OCR（PDF解析)

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      "markdown": "# LEVERAGING UNLABELED DATA TO PREDICT OUT-OF-DISTRIBUTION PERFORMANCE \n\nSaurabh Garg*<br>Carnegie Mellon University<br>sgarg2@andrew.cmu.edu<br>Sivaraman Balakrishnan<br>Carnegie Mellon University<br>sbalakri@andrew.cmu.edu<br>Zachary C. Lipton<br>Carnegie Mellon University<br>zlipton@andrew.cmu.edu\n\n## Behnam Neyshabur\n\nGoogle Research, Blueshift team\nneyshabur@google.com\n\nHanie Sedghi<br>Google Research, Brain team<br>hsedghi@google.com\n\n\n#### Abstract\n\nReal-world machine learning deployments are characterized by mismatches between the source (training) and target (test) distributions that may cause performance drops. In this work, we investigate methods for predicting the target domain accuracy using only labeled source data and unlabeled target data. We propose Average Thresholded Confidence (ATC), a practical method that learns a threshold on the model's confidence, predicting accuracy as the fraction of unlabeled examples for which model confidence exceeds that threshold. ATC outperforms previous methods across several model architectures, types of distribution shifts (e.g., due to synthetic corruptions, dataset reproduction, or novel subpopulations), and datasets (WILDS, ImageNet, BREEDS, CIFAR, and MNIST). In our experiments, ATC estimates target performance $2-4 \\times$ more accurately than prior methods. We also explore the theoretical foundations of the problem, proving that, in general, identifying the accuracy is just as hard as identifying the optimal predictor and thus, the efficacy of any method rests upon (perhaps unstated) assumptions on the nature of the shift. Finally, analyzing our method on some toy distributions, we provide insights concerning when it works ${ }^{1}$.\n\n\n## 1 INTRODUCTION\n\nMachine learning models deployed in the real world typically encounter examples from previously unseen distributions. While the IID assumption enables us to evaluate models using held-out data from the source distribution (from which training data is sampled), this estimate is no longer valid in presence of a distribution shift. Moreover, under such shifts, model accuracy tends to degrade (Szegedy et al., 2014; Recht et al., 2019; Koh et al., 2021). Commonly, the only data available to the practitioner are a labeled training set (source) and unlabeled deployment-time data which makes the problem more difficult. In this setting, detecting shifts in the distribution of covariates is known to be possible (but difficult) in theory (Ramdas et al., 2015), and in practice (Rabanser et al., 2018). However, producing an optimal predictor using only labeled source and unlabeled target data is well-known to be impossible absent further assumptions (Ben-David et al., 2010; Lipton et al., 2018).\n\nTwo vital questions that remain are: (i) the precise conditions under which we can estimate a classifier's target-domain accuracy; and (ii) which methods are most practically useful. To begin, the straightforward way to assess the performance of a model under distribution shift would be to collect labeled (target domain) examples and then to evaluate the model on that data. However, collecting fresh labeled data from the target distribution is prohibitively expensive and time-consuming, especially if the target distribution is non-stationary. Hence, instead of using labeled data, we aim to use unlabeled data from the target distribution, that is comparatively abundant, to predict model performance. Note that in this work, our focus is not to improve performance on the target but, rather, to estimate the accuracy on the target for a given classifier.\n\n[^0]\n[^0]:    * Work done in part while Saurabh Garg was interning at Google\n    ${ }^{1}$ Code is available at https://github.com/saurabhgarg1996/ATC_code.",
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      "markdown": "![img-0.jpeg](img-0.jpeg)\n\nFigure 1: Illustration of our proposed method ATC. Left: using source domain validation data, we identify a threshold on a score (e.g. negative entropy) computed on model confidence such that fraction of examples above the threshold matches the validation set accuracy. ATC estimates accuracy on unlabeled target data as the fraction of examples with the score above the threshold. Interestingly, this threshold yields accurate estimates on a wide set of target distributions resulting from natural and synthetic shifts. Right: Efficacy of ATC over previously proposed approaches on our testbed with a post-hoc calibrated model. To obtain errors on the same scale, we rescale all errors with Average Confidence (AC) error. Lower estimation error is better. See Table 1 for exact numbers and comparison on various types of distribution shift. See Sec. 5 for details on our testbed.\n\nRecently, numerous methods have been proposed for this purpose (Deng \\& Zheng, 2021; Chen et al., 2021b; Jiang et al., 2021; Deng et al., 2021; Guillory et al., 2021). These methods either require calibration on the target domain to yield consistent estimates (Jiang et al., 2021; Guillory et al., 2021) or additional labeled data from several target domains to learn a linear regression function on a distributional distance that then predicts model performance (Deng et al., 2021; Deng \\& Zheng, 2021; Guillory et al., 2021). However, methods that require calibration on the target domain typically yield poor estimates since deep models trained and calibrated on source data are not, in general, calibrated on a (previously unseen) target domain (Ovadia et al., 2019). Besides, methods that leverage labeled data from target domains rely on the fact that unseen target domains exhibit strong linear correlation with seen target domains on the underlying distance measure and, hence, can be rendered ineffective when such target domains with labeled data are unavailable (in Sec. 5.1 we demonstrate such a failure on a real-world distribution shift problem). Therefore, throughout the paper, we assume access to labeled source data and only unlabeled data from target domain(s).\nIn this work, we first show that absent assumptions on the source classifier or the nature of the shift, no method of estimating accuracy will work generally (even in non-contrived settings). To estimate accuracy on target domain perfectly, we highlight that even given perfect knowledge of the labeled source distribution (i.e., $p_{s}(x, y)$ ) and unlabeled target distribution (i.e., $p_{t}(x)$ ), we need restrictions on the nature of the shift such that we can uniquely identify the target conditional $p_{t}(y \\mid x)$. Thus, in general, identifying the accuracy of the classifier is as hard as identifying the optimal predictor.\nSecond, motivated by the superiority of methods that use maximum softmax probability (or logit) of a model for Out-Of-Distribution (OOD) detection (Hendrycks \\& Gimpel, 2016; Hendrycks et al., 2019), we propose a simple method that leverages softmax probability to predict model performance. Our method, Average Thresholded Confidence (ATC), learns a threshold on a score (e.g., maximum confidence or negative entropy) of model confidence on validation source data and predicts target domain accuracy as the fraction of unlabeled target points that receive a score above that threshold. ATC selects a threshold on validation source data such that the fraction of source examples that receive the score above the threshold match the accuracy of those examples. Our primary contribution in ATC is the proposal of obtaining the threshold and observing its efficacy on (practical) accuracy estimation. Importantly, our work takes a step forward in positively answering the question raised in Deng \\& Zheng (2021); Deng et al. (2021) about a practical strategy to select a threshold that enables accuracy prediction with thresholded model confidence.",
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"
        }
      ],
      "dimensions": {
        "dpi": 200,
        "height": 2200,
        "width": 1700
      }
    },
    {
      "index": 2,
      "markdown": "ATC is simple to implement with existing frameworks, compatible with arbitrary model classes, and dominates other contemporary methods. Across several model architectures on a range of benchmark vision and language datasets, we verify that ATC outperforms prior methods by at least $2-4 \\times$ in predicting target accuracy on a variety of distribution shifts. In particular, we consider shifts due to common corruptions (e.g., ImageNet-C), natural distribution shifts due to dataset reproduction (e.g., ImageNet-v2, ImageNet-R), shifts due to novel subpopulations (e.g., BREEDS), and distribution shifts faced in the wild (e.g., WILDS).\n\nAs a starting point for theory development, we investigate ATC on a simple toy model that models distribution shift with varying proportions of the population with spurious features, as in Nagarajan et al. (2020). Finally, we note that although ATC achieves superior performance in our empirical evaluation, like all methods, it must fail (returns inconsistent estimates) on certain types of distribution shifts, per our impossibility result.\n\n# 2 PRIOR WORK \n\nOut-of-distribution detection. The main goal of OOD detection is to identify previously unseen examples, i.e., samples out of the support of training distribution. To accomplish this, modern methods utilize confidence or features learned by a deep network trained on some source data. Hendrycks \\& Gimpel (2016); Geifman \\& El-Yaniv (2017) used the confidence score of an (already) trained deep model to identify OOD points. Lakshminarayanan et al. (2016) use entropy of an ensemble model to evaluate prediction uncertainty on OOD points. To improve OOD detection with model confidence, Liang et al. (2017) propose to use temperature scaling and input perturbations. Jiang et al. (2018) propose to use scores based on the relative distance of the predicted class to the second class. Recently, residual flow-based methods were used to obtain a density model for OOD detection (Zhang et al., 2020). Ji et al. (2021) proposed a method based on subfunction error bounds to compute unreliability per sample. Refer to Ovadia et al. (2019); Ji et al. (2021) for an overview and comparison of methods for prediction uncertainty on OOD data.\n\nPredicting model generalization. Understanding generalization capabilities of overparameterized models on in-distribution data using conventional machine learning tools has been a focus of a long line of work; representative research includes Neyshabur et al. (2015; 2017); Neyshabur (2017); Neyshabur et al. (2018); Dziugaite \\& Roy (2017); Bartlett et al. (2017); Zhou et al. (2018); Long \\& Sedghi (2019); Nagarajan \\& Kolter (2019a). At a high level, this line of research bounds the generalization gap directly with complexity measures calculated on the trained model. However, these bounds typically remain numerically loose relative to the true generalization error (Zhang et al., 2016; Nagarajan \\& Kolter, 2019b). On the other hand, another line of research departs from complexitybased approaches to use unseen unlabeled data to predict in-distribution generalization (Platanios et al., 2016; 2017; Garg et al., 2021; Jiang et al., 2021).\n\nRelevant to our work are methods for predicting the error of a classifier on OOD data based on unlabeled data from the target (OOD) domain. These methods can be characterized into two broad categories: (i) Methods which explicitly predict correctness of the model on individual unlabeled points (Deng \\& Zheng, 2021; Jiang et al., 2021; Deng et al., 2021; Chen et al., 2021a); and (ii) Methods which directly obtain an estimate of error with unlabeled OOD data without making a point-wise prediction (Chen et al., 2021b; Guillory et al., 2021; Chuang et al., 2020).\nTo achieve a consistent estimate of the target accuracy, Jiang et al. (2021); Guillory et al. (2021) require calibration on target domain. However, these methods typically yield poor estimates as deep models trained and calibrated on some source data are seldom calibrated on previously unseen domains (Ovadia et al., 2019). Additionally, Deng \\& Zheng (2021); Guillory et al. (2021) derive model-based distribution statistics on unlabeled target set that correlate with the target accuracy and propose to use a subset of labeled target domains to learn a (linear) regression function that predicts model performance. However, there are two drawbacks with this approach: (i) the correlation of these distribution statistics can vary substantially as we consider different nature of shifts (refer to Sec. 5.1, where we empirically demonstrate this failure); (ii) even if there exists a (hypothetical) statistic with strong correlations, obtaining labeled target domains (even simulated ones) with strong correlations would require significant a priori knowledge about the nature of shift that, in general, might not be available before models are deployed in the wild. Nonetheless, in our work, we only assume access to labeled data from the source domain presuming no access to labeled target domains or information about how to simulate them.",
      "images": [],
      "dimensions": {
        "dpi": 200,
        "height": 2200,
        "width": 1700
      }
    },
    {
      "index": 3,
      "markdown": "Moreover, unlike the parallel work of Deng et al. (2021), we do not focus on methods that alter the training on source data to aid accuracy prediction on the target data. Chen et al. (2021b) propose an importance re-weighting based approach that leverages (additional) information about the axis along which distribution is shifting in form of \"slicing functions\". In our work, we make comparisons with importance re-weighting baseline from Chen et al. (2021b) as we do not have any additional information about the axis along which the distribution is shifting.\n\n# 3 Problem Setup \n\nNotation. By $\\|\\cdot|$, and $\\langle\\cdot, \\cdot\\rangle$ we denote the Euclidean norm and inner product, respectively. For a vector $v \\in \\mathbb{R}^{d}$, we use $v_{j}$ to denote its $j^{\\text {th }}$ entry, and for an event $E$ we let $\\mathbb{I}[E]$ denote the binary indicator of the event.\nSuppose we have a multi-class classification problem with the input domain $\\mathcal{X} \\subseteq \\mathbb{R}^{d}$ and label space $\\mathcal{Y}=\\{1,2, \\ldots, k\\}$. For binary classification, we use $\\mathcal{Y}=\\{0,1\\}$. By $\\mathcal{D}^{\\mathcal{S}}$ and $\\mathcal{D}^{\\mathrm{T}}$, we denote source and target distribution over $\\mathcal{X} \\times \\mathcal{Y}$. For distributions $\\mathcal{D}^{\\mathcal{S}}$ and $\\mathcal{D}^{\\mathrm{T}}$, we define $p_{\\mathcal{S}}$ or $p_{\\mathrm{T}}$ as the corresponding probability density (or mass) functions. A dataset $S:=\\left\\{\\left(x_{i}, y_{i}\\right)\\right\\}_{i=1}^{n} \\sim\\left(\\mathcal{D}^{\\mathcal{S}}\\right)^{n}$ contains $n$ points sampled i.i.d. from $\\mathcal{D}^{\\mathcal{S}}$. Let $\\mathcal{F}$ be a class of hypotheses mapping $\\mathcal{X}$ to $\\Delta^{k-1}$ where $\\Delta^{k-1}$ is a simplex in $k$ dimensions. Given a classifier $f \\in \\mathcal{F}$ and datum $(x, y)$, we denote the $0-1$ error (i.e., classification error) on that point by $\\mathcal{E}(f(x), y):=\\mathbb{I}\\left[y \\notin \\arg \\max _{j \\in \\mathcal{Y}} f_{j}(x)\\right]$. Given a model $f \\in \\mathcal{F}$, our goal in this work is to understand the performance of $f$ on $\\mathcal{D}^{\\mathrm{T}}$ without access to labeled data from $\\mathcal{D}^{\\mathrm{T}}$. Note that our goal is not to adapt the model to the target data. Concretely, we aim to predict accuracy of $f$ on $\\mathcal{D}^{\\mathrm{T}}$. Throughout this paper, we assume we have access to the following: (i) model $f$; (ii) previously-unseen (validation) data from $\\mathcal{D}^{\\mathcal{S}}$; and (iii) unlabeled data from target distribution $\\mathcal{D}^{\\mathrm{T}}$.\n\n### 3.1 Accuracy Estimation: Possibility and Impossibility Results\n\nFirst, we investigate the question of when it is possible to estimate the target accuracy of an arbitrary classifier, even given knowledge of the full source distribution $p_{s}(x, y)$ and target marginal $p_{t}(x)$. Absent assumptions on the nature of shift, estimating target accuracy is impossible. Even given access to $p_{s}(x, y)$ and $p_{t}(x)$, the problem is fundamentally unidentifiable because $p_{t}(y \\mid x)$ can shift arbitrarily. In the following proposition, we show that absent assumptions on the classifier $f$ (i.e., when $f$ can be any classifier in the space of all classifiers on $\\mathcal{X}$ ), we can estimate accuracy on the target data iff assumptions on the nature of the shift, together with $p_{s}(x, y)$ and $p_{t}(x)$, uniquely identify the (unknown) target conditional $p_{t}(y \\mid x)$. We relegate proofs from this section to App. A.\nProposition 1. Absent further assumptions, accuracy on the target is identifiable iff $p_{t}(y \\mid x)$ is uniquely identified given $p_{s}(x, y)$ and $p_{t}(x)$.\n\nProposition 1 states that we need enough constraints on nature of shift such that $p_{s}(x, y)$ and $p_{t}(x)$ identifies unique $p_{t}(y \\mid x)$. It also states that under some assumptions on the nature of the shift, we can hope to estimate the model's accuracy on target data. We will illustrate this on two common assumptions made in domain adaptation literature: (i) covariate shift (Heckman, 1977; Shimodaira, 2000) and (ii) label shift (Saerens et al., 2002; Zhang et al., 2013; Lipton et al., 2018). Under covariate shift assumption, that the target marginal support $\\operatorname{supp}\\left(p_{t}(x)\\right)$ is a subset of the source marginal support $\\operatorname{supp}\\left(p_{s}(x)\\right)$ and that the conditional distribution of labels given inputs does not change within support, i.e., $p_{s}(y \\mid x)=p_{t}(y \\mid x)$, which, trivially, identifies a unique target conditional $p_{t}(y \\mid x)$. Under label shift, the reverse holds, i.e., the class-conditional distribution does not change $\\left(p_{s}(x \\mid y)=p_{t}(x \\mid y)\\right)$ and, again, information about $p_{t}(x)$ uniquely determines the target conditional $p_{t}(y \\mid x)$ (Lipton et al., 2018; Garg et al., 2020). In these settings, one can estimate an arbitrary classifier's accuracy on the target domain either by using importance re-weighting with the ratio $p_{t}(x) / p_{s}(x)$ in case of covariate shift or by using importance re-weighting with the ratio $p_{t}(y) / p_{s}(y)$ in case of label shift. While importance ratios in the former case can be obtained directly when $p_{t}(x)$ and $p_{s}(x)$ are known, the importance ratios in the latter case can be obtained by using techniques from Saerens et al. (2002); Lipton et al. (2018); Azizzadenesheli et al. (2019); Alexandari et al. (2019). In App. B, we explore accuracy estimation in the setting of these shifts and present extensions to generalized notions of label shift (Tachet des Combes et al., 2020) and covariate shift (Rojas-Carulla et al., 2018).\n\nAs a corollary of Proposition 1, we now present a simple impossibility result, demonstrating that no single method can work for all families of distribution shift.",
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      "markdown": "Corollary 1. Absent assumptions on the classifier $f$, no method of estimating accuracy will work in all scenarios, i.e., for different nature of distribution shifts.\n\nIntuitively, this result states that every method of estimating accuracy on target data is tied up with some assumption on the nature of the shift and might not be useful for estimating accuracy under a different assumption on the nature of the shift. For illustration, consider a setting where we have access to distribution $p_{s}(x, y)$ and $p_{t}(x)$. Additionally, assume that the distribution can shift only due to covariate shift or label shift without any knowledge about which one. Then Corollary 1 says that it is impossible to have a single method that will simultaneously for both label shift and covariate shift as in the following example (we spell out the details in App. A):\n\nExample 1. Assume binary classification with $p_{s}(x)=\\alpha \\cdot \\phi\\left(\\mu_{1}\\right)+(1-\\alpha) \\cdot \\phi\\left(\\mu_{2}\\right)$, $p_{s}(x \\mid y=0)=\\phi\\left(\\mu_{1}\\right), p_{s}(x \\mid y=1)=\\phi\\left(\\mu_{2}\\right)$, and $p_{t}(x)=\\beta \\cdot \\phi\\left(\\mu_{1}\\right)+(1-\\beta) \\cdot \\phi\\left(\\mu_{2}\\right)$ where $\\phi(\\mu)=\\mathcal{N}(\\mu, 1), \\alpha, \\beta \\in(0,1)$, and $\\alpha \\neq \\beta$. Error of a classifier $f$ on target data is given by $\\mathcal{E}_{1}=\\mathbb{E}_{(x, y) \\sim p_{s}(x, y)}\\left[\\frac{p_{t}(x)}{p_{s}(x)} \\mathbb{I}[f(x) \\neq y]\\right]$ under covariate shift and by $\\mathcal{E}_{2}=$ $\\mathbb{E}_{(x, y) \\sim p_{s}(x, y)}\\left[\\left(\\frac{\\beta}{\\alpha} \\mathbb{I}[y=0]+\\frac{1-\\beta}{1-\\alpha} \\mathbb{I}[y=1]\\right) \\mathbb{I}[f(x) \\neq y]\\right]$ under label shift. In App. A, we show that $\\mathcal{E}_{1} \\neq \\mathcal{E}_{2}$ for all $f$. Thus, given access to $p_{s}(x, y)$, and $p_{t}(x)$, any method that consistently estimates error of a classifer under covariate shift will give an incorrect estimate of error under label shift and vice-versa. The reason is that the same $p_{t}(x)$ and $p_{s}(x, y)$ can correspond to error $\\mathcal{E}_{1}$ (under covariate shift) or error $\\mathcal{E}_{2}$ (under label shift) and determining which scenario one faces requires further assumptions on the nature of shift.\n\n# 4 Predicting accuracy with Average Thresholded CONFIDENCE \n\nIn this section, we present our method ATC that leverages a black box classifier $f$ and (labeled) validation source data to predict accuracy on target domain given access to unlabeled target data. Throughout the discussion, we assume that the classifier $f$ is fixed.\nBefore presenting our method, we introduce some terminology. Define a score function $s: \\Delta^{k-1} \\rightarrow$ $\\mathbb{R}$ that takes in the softmax prediction of the function $f$ and outputs a scalar. We want a score function such that if the score function takes a high value at a datum $(x, y)$ then $f$ is likely to be correct. In this work, we explore two such score functions: (i) Maximum confidence, i.e., $s(f(x))=\\max _{j \\in \\mathcal{Y}} f_{j}(x)$; and (ii) Negative Entropy, i.e., $s(f(x))=\\sum_{j} f_{j}(x) \\log \\left(f_{j}(x)\\right)$. Our method identifies a threshold $t$ on source data $\\mathcal{D}^{\\mathbb{S}}$ such that the expected number of points that obtain a score less than $t$ match the error of $f$ on $\\mathcal{D}^{\\mathbb{S}}$, i.e.,\n\n$$\n\\mathbb{E}_{x \\sim \\mathcal{D}^{\\mathbb{S}}}[\\mathbb{I}[s(f(x))<t]]=\\mathbb{E}_{(x, y) \\sim \\mathcal{D}^{\\mathbb{S}}}\\left[\\mathbb{I}\\left[\\arg \\max _{j \\in \\mathcal{Y}} f_{j}(x) \\neq y\\right]\\right]\n$$\n\nand then our error estimate $\\mathrm{ATC}_{\\mathcal{D}^{\\mathrm{T}}}(s)$ on the target domain $\\mathcal{D}^{\\mathrm{T}}$ is given by the expected number of target points that obtain a score less than $t$, i.e.,\n\n$$\n\\operatorname{ATC}_{\\mathcal{D}^{\\mathrm{T}}}(s)=\\mathbb{E}_{x \\sim \\mathcal{D}^{\\mathrm{T}}}[\\mathbb{I}[s(f(x))<t]]\n$$\n\nIn short, in (1), ATC selects a threshold on the score function such that the error in the source domain matches the expected number of points that receive a score below $t$ and in (2), ATC predicts error on the target domain as the fraction of unlabeled points that obtain a score below that threshold $t$. Note that, in principle, there exists a different threshold $t^{\\prime}$ on the target distribution $\\mathcal{D}^{\\mathrm{T}}$ such that (1) is satisfied on $\\mathcal{D}^{\\mathrm{T}}$. However, in our experiments, the same threshold performs remarkably well. The main empirical contribution of our work is to show that the threshold obtained with (1) might be used effectively in condunction with modern deep networks in a wide range of settings to estimate error on the target data. In practice, to obtain the threshold with ATC, we minimize the difference between the expression on two sides of (1) using finite samples. In the next section, we show that ATC precisely predicts accuracy on the OOD data on the desired line $y=x$. In App. C, we discuss an alternate interpretation of the method and make connections with OOD detection methods.\n\n## 5 EXPERIMENTS\n\nWe now empirical evaluate ATC and compare it with existing methods. In each of our main experiment, keeping the underlying model fixed, we vary target datasets and make a prediction",
