diff --git a/2001.09215/main_diagram/main_diagram.drawio b/2001.09215/main_diagram/main_diagram.drawio
new file mode 100644
index 0000000000000000000000000000000000000000..786c428104de6f05a41cb3db93e86a78e0430feb
--- /dev/null
+++ b/2001.09215/main_diagram/main_diagram.drawio
@@ -0,0 +1 @@
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
\ No newline at end of file
diff --git a/2001.09215/paper_text/intro_method.md b/2001.09215/paper_text/intro_method.md
new file mode 100644
index 0000000000000000000000000000000000000000..1ac75f270e9cd512a4f10257feb5c31cf1d27af9
--- /dev/null
+++ b/2001.09215/paper_text/intro_method.md
@@ -0,0 +1,22 @@
+# Method
+
+We aimed to mimic the presence of sparse/noisy content distribution, mandating the need to curate a novel dataset via specific lexicons. We scraped $500$ random posts from recognized transport forum[^4]. A pool of $50$ uni/bi-grams was created based on tf-idf representations, extracted from the posts, which was further pruned by annotators. Querying posts on *Twitter* with extracted lexicons led to a collection of $19,300$ tweets. In order to have lexical diversity, we added $2500$ randomly sampled tweets to our dataset. In spite of the sparse nature of these posts, the lexical characteristics act as **information cues**.
+
+Figure [1](#fig:pipeline){reference-type="ref" reference="fig:pipeline"} pictorially represents our methodology. Our approach required an initial set of ***informative tweets*** for which we employed two human annotators annotating a random sub-sample of the original dataset. From the $1500$ samples, $326$ were marked as *informative* and $1174$ as *non informative* ($\kappa=0.81$), discriminated on this criteria: ***Is the tweet addressing any complaint or raising grievances about modes of transport or services/ events associated with transportation such as traffic; public or private transport?***. An example tweet marked as *informative*: .
+
+We utilized tf-idf for the identification of initial from the curated set of ***informative tweets***. $50$ terms having the highest tf-idf scores were passed through the complete dataset and based on sub-string match, the ***transport relevant tweets*** were identified. The redundant tweets were filtered based on the cosine similarity score. **Implicit information indicators** were identified based on ***domain relevance score***, a metric used to gauge the coverage of n-gram ($1$,$2$,$3$) when evaluated against a randomly created pool of posts.
+
+We collected a pool of $5000$ randomly sampled tweets different from the data collection period. The rationale behind having such a metric was to discard commonly occurring n-grams normalized by random noise and include ones which are of lexical importance. We used terms associated with high domain relevance score (threshold determined experimentally) as for the next set of iterations. The growing dictionary augments the collection process. The process ran for $4$ iterations providing us $7200$ ***transport relevant tweets*** as no new lexicons were identified. In order to identify linguistic signals associated with the *complaint* posts, we randomly sampled a set of $2000$ tweets which was used as training set, manually annotated into distinct labels: *complaint relevant* ($702$) and *complaint non-relevant* ($1298$) ($\kappa=0.79$). We employed these features on our dataset.
+
+
+
+Pictorial representation of the proposed pipeline.
+
+
+**Linguistic markers**. To capture linguistic aspects of complaints, we utilized Bag of Words, count of POS tags and Word2vec clusters.
+
+**Sentiment markers**. We used quantified score based on the ratio of tokens mentioned in the following lexicons: MPQA, NRC, VADER and Stanford.
+
+**Information specific markers**. These account for a set of handcrafted features associated with *complaint*, we used the stated markers (a) Text-Meta Data, this includes the count of URL's, hashtags, user mentions, special symbols and user mentions, used to enhance retweet impact; (b) Request Identification, we employed the model presented in [@danescu2013no] to identify if a specific tweet assertion is a request; (c) Intensifiers, we make use of feature set derived from the number of words starting with capital letters and the repetition of special symbols (exclamation, questions marks) within the same post; (d) Politeness Markers, we utilize the politeness score of the tweet extracted from the model presented in [@danescu2013no]; (e) Pronoun Variation, these have the ability to reveal the personal involvement or intensify involvement. We utilize the frequency of pronoun types $\{\textit{first, second, third, demonstrative and indefinite}$} using pre-defined dictionaries.
+
+From the pool of $7200$ transport relevant tweets, we sampled $3500$ tweets which were used as the testing set. The results are reported in Table[1](#tab:res){reference-type="ref" reference="tab:res"} with $10$ fold cross-validation. **With increasing the number of iterations, the pool of gets refined and augments the selection of ***transport relevant tweets*****. The proposed pipeline is tailored to identify complaint relevant tweets in a noisy scenario.
diff --git a/2011.08067/main_diagram/main_diagram.drawio b/2011.08067/main_diagram/main_diagram.drawio
new file mode 100644
index 0000000000000000000000000000000000000000..abdb9be6649fbe74a792d085365ca57d0126c13d
--- /dev/null
+++ b/2011.08067/main_diagram/main_diagram.drawio
@@ -0,0 +1 @@
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+# Method
+
+In this section, we show how the two-step process of hierarchical encoding can be achieved using a single standard transformer encoder. If we want to have an $M$ layer utterance encoder followed by an $N$ layer context encoder, we start with an $(M+N)$ layer standard encoder. Then by applying two separate masks as designed below, we convert the standard encoder into an HT-encoder. First, we need to encode the utterances independently. Within the self-attention mechanism of a transformer encoder, which token gets to attend to which other tokens is controlled by the attention mask. If we apply a block-diagonal mask, each block of size same as the length of utterances (as shown in Figure [2](#fig:masks){reference-type="ref" reference="fig:masks"} bottom-left), to the concatenated sequence of tokenized utterances, we effectively achieve the same process of utterance encoding. We call this block-diagonal mask for utterance encoding the **UT-mask**.
+
+Similarly, another attention mask (**CT-Mask**) can explain the context encoding phase that allows tokens to attend beyond the respective utterance boundaries. See the two matrices on Figure [2](#fig:masks){reference-type="ref" reference="fig:masks"}'s right for examples of such CT-Masks. From here, it can be quickly concluded that if we apply the UT-Mask for the first few layers of the encoder and the CT-Mask in the remaining few layers, we effectively have a hierarchical encoder. The CT-Mask also gives us more freedom on what kind of global attention we want to allow during context encoding. Positional encoding is applied once before utterance encoder (local PE) and once more before context encoder (global PE).
+
+
+
+Example of UT-Mask (A for the given CI) and CT-Masks. Blue cells: 1, White cells: 0. Bottom left is the UT-Mask and on the right are CT-Masks for HIER-CLS(top) and HIER(bottom). In this example, the context comprises of three utterances of lengths 0, 1 and 2, respectively. CI indicates which utterance each of the tokens belongs to. The entries in PI denotes the relative position of each token with respect to utterance corresponding to it.
+
+
+The steps for obtaining the UT-Mask and positional encoding for the utterance encoder are given below and is accompanied by Figure [2](#fig:masks){reference-type="ref" reference="fig:masks"}. $C$ is the dialog context to be encoded. $w_{ij}$ is the $j_{th}$ token of $i_{th}$ utterance. In $C_{I}$, each index $i$ is repeated $|u_i|$ (length of $u_i$) times. And $C_{IR}$ is a square matrix created by repeating $C_I$. $P_I$ has the same dimensions as $C_I$, and it stores the position of each token $w_{ij}$ in context $C$, relative to utterance $u_i$. $\mathcal{P}:I \mapsto R^d$ is the positional encoding function that takes an index (or indices) and returns their $d$-dim positional embedding. $A$ is the UT-Mask for the given context $C$ and their utterance indices $C_I$. An example instance of this process is given in Figure [2](#fig:masks){reference-type="ref" reference="fig:masks"}. $\mathbf{1}(.)$ is an indicator function that returns true when the input logic holds, and is applied to a matrix or vector element-wise. $$\begin{align*}
+ C &= [w_{11}, w_{12}, ..., w_{Tl_T}]\\
+ C_{I} &= [0, \dots, 0, 1, \dots, 1, \dots, T] \\
+ P_{I} &= [0, 1, \dots, l_1 - 1, 0, \dots, l_2 - 1,\dots, l_T - 1] \\
+ C_{IR} &= repeat(C_I, len(C_I), 0)\\
+ A &= 1\big(2C_{IR}== (C_{IR}^T + C_{IR})\big)\\
+ P_c &= \mathcal{P}[P_I, :]
+\end{align*}$$
+
+The attention masks for context encoding depends on the choice for model architecture. We provide the details of the architectures and their attention masks used in our experiments in the subsequent section. There are other masks possible, but these are the ones we found to be working best in their respective settings.
+
+
+
+
+Model: HIER
+
+
+
+Model: HIER++
+
+The proposed architecture for the hierarchical transformer: (a) HIER: when the belief states are not available and (b) HIER++: when the belief states are available.
+
+
+We propose several model architectures to test the effectiveness of the proposed HIER-Encoder in various experimental settings. These architectures are designed to fit well with the four experimental settings (see Section [3.1](#sec:four-eval){reference-type="ref" reference="sec:four-eval"}) of the response generation task of the MultiWOZ dataset in terms of input and output.
+
+The tested model architectures are as follows. Using the HIER encoding scheme described in Section [2.1](#sec:hte){reference-type="ref" reference="sec:hte"}, we test two model architectures for response generation, namely HIER and HIER++.
+
+HIER is the most straightforward model architecture with an HT-Encoder replacing the encoder in a Transformer Seq2Seq. The working of the model is shown in Figure [3](#fig:hier){reference-type="ref" reference="fig:hier"}. First, in the utterance encoding phase, each utterance is encoded independently with the help of the UT-Mask. In the second half of the encoder, we apply a CT-Mask as depicted by the figure's block attention matrix. Block $B_{ij}$ is a matrix which, if all ones, means that utterance $i$ can attend to utterance $j$'s contextual token embeddings. The local and global positional encodings are applied, as explained in Section [2.2](#sec:conversion-hte){reference-type="ref" reference="sec:conversion-hte"}. A standard transformer decoder follows the HT-Encoder for generating the response.
+
+The CT-Mask for HIER was experimentally obtained after trying a few other variants. The intuition behind this mask was that the model should reply to the last user utterance in the context. Hence, we design the attention mask to apply cross attention between all the utterances and the last utterance (see Figure [3](#fig:hier){reference-type="ref" reference="fig:hier"}).
+
+HIER++ is the extended version of the HIER model, as shown in Figure [4](#fig:hier++){reference-type="ref" reference="fig:hier++"}, that also takes the dialog act label as input. The dialog act representation proposed in @chen-etal-2019-semantically consists of the domain, act, and slot values. A linear feed-forward layer (FFN) acts as the embedding layer for converting their 44-dimension multi-hot dialog act representation. The output embedding is added to the input token embeddings of the decoder in HIER++ model. Similar to HDSA, we also use ground truth dialog acts during training, and predictions from a fine-tuned BERT model during validation and testing. HIER++ is applied to the Context-to-Response generation task of the MultiWOZ dataset.
+
+As described in Section [2.1](#sec:hte){reference-type="ref" reference="sec:hte"}, the encoding scheme of HIER-CLS is more akin to the HRED [@chen-etal-2019-semantically] and HIBERT [@zhang-etal-2019-hibert] models. It differs from HIER++ only with respect to the CT-Mask.
diff --git a/2102.06857/main_diagram/main_diagram.drawio b/2102.06857/main_diagram/main_diagram.drawio
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index 0000000000000000000000000000000000000000..224046dd96c8fb3076b0bfba5966ac754615f8b2
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+++ b/2102.06857/main_diagram/main_diagram.drawio
@@ -0,0 +1 @@
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cAbBGKJSHnV3AgpTwWPZAnV0G7OTRvPdAYbVtyTC2n7X+XbF7Zq4yySkqw3esrUQmVUWsXQL2zxMfi1h/LPJamQPxuC9BiUezWSI0Hc9THsF1floRWZxZzIGtDsX5/HSFUjXXm+Qm3MpFR0+q/rnl6sZMHTyOSwP3hI9g7kU+PPtfNWEzskgFV863Q5PTldHkQoDhBJBzdQFPDY8SZPSxH3TQ+pXaFBBt9Lt2tAQ95BfaTPQKL8A50+ACeVmjsEL4OjrlKNvrCf8+iS9IvPLQOaNffz3kuIEudLzymYzuuuPtRZzJQ66iqnuYN3UrASKHrqrn4pgnfzQrnCug7DX3cQ8oBJ0R1JgVff7kK6F0iHwpBMXjs0e0Fp935HjDNCQyLhOPdZ5kBjLDlGNlDzK1Th0enVe33KHfb0aw6G7gLE0w9Al4BU85+/vthliFAvq8GGqhlJc+YTj8+KNWA3to1m0wAF6f721Qq9f44yIevmfahFHRIwFBRabGePqkIg9/pFZEN3dYVSY3RkyTG7e2FmyLzAVR+mLjnX2Usgu2C0B3q61Ji5Zr3dN837O9ydK6D2u0WjhTlQl24IBPcO75SOTuSYzPFSzp1K4Fym+0cb/ZWW5ix0OIz3mhX/gTcxPAkt6qpBVMder4rgKkUhYDVWgMX7+gs8igjh553ASCXyRJGAYeoHOJzb5HLlNQPBJ2KtQ50R56WCMHIdvC2K19MQwrmBct2lsT3I1+so/32EWAvYBQefcXD8aBBCm3wIsv6tuy5FVxRUgUAyeQihmQeufyO9RCwPSMoCayjyCDnftlkMCyGbWR6O5uO+2+Ox0hxJ3tB8DteHsxnaZ8y3GEivRzQ8iFlBW5HJC9drPYNfQiAfUiyCJK6ZpBYmQ+vEeKBzHX+XzYnCB6L/CUFdgEFOGZtANP54FpT7++b5HI1C29+6rqEXfnDA7GSWukEd0SzoBv7EwpIRUarOrwnK8vb2ytjVzZle40HOaY45F9D6yKzTEPwhquz5FNWSe0YBVGpbAtKzRju+OhYHlQYTlqBqhl1oxa5TrOU591ZEweMqRAhQTKuZMnodzXuuv0J7IBIN2JoAVim0Z6z2aWrQeHdC7HXUqUWInKg7I89Ug2wDuCIiYjNdeVD74KPbVSampgiPmyokQ4ep8sts03tfc1MyjUwHWXWrYRhFZlSk0EdTENF+yNbpjNB9XYPwmv5dYbgJy17bKguo1nPeRY+HKvmdbp4cGb0W63JODJ5qCUHIkaVRPBZGhnKVdSdQqW7bAuvH55b21N/LQ6aLXeiCUpEDxf9SxbQ8BfbtNrURNrQpNoN6gCvWlVdmJB4EQvdR9G0zwaVXQNOpb1ZV/aXvCMfDdADbl/0TLHU4bTdRpujwKfrKO4JN4YDxP8eIFqrGHMVA+elu6TjLQIRysHC+FMgK5PrQWAukeQ6QHMweGsBr4Rm/KuWqFtezo7ILnA4yghtvzSqgX1wo9kfQGfpz8mwNFvf/fegERA7cS1/1rWpRLX2KzN4l3fCcVaWVciGx4bEhAWQvwHQ42jR5uvkxttvVIu352ZEI6XtyhIOxFuoFts93Xv5mFwt5J3Cd8ou5IHjqG97ubKTFeRsh9LYBIJ2OHMH3WpAGa5eH3tktEXeRhsHyXFkjuitkBuMWXMnD6wd0MR1H/OmVDHL8J8YgeL/wDw1ob7HjSgUoUEEja3OID8xshscebj/q1Yii2srA+qp2ZqjSKtMjDlBIxVvxclJqTDt+PoJ4s9bBZmZsrejVRpsS6KU80rjxrwMmXvAyBzFCfYVix0lFmB59UaBaVz3I37mL2SZb5COUl9g5SjEmjrcAn3wKOwNgEH+tRNL+0xvZCLjXPEl/tJ2zS5k37eFiwUHip06Ar2sN4vXoIJOoOHXfbl5QHN4z3sWIgSuTbb5McZ7MxsNjtujFi5qqB+zFRQAxD1h5KVopBLAD1GN+aSjyZji/fNMK1NukArj/mTjK9FEOfdnIK0gmb4zSFzRg1sQRZvyZVAVUqN9fdgyQr2tbJdJaMFNQaYKuFC19Hn8B8W3+C5GjKR+aZvB6sDY+nV9RLfmEDsabC3K41KdPooLf11q04GOtdqOkhPSSZ8rHY3e8xxNB/tV3GesKZtRtJwR9RA7ZTajr1BS59iOii2D80d0+F7OWkxvMesESVWTpkZrMhpcmV+fCsFWeZJA/M0vpsqrg84ohCrkXSKaDVxbYAgPR/B/qfjF0DQ11pjbZLY4aH7sKHTJJdhnYyLMQL3iw6h5UrPj05CMQxTX+r5kzJZGAOcoHds63yH9k4QG8QHpd/73iU7DOISsYFaiUCWSZlb23NVXQqQY9h186mxJy4oRR6WkfBp7CKai9dr6C7Ode9gzsRCv0/yj2vPZAGDmKUm0H1/lL8pumIBqEM5feVAInnGXXHvMy1/D9LjP/HjGwSivTztSxIUbT8UNK/2BLFIrAD93lCQEz1QEkT9qsm29eK7QwVeqWqPY4lnWDj8F9MQdj4QCaUwkNN9QCKmLsHlP936/jHilNGgiNCtFIbIt2Nw0cpiaH188xUpYIki9vntK1VeLu9X26c4miEVwanDF6N+eAXk2HtsHySNnQwWx3gtyuX6LuweXb2kG7EXq2DNR/Cmjm9gi0uMSt09zNWu9iYeCie68q3XXyPQmlhsR5csakK3jCFarElNiFSxcavy+Ir45jqFMOaLxIDHONbrpovWClprSKS7GWQhoM9lpARBwj0nrt9t03l4ghUjDdXTD/xGI5Gbgr6rKqSYUjOZueofSuIGVe54F0CybLFibuBaFzNH1oyOYDfDiyL0ibLwHTg9/1vP0jBO/zoWTL6jRxA08aL2yINskvVbuKkpRheZBWfl7uBJY4Ryr2XEHiVWt0pefSfvSAIrMI+MF74FVr/g+Zwi9636diL4cMJrlnN/+qRbQOlWi11zFEJFlEbI/ypUsd4LNRQDpTwHa+OxrRt+7QXUKi2v1ArBZAouaU10LbT7E4JJarUOz1K06pY5XYf0JzQa43WHSFp/AvNIAdbCXI+Tw3dRc/dO6Dlg/D/6hIs+e8dFIy/go/0oEI2GWEVL6fytgnTZNj+VgyaqpmBIrjkr4sSboMcjxLGb3kZSzilQsF7Ypr6sEiKD/HMKe4lT+Lf5jq5hOedVpsaaRGRT6lQnZ22ZDoby9lGE17BkJCD2m6qK31U/SHt6VVW0fh+mX6TyYThnh3oFa80TTyOeamXBu0RO/0NiLnHMLPEpUdhODiMpcnGJvLderuqCjkDXvWj6fnWGAanWfStH3co0G+HDAz1p/3Fi7QJVlNWL6vzKL/9RgHq3JJOLXXOJBob2vlX7PL3v12n9FRA7I3kRR6WX7U6wx4nF+hCI6UwC0EkkigKkUcaBWqBCFtju+vwUwb6V6J+Y3N9oSwcaWHBb8JkfIjj2XfdbWYPDfIwFNqFAr1LJk5xlL/cBV2EsHB34A08PL8Hfine3NwpYeS3QXGYmfe2UdWMPVot8TT98EBokJe5oxffYoB1Yxht2pfkca/XNvUCGNMG3PFxBPV4mB2hGTnyB+RYSfIGF0WoWo7DAwYo74mcpdjTaIllYhLaAKBKYZEtW9oVhUqgCFmffdNEQ8g9bx/ORJu2U5PUV0SzQu0LTA+WPqhW+7c0yV5HujWN8UmoTzt5HOCtf0McPbgRUCez2xkXDtHQsrTu7JcwtVqZGG/xrYrdFFmE8Dmtdn7QVlE95lSFoXu1ls5hfsNPRFj8nVWsMFIgW/xsU/RISUVD/UiATv+ee2bd9d2qvkMYvGAwy4pNDaGEpUxUfKEUdcMMu14A8YDSt+gzXkkhzkc2yKsUH8KKl5ckOgpo7k969+2ZKZZi3AJ5hHIcx+u0YXRHu/hG2Z9FngRyjeVAbxFTk+YSY1PtQ69x80rMI+HA7K1PCOwGbsOJc7r81ZKJNvYdlcgVVaSXQD77Yz8bbXwdHY4GFBOnK3x2psDq6NHbuuIcittekuGyJbdZpjw+FVHbNYhqcZoJ0Km/Re0icWtyEhb4kXk4TCJumD6U8cn5piKkPY9hEB2OtGJh7YPz4ylrSVSv3gtNk8ccjcSHmyWPmTJZAAZYvmjgLs9WCqYWpHRPOh8Mz+Eqo5ygUn2AACerp9Pbkge2JvuaLVGDtX8JjPeDNoBfEXIAq2ynlVZTTQO/l6FfhYi6MqbcnYQjI9mr25n3P3Nv7wIfQvGevCWZrdh95THXA1W4iA2thDoGfoNIhZbWCGMvjVcdDu09uHhMRwJ13f2db6wvAJ7HtqhacJOSMW0PKC674IO3IyN8W5n6tqwoliHwc8sAxSZkIrUSJwVGpvB4XC4sbNrH3D2FUgdysQWkhPAuH+gmSrPp6elywjuHA6huK50FbXZjDnGWzhwPJXuvwYfagCsT7YatNMcNO5NruHyRo3xuomeqTWoEwHW5A8bdzHL3ojKlQBU66lKox0RJUTcQTiRp1+Mln0Q4VjMnhc/0B+ra0HcAnsz8FxgepKftDRDTji0N0untDUUKbCSYNP3Mv4aB8eWrTeNZ/mf3zdybW4cPhVctsvNjXCW6QQZ3xCFid1SM1PFuUm3JdjYkDEJKmzN4S9ik4fmaE5FPKYeDumKgUlCLrjpOpVfuFtpAzOxXlO7pgEkKX119ppD9mvCotsIHiQJkivIOlOK/FAPdNSKaWACUjXARe9ZEMRRw1AYF5k0vvJg8yOSg5mkMv8gjUsLlgJosY5Umo5suyvh6r0E6Bq+bSb3l3RXZtE/hdtbk99Bp8vzfFiW5QH+LygSib+vaSR+vmRzBeBHTvnHReN83EJl2MppSk3EALMGBQm7nPLuBAjXNTPUd1MyzuEBAK81JcSmZrlgUX+Pbhml1mQ+oOCNczZUM3xW0o9Ukg4o0MPbziv7tYKsbHKpyVLkXLwRXTwBa/cdQ7X4BXjPCHC+wRPiCSN0Kod4deShryOKwPPbvKWmGwG5A++0juPcXDt+djt8x2D1SDui/IVh+x+WIr2OskpA5QS4ySOpKkvPdLT31Ri1pQfYuVxrt5Un5yP1AIE/IFms0Cmu3lAyKrWwXp7kj/l0GqdFOcEgrrsxt5vGCtb7+GHMkf0KVnOLDhp4DwkujHB1PfZ2Pq1LQIMtxZQnpyMqRIFemOs5+hkH9FzPyqNipRrk2WR/pOe5+Ay65BF6irtwdgwSrQEBmcZ9LKgyjQ9+xTijrXY08ewAitIibrzQoaGZXRAFjNo5oWVFKxoM0cLMjXwevHD4EYF3aSgkYGApoMGvwAc2PQ7WODh3k38MiNPdhR1a6vOimxOcjVN/pqGmoXv0aX8gbRZdjn5gwmWAgXl19gxR9NATHN+zZQYSolsuADfcesyDOkD5k6KGnubSVru5BUeO5+/Rl7z+o/mUQto3qUUPiIw7OWKZnO8uqrTAEoP5eNioFFs/G4i/rhdRGv37Dr6c77q/d3xashqJA4H4CtHmC56vXJhoN1YlFKJZqzV5XLbRk5sZRkzuaTDmF2KJGJkndsew9r38BS84GvavWxzMrB07C5v9zpuUszrDNu6Ne0M5F8tPXlGbjGHHb9gr9DJCxg7h/SwUj40OUApParL6RTZ5giGJL3Ryl00ctteBe1nlydZMjzKiJzp47IuG59lBlDWHwS8Qygxjt0oo5FLg9vlEs0AnkykkVBDb+zX/z41TuXy/fdSi26zMPLlJAyUcKwlsw/cFQYbq+ft7YJpJW0+nc5SEAEWsFb3+RolEJPsBAz4IZqc3r9B2IFuYADtV9k+XTdXT8nLb3o0zu6arf1RFB1R1okTj26OyRs5YD4hQAoZegW7DQWFAn8Z3HmyZ2aNTdDeWLMS7Rtjy+HoLKT8JrSAnK1aSuzhbqlSdM/nZMl6ROeY+Pb6hjYYGVo3gEbasOsxQvOFt/3a/K65S2kBbWDxbxXMTpqNst5I6URZO6RxmLW4WMW2MmWJt3YQZ/pUsXI9N+LXpXh2flMFHCzJ5aPUYtHddBBbLITFqW9nTDwo3jO38JIL9JDhly8h0rnORFqyS6K8rLAJggNxgMg9L2rKxCF8utphGG+ynVYOdx03GNKY0EttZFw64MhQUoWtANkrxWV1eucVlCHdL3YmAZ15mnLMzgzvaYxZjw2pz49GcNe3iZ2Nav0hbFCmYfCISJaJYwPPHVPzn+0hGhHHc41fMQ2fCOIih9/bs9jhcdzQYKwzl08wV4vX25BndH4nlLy8iX4+kJJ057+BcMutMXjq0yeAfcZO0FgpW6TxkMe4Mig/RUkFulj9Xb2UrYqX5RH9S61owinG959j1bFG0Q0bWG+NmHxjQQBq9yoM+HQKtxm8P04PTcOGmGq5BIsdlAxb/a8igFjniRoXnUvfbbyUUK0rvSX6R7D1jX7JmDe2BQdPh3Q7uNMiwKZ0VDcVAAZIDiptNhMFLfw1hgae2Rr+2NKZVChSmo733S2iloNtebGl9AawSIWwj3KhgRYm2o/rWuom2bzzIQjSv95ECs814BtXQcPuEcXLhWOtvqpwNTK69v7n+duHBCw181uwtRpWJB6797HrTEAWy1YmjoepuEMrEePVKlXFv4t0AdUWbO16shuLW9b7QP9rVbdS8oz90/wCFm6hMmbPfxgjktLmAtQKVzTvSKfcD69TZuuAwq3Hlffa7BsVMqj5yafxuQ9zpR+1uoI/AW4G8cKPIbbKpotWSiz1HyFo1IPFx3Tne2zEm33oTRUMyTuXatezlUBURxpyPtxrz6ew/1jpNrRMBGmTto3qHfRh1zeoB4BaoYQ1PlSbLS0UaZPShogsNF6z36a/1G235TQtA1iSNra8EnVpy3omlT4RkpWBYITbrxXEIQKwke0EehJDV0JKi7Y7NWmEQqAMxXKVRHH18qr1NZO8l5YuXXpROAyatUqnQK04Zo2P+jEg0qEPk75Bs8E3YSwz5ONIQjKifnQQhwoysGF7srupJcNpcMK++c5nlB4PAGnI8RGfSd7Gl9UXxIyt9iddvkfSAltlV0exscmPU19H1pQRkOQVkkU4sHDB9ewTo7d3UYV7j9nmOxePCiNivvAL1ED/LBA3fIvfngYp2sNKVqbNrTzd+O3J0HAsEMdB5Z8fFIaSAhsFS0YKaBVrOoFK0xTjYCtQXxFFRpuNKhE7Jj/sH/+ekHp+nXF2aSoAGDJRUMVOylZqi7zyOMvRPYSP/2AKgz8sHEt8+P0txutzXDV98Xya26eaGTIIzhmxV7G5AG13aDdsQCZNJLlZDjnF/+OgV/qEZfmVw3Kyev70aaAdSrcpHxyC1b01ICie6Sr8phRoBFlh7U2Z9BttUe6eRbt+cPyb9b9YBKeb+k0tAEYSm69jf7sEXL+yIFFjhuAcGP5EUU42T8NbLM6/t4LuNVpKDEDUP3GIaFrVjxtRMsOobQXRmd8fIqDut5zXMswTa3vQyp1U1BI02pwz8v3rDA+V7/8Ws40OXXDvvvoSpTKpE+/Suu3ppsfL91okN2BVgAr9MMfVl+totiA1ag2Oaks9iPsBXaT6Oi6oNxXtDxc7eevqFxdmvfuqDj2+y1JtaJo27G2UCu4H3gOmOsNyq9uLYyAp/RPujzeVR6GjD8jimHYTL5B0ldoSIPqrFIUv9PkC2SY1JE+vduCqUyjoRn8nryMn20ztfHxZV9ZS683ZfecY7eOoWyJWw2NA737I/VXv9IfFQ29z33KwuaUZCoRR7TxNpwUQXVHqEZfUEUra9kv2MGPJfTYltIXR9TgDdZZa2z8rbMSmDDDwvSMLm0LobhX7FKvb7D2mjRr5d1c6Ys6imvFEE0PQioiCO9Ms5eohjwX6K69fJQg7kbAvTwV3eTXBDZLCTzkvdIh9Vo/pYZhtqsIj9v9mmzxBd1wAJgHgyGGxpArbnIhPXa4+UV2DfHEns5GtZSOtsVhHyvo2LpAteF1UpezExPYUUYce6Af8rfvXvyt1sOTFB5UAHt8Qg0lnuC8pFU2fju3200IE9ECb2Qt2JNah9DBBjF3ZXCfMTrff/Vd+gDI5/oaHJ0rVIrF05VSYpZ8aKt+QA6C1GZf51uBIKRMhDlDr1Szgifl2W1O1S/6l4k/3cXoMKu51il5KozWyKunNOuGTrzlK3dLa8+Kx+PLnoZ6QUpZleQ5d9zOHZs+oPgk8jO1QCK07igrLM4xuMsya/UB/9aq7BlL88n+wrke8tJnvYpTlnhW4Khct4lMLpiVH74LT2jZ2Zb7yHYwshHWpl+pzpMRXO4fSsNspHl97rbu3FJnKKVTm88md/d3AfsSewmuQ4AJykPlICnyYthS/ax0XS0RcEVG7rIOd8RLoxMEeQd1lzdcaDN/CHf35xIPJi/MbV3t/QNvMqMhfcj0w0XA4ugABOJDDP37bF+ug7iIskzWNNamLFM45+Y2BYsiUFV+5nTqJ6YnWYGr1WxvRkOTVIZF3ijUk5a/w9l7wQ5NLYpHl8Ky9MtR7YA36tmEyPwFoGyrd9vwHyzHD7lJv8dhyA5jx/txbSvlqVn4UUk89m2LSTfDPwJrv4zGcQ6tUGZ5Nj62/jAs8e7DOYGI2MXJaNqiLwINNNAgLlwsikfEzWb1HrYYRO/ojfUeyiBDuTESdqeefVDv0siY8rD2kOYJ7HV+q9myjOxBNt9BPmrYKjsp3esBbKhmWj3uFmkzERttho6/wXrrA/n4JG/TZ8cf9rxhsXB/O8fU2moWkfSRZSbzsS2tVSDPrEnChy0j58R9yMvrWHa2pmkZg9YCBmWyvf+tKZXXm/oqjTJWiDFaz6eqtb2aBAsHNfZyoCnGpHLKV+OMV8L3zLorhYfKilit+17z5n20tx2w+NetKEy63nfjvc8Y+ty/NXfUWr+xzEald8qLTac2SFI42KewXoRmNWBuMiuOMQcdc8VR4IEXHBtcuUUMEFLM8mc5Ti5kHHqvnxmV9je0HaNZbwvDdlfHt2fC+TFyhxOTf727o9E59gPnoUfw97M/OFeYPHgbSnjPZpfNs9WYi5RBDwiYkNY8SeX7ZGApCj80Z3yfGf0WY9kp7hu+0juXhkTVk/MbsD+uuLt1CWZclVG68TxvBGhShSpcIjHKaXpYDehgVP3j5264mHkyG0Vr3a5muiZVtK8QLwp3zDjltXiCGkiPmO+DjTfTG3rQMXY9oxL9hXy90JcgZZL4Bq/AYHu9FrLdMahtO4v/jUgTT8EamlfONVphdxp9ogfblW8ctYaunYuumb2OynwmDL/SOuJjYFHMm0JKtAT4Skq9dcYXi7BoIP52q4sEZrrdxYmswW5lZDM9gE9N0hDuMZRSZxQG3vsJYfmYN5exYrUuNw1Pu1gSKQq77msSqOqtsGD/33tjf1VwY3kX5tlMyjgtj9qtQgmpxyA8jcsPrSoD7OZKGILoTXFcubBRv4iYZIz32aTsiKrycpqvPbtrer9mtMobvhhtJblLIostsnS/y4UqpnRTFTnyrb6/q8O5Hrr6BauA35rMPP3ILVOlHPugTapmIkc39ADVezX27O3HGtK+RoJKARnlTcyCUXypDeF8mYz71V0vrqRn+TofLxCtmF/IAuoefjfWoj5O/YSHZohPzaaEqoZLaUzFZu/w9fj5Pmm/rbV4SkeFKbWk9eGUE+v7r6J/HjY95vA0k6ZdhXJmRxrPCV/jab72Y8Wg90ec22FFFBfUUrZncox1fIAD6GL5PqJ22vL3DfYRSsxA3WJfdusi7b7n9hvM9yTFJUB11mBqOeFBAW4eV/0zfMzdybQqKxhq9l4KbfAsKAdgso2hOVgBkczj2r/RMsvR9BEKs4tT1tjGp8+u2po9pxpztQnv9Yhtsjt2+GJ9sEmasbK3XwhiK4lMYR/r+8pzcOPR7yf4eFiD/we/5DDUYUl8ssQdyFtEbuPCnktJgpBaeK4kJLyjIuFQYzUdf9W4Yno/yA+6VpYpzC890tYrKjlxLI8xYXuRgkH1hHvb7VOFJM6zwUYrG95ASlEkqD/KgAnvxY3ywO4HWsnqEOkdU4zQTPd64BOvHOszzNNmBHNrpD7mutdxBy5JJBw+QuqsxpGACkefodgUAy2yiaCsZdhF6Cp5UDrMvqLf0Ryv63Nn/uZ/pGH+bRJ6DIq6zbezkB4NsG8AYqoFhEj1IAmseqcq2Mx258W+mVp4UmG8DZda8kDXNv1JE80LbbECLKKtIqhIgdEY1WsZDHr1FQo73+p44fwWN8Y7tGfBV4VwgS2wpTi6IsOUASs+/CRKIlANCAT78CgkBC+ZuPlcJbWqsCZQMPQrezNBDboIZDeR/lkEYsTHsqFzIhGlPT27k9v0/E+jpJHEXtOJpqISpsTncGcvU7uYbSQRYoVaIg4XoxzY1xc3ecU6hunF0ZRm8pr0OysL/VYWRJh4aMp2gxvW/fGCgeKH9gS1bOvujFGv90GYr5Luxbn0odC4R/tJAO1vBSG0e/rTJe5FFHXDM/0dhPu3laxLCOPrQ7KsTQzYo+Nf+T7Obp2XY2UurTQj0ZMUXHrXMA2RmsKHRJeKH/Y3uH8s/sGQyynHZn94EwwFnNa5rELSYqMUJ3Xo4/LNjNPifi/dVkEIe67kviVnd6sYhqH6yf1JFUI+PpRzz/Fqc3YrdvSXHyyeCCJi8HksYM3gq8Wnf7kpfr6NTPbUSvTcpnZzdBS+gHBFSqgX07Ihy0xlxPnwnq5j0VmWVE0jpD3MSGrj8oP0Hq0efFpsrlxY0F/38mWiWJ2mxbr1T9U2X6hVG9lnOyowOWZ9XAdQC3vJYKiFt4cP9uIvcuLZNF5WXIP5wYu72kkKoCgFmEIG6vjMv7bFQDZDGCzVKf606uVoR22mnd8DSO+m5jLem4NekQdl6E5L+QxWJ5GSLqlsAl7QYvQzY3uw8Vh4Z8XOAs/DQZgWfV6ChZ1xSKjh99JGCoGngb2SgAdMEUWCiMqJMiqIzktFoSlXixDP0eD1ZesgjtUUyOaWciU5OEyn0D/2yeCvJ6mViVU1H0hQ3xuWTouqnZKtDmUpNTmzdbOWVK3JCaHvT9nDxmpKF6cGe9OXX8m/7fU2xb9blXmklrktOikPDtz4LRdz+GuLdhGoF/uiy98ObxH3EJW/g3rnkR9X6N1iuxXVU3uBCO7bpFUuBtsCDVQV+GV0XbRVqCDSjjLZlnOTLJpCqChKx056g6r+Bja0ucNcLVxfrmIxsFqIsazar6vn4jq38aTHCql71AVDmp5OagSUpGdPlSCElc+0rryUbAI5Q/H2rXuGJ1sXkSifPN+3CopNlI59V2bz81R7Rl9DplinP7rAeYuCqV0hCA27VEhfuOfnTehnNBABlT5ZFX0456ZXf6ZIzE31RlMFCknYPlww9tXOc03cqZl5rcsjaKv+I5X55WURbem4/r4KasW+LVjz1rCClC15MTnMG050FgaMfdQTWJOERgNHByRP+FS/tlDeoxLOZMzIZ1QQFykVE8iqme6ygANdJr05oC4jI9LS5Bue+bS9UiPs9uSEmFgLvKq9DWGJFfoIhyFy8cScBNrUrZehwLBuIBBiqSjNjKHug8yjCp+9+TiuG7n5JM7jrZmWVM/Yb2dRWLZVtsnsDmopIkHW+f3lpOls3isBuKbx285cEdI5qq/xCdGGannvTfRXmOTHfILcZumNYDjY0r9cSxgvdntDNKd/sWDsM23E3i3ZOsOjGPSFoEDh7VR+uxWk1BLz5DoiePeOvmMbfUkn7g31jLXhareDfH2fnc9m1kpxqfZIQaI7VeYMwsn2ZJU5eOhJjPaZOBCmgh3WtDNP71kmj0ooV7r6WuLcD4UpMVqcUP2f+Gc+v9ISQAaD2c3r+M6Qhf6WYp6m5W55u2iqVxHcwmm9lZer1i+f32RM2YutvgoOVP9uFuxRk70APck/VqJDSnXzakkaubGu1G+NRhTS995ow3Q/bKRmMco9MAKSUqfKtGsx109G2/D0CJfBI9E9cMHz0YYC/48BWJ8N1XQLEa/CuNpTgJxlkGW6RTJyzu3HxKzKEbvuwz1CD4ZDsG2ImAbQO5+XKSuONOztTMKJZPSetoQBI2nu7e8BO8qt4PNFNvnFtBCUQjYQLtfM4WW7KMb4YTR3TbzEWqCGcz3Kaug8tTWfGJSuw7E1pB7Nhssu2iAW1WrLG9tE7B0whAwTkgk5ApoalLehEOmJ1N1WJpfrJxgcWIZeQQXHMM4vmAZJA7V6IWB3EcE+xEW4eYRtrBl/DY4E2uF7xT8SCJLm/iCfs+pubiQJ8VCVl0pCuUDcjJVvLu59bZ36cWL9BG+/bJxNRlYZgBqdlL/PL12oDAgHv/5CJ494n77aYuLExWPgYf/4RD3v2pGsyyGPpR1pU5MFvwJlEUVRWKeec0D+uNAmZdS1bbxLTAuM2vb1M03XJ3xyu7BfbXiAjGA9wgQZpmMOExPOX1FLXm7cXKIzcgi7NuKXtXqN2IPso+ZzIMTzd9ibwiCyEWxOB1KHJ1kw/NY4iLsZvNXxbTIR7RnnCTT0OmL/wtPQc5SdcaLhih2eBaoGfdXxJH/ZbvC2wrZtgaBa99Ev2LhSMg0PD1Lsba/i9Hn9th0kXBH2PI3G9WYD8CGiZAJ5c7kQ8p0B9I516Vr5TYu8Y59Dv6SSiQRVDQjaFhKC54rSr2GEPD2WKSdkq5gVoIdtr0tGraNg3GLn1oTAH1h2XC+iel25G9m29IVCL3wuV6ZkXYUfiW/Mvp83szBkb7pfkFRvkJ/9eetIn62KojUyi3vrrjRIl+pcxqe1W2W01dgook+5eM94ySdlcJJSW7gH94uU40+kOGc+jeqeDLdv70iKQYCGchLpNyaTWlib1FOsU++vHWBoK1G5tGPWyU64omJQX+TQckmxLH58gJdnNC3oxZOPTH+IS+O/4d5z9aRK61JNcAMrP99LecDxQO2YVQowNim2zFEe33AiGGCrEpkmow9ezBRIkBwx9cda4ZuAn9TRBywaQqJvgJKeiC2/eNGUg3g77DXGvsEr7VUX97sRCipiOkmewXaOdw8m8HUCpRpimgfLOf3b58cmCAlm0q5+LzWDu0qW+w6lClpgs5kvCz98oAqKOSylWHNINEkFykvaN+PnxW8x0yUNzlJj8TMP6KBV4K2tHqzlyVnqKkJzQwufYU/qyNg88x4NN6iI9ntrjZ2u4OweUVs8nlpPKByuApxi9uxrMVfwIX/tGHqR3ToFWNU8eYUusKIoEyIZq4C7Ncp55wKct7/3m5RNRtjm5RZsikwtb8THanTmk0DpQ0jHtqOUy1M+nsGFNhKUqQ8WhR48hQW3jW3KKwLhJ6xvL86oR+96VO1Q7uli57rxJ4cqoFQo4bOEE+DNyAZexQ6N+K9/ZIn/jeZcA2/ldNxv5A891x70usiNeNUGLlOZUk9XeyvhM11CCpVJ9FHuXygd9idqjmwmuKGa2QsKqgm439/tuoe3X8afzNR1t2mf0/cezFD3Pu7VNRV432k1msChev+R048RiSmq5aJUIXOu30qYkEG1JgTegmQOVsDn2v5zZ08CNIKRRD2NOnL+5Mz9Sdr+D1c5CaCwXW7b+xFUuY6WMGksvETmMkb3jS8xIbywXIBEHvYMm8/Buwzadj+8HMePJNM7HyHgSpI0e34bIuU84iiFLn7+KtmyLjMiQpihfXqxi9/r0ATB8HB3LEFT8KqSJm9Jl0guKSenL3L0ILmT/r5v4IVYAvDLXoGuxDVdP8hWnYZRuxTG8wTD4sm0AgDE3rKRl79dDiLe4BA7diXY+QJGbnKIardFZaMwzieGcDMebUanVFEtPesqcsN+7ec3Ps7OMNLp9WTDroWW+2FbeeSr74Kz9tDsH1wxYP46KmCqUx7XWecXDOUr18P2+PU6wXIWs5j2KAb7fL95eR6Z772wDceD4K7IAuazkfnzhsYTrDe1/lEiGbH1q8+RczaNBOWHF/TQzsqtYzOl/TrK4FZtNXhuckS3hXMzX7/q3WYwKQW4ITMjMiEyp8ONQMhhR0w3sXScrgtjkMasCDKoNOkeIZoQK+EfugxBFokAzH+zlub3dmwabRYfXQ+/5v9D03VsO6okwV/Cm6XwHoSHHd4K779+qNtvtn1aV6LIyoyIdFV5EzEzsl3V88M4pp4XhVuKjuYzHJK423qyBw1qqgv/1+NjXuZn3aNiDQxDYl/+1Z7iHI8LyerBy8Kp9/vSWWSteFqRT51Elom2lv5DWLj5y1HUf9IcGi7QkJIkNNVA17AU99KVDWSekgmnmFakQD9X8xuIwD+Be1TqQyNM4VsfJapcI21uNsdvjRJzzNcOHqppf7OpRNGxFfqLyJeO7eguH/vbyA7trExRQvagihfYq+Y4vLkeMfD7251A/uF50LOCln+NB8Du4MJlV7wIySnC/9QutvKw7QT+vy3a46ANV5Wz3s+5K4OmQ+HuVG6T79QpAskS4ZGcS4KR97Rw5osnMCLMcuL0pvEGE3xiaqeBhlL2T70dT1BjhqICgSYzlWtgf6ahpiOp/GqxsNtJMeW4Cb0Klbz61PHy86RoOBMymLMhzGf64UvVCLirCPKrJGaimgx/5K40WzvM8KZ24e9Of7zFh/DX1ZfLXy9lTn7jRWjHvFtPtKIs6XleAwaaU1z2YPoBQyufEpV+Ej2MJyhTPdr6jK5I9OtuerGqY5ylYzxp93POEZtIK822PIwucg7Z2ebbWe6bpFB4YqmG/PvtTbpGWhkLVmF74xO4N/0g7qERmmKQcG10fFD3QHEZDdHe3SRKNvG489zIH7huF+UEkBO8d2CV28xLW8p945H5r5kSygQlCS1HfMMTvSbTTPCrW2WsTITa9MlJOPG6/gUAkap3Thc65Dfove5nDd+wJl/eioVaDzIJLOIMEwiO1ax3KANnOyfZHFP5/Q9jBYrDgulTsqjMz/NYSSrP8/QjSbdFPI6rGEmoWlfLkRwy/AJ+Jvzgck2dwhzfzKMHbasj0XKsxV8+NN5PjEgQ3DThewK8ulJDsjMd7HmTHceWbyb765wdvGNF+YgSUs2dCzGnF2PrzU+xapDX2B0pgRS4Q3rxolTuFxB4h2QrNNatHii+9kxOReT593fOPNb//BqggiIQkPv5FBb+IHPgDCw9ri5ooCOJ3U5KLfW6hf/xz8bINoFeQYfZCG67p1kAWC0EfYfvsFngB4ow3RvMoK6Y4Onefy1nWiuWGtXgvqEFPWU68DwRV6YS/XZwoKWCgjPWmK1fWBUMoUK48H5/wi/4DKQuaqRHuNMqycV4lqGDv/fpVMs0SF5bdqgYjb93kY056AEblMat+6ZwCd4+d6A/SmY9p7qij2oTrfB3jRFxm/rYgdoMXov7y9XjSySKmpx19eiv95yPR1rm6QPQdaT6eyfnZXS5UQvpbWl16vYGLyoU/0SsniOpBrDGCx3wWAKlKqR2/DVVDEiDWfi3We5v2RMz613uY+3ec39UdiGZ5/iei+rlrUQRV/yxokCOSY78MjOUxjGcZEa7u6YUuonFgcgfhhtKD30NCn2AttQsjapk3d04th7HbRPWexo9XJ66QvgatS55xCBbzClCZ8tZP/CrT7KWLaldhu0CelSEzeojTz9YU/fpq/4DGPbhq9vcGDrkhecKwdhqPjmi8FBClHkFZCD1kkw0rwqI6MUbOgimIdWED7rES7vDQyApsaKfc1TjUNGwvkZQu9Mn4jaGEDC9DBuPvRip7bUC+8sf8Ae5yJgHoaAM7P++b7AOxymta7DunqS1vCiTT0IGhSNdvtP+DoUlCQ6Ve/woydZlvH5Ye1bR+Vs9hOzbSo3Z8H8FkQYR6D2Wnm3JkoklU+r8V2hzNYSpmc6Ez4Wyqbk5TJb82n0Vx3tvVUzwnY6nACSHABHG+tcoLrS+3+d5gnweWEUvyV/VgH55XTydR8NIMeBlFvCD/K0LLshKWcYAogXON7dTzF2nvRCj6Mkd1eARsUud19r0aRN/+JI5DN7pqoUWewA5TgithHthZtqlsxsjb0BDkgWuirisMdv+gokIJrDPv54vxJf9vvyKVoJCDsgWT40viTdP9eOII6yfOx93DVQeQbsMND549kSHVQPN2JNALzojcgMiXD7L6cbXR8IbHBQf5MNrcEAKbEzLrcGaxydRb1XEU0Yd6jQz25ndDoxscTHpqWiSPSIVKQ36dM+OBJkBsoWmRSURrU4rXCklyucb59ObbIfm+f24ngyuiXtTBqhaEITqt/I+owfwa+805rYeCSYCClmW7N+nTfUO97AENfGzrlC5BRCJ9WZdJuiDz1v0HmQaiqTuCq+/jCvTlRmQ5z5Pb8sWO/bO075une9BnkFim1JrLYKn9LZWYcv0u98nrwd5oT0HISu4gXuFvR5zB/ZpzUGEctQLPJi0xp9HJRbvRukT4/YzTm+TADMWl6p9XXTIw9+5tmsoCINJAhRhGkJJ1k5ghBcGD/xeJJkquroN33PxnkeknneTJR/vtz2SznZI5/nh/lt2flPOL927F2fvSgpmmkQ/HJhGsaPSsLluR1iKRh8h7Pt+KZP1lidz0GynDsmPxVLldJcLKDBNkDERTOR7RsROUdOjfrHS9e2hhmWTJsLoAEG9zdqH322xhA7jIZque3Qs1pLQ77yvT/eB4OE96iycpcuPOwItqInckwHAvaftmN0+v0ffpCgtGTaBqx9WGVEFkfKt/IkUZzPdtaIQcv7rGNK/0rxqqSA14NivnHRB/fvZ8VCXz0C3cUxoJwDTt4DNxtLsVhHrZ9RepBHoPJ6rnHarKsaUdtu23f9J9hxOuP+Q2Ys9RGQbwNAa/66/RnSigU5jpztGeLjGb/A2KXV5KZGOB2TinI+u+0bdTm7XaPLdLJM8QKsxtaI5HQGnWNsQdUj+8wtxS4uOonVYvDNId0w+JPiBBmSdcp9VVL5Wxpzet3mCLxp+3MyKfAphtzJoosOKCVxPt5Xcq7+ZmDEIlygFaJh6gfhBjKXlDFajHEd4WdJCiuG+xy1DGL750n5g0o+X4FTt/o1SJZ/jWbHEZ9TQ7GYMf2PWap/0y0rsLkJ6TZJ9kboIA4295Pvf2DY93d+rkIfeD0kp5eWtoz3dk15UhkLATzNPEfYHtEvJ66iSc5asdXBtG88tiO21YZNd9IPH/egn2WJ/RXMuXIMRH8IMOdCQozO40E1pk8kK60l1uAGEy/+SKv6N5kvU1z7syu5lefvDDCPUL2NxJt/q8FCpalEmkgpJWaNox6CXzII3iPyGxwfYntBJBvGpY+FC3UddwSUn/ytNCasT+Le28cH6yZqsu2aWGRU1sLENvIqd/kZp6TmqFM48RpNfOkPJ8F4lPNIg+EXeHSIBT4JBvd0vfTcSgrcYbPqYqMm9jHHpbyvD5na3ncCDbehQU7VGzNxcF1j82ccpPK+rmbORQAhNddXrqX9wLcw8DVPOrKUPNEObg9rPpjc7oIgaKnPEfivphj+OFktF2x/iYMpfhlQqXKUBzCReRlR6/p7xdEbaKT1i+/m8rn8xdQXq/X6Vpzw2PejsRX4uMr4u0ThTCtIki1AipGyk3B/uJY0j8p3H6TQ3d0e+W85JI3GZxxt9i1axbY5rYq8V/6SMH22vLm00uKobEornbBEeFbEnIOt250mmg57qR27n05bGx+QyvvRCqR34j5clAGqiZC0QjjX11yMIaVIaxeWdZU5qvuD1T7VojaKG+dVDY4KreTJRph21KX69AZwM9IXZpThmShJbG72AhN7YZpohMD9w2LHzjeAOsLO7v/vwmXqx7uStFlrMIjKf1B24NDcScIXHM1CyR9Dzj638GP5pTaPlZv+SOBKn3OMGajJDH32ibxc/xmRlewTRuFTAckD10hhrc0xh+w7q58vDeaFqWbeIDT/WGmlvGr/730mRX9hqvHg5eDCoXuQ5U/F4Tp3gk5YUFYjb/UF2i8eaOmCRvFYGlykRVW6d4BffeNGbDMj7/UAza/lc98H4fzVqZ6vuBtJ+d5muIFXGZsFZdZlupp9J4xenpSierynIXoSCu38KIzj18tfWxJx4V1bkYek0H8xSjKX0qECq79eLOCS7W6s12QPrvvRx8OK2tenwl7kz+azPFL3osfZcLRBSiLKjVJSEvznD0nnqdwaArAXVJPAH0Il9pkdEki84QWl4vi+tXg0JA7w4PMLTdYbjyOcNikUPI8VZ/X3C9DnR+cHIvwRn3o4SHjSgemHWeUrQI9lcr5nHr/lG1n622kvcf6TiBlQ1br1YTXZbiL/ciTd1AhN9b+wY2EPO4ZXwUY9vbPj3p4OKXNK8fAUwiil6Yek2bMsVNkNtB2wUNnjfcclJISDjzogO52dj0xVGcrvu5tRBnZODgqRoEotpphJszrshZ3vAXcKSk52joNPybBDdTzi30/LvBYk+4r3IEE6B8agmkLULZyuUyjEPZTZfBhd8kduycHSzt4848XDl5Ef6sV5bR+cSVLdAfdK0DTHfeOU107TOoldjVOADRyl5g+Xp7VboDErYwtME/O/LrDSHUMbCFIT+AVBUrMNUTMwflG/fk9NftA00EQoVj1v+q8qMyg4l4X0D+ONv1CZ1kAA5fEGEnCuDKjv8ZXJrnBQJZwiR9Vz7JH4GkF9yklhDIPlytTWylRWK1/LjMrP5mAsMY8v1VPMiPydOS4ERISp9M7DRyjkWboJHqqgerhversrWDkz6CEEEiMbHpM3RAKChK13b2zhBFLQKRD0kLuiwJHnwWMS6BTJ1khyo5sX/usp1zHI3WDde/g7tMmAo5AulKaiobIZk9rkVZZdG5s/rfYTBldyz72xyIbZuY/ObFhg/O2ej6bD0koylyNZy+zBAG1NQrJQCCMNMWFGFBgJzrsnXswiUxXkKo0NA5u2of2HzSOD7bJeVZfYhsUoI9aEeXqQu1zCNYiravh8wK1iArUuR2JSpU3QG029ffDRccgSb6qARX+K+L2f5KkhYv6yLfKi87Lzn3zd4cxlhiTpHwk7RUkuRvh1ByvMlB/ejcnbzHPRNBXMHimKtcQ0fKfPZzDGeZYew92vYlC6GbpUHRhfSewP4gAOHpYJjnfZQORb4b4Se+2sEKL5gN2STPyKQDZ5LOKq4KTphIm88JE6FvOgZc0HX1d80C1dCe5fesfLeIJSG0/nM0Z4k8YRt1k/V6j7bYipQYqtrd6p5I0y41vXwvHsd4zmcRofL8mlKujIQz8XZ6/CJegzs6043y8t4bdiXQu8NI1gbac1enwnzVHNoO8YVjeHw30jOE5Cre8qE6ygzC8mOltE9ug74yL5D3Jeto1r7Jzlv0MTYWppE3qBB74lVKHh82izoWtCdpKE9pDV30KjxrJ9nmyBLtwY3YsfQkBhhY6704z/pX632wK2GfveHlg24z9d4yrLPMt+d1PELnmmaWQpl+vxmOfeX+gBgI1BeDsnKD0qHbDjcNni7kLoqq8zu3UGHh2AK7hRRAYdDEhHZBP6pEPULvCjHZRfH+ZTwyKRiqMU3vEzaH/qSJI51LOFdFrVcIS0NPspIzEPVVKAmJH+cKLd7R5hgwkxCdymcymTDlvMjMuZ3bdaqetrf7/0/AHXqhPWyy+pgbUewg2wO1anuipjkKKLrwD0lPzBgx75x3PoKDCa7WeuI3dd3Wwegw1xX0Y9YQKUT8gC8jVOUuHDzMUjbj95AAzi33seIJbnCDyADmz0nMhZkBvtj08a3C3SNGYe95QImOo+SZXdNyAxnZHUskdJk7tkxdeIud1yd6C31/e7wxi668Cy0kga7VBKzvhlmBjlhN5eAwrl2/+b3+4S4PL8iRagnrtQaxqfEPFZzBoxP/a/oi0GIi6F8GBnaQSeI9QWDm/ul/mrCmPKy0LgO4YnHEeWvjFczYe8nybP9N0XfAoaTSOBRwh6nTCCClqosfg+C+q68l32+kQhchxmDO1iUcyab8LiuFwaZrkXuLA+Z4hpRbbyAmm3m0wZKtrb8mpFvMEmbL/X7YJ7rJ/RIZBhswTkhpjMdpl2oLQGAOfx869COejc+0O+5TA/KYOPSKL/d+xuQpW8VSHfk2h5EEFpPmrL5fPlBySJTF9aR0Q25Camwmq9ZasVvjcCkNMv+54f2vOzJeSOzw64Uo2fkzK9334V3Re6aQogiBkpSywsF1XwBvuGAb8K+8dhLh9MEo/MByaSvl02J+hV/QWrvH6wOfvkUcdzZJSg/q4MNUtfaUfg0G/ka99WD5mKE7KWH5/KjsGYORB7vvM5xehmFzA9aN38VVGsX+LVrt6RsgfSQs2aCF2PskQYcBa6rZkH+o6nn+cdPqnvWUQbaVtbHBxny95N7ZrV5krVekFYdwcwnFasYeBWjHEj2Hn+GN25vj3qlSP7xxcRpGZIdNh4zai0kP6KV0EHiYc7y6Ee61+kfp3w95h+ql9Aycwf6a2vIWPfYQMBWhIL5cZ6Mfgmac1/kaszxE9XhZ6VAWi8ELhNQY2B55k9BiIhhAb+l5AULod9B/CJcynYKdsDwO8oQJIhSJR5kvqidqKUV+c5re3AdmES7vl6afkycvlsXA6Ln76876m86oVBH6iy/v+noEK9Go+9ri50lFEam0eQ6B5jBH78U/Iwcp/YSfBtEGMlVhR/7sMG+iiyMKZI3jxvL/+Wm6tA/3BINfArV7EdXQ/sL1He/7XQNJw6f3Df3p9U24psSPXbsAOkM4RFad4kd5PyuQUPLnXrQrIOJuZn/oI82Ftsq4mcV4InrZ6ZMuk3oQj72KVtDt/m27lhErlsz5yAJ/gzgEN1os0+0mTsP5fOzVbLyOV8La4gEaBoThmQRU9MafZ3x1QVV5cMzu83M62WcwqKnD70zBygHAQVHpBFrRiVoRxikW7fXPyvSjHELXUqTt2anW7P7ppmpEkmCzOcEFDgw+4NUWYsO/ikPf4WdMoeS6EOBqUiVUBJqhwmmt7IbI4SIYXqy+awE81cGSFv3JvjrcmbmjZrztGqMRxL7IKtPSThV7lIcTc8EJ+CKB9RBbBRhOvBYwT9zaLalkdzgWKt21hW7TfeVrnmBkI1MsUhobMrWuERgrzOjj6IztzukNp6bHt+9oCKrFwz9fVqRoTQFJ8Mf6tOBjdb3M7d85J/wccz/XAJpA/sG3QxUbS62rpShNF59K5/zl0i/XphQncU4akqmcPvZ9f3qE3Bd//aBmFwH/OBQHPd/pbYA8oX+D+ShtzsnuS4HNZXZX12ZqlsjkO5kHFoXzf37pK/kpmY838gG0cScbHJAuyolUcQDyFagBRjHiIB0TFAFvywTmNQ81hzmSI4N91r9l/45gxfvyL3O+dCR51R+r4H65Rpzb4mB3/GYO0AGT4VTnoAsXJYTL8nyn2Hq6xeva48H7xa0LhufKqhPFiphz22TcxUnc8T4Y8Jfkr9eqnSOoRDlIOxH8/aZSIChVOxvCUVCXmqHu01s8FMEB7q9n3INMYPzTZHqRUX3PrXn+7b0j5Ussu7QLQv57Xy44TGc9rxP7E+HjHkx08rY6iQl8tVW8hrsWKAKbh/d+AuFRZIiMJhDDS2sMH+O19+ImACy8vWIPj4ilKyStNHtl5xBG6jgbgt28eDZdWLs2t/0AhCY2irKfdn7ftkaGoHbcZImpE9cOTq2Bis8giimoItOiHiQ1yU5LjcD8/UauS5T6iIVTC1rK4yytWCQZ9SXF8DPp3Iq7ZecRdggxh/gJF9VzHyhxnMl81+GDc6oQuZfHjYPtwhpjmhxii41Blm/G61nsB1Gwq//zEidKIrNi64HglMOrHM/lmS4x0JoP9itlkYyvD/M99lREYyrcjw4j+N5TiZnwqCIe/R4we8BdQMnSdQk4ze7Qr/L8ImnpNmcytrPiH19JWAAvY22L8zZP0fBav0+nM5XkFiKqFwQwQUCh3x+1yoN1K/8DVFhIUdosQa7HxfHajaGIP1vY1CPy52b+wGcPZiBfX7NtESCEBhpRpTD+JxR4+z7GaQ97JoymvN6Rmz2E31J8fr+Mh3qa+19IP/qve5BpkLkgk+ryoVxY1mDxGIRIC1JMV0UC0gLtzlRXIEPmIdJsrtR/7RYKK30QVsu00+3wukFuHx8cKkF3CRLvZowA8KEC3+LkwK1XgzvgHCj6HyAMRfVVACqgqdOzZAyYHD4x/s0g4zpMaKXFbo221zO35/NmL+O5gvsb5z+75wjx5j5Pia2k7fi0AITf926MRMRfqTq4HJfY5nw/XzM7H/s0t9Bj+pnPiUC+kXx70VU0P567/GeCyeeED0e+YcEL5489yuFBJI0p+aEtJpYHS2cQKUI6MHJ/FBZYRXJ4esw0EA1Pt+gqSfoN50v6YtGs25WzdbXIBtEwoHUSZcQSSREDtImWDZePlnBUncgxIfTXH2pHSNLTeljUIcocv1t5nziK9JTwrfwmkFP1R9TcpiyaVQMTKlnVHy5mW6f7CMJjeEvfAaFDIMSj7RyNfKFLXMIyxzk/02wjzoSD1uBYukLwO/9UfWLMvzNPwiUOf4WWTEPgJ/1g3/BCcEvIAY0TSh6AWv0uur4258YgeqGsWlfPh2JHjEg16qvoBvb7l+Sdf71gZLI1brIloRxpUi2B1SZXoPmPND6GDcmZqT6qMsK+7J7HpRE7t1rfZTCpgh7FWJGx7DcIZQW+veHnJVdaxJ1kjty6R8s+fD5dCc/Dn7vtLSzgeGCNBqJN52eQoFHcetT6jM25zBOTlK/QJyGMbIzjLxQySEh81zcIA4WZI4dXt/oSUWXA+/6iBz3ZfO5bmHF0OLkFmcfy0mCZbhozy819zVjTvryWd5i0RzFtSsOsdxWJMX71pwL5SztTzQoE2RTc75BtJe66NGibIS6418t2lMOBgg8EkeBIkeGlMsOBeiAAoesAQZLU2UPiDhdorCeu+yOZwjtrd5rkhIV9T6iS7purue8vzd5rR5dVzBvzZArAUjKAXV4uASVfbsfFq6G1iEGyYdmzdasdZXdH9iGCqZWx34iBiXPZYFW7JaXuKU70WRM8PiMEl+2izsxWXJ4GrfoRcYjvO4iOf99WtiIMaXGobRvq/l+sttcDqUN6Zvbq9P8QciAqa4fjXldFZRlh079o9LzKu743le9ovtn2G3pZ4Qy/kUqHYKoFK6lIP52EsdjUUGzVv3UkgqTw0R8WDnGa5EthIe8/JqZrGbZwQHKN0UVgnWXREoTrurcP/OqDb9wfN9v63qAsHR6eTd9w1sHR866qF+rDIG3IWeIqhl24DtumckvOjQwGbWgRQI/t6uFJLLvK+akEYvnz4ZjaXo52JWb8JxphnOQen4LbN8STVhgcTo+i6h9fV5hdG8AvY+dIq2dA25a4awUqrEFjjEOZb8iKmgosdtH7xC5SYkTkKf6y7cu7nEu7ZRynLCDWt/PtvxE2o8nEFFs0b9CNMEC/7m1dzQOy6HuIuXSDZ3f2k+s2IohA6haW4GyChhtKRoBea1aXbedubHqUlNfk8v8gHICGUkQ94OWya6TK+f2QpRoCZFlLmaLuaXnw5eq8WpO/uaqc89eD46iyOEwTr5B5n8jPYFTprXhTv9LWochcfzb01aHnaMNjWoAuK0SX9FnTI8v1RoycPmlPt9WbGboYv7cDAqoLiPYD10LaLXm0cS4SdtGuma4ytq+oFvhP7nkeT/bUeR2cCRVg7z1CKtzvuegclwnPvVwYH2Y/EUgEUs2Rh8Vq807EedTGqXK9JJ8iIjvZjgjAl/YJ1g4Umj4EFwWKQPSF/KXLwiif8/+r8b3bw699uB/nRsywJD0OedeS+OErJvcGNu9EoTMHP4uzlRkwM7TZ0nvwo8CqPRXNO1GguCB3yyuQ6uCpzwahA57mqhuyI8JvyIjIOTgpLgr2YjvU4ewyOCki9S0GPxbBRgOv55LRK7r+3yreJe62BCQTqQ0CfATVf9ESmANCZiVKOxnIjHVhtaUYoRImgyv253LP+r4YkOQOjPof++p7lr/Tk/GiUOsPP922IgmQLPgQZm/rWuBmtAf1A/hVcGaD523ILXKZB8A1Y8K8rm7WIBvV5+azE4+GaQGY4wffY7woSApjA5oqqE7EDb09dKOLAYxB6AAOnK7CExEWjwNhjf4Ho/XAe3ad7bJOy5+sC/XpCTEfR3BZkWayacKm/HlRYKiteE8zNw9/Q0meW30//sKWRK4zM+L0tzzgMuhubHJ4mLzE2hSyGQ0qGM5jIlDE1NWjxw16f1HG8rMVQK2i7YzVYXErDRz7RCiHfZEIKbSQodWJhWrUn7XXJbvM6OXGcQl+VlIBaEtbZfBFKR5a+ulT1K66yew47Ja7LQ1tZIp6ir5m/Q2/+WwyxD56p59892LY6IF32NqQb1vqztdOlL+385KcPJSPKv8GqNLN+CZIF9/iwoUl8K60QjRyV3PSNQBqkUVkd/NaU62rMraOEz4iqsJAJk2+5IM1shkAfqRTeOr0Vh9kYGH+/sb+6Aod9x5utlnliCuMqfPMuptIHLR/BcnbfwP619j+QfmiQDcYTDr7/SIT7J1ApD6l1RoK2io2JgPvyeiGXxheqV0hqZEYfoP/LEO8GLOZ0jBYcABbGRDDnxR0YisihbQxzZgtqJ2ePrcrvZN02oym/Wl7CHFvxwQjVQnd35sm2gvutM1ZOKKj5vxAvRXi7nJvmdN6e8sYcEKlw4ETGUdDeSG8T1rraBnC0td25z/pqxfn07jBNj9N+j9o37nMHd9p4/KuBodur/LRi/Jk3Nq4padagXFKEsicBrZYRsCSekWJ+A8QuI18v2SZGRpAnxfKU38m9Qr3wuonGLYRx8JLYuhXgzNih4mQNdvPLivjGkh1BhwuB+tiMJvI9fxGv68MU2Vb5KJXzwOzuuXOCfWJhLL4U5Cd5DZ8HJftUPj2umGv1ED/TvMLxEEterNdkxeaeZNvmykXZPvO3DtoKYS1LNcheS3JLT+5f8z5ZnhHKUtcqFxIO+BimtBjjKpOnvrmhUCamsTgbHo+zdiHTA1kdGOT1snr9cGYazT0Bx4Ohe/g08FOq2gHAkHIin6Gik2QEYlfk1y1Z1kguoUB02gm0ki1xRX/h7i7/jc3/lqbVb+hbFW3Jr63ptEEJrh8aF/k+45Qqd4hipxdYLUzCiaVYTZDPdlf+Cj8H6ElZm/ho/c0HjQ3u4lSFPa4I64K/O7QMKFbx3HmKoLNMgwYj4374sEntIj+qglTsN/+pUt4BLiFOyIx11ik62R0+jDecc0/ERIJOID9W7qs8bXIZxB5nUO3tU4CzMz/WG3OncXLxHUevHdzujs3RDRkb+JAM1mcLmsJNlWHNwS4kvSow2CANCFHzJUAizqT/u/MbwG/nOt5G8r6m/Eur9Ge6ikRixtv2qpmU2fgNdQFoon3Wij4eWkHdha85SPH/ikRD/L7aa7ndqjMgUNMn4aKFNmUuNFyy+P+ymua0HSGsYttKonNnjRS8fSM5Pbg5EsUzrSWr+D1bXciWh7L5T8NE8+YXELziT6QzHrtPOlCkdxDMWeLcUnGlXfKzCqSOhXhhDBbxew7FYJIEdZRe11TfJeJznfPF871V8Gm+UZN2L1waonkW/fUtSOl390uj8vD6qHBC5bGuTEi84itwDXdnJtNxAoLuElQbhHJmWBxmKF4tY2JK8PAO5rzuXdJwqRjKINgpK1/30xfunRnFB+LrfzwAsmcA1WnAth8dfe12qR/t/u2EJ6eEkYeiHP3dXRWZoYyqvCYVC8nsgIEaWiMo370KnGmJqCeWcKyfTGx5jZ/QPRITjjwcea4HoMks/nxDP8DnwJnh9YA+ED0e77YLVXmbLufRf8kVynO+NMwTU7Ex9YDSLFQu3937qu3g+nn/enjrNBgrbqphE0GcgWPtZ2wf2lMgVQpPTBHownfPXBWEoVa8FIIn1g13OpBZ6wjZa010iO6C7nQFivzQ7Kw2lum9icwnKZ5tQYDmWj4nOyOlM1zYtZTrgoepeUWPjUA5JQ3UQ1cnXJuqEo+NW37Evdclo+bFrJbUbLI9fDEJv5HVpG57MMtgC3+sZ0ReBIN7hlGrQAe3/vdJgUGTY2i25A3sWP9BWvY5hk3xR10dNXLnU13LGMBAWBQ0scNyWOp/RegnCTVXc/7R5G52lxXlcGtg4DZVDJMQVTSPzI2vYHqgaeDMeQiusn+3xZcMrQ0hvvtK3CNlIWwQIUiNxeZqEgjDmgCo+5zjemnTSTeFSiFc6FoO9sfgVsu/r9r0VlHRjCE8C8sqXptQ8p91NwcUdwYiA2x7W2QpygVqXAYxYL9RKdaOKj2EmqgCaCv33PL2tPKJY8gDsXTF6JP1EgjIbZLH645Uz9Q7IvGfasLwSEtYyRafkPczVE6f71341JQLB3L76c+BDBuyfH9UXNvpHhXWkBEDU2CCg+njXeKBzcI5oINqwKV+ezXY+LolZ04TjQZig7JKjr52ZCeq7QSfyL4bOLaERee3l27+FgTAe+gHsMejqooeTMj1GNt/bfxNWRolL6cE3NFdz5hcpkwklJUbF9NWGYR6Bn/ZVnLHt9qB8bvX4840X4f1Wp7Q1kB9J4EVZELJvtq/nGfww4hYpkkinNDbiwmNDGd8sJWw8S7HRqwRpLKvax6i8JgZSRc35qEm4yAz/vHsVeyGwrqQp/gTMNSvXxM21rFODzM1oLRVBCxHfxasQ8SSDlrzwmuvjJIEXRmjOeQwt+7sUuFOZWDakmLpNZtz4UBoZIe+08WLqrc8YEwH57BmEuPXPZC67MMVrfKwkkkzyM0JRQTxhh2i0R/OM6bdh4Y94+wWnp0n09Ej2IwbroXIWHdanq3OUfz+CTzIG07MuJTa+PXc6VDnclC5pARUtXMzVVvadCsZ+ipiE2J0029uRm71TGQRTqXorhpTr7oqSzLJP2qvFar7c4h4wwCeE/me2pX37/q11qlwCWnFswGwk/raekm06Z8sYSztltoSgl+ykndGiwvRTR7kQSf9f7JpFlHUT++ZGsdXBlOpbBz19crH3/eqF3iG4URuGW+kmJEW+GlLINQpEuP/TRZNqN08Ab4TE5lkIbN9rLom9IrgVydxEohxHEeVwVQkshqXYmvON3PIR6JL/b+EPH5HKn8eEMyuOvWjMZHtY0QfjHNHoOKwIECMPw+rvqGV8ebXrDspH9pYYCZ9JVYS7Ga1ITEx8CinR5LPntdErYXzdwNJDL2JTe4pA5/CI/KuqvH/lSStB0JsdzNmZtTZ2Mt8VTzX++j6TfJh5vPK1J5Ri0+Q6sS1U8TWNcoEi+4Z4L+ltx2T8lWIKAs9tf6FooYvZ9cNmU1HWZQ5O6+klJ+B8OXL3w+umifSCs4sXJP7UjvXdHUbF+6sEd7H4rGGwrbPw+9tO2utMXGx23sQYoyVAiJ7XY/aelPlNG3YhPxsi+iSZNfS3iyBs5OHrj/vk3RWdfpbRmHj4XUvhJk58HZ+pzvqt4wcpJbZfYUnvHaiSVHfaiJKmGlQOwoyD+UeNTYl8nugZFDPd+YeJ0dF6ODefxAr76g2Do/s8xZIW934DbWUicC4FJviZwQTn703QHYPPHY5p08bNPNKSDldMFl2faF3yCOaANIomwW8xkERSG6h55QcH6H5qeRDhaLv+n0XA23zVxLVcM9DDoWw5/qyDnQanGB6KeXsgyniHdewBbUT1C4Kmv/gITmclvpB69lxPkdi0eX/plhX7eYW8cnUQq8qsHgh/5E1/r76x3bbmAckH+TepVALC43QFP0TLLLSeNUGgqXYMwdL8I+l6o71r57odb+SNPMj5c7Osby1rxGH8Ozv0iy3lDJkdqLOeH6dik4oPX39ev4dzphFhIjoJ6orxiOZi5yZNOyESXNqbi/M62UayWk42+YBN++puNOuqtlPm+jkKHG9cT8UaicNg5nzVMJ26boP4pdTkUPlehwEKTXnrZMpdr36a7qNo/3t8vkXkFp3UiADbXAwmXwZnA8EFKewh1oTg5XSHa9M4Bm6QInVCiBzrsWu2N2Vaz+aFfy3ljn5QKHzz8xnOc8j80F5hhnX81J0PzpuWybNRiqNneomvwmXJWVH17czLJshs01I5xb7WFHOBEYOH83/yPspwBWXN7HTuijFjAL6ov8cxGgv0IqaHnWsR7YqjPgns7y7j8ijSIw7PYXN8viLs5fhbuO9gb6YnZ/iQ4LD7J8OBg/LyQgxbLiuBfDznzTKkc06lmeHwCBrulBORjBMoSVW+1wPc7FlIT/krjpjLUra4Yy3wbOtL89Mg/uWUO+Khe+BhxmpDk2LCa4I4QkKt72ed9HtiHQv4KBmLka9Dc32acajA7rXWEbRPliPUe/moiz/zJgWU1Nz8SYvRbQ/cvW0GLljpO4stnpnWXtcrp+uQkf+xJhLUyiTxWEoiiQQy2BQ4uWtsUk79f6UoQ93HNiOU+j88NZjFm9RRA9e17NRhxmj4wC+EKbfiSpd3m2ZG0ZQpKRtaSK3ajQv9N6QfOLM7k0jguZoxFDWT2/3HG8lyiUqaadVpPLTZdAz65k67Nsj6qBeHSJFgww9NRwWnHyMHSyw7nwfuIxfn9Xg9he36fudNvGBVpP7YdJj3lc186lH7XlRIeg7pbRX9u4HV0FXBQ8q+IjHuqcjw15GEHTGXyjbVDT/GpXjEvGxQdMi4d3U8NlkpAgZn5lXg8UX5K29gIqBZWxwYS3aAiZqwqtCbtptO5D13yMiR1m4MZYH2gcOe9Wob0y4U6yGtuUCRJy/lAAXiXQhzC4ZlMELhHVyH9zRw0rOcptdurBUQMARVLNG0we/AtjA7hjdApPoJThswKR9M4mp0swoqcF+IudYklrwuBy9OE1ImPI22IiQAcTY0QJWLmLiAypNOPMkuEnijD2MVSvZ5Pk6gYV9dG7Ydv6CnUcRPFF4RHlxM2PhXoRJWYAX5pSuTN33i8Bg28T77rSrZMzpDIUlwZYl2JQmhl7TfwZtwk/AmYLWhULoHG1+t6SzpCeTbd2vERg0PogeQJJPZjMlD1cZKDNwfGkBMUcQbx6r5Q3KMv2RzYMvdktoA0oTGeMl9XrLcB24qf+2UiuWCuOW3KuCbR6bEqULF0+l+jbaAb58OZAGWbUk2CKtK83OcCp4jWx6nLPPLfctDhfucIbZHvBSd0jCtJhyMBW/aKcUeg5w7RtAA6g93+dBWfkZyM8wXA1ccZnFkv42oabdK4xlrRIX1YcD/5idEbJ7g5bWZzpmA5QvKF+3a5eFaPtIKMtE2HDDvgKnwt4gztQ0kmAWcVGlg9KlDfNbswdDppOpCs5RsfnEmxJY8dGPghn53csxiSEWRy7YPtnRGbtea761inTn8j9UVfObmA/5ZqAKH4LtNEYqL7PLuEcUGUddbQZkZuZYM+BYseY1m55Aw1wr9VyH9zrg8PM5KdUwhi8oC+465PabY9NEfQ8ijAo1oonZQ7EGQp25xH4naKs8RtVfBLDl+CjpRV4XWuhei5OhnaCFT2yZgfx4t4DFCo+9XvdFgO/0dLO/b5mU5r6joFmrpnqyKOcVTaNnYu7n5ioy1k4Qopn10uU9IC32lwJUC9PvEhwohDUDkuY1av/LJk/T3uZ+ngvB8X9nd/dKAScG0sE55yPQF4loePNv9QcSOCHLKvUDIqrCCQcf47iUyETdkbEkU8DQ9WkdVzic1LUBFd/170V1jmvWdGrGbhyrD+5ms2S3i1JNRCsNP7VL2l5qliA0ZrWNiqyqUVNGpFYaoVzAWqPH945j/kpQMaHlKZINvDLs0/JUtUy9b3kFsDW5KvnqOo7wVa6JFWCjncAgftuaTAJLeyn6SWYxdxDRHsnJd3OFb5iKXXDpOZpx5AuYkaRFIUE5k2KFSu1U0ok5mTLzhIgUdo+N2F8Q2yQm+fTD/uet2oEkMs2F8Nll0jL75dNCnkKxbDv1Nfv6hdUWug6MNiyHE1tT3i39bZ9aNZUJF/A12jS99idlTTFky0KnBDAGAdZPWAVVUsL40PLEO3J+aymc7M/3SSQFfR37eV1lEX/dLKb3WTC1rGod9mgk6hv8kBlO71RQFZTg/jyTLlZb+7ZggYKZbNf7tndYDUseWZcNO/muUniCDAX3+DMZNJp6rRKmJzGju+ptLU9SdJ8euXGOnbzCvZfE7v393cBedUf/7K+4j0G8UBfk3Q9SoKWUIQfS3KyfWr/ulot172Ck3+lVBo1ZMifRzV/ao8HI1sxt2U1IcqJCGkwSRUUJCS6y/rs/OdFSp0pYG0SfyIxRQ8wVeblHMjyWtf5+8gky6Y4Z/cXbpwOCvPEMIRCx1iagwIDH2q/daSUc1bxlA/mOOEMTr1skiYji2t1dw3TGB5nZncPX3/1X2uhg6Hr8/cCHos7WK/ywvy2+YkeRnmLky2tNFaSY08jYkqL6aWrNzD/RKoqkAeBpM+WyA/lWwL3GLXUsc4hFNBjBrMvPDWthfmUd8gtD85hc1erhDsPJsu3JJUk6JPo+PaI8XBmbkBAqHfE6CyAjjBF1sLsJqtXId1YqUfOJUIuapT49Bl7NcWPnSdUcnJ2GFz3rHHQNkzb+9B9yb1FMbQBP0RhD8FI7ruIfnVVwxTFDDbxgIhsT+4Aio4BUWK9rPI9nRBXaDEExVqjzozkMbLxySCYOpQBEoXUJBAupM1hiFawB5bxu1BkxZTOwuFDcRgmPTTIWWjjLN1yeEFKc9HHxjELPXLiAxJe/Y3bKXJX/vgmLvYlyiO2WvIuhIpoKsw6GXzDIaROZuvLlAgZxj/gm9aWA9ACYtpQJodWDRaY+sbwhXqtHNTMOkVQ6quEbcR6r++kjITGz7m9lPMCa261wiAgP4SmsAIgAAJ9ShVp78UhzJggNUtrO9nfbtVsBBnW7no//pxf1krmEwK3PHHEbIm2fmhJxTYBweSsZi7u8FJf05O1PuSK8NnhDan9GhpM9hPlw+XT+Sj1LzwJ6YUKvX2TqZ/vd4zFBKaaZugc5RXtK1l9Q73N6cCODQwHr906BkMeTY+/veUNAS/nVTQJ+XL3xeW/RZHxKBMandtVMvX/q3PbEx4XWDoBmtSZaTCenURKb0kBRl0sqaMJ6mwEl/3iHat54HVqFFKEjtcTJlIWAYLRf6Ufgomv8oU56F+bX6h55czmpr84u9vB9YNCvrW9JmT/u09xqe6kMyRzdOGu76nHVeil/LMI14iFzVurSgRlxM7veu4ectwHQnsOaUnHIgJG1eFWF2wlZzQwvYYasteuD4STg3ejyehvew3Fm/n9pP8Nv0rwZ7TetMa6jZx1e9PTZ4fuYM5UPUKvRQJ/bUfZGC0DXWS5Ukzsqrq31gXhqwo4jqAKDQhCnXIcrkOjC8AFxWBTDnjQi75iQ+eovJrgY8TqP0TKkEDsD2QTBnvdkOn/CRDw6090V6Se8HK/agVvIo2eYhCL2iG1qFT3/e0Lzbr6QZnB7eJTVmvhxuIYgKjBnX42TUSUwQwFpbqI0CtwzxDTY+4nfCZCznGS+P7tC1bb8/Qcrp8tTqL8Cv7DV5vydIoqTtENi8Mp08M5c81AXnfDghprPbUllyKOD8azaeWkMLKp8tDFJPyg9mp991AiDSNoGoh+xurQnNwAhzyQvPMnzHXPEyoS6epd5pRY361ltwkfHuOrpFUFyHo5pesX/uRvUz25ZUgYtCRtXUhOEYdQnNlyP+qPf/yU9Skl93fzojzof7mTf1NDRtRigD/in1hDX/AJ1ePEX808Vrjl/ihIUf6KvCmGClHcSxbNq5aV6+e9fPcBShAEszdm+V0lZl50TwWIuPWcsbpval5MTPy8l458HEZbCNCadNAW44bD/ZMIIwYL2kRQTjWzPJvHzdh7emNsnhm59SRqgjAhCeMx/5agxGcgpc6x9hZZVVZznuDZ84DL2wt0thmy5rwzg1NiDWTsedJsjX+tSqLyAHT+qO2jB8ak8QPuZHZz/ubgjk4x2CDSDPpgYIArO+eT8vu+lodgZm0Tx3+HNLAH1UJVjFBI//fPnsQV8B8TOFZHuq/glyQ8RTwZz7jg6TWWKMRG9IVguqwbwueopUIzTYvwt3qFSFi+Vgo+mNF15nX39D+xPrvi3WEjfBiW+/DGz1u3q2fQIgNrQ4Q/9sMu/kpUAxiLejli6Xm2E0jN6FlFhqROnQ5CQyWuYB9bF+LJgC5I2hCVzvssIP8kfo7fX68xZCmuFLfye7vXnUw99lVal83UNGgOQ/I52GIF9VgYCi4dHyCaodOtRJL4scFbslZ59Xp+4lxv5gPwAH3+qbewuq53D5JK9a5037HqcZ/YgC8cSSvhL6yMFOaCPTeaSbctwQnKpG7xEqwNmJldYVeDjQ4XlQhWpFANxU1fF4kTNSGtEldfRuK3lcpzpitiRqn/8Gm4EPfEzyuT+WAICi4Qxaj7GdAFPwMC0zHs8VSdlBr8iJFwjI1xxdkuq6+XAif3sIJoD18ouUTTE4f9XbcrzPEjU8NLkMSG7kxOk9J1i/sIz/t6vlcIvwuZeUKCARcY7VQ2O16bHYAN/dRuyiGpp3o282luW0URZbdPs6FSdus7CVN6v6VKi/17CL+a7QY52iXwYx8STzGvcPPxPTU1hei93Lsz2sPHTGNanA9RCPk87gh3+vmqKvrxCD0W5HUXaqeRSX7/sN30fW3z55iHqoGVS4DL8UwW1GLtEenCcgzFBecwjqZGqrL13mizq+qOejuXxJ4UuT1OtnkTKy+T20W8E7Dbvq6Ny1hVe143+bgan+Sn7nen33gLDaPlC9iiuJx9OZaraaS0WlLeTb1kmofhuWgTZYm5xGKPDoeJlgjLI4Kj9OcCs6Zy3TMjPPer7m/3eEDkCxABQRbGrhPNKj6ZAyLe+FmTvJxF9tAzs357Uyh+B9N17HlqBJDf4kcljY5YzLsiCbn/PWPcs9bzJye090MrlK4kq6kCzYMAXEYEDl1HGbRX0uIFGXMXng+h4q1PrcI0iTf9wM4CHW6sgOjwhBmrVM0TIcfvBnfk8N3vpkE/E+lx1V3BaGDLagLbCjzhfemT52Yy5Y9h/xHF2qxBWcKBnvz9DwHUM/TkMrgRKM8WI439XPsN+Ym2E3notQ5lT2EMJCHb9piCPTMOCy0hLXZiiAnxkEoUp0CnZUS7MPFW/LyFC+NTdzh6vwewaoQOFs45RTWReyhgK16ik1nR8k7Q829wU9PdvglQWVMMjoaZJYPq0JfTa936y/P4oY9ZTNBLm10Q4r2EL3w3vJM0sWS2ScFM2ngESWyX1KacGHqGoDsEMCAwVWBWWXAE7+psYbKKHquXW76lsPcS5t6thSf5/sCI1d0wVth+hQ9w0WbhfbkDGlQN3DkpJo88urvx3XSkYp5KUNE7kH2vEqh52git+nvXU65EBQTxjKkoGPXbAqzpkWcA2e8ghEGzf3jwSQXM0HIYUw5bJZ6mMa9cCrr2qLH4fSmDwscnT5R18tex7BKDccnAenCNUQ52BNSouFsnJNYvikPzAqaZ0XShyORZ+2zuUZaMB+6fuDSE6B0PkolLBke/Z9HurUHfeXF51eojQbDhtguPknfCMBev3enQWtuO9Q0ArcUWSIHt5oZBLhJpYzDQ+43L4zRg1MjCAYNH06EqU9r4YxTfuvUuTveERZukVsIhgyd6ULDLTawC5OuhqKlej4XC7IZqMc4m4iqCwr/clHXCyC8eFInOthtVhbzzbSzD/r9zMiad+MA2fED4KOozLa9WfjysQ37gXlbkeJQhBFyPlnPmcL7yT73GsL6C+BLVSkqhcEILb/kExsQb33jBLMMoWUE5nS/Y8958RZ2zBi7DynI3CJf/QzW2+iHYJtUoL6NouYu7MrjlaY23jYLNpSC300cEu7xF3BokHUAXpfPMNEnyo9HJs0qXqDVyomLogop9Q0+IhEl1KMbkaGfGDOrbG5OTQhfggWlTJxOJsjLhXqTjXyua/Hs16LSerbne+7Kd/l7q7Lmw9ZL65w4ScxOua5vwFWAbPAOEBtf4QHQPNtmcPauECq53lLgDPD7fo0SVFXJ60FXQjM1ZaUK2uLndMDDjKGz3zXMfIzLcoz8VsV0oVuHVXUjgj6OSOWrhkVrUJvFMTRSc+OiGLOgDpGK4kR3vqCk8GbFy89HeThxk9Bd8QxA3ZufSHO1uLNBi8ozqJRsrgSkWCVJnXAgBE3gYPAV0oss9/AcnzsfVk1qblmoswgAxMuvtQzJEsSSxWJc4EmUfH9kSGM3mairbCsa2Nrr2F9tsH+vyBFGh/9LJQN14Y7e5rpV7U1MlLM+Cyr4D9Tg4LkR6NEEJUSQ1MEj9/5oCpb+9VldtQAnypQZTMpvTcnpTm4LXUPH8RYwTBRNbrMrmR8yNJ1PRNpNnY36wq4YDsWWeozhn+TkUlX5TlrXYLMv5HHsFzn6qLSm57UEcjEZw+LAyQqE0fYpcgVChmRlN9efKRuxCIJIkDLZmt+A6LOZKld+fAfAd+HXGARs+JL6ca1T0zb9hQ6hsCQyEQ3Cpf7mu7IQlYn9lmeYkeLBlqwfbwKnzuyBoAydhSDUAZc5OsdacYaJ955z7xwEtKD5Lurf8xlan9oo1Nn9TX8G5HZBrIjgZWLKu+sG485iC4KwuHc+Tl/NFLBr25st9BusLfz031UuLj8Vv6QdqvAVdcxdPC4FKSAt+wjbzN5rdLhomIME/OgGL2LpTbb3IdFj5obe61xvbSkqRZD2+VVhZCojnBYTox/9mdIwU0y7v7iAbdMP7MWDrW/Rt3dQeuUzMnA3D+SDYJ83kzF4y++T8K3gG/Sc0apV8pkMgW85DYMZZ8GlpnM+ky1itJx2/afJHHXx3oYpa15vyyvzt7veJzQ9Fd/I8JVG1+lBMdRVq017i1fsL+nVC2VufRr/12cfT1x/+oUfUzAtlkFxBpt8k8Sujt+acioEPBCODzJWo+VG65eGnR/qXWaJcgnRiuU5D9e+kh/fJP2+sklpVz2TujokguCVeuRLxj4KbpokT5WuRLlE13TwEF/HRelDbh8N6St25D3ocUk5E/piNasbwvHBhVk4s3wnjiLAfisSy+qMb9wHE20lDOhPB15NwnLT0B4T77uCiISU+7kKWSF6YsmjqSz6lIEwuyyRyJaXL4aU8TrEp9R/J+IzCoCm8vmlu5Yc6xPN5g0X9vbpotCgL+Pmi3SefIjB2LuGQnZZIHzewTVIBsQRnF8Ql1z9qnXyEsZbDL0DLng714p9jHb84Lmr3FXlOt+ZLTvuxwRYy5g9tpkXpc2oKdwE0aHMUg+eWcL2nr2mjuzSxqkYW6JaecG5S6yDmlQly9ZH3MjVxqbU+9KGnQj6IZ1rY0BjhSa95e0ZjB1AQGlpj1dTkVZynuY4JDXS2P1gPa4lxOa6ayvo/fzlIZp3Uo6WdOkNJ5u0CHwtNDJumMDFPDpgCPWCVp8sYSH2bZQYOmklJuZjzru+geF31bo3tIUPGAIJmvc9fUih+dKiuwAk59ku6Zvvl0O+ShsTV7tU5IwduiDzj5yiVWben2hnEbYXuTBsaGJJxZg+yPzyBcoy6+uAxkn3lB0UYvhXTyCs5Y3zL6aWAE7BZhI20X5e99Y+3hQufcU4L6gEquBDcOVJD2JMfxFaJkws7j8Ho80XvRUBAxFDctOon8WqA5Mx94U+IJAS9XWJtKLFLnMUnSaT3m++IKDMlQk64pqaJlJFmwxLeIuIVT+4PoR8G4ff4HMcTcY2Rv+5xzW+GTILlGXIN2XBfAZXoMHNu7o0CmWFUJ14ZL0DUmf8iB1xoAs+FZZghyFIEIda8de/BwKhJAWKq73MJuXXdAgkq1aUKx3yBlhk4PmkGblKvxI6tnUfo2MYlJGOfBc7uv+Wli86W/4bjHglhJJQxOV0OMpLnIL0CBXWq1lJCWbYxVCk3U97GnymRCWcz9+pEMaPUXbuLGb9F+41zOrP8PHT+fxB3dkR3/Lts5bZ9o8b0jCheELBG7T/nH1Q8ygJfUgVxXtIJkne3ozSfhm8t1Jw6unfY0WCjhNoEasshEY4hrIDeipffLtU5jBMil8qxqyN5X5/r07ohKsBzORePcyyWL86+uH4askrUpQYfRUWgf6sj9+L21PVfZntsL2IChMCIjnBDr5NmRrcC7liDcKoUc+yJRGHqCNpx28Vp83mfB4nHZZWdRgunU1QVEJ8FrxW/QZ7NVM5jpJtBucELdZjXbzMtucujbDlNyHrsagziZUWWuzOYhuc+hF3ZYoUQqcpRZIhR/LNzsaLg7lbrs0i53FW+VfYx6+yVmSOPg9H8d0rwUUHw4Dkv1H/LxLHtGGBtbzV9GxylgRqrBFW0cd4frIVEWUImB0q79+1IiVMgjl1ZbMJHGv8FMsynXx+MpguGJ9qXPeo0XMRoLJC0UNTre9bWiQPoZcRIfkHT5T6pKra62RxjVRBNlsIKBaOHw/KUYsUR5PBNUsAUUJzpdwHyz5uMuWM6zFVDYsQpnBN+UTtqtBzSr2oYh/6o9GLO/0+nI75+DYfB5nCQG8SRN9sldG+I9OqJOtJIWCrw2TMp0RnUy8O1rWXlpsrY32TtQalL5vdhRSDrRlMNP/tZ6XBQ0j58sieNeZfPSdTlguvCOV9Q+9EqxYk3v3E78dPtftQXNnl7gnkIp/huNEbbHQJVxfM5mPXFA2nI1X3eQc240clkvuXBOUNH3ab5sJ93nErB+dHJsGTr8WHkZ/N8G3WsUmc0183QaMLM1/ehrcd8IJPJfPeBnhOGIZAkL6PS2/7yRpGzvfnT6sx0PfEoZFMqmGEFcv6cbQgVbLZVuGcL82a9EjRgcK8MUaODuvZkPEKjn1Dv4KF5CA9wpTkzTJihuibhKy2JgEcG/E+vNhkfmzx+AYM3/1+lD2gaSduHyP+EUmyT3FkwqOkAVSipXXrm6x+8etCOXcdsIK1Vmi2+Pga6UqwhlMRvJ+Yh42NugHROiuzYTXG163rmTOQBGynI+5NBu9/eFNBEKIFdu0uwN/QLT1y1cKL+dFeBzuDrHGfTgAkYRUQdl9WQq2CkzfjDjn6mj5eAp1e17nHaLRGaq+4fYxoI8vY7mS6rY+KrkA915peGIvjHimXGd+SczmPsxBKpMUzszuRnvQEMatQMEbCXmv/TNTn/i4uFYIJiE0bW4RcUlf4DUY0rhiyvvxX3WPXfBnlIb+DYkgryhCcWKB56X5QZLKUoObkhvsc146b9Av2ZgyixnUE+gUKTKfr7ywBTUccQXp9CNNDAanTSx8XeqP4DtrI2TzvzlkamvwOY+LUDm9jpC41MjfnYQn6j3qvErQIjupby5SuLgYz62TDeNBHzeKJt8l2oyqfqCYceVwGM70ajMyRz269fMJduhdRF7X8W3AD0vN1WzD6VjoDbc8YX/k+pbffFK10ZCO9ZkvuDoQWpKQLsj6zNjyL0fdX4maL/eMQ4PTyNhVAwBgFmbEjdf0jEBhl+KFTD+R4fsnsZG8l8AUNEy2ydkrGjLZ+7KY9TI/iF9dt9vr36xhUS+zG2Uo3daxTFMqusnrc18922vdQBed/Y3CA3RkzuhL01CEQfgB1U7aN6NueNfOBfp+wecBAZEbXZGC8ii/N9OoDzf7aJs9fC0pq4iYsXDxbZwMiKJ5R6hKmlS/GWpoHClcdT2SsGn59EASQObVr1kzqtUgciJinyG7AQxLNifl+p9IO6I5JYOvp996YmBz4CZ7WKqdWRicWezEJP+kM8niJEVBodAJbV94V3az/97Bp9Z9Lr8TC+LqxsH88XU/7tkE+1D2+7GWJ0zxEYWPZDavL9ioUdUEA/WJtzSMvRcpheaGdBlSjYX17YUG88B5KtZwCy6fzF70++n1U3RVPtbYM3aQsaRlCTfslEGFoThupNFsF9Ff/14Gn6R3EvvI3Hb0P8uuX+pqKwFEGM0RxG7+s4JWHSgokab0D3okgEn1CwkjsIY56QjUvdfQJI2xsYM42aOiBSKT6RWECAkNWyoldaIs2EohGWFk0IJy+tVXCmq/nH+JLYyIw0BvUcPhoim/oFXd+EdRN8Zjpx21EFkdY1bx0bT5fsihssZ32397wdNh+Ry7gob3zz0iM8mGXLuJw1oi0gxEZNcIbVmT/BmkV9dbAw0QKkIzjWEdUQxPka0BDeWQCfi9C7GLKoM4Iw7lHqX1RIJkjo5aByk9ci0Ho9pd/p8p9Awdv7BcRtzS4z7Gjhc+SOl7s8qC4MB3B6NWTv4fb5FwuFHnQRrdQQwuU5mXYya9Nn0BfttKlYM8IdAEi6JZjmYTAFdwtHVqQt8g+7qlM3YYxXeoP/hzlS2ns5gi0cl0HZPWpQ7pHWIoCwecUlmcTBTlnn0GYomr3yje3OZUpZFi9bSMxldXz9jqbc9kpQE6P4G0r6rtVfJqjm9+0pjBKknZL+KAqBJFaeYhXjt9A3y9uINi9ezaU7BWstpRFeOSPcls19McNkVZB1Ly2cJmmmZTwAP1ztROgayjgpZxdFRlyPChtSFz3EGdxPyDQ/gUQYuNQKcqR/ZCDYkwZm84t+DSKpFxiu15yp/aaxLofWdq+gLJyLOsG8tRCfpaxRARPGHoEQyJEMT/a+zG6WXRUlVdwr0a3uy2q8rh4XegTU70oiIJKnpF2I2VAVvvbJvtcCOYdvxmP9m+e95TnlxVu+n5s3BIp4CzliTj6Yol4CHPQXxHCGaNy0kCStiMEmUqQbnMnDDdXf0jYZqPWRpMfS9lnLHjfP84YIoMptG9CKFqASoh9L0y42J9XCP4NfNWYa8vd42N1TW0Z5iPUfAhZ+FAmLS2IBYLT1R6hXwVRiFaZZtm2ocu6NjrBNBvrn9i6hDTkmyWHubVtIjEQqnW42kB1St70J1as6m0rnHt/wx42RfGVr6HIipuYSfHK1jfd+L8Nw1Kyzvy7//Vs0WgBdm+eSwN4L7xMQCYE6Avohag2Fh6zRHw1iFAUwJd+Q2/5ikSOV4pPFAIzikyov+Til3dY4Zi9L3CJi2HoKKu7mEdS1r0lDJJUv2pUbVkz6AB+I0v1yiqF+pztFL7X/jR235rfHLbwGzC8LLWoeXGJdXPqokzBmuxCTQXncD8GN/H2PJq6mqighQfXAkbEbxbPYi5HSvaoejYNHi09u2n3IgYuKMDQqUiYX7NxNqrJEWbL2i5O3rdvfL/6Msxb0meB9YJrY+No5wXVsl7XawHLilc3sZm68fuDeUGPh3JlsO1o4kZjYkrdQJ1zyMIbgnUBDw9QjRQW0nwMVroMEwIiyOc/V4ZsbpR4P5GsIhc+8hOpFAz3Y4lxi0PdeyCc5MWTr8CkRmfGE+qtiqMl4M47egK7wYyohGv2XQ2aknrDGSzED6bMOTwRBTh/J7Ij3wO8JkUWWUQtDIhs9OkyZsaqWLcBf+PgkCzFYBjFMkG3aLlMZ3laRHAUeSGymHy/PrAj7r+BU3ewkjnc2Pj4fcL57fUHWmmL5eDCwlXx09Ug0HNgze4Il2wgT7dtW+V/ScvvSuGVhHOPMryNYz7pwRuiRZLhtZe9snCq24AMYrLp9OZt2LLsT4M491XxAS6/sMTFzGLcObTMkjgJfP9+j2QDc3or92pU20CCaC02Mas6Tnrx0ztXPB0CtovOJ0AeKp8I+GwVw6r6SET3hBlGnHYWmuTbL0BxgvPhnXxjssZilmZRVeUrYM7qAGzII8rLJB1OLj2gFhUkNl5N+Xz1r5MAjCf5kR/GHzOcQ68fMRoHlFLvb6zAWzgLoBMAyR3oXyUjU5lKVJT68noh5npVNCCGMZwux6hyFjcASilm9o2kvFy1LCmZuD/TL1Fv1YYgs0yTm26CKVc3WFl1xEfJs/3LGSn23rq5Snl7gXOHfG5OdjRREAFQ15RjK5xFNmfj08C8RgdYZxxYeqnQ3Y4qBDh0zisj5rmlU6dzaePXKZ0N7o/y8pze6Bi/zDqLbM0ZzW4p0kbTlePt9lUPXf4OkrcKnBtRLPU2oRzTziWLnYhTZFNQ2r0+tA8nyaBld39GQyO0vMFj5YECkJS53S1sA7kZX5DqNR6UNqzh/avwOUnC/KYtHs0dvDKSf10tE0X9a1bQCh4FKTqvgOtev+101A4jI8SG3+0xdBnS6Evr3FB9kaY2bQ98SXZSyZ6IFxgcCI3y/O7HfOXB8617SBmfjsegUp+viQnzQHx8DgEnzNPGgAtFfiVHUPFxp0LDD5evY/ZT4bWgxWOo0VhKHqDbhBheNFv6ighEYPdeu9VO6mucX2khX0fP2RF0RncBf2ESdNweQ7/LVzN78a263ePchAubeqMp37p5NcScgqke87AzpZF5IinOJ0SItUnADatT8kyK3lfLO7VOpinQHghSslxCywNfTdqoTIeIpSeEGwxRBhc/ywLXWpW8Hoyfb8K+Si5x9rsA+niBnaW/vwzqvzVaJLjyOd/rrgj6G0KoL8CDCU5enySp7FGD2sTuXugk1/ujPwAU+Ux7Haqapdb5orj4w3T93kEY6UPlWBCzTCtj2j7hPQqohtljBtpLKnJTtOlh6BnaWDYXoZsnGvpasT/li8xlHupE/F6czgFyYlNz8ufM/o3wtR8YA0rWpRKNYbg6kboRwVc/4FqXDeqaK04VHItrXhmKWgUUXKD10tGAvrKfr68orYpo/iispSQ88HFqz2lijttLAzR8iQEGCAsGO2F6aptEaAdUZwplbdBUND03CVRkEaI8SF80/P3eayQWnR4yHnT6/T2D+dW8MvwdpYlcP3F2+FeI5V9gQ6Lx3NA5xBF5/rMWcTGrvGGTIU7UbVmo2/41fjZus4N6l6O/J03oTnmLfhxP2EpmVIXeeAvUBi80NCgktAiXg5XRuJ39sc66BZ/lx1aAn/AYNQnL3Ssck9R/pd0RzOHkz7HIZyQbsksxUHZyoHqYEvXbguZ70ugKMxNe+uNY8/Sf/K/3avxMXh2mw31evxaDOL+crvloxRvbmrh9GXbvwjtrFpBf1/y0R69Z9GFW2KFHp1RRPxkf2Yk0PZxPcFJRj9U9vtWyzEF40zBdbrWhN26b7p9k16zwUapMZ6Ca+Fu4DI62bhVAxPSg6maO015hnuZWP6/UTJzyLBkRvLAE7m+qP8BanVs5VvsPeN2tbZppWwxFTZDkb7PuB0SdMcgt/gLn453BjvXuKWIt0idE0JIKEYcj35E1dRJVjOZ40QWyQn7bbKQvXeTNKpY2sJz7NpnboBVo2dfYst9I4PymlwBQvKTQHl6bnnYSoE3IpoA0QM9+AwBw8UYPQskvOZtlp/sd8qMEk3qMwuM0P3M5cUWrVunt24uUkWVi2uwofi4YDQSsLXddHmxQPBJxsi9+I1cwZrAQ7NQX8ZvVso2yQpQ9sdvwzm+8d4SmWPF3N3AxLhmo+pv78p5o6rjQPadOEj9e6S8N/Sv9LefWf+Bonck0x/uvAz02HXzHOPMfP0n8K01eWE50LDH58EpH2g7l6lAAEN0WnSkSBSAUHNxaGbx/sidzxPljCxUIENNuUuTYKZE1Co/XqxnzG/4Gv8Frgl2mM+cUBObRx9iM41K8ob36SPRl71zhZBJDk9MF2UhnmlUQND6wrNFC3Lgd+TpOJaBWv3r0HYH7p5kizdVFGZ9I6zH2mmB2Jci0oo0OCqs7KotkJ3wQQAfSRxMMJ+ExvUCOy+fA+1QKiTPmXr9/Tf2r9agrqSfdbJHCGAp6YEJQP32XIBZtM6+E8PFfP65adBgU6e5gkh84qQKYNIQsaYRYMbBb/h1s8UG7P9ZQKMBql3I15kNq7HDObqcLAB17Af/aYepSDuCYlc4AZBy+tY06n1HRYRNY8lblOdqrAk+TYTQlLB8i0qx9kbzRlucjtZdFgfUZTAoXj4lLZ7Zf6KoYD1Sp87ax5GpnNqbaw6wxec67zzpO233vNLULUHk+9lw+6EcutgRhsmrjp7QEpDWN3/Xv7HiL4jbNPGZfpxKRR+Y06YZRoBmr6vSxgT8QYfrthU4wq17fcNE3nwDNPHX+baeKSl3VmSZdXyv8Ovi3q0ZXB9d0LKBpjIZJP35L1bObByImUBEywKAfKIQUBK1+M2nUS4NimrJGGmqTN5ZwlXpxixRghDErynmRjprJxOttaNUBWgM6uDuxQyOZPbajShZ9BrPrGrNAEuxj+fIqLYzemD2NrlM4V58xmawBsZx9nzpK3A6lLYLbL75Ju48CIBup1ukEXuSUzgOhB7XqLXECgSEy0cW0SXBavmEcIm1ySeHwpnvqWGI+6oBFSOkHF2pd0SkGnnRk8kGGMEhYBlLCDWyNVlo5IHkhuJWK+VK8MIcxvaNw+c3M0fv4fRxIk4U3Lnae5yKnDzrgR6wSVeOMt8eoSc5QPh94QVm1V72pL2ZkaMpWs0HdXmBeUMzHT9R1DCKnJs8Zm3QopSVESNUJ7ubkkFecDLl0IulXxsLYmwT0PLZa0yzSn0LZ8XZXkNpViqFXufvo8B7lIb7X1Luzbn2iYo2P7wKlTeubsaU+41NMQzEhduUHmI9PQqz4l4jruCGL7C2G72NBg+1GBWyDF65TXCjRh7kP5qMCirxbEBK6rH8gsP5Jq+jRyEXI3xxs1Mb66KNcYF8KwPkIg2zbs35Zo9cVdo4tTuRUOCTYCXUvD/KLfb1Cfj6lXaZ6qZRYMcL7C4US9CUL1eqqm/tl8rgr9widAXYWpHRA9uYDNBIlmQoEVH77PzE6NJKIE+nf4PfGKqQLk2PcEYC9T52PW8zh+tbG+O1yhgy86zdw3ns/0F+rkWCbz5T5CUtjtvjmD56mJrp/f57wJeR11z4UnICHN5BQhukxo2Co3kPnDHQEw7KJpbiOXq0e+hnQM87KRSagCP0f1KB/20Cef1wYSINdTJjTrFmiPx6D2uideZ5BWjvC9zyv71Jw0F38MpqQxo3IRf2WJizgXCf6nAQojQDn3GRQ3a6l5SVayrrqKgl99pK0Fjbmdr4LD8zzlF3kugdwNhx+zcpZml4mT0o+TTa7mvjHzZQKBlfwPJF+zbmDhYxAhOyJiOyh7Xeg/V5iuXarjlYnyWmJWhR12mqcJwK0n2hXBh+IAmlMCb9+tBqpA6eEH1uTW7l/kzQrh8b70tecE0uD5qaSN8Q6gz5gsxqlPl5OiFIPhjuWB/zF94EYnIPB/ovDF/cFHJ0tCq6fwgbnlUhFF+Z7ggFSw6emLGJ/vL8EyCIppPzeOaBX+fUEl7aNjHJSQ+2VoslvNhV0hJPud/P3i35Pis5QtjqpN+Az6PlHWqjGAxkrpXaF7ZWOLUDf76YBPuM56Z9U4/hge6SSM1h0rrqe1I21Ius0941KJCUl+E/4gNHw+r2Cx52mzW7jOXMmm1DVxGu7JMTVDyvROa5L6S50z7hpEHKbBOBnRCv9EI/eGKD3oyKOuV7Ktn9jiK/dTDDgA6HjrKJ50dd6wh62k6Witv0NMz+S+Pbg03jZYdoERJNRm4M1bo65OdQZk9xN4UDfKrDaUOYoP/MdHXVrHdQ6jCuelgUzhYwMm7+su0RCboiOGZbzRUUqokvfRFnNGun2d3PiUL5XIu9ftJwXFAxi2SPOpGEs5kI9ZdAh7n3cfqXXB5rT7F8fC+AZ45IJChpQj6PgSk8D9d2lTYBnzhEjZB578JVmykgqyAoRZNlA/jT4tK5nGpUZy2bRRCb8adx2Gi/n8zErZJtr3YZc9QruO2zmav+QadfVjZe4nQEwpi69+khjfCkmJcQsLLv/jHcoOZTtuynan6KfChjF/gZqaTjWNFD85XOZxBWZkpnOjThdtjFbbQpHYd+j2QpqVofjJKY4JUpDKSZiCwDZIuWBh0Uon/GsPvnTfKqYSDO9+mI7+3gdwfJiPm7yiW3mMllD0ISz0LUjMbobXwtt+244LdcSORolmG8VZ1UguMMXVZUhfsf7RifgzvR/JIUqKQbDjK1S4GmQ0mc04rCKEwCCkYHhNvBe64HprOeRr8lX+Z4dDbRvSmK+vzVBpy4Z+L5DC1NqtE7Zc3SuYybzBGSaUFNfNTir5vGwYXXMjXKseNRbAPBU3gH1qjL20EQtl0FnJO60+fkESzcZhy2aQP2+3LTZJve9XMHz1iLYuGqje5pApoWO7x4asz4mymhxe/SO/e6TlU9MAEz4/ccqWhsQ5XE/fgUtAAs9+UjPZG1QLxSF/+gpXkKYfOmB6xXN802iwAIz9Ut5grnTbjRTNA6OMHNp8k0OG/HWIT8nZ+4L38SZ3fg56nHN3hB2QZAKqhAjxNB5VtufnH3eOqfWMWPesobLXcF+53oHQ4ylqRBSwl6OH2P+RMb5UOFVTdL33Bu8/T5qZ35Hnsnykp7JB+XNI+kdbDAsLYJ7nUfv0E0WrG7CTgrlKOalEZROtMf3Vl2afMLdJ2K+iG9p/u7f3caTqhB5UdmAYD92BBxal7DBKnDhSPFEfNbClfB5g6cmjs7fjDYLdfga7nRWvIeG4/wIGl6n75382MzoieoqTPmLmyLTkwP/EzxWmLegG5JTLQ5TSNBomcZCaHdiMU32GsTL5mt7os0pjMVmRh91tNBgz0j9yghKHbkMpvNGsNgmyXKu7lNQ9wylurwNKbrmoiWX0Gs7qHIyEJGrY6E3avNFARp5QPFdpLBUOheahDtGeKm+1oZ5Rt2heP1oSQU0WPDdXPDbrStfkAarDop0Wl44b9l4e7hYOyQp5QvKjKrhhmVOyFNaLqvA9aaCrzxQm5vb+NDdtRR/YyrDTCscxqbNMUqkOc+9uMmQtbPwJV79z3PkK0KYnkBv1+4tAGlg5+uHo2ugdCqRJxNyJ6rbqhaXE0bG1TDBSsLaS304Oe6hFelvkdKWGAEbFW+vebACjhKFbK0KRVmtRxvMJproQEP7ebbQSJ7IYDwivxUwBcyuktRUYfhvtCQSRpirTdnHVaUwJ3IYgRp+pK7Pl4PtD54YuzSqrCrTa9IXgK8tcDQGZtXw4eFjZxQqyTDkLshkKVTdRlGP1FwYKXu4G4lif8UlTgVYMlTWFLE2vslX+X6ZROrKD+4Kw7USylVDZxtwknnPtz532dJ/IZREsj9yT1Gb1X1iAIcJtG/BL88mrcPWR3bjP2Lt2OTbOyh2gjyoh2F1a/a9RQo84hGmBHnEchbEHF0udXohjF+jX6WI3RoOUpPy9WCLSojC/CO8zhf8/iBG9EAy/GU6Xw5B2uNz5s6lzj67jeJvEUcaDwsJv+KCa6NQKiFE0AgJIS3rJlp5dtUP2Iqgs1/SFVOluB3QPPkeGT5Y9Gblm2uRYUgMW7MbDonGlHz++uuLKzmLaWkZBhlMyLF7H4I/SEPgxP45V6F7Hht5svxALLi29qu5AwbzVv57XRlFt9/HgTaHSnuh3UIwgw/lLBFWBzFo01YIPknhcwQxquNwWIIZzPf0PNFWMeLAvps2EkM7O7qnG6rLYaXjLMFYpzJFIvA4hLHht8vdBmBKhDMuAGWqbMQsDq7zc0L98jSNcb7vY6DuTi/E/JRNZ7KJrTygGiST8vzRftDAFfeIuHIJ1/GiHBHsbqtPhFqaqcS/u5r8ihBhTm/fYg3T3PlK9t/X1G0O1bM1u0lkaAt2zcAD06KpmJOcvMs504LEi6NvPpTWYG7Hm5pUkBd67xMFPbC8xpQ+OkIOakeiYjLtc8OGU1sRzEtn8UA5ksBb3QZI4tbibvnCjmk6vFNsQ2MiHQJQhIRdAB5gAAgZLwG0wo5UqxOEwjnx1MikN8W4Jb96ahVH12YRISjnTmLmUDHbI6x50anRVWvZHJELFCbhbsb5go5BIoBDUo34lLv1j0fIBTrJcLUhZdZIJiHhiR+0jvSa2oDPEk1MG9zZJ9/pUt7BB0UM3ndtmIh5k8Qu19rwjG9Vdzj70aDaNFk8APbs537gG+Okvr2H+tR93oCSNsNAPmxbnLedjZKxvSaBmAbhi+fffKuGoxDVc5WLovxcO0y6A4mM1lEc719diTo+LQbjH69Z048vch+8FJXMwYDzMInIby35Zjg1Eu3xrLH2/TguQcfmZncfz1XgAun87RNK3ffKDHdqzaH+aGwZVqR7SKURc1DOl9EH0rPhqPObp+OXYahj2MahLUciZ+tmFP3Y412+I0ZlVJDHgkyObcgsqUVyqjPYvodk6WaHgIF0GbRTbmKE81/VX1gkrTTu7lgOjTSjwxW/swWjyu5TYSGMuU+wPs+ls6jZO/Hgv65C9jZdleiLCNIVUeLu/iGkzfzH9gK0C7/aRf8J6eeML1n4DBp4ISDcyZWy+nHgm95TdbO0nu/Z48p4idn2DDqQfJ8SY+DdZR5i2UVwaa+jgamon7uuGq2OZgiutfxN+yH8RDMnt7XkZx0nmGj1/n3oopx/DvYO9aZLU60lewy2yJZia2yqIsqbzAv6YVSz7EsBhWO/tMkMf/Wb6MEuDF4c/ijMbr0y24pi4pyaDz7t4DzfsFqNsponDV1F5mohS/mV8eTtVfBv6CWHC9913Dw+KtRMHQIVNPxme6bi4mweUuesAWh8ULv7TgINz0j2IhCqcEK8F2b+vHuBo/L0icq8NIuQhlsnxd6WK2Wmb27bY114WSPVHrcdSgKzca5dcriuIE13Rr8i9aNb+vJRT6wIiaM0e5IIe0LRB7xn17EVQcUQKixz8U4O5tXVr9+XWkvXb3QX+YGOwxjpk1ZAmnvOgsI4hllCfd6JGyxyHLEFV5x+i0UCM+fSXIYLKxmbVR+Z87jiwxm/dTbx06jN92sFKj22CQ/E0+nV5z7KtaSHftQQAscTZYBVRSpOkekEwqJx3l38g4UdJiTEL0p5iX75b/M0WcQdRORHz+Rfq2HbKqhRJZiWb8XjlT4kbLApQTpK8A2WIq3610msaRZgBPi5s7W/Ln7KnuA8J4f5dWa1ffLrEB2/VPyA8CvWPsILTWrAZNj4CWCQwMjbbDfJBRscdx7HyN0a+jtPiXNW3AjDPLdpUbyQwvJ5EN+lwDYSP4IeNbgHW9aHHOzLtT1Jw57gpXFPqA3eiDsdpPAm2j56V8uKa364mg69Sqs/Y2ldIq0h2Six5WOpUsHbnCohE80QoTkpVirHw9gznmK2DynNh4Ft/77sURB6rnJ8WpXdtuhGOGFwNU/Hxd37fKZZRmtQM7UrscZ2wQGZabimflMp0D9M4QLZGf7KMvymoXvdEPSMh65Kjk/oU0RqsmLOIqjx5ABTX21kG3ZwweALMqlKHhtWZiGU2HKj0NIwVWtFBFqAK8zb72BkYZtGo82mGenPPFzIZkqJF1K/PuHSCcjqNc3TxzRuYrC3noWxHq2bD3CUES4hj+K8L1n84Dytgkks5qi2JPhd5/tKAK1IjiQzsIOvhFxszOb1AeW3UBL18DnDEVIBjnS47y3tvPBWvbJzx5h5UWoZsTE+WbWenWQTf/8OQ5Q9ZfDVuu3D1691zEmAUs1DbXsrifbKb6X04/YADyBXk+gBRqCKIEoXlusEO2RgZ8TjZVvgKLfi2gqa3ml66K5fAm0MNYT+9BYs3XYa16caqcFm87TH6fBUX5+bBbkSHtbqj4YR89VPNHg+f/s/qmGrneNCE8BPwZGx9iG/kMFQou8d8kSOKy8XomaL56GCVcuKmromOs/M0Du3nNb9ZTcZ+moyx4uHA6lmxt3cbMZLdVbyFTsfR4rD2O5WJ+20kMx3HKKAt555Xc4Ps9xw1ZH5BE7ftpeug15t2QNJ+NyWFY/ruCiKuk3HcvEJ/4tqLWRxQMDcTWEr2J+8+fX/Het/CUP+18qwFHujAziRlqpWD7FdJ4Y8TR8dyuOE1Ot3vwXY9UJC3U296IH31Wuf76ZVU2llnLXyVBNfSQ0MSFGXAVmq74JgUBSmTouEa/aEhRH4ngy3ujl9QjR5gM2XohAyts9p1dlZMRXK7ChhcmS23CkL2sYCbSiIbE8fl5rGtgzZCEkZLHcJzxzvx+QkLRjvlghsGARoHERZ1O1Od0iw+qK2Z7TX1I4iO/mmO7t5IutpV0KyOXvdvCjidqh9zUiY5W19r7t0Q4X18gMiq56QTIEfcxes7JIJ6ilp61RUOYbH0IA4x0weFTJ+1ErvVogSk/DqsDjEbximZv9V/oYbPxA4hJPU3y4oUvf4w56ql34COTlXUfog3mDL5zWrufc4sWynmUD9sSPL35yLfAWbh9/m/RtDdPKF5IA74pxoKf2vplp2u3vndvvpWs8tYLTz86lSjurwy29IArhsYKRvXcST8ihGsCrlTaaorAIRWERgdFRvK/a/Je3vO79yZBdnCExte1ND1mfTbpbX9KE+YufC/BBXMcuDDT2c6M1WOnzhelv4tdyn9PU1motPkhOzcVzvwnOonbTVavNj4V49Jm4o3R7pN0r2qCX0eFrVgXyZqUTDZu3mO5JPwM0n4a3m5IAVJKd/w1qUrSVxt6yOgabTrGmTCC+m6cfZOENxJNrBoW23BecV7ErOGRay5yu9lTNXTlC5f0Gktjs+QrejE2xsleXTrAbHKGowj5ksj5BZunEuZIxOCYok9LbfobrQ3/1PMaS9PX4b/SRQI6xogUdi5B4Ck7sqt6+1z7mhAR4yZkBOcJYXft1DCWJke3gMeZg1zh2NzggSxwkAWBJSV4a79SeyXG8/RK5Hkz+DjeTzKU6c57PX0icLSMEdtSMQLQ3Z60Gwz8EHlwbPqBCSXD2yvHx9s+OMVIgbOvqVAeoO5no4gmc6Wcw5iyIBbJjlKSS6K8pNxlVn48FGd5TkCr0zG+R2p9Hgoe3jmLl5ISvUa5A3LZVw0h8OP+Fo20ehPlA7peMkAhztF/TKAICARiCNs4dEOB55Af4G38SsvdmA6/pNT4b84XjEEvmthrVUn/ew9uhS4qzgSpT/KB5vwLjhIanwKQpk9EDrKg8PHihQYKANjgw8tQTWXGW3/AqqEVTLvhWg2CUwz14HyKyzf9eENDCL/jbSqW+Mv0driFjhCec+e88esHmz8oDhOIFjmy1usFrQBf5d9IAkRwHUXJv09OnBC0QVe0VBqiNr7V6B3hfHrRtE353rla0/l8QDFxdQ7CkCTZxpSlzjrNNkvbi+KNA5EpB/34gRgAoHP6VdwKWQObLUj0va7XQCBIkmUXPvfizHBfBVkwg4qBs8/MzLqsvgOEOo5DFdHWsa+Om373zzQvq5vgutpNeEl4KSC6WbICH7+VWGqhDkCz0MyGKd7UlMextrbiW4lBLIXvJbyGJTAa2HM7ADtseSFAX5W5FzaL3g3Bylmla0ih/sdgDHEkjnNuzySXFll9rwO7ns+7fAZM5N/9BM5nES/oB3AkozAkzZWPcj51vikALbp3Xf1WjbwCxfZdm4vfNqlNjwjFXJD/g8Lb4uBvdwnST8hIiQfoa9d6YiwM78DVmiJtXB9GpCMAV1xETkeG+c8EuQck+YXiVZwqHb8XIEg7hzPq52o0ndWsyBnRB5UeOS5X6QVSA4+JxrDSQ/kfWLDjbV4OXkhlo7oBNohBLu8uAxM2+59d8abzVcb05h8cWS9+fBWN8UwdY0pxOWMlcXJt5I8mBXqUGVDHVx7LOfjZWVcgLDyt89vGUWI+X4C3G/wYK/XUvBfv2J6d7t19+yKpoG4n6sv9VuNP4vVIuk4olkL8FtFEIEVZpUq7PMWADCsAw0FBY56j+ptb6bchzHd3GDc47hb/5I2PmFsovSp2Sax6+EZ6pPZBN7zEgDbZKWTxQ/4yiDR4MyVjathWterMDKPbgxCuG0PaAEClzC5YZDrf/RsF40VaL370McpszRt5A+cFLwd1eGxLmTTwq6jQfReb7g02CDUZ2JpS08HqaFPuOFL8at4oacA7GI+UYis4ZKJThgxRfTGL0xbV939YZNlvPALWPd+yLGMKlhX96CXojs6YPrfAAZuR8rIVmeyCC7ewZV931lQGVYyvIv6XOeKyrPovUBhx683OI7nuI+bKGKfDef70KbOq2OMTnxHmD4LRrOgZaUwc32gxNl3cLo7ZO6Ecu4nuIVBh2pGXc0CLA4wRWwEtbmOYxUMHAFL6Bt/Uck3ZigrBX0hgh7ltu57cnUscPowNPvDeTJcvt0yh2Mdk3LJL4Sm6ZRudJdxbBaicrt3jsks00yGphj3v1ECdec7VRCJF1p01dFxvFzKZNvrz9C10G68xGGDMJ0Bs1LyQfeeNqpa1jCsGioIASS8U+IC9wvAyLTYp6vO19WOAljbSnwOR/yZOy5X2LML+Y5OZjHu4wt5tZ9JH3xb+9WaPF63+JyjcnxG8t1xZ8gwtWrSMM/bgErzgSHrOu7d7i6nnx0I3TVKbsBOmLEXDQCZveGAKnQPRMP8IiPgmAmnqR7/pvs1Npo8SfuAJk0rvr9NcL79G/ukgW6euTN/nrQUbCffqS2VK9PZsBZTTtAoz7vD8JAKQ55N17R4BF4iu14VMbUEeZXVTreoEQTZXmLSti31JC2tuVRoq87ny7W2mL3HZUsQsL0evvvhMl+e/o4RAdRDNN/wzFbmfH9EjTqwePqm0laaS1vmoin6k7D5RBih5+s15iLoU17spzQ18fg4AbY10iPs+ozOmiyZ+V3HLDpLo0tDuTsD2W/g6LVgBcVZ4zORSZTfy3f3DdL46hU2QYeIMJ6F411ZoIgHeaQS964fO96VzslW9aFCp6gyedDnLm3Snj5uR5/W/vCHwMl4fxLngB6H/pXigxaf1dZ+TpDDZO9WNO4vN7y7kzfYFzXcsyeLntuFCbDlrAFI4BXnA3JDbOqzcgnGBKzpJvyot3iaEQFxTfUdd+tStSL+Q7rvqU4hOTzifXFGXBQf4xd7+CfQ8dVwwKLGPhN/+qkvPMXJ8nCkgjOEqCicznOJEyxZeg+Oa8y+iAgEEtOxslKUU24GqhTiBk9iSnUmuVXMZd9B7tw7KS2IFOm696eOe+YleqFVE4ITNVdr5KTqOS5Sg1xg4HqAbHswSPCQYR6yJRvjsFR/JbyM5P5CiVjyC9CpEVFt6nvb5kgwAKy8Gf6CFCNfUAlIN07GFp6Ao7dO6HPEwJbM/BKZScswLFSVxUZGMjk3B5AYukAMrzfc8jSYooXu/xgmMNAsN8KwqIVfDIzOlWusdJyVLhkBJ+q0I6f7Lvj28bwL7C14qX5dWB4RNWlNhsYI9tLI12DP0bzDVyshFrHDufad4OhwwYIlVoUXquQtyaoW5QAyZkQDe7HDPc7kqcNOoQPKDCQajc1hsQCp05DwrJ0dZ87TDyBfWYRpnnCpCxEwEJX18bVUjuclnyQPSWjAFz4TJpUYxRU/GDmoM3khQxIks8kuoTJ8gXAZgg+NUY3vykSrB2Yr2iPYmgOZXoapihhCpF6B9yaxfwIn10jPKF38IAv63kgkkH1LkYUzblpX4IHMLpWXhDkkaCce9/j97GZwG1uZvZdEYNnA84Q7uNNonzC/ixufeZrXO6+pp9YD4/9e1PqoXc6oGNudEoeBxiARL4iDKJKYPnx24hcshM7mqCOD7qnPHNZ0WG34CJtAoXWhslmlmUiDWoy4kvSz9H1OGXV1XwiH07YoSzgoUvgTu7E36e7OZmnP7qT53gEoxRHSuyO7TvaDv3Z7zaY02/0pShEOUjL2pySa7tavR5txDvXDAK+xChZwWuErbUHYO8gqGNdLbwbxFilo9Z2afeK/ZKRIADODjxlRknqb/vI+7fMadt+XYwvtKeBVG69yY8KoNuSEtrflIAwQ7y9Xd3pXCk6ZfVTqQEi0Xa1yhAsF8N6qUL3wIZdMpVLwVgG9lJaVowzeEn/8XRdS44qWfCXQPhH4T0ID2947z1fv1TPjY2YHtOjlqCoOpl5bCB6tklKDfhs2WqkVPhOfj7BW55Kc5F+0wJnpJQ8HC54TtuwRkUEF0N2FPCkbFLyN3Ls46pKgJDqvXSxYE/BREnepbcJ+d2OMtDDkYJd6a9y8bNINRePv6UQQ7w30GKs/QsWf7mLnEIzTurKBvyEsmPKvpYM8K9ZmfGh8PtPNns4GugulSNDy0vIv041V6P7grtQs5nFt8oLj9wI32GTJEtB3M52CFesoH+j8kb1SqEQwBIj7xlvSKV63FrOMo59r0Ek7kEsIRiqMca2KM5mlBq6L3XVhFzWYnVmJs3KASPIVh34aO1cwNCEl6+R8nux7zodNl33R2+fyMQeUfVXynD+wu1J0tfgiJ66ClplZuav5qlsittMiLUYtf/GmcvQ31z5H7d3UBeGf7x39z9CGJ08fJjqy8pU6/veDMDiaYDzVJZ1JE04MSA9ywGS9Ge7A/+eu916la3chP0aS78wLDFSPRnpB2cPOG1gaIQ935Pndz+7IWM7UjyXvYhvi3KPd7+kPJks+aVm4d4CLUOvMyU1yc7QSPCrftlXJea08b9a2nHSgfEgPEfl1peoha0rsXmnfMHBXYp9pYfpeAPmjY2xxeXhniDncD6+LFCKOEtu8J8sPIL6EIzvs8kZLwZNukSSfVNyDTcCpLEa/5lFkoOo5MnFxXK7V2lgwIB02Pm4GQTfHT0UZe56Gx6yWOcwToTJg+/hzcoaKvJBCBNXKPKSBNILpbgYShKhw0GRo78qAHh8zuqEqV1IKJa+dUIpKiKU9MCPEdC/7OLnXFdQhAIvTjepd0wV957P+vMdLZmSJt05EylFTFVakJ/VToiZD3id07RK68DswIgxA4Sm0KuiztQfcJDTx1TsCxlkFgEOtCXTSj8LU30RRT4/IqPgs9BXrzhk/5usp1oOuaMM47mTmza5G0/ZdH+Qk+38LAqiBQcmsXSU6hwmm14n5fDNGnO/wthJuv6piBIKxG9fdEhjHI0ZYOlHEgd9lP3gBcogr2HSwiKV0fdrIQlxnsN1Eng+8GDZzBN7IrNuZ8IdwwPg8y+KiveRrBIM9MZMrfSPLgI3/1f93P7NG++UlOOmcwSOZk+gCGufh+2v/hufQDFEVIkL6E9ydV6VsfX7bisBpvVEaB/TvU09bWglZaNy33hDGXW/vy6ubxmmZn5IzYEdOT5IiVAlf5Yll6rTEbJBIyze5fC+nF9B2bpX73YTkQFTKV8bWRgcvYT9RH3Yq997yfh1JA9XagJQtiNc4BIg1RQxlskB/IG+p6EDXglu6/CrOgTcpyOYKGOWl42fWyf0QbLz3hG64A/83p6a3ryf8IXSITBbF49qPNgrhXK6Tu53zznFOzqnv8UB745JfH4xRWOntYr601/HBIcGTgdczwDnbcEFQfB4Ic2iIc+YgR87DC3EdC2dW4Fd4Ks1p8jlRWd7eu9FenS750d/bENO29/KBdrLNAlVYMZXkLPVauWBL21spP0ocAbHruIptNV/MYaY9D/jUJ29ytbr5gltbdMo7KmextE/4m9WJWC8il1G52zO63uqv2DBfaTjqBtMRaVp9xIEdj9eXa2Q2+Lc7uotVwOyfRvQd4jrG0HBRIN1xekLt3nD7Id/c18c7lGDz/qd9j9H2KLdg/thYga1omnVY4AckqLL2/fABK22aOoPzJp8VjNAMItYiPMDZQL7lxLyl4iHHHe1FgUMnh2ZAe07KkYcfxz3LLARLjfe++HXeqr3X/86F7UXEps9jIod9jsSTTuwVflaOeDzMVh/fV/aCDFVjpiAWk0q08BphTP6QAuI9FO3L5RpS7CY3pXizNZlXI6kjclkMn1aQTMTvCbkt1i2rhO6goWU5MFkZHJD/u8B3qUChrc/B+9FimsHmGhrPhkJsxMywNpEbw7khBLnDpVtfHsADEvbYN7DP9GUBpEZfG0nmzaSQH9/Q16rTq2u404ttP+LRGPnfAQ8exz/Si7pCnLMAjE2ctgfC5oaNFKvIt019LPUEjIa/+XuLr3J+/V2gL5IwAoZollvfCR37CpP0Cf+K2vrc1wGTosWNYwOkH7hK1G/6JppjqxatDnWeMGk3bc6rn4uFp9bazE+8DPR874YNetK1wXDao2bSJSroAISy+fLjC7VRXqP0z12z45Go2iV4gP9mwAk59C/pGFsviICkQFPpA1k3hNtelLez5wfMmLrgEfmcWlaoYK9ci2IeTDd+xsyW5pJjb7dFIpUKx8P7TVB0HiqO+YWW7pCKqPeWQnFhO3N/aikJHy2FlpoV0oOIXW+sxP5k7LVmzaaQjENTlXz8PKwnmTA/Lm7FyShnsmSwQVc/0b9/SXlGliAkn9+ovmUJgAFcFr2UtJBpOZjWDfxXQi8yqiKu9PfUQPR19y7LBucuzcrsv4IAtqoOrH7D0XQXKBPar0WCN2w50XoNJhOBfiVvSrVmngYyNKddHraHRtW4Mp0cQFfUcQHG7e4m6Y9iBgPGhEYc8jEwGhOniSgBQYwgitZ9zItQAkv2ej+r+7p3zf5Y6I4mFGla+oGG2BG1SuKPc919Pz53cTsy9EEFCm/6D09CLmnVnvywxYUEasQJrWWUJ4V2ozsTBzP8rbI4rf4C5kuWBZsJ8ypn9ylrJnEmci7PHjv8J+/Rk3vksAez6W3oDh2hUzUDbjQu3mQzY6ketEysQcm8+YmiWLFv/R1PvDTbA0gC5eC5RZqQ36mP/9EACkrXj5gIAGN2arDID9Kkx8DaaJCZXFKUQviu81taHcfQ65n3mX/BTGlRkpSIYgJAr1mGaEYUI1DOuUFMr7ZA5jI23Iw/8V3Zof5lt8Q6lZFniNveI6EbZ/uHCssbT46rayIz1eop8RRZAUR9/1qPqGJs++pdhFQRAQs0J5jpG4Pp1BQo/3X4tI6lV4NKeWnoWMJLAiTn3jA+/Z0B+tRVCx5vGLnfaV02vthH9EJ7IKUbJ+wbYGZpJHTRejT/TXHi4ZG5UbiFowd9hfh5kqosD2r5yqq8nMbXVqdw5qDsSBCdxWVHD0dq8TyOAucktOBatAGyronFr1XSBYcqiVbF9BxeniTcdxY6xpZ+OuCymgXlK1S1SaQo08GVv2A5C4rRefWT+tWjLlTlUhTPi2BzHdm49FZjNbBqg4Oml2bBPNe+mGOuJd3ixpTD6zG4eYrqoU0mP+Uu4iRf/00gEUgMBPsZXKDHoiQdgTz4ILUFAY5cAyCMXhQZ0a9Qbofwh42ctdkJVL5wtEQmcFg6an4o7SoYxe7ylSulHmYdAWvVX61VQ58CkL18kVLvuzygVT7mcJBAJGTeXaYrR9vNtLVV/Dp+F3McivHnHVa+gU7vSY1mFveivejmoZoZtXqtk8ZnpareMQd7O57hfH3exI12ufbo1yjg+CARR23bUxJh7m4UyPmmopZJiGKFpodAYIXN7FOxbsWzrxyx4u8V34qdsNCZ8Lfqjb57NPeKmHViIgKI4OT2j6tXIFUxPZNzryAndf+uzjfOS2qjIv9Ipi/WYJuI6H9cxpGCdVaKFrnSUFtzWLzehGljUM+gBdHysfigwtkNWvPw3GEuF8+dXd9aD1hJIZhnJoTS28fxwBChbb4ZWIhS9+C8Hm+x4t+cNK89i2Hgr9RPiQ4la6Bp2ye+VJ4tcEA7CfCI/dhHgbkr5v+N7F9ngW6QztqTrfzWAl6uD9jF59hoKsb23SSlcIQCvKKGHq2sFAyO/rSeQfOlgTONf0/7t0zcCboqQtadOK7H83V0GnIMlvzE6PjuoN+enwG8tFpg75XbcnUMzxUrQZeDZnoDARLCmoiKI8+YDfZnhTfnJ1o2aY642OtKy/R2DmuSpWtPFXKx4EEMTfO9WbuysYSiQeVodFmiGHOQulLoGhBtqh9jHYsKArd6lkeWy7jb/TMqSHW5r/WoEwF3xJhskF+40uAXcQ70EekQTvLALkkgjVW7tURC/tSzxXZtu7UeUJugQVZK9AZsOfWb2kyf9MBNL/ogS9lZkP18ZyhcTd7hBt5nmbR6fTYmySNiqaxkT2/5oqqUv5ZJ3oMY544ypUwEr/aB+llehnD8uMQ2c+nClyG0VUlzSrm8xrtmC6EQugxv9RxUs3E3YmyL03BA3oIm5yO8vNLHEGX9z6hOkGL/grq/6YeZmTlEMjW9c+2aETQ057jOQHcwLUSEUnILymfpbKkYE2BRNCxoDx2dDlTqOVmWyo2AaBDYhiQJ+QvGYsHXtsvtjrJB5YyMYY89AMzlG0Wyt8C/od6yxU0Q5O5zjDiHUXc11Bnr8AOAwkd7jGPgKXo5SgRtaOX4ZnzURVwbtt/EeiLsPsXO+uVJzuFdBVKsg32PIoDJbzik6kZcEnuiVFuWqHoslW5Wihf0R2HVpEyDHTENsVuFyodW+3KFudLGLq1+C+KZVgsEZzagjaaMstG8kle4U1JP9r6pJCBWLVs8pm8sb6dm/9gy0QkzI+iiIXMV0+eqqM06CTB7r//bfirSQJUD0vxpjpJ0DIr9EK0WlyJ/mSBsDZux+p042baFkmdQQGX6NSZjwDcP64x2nGWp2jDw795mjzXSJeNm8I8VaOkpesb0B0HF2x5VLN6H3kplJUlQG1AIYMaI0bW+3z/8rx35gGnCvAMArCgvzE0G3hiL5Wz7F5twXbV66DC+F80/pWP8+HLBbc9hqrftgW/srQC+fJe+V+9Evgj5yefcLZIC+P9kRWyM+TxDmzOECk9p1Q7zg8HbAZEQW7V/AvwBY5mrWb9CnF0NIn1YEGo4xSB7UKoGuyN3sXqTgDwAX/JENcyDAsfEGAdXqqDq3OL6Ms009dA92F4zQGd92NYDRwI1OpgzdChABVFfPscgJRtw1/sDsQu2gZV6vb2LciDN/m4M7htFw4Z6eSLtr8EWVXUqW1Z4Jve0ojOWUWmd0GMSKCd5Mbuy3bxwlgGJ4LWZSXdKVVw5xPa4d/0oSbyEMn5L4B5S98IVm1WUp853aexwv3fmOrz9iEcOKr+8wrvbPv583tBkjUWS/uBLfU3uHrue18oUojcJm/ztiX67M5K5EhKmj35rJ0lc+1Ut0LzGaxR1ytDb5qjir992vL0vTCb5j5rdQCicpJIBPRvZNXD+UnMna1dy9Z5cz7ykNv/xeon8GfQg96WUQ614PU8MvCgCc/cPVIekTfI+NExqoMOVAdmX1Zh6Ofw1Pxzrvx7P4GMpwBgQo80Vrw/34smoAhN1fYqD/PxqzOFi2aoAjhlmosYPy/TvdVm6zcIFexyAF0O6OJvGg8gxD4PHuGd3M+ITBB1Z93I9n4TaMmP9LkeVmY+/vmBlmIqHMNZDEOfRtdr83fqL75rIHeAD+EhOxbLbIbIGv4yuerDpLG/HHHXvUlSIck/FWeTKGIWKpDp+AkSSP0I0ENkxW3ksL0klWvAhyX0NPoJeiaNHlSQhtkLqEl0CIvsfedx0E7bTsfQBG8rEu59elTktiLeeqYlch54ZuY14HVzuRGyunVpE9KOgZUKtQ8o4Klo6+Z7fYU2QqdcyqAiXGcVJPNcYaZP2/6al4k39Y/Yx5hGLY0Kx1lhXiLgdanVGFLoBtdFUTxrG2DlShdeOEKOKkEL+0dIDNHrYZDA2xu+4yQTBV9dH9GWCDbJe/Rw2jh/jqdIvDmV5L8GDPSDbtVWFZM+k4678rW+d98WZETwW73EyOSOhNty+SM0uuUhbDjqDijd+73vQByOLpSx0am/5SPs/GiQusygbVF8rw8Oej3yGZtDPmrQ/Z1EoNJC2747DswZQybRJzDxz/bbzeqvQiawyVGwYMVoLo7eNings9WOREIlSKP7pKsXxUhlnsHR8s7pNF+5eeBzMO/XyheQmA4QnsWduku3s2MNa4I9ryDOWqjYzxenF6Oz+aSO4wAmvd7qsSvKYDNSXsN2g/wMu9SoYLwV2AVQmkm8pu4Goi5P1u+Z9ip+PYl/goNs8IfOFHI2HPEfyhFBnJVno5vP7YJt7S5E5xMYfxwacVD6oSXTV4rn7F1NjtCj2TCQFx1AAED/IJfmoxZ6qs9//UnK/BMMVAkU5TY9FTLz63oJM18UlgKeovQcxa6EmYgPtJcatg03YGgjzaNbkGvU986NIy2I9DthLCXI0FNhdSbbQFeZuLZUGPfpXXJ9hsMzX+E3fNhPgCTJX5kWVQ6TxD8zYwz5t/170zkcVBJ2CaKJDJDPgQW2TrIE1e8RLDRkLquKJ8kiF7hUKu2fGWpjeAaxcf67tSEZnY8dph8q96Gq0w0qiSFWTn2wu3jykif/LOOVNtJope/JtSCNxFQm80WLMNlM6da70Mgiow4A9TIBnFMWgf3Nrqf8ov0rXOXAdxlY+Fs8AABRjouLntxnB+gP74OhKHxwaqTnS8Wfpz/8w0eIe6k19p8+RyniVRiKaLZ9DIsfyU3mdIpzshVzkTToziTactm94BMPLmwVLLH0c1Ym2DdIT+AMpOHMMbal4svhTi9CntFjArlbf8OkdC/I066yxwXdGC/7eJziXupiEETqM/snZxABrSIEydHlp99ot0PBNBvH9wTQpr2mb3plr25c4dfZOxee4IHeikF8VZkEJ1BloXy/hBggA+QV/dXVuG2a3awVf62keix8YAgL6A69mjBpsulj0kXetMC948AldH+sgrj/0sxUcugLChluUCv/12EswFXoF+wtihYzSK8k98nliQY384VmScfC6hBJFiAcJWBcyexjszk3PLyYHSiliQRViCSF5+IziAhIdx289i93TAfeBqaWKRSp58PLw/1OPk01jKRC/RLq70WYsjdIssesYAbOkwCJ9Mro7Hr5HqAa098FDkNnEChbbIeUwLCxL9pfbv+HKKas8FHH4QaVg5tlHiEdZHICMYQXAbYx/6WxXXErwxSXim6Dd3/Z+8vTCn+jgwBo8gwAFn26xx3Ipgiwt37+o8TpQYzADHwasv4bdQUqrNzsL/cPASuXuqCWHYN9nxF+AIgunH6qrPee8/uygzJYlPwvE7nTxL1pohpg2WID62DICr4oTr2K+Uci+CCcvj64uK/YTCVzQfeAm4yBiDxG1H/Ybi6maRdXyLDT4q325JbgJDoLvw7Z70pBfgC8uNp5PhNV9+73WsZV95+PxHoyrHqCZZM2hjataYLgdXXu/UjKwwx5wPMpXMc6K+G8hbwqEB2JUimJb2w9fI/OqMLOyyqDdDbZ7Qf8hor6Ez1X1fMpX+AsOAVMAsZV8KREUOwwEFccDCJPXM14EP5PjQ6Cu6EtOBgo7WMsV8zludJQlkQcHhGTdSQUjqkX32rPbeeajbLdbPYE2lJVuQ+Jw9sX/u528tuNe421x8kE75LGpI6nB8tsSbZGq6LjRfp7of/6YT+RM3+/SOu1/kiclO/KYGDOFh3lAZKBDmN0d3Co76lHXNVmFf/xhv88wIX2+Su7hlS0PpMNowi7k4HJ3jXqnlD/4L3hEj57G+59pR9hyXoT+n2uXSCNbfi85hPgcXvCSRJw8yX4Z2sfGeJ9Fsda2KWcXsJEiORy6LteQ8wrWn+UZ03tuLxonoeAlqjIswivEhnYetW2hK5A5eSt/uuJRzeF8tfkBHwMwfuNmirOJlYFCzwDJ/gtiyhcZadphzW6KY288unEYYMpy/tOHgesb1jKOvBcd5RzBPNC+azrKwRE4qR80Hao/dVJS9TAs/hQBHZype314HgTGVORXZVRPR+lwvvVS75z8sYvkH+Rotw6dWL7LTORS2DpNaCT89BfbVZ6mXNT59s7nzriq9K2jhBJ++r3l/4KbuSBvSCDHbKYiGnY+yIFdvdzeYX3/oeHVEDvLKpX3Xxavhz4Y0XHu+amTlyPAY69toz1ZjV/rRz0zQj4qMvLJZxt3rf79kEqmpNDfNb43GSxtjay6mfC35PzO2aGsMa7XJM+f9ZiYDObkOo1/2G4TC/Hd+aavJGYI80+ljdZN9msIxv+TbKNtE6iH6kKjnhY259RoGgOeGJd1MfCMjX7XDQc/vJ41n17+6D5WvzQtsRyXTziXjr60f2C5Qfgj3YHvicM7b07zy2n/RbfWxCq35gX2rl3Os9kqDei1jlfCdX9aoCAL2TjipHUhoS322m991jmzZBvC7d+rn8mnv9rJ/cFpkvZHYKfixJYxdOZQuc3PHk1nBn0FRbU8AMWWry9NCt+/iZQh4NjgUk/L2U3ntoah/YVF9qJsR3FR7iiCvE897uStaJ/f4cV2aIL31B3j+rPUaz+YL7Srzc0YB++Z5Phjx0A2GkzcLct4bOFiAdk0VOXWwRajKlJPWaK/CJwihNDlQOsO+hFZCWG88KSsoe8RR4v8cUwcTNdYKID3OP+zVlhRcMKUUdgpVHk15LZ6u952TMvYpBgSxTQw/cOpMrCPjcXu2SOLc9ztAJiJqjVYJnJT5ldVZHsygMG0tQGwokUJBzS7DwYwGrdy1EdTNETLFSky+km2J6xjImrUvntutSAD/98myCbyHxI9zHHFYAofAHxwjZRcgwRqUDheJgVQ6ar5mG9PEBjF3KakJRLbk7dsicONGnUKx/lpCDoYuIh48aHzTRy0aDMeSsr5amaKxvG4XD8FSgBj0ETQmVbSFk68AYVLjD8XemHqvh0NbskX0iwkBpXrNsnvnvizj5kBaisin1xv7vFIsmlrjX2eoNuHn/Ujj7EaxjHpiBFv6ZZSff/Ric/V4QGzF+HboV+GhHzo2sTPFpFuxD5yeVGT4H5mQiKQVRiJueFznVPdtrHvgpn1xPqM/td/Wdss3pOx1hlckeNm9K11LCScMG+eF/jbDxuhnL9zxr7pDyfwwxLKuEJgrff5x+FD0SC6b1XJQS475Md7etQVDSdQyCuZrt7EyMMy409SWat9Mu778YlXxOGBCj2X3tBNnH2F66hFNNOpd9iYvh/45/sLssyy6Mw+9UdNs8o4Ymno8Nt98y+J6LLpOX0/xBP1o3zz6uRUAhF4/HAPMogE7Y2v8SM1xkoFmnCt08xgmln2w4K99Kqk3OZFMnbbtN5pHCntvgAeEMfJcEGV13P8JfBRzL5rNJNvJDd9hwU4druwoYNy82cNF038X/izpnD7I+DTlFQ7NJioNiEsMeN2INNx/0sqCAyTVDU5laWjn8q+Id5vkyZ5RkCkCIbiUSSEfuqN954yX5Q432wnGrlP3Q9gUek1B5IAU4E8agVBzXpqZP7jxo62F+wMZkEVSTI1TpzqcUS9c6AgzCDL2wYDlZT/lLjQ+SYxfyvTd7VDMXIKZz72jpdCp+mu2TQBpOWIm6jYmF70L+eg2HDo3W/jZjdddByAvIbWuyRouc3xqXwn2Wj+7PgHgagkgu8v5csmKSP/LD3p42KP2XIDY/RZJxUNUjIl0Bwp0Hy/FKrRD/Wb7wlID50w/gHHiR5p6nRXSfeQilxpL+iA/25QQ9w8mWDLex57RXhCdpgEuFLsYai0zzJ3h9GmLjMJ9CvwThseJ0tHzrwpHXsy8qyQa6A+UHWirZI8ZJHUxE/hKm0I0U4AV5Cav5v3xEIBLXD4P6OfBPUTKp+zPE+84oVSHeVhVhfUvdbebNC5D0v5nWkYzABKQvs9hVFkdKawvDRjp7EKCvs/Tj3r6SnHIwvbBlcFAicz4FiOPrVst52WH/FKxgePhk+gOii2GE8XQU0VVOIY5U2iVUWTr5yZY7fE2joprNpFKZSmZrCg6Ylc/xE3TC0fw7jfHpV8gfPnAk8XAzBlDAeh8YdoyJND/VncL5y1ZpXMVz1syMdlp7g/Tv2G5U9ARSbLvd3A3ioR8hfxjhjnPdueQ0n45GZZytdMxAXM/rFAy5Eom+206K2e4TabRdjIt3k/NC1Pwf9N2V+mljtNaCToogThsYl2WGPfEcXaOVEF15hvmyknFtm0dDUeo3rkelOY9f9nUoozpSYVXO/v/7/QnNYL0bm8sTLbdTH8k+T/eRpxuJr5rEXNj+qao76Nx6mx+mkTXOiVELKvvy17Rj4ZNuvgs+JeZwBVge+k4jdPb8cHCq79v51eWM30UtSf2kP0sm4/qLyD4Gp+vzy2FfwWmR1Xx1R5lefcwaLmOQGQX6EsfxOsr/eFbPygYfiLOZTJdOcyZZ1Li0D/V0HeqNNGGm+la7A0AFyWz4Oe43h9ZTkMJVN89JEkP+xhOF+dlzRUl+S+7POJxDVZC5W33e7r7IoL+cZQD3Ca3XJU+A+hbHBn+afg5w/R1pSrvckvp92HsL+vO9nuXwj8Za6EHiAhoB82G3YOGMMsGjCrd/N+K413IsW2+VMGdKhlx5gTmXkTYu9wm2HQBzyPU4m4BRGYv56NbHslMPpj1b4T6FlnuBDPri5+/i8crQe3E6ROM6L5WQrbuY2+fAYFtn6US+ov+g6+ytJe8rhHhgmcmTAPHCdYwwTfz532cKcvdVhflvDdJH2xEY7qghNA1UTyT0PntM8Rpmmm+bBTS+YNd77sEgEvf20YSEoDo+QAa3ZTmeW9NmTRv0g0xE4l6SzkwCaHdK1i7MPZs7Q2nFNUInAU5qi3ue/9k7jSy3ahrGvb2xdE6uCla1f1eDixqfyWVlWwB6CpkuaU+aHPRJd4MFBo08T3pejoEf5UbYNvnWJt9dqtNR9W1nlae5BXQ34L46VAifPCVqr0fjc08977154Pr+B0IoyXOkP+esR8wO3n09+Phw/auF1ycDAZoNuD3rZJSBJTUcEWC/T2c/Y7mcxvtwPpb0HPLOsXmvaPqr+BMGNnV+l8h6ELUQtNrFvtPnlL4k/M/kOC+0LAGHoZqIrCYy+jBZYBQ51fKkJGtrlfuR+kSQvKs8cjqbDe6NwzpazfhIvUV5DihsbpXu0Y6Znl6WlVQbMSXlT7FdwZBudaV0aC1Vt+19iwglbpCYSbk7hVGJLAMDRAp+Wc5DjnJRUOMHie/uhUGqNedIaefSMd0N8Rh0tyzHz/73Ly7toY/3Thj+tqlW++KzMpJ7vqbGnqMaqjWz7MFFbshV1ykSL6JCEaG8fBjeOT/E+4PxRpaLD61Y9L88Ffjt9vfQa+21m6ol7Hm+IFv5qE+mxEC7Hz7/uQrDAzeFjni6gPon7W3w0CFh49cY4fE5p7hgDRcxj9x8Tcx3qryxCxPrxw6AuAZNeOXpc1B6sgn89vwq4DbV5y5CRsTBp07vQv5Eb75d3gKeAdrdxPB8QUOvGTuWlnv9k+LYz8SrjWPA+PlSe2+Pvn4ub3Iy1scwyc9/mm38YUPFCI9ScKFhy1EXzPh9GQbjU+owhM0plzNPS5Ci062mUUmKsE6iSxUnLLfYJg8owPerxf7lYl8ZHcisLg0ZaaH7lUYphqvHvUeA1GeZ5W03oxoslYKfLZSyTV3WfyZtFK/Rjagr+HC190KkgYhDX3vAhzdYZcdt4Nj7p44+rdy4kSyqIGfA8rPU/zdHla6uM8T3YkP07nE8OCtGWYvCYnmU3oXPp9JSUVl6usv5Kk/XXIkkQ4y1v6XQOB8WBLq9mRHDYCvdS7HkxhPr3La6tVcW9YHZXW5pMsfXchtxvK1yGuFUIEy5ngoe1qfXVPJOtVaYSs0cTXyHtLrFHyB/jAMwdXRI5fcdVRnxJntJyrZLYSLPeb5IULP6JrQjco6o+9sJ/2MNr5XDsJIkEQvDS/HJ4yMQjFKpn24CDDOfBzUDj4Fdl8Z0U5dsAGqSq6/TDYisCrobnftk9cIz5xPwXSJF/FQMIhJHVBGV0wCN2QKWsNxzxa2qIYBZr+dw4ZRUQx1sDk9PZFX36ya5kXpZiQWFbchEsmPL+rBH5lPXKYTigcTsMr6v51Q6j9fXD8/S/ITpZGzzMNTHM0oTilEsRbMyxQKVSubGVZAiezf+KkP+92PAXRyyCzsSF4i+eHAbwZ3RL0oqA2xtvby3ye/Tq/RaanHy4bCWLHItI3WJakM4Wiz6e14e2Iitxpb8mFe8v+s8hgdZzR3xNpD1MLqs9RG+9/VNzCEqIvyYKRATlGRPBYA/rS9ljQNyOrndIUK+ElJNiqwIgOOfsJWQs2B+flSzbxQ5KuawZ91pXfO6ObOUVNu6Cbh85cpNGpWs+efaYq2C5DXDFyqWPFQ5neN+XYIk4Ul7DGjWnU/mcuYu9+7SRGcDuUeQ5bAafxcPv81vwjnkaMJMEvIuutloX6XuqsNj0NmmTmGYpIeGhFLvlxdPooj6w4AliQvVHzHbvTs7oVJa0/QhuR2BOBcxZbIBXLAlm1r3oiiJC1cSfj2ftsDpZ5LsLpNqe6qdjrT4Wl2REDLcPakZeIwRK68/1GaDah4GrtXBQr511s4lEpx8HJEiwyBkyKoEWuJiYyAPsyCNYpq55BnEr2juN8VZUvn+vdF20aW0eSaYO9eFFzb4Pj75B+cdtnArdnp1mv+ez+sl6u0Oj/srxUS6PV1mqdxhEdz/80TV8VHDCVeMvIiQgdtiS7OrRXfvZ62dGCNqY/QMJswml+k7BIZL0gISyAOVcjwXnNg+hkOZlEDtgO/dfyrCjTTZpSe9BoLaj6RViidmKOxcEJymtpUAhyyjZl5te6K8eJPd744amOzWOj5ACO87UuZJcAAzzEAs9f4M/uJUj+L2x3N5fIha+eymhGllHMhjTWonRJNvNIvLHhOjtRYrJbvUw1myk4kAqmq7fCZZrW2um8+fd3ju8rBKZ9K1GSKR/r99bVBU94dH+2lVm16fI+wGN2yk7Pu4eiuSp9IF0vpZt+yK3KIJHr5R7srislMpvKoMw38PUFelQmMh69SRsnCLLIJWHFSjV0ogQhcmAvS66E46lPVVw1XF3EK/0foKrUvXWZKOi9YVg38s0pdMJiYihaHs7aUM1G6+YG2JdmzN0QMBhQvBlsG1OCKRvpYK5WvxU/kZw0oklylAV9ZEAFg/BDw/wzYvs01L6klSFSBFEKnRBykqPl1qHC9MCQhBnpkGAfJm4qhkQ+chavYBrr7TZx0UiJ3a0jpAZZA/l6aqs2JIxfu5omfUbn++ozAzrgWAxHasnpODj6oXH8gq0E+zC66fB6L/z/l1ZNY7GqPamplG1Tm59e7ijORrDTafsRU/EuSCWY9zNwlZBvKndmEEYprv8aCwtzuzH9I+7IneDe0Aev9uv6TL0bg09uqpl5nsk47XdXSISrOGLc7DgAEA8sZTczFKZXNEKBnvVau9Z1ua85Se38wiYJRtN5VT/cV+70lailRVPtNZKnF+8X2ip4jC2cZ40y8xhIIV8fyU0/ZuFmmJvv6wx5JOkK+At7ujnJ0sPIj4En29GElfxhAMvJlHHSI/uLg4FsLPSxWF44jtR2Jd7wkrj3wMLUQKR4PGVf/CuTyHzZQrEh9HcgNDEy0tWq94NA3Vt5eV7FIYRStz7dPChcs+bCP80lvrXQJbXplqE+a00oqoe0otSu2Ldvw8UvT6jN4zQHJrr7KPVwYRS+XwNY/p8DOPmPfKHeqRq17OCoaFX86fjP/DVDRBLDEdyz6uMljAHti1v35Ca4CJLG+Spv1hTp9Jv2tn+EG06+pUMpzhMpgOnDYBOG9ckNHDwfxznz71AjKNcueJY1d1qWf6jZICH/fRXiQDmSUeHA7UR/91uDzwdyAhA/m5llZlNPmGnKN/7oCRszDnbv87B+slVEKZefehElicRadlVK0jTAaw5Xjx9DFwS7V9CRVXPC5Mffu03OJ8GuhymAOffRYUuaCbV2SZIyOil5lwJGL7WSZLxbobbY0EQ8C6DD2KuNjcabGRqeDci174x8cO/WyZQiUjtJ15wB3nt9zXd8gSZmUPrrHHqprRJCnD31l68YAzcCC2VLillHYTLfU5fg5yJsRXAaCLrfXki9++Kk3Td6X/9uP150elDtjFVdN2EL+iPvtAbFfENbloj0LEShTcg7z6scRumOXaW74iT+63E3o/Ppd+sMXOTK4kTfxNnRUfToslB5IMkusnZtO5WYtDUzb6tUt3aMKtZEjerfLZSpf9OfgyE1sePrsxzrcWOyJlQ2PIt3d2SyPaOYw89G/cgyEw6/S5M67EksLSFxyfjNqLgyngNoXoR6cOoIsJO1ggNQiIyEdKVfeBThIAOL2g2HzesBD1yaRo8eK8hvxBrmlLGym1e4tGyGgCPd7tW8G1V6hwShvjGZePnnSTCWoGMabeaemoLIMt9aM1HaI4PmdmaCXEM8BvylXncq0ICeY5jvo9ww2/5rq7BTNw5cTzpsDqdGpjD0Xd8+VjinmbOsmWrolL0CxJmjk2re98cTff3tNaYGOlQJazNOo68eqa5F8e9YE34lXLSagYVoA6hz2n2ga4I7TguUwclxepJU5qkn4YO+USESytiMdlsXevSA4/Md5cmbuescqgz3/nz01P5rz/SUWDWT4NfTgmfpkD2iUQQaj7lOnT1z8ushVwH1Ur8gvjT5iDUCOelUXnb3H1Vw2lDZZd/UN6+4Pd9gMIgqRFRDqS6u2J0Oe7njx8tsyXpHKQeyjYWuEQ1x/DFEvTU450AJwZs/GnK9nMvvuzwA+axkLjwe3df1SVj5ot8BaQpEDJyL/Q6TODK7g+xf18nk+6Wq8Tnka7vNS1yGqSj7gHBHm+1qI2yH46QASvUbCtLMdXFjGr6Xz3sWDAvpwPeUB3DQ7J+zyTmp6f3l3tLkVzizfOauo2ec99f5eAK0n8SUca8tHqgZSRzGBo960u7Fqt7XSIvlLR6L6AF+toIHew10VGKx30eqqQ9r+k/GNOQnxPWonqMNncGxNy7ByPoFu3RjOSOPtGYP9yrt6vCXIBOw7xe3Pp1u2s2Scwh+s0tW8YbPJRx1QuanXp7jIFBHPSc8AeqUwzzcSTL1kYfnOB5Q0w0FWPQtInWdDKxEafpIf8oMzRV7hQWv+TagNMd0+d08ht6NgKMTTRxpinedRLcDERj4xWxy5tvCm3n/3oz3BBBZIIGwmzN8UTGHkRysAVTq8BaOvWKDSp2H+rNwbVpIuZtvahGc5LF/3qgYl9OfDwkBNTKj30IrInUmCpK40XCvML8j/laWmSWP8MLe0zgtDP5wpPf8Ew3A5tL4BAxfnWj8KAr7o8DzbQepiK4AAkLsqAtj/nYvzWu74rlwNLwVqFUyZ2/kirH6HCiO/KDvVQ007NFxs+Bqcg4EW75CKfwdwXe3+gDfrJU0eqW/Gcz29FzTERKZ1cOm+hg/2m4mmmNYoow7L95c38d5rpfjIiQt2T4qZM+wX1+FYnxG/gJ3ZqzGFPqpZ+jWZ4XYhVUPxG6r6dmr7weqaYZrK7hxznS6wXGBCob4Hxqd7+/VTX8CHMUv/o4OTCi/T5+OvcgQHAFg6VRDXqlPWvg6Bjo/VOJqnQmvWX4gNBx9w6cw9Ao7HgNuLocloyBaBPw41Cy4vdR35C1xXWTkJKeiTWqfUsFzGXa3x7EXgIXvrSU0+e/8TT4Wn0lfA9/sCidqWBX6HL9uDAXrBezmd+iufEZyeAQEb7WbNYxrEMNkyWh1humKOr7eO9tf83b8LLaQUk1dx4yTGymdLjewxv7XiyI36Vdy9O4nrI4LYvZEPzRzWHkHgf70GY7+nvjKeFDVH9ZvNmQ2HvpM31nwL/VnWp0ML2+r+4BP/YMMWyYYU6JRJ74iE0DLplb1D8SbZpzJYju7BkvKeV/zPaotY9Kpcp7gt5QjPGElK8wU5KBETsgj8T9864UhvYeqv8SuYGI8ygtUw9MdD6GmcTmTrEw3b+MdEcxViQTZXj5wQSn2w9rdQ5qF3ocJiqEg2Va2G4UvhrtmUoJ1w+EaK6n06UUa58ukxJrFAecH2ABNFFCL1C7+RBUfupXrpQYbrqLpKIdw0kEoDYcFaygsQSvk8ykB9a3PldbWudarlgaRJ1GvxgFuSqq98SzeTsqO8swkm+xUn6hpvOCf2S4YxjH7h6+iMSgXlglqvYTOJI5JpxbOU0ioYb2Xz34hdn/mDgnmxMei9Hq1n3S7x7Ha8mC8pnn/UoHrkPgHfquTiBNJW0HsFmZOmc40fjSUXqpzUpNMdrNlcpjFHch2dr3PDUiTHthbuC0ZclUrK+GUapqOD2U32wNxkKbsTapPl1FvoGUS14xshmf+aXMB8Q8+XbvlPFzs4K/FVbvW8s35+DwV1ZZO1rBFc5CKpdoH8pj6B/WhR43keUxcyHrhgdifuERUB+s04IW9wepu+TAanoQBUwSCv0ucZp/TIoO39ftHasb+d9Y7a8eT8nvuISgId2cMOb+K/IfjqqZQnKC9DeYeikMO9Xn+mqTtVabM2lofb4AZSFIq8yCv9gx1LHfPQolZj6WEfm8/8xB1N+oC1Ur64muWPL5fHulFUdJscsZ1r+O3/u3TT3ut1x1ZWQgw9MNP3A61krXBQLPoECIhdISRlLOlqsL8uWzomL5n0wthb+BXH1EENWUjmwMJ3+WY2+hc7khZZlMNpMhYOBpNxyd3a6MW38N8MrrrsBLdEbd2BZ1WVf2dRQJxp2bj4tpYXZLYB1hwhu+WJDW9/d8PlaoFiwVR4NQ854EIy8vOUzuVfYun0e1INhziPLDN5unz0vf9ohnv2AfWLzKokhRnwfTuTwJOKnk2L4c6e/leQJgEqf0sWFLCZr4PUy5WYWcAjf1OIbzy/LzH6G9gohuXi3XPX3BDOpZBeD8+mMSD1i0tRSgjLZBQB2/ZISBVhmikJNOM58j+50ExUyWmMvAlSX31TjexlRwljVeyXa7vf75WSLp8/LGL4/zS4NhgerZbbMtyHTpDLXyoSmsmEKhjtAddr3aM/Rkg7nP4HHex7Mh5T7xFnh0LIP2GpUl6HQMl5ITHS90fr8mTMmPH/cT5pFEGfIdeZVIFQXEUDNX/ZTrKbonVfsnhnqO/NcYhScy4ICA/5IBfmMNsvOP1wbTDvf8/JIi/hmxf3aMHlrQaIDuQ5SkvDGVppxFZyOU7OBetOO75npmf6vh2I2n9nGIDhGh6knZJsRpNZAeJ0tI+OucfWWTH6t4vCxobuSZP+XjMP51AUbN2e3L89W81oe4uVwnzmqHa25BC4VowHX8sEZp1OQHrkR/+u2lEYuUCpizfoS6j+SFDKiMVZp8++vRbTcsxSrlSdkk5kkO+jjXoufjVNyJOuD79UsEhYnoJ7YEe1jTOO2ZS+rur61lQb74h5UBf/89gf3eZsuyi1VHQWOHKrYJHjS4JMekFYgt1aIhg3ujXpYK7gSFCPj4a4j6EOND2WApDYsgfMdO1F8Wi4bJ2qniyG5ppcXzPgApSuIJhwKBd7XbSAlQLlpNkWIJSmJWtAnLkp/nuR1l2dZ1m5GDS8vI7uE8ckZ6Lo6M7lu/Z4BbScDvRPTxQADcAm7NfHPbA0s4a8onrGS21q+TrchK+hfSfd7fTDXGeGxsGXgMNAxLnFVIGEI45Sx8QSIJOVKc+DIS1x1Zos/VIqlNnVxVlRZwAW7JTqgG5GUWxKKFqFc+werT9DVS3wWZDgSrH+LWRorqvjkdhwXw4/Epz7+POx49kLlkfVNQ1SK8oPVd7ufTbMRfYe730dxoeZrLrKKswaYsaJdiYQp5+IAf++XzylUgm7XmIeIz/GwCbOk90pJaWVQhwxRdDcaP1XuA0NAY1TAacWq3BtuOoM3hQLLVXRt9gH90V2Y03OnXDNPs66aaD6NVq3o4gUJiBGKqS/euOwqKJ7pH0i6+UDasEnpnClXU6w8Mmv76ay9nL4uzr2VYowd05uh77SHEnThzqx+E9jcMsGmeT1982WuRBuWLc4GbiQjfXfjwl2Q3v1SNVBN6SzYr37NPM5PUBrwcO+oLFNJdtZnBEMS2B+xujJKpky7D51kOEPWfTz0IUwmzm8G/vPfECOddX9zPykISe8zZbARJxKIhMHmmpiypiqAfXE8jc6TVZUFMqtbkjiEHdhhGnqbGizMj4kuuNN10FypkjCjAK/gXM/OYZa4didKhqQ2GvyI25TAI+8YHYuZK7UMkQZBPseWwKaoKdnMWRUyrwa5w82/GWfVTf0aQnRtDHv035qwIZn0+lBxJ8oAL8lYzX8whM2Sh6mgo5Mcf+K4Ia+LB/xkmG3LyEVsQkbizp2B/KBPotb4QA2WB0PzXWNqP1La+nQgOxUXVgMWL7+NxOOpYvclKAoR5Ic8x4xCaeQxaXXrZhGG40sEYFvlQfnizaCvub7iX3K3ojqLX5RhSLHEeLiRE6erVU7NC1+N+WyJFZj7I4MLm75e2oN02EyXaYzT2MfjiPBo/UeI+7ohPZ9wY77AfkyOufM0Tz5PMmh4nZrPjqevItEXd1AcYAprAjwpO1s1uhWCAkKv4TH5YSFf4tP4PCsEJeP5a60Vh/EEPU4enz/eXZSCVUhlI5gNtX6JwHOPhDaOI+WX1s5oCpZ20laJXfJwT+QtVCqkTxAbJB145oz3IDDNfDmHjtWi/q3JZVvFdt1PR+2T6G2QKvF1o1u4pMhAfF+rMc86UdqpDbE1FRAE26q+7JH8Ff1m17hfK/418s3DutlYZtmfc3Pii6BvvLzYG8iPqE7Xw3rvTSTWMfQXHajP+CjItthIVl8SRl4/vlRnyIr6uw3LZzCpaX2yEoa/tSuciCbUdtV8u5lPzCpwi+EttCyuvCfj8xV569FdzR9vPbmgdzAZ4E7grQhRrK1XAsJil+FnKwazTnbndvG415FwvyoZv97nZZJbDeL2dZSPK9DGFfxuR42LPkaZX5ebRTjFgbTyhdgs4H9SvTimyKTf7OYOMi+BqACWxGBNBoPT5vAf/E7cT2VwFn0r+THy3tObm+X88fcVy5ErX7dPcuRiGJZWYGWZiZq6n/5Xu892I7nCH3balzA1rbdwjJXV+q4G79wbNccM8z3HL3c0+Rb6bH3TcVhqcMcyjwR7yf7MxPDpxfzHpozEVsuM0zIXZEvmgd/4QNy9H/O2q/t1i5QuCPPxuYb/8L86BBEh6dcbCk16LCQwueVWL5KDkmof7TKqwKzIGNrC0b3PEjVvFqgp+QGc+xadUifoy72LAy2pEpe9ngQvRUCjX8rKFqsbDK/+rmb2RlzyMz8H0WiZZo2HFxABsngZ0P+zmFmS+mBZmXcGssVPJaYZ9Hsj8K/NJC35v9NYf6p8okFxEJ3RuQDmYgsrQxlD0lHv7QJ1fXi4XJqZjf3UQ4DPVT1TZRrZJy3FqlnO7XHMZlsOzuDzPOBf2V0m+MALoAqyMx2EPVOcp4s9fSn+Xqdr78l5xoHm0584yn944Kg+6SOgGsEHu4CtG4huo/eRZEVjqHFWze3n58ULbLsS9/EXAbi35YC7bCd6A7wLNJ3Gk4mKXiVcUCfb4cLdJCWNNYQoAnj40qohl4XOMg8L1VuykIHgZPN3SMjl89oX+X9w5+VD2EF524gqO9CgYRECFPhPopSvPl9F+1O3oc3Ak32yxyO+nByUrzE16lJ61apOX3fSZyOj4mx/q+PTSt9eaI6aBj8FYcs5ckP6CjNXF6ROlMN8nlbVtZx6lkdqxZ1gY1icmiAbucL9fZPoI8Q80yfFjZnDrot+2tBIFmD7FhCVhfnGrYQklJmKfU8NzmXrLnX1J1p5YCMUifA1hNo6gbkK1DFfaqHNGZbSEr9jOQxosk7f6pvlANag1Lrj//B1eigLyN2vRp/Qlz2R9+hBJP7eThrcBG4C0o2S+ngrr1a/FruVN3k5SEQ4sdJly1HMFOTIJO7BuV+evCAJKSQLPILaO5VRSahCilnyBa3p2Q8dmbHdA6eC/aUZKt39pBUVP1Ab7DXhKE9FVjISj0AqRkBa0xNrRhNyypGgxKc09h82E9qsjLtbdXvVNmH5nQv9SVbRTRHKhyAu2sSZ9NeCm9E7zz5GYUuzGsIcGF88Y4jpJi1VocORpHn6NvfXZeRNDTTn3fDh/0Yip+TghDFFkBPV5LT/sEVC+aabwxwxiiaiEYL+A2jDk9JqUhxdh9RTbu3G89/3DMHS+HAKJwsSk4lbHeC8jsffCQXXG0eWK4ccu8WMoY+mnbl+fw5MyuT3HO/tE3cGJUAlZ3jjuTTG8r3Aspmi3/22uldyjqFCkBirKjI5btrIWhtBwoJAGnX+curhx9rd/i1j+0W4DBvaaknFccWzcqCfxoPnWJlMOn28qHdniy2P+/l0OvuYjdFOsdquz9LaUZ/R353v05JXd2m40bbDG5u7GgU8o2ay1uhii5wS8X7u+1vUCMzR4OedpcbsV91H/qoF8ZxAk3bcW/atra+hn9idgta5NmWt+PTcYCCJpsVBfuf48C5Te/fz70G4Uyp+KlQ4V1iOLUUTXCmCUoyFbsFP+wHWF0LK8euTvw07QL/ka0YttgQUknPJnzEA36/Ub2Qme4N+6NqWsfR5pQZJgExYMou0I+VPU5S4sGF116ra3jTu+Guq7j9IuWKGhL4VvSJR1D7ujVOneZHnKmEAFBeZNk/3NhzMNqEFOqpLDo3fKSOkQmVZSVIy29wD4or2rg9VKfBHsOgXZSkAjGJpeHSpXPspKeI0lBldLfxjrDppzAvja+Gm3lZGQ+rcz9JxCYJh1OczWFf1rtZroZK7zsli0NKt8txmV1vAoLoZaazNtEiQI+OEyv8pyK/yh/3C8dyXU6Jz1wzZ7gYlNf3Efi+H4WhAqP1Tajx7JoF9RHqlxIiFOl0ejOB12zv1AcprbHbXMOTcDN/onywqV/nr9AhNM8nIhvRjvWWDXNu5BLDinRzEmiZWapUKZ+XP06o66kccVqVBdSKfLYohfxqtlr0lIKFcNGucjkd4dlhettxAS8Dm2d6wgIUCcZsLuFRci7PpeXu4EpDvwcGA3MiGSvdp3QSZb7wp9aDzj6ktMRUdOQ5/QzXHbw+sEsSLet78ER32j//ZtV8PT3lAOdThoQmH6rqDLuXWR5l+wcVWjdSIagGCy5jpl6G+x/SEC0251z0f1lpzzHGwryaegizrOyLClshbaVL1IdnWBMY3FwmvYP2jt8ZbfJJuc3zQTLZ+vUQsTq6tN/QFD4hm1WVHb/nj6+mLqTgPdncwwZ6K9vaJ0RUK2Hc8N5y0y/YfTgQBs8rR3sWyZ5UQu2+3x3K1812ucP86DnbQSa0bNkPuw7nwv9OSqfEDPa33jDH7jLzlJf1R8Fre6ufUtNV2EEBsT81cNiiXZNcA4QaGclS+mlOLvz1OOnztRkfIZNvgyAWCdDT6m9O68fTlySFaTw45kPbzNEG1OyBgXiNvpbL9sHY4Ph58Ejw4Ws7nH3+gnzprupTBEbRcTDcO0NTGgcZZa4yr0OOt9zBl60ae17j/sOM35q50GuPctBTv+pM1iBzkrWz4EceK/O4EKa/9Z6BL5B93bYYGuS2xfvYV4n2xl/lqPKAv3VNNnCgtc+O6HZP6DfcSIdZc2uTTN1tim8dEgqWvxcLQs4PTyUt3mgcmth9sWxnhL1lzQkvI9OL5Fe9k90oLBWdnvD8p0AoLCpv4JMpP1AkRdrJerKoP9GvDhkVrtNjIQIGSbWlgnxtesmkusn1nyMr4bALL/Ol1DNJf+agHLwH6i3jITtdbkaRcCIhBU+9erY24qwIDlxA+LgYiv53c+CH7QfknNaumHlu/8dY1eexJPPlsqdJJKQfyiP7WlaURDnEGL41bSdxmEWP7SslQbU1ULLsHAw94DZpTuERrGvl3n5C6Ncuds12T3Q6ABl/s7G5T8LiXbLHiSQyf4FY/OT6WqYSEDM8ivs8f8VvqjVMkBQvrObn+vAVhkSa9NXwk+ONHkNh/WgHvyuncMLxxkTYjHO1AHeUOzwq6jy1ujtf1t8uWeNs6Hr84UBWaFFVepf5u1PYnGC05yECuY1xdhtI+0g8nl/Nj3BFGJKEk9T9jHqsx87DwJ/aC92UNLwrnbypqXH0kYQmSh2OPsxLv5FsaIfRGRvMSMWWLm8YPvbfQcUZPbDNkooCLA5l8qBu2i8IU2VqBtqxS1Zu7n9W4wFMBvHvhaPtv69HD3uBCoeW2fjAdddM4pHUUcPyu+/ArroPP6ZLR1YaCXrwD2zlU2E0hgBfjfQPYtiOmhBfX4Dpr6FicpLxgJ58V9Jj7L8SRr1O9mjCOTXYDwsZGI9syXYu+U3wjf03irgQ9fZtWVsK04nJZcbB0N2bUSSMisCZigKcm+/kCeWvwEZpVfZblUmIyZrlb42/wetI/H6XS7zE2zjW+aL8cP1/e08pfLbp1xJj1gTt/rbwDndHAiLsodCBtOPX16zCwn18BCbDe5tPIH96bXHh2eZVBmZF8y9bc0rbIXtsxkVvayuRG6mGLRmU66e1YYlfvYdReDErP88kFOBTx7KaFECVYhMLzC/HVy/5owordE/0SP0cLfDbqMIZckXXOtZVKdoNebA16qSaUXNHwWRHTSSp47oebvmHo2Uus0+twvUlzxqLsqGdjm+Yupzm4E3z0OF/rZOiaZXtgNfUbN7XCb/MtHQNtzaPGa7av+EcpI1nFtkHGkcfU4fNqFGW5LFYfyscm1SDjSnzrDBEVF/UC1Uq4TxTVAqpRL+q3+BWdlf5ZudCeVPM3XMncEjC7pACZw4Qjx2U2ottZRlD7dZYYH9gWxrdxmTsjRV8qh96C4vrFv+Ms9JcqR7wz2B3VzgrC7EbNb2yPbqiybyGxrS6eQrJp9fXoiLMWd+i+9/JY62gorLu6zXiu70F1i23tu0Rb1cwmGX4JKWkYywEWaB4VYN67kcFStsKXM2YueqrZn4rxNxCMJ+L+Aw8z810GbtajK4a/NnO2DxQjvOLfZWr1aqEGlGS+nFlxP+Mf3X1ij3YwLfy3Ie1Rp3w7j/qyDYf22Eu2e91GIBNJ8x9M0opBKi7w5/YQIUOdDS6TVYv+oLFH5358We40zag30dWXcbKHFLKgejvSVEEFcl7l5BS1Tia6PruQwrtj1VgasS4PC2A2z2fC3cor+5mST+8cDBIhud6I5yhqpzrsr0ZEvqCpFZJT6W0ddTK3ktFfNYlBNJMirVbIIqNUQPPPDFSzhPiTuwL0EKvlsrgv9aVY41J4FRkj7mK9DWmVLMQ2m4KdUAXrPe4VwguqCdWKptg0IEp94ajMLP6+/lyD7UXt8JjgyWcpLFMHEygvMHgZ26js/9IgdcZ6Vvi8WQ5O7+aaptJ/2nbfwwF5rGFden1454W1UxUop2VmK8MC5kUl+6ucbpG7D1ZHg66zVb43ulfMMncwXuoTTwjbRrejjGpIfcX7MJRlqFIHsE3OjRYDSv+Hiz6sMOuqjzGnsxG6MqHhhwXN8sLaWl956bcOfD3r/xP846FCSV/2jLW0ePRAsbd+38LMAVSbt4OLvFEJrRb8XIP7NQHDjiPorWYhgpCZO8eWJX+h7hi6QAJiY+0u1qurgA5j7MxwZ07tdxPhQkJ882cwg6sRHrvg9wH0MitTskxSqub9nJ3GrKq7MJ6bBD32AR9SnQUgs1u81QVK5cPr+5S/C62azH+IF99pgwKW16OkX+gvmTzYZXOE+xmon8UKlkHIE+X06lBCScSUT9aCofbbX2w3WE9WKqwLGjqkN5coLASO0Rz0ieGy2hUEfkQ0XyyCNKPFdQEgSa6f1ymluxokTOG0FDrs8+7mqAGrS5twMphA1Nqo+kdf/lFf2s0ncrSGMh5zTfsmlrMMEQbzgmm3AL+bTn8XkP0feNjkG73sLXohS64+M6Dyc8vP0prO30pwjJJin6wTn/xB6MBgUesMhJl7s8albfuBbQtHpL3tWamWj1o1E5eNxE71Sgf+3nEG1VEB1aoZuPaeK5NK4RLDRhT+cHegfMO3sN9eK8OwUWRiv9Zfi8MPsfxV56V9Nvi0+3jH2MeWJnGtrWUKqquSMenxpy7xaAzNAjNe+3F92OWNV0ca7/woZYJJgTHJsI9J95IS2QcdXKIUn/aW2HcrslieMoUc+e7nKuQJiI4xeJjsZ/EXqb/yKp+xxf7Ss4uFIUVk5kwF4dEC70Oev1wBILn6rRcL8rrUVjOCzttnr1e71ryKzODCR5m9YPX/IHS5HZ9IEIO701/SQXnw6L5e7ATvGl6Y8h1Mr/nfskvNAU61Mkm76W8wN11VqLRvn5ur5AtnkYLqRnHbUD+qmtJCU2IQYx1SlhYYaiyZ017/1PxVTwfHV1uTj/zSPPkvoAD6J/p0EagD4MUMGl83uT33PM+EyC4rr+u75OglhmjLXA9pWi5ecvtath395Le+wvw0A1Gkl5uXxrc0OYmJYXwYo+ENMtT9KJNibA/ip2TPTDMTLpHHGuc4ThN0rVYdZH+/hcLfbb2XiYFaX3wXB/MEVJ3C39aLDwdP9rH1B/n6YKs0RRaeyq7CwxeZjTiY8xh57MFCEbG4z1ML6FaXNM/IqVlPZD2guWcRiOZdzJPyKRFRWTz5MmHBORH39wMnGBHEU1gLRy/WQN+Op+jibyha1k1A4TIddq91ffxwA8kwL15BEreWgsZduFX+Fj7Tt+/5LOJfbq1bHgzGAWPgDBDd7cYAGxwtJnvn5f1oOGgsYPvITShZfurHbJDHBsKo/39Az12eZC1UJx3NEdmirfcyQ0l9g8KL1V5kt7TLh1SyXD+H6wc95C7fXAMVIP0SjPPvDvaXyk6gkAPnMPKjs3XuvV7dsX1Nixq6B3bIhPprWAlnPImlTnRbAHHomSw1H//UGuTXG5xymDBjGEQJ32+mFp4b1A8HK9F5tzviYO9CfZhx0uYuFUnnWomPhv6qBBWz/Zrgj0mym0DmmU1Ts+ZyQE5b5tXLpwlHank1DJmKhAg0DkpzuGU0RmQJ9mz3tgWOWJXCLbmQ8Ydw++Gnte7qrT+7O4n+s2ggkYhlUDPx//IU1iJgHigJbvSdx98Jbk8rYk+kAyg/b9XODnMGnef/WP1bLEDZLUVZytG1lIyWuhex4FFbPV70T636eapYMw29kEvDy1BdFLC/PsWWbJOk2nyjCL1d22XgqPETC7e7J8H4GXmxVIrCIG2mu5Ga+t2aNKItjlcXW31Y48oFrHNDnTU22Rcz6wTXIZYgypy5qcKuvnRinCJn7zU9Eo8WFD+UMEVMHH4gbroQPEg8a6FThxIUPVvE86wezxC9FBiVewOPywOM0r0x0N/KwCZ4Z6TRB9b1fl/5iBavAYqoFH3axQFvMVNZ1R2vPJxheEmq5lIXATqgCYFQepIydMNc0xzaAPH99TR3WXTgff32FFn9SNFznQoLF5MzGVQDDSaDbbC/+BiOqCvPTWCnUaDvbv/rEYZdk9YFzhB8EautiXBXWwX7E08VJSRSNPcJJopxUvE6/w+JuKxGXal3tkEfSJYQ+aDYfttni0m9TZDYcm9cvuc/goYtaO/4PM9+XPX/bsslEH7MJFYG2qzMMRwk3U3GWwvW6+8OHdu2nfyGir7WnU5VYFY/whyYOTTDNDxZb0KSgjlV5drDbMvor93kjMzWOw2216HQZLFRavPbm1HmaVrzxOb/eLGzzrvFb2ij9y+xAyoFoKFxMtkgFmZlSU/irjH6Mp5i8hw996/1syilrSvUTnxzOlQxMjYeBbc57KIBpPvOJYlL3Wko+Kd1FHIREOvzeXMB3ZlgYIh9BXa/ZonV8j0GzUA75Z9df5r26wBOtNKH+J+tskcZHMVWP9gyJGFPdD/U94sGlNht6YMhuTwbUc8DNZzbt6JU//WQtm3KHu/6Y1ERoqdVu5fwEmThEoS6f5ucDxn0xNI6xRwRqYFzQ50ID257/hhrJ593mhunGpLoqmSyLOZB1FAtEvbfwiHddbuC5KdX/uuzzIR9MpZGi95+/z9JgXE2Jkhpb7W4jCqlmo/JIbLHW3/bVJGBVzk+qf+Gjndj9364dXi/sqaCo1H9lPRwqZJVxt4IKcBi5LpooWdj6Rpbmj/8uaLuryomCspJwVX9VxA/v6cpWy3aVLXSr0UgiFMw714jUVWOTZXPMpODqSz5+PEGfZxH6vln8haRoO2al17HLKuoL/HtH1odXqX23xa3onY/0QnLY+fj1FNQhikycUBSHnjCWcrfSuFEnWZ+ZMU4OUxhW/ekWCEJcLOWuOOBi1Ce4v6Wp66f82PQz6qidE8+n5R7XCaqEfKyTQ/wuHvU+YvQc+/2FQn3fO/VC/hfKBOGxL2dP/HYQLO8jxFVS6UY0wW8m6U89CPPmM5hdzMUJCzNofueN5cedwK3fwQfgjDm5NoPNDdgtKvRRgmB4Wt82RO2wo1+17fBnEG6zrLG2720oQGN5+lBJ7bzWEfsUL1Q2EO4g14kmSbFGk9j4oF7Jo3Ken5NeHwlRj7f90VP9KaMT+cavXfybiDM5PL7U++9+MQ9g42lWJVfR2XFWsQoqSByJ+jo/huNWl0UHwkSEgLJma/wNXjcam8TscDSTvz7G2IeNV9kyeTqlXEYoQ4UHqeMG9bM2AYZ+05/aY3HJ1OGvMWYZMhZ0HLZimUnivw15ZRirBvL72JmtG6lfGgOf0bmvH4bn02Y1gLiTeB7rN85aI3ejPrq/VlDtDU+YpilLxxkGAaj/+BBt218HqP/ATLfCg26u/NmkFLLvmZ5Aouyci/FWUgjqicHOfP8TZ/mGjObX2FcCUswu7rnKtxpC9wn6xphCOqaYUEw79U+vgOxBX8KDp67QhH3M9hgL2sCQXKZEk8HM1BUyNbjZwtDG3S9yb9naMuVNnP2X3VFhiiCV+phVm2KRE8822EDNf4XDyBOUWde5/psi1Dks9XFBVIavlmNaPjx/vS7ybKmv6W+I0oSNkJ7nBJk34R5oqY7aNKFGghvCsLrf328P3BlSDxJTzL8iadEnlhKN9gFKz6LXU0Jd13U36qvtOZ1exBfsfofBnV/LFGjB+VADqq6LN28pKIyg04nukaxdAy9FvdEMJfxsXglGwa1kwDrlsHihBR3hJErsf8FIAH4P1YQyUtwPqfa1U3lZT83j5PoYhlA1hDFKGn9Pykh4k+qFP0SiD7TI0A8He9F6JyZTaMe9D2G1QNruEQY3l6FfkzI2UgBib89YbQCb/NPFEmc38iAqg4aUJHzV9f00447dX8s4U8NenGxXINJOOGabzSd0tqCEEhp4FKVpz74P750lJ4is4VmvLSkcOavKVPYuIxDSBwJFT/zKJT+SHfsw8sMO76ZxaTFdAcyEX8ocxfVP6ATNJxYCSUBtX1hCV6Dw0CyNBWSBMtJZY30p8Py7rJeqs9lexCYoskAROkxtL/+nDJxGuEL1t3BiVF5/l74MQk/R5vtXWJIR6LNSNhttxr0ik51Y6mGxxzYgcWyqMHo+YUkca8dtFsNxVm3GYzE+4B6nlHiuJa6L1jVdj3Yweke6Z17sUckqBQ60WhqZK8URSOiVVJQx3RvEJWUl9/j0/uyzYJRyD1Pt+Kud58GTPOx4qpUNNuE/ql4ZIZbdbYVIEJq8jHu9izENftlhD2C1fW5dvOQOm3w9LPRaQN1DYT4PlmVnNMgv8kke5ibDz27wfT2Vob2VXul9Ygq5QPAC3UwwWop8pf437oVggaxFQqSvZhy02Qf4D1pw/6pPnciE42G8QNi7eWAo6uu9LnTbVXvht5q5UyHk7m1n12FDt1Tcjgu61Yyz4src8EdImvQS6dow5VFjk7rzb3O73QxPk4AvbGrvfA7XWMCgWyF4L9GsP7Tcen7M0MVVq2x7qcVAbF+xWwfE6hvbC1ZGtewPZ/nN4mihY8335q9h4LXeBUGi+nHDq1z/JmbH3r6K/DK/iP+npJKrlLID5NvpndkZWlqdw4Z+OZ8jxPEXca1xXJl9yTYrbmrBj9kLO7TrtYcYdl1OIgriT2wCFGbshv6svt4kypWff+ErxG0eRA24QZbpc9enRSaN8XS1es5ozLKZpDnlJ/4kLyx2JqdeU3FsInuNt5kqjz9SKewHqj1Z330Axf8yhPLUxXn1mh4xw+wVxo2X9VcAhS33vs4qD9yf1hvCPGU5zpPyi+jPk3ioRZOR5vRJooBnHF53HCQKHaZ1Xk4BMZd13sWa7x9CzDFId5dB/3Yvf0uYLoZq7dYB5Q8YTw9462nB0K9RfcrKA5ygNf96/l+vr7mjlC7u3HovIiN+EdCtzS7ZLyFa7C0Iv7aqXumE12QtPn09qbNaJPU1E8L8FZqOeo380b46jeB/m4T3WVv/djc5fwM6zp530r9VE72Pryc0zmSZkEQoGQrhlX97ieziNjjcO7Go0V74zECGONvrrSwu22UaYh2zJ9Hwo5FcBhKzDeaSbOTL6TRpTg6Sx3NZaYDZjjBOfVMsb1gMMxS61t4XltmBidxWh3n9y1JKVU8ezH7iMtI7hjcXBZMfYkR7uZzVfHIXuztZTvkVhmLXyLhWoASxw7/re0YxbCQzWrjkRDeHp6ztS8shufaRIU264CdgiHORoguzlzjc4Gof/seFVNao3Mjn8zepUhK6ZqYmj0Ands6dZ0oFcHZJla+FxYs+eI1l/HT3xZLnESmsenL768mNhit4ZgKN+sP86SbSLmrasILamYkwToXzinJPhenZ2RXIe21FIcJQRbUPdLjG3FQ/PQm7mVe2q+Gx64S+vDYzcxPyTHQws5olIORBZvqglPh7M36L9mSx8Od7itNq7x30cycApJlyFR2wyY3JQ5DMXJzvnSXlI6mL+Pciq5izaE6ArEi/a6ARbzDTK6fouSEFXp4uebYKsD6DqQ+MIJxjE8HODh7eq5WhLGDic20BRSIvh9CrU0DFFSS2Mox7htQmnAzvtsPBNMTFk5CsHVij8NqV9nlCyQ7Br9bxM1PCsMjlPIikPoBfLIiSyuBxYxfqjjtuxmHGmkeJfYYQ2eZncg8TyrmsV2w1UCq7Tw+H68uLc/du4CYgDRb3GpAsf5wxRjN4DXwd6qRQ1/MglK5RDlM0N54YO84ooyh5uG47mzJFaxJY4SO6On/EeR/P9NCoGQV+G/HH02bsnxaS6OqiI20Jy50EN/XDfOgCEXrG6/0oJbtGTSduCl/Xy10TU3XJ63F+2qfHvIVnqx6D8SNF634PJ5IILnoBA1z1JSZV7XbHHy9ZutvDT3dEC6exGe2GCLkSQoFG6q1JAc/YPvX/GYQXa+6Se1HszxTvyqxn9bFa4Xq82cISwLsUFVhKlwPwLo2I/81oADCHP7u+bEEIR09XEB7uURyUHWKACrzI2ltXEMB0Qcc2hHb8ue+4DLpQ3V9pp6Gjlr8zx5CnqXFUJNj09Nc1PjEEeXmikss0rqIoWEU0zoZJAhCwqnTMyZHp9fo3+Fxdl6pQJj8V9L2Wsu0kqA2pI1n0m36tqXf4iEla188hSKt/jIBIxrjq4Atez9MQvGS4Ikntty8SNAl2OhNnBnnVLQeGHcC/SB0C1wXgAeIiOV2If+iGE2kK1sCwJEbl+Ux2EOJjoKxWrla3/EW0N3T5/DrrReKvv/hYaef+mfht8S9Tmddu0X0ec3sk6ec5d6FWx5oW1/8666ajCPCgcfNqRthQcZN8aEawUwE7sSf4Ed6cKynVb3/clrujgHVowCGrzbBLVHQUi2Knnvj07mc+QtnVzCDqzDqgfT+xxo1FcRXyB12y4tzquyHxJr9w79qEpOqe5uhnz9MTT1MmQUaB+WzQw7rQNImmeSsAesoFO7AZyBySYD/7aHWDMyExL4E3eQnyg9ZjaibxT6C/mXKdMgPiEdX+WTSWkJ02v6ut//oQRFCRfQyE+qDi3QuBVUndvVf4Ihy2ZXWdb/dJenXPR47LuoBUuNrKioybFAsDMWSFYPRm+++8lkn+K3DQm8X1FhMBqH5YhysVtG0wWjuVbDKPu0xaHrZRDgrueLzP0djNSIxVmgNBDppotFssuTr6Lgl759rQzU33MygQw6sewtPUCLwfDtIk/Oa6C/NFsS8mHXCgTJscDXJKmNwC/5D3leRUK92MaoliOv+23JmDfVLe1YP4rlleDCHlFrJuDxCv/LNMgkPF9SazEon1vpJGDfnJlnrBdkJZzgkVKYfxvvQ4E/GvMV2I8PlKmgW5xqD6xZxs8CstKU34XTcU05tnp38BWnWJLeWq5gjpTD4759ywEU21NZ2rVE0pN4j6oPquheXUYiHD8p+/aa2jHpFTL39fnDDM3e7JVhU9tMYw3tieytSq2aWouZ4UYfF5XVl9gQMp4iX/JfUMKRRpLjRoAecDoclu9aULAFGNIFQKIlRfaTDZ1XKbs6eF3U/NiKJ/r0gPIBHAcMOOzDimC9f5Eo6cOL3gE+U7Jdb3c3fZ+hzrDsE7JB89FIy3yr2YuYqkGs6RqjkvtXcz3m9gDp7+pkBIOz9Q2m9yP/pCv8S5l9yAz0lRB5Z3qlSGXBl0Nyelbx5DJ3SWf/k4SMulPNEpalc6gfSpchskjnh2vHitGdVloT4KBMITLqoBJrdp5XP8DWk4/dzo7YhV7CYq/tOv6i8kYOMLPHIPWI7N+36W+E01/ryAs7QiCaloKvNvwC2Z9ygeL7eT+sOQCUQ1Yf3PfODMJw+znxpldrTVBEtauADdhsm3NHIp7NJ7OXTjYHDG3x6nAA/ZFlSq8XUWQHUXnHD/mbvhXsfaaFK8QA6k+mCyjm9D26NILJ5rJlPTcobQ6uYw/FppsK2ELzK4J+pWxxnnIH6dDwqjsQ7y9dIFNWpMfZLe08XrVbHSJpfsBL/EJfb5SQ5trAF7IpiFj8AJ8AFCp7KqlkfhkEE22xPm3E3xglvDOZJWCyzS96Y9tMaCrrzAD9vywpLv9R2M5lLzINWf02JaEmdfSRmC67Lklj4T1/jFDkBLivOdVY+zllG8lx/+ebk03VYSTmgBA24btn5WSP4NXa0Rv3u0O/DgQ8WTyRXDBXek73X1OsiENQIFt7UN6VmTfZVgeNk4TzFScfLueMu8j34lBebuIxuIfkSKExQkoLFYOFVCaJv2t1cP/6t2rxtTU7lSUb8+LH3IdslKZer+joMcKZCk0HVoAQDTwmjsS/MB+bq/Gj1+0GYeH9QB0BYOCHQ9AI0+VDoAPnaV2OucpaTe+AaC14JzGw5VW7X8ogn1K6RG1mpNgWqccl0bJvIuf/z5DkkSd52/VcPjXyEiWrkcbqUiO70UjCQ5A9QQuImlubZUkOScBgGM14HaIQPeSWAKjHIhSo0KfBDxKqn+pZJGyyb610Gc7NOpt0gtobTbGGb5TupRGOpcV9FnoMbMgIyMGn8Ju6JEAxIcQnSQr5BvfozD5gKTeA+mrXQbIxQ7x0n28uNI3WMvW4HZ2vAd44eGEHo7i9HlmMECz+v9jhQyeKkhSAHMqfhWwb5gq4/gpjX+UmEHQaL2PmxFKJqlA8Ha4BuCFovJMB1NkHZvZrDI5BUs/t7zxAWhnfKt5fItU8NCDiuBGuGlVkXTMCRY6/FR2dV6ofEvHz5Ny6BftkVMllvSZZdP3EeqkPHbyl0FXdDPipL8rwnwMYkyany9O3TDE3danrdSJRQl2q+zVO4ZjI3mZ2uDIflvAqhJ8E5XL1ylf3IoTMuMYBAuhdABV9QU+LY/K4Jl4NsA+gphgbEZyFNfBR/7Q9ogfKgF8wgIxFYeCllojxsIWCMukhSyuJXFqo5P0fxiwVlvf4Pvlv5DYbiqE1AExujwLAciRGf0Uq3sDo+vU5dTvuZrojKfpd/ZhgYCqepOZSPbTxTzJVftT1OSBrklyEuXoL75SKuT5NXLmI3wbybNZL5M0DCx4yI9g9lHZnau8VeliSj+7KJbnwv9G93hGnUt/8s42vBHWSrFUaB+XKkbAE4deo+i5O0TcqvZGTX24uPYGzFNc14zxsQvq5+hMHPVhvN9VLmA6V4Exqe/p5FOa/5tfU0INI9eotkbCrfenY31JEsAOSrpCmGSToEdHXeUPEI8bEKXhL9apMRa8VTJs2nHsOHwN8mz9oq38YnpVgjtSPgFxZx5rlXVt6uINkiMgJBQBH/uACYocTUAMs4EmgHQ2K6U1jOMW5dfSs1mSAVZxOYWpc+vRIFuQcEHryCDi05OQwzRPNZnIXDqTKp36Slgv6hAFgFIOKHwgHvVRVAFS6g8PBNwNGwt1IXbFdXbbaoDwK78cCEfHo5+qc0QiE6eenw7HP7wkPrycxCFPm33JJ/DukNx+DdjnIdHyyee4a8CJKDqnVnmF6u8nOeRcnvDgzz6m+qZne8zuUMTzdb35aLRwttTYXjvO3VQAcvvi2QVybdpQMk15WdjGBl/wzvuiAV72Zh/3jJ7gZGls93rZrvatwlc+ZRDX/KN9BdJuEKNro/YvbXWbV3YVBaaKFf2s+WO+/UYloMHxUoTa44XV5J3a3BlY1wWjlscfYcCLmk/XSaWOqM8D+d78Q5k/OCJ33I4cB/cHcd2Wj2AYQv8S5xWPOHGYWnZFKf9UOsEIUWZlxu11tg1wpR+IkcsSAc/Ifxji+owPlpOSB0sPpJb6k1WaYI/F8U8N4htpbWXgTw21oy3ggVeRlqf3mybZO+WVWi0AKAxTXqNffUCZDivL/6Lih+QFWzWJOjH46b+aRVUI/8LhQ4Qe171iqpYrE6znOmqgoSRSUt+sS0fmPqBUSdKhP1ehJ/Bdc0iRr2hH0yMYUlXCnQ/AgVk9V0nC+sXWqi2pEBRg5bMdbLaJsjqvSH0ywBfXYbLUbh/uNmTv1G6fCV24jkblc7iezO+oqaDsFTjZwf21OCBvwLuWbGCwzrQ/E3EELJRoShLSe76FtENNiXxKkR2sBDtzA5Rw40OQpxMxxhJ3OjxDA9jlJ49mS/mprJrTyH6lwZ3PZDk3UpGe2t7Zg2Hivb2V4E0uDXSEJQ1/IXfNjHPKYPiBbAPh2EEf4DKoJpwRZhi5vh+Kkrq7es9+2yjWuGKwWhnJgytqO2n3b6dAyBYUOT9BYgBGRxTa+D0yT5RlyK0kflksVvBx4r7GEmWp8MGulz0rRwdlWYHF0WgP2fG5ctdHWplOwpgXEaelL/PQMd7HdSHKvgcJs8BeMKRbY25tpa5DvaBf8+ocV6KUcrZje5x2M2FvKLaZQlWXUJta+oATeeuEfbSPzUVRs/kUVb+q6nh07/ad4EW/+yuLal3FUrP+hUA/bBc24DIYziHRPsCAaGCMykFwkxcXdNsI1UQ7++Q/U16b92Ga8gnvOeXvhz/NqHoRomoWH8dMAmSaj1epVsQZ4Fuz1SSBL8C8/YYDbOFfSBCq0PSUGRfiAoNrOTlTWfil2M4dX1fyxQLyRnZnl27ZV69Bl5lfEcboL+sjFlt7Ofg4UH61D4pedwPol+uvH433Eaixj1hE5kOsJqAeYbdVD8Au/UxiZKh+EX7PKH/ZhXYCYgmNS+Ob39RMG+juYj1tUDkxNJKjL9q3T9LDThM9/1riKVdzN+EzRs+yB5J+3ScI03LG7eeu+OA6YxgixKzEnvzt16gLKZWyB5rUL4W7vN55FILM5zm32+Ete9GWz/DhX4dDK6q99Sn9axOCIhUu+ZW6XGq7XM5/F6786RKAHVEcFyxrzwl5hv+cKdHsG/6bb28vSESVFjCGlw+dczL3lFXdrjCL4pTm5tuVbo/AuaLWtH/eR9q6YmlzU7Xpz9HCVlnl02gxAIyB5V0XZQL0l+Uz32j+027UxNeARPFSwoxYTK7ykqVKbsABllNFPbbX207jxPAhVnn9TN3XmQFfGs+7hPE/AXtyvq8ADGqMOpyCd5Fd+EjVisDtMlEX4b3LxsFmFJwEtZedvISwu53WZhoPF8Sc36fWR2JgDWrfTEHSPykqH1tx2ul6FzL6NcHf4f+ZSI/vSvKq+DGXqOYGnhCWaUgVsXvRzUUe6f886n4L94H3CfVdFxs9b/BX1KeQ/bCyDGpJGCddaN+qaReI44Vca3X3MITiL+3Ih9tfO8C1EYwyGlS0A/M27dh+Kp8fPlaNE3W2pKRnwRJX9cItAEBCfOmx7N/q9XAH2CoPYg2vyRaf4FYW/+rzmORiVCfwConJwBL7ximRE9+QDVf4uHMQVFVgJPsq9E2RYSSCdmXBPyS20FOcBfF9mM01xTD6dNM2IrM+X18TvCsW//lSJfq5iAdTw0Hdpa/hSKY3cubGFnclj39eTmDRzfQDwSpJPDYRCZydTCcPtF7KToidVirBZ3gKJSO49jtohgGHsiwSSnpmyJDdN84diZ3vn/z0WFkfa1maaU8d1ah/tMtTV3kQurWiPoQmO2SJUyq9m4/45KCPa0MNEI/5cP7T9DM7+MxILgsI7+U//q2ZevRGgwxTy6cMt9QeQc6c1l1kn+QM1jOYULsMT7en4DSrEoGxT+pIkEXSWFrIFA49NRe0Wfq3heFzJwqe4Y5dsAaDstCEI2f7AFhs7Vo+NRCqa1UxQGLwwVkBjNs5LP70xyulF4hSmmFnpd0McRWgyDLN7u7HVg7LIdDyqGnYvddd4GTqEjW1rrfo8WVyPwYpH1MjcMOTy3t2VqI7bCtnc75CootSA7rDtgaokc+YbWym8WK4fhPZCiQOVmh+ffn6DD7cTRlPcBcX6A2d/jfy+ocov/Rcd50NdyqN8OAPn7+7Skx5uSafv1RbXqH5eeF9KOo6v1Yrdf98VNBt3wWWcPPdZg+yaZXBUFQ8j0EkVJFnM6nLklAtJw2lEqUDTJuNVfFxSDHfjQKzWjnfC4TIvWCdkEuCmaacVpmWFXXgfMXunATmoCTHWr4j0El6FC1QC4VMOLaJy3apUCkhLHoAmQJvrjRQdus7WHc8MJ2BFVZANBjGMCecHlJjPf0wgE/gYQw69htQQtrU6WFu8UKcu4QEoILhDxw4AhkE+bG83jYJi94nuSZxQdX0yqpOpkyXJ2UQz8ht2yID514KpLvNe0DTdQ9Hpkpiq6AVQ7wikK7GIocB3Z2SK8hPs0PEQvseqbt9XyIhZM+H1FyHc9mwC/gMZ/2EwcOsEZiUyP4m5GMCHrLKkr5kwIOXbcFbjsQ7asXqGQc28xifnTBM/NjnzuTtuzOBExnTUZobHlSfrDnTvhEiyumWIjtemPAys1zHv/gyz/lZFuRHT6T/Bcn7jp1sHob1HzfNyzCME6qZYgCxd6syf71pbJ3kVRFgRgESSeGZA4S68nnb5WgU+vjjPY9AhI7q+7m19Dp3XNrwRePx26YvhLfO0hBvxYXjUzbbRHShgsThrq5lW1Yq+lPS38ZGklpbXUy6cGGf5Ur/G/M9X3faVE0z+j+m8Fxy8XP85Tjte8u+astGpSommrZczRwrJgQwXXVX4hn2rCvDb2fyAljc5G9jvJloeXZTJFOAiZIf6IFrxN2Xdnav5lXlidlITtFryqJKbkbdzc8zphxJSnAj+B8cF4xkgMCf0iAhMpuqguK6PkqjxnsgK/f94eSyEba/3QsnAiS/P3Avpkv2DfTSHxYoGJJoufL+knUVzfknLgivo7aGaoEklQ9N2/H+CT2jGmqmPIW8Srvn19gcgzaMdtMnnO6WidS/NBirGmtL7dOalCyAaMvaCvLzImS47Eoin4RJLBoL58WY8FG2pOGkQ5IaiKJ71fBF8cYgAFaKVdO4TKwOehD5OJ7Ikl7nUbBmjzW8oprJ4iuqXrI0QwPVciRfFXsLJgQQOVTcDHuC3DjF5z/NhPMXACq4s7zUoxE/DNL/2WQT+xAX2CDwpNl37PXdfr+hD6cGx0QsbEk9gdNiJcOGH8RrX2fFeRl4ASV/ZL1ECc1CDKf1eK0ZyA/DzSzO++qh/mHxboJ4BVCsJyb5UytrToVf7mWMDHVgodsrrYbOf2SGBcmQvZj16y0YJA59g4IGKpHqGBdBmKKoI2mvyzR1pSq/Vr0wLXEKissE179QT/TCM1hgk4S6D3VOho3xdVipuTrKfHBFLizDJ/P3L3neLDQt8lHnouqTjn+RRC6Vd5A7Kj7sKd0oaNTEOI9V6Puxi26O0jAsMFc3maxRtCXEQzKif5GTCndz2eWFu4pBubeo0JvhcP/kNZ4dkixm/dKIFMTdVBbPAs6o0VUMzqEde5fn0IVkHx0Nrs+hdhidia18a+Ttp1ozpuGgI5fSPAkme643jSYsx+/hYuKsr5A6W5xl0+0NIHY99uL252ojqfC5rqfIvHI8wIg8PM1EMp5QppRIBjeeSBWUpkQoJjdXquDj62WQHPkia09LxQRmmBKnzAiA98s7ETXqOsU9v9BO4rhEMM/iCATVBJuvqoaRUTJBWhmOEG7HAlJXkN9eXFu8XXzEB8KYw4Fuov7wtezK4rJlPbjSARBfcxBo07kGiELLLJmfmcOnhQCtGxOwAqvYirFTFFkN6XozzcJ5q7mSBz65HBinj9Kyw6v8mM4qw4IX28epinNRV+0Dry1RmGYhWUSbn/LpMHV1fNCaCIv7EzhNaJDOkHm0N09yU6FE1sJ/4Pl9V8Y5GjrLbQ9fnz8v4E35kQ39cUu8Qa0A/R5VPwS8tFD8gleHrpp3c9FKgKrLtkUk+K5rUm1i5hysIRZNsZhkcZLAv7fv71lulV2AaTLDUGC3Y387/eDyyIvWAZ8eQKq1gQU3C8RtAtJVBJkrfO15cV6j7xwNKMI21XccHe2n35ir9EAMSfHhCC+t+ozn45j3bGCOL2HN14pJwHzaSXMmWI/NiJAoH14hlT7Nfcv04cyCEXpByGxdre/Y9m12vjTuEsMFV0XAl3IVotn9qSvte+US5s73Kw9xS5bk3wq6WB4zF+NnL3eT3/cvN/oApk4S8WbgwsD1xmmZ2qla/KlVxWarhyzrGsWODmqvU233o/iw97cjrl9L09ZDDoCINj4MoOpaMnK+WyDxc4GQtFBdx7VgeCxYP3ib7W96pPlXNT7qlB62l+DCE15Zb09WqTfu8I4fxJ+IN8GlwiNn6ZuRkbyR7oOTgQCgU+S2iegpLwFUfNZjWyDSQkfkoICVMyHyYrzL3r3AqEXMg1ib4vb/tpM/zTIcp/JDp2y0Z5Q/06cT28ns9R+fCvUVb/gjQlNEGdk183ya7YRKPna1UFMgALWOX6RwinhpW0Yre96xcUFByzG82JmQSLMs2Bwc0ST/lKhbP8vdkZG0tb0wmsZ3J7m+X4QZmiw1N2OsWYlA8NrRu77U4R+VLbv/SgBanjarHXB3ifxMjSKvmgVoqG112njQos7fuoXil3Q9GOqW3zvonTEWu4QmwkYjKIP5kubDD1A8xLNkqsvxFmrDy4JxiWUzQJ65HqwJdb3sWqiBbkHNLDPJH/3CJm0sDOwjLhJxj598iQePlb9vQfBXsUm+8m1jmb611mGG8/HHIUldg1Wbr/suii/0QdwIQsft1JVji0ZvJBnWZg9rdCbanPdttnds10qowNGBJz/5vS2Wx/o4nGfDnzHtB1edYdrrZtE83yl7a3iuLUp1Kw/67yzhwBYxUz43UMGHw8L5iX4tTmcF6nPY33reH8zzidlZ/pulgmQ7qFNmofR5S+9cNLhLuuIJ0cPAVCyzvdPsfwU1OQwTOJMleKuB3pteuFvFIojMkQzm0n5o/uVfnLfxRX2bKA0SthcJzbX5JaJ4oGgxfrZi0l9I+VCZS4IM7mR3IDjazLMsuEhY0k5kGgpYU/pkAbHJWppdUWJvv28vVzeeQIvG87RcuLS5lveDYQ0lbjNRl7YUYf/x9R1bLkJZduveXNyGAoEIoocZ+ScM1/fXJW71/PAy3aVynDvCXufCLzwzBryDcFjFvbSFTnUh3jdKo/gw9/Sd/5lpxktkNM10XG0Z0ZV8TULjQoTV96x6H2fQi/Y1aAMv1/GFBSD2NEFidlXgXC23wHag2Ne8Is8DfY9mNoHatfqS3aG1djox2QoB2Yl5vM9XvZiircaSpCmxOLy6UlLy9Z4MeclzcX3NNVvh6XJk5+9L6R0Ynz0LLWo0IMxDp9poyjJlckKCwVyvBfA2jEg2jApxCw+xUY00et38kjTtTg/QmeflbM9+dwlf+eHbuziHNGU5xChYK8jPX6ECxoMb2ic9NU9wB6Lv0Mg5fdHBlfOker7t7B3UEz9JbsHwYVqAWsc0wTrF2thuX21pPzbY/12RrLc5dzHLHOoJhPqWV4BEezIvNe+2MhoCZGcPu3v3Vx+q6k4rou51/hkPoaJAqmZDellQjqaPCIHSDjK/VC9Zn4UABW6snXWvhZf/yq14uXNPxRGGgfWtVcyyQFiLKXUpVrtDj+PsQNXojYgsKOgWMOqp4mEeYl5GSKq92c7V2KS9kN7Lus3P/l+5a0aszH3cX7DJsHnrCgNlrsrz/g3Xlsl2/WjTo20VQpg5WUrV30ogRV46qDeyBwuGrs69wcwnhNqZ/EmFcGWLVfWSDv2t22M0Vfb53LJ4p6WuTvJd3eY54gjmFsq4hT92+p+lbayXJ3uKO8c+AjeqMZBeR8YMA8qTRCTkmLYXi3eo/D4qPi/veCn59gJ2rQaB3G9pE5IdjFYBMJzqMaHKffuqewR2Q8+cOY+H5iplbyLsA1kq05fgQja1WvONMgLBzPmJBnJH/Z51PNreg8+y6qVzBRyJ2mgMK4V+8dE+30Ewdfs9Vo5vPaTZKlLJ5IULvUpOc1Nyw8hlxyzPIxPXK65ZOVG6X3QAsNAjbZ9Y8LaffK5QXJzuMoKLNELzS8BIQ9HLHg29dyxmgm291TavkQqBIUrg3R34eCXms/h95/6Zu6ExiMUSfzD69jEiXoy5gC8mt71oaCz6xhKKkWs8LjQXq5JtBVvA9emzn/14YrCcxUHe8q7KKXG5OYXLRrXytbA/mENPPvA5Jrn5V81a5G3qWrHSvt5TGPJGc2caMuQ0IkcqHuXJ/C37Xi6/0ZNfXNEQaBmbnM7/pUK1cB9k3oxmX6qoaKcOXuE7oV67oTnMx2+v/Crz3O5jnqs/GXGbYHIF/PLpEEfx26P56wRfZUiBitX+XjZgHWObhr/S5jxTpqMVTQf5uRbpGDkYC8brwUohO8ZzYYEXmv55GhcMpT718w5KkIXEZxnWRh5/SX9t6GpOfDTP0BK2UHJY/LnS+HmRiQ0C1MySscngRTEYEtCaSfkyoPuNr7BZ0kvHl2R1u6lU4/P/ERl2+4iyON22UcJQ3xvV7SMKJ+FGAzWebXe8DWTBo3dw7lZMt0DCbO+xYSHIwGZfYHKtIYIuVPbdBvU2aXfNgbqDIEwTk0KNRz3eP06NAgl/2CmTUF/4qA8OJshq69wuP3HnzwqSZLjOakavN0QEplQQlXLfamJ7IpGcMbeihqB4BFKhgZQz7P3FCaG+ULpMcJCyDRyNIfpEIheSZaQoW7QC7LWd7u1ybfX4TITtx5uVGdqgPhj9vgC7vM+sm9/xMcBPpYEu5cFTKIqfI1kjeOr+zbP/rF4/PiKfbTbpva0WDQGMQCDh8jYQ7t5uiHUNkAHIFPgheOutGyaJy6tNE5Mjw7LraCt9DC3pSQXizz/Rcij1nc9quDdsn7TUo1qInqiYC45b77alJ+kgwUcLkjRJoLfoSAxQRO1NnQDiWkfCwMIwG/I9HFoslHhS4TqkFKUrGhLn/Z6QTUaSy/O+c2k/PzGy/eO+8VmNcNFiIUOiTKvcQEe5IV0XcL3Cg4hxaWFU4u5I46y781iTN4C3+HPn6sFTZ7S8p5PINRfZPh2Zfwha2NE0lShIqwh/T4QY7sfjNwmdhS5TvdSjGDAxUF/W1vY649birFVsujYpCSks1QYO7wyiy0lfjh49NiNgXvjtXr9Zl0xZFEM3zPsUCMbj70BHFg8X1Miv1u3IsT0T6SCnpndYz4fKo2ODGXgcNgb1fxBu7Fd7DJW4Nu/nPNrZ8OOvDA3GTzadSqEDxz5NSoJN9gdFCCevYLjJ7LWY17WyH9fkNM6sxgp/fKG2bkInTGRS14eXX8+JnJph7AgZ7LsNRlJUYiYgFNSopyXNdPCJBKkhSB50JoXwyM2ar0WaMgfxJwmFw57v4qLOr3i3ASac9XE1w6q6LCjdxqddvaZHrOKWbSmOhg0vNc3yhLA3D9uXfic2ghS+UfWolQVOVUkArYVVHWkVvekhrpzZsO5XSUfWY7Ty+7wALvRlLt4PT0ON8rJ5PrfVlPmbLcgQzGnUTpdItCHrxR0hMuL6E68uyCxgVkvsygYXs07XyrP9gqOoSu8LuX5ALwiqn/My/2KUioFhid9b28sP7YOc9t18mJAxCP+L6zlvckdovSeou7nOiYUPMknk85+NplXBIqArCyYKXn9behzDQIr5Ye5WkAY8JnOef/ig/tBxWmg7dZp9K2s8VKIF/47ex9qsEZX6zCdUGSsx5a2A/tdQgZkqhNMFMGODbebhindec32wBdW+V1E+b2ztTNivOlDMy3/NjvBoZzCGglJ9jUxR6tf4y+1NBS36bgNDn/x0B7I/PCmruYZD6UoAFAHjuMHoVUMKbNLizdCjk3YYp+7A7x95c6/Sbs1rdqm+TDRsG8WbnwnGypnseDKnD82esaW88F01boiqbahHQmFepJnNU4Wm/PeP9muFdIX4oO3/rpkpvsUK6gFsBlQrfFaSkdk1MFS307x7Qo6h44JrayYtSo/MGrPSl4bkRcT3VDc8n6b01A6p/yJsFDasmZy26A+1b6L8Q1ScT4g6u9eFUdhuKMUn2+wuICX9kQ+3jqhJ5y+4W4kj5VHjNHRjeHDaD4ZuOVf34CaMYwoMSLAvmYYWF+KfbcTIZfb641UfFy+1anoNKXPEkWHTPnnExjB+epKUk+no8RezrOuMjtLfSMW7g+YifEJfz5MQ9yWMSGjLwm/U6cNbNCxkEqbH0GSMaUmyW9lSH1mA0T52WEkXXl9ABnV7SNAyTiOJ3MQ7wPIduiMMU5ym6vEHLHWEMbI0r6k9SR3dsFTxXtR4X53mjBLlVqJSG7JYNI9No+o+ifSUEeXFtg3+vZj/HWoLSUP6i9DPUqfolSLCOA+nosHFauMB7QX+p7ileYJQairiURlQ03Ow5Q/yOsbRwUWqfk29rQr6uSlSwruMkOTtIUAvKpVl/nxm9TgjI1Ys+0ULt6DZNwKZgzz0fXfToTmer+8Uk1ETMCaOfyYr8ATZ1s0mcALqpIzI3XsVfkB9MPRoZjwhdBzG+gHxSwMZjmYP4TOCZpq32XyeqDSCI9lxr1LZ4G5FC4+Yaa6+kYPZZs5N/ta4F75qoydP2b0U3D5S4Xeg/eAfBbHYB/gO3baWw9jYUKwoRW/PkhjCbHNrjhZ3bt0vrs2vKhgNN2l7WAnXHfYA4EMi4560CnHj28DkIvXfIYYj15970XoNafd+LGxj8Dt/ZoaDy3oIUR93zabKmYV2e4dvicry2TpRaqOkOe7E8P6zEJFjGrPz3kethkeUWU+cZVk5gy9yU6fbcuDeJ3agwl6jXslzIlQjgdD1IIFy2hLsFhAfxwdevSS0D+0OhMfqVEwVAmjmRZDtn4HzdfB+kZdytoeu+oBHp/X+eilZxc747mshjEkl2eoyB5QBWqo/FcngGk+TL3LoR9qvwkSvwVzD1zLS/tyR6KuKGRWcG8AUoc0MsBPCDcpwn4olfcDPMfHojwU4/tjHvxLiaoovoBKvclj5nTKz7kG64/tjfFKmbNvqr80lOxv2v6ABChhdPn8YSJTvU/thU5OILXJPrhGsTfTmzV2fAZiV/jCxXLbjn3JutltEdGdb1rtx+EynDT9pukE5Z3La6VDTmOr2hqbQJIEQCQEEia1r81jSRvVFwpBQ21Bj/9amrflLTDDzgPHpKLJDjhgOtiZ8c7nl2BT+W737VzVgmnwfMsOBXh9ruB1286PEkEl2fU1iP2b8q62ieB4Tfsrxlmt8qEh8pQCZR8XSOqqeHHc8bdK0vLP21BeGnd0yKEHJh3EdGlUpCqJXlW6eFm41TlMqDQUbovZWGFEhYvyGmY/7zv9TB2KgeqpwxN7Sf41tPov7OpSNeNgLOe4SnKLslMs79D0bhjo9m6EYxtx3e0mxPgE0xjZH8KX4Wp5rnL3i+fs65RC1FoKnaRn8HTP0FZlSaD9lSeqx8NL2jLsfvN5zEieFqlWZ7/wCcJ2U3QvZxHzk1w7GoW/lMuBQ+vo7mYDQH1xWGCXytcFfRC8ou77ECGTnhb5B0BA8noQNF6Y1gW4uzcQRIWWXcoA4+Be9Tu7znIhMZrLx/K17NBnkS0MtUjow4pfQ+4roUksBAHK5U2L3aSlOL18SUQODVRKyS9V8VCNweleH4DAy4LD9WdA7ZAqAEzcm0YsUITsB4meAsehblDuQdQ7BGlc8DiVChHULxg9IUci7zFBOEyM1V4GBAHkbWPknaCTtWKcdt/E++7dfECtKsVi4u4C045r12lfnmIZswu/PBR8LuAeocMOStllZjieOx+/6wNcZgcuWSCJgY5xPX9ol0VFhSxsyWsUxDsuHQG1U7rcgo+Io7oV5CduIJm5p3q9fU4MO+LJm6G4z22pxmOHpf+ITzwDWNi/1r8QBiO9StCx51tJfLWFLwuyetJkR39d5aKBcl8h5W79Eji34ihRFvTHAUHIrXYGFvlS/5vMK21//sj0jlopEpzAH3yjG/W9Na9e574a0wIr/5m2pWdVah+VKrPvpf+GdQFpXJSgRPWgsf3RxBLlyxHsDvc0+3U8gHbBQC2HEBkLDQSR2b5cXH95BfpSoEPOyrH3BylqwbkE6DfIxuwnJrHuNUSuZvca+p33b1jLUQgGSjmnopxjzgRcx+Ev2beNhjw26tGY5aMQd5kfDLdb56UHiMP5lIkGyG55QRoEvRbTI1pWbQr5GgNvEwnMPUxmEqmD3K7mFN8Xb2y7/1rhnROEj9cl8nWE3guf1iBlbz/KJsH9+hPdiV0r0AbJoJ0dteOUI3P0zfePB2CDD8OtRPxmWyHFSWfOl7pI5sFhstqeNhEJJmwu+9bCbtOCLuXAx7k8Wm1DXsBF8NGwLbw+53rPjPvxB254JBrG9l07uBD5PXH5h54d9WPowufGujaVIih/Xo5PHqffseKrXg05x91+qzgIa6F2vA1qKmbo87jBdlds+Hi81QrbhRtcQFte0Y0j7raJmvK602osJb5Igrjkby0fSL/r+2HEhBn1VtcNQsOMMUtbAIss9l/18/DpIqf7AY26oE902rqK7i6+ZEK42WFoZVu5kMqAysZVwJtPiSkek4aK9Sm/CZEtSP34lxWnvT2v3TTTPnz6LUuguiBNe1I9HENTVVIztu6uIZx6S3Aa7hsPNz7vftYtmuZf136VlqejsyZpav+wzOgQGIOcwgZ/VHvgHlrXU5kc71lwpitHIiAG+fksxSVOS7W5QcbhkMaGUgPshOtv5EYea/eVHwPtO5uSYgGCZi4xTkaVuIZjacF9AGN0vwQQh2POZIOy4+bOHYHimW63XKfvxOYYUCygxrl2SzLItN39SLuUZs7wbIOKZfpNyO8ffVhwo1Lvkf1Xb0aC77YsR+x7PaPx9Pv8IjpZg6QWVGNZeIlDWF9Q1cMDAFXPsyr5vpnEo/7NbaYh/tIezdoDuDbHSAfFfy8x5dvgHRuRufDOMVy3SxTVVF4Y5qC/vlYrGL47xwBgPWeY738K171M4K2mU+MkUtrmdWVBAFaBhpfrq3nxRd6Y4B/UHK76Zt+E2ozNQ3ljgeMt8+R+k4dzvwSgpWupxqRldN6GXXPJ4UPqe511PToPIk35LCXhyaPyuiKCNvDe84B9d0VHBMnGN7bm7GsWHzkXMsM09y+qFW2CrsxNkMXKD6Yv5K5Fj3ahuxaMDYNRnf5nvj8R5OsuETQCnz8WqsdhyGpFVyi+ayeVNtwPxJjbQdpb9INBqxqiCE/Wp+DU7Xp8/8ZZAj3M5qjQKpoPAtdGFoagSH9FMBA4JHzsMdNuASQBJkB4AXMqSmzTisps7Z/9RvcRxAlo8pMQplxyGvi+CYswp60mEWYYBluxIUEpB+I0momQexAANMpoIAm1HkDwCmDNOfHyCW891IGmVM/hBdlljsCSu4ORVmhTWvhQ+s/KcJ+hj8p9tfRfxQoE7cOWOsZ+jpp768xvbIy5tEnrMW0Jmyi4KHYaPqeFKeqBgL/q3XMpg75ldBcaKracVqvkKbq8iMHLaJadzgPrsA+7U+IhfeNQvGXG5Zaj0vMxmSuzX6CxH4myD5fsEtE7mCTe4AxQJSA+vH73gQFiogEms2WvSbnWrmn+cmbVNPM8jwOMpI8Tmtx6Z9fHUC7V+3SynTW/mFu/YSMvwqxICg1gzLoipNE0y1xK0J+7cMSTlmErgbErPvKeTREN+EuPlVUk5xFLIxpo4DHlZoGwP9g/ft2KS/xCqwmc6hLPw9KUSxdRwqlxY7P8+Am13b0l6/dd1lx/iP6Kcia9Zmump4bx+28XZ8kSEEuVeTVDNIkvbQMZw8uASSWEOeGTkA4lPdL12CnyoUPmt9TF5V2kLmtj3e2FYeeL4ce16nrSmUzZiZMcHGKs/HEsRu+v+6BkHK1ULlZLYoK2nTfIE5X5yegV41a86/42OQ9s0tTxC3OFC7iXK3XmsX1lPavAjZAUlWIYlSWwihaQiLUOLUkrj3L1Zfxh/RJMfKSk3pi3An8fS5JAv909abGV1DdmNjA1iWdMTqsowt8Owlct4NYJZ59ZngRbSIEVuu76wMxrEVDLQfZCfO9rOu73x5H7PqMGIYg2xcDo3xon2MMp4sFuA4egKAwbh2hRBjA9go15vF1F01gubdxCGjHHNxWr0fgb5Yw9WH+TicTThBQ81PMe6E44SB+PRS7iYd45ndkDyDZRQ+P49DhyeKXaIYd2hldh6HsLZvThrwwaURhDCW9QSkKSCKUTj2KeSGFAw63eEivB6GXGEKQjM6y4r2lmzoCBeU0DdY3yXVEoCsnK3IKCEL8QXlWL+V+HgvoqI0iijnuVtdYoNSds1fgzpaWbovgbhUj6wL6EnLEluCeyQrE3qAp9fKi+YytzjTXFdA+ZD5oHCD/A3IdIF4f09jUOaDa0tGRDxASrNfa9x1Zf24eNN87Sus66jUxaqaCfFDWC9npelNWLMkx3EF8oU3+3LJKEmHwOOxpn0UR3PfjNLNCv+5UKKJa05fV4exU6WplC8yS5t3jbYCiUpGloQYQ8mHubX18IFJMwAjGTSZicFbvyPtpph5t4fbVV9Trvl+PV3LBuu8itSIoqMtKtJnuDARQFTSeu7FWFTGZ+M37tlD+W+kW3eBeegeoonJE1julDcQ2cJt+h8eK2NodbLnDCMP/wQY/+/iZwJXnOL7ti3sC2tCG0mWPgPsS/UXbMz7Zfiwm05HlGorTz8DYf86smtCxk36BYLJyCb9wApyClhN29iglqkjMz+izMyxgcWIMmWCrWubJOxzb5YcoDsgYc40Ndjmh0CHejC44iAyo1sSBL+EsteuFzDEWFv+sFRF9a3PQyY4ANoC51RM/YTV/IiTjdA9C+OLTKjubuejddlHw0r72wFM6Ri0FeISlMO46Nr88Ix1mRPaSdlkljgZSc/Qfk8eyMi5kJUm6i/OPK0sTIe3+mnETOVeA+cmczhpwnr2MxfdTyq/7dL7ZBCF7M/VhLCJbb82nmX283KKscFLCc+ds892FeZgnff0V3XsXHmIs61IgSM78QbEvwz/napV/MryBVpL1wMCyvd+03cpKvFhFxIqbDfIZCwtZfUXKiI2QIab/2iJeJYuP26yUXjj/D4YRz2F+HpHIjMRve85M788UG4iJapep0USOVK69Ob5CiYLb2+mL7Qn2/ynDRa6U/Ru7BrZPv920bmwTtDak4Wu8Z9r8kLlF74qHTwPTsRZu9YWwyeiiwwJFfydEB2cXo7xu7xjGX1ftFPQ4UIax8AJYXZJkqI/RbGRvzhXo8sLA/vrpL1C/ikVzxcmMKuosjyPJaIr/ORFBJJHXtfG35TOBBL4xgXmH7PHknJtkA81vnEXPlMGeWoje4xTjBhlghDh3yV873g1ab49PVH0Q7lzPm7CnlI2QGNkas1BsMdmYs+6QjksbbqhN1XDCboC+STQ6oaIUeiHWr7p5V9LcWyXmM++VhLO+lGukIZRP0hDgH1PHyJUb4+Fy/fS1CCHb63Ac4iZqilusyyHj9S+oSorCjQu/ZPDV2riamujfTmfIQsh0AHSb1H3jfjQCy8NtkmhlxOzC3f98t/W+A7jda/NF53mtQbt4JYOj59kV6D/nbO+J5x+HNbiQJfphU/6sp52krSMQ2Y2YpJl1TVVQawtiWdNPc2hvbyqiui/rwLtJv0iqjR79RCcJy3dxe3owreeP5D2uwPv4b/YaEDp6CIpscWjJdczWVNpdeHQdxVy7ZVPhfafYPe2JXhGbYtp2RmWzVQAiw+LP3vwm8mXIDSYAUHMYwCIVK1/e5j++jBP4uuelIkyknuUrRTpejB9Zk6vK4IJE0XqrEm8FxaQvl8mDFMWB5MfPJp3rYA5wUKOrl1+fb9LYPL6/u13lO5bzuh9e5kZHnRiX4mDiVUzX6Hz0e9Dy38HXf3beI6TVHt8fhjSWeXTuf1Q8FLN1fef4qL6B0K202pgGh6NcVKvIqhm0tjomwHXM192pxyWkTh69XdaCsxjGYabzCZklGuI4rZ0o9NJoQ16cmVPze6Jr/FvCwU/Im6DOV1k/OLtS74YypBPHGhXilELwMODMW+zX1uUFmls5zNQCWWYCvomx4vEf6wsst/iCy5gVluwnBvpfbC0/f53Pt5ICNQlxodNk/htEzKZea6+YHaJ1WFl4536/e5M744CVr+1v7Uc1E3OryQgkdCLcxh39ehWj0/nFrMs4CFF6fmZnX/TLu+YWPeAzAgWpq11zcm59bcUMKxpevlZqkgp2S1urgGHc17yDFeCpzSS41aKGsLscquV6Qx/Ure8AQ8d7XcwgNdTMY1M633Di3ubhODJmzMzTv36yEFK2ARK0IJkrmE6hWtDSmDoE8e06uXe5kNk3xrUHjWKXgNa4jT/PYhcI4jjTipIB0TFUe7lMFsW4w30ueOB/Vjr5V67vvPvqckijur4nHDyru3yHn+vgeKRR2MXlC+ZNIMO5tpQ+i/uUtaa31DDcYBbKnDcHSX6DIkCB2N7YvJUhDVhRAiE/rnWk+JnnrOyIAsSP+/abZnBIBPsP9vKLXCSurt1tJgDAFxKM+nbh4HhebLE8xDzGCZgmADo/J1y0HtiKam8S5tJrWh3lvLr8XDuS0y9Ntzi4aJ12zC+RNQ6XaQ3Ry7IPjAEdbQ6rut8G6lZGd9dC8tcd3FhcudGqos/frYvXxX+UXVsKYJf+WzMPuYcxYeMZs9rLoWSMiFN2143EY4QzI5cSu5T10RfejJl0Mf9F0TGVQTPnZnRCLd74znLKmTh5Vi0LJea2rTde4KC4SAsCtSeMGR7dR2UTTBGuaP9r2m6XBc2S4QiE506ukvDJN17k5RNzu4o4mPfZUlCaEeGXB/j5EaHkzIxQ/ZlQMdz0XgFPm7c+XJMx5/osIZbOzuFxITWT/ep3snhpjKfUo5LnD4jwEDOWn6aN+GeLqkU7Bivxn3r/ccgbBtvVc54r6w2VwgrTnVhja60GsMkYqwPX9XK9OS4phhfY1u1L50nXYFNfgi97esOfh284TMK0PPAkNoMLYNMsGWTwrJTPubk3vUuqja9fC1iXfu7YBO9hHKkqlyJsGldnv535s1oLEU0Q9ghlsrZwloJ3pJlDMLd3jRkPZeIyuhBEoH+Pw4e257alwyjmY9hgFgOBueYQGL/3xnshU+1DRHDv/koZQxVTN9c1jIFtgW9+LlUPnw+E8PmVaYjz5/UZKQBO6RQv89CHseN9Na/c6hVr2Dki3y2vh/RDW5ZSaeCM43hCs2/TX7nfRQeb2BseJc5NoY26vj6Mobr6KHQmpac8tbmirCTmKAK4ZLcHDXVZUiWisL10ibMK6zIObLo+snwj22AhXybjPjJOOxbH76bIreae5jTj9WFQ9nO/ftX4nTcSCsNTXXOp9eY+fXflrawCTspmCjcP651wRMx9kgsb03FFbAne4zm8OIx0TPaVomra9n0T2PgeqNrXEuU9CTyXDTQWuH50PD1efASIsMFCQxy0Qthji7E+TMvhf+Q2KEjBbcvu1PMSopPLGRYmRlU4XwnwZAWbUUXnBwu/1hKzQOiI0SRRa8r5mCvEpgmNWQi8x0LKNvvDmObvBnFqo9QJzy2oHe+P33w5whnrke573TSuhyRXlHApFPcr+pGuriojjJlN5iBwskkUyR3Zh9Gyy+QgCRKKvf2sBsV/2E1gDWCyPypU/aTGa73wq8Gx/T+3w2oWqMSsZvefV++1ROJBdkAOBJIncWbX7V3MdnW6FnEazteiAib7/MhyhhDwdpfrghn5hnEbhfCYElmSzgSOIJdUOYIRIzcQKSC35iADavZjET1UfCtQBk8QdBYUZ1d1PsieQNx6Wrke01pBmaQjxnkaSaPdOrRWIUpZtWpqpy9pUzcjAtNJP07YN/b5twW8F0vzRQX8gt0nxHXk2O4/HLNa5B3cjJfiLD31br6yEWCH3lzP10GrEKiquSfM9EpAgZag5XrvNEnkjcXj/O6rtb8K90U1T7huQMKXrb8DOr+UYcADIf2HKtojn1isHaRIKfq1qJoCYiLtdOGAjjvZOmVnfEbMrNmJcu5/Y7t/f0lgLCh/FPvVvPRCjrboypq8IgF/lS9vcWsA/7Fz52m+oOuHYc1G89IF/VKk0MPN/Vp2YInYGVoIu9kcZQXbXzW9QR1rzeUUEczS9pI8ocNOo3eDZy/lTer3vuunL9RIz475GrBvFJ7ChVq7MXkXAeGJGS33L+fgQoxSA2NI0ie4j8Ssg3Yp/1VnwjDIVAfF+4bO27hJ9JXCGVTLuSdOpOyUfk/FeB3EaXxallHPd5SAkFiepPiOHnQzuq1U0zNu2O94Y9uOUHqe4q8AKvxbLLJ5ruO85OJG1cxonWVBaHx+pgqbk0u6JcD+ylKmT8TwDTJQW+yJQj+MwAftmrLKQPlequZzPE/cIcTNo721YSw5jeFv/7djeLUNkoQU087fBjMQCzD7wu+43l5zJVkBt9I6TETUd0N2ojvBSG2dHfMaa0+2Zuz3XFZGH5oj16/UbFPfqT9hzVU75Tc0q/CajiWj7rZVVX+JJjgbjDh92STFQ0XjGcY4tnVMNbXuASi+rulKsI49DWeIoT/P+q5TX6M1S6cib2XY4ef9q1wb2y6fen4lARvdinSXOX4vf7+vq29MU5RPH8YEnRSSoDWNPhxdcayu11uFWiZcfhv5lkgl0E04OvjOGFOdSkdshysmbrJetY/U7dMpcWnzokHjxVAv2QDOtkYrL4Wdx9Nw1u5dOibGBZHzsSwA8aXZBRqFuGmAnPTEQi/al6Gwp5XMerYNQTZ+NOwclIkbXMtkhSVL90/C3gb16ux9VcMiebwlgh1OHhR4J/fa2bIPfSgyDfFHbgePhXYCxhHyf5ymKHNThNfFj2OUY71LajSZcAj0AvCCF2tFIVdK7bkaWJ4x0L8xkZM6hWNSIVvrS3RROzBVJsP0ULg6eT8dSlm/fAvU78HXIQKjv86sZvNaHVSieyFXMTcqhHRPT4gQ7u9wdNUiiAehFzd3KxEQ11wS4o3WV+/HUoOnbWiVrYMOHGUWQYfkwRQhV7CMNFrO94S1NfRDnD2Ked63vMc/3r8FGhtBq9Fxgx8HRtoYBrId839umfSPkYffJxFexGpg+DpvnLjdovGw+O/yhjHTdNuxfd1fW6hMcpkYHHzvv3udvCpNrIsnQB7l3KgKiXVsKUqrn9TAUTzZdWI9WBPaZJswlfsZUzN7hmGMDRlUG7xXCUqD5NrWeFR28jaAgcLVKK0XADgJTIG9QBn1rEoXqbJizhYflY1j0fsPK/WIRXJnvyvrlnowrSzgf5834hMoClH8zPEu7Ek5sFZlsWccE6rVxfUkpQ4wgrf5ND+gLDb8uBJeDlev47ntGk/Sns8N9wIZlhyue/SYW2hWL3Hwn+qJh8LppiFXxB+vX6lK8hC83/MMxTT21cu0tY+xIZVpsHkdPnyAaeh4ttW3koJlmM74kxBkYCFH8YurpcHMZMnC0pCsI1875nnOewAh3h35TG0Xf8GuSALLqUSC7PQiKspSCpy36q28HCY/iEwvTQMfw4+stOaV9Tb7GhPi//84fAK2L0PURBE7cgfj1ShqUxjv/rPigUCsypnQFIUGY8Q+IXBtJy/JQmh5PktlZZ1BtYR7Y3N8WLCyLVUqehO/EiOBCDB5gAcqz2HtoxpyMwdonVYQ+z0wBpRnlt7pFV2GcIbKuiKhQloSJIELYz2F1Zx0Jv+t91/X5xdxYocTzNEjn7gaHfarM9Dq7D+lfk7cK/Vy9RIDX9DdAHtihZ/BoE5q/a0arNdZg1s1bxvwHPbpHtU44QCEMRU+wkmfZ/pitnCDJS/PpQ2PJIlUayJjn/IUAIaH2XXMPC/ng9SBv6DtggsdNTOFiR4TgZc99TWFcIvG2+9HevWxRBkAo/s1le989ioYEUaFlcb96P88BuFk0zX3tbZRlxYTLt3ZEow9iPV7/a1nqkvdDgAOJlqsJjTlwDc8/y2zKkuPBhqs7me/sGwXKSoY74hCdMl5y0PdMSvT6sbcflpyc3w6/dmAyQuft4f38O/NyczEuySbs8JviBKje/gSAb1INSA87gzwmTcYQpMITfcgjGUm/8Nve6cTlMpeo95jS67lF0uR/5YcBD/fXR74agzlXHoW0qLERyauLHbuvVy2jOznq2SXJmDnIDGLzdIsOHr7xG0aIgFGxPNpJ6Chx3+k9Rc2o7XEAypH6rgM2ycv1hZAf1v5gIU6N31GkZc/TBmH3WLY6UP1DSyj87qH/djB0/tQi/v5t39rhDOXuGg/6/0LKVs7nbINXwjpoatB33G5oA9qQYH0ZPu9UKcAVHFEktowL4y7H+X474ijN/ZZPVOtHXJQ5rhHdNofzN6w2woxtq+ScMLPWaQR/bmAUTJFIONbu/Jmg2zbDMnYFn9/0NYtmvI+kN7hwShFg4RjBHslOqoJBe3gqjLW7Ft86iwMwyN9urijcSsw2WbjpHR2Ah2ATSFWc1Jfwf6j7sfcX6uof1bf3oHV+oyYukUw8vAR/Vp4n/burnFGTtKICqT6vCyHyx5v+sN6GjVStQC6GJt8ZxvmHdKDEPTkEuJrfLAYi+pQomIvH+KNgTgUMoxcbQw0rwvcfh0VQiEr+jr/n4HX6W5R+qTyUfENTyKeGIHnU4vfaz23ds/t+iz5DokTmauS6Ci0EO584TQobzBkLmMzDH9hIMEokPPSsZHEUcP7opLb2CiwSzCuoevb1cct99VaWDzAJ/3pSUVQvqIYfjxQ4P3Hudch1fT9y3yU/GSWf3ofVTjAmbgK/1Wsm5fkKFunpB/qcI/iMxpT6JoXFi0XZHmM//P1oFIuA6fAvgLQkDCoW07gDxNM4jxv41+6dtHGA/bZ8cAjuXH63jxlKHUwRG+c+nwaXPcUoJFhht5G2d2f2ZGyo/d063Y438PVI6dIJrmgSI3MpTRwbJ+I9N0kVhuVTSDGvz7kxu6WkegQTH80pJy41b3zBJQahLhdR2A2l8mgXeOEvsjEuguUqsrhCTalAqvWPo3Z9xtPM/jWxoQZOMSLO2Fs1w82c5JGvxNKPiXm3NjsUwvnAh9Vqk+qlsOSgrq/aFpmLTwBZiTyMFOHdtRBpwjhZiP7kCypzqKnezgk5vu9YgvC4oT07W4VWS6NCj+W8vnw1VHvrpWlFDtEQAryR6+V5zTnle8RsFs+dyxwy8HmZYi3MYThKDIGEVQzsBI8EGTCIdXIty0m1/7x4mdtLCGQwvmuSjBmogjkRf13br5QnXFdtD5oB9ZTCah+ORqFdNnrHF+XrtXG+bHtA71MIk514i9Wj84jlUet7+Xjj1toNcP42qvhwinBtv7a/jT2ko9+nSOLPrUBnru0pRbQPWf2Vwo84wSoNhxHEdbsqihkgubLqWxQ3XAriRwWvuDMTwMDW8pET4MByKqwLMSu53J9T6rHuBAiPl6KoKqaH8t/4tWMbQirvqjDCeJvLty+OpBoChENGY48iqPdYnoeRPdB4d5bHQNy0tXH+HKfA0RsWJhkaWoCBAn9TaAGqAUqrAo11WjrvD8yNGh708EARxPlKiTxM8he7XyPJxE/Wwb92tJLABNxv9VjpycN4HRYkmzdDiAh8kXSraWBXWe3nmWi7hsTmURrSIrQiajkUqS+J30rl1PqwwfKWNBu051gVT8LiViFgWgDjUhNiFhVTBnFs1oabDoHMaukZMhwo3G9xQfgL0GXwnhP6RnZYKq8WKylXZN+Ll2qC9Y2+F1NMhNlHX4bpOG240XnKeOyzyrxv8mwaSOsCjsF4+Kbjp8+1n73ZAIZyIsmZTwIfQ4nv/vNwEOr/Gni+L/bhdmGtIt/m8bfocaF+XdcGRDfUYuotBff3+5yzGdzx8+zJg158oiURyWzyT6TliBB3p8RxR99ZGFC/t2eoQUKDLCrkaDaXd1xPZY4o8dsKhlIOXlVi5jqdEST7PfAgtRNmumL7OEq6IBLMKCgpCNerA60XlVd9FTBRuNT9EFAoLw4gY3c9pui+J5rzw35LdByTiKvvWngT5TaINoG9yyIuTYPZPytRvddHadcm0eWPNWu/LakWOIg3dcBEsWE2ejkKzxLxyLiQqbxTcb1iJVWXTMlqNqbE7CiG2vHkMAomnwUA8JOajzsB17vD0MZC/PSzzOgOReEXKEJdVdePxTny2urqKlt4MiwsRekYmGbEqxYjf/BBjD+GkaSS6CYP2QPhCCYczgfDpV8ijrTNi23j/oawUry8qW64zHOJqLUmg4hiEXsZXqjI17sbB2bs+DV6KIOc3vnZ5OAZMgPzPxWECsMANpozu2z8MRp6Qgnd4YPsg+HjOLvcx9+5RT7aw5jv1JpMlvfVL6HIOCdOeCT0K5GjGlUheRllzXLdwm8Yo+YvU15klz7Djg41QpNVRHlwpkfIb6NGTUM2VqRoPiv2eCvXKUvvdf8iauAxWvw4GfmlUQPNA/vy8LP909x33wvPz1pfKq4iMBtbseUYX5thObp5Wf4gGf3eE/C2ZlQTEDhd+bhv93P+gDDiRb63055hA5SJxZ/76Fo8CNBkXtQ9mLo/fgzCa/FvyqGyJVaFDhlABMQvLLV/WGK8zPnzZT5cOGgcqE2sPFGyOiR05OXBeGZTD1rFdJkzeWVEdLo6EHIyNuHVpb3Fu6Zl91/MkenH1h4Nbbc3rYwjVVa3AiCUC357TrHLVvJU5ztH2vW3gZG/43W8gDGBcNsmKZaHKaR5XlXX9lzUM6NkIhYnJYIgyLrBKMoxUrEoWf591AMjPDZbCCX8vYdrWz/cXZnH1UxmKSvckT2/FBdYzaHaPFPfKNIJhokiVjN8Fgkqjjd3WGWWscBEq48NatTnDlj2XX64M/LAwbw+YSIXbo1oE03WH3OJnYy/SkVeL2B11xt06/JIDsyNpVS5h/JdmohKMRCC8Vl2RbbsWQvtc6kbfJYvX0x0nfqEj+gZhxJkDYQZSOibUCvEC8JxgtBXxbvkONH4zXj1M/J3bB6rPBQV4MxfKVIVX9G0pbOgJ1NokOWXWuhBO2GGmWSRkY4dOTxuAVanbVUQUB3X0OjNAST++nEO7JPf4Iqbwy0D9jd3vm8KNWGtCtBGNLxiI5T0/B+rRMaVTP3yqFhOlDFdt0Cxbs5W4Xnq970PsjQlHS9lzkL4RiuPni9esi3S9MgYFPNYAEI6YsIjFNPixuTWgbXGb23rfhPaYtp/TS2Y5yw5Uc1UgWjsOrAvbDSMqeDywbmD7YJCX3j7B7y+LrxzaZ7vtaeGMJVv/D2OaJVO0ycZugv23j7aYoAig7EHiF/SzplxFm05Stm5aIJ+rODiUgF27voBteARdv5oWkOQHKgwkOHnVbOzT9XhEeCaHFnWeSkYj+8rtetEOwObwl9VNb4qF1qNPa+cMnTiMmH6Uj2XzmgcxOwcdI6sh/V9wCRq5rhAqDMbqBZ1jGIPyqbL4PIB+TzWa7KksR7Z9INieGU+6olqWMU3l2OTytd+rELcpEvhlFnHcp3uzrrLYVqtgz4ZyP2a0SBb+b30TepMI68X7mBYpYML2cP93rh8LKIjvh2sca+wMErl8b8t8VDyccCD+9dWGtgImhukeqkI+IFcdgCoA71sspZHomgO4/kIxjoiPswY7ENRDDPwqjJR+9wMJgFkRRMwrayY93yTfDgbf12sxgHGIvnbhN0mphj2cOk1rXuzu4+YqOog4rLgLIhTc0ktHkZWONRvj+DftKsx0zQNIgRWFI1iS973N0NVAZjx0YNNevB0A3fr/kE8fPnwN73wQk4ncJ2nlnCm1kAxeC78mKNtMKFRrC99beaQ6b/PS+RlB62OTTO2UbOqlP9Loe/TZXP34e13Z9tbwp199v0NmyHx90T1Y9gCCjvchluo8UziYT0Pg6gNG7nG5VGz2Cppe+GXmRaStprQGFy5fdHNbllzBFDoNZCvDerFs20T+8qhj1sd5k11yolb19i0QPLSbSd8OgAWmBxb9NsgvoskFiUqmRVFBFE2EIiPZCmpj+4rk7MyEh//2ny44Y01xcqk/9PcB1bl7/ebSBtnHmkuMIY7F9ffmGBper+Ytkn8b9cwN6Wjwdrav6pVwZbSRaQqTGHNh8qhoKueedGg35JpdWhFhnTryekVGzD+gTGDRl345j9bFeZpoUhmuhGDTBwtK6XMikVd31fz3oqSYbLp40quR87lUXiXlSImrv2+/PzYqEZ0N/bUF4989EHCIUjH8Dk960eXz7LbR0AtyGPCrz+sq6/5A2zBadkDYLlf4o63llaBsyd+QxNPjzsdhrhPloNq+70SUT8OWjh0lh75TDQNotoCY9K3pg1mRve9N5S2ujAwxLcVJMG0UzIr/cItjQrn6FvFamJe+VkPnsOd+yFyB6Iw3uBjZQV6YCJMKeY35gtvhvHtZUmyaXEI7hc82PH6t6MkWPeH93QcdihLWxe/poW4e8eVByUpBWrzmqzWqIMa4MvnBCzNK7MZo98QpGiFH0lVO3UvI7dffVJNKuTNE3l7ySjYuxMSzc4/nOIrjXZ3glEAwlm+SAm7O63f9AfP0BD2+bTbRQTI22V/ZfKFByI1/KxEUZaaY5o8YGxFldJ8pBkWZ9LNvzLxTfmqSPYmka6vBe8C7noUPp0+j5rgpFXLm/3LPpgvGxKjin6jtacB0ttAwQIo6c3ITBinsJuiDBXIq3R3c7eiBZOWl/7NCxST6FMcqctXU2/We22GOfhNUeZvTCiwBznsVNxvYmZoEdNWYovxt42VwUAgQS/lL/gPww/YlXu7EELX4P9GUbgliaLFTO2c58F17DfoqzctxgmRRthN3J9KfXwJyWKYJlHLpS1m1H48GGHYNM4V4wvq6sf7tloCwq/AVy5ifND+w9Fc2FSUIuK91BF3Oizu+wsV1/BIdx4FtOe9F9CCbOuY3trK0REkBRgTD0ukf88bENSGveZsW+tU0LHOprMZT7gh79R6ICaaCakJ5/uEbO17Tqa+5c7lO75MZkUCkgu+6v4CNEoZCr/RWYKIoliWPkFHNSvMBf+PMytCwNcx84A7YLvocLr9VjO8vMYe0i8uowBF01Wwl/mdkJNxEbyuE0HJXeikxnMxokDeBSDgx0GaFtp/0g2pbNwZJn1+lAlo0PPF0n9nU0OBjjOFMUhwWESzUBgYhsSTBAxwnqwhzhts2+gpbxu+vDgm4u3vTPUZSI7O8ySv8lz6OvK2KHnUvtm7zR6YzHkRi6h829Fy9RLfwUY0z/U18+OUP4aV9o7Qb/6RUcAsmKEzn32kQZ9/Uwo+CO1KPQ6mnl+/LIGslay5gTSqCBKTC47u0/dd6k2xr26SiIO3EC8FBbUPejfRe2neJM1kE99CHyZynQpGjTB5yPGoCGEKDBBYo6mdNAZFndOt0Qpdk0FqXVmBaB7MkVDDqmhfVds2aGi80pqg70YpWIpaosCyfnPqcb+FEnyYUnSXlLwiVmSP36zOPQYFusVoo53MC48JHYwDM3x9WjPUUSv+ViXjzmW62VeGqx/wzT9INeIg0/7NMVvtRXmJXOA0BWoGD8u5qn19oADYn8zrv9/pG11E+RO63yHXB80hXOO3Z+BOQqjChBdkly+3C/jCDVrHhcelhzqkFNWr9WLMfZ4JXgfq1SO3r1h1Mw0xg2B2JUDWp4GddctC+3q5RiZ6nrc6Teme7CPVGwuvymk+DgBKcZUgHmN9m9+U/tumLeF/zisFfmw0v5om8pq2NQ04MwYj42ggk1NJp+Dm41RFene/5rt/UHqGRQ8yiFecb/EiscwGlX7Ei+FpUOvFpSWzvL+w/HwdkvSSgAhr/MTmhk/kS5eUstobDYmtfq+znY31C4EzMIC4AV34vzHghLXmMLfivzLXaCXqiFzd15x3aJuWGScN323taPorKVGifd72yHWvH5F6hH5xRwiz99LLfYIfGLR+QM+OoMq3FleH9Kpi0AoC3t9vfqhacjWP/k1hfSWZy9YFruTbZ9pCZRvtVBJ9VmM9SwKjEOIKMAfk67XGiZsZ9giTb0nWOQQdJ8HoQ75DiIczBeGbbJnfJGroP+y9R7fjwM4t9mu+6VvMYSgmkWLOYUaKmRSTmMRf7yqevtfPHnktTzzwWd2n1aLEUIUC9gZQAO3ESzQeQ2mtl8Ygd3zB+qI6HKmmfxgrX8wlOA16Nr3UlQ5WEDJalzxuh1Tw0/kxLqHpurMNAGwuat3pkiel+3u5PGfpBh7Mr/MBH8DViQyglz9Uf7zWHwWVvWzRGL3zMbhZKF3/zSkZcUndmvRWUnDi98tLQ9iRFyUpEqFeuoBb8SIhhVQRR5DzPe34A7355+2YxrvkE6sXQut9e0Ls+DrTK80ZQUA9jYBZckqwlJTw4VLZwzSp5Wi8e90Uy/qTNvLCfXZgXjAdxLqC8qFJKza1X3umju+YbKqHWvV7t6YZiKlv41EZlelypBFQuZyGoAZgV0lefzjHOuVOag+Cph5Cb2GC3M/oxW+OLqBIyUw0MVBjstDPb6n7TmUCvrK7y2P+afu+onIwpxdcvD/Bs1FzTqPfqtOGeJeMZeeITZTK4HIAy3aSTEpvpYke9754AwTrzYhTOP/4ZeVxcjV6ItMxS7IsMnnyWieNMozUEmebjyhF5rn5hFPFjXacUT5DYWrSxM+g3rQgF4JGEV8ASkJf8N7KFgW9PMS/DDr2PSac/0JfphyUXNqOjy3iFkpK73pr0IOIR/IipzDTi60FC8hC6BDqMozqRnDpa6J1QhX8kiuMfU4hj35+v7883c4te9fl+PVzYEbq93fbiPOTfLIR7uGDO2HIcSeC3UhaRdlKeCvxOe9ttyXjoj7uwmj4uPL9Z+T9WK/DUwYSb3EUtFA2w3ZigtOzukeJhQ9Z8S642HD0WIA2SoNAgiMqly6xdzvcFbI81qAWMm4Ah9iqC0WGyPM1GoyyJLG8Fu12Y+5D1+GhokMJAnphsswJD9/HV9Y6pO2L5yJFNuee+fEJsxW6JkeqfdYVFVUNWW+RnKZp6byS/G3XX7wfCFnEABJ9si/e/8iPStfS862CeYCxGhlH0cgzzaIcX75o31UH5owOKvSVJf4bbtgAvKfeFZXG6ot4y4RbnzPXjtHJh+ZQYQiMwzBhWOSTr4t22CTZAYzDEdCHn9vrIwiFivrRjmbsD7fmOli7jnRChKJ+3nwsy+rpY23RLI2z1nmS9PIgnNoMjI/4Vj4bm43uFjCqo6owD+Fu2P1LBv4I+aWq7KA2aeNHdkwmn9jnb82VmQeY6dIpsJeT/US7drU7EbYyn/1UktBjLZiHkitVq30uoebe7FyHLFm28TB2/FMPytwZ08FUaP45VsT0+h3yw9aOBahytyhGyZ6dYo8W9l1XC8805RTCW+NY77uTlKTYZetEqe5LmHsWevkYcm6QELRinP1bqK+uO/brOLvsJXO/rwqA0zUL2THfGWPp4DrZUTnPJsioJ/TSaKd8pdbDPR8Q9a+DVXbpMAzT/GOJtbYfE/UceR7HvHVxsNGFWMY6vYRGl2cxZR9fHB00EQyGv0qyYGEThVuVSk87bW7se3Uv3ANIr9Q2KrLX5ur2fEjDXXJMER9M1w/ExRvtCeo/5nOo9mTfIIKXkWF7Ikb8XaGftdbbu5oJCwyCKrJ2wSQJybJrT7jh6Pu57MdLSgTaUfME0uB1DE4E/c8o0Q+xjqfwnER8pBe/FxFCZ0TbnsfvczpKKU0xv73+8U9TL10aqOtZ4HRWUnFqLrEBHWK5USCv7jmIj0Um+1QNTArlemGMXzzMiP4/A9I0i1yCLFMcx4UGUZUfNsC+Xk0eqPs4P1dkxd3MKXMUZMMYPvSlW2Zqw530mHiSlsB9QdpHfcqyzAPirSzpq0F13UmKKHZRoXLsNe9dBM4XT8WhhkCptTf+KzwqrW2bhrdGtF/3Kai+U2dKzvaA+9/KjgnxtLLwNK+krNk5MuYGA0zi9C9mnXNTavoOjsuj/PFDvELfSD0ld4L9QheTEWtQYrbfO2/i/bHWMFR5wrwQ6UlUmmOFr12q2cXkKTV5F+P6K58kdSdLlkBTmRQG1t+8DyzRCDRFl6zDEJqthle2jrh5h6MZtAhuK+198wswTAYZPgw2bxoeEPJdnG5tYZ6FxM/6XRSrwCA9GZBLe/G3Zzh9VupefZNd1Gl6d/HNmHGYkoFpAWWtlENY6sBXDnYBrMMdrvAZrxVH8gJeNX6mC5PSbYsFZTjFcF9k/HFFa7ByGL523a9xJ3pK+/hZHim+VvGStXOCz9NetnfmB/mAsdFXhLOw6YsUKb7a3uwBOcCVfj1aPOhOy4RXeIkrWDRGe3oX6g8y5NFD/uZLWM5CChzW8JBr7A7xByEci/JvBTBW/9g4Gmo4zIEfQxAw65ZL0Jqmpc/kU6/0NezlKKqS89v78AxqW1APTJrnaiY7mCvFpwxf/Z6N+6ft4Lwr5JBfqfqukoAv1S9G8L4GjFkQfKn4j35R83UBCBLxAkT2DGcSm/azBKUM38pAwiyOiS6+DaKM3YgyXWB9SjeN574AYPD1IsDJBljK23Pfi37NkwSYTc4argBzD86hXMfmHy8ec8p1HlSLfPc9cNmYppcIKgb3v+NI+DP1mNgZrM36XtQM8UHIQpJZ8BxHt1t7bd31M4Ot8W1mTLPTEV/BbNTsRUcUgijPmiinL4yjMg3sPcM5CB8+Jkl2WeLE99EA2J3ATIiwSEJ2FQSiFAb9jGAIn2WxjweMZStSwAUM2xOkkL7w9sHa1D5t9QarH5BeS3eh5+cSTUYwYjfZS1MlhmVN/zbqr+GOtDRNq4DF+fFEorurqbVwKGCVd0vI2ETD6woPSQ3dTDmb23bN8MRruHLIoSp0l1/SV+Ww6m03SXM3ZYA3XN0u0N6Itu8vBHp2cZorHPuDVvm1/BC+AXeoYHkYBJc63pv+Zmy3ToXQnToYekPihGkWq5VfCWeV1rGEHYI863FpWQgwgroedF/BBd9a80fKdNsFNmd7SWxKBvMaBOrqJD/+/XIfSYgiD43kwKBW9m1xNR0/tDo4nsVijl9K0Ind9muT2YJ3JW3DSqGlFIhs7NUqYNNA402BipQ7SsmuinTjfCp+kfq92XL944EWYxjFeNjU0PsAbO8uAcnX31SWHr+Ifwe42b1oMngeuzPOOzBd/sMoXg4iR/IVveUP9AH55MrNK2LabtlG3h2O/eEGyf7WAsYcShY/kfkdxK9c3R3r4pSexQ//RBpe3LQl2HBkB6YSBoGoKtFwaJ/iGjByJlyxhiff9SXz2d8KYsZfhLybTYQZOxJdimuSm/ImdN8vC33DdZV9kv3V62hHUZYgpE9v8PYF084dV8LBWb0L4K3XRCqYQf2iJ+BXhtiL/n5aCQyqv9qadpbmR+lXFAVBANT6zIvkX8Z0dKZ4qsBVgZHPpKyHrK2+AdNIunMsYogDy7Iiknxd7Rm5EazaLEq7RwBTseMb9EnA/hwfn/mhE5L74rN9BmFVedHeFAEEwhHsW8WdH+nC8e1NhbaL1bS0AuO5QQfXUHY/c4tGgrSLsrSdbdPLmyvAXDRJMsMUaIRLoVSqp6r95UEXxDEv+bka+OKeFRKsIVHOkd9rT9xGCwJY/qOmyZzHaN2GoLdmwLpk7pINPlh7sxN5g4VRAw3mSE38nXxMz0p38Voa79rnqRzwcNsWkLbi87Abql1/72H/Dg4MqhnvNav4QDOh72Wbl8kJf4ogHJrqkiSHmoAJRpZqAXxvfr8MDXXTMjtVy1Sh86lH9KEYnPY2QiQ+//kGCJgcKQ1GblIkiUzRs8OeEUsu5fCNcGq863jNS9horfjlL3JbM9Xk0m+k9h/IXha2/U7Sok86YfJa43WB/AgELTLkP21COndaFDlhwwBzkVuFMqjZO4QDsZDhzq+qAU4C60oYVpgwsJpCAVUuSZI01fP1E4itkbDh2nnPV9h0QXuoWwBuQJRxfBAjLJt/0IxfemtMO4Ce3N1K4XSV77Zovq1ls0i+PyZ5Hrkkc5BCWba515K2w9zQsyA39nYHs9VD/bGf3amjtmp40+wMAigoAfKjKfHXLnoojEDXX2jjn6tqqI17culJJytcRg3DMKQXFQT8z3yodeJdSzwNof37GjUPi0D/185RR0Nr2bWu666sHyRXW1e4txztzxX71L3tAE5UbjvZg0W8S+5z/ZAuzAHosVKLCcz5Hc0n+yWACZ8GTWlJk/ZLNgRcZifvSpPfBRTi8yz2cIkQBKAmXtifOvaEeaurpVYN/c1pKtCJB7R/6/UCHLeCF4AGret1yGsBKwgA0o81x8wq8YGz11UCNADrAHH/aohESyA6LTAAgw49vBCLudeYri4+djXX/3ik7f6spfC/W8vrzlZ8ozq0/yJYOOP80O49unQ2pRwfa7Y5/8vBHWaKXaeFeq/wkRbFFJNZuvNZqK+6DQJ1EJeSTiWMoKqmCdbDUk1JRmgwZ1UcKOaarpO88DDR2Sx4z5X2i1S3AjM3E8lL02ap07t+L4s1y+PW1sBK/MAg6gJ3rMA1tk2B3G5UUdrL/FKf/bfEEaWQyTVPpNenLpFAwj00dwu+tpuNYJWigFq3HQsxyPUe3+dE4FIIiJtqo36bZHm5OUODyJoa7Q+Mut2ZuKyuRHvPy7yOAqSzk6sK5AWly0f3hk4nwQW4e3FdIhIv+ejxcKE7QtnstMotdZdlnGHKcs9wKkEpNpt8VUruXBEPnWLmxS/9YsDeMGdhwhLn0lEPkWWa0fQSX/HA8D8G0XF5RQUleb+JMfXh5i1SY3OtYyzesuKyB/yKjY2BpP7lMbqfvCjSxOxsu2/y+vVx130MeG1w9yc2MSRC/Ab+l1j7VpKnp+SZKREL5xKvHeYqcl5lBvOVQxgRdB2YSwAjVMEjbmwKT//9J2S7gTb842v9o8DBZ+uPphpJUYP5F4HiAs6FjsYKFk9+lxQUNZcJ4841nJ//nB/5Dyxj2jECRhAPpyZ88EXzZpz0Z8aQl4Zih/upSrnyArnaXtmaP1WpRwi2jHytPWFhNy3qVDXEXxJAgHOZVP4XM5m2ov3LQrY0F3veCADSBbxKC/fSxVl6gMIOfTw9KRWzcFEDEUzG5MTbVgyIZj2jssJ5LigLClhNiPD9pTWJ6gvz99pUN+aGv671A1MOgjfeP/Pt16n/FsUWkOmzPylW7xDzmj89s3iG5yhQIbN5QSAWmnbf3klyqNvxFbozP2lKkQ7uP16XnzMwavNBYPWRkhPoRS9woHqJ2bNJPRAx5sq3C0Od6EmXyPzSxOcy/FVT6b5jWepvGp8loX4m7rUUcLVopFEFA5zLVAqZUxHjUrr97dLd9w562n/keChBsWT4p3DZwdeegasjS2eGHpF1HdlNRFirKR986/Fyo/3zfv24ovsmHADXaTuYkZBQjHqGZhneleYKgUyEoIxP6TkGKdZK/I5PSPWUIv0DYcs8z5PH6aFEP1a48+5Ow/2xr0erQXJRh3scvEfpF9XQDj4IZoHWiPzdqWWLZplII/N2I0qRj1VY0om+rXucKrNbucwz+Norsv27gVumXTvrycpbOhGHmgS1fu4nHC4Ol9TRRyoMRhrki1qR8Fcjzy+OLRnnxh7+2K67ItdtvzydxslxncKrR/OoWPp+hwdWDeiQIZJGrxeC5b/Vm0rq3bbX6dYHHA2O1e+iEXSfr9DE/bbBHGWHEFi7RdCVq4LajJ59g2aoZlL5hqaAPXyll+Khz0QUEdwwuoQTX9Dfhn1CDIhKFOQqGCuxBCOVRc2dWCTLeW1rbPik8c3Pc9X/2QAO1IP6VVqBwEX6xX4bDApdBd30AIo1D59hqrZFH79P6gUzWi5wP1Q0etMSTQyFdZlAJZA2Qqr2xLU0tsfQZ5doNBiv1fPpoml8hFNTFp/V2u99SPDzPVAYkvZ+67rBatdgQcAWfXrF7uvVwalZTqBFQRKDCHt71wLmUkz18/429NN503YLx73wAwgCyo3DJrit/EQkpGZVuGAkSsNhTcPf3d6R5XWKIA6wjMlP/U6daiawRID7iFueGIWbHzJ/MvZpp7Xn4nW0k3KJCLRbLZMwCta4Bv+ac2JDfqSs8TP5m8UwHs5NOc1dsG3w6LC4CYeyAhuVPjTBKsAKul8TblZIx6A89yqYCzsp3t9lynUYUpP93kNQq/vmBbn3ZoV84NKYynLdasJYzjZ0Cc2G3S4fHqQVQRvUexRLuJMWb4Ygi64D45UrCQGOGBlNN1kuVQt/jMYgLejgaoXUELqfa74zxjK3895lAxYMXQ3dHvWtiUomzfDhsE+AYIJr+xi2JOjMjmY6esfzwqORfOYmrqdddcLtiNrH4njBN7hQoX0C3atr3ffxiWbr9unDETIGxw551sBSK8g8lILQua0IrzbSi6ih9Zn64G8Z9GgQZyjGGUiID0CDAbTe/x53nHieAr5Sjk2DQRjTtOYzU/6K0sA/20E4Uq1AD2J76g9xzAxlCl4IhmE4EpkFA9Ai9JI2+DTDKsLSF1eD1Frn82tsCTm0+dwGC/X4tx85DO4ii2aQ8A/VbjssY0nd8hAWxva+/MmZ816E+3P0OBlynSBhn9YETRplxBZHMjAf6yqvLb8f9j87NWDk5kXXy+PbBJnZySdvF5Xuc31RfNnYAjhmRrBxxdNaeTTadffJYwDSPOy1UB4dUHC9u2DsimdT0IYdrl9Ya5OzsJoPtzYmbiUit43TI1I/g789kxd58n9X/n3LnybnYf2Z7o0vsIYY7bf7E2jlaSJVqDM1gSQuxNufbjuKdSyrRT0/vsgheyLMPGJeptjn/GwUFeBF/ISu0U4xbul1b5YQaoxALwqJ31jhbr2v9m9C6nmSQlTBIjJ1SeFurQ+V/+uDKONVxXEiP7qv5ddhiFqL2OeZEWGgQ3Fjmi3KnIb0RxRnaugIOkWPYSu8RosvIm68ItmClAJ6V/hhsZHXQK0C95RJ0fJXKM8V8i7LxHUBrkSG5anc/Z9/D7C+G5i3x11fzUBhb0jk5KQ/76lAP6DHgsvJrMkGAlnbi+Q/kZMuQT7h2WID82inweDjudlaJ5AxZ7SsvaoEmzARIJ4z7ioKf5RHN4Z9/gJWBE5+IkwVHi0p9PkGaTG0V7Is5FlvQUlJDZTsuveD8LkjJYtAayB3hG6ZpHsLttSY08CLylpqMNItUWHb0lRqdrDmHlcrz+9QVkzpY2yw6SQ3eM4jtelxjeRfNz43h7cRXSBEhlcABg/+ZSyWwe0z7p69avDjFwowl6HhIm2wjgkVN3qlopGeOwPhJ7UJxRaVx97mksbgvg7za9/hsuPR4I0+yf6TsPKBl568YYRh1bY4uoubpc8jUgYqaHvzLpgMQfM3X1dUxLqj48cq8R9w+ePNC9VVKBnyU6N/i1Q1zrBve0yYMAtkqVkFKcuAXb/kNT3ruUOtWgwAo7Z9/kl4wzCoZnTPvhTPsN4JTBWn6C5Pd3w2ZzAfxRpraIFPNIwO8hbcZ5lQF1oz6pR+GTGcGScobe3uPxZzvDY1r+QFaMunPH/9nYmy0kDISvdL0qjGIc36gG6wAwo1KUFO+cEr4xnxl3ZQahFkE4kS4oa/v4G+d+n4urvzRK3ZSro8JO038nOirI6POvWPPN0a0iysfsUoTRgGhShLU+56cQR3ypMHhRJ3CjnbT6kSYMPqXOdSxALO9J8vPk/5wFHHmSRTdn1W07AmY9320WeiB8SJyRnk5rphPI4szDCcSRav7okP+LZHyO9v7hjhm6vA8pVi0/uD5/t+6WRZmb+GrucJyJhNjr0aauwZttYIye2xsGLLHXqOfXAH+oqfjt6zva4gnrawiA13xgB+UWq2fRfADqFkA0kIw53lqN/+CXdBRBpvKXhcexyvR/y53YWwj5xcNxCzmYDxUclgmfofJ6nKvnxsUiSsOK1ygCHN8Zr0+ktmE2BLsk4cPbA8tOMXMBKwFQLLoyhpYX67dIxudp34tcnv5+Ml61jIdcwvHzGx3wxPk662gI9CHb0RclR511w9YMt36dkT08X2LgE1kGVNcM6hwLZ1EEdobaaPFrI88yH536rXhHtrU5CudXChSoORgMmck1Xo7Mw1vL3KS4cSZMAU4zNh9jTXwkF/ARn7dTChg6mfFEW5GcWq8BqLb4rBFzbQ4Lwo4mpYVQJ7T8OU5c9n9l6ihmXRCzDOm1Dw/TeeSj2qzgRMJdn4YYQpX5j1pt8lC9UGVufgqIB4g4nmSmsfXh6rUnmxj2j6t5+RcMJxgN4R7jMPBWRMa1obLfN1okn12msrciDRDviV8VH0Znq06peLfHMYX6ZbZza9ltE6EzQ6sZ25waL4BKxyX43jqd3RnN1xi+R6udHCLfEeLaWGEQTxybDT18hIlrkfwLPslwZgFrBofDf9lX0BJCgWMq9iInFZ5+LGF5DhfIp+y4cvVqzHrcB+A6gxJvkBpEO+YwhXykKstxdC4hRM8t8MBsxnyEfYDE82MAQd2P55WikeKBzIBETWvJ38EPd9eP5H2WAMNEf4t5sWrnAjyBAuCK+v0AOyLXZYG4Hbieqwlbj8wcZ+M1UNnbVVWNTM6hOL1ujnmenTp8Pz4gt9CH+tseEdYfUf6jUn6xu/mc9nan33jEaasu6KP9QskaM8zMPwn/sHSBbeoT+jVP1K25v1MGRv2UExuWNkFeV4pUU495E3ZFmx0IMzTHOJvg5Zkg4imo5voCHLY/Ya1PaOIkA23MKdklGeB2k2fjVT63VR70uDSc9wI2mGq3y+szBzgCAvs/3SM35qW4O+zf94VlguQIil5L/xyszTRPHaV0coVV6h99l1AZJZiVGU5Z4TYFT7+st1g7tfFrXIM2ZND14XHrE8V+3+ftmOkD5kJysMFvzI3rDHCOA2yuAXLF7rn1175LWobn26hmRgewPvYIN6qbPivhtdQ/RW0cYcRgr+Zbil72x6cVrIzVXHf52FIpU4aBvmmXvxXVF+HvdoPmKGv3xtSjgNtvxhUvRp+0mvql5tz8/qdyVruBhUfi0joVgoNiqVoQO1gcJI/3m1nPPjHdyI9zlUHhXAmU6GCbTyYfTmYZl8E3843teGyllODV+c5ufVL/dYCGmr4pzliyTeUGXmBVGHDgNYtkA/wBVhzkJt20X9RXaoIIdcXdgm35UOL9rv8MP6T7PmpXgaP/4raXzRRwQT3x1/brzYOUkNOPgc4l8oQZ7+GVD5A9Y8BBN3DupSGHfAcQC8E866/mZl54duaBllZ3HHX2oBZxWSBE95e3YDawXjyVSdNGbW497pe1e9xPRovxrp9uHH3tMZjFFyqlMXhTFzo+r1I7Axo31XkrMV155e8K2gh+H7ch8uhtAy9+akSA3RhWkN+cu/RNzJPyzbJBddkiIVuUfp09cTr94XtFtUAxfAuhoGrPnwLQsu0Up9tQDjZDRT0jPaPlOiygX1epLk80uvqyZx0Jv7q/vkPMO14301VzcxUgvG/d0b09IH3lrbvq8b5b6KUMpuTPhYuu498i9cngAVCHQmUdFiQ3mZj2EPbB9rgs7kK1sEDyuPlpp+psl9WYU/eaxYVytPouZLUBvLipJmyyvYvlfSoV9b+kd8bOiXp8nvm3I4If0QnTB0NOw5xp1l0MIgoIkTlI1TsY5SHKm5QUdKv+qr7o+h7zzxMiV7EdVR0A/AY9BVsJHXwxRjtxeiY7n48wsbckintkhlrpn0yc4oqxi3SwfufQIym/PD9pon2+lTVcx/z9EWSNQqD/GMkkzGatSR961ni7slQxhA6GMCg0W1McGwwbBY4is2GPUnzZI98uJrsQHX/E/ehFSvZIHWj3/cDDIVYMEhvXlpPzF5GzzqkmK9SQH0s5yidvRw0xH3aN+ksOPZa37A7QFS8XySSFrv/btNh8PCBMpGAUP0RRcRf1cthkmcK+TU7dThUB4qhCIlFrHzuKW1tB46cRU6Qvxzd9u50hxY12+0bQqfiKr/YiiP5FU8Bq3xYEYNFIoWwOvf7xI/0mHzS9+JMsulgC3V/3DZSLG54+VxVWlP7M4gRbKTInglr3H0+j0ly0yOArMIAi/x0cY9dIVrzkuPhvdfr1kjDMkTWQvBwY+fWwW7eICuVpqMpcAg95SJr2hkQhDPF0Y6A4jho0TAF78HeIhVHGlf43wpjvh4gDb/U9cn1bz+/F/eJIKZwdo83hwlrUIEKhcaGrlD0yb89zPUHHJq7JPetVS9gFIRKd7vGvmzoZAV3liC5Gs5i6J3WRySJAnEFJrRPtz6TQ0fJxRrYM6CYt7ojVxv99VWwioKoah2OgGwCh2Ade9J8/HTJgAMfvPrXKNxHY2dBxiA9v7kR2J41K/HfEALI7M8gGI+TxaDyIWA1aLB1KjlfrUysu/PEHvuovh9PGV+Rol7p1rp8/DinGGaouhGX/WnmFO2xjwjdJDlKHMS1DIyiFVfiIrbu9dxuZMbo/7sRAh9SnxAJ0y7f37P3foxQ9QpJ96/FQvJVcl/LAnc4/PeJFgXkDNZNHbm1baOqP3lPozImkDYqWzYVWRiX/kH0i07PAatzVtbFD5eh4YSm8S/I9eDfX1IIsYg8BprmvIDggtDKhNxXC6V9VLoc+D96lX4VU9Iy2/BhkXCrQF6uTKHPNl0Pob5pnUwB0Ea151HEISG+WK0L0hPMl5D0/ZXUq8/X7pPaz2s+grWmeiO8wcsyjvaF7dnX7K1K465/Y4Dhy5E4bgLtBnZ3xppQjr0fqHn/9Du5BVjCEp1Ja/rCjNgJdyZdWRvwTHafxNhRX6YRm5wn/kMXt+V6M5ziLEZNC2xmKaSRNG5bMJtM6dVvyj+kNdP/GFF2XQkPgWDSXpXT3kCDPfBzmF6/ldsAUbb38y2PZ/PO1t+h0ZR5qt/+fuLq5aO+bE3UwnRvL75zCDLCEXlG9bS/Yx3xVfEpozTGzb77mP6DKhfN6AovthEUFt3Z1Ac8O7rjc1TqcYH4PPFBHVjRxvwHihMdLowraHX2Sc+sVGWl6Mu4NliOxxj6cnB0IVW668fnHTOV0o9Cae5GQmgHRrIKos0e+VsJ8Lu6i1BeD7QYbZamjHVWupm+j9GseuNw1t927aq2bY4pRL0o7vtEJ5BGwjPA2v6COH924dxMZ5YPxcjh40bmP7aH42v05wLg/BS4AFsvCzcz2mi0tw/oogN0TWhVBO2dIzuClRqOTU+w2R9ZgY9zK9Uj1QJtuj2Hn5fVRIX4J5/HhyX6ZX83Ub4fCdAyhO40cbuvmxB20Ges+fJ4fg3vvtqwpwkfHSuSQzFVlzRSdhiZZsDYA0sc0jA56trFgbMEkIANJeFptIpmHiTAdaBBow2FRDU3UZxZdzzQQ3Ed9+nCsvWE2APaDD3inCk6fXb+8i5dO+RVoUUhpIBgMvbGp1areIPsNEa9LlJdHR3YZLQCeHtzXppZ2pTnfqcwWm8CYj74+seo7rN6YdfqRlahKyRBo/F4LfZzzl5zMffC7IzRSd2mRQPfXXmuSnt/VmsrhcjqUWtMjjzsRgjkyGS4d1B2LaidOt7L3PsLQxjTrn8WAwH/0EMXTyQ3l3uR6x1Qr4uiYIfbdcMZxbiJ+7iFjAuMpVhVf0AZr4avAMMsRCeuzQMOIZFtSpO8U70DY9I7RZ+Ur2KWXHAiQo282I0LlUDcjfFL0IkFn/NHyfBnghXKHcx1nQtkXc3DNQpcE9peiyMqmI/JQPWFhx+H++iacml0mV/YNbprW2v33tIS+tz5S9p14SugkVgar8RGRfwAIDsolUUijfDsFPCL0Ly5Jc6dl7LJR4Pt/6eXSlT7zWkM1uJ3++x08U4BErARTnRHEUukArnUUPD/mMYSksOaACnDC4Yq4a5L9+IAZ+94z/e8yDuzTJizoVJcGWlXcxrfBVCmlJsAJippl0t0JlF8Up8lUtluHQL0SqRehiiMIxqLeWjj5dykruhD/vWhv/2AQ4WlbqeN72GMGhdV0UJO63KkWasMu2ksLmomxDp8nheSgWQddq1a6FIoy9+XmskslihLLJj9i2aBbv5+TWmCpDkfyvhuYTRZORLrWP1MHS4ZUvSby43vcBnfB/mHYmoZqO2BkbOqSlXgH57jSvF1U4bSACHdZsUcWV/U5LN0yP98mvVPfFPVWLUkFgdWwTj2tHZcfBG1JgLvW9mH3D2yYf60kZTkDKKbCtSQntN3L2K3XOg93H489AECrwzRdof8IqQbzzxGh/23bMBOQNrOuWLblT31z02QUW1CmYgZXbnSnyq6qBdeAb2xQEMpdkvQSiCpvGd3h9J2Mo60+THPKBgPcQv4Zel35//NfhvPap361hg02nnJJBspgs8+Heme7Ryq5U0nOReAHSM7WUd0+tZil8we7RbIAmtHEHuuQqivZoIqMZKe2BgDcapAbt1ShZMEub9xptDpGZ+EbLxj1VhPXWpJJhKASk48365r23KbBlItDHG1ou0Ga0JnejGJFScBsDOEj3/QgwcblW+19EcnfzoYTBy9Bk/S0F9T173OI6Sf3bsVi8NEycF4rIUYFpJ4usgEgRdcn+1vMEETOjfBYpOA0iY97scvOTTlmTRNzE1r7lU6NeiN8lfDEry+x7hluSp+XwbrMRb3D+Wglkta4ZasI+dqJuI++S/mvsImOd1DCcPveCfa982xcLwKC/vmHRpItQP4UjFDt6eaOterUbCT68UGlbjOQASezxg7YA7olqFogH0lJk1W9iHatN4zTzCvNj6HlmCNgHi/CGdy1iXbxyuseR2dH1gdwo/IIMhx9eDAGrZl5B+cFHAxTReGIHYDwkC+N859jlWR9HXSC2TMVoz6wSCnmWbhxeX0bDTmHlMymu03nqgUzaJ9vHc2UvC1KwGbGALObYFn6XwOmOGYS7E1rWllwyzC/M01LTHF+gpThXJXt5UmWdzRSZbGNvhdCGF+WrZk1qLzwcw5xMlpM6woM6A0YUHB8b5HA4fdtVrA6U+jyGXJoB82p5yHT6UdyBIZgfXCsMIpwbw2uoxUdzXuttN6tjdvqS/wA/X/WXwCYf+QAyhCEXSe3zlO8Emf0cdjELDRmQQ+Xw0yMp/zXN1ylA6iGs+SKT2ynnCcViRL60XFNKOF4nRWWBur6wCVIl6y3kTviQtJdVOPJ5FXJ25Gfh1fmxca3SjkLJEYrLz7Wk0PP1NvfRz44o+o6dgiVURxWGU7KKwv4g05/QATLkYWyuBDPNXLv3SEe0umhcSqcwdXwr9Co/D6MIkAfZI8A/L2Sii+YUZ4oudI389SvY4s5SCdAbKqpPPz52OdEQZ+FI4CBAf7xPCIpRgkx68ro9zlqBTZjcDjR3DnNES30syH9YKsf8ykoSLJhQZx77wSSBrDuwJRzQ8uVqUqQ7F3jcBoKj/+b9Vw9LDDnr9WhGXjucyrOu6jci7KHwHzc84iDaBT93UQ8LN0xd3JHw/+PQM4cSp9HkxlcZd43cX9DsT/G6kSqMwO0N7r7bcC+rhE/2GMv/8w5B39RhtNDW95y7e+pjRpSZB1aiXG/4zrp3lN4YWvyOLbzfUi3wONVtv6UPd6sO3Jf+jx7Ve8C96qIIc7Sq4zdfZWzvOaojL9aS+E0HgZLD/ipHf/0Hgb+5N4pDYfpQ70+5rdOvflK5m+W6JoOiLGB2VA+/YuDgJKdofraRc3Oc3Bt1TTBsm8nMgV8fw4gC9JMPAD3vfkB0aOldhrSFdEA19zCORQfUO33ECkQgxyN9vO1hxmsqt3b37cXV27In/Msk60vTmhsdNFQlYF/uKJAnXoST4n99CSi3lNTUGNBjHMIpeTs83S6GDWyFp5usUrn5TNf4IBke9fT7jX8CC8cdvi5C4nWjz/GbrVgwT9T7AvBkZeWzQPbF756eEs4fqZQQHDGiQ9/A3XDsUxYh4Kw3S/XcvcHYd7iB9+Jp4NbDWeJ181Ay6O5J4BUhpBOLEzkNVxAccnk/+szxb7rwkzVB/CIgrNX1rXunVPrufxumWgkqRQPf49UaQElIaGu9yGQgvMtcIgFN0ExKrXAiTcwDao9ltEfAUy8V+YOLCRM1D3Pn25WUY0Fu2i5xfU5nmGBW/aeSJOLptNtz6DkTzsVkKtHTPc7d/1q9I6ustM+ed/mcq0Uwup16Tv+IHU0vWqBGnywzcLWTU/e5HLHljVIg/Dk6wFmzn4wiKVfK2bvr13AxdCgd7nH49lWdnbAIsxK6HsffAvuWb/1QUGPuqc9LNtrNprnKxUi7+QDZDG+yHshsevbCwt8BEkpQ64OTdGPAjOF+M4jigL/shVn24vsv7buYJU+RtjtWV8bXErI0RzkHA/PCJ7YyRoxKaO5Zy4XfRGfwgV7swyo4rap8Yf0jMgueZYyGCYe0P+7fU+RvuXa/DKHx+NponmieLvpbKL170POcN1+ofG1/pYgCQKyhOMOfab9ybEHmSHfHU0/gXPalwrsbnBhc3YzJJIH1y0WElRRs4gD+oJxkNo4zZ8m9hjmp/oCMTUHsslv66PNjjV+xr6i5vr+bD51Xm+UtPsc2j/5Uih1qtFsPPvds58J5PsbKWUn9pjZTUk33s0YMyBPK1WYTXXzqVm2jgkZD1M6TL/mT6DRDL7yuZr2ywmU2JHX6h6qH3t8POtbVmH6b2rDkU46N5gGUBNswQE8cp8rXvWRXdS39btQamR8IWjg3L44QYBuXjC8RjC4C9WLuoi4veArRAI1Oh+r3yZ/61wBg9xnCeheVz73GYuumKorauPcABn5e4knG0KZ7CIFYRgdnT/EsY5uRheagxOoqSNbdPMn3C+X0/ZKuDpp8V/5bopMSEmM9/0pbGzgOFEKZ+5cW2I3Zfb1GEE6TnowYSQbvEocTtz/vPWPzI8kC6DZeZV6PnOu2r/Xcm+JSnlyMOWvvM8jkTbKXlEmQv3ixNSW3bfp5eO/SSfIzrQwYPqKz1XfYYCLyJ6IVa2ts66daaGXe1ocBHcQawBjiJgEcNvcq343PPXroccD1N9plyRGGcQO587/NAkcCDZotgUgBTnDi7c8WXxxQXdoRqX0iwJGY2ePr7JJRzEM3dHWaPij5T+p7GAFGKuxz2R17rH5LUjDvcHgJBEI/Yo6t1koCcOCYJ+cys7uUeLqv+L5cJxp6AERGnBON7iO6oJmSS1MsESnKSMQnH7lNHzcTnzDfptur3EzLUrqWLhtnEnFNYHhLcQYkYUOcQoQNofU6DBdicGRmAngXGxeo+dnFBzwRBw+a/nik/suXxSyheHa/pT/tj7/znLS3fqJCGOD+aHYDxn03o4+EEULBFlMasnVn7Kxd8c0YvTLTfWeyKcfAJp6ohxA8sTJtVg+/qBUFeipXfzXafzGtxXbBC+XNkm2WUH8lABDWSUB9fEgW89tAlrUvpzWpACHkdkcH05UXR9OBXGIAZGTkGCNw3HKLq1EOxPjZEdum4OELhOk/OQr5TwbYW/dc96LXn0vM/lVd5+/vKlkKirFigtn2Bsdmne4r46jIhEAAhGfV7F24B4PA0tTfP9dcUgUuvgXl+U6mDp4KL4E7Lnf6TPWv26Hk6EaVTYg7nDQDnd7Y13AgUqly0tXI4RWCeli9LKY+a+upCpxYaDLpatDMRvUMfyR3HkwJWHSGukvrfZbw0T/MaLmd3JFa2gsvujK/iv6wYoZxnBVhRIeaNy6AFJ4xBETAUbn8p/ph+UJlwPlALiNruJgtWkPXfWOXchj9E+Ns37VFM/DEer1orptz61sYDyPIsASLpR5EKlJ+cVoPyDOzh1KiHc3wQDc33rwO0crDtmFLDVXyssmcH+Rx0gI2XXd0SeaeWEIR9h+oKzjJh3VpD8hixon/bZ/PZ3GmCQIMlUYFaAePW1zPhBc/zgX3GcEC0NDMOyGBVVU6l3fGQRRALyRFOnui/modLXHDI0/oiXhQ/eGrfO2MqLKhkWJP7Dyi/NyR+60/I4GEWHQAGvSKsvz7VYegIn6ge2LxEAdKmSfRFW9aeXGi6brOXHXt6x6LLxyIditt7ck0blJW1rDA2VdTyDGptOzpmd/PMQb5+v3B6/Ate3MsQViFGxv+s5r7VatrF83KxB8MJkCr8hVuX49ToM01J1kaSvsSwY/2YOkZzt1lnH6dp3X4L2vLmrR5eccWZAlavD81h3asBEJXq9xF3DUYTHkWyiHidkwEtfZWeGPMWjOXIbypx2FLkhdvzTBA6h7uYif9oHDhGeH4VaxCctazzQ5S6WO+SaEcJY38KUbW1fJvZvpPBjz5gWScHbkR8ROLjwyv1HFUhRYuI75RW9zzDUyToV/rNnkjhJlcuT3SCq7v+7kh8ZSyhLXHcOCr3TTNDNJiW9ZmKXaiqhPWSzQ4XsSGNN/1sh3SYsyYzw8AzIZdwoRMTJYc0+8GZFeoDn6A5Mq53JBiCtky/GX+jvPeItSl7bk8YvhtnPswthO0ZuWqQfLWu7Pkj3/tMGXKLTdlHfW4Pbn1msEiWNIVhTb9LCttqHlYSlu76guAvQb6Lcj6/eNa6Fym+tiOfu8SR13LM+yAai4UKSZNntee1NQQXkIa5PNumGWt/0BZfXqKiubOAy7ikzvgbQc1SdjZGc9jX/TXC84ugHmWKMb2ycz59VCF+zwsaKUs0fo32ZRahs9fH6GtodhdphTCM7t69FmCA6VQjx01spPE97BErKYh5rRlOoTi9/khgN975I4p1WdGsmY19mLHMAT5vydk2kzG3D3sQX1u5SwO5D8OBZLO80HS+z3CgPyWFmHJ7xddL0cAEZcoPGzCjOq8RGKr0rso4YCfQG1g+u4wLycQE5G7avGBMw2z4CQPs41vs/XVuFwKIe/4x7hJeZP7D+1dwn5RCBlNj1DAIwDQq88vJgUnV87mYwLUc9PAyLtOwIsjUxQJEsDfb5k98T3xB6RkpLHn7FF363HBzYS8nqTolj5tWCqE6/TrKEVYrDJnd32O9iG+YvFhDwLUvhPm5DUU8nlhGJr8PrjzMU3oa8EmuJ/W21xy6QGW22HEMsH2FFI3NjvpGfafPUxax/GvnCTOESwA9R4lk0MIzCS09G87PE4jzD6bLU37xg8VTAmJ7NXnhKWKOXHoT4qPwA1cz27OtMhNTWlepepwNJrXLVLhvwI6f195YiQEFWvtXWwanc7OwPleyG4IwQNEvcPTBcZ9Ac9E2a3FijwCJbWsIRqIVYwu8aKrl60e2iLUwG+Hru237fN38CcL9gCJTqw6+QHZQOi8DKKIRJC2zbKEZhHZNvL+r78dh5hV7enGPfk9/SO5d7jSCLzXx0dQJNlqVJuivTxj8iYbUNwX4oNjROo8vEt4Nzvxske6k7meGb+m67lT33tKBVW9/H6836q9h+Cb1fg5IjrdXw/SvOkMKZDK+noy/W8B6Nry4qyFDejItHvKiovUk/QWWRJq9czUH73pfpGovh5I64cUnyKqWvRaXE0Llc0WUVn8BLvwnQh9XHN7VGnvBQr3L5zEExiTo27rxTYLX78z8qRm2DME171yLGa3DNjquQDzwRo9xl2m2WALwcOSnny5FCx/ot3Ta85ka+Sc2yL7Qewk7edd9MVTJZeKSGEmdvT301K/VOL2hGVogw01uDZ8a6KTPLcOtI1WH1jZfSXJh8X+JYE+CeKs8VZYrZngIi79/bgflFkm2R2GqB5Lc8wnWGs6WLPHPr7A7U3ljeEkHD6lhnc2kd4UriDoTIzxwcbGIS/5pmdKedzPbvJe+QBT1pn6Ca0Vav09synxW6kWetNGc1EqxBbZT5nyUYPl+e9xTGgXJK7QLlAuzUPYFhu1UhjRpRiTpEArINquRAJ3+Xvtrzj7UlvWoMkCtDW940L78o3VU+XNWGV5m0MCU1oC+P96L+t53SxDMe/ID4mEodPH3WQaFa/SUSOPuQRPRBad07I9nakd+kg00VUtVv+ETAZ3Ch6uKfkPriPhvQWDsOq2/dEiwuxq+9sUyywh+Cv6udsXzFv98ZEIvbk5A6EnfZfqJgGeAWORA13xSR8yHXAA1faw0B7UzZW/y1x/ReShGos76eVSXEnmTXoZL1pKNFQBLqUFQsA2kWS7VI3M+BLfFuxAop5WVX+d2fO5RNvmDV5/j1NUGfu/VfWli7I5qUIq47pfGUmbP9gOGpw+tO4qy5XA3RwHkwX6ClVWKvZsNqAqwDUEzGm8vIbFJNUwGDaBDwh1U+u1vI//qH+SesRQzR2/s7AiL2TwR+iC4NvtGr1DzNtSFUhKB+4j5EI+Xk2LTJV0/aCZzWxYwjYE/NCLDSOhZYj9uIEfZEDyxmBcUwSC/tla9NEWzOVVZ3g9V7MFhPLceyuP+ETlH0sbHv5+x65ToP/8BTGN8VP8OiJUa2f8OcM5rtL9/r5VHAw5w/w7YL8Xf/vP9ZvZs7t937Jfob//O9eAb167f/zmZBA7w/w5U7mPK//N9SfzU/PH3uuIVcED8d+DR1S7xn5OpkwHe+TvXo3Nc6j/fV18vXvr3/YcYgAP/nlnlFL5H/vMx301F7d+5HkoDDvy7Z593Je0/96zMHfLw/52L///H7P/JmP2/+uEfgxQFO+VmI8LMxa2k/u6ql7zO3ewPz/8PDqwHwDbIXixrcf4PDvAohv69NaVLMax/b2HwLVz8H5z/nM9i/BTrAug68vs7SlP4/2JYhKEZjCZomPkHv380OYArf19nUfR/sSxGEShK0zQscwI/UQODXv+7AkVg/4tCwA+GogzK0sTfR9Lv3+HqvxeFSOzvVqDdO/mi7/9zZ/drDGnyv+/8O8We9lvx987fG98Vptzfb3zrdIIvm09agX85OAzNO+0BbSt6a/w2azMO4Hg2ruv4AR/o4QEufXfVMm5Dzo/9uNynwsv75387x6NvKvjddZzAuykg9W/4rGVzFuAGufuSj/+8i/znHfA6T9f0f/DH338BFIHbJPkm4EznQNRnda8bw/Vr0YdygjNwrjX+EYN/BSfiH7csc7LBu4Gt8I9KKR911/wJ8eV3Of9QaVM2u1sWEkQybCB735i5E9LFW6r5iFPCSAevvi74pYmV7OIRDS/1MGVHCmXHy7AEyTHpl9jcGkfOkT1ZJHv2jSIn/XswpgwjLq1VNr05qlR2kLcw7hoGUJTMfTPc+aahsmX4a9DIuGyV+27qXAziXDR+SSghachuiuRMWeg/9Pbx073x0Nvx1JvHaXjjz7jAX3qTvx9wnxbTub7DBXJd0Bb6mjE8WzcGkHwyQxXdubIhjOStfLMEuzd3ChdpfBxOV5PNr9Wmahqu94Ai8qr5Udqe/hz5pDi/trATj5dOvBIiwC2r3L97rJAAPQnSALMad+hgXWOSWQYrAKbgUrKniWHRui922vf9NM9FULoHrkLKs9NsiVPlTSbAn7uc4Z0a8L7DL9tWMoCIl9BHcjvK/z65QxAA3RAQ6EX/9/P8X49CNsHmN6fYLBxuMXtDOlVuFqvhxn+/+j9/OU4SbKzKldkffv7fTv3/7eOQMlN0wJZGuVvEv06u/4JUuWoOzNqviRcgkwsBLrqoSYCHDJV+ewxNoiDpU5/saY0K24HTnn5/nDc5YFTli6fid6ZFBCGotza7xOt7vqdNHpCRxOvnpTbb24BJpSXalW842t2YfKjS9ZZjaKPXBH2Y4a8zEbKAhGutyoeVFMlllOsWvGk2o8KExUL2022JSdLBrsYoy/EDuEfVwB1DKJ17F/Qr2xboq7cV/d7UFPN5ugcqfJ6leO9DIGbeNF8QWXV3UWvGzl6eVbdJAW/h8yj8b0qaiYeuSqE7OIkVea7D1K368YuG8LIE/t2x7K+xd/LdpeH4o0PoK++cfLE3cnxudqfhgG8ZuqDxf9LV9XfnaXIq7S2hqwfuvWCFEZNZdPp4GOS9KfeEsRxs5vY9RRf9nTHLt9tPtTA2h560xUT/D5auY1lSZQd+zdvjzRLvPTRmh6fxnoavf9SZGzEx9pzp7qIkZUopyRKKv4Wfzfag9CBXd3z0JeZs1OYTNYzk0f2dC91/VPZDmeejWKeImlUykglaU5Q027Vs92Sy3OGQTF0Mql5sShh/STaqg18OtR2/WypwHZjQ/CBZ3lWXkIFsZzOFnxTUJzLRGuhK2AhoVSx1Z8/lIt3wfWU8XEiR1rizSTAYpYBdNtVcqTCxa9SmrXjw4PJivj7mvwsHLsEj03+yOEDo4CoiwBNsq+BCFTROiE9BZv2pNARR+CcA3HP/mD05thaOzMy4lwHkNy+NxKMZsvDWZnFE5vkV6UCzj2io+sj/MjuCPmq80xmaQN+v25dllK89u3+r1NvBAriAfgiHrSi0SlZWGs8mX63D3dmVo3L1qr7UmCgItXCt4m6ZmkmLuuEcgOuuiugfJEjKYY8o/VeFYUn8onknAvxsRHUM+E8HzrhX8DvpqFiLtitLQfHV87HCiGwz8z37S3Zolppsf/z+2qxwd1WbS4UUYuglsx+KbLFKXzxtCBy/O5SesD/2nmI/XrbnEtr9cLCbR1c1nLQpeaO7T25n1/DY689LeAcEQpY8yZdnfn2OhJprSq8DNP3NBJJGRycbHipLp2b96cc6kPdp25Wnom/zbyu0OBulrfzQX5fMNFb/GxhF5b19bm9QLMP+Whjg0h1lX5BgQfNNt+bvLUhJf6T9c1FtGya4v8/jZQN3AXZ8WcfpVtU9jhLyHaO9umJBKsWZyPDaK5ps+o0O0MPqmt9Un3GziN/fVJ8wecHBx2jv4wTEbTIqh3/g85m+RFDj44WWoiLhQLh/S6uXyrXkE0BCRZz/2umAQF4k+AfvQAS4gYdfwJ37Ups47mM/B3nWEh/301eXg2PT+zTef7U/OipX4VSx7MkD79QD74qsStbst++G9pqbMkiGibyvFxBXXfDNPD080BlIO3OsuNj3usyaA56w+Tf19eyZU0TcwQYpqX53UfC3i0pFQ8gMC0N0/bjgkJehu4KBZgrv1EvU//5+Kk9++AOicQ4YDu0t+89PQxgde8RxkpBo/G4lROvOx/onUppPDXyxPaNrY6aOZGCuKYvG+0hzPv0tpWNOzqV+4GbvZB+rufDDoa9V6BH5y1S1YnUjT3mrVLwS2ctP/iyJeCgBBW/fN0jUPtUPDggy2tF7+4xlx/26qTf6+Pp2jaWZL3slzLm+q8BeSJ3HTnS5Oqg0UDn1PsjTUoRHk7EOPdpWpnV03afujTPF50h4RVo6or4NCbL0ZfYGNjrBZFE4IS0j8YCL+q5zrh5rVrxghVjYfWth/1efuOpiN9DPhZFYIiukJzwoW/Yun7qmjaAlJSJF/vuO2yKBWebiKlHXhE9/s4mOKECujPqrdaduNHwvva8C4E9WJaGVzkZGIE3kSLAwVaT+ubKGjk+GBojmX4GGTf79kt3vFfogMAhEvNtK2MelDDAqy4uTNCF9E1qEdg2PKlzBris826lFfMjx81z7XpmfhaGT8Z7cXwYOuXOPiAO3tYkYnAmNYaBLEdk68vUQH1pHApVWgQbdw7sXc9PpY3KnjtrpD16hPYM+NAiy4rBTtKHpS7QTLxSljMmlwP2y1pjJ68T0qFnxrEgAn854PuUaVl1L4AbUU0OKp+Sh8ybz2qwzeDiRJPSk6BfcnohiEH6xo53QnLjQYFS/CriO9k2Y+CzxPstSCJPjICH9s/vPDY9c00Y6jvJtlOuJi8G1bZoehBXaNyVo4iazA5zhN9p2F0rRORpeYjPXQ4WHIFxBNF1CAL/c1Arv6gNP2weOZzyX+KS4PWahDBbVRl/NwRNy7rZg9eGQGtrjT56zK54tEwf4ghvUjyFydnoCj33KS1YnllFv6Y0DXfh1EauvducRoax7Vv2NdgPO5U/YQ0bB7mq/ki5SXGETuYnojyc++XVIRvWpWYqHeZ+D7ybYteE0wrskssvpd/X7N4myH91wiTvm5wNscIc49+1cumbR2uE4jOlyFAsu2P40IP+1EmzLR8bZSKNc4nj7mMofwNu+KQ9iZyDOjKDGpS/w+FVxMG9J5xH+8ALa3dSod9zlhVwvX97MEsHfyFAFHr8cc/cQeqYVh9K0YO6BohbHF9WRl4abzClJVp8ialB8K5e78RmqiliJvt06hNprOvz0MNxEuz1LaLhxkWrzLKIyJ7Am3o/5Qditv+O/Z1jikCmgH95LPw73odk4ZSfeOBlrYjnsjjDU75bqp8dn8yk9KnwWIWj/9hV4N6M5+q89mGfSub67P1YphfHj2TjyGS8lrOEol2fGbJZFohj4Wv+Eg9iQMIWIfhhQB2J7236tJckMg1sISj0uY8zBPAhWEHHWLCOYF3u/IR2px4+7wpAyuMnxNnWAImf/mK0TeSO977Y6eXOio8ekjb1eqX1QyUSEiPmbtebZNEf+3Bc/MxH21w4oQklULSjUiHL1xRiHOp8AJCObA2Vb73WARs9EIDY1qbl641RGpNh1tJ2E7N4G20xqyYN8/HlVaPvfzK2YdU08Atch/WztQvfIU/QcqE+j1UjT2nxEP1J7Y3cq949+rQcdJpHRRIkLV48F1muJ0nwvSEVJFYim4gbauNif82VMLRMYnK64h0DasQZtSGUhT8F6J3SLaD0HyaFhxlfWOjX8qzM+TzKO2F8w+EvrN8DP4T2zz+TA7qP+IsYdpXF64+Mp6aT+qwU2UZKRCQhKyC4IMK66hyihFt5AqwYdzNGzM5qGdXgm9Wevn1/cWEx374wSi9gzFCDViwg5Dx4v425tc17Kwttjwa3OEksrE6M9F9StjRMFvX2XWDRp57NILfGyTaztTcSEHrZWNpd24O/BoL+x7iDsfTebme78gZAVxBX2YE4vN09lgaWY75iUTPnpyE1XZrIyWoWteFh/78FjSqPGKU3DOSe/+n7NZdr1w1lqDJkR5biDEQB2EZLf8oH09/XBVf4FJhly6Y2xGOSMykFB0V8Nerw0UDXxCQA0oLpgQ4eLFyQLVT83/Fvd9SSnRJ1eSsxFzS6xDvaGPsxrpX+T+56dcC2LS+gUHgkYiqO74bRGLzR89jwTv3OrLM/hErEPc3+/ilzFhEPodyxqx2LWesXmkGnUC1QDTJeKvPbZdnA3+Mb7dOCdsbFnCDVlzSjlUXql7L4ypazgfAhTORxJYPK/pe/35Z5cMXnPFgzabxOfZ4XYjfXL1Li8sQJh0eUbCHNEU+0EEvbb7Wcrl54IAQP/WFkpMrBTpErPEC1wMmv9ft7S31ddfvDe4Q4n8G49aTuP1/GdC2zHOqtXsy5kZWDyssXckLTIPfTV/Zhrs/V7MXW1XUBr2usfVq8/btPJy+QiHO7sPamFPE56hPFVRg3HIwYa3f793r+YkDwoOw8LdqFc/034WJLs4VwSqjwG9cB/qF1mNMcTqY3N+qde2/cBODho3BGX9jqpSNhIkhiYRg+w7MW94Me2gEOKOSNgFEXVLzrCTYgd+h1XaESiHuR1wX22EggRsB0Ix9Kv0PoqNDKfy16gAOPd+fxm6Dw+v45n+EN1WUV7g5/WgFwIeHi/9Ufmycjp8uu6BXLO5WEf08/Slnmj17jb/Uleltu9Ruf1ZMiX8VU2KOIfVoEl70b3wrnQEYO4CBIm2mDOhEFCK7uVk66lP4wgGxQGFlCy+ui85GHOiEhrvmntmwdJtaqGnjNf72rZIGf2YVT6wUrCIFg+cmra4I+7k1vQ6yc2fjT8Rui7qFEtQFACeqcMdd7Mp/6cnMZUJ1H0crdku6NzZHFwzk/VrGOeIZZTV9raliwJhElAncRftc0KI/+ADgxfQjLkPwJA1IKWn0oR/u0pEh1PBb90SiSIOpY2FrZbgIbV32RfMLwEnpEyToya1mtxTj/QstotVOulC2lm7U91HV9eKq5s6HXb8/awz9nN1tlSv09rEjPMis8beXJctQWBxpEhdKeSDMqjIGfQpCay+svUxi21Pj86AUMfVH2ov7P/ep4uLwfy4vlfkA7tX+Zi3QZJJrOKQO1sfpFPPqgyd12mJM4t733O+Fq+nwtPKTlsRnisI9Rjv7n1BtC9BCUmUBTCg9P+DuyWOsCYLx7kIV6s7JDcVDIqI+3hx3H5CZhHGLum1iCxmzExg2CcX/Ok1eOWAzwxBZ/28MMWSeEW5YUet//VGclUlIvhGJBnAv1Iad6w5r9JCLeNGtkvWOPvWHnCG3scZ4XEjYMGLmUM8VjyUGnQWIWz42EPP67zixnTSTFsh7QdmFJdtAVP2xRYH3wOVmx/T98GpZXLS9WfhLq84R5j6Dqph1rh0Unga7U6wsMRVqbbhcC6SiOnw5mR/vIW0wfCXtPyQKRlfzR0ehHdn6rnf7cQOy/2C621cIQSkfgvgc7GW2O+pvkTylDOOdO0BE6xJRB94v4QR5Nb5e/qCxLIXXwtKcGJv4qZJn6bP4ERLK11EY6zOlg0HCFTzFeav9U9CN8DVXlnACRCtYlekBQUyckyjhtcV0drTAHwLsaP/TQQP67b5JUI69ElnBY/UmB0NL1TjH2l3Q0FWEyu5j2RIoadhVma1+QjEuerslxMtsdzcNjIg//rpzrzYe/85QxNoebfnvsflqIUgYH54mzZHxow7DLgyQnRHzqKWiIAaTGzG4PUXD7+XawsI+hhzcFQGXZSuoNLg9Uthmzv4+Xfs9gLENJt/oVWWABTmNzyGYM4bI1KhkK2Uk2wDykGDEiqDpTQo5hXdRbFR0oEKd8btZdCxHSGz550B91/n98xgv8x5HN2YaQHho3ishYHMY011MQfG02HCzGcIdVX4OJY5ggmQAai9Kdmrxm9WakUYt2c0HlnFotAmtjGiCODUYbKopTTXhkhG48mStVp2lxlrsEpoDV6CxVjSlAxZIUJeAYJ79+/GV01MGPT8ogL2WfGTZD5QPGRRLigdKbIgZbrd0ZPh9bhcQV9Qbad/5giYRSC+aVttWpgnNYddNYnhOA+zHOQvewUtRpDeaweCGcQLfr5vjZgT//2i//he86zIbgZTh86VyICpjyfL/amfhVQs1Q3MVbsc5cHW9LVxbPkWlX3t8ZEzueWx2xY+YXaite0gdpeulhDhD4pZYRLBbnQ7ncGjtJG2vPL8FeoUE0jTdIW12m2SfzOSLJn1LzMTlYQUsZqp92vGcy1GYgKz5kzp0UcourxZQ+0vRoUgGZ1eP6arTzx+EO5Ge7tS0E73PmRO84jnAxpvdg+sh5T5NIioU0amIy3KCumpSPSTAbjzLqbBZrPpMbPHWambmrmIG61xOckDj4Gb/ikWYnKnojjxMO0rT0KUr9BPsWsmW3bEakELrbPNI8GQi5jHqaYNFYVaF+ZQum6RqmT+UQdeSGbYBF4YTCwX9rhluRpchgNx3ap9JrB2pqpXfgmV18vcQapN8ySg+ne2VwWvW+iCor/UljCcZnFYkl8zIWIwsZPLBrllbMxA6Yy/q31UhlPP8OM6YreogXpF2FSl9vb9xfLBTXh98Ar5tSeJZKcmELd84EsULI49MLN43dIXza4ym0s85LtBCgtGTQxC3+D9jz2Y7urYpYo96dhYy/0hsJxLX/QocYpOKzcZX3BFEvs+fIM8ys/zd5J+pS2Vbd2CqPRTEt8HCY9j+VT/2xhoPoK1WKDzhtDv+nNoL4CnxdQSYJ8WWO9SDwAr6TJLeGcX5JpfJ4gP0zkGOk60UE/42FZtGyLAaoDrHF8D6lPtGPgRJQxomEycqSPr1+BKwhrVAE/8hFKzgiYoyX+pZ1DksyLI9I/G2PJNCvemymf5aXTqhqO31pHliHookkFeivk2xpsdWYY673BzUSg4bS5dewrb3h5zc5gZkJV68SSvQ+bQfcFM1Mo1VQ8d8JDRwHMzYDJS/2WQ/YzbLeRGEHAoJFxdIiZbQ3dg5TxJC+0zivQiDIXGJM7eAaIo1gTBcNbb0FKzbPuXxDVWdYpWTuHcSriILFCETzNwHI3xJ/I0+Ps6/VD+FGP0XCoDQx/YjmQCfAFVSgqL2ckLOiyrB+vIUgsmCym4XrAp6QI8vbuRIa/d2UM3xKXQmc35tU6dX+3ay+cFYkq3B0I1MXnjuxFz+XNJnix+G5V3OXuD5McRB9Dx1iOtaTP9Wi4+/YrTUNndcWTpTi6sTiQO6T/hJVoyhnurxMoBu+4GFZP+dv6j68TQL0rvtwoS4pqB1R0mtNGGIQi2DMnfvlRDSQjE8iqmHLz+1tdbc6ohPLSRWJziQDGm2XVZA95P4C+Gzj4a2Kh4YmtT5cj/NGPzcx5UF+RTCcIFCO9RJFlcRjGHof4Klmz4djYCjGd1wqLoQGn0warCC11bC/UkfOTCRqmMcMGcxR/DLFJ4L7CcAWwZNf+JMEHyJhYdBuX2rW64dwQONdcJ24V0An7RyXS0ozVntNir4OSkZkb0D4oUtBHIOI0O7j/aobsFB+fbGLP2kXKoSAQOAjvBo/DWlowRqCxiChkTXgRTlzrFPtHXadIxj6boWlWaJ9XuODT6+DLAZzS88cgsOSGqnou3IUyyecTad8aSZYAxtIIHNbrGeGl5+N01DnXCtntpUWD4zX8jamD+bMrZxGe+eda9Q/Zhq8Zrr/roIHe3D7ka2yXvwT8Y54VpxXOBWS1Ij/z8VhS818E3RkScRrp5SYcemI99NgGh8/Jy8e9oUX7cQ2rZXz+1WLQmplSiHOj+tTxO9QmWUiJwsaFrQhDnKHx9ebsDKRgOLSLo7/Bsq9L5UmNGluQGYJ+wnclnCYjXNNOdGj0cOA0i5jBf7lRawUPkm2EYFSE0FaB6fofdx5lFBTBotEl4YCZXa1+H9cnHD3tOGh5ZLRlVbyB4/X+V3R+1W9gC4TJ+yet1T465DyWgDoktiGkGIlfSbgY5+f29LViL2GFKS15v3baQcuCSDDXfP+psP62dVQkjJsGgAr6HPg877CNjIV/lY+oQg0nJX0G9jxvfaTpF7C4h4K0bW1al2Ja5aFRtUcxKSNU28JCt8PnTPCGPVvsymrnz+ExYGNbgIyRzty57gHwsMeLN7WYHKYqr+PUYRVaRqVoAFt0RQ0fOx6/LRqTjH5UJ1yzd+N7pK+dpsY0yHPG3nPFh2PHMZCPvI5icDXOGTNtVpixMApv74nJgAx+4BK2ZRjV4VwMgr87hIxvgGPCFdtLL/x+P4em8lYLwK7J1tkVCupfuH9YGYEZW1OBWBSY9hv4/O1LRtMgipiGdFXtl+7FvpydGXPQNcryVtnnAeXGkXNEOAkYT7X7leKtGmGztP4Jrvqc/tpH17SOnPSojZiz30fXJjBbLpRxm8FVdl2FOfqXKWZvraTtb+TjmsJ9VRuFqHIOuT/aFHXRV/iSlw+D3D5oKL9KOpxkvZbJv7VF1pcpvZxW3pekVcv/FUwXjk6v2SIl0fu2teb6IjPZqadV3syhOZmeSXNGpNy7cc+ETzkAMp4UnuU3nnzG6FaUcD2gON4UPxm0JSxUQWs2c/V1SXx5gtTBjHTKpu8est86BAK9PuKyiY0q1UPbzAB8kL2XaI+du6e+Lj7WP6aBVBQ3ZzNRHpC/r+0nEI4pkE6gVxhvORC9C899hxUWGCQ2TfVckCw6X/hw5svvNzNCn8qxdsCYbanDnX6rmO0r8nh0JqakosqSPpDFbvgiVDCm1DN7cwdcOEXSjt9zobQZvatfYmurO/20ZkJalLQJ7vESWZTq9XpZxCEtJTSpvPsxNnV0TNtYv+wz32pJeJopJ3tKJzL3sP40oxpq8P4dVk7/vjOoFc0zqc1dgS6XYj+pAOQKBbTs/aMy0Mqz1qLmsDIQGebI9+ZBCP2ZV+p1BYzIjfemwJG5oGuWGZcSrW88nhOCo8vhs4/xYRLqyEEUTgp235bss0VEA+TNAdWT1S8YcYDORxgx8WqTpSlGKsjsmcfkUSGlmCqMVCCS3lPn98/pg+dDu6g92mc30BfwbmVAgsNypIrLS92xYTHiXhdQaomgAZsIQUS97qgMTRnz2z/FxNejL2qBDQpKC4v/eGEPkoSdJw93ln90BuiiTe+H/LYBFrBCtyavO6FM/l3nqluz6lU3QAbQxOQSTzL1RgeTJGM0R0X5oJSe+6Tpb2h+VTLYs5D9LUUoI1PVolvEvGZdYvoAvnZuEUng5nPO0Sm3wisIiLJFW7o0bbuvpQ07PYP9myP3cU4S4sO882xHPL2jIm2OJO/fCtKYSoje233P8k18Qn92rrqJVwR6Th4oUpqOeg16BQfpLjoKF+UaIg+TDR948vrnpYI+EVfW7VNrda/b9XryfrSnU/R0YF6EwYfVLioa9gxx3/qOb/JjLVa3sPi37b9ku/5moePfZe2hgh7/Kq9XDwe2pKOVbu5BU5kyvPBvB/iM+qGBjYl539NocLJGR3i1npGiUHbouJE81h+j5AxxqnmjMMKfv4VRR0Gd1Q8+xKv7JE5CkfMRHUszUw/i5arYJ4sfT3lp/hqOk5RiMoa2kIipoD2ioHu3ZPY5wcZ7aAk+u5hi/YJB8x8cWv3bC/Szg414QFv1YzJp+a1RPuQ5ohDu8WU/KVtsv3zHr711LaRvMRV6+fPNE/TWraiwYJcfKxgJJT5xj12X1TFkTX8bhEXe7130ANTFFo24RA5OPd+zz09WEFTig4LngXxMoFEQ8wr7hKzQl1S7tRw68whd5eByxxG0mj6k8zWz1wvLatEeccw6MVQjce5aM7cdU1AL4RfvhyEIWUw7KCd32+7njSjA0vCSSX2t1njK9WvxI/gNl+xwAhLeT3uU518GBeAphyVcFQjXuwORfHMI/aJFGFdrqG29pelDplQsn/PFmsYGtt6zanqBME3tl3aGw7X2XxEAagYMpWGxtbwnQvPhx4EI3D/FS6B37aBAKXrVJWqaf9+khqxsHLpFwcCARV4A17VbYJFwCftvi71OMDiK2H6K3DVUPu7xNxFHA5s9WBd+OVimdSxZ3bUFMeaFJNLHAO4Lhlsv+BipZWmdWenZR7EkPO3em74R1BmqTs7Y18/4WFm+AjhDorCyUqoG1zmm1UoL5uCxYBy+KKQwUXVSSAOREBtkIylK+h3w5d8G+DXZglTLLmRnlJFpjV8n9WbOHD4iucc2vXFmEe2sQ1I5RzZDp0Moe/FurnRp8lG+EZ9NfEpczeMw141xHkxvSf3xE6gd56B8IKcfSfi0TQcZKsiHQcsjy5v1GbV/ncBgzP0wCpGeWByWozXvjUOTMDepPhBzOH/sBvbb2fwb+FQqeOVtJ0eqGe5jjqfzwfK5axCW2UTg+rL+QP2P324rNLxrRvEZmlPBsnZBarXd9XAiqqOnXitvJwJ/O16PElIzedpwUHvujHhFd8aVhKkEkuJopi8ia7D3pfVMvdo/iAyF7wLOMIe6UXbOF8U8sGCvv+cYqBOxUhx3sPdlf+Y9jJj2PT0BsTPph/rWizuDSiGNoxlt7gu55Q6QULDn6DqWgY/OtNkYS/lB4RkTX4d6Q6e4+HxbBL9FwkG46f9GloXX85W+WJxWeCLHNu2uxDMGEJ2ouFvIrX/PsTESnr1x6HfUW00xSa2fb5j5nulNJLvhFw4PvMpw1lxs0sRT1Be1k2EjVf2lK903T2B8KRlrk2W51KzCSievgsdDJ5kvcNRNl6im0f8mnehav9dCmM2tLnVyiTW+QhyExEWlaC2Bp6v5zvX+ftxm6kOyO1tcPnIak26WJGQwHfloMLyHv9GdpzuJuyVLLU6F6CX/9ATdX6yAixjxeZ2ZzelkLfj4Rnx2r66gsTbTSUTVnUXVAhsYvaWM+cpXiKRnYM8a9Su/cKYgRI35n/FI1jfiYwJ2JUggLMm9bK4VUXyNhN8q5zY9qB9MKGnT5Y5fsCU7ZxAKxANIqnEUOy9SeTssDTLAzqViXnF2xXNL8xs038sZZD0bRkKhjNCLx96bBlyA77n/HEW6krN5EllTQPyaBa1BKoX4mcKfcdSsjwtuO7m+C1+RqI94D5dLqkcztYMcv76YwEnA21NNXzwqGAlRCh8esGY66EyRBnOxlTu3qIFVRlqC/ZUyKzyKWnRqdGp+rxAZcmHgzPpowb4aTjg69nNkSxa/aGzflKU499946slETG7fMWvI7RhgcguV6+QyAT1KZpggKSrGM8FkY67KBLeM518XGS1B9L56/yYnKBYM3BAhmhQLktWfoDhqWswvpx0w/k9IaoOSSfA+FDH4oE3Z+dlhhAvzWzVAkKQexrbbBThj7XDhAac9FPyvbjLBSLcUAf3I/YQgUC5p0M9GT4aqeMYkJ7ZEJ09+31422p5oq9RLfgYZ8AuKUL6MxfRoGrvj7etr8MYAD0CqlzlZNVDSiD/zMLmjfb3VtXfy5Tp/RC1UWQyaTfQO6/Z55hV/OeHcQ01N/O339cvePKi7oiNprPuinB1q78O0WYuPz4B5CWz/ezjOSTAZNWw57ECh/CXm2ArlkJYMp6ycVCgSpeCTPZZtvNv1vnX2ebJrn4FOzTiCrXBpYWQvMA87bj9vjdMyEwgG6p9yGz4oiUKzEWHwdMhukEWul/eMafEgySB47qJxjCBa5U/oHJ+vWKcqCqUEITN2uV/CRITKh2W1jVAjYbHgsAvvbPgVs+oHWVRUKl6kwohGdWzBU3wonokvlmkf6bhc8or3vDmEaUPqWKrq3fko4yy4r8vNMVZWx+VKaCZmsbJ7Cu/3bOzvNvtJ6ni2X5hWPUqLg/1Nzec2TsnfGBRjztwxkxDGFPwY8XuBC6cMMkn+1ajZFm2MEuk3Q2WkUG5zAT4/R90To1fPty5bQ11POfv5dkEGx41SSHKt1+TUaM0jFy87YD4X5RgdzcrGnCcJOreoIQ5JFO64a09WIVmVsgOssmx/Nk8UbNMqaKTSL9UnIWbFhMnqiPyvYFV5CWUFWf2ZochQxhTfZ6ZUW735eUT2ccYzzF2dwlY3JEpRttUSksuwE8Lba8c7ZaIzjJ08/ljxvQjAINa9WLnx82JQGCq2P6ls97ezu58OppTfq2CHB/L+Z/3PXLDB6BYKus5jg0DBn1WoUwiS9NOCGaWim2E3nZS3n2LurBEP2jMvTRHONfKhvsvsdGGb+B5nxvsRnXVN5QvK2cABo7LFER8ZbGJ4rgW1gLqZSGsTwuv983301FdqYt65P8LAW1Cg+99See/tpAwjS6VbU1sX/+13dW+W55RgFpMWMWs/Q/lCtsfr2yrum73cFOx8YSaDcXuklOhJjcGiXaqq31KuMA2ja7w+00U2fCtJiNMMf2FH3wetMBkoDUEOGI1VHy3DSzTAhccvsdUUdlkNJuLv/TrZEORSkHU7J4op7+NESzPCrYjwJ0sH31wkL5gfxJAC+aeyZYX4meikQXbZEmwmJcPXAGm0+gZwxi7Top5ccF88UzTk0lLr0WzUHXz4XULnE3ABPDQDqB4LZv76gdVetpLAq+sn6PMhlKA6j9OA0ZQUEvZL6NDffs4vnrWKpAVZzts1PI45NdOqMbSGVBBpfPipd0J38jc1cY61l9ZkvYX8LvixpJ0uNPfszYHMmT2mYx2oxtkOIoe/eUw9yb0YoL6/9W4/Q1Yg6XmI5kYRhvMSBluQSJBApINqJI3lj4qKTVtyWdkEgSYXCGGtZymzmGh2xob/pWdCJjMehSoCpCBoHro0P5ck3Qd5PsiyVqt/4XUD/JE48/z1fRysTMWWETHo5Q1T5Pgoc6bVvvrZBNTwtJ4QkWS+vHrUOLdf0mJLt0sB27fYLn3WNaaMhyCwsTXFdDSJUiplWiroZiVytHKE8jxFS5ZG/+F0VBB/9zHcYn+Trp+aLiN4zGTSyW9eUk5qMqg+NymwjuC8wg760tx8GSXVgcVkNvtTIbjaGu7l0PHm4/KOP80Lo6xn/2bqTPrKET1ci3Zsy1kPc949j1Q+zZV7fezSwOyER2phwKBp/tJ8OoBtqr4dMaBnXI6CT5YxdLxGKVPmtmM/BcvqPoSJL3Xz+eeH4QzrUTrH0AeU4nZ/fF6yV5lq6af0LPesTswvtoXdHlxxwKwkiUe/xi9BdQRh8D0PDQDg0qIJGRj87iVpIMspblAVZ4T4uGuPki9sszX1eCwnBb4Etxy0vUwYEWpdpYk175BtjiFPtK7k2cX6RfZ2VhExkmDYfbEzpaiCbiYC8vEdFA0WTCu180hB0WIe7vtgWlewrsvohGbFp+4H5Dl9LBlwZNcfHvOYcBi9R+4pUGacj5MNIXMVyFONnseHjY70VFwmap6Wv9yENhXmxfadkATyIkjFWjW1PXjxUQWWfQGbqjgdSPwtAiUvP4ZQQT/ziKn7egdqLDE1bzLPiIOEX2Z9OikWW9MBL1HzxdL2vx+l8NTXYTK781+ykW4PLdWkg33DCzV7N0PF8blN9+A8lnAiSJrxv5JtBtgbvJzOM5aHxggDZNOqQwVVpqr89eDhz1MBQqIUCBLaeUd8c3zQ9xLs7bNY2xGW0+uErp8JDTbOiQ0+FaW9mWlFvUgMCG5WLcPj7K8OlBUzCnlGDgkTeeJHgZb5t2fptiJAYgjMn9X1wBpZFZTWwUJ6kXvpDBPj3/XH3AjID7EjjekdC11CDE/JnX4G72NajEYHOqZKMwExbhWM2ezVfGa0PjljMPi2uBpnFZ0v+gJJ/ohUR0D9aZJnCF6WaF8gxZv25S9hJqLYtgCQ8rP5nhATzHEblmYySdG43ozMqz7xCW2YdZnG9BxrmiUG3dnDrz/d/pQznFjXTUFLPUO0iKnRyzfEQfBMW4qKWBqeD0W1vFGz7FxMv7pmnQY4hhn3vRseau6cD7JTzLzfGdiqRe9umddFJ4hUiOUH8ttihxzGk9X59IPi7Nun8VBw+4TqUoRvH8mFhAYF98Ozjg1M/GhHu/+WrIRxEj0fQ3qDnY0Dx80P4ft4J2kpEmpx2K8K569iGWRvZHLSknHb153iAdPLFWDXLFMOS1aHFG98aU+8BVCD1Jm/FULJQkzInVzbfNqhEdkxHxQ5Q8Pkjj7VEuIEJvvqIhg5oqIIcQHoa7M0I3dwA4lPa407wrlzTbxxU0zY/AiM7HbKyUups01GcPfq219rlCJ2vw9Eh2G8P5lSKe4knzwlUWqg3KalRu3vnDKISpgYCPXEJyEELUDd4hCUnffTuNwsSoSpq9/Gpq2zCMUHyMJE+cz0Q/Si7aOwGJEA+TIXnUVm8d9OH7UPR5lyXmsTG4gkoCwaaoo1o7b+YiwmGUtY3R7l4j15p6jS0+eB0LFjGWGIi09bJUrJ3xvssAhXecAuT/IQkt90lLJ0FZawkQ0BkHNqbNIROcXEsJcPz2Rq4lZD6k804YmJ1/P0a5iXxvpS+cVQW3qJBxZikNcfO+mLawpZzs+/QoZQBMEYkRDVKNsHiUFRmSPru1GdmbrTow3qCT/kcudrKjqsWKB8FFtzmeFeMdVLQu1dk+K1phkV6V9CUrfcR1c014wNTgN7wLixOzxel/36QeOqKuueRmsN+9PjgfvQ769jeynvYPPUCw7GTjNTWkUFKy8yudvLjt58TKGtm/8eRa+2TECK3bRwBmx9C93u0Lr3gpzfnRzirHs4Y1M+9fBhvj6mT5zrb93FABHQcAMc1BYL0WzbDR3IC3/d9U+nk99M4QhtPuj8FGtec6GYKfvJb0f0NP7OVfVCUUkGoQfxaXZdQkb81b+ahA2vr3oU1bIxBPMaSnAFoTyHLlCUIytHuQEvuijNLH1wCT+SaFmss2qONTt2L4Yn6CzwqbjdnRG9zWa/BJMg4A/ryofI2byehMK3Haus3gWyfSm/KS2euXBnI6P6GSsHufKruP8ipcNelJWUBz/dL5jPpPZvLjzuwvBhHk7XpVbetQR//F7mVTySj8rIxxWi/Rwmayomu1LaP11ngIEes5s1D/rvLRMUye3cS2se0kRxaAL0faIGUnQs0fx9jtGofrYWNrtr8da5/AmpFUR2+IeC4pbr1DF8OS9Dd9+gmc1qFZ+tQVGAtUo1mwPD/T0azoIeAFEQfje+xEpNBx7+ftCHnr93Yhp+ofbEM8E2zBWWiH7q9EhIXkhNw1DWgn4xUGBESbI0LlKu7pCMkfw91l1z4PYLrV+oGg36xPABuOkx0KOUsL90maE/e59d4+J63p1pFOK+qONZ3sbDAbS2OJMWkpOjyHEQPoJrlBiT3Q69/ksOmJZW/DIruDNgL4JtVM8HfUY1FTvb+PKrgoj2u3g5kW987zt3Po4y12TA5ek0CLtzuWmsZj8I47hy4hXUWX8F4vy6NxJ+p/5ZnTQntaMLvYSWG6P9cwahzXboomx9nCwWbGysVNyfaLd2HEcQzATQuBPF9U9VZRgY3Y1vfBgo5qVLVqEq1eqbH1n3TNCkDrPMqGe2usCKHDIYyl9OWblYGuS55UYT26d8bpa/1sPHAVIlCBIRqdfybWf5bOKMbU4VkCbefdiBadEGAqqPr2J5eY8o/TG1G98PyM9ptqVeY+dkEqnT8FNMXCclW/dcbXN6Uov+Jo0857aLxv1vmYBev2dm1KfdDQJj+QC/i6M9w/YcXHiZPAzAfSdIeDgcSk08PoFEadyin3CEsniKIeYA+cBk7mc978Qxa/5kqkeWZGWAEF6+j5R4OEkL95RUgusgaqSWsK/PEU4xOcevC+7+QJI9PttjNI3Rx9+eAdGTZmt99JL61xLQAAdOnd8VzCyChJ0FskLPpJmtI82VEbqIc3XOPlRRrkSGC0ifhblYcAKBO54K2HKF1g8J/4jP44doIPtU8qB13UDsi8KvyhmOFLPT924o2i1RGbrOoZ4TUOSmpuUbs1MKXDU5kqrOjyoHkvMIPqPYzcLKIMO0RzI9/sCU7/e+DAkjTCX1485eSQjsjXYi637paPmayt2U7lJS7aVlMVOOW0W67jNzwd9Oz/0phpg0DP6udRH7tFrmuDrafx1lekDznUhrW7w0DClm3jGm+/eT2yCLcqhwXVOc3GLid9tT/3dKU8lF1BiOI7UOlF0ArCauOSIAzDZgN8QZ2gt7Q3pe7vBDc918n75RSmvUHmznmchQJorU88D53hPD7RO4btpdBlhttWk92klPFYjxqROEAWqqqVFkua5L4WMO3yvqrhWCiP7ytdN2svasf39zhAlmkDYZoVlRJSZUcN4vmhIJWvn3iAfLxPGASFjEjjOl/TvVGJnFuxa8PSD+9pkZJ6yGPEe7WtWdfFq9h/BrdBVEE6UH9eMib8wUaA1YulowkndZJHrNJnFihjhgwx8nEu2aCgecu2BcAN4J+YPTdDuv0cED6dA9DB8mYL7eeVlD5dG/xSt26ZBZuZvoF67j40bIlu1u7n7S1dDYfYV8rHrq6JCPeh97VHieMJKSCqophQofhWUKWvFEJl9CmlVEqYvZjm6XqL8ET7xMBKnyZYlj+1HR7bkmFFhEI5aWj4b0uFkd/7VS1oGslrjSK3N5nOjVA0Aib33EfoXlM2R+wbXPgq3o45y1Ambs2LmuKXdW0S7p9SeSOI/R2/KzpbYBvaeyss3m76O3KahMHLcZrAlvMDTTAyhSMFzCtKgTUTgTbUJuf5iQkYy7W3FS3YX1jRUsREP3OkUnsYbExsyoBKHnOF9iJB0m4u+6dFHRvLpzUm7ltCMPd5XQoDEq0+KWAlMQBHL4HnbBZ6RwSPCgP5lF00WipOZfbTqQMIMr9Eihc/KT5Jf0NUr65rGR/1lOgzOrwILzq9ccOsR7QHDytYFxxoaX59xm0enDgDrCrfktrXFGIxwVO9iv29llnqp1aNU3DBgFzjH1azWwMUzq4jj7LAX4tST5Uuayzch+Zh1U4JfnOMFk8nws/rIptu5BdPjyBPwom8TWqXkjF3kveW1x8wahYtHvJs265O/4NjZlHIXIV9mxuUyNkV3+HVs5Wq3H36jPIOeKZcTv3bGaJV3lb0jFikShGYAwzpNQ0W9d/ME00PAkoRkV8N9woxnsTmgE6lAMvdPaAdTI49ZeaOq+MUubWTyKycOCBerj/g7+NxLAJt6ID0oSrIZ+kwrkD/OEoOEWU90FRud0rpkuwCSL/RqzOx039uIzxsvgv37Twa3523RgFI90tMYTVXfNH53FhANFqWpqVMTYLeSFZvMXOJsn4UspKok0GIjRtWHy7iM7MbVkh676hEHKR4vRuNsbBFWPrv5MD+rKjgqZFJ1o0iZBBcdbicZToqTDisAUAWODj8m3vQiZVv1ep3WT7y6hbqQsrtRoOXEoPxqQ7I3UZJvGHpUpj5L7OGTnqtT+nDNwhae7jipuyTuKT2AsVPXtdHc8BpqcILsueYIK1krY3ZsDmYwQ3YHjvUKOS+4lKd4ws7nbtLfkR6X9puc9fU5fT71g/UV734pH5/lkSijk6I/eRrlChcFUgJiaCrwcqJV/mxg56LI9miaS8pX0SUgMLbRoFsrrTL4gdlJbG/xtyqYrFXsEfgsLbQeVnHlBSNDORlC1HHyLugKPJrOYKaZdnbmVj6DIe4SmijNrcMwPX3hM9OPAkRCn0l+0NfqRxZR7WkWtF0fAqvrfFkAjubHABCBmHQ534X7b76j15HtWflF7BVSOS2luRVeQcyhHMX7ULZNm0EucjJ1WO7lXA1D3PtzvUujD9gOxrM/ZBfUTFFbPfi8JTApN7UE7CWW7CLzSD1WVEZyLth3IS5lBnaLR8dtRx6Ga7c4eumEl23tDl+/dGfBvP1962WVperTt6Zj3kV/kyenFLBVfyigSBs909Op9CiNN0VFeckMi372wlvOG1pfqlj5RyAU/dE2UxjsXg6iFNaZg2ES6jcpjUBxG49ncr2mXHtQc9Aq1lnSwpidazdZHUw+tk6yK/aw4pons7DEuozF6Xy/WxvqaiM1q4bSIIFOm0X4MSZBnmqjm8zsfBafAEI+2F/XCT95OPuww2KTXN6yKTWL92VhDvVOpTNaVMQTt4FmJdQwhRMtKgEtu6aQTARNc2BW2dkJsmEQPs37aBOb96XOlW4wdIEGhGuNGy6UicdB1mEB2Y3f9PcRaNxgRNU2DHlLl0d1zbI6AP8fZ8/M8DbAAA330NLVBixFrtYOPOANh8AwBEw1CwYFtVUuONU4UHdAva2kV+Q5b7Yq1Cn/sRh569RSoNzj+728qAQtZYgjhXSlj6ypAzFPJNuQ3ShW1I/YdLZ+uekfsmwN154NM++SSUrnJT6SH7rMYjEpSj3rMbY3R0FvUt3k3JjW6QNea8UKaKcC0+tdPeTzKdFWyJbsYEvZ8XgTjDOPlC/L0WI4r4WdVJhXLorqpdorvbtjnEnrVek6TxpKxHHz4RS4V1Jm0KybbxCcSARQmZbIv5/Nzu22SZSY+r5MF+qSu+m/WrDPU4GEs8O/bWrsiJchYpNhvLiMrMp99s66JTJucW/VSEExvivn4Kk7TjgNK8OyNt/2dPEm0NFJK0enaMzDnXxK8iotG0Pkxw3XllNPR40e4V2h9ue+5VGGOA9k3fwp0O5TzEA1bimlutAZu0cTSceu0sYBUl2aq2kUXY2yOXkG0zqAcwBxEIGE3IM3uTQPGvhpwbDjWFox4MbyiUExy1ff4OQxNNECBWcTHGQqDsuZsB718axe4bNJxmEJzRWz9cTO+gn2ctk/gIfhyC5I9g7wyyP59oPQQbo/2iRQr6hlnnYI2gOhMPqYN1TcaYvOpYcnZfcE3c+F/+c26g9gWxyUTmn9/AzYlIkKdGH49ZnCR1zXl2Y/w+PWjq7NTCdb3JwBvKX+k+pI6rCfh1S4luWKtKNgxznk2OUsjR3WeGTn6JWYS+QY1PH1oGvJJU97dBVjG6MG6kAEZUqy7LJk8rvKRJ5TlcxgpF8mvfoSreDBSt9L3FjdbzMa7obDhJJ7smhlj4kCy1zz8JsC4uoBlZ1mrz5KlL8hJK0b5w5hrVDKSNmsPPNT1uu5X4FjSt4ahxAYkrBp/4UsfjR2SlakHPCesEw1OiV3J0Hw+tVkZiNMpEqW79hLl4teuzr4/3fX4+dmS+JBBEn0K9b8hmV//zxKxxVlnalVjDg3prj65nk9Iemc9DiFpGy0oA60eyHLhAa8rur+zQd9V6xf+cs8zVJB3xYJpYlfeqr4Dz7wV08ppWcWnswF2HqaOO/ktRceh4HN2HkUwwONlx9nQWN5h/rVHOdMXub9YzjKokl382Kc056w66epnSOnbACDJS8Ws1xK6MRC8JH5ADco+4q5d1B4D6SM4/qDLg53o+MzgjtwqCvb9skzk0OFruAPE0wvwX9nrVwPsFKPviIKNnuIMGMSvcrkKn3OK1Io2xDmEEcF5u0aCqxRFUOAErNNWSCttFsytCXxwf/DnkFyEeAMsFA7YcLGEQn6K++aUp/CVfKRYA4f09Cvul+HO8Gz5+8EkdPmbUD8rpR+7cjybCcMjHEVQjC8H8LJjqnOqe1qj2f+GeeRMNZStBpQx9dgAnYN79xCpd51IEBswVGFNR57nHf2RUtrnTmik58S2snLY8kNjqXtOo6O/2qhnCzwex1Oh0N8OwugHyzxu9mar+T9L17Esp7IEv+bt8WaJ994OOzyDh8F//aOP7kIhKeJoxDTVlZll+Z28ngqzfbHUILW9T+EnOHD36h0LFUK5jfqtW4YyV8cVmQ+qJ5LQKXSdFsSvGCKTsbzSJYOVOpqQKlfkbqx2ZK1VMdPPrTCEBzAk9vb79p/78rPdsz/ltkmSTd7a+Kr1JKBEOatIcPervcBpHZ8/g/HD4G0rcJHOoqaX+tUcHza5KXxTa1VFoAVWElA7g0zxNkhF2ddgbxwrpHgH8ozo+2SUz4DvWtZ44+h4091Y/JBK1B+LCx5FNwB/Dv7NZ2ed3r/MFNm9KkteYA4PBiisq8cbMQ4LO7Vy7xoTM+ZIDjk2TcKZApbc8wsSq1Ia98n8IsXnQNBy371oQwniTlxY1nHtwAtjxp8BzEMVb8ckhL8F9DudK4DcQFVUpvj2iZfBQ8dyvdZv8drYXIhmc9Pn2XKWkVzz13/a8CVrX5TAYAFFv6UF+bmhwby/iwEfJ/xA88NP/mCypl5oDMEhrgyoiokdgH1OtNwoQJX8y34irFZgXhcFSRN3yv/20N+A4GRHMlMHtt9mFfCaXNNGS2nlQ57KaEnFi5sV9Dd47wBSM9kDIqmCEtYfkGNYMZlb0o+n+z3XE+b0PGcCnV3HjHOgR9qQ/T6tZ2G3J/8lvmwRie5ymBj3Dpp/L6CQ5lHzVz2kiAI1rCCKuXVqTOdvCvSEjgQl5MuHlc8AYb7AqjjsKe1o/hskcFwuiduitUfouYQq2CmVizxRMRHKDB6+Y+tdWdlvPaLsAZZoF12Lqv2gtRAkc53My90wEZzSCEbrC0syzd4kYmLr1hcuLawY5WQWQ02lI8ErlMddAw5GEDtb7KYMxC3EDrWXo3StpfCJwU4e/g72IzkHh8b+orWcaj6ybmeaMXM1d2O9iz81j9GzRq7Q+QoRYpJDtXevhQn3/ZfC24wZ9aqh+ROzR86ulJ33lB0AD2doJ6ed8t9S13sKt0vdXkGrQVo88W7pDuroQuVBgoAcBjY1lEVQfgoqFJ2h+AiG/mMg/vnp5JEuRy1I2ylSopnbSMttlN/EOTFDH/bTDvE/LpPTlNC/8knKR0nw3Pj3ynPPO/RMFw0qdCbCmRXso1zfzC7A3WMcqU0gkyQmUB4iGSpanpfAYF1NfPvXBdcFrSfU4qlhIqrzRvNzSpyfxYp1lq47e6lPtuQ5TM7Fg99pP9K2fdkw7HQ/1ASbCk6Nq/S3ErXNixHDiHSzi8XiWF6zjp+jre5yK63/45qKV0xqU1ZrI4v+MAqrURicIT7F/inLlJPRCX5YuRKLsLRfvRnCCKG0nWdLLaTAn+/QROnYSel7QzjR/mmnLv0MUHcjDOh6QiE6pQhMUWrfPvV2YWT+bZWTMotTHLFOA2saRfV2sh85ZLjdZGGRqUR51/yUk0iffCDUH/5wkpC9jL3mnvuYcVzfRCq6AN+VHcKMM2pj1Cd3kjHiV83PIN0pWsKKF/vfRMjwkwRH1hOvpnflWUAEFFkCUMj8V5kcBiAfKwGWr/bf3PgbWE/sYXAjd+Qc8GwYh2v/TTVnbpJmL/QKZ5410FfGFbHrU0JFfP1SfVb0d1OsbYpn3Rgy372vU7upNHnY/Oq+gQCaGX16P6Gq0tPzJSAuzX+PtG2oFq5YTrg1oeISvEj8MBiNVA58YyEIb6MbLseZWoLan/5pQQ0dKw9rCnKFxrgup13x8/Njv71wo9vvzPoncL7L3crNPf0S2F3h4VkY0US9iUSKRjSEbwZJ+0BFfoFDLNGG600pxD7NvDZ80hCENvQgRbBcedBlY1iGvUPswuh74tzXkE52g9hxfg+/dKEpd3jhcDhIxzJTXj/crZ0+IUfvGRswLCkcyEhueJ3y2zKXpYIi9AM1cqd7RBXUn+QuMHh4f/xnAO5eQkw4ATIxYblaZandKMElzdrOZEZJX8sOfwXhYZdxSqLTlv06la1QfCANqPlxcpIwAmH251qFaEzGWZqFvtOWw59DW0UEWPBN3emJsYWkgoSWbUE3qXdGH7wa4crCWl62zN9deqZoFW0+S+PHrT78pUFF1bXwDnlYq5dUArvNFp22W9GkAmN+Iy4ku3efk4HT4xiBiBjT57oUIEgiySBtMe3Jx7ruuRYbqEjEL8tjEsQ09qrvi7X3rOVza0GpQPm5Z+bGI2XqCzTWcoIRCwXCKgE9QkP5utGTWFiBaRpq+YXCJtuv2tqoJYHz6cn8mrl51ZP3Q5Olb9YP8CZfDrEKNqfUJq5vNQTCCgP3SB4CSU7mlETuRQqeqXv0rbTePltzlkj8NdA53Eu3P6Z5r9pcv9L9jz6IN9C2EPuQ7c93aeLWxbrPsL8u0Jn3OSgbH1V2qPx8Dfz+ac9sHGjGkTxJXEnPFxlLJSrAI6znj1U3Lc+kqt5IXEATRBW8yVeo1CkHx5lyUdONEGhvFPSr+LHEiy9eEoikHkAJTPzzCy3y6Q+bBUs2EcSl2l9Yrxa4OlRz+F2l5Lkb7tnN695Qe/7FWHjJ+ocyRkhAz58TOTXmkePsK4vKcEk/XL9/E9kLgFeU3tdFl+JcOr1iJtXQSThmw31IjVcrD2c60e4/zI/5CeQMR5Lhs/Nh4U4aLpz2E3Xubs6y+9AZRtvQTMYNqgNfv+2BCuXIx7zJrKOc3nYAI2f7oXKzuUhJVaHghMR9hl/ZFCb6AiNsGt3ydpkdjv82sjlNTSuw/F/3kenpQ868ZvK1kTxXVFGCymWM/dsHB1lhhl5dLIRK+o8TDOUbfztZpxbg+9yhXBh2chTWt2CX/cbXVS5t8bdhQs/qAAWjJ8UgjGpnVQCLZECcGDUjiE1MBmOchv/BRvRNjnkjiJfx8T5BZHE3Lc8rWPyZ3SsUU2bA/Rocdo2pO6NSFZ5rVnKsfoWvIf5N85A+zIJ+PZ1dINgbxhJEDuD37THaikR5hBeg+TFO/iYdBGllxB6uWweEo41HMUZL8Vtzz25LdgTZ4malOCQgQ7AoUPkBJtaxD2/wnLMx1QJyH/rfTKXoH9OOvkh+ul2CCwADHtjlnOYRWTT4VtRi/Rb+ayLoSnEy7bf0WXPU3EG/Kem/bXmbhLpvEsiA420c0muOkc5fOgHZP9pLdsluzd5/tjjmr+CrzxMiib/56Myqf8taeRk0OVWQwX7we4vVAqp/5aHpxgjj/F9jcFjU4D2JCUg3W59Y/iukDiYkjsaHPjlbDX/uNSWkLuzG38ivjsBhLPoOAdblpVNhZzQw2Lmxj1IiEx6fdFIBPNwh9edUFGSVGB5DSCU34uuWSO0Yf5bWea+gCPdaOjdGMm2W2IwMy/HQuraFtkOehgiyq9veCH9XatH02KmfJTwslCJLXplQmphtnCZPRgNbBr1t6bKlmjbDjXF2Ll1V0Irmwt1bIsvPKmW4R3LNTMDlnKQSKmOPoeIwhUSLyBfeOlPCRKpZeB0QyG12Ao3lJEit+Cw0gyAOE9ekiUjiI4DM/dLSzLcmhbuDs0gvdyGnQALzL63K3mWalpsRPC7+emaOnv7GrsTKldX8EBvul7VRRKcOtJoGd1bUG4RO1upAwe/bvfA/CJGl3CynuT5TGMcCXA1jU1P4UED4NBud722aDx4D5y/WHD7/QGzwfm2LkgvalT8guLNHW/zr8ikXNj0O0OBwkDJZB1eo0vOm6GNTwKUzqqY9+78A4B5cIRko5H1JYcRbNDAJRXnG+68Ynkeo9lf+OZA7KzVAfDRjlweKkHYfNnfur3oIrVhejoa81D4Qp3Io4aDMRRou+kMFEXYEVhTju7vkCI2zCjRCo/szK9rY2v4ib/beqaxU3c53c9h8EvLaql5CWhN6KHVyVP8+WScYxp1nG2xX6ncPle0S9qo+GbKJyO9UQsT000giAAfwwaePrmi1Iz8aLMDHhyW7YacT5K930ZRWDFZ+Xkt8bzeerTWHIeO8zIa0c96j/SXh3Ux5wFZVVoEhyw50lB6RZbiTaj95z6pyzV+EsZ/fO4wmvlXanEh7+AL3l/tq4oehRcSe9T8a1Z2khPX6cjhZ+ZMdPk/R2GWO+r15isedYwsTrsNcApdntVYzLYiPnHjPSGi7t3FnUo2RYGJWC/uhFzSmPPA5E5h+lPYyhPI87FiJQz7YVNPkfA9t9sbxxS/zf5gwJBoTQ/Ir7l8Q92fRxpqjsYCl+cZdPtcpH/3L/61olmSFm5ihWYP6sZoW90XY7OSjXCSJmQfZ8cwaEDZcozg4C9WIgJskmMimdFEoIDGhocA9of/Ko1tbXW34c7Q78oUn85aq4URzsOaQ/Tim1ywJ19QpJENs7Givzk+eXCepzYA57/yMWVXOOz6Xc4Fis3QQkERllP+HwzHNA44k4tFi4cxd3dqCambSJS3PB4bvJIAFruKpf1wcoMjRhZOsWXVgwI0tX4hz9Qc/EPQ1ndQPaF31V/5SmmhfDoLSgc+wRb0PjFT3ToQK6kd5ZeKHU+nLTnieNj2KVf5cu4aiXX90fX7fTlF62IEl1XtyRiWAaHrmu6MRaleV2myBzTzIg7GNLNkKSeT1UTrn1q8SFs8woozt/fzl1saJQ+I1/GuMVTYO7SRRnJC/9K5N/GZE/TH9X1fNwCKhHmVt8NJfC6de0vaL4EzPTIj50lJ2/0lw7pNQKYhxpGN1Ngc3f5EvyLQ5LlZhRJJE63V8ixsZSmVbl6swPjNuXxm/m5+OFXZYxcySbfHOqiCMxvDLtY8wDWBc4uOFG+c7QJu8PMcJTFL4k7bsKzqxWvH9l45O9EASQmTYTZSVkhXu11c6h2ebBI3DYRNx9VJ5JcandTix1BPSfKLjfnyXdZSFBDV7ibERBxs/iO7X4lOiVVz1QY2HAARnxYUIgVd3Yv64FqVVdUgJ3seAkStitpIag2leOJYdN0r2xajO/9pnXozQsrnbaEbU8DSmfvxFOJbNbiYmavF9xC078RofYFbUjzTrz4+3dCyT1ipGlTc1Llzb/PbBvOf2qH8OoAoBntVZ4YfExY/wZocZQxmdVXIdw7e1AVzyq4jZtF7kJlDHU+ThaM+oc++vOH+a2qDS7Trnop6hpSyO2d8wEEi54sASfq9ivZBH+VL9zT1TQaX5Hk6MR9zq3EWZ9si5Tf6VAb+/fnUBmYVZ9+ykCqd4zMERTr5fMuC6YZ9Yu6e/1TqviAqkUXndmmVY36YPyqTs1pRqjwfhBpQ5C2IWis42TSqL6oq214ikdvSJUHxfHj37a0kDoKCCoLwb1TSyPcFF6mvbtuQJ0KilWkUIWMxaKBAO2S6WvhI6+eQsUL0+8+LusvxN2YzGb8CVCITgxd30Xfti63fRcI3G9Eb/fb0er0aRUh3+wnAhZLgRjjsrZ3Ikq1pMrxBnlLSxwa6D2I2vUoQSSPXEAcy1tD2+riPQX1ZW1i4XaI9bmG3lKAorP3Z4Mn9T6ZCv5IDDs1fSc7gvw/pE8uCjiwoeb/sp2g6pipBu9peCkaWDO7w7AA/+SWZGIOS7IlYd4jv+lL2ARtsp7EEI/VZC40r53ygZ9+g+zvQDsOiPr2BytEpy0wRfVjd8Ft//FCe+xR61l4YvR57MNN2Tlt8rA+RrvzBLxn9SL2ylQ23g5GEDBvyCxXkmikoDoGc9oSjerN+K+WZba6bD3wat/9bT2RhPqcv93ui/soOB4Nty0qwvhHjUxGxFKwijwMYfjlylz2d3+S+MaOyaCdo+QIs9bmYMM6nhLrR0WpGftVMAGXto1k9UKQhUSkcDIvbfYxoTp7nf20JSLDd3hYfOImQqa2G/ivX9id/flmYJxsn8q6AwIEPS8mqBTgD82RcO819k6+zQQJMpX+8OfdD4GYb5ZfMxqzx+mGXtB8cYXDupm1aMOXb4fz05EAJARwTXEesQrsfQMvppxOdT8pILz6N7QOgLedfEr7qX02fT0PiS5wkHjcYWfQNMJDb2mF7XC0YmhvaD/r6qu6mmsYh+9i1IvioCiVu7W8RNe3zF46wjR+eQ6zfwiopPmM/5W9jsE2BGqNeehxxkQBb8XqcxOzMIyhP7wioPHkpaq2+QSyZzkhEFiRz1g8PWYi8MiVoFkUPWCLvDpiOf5XaPA2KJaNMuj6OlgYu8EB+Ilr2MIwo+ndociu5pf5LjJmIbU0W4C4Q24TDmKztcpYlJWUSZTnnZze47++EQSqDsubeQJwQCJMmgHiteblbD+MQAtbJLgoXH4qOebAS334Ygs/2R1swI4STVJHVGT6WQNtItadaJcursAlsIrLCYyUYroac+b6jKkKTvirQXRr/n80AbZ+jvkk1VKNxBS5ZJzyLm7M7/bR0WZENAS/Sk2yJtIYSBV9SobXSVgI1cIy0QllqPf/OuX72Q02GdeD0buLpOPkofGzvp/007/tfGDYgBQDy29+w0pSAvDYBx5Ctjcxzaoq65o1WiUWVMGQtwT0lD6V8rtzWV1GJb+VubupyPd0oFvC8MKJGolqKQDcxa9b88DfcVv58i/dIbHM/muJIxhn+ROzMx/dPbe6FYPbvB6qFNttlIWW7hvl5qf504qoeuxll9JosTNwRpiV51Ky9rbpJETYqGkPWEK+SW7EwpIxhNS9FA+Z+emNQsjPiC7IZGEK+jBakywwnK6CFXF8h8jv3ZEqkOSEWnCeHj6JFsP7QTMhxyFQdRtK9/agl6XyvvzQLxkD9YkfegmAMwSQoS1HSosMWokH0zdzJ9cTmZan8SVpzZMg5YS/A3ZR4Qn6IUQeO/+GGJYgiMGwqeOlQiBGgm6W/aOVANG0p8u9tp++1bqLGTkATM1+B08wvKOU5KqDEDoEz3OMAl/tQfG+mKGQcfTe8b4NwkfDM72yaz9solBfD5Xgat7HzO+LhxUiBYa17n1jK5+Wlh+EESR5s4p74/O7vp3ykIpYCqm/YAP9np5A0p3zYqxXNTXoWGYR3tAGPRKLbysArElMAuBjaiG4OJHXVgJhDcz+GGwkobb6e9cDIUl+f6b5jJN1uRmBSwELlrvSvdgT6d0KO+dD9D6IiFrd+wXuyIxYveg+Ebdi9HyT300iBx81xzCe+wkNugjMrpQIjZeX0LffBX7/1zlMAaZHmORkxYU1Kq1iimkPJokwliDIcmGuAxo8tx9lLbeAMAD7bcgnxi4k9svs18rr31oNbAseqBRPHmlD8uZz3t9wMUgymRgCkxvwcREGYheKP4sE9z66jPw3jRIsRfFDD48CV9x69xfEoY9w+Kei6RcLa/jk+Mnr82EwKWsxQ+S9WI/qqh5cHUDlq2wnYuh5c8YPjk90QPBjtk0GgIO+kPA3PSRU30V6UjdaSW4xVum9kzGsOikRENWwt61cRJbhLtgMPBtsDf+AUXWGwp6ZRWQHCwXISo1NraHbe/KgsZDQia/d33BlO7PqYvJ8eJiPK1414LJFz/MmTlYYKnyBZSB8lIKaLYOsYxFGj6qM+Mac0PhQB/ix0UT9oSNsHhuijz4/oRnZDnn+MqGupPQ/XI+Kc0O0tiIyHK0iotjzOrfipbJqkpNwR1DS0H8wF1Yal+cHZcv7IlohlQZaBQUAe7LsIlr4d+/56pr8NFaKCxnVmJOMGXVMEf7zwgFdHCa1CHxXZrHp5PshFFWzomJa9iMINClnm2htjE9vPFYMAM+2QppoYSSOtuY7Tze8qEzXC+d+yZ4hnblksPRxjir7Fi0rWCdIcNxcFDLca/rNGZGVWE3sdU+ldJIsGr8sEqTJH6vnLJpKOJ9IsVo86X846V/EXcIr6odLgIXwkk4JJ/rP6xZ7ysxT4bJTUAPGriKGm1QvU+mNLaT+Gr2nD9EtYXXQjvkSDTUkZef1wzyhZR4bKPl6PzKpnU95QrfLfl7mHNPbavZkaK/fM9oHRMDCW828xDClBa14Rk7NCpaSCNyYgdOpDCl5T5wOlIXhROSWdZNacmElOuA9QlAQ94nbSGfSJAkkZM93dE+NLUt/uY+iW1VpQy3CyXUp7gL+khDEgjVL7qe5jHFbMksL+1NaSCu2Hfohhks2MNP+nvLjoIN9cNK4SM5Gk+VmyWpY/gc/e5ObiP0dIJ5O2XEj3d6sNe9JEGYgXxbtjHg6NdzK4DTm9R1A2iTNUm6G8hopZiDQJ9ITRsRSYJt8RSYK9y02B4/9Z3VyHZaE+zsHt33KTGgzzQZUFDQCcBMre0/Gi/sxQbwpGGG0CsJrsvwRxarG6OVRa6Rjyrx9BS53edoxMOCGgFmroyCor0bNiOhDlbmK3KM8HFTgaK+iOaBO2GsHh1Z5L+kNw5kL1Trdirm1cEFvRA1bEH0t6iYeJpT8ljO5iiVVyEeWchqXT7QDYZXKstyjweCROmmzHRhjyZX5xCxc4PWJPKtn2LMDsHgwMPVDBSm2VlZgU0eNrmq78D2u6cGL20GC6pqhEmER69i5bicRpbqubC73VXX4J/j6IBIS2FGUkRJ/QtJXbasJl8pKNl+twLhsEwgHs4jwUwD0iSwf+5mYYkK45ZbQH2cgLmmfjw1vyIg9s2mJPxXIA4hTwc7hlwKr30Oa/KyY3RICvA3w9eMjoQpP7dmQrhgPtmTPw7ANoNWSKhWe2+csLjpQBhEGj+XQiG0fFCH5JyQ+GijFXar4u9C7gfYhQxaPNodZxQqljxZYRgz2KXM6cvgi1/iyfKWtfo31/XLX7uDbvdEHKrUUWlinc9vmktjOxtnsl8Wm3P1qzqfO+QLxkNu1dUVM4pocUAf0SNVeXCjnbK4XQxYVkRidT+mYh+rKkQn3jT6I5DM6pfROMszl2qN/hYdhvAc7NKhaXUFWI9WfFSZZItl7EYXrO+bR63KdEA8Exmqwv2J/ek9qk/CRYf2hn91e/dOZTKFXmUL6RRkP4YylqHZFr+UJZIt2ShGpRI11ojO3dEF4jUAGSntufb0mPbv93j9uH9pulf8bJUwv7ginbUnyqMpP2sRiOeyLQqlXH9lfLxqXK4Y8s9uKvzJ6M0fOJU0I3fwc/IaTk88zZdLi6G2Jq4AOzEEN8zyB7AkEo7zRWeLlAyzEbcoosUM8xRPDnp/Nl10c75IfaU8k3TP9oCq5y6PlTTGSiuuvnsaKe2vFD+PZG2jkAkGeRGtIiQfRHl5TN7ibTA5/ZZDA73u7JTccIo/K7iNSVcs09WrclosncwxjI3hhw5A2m5PA+turjyeWGE7VtndjTsd/Ja0EyoDLxcMTMibCzqknGpiKQx0Nzc+koBGWpotBGRx2nX7jsjCrgkY3O1R+TdyaXTALRNYXmd0tYhXlcb3eKvrgIubFXgL1Y4q5fybBXROXj9+KhSnWBf9cc+6YyQ85a1EtrYqNc6ep4iFfNHQvwnSSo3nYf4XAWHHmzGk+uViVYXM3v2kj1xxl/RTsjBGNQU/jyDUb7QkUxKd5mz9cThUxVgj10UkawBAKjrYTdgG4zEPkRFeY1EN6xzrnPiy63mUIZOvzL6nkLf82ThlC8UZ1eGY79qzUwQ+7cSzxS2RysruoiXkzlR4SHdJpXDdKUtRv5sYiDYMiJTrX0tvyUUrD74jvRTlaEabHdlxb8OvEfupMQ1Hi8HtPKAgQfCUF1rlxV865WUx0AKSzmQgJVVvro/MRbNCgx4j19ldjw2mhLUSyj3jN5lCqgQccoeb8kgBqmV2giaCkYvQoGto/7ZdgVb1kJvt3Zst9SNpA1SQKz4s+F1utbxoCndquDK71D6+iIZ0O9c4GwVKHGbYZknwhzd4gcMBRP7yjb/+pFf7sc5BfrTIxEJwQ0LAf18+RF4yqD8K9EEZKj+MbRYnpdnAmdPelzfd+0Qb6TzTJ/Qneny52Fh/FX8k/XfOwjPg76OHkbDR7jVmmrIkPEqemx1gyTOZpaJGOh7kqicwy/3lTz/b2ln8D3IbfD5SfpCR1X/lDHScXQUlNl+nXGBQe2mq4Obzel0w+tM8Zk5qynPDs410ZPx6oI5lT4fEJgIDBZP2EWP2/W+EeIridqWYsd02cdI1FGGvrpxM2igSmrVrEEo/69TqTyIiBnvoZKFqx7GNLHZR/yG5togd684RPJBOhnzyjTSpGMybScfMi//S2lXXAQIJeHIv/YgyJpYStYvp7xYZuJEaFlCreurfExGHzAHphkE4Trs/Hh4UOTRt11r94QvEaY/E/XlyuAxL2wicJsZ20c3igWSxZGl849ZcOFzmG28or6IC7r58MkrVXvMTk46TnnKgW3gwEydSHLednjqd5xUDsEFfzV3hwfA0IKsjX6MlCvI1aXD394xAkKwoyynC7ODgvA+NHHRYUmJf3lDTZNNWvKZL1nuYXJc39X5mJGTS+iHWHzLraoMSHQ5X4oQqEFUYln75RzGMrmwMrEoGX8uic5cqLEhYFybnd3zJXRq6oIeI2IixVOzGPXZC63FEf2kk6TQ+LuoIDjZdMwuya9Ns3Zkfv7C8P8DBYkMs0xSLW7TL4cUferHEnX5tiOSvta/3nTU5/V6tdorzB1fvG3fHrdqZrfkJAhTcfq1YaMMmjjxHop4Wlg5FLVE3OrbG0Ww9SJ9rjLh/FXA/bDL3EQTaWGorDGdSx5UFfjM7bljT7YFQD8T8yhjMVFJzY9Ph7qR8niucsicUDdwOeZJdxZFk1qGD2qWg+nTafrI6Ok+GFn5jpkGIQHJGNzbACDt1813kWRDJ+0WcsyHr7vKXy+lJ5EqsK3TQcEms2cAtdKIFm7hE6/yx66e8SuCy7eS48h8zsd0KYIle6lNbcSVGgG2dZXHp5tspO7JGXAhImr80ss9YQkHB50GqxHunSTpB/I7W1eGs0JB/eYvhR6pS30DN78twW1dvGqCT0e8zp65LEeOTzHVdMcvXdy7jIbRkXXqjv9HU1q62bTBuNGNffHMt3fmKaiZReZuX1kmadfvtUGonh985tM2uleqUzvxduZCgQ1VBoKjgWbbEvWROh17if7wKW5CFD37Jeylif0WuNO+jDPi4OxIUnJViZhqB/65zyxuyWQ58X4tZP0ols7Ubd4YDAv33thgozVETSv8HtppKMdN3eNvT5H7vsgGS0BbMevzICXfD5X3fZkoKNYzt9+Cn0NWabdPguszxUEX7+pjoQdrW0EYo/Ugp75UYMdRK9VmlQ/05zs999IGZjO681vGpN9WiZtCSEeGeqUXG4PqoY/0iofTWR0xM9dXsns6q7jY9IIqzg9+ZhvRaqit0HsgqcUonOelMstCefQQqAmGCxKnojcFhPyG6Dvv/nhmjQtCIGYqg4MYwrODEJhR01AGuL/90ZAY6pVbsx2frSX05j0hsIS/WdjK1oTRGTYIlLv1AzvkZ2zJHvv0gwxIkKFPwVMTxmeUd49mYG5nHE4SXN782/2qyoBAGQrnMMuiEmc0o0gSljXXfZQdsmrYJ1jZklboJQuM+L1A3l328czvRMlD++cr/w5/q9GZt+Ssaxp5YkVn/UY+1nVKuqu4Tz0t5YIXwvAc4wccR/QqpY5iDhSj82IG1E09R/iNTIRKlICHWhRATBFjPJ/vYKxTDzHX/F4oPYpbhumeQkPj1rv/+RjY24i2Y3iz3Mg89K2M+NZMIRwAXfkev+BNOV4489prnNkBx3MwP3J6QdeBJpMxq+RJI78kJ9e1rhBsTGVZonllqgixac2BGo5csFj6fEm7wrZVDA59VOliHtylNBmWHWEznnL0Rgzhc+3DrFHqp7qTY3+17GeKkMsbZejHonbyEjA6OV7DvP6CeBf2G+TVitakQMAUnok8TOVmPnEj2x9nxw3fMh8Hn+rJqewcgrAqbg+ujcet42yN0pyJNvY4ZSJ/7nTbZY+1RiWKSV/t+3mgQst/7lxVEk4fiVBFwyFxwZFDlA2TGuPxmDdVWDE/2ucDJ2W/aljWniU2jJSEYDqoHwkg+Md/GOd31LyF/giquoON8dwmXqm/hclHFvRkTVX2pzerR1o4lB34W8LHG/FshvzuDHkfFYripIZQzEGZeJiTwXPWdLaTzavowoEYJpbrUzvGWTXYXxFQvSbNHogvd6GUpyCzgDrXt9Vv/iuN5XQkLxzPNFSrV0NIA0OT+eesIZarhXqEUlGxXpshi7JKc6NkIW3Mxy0Lx6MbKZxjSBEAUzp7d1bJHyFe7mRDpe7uxNKOti/2Md0HVOYVz0M+rkJ/LmzsX6gbAxMEzQQoUYPT/lhW9xK6L04DYfmoP75HxmPUd/lC7AD7pdZFd3CNa38zXAsDUGzUvvkjmhZIybmCXYzsh/g8byreIi21ZWqH+aeyWd61SOb76L8G8Y7hTu9x5jaFW60YkMQUSq+8GJEaHUBUvlNRRodMnpg4Hek171f4mPlLg7oE4bXavBvT1uIg/lkN8bfTM96lb96J+xajXp59oCpNKrBPRBwkCPAj03CwL241PO8YhPuRTZdEAuYldS2E1u7Tqa/5MaXM+1o5J+R1iHb2s+BRdeZfOvcYUxfYN8es351iDVhtyWKYp+4lJAWtk4g7q1NWINUnpG2jtzMUFFG1n5hlUd/aN+dTeusNt6TSohQG0zqutXwm6XrOThv3oenbHsT/zHArtd05erbme8fipI8dG48n1fe6hpmpKAW3DvzaQcmJFL6JdCifepls8mg5msK3/nFPXx9ii5aAgvnyrbYzilf4AfO4ed8zWX33koKxXQldKck54jvqsVZXIv/ybVd10c+iJs+KaGvTihnzMZVzRDpfGpHNMWO3qDS37UB1XfPy2itCXsR0Ci6PlZqW7++qG8Ozl5/TbFTpMHCYfz2rt1ksa9mX2fZ8na4TyXQYST04+JQ6JmxqLxXWCtJhczMmyz/PuCSmoFcz8je5WNRjEu5gXLCLIcH9UIXTog1DjeD9XH+/wedAlJRkZjCaUa6PbJH6wXTQtqalCaphOLf+Zu9dFqz7dJ/hHxXaXmq8pGXd3NPIxzgP8scfKcIC4kEFsu7T+efxjiRC8t8cztkiQ6MZLW0bgFcnmsicnnMJcb9QnhyamgoablbCWMjWK5g5t0NiJYf7FLNWjBz8LMrT+5X3y+mQnDm+1sd1JtXhiMJ04vUvuGa+lmbyUkP3SrOgF7UPcplAtfN4lDrGhRgPlSGBJzhv+HPOz8WlP3xBsen5NVpYzs0aO2eHyJgCAxBfEN37ZWFi/A6o22Ov24mKeLdV5ibZZEtJ4vXb3QzadMX6b+ioqUE3UfX88/WrnzWDN0MV8lFnzhf8+TyLyajRvltPbjSdBFdDKEt2DiNdN9WJAfM+N/XVSDJZkE1SVtg2gm+wE6/EhvhKwT6fc4VzFzY0QM1fx8L60nefVOXst/FeRHP6i7Tp38/4t3L0qosZ8aJtiwqjCcG1iP2yBTEf8Ess9mV4aRjI2GjgXkNMAPs0KO5M99brbEvpzvlvu3fdgOmKYqzn1M+PP1KedMb3lk5e7EAgnECVDzTpkS5tLIDCLW8RYQpSnP7oiNzjmUmzXA7AIraBPiREDwC46RgOZCa16GJKYPvZ1R5scT5DaRpMjNRcP1lqLFnISdwoA2LTX2qb1JBg3dweoq2fUix214s2cKb7HH9p7OE26npcpB9jYtr0qZaA5pHRbdlmn5Q8LQ/4x4ilNjOBbQjMEY5OEn5HB0l4btN+Yzv6qmCVYU7lyedTAra6YVktlbUiO/dh4dXrgj4JEk2pMEz3vjVzHtnxVALbY6BziPshgBFKdoVaXGP2/VzR4VPL/ivfKze8m3FQp6t3nO6qBIojEKENLb84DYQIrm8a/mZANKOa0Cqws2C0lvzrGBa6bHn0ONurNYPL8t2G/PESe1YAWWFK6DimjIWHnCT7C6xe7ttVjXJV+qYgDvSBjqJyWWuEBxq1XYieCsN5Vry1Ig8dzrH2ysZyEq7J6mycQWQLTqQZ0V+ft7NDS+NENppSvAeWf0nFuuL1h4ysTr7BSFIWEsYWsGo8frSaexV4wiYMQ6XOnwmaziqJPUhLBg1/HOxYh5cfGaCilfB7oqFEsdLKhnB7WFv+NranTWzSppvxfoRxUO0BV0E6UrnX6YcFdk8gc1A0SCmgK0H3yPAA09BHG6SX9TtntloCBTbAyl8L/daqEiAqSVT0midQEdWFYtkgs4OMylxYGVbcTiOIrqQLMjChMd/aqnfsGajbg331bZ85DhQ0h0zBriU++IgNWiGHspegRLXohp+9rNxK0ieeasAcpogaRX3aVxshvXElkkfCf82vuaRWlwFnjeVykTNLKuIU+dt7TsFoyxmmtLpxYY2eqYer9bdyu7ki1Rzb4yTHFDiYA89C0kG0HzfSpVGzFDppPYgkvwCN+/Te/ti9BrVlcqx0TPoBR0MVph1MOQaj9gV1i35QZfSI+XmNdLrhe2YtmOlQn/t2L2h1jg12TMpVbUO/dsLyCe/1fVctMJLC7GSvshB/Trb1c2GvAQK9FSq+3T/aOrwyc5O1W0upsrbLG9954EoMISZdVZ9glLq+ZYWFR5H8je2jXX8T/2XqEgL52A6Kc3YtaIwG4XU7NXG1fgHfFaKHv5P3e1nd+C2JB5iOjFnKehevu/4in2sq0VdIQKCilg1/DVFwP9VqYGL4MrOKTKatGyRE6n1CNmKIjsY9QHodAqlfBZd+FuUySrzOLdmr2CEthjh2owUJZwkeWmIZT9IWRj/nXwh5m6FCQLsak7mthy0fgyRmfT97QJpXOYHrcBWt2yFp9Q18BkQcfiBRefKwrVOski4QticC15Z3J710RPyd5eeDILDFCCCeuJfNWFIbBtFaNnqsCHGQ7KLXVDWCb08c9NBusQ3oyhlLmtMSqsDed4NtS6ikdNkm+AuZTQhD04ueChiVyzo4Wz94eSbNYcRn9pVtyoDhwrGRerG/OG+0NYk28wfEuyAH4ToJGUets7FhGJnNJ3+BrzjEK7FYDo8TdiOgTy1OvIhDPkZfKGG0JsPQ4JUYRkZLEX+63YKkBTHmryGnIauraKctm7l1kjW8hIqvreTHW3G0xdmrHEciKg+nXz7XBiq4gTFke7b8WLXy/edIxeKKX2al9mziIn67vy8atRaXGgilh0ocv5z0k0t8fMrzlyAjfMkZOIckiSQrEZNpIyIXTtpnLEOy4ggXOSXmyTTkWd+i7si2j2kGUet8MiqRSpBsO5KNMIyH12DTYYIiwa8k/SuI7ZNCeBkh+PNcq7OIriVDLM5N3oL/vstD2SkJy+S6jTAhJAQVF3gIZrl0nljglxLbW753Xv1tpAesryiD/m8RmuG9PwXERBqgbcxwLueMjAAnJowxvdqtArUwWZieIHFFpvkYFFLZ/V1xYee8DPT/i7MF6idA0QnRWvxLLUf7E0YYrkZcV+M64G2dRTpfgWYTk9BY9jdVOuHPVNPZgO5BIlH+aJ5pu6iYorKlfZEy+Anixk0F/op0Q8IHno2EX+kEWB7v/UZh9MvhOVxj/HBz/TzLnS+8eLS32tD7iTdrTNPPgN2xztzr82K3go9xCML+0pj4BYmMRNiGc3R8oBnGga/YA2MYXzEIDixsiNzkAPkeVvAF6iUVtpqqf+AVlBXT6W6idqPapgQ4U3WAUDrM+nmPtU81O41J10jkkodHVjqoGdEFY+MRae1tIyzTsaJWmqFNegM7g3a8WimK5ENYk00F3SXGYV56ICbf0v1kScH/LuKvPKiv6jCCy21gHBNjFeFTQVjxuAshGszfIrvB9WllZD8w0b4ynJKqL6Lwv16qc/Mb37cnf7P9/m4YLtBWpko6zEBf4fMERGzzkpXSsjJoD/QboIviw+dxS94HhbtB/H04QBx+IhlglSmibahQQRJRrqO4esBax0RxuiURIk+JqntOAh/hnvKlF2lSYhP7OS/x1FmZvGx93hwG2vqHDZSaAfPY+eGoC3t/ASQeaoR0aO7hSp16n0IFeKuAoGFMcU3pxG3C8F9xJD7hx/mAIpsQI3Q1L00dLiK3kNFD1Kxf8NQClApM5RkbwsSjau0pq5zpXPrvlb2Bm1wNhc+3lB/9q3VjSMsmYWAycjBSqqqfVYpu6HsnB/cDpzoPuT9thdvYwGkLwm1m1V+Nd8j8UQweRHHYfEs8MEFchNlrbL4Z9XwFVjO8SdGI1kA64+6gbI7TC7AV1WhlpK7XugxsNF5mmIwARfOyXfPhqvNFl7xTHaf1nLRsmlhIvLJkH4kfjCjv4xq1J2P2jdwRUus5HtYrhtUA+SbEHw9YRE/SRX+K7OPr/UXM9kb+MsSNkaXmZSlloERIYEdbyGewg5xKitfpeszoZHPLCyi6Xa/yfVkfgCjw/b9XVX2nGKb487F6TTsSA7k0ZjFZJfrr3ko7u4SJPh5SbaY+UoMXHjZ3xQtnEuZ8FknQl4qm6qDgDy/pCuEX1W399cxk9V3uUTI8fZVsh5iav7S+nncHdOpqAMug9sLsp/uyaEqT2M/DAhZiZO5a+EgCe7GPSHVrpu9/+TM+LOGo9jqtSjsFn6gZ4haA3+vVTE9HJAsKa1apbIut4F6ehcKRk/YG6eFKzanQAgFaFSIYZc2z2/u21fAhNBlURYvaF7Cho8/u3ZlWfq0cWudDtOnkqA1wp/5r43KbeEOHqIX5OKjXU0KooyzJrHBRSkgZLm79H5IUCyQ+vWIVXNxtuEqZCdXK+flsLKrsL+l9INjweMxmrW6lnXnbvvUXHz19NTmFbjt8fIKoSOhChrpvIfCFxA9oVDb2tJfkDxeI+dIoHVN/Nzk/9AeEXy7Do0x5kkJ2hYoc/avGMIDLqrO1WtmrCwBlv122Zzj1vBHhg9AqVju0X9R/hNR6OK7bLNfpqLxWW4mVKY5vHQNKYgkuWL0kuC1qy7joc4q2IaNju2uwtOLUuKTkMdZZDV9/aY7HFRITB24gcaQaEEbIwE3tyhBn6ZPvDHyicsdAex27eVsvGV70ao6fY1rFHQ6JEJ24LuxRqZzQ76OASPlhnJotCfbgMEenGDMb8966Vtixb0s7CROq8iL5OdZPere87dc+n35tm9lu51WeQJaITgK9ak0DH2YJ+vk9EgdIri+rI2EQHEPk87tiloPeTI5B3PNZ2mnbyikEwmzRC8+fCUCn3247L5U5WO0LXItFJORV8T8a5lWmZNKDNh+Ln5vFEWcAlBB1TxIyJc/VAk08zGhExKYKRUT3QaDBKUA7KTvsvRrT/dMAHkURT3AkjA6Wp7Cm2m6Cidw7nDorKoW9IIFKFyZRHRzjtLbfflS+ccLNpsigX60dfRkBXNN8LRz0CACoLbmirA1EjnzVNjtUyiV7S0bLFQ/VLkYURkC/rGjw2k7G0Fp7VwLV15ykpi3wiJ79EOdKtD80Txjh6M34leBPuGYJDR/YZ22C6PhG22CTrfbowFFr3/Ump7g8QLC0dxEMH3j6FGhU7gdU23NLfUFP++yqh1TcTXrx77KqqMFhS8Sf7hVBksgPBOvkSliqPjznNHOCIbsitoC9o2xzvnQKpA4vcHjRK668l2u33OwFrGpSlGdhxfBKo8jZnLD6do38bC2yV7E9BMrNG6C4U1E+nRFvpLrIpat8HVZFlnXz3GX7Um4I7gxnFzpMiREFfG0ArOgi+SDf8rVW7b86x5zhbtXfDqb7eUP8N0bzhv0bWz27pbZ5PuYhAYmsU003OqEzu+nWHiQe2nb+To5zkdDu/Y0b/u3PFDTa6G9sMXdI9sPPQQBFl+shvpySj+9UambXxQ0Y+8R2P4/4b9tGlDR9eKENkptGmVdH3Gdpph93J+orPJ7T53sAIycR/SXqX49c0ea55kHJBoEzVt7iv3EwunSgM2MLP3s7C9ip8MtP9qB4LPB+ZzG9uWbrp5EbIVYB17gg6Dtg4powrrluhy7FDtftGWNdr+WxiugxZSS/rwFGhhLaGxQDebFjBnNFxLIpH+EDL+VhrPW+Tqw9IBxDzp5TRPwqqy/TJQmfBI9c2JlmQu9BiCrtO8Th8ieH1b7E9XkRGolEasXmhnwTaBV+O2m8sYvjwCs6Z/zymWfffh9KS80EU4oy2eYB/8TVCKAbG/e0OPxd5RfD/1IhyY9bkL4qr5YuTrdL8xUbkIgffSs9JbJQnyf6fbiTGaJa5S5kwNFBk+uwKbkbosTAXiBmlzuEKePP6+nQucgKpyyb3iospf9IjKtjadeu5vz9sfnyu0dwq7thfxEdcCy/TXNlQFLqYo5eGUGsgIkwpnK7AkvIGG5D6i/sBSrgWWYGxhICniLWA6YD2wIzNNlIiHEFtHlQAcuXExifxGJs8je4+XC7aCgIEBJjGSiLmOhjqpHjawAw4hTCGhTE0Yq+QCn5Z+4hGn/4ZtuD6OIr9EPZeGFU9pxXO/9PTzxJBaz/L3YDMDeUEfuZQbaaduwN3m/gflBwT8PjxwEw9ewDS7N8tmlyu1GY1KofXbMCnzAsQ0+qyrhi/fMkmj34D286JmFUc81ogVlkXlR7rRkDJt9XQTtZ/mwk2PhgP90xhQ+mm3TpXAqRvi4/d0wHnAaPjsde++sTkChwqOgl0evnU06ZyFkEeFL2L1QB1MhL1K7jhaGFYGPd+uoKAbjeYXMGxf3tKvSCUhjbWz6RHxMpVYMwwj5mS6+k8JywCxjcyc5Ir9CoNP0VNC+HB4bvi7Yj29zt8H+T+O9kXDh4pm2vli0TXnjZ/z9LV7EsKxYEfwmXJe4Ojexwd+frB+6biIkXs+i+DUeqMrNsY/qlZBCmrvBTG6lc9whg1CJLcu8sBit9oqogK0AnKZUAY2ehyX55iQEyDn4o5VAQCSDITjMrw+/GwfCt+G9w9dkVqoTX9UHP7WEfjMWt1DIbNic7U9vc7s4Lx984FtmKmRzV9RdMtzmW0WuTlxIVmgeWUAxsDh4F9kNnsibJdPg0oxMvqyI1gDMtkSvBX5TxHC5fl70KE65O/PFKT8kYD+HnakHyAcq4/ojdRvj9nsGQ2qxTeC2EiCrIUcgnbe4gs9IfIsUYxMm4gA0f8tZVv2OZMRSbn86G6mV0ETJ2X3bTwGFDwFlvChXSzuFXymeDuYPhhM2jBmpf/jqjFhZNFDyZQaHjtNrwUUu/3NSr3Nhai3hOLqvSCLQphAr8Nu7FmEJufKapBN8DSsRR+f4JSog6EQZ2KXYye2+6ZVat0Ct3fq9LkAZdqyPlxvbM8MqnH3S0hiiZPefT1+3RvqYA2ieEwiU9SdLjQqado0Gq/YpOzuG41E3go5/WdnHXHX2pUtNlXcNIipelg5Azlcn7BLSF3RIYCftfUs6NhPZElFaKaqtKh6ugmwqdhR1kmTejfFw8xLVUej1XmBS6eILbXOl2BvsvS0V1B/kOzA+e6jNOTLH0cMzfBUjFMzUby/uMohErgSaP0My455NApUsFpmUioMbWChcxT90q98aew/A8siO1mGyDRo5hTezJzJk5VGmim8I7dz1rTHHNHLag1Zn5DISLPayZ9Ja2xWcxo68ztwZoOUufUqvi+hyKtmsFDqvsjX1ofwofxxpLKjwLMd/HTZLFgTjgJigxX2JtReth4SFKolpqU6nZsv9Uu9vMfoL40xA0Sm7C+iKNwbqO3BOUnQe3VwuZL/oWDkrPNGYuIPviAcVez3ilaHLSo8+ofVhtxLBbtNW2+RB+/tlQdaeU4QA/9slL5PVc8NEfbTeaZFD3UHMNOpllyctkQwCce29Snp0IOnNfXww5oWAJ30c2KXwlfk6QDpguCNTkm7ORGraw6xUJlGOm22SHrlOAaY0BkZLKwqDznBDgKjbds36ZJiDInkP828tmEX/c2rbxsIe4yQ8rD+rFJQU5VkNPksUOCv/1sMkGAJUgQv5qraTDYjyrao+g7QZ7ogdgsVaqa10GE33LllUIIMleQW92hxVa8bkP++wECQdj+/tnKtuU8E+19nX04Nu/MED7l+FSxN2XNMlskyM/wGW/4LZbgH/fuctqEtYXCrkz4mk99JjSh6Om9+dfYC68LhmbWgwgj61L5FxF1E+P7wOmxDdqnF0AaBrQ46hrzzn5EgZvR4x3eZSa6mgNTJuglvH4d1nK+dozroKY64FHxOZT1wD+eOMuZzy/w1nqLWt8iHVPxjhDm199EbE8K5aNzCaYcuph6wC4JWAc6t2SsauH/Z6nh6A2NHXi2IvrfxU1fiSCHq0XGZcxs2Me+5u8hqmomANDZE7z/HMmsxGYM3x52sWbv+NgmeiecMgAX3oh2TldFq/lGfRS8SjzVnRS1p3oqbFhhBQUhfFAVitH0PopCozOG7uFNxb89wAj+4LvcEruW4XrlszDyJ6N86SFH+g6AsOBlOHaLgdhva/5HJ8BmE1brEhhg6h1eNyPBdHVCK1Q3rAwuuBMHBDjZ12YLItFrj/AP2smWkwvfW8gUhaiqO2ve7c2XONPPYIF5L8A2n5Fj+xWj3VVd/0yQN30kKl8LkjmgOv46xvLwNQBz5QXK1yLWangCGNl5bZBzB7rh9Ls2J0/V7lVSkd8skxGrUXQKGS2D6UKeYORTp9N9rrPA/SxHnMvlP3gJjzkP/FwzzYvAiCQRzbyNJRpXtzV7KWMTIY/Mc8UXLCObneBa9u3GSllUuyn2dDs58cnvPhgZY7CBuijQRze5IsSmueTRT9i9nOf7IMshfti64dJVfhQ96APf9nOBCu+aIeupozzv2zsJllH5k6dVNeqwbwMASjvG6i3hW7SAt/Pr/drx9cNhiJLs0iLhVUrLkkAK6vzgam/oVvfRfG+60rBilNTfW1M6QbkcVEV2s4MVSoiG3WzYhYscVpAAr9pzrDeIvIU67yPnRA0ResXSXhSqGwnjROyFlWcMaWgpSa09fhAWWmPjSRzWDLTj0VzIq6PEOWlsW6pL1Ck23/vAwWA7n5Urxg05cfFQ4AYJX3r8pxpRvpTuuy2FxClPowHjWUrBlreV1zm2pFyiB/CK+MXInhJez9QzwCMNfKDReXWj4P6VECE6q+wN1rPRSWeK2v1RLGLVKwIddVURN8ARyf0+JmoReB4Qca62UBsnV1vJaVPPBbzE67EO001YMfLpsgB8qyVuHvCaQPXaLAG7/kWkqm4b5QeL85+y4Ojz+jrsb3fSp8B/xJf+k+Apn61NsQePR5S3eJFLwo/qzQ/SJ26MFZxmPmrl6yxLuTeMu/FoB8Nw+hftsR8/R3H4/8WPhadr7hfin9ZG1fJUNWhR4QKMNMvT4g5UH5eSO7jaSM4aH0HcRsS0qVPTtqrb+oWDdel+Pz0tZlxdQQRNgdLB53ykWC0XUAMDRoAirL8DNp4F68gtVoHSzGX6PAkrPJcxuzyJSo2mIeTaeNLMfCSeKKwSkKNksEly0rAwGj1PjxVMLJa1DU2QU38l9VJn8COabxAh5O3V0G0uSx90x+l9jIWb8yOycn3GH7+Q5FNEN7Evf+/EyT11yo8FwAkbM98dX5yPviKhNKJul4s8kj5GA6J9QmfOycIwEM8ACd4wvfHT5tkNpREWpjivX53smv6i7uFtJccHQ5l8kDV7pcOsRanNN75g8YQxtqPmVPfMYueHIAb8fnSy9/nbW6h+K3cB7Abd4CrzKS0nOegPGe1uqp2btXA0Tgw9wARdcJ/s5rfyW61lmHgSepMIXVg1ATGGankuXj2qj86w9fdjx5fbJJFOsisIKZl9gVMLniRVPXCTrama6xgptesagcKS1jE9pR2YWN2MMC+zEyEZdjrVCxeis/5voiHWn3p0wZCbpS0m7Y0JTejRNLNxKW3ar5+4BRaFFMHPzVMprU2c/U3AgBiZivFyuV7VtXS/j3dY2kikm9S42uhTHvld6Tll/US1Yya61qh5rOEynx/CTCNrYS8+hdB+iScfdtauIWEDENyg3YgK+8t3NSgajduZcWzyJAxVtMlcDA2lR/i/tcecV6bNsplMSQEMO8Cijs04SWHwp5TTrWE0guCpGRrdzrs3Yqp7A/wrc5mNDvF4pjwEd3INIbxr7ZRcKKy0Dr22BeK8Fh6MXbdNu+J/bhkdt8+X6RUU0xbiLeSm2uKsA9Noa0GXgvfsePblwzgfx2Ho+UvznmaMvWdSVHe0NB8Igvb9ZDtTqQHCaeHrRpwDJNMNGhFA8P6csoSI4IjDPVgjJ4QvtXlPefZpreDrkFPS+/Q4lweGAUt+nkd/5mDgkbh5xXuSuG5a6k+0rTcDz67dmaWeT101hbspGOqsErmBORJzDWdzEbEIVVdwH2lTqdxMRWiBrOFJa2odWvxLaOHnDdKVB3QDwvkNpyyXHgVf1mchDBSjxrh+JKkYpitmcaa9p9p4xDpk63oQaUZRHFhz6KnYLGNoTQRmg8ZMUMk/niAjAylcHMO8oejbB0tmESQpx6AScV2t5HyEkNTXOAgHPS5qd0ZGvKzRXVn/ji6ra2JYE0lhBg21ZvJZ7xQ/IthTAsEfQl7DPHMv7HHpXGjYnf4a14GtUYyhUxj9zQNlHIjAkeuzbG5GtayWEsXjMESNM/afTGG69h4Q8l5qUnOtUcEBJ6JnB7lBU7BUdYIpIFIcGzFi0ynebOyMbp9Jargjvh7SpTF5pdJfWJga8krE+uiktB94H5WOgeAu4Y7U5BWXYqSBLfxvdNo+SDA7oidSO9olm76WbOG2pQM5y8TyfiimvTIxATPoakOVYgDDeVpY1bhE3sjqE7Xo2uLCMCKAPy+2/DVtb4womQ0xQ8a8fAWTlNuT+LqHCOmjl+cqK6vZy7/dEZFYYp6WgR3QA+y0N2FzEEraDguwHJU/NwVukV3ERDHhTq/hWgjCimsb63PCrl+JxZkOXoyB5geRcNiHm0owN8wn3FAjVXMda1qr1zL9vlO4h42TvbHPkVYCHCETy9veD+JK/iakVCBIh6csLTHIOpTpF9ysqB/wO2oYTwnqFLSRtLEc4DPiQz3P8MsFTg6+Sf9gw8HnnzbBZhmpGk2KvdCv0vyCAgW0U7ZHWbsD1vPaaz5ZcQKfGKJvkC/DOucTJIuYVLSQeUomxMPl4zHE9RuGOCFbC8vDBSa3y2HTb60LeC6mjRZJsaQwPCxY3pIY7gHV8neIOy1k1rWbZ8oT7O2RhmvxUWPk3+tAVrGLkpL0MF+Gr+6hkQos9D3F7/A0kZ3pppqbDqjNHIaFDfRY9++WGM0nGYsuTbIKFxKy8LWDCFwXbRaYuQYWqa5TjUQecjxthYlJnjF3J9h9tdqg1xQY+gdTE9+JntNHSt9gikdVi0U7TmcRuWFnPj0DTLWehXTN3ZHnyrL0P97WgOgOe7tZ/4gibbrvNJN5+EwHc5IdbSrzaBFCzIBbyd+PaGte7OPChJpJECvRupBNs7Ch//D3BWwOucLlAuD9anUvQEDcMGuj5FL3zrd7vXXsU3PBR2JZUtSGMgZ1J4JRRib/zz8ipPH85kBcsxf28b+1UOoDsDlcbTt8rtNf/q3Q38ao4H8ziZlCKgsdnxd9YNjvXt0Vz1t5qSMQpaobQqdjCizGnpvCmzon4NMkLbCkTnPQPqg5FtUOFq1sMshhba6RLExth/6dRnNvgM5nVjP1aJQ+/kV20by1a8rSNpm0w6bHX/MHmGwOM6JWhHfnjVUKMrficoFNG8M4Hn6jqyUKmN6sIBpFKjWCgQpRNlI2Bfco0f/pGzRU3SUG3mid7deAGygUsTONMhYphsQE6dhQTS/lTfjT3r1qzAOamiTwXnjpIdi1jxNAA9rH8DehiL0GTzSPlT1tZOmQzXigEdHCdR6us55wdM0g31m6zdcBC+YUO3nFNFghNFQNgyOyLvx85YACpYvkzlzZ4cZODIikmTNKHKhuA1Adw1zes6b/Ybx8Ddgep1nc76nTkiFkWVmKRYJt/dHsoNOVNlVCA0Ii/ChD0YDh0BXWO2drb3CHjz1ZFX9kzwUhMJmAtBVZ6SR9c8e/jwIxcKb7pTkENNmb8pNZTliAb56Rd6MDtcxx6m3nNcxZ8MGeHDsy/bfvFwgfqJBDkUw7pCkCSyPKDTq0QlNCdk0Q9bsMfYX5GNjnmWZd/5cH1dk9YhkAZV8Xy0rJTiel35DCzWmc0FMk3n0G679EKTTybhYvvwB9YS/DWldNmE+KbdTAlsm4UCZc/iD52r4m7XjIKmc0EB8nGLiVDye1+v0cv0SKHqX/cAYTdx/o1LAnCSYqxbUU5TLymOwC9Y2GtLVw+buno6JbQnHCBYvlr+inzB9zhgOhhCYBFeJ47ju+WiSn0/zjqfgBJeCtbL+mpbD9now0scClr4M2HZPClkUDzMNQV4LH3j8ajiIlp0b72MTYgcwZhIO1C/3O4Wik+SInCjDmhfCwrHOAkfaME+PlQcb7/lY6cy7Pr+buJHobPvv7qIMpAOx4P+ibxYv7dwuUad/aW9V6KaIv1GVtBz3qerPYQPq+QndHXe8mKEO5EGMJNMFupeQAF8LnU8TyxR8A6lySY0vS+azqlfP3h1fTHD27rkn3pE4QN/BVc8EvCOACPmMoiFeA3+ClvCzya3V2mL5i4cYGbMUQNxSAl10yNbpirqwSJzhCR+kKLDJpiYX5xTMzHjsXQwAQ3X6Zw4oUsxlNEnzGA7U1+ZVzgsXSBzLsUGE230GX2D0wUvuTBvnk6Z6wFFxFRBEgpohjwsTGedeJktD5KeTcnXY1OJWUu5pLYL4OEnycMUX/7y4W0GAh6L2Gw0JfbhhyWo0RCpyj5Q6T5cCjDsp5+OJDAjpo6EbVjf86XoCfUkIgZdGEaDGDriaYFLTpAfPhDYCbX27K9seGeoaPJNx3LLCkpbCb36guyhMz7jEdSv3mEmi92D9UsbMsNi7G1OxL/yA25XMkG0hTqbUCBo2M6SJ2eLqGdirX2Rud8wsveAyDGDZenKh9CN6YeiQxSoAkimAq1XvMoDsxzDaIUewuWDWtxJltSBdUwSlSjROXxnLtfDT/hdp4vctMZXojzJzNHZrLwpXMOLGsF3pmSE/VQoj7SznlrtvuYL8fXlbwB79o8XtS8j/GuncKfgrjteomHcnmvglEseD/Kxci+48+CTyjom/olPa+35Irb9/kQxQR7NZQrTX+hNVZWtXPVZddnJbTV6CbkMxWYTS/0JbwlamSEIxLBvWG5yahY8jLKGuiWHeMAsFX0LD9YcFRD4Lfwa0AWZULC1tUIpfbHOAZMbOUqyEuDrFNS8T3D7v8Jexoqy/6q9HhVIc27y/8GIIs08owvH1s9U8BcQmBjPQazgSS1969Lf34TL5cnREGdMUEcCUp8RhwGQGwNIBAhuSJ3MtBB4RNjhAX09jOif3Ovop2CC4tIcAQ9ramQCeDAXrbIWFXKpvjIJsyAth09BnC2uVxC8foUfYIDAjCyApBQmX2yrGcFECNuCgSSHEdRhNxCJuODJ7dIei9aBSOcEqiYxYQH/yMS+vqaQXqoWDoAxKr9qZMRl/5ho2ZK7tk0w7v+GI+Qmc7DlA5+cQc8dYMkHZyURyxzGw7OqWRmivtWd4yZBNbDbuJfzjjn2hkUf5l4VtD5Nzr4jcC6WMftrZb6HlMEY/UKZpMZjVBgtgxHQQCX8cxvUesPAoVYv2qHvHWnxhCK0DXEvIr8mbbc5Qni7Htk+Nd9NCQr97a/As+n7bLJOBkzWIgkuvoUdT19UPh0NlB5p9zMP8eGn7cv9d9L3PptCZpLuHI7AcIeAJ+aW7cxjTqRPK6frbmh0FHI2i1QW9XGAXvhhRymzG0RBbZDWnjkE1g7rRHH77MQkmsRUf/8V/WIlS5OF7uZ7w/AGnHwAvAV2YkAAbcloeBeUPTwyRrFmoN2Yz94LGm6OSdi0YF4j3U46okBZaKk6oq4igAX0YnOOkCX5PiKPKiCUOUy9uDHcIrRXGf8hs2oWLKqKsmRr1a330qwtBkOKXPYLz+DfSPWQss/EthSMPR/T3TO6ZmGxBDEiPq3YlNtgZ+rmNAjOjqnw4YzYOgJsQwlxfBlBCup8QYhKoRZw016/7Q4tSt0RsgKgHa7dBpNtFe39OoL8KKplaA/F/f1NcXB5r/4qGZQAMfXVt/ZjIPuoYvPaJyQPHSpvrQxcPfwZZpm59gU7DI3onWWCxkf72HTjzM1you9V96WjlBHTjaKqu+aKOUg/9q8kPjktcnklS3W+CHyt4vffg3TqHWioBAurv8qyA5dE1OrGOwHwgkskt1BZVWp1FIsrxMTmphcn6eJoXXlP+CQaf1Dc729IiGqHo7tAIJ7YkPHUp5myKWs+WakKiUlYlHBbvaCjhwjb4A3zaAf9/F8O4gJPkc5KIskHdDjXLDrq/oRdcI7wX2q/NwcVW3oSZ3Cwd7xOzkL7z0P1Ly6FX9ERlYXyZ0wTZJSE8LMSpKSUPEDu08P7SmnHxj0DVUvA4fpMQXx/b4cRpLOl0QBvkoGZrPWE0WCPpxco9GrZfzSajZIadxfjWQYkaZxVCHGeePL+4dbcJ0mOpfTSk/gRfxtCpxt+1j4Dz46QSNV5237oww7zHfrKNHvd+GuMisuPqws0EpstT/5fBXQXdgIA/DrZtfcJAZ9F/LELofmT/Y/dG06zjJToWkuAU6KYvCbGUc0vx1kTmazkMnCgz7AUMUK6N+NdNcDNhzrn/8kM08K8Uw0ptTV5+LwvMup6oCqqkEDe+hNrFhQnrXH3i42rAagSNUla+YbmFGpj4UiV4MDgTuNb+Rh1+Ab/AgT578kUPxyPfiHMoucn9tM6UsrgYdGETk2ZER23xtThSD6DFFQpVcn0uxnuACt47gmf4i6oeBCC0WqLdk6HpL4ZW8jWPkH2pCC2DHq3odzZjSTy+OgK2oX9Vf7reIKhkfeyHwC5O/Yk0/hikVt9WdnjXLivKJOJUReB1RnYdLftd4mfXQ6L+2dk43YocMx2laxmz/pm5Dsg0Wh7Iu2obF6luin/PzRipYNFpBQFVP4BasDva6POqlJD6VU5tUa/Nfyrpqw74MViT+hInjdNfRd+XN86TzDibOnzXGTEywF+bX0ANZzDVhqoeyr7nAQmRpZAaqGaZuOtB+2Ip1zvdzKhmpqCU7oBgHTFy+YmMUHghaF39lVgfgOJPBmUAX2RlgLv75dcs4J9pFmrwzeeyDmq8Zg7TBEfDz24pVelzasx07Q/JEBZpAGmdPp1k50r+44JBUo/Joz9ubdksEfvPvlydr7NECSLTiZAtKUFLdqMl9ky+hIxurS0/xQn+BoKjIzRczkGhjFHL4qc7fTv0IY4SPJ1ROIlQzYgciI9BW2DqT2N4gcUD+S1mftfvD3nwal4M65r8w9LXcQPHhfXEwEQU8qv+6r2wbTAbz5zIAVopWy6KbfpdfzqWXJ37NkScEyWP2JNPDhKwzgnmX7P9EYaylP3+L+BNvODkvzEhQSG+VhWnBwogjzniks5+4bQAlkVqHiLQqqGeUsyWUpcdmI7KrOJcJ+7zum84VzsKlpAS+tBYoVvVmOb5WVFZSsJwOTD6sFLyBEHG9bxPBUeeC+7+Zx8ts16Qcy/5ksLxpb9PZTd4XN5XlXyAUnuSZT+5kW4+hwojnz074ZKApTFH8SLjdtRyop1SeDzW16Z+thYXQrSTD1CIaIVBezMK0zX5Pwo9HoOQ8y50C8NIce72t1A9OxqFqR9O+VczU6zQF6y0ZE1jfMfZjr9BBr7mJ8ao7npmlwe6hJmDA3XfaWxHYlqbfCYgSiPdeE/h5ec5K4puxAUmR+gIfRozVidnc4xF/IUnYd37MviQYItE/c+FvQwkxbqfZeWpVRIhkonv6peJBPQrATjCPwGK7x+A1EvD+DMWcBxzCq48DLaVtP8igy8WqPsU1VyT0KKQ9T5np1EgPfkHTppKusFrPga6rEIrs0cXI3yq/qdp081oE1PnBC83+BRkUlULoNjsJeU1h5QQPMKnXVc3/ByzpDEx+rmq/XWAjQBVz1KBPxkaSgzw7EfoOSPwH/uXYr5QmWEygGAz/NRtJ1o8Ju7iQ/ln2LMgA92jIu6vhdIHZZQuWL4zfsviCL6G66v5JfJgx2JzTk3VKXnwK8O8pDa2iula0Zb+1HvuMObrJaJT9dLokTJyGq1B9KxhtZP6v7P1EhROKZ6lAw8GaSxPWqtH3Aqv+m5I/BSnyYXfztnB0UU9ttoy8GnArAfNSXpT6EvtUqogYXDj1Z7fmyUZ98qCyIHzqpAw3NieIc39oDzri3/XtmrRBszj9KBa3DNYrUMpTExZsYGRPkB4HFcSTYBf4EEaAxwoI5lhDkyoihgXm3BzFnjcMbUPsH6IWPm7+VF9wUlgYYiX2cdI3JCAklJEOYi4c7ktEiYtnaSa/UjgB8V/ycJP06kq4roi5cFQP7slhxMNvv2En2HsIx1ZgwGhumuP1wbh0cap+FRmFIJenGy59rSDie2SW0W2Q9kVrSzSgAZIrGbXhA99qPnPinOSIkLhaX9HKPju1lbb04uFlnMEkZ/G1A2hDGLSiYaSEKDQIBVDBstUPopALHUGgfFdvRzS/dZVgjm+0OUwuyvqg8iU6zOfSMi7Kw4sf/Chh6ot/yFD/wWYySVXOkLpDmWEF2bbLartIWHTgE/0rN1tbbNT/OkWHFDUiHSh2IZugzXuooMZQXxm+H5p4XcBxV/3mfd+7zl5PiIn5Ev0/XvrqOaxn6Ojz5pTVwsB5mi/gmJhcZrLWh00rgFQCyYyJ+JXyUm1A+4l0Zl5ZIBdH4OPWeIA/2WLKMbsfmGGG3b7SmzFh+ZMOikRHdLjUfWkBfjyKB14KDM2/Uh40Bfri8kx6n+tf4xsEL6BGjS1IBsPVX+BkslDFSkiVWxMrztDIw1p6jKU8Vu/juHUCA06uuHw+sZo0PhHtyXZ/qK/TJs2dmEMZBv/cyVSv1JPSIpg22r3n3bkf8l3o3jiuHYswD0lHPuxjsw8i6OO2LC4OXrSUHj6MUWdYeSFGWwkQPmH4kTpr/BRHhXJFoZsX37EkJt15AkSRUknTZVA18oF+0lK8kp7B48xXFrahlFOpAy75l8XrAf/dfFg/NoXUO7Ti84WOy3D/Qf/wF1h3/+Qz1aC095qEclLdL1kr/n5XQ50Grn16aaDl0Nx9OUJxPYXQqfimbGb1iLaB6fFkps5WTU903o86lyG8uCUy9UjEPPIfGDSkktjlPbAQ6SgKb/T9FldZv1GKr92BAFD74XoTSGOG6eY9VQLA9CaIdoDJFfPySmH9cywV2mXWg+i3dC0eGQHGHCX262YifHUGpCatFx4ZUoNFZg9IGFetxeDNgvInXS6vJWaWPhDqkkpyRmAJ3KN9eE8B40RM+PYOwBJSriAto3dpptW1r/k0u9lAWrfE2tToENv9qhdikORzPlZHvBQutBkDwf2ELh9PneH9UCbQI2/Qlcu1H0/4nQsYu2L+uRLQSFjpmavtUQt/mtf3AfkJzwuiWPnmiBuUebm/dpG5qq1G0Hft3c81ndyGPmYwauRKd1YoX22XggQSSYxy1n6FEn16ToLXKvCTg1TwAtlXkZn4kRPCdi9haLWbf+LYwUASibHrc+4CkHcXn12n5jsx4dgJQR3jzCcmkVKiQpKRuqwIlibm6RtOm82l3wc+Fc3/PsT/cej+3iwOghd8lp3phSBp9TFLbapPkc5xD8A6/QmN+bv2Sjpq0EvqbP6UfgtSaiZxZ/lGT93nf9QhqwWKvwta3qahYhHne6RukWG+PfMYWdjAGhBgJDLpY5+QS+ahcp0K1HR+kvoBfBTR1M9EgCqq446McMwi7fAYjxnpbwVbnLKOcmMrhosv3dEN8/5QbU+t+7BBtg0JduPOIG30IQaS6lLMgx1sHZQRkWKsWlkt5+0oiUZ/NP4nSEartSfNGk/yXWDidEA2DIbxuXTrLVFkv1TkNhkuwtg7oxO022q1AfPnglKDM1y5UwohRowqEAaykEOzmim6Ldyzp4NZtiY/LV5lwveFy7lqVue+xjJ2yyHBQWOmbJGdEFdi3i0oYUQZZWU/Wy/NWDlWIu1HFhe8pVohBniTj9PqfMqG6GkwgdCgLCqC7CwpQHKXQ9o5PME40csPE+LJbFMsSi8fg1Aj+yXh85tkAGMmEV8WggfgT8ukwqoVWAgOZWp08Irf1b4feTP+H3b5GFUIoFDk1Jomv/lls9POU7eaxLWA16pssKLys+M6s/17Ohnk5iicWrMs1FELKnXT9kzvuwDg29jGY9fpCMa82l0iVFLLeVG0pnPdkSQKSXD+JFEVlfF524Gv+bJNJ1CqNXhQEK9Tl5xQbsDZ07onUhz0gc5Fj8X8JITUOBJB2ljE4QICxmyNtLLSSl1rQgkWej5q9lujw7NxicKDcZ+0EZnRhx+EYd9Z1R1ajyGGmxmL35HrefsvIb2e5WRCVCy9s54A3LI6Hvhmm45iohPjd7Ggvtq3GO7L//kBifW/oYI64AwbtTkUZ08mnj8w46euRqdPIKQSryD1SXGUE9Bv//k7z3h8C8YiDWDX5evKU7TOxt5msLFYmI+dLI5+oYuYOYZRh7oz18MGhX3w5X/9e04jU8YxHTZDAtXZT4OFEF59ZtuEGNnXrnrnme3r5U4X35wK1tCENobKSG3Zhq6afrGm/N3GUwMY/3l4/pPBJjnwybzvth7fm2kxBYbjj497rqFYNSehVEKZHclIbBTm7t15tm5KDvz0ImbUkjlczWwdIxmvnrNl+fHA718mffm/orxj9DR31JFpFaxDto+5rxHWENL8PBFTOFmdBmDOMLPkWPrBvsuOZp/XcLIiEzd5F9bTPnwKzL3U52fhROyRXAmCG9fCwwBpcLPT8Yr3L1ScHjM9XUhDS/LBDueTX8dZBmGOrr1wN3epR0SSDeUh9JfnmerLxbcA7npkUUDL6T+UdPuMmQn/x1x0N9joi6bzty1w+9RJ5+L0ko+KcLBzEP5UsxN5T0LOvIzmSzHGOFGtEOY4rBO9kqGAzphoY6dBAFVPz0S8L+0RVoj4LOsWU4PYPb+ayPzqHT+etaoFVMfMFVsLp54WCuxmCsRLvkFLNnFKzwYCy3/bD8hBQs8cBkWCXYRvKGqKN7wmbYA777weIr8APj1/qdQuNUOP53jQwcgfH7/xvV+ZzkHxwF5BLDgL3uJW9Z6ZWw9d/GoBNV4epQX3Y8t0QaCf+EKVxf+DakkTkbla3Ho4sushAnp7NO/oYHu65XB9nwUGm3cXI8p/n7ahS11k2B542osQs/K7uaSRsSDrOm7BNGrBEhhQ/HNj1+NsoSn1NOsykYyE+PbJScSgQEKo3aQqiU/+dkDxE89LpwoE0xkJuA6glLCPppybmiIM/ZEYlf2j8QGAuK0xdoNPo2/QL+TfZwaR63Z1DkPoJNhEk6pedJ6jdDYDYnrCg6U9OjPe27rx9HMJJO6HvkzXwUJ6toNPRjGZ0O1YnMYWcD7eYySgPjTezm0oJG7RczT/AI7XTtg98eYHBNUAxXy1FQ4+QdoC4C6dSJB7fe+Q9Mwh+wZ3aRT1gZTvWd5AHxdOWhsahJZPqbMfQ/HKYqxPYlikfEapZKme0aFFmI16kavu2l0LT538m8kDeeT2lTcqGxQ0chh/Gty3KNQ8KB17Qqk+uG9AiLGuWMGYECp9ytUO5tJqifmywzm6cjYYtRZSN/6YQSvx4rshvcFIqG4OIYhy7M2/g4sLfb5M0P3yAKXSdxVRuckNDomHlS7+bDwhvZm8nhPa34V/u5h+EXvBlaZV9uNmFrCdFN5m0VH/hpjOXNtTLiXCPJ/MzkD8ZsByTPlCUOcIECKmlP2uqxThYyztOO+JooM/LugDH6GJNbT7ojI5ojPU4jQQNNz/jwNudjMNnVdk40pYgFh5jQFOsUpxs8eKFh4Bk/YdlkQKynLSU1YB6FU4yfyOjjEFF1KTk9Mp0mejUkjIzH+DFylj1Hg9TXAdmLItKSATSn1h0Q6mSIubVQSf9qSaaHQZ3tNwzc8EXAuhXmfAVokNsyZrxPqkvDRhoTe7gJmgyBSsrPSmLwuDDnwl7a7O7ABqzZUBB6ZGEm9g1H73kq42q7DGk0yDdAfi5rbD7LRaYDHxkutpDHv1QLsj4UI+Ah4qeFIlGMQy7ZxxDcUcDX85XiG1iiHH7ANY5a2FnPN8xeAE6NQWFgSe8w7kICVzQ4o1WrPcb9ZFNVOTW4mFsMkLG16AjTCWgKZT73CH0x3IRlkmcc5Gt9P3yn0owUMcNw1bWzVAQkKeM/mheDcFRDSkn+DchCqUxVwPiRMkcZKlm4jynaMS4nManIg2qO6/C7YEOYlcejhKZQRAIU++Lq+uvg87vKjr0kEFEXUpGgxdsXMLXgvhFZ9QeZngzKE88U2H+yKCl/TAatmB8o92cMITS+qKiZXrNYagfwR5CnnxSXWBiCkGMps0vFXwD9JzLGgxr/pKLw8fy2Xg1sOWS2f/pKP7wI8yw9cNGZ5KMaLOAnaT87PA7OZFZ2shBXRv4B/oha+OszDADR/E+I/PinldCcNcPL3MpOxgIE/OgBdusM8iRnQb4sg/NX14gYRkxZz90oHgoLZZAw+siZSiVjWzd+rgr8W8n9w3yRt4czbh9Naq4hP+rPKEdVymBcY0pl7Ltxz4olyGX3Eq/fXl0j9CbMp6/00UcLBXuPKqdm1fffvyD6ts5sgkm7ZKOF3mKqGjktGkDSDFkPL68G156e0v5jMm6m9/MIpKWgRWyIMtvHJ2h7w++QP8V5tlpj7FV5HWmjBNwasbD8wkpcvI5bUUYD2p3HYPvf7DmYjUfQ2B5uRaPkWk4rwAmUMmIW9VdhaTmGNfU9poRgDIZCfX37GPWWHcSke6axec/CFfU0ZD/1CewQ1i8VRTg5ivWkrhJ1JA/B1yf92xSpQVriZ79C2Hys3HHI9XG2TYZDReoAWlXLjnW0k0SgoZr64X04ONeX1opgGFX+bBjHRt4j1MUuDm7k0GUdkzj4lFbSttNw7Hn2iNO+OINNi3ISLM/ZkY3pzZWqL265Mfy0uNBR0zLsuGGzvoaGBscupGd0T2XjYi61SrF6MheAl6sm20rkfYWPLcAaIrFVaYOkFpXgYeFgfgpPwnctTBjEJJjupkds603tXfu8if5STfPHg9ZHTU+Pu/u8k9lHMMxcFiKHPKqc2d3pKS1Tmt4t70DLWDsEpIcQ37PEPbco02N70b4xIfNj8631rRlu9BVeIi6U6dy6PDX8+oeeKYisabHloA5PovoPka+9TQOW7i+2Qv1TKnbC/Ue+cRn34qSDOdrtHr2KFDVWpYgJ5AGutART+2l+s1+CBG7WrHlZk2LJZx+qggu9L5lgZ55OoS+cW+1i/cPi0q7n9K+mXw2dVVUJqI019ab2qqi55tw8OhuTa8YcjadCd/G3B3nahyAo79icP/CGGwjyyD6KxXBvX/j0L90q24smBFgranIBQyYaXwvMBWVaijjKs7gEQfrM4htjhzu/9fu0PI6aZBYeMY5ZM+CFsjZfqu+f2YREc81iUwQ0xPUCb1Pta/KnTN4CG/iWlVsjpA3qUwocJbNEVL8cXrn3AV+ZguvJODzQ/g0TGlkyzLjFbFPXSR/1jTXNa9gO6cpUknvoHQSqJko9auc6D3S6pKsX3UhBCb3HoZ3te9+vFN8cjdfQiptWY/J9gMlNWzFNJRTm90HY326JvKZYS5N7fTOt5mdLwY4mOi0U301s+b/EnHeQ+KWmX7r7wql4KWSbSuJVxsLt7UGk4rXHK4TOYCpO7yBP9pa+RrPbVnZpbVFxcP0mTgFeHmwE5fgu6IkPtkikPPv1Gz9rzL+ICMTm8U/R6yCL/oJwiSo5qSS1Br9LCHkIVdbKWorKIEjye7+Tei1/ymLFWYw887dZStD6XrFpNumiur7s7KQyotS4mh8nI4gXJVdnDaBmALb46OOaZIr0uRLPZWnCuyv3idcPaV/H4WUYN/CUQdnDCxy/h/ryONl0o/QBhYgMFhTlGPqyLHHs6oRNP3SkajJqGWdx3kOPfQlbPGotfYh0A4yvO65mHTmZuoHjhURk8EL1Nzt3iSiYNP6k5FQ95EIm138NXLjhzwAAT8xOnB6baZGyTowb/kSMjUUWQofTnSbrC21I3DM96om0WX+T9sgKhOnr8ZMroMj+ER/ZeQRWr2qwajVzGAe1FIjEVHVD+0QOU29y9A+pj3gp7gXCUvrjFp08/RUuhpYfQHgWNL7PS8BEU1UUjxgZQ9G1WrePLBunRXcOHfSeoghK6nFQwOQPXjmMv2HeEpz5xD1JDNIgzmAIwHMUgjAm1odNfR33VlmpRLyQY7jg8o6HFIn0Ov2vaplCQzdGcaKF7dizwvfcCxGWe2uMo63axa1NJuBcSZoDT/PlRid0qqjJv5AUyNWfhj8Fzs/SZwRAV8ehwl8YrB1+oUquiCzVvyKKnjXYLDXDbmwiTm0Hnug2lxzI/IFRIupXUFIAx3Wc7kdOTrGlzdae4GYcGoYY196agqlwoL5w2ssJJb7Hs8KiZOl2b0JLIqC9EZeRNbtMIX5l8BQEfz0SAuoq2tIYKRd51YUpbbLkweWpk7AXSbEo3/Q/S8ZliiUXFz0/a2afwQJL++f31B8i7Vo9iNltOO/ybL00HQOurmwhLtXj9G9FXk+gPGz62JgKf1yD0x+kppcVAW4F2JQa0Ku0jNqkRveetM/wLp0ra/Ul11KxXFXLP4iJPtW7od7pNjx5n5QXNNetRzzOJ9czAzyxPsV7sJV2QTw5g1qQDlkZlOgr40YtjX4xcgvgCQeGTGl3gbomQ8/d0CvCwv/zcGSdchWLBfu3bevBkB8DchxrQlyYmzIsVjXCf1qYUYJwSqNb0ABYjii9y/CJU79jCTttb8LKzSUAyih8Nc5L0hBUIRI5u70vS3kE8SCfKGU1IPqXE9DbfP/XtqsBv7jZtwzUp7HglJu/3In3zdvNl+YwaHsrfrYFV969Q9/TSeW1hqyZmAx4S+Avv7MwPDmZ9J9YNypigL3FxuDcvHjq5gJfFfWGIkk+S7wpx9gVPv9PYNzI1tRFqMTzgqOY3KgiGWVbQI2r2sa9P1KcwqG2HgvHoEckjymxm+CQeSYM4JwdVHNBtqVkDTx09Cj/76n/fsxz+XhSqs6zcEf5ISlnr+KEyKEzEZfeBwe+DMdJJtSJYa8YwporcbDO7wZ20BQ/q0cCgcfdBlmREHDS3Gt0G208Q8RRkkoAwyrpQfuav766jxM14YZwUYUqM/zypU8KrZSI8Z4VORSuXaZl5RIBN5Mh/0wcXLYL/fZJPTQG/R+xUSEb4hSiz5OmAwGXHqnbTgK3/O1m/7mdtUolzR7wNHU3CMVcmE2/IY5EJLv5qRa3HYr6U+VaNECKoAStxLBmhMyMyi5IHXnPUHff910yUNttv4pig33AyQT+/I5W8S3fgrw17lJ0z5O5z99CKn8ptFyX4feu1MEbIDLCSllFV6XirqhWcpGC1RoIx1vh6DMiCqu1+PVCIBCVJDoJPbEIxxIpDP27o2p6iBtQv7/7Bk5QgUP8yPx/ufrSuFswzV9eTHPLpVY3xc2RUyOPX/RJGVL3kfPGj74DT8veiAbAgOIgayeVqsXvBtqWT/P1FBFf4VCRcfncwBsXawKgGvGFvOmdB6UciH2XoH9KhfZl6uYgeI7UkgKaViSLH+V7Xuyd85JA22qUVtVlh+iRWUZZUxoZwIKrM+hzfP5E23aNCUJaeb/VngLGFVqGKin5a3bjiTds18ldg9eV3RBOBMcm7ldvU1FrZ8ZpHW3SkYTXVviAIgvoJ2D2pARH8wwgU4LNgxZOqAgWMJzk68wJMZ76NOvOhryqRzkabY7ujpK2R6/OinEdaqiFD27cKFI7/T19n31Rfq/a0t/jOipUfWkG7rUToDW6PxET2UAeYIRbE8mbtKIc1cxi9mLcDNqJod1R+lnVJFpRPVAEw/CVBWfTYxD9oeCFFgOHzVrILBZ9gUn8WBSeiDvRDzr/LMRLqeMhNLDQWdiWarmwDq1Yw5wioktADOZ3u/BiBk7/ES4Ieuhk41QXFL2FDWCzHPdDoi0cIaTthxC1zhV6fwW3KXoOLXcdlDwlWf0NN3QVzLfIl3jVCVD9Ueg5qL/L8qMsO2lFYSdIli59WAkwbF04DZL/0CJ0a8+BFVwOPxYKkEekTxAArFKpJK5woeHypIoHWQ1L3N4kRKOteijXN/nOY1CyHRLELLNC9XLKecoVe/WaDbXDUTHxa0thPGpzzvjACDfvnWBrjX/LN+Uk64sMB7JaCX8nkJ3eSgF8AFCacZ/NpkrQkDDG+AUGsZPr3pfmzFHxOqTlC7qtpJ1Vwsl8h3eHkuf/AlgQYAmoZE22MmRaTfAhzbPPCChTWg4YxTk+rlL6s4wy0wZKgwhdqPAFxvj9IjCzCicswPKvFnM/BD0frCXnuRbnRpfj+OxjqdFUxggcetsq/0h5yBINddyRwLhlG/7aLBpe5MjNuh1XhwFzBAwIAXUl2KgZLuySKYFXz5DygX+LXac2tN3MHvQuQ7Lb58bH041sG7USt1M4w3Xmg68KySimhurXAYr7NxnSYktWzg4V62MmJPLIfBGAp9rkbFg8MmxrFX4fYGfQ+hgttQRSSySUgPUNAPC90OzN8WQjiLFdQa53FIsaEyIPnC1tPneJ57PUpDLN0RNjXn4/FAnFtDakHXhpfLyjG+kbqf4G5QovVM863HFe1qQdEksTtYqUXrEi6hgpWwE6NXG2MxzSDvA/8fE5WdwJXTHae2cqqIVMGxMfIxXSsP63kgyD9+LGwPPP8fA37UGN1M70AVGJd44Rzd7I+pgCjU9LIzagp0LQz/EjS8WcEKW2XXlJYAXsNAwZVcgmuV0walwKQrrdiJUtey9gUspmGUa2ar8RVtDvMszlcMUOEThxpXdJPL4BejEaOk/nynCkNX4GISnT7tu/8ImxwV7qzoVplUEqmaIyVWQ/NpjYZaP5x43bb2U+HH1Ah+FNgY4qvmuGTPa2EuF/SgV74Hc7Mdrysz5s6GOyaGSsLxuwMUjpE9rwpFEG1mQgQ7omivnHnR8iAcJDC9QV76DW9eM4Ld2bOzsB2dosW17SxhpnJ1u9YvpCD1zvDBDM2URrb4m9KLZX+A8vVXwvqZwchHfI0jPXiZAN/wPu5X6jYQyoqOoIggISWA0qjxoUW8BcAg+CsJlWPqmyf8uF3mUfwP5aua0lSJAb+Et484r2HxrzhaXzj4euPmr242LuL2ZkeqCpJmSmVlHN6FsGNbX+AD0nSKP3iH9Y6omJP99C6fRT+bfmzNxpOsV93Md8j8jEiqvdwYmzHpmdam9UZYgiIAMr6XbftDkDKcCTbkkpwChb2ZAuI6e/qPt+UMPMUy1zuU4r/YVAm1F/wEV9JfBFTiUWnx3izj3OGUDtH/QZmEIewCCVvLeC1cgDXHfxFDh7SCZJbFhiJnlm8jJM1qaAcy66sUCTeaYObDPz69O1sMOntQwcZuyAUjJN3AKBgboG8ekoxluMb/Hn1lux0BlNLUgPE/fGyIJ4nsfQhRxCc+FW7lw4vb7byuru6FPVnFVxQdXdJC8a5OWo84jMQnpqKbMMFx/bxlv3X35XwFtKqu4GCFnSkJX3mEnRqxua7IIIIV5KivhTqh8ID/Wmirx/tlRnMzNpOQRjFFTcu+XjP6Wfb6eD4hQtshB4qfyLSVwGCFKfQBzNDXg5TP/oyRJ80WZGWWPnJoRVtqXR/XUFGcKKWLL3Gb1t+FoZvNpSRjpLlm4IA48PYjczWoc1CDscG+XGii8vA6dezOoMMe5X8WpLv53Hp8VvQuAFqxNTaeLHERB9gG39/QYWWreGberZgqDuX0IZtTOT1aDC7NHl/Cg3DaFzKSBtQc5aRlCbgB3J6iLIa9pFhia97H3KXtPtyFQnnm6dT6Y4vCzRL1kqy8yOq33BsSF+G+qzc9YyrckdnvSQ5AwL/uLoz0Lk5tgApsA+5EJ5e/zWcBIeIeh5kqCUbJngn9N5ILbC8y1bbqvwcl/Hf4NsziS5vH11laecGiEykJpZiWHHfOVUJwMOKcn1SZ2rPXPB80ViVbhL9AQdbOcKnOUL+6pgXOJYxq85QJhlUT87IKem1EbLZGjFeXXsF/kH7OWRN12LGSZgVpXJSjYEeG7athdkbFRoI0jc3tERRZVxeYhZoi/u7pJoxTJtYhUfyTbgDiTW7MNn2p1WgNrSRAJT0Uc7Vh806ZSat6pHqZUwjub+qyzVWPM8hHh8+QYnUzxupBnnxA/T8dXy7KYj46jpc6y4u3QNCa/X2oryaJCL2PHNY0neKfASN3Gl1YOEUzP0ranF1LKrTAzTfCDlMZxijeQyP+ITM9SKB2xNDOi2Bwrl42L9GMYT9uSliyM3oCiLui70kKsIhnmX/GNFyvFSV4q3d+DiTfo69CbngtI0nmQ4gI3cMKHXo41+FMPyv/Ec87sOOQAJev3vwtb8ZiuBStmgnMQ9kSYY4rQK6BBCcrxDcm2JSO959fn0j4vjX07D6PjpxbpooYjhfBnbNl+zg8VKaJpYNoA3GK+138MxCM6LPu9Uq43wNVEmt4jDecH1OPwCG6L9KTVjVcAS1N/uFDHeBREBXBHUGXeqUFwP/jXrs5EPzSol+9xRWUN9EqRV331gnUue4Lq/76Z5PFOignTlIMbDoAY5k8GBjYcZs3gZH5GiaNeQi9cORWsoZBVoOF7BPjMzNfSdiz++YSi1f3vWG5Jg7K0UH04dZn2q2tTXOrPsEVDeNySUwTrvjd5kmy+TKhfqeUR96EnQzvMzuTRGhi8pDaeJseD+2DYWLmabIChbk/wPD8WeOD1BbHd1isJJlHhNmBdAvadb04Q5/k+1vl1NkIE9PCAf+awuyzzieI0TEV3kaV76ml4bBX/RLxcypj0g9Rs/ywo4dqsmXkZK9a7d4+TKWulSQF/SoYDkpXsqggPPHhGTOFnBjBC+OOdhf+HvRQ5F9/bCMiSABUXNQGO5DgHap/d459Dh6SSNY0sxCKdZBUFvH7Ijg5/v6Cw6VNKwFxRh5/JRSewhKWflJlUS8XeU9LkcyExFVYIzg/PzeT9lT0b+gI8Qeyqw+/OLxJKx8woPFQeD71mhKSupeymqSifJHXxuQSDHYoVSK2OK9BTvGTJ4lrJFqcey5CG2Eb/hXrz3Sqb+l4qQq1I8vbVcE3v5TKxofjPyRke4IM9MpqHIVlgTcZKlivmHHr6YIyPQ5it/ArHLyZR+Bxzy6MxeDGWyrsbsJdClxncW0uAJA/hgv5Ex3JQwMt65Xcp4NSZZfgHPPv1I0lKXHnfjEwVHkGGNlECa18q+a944JYNG88T6uwPX3S78H5wmoem6LPIZo+5Ommwa+JWgQmI3vQRIloRiMFlp4m4Jaxi8FbbcQLS26DHoBZi4peD5gpG2N/YHcokk6pccNMKFLPZt4vln2REs3LD4JeQGZgq/K8Oipi9FD10Drn3sh5MPXlnIfRa1H/yasdDJH+IGNKBb6vCxS61cj+xLjd1yDpqygMiDmmjTTtB2G2meT8gmrfEqrlAzzZdiTmK1sLuhfMH4TguQVdPrdU/jGf87nPRJVlNcxk2L32OIsMDDwp69oRjdXlk87rbg/AfrEzHjXOhXR5j5WRrdqMUvN931Lx88HWUnPATdlwjRpNQCjT9hxciUq851Sjqw/yMhsGmbuHq1F1mKqWbyF7/o+ERJSiCBEBkUJ/4RRLFmq2oR6uV060gt04AmfWn/cziUrx1YAdWiHvbYLot8IOit+RfqxQxUgt85lgpKRcrL7m3aAP2nlK0GnK8KCFqTQEhdfnpxXbl8k9X9Sk2Vx8ykJ7AkCwpzjx1wpXa+2i/ecD21FJq34+3BWfBP34ALd38Aj/LfwTN55P7qfAkr1cweNojBA1xsCmKZvS4BG2xwrJHTd7V6QaaEgXwPb7bSSjypg5r8sk1DB1LGzz18l1BjP3+/DEK72egC9iPX5duxvettL5vuszf8VMnZf3KuWHAalnV4nTDthq+jIPsN4Jj/kBgSEzv+67SaImbQK0yiE524/qEZ5LSgbf4Y6f91d64TwZARXvGOges1X4rTFGAgyt1cMVq39gZJo/jes0AZu6acXxZKGjm72pz5YhrT0xN8EhVVvDZOBuL8BOwqbFbyKXtouz9saZ5xz+yHd1JqRjRKfJwEUFuxh+8H1HRg7xvpoRdcfA/aTdYFCIlSqU7bEIO/y9uzlbEQSowrn/j1qw6NmJmRVBHq3+Ah3PbEDbZQXsoMYra1ei2bgyOi/zF3E5KXD0wcsqverBdznzFzbZwXkuZnCBWunSpDsyeDNsND7Srmhuu3pPQ/H0aeGCXWNl6CczhrI6YbiI5HUl+2Vu0dZNXjOs7xL5oHvXEMY9wV1BSFSrvrFuIsZmB/3wcwhWn7DV8gARw2lZUWxP610FvEQx+QPfyFe4XglRPCww3wasHppEuWLTU3G41G5OEKb8zfu8LWZsVDHP5mjewQxjnSu568lAJjn+/4MsaCnalgJibXj1mf6x4xNoloR/lqtCr+in1WFJAOARnIXswo1ZOae58R/Pl1z53qaxXqa6+NfudCIfaWkB2XJh4AIwhTapB4pmHl9C+YreyQ4Meis9Qb0aWFqhaBp+qSb9Ciun6ZrJabGOlW10QMrqRoXJBRsP31Gp3254Vi93raBtoFyfbaTuJC0K5RhsBQYFkrjfTp/S46cO0yrc1BGAWTt6y5kgTCmKKcgFa+i9cCQJx+PlHlZsfhPNnR78cvJNS3sAXR+zrMpGfCRurJiChs8my1q8DJyWrQZwZbvgzUVXczO5N8ElmZKmopB67A2gfcSbd6taYyDQ0qoFevlf3TzApqGw1uS5vN/7Joyjxnnqz4/XqJjbboGbsth4DiFXyAESeNxUxE56jaFkWhtjDdEgz78rE0oa+0rfwwzO7QPGK8o3t88szgeqjSy9f+Gu6HofejyQfQLaIg1YYWdOPH7nD+tFiVxh/5aC1t/l+VFL9RpHhzWNSyfPZbCCgMaQIR90bMCt/PO6sbQA1cIHp2fo5N1TGObT4R03CBFn7OQT56nLami2+AJwQ9k1Cp1Vm3N+vGisJNtoiNdhbUBE4nEnp04DPW4BecLKPtiT57tZW5Mf+0tWBkSjZHECrXECWkEPTrErDh43ITU9Rc+EMt13Y8R5VAev4MOnxZhfLWlXe2Jik0UdM90cTqFy1UtXhr1Pkvtex9JK8ZtoG3Du1sXav0fuIkBLiIxVNPxsZFORfEbr2ElvMOz8gIWTau04o8Hv0vMUl6v2P66+aV0BZDjluGXfjCkVUK50gTYYp7IcEmI0kWwbFYQnYWYyX+FrcHswYQHbmuhLUc8PDr5pKZVEI/q4oas2f5ii8nJZVzO3AgNSWvQO4M2yFNiTNs7KK3LOlEwh/ddF17HfBJ7KpP4a9k2mdbx18MZA/AeOWuOAEfWgk1asJg8YJZappXowrIP3CtQAMs7AawJpAcyidQd/Zg2Nj/LDVHN+WBGFSEWRZSesBm/nTufePVhf3fHpC+6dY+eqv+qLc1CJMcldTLzb3pPpch/fS/Sf0RkkToTM4GjQgo6UEh1EgkMHNu/K7spWgmEBhmxU3TCXfa5V0cN6l/iXp5g5q94zbw0QUieq+FuFnDHfTlb/iK56PqHOadGjkuXi5vIPWCcmtkOH/nJlV/P7i17EXIDJgzU3Be7/RF3iYRoPaS45gsuEmEFU02N79k56hz0Uov8jvK6LHdVFLaFE5aD9X6uh5LgpZQRAbqoCL/usFQSb5AjEMfiscS3Qf79IZMRDecFsdKZrzmdeO5IeO4G/XSc9hVr9HDd5RfxGvNUmlq9lLl20HCM40eh4sEpSxBmj/lTtt35q4lfB4J+fRyLme60zXpjvSkzWMWy/ev7bcy6/dpen18LRtYcUqkFXdf7eNWfSWFLpClDwTYm+WXt1MfuIHLituLMy5spwKxNNvyxsJOn+nedCAfAAI6+XajSC3oN6a6yxsypJhufe0wjZL3njE3vdQZi60Ub5PqOV4FCHeRjl0dLH+s+oL8CwlbHLGXhef1ulqH9XwdzDkFvy2fAINLM61AbI+LgBtdyVvwS9BfrgSCFEq6CFcKfaJMWO5sNHPbzPn0HElgshIAeU1qGOOLQoToQVZK53NgWmQUH+DpOGvSQAQktdlADJAh8Npcde/Lp1SC9LrvrBnqKHGH/2geHMaGGDFGm0JbOmyPniTaA7HA0YQxS3cAbn1tr+/gCUS1T+T4leBO33IqOekmLU8n2G8kixsqZ70HcHnWZWjPpw48hz3svBzmM2WKrzST6YC7/KhVBGUvORRwKi2zf+9fv97A6ojOnBHS3M8qmIo6K1DECpvSmGrf2xyCpMaVXjLnSb/FGrwwWUUh8CCL6k7h2uvgXpoBnJ3m3NHNQhfWDqjUNz4R7PRhQZSU6j/HRVQRIX5FWRMnR0bqhHB9v1V1OuKmhg3/XGbis73vrpziIvU4ChgqJ7HOoTCnnu4GVAQkdHkmFeYBjz7U8IFJ0XSxH3iKtN6NjkgSMfSJZ8pSn66/JxVT8jfN2LS3S7BIvJ45tSy9NnvBu9jmnCHQjt63ciBc3kFP0WVwsm0sfyHKeL5LmZiDjc4YU9EtkwrmcsBLwDmgJ/YMKzqwirVRyaixozepcivR6OUmC4rabpI9ym+lj5C8qM7/PjTcZCd3AD353HLcfwr/gjfH0C8IXqZCNqHn9+sHKnykjur4JjtYAtIwcRnbRWZUSk5BM47hosOvewCyl48KI81fuWfYAI2WBU3/4XbiPz2mrYulShbHh2SfvVxsSMHlmiliKhF9mBUeRQ6iwO4XXfuZTtcVrIOQ/UW0P/nnLGwgl5aygeLZA9TqFDCgRlbYRGh2GKbAlp5aYtag36gLsJp/ily2W8Jdd53lNXLTbSxshQoVOs2GASpE0hg/uU+Bar+Lizssvawxs9d/Z+f37DwP8rL098F8eCvwLoY4iw2jay+h2wo+f98cTIJfnmwHuXtSxtHIojqXmMuJ8ao2BR4p81myUWJbr7ptcKzlV8s2lYs1pfTvqqWuK7QLLEgPsU3oDjr0reZJs4CsfexCDbly8j7H0re/Dv26AiGxPHuk7ngP1GZMVkFV70hVwpuc88xIVu+2xI+MgaiPV637hHyx3aRzrcVmwnV9R2Z9TBRvZE1tRTXFO5PDvd82YhG2GX3qKXbfVxGfsmf6iazLN/X4+Yn9V3utBgDBdRsDvi3KpjQ7pEeULYSF7jJliryTjeeznc+TAX0l/Mr75HP7ReZTx0zsckStONHG7ukHMQr9txiWoSp0BU2EyAmlBw0dW2D8F021AyJq6SKPqum6Aerl3hdFuzTUzGCPPyLck8hN6+icrZPkntp5xyQHA0uNfly3MXPCfCy40JLGUX5zRBQTf+RYxTgsqrT9Od+jbxG6IcMWbpViDLH3cVaHF8fvFh7vvOJX7gGH6U6DKFZMS6vD9BsCEidi3188QAEzg9527nPym8VKvs7Ff6oatHRyh/V9MBO4ckNNkh6sfaALFHuMB3uwLTpOefz/AkYOACeAGtfw7d7ZpIKW5xD+XoqmcRpaye1rwjT+UDFDkkX0HnorlKVV18sa5yqdpP/4ab5c7IRztRIvH7wPjgvMXzwHw6Vx9dD6pU6c/LXdrhOdzurK0HYe9r01dPDfCpwpxH3LIRy5str+JkblQfgn+0iLmpP2gdt7FVkj+aTgCIT/gUxnXsZQv/8ZzB6GHI+iQksKLmkvlLNjRBepZgQEoXgQ+2YjMwFrqGg6V5hu+5KG34dae5UR8yJBhv3FqEYhaNHBpKlbj9zi4AiXKDOpNaLqJNDRQlscUpVAw89xKi/D8DM8002Lb20FvfoG/7/p1cvSQBXf5FWYYbaQH7QUGQU5yOH4AUDs9XSogOCEd3YtkmtosgVtwEUFj5SRfHTrLBorUUWSxktDhLAK89hN9mJ7M1XsECfep3tgLbnKo2LghTou/SdQmOW8YRKhPqDlHsWkBrzMOZA3zIGDJiDNNwUehSZZhAJzat3fHEulG+as6BZNIxnYuYYcQja1VpcEz/Yu8EFjBzsdtHP+IOQT4mf3eMiy9u43YWor+yAtYH9evkTaR7x87QujQVwxs239SARrbJBCiURAIq8KkV/P/yAhu91EHZXg4UyXxgAgA5gZWFzXCc7nH7964teHJXsHGVfIZ5ocnWbfJYPmCMCVg32/Jub8pk0f54K8ADaWt9senrfidnW6plPlj7GRhBR4kvof5RSmh83UGjpinwGDLBuRK2aVGmvhmE9tGjmxKaWemJ1iiyL6ZrSM1xz4lfM1QDB+F5/HweLtfGV/JyKe+FeQxg/MZuwKP8R5YD63y5cvRKZNF3GLw/gqzVqq3cBXHI6TloRtEhd7iOMNYl4zRbUWRJZ9HYDU38W9IqxI0VxrQD2wV3HUfIkDy3C1chQy3gpWdEQTxHmlVY/5omL9pPD1tVqiHxZ9dMpeuR6K6DE8LqlF7MsBUqt+Vqb5rWkFSqL8oSXz3HslJ7VEf70YOh2uX+qaeTyik0TSM/PUSyhzw4fGKOWGh7iTV+1llq4yy6MxfeAKwUmHQFBd8cqJXz5+Q3rY12YTlmrQKJX8yseriHKkBbv35t9v4H8G4NiQDzosoB7dOK0R7acNMba4kVCIjYEATukgY412RJuDn+6skLH7csYBAAEkJWeux3/kDZxcjN18thHpWgno3CD2XfzDKP2IbHBAn4eHfTM+9i/5C5hvmac3BE7IXtUJya/GB2gHF0KFlzPN3QNevoPfmm9WMH/tHhCeDvNnGvLMdeQT2x7tKu6Ue6TYjf3ZZPaFTn/L3bPhdzWEazPAAaAuy1WU3B8OAHRwbfaFYdqc8o8zYDcLM/UKL27feNnvhgyV8UY4sKz0YKI4mBm8wzXzMqMrtmNXFMyzrYQ2Txkj5yQKJcVptIRxwdbTl/Pj18HUQ/ECW1/SspKwJV5/1IEZy+maL6UVUJzPjxG1j6bGiW3Y1JMNZ37DA+yXfM7e24t/4MeczpIfP9NUYfKJ+tS3VFa4pVgghA1o23falcSfNt4u/pzV7mTZxVk+bZ8qI7+tLCltWGiZZ+Ga4rBigq51oHAR5NRp5PvqcMl+y7/DjTkwjggCludXG7tM+fCEfSC0JYdWWPadQ/fU+LE6oSpehdRO/ePdbZTaYx8rChlJqfDQYsn86pvrMN23IuKXm6O84tlaUTxy4PRDlbNO3KpSIjKwa1wXzWugPlNiqumrlfQFOdniaDhqHmnHUi1BG5H5ofZyn5oZvvASB+qtGWPhqbINzHueZ8OjoF9MCDPmSfkat5Zl6qXXFqPnkTdmK1cCn3bze4GrDa7ph2ptatRr6kcpmZsBfQmKUxh5A/lW2wTo0NUNUMtKnGJ8QRWO9M1F3SvkgeFG8C4IdIfp73HLJF5aXhRngP+mJ5QxG8Htiyk2iqWNZKFl3r2AhbL4HeJ9vJpXAWxAgvZRMUpZUkNJQyyV2oZb3FFRwswPWyfocHv1GHONgMjo/soHU7xoKZOGvrb5wJbd62LRXlUQJvy6EIc5p8yMVQvbfp5QbTCmBgu3skcrLRd6ebSHjJqxcPWy9ftgMK4TRt+Y78si00myRNSnP76O+Vk75gYQHWZxSfqTIBi2oK0EaNyQQ/KfoabQI7DgGjRpmUuVtBJISudCJCw+TMmfpLwzMOcSrbfOvZlxyiOkxRcbl4zNtcoJq1cth3rUMCVEnNnJyu1PO049vt0HS8FOPTOduMvxaNgHDv3BRos7y+d0tPO/i8ldS9Omh2wymF0imU9dxFmuVQ86kJP4cq6vQKVyxU8OnQNuaFNXKPcfm8RWVRbeJNYP3xKqliJdcz+e2yj+2xTGxRzpSkmnLzknCjG+GHazdDTEhtIjvL77JI6Nl46nhO6DRIzyvstXSFMqtSc5/wues+fL3sjOsv79Ecludwln9Lq8/QO74JcIwjnoIkr/iJq7M4uyAgx0ZfhFLwS6I7nWCa0DCJYYgyYAwjgvzxxPTsDwqZyYIDns1La6JtjiZ1OtAt4/PX+jnV6N11U30cybey1qB0yDiiYq/Eo7G78YkDmRPkoVUdFxjmzKRa0OGJq+XR1lfqAGDq2A+85ABYwpd7Z4KkojFOco2DMomXkwcX7ZJkWyihIj2sUIyqvLUt3gBXMtnxyN225ttbPumpxLu9jYn7hem/HIEitf5GwG9Ub6l2Sd6zjFNyxq2vyGnmmVXNgSk2OqSLPSFQf02+8DsIA+rvDZz1Ysf5TLgjp34eMzxI8AyouZUPhPw24tr/ON3E3E3OjtnigvjKHZvzYN8SCvkF0ZItslqF8neztelyAh41Uf6/JSJKuSOEXOgqaHugWhZsbafWsOUQMvMlD+ypvUU1EWD0IGJ7xf0mNcTyIjG9zslPQFDt1m2l2PmLIoUGdjYkCiqimfQdoPmzEVsyglAqPhH/y5Yv3mfdrbZIQ+slW4fEq344DOUkvRFDNtKwOCH7J+fs6RyFbp2L11AxPgikK1KVikhz6K3Mz+7tdCnS2GAfQnw0iMyfWiybRizmuQ/8y/kjI2FFMjAVMzi0A31Yn2nxCB+nSCPEp8f1/vjR9NvyWvIiagVVd/jPrppilJPnkDsM+9YKfX0EOmGADJN6qNMoaTRxvP9HDr3RZ3B9wIxCvyD9zrHsKE5l8zx9UKGljDqjNMbMl7ogbOxM1a3b87a+5L0FVNDhTEoDSQDqt1L/69z2xLD9TABRMcigXXT7utRxr3W/EoHpRhU/uNVagX66/5ao7kcmtV9PSO+McZ/KMR212D7CRkI5PlqTU6foXex4XmP4H95PW/GiAgl8WjF05aMZ7CmyjO2x+8rU0KHJalKvf5cjphPj6WfhmhUBEEt1/DGBNBQMYIiJAjlb9A57Kr8jpMoP+95TT/6cUfVsq0/HkbprwEhXMEwZXdxB0T/3OIU2NF1BGQQBReaanddNccSkjxbxfi5pQO735UizXJGMXsJ4j5mEPBsK+MimKNQ/B0y3EjK4WQX1THHd0fk8Y8clln9csFgKHi5K8SGFJNj1SehWfWRBPLZtDaRsjBawC007Y5gZFDZlgdW2DwMwSPZ80Qt+NG9yXkH4TLSvtgDLPNOe+zTwo9iuCWn0uV1OlUEpR+3aUSrIKE3rDvRKnmJ7WBtUvjLHJeGKPp60w8oxUeyG59xMhYurg6eY/7lBykOM1sGDMfriEOQ0Dn+TPkbr92/GXm6tsu1cIxnr6OubBnHYRVRNefMZ6pFfBYYFxf1OKdC79YEQ7jSYRyPapSWH15hXhRBna54ZsDLtfI3puJFn/f4oTM+1dEgCqQGcNdviRN5HeIBW7zEimD9DhgkENAc1pSibsWgl4rQDd5fNxON4k+ef7mh+VYzQhhhawYlRaElBroMTu1LIFSCPHAQeJSYT4tTRigDzQyt+pMYpKXgmkpAeHAap5u6/aNEPN0ARW/5dK+GXe7RiV41dDN+WuQalp4cJ2+f8+IZcupZ+zYVFo8K4uXkEotQLXmyUoOXlwyyi2y/lX+4IdqAmnX3tk36AToSDfy3nhAPorwCcHr7qxgz1v7qwaubPsL+0IkS0A7pHyWFLDVqYaq6y/Yb/BVW0y6tEvqaP+TrBhWUpLfP8y+BZhVe1BN1O85XVXJAeRig+aD45x9DALQwUqDsVFxZ41sUQBIl8a4W1n7P7W7wj+JXWGh7N7lcdmamzwzWh1B5GJfboWZvUDPACsQxGtrWtabtogocLDX9vbu2+mMNk1ZB2LI9A1ba+7k3WXRtIV+xqweqm44LKVQVu2N2u7EzkdkqjMFb8tc9Q2pLffMJqH0tfRo1xWDnl5H2KaG2ZUZ3ruONz7rtvWQO2jAwkpmktPek2nDGb7Ri/nqosgA/YJKc5ksxVTkEBtZIBgNJKDn/rSpp/I2sqGVsdNXVRIS4PBjaDyMbv1EbKJlllLfg2Gv0Gp6oROrtBhsuKuoOez83dJIm6h1QbncD8bhl+RJ8H5YYqtRKHorNQskqV9ntzhyDWidLUtJWkRhoyGnEL2BfZi1t0tiB8URFkNp7EYugYcXnyRV5qiJwamqvANJWAdjHRwBnspQMdvKXeoMgEEzJoyaTO4hPogalazjE/shZkBa4bMoIGdo5EGlNBTrRZJZP414i3KNmiPQ0BlBsU9LEh0bq1A8Yjo+t4QcCM5bB5RiLKLguutlb4Dkj8egW9CIat9mQVLjOSJT6YaxEiCsJqr6DkSbcgFiBmLq21WoORxSB7HKqNX6zne3nlAyVBzyBxZ59VO0qfNEANshTCCQNpCdyZr/qSULv71+ViYgygvHCX1tIvszZJsrEaxeqCxPAdAC9r14CCpiQKGJU2fWBrfMnIzt/OVEp3zcQim5IsJG58MfT4h41x7STn9hmrCC2/JHP4WVJjHraZ3s9MV+AQ3vmgZmwSblVXrPFGEAuRsIb3DOox7fIzboHF4lYuaw9+77ENjSkx3Uq9INIKHwbOqmF66U5NnEL9oM6l0wSEn9DAB59QroPmK20hOijZVNJP15jnY4qB2lqAqbPAS/AauMsy1IzFPwS0KaG0tjntCghs8fr0FOR/0HFdtI3ROUmp2/iZ3ym5FyvLOLB4grDw09/47txxvZlkG3OK26HClnnlhnQqzuznKolak7S2FNLjZv95zHW60v+HCk0y2/aS0ZP8BvyeZk+D1OkVKsFYe3oTAdJSdvuPGmmboc4nxHoViUdo2a+L0Z/M8LibIYY4eJ97rw/0M/e0z6rF5Nm4FsZIeGe4pvTrq9JDKmCVyETfXRL+iRhDgQM3n8elN/QMYehNHJjmj9uyV99dIhqmCe04vuGl4zlBsguRJRl5ajLfHWMDIO62LwOHKfJyqRFlDRyyGpm+iwqsf2UtC8kcDT/V9pNH4mL4mzVx0jBltAjmQtPPfb1b+DcaxJExO2x2XlUqTy4PLORgopu0eZm98GqIE2MLxFt17x1UvFZDgNObWdT1XkgNMCNZ+3RX9S4rDM6DOYzlc4E4UUGC1pAwQB+/iRTgMChxwUJjwStUL4gNDzOVtMT4nkG0x4XcBDij7O7Fx1NwvMGH5ug3PtKNjU0xNv6okvNscTtGLXz0Hiu9RoDO+JGDopPT2xVXX/WdzIEjp1/bbiMkz8O3rL25Yo74yfKaKODmGNQ4s8eV2r96x/7/1xR8/0HSCyWzzrgSx46mVJIlMyDaaTGhbJN6HJS9R97q6Dp0My8j6tULxygAfngUirl4EQP7HT5eFFbEMQnuBsXWY4+P1twFYtwHsE1HGCl5PXSkwX21RkhfmZu8vHXc42+cZg13/CC9PS1F1a60K/aaRx2+ZmmW0zMOWTBTMMOc37/OoBErUj7AwcgpOJzRjvWjPKryC/G12jvwCc5Xudx2pahqhqCCVib15wBW8ZL8oWWTBsT+QhRwmySzbnFXudMA1bEYlibdc6w4Bjyr+9RnCS/PjACLx+7L85GL+b82D0JH1i4GrMi+nKlHvcA4vOLfGYrL6dyCY9bSX7aV1msQTkYBjNVhp2O18pEbotINxt885bNjbYWpqCEChY8nKMOX6z4k1TB0GlxSrJMv7flxd6sHn2sxZXUuHDoGzO7lFYIv1G/r0cbqt40udIYv4JbRKv2JaY5wG9byTX5WvAWbQMVicyrPYLfs/Vcm5rAbFnpMJlPoMQ5E/mLAA4qV5FHxb+w7KgyBeKE+LAm6W63mrQx4AQdDxO8WHse1dsGCcdc5G6Su9dnaHfQDqEelGJnR6gkS/CbtrsVH1+o7rnQinGgirczWtkzkwJkGpup34VSqVmOezW2kzrygb8rEN9y+jhS+GVKHU7pP+rl+GbuNw0hzfpzX2thyaLLs1ZBK6OdYDfBQBn3jfGXJ/IMCHti1dYfKsAI5swqeUN1jO6XMuDZI0P+xnJdwV+LVmfSsXI++LMA2OBoJ/8BO1iJeTTipHbTVVllWfqAFDbhHhifQYynT/lUhpGK1S1A5NrrIZnrKvGR4Wnp+fzsoCuLSTiLMN8uYFe/RflT2rZTlz7tvImaQCrZnKlXKEHxQe3fjr1+dfZQ7vNTL0LT66LucS075ZJ3sicjabGr1Gp3aszAAxXPaHRoXaMofz1V59XnNSwDl+Wv+dyVHzj4UDEXpQEwA8pTuhoX9E4lXSY4dBBtPkyFba6T7I8AjXI+0jLiC6BAPL8swAewsJyoHRtteAhBUqfKlXqeUEftY7SfXn+CPmVHJTbOlNNOtn1FMae3kaxh4mhJ8UkUb1LlISgPI2CtyXPmn2oB0xrErD8OMovGSV/GIxs764cQDkUf11xc9V+blczwijjj+bRE2140RBY8kY6Y83ZvDNAw/orQUscpq7zGluPMCeOXB+X1xSFKh/aluVgYPnL3r6sngkmR2c7nJkKUomAZUc5K3QAmJXaFgo1V9XrGm4jDwmiSiPqQTk5MUIy70EdF/aGAkX6EeVE7Xugrp9ETpOSaQ3wElRgPF86L8jfmQAMgm3wVYuzgGvr99lgpYhn5qvfvBTHcidx/ORWf6icC+yEf8VloMoekSZLtJiFGdGCPoQqFEKNRWiv3OTM3DWQdUfBjX403xmx4jnJy6S0+GSP5vFSqYFdzb3JG1O3Fqw6sXwBsmWNi4gl0hDnKxNklgnCMWIaRrKsbIi106umbLAitYkomxHbj2YuIGD/CMt9NMH6WuR3xZybkv2aPdU1uuzVxNfxa5JIaWxEQpA/gqIxq0rH3Mg30opf9GDyG9+7YxY6FrWd03pq98QYKNz023yXpt7u0XIsyax/0fEh5F3ezDD8h86gE23cSKQhi3H96q8duXr/WTFDyGVvJRxDZu9kIaDeB+rXM/K/usKlPLGsW+nhL3MBYEAbFSu7CMvEsFdKdW1GJsv1qa1OeUGifpS9G9J/CB2SH0b9T+P4O38T/uo8RjiTtEMkWTNTKJ2tCGvcaIKUJS4T+BnS2YSPEF0wfmUdPK7olXJapPRYU77TmY/ocYaKKRi81xpCKA69StuV6lnA6XTXaXnKsLVgWLjAI74F54Wwa7LnMgAN9w5PCmkdTd68rU/AGdLg9AwTWt62JsN6sHeqOz+Hugvar3xSgclXMOsWc6i9mtwM7VmUCsD5G2zBL86vpKHySciY8r/5PNJMzOUZaYmoTH5S0pigpRbts04vJ8j1lnrBDpJNb7bDzbTuc4eeZ+Vn0DlcscE/45dTQDp0khlTf34VjPhAEpZNy/9bnpeMNqExj+XCnrb79WSj5cRwFNpVFlNyJMybJucPCMaPOj3h2DG0lgiV3ZiVo+3mczeR2DvU9XqfYmLXXRXHs8myMqTZ7jYcIMYPivcqPvpVVmPIx1jh0W/Ut1BzyQoGojh0uL9oWwOvLQ+PEHDjDyqWd5r50P4UnMr9MOZfTknNpB54SXuTtQY/rzmYs25wQAldssktlcMjPZlqA0MXiASj0KUtZmeOT3YhGKAefoIDnF1u2zXA9afbN6w2QAiCx7uOtwVlUHYd6X7sMzPeTQ8J6oCLgYOQcCk7cCt3tlgEQWugqKpC0eyNQQNL62uK6xX3+hoP+6PvfNcA9GtsBTGb5G1TxDS0om5VyeLrgKEzfoJrhZ2adZGNawDlkKX+mTvuJTFSr2IaeoZ3V2bcQZdZzeAep4bpScOd2JEPMYI6pTzQrZJFsBOmO//LElZF8O4kYCmKmVGtUpG2VB/sPDWJ9Zhj+JfWMF5qS24cdpPXyHRkiH+ReIdSflOk1VuqSyQO6gjkM10xkBjUITmzwa6umXt364kQF2ZnpZVDan1+6SODgW3fIAZgRgd80QZCjIcxqY4wHRSfbl8cq7IzNXE58iYpgOrVxPAdyGPaUh/UHakfnZSU/eezU126tp/B/G/mXXMBjuxdvh/xUpbSuxuioth/s66ArqnV2RnZ63SE7JtQAeYahVMdMSnMftP6gJhj9yPivl2L+BRpnZrKMlVyWNre/zzh5UXKgvX3n5Heky/bpa7+E3Q8BmRl8XrW0vhjFntGgqR7rllBMfb+UZtu3JWZjpKqf13+VwHjRQKblR2NKiwYofsA91RwmkRk3sFfSl2tzMyph282/lIPhvA8U04QfefcC0pfJs3jJRdCAaVSeIi84ZvJWjWuw4f8NJBELIC2Mn6wvGI2J7wdG0yyPg6cKFDpr9YwmUzInx1aiaWRsSEATdo+WrR8zQiNOWfj7kmBH7AXjyDU7KcCntRJdp2VEqj0ANKmVqJKaPo5QAG7BUmHwU08e4KhKECbmpTCHa/3d1Ajdjm+/ET+OfzPCOIVJKxT74X+MB39AsQ712npnH/gvvwMsUphIkifL1ICIY7+rzVvFZa1oBro6hQz1xljlH2F1uYSZY5Y9v8fig61vcORLlvTCejmvxM+GQuPnDfkvVSNeZFwxHF0tz4NL7ktrMGy5cPi+cX6lcAqWp6wODyYEVWBU+VoE9QNp00NtGS10R/MHQOpx8m5jT96PRVVQUMElmp+bxig/ERtGFyglFcfqJ8/kYRpviLKugOCdYzw5cURa0jYoBEuS7UZXg6i1aUdbABZ8GC9t84CLP8gzywB1fhRkxGUnahm0b9GaRJP4bpAB32Dzg5DZXtwuXKMujXGK/aER6nV5sa4s6u1kjiBmkvet2QXJbsnTvwzLIPUgtXHCO9fw26ClqDOCT2ISIYgufgAThTcqNuLsxzijs4yJKX2TNUmFVk+JHNqHwUhhu6iVVJJYEkfH3+ReLMNAiTY3xaJowHsSxXt8ZhjdHu3TM45BNG847KSDLqGxYAni5QdY8bH4PZONDS0Dzoe6U7MfIIOaFOIYjqn5SLl2gl8SgZf0ONm4I4xExXms+cXhDKOP8g159O5u7bMZvX/KvafCWMB7noC/8H4p4z32P9PfFf1ylqGjTbv9Ulaqvx1MIVB504vsyGINUwrkTAixM/ko6WfInWBJswDEKuPBLweSDh1Xb/fz6k1h3IzFSbuRWAnMBwI1hChTuZrN38AamG8R/OXFPDQ1h5XGXsFQqDP/Ck9BxX31ReZWkJsJmgSFOL5ZEpoRksF/kcqV8VDDGFjSQC94qZJYsmpwH+XY9AiZ5E6HTlg+JN7l0yfRMQubBihNa1Oc7/boRX5SmZITikNHSsRzu/oOSqzYZYUqF8ezdxYSDAcZsTblAqu5/3KWjkNEnS1QxpM+yLcWzQiaL+fZoWvkxnFv5hWV/Ml4EAgKZYhPvrhhVshs8d0VOLFeMRUaWttr1TNyWKCECEFDjHsJpDOWWTycX965b5LdgFN4cEB4KEiMW79UMU9+QAUiI6drZ04evSjtZkrrZSzG96kLcxAYkau3OwBoF0bB+B1QGpeW5Yg2eJdbdrmP8F2zLW0aH1sbbdoisVFX8dDz6JSdwiGKZfhRe9CjTww852/iNDVST/vyd9vpCGe3gC9j1+EUSrUD7qox+q1ccJ+evgdq7W43Hem6ZyMmHjeuw6Wyox9ElqPZdhUrUhzBasWT8NRjY/fKoTISf6rON+xFBSiIeOhZQAnWH9BzsXffPBuD0TRCPLrbVzC3bot4M7n1wv0YFy1WBF5QAQ3DRsrDr19RlGZaTgJWiyf1reDhjou1WfKAReXvOAs/ELE4a2cgeyA82HamA070ZZAINqGoOQAZuflYJyQphhYoMeNmX+Q05LkeIgFP+e3zu7H4t1tWW27EVqFEzCJD+hIbB52BBImedqzrJsRNfi9EH0VRZoKOvpJJnmPKZ2nnV577BoYN+2DTkmpG/M3yY52j2G8hJB8UpXN4OPqxxTSe5oelyguHXMVG06foZCgjxXyrFydpY7VY/q77hYSR6d9fTZyfsQJlHB5hFgy8cvfqOU8rM2w78EzztaGVMT0B2KgGeD2/qKH3RWsF+ZjZfR813k2MdarBj8q8jXFDjhMUTeg9/tctNlVY00O7CrbsKouE3/rFZwrp1YKyM3wv8YzwbNeXCFmd+Tw4xbDd5+O4kCZsc9D9CBF5fnDwe+E709RogAa/NIFnWBM4brgwp0/H13mGnR5J7CJ3qcZoq6K4WtSiNoXH3htFRF8PYlECWSZx/5laDoHNGsU6yyF9fB4VGubiOM0FICP3CadQc2xKevAypFNoj/BvgzdCSufYV6wpIz4TdX2Bh504+LYwsHlcvJUfJrOIGFM4EN2KgsQdeCBzTEGRAthb+10RIviOO3KFhobSe4oueOMa0BxFEOifVVFUt9X8h1rcnDCj+v1fWEqfKB91fSmgMG9bmGl3H8tPhr1POplwgUu+W/Xj7YlUpaRZ/AVy2/tyCGNA9fGUAC/RtjYwLIZG7z+LtvrIZL/MimoO/9EVg+JcFA1H6K8qZk4sXyC8JM0W5YJKerSXgZ+Y1NNI2EotAxLQKShn/Ay4mI3gZ4hwUjcqmYH4lfcYrKfzDCFq/nVV0kT/CuJROR4U37afSCY49Y1PbSXHspNOLMF/sfQnO1Xz15iIYC9PCy6KrUZneqlxhWFDHnAY0DBV7FTW5Gj/0M4bMg3vGc/Sv0y84u/+2ATpRd19PxyyCitM9t3loV+0LzJ6C0RRcWQl4936SnFbPsTzvETcUo4feCx2eg1ckvpIOSsEypqzTWksuSPp1Uq821kT7WUwgs76x4Ek6MWiyp/wATFBm9UfR5vcyO3jEflhZKFqA2bKrVTXgqCFNNqIJU8YJJ7KJLXiUJnRxHt831PDuWzNQotzcHwHH+3AIs5e9P1NatgZI9vc+VTYtG0dE0kZ3nVFUyBXtCaiwHrbuQBK2dqbD8zQSs6dUGLUd2e00nF6VS5AxrAQbKesjkZsurwdNqVD7q2tohn1MW0GfReshiGpwTYslq2qLVU+zwhy1KwNUXi03Ey0fPk+CAVzb6szQue8UBvhRJTjOKdFfS8/RRy25qzwU2md8zLOhrywW+9LlXwt6Wsu854HIEY5y9oVcmiHvuyr+WTY8FfhGM/U3ZVUjKJaMPg3svgqb26bV3SkEyQV/Q10rskBaJUVRJT6jHjARgpaTI66sMlsE2ibJsX7Vm1Y3Db3acsgkRXzfbC73yYNL7P90ac0MEIW4LxwQr5fwl9+nY/pDAm5M7OvX5N62XnID9W5lxin4ysTli1BDgRZfkJPpuT5BOXkLOGC1TaYPkupzwQy8++T5hu1PUeGDlJlLZ81wxGT9j/8yDCODoKvph3LR13ov4ajf+0EP2Nhuq6HIgQ7anBqcl5PcLrZngBtQ6+9QKi0czZNAIDtfQ/xX7sx00Nslw+JtD/InfrFm+eS59mITyYSY/DMcF6DmndbN879eiO0HnXpe1J19JMB0kLfXrcbvuJ+9gKvZ77/CDWqqRADLHPA2HaBy8w688OjtnslWyPIfkmeMsFOQoA0s2eIMBYttXwrBaSdhFYxNMVWIpLJcNItQfoCX5WFssfP2LdxEn93JJq0RlqRqQ3rS7AdpQ4ZI6tdQNnnJqsKXvVYtBkLBzyDCFKOEHr49JwSfjdZIE3+JYHX5LN5/+vXSKy6jCIj+mwSjT7LOKJiSjdW3ymqxP+EyWC+5fzRvo44nWzjODmEBw37LXeRSVaDY5Tk3RSuTnJEw5g1XrGCT+qMExzN+HrL6MIDbVUbOqZA6mNpeJuWg6Zj8pJx7AWu4MIvQQLAYOU43ZPLE0WjZFkkSA3Lg2RokHKGXNeSyRQoUTPYWroQ/WsURsnKOHghuuJJ/7mQr0WDzyHAeJH4qVZugP5aAN29jumAtYEMim68foqnkVU/nLJkOeaghFt+DR4cw6WswGVJ9pgw9KOd0/c/lq5j3VljCT7N3ZPDkpyjEGlHzkFkePrLnN/e2dZ3hIYOVd091e+pYJH3A7SU1UHNBnUFONFeGkArmkdbSLWiOHccdd8bNGnsC/iKtCv5h1R5Li6YdmOzguyzhehGmup1WgFTrNufEvVfdQYQ3o14X1wLvbzvydTNNqn587eL/g82HAnXhS8igHobH+oCZerXC12Bc9OSWcGRqtQdWya9AnYATVTWIpDiDM2jhxonr1nAtRrGhiNA+C89Lxb0RaCpff9VYMCzwJAzrmOYe3q4m9EmjVO3eXHbcVWvoIlML7c4BmZu0Hhi0ZeRpVc8HxT8jeLZhSC4Ky4zIU2sIZi42gFcFIdhzH9WGq6ASKDqMdpeH2L6T+ZzmpLtfd05Iy2K+AceokzYFTo9G3vyPVPRUeBf+3KS0qbmXnXKtdZ39ljWQdFke/jZQC6alUad/SJLdOzXA23ELzNzTWXsrgHR4WpCJWY3rJvTH2dhXolWtGmq6CmHA3B7+09yb7K5j5KbqPLLJMjurewhwV2Dm4H/Zv5w28z/hhnu3/HTDeXXdTHevjxWEDfqMpq8+0R5OcP3sKbMYGCKYtm5JRA3hKZfQhXJh4kC3pKKWAQ1sNx6/WA5Jeg2aUL7uGpCqn6/DKLkWkMHYkl2+9FM1IZLojshtDXDGCcrGJtjuPOdGPlfT4BCXdVQNLXSWm5/bMZwVF5Lq1/U1SICpgZK6OOUvJ+e9OcYvmvAjqxk41Jstsc1xKnTJcHNQPi1vEG67QpIvna+BzKrE7LSeG7rU435Dvt6CMoCqf3zoNZ8CfMvzY/swKTeZkVfzH1+O8QRxr2JKIQ05sx0CvyhOFBe6Uecl9EAUng2IX9oyTrqXJVEoHS6QvuVTr5E0qtynX59Q5sS3AtuNa8+9G/8DrnVEYPHeT6hFnxj/WgvDJs9qvaXmiiMazAvHr8KvJnA4HpemSM9cfapzXZ5X4kFtKRZc5607FEQ5Zm4fKFROxLhjMCLh9naxj5fsM6vxd+uGqlqACWLCvuzoRCIE50AeIjsLmlOrU59hjuZA1sgzZg7BwlHrGOnoOJlq8rzYCRu88l3Bj6aqoZz0qCXyfhl2skKbTyTZPZcZUnoY5XrpDSgyuoXjky2Iebgo77fjRcyxIk7Wz+t56dMfwxITr8KyYeXJr+n2koyb9jJZrqIANXbyqg75kuItrpN4Q1/q7Aa683kDW6p62O9eAPBgu6rUq2YKu7uVDL3g19rws9GfJqgnymkIDUOrb+abxXVuQ5TVrg0eghBuGoXMf72yv2eoSt4vKZ/wU+fxIOGnsp5NriBJgZm4317Gt7Cl0SRmqITe+4LCUGXfsWJZ7Eb3Hvav/JW6OIHVLPtecqaahrTEnOqZBlI0HxkrQeUo3UMSw+3tDk/UaPDUHR0Y7ZDn0mRynyyMu+RMblRs+ce3HdnsXz0zZCgmv44V1vIvvIPEpaLlrVrLP/0FUqM+pDio6NKo9oclXDVOtAJxtGmEGHYM/loaXpvqCvddoGekYsAAPmFPzTV+qGEorBZBetXLPSDxuny66XtlI1ToHKecF/IDD6sZkLHE9EluBMES+yUUvKhx5nyWDGPCQWRMERzLfA5M/UmejQ0DntkYC5l8Q1k/0rZmZ3m9RW5KyIbLIoX69HMZDrCgMqw2Do1lmufNVbbgSYCfmZUHdx7hzC5P/r7L5Js0j4uNyNZMNYjAzjH6etwlN2DZLJCIADulLR0IS2HfD3qJje9r/gj2SawT5m77c/VAZFbdg45pAMuKzdnTF7kyTe3rf5Igxn66bvvRmtba7k+dmDYikFDsS+Yif/jGf7HPIqK5wi24CIMlku4BQsrB0rdzaXHxrYEbq7It/QZPvlLYhyp7apyIKz2dSNH6ywhdn+NaRoAPoZhpw39asxfgLQ2XsxPybiEs21E82/MgrvWWhUtuhwzpZra80MRd+7lw1HIwpdx5S1W79aw8T9zBeH/trCaxKLEWUf+kdRB0KHcIMpDi/72H+eeXAR+wbaynhOgUC398mxbbmE/oLJn0aNbxTs4IxJKgLjKwvHlnFkAKl2im5Hs4V6x+f4+pjev5VPixQfm1dVfptqyD21eMYpPbd88KPT9Ay4LSV4yiyKoEbntCHlAG0aJDddff6NV/ya8qF0zCO4vzjjmC/zIQBnnzEVwz/mSHyv8A01/GlJgjGsTq1ofA4GvsGJSeNfa+W+GwE4HFEg/BPF3q5UFMm8H1JqKCuuWx5gDfhC++51HHF4GIb5u7+sTigMyysyXEBJ+oNbrCZsEGAGd17S50UFduKPaYJvaTEENareieDIX2WbHXOLwv4H9/ksB+YXJtzbxJ/ySZyr/HPyIPU8XdlYPFIJE14odAE65AE8ErToqg3SFvGBxdGFDg3A30UQdANtotht72/pQBZ5XzicdrCbSJV+wMTAP6G0tYF7TswkxkpWrsGUkaQhm3OFMAnmL3JXUHUWCyUd59yf6JwDT9nhKAROpLEPwiSLuTVUEo826yPHgroxn8FhoNj2LdNz6/V/79Ia15IH+xpQeROPYBr0KfobXWpKjAzunOzMOJsbkH5jzdc3ii4LJ7BiD+E0SUdseFsV8MNCACiWzg5Om0bnQuWN3tF0dtIedwUu8vHsSCC+M3yHi0IohhHDZ4eyAKopYY7e1JZMWG4ZfUkPcYUgo9lMSkLkjQ4BWo1EZu9TprgdJMSlkcAgkyQUg+zlu6edBBBxJ8FQIvT/WHHeDw8Xrwg+7BwEPg5deY5ipTBz7IdiSzT+kPKUrN0JCOej0Uipb6+LDyJd3oaAgLm0oroDoUdLkMWZ7pqCknxgIAsG2Pm57ZdcXVFdPQQF9i5/Er8cPGxzWGI+0xvAM1C1/v8PJ7f2BVlney/QIaZx/7VdJf4mivFmjr3TGnBr5ar7YlC6xEGVsx5VfQ1c+zwLnfJCbjGCxojEPmzafNzF1zPhjqmBicMwCM3Zs++BQJMrYUnK/WXAYjyrRuXz+xICgCju90YXE4qyCsU7PS/VE9qxqyDfpksTQELo8Qx2di2WDO3SYcgM6oAdSOox5bT1aXht7w27QSNXvZ0H9qnGb2HDp+ZtukWoifzCacTxA40+5xtj/aCR2MdK6oOMGKra3bMmQ+l1WFGa/WpxSEhFtumGq9TH6Gb/ay1R0+AU9URVFlIzLsnuxPJIdYMjtRTVnjGWOcgO8wZLFTzj/Vj7rhmhH17LpGd0cHZrj/CHrD+Mr7FRvDKfX/o2J6TyJQnTO5UGixj52PwYAoIHt4GcQRb75AD/tM+D8qyJq0Dpsfj58/3Wj4iueZARjHh4wFey+Pgy4FHc9yC6VcrYy1oBfyu5i5IDYYaWGA+pl5+j9DCWkpkjMi1vnR9Bvs+bIuxHi0v4uihXjM34D1tIPEJSZPT/QsX0zz+3DkhmabIWO2krPBRxnS6SR9xTcEojHX/H2DK1epMMvGo4XX6QVgx4Aq0LbOpqwRH81N1UYi72y2YWlw5GxasUNOZhPmj8FxP+AL9wFXC+HfM4/eFlp/AIixocmTibhPYcis5UdVPjIwVYtsc4MrJUMhl/5ctPiUYDu386XGG7OnkFLmdntalOe6+thLf8i+MMRw06M8elFKD105jLJSqYfa7av1QWdUZPBPZ7jf2+I/g6SuhKZJoreDDPsat2tqneEfjwwovgGZvoRd+3HiOq58lKj1/sWuLARo+xY3qSGGtweeOY/rqgDwrx/r5bbeo+rnaBcpsouZbQqipb1Qoe8zUp5vxlB3ozKMARqXhFFk58f6bfjeuJWEd9VGbaEZcmUQuZfD0IqioUIoUBl9a/AW+t8VVKcCqUGbhk4ap90QHpkg84CTY6/rY1GB2yjoR2F9K1MBZtxRC2oA6IyUuzF/SzBFeaS/CSfFaCelKUjiVJa83/y6hD5h54Sg8u81h6mHLvn7isP6ek4knFfL3DpEk8zVmcDMliiE0Wq6Cp2bsihExkdxQ1NJwFIlc+nb7axaIxwUNikY248UocDTT3NamfgxIpHKob3p41UKBiW7CjK/Ye+vOj0ycm1FgJfvZ9MD3LPcYt1YHQHpaESOJEYesyLFCUYn19sSFDOl7LzS0sZV9PscbBMVv85DCXLSUFVT06JKfQiJVG/OrXCKFQxbADQ2oQcE7e5L8nT0C/RLA/HsNP7rvVp5dyH7Qh57OHuGyPyd3tEMKgKkhljN7kyL5WAt+pHKqLnQ8z4Gwh5uzFRGn3BuDcsO2OX+67/mNhl1tGhytgUzQlSM1CZ4Os9UDyE+ZYU6mRYP9h00+GB9kPG+azw0hiiMB13zmvnHfcNl2o1IR9U7NNTxnSqx1kmGycjAszP0JcR159xKHWLLCLz9dqFdb/RQny223fQsfhe6s8jMjtrHtdD1rHChj/t2uKjXotIYRJkyPSfkF5IW83Qilj1fmRjmRdRonlTkiXBfb9GnpF+7x6Ff+/MijB/1+F/7RRHVnjWBbl0UViYzHnkccwURPBm9YdCp7breHeebm7Hsx8dVkV20IHFSiFkjl+0nzms/rtsXI63R1nMBg/5C6t+X39H35TnLDSzRdwwfDXFq09jAKkAU5J1w7oq4Ut2ZlE8rXx/3HvxSD4ijlc8kS8bVQ8Y3a8VRutlnS32oOQVmxu4ImisfYXn6AB85va0cChQsy1Tulc813VS3y2VY7ZvGOcBkgtzk0XewPpXpC+YKUwgu+jbc7e4mQZwqCZx4++aj+y9JiNPaHhDTeqnfJoL7AsoWPDEbarhJJzd3gc9hYOcQZ13HF+ewyZurHr9fIMxeo6zIJBjIHkxN316vpVnmx9O5vA2HKsKoJtjPL/lxABn1hlyQOnhCz2+N9HmYIimJB3DmARgCAg26XxGyXP40gkevifRUOFE5oYN4LOdtAL/qY8DnAGoxbEpGBiiPf23fOUm2Izk5dg0FTCC5mHmcCvejJdJTtIgwCXGho2kLdA0n9Vfg4ps0GdjTc6UPuD2rdiDC/RiHb2QdP+CjZss6BTWhDLioaL6SMkRYqu3RGOffsW9YCHBBaz8lqPgklX3i2v0YGAILpn2Fx+YiW/4BkQUxPLrCpW7FLh9hlb5t3kdZdRtPjhV3hqlSIrVgEOjokkflwjRM5Kg1OacXQwm7BzXSr4pjGp7ejQ2zq6frazL1WqLR61pjoYuuTQY8LByKpO78wWKsqyItSF++8XM6CuJ9wf210AkZ/CDqE2/zTuC/5ay2odzZAGTvl86t7LlA/w1muiqIjuaYR8mYiNt0KxPxjHRbpdPCsJImoGP+UJiPpQQz/OhLU27pKGRBX+3DKVTulHdqS9qxIXLOjFMZkxp9LLFf63HyQx6FegiIT8HGuzyVwrLmRX+JB0w9QsmoPcILzCFCK+Bw3tM7afF70MAPe/W5UfP3rVTlOJUMsKCEtHrj9t3bOmnj/O3xe7G8UkYp2DW0EeZ2TV+ZFdGmxybRkgH4KMuIkuLQk97QgQSv+wQ2q2xO0nG3OixOuhejIHNvTwWKxky3MMSFHowRNiHZbItEHM+F14zsUMlq11TmcBcw8IiUYpq4yfXXmIRnz3x9aaz3mS7ZGjF3APkb8H1I1fymB9uAa0bXdA66NmKp5cLk42v0fWRS1Lz0DRqyBelGmJ8zLLMgLHVGqWE3n9N2NM/BF0xRZcocwzP00u9VayAuoATqpQSpwBcstfo+TIy9k0eL2D+0TzHU9l+73sNTAXoLP3p7DbKjyP39aJ+vIl+WZuD4pcw0IXVrQBiPetvYsJqrvhYtcM1CH8tY8cgAbaQ1lQfDDLvim/T3nM2Afak7TzgzHcCJC00DRIDOyWj+0K8z3OYpBi/jEx3QEk0nLu/hqElhUfTt9TdSzKMXcbuUsHjgYtW3y6ylLXddRYabaGoYPspnJKBa6EaG75lBF6fz1Vi8TcTG7hRZCprKfLutlwxGmFlfIBrR3Waw8A6OmWzY2P/+ZMFCTKQLxWFtbr+5IiKe2ogwWlsD2av/cxsjB7qEb1UBn2QLP1Cox4OolQAfHet/oiAPoAXChNYMMeilz00f/sjH7eMMRIm6LN29YuSHmb3Z65DpGDAmMbDy6nZMhtcFmURn2yNM9Tn7Thzx2bS6pPa3VFbRENDscDBHzlEHaswM3UuTbemhUKWcn7BqfkSF4+98xSL6TUxb6uxhin4mWJFKF4qZ2lE+yyNP2Ba5nuV555hI7upj2ybALMKkhuXiJnLNSD4UqMJJ6oM0E1gFm49JoNS1N/a9LaBgcVNkBDX10Jx9rwtkIGfpAL5dDmlZz9/w+s5Dx59yVplTgaNHNHP2c8WNwxNG7r6FwtvvIIYijuaNzM80lgWqdoLTDgl5/enmFXkdu/39FRTin3NytpvoCWQrd6zAr3J3+tJMWYBM1lB3llCg/RLGo0W7e5uvcUewcLAm/y9gUuKYc/LF2Ry5WlXZh5t4pNxNpVVqXqnTNwF9Y8K3bJB0KSh3bkTpdKqs1+3ZNafzXBD6R5+L+3B57EXhbhr1zmBHSaf+ATubvRKSzr+Y4HIZ9ZE2AeLX1WM/mbG6Fsq1KKq+C06I54h+HdMM+ZMGo2KiM6oYkV74UwuyOTHRKbtrrQTd0nURSCLYlYP/hPpu/rGR6QWdRiXID6gMnvXCQpkBSS7HMJ29LhiAFHoFkhrDX9MOKM4S+QhdNQw+XHrAMGAqW8/u80JjA8RJNUn+X37yXhpq4C9TiwsQaEKREoZjMIVETV4RYSJQT8xiNpq1kIJpUqLqstwthWBamDRutfofdG6YjikypkvNoJ3U//JqzvP4OB/2oboyxKQBbC7nAJMFXV0NyeLKJ3hcHQ20EHtVugajVttcRN4Ju1DzsRQWAaqVHZ9cpXUtQKSK1CtvjjILiHIq6HCgozisceQyrWFXiQkxB9gDzAoJUDRFzziGLxMT7J7J69kWsiItqtyMigJGc5yfr2XBnMplSyMTLjA/CDflEmEjvHTPPoavXQ/hlJ/SEBWe+FGF5YW/Ca0xfKtzRbFo2oeOgaXtn4fM19N5SVZEpxARdSz1AzBzWHf/YTT456JpNeD3PfXTqT0h3Uecrf5BwtWyzXvWg7V3GXHHlUo7ypE4nlYiHP+xChvJybbkAEXhxKAo7bKdQKF6Zq9xDHHSKlWxqIp0VPkg4WbbVmjldB2bxTCa0jm4bp+5cCoP5+gGVbgPg6xhd7QJ/VyDBMKKj20IY12PKouh0BmUhwotLCN8Rhps2/5mNg5mYgwGOu8QBL+pTPdj/BFVSY+WyhieVheChhL6mCoPz4aCHw0wWhvUvCVp6m4+2EAOqFJo9dtWfbv/3nCic8xedK8Uh1itVL70gHVAMA0vWJoA2Fhq/o8S/OI6kKhIY+2LbhbY/TjbR4CQ8IdksvfHeN4/Oko5SGh01oFoNOrejOEwdF7Vk4jWalSS0r2py+xux2IMCu4MV+GIyDbCc+KGS06PoKEl78tP6Vn9kLWbegb5MKP0UokScheymoFcyRuAQUwa63xG88I0+Yq8Wwfqsf/Hlv04GwE1V7knxyOiFNF4KmgJsHcCyRvxySQZX+cbuAvxCrsT/fClgexhesZh6q7rFDUYziXqgkbGaEw2oAXPmAAoZSfCSNSWTbYbv7B0m51FLaIjycPp6U8G4Uex0f8VOQH4k3LtbmLV7obXAuoCmxHSqN/nuF3OkLBtNS1/9rrHsPJqhlqhtv/ljJN4CYblJo/iAlr/UPSK2KwGgPbQIZP1NeBwQqcFXlIBz33z7YbUto2m1Bv69f3Npa/pvC2JSU6lFkVbqbY20bdpLH/wNnG12jChK7yfUYdvB+RhEzn7/q+XRXK/b3OP5JGUMxjYSxq4fJ6oyg/p7HcpYIvhJPCM4mR4M+pjqDYFix2zZstwPyKmMY4Iyifb17fYCIhqYjhDwmypKoXVJm02+q7a66Bcd3bax5IQJHyaPC9q53npW6jcjAKnyYftqYrMiciBaHKDp0dDl8H6JMvOe2Rmt3tCROgGvto98aCVbH0gE6zCI0zUaiobZEyTDD0hD3oZushlLsi58Ap/gZxzU65nEARpP1bLmUe3Dc7Cf7FdM8xh9eHih/vWbkELvNBOIY3lPoR+S3l2q7xhyz94p+qJ9sj9vFLjFKH/xbE4eVf51AxCPuTaZbgf5v5fMnWtM1SndoMjPA4PNdRbBi0P1RruTTB+IjMIyjVSRpq2TkH2zafMcpCS2Uil5GEnpya27bvgnE9xizPzt4Dk2GCgZJjg0PWDHF2hsPYRnZFU9tWIIYCaS33Wbguw7w6+34pH5JwtTfqJWrmb/63QIB8bmtsef8LqtaQJCKw71f4j4Ekfjf+dsqQdp8DzJFjvrOQCGrIf1sS7essx71QOOr71ZnwM6kvM9F+FbvMSxPX8xhy2yTswbAeLhhWkBL3yudZxojyq570CFDKEccjrgA9P9YPaxCiWK/F0Op75MX2jWRrcErAexI/rydTHymUeMMLY2oWqNl8SPilxScK9hPI5KwO0FEFa7J9MBhR0nA2XRcnj8J06NdoU0dQnY+9Jp+PNfZrglpCuJ1lRpAmbzk4tSQWcuYsuSjPz0Fv7DeIcwKR5f2+tk3JVAz/yC00oWQkMJ3XohJKc4uCotULwKfGu9S/y8+AoOSAmkPdQYbsokbRFTAFxPDtWa7Sf0P+wqnCY/tr1RLs92L/RO6O9LvWnOXG9mGCnwyWMosORPKAeMQEfgKTyn/2g+bj8uVFGtKcv4VpW24ZuJ3tUndQWqHEL86FaAPs/uMxJUilTA3zJ8Q7+VcNFqY6KDqO46KiHBdDJW1xNW8ppdWUDk3YzKWjvSWp7vWnpEeeecQdrJPK3oJjEbMO9rzAHO+BKWMHQB8AHF4mWAbtjZYb/uWT79/+DG/PI0oyFCOH1GNFaL3DjBvn7oIKTK+72xbUMb4j+PK/29mVh3sk9MNABHE7Gh7EyFI7bxtGPiGpX8bXgaw9rla2acQGQo6hh0WgQj5ibs2u9i6GwXPaq2SztJqSoH9zMLV2f1v5JQSYwYJfY0z08icaotSRrugtPnysTMSNfmNpNFBDhBGOxLXNUhuXklOadZ54xHxIDrd2KZuBxVU6vkl5H+2DfsHdThGGC/013U6+RoaFIzp8Sk1B59HQ5oTW4p/ZRkbTXgsrJlI+drKUR/XJNo8QBPeWZCunrPkzfNOQkHnwDtsGn2Gm0I2hTTFin7QXbGF5g2MXkbuwIasRowcrDpEb8oAzdOoJjqgwP+oiBObg/YtH0UOlzhf+YTq6oauhoh0t5Aj5SfZ5s+wqOuyXGpDS+qDWQS7gUD4pWvVx+U/FigVU4us5L1+wpWaBYzsXCfCnG/x2lAzUl8yQl1dCIh19Ef+uqincsadhdqQXvi6NdUw8SMmPIBVPmX1SHQw1iRzNVmHKos9YQ+pVUt0ZMJEKcsOHL63/8l0q+yhBVM9Ak/yNu/X5HQr8LqOvTi2GSIxHCqRe2NkUz0ulECY90higsdf0NRzDR0SPRPfRe5oIPnQrzeANMa7WcbX/iXK3mJgDy48yxucxiYOhJX/ZX5WpcKkMu/0MIHiVrdprQnmyZB0w/LKqSE3SadFE1HSY2XxR/ehV4/gnOOI5lgNO59j2CsAM/tRPui6voSIDjze3egjJjAN4IYSCrhuTrfTwJJ05/rH0dfoNHJapuN7+dwAPEMBkh8qbBTtbVENwU+zzMaT+ia292VNA/520iN07jXwbWQDTIFoQL/B6EeY8UGldOG+MYeGNt4ts+P1yZh2Jrz0cT7fg5f0BcPeLx2d84dw8W5NoYwLKlmaGo49fJNhoDGiJO0P5urUKPT2KNQc09vT5/O0/hf+6QePgHseaLjn6z2iGxJSCkq3cLSIrnsURVVY1R9y0X6fya/BlMdyZLDL4LaAe5EryfDWIVpjUtX2J0asthuq0bVP0WrF+BmshLyHv1pZNcBtSme5jVTmMtC+awpy4dgcQyCaqVjqS2OWFAeXERfpA3J/GvGk7l9O5tX8mOeDtflvuFabNTM25d4fLriFwX1vMPsWD2SowPbHqrLVDiiAHfuRoNelzA7lPwS36DcVyBmcaUhmZHhjmGebJh9nkT1rxC2wHJ/ynrQV6etxM85nzNB0JY37GDxJ+2FZE8VbQSRmhwNRRTSIxG4oBqcnxI0CRSTaBt7AlRH/lQwC1VvzlwscAowLbpCRId2yQpcdxJXZs9MQKcoy+kY4jO59fO1fBgIoZKzFhakc0i8nwyyZSBu680giz15e0FEqHjBHaFpzTUX0gpk/sP5WAwaXcuarsJNPZHWBKwrHRbPmiCBYtSHSX5aNmZ4sxS5nrxANV3sWsX9UBvHyrLcI7gxtvghZXrYLiKCIpP6iHlOe5MFf76BkZHz8ZD0MuM4OHs06E70QrQoumOXODAlc62OQRvSUT1POseOU4kAzkBC/LPyyPX8C6wN4JFr1BmbkkqH+gXhoR+rssoTak0oQrEaYTX5ftjdw0wQLinUEtXqHtivw+81XX55sIop4I9xG6/ubTp1TZLW47tR46cO5Pt9qnhlWsaYSfdSsmyR1ZHoa5agegPJZRpvv7GguCIVj6b8zATSvic39qXo7P0QB7sgns2msHiysust26oDCwiojxA2lRM/Bn2PMKIG5jIaK200fXifBeWr/vnyqD/vin3QXXzh58oI6orfbzCGmwnU5CJAEMVhqFbCySxhIagBpYvwuT/AsgAnr/YTilHZF/4shipilTRCBasiYM+G0exEjfDLxvAX7q4drJMfytmFlmAAWucrYz+kQTiUuoNR7PKA1iepfY5LpK3d5iJGIh5QtBhc0mJL9Ey79OhSrwL4oaSC9QpAqDo5VgyXyS0WozjEdTWXys+GuOEoATzhmVjg+G4NDej5Qa3ZY6C2Kdf8d9GBsUy8803KjPmd/nB5RDIrMo7bEgL5B5SW8wcl7FQwo4h4uf04hvpE62QaMgOtPDjIv5LtyL90NMy9gj2RhM0OPFv3GD/0T5ohGMIugsiJY7WlaZXbz4/QHWNR0n653Ftoy2w8P6ya+GKSt/NsLcZnXru/57mgNYKj9RZq+MVTTdEvGGI921oNx6CCtRAlNOFMFL4QVifByNWySEFhgQoRsiBHnLsI4NiMFhpcdscWMBL/CryxVEgalgQWXMiAmJAfIruoNeqLIZBPosu7m/3E6eaOYa1sezXMcoVrxKUULjB97CHas02EcWO36A0DNGXN6RvhAE+3eJqARB3nV4yf45BBkIg5kgyofnMmVX4q6w5IJXVG6uj9yYCtYDh9YVGduLeQ+rgLwZ+wnKGIBZCWdqI6ckLI09jG8gY/SFuxtWL6jg1mFsLijIsdpuIgDLmmdtYjwtwhS9Qi/UEAOTr6+/26bYnTcb3QokGWXNoddwFS9DvtXyqP1tlYisYi8zc0jNbsdlJUu/giq7rV+aRuzQiWhVhkJY3LeGtYUOC0lD5pn5W5riMQSwWN3wUsPq7XXtFLeMPwPdc8UuPZPM9OmEin9bw0YxZ8a7PL3WB4H1p9qCKtrB5/u9i1CmzZEOM6Q1jN9PGjzt3IWN2QwxsJX1ufHyXPOrvXUkqxFZW+R9dyisP4fJAEEr0Qw/zpiBwskHwCTqCX4yzR4ITtni36ooAT1mDXxtDlzo3GgG9oYhb70f0V+RD7pOKkqzzVcjW1yvOEfMLv1wUYqznA71R3kr2GeHOZVK8koAMLK3bgiLuL6GhR99pYMUtmeL5z09ZQZ1aHFu/CnnAyszhHaG59lH/pAGi4mzBDoOsEN+Htpb6L5gTvbsI0n+K5xqlm7PONK6JKbbuN6Y09YBh0Eqn0F25SlyHSUb2qC4ZcoqEqfKgbMz6tTNa0zbv3IPWczdZTtnbN9hw6d67ynzM6LhD7SwxDeNWzQbRUycr/cPAFoN3esbK4+rxLZcVMuNU1NQXotaQFAvAoAfqlFWVP7bnFqU2xttzNEPRTxyVcvE8XzHDh/7TMc9TfpoVgeZBNXffLAvti4IBc3sP6VvtbZh6J8PHds8TOX1fX5N4uMeJU6GGvsaT9oTxhIuONhACvYasHe6HWYCXmJcRsq2XWeU4XQ4FpjlcRnVHY9mQRuu3WvvXmhWjpoVFc7HCkIVaGahw3aJhUKWia1ZPZfyLyYg+BbJEYFEX6pX2tCGLhkKqqykB7+IePz+UcGwXAFusJT/yg3i9lv+puYu4FNuGDWOjRGLnF/SqRgeCUK6yBjCUYT0Jww18YcziywLFcgwAgW3PJf1vFwuLf9xwimLKBe879eAtMQkLDZDvLD4kXV2o4odbmPM/M4znZihihJVrxTM8YE6ghYtG6ZAdkQ9RNO2C841ti51Xp6oCZ+JgErgCAmT6dczP1rsrjIqO60T2tbWw8oyGqKhxvNjtIIzQK4rff4muUTuT0mxyp2BJwiWYOPsRSY7rzRbQxSMAzaDEc1fcxb8eoVqUwWPBXWrzG1/YyFmaxLNzrSsfSQOw/3XBFvCtsqsNpRHp176AzK3jT3mCAoOanol8fWaAn6ZB7iPwojcTcs0amKQyx1QAdJuG9pv0OrRTPGnng/PP+UqkfYdWZx8WrdO17UHa3WPtegswyU33ZKyxJ9v2tuxYfCbWUAUrB00rnSmhS6gSK2LjD8mhmM02IymKH3fnKAIPJyFFUvUFp5f22j6YChddEc21Qrj6T+yMQqsP7RWuJm9ZOcFc9B75IOywqetfPrDGUr4WQZwtZ7Yw+wObIpAMjJ/EVoKf0oDJllTUSSMhyOv9KdLnnkSP70RyOOLbJlmJPS60yqDF3EGAOvP000dPniMvwFF/C+JAgSWbJuWcxDMhlY7Gi4KEdcZQxhjTiw4RqwaIqiBqWsHCZc1vk48ihCDzZUuc9LhZZkbBwr8ulsTFPI10xbVyYbOhO5sEobq12GHBJhE0uQf3Sw7lvkOLcF9WeHkjLjcCeHFaVXwxuzzlDTj1KOaDfC4Ak1S9mu+Mbsryw98At5pyYwJI/Vk9MownyMF/0AwmbbzkvQhGXOBKlT5r0KJPD3SdWDeyJ7+fq54PoyIyTVZxtRyPuPXxRp060/h7g1+Gg3VZFAwXdPpmwH7Nkv1CKsx9+bY6us4vIijSMmGMFG+rH7CqGKeMIXkvbjSTto5dtAT2aHA8mlm7IzjaMPzzaICDDsfXV8+cVsBYlmAIpBkht8kt17oUbp9eQbQr7xdCXEFR5uv247Ejox7G/WcwdgihIgl+G8rlUqwEOte47RiSpStvL2kibKZ2Yc2XaMigSaoqK22C+X7qmCEk9PWqWdfWv9eGfbIg7U7w1H9MaQoy89H//7tWua7xPwY+fyS49h+ubbfCVWxrsHsuipwD3CaEFTnmiIskZZIyxRLntXQEZhm+Bttaf42shbAtP7bJDdvplxPlLyan5Y1hSwA764/zuKkSn9F8q3PW5PCG5sJyt8u/2bVOWwY3g0bC57C1fMGGddBtbkylD+V9dKOMDKDYaHgmN/l0EpuxAj3LCio+eRN/uuDHsUXL3q56ditfnLnxDeO/xuzNVpa0pn0+hIIf04XDs0u0Ofm/B4ULmk0N8DI5Da2MH7CPsKUs7Y7X4efK/5B0i+X/CLpXHktK+/5dWNy4sWIrfGnHMf5hbVf15RGDL53pfQJswKU093z0VS+NPJyJcXaBNZjbXQNrZ/TPn/FJwYN2O77MjMXn0roT7Q6Ru7mhp1lZswvocKDvcrX2GbXcBKNMUBNLnnwozeBcVci6HmHNBoMRR+htR8Vjw585XqoDwyKdi/RDcUXk6HhwGl8piGkLEHbi0Xiuhzx0ooUkbf5nx3tQQ7SxS45flNO0iRMpIN+TXejXibme4SgUHTMj9u96BF6I0yyS54aS5kkomf4cTfmBcBScWzdkRTgD6kx/NpkiGZVRuzqF6bTiDAcQUjj7saXvvJSRMnGN4AWn++fI8CTZgZni1R6dbecaJvvI4Kk+fWTTfx6gv9ne0p7FRXREzl9fzg8O821UET6/JuUzG3RKXejjfYdjavwQffi7632+QeANJEAebcFOd/iHfU4jJsSHqM3mTxRRDL7slJ+lJfPF74RMY88AugZMEVDCXyp9ohcG1SE0htPQXqJaLYk0wEafSwplJ/sfYvczRt7eC1sx3C8BZ0hWTDchmqoVf7dFUJn8pYOzlG/+/Lxvh/iyL4Ct2pENjh66a4qJraU/6CgWjRje/C3jtF1VNLTq/bGxoxTQ6grm7UtUsrja3qwFk7XPiBu0w+J5kdiaOtWMoYhKNsM8NWfJC+U4pUJJiuG6vU47erq1+Gdpsr6I3dAWvbO8EWXZR6cmBGaHUX6suddGPao0goLuzeO1RHtn23eYpeEWXq39b8tUX5hFRpOcwOmTgUuP0oFlXM1w590KmGuvlzQcdIUm9EmY1LkxUX47Dbo0THhSgKqTWLK31V3sQd67WCIJfOQXC0+f0OFfzrPfdzLHSqwLYJyFLSuh0C04+dZni5iv8HuPzZKINvxcZI0EUFcGahepIhhIT7WyuB3G3/in5CnZ/ClQVig4c/fLM4E3Obz5k0dlSyMK1A86l1C/ADpP/Ea4L9d8CDlBP9iWE+fHP8eN6hGEaUH2q0k6bIhl2j0Has+38p+ZHKsXvefFb+qR6PaB2iKsomKcXP7d7XgjUOYSPNsprGr6dLzEdEojyWGbDbnzZb4lEyIofA2rZXbnmYlwtzFyn7FNI7KPyE4g0zO1lCAwDhrttyXwRDSMyHkeAELK7LMkFnThcX9R0srjjDHQ2rZeXKVUlmq69v83Xzj63uQIJXXu713LMmvuPdb4l+vMJb48vek+qTs3y7FOUwffVoY3zsIEpO0ZdcQ6NOhebWett3mvmgYvGWXNELiabyeoZB+ctl0bofzdO11gGB/P7n4mtQs82/GhDMlr6DIC0kJAoRyQTEaUzCSt7Nd31PStIMXJrPFTa/wIgGQYjWtnYkEvwt9GNuMwCQ3h7ZHvVamNlA0KqoXkkcDvBrwghUQnxAn0gFSpAfQCTPsxfY5s+Q0eUYOXXkMkzvMV+1uzfFpMpoWfNcyI+MElMsU72KGj+WR1eeIS45klmtoab7gfHzZk20+223EuzsZ4MWZ8zQCb0D1l1mmuE+5tigvbaKr9JTwvmNWHn8+lGHMMV5hioM7KNzfeJ48J38dknTdxjPnIdExS4ZxMVAX9lAehO0G5NC/5ZAr6Dy1AiGrTwjRNdNnxse9sZ9pCXlcSyHT2KPla4NxjwOOoROeN+bv5SIsJ/UdFYcG7ZcxuTOtnCA6hV0Jbgo2Ux+pQw49c04KJf9g78F1HKQcuoxYayrqbwX9Kb+to1xlAlmpvk/0bNbZLaHgN9xDuDpP7T2QGEK/EWr+nqX44WLo17JSCn/PaQ0WLsRAsOHUT0WOmnl8uRiE7q/xi7VOJ42YYtedALJabNG+YdI1Sm2MkKnLFtTqFSSI2GkdG5jJ2Zv7SuO2pcnW7QlxjUcWSIAs7fzFDlGljjHbmtk8aYw8bq1kevmC2aVrnhBwdur+0p9jXv4WUDvE/guOH9FSMjkTZOslXM8eRomna95NzjMNCGfjSJeT7BFyeCQEWvqJe4Hh3fGJ63l3m5MtFMXjY3QgBcAfUMBaQZ1Y3MjxTKSHVCruG2FE8GGZA/z3WYCW7x5pm2zXdBibA1Si3Y5hGJTEeeakjHdDj7Xj0+tHfmuAOYZh7/z2T4WB/rASMAJSgVjIwxXoF3cLqnQYGOne7SjAJWYHpmL/V6nfVLovt7ws+RI+pPQUyMPytJ3BuSBekEbB/zUiKANDDblk9PFiLfbcISlUUDPH6w+OOhijD5k9jTTO+MibuGINh+9RgLY9CfHwiqBzcVi3ePPGYeqbF+jACoFx5z/PfpBD/0IhzbrToC9fDF1tzkwvQ1Ejsr/WVOcuRpRxYRwjCMWKHeOeiBFKMxjDhH3KnkyPZlldIfyAVsKDpbnizocZ/74/zPqdqOX+3WZb1G7wl61iMErx58/mbNidYPzRoyijGNTXAD0Z3DyFZtMNbi3f+CFB+jA3we9rrWhtUYiVURvMFZFz58aXOIwhhaSmVF9cmBOpqaoXTpGVSOBvJFPLSMBpUJEJunqO7E9uDlNYH9UzLliORyAXKAFeYKL5rRTeNMCz7z+UWdvEAnHiVzh5ff+pvnGjOxVamJw2onmczh4Smu6KcHDh83eXPwggpBXKs05e0KejeOCc7+ualEpgZaEYgIoXyErVm7LKIo9ndDc7nMSjBQH5aonchPlgAzmj3UbLvOBN2BsIYabxFx4f+GuCEPj3V6Lh0D0G/f5IyfDh/gZDQv4YZeW9LPcsbk8JiLe91e7IN0xoWmkVgCBy8S/mNaCCvZBsDzsQsmFwVEPWIdPakU7vMy3cX9cQZCvggdC9AllMkEFxgL26NyCFp41CQa2PkxqziJAhX1xZjpa/03mjAHc3tPHDyJWIEry9UqeeGsyf+gb0+bqsr2Rc6QcQd/VSxjHeeK4rJtHTSXP4hoJarkDwj/1nP7WSL6RTHUncMB6MB/pQoRMqaPXiQu3Mkekv3bTfQejb3abMsGeIxO6FigL2hmONnagc/HNif0M8cV9tnw1Id3u4jvYpmSB9WiiR3UeZ9vu3aprOvVy1spQSMbCt+nU/+fgBGjbF6ArCBdLqkVCn/Zu1S8SJcWThQTZQYMkwIwFIFLCG+SDf8z3OE51o4NoRjrcGaXWAuzCjsruU/hGn4LIxsdygiHfKf6f7v78lyaLMtg9h54DKOS8v83T5I3qe2k6alx+u/PIaFW5EafN+cLN8xp/CUpwMSVXe00SWonS3p4jncT6beBXhuDYor+VRVtf4fbm/JJOxOaS4dT4f4jstj2rS4Jv53pmXNo9J6omZJ9gXuNtCG/JHAnD8XwdtuZJbCzry9jYFf0ubNXT9fJbxfsMMwGMvc1BOCJ8f7aCIB6ZWnpopBEnjlzWCyqkVVvgEeO8loZj0xzNBzNscPThUd+6dRMlfNPCj2I/kaEoVdzA5AD2OUnDZbvEbUa6KSsx3slh9t8Gp6bkFl48cZXDBF1QCmJqMkxcLkFUDe9zpm+FQym02RUawt46qxFElFZ6Yr+75ZaneyHMvQEliFQRvIT9cV0r++hNuxmqbduKrxMXJr4IORbOqc2yPeVwy1TSE5kGRiskqNBJLRls4usa8TN5XKaYyswu1N+NAfIfRhQ9b1AIy5ERPOzQ2nbo5oDLAEgbSzHG7eBISlIw4/4Syaj5svzMHR9sOTRc1isUpej3fzJPwBswPIjrSCdevMJVIo9xLRJlkJL7hGKBBlQICsBLogmxUQUkZaCSGBK9fGyjGvb49P98TNJhwUupl3yTwk10eOIOeYMsX3oZ/q3b2dmKO1cawwakOs1JI8rNX7kzVla2GtP41xS9LEmBo+JaFRC/11UBHkTBBwVyzx12QMmbRnCXmzZxImqvqouR+7qHi5dDvx8/nb+rA0O9+TtaCa43+LFcBrmsS+XUMqBgTXv+nOnTZdZpJiLAOoCXgmRscIlzIno2T0P/Sxx79fbI6SsubqKesxkfG1MtvLQpEmUi1yw4CjcnwR5c7818NYze9bWVVqK/g1VJXf25ScrVauD0tJmA/dZR37Wh5c6Xz/NASDitQ1YmIAkABzce82anCznAQtp1QIbfBUApS+n+Dc+UogvHPCPo5H5Nlj2wg2AF6ljMo02g7RgssOmH3VFMVC71LuKQhmnHve7JJhPggPMecmJ9xCnNZc2vc83ofPibVhujvXDAxLs/SHB2fRR8M8ig7qVXZzVaicvwErHRbIc6imDwM18YEDNXKc9MQTH7YvhZ5jJr5zojaFGzO+3tgRvLbsjP4bPsbiPH175RUavsFKKj1UKLFzkZ4qtyZDGI83hXDNLIel08JGIW5A9ty7U1D3GBPxNDt0EXDGsR2jAQYmxShfPJ9CrcmPqmdLk+LMzK+PyVKBGkaQTeK2FadQndpB0JdL18+zS6QmHafZp0PQsNTPvtaeUfwMnx0CZ7gu57LASeW0W5S/HFYGRy4/EeK+2eDvN35690bKFgpffDOo4FFZ30+L7n0aZhzmf5u6pll9hLL1fMf6TBDCDX6KCecgon+JB6yhC+lrQXx+m/orTldGvdJfUMppY+2Om5wTWa7NlqG8dPznI2OWvYwI/DmEhuEpHxTZ7v8pEc3phGKfDRWM/bgSICjWXWbd3XucF4PpSDEUej6E34mqbuyFXgYSHbahI4JXdKUdsYjC4Gz/paDkH8CKHKua1azPLvdRPkQY+410fhgUNtjqS4bR3QPUOebHh25Rri2e+xBwtgzx3Nxai8BXaw+VOXwzSb+GuWZpWcgDvMNsu66GTonOP9H8+2Mlp8xHimiYkp39Kd+aI8otPWFITerHwvnFp5t0etPk9OadXtvkBd00wfYUexMqGh9FcEk+ctO9hqNZd4YO1+oEP1p7e7nadCTzaEI8dHRDcUS1WS+A6ABQopqlS6GMXBZTl/3eQns8bTSKJfuMXgwglwwVnbq4R2jJ4UNO76UuaeIZgSnyNv2Ebtj8mnb/qI83vROh0/DTBDp9cXl5q+8aKz6W5YdB/B9sCi/9G2TVdb+qxfyb6sRQAuNa1O/KEZWstEDbxh426QedN8Y7ssyfHgrCHOqXgqIyOiONwgqLa6VHytyEWItCg6USyGVtpAsefq/xKWul0xCOvqm5Rq5QfufAGN0jeyMnMJHX3vf5//TdF1LkiLL8mvuO1o8orUsoIA3tCi0hq8/ZM9es7WdmZ6e6qok0sPdIzLyws1EJOnzvn87QazsnN53RSXdf9fbi90PLg9wIFOkt5QaulHtqRbYuApnvlp3d16eydfdpRCGmQIjlL0FBnwoa6UJyydeSAN/mG/zh3FQBYOAqr8odmurDxeOAN3FKQ6YaSA+0QOnRNfqQE1dvlTwiwYmoHCroDpFTAOJOA990IV8HV/0TqyJAFbLPow9+DUNh0QDxJQ5nGCmv57SV8pTRoG5gk+NKKkzgUqyiuh6cJfr/N0/+fIyycEbv4NcpH+9uaY9qpJ16iUZj1cMbTruM12KN3umSEzIkg0Nkn3O9PL0NBBC2FRR1+aND3F803EfXMgR9ucA6BOtvBkqA1YyW2nwL7uXVzMSzTe/WogmtyICYorddi+Vczu96YyUA1pudIBjQvY8pdnwHb1JKOpbWREdH6HMqlEulwlDO9eADmBYGk9ZavomYA33JnA6niih7Prsfn5exYdQhBsxY0HZ+KPM8GD/7mvoemMZnTpUZxz6JR93g8maoXDv+UFogyfKs39YOvwi6Le10b/49JZQ2GwRPVdaUr+fXM+poFHuGfLoybbXdph6125QcI54VI+/69eOfzIjxJOSfDksAIgHB1YqpDyLcTVvEJ6z9Akw4Ljlvs4y6LcpC4fSU449LJUiiW3Yy2douKM8fWVTOAUx75i986J2e/wiOlOhTfqZ+6A7fV3SM1aBt6bhhYg9P4htrKVExl+l/j27q8J1BHUON8nMEjazCyM7UuqQlGqJw0mCWE2DwsWCZ07dwU2ptyxuNY4oYXV8mmqaVYmaoXP8l8uhxmFCIW6aFY0WCKNn3Wcityw6OCzkv96rVRIZo/fdrNxZbgEny9mRuPavQTm9hcxDQIkhNH9voPz1KDSlD5W6TSnlpPzXyfuTzZJUf4TEnnc2Y1JuKV91yint3fUXkuUNgnUxfM9TuurhR/CpK+kw/6sz9JVoYWVSRlhFHFzeoR48V5imOfhJdfChXH/MHQ9R6LNm49yv5cpOfoG/ey8vfKJnRjsvTcXEMCLHfvO6/fF2dthXyAdkR58TMisuUpFnw60Ghxf0uk+z57O7pNwNGHjawAZak3dNfUYaE81Rwz5Rf6bOeMjdUTSvftQQ7X657G6SzA5nsDLWcZ6u3CaCF+8vm+CRHy8YVsQmUwEqceA0kJEivNz8XZiVHfTL68pM5ceeGBTmAGlYeTULEC5ABVBAC7Cgfwes5V8JC5of+O5aclE05Xn86MHN231WuHup+QtSdCdEdcIyisVrdsTyDo3KjCc2P63Pi+mXcBdEDUraIvO83Bwp5OB0tFufD+p9/CASNO/sKzPSumRJdGkVquHfQZrfhsfY1pi5w9c8Tovyc6ANdt5SLK97QzF4TqTnkEu0XH2lV9K5QOiMHyiF1pbRSyV8Jls0KrWmwMpaOLPnOsZJCmXZfzV/NKfwn/ZtOOTSxctf+ipDJ8u6qpnce+y6+MEyjSpM+raF5jpJ4ty8Uoxn2OvC5vaQqzByHeAp7uE8+ATH8MD2xJJiGIv8OZOV8Wa+tRub+CH8B5SZGCJ7KAYm2lB4mYfyMt6IFyQpgX+2UQY37gZk+rSOQlgMV7QxVS4WxFOJO1Vn7EiTBekY7AxjuP92OidLIUUExsy+IisXU69pDxJfT306p4bDs/wtPA7WXEHe2HzEyxfa2HXyYC77BSihhK1iXD0eQnav2Hbs9mUHSgE7uIuLLWGjoPCHNGIvses/Qd8BN4Ef3C6347rraIVF29Qm2VXkbkl6ySFp7Bg7/F1ahF+VjArSgIL6Kgr6wlnobxwlG5JDCEd/Gfgu5vGR+p1pbetEtfzpkp0yxvSuSugJrTQCr5T/PhAkZ5JHuI9yPUnP+nm1JWKMMSUdrOJw0rb3ZbTf1AHui+cTSFSDGjw5jX/1ywskVf5x6HTopJQ3En94AuGUQ1cURApaBnOCVcZPN77PC987r34T9UVFDL20jEM4GH3KHKjgYEvET58ce7wE+wg8X5K/3OM3kw30cv2FJuRVQfn5s40LaD6GQwQs+0PajJn4dJYPtJaJdx0HxM8IlROWmc+08Uv9zwv8q2MgfBuJp5BAfwfhC69sM5KU0TcWx+MY26QdQOZZ8pY6WP1vLNX73xXfq9Sij4VszAbxIfcjBSmhW2shkvBT2NG/V1e9azIFjl77cCUfxEQzyEJ2kZF+6BoBU83lLjiCA0bUb+IiXLw1wlfNvloDhqPUWJnDAjFg2HnuopBdAqV01+vnFcax94QtPKnmdwMfRSWjLGDpT/qQjDqvKaFhhUJf1bmNL6UF27qTpgeUeUMjUMuzMG3byS2MnDNGsKHGeCDAVVjSlOtwpGGXCBUHel5wlCs6vi5wLkNMctCoOGaGi96yb/pyYg+T3IhLdTXUgtP8R3scNCQsQExZvXUgxxdERML/DLqqxjvgBCkgDDMAf90vpVUZgdgDfW5A1KhkOMj5KMn970wDSmmDC5TsWDgu+0SXl6eq/12s2hlpIwaUjbWV3FPznv66o/1L0F750lA7k905+meqLYhN8EZbN6RBDydwTsS/2zkCkjAeg8ByovV+a/6gSGeKJfibD+9ZVvgsrLdlD/md1sbiEJtaEvyskiB/9TF9o6lrZoS6bzcpbI4SLE9qFAFq6IJdsXkaGSlaavH+1z4vMNclzwg9uhHThCibi3Plfmw9K02pz49gdFkyeaEeHFhg4+9LGQDhSAsm1j4G5qzn9ibi+2jfN3lr5FfxsKujGhOfO8WIC0EmItdeeMAiRar83U1rtFl+ojVYcSRPHyxj0QN1bEhr8YV1XjjGaaJ5hL9OszeK3LUDWwbQSwVHxcag+t1pV51t6E4j4qIZ33VgVbXEDhlqmoH5RIMtziPek+PPQZcPyjWAVKsFB1FxJC1J+CvFC+/clqKuvWZC+gsuZjQ4m8C23eLUHU27wxa9ByKqTzqCN1p383zrSzhg1oU6C4QyQ2lrh/XJKBdURg99p3FKMpwSh5blm75kF/5ymGNJ+PhDzA79Pd7mg9vTxIkEoZOq4OOB0vDwM7erxMMQnCVoPwY0CCFRdadCTdkbfq14qqy4Vqz/MiU5zd3stvhHuePDFSmhQHevIE/zu9uDex8EP3W+xVQi2EdfMkvmN4Rg0iOpMlIJI5Y7PghzaW/r35dzXRTjxoQX9Y4LikL6u2WVMxTGOgNibyzZ32+8Fg2CLg4NgeI/pfJ5UCGcix767HiYxKwf27/DzKSdx90lKNw3Hx+ICbfWm9uOdMRo0YfQ4c+kWb04FcUZ+NpTR+yXpH1+P2p01d2Pq14Nf/yIhUGPMUUChdk/fMrGgBm5kxFoJD0q+dsE9LmOPSqojKYg2Iyh8B9mWNEVuddN/HWUe1JB5lEk79Wm2Lmz6MRCHjZW6l8DLTHjTN4U4XFtupLcLyVINyE5IuYR2/F5RQs5gwgfw9BeKicSbBTFffNhaoJQJLoIWyYf2iPT9p23AzGecGnu4xvK9yG7UMsU5Cu6cPSwM/byxeN7vcz0Mrg1usk9Olm6iy+kJSavXPq6IXiVMzFw9E+mh2FIHSNrKZJJGeSUwXpxBMYuPNdBW9CYpaT+kH2oV0YRmHAz3cJzqTXC5O91coQOT4Z1QfvPcTxP/DDqz7i6mO+Rby+RhQYv3eHDjFj9HUOwYNqk7OxvtAlAAOBi9C1jni3Ie/zYojzmrrmZs3RRjZQkseY4UgrcrJ+/++PILLxj/6ek+0OtirAunMgTubvZuFFD7zaT558/QzzGyQiinmU901JGtNK2LL73yHKoM3koPjKVxc14kT+s8La2KZiODXeY08o+CY7P9WVKGHbkb//u78EpG5eAP/Q0UPSBCBnDJ8VyymBXh+CcLowHesHd7DBROvGmZ5y0sgcoQKsyf8OreeO9Kqn+xHtVoJiJUUCRUpL9aN2w+OsEKaZW5CZOZ10NnK0S/k2bUGa/6INpsz6Um/AtyOe7X4JVkMpurZg5X93dCm9ifmAXGALEB0JSrHJpBj2bAbYygofJAf0l220reDAjGPQ7sJ+wq5vVHMjfaMUN5XqamliUcTUCdoNWIdOpk01erM3Ta8XK/ZfLXX64S0eE27PP78MAFR7/zFaz5RtAbBfXvUBbeyfoFujUce+0DSu2tAMLbyzPoX3XfNrBenE81ySwLCVt2bGUiYCiSt00YevUJt/Z4sfQpvJfolUSIKXfZHUbfh0vYbo/E7OW6hiHY5XzUvO19b8pDHBZztsL39rfUF5QOjgekYZqEFyKzzNA0B+JnaRj+Jc9fGAJiZ6x0kw1yyKRMVGz2c75yu466AqPnLgUftJNhsN3zxTIuqkTO3lWRSiXB4DWa1YAQ/UzbURg+OHe/E6zEyE2PbldwdXyi1aSWAMhcc74fdx97aIfnsJ5S0UTCBkmgXkuVibW4M+SyJaMbyaSADbRwUeC5UpFLk7F4HyNMLRy8TrjqTouZKwb1o3x7Wq1MIGseGGKp6Yuck0R5iabBRlRYC6q5pGP3TXhPz7BcWwpnwWVw7LJ7PZJ6bjycyWQXzz5yKB4ozNXH/v90daqj/0UswbRXBwndgwLJR4M3ybUxtLPtWyE4s0ki5+WJqp7xACKEj6jskVCHzc56F9BQA2Ty9JrEA/Z4TJzh8qi/diLwU/Ev0jasT8SU7vkhkfKACwgk5LTT6h3qTV+YGvas/ZTmV0AI/VEmFpVPgqrY1oroVqsS0Rd4NS+lJwWmjd9XkNWdeYXYk9GfNkIew9uZS2Bc23s39QgR0oMaBlDKmU94CRa5clm+8P2AbUcecLqAI+aS1qfImLJM8WbXwtqGS+pNZvnFbXyR8ZLO3tkKemq5d18ICdIcceUxJuPAwq3xngqjeuq6IZ85kZTPAtKFZpFseAUrMWYZhxwqr/DvX+TvZqg+xu5QS0lmJ0pPlWnu3nN6rB0X1ZG4eZ0HewrmpSEzoK9Z8mF6fNvEvoX28zTdpkQFHt3YxZWqoOAZ0OthpLGf4g6PwWzLxV8AJ81aeT4qnB5OJR6RBZHX+dYedBYPFA/rumRl0/ywwgMHTpaQ2cWvgkkUi0J9jdEKzUJFSUs+6/1rhBMu/DLQ2VAa5F4WejhP4nKo9JPeIy/Q8NWQCwfLaIgbMgMJEnLAtrX9RetQ6UcKAL/Xk2SCbvdCq4mWH4Sr8txRMDWVPNthJW+pD9ILvH07buApPQrnYm2gWkFXE6dtHcdoNclDv91s3tPVB29ZFmel5ENqBLRKzZgchvAq3PE9GKj9570Xj7uv9ZnbDeOGdg27/bCbQdszXuq2PZFzujvjFBUfBPOMZerC+E3EfUNLYbUKw7M2iFzMcQXciz9qcLbC8kCQD6N6eobuz3+wOQDihcbxhqJ86HH6VW9yCxTzksncFHV57hiz8wTkSgCti6Cyg+Vi1li3zIxLjQjfvxhEPSVRMEWcbyvU1r53vb3tJnLou8+0oF2N8Q9tqn75vBlzAWAUKUFXNwCrireZ+ClD9LpYBLoh/NYGX4248gRzWS8WPWvCJIV2nO/jwsU3MNBucbvR/Tm4WtJPMl0zD1mNQPd4ofTNeOZhmWPqG/n9ovWMnryLctBKvc551XnNLqolMTu2b1lJPramErrIJrQHsTtydh5RHfJO95v79Mfn+n96pgnxglfQ1pJIjPvq3LXPnO3R1nkzSh+mfDd1YFzHVObO7ujyuNEOLI9ebuQJVeE09QrB5uLr0D7++hyz9QlfuoEoqA5d9OPbGmXxRg1qPnKUAkwf7yYnhul2TcGT9lWdDUNCG2BgPWWHpEJ4fyqZW8jUVUwfSIo3rjFiz2Q4ipv6V5c03joRU15leWexzigG2Zj4/oKd9doYb7lgonmtb7uJMrgz5mjKkYR+4X4rbzzBMnTr0/6hmIOA9nBfbRXW9JhHGuvsgyMNxVSDIicIFImjE/GAuSmvrRN51v6gcskef91/k5IWiIZWMrhcpGtVvmMXXDqCz2nxGtBljEThHRWFH/Xoc0B/oI48Bj1s3gZCP5nZf31S1zszM4Mi8FTZ77Y3FrjhPnL32mRiU9HhclFKMEv0tRP67ov7rKkvqUMudeGjZGqqV9eSiFrHtNc5P20IMmSzQj6wEDSdcH/js3Uv4yD6T/iAsvPcOb0kzXbVPYFNzVTZYbKvGuWNyQ3+lORBY+JbJWosIcX0jkFun//fkez59IMFZiGRdCHKEF/LtsWTyxX31EhDD6XncWo3TBxFun3Kxn0ZfAi0q3vw753BaDhVOu/7qByNSNpq0XxdJEUgT/PI3FL/nez9uS/VGqEMu2b9StoxgGQHh8HK7DSHahTX+lsABPVL5vGPgrqaWQQJlATuWytbT8BOGW79tdsgPxN31smGi0AU8o4F055HWMgoUqdp3SFVtJnY29lL7wkGwX1vGrTVnVc3O+XpdpCzTCvKR7CjNEeHK9gc4LOHQV1RFI+ohQBrmeKC7HXvZAVlrlQU/KRer5t0PBe6Bn99OEk+NrSKl1FYT/PN8PlfeBLHXJbLW8l5PnsK0yRgksv4EzkW1vqqIz9GJMfPbIjF6P0UwDgAn6COApfznISTpJn3wLnvhfq51yKkX/hyjncpsy//FPxk2SfxYmr00lr1p4zDvIhPZSf1FAZuhuVCQ3IUNqMsgR9EsGnH8R5lgQcGGRt5kNzhFsEdprzcip+qQT9ou1/bSo1eqNwuHQhXX5k6TaN4DgfruG3deK0CAq/2UKZ2MtaQ/gacBFTAPqyJHrROax6xyhDhLbWCtN7PVrZSVtGZW+ApY3khg/jDSsZb8OpmcpG3swA3Qk7T6KDi/2eh20FdYSczO/i1ia01DoSRz6Qm78jvKp8UCQyeetU1c5ZgkoOE2YNxxCz3kr4bdsawVihupyWk6JUe5nyuv4Nvnq6nBdn4exy54a++EjW06ma/IrFBbn8nauIaiqbGVEP9ZMTAMYV03R+lnvkdN/T40reEMayau2vwFZ0yBGhF9EPGw2ERMmQUFq/hJJ+hU7rWFHtgqxtStkWr3MQsMDiZyzY5MBT7GJi+gWVtFtPAWdoVl718H6vbU9W6cxYT4oS480aepqOgr00MoeibyZHprdHSB5YcuhZu2NYRTeLQbFIYjru2oM1BfkyGHzjIjIahl/w6Oto9Hf2Nxzoe4CcM4Es9L4JCnO66m+yvZY9Pbbi03Kx3FNVcaa0RumuxXoucEuxiyt64733k/Yz7Yg2C5oyQCcV/2H1H+Rvg+ZJcagB+X3GAJUUq4egGvkaf+O3K/4RuVMPmMxgMKNedPqKZGSCM+MFXCgGqnOTS7W5MBYqhRUomTrQulp5+N4FjQAiRQBsw7W7T/+GVQDCDhejXgwZ1Va+p+TRqd1ARuYoXCY+qNm5+jyRo1zOthXRM7Tez/i8SkeejWKoS1suAqnrnIrfnQIH7X+iPdl6EvvdPTidZ+8e/6vqnIr+9eG1RLutN2Z07k4JCPbMVqTzVxOPHjOaz0w+ioVz+/Lcmrc7mfH+wVX25Iiw+Qo1g0ICihy7l9XdVAlKtdB34iLmnCKcHYaEZ1e2cb6OzvHeND49ZfI58rMlHdRxOYBo6MWlB0Lifxvzr2vXVuxjRVqTnyB2AeIdH5URRSld7yf0I/BXlL6hBZL07+Eevo4h7a/MCG+8XDo0Gnk3uo3mTwoH4ACD09Iu+VxbmqLQalcB6jd0r3+CeP49cjAgxpqPNg7bKP0rnWSQd6mW23LsCEuXt/4JrwI2KOJryjN77DragmNnn+1BRCNMcf1ngOu4sBvs/3AgOpSd1g9/do+NYyqPI4ninSzdphpBqESwjJmXch5Vn4+mYVitkPbdSMxBeqwJnUXb+iZ4oTejHG+MTSs7MF6KzRqIZYm0Yveonb9g3P4Or28roLdnsM22yoDvkSXHAxTFAFFVPQyYgyri92DG6mzMLTOJmJWG+egTNLVsdjFVXGlhSVX9lcJX1lveMLilinLOLr704qy6U76OwxSv3Rji4Ck9xhwXD2JwABRh84lMjoe8L5BZLvCuiw/anAnLIjjcMVOtUjd/0D4Lzm+LRgeeU7cOvu6BeouVmTBxGgpjlArhpYgQg4jQipUnmFMyllZ0e9NfmKHnL4Vjg1vt/fJzsvkz0odcNrnMAnDS3cVwDcistH7F5ucaHQXM7mWthp3ggPtppFqDxRljlhpmsccGl5FC/x4gobkqfrUz5oRtwI2XGhNiKh4EDVZ+kUIbguKQheaedZi+lMwSWx93Cd6MZpgB3vnXHoBn2wgULRvhE3z+qj1XsXy1SvDMgPFZBbtPzqpSEvHCzdjrn10zhBHIY+v/fjKOLHlByVH0eUZrV4kRbwlozXuGrvjawsg1+dK2cyK16eRXz/uLbGBRfPPIHxsfefvlK16SV+JaRZIfHAowWC2A7seFalv+knl/LCjMZ3YAzPbNLZL0cj46bq4JNhi+3LGPF4YaahvfTKAmWpqCtsEsljAiGDfHU8kySPR3j/q8m9rWJHsvHkZL2hWsMHiIzIL0Q7WleaTk7WMbL4v4KAl40FcBQrGYSKJv5ZsOQQj86QSo0/4uRhtzLoLPsY0wzzPo4G4r130ayAKnb8TycVqWEYplPTi1ruFCznHgAYjybXSl4JuSIZEds4dVVk0+BX+PnIm10XksIYWyEfyASklCrsFBeyGi3yPYWZ+ozerRRXZtDkRtEhXolSeaMjDAHDHLJbTwlb21Y5QI3+ffEPtUh6zB5SirkPRwyvws0LfxhNzMIszXpXYgO/QnrKKd6xzsm96nkLytAhk3UH4aU/l58Ks8XD15WmZHCGFIdhrUWUXcu3KBvSQjahXrZQHSON2bTPFpu51EGs+e7tIdLD4KeaOq9/0OI0gezwPUhyG15/zGxpPtuZvq2gCJ1UXodJDp+nLojrWACW7buhZp3H4Q+XeEK/hSkUes6hQXxYHn90oR8Eu20Td2g9fABgZzCjaRlRGnBk36FPzh6bQ6fgT1GRZP2ANMEYtfmrJ6av0yomgeKqYiA3lm/YWyyo8erfkRRJ5REybc9R0mBJI1nT3ilnWvJKn+3WU+WE7YuocV/aJ5So4SChPOWwhPHVVPDlzcx7jw+mBQx/9r3LMTe2cUBCp77G/+4z8iB/Jnz5jlHJQwyVKTXfwELmusaUBVnLrCT0rnT4KtXYgjvUOUmDbKKfwxUtptw0rAX2h5RXt9BszH9Vk4oW6LHhVJF3ZCzO0gRJLna1moppLO5PHy51OIh+zu+oR/mPFGqDdToa27oJnBZnIQFUJ6RCe89yD4A5Gg2bTInSsHDlB5On9jVlzTx/kueYAFZE0zOC614aXyPAr4a3UiPxeCzhXgAogg4lYesMkEeGBbVyRSCWt0Os0QobT5PtONR0ktUSRrDF/5UbpKkf1urbuP5XBJGyfhv+vBfmzGxJjxzMeyz37+AIiANYuCR7uRI9smcZBEBG8DdpbWkE2p0zmiYtllXdpsQiiND0crEe1DyCP5VS63LNodBmYJxOlLYOsUIYmR/Mjobfcqmx+4SEO/e4DLnS427EmoDsGdLZUt46R2LLeEroqUkhmHnsCxI99IO6AzZpRr+niZD+363vX4nGO2a7M7CHzi6/U1ktAygA1jMod3SR/2XPQJGDWSzuD1qxZZa3z07oSkk4H2YKP7bsAShpMq3tmrkDbH+UgjA6U6MJ5WzDCJ0S9ChnL070I+Vue21lP9FRNJpQPx1b/7zYI0I/LY35DB8x658pgC42p6gEcnPodPNU8HrullmaGkkKMEzhv2N6IRrLzKnZ4Ji2jiHQFirY3hBNzww+CM1h2yZSihqwWvIb8j8aMkQjSUjZtjpXDwhpyUMvyl1umHk/FM8dcPPookUeNuxPKhgcC/R90/qim8Ku1dYfqGi7omIfmAoNjvT5hMtj4QUWrDkd0kxDF5hEWGP+EreHOP9Bepnul0ZOrnB5neBi8d7CzEXA78hkF/NSkSfczCBE7USTfY8/zJ2M/MgsVzNSfRy00tyTPws7IvhwMsSB4BMPkxFC/VCQe82I2gV8IsSmm+WyM/sGAFMkes9nljbDp8Loz/UtGrMcMVeBH3RQNyg2AeAhlySFIeRVF0U2KDXffk5f2NygDld0pE1mW3a4pJX+r5YCQ83w5yHe5AxrbFI9ure1OQK97l6REaOMckB8Pkk+UvKLwcFtuXmvFWNU1A1/1+FOctm90U9Xr/jeqZ37s6y7VAUbfqQ7cRz1yVie3hjpMYfh0i3FFTiTwCvXbpFwdMbAavc4zpowtIZ4budkHf1Prmp9AmzQt7UthfjWr8JSXwKRYKkeYJ1Ea3Edgo209EH1207hdq3jixN1vtTCiuk09LxaZpeuDTvDEGmrnZYfk3qYo9IrnNeGEeC5zwMrZBDzETaoioBaZyrsIEejwWWaSnCPzdP6pTXioT04sMeJaPDZsmeUdWUFZc7Pr5VaZVIo71VZ/skHchYRvlwTIrJNYrAyU3jglUlv04J3Wupmn1hJIPyXRbp17WN1V9fM+ltzBlGvmqR/HjzTvTri3TEp4JtfLnyAL2U8zsVV+7EB5Mz2wvW+YlHmo0c2ZVmXyDLDTlfrNfkOoPYmX88iE1Sgx4FQ6Y5NaLv6LHFZ+Uz95n4UvR3xV90VEHx5HSuIshIDyWTyHP0SX2cPspeam3g8lUL2846Ag0zLL2OrRXh1e7jP9oX8rgvz5ebHTV+OX4g/M01Pf8Pvg2UIWOvAi5U0307Up4j/pK01MvcoD+eHf0TNEVaxnTAfkKI6LMz9CLpDlHgeVpPnkIh31Cxj61rbiqx5lLNtQ0bitOmxJ+aSGa2bUYmFOLvX+e8O9U6mZXb91ueE9p85aw2aS8Njspi6TykcVFhTGX65Oxmz2/WqkcssABzTCCYwl50VQ/UflP0rk3YD1UEZ0eYqsqWcx74tlK3QXmdfmWGRo3w3i9vRZFcygOCCtnmfHDLUcQn7Q7SKZs+0x+6/JhmAcay6hbNzd3fmjpX/AN//3at/pL7hb6b57aqj8+kZ/g1BGLznfH44//ZUfPKK+Sj1pTiz0ibsyRpbPeFNIa2YjhOgr2IopiSGaWc53AEsJb1PcPqZycXXYGeKRcOuAQvELfpKYMJpARd0kd/AQw+xgh+mUC+sPC4ZCVzQhWpPlHD2rWrWCyyCt4fZJavqciII9aHmi/7Kc5uKEHjvx+hbVfC4BN1iXmJLaD1Pjn63ytf7dVsasQoxxW0M46cMrJ2lXMFDsmUwCP+iCaJCG9pkLwGM3wW/IzUSvcKu3Haq1hBH0/Iqj+jz6g5fp4Yxv0KjHgMkQvO75utzyeSQ/b9IaMju6tq62ni2iMb7XtaPTpgGvgf0wpc0WF3lcJycHVWmynJMVBFIyet5/ZQnzvxBZ3TTlzBI14A09OvdXLiIBDwskzOToB3Sq+Ag/i5a7WyPMwVUQCjWOkgSPHuXlOR6VGPnm5Z9LqdDLbjHBRN5UBAOUBdCvMiXeTPvGKuvT6FUmsfj67V90wDDG1ZIOIo82CM72qiamXGieZR0jt89iw+rGMitnbFVNV5bERx8m562bDb4oAjJrWOeY6ursW3/wruf4fKHiblF4SlJIl/u3Cf4kHRNXpjkggg7H3YjktJicC6ykmi0pdenLg8vvNytsDSyvvcHPOI8P2h5xEqLvUvJ+98LNK4LwsxIvo+/hxZey0f6Ui0PHI+HKky8lF9VMxrTjypXGySYjbqCR5OozQhvty58zlRxxaGhp/Y0Tev67mZ8cPCScSwQGSSAeNypi8Iaa9/a7na65uBXgvS6Vv1EfQ+w7wlKEUU9BDmBLhzBfwv/sEDU2KJSam8q5es92HjWt8QX1fKyAOKD1XHdA0Nz8Z8pRwv7J4jq7eSBueLJSruBJY+S3B9hX5iDWXfUH5JCWIZldeObi2dgusgGfqC1tQkTUpd5s6b9AiS2Fua/1SCg0hCp9B/BSNTf6c77UvrwKXLR05HHpsnyhKaepRdwE+S58dtdHDw0gGjAFHaFlArurne/oQReBJYWJtfKdoi1phqPBB1IjiKY+VQ4y0oP7mGETujUEilfP9uprDA6qXRybiHauWR9ZknKCaAkLM8mf6iUTRGlO6eNsT/PCmiZE1S67iCb0bZkT6obMAChSY6zbexGpe82yvp2Vbt+Py/OkjeCKIY7dBVjrZBodthjOH5l4fYf54ON9kguBF11+9/DMx/D0fsuo6OSqhYmnQF6CIy5xA1tPqVVvtmP7ATR3/eTUC+Ef8UWl1FmKmL/WJffzd9z1X4wxjLIwAGwonKvYrKEMAAzZPG2V78l/4gTKjucKySJUOW91r2LZBXabtnjRpMBt561Gfgzyu+Nmp/RGai9dhqqPa299oF65qD7uoL/+p2aauRqpbg55q0XtMSFA86G6s/mRC+o0CjtWMQFuH9a4M4NIdysCinqm7EWE9EElzB9lBkDF3O5SlPyQmz2mHYqW0j2wolOrcPqVG7ffHCDshx90DtRn02u82tD8e3Gm7WiGjKlyYk00a4gKTckg56w/k7B3nTqEOCBBoGw+9JMIYFsic0YXGxPbf6VHWQiKPi5jjsL70DNhNFfrlZac7P405VgHr4PHzqzzpnACIGhKca9+PizF/7Ziggfx5Qd3bpmX5O6M4tY926VeQX/W0Xpz1svvjb5YuAuZaU3BTgTfXU7RbVB0EQAfS6SYxWODXUy32SxCoj57c3CCNygfXavFEDAUeODx+3D/nUAbT7/Y17iJ3+8fJr96QUuTLCwE9viAHaXRzNxfoRMJ/yaGT/DGqNfV3dcmRcJbwcYo+yL2HgjhJWkGnhJQL/oRfu9iVjtkLMhqJpYmUI/B62NDkdTalwXL93RfD7mAgh5h16NBmMqu5skvh2ukTYgb2nslnPJsrakbEXGGokTtAP/gN3Ggr5iOCk5k2F79q1x4kywhrOORJh26EztosUn413U8vEPEbF73BVxAjP23BcFXbFLZEIUM2nH9WnZnQH2bgEiMbByhnOD2lwp4D1+uIPSD030d2mlxa+Djnz0I2AsmpKn9g/zDq/SbUyAFgsmMvsKaxjYeCRu1paWCiIP4cKn3+8bvcy19+xK1MlBgXKrBWLQrv7wsPefMoEK/by9ekRYD0j22qjJNsO87/TSQVEZ7o80xXTJTEuw/my7PGPPhkLhIpU9FUtfIGo8LnR99h3kmv5P05V734VHjl1z5jzctAG77oxef/T2Ga1W9f7UrfVAP79yVsB9zfBoDmSqEnaMyBQp4gnQk0zZXagUcu/LywRpY2ZWNGX21Fq4fWsOX9ezWoELpw3tdXP7CYY2GpwL2MCLXIYItMzkpwp+Irz9argsF48WkNYWOsGg4qvEOnzxHXiH0fFvZ3A3b08o6pLEbFVcKvxnNrSIWRsSG1XDE/+mp6ZYpOEscRPP6t3M8rW39Kz/Q7ZjIG/fg11IgxSsgaG7qn63WQksIWYdq6nv6GSXwlkzaIApXY8WvR+Didit23IIOfjJ7ZO2Fjf23vB51QkwL++QH/jXQ+SuBagRSuQY5wlMGnyBihKlVn/I3Q9ibeHbQniUTWX4TV3Ecg/3nJ2gnCiJbLhZW2mHGO9kUpVZ2rFCs8JhCSn205NErYSvv94B6IBWbPjTh1XMs2drGSvWYPCNnLvnFksNCnK+DMeWipoBDi4yFxV38qd9buuXl3pOx+flG4CPdMuaw/l8xuOK6Eqpz7Zl66HAGj3AuhQHPd/V7qybl4Zmdf7BuLVk1XdFr9lB3Ua1mWd842A21c7O+pa+alN0OODrD03Mv7Ydy/4WuhaMsW9fhNeED1w3u3lbvkeeIVAIPngUlb2/Cy3nP3ermKixknrxGrYgx4aDb81nyHwYH/xuYBgZuLwc13EDiixSb+mN309QjYiy4XHfhfjtjD+GnGkByOAqsxgKFePtjly0mYW5UClYPJ9lwNRyQ5Fo5+eHsQ5Ea4Tx0VMMoq4GVbx79zRsEacCII9buZCIupLN2AN1gJdW5rspZDd2pFNtglO+h7qisbbOBiXNGsotIfLmI2sh6Bb056jWhPs8/d6BPnIloHINtA2LISZypYmpIY4u+5ni8jCYbXiIkMHq/y14GBCTq4OZbl/wZUgdoFAEYZplXkc/0N7rH7XSRFecNSbnQhsvX26UfRxQxtveX+ziHrPswc8U2Dn3svL2td5ugrYFGXWElCfiZrBD+o9mmZwcBPf/N1WtIUbPds2gsX6q15xVL+RdH5q45YAkufjQNvxHzld8k5ewfFWLpoVzxS1PoZJbBLPK9S9sQcUaYfuk9MA/lyuCNf4dcrQ1QIHQLFT5Ce/Vvf63H4JJbjdNyfNR75tcEuHDN/7L7T5cElHE6BiTri+HwET5on1PoWAJMm7MQLear24q8Bw0RVtiVJHmDVefBLiRLMnolEnOe0Ec5VU+Bv/jpPgqgaGtUR4gV54Ob0wl2zCXfnjyptaA6SusUYR8hpt1NW9rGe83QMEHDEoHj9tA8RbRkTFpv+g7P9xFvr+yzQh8590d1BE9GpmgxZ9TRWoiYwQEWy+d0iIa6ZdmdhWLOpk1SWbU3p3/Qny/y7iOYEs4lG0lriAs3SKGE7aOsaV4vJOSkzWK4AfcDn2wAtqk2vDybYPhSyipDxPuEjWXY3rVK1AAipNojSB12JYrvUYqWihm5bbLL5xE6zQd43nh3VLtBf8JyWrk6owBRBwtqxI/8461V0vrTDYnOi8t+Nr7V2fQX1FNyCSMvUrNXqJKKW+bsQSGK35v3wdLm+z6aW8io7H3wkqWBq8zGx44b+O2tA0D1uUQtYcIV/XBIn/s5Rxc+Xup8Wpcwxt7sI763sZTlWfqSY60gfL4vJDTzPSptt9g1XFE0ktTXJdR6m77C6K8uprdzJ4nr2jRa+5DZPY3kmmnbzXyWJncUGiyLNYWDXiXyanAHwUg0o+y5ZNUIJ5ZFj00ORiPtW2/mpjprHgI06Q1rWCLb/6A1z7wocHw3ntSf6HgH2HlBxnYHqh7hsffFldXFhPtk3nYsWTG0RY+/dN/uutkx+Yz88FxaC1488XxX+MyebUliEuk8TCs82tfnfuBbKRN77jIT8kpcWOL1Y/o8uGuA8EMAcMVxOcH+rCMns3EjUaoc7j46tUdpRyxIIBdy5Li4V9xMj7cXYLYk1Ydn8ufHIJz8yZham9ykSeAO3DppmbhPnZGK28e2qIZUtmLE9+dV7HKzES+b1POuMibrfutT0dtLERE7Ar3haq4MYJ76H3piU9eBrmWQ2QUB0P5H3d4ZSMx27hMKVoLOc9Usuwyjn9zFGQD6DmU4xQvTXCyTJsySB0fWSK8oxt4J83mxgPanwF1kn2ONkyWyY0ZmAKeUkFNgB3Pw1ulfzFbQPbTUe9EISKo0LFjSmXvILgZkHTJaHf7wp6YMT+z/6snnguFJIoM3Zn4xQSPwh6FtfE1py7WglWHah+Z+P4J8JasSjHS0yuC6xEiReDfkT1Tc6yD49M1Xqbr0Msy9hMFePnaSPeGvyRWAf6do/mkgh6jwtQ/BMVI7K/rfiZZvyLUcSm1RbZ4FbxrtqSg5u4Kly/zWw9umf+4MHtHzoFtc46vxNhg1iDA0b8s33sB3X1Z5JO+xgeCfBAUqRCmzYjKxZE7hUMW7bL/Il/2bb4KNsBc4LR74s7ftNN3kL7qaS91oDUHzzZLhEuyr6D+l73guk19y0PC91f6FNHFirrn+3OgOEEXq43cE/D5nwrxh6b2+Adqwj+T9ecfXFXk4KhKnwtYHm4mJm/FVvBmEd/tX27N1/DYa1ySW63ALJRt31GPE6n6dCTfULpSI3/a4eaf6msFGXm0NimTAUg5ibHdGAVtU/AfuLHl5ijB+QA+d5tNvflC3jxzcvT0xcBuzLhNjknjlGbkYdR2Y5uzSfcy8sj9RcM++gIOLDGGNtlztxFX/O2WuFTVQHJLfuF/v3Dn2CWoJADvscby6QlR83IgfUdnhShkH1c0f9ygFH7hUIANtuURT6plQjVuuPrb94y9mHkSkECnJSj2RgUrjo1CtLXngdkjH+7dxy27bZiWnbAi1wBJ6iuCmVHXGU5L8mOnGwzbi/WfiVmZYNh4H3Cn33x3IVMxS4773r5jMZBkvCM895Y+HTZ2rlgaLmDFQ3674GkfkwXfibegeB7p/vzKS/aolv5HLWfxGMm3+XsaEqzd9Lwk5wa/8ExsS4WzxFjwlpahizdREmha+cZV2Gwvcx7Yqau4RhS+Ry9sooEIQY2d6TWO6FlNEv10lyXnv2JTRKcCgWSM9JmbqIoc74z617gGFgayy9fue2xnG30WSvl4EZK8qSQtUJU3D0ph78B8t+HPurlqb19duuVz410iOH0AqzEzFgOLQdsQiGjGoNfH//EfI9pdfcPqBhi/iMD2TTWfuwWYHzJxbr0SmE1ewbWsOtlwmxZ5QqSUQP85elm/OPWGqFSbTcKzRo3ZDLk3WtZRYXzfPOZe+f/TsZBZQI2ehjsvPLGQtHy1m6ZjLRmqrUE2zKGF00H5rxAmlmsvEb01fifwz2vyLuwMtMq60V2EzggMFeJRKzCabi7YaY0m0wXeu2snvuMCqfyOyi/ki0Y7rFppaJsAQNhUAaS6ENHCpiw7yyAzMxCvf0iLRJ1r+rrhiMZDSaxkFo0aAky4vgy2ViH4jshYb+kXkWO3B6EJY+XzbG3QiFOM6mHqnjl8GF2N2beaxRhiAygardm9gmw/bk0VmLB9fDFm5VbP/AiCWfOZcx/V0wUrRrjmw633ZqpdgPc3EzgJ/b/6nfXKfNOXYBqoO4PIzJm12kq6iPzA61gJ0TUEGYSnNfxpa53BEcZprO+tZQ74Dnjl/buU6fUPg8XVIks00IJWplGVzD0Oc0oyrMJFqUY3OALh8/jFaRzfXRfr9wLggUq+bT/r4CmCxd2/tcd8Oc7CfG/25zjvm0L7dlAPKryPM2rPj3Z/O42mYnPzRJWfDWff2NsgZkQB4Zk0kI3iUKJdpcPRKgj/2jS9c1xUwvmJqhUWAPc94i3rJBum0FWPpafneNF3ttJmPojvm6vE3ea6DMQ9htx5BffDWrrVkCrOZnWJbw2g6R+hXJ69Wg3xK01dFP9QWwVI6TuP1CooDbsCV/d1eQo+ArmrHyE/An8wH7oE5+GED5RE8NEjOXMDfb8r6EtAr6wCtKCa0cO61yQwyT2C871D2WaEhLdrNDodrHvNuVvvg9XqnmC/jm75giQcQ4f/y7n8JOjQMXC/zzCxH68moTXzAhpPkm+uu1/v3ow5kq7yGy9c6KjKwdF+9sw2fQ0UWoMbD2PZwer8Zp8PaoDsZVJHxIEDHkgi9sdN1dOxCICzD9tC7Uf0pFsw2lVRkq4jnTYG3i+bvwgrIoP8V1IxL3rmYmz7Awc2sTgtQ+yWkyAvt3G8D7yXiJ1HgGaHVlUfScIapI/AN60MNiVvuK8RDQOQijjLG5MaIrVEMI1WQRaZIxfb0DQmp6u0ST+90LYOxyi32NrAm27EQEEQaTVpapN0m5IKzHXQ2RWedwb0GG3zTU2D18G37fIc3Bis5aiQvxqRXHJolpVxpagGfGzsIpIqt3qUSqIc6tksskNoUWPxRuQHX2gR8xSITU6vj7gPKAT5tENRPgOhmRGSK0oNux8KyVtCnBz4FLDyNrwg5Xkac4Y8ZgRWncPnO64hFZnqOJaKmMoi/xZAsCt1J6RgGug2pDxIYUIWr0OkEJuDFdfAJiODAtemxO+yBG3OcwOIQ0bfRP/WI3EekWDo+JyQTdqWSIbLpFA9kUlporPiqqp7LujpqXP8TUme5VnUN+4e5pc7u4SOoGFiHOy1rynlfJD/n8oP/ss57ZSBcOy28SlHzN92i7AxrDDYjyFSeLeb8G5ekHfwR7Z4pvYNVtQi9dv8u/giLeBVRXXRwO6DjXK/6RULzFEcSV1V8nEPqdOm4WEPCKhWMX1vRyxxD9O+hOHsR/AXKY5d7D+3xSTB4ZAlvajW1JDo5ritmhnYlAL7XlPU0NYW0lR3h2S+akXUHz583d9wT1FwWxvlSWM5jpwSy+R9gLdpLOqRyys5x/5V8Z+rxk9yufwMlUEVcy7esWakzyeUmt+UWQ+padLtlMQonP3FVePxcpj4XZUj1LtawFLeqtmpERwILyZy3aX4b9O+Gtr0liHqqT/7ja0UUbHP4kpxpibcf09tqzvrWpt0mzcXtVXI7V4ab76Dxxf4QszjhKwCx+kbiAT0yHN7rLnxFIWSCVAXq3cP4cMMHx7cdUcQub8/KK6EL+SuSoJzxE22zyTJ9uM9biFIYhCVqR8xfsJWBUS2NK5QdMXXGV7LqJyTNh6WLFRYoN32KMy+Py1yudeDIDU6GYjT/+Jt4vqqLhVDSxFcHMvP9yFCZ1KNmQAQBZ5o9QvapjJ4IGE6LRY4+Khy+M/MmK+Irnv4pWiX8FDiVrvHVs7DuCEQkaVinCkpg+60tkplH982eGEhj6WxA9YIQx2hanIWszYYz0+2kWtbKHepQJOaHh6JozorKjkTm18TIQ+PvjCGU9K7fFyOdILZOJbEmCrYlo9ExPR++n1vMFGrvB6ZJqQEfcLd2RAAghdtut38QqvcIR7PLhwJeKBOSXGsAGLQuJvatIvU0/kx9NKR+2veM4sE6WDAOHWx4bHvRXv4pgRcyoVkbJHDyqWB+c/irE6dSoCn4sI1sL5I4V9CzIZ/rHVNr/EXUdy45qSfBrZo83SzwIjxBuh/dOeL5+OLo9MRHdHa/Nk+CYqswyWUVoNAIX+ssAxUoovGWoqcR730M11148RzuTbofJbzwwtKn4uyYYP9i8yRhwbbS62j6sz4z713D/AVrqPQddPA9SLYV3lumaIWEjTQCUwnquYvYvaliQCBp6tr3Oo/8eQrGcpHPyNa7+GtF+wf5qYt48MGrDawcdSIuYR4wpl2EL3XwVnOnUKE5ccW8WOpXSSBKbGZybZEkYs/SzqGb3KxPxTSVMPJaH2iaPYyg2xkBLKePfZ3iaPAxP/tSXEGaOUwh9QYK9AejUOEQj2ORxyy6e/UlKYT81+OfnVcGH00uCPBiGr7pzJQ3UYCN++e3VF6prsYm69n5Ddfs5y8aMaCQNbJgQiNL93pL3gr9RogtD3+ut4wQF2vuG9XrI22iQwYXAAsXwCgzX3+VZMvWnMQaWINahggTJJk19kTTSsvw+f/+k6F1zCzfrnkr2gw9e4B8quSzhL1wxzQYxFPcFRJTYASRrPWgP1J3bud9EHCX4wzoiBIKtJvOhfznZV5eyJmxq9oHUBsHXV3nAYpd1ZPtTzzhlScfvSPlaLqhd+6tKYBXNNT4YdKVnF9ZBjdiMiNpdXF5MWaJS6DIZOPpo19J5bawekVakBrDqEg4hYX8RdIEy2s9kt7pfbqXtxvpKDv4wljfJGDBD3nMfZD0WXxuJcJM1UbwmtweEw5dGVTf6HMcoiS6XmKnGPPJuWdMiSgYL1AKYGPRK4wyLv5Eux1J6jACJKF9DUDkMDyO1a9EGnWeNr2Ne7thza2/VbFY3kw/5hdMi/sCkdRETx4bHDWfTqixsQYP1g26bakfTvLh1BR4A68Egt8oLp/Gkne3E0iG+PLakBN0lh2ZESPrAS7MAuOoTYHLKLFLFb+9q0qgrLzVcCr7fVMDXDhb2tisILu9BSbr6NTD3p74sR6TRO8+OY5c8k3tooY9LK6eYfBgv9Jj4dmUaMfgVbm1co77C4jdyVdyq7IvXGBMcwMG9bwkrhQQQoV+HsQ9Bru+s6dDHqkfKmK4kCY73M3xkEGyWWw1gjWei18tFzxvmb9mJbX5U1RH+yl0/C+JuMIGYuvp47yNND5SliBTcR8cXD8xJvlTlhqGdKC9CPNfvBqYmsIcUNqQEFHMA/hADtck7Hl6om/nzwrW084vbnDkL5EFaED4VA4keIqJ0cBSQCFDd+xhIcCTfy7evfpJ4JYXWaJxFi36JoEpC7qKcyauPf9yvMRwYtQ9snpnunbHpFbWwSk4ddJgtl33ABlmAinKVrC8/h61dkui93ijCqVMDQx6LUQGdyy8s8zYaCBXk5XdRMQHsZ75/b10jpUxInpr7IOyBRH3mCKL4DuODYHJMTmipaoWLe+cIWj2reEXV3KsZCPGKIgckgl+5T8L6MLr4AkHJBmIyxSR5OQEzcReLhxBKsEx4q+S3Xxqxmh73QcTEpNOJKLkALlsuQJtCcuXMy+PeoMz91ypTPziG+yD3ZZTI4GzfzSPJhAYAPN1dpSbkDQe7gA3yQtLFeeSgLMZuqIcUnExw9qWNrtjaWU0CEZIeLQZGOXUB4PRJPqAjZlBrA1QfXefSLrke/XW9pYh6NsXzVp3POFbQBWUFMaGiAEMa/qkdia5KBxNdyCD5m8QPPBEX45WMsqKAcuPW/81bq/IeAZa3wejXYIG8RVk2CMdKJz9m+7ENr62+8Er63Dhf7N9xovN5O5vFI6VhSJCyv3vcmkigVbQfBsmdNOYRY/XCyF/AFWJctA6nHRQgia5pCJfFUD2OkYO5YAsDdTyoyPqdpTGkadXgA/fAdmx+E1VHyv4rZj7BnRkBST5EziIYLDjRLwE+WhGLLuZD9csb0T6DKECQWzv55ybZtei/avSf/80o+88/5SjxVw0ag41T4tcR2h/vFg8KT1TeCWZQGiA6a6z7kgCZcwgNgv/SBbh8qdK77jobl7tV/PDGCJyDjFY4WrDGjYz6Ho4VC6vx0kPmshVRyLCos2CpU0/EyxsHO8HPU4GNL7LbGc9wR/l+UdNg7oKj2krtUqQtT3lVRktuJDHywr/Zh0QO5Si44hAo/vzzAxb00eytuGVW4m0xon28bru7GyhNe0HX4PTaSjM4Wi7hF1HT1r158oTj/CMWN9oi57gOnF0bedlC8TvxV7W9btfqt2rcT8LkpUACbXliErOqWMvqyhsIjpKftyGzfpkAu/de/VrFyXi61IxPaUamnK8EDp13y6KqJ+1Q0BESwG5iUWcHb8WitAc3JoPcajpxMcNFqL1/dsEnwCn9I4/vFoYH1jMQ7ND75eEMLm/b0JwkULtLHmNo690qGOqWIjZltJzZtdZHcCFhRCMtwESZeM5tPQaTXUG5erhxdJbYGnWId/MGiZ3nOgxcJJxZrMGyqoXM7Z5ug6Q8npTFe3mo9AIMJtfwksrMn7YS+hQftLRCeLJsVVhSKDKdD2Ahz6iwc+x9MfjhlqY0kw0CYXC56UeyK8IokUYRfwAtVG/1DH1iEcex/IUtknBK/N4IKW7fPguZ/CRnogZgtv93uqAANkhDDz2IrxouqcKQpom6154hVwGNfATtXsTxhCkKnMTOssvnD9WEO9+T8OTm4XfPNs01tYtmz3oFW9rJhlyXQTqb3nyg135WScmgYTUWCyQ0IDt35IHPyCvkVNSb710zY5uHSswFpOaCgLEmu6LowFL5rCeQNgGFQ9ZqSho6dDjvuvFs5NCip48eTVJsZ0mFiw+uBUZcU4nnMwdeO2fIlTqHcjvFO6cztQzCapeyTHUOcNKuO64fWkgmWTfbGWW++ectfWfuUDBi/jLK93xVNcLfE2vuGihOdb6kASxaLdqdGyP1L7cDcsAKBu8M6EhISHlx3gyX4zZZEWnydvi8dyreCjx2E873UcvyKFUPazAxcwmr/PpWTimXGld9s1PH8rwX6K5g8q+2b1lF08xGulmRRQI6UVkU80tHKSDyULbhgAZbWYeRJNWxEkn46lCZkn9ge041x9a+cY2RF+J1wkNmLrselEmnpTAxrLVcMh9UnOQ4NMkWmm/5ew9THrsSu7RDf8KHmpITJCqw6E4NqktBhGLKvkjSgoAk3/zZORo4TdjYz4NhlvA9uMD+kU0m4jTL+C1cTDuGKTTfMrx4dCiKTirGtzh1tYaci8AiKqUKxc/KmG78x3HJRZXQzRViU+9rS8MTzUMSCN2vPoZoc2RYMqvTNML4Ml0AawWJ/k+IRzIpEeUrWCNBPF8EAuqKqzY2ARGS8gL8y7DQp5zZPtjsvZ1DGbGREOYIpgfWbyx3Khk46zXhcDPyC7a0g5pPKNFyK402rjUk3j8QqV87bWsDccgVyLVeALezPCtF/gtlsPK8nT6iWDVD1OsDFE/FPcioLazrHQs5PKK3coshxCOW0dqndwZK606/caEp1Bt1Z0h5epDRiNtlW29dVkzJmTNiXhxYrqM/4ewWuGYN9alyuRueMnSm+tIJerERIeop9PBOaSXudiOSTBsxNES5KoopasBRdm8AJi+1baXL7DCjXczGwbT10rxJAWSMVMIv5Zmp+j50Op1aDBgEotnc0hDrgCuTIU6MaRLLILXFhnHo8PA0gIr7sLC7L9Fi5FCYsYZxK3suqDelvYaPFxNLn8JFIJXN4+dIjtvHgtFqnngbjD58RZWFa0LVcixvZcjd6LdU3CNDfE68aqyozfjhNaxhCVAq/xWP/Eb8iV7BssPoFQfTQxVc8zWAwXBspcuLbX8khMhhENg7p9aPuDpx8jGPWJMGxRo/KRd1Cj7Nv1l7FrArzmD9/a76EFMqDQFVRAREP9gQlzAefPZIhcOVG46+jVRtol6S3t/fZ7FpxodfdoReGhZ27JbjrwfPEbGqFhqSK+5g6OxZEWQyfNhSNlZ3JMjD3EYSOvKqP3I7vxxpDkBX2xRncSmCYxMz7+GT19WlxuP+YRC8w5wv5tudCj1r9UAe3cqcHIB8CmBMqRzsmEOGnhJEc4q/ZIp9AMjounLotNp4r2NufBSc9j9BuIKXyMHbS9Mw+2Op7bH8iYLQv8ECLXIzNfFQgcm34rNZkCg3Y/mFUT/trOKcswy3evZa3FuCo4+X/qDXLpC/wcchmN4M/BniujNYUvzL6pb2MEfmfeQoWmkLFYWLvfYcxRXafsJNfpzn46G9JmUi30VffeInhNW5850yG+CVgQWDR/mIC2S1IZIyQTE8XK5V5OjwvBH5kLSREW3h7hyltqrNn9TyGLxdfpn5dxVMip4ehtIgDpWWfVm4l9UDVwdNHjCAe/h1eesIA7apvZ9bPy5M8/gv+S2nlq6m9iPpOfdl7tfHV3zgX/LesIV21PtudzTv+6oSBBI+F+c3rVYVM5sHpTru8ODj+clU1MdSqW/6Awtg0e3C9g6NR9W4cb4ijrSnvuN6FnzeUZg7swQDIs7y4GgFxU11YBK9eH13i6xxmp68qtoVuPdCkKF/eI5wF42NzreZ8bZUOrJl9acAi5FWXoIVueAb9avXjyWSLktTGJaKLig2YkFxyBcl/2ZtgcufNJNc8mMzKHHurGyWmGQT4oorfZ10hB9WWn1sd3u+h2Mx5DNX4zZxqk79RNPYo2fgb9sJCvn1rwml4iUAkY0x5k72quHhxr4faErfdspIPhRvtxoWsbOqXHCh5DhXsxCmv+FPtbe6b95TRiNq5a08k5q8E7p/IBPXYXWFTfW6WCvlZcd7OMA4gfeXLJbTltT0IlEhUrD7K8whSDKzGO1o7KiTaaForARSHBNklcBFcwqTFWD+OasLoI5dxI2M9BSR1Z4N8eNcuRucDUObfd7Xgx5jevvMS/3GG2E7XcbogLeHH1fOpwUjILypIPGx7Yg2HK/xeZY+0rTvgX8RVFsdZpws7GDHvueWffcvCaHLRcOEY9+m7t0CXhBVPA7gSEMaWPqLjxEr1sBhySz7OD7mFbl8/T1LS5S9o91+cVuNbVUsCfkALVbB5vEW9u589DxPQQTGr/x8dove7tTyNay3GLK0Y4ieymp9Ir6LgzclcupcpwMmIpAYTBS0uIbfF0ILbxxhhCQudB5L7CviKEvVhfE1quYuKiADk+B//azs4t3WRVN/AhD07gzLZ9RVM6VDPniBmxVRU1vWjETas6/CmF8ZIkao18DKYuatA5+qfm3Lka44cCmLZwagw3k3jkjNjPbYx9OT6rkJ2IDHODa8fIorD6fZIPQNN+YWPWe1cLB7tJNrmHD1LaXF+c6saB0/z5na3YafaQ+Mr2QxFrAuIU052mwzscZBLc1Ob+hHKjxc4jtpM2w8+A0tDJCg81vT0tZ9TXHUwKbyG4O7q5VZRDbxnMofdnOucu6dyVr6Owf0g7/d0brz60Vfp7UtJNfDm3DwiIm7E48mzgNNHurRCPBONLhBiUZz7sHYjIhC+HdNMyhhwUvj1/IOHs12UhOQAigkH7z+PoSbr27WnUBhLngUpj6U7oM4F4h0C/oWdK/gpq1QaW7Y5iUI5TUW7quOD8WZn+bPfjABXDOOagqQDyG0TvexXpCWZk7lY7MxIxssvrH64tvoUE7BZ1NtatUsJcPe81IKsNwErVBpXt9WTWcUK8sgUagBrmZ34lHYARTWbryvuIbzckkbX51LHOSNuajGD+w8dXHM+WG31bqIp+19nSCnSR80iV9fGMj8ikxhWDIl59EqvEKFJH7DJHFTI16/VjdOL4F52EuhuLjEZvXNYEvMBp1jpw0bJRGbSF3J6269Xs9V7wrsb8TnHRLUiRlgnc5avrXHRS6fO9TRZa9sOme4bgzMq0EI5K28cy8MKdq1YlLsblmivwcHfFrCprvoPcubyJex2fN7SFVqorvgqE3R3DCRxkfo8XfRoLY737yTeofCQ49864Y+4FpviBuzd3kRtUx+6QunCTf6Fr1OuHr84IJIO2PKQePSJeLkQItMh5jnDJ1in9vBrztG3WnE7beB1zEqwarhgGG+wM7Y/s1Rm3QQXgAoIEVvVSAtDCsbeVyTWHABwytPcpA0ZqFlxLU6guaamUPujfsQYSFP15HVNrCWg7Wph5IY40f+6KjC5KReYsX+qQ6dF+dzSw3G2OnVSXIzGFueIQbs1uCZfg7Jr6LxlX/tzkiaBckZBeI6Drfdgrn7QmZelLX7CUXcnJVZKLRlITyl6HUk51qKkRjshfs8188C7hoBGOyoD0uuR8OQpUlInM5J6BD3RbQxmXR2rOaV4+VBdDHG4Uq20aOr/2VnARZpXhOSlhREXVkJCdEHJH5YzkIvXUnUoArdsnFJYlps9DMk3Y1OSFu1JoseoKjaPKmjMtYXnZrfkkkJ5JXD6G3qaRAzfGG1/4I7AHa13xdOJ8BVp4NBo3b4prrzZVWbXjXS26uMlszeIi9Dkuh8P2FCQrmLyp+uf8PVsxWv90l1vEiXBBGci1LqheOaROPFbZ7X2dkzEoZjZhiUlbIwVtpSaaH/mhddU3kfaK4yOXSPn3hmRYUslfT6FK6T5RO4lAbQSvzyH+rUotG1X7RXEz3ZdhB5MifVBFD8Si9J3rxyX965DqdeeCKTOh/Pb6ViRmfT0QKCfIER6aEnQwtRA7wtkCIs37tgFjN83zcUiB0TrDJg0OgRZ1knEqKQruZgfL6Fvq7aHupMymnun6KgtR9bzKYk98UWkK4SafpXEq23Acg+lc9yVhYevs0NVKEjQXJ6J8UBAMMe0N42hEQf/BYDdFk8y6V8KWYEKIlF4Aeo96hXhw/3A+49LxlcPz8czSToPmxoDQq7FhdlNmPsmPfzthK0OB1Gh72IWOODaumNl3iEaswABPBYlZ7o5CSpj7iZb/gkobCHwCW7x8wJVugijxWUO9pasTPyT1hEwa2v2aurDo+e4Ye4H9BK8Eltg1ldTfGZIrScorvWnWwk+2L6Bndh42BuVHiZnKbX1E/D8a1J2gqlh2xVtgZno5WbqbJTndjzbmn+jh5GwmeU6cv4Lba7Itq72SWtoAMrBICrAN64tlCKwOWHtb9Sft5myVX1uSIfGgEPQqM8+P3bMZfIPKxLiuqJuery1UtqoCeQo6R4G229dSQbqjM4/9rgzQo9njlvgp0EfuYm3Zj9im5X6NX+6sdVVqqTe4Ae124x06I5iLPWX8QKDugBLAxDvGC5RqT589WUHrJDTMCTD/0+2fIh531F2iQoGPxI2BuStlKY1roPA/HN7866D1QY0t/cvrnGy71SggE3YvV3ep415yOJF9Zw8wFAzlubMMQbk4G+wt2IHsqgXPuj18nI1RJ7Hv1rQJPp+vQxo9hIGDle5G7L/GtWAqf7VGQlfeFGo8nNJoYzM7RGWbLnB0xYGhSu4qjd5ofwfMiB4Jq4gf/kKn7QmWzVrXr8NQwdfdnCvCoWEN/+BU31wRSovOMpPv0S6rkwyq8N7zentNZCFpWxItpNyJNX+lnrZf8+/h9S0ZhJnD0sb2YI7POz25D4oFzC2S0HBSehdPjl3qxdO4tzMDOLqIc1y7XBapmKedddQkl0Ybs32/K48OGvwUNk/UPywC1eC2W2XEllb07Pje+GETN+7kwSOYH+U4rIzrZ6O9ilSThQOs/IcS3O6rHHgYl5/si3zd5Dsy+bXy2+oLwkJYSpK5SWW59i5JxmIbMaFz8wi9tSIiG0mmTfLDeADWnviB1EkJYMpakWkv5NjYYiteUG6+2aq16uNyG+u+wC+zeKpzPM+HsbtWFoLHaUgpvF0gzMCh5SWlZfd3xUx9yC+HCmjyfe3OGdZF8k17qYLKNm+ChovKdz90rUusAxXeOt982Jz/p94/cawMdj2tb2ijxH/iiby5IyCOsxp+58rJfh3sX+rWRX8Qvs9RpomKXEELt5nR1SZih8DH1zrGRdJP4YF6+S6zsP9ermT8JS3TCJKu/FkmeX+y6pilzrYq9yPLTXR3snCpsNHyJ7nbet9Xy1nX0MX95PauqFeHwjQuiW3ZryYL9Dbt9u5R6ToWMxX5ieExR+eUOXNeXH682/FLPWie2GJg2PMGZoshK1YFbwTeptAd8o8hmtJaWsSHK3UxhoXum/yyu2S8GY49Vhqe9q+mArKIImsbQ8MKEZpE3szWp7DCoEufS9Y/hU2V5pN8xo9PVEJDwrm7hqezzECtmb+alTfmg3IUAPtq37nPRCopTCh240oU+Uaa6DWuDScBfAfbHxG1PqiGaGx99bU5nX3hAJCN545wYNza8yIfx+o0Vqv3FnfnCcJAtKilYc9wgZBK61pKdvzUyZQ7zL3NypDFdnWXNPUktm4KD9RDrRK7dCptc68aUg5W/+YWu/tv54jHWA0yaQFxJJH3PmMA8ABKkrARlduWHVImXaMhoLq2As0rfD16RXkH5ShhIkcFp5vynsAGmt8WM6PzWBXOxJlbbRCHECUrAj+MsBjIlkTwdm2GTkOyVu9GR85w8Go/sdeC3Lvj6oB8uzIfwGffdLgO4jWdH/LkjIE0l4uxgyjgvtyzIt7aj5gJqRe93ur8QKONVFHzqaFRgj88EcyYbM0Fl+jzr7m+cBkd7rwt5gdBDrQx6yYhrwpzLbNISOyrrNMiRCb8JQ+yD+cF48Hxabe+k+QzTFrrTqdGu3pzPLMHIkzYzxkCJnkjP3iVPGngwRLZBMyDeFhOQBFRw4b0sieJty09xarqo8lWD1G7J2T1hURmTr3BP0ppBmjWx9Ne9jQVH0h/EZ5p29t64EIcu9WqRsSWhFLok9js2MePzXbEtzpb3ue0NI7M76vIh4/LhXkE0UYSHG5RhsY8UddDGijK0eAHRMFz6D0lvG47MA8mDquohLyTXdCe7Eec92S40SNvvrsMkW0x5VyQa0hxjyK9utA9LO3dFV/51S3UOfGApzz6AM5mQi/GCBOT+xj1umGBC1arFdZk74+1P+K0Zpi76MhYRLefM0N51sAhrMzsxkSNmvewnnwyYyfqPE5r4AwP0npAwIegQq0POzsm2PgXrTvHUtsGPITKfwuvOceRuSQjsQ+c7UX6qXEbTX88bucBkgzsVbX5BX7K6BSKHBNSwTfRdECutVqkmo9jEBnB2z6uUG9MT+Spzf18J8r7R6IKQWTOOSrphM4HUOc8PhwPHqxUDUil0p4DwdzsFhRSuxPbYtxBRoRV9WgwxkZnesXkp5JbnnX2eUFQ4IWW8fwGMPQGS3r5NjZmbEJWMrombjsuEdz8JCeJ01Qyu5oFmLdWpihraWxqQ3R4KSsX3Rgc5oCSTTUJHm7Upyuutjpr+gWWa8eC65F5XTl69JngH1eB5MMA9pD9wjPVeEqP10dyVf/l4TBBIZoyLdWEKa1mm6qkrmcyi6TjWYoKgCsisDuGpXWEUDfVwNPJQViCdYOfrOaygO8ONhfQ7+E5xAK2vDbXMqhiw6s6ZR4g4KBJTXTR4k3aikQtEYsW0JZDqWxy1YVfEAFgScTZAPRz5o0Nwg9f98vnXYBbmZPErWuXsnzDyX9Dvd+rshq5buYb7+CrpeuLL4qUG9SEA5N0Kom4HWErHex8fEgdjS9LZHDd0hUU7Cr0KnniIEqEyDm0WfJMJhmKk5vvH1WXvbWfLlFo0RiK+c1Fjvh6/DkFnOmt9RcLxKjpjOw0+Rfuh7bn+73brcZH8BHVvxgS2UFIMaLxHfwK/L+zmw7am/Nm5FqbNG4F1y8vzDXngNNf1LtkksKhXXVmrOXdcK9XPt+y5tZyPTCc+/oxpN+iYNz6kvDqsyPU6l9S+hKEm9KkxdvInVq8iocIZlsTRF5KSyE/YDpu/qLs/mHriOIZxZ+jLCKC+E4Zncx0jkbwoKpqx9ikmL33dTDm9N536jYCzdNnHPVgAPQLyUdaC4/Khqj883Sl/omZV8lpZuk9M3b7wSfjtLK/yUjtHD69+sXKOqsFIxvFx++w7ud80pp2XkI27Fc+mDlH0X8+VN6hY4TzNJl0rLxYd+989rTkL8HvhWQWe0nyM+lpnvtDNxJE3SV2ogK0Qp/4X5lmReKJGGwhs5HKnaieJ89+smj4xCsOlDOOAoUCQ+uid2ZKijbJ1Do8kVW79Tb3lo+nnglA80BFgHR4Ma8S9tgXrl49388G0aQS3Q59ERPEF8rJG3psDSgfL5tPkA9DhL1jZXBkW1Ooo8B37CreDhycBPnVdnBIdpmqR4oKWtHDyd2WjpHE0etGjdPLsk37aAaX21NVfNsX0Du1v2swUU2d3PGyR2D8f3eYmVZXil4INMXIxRcpp9qcZ7mXurRxHuCsnPbjm7INRqUm8MW3kGB6vHb/hlPtPs61p72WsQEm32mKsaLZV+saSiWPVhn2O90OBfTvkNrOSr6wlOkdMLPRZHS5Jm0kYuGRdyrlMkEjfGoRu9UTpT2YfjsRpVSY/Yj3i0D7x1XRXGserXL85ZVal79Pc6JGBLUWELV+HjSEinMwxr2TACi83Vh7nd8A7htgirnbBnR/yCveqRZaKGpmsW5ygSYoRX3mG8eylBAIEIj9r5Z1ejUsx1GOs0+tu21vVwtiauJ5AYfK73Hw6gMS9wEcQlqsSA+RYdjrKJSFuk3ro2qu6DkRzsZRFvHcvai7Nf8Dn0FjSFkBIPZcgdUXiDmtkAktBWTozNUvYApD4CKsuchA4nQ0OtPev7VXllqSVomQnrO+Rj+Iuhv9XLNkd1I3GZR9KLjmInlZ0ko5rbZdAVATljdqbQt3OZ+1bug1SmekXwurVVZa8mX/QsW3BB83SL3VGfvfvGaTDaTQxpGKk+nWTJZINL5RtU4piLiWc0Fwg21aTSToSSI59XGbCvz5dcoDGAtqCBxGLdaG5gOJfy4xZkukRs6zr2DbEF8xgy56enuhD+yh0s3gYwZSbJoWlJQZsFhhkeCYI9YppgIU1oi/lYv6iU6+f0z+mgA6iBwwL4VMZtDqgDfspbD32GudgJhbVS8/c+zqAihLfZlqFLEEa/FalMzqYPfoXXYFmYlM+zJjv8iR6E4Zfh2xPY1YjPeUTKGWLMRJAdLqCDXpikD0EGxviDlekVCbKUDfLtgi9FqNPxOCNJhxW6KM5Lm8j0XL68g3st8QCjKWuHPPo0VAqcjmGzBCtjZyhFsqeAODyLi9mRvsqixvfS5hMqsSXplXwrWjaLOu+blr7u9chtXuZUkA35foeStxZXo4mSZegPf9aeABpoZ+SPIzO3lD7OFblr6VdngvfajTvVQYGYmrVmhIURyoJ9FCam7Zvyun1aAW7cgotwv/D5ORlN4szifTzHcgRPrXS6Tf30l0+fZuT9sMnIFy6O8CeZ/I42tGcSNShKk8TsHAm26dXnLmnW+RVRe0Kq/ZMHMcf3mp7E1l6Z1NHxjYVEcpDUDviC12XmvR/UacRlAo6dg61hMg6LFNHtzq/LABoyDNegXeHidZCjY9WWoEFM+vswPnxPIJnJcPtF+R9bYSrsDN5wjtMnFtmV+vb6SMRfuHo6G25+66BO8J0pypz0VSJQE2770GF//LomVH/VBt75Coq1Dbxv50QI17eCETIZ9Cp05To4Plu5DRNvRVAUv+gQch9gywh138UWMFMThdWU/MvI0hhx7eB6zWDt6U92fcljzrF1gWc9WsbMbW6MlG45mZAC62VQ/JVGROmjnG/VCPQhwksav6bG9qiin8p3pTS+TdT+473in2zv1soI5Q4VoBz9jVAPQ2GEN3PTIH3xcCCi68asa0o2Q0CsTzOSQvUVniBVRLrCtzXEeyBsd6O54EIZkWaSB4sOe7lxZ4Pc6kWXM3TpdLq485AS9zoiL7zQt+eIKLhrtbJD3m+Op8vr+lTqPb4orBj0T4i6C125H0bpEo807DdnDSGGUde5IRKIvwlAJJ2tAz65AXj5LjmTRiHMDBgoWbQCgNKsFrPtsj4flo/t27d/fX7CC5CZVwkPcomg9nK9zyLoxnAEgQR7/ZZpgYaJewDqoYI93XbjzAwas96Ln1PeYSzIOdv67S+zheMzEcEc7ceyBr/WHvd/vcU7HKJGftp1KZy04bRHIc4U3YNgwfnzhAy7fLuh6AsMEUyUY70UNX8v1SqIM2cn5Zf2QoHxquLO321WA3c4Dii2GGSsyFSwnBqsR6I3TDCwRdaIa2/vyCE6JAkDOVMPY6jCm1i9IJIDImRhIhzghAlkMd+I6X74Bd0GfVJk+Tbj2y2gPHh3+Aukl1MMU8rm9etqbaIslQb/4xE/Ud8XN34Cj1+6rG/vlrqCLPA5WBAbE6y6YDcw9eCbn5AQSIWzSv5HjNCqyMs8rN/TZbCKMDqO27KSTpW8uzn+mqb5YoU0JY8CipRaNPH8d7025NyUZSgMirWkhypBtMMr6oPtooGyI6RYGsZSXHJdZ2YCR7k+kFlWdIyxzBhPwSGxjNqhD62NHr8wweI63ZLeRnVzqyl/8TN3mDcDQMucs2rD9sSJ68wm7PPX0vcgyqYiPshLEuybf5w2Fb7bhNUEDjJ69FPW9CEKI1vZ6Jje+izTvPzwiI+LaBZ5ycx1+sfSlaz2fPP+gspMjxgChXajYino4gVAxxa07EWCcmTzGhe6JWrLdWgQvF5zWau+rhryfah2S96IVVlGOjxcY3uQjTGtYai/Z9QppVInRXZ58N4tcgkxpJfDs4SN1wc4bJLlk7DBxvhYK7q91SRxl6u7DzkFHIHlNS3jbBRCpTckF+t7XHjyvCtReB8fr7z6GReSTWp49EoxMBmUZcacZE4zDcfEdF9reFBVepQCJUHP/7VlqAvvzxb8wZoak3BP0L1omrmUgRpLiMoBZAaPj0iKXMxjC4mWecbBQ2rT9r7JuD+fORPqHPTwskFTO0ZHPm0boM9Hl+K2qCw+4cYZDBnvir22y26YxQ9+xz/xZBNlwSzlYt61tffmFnBTNuiChX+ZLwPggnzMLLEZzwKWBm9ztu7lNvuT/gId7eLMtxGJwvCaf1pQhxEQ/xSYfi29gkIxxzXnnMQpA892USVE1EB5KcYVUzusCMZuU6uP/OnLggYbcsjcH4bOE3MeG3QZag0+QqvIK8zuWsGRcogM5TU7AgY0FiZuqeRvAPFhHtwXWfpM4oEfNYhF4Y66cQE38yP3HcsvrSjEyB4hTdNFN15I/9CGkdfXCfF+uvxmRKt9jivX+ut4zFXU/Knr3hR69MucvjPLoOVqYHMdXUl63ANaEZx4cT2CsT+YVL8t/YtnszKGFhCorTSSRhsrDJDNPfmAlNLfY6rdFOAMl71CLOYhRD7yTT/EejyHiCwvUtrDeepSaMExmGqYNIyrNMu0K3TGLcM46K4/R5tOlOOg76K3q5DjaEZDR5odF/yj0v17DpqLqFNazVbZOBq3ozh3t7O65rLQ05fE0zT7+5gaudl4cfh1pfb9fon0LriHqtV3QsSy2ScTqFOnV2DMYSBHn7RSQV0JTDI8ztppIW7yERpCHvwUonTlVfnTWl6GDydl83iqJtAj7OValFXxitRSiDhoITolh9RR5Cxmv2lCEYre1/d38p+/boIFvoA5zDPEiQSOsTgs+bjzRwy0qbFTbjiOr1DRfdNzJX+/Vgh509umhkmUmZpSXI3sum/oKDAYhhqdL81yQmllghk5k3/hYSnBy27PXjn6SWCs9E0BfB0f99CQYPuEP34EYnktA5EcsZkMWPgpZIlGWcXZIIYAnRvQ/vpqP7i3u0stawjjkkxVRvJ6iqNHXp+IH4zSGNfxwThhoc97GOgCg5Eke9jvj/ercHgYA11ttLSqbRFnjrklfFr8BO/Mkm+FZA+8X+O9Zv90pa9zxpxuFr++4qIU8q5u8G/7x5pQ3HBPeMQcnjnXewLgpEcb/7TY9ZmJcewXHmbrOt+2NZMv0oCzs1DEu2+zTlELuAgeYs139AO2LG020/zZQk0O2DYYZgzjfVooBlgIFobgk7eYcYOBfeh0KsREo0AEnGqvEmAYl0XbcFplb7A3YMs0rAhtPWyKXqR5Z4H8Bx66JT3xA2MkNbZ/xhwDvpH0UAFOFgfSytsaUQDxuwHEetUhz7ECj+zD/3odJbJGu8k3Fm1HUbynnj9pkA0XIXbV0EpuH64D0uDtfc13L/oAyOAmHNB8ysmQKBc5CozEDNcMbG6s9BUKCDx2bg8pRvtlibFsgGviGrxPin/93gDaSNq3lumjU9FkcEpwvKqBeW5Bz9g0S5DW41Y2e2Vz/ihi7N7VqPJdEAQbepUnYRQkdttEI39QCxjGO/iTQ0ENvffUGNCQEkAr83RQd3y5IEKPkfroUqQTop6u1VHEZBG4jJeapeNi1QZ+x1k/1hhDfjBtCLjflHQrMwUlWojBWjWrhAb1rV/fnGQL46oRnDEw7MEpDuaDOGt4KiC4F7BE0DsT9cZXkpGkHsTE/Y2dmhiKhxZvbu1YG+4MVPYFkWZ1NdZxvqhmOd7zmxaNwgMvEt1amdPkyKxJV5EVL6QbWmtBIrDeR0N4Dv1gTHDHFtekz0lAnZlrmCvu0p0n5RQCG+JpNSkZYCOHLaIJmU/RLLtzfcEqowKoD5LJn3rhJWmtAdE5yUdHp7ud9y21bw29hxW7t41GJQxZgNJsZg7Lx7c3BXMejA9qo0vgyFa1GMdFsnka5iMb6RdagUoc3HSpPE6qYF/drr/lPUPOXwPQbu2n4d0ldHBmcOJF5t/v7C5G2Ax/dfWLqO6m9KJvUc2HIWho+iPkofehcpZ/gGabrsQeFnvFuhyN0eVN5woEGBWiON4fXyywjLqm3dIQEOX10yz+gvt7OJKZujxiELmTGLAXLADa0JMNvQWUPKfwKzi3IQCjKd9kkazn+yzdB96Cw7PQfRE2mVhffLFqiD924HjtDBVlHN1BiUOdp0q+v9eLrm9ylJzcLIwzZ1PY0unflJqA7noSGYicDc1P6ggtbbJWZEE2j35RKpvRvYr5BKk6CllUQvtz9eybgvEdR2eo5TivIDO1X18ayA5/eyMdRrP8FHxTVHPPjgVYcz/YHmvCfca+2j72ZMHyyXSgAqMF1WHoCRHUOTPqYZj88X73ln2o51+eYZc87PwwmC2Z+9z7PMMzbd5+MgtTPvAGx8qviMG30O1FluRRxhgvKItuVitx32WRiGVjlh6aHxUH8k6i0oJaiqH8kintxMz4+Ky6Vg41EJlDWPq4y9eQjjrsG5jvFDNHS8qp98h1fZxNvZAbGc9ZYal0Tbuy4fceGHL7SmwUft6RYOUb7aco9Q4l6nDA6wGInwe5zgQzR6XFh1mM4NdCc3hs7755SebUAcog1M51AWdlN2VUKTvZj0F5Bt4YjPzZA4giX3eZ3vJG2VN5XiWp84FO2Xc/FHE7EZvptDuqah4Oj0youO7g1LSdbNTq3VKc5JqD8Tcq5CfKFmlTq7E6dpwJ6V/o8Bf9o0MY2rrsYGPIT5BCrc5KP4rAyyv0O8RbtMH1Vg81kSkBX9bLyCFNcMZDRk+kfBW44wrAl7FWEuS3zCvOkQLCaLv++pj0E23yikebD7uAinz2xRM8VknL9tn7kJQHQV6p2RbSjzq47vhuRvcKmBLFjAEeMpBEHGJdzGy5ApcUzAdcaAFcKOPNmfj+EB+MJa9wAfkMblLAzY8NVAtxuvXx+4E0R0rI1EsWDhfgPqZRyOfhwQxd4DGv/2tknn9/4v6Uk2LmHeuOqq9qnx7oY4mJfNlO9s1v4jFC7VzbNrcHXBEyrd1SRqKNecA+/lfqCORzwMdd2p9CTVI+O/hDQ5Ae41XP3nMOw4TC+g5cm/rxR/GzeILU53RyefzrHC9F/v1mgHH0o45ELRGFpPPrq++WE2V5bx1GKOITymzMqDKw5kVi10TaXCZHeCGk0Rc8YYCBWGKU92HG6cyb5Ifil/5Fmc2jHvwu/AICNl6MecG1MKtG+a9f3UBovKt4C3X52QQEtxiQoPz5bML9mnVSipZNWCnvoYGr22o5v44o+GlIncr7IW+RyI2SkY/ADY43BWX8XG5JWh8OxgxA1aAxtMaWJBdDufFazS5MZ6x8YP56jNnFKMIS1kbYHmEIJrGw2k3CuElgjiuNyq7E/8LA2nhsIf7TQtRq1phPrErE8ctyiQ32Lpat6H1EqWvEFfHmsYb3Q9r+UL/GYmR3wqL+oQi1pPDEoNYNGWcZgrbvLhFBXim1fqDLdp/KjAxpluNWQF3CxnlbO9OUeEt2wMfmdtA74e00Y+KVR5IgR1fed9BRILgJgspI56PmpWfKsIVLHWZN0yrPhYsgqkb3WSIkHhPhJZX6ekPbHc8TjFF5zX+B1WTMg3kxzzWGtWZARhgU6IsFMqbLxTpT9udMTtzV6cPbHuLqQVB/IgzWWr104kTBDjSFbSNKEir7GyijaYki7q+13jAV/XpvZsH7KsdRPQbG94dfQu7FXw+j2Mapz7CuNsehoVP74bCZdsQLtNj6UF6Pgb4n3ujQhcKznZcDglGzlkE7JZ9Y1KqXfZ6cFfCoAprNhd7DlY3OMuHxs0vOQvC7RchBAAx04Yt0j446/V0KsipO9jFwUDIg2nLxfOncSYy2+p6I3wEp1pM2Sg/Z4OLeLAxnrTAqDves0H1PWUBLoXGzXkOO7unYpQP/YFSqNsP078D8/VCqGxxkS7Wc1EgzRpTZh69h9YfABpFoO4nG9gsOtHNE9+6FltZyA/h//3p9W7QB7ewkUKQPM59yASL4ZScAoRDlXBaERlbJXH0Dp1zxEXCFukc+Xspc5yTD/zZNwD7rPpJbKQWvRClz7spRgq85bgfzqMWGAuIn4kzorz0pgUXTpgpjQW5huEntMd7PtW5qDqGKnRbLUv0WVnxM+fAA5MokhceFnIxq2z89bqm8SfYmIBketiQ/QZjnACPY2A+nKOqtcXWIpcc1JS/DGbT9p2W3A1ddCZcUbJfjMm9mpRoRf8gEfrvDwuy/kRLgDYmo9R60FXyBf4vW4p0NAFsDjIJ0+G7IQfQeDhA8ibDTqro8YqSoedcQVTAgo+TiRIrfvXNeVgOhWe2rQUkEoxuiV/xw5IDSdUzT5o/kEyX1BvVUnz5VLz3f18/rfpjAzLnMgQet9pP0f9zlx5eO95GKEmz63tYnxa9y1ubkgo2LI8eOb4Q9VOCK/UB9DteLD9AsjHephUk9pw62WBc1QLw1bnACXOA2QrkCK3djc02GjCI2CVEErYtW1GfAomE4VCctwe77IBlbQw4TZodKnKci5T9klYWY4SNoEyIE4773cyp5sniILh87xjCQhIJtCoXusEwSfEy9qcdx6vvXvWtsxassvRlHzk7ASRbPqoO/1np8fFE9rUgIke498k7ywpZDqPZk/L5CwFyfhZZX6/Ub4CMRAllVrRGA6/3Ax0TBQhYXlKJlygA5w9dJrG5+YPna7N/aJMEQMhEcQlMmVBdSxP51DrSBloLiXj0esJdQpI/h0M5dDtWYxdJw4F737mLdEcdoAIVy4AxNdOQWeuQyCQxZPcKe8+o3YR2tpuB34FPq7WZkOi946zmyhDAhK+hWD6oaPoUvMPE7ISCs2SM3/9ziDaKLuWqZWJOC3n2RC3YkqZBEtkfVPUIEv62C0KLuCBGltebBWRIZaU4r7yeKSjzCRJ/qIFCYxHt6of0EM5wsO98W5Y3pvYQHyED6DymRXU82D+QEBzwML5BxFRXS+SmoEJ2O7GrMA/1soH5IGgyXvo7dxke4sxt2ZSXhiCO7KZdQVlPmvbljgDEfTitrtoCVhiBUVEDSdJR+4de+fjwLtXze0w4vCWQn1VlXO2Nf8twf1V36wPB0L9Fago9ZdpxO0nmeVN7y4QA/ahcVszNGFjp3kw9doGqDORUcjb5FQ5ehcOzJNvSViOmhxGKaI91FudQcXTG6m44Fdn9lnbtVNz6/VH6uH0hR0yakVXTLXgqnHFycSZGOMUR14LVvfnXmZhG9kjX0+U9JfymJ3ksLOQEYwdy8NEIm5XU3SIzEduIeku/aPi9GvSAM2W9i2wIxz6pwBekLwK9b0Cf8hTPK6yXX/M1fjJGTsLRIZ1W/K9pfwhuw+QcqhKdth8ZGP374QVH42CKZ/mGvHXQ/VGgaViQhymvoVNjx6XNsYLStC7E1pmgkLRq0bhn+O2aE8YftrJ5AJI6XWP8yxA+bRaiiMNrUGhrKusE5cw/+qnk4YdKfkG7yG+DXPTiJBOBVb0RF4pCX7TJKI4bSxCCMzewhNYDQTTozfQTL5mAViWekFg1xn4eS990gaynmoOjmM1bulE3DvR/AGp1jAKgqAdN2o3YXU8qYr3SM7Xwd+N5XDw3hxDAyOnNW8U4MgCt4iMmzUzSDj/Twd3bMF6vaMfYdsrbL5hCO/WS+iYOnS7JCUl83g1jk11jNv9xPzRMU94p4F0BjQ/+4OL3PyWwfUPrYUXPgLEERNz0Knedamx538/5Jsbg1zUm/70eM9MKFD5zN8SLnjxf/eZf4MRJU92GlLyp5+sryNssevteCWG0xHYIMol8AsXyO5AzKkHENGvK86Yh4NJ+IRECIKFz1AnYgymSz/QGI3dB6Ld/jQ7r+aUywOg3x89pxrn6xnt5PM0RpL7B4wGfd7ccB0PJugrqaL06VWYFx8NN5wcGAIwxoLmY3f9uoTUx3jtowjLNXktHQSBFiBJd8tdfIGw1eC39aLw9dvK6feAQllw5WbPDR72FU8qJ8s1N7xvewCUXVqhWLVDzAc+scYEZQi3sSXYpGxcxko5ByeO0KBSctwa/A0osZTeiOI5Tkvzxdx4KjWBL8Jbw54kF4hHA3PMJ79/XLU/XsZWZ6ulTCvMyMiHQzHJNxSHC0tewCh+OUNrpDlxI1tBuqtac57+MEAxGyGliDCJuBwUZp28FhyPmsxNM5cijbEyvf1jzppIszMy2VZ2Vn93g+hNTvhTIppSZka3cJ322KFxHZt6q0HJOO6D0C/N3ALcA798gyYx/MaeIZP/a+QUiyo2da0PBASREI5x4negE7REl83/NkXykrIPsA6/4Uf8IPdcnYYRcvg57Fidh1jeq9sEU9ZpduP8fNB10S4vvbOPH23ZLtVY8AoavdR4qsxQQIfhapmDVGNziCt984zQzdwaSXjw8mhZozOoAVRSVFURJZ9uPvi9XBQGMcuFKll6Gi9/sDmCumvrfUToAxQgXLlQRF44llQSqq7IUbukNy3IQpUsQbR+B45SofaNn162ZoiMja6Je5wb5SDJ9cCxnO3B+n+1K9W925WIPS3OII27CUxdJFLkR72wLYQzpInsDqdab3T0FgytSXmtVth0rfPSTee/kBAVjG4C0HV2xWEf2ScH6OW6mTNa3BK/V1fYFhlt/MvKXLW7BkrR9eCqOMGmBSYdxnWRekLREg87ofmDV/ceYtZ4O3nnwPW1oSoOU4F/lK0SAnk3/zGTnub74/TOBvLsbm512ETUgcXUd45geaBo71wU9apQvHCsYESyVLM7nzQZf4Jy2cX2EcizcxTskJ7fQMZf3WQQgfvZjRh4OD92jc5Iu+v5tGk9oW0v1vuvthLSRu3Tgp7b8eZwUysuk30QDeMr7TDyZwwYtktH41pyo5mA8xAIhL+AHGaRuKYtAvALo/rVle+lL6PkwELTWW/Zm8jn03kg1J3PfcwBut1kRdi2aBi6wsivIm9g6k8IE+kTcboGjlWwQPWj30RuFSvuRfvxxsGtQ68YP1bP9qfKQ1Ldr864YSh5roPvKxwqtOWEW3O/WyOPq2fuVezM3fcsl/U1ebWcXBf1JAXCD56PstnaqAoXDJXV+FNgE4QHUsEQIyS+qcmQ5ugN7Q3NvpGXKC4H5apA+3vzGZKd0WwHQUetBYS/ltPVq4/oKP+vjgWJXOJMgSAYuk/tLF7A/0cdpFf8vlXBfaEGO+P9BtvGU8z/LrmB5epQ+KSj9BBaeZDhXxPnxpD5ZrEIE68TwfMMsy5y3sJ1XDPJ8dih1OyJhh85+IbQqYNo3iQz4sDr15uSCzWHASQjjb3yJQEMg1tIBd6XvnAtAwENo7bbG4h4DHUwg0t6yaSUo6YyEjF6Tvm5ur2sSXhwrU7MuM9z5NDDgE0A9r1vG1shlgKQ6oYxP7LvHfMo6iNWivnMDQHvFHlfwPxWRfY9yVtj9Yhaxz+YmvOjvqTqGtrLELd5onhEgaaT+nE7GTkzX9OmfvkodfOIX3n1YcWAmlk0NE57eOvWTAfsqxNAuedCcCqKPcS3ph9KE/RIB8/yNxzOQBdlflOfE61TS7J6vM52AZDgxsUQMK1G73wxLu2m/Cl2oGM2XZks1norE1hQL0v92M0FA+jTxw7OLOODrqlNLGLGBjYlZHhk6wxlBnr8Hz+mwPCFM7x24jKgHWtjLtgYK8nbTyIayT15h7YsSXHtmF96qoHMrFzoSnH7F3wzr1EQFO93WrG5NiAE/73hYZoudV+G3jQRYq0GQXEIJhq2WEGQ9zxlWdsfVm2Hu5be9joEVcU6R3lVNUBFJ5ETV38OnaJHhI5BuDPHNecmjAErLHxjYm5M4runFPqd97hdrluQ3DDq7EnrCTRH0FXNwn0N/Ez+kmpSC353epD+0rL2StOW/qS2LcXnVS3Ea6bdoPrgRfhiEFuTFFKx4cIwOlIdzMwUqcjTmbd1N/jEa+z+mY37QT6MHpyxJgaziWVH3vWvZdK0PJB74PRWtuhVw1DWcO195D35urEaW8OWXKsRZUUvWRUfQeE9PP8tEUePwJSHktP2FMygy8wlpGrRnfGVKgBjRyyjevW8gG+7RP3txGS/f0GfoUed7Zw9dyaq3uskTUY045sYuDC/GJdTV7cZk+99xcL1nilys43APdljQbNrcRBWCfDkt/R0GOM1MwHP6IO99wzk/HQfknlvXncNkuQtV0e/Jfhjj5T3nBH0MFi7CdtYZZc7NuGB4DJfRP4NQ9NPj2s7yvNXe5R4hl++ALNqPDZ6X0Ou7G8vdlo0XIb+VtD697Ub1iBAUJSP6TREDQ++6Yfecg1BZwNkycebxKFeNTKzZ4P+VKDBLc8XYTWZ7PcHmOxLEyxGVv+fFtrxI3d5XcUo49qpZNwTwW7Kq+wX0EsCZJDK/DqVizZz/E+JstXIxk9QFJ9kzhMehxNJ8VWw7pLZX3W5CKyvbbpJFBK6Eoj/KodDQTLM9P9jYw0yDnFLqUeGtby7jmzSSl6e63cG8VeOrfHoFflLxifdfgfH8ADzCRXgKv4rRyTejCed1rzHrpaMEwOVuN0rv0HoKth7D5i1WSQbJvczn0tfow/CDOW4y0H8nhzXJ96zMdy2f3B2Y/yq8TRmIfymfHmmOCHi9zURy8XztJCNSi5xoIsG/97aKQ+bxv+1SBo9dCEf2OGvT9xMkl1djN4R7+G90e1du+BVz3rm78iMf0y+PHZ9o8ykDC1264dnxxR3egWZSupBXYLWm+k19Llq5bzIU6PkuVMoLKULJpCnuY2qrrr6kUXAyGk9chE6rUdOuqXaaS8XldvIdaFcUqe6mgUYg1lKi33XyPjt8Yue394U++fOgRfrBfokxvaw8AAAANEwMhrJggycg4lTf0xczgpJrNdCQT4w+j0Ybra563cPTN0Y/vHuXLcWDjaVuXjbSx8+VsQpifmYxt3/zt2zADcgEmk5ASYiV2AkMaZldKnuCiHUzcWwG2SWHxF8yXEy+mRZDhm16JP1n9rUNWB2LN+UXCO/6WGCv0hz1VhCNgxsGBta7iG3BAgQeeXAEvzthdZ/nVWNsSIu1K4TYfb0mnzuvP3hQtfdzjW/pqZc/o8wv3Hm4A2CG6jyP5e1cAKEB8TQ+hOagm9euT6ufQ3PTY0xat2DH4Kno9yeDMDDHkIAHXxft3hvPj6I93GOOIeV5ZMNGo7uhSyo6aLTx+04EaHTBA8obedtABN031Bfg0ufKFK15p8TLlQWdQzeujHzaL0UKcIYAoXid+9/bYRw+nIRtsKYgTsK833ifQA16B37ge/glwdq+0Bn6Wb0vcWfgYlGGj6oWubeuu6yU5I1NOfPcrdekHjHNx8BJKhGxbt9koDkUeyaLH8gOhSR1M6xPCQiuEcUvpug2clLDo87tZCPTcu+ShnPk4jAd6o0PVes6QRS+Kq9UlmaLoGoSNbVlJo8cgO4MpPc+qcwHRT9DSqGjRNyDwqNMYD1ILRcAdsGXNTUkOFF16Ed456MMDU//FMubJ76f/XnFtau+takQzVkqaTguqCrtXMbG9+q1JjmVtbilM474spSN2L+Ot7zhbKubE1NckAgYrpH5A+/ntPa4YrcWA51AP4ENQRAtqVk0uQ1+59k5lpH7YTvF5o5lLolomKEyVoovlRK1eme9slZhjxHCBK+pQMd0XGObLtkIKq+gBakVYm3Brb8g8mbyNvQTqXb7BtHMTTrdX272WGNYYXPfgHZBFFfc91X6RhBaSBEr6862Y3xu9fxyIcW/Wu4cPL1fEDR2NiXdoil7w13h9qx6OB0ZhyXd9m+kTOTMnSIEni78Jk8krsI8tHGg3hoPf2gJgZyR5vZe9Bwlw8V7P2sJQ4ACICol9hEzDQbaU8ZYiujOurNI0+h1JWjvg3BbeND1uFLtKbM688qMa9K8HxW/PyGXwCL4upcZWYVMT5hD1ZbLxR/zJ1jQEZE8QBd4QxbFgbh3r/8a7vERJ3HGSEqBAdxtW5cRbjXYdAx1s4IwDXvbK93A4irTH/dParU60qOlsf912V+Fla+kYI13sC4ChaMg0koLc846hAP/KabLvp/Ux9FUg8U19FyumoqjYn7+1uJIBPvQC6DE+U/i342kA1ylJ3fdv+glISFEqtgECBXup8iruLdhNKl3LoDSDqKVDSLdF3krBDDiRf3AZch77vqOL/6HP1CU5p96SXuapqMh0WdxKptvZkrUEGQGYGSLatYbAZ9csEEgidWfpRRnm3dC6BYXBYG4r082OQqw1WWXtrjCIvO6lvPqV4KkMUQG0Lyc/6jJHx4Z8dOJFwaU1dsDKefQ3uFVF7TSswfM05pKVFSXrwZFw4YR9yx0apIXMyB8mLGD+JB8qdPSx61x+mp8biasLX0ohREqpctPYEjIt3glUaF2aQSx60GMrFCzuljg9IIFfQ6j1o4Q962HwODtnLjiIJ6sl+FfbI37SXwF4nNKOuidFndEDtQq1C9jtcwVlIaD0kWIk7LGwoaCEPEMW0oAuDrFzNnC6WYPF8a7y3z8K03tvNFSabO+VopU/KpFJvRo3lPBzIVgPBgiyYGWySP7o4dpTeFQYM7d9wR93Jei+jXqFqCoPhk3D3OLcn5Zh+q+PO/XXMLvy/gqbPduZuI/10LNETMUitzPDfZg6PsCEqoeOumAPnUZPBT7wFj1ZoVol7bYkyaT4BvLwq2anOX9J502aVwyNlzDUs7wyfhm2rUAdGaXiNmQxw3EwIrt/vyxWjcdDsZZ0KSfpt5Hjcamai12BYOoMZaqnbv8Nv2X7DIANGC7njlF3+SpM4ld3Ldtrae9ofOaCs5poWaimKTdvM8rBp2T/0obuetxhfUpMiBZmDimYux2OBdEhHIdOLXM8RbxKDz2tkuqW3evHpsFUjHxNSQwRQALiheeOkGqZNkhMSX5EqTdkNrlSY0d8Vo/n4ui6eB2sU6QHZDsSqD+Q6tk9hT3+dgwX5inrhPayb/IizNguDiKbYsSXPjQ2zCDUlXVONhnTkJ2Hoa3mGOhAHWJHgGcckCe5p9tUNNf55mJRevzLH8ji3bZ0sdEjxg4iSEQkYcKoQzrVrcpFvCh95DIGp2c7jC25ISkwdcEDa+lvPCaVD44SEmMZuqO+Zu9UY8ZXYoupdmGhMFlQAGDUgaiBvbzzt/GhvzLwNd0ht4Ue5wbNQmnqD4KyHEmIpQz0HAg1Wc/5fPW9lBpneYshFKdqGncbjekB9V50jJbnoa5yns5SLrspGITDy8/y99tCKpRc6Ut86D+k4HrSopYl4Ydep+m9c3qWCWoozR5DWdHh33g/HmFuxFE6sIRBscl7bh9nXLWRRO8DBhO+ySUHkFQwWjcJy6iN80AdVoSoz9F9x5oLupmWg+o8sVL97PwBGdKhnHkW+3tjN4wjeRA3kyNGsNg4QGPnDMcFS4k3vd+/7vQJzw6+fJmzvx8ui3yiSE8qFZMKBhTUs9Hh2HyTPUd649DDPjLtLT9GrnqPQbJxJi7mu3l/9zglA0tVPdWvJ3iKYAoQ9Pr7wemecVr3TYoIEsVjluEBN43HvEI1fgv+XCVJ0vwWB0ns8JYI9oj4oOuzCG6XLKRMSt0yTpHipDBrdUchMm9tqT17EfjSbGxfze7grWN3dW0XqwSXw1zSQwOfH+zVPKw7cmuGrqJFT7z3L88Vu1wplazykEYYSoJ0omoqEoY9JZCTYAQEjenG0fGv/SaR7UirJZgCHNrU40iiqi9s4RPbwla7MmwVZhu/WgWjbswSgZhQqbYP2gxp9d3MjlCAx/uJpges8S9POe5boOKvosuJLBbTfCM5AwIaTnXz+X0NNSnvMEmtoMcCrAhlm0T6rzA8fwDZdUTW4FAI4HosYSrowzlPlDAUrKfznnvtoGZENJodY9ZtQhmuy2Q1zu/HqveJISL9Qdsjo9Os0o/TRJIM3Nw9egDXQOYHQ9d7vzDpr5pOCi5OY/ik6KgtgBwryer7RZfLVtjoUdYphdzYA8blxiipyKVpN+zUvXe4JPzExPOjZhnbUiHpL3Bw8b2bWTVNMtUtE1Rl3VBHY9TBx/7IGTfRH99RUHdr0g5fYIz0wFlyxZfdamSqdvTyRZ+zoW1fK44EuTTv8L3CWcmhRW2aeTKpJN9YMbHYLAJQAd84F+ZA1ENNDvtlk5GQNE+gDjzU+9KY0TDdKVDR5arf0aU/70K6XhpzzkCFvuPYOPeiD/xDG9h3bpnElmrfczi+4tbFmnDnnYxDdJEyzspwv9x4UeEFhKLYiZGfXmhJnAHliuSvRsBFxZIfsjLDFrcQXfjxJyPy/dWhOBupTJuzVCkbZZt6SqY+28UDU+aktBbJZu784MAoDmtSL9Nzwwc1OkwZKTRp4pZ8MdgHozSg/btGk37sYiIjUHa+H00WSLhzUkvwRIt1NsDKP1bVGPt9vXctcuKr2FNQtMhyNKiklRpmU965vAtHvn7NhY4kHkjkX6Y4O+jd4u43kjL4reF4H2/zLrIUL/ECjg38uwMn1X/HuOwhNHTiUyree0oxOb9ddPrTjLQFUCg6mQln2XcKVGbAkTuIu/6d+XjoCS4iqQyTe5c4jrmW+QlsJlk/stLJV09WVwglfI45Osso20mcaRjIpQZ6Bz0uY7MMQVkwHIFNOEV/s/cXf0hP1m8269iSWXhvQZYr30aAyBIKRaZM5wZ3R9XPKoamBU1J8I5WE2TqOVzLR3Gz1m/M6oIgC59lRUP1L1Rk+BZ6jNWoO5fF9Px5BjX+8Pm+T525Moghch5DtI/IrkrWiwpABZ9QAVUanznPzSzX81qGpScXGm6Si58JvtIfFgTBuXapAOhpb+y2P1GGse5Sc1T/MATxLvkmEJRkBYAoQznVAit4hPeQ66hhtcFvzTSI6nQ0VmtWMkDFLJnq8ZCvIoQZpBSRUihtX89f8+DsFmFsQ48vX9Hmdf3c+eUFEwWjVd0S3Kg0w60QgySj2JNR2Zr20D6H6Iy5Qd/ExEptfv2Iby3fHAXwEZA57E/Bqsz94wOWgD0IKD/Q1SiECrtZ0Qg089eVRbN4S5WVjbLL1dLgbklbpX/bJVYp1OUfbzgFdzq4xeCrjXCoQ2YkGGL4sLC5SlIp346zY1+YJCnT5cJm32Rr0vn+MstFiVql89F777tltxLjWbpwrth4D4fM7xcZkqaxxY6LC6+c54ZDRFTbWo+CTW3BOjSdgR9Dp5gMJ+R9nwiqLlYRUF4P70q0Rwrw9Fc6BU+GtgyD/6lh3vK60NN3X5GEcfhHUeID+nrmq4x2meN8sQz5KjO+C6sO7+X8p48RtCSpMo5I+vu1S2mYwfu2y3HIKtZ5ozbRQ1xDG99vJ6RnBeaCMKygDrMaV3IoEDA9NejX9kuSJSS0D81SCMMMs3gnpLftTgcf1LSzao8o/K1r6pTAvj3dIRfQ/PHFHK+SGTWri8NyAYkEQxJ1UHyQMw6fQh+n8SNiLD9zq/CDyL3FmJtQBSwKY3OZ74w0SG1N9kVxqByDWEP76F3AY7g+PZM2xaKVLeEE4ocXUi6ywLAvFDqpqWFPaDw7EXYxyqPYI4jD7MVEZRr2gsDw0ivZyiUvv4vgVBLhy53nS6IrnEdySndUkoMQfuP+AY56k7YHa60DVgvvNPeK02VDvSVL7i1hjiNtuh8+2JjKo1sQ7aQy07NTV+ED4iv7W0IEhfJDlz66iSG79w184kN7zlUrnC+9LS68Z196+BrL3aYuciRWYSLX1zvQb1ToOk8Gu3yzNzfeGSCLllMNMi9CcYgYgIqXo6lRxXf6S5VFuFBcpEmKFIhq7QPBeXgJXrRgB8oOt7WRLBsNZiizUp6/VjWg5eYc4+Lx+DykDHRdKw9abFvvI04LMLN2E6LF6VmkRA9SmkJKTnFTFuSGBIoYbRWwNL0Z7IbZcqyA6jqBnBPNe7TrzHg+g6j4Q2NE5cxMTfBcsDPpTrQOLqk1Tt7zxjQCXavzbFDHj53uDFFw+mowYXcfM1sR9zAWzEi9R6FwIsRHhhTNOVJgAw8q1xu+UrfTkaUcAoW9VVq9wwyV/HMh6aBDWCApvAW+hm+lrpTiLTLQKnCkZFwDJERxz0y4RSjRgvLgiQmvDsz2ZKfchqadDPlFFY7VYOmq3+K06iEpFU1QATM6AFw0D0v6FGd0HUwDY90XmivyNhA90apEeVdyq/rWW1ctnP2M8tmBh2EkZ/ZQFcRn8K7VxKb9yRwGH3we4y2QT+hkjv4YjgMMZTeiZOW62lf5Qrhl+ok5A26yxwRBpIjzO7KTv7EmD7epMrLzjEOf09+hq8PCMT+vpO1d/80FL7xzMeJAJEZeb53Friy/LvJxY5+7BYiOse7I7Qnrg/MMLFMbv3PolK/RNK6ItGcYtV/Lqczb/dgQgGyYOQigEKFO58m8bYxD8YJgysMhQhsfcYtG8m+PPj9/RqVLcAlV2qHMCKX5xcehN5sQJ42Os6r+PbkR3hAEFiHs0NyJ+5xB8B7ifMh5vqaPLG8pncnIyKghJUmBV7eOfzmMIadmEo9RSiIHv69j0Djz6+MvGTQ0iPYNxw794v/ti1p1C95rl3WH1EBqKGtqqa34F7gdq0/dM2EfDpea+ostWRl0L4sSY86VC6y4LI2DfZhPdRX9xYYQ86kRfCwOWTap1KcilGN3G0iXG/H+XLxBZdssoG7egw/fIvba6xH7MId7lwsnhymwbPK76HwU2+wDphjHqY3JNWV/786j2Iotgsytis1av65fICvnL5Hol18MnqqPrY2dAHksy/kbxgr/23zgfGxlPJt+agDYuegP4W3gBk+0Ze+LfTwr65woc1gpwnEmwWfAFXiVr5oItz0xybSsQexr+iwrw67FB5rGm0URcvm42ceYlAKTxUZEdteQO93NORVcxPlKElqIGI5+O+1S448pfvvkAxd2nRJOb2HX+pz3X58gw2JMScgwTkl6ICoUE2R7EVEgK8VSbCCZQ7+doY79VkH3M/yb/S7Lgn7e7a9P1S7os2AmNWSoMjSRW2UkNuAZ6bbA0+83jiE0oPnZe2gNSrNmjOjfDhk+/rkJ10+wd6wxjhR+TlTvqNNvILgfG63EkKhtjLSBgc6tKRyiXQCqbw9+bWcWn8gRJrLt33Xz6dnKGKLncUl9KrxutNl2yg61QgkhgbjFJigCmmHPwF7JPT4UEpOYW+YElefNyPHKC5WYF+JCyTD5+DLvTI0pEqiCFfHJAVMI2QvkKR8mw6IEmww4rxJ37WY8sOzBsLybFdgjeTBD/6GNE7KKw+vTH7RXejlHGCaCIzUtoNvCCPkIsRuwgu+xdBoenfTGQOdv33lkCFkN/TLcQm4RvDjFluNh8oX5FBcgtQzL6Izh7NtDsJao/3abzie9DiTcOuLyMKy0sOC3QsnJE2JNhvtYw0ZZTbGcKVUUDxz5UFZLPIeTd6rOAGkyP3T39aQcoMw4ZE6+jd4gtEPTkPhLaYeKpNy9zciKbQ+5p35CK0d2jFxsm7f6VH7Vln0IM9LzG0Mjt5l1Zu39VB5eCIdXRDk2XikwGJsiHha6We/gVZ3CZy1ysLNSXNO4978GqW1PXGT1Te3GOWDZjRYzIdTr6s2vM0O64/pd3WvXQ4XeWwrXI3SytbZ+cObj3jW85Blr31jAsIHRfXrocXmhJaLinentcO7i9fZLysbp5xAL50PsCstO/RXNI4ib2O1udbcbVKkbLGfQq1vkjYVmZ2PWfFhYsPsojrsov1UxJ8t1QQqkjUz2AH2RLRhAm9r8a+Nc0fMVLDEPPtePHzR00Ae5y1gJKl/3N+hqqVgxlm1dOBrK6vxmPrzVuYjhRrLT7FSTJfLzTLKI8Q5XqGAEYxaWcllfOhi4cOIaKCOidSmbb8sZ4w6rbKXMbwMDv1rw4wdRXtq3DRzdnH4bwKl1pQyeios+vP1hZArEfLXj3NBCwpgX/hXVOsRm10Ec1cTePmZHpux4AiVTyVDbH+T81mmQyM0K5dNAMaxtVr3ck7qhgyhZ8V4Ph33+uFFyqegeUdUTemvMzT8O+ZqNinQtVmzTSPdh46pvhWD1I5KPLomlB9AoD2Vg5sIdiIHt2TXOoujmA7PiGIZV5AcIuFjX48MYl5f8CqS/l+sXqMtno89mnkdlparAilHpx0eFHpqT8LoRfjfW4FA2ZruR9qsAi6eYrNDfzpml8wuPzvu+7UVs0mDHstGaEZApjYeZyMyu8pXn0kyym3AUV0famxAhj484o5iK3he2nIWdkensAYFZ9vEc/Z0xznk+PK1lMv2FaQefZIxQmb7TQqyoW677vh7eGveb2pDXkPXSTQ4FDGIEq0flxQJQl92SXIqJl2LfUJukxZql52tw8hvj12Q48ETBdSsLgfNF47te215wCl2p5vxnjwMDk1+RkYMJK9ADon2EAyGqnCzQJWb4ZJifw5sGz49RbXrpfztNnL2nZompqsB8tZmthiPfWJI1BPLcLUwcfq4ovsnjYZh0g7ulktiJLkCdgJcOoBY0cG9qxfUCl8Bv+lWc+LHefER9VndVhKJxPgJDELdEh53WkkuY3tWyY8FvOtKK0oZUtMRekOH891rRIx8woefkj9tpa1WtZbS+3QFVN33YBU19j/hGee+xZlKDYpoqVo79MEdizMk6FY9KG3V9uBNN4YoMHxVjfUJ9WNrCpdZTq6XbfJzTWEl2Unr9JlMOtoWvEVaVPviAqeauI3kBhL534Q+bDAJ4QzPMvFq9/ICSVapJmcOwjqpWuddVeofUHpH/Yv0J3agIviZLsGm5C6ZoYix1raS3dOdTUUgedCauRVa3itSe0z6hl6HmFl+/8voK+GYDUQ39+1YvN1b67qORkyoarxqCiz8M3yuco/pN6CxpPm6Jw9NGGS/JiK7v5ZpKd6zOOA7pQP7iunWf+53nhENLU4W2ldVS9HlegxWUfo28UjL6hmRyy1iyv67WDlDo28NsShS7w4I7jo3OiTVZ1QGmfhFJbOz09wmzDbLjOmciDqjgyEDoMbRFipr5g4q4SyaU//cao1DIbtx9+/2LLDul6DOj6uCrXQjhYWYdTuNWDzZhifiw0DiihfwgQZlnS508qFSExCyiOsJKVkdzihUO0Ywmc41UZDs3/23iYaG/fx1e9jbW9O8PI4WgxUJmPHnvUBzoZbo9gKMfaUayDwBTqk92D6oBF8Yb091b7GyzqWm5v+vgmp5IYcCiGQ1MKXNyc1S99jY7nP9aV9Wf2nvPleiyf408DhWdggPdHY6OAalxF1OvkSQbaF9+ofzd53lQS71VVDCOvQKqbC4CxVrxuocKbw50pcTr9e7AJashLN6z3MR32auOc5KsGp+HzuTddQhv+iIgKkjvUwmjbxA+pwfjBuMlCsueGdBOYKqqC18QD/CzIQ47oG/UaaK+Ines5EOBEEF013G1t5HV2Z8QbceMoqjHYzwILJbsfS+mbLqS/1BjQZL5s0tAPJZDhCcgO3vrkNlDfFRqGl+jfC6AIKIEtD6PbqLxIY+EM0Dk1mCRB8jJclxdzDYrae1Or3I9mRjzN9Pr11kHnxvhzBZD3r42J6akG5Vo7TP4idKorQk7fclVA93fzB21PLwEZsifhIKx97YEpRLM0k/jZ2dcFC80IDgsryZkY26WoJY2jX9D4mnuRqMxprnEPolyURAC/+6xXzwA46F1vJyMYp3NYpuhwQOi/d6tratOrUmK5Ms+C8Ib+BTLwUI+trzhF1I4