinjaNh7EYryT+wtQ0jw1r1xqKM1/p11bPbXIUjIj2qCpwMjYcV3UXHEJ+1d5bfe1Z/J3+89KhKGKi/bO8tl/281Z/J3v6noGn6ZJaeNtRuQjeTPAGD7eMkjIz6/LmuBlsU1X4uapbTO4SbMe5TyMLHXQ/E2W+msNKj0u4lWSWSSVfJY/OEjLDp16cVy/g2XULrxrZX+pRSJcXTSsWePZvwsfzAe/txWlCElSlWvvFr7v8AhjDAZdClg6sm7qalp6v/AIDNjSpbu18WafYXsrSPaTtGCWJABUAAZ7cfrW58Qmnks44IpSiLb3Fw4zw3lpuGfXmtLX7O3guNOuo4lWWXU4DI4HLcEVkeMp0uTf8AlSK6waPcNuU5GWOzH6GueElOpCVj5TCYX2beFm7qU4/c2tPuWpW+HekalMl7qmvRh2uII7aHzFGWhC5/FSCv5GuK8T/adM07/hFpWHmadeNcWzAH5oCjODk9cHI/TtXtOjLs0LT19LaMf+OisfXPCUWr+JtL1YiIiBXiuUfJ8yMqwAA6dWP4GtaWLSrylPb9Vt/l8z7KhjoxxEpTVl0S2TS0/wAvmct8IbuWO3vdNni2+bi9iYn76n5CfzUV0nhzVI7LS9FsHTJumnQNnG3ax/nkCqZ0m38NeJPCcFozmHy7izd3I3MCN6g4x/FmsWa/isb/AErzGO2zu5mcDkhfM/8ArGlWUa03OO0tfuujwOIcWqeIhioaKSf/AKVFP8Ez1GiobW5jvLSG5iz5cqB1yMHBGamrzdjdNSV0YSf8j1L/ANg5f/RhrdrCT/kepf8AsHL/AOjDW7Vz6ehz4XaX+JhRRRUHSFFFFABRRRQAVBbdZv8Arqf5Cp6gtus3/XU/yFAE9cZ4z0+G98Q+GROX2PPNAwU4yGTP/sors65Xx2s1tpVprMCB5NKukuTGTjen3WH5Gt8M2qqt1uvvVjfDRUqnI/tJr701+pa0z/R/F+rWwTCPDDIv0C7f8/Sty4nW2tpbh87IkLtjrgDNYH222TxfHctPGsFxpqlHZgA37zjB980vivW4dOs2spEJa7t5lVweFO3j8ycUnByklY8aNaOHo1HJ25ZP8Xp+LOGW/vpI77VjczlHkNvhzkFXVzjn0wOPeryprev67YaVbNENK0oWM1wHwCGCb8jucjjH06da19e0prLwBb26IxaFkkkwOQTnOfpmpfCC7fEniIei2S/lAK7FUXLKcVtt96X6iyGlPC1qrnrJQT17ylr9xy/jnRUs/EzXkb5F2wnKY+6wXYfrnrUunafrXi7TrWzuo0tvDdsWBMb4knZQRz7bs9h/Wum8R2q3Xi/Qo3ztJZj77Tu/pXR29jbabpn2SzhWGCNW2ovQZyT+pNQ8U404/wA3Ty6febYCc446tiHq1aK8tE7+pznhgLaeLfENkF2Bltp4hjqvl7Tj6EYrmtY0Nj4h1WOGNxa2sbTsQeFym4dfVq6W62WHxB0W5J2i/sZLUnPBZMOv48mt3WLeL+x9UdY0Ektq4dwOWwpxk98VLqOMlJfaS/DT9DTPMHHF04Tl0Sf3Jq3za1MbVb6W1tPDjRysgkniD4ONylQCD+ddVXji+KLvxAsViLRIjpmWjfcfnCKc59/kP5169bTC4tIZwMCRFf8AMZrPEUpU0lLcwwznHEVKU+ii/vin/kS0VFc3MNnbPcXEgjiQZZj0FPjkSaJJY2DI6hlI7g9K5rdTv5lflvqOooooGQXPWH/rqP61PUFz1h/66j+tT0AFQQf665/66D/0FanqCD/XXP8A10H/AKCtAE9FFFABXnWsz+Bhq94t/wCI763vPMYSxx6hOgRu4Cg4H0Fei1xcEWseJr7U5o/EU+mRWl5JaxW1rFGSuzjdIXBJLdccDBFAF/wIzN4Vh+a5kiE0wgmuWYyTReY2x23c8rg/yAHFdLWJ4T1O51TQxLdyRzTxTzW7TxDCTeXIyb1HYHGfrmtugAooooAKgvP+PKf/AHD/ACqeoLz/AI8p/wDcP8qAJ6KKKAIG/wCP5P8Arm38xU9QN/x/J/1zb+YqegAooooAKKKKACiiigAooooAjuJlt7aWZyAsaFyT6AZriPCHhnTnaw1ueDffeU5DE8A7zhseuO9a3ju9az8K3Hln55SEH05J/QGpvCaFPD2jA9Tp8bH6kA/1rojzQouSdruxnTc/bSqRdrLl9ebVr/yVfeb9FFFc5ocZr2p2mhePLHUb+YQ276bNEXI6kOrYHqfat628SaPdQWEsd/CBf/8AHurNhnPcY9QePrxWhPa290qrcQRTBGDqJEDYYdCM96xJfA/huZ7l20mHfcHMjAsDnOcrz8vPpiunnpSilO6a7HX7ShOMVUumtNLFfQv3njnxVL2U2sQP0jJP865LxVqNvptx400+SZUmvVtnt4z1kJUBsflXomkaHp+hW7w6fB5ayNvdixZnb1JPJqxcadY3TFrizt5mOATJErE4OR1HarhiIRqc1rqyX3W/yNIYqEK3NZtWiu21vXsTRIIoUjHRVC/lT6KK4zgCiiigCAf8fz/9cl/manqAf8fz/wDXJf5mp6ACiiigAooooAKKKKACiiigArkPGnyy6VL/AM89Us2/8fcf1rr65Dx4QmlGYnAhubR8+n70/wCNbYf+LE6MJ/HivMnvfm+JOkr/AHLCdvzZRXUVyGkTt4h8Yya5axkaXa2zWcUzf8t3Lgsyj+6MYz3rr6ddW5YvdIeJXLyxe6X+bCiiisDmCiiigAooooAKKKKAII/+Puf6L/Wp6gj/AOPuf6L/AFqegAooooAKKKKACiiigAooooAKpJ/yF3/3D/7LV2qSf8hd/wDcP/stAF2iiigAooooAKKKKACiiigAooooAKKKKACiiigAooooA5nxhJ4ejitP7f1OexUlvJMNzJDvPGc7Dz261heFJdJbxn/xT2pX+o2ZsZPtLT3EsscD702YLnGWG7jk/LxjnO/4kur+TVdJ0Wxvxp327znkugis4EYX5EDcbjuzkg4CmoNLfUtI8Vx6LdavJqsFzaSXKGdEEsBRkXkqACrb+MjqpoA6uiiigAqCb/X23++f/QTU9QTf6+2/3z/6CaAJ6KKKAILf/WXH/XT/ANlFT1Bb/wCsuP8Arp/7KKnoAKKKKACiiigArM1XULVNOuoxcR+a1rJIihvvAA8isbxpqkuly6ZLE7Ah5GKg9flA5/76rhLqZ5dOsVU/8e8LhvoZG/xrqpYfmSk2eFmGbqhKdGMbtL/L9G/uO41S3A+HtnKEzJbRQTL7H5c/oTVfVb238Q+HdIt45kjlubqONkDBmTAIJxx0yPzFdObFZ/D32FyVVrUREjt8uK4Lw/o8yeMEhjDSW9m+92OBtyuQfzxTpuLTbequzPGQq05QhFXjUUYv1XXvtct+L/D48ReLtN0sXklqF0+VvMQZ43AYIyMg13lpbi0soLZTuEMaxgnvgYrirPU31D4wXds0aqlhYNGpB5bJjYk/i1d3SxDmowg9kr/efXYpzjCnSeyV/vCiiiuU4gqG8tYr2yntZ1DRTRtG6nuCMGpqKadtUNNp3R5VpNxqFt4l8M+HNXjIvdOllKyA5WSExHYQe+MEfh9a6TxDIkPxB8NNI6oGjnUFjjJ+XArS1Lw+154u0XW4zGv2JZUmznc6spCgfQk/nWD8SfD15qsdnqVrMkf9nRyyHPLFvlZQB/wE16CqU6tWN3a6afq7nqqrSrVoXfLeLT8m+b/M1PHlwkGiRYnWO489XiG7DEr3H0zXncmqWWlQeIdLupz9sktngj+U4J6kZ7Zo1641HxX4usksZ97Npy3EURJKB/KLMAM4BJ4+uM9K6Gw8DXus+EtRGtW0EOuXV000U7quU4TuucA7TwPXpXRTp06EI+1e9vXf8jgjktGljoY3Ey2UbJPrd3urbJWPQ9OXbplovpCg/wDHRVmmQx+VBHHnOxQufoKfXjt3dzGTu2zlvHa+Xpmn3qkrNa6hA6OD0y20/mDXI31m9xqGvhI2kaGU7AoyeZf/AK9dv42s5b3wjfiDHmwqLhM9zGwfH4hSKZ4bsd9zca4joYNTijmRR1XcMkH8TXZTqctFPs3+n/BOPNcHLF0KMVspST8k46fiXfDD+Z4a08+kIH5cf0rWrm/BLzDRprabIa1uXiCkYKjg4/Mmukrlqq02PAz58NB+SMKP/keZv+wev/ow1u1hR/8AI8zf9g9f/QzW7RU6eg8LtL/E/wAwoooqDpCiiigAooooAKgtus3/AF1P8hU9QW3Wb/rqf5CgCeoby2S9sp7WT7k0bRt9CMH+dTUUJ21Q02ndHkE0k0/hzSW2FptNMtlclASEMbALn04xXQ2iW/jDxVNdNBK1hFCqgscENkEdPXmmX6paan41tmISOW3gvF9PukMfzFaHgC+jbwbJd+WmI5ZSSgwXC9CffGK9Kq3yOcV/Utf+AedjMrnVzCdRfw5csnp1tov8zrLi3iu7eS3nQPFINrKe4rm/DahfFvioKMKJrdQPQCIVt6Nqaazo9pqMcbRrcRhwjHJX2rmvAM11qUmt6zPAIo727/dY6FUG3P6Y+oNc0IuMKl+mnzuv8j2qdPkhVbWtkvx2/Bm7faZLc+INMvlKiK1Em8E8ncMDFac3+ok/3T/Kn0yb/USf7p/lXO5N28jhhSjBykvtO7+5L9DlfGxFtoOnaoeP7Ovbe4J9F3bT+jUtl4rt9a1XW9Cby45oQVtyG4mUr298n8j9a6C6s01HRprKXGy4gMTcZxlcV57pvw61C20aWeSSFPEFrdedZ3KNlWVVUANkdDtPUcfia7aLoypWqOzW3z/rX1PUorD1cO6dZ2ey+f8Alrf1OXsL5dEked7YywzCSGVlHMYkDAN+Z/nXpF3rk+leBtNurYIZZEjhBYZ2/Iefr8tJo3hiTRvDN99skWXULi2dZihyg4bG3gHuM+9czoKSasugwhTIiGSRlPIC5BrWpKFV83Z/efIVadfC0Ip/xJcyck+rlGz+6/yRp+IvEE154e060WSNp7yMNcKByORj6ZI/Sut8NXH2nw3YP0KxCM/Vfl/pXJeGPBjziO/1HfEoYNHDjDHB6tnoOOlS2VxJoX/CR6aJNssaNcROp6ZAxj35WsakYNckOhhhK2Ip1FisSrKSsvkrr77P7zvaKyvDupjVtFguMNvA8ty3UsAMn8a03ljjZFd1VnOFBOCx64HrXG4tOzPoqdWNSCqR2ZFc9Yf+uo/rU9QXPWH/AK6j+tT0jQKgg/11z/10H/oK1PUEH+uuf+ug/wDQVoAnooooAK4L/hHrHxjfa7fXOnWUckUz2dtIGkDs8eVLTBWUMM4wPQdea72uf1HwXo2pX8t663UE82PPNrdyQibAx8wVgDx360AP8I3QufD0cf2W3tWtZZLR4rYfug0blCU/2TjP41u1WsLC10uxhsrKBILaFdqRp0A/z3qzQAUUUUAFQXn/AB5T/wC4f5VPUF5/x5T/AO4f5UAT0UUUAQN/x/J/1zb+YqeoG/4/k/65t/MVPQAUUUUAFFFFABRRRQAUUUUAcJ4rabU7LXJ2lVbPTYZI0QLy8jREEk+xaum0WPydP06L+5Yxr+QFcnqj4+FWrXP8Vy7uT6lpQtdvFGIp44x0SHb+RFdVXSkl5v8ABL/MjCL/AGKNTrOTf4K33JpFqiiiuUsKKKKACiiigAooooAKKKKAIB/x/P8A9cl/manqAf8AH8