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      "markdown": "![img-1.jpeg](img-1.jpeg)\n\nFigure 2: Scatter plot of predicted accuracy versus (true) OOD accuracy. Each point denotes a different OOD dataset, all evaluated with the same DenseNet121 model. We only plot the best three methods. With ATC (ours), we refer to ATC-NE. We observe that ATC significantly outperforms other methods and with ATC, we recover the desired line $y=x$ with a robust linear fit. Aggregated estimation error in Table 1 and plots for other datasets and architectures in App. H.\nof the target accuracy with various methods given access to only unlabeled data from the target. Unless noted otherwise, all models are trained only on samples from the source distribution with the main exception of pre-training on a different distribution. We use labeled examples from the target distribution to only obtain true error estimates.\n\nDatasets. First, we consider synthetic shifts induced due to different visual corruptions (e.g., shot noise, motion blur etc.) under ImageNet-C (Hendrycks \\& Dietterich, 2019). Next, we consider natural shifts due to differences in the data collection process of ImageNet (Russakovsky et al., 2015), e.g, ImageNetv2 (Recht et al., 2019). We also consider images with artistic renditions of object classes, i.e., ImageNet-R (Hendrycks et al., 2021) and ImageNet-Sketch (Wang et al., 2019). Note that renditions dataset only contains a subset 200 classes from ImageNet. To include renditions dataset in our testbed, we include results on ImageNet restricted to these 200 classes (which we call ImageNet-200) along with full ImageNet.\n\nSecond, we consider BREEDs (Santurkar et al., 2020) to assess robustness to subpopulation shifts, in particular, to understand how accuracy estimation methods behave when novel subpopulations not observed during training are introduced. BREEDS leverages class hierarchy in ImageNet to create 4 datasets Entity-13, Entity-30, Living-17, Non-Living-26. We focus on natural and synthetic shifts as in ImageNet on same and different subpopulations in BREEDs. Third, from Wilds (Koh et al., 2021) benchmark, we consider FMoW-WILDS (Christie et al., 2018), RxRx1-WILDS (Taylor et al., 2019), Amazon-WILDS (Ni et al., 2019), CivilComments-WILDS (Borkan et al., 2019) to consider distribution shifts faced in the wild.\n\nFinally, similar to ImageNet, we consider (i) synthetic shifts (CIFAR-10-C) due to common corruptions; and (ii) natural shift (i.e., CIFARv2 (Recht et al., 2018)) on CIFAR-10 (Krizhevsky \\& Hinton, 2009). On CIFAR-100, we just have synthetic shifts due to common corruptions. For completeness, we also consider natural shifts on MNIST (LeCun et al., 1998) as in the prior work (Deng \\& Zheng, 2021). We use three real shifted datasets, i.e., USPS (Hull, 1994), SVHN (Netzer et al., 2011) and QMNIST (Yadav \\& Bottou, 2019). We give a detailed overview of our setup in App. F.\nArchitectures and Evaluation. For ImageNet, BREEDs, CIFAR, FMoW-WILDS, RxRx1-WILDS datasets, we use DenseNet121 (Huang et al., 2017) and ResNet50 (He et al., 2016) architectures. For Amazon-WILDS and CivilComments-WILDS, we fine-tune a DistilBERT-base-uncased (Sanh et al., 2019) model. For MNIST, we train a fully connected multilayer perceptron. We use standard training with benchmarked hyperparameters. To compare methods, we report average absolute difference between the true accuracy on the target data and the estimated accuracy on the same unlabeled examples. We refer to this metric as Mean Absolute estimation Error (MAE). Along with MAE, we also show scatter plots to visualize performance at individual target sets. Refer to App. G for additional details on the setup.\nMethods With ATC-NE, we denote ATC with negative entropy score function and with ATC-MC, we denote ATC with maximum confidence score function. For all methods, we implement post-hoc calibration on validation source data with Temperature Scaling (TS; Guo et al. (2017)). Below we briefly discuss baselines methods compared in our work and relegate details to App. E.",
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        "dpi": 200,
        "height": 2200,
        "width": 1700
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    },
    {
      "index": 6,
      "markdown": "| Dataset | Shift | IM |  | AC |  | DOC |  | GDE | ATC-MC (Ours) |  | ATC-NE (Ours) |  |\n| :--: | :--: | :--: | :--: | :--: | :--: | :--: | :--: | :--: | :--: | :--: | :--: | :--: |\n|  |  | Pre T | Post T | Pre T | Post T | Pre T | Post T | Post T | Pre T | Post T | Pre T | Post T |\n| CIFAR10 | Natural | 6.60 | 5.74 | 9.88 | 6.89 | 7.25 | 6.07 | 4.77 | 3.21 | 3.02 | 2.99 | 2.85 |\n|  | Synthetic | 12.33 | 10.20 | 16.50 | 11.91 | 13.87 | 11.08 | 6.55 | 4.65 | 4.25 | 4.21 | 3.87 |\n| CIFAR100 | Synthetic | 13.69 | 11.51 | 23.61 | 13.10 | 14.60 | 10.14 | 9.85 | 5.50 | 4.75 | 4.72 | 4.94 |\n| ImageNet200 | Natural | 12.37 | 8.19 | 22.07 | 8.61 | 15.17 | 7.81 | 5.13 | 4.37 | 2.04 | 3.79 | 1.45 |\n|  | Synthetic | 19.86 | 12.94 | 32.44 | 13.35 | 25.02 | 12.38 | 5.41 | 5.93 | 3.09 | 5.00 | 2.68 |\n| ImageNet | Natural | 7.77 | 6.50 | 18.13 | 6.02 | 8.13 | 5.76 | 6.23 | 3.88 | 2.17 | 2.06 | 0.80 |\n|  | Synthetic | 13.39 | 10.12 | 24.62 | 8.51 | 13.55 | 7.90 | 6.32 | 3.34 | 2.53 | 2.61 | 4.89 |\n| FMoW-WILDS | Natural | 5.53 | 4.31 | 33.53 | 12.84 | 5.94 | 4.45 | 5.74 | 3.06 | 2.70 | 3.02 | 2.72 |\n| RxRx1-WILDS | Natural | 5.80 | 5.72 | 7.90 | 4.84 | 5.98 | 5.98 | 6.03 | 4.66 | 4.56 | 4.41 | 4.47 |\n| Amazon-WILDS | Natural | 2.40 | 2.29 | 8.01 | 2.38 | 2.40 | 2.28 | 17.87 | 1.65 | 1.62 | 1.60 | 1.50 |\n| CivilCom.-WILDS | Natural | 12.64 | 10.80 | 16.76 | 11.03 | 13.31 | 10.99 | 16.65 | 7.14 |  |  |  |\n| MNIST | Natural | 18.48 | 15.99 | 21.17 | 14.81 | 20.19 | 14.56 | 24.42 | 5.02 | 2.40 | 3.14 | 3.50 |\n| EntitY-13 | Same | 16.23 | 11.14 | 24.97 | 10.88 | 19.08 | 10.47 | 10.71 | 5.39 | 3.88 | 4.58 | 4.19 |\n|  | Novel | 28.53 | 22.02 | 38.33 | 21.64 | 32.43 | 21.22 | 20.61 | 13.58 | 10.28 | 12.25 | 6.63 |\n| EntitY-30 | Same | 18.59 | 14.46 | 28.82 | 14.30 | 21.63 | 13.46 | 12.92 | 9.12 | 7.75 | 8.15 | 7.64 |\n|  | Novel | 32.34 | 26.85 | 44.02 | 26.27 | 36.82 | 25.42 | 23.16 | 17.75 | 14.30 | 15.60 | 10.57 |\n| NONLIVING-26 | Same | 18.66 | 17.17 | 26.39 | 16.14 | 19.86 | 15.58 | 16.63 | 10.87 | 10.24 | 10.07 | 10.26 |\n|  | Novel | 33.43 | 31.53 | 41.66 | 29.87 | 35.13 | 29.31 | 29.56 | 21.70 | 20.12 | 19.08 | 18.26 |\n| LIVING-17 | Same | 12.63 | 11.05 | 18.32 | 10.46 | 14.43 | 10.14 | 9.87 | 4.57 | 3.95 | 3.81 | 4.21 |\n|  | Novel | 29.03 | 26.96 | 35.67 | 26.11 | 31.73 | 25.73 | 23.53 | 16.15 | 14.49 | 12.97 | 11.39 |\n\nTable 1: Mean Absolute estimation Error (MAE) results for different datasets in our setup grouped by the nature of shift. 'Same' refers to same subpopulation shifts and 'Novel' refers novel subpopulation shifts. We include details about the target sets considered in each shift in Table 2. Post T denotes use of TS calibration on source. Across all datasets, we observe that ATC achieves superior performance (lower MAE is better). For language datasets, we use DistilBERT-base-uncased, for vision dataset we report results with DenseNet model with the exception of MNIST where we use FCN. We include results on other architectures in App. H. For GDE post T and pre T estimates match since TS doesn’t alter the argmax prediction. Results reported by aggregating MAE numbers over 4 different seeds. We include results with standard deviation values in Table 3.\n\nAverage Confidence (AC). Error is estimated as the expected value of the maximum softmax confidence on the target data, i.e, $\\mathrm{AC}_{\\mathcal{D}^{\\dagger}}=\\mathbb{E}_{x \\sim \\mathcal{D}^{\\dagger}}\\left[\\max _{j \\in \\mathcal{Y}} f_{j}(x)\\right]$.\nDifference Of Confidence (DOC). We estimate error on target by subtracting difference of confidences on source and target (as a surrogate to distributional distance Guillory et al. (2021)) from the error on source distribution, i.e, $\\mathrm{DOC}_{\\mathcal{D}^{\\dagger}}=\\mathbb{E}_{x \\sim \\mathcal{D}^{\\delta}}\\left[\\mathbb{I}\\left[\\arg \\max _{j \\in \\mathcal{Y}} f_{j}(x) \\neq y\\right]\\right]+\\mathbb{E}_{x \\sim \\mathcal{D}^{\\dagger}}\\left[\\max _{j \\in \\mathcal{Y}} f_{j}(x)\\right]-$ $\\mathbb{E}_{x \\sim \\mathcal{D}^{\\delta}}\\left[\\max _{j \\in \\mathcal{Y}} f_{j}(x)\\right]$. This is referred to as DOC-Feat in (Guillory et al., 2021).\nImportance re-weighting (IM). We estimate the error of the classifier with importance re-weighting of $0-1$ error in the pushforward space of the classifier. This corresponds to MANDOLIN using one slice based on the underlying classifier confidence Chen et al. (2021b).\n\nGeneralized Disagreement Equality (GDE). Error is estimated as the expected disagreement of two models (trained on the same training set but with different randomization) on target data (Jiang et al., 2021), i.e., $\\operatorname{GDE}_{\\mathcal{D}^{\\dagger}}=\\mathbb{E}_{x \\sim \\mathcal{D}^{\\dagger}}\\left[\\mathbb{I}\\left[f(x) \\neq f^{\\prime}(x)\\right]\\right]$ where $f$ and $f^{\\prime}$ are the two models. Note that GDE requires two models trained independently, doubling the computational overhead while training.\n\n### 5.1 RESULTS\n\nIn Table 1, we report MAE results aggregated by the nature of the shift in our testbed. In Fig. 2 and Fig. 1(right), we show scatter plots for predicted accuracy versus OOD accuracy on several datasets. We include scatter plots for all datasets and parallel results with other architectures in App. H. In App. H.1, we also perform ablations on CIFAR using a pre-trained model and observe that pre-training doesn't change the efficacy of ATC.",
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      "markdown": "![img-2.jpeg](img-2.jpeg)\n\nFigure 3: Left: Predicted accuracy with DOC on Living17 BreEds dataset. We observe a substantial gap in the linear fit of same and different subpopulations highlighting poor correlation. Middle: After fitting a robust linear model for DOC on same subpopulation, we show predicted accuracy on different subpopulations with fine-tuned DOC (i.e., $\\operatorname{DOC}(\\mathrm{w} / \\mathrm{fit})$ ) and compare with ATC without any regression model, i.e., ATC (w/o fit). While observe substantial improvements in MAE from 24.41 with DOC (w/o fit) to 13.26 with DOC (w/ fit), ATC (w/o fit) continues to outperform even DOC (w/ fit) with MAE 10.22. We show parallel results with other BREEDS datasets in App. H.2. Right : Empirical validation of our toy model. We show that ATC perfectly estimates target performance as we vary the degree of spurious correlation in target. ' $\\times$ ' represents accuracy on source.\n\nWe predict accuracy on the target data before and after calibration with TS. First, we observe that both ATC-NE and ATC-MC (even without TS) obtain significantly lower MAE when compared with other methods (even with TS). Note that with TS we observe substantial improvements in MAE for all methods. Overall, ATC-NE (with TS) typically achieves the smallest MAE improving by more than $2 \\times$ on CIFAR and by $3-4 \\times$ on ImageNet over GDE (the next best alternative to ATC). Alongside, we also observe that a linear fit with robust regression (Siegel, 1982) on the scatter plot recovers a line close to $x=y$ for ATC-NE with TS while the line is far away from $x=y$ for other methods (Fig. 2 and Fig. 1(right)). Remarkably, MAE is in the range of $0.4-5.8$ with ATC for CIFAR, ImageNet, MNIST, and Wilds. However, MAE is much higher on BREEDS benchmark with novel subpopulations. While we observe a small MAE (i.e., comparable to our observations on other datasets) on BREEDS with natural and synthetic shifts from the same sub-population, MAE on shifts with novel population is significantly higher with all methods. Note that even on novel populations, ATC continues to dominate all other methods across all datasets in BREEDS.\nAdditionally, for different subpopulations in BREEDS setup, we observe a poor linear correlation of the estimated performance with the actual performance as shown in Fig. 3 (left)(we notice a similar gap in the linear fit for all other methods). Hence in such a setting, we would expect methods that fine-tune a regression model on labeled target examples from shifts with one subpopulation will perform poorly on shifts with different subpopulations. Corroborating this intuition, next, we show that even after fitting a regression model for DOC on natural and synthetic shifts with source subpopulations, ATC without regression model continues to outperform DOC with regression model on shifts with novel subpopulation.\n\nFitting a regression model on BREEDS with DOC. Using label target data from natural and synthetic shifts for the same subpopulation (same as source), we fit a robust linear regression model (Siegel, 1982) to fine-tune DOC as in Guillory et al. (2021). We then evaluate the fine-tuned DOC (i.e., DOC with linear model) on natural and synthetic shifts from novel subpopulations on BREEDS benchmark. Although we observe significant improvements in the performance of finetuned DOC when compared with DOC (without any fine-tuning), ATC without any regression model continues to perform better (or similar) to that of fine-tuned DOC on novel subpopulations (Fig. 3 (middle)). Refer to App. H. 2 for details and Table 5 for MAE on BREEDS with regression model.\n\n## 6 InVEStigating ATC on Toy Model\n\nIn this section, we propose and analyze a simple theoretical model that distills empirical phenomena from the previous section and highlights efficacy of ATC. Here, our aim is not to obtain a general model that captures complicated real distributions on high dimensional input space as the images in ImageNet. Instead to further our understanding, we focus on an easy-to-learn binary classification task from Nagarajan et al. (2020) with linear classifiers, that is rich enough to exhibit some of the same phenomena as with deep networks on real data distributions.",
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      "markdown": "Consider a easy-to-learn binary classification problem with two features $x=\\left[x_{\\text {inv }}, x_{\\text {sp }}\\right] \\in \\mathbb{R}^{2}$ where $x_{\\text {inv }}$ is fully predictive invariant feature with a margin $\\gamma>0$ and $x_{\\text {sp }} \\in\\{-1,1\\}$ is a spurious feature (i.e., a feature that is correlated but not predictive of the true label). Conditional on $y$, the distribution over $x_{\\text {inv }}$ is given as follows: $x_{\\text {inv }} \\mid(y=1) \\sim U[\\gamma, c]$ and $x_{\\text {inv }} \\mid(y=0) \\sim U[-c,-\\gamma]$, where $c$ is a fixed constant greater than $\\gamma$. For simplicity, we assume that label distribution on source is uniform on $\\{-1,1\\}$. $x_{\\text {sp }}$ is distributed such that $P_{x}\\left[x_{\\text {sp }} \\cdot(2 y-1)>0\\right]=p_{\\text {sp }}$, where $p_{\\text {sp }} \\in(0.5,1.0)$ controls the degree of spurious correlation. To model distribution shift, we simulate target data with different degree of spurious correlation, i.e., in target distribution $P_{t}\\left[x_{\\text {sp }} \\cdot(2 y-1)>0\\right]=p_{\\text {sp }}^{\\prime} \\in[0,1]$. Note that here we do not consider shifts in the label distribution but our result extends to arbitrary shifts in the label distribution as well.\n\nIn this setup, we examine linear sigmoid classifiers of the form $f(x)=\\left[\\frac{1}{1+e^{w^{T} x}}, \\frac{e^{w^{T} x}}{1+e^{w^{T} x}}\\right]$ where $w=\\left[w_{\\text {inv }}, w_{\\text {sp }}\\right] \\in \\mathbb{R}^{2}$. While there exists a linear classifier with $w=[1,0]$ that correctly classifies all the points with a margin $\\gamma$, Nagarajan et al. (2020) demonstrated that a linear classifier will typically have a dependency on the spurious feature, i.e., $w_{\\text {sp }} \\neq 0$. They show that due to geometric skews, despite having positive dependencies on the invariant feature, a max-margin classifier trained on finite samples relies on the spurious feature. Refer to App. D for more details on these skews. In our work, we show that given a linear classifier that relies on the spurious feature and achieves a non-trivial performance on the source (i.e., $w_{\\text {inv }}>0$ ), ATC with maximum confidence score function consistently estimates the accuracy on the target distribution.\nTheorem 1 (Informal). Given any classifier with $w_{\\text {inv }}>0$ in the above setting, the threshold obtained in (1) together with ATC as in (2) with maximum confidence score function obtains a consistent estimate of the target accuracy.\n\nConsider a classifier that depends positively on the spurious feature (i.e., $w_{\\text {sp }}>0$ ). Then as the spurious correlation decreases in the target data, the classifier accuracy on the target will drop and vice-versa if the spurious correlation increases on the target data. Theorem 1 shows that the threshold identified with ATC as in (1) remains invariant as the distribution shifts and hence ATC as in (2) will correctly estimate the accuracy with shifting distributions. Next, we illustrate Theorem 1 by simulating the setup empirically. First we pick a arbitrary classifier (which can also be obtained by training on source samples), tune the threshold on hold-out source examples and predict accuracy with different methods as we shift the distribution by varying the degree of spurious correlation.\nEmpirical validation and comparison with other methods. Fig. 3(right) shows that as the degree of spurious correlation varies, our method accurately estimates the target performance where all other methods fail to accurately estimate the target performance. Understandably, due to poor calibration of the sigmoid linear classifier AC, DOC and GDE fail. While in principle IM can perfectly estimate the accuracy on target in this case, we observe that it is highly sensitive to the number bins and choice of histogram binning (i.e., uniform mass or equal width binning). We elaborate more on this in App. D.\nBiased estimation with ATC. Now we discuss changes in the above setup where ATC yields inconsistent estimates. We assumed that both in source and target $x_{\\text {inv }} \\mid y=1$ is uniform between $[\\gamma, c]$ and $x \\mid y=-1$ is uniform between $[-c,-\\gamma]$. Shifting the support of target class conditional $p_{t}\\left(x_{\\text {inv }} \\mid y\\right)$ may introduce a bias in ATC estimates, e.g., shrinking the support to $c_{1}(<c)$ (while maintaining uniform distribution) in the target will lead to an over-estimation of the target performance with ATC. In App. D.1, we elaborate on this failure and present a general (but less interpretable) classifier dependent distribution shift condition where ATC is guaranteed to yield consistent estimates.\n\n# 7 CONCLUSION AND FUTURE WORK \n\nIn this work, we proposed ATC, a simple method for estimating target domain accuracy based on unlabeled target (and labeled source data). ATC achieves remarkably low estimation error on several synthetic and natural shift benchmarks in our experiments. Notably, our work draws inspiration from recent state-of-the-art methods that use softmax confidences below a certain threshold for OOD detection (Hendrycks \\& Gimpel, 2016; Hendrycks et al., 2019) and takes a step forward in answering questions raised in Deng \\& Zheng (2021) about the practicality of threshold based methods.\nOur distribution shift toy model justifies ATC on an easy-to-learn binary classification task. In our experiments, we also observe that calibration significantly improves estimation with ATC. Since in binary classification, post hoc calibration with TS does not change the effective threshold, in future work, we hope to extend our theoretical model to multi-class classification to understand the efficacy",
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    {
      "index": 9,
      "markdown": "of calibration. Our theory establishes that a classifier's accuracy is not, in general identified, from labeled source and unlabeled target data alone, absent considerable additional constraints on the target conditional $p_{t}(y \\mid x)$. In light of this finding, we also hope to extend our understanding beyond the simple theoretical toy model to characterize broader sets of conditions under which ATC might be guaranteed to obtain consistent estimates. Finally, we should note that while ATC outperforms previous approaches, it still suffers from large estimation error on datasets with novel populations, e.g., BREEDS. We hope that our findings can lay the groundwork for future work for improving accuracy estimation on such datasets.\n\nReproducibility Statement Our code to reproduce all the results is available at https:// github.com/saurabhgarg1996/ATC_code. We have been careful to ensure that our results are reproducible. We have stored all models and logged all hyperparameters and seeds to facilitate reproducibility. Note that throughout our work, we do not perform any hyperparameter tuning, instead, using benchmarked hyperparameters and training procedures to make our results easy to reproduce. While, we have not released code yet, the appendix provides all the necessary details to replicate our experiments and results.\n\n# ACKNOWLEDGEMENT \n\nAuthors would like to thank Ariel Kleiner and Sammy Jerome as the problem formulation and motivation of this paper was highly influenced by initial discussions with them.\n\n## REFERENCES\n\nAmr Alexandari, Anshul Kundaje, and Avanti Shrikumar. Adapting to label shift with bias-corrected calibration. In arXiv preprint arXiv:1901.06852, 2019.\n\nKamyar Azizzadenesheli, Anqi Liu, Fanny Yang, and Animashree Anandkumar. Regularized learning for domain adaptation under label shifts. In International Conference on Learning Representations (ICLR), 2019.\n\nPeter L Bartlett, Dylan J Foster, and Matus J Telgarsky. Spectrally-normalized margin bounds for neural networks. In Advances in neural information processing systems, pp. 6240-6249, 2017.\n\nShai Ben-David, Tyler Lu, Teresa Luu, and Dávid Pál. Impossibility Theorems for Domain Adaptation. In International Conference on Artificial Intelligence and Statistics (AISTATS), 2010.\n\nDaniel Borkan, Lucas Dixon, Jeffrey Sorensen, Nithum Thain, and Lucy Vasserman. Nuanced metrics for measuring unintended bias with real data for text classification. In Companion Proceedings of The 2019 World Wide Web Conference, 2019.\n\nJiefeng Chen, Frederick Liu, Besim Avci, Xi Wu, Yingyu Liang, and Somesh Jha. Detecting errors and estimating accuracy on unlabeled data with self-training ensembles. Advances in Neural Information Processing Systems, 34:14980-14992, 2021a.\n\nMayee Chen, Karan Goel, Nimit S Sohoni, Fait Poms, Kayvon Fatahalian, and Christopher Ré. Mandoline: Model evaluation under distribution shift. In International Conference on Machine Learning, pp. 1617-1629. PMLR, 2021b.\n\nGordon Christie, Neil Fendley, James Wilson, and Ryan Mukherjee. Functional map of the world. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2018.\n\nChing-Yao Chuang, Antonio Torralba, and Stefanie Jegelka. Estimating generalization under distribution shifts via domain-invariant representations. arXiv preprint arXiv:2007.03511, 2020.\n\nWeijian Deng and Liang Zheng. Are labels always necessary for classifier accuracy evaluation? In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. $15069-15078,2021$.\n\nWeijian Deng, Stephen Gould, and Liang Zheng. What does rotation prediction tell us about classifier accuracy under varying testing environments? arXiv preprint arXiv:2106.05961, 2021.",