qHYpzd4rZR0Q7zLDnie3ip/sI+NkwzpQzlhkXtF5qqAlgFAIAqevKnaG3ZvKgOZRUpvIvievn+5JUEgFITN6367ppMric/X1dlP3WgY9mxbe44Jt4cv0/RGNPmZ4cUjP2/C6/JVqSh1pSnIA5QmHPcH8mr+cKDCGtziHDxW03rkG9oFcYFt39nyYJ+qa85sEjvctpHDI2Z5IOLIwoexdST449vgtPSC1xsjJkeaXUOzJJFJjl+4OmZvp4gH1n1w+beg4V/iCYDLohPU57zBoQQ5JJMGoWEgvgpC+QvK8k/tgjWxfiGTDR5HiXroUvGvs85vJwEnpVnFCe4B1WCwV9hjG00IVtWAc0aGil+XWbJ0ojO3TvO4J79nXZ+9aw7BPDNZBUHzDfuWzBi/penz6H8/+AD0OLVloXpS2k8VRwFEnPjk9qFFQ0JEYJpSnXGnNooyQ34FkBRj2lWDF+KC7EAim6a6WErLLcXwDx1kIO/8H3WL3JtCJRAlSnl/RYC2qqDszY60urbPIHhIJ7kYd/uC2cp3jmVpDbFusxqXnQbx1ZwW/o9Oz25du+XylnPklH6ywY5fwTT8pbN27tENJmrOY4tchxdQ/fb9894pA/GZZ85AhW9xky9QdXMTXmr/pqTaO5btnPPTWl2qG+PPjXwJffPGusx+El2dvRixULMeA/dhS4euMRplA8AaWuNeA1g2Wxe9laVkbWXRrUpvtzG0ViMu6Kt928s3ZGJSY0rRO4QfI8u2TqG3txOjb2Etjn8q28DozV9YLrnBBgt8+dH3mHQknYFQ2LVAPmxzRu1ax8PBvGumLdQFk6ZgkivjRuUmJtGg89SlQTEiIeETitPQome0lfQ3t9DHWF8NilWK3BNECq5CFA8rRcIY7swtSWX9TzYYIS5ars1aKUvCY9sFfzOpdpy4+FCVYvbNppbbPPgVRbGVXssonTSAH3Kn47mCZvxIgXjs1o/Pf/LTnA1ZMh685A34sMFl07Mq/DaPv5+M4kkNHYrqLL+OBVyASiEx93oNsM4guKrJDv7K4XXQSB3lyqFWPCzg+yDNN+dMyQBbkCzPTm7gMHuCQOb6H+jqp8uo2JYHNFBKxC86DUXrEQniG81xNSOEQORBL2c4sp1jAQwub41k60aldah2XADNIRH2q6jJWMZvuQqprCG3FoKopylo2lPwJQrmSsM97fLcP9d9729zNPMbk3TKum1A5LO1duYyxGqDjHqdYrSxOoaWFhYZt/XERqqvuYbHRD/TqFOx7vmPDQekSNiVMLTKGEbgu9tNBdVBAzAeAZhX7DakUBZvY4WJkMk3jXfrgrCIbBiLWONagSAjNdfD4MWB3RNx033EeNVt2g/lIT7oK7ffJsna+TTtmgQqoBoHxFygeCJ/jpBdJeJtdmBVMPacTXNzmwVJbMgtGjtHTO2d0szxtoTcC3bulD8ljKCbkQtcHHf8RINsYbKE8mamAA/ttY2cqI5672fO22oouOtYN5MT9lerYWzUkSQ1dtTrwWv6SVsBAcw+GeLKZEUTFHJDYXCoV5NiQrqtR59kKFLlWY+AI5MiY2YQEN/lrhvTRplPBl5PHAcN00mTQg1UYpwMOEXiyxLqM8DEEl4UXRo1/c7NbMnX4Lkmxbp53xgQHCQvVhCG6gxNj5Kq/h+DrRl+gj4k+NPr7rc85EwWUYgO6ImignvSsbnjc8KISf0RRAud1sXW/KuyUvRRgIEHFVUWyRvtHUrdVHJiDQbd55YNSEKXTOA+gXaEvPa3vzzfY7ZBmzcvsuBRf3pexX98VEmEmCgsOdQL5ugV5PJPbg8RGDt5zuweOD70+ZUDlMbB+fXHy5B2j6v7qII1nnFtexbPXzyT9TeXF04db53gytPCIf15m2ZHbelIvjnH2dVlCTeSF1mdyCEHsNG9WxPSJ2jhUo/q6bmeTDwYGjS6gzh6j7tFJe6T5IkUxgWZZsig0cqNAibR4cQB3w3FO2mvBU4f9QAbJw0rfMgsr/nx8u7whgm9hQOB5mnVMRqhzJrsgm4HOdVcZfDUkdD8UM+Pl9Td2nOU+HY9PrfqVfy0l38ZEfXM6sLwugyJP/Ky5K5F0SysNHat2J0JQx12CPVDJm1M6fkpOtuIuUIS0KVnhu1+BsEuft89wyNYP0D1+NJY+96sFppO0WEAh9OdgSsacfpsQkSjuuuIlpl/PAfLEBzVyIBKQCxbLwcUx2MfiGbr7/NpvmYgpwQk8fF0jKY65jHminoAjqt+RgTt+Q/v7A9s7oxSvhpUV3ori+VXEXOpLjT68uJuArI+lZayeOcEbSGX6i4XsPtjv5JfZuyfKaZcOUsnPPn9L3FIaOTMirWAtswwQtuabT3clic1mEvCan9pAMzc8mJjdm1A9NoAThQTN4FM7sIuf7seFoAwZjGyASmlQuAdmjVDkqQEEPt/cm86/Wov4NfaQCJ/fmns+JJ+Lzgdo4rRXeyUlP1DUqtCGBotLQfAVy/BWNYwO7l5f8+Q8ZIjKwzfhaQjZJmEK6tIfqrQwH758cGkR6wW27kxRNBy5ni19ojf0us/A0LTaXbEkcDNjjktWfY49YWcVRx+zMYWgOAgEQ3WBoikQ6kBPIZFh2HJMflUjHgnc+q7+BEgx+NThH8f5Dezbs26FhMSOMXqZ2koau2oG+jxwFTdEgSbEYBIs86qqT67EopFC+q6/UEDPzF6ygpWu0AsrXgZUyirKx+m3Y0NIndvMe5+6Nr9qNvSjB1Ia7542JVtj5NS4a1rbM8X39Ww4OQm2j24zv8ExGLu5c43nNfDBsagzRhUgW8Qu506hbWg4wzcTVFzwRZFhBH/FXOVLrTKURWwXBIGECsNNT/RJj3ZXx2yhLjly2Mr6c/rEmqxCoK3O5lfLF/5eHUV+6Yl63eYCqakmXnuoPhhBtmfrp0wWeX8kCVp4ZADwdhjTNN5vKknu88a2kp+PL+XSP7YTserXswnvFJSacQTucBqxZaOh63CpjMMoH15dy3c6M6lSqDaeGyvDvX3CBnfa+VWW0Io+34L2PY+svzYrKqBp2N3yVwE2nYoEaZnrH4b6hTgevDocwKLf9OYdF2SRTa17ZZaHMZxYxN7jdE/ErybcZH9FGnrSTR+2n/btgSJO7OMzRMTYbTnu9hsEILhQ8K99yq8of7Zlx3ETVz9HiO1QUr7RHK4CiF6kHCbS1dooL2AGyc+MxoMuooH3B7MUE725xeG31udO2nTjNTh5GF9UFzojg4t2aK8pPYiuMbJq4zv9jEde2xfQkpKsyL2RIgL/grVfaXzw0Cz/4JNlF/9d2RYiK9ENJ+Ms/K3Vz/dyuJ9XSMgG9S4fFxbp+3Trq5dz3YVSJeUAOYj/dcG4iWFIWpSekHQCABuPmCMjQNniQ4xVL5xjd4t6L9dRpzR1PEBqexzsMbqn9rellwWxj9iSAQSd4rcsgfzTWkARFV5EX0qECwl0VAwbAzOyvTcGJvkvW1ptsu/OAn3HBQmKcXMoeAADfrL29vfpZFqb6GMrnFO68KeePfpQGvjFZz3+5mi22qKbk31Eh0LUdmjKJ8+475IAK5XMLpksi4yRGQSOjcQvL52DgQADTV0u1s1g7kbj2F+tRGH6EGPBjMwrgfqDpVuI9QZDAUz7uMeTJ3+FF4VcZYVlUle45DhwZGvDcB3zOgsg0RatAQb91YBMMsTycobo7a2bgKq2+2qEUVpeJEkiO/OlPuZbrs2yjn65fL012N8wtceQiSK/oi6SlRm/I5rTWDP6GmKNJCb9WHkdvPT8XJWgDNOeWAJKvvFEfo/i/kYFm51VCL7bWPpY3u63j39k2tBuD8Yo5clXHu6E9MFgeTZ6dko8DdaHxEPR+3ZNHIm7sT84dDenDITWvgDX88A85CzZXRxBWAhWHiaYIZJ/yQmTWVsMvrek7LRL91cmRzG3hqmD2ZdPaEEf6zaECkbLpX8RaFl5H7kdQhXO536QuA7jMShicd7GgXZoHmM4UL9xuU/EUKzXLOJ0USSgB2pXz42JPs6A2zN2JvN0L60ufxDM2mgqkHGaa0lrobLP0htnj5sg5flezE07C3xZUDMo3V1GAOgjVvl3wH69oWcGkC7NW+WpMZjZQ1Chly4x63JS1pVsRQePaq/P2FIxHq2G3WqDDfFWglait3w63S/ct6mN9M5AoVnE3eRz/dJ0Y7LdB/LZe5VJP/bNN4/vLC7jNgK/Z2jvx4K6PO0j/rAcnMtu+CSPmNAqHf0cJhGLcFU4BV6qDARsSd3VIa2r5oEKL8/r0XV//gF3OWPTysloz2Xqh4jI4cAb7xD+vhnjHRjSSL72B6Ps8a4jM/ZmWbeMeYsZPRo655l/XsUIx3vcfU04Ki5oS1kq8zLwVJLIeZi3KF7H+5duRgK0cZmLYGhuLDfofBD9h7lP/zbFXqPB+GeWKq5Wu2CTCXUo79wz4keP6vL4DsHrzATtqIyK3WqrYC5uKWwV45VlYtpzWV8z6W7Hc3lnqSGegIIBIfwAklouHe+l72SRnOB3aVoEsZgfRNNvhWNcooLM8d465JAgc47VejQqoD1BDptBVKMdnF2vv80Y8WQZ1V0RZQhn1uOQISMnzruPgWJHv+DRQbuNSbFiIM3keiDblSMdh3rIGiQsCWgJOCPzCrBrCt2/ftHffPwCxbaN59bfNMECnFJrvehdUJ3tEyyow+AnnK16p5pZgBQfpo7kHYLNCxG25LcD1P7IjWTlyy9TEt9Z4ChMudtlUmzOuURZtRW3LjuMo+fUuztWtIlsYeuSrNkgjPiNHXUHWuRsz2gkTniIp3++FB46HlafNXp31+7dZD8BN3qJOnGCWxBjV88DHRlnUJ0dRNdf5JCfeEBY0x1lxvmVcyQDolwAOCZA1bG/wxxxZ7MyE7k/lnqlM+erlWMD9sgHjZTIzs1lN+DZvjZ4Pw59UA2A+P9qHZM66w2OR0laclYJ7l/eQdhzRioPtiqPaf3kCYzl1obQx8ZFkAkiQMUaTBSBkBhv/ef9TVYXaf096JeEW6zx+ZBlh0/Ictsg8wYr/ByjzPYQfEDldrsPYYag49bKQJ3PaL8d9UXPG0Ob/UpfOKIdJwV0g30Iq8yUPveqDS7oD7W5iDEprwCpvvKlFUZ7F9VmrPdKnEKcrN+dp++EpKhApFYEvMNuZaguPoW/MJUWZ5ItGOrU3dDHAdqfNsGWhi5AitZesXtVget3E5qbvjsDimB6fA1srLBSGvq1Z59a4T5AV0C/xeyRFLrR849UNA/MnKLjJ+ZxxPFZUbvX2W9B0fMsUR1jW2OQ/zWien8Xs4O2va+BIp9t7//+lwjeCWq0JrVlblrhGWnOIyPvyFV0YhP/WreJrFkhMiqobEYk9lXtqFBf9m6UBEEaXMpb7nKbkTp26Zq4F7ovw++AdRtohOI1m8G6EHV0s/+5BfGx4eQqZsfKOWUOcb5MRqqEIsEqpBG3B1OoChuoFBD9cR6rPGEcqfa3zz3MKVsexpT3Bk4HK+lBmWaEmKbv8vn8ShLMPBSD87eqIDWm1c8jWg9/jakJeF5YABt3yE/s61TXA2kE1DmSqwt587UjWfC8WlI/XVoA558g9V28zfiTBsECWGskrYYeXcyFJKuuUzDpSfTXGnePAFDnjseU1vi4TekOe0l7U29oOaIhqyRR7hfvlhiW1zQbPeJsTBzYwVUAUY1tFv1Qa8EVlPMoLCLrf1sdXwTYiMmu1F30GbibWY56McQBZ5lJrKNvFvdR5m6Q2X7d4FEd3yP5N+sRZyryTZtfzUzsYaFrQkw/d4+egVzsyiWhZlEc07APGGoExdkeVs7IXCySaMiRZEQCeSe/fTKiIzPz6XDn8AEhCqNWu6lENeW8oC+CZBGKBCyYdrhKtxIpnB168/Br3E3cRPEi36DXla6/bG3dS0XWOoJNEIWFAXz+dE5w+kDnmyj6HEiSO+af08GdgrLvyjg1ABRvtKDGv9uysA3jfg7IeECux8WG9hlSZ08x1yplJTx4Lfd7kk4e5Fu6OveeCZgBT7HyF5ROhByEMQi82h6allfGFlZAVTmWg/lgLHviHzi3IJ5SuF4K/L37bT+JdN8sI8NqJ6p2B6K8S5UvdbHJxm8KkUFkw8heeQhJRHitbvisAJ2GJbp7hqdd9A5FXVvguu6RyecV63zW7Yyq+aUMjLaWTganx8+h8MHhum/P98ScSmdGIOOgtR6eP9/nQhSVmFhg+WX0bS06ULGxk2NOihzAZDC12WLcNu1J5/LfPHmH8C5qV5hKAUrEZLwjClCDYQaPHMXpK2hObqEZOE5HB3oRtE/6yIrKgZDUYREgTFPP9C0xmbSpkeTZHyFjMcMGp6tHiJvvYUERU7jOV+6Iup5XbJx86EgFsV8bZBk+SACUnRPc95nmwOYZs/NHAx9wmI6uc0PB34sBixc3b1gewMgaQes9A9/W9TG715QuthFq726YZljISk+ShkJl3xSmFRqmuPX7xZd0QJM++4BofQXcRHF+o7uoildudmb4UKbwXKnGRK7VD5w1izjwAVdvwAOaWCTT6UE0jI3Or5aURipnNuaJnVqb9XQMAh368Vth+tr/pyvsb30BMHZiMneVp4uO/q2xS+Jeppj2+0XD/beVOCUZvLMdyxqEDF3yuos2RrUvdqY5FXQosy0tvZl4/uJFYbr1zLGQpmeGEpIcg9YtufQUMzYsVZfWYGbNc+ro4cGgAqNTUVi926WNv1YXQDd3dhFZkSjpvJ0KH2AjATpVWS9n1TUvGCk0SWyvTTNgIWZ4ulavtIpv1+HcXyrAvZKTomhsbxY6VjmS9cZA+nTE9IQTnGpTJQb289sNs9bpTqhI5dfzoE/t3b7k8VvORFR6D1ZFY9qTyT3YAuaJhjOFsXKSafl6sG6Rlg5e6wEC2CFujb8VLL8BwuB8BMU3Bz1NWOlfWvLCGO9iU0Um7pF7VbeIxkzECD1CK6QnGFL9IurGvqXKxui+ODR8K6J9z2ZyEkcotdocDBdtUnpppQdtZ63zSoW4c9h2axqJ5W1af+jWAS20gjtPWOZA3qRPgLIoSAmRrVd/F2IHEngR/14DerqBR/J2ErhqAt+hG8RRFuRxiKYM8zdM941F4nAw5ZdBRCI9K+n7w7KFQqG0v597sW47pVcyjfZg04RIjBTZMUgOtjOUQmD/j6br2nIUi4G/RA6PZEwGk9/IJmMyfP1y3bN75mGnz64bcyVVlaQrxT1SGm3xaUMQnWQWE8q9N2PdHfcYDrXH6Z3Xzja+ZZZNrL7ZwiJsGO8q6daW3+qj166uWwgY1S9H5bU28zEsQ8fbd8a1kG0Gr3YLw14bo5chcZfNeLVxin/FKFfd5mtmWpCqVaotI10dY607evQLUV9U6Hgq8AQQiCErhdOHbTyM/kZohAoCCvj69/2EKD8LrnMkEgRezpPYq2iQK92eBDn3M86t2lywb2YyEbaWVXe3bit9DCpB1tEw+Yyy7Dqs44RrrCp4/2UNxCum+wKZH/lbjQ9XlPqhjnTQpGGCDbngUBQLVzWc8kC4vfbrU0xqt0fiZuY5nb8HEGAQstq0+Qk8vYBdciKnNLWHr2hUicfuPGQ8BhUP6Y5qXYTJdOvoI3hMO9TTIWXXrzXSVFJfEHOScRD6TtB1EWr5Ft45oQ3QSvC/fBdj/W5nyyiKIIcGh3mnflWEOTDxM3UpHO/yhHMpt5MqVjSPY+x9CJ4/+xKO+YslObFs2dRrjFHHZNSVLoedHJbLlnFv+gZJkK3g4hNXuFjfV6+yt2v6UEUnuS1oibkkAq4L3o0imeCXm4bPVFnNM3PsYMNpy9oTyg3FQkw/A4Xg35r8vNb2Qk7QNMzqi2Ry6EA/uA+PtPu7t0ODh3KtIULBygUxuYe2c6J+lJ39oIz4s62GkzIxfTSSxRf5Ixf3L6HHqtFs9UoSy0MmktZv7u6GEl5/NIoNeDA8XP0N0bF2C6cpopLBloWfDQUWarS55yXZh2CWhjjNdoR/HOYRvamD4Ht7DCEQG7loG2iF0RD/zkDVJ1DLdA+Lj/lLjqDpRRA5Sp4BDjCuyP6tMuOwTUOnK2zoQigtBeDDe8tn2f2t2UHpDo6NzFr0uUwTclXZ/gVAn82DFd8WJENErdUH/7iISDpLhckf6ZSZP2VXT0NkvjkboW7mE5d3lcve1AIqzULGIhohHFu/xc1AqlMFLC9+TC/CxyvJyVqRgLs+SEpkuwMN3/fysqfEbbyk/oQKzeYFgt99MhLL8iKKybDYJwbnNYZcU6r3QhpeYekOC1jO8W2JppFlWg7i49BLn+33M5od7FEsiDIkhtjz8ZfmR6MUZ6c8RCQn1k2tnRDZeCLSv9ieyZre6Nt4tHhq8i9+WT3I4FPHzyzlfn7r5pTIrw/3REp8OykPaFoMgRFU4EpEBN+vTK6HOZtj2qLL7n0l+QL1XXYUqBR/NSfStXyWSvWOFAQFhtFl98JABNGVFNhZwwq5BIMo86128i+zxx5g9AG71l4lX7+bZahG4oDGPxZ2+I3rYQ7O9Q7VD6bN/DL2dw0o+kaFObXsn/dldUIWj7EgEbzv2bZeLCO5e/3OijMn6vdNLNZv19WcYpcJj5fT8GdEsLOz6KZn7nTKc3ewDHhfpMFaFoyjpBqn2DqsphfrhUxkxGf/WMcsMuM1ZCHya/soG1UvjjG72Ii4e8Gl5Ps7zNHNClQcEuYrgLgvVDm/GTWhwxR0/ujZFhoUlqEJ5keezE1e8lGBc7cyjOU9dkKZTzECLkxf/dkznMYZvT69yK/lfFOZqfubr7cq/9WhxO5Dw+7CATr0hAtANFwLidwhG5dJGZdZBvBFUN+6A8EUOEHNY7fMAmrIHhEmdpglX5HhpjRD3nYKpsu8kAkv4LIkC0W3HXSLwRlCIiUgiig/xAZYQIFw8eH0kKhmuBSy2C008Hzn58Kpb2WPykbHlLjIE4n8vhpKilHPTg2pGmIUfg84rqh5ZpOrmKL7gAERDqCNZa1vfru5678xmAdwVXR4TgCKC1Rymp6ky+LFMGOQoVH07p9UVx9fn4ZFUzBm6XyvDfbL61lJhx0UyNh0wx15ZjmhtTx8CZrTWenUvT/M+QAoYSELAbUc9C7+NTKvOLVLuCG4Od7Zz3kXhug4t/aaFY5n3tfi+PgAMj/SiY5rwQAtnp7n6VVB+kkKqjfgwYd9g+XHvTSJFOFtibmVO58Vc9ZG9ncTgTQq7YFUnfbCtVjOUFsYlcFM6Lz0FSl3dFHZA9x5DGnmils9HXfAmQo8SHNvyqOD+56t+hIoEwFUOjzy9ruVaq7NhOHsum8QmXFkL3+LWx6B2JSZVINX4fv1WZ+TSXTtgg33axx0GfJmytk7xt54SWZIXAJ+G4RVTOp9mN/2VmGSl5uvLBM+Ap2ubg9e8C9fFBYBo4Fp9iLq2Iso/2yQIkEau0TH7xW9WA6nnUJW7108bRHK+6p+n5c43PCjnDQk4Tn0Giov+rWykTs53WABjwgqMfNWdQgS6WQJjcHWyxT561wvajSwFd8dygQHLA722p4WfmKHMiEDTSH4BlcsMH2V6dcLexjjnLBZeeVrFGTXvFfg4OlSiMrDVhhyL7s7f9cTSXwpcsxu9j3oOiGBpcf7F8Sm3OM7hmPuDiUg4F4AIQG80S06n1AINqCIdFJ+X8tv+QpC5jxvZzvM77j9ZUoxXuX62G7B+VBXKaIx3f2uEPz9wTY99uj6slnla1K5NEnH96F9TtoGtiPRYsekBMv5C0XsQogyxRY307BwP4Y64tsdqhSfV9G3a5C79kqUeROMQ7H3Bp5ciY0gmbrpDSnbe37JMplpUrCUiZYDuy+AqWstDJFKgfMsViaLbSUc4eglvOBnrYKuN7HsUeeKFxV4JwPDr8+Ifd6ifqV/SwzEaDSvF5GqVvBG020uKbJFYIUMFyRabW4m3TR5v9LVQKhTc5bvB+Uk2hXyK83R+uFISI3EMGL0hA6O0B/+0pDsrEBXYcjMvAZ9aYjD+97Mqxga4WDXq3Og6G8hEmKsXSHZd+KGOR2Pc3w2A3P1IrFD8ieuJoj8XSnv2iKGKOshD03B4VtLDFXD8eAVtRQLbfs7yNfiIpkcSous721Smk9a5ofWVvFUVBCb5uWqJVGemwcHgkmGYIxll2CQevq6mXpkbiohYSMYvd9Uc4Mlh9VDkZWcRu9aBRVJyhorfaXq/PI2LAXW/etEkOD+/jZ4A6OCs0wU4C0VkXLQ0Miq0WI3OzKzs0LbTYVdiAKdhQ4Kfqty5OZMPHFOk6Q23oYtSfR3u1A6pKYNfvgVoHNlg1p8B/4NAw5NhXcR9rnFY1uzDgb6LnYM2rzLz15tl6Hne4DfOUM5hCUMKMLkFPkRjUxAhCu3f7vWTm1qaYxHG/nsRrcolIpipbXDzKX0rixdf/ndqP/ErbHrToD39G5SOsSGN15ZNhSWVWw5IA0vRISgCKv7li2FJIh+ZI2tb+l5uZuZQRYGcYapN99VMXYaVUJw41RARaXKZ3xEQhYVoPhGxziNyR8OZEp/u3D28FYRzU+oMhRJxg8nBahiIR/RfJppCsR7rdpI/Ix4I9fHwHmXel4/5OeYv9hHnokXbaCPB02lBNaqi52JMAPcRbmIsl1yZW6SP2RWp2oLJeJ7+hJx7lP36AnI0H8Nd/remrnNYbfjovvZU9gbjGEGjObSLpzup7TMe4aJJbac56WPB+X4LZQXdgbp6/fnjA0YLQ5tibqJbPTRF/v+O4xbvbv851OPVGeS4yjBn1ulf1r1PaLLl1rgnWoyfms8n5GP15zKO1uRpyLmDkpltwNe1SmBqRgikV53rPpDCH/2oYTBcNxpqHmbujFSdH/zp4FpIQzdHHgKSBoYSUkRGT4comLzr5BcPnurs8pZhsxHkXih+lSkTeCb4hr5kNALqBGvUHjwhvdweeiQWZAYmR2JUwld75JXZLb4uccWJaBjpYuzhBoPo/S6prOsWTTJYrX2jgyPZJc2BuAAqon8rjwRryf9BKaDM6Rhg/KYMDlW4eLd6TfFtDeheHIDQSyGW45a8KU5tiG0fb8LK9iSc6hwenJ53FNSaxpVmhs0c/f51c/jI/EQ6bcKF4LLgdMr4UvKoJfAdfG362+45ccW17AEL3g6bfb7o0ZG9ms/HsIcRAg0/dJqLjNUGhwUyNTZQbOSFSTj/i7n3Qz6wcVJZFks5MmQzvtas64PISCIReA3aCtLcDQDgyo+Svgo0xOT7MZ4Q4UYWamawLeqMVlnYfOOyIP5umseL62erulikOFv2Wi3h4/b4r+pIeAhdiaLdL8r8nuMRWxd2/gxzNRWF+An/ZzFHyblHR4lQAVbxO6g51LdN6ecaAwKaYZg3DPGw3R7hvASws2BCekxssaB4UpeFTayQImzEEFVNciPLBZIZ2PiwMFCeL3nhRLnw+B4DwXUVeuoYn/+K/H4TRf47Y77hLAvlzNA+/2PagHV8CJu9A07ifej9T3S4kgtL/FxCppn9RJ0ccr1nAfahyNZ5NIS61EKL2snn7Pxo17kBjB92UsNNEiMd2wHSZe+uF1e9ZYdOqJ7PixwWqJ3wPQOIGACMgi9h6CV+jAxQGYAYpwO8msL43Ped0DX85HwIOz2jOTleVQKKecTk6tk87AHSGgLCmjzW8btxjXPYeQgakC3371grjq/rssFEDM/mHiWsnsTsevRRM9xw7b+xoz1zJwQzdsXrR5JPr040zuvglMifN27S94qFgCw83l7Uo1k7yQxg1DH3LolGdM+lo/BLJUeYjGdA7YzJbXtn2xwiPHrwvhwWkT7b9wUzsy11g+6iNl69HAgCdD+ZpO/RV6Zr/dcrdNAXCeBNm/YS1/sPgWfmQTJ6rvLyvHTVaFQ/rr2wpd0ks/ZyoleQRlh/6Yx2Ibt/bK95a2y+pieUDxqZE8m4HXQwwp7Gsj473mQQrRO96BuMbgqlQqs5Tsxt5pCog50/pgvpZUMayG/2W4vxpFESS/JSA7Wh9D5Bo2RVcTGoE/lkm29JTC6XrXOOj3ol70AhlTKfdkBrf7LauBWb39NWHoCFLvymKpOymqiqLoLSGAYQ77rh+X4xuuIosk8By42fuO02yu1f0ukCn5c0++Lv/BA/g0HnX9r7x8EQRJkmstcVFjSnPy9e9hFru4eEpZsXYGmWZYKL4gulHQRPeWPJwX0Edji53vY0WjvcW8g+6fdntDIiI5SaCa29721OY0xxMzEWPARRLWD6QWPHLrsTQi51/3chYPuc2pIYTRDj8yb7gXJ5V5bNvpaPBdEWYFf5bgVUiYZz6NOaZuxiw79h8xoLWK86JeuuOr428VI1PsBOmek66FfQvgyBaiEW/BlcVcttJzIQkLjdSuto5ufFzfi3qBEqVdBTdni+gYe/GKtS3ggaQVKq9inD3Pn37Gn5iJQzYgkAanrf+O1DXphXmek5IzMWeH+el7uwK3kIsuADotajRUOTJsWCcGagNIAHVEqJ/UBfU4YmNo1GuUZ2CBtwBkOeWUBeROKqM+ahn7jmxle51G5HhW8mLc2c0q1EMjbS4mtkcaI/Q1qbx5/5kNfuAdqwSuvcb19W8LoM7DZl6lZ/0quacwCbqET/NQJ6NrWShTzwYL6gQz2Bzth/KY4jt+nwhH8x5V1VIOoLaf9i6ZKhf5TlX9/MA3L1mUzltf4UA+9ak1Qj+1dJEXTWyvzXF/HfOr2z+vopjbBFOM3Ef3xM/71ektoRvzGUvlK/DHNOpc2q2Pvdbfyq8eUI+qlttkod021lzu1yIbdXZqq0yFC5X0O2PenqLyxuNFaGqzXI5vbsi0yQ9mNNl7MeDDIPb/uA88uN3kFYR9izch/SxjuQI4xQT1lnhkhjnOdHR7GNhQYnZcyCm8BDsL0Krx9QMJjEx8ID2QEZh63Y+dyhMM9zh1HgR/MM5sbfJz0zDqE3Q0LKa5gOp1jevL9djsKNwJVEzJJLno4Uo8UWl+qZSzDEPpFVdEmOHgZQnLSg0EfJqvquY6dMVcQiEstQ98TUG6YG0hxYKK75fGaKtE9/jqCgc3YRYndwmh4UfSrwRwlRqET5qHrNT9RnH4Tv5RuBpQeG5pjTqwekuk8+e7CCqK97GUNnkRQPlNwfUHwyIXtuVqyvBGzr2H+8k1iNBq7t32FL2CP/eFM4W3ekwcccUxgiZ1Rj37FB6MNICRyP0bOB5aFpkBJOiVtzi/lMBzGZtmo7vMzypiHq9vKOM5OQdrMdCjXB3Xf4BN7m0iEAlNu1vEA1klHFR0VihEeggVTs9EE2Yeo5BFNpO1ggH0ZzgCMS0IGuWz7ZSNPUAn91ZJ49obXBawRZbkyR9GNwcKwsqGevMmDM9TxIXwFg3iIY8vS/Et10eYZfnqBQYM3XtTFRU87IU2Ff3nji6jKip55aufJ/SH/Faw5b5KuulVSJbbidE1V9VfKaQ6oPLCB3JvXwJWQQ/aWvaHCIXBgvQS7zveuN4CdfDfy3JPvdXHxSVRaH4vrSPpLTVsyPs09yIbg9PDxUoo9u/7uH48HMkElXN/0idLSN9DWSXeenLPHXWoA4K9CHohoCwO/0n4q6X43lQSyPpG3TW2ZkHFQX4ebnHoEQWIvByyM8OGCsadClG+YJIRKLZ/3+o1sHZcXRqW/YF4oOxiXK/hSijKDteofEmPyPDORBN70ylxuQm6hCHEYLFnD2DWcHP1wj8wpgtdl2U2lgSw1/CjYx3ipoe7J9riamwZT7MT3h2DuewTp2FyVo/vIpIgYxPTXqQ4iCr6iVwYU0k1j4O8PkpbA9qEr/k1tlfY4KrhxGmBebVKURN8fXun1JHUzvvYt5vrdDbjY1+VUFHG3TZs69VeN8dOymfHwmpan5WmjL9ym3fkAqRlJIxWvbEHqli3LtUd6jiCM9pEa7xTK1sjtXPgoOWfcSEObprw/oUINwzJ9A1o1vuz7VnlFr8Ex3xWyEEHWQGr85ezJWlOTueT5MTDpIog3k+gkZZ8fWJQe4umdhrLyAMJWY7fTR1ANHWocxfeG3wMzC5atpJVVLQmHOcQbGdx8TNC3xJ2GOUlg201yfmQfZIFlRigexp4/SKy/7PzTWmqhl1+VHC8z4BtkL/mXPgiQwd/qzrTOG/kufND3KWACcvDl5hjAlkvoVumpAeMMxttswkBE3tYsfZDLog2Ysskz2Si/VRljD7OtZeXjDg5dLCY5Dl+mfkOh61bm32LKyLSWe+1u7L5+FWU1xTe3T3mXj8rBlaKAA3n4hs/yiYkr9d3XryVYAa6dozFODtzCpytdzRhMBt0FECw/nIzmAAQThePa1OrCnwxtDdfH5QgCIqf8LQkVKZRHgYP/9h0C7Seyw72V5AEGPYvSrzsERNS832yS4XL3qgxxiEFnRhzCrQwAozzoi43UjlWUD0TnRh/ZKDNx+HsTa66SYWJlTghpYhFfrq1By7MHqrR6+HG2g9+YuIjrk+JFmzEn5MG/RcODenPkE4p1ErrY4gCUWN0L6N7ib/hID2PP08f70+TFMFP1/PpjQFLyQSR6+/v/HY96efxcdAdVmW0SZiwS+nT77Yll6bX1m8/5VHopshjDoSeOJCBc0uUgI3OZcjlFKd0pz4uc6g+AD/Cu4TwunHBW2/N1GWDUs7G2B9ownIZGqzXhz3O61lrjhEsib3FIXvfSsygA9jcUk4+iRyIGIg7elovvvFwz7aH55WAiNdZ5mi/MDPrl2F62SqwWLCUCyyiVo7D0uj9z8REsHTbKmdrlOKGou33ohulAa6dxk4u+yrepzRaf13dG0Xu7/6EgHcEE3vVIB8MMU3PGulcJaji2GY1JA4WtkZVrzq0Qju/dNoyAYrtSn5U1sInwOa04Wj8g5ekmkEsoOpy7baHwG861dfr6fndrjXqyX9BMxbarQ1cIQM7LWdNpAK+tfB1hPRJi2j+c1SSSr76v4EuCzkNx3wFr+uNOQLH8GsghtXeYpGryAg7QVMLYqO2+Ad3rfhtX8BL7pSIwDNKn4TIvktyW2yZBXGZn6gYFQyyTyihcXrxO3fXW328Ksa7SWt5fnUmpj29/LxLp7YdVSBpKyL9xRZqOWQ0OZM+hLfwhnnpzy9+UN+BATh5Wajl1CX3vCM9Rs8/L1KRVGmGi9goKo3ksZDcl3c0YTaJ1k2mwOBc+Fg6bxGmEiPIyaoVduj13jtlFz74HQQR8UXkFEfuQS8JQYfp3e0Jn2BcArm2x3JRytlLjWTMdTfvm5NeV3Hmxg9dGkQY/CdOgpebLQgkFfk81+xsZWYVH5NHBBMsZOovnRuhFI+QEaMqnZ81+m55BQh8IAIp/9w7inLmNYmJLFwJwf5ysVN3roB7NZvxht43kF11cLjcPvHwlxyFCstuwJCHQP0z2fL3EOuCv8K90AiIZFIEvQCTePMQKQhofxQlwlrqHee032ZFSd0JhrsnGXE6ZR/zvBmNObXG1pD597q4gy54/duEN6rq/8SGUvPPpXgIEL8EPgWGLVDlUJsoVDTqT3iF3mocPF8Fd4TBLnr5i8qdgHgx/UXnI5pLVYkNMBtC7ndjWlKa7mkT260fxaHknbqsStzIiajQywu0WMv/mLn2JS9hmHzk0BHCWqb3PohWwSfws5MnkO69kqzuHHyeT2jx8aY6FBBFWl0dJTw+Wy0kJi/OEkzy+68I+T4ia247KvnXWXJAQJjPLIg4mUQUJk5idKPMQnpZYcWqlYk6JKL2XzdXdtYX65rQbSsD4B++w49XRpQFBFsV1I/JQbYTOJNnGVh698128C1F8qS0CJs6x5dd9wH8IsD6462jy7BuJDpo0C+q3nmk2zRs0wuUB6AFGtiBeXo6bPW+7+fU7d3/xg5T4J97cwdd/we4GcfLnt0kWUX5T7Mc5T7WzOKpiC9uPqit51i808G+BvShQlxRHFNSIEmaD+CQ2hvryPx4Ek98TlX30Xunw/dB5nJLs9vrM790fQ7TF3l1CsoCbTI2GmPk6Kgh3+F+aBAXKpkdCy7w7x+HAQ7YNfWm5e0IBf8dSzsStthDh3hiUrVInjZBnEU1Il4Su2Y5uETbYY1mMQZYXZO3Iccmo4M87BgkcGWW/iS2rCHoRH7QAdL2EXoI1zxawvccp0Cu4bMuT2DZ+4f3jmvocUbi92eVhejPTdAyCx8wK0jyUNjHcglbmZfEPGVrjEr3oYTsGIdfg49fnEvbqWQDqW1a4JGM8++JFRBYcM8lJo9CnsecSfAu322Skh5E0lnZ491SvUkmxW6N1Vb5wzDbPvFR6RqVARl9ODXXHOZ9XY4ysXzfCodxeBX8h8+3Qkt8qyHdaACf1Ji6bXog+pqnnTPE+z7ojSx1d8AkHahNyMGb9NwGPZ3hIojqkVvtGhisW0uV2QQ+WEeMdA8yHS6y12FWpF3iTFiHVNwMKiItppFozeEP1+5tc4nHQnxujQWqq7xOv9EHpN+szOLKti9mDDuQJa5HgMHVt29ADVlx/MU/jp56ixFFNGCQYPNVyWtp2sBp7AeBw4PsQcHkncqgFRauzorCrc45NCj/gZiTr/zI4nhQSKogdq2cifsme4UVodBUFE3PXtAznYRWi750KOWaCa5T4Jho2JxCN09jHwe5ANsTmSHSDLc4oXU0GpHbsB9KCbzyC4pPfxaSZXA0WfIz2lxL55rdYu4X6+VXcUBB0tdFiLWS0/iVA19/PngAVxFA48PGMyil34NsINsI7+mZNs4TuQpExV8NHyUo+jDN/3fajHyqhjQcz5HLy6qkh5eUZHlTgsMuvF5PBf16sZEjKq5EdRNosJYm/57odQyCY5+NRBayzYZC3tefDpd3oq4lOvmQwIgpXyg9s+76i5ncXJJdiaJ6/i5UeyMhp6gsQggROEZSksSVcQCcaVef2rg6Zjfp6fwQGHWy/K2sQ/Uo6FCN/Q8J89FLeOxyox+nhW10FvB+9kia5U/cmNyaI0Pq7f8t1IZJj+3ycRdUrKyOx1R7I810pVHVjvMa+5ff+/BNjnFj5KL2GN60Z1xgGvEZTpIzAuFk4EHlnqxonvMJaGtNR6mwVvEVMFdFQJFxXJwXFtcm6XZ+ZTY54K7doLzMGebCMXijYfCicE/EtD/p63t3Dp0t2HbyY0J1cOW5UfWnDpxZ6lPTLVsbW/bUqaX+JzZo7955MCRx9inAcmjlQqPw6y6JoYMXdR0G9T/wL+FVHSreSoJRfpdajTh0nZ4YmDkdTAhaDh+V0Ai8CBPIGl9Vwe9qlWkdtA6TVrvfbBKMPWF7zrUwTjPTgYJaflhm3NCUr+fyN+1Muy1Vh0tryFkMTP6sSMx8QyKrZUqjN3R+Vypw5xQ29gig5AWkqoVx7nGPBI+FB/SJNgPJIy8SUXN5KZfA2yr2JUCIO6K+PFli0/+i8Alv4ZwnfW2yF7nyDy/qiHS2yg37A52Bslz3Kbib0MB6BtEgQ9IpiUlvFHbclecQLeH5iXTHpDaM0S/hB1PnqHv27kxRk9U9g11u6dHJS9VKhT3i26N3B90EfDCMW5hJDcOMCrM4Er50Vtoebm6q3SU9E5VIKKvpE9vCo94MeadB4P/1G4dzJZb84OKS3RUwOvYzuCKBWvuXMSueulCzvnP58iAZGzn2bCZrsvNWZlyJiqa//8BwSR6LL+z/zxwo5FJSu6WrbKsNGQLOgf/GXpt/Ft9WY6MtdLoqyBXo990KbP5sXCRucBxEpJPc0BmnT/NaanuAz6fMDJhKw1vJbOQpc+/0i5lXl8VWGbugO7/C1N9KHxbtSCWOTl7RJJ17FBkfj+xtScb8/wf4NakBibeyoazwUYru0R+7Qy0UeVo8WdFlqp0tUET7JeJ0PD2OdiNgedY3BJFgYeKY7GlBRPcYXmQ7+Q9y0RmGMAIELEd4h2NiFnWn0cxXwWZbkSPkNHk97tRY4aU8w/suMKzkQDS3iep/8UoeqbvuSfRgFeD9zZIXIZ+2AxCum0P3ploL5RPBLFU5khhKHkNag0NTaSdi5MP3ZwNemDohBwzx3VYd+dogV/fxabj28AFzezFzFPXhOhoCoSSvSvz404BQDacHDXPQOBKqFFnhWSZjX0TzwuoBzmoD4lqD8ptoj5BWOOirOd36wFDMhig8CO7l/CRPp2+Sm0e+CNse7QI7Rw5gnHqOGhsAoRonreaQLZNErHWWKfFnwuuQ60pxh8yDh/qYm4vkwO+zdUhe4XW6mWV0EGhZBD/+CaY/thIVR1VKjWun2xoa8Amo9j8ukB8nQMxMRPGsMOFWao8j0tNJhlkAXQOMVJtDjWsT6jsLv3ofL2Qf72URkH3qwj5QdWOHRcgYcdwyLBcHF+RUB5oCzdHl2v1tsIhbv08k7J0GbTQ34IgI+me5COBbK+V8vDCv8G0GWKsIFvwGcz2mQfmh7yZaBkWssK08JBTfw2M9AxQsCm7aaSjxhQL1H2Fd2V0VAmEUK1Ev4PVThcx5DzQbOxE0v+WBCkKmwvvINMzbBsIclRQk5gW52kQwFtrnhQ7YAvJdhEiPXdIsA9oY25ghGa80J5TDlveSv+KjYJeyQqYe8xzvbxDr9UQpRGZQUFlCknB+8v++et8pvWzR4czOVUeuBPuquHC1DWJQQRTJho2yeZ6fAhPE2t8o1LGLRn2XS5lyyhcFK9uJX4xsHBSn5PsjdeBXNSm63VF3MZje/mMxwSDZRQ3/pceeZCv5mHygwpx32548okS4D394TSyth8CjKrN4jr5bSzPWrVvRaK4zIFxStmbIyGpIhP5Oy5I5s4w+fBtGw24nuQ+6thNGdHyc9Syp4IYYlZSr0qH/O26w2cy94+NLR49zYJ15Dxkmc4PjGNVyxYGdrx1kotjIGGL+UV6rpeFPo0dD01Qc5hkNES7JUJqArGQv4FN27Ab8qZyinagqXgrmX28EV2zD8yQiuBuUjfnhz/YyLvx7CfQYsaA4SOgcXg8QF3n9+H4Ug9flFb4LetV5B9xBVP8iHO4xlJKwp6j8P0L2tMAKJhnhaWrJsAHEMPkMAIvp7EfpKabiRVZZAM9XpBl9s+93gJ2DzUxo6gB8u7sGDk6fBz2deFntLfkWkzxNMoI+MOQvfoR7/uFEtbQn7UYWxFlsZ893VL3LCC9FZ1ANRJOUOsYF/INg034abrFPAPOdHEjNq+tsx0+jr0chJXy6TFBkkmhaH93pIBZMgcOIlxJqEaJPavIxtqX8sshLp+7Ty+WQu0hsJafcI/XnSV3p54lwIKVmQ1lrNd0qH0PsyFRR/CNyGkDlY3SSScoohpoUiwDeH1YMmbeNNW5kK57c01oVnsnzc0RGXdGpON+vXh2sKE+Ainz7NbSGfA37um3Ddp8HD3R+hjrQ6wO5Kj4JCdMoRo3Ml79gP3ZQ1SJ/MQAkeUBB3p2pMAoego+K0fMVqhusuD9MeR8FgynGkltm8qso8Po8rpKRGXHEsi1oVDdSsnSOvETHNTs3DhbchUmzJo0nmmF09avIWx++OvnfDbzN5Re/OXKWDazDS+Uv18SWtbzdN8D8AaH45RcaxJlDuDVVJ/kAZ9QqRShyhQe2ThH8iUEeTVtdyXazD26dr1PFbqBTHHGOwCYDziOMBEolnzDvAzsl+fyO5S85CB2NqjC4uHP5GfpnVKzE4vr5wT1xYxV/JWXqzTSvkuXfZuMtjVHSMQ6UrW1uvzCdEt3gPT/9N3i4aES6S+CRJlOTrNY20eeybvppx3QE+YdGvz87U5lC2aCfcFPPrjWTnVMmWacKzg8EJGLz+ZBzQV8PGFF6c4xf4kb8XJDqIQwigEEc6XdZ7veuvnV8xCv5Mw1dtvjoA32//b252XuE11AVFhnmI65zXMVhf/dJWIx1hCy6YMt15gZA4SH+IUPd77Z13bX2ZW1LCT8wuxw5DSlxCmrNKb4xCz35BJETYckWxfJKyGBJe5cDFP9HRb4ylSexf4mhCKDtIBaegcc9apNSait8oDJQKD2rE89eXRvTa7xX4U2pBvWgh7p1mRzigjhMAnf5Qm81brB0YgAUIMwoE+7Lc+ASpmD5sAGH0YU/EW09/4QKgaQcUCqyGzbVZHb3wNaUiU8Tng9sW4C2KvFxWi1kU/DcW9cWOP0cjqNwj1JEknW39wjEAICNyvmX/2BlUowu4pkB5pnpcTwph9P2WfEFpZOENkL1s2PeXMb+psyoNjc+6pqu+EUbQxQVvfaZw9zD1iGIHZ+nWEV+hmpiG99jTjB149JwSaFG9lU5vIchjQ0Nhndl4Zc5xA9ly11z85jZgEKinOgtuqwHCpJ8bFziaMg5sex6TyemCDW10OCjMTNBCM1JZNn7z7I3LBW1cfnOYHYikrfd4YxIpw2IiKhOuoPS7WGPRkuS8UXLRZMdFyLnRahAMOJYBShdioW2YGw+JWxpvVqBTzmkqPDqJKYTx/YEb+eWgOMavXldb8pEPacHCrxeShpAu6agHlM14NOlDjSk4zD4PNpocJnb4IhHmHfJ3JLqJndMdrgX5W9AI+mjpFTRRyY7DRjgj5aH+SGvtBBee2WyUUMBW2ITRTb4fwhDSSJT3KrKax8YiLab8KFCZx8jRr+6EzfG9pJD+Rg8r/IOueWsIYmE7/dsEuiR73omQM4v35wlZ/sAEdDcQD1NKGi2vin+zUQ9wBoO2/roTfxeu193aQC4Jxi5qTgWYKJ1fsQW/o6oT8exGFI3N9R2y8tZ1+iJv0+Q3CoRce8o7KV4nWTm24gHIAU28mXOTB4aoDnMatUw7wPhYz8qpx8zU1TYN/lHHoroW7E5b63V1s7NWV2GkMBhgzN6Cm6EAhTVpk/1ciu5V1oPqTsPQENl4/lQoAzRLWm4pk0VJzhDob+VDB9qcn/dzM9A4p+DU2Y6uLMux9z19lV1ZrKxQDdp35q/6Lr/kjPTK95qCBvjJLMvCL+dD9uBs0FRdNVJVGnF5UeW2WMnsgwYttpEbtYWQcbiV+dZzEjue2CexjJ8pxBVpQIuYPvNVubGJHhwFBYev8+iTy09Jhd5lcGdHVPpZKqxQCq7BCIB7dAlh9vLg7Q+8kFMH+hbPWNdgXdfF/vFl7pvQ4wUCSOk7u/v5AIetkfLRnqnCpodpOU0+p9f82l/ITTd5gnzFexlnaCZJ9VNr53OGhMasXzBuga1o4r0mhky/bjbReTCV6OHLmYfhFFsX9fmrr2VcO9T3i7SECK3O+u7m91xtm3G/5+9kM0xgnWS5FTuUqha5WwMwGSsVaI343NgZ8Sl7WSnJKRdh7mmC30QSfk0YNI/oWp86rCNRS8rD2xjZCxHBDjq4r1uV49rNk9DPgv6TFgUH+dG438vhUTZdfrv9t0JtN625Q5nKZoVDySnoUfSNlAEPT3bykTI78xvDJN680YxUc9Fd7ZZyfpwmIyQWRk11TiiQUlpgjY2VIL0FCXRVjvoSKtk6RSZdMvqb2OYFTuPjWDcuviGhjz3cHlwi3eYRTizJbmqbB/VHMuUeDPscArGGgTkIfEFIfoXzE92P8Ny0FKvOeLSmQdOvKfVuBKVwDQ03H0EnzIM8pZTcbXhj4VXE0f408hvZdvYs3y4b7r8zpyziCR93H9EL2XGGGhuF2RNH3AovZ40ErtW8h/wbUJ61cbCl9EPASx/dElC5wTmNIDLq05NixGEOaZAQhzOX1HO/WeMFsnhNWtiivPsZvtBfjkKBSSbUQZrS4DAEIc56/6qI6NGmqQ6AkTU35jEJP7cyobBn+94WlQxZX339ZoU4NFzlfiV4oPOhLilq/DU6W2hgHTjbFNC1SAORv9QG3BjAUo8rkAFA75mcfvRaOIb/iBnlhnl8GUMME8hCPqEMt2XWeIlbC/wV7+scbQwb7izn3lgUFjQ5UZPiw9vlQ41r333BD2GizXv3oG8+mmt2LmdjAL7kZK8kctsv+pmz41yMnuxkW/qtj82ReiYn4CP0F83uTJYfZRKaTNBAy741KC0BWGYDykfxDL/2jLR2m035FUHCJArRt7L5hkkYijW8pMd+SxAQ1BgKNKegxqiNhHSV9wXcSKHDCf48SLZ8UyzrlRfBKbQx/SrsNxiDzg55Gf7GqlcSFkmNubh+8qtt67SB046bIzztvciFph58GyF4Zbtz/myaKO2ymEup63F5T3xwbkECV3iBjyykURITPBZHUVqdk02g8rJLaoC+EGlLEeQnWRNbDmPSsAAgFbEJLyvn4K2x1/1GspnPMZXfb7Xev5ZfgBS2fmuxBx85La9TUXQGgJeYcwCnIXDf2oaZx/i730H+S0S6EqdCv7ToEiUTYNvv8pYArjLuSm/poB7iO/TcJ/itFiQrFtMale5IwuWQSsk3uuB9QZSWD2UWO7Zk7clkMiN/mUHn+l/JmkDLCVuW6fZIoIBHSWbvjN+FuDiraidyCWllKT7wargsL2g0+5vnPbl/c0fDem2UmYe33k3QJeOhqZXcM5z88OxW2/VWnF24NAY7YY2kf3/1VYM/kfl85gg6ETi9WuTKpCSOiM4BMT7uhDhmpTpG55NuoWeonzBLMiemnH9XVIx0U1NdsP6UXdA1zMP/7xs/cgtPsGiaJv95+6hFHuDjT7kPPaNtXx9pmd/tdBjGnuHTegSLbGcUJkDvgHr5zvt2VUKsSby1KPnQUpAUSubzbX1IFyEX2I9S7zJea/rxJMYJeEiINyTLG/lSvZteM+Lyv+y3ocrsYZhKD1VrhUzGy/kmTTW1iH9JeDzfKeOAzIa/rAnkUwiTqDNFI26yMH7uhhMKse9KA0cDgPmWmYRFp6IS9fRTmHBX5bNp6HGeXEclIUmZdyfNnMan53BqHEB+eafru3nvdJieBTl5S/ynpfwOZqEXX2YlodNmUrRlkN+gd1e0P57K68ebNeZweGdC+eFJ6JYKMsQwEJNujL5X6m9WvAjWDoJpcKx6/W4YfFSxsgkorIye5wP+b+a/2CROXtUfc/hgAqcqdAkTfQUnwe0fESboTCYczifB9XPxWcpVKL7NQlfHNCvYGjE3eA3ukau3sAIlAxIMu2FjiXyP2QZNHalEKrxKg56q6pBLj4MY+6CAC14K8V2G2WK+ZhE+n4RCwo6oiTF865IP1yHYKAgvj1e4K+ZaWkYP79Jq/W4TFTK+dvRVfIPjMiQokleTVbdp33DXXFmLFkM3hIchyFEfuS/iMzWZOer3hJwswP/tTR12jZXWiizpvCLZVGTpXLRHoIuN/o2R6bu1Yxk/jhO/3y/EeTfDHH4GdyLiCdI5/NwNbIcvev7MWrG4IjYHyaNzR67jDtEhneJA8puvSpT5Jv7JbPN0kylh96vkgATMzqqpQ9wkYS7FQUKaA+HrQnpGt8uR9C1HFf8qRsGXMC4waqRAj0Wiu/1QEpZ2pDOegtBV0+MVLXpQCO2ovHEVfV3hqxORKyNSEgrDtFpm7CE/Jo1bUg0EnndHXGm7s9tLGe+8XrZRIs7kUr3esCl51Pn0r3eWyFEk20ChjJXAjxDpSMr2DFmLZcUdx4GsOhyRDtDnrN/6S5BGPViWHFhZEt6JUnsYW7ala6ner0XueQpgeqWkN1+5xB5ihZSx6CqBIMp6412fsVny5iRHECTbrwDofmCzry1Ik5h/abSZ1V8SRTeETEYFYqm8hnwANp/CyFsZh88TJfZLxOE7iM41EFsgg5IMrageebFq2SMNP3XmE+WuYVUVJAssbU9wV/X4tP99bRU9CmOzGFGjY67P12ufIHIjTW/kWiXsds29klnRGeUVoV9S9mfZIl8ucTTxRsHmvXgaYQhl1KrI9hspEeBoCEldr85NOK8itcmAZwA+bGow1ou0R24QEStdxP9GvAahJn2Id3iY3/eIviNkg0+PeDE1OjRpX1tBfXxh0n0r0Dl11JV+96FgdbryeXzSW/XU898MGgHJRjx+Kxm6uwtVX7gF5XYc5x/dwYl3/dZJ9Qad25qkkwi0LWitc/wI8iv66pq0rIeH9KoQM/AKTLlmJ+l2usXCcy8wtVJhNhdqHAjy7KUywlBmt/kwgUg5Nn+Ergo6x07yFwRT/wzp8+uSUeXwdv9+EFjwxSeI+UhqK4hskOpu5i1xLUm9Payx5McvOiRUNiIOhYm6k0h6S9pK6RKxL+3mHFlMTP2oRcnrxE3/4XAkmtqULTP7aIsHw3p0z0FjXN1CwcB2/6ZoFLFKmhyWoW2L9pYIKsXMCmKhzNU2ymnWM7HPr6ZF1WeOC9zsInAxdG4RpKOOKI2/44juIXhBCmDZ9QEvuuRW8XAtWT+jsWb6ePjdWzyIywnUhfS9sdFU/KBtrlHNAa3TJbnrO5jb3zSLsTzGoLfHJxZgZwFiszX9htvyiaZdnssO39L+CugJMtvoRa0M+uixet/t9U/Sfus7l4c7Ro/I/lBDq5/fBgqu9wiuT4fnw+LXKdn1gYdekV4c2zj0uxMFINUiEhqPtFgXR3BN1i9/iDvG2MybiN96hpR0PantPfyWCUoMSinDDuekemlMU+QQVdJS2LIi+jbcIzaPCCp23N7ZZb8UMPpclE/Y90lMdui2vyH0N7TSschFvtklckZL+RjpBpJBlwaEeErKA+Lfv7TY8mcdThE+5EOr0DgXN5C8390vdviYkfG+687LNXU5EUGHaLEu9g79x8LZ4zMPwW+KOE5qRmtTle9f/iHsc6XYDzFoXFZwQ6Un+kimF1YfdF5tOp1EzUhUW4vpmOe5GI276OWxG51xdSQZFuwetfpXkEp/r9Vd9on8ZlxaH2JPwnvsf5KPa0fZFN7CUim07ulZ/vG35I2BEYWsNnIoeyQzdrXzwlPx25FtkZd9bWq7lx6W+UxAkH18XsiERRtaPBzjrqvafwSGxyhz4pt1csjLMOGxIJUt81B7QFELbaqxKhg1dEYyq36eAlgQhuu20ODHbhFZ9C7f06ZSvvtLB/M5qBMH9IUCeSLsb+E0BeWeIzLNMS+ViQTzMivIF9SMWwYKd1t5v7evLQe2IHezaU5UDq95wrJtCddAjq7hAJJUaSW/GBKMFWdl+JUAtQ6xl5EoZrfu6rWmKFzAcgC4RdigPa0en6awHL6s3SxcIyRiBzmsbwRxLyXTQ42Ad9iqQFHsseZZT1KMejz71fPfQ/Y3Qj7zgCtHR4N6jb0HTt9JNolHCJJocmfsIEji+tJ4k92+LYW02UEU9etsIixGSiJQ0rAgxj//QYHxuTMxNhZcl4CHGgaKi8+5frpGign5XQRQY3rll2IE4Sg/qIu7crIM95BUmfxQjHLhhy+xOcQQN3NlDkhKgyTmztyi++59ohL6QGCqtDIGAKG++Lw0dxsmjOR9bimtrD/EHCXtV93WMnrOA/FNkeZ5Rla1tLExy4isXlGkhWkHwRnyAjUm/ubmBgY37azjroStMiCj1R2AgeCHcFl8KDiHeV0CYSJjn8BvJMk9gSjiX0uJbiRMZGGdW8wuF7hv9xfsMVbXvdhuvcczS76bnjGK44TvdCY5ZjAKaFqUycEkd1G77bK4AIXCoF+ZAWOcyUSoaF7BVXHx/mCJ5D0iV77hd5kiz9ML9516hIIWf4OcKAu8fGCJsNGdnIB4nKCnyFbacZp+1ZwUVCuubQ40/NETpfhtpJfO68SGVjIeQsk4phY7taSPylS5k3Yqw8hzqSkuZkiMg3ngGJ9T2gOWTX9c0/Zu9y+/xvypi7Znd8ZAB11oCyFkTn0XgNZzDZ3IR6NvwhIHk8l6IZDZg4KDQgraV3xnvUKyQ3B5LTJSaD/SxKdek5AahqDkT2e4MPhOO/UBZ092+4kty2tyEMMVW2jORZedX93DTlAai88BKPG0dRr2QwkO8kTTcOuJufSkNM2miXOg92ychUi7A6F/2kfTFqQhyBMbC0Ly6Pl7MrdTA8xV1jXPikhoL5UddUtaF3PSFfMWxAGXiZsKRU0/or5lsdSK8UFf5Tzt6hzrKGPTiM9XdQ+dB+ThOvGxQzKjwUCyCj0UM3LDyO0xxAH5SJuTBCkuG1Sifgmz3c9Eu1Ey+PPEzyoyL8PUP79pbYmWKdX9H0tXkdg4EAS/JIajmNGy6Ca2xUyvX42ze8hSYsszDVWNzVUpo8gyyh2xDJOVN4cwBG/5INmnT3xG2/kSmNJv6Wy72K2Ed2Clr0ittiyl7G/CKf4bcdqN/1ekHuYauLKeUjlNRmjHbmUKyv1Z6HIvODmC8vs2k6GEfSudhdyoPtkONQipnUR9hyG8pDuIstxOFfUrmX0FRTQMRkF7D+SFxXVr5Onq9JusMymhfp+lrY5UmD7m2mSn6d5SlQvweqNb2wI9cyLlmymGtv5WqBuFJBeOIPGZY7DeGuTmmHpTI+q3YCERY3EF73BcrMgvz3CJmErLWkuGXt5TGWtKCmrpmADkvsShP9T9fxgFAFekH6bH8QLTeURdH2fVaZByimQxv73NQZPC0XU2NS/A9KE51Le9hrbggwph6OrwnQvlRb3lXR9X+PkXLzQ/+iNpOwrGH7II0/E3nhvWA+Q1GJn1jTZurYIZtUgQm8TsRaTzEdjF7eCSa+mRX5UqCOLUakxp6wO+L3SJlb29Ol6fvrO1gkEsrADwEaFnxopuVwC1EvleLEI24SE3gSt9By/iva4kPUPNocOM72yM+qZfSDqM8wcMFBNXqzqEL7B3/THQ0X5vV0FUDBOf+UC1ge3+DgdK90XZs36hCDuw6FUxJjqIswn/cmjZFDoIIw2zcn8ve2E2a6hIbLGYhZo4pagpRJM+c5hcbPCAxJClYRMfYvQF38ftnG+L6tYWGKowhn/jMFjGat4yDzErcqjS2ZvgkvgPgTOqjk7X+gX2Ft66T4qEhi+isHuWq/Cihvdy15JjCCRX57FgdhjLrmdID2+PpuQz9r0qMO136XXhlKSz/uudzcDtO0JG7QSoSMxAPYTWd0LOm05HZr7YHGP02xPCCx27HtY5Ue5Wv/f5KBOLeX8H4AxoYoMi6S1gItWKU22ksZJIMHLgr3ykFhF8gGqyZIv5jCIz6JZ+LAzX4BO62USB2BQa/MpuBWNyYRGZr3HAXYb59Y7P1udBtzKBG78JYMdZTBZVTOWngRThAuYb7ZB1Zd9Q6dPsuknakBAPwz0eP8/w50OQgCy5D8T5lvEM4dfMYDPrj2tPrFEA59gmIPIEgPKI42slfAfItRrY4OI0nifh8vPIwMCEYjsINb3tZ0I5aAj+tEJX6mVrfHIARETk/HXJjgYLJ7s5pNUmqNGCctHiH9Lgkx2ofRWjM8ZLFfFTC1LjQ23OfIMe3NJ5lXYFfUA2LDqV83w6fdmqdH0aBqmUHZ2jhLmabO2NknHT4SE60EB+0wHhKbTQO1pSf4NWA7oFz/B1t32mEHQF51fCJGVBHr/VemGQcUhbcUu0opiJjW5HdknGWB5C6Nr6og67rPvdbM7qv0Te4Htq9oYfMalVUJSLeeUe7cXwdU1xD3LogqRy1iyAozGdsFgivwc+kDciWH5dHlR9pTOyXuSEuNg4e5YFHHdXhDC1rC2wky8hBTT08XamilcaFfyWKfdlfJFlid/hn41K7ynL9lVu34cU1IzGO2ayoySckooUX1L3Tqozdjub9lW6zbSDRAiXZOOZ6XMRZ7ugs+DEImganeTyivcpX7ribnmiC1HlMvruFhvsSCSMZmmTPl6oanTB+/uS7Gq/fBzd7cVpbjS1e9bBPJMnpQegqRuM08thdAeFDnCSxaZjC1O/dWmb/vKAiFN6zjemx8fjyhHPs6aLNiVZ4HRV1NOdr8zthBWf1g0tr2jVe3YTv7WFayxMckmS7k/PS/iDuDfL11nawAjJC6VJwP/aLR4sETYxvN1FZZlQ2nCJ8pIj9ihD+jdgdVJbuDoI/fQYsWXpb9xRQ8GYNYo+iIutb6TXn3uFatXw0ENfx5fMJJ8VwPVqo0MsrOCH7/JcGI60iCOLdzqHLbq/PpXbeHGEh/yWn39N0l/XFVpKArNhe6ZSIA1xoNs88k30wlHka7vjs7ChxL/qN6P5+nmjdl6sawsZL3tktNeNIgn6lpwMwd0FZTKGEr4lMWSV06ZTkCcTXRt8gwpGBJoIrQElQ8yh+FDgvqgLUNFvDgyCEJku0/uv2bY20at7Bdt1gEikGJwOWJAhGkKHhXIAU5/GqCyBU2tl0XrO0SM43HesWNHbHbn1RficQSnZ8YoVZXBpD7HkumJSJuTKj37cdJmNkG1wkVVZtm/2We8b7JmA8I/gG8sLd/AdiizWF9JN9616IZC0KGSaVlHqA5d5E15GLdUeMsjFRTDWRyCnEWU0hP1cc87CGEIq7gyrIn2JuJlPNhNc7HAOFzxvRKq/nCH3PR7E9F2+QO9wOPeq8zBlVO3dL2dOrzpp3UcX9XM7h0ybOYlOD3BQ8bTGr3r3lrhtq74j09pXO6+O3UW6ExS7qobktLzjL4dg9528zN/8gn6tiaaiyziwU8Xau6GJzpfZO+9pUnfq/SlnaffFjbF7ioxPN7viMN8/QAwFBCK2HESo6VQ4XNiZDXLXRaPJ7jY5kT2fOxCpU0CgZsGr4G/aY2lDodgYfngQ/gfPBzVyo4rLw7cWQlltNbxFb9uKXcDtJNU3EnrKahekvQs+pmcBZr54E8bcqHnApk8TINOySnGlp/GVlNQZzJWVOFUwRolb50Uj9xBRa/3V+WxAXJIYBTz2V09Egw0yoqQ7QMbYPr3a7Lz2wJ3JdZkV0MQlnhTyirhB/XKlow7lF7RXgNoaROsZJRo71zuAbYZFmzDMdTgWjV/wbjsT+KA7y60aVJlOWyY/75Iyg+fWcWE1ZyTcCSDcGs+Nltpk5G59PCDbDluoMBvZc2R7MlwJ7TUnIr0saYba/JsxelGdHo6p2zTbNRNz1d0vNvjwpjgfmyOBhRUdBnm72mOCcEFm4Klm0wXtyK6RbmswDgKQFYywUaIk2U/A6ZOlRw1OBgzrwZ67yp8sehwVTqtLnnRrDXHZ0lhe1xDxGm9YLLCvUNT3R8VCTHpMEdyjIfUWAdhyP29X/xWP0nv6idfM/ksF7HhNJnfGySBWkxGQagAAGBMFIOfRznAxGxGRXYCBgSxar2JgZfnSgGtCFGUUQL3C9zoP3+gYujgt82xGMm9eO7gMw815UV5bz+ZlzQztFzDQu2uZKHsHW1ibpfLYHKh7C0eNrvavghPnFR0kuq5h7BIOzGxiZ4DMoJwi3fAYfnb4pu5fOyNAUBdjm7lbUx+ooOcNchMafgTu+Sa+0fnK6KGIDz6dH1+mY3idSnnnRwgYMkNo5H1fFM+57iaworPw3C23SxVpbf3ON7vdlr5S31Wy+gcD2bMneapnm0A0wiOJoQ3RjgWmSw3UTnRpigJ5dd8vZTEhkr1Uk73PvVUNHKSTyKtiwopMW2QOR+QrF0v5NWEm3e7wZfAC/IZuWP4lcdIb2ZepSpAUwUrGR5B3mg/BzMUHQuY/Gw9Ylocxk3lw/Cd8Q0Bb5LbVAEbrobOslRYXt31ixxMu0ffAMBQT4fXYRNiNSDGSlsmlCi+w9uuNQFY1AtULq9aMl9amp+gKAfFa8JXKvsXsVKzc9cm3hGSLJqiERORi9qhSINS0B+SIGxyWuxKzbxVgSVfJ6g/mUNjvN68RJR+tNq70oIrI7cwbHTExUUteBSkVSLm2VOzoiIHOiVTGqb+huuAc8Rw5lhY7DOyiUq6EC6i8Zs6DdChUcz22Tkv3VqI7I/H6mrnwcsblXqL1IJjMW2/m/Xv69jeEIeeoxHXLr4VvGMnMC/EbALWhZMEWbkcCM2IhH6/YC46jqKS+JqAHY49tMGs457h7D2xpfdzC6cI0cbnxbrL40yNgWkBbKTsNJBXJIPiD8zk10tUcPjjQyimbIJmIWB4ONhF9h910wH2h1xob882baR1RXVSO2oF2kKqVvJExo4qdZISxpgO9HPGIr3jUdCS0Jv0cy+OdAa7OU5vhn534GwKJu3UwBcidQjYZOjvIuO5FLd1ed9ytGx131MOnbshYvYxhfbDyhpeWk+tWDwokxvEkvDQ7lXmlglhtrWtorl1yf0NGSDRM0cRysCWCZ6CQW3igYwjnVfBbOLuo3KdNYpzFPzhRMOel+3QKeXvRP4hNj4FDMRfw1aKE3iZQh7QG2hIfYSmTjYv+s/4vpXFDKsIVPdi9HZY0jeyCIONtDH1mtaz5dyjTwQ/YJStEEDLQ/rj76hNeiI1XEQ+ew5R9Y3NO8jt61uGXky5EZ1LBxMH7GuPFprC2gIf9KZXIHGNsFcdXmxTXEfyqugh0YHZ71/SiRuJK9eRc6uhJq6NjzEytqg70u/V186CwQX8b/Zfl3OLW0wfAHkMOOH0MNVpNmB3NY6CNQCz8t3tH2KvoZGRLqiTgX4bzaySpQSiiwOdr+Y0S337NRXoW+EvLLnTlL5Z70qKW/4K8rQEPhX0rF5OuqUIPxM03kCeqnyxv22aeTR3ydydxUunxLrlpkf2i92pw+Tbij31kIMk+EmSxgLkHOwi+qO8cd8C1w9vrVUdIR+iQlrCX6XEF+aHGzC2tWG8T3N0n0AwidhpTlRdOfdXUASx96ZcJcm6DHmEaAtUa1anIS/WlaiqJHS9iw15m+EPbUPujrXiGzpd/2n5tF3KNKXBh0oxP0qLCB3Y9rpWyLyqDqGsu0hUpWSL2kdYyQIgGvdXFvi5MNVa5AG+M+j9/QBR8w4SUDidsMvDVuIAxWCLEGA+C3wbPuh/PdhYl7q0gEXV5r2NI3/N9inb/iREQ1wrs2Aj2MsnSmHdJg+Iqq0u9LjhGi6jotafCS1tLw/oqPL6+LI6+3+Tap4HILmHgmLRbOxKIA8IhtM7VsFiyvWel8c3UgZ5djBzpcW1hPMDmQzeZGroAAR7HNRWXlrLJEwsSrDwugkRn2yeMcbg/EZLlAtJdouvSS1bVxxFCIH1b9DMbQwyv4ExrPp45FhRGIjinxh+DdVwglwY8uBj+cTZyLymjvG+vZr3HvxdujZt7GNvYRb6wOJ/puRKGR6/6i18bjwZyqkalgq4giYUCv+O66WGr+3K999Ad4f43AErQTpSW7Kvisevjp7fDpdZ6nAjkk5cwr7SJNwDc7rJ7GAcup8GXRXTpWn/QDT9JxAFgGcrQGE/YsnWmIHRXBzQqiS2q3RzVAd8zbg+M765rqnCk/IRiXGxEy1+4SO23wEmnhRtx6W0om+85gK27AfsuL4pkjMdu6emKkdtv0ZXoW36RMaOS+7Dcpycmz3vimJN3me6RNNub8c7VwGzvDfdhdQLKgMuM3XhsDVCGUTvIsT0c7bl7G8AXaHqjw5giYaerFsyBTI+cf9oGnaatG+1t9z918D7K4iuxGLcRRaJDlK0c8zi3qE1sDbk9evy6hG+zHydF03vFWAxlamNhVkA01Codo6Ncr3KqN6SEfIUBMEtDryQCpnNWtY8UVJWOfsV8fj8MtjIf1Oh3b3Q/R9eWd/HLSOJ6JmT/Rj3L3e8cQKJDbuyeXABUqW3iGvOUq+yOgmvPMCHVFd2+hfnu8DF9XWFo58j9K2p4F8gPAWGcupGjYX4P2g1GyrEuYXpoZfKBKLyYMcZLQ4LyuRQWaPOVzz2sL0CLH/247CijSdkzYRJBYVY5tLS5Nsv6FTUv4jeWjARj8w/DZuxeR8ZXbhRfw1+dwBue4Ge78hHs57c5eHRHW9aHY7TvYMP5klGCiVqX5XJkUFkQkMdrWNd++bACc+n22cBwXUkBIg6YW2jXbxRJ9e7472r/oAwN7F+gv8dV5S85fwAR98C5zioAevUBiKmU8gfjgDa8OrtwW9PcYSSv6hF/OCDmfqjvzvsP9RCRI7wsl+wPQj0OeoecRDyEkO9TBwmruzQ6LEI7EB0tBz8uasOPZeKy0NSUnc9LfeF8W08/qOawWWgJYvrqIsgaNngDi5XY+y5KH8q4ebv7RA/2+m4DqriQbA7cQzNKFzZb47k6eDPPtpINQPIMPK/CnAkVnlMASADAXnloaAmb31IZCv94z5/qeXCBG2K+pmOqq7AJO5mzwXRtAjbAZnLFzHQ0Ac/g8TKfXSfOUAGxK1pAdfxIwN+dQDDkblM5PTG9r86FGO3HHvQg2IUDnX7A42MKHmv0hlH60cT2jRMvVlW79+3WcMsT1TkKHsn2H2KdOyrAz8IWVar3k/S3Q/O3I6h8SH8wifvVKudO0tjGz9ketQjhX2z1UYZRGpF931vr7Wj6z3zxYAi7GKQ0JawS2SGe+PgKEo1LZILIBUwbk48QEwhH+NWk78e9qIUdAUtu576uJyj+PMrC/3Ihc3hTMx4BuzV9YaJ7/rOjTq/d+zLirfB5YRql/LvUJaIody+rwOu3hprCHgnijIz4qonrtq2aaQThgaPNDAPxK/sX00tdiL7SIcIksrTw5SY+BEnerIMfGn3FG9riVHvDOF2ykE4ardmv6QaG0omVUFnXvaJ2RfceQtYkMIQiVF4fuAd1lTjCwxD7FjijSqi9/6wFxasDEGnFmgS9nJBvLEwfz0pt93vPRLK7uPDibxxxcoLefmXnRN1Lx/IhgiF7cwyZ+jP82r8zScwtESPuvqFE9H2Fn4R5z484gWApBMNMTw65n5Ox65P+EZ9ZtJX+Q8frD+y+dhr0rB9sQwdx0oyddtOkTyh6MLzT6k0kw9x/t3IGaKhIad6/CeL+1bRNKH1nzstadEM8D4Ups+sj4S1S6cW9W7VaM6kcoQpsXPXUbW2b39pCZYtVe9YVkGCtl1gN3Fq9k+YOTIkFtntCettZcv5uCWaryCV4n+fME0w6JTOZajgSiXTRgcZzkdBk/nhbpZvltCAjSPBqMK0IIeAVSMcq5ktyVyFiEi8lgNBULe5CpD8WyPXp7iktD1iP5ogbXwuSvjuI9/hAIN7xgTZlmFJZDPgzZJXEq9hHfMYIwHDzJvA2om7SrL5aeDkukGdZpaC/hUgrvknEKvCwG3rRmmI9mJWLOKWfTrP3o/cw+r5T56UlUle1U5DmYXgS5G8R1x7+n/Hd9dlspfWRENQnpFlH1jMDr0S1W2/PcPQNr0Ja11vnsCVaJ++hqOsVpyVfEBokXTszJRWt8mrbvsPUKsoj/RZjZJwFos7xxSdOaVO57Ud+L48DT2v+zKIQd/O6eRsE7N6bYXJqPc5F8yXIZOx/M9TEt8N9g4EJHYYkSxACYMQ1gCbiEChvFu5BzfqlONS7ZWi0bwnKYxT5jMjUTK+0dVHU0zFakUGkpQfXfiU2/nmHlifgl5JzKTOWTCwwunzaRdMVMDDxLl6SRLAR2VIbD8bFFDNRKWPmF6T6AGDr41JZq5qYIoRNKuQQ2HwzlVKo54/ZXcYTZcSjemHG2mF0gZsVEhEhctkFt+IkhFaS2qgm8v0ZHg7FCzPOPotZmEBSHpMGgctOMqo3ijuKoSBlQLHYjs3lpWJZO87QV1M5LIwjCLnt98vRL03fPqD4okvEqYocs6rBSJZSFkPPzLtzSazvByLEj1EgbUG6Yh1aR2G9lqQPso6u0iEZgcvx8IYuqW8glS4RfgASUgkrdOJ1AVRkOBHfv5AdjRPvTTqwsPllgeazijtYtrHj2etBRqiCLfEVq7JdZKSiS6fR3aOTHSIraFK5/MGyHyBzWLg8BDcy2002LKUHbnpm9umVxvVQKLdOPFCiiYeITOhC7Oe3fr9iR+3tU4zk91dMCXar0dBAl6N591BwXNdqjPb+mz2Swj9gIrVMPk+1R3kWGt4qyKHx9v21b/t/cpX6D5IJQBqxx/M3Tv719M1j/f8KkSsC9OHRUeNnVxWJo0jn2qr5aH57bybVHoz/MuqsB6fzCbHLn2kq4BQoOtSTcZbZbqnvWCctEtYKCLum1pDXmy+c4yc4K90h9vItIbWmQF5ktYPjlKx5hZ6NFRdzyS0ZWqfROi11Sw+PehOcqoQTJstc7ArtUHf6jTrAzKdslznSu29p36ZfWfXKKBiT0OnYsaDKqNgxw89FbfJ49TRSJbz4eTnilTbjPZWeVDcVjq6kZMivK0cwA5ieZc6i+mzsrs+v0c8n6y0OtPQxh/P7Hh78Wd85LAWsWKbdKg2p2o5hS7QKwzzgKijgeT4Esuw/eSei5sRPYfDXzCg+xC7W36/Du7b906676vaqFGMWJJ4lP2wiRLHbhn6Sa9L5ZKTtdKW0jo13HJTg7reX4TeIln0aOtTOLRbWhSCOfPxNiy4exnqlCVWCzbxsm8d/bzm8WxHL/WEny3U9ICuaFsR8KRb9tjDRga4LXcYmCyQ0/lUdXPBCGLVBsWXojekXhIstzDDdeluW+OarLruHQQOlCW7eLXiGPx5cXCWenIqilPPqPccJ663m37CD5GOXMzOgxKqX9VQYVzYX7+1WV35n+3JP5a7syf8PeqY3aUfFgX99Q9JQ5TEyfvTeHfW2r9xAUuixOCVXf1qq5T8PthxWJmseIxmCQJY9j13ZeXzCvCj2h8oB7NtcyroHCi4MMESJTjYkZDIi8jNl4CBrehzb14gR4NBAOs+zaFPasoDhuwfBETKCO+Zp7NCRsaHuQTI5fDzMCmzDkAsulvRX1ulT1xddGF9EHYFGUa05YkoZ4RNgJ7ZF5BgunQ5j6uDjGxdZMS96lD8ZhYowJLoT4hvw8+vX+KlShOQQVorWxUoVY57tvXpdfXp+cSj5XOZnqnd7XLwXO91jg3SvBUXtSQ1vOpNeNTozxeLJ8WBMDNyzOs1UU6UdquGSSqfvCUhwAnfZzaYMNNFl9x12MjsxrDsP52L8zQJJMra16rEeq5dZwTkNY8w92EyieN8P2Rb7Go6MHLGIFATrXYwGS0J6z2DlBGzLRmflonODJ0UQNj70odI0Y+51GC1tN99eDU3hKFJy0yuDZtCRkhOl5tigjEEErJXAEIbMQPbOBniQPor+Ase4Un8z+MRfeR0AKtyrdKvEO++uU2SYY+AO20XqKwOOSu4QeLHYaDL+cCLbWx8AlHct/004kwiXPnEJw0q6MWRwbyQD3umWu4DcF7oPvxKSmfp6S7/ZO/HepRTSj1L5jXSP1nKq5r3sDus8GeCqvm5+b2pVnYhDpkhyFRECAUqJjgHeC+CUtb4Qplrd3AcJKHHzDX8fhyj9LXIISTfz25Gt87qz/KcuFGRvVi3UujjvBIeAMOKn5K2xnUP4to5uOgZvZo1yWn9DyPBlbMkbf9zTMBVMritym/nSXO4yiS0sq8Yi48FgOiiriXVudqxMJ/wjpEY12ae3ItfLneCZmT5orTGVDh6dn06UdpTtsLXR6bHVgk40/RLl0iy8xN3Jb8DPY8wn4v2L52px183BfRHFJYBIVHmPWa1s2h3JLV60dTauBrV7PrFM/pnZYgWO8hzMtY8LxY0RADZA0H5umLLNDUaqymS1cc3doNWLXTzvFYY/HyxVyH4dJdyR7Feg21ZpPgbYMCI15RgJPQU99NN2jORc5jCDEqYQJ+7dLQfD3uoJwz2aE5gvT4O0G6qFROCCVYg/NOTtdza58/LGbiUHTIEexAliOZl8R4ZhaGwcIScnzgNOMeHu7lpJuMC+XkAQ90GYoR+JAPYL596ttPYSieJ0j5/E3OgEEbfQ9yRGILpQigcGRPjaZYwdfc20IYSv/ZJy6nyHZZsgGeJdHX4Cvg1Bum9uvu/V84MdMGFS21inJ68X0Ef18PYTTPvNGkeuQslE0Bei/nady88TDUiWvvqi787XajJFy9YMFNqELkvf/EMjBXpp6xTxtKQXhAx7DSa+GCMgQXMbsdHHA8+W1zdXrTx+F848Y9kDGqB9Mt+fT0nlYXDrscl8K7yO6NBd3T7r5MfD010/OnaDeHwb12Ymn7jEoQIvkfyHJLdQLYIEXfcHfvFmZQ7ZlJkh5m4EDNFbWoFoAO3WuHGy9U7ljpgYoYMYogJKfuI3R86GPscgTuKpL8rfmgf5pCRzt9RZ1D37GzJMNx/glW4R8COD3+oQkr4dW+aWDv2tJBZjLH8tjRZOj98uAwSxnB6EnQE5hj4hQEOvqQRvWf6vLpfjo8Bl6ML0+ZOn8it519KXi/axqhcJgrTVu7vPaHKSuJX4snh7MmmOljyoGbA6CvMkCA6URKs/jARUNsuqBsN60OuMWJVTaQLDxBHXl6TG+HzEvK33OIax0IiwpN4RW8LXdPueZe6KYqiPat5wesihpnyJeQbu2vbgryi4wgfTv0RRS2CSKs42zyuJZsxk912/7U96FilyOB91rWhaB/OwVXMxmo/NC1HLSEqufEQnnjf2raC/yffPYz2OZOfTM1se9QVLpFhP3mR0RO7ZJPWrhgtS3SeKldkeuYpDwoNgiyqGqsxyonP3FHgT73ZrG28fb95jmVZDij1uNOeRFQ6Z3ExRtTopn2e2ezl6FOVMlFtczU9jt1mT93vJDOKcCeOBiNt0vtnJg/CmR6rvoBYP76j0eEk7EYm9b88n9dySLZemUFHZcj910TfejeK3NBZHTckWywVD0zMhoat0r+SzMTYAtwL63WqBz4Z5bBmCsqREMifnwrGiyBL9ddHN8NshAANjLywdwnuAVQTutz8gzBesKYBZRfMLPQfDJcWeCkGlhUOlwsMbb4uwa2yXGZPawvZcXUKVxNUea0GhEFXbxd9uDeAGd2vIqAvpi1K3SZB3FuNT3sH1jgCYbyZ+2fkMpr/QY79h4foBTrLoGlO3TMR4X3ccWiXywB8s6xmD04yPwa2ARJUFXuGrgY/5b9oNY1O89XYIRnzxeMqUCrNc8G3SJMa/a1gHLuiBNM10z11RSWuqkv3EW6p4v81OuIt4ya3kFxEUAi7xRJPuimZ/JFqqKuHdWQ9fMS6KpI9ZIh+mFR0kB3/BCTItxdfCBNz4zxuB3nixQBhk23qShfhRsIu46PpkuM6GqYX4MphYqrReexxqUQ2XRsU9qVpfFC9vukMrFYUhvfytWpEBq5KToCqJhJmZ187mC9LFjZkQUxgRDgj31i83IK/X6PaP3r9elUEOP/f1HJ8QHMy72dBIXSaLLc78s+83xJQYRmlb3LZjXC/7sQuR5SiHVQ19sqffvX/zC4Gf5u1+GXO/h4dM7h3oQGp4a8OJeO3DHbhKasPjT1BM1Z7XrpCe4p2bVoykH6nEvfhzGTlmZUgRmvg2/cYNFzo10KgTuh0nGKLuuqMjC66HJhsQsOphmeGjBr8IkIaSZuHFx2FtXDw272XVfIKdaIYuuILjxJTZj9qI05QczbXL5pIcncCzuRBUM7qpSd0uU97fKBxn0iD7X3uxYr8ebqJx92fZ8BskFQ066EZMjbp3Hl5lvvhfhfFesfE6XCEux2WqmZudb/43X7drNRJOzRzJBwpihH4fp7PxAi66UxovxiwPOHqjekgu1hMdaIS59LH3bGO5tcYm6rhdQJST9kBqJawfzPzblY5NEHFo0QPwXtLEnj205Vn/giW8baxocAQoKVXxNIHe9H2wEcGdKBmnw3WZxsaHbwN3iXDEdkGlKIVsClKtM/AGHPgSvSxzxPis14z+uWDKTzAHEo5AeShodsRRFQwyeRgv3K5YxroSDM8YKnenVPouX1yPPTkhxAdy5+X3YPsH89qR8GaHnoElX6Er43FEyOnOPEC0n+q5fjqNhRECTUUzJUWBDxTC6Yl1NdvhCLAwgED94QGrXpjyH+e8iQXDuuVkGajzWJYTZZRGs9vOX4/IEHBHfFkyD02ydG92l37b204TXqsJeUnkY38ESKK7YH2FIBf4sLHJq0i4pPfFoVGoLAZaENDuctYp15eexFI1BTjOEBvKU50FpxerQTBEO6Ha0wmvTr7XwZUvVDT87a2jM3jY+psMaSPTeAjKc2pPmmVe0BGUx++lGsMznioxhbn8Whkiqlu8gf2cIRnmzruworZCtGNbMRpVMfOvXtuPE0vvgBa2L4DQvxQhmBXwcHMFn0z6y4+HGRFijFQF6sLDg9TRdv/SKolxIDoGnNUKxxFvrVr3LU0lXarNQD7nwHYFMQUPleJfRj/jIZSeu7h+GlxVa0bYEXlGh0HqPWN9zLBiIy/YM9KR5H8j6MJo/ZH4ICRS/WY9FFlixwZKcxQVgS8BbelO2kUR7V5k9EkFxq6n6Be36ocpziFg87YtQtEYY5vWPJkVD3pBGpHYXHHumwoltSEeQJXG3Y+K25YMKnlDcpm/1MHyVTsXQhdpCm7OOPetUfbpVzo46db1Sre4VgliTvvjBhdoTsJFzCU17W7+Uw2aYzzNUNHmDiyvqX4oqQX3RTGH+RcHAvoAzPXORrLcxr7K1eWODbEU715xGFSE9DSBz+7ENjRp79znS30QbesrRV4Acsauq+bqEN3riofrBpeeC83slmBreHF83KAkle3j6TKOeIQZHsF/UV3+K2rfS6/WqEjjz+tWji59cQXGHW5LJlE59IfkAT/YoXvI2gxxiPFsQkREzpnrXOQbjWV1GDYZ08+RKoS79MDnqaJ3YtkJmMLEsqvRFOh85vPf5iM24GVfpQ1f4+GOFghW8i2//87Kp475FrGNbyQIx/uXAPmN8Lkxo48VCX7R7DwqMk+YM9xRLWJlKZA3cfyWkb4GbZ5GuDGC+po3FzqKgR8gNpFdq91TTpoWaEzY3JsO+2yPW/cbwnTgwdQS5PP7YFTyRdK/ZuJ4Tw56LWQc84aX/2AbUNEkT1c7M1mpqL8eto2hfEnlsFcQrqgUxV49MsUjbKXfUa9FPYG4/TgoB+Zpi9pZY92KqxO38I5sWxhsUEwTzdEy4C5rkB+vTyljUICdWtj4nTu9peevLAnzcUuKdyXlB8Ar/HkG0N04iWaELuYcyWb3BGVvmbo96KizrvdtLwzZnOQIipGvCBrX451cqeLpWLkTcTAv6ZsvBfcxAf9heQBK6ahv/Xc39/9/Bf6EBgLape8Tdvn33ZqMBzG+/OKwpG9FzAQ8LF7c7jgrt2IGJqkE57bED2p+TPFLFpLw1kbTZbzqTbzW5QFPpvxb4SDIggQ9VPjEi/3xDjnBkEuf4DtONs/Z63ebvrmVVy+ButEFJdG0lynctPS3c1K9T0alkucvQ5EHOwTbalUlqNPU9L4eFcf59MDWILu+9oPoNdNkRnbUD+UomtSiuULuqWJMsw4KnW3tkgc1vSVbeZjKZ08tj5PwQht0PRnegflGxYwYqNCnMd9zqURmlpvIwqvw6HAtfuPy94kzEsTku6vvWCS2btnD5/GU0esxSaI/4F9wRlT4OksaOqkuaebfqE32XNaBz16x/BkY6VQk/HOhhkrKCz10Bvnd5dpjf274q3l68PmuFWQOt6PR5XDdsADRHQ4zTOMB4hsLNkyhaKrOXGlNuiuaq5m+tsiekZU82K/zUm5jskDvSqWhpSXsDjAzWZvjoN3KqMPickzxuLYxz/1yv8H1e599gKUXR3+d4n7jnYWpu7IDSPJbx3vm1l6DTKoBIDdhdnG3uPcVPKDEFbqKebyNzQiQ46G4+YJehOfFaEG6dbZHg8KLF53Ie67VOzM6bFZ0/vRWhZyJCUJJ3QJV8y5ScsDY5IzJ+WXhu5Tp4MKTmdsJzJaJL5BF7u39/hWIEB9QWcbp9RE7WiGquy2qTPxyco2dX1128LYa2yPa6RZV+/RSzV86C4vbijaz34euEz3o/vRXe9IPK2Q0ZPyg2cJ8Z9kPXAhRvOYoeCkNV9fKiy4Nq7ZygeMvWTAJkb1XKoeXfFFQZbgr2huLW/dE9GvXvw6yz/KiaRN9wG7XlJQQNpeF13wovI8W4dIVzaLQogvht+irfbBRDNvAmu/7GuQkUEqIeivzFhzRBSK14vpF92i+iJowIAEflPsID6YDP26GVV/Hr3lHQUwkWxMQT2MKkGb9ORbQa8hO0/SQo6OEmOjF3IjHHir9+OCDjMEZhgZs3ZqbmOPrnvSeHr/HUc4vLiMRcntcQiN88cgaWEqdYIwlQh++EzKhXD50B3LicKhhkOhEnHMwx04BQWDnzu921pGsC0MUS4DWiCfaSDiwM5gJQnJg5WLykE/ojUXDq7nLS/XYB30fMvRQ3seSewdJQgwEHWdGPdRtsJMH/R60OOt3Q1MPcFtv8ZqobNtapV4Ji39gMOuA04pbOMBfEQudEUFVEsSEsPdBk2kHPArs+zteWVlQRxIJDfmLPzoV36AwLu+Warnl9iNNNxQfJCVvLXMeD63f6K+ozjEZL8wSxaMl3ah/xVqPblNMQ0hqTtW9dsQZAaf++jxEBjQpiweGiQFtex7kukDFeB0mfnXb/d3gZvEFYGbQewRzDWLYeug3H3sYCG2B3dPBcHghFdb/zeqXSdqkLcChvhX0YjrX7s2UhaB3xq9c6vJshUqiVmLx1wXhPWSgnOwG5JkjaWTV6C+ea8fDjZqtC0rETqOFQpLP8mvxsmHT1sAEXJGQl48JDmoeFg83z1m2U11Bfx9qMwS+Dgp6aNC5Vx8PgxM6KGwHatm9V6JI24HCkp8L91Xxogi7i++k2JCuvA9CXPGSaH6XzzMyOJ0HLT0/uyEl2fzWMY2bnU5w/umO/fuQ+EzhoFtjmLJa39FjpTOzjt+Me00DwxSZ9DJInRkH9c6lSKK+tkF9CDf0AftsMogSKWVJJbKPSE9mwQzF4Ss8Cmm8WD8MqADSW4HwgbzMVXak7tdKfIf6ZezeeDyvlJ+xfhZ3X7AElOW6Gj5zFfP2j+93dfImWIjwvtmrLj/wazbHMSqLuxZ36pEOm9056kLbTUjvE39wHMOAuCcttILjO8hVgdxRKT9PXqkNR2UyKuOBIcJM3gyM9pmcNgqSO4iKYZRMX08fWnWGO8JrcR3oCaYW4mHkF264H2hB+JiBfLgGTMscPqQWP+qu7jeASPNJ7+LzMU4qdn+bVluKCudzdQyOp3B55UXr1a8EwqHbvJJdx31OvtP875Ea7IN2Umd/YwcCmqbgjyh1uV+Qv2lQOfcwErmhpaI1ukE/tpDXdts1V3Y95RQDoTYLp877N7jcqB0mLRFqH2ZEFmrbam3nrKvl+A1lXDu+trtWeWlavPEEV/OPscMhk6l0te42B8+t7HKcJE+DQkfHPC0LvXSk7xGZ/JE9gEfOJxINpIhV9OH21Pu9kdM6xC0rrSrziL6XYqFmymF3HRAVJuWMRuR35tOpBGm24w/3/EaYnn4YNr8c7e2S5K+70LAyeAmlEhEQe6IbjEvqPuArehC26aGevP1udlRlrThSJ6MXWM2pabGNOQrqLiMPrS03ytR3cvphqTksrYAPRsJ36XLLMvyXb+bGc4gSxzHxzagaQqvnBR7lPjn2sYfPnx7E4vSCqNqnALM4Tuq1ZuBhCklLJASpHWTTOMB5z++ismDWmI/u8xI9xrC/DF9zhozNhOCjPLwPJ3IyfwChPyETRcvPm7N5sCEVYvQdZXBzt89psCumR1+kY65Wi9AHczii3qVrFMF7BEXU6KuLgJzhy/NyWc6yb+dBSQgWaTyoaYGRN1tIqEoZ9NvrvZoIZhtWqM6RZTgzrzvA4WpG8dIYcolX3mdqquRINABh8AIj1qibKcyvddGGKE1lcG54eJ49biUH8xBmV7ZpxSWwhpczo7+Elqzf+K8As7nH/FV1t0ZHaOkdViV46fvD4MdCiwRDOcfjBvC8F+B4diCGep3WA3bCg3vR5l5atOkXxvF+zd73cZKaT2cBUJi7BubKRtTfFg8Yx9chEsbCE8+FqUzutGFvWQR79ESI/bUWGTKMuYugmwaePpxSieVfjifufhVTjTGjPokgHgi+f2S24lrukqBkjO5EH1nYir8kD2+O1xFSeJIyyijDkWPCcPB5w6fVb/QWnqhpkVYay8JGCMriIwCaNtg4LipSQMBe0gzIkhTI+FjX6gFj8zBgFHnYRbkcrJJCPk+kCezoefOgrhMUuHUnXbeF+1nXXGLMNkepWe/C9nUbu8J/UsWvBS71Ei22cM+crnFlvylRk9v1DTo5BXqOXUVIay6MBy2aL3ZtE5KC5Gp4evbt6VJzIEQ90SYXXorOtcYJYONO/HZep4/7BFYbpwB6Ymtb15CBLNfhOKMcHJV1mtVouVc+XdBFkh7u9sSpklcAQ94vowTjwlSZ6eygk5dEYtZF4qJDN5sW4fxd0sVE/bgRUfnREIrvcKM12TCFyTlShqiMrENiQ6h9DGfGR/7zbVNykj3+7pr785gbedLKPvLs+IK7Hb6VrWBjvA/aHg5S9YsCOLhICZy2bW/gH8/N8MWoX24PQ2zSlS1hbEv0q8QQQTDj6kJi/YBc2G+PkL9TNLXY4z1k9g+zgQyk9L5STKohRL4RmCj33JJeiycrFgAXcE5CnY66HpA3+yOrADmGnwzMUXt5b9qaY2im53p0omFjbuk9bpNNMZKsgmnnrPIJ4n2JpSDKq8Hx7DOTox2Oc94XXiMyENHOMN9TOrmfOA/fEi/M6DsY4K97ifzGJNcB0W2nktrgcnKsm2Mc+3UUNdO1ziz7ksI4Y4ACxTj38wc1y05dyvu/UvVJZV32G/cg+L3SNm4M12u/ojmvv+a9WuReMpa5UnIFApy9CWH+sKa/iZVwWvDy1XKk9kgfJ6K9sOSy/4WXqfVPYQsfnk+LWbwW5gXJcwhFeObVW50g4QeNQEphAH5yBjtGxfLFBiUx7yyAVLIKWZ+LB/ko2iAN6Dtk63L2xwzf1XLlvWF4O9GZZBaKzG4XzNm9CfPxG7/x/xcsmukHQK1fg9piTkJImIcFDiDHIfTEqt370LQMXdD+5tEDFS4+TujZb7bTEQNdewysVVLpXwWQOLkl1VuyA9ByRANCAcIAHjh6DDxhmWPZznOcV/mD9DUTkqvVCwF6zpSYLpI+zL6VBMLMh8LLSV0+1Es7OGx+sERSq0VNWtu1C8olW/CXFLJgTAsp0m0Yk3grFKDjU0La72LNDuGJ6IgIiFfB/mYxyOFeObFPq3UpDGPYOU2xjX+/RX9bRl1DisbBGz3sj9D6IChY6rdk+9fi+aDPuswPzOehQ9y3RBg0DSTgWROhQp7YNZ53v9noLM4DG2rEsOPfMG+oE7lP0IOQzox8TCxrbyaVhvvwOYNT+EZ0E0tTv7frqMNCpcCnazPCMczMWF/D3cpplCsvYDIotqTAkXQNj3hC1T4VQWJIjFxzcYPMzoaSv5LjdyIihftmnEQGYcEDrXVL8iRyOiebf73o4P8o6A0skhVjW9gUph64T7Xac+GphJjH1WDrh/YYiQYrSRxRhUATUzjMQRFzRIfsQ0qL0LMeN4vsZVFmWxENAZ0hyantWPfJFblVNUflP1uDdWiL2A25avpXvSoHqheVgL/qjrmYGF6HjiWGwgQgso2XLMLwHsMn6oodpJs8+FOjP19u1cMLQ1sa3oj5Qy3JFw5tajsYwkoxYyQzmHBsMmrQK4Tf914J6i7B6xcvfYGgELbKoTlm9LV9zYdkEo9x+S2H+Eujibgcwr5czoCY/5Il6W8pazxuVLTwlrYvOMacfHO9vyCAZjLWIG0WGcJIxjCCQumRqsUS6TUCiZsz4tHtrRAZ9cpnMByajVCj2gwL+bwhFgikt6YtwOcKJb/LQzxPg+0wxJGgpTl51vVkigmcXVpzOclQRRSZa23OwpxFpjQTNC/sGLYcuXqwWtOI+raTMWsxrsIYo2Bn8Y1wVvX5rQutEpFhNKL9MJVwSWnEN6buIFNrlzZwUOSDq9a/E3ihNJ0z8Idptt25WWqcEyp+n71+Yt8oReTN12ALLo0q4HR3Y/DH0johBOukWoyN1aV25XBR+p7XZr8vtFQO1W1cEq6tB/u8lRK8Wz/i5FpG+M6I5L7wEK8LsLkWiQfKmAXKxUyQz2jhmNfJ2T8IdiXMHgRvPyQjwlA7hyRlEouU3r/9bIbbWCX9OBwnltf8e6f3bJeqjyJp3qTl64NUD/4olhbxoHhH/P4X6gFWDswgF5nqVb20GRDM5TGGuN3vrzySNPzsi674FUSEJrbL85iHBNegRgMpzkIVX+DfB8LO6Ln/CzRKYWnZOLHG+CZPAEBYIfvgBMrjfls/aGBKdTtu0KL6SvFDxwnNrDM76yFmK5ePEHsaKWPIl3eAG/EzYhESa7fDQsnr+CMojqYzJHlSHC+9qrD0pnX5yL9u1d67asdBZsR1b/XX8TdRlWCz6HEzjUZDeWCuUUrl4LhY16gx2aetUloDlHlwBPbbgfrQnAddpSEvu1GIfLSxfE5CvPek29dRKv3k1YlW9V6rpmFi68X2W6cSR/xZaoEJRzlj1QExHYmJhzmFWNcRFq+9D6AkUJSWq5qi6khBxDnfRUyg8Sp+KNU6CR2JDV/L2Tk0pKHBvlSZzCSOni+tZzYERT72ikQ6XR0wyXAm4uxs8oLu1mlMYgrKYZM7vJBPItRPmkbjE1SnQQ6rDPa9vAH6kD/bfH/ZOkJOXx9JpObjzhwRwqLi0cISzJglpKhbu8ULCGsnVHiFrw9Eb7yGmeS1h1mvmO4jESAU5Vxfz66EBkLXUC4+CHYH9JtGIKgLzGYagWhzDSbCpKXHqD+Kty2I6279gkUABDW/sdhAFuAvcqBBVb9hfCtq6bEfxC7yf9RNqm/7pjHwjngAYgr6XYodCpXMb7cMoYF8ED3TWEYLvwrKx0jcZ2NHD/x0QA8fwYnM48G8KwNbwMTFfaG/LUKqUEZ3+ZFNumOQfGPv5eUxHr4AyPPOdK/V/K00r7EHEpQNgG4RD+FjzZ3X09/isD7T1OaO877TAZApSErGdBsnU8TyfLqnmZv6KL5NnjbnPfba9gxxydqq9N8gp0a8mG+n4RMgcqz8lr7dC5lLRvcS9LEW7hQf8V445aOxRcN+8ETSgYudh20vge0kiCaAzKw6LOofS1exLasRRX8JlyEO3XjjM9zd+fpQ92WSldW56YaqI3sfJRHF1ZI79POXiGnIToFs9N/A9uKXNdjeEMGwFvAPMWb8JEwZufrMElGz/Vuw+vvIUi4TL9pGOF1G2fv5YplcQl4PBTWS9u5gA0zyWLTAkTfAekvLReGQJOjd7SRSAXb32Ym1WiIn1EMgZjVLz1HdHDCYlebMN8YTwY9KN+k7ZKyxd1nmJv4PUdFWrSYiaWIWQhJkdMadVv+OnxCAB+r+ln5kDOp5mMQyZ39MhUwEZb4S2PbS4XsEtoYE134y2vD/WuG/bjuQlEo/aE/dZHPZDrXlTy7n0zNTDwF9QjE2UXf+S9LRtzgX4l8LW4dUWcPqVOJHu5KzdpqKYcB4W+5iigawzLSnhUlVliLZh35R0Y8FdYGK05N3hZyuyT7fb02oe1nb34hkxxq5IHoGFChwxZWv3Cw8wvWu6VP6K17rYLlSbsL/ShmKEtZGDYt2f0zBwfwDpcJFOpY4dsKuob49V5mmuMunvODQox9qa1K6L31Oef2kJem2XFL1J4jwHfHB7peumZNgFWNG8ENiJOBYmS5Kj/32F91Np5MDlXts5RpxweZ2CiH52UVJQOsOO857d0lg/CYRocI0McgPZ/cGT1J2xbr9E5NVXgwqFToZLQGvEdRoruz9Bu3mQFPB8CA9z7avWY+THSJN7gzoM7gr/OkoElzsUWYagtvL65gPYV/GYpjrQ5fj/YVCBa/EBXMKR3emWUftmAzBpDNKfAWPJmxwkEv2PAX1Ib5zGkLaSf9ycv1BZK64BsmMDwFimYO/WpJXAylSP0QpJc1wRipeOFdfy19ssi0eBWkSqr2j7fNABZ+lHJm2p4d69d/+4c82cigDwCBCp6LsRjIr0U+E1hRKi1hcTPiGMhlJfEQ+nLofmmEHMFFOnIt8ne0kaSgfOFdf5AXs3f/Ftt+RoMa/7sDQBEBKXG/SAj4B8EaZqaXvxjLc0+zPcwUsnISXr2Ow0KZw5NIJqY3MYztmgxsgrFVtvc07mlfLxC+g4siRCssQZEfzoVRj9xb6qksPWc2Im8biXqxncswpg7kn+YTrjv9rCZFmrz6Yoxes5LiNkiYc/eiSbEy8O3weButqxeGCWvfmlsGIDUTr4B5+Xr50+t3d28oWaNHNtqV3DU3lj6gSlZkdGPmr65CnhSIrT/Ipqokk7BBk7PuDzx5SNky878wPYI7gM+bXThYfSFyev8ldoczfu1MyMBdReoDcljJVwy7vAfdA5OuTGsCh+hBrmgGfht6XO14nqdfdRnJILIGuEhE0zojePba3meXCUGlF3wvz/aCAQAnMHeH/D8ITm1N4Sas1jHmBv8SSlTt7KIlxRF3gKczjbFoemEiS4GkZjigeV3+NWVIlmaWp+TIfesFv0sUmt7aApXJgmkSMmCjayr9MegLzPoVXBcF/KgcObCEQPdZyq32IfBIWjUIxy+EnjCs7F2tU5QSLHOmf/aIKL6v+Goz6oaD7o7m9toQ06VGV7vsL5L+oiQAyy+PdIyujm4/HKjaFjcc684Gdhx8ia5iuiMPBfoF96xqocjs3uZvoTe9/65SJ9+YT4njkzP1ydvvk3k9nY8TboGXCoxiZzg/4AVvBhk9eyG5OHtS0a1IiYKcUPvP5wAhNRqRlSPz4JW7HhoSvHorFosQNNfAyj9hOUIDwE2IFhiikeFs0RG6GHcXKa82M2wczWiQYeDqyYTthHeLMD2joEwk2ZFL7dhAHQMJlWhRADPCCK0ONMKvncpjRoEYnjo/nPp9BgI3xkKW/wcLeHUUhiBiaY2XAD+VqHWewu9UwCPqSZ7oHZHNFsQgT1wM9fsIpPypOh71iFht299cPIheTRAE9xY84RAr9b9kol6AWGdCo+zJ/n1m/IxIEg8gRhVwlZPICU1F/2oxeaSS21ppM+2DrhErpsxcQkb1L9GIoj04ihSN/6fUIiYV8BVl+kck2FF3T/vWTjiKCCH1nw15ET4euqj5JRnfvFkt9z71WYnxdIlNQaE2mGgTEKuUWYuZX5Y0YHv52Xi7bIuRJSDCsf2tkzBRaR7fw75xaF7yRoKNIWv2tbxq6/5kerIjQ2BgQ2Bktri+1AyYJxIMuvaG/UW8Q1N96uETydbOrD1Lf1M+vlBZmFhYyPPcfs3DZZ8Q3pKGftW6FT3r8bBeUEP9hrjslskmLTguoYL7j3XDy8gvjCmGBoZ8UfdGFPfA+50or5llQmTT83eRFgVmEonEEkc+vr/3jvgz7XW/RfPC10niReWgWb/4qPucek/fTGBg4IafxZHeIrxkuj38tNvcevvwzwSCIs28puUWBln8YfUccuR5ssOZS3DZLMKSFjTU1+GVONLMCSUQBl/NNfT9Eus+GfkXAzM+hHiV48YICG361T2OaHAUElmyO0qCQxXlNP0vtMDNycfxFyjikJBDs4a1GYQntta/6Xk9HKUVoYnxqDC4jlY/wzawUZwnQXakznlX/GiIgwlGtAJyk/DpcEtRN+efgQGh82fa1MjjiVoF27YG+/7XnJei5bOXQO5KlJ+sJEceozMaoSkjR/8hIayx7QM7+Ws2MAImvPHNp+mn1djtRYvULYmReUTmn73B8AySsYHpufTq/CCrIKFB6I5azkhY0DPxa7LiVW7KAnxLxoV6xWmFfUpIIWoZ0kueFozlbYhSjC95FHd5f1nhIgM7Nl/QX1L/xqZaM5mKUn3S9Zs5Ys1UqGqVPH4enffmKRcOEUJf12iumKbLJUrr/21kEZIQzCMlNgv5TBhSQ2Y8sF0c4lVDGQqpugIDpsdXQR7V8zaqFIZzuz7EGLSnxUJ3HPWMuyr/V87pscepxQ4FmAJOXo6jzWa57BiTkeD7dVSzZQ61l0rUyX1OCFSZYWr6MF5MQbWD+okYKYgE8CX6VGxihLctT8upEqRo/M6qX0ZCF6P3tr6zO61pQ67u3cX3Tk1CGVnmSK3awtRN/85sYNxroWxzEezrbdnKAL7YVkwX0jqTfVwdh1quEF9HC8HXZHoZ7Taeq2M/zAtee27JcoxpjpNgXqXkI5fdYfsCZIrC6NUxMu5fWbx2P3gfrKQeCzNWK2EsVe73sbgyl2V1wYJCbbPQpLj6GdNcLKGLPbGSshxB5wYaOizIwpAOXCcSl808OsKeNEnBIBgPkNwTL64lx8rQAI6mNbM3wNxpCJ20eJMURKN2Jg/ab52Mwyqn5Tg61BoK8+CxW0jlm4V5jUr97kR/CUARZsGUbZmAC7N/KkVMxuGJqhO59FFMVvzWD/V/gtP8t5gGcCof+qnrPGgRLNpVvQrFjUqb9EQprSu4XeiI+5rXcc8Dwdk/kzOYT6T76OjzeNZRUG+ePufWNd9zpBe1SvinXiRxDia/7SVBXUOEVOJX+DGGaqJwyiwljTTFoghlIuOBCosfDYUs1Wcje19zuU2uzmyOPgIfjKrA9akdB5n42hn0Zg5T1CSV+jJEn+A/kwgk9pE9xoug9rKYPWkr933zaRFbWWXYNYw8G7LBHiD5MRBfHXGagfUaE9QUoGGOK/PEYUO1et/sMXxWvpH/ns3K76LPyh+NZVMdySnY3fRWtYOgCITK/98Y9RRBb3xC3ZU/7BJD04jCJ8s8b2Je/Qrw4/2ttB3W2omN/xwmGvGRwBhpjDRVpCqCcZi7GlvRDo5UcHP3HNsRc0dp8NJWBckQ4ELhjNB1Sv75PX7XKVOTeAJ04GTHOQVbwP7WnmLOJcMKL00WtVIwjMcq8tWgL/VAjupDif3vIAPb5A5lmLlDSbADurAavmc79a6T4XI7B03yeZSevv+X14T8JgjIj+1cdJ94PIH9LYJM81i3np/0gB4ucnc64IMYdrcBGRE7KeExDEBewdnWV3kO7kjNUvQCIv2PIXJtBTgftCbWUSKfNBh3dHtxJM9XHQkAg/XFyzRxRXD8XXGsX4DpsK19fFWUUU6Fc7vSGBHpgFsFY7/SegA0WEhmQ1LKK5XKJ4MUmRLIs1d4zwMjw0tahGI0H6/AjZK0orW76I+q6zccgJbwnHqLxi4HYcnRr4JBEldSTj629dsX4fpiIkyxLj6H8pa5p02x7KlQsMbQWIR9ktOBNh5PmIBefdgqkkGT4JPh8zeFF/xY7ehvBgVmOrPlgycr5YEKXKGRdRoJNOS8UdkPSxnZ5vnJ5fNnaIaxk4i/zzsgm16Qrb+rOjyoIIFUPDaAuu/suF9lZuaRxsFCsq+smMIPi1nScAvKrOWMXyq5hB9Bvk3V98OEu33M+CE5xL91H7kImfE4H15FLoQNS0bhtGYq/XrECTp1XnlPk0Ws5su1idiZcm8bpb0EZnumlggmUOu/oVx4u/NMNMSw4pVRKVsx42B2xnbbrhZUVH38n197lgl+uygZLlS00SGX7Qeke2JrOf/AvWjw0IA+hGU1kAwKgIMHiiKY7oxNLHm6Vb5X2GgvGccwwQh2us3gZbVDGwhw386UWHKixHeFdUeTNho+0bAmUKhfDCN+q4UXsZmpggl+imRAG9qvKJuWLxcxnbGd6d9UrmjVJCLXl3ETn2Jn6lMOfyOgHFTPem2TLHoj/WG3rKvDf4293DM6uA5ssQSIZrBb7xZWqteeIDKI+RIFC+5U7JTUvauef0tdsQKjJg0+hCT6t7u802/YQZfy2vN8dgzBO46f1OQ0R3iiySNNAkskkvpncc6FLrxHoF1daqFpBYKZs8ZhGLtlHMnXt/DsMTAVQxNRLEKNlUnWqLamioNjX6clHaQ6KkITOi1+K4QWmPFgPccXigENTzM/O480TU3EH1P0HwN+P+JLVkKzqEIHWWlYo08CKenZIyObRy+8iPzFHkKDDiWn09hrviVedpBWmbITZ222QbAS3CDOxV5AvfwgRSeQZfibEl/QRecw8O5gCIHYVTruy7+DaLlHE+Dcj/r2CmqKYGITjAPMHI6jY0QUzZFiSLJ7/t+OC1jkxzyPY1vGglkU2kTscFh1mlLjBMs9SGlci9Wk3GDVfUgDmUVnyvf4v1lAz8WWgwUDwFU4xYYFuN9iAe9ofiJ49wdJVDtah5PQpw23zLRwJR2Io83HzRyEQctUgBJG+uR6dBo0x0/5o4Ok0M2PhYv6UXFLq1lVXdw0KyqpnTOtzgcy2G0Oa/yWjy/Z7enimk1xFvzE0NYm7KnUjZ8xheNfkR+ooWBYOVv/1rD4Evrn2Yfg6tTBdNNOTHZpkHqEEp6UdAWSX6l81TSnprodBs0G11zai0D3yn5LVgyYkAvYW1i5mbePIXthI9/dWGokiJ1+yqWz560NiTH8J+espYXz0SLRfkuXYn5Z3HS7kXVb7+o+v0DY8paXomJeuf3Kb4lNsB2K9GfTdlf2gPyw0EiUgoqfXG9zK0IfhwI8RFiGeHbexq98y/msQCNCvv6JdaALc6JqNo9bPvR3djVNJhbRQM7MSk4FsmtTTDS6ntNkuDYEP2HUlzBEGcZwbG1536YnnOGNv32OhHNBHzgaW842NqRHpbS3DU8lXfPCLluJlVgQXgjQaPbXBXUkkhi08EbUO6B4Wl/hv92GUDBCiIvGvfEqfmoHRBcFxAOLpChQiXQCOLwpPJwOPklnyXEdafWU0hwR322ij7h14CZqncJmTrdKBKpWIEuLPl8wi+nxJtYOq6yStaKMb4o6yZ5kHCzIvUgOYXPy6QsbpT5bg74Q5bVmY9joHv24hWpITzhRhyUgkX+L4oowg+M7Eqi+g0trU0NRYazm1fUVy5LnBFLa4Oaay8ody8YQ7oweIAzG1yXqg0yFz/FbzASXMbE++9F6wMXDiiOK5OdhVlQL+9ro/Cr38UpRZAcKH3bAWr0ixlMx+Q4BUSeGeQnTHZU8qf2lxqSrS14w5g6djxxNjxlNK9if6fUyAPCJoRtI7ctmERCzhL1eHGy7tPbf8V32K6wwnWlBU5G0oqcNhGI7HzSGRhOlro7CmW/jy8IcZehiYtxmSEpl9WSMiFrWP6pm3qX13Fv/Q9WqithOmrQCZFf3SEWGeDn4ecKonfY2R+K0XXxS1cL9vIRka/UVICfRaZQrqO3D71xQOBFt/6JSsjhxjGx1E642o+WsWdjvrqcrtN4oLYClzgxIpBoAEsFh2UJgC2uN0FwN8+dWgbkOFKw0qiOH7FwgdM50bU66rPNT2SAjx31INOKQpPZg1gj2zF9PDfliSBNN7ydhP0Tpa+oXNY57qQd/BDdH3Cu3vOWiJ7Ba8kAQUby93GCovPQb3e8gximru4P8MvhYKVWMmhPTG0f9EuAlM3ethDkaB6QKI9hS4zQcGj28lnAgN16MEGvUpjNNFZcVuRC58ESEmxIqY9ba6cOPfdlAFnSplG0MKatnyL6ILKKAEklXiGCs3n8Ty71gJCDLqS/e8YtV/NACUFvw6JQ+HQ6mPTeeHGRTY8iA6HN7y4eZ7wZYA3vO1hp7yaBPZtnpIYh7/NoV+xsEx0sUXAllRUdRbDuI/Yp3zoHwbEdnmCzFLrzeQ/DXqiN12hk/hgcfUUTOdcU8LCIMaKcY82EKLqaSEKr9W9uLf5/NY9GAuH7I75yfS6xfJSPBY9C/MoXREpzQRgEYkX8H/fnE9FoC8/x13na0LQ1v8nINmF4156kdJk3MCmOuvxtkDUkImO4zkT1c0GpQBQUmsPF3qx5cAH9wMpeE5HfwClfxVLZOpgTwE2PgBsAf5V49bXMGzuKBMQ3W2wFYz0uI8IJlLFcq2LtaT2Ms/NDXDSRXSqdCm2iScZ5HJTkYkNaVwJyf0UxuPcO1BzrP5q8B9jamyZXCo/lHTOHKivP6gL9kQT8RbBuXHp7PeUM3QdtLVFvJJDfMVXbuH4FeATlNUulk2vfZQvcMhLtgbvB4kcs2yR11sqipAobALSSn/1X+6eRSWZ/oxMY5SiDRrFAzoujeBOg37fZ2c9zvqjgnJD7zVJVEWoIukNxgRpGYw1Ypkh3x/EyhX8SH07tMa9CQajYKeo5Re+QjqMlSrjJa24ADNIXdV1jPjuv3Qy+Wc9Z1+R/bBFwrfpXdW2VloWp3aKpwdC7B5L4iPhEr8s1ldgEWS0WSLFh+JLk+LNecGIV+UqkVsYSK7jPFh7gVagZ4ofLKbi+0ZlLx/JBw8dgvkqdBHsDko8fVJKCDuGzaNK03UWVz2qYcKsKaXnc7MpHH0IhwMyeEyZPJjTtXIv0x9S78Uh9+/KE1iQq46UsBJ7V5noEcPiBhibJcPf8gJpQjjn6w4lELRAVQyXG2AEOGxdltf8b7YojIcxLylnKRtdphYlAvikFnhvz8F7HPcylVMYlc+Hw6xzs/J7KrH/vpoUr3Vt0A29UBLZyY6rqy9xiB0dV+9gthBrS6bozWey9/cp56cJAnKVxE4RdzsJZa44KuTezK6Jrb0ATXct++gzxxeI9d6mLtI88336DfCLkAwZvomBu+iUCPjInyogtH8zVlbtJaRgaBTFMzz92Sul0THZopHFtb2BlaNiDNPw2T0ZOZ9PmUXKrmWlM+HSk/Iug7GkLSDUn3Ls65QZXHH0yK00dgbrp2atgOrpZidaRqAApYnnhQGzbA9eOmvZGyvDdrEY0z/JqZuqxDZ7rjTGy2uQAx7Z8xzvGAYFJFZuItxoTm/n7wWiOv7GytCh+bChfA51DUXv1KC7r9g7QDr//XxaEzOGgXeP9Ks1wtzRUPgRn/9DzwjBnOBTTmOqruQ2GshW/k9z5fdtQyMDNpu8UeZS4FgaBKA6kjs4WEZWus3L/2AacutRIvld0MV/FD1/mk2rEh+fATd2ROofhcZYq8wfR5104YWH9yU9bO5jpNP/el+PmnMEJsfqWzvKBUH1A8OXCn21/mHOh/fPP9GvPNyAbt/hQ4y4P9f6W/IKTxIRMl8rUdStWVo1PWFVWkSrvyQm1vNfGgGEytmSxHr/p06xvby+Ng0SYJQCWt3mRTnocOu+HnoeIUJgqaue6jWL6trNonvDD5FCHwGwBs/qlvshU6bHODOciGsvNoQvEDg8orVNvdRmj3aCl2/Kk0Rv4+7IN34UJSkHI1Q6po90FMX119cbnkldH0TwlZtnT4kP7PtTysuO9FYt9N7xzLyqk6iCiVNTzSjVbkxuu5qi7PiV6l9JSE1vRyYHUTTjHY3luccpnDvlDp6xEX0U0vpNTpZ70Q4n8yIU7XCD1mlGZS2KD8ivf7J1SqF1xnAoV6L461XotnJHW/G0wfONMh9dmGy+Ggf9zwQYjVG/ik9HAsbBGXwSALjOwlmTHlPpuFTWpzC9lxzIfU5b0/k8epr7QXRnrx2LNPp3k/0qVSuBSbIPDK1f2zGKOcmvwvbG/M/7Os37517oozCnXZiaVsojzP/dBdHtBhY96hoGsfijuKF3h2r7j4MioOiRdhOvwJtaixgbrSC0ikO5kgC34hdHV0AA5X+JVcqD0FNidEWbLY6qQTNfSPtWaUMen9uhC/y37TC5oKgdTp8qX6kKOSKU2XbIXobcY2jCLOiCrGEWwQBcWZOZVda+RIMow9hgOzGrA850s7s7EQt5Q7d18GNbjZuKrOYsz9X64okp78Jq3nhf35/rAjPCkeCEXyMWPQGt5oVdGOL+JIDsI42V8zSmyg1oGJSjI2MdfyElvv+DvyM18cPDZcNT4tje+J6KaIkkX9XC10KGVpxlMkpkpsIJJSR+ZhRpDBzEDJTCzCDUJSr504w/oXXVaJlmhWf7aF/ylO0gr5Xe+sgKD0zd3olulQvnLt9rREC5iuI6OFLXwLS4TjfWm7+enqkDabmOEjnmuNGXymALcbDhavp7jubV4jpsBtvUSPmABWOf5oPofAQz/gUoBagBWVJBLRt2QgSf/sNL2/OfnQ2OuSzZk+tP6Tk8zPz6WcgWo3ovH1kn5/vP2ip+d+OohS5HnbUoGwJzb0vESbDD4lJjVQ9ZE/+CXCN5XpGdeKloWBJwF8+DBSEWF0Zk2sWH69rdB4VnXnTAeEOmDA+wNL8paD98K9VDazZAZ/ZPms+kxtQsbyaKWHozl9fuk80oBuI7lrXpoSiokLk1nijS6W/cuWxGKMN3T36j+UBUi2fkAZF67o/WP+BhVUWQUGSdZpTZMtLn8BJ9Oz3jgRyC0LKpV28X0kNPa2jEdnrKIy03r+ARfb724KVrU8gfO5CeIGcUKpde9fS76u0XDUkcFakOPVp2WycrqzT3XZEbjqXParooRMRW4BxN1z+pAdF0KwoIZusbVMxfZ/74tQjcezYA4c5XcfdMBH/WStfXXQT7LKFCyu00C9VKGSWgVczvsRFLa9+4VErl/d8FyTfAkj7s/zlwXhPLkHrPzvPIcVC6Hli4Y8qZx4x2SYjo8oo1GWqMTWU85aAsVItabvR+UAN9eBFKOYkqLSyaNTYCi+RkHm/XyweZ3R4juKcLbCf63wmeGgNB4z5ExkTU2rGCRPOEJ4dK4+/4TPshpzGcWO4hqQ5vZ9yX37PyCAh6hMACLRJ6zkeGcN8N+5K+dHQelQAmb+COeOfPelV87fR92/UF13mIDdeHMuxo4U5UcXfHpIOodwd+8rXRHH25rHSnV9KbKP+t2IfJLYFS1Y+8MtwUfsxnUuu8FSvC2BkO8GxyCgmjL9dqZKR/01lN3kjZsHcWTZWGGDXbK3jp/KxKYRcDEEqBgcZ5gfGXi5H0mHv5OrrGWEzV82NRzS1Z4ZK44J9k833IjosCAoNVtXnSkz+EWaW/GtvQE25MqcOul5AS6/tlb4WhSxd91JAsRJeek16KaPxdCTPT74yeuWn/8HfZEjS5MIi46XVuTJwbPcXi4bDbcLliNV+AnpE4vi77blLMpetpebF/d/49iFCJSUZu8IYj+eaLHp99l8bto8vrSViFAnHoIcLVSFIndSWKZHF3vqoPwPOfVtADlnaRx+oNQiozFdDkXOBE803/zin0CLPjf+c18XU3krBvwQBiUtp2P4mQj/8cFscEwgi4Cpi6rj4aQPGqTpFb0fY/J3MEeq+KdV4gZdXDpAMOvhZS2UWwDtGpigM9MtUKTUvBTJ6HmMUE910ytNER6yZDcm2dgD8RJERWFgn5Zgwec7qI4HrKgP/m3ZlsL7ocu3q43S7Tg2eY25779XPSnHnE60jszFUSILfu41sCHMZIlCMXp0XzubYV0LcZ8QH+XqdQITCv8VcSPMvPiii6JLf31gXRETrDU4b6S5MKHU8mmyfurA7CmE8+qS8jl8HnKIO0H6CMMNh9cxjArzaY1yuqDQhLa5Q1L/dZqxcTjlH8pCnOJ4ha4CRnJzExW/gi7viIrbB/VLxugIaeLsbTsrQo9Go3SeWEQXJdDQuGVBa1euW/uOwKlvJY0hM2wq/1qEN1VMpH3IrBM6VdoWma+be/XNqzggY4DlXLkxwKHYjow4uF1cPP5cWPBabNj095rvG6Dl6r+USMX9xaHqBB3SuVbGVFSkYSzVw4Nd+yhrxYBcys+87TohzR4djc/KAYMcgPsFa9po5BRb7oyvEGurBKnVyrB/SVV9GOZxPr+q1aZZH6bM0INKT1pGyX7R5QUQO2sKF13UmMWNyzBWBxncBlDx2/zm/zjSTT8PqAf1aBuD3EduDpKCd8u1x+pG93F9WeCePTPKyO2r01+KBWoU1vTTmjjbjhdXfD9Lz8aujIDgCZGvYKuM4U76CNJhU6q6cONYTc7CrUZzar19aZyiqZLH+34wZFzlwRX/tTcd9gUjCufxt6b6uuEH4ibHpDzeRfMhkym1mSvC+E87Ef0XD/pXq2dqHiolYj7gd27akwfi50jHLGVmE3of98s+LpW5LQtpbbryR/zF2zDQE63wpaeuxoEazg6GRZJU86ieHCNvkBH4/wUi4Zh6JcNYVUYsMofTlQH46Y6mnFqMd+zm0ARDRqQ/ueTiAHDpTYoMRINIrmpPef60r+6DD8aN75nxRzf7XLa6uYfRXa57+ECgVL4HQisDJMtnmWgb9K2o+fmpiOqZbelHG26l34Z6nvDorwKw2bACU2V2lq67Tsv4myWwto+Q4wwiUwzWFffeFwZpWPz2x6e/SQcEkeZEDDiiOl6UAThzPFAHbbUP15WxEDBUnEg7TbwTRRh5PMRl5behtJy3j+shThk9kbzmnktZ4fwhGD8h4jYBeb4k5yPp6FmHrbKCCXvRoMYHLj87n0QQAD19qznZgk8cEVDT3GfblGR8juz6Jw5myguBSYOh5kZ1qY75knlzCWB85u0q3O6CfqhzVdeBSLmTanqfN31bn8YftMvHx0keJBFzdmOCqDz4Ogdn4m+OCZ+uhonA7AMGim4SiHVYz72x4kY+tBV0x4b0bh85P4QdLX1iAFMQDHmSvIayTyDGiYFl+O6Ca+osXUnrACBzbXm0JFgKLhhrnqPjzPZpYrCNcDqj6RirFh2fUNlN4Mp+lPRbFB0StC4FiWZ1Bs0ehjaaqJ4dOXHjOFwv6+eHYsnJ9y4ShTBgOahjhq2AwPuAu+N9CyWeb9rz/JhKfuMb8Woe9Pbnqrbwlzc3yGQXNY+2HfLzBpmPRI5mPYy8JuezkyHg3J6EdPs8CusoXFzfjhB5Wjp0lbCeyc+kHSLMSB4GQtNmT67GHkXB9gVZiIDi5A1fmI0F46ovpJiBpWlH5e/OiqAuBfOMRCBRg3onv8zMXNYsgqbgo1RX/XmRJtA0tsMvUwFUlIll77HTctzLc6VpaI77m3xPfaWa0fDrjE/C3zOQ0DdT/g9Nbui6HIg2RNCCKKIytuefdFg4PxmpkKhQxsHOs/oM8frrMy/a94hpc77JJAtoIE0EOFCv+6iWL+vlJ5JPlJCPUqnzyQmy2IMMvuslOV+CUj4IuCtIHBNAo8HBriFmWBhufUyX4hoLbStiYctMEIFfvzy3wyskqnKewaPRO5h8Wyn6thECE/WN8gMRUZk8Yo5B23v36U7BdHsQ/xq6FlrVQzg+GCTXnLwi40Z5/uVvlDbRuyi4waRxtZAx8tJWd3HNS7ImQJM/jaqyDnZ9JGI50UPv4i6Cs09MhSuxAp4dM/t1ns2gENzVOWM02am7ujtzmezFYFsB21SwbA2BJovPELwHzAgMisjptraQgoZPlQ+d2X7O4b7SzBwbjiouFFWVOPKXk2zkjIB+dNroVoIQXPksqCqFZB629/jxCQAdl8ZpOdkGTcCIL82WTrTFP52sZ4h4/FcRdffduQyqhAUXIAbBlaaQ/1Vn8DE2LEze58cOUsnLI2XNOxp+2TKkirDb8YRmrmtiXultKFPlNY0dIGqo3MuoZARgPnItQswIyvEXPzJbg3uMpV4ehFGTGlHJ6Xh5dqaZjn6O/yMsgV7pgDJ37EA9vNnfL4l6jGnO4wlevxlLxvVhhozByuLs7wadPrUq+2VHcQF42s34eNtgKBE1WxhrJ5xvLZsyo/XOMpAnfrnsmD2UqS02jDYydkqGF/Cu5m4tBqEN1I5kRB1qDUBb9fwGTRBJzc1xl4T6kqDkmOQ3Hgvf+N+ZUgllVCsQV/KX3FclgoOz5QJZy85NDgXqXBmMVSfcYOYu4C3ybdt8cdLfYfJehcX66e5wDfYwqYc73Nr+wlhFC7Bukle7TiP+lMVZUGOJmOIzg0ujWfd3m+cuAuIejXjsokUvgiLo5IJCCPmieUXYeQnVkJO4rbaK4ySyfFj4SikOP0PRf2D9daor3it8pS4jfHFN8jZSMf6cBN4y7eo0lheekSmx+z6TOJywzP2NQGljwGyFoCx+SYgL5hNgyLdp8jYl2b0oBAIgFQ3hc0R0nNtWI8cYh8hJpJYLu9zFvL7YSyn9Ln4F7cuQCRy6GTKmpiSFRZvTzhaF+WkwhzEeOdfqCGdATYK+edwzzMJhpgyBVz/0NwrZ1eVQPfjqKDE6NikFMm3d1h1xr5OmRSci/hMEEyUPqtxFkQSZTFHtBOntJ/tfd8no6/5B5v8cfFB66vylhpgFioqpSrPcveKL7Rv19bKSo1EIQFnEIcQZQSmlf8AbQBTjMlxwVJl7apzKvJSzvH9ipKuwFtX9D+wwIBKG4IxmZkq6+GQO8PUwlMtNX5vCik50/C8osXYQ0lMn6lPwjb8OLJ6cKmRuKYdRgEnhdVyQ6GoyqyfvOSl6wN48g0Syx/AICDtxGfKRrs2LHn/U5KwrGyNDvT04fm2fSOM1+/lcQf5jdRrN0M1X7RLRgLNJ6P++XMIxUrBXGho5h6lZXE9tiQqJeHOrejLw9EcP4xNae/1UZJ4ly8mUivGT7N/mdgKnyk8jl7qcCCbBPlBQhS8hOe8Ygf4mMgekohLLKtfcCT3dJNTJyNuNwXhLI2ahzBfgrROPqQZTJYnzSeSY9bwEAYyzwcJkagJ6hrD8XzulcWa9RT1AZynMVx6JPFqxeKEhI79g8PjPrwjY3yu8eu8rUkKjBY3vAg7GPwBDCgajm/hBMeMBnpvXIs7vT++dAKeUrdrgfSzI9wpB3n3zzZpGcVwXN/tLKQDiHh/LW8BomuBv03qTRSN1Wq3/VNIBe6PB9TfWqwm3nTyb9Ovp0uADgoko/z7uMKaDOQN3ob3TXFz/ODPizOTHbv+5N1iyoY33UxL//lmQgT2P5OPdTtQadHuosZ2BhMIVhhhbXN+fEZRnXZKOJHI4wXs41+IH+BdezQ1iJ9x+d4+hfycFoGzhIYc3zzm2LBHuNfOkV8JDvjah6F1/ViuMH1iZOoXcCndkgfdOsZDsS2ofNWXjxmrVYEotvyxg1TGngwZoRsSSQ6OSvrFf8Vek9QbqLq4VeB/M3/sKVMK717RjMwRE73uTlxrcJJCSlAEXndDH5nhsVqljHrRfS22YbVB6o5yHV68YH3bBxjLtoEctxk5qilVV+qTFGKFvFkme2hfytqPe8qlCVJRioEykyaKJn2jZON9wrfSO3mFwT0u+cy38LhrSUre84HNAo93KccHW67jRaptCwlIjLoaNKLSWE2P4tAYAKwiroOdkYbH6SBiV9vZgTBxgIbD4e4GPXaWwdEjNz3lyUoY3SWpymNbiaWPZQcBGF/dolhh5p3NOeljX/JgfoKEE4cFBZ/t9gPxLXm63CT2DNxAIesBfaNGX2jYioN1KGWHckRQrq9xddfG3OboKYj1j0NpCezzF8KEPKYq2vnwJaEkK7pqg6lWKvEWzo7+gbpfh3TZ9bxT5Dl6OBnY1yR7X1VfpajZYHOdG+cecH3FjMqyrVhwiuYDQYSQrTe/TZ4uYUgx8yc8fncpQANdlJe6JmmZBn+iOJ5P6ZCquTHKBja22QsOxtKy+94sWxPwMiGf8+C4jr/jrJ0DlYoAWTC/DXnsmoFNPeYk3dc2l3Cm/ehfMFybWpSqINLkq6qHnqY1nsGVqZK9G2O81t/Mu2tZDU7Sq+WeAobGuYdW4eGetCx8veaoH78bOGglsaXAMRVGy/d3l4NN8U/B27fqGPIvnqYvhIm8Dc82ocfRnhTHf3G7OYz1+ChIFyHCjbmcf2CEZ0DKh9lRbYfpimDc3rEYtylzAJPs+x7NNQqniSfn4SDju5tkygFJKt/RxKJvhv50Hk/uCUB/VqhylSG+ZKjgjsQ1iZ8MDlgDmBxQSoZ0i5iY1X6Ywzqa1Zlf2kF2IwztjTQNQ/4sbZJVduMiZ+/tYz10epnwh4sjIP0YXU1z5ph0pf5Tbzd/3Ass4tHoyMNcXDJOeVmCrJ4lGz2qivYVbcIy3Gu0/PcuAsNNILzDgzn4ygnaGykVdFOCaVrDgFYBwHBIsasQFMlhMLkEFJmSICFSnmY3Kv0fOzDJO0wmU3sbwGSgHaFzLPOEojF0OSEN6KgPrGa+sNu5R/Ue8NSUecmqENVpB8ZimZNcR7lo/L6hbHMmFWCk1k6qFxkeLaswLDfXVHy1YsBRFP/2SWFwNi24nys4x8GcMSXpb67WdBcLh4YaMFMRGYDi0OhDg84C2+Z+ABO3yAL4g3/uYiOiJuvHvYe6IaxRr/sCrkgAtSCUcIoX5EKzkwoktyLJwcvoPsc4ME4KF3qi3hBbDkB53Nqy6o6kW6F3XqzYBbsP4iLRVTCSMfq53ImqwUZdXbcHWZEflro99lWdFtylJ6nPe7jUFW5NUZ5i+tEJ1Zi939ehqAA8Pbd/+BMn6xo6MWqUZVlG4t1CEDy+3Hj25c4u+yQNY6ZdDF2ZQ8F7ynXycbVuDQHdi4X8r9mTKKzswMHMSvt+ImZbjVMmdT+Mv71L3IJHLzmjuDlB3aML1E4NEljOfGO2zHyAROB3LIkMT3yjOGLZhwiQZoLAFlIA3neuExtPjmcAFTZZuxhv8xvc4Bb1QGw79ZDWwOl9ToCZT1Uo4WSo2UKh88E82/VdP87tNU5aM/IhVXdlLpIebGDaPb93P/1YQC+ADXphAw1l3UWb8d7Yc8rR+vDOXmstbMfa2CeghWoEWtT6WO6HyuqylT7a0kBd2ktjxKP6f7SBVSK+HHpj+Ue3of2ejFCFNNAO6wlzQejGr4+evWnBN76ZN+lQqLtA8SL4V0BFeksm0paLTArV/JKXk5OoDAI4C530zL4aTZHS/ILsdcFleGBJ9vBl64umsaZKEG+EJITgekkX4x1EKkw2tqf7SNWjCfCcQ2mPUSw3bESHVwYcvMSHEOUTZDL0HvppHLaqzvHqfr4/5mPNT8jRZvKPcJiHDP8fPfFsDMDWsgrOwOvPOdMuyOIW2abCuCH6GqIoGLG+vUm4ifUIbXFZ8UpE7Zsr4R54tRmVP8Wt8RcqOm9NcONymer4NleuUDg1pzQX6inRl84lnyn0buwmPMbEANyWqs7DUzkAbFt65yWKi9FxKhkXzkHEtE9yNJnr/YeUqk3t+EBwaRKzK68xnVbhU/0g6EVjggP9iTNqRbAFqWAH9I88f/C+p3lMFj2AhDrfCSxNJfsPPVfQC+r0Hgrdeyxzf1JR2N4AlZ5FzCloMhu0j+iXz4TFjBae8MgGlGyv86FJDoG5BZb48Lhx5rttY6NWytAVkE89IA1VxpqDCD20+Qf8Oqizjlz/Ne79vRti06Vqq8NQwZEFy8zk92SLwpegyiOiP+8LTersxyDh356Rj6vX5UbRUdifUtk1BkNY3qXs8cK0LuIVyOIENibte1DNAzouY1NXZUE87DjiPy8V6u+ZnevzGqQSXkzAgITd0THxf1tgnwbXUJKM5dLbIYslzxQcWja9jqs/BBpAG50mOowTTBJ4HUZi9Zft+9DVtRom6VmF2TAQ5HFyGik10OkYGweJO2TVv629eTV9xO7IhvLs+G2Vjo1VoyISmSQNr+JorWCjmCNj1GWlbs5IPkB6Yu1EkvTs8veYTHJACGyfmV98vYKNiMt5MCdhLIV2hXujhdQkH7M1H7WbtJJT6sCATv2GJTSZys4dOLO03q3r+p3WxYnKrzl8v4632kJ9w9Ru99tLkGG3UrGE2wvXOvn24Rlg1V455fLu1YWWp+4Qd9AaNjo9lfRBBmQW6jTzERUVOsSsJPyCSYthpaIhtdKWdwDQOBZCBurE7gFB26t8SQsO1j8XkE7p6rfYYWbV5tJsl7AUISRibnHapnQRO7R+kHznyVmQZyGfIC+trpQxzy0uymAv8fcugp/6+YFcQ+w8Opwb8HZFfAeqpkwvHKtITy/fjAbhUdrS+h1Y5eLQWiLMurgczv/HynLkRXR2NYp9RKwV4CLHTB1xfaX74aaFaibH9T61ybFoJhr8VbEvCnPugQs0Eanl1V0mmmRDNf0lLpZ/ygKPgY+byw4kPFrPQ4e3TVNHO47+W+VCRE8IXJXYztLA/RgIpCv9fBwfu6/c3P/mlpB4lSlx7OeGzqrwCMw/OtOZbknnKJYqVdeD9AmlQ85r2dTx2tPSZRH/zCQlZ5mWgDZiyx+7QkXlXANDnw2uZww0J3RU3zehPXjkG8SIZDw+OvmSDFx2RXw8awTN+QEcPjYcJEB80NSD96+mKC6qY2+rEcuwiqbSAG/fQJuxijcIKaN+dJdMZ2UBAumPStBgTKcBSRFiqfyZJvK/RpCrspOVE2os/Up+lQc/s+icF7ySEPIJ4pNGjBdziKgthoZNvoCr/P4V8klbIL01mZ8lRzOognI7EO4E3ZN0+fDd3k9iOAvbOshTH8l9QH1d+vx2ctq7eguG2h/ngMesyG3xYy9iVp4xB+8ThrjE3YWFXGDdmI//rsBrjP+6bL/Xyni0zD3L9HwcfWwncbt3i6cOceHjnjKEvoKgcQvMOJZLYVvCraVv4Q+Pb1QaXNIQZJBK0DBBThKWuAWhaAYWnv9WuNzFm3KQHyvoq/vxLBbg8Ws9Yo6wuCRI8SXsQRv+Q2+fRdEpSSwe4tEES2U+iiPdEu8Ab8W+zENGbWvB0WO8TwRQaLNaznYumazgSlnN5lAz2De0t0ukArO7AeXID9nexIXiiMASxGLT1xh8/UXdR2tXKxhcZTP8zH8ewHgvFFiQRxgoo4ovlNgtZgnBv40YAR+YXrMhUSEm2TGp2DPv2kxIyaQYVHP4dZpPI+EfTXxBhn5GySNFNukGrelIxLs7nnO1VPjIJfm8tR0l7Qf5AT/IOwSDBx0Pmr5wfNojGxNjPsvJq8NS8ZSz7e1K2fT28VN2PEOFbXycvB9b+8lAUuzYRsmc7KCG6HPdJfrYDMIULI9+ef7m8hxfDAKnZDu0x13478m1gMQlXs8br8vR9uiNfC741EeBbTHMGT62JJ4NHq2blBiUdPWqr4txv2SK7AZ2/yxccgbrfmfSs7sJE/pVv3lAFCPPLv8fkzZlToc0k/fsURkuxWRGFZYLUmAjzx/Lq+b/wbgk0SBHDRQHK8alpznbTZ1I7pdcNgY2uQYnNpgWo0yfgodJ4q78M6++tzDxgw6N20ev9FSm6+5q4r6byBiT8wzolYq/fl6he0M27g04uES57i6KK7cciSwSiXTTxgnPQCqYq+rBdHlPeCFixEqN51cirJEkjimSioXTCpmiAo9yeXS/U3T+3nHbFszvFjeQ6L9DhkYVofmy95wQeeHGo5aPaJqelU0jdNPvVw7+HmuSchlzmud16W5lkygSvkwtNIJFspTwR/kz6B4lxNYb7KzjVZFD52THG+b77KFV9BS2YrMB4x1uYSngB5lmgrXj8Fheidx65y7LiMFODYL/3LkwF8V/I/HnPZc9CC89cW9BctCCs5hsdJ8OPi5V/IhChxAyZHLDslsUoLTjZhcaR85su1F+vbr+jP61Yr+MNkU+uCKkeRgyLgY1nykfLBPNQcdyxiLhFS3OC/sXfD9lcKcCUCqA//zKGQX9rM05819WDZfGqKO5Wi0283cY6jfrZxCc/wrCOUUi2d6lISyBiocRe9MatJCL58Z1QVjbD5AS5eotg7NCS7MrA1Z1qbnEighB9cLh7cLXIyueO8kJmEUv63dI8nRz3E2CBAqlPj8A1D9Xzdcv815Cov6L/B9QQSBM9P31rJ2a9BS792oyyL85N7p/f0xU/pIdGyglGz5q/D31A97WW4ZMZGBfN6sqUqYvgM+YiPE6aM6RhLUnJzB7IM2yQLnQPXqVc4sxrhfaS8uRt1JJ3UzNvMWpcx1Bp5PC9Ef/oJxuKwun633o84dTegFrtTiD6dEMf/rtuEfGU/YzwAzJv98eOc70nMzHL9Hh1w6kbJhN70BQjWx0k4fWQYPk8fboO/+dP3z8xrCoHx377+hfnoIwHjuMwKb8XW+CtOq59chTRgQcCW47+dpBqwUg2P8K8d+oe1noH626X8UdyCzPPS1xy90n7UaZlVxgSn92slsLiIVX8EUTkn/R9R17HsKJAEv2bveHPECCe8QJgb3nvP1y+tNxt7mYmY0LyHmuqszLKZsh4Jqtbq+05WXD9ADr3fG+q9DHFuGcPoNBPMrbcGz3VP4KAK3D4bszQevQBhCs6roucm3XY6t3p2t9jh0uO0HfOz+K/5xynKTqd1WlzMERzEpgKt48uKxcx9rPaO43FMQwM1T7Qj/VE6g3XFngwTECNqH7RWdiFBwBp6YO4bK4gbUmaAxo63rimjzb2nOnjJrobhBSbOafC3Dh2tiuVFbiVWv4YouOOHNr8CRV8lrZtrgnwuivg1DDSpqneY+jRBi4Sqc5xtSNbU1WL0YufteR2FTc0BgjqI1La8To80P0vv992FhPbVB+usnQCi9h8r9OmMrFaT9Nk14MmsWKyd9hSknUhpMuLkHOeWOgUpXBEjn7glThy8+zw6DUTA/YSztQOm1I7MakXjrz1+ZJDJeJyBcw0fLUb8xYrldvszgqOQgStJMJgo4u83QHm0SgeNJMDNG8f38sF/vTYwQoGIXZO/YLiHcoKmNqDZfkM+ulLqnothe9/gItuIkOjyGDtbWRk6gS6Wwb+AUNU2HWs8yL6wJ0j1WP1vh6i1S+UJwt5sZ1sBAjGkMkNk2DO4OuN0bmPsb5YyaPAWpoGkAgn0FJ3AtvDUS8KNaggtkbkukN/L1hEYNyheXUrR8WA3ChvsWsi/ZjND/KCsYhn3y6s6/jReJ7SDtUYCwx42bqjFoYXKEZDLkCI5i4Yexn3eGddYr1gS+J3xej89PLGjh4ZgOQsvDimWxDdTxt7epS2NBDFhDB1W61wZbJmD0N170vvnszVN4itzYZNDhHbBK8VSK41qfmtiOhbyOIfZuB/SvCsKoA1se4fNb3rve1U7xJYXGep3JRfd+cToS4G0cTADuXIZEm4BgnIjaRF7XcGZm54zYqbrRtnKLtMY46cFv25IEtXI/VARxO08XC8ULIPzUc0QZ4RKTcwpzCUVDPAZhqQr9Zogs96JgKEd2HW4vabB6+oyXLGshz0I6cNZ0vp1X3GDi++uj12ldXklJis7oYJc2UAAjvECzbUQzkPd1cKHbp1JKwqsnOGaEF3B/ePdEMQWI8NRJZ6XfJjFiF0uf8PiDka194U2YMVIiOlWb41p+Fc60mMNlAYO1KDdJT8w/AsOAH7q55QZkqPk9cY5Ty+Rtx/AV34Oi6deqcsLcf/Kbs9eXP+jCGZjFaD+lq1HJDMeaUqTpMnDFRQvcY4ckJQtjcP0FvwKxYPZHzbn4auZq0opbpxr8pDLcw5YgMnOya9cSDoUeekLyYiNakZdCFlxengj+iNgu3aKipv/HOkEHxT643fKGMkzqZTdo4V09vlzFo59p1uitRCzyJbt3nh+Bejkqqc12rp227OZGg0AuHp+W2EsuNbVZUZgE4hJX3i4E/TyatAyB/68OCCUb0h12Fre/BD8QTPQTJL14i/R4mz8gKqqyE8o6E+aRWf8kMhULoX49TM6jEUqXoh9v7EW0sV1/WDS8UVBBm0q6d/ebVcKvW8no7k3ByOKaRY4icfwgtqhbt0JSMMMZvH8jjJU0sQjhBeNQSYhsiI0LRhP80uAHxxs7hlL2I2AWAsbLHtMuvsyttz37En8HVs0p4C3nT+nHdKNkJl0CPzWJN99yL4dEEasRhX87gWU/bNwM/E3r4hR7LB75m+UB5j357elkJH9U+oVKAqz9xeZBYPwrCz0HY6Ou/Pn9Q+6t86RF2Q/dz5BxgJNU8NrgcU3BjiwUGPw/W/yJYhKU/RFqxo5wEs2H5+vXWAZhzN4ifi/ETZWf6JCATLoFGyUrPbYfg+hjuM8x6VXI7B11D/tsNExOkhEbCiuH/f+MOV+bSovHTN3cg8ZfbVju0lQmiyHVmeEI5FS9nDE6A2hMTSwBWalHurNu8YXr3GV0xakNT4p6QkLZeL7OIabGrKl7BkAMc7A4kNDzZU4al4+OJkj5wnrTh8NtVOXURVpn+bf9eyD4mDWWkmxOdQS/KOnz1GIj7PM5B4t3q8FZQ8+IDgM0WbIol+R8cD/iCy7krTv7jviGZ86WuUvzItdUcJigOTeCQmnMmI1FXYYGdIZlyZAp3BWIlAuilxewv3SJ22lvEH2bAWPzLhsPP16FaamR3LxnV12S9r1TZEjnZ8Fe8ddaolG/JaXdR9y3oI3gwx1GcS//RHdsKFnO3lboV9NzW9PAwV8ikKUHfTICGwnDXEg9FdpkO79JniUUaWGLB2TUkRRhI5jtOOAtaK0d3pSpKzbHD1tWdZJMtjYT6eW1Jtj6VUZEl2GijrM66lt39+f470nqaD/zPjHsnLVvgFzmQYgxX5yB1H1RwSSQIKQ71BNO+xOsGHBSVkm7xl6z+JGamuIv6CSVAU+gZOa4VAWM/xUn+qjE62v4opp9OVKgmGZw527/vY0mA34bA+mknMfRMeOsa0HzN7dsyTs+xdSnc9lquB6KL33nJQWnIU6NuUE0UOM/GYROWsLMXWTg/lGjNa9j/IV04+rrjZ+R2m/q/sxA6M0hXB//uuORtSCObAe3adwRqhmewgoJlHmw65YV5dFiPeg8HuD14r1i39RqcnUKtl8GPbLmJ8x4DKhQdvLM9Fvc3CQDkICDMPW4u7jWwvX02+p56+HVzHgJrOIxWSpSzQlLtX80SOto9iqZNa2k7MjpKvGSeAzgWHKx6219cz0lNQaRaaKONfZokrrgnWKS07FwiWuXDTMjfvh/eUWJe/Lo9n8XuksdbNbG0Aw9eFedhTjmJJ1Du50EDGfiQEaWZHpRFB6hEtxieC9dPyCyDuyjk83ZegSvPV4JbJShLJTT5m9g/Fv1/2GIu1SrSN1nwdK6jBRbp1BLYUuaneBoPx6c+TlRG6hV6W3lKC5XUMmk7Yt8+ucGorAlzvZgORSA/L73BP8jR/JQAKQzM1QIcM89M6a5VAa21PM3W3c+8vZdGKNGugN9CGqkeRo8itlbxrLJF1fuduVEUTIz7xOSTKyHbcuFaXsD1yOgiGV74cMEw8dlE9KQnXo173hU1+0l8WEiFQLGn3L7xGRR0LRkaJ60zIeuPup0JI2xGvdRRJeCrUh2GQ0wCWe4HvdqIlTf+G8xYhqZAcfoTZrRS30hFIxPkQDLE0E5zX2b6P95PUUU0lTwEtf9vBV9zohwCvBj9K0kr+u5Vfeuq1vvHN1dbeQH10p7hEjflU5B3EGv09tnLAxni2kLCXsuHZ0L4cXJO7E+1079SOt7uIjtVZ/vD2aadQJHrPNIMrC8mqQhgWX1aGkFBF13FPg3shejZ4fjvaZ+efJnNnepEBn3nc6/obcX5HYZas4QKpwqb2HnVhcbRPlI1VtoifwKPwETxd0KHaWZMeZs9fBUwRo/+FHFL0hSpEpvuKhPMe/NMF3q+WuTngIBlKvK2kSDPrognMxCEy/8dY0bQdYJuTa9IQhl1YU0TT9ErsH8GSHivTRX3YXzD0VmPvu4rpomEid2LNM2Mc4Vufz2zfLLv0hgkYJYTg/CS0nuWNhoEdIIDamTRhucFy9shgJcUioepjx4ew3cHtqHkmkgJpJfscET91vU9Un3iU/e2BtvrAhD6WVCWZsHfOr7X0oRJfGs0MyjWQ4cV34Wq2EgwJ91otwvpOPfb+TPimlJoVwheQu9oP65uPUHhLbEfog3FVANhKub1yOTbzQ5fiVPeJE2pcGOjVfT50ACMnbiIGJsD0RRHZ0NpdopaFVa0Zf6I84Ur7c+ljg2YPOyCqPwx7V03KRaxZzwL36UMFBIkQkEGGRr8O3g5QlHzyCbWx610nfhViKu4Hb8tnEzgfnjWZmF5JDDUd3FuDMzmN6uv/GKYJtdoAr3DvFD45yIFUXvDDn0RUzlca/UVcQZaaH3/wmIUgYRXFiaIYgmm98X6q2YhUFZa2Ypz2oLY+TNHf26fRE8KTbt0+ubPTZu8F+271ZYu0KC+B8VAveQ77XKPs054IQhHIzW7zVEvLp7RYfXh3NwZ/LCKNdeEVav2Bb9dahdF80SreyBbsxP8tG3HX9lRakstdrlyCBbJxz+hzoewWk+Lcu/Bf7/AhhroemW6gWc3boGTsnoZzL5pAOlcn71qqV5xC/PQcMvTS96mjdVsKvfikfrKFpojZIxaZkbCnohMmgptlhzQ7izDIrxiLlzAykd3Cw+avhvuTQjsDHD6uY7dX1pkCmFtCtkO2o1fstfBYM2aQfzq+av874mYPXWVM0vOxeokk0KD7mfyzP4b7tTyjmFPNYL2PnNIupa6ksiWHbWsDB6tUHCQQhGVW8m+q7wya1LxQphZUpoljgQ0GBnSQM4wozkohTCHVSFOLJrwNuIlTb0L/l3lntHvyiL+uhEQAs2awET4BoNUSJW8olZuTF7W9WymRpO61BjzXty5kcyBWFycwY+ZfcpkcM+9y2/0inDgvFEra60u99QqR/QgkPhS/KbswkkRRrxuFIbH0+q/m2TVrJ73EzCBscYSxBF4f1NaikHOhcL7toR7+eRxhsOukAQZRmBELudG8k1qOWKARZ9DZa0JNKjDnk6FoBaHU0zwlMAK0k8QsFxPQhURcKpdHDz36Nj3OOtZ9tD78mgIwKMDbaDLEMig+OJQ4+DRXNYLOsy3m1153Kkn14rTbxi53M4+RjJ87D/DKEHmeYj5cwkXROb9cJvtm31ugX7J39xgT5x57j8e0qN93SH3OxFIRR1Hsgh4oceG7fczjgc5vZp9BoxGE2vStDkrQUTIMHyGik3zS53a9Gw8HZlT2QBlvSknB1SQFbXCxCIm7mI8rwoneV0PYv9Zzl6+0MX/Dd7WHbvd9S+eyLJ/ON6+9fMdX4W+uJTh1aqR5TQbq8fN+u99XFQVRlJARUqcILZ3kJ8IZ7IdF3oB2iGVinEZhjaLeKyKMVeSXvbvNfPOmNPRlXHnyvyks72Kj4LNgIUePkClpTkwm+fa3zQTpqaNJwN/6N22NtSkB5F4Cjp2bSFzGAgjDBXGzhrdtmS1QtO/v4YzBD1t94bAp0fwGPMLn356EBn+o+eJiFe4fVH6+AZrk/noy9bJur9Tn3Tm3LW+T2Z+ewkiQdY+IMHGkIMQf3zrNUBpJL9RwzCbyJJgYXAM10iSVRSUFl6/TGZO0/sKeDQVuR0tfLro26310D+lhbyPMNRKep69ota+kcmZy3LD34+1a02wqDJVKVlpZRftX1MfxitKhBX1XzVBAanT6xeca/FYY9/DZMYNy6/vNueU+jdolP/mHXfcsKZcN/sL6Jh/bNk46i28fxyl4p66qrOSlT8bBPYPN5SkLqzgyyDgMTlxl0bWB8I/CclyZRpTROZs+reAeZtk8Sr52erHskRn4IcoVlxFyHWhLVM/oFhCi/RLZQu4Kl+1bUkdK/CWJhC/sluRiNjk8sFKcRoOEHVxVjYqmUXKGwA/ecGW39QeUW+03ndxMGvLnOjuT86xQ28Yc/iAbfm16E0dXoPIIga9UzbOAI124EQSFuJbPoa3roJNlbEKkmCqsePnKHyPIUO+GeCXTNHuVlR9ePAJlzmEQshbX/sOaQz9yn3Z2EVWndYpQF+QUlMgImUnDR9lpwI9AK3ZSiHstkSGzYxJENI/PCAE+I3PDcvxkEW/NZRmnS06XjnQ/gXXX0lvb9gfBcuOI6wPdpjfPRrlK9rwaiLlQK4T3+W9K4IJ/Ot9x7Mg97Io5QuxL9PCSms6cJSC+rwjg4afcTxwFG93hvev52IxGF2adNffO3QhxweAT95tInB99YgHeSw95ekvhxin0/66p08qOWec2Rlunzm3PLcF5cTyefzwZeCWjHGn74aUgwDAjB4eY7yJgqE5SdT+HBXy9b2VumC95iE0jNVcdHUeQzXyAdvYTNq0MPbeD67Nx4Ffm0PeHvTNHFQ+5BmFSjUnkw8albWM9CK7a2tdIV8a2+W3fvY+8//9s07aXPYa8hdw+Xtj6vhb4TdcJ3fdh9FaakuIqh+5P2Mf2J44xUsMmkChplXBg/BYEjUSsyOHJG2p5220HwmMheis+DjnX/vlLPLFicCLHc6jjy7b/6BHmfpW8fFSeR3zwQhyb/wunOBJHUDeqiIT2cHffHvrzNJKVjcxlXZMcFckJd1yIvJc2il5dI+L48T7UjAql+nGIP++X0XUrgLf5+g04A7JYCAwht1zCsmvkkcabXr1hYYnRx5yQYlXVWuWuZYxZjzDYEva/XnHD58ZtTbVIZumFRTxCflUHnfOoFhPnluR1brVO4j4oXis7IBCHwScMPZ6CXWwAq9LdNmPlIWB5btisiq7r2rJEaJ0lnjh3KN9j2LXznD81EK1aYBeMTHePc2JbTNZB33vGlAP8QcvDVfmWL/ks1dTsDrNAZqpnUkKmPHCIRFy5dppRNlIcsDhB0O6x2vXmZEEgoUSQCfhMlizLtFr36jx93dUlNa6DTkpiUNEm3chKFAwYjV/iNBQiw9+w5l3vhm+gLfpkeXGd5dEbMPZ+xTSTN+ZFPzYPbA9psaOETdV83NdAxRtHEvI4MnXGwaJElJ0GHGvzMU5TGqWUtMG/rBYXQ4uUk9Hhq0P0TSLGuf2o9gn/tbO8lQQbv8LB68RuiZmZnliQbazxh6tUWDKV59blh+OjajQ6aHqdIfpB9Qvt5a5ocfuO78MD+VvzGCAQF4v72zE3ZgrfGtUK00/INL5n+bgE43qmPTkyvfEmOSyazP1q3QRsMk7OWvayv6YuTyo4aT8C5rvICXVXmPr9VL3SEgCSTsYmNV+nPRmU6NRPcKhfXEo2426sV+zFBiaR1kvgDl/t5kNmBmCAs/RvgQwRoVixOazK/xawLZ0JZd9L0F1kUPb/96rGE1LRekAxQJuS/lw3mPiC4owr5OxBSFEUe9yzC5EKKCfOKJEFOl/r3aWJF7zeIxX7dL/3KKWKmrG8zWWzyc544W/YLRDzXvVB+XS2l6eB6557LKDHsa1pWdEHUm+7kamBWclhRQEjW506yO3EvU58h1cGUjCBZ8WD4ya/DJTGzVwX2QwpkiF3qmqkQtpsjfYFgBr6/+dSvu+40udmmuJh/XL5TTOnY7NvRCEU3wUQZFN7nnbUnrVf8lIv7poopId7Mqd/NZpDHI4keXtIfGE2f9yDXxwMTkF0lXOtHhglyeeBqJhmHu7wULRpzDid/myjXouYFJK/CMtHmUkNp1KQ/mJMoL/HztT0Sj+qXxDACtCZwpzrKBYoAPPLM5pTAncGQAaHZHkpulBpqwP70lflDMhnC/20khV4CclXSyI90bv/G+rfMKKPKmduYmWLdhw4ziA9YIq3p7O7Bk97Mld+dyCRgeh8b13p5F8r4qSrt9Q3vekE0KF4JJWLzx9QwCTNAqsyIzH1KdzHiJbKpL/1whdlFbZTzciM5m5XHpAYi2gLtX5r5keYgmlWAHvEtvPv+V7c7txXXGy1IjwjZrzIDjK8wSA+wrCWI3fJOs1nHtfoU1kjkhk82r1T7IdeQvItYROwXyWOBa40FgWYMiRQ+pNE5jEVubDwP0dfYy0Cr85KOXPJirEFEvRMXFd+02tVRi/btqR9XU7DTtOJp7rcaDTsAKwDekwKsaLtvYaaudFwHmi3zB3b2fD4Y6tXl1Psm5wWCOE2wy00rlZu8Hr95fKREXZbQ8gnS9L4jj5LilznfCBHKha8/XBc2kPriMt0ewmSCcpSWPmIDusIF0Uo0qHyxP7/sapVQDKwhOujD1d42b7x4C5BfES5Ix91zel+Q0oyc2RyUvPiUzDhDDm0G8aSfEApGUAv1bbYM03zFvH0Jj5AceRnU5QFHS5C52aAxZr6Nhng4ThJRQlnumYE+B3dnrDjjBK3OLKlqsiupaM1wk4++ZV9EfSgBbuGMwJ+onwGy/5XBfVu/Ka6+vwS7Mh3QgHu20U6aZt82PInKSKfiOuzMAN2rDPwu9kAKfSSZS/BMSGbOxO4f3G8pVnUB4+1DGVnEjzH56T7p5g4791Sh2TTvyZqRCSoid0zFIyzn0Punydw1jpHf9GSQNQPSdO7GvgdVkiyi+ktf3r2H518sWiov0Wav5y+CGO15P4NCTT7LQarMIfhGXee7D/mar/3CUTTIAxMqDwMbnhYq5/ar1NEpFKucDy0HxT6/PsDQ8NnM9Hr5mC8kIC4BKjKQazSFsIZ4daK2BVfzlf8W9UM/hH1OwFQZQYuG+aNP93lKPeqZPjQZc8ahfXph2FJjvzeZO5LnM2EJJqkLBPFtTlJZ26+c7lsZcK5SmwHHpZoMfl0fDRD8hrqdvdwr/gzpvoDBw5CR5TFee2zqnEvqwufZ7ehqL3nnFdXX6UTorVOJ/5VDhrQvOVgTfZ4O1WhQPGfmF6NenCoyPMIrK5X5M9Oqc+xt6KHHRp6QzXKRelKoc6KGXc9+MD8NjT5SzF/RFblpH7ABnh0Qk9edGt2l+rkQ4ssxVCOe91T8Kj82Hj6/07I4vx6I9AOMCre3TXkYDDGeFHK6bfXy2BO8j+4dwi92e897sQRLKktN4Is3F7AiS/cFgZQ37ZDWAzEtEntldqvHaiTkCzuXOvfBEHp2gYawHvSeyP35lr+0119lGbPDaQzCMRNlXkQigzS3w2R+6fj3NTv4xmHtGrZG1fZeEHC2LKDZ3VK7sFEWIc8kLCGpnwmoFW8qs1Hi91fFVqanRQE93j8EoScV1oDnDUOE6z2OAyBo6+8yeTUso7+2WGDEczS8Auo9gGODMysWmyOjhLQKsmqbbTLpqlNNGSCq5luMpzCHOiLATiQ7s/Cb18ltB932jiWhzU17779aeL5j/V79CoLstKMllPr4Kl8X+nusKO4uyBRHzdfHC3DfQ1QrlHP5dXqoF0bCL7jUIxs6BfqyPgqC0Actqqo9B8nD5/H8ECnpfkSw7y11ni9ZxDnH5qqpEI+/j8pawzlW7RX1L6tKDLBo52EaYVesiCISKO2vOG3Q/YBYQ4t8nXA1IRSB36ySGrO0JdrocAhWqMLo9PE7sWzoPIKbbZTUH5I9XLnmpENkHq5tmdRHbsAmNOcMKlp3Bqd92LZMwC2ck1KqPQBylWoDjUk24/q+xi8d60nhuEwGzWKOXVoihYByblbYYFG/IacO+MtCOKN/fqTZ0d1g4GOsrPD92c7TB5bUb1McRXNjh959eJ9HuejXkIDgLFxX74NhRxHaw1QskDWR56wClM/twl+FqLxZ6ECcfR5eIXHmmKvHA1sNLPKbTftaCh02xnew/kYZTLsybvbzxtMfaIJ42QVuSTmBCKEJsCKn/X8g39/vgT1LPtpf0zveKJ+gAJ0gnvNP+IYQQxPeFn86Kgh/BWe5ZOPbkT9cT9PaWLtxWL7NCLt7H1Y1G5IsikmJe/Irll0kat9YegFbHZjHkuaVB7FcorOYk1/uh7eUjhASCvaCvr7KPPBCUuPNr3D/atgMJnJCUnhElnhxtkim5qVfuQVh25PgFhf5sgD6D/B5N80gRW4tdJtqvXME8kGUMzzrSUK64hwEPw0yS1RA95cTikgO8uvrqQyoAAl7H9HdBUow5hV7xTUWiv07Esxw2n+7B7+/xSIgiBQijySwLtERD0H4QBfc7u55KxkYCXCBNmk2hBCcwmE73ZN2UIgM5H/bo73JpCkYzJHoxKyUSYTF5P6mad4TD7Zx2O456Yvyvd3Uh9RDTuyi9bAhW0q/+DljgF7/hjCclC9PwmMCOS8cTDX4FR2C6HlEp0gxBC/HLzAIYx184HtOjcZJ//bUp6HZ98hLTiNPfs1aifDaJRfwzk1CLsiz1J8nTHahgc59po93ZacvXgefaIL8Ncn4fOz94aO3hqSttdL6mdnOEq8tbwxT+jiB2d47a1Ry9t5UuXSDCZ18SvBfQS3ml+oz4h0pX3lg8/plsfRZbAyJTwuc6MxyacAUFxiAmfP7QjaKH/lMgVY3oRsP0PFGvOgHSnmSEcYbbnnf4akI0nOzRN1SuuUr9W3mcFE0lBNqhZBMWn+jW8TUxaiwpWQxxDyHmDKLdcgVm/zbzxmnBRqgOBYTNb8etnf36jSjLTyU+cWpPq59EhwH6LNfYwFntAsuitJm18yvYkoSSdfu+Dcb0oXEu/DbfpXolGY+so7HQfgAivUFuxnIyVci584KRIvfW8EpiukNqQ9UTUK1Gupu1+MwHaMJXvcdghDqoyc7mIujhGYYZBCDbgo0iraZnTG45TbPw9wKw7l2P01fDQQfObo7mjH2wH9IWs5KIBDoE21LbwSVkedsXPsKbTlH77hZoFNHzYV+R6/XO9R+JfDGllE46uhMLPw2Ek4vQN237S/Osu3HixpklD9Dmtvd36qJZB7pwsFgEpXvjPsG4V1+Uxo1LV7D4tf1UdxCrsGNomnzV+MCpACyZo9ieJQWluY2ZS7UQ5ziUfWr8QIgOh0s+FjGXswW7Enpp7mlXoKTtQJFtmCVMXgaMAFWyN79AN7wSsFAPEIUXG1gTgor4ZMWs1/oTD0H7mSOGHcRkdicQi2bU+o6HNkN7Chk+ZVzk5F7Dx3HNcf5oYv4Da75d4gYj4p7LXgHdZXKXSK4JLVroaq3c+f4LIsOxq3XWAkOLnohMTWhqXwkSDz1srmjJuB3jlErSi0p1b0gQ+x7QCtQFiuHryRdci1zLngz97aK6Vy8PrYHilsFxWbaj2E8Hg6Jm6+ws/VXIKzfilKiqgQeA+cZk5rtUK6Ga1Pny7zlQmK6Du9jQWHykb/J9Tj2hSSOR7y9B48njXYIqb61BzERQS12VyDHe2Gy18izOwqbyY5ny44f56ZM+tCauGV8nOxjbp2z9gLUtwjTKYV3FyUo1cpc09HT23R8I5RrqV6a56w4vt9RLiOAuSx4tK9fvBC2Rzw1yKQIGm3gwzzjbXsS2dbGIQyKD1KUnfEd+cj9bp3SwxMkcRdygLbJ7RHrc4Orxu6g1nqIQu7rdGo7tOAeYC01u82iT7fzt0YzmH24LDuzrweS2YvVymijSgMhpFNL2xY+oO6yVAMHQoGW4UUPtRoTU4d168tbQbgniehcr/b7Nyb8exbzb6WK3+exUdKgeM/f5ejquXZvMj8zcE0Pr5NepjPclvCW/2Uckqvg0Pm3S5acs284jy1nfluUekHB1CDk36fuTP146m8sd7iQTY42B8wFkdaaSj9mCxvsR+bSMQvvQyDf9Or72druyspFTUq9YEGt2XoQJciO0CTfDBH+TcOC+rH9EvhewWKb3RBFTlMOqs6Al8+KGrUsSl/1x9YucgFV5TMjk3A1QFV564H3OS2ZiI2q2Teiz9dg/tUzTh+sfbuyokHESmTS9X3bHYLC9edW5tY3T8OPXLxdzgxutms5xdtv6SBuyuSOsLnFvHid3tcpIGJQlAKpXozEPgj9MDZYGlH/DF+v3GXoAxh9Spg+0qhI6TK5N7FIYOVeqMBJEDY8QV13fF6peISPhoM+bvyIZWayAJIiDrDm3iENcuan87eOCAk/Sm6Tsd0Q3bxNy8xtjIrOESOXKfSlSBCwZ4uiyJUvGetKo0PId7QNgrwWL0AYI/N5SbjcntJf4qVSByrWHu7xN2FzB5fnIFC/xH+E6XvxLiicADs/f1GA326sESYqrLDLgHl8380L3LXP03YMwlUW+sOJNzpkh+ziYe35qd9E++2C16OY2PCwoE4GvqT25lXcBt4j6/lXy/2GEMULVTWxErDltmipNrF4FIXdCpXb/SGLTaHLAT9bprlnjz6DyzALQS1Yq06kmQpeD28D35tAUXgjtf1+TwtSHVSLzb5xNZjEhdBkKYzsMbFZ01mqhK6l+iyZCf3wYjv/DtkkjXwBRkn3W5wdH5MEGjUgJFTifEj6iJ/AMltS6BjO4N3ME7VzLxRa7m0ITgfxqcOC5TgGlHLUIQMHnn0bMCXlJYD4NeKqFH9Q3Cf1qy6CMaEr62rCd/IMtplVWu6ihk46+DmhkzSW8CR/JFyOfPvby3uTbh9/S56+tBvOby7FLhw+976r23VQARSoEpkAohxTsO1KUR55YCM9ybxmhazhVOner8+6iMHX4yKNv1uPm8W0oJFaXTgzhHNbGfFs9693HVEeguckWpuT6FRjTCXuTYCLVhg4FNvjfMykEYF98c63A5nSVzHSI8SVTrcW5mwPcW2fUhN3PvResjsLE79+00hSe43Y0W2o5ArB9ZEvRU34ZSaDBBGefMzKD4eNj6DeIyYFBIFmkS4+db0eLIPdcZpNQIwWEEEadM0J8P93rYCihR2wfAO60fjO7HWHPtdOmh6z9bbTmIjnstzFRQymQOIXN1guXMokVDt28xska6XXieXi2+8yLcy+lCGPDnC5hTQz6DFzt+h++8XzBkiWcxtPqmsg9NuDYqvDfZPxxQ+vGGNgyVqhBdEpFNaNm7+t53KOjoS3hYc2klNNOtp9ouNFhXDYiFMyZ10sT8lByPAabN6k9Qslj7zAL0baM4Pb0tn7Ny4cQbZbaTtESQl9cwf61TIQvzEsPgXckHjoTKv1BKYdrImLlYj7Qh9FrrkjQ1sdsirf/JPQe+uqvvZr/m29QC/vBpVG/ciJtVYsvmBf4cW9C1IMK7ED1Y58WaG3m1s6x0jmSI1ORE/DbU6wGdPZb00mYbL+uP/aTj2d3eROpkFdwp33jU6Kbv1r2q6/pm+x+Pomysh/bCr+CpUKt0CjNIO3E9OQceTNs/CLBuJDO93duBmEwPhcAEmxemKOGhZOao9j0sq8OgAJGvklDzIwADKn95sk44GmUgXEoygJIO48kpBPE4mqcjq8yGGpgpssLwvE5FNXGSaI2YfpfQesSt5nxlY5Rabeg9NvQ6kdPoYNsVpi0Z9JJUTJ37J76GU4OY4Zu070cid818gVpakpm7aDeCXqIdKyvbGrWpdyckUNawVAAOBhzldOvjEHxmChwxsiQlDTapsJfTxkVDyXx9WLYU1h0oade7rWomK55dyENzrulDHjqc48WtJHcorxR0QeLyReSkuxLabZvblYXr7/3T213u87CSrWlVSBoOQ/h/yrSApIG6kcSVEK7lv4e0HxTWcRv2k19o2QGtJywt2TxwC9hiO8V1wD0yDo2ePfSRlIsZt7OMZ9gvOTUYAS7qxwfYn8U1wfvLGpanZ8B7oJHsR0XI1d8GaA7BQTzU5PNxv175vVQfFUt6S1TPOgqpiC0T0QN9vO7w10qigG7CyM/nhSXqchMp7fTtpdOihrYFNMQrsPnm8IdC/GnjlSq0r7qZU1HRo1iLiqLDEFQoJnjOVSY7YPs8umY1tQQ9HR+Hckxp55Jaz421U/YtJz2F/U9g0Kg8A/Qd6v2DG30ZxryTXui7t4KAqB5CvzNzkEGAAhgSKfNu5JH2QtLgPRwv3kWsZRKMVZ04Zirk+ulaBPhi07cLqN8aox5ld2cbszROEJCuBOa5LwTtVPVoppiWI5WZKefudCYkK36sLQKnR7GNsUeiGOw2zE4VCevAKbdcx4tJQuc/lLT7sdF7vHtf/6SGPCMihEN0hfAJEWM/ajk8ojI8aNhXPU9QG3Q37x8mmJCM3rc+NQ9t/MD2ELy+mmY4XRIzuWQARHbOuUY+qhQSeECmvvluXaGIGAcfhAO3HwBk9XClhH/34z/GPd9618iSwP+myCHay6wcjVMDS+NG+naxVHlrYx00jMSDttgFz4J5VArb8QmRqaZe5IQy/5hCRk09cIj3bcIY0qpv7Ea5ABSbM8ZjtXoaWScm/TD4iLVrqNmDhe2rOzTk3MZlIksknnasJArHpnXmKAnZjXZqBhGyIW7RDhDxUaZtqZn3cEv4lQi8RsLgxaGyAnPWLCz410Vioux7/pujXuzun30lAdFa56OdGNqz5wxv/yROA1OhAPxd9VMdd5RcJ+qi9E7LLC/LIrSHCycgGEXBw3NVhpyP6KfInUdrEQlE6xJjCRFTgz9A7h5qf6+F/GZ9ejgZEP2+EKg/+6mDInuMfcSI8vfEEJ9w9AktrRJocMlzHoWy8rYtGYITyJbXM6ZCuVgu2io4aZUxqG2paww8Af+5RvS264Sx+Km4u8DIiZaN954NKh0NusGZnriJR7dWCTEGtsTeTlHx56B2p1GPTpie8gk33BvtLX84rfM3IhvJLNsIpF+y4KxYH3JNyYMvvCHh6iWeDymGFaM7eGSrBucWT/alFjpmmhwDMVMMtwS+z3tzPJ6S23zB0e6BdGHDF8tOjr+TvwTqZaehee66JrGZzwBdLyCCbUQ4wwdfxRHI8/IkHZCk9suxhVEjJupzI44L70YjeSG7f8mko98up6HEREbHQ3RSO3yFp7bTs8PjqwSKiHgftZDLXgmodv8ZjonQggVMiOTLTxbZZ0IXV2fqKOzxJeYxNndYaMn9c9BDgzvAdTJu/9RSvDBPN0mwqVtmOrD4NyYvZw12ZE7F6439uOUeFZ65h7H6uTIS2AxtndDiLZl3vtYJzIMgtfh0wdvLiMXVMFrQ+sVGrOpuoIbp2Q2VEbxsN0unAiwa/bxA9y9Ct+JC2nXZ53uj6exMRPahWwZb6A4lmHMe30KMmho1I4drEiTgVv2QmQ/DcAM89/qR1squcLJCJ9XASsKnUQmcPqXvpqnGOKxy0y6oIgdr4Wc8qelYFlQSEajBYusbE+6pGn8xwHoMcJa4JzsHmyIvtIOSV78VUWWoWaDlGVA8hNFbGi1etKXlG+kY75WAZGKnnmIqlJT4Hre7EBL4+o0CdTbomA7agO/o3iy2hPox/46UsSyV75wfNVwoN3i+qEMKE4D/eV1zthAic+7vkB7W5d8glt4C4UOkdGc0HcRrm6+teTZqf3KznbNwgf293UbS3z8qO6rsj+wfBDD0mA+cCcUTRHWUP1DSeC7FoPHd+uRjGdQqA80Z3yVKJvDqRtQ52W+rmOYrSqVWLOR6/gKukj7Tsfo4URR+7ZYvK0byv1mHVy4L5OlmghLGDBAdvl89HyMVTHamq48IOJkNGRsMPRGhJffR2jgENBfXfHFp2wdtuDaPC4o63TMEaIGDOvwUAMIgmmZAoFCTMIP2osI6Y6Fi2jcnaTzDCY6hJiD/quA2S5uHjiyHlAUNRETTtGP/rjpoHHH6fqY/7GmDg3bE9zZO/eCbIzv9B6jyIm3v/bFY1QJLY1sMpTFO8zn47tLC55BDQZp3ZSSIhX2XJhprieLl6YAs8KrFvmIa8KDq5QPmwB9lCzzYObgCm8XeajoeGiX+ZA4d8FUJoMhDtGrhsWTWKTWCQzR2jj3cxTOaIch7UO7mV5LRrjO3zt2/FzdCxyppsJtmSLsWOVxDkEZIwt07vvq/cec5aBENuWFKHyrnjyu2/+9lWNlIRKwfvYNrUBEzAB9X3BzCJJpMmpxTanWD0WNpI9PK+TDcRrFFIWNNK3aDVv0jpUJ4v2H4FB9RPXZbNmvi72CzjPF07L2x3vjQH5Lqxeva6cHy1GfYIIBhl4cBsT7VXTCUk6U/ehHtUHpE/LmhBhlK0NCpES5Cu1xMwj3zDTEPdc9Qp3cHci7HRLXKMBmZKs3fIMpX0P+fre+4pJX7GXYAMt3T3snwnfO/HhXSwloKQnODX5hdR8p8aFlmHO1eLFk2D3HYjee0yk87cwoOiqKF1Ds+MZTvmNz8zByRR1dLEF2mUSbw7KXEN1oZC6mQKP1V319zcxzRkkBvccgme6t9SXJ6nYBr5TFAJyPASdep/vHzOZkXCfTLQIivAVzkRWNc4HNZggMUV/LXBLGsiyKSiAJLQMbk3f3kCQ/NuMR4HsARvKjedH6sfG0yA/jKky3jo5USjMGkoDt7bEXMhlivZEczxxqjXDlTTOpNbXWy+gN5n+eEMacr3BRWivjwCOzSIfImAy75g/uc7Nth4grBHp8ytcqfgezC9UkbcR/yooKtL7QISEQvBE9KXYNYuJW162r+zMekLdnKztyxoHw1dT/mJT4MbRuX8u1FRrR0hARS1ZSm140/H66PNw+i1V6FG7rwMSukl+Noz1AUaeV0RKSGMBczwXTiK+Zx3eLFJs6IYDBSTwt+gHzkLFXB5OqAEjA0hPZUei4gvdDEYSZTycka6eQla9L2ZYMwwgmJj1mlDwbdpRxxXiRQnAbamBqaT6bcwuLxcNPzvHL/Asg6MCP1wsIG3iXjERSvfDSoA+4BxL+WIC8A/CpOT3R8i/8r2i3nwtfqF7OcszY0KJd4Kxb8zI0vpuFUtajQ/YKCuM0/QesfOFJSvW+WdhndH9yKYv3fUg2z8LbGWxFpxRTEDC/qOmAg9VJPqi+vfZ3tUrszPN3AZQD87eaf05Mgue8zCJfcQUPLz+DUmHQJIG7TM6pr4grQeYbwDY2r33xWFa5LE4Ly6tc4Xb6v01v9NGq/LvbZeMAXdwaMIp/wWeJoe5isU1vfqtJLxTV8RvYeNE2e0x1TgvCQ+nt1qP1OruW6rzFSLO4P+9JgTftNmTJL0gDgR1OtbZYg4ukPanJqqM837xdvf4MEvdk5CSVwOaw2Fm5ZyQvJIlTJla6bv846W+U5tq4jnCUguEdri29W2EPlF0nniNBf4X3mMDYOwgI8aS0TIDOwSn8JBIB833GW31b4xMRTUzH/EQz7KAGUdi+aJqB44pqk9QlYVqB2zxwN2HlaywLqnRWoLXUC2Rcb4lpVGKgRnGd8FNI9169N8lZ2kfNXczQbORpsgeiPhkB8+EA3RzAQbuOfAQMUqSc9+TKop4vamT8975kg4HFtNdyMiT7ctx42VEP5drKCq3vjR0WjSZLOlDz/zAw9Svstvna3yNHBEMg4YwVyTy5IIeKN/oKMMwEIUY71wPuILLdJlPDAPkx8v7ZRoyL1V168kOB6DbCA1LQIRFHKiE5bCzl6a4bHREEug91Uf2KFK2l9LLgQ9wqoMx4HM9C6bNvG9jHFcmJLRBSjg7NcqHB4/G8bA6sRt0pdvZ7Y13o0pzIBfSniZ4EyFP4KC1oB8lnO5o7nUzTpNk1DmNa86J9rdFOBbioBEuOT3rhPwR4TOI9bgpcxgDix/fY3iba2VMpcFqh2q5Kt6/JRtx+lZ80P6wL41iu2+4EBrKqIk4nP1vQ9HE7QgX3ue+gOt1m2mymL8NR4+DXIbzfox7/kahaJhhwRzfZMwzHgSLw7G/FpD8mXSA+b7aNaUUtGQCstIqNXGM8LpnW9jUgHreLP5Szmj8YGP7dRO5ezubwSdFe5xVSc+0Yr/tvqxq+Fw5XQYyio0ZrHJt7RWQBOz6qlVz0mu0DLbOJsE+L/0aBJFfCoHAZhZG1kiZftPV3Fdv42OdU5V4W0yW+BQi3cVOTeF9qD/8WG7vPa9KmKeskgD+a0OkOBGMsylw0kWP5mrRtxlo/dcbPTBiS3gA9dhjc4Rg79E6BxXBQp2xYqJjBHtKnwLc/WJiihevzgWJ+gQGvfhNUj+M2ebZqeMCCKqk10m3/LvmmVzROy3R3x4LPXpBwlLxUG2RyCSwifcBQK6Llkia9NVwdqb4braGI+91NiPNkIlRRTYbCRPiogqEQn0upXs56xlB4vH2kLmRFlNbah/ZXUBQlRm3l9g4vWfY6b1ToaEInK5Heuwli3kg73PWITzcamc5M76/ihmvh0ZCMjfte7EAQolFy1iEWAQ2YSxORfHCkhoU8bBHiquZ7ZgNd1k/CsIFNd5rEpDPOjridOA7vfYbE050oB0AMi6w0ZosAMKZ70yNt8vTt4cSY2E3vj4/R4Ujwy1gX3qxJxBmWeGK8aFEvnTvvLSbESj5ZVitFPYrXzLt/uVFcxEr78N0XW1+WBUul/Lq0Tg2fBuyaOUwj2S1HCglRIA8txLzHwsMFBYQV3P5YnIwUZqNSvNShxFu1dhQee+GIZive8bHoQAgZXhv7SFknS2XO3XZUCCuJySxA5b0zrC1EJRpbyt1tRBOKoRk07EU4cK17M4HN+VDwjXnlLM0ZGPU2epDjyeU14bAJK6iQuNHvGCO5G9F2QlfPGcRuzj4b5wrbqpt1CnISmB5RbIeDK1dhkq91y/9uW8QfdRHcGgGBpDgTZiR9WXuz6ctXt6dkaFVGey3YImeox11TslYJsKPfKG3p4DybOV0kFKz6ocoWm9xhhSARx+yM1eRxkVn3J3029QMCb7DPOk/hod4UyO6ArJKTK05dc379n0Hxb2UFPkelZF2LYYCn6QVkMC3stdWFATJzhwdD53Mb1DREZ3pFoFQW6AylvV4jLTM7LnVPqS7GhV0HzHjUZqBzivTnZXPkHc0mXoRuvr4BkkCgeQuYziIFkXPHjYy0CkE9MSOur9BAm8yp70cNNWy4t0XjOFFP+l0QlFD41H48nJIt5nIMxvjkjtKLmGudNy1BMVl77iPNErUkLE9IYyzbQWFmFecSFBCWjzI3xtuxoks6SM1j40pKrErikFGMmeYIgqQ5nYqL9zGYput1E9FBrJ6LzYOOWv6sMsYrLimoTZrVm933vlbMlMy+1zP9aZvf8mqwiR60mdtSmEldqy2nGWKUjyJ/naK10SucF5/mBaNauMtZRjT6sagUTuIKZ1UBbtWbSBZYE6AoLHCWcVMwCR0Yeu+ZsBUogV+WFlrCTj03MK7lS6KF8TSEPiFDfieVylkcWbyseGsTtv8VYUQvI1hzeq26pGpwCblnVh1hueNFOrGTS/WYmau6dM49JlMiqiCW04ImnOmX2hX8TVr1V5Mi9+v4YZTQ16E2IIF2vkvTdexJCfXLJ/m7mk8Szw03jaww3vvefrLGX2/FgppoqMHUycrs2wZfR6IQPxPwpfWdtzHV4AotIs7vpuq47wyKJS73/ZJPixw0r5wwGH0JNuMZl28jqQ1/b0vEIdbvG9IwzgYmyZEf/tcHle6kKHuss6jqFUuRwuZ7EbaS9f/Idz5TYzq/bz6aSTc+atzjXTg9Eyz+oGJXMJDRDj8KrqdOrgKpv3T53SOdsrHVDBc10vEqqtkvxAg1852fH4zbdxCmuQ+zdBV6Tu44nSUsVd39fGjOZRbeEzoprxaFK9TQbw/bnNKY53vZhGeTq1vO1Lw2vyQv4mufr0trdmZveSjIA6TaKzZQ3Kq5PpIYHVsuuXaa5NcXCOLvtha/RvAgHzacQr/CiSaoDk//hVgx/BU0FkJkFrxjEXHhGjFnO9qhwenGKYf0LiU9L1qEdzvU3pa7BnOP7GEh9n9eJrWWO4XYtjf32hC7URfPSZA25rSwY89xfjIucp0FBS/0ObLt5xMPx5r9uewJMfOfP7WGLOzoVkzkdUy5rK/WhnPkVRaxXL+lgE+jrvR6iGZBeR+vlgm2B/axx4FbXl8OZmL+Js5KsT0l/zbJ+CSRplVvNIV07TLv1xhAzNzLtSEo/V76Z7e92eknCcB91AloeEeBQqyE4lULtfZuBqdKrpBNAxlaRo7fJuvLN9YtWeXikDjaZ84Qp7UhUmyUwVndA86PpiSMLVX6UAainPIbEhLo6+wbCSmEbJWXtLw4EjQhXfGZ++n1p66g67hOXJl08qwrh4cBuNQVbIUXmZ4s3i/GWdaoT+vJ+z3xw2Rc+jUxT6tVICQv4nUF0wN8J4DWEP/Cd2VEU6RgV7/FpdskLrfeORmSb6jQhzpPXr+FqLwe7Zq19NY+27/JVMVpaytMbJgihsmHhoAdvqCNDIml7PfhWCCH9n83us6eIKz2cKwVhg9547x/o5E2XW4MYdHOD/AfTfaPeWsmR5K/rfPV0AbXGU1CvUt+ZYAlLPtbjYS/zU436Axp4K85VMqDMd85jNdnWt1gN+tcdlpdP192efuip01VqyAahiBseK+3QjVOQ1BbNWH2L8gT0mu3cZ+Xnpj440Xq9wHJDUwZHID/QcezUmcZLBatIwDxZeJF2u8xxOMVATFPneOwmRPyfaMudA3k2P0yWlnaRoRQyeF0/xPRjU98B4Wlt4RNHtVnzG/HnEGcs6WTdWDgGpNuImFGlqbJTOwzD1ZA69jZ8auBy8QUfk51/6pDBPbhNPaTDnFFrO8wroOu2+0wNNxIX0eG8csH3gOo++hizjGUHEp/dtLIv70Xkr5G/0s2Pz0CWkvGy8Rr83Zr7DKC0tuLN+kVKZAXm1p3MgmRPFLcNyghoNCMokAEPALmAscR6PWmDCbmwUKxnbh9l3IWlrH025+IDO/nJIqws83PsZAsg85pavOzntPMtom4Ofs1UIqXJPS4M+XFzXX40Iy39xKgHruCfZHCKSOfqvT+ht5fN/q0h2dhl2FDHGSlDLWhCe9+KlMT0IhM3KHbaWkbul+6q/t3CLfOClQCDkztGPhZ4Ar8NJNTUl0TuDYJ+TzH68XoyzWd7ol1X0qjXjjXYrkv6t+e/r9Qsfr0lgPBe3TxA5DX1myfYk6mJWyPsRS2O5OIObXhF+F7EempaIbGmW9WvQF7LvnQf5Napk0IrCPLr1fxcRIczzM0d5CtthsWmK89GhQJq4epIO397Ng2GZFvglb2LoLGEB7me6XXtN7JUkluRbNwwutIoQPZ2JVM2xs37rt8LzqxPkgc9FAgTYkZENLWPtaXTxDOymdO3BqfW70vgNnqTmYdCoQoseQot/67VMKd/nrcYiyKylOzwdJtoB2WD3U65clAoynATN97YyCCMkceAkZg+WzyFhLB6weEVOg4ZkW5lO7oRSJQ6aS2U8WqWLtbXaXqKUrQzAIC3xmsTPQJ7MaV/rblkJOWcIcIvy3h/eJExg1SCZvSyWlhQZEbI36d2WYdlWjpj2Bk6vu3NLkx/LIjNolVOR11azJEhw9dV45iVSD8jvc0/exf/bVdkmjZa3YYRDsKZI0N5pTCygvmvyHMyj3rxhdlJRpTdFcZHH2zHrQD0XzNfalfGwDqHCZj1hbDzBCeVLVn8gSP7b8Yh+KviTtb1Dm10JBOEsA3Fj6iplxVe9l6pJghE8xGISFJW6H2NsqSclHLEQiFivvseRE5XzklRZrhRfgQVc5OpIJL0Dj989yrE1kNbzOcywWFwX7ldprU6ERu5AE1pCkemi65FXww4hBilnKqESyZoJz8F8tKT2MSEqU34cUIh59HHCzPdy3f8F+0WyLIH19M5l+QuZgPjQvjsLIv5IfBXR/ynZ9nvZ0k/qNLlKtJdlv308+oSZoX7ASi3n7lj0VI0APHkq4dPzmNZGN6PXHLreGZ5Jh1ocBwRZ81qXfZV9bMcapJ+zpoctYqf8sV7LwIk/iGf387qb44sunUEBEdIG/BWx304BL2+3QafKJfJivCzDnlXEQ8i/EpPUlITKumqndzAMGbl7GYXNBzGfpT7p0hBik84gKJaaLPQYRN83oGao8tT2s8CNolRKCVRLoSyUVQ66LMiupZU8W78jnrPz9KPoS3yDcLpJ2Ec/uhfapKNDy5H2J8YZoH8XppXrVOXsYrooCiKF1Xo87ybcOIp3uvwb51EX2PAujb3kPhmGeeqiQHApCgftSOgOjixr7haii/+sF+X1Ktg43eWwVgLUM12q81eUCVBpw9DyMMazZIBhHaPutmRvYfi7l3wxfV5AvzGO7SxPvCdzt9Pm8ZOKPNnInCSJlG1PZ6sbpcGN6k8YFQ/Obk0y/PgpX8Bd19fslDsKe/CwVb8YvvdQggrfrBLa5DiwMzNc0Dqkn7eO9QqPkVNn71tRf8YwbSz6U2cHQ995WxCiD2lUHzSH76mrL3sJ8TTdi0QitapAYinjSZepmm+VIMZgKJ0qfr6W0Eka6QNfsq+OUwFtBl09Xs0GNDl2AnAQYFXFXAloESjGNIpQjTyJSj1bn8qwUt89dyuJXwKSfCw4iJiwTpymTKGhSVH927GBlitaqrE0UyyJf5X/q1TwsQnIyt6GYv552rB3OGMoUVQWybmQfoPfRP/fAER9EVP9aGGlvJGBbrKZ6oN2r0f2g91k9XS4WJ6ofWVqmO8QZ82znS0xQ2W1tmw5rSjLHDNTzDf+yXFti5nGsHUJ/gFdFF4XHp1TMKPoxIjbSfgfhiZJ4xYdN+42welfIQJTN41oZ0gifvwUkYwUJZh/zIW4/9hS9mkCi60GwbXY77eXeoPG+u+gZIN/KmMtz5zjGhyn+Zlr9XeJpfppb9G712fzoi/lwYumMtT9IbCOBuHbYuNfoheAilDvI3ULlDEua97Piz2JSp1VPGfyRLjKQu50JIIOhlDEf3LE2ayQjAckppvMva+5pysmSZCqWfSvd0NaJcf4k1OTHkTJdSSzp9qSzTtb4DX7oXLBxiGXkNqZ0vyYb0JXwzk8X4rIU6E2C89UzBYGz47LVjC9jI9QjsvAEJ/A+dY31NapM+SswZQW5Poxcl2oSZIIGKw9s/0ezPR9Ju73V2tdYSLZkMmfUakOjQ5AGFLSXnoNY0L3FaX3d5ZramApaD6wHvwAsPsNhAvHObKF2ZpsePP52hSB4pb56LcdIOIALrWl8T2XJCbVkGq5BWRAMp8y6rhjdLi60u7YWkASH/3GpmG6E8tX1zhCSG0hhHlaqS7vOQ5NxmW2r/PA40AHnAo2Xwpb/hUyXY8a/6fX4ycV0JUvsdDevPJ2vlpWxQKPZNVNSkL2VB9ZQ4IrtbrC/PnaH/Q8b0WeV6NLGjet3psAw+WiABBKXt5evy4/A8DEk44UtyIRthLCFuZXbuolyl7NzlnJCzP7mp//LZ5Bm7cV7YyzfNSKEn39AtKMkrS1QWoPsO++J/S/DSpMP5XCZIbd0iVJ7Fm+XtmEAOVg+VL4IEtS/2TQFGibAkBIisGJZq3ysVCDZWFt38LUYoHWiGTf9Cqg59R2fvhYIyQRCggIRjaCYUJIzDpsM5BteRVuqX72v71uXKarTyhmBqD9p0hZ5pkmWj0FLHZEbrmXI50DHvGWyvjtYlFVPwr/mwxo+pSrJD8o8QAFILUf/+NfNKRAiwsxIIxYxfCJbFRBW23cCIzw58pskS/78N90zA85+NM4wW3tbfm1GfXWkSe2kTPPxjFAETFJjxB/Z4x/VzKsVsIAfaq+g8Q0SkvJlLMUdMtMrUBu1o5NRlWmikvgOmMMINSpkosjgEBLAIs2UK2sbwnTnuh22+ZBwWTkyprhHlFINRvnjzdGRzkSVL5lwIYkTutsXVHdGZvs8dfDXwBx+NPZyKvxvLt0FyEL1bACmmJEAYNVhPP/a8QDJyYzg/QuU8YvEMk7TkQGxOgYVohG95CYv+eb3rUqCJ1QotsKWXKnP3nn+cBn1qRUazI1qrjKvl7gITR5lJPwx3EvoUMNUDESmSktmZ2QqrzjMcsiZzpsVqRc19gN0PwmxYhZpBTIoG/LcPV7w4OoCptKsERPTloXZBhEW76r7U5Grz/taMY9Uh+AMQ9ZsRanqVALv5NmtZshzjnO3jrUIPrKj2xxz8mwRJ1Lf1SfWs4puqvUh7q9ayvueno3NKO59HvJ7Dm4DG704eqoTl90fxbUNMvEKaZ97Rox5eTgEAAA7/fUv4QYB66T5GsIDpdeJ4eb543UtIknuGuW++ellMzZb1YaxTZk9z00BFJhUMvwUrl/1TQT9qeIGq6oNt/PH85mfb//FZBkY/DV69YNOG7J9Rkimuk/yjCTeS2E7D5tw+Enh+C9MnA4Fce7PSs9uomeKS1+fuRSXy2lAdfGKhhlKVLQPqhYcI2xkVdDgpEukaE7ToQiUGlEi0u2oYyq9G9pzgRlMEGkqCKBsWqPXu4khxopK+d+moPdXd47NgBX1wl848K/MeiDBoBxjz0KXztH3uohUWkWxC6BiguNyK3nFLjVOzgq/qSzlVVuPbxxwQd1+qDOqM099PqGZd40eqt19Z+Yo+3GElrflj+ld5+6FCV9dhHXpHkJR0Odrm/uZy/LAM2X6bR74UfrlCI+ecxOkVCyRSUovo+2KQDlvvkPcJzgIbjK6nJDU3qkXDKnoL6eYQ0KjDPFUYKUIi1zpshjlS5VVtAntliYeTXXEJr84BseZBil4pSBNGA5N2dBYWdu2oLpypu852MNw+omIL2oe9v06VUqk5wocOiUz+mCF0fWTW2dpa6zViFrnNkihncHL/Urxm+2LhwpVJnon5618jN0iColthqEG9EWv7Iy1Iqaa2pQZyYDVdugboNzdv0Xi4F/C5F2hw8OiYrTujP6NvKdsj857PAvpozHKQaOuqygTG1UJlLw4WiwTxNMwyayixChN6/tXYVR+qMZYLdivNfFVe00/5xoAE8lZJFXxFk6uYWjE5pK7oFyquY2WIbZ+KZWWqxwbQtpoCFO1GTy6j3Wuc9J6F9l4Z5Z3Dciq3H9l5LP8uYghZPzQvpszQohsQrpxKfl6GKjw0vnqZtjCurUsuyAeq535A5ahDzxB3UClEQgpjf+SRAQwRercY6f+NV7HYOS53icvyU2ID6ac0NE12tfRernR0L9McLBX1hrDe+48ejhfN9TGUynjP25rUTz6m2w/lcHMDfX2jGY6t0brHRIWt6I6qY6Um2a9PyXRLvlfWJ8aKPZwTrWhL9tiJpJralWffqf88qufbTiGXkRnc8bXNOg3zb1K5IeyeZELmL05NSONYWydBwFrgmuk9hUf7Eek1Ye2m7/Owa9VQrOnYi6mw0dkToKtJWigpKP8ckYfmabf85zIsftSK+6ZntAxyWgMAIkSM/uBPepz7tsGFQ4dDdqf3X7a9+BHBNJLQRnPKmLL48DU9DwgX+pHnS+LZaOPi2xgJLoQQlBWiCXiouOZO4J5bM88Gp05QIgduO8rFf2tKiXrDDFSKgr35etVDUYDMa2ifGexyNGGHJUBRNur1nVOBAofqp4qoinq8PX4ZZwYI+NhZrK8NlGWjw2C0gxe66T7Sv8V38gbABnbRkkxBjA/FciakX8rH8UWVOWhcLnUbLZiF/w5Jsj6foNniYhZu8uj/6pWUgkLMOvPf3QdhmnAkbSf1iusz8Jf5TsyzDGyAsN0pIhshiioZ4t6FcOrOG0VvQ0xra19fWKLhdYv0h3nSUvd0L5x14n+ZniGMeKltcpWL3owJ39bpsEjEDFFu/PQuhPJL+RmsBvu9Z65Kwv3+DMVjOmpDJO4z88dINc8CTUz1sDsfl/XZBf8E9KTTpZOqm9YsmogWWrgJAiu0Lp60Afw8na3qcFnfarFgovLkerbVFLSORap/Z0jRjL20hRWeexO+tzb6ODYewj6us/gHLuKv37N49l4QUb2atXHSg9qVgTOh+cvXecG68O9OgKfMhzIgBJDVpogUYvmtJhhmfv2f+Xi63sajn6B7PDU8eiZNC23B/IgvDxSJjdMX1GGoFfUu3OAnlpY0ktMLEWyKYpJteTwtFUjGHf5XNjvb+vSNuY8ayPrOZxm/zfhymomUSrU6oC+ro3YWMYWsB6/uhZcFbI4C7dZw7hIkJEbqWyrhJCoizt/6uf2/njWWjA7V4UTNxVeaBCKZsyS0jVNSv0kZI8I/uChwr6sF2+G38UNMabEfIMykhR8ucYaIfXHxIGnkvPoDmRgGfyDgOo8bu+S4ihfVGg2UNEBd6AwZeFc+/yeW0liApHxfQcCDa1UlZyG0CXoOWlmo9w1QmCBohDsTW3NeyXuPeunlhVbBk7VvuYa7W9gaS0e7D1xlSs9ONFdaRxf4qIywimgM61tS6eyKhRyJxJ9gaydWkxEz5RbPWqK4IusvyhvyuAFsmY1a2evUQxLe/qa5KbhQnV2Cq/wTQNXHenZjGVQfBPHadQquKjQFk+VvNFvcmrepsHq1Pg5877GbwzN1QFjN7wiDg2nEiWRs0UhENrMRg1hPjzQan1vfvWCllAcKek/FjrxxdpARoXT5M+gNdC2IzQmi2KQH2GD2EQt2xA+KH5mQEI4rsYHs/mXyPGWikGk10deR1CkXNrcfTCicKewv1utxEdO9uzie9faR/29jgjQXabYXtDpe++7UHqItVLca/T6G/ESJAGLIKy4k1sM1QU1j4KjOQ0HzO2YrOok0VJJje6bkwzB/HU2INgC4V4hPcJv+DT8ri8Ruk+HER5ko6DcgakrSlWAIZJ6+WUehWNqodKyDyo0RKKA4NTHEQ+gC3PwoqRaprHgWyLVn0YiTB2BJUS2/lavGsnVHuRDpow0gRIbgiPQPTfzIf+//6YACmCbjkDYBUxb36keBYhWqs8osCnnIu6AfHDkaX6mS0AcxK3nRD5a1QYyVGq0Y58ReM1pFBVu83l4kTZqfYtDhxlHiO7YH2dTe+aObJVYq8bXnIK7iJmykCHXPPoqSvluPIdGk1UwV8hs0OA1BB1DIdoN4wmbBwNUa0zigZ4h7Zwfuwd8sl77gNQgetQYdO+zYHkN7ULs1fusjdwmYHC+oO3MX687013liVqlAWcMPi2mwNERHbFpuk33Hp5X7uGW3RnnV5UdQfKlzTj7uHQFn7mDYwfZHvCcp/KeVUmFbLlAqQt/hb/3tzrpSNx8EhN1c59QMULrFNsWctH4DlwzVDX3kmS9yH4XlTw8S7fcsD7YX9AnbZZaiPATNTqiHGh1jK9fBsoarZGhwTdHJ4MvWkRfxKJnAeA6TPLAMNk9H9u4KmbkOFn72xBYPuta6jJTNPiFcfWXJqvVfChilDo6oB8SKfUfHBY7QGmOmXTE4ZU4zdBGpWvWwL587n4aelIjlCbn0sBPSJt5iMaWUocqwG1FB3teWTjqfZLfI0tGhWW2im1qLeHOvmnZLeVlWltodvhwBq02xokp2Fjv7LIgYunkse8nglP8G7osCEeOki+0//7iXM9zLOwVz7ZtaBLXHTmrhGJB86QcrXOtUIz4aImMedLQSKsAoioYlZ+j1WfC73Y5xxCstLrF1BDwe1b6OG4UfzGL48UhkvDWET0QUMxt8prPpO53ZZphKBPPM3YdUwfTnRppflZ8IYxBZP76tDASkgRsXKvG5RpU5Dampl5BwsZPcpVm6XUhjaCqqahlpYJKFgaPkksqUozo2TSyOFPJ7yKYc1Q3xyA/GUxFqNr8YGJlimGxAvDaO5vLZ/UYPrCpfz1xKDfqM3QpbPUliGeXAagkWTTrx4JYSlo+pdkaXhRLyUN8X94comK0gYA+uzUz2+ay8mDguOPjnKvIr/jbsxCVffBnvXp/+TpjxKfeZJZtHCsO2Cn8+IuFnJ2ophlvAPmlcoj5zU8NlAK+MoFpaghh6KYjngovJYzHZBjb5nkIh79C8VlMrp/MVwh2dFnNIhf2quYY4lDlbzh8ozFOECDdFcISS5EPB7CxzeCTwy4kQq97MQzIe5VF2pjZDwepnVdf6KwsO45Sy2jjEy7jf7gPwO8qQovhFTnRZ0fy7ShInfDTNYz78uCiCing1mXIsxnKlwA4JstEuIsdPC0eMq0/4uFMPD9ywhQ3GxkWfEDFmDNuN/EDWVpOufA+/QjbTLj5OR3p3GnuRmoJsa3+y5GClSMhtGS3OKV8FOsXMhtFUfEHu4wQ5S8ANzcK3g+gx5TxH02LAxt4jbLcO9p+6OC00h/8NNKtjDqmy7ZRjb6xc/Tcl8uApGZTLEbjuwvlRiteyN+Xw9jP5J41rNoZPWHa4APe1jFQE/ChwEjztZDF4e6leDrmjdtEbJZPs8sfi3VDUMMXzII9VawqbSMxOfhvD4uLwxX+ETAfFOe4x0Dent7DyxZMEFa4wobscyNVRkXMi5QH096tYqAeX3a8+Fe37y9b4z59iydK7IvW88Gvy0aSCMMFMZKFn+0esBiee3TQYeUWyvKwCqei/P345qyf32WWtuwvbGrTophr5A45cFVj3lKIo+WeldFDxlaC/MZYJsL7RztHHksUhyh/uY8yXuF83LOwdFkgzsHyEH3z4CX4o5mRe7GIii0u1nI8QFFXDz7xX2ckCFKxw2435TjBZLLEZptqsLLtLUCfKzjWfUeWJcjAf12QLATwnDPXLSMnXBrox+rHrclu91QJ+4Fo66XqyqksnbY3JK2fuWGJEyHewyt7zfVpy5R+PcBoECGTQ868RiKFwqjLZ5+wlW0RbtKjFmw75lMJHdp7axxIeW91IM1DN1TyEzKCDBWkKPBPX9XToUPfD34QUJ5JSymGVEm/D/Ll68d4XVtpvgJ2KKssxWn8/FtvY/9OUTwCrwSVSMziQknnkiE/nbZJhel+qgBD/znHLGRf8Ps/sHYACKsaKlmKnyQvqI7OpwehrF5uX6Ks+oAysmbYFh9rlJs1Pkk69qZxRGf4U7W0Lrd1HedCthgL7szgmtklg+VOBklmF+tLOENV+Xm80jyFTbvWvqcZF66a88oYqdgLN+n8prMCqAyAbCuAq/o0oz0XG+clJyeY2pK70y/4AJ7nqTQwE/qUl+aktxG/Vg3ETMM0j4pnTs8e+8Spz1LiTuu/MaqezMLWXSwr9JOLOZ13jxB9R8CJ5xfSqWjU9pz10kA83JotpTRMMhwCeITTyCcrBB5uCtkg8zSrqO7ZquGVGI3/UKpjO8XWmuvvKCjQaHncBaf3QPEi8P1XxUJ24Of5Lqf7vdsHbYfNS9dOnNfh8lv+LYN15pqTv7FyW6KWRpwg7Hb3NbTGPNKkJPQfrWta/fy5JmsPotImOXG38nNPsbKuYhT9SjRmXjkNhTArn20Y0jmV0zaL/E0xqhKGEWtsrHDbeO5pqSSDntW5+muIN76rENZsw8s0HAplUwDWxg8/xf+6VLNAYEDHQRjrODJFnoi/lm++jzhTduT2lVRyRZ/iKNMLpTb5VEHP5akNOx2FQz1ySGbdw3rm5lXItiaM/UiPamUuoFDGfqiQ1iKqRtECZd1Sis/De5CIIWt3dcfnb52KmDLeDIF2vPGEW51H1l8wDiPdXYZIgYc8MRLUqVIm3YVrGVtOX83NypNFl42c0en0Xc+/iTJ6L0v3IATmq8OStEcb5tWF/0YhYSpTH/bUXWPkgHJsJjNzmL9De7w6jD2Yhe3nMIc0gpSxQ3yplepROJe8V9Kbi/ULpb8Vy61fh7OPsXtTBH9zs+AKJhBMwhKBhabyhRV7PfdSlXPFEelGlXGZUhhgHwErpvEJMuwPJi7xAemSj5BhCwUbJXkxzVM/ZgL3Gu90HSb0FyMjfBMryfvtJWO+MMJwZnIGMtIPa3J+ZfUZAc/Cgwdz7Q1fP3/6Eo2p9fUaht1yVcX9TchyCuN5aLlt3eEbAcwDXnYQNPjb56Njq0wJE03aebWWe87xvaX8OlD3xVqH7WvpmKIvmYug2tplt4/vP5ItcFO9iLRHMQ28G/Zxw2so7KzRiumoHTDzq68CBNkwVMmlsxBVphvsOlyVmh0B0a7PMoUcdte7qrunAPmdw9VezQMqTWy2+rVNb2g1SSrL4s3heNs37pj1NBP01kTmNi715kIpfX0eKegnmSRnyHRZmsB+ryic7cGpKgpk0xh9lVGLtYSpc+mvFgDqIXQTSl6EbUoAQ5nXz/1CTnUMe0ikoTdmphazmbuqKu69apqE3ImupvPawojiiGYZE0wVYdRO8PAY+lsGV9op9IzWQME9c2Z6K2TNWF/g0W+fCVskvvWO/JXSI3JMYdHUBLSTMaYviLM+PKr5rRZf7YBfw09WgbgnQH8BGJDqCJ80xu3HqdZlQdNefIVzlr+0AgOXbhpfZvmbmWeBjbjf4G9TFZhxyYSsGS7YxhMBA77QCShKhZhiudnBTtPvx762DN4e86aeT0H5gS51uOEMi+UQrdZqX0L4BO3LzuHXLq9m3XMtmeYlNbivpxU72YiJJG5+xPoKNrNf5BU8PGbEivVFfefvsZtUYKlfriSwfog+DLiw6qYLtMyx3/yXUV2h91xJe9hA2vFjOYHGbpV0OZ1ttGrkojus555qC0m9FaoQ0diXds2Byo4ivwksu3hj3iV8wRy8O3QxpH7QZvdf+SBF3nytcLNmd247InzVPe4UjcZmQdISSd+HVItIbcaPXTZecKMpk8MkR0DPSXPfC0HI+v7QFNHkIejDTQMYrbhvKhv9ULd2SutlWFSUrHbq+1YTTOUI949QI2KESQX+QA6jGBvhkBBOKYaShA22/jSp+NvsmdBYez0085CKuSNDWoJE5UhRrSV6S84kiaIh3oUV6au+6E8XEV2YnART4g4bUNUXgTAJb8r1MuaOfQSHvueaiNUTzDlmIvoUVvV64YvVmnNkG4pub+EJNV4SLrd2BQm1lsHVbEe+fxkXCy098IiE6D7Uiked0/v+B4sn5KvE+aXZUKp/C1yQbIhmEJVY8jN211qiCIC9JdxiEUSukJza3AWFzhKIWYEDx14l/lzksyGm+dddDNRfQvrO/H16kfrsh/WB+2gNMtzOmvr70Obf7r/SpqSe9Kk66GmM4SWNNkMERnPk5MiDHe6if9rfXxF1G1V6kdbEJtBJQiASp2zhoe3Fjgo6CSu7kcIR6tCgXF1/4pcPkPcwh9JeYpD3twrxkQNYEwQXq385cZEQRhFTGBovK4/Ll95NZ0oqR2PbEra1I1BJozxCURGodamHrJZ/cv9jcbwpSmNPC8p+TgWXvdy9B6be6StqXiDUqK26MRoFNlpy8QkV3zgD85i/Fvsk1MEbea6gNq408B1VYgMyYqewD+etxORfF2pULNt1WnGy51lcQ+G3rNrKlCvKQrK/giMefgUR+OTEXB19kCB6wE8CJnrgZaTIb/D/RjgLAaEXjdl73ToQY4gKK9dm3MkAb7AkpsSk+IfqdiI2pnV4pFF+ykiYNhtBwg0LKTL/3Vze8Us8SbTeqSNi0Byl00mjFxqINRUFa8mbW5uUAeOZ1ZRpeD8GGfWq5+D7j3UUJ6R3jU5MIW9gChhE64/Ok3+IV3rqy9WnvQOYQK7NMAH9ctHvXpMy5fGIXD9kivGxAosVXmaz8KOn/OsXZ9p/tgXO1oP9c2wuUpQkytoW4S/8GYbKQD+UaHds3mQHQ9UVLeBlWdOo4de/bL18jXl4085nrffYnK/7hmYIE3OPD2OFIF/JxG2pcCkoZ8mXjwL90IJeOtNTovHL7E2vh6VfR/Y6HI52ZqKCa3pWKbCPPlWJZTL9WgaWT5PI7CoqfdUmLiO3bkyeOZjVl2shfij64uEK+D7C8O2+k9B4eryPT5Rf7Rt/l69IqzEu5BBjlR8m+44RyHsX0QWjshet523RSS9/CJn522ozVqNgc9dohKBmZVHIW6th4LMqBLo1WTB7lWFXCPVLQJkCiajycNOiZztdk1R1z5ARo/kpBRKxDAW+8G+IDx1FTwadXWNYqYBopkNjFwZwx2mlr54JxkzhayrLFCxCuLU1dcBXtthSV97+DbRFLn59ljlyKjDEXLCZbuE2DnwB8vGU4efSm8We0SmCCo2vLaav6wuFW8Wr0q+WC5hzrLZHnwYkwOVaN9Ak4aexi2HOPF3AmV1RYRTOt71oGUHluPXZqxRFMBMe79k7a8sV92pkFrxqLiTwflwnvO6tEGVvL8Ozc1fcnnYFUveUGOkzrEu6p5u0HE3WCDsQRM1azZGiZnndGP11IFSi1vGouZ6rcuW/mOsOUt5fTQz6J/ijY0B7EOp1fOap9WaKmSWWa9wEuPcqMQxNPHl47oPWUekfN/sK+Hxn7b8WYoTqV49j/aIxgjg5xyAxlNzzgM0VjECkrC2qy5/VqQavN6GtrWx/RcLP4YonFnwDmNQ+gZKBuQ5VZC/WLESDNghq8QMN3rlEEkOzF5E0ocBl77+qrgS9jEhXG68JigcnZcQ1NjAfn6TnXoT6i1Uo8wo1WO4XCUFGRCrvDYUzfAi2ZyzlTj9bsQgT29HSMdXjKO0Cu/D7va+6aAtZA0UP3z7hPnzJArTJIO1VB2XmerlT5jojYWIbrw/R8FrytbWR0abWHVPSvfVBo+VR+z0ZiwK11oKTTigURTgg/wN0rhtgQG6gn7a2rlMDt9hXCzADHQsQYFQUNe8POafaMXMxH3nPXu7f1yix12FzeJYFeTegRRNyDSUSGDvnX2JjzwwKuIxsjkro5+hRboi/Zx2d2vBbYdJBSMbqDooOpHWYX3BtFnEK9CZTa505V1/L0LC2c+Vr2QH3VGKq6+bs2My/TgcDAAzywlKKJIGBEOZPAjrRWGTPUn/91x+k1jPpXnRz/0lzg6epUhB5x/5yeHA1s+kfA6+gvdz8tM8TZyU3Xmg+43+r4vCWeN0s4H91nrvuHL5YAn7dCuPc9/M6FfIUazsdnayGytNU1mdTHiVVZ42ViWk4d81IFovilWb/JE2kxw0pv36MBxEC09r2ZpTtnp7OjJV7k2WVsRDJo9zwRhOUzF29kriqNKrCzcsPcUHg0yg1JBbT/VNIf9lBSftR+vvi8RUi+Zv72swAUl1CM3l/HOO5ihq3y2dsUBzP1l/N52bF8KmFnZt4UyDeU75gq26ZLQYsBofhylqWAH3MMQo7rMxoAC8f+TKhhO10niW5Ly+czYiZ3nNtjffJVydAQXZtFta0Rj7BcuFQTmaNLUWetRUJjdJO06Cl558r91mdv5Itwd4/0EcNKiZxCI0qKusVFMXgo8yZwhhUZH4m56qmkKGVZwbkOeLUxbGw5vTvy/vGNmOlvMsOl+KMRdoqJT58m/5WxATcaRWAbat2txcqTS5Xaw5QKkLqgYFIiPgkgc4cGLegJ1p9jb+2Tytr5lajpbnMCwdDmxnX7fpLXVJiu0/1HgnJLYDx2QU1Zzv2n6P823umBieOa8hZ9ukes7cuCmxRL81vBBb0Q1ouWmj1xkI8LhMnE7/RzlkU2rLkQJEJxptSIZzo0XmrImdetH1ByioV11Vb2iXfLmOtnfuioerY+nB97p36IyvS88wBffcF05Fjv64PdILEvCINs13sE/EDjUYjbsNRGYhRMPQdD5bXU0xrgbmDzCc3YJji0k/tDC0EQkjNIYpuqezgvKPVtswomL7ws11h9Vfv2yXOnoZz1083bWNmE/7YZ21jiKKV5AfHMfjSzeOuRtPY3y1qtuwBhrH88FJwFZPbFHh3a8aXvZycLTpyDtECSwMeAexp+1vkDNKDUHiUv6J3esjek3pWufeA6H80Ddtsj2x7FU38zA4T9T35wOwKLWHQ6phs0hjzuA/iWGFVmincxkjrjqwhWSF/Dyf+gl7nP7x3RrapWOMKlyOI42GfJVk3WotLa0YFDdug23tBylg95qgY3LKxEzlz0J3A/MgGyz+kfdDg5A2zHuqOvBp9GXfm4LA0wOLPd3oIsrHKpsCl7bsSTc+6pPx9eVu+o9/Cjm6wYuYlS8B7wURAlpFpRPSxuMCWhIN2J1uYEiPCmE90eucpC/FI3FW598p0ZK+9LJOnfWmXx7OJ0msa0x7Z8Xa6C5dmEEdLK+0hlEE0M52pSv9gVac/fTIKA/k5YQUa9wkTdB4uoy8flqmAMbh5yMxMyykMH+AtYtn0/O1gIyKQG6zadA9uWjZvvLwqv4w39KwWSxj2yhITJWYeMxYyeCi+khGrFohA7Uh+2D4hVEXEx8Cn4zAHGWmHzC/31DNf1zWf0qGlqgia9lItdHmL1JJSJo3VSVhKKARpPYwMTeU02hUxZwIfXMrJc3Wxl6AaV3hYs55GzsqYqirpqyM+vtPk9HXdZ65hznk1hIG+UgH50Dhc1QIdlTK62NA1cvkQubfiJdVtcbAU+k9PDSfkcqWSXtaahRFPFzRvpAokLiW/45Dg8ajq2NFfqBXfCJYp0nJgosVzhzC5NyRf4pfzdoFqJuTo1yJ08axOD41VFxM/fBzolLSv0aBW7Smp8b6PXcZfTesxXH6NNYec2OH1+qxawuyvmNb8hCOZLqSHLHnXGjOzYcw2urCxLbpey0nndlOak0GULdajUJIOHZOnVmlTH4O+8Vo+67+AZX65Q87j0AM30IOgV6wrrup/OLil9JmiXnXo19ejJsdy/fFY4cEcG5y24/MD7njwycJ5KahY8wxzvHrrtwcABRG3hCkPs7e6/74KGTXby0TEchVQ5SXoAasoYzUs8vZJqkn50nnSH5ZV5M4EzPK1BCm0GdkRi0EzQsMeROHeaxP/ebHvlyqy3SLoVpjyU65GkuuRS/3JTKBNEm6CI6vvUNU2AsZNkHAyC3JPPdZRAvCpjsiTS8jY5vaqbiyObl1ep2Lb/XkGSbxT+sC79vnwFzxIGvqKULGpigbwCFNRGUWY294d/4aEEC9LYKyIjAW6WDte1yMiZ0Mm4vr1QsljzTCx7m6KNycTFuivBNEWsTQkcw6ATfHJSeahSwsRAX0OpNRScOd4j3lbgOUtF/5N7wkoT3EOV5XrteN+COl+EQ5hy6ReM53/fgG6AfUNUSP1e0aZk8cJuoGIanIV5ePgb8i3kj0q57GnvtnmkDGWuqB3dT4E4iWqydYaZj7l2fveXt0cN22dKQP3+2EPOTbzpX/tDXwLaRmmGCoq4SKoCs8/kygGZDCPeuHYwsm2BttuTCsC76harNuy7OBZpffXlHtAcRRpLvidVn/7O1fiOM5FxD7gJVIPzROQcyRqMqXFiV6/vLBBKrY4twIfnVTfD+js+6DrODzvH7gujsLohy8CWMyz8msiKbeDXK3xzLtrahDATak4pk6D2bgofDFBOGdsoO2Udw/3wRoyBirp7ukkM/vJUpW7k1uWvfplug+kizLjgmL+rJPEPJKQzLtJH2nnXn8vyP26X55ek/RYlp4bxCT+mvvPLZcoCUK+nHwxvxAQ5f+8XK9Jl8nU4lyIMfXW6K/xJLzbkzjIqC3jEJ5Ai4fETZ45LRJUN0tR6rDB+6RGf2RAto3O9rimonN/L4qdU2wccc0ii6iKUDBLLaO3g+CvkzLFuhCQLQnjB/akAPXOXH1wvGlUtMOolZqyrxWkhod+qwZPWpjqNkWmZvineZ8PlCYcP0gP98EUBEeftDft+JZ77eBe14VPAUeRmLYi0VApUPdip0lVdagrFY0PGulP01Qj8C1p6d8msPX+b7FzX8oSREzaLLMvb+b3b8pMCOoV+U4Du+3jWNVRYgCBSpMFZ8954IA3VamAtU08xDG+8eHTDh+xceWX9pbvB7hZj1XpGZ+GNtEmiide12oceZUQ5fsDUMBTJElR8GwUhRjjBZg/+mSk7qjfQ5m5957CZOXS5ch7sdelURNIjoNCClHUBe9IquD1MR58t4Z3wZyLv/FksB+ySlmgMrXJoAu+9O9Qnk6mrc0onEs10LjBspupn2IqSV4ltbXpxTwTugKc0ITh1U8DYug/SRgvxuV/wbdcasqTX+oj3Tsqg5oEVEY+q/0hvS8RxrcSV5aK1aJhz8kS5gf4tQ/xNWwJaZQPKEvCgXmEJV2Q6UrK7eZZkYTL5vV9PXd471z0KgYFpUbeZMbmtElyge8dxARyhuZuU30PGmYPVQURS9+6/arZeoFeiMeYu1wr5pgO0aXRw4GKhbXi93uHMPRr6J85TIvLTOXSv2IXaO9zb6d8s0wqOXKVwJ2e9qxvdBHUzd2HmeHIIv4I9fRK/3Dq5Nj2aRCUvLI+PFifMj/Uw0wYdBcwc1VlKaDS4F698UPND/by9RryhiYG1RtVZbGKXWcg0jn9DZP5tmkj9wXtOv5WQ4VsEPKSsoaDvRp3ImFfeHY7+W3M8DlK++jcZZLjZZBfenZtLwNUOouQVP9vwA1etmXR/3DpZx5ztK59GVaoIEDZ2r5ADtTiHhyUYfxbBxGAyBBMBaTggh+cLietHfWYsnY7qEDqp8QFNgwNmo9m/rmlRcgwiEjHr0BS6lQBoyeZaeCh45UWMkqa0IVqiF+bPCN7Xwx7ONtscuVpdDoUtXCq6DbWGejqBdySb6M0SQJDWZTvaDBrihlAPcdGUPlwGiNUWx4E7AM4YUuXQ8Rzx74+m+9mTeIauWyTM8zOsOaBJxnxfnx88EsSHE7SlhSNZDqbP6+S/eTTsSq63S3GdL5MUCsQjU7oBevKWEZ/pixZuq5OXVmGZkwNtBqyHG8lruLQ9/Ux6oTP8ZcpKz0f65KQqayp4FJJHjOnUlA8I0wRXMnqPfmrWYzFQRzDY1JZYE0bZS1ylSDOpWMuVBQDMify4iaufx8PG/Om2D7h63asEmog8fp6xrCeYcu7uNAqryLJueX7mbRR/Bt1jkCQ7YZykdHPU5PI2IyUzDiVARxP+HxoUqLVIkyYH/PsLVzwEmflisZfDyzu1cuuhoDc+cZ9OaoJjuBKMygWfUO/5sMmIzpuXE4QSip3cksz7iSRvr4Fz/NbqvOdkeEo7IfjBohUmIutGrXkwUsWBjLzzVGu5M8l+2PDmRa4PhzLx0iKWgQpzZicqmn3nzD89ZcCpRDSL4WQWH/d0t+X2AGTRNvjfx2OIBOwk8FVlC6u1/3E4+9bDejwXupmeGX/1hYiNU9rNkVshZ2ewWkZcxmx6aHUOTUyanTMU0k1H6wfXyTKFtJ2UYyL3BaLXmG5RwzK7IU6NSExSHkqv5J8uBA+Qg0fwgFTsKHxXIrBI+ZT0xejn2yUY3Z67FgsM1eyvLUsCaypiLtDUcmZgJKxO/7HOV/RhZmFjDSUQOto7aBGdkZSKEo+hJs2I1/mDw47ZJ7fGOB7Be1VfyHa//1v57K75+A4Kn97gvqh3o7LpdldGuGqOXIrGUUfFOwzxAIVf5EKYoCavyUPdOUg3/Sx9xx7eUXBxI1DhtHyDGtqYfdYgdWR6slwL3Z8ONAY0hdupou+e4IRWULQr6edGtRsno1JJqmtbRBbzqI+EZg4U+VJxQYY3+IgQRcUGGIDQtdGGl8FOZvBy0o5s5QZlMi+Ls98ikrDPqIEFTGYoyrg9WbKkTAVIHmQqaoEGpuEgdpB2tIlbHs9D/WjS65to01CflYGtcPWtQQsY3gy7LkIh8wfn5allWW1ntsVYItCGy72RVkFxYNAR6C+fM6m8xkmBy+lipdq/QGr2OrM1ZzUnRu9zbj1LycjuERxPBCpsnwtlYbttONeZObOEet78fgu937S6wFCgGUKxfA2ajDlMxiRPv0ayevgI9HzuaHywntzFQXaVzRI5IWzavX3K00HFjq+TPWfm1FegZ484AWvtw++RvxY1ScsNoPSEu/yqkWzozPB/0aGRk8i0yW6xlNfCGY9xF4W1pN3Sb2ZKZFS5FMq9J2NNeW9iC+FMeVG7RA+i8iHeAhFcijBG1bNRAT07v46GkKaIRDDje1svE88uyeYyLouxkF4fvxof8VeFxcxB6MFAoMKvziuVf9EN1xcxtnyt/uj/xD1HEemuyPcgVC1dQltkRfrQ6oXGGvH0IlSG6aDF/L5FxU5U1D/V8fjrmvewr9XnTk0h0rez7vl+WUWaHxbZVmpnDD8P0tXsSU3EgR/SQxHMWOLb2JqMevrt2u8t915Y1tSZWVGRFLPUzyO/M3PQeHOSLmzycRFDXj5+JtyLkXq9CRQwG12GFYG062oZ01b1EyzXzcT6FuFsIbPjgabW8cE4S8XOJQ5Ew5GTf6razLPirOfUpfY5CrMHEMH5r6xqVEpbuFLLmwXzYFDk3+NOWRm9XRyR8WW4yowizoj+CyAur1aALRevdyXqjSK/CuuXL0a3TvZE8+K4v8mZqAlCtgBeKqqBHFrdQb9o8IUef+73s3oul+AM2xA6T1IXOkwlyK2Qx7fpV2b0nw+W8DKOfYTIsDE+U7evblkHTfZgpplaEy/41aW6O9EADWz5W7ufZvOVwbvr0hSUOjzsCLr3MPhk5eXbcouyS6uONexq3s7qPQVXZZMr+n6nkqFX/QnOrRDG8z7r2teO81lcskMPui9+ijS+yndaFtgm/XRJV79QNsR+czWXoAil3675YGgu9BXFDi0YTX8ZI4uRFsyrEWwyRFTB3y6PC23rGEiaKAUuos3pWpU0NKAGIxcnHut0Ya8zvE01jDUUh2nMgOSQaE9dCtGinbE0oRGHz//Rp6dQaScGW4KidfAcHQHKPc0m/M+jrIX0dopaZoyEVIMRz+AnjIwxg8WfzYSPNERPwCh7gVXQP3ivRV2bv8WSj7WUzaZijTAKGgxXk4INQuEM4xlQICLXGQkUBlp1A5WFhApl8kqVhrlTOn+NLFBTQkmrbz5PIbXlYB/279DeOLNtU88ftjARZFlGkEXUX4j/WwxM0/pZ0nIARIdSKo8XIHLndRn/MFaqKUJ0mwhoJ4JRlJZfwNPUAYlwMPPB3VjSu46T4MwLO5xnxx7Y8ZyOaWc2+jDkT4xO/E333D4Y7v5Krs4y7wh60xFcsUsNebch7ntL33zLAFJT+8ueAu5PEPdvnqTtfm7K2LPPz1W/jV1EMhWRhF77pfYwaViyIxvMAXYhCmqX8ms9EAZ9TQezN8/ywnsXTNa8hwid1ybY2kCtVAl5xLjqZhN93a0dlaNV85kw/DS64GuKRDMYsIR6NfV6z3RtJEMvoSFfyTfoy9Q35rj6lZN2MeIozrMKMQglo3rHHG8IhBT8UZkqJJpYvu7U2wrCVwCMzQO5kCHdX3U1mSE2sjGuBA7C4VjCcLteeJ6/eM6EwH9jGy01cRbTrQKkVccWMpSY14GuXnYZNqHJXmGtTDiOFmSFNHNEklJCzrBveLdidoJ5cNhevF966xSLSUX+sUPIryXjBjJNgvW24+BR+tmW70vvXtz+eZT5DIsNrpHfBVPwe0G9t+HuPB5fWtpY9MX9qI1PLQWeJM0poTKuPmoUopaX7sh7h4Jc5dvW8PIV9temjmzVBRHitqy1QycF2AJqvYhEHZrFHsDirWhH2A4DphA/6qGYQ48VdVuW6JGhDs8Y52pO8/Z8BEAZcxyMphLtelP7b3/BtEJV1JmJhgBxoZvhYZ95eD8EtqXGF2UwtOduYGdk2xx+L/vXV9Dpczx+V03AwrqokcneFvcT3vqxjPdOg9p7RZIib2LUi/ow3xSJh3/gvNXjyMa5cwmEaH+6yCRc/fsxUGBM7r6s5/NeSrU5ezRD2qAy6F91souyxTBqo2ls3Cq+Kv24FhxKkSpFJIS85IwfVw+fD3eBVkoDSd7jN+X4GTDL+tRvlf43CVR2y27uzEngN683eOvrPj6lZLnQ1CamqbRczQCyWFkvO8C479eszNsbS0MZW4sentGWyUq2cmO1lmIzBjVWLoP13taoYKvFy6eezH9ya71Nniw6RbFknvPX8v36O+cIkhx/z5WjLVAp1vw7bhe8WNWC/sccUnimXFW1XHbzF/vkwVG50+Fi/XFJQ75B2nAuYCTXdOSbKXQqccoZVwiPSnUZrsvQ6rY3s+O2pG08temklUjV06MtAVvpCcn6f1wdbJMGCn53wEmeTwXvDSkf87HO7H4b13zYsP0cTgiGBdNSWHmNplJRU21Z5r6r2FWBg//pvhSLqSD+VTULa8HzqieQcKltkT/R88ouZwgq9heXZmskdg7IB5M7B0dw3aEiu0bnQOiHEYK6IMzmoF1M8A18u9r6irIfYGIL1g5B1AmFWAE6Qjz7zAnWv6B5USrFcJcI+kIHMiZce5qybIhZE9mz0o7gOb/aVGTpHOj884elFNQK5QonZZNRgppKlqOkQYILqXF0EoAdOMJIlJ5egcXmWf8TUku635B+A9FtzYGyWZCL+hhDLTxYThGbn4o11fz+0JPA2Iaftoln4BLHE8t35dIxqbthnQy7P7aElMTNQ3XGNZaW63NYcIsbMmANNcjRQQ/s4Y/Ztd+oKBIY2K27SO91D1hzUrqxhEn6d8Ie7bXaxE5MM32LKn/AP3wGw1FjrSdamGewODk/LeubaCd+xeCOLqseRN1jhG83o8jYXjFEiT4jYTBzUdFR4O4ieDhybK3B5C6YmOa5U8EaQwY7ezRWfKXE38GVbkfG25lA3M/lZn7AAYyBh3bTN022bMEL0oEU3E5Po0yX2lIHc8OA092Q6b7xQUhZFkY+/CQvP4NbILtVNLlQUrk8tnrsFZP0RZojxYzmZY+nUtrbIo3AmEc7xDmXfy3H96OqHuR5exv7tz2/INd6z262XLTx9PAb/NQqdUDynTDdKtbXMLoBmddxX0bKaFI9ZLJ56FUMUAOTSRooq3VXmZqMzfg63glJCIPAHcMWnU9aL1JNvMp01w8ubMOXqJzT1mA/gZoQ2MVM6UNKwyP2k0xg52mbFDQoNACXJOw/90TnqPUQAxUx0Te4hf99u027mkd0/4CDYEIJryfAqrGA1AEA6jXd/hoGaLaKa4B/YiiiFeWvcQdECz9wQ8GTUuCSmsA7mUPGPnbEW1GttP2Q8U8VynetkPSS1CKRzAJ+eoW7I4UjbkrLXUPtyLdp/uKWEbPYHqKgvQU844hyAErxISgnw9aOIZKmLhtIEkwYqaVnR/DEtUVZVWrZedNs0ST8cqOB+M5RaL0s8+VUzcsSpOa9kaHt+Rxp5lkRQOUfVLPXtTECuIJTTUd22slbHhHu0vONP4CGPnjb0Ck0m/HloRABI/SuPP+u+fT335A+VwCnAZHLoIMgtjx1ol01mkgQR6N/F8hzsG8bF0n5HZIhr+43vMxoukvLfG39j1b52TZTrQj7VYr5r0wjIEQS/AR8ZyTds/D+9Pq9la2NSEt/yaKBMDkrMF7B0cnoAP8KE/WcHOKia/GvVWH66MJvEAwww9q3FLobqLikSs/o4WxPGLj4hR//LX8maPbSn6fbs/9fDPGYxim29/nOfLttix3Ej/XOUi/m9PJMuYvA8jMza1EyBDR9NxoO/BTyTFzHdBSDJc+gBfjDOkMC4OfA4fjVHIV9CbasljoJvWks4uTLbi3VPh3d+/VCVv5Y5gcSE5njSLsv4fTwPwjLatK3+/cYW5jynGD4UR/Pqwr9djZ+lVeDiW1O/9xchvehGumW/ShFE1t90j9uNPlBCJR1A5fOMD2/3Y0X+vX3L/Ox/wuEZSGCsUeDpeIpnVi5PDvwmoTN2J+sC4+8wtxGjsXkDufBObJUjocv/dqoleee/mmPpqRmtcXKjXcoZtzKgjXZMKvsNcfETI0KKCYYgxRknt4JOJvnP+HsNLylqy/qdGCuLOyEtRtxnfUz13/2Dir/KmQaDzjvnu6+OM1W4Y1LjtzqsVfz9Rirn1k16m5d2kwJm/h3x9dVxn+KFmqo6RdBK1rTPZK6SJ2SoKU+5DP8POD6slIX21Asj97+qxqPh880TSG0SXRnTfP2ov1W7DjDOdOHpfwyu3UQzcUZQPOwpZsWPiX/a5GLclQnCHW8eWBeAYHO60pVQ5Ixu8aSh/V1i5MkeRxuK5cvCtcHLPyu5E/YOsrkMu5l+B9rw2wUAm9DGJiQBHaI9fhF8sVx4i/c2ua42exSRi+9vurfdmZDKRICHwrFPGhL+qf6/9ytXfKwO4jgRafv0FUU/x5zAphrq9lT42k3NCHB3OA2Iwl/yrN3aPc8wsokJ85xsX9aLLRLuXuK8iE3RCdoArS3x56rtNDT26ApzRgOk//jftOzF53O11LpcOYVAUrO4IFkIKdCevVhxX2kFylqCrSgTHV8mFIZtd9RLk/Q6hyeO9z/3mVqC23Flbl81VkS/5A+N0AJ/PYGowXkiNUOei0UtnPo1pAjbOdTMnP+nvLqhlY1mTvsttydWnfJLtJ2oGaTqEVUZ7ggzdaO1XmiNNdAelnSEC+Acm0xZxKQojTOfTZIvBX+lHaCyNeVyLjoTmWvG+lKZLkKvx2gowItjoC1M+8O7ZsDHEh1n2U8FSrept4mzdKnodfjSLLV5MbQVkRoxjZNjVzvLgkTgU5dxBp7I7jx1fZa/OjAogjZoFmxlFklcqZ4/dh2D327ODTZjsNGnDADRyA6we8LBmj7jHFTPnylJhAX1oeuX13+C2Zh1TqZtr0tYTi3L9NVmMPsUxixkMUsu46r8FjLpfZmbIsfEMTz7Gr3VHeULLrla+/nPDQFt5OBN2d3Hiw9CvaGDaGJVw9Nl4/e6hmq3AuE4V0UwLwTd2PfdzSzv1I33a12vYFyopPlqZ2cATqlB52dYxel9Ei09g+Xz8f8f+L7PekwruPeT7jzZU+tNJ1dbLkhYI8qRJ93QWaXizIZjJA6NwiPJixdTzGZfcJnAgxseuREqObWNc0WWbe17SSV7nTlWD3o245PXkezFvJx+olJ3Mf1/OlMPkhPVMIZKudZGOKTc/aOCFuTzaNFfwCxzErlId35PcK0yw68rQDd0VZNh/KDvflNsEyM4wv210jOKuDOi433jsmolNoXe8a3GXXxc6qpnDfG32ipoFPC9c5S+fmwpMSkHFQpoYmC2aUQ0mP3LoxpRA66JkBGx1a8cdZ7w40Ajtc1CNB5WuE17n4/Gk6somQyGLfPjLeIeVJYBO9Bb9ka381oM0G6LjzJutVN1rvFzifkQMwJR4cFunDvdZVjccd6N7lMcOLHNZF3rGH3Er6G1gZRZGF3VWqFr7tWWN/Y54wQOZOOauOH4kD6EGtunnhXEIr5CJvqh8VuwZcokZbJHs9JnyU27x5HSgW8QgS28mkfZaQ7r58CCksFZOFMpAHj0K9HyFdsRDi8IM0UPShP3MeM8TW6IE5ILy43VT/Kbu4iyJC50leMeIqqoFrEZBkWM59nQLmYJ5rrp8kXQLJZXGM7SxhdRmDkztnzyDk6z4Qv9JtTIZ6rQFhyhhbCVSwNYluTGkbFC8BCQjtps7Rb+sl0z67hxYE578nQl91Q/r6dZ4qcoz1AgMqcBtRxFoNvbN2gC0jmuG0DhbwqaA+mGCq8uLsB3bXoLrfILkfG46IYPiSEUZXoj0k05/89jlIyFggeEQL72/QYgkOylGBHHi+osN46ikRpKBOnx/6IaApexnp2KWxF/55BCujOOMuMrmgrBGGLllGuHWWPx94Dz01K912E8NGhF03VxLJMp4ZPC1fImREfzb23NEuuRSPCQ35vI4urIlpckn8pgrc6FliXRBleZnN74GaI4FrjTCCNRb0MbSiXHcNW/0NrZiTKvkT9SaYrHdnJ/Tm450zAGoIaHJiT4DKyjsXeRY49KK3weMzK066J/gLQFrgb/Iu9BwDbNP0theZ/e8lbSlhrSIjflDq5zUb9kMJAz8hjeU6Eru8GfpjXMU+WJ8huVJHK1UYF1EdPnsIjdkyViKHsQ8Bt8Xoh5RmhsEOOweefLpVHZFJrSpTflobWzAxGzt4zRGd3CFCKJv2Z/wayG0Me5k1lZVKlGvX62QzoRV9DMj9JPMSdE1lM+x7uNCZspqzxY6rY3j8DFcaFoMrORIjX2aUAeb1s0wPtnHiNoBnnkoS7RFgT+BUXKr0go5JmL27OyDPGp7gSkenYJ2uyw8DJRL1VlI+CFk6a1Vs3bU8kKkJakFoXuDSRohKpF3ii3GSJZCPV9PKG4NrFGK4SousndedzuiE6u+r3pOwLRk6OfzvSLUSwxbnduTl8pLgeKbi2zVGsI6GFFm9X4EZAxms79IPqYugjMjIrXr9uI7DE5Boiok+zmNd0OQi2fmFTIVHjdtUMgvD1T1yEEyYFhLS6cW9hu8E8qZXxZV80J2lhU9fn2BM7GQd+4bkL8SXwxhV8afMhbNK6XUmNVh5gMYBimauYOFUZ/3xGi4oqZmrQUtEOpDcwPvyX21WYmfkCcv36X8kXXggJ5pdKyQHy54/PKwXl1AaxAP98ASGQuWP98yhIVQEdiFLKJ+UwYO8n6PL4wuTKOkZSNS/0Qy0E4DHEDVppWKLPn9bdgFhnGIGdEmyuTxD5BuL5M18+NXVGBbl/EJ6KHYUP/5u/Iip0N8PcnFO7kY/B3EVkxo/rcIptuxjUCt/xateL8ktfZj8TLGyKXTvzsrwzAmCUeCe/K1wEiHRHwVEjuytCaxYRms0+ZuJdkFMx2NpTZ0P/ZGiuiLS9a5UJ+N2pfpmggatiNoHhKogCAwYVj5zDStTRmmd+yaKqaZtgSAdn9QqCmbKgdcnwKR+Oue8SHyCXwTiYJb8LIg388RV2b/A/ln2Aa7cdOXlQgDluo2/1goRoP3hkYl9Ro4Mki9qbTh+cY0df0Tt/gtOxq3IL4CUL4/FLnt742Whd//NATAoibsP8/yLizVcHxozA1XblMdQFCAGndLZjFVLIeQo7NJrWd/1Z/TKWMThXYBtBeLUEPyrGiCEpusM0uOYJArXvS0p0502zdrKB/AJm5M/FswKenWeW3F4yFRCNo/BY+qgjrmJfN17c+Zt7zgNwxZVLOvmt06kMtSAhFAPWfnPq8movk2YeTrfAV0o/V+rHOu1Zb7vf5os9e8n5mgmeLjSTCP/Hr33UuZxdBxZe4anZE9i2VsvPlUVPl/r8IQi5ArHQjTX+JiXirYXAy5OXGXm4O23reFMRAGiDYnZ8yesoB6anCttiuRLFvLX52ChY13mvRfcbMW63ZkZKraDY8OXS+/5iZDdVvPp7gmhT2cPEMEXMoaJe6kOQiyUQUDpgbfvvLaKf8uKc1cZ8pU0Dc3FoKNhibtVW8oYmNjFOF6jazNLljZl6W68TesbSOY3EXpZFdQXjb7oCb3kQD1FMJeZPTj7+LkwFlQasPDFf/v7pqhWfoi/hYqW4FxDDlf3YW4mZ8hSBym0EmChVYStifLRZHsJbSBzj1j4bQAT6wlGCjYGfOy7ZWr2m6iVErgS88UrMjBPPuVYBCIMBTTdiVs2DONnyF6hzd+nYLLH+Fn3aY3XFlkDGNbE3uv35yS2oifq1jOIQsdxvlciUy5dvc/nxzoXTo50auTb8khDIzvjv+ytfeaUDdPsXzEPjw3DL65dx3WbZWx8467j8IH7PH7uAilDlMUj99FPaa1I/8oW1YkmZmymvQnjZBcZBryeY6FvWLwQFpQgfloOSfBafjfgvm2pTkoKTvLi/d2HpOTv8Bdauxu+8K5QyKb4EmAPK7vsc/4jsIkQEHX5R7mBo9uAS8k/+xfoxv+q0XPI2rz0LpqOH2hLykATQd0TpP9BRydAT2dt5+bV1Et4FBFMqHXAPaOdsFCh2m0fL+XPkXM/5PjXB78qJB3Sru/fEZ45asFUW2nvS2gKvHjuGozhNbuAQWntwuyfL1Or+/JXgVa+6CCIdCDToh+SFh5ZDvZXW4mUMTMW44kqoZ5hpqj84kvOCdHUN+CtEsNnaox3d6PGYLy95cqTR+GHBzp4dc9n+4MeeD/CvZ7Ol/ZYyFU0+S5zhrt0kBFD2tFQ+3sy2QAuLxuFS8Hh2J5R+vgo/IxW2rCkS3L+bSxizrWv5wjASSpznpgTcGpbNHgtEZbg0NXGOjk5m4weBDePOv/tYDHOSuSudgP9AoCYKiGmItFwDKyJKPt0Cdx3LlJWpTifLWA9yQpehWL2ahygpAFFGfqbX80OFvxRFtPox1c7UaerSP9RMx9MRgvIRoQkeFe4E5LqBqAMUqTQcuVxF79p3Pyivet7Q3eX4YGzg9GipNGDoPCFJnA/8uUoxkfKoOshJqB67qWFtX03Qe+ZdiMiA0wYCSxiBCyHpsGBmIRcnDW8XmK53fQCoiXInol081jxy1buDgLK0NEm7/N4c3b3dosTzwRqV+ozOpUa026HIMUsyue6Zo/Rh6o4FDYxLt2ugFc5xoNBHK4T3krCq0mMZ8fZsuPBst5n//HeT2PbylxLs/uIBBosSAy4wtV+cPoD3PKF9ig+SJyk84pU09MXdYpex788k2dJy5MyASXbADOiGMn8SQ4B5vNqOpqGEDAzN7A55d3WVMqecPMRVBBarHxl1lzqd8HkOvW2aWGUGdgCwok9ymKczoqIvXloazs0XyEAMUTR5jRdPMfuvsRQv337XKbPGw3ecUVIhm86imGiyhd83PTh7csbi2MrbMfOPe/UlcM9y+eWVPI06vtc0ehN0fx2rm27qMl4JyW++YmfPSeD2bbuUz2SF3qWJBVZtdnTBSX2IUHXjYvDl6T+EVTpbfA45j87amXYhKfHnTYNf0Le7IQlU6wKQUA1w6NtS8aEawX5xZhfA6AxMXNzts7HNrUiR+SMqLXd7r1K/EiXmOIJPl3USzKhesgfw5ELrmKiD7L9vBP5l8wAQjhnjorfuj3qjaetxq+HaL3CyNQTens+zYHOpO799nWsCHAcSIsdr+X68xQHE8yQLJony8ABqrEfFlitDQrVZLBOmZUcs5wAK/nb2hsSUYvxM09hgFZQJ8gF+L1XrXTxJ2EV2mBZ+EVYDleKGc8TAUWknlyGqtFo/TAfzDx6a0/snxcYPpM8ad82bzMrq/x3GaPYuCTPMXkyit4G7bocZV39HI3gjHceSuOEIDMenD/7sdZpZOvCjnw2i9jsr2Ns0GHhxVQHR4JyfDxDKHUBGsESd5GVPGUMTsqf7ABfZ5GHyFooupUdj2qM0yz9srKG2oZEphQt6E0yFSBZ02y2ceIryUoDx6X+tnzlT0WhApP7X0jpfWFsDIfV/7CVC7vvecC37shRc4ZL/rMKUnbIieaIo/3xpMmBG1zaK9aSBaGGeJVQfOGHqR1A7xDIaTGqeE5GjKhsDWCYPZRNlscSgaqU6lnyW1E0obv9as08Dg21L3OFlvacubsSJKaXjrOPJ2PIHn6QelNRA+kWZMG7XL6lBnldMey8wqjKBz1J4rUopDlMl0Pw90faX/zR65bOFboLp8TpIum9wFpJcbwO5YeoBXi/SvvHaSeNVcCwg4pL5wiGdf2x02nPFaiBM5ymxedDogDudiuRBtx3+WYYa5K7hD1ltUiLEWUoX7EE1cmjvP71BYPkHlBgujIo4a7zvsbDH1XMPHhISxkgzOUPCwCyN4yaw09x9rUlXC6L35tbFUgJOymbqExB6fz4OdZX7v/6nXYupw5MotnLQaMh09B7+E694ucHWbEfoFO6yYv9HgKn7IkS+cboO6s28/zaSE650rOqCNVrdX2ZboBkmvu+fE3Y8nK3PPPiIofOp9/hIeSPRP/ta9MCqeEKoXku7mMRNi4AcZslA25k7rTmXgK0xz31eIhaKlh5on59E1Y7JL3+Zt7mKpGk116ZQnymJt2JaAr5yZQ2NfQgM+rmmktgE0vshicq9SZDSyOK7MQ+QqBW6ZZSEVcszX39gln+L8aJtTKOliht4MqQzPJvtRnLEe8KJbH+pkHCawDXFDRTN+EujXxl/kKCr9X7yn84EY4U1lnQWVqialKAqyH+Fsjz5zcpo97+sQ+5ogZySs4+A/u2QJBrzaf6MuMlZBMsEbc9FtCEk+RBfn4R2uSEtMd5C50P9BcjLPc6ootQDeBzionYpW5a4+u2Rvf7VH3A2CvdgeRgcJdrqekYzErd5/1TkEaFHMwK8ne15yzkc+hP6h1ybETkrEhi4BBZKR7fV4lSYgkMzZtrVnbrYC8nT5h7b6TQi01XaIcuUfYJ/QeqwWd7tf+r3UwAXLqXckg/xx42zxqGlP4ECCN3XPtLD5xsgdyy+9IwlemuChFPNZ/6ga2ztY9B/8wPZiyCHQAQq7lQtBQ5JsqjhcolJjn0n19SbvHGUUxNJB4oUtl23EYLAOqANJNj21gm3ak0iuFZYNJH0EQMVHEdRbdvuPPFCVgKal8kV+htqZ5ToTyv6q1KfdEwf/4S3cfK/MWFsN5KVaLFQkYWjb2gcfJNk5mx7ag/1IGWaJyhRFuzIu61PyDPIq5j/PVLH/IwfCbUNDLvai4afltg1LJH7Tx9VRAjGfcwpwke5/lx1VAqZ45kDxZOfs+XsBVJNWIGXeKzZM8sdV2SgzvEeiDrr61GJLrrSwLb3wWWrLlkmluypmq+RTqsW4WYU7yScKOHOQ3+3Zu+gjrRU+GSUdkSWQt2m9zrvYpNjGLmpW48g+Y2a/XmFwFfkxF+JHxZDOqpagmA43i+yqlClAx3qWqO6Zlb+YrLn4I3+/rn293SLfmO/awbQmpRcgfEpkIOgNFDkxeOYlR//ezRh3XW8cNgD7ZeAWpP2ZiCFSaQlv3t3nVUlGVpS6vWfIbKdZV7QgdiGf3XnTeCWxTUUklF42rvl30QyWuk2dyOEKZ8JO73jBxPmF/Mj1/+wzyHii1PjqmLc3t2og6E+Rq+qkJCoiLJqOIkZmgfBSVcsdbFsptd5Yeu2uaQWt0OGRmW4trRjKHuWt7Y+0SEkm6dGGaTJidvLv6Gs13FOXpjCqll6IvBJaNO7JoqmR/bcgrXxe1DxHhFd53xR36fJY35gmMk+ZP1s1//HPZn42lFVskN2jTFal+EvspOYeDHO2CvXS3zyDMcSAVcx1m82dhjU4/V9CppzFkc93BYhL0WwzvH5mb1khjZULGDXRu7EFWGwTtU5+UzR9S7WDS8PGHX0WuMQcJRmbSeSLH2UEyoxx70gxDDtSbx+mAfpBSMC8/Hl7EbGvzryekwEGPJw5LMQtIK21A/4yGzVK1GNJC0rZnoZcfabnujz7z22ffPj0KWp4D86mcW33Le4ff6/f/WR7trGOXz2UqTxd4laQ1N4+TgNbictDGoSEh+caSLrlmoJoeRNZSIwS/z3oWL3fKkr9n352mqxd9I8jz8Gb90RpcJZPvzx99kXyaHIYlMpNoXFdA1PpUFc88+t6iNWbuYtA2MqeNXnEC8xVQMlA/0fCmZd70RfCK1Xhsq2aMwp0B9pVQO269MxHZF45H9VQUU2Kz/j8Faynm5xehxE/A8XbLA2uw8XHNiT50ehZBYg1FX8rhdRRqYKfSCrPf0BvXOaHJZvj9KHYoXLR0SJYmIIoZfDIH1l72vkMk+81gb5sQdvD3RY+lTtpW4bMSiPhfpImQp9o+imbzVeGHVJ1EE/M7YPC1zt9+3Fum/dtPEXrKHILj669ueUVqbo+FuALmpJkEEPCYfCXjTM+IZDOuaxRc/y9xSqjDDlBPPoqncvntmDBiTc/hiLu2aVx155Xwhk3FNJFzxX4RrDOwWc+oH/VHiGpqycYX7LbTAgn70VtNkxze+k6LInbt+QMDvOIocZeDJ9oLyrB1CP7T91+c+sfh31QDVlPreS/T5fPsWiAC4zGGlRb+Tbg+7ZL13eA0VOY6dHFRVKHCjQLEJpUKMtllA0oYWOH63NqdnHWwJOoym/bYhSUYffon+UFG6MFH/tw8oLbrgTPsxgzI5GoVA4zpaybtnrJtq/9IVyWsmQx7jZb01iOJtaGbsjHWHENaCAiDsmeLrgqW0G9bWggNZ+LsKOb2TuMR0czfJ514lFehQgyUUhwn4f50Otiv0c9HlhY/Uj2v5Y4hz+pBPIPFVsPe2PvEtjntgSGn4+/BoaqLdD1kcttLxxE7Zd3KqmUTZaF4wJ0pu6EK7PWRgImag9NaN1YEqVfmGru7mn6+dv/kXrQ21Jt5gG34YoVu0ohg8kuVBVkCs1kL4UWThmGzaFjuBjw7MBUE9x6ox++Q4qYB88GEU1sHvthvIv/9W5jEdY982FFY7vvaXBL2zW26jmlmZP7iEBDP+MgLKaP68uuMtWtePabnttA99+eAA+VRGEMV+XnE4v7Bz4q2ei3EtkFswnP5B53apEveQ5xanwdPM4ZyMcs+haPo1t5C0xPiuhOSxspQCuf4ZtlagVzpe2Nbk3xYDZW4CRmfE0Nz+Oma8ZMXxETSV5oj7r8lstEF5rDkv3+u9VcJrRpf90ZbKGqnnyIEjOwB6AqMQAtamXOVmj9WL5jgrlA/0J9wPf5s86gz84wp1fo6k7cv7a+l7SdwIRjy0VVSKk9pCLgZh7zmqiNMrTM7pK6SioDuhqmgxqmpXRfxi5AIRAl8qmLV3C+75obfSTFV3T+7NC598G+3HiIpHvPuXflb5MR4P3JTdnl+0iifCSWD0s47uI9Y