//AFyX+ZqegAooooAKKKKACiiigAooooAK57xTokniHSb/AEuKVIpJo4SruCQMSFu3sK6GoE/4/pf+uafzaqhNwkpLdF05unNTjutRLCyh07T7eyt12wwRrGg9gMVYoopNtu7Jbbd2FFFFIQUUUUAFFFFABRRRQBBH/wAfc/0X+tT1BH/x9z/Rf61PQAUUUUAFFFFABRRRQAUUUUAFUk/5C7/7h/8AZau1ST/kLv8A7h/9loAu0UUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFAHLeLIotU1HR9BmsbS4ju5Hnke53fu0i27vL2kEOd/BzwM9ar6Jp1r4Y8YS6Xa2Nqlvf27XEMyF2nXyyissjOSSMvkHjuMd66DWNC0/XbeOG/hZ/KffFIjtG8bdMqykEGoNG8M6Zoc0s9qkz3MqhXuLmd5pCo6LuYkgewoAtavc3trpskmnWgurssqRxs21clgNzH+6AcnHOBXNXXi3UdDl1O21aG0up7azS6hazDIrl38tY2DE7SWxg5ORnjit7xHdarZ6JPJotj9s1A4WKPcoAyeWOSAcDnGeelcjHo1/f+G9V08aNf2+o3CpcPfahLCxupkdWVSUY7R8uAMAAUAdFp+q6rDr8eka0lmZbi2a4gltAyr8jKHQhieRuUg9+eBW1N/r7b/fP/oJrnrJL/WPFdrq1zplxp9vY2ckKpcMm+SWRkLYCsflATr3J9q6C5RZJbdXUMpc8H/dNAFiioPsdt/zwj/75o+x23/PCP8A75oALf8A1lx/10/9lFT1TgtbcvPmFDiTA49hUv2O2/54R/8AfNAE9FQfY7b/AJ4R/wDfNH2O2/54R/8AfNAE9FQfY7b/AJ4R/wDfNH2O2/54R/8AfNAHK+LtMm1jV7OygKiXyHcFzgD5lz+maxrDw+dQ1fWdPEgTymAViOMb8/yzXUvbQN41jj8pNqWBfGO5cD+layaXYpcSTraxiWQAO2OuOldKrOEVFdv1PDlltPE15VZfzWfmuW35ll+I2+hqlp2n29q0l1EpEt0qGQk+i4GKne0twjfuI+h/hqOG1t/skbGFCdgJOPaudNntOEZNNrbY57wjJBd694mvIWjkDXqosq4OQEGQD6Zrra4/4dabZp4JsZhbR+ZPvkkbHLHcRk/gAK6n7Hbf88I/++a2xNvatLpp92h14y3t5JdNPu0/QnoqD7Hbf88I/wDvmj7Hbf8APCP/AL5rA5ieioPsdt/zwj/75o+x23/PCP8A75oAnqC6AZIwQCDIoIP1o+x23/PCP/vmori1t1EeIUGZFHT3oA4r4eaFe6NrfiCO8tHhjDotszL8rJukPynpjkfnXoNQfY7b/nhH/wB80fY7b/nhH/3zWtaq603ORtiK8q9R1Jbu35WJ6Kg+x23/ADwj/wC+aPsdt/zwj/75rIxJmVXUqwBUjBB7iuL8Gi60HV77wreTmZIUFzZOe8JOCPwP9a677Hbf88I/++a5fxlpj2yWOu6dYLcXOmy73hQYaWIghl4+ue/euig73pv7X59P8vmdWGlzXovaX5rb/L5mnov7vWdcg9LhJP8AvpAf6VuVwXhbW7PW/EVxJDaBIruFWKOOYnQYK/qf0qbW9XstM8W237tZYI4SkyIR8hY9SPUcUToz5+VrWx4axMMLTaqacs3F+XvP8katndRXPjq9ETE+TZiJ+MYYPk/zroq8/wDCE1vqXirU5xEoSSMusbYyo3Cu5+x23/PCP/vmprR5ZW8kVltX2tF1O7l+ZPRUH2O2/wCeEf8A3zR9jtv+eEf/AHzWJ6BPRUH2O2/54R/980fY7b/nhH/3zQBPRUH2O2/54R/980fY7b/nhH/3zQBPUFt1m/66n+Qo+x23/PCP/vmoYLW3JmzChxIQOPpQBdoqD7Hbf88I/wDvmj7Hbf8APCP/AL5oA8r+LD32l6tFdW04SDUbM2sqgZJCNkg+x3j8q1vh6fJ8A6pZyEC4tpJ1kjzyvy8f1rZ8beDYvEujiO1CRXsLboGJ2qckZDcHjH64rK1ewi0bxLD5cUQj1DSZ4ZAowDJEu7f9cHFerGrGph40l8X+X/A/I9qFeFbCRoL4ld/dt+H5GQ/ia60z4SaSdJk/0qRzbs68tFt3McD1wB+Brsfh5HJH4F00SqVdhIxDDB5kY/1rzXwZ5DXmjacdrS/2iLgx9TsNuDn6Zr2r7Hbf88I/++anH2pr2aW75r/eTmXLSXskt25X+8npk3+ok/3T/Ko/sdt/zwj/AO+abLaW4hciFMhT/DXmHjk0P+oj/wB0fyp9VYrS3MKEwpkqP4af9jtv+eEf/fNAEsiCSNkPRgQa8j0zw94jg1X+ytO1GO0urIDfMTlTHweBg5z8pwa9X+x23/PCP/vmoE0ewjvZLtbdfOkUKx7YHtW9Gu6Sa7mNWEpTpyjb3ZX19H/wDh/DnxIkuzp1lfWckkryC2ub3IRFlYtsGAOchfbvWh4usll8Q6WiAx/bj5E7JwWXcv8ASuC0u3ih8N69cyIuLfVrc7iPugSYP6GvZZdJ0+4ngne2jZ4STGewyP1rrxUYUanNBW3/ACT/AFO3P8BTqx5Katr+Vm/wdjnvC95Hp0/9iKjPm6uFEhbkBMYyMc5rKuNfkvNZsEllDPDqrqFAxtjyoX+bVl3F7/ZHjC5uDAJYYrmT92eBhs1RtoJJftWrBVEdpLHI8Z6nc3AFCpK/M+v5s+DqY6agqEPst6f3Y2f6M9fuesP/AF1H9anrB0+/07VreB4EiEvyNLGozs3A8Zxz0Na/2O2/54R/9815zTTsz7KnUjUipwd0yeoIP9dc/wDXQf8AoK0fY7b/AJ4R/wDfNNtUWN7hUUKok4A/3VpFlmiiigAooooAKKKKACiiigAqC8/48p/9w/yqeoLz/jyn/wBw/wAqAJ6KKKAIG/4/k/65t/MVPUDf8fyf9c2/mKnoAKKKKACiiigAooooAKx/EevReH9PjnkglmeeUQRJFjJcgkdT04NbFcN8SZgkOjoTjNy7j6iJsfqa2w8FOoosqPLq5bJN/cmytZ+H9f1fw9Z6fLe2KaNMUlcCNvPVQ28KOxyQOa7v/l+H/XM/zqPS4Tb6RZQkYMcCKR7hRUn/AC/D/rmf50qtVzduiM6U5ewp03oopfkv8ieiiisigooooAKKKKACiiigAooooAgH/H8//XJf5mp6gH/H8/8A1yX+ZqegAooooAKKKKACiiigAooooAKgT/j+l/65p/NqnqBP+P6X/rmn82oAnooooAKKKKACiiigAooooAKKKKAII/8Aj7n+i/1qeoI/+Puf6L/Wp6ACiiigAooooAKKKKACiiigAqkn/IXf/cP/ALLV2qSf8hd/9w/+y0AXaKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACoJv9fbf75/9BNT1BN/r7b/AHz/AOgmgCeiiigCC3/1lx/10/8AZRU9QW/+suP+un/soqegAooooAKKKhurhbSznuXBKxRtIQO4AzQJtJXZjwSJN43uijq3lWKxtg5wd5ODW9XLeDbK5C3mrXPl51BhIqqTkDLE/wA61PEWv2fhzSZL27kC9ViTBPmSYJC8DvjrW04OU+SOr2ObLVOtTUrazbaXq9PwNJmVkcAg4BBwehxWRrmqHRfCVzqAjEjQ24IQnGScAfzrkdHhu/DWraVd31zK/wDb8bi8D52pcn5k47HB2/ga1vGt3b3Hw81SOCeOR4liilVWBKMXTg+h5rVUFGtGO6bX52Z7CwyjXhG/NFta/Oz/ABNbwfYz6b4R020uY/Lnjh+dCehJJx+tblIBgAUtc05OcnJ9TkqTc5ub6u4UUUVJAUUUUAFQXP3Y/wDrov8AOp6gufux/wDXRf50AT0UUUAFFFFABRRRQB468h0fXtbljyRLd3UIAOCu4A5z/wAD/SoZL5IrC4kuFkZ9QKwwuMEbk2s+7nPTH51q6tZRSyeKJ23eZbagjJg8YdEBz+QrTv8ASbBNX8KWEG2W2khvGLqwPmExD5sjr1r1+eN1ft+SueTiMrlic0rRfw8jkv8AFKD/AFTZU8A26weKXkVywn0pZeRjH70jH6V6XXmfw7cyavaMTknRFz/3/evTK48arVbHu18NHDctCO0Ul9wUUUVyGAUUUUAFFFFABUFt1m/66n+QqeoLbrN/11P8hQBPRRRQAVw/j2RINT0SZ2VVEd6mWOOWgIA/PFdxXGfE/S4L/wAGXNy6kzWZWWIg9MsA2fbBP5V0YRr20U+un36HXgWliIqXXT71b9S34R0XTv7F0XVfscQvxYRp54GGIKDr68cZrqKyPCqFPCOjKwIIsocg/wC4K16itJyqO7MsRJyqyu+r/MKZN/qJP90/yp9Mm/1En+6f5VkYhD/qI/8AdH8qfTIf9RH/ALo/lT6ACiiigDnp/DOnWXhrV7G3gJS7WWWTdyWdgSOfbjH0rL/ty6sfhJBqttKGuo7KMLI3zfNkISfU9eveu1ryvWD/AGb4Z8XeH2ChYLiK5t8HrFNKpwB/snj8a7cP+9dpa6p/LZ/oejhW675Z6+9F/K9n+hT8KF/Gra956gXZWGWIk4UOAwbp2J5r06DRbOPSTYPAhR4ljlIHL4GMk+voa81+Ea+V4g1uI8YQDH0civXKrMHy1nGOi0/JHNmeBw9PGzlCCV0l8rHC+G9Mn0TX57KcqS6JKu05+XcwFd1XO3vyeOLJv+elqU/J810VctWTk1J9TycBSjRpulHaLf8An+oVBB/rrn/roP8A0FanqCD/AF1z/wBdB/6CtZHcT0UUUAFFFFABXO+LvFA8M6a08dlPd3BUsqIh2KoIBZ36KOR7ntXRVgeNbS4v/Bup2tpC808kQCRoMljuHSgCxql9qyX8NlpWnxSs8bSyXNy7JCgBAC5VSSxz07AZp3h7WDrmki7eDyJVlkgljD7wrxuUba3GRkcGsfxhqWrR3NtptjY6l9kmQvdXljCHkRc42JkgBjz83YdOTxo6Lc29rpmnWtnot/Z2zM0KRSQhTCACdz/McA468kk0AZ+neKNR1XVpILW20oWqXckH7y/YTskblGcRiM56HHPbrXS3n/HlP/uH+VefXGmC8t7fT7Twq+naxHqKTG7jh/dRBZdzSib+LcgIx1+bGOK9BvP+PKf/AHD/ACoAnoqD7JD/AHW/77P+NH2SH+63/fZ/xoAG/wCP5P8Arm38xU9UjaxfbEXa2PLY/ePqPepvskP91v8Avs/40AT0VB9kh/ut/wB9n/Gj7JD/AHW/77P+NAE9FQfZIf7rf99n/Gj7JD/db/vs/wCNAE9FQfZIf7rf99n/ABo+yQ/3W/77P+NAE9cJ4xiGr+LdD0pRloy0rD1B6/kqk/jXaG0gAyQQP98/415zpFzb618UDNDIHhjglKoGOVA2qCfTOc/jXThk7ymuif5GU4SqyVGLtdSvb+VRd/vdl8z02oP+X4f9cz/Oj7JD/db/AL7P+NQ/ZYvtgXa2PLz94+v1rmNS7RUH2SH+63/fZ/xo+yQ/3W/77P8AjQBPRUH2SH+63/fZ/wAaPskP91v++z/jQBPRUH2SH+63/fZ/xo+yQ/3W/wC+z/jQBPRUH2SH+63/AH2f8aPskP8Adb/vs/40AT0VB9kh/ut/32f8aPskP91v++z/AI0AA/4/n/65L/M1PVIWsX2xl2tjywfvH1PvU32SH+63/fZ/xoAnoqD7JD/db/vs/wCNH2SH+63/AH2f8aAJ6Kg+yQ/3W/77P+NH2SH+63/fZ/xoAnoqD7JD/db/AL7P+NH2SH+63/fZ/wAaAJ6Kg+yQ/wB1v++z/jR9kh/ut/32f8aAJ6gT/j+l/wCuafzaj7JD/db/AL7P+NQraxfbJV2tgRofvH1b3oAu0VB9kh/ut/32f8aPskP91v8Avs/40AT0VB9kh/ut/wB9n/Gj7JD/AHW/77P+NAE9FQfZIf7rf99n/Gj7JD/db/vs/wCNAE9FQfZIf7rf99n/ABo+yQ/3W/77P+NAE9FQfZIf7rf99n/Gj7JD/db/AL7P+NABH/x9z/Rf61PVKO1iNzMNrYAX+I+/vU32SH+63/fZ/wAaAJ6Kg+yQ/wB1v++z/jR9kh/ut/32f8aAJ6Kg+yQ/3W/77P8AjR9kh/ut/wB9n/GgCeioPskP91v++z/jR9kh/ut/32f8aAJ6Kg+yQ/3W/wC+z/jR9kh/ut/32f8AGgCeqSf8hd/9w/8AstTfZIf7rf8AfZ/xqtDGserOFBA2HqSf7tAGhRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAVBN/r7b/fP/oJqeoJv9fbf75/9BNAE9FFFAEFv/rLj/rp/7KKnqC3/ANZcf9dP/ZRU9ABRRRQAVkeKJmg8M37L1aPy/wDvohf61r1heKsyadbWucC6vIYj9N2f6VdP40c2MbWHnbezNe1t0tLSG3QYWJAg/AYrm/F6LLqXhmJ1DKdTVsEZHCMad4y8TXugxWdvpente396ziJApbAUAk4HJ6j9aw7ebxVrHibSLbV9GSFbCUXEl1ET5ZBQgDnvn0P4V00KMl+9bVtev9dT18JhpQiquijZ21V9E1t66G/47tpZfDEl1bruuNPljvYx7xnJ/wDHd1eYQ6Pq/ifWLibTCx029vgJyGwq7FDAtj2c49xXuMyq8EiMAVZSCD3GK5T4e6NPoekX9tNC0atfyPDu/ij2qFb8cVeGxPsqUrbrb5/5Wua4TGexoSt8S2+f+TVzrqKKK4DzAooooAKKKKACoLn7sf8A10X+dT1Bc/dj/wCui/zoAnooooAKKKKACiiigDzvUEU/8LBVv+WaxTL7MIdw/UCqXhDwbq2ka9pF2032nSxbPMGyFETyJgrtznsvPtUXjiXU9E1zWBDbNcWGt2gVykZJjdU2Dke+PwNem6dE0OmWsTjDJCikehAFenUqypUVy7SX/tqR61WboUueFv3iSfoo2/Bt/eeZfCec3eoTllwbOxS2P4yu1erV5z8MbVotR8QzkYV7towfXax/+Kr0asMfJPESt/WhhmclLFSt5fkFFFFcZwBRRRQAUUUUAFQW3Wb/AK6n+QqeoLbrN/11P8hQBPRRRQAVFc20F5bSW1zEksMi7XRxkMPepaKE7aoabTuhFVUQIqhVUYAA4ApaKKBBTJv9RJ/un+VPpk3+ok/3T/KgAh/1Ef8Auj+VPpkP+oj/AN0fyp9ABRRRQAV578TNEvbsWV9p1vJK7EW1ysSFiYy6uucdgy/rXoVFa0KrozU10N8PXdCoqkehwOrWg8C67N4jsNPM2m3MRS8iib5kkLbhJz2PTsBWaniPxNqviLw/eXVi2naRNdBYlV+Zt6nG7nngHsBzXp7KHUqwBUjBBHBFcr8QIWj8LjUIGKT6ZPFdQ7RxlW24I9MMa6qNdTkoyjeT0v8Agv8Ahzsw+JjUlGE4pyel3frov+H7F/V7OSTXdGvI1YiKV0kx2DL1P5VuVy2jeNdI8S3KW1k06XKHzDFNHtOzpnIyO47966muSpGcGozVmjy3hpUKkuZNNu/4JfoFQQf665/66D/0FanqCD/XXP8A10H/AKCtZjJ6KKKACiiigAooooAKKKKACoLz/jyn/wBw/wAqnqC8/wCPKf8A3D/KgCeiiigCBv8Aj+T/AK5t/MVPUDf8fyf9c2/mKnoAKKKKACiiigAooooAwPEniPTtJsri3kvIhfPFiKDd85LcA49P8Ks+HdKttM0a1SKFElaMNK4X5mY8nJ+tcbeWzat8StV00bFSSKBXlKBmRVTcQvpnIFejgBQABgDgV01YqnBRXWzf3GckliHy7KKT9WlJ/cmhag/5fh/1zP8AOp6g/wCX4f8AXM/zrmNCeiiigAooooAKKKKACiiigAooooAgH/H8/wD1yX+ZqeoB/wAfz/8AXJf5mp6ACiiigAooooAKKKKACiiigAqBP+P6X/rmn82qeoE/4/pf+uafzagCeiiigAooooAKKKKACiiigAooooAgj/4+5/ov9anqCP8A4+5/ov8AWp6ACiiigAooooAKKKKACiiigAqkn/IXf/cP/stXapJ/yF3/ANw/+y0AXaKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACoJv8AX23++f8A0E1PUE3+vtv98/8AoJoAnooooAgt/wDWXH/XT/2UVPUFv/rLj/rp/wCyip6ACiiigArD1v59X0OL1uWf/vlCa3Kw9R+fxXoi/wBxJ3P/AHyBV09/v/I5cZ/DS7uP/pSKes/N4/8ADA/ux3bf+OKK6iuSgifUfiZdyyzMI9JtY1hjAGCZQSxP5CutrStooR7L87v9T08RooR7R/Nt/kxr/wCrb6GmW3/HrD/uL/Knv/q2+hplt/x6w/7i/wAqwOYlooooAKKKKACiiigAqC5+7H/10X+dT1Bc/dj/AOui/wA6AJ6KKKACiiigAooooAKKKKAMDw5or6G93C8qyGeeW4yoxgM3A+uAK36g/wCX4f8AXL+tT1U