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      "markdown": "# APPENDIX \n\n## A Proofs from Sec. 3\n\nBefore proving results from Sec. 3, we introduce some notations. Define $\\mathcal{E}(f(x), y):=$ $\\mathbb{I}\\left[y \\notin \\arg \\max _{j \\in \\mathcal{Y}} f_{j}(x)\\right]$. We express the population error on distribution $\\mathcal{D}$ as $\\mathcal{E}_{\\mathcal{D}}(f):=$ $\\mathbb{E}_{(x, y) \\sim \\mathcal{D}}[\\mathcal{E}(f(x), y)]$\n\nProof of Proposition 1. Consider a binary classification problem. Assume $\\mathcal{P}$ be the set of possible target conditional distribution of labels given $p_{s}(x, y)$ and $p_{t}(x)$.\nThe forward direction is simple. If $\\mathcal{P}=\\left\\{p_{t}(y \\mid x)\\right\\}$ is singleton given $p_{s}(x, y)$ and $p_{t}(x)$, then the error of any classifier $f$ on the target domain is identified and is given by\n\n$$\n\\mathcal{E}_{\\mathcal{D}^{T}}(f)=\\mathbb{E}_{x \\sim p_{t}(x), y \\sim p_{t}(y \\mid x)}\\left[\\mathbb{I}\\left[\\arg \\max _{j \\in \\mathcal{Y}} f_{j}(x) \\neq y\\right]\\right]\n$$\n\nFor the reverse direction assume that given $p_{t}(x)$ and $p_{s}(x, y)$, we have two possible distributions $\\mathcal{D}^{T}$ and $\\mathcal{D}^{T^{\\prime}}$ with $p_{t}(y \\mid x), p_{t}^{\\prime}(y \\mid x) \\in \\mathcal{P}$ such that on some $x$ with $p_{t}(x)>0$, we have $p_{t}(y \\mid x) \\neq p_{t}^{\\prime}(y \\mid x)$. Consider $\\mathcal{X}_{M}=\\left\\{x \\in \\mathcal{X} \\mid p_{t}(x)>0\\right.$ and $p_{t}(y=1 \\mid x) \\neq p_{t}^{\\prime}(y=1 \\mid x)\\}$ be the set of all input covariates where the two distributions differ. We will now choose a classifier $f$ such that the error on the two distributions differ. On a subset $\\mathcal{X}_{M}^{1}=\\left\\{x \\in \\mathcal{X} \\mid p_{t}(x)>0\\right.$ and $p_{t}(y=1 \\mid x)>p_{t}^{\\prime}(y=1 \\mid x)\\}$, assume $f(x)=0$ and on a subset $\\mathcal{X}_{M}^{2}=\\left\\{x \\in \\mathcal{X} \\mid p_{t}(x)>0\\right.$ and $p_{t}(y=1 \\mid x)<p_{t}^{\\prime}(y=1 \\mid x)\\}$, assume $f(x)=1$. We will show that the error of $f$ on distribution with $p_{t}(y \\mid x)$ is strictly greater than the error of $f$ on distribution with $p_{t}^{\\prime}(y \\mid x)$. Formally,\n\n$$\n\\begin{aligned}\n& \\mathcal{E}_{\\mathcal{D}^{T}}(f)-\\mathcal{E}_{\\mathcal{D}^{T^{\\prime}}}(f) \\\\\n& =\\mathbb{E}_{x \\sim p_{t}(x), y \\sim p_{t}(y \\mid x)}\\left[\\mathbb{I}\\left[\\arg \\max _{j \\in \\mathcal{Y}} f_{j}(x) \\neq y\\right]\\right]-\\mathbb{E}_{x \\sim p_{t}(x), y \\sim p_{t}^{\\prime}(y \\mid x)}\\left[\\mathbb{I}\\left[\\arg \\max _{j \\in \\mathcal{Y}} f_{j}(x) \\neq y\\right]\\right] \\\\\n& =\\int_{x \\in \\mathcal{X}_{M}} \\mathbb{I}[f(x) \\neq 0]\\left(p_{t}(y=0 \\mid x)-p_{t}^{\\prime}(y=0 \\mid x)\\right) p_{t}(x) d x \\\\\n& \\quad+\\int_{x \\in \\mathcal{X}_{M}} \\mathbb{I}[f(x) \\neq 1]\\left(p_{t}(y=1 \\mid x)-p_{t}^{\\prime}(y=1 \\mid x)\\right) p_{t}(x) d x \\\\\n& =\\int_{x \\in \\mathcal{X}_{M}^{2}}\\left(p_{t}(y=0 \\mid x)-p_{t}^{\\prime}(y=0 \\mid x)\\right) p_{t}(x) d x+\\int_{x \\in \\mathcal{X}_{M}^{1}}\\left(p_{t}(y=1 \\mid x)-p_{t}^{\\prime}(y=1 \\mid x)\\right) p_{t}(x) d x \\\\\n& >0\n\\end{aligned}\n$$\n\nwhere the last step follows by construction of the set $\\mathcal{X}_{M}^{1}$ and $\\mathcal{X}_{M}^{2}$. Since $\\mathcal{E}_{\\mathcal{D}^{T}}(f) \\neq \\mathcal{E}_{\\mathcal{D}^{T^{\\prime}}}(f)$, given the information of $p_{t}(x)$ and $p_{s}(x, y)$ it is impossible to distinguish the two values of the error with classifier $f$. Thus, we obtain a contradiction on the assumption that $p_{t}(y \\mid x) \\neq p_{t}^{\\prime}(y \\mid x)$. Hence, we must pose restrictions on the nature of shift such that $\\mathcal{P}$ is singleton to to identify accuracy on the target.\n\nProof of Corollary 1. The corollary follows directly from Proposition 1. Since two different target conditional distribution can lead to different error estimates without assumptions on the classifier, no method can estimate two different quantities from the same given information. We illustrate this in Example 1 next.\n\n## B ESTIMATING ACCURACY IN COVARIATE SHIFT OR LABEL SHIFT\n\nAccuracy estimation under covariate shift assumption Under the assumption that $p_{t}(y \\mid x)=$ $p_{s}(y \\mid x)$, accuracy on the target domain can be estimated as follows:\n\n$$\n\\begin{aligned}\n\\mathcal{E}_{\\mathcal{D}^{T}}(f) & =\\mathbb{E}_{(x, y) \\sim \\mathcal{D}^{T}}\\left[\\frac{p_{t}(x, y)}{p_{s}(x, y)} \\mathbb{I}[f(x) \\neq y]\\right] \\\\\n& =\\mathbb{E}_{(x, y) \\sim \\mathcal{D}^{T}}\\left[\\frac{p_{t}(x)}{p_{s}(x)} \\mathbb{I}[f(x) \\neq y]\\right]\n\\end{aligned}\n$$",
      "images": [],
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        "width": 1700
      }
    },
    {
      "index": 15,
      "markdown": "Given access to $p_{t}(x)$ and $p_{s}(x)$, one can directly estimate the expression in (6).\nAccuracy estimation under label shift assumption Under the assumption that $p_{t}(x \\mid y)=p_{s}(x \\mid y)$, accuracy on the target domain can be estimated as follows:\n\n$$\n\\begin{aligned}\n\\mathcal{E}_{\\mathcal{D}^{t}}(f) & =\\mathbb{E}_{(x, y) \\sim \\mathcal{D}^{t}}\\left[\\frac{p_{t}(x, y)}{p_{s}(x, y)} \\mathbb{I}[f(x) \\neq y]\\right] \\\\\n& =\\mathbb{E}_{(x, y) \\sim \\mathcal{D}^{t}}\\left[\\frac{p_{t}(y)}{p_{s}(y)} \\mathbb{I}[f(x) \\neq y]\\right]\n\\end{aligned}\n$$\n\nEstimating importance ratios $p_{t}(x) / p_{s}(x)$ is straightforward under covariate shift assumption when the distributions $p_{t}(x)$ and $p_{s}(x)$ are known. For label shift, one can leverage moment matching approach called BBSE (Lipton et al., 2018) or likelihood minimization approach MLLS (Garg et al., 2020). Below we discuss the objective of MLLS:\n\n$$\nw=\\underset{w \\in \\mathcal{W}}{\\arg \\max } \\mathbb{E}_{x \\sim p_{t}(x)}\\left[\\log p_{s}(y \\mid x)^{T} w\\right]\n$$\n\nwhere $\\mathcal{W}=\\left\\{w \\mid \\forall y, w_{y} \\geqslant 0\\right.$ and $\\left.\\sum_{y=1}^{k} w_{y} p_{s}(y)=1\\right\\}$. MLLS objective is guaranteed to obtain consistent estimates for the importance ratios $w^{*}(y)=p_{t}(y) / p_{s}(y)$ under the following condition.\nTheorem 2 (Theorem 1 (Garg et al., 2020)). If the distributions $\\{p(x) \\mid y): y=1, \\ldots, k\\}$ are strictly linearly independent, then $w^{*}$ is the unique maximizer of the MLLS objective (9).\nWe refer interested reader to Garg et al. (2020) for details.\nAbove results of accuracy estimation under label shift and covariate shift can be extended to a generalized label shift and covariate shift settings. Assume a function $h: \\mathcal{X} \\rightarrow \\mathcal{Z}$ such that $y$ is independent of $x$ given $h(x)$. In other words $h(x)$ contains all the information needed to predict label $y$. With help of $h$, we can extend estimation to following settings: (i) Generalized covariate shift, i.e., $p_{s}(y \\mid h(x))=p_{t}(y \\mid h(x))$ and $p_{s}(h(x))>0$ for all $x \\in \\mathcal{X}_{t}$; (ii) Generalized label shift, i.e., $p_{s}(h(x) \\mid y)=p_{t}(h(x) \\mid y)$ and $p_{s}(y)>0$ for all $y \\in \\mathcal{Y}_{t}$. By simply replacing $x$ with $h(x)$ in (6) and (9), we will obtain consistent error estimates under these generalized conditions.\n\nProof of Example 1. Under covariate shift using (6), we get\n\n$$\n\\begin{aligned}\n\\mathcal{E}_{1} & =\\mathbb{E}_{(x, y) \\sim p_{s}(x, y)}\\left[\\frac{p_{t}(x)}{p_{s}(x)} \\mathbb{I}[f(x) \\neq y]\\right] \\\\\n& =\\mathbb{E}_{x \\sim p_{s}(x, y=0)}\\left[\\frac{p_{t}(x)}{p_{s}(x)} \\mathbb{I}[f(x) \\neq 0]\\right]+\\mathbb{E}_{x \\sim p_{s}(x, y=1)}\\left[\\frac{p_{t}(x)}{p_{s}(x)} \\mathbb{I}[f(x) \\neq 1]\\right] \\\\\n& =\\int \\mathbb{I}[f(x) \\neq 0] p_{t}(x) p_{s}(y=0 \\mid x) d x+\\int \\mathbb{I}[f(x) \\neq 1] p_{t}(x) p_{s}(y=1 \\mid x) d x\n\\end{aligned}\n$$\n\nUnder label shift using (8), we get\n\n$$\n\\begin{aligned}\n\\mathcal{E}_{2} & =\\mathbb{E}_{(x, y) \\sim \\mathcal{D}^{t}}\\left[\\frac{p_{t}(y)}{p_{s}(y)} \\mathbb{I}[f(x) \\neq y]\\right] \\\\\n& =\\mathbb{E}_{x \\sim p_{s}(x, y=0)}\\left[\\frac{\\beta}{\\alpha} \\mathbb{I}[f(x) \\neq 0]\\right]+\\mathbb{E}_{x \\sim p_{s}(x, y=1)}\\left[\\frac{1-\\beta}{1-\\alpha} \\mathbb{I}[f(x) \\neq 1]\\right] \\\\\n& =\\int \\mathbb{I}[f(x) \\neq 0] \\frac{\\beta}{\\alpha} p_{s}(y=0 \\mid x) p_{s}(x) d x+\\int \\mathbb{I}[f(x) \\neq 1] \\frac{(1-\\beta)}{(1-\\alpha)} p_{s}(y=1 \\mid x) p_{s}(x) d x\n\\end{aligned}\n$$\n\nThen $\\mathcal{E}_{1}-\\mathcal{E}_{2}$ is given by\n\n$$\n\\begin{aligned}\n\\mathcal{E}_{1}-\\mathcal{E}_{2} & =\\int \\mathbb{I}[f(x) \\neq 0] p_{s}(y=0 \\mid x)\\left[p_{t}(x)-\\frac{\\beta}{\\alpha} p_{s}(x)\\right] d x \\\\\n& +\\int \\mathbb{I}[f(x) \\neq 1] p_{s}(y=1 \\mid x)\\left[p_{t}(x)-\\frac{(1-\\beta)}{(1-\\alpha)} p_{s}(x)\\right] d x \\\\\n& =\\int \\mathbb{I}[f(x) \\neq 0] p_{s}(y=0 \\mid x) \\frac{(\\alpha-\\beta)}{\\alpha} \\phi\\left(\\mu_{2}\\right) d x \\\\\n& +\\int \\mathbb{I}[f(x) \\neq 1] p_{s}(y=1 \\mid x) \\frac{(\\alpha-\\beta)}{1-\\alpha} \\phi\\left(\\mu_{1}\\right) d x\n\\end{aligned}\n$$",
      "images": [],
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      }
    },
    {
      "index": 16,
      "markdown": "If $\\alpha>\\beta$, then $\\mathcal{E}_{1}>\\mathcal{E}_{2}$ and if $\\alpha<\\beta$, then $\\mathcal{E}_{1}<\\mathcal{E}_{2}$. Since $\\mathcal{E}_{1} \\neq \\mathcal{E}_{2}$ for arbitrary $f$, given access to $p_{s}(x, y)$, and $p_{t}(x)$, any method that consistently estimates error under covariate shift will give an incorrect estimate under label shift and vice-versa. The reason being that the same $p_{t}(x)$ and $p_{s}(x, y)$ can correspond to error $\\mathcal{E}_{1}$ (under covariate shift) or error $\\mathcal{E}_{2}$ (under label shift) either of which is not discernable absent further assumptions on the nature of shift.\n\n# C Alternate interpretation of ATC \n\nConsider the following framework: Given a datum $(x, y)$, define a binary classification problem of whether the model prediction $\\arg \\max f(x)$ was correct or incorrect. In particular, if the model prediction matches the true label, then we assign a label 1 (positive) and conversely, if the model prediction doesn't match the true label then we assign a label 0 (negative).\nOur method can be interpreted as identifying examples for correct and incorrect prediction based on the value of the score function $s(f(x))$, i.e., if the score $s(f(x))$ is greater than or equal to the threshold $t$ then our method predicts that the classifier correctly predicted datum $(x, y)$ and vice-versa if the score is less than $t$. A method that can solve this task will perfectly estimate the target performance. However, such an expectation is unrealistic. Instead, ATC expects that most of the examples with score above threshold are correct and most of the examples below the threshold are incorrect. More importantly, ATC selects a threshold such that the number of falsely identified correct predictions match falsely identified incorrect predictions on source distribution, thereby balancing incorrect predictions. We expect useful estimates of accuracy with ATC if the threshold transfers to target, i.e. if the number of falsely identified correct predictions match falsely identified incorrect predictions on target. This interpretation relates our method to the OOD detection literature where Hendrycks \\& Gimpel (2016); Hendrycks et al. (2019) highlight that classifiers tend to assign higher confidence to in-distribution examples and leverage maximum softmax confidence (or logit) to perform OOD detection.\n\n## D Details on the Toy Model\n\nSkews observed in this toy model In Fig. 4, we illustrate the toy model used in our empirical experiment. In the same setup, we empirically observe that the margin on population with less density is large, i.e., margin is much greater than $\\gamma$ when the number of observed samples is small (in Fig. 4 (d)). Building on this observation, Nagarajan et al. (2020) showed in cases when margin decreases with number of samples, a max margin classifier trained on finite samples is bound to depend on the spurious features in such cases. They referred to this skew as geometric skew.\n\nMoreover, even when the number of samples are large so that we do not observe geometric skews, Nagarajan et al. (2020) showed that training for finite number of epochs, a linear classifier will have a non zero dependency on the spurious feature. They referred to this skew as statistical skew. Due both of these skews, we observe that a linear classifier obtained with training for finite steps on training data with finite samples, will have a non-zero dependency on the spurious feature. We refer interested reader to Nagarajan et al. (2020) for more details.\nProof of Theorem 1 Recall, we consider a easy-to-learn binary classification problem with two features $x=\\left[x_{\\mathrm{inv}}, x_{\\mathrm{sp}}\\right] \\in \\mathbb{R}^{2}$ where $x_{\\mathrm{inv}}$ is fully predictive invariant feature with a margin $\\gamma>0$ and $x_{\\mathrm{sp}} \\in\\{-1,1\\}$ is a spurious feature (i.e., a feature that is correlated but not predictive of the true label). Conditional on $y$, the distribution over $x_{\\text {inv }}$ is given as follows:\n\n$$\nx_{\\mathrm{inv}} \\mid y \\sim\\left\\{\\begin{array}{lr}\nU[\\gamma, c] & y=1 \\\\\nU[-c,-\\gamma] & y=-1\n\\end{array}\\right.\n$$\n\nwhere $c$ is a fixed constant greater than $\\gamma$. For simplicity, we assume that label distribution on source is uniform on $\\{-1,1\\} . x_{\\text {sp }}$ is distributed such that $P_{s}\\left[x_{\\text {sp }} \\cdot(2 y-1)>0\\right]=p_{\\text {sp }}$, where $p_{\\text {sp }} \\in(0.5,1.0)$ controls the degree of spurious correlation. To model distribution shift, we simulate target data with different degree of spurious correlation, i.e., in target distribution $P_{t}\\left[x_{\\text {sp }} \\cdot(2 y-1)>0\\right]=p_{\\text {sp }}^{\\prime} \\in[0,1]$. Note that here we do not consider shifts in the label distribution but our result extends to arbitrary shifts in the label distribution as well.",
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      "index": 17,
      "markdown": "![img-3.jpeg](img-3.jpeg)\n\nFigure 4: Illustration of toy model. (a) Source data at $n=100$. (b) Target data with $p_{s}^{\\prime}=0.5$. (b) Target data with $p_{s}^{\\prime}=0.9$. (c) Margin of $x_{\\text {inv }}$ in the minority group in source data. As sample size increases the margin saturates to true margin $\\gamma=0.1$.\n\nIn this setup, we examine linear sigmoid classifiers of the form $f(x)=\\left[\\frac{1}{1+e^{w T x}}, \\frac{e^{w T x}}{1+e^{w T x}}\\right]$ where $w=\\left[w_{\\text {inv }}, w_{\\text {sp }}\\right] \\in \\mathbb{R}^{2}$. We show that given a linear classifier that relies on the spurious feature and achieves a non-trivial performance on the source (i.e., $w_{\\text {inv }}>0$ ), ATC with maximum confidence score function consistently estimates the accuracy on the target distribution. Define $X_{M}=\\left\\{x \\mid x_{\\text {sp }}\\right.$ $\\left.(2 y-1)<0\\right\\}$ and $X_{C}=\\left\\{x \\mid x_{\\text {sp }} \\cdot(2 y-1)>0\\right\\}$. Notice that in target distributions, we are changing the fraction of examples in $X_{M}$ and $X_{C}$ but we are not changing the distribution of examples within individual set.\nTheorem 3. Given any classifier $f$ with $w_{\\text {inv }}>0$ in the above setting, assume that the threshold $t$ is obtained with finite sample approximation of (1), i.e., $t$ is selected such that ${ }^{2}$\n\n$$\n\\sum_{i=1}^{n}\\left[\\mathbb{I}\\left[\\max _{j \\in \\mathcal{Y}} f_{j}\\left(x_{i}\\right)<t\\right]\\right]=\\sum_{i=1}^{n}\\left[\\mathbb{I}\\left[\\arg \\max _{j \\in \\mathcal{Y}} f_{j}\\left(x_{i}\\right) \\neq y_{i}\\right]\\right]\n$$\n\nwhere $\\left\\{\\left(x_{i}, y_{i}\\right)\\right\\}_{i=1}^{n} \\sim\\left(\\mathcal{D}^{\\delta}\\right)^{n}$ are $n$ samples from source distribution. Fix a $\\delta>0$. Assuming $n \\geqslant 2 \\log (4 / \\delta) /\\left(1-p_{s p}\\right)^{2}$, then the estimate of accuracy by ATC as in (2) satisfies the following with probability at least $1-\\delta$,\n\n$$\n\\left|\\mathbb{E}_{x \\sim \\mathcal{D}^{t}}[\\mathbb{I}[s(f(x))<t]]-\\mathbb{E}_{(x, y) \\sim \\mathcal{D}^{t}}\\left[\\mathbb{I}\\left[\\arg \\max _{j \\in \\mathcal{Y}} f_{j}(x) \\neq y\\right]\\right]\\right| \\leqslant \\sqrt{\\frac{\\log (8 / \\delta)}{n \\cdot c_{s p}}}\n$$\n\nwhere $\\mathcal{D}^{t}$ is any target distribution considered in our setting and $c_{s p}=\\left(1-p_{s p}\\right)$ if $w_{s p}>0$ and $c_{s p}=p_{s p}$ otherwise.\n\n[^0]\n[^0]:    ${ }^{2}$ Note that this is possible because a linear classifier with sigmoid activation assigns a unique score to each point in source distribution.",
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kj2FudMsby2S1ubOGWBMFYpEDKuOBgdOhqJ9D0qW2itpNOtXgiJMcZiUqueuB0HOak/s+T/oI3n/AH0v/wATR/Z0n/QRvP8Avpf/AImjmfcOWPYhfw9o8kUUT6XaNHECI0MCkJk5OBjjmmp4a0SORXTSbJXRsqywLkH16Ut5p919mP2fUrzzNy45TpkZ/h9M1ONPkxzqN5n/AHl/+Jo55dxezj2EudG029uFnurG3nlQAK7xhiBnOM/Wmy6Npk16LySxtnugysJmiUuCMYO7HUYFP/s+T/oI3n/fS/8AxNH9nyf9BG8/76X/AOJo5n3Hyx7ElnYWljE0dpbRW6O25ljUKCfXjvxUQ0jTlv8A7ctjbi7yT54jG/J4PPWl/s6T/oI3n/fS/wDxNQXmnXX2Y/Z9SvPM3LjlOmRn+H0zRdhyo1R0rO1H/kI6P/19P/6Jkp406TH/ACEbz/vpf/iaQaYv2qCeW6uZjCxZFkZcZKlSeAOxP50ijQHSikHSloAKKKKACiiigAooooAKKKKACiiigAooooA5IEL8WpfM6voaeTnviZt4H5pn8Kd4HKPBrUkOPsz6tcGEj7pG7Bx/wLNaus+HdK1/yv7RtTI0OfLkSV4pEyMEB0IYAjqM0o0DTP7MttMW0VLO3ZHihQlQpRtwPB559aAMXx//AMeOg/8AYesP/Ry07XQR488LM+fJxcqM/d3lFI/HAarmu+DtE8S3UU+q2880kQUJsu5olUqSQdqOBuyevXjrVmfw3pN1o8Wk3Fq0tpDjyw8zl1I6ESZ35985oAytEBPxA8TvGP3IjtVb08wKxP44Iq14lsb69bTorbVHsQbpf3kcQZxhH7n16dO9aek6NYaJafZtOtxDEWLkbmZmY9SzMSWP1qa7sbS/h8i8tYbiHOfLmjDrn1wRQBmppGoLpUlofEF21w77lujFHvUemNuMfhSQaRqMWnXFs/iC8lmlIKXLRx7ovYALg/jU/wDwjGgf9ALTP/ASP/Cj/hGPD/8A0AtM/wDASP8AwoAhtNI1C3s7qGXX7u4llXCTSRxhoj6gBQD+OaLHR9RtYrlLjxBe3bSR7UaSONTEf7w2qOfrU3/CMeH/APoBaZ/4CR/4Uf8ACMeH/wDoBaZ/4CR/4UAQ6bo+oWRmNz4gvLwOmxRLHGNh/vDCjn60mnaPqNncvJceILy8QoVEcscYAPrwoNT/APCMeH/+gFpn/gJH/hR/wjHh/wD6AWmf+Akf+FAEGn6NqNpeCefxDeXkeDiCWOJRz7qoPFJaaNqdtqIuZfEV5cwhiTbPFGFIPQZC54qx/wAIx4f/AOgFpn/gJH/hSHwv4fOf+JFpn/gJH/hQBBBoupR6mLp/EV5JAHLfZmji2kf3chc4/GhtF1I6oLr/AISO9Fv5gf7KIotmP7uducfjTNL8H6JZ6TZ20+j6ZLNDAiPJ9kT52CgE9O5q3/wjHh//AKAWmf8AgJH/AIUAV59F1KbUjcx+I72GEuG+zLHGUA9Mlc/rS3ujaldagbiDxDeWkJK/6PHHGyjHXkrnn61P/wAIx4f/AOgFpn/gJH/hR/wjHh//AKAWmf8AgJH/AIUAV9S0fUbu8ae28Q3llGVAEMcUZXIHJyVJ5p+qaRqF9cpLa6/eWKKu0xxRxsGPqdynmpf+EY8P/wDQC0z/AMBI/wDCj/hGPD//AEAtM/8AASP/AAoAh1LSNQvDB9m8QXlnsjCP5UUbeY3945U8n8qW/wBJ1C6gtY7fXru1eJMPJHHGTMcYycqfrxUv/CMeH/8AoBaZ/wCAkf8AhR/wjHh//oBaZ/4CR/4UAQ3mkahcWNrDD4gu7eSEESTJFGzTfUEED8KWbSdQl0uC0TXruG4jY77pY4y8n1BXA/AVBJ4O0RtYt7tdH0wQxwSxtH9kTlmaMg9OwVvzq4PDGgf9ALTP/ASP/CgCGXSL99HS0XxBdx3AfcbsJHvYc/LjbjH4dqUaRqH9kvaHX7v7QX3C78qLeo/u427cfhUv/CMeH/8AoBaZ/wCAkf8AhR/wjHh//oBaZ/4CR/4UAQx6RqKaTNaNr129xIwK3Rjj3xgEHAAXHOCOfWkt9I1KLTrm2k8QXc002Nly0cYaHHoAuD+NT/8ACMeH/wDoBaZ/4CR/4Uf8Ix4f/wCgFpn/AICR/wCFAENppGo21pdRTa/d3Mky4SWSOMNCfYBQPzzRp+j6jaJcC48QXl2ZI9iNLHGvln+8MKOam/4Rjw//ANALTP8AwEj/AMKP+EY8P/8AQC0z/wABI/8ACgCHTdH1CzeVrnxBeXgePYqyxxjYf7wwo5+tJp2jajZXZluPEN5exlWAilijUA+uVUGpW8L+H2BH9haZyMf8eif4VDY+EdDttPtoJdG0yWSKJUaT7JH8xAAJ6d6AFsNH1K0vftE/iG8uojn/AEeSOMLz7hc8UltoupW+pC6l8RXk8AYsbZoowpB6DIXOB/SrH/CMeH/+gFpn/gJH/hR/wjHh/wD6AWmf+Akf+FAFePRdRTVBdP4ivXgEhf7KYothH93IXdj8aJNF1J9U+1DxHepB5gb7KIo9mP7uducfjVj/AIRjw/8A9ALTP/ASP/Cj/hGPD/8A0AtM/wDASP8AwoAr3Oi6lPqJuYvEd7BCXDfZkjiKADqMlc8/Wk1HR9Ru71ri38RXlnEQP3EccbKMdeWUnmrP/CMeH/8AoBaZ/wCAkf8AhR/wjHh//oBaZ/4CR/4UAV9U0jUby6M1t4hvLGPGBFFFGRkdySpPNP1XSb++uEe11+7sVVQrJDHGwY+p3Keal/4Rjw//ANALTP8AwEj/AMKrt4R0I36z/wBi6YEERQp9kTkkg56egP50AO1DR9QvFtxb+ILyzMcYRzFFG3mN/eOVOD9KW90rULq2tY4Neu7V4lw8kccbGXjGTlf5VL/wjGgf9ALTD/26R/4Uf8Ix4f8A+gFpn/gJH/hQBDdaRqE1hawReILuCWEHzJ0ijLTZ9QVwPwFLNpOoSaVDaJr93HcRsd12I4y8g9CCuPyFS/8ACMeH/wDoBaZ/4CR/4Uf8Ix4f/wCgFpn/AICR/wCFAEL6PqDaStoNfvFuFk3m7Ece9h/dxtxj8KWPSdQTSHs21+6a5Zwy3Zij3gcfLjG3H4d6l/4Rjw//ANALTP8AwEj/AMKP+EY8P/8AQC0z/wABI/8ACgCCLR9Rj0qa0bX7uSeRgVu2SMPGAQcKAuDnpz60600m/trC6gl166uJpQPLnkjjDQ+4AAB/H0qX/hGPD/8A0AtM/wDASP8AwqK48K6DJBKi6JpisyEA/ZE4JB9qAEs9I1C1trqK48QXly8qbUlkjjBiOD8y4UDP1z0qqdJ1Cx0rUjd69dXwa1dVE0aKEO085VQauw+FdBjgjjOh6YxVQpP2SPnA+lK3hfw+eDoWm4/69I/8KANS3z9miz12D+VSUigBQAAABgAdqWgAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKztV0e01iJIrs3QVG3L9nu5YDnGOTGyk/TNAF89a5HTp9ZtfiDPpl9rDXtpJYfaoozbxxiNvM24yoyeB3NaNj4U0zTL6O7t31Eyxk487VLmZOQQco8hU9e4469qxd2vH4ijUf+EZu/sP2P7H532m3wP3m7fjfnGD6ZoAmuZNf1fVdZGl6qLGLTtsMMQhR/Ol2BzvLA4X5gOCO5roNB1P+2PD9jqRTyzcwLIyZztJHI/Oueuo9f0jVdZ/szSlv4dR2ywy+eiCGTYEO8McleAeM9619HtLrQ9P0jR0tjcQRW+ye63qoRlAx8vU7jnp0oA5KXX9fXw3P4xXUlFnHdsV03yU2G2Wbyzl8bt+AWznHQYr0lfuivNpPD+vnw1N4NXTQ1lLdMBqRnXYLZpfMI25378Er0x0Oa6d/BWjzO0rvqu5yWO3WLtR+AEuB+FAGjqtjdXqoLXV7rTimSzQJE27jv5iN+lY3ga51K+0i5vL7UZb6OW6kFpJLEiN5SnaM7VUHJBP41b1XTrrT/CN1p2hRzy3DRtHD5t0zupc8sXkYk4yTyfarMcEuheG4bbTrNruS0hSOKDzAhfGB948D1oA5nxVrVzp/iuO3n1vUNL0sWPnM9pYicGTfj5mMb7Rt9cDiuy0uaO40u1mhvftsbxhlufl/ej+98oA59hWbq2p6vayiOy8PT6gjxAiRLmKMK+TlWDkHHQ5APXpTfB2j3GgeFLHTbop9ojDvII/uIzuzlV9l3bR04FAG/XJeN9SvNOOl+Rf3ljby3DLcTWloLhwoRiBt2P3A7Vcl8FaPPM80j6rvdizbdYu1GfYCXA/CpJhceHrK3tdJ0m91GEFtw+2hpEzzndO+W5PrxQA7wze29/o6z22sS6qm9h9olREcEdVKqq4I91BrO1G51TVPF0mj6bqjafDaWizzyRwrI7u7EIvzggABCTgZ5HIpNEt9Y0yw1jUptKT7de3P2hdPimX5RtVcF/u7uMmi/t9V0vxS+s6dpTahHeWiQXEEc6RujoxKtlyAR85Bwc8A4NAF7whq9xrPh5J70J9sgmltbgxjCtJE5QkDsDtzj3rfrn/COj3OjeH0gvGjN5NNLc3HlnKiSRy5APcDdjpzjNJL4L0i4mkmkfVd8jFm26vdqMk9gJcD6CgC9q1lqF60K2WryaeqkmQxQI7P6cuCBj6VneENRvL631CG9uku2srx7ZbpIwnmhQOoHGQSQcdxVDxLa6zpui22k+GLG9uIpXK3E4uw80MZOTtaeQEsckDJOO1aOlyXOn+GjFYeHLi1e1XbBYyzxAye+5XYc5J+YgmgDD8Wa1eaf4rtraXWL/TNKNg0ry2lis4MocAbiY32jBPXA4rstJniutJtZ4b77dFJGGW6+X96D/F8oA/IVn6pqurWkypZeHp74NGGLx3MSBGz91t5H5jPWmeDNGuNB8LWlhdmP7QrSSyJF9xC8jPtX2G7H4UAHizUbyw06CPT3SO8vbqK0hldcrGWPLEd8AE4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      "markdown": "Proof. First we consider the case of $w_{\\text {sp }}>0$. The proof follows in two simple steps. First we notice that the classifier will make an error only on some points in $X_{M}$ and the threshold $t$ will be selected such that the fraction of points in $X_{M}$ with maximum confidence less than the threshold $t$ will match the error of the classifier on $X_{M}$. Classifier with $w_{\\text {sp }}>0$ and $w_{\\text {inv }}>0$ will classify all the points in $X_{C}$ correctly. Second, since the distribution of points is not changing within $X_{M}$ and $X_{C}$, the same threshold continues to work for arbitrary shift in the fraction of examples in $X_{M}$, i.e., $p_{\\text {sp }}^{\\prime}$.\n\nNote that when $w_{\\text {sp }}>0$, the classifier makes no error on points in $X_{C}$ and makes an error on a subset $X_{\\text {err }}=\\left\\{x \\mid x_{\\text {sp }} \\cdot(2 y-1)<0 \\&\\left(w_{\\text {inv }} x_{\\text {inv }}+w_{\\text {sp }} x_{\\text {sp }}\\right) \\cdot(2 y-1) \\leqslant 0\\right\\}$ of $X_{M}$, i.e., $X_{\\text {err }} \\subseteq X_{M}$. Consider $X_{\\text {thres }}=\\left\\{x \\mid \\arg \\max _{y \\in \\mathcal{Y}} f_{y}(x) \\leqslant t\\right\\}$ as the set of points that obtain a score less than or equal to $t$. Now we will show that ATC chooses a threshold $t$ such that all points in $X_{C}$ gets a score above $t$, i.e., $X_{\\text {thres }} \\subseteq X_{M}$. First note that the score of points close to the true separator in $X_{C}$, i.e., at $x_{1}=(\\gamma, 1)$ and $x_{2}=(-\\gamma,-1)$ match. In other words, score at $x_{1}$ matches with the score of $x_{2}$ by symmetricity, i.e.,\n\n$$\n\\underset{y \\in \\mathcal{Y}}{\\arg \\max } f_{y}\\left(x_{1}\\right)=\\underset{y \\in \\mathcal{Y}}{\\arg \\max } f_{y}\\left(x_{2}\\right)=\\frac{e^{w_{\\text {inv }} \\gamma+w_{\\text {sp }}}}{\\left(1+e^{w_{\\text {inv }} \\gamma+w_{\\text {sp }}}\\right)}\n$$\n\nHence, if $t \\geqslant \\arg \\max _{y \\in \\mathcal{Y}} f_{y}\\left(x_{1}\\right)$ then we will have $\\left|X_{\\text {err }}\\right|<\\left|X_{\\text {thres }}\\right|$ which is contradiction violating definition of $t$ as in (12). Thus $X_{\\text {thres }} \\subseteq X_{M}$.\n\nNow we will relate LHS and RHS of (12) with their expectations using Hoeffdings and DKW inequality to conclude (13). Using Hoeffdings' bound, we have with probability at least $1-\\delta / 4$\n\n$$\n\\left|\\sum_{i \\in X_{M}} \\frac{\\left[\\mathbb{I}\\left[\\arg \\max _{j \\in \\mathcal{Y}} f_{j}\\left(x_{i}\\right) \\neq y_{i}\\right]\\right]}{\\left|X_{M}\\right|}-\\mathbb{E}_{(x, y) \\sim \\mathcal{D}^{\\mathrm{T}}}\\left[\\mathbb{I}\\left[\\arg \\max _{j \\in \\mathcal{Y}} f_{j}(x) \\neq y\\right]\\right]\\right| \\leqslant \\sqrt{\\frac{\\log (8 / \\delta)}{2\\left|X_{M}\\right|}}\n$$\n\nWith DKW inequality, we have with probability at least $1-\\delta / 4$\n\n$$\n\\left|\\sum_{i \\in X_{M}} \\frac{\\left[\\mathbb{I}\\left[\\max _{j \\in \\mathcal{Y}} f_{j}\\left(x_{i}\\right)<t^{\\prime}\\right]\\right]}{\\left|X_{M}\\right|}-\\mathbb{E}_{(x, y) \\sim \\mathcal{D}^{\\mathrm{T}}}\\left[\\mathbb{I}\\left[\\max _{j \\in \\mathcal{Y}} f_{j}(x)<t^{\\prime}\\right]\\right]\\right| \\leqslant \\sqrt{\\frac{\\log (8 / \\delta)}{2\\left|X_{M}\\right|}}\n$$\n\nfor all $t^{\\prime}>0$. Combining (15) and (16) at $t^{\\prime}=t$ with definition (12), we have with probability at least $1-\\delta / 2$\n\n$$\n\\left|\\mathbb{E}_{x \\sim \\mathcal{D}^{\\mathrm{T}}}[I(s(f(x))<t]]-\\mathbb{E}_{(x, y) \\sim \\mathcal{D}^{\\mathrm{T}}}\\left[\\mathbb{I}\\left[\\arg \\max _{j \\in \\mathcal{Y}} f_{j}(x) \\neq y\\right]\\right]\\right| \\leqslant \\sqrt{\\frac{\\log (8 / \\delta)}{2\\left|X_{M}\\right|}}\n$$\n\nNow for the case of $w_{\\text {sp }}<0$, we can use the same arguments on $X_{C}$. That is, since now all the error will be on points in $X_{C}$ and classifier will make no error $X_{M}$, we can show that threshold $t$ will be selected such that the fraction of points in $X_{C}$ with maximum confidence less than the threshold $t$ will match the error of the classifier on $X_{C}$. Again, since the distribution of points is not changing within $X_{M}$ and $X_{C}$, the same threshold continues to work for arbitrary shift in the fraction of examples in $X_{M}$, i.e., $p_{\\text {sp }}^{\\prime}$. Thus with similar arguments, we have\n\n$$\n\\left|\\mathbb{E}_{x \\sim \\mathcal{D}^{\\mathrm{T}}}[I(s(f(x))<t]]-\\mathbb{E}_{(x, y) \\sim \\mathcal{D}^{\\mathrm{T}}}\\left[I\\left[\\arg \\max _{j \\in \\mathcal{Y}} f_{j}(x) \\neq y\\right]\\right]\\right| \\leqslant \\sqrt{\\frac{\\log (8 / \\delta)}{2\\left|X_{C}\\right|}}\n$$\n\nUsing Hoeffdings' bound, with probability at least $1-\\delta / 2$, we have\n\n$$\n\\left|X_{M}-n \\cdot\\left(1-p_{\\text {sp }}\\right)\\right| \\leqslant \\sqrt{\\frac{n \\cdot \\log (4 / \\delta)}{2}}\n$$\n\nWith probability at least $1-\\delta / 2$, we have\n\n$$\n\\left|X_{C}-n \\cdot p_{\\text {sp }}\\right| \\leqslant \\sqrt{\\frac{n \\cdot \\log (4 / \\delta)}{2}}\n$$\n\nCombining (19) and (17), we get the desired result for $w_{\\text {sp }}>0$. For $w_{\\text {sp }}<0$, we combine (20) and (18) to get the desired result.",