5OcuZ7lTm5ycpbsKKKKkkKKKKACiiigAqC26zf9dT/IVPUFt1m/66n+QoAnooooAKKKKACiiigApk3+ok/wB0/wAqfTJv9RJ/un+VABD/AKiP/dH8qfTIf9RH/uj+VPoAKKKKACiiigAqK6tory0mtp13RTIY3X1UjBqWihO2o07O6OCi0iy0X4jabBZReWjaZsJJJLbWUAn3wBXe1yesWd83xC0S8giJtVgkjmlK5Uc5Az6k4/WusrevLm5W3d2/VnRiZOfJJu7t+rCoIP8AXXP/AF0H/oK1PUEH+uuf+ug/9BWsDmJ6KKKACiiigAooooAKKKKACoLz/jyn/wBw/wAqnqC8/wCPKf8A3D/KgCeiiigCBv8Aj+T/AK5t/MVPUDf8fyf9c2/mKnoAKKKKACiiuMuL6ebxDDq6Of7Pt7kWSjs24EM/03Y/KrhDmObE4lUErq93+HV/I7OiqGqavaaPAst2zDcSFVF3Fscn8hVh7y3jsjeNKotwnmb88bcZzU8rtc19rDmcbq638jmdBtPM8ceIb8joyRA/8BUf+yfrXW1yngW9OpWurXrDBk1Bxj0+VTj8MkfhXV1rXvz2fS35E0FLk5p7y1+T2+5WQVB/y/D/AK5n+dT1B/y/D/rmf51ibE9FFFABRRRQAUUUUAFFFFABRRRQBAP+P5/+uS/zNT1AP+P5/wDrkv8AM1PQAUUUUAFFFFABRRRQAUUUUAFQJ/x/S/8AXNP5tU9QJ/x/S/8AXNP5tQBPRRRQAUUUUAFFFFABRRRQAUUUUAQR/wDH3P8ARf61PUEf/H3P9F/rU9ABRRRQAUUUUAFFFFABRRRQAVST/kLv/uH/ANlq7VJP+Qu/+4f/AGWgC7RRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRUN3dQ2NpLdTswiiUsxVSxx7Acn6CudsvGK3XhrWdamsJbZNNeZfImO12EahvmH8JOenOKAOoqCb/X23++f/QTWVoWrarqcjG80+xt4Qmd1vqHnsr8fKy+WuOM9+1as3+vtv8AfP8A6CaAJ6KKKAILf/WXH/XT/wBlFT1Bb/6y4/66f+yiuf1u6aLxloSBiB84Iz13cf0qoR5nYwxFdUYKTXVL73Y6eiiipNwrDl/eeNrZf+eVi7/m4FblYGkxrJ4m1u6bJdWjhUn+FQoJA/Grh1fkcuJ1dOHeS/BN/oUPDd1De+OPFc0EgdENtCWA/iVWDD8CCK66uR+HtpBHpN7eJGBNdXsxlfPLbXYD8ua66tMTb2rS6WX3Kx6eMsqziull9yS/Qa/+rb6GmW3/AB6w/wC4v8qe/wDq2+hplt/x6w/7i/yrA5iWiiigAooooAKKKKACoLn7sf8A10X+dT1Bc/dj/wCui/zoAnooooAKKKKACiiigAooooAg/wCX4f8AXL+tT1B/y/D/AK5f1qegAooooAKKKKACiiigAqC26zf9dT/IVPUFt1m/66n+QoAnooooAKKKKACiiigApk3+ok/3T/Kn0yb/AFEn+6f5UAEP+oj/AN0fyp9Mh/1Ef+6P5U+gAooooAKKKKACiiigCC56w/8AXUf1qeoLnrD/ANdR/Wp6ACoIP9dc/wDXQf8AoK1PUEH+uuf+ug/9BWgCeiiigAooooAKKKKACiiigAqC8/48p/8AcP8AKp6gvP8Ajyn/ANw/yoAnooooAgb/AI/k/wCubfzFT1A3/H8n/XNv5ip6ACiisjWNYeydbOzt5Lm/ljLRogyEGcbm9Bk/pTjFydkZ1asaUeaWxJ4hvJLDQbueI4l27Ex13MQo/nVHVtLW08FTWcP/AC7whwf9pSGJ/Eg1Un0jXGmtbee6F7aPcRzTu2A0ZXlgB/dPGK6p0WWNo3UMjAqwPQg1rdQtZ31ucShLEupzxcfd5Vfzvf8AT7jnbeWPXPEccwAe2tLQZ7jzJRyP++ayrNTc3Vp4ckUvHZXcjyhhkGJeUz7EtjHtXX2OnWmmQtDZwLDGzbiBnk/jVjakZeQKoJ5YgcnHrR7VLRfIX1GU0nN+8373mtNPwSOY8BgNo97OBxPqNzIP++yP6V1Ncx8PAf8AhB7Bz96UyyH8ZGNdPRif40vVntYv+PNebCoP+X4f9cz/ADqeoP8Al+H/AFzP86xOcnooooAKKKKACiiigAooooAKKKKAIB/x/P8A9cl/manqAf8AH8//AFyX+ZqegAooooAKKKKACiiigAooooAKgT/j+l/65p/NqnqBP+P6X/rmn82oAnooooAKKKKACiiigAooooAKKKKAII/+Puf6L/Wp6gj/AOPuf6L/AFqegAooooAKKKKACiiigAooooAKpJ/yF3/3D/7LV2qSf8hd/wDcP/stAF2iiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACuLi0+5XQPFMU2kNe+fqM0iWjsY/tEZ2dD7gHHqRiu0ooA4nRrK3m8YW9/o2jT6VYw2TxXZktDbCZiy+WoQgbiuGO7GOcZrrblBJLbq2cFz0JH8J9Ks1BN/r7b/fP/AKCaAD7JF/00/wC/jf40fZIv+mn/AH8b/Gp6KAKUFrEXnzv4kx/rG9B71yOtxBvGVo6s3l28tvGw3E43sx611VtqEDavdacCftCgTEY42kAVy12WlsNf1EIQYtQQofXyyB/LNb0U09f6ueTmc4zppRezbf8A26m/zsdn9ki/6af9/G/xo+yRf9NP+/jf41x3iXxVfWOttZ2J4WEKAFDZdsEH8B0FdPoN3JfaFZ3Mzb5HjG9sYyRwf5VEqUoxUn1Omjj6VavKhC9479i19ki/6af9/G/xrnNHkgg0rV9ZuhIsHnzTfLIc+Wgx68n5TXS3FxFaW8k87hIoxuZj2FedalbpfeDfDelyyMqX+oqswRsMyEuT/T9K0oQU9Hs3/wAE6IQVXG06belpN/gvybNz4f26y+D7aZ0lQzSzSAMzKcGRscZ9K6f7JF/00/7+N/jUkUaQxJFGu1EUKo9AOlPrKrPnm5d2dNap7SpKfd3K72kQRvv9D/y0b/GmW9rEbaInfkoP+Wjen1qy/wDq2+hplt/x6w/7i/yqDMb9ki/6af8Afxv8aPskX/TT/v43+NT0UAQfZIv+mn/fxv8AGj7JF/00/wC/jf41PRQBB9ki/wCmn/fxv8aPskX/AE0/7+N/jU9FAEH2SL/pp/38b/Gobi1iAjxv5kUf6xvX61dqC5+7H/10X+dAB9ki/wCmn/fxv8aPskX/AE0/7+N/jU9FAEH2SL/pp/38b/Gj7JF/00/7+N/jU9FAEH2SL/pp/wB/G/xo+yRf9NP+/jf41PRQBB9ki/6af9/G/wAaPskX/TT/AL+N/jU9FAFL7LF9sC/Pjy8/6xvX61N9ki/6af8Afxv8aP8Al+H/AFy/rU9AEH2SL/pp/wB/G/xo+yRf9NP+/jf41PRQBB9ki/6af9/G/wAaPskX/TT/AL+N/jU9FAEH2SL/AKaf9/G/xo+yRf8ATT/v43+NT0UAQfZIv+mn/fxv8ahgtYiZc7+JCP8AWN7e9Xagtus3/XU/yFAB9ki/6af9/G/xo+yRf9NP+/jf41PRQBB9ki/6af8Afxv8aPskX/TT/v43+NT0UAQfZIv+mn/fxv8AGj7JF/00/wC/jf41PRQBB9ki/wCmn/fxv8aZLaxCFz8/3T/y0b/GrVMm/wBRJ/un+VAEEVrEYUPz8qP+Wjf40/7JF/00/wC/jf41JD/qI/8AdH8qfQBB9ki/6af9/G/xo+yRf9NP+/jf41PRQBB9ki/6af8Afxv8aPskX/TT/v43+NT0UAQfZIv+mn/fxv8AGj7JF/00/wC/jf41PRQBSntYgYsb+ZAP9Y3v71N9ki/6af8Afxv8aLnrD/11H9anoAg+yRf9NP8Av43+NNtUCPcKucCQdTn+Fas1BB/rrn/roP8A0FaAJ6KKKACiiigArmvGfiK98PaRJcWGnPdTBC5kbiGFQQMscgk88KOT7CulrD8Yafdar4S1Gxso/NuZowqJuC5OQep4oAZquqak+vQaJpBtYrhrZrqae6jaRUQMFVQqspJJJ5zwAetWfDurSazpIuJ4liuopZLe4jQ5VZI3KNg+hIyPY1Q1W3v7DxTBrlpp8t/E9m1pPDA6LIp3h0Yb2AI+8DznkU7w5Z6jpGnwR3Flvnvrue6uykq7bYyMzgc/exkLx356UAZsPirVns7XXpI7P+xLm8W3WEI3nJG0nlpKX3bTlsErt4B61115/wAeU/8AuH+VcNBousf2LZeFH011tra9R31DzU8p7dJvNXAzu3nCrjbgcnNdzef8eU/+4f5UAT0VB9jtf+feL/vgUfY7X/n3i/74FAA3/H8n/XNv5ip653WnXTL+zuhDEbLlLlNgwoJAD9OxIrb+x2v/AD7w/wDfApuNkmZxqqU5Q6r9ev8AXYnrC0cC417Wbw87ZVtkPoFXkD8TVvUpdP0uye5nt4sDhUCDLseij3NV9B0kW2mA3lvF9pndppF2g7Sxzj8BirWkW+5hUanXhBfZu391l+b+4r+O7mS08E6nLDIySeWqqyHBBZgvB/Gs9l8QeFXglNxd69prYjliEQa4iPZlx94djn61L49trceGhCkEatPdwRZCgHmRf8K6b7Ha/wDPvF/3wK2jPkpK6vdv9D141PZ0I3Sabd/uXXdHKz/EXS5LZI9MSW61WV1jj090aNw5OCGJGBjnPNV7jxVrcN1Gmp6VFp9ldwXAiDSb5VdE3fMRwAecDrXY/YLMkH7JBkHIPljg1k+I/Cll4hsI7VmNqySCRZYVG7oQR9CCacKlDmScdO71f6fkOnVw6kk4WXdu7Xpt+QeCovJ8FaOvrao35jP9a3qo2Wk2djYW9pHAjJBEsSs6gkhRjJ468VP9jtf+feL/AL4Fc9SXNNy7s5qs+epKXdsnqD/l+H/XM/zo+x2v/PvF/wB8Covslv8AbAvkRY8vONg9agzLlFQfY7X/AJ94v++BR9jtf+feL/vgUAT0VB9jtf8An3i/74FH2O1/594v++BQBPRUH2O1/wCfeL/vgUfY7X/n3i/74FAE9FQfY7X/AJ94v++BR9jtf+feL/vgUAT0VB9jtf8An3i/74FH2O1/594v++BQAD/j+f8A65r/ADNT1TFpb/bGXyIseWDjYPU1L9jtf+feL/vgUAT0VB9jtf8An3i/74FH2O1/594v++BQBPRUH2O1/wCfeL/vgUfY7X/n3i/74FAE9FQfY7X/AJ94v++BR9jtf+feL/vgUAT0VB9jtf8An3i/74FH2O1/594v++BQBPUCf8f0v/XNP5tR9jtf+feL/vgVEtpb/bJV8iLAjQgbB6tQBcoqD7Ha/wDPvF/3wKPsdr/z7xf98CgCeioPsdr/AM+8X/fAo+x2v/PvF/3wKAJ6Kg+x2v8Az7xf98Cj7Ha/8+8X/fAoAnoqD7Ha/wDPvF/3wKPsdr/z7xf98CgCeioPsdr/AM+8X/fAo+x2v/PvF/3wKACP/j7n+i/1qeqcdpbm5mBgiwAuBsHHWpfsdr/z7xf98CgCeioPsdr/AM+8X/fAo+x2v/PvF/3wKAJ6Kg+x2v8Az7xf98Cj7Ha/8+8X/fAoAnoqD7Ha/wDPvF/3wKPsdr/z7xf98CgCeioPsdr/AM+8X/fAo+x2v/PvF/3wKAJ6op/yF3/3D/7LU/2O1/594v8AvgVWhjji1ZxGiqNh4UY/u0AaFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABUE3+vtv98/+gmp6gm/19t/vn/0E0AT0UUUAcLepfWXiq41hm8iBLmG3Jx8skbfe5PpgfnWbofiNjcPo+rWgh03VHl+zXbEgO5OCvpnOfTtXV+JViPhrVzKBtU7gT2IC4NY39hyaj8MI7ZAq3cYN1bsy52uHLjHpkcfjXdSlBxXOt3b08zgwWGhTx7p1VeE09X0cmrv5K3yL3h/wzLHNHqGpySG9ikIUHGCoXYM/gM/lWVoviOXRtQXSbxPLsoHeIvt5DFyQSewrr/D+pHV/D9hqDbd88Cu+3oGx82PxzSa7ZR3WiaggUB3hJLAckqMjP5Vlz++41F5ehjXy6WGivYStKF79ebun6tI5bxPrjtqOo6PudlljiiiVQMByQTnv0OKf/Y1qnxF0e1O900/S2liyf4vM25P/fRq94Y0m2vtIttSvoxNeSTm5ErcMGBwOR2+UHHSiOVJ/irKI23eRo+yTH8LGUHB98YNaxla8Y9E7/kdeTU6rlUxU38UXbuovW34nV0UUVwnUNf/AFbfQ0y2/wCPWH/cX+VPf/Vt9DTLb/j1h/3F/lQBLRRRQAUUUUAFFFFABUFz92P/AK6L/Op6gufux/8AXRf50AT0UUUAFFFFABRRRQAUUUUAQf8AL8P+uX9anqD/AJfh/wBcv61PQAUUUUAFFFFABRRRQAVBbdZv+up/kKnqC26zf9dT/IUAT0UUUAFFFFABRRRQAUyb/USf7p/lT6ZN/qJP90/yoAIf9RH/ALo/lT6ZD/qI/wDdH8qfQAUUUUAFFFFABRRRQBBc9Yf+uo/rU9QXPWH/AK6j+tT0AFQQf665/wCug/8AQVqeoIP9dc/9dB/6CtAE9FFFABRRRQAUUUUAFFFFABUF5/x5T/7h/lU9QXn/AB5T/wC4f5UAT0UUUAUry1ivXa2nXdFLC6sPbIrnEbXtOlvNJ0+1a4jQl4rieT7qsBgAnqQc/lXTSTwpqcMTyosjxNtQsAW5HQVaq4zcVqrnNiML7VqSbi+63t2PP9Yl1fRE8y8P2yDTInuop5AcSMQFVWPqGPrnFdV4e1+z8Q6XDdW00RlMatNCrgtEx6hh1HIPXris34gfvPCklqOt1cQQD8ZF/oKNS8EWV7qsd5a3E2nIyFLmKy/dfaBncMkdDnqeprp/dzpJz0d3r6W6edztwmEoUaHvN3lJ676JKy/y/ETxqRIugwA58zWbcEewyx/lXU1zdj4F0PT7+G8hiuDLE3mKJLh2Uyf3yCeWrpKxqyhyxjB3saVpQ5Ywg72v0tv82FFFFYnOFFFFABUH/L8P+uZ/nU9Qf8vw/wCuZ/nQBPRRRQAUUUUAFFFFABRRRQAUUUUAQD/j+f8A65L/ADNT1AP+P5/+uS/zNT0AFFFFABRRRQAUUUUAFFFFABUCf8f0v/XNP5tU9QJ/x/S/9c0/m1AE9FFFABRRRQAUUUUAFFFFABRRRQBBH/x9z/Rf61PUEf8Ax9z/AEX+tT0AFFFFABRRRQAUUUUAFFFFABVJP+Qu/wDuH/2WrtUk/wCQu/8AuH/2WgC7RRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAVBN/r7b/fP/AKCanqCb/X23++f/AEE0AT0UUUAeW/FHUILvR2sbScS3EWooJooySRmJsAj8D+Vem26bLWJCPuoBj8K801q2mg+LmmSbR5F5NG+fV40I/kw/OvUK7MRaNKnGPa/3/wDDHoYq0aFKEdrN/f8A8FHndjrL+GdA8T2FtsabRrhnt0kBwIpDlQcYzglq7bRrx9S0KwvZVUPc20crqvQFlBIHtzXm/wARvI0zWNRfZ5Q1PSlQMBxJKsynr67B+grp/BWqGFJvDN/LANQ0siFQhx5sQUbWAPt1/Cta9JSo+1S1ev4K/wCJriaKnQVeK1er+5X/AB/M6xVjgi2oioiDhVGAB9K4f4aXjaxDrWsyxIk13e87R2CjAz7Zrtro7bSZvSNj+lYHgFFj8DaVtUDMW44HUkmuaDSoz7tpfm/0OSm1HDT03aX5v9DpKKKK5zlGv/q2+hplt/x6w/7i/wAqe/8Aq2+hplt/x6w/7i/yoAlooooAKKKKACiiigAqC5+7H/10X+dT1Bc/dj/66L/OgCeiiigAooooAKKKKACiiigCD/l+H/X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          "html": "<p block-type=\"Text\" class=\"has-continuation\">In addition to an analysis of performance on counting, a density regressor can also be used to locate objects in the image. As mentioned previously, if the regressor is accurate and precise enough, the resulting density map can be used to locate the objects in the image. We expect that in order to do this, one must regress each object to a single point rather than a region specified by a Gaussian. Perhaps this might be</p>",
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