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    },
    {
      "index": 19,
      "markdown": "![img-4.jpeg](img-4.jpeg)\n\nFigure 5: Failure of ATC in our toy model. Shifting the support of target class conditional $p_{t}\\left(x_{\\text {inv }} \\mid y\\right)$ may introduce a bias in ATC estimates, e.g., shrinking the support to $c_{1}(<c)$ (while maintaining uniform distribution) in the target leads to overestimation bias.\n\nIssues with IM in toy setting As described in App. E, we observe that IM is sensitive to binning strategy. In the main paper, we include IM result with uniform mass binning with 100 bins. Empirically, we observe that we recover the true performance with IM if we use equal width binning with number of bins greater than 5 .\n\nBiased estimation with ATC in our toy model We assumed that both in source and target $x_{\\text {inv }} \\mid y=1$ is uniform between $[\\gamma, c]$ and $x \\mid y=-1$ is uniform between $[-c,-\\gamma]$. Shifting the support of target class conditional $p_{t}\\left(x_{\\text {inv }} \\mid y\\right)$ may introduce a bias in ATC estimates, e.g., shrinking the support to $c_{1}(<c)$ (while maintaining uniform distribution) in the target will lead to an over-estimation of the target performance with ATC. We show this failure in Fig. 5. The reason being that with the same threshold that we see more examples falsely identified as correct as compared to examples falsely identified as incorrect.\n\n# D. 1 A More General Result \n\nRecall, for a given threshold $t$, we categorize an example $(x, y)$ as a falsely identified correct prediction (ficp) if the predicted label $\\widehat{y}=\\arg \\max f(x)$ is not the same as $y$ but the predicted score $f_{\\widehat{y}}(x)$ is greater than $t$. Similarly, an example is falsely identified incorrect prediction (fiip) if the predicted label $\\widehat{y}$ is the same as $y$ but the predicted score $f_{\\widehat{y}}(x)$ is less than $t$.\n\nIn general, we believe that our method will obtain consistent estimates in scenarios where the relative distribution of covariates doesn't change among examples that are falsely identified as incorrect and examples that are falsely identified as correct. In other words, ATC is expected to work if the distribution shift is such that falsely identified incorrect predictions match falsely identified correct prediction.\n\n## D. 2 ATC PRODUCES CONSISTENT ESTIMATE ON SOURCE DISTRIBUTION\n\nProposition 2. Given labeled validation data $\\left\\{\\left(x_{i}, y_{i}\\right)\\right\\}_{i=1}^{n}$ from a distribution $\\mathcal{D}^{S}$ and a model $f$, choose a threshold $t$ as in (1). Then for $\\delta>0$, with probability at least $1-\\delta$, we have\n\n$$\n\\mathbb{E}_{(x, y) \\sim \\mathcal{D}}\\left[\\mathbb{I}\\left[\\max _{j \\in \\mathcal{Y}} f_{j}(x)<t\\right]-\\mathbb{I}\\left[\\arg \\max _{j \\in \\mathcal{Y}} f_{j}(x) \\neq y\\right]\\right] \\leqslant 2 \\sqrt{\\frac{\\log (4 / \\delta)}{2 n}}\n$$\n\nProof. The proof uses (i) Hoeffdings' inequality to relate the accuracy with expected accuracy; and (ii) DKW inequality to show the concentration of the estimated accuracy with our proposed method. Finally, we combine (i) and (ii) using the fact that at selected threshold $t$ the number of false positives is equal to the number of false negatives.\nUsing Hoeffdings' bound, we have with probability at least $1-\\delta / 2$\n\n$$\n\\left|\\sum_{i=1}^{n}\\left[\\mathbb{I}\\left[\\arg \\max _{j \\in \\mathcal{Y}} f_{j}\\left(x_{i}\\right) \\neq y_{i}\\right]\\right]-\\mathbb{E}_{(x, y) \\sim \\mathcal{D}}\\left[\\mathbb{I}\\left[\\arg \\max _{j \\in \\mathcal{Y}} f_{j}(x) \\neq y\\right]\\right]\\right| \\leqslant \\sqrt{\\frac{\\log (4 / \\delta)}{2 n}}\n$$",
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      "markdown": "With DKW inequality, we have with probability at least $1-\\delta / 2$\n\n$$\n\\left|\\sum_{i=1}^{n}\\left[\\mathbb{I}\\left[\\max _{j \\in \\mathcal{Y}} f_{j}\\left(x_{i}\\right)<t^{\\prime}\\right]\\right]-\\mathbb{E}_{(x, y) \\sim \\mathcal{D}}\\left[\\mathbb{I}\\left[\\max _{j \\in \\mathcal{Y}} f_{j}(x)<t^{\\prime}\\right]\\right]\\right| \\leqslant \\sqrt{\\frac{\\log (4 / \\delta)}{2 n}}\n$$\n\nfor all $t^{\\prime}>0$. Finally by definition, we have\n\n$$\n\\sum_{i=1}^{n}\\left[\\mathbb{I}\\left[\\max _{j \\in \\mathcal{Y}} f_{j}\\left(x_{i}\\right)<t^{\\prime}\\right]\\right]=\\sum_{i=1}^{n}\\left[\\mathbb{I}\\left[\\underset{j \\in \\mathcal{Y}}{\\arg \\max } f_{j}\\left(x_{i}\\right) \\neq y_{i}\\right]\\right]\n$$\n\nCombining (22), (23) at $t^{\\prime}=t$, and (24), we have the desired result.\n\n# E BASLINE METHODS \n\nImportance-re-weighting (IM) If we can estimate the importance-ratios $\\frac{p_{1}(x)}{p_{s}(x)}$ with just the unlabeled data from the target and validation labeled data from source, then we can estimate the accuracy as on target as follows:\n\n$$\n\\mathcal{E}_{\\mathcal{D}^{t}}(f)=\\mathbb{E}_{(x, y) \\sim \\mathcal{D}^{t}}\\left[\\frac{p_{t}(x)}{p_{s}(x)} \\mathbb{I}[f(x) \\neq y]\\right]\n$$\n\nAs previously discussed, this is particularly useful in the setting of covariate shift (within support) where importance ratios estimation has been explored in the literature in the past. Mandolin (Chen et al., 2021b) extends this approach. They estimate importance-weights with use of extra supervision about the axis along which the distribution is shifting.\nIn our work, we experiment with uniform mass binning and equal width binning with the number of bins in $[5,10,50]$. Overall, we observed that equal width binning works the best with 10 bins. Hence throughout this paper we perform equal width binning with 10 bins to include results with IM.\nAverage Confidence (AC) If we expect the classifier to be argmax calibrated on the target then average confidence is equal to accuracy of the classifier. Formally, by definition of argmax calibration of $f$ on any distribution $\\mathcal{D}$, we have\n\n$$\n\\mathcal{E}_{\\mathcal{D}}(f)=\\mathbb{E}_{(x, y) \\sim \\mathcal{D}}\\left[\\mathbb{I}\\left[y \\notin \\underset{j \\in \\mathcal{Y}}{\\arg \\max } f_{j}(x)\\right]\\right]=\\mathbb{E}_{(x, y) \\sim \\mathcal{D}}\\left[\\max _{j \\in \\mathcal{Y}} f_{j}(x)\\right]\n$$\n\nDifference Of Confidence We estimate the error on target by subtracting difference of confidences on source and target (as a distributional distance (Guillory et al., 2021)) from expected error on source distribution, i.e, $\\mathrm{DOC}_{\\mathcal{D}^{t}}=\\mathbb{E}_{x \\sim \\mathcal{D}^{t}}\\left[\\mathbb{I}\\left[\\arg \\max _{j \\in \\mathcal{Y}} f_{j}(x) \\neq y\\right]\\right]+\\mathbb{E}_{x \\sim \\mathcal{D}^{t}}\\left[\\max _{j \\in \\mathcal{Y}} f_{j}(x)\\right]-$ $\\mathbb{E}_{x \\sim \\mathcal{D}^{t}}\\left[\\max _{j \\in \\mathcal{Y}} f_{j}(x)\\right]$. This is referred to as DOC-Feat in (Guillory et al., 2021).\nGeneralized Disagreement Equality (GDE) Jiang et al. (2021) proposed average disagreement of two models (trained on the same training set but with different initialization and/or different data ordering) as a approximate measure of accuracy on the underlying data, i.e.,\n\n$$\n\\mathcal{E}_{\\mathcal{D}}(f)=\\mathbb{E}_{(x, y) \\sim \\mathcal{D}}\\left[\\mathbb{I}\\left[f(x) \\neq f^{\\prime}(x)\\right]\\right]\n$$\n\nThey show that marginal calibration of the model is sufficient to have expected test error equal to the expected of average disagreement of two models where the latter expectation is also taken over the models used to calculate disagreement.\n\n## F DETAILS ON THE DATASET SETUP\n\nIn our empirical evaluation, we consider both natural and synthetic distribution shifts. We consider shifts on ImageNet (Russakovsky et al., 2015), CIFAR Krizhevsky \\& Hinton (2009), FMoWWilDS (Christie et al., 2018), RxRx1-WilDS (Taylor et al., 2019), Amazon-WilDS (Ni et al., 2019), CivilComments-WilDS (Borkan et al., 2019), and MNIST LeCun et al. (1998) datasets.",
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      }
    },
    {
      "index": 21,
      "markdown": "| Train (Source) | Valid (Source) | Evaluation (Target) |\n| :--: | :--: | :--: |\n| MNIST (train) | MNIST (valid) | USPS, SVHN and Q-MNIST |\n| CIFAR10 (train) | CIFAR10 (valid) | CIFAR10v2, 95 CIFAR10-C datasets (Fog and Motion blur, etc. ) |\n| CIFAR100 (train) | CIFAR100 (valid) | 95 CIFAR100-C datasets (Fog and Motion blur, etc. ) |\n| FMoW (2002-12) (train) | FMoW (2002-12) (valid) | FMoW $\\{2013-15,2016-17\\} \\times$ |\n|  |  | (All, Africa, Americas, Oceania, Asia, and Europe) $\\}$ |\n| RxRx1 (train) | RxRx1(id-val) | RxRx1 (id-test, OOD-val, OOD-test) |\n| Amazon (train) | Amazon (id-val) | Amazon (OOD-val, OOD-test) |\n| CivilComments (train) | CivilComments (id-val) | CivilComments (8 demographic identities male, female, LGBTQ, Christian, Muslim, other religions, Black, and White) |\n| ImageNet (train) | ImageNet (valid) | 3 ImageNetv2 datasets, ImageNet-Sketch, 95 ImageNet-C datasets |\n| ImageNet-200 (train) | ImageNet-200 (valid) | 3 ImageNet-200v2 datasets, ImageNet-R, ImageNet200-Sketch, 95 ImageNet200-C datasets |\n| BREEDS (train) | BREEDS (valid) | Same subpopulations as train but unseen images from natural and synthetic shifts in ImageNet, Novel subpopulations on natural and synthetic shifts |\n\nTable 2: Details of the test datasets considered in our evaluation.\n\nImageNet setup. First, we consider synthetic shifts induced to simulate 19 different visual corruptions (e.g., shot noise, motion blur, pixelation etc.) each with 5 different intensities giving us a total of 95 datasets under ImageNet-C (Hendrycks \\& Dietterich, 2019). Next, we consider natural distribution shifts due to differences in the data collection process. In particular, we consider 3 ImageNetv2 (Recht et al., 2019) datasets each using a different strategy to collect test sets. We also evaluate performance on images with artistic renditions of object classes, i.e., ImageNet-R (Hendrycks et al., 2021) and ImageNet-Sketch (Wang et al., 2019) with hand drawn sketch images. Note that renditions dataset only contains 200 classes from ImageNet. Hence, in the main paper we include results on ImageNet restricted to these 200 classes, which we call as ImageNet-200, and relegate results on ImageNet with 1 k classes to appendix.\nWe also consider BREEDS benchmark (Santurkar et al., 2020) in our evaluation to assess robustness to subpopulation shifts, in particular, to understand how accuracy estimation methods behave when novel subpopulations not observed during training are introduced. BREEDS leverages class hierarchy in ImageNet to repurpose original classes to be the subpopulations and defines a classification task on superclasses. Subpopulation shift is induced by directly making the subpopulations present in the training and test distributions disjoint. Overall, BREEDS benchmark contains 4 datasets Entity-13, Entity-30, Living-17, Non-Living-26, each focusing on different subtrees in the hierarchy. To generate BREEDS dataset on top of ImageNet, we use the open source library: https: //github.com/MadryLab/BREEDS-Benchmarks. We focus on natural and synthetic shifts as in ImageNet on same and different subpopulations in BREEDs. Thus for both the subpopulation (same or novel), we obtain a total of 99 target datasets.\n\nCIFAR setup. Similar to the ImageNet setup, we consider (i) synthetic shifts (CIFAR-10-C) due to common corruptions; and (ii) natural distribution shift (i.e., CIFARv2 (Recht et al., 2018; Torralba et al., 2008)) due to differences in data collection strategy on on CIFAR-10 (Krizhevsky \\& Hinton, 2009). On CIFAR-100, we just have synthetic shifts due to common corruptions.\n\nFMoW-WILDS setup. In order to consider distribution shifts faced in the wild, we consider FMoWwILDS (Koh et al., 2021; Christie et al., 2018) from WILDS benchmark, which contains satellite images taken in different geographical regions and at different times. We obtain 12 different OOD target sets by considering images between years 2013-2016 and 2016-2018 and by considering five geographical regions as subpopulations (Africa, Americas, Oceania, Asia, and Europe) separately and together.\n$R x R x 1$-WILDS setup. Similar to FMoW, we consider RxRx1-WILDS (Taylor et al., 2019) from WILDS benchmark, which contains image of cells obtained by fluorescent microscopy and the task",
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    },
    {
      "index": 22,
      "markdown": "is to genetic treatments the cells received. We obtain 3 target datasets with shift induced by batch effects which make it difficult to draw conclusions from data across experimental batches.\nAmazon-WilDS setup. For natural language task, we consider Amazon-WilDS (Ni et al., 2019) dataset from WILDS benchmark, which contains review text and the task is get a corresponding star rating from 1 to 5 . We obtain 2 target datasets by considered shifts induced due to different set of reviewers than the training set.\n\nCivilComments-WilDS setup. We also consider CivilComments-WilDS (Borkan et al., 2019) from WILDS benchmark, which contains text comments and the task is to classify them for toxicity. We obtain 18 target datasets depending on whether a comment mentions each of the 8 demographic identities male, female, LGBTQ, Christian, Muslim, other religions, Black, and White.\n\nMNIST setup. For completeness, we also consider distribution shifts on MNIST (LeCun et al., 1998) digit classification as in the prior work (Deng \\& Zheng, 2021). We use three real shifted datasets, i.e., USPS (Hull, 1994), SVHN (Netzer et al., 2011) and QMNIST (Yadav \\& Bottou, 2019).\n\n# G Details on the Experimental Setup \n\nAll experiments were run on NVIDIA Tesla V100 GPUs. We used PyTorch (Paszke et al., 2019) for experiments.\n\nDeep nets We consider a 4-layered MLP. The PyTorch code for 4-layer MLP is as follows:\n\n```\nnn.Sequential(nn.Flatten(),\n    nn.Linear(input_dim, 5000, bias=True),\n    nn.ReLU(),\n    nn.Linear(5000, 5000, bias=True),\n    nn.ReLU(),\n    nn.Linear(5000, 50, bias=True),\n    nn.ReLU(),\n    nn.Linear(50, num_label, bias=True)\n    )\n```\n\nWe mainly experiment convolutional nets. In particular, we use ResNet18 (He et al., 2016), ResNet50, and DenseNet121 (Huang et al., 2017) architectures with their default implementation in PyTorch. Whenever we initial our models with pre-trained models, we again use default models in PyTorch.\n\nHyperparameters and Training details As mentioned in the main text we do not alter the standard training procedures and hyperparameters for each task. We present results at final model, however, we observed that the same results extend to an early stopped model as well. For completeness, we include these details below:\n\nCIFAR10 and CIFAR100 We train DenseNet121 and ResNet18 architectures from scratch. We use SGD training with momentum of 0.9 for 300 epochs. We start with learning rate 0.1 and decay it by multiplying it with 0.1 every 100 epochs. We use a weight decay of $5^{-} 4$. We use batch size of 200 . For CIFAR10, we also experiment with the same models pre-trained on ImageNet.\n\nImageNet For training, we use Adam with a batch size of 64 and learning rate 0.0001 . Due to huge size of ImageNet, we could only train two models needed for GDE for 10 epochs. Hence, for relatively small scale experiments, we also perform experiments on ImageNet subset with 200 classes, which we call as ImageNet-200 with the same training procedure. These 200 classes are the same classes as in ImageNet-R dataset. This not only allows us to train ImageNet for 50 epochs but also allows us to use ImageNet-R in our testbed. On the both the datasets, we observe a similar superioriy with ATC. Note that all the models trained here were initialized with a pre-trained ImageNet model with the last layer replaced with random weights.\n\nFMoW-wilDS For all experiments, we follow Koh et al. (2021) and use two architectures DenseNet121 and ResNet50, both pre-trained on ImageNet. We use the Adam optimizer (Kingma \\& Ba, 2014) with an initial learning rate of $10^{-4}$ that decays by 0.96 per epoch, and train for 50 epochs and with a batch size of 64 .",
      "images": [],
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    },
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      "index": 23,
      "markdown": "$R x R x l$-WILDS For all experiments, we follow Koh et al. (2021) and use two architectures DenseNet121 and ResNet50, both pre-trained on ImageNet. We use Adam optimizer with a learning rate of $1 e-4$ and L2-regularization strength of $1 e-5$ with a batch size of 75 for 90 epochs. We linearly increase the learning rate for 10 epochs, then decreasing it following a cosine learning rate schedule. Finally, we pick the model that obtains highest in-distribution validation accuracy.\nAmazon-WILDS For all experiments, we follow Koh et al. (2021) and finetuned DistilBERT-base-uncased models (Sanh et al., 2019), using the implementation from Wolf et al. (2020), and with the following hyperparameter settings: batch size 8 ; learning rate $1 e-5$ with the AdamW optimizer (Loshchilov \\& Hutter, 2017); L2-regularization strength 0.01; 3 epochs with early stopping; and a maximum number of tokens of 512 .\nCivilComments-WILDS For all experiments, we follow Koh et al. (2021) and fine-tuned DistilBERT-base-uncased models (Sanh et al., 2019), using the implementation from Wolf et al. (2020) and with the following hyperparameter settings: batch size 16 ; learning rate $1 e-5$ with the AdamW optimizer (Loshchilov \\& Hutter, 2017) for 5 epochs; L2-regularization strength 0.01 ; and a maximum number of tokens of 300 .\nLiving17 and Nonliving26 from BREEDS For training, we use SGD with a batch size of 128 , weight decay of $10^{-4}$, and learning rate 0.1 . Models were trained until convergence. Models were trained for a total of 450 epochs, with 10 -fold learning rate drops every 150 epochs. Note that since we want to evaluate models for novel subpopulations no pre-training was used. We train two architectures DenseNet121 and ResNet50.\nEntity13 and Entity30 from BREEDS For training, we use SGD with a batch size of 128 , weight decay of $10^{-4}$, and learning rate 0.1 . Models were trained until convergence. Models were trained for a total of 300 epochs, with 10 -fold learning rate drops every 100 epochs. Note that since we want to evaluate models for novel subpopulations no pre-training was used. We train two architectures DenseNet121 and ResNet50.\nMNIST For MNIST, we train a MLP described above with SGD with momentum 0.9 and learning rate 0.01 for 50 epochs. We use weight decay of $10^{-5}$ and batch size as 200.\nWe have a single number for CivilComments because it is a binary classification task. For multiclass problems, ATC-NE and ATC-MC can lead to different ordering of examples when ranked with the corresponding scoring function. Temperature scaling on top can further alter the ordering of examples. The changed ordering of examples yields different thresholds and different accuracy estimates. However for binary classification, the two scoring functions are the same as entropy (i.e. $p \\log (p)+(1-p) \\log (p))$ has a one-to-one mapping to the max conf for $p \\in[0,1]$. Moreover, temperature scaling also doesn't change the order of points for binary classification problems. Hence for the binary classification problems, both the scoring functions with and without temperature scaling yield the same estimates. We have made this clear in the updated draft.\nImplementation for Temperature Scaling We use temperature scaling implementation from https://github.com/kundajelab/abstention. We use validation set (the same we use to obtain ATC threshold or DOC source error estimate) to tune a single temperature parameter.\n\n# G. 1 DETAILS ON FIG. 1 (RIGHT) SETUP \n\nFor vision datasets, we train a DenseNet model with the exception of FCN model for MNIST dataset. For language datasets, we fine-tune a DistilBERT-base-uncased model. For each of these models, we use the exact same setup as described Sec. G. Importantly, to obtain errors on the same scale, we rescale all the errors by subtracting the error of Average Confidence method for each model. Results are reported as mean of the re-scaled errors over 4 seeds.",
      "images": [],
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    },
    {
      "index": 24,
      "markdown": "# H Supplementary Results \n\n## H. 1 CIFAR PRETRAINING ABLATION\n\n![img-5.jpeg](img-5.jpeg)\n\nFigure 6: Results with a pretrained DenseNet121 model on CIFAR10. We observe similar behaviour as that with a model trained from scratch.\n\n## H. 2 BREEDS RESULTS WITH REGRESSION MODEL\n\n![img-6.jpeg](img-6.jpeg)\n\nFigure 7: Scatter plots for DOC with linear fit. Results parallel to Fig. 3(Middle) on other BREEDS dataset.\n\n| Dataset | DOC (w/o fit) | DOC (w fit) | ATC-MC (Ours) (w/o fit) |\n| :-- | :--: | :--: | :--: |\n| LIVING-17 | 24.32 | 13.65 | $\\mathbf{1 0 . 0 7}$ |\n| NONLIVING-26 | 29.91 | $\\mathbf{1 8 . 1 3}$ | 19.37 |\n| ENTITY-13 | 22.18 | 8.63 | 8.01 |\n| ENTITY-30 | 24.71 | 12.28 | $\\mathbf{1 0 . 2 1}$ |\n\nTable 5: Mean Absolute estimation Error (MAE) results for BREEDs datasets with novel populations in our setup. After fitting a robust linear model for DOC on same subpopulation, we show predicted accuracy on different subpopulations with fine-tuned DOC (i.e., DOC (w/ fit)) and compare with ATC without any regression model, i.e., ATC (w/o fit). While observe substantial improvements in MAE from DOC (w/o fit) to DOC (w/ fit), ATC (w/o fit) continues to outperform even DOC (w/ fit).",
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        }
      ],
      "dimensions": {
        "dpi": 200,
        "height": 2200,
        "width": 1700
      }
    },
    {
      "index": 25,
      "markdown": "![img-7.jpeg](img-7.jpeg)\n\nFigure 8: Scatter plot of predicted accuracy versus (true) OOD accuracy. For vision datasets except MNIST we use a DenseNet121 model. For MNIST, we use a FCN. For language datasets, we use DistillBert-base-uncased. Results reported by aggregating accuracy numbers over 4 different seeds.",
      "images": [
        {
          "id": "img-7.jpeg",
          "top_left_x": 290,
          "top_left_y": 226,
          "bottom_right_x": 1405,
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cl1eEL86Adhhs4/2RXT6f4QSy8F3fhya6M0dys6mURbcCUsemT03fpTdKsJvD2jG18Ra/b3ln5C26eZAsChFQg5JYliVBJ56A0AVjqs2ueP7C10+7kGmWNj9suTE2FmaXiJG9eMsKPHeh61q+nzm01UJYJDun03yQv2sKSWQyg7lDDjgfzqv8LdHFh4W+2t5jPqDeZGZRhxbqAkKn6IoI/3jVi58LeI2tZdLtfEwTS5VMeZrXzLmOM8FBJuG7jIDMCwHUk80AY66na6v4k+HGo2UXk2ksF6I4z0jPkqNv4bWH4Vra383xU8JrH1S3vDJjspVcZ/GrmoeDLebQNN0/Tp3sp9KZZLG4xvMbKCPmHG4EE5+tSaL4bvbfXpte1rUI73UXg+zRCGHyooYt27CqSTknqSaAOlooooAKKKKACiiigAoozRQAUUUUAFFJkYzRkUALRRmigAooooAKKKKACiiigAooooAKKKKACg9KKKAK8zzCaNIig3Bidwz0I/xpga687y90P3d2dp/xqR/+PyH/cf+a0D/AI/T/wBcx/M0AG26/vw/98H/ABo23X9+H/vg/wCNT0UAQbbr+/D/AN8H/Gjbdf34f++D/jU9FAEG26/vw/8AfB/xo23X9+H/AL4P+NT0UAVEa6aV03QgL/sn/GpNt1/fh/74P+NEX/HzcfUfyFT0AQbbr+/D/wB8H/Gjbdf34f8Avg/41PRQBBtuv78P/fB/xo23X9+H/vg/41PRQBBtuv78P/fB/wAajia6k3fNCNrFfun/ABq3UFt0l/66GgA23X9+H/vg/wCNBW6/vw/98H/Gp6KAMa+8PWOpzLNf6bpl1Kv3ZJ7USMPTk1eit5oIlihFtHGgwqrGQAPQDPFW6KAINt1/fh/74P8AjQVusffh/wC+D/jU9FAFSJrqWNX3Q8/7J/xqTbdf34f++D/jRaf8eqfj/Op6AINt1/fh/wC+D/jRtuv78P8A3wf8anooAg23X9+H/vg/40bbr+/D/wB8H/Gp6KAKz/akjZt8J2jP3D/jQn2p0Vt8IyM/dP8AjUs//HvJ/un+VEX+pj/3R/KgBm26/vw/98H/ABo23X9+H/vg/wCNT0hIAoAh23X9+H/vg/400i5Ix5kA99h/xpbyd4LOaaKFppEQssS4yxxwOa4nU/GF9q72+leFU26jMQZZ7mElLRcjduHGWHPHT8SAYlOMXZkymouzNCbxiP8AhMIvDthCL24TD38sS/u7ND90O2fvMcfL15rrB1rmpdSsLfWBZ2kERurmZWupY1Aywx1Pc8AdeK6bPNNST2BSTFoooqigooooAKKKKACiiigAoPSijtQBVja5lBZWiA3MOUJ6MR60/bdf34f++D/jRaf6k/77/wDoRqegCDbdf34f++D/AI0bbr+/D/3wf8anooAg23X9+H/vg/41HM11FHu3Qn5lGNh7kD196t1Bd/6kf9dE/wDQhQAmLrn54f8Avk/40u26/vw/98H/ABqYUtAEG26/vw/98H/Gjbdf34f++D/jU9FAEG26/vw/98H/ABo23X9+H/vg/wCNT0UAQbbr+/D/AN8H/Go0a6aV03QgL/sn/GrdQRf8fNx9R/IUAG26/vw/98H/ABo23X9+H/vg/wCNT0UAQbbr+/D/AN8H/GkKXRH34f8Avg/41YooAzLHSIdMaY2NpYWpmbdL5FsE3t6nB5+vvVzbdf34f++D/jU9FAFWZrqKF33wnaM/cP8AjTgLrj54R/wE/wCNLef8ec3+6amoAh23X9+H/vg/40bbr+/D/wB8H/Gp6KAINt1/fh/74P8AjRtuv78P/fB/xqeigCDbdf34f++D/jRtuv78P/fB/wAanooAqBroztHuh4UNnae5Pv7VJtuv78P/AHwf8aE/4/ZP+ua/zap80AQbbr+/D/3wf8aNt1/fh/74P+NTZGM0Fhii4EO26/vw/wDfB/xqrqF+ul6fPfX13bW9rAheWV0OFA/H9O9Rr4g0uRborcgrbRtJKdp4Vep6c/hmua0DV7/xXHqGq6vaW1t4Unj8uztbuENJOuf9bJngKey9wQfcwqkZLmT0KnGUHaSsa3g/xFP4osZdRewks7V3zaebw80R6SEZ4BIOPaukrG0TVrfWJrma1UiKIrECe+M847Vs1SaauiE7hRRRTGFFFFABRRRQAUUUUAFQXEjxovl7dzMF+YZHNT9qr3PSL/rqtAC7br+/D/3wf8aNt1/fh/74P+NT96KAINt1/fh/74P+NG26/vw/98H/ABqeigCDbdf34f8Avg/40bbr+/D/AN8H/Gp6KAKspuo4Xk3w/KpP3D/jTgLsgHfCO/3T/jTrv/jzn/65t/KpV+6KAIdt1/fh/wC+D/jRtuv78P8A3wf8anooAg23X9+H/vg/40bbr+/D/wB8H/Gp6KAINt1/fh/74P8AjRtuv78P/fB/xqeigCorXTTNGWh+VQfunvn39qk23X9+H/vg/wCNEf8Ax+zf7if1qegCuUuiP9ZD/wB8H/Gql9pUWqRLDqFrY3cSsHCT24dQw6HDZ5rTooArJHcooVWhUAYwEOB9Oadtuv78P/fB/wAanooAg23X9+H/AL4P+NMla6ijL7oTggfcPc49atVBd/8AHs31X+YoAQLd/wB+H/vk/wCNLtuv78P/AHwf8anHSigCDbdf34f++D/jRtuv78P/AHwf8anooAg23X9+H/vg/wCNG26/vw/98H/Gp6KAINt1/fh/74P+NRo107yLuhGxgv3T/dB9ferdQw/624/66D/0FaAE23X9+H/vg/40bbr+/D/3wf8AGpsiql9qdnp0e66nSMHoCeT9BUznGC5pOyHGLk+VK7JP9JH8cOfTYf8AGuZ8S+LjoV5Y6bbxDUNXv2At7OBDu2Z+aRjn5UHPPfH1xW1/x4tpp9vDodjJqOsXztDZ2pQhdwALNIegRcgnnn8yN7TdPuIbC3uNVa1udZS1EM95FCEL9yB3Az26d8DOKITU1eLuEouL5WtTUgdnhjd9oZlBIU8Zx2qWobb/AI94sdNg/lU1UIKKKKACiiigAooooAKKKKACiiigAooooAgf/j8h/wBx/wCa0D/j9P8A1zH8zQ//AB+Q/wC4/wDNaB/x+n/rmP5mgCeiiigAooooAKKKKAIIv+Pm4+o/kKnqCL/j5uPqP5Cp6ACiiigAooooAKgtukv/AF0NT1BbdJf+uhoAnooooAKKKKACiiigCC0/49U/H+dT1Baf8eqfj/Op6ACiiigAooooAjn/AOPeT/dP8qI/9Sn+6P5UTn/R5P8AdP8AKkjP7hP90UAOzzj9apX2rWGnlEu7lIWcEqrdWA64rC8Ya3qWhxwzWxhFs/yO5GXVuxAPUV5Xf6jc6gS8rvIiu7oGOSm45IH5f578dbGKDcYrUwrVo01rr5HofinxVe3Aj0vwvEbq4ljaW5vF+5axAZb6yHoo965KwubuPVXGl3N20lxhJJZB8zgj5i/JwdxPPYcZPJpml6hd2+nzWKSERysGcg8HIPcc+nHtWlp1nc3UrpplsWkf/WTYxz7noBWUqlSs1Gmrvr2FCEcR79X3Y/j8jo9LGnadcwW0eLi7lkUNMegwR93vj3/yO3HUVyWmeE/7OeO9u5zJcI67VX7oyRnJ6k811wHNdlKnKEbSdzo/drSmrL8xaKKK1AKKKKACiiigAooooAKO1FHagCC0/wBSf99//QjU9QWn+pP++/8A6EanoAKKKKACoLv/AFI/66J/6EKnqC7/ANSP+uif+hCgCYUtIKWgAooooAKKKKACoIv+Pm4+o/kKnqCL/j5uPqP5CgCeiiigAooooAKKKKAILz/jzm/3TU1Q3n/HnN/umpqAFooooAKKKKACijNISMUAQr/x+yf9c1/m1TE1ADi8k/65r/NqzYvEmnSXk1t5jI0OclxhTjrg1nOrCDSk7XLjTnP4Vc1mOF9O+TXDeOdbBsmtrDUkRl+WdUbnnoOO30rP8SeIpdQunitriRbQAqNh27vUnvXJyvEv3ykkqgqGK/ewR1ryK2Ye2k6VJXOmrhVQp89VpXX9fMitbi+gdJ0upICn3XzhunTjtXTo99r1tCdRvzbaZCgUNI+4tgY47u3XmsfTtPutXlAt7N3YcHIyB/QfjXc6Z4HOEfUpyQoAWGM8AehP+H5114PANR5q0rR7dzxVOUnand+b6mn4UfTjFcJpkciQRlVzJ1c8nP610dZ9jZW9hM8FtCkUYjQ4UY5y3X1PFaHevQtGOkNjsje3vBRRRQUFFFFABRRRQAUUmR60bhx70AL2qvc9Iv8ArqtT7geAaguukf8A11WgCx3opMjNGQaAFooooAKKKKAIbv8A485/+ubfyqVfuiorv/jzn/65t/KpV+6KAFooooAKKKKACiiigCCP/j9m/wBxP61PUEf/AB+zf7if1qegAooooAKKKKACoLv/AI9m+q/zFT1Bd/8AHs31X+YoAnHSigdKKACiiigAoopCcCgAJqCJlD3JJwPMH/oK1MSK5LxJ4iOmpd2tqqvduQfnHyqpUdfc4NZVq0KUOaRcKcpu0Vc25dYspYJ1tr6AzqrbRncdwH90cn6CvHNS8Raje3TSXFwZGBICldqj/dHXn9KbFqV1NcxxW1uWnbO1VY9fXPYA8/TuOtdFp3hvTNM0mJL55L/U96zTOSFRCOfLAGcqBweTn19PJnVeIpXrK3bXf5GVRSqTtgZvTd7W+Zd+H2o3W26+1SAWkYzvKrgyEjvwQcAcVqav4zxMLTS03SE4LkZx+FcTPdW0Eph06MAkbSYycNznGf8A9dbejeE9RvAs8w+zQ53ZYYJ9gOp+pxXZho1qkFFLliuvcxdVwfsr88+r6HpFiWNjbl/vGJc/XFWO9QWaeXZwIDkLGoz+FWK7kraI6EFFFFMAooooAKKKiurmGytJrq4fZDCjSSNgnaoGScD2oAloqjpmsabrNt9o0y+t7uHpuhkDAH0OOh9quMwA9+1ADs0mRWB4u1u40PRVmtIo5Lqe4itYPNJEYaRgAzY7DNZaXuv+H/EOk2Wq6nDqlrqjPCGW1EMkMiqWBABO5CBznkcc0AdnketAIPSuJa+8Q+I9b1aHRNSt9OstMm+zB3txMbiYKCwbP3VG4DjmtrwjrkniDw/DeXESw3SO8FzGpyFlRtrY9uMj2IoA1n/4/If9x/5rQP8Aj9P/AFzH8zQ//H5D/uP/ADWgf8fp/wCuY/maAJ6KKKACiiigAooooAgi/wCPm4+o/kKnqCL/AI+Z/qP5Cp6ACiiigAooooAKgtukv/XQ1PUFtwJf+uhoAnoozRQAUUUUAFFFFAEFp/x6p+P86nqC0/49U/H+dT0AFFFJmgBaDxRTSeOKAI5yPIl9Np5/CsC88X6Xpl19juTKrJGG8xVBQ8Z2g56/Ws7XPFNxZXl1YtaxtDsKDcxBYkdfpXnb3ENvAINicHO1Bnn8eledUzBL3aSu09TplhnCHPVkoq10bXirWrbX7+KeOOZbWKDCiQkfMeuV9ecZ9qwLHTLrUZ/IsbV5nwSAo4/PoPrTBbXl7bSXqwu1tEwV2Q5256e/41634HsLyw8PRx3YRdzF40AGQp55PfufXpWFCj7aq51Lvy6HkSnDET5Yx0X2rblDRfAkUMMcmotufAJhU8A+5712EEEVtGsUMaRxjoqjAp5BPGKXHIr10klZbHYoqJFd/wDHv/wNf/QhU9QXf/Hv/wADT/0IVPTKCiiigAoorO1LXtK0ea2i1K/gtDclhEZm2qxGM/MeB1HU80AaNFNSRJFDIwZSMgqcg0u4Y4oAMijI9a5XUtd1a48QTaH4ftbJ57aFZrme9dhGu7O1Qq8knBOeg/SksfGA/sHV73VrcW9zpDvHdwxPuVmUAgoeMhgRjP40AdXuHrRXEp4o8Q6c2nXuu6dYw6ZqEqQj7NKxmtWf7nmbhhgeAcYwT3rtR3oAitP9Sf8Aff8A9CNT1Baf6k/77/8AoRqegAooooAKgu/9SP8Aron/AKEKnqC6/wBSP+uif+hCgCYUtIKWgAooooAKKKKACoIv+Pm4+o/kKnqCL/j5n+o/kKAJ6KKKACiiigAooooAgvP+POb/AHTU1Q3n/HnN/umps45oAWikyKNwzQAtFJuHrQWAFAbhSEj1qC4vba1XM0yJ3wTzXGeI/GMRtJLe3fbv+UsDgkd8Vz1sRCnpu+xvSw86m2i7nR6pqFvYrcmW58ljAArKMsDluQK8our+OJTNcT+a7ZYsQNzH3A71QvdYkSMuh8tBk7iO2ff6/rWaVMjmRmLMefTn0/8ArVw16LrxjLE+7Hst38yK2Zwy5uOH96TW72XoW5Lu4vJRHFEUzxtAO412HgnwhBqkf2++2zWwOFTJVt44O7jnjB4NVPBOj2mpXE9tKkqTqA8VxA+1rcqT29TkevpXrqIEUKOgrpw9KL1irRXT/M8ymqmKn7au7jILaG1iWKCJI416KgAAqXFKKK7TuSsQp/x/S/8AXNP5tU9QIf8ATpf+uafzap6BhRRRQAUUVmWfiHR7+/uLC11G3kvLeRo5bcOBIrA4Pynnt16UAadUNcvJtP0DUb23h86a3tpJY4/77KpIH5ir2RWV4o1K40jwtqmo2kYkuLa2eSNSMjIGcn2HX8KAOFuZdT0nwlp/iuDxJfX17OYGa0eRTBc+YwBjSMLwRuOMc/LzW7q817rvjIeHbbUbmxsrWzF1dS2j7ZZHZtqJu/hGAWJHXpXL6joGm+G/BFr4x0xg+txCK688ncly8jLuXZ90btx5UAjArpNPbyPi3qQkXY17pUMsQJ67GII+ozQBa8JX17Fq+teHNQu3vJNMeNobmUAPJDImVDY6spBBPeuluuBH/wBdVrkvDv8ApXxK8WXcZzDElrbbuxcISw+oyB+NdZeKGSNTnBkXoSP1FAE/GaXjIqs1rHt6y5/66v8A41yWleIxLqMsOpEwx5OwrI/Bz0PPp/KuetiadKUVN2ubUcPUqxk4a2O3oquttCwBDSkH/ps/+NL9ki/vS/8Af5/8a6LmJPRUH2SL1l/7/P8A40fZIvWX/v8AP/jQAt2f9Dn/AOubfyqRSNo5qrdWsYtJiGl/1bf8tX9PrUgtYyBzL/39f/GgCxRUH2SL1l/7/P8A40fZIvWX/v8AP/jQBPRUH2SL1l/7/P8A40fZIvWX/v8AP/jQBPRUH2SL1l/7/P8A40fZI/70v/f5/wDGgBIyPts3P8Cf1qxVJLWM3Ui5lwET/lq/v71N9ki9Zf8Av8/+NAE9FQfZIvWX/v8AP/jR9ki9Zf8Av8/+NAE9FQfZIvWX/v8AP/jR9ki9Zf8Av8/+NAE9QXZ/0Zue6/zFH2SL1l/7/P8A41Dc20awEhpc5H/LV/Ue9AFwEY60tVxaRdcy5/67P/jS/ZIvWX/v8/8AjQBPRmoPskXrL/3+f/Gj7JF6y/8Af5/8aAJs1FczxW1u80zqkaDLMxAA/Oo5III0ZmeVVUZJMz4H61zHiTV7KPScWzrdeeSmGkLrgdcgnBrGtXhSi3JmlKlKpK0SMePFBb/QdybuCJeo9elcP4iuLzUtauD9pEtuWDRsI9mB2HTORnGfaori7igRQCqkdBgDHsPT9BVWFZNUaTZcCKJVyTyWJPGK8Wj9bxMW5Ncvdr8jpzT6lRUYpvm6pPcls1sLVW3yP5w5Uqf6/Wtiw0/V/E8ghiBhtF/1krZA/HuT7VP4T8Ey3n7+8Uxwbshs8uPb616VbaXbWsCwwo8aL0VZGA/nXo0MJTjra/m+p5EZVK0OS3LDsuvqZ2i+FNN0ZAyRiafvNKMnPsOg/n71tsP3bc9jUf2SP+9L/wB/n/xpHtI/LbmXoesrH+td60VkdEIqKsiS3/49ov8AcH8qlqK2/wCPaL/cH8qloKCiiigAooooAKgvLiGzsp7m4bbBDG0khxnCgZPA68VPWdr811beHNTnsQWu47SV4ABnLhCV/XFAHmt5pw8VXn2zwx4Tl02bquszytZZ9wifM49z+ldDA3irwq2mpqGpQa5Z3NxHbSf6MYp4i5wCCCQyjqSwzit3QL62u/CFjdvqAnia0Qy3LSdTt+YsexznPTBqv4DvLnUPBthcXkzzyMZFWaQ5aVFkYI5J6kqFNAFf4g3gg8PwWP2eCZtTvYbBftC7o42c53kd9uMjkc4rFk0s+DfFWgXMl/eawt7IbDdqMnmywMVyHjPGM4w3GcY5rtta0az17S5dPvkZoXwQUbayMDkMrDoQeQaybDwclvqsGo3+q6hqtxaqyWv2x12wg8EgKoy2ONxycGgCj8P/AN3J4rhfIkTxBdOR7NtKn8qX4bZbStYuB/qrjWruSI9iu/HHtkGrup+DY73VLjULHVb/AEue7QR3f2RkAnAGASGU4YDjcMGtnSNJtdE0u202wiEVrbpsjTOeOc59SSSfxoAknQvdQgSMh2PyuP8AZ9aYIX+2EfaZf9X1wvqf9mpn/wCPyH/cf+a0D/j9P/XMfzNAB9nk/wCfqb8k/wDiaPs8n/P1N+Sf/E1PRQBB9nk/5+pvyT/4mj7PJ/z9Tfkn/wATU9FAEH2eT/n6m/JP/iaPIf8A5+pvyT/4mp6KAKUcLm4nH2mXgjsnoP8AZqb7PJ/z9Tfkn/xNEX/HzcfUfyFTk4oAg+zyf8/U35J/8TR9nk/5+pvyT/4mp80maAIfs8n/AD9Tfkn/AMTR9nk/5+pvyT/4mp6KAIPIk/5+pvyT/wCJqCCF8S4uZR+8PZP/AImrx4FZd3qMWmafc3UgLKkpGF6kk4qZSUYuT2RUYuTSW5c8h/8An6m/JP8A4mjyH/5+pvyT/wCJqDStSj1WyS5jVlDZGG6gir1EJKUeaOwpRcZcstyH7PJ/z9Tfkn/xNH2eT/n6m/JP/iamzRmqEQ+RJ/z9Tfkn/wATR5D/APP1N+Sf/E06e4itoJJ5XCRxqXdj0AHJNVNN1vTtYRnsLpJtoVmC5BUNyuQeRnFOztcV1ew+1gkNuh+0yjr2T1/3am8iT/n6m/JP/iaS1YC1TJx16/WnQXMF1CssEqSxt910YMD+IpBdXsNMD/8AP1N+Sf8AxNJ5L/8AP3L+Sf8AxNRSatp6QSTNdw+XE5jchwdrDqp9/avM5/FGq/2xLqG63V/sxt1ESuRtDbtwB/i7dKyqVYU9ZM0hTnPWK07nda7rEei2jyG6aW4ABSAuis2TjP3eg5P4VwTeJ9UFxNP9qfz5VCsABgDsAOMfWsvVJ7p7pmuZw8rqSJC4YMMnjI6elZLzobj90H8kkEo7Z5xz+uce2K8mri51VaLt6dfmVWxFDBVFGouaXnsvl+pa1Ka5ciVp9xc4zu3Mfx/TiqkFjd3UbyW9rNKi53NGhYA4J6/QUSROsCTHiObcEVWGWwcfdr0fwLY+ZdzXkdytrtPly6cqnhQMKWzznvnv+YBRjKpaDPFxFaWNr32S6fqa2heHrU21nqISe2na2EbxgIoZcfxKBg/jzXRC3kGB9qm49k/+JqcD2pRXswgoqyPShBQSSIfs8n/P1N+Sf/E0fZ5P+fqb8k/+JqeiqLKdxC6xAmeRgHXhguD8w9BVyoLv/j3/AOBp/wChCp6ACiiigBCcCub8W6ppVjaw22paZNqb3JYQ2cNr57ORjseBjI6kV0jdK5bxBqM1n4v8MwPc/Z7G4e4EpyAskmweWhJ9TuOO5AoA5TSPCPiNr0XejgeELP732U3DXZc9cmIny1+nUV2Ph3UdWmv9R0rWIoGuLLy2F3bqyxzq4OPlPRht5AJHNQ+L7+Wxn0AWt06XUupwxiBX/wBdGSRICvcBSTntgV1HUnjvzQBwGoQ3msfEG/h8P3v9lXVhaxpqF4YxMswcFo08o4BIBJ3Zzziub1hxD8NPGGmTKx1W0vIjqFwG3faWaSNhJ7Ar/D0XGK73UfDN/wD2/Jreh6lHY3lxEkN0k0HmxzKudrYDAhlyR15o0/wZbw6LqdlqM731xqrM99cEBDIxGBtA+6FA4HOKAKPxRYN4DnWI/vHuLYRY/veamMV2nbiuMt/BurTT6ZDrOuJe6dpcqTW8SW2x5WT7hlbcc49gM4zXaevFAFOBZzGTHJGq73wChP8AEf8AaqXZd/8APWH/AL9n/wCKpbT/AFJ/33/9CNT0AV9l3/z1h/79n/4qjZd/89Yf+/Z/+KqxRQBX2Xf/AD1h/wC/Z/8AiqhuVufKG6SIjzE6Rn+8P9qr1QXf+pH/AF0T/wBCFACbbr/nrDn/AK5n/GjZd/8APWH/AL9n/wCKqcUtAFfZd/8APWH/AL9n/wCKo2Xf/PWH/v2f/iqsUUAV9l3/AM9Yf+/Z/wDiqNl3/wA9Yf8Av2f/AIqrFFAFfZdf89Yf+/Z/+KqGNbn7RNiSLORn92fQe9Xqgi/4+bj6j+QoATZd/wDPWH/v2f8A4qjZd/8APWH/AL9n/wCKqxRQBX2Xf/PWH/v2f/iqNl3/AM9Yf+/Z/wDiqnyKMjGaAINl3/z1h/79n/4qjZd/89Yf+/Z/+KqcEGjIoApXS3ItJd0kRG09Iz/8VUxW6/56xf8Afs/40XjqLSXJAyuOazNa8UaZox8qectcnG2BBlmz+g/GlewCaxq50ezMss0LSOD5Uflt85A+v41xuq+K7zU1FoCICgSRnhZkO47uPpxn3qrqWtahqKlLm4Jg8zeIwq4B64yBkge5PSss3JSNk35Gc7f4QBwOPXr3xgivBxGZSqX9g9Lfjf8AyPbo4CNKzr2vf8LHS+G9c12+1vUrkx2y2zSR+aZmZRGoBGUGec4yav654wSGCW3tJkcsCplRdu36HJrgzqKvOUifLuduMHAzk5PqBWjPpst7p3naeXlntzGssXkF2yzEblPTbgdOT713QrVcXam7RdvPX/I85woYdSqwbnFPy0/zM+a5vbks8QJf/npIxwT/ADNUvJuozPI0Ucwt2USzHcUXdnHb1GK03EVhpEFxcags1w7FJIChV4iOuR27VV1TUW1TTbaBYWt7NJCbdJDgyAZG9scZyfoO2K56Evq7qU3FWXXv8znzPHUpUoVaU3zPppp8inElrqVzbfbZXSToxI+TOeOBk8cYruYfhxN9leb7RAbwOTHFtJi25PBPXn17Vw+nwTuZbmOGSW3h2m4Kfwqfft3r3my8r7HB5Db4ti7GznIxxz3rooJVtZrbax4+FisS3OqrsqWemGyUNDHaQuUCtsiPbPGc89TVzZdf89Yf+/Z/+KqeivQSUVZHqxikrIg2Xf8Az1h/79n/AOKo2XX/AD1h/wC/Z/8AiqsUHpTGVYRJ9sm8xlJ2JjaCO7e9WqgT/j+l/wCuafzap6ACiiigBG4UnGfYV5v4ln0nxJqM+n2Pg+bWb+3kMb3ePsscTg8jz+GOD2GfavSG4Hb8a5fwjqM97d6/DeXbPdQapMggc8xQjHl4XspHOe5JoAwtP0Txv4f0qW7/ALetrowqZP7NnR5lCgZ2rMx35wOMjrXb6ZdDVdEtLtrcxLdW6SGGQcoGUHafzxWJ4f1CW78X+JbZLo3VhC1uYzu3LFIUIkQH/gKnHbJrqQMEelAHM2/gLw/aXkVxFZSEQSebDBJcSPDE/wDeWMsVB69uKv614a07XxAb6JzLbsWhmikaKSPP3sMpBwR1FbFFAGdo+iWOg2X2TT4PKiLF2JJZnc9WZjySferNz0i/66rVjtVe66R/9dVoAmPPFcL4j0KSTWITaWRMUwG9kHBYk5z6cY/Ou6NIR3wK5sTho4iPLI3w+Ilh5c0RsYWJVReAAABUg61zutafq93qttNZXHlwIBuG7GDnkkd8jj8K6BeAM+lXTm3JxatYicEoqV73H0UZorYzIbv/AI85/wDrm38qlX7oqK7/AOPOf/rm38qlX7ooAWiiigAooooAKKKKAII/+P2b/cT+tT1BH/x+zf7if1qegAooooAKKM0mRQAtQXfNs31X+YqUsPWsPxHraaVbqoCyTyMNsRbBwO+OvbFRUmoRcpbIqEHOSiupuUpqhpOox6pYJdRspzwwVgdrdwferrMNuT0pxknHmRMk1Lle47NZ2uX0lhpE1xAU81QNoY8dRWNqHjfT7VpI7dHuJE6FcBTjrz/9auJ1rXrnWFeeZkVYAWCk7VX8e/8AWuOpio1F7Oi7yeiOunh5U2qlZWitTWufGdxJYyW94sRRkKuy/KefX0rirnVWcKtsc7jjoR+PTHQVTXVHk1Ozt4LZrm6lmXy4Q+3dggH2UepPFa+uWml6Nqt5a6XCk63EarLNMiu4cN1VgAQP/r1EsIsPDmxPvVPPoceOzW0WsK+SL7LVmStrNNcKnMk7Hp3r0LwZ4VuBIz6raNHGmGjVmHzEjow68cH8ayfBWianPqUGoQReVbhtwmcAqy5wRjOT0P0r1eAfvrj/AK6D/wBAWqw8HVftJnl4Og5/vZkqqEAVQABwMU6jFLXeeqFNk/1b/Q06myf6t/oaAGW//HtF/uD+VS1Fb/8AHtF/uD+VS0AFFFFABRRRQAVHcTR29vLPNIscUal3djgKAMkk9hUlZ2v20N54d1O2uJ1t4JrWWOSZjgRqVILH2A5oA8qu7nwBe6oLqPwPrt01wWmRreycRXAB5cJvAZckHp3Ga9Q8N6ra61oNnqNlbTW1rMh8qGZAjKqkqPlBIA44x2IrEudN1ZtM8P6no72NzqNhb+XtLkQzxugDYYDIGVRh9K0/CGj3eieHLWyv5o5rpDJJI0X3Azuz4XIzgbiKAN6iiigAooooAgf/AI/If9x/5rQP+P0/9cx/M0P/AMfkP+4/81oH/H6f+uY/maAJ6KKKACiiigAoooNAEEX/AB83H1H8hUxYDrUEfFxcfVf5CszxLay3Okssd1Hb7WDM8j7FIHYnt1/SoqOUYtxV2VBJySk7I2c56UDr1qlpNvLbaZbxTTCZ1Xlwcg1e704ttK+hMkk3bUWikJAo3AnGeaoAPSuZ8TSagmh3v2K2hmjKymYyvgoAMgqO5/wrpsjFZ32+zgmW2uJo0kuZXWNH/jI6j/61CaWrVyZJ20djM8GSX7aHbC8toIovIjMDxtlpAV5LDsen5mujJxVSG/s5bma0hmjMtuqmRB/AD0z27Vga74407RzcwKRJewSIhifK5DYJOfQA5/SrhB1ZWpoh1Iwjecjqc1Q1bVrbRrB7u53MisoKpgsdzBRgEjuf0rz/AMR+LdQ1G426I5t47WQ4mjl3eflRxt24/PNYtxc3viW8gvNQgjaZIBCiohyxBzkr65J46e1YVa1GhFynLbp1ub0qFavNQhHfq9rHUeOru4efTwJ4P7PcttCOSXkAxz2wMj86zvCVrLP4mzaXk1uFUSXCRrlZFU4CsT6569aydet7uy0+2S5g2Qzl3gBJV0bIVsqfUBcH6H2qx4W1q60iG4S3s1lublx+8ZicD02dz1Of0rzHVm8QqrbirbCjXblLCQgpJP4ux6HrJuo9CMtrffZJYj5mSoO8DPy/N68VwdlrV/p2ntZ2dxJHbBdkSBFzH8wJI4yc5I5Peq+qy6mZN98S0qLuEcjYwPXHQD8KkuLddT0s6tZ2Gy0tcIVR2eUsBlnYZxjtyP0p1MXKc3Km2uXp3+R18tOjalVirvW/Zev6Bb2AutPvdQuL4xMJT8vl72eRud3HTJ4z7VjRGWa6W1SZdjy4MznaB7E9h71ee/tY7chMtO/3nB+QRjnG0d8+5HtWVam5v7tLGOD5pyoiAGVHUZ6cdySBxiuNL2yU5dN11bIxuIp0/dpTtf8A4HbuLqNxcLMbSYKGjkIkUjjK8dTyScn0FPbRbuLTf7RltZTYsxRHxgliDgnj7oPHTntXWab4Ea4W9k/taJtSglxGYGysbjn5uM5/lz1rsdUs9WvdAhso1thcTKsdy7/MqAj5iBj5ufp1rsp4NJO6t2PBnSq4mUqlZu/Q840fw9OviA201vBehIz9oHmEqinjluzgcgc9QeCK9O0LRIdJhBDmaUrt85wN+zqq5A5wOM/5GfoWhXPh/wC12ibJdOdDJHIxAkV8YKtgfMD1B7YxXSRf6qPj+EfyruoUVBXZ1YagoRu1qSUUUV0HWFFFFAEF3/x7/wDA0/8AQhU9QXf/AB7/APA0/wDQhU9ABRRRQAhzjiua8Z3uh2+lJaa3YyahHeN5cVlDCZZZWAydqjoQM88Y9a6U9DXP6taRy+LfD939rt47iEXCLbyEBpkZF3bPcFVJ9s0AcP4Z1bwhpes2raZ4O120uru4+xpdXNsWCPu2su53bbjByBzgHNerr1HTHt0rg73wxr76wlnbS2S6I2qpqhmZm85Du3vGF6cvk5z/ABH6V3Y6g+v50APooooAKO1FHagCC0/1J/33/wDQjU9QWn+pP++//oRqegAoopksqQxtJIwVFGST2FDdtWCVx9QXf+pH/XRP/QhTLLUbTUYfNtJllQHBI9afdH9yP+uif+hClGSkrxd0OScXZkwpaQUuaYgoozSZFAC0UhOBmsy91/T9O1G3sruYxyzjMZI4POMfmRQlfYTaW5qVBF/x83H1H8hU24dM1DEf9Jn+o/kKBk2cU0yKDgnnGcd8VCl9ayvcRxzozQHEoB+4cZ5/CvONav8AWYPEy3dvfWE8kcTLFtjJG1iCB15OMcilJxirzdvUSUpP3Ff0PTNwwecGuH1n4iWun6m8NuouIoo3DqyOjGUNgKGIwB15war+GvEtzFqS2N4XuIbqUskmGZo2Y/dPXjkemMVZ8cWulXEZvXnhN5aDa0bOCQpwfu9c8g/SlCvT9n7ZLmXQJ0Kjn7FvlfUXwl4g1a+1O5hnsGMEszS+a10G+zoR8q7epGRjjpu6V11/fwafY3F1Ox2QIWcKMnp0A9a8+i8Qab4a0tntx52q3FukgBOFAcEr78DBI9q5i5v5dT1B70Rb7mX78jD5fTgdOMAVlKtVjTU60LN9unrc1jSpSqOnQndLvv8AKx0fjHxOlxJZiOR/s0tsk6pt+bcxPXB6gDp+tYD3r3TGVQXdgMySjnA/WoV04eXJdzRtIAQGZUyFJzjjsODSzXS26qkSh2XO5ieBj/P/AOuvJxdWhXalSi7vd3dmelQVTCRl9YkuVbKyuv1LkthPbafFqN2G+yyPsDkjrzjjPTg9KxLuY3NwAmduOh5BPfHtV28vEXSbO2tr24lhZQZrWVt5jkXOCp6BTu7enNdR4A0iS3vZDfWIMVzbCSF2jDKQTjhuxIPTriqVGMqijB3Xf/gHgYnHVsXN0r6Pt/n5mHpXhvU20htZijQ26KzEF+XVc5KgdDx04rp/7Y1Hwv4ehaQWknmbXhgO4OqMCW3cDncR+v1rY1fXbPQ7ZrPToohPk5WNQFQn1A7+1ea3Ul9qdp/bOo290ljc7has6KGlbja5zyqkZwMcgDHGCej2VOjecNZ2tczqYiVLD+xhtv8AMrNN/al9Ld6lcQo8sgLF03D1yQOgA6d+gqaJ7jU5LbTWkZbOOThdpcx7iASFHzY9veqdxY3dvOgubaWFpOVUxldxz/CMV6t4Z0MvpNnFqemR201i6vA8ZxngHPBznPBB6kVlSpyqyszzqFKVWVtjN8G+GL3StYujeJ8kUflxur4EoJzkr36dyCOnPGO+VduAABjjiokGL2TA/wCWa/zarFepTpqCsj2aVJU1ZBRRRWhqFB6UUHpQBAn/AB/S/wDXNP5tU9QJ/wAf0v8A1zT+bVPQAUUUUAI33T1/CvL/ABlf+C7zV5f7Q8O6pqlzbOtvNdadA+1ZCQBG0gZQzcgAc4zivUG6GuKh0Fb3QfEWnWepWsjT6lLPDKjZMM+8S7ZMd1cD324oAseBdU0i6t7vTtI0O+0mKwZFkhu7cREswJ9SScAE555HrXXVynhbTddj1nVtX10WcU16sKJBaMWVRGGGST1J3H8Pwrq6ACiiigA7VXuukf8A11WrHaq910j/AOuq0AT9KM1BeXAtLSWcxyyCNd2yJdzH6Dua85n+I80VxeW5ilSVptsPnQhfJjx/EM/ez68VXs5Si5JXsQ6kIyUW7XPTMijIrhPCviJ571o7u+aZZyBHu5Bbpwe38q2r/wAW2FldXVoSTcQorIOMSbhnA57Y5rloYiNWm6i0tv5HVWoSpTUH12OiB96KyND1231m23KVWdAPNiBzsz0/A4Na4IJraMlKKlHZmMouMuWW5Fd/8ec//XNv5VKv3RUV3/x5z/8AXNv5VKv3RVCFooooAKKKM0AFFFJmgCGP/j9m/wBxP61PUCf8fs3+4n9anzQAUUZpMjsc0AFZGreIdP0qOUzzZkjTeYlGWI9PSr91d29pbPPczJFEgyXc4Arx+/1X+1dauWaeKaOZXYZQKcBtqrwTkbR9ayrSqQpupBXtv6F0lTnU9lN2b29eh6PF4s02a6tUjniNvNAZTcNIFWPp8rejc9D6U/xHodnremyBkiWdlVY7jywzryDwevP171x2mafodtYwXd5cnYrOWs9gYuc9v9nB7/nUet+MLyeVhazNaWmzy/LGMkg9c4yD0GAawhjYxgqk1yp7d7f5mlbDcsnBO9t+iX/AOtvdX0nwzZvHYQWryK+Ht7fah3cAlsdDjHXmuSt9Y1rVovsRvRHGz5eV3CgbjlQWOOMA8DrWZYJBcebPcXBIEZcxqcPJnjIY9CpIOcHpTzaXUenm8SFntIuZJVdT5foSM59eQD0rhq4ypWaVNXv0WjO+hhaNNOc59tXqtTH+126Xc9vO6OsblUaM7kcc8r6g4puszXukz29ldSxia9txJDEI9xEbA8tx8u3AGR36Vragtx4a+xXY0aGe8uSxtIXUFN2377kZChcg9ieMdyHait9qukQXGu6hb/abeFtkrRBPPb+IKg684HHf8a6IOjyOcabi+z6W6o8jMa9WEfZuopb6rTfozMSb+0XEVtp1qs7xRWy+VEAxKnG4t/Dljk/hnOK6HTPA002uyWmr+bt8jckkWSrdvvY4IPY+lcvo8In1a23uyoJFLuqbyoyP4e4zj16ivfFHI/QU6EXiLzqO55GDoxrLmn0MXwtoc+gabJZzTiUeczRkdlOMfQ9a14P9bcf9dB/6CtTVFD/rrj/roP8A0Fa9BJJWR60YqKsiaiiimUFNk/1b/Q06myf6t/oaAGW//HtF/uD+VS1Fb/8AHtF/uD+VS0AFFFFABRRRQAVW1CGS4065hiEJkkiZUEy7kyRxuHceoqzVbUFt3026W8YLbNEwmYttATHPPbjPNAHC6P4f8f6LYJY22s6M9tHkQpNBI3lLn7gOQSB2zk4rtNIXUE06FdWltpb4Z8x7ZSsZ5OMA89MZ9815o+jfCYxkf2rbJkcMNUkJB9stj86674cPbt4HsFtdxhRpUR2JJkAlcB8nn5sZ/HAoA6yiik3CgBaKTIoBB6GgCF/+PyH/AHH/AJrQP+P0/wDXMfzND/8AH5D/ALj/AM1oH/H6f+uY/maAJ6KKKACiiigAooooAgi/4+bj6j+QrnfGml6lf6NOLG7dUEZD2qwq5n56ZPIroouLm4+o/kKlJGOtOMuWVyZxUlZmXoNnf2Visd/fG8kOCpMIj2DA+XA69K1etNBGc5pdwApSlfVgo8qsgPrXPXniywtNThtNwKmQxzTO2xIcA8kng8jHXvW00yXEcyQXCCRcoWGG2NjuPUeleWXulTWXiXZq2oCaBZElZ/sabZQeWypOB3Hf9KyqS5Gm2klve5pTjzppJt9LWPUINRs7qZobe6ilcIsmEbPynofocVwvj7X7S2sPsCeTPcyTMSRIC1uQRzgcg1zNnqraZPNqDahdxlY0SCJcATbWICHAAC7eeg603VTZeIoba9hSOC8YO120anDOccYJ7c8/7QrprLD04c9WXud0cSjXxP7mnH3xvhzVrm1N1IzF7aZDHPEXK785yc5yHx396XWdC07+0HksrlWtJtrQ7HLlF2jhtwzkk568dKiuEgsrYSQKIHXaUKE53jAB5PqM+nt0wyC8uJE2ugaY9Wzx+J7n6cV5Lx2I9jOrhJWjeyv0X6/M9Sll2Fpyhh8UveSu7dX+gtjYmxjk3zAqTnaRgD/PFX21aWIwtYRLGVCk3Dfe3ByWwe4IwOe1ZTyrC+6WUu390nhfoO341HJeefbrEkTiYtgOW6+gI/xrzIzn7V4iT5pvy0NcVjcNSorDR0S6J6vyOlv4ry/tRq2rXPykhIc7TtDnHyrngZ6nrgVWurq10XWrH+yr2eSeN5Emk8s7CRgAAHgr159OayLK2xctDcRb8KNm/kAZOQPz/nUtnpxluIpGi5BCQxLkD7xx069T+fvXZHFYPkm6l3K2nq9rGNPDYrEKElHljfVdku/qd1oxTxKVstVtppltz5yzhNqNg42Mfxzj2ra1DSotPS71LTjFa3ckQRmlcrEACPmI6ZAzin6cLPQtDWWUeRuG9weSXxjA9+34VwXizW9T1e3a5gbytNikCYV8Hf1ycdT3wOldMbUaKU17zOTMatKM5SS9DK1aB7eOS301bmSzO1p5nj43jgHP8IO44U/rWfbSrEixxHbcEhmmbh4yp4KEEEkjII9QK2fDcV3qFq+nW1+lttTYIncjesh+dz/eOOAPfIxjnoofh6ttrNuVvnW2A3BwwWXeMHA4wfXPbHfrWCpudpRPFVKdW0ovQd4ISSHUvI1DzbW7jiBhtTlUkVhkyf7THHOemPbC+hioVij81JNqF1BAYgbgD29un6VN3r06ceWNj2KVPkja4yf/AI95P90/ypYv9TH/ALo/lST/APHvJ/un+VLF/qY/90fyrQ1H0UUUAFFFFAEF3/x7/wDA0/8AQhU9QXf/AB7/APA0/wDQhU9ABRRRQAHpXJ+MtA1jW/7OfRru0s7m0mMwuJkYuhxgbSPUbgwOQQRXVnpXK+M7TwpdR2Y8U3UUCqz+QZLpoMnjdyCM9BQBHY2njxLq3+2aloctsJF88R28iu6ZG7BzgHGa60da8eubHwDY6rodzoV8Li+GpQKLeO9kk3guBkgk8Lnd2B288V7CowRQA6iiigAo7UUdqAILT/Un/ff/ANCNT1Baf6k/77/+hGp6ACoLy2S8s5beTOyRdrYPY1PSHpSaUlZjTcXdHGrJJ4b1SDTrKBpYpiGkkflmJ44xgDH0rqrk/uV/66J/6EKkxluRyDUdz/qV/wCuif8AoYrChS9nfXR7LsaVqvtEtNVu+5PmkDAtxzUN3cQ2tpLNcSrFCi5eRuij3rjPB97aprF5A+oq0rOUijY4MwHO8Z7YH86qUpqUYxje/wCAoxi4Sk5Wt+J3WSBQORVDWNQ/s7TJrlQGdR8q5xk/55/Cq2gau2rWTyyqsciOVKg/rTlVip+ze4lTk4e06Gww+XiuD1bwTJ/aBu9NQOszfvVeTlCWJJGf4eckZzxxXdZzkZ6Vz90utnxJFJA3+gZXPTbt/iB75zUYmVocrTabS0Kw8b1OdWuk9zU02yawsIrZrmWcoDl5Wy3UnGfTniidDLFex+e0BZceaMApleozxRBqdvc6jd6fGzGe1CGTIwPmBIwe9ebePPFkrXd1pFizrF8y3KtEDuIx909e3+c120aMpSUV0OOvXjCDk9bl278P63pq6xeDV7lIi6YZ1V/tKlQDnBGMZ9Kd4aNpYy2LanIZWlBhtSYwggWNc5Y7hkYIGSOq1g3nie8l0+G1bVbhYWbJ2cMVwcqe+OR37dKwNsUjmCCFf3h4aTBYfT+ea5XUdaSlUXupbOK6aXudCpqjFxpfG3upPqr2sd3quoWHhzULmS0uLie4urR5bZ4FVkjDkhWJJ+bBHpXHSarqOpyOJQLieQ/PIR1GMc4wBwMdOgottFjjaNpnOR2zgD8e9W5GtrSCZTHmVoz5ag7cHPX345rz6uKwK5aGFjfX5X7nVLDY3lliMTPlSXle3b1ILbR1im/0gjcSMgZwD/X/AOtWjDKLKKzuhaXcsjTRhkW33REbiMA5yXIH3SO9ZNg88l2rSRPdbVY4XAIO0nOegA6nPYGt/TI7y+SG1jt/LnMnmENKgVmX7u0ZzkDJ9fyqY1KkpKtiLVF0V9reQqNalWouFC9Lza39X0Oxuxbah4XufsFk1pNIoka2eMRy7Vbqy9ecHHrXlV5MsszttaNkADKRyx7g16B4qEs17HFYny9VgtS1zdK+xWTafk/2icEj0615xFH9omjj3KC7YDO3AyR19PrWWLpRlVU0rfkePjMfVVP6vur3v52LuhwzyapbG1t47qUPu8l1BBUdS2eMYPX1xW9f6pJp0t7p2h3Er6afmlKAnyP7wQ5+7+HaqVnZzy2kbKyWs6sUa7kmIVw3AVj0AwAMc+9Zs9xdxa/AtxF5QsrgstoZdqyS4GGkZcEgHdgZxzk55zpD3Y2eh59NKKtewW99dRagl6txPAYEK2kUe0l5Mr8znOduNwxjnnrzXe+HtJg1rULybXRLLqaApLbzDCIpyAVHpjgc8cnvmufjsLe6eK+vLmXfcXHl/bJYh9mYYwQMYZcdFPHI7dR6D4au7eWCW0tZZ7pLQiI3coGJOpChv4tuevv710UI80veOnDQ5p3nt2K9v4J0+MSi5aS9Dp5cfnnd5MfZV9Metbmm2R0+xitfOlmEYwHlbLEe5x26VborujCMdj0o04x1iQp/x+yf9c1/m1T1An/H7J/1zX+bVPVFhRRRQAUHpRQelAECf8f0v/XNP5tU9QJ/x/S/9c0/m1T0AFFFFACHpXAnw34ysvEWpaho+qaRb2t7LvNs8DsCegcjPD4wDggHHSu+b7p9K811jS/hlJrN4+qajbpfNMxuEfUpEw+eflDgDnNAHYaBF4ijM/8AwkFzp02QvkCyjdNvXdncf93H41t1558O4tCtfEHiG18PXBurEC3kE4maVQSHzGCeOCM9z8/PSvQj0oAXNGRXML41sJ9Qa1srLUr+NJhBJd2tqXgR84wX747kZAq5rfiWz0KW3gliubm8uc+Ta2kXmyuB1OOwHqaANrIqC66R/wDXVapaHrtl4gsmurJnARzFLFKhSSJx1V1PINXbrpH/ANdVoBkWrEDSbrNvJcgxkGGM4ZwRggH1rxmTQrDU/EITTLsCzlPmNmNibZMc+YWwODkde3rXs2qxzzaZcx2s6wTtGQkrDIU+v+f1rwnUFC30yrP553sjXAbIlOeWBPPQ8jnP5VlPGVcO7wPMzC2jauXY7i20vUbe3stXtprYMWM0ts4EZwQAVHXJ54rsdO8CYvc3UMAgCkiWI4O4jgr3yDzzUPhfUY9R1TTTDCsLWFm6XLTSEgpnB2g89QDknABxXWan4hisbSK/gEV1p/mbLiWOUExg8Agd+SOM9KVWpQqqMlo93bS7811OvCV5RpylJ3VrddEcjb6+mj61HbW8k32SECNwbVt8gUYZmwMnnJz716UpGR79M1zGmXtnqupTa61usFpDF5EF1LJs8wbssdp6DOOf8jV0jWrXWLdp7fKqsjphiMnacZGD0PX8aqlSjFOcLu+uu3yN3iHVa5reVuvmX7v/AI85/wDrm38qlH3RUV2R9jmHqjY/KngjYDWg2ONG4etVr69hsLKW6nLCOJcttUsfyFeX6j4p1LXdatoLL7L5cWpKbcPFKrHAOC56BeeRwfatadGVTVbGNWvGnues5GfejNcZrXiq70Oxt4LtE/tIuhmaGF2h2FsHaT3wOnXNdJpOqW+safHe2u/yXJ2l0Kk49jUypyjHma0HGrCUuVbl/vSZoJ46/jXDa5qfiCLxbpcUNgoXfOIUF1hbhQvVhjAxwee9KEOd7jnPkV2js0I+2THP8Cf1qbNcb4vu9Vg8P3M0S2kSNaBLtJNzFdwK4Qjjgk8mr3hmTULXw3HNqptfs8VrG0Pkq24IEz82epxjp70/Zvl5ifarn5TpCRwK5bxR4ztPD6CJPLuLvdtaHzMFBtzuIA6fd/OsfxB8RobV7ZNOWUtujmkLxjbJCybsDnIPK/lXn8327XL+S+upC0shGWAwMYx+PH8hWvJTox9piXaJk6s60vZ4ZXkX7/VL7XJnvNTuGSJ2V0s4ySmQMZ2knGf6mpLSN7m8R0t13EGNQYwxOcc+x6j8auaRosJu7NLndtuXMKOQD0GSee3vWt4rj0fTrS1k0263agWZVMRDB16MG7AdR/k14lWtWqy9tF2j07WfZf5nqVHQwVFxqK73a639enyJ9B8PQ3WpXNpdMVktNjSRAY+8Mis/xDarFru1NNuIRzFAq/8ALULgEqenOfrzXGwkKuSjNEpDFVO0g84x6D3rplbW7zS4NQvIbqS3WNVWUKrbUAOW4IzyM846+1T7GFejKUI3cWr3dmc+FzJymvae6rPpdeRHrHha4tNKnvriOW3MTAqmAd7HvjPGAMkjmpft9poPh64s7Cc397dpFJLNFCgRMY+V8ghurZyM84NaPibxHFc6HDp9pOdRu9gkkuRHsxgHJwOjY6+lVdP0TTtIsra61jUrV4byInaqFtjjlWUcjAB5yMfXNbQq1KUPYU5e6uvXc4MZWjWq89PRvfsumnqYMGsXUV2k2pyTXqqWYwyOV+UnIA44Xk4A4HbA67dxrUviXSotLk0+GS7efbA0BAMKjknb2GDgZ4IBJ+7zz99boutvDJffaF83D3UeWDd92PYZ4z2613+ieDHs7e31HTtVDXR2srYJiZD95fUg/wBBWNJVZtrc4qEas5NdCfw34YjhaMXNk9te6fIdtzC2FuQec++R1H8ug7ZRjAxSAYPtTuK9GEFBWR69OnGCtEWoYf8AW3H/AF0H/oK1NkVBCR5tz/10H/oK1oaE9FGR60ZHrQAU2T/Vv9DTsj1psh/dt9DQAy3/AOPaL/cH8qlqK3/49ov9wfyqWgAooooAKKKKACqGuXiadoGo3ssQljt7aSVo2GQ4VSSD7HFX6ragbYadcm92/ZBE3nb+mzHzZ9sZoAxdJ0nTL/QrO7l0fS4rme3WRglspVWZQeOOn+FTeEdVbWPDdpePbxW8nzxyRQ/cVkdkO32yuR9a5ux8M6w+jw/2N41vodBlhDwRPZK06REZCrI2GHHTjIrp/Co0tfDNgNEYtpwjxExB3Hk7i2ed27OffNAEXi/Xn0Dw/JcwRrLeSulvaxsOGlc4XPt1J9hWP4Xjs5dW3L41utX1KFT9qt1ukMROMEiIDAUE8Y5zjJPSofitaRXWg6U90P8AQoNXtpLsnoIiSjZ/77H+eaPFdpbWfinwbLYwww3v24xARoFJg8s+ZwOwGPpQAsEOoeMta1uRdbv9PsdOujZW0dk4QmRVBkdyR83JwB0wDWx4J1m61nw+r6gUa/tZ5bS5ZBhWkjbaWA9xg/jWb4DdLW78W2UrbJo9bnuGRuCI5ArIx9ARn8qd8NP3uh6jfrzFf6tdXMTdmRnwCPb5TQB1r/8AH5D7I/8ANaB/x+n/AK5j+Zpk8ayXcIbd9x/usR/d9KaLZDdld0uPL/56t6n3oAuUVB9ki/vS/wDf5/8AGj7LEP4pf+/z/wCNAE9FV/ssX96b/v8AP/jR9mizjdL/AN/n/wAaALFIelQfZYv70v8A3+f/ABo+yxf3pf8Av8/+NABH/wAfNx9R/IVVu9Ys7XUbXT5ZcXNzny1A9Bk/yqSOGMzTZaThhjMreg964jxV4TW81WGe1MlvGVeW5vJpSyRgY6Ang98cfUdQm0jGtKaheCuat54+0mzv5LcH7TEsaFZLZw+9mbGwDoeMHINV/C/iW68R2E9mJJ4dQRXP2s24MSfN8oxnk47H3rlrXTYrC/WO+tLeIeTsS4Ub1ZccSqevXBz+GB0GnZw3mj6xJai88m6O1orRRtW+47OfugZxn61nQxkasnThB3W7fQ0eHqxpqvUmtdlbv5+RreF9J1y11bUJbrUWEP2tjIj2oX7Sdv31PYZxwM9K5fxa2t3GtutylvbOtmP3SShgylyMjIHzfTt+Na+l+KDb6j5d9I/2e6mJ3y3TKsAwTge3GKwtY1e3uNYv/ILy4fEDId6sMDq3bue/9ampjZTp+0pwUr6a7K3U0WEhRqezqzcWtdLu9+hTurP7VaJDKwVlwS2OPf8AA/4U3zbSGNY9PFy7jcXSSTdEucfdHrxz+FTQadd6lC0rIdsSF5EU8DA61nS3n2fMVuFHHJIxtPtXiQqVIUZUnLmTd7dL+u56eMrYelJYhrlaVr/afbQm8vzWAupFJ67TwoqncXbH5EG1McHuR61EA0rE4kZQAznGT7mukv8AQ4/7EsruO8M91cFVt7VSXMiYP5EflxSjGU3yyen4I8TEY2riKUpUfdXm9Wc1GPMdVLKrEgBmzge5PoOtdd4ZsWnv5NGkEd1p0rlwwUAMBn51bG7PTAz7d6wdO0e7vbkRWkLyXKEO8a8NGN2MnPTmtzS/C+sP4qubN7xI1tfLM7RTyKTG/OE75wD14zXbhcJ7b3m+W3fqcWEjGi+epHmb0X/BOp8TeHIY7R76wQR7MeZGq5BXgfKB09Sap+H2g0qzk1S+8tmdttqnlYkJ7nPcdPyrR8T6rBFpxXTrxnui+3akpJwOCOvFcfZFfELX9nca29vqcNoZhO8ZljhGcAsT8uR/dz2z2pyowWK5qcVt8j2KmPl9X9ipXk39yM67u9Q1J7iK3uf9GjdS7SlnW1Dk8kDJI4P5YyK9C0rwFoNra2uwSXKoTKzySblnlJBMjDuTgD0xgdhWjo2jacmljyUjdLlQ0sifKJjjG4hcA8d8elaq2MSIFTzFULtAErAAenWu2lRUd9bnBSw6jfm1ueaeIrCz8Mai7NpkE+nXYKr+8ZXjGBlQc8cgEHHHSsHT77V5tTW5hMk92ImVHKhiihSMgdN2BxnrXp+peDLDVNTW9uZLmTGP3LSHZgD8xzz1qTRvClpojTfZ5rlopMfumk4UgnnjHY4/CsXQm56OyOd4Wo6mjsifw39jGhW32GWSWDB+eQkuWyd27PfOa2M9KrJZRIMKJAM5wJW/xp/2SP1l/wC/z/412pWVj0YqysPn/wCPeT/dP8qIf9TH/uioZrWMQSHdL90/8tX/AMaIrWMxJkzcqP8Als3+NMZaoqD7JH/el/7/AD/40fZI/wC9L/3+f/GgCeioPskf96X/AL/P/jR9kj/vS/8Af5/8aAC7/wCPf/gaf+hCp6p3FuiRBgZMh16yMR94diauUAFFFFAAelc3rlzA3iPQ9Il0+1uReeezPcIG8tUUE7Qe5JUfnXRnpXMeKrKw1C60q2k1Caw1dpXbTriBSzqwXL5GMFNvDA4HPWgBNZ+x+HJtLu7LSdPVJ7+K1mZYQjoJPlDKQOoYj8M1044rhG0Wey1fSbjxb4mn1NRdqljEtmsEQnwdpfbnLYzjOBmu75x70ALuB70FgOtebeKNZstT8Yy6FqNxdjTNPt1ee0tA5kvJpBlVKp8xRV5PTkjNWJ9a0fQ/hfqeq+FDthXKoCXJimYrHgq5ypBIO3/GgD0HcPWjNea63oEHgfSrDX7Ca6F7azwLfyvOzfa43YK4cEkdWyMdDXpXagCG0/1J/wB9/wD0I1PVW2ljWIhpEB3vwWH941N58P8Az1T/AL6FAElRT3MFvHvnmjiTONzsFGfqaXz4f+eqf99Cql/a2GpW/kXaQTR5ztkwRn1xTVr6id0tDO0jxXpmrRWO2QRXN55nlwH5m+TOc46cDPNO8Ra0ukxQAwtIZHBwO20g/wCfrXJ2/wAO4bK+09odRkKjzPtU8U3lMOPk2gdOuDyeldrcCzh02JJpo3jg2fvJnB4BGWJPsMmjFQTjahKz9CcJOSlevHQndI9S03YysI7iLn1AYVn6J4ei0dpGaQTzMQQ7JgqBnp9cnmrd7rFjpth9rklDQ8AeUN+c9CAueK5fw/8AEC11G5a1v3gtyFd/PdxGgIfAXB77SD1qFhXK1ZrWOlypYrkvSv8AFqXPHV/o9vpTR6isM1ztMltBIDy3TPH1rO0S40HQfCUt5ZXVt9qWIwtOwZRJKFLBcHnv0FJ4n8WaTeaXqmnASySbXjjkhw6khQwbg8Lk81zUnie4vLi3W8sLdYBZtF5VxIUjkJwN/I69PXr14r0KdF8lrP7zgq1o+0vf8PzLHhXxTfXXiuV5J7VJNQliEkflMd4UFflxnBwOSfUVv6h4j1KC6ufD8r2zXc7FYrsttRI2z94Z4YDoO/H449vf6X4SvBPI1pfyHCxrb4YoDk784ODjjAPesa3mu/EthPZ26xQsjG8uGyR5h56uxJz8w4rlkp14e15eRLv5BUi6UlShPmb7efQ04n8MaNqV3fiVrlY1iks41kkUs6j5s54684Ncxc2327UHurCNooWIdVMm7y++Nx64/wA9Kuw6AkgJkuCtuYiVZSPvggDI646+9alzYrYWEcioDBtCgL1YgDJx9R+prjr5z7GkpUHzTe/kj0sPlHtZ/vVywX4szorGJpU+0j94oyy7lIB7nIOCT1/GnzX0dsDbQ28bSk5DMB0/x9qbptkmoapDFqEs9vFNkRGMA4c/dBPbt2/KtfS/BF/dLdpf3KWzgxskh2uGJB3AYbtgV5lSlXxt8Rz3X8ty3ilQpqjQp8tuttX/AMEzrV31C7itrFGIkKIXlUL8xwDxz05rR1fwzMq3TRuJ4o5mYTrcbvKj4CqVH8WSTn0pnhsY8U29naC3s3t1Md1Ip3rKq9SA2cEgYyCOv59lpttpHh/UmtZdQR7i7HyRsMDaPU9M/XrSw9F04yjTitXa/ZW8yZYmOKpXxU3pbTZM5bQDbHSdTsJLVxdx25mR4fmMiKQcbc+uPrz6Vm6rcafDpenz6ZfvNduS7qFCmM9mODlSD05IPNbNxoOg3OtXZ0y6tJBDabRBLdSf64tkHdnlccEA9TXJ6gp0/UJxb30d6zoRPOseRubO4Akdc/xD1+orathaVGMZR1lbV/P1PPxeOrezdGPwPzuR/wBpziOZJdsk80iy/aWP7xTjB578cEHIHbrTG3LI8s0mwDJmkJAXHXg9M4BOM9uOalutKuNNtLe6mjRo5sFTv5YdwRnIB4OajubptSmhjubiWCxhkBWGN2CLgEDC55OCep9M8Vja7tI8h3vaZtaraRajG89vd7/D+mW6Nb2cAKs88ikF7hiMHbk4Vc9e3NM0Xw3JPrtpb6qs8EN0pkjLcedjnBOfTPv0q/Z395olvGbQLc2soIitpE+adCWJdlHIZcDk8MpHpXc+GZkudJjurjUI7yaRzKzHGImI+4o6gD8/zrsjBVZJS3R3xpxryjzaNGc3hTUvsQ0f7er6R5mSWGZvL4ITOMdc8+g9+Njw9pV1o8c1m8qS2SHNqcYdQckq3rj175Na3nxf89U/76FHnxf89U/76FdqpxTuj0I0YxfMiWio/Ph/56p/30KPPh/56p/30Ks1GJ/x+yf9c1/m1T1USaL7ZIfMTHlr/EPVqn8+H/nqn/fQoAkoqPz4f+eqf99Cjz4f+eqf99CgCSg9Kj8+H/nqn/fQoM8OP9an/fQoAYn/AB/S/wDXNP5tU9V4mV72YqwI8tOh92qxQAUUUUABrlNDXT9fm1a5uNG08eRqEtqjGFS8mwgFmyOpbP4Yrqj0rhf7FmvNX1W68I+JpNNY3Jjv4GtBPEZwoyVD4AbBGcZGRQBsaHeqniLWtEWztrdLLyZYmt02h0kU43D+8CrfUY96l8bX02m+CdZu7dis0dq+xl6qxGAR7jOaqeEbKysLvVYBqE9/rCyp/aFzOm1nJTKYHQJgnAGR1rb1nTY9Z0W902ViiXUDwl16ruGMj3HX8KAPO7TUfEHhHwPpWr+Xpq6JbwQ+fYxxsJVifH7wSZAL5bcRtxyeT1rdsH+0/FnVnYg+RpcEcJ7BWYsT+J/lWTc2XijW/C9v4Ou9Ha1O2OC71MToY/KQj54xncWYL0IGCa2tV0/UtF8WR+IdK09r+3lsxZ3dpE6q42tuSRdxAPUqRnpg+tADNBPlfE/xZCgwkkFnMw9G2Mv6gDP0FdXeEhIyoyfMXjOO9c74T0rUE1LWNe1W3Ftd6pKm223hzDDGu1AxHG48k4JHNdHc9Iv+uq0ARXKvdW0kE9qGjkUo6mQYIPFc14n0SW60WPTtO0mJS0ihSrKoiAHX8gB+Vdgc815t4y1zVdNnvtPS4guba6jYsrR/PAjDbtyMYzngn171lVUWrSMMRKEY3mri6H4aexFxFe2x/dRyb7i0mB8xWHzRuueSOD+VZGl6RJOlneSwTz6FJcnEEbZYHJUFgO2eD/nOVBLfuvkFZI1ZYkktYcxyXIxxhcHJ2g5OMevJ59D8EaCbHSILySe682ZG/cmQ7FDNkEKcYOMVzUqFKeifw7HFSn7X93GNkY+uaJK/9p6TpV1LChg+0yacIRIrHtsJPHI6D/6wytMceDLu3Wa3eS9kmRZW+zN8kTLllU/xN93j2NUpdPbT/Ec1iJgJYpMJdNM/ygfMO/3jjhfeu+jm0zxhaiDUIGUQShlxNt3NggYIxz14ruo5jh42oLb0tc3hhJ1E6lNfD9w+58Y2Z8NPqcahRLG5iillVXfBKnC9evpWJp2va7d+JzLFbWsiPbKvlJeBkA3ZJHq3t7VLH4E0zT/C8y6j5CXpjdPtAYuq5bI2hiADgAZwD15rVTwhZyqk9jezWx2Dy3tiAF9dvpn0+tXVqxhJQpJSu9b9jopU5zjz1bqy0t3Ny01Bb+1W5tVWWB87XWTgkEg4/EGqEc+m6NNdDfHHJczmaUNOCd5xk+oHFZXhnw5q2laDtN/Pb3ZjdVt3KyRQkvkMAOMkf+hGuZsdA1bU9RunRts8mZJZLmIqhfI4+UDnnOPaufE1PZPkpat7djfDw9qvaVNEt/8AgdzU1TxBpfim5k0uWd47aMxywywOVZpOQQSV6DI64rZ8IaHd6HpsRme4kmaPDQtchol5z8gxx2/OuT0vwpq15Pqt03kKRdSxtGFZSzDnKZ42ncfyFdpp1zf6T4YnuNQt2drdSUiBG4oAP/r1rOvUVb6tGPutJp+ZnCjTdH6zJ+8nZry6Fbxb4puNDtmWK2nWfCOJvL3RKC2CC3TOAf0rPsvHsV7dCZ7JBCs5RZnJDIhA5789cjispfGK6lp1+NTsnuLOW53w+Y+wIo2sFG3kkcH8a5KXUJZEmTaWeeVmZyxckt6emB657+tVXuoOnBWmmr+hGHs5qpPWDTt69Ds/E2o6brFrbawkSsGGxJHkOVALfw9+Qeorjri/u72MWVrGyQLK8jEOQHLBRz/3z+pptnpYh2yXUihHIUr1wM+vr/jW6lv8h+zwuY0XLEL93PTNeXisyp4au6mH992suyPUwuXVcTRVPEe4r3fdmXY6IAT5qtMwwcKpIGB1reSwCaKt5HIUlEohjjMYYSseQqg9TVW4sJ200TgskE2VEitg5HY81Jrl9JqVvHcS3bRSrcpts1bbAFAPzAdmyevv7Vw4fErEVlWxcrtapNafLodeKpLBUHDCxtHZtb+Zk6XpmoXkdwDpc08yyMJC0WAMDJGezYzge4+lQtI+o3It7S0TZknyIwcOVBG4KSedvGAeSO5qM6nfwPOsc8kDzL5U6AlQfb6f/XrpfDehadJor6rf6k0cRbb5UJw6sv3eezDrgdAeuM10zmsRJtKye6vp8j4+tVlVmqUXdRvq9C5qPhibT9KS+CF92DMu0KEXBO4jrxwMe9Zcniy9GhRaRao0ZjyvmDlnGSflwAB/9atO98Rrr3iGw0R7lrf7UW8mJULthRkswH06nAqa78CWWleGbi51G8eW7UiR7mOPHttVecAk+o7dhVrDuLcqOie/yO7FYmtiKSilor6/oZ/hC2vLe4a/0u3S7Xyik/mlY98mN2yMnkY4BzweTxxihqVteaHeXEkLxGd4SLtI4/3cHmHhFz07YA569qhmtb/w9c2lzdxyQTGPfaldowVwCHXHpjPPGe/Suq8IwR+Ir+fUNV3yXsTB1ikY7QpGVIQjgAZwcnqT70oLmXJszzYR9ovZLRlDwboU09uk02nW15YXbZ3lwHiZCeT7EgjA/GvSYvNhjWOO1CooAChgAPpUOmaPbaSZ/soZY5pPM8stlUJHO0dgTzWjXoUqagrHp0KKpRt1IPMn/wCfc/8AfYo8yf8A59z/AN9ip6K1NyDzJ/8An3/8fFRRSTCSfEGf3gz84/urVyoYf9bcf9dB/wCgrQAnmT/8+5/77FHmT/8APuf++xU9FAEHmT/8+5/77FNeSfy2/wBHxwf4xVmmyf6t/oaAGW3/AB7Rf7g/lUtRW/8Ax7Rf7g/lUtABRRRQAUUUUAFUtXsF1bRb7TmYqt1A8JbHQMpGf1q6a4u9+Idm1zLZ6Fpuo61dxuY2FrCViRh2aRhgfXmgCrZa74p0nToNIk8I3Nze28awR3EE6C3l2gAOWJBXPBII9q6Lwno82heG7XT7mRZLld8krJ93e7lyB7Atj8K497nx3r3iGLSLu7tfDsUto11tswLibYrqu0ueAfmHK13eiaYNG0mCwF1c3XlbiZ7qTfI5LFiSe/J49sUAWbyzt7+zmtLqFJreZSkkbjIYH1rI0fwdo2h3Zu7OCU3Gzy0knuJJjGn91d7HaPpW/RQBz+seDNG1u9+13cMy3DL5crwXDxGVP7r7CNw571sWdnBYWsNraxLDbwqEjjQYCirFFAED/wDH5D/uP/NaB/x+n/rmP5mh/wDj8h/3H/mtA/4/T/1zH8zQBPnFUb/VbPToZZLiYL5SeYyDl9ucZ29cZq6eleZeIfCWu6xr3lyXxmBtWMc32fy0Ubv9WSO/Oc+1aUoRk/edkY1ZyivdV2ekRTJPCkiZKuoZcj1rC8SwavN9nOmu4Vcl1jcKc/j2q5oeix6JYeREXZ2wzl5Cw3YA4z0HFU9IbXTrNyL5T9kw23OMDkYx36VxYtRf7pXs+q6HZhXJL2ul10fUytP0TWru+t7+9n8ry3BIY/MVHsB3+tRa3qOr6NrjSeeTC4/dqx+Qrjpj1/Ku67VXP2a65zFK0bY4w21v6GuWWAtT5KcmpXve51Rx16nPUimrWtY85XX9Z1nVjaRtLG3mKE8pHVclRnJHTnJ5p/iiWc6vN5wuFXHlZkj2q/AB2/3lPP68VZ1d38IzX93A9xNcSIfJbyCyKSwPztn3rA8U6tpms38V6trMXCLGyybkYsOfXpz2xzVywM6tH2k7rVLTXbS/TciONhSrezg4vRvXTfp12H2N3qN1qdiJNMnvrcxM1vFJKIwyrjox5wp7E96o6xMTqpMk9xNewsRGy3BcpzkANk/pUtql7em2ht7eO0s4iVjhjUnarEEgEncc57nrVK9uZNP1JYPKRbhSARI4O3r94j+XWt3GnOUY4S/NFa20v095nPerCMpYqyjLa+tv8KIG02eZgLqUGONOVDE5x3JPerVpA093BbW6BEd/LEzkKoxjOCcZI4OBz7Vp3DRGRCUt2QopPkqwR8jOcNzznoRS6prUcumQ2T21rBbxuCIYFwCccZz1xyPfNePVxntpypyWi2S2O6rgXRpRrUpJJ7ylv6X/AMiqmr3U1hFpzXTwQQFyZY9y/aCSMK3bjnr2qumh3WpXLvmG3Q5WN5jgTOBwB7n+Wan0nVNJsb2Ey6bBdwkfOShLoBggr2OME/19b2tXN1q9zLqlrcXH9iWtyNzLtwhwAWVT7Hv61vh6NOq41KktEtmtTwsXWp+znRk+dtp3Tv0/rUxrTTYLPU0i1K4ktk8tneSCVXJ+UFQMAjJPb6VWg1i+s5i0N3InyeXvABZUHZf7vTGBjoO1PXSrm8llmsbK6ltEdVJ2jec8DoO/oOBn0ru4tDW68E2lpZ6fDNLPL8000XlGLrlyM5LDpx9a15ZVNFpZdtzzYRnP3YaJGx4ZOgRhINMlSa7EAeSYrmRgT/E3rn+E81znj7UAmpxiwikju4Y9s11GdpKEH5Pf19qbYyXegSXGh6WI7kyMCl1GuH6cqfUg9+gFUWgguPF1j4egik1O8SQTasY3CxWsfYM2OXJ7dfp1rpbqVIci0Z1vnq0/ZxVu5B4NuLa/1z+yjZXPNu5+0w422xA+82eMnI29TnnGKrXmhR2WkTNY3Lra20y2/wC/mMst5MC2ZGAOCck4HpmrPiXR4/DmqBNKM9pC0RcSuxAJ6EK/U/L2Y/n0OVp11d3F1bx2EcUdyE2RAKoBIBIJ3cb+eCMHr+PNKfL+76nLKpyx9lLfudx4Ku5b3W7iTUppU1CKLy47YqURI+DwO5yO/wD+rv8AvXOeELfTxpFtcWcGJQjRvLKmJSc5YE9fvV0dehRTUFc9TDxcaaQtFFFam4UUUUARz/8AHvJ/un+VLF/qY/8AdH8qSf8A495P90/ypYv9TH/uj+VAD6KKKACiiigCC7/49/8Agaf+hCp6gu/+Pf8A4Gn/AKEKnoAKKKKACuV8UWGpDUdJ17SbZL2401pVe0ZwhljkUBtrHgMNoIzwea2tY1rT9B017/UrgQWyEAuVLcnoAACTXKN4y17Wvk8M+FrpoyOL3Uz9nix/eCn5mH0oAbNNrfi+9022k0G40mwtLuO7nnvHXe5jO5URRnOSBk+ma7vHSvO/C+ma/wCJ4rTXdb8SXColwzJp9gvkw7opSuGPV1JXoeeetehjr05oA4XT1tdK+LmuyXjJFJqdnBLaSO2AyoCkiqT34U49AK5vULKXWPDXxCuNNTzLKa8jltynIkaHY0pT1BKnkda9R1TRdN1qFYtTsILuNDlVmjDbT7elWLazhsrVLW0giggjG1I4lCqo9AB0oA4Txvq1l4i8IabYWFxHPJrN1bpEsbbm27w7EjtgLznpXoOeazLPw1ounX0l9ZaVZ290+czRQqrc9eccVqUAV7aNGiJKKTvfqP8AaNS+TH/zzT/vkUy0/wBSf99//QjU9AEfkx/880/75FBhj/55p/3yKfmonuoEmjheZFlkzsRmwzY64HehgOMMeP8AVr+Arz/4jyawLeOCzt/KsHIWWZXX96zcBduN3Ht1zXcalqNrpdi93duUhQqCQpPJIA4Hua5DxrrsltJbWZ064a2+1xebI0SmOZMElFJP3v8ADtW+GUlUTSObEuLptNi+A725aCbT9VlzPbxwqkTxxgIpBwoK/eOAM55BqVPA9pb6mHxCdNXLmGRd34cjp15rmrWTQ7XUpdQs9LDzSGNraGRSDbuvGScndkgH+tdANK1/xCRJqM5tLbORHjBx/u+v1/XpRisPCpJSlolZ/MMLiJ04uMdW9NuhmXS+F9HvbyNbeK/lvGYRJHHtWFSuGXfnPPPSsfXNH1y9svtq2TR2SIkccJbbsUDA+8ckcDk+tDaXaNcvJHAsBh3b2mYnOcADGMA9ew61uXGtStoEFgxDoVC7s8tg59O3H5V5uIzugn7l5Na3ffY9HDZNWesrRTdrLtuYf/CMWMcNtNLfQTQl1aZEJ8wqPvKB068fj3pbHw4b+7m/si1cRxLuHmSEhevGehJNSxWolu7eOO5jxNHndJ8mw7iNpPPUDOeK6HQvE1nbai1mZrCGzJYLIrBdxHGevJOK82GIr4l+ynL3G1trq/ysdtbCYehH2kUuddXZaLscxGjITC9vLG/Ty3Qgq3c47966+z061n8LiS9s3BtkZgoOG9/zo1fwldXfiSzvLfUtSWF3kM7pchTACvyiMY4BJx34rdgubSyvo9KluGadoxs84ktKFGCS3QnvXTTyqFBuTmnzK1rbdjCeazqQUZKzjbW9rnmcVvfR20mpwyx+TZFJGMmP3h3cgZ4PY9B6da1LHxRqNys82oQ+bYzq0QjRQhXHXDDnIB9fyrastB03UfEFxeLdR3MFtOQYApCiTHQ9jjP6VlazN/Y3iHUYYTbXBvyjtJImRZsWwGYYK/xcEgH1z35oUakafNF8rXbZ+djnxOLpRxHtZ+9B9L+Rs6tY6LBoNhHHHHZuwH2GYgqYpCu4ZYdMnrnrXFy6le+LZLHTJltklgBL3IALPgHkY68D7o6kZ6dN3WPEM1z4cGjG2MuoufJnSUZJAGfMGBgggZz29+tc+BaI1tb2gSTZOrW12qbJWJ/5ZsB1IPf0FdVWd3aL00PCxFRSnam9P60OckVo28tv4W4OMf59eeldR4Rh00pqF/q5t49Os4d1w02cZPTHYYwRjqSRitq58G6HpGm3Fxr2rR24YFlcEIqYGWwDneeue5GBXJxXdpqsNvqOraSk1i20Wls8giecIMxzyRjopw3BGMDPpmYUHCXNNaGcMPKnLnmtDtJLC18eeGbDVLWG70u2gEn2aCZAoljyNrbQcgEKMfU8Hg1mr8NtVJkkFxaxSLt8pBkq3144wPY5ru9Pa7Ph/feTQzStGXzAoVMHkKvsOmfp9a52/wDHf2KeO48seRzDNZSAJPFIMnPutdVWFF2lM7a9Og7TmdJpmnssMVxf2lqNQCeW8kQzwCcYOOOOcVdt9PtLXeILaKPzGLvtUDLHqap6Dc3t9py3V/5W+UmSNIuQiH7oz3PvWsO3FdELWujrgo8qaQzyY/8Anmn/AHyKPJj/AOeaf98ipKKssj8mP/nmn/fIo8mP/nmn/fIqSigCqsUf2xx5a/6tf4R6tU3kx/8APNP++RTE/wCP2T/rmv8ANqnoAj8mP/nmn/fIo8mP/nmn/fIqSigCPyY/+eaf98ijyY/+eaf98ipKD0oArxKq3swVQPkToPdqsVAn/H9L/wBc0/m1T0AFFFFACHp1rg4ZNa8H3+pQx6Dc6rpt7eSXkM1m670aQ5ZHQnoDn5hnrWvrvjjS9CvRp5S7vdSKB1srOBpJCp6HjgfnXM6x4j8dXEdq1vpVroNpd3UVqkt2wmuP3jBQwQcDGehoA6LwrY6nJqmq6/qtotlPqPlJHaCQO0UcYYDew4LEseB04FdVXPeG/DtxorXM19rd9qt3cbd8lyQEXbuwEToo+Y5we1dDQAnOaWiigBMVBddI/wDrqtWO1V7npF/11WgCc1nDRbFTdkW0ebskzk8+ZxjnPbHatHvQelJpPcmUVJWZx6eCPKiM8d9IuoxEC1nHSFBwse0k5XHXOfx79XGhWJVYgsByQMZPepCcCue07Xri916axe12Rpuw3ORg45+tYOdOlJLZsulhrpuC2INa8PQxeHbiz06zV/Mk3vn5mOTksCe4rPm0I3Giw3Vy4tZraNmmjiQZPcdP4sY5Pf0xXVapqUOlWTXdwJDCrAMUXdsBP3jjsOprmdBhtbTXprVbw3xnhMh8sFlVTyC56ZPb/wCuK48Th4yqpNXT03sl5nVhsTCjBpNRktdtXfoXDPa+K9ElVfNiNu28HjqAcH9a14o4dE0bapYw20ZJ7k9zT2gtbO0ngt0SPMbMUUcnipL25tLaxklvZY47cAB2lOFGeOSa66dCcVeXx2tf8jKpXi3aOkL3t+ZV0fWoNYtZJY0aPYcMr9q5ubx9FbXt1G1szwsv+iOowZGwBtb6nkH0NQR+PNJsIY1gso0SSScOsUy5XZ0bBxnd25/PpXHeJbzTri+nk0eIzRzIGbcpzHk5IQe/v61bwuMbjBNX63OPEYzDcsnTbv0O20DxFeW1/Nba3NtZtxYuwIjIzx+PtxxxWD4y8Xx6zJDYaTPcKisyyMjFBICMYI7iufGl6kLS3ka4y8qcQqzF4055bI+XOOF5NSW+nQRlZQ5aXu5OfyrnWLo5dFwqS5562t0OinQr5hJKMFCNle/X0KUWkXC+XlxtyTsOTzj+Zxj8q1YoYw6YAijXPKjJ/mO9QvDNNOJYJ8eSVKJjOWHPPrUV208yvNDv3um+62japkyc7fYA4PvmuDFYmpiqanKqnJdLWsn+Z2qNPLp8sKT5X13vb8kS3luk80BN40LjJCquCCO4+bnr7U2WTUIrY263EVzbx3CzPGVVG3YOPfpnjPeupg8PXEumW9nBbQvLcMs0V4hYLbR9QecMWPp0p40PyLP+zZoobfUYpWMM27m/U5YgHOdw465x071dJYmhS5XaSS2suvnv+JjVxFCviXZON2veu/y2/wCAaXhzRdOOjvqF5DBc3Chg/lSCYIF52jb37+vOK80uGsvssC26yC5Vm85usbfMSpHccHGD2Arp7HxBeeGHmtV8tvNyRbNH5bRSHHzuccgg+vQVypjnvZ5ZMeZM7FyFXlyT1AHqfT8KupUi6ap042XY8TM67nVcOa9m7/5l7TLe1ltmBWRLpCJI5QN6sP7jKe3Ug85HXpitLztL0jU7GG8gkaS/cFLK2JLCIH5pWyfkjAzk9Tg+hIt3N5DoIsE+xHVtf1AD7DpSnbhcfflOPlUD14444Bx3kOg6dp19qeuW9gH1K9QfaGDl2fauAilug46DGT16Cumjh7LmkPDYS0eeRnaFonh7w5NPrEV151xqsuFu7hwzFWPyRIR0UY6D056cZGr65fWWpX2l29+JreSRd12ykmy3NypIGD046Y6GsS11BNG1db6e1RnEsgOnMSps8kHcM5ABHPTHuOg0tRsv7dvrybQbTbaRDFw6uVS9fcDtGOPXn6e1EqrmrLcJVpTjaO6Otk0W012LTppL03llCu4JkMs7jADsR1xzx0q7ouirowmjild4GbMSOBmJf7oPUjJPXpmptHKNpVo0Nq9rH5YxC67Sg9MVfGa6o043Uup2Rpx0lbUWiiitDUKKKKACoYf9bcf9dB/6CtTVDD/rbj/roP8A0FaAJqKKKACmyf6t/oadTZP9W/0NADLf/j2i/wBwfyqWorf/AI9ov9wfyqWgAooooAKKKKAEPSuXvvAOjXNzJeWQudJvpDlrnTZjCzH1IHyt+IrqaTIFAHnr6F4z0bXk1mK5tPEIjtmthFMBbTFGZWOCBtJyo5PWu00e9m1HTYbu4sZrGZ9we3mILIQxXBI4I4z9CKulhjr1oHWgB1FFFABRRRQBA/8Ax+Q/7j/zWgf8fp/65j+Zof8A4/If9x/5rQP+P0/9cx/M0AT0nQ+1LSHpQAE0E8VSfVLeLVINOYt580bSLgcYHWqGs+K9L0aNhNMJJOnlx88+hPQVSg27JESnFK7ZoajdRWdhNcXHmeUgw3lqWbB44A5Neb6J4q0vRl1FvMuGkMpiiXyyBtX7pY9ic9O1KfFOpeJEZRIlpDyDGhOSM9z3/QVBLoUtnpV1eW9uGhRWkkkk4Of9n/JrKpHDRmlUTlNbJP8AMunLEyg3SajB7trt2MqTWtU1aFree6kNrM670OcMAe3f0/wqW9GmW1skjuq3DhdpcEsU3YPt2JrfTSZbPQ2uLyeC2uhcx3FvJGXkxxkAjgfzFZdutzH4dgjuNRgePz3JtVUK8eXY7mxzjvz68Vz1akYU37R6R+yna/qzanByqL2a1l9trTTsjttPhtm0P7R4Wa0llZtqzXO/acHDdORXmqWwdJZbnYtwzuzSjsSTyM84wfqa9D1jU4Z/Bf2i31eGPcojMrLkStjBQjG4Z56YI+lcRYG1uS09877NwU2iAeZ6lmz2549fwrizCtKNKNOh7sXrf9NC8JVoyrv2vvTtZdu9xNAtJta1COCS7W2ARmXKkGbGegxg4yOOuDWXqUObsSxksTtVGRGGGGcqQRnOe1ew6Uul6jptve2lnFHHGzmFWjVTGykqemcdK5e68LXVzdyahcNZvqc+Wew3BVaL7uNw5Bx/FVwwSoq8d2efi6tedL2UnzK5w+laJdaxfva27whl5LNJww3YO3HX1yKmntdQsxcWaM8kNrLmUId0SOTgNkA8nGOec9q6XxJp8I1Z5dEieJ7CH/S5I22KgCjhD/e256ccDvWxGfDR8Hwq87W1hKwfaCQ8mOobHLZAwfpweAaI0Er67Hlww/KnG+qMjwz4gsLDUVEJlttKmUJmc7h5wxnGOFznk9Dj8tG58Uy+Irs6Zo6OitnLtwXQHBPsv69uK4mXT5766Y2NvcLZSSqkW7LbMkhN5GT3Pqe3PFbgsvEeh3cvhnQEt/t14ElOqvC223h5UscjazDaAFz1bnrWlF1JPkWxrh5VZPkS07l972bS9eh8NeGLaK81olJdRu50Pk2cJIPzYPVh0UHPeu5t9L0/TReSW1rBbLdO01y0ahPMcjlmI7471PBCIghZlkmKgSShQpkIGMnH4/SrBIYYrvSSVkepFKKsjxPWlaaSCOB5n0wO409phzglQec52ZPB+ldT4P8ADlncafqNnqMTm7EwWeNiQY8cqwPrySGHatrxiIorG3eOWSPUElH2JIhuLuexXoRjrn/61M8ISvcXt/LqMkg1jcFmgc4WNATt2DOMe/8Ajk8caSVa7PPhh1Cvrrc6SzQLbR7eBgjH41aqC0/49U/H+dT13bHopW0QUUUUDCiiigCOf/j3k/3T/Kli/wBTH/uj+VJP/wAe8n+6f5UsX+pj/wB0fyoAfRRRQAUUUUAQXf8Ax7/8DT/0IVPUF3/x7/8AA0/9CFT0AFFFFAFTUNNs9Vsns7+1iubd/vRSqGU1y7eBZdN+fwzr1/pWOls7fabf6BHJ2/UGuyyBRkUAcH4d/wCEo8Kx2uj3+jw31k1w3+n2EwGzzJCxLRsAcAsc49K7scmg/wCeaB1oAdRRRQAUdqKD0oAgtP8AUn/ff/0I1MelQ2h/cn/ro/8A6EaZeahaWEPm3VwkS9tx5P0Hei19BNpK5OSMfSvM/E8xsvEqbtTd3MuZJNm37GhII/38Ak4HPHNbl14wuLyb7Notk8kh6O6549QO31NVx4Ju9YnW8169k39NiMC2PTOMKPYfnTqYaLinVsTTxLjJ+yuc54w1vTtXit1tZrm4mEaoZfMMauFOc7PUnPNOg0K71XQI531OORBIDFG1yJD5np1wCA31+ldB4s8P6VZ6PGINPhHzKhk/jC9evX+fWqF3a2U1jZRWdukEo+aVIUwm84GR78DmufF5lGlGVKne6/Xt/wAMb4TLJVZRqVNn+nf/AIcfp1nrWhCcxW1qu1eXuzgH3DDnP5iotD17VbDQI4JxEyRqcSDLSct3zx3xWpqllfQ6PLfXNwqyoo8qOQ8E8cH8KzNLuJfFyR2KWVvbi03SSPvZfmIIXAByeeSTkYHSvPp1q7j7Dmavqm9X/wAMdNaWGpVI1HG72stF/wAFlO51IT2dy1wjy3bBtnOACB8ufUdMitzSdIXX9BtEktLmx8lQwkfBE28ZYj+lUNa8MXGk6Jb3SKLiSJf9MCnj3ZRgcfh0ra8Ka1dy6K7vas9jaRbY5VHzybc/Kqgc4AAqMPh1GcqGIgm39r0NMVjoe5KhUaa+z8tzE8S6fLa6osaWpjjZVSKRX3GXAAzjHB68VH4O0nT4NdY3nM3lmNIJosgnO4nnoeP1rsv+Ek0i5FiY3+0SXTYhRE3OvqSO2O/pUsulWFtfSaw0T+aimQ7cnoDyFHU10Qw9SlVc8PK0W9V0t/mZvFUK9FQqq7S0fn/kF7r9np+oxWcqyGSQAkqAQuTgZ/KsPxRCNUmWz8y2hthKvm3ruMxPgYRRnIY5HXtWjcaj4fuxYag5jmkndUtmCksT9B6d89K801i5vbnVr+zhtik8jFbyK3BKzMrE7guMjpkn3zwCc3UqSipOWqe1tzysbXpKmlFNvqaV9qsUfhi3t4Li3iudPu8QGEfNOB/GB1XOeScg1g/2kZft1zO7C8uDn5V2q+SQwIHqCeOvGc562I9Mu7GG31K4hXawE0Cyr8jqpxtYdQcYI7EZqDVbmXW9Sn1COBUD/N5UYyyKq5JOOfQkkD1rlqSk9zxpzk1vZ9jNVmYA+YwAXarckAc4X6fSuu8PwNpkk2uSNbrJaN/pVpOu14oiPvgnnOPbkcda2fDnhm30q1Oqale2M1kFEgwu9GAHyvk9CCWGB7d6zDfya+99r2twnTdElgNrp1kYwLu7IYESnuvIGE6c5PAyd6WHcVzSZ00MLKK55PUkk1bUvHGnzPcaHGml38gh0cTgiUgAl5pDn5VYdF7gfieh8MaMbS8vo9Ss3kvyPnu3y6ToSemehwACvoBXHWh1Sxv7RdRd47yCAPZG5+aNFGSQw/hyBjPVcemMdv4Z1688RXz3O6O3tIV2/ZuDIzEA7iey+nrWsJxnNOW50U6kKtRSe/Y2YdOt9K0iW1tI9kIDMEZiw55x9K58+Dbx7gapJfq+sb9zMyZhK4wU29cY4z1rrbz/AI85f901MK6pQjLc7J0oyVmUNL0q30mB4LVWSFnLhC2QueyjsKviilqlorItJJWQUUUUxhRRRQBAn/H7J/1zX+bVPUCf8fsn/XNf5tU9ABRRRQAUHpRQelAECf8AH9L/ANc0/m1T1An/AB/S/wDXNP5tU9ABSEZFLR0oAwta8IaLr0iz31mPtaDCXULtHKvph1IP55rmtU8GeJ44rddN8Rm/t7a5juo7XVVyxaNgwHnKNx6dxXoWRSEgjH9KAMTQNU1TUHuItV0WTTJ4NvJlWVJQc8qw9Mcg46itymjr706gAooooAO1V7npF/11WrHaq9z0i/66rQBY70GkzzRkYoARunpUZUDJxg/SifzfIk8kqJdp2b+m7tn2rhtHj8RReLtWlnfTlANubttjhSm0/wCryeuM5z3xVRgpJtvYznPlaVtyPxt4m1HT7qO1tIbiCAFklle2DJJlQflJznvxgciq3w9uvKvriG3lt1s5Hx5UsgE24KDkDHOe47c/jL43urS8NhOGSSz8tmSXdlGJIB46ZGPwya4k30VkxudLL/aNrCRhEv7kHjKE8gkZ59DXDCp7XEuMYOy69DnxOHnRSxMpJpvRdTt9VtfEEXidrp7+3VBZSbbhrfCKhb7hOcbq4/XfEN3rt4HuY0/1ZiWGJTwN2fmznJ4HIOKqTzzsU2MxhjG2Jd3yr3x7dyfXrT7W2m8z7TI2zLbv7xIP9K66+Op0afNdc2y9fzM6NOWIqpU7uPXrb7yF7JViUSttuHdiAsYO7Pdsdh2FamjTXOkXjNYXLxGbCbQB856Yx7nGKmnmLym0uAySxqAQ8IQgdQefrRfLptxBE72McUqJh5VdwXwMcjOO1eLWzOVZ8lWTjK1tFvr0PoKWWUaa9tRUZW7vb7jQNyJ9PkuZvtJuWb/RbpNriWQEfuyOp759Bmjw/oM1/rd1b3oW1mj+eSEY+6eQFGTkZ789e9SaTbP5tldXNnPJ4eNyWgtw2/yXOAGZcZK5zj685zz6Os9m140CTRfatuGUY346gevcnHvWkcHRqxXN/wAFs4aeKrzxEqi0t01svNHlV7ZNYalNZv5FvMGLyKkYKgHuApA9/YVtaX4Wl1HR1uI9QE18krRzIZg8URBJAXA4OCpPPepPEHhDUNV16Zrb5EKmU3EnqeiDHPb8BisHRvFl3okJsoYoP3UpaVCd/mDIUqpHQ9TnvgfjhCnyOca0dJdXvp5m+JzXknFp2UV52b06baam/wCI5ZdKvtNjtbsjWRAsfmEgI685354PI4zz1rkdQ1aX+zRp7xSx30d0808pbcXfoG6ZzxjjA475ra1nxFe+INIllTS4WtNqRyuxDFJeuR0IAyRz61x8sbJK0MjKWJyxX5uRnpn0yfTPet60kvdi9D5vF4hzqOUXuTRR3GpXQTcGlnkVd7t99ie5Nb8V7feB5IrEaXDNrd6He2GDIQoByWVTnGRnPHBxVfSY7/R4rXVNM09b69vTJb2cckb7EkA4fcBjb1ySR0IB6kd5PoGrXPg2C21G9W/1eOMmaVFESzZOWjyBwvAHG3O0Zx22oUPd9o9zTC4d8jqv4uhgaX4tRdSOo3ek2r6hJai3+1WsLB7mQbT5akjIUMx6n+76c9dqN7q1roSX6wW/2iIb7qHdnCY52n1HB/Dv343TNLsNb11vslpIqMpF0jqqCzbjaEwPvhlyD0x16mu5n0CO90u1sb25nuEhZWkJbHnFegb1GefwFb0XUknc6qLqzTueWzzSXsr3s1zMizl1+1hGLznAzEEDYUdMA/mBxXf+HdKvtN06QTMsNtLh47TJdrdi3TfxkYx68/TmV/A+lzalc3c0e+OZNqwABUj4ALDH8XHWtS2s5bDSEtZbl7kxkASuMEjdwD9BgUqNGUZOTDD4eUJuUzSA5zTqB0orrO0KKKKACiiigAqGH/W3H/XQf+grU1Qw/wCtuP8AroP/AEFaAJqKKKACmyf6t/oadTZP9W/0NADLf/j2i/3B/Kpait/+PaL/AHB/KpaACiiigAooooADyK4TW/Cc1lY6hqreLPEwSCOS5MMV4oGFBbavydOMV3dVtQuoLLTbq7uiFt4ImllJGcKoyeO/AoA4HRvCF7qXh2yv28c6+ZLi2WVnjuh5YJXJ4IJ4z6j8K3/h/dte+DbCWS8mvJMyq9xM+5pSsrKWz6ccegwM1w+h+GtJ1vWLizk0O90iyltFvorRdSYJKrsQC6LwmcZwDxXoXhC8t77wxYy21ktlHGGhNshyImRijLnvyDQBvUmR1yKgvr2306wnvbqQR28EbSSOeiqBk15v4Pu9TvfiRPe6hLMn9o6N9sS1ZvlhUzbYxjoDsVc+5NAHp+RjPalBB6V55p+kWnjrWdfvdWM0kNnfPp9nEszIIPLC7nXGPmLEnJ9BWz4B1G6vvDzw30zz3en3c1jJK5yZPLbAYnudu3J9c0AdG/8Ax+Q/7j/zWk/5fW/65j+ZokI+2Q8j7j/+y1mapr1hpV0wmlDSeWMRJyx59O3401FvRIlySV2zZyB14rI1bxJpukqwnmDyj/lmnJH17D8a5i51vWdcjP2UCxsuhlLbc+27ufZRms8aDDeAQWySXE+QTI3Cj/gPYe5P+FTWqU8Ovf1l2Q6MKlf4NI92ZN1pP9samdQigFpaybnctMzZYnOQc8/hxWotlZ2cCySmMJ90T3TBVJ9FzwOnTmus0zwvHbQx/a5PNZRwmflFU/FXiDRtPMGmTJaysZ0WWKSPPkxsDlxx1ANTGpisVaMvdj2W/wA2OVLDYW84+9Lu9vuMXw7qWgRHTklkiMlwkkjtJKoEGDkKfrnvXd3+owabprXZG+IAbQn8WemO1cvpfhbw/Jb6dqUctvcWcEciu8tuuLjJ+8xPcEcUeJPF+nWBt7OGJZ4BIqzIItyNFtzhDkLnlfpVyoR1p4bSVupEcRPSpiHeN+hozXulazo8lxesYbdn4L9VYDHGOvSvMLwSx6ldQWiyPHGGLlXEhKg8sCOnHUds816fb2ul+I9Ji8gNDbkJLGEwrLkHqORT7mTTvDT2KvZKLYqY2vSq/uycY3HsG7np0/Dz6mGcpv26WiSv1ua4msnRXsajir/gebalod1Bp9rqoitxZTn5YFkZ9ox95yMDovOCOeMdhdhHktZXvim1uZLUReXbvGu0yHPG45DdOQD1HrXpGj6nBqsM5gtWjsom8uGRgFWUY5wvYDpyP/rc14t0rSdc0qfV7WS3a4t2CvM8zKpVScqQD74z154Nbxo0rJ3t17nlSoezTqU3qZdtFbeGdGtzp0cN5q13byhporiQAR8sGA7YwOeDkfWlh/tvWbPSo47F4r9IFeDU/OLZTGCXbHOePlyTzXP2N3deHNRnsoLi2QyhYZZwokCLn7y456dQefbiuzXxpa6K72Lk3kEESpb3EZ3NKwAyGPTOSOR/PiqeLjVpyjPe+5nCpCa992MrUnt9M8LxI9ncWurw3JAkIJLS4BZyx4ZSvb3A6DNc/wCH7K41bWkC27OjODMUAxErHqMnHc/0rqZNL17xdBHdXTxW0bnB3chEPJ2g9TwPrVW68J65aTvH4Vu4VbzvIe6aUCS2Rgd5ZcYfsR3zg9q5HSc5pr4RujKpUUkvdOg8SR63a2VnofhOz8mS9LJLqTAGOyQAbnIzkuc/KPUVbu2h0DQLPSJ9VvFd4fITUZjuYMB1duxP+Ppmue8P3txoVvH4RsI0OoRzuXuJ5zIrAsWZySc7jk8evPHSuq8Radd6zZxWULRLaTNm4lI3MFHI2jpknvXXzpxfs1qju9pzQfs90ebS+JNWuNQuJrG52S7MzMkvyTbcrvVWxgkbeAOvOKvaL4qvzZ22h2rqkks21buVuUViSQc8Z6gHnjA60y18PX0Ki8ezuJNLEzNCjqGlTdkCRk/iwQCQetdWPh9pZs7aNWZZY2zLIRkzAkEqw7dBj0/PPJCnWcr3OClTxDblc6woriNmUMy9GYcinJEqyGTYA7AAtjnAzgfqfzpUAUADAA96dkeor0Uj1ktERWn/AB6p+P8AOp6gtCBbICR3/nU24eopjFopNw9RRuHqKAFopNw9RRuHqKAGT/8AHvJ/un+VLF/qY/8AdH8qScgwSYI+6f5UREeTHyPuigCSik3D1FG4eooAWik3D1FG4eooAhu/+Pf/AIGn/oQqeq92R9n6j76f+hCrFABSHpS0UAZ2s6Y+rae9ql/d2JZgRNaOEkGOwODwa4HVvD11p+v6Hpa+NPESnUZJcyS3YxhFztGFHzEsMewNennpXHeOxY3kFlpE2jHVr28kZrWFZfJ2FACzmXqgGR065AoAx9V0S78M32iXh8Xa7cxS6lBbyW09yD5oZugwBxnBPqoNejL2+n0ry7QrDSdN0/w74ml0+4upry4S3D3d68xs/MJUFA3B+baM4B5r1IdaAHUUUZoAKRvu0ZGcZ5oPSgDjr7WtWkvZtN0m1JaORg0xG7GTnr0HXFFp4NkuZftGs3bzynkorfoT/QfnXU2igRHjnzH/APQjVa/1m1sAVZt8o/gXk/j6VUsSqUexnHDOrLXUsWtna2EIitoY4Y17IuKzr7xBbW2UgPnSdPl6Z9zVAnVNcOP9RbH2xn+ppNW03+x9He9s7mOK4tyJGecArIB/AfTPTjmvPnWq1f4ei7s7XTpUFerq10RT1DSdZ15ElfZGAfljkJHB9v8AGrt74ZA0KG0hdUmDIHfGQ38PPtzn8Kh8OeIpdYlvNSuLqK2srZChtAQSncu5xnscY4610bzxXFlHPDIrxSNGyMp4I3DpUQwdGTlN6uW4o5lOcI8miWxlahpdsnhN7TU55PIhiLyTR/eXbzkdaNN8O6VENOvNPR4hDHlGQ48xWGcP69c07xP4fXXtOeNZZVnSNxFtkKKWYfxY6jipvD+hpoenrEskryMqmTfIXAYDBC56Cu+OHoxgrbnHOU51WpLTe/mc54z8Qab9rtdJnvJEjFwv26NY3GYsZxkDkdOlbGlafDbW1xf6JP5ltdxeZDasCsYfHDDIyoPpj/61LxnI9jdabqcjrLb284P2YEKzPyAynuR6VWk1LU7jwq+q3Elpc2c5Ly2ytjbARt2q/HzDr9eAairOkvs6o5udKo+bf9CXTPDV/pepQ6lFPBLczsftqFQEwxz+7wMjH6119w0i28hhjDyhTsQtjcewz2rzTQbuS41LTm1CW5bSVlaOw3kH94D8okwc5A4AP4cZr0qWeGHYJZUQyHaoZgNx5JA/AGppOLi+heHlBwdtDjbDwxqem6lFrAjtpLq4ci6txhViVj1Q46jv68/jxfiGO0stYntbeSeREbNxJMRvkf8AiKnHHUg+vbtXf6940s7MG2sp45bgg5cHKpj+ZrzzVIZzezedP9s43SujEiFmx94jg9cAZxnFcmI5LcsDjxPJbkpnXaH4cPiC0gutQ8yKyTAhtwdu5BwBx0Xj6nr7nqY9D0vS76bVEUW37nZLghY9owdxHbAHaub0fxpb2ej2EF5cpcXbSGKTcwj8tVOCzluMgfme9VdQguNf8bznV7pY9K0cLdWWnW0mDeNjcJXbjgHgL698Z3dNL2fLpudVH2PIn1OUv7u+n1m5mtbBdPsDmG1swu4zpuYeY+G2kMzHYmD0zxgE3bDTp4beIpBcT3czFLR4mIa2mRuVcHA6YJ4xx6cja8O2UrXlrrcViLiwkldI7WMsfsmW+8oPBHrj8OnHpG0J0wozk1lGi6j5noYww/tm5S0OL0HSIL97211y3ll1NZVknZydkig/JtIwNnH3fz7Y6pNKs01JL9bdUuVj8revGV9CO/Soxrmlf2f/AGh9siFp083PHXGPrntWirA4KnIPQiuqEIpWR2U4QSsiK8/485v901NUN5/x5zf7pqatDYWiiigAooooAKKKKAIE/wCP2T/rmv8ANqnqBP8Aj9k/65r/ADap6ACiiigAoPSig9KAIE/4/pf+uafzap6gT/j+l/65p/NqnoAKQ9KWigDmtZ8Kz6tffak8R61YDYE8mzuFROO+CvWuV0Dw9c63c6uv/CaeIUWxvntBELpS4CheWyvc5I4HGK9OPSvKNch0zWvGEclto93bxy3/APZlxqlvem3MsoBLLsH3gNpG4/hQB0XgyOew8SeINIn1u+1U2ot3VrqXf5YcOduOzcHJ4zxwMV23SuT8I2+naPqereH7HTxam0aOZpTMZGuFkU4di3O7KsCO2OK6s9KADIo3L6ivKLnWLvWviFoepQXEi6T9vksrZUY7JgkbeZIexBY4Hsg9a39Tt4/FPj99EvXkbStOsVuJLdZSolmdiFLY5ICg8Z6mgDuMgjioLnpF/wBdVrk/B7yab4i1/wAM+bJJaWJhns/NcuyRypkx5JzhWBxnsa6y64Ef/XRaAJ6p3Wp2lndW9tcTCOS4JWLcDhiO2ematOwVSxO0Ack9q4LxrrlvfW6abYrHcNI/zzYyI8Efd9WPtT5Jy+BXMa1T2cb9Tor7xJa2sdy0CvdfY3RboQ8+Upzk+hIxyByO9Y974/tWuWt9Mga9OziQZC7j0GMZI/rXI6Pp2rau50aSd7S2QlmbaQSMnJYDq3oSa6Xw/Bouj6rO9hdsbby9rRzR5YtnG5G9D6f5GXt6FP8AiS1JhTxlX4Y2XocjfeG9eu4Y7yS1aC3nufktFDfut2Tu2D7g45zjnH4dFb6RaaJoAgsJ7STVZQ0NxdRkviMnJx2BxtFbt5f6jrMcsNhCyR7SMngnj16Cq/g3TYZYZ7qaMOyymNQTnaV6j61y1cbWrP2NBWi93bT7zso5Vh6MHUrSvLt1/wCAcle2LWCJbRmQQEmWKOQ9TwM9Oc4FaM3gTV5rONS8DM8ygrvI2Jg53dM4OOBXc+ILYy6ZLLDbpLcRLmPcoJA7498Vgm+8UC2s2WBtxYg/uxlhxjcMcd/SuL2UcPWvUTl1Wh28ka9H2dK0Fs1cw5/C9tod48V9cCWwaJpQFH76UrtygGcn1z2HpXLGxuZBFcOs0FhJIyxTygsq/wC8QOAOOR6H049E1Pw1q114msbmPVLvySZ2MgC/6NnGFUHqD0/Cuon0u3vNN+w3iefEVVX3cbsc5OMd+a76uXUmlKD3PBlTqVLQeijol0tvb0ucP4DuLm5v5I0uI4YYlDyQnlpzggEZ/hB5z9BXR3Ok2+l31xrqrcTyIGfyI8EkkYOM+xP+cVqnSbE3dtdfZkE1spWFl42qRjHHUVzfj25uNPsrO8tb/wCzzQynamTmXPGMdDj3B46Y7z7CFKlqr8ux1PE1qVOU5Su2tX3Q/V/FOk3lgLeE3NzHcR7pTaZ3QR5wWb0IPGPauX1K38MS3ckYuLm4CRh43tiuyBMcR+/UsT1/EmsXStQu7OG6mt9RVJ7rKTRupJ2j+PPTdyQBg9T04rNnQ2t1timVsgFHUg4BHQ9s9D/OueriHJao8arinJao1NAsIrnUIHuzL9gWVUJjIbYxJ2bwOmfXFaOv6TNqRnu/D2mObBHSH9zlvPfn59ueVGcZ+vvivZ21xrVj/wASqKG2+yWgSYNPse55BcNz93nOTjsM4Ix0vhKzh8HeGf7Uvje3N1qMqvezsMCMDKqQgJCxqMAY7EewF0oKcddjahSjOFpbdyl4Y1W/0SwbRPJnl1a4l82FLlz5caMoPc5VRgnb25963dZ8SxxLeWNzI8EbJ5cd1aHeTIAC0f8Astzxnsc8Vzet2dvcb10OK4vrqB/tNxqavlhn+FSOGIGOB2H1qfwtpcHiA7ZrJvsEEHlj5tqLL3Ze7E9cnpkDsKuM5L3EUp1L+yXy9DpNB8PpFHYagkD6bcorCSFH3eYhzgPxy3Q5/wAjqh16VT0uzlsbGK3numuXjyPNcfMwzxn3xV2u2EVFWPRpxUY2QtQXf/Hs31X+YqfI9agu+bdseq/zFWWTjpRSAjFLketABRRketGR60AFFGR60ZHrQAVDD/rbj/roP/QVqbNQw/624/66D/0FaAJqKMj1oyPWgApsn+rf6GnZHrTJCPKf6GgBtv8A8e0X+4P5VLUVv/x7Rf7g/lUtABRRRQAUUUUAFVdRitbjTLqG9ZBaSQusxc4UIQdxJPQYzVo9K5jxrpmsaholwmlalHaL9mmWeOSBX88FeBuZhs7jP+17UAc9a+EfFFnercaZ4osWtDZpaQ3UtsXlSAEsuPm2uRu+8eoArsPDNlYaf4es7XTJ/tFoiHbNv3GQ5JZifUsTn3rlNJ0jxgfD9k8PjSyjt/sybdmmpIEAUcBg5DY9e9dL4P0+30vwxaW1tqI1CL55BdKAFkLuzkgDgDLHgUAVfG+g6n4h0m3stPmtUjW4SW4jud22ZF5CHbzjOM/SuVtB4o/4W+BcnR/tI0lBL5SyhPI8/kLnnfknrx0+teonkVXFhbLfNfC2gF20flG42DzCmc7d2M4zzjOKAOM8H3dto+qeLtOv547eZNUlv/3jBQYZQCGGe3ynPvVn4aI7+G7nUWVlXU9QuL2MMMHY7/L+YGa3tT8OaPrMsUmp6VZ3jxcI00KuVHXAyOntWkkYjVVQBVUbVUDAAoAwPFEN5cQQx2cioSW3uzBQF45//VXLW2k2cFyEji/tC425y8fyA+oXv9T+VdvqmnrqE1vE0hVQGJx3+7UlnYwWVz5cEYUeXye55rKcq7/dwfLHv1NIRor95Nc0u3Qx7Tw0ZiJb/avH+rTjH+A9hUZ0C8TxFHPCI109cHbngDHI29yTnn9a6rBoIOKw+pU2l638zX63Uu/NW8iv9jteMW8I/wCACsu90nSNYuI4j5YlsriOZ1ixncBlVfjoQelQalr7Lqh0dIpI2lXyxcKeUZhwQO+M5rJ07wvq6azqzyardJHIUAlKr/pHyYycdNvT8K6qNWE4ycJao5atOUJRU46SF8RaVcfa3lEkUemJGW2qyrgKNzALxliQcfrXG6je6VcJb20bStG0O/8AeQ8qWGSRjtj09K76DwakmkXFrqDRzu5YwkgkRtggNjua5qHwHcaVqhm864eC3tPM3wxKTI/KtGFJ7rn8+tYYfB0KkXWq3jN677GuLxtek1RpWcErbfidN4b0/RtJ023ZVhja6jhAeQ4MrlcjAPckngf0pPEVvps0lpHdXccFh5+yZI0xukxlVZx90eucZrjLyPVfDpe8axkUCItb8GRLDLDjJGNxBxz7U3xJePd3czWlldabFcoWuY7kCLz2X5sgH+LpyDk5/N1qs3S55LRnkTxDVPlcfkODxW97eadZ3M0ugRyK88kUe4xITyA3Xbnr6gHHfOTfQG7vr6XSrKVLKI+Y0QyQgHAcj3znBz+VeheBrWaGwli+yY02b97BNKqrIwJxhgOvHIPHGPwt63rlvYGS0sIUkvZjhhGM84wPq3QAVzuinG7diXQUoczdjzi6uLRLBNJhjtmjEgkW/wAEPtIztb/aGcHkjjGOK3vDnhS3kuxFrYCNLGRawbwnmDGSwAORgYptrBpFpoNzdXcAuZ1kMK2HKMshJ7dc9TxnjPvVO1u7nTrnVdIs5Xu9YjsU3XNsDI+ngg/LhuMgAkLkHoO/EU6a5ry2Ip0lz80ldf1uamtyafHbx6Lo2rPPr9g6xqkRy4V+zY+XIUZ56YGcZrT8Cafbx6fc213LPPqUREVwLli7IAMDB79+RSeB4tI0+KPTNBtHuYFUy3eozH53mYA5YnlmPfkY4FdhbqivK4VQ5cgnaASAePr1rsjFN8yeh3wgnLnT0PLdQ0OIXNyLOC8l0a3lX7XIQDIWGQxjJGSAOuP5V6bZ2tg9pC1vBB5LIDGQg6Y4x+FXNq9MDnnApQCMCqp0lBu3UqlRVNt9yL7Fbf8APtD6fcHT8qPsdtnP2aLPrsFT0taG5B9jtv8An3i/75FH2O2/594/++RU9FMCla2lubZC0ERPPO0etTfY7b/n3i/75FFp/wAeqfj/ADqegCD7Hbf8+8X/AHyKPsdt/wA+8X/fIqeigCD7Hbf8+8X/AHyKPsdt/wA+8X/fIqeigCrNaWwgkIgj+6f4R6URWluYY8wRfdH8Iqaf/j3k/wB0/wAqWL/Ux/7o/lQBH9jtv+feL/vkUfY7b/n3i/75FT0UAQfY7b/n3i/75FH2O2/594v++RU9GaAKdxbQRxBkhRWDrghQD94VcqC7/wCPf/gaf+hCp6ACiiigBGOFNc54l0oalcabLZ6nHYazbPI1k7AN5gI+dChPzKRtzjpgHtXRnpXnHiDSPE58Z6TcReJbSFHkuRa77WMGEMoOzBfMpIGOOmMnrQA/SfCmoaemi2niTXLN7CxmX7HawRlBPMMlC7MSSRzhRxwK9DB45rg7nw/rUuoaWniDxlbywJexzQ28dgkLTyod6ru3H06D3rvMccUAUtY1W30XSbjULgkxwrkKvV2PCqPcnAH1rO0jxVZan4PTxK6Pa2ZikmdZOWRULA9Op+U/nXO61rOm6t47i0y81G0ttO0ULdTrPMqCa4YfulwTyFGW+pFcrFf28n7OUlvBdRPcRKqzxpIC8Ye6/iA5GRnr1oA7uz8aztdWDaloNzp1hqLiO0unlR8s33Q6rym7tnPJ5xXYEjGa4r4pYTwLOyACSO4t2h7YYSpjH4ZrtM56UAc1PNqV3cS2dopjiWRsuOM89yf6VdsfD8FsQ8/76Xrz0H0FXLf7RtbYsZXe+MuR/Efaps3WP9XF/wB/D/8AE1zLDRcueT5mbvES5OSK5UShdowBVa+0621CONbqFZljcSKrdMj27/jUmbr/AJ5w/wDfw/8AxNBN0escP/fw/wDxNdDWljnaueceJ7GT+0bjWpLFIoIZo4vszHLXmGAyecbc4x64Ga3PFOuXdj4aEllZXMbyRK4mRVK25yMBuev0qz4k8Jp4gVZGgt1uxsXzmkbhA2SAMdxmrZ0a1ttJOnJY262byAtCWLBssD3FVThTp+8tzkjRmpStpct6LqM2pWfnT2VxaMp27ZwAW4HzDHbmtIkHIzWVql/eaXp5uY7FbhY8bkifkL3IGOcDtXKH4hy36fZ9NsCbuRcRhjvw5PHA6jGST2rOrWhGWuhq60adozep0WrSaZY6vYXl/A5kIaGK5IzHETzzzwTzzXn9/qllLFe6fHaia4a7aZfsrsIJsg/MVBzxjt1I69q3W1W98ZyjSEiS3jiIOoZcEnDcIh54JBO7txT4vDdjpNve2F9cWfluwmhl8zbcR4HqBzjjGOuT61yTbm7w2Oao5VXeHw9yj4b0eTRNYtrjV7NxHcZNsQ25YH5++McHaBz+frS+OteTUrdbSzTekUm5pSvcccHt9fw71m+J/FI1WN7IqY4rc5j3HLOwGASR356VT8N2qavqdtbz7FkMm8S3BJ81V/5ZqMbfr/8AWrLnt+6p7HN7Sy9hS2OdVHkdAql2c8d9x9PY+3Wu20K7k0Pw3q5n08LJDIq3LznHysCASpxnBPTuDXa3tvp2jG61y6W0tlSL99M8hC7VPBIxgkHAHft7VzN7pUHxCstO1n7ZNaaSg+0fZZkKeYR0kYHuFBwTkYORWsMK4Ju5vSwLp3lfU42fSZZXkm0yKe5somCmYQlRuwS3HTA+gxx1JzV7TtN/ta4hsZp1hjlDGC/aNibggbVQM2OAO3/1q7iystN01DdW13apot3GsX2ZpMxsx+XIJHccEd+c4xXQtYLJDFC1latFEwaJSeEI6YG3ilDDXd2yaeCvq2JoaXaaXFFe20VvNHlCsJ+QgcArjoD6VovkIcAnA7VF/pQ6Rxf99n/CjN1/zzh/77P/AMTXco2Vj0lGyscEdC1M6kdc/sxNn2jf/Zm7kjGN/Xbu7+legx7iASDyO9Rf6Vn/AFcP/fZz/Kgfas58uH/vs/4VEKajsTCmoXsOvP8Ajzm/3TU1U7o3P2SXKRAbT0c/4VLm6zwkP/fZ/wAK0NCxRUGbr/nnD/38P/xNGbr/AJ5w/wDfw/8AxNAE9FQZuv8AnnD/AN/D/wDE0Zuv+ecP/fw//E0AT0VBm6/55w/9/D/8TRm6/uQ/99n/AAoAE/4/ZP8Armv82qeqKm5+1v8AJFny1/jPqfap83X/ADzh/wC/h/8AiaAJ6Kgzdf8APOH/AL+H/wCJozdf884f+/h/+JoAnpD0NQZuv+ecP/fw/wDxNGbrH+rh/wC/h/8AiaAFT/j+l/65p/NqnqrB5n2ybzAoOxOhz3arVABRRRQAjDKkV57qXhPUrnU5Lvw1rdl5CaibyS0uF8xI7kAhvmU5Gd2Shxg12+qQXtzp0sOn3i2d02Nk7QiUJyM/KSM5GR171574d0TxeravHB4ss4ZF1GVplWxjlLMcHccP8hIx8p5FAHVeF9LGn3epy3eqrqOszuhvZFAURgL+7QICdoAJx65JrQ8SWV/qXh2+stMuUtryePy0mfOFycE8c9M1keE9JlstX1i8vdeTVdSmMUVwI4VhEOwMVG1SeSH6muqIyKAPH9V0/wAT6Tq3gvTgNCT7PM8Vl5Sy7QRHzvzz0546nrXTW0p0r4qStqDxxf2ppcXlyZwjzRsdyKT3wcgeldnLY2txPBPPbQyTQEmGR0DNGSMEqTyOPSoNT0fT9atvs+p2MF1DnIWZAwB9Rnp+FAHL+FZF1Px54r1e3YSWebezilXlXaNTvwe+CcV0uuXAtNLknMYkCEHYe9WbOxttPtktrO3it7eMYSOJAqr9AKLsZRMjI8xc59KatfUTvbQ4dNI1jxIwmuVjsrQnIUJjj2Hf8a6Ox8OaPo8LSCGNnA+aaYAt/LipL/Xre2+SEefL6Kcgfj/hWabO91FGu9SmMNsgLFQOijk4H9etYVcc5v2dNXfZbGlLBKK9pUdl3e4+61KGaTytMso5JCcCTywfyH+NJp3hKGNjLdqpLc+UvT8TWZp3jXQ7a/vLeEq0KiMWxhjZpbhmB3LyOucY6dat+H/Fs+pavd2k1lfBftBWJjbbRCu3O2Q54NQsvnJ89bVrXyLeYQivZ0NE/vN2/wBEtb2we0QPahsfvLfCOuCDwfwx9KwvC/g5tKlluLue5dzNJtheYPGyk8MRj71dj7UHgV1xm4x5UcsoKUudmB4ihnt9KZ7C2TzAQHKxhmC9yBVrS7dpNNhe8tY1uGHzjaM1fuh/oc/r5bfyp6nAHArmVJ+19pd7WsdHtf3fs7db3OaTQb1fEz3jSRmzJJ2+2Pu46dea3/sltuH+jx/98CpgQOM5rA8Taw2mQLJbXtsk8A897eZgDLH0OD2OenvRCnChFuO25FfEXipT6I15YbKCMySJDGo6swAA/OuG8SahpeqXMVtYxxSSW8jEyAgIuByPTgc56cVn6hr+o+Jre88qF/7OQAeSqbmzkYLEdye3t7EnjY5As6zuiygE4VzkMARkNgjA9+/51zV8Te0YrQ8nE4u9oxWjNm+8PXsoa7gt822wuZ9w2SYGSRj+9kADv+dPtIdKuNC+xNbTyazO4eDy2D+ZkEAk/wAKg8Ee3fHyw22rm0lgivo5LizjLSxWRYDEgUYJX+6SeB0547it7w5b/wBn6jp98L2GW+vVaa580YR0J+YowHDr3UcfTGKwpxjJ+6c9KMJPT5nO/wBjTWTXX2uOVI7cAXAJK+cvmY+QlcHnHB64/KxfXmp2ptk1CJoUNuVg2qF+QjBOBxuI4JPTPSvVdM1Kz1hria2/eQqQm8rhWKk5KnuM9647x/HbQajBNAZ21SUBUUDdHs5BGCDndnGP8TnapQ5IXTN6uF9nTvF6FjSPCF6+m/ZpL6GO0ucSSRwrudScfKHz0K4yR6DHHXo7DwxaabqLz2p2Wzpte12gpuHRh6HHX1rm/hzbQWsl7HLNMmoqcS2smV2r2OMnPpnt0+voPcV1UYRcUzrw9OLip21IfsVt/wA+8f8A3zR9itv+feP/AL5qeitzrIPsVt/z7x/981Dc2lssBIgjByP4fcVdqC7/AOPZvqv8xQAgs7bH+oj/AO+aX7Fbf8+8f/fNTjpRQBB9itv+feP/AL5o+xW3/PvH/wB81PRQBB9itv8An3j/AO+aPsVt/wA+8f8A3zU9FAEH2K2/594/++aiitLcy3GYI/8AWD+H/ZWrlQwf625/66D/ANBWgBPsVt/z7x/980fYrb/n3j/75qeigCD7Fbf8+8f/AHzTXs7YRsRBGDj+7Vmmyf6t/oaAGWwxbRDj7g6DHapait/+PaL/AHB/KpaACiiigAooooAKz9csn1Tw/qWnwyeXLdWskKvnG0spAP61oHpWRFHrSnVDNPZlXJ/s/ap+QY4Enrz6dqAOV07xzBpOk2ul3mh6tFqltCkH2OGzZw5VQMxsPlK8cEmt/wAE6Zd6R4TsrW9RYpx5kjwq2RFvdn2Z9gwHpxWYLf4ijk6j4cyBkj7PN/8AFdK6bSF1JNNhXVpbeW+BbzXtlKxnk4wDz0x+OaAL9FFFABRRRQBA/wDx+Q/7j/zWgf8AH6f+uY/maH/4/If9x/5rQP8Aj9P/AFzH8zQBPSHpS0HpQBAYIjIJTGpkXo2OaoXmuWVhfxWc7kSyDjA4GTgZNahrkNVtLyfxbasdPMkClR5mDjHckjpiuTEznSgnDdvsdGHjGpJqo9Ei1rHi21sbprSCWCSfyXcN5q4VlP3SM9evHtWdp+natrF7BqlzL5IicdQVLAc8DHfJFaV34R0mfWY9SltrVY1VxLCYVxI7HO8n1/xrfDRLsRSozwqjH6flV4jDwqyjJt6dPMzw+JlSjKNld9fI5LxdcvZjfP5dzp8pWGeyfhpCcEMhHORjpVe30m58V2X2/V79GsHQywW1sceWSCPmbHJUfqT6c9NJYWTaq+o3CL51um1ZHbhFIyeOg+tcPf6zDb6jJaaZB5FjcTK0sqlvmfvx/CDxwBSq2T956djzayUZ3n16G8ZtcTShYwuJrjJX7UFK5TsR1+bHU9P51k6ba6ppE11N9jjuJ2Q+U0m4GJ+477h3zkHt713ERENvvdce/wDnoKqSKzTYyHYnr2pygtGayprR3OGk0jWpdQi1BiJNUHLsI9saxgEdQOWPr2/lZs9CvdG8PTadoFubeJwGlu5mL3E8xPzMzD16ZwccYA7d1Bb/ACGJlKkHJI/i9qJEj3uisYiRgg9CPUU1St8xKglFpdTk9D067sNXgbT7d7eyMP8ApcBcsGcDAK5xz79T6VNqH9uW1/dtpspMVzkus4J8g9NyY9u3rXT29uvmLNHlV9Ksog+bjqeacado2RcKVo2TKOmh7XT4ISLq4KrzLIQWc9yeat/aG/595v8Ax3/GpQoVcKoGOgrM17W4tBtIriW2nuDNcR20ccG3czucL95gOvckVqlZWNkrKxe+0N/z7zf+O/40v2hv+fab/wAd/wAaxofE0YvoLLUNNvtMlnbbAbkRlJH67Q0bsAfZsZ7VLquvx6deRWNvZz3+oTIZFt4NoIjHV3ZiAq545PPbNMZp/aG/595v/Hf8aX7Q3/PtN/47/jWbpOvR6nPPaSWs1nf221prWfG4Bs4ZSCVZTgjIPXg4qpL4o8y7uYNK0m/1T7JIY55LbykRHHVQZGXcwzyBnH1oA17aVo7dFNvNx/u/41N9ob/n3m/8d/xptjcfarOCfyZIfMjVvLlGGTIzgj1qxQBD9ob/AJ95v/Hf8aPtDf8APvN/47/jU1FAEP2hv+feb/x3/Gj7Q3/PvN/47/jU1FAFaWdmhcC3myVP930+tEc7LGg+zS5AH93/ABqw33D9KF6UARfaG/595v8Ax3/Gj7Q3/PvN/wCO/wCNTUUAQ/aG/wCfeb/x3/GiO4DyBGR0YjIDY5/Kpj0qCbH2m2Pfef8A0E0ALd/8e/8AwNP/AEIVPUF3/wAe/wDwNP8A0IVPQAUUUUAIelcj4sjurPWdD8QQ2U99BpzTpcQ267pFWRQPMVe5Xbg98E10eqLfPptwumSQR3pX9y04JQN/tAc4rL1WHxM9nZrpN1pkVyq/6SbqN3VmwPuYORznr7UAc7e64njW90my0awvjHbX8N3PeXFu0UcIjOdo3YJY/dwPU13/AEHGTx+JrlLS28ei9tzeahoLWvmL5yxQShymfmCkkjOM11g6jn6UAZ13oGlXkkk0+l2M0zg5eWBSSSMZJxmub0PwDFa/Do+G9QS2FzNA8U9zbLksdzFGyQCSuRjPpXb0UAcGPD/inWRpena62nx6fYTxzTTW0jM940fKjaQAoJ5bryOK7vBpaO1AEFp/qT/vv/6EanqC0/1J/wB9/wD0I1PQAUGiigBD0Ncx4uvrnToba9t7oK0UoBtMZ+0gkfKO+eM10zdMV594vtHn1+OTSIZDqkceZpAo2qp4XrwWz0xWVZtQ0MMRJqGhoaZ/aXiTR7qb+1RA9xLsaKJebZBwyAnkPg9fpWdH8N5oR51vqxhuCjAlEO3k4wDnIG3g8nnmrHhzVtLtktrXSrCd9RuGAukkY702/ed2P+8ceufwroNb8QW+kxlOJLlh8sfp7t6CsUqc480nsYqNKcVKbKN7pui6HHZXhBt5bMbYhAcNKMcqfUd+T+NcDqcF74l8RSz2cRE+FZwvCqAMKe56Cur0qyGtzzanq10hjhJ3xhvu47N/dA9K6qzFq1z9otUiKSQrh4wPmGTjmlye1XZEul7ZWWkTzvTfAt1d2siXRMPlKfJ8lQSZDzuYsBnsMf8A6z2FhpiQaDaQatp8RksyGBhHyhlOVYc5z6++a3DdQxxsxG3aeVPWmx4voy0sWItwMeTyfc1tToxhsb0sPTp/Duc7qejWfjOKxl1WxvDa2lwZktCw8qfAIUyKcZx1x0zkZIroFlaN5z9mmI4wBg9B9alNzEu4HKlOAMdfpUsZLZbBGccHtW25vfocHZeHb+11xNTlsomtPOZls1clYc4+dc8FvqAPT27cTuD/AMe8v/jv+NWMc5ox0qIQ5SIU1DREP2hv+feb/wAd/wAaPtDf8+83/jv+NTnpXPTeKGXWL7TLTRdRvpbEIZ2gaEKN67lxvkUnvVmht/aG/wCfeb/x3/Gk+0N/z7zf+O/41T0nWrXWYJXg8yOSBzHPbzpskib0YfqD0I5FZP8AwmPmQvfWmi391pKE7r6IIAyjq6IW3sox1A5HIzQBvXErvbyItvNkqQOF/wAakFwSARbzH0OF/wAao3uu6baaF/a0lypsWRZEkjBPmBvu7QOSSSAB71DYa5d3d9FDN4f1O0ilBKzzeUVGBnDbXLLn3HWgDV+0N/z7zf8Ajv8AjR9ob/n3m/8AHf8AGpqKAIftDf8APvN/47/jR9ob/n3m/wDHf8amooAh+0N/z7zf+O/40n2hv+feb/x3/Gp6KAKiyv8Aanf7PLgoox8vqff3qX7Q3/PvN/47/jUn/LQ/SnUAQ/aG/wCfeb/x3/Gj7Q3/AD7zf+O/41NRQBAbhsf8e83/AI7/AI1JHKsse5c45HIwafVe26z/APXQ/wAgaAHJ/wAf0v8A1zT+bVPUCf8AH9L/ANc0/m1T0AFFFIeQRQAEjGP0rz2z1lfBGo6vZaxY3nkXd/LeW15bwGVJRJghTgZDLjbz2ArsZk1Q6zbvDLbDTBG3nxkHzS/8JU9AKw7238dNfXDWN9oSWhkPlLNBLvCZ43EHBNACeERc6hrmteIJLGeytL/yY7aK4XbI4jVgZGXtndgd8KK6+sPw/F4ija4/t+406Ynb5Iso3Xb13Z3Hn+HFblABRRRQAdqo6rCbiyMKttLuBn0q92qvddI/+uq1MkpJpji3F3RTsNFtrIA7fMk/vvz+XpWgRnjFOxzS96UKcaatHQc5ObvLUzrfR7e21W81BN3nXYQSA9BsBAx+dTWun29nLPLBEI2nffJg5ycY/CreOaDwK0cm+pkoRXQKXNZw1vTjLeRG5VXtOZg2V2jGc89vep7O9g1Gyjurdi0Mo3ISpGR9DU8yY1JPRMW5mjNlOwdSuxuc8dDVXVrm8tdOM1jbrPMmGMZOCV/i2++M4zXn93DKTcy28F6PDP2jMsSnDMRkEquMhN2Mj6/hn+JNSvbe9ksItQeb5RGGibavlNzsZQMAjjkdvyHLPEcqd0clTFcqeh0EXxKR5nIscRANtG4l5X6KBxge+en5Zq3+m6x4ykt7l4Ut4gMhgfuj0HHJzWJpOhaVc2putT1QW5cSBEC/MCo5yPb075616NpU3/EvtgxLIoGB/KsqfNVX7x6GFLnrK1V6FHRNEfTdMlMVgYNSNuYWeNyY3YchuT6muHbwjrH2ndlJZnbLkux3E46nGeuec857V6zPfKCI4k8yRx8o/wAal+yR4dtm13AyQen0rolQjJJHVUw1OaS7HkUvg3UjfMyIjqzA7J5Gbpxszj7owB9APw6g+GNQ1DStP0u8Y/Y4Iy0kgIMrPyAq+ijPX2xj06qVYFibAdXU/dJ5NWLRmZ2DMQVGAhP61McPBEwwsIuxmaDDqFjafZ76AM0QWON4cAMgzgkdjg9q032ysjyWbsynKlgvyn25qyv3j9BT8V0KKSsdSikrFNSqzeb9icSbcb8LkD0znpUv2hs/8e83/jv+NRapfxaVpF5qMyO0VpA87qn3iFBYge/FYn/CYJBbQ3moaNqdjYSqGF5MInjQHGC/lyMVBz1IwO+KdrFWOg+0N/z7zf8Ajv8AjR9ob/n3m/8AHf8AGqmr61baRaxPIss8s7iO2ggXdJM5GcKOnQE5OAAMk1TsPERm1OPTtR064028mVngWZkdZgvXayMRuHcHHqOKYGuLhj0t5j9Nv+NR3EzvCVFvNnI/u+v1rP1DxFFb6kdMs7K51LUFQSPBbbP3SnoXZ2VVzg4Gcn0xVvT76XULPzprG5snDbWhuAu4Y75UkEfjQBZ+0N/z7Tf+O/40v2hv+feb/wAd/wAam7UUAQ/aG/595v8Ax3/Gj7Q3/PvN/wCO/wCNTUUAQ/aG/wCfeb/x3/Gj7Q3/AD7zf+O/41NRQBB9obH/AB7Tf+O/41HFK6yTE28p3OCPu/3QPX2q2elNH3m+tAEf2hv+feb/AMd/xpPtDf8APtN/47/jU9FAEAuQGAeKRAxwCwGP0NTSf6t/oahvP+Pc/wC8v/oQqWT/AFbf7poAbb/8e0X+4P5VLUVv/wAe0X+4P5VLQAUUUUAFFFFAAa4TW/h34ci0/UtSTTrma7WGWcIt5MN7gEgABwOoru6rajexabpl1fzkiG2haaTHXaoJP6CgDy/QvCXgC98KWuoT3uSbdWuJn1F0KPty24bgBz2I/Q11vw3KN4F03ykdYh5oRnJLSL5jYc55+b734/Sua0bSdP1/xNK2reEdHtZJbKO/twp8xgGYqPMwApJxnAB6nntXbeFNVOs+HrW8e3jt5cvC8UXKI0bFCFP93K8UAbdFNZ0RGd2CqoyWJwAPXNV7TUrHUFZrK9t7lVOGMMocA+hwaALVFVbvUrCwaNby9t7cyHCCaVU3H0GTzVkMD0NAEL/8fkP+4/8ANaB/x+n/AK5j+Zof/j8h/wBx/wCa0D/j9P8A1zH8zQBPQelFFACGoJJ7dLiOF5kSWXPloWAL4GTgd8CpzXk+uDxdJ4ygESMJVeZrBA8RAj6H25GOtaUqKqNpuxjWqumk7XO18aGceG5zDBbzJkeYkwY8dtu3ndu24rzrQ/EP9l3lpcaji9SGFvIQTDdBycgg/wAXXr2x9B6o98uk6PDJqU+6ZI1Eh4zI+OcAccmvOdUiGvaxBNKIdOhmLpHM8OYyxIyGbjLH364rlqyjF26/1ucmKT5lOD17EOteJbvVNRu/7Pkmks5FEjRbSRGFABY7c8cV0NhpOjrJDC+pG8ljtxLHGwwjs2f3g/vccAdqh0b+zNHu5b+xu5V0wOttcSSoXWZuzq3bDE5J4x0rO8Q2UR8W21po9thIYvMkELZVBkk8fw8HPvkVyJNe89WzOPMvflq2ei+eB/o8G6TrubHCr6e9JDCUdGdMxk8e1WbPHkDjHJHJzT52ZI9ysBt657136Ox6Vr6kOp3cllYSzw2sl064xFFjc2SB39Ov4VyHhfWtQ1WSSC9tbnyhPIFuHA2pg8IcCuxgla4JY4CY4XvS29nbWqMkFukSsxkKqAMseprSMo8rViJQcpKSehNEGCDfjd6ilTv9aX8KRO/1qEbbDq5Tx5zY6Ng/8xuy5/7aiurNUNW0ey1q1S3vomkjjlWZNkrRsrqcqwZSCCDzkGmBgfEPDeE2t4v+P2e5gjswOW87zFKkfTBJPoDSaQfL+JPiSO4GJZ7e0lt93VoQrKwH0cnI/wBoVrWXhjS7C9W9jgllukXak11cyXDID12mRm259sZqXVdBsdZ8mS6jcTwZMM8MjRSxk+jqQR7jpQBh3ha4+J1itmQZYNKmNy55AV3XywffKsQPTNP+HOweCLG3wVntzJDdIx+ZJg7bw3vnJ/HNbWl6DY6NFMtnE2+dg000rmSSZsYy7sSW/Hp2qteeFNJv7uS7khmhnlUCZ7a6khMoHZ9jAN+OaANlOQDxzT6rWNpDZWcNtbpshiXaqg9B/nvVmgAooooAKKKKAEb7h+lA6ChvuH6UDoKAFooooAKgm/4+Lf8A3z/6CanqCb/j4t/98/8AoJoAW7/49/8Agaf+hCp6gu/+Pf8A4Gn/AKEKnoAKD0oooAztZ0Wy17T3sdQjaS3ZgxVZGQ5HTlSD1rzfxJ4M8JaNrmg280M1pYXs0qTTtey7chMpGSWwMkk5/wBnFesnpXK+MLwubPQ4dItdUutT8zZDeMFhVYwCzucEnG4YAGaAOT1bw54T0PWfD8ulO8movqUG22S+klMiZ5YgscKvDZ6cAHrXqgHIrzbQDpei6BoGvWeg6dby6jdRQXLRqS0YlYoChY5A3YyPTNelA0ALRRRQAUdqKO1AEFp/qT/vv/6EanqC0/1J/wB9/wD0I1PQAUUUmeKAA9OlUtRR3s5FhkWOYlArkZCtuGDjv9KtPIqqWZgFA5JNeX+ItWnutZul0CS6cSxgXPl8q2MYK+h7ZFZVaiprUxr1Y01qWL23i0m8hXSbueXWdx+0zj5hMT1DD1zjHpj8uj0Tw0Ypft2p5luWO7axztPqfU/pR4P0nT7bS4Lu2kE7yLkysPunoQB29/8AOOm4wRjr61nTpX96RnRo396X3Hn2s6Jf6nq17eWmntFbRELNCzlTfFSCeB2wMAnr+ddfYSL5cUnkG3X7Mp8plw0Y5O0gdMelWLjUbS2u4LaeYJNOG8pTn5sYz/MVyvhDxDf+KNc1G9lgay0+AeRb2sq4lcZ5eQH7pPZeuOvqdIxUZNpmkIQjJtPU6KKKW8LXDKoU/cDDqPf2qyLnChPLPm9NnT/IqWZiIz5TLleoNVIEaZ96vgg53Z5J9PpVvyL2ehzXi/WNS06SMW9pcf66LFwmNhBP3Pqen410+k3c19ZLPcWktrKc5ilxkflU1zZw3aKlxEkiqwcBhkBh0NTKMMwrVyTiklqRCElJtsfRRRUGwVyegnPj7xeexFn14/5ZGurPSsS78KaXd6hcX7JdR3NyFEzwX08PmbRhciNwOBQBzGrRz33iHxgNJ5caGtvIY8/NckSlR7sFI9xkCun8OXlm/g/TLuCWOOzWyQhs7VjUKM59MY5z0xV/TtLstJtBa2NukEIJbavcnqSe5PqayJvBOjTTu7QzJBLJ5k1pHcyLbytnOWiB2n34570AcVYhoPCvg+7uYzHpSavJNtkBAiR2l8hm9ssvJ45FerZG4dKgubK2ubB7Sa3ie1ZAjRMo27fTHpWbp3hTStNu4rm3S6Lw58lZryWVIsgg7VZiAcEjgdzQBuUUUUAFFFFABRRRQA3/AJaH6U6m/wDLQ/SnUAFFFFABUFt/y3/66H+QqeoLb/lv/wBdD/IUAKn/AB/S/wDXNP5tU9QJ/wAf0v8A1zT+bVPQAUGiigDm9a8DaDr999t1G1mluNoUst1IgIH+yrAVw2geEvBt/quuWl8HhurS+eKO1kvpFKQqBtYZbJDctnnr24r1xvumvMNbls/EniO2Nz4a06500an/AGY15cE+dI4DElQuMKCpGSefT0AND4f2uk2HiPxJZ6HK9xYxm3xP9oaVd5Vt0akkj5epxk/NgnivQK5vw7PBZ6zqvh63sLa0gsfKlh+zrtDpIp5Yf3gyt9RiukoAKKM0dKADtVe56Rf9dVqx2qvc9Iv+uq0AWO9FHeigApG6UEgdaNwxnNAHE674av8AxBqs1yY7e2S2ULb7xu+0nIPz4P3eMY9zXU2DzyadC91bLbzbP3kQYMFPpn0qDXLqO20xg1+tlJMfLhmYAgOeR147VwcXiXWPE93Bplq8cB2EzPGcefjjcMjIXv8ApXNKUKcvNnJOcKUvNnR694jwH0/TV864cFS6jIXPoO/9Kj0fwRYxWrHUoY5ppVO5DyEz159fft2rn9Rln8IXE9rbvb3UtzECXK4a3yQDu7bTngnvTrT4gXMBh80+daxRKk7MoEplKseAGxtJA5x/9fJ1IX/eGDrU3P8Afb9uxX+I1jFbajZ+RJkSxt+4wMJjA3D3I/H5a7DTG/0CAg9RnP6VyC+LZbgrPBosNx4lnYRxtHDxs7EnOehIJyAMc8Dnt9OhltrCFWkWSYxlpWC8DJzx/L8KunFOTlHYujCMpynE07W2MbCTIJK8nFWJnaKF5FRpCqlgq9WPoKS3VVhUISRjOTUhGRXUkd1rI4HTte1S78T3kM+mXeMwBYzt/wBHBzktzznrx6V3ioNwYgbhxnFRR2cEV1LcJCizTY8xwOWxwMn2FT960nKMmnFWM6cHG/MxF++foKdTV++foKdUGph+NP8AkRfEH/YNuP8A0W1RvPZ23gMS35QWi6cvnB8YZfLxg+uemO+a2b6zg1GwuLK6j8y3uI2ilTJG5WGCMjBHB7VjQ+DNFhaAmC5nEGPJS6vZp0THTCO5HH0oA5fRo7qx1LwH/auf+QXLbqXGNlwVQgH/AGiisOeeD71teNG33vhm3hbN22rxSIB1EaBjK3029frXQ6npVnrFmbS+gWaEsGwSQVYHIII5BHqDkVT0zwzp+lXbXkSzzXbJ5f2m6uHmkCddoZySBnnAxQBk+E2EfiDxVb3AAvjqImOeS0BjUR49Rww9ua6x8bT9azNT8O6dq00dxdQMLqNdqXEMrxSqP7odCGx3xnGecVNYaTaaRaG3so2SMuXYvI0jMx7lmJJPTqaANAdKKB0ooAKKKKACiiigApB95vrS0g+831oAWiiigCC8/wCPY/7y/wDoQqaT/Vv9DUN5/wAex/3l/wDQhU0n+rf6GgBlv/x7Rf7g/lUtRW//AB7Rf7g/lUtABRRRQAUUUUAFU9WMC6Pem6haa3EDmWJVyXXacqB3yOKuVz/iDxPoGmb9N1HXIdPuZ4yqkSYkjyOG6Hb6gnigDlNL8Hancrb6no3jG5XTriySGDzbZXnjgyWCCTPB+Y/NjIxjtx3mkaVa6JpVvptkrC3t02ruOWPcknuSSST3JrjtO+G8Nrbg6b4t8QxW8pMqiG7jCNu5LABMc9eK7LSdPbS9NhtHvLm9aPObi6cPI+STyQBnGcD2AoAr65oo1yzhs5pStsJ1knjA4nVedh9i20n2HvXJatpdlo/xD8MHQbSG0upzKl3FaqEV7YLyXAGMA9Ce/H06fxX4ntPCmhvqN0C2WEUSZxvc9AT2Hcn27nAPMeFPEnhdtS3vr0F/4h1FgkkqxOBntFGCPlQenfqcmgB/h3S9P8Uav4o1LVrGC8ddRk06JZ4w6xxRADC56ZJJ4q/8OJ5D4fuLCSR5Bpl/cWCO5ySiN8v5AgfhWZo+t6d4L1nxLp+uXIs1nvpNStnlBAnjkAJCf3iCpGBzz0rW+HVpcQeGnu7qJ4ZtSu5r5o3HKB2+UfkAfxoA6SaRI7uEu6qNj8k47rTBdW4vCTPFjywM7x6mpZBm8h6fcft/u0o/4/SP+mf9TQAfbLX/AJ+Yf++xR9stj0uYf++xU2aiNxAJ1gMqeaylgm4biB1OKBXG/a7Yni4iPtvFZmra3pmmRi5kkhkuACIlUgt789hUWteI7bTleGArPd9PLXnYf9rH16d6z9I8Pz3tx/aesEvI/KxH9MjsPaspVG/dhuYyqXfJDVlKxtZPEN2L/VriNLcfchLgFh6ew961dc01NV8iwW7tYNMA3TBGHmEj7oHYDvnrxXRhlCgLjAHGOlc54hudV1PTZ9O8K31imorMsF1M0gLWasMltoz8+DwDj1peyjy2YKjFxszCu4tZs7A+H7Jba6guHMS3bOiiGHA++D1ODgEA/dPtV7RtIfw89zaxz21zaTRBjcZVZQ4AG0juDyR6c1s+GNCtvDWljTLZ5ZEiOXlmYs0rt8zOSe5JJ9qnl1fT7nTLiaG6iZI2MbHphs4xz3punBNNg6UFJO+qNMFF4BXr2NJIqSABzwDmqksjCVvKcsGGWBH3aGmMAUq3mKw4B659a0ujTmRLLiF/OjCn+8M9RT4JjKS3yhO3PP41GVQ24kncbV+ckdKraVqVjqbzPZux2kblZcdehFQ6kVJRvqzSNOTi5JaI09y+opqkDPI607A9B+VIoBzx39K0ELuHqKTeuM7h+dLgY7flXNeNby4t9NsbOzma2m1K/isfPTholbJYj3wpA9yKAOiWeJnZFlQsv3gDyv19KUzRg4Mig4zgmuah0Dwhpd9ZRR22m2t/CVaEhlSdicgZOdzZ565yaytG0DSNX8R+LG1LS7O7cagqq88CuwHkp0JGepoA7suo6sOmaN6E43DPpmvOb5pLDRPHOgGV5bWz04zWrSsXaNJIn+TceSFZDjPODjtU3h2x8Lvb6Xt8IyR3XlxEXDaQVAfaDv3lcdec80Ad+jrtA3Dn3p25fUfnTEGAM45/X/Gn4HoPyoANy+o/OjcvqPzowPQflRgeg/KgA3L6j86Ny+o/OjA9B+VGB6D8qAEYgqeR09aAwwORQwG08D8qABtHA6elAC7l9R+dG5fUfnRgeg/KjA9B+VABuHqKr3EiJNbs7qqhzkk4H3TVjA9B+VQzY+0W/H8Z/wDQTQBHcXMEkW1J42YuuArgn7wq5Ve7/wCPf/gaf+hCrFABRRRQAHpXJ+Lbe1vNS0W3+2XGn6o8kp0+9iQMsbhMsrg8EMB0PXbXVSSJHGzuwVVBZmY4AA6k1wesSeF/iHNDpUHih1ktm80RWU6gyMCMMMqdxUjgr6mgCXQfAd1Yf2dHq+uvqNpphDWlqlusMauM4d+SXIzkdMGu3FclY+B57K7t5z4t8RziF1cxTXassmDna3y5IOMEZrrQMcnAAoAdRSbhRuHrQAtHak3D1ozxQBDaf6k/77/+hGp6r2h/cn/ff/0I1OSPWgAPCmuWvfGdvppvIb23lhuoHIih6+ep+6ykdj+n14ra1XV7XS7YyTv8x+4g6sfavLrof2xqk91q961nlGkt5XXjaOioP4uvIzk/nXPWquOkdzlxFZwXu7l/XvE9/fBWkje200kI6qcsH9CeM/T8a6jw1YvbaCk00EMcs8qP+75OwsNoZu5x6cc8Vy819Bpl5pd3qGmSmyezMaI6ABXycsEzj5hjg9Aa3/Bkjy6BJIIJIbdrzdboxyFjLLgAnsDn9cVlR1qXk7sww7vUbm7s3tV1OLRLVJpbeQ2/mBZGiUERA/xEemfSs648XW4+0mztpb2K2C+bNARsBJGQDnkgHPFWPFKltGk36jb6fZ5xd3E7BQkRGCATwCcgc+tee674VvYrkQRzH7CJx/Zloh84zOQhZ2yBtxhjnNa1JTjsb1p1I6x2GXEuvfEHxfti3Wmi2UmIEVgdxwR50hHfGdqds5OMHPeeFtPutJ8/T7mFB5Z3LOhz54J+8e+7sc+nFYfgbSJdN1aZHdoblE23dvIgO4Z+Ro2HbrnPf867lRi+bufLB/U0UouX7yW4UIuf7yejIZ4mllcxoQB97n73tViGKPiVVKHGMVMaWtzpS1DtTV++9UdS1iz0pUN1IymTO0AZ6datwSpMvmxkFHAYH1GM1KqRcnFPVFunJRUmtGS0UUVZIU1pEQEswUDqScUrdOK4tLSDxN4x1ldWjSew0gxQwWswzFvZA7SMp4Y/MAM9ADx3oA7MSIQCGBB6HsaUOrdGB/pXn3iq10K1+GviRNA+xiBosypaSAorfKPujIHA6YFaOveFtLsNEutS0ayttM1GygaeCe0jWLJQFtrbQAynGCD65oA66Qjyzz2pwI6Z5rzHXbnT9X8ReGb3UtLa+t7nSZZxbramfDN5ZB28469feuw8NWujwpO+kaIdMDMBIDZfZi+OnGBnGaAN+iiigAooooAKKKKAG/8ALQ/SnU3/AJaH6U6gAooooAKgtv8Alv8A9dD/ACFT1Bbf8t/+uh/kKAFT/j+l/wCuafzap6gT/j+l/wCuafzap6ACiiigBGGRXnE3hr+3tZvbnw9rk2mPZ6oZbm0ntFlQXKrjzUBIK7lcnrhvbFdvrGu6XoVoLjU76G1jY4QyNyx9AOpPsK4638IaX4i1G48RaT4v1jdcsQz2V0iqAD9w4XOB2DcgCgDpfDvh4aILmae9lv8AUbxxJdXcqhTIQMABRwqjnC9s1q395Bp1hcXt04SCCNpJGPZQMmszQNAl0Mz+ZrOqal5u3H2+YSeXjP3cAdc8/QVr3EEVzbyQTxrLFINro4yGHpigDjj46urOS2n1fw7eadpd1IqRXkk0bbS33fMQcoD75xkZxWrrfiOTTdQt9L0/TpNS1O4jaYQRyCNUjU4Ls54Azx7msjxuD4mjHg+x+ee5aKS9mHS0hDBgxP8AfJUBR1PJ6c0/Tj5nxa1pmwxTTLdYyey7mJH5n+XtQBs+Htej1yK5DWslneWkxhurWUgtE+M8EcMCDw3etO66R/8AXVf51yWh/L8VPFar91razZ8/3trAfpXWXm4JHtxu8xcZ6UAWe9IeBUGbr+7D/wB9H/Cgm5xysOP94/4UANvvIOn3P2ogW/lN5hJwAmOeR7V5xa3ktvNYSaq96dCjkL6fvGHdhzGHwc/7uevGa7PWNeGlQkSLDJMfuxKxz+PFYVlo2o69Ot9qYCwdViyRx6Adh/P9a5qvvSST1OSs+aSUdzF8Uajqet2Ek6Q/8S2JtrKcY56HJPJ6dO5q14B0sSXAuZrJJLcEy213xvDD5SGw3HXgYpnibSJbzVpYtHtHElvGJLpYTiPIHyYBABcAnp7V03hewtLayW80qIpHcIqmOSRuq8EkEcNnisoU26t5GFOi3X5pdDG8cRSaZA0trZhLS7cNfTI2GfsFPXA/TsevPMTaPda3bS6npOm5hheOC1tEdS5+cBmcs3TqT3/nXda/Yw6rqcNvc3kDSW8DXI0/zsM/OBKR1IHIHbPpzR4U8Jy+GUmkMyXE8yqrPyoAA6YHXnNXKg5VLvYueGc6t2vd/r8Cp4f8Epb6DCl95kWoeb55ljYBoj0AUjPy46g56np26+zVRax4UDcoJ4HPHfH1rnZ/F4tJLm2ubRo7yJ9sVtklpgThSmBgg/p3roLEyGygJUKdg49OO9bU/ZrSJ0UlTWkC0qhRgAADpilpuX9Foy/cLitTcdRWXb65aXOoSWUT5mQkcggHHXFaOX9FqIVIzV4u5UoSjpJWFH3z9BTqjBfeeF6U7L+i1ZI6mGaIOEMi7iMhc8kewrK8UarNonhfU9TjRWktrd5EGMjcBxn2z1rAj8L+GrTSLc68unz3twgaW/vZQJpZMAkrIfmGD0CnjpQB2pdV6sB9TS5HrXC3ul2Gr/FBI76xtLyJdE3os8SyKD53UAjHQn8z262La3Xwx41stNsFEWm6pbTOLUMdkEkW0lkH8IYNyBxkDuaAOy3L601yNuM815N4TtfDcvh9Wv8Awo9/c/aLgPcDSmmDDznx8+05wMD8K9TjQRW6RRRokaKAqDgKOwx2oAsjpRTcv6LRl/RaAHUU3L+i0Zf0WgB1FNy/otGX9FoAdSDq31pMv6LSAvk8L1oAfRTcv6LRl/RaAIrz/j2P+8v/AKEKmk/1b/Q1XvDJ9mPCn5l7kfxClk+1eW3yxYx/eNAElv8A8e0X+4P5VLUVt/x7Rf7g/lUtABRRRQAUUUUAIelcL4PtdHudIvdQv0s5LzUL25a5M+1iQJnRV56KFVcCu7PSuSm+Gfg+4uJJ5dEiaSRy7N5sgySSScbvU0AQeBLmzsdO1KwF1ElvZ6ncQ2ytIPli3ZUDJ5A3HFdjHLHNGHidXQ9GU5B/GuR/4VZ4K/6AEQ/7ayf/ABVdJpWlWeiafFp+nwLBaRZ2Rgkhckk8k56k0AW8EkHGKCDggU6igCJ4I5SDJGrbTuG5QcH/ABp4HOadRQBA/wDx+Q/7j/zWgf8AH6f+uY/maSQ/6ZD/ALj/AM1oJAu2J6eWP5mgCZjgVwXjTUYri8tbXT42fVI5AY7iP/ll6rnuT0x71oax4gmvbj+zdG3SSNw8qf0Pb61k6Vew6K0lu9hLJr5fy0tyMgg9GVum3HU1zVZqXudDjrVFNci27lrwZoSRG4m1C3k/tCGTDiUZUtjO4H+InP4fqdHxtMlvoq3Iv5LO4hcPb+W3+sfspHcfy61g3etahbX9xpC6gPs8kwVtQbLNa7skoTjGc9D29scR+IPFlouo6hoLypNPBArW9zAgeS2mYEKW3DaHzzx7cekpxVNxJU4qm4LTzOdg1zW5dQnSTUJraWXMTSSs+23DNydvbBOM9u+OCOrn0mx8IaNaHRbmQag05lAU72v5HILlxnnPXPb8qteG9F0rw7oljp+qRW3266Vo2kl/eNMTzh3OcnG0HsT0HSuU1+9n07xPeQ6fJEHY+XugXbtUgAKAejADGVPOPWsnzUob7mLbo09Xe52/g+5kvDf3N5cyNqBcLPA3yrBjOAoz0Pr3way73QL+81ObW2toLcRzgrZsc+aqkje2Djdzkf5zTS4GlWel3WiS+beSxLG1osZxcKB8zFeoIbPOf510vh+Y3PhyW5e+e7nmJaXfx5T8Apt7YxitoOM7RN6dppQl0/E6RFABO3BPWm+RGGLBBmpe9FdNjssrWIvJj8sxbfkbOQfequnaTZ6XvFpFs8w5bnP4Veo71LhFyUmtUWpySaT0Fpqd/rTqanf61ZI6srX9ETXdMNqZnt5UdJredPvQyodysPxrVooA4y60XxFq93ph1K20eP7FdxXDXcEjtJIEJO0IUG3Of7xqW30vxFpWsa3cWNrpdxDqF0s8bTXjxMmI1TBAibPKk9e9ddmjNAHJP4Uu5PD/AIhimuoptW1qCRJpsFI1Jj2IgHJCqCfrkmpdOTxfZ2tpayWGheVCiRs6X0uSAACQPJ9B0zXUUmQc80ANjHyDjFPpqnCc07NABRRmjNABRRkUmRnFAA33D9KB0FIxBQ4OeKUdBQAtFFHQZoAKgm/4+Lf/AHz/AOgmpsg96hm/4+Lf/fP/AKCaAFu/+Pf/AIGn/oQqeoLv/j3/AOBp/wChCp6ACg0UUAcp4+Al8OwWbsywXt/a205U4PlvKobn0xmsvxvaaNaeFReaeljBdabNDNaNBtVoyJFBA29iCQRXX6xoun69YNY6nbLcWrMHMbEgZHIPHPBrnT8LPBWP+QBF/wB/ZP8A4qgDqEv7JmCJdwMScALICSfTrVknjv8AhXK2vw28JWN5BeW2jRJcwSLLHJ5jkqynIPLetdTg9xgfWgDmNQ8JQ6/q1zca1I89kgVbO2imeNUOPmchSMuWOAewArk7fX77w94L8Xxx3Utyuj3bWlhcSksy7iqqpP8AEULD/PTpfEniRI786BZaha2l88Ya4uppFUWsZzyMn5pD/Cv4nArM8S6Vpr/CnVNL8Oyw3K28aynyZBKzsjrIxYg8sdpPqaAK+t6BB4H0qw1+wmuhe2s8C38rzs32uN2CuHBJHVsjHQ16T25NefeN9WsvEXhDTbCwuI55NZurdIljbc23eHYkdsBec9K9BzQBBanEJ5/jf/0I1ma54hg0pDGv7y6YfLGO3uaydX1hIJWsbK2Wa9aRwW8vO3LHp6nn6VPofhOO3Iu9RRZbludjAEL/AImsZScvdgYSqOT5aZW0vQbnVbgajrDEhuViPBP19B7f5NvU/CzaxeM95cFbWNAtpFD8pjbHLH1I7dq6H7Hb5P8Ao8fP+yKPsdtjm3jH/ARVeyjbUfsIcvKzkrrwdd6tcWj6zfJPDbwlGSMFd7ZPzYzgfLjPvVvTrD/hFPD12L+932lvN5wkYn93ECpOePYnA/CtjUJNL0uwmvr3yLe1gXfJK+AFHr9fTHfpXOW0Vv4sj0XxHaz3tpp4DkWEkIiFxubarOOpGCWUdDkH6ipRUuZbjjRgnzLcZe+HY/HOqaXq1zqK3XhlIUubbT0Qos8pOQ8mfvKARheOc5757MRqAOAABwPSs+efS7W8t7W4ESTXGTEGThiOvPTPtS20+lX01xDbrBJJbsFlAj4UntnoTx+FVdXsy+aN+V7mgAqndgA4wTUQP/Ewb/rkP5mudv8AXrLSb64t7/T/ACiq7rV1TeLj2GBw2T0rV0smZY5ZrNbWV4gzRZBKnJ7j8KUZpuyFGpFuyNWkJwKTy0/uj8qTy1/uirLKWo6TaaoqLdxbwhOPx61ahjSFRGgARQFVR2AFP2KP4RSBAXbIHaoUIpuSWr3K5pWUW9ESUU3y0/uj8qPLT+6PyqyRx6VzN/omo2uuz61on2V5LqNUvLS6LIkxXIVw4BIYA7ehBFdJ5af3R+VJsT+6PyoA4W48G6peaF4ngZdPtrnWNnlQwuxhh2qq8ttBycE5C1p6jp3iPXbJ9NvE0/T7Gddl09rcyTyun8SLmNAu4cZOeCa6fYn90flRtj/uj8BQByusaJq0ev6Zqeiw6eYrO0ktTDczPGAGK42lUbgBf5Vs6VJrbvINWttOhUAeX9kuHlJPfO5Fx+tX2VNhIA6dhTtqnsDQA+imbY/QUBEP8I/KgB9FM2J/dH5UbEH8I/KgB9FM2x+goxGOcAfpQAv/AC0P0p1R7VLngdKd5af3R+VADqKZ5af3R+VGxP7o/KgB9QW3Wf8A66H+QqTYhHCiq0NvDIZy8MbHzD1UHtQBMn/H7L/1zT+bVPVWGKOK9mEaKvyJ0GO7VaoAKQ9KWg9KAOKWGxv/AIkalJqSwSf2fY26WqzEFUMhkLsAe/yqM0zTzp2k/Eq8is5baC0vNNS4lRGCoZlkK7vQEq36Vrax4G8OeIL43uq6XHc3G0JvaRxwM4HBHqfzrP8A+FWeC8/8gGL0J82Tn/x6gDrYrmCfPkzRyY67GBx+VQ6mt6+l3S6a0K3xjYQNPnYHxwWxzjNUdB8K6L4ZNx/Y9gloLjb5oV2O7bnHUn+8a2aAPP8AR9I8eaNZmCCDwy8jsZJ55Z7gyTyHq7nZ1P6DAGAK1da0PVYvEcHiLQxbS3X2b7JdWtxIUWaPduBDAHDA55x0NdXRQBzfhbQr3TZ9T1PVZIX1PVJlknEH+riVV2oikgE4HU45Jrcuukf/AF1WrHaqt6ypErsQFVwSSeAKPUNFuWM4Gc1zmt+JltnNnYDzrtjt45Cn+p/yapalrt1qlwdO0ZS2eGl9vb2961dD8OwaUolfEl0w+Zz2+lYuTm7ROdzdR8sNijo3hlvO+36ofOuWO4I3O36+prpyvGMcelPyAMk8CkPPStIwUdjWEFBaFLUr+20qzN3dFliDKGZULYycZOO1VJddsDexaXbXcP267iZ7cbd6/dLAkDtgE9eQOvIrO8VeJ4NOubPQoAk+r6m3lw25UvtTBzI4APyDB/X0NVvDHhO58N6yZB5N3FPEVkuCux4iDkKBzweM/wC7z0FTJyU1ZESlJTSS0H+GvBw8P2l/qWo3P9oeIL5C17fuOW44RP7qDjgY6D2A6/8AgFR3f/HnP/1zb+VSr90VobHK3PhKW9uLi+uL9/7Q37rWZBtWAA8ALk598+v59DYhhZW4Y7iIxk4xk49Ks9qgs/8Ajzh/3BUxgo7ERgo7E9BooPSqLM630SxttQkvYoQs75JbJ79a0BRzmgVEIRhpFFSk5P3mIv3z9BTqav3z9BTqskr31nBqNhcWVzGJILiNo5EPdSMEVxl74c8SXHh2Xw9IulXtsYGt4r+4ldZkQrtBZAjAuBjkMM4ru6TIoA5WfRdatPFMGradHYXMa6ati6XNw8RyH3bhiN8j24qzpmiX7a22t6xJbtdrCYLe3tixigQnLHcwBZmIGTgcADFdFSZFAHFaFpfi7w/pS6dBa6JcRRyzOsj3syMQ8jP0ERA+9iuuiE32SI3KxrPtHmCNiyhu+CcHGenFT7h6+9I/3aAHDpRSAjFGRQAtFGeaKACik3Drnj1pc0AFIPvN9aWkH3m+tAC0UdKMigCC8/49j/vL/wChCppP9W/0NQ3n/Hsf95f/AEIVNJ/q3+hoAZb/APHtF/uD+VS1Fb/8e0X+4P5VLQAUUUUAFFFFABRRRQAUUUUAFFFFABSHpS0jdKAKtxIkU8ckjBUWNyWPAH3a5DUNUu/EGotYaWGEBXEkp4yM9T6D9TW54g0241Vra2ilESklnb2GO3erenadb6W4gt4wqhMlu7HPU1lJSk7GM1Kb5VohmkaLb6TbbY13SsPnlI5b/AVkv4QEgku5L+b+1jJ5iXoHMfou3ONuOCK6o45rkNS8XzP4ttvDXh+1jv7xHWTUpXYiKzhzzuI/5aEfdX9Kr2cWrMp04PdFS51/RbTV5fC1tpovIliefVZIFAjtFxnL+rseijkDnsKj0jwt4P0K0hWBzcLqTFIjPIZWkZvvN0zk92PI6cdK6i20i00Gyvm0jTYhJPJJcPGjYM8rcksx9Tx7Vzmk+G9S0bVrfU/It5muGZZ4V+UWwY5yhPYZwR+Wc1nO60S0M6is1FK6/Ibqr2j+Fhp9ncLNZRytHNdXT5a2APofmLdlxS+H9Ds7fT7+K+jhmsbja8d+zjMqH7oOeVYHn6mqWtaXcpqR1u8itIJWuAtvYSDK3G0HBY9N5HQnpwKzoHR9S8x7aU6QsxnbTA5Lxc7fMKAdA38Ocjriudtc95I5XJe0vJa/oejWOm21rLhIwzwxrEsr/M5UAcZqWW1htrW4MMKRmQ73Krjc3qaltmV5ZWU5U7eR06f/AKqdeDFnL9K7kl0R6CSWqJ+9FHeg0yiKe4htk3zSpGuQuWbHJOAPzNBuYVmSFpUWWTOxC3LY64Hes3xH5J0O5Sexe9VxtECDJYk4HPbnBz2xmue8M6bfaXrka6vBJcXEsGILrcZFiVRyh/un371nKbUlGxlObjNRsdxTU7/WnU1O/wBa0NR1RzTR28Ek0rBI41Lsx6AAZJp56Vy/jhprvSIdCtXVLvV5hagsMhY8FpGIz02Kw/GgCHwr4j1LXY9Rt7yGC1vkjjubUbGx5MqbomYE5JBDBsY6VBeaj4ts/EOlaS15ornUI53EgsJcJ5YUkY87vuqtfxaroXi3Rtav57KS1n/4lcv2W3eLYrnMZO524DgDt96tLWyD8R/Cmehgvv8A0GP+lAFnUtY1LRdMtYrhbW+1i9m+z20cCNDE7EE5bJYqAoJPJ6e9WrC38QxXyvf3+nXFsVO9IbR4mQ9tpMjZHrkD8OlZ/i6C4t7/AEXXoYZJ49Lnc3EUa7m8qRChcAcnbnPHOM1ch8VaPqri00fVbS5vZI2MSIS4RgM/OF5X6HBoAd4q1afQ/Ceo6laiNri3i3IsgJUnIHOCD3rHm8V30fgDUdWMMCavpweG4hKkokyMAeM52nIYc9CKj8YJqw+HuujUpLNyYQIxbRsv8QzncTntisb4nI2i2ep3sSk2WtWwt59ozsuE5jf/AIEoK/gtAHUarqmsnxhb6HpkthAkli928lzbvKcrIFwArrx836GtjTY9WQSDVLqyuGJ+Q2tu0WB7hnb+dcV4pl0WL4j2D67dJbWg0qTY73DQguZUIAZSMnAPFdP4Zu/D0sM8Og38d0kbBpdt08xUnPdmJHSgCXxHqc2n2cENpj7bfXCWluWGQjN1cjvtUM2P9nHeq2q6lewX1loWmMkmozxNLJcXC7lhhTAMhAxuYlgAowMk9ADUfiL5PFHhOVyBEL2ZDn+81vIF/UH86q61KuheNrXxBdkrpk9i1hPN/DAwfejMeynLLnoDjOKALUGp6ppet2ul6zLb3Ud8sgtbuCIxESKNxjddzDleQwI+6QRnBPRCeHyy3mptU7WO4YB9DXHahf2vijxTo9vpM8d3a6ZM15dXULbo0YRlUjDjgsS+SM5wK5zVdSsrfwj4s02a6iF++ruVti4MhUyoQdvXbjnOMUAeqNcwK21po1OQuCwHJ6D6ntUa3trPNJbRXULzoPnjVwWT6gHIrkrPS7K8+JOu3l3Ck8lpDaGDzBkRMQ5LAdm+Uc9R2PasvwhcWVjrthpdjcaRq0Bik2XVvCFu7UAZ/fYz16ZO0knpQB1+gahcTTahpt64ku9OmEby4AMsbKGjcgcA4ODj+JTjAOK1Jv8Aj4t/98/+gmuf0r5viF4hdPuC0s0b0L/vT+e0j8xXQTf8fFv/AL5/9BNAC3f/AB7/APA0/wDQhU9QXf8Ax7/8DT/0IVPQAUUUUAFFFFABQaKKAMm88M6HqFybi90XTrmdsbpZrWN2OBgZJHpU9ho+naVHImnafaWaucsLaFYwx9TtAq/RQBlWfhrRdOvpL6y0qzt7p85mihVW56844rUI4x2paD0oAyrHTLaK7nvgmZ5ZGyx7AEjitPuKhtB+5P8Avv8A+hGp+lJJISilsKTiquo6hZ6XYTXt9cRwW0K75JJDgKKNQ1G00vTri+vZ0gtbdC8srnAUD/P58VyWjTn4iaXdXGtaDCugvPHJpsdwSZJ0U58x06BSQCo7gnPHJYxLSKw+Juj2d/qmlXcFhDdme0t55MLdxgfJI6dCDnIVvTqQa6+4XbCABgb0AAPT5hUygLxwAB07AVDdunlqm4bt6Hb3xvFJsDE8T6ZeazHbafBCiwu2+W6YjdDggjZ33GpPC+n3elWc2n3EESxwv+6niwPOB7kdQw75rdBViQCCRwcdv85pwFR7Nc3MZeyXPzrc5y+8JxapfXFzf3EkuQFtVX5BbDrlcdWzzmtHTbee1McNzOLiZIArTbdpfk81p1X/AOYg3/XIfzNUoJO6KUEndE9FFIwyvFUWRC5gad4FlQzIAWQMNyg9MjtSW9zDc73glSRQ20lGBwR1Fch4m0++1PWgujwvBdRQnzbzcUV1I4jB7k+vatzwuI10WFI7KSz8vKtDIOQw6nPfPrWcZtyszKNRubjY2qKKK0NQ7VzPjLX7/RbKFdJt4ri/k8yURyg48qJC7njvwqj3YV0p6VxMaarrHjDU9R064s44LJP7OQXVu0u5uHlxtdcclV5z9ygDckn1TVLSzvtCvNPjtriJZQbq2eUsCAQRtdccVkeFtS8T6/p9vqM9zpCW7TyJLElnLuKpIUO0mUgE7SRwcZo8ANLp1pf+G7x1a40e4MalVwrQuPMjI5OBglevG2pfhwceDIf+vq6/9HvQBJJqurazql/ZaM9tbW1g/kz3k8TTFpcAlEQMv3QRliep6VuaamoLZqNTe2kuwzAvboVQjPBwSSDjGea5PTL618KanrVhrE6WkF3eyX1pdTHbFIsmCyb+gZWzkE9MVuf2ldaxbx3fh290+W1yweSeN2Vsf3SCMj35FAGTr/i290bxhaWXkwNpBgSS8lwfMiDyGMMDnG0Ntzx0JrXOq3B8aNo2Ivs408XWcHduMhT16YrHurGPUfH1zY6gqSpcaAIplHRgZmBxnp1rK8H3d03j2XTtQZmvtM0n7HLIw/1oWXKSe+5Cp+uaANTw5qPizxFoNrqqX2jQLPv/AHbafKxXa5X7wmGenp3rsY94jXzCGfAyVG0E+wJOPzryPwbceCY/CtmNU1aKC/UyCRG1GSNlPmNjKhwPQ163DLHNCksTh0kUMjDnII4oAyLS/n1LxLfQwvtstNCwOAP9bMwDnJ7BVK9O7n0rMstQ1/xJDJqOkXdlZWAdltRPbtK11tON7EMNikjAABPc+lS+EgN3iWJ9wcavOXHfDIjKf++SMVm+Fdc0/wAMaAuha7ewWN7pm6IrM2zz49x2PGD98EY4XJzx1oA6HQdcXVbKWa5jS1ubadrW6jL5WOVTggHuDlSPZhWu00SFg0iKVXewLYwvqfavMruC4TwVrms3UTwDUNWgvhHICrR26zRAM47fKhY+gPPSrOrajZ6nr/iSaxuo7iJfDJXzYWDIW3Sngjg/5HY0AegS3trHE7vcwoqAFmLgBc9CfSsrXtRltNFbWbCZZorT9/KiFWWaFfvgEdwMkYPUAVylxoujaf4Q0GUXtjpkwMVz599GJIriXySD5xJGeGbBJGOMVp6dqEN38OtTuHtbK2hihu1Y2g228qqGzIn+y3J7/U9aAOyidZY0kjYMjgMreo7VHbf8t/8Arof5CqPhlJIvC2kRTbhKllCr7uu4IAf5Vetv+W//AF0P8hQAqf8AH9L/ANc0/m1T1An/AB/S/wDXNP5tU9ABRRRQAUUUUAFFFFABRRRQAdqz9ZtTe6e1sH2eawXd6Vodqr3XSP8A66rRuJq+jK+maTa6VbiK3QD+8x6sav8ASjgdelRXM3kW0swjeTYpbYgyzYGcD3pJJAkorQkJGO9cz4k8ZW+hX9jpdtaSalrF7IFhsoGAcJn5pGJ4VQMnn0+pFPV/GkbabFHoYNxq12dsMGBujOeS4/hxz19PTJrP8PXqat4te9tbCztb5ogmpXHDtOF+ULHz93gcj0XPQZz9rG9kZe3heyOo0vwvpml61qWsW8bvqGoPulnlcuyqAAI1J+6gIB2/0Axtgc9MCkGc06tTYhu/+POf/rm38qlX7oqK7/485/8Arm38qlX7ooAXtUFn/wAeUP8AuCp+1QWf/HlD/uCgCamyOkUbSSOqIoLMzHAAHUk06obxkSzmaWMyRhCWQLu3DHIx3+lDExpvrVYo5WniWOQqEcsAGJ6YPfNT5GcV5zpml3lnqNlfXmnTNpbTMbe03FjaFj8rFf8AOM16KODWcJuS1M6U3NO6sC/fP0FOpq/fP0FOrQ1Eboa5HTfFNzd+MJtOlihTTZvOjsZwDukkhKiUHnGMlseymtvxJqjaL4evb9F3Sxx4hUjhpGIVB+LFRXCatoeu+H/CmnXhubG4XQHW9Kw2zrLKBnzcuXI+ZXcnigDa1/UvFmjNYFbrRpFvb+KzQGylym/OGJ83nFas+pX3h/Q77UtentJ0gXcgsrdoixJwFwztkkkAdOtZ3jOeK5tfC88Lh4pNbs5FYd1O4g1oeNNMutW8K3dvZDddI0c8SdnaN1fb+O3A9yKAGWieLJjBdXE+kwI7qZbIW7s0aE8r5u/BcD/YwTW/KSsbE9AM+9YNr428P3McMZ1COK7lIT7E+ftCt0IMeN3HrjHfOOauwLrS3EjX02nta4OFhidX68ZJYjGOvFAGF4D8V3viOxkTVoYIL9USdRACEkhcHawySTghlPoR7iq//CWaxceFPC97bfYo77WLmO3kaSFniTcjkkKGB6qO/rWVpMb6Z4F8LeJ7dSX022Ed2AMl7Vj+8Bx/dIV/+Ams+R7Ffht4BfUJRFZrfQmV2kKBR5cnVh0/CgD0rTYNdjuydSvtNng2EBba0eJt2eOWkbjrx71Lruqx6Jod3qLoZPITcsYODIxOFUemSQPxrG8OXnhFtQMOh6nFcXLpyi3zzEqPQMxp/jzK+GllOfLjvbR5cf3RcR5/KgCS/wBTvdC0OySYrfaxdzLBEPuI87knt0jUZ9TtXueaq3Woa74aEF9q11Z32nO6x3PkW5ha2LMFDqNzblycEHnuO4p3jKGaL+ydZgheYaVeedPGgJbyWRo3YD1AbP0BrO8V6zp/inRRoGh3kN9d6kyKfs7B/IiDgvI+Pu4AOAcHJGKAO4Eib2TzFLqAWXPIHPOPwpgurfG/z49rKXU7xgqMZI9uRz71x9zqdnpPjrXDfXUdubjTLfyFd8NKQ02Qg/ibJ6DnkVg6Rplpqq/D+2volmgGkzu0L/Mj4WDAYdCNxBwe4FAHpjahZrOkBu4BNIAyR+YNzD1A6msy3v57bxVNpN1JvjuIDdWjFcEBWCyJ74LIR3wxHauPupdP0nxVI1pcaNqTXOoxeZp80A+2QtlVDRHqQmNwyMAA4IrpNUBbx/4bCffS3vHkx2TEY5/4EV/SgDobz/j2P+8v/oQqaT/Vv9DUN5/x7H/eX/0IVNJ/q3+hoAZb/wDHtF/uD+VS1Fb/APHtF/uD+VS0AFFFFABRRRQAUUUUAFFFFABRRSNwM0AY2r+LtA0K5S31PU4beZ13BCCxC+pwDtHucCtaCeG5gjnglSWKRQ6OjZVlPIIPcVxvggLf6j4uv7hUeaTWJrQswzmOJVVVPPTk8U/4bO0eh39gM+Tp+qXNrDnPCK+QOfTdge2KAOsf/j8h/wBx/wCa0n/L6f8ArmP5mmTyrHdwk7vuPwqkn+H0rmvF134gnSKy8NQ+XcXWI5r6VOLOPnMgQ8s3YcYyR70AReJ/EWrPrcPhjwvAG1aRFmuLyeMmCyhJ+8f7zHBworrLeygtpZpYoIo5J2DzOiBTIwGMn8AKr2BFnZW8Es9zcyRRLG9xLC2+UgY3NhcZPJ/GrX2qL+7L/wB+X/woAlY/L1rmW8caVGt95xkgmtGKmFxh5PQqPet9rmMqQFl/78v/AIVwmpeGtS1O6uNWebytQikBso0hYoqqcruJXkn/AD6DKpKcfhRjWlUS9xHQa4LrV/D8UNvp2570AMlxgeQCMlmGc5Ht3xXB22j6nZTXyRxXdxpCSD7WUHlvOFPIAPJ6nOODivULOdxawi6VvPCL5myF9u7HOOM4zU/2mP8Auyj/ALZP/hUSoqbUmZzw6qNSe5Bpgi8oeQgSEInlrtK4XaMDB6fSrF7/AMecv0qKO5QTzfLJjI6RN6fSnSzRSxNGRLhhj/Uv/hXQtNDpStoWc80tUVupgADHuwOuyQZ/8dpftkv/ADx/8dk/+IoGXMUgXBzVT7ZL/wA8f/HZP/iKPtkv/PH/AMdk/wDiKQi7TU7/AFqp9sl/55f+Oyf/ABFMjvZTu/cj7x/hf/4imM0D0pApzVP7ZL/zx/8AHZP/AIij7ZL/AM8f/HZP/iKALhGe3TpQFqn9sl/54/8Ajsn/AMRR9sl/54/+Oyf/ABFAF09KaEAJIAyepxzVT7ZL/wA8f/HZP/iKPtcv/PL/AMdf/wCIoAtqA0ePWgg5NUYr2Vo1byRz6K//AMRT/tkv/PH/AMdk/wDiKALmCaTbnkjn/P8AjVT7ZL/zx/8AHZP/AIij7ZL/AM8f/HZP/iKAIta0n+1rDyVk8meORJ7eXGfLlU5Bx3HYjuCR3q7GsjQIJlUOVG9VJZc9+TjIqv8AbJf+eP8A47J/8RR9sl/54/8Ajsn/AMRQBOkSwxbY41RQDhVGAPwFOCAcgYJ6kdfaqj3kojb9yOh/hf8A+IoS8mKKfK7D+F//AIigC6FIOQMfSk2Y3EDk/hmqv2yX/nj/AOOyf/EUfbJf+eP/AI7J/wDEUAQaLpTabFczXDq97eTme5cdCxAVVHsqhVH096uy/wDHxb/77f8AoJqH7ZL/AM8v/HZP/iKRZi0yPKrgJkqFic8kY5yKALF3/wAe/wDwNP8A0IVPVO5uI3h2gSZLr1jYD7w7kVcoAKKKKACiiigAooooAKKKKACjtRSE4FAENpxAf99//QjS3VzDaWk1zMxWKJGdzgnCgZPA5NNtubcgHBLvgn/eNcj4en8SeIfEcusXi3Gk6Hbh4bTTZUAluieDLLn7o4+UD65x94Ab4a1HVPGst9fajp8EPhe5j8qys7qHdLcrnmVweinoFI9Pqe027SMDC4xx0FKq7cAAAAYFOIyMUAZcutWVvqhsZ5DFJ5XmqzjCMB1wx44xn/8AUa5TW7+PWp7K40SO4/tHeRFcbSqGMMASxIwVJ6e9a/iHw3c+Ir1IJ3ji0+JN4ZADI0hyO/QDj6/yv6db3tvpMUN+sPnRSoitD0ZAwwcYGOOwrB8824vY5pKpUbi9EUvBqRx6fKrRTR3olP2wzZLPL3O7oR6YrphSAYPTr3pe9axjyqxvCPLGwtVx/wAhBv8ArkP5mrB6VXkil81ZYtu7btYMcZqiieg1Bm6/55w/9/D/APE0ubr/AJ5w/wDfw/8AxNAEm2kUfM1R5uv+ecX/AH8P/wATTEe5aaRfLi+XH8Z9P92gC1RUObr/AJ5w/wDfw/8AxNGbr/nnD/38P/xNAEpGQRTdhxj/AD/OmZuv+ecP/fw//E0Zuv8AnnD/AN/D/wDE0ASbcdBQFI6cVHm6/wCecP8A38P/AMTRm6/55w/9/D/8TQAsqbon3gEY9KeBjpjA6AVXne5SB2McOAP75/8Aiakzdf8APOL/AL7P/wATQBJsP4UbT6VHm6/55w/9/D/8TRm6/wCecP8A38P/AMTQBKRn1pNp5GAM1Hm6/wCecP8A38P/AMTRm6/55w/9/D/8TQBQj0qS28QT6hbuqw3cai5iP/PRRhXX3x8pz/dX0rQaBJGVpI0ZlOVJGSv0pM3X/POH/v4f/iaM3X/POH/v4f8A4mgBxXLFeDwM5H1pwXCkAcdh7elVw9z9oZPLi4QH759T/s1Jm6/55w/9/D/8TQA8pkEEA561ma3pUus2S6e0ipaSuouhzl4xyUA/2uAfbNaGbr/nnD/38P8A8TRm6/55w/8Afw//ABNAEgG3tgCobY8z/wDXQ/yFKftRH+rh/wC+yf6U6GJokbccszFifrQAif8AH9L/ANc0/m1T1An/AB/S/wDXNP5tU9ABRRRQAUUUUAFFFFABRRRQAdqr3RwIyenmLVjtVe56R9f9avSgCcnvXMeLvEz6ClnZ2dlJfatqMhhs4ETIJGCzMcgbVBzjIz6jqLHi3WNS0fSFfR9Jl1LUZ5Fgt41X5EY9HkPZB3/LIzU3hrTdT0zRIYNX1NtR1AlnmnKhRuY5KqAB8oJ4z+nQDB6nPR+Crxdly94s15c5TUM/IHRiNwUgcEY+h9hxXSJ4d05JrKWOARPZjERjODtwflPqOc/X6mtUA59qXpWapxTuZRpRj0AA5paKK0NSG7/485/+ubfyqVfuiorv/jzn/wCubfyqVfuigBar2ZBs4RnnYOPwqc8iq0cdxCojQRsi8KWcg4/KgCzQRxUObr/nnD/38P8A8TRm6/55w/8Afw//ABNAEmKUDkcVFm6/55w/9/D/APE0Zuv+ecX/AH8P/wATQBIPvn6CnVVR7rznTy4vlUH759/9mpM3X/POH/v4f/iaAJiM0wocfh+tMzdf884f+/h/+Jozdf8APOH/AL+H/wCJoAk2kjBP0pQCO1RZuv8AnnD/AN/D/wDE0Zuv+ecP/fw//E0AP8sbt20E+tK4+X8R/Oo83X/POH/v4f8A4mo5nuUiLeXD1H8Z9f8AdoAn2549v8/59qXac56fSos3X/POL/vs/wDxNLm6/wCecP8A38P/AMTQBLg5BIqtqWnwarplzp90paC4jaNwODgjHB7H3qTN1/zzh/7+H/4mjN1/zzh/7+H/AOJoAi0+K8hsIYr2VJrhF2tKgx5mOAx9CQASPU1NHAkTM0caoXOW2gDd9aTN1/zzh/7+H/4mjN1/zzh/7+H/AOJoAfs5BwMjocdKFByeT1pmbr/nnD/38P8A8TUcclyzSARxDa2D859B/s0AWNgzuKgnHXHP0rMtNKdNdutWunDzPGtvAq9Iogc/izMSSfYDtk383X/POH/v4f8A4mjN1/zzh/77P+FACXn/AB7n/eX/ANCFTSf6t/oartHPNtSQRhAQTtJJ4Oank/1bfQ0ANt/+PaL/AHB/Kpait/8Aj2i/3B/KpaACiiigAooooAKKKKACiiigApG+6aWg9KAPPrK+l8E63r1vd6dfz2uoXbX9pLaWzS72cDfG20HawI4zgY71seA9JvNK8NK2oR+VfXk8t7cRf3HkYnb+WM+9dPtpcfSgDnfFsPiG405YPDrw293NmJrmUk+Qhxl1HdsDgetS6Bpg0DTLawi+23CQRbTLcPvkdiSWJJY9Senat08ck8VVGqae16bIX1sbsDJg80bx/wABzmgB/wBpb/n3m/If40faW/595vyH+NTgg0tAFf7S3/PvN+Q/xo+0t/z7zfkP8asUUAV/tLf8+835D/Gj7S2P+Peb8h/jViqkmqafDdraS31tHct92F5VDn6KTmgBEldJZWNvLhiMYA9PrUn2lv8An3m/If41LJLHDG0krqiKMszHAA+tRWl/Z6hD51ldwXMWcb4ZA659MigA+0t/z7zfkP8AGj7S3/PvN+Q/xqxRQBX+0t/z7zfkP8aPtLf8+835D/GrFBwBk9KAK/2lv+feb8h/jUcMrx7828vzOWGAP8anluIIWRZZo42kbagdgCx9B6mm3N5bWUDT3VxHBEvV5XCqPxNACfaW/wCfeb8h/jR9pb/n3m/If4063ure8hWe2njnhflZImDKfoRxU1AFf7S3/PvN+Q/xo+0t/wA+835D/GrFFAFf7S3/AD7zfkP8aPtLf8+835D/ABqckAEk4Aqo+rabHfLZPqFqt22MQNMokOeny5zQAQSvFCqm3lyPQD/GpPtLf8+835D/ABpLvULKwjEl5dwWyE4DTSBAT9TU8ciSxrJG6ujDKspyCPWgCH7S3/PvN+Q/xo+0t/z7zfkP8asUUAV/tLf8+835D/Gj7S3/AD7zfkP8asUjMqgliABySe1AFaSd3jZRbzZII5A/xojnZI1U282QAOg/xotNTsL8uLO9t7kxnDiGUPtPvg8UXepWFg0a3l7bW7SHEYmlVC59Bk80AO+0t/z7zfkP8aPtLf8APvN+Q/xqcMD0NLQBX+0t/wA+835D/Gj7S3/PvN+Q/wAasUUAU5pXlj2CCUHcpyQMcEH1q5RRQAUUUUAFFFFABRRRQAUUUUAFIenFLRQBw8mha5qPjRL7ULmSHRdOcvY2lo5UzyNnMkpyOBuIC9Ppk57D7Q3/ADwm/If41YooAr/aD/z7zfkP8aPtB/595vyH+NWKKAK/2g/8+835D/Go55GkjCrby53qeQOzA+vtViaeG3iaWaVI40GWd22gD3J6VHaX1nfwCezuobiE9JIZA6/mOKAD7Qf+feb8h/jR9oP/AD7zfkP8aY2p2CXy2T3tst2wysBlUSH6LnNWsj1oAg+0H/n3m/If40faD/z7zfkP8asUUAV/tB/595vyH+NH2g/8+835D/GrFHSgCv8AaD/z7zfkP8ajSRlmlc28uGIxwPT61PNcwW8fmTzJGmcbnbA/M06SWOGNpJXVEQZZmOAB6mgCL7Qf+feb8h/jR9oP/PvN+Q/xotL+z1CHzrK7guYs43wyB1z6ZFWKAK/2g/8APvN+Q/xo+0H/AJ95vyH+NWKKAK/2g/8APvN+Q/xo+0H/AJ95vyH+NWKTIzigCpcSNJbyItvLllIGQP8AGpPtB/595vyH+NR2+radd3L21tf2s86ffiimVmX6gHIpbjVNPtJoobm+toZZv9Ukkqqz/QE8/hQA/wC0H/n3m/If40faD/z7zfkP8anyKWgCv9oP/PvN+Q/xo+0H/n3m/If41YooAr/aD/z7zfkP8aPtB/595vyH+NWKq3WpWFiYxd3tvbmQ4QSyqm8+gyeaAGrIwuXk+zy4KKvQdifepPtB/wCfeb8h/jUzyIkZkdgqKMlicAD1zUFnqNjqMbSWN5BcopwWhkDgH0yKAF+0H/n3m/If40faD/z7zfkP8asUUAV/tB/595vyH+NBuDji3m/If41YooArQlnupJDGyKUUDcByQW/xFWaKKACiiigAooooAKKKKACiiigArO1v+0BpUz6XFFLfIN0CTMQhcdNxHOM9cVo0UAct4S0a90HTZP7RvLzUdTu5DPdzu3yFyAMIuflUAAADHA+gHQ+ef+feXH0H+NWKKAK/2g/8+835D/Gj7Qf+feb8h/jViigCv9oP/PvN+Q/xo+0H/n3m/If41YprOiIzuwVVGSxOAB65oAqzytJbyotvLuZCBwPT61IJyBj7PL+Q/wAabZ6lY6grNZXtvcqp2sYZQ4B9Dg0lxqmn2c8UFzfW0M03+rjklVWf6AnmgB/2g/8APvN+Q/xo+0H/AJ95vyH+NT5FLQBX+0H/AJ95vyH+NH2g/wDPvN+Q/wAasUUAV/tB/wCfeb8h/jR9oP8Az7zfkP8AGrHQVHNPFbxmSaVI0HVnbAH4mgCusjC4kk+zy4ZVA4HbPvUn2g/8+835D/GpWljWMyM6iMDcWJwAPXPpUNnqNjqMbSWN5BcopwWhkDgH0yKAF+0H/n3m/If40faD/wA+835D/GrFFAFf7Qf+feb8h/jR9oP/AD7zfkP8asUUAV/tB/595vyH+NRzytJEVW3lzkdQPX61bLAck4Aqraarp1/I8dnf21w8f31hmVyv1APFAD/tB/595vyH+NH2g/8APvN+Q/xpk2p6fb3UdrPfW0VxL/q4nlVXf6AnJq0CDQBB9oP/AD7zfkP8aPtB/wCfeb8h/jViigCv9oP/AD7zfkP8aPtB/wCfeb8h/jVig4AyTQBX+0H/AJ95vyH+NRxyskkxNvLh3BHA/ugevtStqdgl6tk17brdtysBlAkP0XOamnuILaF5p5o4okG53kYKqj1JNADPtB/595vyH+NH2g/8+835D/Gltby1voFns7mK4hbpJE4dT+I4qegCv9oP/PvN+Q/xpHnYowFvLkjHQf41ZooAjgBWCNWBBCgYPbipKKKACiiigAooooAKKKKACiiigAooooAKKKKAM/Xr99L8PalqEYBe1tZZlB6EqpI/lXnkvhqwi+ES6rHBGNXSxXUxfkDzvP2iQsX6nJ4+nFeia5YnVNA1HT1YKbq1kgBPYspX+tebTeKLWb4XDw4mT4he1XS200j96JNojY47LgFs9PegD0vSbw6hpFjekEfaLdJsf7yg/wBau1U0y0Gn6ZaWQbcLeBIgfXaoH9Kt0AFFFFACHpXCat4O0Gx8Gam+qxQT3Ihlnn1KRAsxlOTvDdVOcYAPYCu7PSvLJ/HXhbX9XJ1bV4YNJspcQWTo2bmRTjzJMDhQc7U/E44AAHvJeaxZfD/RNZRpFv1e4vY5P+WohiDKrg9cllJB9K1za23h34oaXFp0Edtb6vZzRzwQrsQvFhlfaOM4JGfSovE2oWrX/hTxjbS+fpVnNKk0yKfkjmTZvIxnAKjPHpT7a9tvFXxKsL3S5Rc6dpFnLuuo+YmllwoRW6EhQTxQB3lFFFABSHoaWqerWUmo6Rd2UVy9s9xE0YmjGWTIxke9AHlevXkviHxJoevBj/Z8PiC3sLAA8Oq7zLIPXcyqAfRK6nV4Ydc+J+n6RfRJNZWWmvf+S4yjytJ5allPBwAcZ7nNcr4j8M6volt4T0+PxGZLdNVt4bUCxiXyWCth+PvY5OCTkmuh1J/+EY8d6Vreq3O+0n0w6bcXzKFRJQ+9WYDhdxz7CgCzoUUWi/ErVtFso1hsrmxiv1gQYSN9xRio6DdwT9K7euH8NTx6/wCPtY8Q2pL6fFaxafbTgfJOQS7lT3AJAz0ruKACiiigDA8b63J4d8GapqsOPOhhxGT2diFU/gWFcfoFzoGijSY7vw7cYunQR65d26N59weQ5Ykuu5h8u7H4Dmur8f6RPrvgTV9PtkL3EkO+NB1ZkYOB+JXFcn4i8Q6d4x8PabomkSCXVLu4gLWyqd9oEYM7OP4du0j8eM0AatvZWviL4la6upW0V1DpFvb29vDOokQGVTIz4PG7oM+nepvA+NP1rxLoEPFnp94r26doklQPsHoAc/nVY39r4T+I2tXOrzra2Os28EkNzKdsfmRKUMeegOOeateBA1/f+IfEWx0g1S9H2feuC8UaBFf6HmgDtKKKKAEPSuW+IFrd3fhSVLdXkiWaOS6iRtrSW6sDIoOR2/PpXUnpXH/Ei3uJvDls6RSTWcF/BNqEMa7mltlb5xgdexx6CgDB0+48P6z4x0SXwZFAgtBIdQkgg8lRAVwEcEDcS2McHGOcVf8ADml6d4n1bxTqOrWMF466jJp0SzoHWOKIAYUH7uSSeKh1DVdO8R+LvC//AAjc8dzcWkpkuZ7YZSG224ZHPbccAKfToOKXR9c07wZrPibT9cuksxNfyalbvJwJkkUEhPUgjGOp9KANT4cTyHw/cWEkjyDTL+4sEdzklEb5fyBA/Cuxrkfh1aXEHhp7u6ieGbUrua+aNxygdvlH5AH8a66gAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACkPSlpD0oAx9Y0C31y7sTfYlsrZmle0cApM+AFLA9dvzcdMkHtXJCC28P/E2Y6PbpDbf2S9xqMEC7Ygyt8jYHCseePTn1zueNPF9t4WsrdHkiS6vXMcDT58tMAEu+OcDPTqelZfhjU/Cd5Fc6Tp2sR3+qX6O91cOjeZcMVILEkAYAyAo4A6UAZel+G7DVPhVLqd3bxyareW0t+1+V/eiY5ZWDdRjAHB7e9d14V1GTVvCuk6hMR51xaRySEdC5UbvwzXAWHia20n4az+HbltviG2hk09bAA+Y8hyqFR3Ugg5HHXmvQvDenPpPhrS9PkIMltaxxOQP4goB/XNAGrRRRQAUh6Glpkqs8TqrbGIIDYzg+tAHkXxNvJPEFlq3lM39naG8MZZekl48qAjjrsQkEer+1dV4zVNS8S+FvD9wm+yvJ5ri4jPIlEKbgjDuu5hx7VyHjHwlqnhn4c3kC+InuLITIzwtZxgyM0yksz/eJ3EHqc4xXSeI7e90O+8La9qV499HplxNHeXIhCbY512hyq5wFIUcUATm1tvDvxQ0uLToI7a31ezmjnghXYheLDK+0cZwSM+ld1XB217beKviVYXulyi507SLOXddR8xNLLhQit0JCgniu8oAKKKKACuG+Jmrmw0ex0/zZYU1S8S1mkhBMgi6uEA5LEfLjvuruT0rjPiDbtFBo2tiFpYdI1CO5uFUZKw8hmA77cg/hQBF4cu9Dtddi0lfC7aHfGEtama3jBnQYzh1LEkYBIJz35qp4V0XTfFdjreq6tZw3c1/fTxB5VDNHEh2KqH+HGM5Hc5p0+pWfivx94dl0SdbuDS1nuLu5hGUQPHtWPd0yTnK9cCq2geIdN8FW+t6PrU629xbXk09tG3BuYnO5DH/eJJIwOR3oA3vhxfz3/giwN1IZZ4DJbNIf4hG5VT9doGffNdZXLfDzTbnS/BOnw3qFLqQPPKhGCpkcvj8iK6mgAooooARuRx1ry+8uPDmj+KdebxpFBJNeOGspJ7fz1e2CgCNMKdpB3ZHBPUZHNeoN9015vpWsaX4c8Q+KY/E1zFb3lzeNLFLcjImtSB5aIcfNjkbevNAGctteDwp4U8O33mRwanqJ3RPJlvsilpEiZh142j6cVvahZWnhz4heHJ9MtYbRNRE9pdRQKEWUKm+MkDjcCOvoa5q0tL7T/CWha7JbzrZadrL3SQFSXhsXJXJA5+UEHGOB9K6KTUrTxd4/0E6ROl5Z6Uktzd3EXMas6bUTP97qcdcUAd8KWkpaACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKAA9Kydc0Ua5Zw2c0pW2E6yTxgcTqvOw+xbaT7D3rWrD8V+J7Twpob6jdAtlhFEmcb3PQE9h3J9u5wCAcvq+l2Oj/ABC8MnQrSC0urgyx3cNqgRXtwvVwoxgHGCe49qXwroum+K7HW9V1a0iu5r++miDyqGaOFDsRVP8ADjB5Hfmjwr4k8LNqJeTXYL/xBqBCSyrG4HtFGCPlQdh3PJyTUOgeIdP8FW+t6RrUwt7i3vZri3Qgg3MUh3IY/wC8ckjA6HrQBu/Di/nv/BFgbqQyzwGS2aQ/xCNyqn67QM++a6yuW+Hmm3Ol+CdPhvUKXUgeeVCMFTI5fH5EV1NABRRRQAh6V5T8Trt9dstX0+3Zv7P0a2E90y/x3DcRx++1SzHHfb3r1c15F4q8Iar4d8Ba4yeJHntpS09xC9lGDO7sucvyfQfhQB0Hixjd2HhXQnYiDUrqJblQSPMiRN7Jx2JApdQsrTw58QvDk+mWsNomoie0uooFCLKFTfGSBxuBHX0NU9asr/TND8N6zdXb6iNLvY555FhCFLdl2t8q9doIPrjJ7VZk1K08XeP9BOkTpeWelJLc3dxFzGrOm1Ez/e6nHXFAHfClpKWgAoNFB6UAcF8SdUWJtE0OQziHVbllnFuD5kkaAExrjnLkqvUdateGb/Rl1qbTI/DX9hanHb+YiSW8amaDdyQyHnnGRnrTPHMa2Ws+G/EcsbNa6ZcyJcFV3eXHKm3zD7KQpqtBfWvij4k6ffaPMtzY6XZzC5uouY2aTAWMHoSAM8cfjQBS8LeH9O8WeCLvVtStIpb/AFiS4me5ZA0kWHZUCMeRtCjAGMV1HgLVJtZ8DaPfXL753twsjk/eZSVJPucZrkdA8SWXgzwZe6BqMwj1bTHnigtyDvudzM0bIP4gdwHHTHOK7LwRpUuieC9J06ZSs0VuPMU9VZvmIP0JNAHQUUUUAFI/3D0/HpS1FcrI9rKkLhJWQhGP8LY4NAHjQfw/beFL3QNWtEl8ZyNIp3Q5nmumJMciy9APunO7gAiup1G3m1PxN4T8P6vsuVhtHu71G+ZJpkVVGfUBiW59qwLHVtDs/hfd6BqKhdcMUsU9gyE3ElyxbDAdWJbawbkD14rWulu/Dl/4O13WidkFibHUrgjd5LMi4ZiATjcCC3Az9aANHT4INC+Kcmn2ESW9lqOmm5eCNdq+ckm3cAOBlTzj0FdzXC6RdQ+I/iZcazYSCfTLDTvsa3CcpLKzhjtbuAABxXdUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAHpUYgjEvmiNBJjBfb8351JRQAgzmloooAKKKKAEIyKQj2p1BoAZgFdpAweCKbFHHEoWONI19FXArB8Ya/LoOkRmziWbUb2dLSyif7plc4BPsBk/h71mP4S8Qw2hvIPF2oyawBv2y7Tau393y8cLxjOc85oA7UkDqaMiuT0/xraz+A5PEl1F5X2aNxcwDqkyHaUH1bAH1FUbHw/4g1zT49U1XxJqNhezr5kVrYMiQ24PKqQVJc+uTQB3W4DqaCRg81y/hHW72/k1HSNXMf9r6XKI52jXasyMMxygdsjqOxrnfBdlq3ibw2uqXPinWYZ3mlQLC8WwBXIHDRk9vWgD0cjd065zQ8auhV1DIeoYZGK5Xw5rGpQ+Ibzwzrs0c99BELi2u0j2faoScElegZTgHH8ua608A0ANRVjUKihVUYAUcAfSnZFef/EbWdZt2t9O8P3JhvYrabUbhlUE+VEvC4/2mP6V2Wj6lDq+j2epQn91cwrKB6ZGSPw6fhQBfopMj1oLKBkkAdc0ADAlcCo1hRGZkjRWb7xAwW+tSZHrRkUARyQRyqVlRZFPUMMinhcYAwAOwpc0ZFAC0UhIHU0bhjOeKAFoPSk3DGc8UuaAIo4UiB8uNE3HLBVABPrSvBHJt8yNX2nK7hnB9qkooAaBzmnUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUANC/pQV7YGP6U6igCJoUaQSGJDIBw5AyPx7U8ZHJp1Ieh5xQAZFGQO9cVqF5qXiTxXc+H9Jv30+x0+NDqF3Ao81nYZWJCenHJOPaq90+peBNRsZ59Xu9S0G6nW3n+3MHltnbhXDgAlc8EHpQB3u4Ubge9cf4h1XUr3xFa+FtEuRa3EsJury7CBzBDnA2g8b2PA9BzVDV7XV/A9qut22t6jqenwMPt9rfyLIxjJALowAIK5zigDvuv8A+qkxldpGQevHUVxXjjUrsp4ai0zUZ7WLU9Rihee3IDNGynpkEd89O1O1XSvEegWMuqaTr97qLWymWWy1ARus6AZKqyopVsA49TQB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        }
      ],
      "dimensions": {
        "dpi": 200,
        "height": 2200,
        "width": 1700
      }
    },
    {
      "index": 26,
      "markdown": "![img-8.jpeg](img-8.jpeg)\n\nFigure 9: Scatter plot of predicted accuracy versus (true) OOD accuracy for vision datasets except MNIST with a ResNet50 model. Results reported by aggregating MAE numbers over 4 different seeds.",
      "images": [
        {
          "id": "img-8.jpeg",
          "top_left_x": 290,
          "top_left_y": 226,
          "bottom_right_x": 1405,
          "bottom_right_y": 1834,
          "image_base64": 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    {
      "index": 27,
      "markdown": "| Dataset | Shift | IM |  | AC |  | DOC |  | GDE | ATC-MC (Ours) |  | ATC-NE (Ours) |  |\n| :--: | :--: | :--: | :--: | :--: | :--: | :--: | :--: | :--: | :--: | :--: | :--: | :--: |\n|  |  | Pre T | Post T | Pre T | Post T | Pre T | Post T | Post T | Pre T | Post T | Pre T | Post T |\n| CIFAR10 | Natural | 6.60 | 5.74 | 9.88 | 6.89 | 7.25 | 6.07 | 4.77 | 3.21 | 3.02 | 2.99 | 2.85 |\n|  |  | (0.35) | (0.30) | (0.16) | (0.13) | (0.15) | (0.16) | (0.13) | (0.49) | (0.40) | (0.37) | (0.29) |\n|  | Synthetic | 12.33 | 10.20 | 16.50 | 11.91 | 13.87 | 11.08 | 6.55 | 4.65 | 4.25 | 4.21 | 3.87 |\n|  |  | (0.51) | (0.48) | (0.26) | (0.17) | (0.18) | (0.17) | (0.35) | (0.55) | (0.55) | (0.55) | (0.75) |\n| CIFAR100 | Synthetic | 13.69 | 11.51 | 23.61 | 13.10 | 14.60 | 10.14 | 9.85 | 5.50 | 4.75 | 4.72 | 4.94 |\n|  |  | (0.55) | (0.41) | (1.16) | (0.80) | (0.77) | (0.64) | (0.57) | (0.70) | (0.73) | (0.74) | (0.74) |\n| ImageNet200 | Natural | 12.37 | 8.19 | 22.07 | 8.61 | 15.17 | 7.81 | 5.13 | 4.37 | 2.04 | 3.79 | 1.45 |\n|  |  | (0.25) | (0.33) | (0.08) | (0.25) | (0.11) | (0.29) | (0.08) | (0.39) | (0.24) | (0.30) | (0.27) |\n|  | Synthetic | 19.86 | 12.94 | 32.44 | 13.35 | 25.02 | 12.38 | 5.41 | 5.93 | 3.09 | 5.00 | 2.68 |\n|  |  | (1.38) | (1.81) | (1.00) | (1.30) | (1.10) | (1.38) | (0.89) | (1.38) | (0.87) | (1.28) | (0.45) |\n| ImageNet | Natural | 7.77 | 6.50 | 18.13 | 6.02 | 8.13 | 5.76 | 6.23 | 3.88 | 2.17 | 2.06 | 0.80 |\n|  |  | (0.27) | (0.33) | (0.23) | (0.34) | (0.27) | (0.37) | (0.41) | (0.53) | (0.62) | (0.54) | (0.44) |\n|  | Synthetic | 13.39 | 10.12 | 24.62 | 8.51 | 13.55 | 7.90 | 6.32 | 3.34 | 2.53 | 2.61 | 4.89 |\n|  |  | (0.53) | (0.63) | (0.64) | (0.71) | (0.61) | (0.72) | (0.33) | (0.53) | (0.36) | (0.33) | (0.83) |\n| FMoW-WILDS | Natural | 5.53 | 4.31 | 33.53 | 12.84 | 5.94 | 4.45 | 5.74 | 3.06 | 2.70 | 3.02 | 2.72 |\n|  |  | (0.33) | (0.63) | (0.13) | (12.06) | (0.36) | (0.77) | (0.55) | (0.36) | (0.54) | (0.35) | (0.44) |\n| RxRx1-WILDS | Natural | 5.80 | 5.72 | 7.90 | 4.84 | 5.98 | 5.98 | 6.03 | 4.66 | 4.56 | 4.41 | 4.47 |\n|  |  | (0.17) | (0.15) | (0.24) | (0.09) | (0.15) | (0.13) | (0.08) | (0.38) | (0.38) | (0.31) | (0.26) |\n| Amazon-WILDS | Natural | 2.40 | 2.29 | 8.01 | 2.38 | 2.40 | 2.28 | 17.87 | 1.65 | 1.62 | 1.60 | 1.59 |\n|  |  | (0.08) | (0.09) | (0.53) | (0.17) | (0.09) | (0.09) | (0.18) | (0.06) | (0.05) | (0.14) | (0.15) |\n| CivilCom.-WILDS | Natural | 12.64 | 10.80 | 16.76 | 11.03 | 13.31 | 10.99 | 16.65 |  | 7.14 |  |  |\n|  |  | (0.52) | (0.48) | (0.53) | (0.49) | (0.52) | (0.49) | (0.25) |  | (0.41) |  |  |\n| MNIST | Natural | 18.48 | 15.99 | 21.17 | 14.81 | 20.19 | 14.56 | 24.42 | 5.02 | 2.40 | 3.14 | 3.50 |\n|  |  | (0.45) | (1.53) | (0.24) | (3.89) | (0.23) | (3.47) | (0.41) | (0.44) | (1.83) | (0.49) | (0.17) |\n| ENTITY-13 | Same | 16.23 | 11.14 | 24.97 | 10.88 | 19.08 | 10.47 | 10.71 | 5.39 | 3.88 | 4.58 | 4.19 |\n|  |  | (0.77) | (0.65) | (0.70) | (0.77) | (0.65) | (0.72) | (0.74) | (0.92) | (0.61) | (0.85) | (0.16) |\n|  | Novel | 28.53 | 22.02 | 38.33 | 21.64 | 32.43 | 21.22 | 20.61 | 13.58 | 10.28 | 12.25 | 6.63 |\n|  |  | (0.82) | (0.68) | (0.75) | (0.86) | (0.69) | (0.80) | (0.60) | (1.15) | (1.34) | (1.21) | (0.93) |\n| ENTITY-30 | Same | 18.59 | 14.46 | 28.82 | 14.30 | 21.63 | 13.46 | 12.92 | 9.12 | 7.75 | 8.15 | 7.64 |\n|  |  | (0.51) | (0.52) | (0.43) | (0.71) | (0.37) | (0.59) | (0.14) | (0.62) | (0.72) | (0.68) | (0.88) |\n|  | Novel | 32.34 | 26.85 | 44.02 | 26.27 | 36.82 | 25.42 | 23.16 | 17.75 | 14.30 | 15.60 | 10.57 |\n|  |  | (0.60) | (0.58) | (0.56) | (0.79) | (0.47) | (0.68) | (0.12) | (0.76) | (0.85) | (0.86) | (0.86) |\n| NONLIVING-26 | Same | 18.66 | 17.17 | 26.39 | 16.14 | 19.86 | 15.58 | 16.63 | 10.87 | 10.24 | 10.07 | 10.26 |\n|  |  | (0.76) | (0.74) | (0.82) | (0.81) | (0.67) | (0.76) | (0.45) | (0.98) | (0.83) | (0.92) | (1.18) |\n|  | Novel | 33.43 | 31.53 | 41.66 | 29.87 | 35.13 | 29.31 | 29.56 | 21.70 | 20.12 | 19.08 | 18.26 |\n|  |  | (0.67) | (0.65) | (0.67) | (0.71) | (0.54) | (0.64) | (0.21) | (0.86) | (0.75) | (0.82) | (1.12) |\n| LIVING-17 | Same | 12.63 | 11.05 | 18.32 | 10.46 | 14.43 | 10.14 | 9.87 | 4.57 | 3.95 | 3.81 | 4.21 |\n|  |  | (1.25) | (1.20) | (1.01) | (1.12) | (1.11) | (1.16) | (0.61) | (0.71) | (0.48) | (0.22) | (0.53) |\n|  | Novel | 29.03 | 26.96 | 35.67 | 26.11 | 31.73 | 25.73 | 23.53 | 16.15 | 14.49 | 12.97 | 11.39 |\n|  |  | (1.44) | (1.38) | (1.09) | (1.27) | (1.19) | (1.35) | (0.52) | (1.36) | (1.46) | (1.52) | (1.72) |\n\nTable 3: Mean Absolute estimation Error (MAE) results for different datasets in our setup grouped by the nature of shift. 'Same' refers to same subpopulation shifts and 'Novel' refers novel subpopulation shifts. We include details about the target sets considered in each shift in Table 2. Post T denotes use of TS calibration on source. For language datasets, we use DistilBERT-base-uncased, for vision dataset we report results with DenseNet model with the exception of MNIST where we use FCN. Across all datasets, we observe that ATC achieves superior performance (lower MAE is better). For GDE post T and pre T estimates match since TS doesn't alter the argmax prediction. Results reported by aggregating MAE numbers over 4 different seeds. Values in parenthesis (i.e., $(\\cdot)$ ) denote standard deviation values.",
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    {
      "index": 28,
      "markdown": "| Dataset | Shift | IM |  | AC |  | DOC |  | GDE | ATC-MC (Ours) |  | ATC-NE (Ours) |  |\n| :--: | :--: | :--: | :--: | :--: | :--: | :--: | :--: | :--: | :--: | :--: | :--: | :--: |\n|  |  | Pre T | Post T | Pre T | Post T | Pre T | Post T | Post T | Pre T | Post T | Pre T | Post T |\n| CIFAR10 | Natural | 7.14 | 6.20 | 10.25 | 7.06 | 7.68 | 6.35 | 5.74 | 4.02 | 3.85 | 3.76 | 3.38 |\n|  |  | (0.14) | (0.11) | (0.31) | (0.33) | (0.28) | (0.27) | (0.25) | (0.38) | (0.30) | (0.33) | (0.32) |\n|  | Synthetic | 12.62 | 10.75 | 16.50 | 11.91 | 13.93 | 11.20 | 7.97 | 5.66 | 5.03 | 4.87 | 3.63 |\n|  |  | (0.76) | (0.71) | (0.28) | (0.24) | (0.29) | (0.28) | (0.13) | (0.64) | (0.71) | (0.71) | (0.62) |\n| CIFAR100 | Synthetic | 12.77 | 12.34 | 16.89 | 12.73 | 11.18 | 9.63 | 12.00 | 5.61 | 5.55 | 5.65 | 5.76 |\n|  |  | (0.43) | (0.68) | (0.20) | (2.59) | (0.35) | (1.25) | (0.48) | (0.51) | (0.55) | (0.35) | (0.27) |\n| ImageNet200 | Natural | 12.63 | 7.99 | 23.08 | 7.22 | 15.40 | 6.33 | 5.00 | 4.60 | 1.80 | 4.06 | 1.38 |\n|  |  | (0.59) | (0.47) | (0.31) | (0.22) | (0.42) | (0.24) | (0.36) | (0.63) | (0.17) | (0.69) | (0.29) |\n|  | Synthetic | 20.17 | 11.74 | 33.69 | 9.51 | 25.49 | 8.61 | 4.19 | 5.37 | 2.78 | 4.53 | 3.58 |\n|  |  | (0.74) | (0.80) | (0.73) | (0.51) | (0.66) | (0.50) | (0.14) | (0.88) | (0.23) | (0.79) | (0.33) |\n| ImageNet | Natural | 8.09 | 6.42 | 21.66 | 5.91 | 8.53 | 5.21 | 5.90 | 3.93 | 1.89 | 2.45 | 0.73 |\n|  |  | (0.25) | (0.28) | (0.38) | (0.22) | (0.26) | (0.25) | (0.44) | (0.26) | (0.21) | (0.16) | (0.10) |\n|  | Synthetic | 13.93 | 9.90 | 28.05 | 7.56 | 13.82 | 6.19 | 6.70 | 3.33 | 2.55 | 2.12 | 5.06 |\n|  |  | (0.14) | (0.23) | (0.39) | (0.13) | (0.31) | (0.07) | (0.52) | (0.25) | (0.25) | (0.31) | (0.27) |\n| FMoW-WILDS | Natural | 5.15 | 3.55 | 34.64 | 5.03 | 5.58 | 3.46 | 5.08 | 2.59 | 2.33 | 2.52 | 2.22 |\n|  |  | (0.19) | (0.41) | (0.22) | (0.29) | (0.17) | (0.37) | (0.46) | (0.32) | (0.28) | (0.25) | (0.30) |\n| RxRx1-WILDS | Natural | 6.17 | 6.11 | 21.05 | 5.21 | 6.54 | 6.27 | 6.82 | 5.30 | 5.20 | 5.19 | 5.63 |\n|  |  | (0.20) | (0.24) | (0.31) | (0.18) | (0.21) | (0.20) | (0.31) | (0.30) | (0.44) | (0.43) | (0.55) |\n| Entity-13 | Same | 18.32 | 14.38 | 27.79 | 13.56 | 20.50 | 13.22 | 16.09 | 9.35 | 7.50 | 7.80 | 6.94 |\n|  |  | (0.29) | (0.53) | (1.18) | (0.58) | (0.47) | (0.58) | (0.84) | (0.79) | (0.65) | (0.62) | (0.71) |\n|  | Novel | 28.82 | 24.03 | 38.97 | 22.96 | 31.66 | 22.61 | 25.26 | 17.11 | 13.96 | 14.75 | 9.94 |\n|  |  | (0.30) | (0.55) | (1.32) | (0.59) | (0.54) | (0.58) | (1.08) | (0.93) | (0.64) | (0.78) |  |\n| Entity-30 | Same | 16.91 | 14.61 | 26.84 | 14.37 | 18.60 | 13.11 | 13.74 | 8.54 | 7.94 | 7.77 | 8.04 |\n|  |  | (1.33) | (1.11) | (2.15) | (1.34) | (1.69) | (1.30) | (1.07) | (1.47) | (1.38) | (1.44) | (1.51) |\n|  | Novel | 28.66 | 25.83 | 39.21 | 25.03 | 30.95 | 23.73 | 23.15 | 15.57 | 13.24 | 12.44 | 11.05 |\n|  |  | (1.16) | (0.88) | (2.03) | (1.11) | (1.64) | (1.11) | (0.51) | (1.44) | (1.15) | (1.26) | (1.13) |\n| NonLIVING-26 | Same | 17.43 | 15.95 | 27.70 | 15.40 | 18.06 | 14.58 | 16.99 | 10.79 | 10.13 | 10.05 | 10.29 |\n|  |  | (0.90) | (0.86) | (0.90) | (0.69) | (1.00) | (0.78) | (1.25) | (0.62) | (0.32) | (0.46) | (0.79) |\n|  | Novel | 29.51 | 27.75 | 40.02 | 26.77 | 30.36 | 25.93 | 27.70 | 19.64 | 17.75 | 16.90 | 15.69 |\n|  |  | (0.86) | (0.82) | (0.76) | (0.82) | (0.95) | (0.80) | (1.42) | (0.68) | (0.53) | (0.60) | (0.83) |\n| LIVING-17 | Same | 14.28 | 12.21 | 23.46 | 11.16 | 15.22 | 10.78 | 10.49 | 4.92 | 4.23 | 4.19 | 4.73 |\n|  |  | (0.96) | (0.93) | (1.16) | (0.90) | (0.96) | (0.99) | (0.97) | (0.57) | (0.42) | (0.35) | (0.24) |\n|  | Novel | 28.91 | 26.35 | 38.62 | 24.91 | 30.32 | 24.52 | 22.49 | 15.42 | 13.02 | 12.29 | 10.34 |\n|  |  | (0.66) | (0.73) | (1.01) | (0.61) | (0.59) | (0.74) | (0.85) | (0.59) | (0.53) | (0.73) | (0.62) |\n\nTable 4: Mean Absolute estimation Error (MAE) results for different datasets in our setup grouped by the nature of shift for ResNet model. 'Same' refers to same subpopulation shifts and 'Novel' refers novel subpopulation shifts. We include details about the target sets considered in each shift in Table 2. Post T denotes use of TS calibration on source. Across all datasets, we observe that ATC achieves superior performance (lower MAE is better). For GDE post T and pre T estimates match since TS doesn't alter the argmax prediction. Results reported by aggregating MAE numbers over 4 different seeds. Values in parenthesis (i.e., $(\\cdot)$ ) denote standard deviation values.",
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