+QfytiLjsirzNEy0PwEVOs2JK2Vmpe/+T3zCJkxWIkMMf7UIXw+5dClm/thPsDYvn63M43z2N4J+dqgLAw2xN7H6x5ExIYVnMrHAoc03qA/Gl9O/OZcDjIHaLwYtnl5V2mAs7yYPYDAt9Nl7yhrNdv8z7KFQxhUgxlYDMwoA0wJ84Anrmn1LecYDvMNmWqxsr5eISsPCfHTLkldjAj4hdYdp5DFBl1IjKtmR99PluAADXyJX+IPVfKvmz91ix7hKRo06uwHL5cf/i0/7FRf60bzQTjCEziBQ0Axh/88+SZ68gF54UiPuM9L7bn7Ez3U/IrtU2rslM3laTgJmxUEc5Frs3XyZ9quSJ7nv39N9PIyfyTBUZ5/l0PwBNrTbxV12TvAPBjtjBb04N7AZYUMeetlhlxP3KgjPdBpoeXlXS10zTu/kVBPfegwuIDezIRIqamlvniHI4FatXUw9V3MEyd+3kp6kFt75zUfYani8PUwIdKLQPg74j/4U0Wx9pEm/3uc9uIj54HXVdoMvWDRGtYNsSeJiBjkWpNJUozgSLnU7iR3Rp8u66VnDUQaUW0WKGr/zl2cq2gXUR533Z6QwkdA8m/7iTiC8g5A01EadlD6LIzSMJTKb3O3ExQQ0PMobNwrCCzfT1Jg03xMcjXxgUZn5Awwvy+1js1K/eGeHT0hMV52H8Z0yoFy6Qu4rqHF6GnPi+ms42fg3Ok7pJobpxGdQfBPHHZ9xTeRQovH5BFYR8m1qSiq1N98Cxnv/W1PgZ3ucEf/RKJFOZGPdzM8ZRkfWGpwrjcnTnhLWo/3mSR/+xAqCasEMzkYHDodA9Zz9c5wqaE1h2euXXqVoDteb5KgSft8E4LhV2xrL/ZkhhBmVFi/Q7xYlQn5G4WJk1pQ/gcTduzzSh9frPvK8dQYhUz4VOjd2Pv+uRHEZ0n1IowrSx+lqnk0/YDyg0j5hqAg/JJPsg6679awM8VKhMCsa//PeIyy3ouPz9lr+AbG8Udkh3uC9EYoclkCr6bVmnI8dS9UJEgrEQ4ZV1b4k11ZghT2Z3ftSyjD7H8lHRenjF33NBYjKuNXaTthg1iNpsRwpIci5Idel/mEOY28GimqYQxLpCTEzVlA5NEZ0XwB0FkR7ykE5F6ZTrSSJFoSH3W1xs6WcSzjBljg1SxkMq63XgMA/iIV0Hbya2iE9Jcj0Iwqfml0H0JdthGxpneZM2i97zpKnZ9pOOYVtJ6uqoz+r036T0zPJE/iarkd+ukI5W4kSj/7Hoq5rqjvetkm1X1KqVkhnXQzzFOCPNYmm1ihG6CWOA4Ej6UGV/h2gYAmfNwcAsHG6DXul5VbJzI1mB47r2H0DD/M10Umef3Skc42mfA5H+haMJDUAma0oCz8OJlBc6hd+BlOQACGpya4fbbgL6pjSs4+TfJajt084Ku9fXjnxEhlTfL/NJqNBBICcbEuroYWtXDflE24zEolZfZY9nvgCkLNg0QfyqdZDpx2V5i1JbZV3HnWmLmPsPusiyAWX3CPGd45xVAoODqQvVkFboi8aJeSKM3XaJJus8dbmm4EitCsu3KH99LNs/pyIi1gxL2HGhQTsO8d/tRpWt7nk38AJEae/DyhUst1sEqxuAtA5MmfkeRh2VQfFxsPScaV5sUCYUFQ99qxWuix2J6m6DKPqPO2eJKys8L5eRaAf11/yR2InnYdOC0XfIafjJI0XJ0EH/3SYHIIMg0T9/A3d0pBuPH3IUVKJoubBaNUl2dFZ9j8x5py4QPoitjP50/1gaxE7P+6eC6vk7h9AmId3wGf5toGBnuEHR9a9+nAA89MyT2QhSJwG+/PCHrGBIEiUxdkq+40uwGM0Wr2mbaHSyBqYS3cPEmOegjxJkJ2Eqjbzk9Lt1RcWLeBTzKx266tpIWQdYk8j8PJ6TlPqYTaAuxX8yR4iLR7qWhDwBYn4o8wdLAGBBRwglODRQh4Mjzqdm/0CPcaC+j0ciWnZOR0oau5jCVBpDMKX+WiUN9FhxUUUHADRAJHRxSz2ECwm/N9x1NUaXsUpA/xQvMfsZYfa8P+cncGyUR/3MExZUGT/EmND7ole8/g3UdinjTyq17yeMgJwMyk1eeNj+EucQWhNThGKmUnlgzOktntJpLUaM/4BbEMydqs6xRBSkAlt69nkIceB/f6KbNTODVmwe+qzuGC83YJ0ZBCSqnNOoAFrZJZOHpO6v/3m0U3HRPuUBSsZ/zndnx8S/R0RH+KWui8oOJ2N4CbRv8Di/DRXhuV30PLhbFcEsuFWpAiaa6g91q4xg9prtPPTdfQGjQg3z8EH7CvsFGVNDodeDJI0rwoTKQaZVrgKjsf8qs7iB+l6SNQCBX2xXxgLnxTbgK/MqkV01LZfClQ4iuW0u4a3xMZyVg41YzEeM0kfae6NGChKa06HnRDZS4wtv1bzpRVdLA1y/48/zWfGk9OkZ+mOAPgWWKJAJjxC8BgmSylz+szvI+ocmfuSCWZPKsM+y0BUnQJiKuPMln48ku80nCMPs4XOE8om7fVq/nMhYC8VtBpjrObJeCaOxVonPiu7KX6+CHoIeBJF7lb+qPCd4sONbc4qaqaM2KCogPvQMmoBz6iRbRRSoiOug5xrK+FkLlse7x7zPjsUA6nd+YLalhozcZMhSBUyffnd2QZWPlYmZmj5bMiUpJPwBQEX7GKhFpTwOp4nBw9UkqOR3UN0VgSFf3sV2Z7bMipfvKTsOxfNAhCSNhGnJhfmSrnPbKN3+dZm3Zyo5Qgn6aBY1ks+BzsVp5B1qei1Ob+FKwlBP3kCmpdSOx4Gs7i+BqbgeJZZX0s57CtX270r15F8Jqgw1QmohTPQNRca4yv61puVPLk5Mxilcg40wEPk/X/3DKC+t/W3xlMRKoYatIPg/DQVDD/Nm9UAiN3LinxPjht/vkMWpJBsFug6pLqewl4HlqUy+nL5B7VantTkHoe3a4/IqZ9LESeUFKf7+jS4tM5ZEvt7xttK7ZF6z0exJfHQhIiLWcHRqVnTDVpkSJ7r2yuKwWrbM7Mf6xyeJVjN6v+9qb4xcFWILRSWCu0dKWt5mm72+wh9YApku9m9A/EuSmbwZIIVr7+OKAiuhcHzm6bb9Svj1HWyGQi2d0UVFTgoWTr1ycaaVNlvwbg5l56ZJ8b/4f2u18AnxUJ6En4FLpZpC2B8atfeoxRTuR8LeZnGgnQ0vL3KGn5nZ8pT/LTfqrpxS7usvjtVqfriq+3uXPPZVC29Ssm0VDV8JPjZtuJjvtFM3D8b3gVEeMveyWir5aAooDrFnh/l8Eo5pSgatwfQgtkYlyWbI2tmrSNDrtaoAIuguvTBSUVqZj8uYAjbIyGRg3xUGX2ZEO6hA/cEwqnSSPIlParV8KUS/oE8SCRfgNHmlMFZsqc4CqNIV1tjSS4o3IdmGDxWPMArFZ1+zkrifI4e8LbJ0XG8YuvucFPU3Jv2KyXEIfFfz8nAz4OetIU6lwA2eoGsgPDGevwULPasZijQTTL6FQ/K2BbNPLHCvEDy4E5/K/xtDY2jW9DdI7EZyUr+V+npF/eqsEPP5FRHjvKCuVpWew89og/xjIddNsMINM4bucCTFqFN0kF9ky33tb7mzgMgu9QRdKNzPscXF3waWiqlNRUy6zK6hzecgHe/k/cnqXPBYo1yQHzxgNh49Cm2rsGwerofbt6vjw4PKm9zbdY3mqouJyeGc+V2U7F+MZIL+M4wgQMdiuF0cyffv3pSxSQsmhUGvPP45NR09hk6/k/X/6B6htyrQAlcOpTbjkC+pwHGk2WftwpcHtU1+MzING/efy+fT8nPnmlf7il0IEcQBH/G9pmDPsZDbKLq4VPVKdes0Zlmwypfl1MkZG8uFiUtl3jon8JstZKU7t8xTc8c0HLx2FvQDV5ZQ0VFcDeQNvwkPh5v94lnOtmIKs7Hc5s2PX4syWDsZ8iyjuB9cnqP7XCcwz4zVfa4SzxpYxBdiUifI18iV3hHTM83uWogOxH2JOqBOmG14J77hB70awHixZA9O/s3LCDi+3fq7XWpntDs9yDCvCoKEJbo+XkPZbYFPw8hKC62KZcilwxi+SjHOYLUfiMRxLwGAZTpfDwQ8Wk0gC5doxciw6psd44FOAu4kthDsbHqKfZ1IxXabZzVxVvuHHeGnbBB7vHbuSzXP8qcPDhALj77RucPBtOP5doyDg0NWrB21xLUmbey1x/taAsEZa4vOqdpx5yf8QOVVmNYPHz4AylB/jTBlbw0x7NUl5rJiMdf/FrKPdT+cY8TsSIdii+zdtY2nqZHcFp+Nn65ZtNP+W1zdOl1BEH9VIBhS0S5FtfeX2ycUgtDX+haFVcPdHs/QMjS75wlKuOdRCFSM6GBHE6aFIPdL7yQK2psL51PNVVX0al6w5RpGM5CUf5dpyeuLRAjEz+dHfpAIYAkZ/XjfrWjbA8vvgDjlmhj4AeZB/lvuM24Dv9SLRMdYX0gyC+iLTBnhZYu20ja44DqtagAUIE+q/60CbEqiQAxKpQ0pQbRKzedvuWSnwVeXkOHTv3znj3rME0S8D+nlLwBzhko66iVl2+4TJj9zYjdcmYwLkdoYcumpqnBEt9sx39zmHm6qHzoiF5YDxRHpxJT35vTfv3hOfrDrx2ZkOl/324I0n+B4txCL/K+CTsRLwb1TXRpV+q4c/Gvd/rsWD6KFd/d6VfyxMziZN1Rolr7HJ+OEMrYkZbPwL83o/hZS8hOz5TYk9ewb2FO6u5fSGjlB/6IxWnHz+RBnYEqtriPbq4eJEEpZitKUk8w2wsYsnh400wvWzgw3ZtG13ZeY7ZGuwEdO89YfnPBs6ucD3VVlmWrR/gGuTVzGgBBoqRP2L6iz99gip/6M23nen+tlPMLKitMO37oOSVANJmIgXnXYoic7F/ZPTMKxjFoRTl9AKhdlxHy7osXJVJFCdnorP3x/MBCMShqhcHqV5G7VfaM47K+gVUrwWcOrKUgtR57J3Qz+Pdn/6/TCNfFM+49mRPS1TVlFmkzw6O1q9sKIDr7fD18yywOzswuVRUe1+jqbMy7BkM5QfXnI434nSh4s2UFgYfn+aCisf5kfcibdNGW6Xjke01bmnGMMEeoWy3oEV0DiMDQjJE3BqKA6YLOw7fVdK0uqF6gb/1cZYaq4CWXo36zZL7hvDwqD0mL2CXB6xgtFjhCQegGYS01BOlDExio4ewr9VFZ52VvTrIHq7t1WB78Qc3JrLNX6ZIT95wc9yXc1iC1UWELfJ9EXuLDBMPL9OViQAmAAdM2lcFyEv4EoSRxG7PfM5Wg10W7ZYfhIFR79vDXT0j+sKKlw71X+SzUt8eQg1D24qSBwPdYoYSRBm6byR0zRDhsbtexUQ3RCS9BaRkUGQJGSaYl/p5aAIHqTg19xSs0XvBXJI2BPhwWrd3bJVvLa2+kFNiVavQPqptl6LcUckaLXQV3XTIpPG/wtcqCPAy8NCxbr/ZP12RTR5hdPZpmwUM1jPGo8oHLEATDhZH0vEQKCDB++m52aFryr2oOZSDKyVVpgPtmMLZp+U0FMaxGNvEmag8z2jJ/zJBAfmx2XYSHp+0/tLojBEDf22JF3Kci/3ioN5eBVw0HDHY6uVH5Lf1ix/5Z/kt5jUy82M80wTgVKEkbpNUae0N7ZxnhThwczEvxZKfHd5WvDRyCNFd3Fi7lOrg/4h4Yn567gEEO862W+FgAsDK9LXCwtkEu/DnnWOSa5chs0Tuj8Y7I7dH4lF/uoKcrwe7euEX/2U5G46F/4NI5Oqf04GR1R8/niI73TQdM2bNxGc4FWctZFv+i4Q4kR9VNS5ZP5NykaFRBMvjlts+Nr22FSs2RTZOOF8U2LcUARHObhQjJ1t3jlPSLLVXz6Db1al4fx/lXJIeu8bbexGztqrMwAeDQdEmNs4XiC+89nOw0imNfQVUR/KZHE8JUmh6b+Gmqd4zSv8TbG254xnmRg0Je27bgEGgTa1DYLqb3ZnmlINCmTgvdmRYbebxUZqOzY1u8lDOnxXx7h51uKqGjcNswKNbzL/U2U9CgAhvWuOc9boJhtu1k++tMwPc8RktfSFVnuI7pg59bHqUXp1d9pD1wP6HmFcjkTzwSfVrIgiczq0opuSQmB5TJYByE6vDKWW/EfTdex5CjXLJ/m7vFmiQfhrYAd3gnvefrL6fn+TUd0TA9CUKcqM8sFGmwS0SR9Nnbi4gYVdEiKHZ7xhkLCUWODL7uspGrkq68SD3to6Fi2Ax9wOdVPdJJT/XinOP4N5Ww/ANNJAjlCahr2QaJVe2v1mnUp9YN/x8PaVdQl1rgXAyz85G4Kya8TAriFooA4L07jTX0BNSypEpjv1krcLx1Kp1erj27RJ92k82zwIKrPwNSvURSEVGSDZu+1PnQf+xTmpvqKIfRrQFjmRZm6K1K1aw78zuqC89gWHqsxPN63AD1X7aqJp5Z+l4VGegpI+IcqiZo/A6tih91JA/KXc4FTfzevJSwLD5nvqLc6DW0BBBEn/1XVCnWKQRjxIC+roanaC5a54lCicoSXsaQYhqvvwSnCnZggBpFJTDkbj53Ce9lJN5ImPClQnTrdqKotDQ6OER+fS9shNEITXRqgMDpVHxqjabmNbhttZNm0j+qMk5346fmR/cwYt3m3Cmj2JbHRq8aKExB8VrjJCKc4hRyc498s+oRASlUX8ESotZLxp+U8h1+cIuoOZSFV172MLNPO3keH1vhXf/y/TRSlMriW+lMHEN5nQyzKWwt4vLNDWSKFFYEthiZckId/7zYxhmQxRueGppnOlL+h7hHq8h56Na+ZhAbN4bjt9A3IehjsG+TjKqGHQG+riPxctkri0Tz8VW4BJ3fdZXlqMpw0rRAcqcAxqq5Awn7nsBPsdmOt2RcHXoKS/urOWxMljy8NwtrqP1I0aBGZ0pbodDEuOy2OZV6ZHySu/ilF2CXJe+qVkz9nVhnebAcPrPTsQrz5JXCcpf1f0bzMb9CJ1gDv/W2uaYetrPe4lAmJl6xskJJoqkXIba5cSQkZDn8tatr8tlxaZ/EAwgkvpr3Kb5HZuixZoffIC1Ew/EsuW4o9QFrjJK8BnE+uqW7L2zO2tbl8+QDPxAaIe1TYiUIh8mm5o4W4dp/YDZsoGq77PvBkjXFKZ+5MuuabJ4/2/KMOQhL9zJW7oH+delzeXJFYSYmkP2P6R9xvwSfuDohC++SgHnpEzO5cxXrOc7EinGTn7J1BrmLsRUmG9XU23TNZxMk08mAGcfGD1xaWRj77NLnvZOjhfgVvZqngh93N8UOhRsRdqsUQSwo5MSAWd2swU3/BS2uoHxa4k9B26BUpBzdYBkb6QVEGJvmKoSL21uN0j4VpxVNGUXyNM/cA0UNEjLFWDboKzB7lye6CmiQ+FykaCYKMenPtOQF2P/lt7u8RSKCViPMRFp7c+PokTItsm6RetuDpOlOH8jH3TrCtE1hRMQ2XNPwCEe4+hSKaIQZpoj8TF4pw5s+qtakl7SB0YQhOKAMCMmUqFbZhGdF9MCDiPWwD5ZR2OIoafOvbpzyLw3a7pT7cFA4iPnjZT+1epl2y7BtrOyE8ZJKnUfQACPFv4ubfyq1BZ2Kp6unVJWsPKypxDl/fHyS/lh+fTvI71KVQOhASVJPGu96inV3qUH95WY3THIN8Bov9NNrK8K1el1TtRvZTcAUFh5BFNDnep4CetOmKX/Ty64qpf78l/W0iBxexEgS+BtUVodn9ncVHx3zxV4nsYqn3K6tlqdXhGEbjsMKzTYuWDgIpsYQkTKhKks3Wsqb2VpjXEOS7Z4Qi+jfPItstDiHVKfKOnMozuWVtKyYRKgFrkz8sEb5BHDHqhlklu8mErmpVkv5a1He4UlSUDj0NvymfEVL56UooHU1GWrAHawTEoNKix25V+BkSsUT8J8ExEEKdHAzPej3Q+wOsaNsbHmS6PT2Hfd9YyJIkmtfAfhv7fewqqxysVX+1zCjHsqFeS/paFAJ4L3Yb+5OPqbVeL47da+/+oMygrgfVWLgm8RGhnf6L9n/DG9AawFJwonH9ij7ay/BxyQBo157JrxPuZAnV+PeXErCik+t0wYgd/8pRNuWhCxXJ8K9jJ9mWJKgDc3uiIyyImYgG9yP8jGhCjtAymMG067QDB4gmIBc4PyNuhZ1GSxWUvqsJEAOerE8XuWA5jSsWoBKL5H5cHODf/smW2V+jWm3Y4ZwTLN+wiZ0xtbv1D2Wp76GbaIlLn3p7xFN0TX2nVbxiGdV0vfO9JpDRtkydB4FNjqv/6aWHPbiNoyMVRYmvCC7Ru4CgVbE92MN4amxOF9f4o9Pff5PmrkHisZ3I/64lsy9xBekg3TcrL61K9ZEeO2iGdkUi4WzNfhS0k2U+vwYCeJPpKsr2lBfzFuKk2/SKqTaTYUTHp/KAngTTWhNlfRm//WtTpve8/VGxarPYmVw+gJ+e2Xy75JPWI58uyhufOx/9XBabjZj+BeHxxwiz2QVtbyRjpgdOac9KOn2caZkI4cXzPTRgOAZR4mRJjCq6IMtN3ipmfdvxWN6voZUx9Cl1l9v+1hp5iGDOTuMqzF/vs/pBwOSBX4Yxp0WCyQ0i4/Bc/KJLi9MdkFUKYSqtC/CaMSetrtbKJCqLiEV4OU4lNfNEMgwo02QV9FEaJjTlEZRlfHvUTcqmzb045hsiFDf2Qm6/rnVAQkG+xRHczQyi3cDvspKrXSUnbuZx/kPc6JU4plNqsHjx6husCU4O+iKDoR5nBwIfIEXDe09Ag3Y31MWg+LECmuhf3/ZU2dYRRThTVijUldXMqwIkRucvJeH00xE8Ku791/K+JCq/VJzH3tiSq60ePcJWeD+pSZBIkasYp9zW5EmsTP/vvzkY2h3/baUFillOFYVeWIGFhtxvPdNQFYTy5KL6GTh8cGwh/Ikm8xkwCeKyJgSbVJiVjx6VuzuCOaHA/LYVpNYtKv5VZu8vZ203Ba/xfBPur6DLjAEQggNiPfWeCchtkQa9CcHcoDYQ6/JIXS54HtXKwWFX5gLyBprnKzprD5xErACWUq0f4IM3VMvQvrDOjC4OgTeFvDGJq9e0al+8smU1nvT8yYhDI2F+2afqPNZoP49RfGLlKlwZ0DMYCJnTmCCu+b0xAZtc57ukv31AJcZu2XsKtoxgrLqj/1pcLugosA+q0XmORRH2ranHYEmsvn6pDV9eRRMV1uS5oSxSCFv5ZjnUxyWIuB3n0G0X+cnsHbDhD3nx28uq7Dv/m6K6C8RXjgQOGCb50jkAawy50DKucQzXMd1ffi2Z4+cpsfV6O8N6OPPNXy1r17p7HRNUiEqiZQovXvtLJWbqaKmG1w9WLiBQUUbTqFjwfeUh0huSkhjkbx8hCOfJNAVCuRBptby3U3EmbE+f52aETrpsOZ6bN0Kge54eWxPIgvg495OW6HmhuOAtNbz8w3EUwGLsoucLfnMv21ILZve6DCLKMlS/VG0taVCOdaxnyuz2ynBCh+W73wVvl/xRWQvrqGNCyqz9fKwzMMjnYE1FkQde1SQVOa2b/roUfysKxNjDNm6QWBKJZ3RGGGJGdmGm3RIaJcqWnwxPP5ipUYnyxqJ3quCRfg0US4L8MOtAGP4c9eHWZxl9ZsvGaGgufXXg8Z6UgATjitrwlWJQiZNi66rVzd90LQ/J/CvXNvIHo053EDOnw6C17+Vo5hdK8nmQvPJFgnC2xMIywlFrf5jlNXvo8tq+fRGSpZrfnGTzqcYOwfhxK+MfofE3esdpUg9f6WUorFF7zBNl+iQpZqji7Uk+IerqTJJrc4PwjZrmnie6fSSh6DS612EsvVyx/5pSN2OuyZLRcDKJNwuaHgvVC8xZiwicbwQvtoIhojLbCfjSB1nP1mQfRfKiTynVbaOTD/HwJCRviaL+EShkjkIDX+GGYbn/uS41MQ98l3PpGy5AzhIIgsOoUbZp7Ko1wnPaA0qdKFU1cmKJlsfe29YIdIRL5CUb0nBQ6sbNyP387eSl/YypaqTVRoKlWrWBWlmasAsurxwa0zJFuY3Yhd9mx3T/6APxkjc2JDUVfb5M7sYvw4HDjIKOwxqzcvogQy+W2wUuDaoeZZlseIim+J+52SGtHglS07uGm3+bsXAmTGB8AgCEjP8JiRi8Tj3IAbcUSdp5wK/uBXLkF3jHvPwkjRjVTbObsvzA+kIQO/Zb2Or7u52XG1FTpX3yP0lZOtmZ+ZzgUn9jKmxeR6Dk2RygZmqqN4zjj0SUm3ix5jPPDv2XWR1mgfvouWoGWdObG9v6jANr25NohPG3yf5HHkehX3dLnMM18ST4qMoyyrEzJEpXaFo5mdPUR7GLkZ+/9zDFveymgSLoAvqjtEmigMCGqvw62X3pxmtchPw3EVEpsbQGe7tZxPubxVi39otZZTy5mqRSzTrlQfUg1kg/4X7fAHSRMZ4TKKfdFYKRZ6ulTnz+CGNjtSKUjy+67kMs2uaaolNH89PYQNIow5RsW9pTPDGTRzoOZ6NUiiEKyUvKVaiZc8fi9i8/ZjqYUE6hZ8gil2yrHMHyIPGAjDzGtENfkR8l0sKZ66dh9uazkDzvEQZdk16FfOkcVX7qqhOW8NXIeEoC8Jz8v7IF5MXhjrZd+WTbe1n5hPuCw59cI1fsECd47g4k+C8k78R/Pg1e4+4hH/EmYqGgxljMC/uFA6OIGSC2zQT9AufSGkY2Cd11DHsIOWOwhm3+0dipeEQ5vnEk0cFcAZs72TXP7fs0Y930879maNDnLKKLgre6SPMAUEP/jDHbZS3jBQZPcJgWuN3LpXlPHF/6IiZUPO2iigdahqHOluJJNrAUx17LKDg3G/NNDifqKNwaTwMfWqvuJH49nALiGw4EW/v6bH2Wz5XPK39jN2hJWEGt49LTpu+fe23gPDCDkPHUp3QNHSYlbRPDwYmJViiJKtz8pGPaPdErZIbdxpStYZU51SZXIOdujNKucV3XvdJ8FjIb329WSrP/GJV0Ekd85oDBh2zCoK0XeNPWG7YaYLG2Yx9YxRazyJNPlh2U9HI+1yh5PPwqfh7dhW3ZW1JrEVh+yAeuyohx5t5iwpJsBZx7EyZm9uVVX5fhzE7z9XHDokK0KLoY9ad8P98wRyRZljaAdZpcZPqHNRNr5aobdsfSuVBOeaNdrll597j+rv8OWyLZsrTzHzhESX3ypW3bsB8HhMM8j8/XFbowulabd1ReEnb/vb2PwWD89F0WtF0xkuOiPuhT0wwtK49ADbiCQI/JF+3f6KXLmBz+Ss6CiWTo+P5NwqhKk0YFgjUwjPdh9MWxIDHbv65epuE8l6BIp4eYdXMmiQp5X77Pp5w2BqHMWaJhQjSqXMGYYCJapkgkZ8VbAncpmlH0TpV15rpVuGgE04PVL3EUVydDbCrjMmgUxBf6UrRyp0OAPCmjqNv41/5UmfxTUqh6nq+Yejien568fZBr5/zL+KEQi5YMmcgiLDePxDCzFWGswhgUPkykaTPGQ/Bc05ndkImiGXQFERvc8WSmCvEp6jlVJcVchYuml8ynZZESU4dTfpIR533+EGPmJ5m4gvdsLlTytba112nytuT7dyKeD5XbHVyC2D6PFrXfy8E5bbFdAjrbU9MlmUw7RkDKznYstsRA2ay4/WQmOFYWji2qMNWj7yCntdPWy+nMpjvpxWWngzPQbWXXgCr6en7draVlbgKDL9KL6Go8Nav3bcZz6kUPixOjBMUdwnwq4nvlNYnFMOWnWYHrsGatT3Ci/ZpmMsS8dxhvcml1a8wBdWP+qyxbIZ2SMPppNZDanHZDNAf4VhZoPUYy9z3zRSS02UNAxmHc09imBBRtVM06Vc9Hem1jDd2ICWHvwT12mV1m2WFGKzCMdgTC/DmVa0iyuDh8CMxZFClHyRNGsn/QE7aU6jutk2Kkp2fLTWGfLKpzutlTr5ITuYI52dhFVv5bpw3QhrF91sAS/0vHNRYSBylQnJfXdq+PTykydqNZLRLlb94V2oBJmmON/Oevtip+IKqoEYi6HdjqSbCVmf3OjIWeAVYo14P1Kg/H47GXFP6S9jujBgzNCpiwH+/rsiPZOBTZCmaFAMg2KKXkst79RfgRzIBvB5/YXIrzlZUqHTBtiP0wDH+WfZDiD/TN1If8RYrJxn87Nl0U8z2NOUy+tTQUhrF4Uyfo3Ad9/lDynBLSmKZjJ1gLkyo944YapYVv6Bo5qL60vz1WfzuNWZiUl/be0drlUEOsuzJLY/oQO7gKNm9v5JLBtMmFEQo7TUT4q1HIBStF68LbM7VQNUtSYGq4oayyTRCEdo4IBmrkoRSnebT4G2fpAbywKJEDhlOLvw6rALiTMQJjijimAP22wL/Ux74FrQGdyuYjNTTpFNwuQcw/P5o4VOo9/O8fEbzlD+RXS++y68LVrliRgAHeCGAmTjgspUsFfDMjHrc3FMSG9vHlbIQs4BxcWe5pjuAI7FtwDyHzwy7IsrbwgWxT+zj0PasvGtipKuYQXYpaBLSVo7rJ+Jt+bLb5PRiPDlUeBqH3kEdEClNV0k3XPuMbJYu1ZU68ZLoEOD/7wI8AN4wrxzeMv/8qF39Y4T9PuiBqAdUf6ny+HRv60CKydUxJpij/zS/I/zbsAaa/kjmt24LW8koe7tzLjrlFwl4qAQ4RSaMqEj4PSooBFeI7h31vjhrwRmlgvTG6AkxiShz8CYmyKP7mvxPcR0A976Z1vO/MnZ8B+sTLM3acuIroXBWl4XrBccKb0vtWq7/99Qm9Q3GvKOkZmdRHYOcWiiL7BTjSZizWabtVLbVxg3fSIbLrraFEL2L9MXyd7ksQjoFTYtX/rfxT4pPj9AfMIdUjznyRxn9igS0RP+0JmOjiGfWie8ojEEdi02XJfztFATHN5bB+fB9/C7fELGpVlyJyQseFH4GZv2z10I9KEOqU6+o46Mb3ywl4JsmMTs6ZL1oWtvK+/gP9XyP3rYLKQYoluUbXvZi8GUbsLky0YPFqDmXzPKqvGX6zr+5hukVn8dFI7IRpp8701DJljoZcyPZGbt8ltnOsIIv6nRwDqdV2KJaBhdak1dW4lShkQyq+Ssa3/3UqRT2E4kfy8BUeojvDeMf14UTSdiWBBd/NigOp7q8P7IBbHk/C3IIg4ei/gL3wJD6IkriL3ou9F7Ybm8dJ6Jdixci3dW32V5H2bnya2fQLi9vxDNfUqnvZBmoWepHRwzSbX8rc7gKqAIfzmqb7ZN5Ili0lCRJhnkxrRUE8DEf4z0eiplkeEPjsfMAC1/JGM1tt1rrhn6XwuvhLBY0sVY8rZr3FWN5hsCeUfX4xlaTucXNIs40IOWwt6UBe/Z98HIyoFMzJyZMZSaznyUrxmJk4TQLEfP9JOjik+e9Z6v/cAtLO6ottdH0yKca+uBFxW+XbxIQC1ZqsVgwDiREYaG4zS1NQwNMQ38YuKUJIoaxu2g4CZNiDDeas0PBv1cpLiNK4qn6tVWkM1D7obXrh6k3RF3L6ByuJWnk+urYFGgbpdPVXov092crJmBCSm88Oawy3bMDqIvVUzpxhMr6EP430YRqiQfaTrpExsLwppjQKS/F0ty7+4ZsufvnkmZ34JijO/Mu6VBnd7I1Y1gldsXZoP+MgB8I1Q9ipfC5dMSJUPbZIHyEfAnX/Xfo2LZ/eam2r2tRLfaLIjGGX+ejMjczdVzpebn9Mw7UFXvjbnOFIatPGpoWBY8WotFXbY6/cRfhSQbEjubhi0VaMIoO+/PNv2DxUeJCEjHbo+SEqCfmYrFOBMEJ6eJAluRr9+pJUU9pw10/f/2KNzJVAch5U7vvBtyVAN/tSlJKjegxGpZHBJLv9pTbgH1k9cSMjntbcPEHBUTHBS+iiRxiD4SgnkT7ThzgYDOORp/XXf/m/6Xv3lVOfXFceRNanu+W1lIa7Pfj6+JChSDHXBkgTQmQ8i38rA7SDKbDyquRIsbCxUXxdoygZGouPa57gT5m4FgIVKPOER3La2l/nLHoNGZtRiuNTru5yhVRE/bIN+3PsL2vjtksIHE3Co73FiwtIq7QnSpmPlRaJ+5cJkd8d6Ft/2xZs+sFU0q9IjzCdbfOx7Xm8lcA7LDXUHpjkBmwVidO9WozBbUUV7j8XX4z4uvKMHp4g5ym7CvTxhcGDVnnswbZocapnQnwN0IAUy+GE6NoJxLcO+7y5EBK4HqzpecpW25iH3WKfYiMmdoBDQRUxUtHULvilhrCrN0mqwpRtMncab/877lmj8lbO0dL39DicUKUywlusivJwlJyMSPhjl4pZSxWER7RH1bHwU12kf8iWqpe+14cldusH6rreKUKhfdqn4uOOkpnJ+6xIEdMXbIO+Fo6U1Yei+L0ZU5CAoM0wajnsYSC5nCVSlRTvJZm77NVW56/sgBu+f7Fg1KOI54PnW4EWsjq9yfivyKiNHzPWI5+9wJYVvFH3+lDAerbG+keLpQ08cDQY/kyjX4rjh0gjQKwsO9EP0xI5JENZW5+RlQUxQRjdS9xsdRggwHctTc0/+Ldq6/BPeJQI/FiQT/6jPVQmcyHlUZZrs0XPMvsZLG57HT0DiKoBMX4tsAPvG+Oh5RKI6FKHv1DQjBliMZmQIZIILgLiS7zGFqzfbng+EcHMzOv/EbnDgg3LaYcBc5bFwTVCrEMM4BhXaJ8LUMMeG0MbPiPdYUJKeqO7NOaN21f7UsuZQjXYU7eAzjCVf9GCITx08hBEKVjsn4Y7rC8+6Ug0Pgwofj6fH904a7N4cDqpKvJ8Xp5wwYDgP0w9IqaoYPBeCSL0IxGriy05sjoZ3mhGP5M4ZBXinmzU+cPWSao2Sq8NlU8ZNo/rHF3ZX4aIloXcLClpa0MCFAk5ZdVq8rqBYb9lVol+Nrz/BRjuskF3YX6EHMxZBtZutsEWeaWfugRX+C3M8jMt4TNQn2XfIM+199INGfBAS7tYt4rNPrI8EFha7tI31FjrXCSpI31gbGoFQwI+gsDfgJU2niOWHS6nN8v/ooj8fVNa+dE/J7Uv5nMsjzkIqHuw3vRSZdWERFFOpb8ZfAlW5jMjRQR61USqec70xurnkF82YhISyC3Sx66PlFVYKZmApOZAhk5F3z9Zg+76FEqcVCFMPREGIUAvOmrtvZPOWhtiv4GIwud7Mwf5Gsuf5gdywil6WD+CER6cySCN4B1qvJeEOwpC6hEN1R0/6NIjF4aX0rFLd69TChA7s+gvw7u2TrPDkUFxLF2UW+fDJC2L04wMM9hZoJcRNPwiMHV+niVD96qqFF4mPxHDFts2hhEraQPnvEG1JUGSrmW+a7serUoMp8bj/ff+5XVAh6lwslIFkg9bar3A9kBevukLbkp7groZxmfsO1lwNuMKCFu+CRD12WlI8wmjNncNIv3wnIEXSV5vhCx4cfHIGseGr8jm9KRaIaf7UpqG0+DnjbO9ZiNpST65HbS8nuauMV8XP94nGP50QC6oVnTky0xefx5cL0Dz3PlFswAFjm65U+atUzbqA3fR6OBJX1ZDQdGfW6pc+vK/DElkmdsjJTWeo7beM/td2FDfKybwg6WHiKaJOGTltxIf7K0SN95Z0MSK62d+fs/NkQjGh1wPe7IIb05J011sBp58+Qq4C+RA/TSpPHADLP8509jQuW9NHDoSrZP30q7efcESYqwkOLNJpZFKyZQjQ9FYp5Fp5KBXiSIx9ckelTsiA7lSl+vAQ0UzIOPdcKlftFW0seeV6geUskwbhroaXvPiNe46av8Nqqz+ZnyDH6B9sv5bU0VH5H8eFDXmNNfzaGg5TXYlJWPOqd9hVIgFnf6MBM/DZw7lQLUsnt42KQgitEsw/YcG7bvdD/3iT6e06oK20fsCjX6OjppshJlTFlaNX/Yq2Axf7sUc0ot9ZZ5fRJ962uT22gEHgSnm4SBGoE2UtB5uOo0lz+C/qVQ2fSr8lQ96uzJgx8vrKJ+fKtoa833q0rSIhS0zduNPJWzYO/tRvvKxPxT7MHgtSQJedAU1OuI8aDylfxGtHlbJUp5HuKUyPNmVn+bpT7PIVj/gJwQd5I0x0965k7D9IXoJZz8BOXKDqYxif122MXf/kKzC3F8BTMJQManUSxb3UOIEEcqI2mRbBhp6rqWkr0rH1K+FpRYRA0eAS05r4KbVCQBODJry2Eii15G2PEgMBZsJtIyDVVHvgq/61w+mYd5YOKnlKa1mbf7w4mf8FSzGTL8wvqjj6GPvctGELIar5m2wzPrZK0TAlf6cBzuxl1aBsOqYns/6l1DYI402mCnaSCvcBnxfi79wCbMlfP9N26AxXt7by6bqMVNzEG5ZLbjYIw6jHekIDbFctcvUucIexdCGGQl8eSJ+UxGupNwU5ovlelhsuVPFPRgUVGxyFt16tEzX8U2y0nY3FN8H8H7Jz/bAnrMRKwyQOHUC4KSWfjDgAiUOjPSXxlOml3bMjdquuCcSKeL91Vr7BVAXeCwI5n4QuJfC9Hry0iChMQ7FptD0zKsIIvN/LFS6WC0Se1pxn0aBiFkXPHsNTB3ghl+LoR8facf3RdZfOZPOtUQU3JH/Vj42TWVmkdUqKwf5yoIStQ0OH6PZGUAt00yd2RsEIt2rJcwEUh4mEco5FDpTXtR7nPOBy+KeFD7rekW6ocrDHZOb+cbKDxWBjGUvOZf7t/hd3dR9eaNWLNcOIsbYybjcNuFiix0jdW0nGMvQPtp9sCXV5ykoJEByZsasaukJs5kY0hMxx7M1Suheb839DYbK2Xx9Fi/+Oolf8nAItf4nanRdmj6bWTAsEDTuGkSJCJo4nvEleo6NC7HF1IHSZjQEGboXx9lX83DaA9HEr7dD5PuUBFwAUFaSOk7ahbM2k2z39yukKbA9XcQfjcBYXCbMlSmA1PzIVP7yJogSJc4koaM/S+c7EfZ29oYv5MtZFcw11PBG/WuRELcLCtxtR/Dzno+9JiDjKstmP7ayAIJRCQ8yuecs9Jc7o4l86rXV6QWYVMjyCKid7dpQQR+SHUGBAYYbYl+xL9Rltb/6vgGmy5rQXyANPozBwqXwfUtmr4TTY9/ew85QrB9SWDJtMvpv+1QtZmJtyfQyySTzN35+BQCDvYF6TGYq0nn2SOy4wTdBEUkUPsgA70Vs7D2/tgQbKchSl3zeK0qDjoGhE54ToCRZLQQAfikz8FUBydrTGtMeFurhjCeNkQ5T90UHp9VooMBrXCiFUKa5TD/lSp3c1D+fDryG1VkHO2Tsc8lFGuTdEcfXz6cyrLHuy4ZInCLX8eneM6fP4mtLGuFnLw7Z2kyYSNa+I/qwZA3BdJIEyWTrRcEOrnsK9AfZxI6nFhWqCFwpvToVuwR71OvLARWNX3dChcQnq9LYUZRN/rKtQOjCN4bzfdsaWfRWoDRwnIj5V2zpsXLizKLuTW8N6svqK4FJ+B3bpDn+ph3fHlJ0kxiKP2Q4Sxa1zCTegO6r4ZF7HYwvwmO4pNNNSKqYHBhnEoso6Lnh1MH5K62Oyaz0Bwx7wZ0w1flMie7hpYOPFUWHp9qXEwDRjmRF6DjLg5eZvwVkHG2u/IWulqVvKByXOCU4x1lJeYHNYGsHG3dFG75/0WF8LhZ6TDqPdpbU9WkPCPguGZIr6/AoMKnNmhIgfUrw37D1fV++d5/+TW8pSzlqkT9dXT0qtjTNph5GWG/Uao440NjNZiHEJ4jXMZZc1kHFXCNwzTlvtUkCVVvVGIQENlGLxSKLxqPiE56JkA9qPL+ESmeU6dFe6oOP+BL+9PBEvT7Ab0kI5LO4BB4i335zNIiU4s4wzXTG3fdcEyN+Jyt+T4Bg4hNoSZq9uw2Dso/5YBaVofBJy+P3b+q5aBZpQ3OwwoWAhpVjyogCKSuNCiH2lpXfj84VhVvk0mwFASDnG9s72ohSAGaOWT9JQ6YMTnalgh74ML++4YJsI4R7EXmUz4U3aaCCEVPsA+mMCAeYdxS8ZsFurgZSOjO4Lmq7ELqveD8Z5+/bh8D3xC/YIxG27qElAkCHFSnS+51YotBS9UvGsY1dZPFuZWrd51PP6tUilAyxPb3um0ZxQbrCk0NLAo+u6EBglUSU7As4RlnyecAxUp+SlvxXMnNM2HX10fI849D0c5F3S5s3lxINohGd+14ZRlLyW/lffoRsTFo2R6t6JhSTkwNOkY4ziGdiyHPZqAMYx3X/gER2fiE1LSSQjQB6FFmzWZvmwAy8cEoj5vgGdrxphYkXun8AlAmDsq96I2UfFO3gxP1vvZD4WZMHRhdGTRk66eRqf36eqSDwn2B6DfYZCYk2/uwRU6YPIfC0mRF6TRAL3LPSAz5cE3WN/Rs1HSWqgfB868ow8jPYELqt1+bZWzoxRfW5HyzCNzLITNlRyVokdZf6kk3G9ZCVaDO1aDYUEcFeToa5zMMw1rYUTZrEvveZ/yErEJHHJzj5TCXTrihSCvvZxnvGpSf21LuSnafz9ijScK+LAvnT7YgJNx+DW48ca3K/pIwFhh/f0zDOFLJqzyWpzNE71h4jWli9U5QTsDxWZ1eNkyODhg28F+9MqmAQRyD9qsz35isGJhmOf57aB3sjWISMj+LW2U92GSa6C0LEoNHvbm1XKrwVEsz6dETmhnnZK6hWl1DREamVNQZ31ZbPjXkHiRRxgpGV1LDxVgBPkF2W26T0DioKXxgybeL8NypqcH+1HH5s7D7yeBID9FuCTBcbzvFAdFKTqaxumqSrNzMc/ZjUh1/EgNWV0bLQgyLJ2O4CywORd61H41SLv8k8K79Md6gLRbxswyFUrAp/sxax7s2JxWQYXGB99n7qUetZVLzdaIzgAsNVd0YPvybXCxVo+rVKA+z2MuOM+j29iD4f4Czc0IwkL7PAxiPuvF30tOnjZCFH+U+C+uXP9Q/rkX/t2EGGMC3UMray8bEgt9WebXXlMsGYZSPWRJqr/woK5n98EJQrUz6KBXKNGdMf6Tg3gScMDvrAd2bepV2pBSe33NT5H4/hg8VsA0oyeRk7fburk2gWDKdZDeJ+TKzb121RtXYizdE/Pkn1LSH/XLmR0u8xY1pp/dFTtyfwLj4eMwRJmh5lfBqHsYz1ydr49ySQs+NFEXSzsdSPdqbJogVDTHjPSmZO5i2kSnepkLvkjcqRYFhYDpfe+KNY61t1GAr86qyXZRUfGcJZF8S1iI1Mri9VZYgj28iS7lGhFWQ2WKD4nQVcTjJSc2ZZXZlIYwjLU0UT/FwNtBbBItSuA0o4YdaSngJ6icWIJQ4XW1YPXwEK3OMqzveU5ySxM3fKHWUIyscbltpYFrqEt+jaVOv8knLRNTk1nEpZ+BmySWGZI/PnV/wrXDX/2y0g70ro3YfVceWgd6SGfSuG8Wfx4wj/Clxqut6hJPG8ZPlgV9PnQufv+LLAX0Ul+OT6g5vfqSBZq9Nvk3OWWL9ShU7HY9g+z8fK1c8PIiJLKn3/+tsRZu1/G4bIhV63lD7mEFhRaQAf2r68GPi+2WlpltRJwg8hfDTYF9kzHbs3OHxBsyyME8bhZD8Hw5qxNH5p3Nl9cpYwqMFlj4EKAAmx8XKgMmufuJv93ZEdyg7VxEkQL/gyhUvDL2YA8Rif5lE56cf7lIHx15SxW/ifbIdx/3HEWrFuG20KaAPlo2J/JNtaK0xGw5n/Mlurhq0hKwA6+FsbCvKpLup3Bb9H8UVpNJDntJaJuzuZlSf5eEaiyeL4gcuH1J/v73n51EY51VeX15PZ/bTKBhdmC1N3pXbwovqCRnjcgZyt6uTRI1r5yRmXgY0OMkEPyr0+4kf21hzSSru+UNp4KaN/h3ZDO5J1ZNmhNDbAMOY4idl/Y5qxxTDQLW1FkGcQjes7c1370tt6gmoPOQjKeniHZs8fD/7DMPuqAxLx/DGj+pfWdnZ8eSOlPKdxHruD19Tr6WEwpEPEj764nkyOPnMx4146VESKw+n8081bl6OBB96npH7d+lIq4w/xkrkzv0F/YYgah8Da8UJhDb8Ni5PdpMTeVny8EQ0FduNDsc5kuIf3Hfb9I3CjYuIwYXD0x8hVuTnRpPSW7vwNMFw/GLQfsR0kUb8+9fCUWQKzzIOG4l+bf7aMnDFVUynk/N5DoZqgWUnLhRIFPyojTpppGf5b5SSZOveyxAhxQFAWVcQwtbTwV98Gxp4ssf/H+P8gYjmXf886dRlLqGLCZEfu5E0OGPZPGJQhl3rDlYNdgOJAsyf6r/ArJIegPITFYqwDzz95cqjS8nPFyhXF/ueUle07/b3DTz41vrFvWXbpe4Rnnu1AvpZmrEAylUZtmYfihVWJoPWu9Y2YWlbufCngl1b6HiP1ZiD+BoXUnztm/zoXb2KEX9z8Tc0XBFkKbQqJgvW9qK1YtAV4Q90YDqoBvvw1PKJmeR5OojE0306wZJ9eRypz8JiaW/Ig/VtQaU8Cc73EnJjuu7mNMLJB6nSCqPp8P8ojbvol09p1yzuNy3UhD7yyk0Hkp53KBvzjBSDLBRQllzcY/2pGgphy0k4R11LBKl82TIfZAvJts4WKewgAJUXS9h6SGpPxwSwdnzIhI6LbQYYYFM2lG/0dTevW3QNqM7pfdbFNpp+hW1Y9YJCGCyBW7o5Tw2qjlP6+pEEFhyOPMsGnh4Bf3/dOCHrUUP9XfDX9cRBmT3hAR/txUR5eKCTC9nypmBMqyE3zIkpCKBLW+NsIQkD7fD6TJEa048mVdoyLIcaFp03nypZ9kcbt74PQ/NHqvrIFoeAlea6s5S2chvZP+VDh9mlWyViHMkUfkILDOn/9iNJHYZhIVz8ErloZ57UH6gUARSZl6Gg5IlUGd32x+uVINnfZql0F1WqHhiHp/N2hyVzo1jWNuM5XaouhsucYijhaG/sZP9ALm7bUsLc5HhmWRu5A/5iv/V13RhRd+td51H67FH9RkQb05FJcNEWWXkZM6dPzUAb0N4RLDleeWAXjmmIydAaDrPjmM8hSW0BgppuPxhcspvZ6/vHdQGUVT26sjClZArHa1so89lJGapGYUmR49ilhqvQ/jW6wREGq1AOLP1awWY2h+CZuYeDexuz57KbRc0wPMNPMr92c+WdDm4sYenBuxdh+USkhZ/M8ILRTonsAIPqJi9Pcxsn52IXDOwYLtUGuIrlE2bZCDrnBnB9wvKsCV+avt5by8D21H9+89H36McyPhhJMBh/5ZdZVT05swxYWm633rBzB90edHgoftVhILzhNCRicfZ4nGMuq5MoXmKoQbR7LOUv2Smo1c5J+SHc60h01NinWMr7EQQCXjxWolq6C/E4gUr3+DqyvHqX8QnOGQfvqhisyg7jHrdVCr0LkYR2Fhj07LqvhGQxBKLhAPykFY1k5oV63MjxuDrTNeo/Aq7wItv6Ac5/Py6LtEnZexi/QBDYyc0XBM4AdqRfXq65xwPsGdAFaORL68sbNMh7Ndlx0ImL+A1Hy187jCobVl36dkGepFfVjQxSv60nAXTy8LTPA7NFuuTw7MrfvJsxesOa7ljQib+NfF1jDi/5ZNTMrblYy7fjV2hOssXy1T8TdQCrkT7dz8axQcSk7HgXpwDyzCdnTD8lkE087M6oGrEsQ7LtmbxxeKuU0e3H5qQP9gujGCsrap7glbIQ2WXbdvTwFqQLrdVJzxDNgwGKWWVJfsYH11yB2PsMms2Oo69rMni/I3/7qEOwxcNpBJynEvGwrUZ1Vhvy1dK3xJjB6fta/YV0alZVZ9kGYgMpWv53xaTHlJAjXViyRIKITKXdj4VvPo+yrRjjuv7kyWFWUusotqi6ccAeWxC1Vf6kl55jSLl83a6E6deowRLVC/MxYxR3uJoljm78x0wCRlphxqbDRlWU+3XB/DVKcRKaNwjprfxbAex+M/dJ/Q8xaEFxxG+ewBGNrh1TTNhwvHAo+sMfklFpsF09owAjZPw9X3oOnFOLf4uFW31Ojmd04nGBisDOjz94QX5KOiEor278u+f2jlQFSzfOzdhQ5/vk4w0PpgIpl1SErxq8PHmVLk75uYmeHIWFyzgdAPUf790okRNY7Rj10R02A2nYvFca/hgyLH1TOPGjtMQl9z7pyN5P9N8nalFKAP6snzy9MJAFBtY+oHg6ddA6P9W75+JPQlwPE9M21Qps/LDZ9tEH7yxA81m9PEnbYzuVCrez7FBJI+L8A8cEvVM/izPOr1JPyCc+YlMlXnIHp8Pz8Cc2+mxEkDwFJQAMG/fTpJyXASTthDBPTsx3SgMUUtNQWvkqVTMVohCIgqhEQelY4WRXTPLh9h9QXz8wFjGGJHeXW0GyuZsJhVjw5/5PjWu9+VtLL5ExhoZ5EMm2cZU0JJWWXQWEjaxPIvh8ZHZGNUL6xLaIsKHHAyBfgWIwfTa037wgvz9CreSgzdCJrVKQdy/imubmYH/iIwPgwsVWvL6oaP/Krp+GuxAVNpOYxJq87ZobVGq3dTmaE2EP+tdxLvhwaHyot8/he6hZUwv/h0Ab5ewRmVJOBqKJnTAVW+oX9LHKAZDQzuQIHaSeHbHYSPWCoCjTjxY5xhWZucB1usJdUYSZ8zW00OnLrv2m6oqABBcRIsguKvQK2vw81tdP9wtLguiBfNJhDyNdIKx8xHf71MQ+SYlWY/bdSkVkgQ5owSrqUoUfrvhD35ulhhgmhmitz+EHfsw+RfwWT4LEq7WAHJFuA70V8E9+i8BPMzQCGXcxQgfpt8u+3EqSaO1W4FafWhq8fcjecxKb3wsPg9yin/SNkv7ELZUExn3tPuFcYaIn/HHlHhGfpHdkEi//YQ0ms/rH85KW3M9txCx9nLoEUoJBvGoWo4Kk7d04N4w0zzctxOB+hkgfaDLOMJlrCGS19x/RDLtKFP4w7bQ3uQKOq51oSMETW/Wv0jS/swNdh+Lws+KZog2mR9udZcpmKX8YenoWA/K49jvdiNVEtHrWvvAUhlFJt3DCu7oyRbk0W9Ixyr0OFlG/4HkYxlRaLD9zI2aHrA6UuFLDZgeU9xWXEpTbfcuadSy2rBxU0l4ebM6vBEJ0qnT9vcJwMJPzs117tRAlLTrnrSsRghnrb2h8M2b1PpCuWeZg6ryB8ZA2YxvwxXxlFA1BSmSvs8TfNFRUnop1QklF0qZOFYedQ8HCZFZ2NlVhVks1e+lLmZ8g72r98RqPcFW8ZgQYyR6GIKKHt1OyywTEA1IeDObiWfdv0jwxhK/eMP8dFkI5CyEukiqFF0DL0n1EZj52DEWHBgIcYedqNx9PDB1wdEDg5mkFCBuCm0gqYLJFp9i+VEsRKFNPxV6lDC4Zx/5Z5g38G9gv49W1Q5N/KeAACG8BLXju7mPpAw8c/T1eWx9c36ENgVqZRWCOtn7x8pAyrekKCSknW/3c0+QJDR9OnCOVqB+xMfxnFTucQMKrd+xbvuT//sJj9RCSOIe6HPUmZwdCwUrvvxHQxHGS+PwIu/TeylTFNK9dlRBcvBn+gvIzkYFQgd08fWyOY80VuTCPaC9nzHZVBrKk37/0LBrd8viJQBxifNSOJZKQWz0pc+lHO+dv1vMoImGyXvxQcKJKDphMa7hZhzHHIdYjXj69OyjoFZmqxIEFzrUSk2Gof8JHi2ujYRHPYcnbTzthJyQA6Zx9B4nAgydkejk0fETO8/lHn5AGJG9C3nDymBIWsFMVnEW7xdBFuQYC4Xqrc52HVdvd+Y1WctsBN92/0g+EF1WQJ5SO/ssjRL1pDk+zAs33A1oUWwt7xN5UbTClgH9HZmaXgCJwGUoQYLCMZnhCwvJGAZVFgVKaUE9CtXRatX7XWJDoH47sfyfUW/Yfl9veATPLp57UZs6O9B8jnqz6Vg6HKlIrfjRRRj0k+qHHmzFOhrqGNmF+OEYnO8wiKd1OAj4+SabyYrO5U6MC4BCi3Fw/t40lXQSEGy+kmHr2HNml9CyI80R9IStJKpa08wrbzv72Y1u41RFTiR0lD/5swDZwK0PZ/7bfnqB+5BoX5CH76whIq5j2Hq8jELMLyQGoyIa5r9KyqoQORU2uzrXFxJ+SvzLUqIeh8iZqrqtc4pn4K5XhdGTdd181v5jTs+CB8PB11VfbTFfILGTat6bs4zQB7sh/jIUKauq+RG3gu2ufRU9jS4buCj1sn3vsNb9jYDvUwT/sreB2N2nAPz1L3x3iWSPeLc9l0Kr7sv61Xc1nlEVv9yFOW8p8qQ98tJ6S6zJSFqDJap+hrmT5/HOOFM9jwk0+X35A4Zdy8g+I0MHRk8zwoIfUJHUc4UMMAgvq2bGAInCEcnpejC3xhz066Ce725vvGMLMXu2PIox1O3TYblvDHGqnDJArosrl7+Y1p4wdJR6b2o/8S1o9xWQjDHBUTWtfxPL4pdZg1wm7OgJaDv6ScH2Efocit70kvItX+8HDAl0b1m4fFsVDiOKCC1d6YrsA4obTP/rSPY+Geu+GCltq/90DwOk53A0edmLfhhAlEvn4zOTxFGbofgebUryDkApe6DOiVzqc9Q/B3m0LWisP9p+ifOANh2qqU0YB0TsOeh6ROFKeJP6EFm7+o4dEEfaO6c5f08jnklS8fzmkxzLzMYt2IocDWH8ao+PiS1lA9MJnvHhAfReUXnzXBSki4KiE0jBsM0a5Cn6uJVxA0ZxuIFh3Bf3NmOoOZwgjUDTZD+ejDErYUuWEclIqt3aThS+WQokZvAOYSAoLwbsihTnUPsVC+MWwRmeWc4l/gqORTSJuWdCqHJBJ4X+rUVrhy7uITsTTLsV8nH/e2aMFrAG/hvtPGpxhwVkaHfnUSDDwPL4GdT/niP8sK/p+m69qSU9mhv0QOj2RocoZ+I+fQZPj6S43PXcvL9tgzNBQlae8tldTlF93d5pCr/TN/hqg5fwlYVXNlH9uN6A2j9zxjSc7Bps72tc5iRLPdtIeiBlP6bLluig3Gh/cndHBuAIZRKdmehcUyjqHs82qDPui+ktPRnWV1fFtk3IGfW9uxp9lzZDFObzNmVg1zZBLrr9B/9AyC//ZH9zvXl4k1cO1yetbXbKChpwomzbNCWGaGcf4Nma+QkS89Oj6VqNCmdhhlx6soomxWG96HFeRrszdiHFrCPpwN0T7N0PFPhyd2o4mAKF8CCWB7x3MECBOaLcQ2rK0FJT8t02qWhtwv0aZ77+cFJiHnEgYUSJZ65h92k/hjgSNzjUjhP02fL1LFtCPa5RQQXJE80QAYhvOj7oO0SYycLSZ8/PpOaN6gKJDGg55rjCeqdVnQQJAVtQg5Q9W1jhLZtJjmYXLJ5kVZof/SeGu+zn9902/3r1E0GX4qggOYZFItGchRDUqUcZ+vjfqCWY6TpxVpxWdKNflQQsNXi3okONQ51xV1NIYuVb5AbZpoREbm75FelKIOhFuOzGPNOfXyVT3QowcuO+p+kG0+grkmnxBBJMNgAoEpZO7ovViCJPvdKSTxYusLOaka3FzloxWi/nze40/iXBXYrkVPsUKO1aBovTXjO5b8B0NxbBk1PAq7ql8UByhymSHcPbyI0v2aSPQpyVUIOeUN99z+y6i79oV7gCzlHyQmC6FOvLKWFl2yDNpFcnvnX4Z9/GSfgnk4K2sKVYrdT/Y0LbTiapPks1Y3t+Uv52EvGvkkrZ7RQ/Xx2G4s9yyT9ZMqJu7d/8pf3gR1CYwzqnwRsFR1Wtzb4O44foee0wVG8kwXEd02Z8iV2PUXff24BeUmuWIYwAyhGUI3XuvJVVSWi77AaC+ncnZ4NTw2XleJUzshrC36SFUOcfxApx+dXzBbNFVScjv6zzi81vl7n2FVkozP1S80eszu8BY2Qzx62GGlPUbCea7ZIFyIB6XCgyAJx62wgTvgIx2QCXNzWfQugZgM/fXw3x/xjZKvF9jysdTDMs2fBW+J1VRyywz6EUtseZeByaLd1TcfhcJUz1CUJmR/fMDaTpKbvvKSaSLXZmzgl2isLT6PIEZqeOtsFOn+LEng6f7wY5dW1FCLzyR1qUjDu1M3XH+tODyenqVocEvEF2iCXTaHGcP94Gc0Mu6qRykVPQE7VdZKVghqa7vdpz/TL1nhJ1jcnznWsNqd4qmPPOZBy8qv3GGFA6PCX2JDR+qyWmLXYkqxAjat6pAdmZv4lSA2VlWntD3/aaLb29pSRK/TMBaaTFntBefkPMERUxlD8BBQZehSKa9yz0EiChaw7sbNgp5c/NIWo7JyrzSmFlBw66fgEOUpti0ThO5a1uYYhtXw+Qv2NPsLbSz3061eL09Ws9Ol9IyxQZXdSH8VxGardy917QlQ2rNqSCZhLFrJ1SDqgedMGsP6c/89In3E+zodwORm8XwDs7jJJapUprubJydx01qsZuHF1KRoGyd7si6vMBvbrYhD9PYJX5oT8y+XGYv2sk6jWCezeLfVsCKjDmJufqpzGzxQi5i7SI67oh7TR6axBR4lOTn2NCHxaMMYP+fLVoUh0KDBg4OoKXH7whlBBtp9inyFR7cw3+smdjShPEB6/o4jq046ij2Bg/xrD4iAjiaiD6XFYuSAgiBBye3fl1Fhito8mLLz6VS5uTPn6qVfk1zEtclz/s4NAdOuLd7VH9N49vHSfULhsT3+ajW1NIytnq45tukpl0ISDKx+BFGIi+XQaPl0CXkJZFR1R+qT/WVyYyxz+yO+NAfVSqtAMek4J5IVdLtWqA7hWw+JopDt9ukoOvKwD+SblzeDa2yNH+ejswnyVUNqcWYlZZ1SRRiG8Vn2Vpy+EB6AIse/ATL5UtH5Gzb/UDqBw3hgxEspEQLEZxgkAJFQTRkerwBF+2sbN67EUF3lWkpRKqsHAaCUct2MNFhNPOWSJ0nqEZLKGh2ULOvGgfSM4U4XOKJcWcHJbK5kOpJYsFY4c+v7MlgLAfvUv3IxPr9EZJcx6/JxeBJYgB0hq/hFjNVRDF1SLHuc8JKWGSuvGQDRCQ9BxI8NaRx32o71Gxu7ZRmKLnGJMlEcjKyqnAwELlnECbJI9n3nSbE+GCy3dyfk7M0AyCj6CqGgyghs4yeEv+5pPwvUDct0KlWWAY2Mfdln0roXOUaH5wnH8y2dRJ139XfsDfiGkOK4hML+DtYuIsoeEnKAPSU/ZYuDcl0R48IdqwUo+bLGmReunQ3NN+ignnjtrmH/UfPMxbG/sgQt4lpW13Il5L/JQgvAT8hAbzwAWgVPL21/h9MzF6H/mg87SG5ALMjQo71Sxv5RndX70zZlGam30UfnWN0yOTGI1P5vCdIlZF/cidPkxaRnZ0aE55R6HFuyKQYRpSXyqdFx++FfJPbsg/WBwMxNIe8S2OYoqUK8GX+d2p0oKpuat6QQ3reMX2SjrdMy5jsUJ9cZVRd530xqQe6RW38n5SKHdxEvFn8dX3h+8OQlfp6vc/IO4yUZwnwhBJvJsg86b4q/hdy+YPdkYXRIlI78pfV/mgh11LLcak/5xUjTH60V1kJYq9czDPsy0Cf+iXp5XESlLi76JHMaIhmCs/YSrXGNRlGUB/Nq2f41CfoxDK2XK/Znae6AbTBT6gG2iPNyi0VhQmCxD3AC7cXp7jWXDJVIFoTggVN19tYMsTVOlgAcJZVw0kHmDkIbvtvtrdaWnrb32vh3FKWfBwcrMBoGx3TE/mVcw1acIrEYNyD/Zcur/oDXnHxvBqFWNSyrXmeHPN2cEuJlE3Kv5vhD1c2w4iYvERX1xAj+Uwkd44XjuSz32V+B3p7RkDCs6NTQQg9MZModdExOmT5O7LV4u8+oHApK6zHHcmUj72L+qtiYlOG4gG7jB33O4lhED9a0dDuWEn0s8ck35K9qv2rnFlW/crjzSvMBMaN4MHf8dCrySxMrR0E/S5FEfemvpfqCXKdTNimpZ2gVnyX8E1z2dJygqcC+1X3EKTMJF57SAVAn/+edhs5b0HhHK9h9uQqlO+amTN38V+uCsN+SuKawUhVUysfeXN2w9GKVnKW/2inoLli7kHYrQnPDPGPztd/3x+j2xuTj0xnSFj6GshpoGFgtqAEEMO5EziRNog/czoakYIo8Si+70pqS+oKXr/pP3mYeA1Clymb7goQPHsgTeELkTwROTx9dRwgUP2oC8aD+0PYt3bEHpJGuorfkSbIzbpq05mO999f0yjgmIijwTh6TcWfXQ79fn4Q8aR2m/dagTNkBxQaprdLNfCqSRTGFp6GETHB6xGxCq8c8+XiJKrD/Fv3OY09E7nZoY6304VVtVVjoIfwtPAux8g9GkKbCfWsZ2YC6ZrC/kV9spvy5josof9Iv2JpaxSXQgnP4Nbz0kzbH8arzQNC4c1Sx32Wh3Sq3dDLxfZAoiTFBs/3dvvZOCCobF+dSW8kgMSvlYFd6uOtkaPOSi0I8T4kJi+E+xk+eTCo97zB4Le874ememv/KxGkVlIR4tOmb05axa8sfavnEeOXjjvxGjkvl8OxvihVDNDm6+gvadG7zr4lRX/yGy1ggq+Ot/hhNs8ISj/8tc959544RqojpIQ0+fW4WcUftMD+Sz+LzfhSVzZz96FC4npOazXVFyk/iH4zwWC4LVef98174708uM0gtDWdTfR4ro+6pImHX1NRPRdsSc3SEqKUEW9IS1cXvEjDshP5ekluvWEqUzt1l5F8jmVkCb6yNvuzeQZFYzhr2ruge6Vic6qrp4cSP/hr51bav30wOH3E/uk97l+9nYY7ElnB1rIma1v5Nd4bwNurE+XqTEEYgDlG8WOuqpJY1JZT5/cq/sZbTZ2imkG9euCg5qwAaBCoMqFIgfFn58lL2S4lpskOVi5ifjFjNDCl7wdt2mxWbQW3ASjCJ4zVwxFDRGS7slp8ZkIfWeSdY+xTfuy/o1KI3tI1cLThwLtbZNq7OF1fOBSY+2OVQV+mIsADiAKP/WgPOae4MHrXclLGRVPdvhHRyD3yJ0tVwo3ZGvHiHJjvjClkZJOeShLITvpo8mfpi00RbLAaeFCc5GcAUdo8nIezYYb7t2oqxfo8MBmtE+mvfDXCFLzb7oGYD7zTbI0Rdr4Lzey1qJAeXhwPV/WGOR/FTDSQa8vhrcdd55fICJSCtCpHkEu/2pgVvel2GXjro3AjpiA/DDFnzY7i6CiuNn79kb4Ah2+KrZlkdGbaW3BLaI7DGv45auXLx94t99Hr4QRazHnTdNLXoFs9fdZm/Xy2RDBRwreh4974VHR/cjAnVG1GxjBUXeJm/toG+pvrzawAI1SbX/KkCpOta3W/oJl5/Lk/Ltgi18E9t/0Quc/ATw6o414DNxR1lEQXUylaA8nxFJGk2PABEYR45IqQZPKsKtv+zBNrhNVUxOVAMhkPbi6HQVhTOn2P8Omckk+/uuM0vIJPCfL/Qzct5SI72XwbrQyrmIDY3Eu+11KH94aTwp6GUm2559vVxHAi74UL7aiAXZUcsaCnPEpyNWX+6oSMs+NYYI4uzK5AdoM/+cw0w614XNMjPGciRMpUUL0Oc+V8jLRgrV+PIqEt3LTgd9cVMyie6tfyF5JO5+l9pVwQ32qUIjM1gJ8vfYjRYGZfAn85XJvQrieK7a1blO9LQ6e30+O2RH6jUB2bjUGQvzeRf6WB5N3QGblPFNwihHeA9vdJ6KZnI/FiRhi2OD/F9BrI4bkTo5pTFuoHXVh6y+F0fqgi/BQ8Qxu+hn/PAkjl/qnhuobJBB+L8/Ne8lPFyIOjpN2Lz9hsHxoc99n//xWJdSxiUS9yWDEOWAB2pusW10XroPhZHavOH2Hbf87I42N47DdoqFfaBjC2GiPI11F3C5WOx6+8SFQqYzCDObCoLBypoXZNO9vE7DO65cKQ4lUPaO/JhiysGgZbtbpShmPhIzrY6lD1vlcB87fWhjwmTXSsJEUb7q6nC5J4f8XamE6WgYmDH+Ui0j5iqpEl486X/Etb7qqvi5nH5ASt6WRYci8t15cFhi7Ac/oFHzZBZ7IS8/m/83HTYhvN9/IrMC/LHGX3GJBhN2XF95V05k3lVNqSKydQCzCUtzw7Jv2bHHGzlN+hmmoJLY5BfAewm39HTP7Nnibrs+qTxJycDXMbHMAoYUy0aec27RLz8ErPLyd9hhhByD/ZJW1/qkT1UNOrSWPPNpKeu0k5JsXb4uXdv/sTmt3EAFU0KiYi1vdBaeeRHlQDlW6wb0pV4Kxiyy+5hlfWRaSvwnnFcltXK7jNroWXJ3/oGBufSCZq4ERNR6K3vwD+F07C9Hrl8uHRmDOKhveW79KqMSlfLp5UleVDPkg6Olsf9hxNoV6a5DxVYRskjAPixPGoaWYoIwY2sMeaVSqunBC/yJXmn2Z+O5smz9I2DgxJTEMDUUTwCt+kA+mn/6mErcH4YfEm+VlMKUz7q6UnYkCiEpdpHbGAOGDv36u8aq5C8kQl1YoGHJAcDzqC142IiE51ZuJ3l6jIIj219WYgYTAABQZ0ggwgECiCIv9Ev2EnHDdEekImk+h+n+YtpgpzyINx9POIy1aK1k7KkzpSViY6G9drO7WHVpqthG9QRC6NsdDXmpLW7dVOnnfqQq74dopI82pKrvb8ecMHe15xbv/E1wrvc50UEnk/sVwRT06sYa+8f9OChfOPGPx2XQWw5LfWCcVWMjIi/kuF/LexY/KCFC+iydGzlzbkzn/TEfaTHx2K+JT2rF+CMESbSuXqEZA3Wcjtv9SXK1ddXgB38CTgzvSRjFg5sawEsrQ0QfDsrl3RzK41h0IBhgt17jscFjJyDNrZ8lkYpSFUHlHQknzmHcMqg+4cpMHF9fly+85erLyNwYMmJfJ4qknSN2DhoMs8k/0sNVJFnY/P1+U7jyoZG8HTfiNmVUCkHSvC3bazBDSnRfqtYrUebe5v4F5yVEV15PAu9Xg6ml1vwjMSGsfXm0JWL2ZZK6M9OOiDdNnviZd74G2YfCM3J+4jL4uMV4IUfQHt2vEmty0/MBdE+reDSkfFvbY+SN4Y/kobcMA1KdEkrg3yvfyzoDqHjXcnOLLroN1OIGn/tlqWLJkt9LlWjPMT8W/SWaK5qkuJrXbP40z3tH2MCB6OIsXnEN110CncSO3T36Xx4ydf7BbrAyBVWTqFqthYqN0RceCCpHo4Eps46CGi3LczT8Fzm9HLL61QqWoXTE5y/Q5ikxVubCY9dI/TYixh7EkyWrO/YVCT29/XH2rgDlosJ7ncyo3G91OnJf8zRRPjtXo8bP/XsaBIxd4kqlWO1o6kgdJZjf6qkcRa3TRb+7P0TcmyIUSU7+90bzsmojF3xzZobNoS/tpxomu8+RN4hGXCx69nJLihUFQdMxmLKuCoinGOksNR5/MHegV+XHGOSbWVMrJUNnnYTkZu+ighJf+vDw/QMaDVrKyttYiwO3YFMy9njFdllG4oyXhIJALyJnMxSwpR+GzE9haeZowmnMwJWDApthw4vg7In03D7ORA1O3/+siTE8dyCwIVP877d9xmASNWIfnzy2KeTOaqPw23/G/T6N9xKrqdzbrNBcjsx7JxCaNqnku10k2opm/Sjk7JzlSYVbS/Xa4u4paW/Ri8tH92fEyV5mfl0eDoOPNi2uiBg7RSk2LtgdPQGgWY0ziFXOKmVd218EsP9EKBfC4jYW3ki7A4O7x5gS2Drkdh2kPqulvjmOQUVY4njfx2nR1sKocUXGu70KkiWfAGWmlZtJkf8aCjnK4K94IP0qxxLylO9BUBWgRahpUjcl/mWQhspDZa/nvAGjrGfYDGHKDgkiNQY5qqjLVSRZsGUYm5+31Xkn5YxVF/GnFUXrsmPBVclEHOmCrMAAst8CRJ0NukbEDRXq0e+E4GLtRgx3nl1YFgZD2EhMPMYFNsB18Tx9KOJCZIbUZmlpaxp9kKWWKx5xq2WEJeaH2yYp28QGHjfS6ipZXZMOHAgvohEBgAIFbMxFqUrIV0a/jW5E2BCw84ICD95DqfB/bP2wJ1JJ5b5/DkbOUacaUD3DWe3e8Is4AkKuKaVj31NGT8ta8WIFtEGSuPa8u5ZhVu3G1SPv/d1g1SbWYOQAdEFpSGtaGREin1+42Tq8d9Jj+NJD2kqzfJvKpp+umgUDFWysd4T6XnxkQadmzDIK0bUkOH3mkIOV77I0cGUssj3NHy6qJT/ziv8Ylz0Zpk98CQH0PEGkoN1BlkhcVCePzf/IVqaiHZy57hjiJaf9Th/R0cF1BpPMOF1jJYnIoWvWIxhNj/E7Ri5o2KG0pCX8CXI2w1+ibpeFPHz3svGvrc69AU8fsxUIqbsvl9fG4xrGAWCfmYMzsYOP/ZT1miVMhU09cpApu2YrkW3NAtbq0P89EK5qo6ngrTs3zHbCfytCm0spBUpI215KrUNKMi9P2UTaQb7/W6Fk65k9sfzn46OOX3WKy10HA/UYejUOC8QscFF8ss+jvHxINCIBQcg7Df6j89+DIy11F8Qde9KwPJZgU8kEImQhpAIDaQTLXQ792q1YRp2kEYZC4JI7Vmf02R6H/EmZ4V+Cmf85gBe6dSWjAcRUyUMSND0NzhhyrLwkYO4yyktMR21wLDtOHN5yDVastlOFGyC0qA/+YpfYOyZ2YMa1LZtFB0/Jx2o4auKcr1QZSm34nuV65HDBjVkylKLuBghHGWq+zsxZvOI4CSqyzevEHG7HQ75LTFv/Ln4yDs2FeEsZjhIz5fwhR0/98go4fPgazNR0W2OCJd3aASjdDwB1Sfj6aBFLTocGlGzTrPEvvOS/nh2xm/ED2UoKpo1QBlXO8VRQ9w2Z0w7pM2RATBNZBp1gMG+ynG0W0/bKoIbwyLLXKNvtOeV+IFkm7wbg0vapSt9ljn/hhMSqV7RQ+kcSOqTgL+JCROHDzkQoer+nT0C9Jh4AT3elvS00gvLzskTVzqV7z6w75Jt3muv+9pJj3S7ypzc3IeXe7ihjRcxhmar8staGslgSfMDkMsqyh8M/6VkDk6SubGE7WohPvw8vK/r5ajgxgK07TmDENa12FL1U30UcKiN/Qtf+Dalytzz7C1vUQ9HtLzixGk8T3rl/esc8OC7hStq6nihEMctFQwTelfFPUc7iy+T1XCw4GUll3wKO9TfcHOJ41xd6NTGwsIIe4BBGFhL3+gjtw0P45bOK9gT3uni6Uz7tV5/4Tg74tfx+qK6eoPGQrvZOFoY6amaT++U7Nq9vzXDWIlbzr5ByUK9dTL58oRi9Wunvytj6Y0I/pMpXy6TG7eVfKgZmpS7CkUh+3YvK9+7FoHI/hEKsjUCmBeX5IwicpIX5XOYc8wmB2JoCPZRRPzMDzo0FUL5pV+wx5FhJLXJF9L86Jgy3mW5Ad60TPv46FKG7+G0S8iEiqcRf0yAEpa2c+Q4mHFchsFu+MLK2Q4SY4XdMmnFGWg7uloh3Jo/vLBYrAK1+X9D3FyW5lKkX/5GCp8+UB5yTfzIIzqXHRX5+EhIAsl6zqmAabPxXwdedG8rivL/as/fX3/H8AX2lvzOXRXBOp7+Y15q4FfuNcwKNcu1HP8i+5v8Tv4cUQ5y0nWH0CUupKdYsMNHjaFZuMxurK9+x4624UiKG9jGEVJmPMzA7m4Wu79Uk4NbudJHUC/ve8LPmO+/ti5eH1Db0hItWIpQ9WP6FHrKTSVnwLT0iSE2Hq5DnvnWAKD8DXlHBVfQ1E9e/Y2Grv1VfQP6dy7M3eNyL5AOht+t/X0Rv0IpdIFg5zs0qCmqv3n5/rnRhOrjiHx5AJ/QC4SwPhSqjMBN/FE4cxvD3DwE/km1Fx4JMV/wNiEWUUBEesSYqdqKPohFMCT8phKXm4ZZNtLWDm/qeQa7dmPicqw3NPXkcGMVs6P4zhhBE4B3/dIKffezUOl1FvJ3D16m4EjW/VwvBh7Ohra3BhRfuRczStly1jd7/sSnzJfqYKm4H0HziW7LVXIYjMTNtCPjoVKeEjnw6157zi1bvxO7zLV9bcnjlROQEk/pm1xdMLOBinPe9gZwgKw8MkxT9/FpBkx6CrzMfvmFaCWyq35p4WCNOPjCgYxfSm/i7TO26aCzdZu2II5nGq4ekZI9g50tiBwE9RNLYLOzESO4O3TseLsKFiVEfPTkWlpFExCdcuhOpQ3tQIZCRCtlT3OsrYDLI/+4Bt7d1JDQ5X78y1pJcMQEXTb19CjVGn+WXlbKuXbEILsRr7uimtjGM4VDBkIcfX6S1UdInl04/K3/KgvEPFNEDbbOj9P2/Nk+V4gg1qOnQBM0hM3FidfczC4ONm/iTrHtMKtL7KAAITYlP1TP7XPHqwaU1sII2YcW7arljO58pVIu+GljXM6PaLpfLHXj9oKX0pRL6dvOFPNocpgY6cuvRe8xqF9oGF51/kV9ODesbK4/IFdZPVbgk+MZdQ6Qbfu7pOyiwcSDdCFj2sk4fIEGUb+fFnovz8QInUYX5SUKXg4BM1FoWa4ZNVcrteaLQKuRZ4mI89KOq9FApDa+zBJVbv7TRQYoC2q4U6DyLgrE0GQnSb+fzIa61rwnw2z0PLU56Qvr/fMRv9esfs5jDCtjPUOSVRou7n+qnvuc5RCrEqMlbaD8ty/M11w+JiRRbVeJ+scpU1Ev9O/UQqzeQcs2hbd+KJrOXNl3NHUaaFP6X2o37lpjZN9no2GYXXopLx2yaCJGNJpB/QwyeiA1soqroYFRjeIz+w5nkkCJAjuqB17giKADejC6gIiThNSP462Fl6RzLRMXJ8yoTwQLCgQ0/0u1bjwiXlTT5iqPrSGVJprS+Eb/uhp6CUq+GbqPgnjkgOLWe9Av59Od94i8aLUotvDA7i48VXpO7d9BPqb4QXBrNF+qm8TBS9oQY60oQbe4XiN1mpvW++T0v6JgbZy334hmBG1CGcNrPxYnt6b0OLdhrDkVIeYn3EbJ7iZeGHlH48wfZI6DvW8RprDSsKM0HJRZHFUTWZODWkxp99HaezEfRh7EiewCO/XXMjtN/5AfqA3N51bTGd1f1J2LIbOFrP6LfoVNbdfas+telyqy3b6dxGjDHtDAqId3Pj5TKkxVabRCaZ91yeQcSzdP/uUxGzobFaEgwdi/CIu97r3I7TIlYSJtUML7jFwIpZQMiddXvByVwIut/XSWj5cjbDlltNk/4nuV1kjEcqXdHny3/kWIAPgmMs3P1FhJA7zxvmGin627IgY0LNBL2i8n0XINY0IdcSRyikTkdV4DIt7EsS82yMjaaaRybmLLuYPy6FCVHfcOtGsJ6YVoAORYsz0XGfMk6VLPeE9ugLfo2xARTs7gcDZQpJtGplbZ9co0/Z+Ig+Nwix4lCyQ6lg7dEJ9e0jdi17eAtoH6GzmIy6MfGUoPX4tjifxmDPz7IKwFYtb3OQMADWRv2Pl1s8EWpj1rOcY1sP66XbL/nzP8AXOGY8GTcx73Pd8x4B8rDl8fKp0nm2zGhlrMd/AOfOT6Axok5JzB52crmQqOhYsAmdq+s/+sI4Crlw54yjKX4jGcZhPR15Q/ZCASL3KY5UOOGNdD0Uh/+S9PkaGokJazH7NDeua2aodowV7EVSSE5TNESujq5lYNCATvzFTzIeIAcG7tdBVZNsrPlKgFs9RHcxEVxfLRRNEgmUu0gn3lh70Vay4KawHu8MLmJ7rK07We9JPu01q8NNaSnZ9H3Abd+zlnYnbbiWX3lw4OeVZDEobzd6NvX4eimbqldsc86Cu/6yc6nA91bHoKvvkCXUfEYIf+MmjVXyK2YvnS/kQy6/AQ8K+rDnGaa1Y06xHvjksQuNGlr06ISx+ydAegamR58RJa5Cz7Nw80gvmX7FTaW/hpMblOg2xrpb+ojJfstiRsl2LG7mrhgojP8KNujtVBdofrAz+06JoDKC3GibTdmiFcf8PsPrv3rTLM2OlPJb1AOqgvjUraLee4Fo2ZTyK+3p2xbWIBNJE1UZJUT+ib743RsMztbSYHk+tdlh6RlepZWNnIra5h9osTCPyODt2zXWnrOvYHGvGkAe4HLIkIpD1aoDljpua/Y24EBN7Jd1NARGUuAqO8U5AlCfN+JFFEaer3jCJg6v4M7c+rvvYdL77hvy5wdDe+Gk/d9Z3gJOSh66+ZayP9xgHUiRpFIDsh1Nncm8GYeSiSLNRxi0NXjNhXJANrytTUzcnHN+FbfwVRnKFN0fuWZ3+MEjtoRrYjhyrXXY/y7/xSfCRymWWTZcapzkoonVLkwAqUM9P1zlDJpHX5+EV/8UtngXoo/LbnsbL9jzVROHyfL38lcYwiTNnhk29pOOOUDbRfHRKuTnbTCsY0JI80nQ70BSYFjD7AR07ixMoIdKmqcEb5OZtsGtRcMxMuJSw6Vc4HetK5JUm2jmCoyWD4V1MYHzPQxS7v6z0i4cTJmlOj0p2yWFlElK7RgTTp3xCdpm60NjhrNvhsr2Ps9zoHQqgjwnBonoG+2unMYy7MZUJgZVXYh/ICOSBjh4HBeDH7MVtBgRmIqZwt9++LpMX1CRh+0K6xMadwIJ/UFkatHhl8ca/LVM919p6YuQolrrT5CbqD+fkPnM8ZMbg8On1vd5dbrnRnu24qheFoBjW81i2hgolqxDtv2+DW+Pp+yd53XPavS62X3fHnm7z7l/NcVVpFz/YqqAIHSBveqvLxkjVEcWyY1S1I6fS1bnrtp2UlUQODamSg55BwCxKnj7w1bpdMnc/WIfmcV03KitINgcm6365UnT8pCf7zeE6XpvfocS3/TyIiswZ1g2zApIHqVl39dNWZ6L31tBYvsMS0jUNHSC8ren+WxZy/k/Wi7xR2qLQCZ+jxFVnSznRVEEy71TdfEHEjcUK5rl1AnFpIpNRzrihOK9/OrH2J6bDJP+Z1GbfkxMWaquBM/HuDKhpKE2Nr8t/cKLR9pNfj5FR8KPKgK5mS1BJj2o/YAFToOhgub8FeagAVLc9fxfEKYkgNO9qXOJj4R7eL02Ku0t70J4tRo7LUb0JI/NV5MM7UjRGqElhMg1VGgI4C56VG7OnbkMezcZXz2tSYya8uEpA971yc1PoQ8VsgPer2uBTTSuKSX2qw5LMbs5TfcxqsHOd14Cvqm0jCOrQYy5k6xJvdtRzmUToSk7BSTf+aIqG14FoiJ3bEwz0hYhJCCfZqYnx2BiRhlQjpnfrQPZjiQcoFf1AeBFqxcmAT6NnRh40pGv7sL5wSbx149J/F44nKnDo2r2YWLk0VVzgthj8GwvhearZDERVIigvB/iuXuMruhVFWw6HgyqD38FRiANAnYE32kES3ILbKtESXvbxHiZXbo+Jlimqr+7UteeDUP4QDIx1LYnyRHLebL40oEBi8AF0E+ErBJ3kunLeGJlvh41TGJW8EUX5OE3Zl2rRs+DOsAsa81gSeyspvywqv+6ov0mH4at6FmJ50/Wb6R697KJMIqDj+DkOfEDu3uNWYgfcS2nUTplUHp0rE2Y9ItlQjaDB3eaxlbVd/MPF3GFYLrekvBLqffCoV0Rzc4LflX2XLeI5IrGDnaO67XDlGV79CK7rWxhD6+y1jFCughrGFo7Q1cdepI2Ku+k7ynAMKwcR52fagXPEVSrqtbQzfl5zDj1hgmPpQLIiVzkEmUWGlXrbZ6aidQSbxPf6s/qqUIrIA0h8zms7HY/+Q3IcWuVK7ChvHUJfFYPO6QGRqx78R8d7LUjs4y0wZxD1vb1/WAAd9C40PYdpJfS4QpG+u6b8v8zoJVbcyDXbOsh8Y8IK9K/O76FvIhp6VWSCbzzNhgxPRpif99Y0Zq6WsVXIpV+ooQAa2veR/bmNWPWkhUMsscPpFzC8PAI8AVHzob7Y82BAU0fM1jJaO91iHRrR3MXoCap8OVZcGBiirSKQ6GNU9vh4Duy6c5iLg2WsU9IsBGC/JBbLW5Q2UQVAUQ8uYXya7ApcOzItI+X2AJuCPqPbg9x/SB6/3CE3Uq2gMHvQdTC2Rh3FUCgsHjoJ7d6962ATxjYdcJ8eJfFCDpZYV6euQKP/ms4PPAvz6ZwjDLW5SXPnJxyM3heK++S/mXG3YLh6OWrQgN4tdN70TisV/TVrubX7VjfDmwzPQgQycDrnJF3hB+EslEwk/7KdDt2MVoEwtWa5T3S2pUzaSYGy6fOtqe31BfGGnaNrAy92ymR6Vhl4RT7KhsOlfNsvJF29NeOA8TLfrSnCzJHU8Ns+IdYt1Vo5eCMSE95F6p7gH+wRndU4SH+7hP2vHyMCR75r6JQAiBY2FWZwHFdUFYZplYWBHjurmmJbFN3HWWCqt7bUQ7vZQmrYe7hPh244zGqVbMqR+S1P8dsrJoCO0khDUGuomD0VSEzLwHWyRVUzDYjiQgBSjPcd8OU+K7a5AAN3p2qzl+2X5jDvURchdCGeEeLKrChxaT5Zz9fX57R91lPeoLW0C7sJwjiGmRyWBRdpSMukwjEMpOnQqIwiLvOXfs4zzzye4ffA59HUixGvrR90L763vq9eQov+4y4hGCSkqrWVuI8owzLsNs5XUCJUmaeG5RJflOYHpjvahMpl2GUODAffga0LXgak6X41gKcX/hTgQRmZ6zrgSp0ENYQPv+yAfNDOGj9U9mG3dGizyyV+7eQrRjaia4WQ8pm5BS+oe6G0YkAIFaYieKfTQNitYV6R5qImhLa117zMswZA5fMGmFumnpAvRi38NkAkAQNlbB16nl3lB+Eqf0XKS9KKuLMcTZD8pVu57LHlSLAXsyYb/6PytngyXr4lSWuTrYfVYgFizf0r5+dAGxqiDqQuELPReImgxbzYy9CC77b785Ykw6mT9ERKYkXeQoNdnOUBao4KDNfZJre2e121QjuAdbfZGc+taZ21sC3ED0wvDRxhsNrwrzJAV7L5gEkolSm5P0MiTFdCzjgrOkjJsLT0+4xDVmuro27ZCGiXuUfAuHHM0++4lnQwISMxmmvpUiMSaFSjPYnm5hqf25zu2nLG7hmTGBCHMbCegbj1I7XMbYdxRr03C3NV7BghK7eIpdFeV9+z3XfBvOSQmPYEmQGz9E3Un+jB/rf4YhfI4ytlZPfKxzOmQscumW2Aq/WMXNGAKj2D3azEx8I/gTCk1q5IZGZ1Bo7gTGDJuh9jkyCTT0AiU+tcqg/87mOAOh4Y6S8hDkzuzOET+ki1w5E/ulsLfZIulPSuOz34m1xyW8AaN599wPchDG/tkqulT3fJ0zIrFDfz2FfvytwDgYbNKKFd0DJ/V81eydAP71nqmw5vVPNiylJYXsVjsCYHphyIhxwVdcgb4O//EQZ65ipPiifNX7zfMkPpUQbUwRvbYd2VMcBLVl697zTmug5S1dnBInLNJos63LQoMhEo4jF/4vyFx8O+Q3+2iTz8Isyrh81r6ZwglldNMj3N/aHJlClMhj32NZYVJxutKcMys1w2V4qcNo+uSG+uZV32+eaX3slKzhlmxN/nCrsqSzGs/UT2uXjzPbRkzKs9l+n2o1IGS+eb6+g6r9wlc/yBG3nK7NREo5B2sjCPCh795HO9cgE+0r7kyVC+V5+vGIbzupn5kfpEHPWsd0LQZwlO5nnJ78UVVc9M9/SX0Sizc/DNMSLectD0ez0Si9ZPpPPe5W+Qccqmx1Q7yY78JRSEO7W8HBQDHcUmWHAUktTJ7dtlvqXCe1EdbetmO9ImeTw6tMBAdQRBu9ZLwCIxp0Fg8OMPbVqy5H+J9YqDZmi59mOrTsjjh/BXAcyUquJ8Dyl2iPwK5VyM6HctgpjVe3g1bPupTO4qwLGnmOGoelDvEEoWLwEf7yJG8UJG2Oh1kSBxCrv6a1awo6jGpeFWJxJyzTYE8WQSXJEp1wBEV+fI3igQgV/AbBS1/6MKgJ4ZK/saDwBfR9jBRaoaW3n30V299QJyZT1UqFHaqGx4Il2aaMx+3rf4GhoXSnd6SUU5YTvnxXxpKpfe13SyIAOEFnJ5E5EefyF2K/9IdoXNRlK++cZ3wdPsPiXAHPeyufww4JjAx5T67hGvWfbNlN4xBl6309RtFGhQl65a9KeoYvS/lRj9bvnxzUNxLziPjfqNtF8BG+aZjrzcbicFdKqcaJtIRRZX3JyPjiB/7N/hmNg1XP4Qs/wC6vhYurckLawUFpCefo4iEL6bTU+0XTSEQeyk5xHaJNE1Cm3GvmTTa8IiC9WEHyop0RBbphNesF+ZXYomOKzeYpG09UUJp1gQ208ucO+tv1Nwmjqo1FvobyWDj74CQxbTzXyfku6cPjFpD6umZEAIko7kpRQZYznlwI4X1MjZ2A/1B7T6KOvw315ZH270bezoNmsohqxABJMvnyt/ePWNtbj3ID5aCIo2tf59HRp8LhVmCVZbLQyj1Ucea8HrAtf9rsa7pbL9lSbYWuMzG5PYR0Im6oxf7VJ4lfTLpIZe/buS98rxh8uSk5rwY4MD0vzSApuTzDuFUySN3hsEuSaFqdowEC6iRyH8ChCpmwNQ+ltxnV4qy0odTU6qgNUgiPUyPLAwDj/VNNz2FlL9Ku81iLnHJX0CTJZjN0+Sixz0A4TTIjLN/vdaMF1P8KUtjq6EEdXxCADY9mqIbeoYlUdptR2dLq+rs4qLxB6eShjW5zEKXmGlMUBs3bzP+/LTnZ2KQEUipYlfhLEEMCX+dUkJgt88DNqqwebmOHUqfZnTRQY012frVjCMPKWA0WSi0L2xhAMgFQn53nY1b43PKplom48LyZTmcxiu5+pKMU5XFBqo/Nxe1cRAH40JFqsQOtLTe0cGipO31nwq58MT8DWj+2wJb5kUOh3yxtNejsQRVNNw5wRgBq4vK6Rjni3ocgmd4GQJjYEP3OvWWFjy5DxvjnpuRsExkrvR1+pULV1Yd18aaJSUFk5BGTv01RYQfzox4VvQ9unfHg2BkY4pzB2/vP24hMhhH4uhHlGHENthl3vlQHmgoMRA1jpYbQzFlGpsBe/cMhrLbWAdx1Jmlog0cm6AvtYIdHMZmo3w8IwC3+TGTnPXpoC0vGnFGanyqjtreSOJYmB/TEBgLwmKBKOXrtq1QDWSLv50eJt+PEbsGV8PAmi5GOZhdUE3UXX+VtqKj/yEzBkSCVnmtWEHnAZca8lSirMlyeqvVIz7a2N7si8fOqxk3BMeduMy4cIV9ZF0BPa779Bq4N0qEFNJ3xnE6Ofk9i/ez9MgGxMeZhCI3wEkA9r/WSGyUyT/0uWECBAk4yiCyRrRNgAe0a8CJGvHdAclU38tYQKEUey0HOXK1wpvEMj6G0TL8faP3qWnxdvZO6VBuT1ai684Qx8nSAA5HEv53YjFxqHDXLFnyddIsALvAfmT8fSacyV9f6zTpjPMYNfYbmtIarrz/kLkg4ShWGJN4GoAAYQl6jzIQMhDe3kmradZoJd67WDH8JaTGpaiEMA2eXdwOmKqIfQQaP9heTMr3hlWf0k65X1nTXJGV9rLDFijxsMsacOArP3PqNUwb4I6eW2DjEa4XlP4N1O7pQKzghBkL7EX5nn3HycU0OMR+IIGHSVnCLoRXOZwHT9ayld8HXbTJI1t84INg2785WUoykLUsFBANQmT/aJdgpAphF60BY7F2Q03kRHKgJhv4f2Zr27Xki5ctNCuzFXi6GfIOp2LOE5L545gPZndQUZgK658tUoJARn5RDcDgW86zfBkBNd8hqhzL/VwmaKs/jLEd6qnLL2dtOBv5cMxpA/rG2n3XybKCXryiW1ZVZVJRSJ6iMp3pO5+U3a24r+3MJZcjA9ZdkptJg1BdvM4TWeZQgLi9Mn8NOY4tQdIEnm/hEoT7FiW0/1MnG7HcjBhPGIfLeYUWStxk8csK3ITxbhfkTP691pAxoF8afwtoIaTpcDNXF/vFCq2tbiUrbKhY+lwkYsmVVbIi7SO8fsTkKe/1L/oosC84ko3POS2fAvP7KdbP32iEumAYQf8G0sotdA1DzUymqCeDQlbSi0qZ9wLd1q3Le1eJkcz6YXh3PLIBmyRF8gFOF4REvzQ70NcVAArDL0sWCp1fPavCJpZgzb+4lL4x7zVNjHHwpuOu0mxZ/hzqqafzc3ZZEfGOOdlHXV7IBvrsuggTobceiwnVqzQoMlB+xob7vb5XsPgHduuWFwCaKaPCsys4ZSFjEJngpbayxq/Fa4JSyLFBmUgBijGD6XC9u+tRPHEKSvNbxkWy6LT4UsxK2zsYl5IhaQ1WQ1+XIyg1EICMe+8J4mRD6fzkPDc7sWtCbwxRCFPXj0LhBtuln7+CpOhH4aj7d4+2FeBF3wfnYEViOWW5OwifErHNR7cpJkg9kY1fNO4Hn91Q52mx6Dx0PjbY1yI7eRa0FyHYsFNW0gW+/AKZQEzYQJo9y0TYSix2Ih955RO2hzEatPIXoYbjNmW7rFNF2eJgvkhzMBl+Wd+HBD362S3z6RtnxC8XkTDyHX96w0Q/gH+g5YLir+36nB2OTS7GH7tUmGj6HkVjs1BTuGvbSFLX/RwNM8vWclwMoAWLJMu8369D4tN9+2vfB1RIiusV4NnvUevcjfrg1JcbFlpaRFb4J2qxeLO0JsuBBb4qfObrFpP/gNVeAs877eMI9hFIo7g0TXi1RPhkcnhOi/Z3tSLqnpbpnL22O1fDVp7xG/mAJmYHdKbIFHWLRbz48hhpGB/RA3n/EsUC3R/EeisL83u7pj7+NUhPSEAWm9WIv0z3SG+Yhedh+CioaZIg/HNEcZZyqeGx5lPMJN7DtxWBeCiayqf6Hs0gEMkgYX8HTV0WmHys8aKUSBvrUmdDgIpxNhzYRmk1xibK3ho2/EQZpOU+TI+PE7LOdDPkWNI1ySUU/I2y5D5FLzvC4mhNvSCqKn0rne39ZhsEpeBDFOJsDrfLHpizYViGPyM4a2sjIHRR7Amj5FVeMXTkKWzturksUAwC3N7DGYTy1+IdvRtb7I/Vope/Hzp9R83M4C1ILJU0OZU3aZhjaeJ1YBHoOkvIjRTHKZUqxI1/fXCxkZ5GFPUFZMzF0nI6i7CoYlggrnTz9F2ipPk5nxipW3t6tvWLSdKc6nwK8SPlKgOiUPwjTykJaNEEYleBEUMmh1x1VD34x8drSwyh9kkuX0D1MY74hHa8VbW1pC8ZliAN0UcXu9GNpqu+LZ3jW8YlQe6kSgNwYwLu1EFRaaKcxxbBmTR/UiwQHMHm3eDzb8IAyGbw47GCL4LnBCLHoYXpIjIaJFIaWaFfjCxs4i8GOvQhMRkIVu9VASaH6A7GgsvKyYPMXbR14gtPNuvY7pagXRRcrTbkPObuSzVULPTHcrgG8PnlHQAmhQ7vCwRqqI49VUIYgU5umB3S3yT4BHU9kpcEjEZDtMb86a5uc2hox0esRRTc3fY/3o/1nRwrO5NN2gtBq0u9l0+jOc9J0lhEby0mEuJM/s6DJkYyDMS1kl7f44j7yOaMYjNpg9ankJEYaTLDYzJiO1QB96dJXCKJ2/X8JeYP1kIvSOiVPpRq4uEPi8NYR5/ehY/wSapRc8qlmmUezA8AiUKGFHGvHPRP2nUUFAeG3xxRitNT9NfZhKFPjZ2Z4r/MCUt2NcxPbZPw3C3sX6b2tzJNFAT5jaaGDIP0NKcvSHU2fKzi3QwR9t2cL0eiyfkff2cZ8ZlfME1ULbWSRTOyXEsyO8+mEyioQMYstxQDEE1IK0sTSDXjJoWIM8R/kwKOkwRIpBtJFuXddhe+NjkvaSO1ZW6H/2PvvZYdZ5I0wadps92LLYMWl9CC0ACh7qAFoQn99INg/tVT1d1ju7Y9K2xmTp48JIMQgQgP989lMPqn3CN+jvZyJQbxth6VHXavjGmGNPba7o14Xjd07IOKvKDiqXcxooezvJxtOgbGn52PYzBSYk1dwRxecH2rYA2ZZgTRQuxKgMIOq3xYx2851EwN1Qp3hmyBrLqS5qOWaYVgSa7DjYts1XcaS1U7pO2Xc5Yx9S46kBaIqA/nfaGEDRXjuUAvLZ3norTkOlyn6Jc9k+OEAzh+fl8m40IV3D+qTH4bECo2CYvk+Rcy3iZQeWbdTOy8hlpBE5iXWHO63y5Rc0AW5Npt/BIZ/sW72i9c83rkiH7WW9MybNxjrQMwwdJ2xVFMrTsBJIvnknB4BX096mGuqPMm9XKaVOXofd6FgrwRCPUUqHBHlkUyd0JJd4mmqmJqvs0TariegT2EKHsLdldXojSzNHOUME8u5gI/zAfSM38UxFREXnhfcxUfsM5iM4r7EX7l9ARlpEWPdSUMDMPhjF3GOFjZCuvAsLwUP2yffcYjcWkcZ5q3b99T/mrD82Kfh08bkYtontDuZXrHcPUSZxu4XZb6ZmHCxYTOCk5WYniqYrbzwvTLti1GITl0zW3o2okFhnCbV9YxMbc9Jhi+Y7vmPk0mF8/AUMx9Mk8LlPs5zJdVhnE9PlB7XNY5MtYYl1cWYnnHn6KgkCfHCgzeHQ8cM1Sr3wD7Kt7msAmjPVVS+LaB5if2zpsLW5ir1nauxyHFPMg4q7yY6x2NRMHuD1uWq9YukgfdX0r+fadtqn9ePELZYlPLgEFJc0LRFtYujPTxgIUn92oIMBxOVISLj7pb9cQCV4O5D1JYMPTwQ1W/cvd2bKC1cf+8mucXdVou8+V8sYu3ktL6RMhM9KsQOxskSn5kq8cuUFlFpDN6nAXlnX9sO7HOaZapaO8bV1Qg7TgW2B36otNAF86ziSOqiSfdHzzjdp2Rq7gHEHkUt95ndSuFyFR2BL0p9kwLoeHQcNFzj4zHhLAy5d7lMlPp6o0z75Hqery3iijU5Q87k4ISi/XtIx83p1LlS6JGBMFWmL4nRJgaS5SxF8fAxzbYH5vRXweossaWkYnxnE4WXsQp5gy6KGJkde+IwO7AUV98g+wSP5YntWEvfakKKYzcuTFNmwKOHN9U7AV8vl677aDAYSehkDazI2bqPQzBn2eAagaTNyz/ciJfsCeK0bjd+KxLs81pYGMFEB6DhbPhgPTbsxu+Th7aGI6YY4nCcizZ5886YlATr9AQMcmlWio1RLUgtAgzFekc2Ro2PNOEQ23XHzmGWXv+8b2IkJv9A/XVJVz1bMgnQNzvKkoWF15MHR1Yyh3w9N1u1b59oKxbSkatrHx5VAVsEdWczt71e28eAF5Vn/IDVqAaobvOOvG+6mSapjY0kVcQMStiJClgKSvQKl1IgDUWggqZFwRZBuclAdWBQAIGGgX3EQhEjVKA/QM12H6jVogOJDmjbUH+6uz3AExqCjxiBWooyzustNY3JqT9VSiOdXSmleCdjHdxCL2WZ5QsA0FTQ3tze8AOxU0ASni0WszLL9M5pC1Itawjxr+nX54n9KeeiThobo02kcRs4+xjzoOMljmenFDyqzBGUuDs93bqpb1zHdu/UcVFxvaG846+yVfhUSSlhr+qP11OJ3q/Kz5As5FIrH6bgesXEs+jmKIc4QPhXgWfyuUJtf319cNv5H+PqTD2Y7wfIfTNOeZ6dMmr9+9CN80JmIVfHZU5R/GtJwwW5eThTHaaFgr/PZo0MhPoowup7t/ZDStiJEesJRJLhJbj4Vcgf/eQsa/QcomEtAGEsCmZSnpBHbssSxxRAknqvhiC6x9AIF/OHwu1KHAsxZzgdD8j3XTJpbwm3VZM8JVoM3lVNcukM1kHHIOsmb1gigSPrI9hrNARlKDZTG2CPRJrNmntM1FTm+sKPeLx+yZPCObelUIkaXYB7Lub1/mmNkQ35dziw5byV0MUeSv3UEQuNqiCDoSBmjgr6lqW0J/3e8w7yStrGxoxA9qCRZBZqYwS77VUOoh4eB9Fme3h6YR6gIkkWTfDq4TwfAeaisbnbmABSddk0X6iVwZ/xMHgAiDuXoVOpbVjs5xsyA9EeVQQluReBM3tt1lkQE/efvUQzF0eJEQeC0Lb7bL6VUcpKtL/dESDAgd5UZcaQZ07/9u2oSRyzPrqf2gOUIj1E2ePWgQr/YF0OuKcv9LKptu4tT4iSI+dggUwL9eREEMrGRQEGnaAi3GjaqDp84ww/Rotu8U51BwQTL8PJiPacPYhHFOn+7izOTQs8eUStXxvqmLkG9qlKrHBxFf54Eigdj4rQupdhCuzhyUh23xKxCyKp3H9SXHvxqPCfUWhYWtvnJc/IBhmo1mJc1jGz2Eq76mGhe3z4+snSoxjWXyR0n8h6ZDykT73sUkB/wx/G6ewzGw4KWWsrYDPFV8GeGhFJ/f4nEGmFx5hTXNSW0ZbKfKVxKKwXOZTNTHg0u1yl8/MeCr3YctCKz/RsAbkC6Qfmbc96cC+6LrSszAdGhiqeh/ij69gwZKOzXqpbDpthMkwfEeUzr5z5gIF8gEfAD2Ggusawwj9ahEc24vbzP2kkpvTyzjhotlsNUN1eRwigIVgB/pxEVJMgS6CP0/larQi/IFSL6SpfM4oeyJMqagfRey0XSyKS4YjU+eZaxLC2Ykgdt3AzXVe31VBNtQRbuiLkZltZ4dLHlz+yxFvgkI/QlkPsqf6waW4qCWk2Vuma6x5NP34mU69rl/0GYGVthqlth+hLTWi642IhaOIQeG/kizZLS9L1k2/eh2yt7JWaSJ7aL+Gz9V2y88R1bQgiCp1eCy+L50CAwlFMqdMoyTBTsEr7LyukuSL7zpaCImlIkZclFX5dgLvCuEBQvjfXv5A0E2sHqjk+pwdeCMg9HRwHvjmcOzIKrEUC7ZVXpEgcviDKQFaPmcxe2UPQg3WTnsJTClcklt2Dr4CtpB2X+NtFGZX0CKBf70FYkjSWb9MIG6RFhP14vv22mG0jgyBsaNvamaFRb95mePw/sHifX+JTEwdROFkSG3JwspDHO9D+rV5IHmJzSEkZAUEgkPDAERmg+X3rBacDcztbUCKaHYhezKle3jrsTEvXlde9pe9WUS+OC1fIYUE4QqmxKzjxujZliwf+TP4EAr01YpJIDtg0cqKc/6tv79VRueJHG/CBSN29VCC1JK29EkiagHW9UyxgDEqiG6lSWP+qjQSfvQth7/ur3tCJuBr+abnfH1X3KsyZeIX3rA8Cp9O3IgWJJomOPxcPVq2JxRoxeZfiaJYUqGKRn9hPVRJSxZgUIuz6Ir7ylbZxRnUdSJzIxLMTHAYrzAqsVXS3wmvBsJtC9h6ZuxZDa3lKNUVX2knb9k30aWRIfTFyQzDfJnyqwciN5QefLKn2vuK232m2Y1/6NsupvXgFWtGphV4EB3AGjehxalW9kvyeqGUjnlCwjqHYh2SSXx9PwNLWM8AMCoV3zNZJyDBUu+WTiWrVGGrB45t9G1qQVQ5tU6p29d61AFu5iC57wM63Xuj16Wg3nzafvB/LHbkQbymhCMJPoKsow9Q68awmnkUBB0yxBdnYurCX9ujtkhf2GXvknmT18+fcMnanVFg9yjRSUAwUGbrczgOPIa0WMQOKsMZY/Khv4ouukTRtHDOkhmOdH8Y/etGb8mX9At/SfS3mpXOz2t3KdBw1MuQlJlJVrMQlZbdCVAy1A3ChDkRyyBiNoRHLyw5On20m+5NDiI3vf5IaFdK3rXyJmH6kyGGWRlU0Kw0eosWmuUDevl+oeWUiBq/cIgxtWKVwxBppWz4F8DFpUyu0YVLNpw+P5ewpynL+i8vv+HKpA9sHwqklmuzvIYYL2InWdtH2flVXWLFndsSK5NUz2A4ew/4QnQal4pekMFgOEujg2LwnpPxXtA3M+eNRtaE+IcDiA4u3uycsFd2lIkwwNRBbbizlUbME/J2SpTFiNY1ReHNFcMdYoqxuCQWckbszNvaapb0KdF9x4kitPqyaz3rVvy4BcTnWkmx7p8ECMwTD8uqJPax2wrA4kU0KQcNtXQ9+GIRYOA72ki5xkSm1u5chUOJ+RFFYxh26bcB2GLOLPsgBvXp4JkZpHRpu2z55h8bKOEMKySmx5GWdVwsaIjPR29Ixu7VO1EC/bIGBWP59jBnmOCAABxfIXnU5yBgG9YY0q7kehoW9lA3zpo15y+TFzDze/g8gla2VPtEGvfRNry3UMh9OUbfxHNmP7/FIoDvSFP29zbNHyC/Gh57lipSrzWxvzrsGGlnACEJwtuauTD0Gala8LGvP0cMD7hqRdzO9JcFmA2r0O/jOIAD91wkQ0ApI6VjjGrAGPEDY1Q07jLViXb0Qv+q7YXAmJa+y233TsFYX2W2QU5uyJinrHisufQKI31H8KJWesSvasXPEpzl6KoP9M2YShlJVmlMtoFamAk5BsBDTt0DaCT69nasFsZr6P0go6dX06o1ZHbUyP4Cuth96OjnHQId1y/woHhBXvIJfZr56KjpzB1f4MtN4NVyfQuubHdJ3h99ucEzTX+QKRBt4a6VYEJe5aaFaGhf3UgnPEafn5aGa1Qy8hCygBrn9VAvWktzTiRb0k4eCfdslpOH6yCjcaMBrjgAQ7Pe/hhly7FBB415gIMvEcRR1KbA6B7Lku57vPgCqpp8FPuwKD1/SrHmVdNdRM1N0Yzq4iRAdfYKDADu2pYT4HbfX2SFeMhUevskedd59ivkEhURKhiN+rnKqcKyridQB3qQqPwyJKUeT+xyCKZe8Gp9Ayu9q6/S4WCPfs6DWQXqE+hMBSIeoF/5OFs9F8yZoGxRDHRz6IV/ZPadm5+AZ+QVGaI4IE9lKwD501llHBGpTzs+kZHP6Q73AlwS0hD4TkLFPmcFqC1mj2w7p15vMSEEBxIGi9/ybt9ITpHNdOiffxP0AHvJ0mWHYXtlFXDqNswawV/21tusvNNloxJYOzKXZ69kI1UmdeF7anx1lfMunVgon/oyDXZs8fArSRL3CAxtojAFI0T7Khq/TrqwkmgjZsFiotvRDs0RCR4lAxCURucjiMh95ElckOZH9nunQ9nrkFw2RN/kfYCk5gck5I5VLEx1x8f9ZpUOnzaozczjWIlgXR9OJ+5706sP75zJEMEuIz6Pq96vKDJLfkt6+Ybe40Du45oY24q8soeUByvJBu3UxNrQWqnV1MyLMzRzGKTiC5J4ATRy8zDRzseEsm8yMfi+rqXXQIk8CtncBjJQ9jfkFBeRXkruTCfdxnz6COoO93PiI3mq411U447vNDqn+qDuO3P2tJK+EVfTguKKg/95EMPwARgiHVHp5T0vPeUsKff6GYCeX5ENLi4IQ1lT1TjPsu+n6+Dzutz/L9rAoAzQTwIeG6FkXDYs8gIdzPPDqs4bF5aPWlXVv6D87/dhLM96gvZiWYsTtCBgS2vQNCVLMax/mhDQhAr/gnL9KRVjX6zLsxahv04goL/RNETiOIUjBAbB1J8LXH9dDqX/RkHYcxQEwSiJweifr48mX+u/Lk+SxN9wnEBQHCOovx9QF01V/9UBDIX/hsAU9rwjMQhH8T+HJN8/X1f/2qdfmf9fT4GZ8OSKrvt7x3/vEajJ/5zz1yX25JF/f1r+BSG6535s/YwAUa2/wfnT8p2Sh91B3/Xq/hxLzNsIvijHYf0/yqRvuudpmeeQ56ukn36noij2vD5dW6om+Xft//Ua//Zm4Jr/7Zt9m7v4cysUns5/fxV33BZQCenPtYAj5ne5f74FYJy/R/rn5nT5Dw78x9FA/qlfyEMyv/Fa+2eQeRgM1LqMn4Ibu3F5WoZxKEC/m677N01J11TD8zF7SKx42llAgE2WdMxfX/RNnoPbsEfdrIU7JRm457EkYAyXcRvyAswj9NewuL9R4WHsvw9FIxT8kDRNoygOIdRDnv9E0fjfYOLf0TBM0n9DKQwnCQglYZL49zQMP+vkOQBCn/80jiMY8p8nYuI/IOJ/M0n/PFj/0XD+w/z927HskrTo2HHJi+XvM/gQsPj7+evw/6j9z2lJ9ql+d/+HQ8rfz3+LUv47TB0MQejfCAinkYdPkDD095n4a+4Q9G84SiI4Sj8TgMHYv59IBMb/hhAwRsPPAQQM/31I/2kqEeqZ7Ic24IfpkSSOUv/5qST/x+RHXrJUxfq/+NF/iqhpivrbIz4fuUdDEPav4u8vmib/hkIESVMogdPYI2jp/58wJ+p/UIpekuFbFsvyzDgC/W+m97//L+r+z7HshyM/4pZEEQrFUIr8Z/z4SFtA9TAG2Dby78EjjiJ/I0kEpiEKhTD473z0/2nipv+nIG7n/zZ1/09KywhFQn9DSIpESBzGMQrG/x0xU883IEKWwDH471//Az2TFPI3GCbA6QgCUST2/w49w9D/OZT81skE3jZ9UhX/MPQagHvW+G3WZgRTkI7rOvb/13Dgv52+dQTzlHynIgNPWzYnmC72d0vm763Q31ue93myJg9F//mIiNNQ/QvCNT5rOgf0kqoRaL6G+66Fd/W86+3nj1RwTATaCYqFi59qLOWs9xYYRpPAhljiWbPgQI5tXVE1nu8J9Tm7Up5GC1yRY4jnb+49f8wJXJfoQf4nymVbJ1Dgim/Jj9wgNjybmZnfGc+vozDMTxV/7vRcqkY+DMv89fnpG/Pnhx/B5384bmSUfzhO/YyM/g+fpd8TMJY4M8ob9CJOfCfKxdrw+KlNEWiLAmeP+vdWSPDx1z2ogp+wNGTXOHRqRa7XVMJvc6hopRc/KaJ2Zv987uEul4RdaVjMHOo1k53FctXI85nKRugrdhlEa5lN54A9l2ueAeLPK5Yi4v1xpP96VfUTt1CTyA6U8eOuoTmaXziqX/ie9dmuex/cdKlDb6hLb2Bw/pqh3ZZLIqYF+K1c/3p17r/ewZH8O0LV6enTlCJYlfVOb/Zik6I+ZLlK86/H//3/v/bGmeI+ep6zhnKZIbSL3rILr2OJbmIXB+O151LXxw11Ps+2Ky32+sfrWPLvfMJ77h0HOBSFv/sDX4x3/Dmznays77p0cOrn857+wxUsjv6Pev+vo3fsv+foVMF5znqO/jPu0m8ewFF1V2B29pt49+2wvlyxpAann8tPP5MbB3noKzzM8UcFAsOsPxaqs9YvkWBSJl5m85fqNc8J0gH7GE3R9tRHItGEIQdTvu5naHCGMOlIHkhdKXEez1fgCPZndjlDLBilXSzrpEoo4AoW/7TOBAr24xDx+ZNSXPl8go+JWjI+5IZrPYzsVb5+bRib4sWlYeax/mvbPx0Homf8EAMbpoHkMuq3ZU29w2mVHCA1w9/Y5blGgElfce/SisDCL3Gc/9G9nja2LH4JHeEXGMqvej/EEv+n8//hqiXZ56BSFHBf5Pr+KwePeeCFor+/bP4s38HrMpR4CszaEJ+g402xGXrDt2NpP6/Gve0Y3E+lNMUOnaIYeSs56Wo73foF9DP7gogSOJ3Lh3n9le54EU3qk9JgRfMg/gl/cIAluYmTCjEo36J9CurW3SrUYYhPL6Tjc9lhYKs2TgQE2oJ5Zn4uHqhTO9ATUUeRPOccFYzgTrV0akN2oUaJv4cnFnV5fujJ8hY3hc1B+RIxjUHkAum7/ASIp7bypV9K8M1vM4thDEFtNnog0A08BrBo72XhWkRAZxLa/9WJ5oyT/K59BCOVMAY20qFYXdQg3MIYgOW/NuiVgmrkS8vWd3dfPNFjF1Yecr5KC7XA07Ag39AHFSJEqB021FhwbexABpBomVUmaeuNZyAw6UOgvrpvEXqfndfGKTSDRy3aMoPrzXltdLd/32Hbod/Xkp1rWYBhAdGfb06b3wMtcqAICovdexfSPmdNRwqir04B+MeA6BQXKqXpP/OB/TVVZfLbplqSU0AZGHL/YilodAeLjfqS5jOE6p91B5jCv/wpBLH/Vfvza/7CXuo/nzY5BZ8gMMYQujsMyAqQQ+23eXPd4AJsRy24v1BccTrU44cDsbxiAHfoBxYi1NZZ1DjFsbxfU0LQ7PkVt4U9vAS7C2tEmSlaCpYipKHvfy6+5fsGw9pMamOsB35NBIzALSI+3E+niNC4ew71VAiTfotAqRvBEiulM5w1WN/WzQ7DvOrgCdjYcUvmYkgS64a5mHwGC4bUe8vYHthU1L9AltBlvCnki8UdX6fzdebIKyf/DMtBfleSasd4xa0UVcOum0OZpiG1OYUwP+iA/hTZKQ95T7kklFp5+tZEiYRWbgt/+cxmkjJ7Bmq8sD2ywhch0aUa+3hiD+yOgwKNmFKmi+yXGDvvkuKAulL6QpIv48LQBgQxyONQk6S+8b+a6dSamcNWviZbfkHb8GfrW/ntw1+CHiMITVPjS7JfYmzpSa3KAVV4D20UBPZo10SuAYQ7fd7EHj30y5s7egKmLnurmXEDRx/puje1ZtPk1UmSDO9W9shCK4NbdOLMqwqTdLn4IvJfM6nTm47vakrSX9lAsjJ9h5M5oaMrtxY0WnRmXTLXbaUFKFReAuebGgl2pd6fnRFFgR4lgkoVTPws5ATngxFUg4eNzFVIt++ichp/p+yIE9uldjQ2la678gxJOOAnPT8UiETzZ+w+Sw3n7543iB2zO/wCBGuKK04fIty4veQQqZEd6bY0uer8SumNU8uE0V/r5Jd3DmgXBiQOdqR82OiPff42OPjzm6CgnU5BKsqalOEBAn6x30Hn36PAxF8WIBBjf1gzWOK/cs1/OWBAQ0Tu0flLgUOb0DfjWIx1/gLxM72JyYCnlzss9OWitXa/Sy5cdA1kkddEi/VnY//EuJ7apNO5AIWXe3twqnwjOYihOlIGhsYvIWSFC8vpsKr+dKfxpiYzlneGuENHv4ROXEutMIK9uHtDgcACaJFWHPMSAg+sZbjjH1yus0EDEfs3l3/RbR15L2YRgmJH5C/xPdQ2VO6Uj2Fk56aX2Hx2hJbll1K9kPYNp8kC6+sjyr85Z0IZbvRT3iYhTEfisKfAQ2pOVAAy0rRle7MWG1Yzj82Id/qtsvx2C02cvT7C93CbtdFLwN3C9s/iYoS1SL8TeuN/2BdLcF830pcJ++3QuKz6p94yUSm/F7qZ9ruFvQ1RLBCVE2oFelfySEMZbCxX1PzSekWHsL5CqtzOGudLgHFO7uQgkIrxgAT8amHarYfsuLYf7YavBRff85dLgSKLbORdFmHWV9vNS1bP5FKENkT5Rf/OfqmjUMFkcknqTFlYHAaCVy0liVjzLhdfkDGAHTziy87rJQ+tDecB9atRod83vsRFOmi6auCaq0+w0xpvYeSv3LJ0TA2sxrt+kTWYRdNU9Qrc3PCYN31nAdwPmnpKykRajIlpYTInX6IAmTUCOSBCWBNO1zNFjm1NW8E2FjZ71qrfnTGz3wMPvm1AHiEeHV4/cEVE7nga6qaUeeLYnIwIfAMjB24RFuddhCfsHVQk1Bl6Eac+mBhsnY/8U77+LWmFFKe4IdZ3hiM8SSQMD+ahq6GSXYEQYReyt4WhSbZ4z8e7BhPPlUxf4qfqVbmbsZDVFulyvz9+W+uxPFaAYfHhAFCfcyz0OUbslbEUrMpHS2NKXKorWoDpszWORuc7phOuo0PdWs+50HCw1OlfTeGVVsh9+yvF2Nr/vJLpClpwCVTEVsp98AaSpKEfyCL/WsjXD2udf23VTZR/Wj2IHP++8lmM8Cqov790C0PT7NBEVMrs55K0ps963Eq6lFDmB001j2jEWUrZK+T6aG1kQNxZEPGBaLBiuSwSrGYrRvaV8G5WHi9yDHlYaqoko+vyguwvkCypdKKz+z0hhOj8FsMlSPSdmET1xkPLur837suD8ANxJCeaFBH5ioWxFGDRwd54ZDrNs7IDfDPCxt3p2YX47sxt8kKfWUdS4T5tO8dxs9vJQ3kd57ua0vpGfVk6KriBA0EJZTAX8VebUNMxFLj+wvbIqi4OuUPmW6Q8TZ4z8Evc33qfH/iKyCoCc19vzjhHecgYxwPLCsVbzVLo7ZzJGqAifuaxESeTAJsxU4ZU8/mmAypzsmQNr0fpy+DxrNLIjQsRgEBPMkFwd8dLW7p1m5++YZb2dfg1t6Yb4TpgsRXa3JLV7x1i6rO4X+cWStspO3YdkEASR4Yrk9SZjB1bBMizArBTw5TI+X7pOdpCfZzg48VODP4+wETv7MnqCHVEr0Dr3vDdzN+CxmL2k/JM4Yr8DDDbwgBzDVtY1dUMhkWaMQz17IiY0d3y9vugTsqG5g6xjiLLbinn1XdeS6hhtlqwCNg0l+XwMU8y+kB7wK/xmBtkAZSP5FsVPsrlNLacu3SAza6NzPcaq0qZexNOf6LxjbIYi6kBpLMIMj31pIbSjI9F8jDfPu42qqCeMw0ihvS3PqwPqCes2cSYh4oI43PpG+GtJBBnfVZ/9Pb2IDRimtydkQjvhbZX4Uk9M8rECtGTL+yhIZtIL5UISWhbJpxfSdJ3ZgxEp2QkSMCMPpfiTCAWUMhFKrwLOxvm1+mUH4aLJ6IADNzSa5jjQDqo9hk1uG5THi8FNbBzP7hjIIuGDrMHwKl8RQZMlm5Lwi+CP8tx/vNCm1BoYSW7xwU64Ayt2H1gcEA/oEA4q/AN0fAKYAxpbk7r1jFl7Z7B9OLTHzc79tuafXce3T6hQuEBd8rQsXdYXusv7aPhzSmSkODQkyB/vq4wHZn1RdWNgpkeHf1d5biY6BqdeX/dRBadL/PbjZaNyDjdyOEDcByxhW9zGJcAQnEH9dARf8DVGIVA/yM/PxBfWRKlv3SIv9Ce4ioFN4Y5nD/Je7F2EFAlBij82jMjZur4ogoYLqmR/nyLfoVlwR79oqjtlRSvF45JpV63PhK6PEZKX0SP2Qhx/fuBfEDT8HUThqnkbN+x4dzS+0KwPJqpIpqs5e4ggYbAzrxsNmbJoLtz6xb02+uK8vUrUlNjt3aPAhoIRMely1mQ57uYA44bHbOjXIm1OF+NIUcAMWqOwBwEQR05nLMDXHue6hDekd6Euw6LEeu4cfZ5ZMDeG1nD6zkoC+lh1zFFWHeDncDISTyMkQdzQFf+Dj/7JcA7GWvFKlfe67Qm60sFaWmeS8/DYSaCmw4RTtW5ki0gFmz61ed9lyM8tJikMziZ6QagSHLUp3dpdlwB9e6hUp+9gMrVrTl/UawQb1COAyRW3TpQZ61CMwFcwjEGdjkCGs02L8gFPjQmJJ1lgoNAhjoJbScfWCVKFCYYylIMuPbbrO1YpzKwDR3Hvi3b48bor3HxVexgSVIvuZWFZc7dnL4e07MiZ9oPQyt0MJDyo3lWAsuWN4HQT7TuW5DF8IYdnNx+OiM1Pmiep2ANdng5hImw9+PjFKwmBxrVLx/n0TA98X6BJRYUV1pkfPF6u7X1sdsjiOh3s1RAgy5O54tpYNsxwrRzeczjbyAxkoJwQLoSLO8QrGEMUXk+YqKfK0thWdm5wLZFZqIEap+IiZvJfxIDWovUsC8o3e/pvVgrKSk/iLDcmNVWkTi93219Nnb5OeIv6wXio4vJim7v+xrz1lcn9oFxEFSMYTN7TfGAQpFNHMWInUAUS74k8B0DwmNblzWVycnTrK8OSi8AN5hCGTskA+5rAVRzNSALVvfcC1tbYGiBmt3Ic5abMoZ7Cs9j0YZ9lQUaze1dPT3q1Z91vaCnGzohmEm9tg+7onPP+pzL1Jhpnr1uCCX7GcPq6liBGNJhddbFtAqvkoWmWyogZkpe9jo9S/fkcc0LHw730CasdMvFkuLnex7dxCfmhRjp5p8byFzDJwlno1FlAFN8nX+lmmBw97601LmQTz40MVVUK+Xy1vjZ0Ju/jOvWMznS8JOhK3ytfK137jtlBSv5OG/lQULMiRhYfmzs4pcUVypVorCF5hD0i+nqYx734AGXnQ97csMUAAN9ePYK4/e99LkGDDfJ4habzaNKFP9qYSVTta9Roe1JfEIt90YBrEOPaDhMZ4rxUHuB/bdYS8gkzD/CSATSq2el5Ky5zYluhTRLTpdvcK/byyfl+9uD8xVsTlIciaD1GJfKwxVGczS2wJwSZkW5dMKgGRgeCWag4t099iXM+QpMxgsSmthYAMYmqnY+EMlJpIX+AHv3utsOIY1hTNo6fac9HBVBNhYeRTzj/xLpm7LC+nkc9WxJ2H99nWJlz8GevMwgl0ft2AqF7FzvKkc+PtMbhCme94Ma0B4sJdgKv5VEJjzUJOxHlmoqrZgvwX9iwF389mjznoiN4yS+BkvoC3wjnzqLsFrXwibApPin+dfILyxdTstzgQp1GXFPYbdBE7xDSyeDaCltWSNxUtRQIh+NWf16DpDQzSwFbVZ886+xSfxMlAwQIa9WkoLjONpKSzhRlRLFb/di+7wFQpX9oqGnHdx2+e793p53EvaFz50eikGpweg39BK0gHodDT43J6zxfgeDAgss86JvNYmOHRLqZhDWU46nciat8Fy+rlMvCsMzYwTdJOYjY6onxFE33+CgffQZhoC4MX1ZMHpkp99Dw8o2y9KG0Qtm0OMNGe3rOuJ2JvL2IAwGWESQVFxR86E6KSICxJ2+LvQHZzcrsGoBuiT1gi2aEwC98M93zIS9ncBqPZ/8lopJLu22NqiUIzZM4EsEmXm17fk7hzzR6vTDBThpbXfxV3CkfE3kygIm9nxAvs4kkEmaShJEj9GXh8nLVbGXfDLJMq7x1EnTXaqixOLqV0+ATonHyQNR+5lrDzhBHxykNbkg467ANnXvbm1neYiU3ufJ99V2baSsPrSbxsa3vSyNnCnpKy2fhKjsN9V/Ox8ZcLnpGSVoW//bu6HIgScE2dNk0bOh8NLgajBEYC/KHQ0YEt6tHcPFeevwr7Z7xVE9hTcVTjsUeFAPSZ2p4OUOebuqkno3c/bsGauo/237sonYt8J6GiLD/hfHCmk1MP6RAK9t3xtghXCren3jhJ3p4meRV/DEy76oHP35GKgaM1RnQWzYia/ehn/b3uNF6mDqFfHMlW8qUiX4vTUS2YfvNl68LY3LmRlYhjpVIfM8foOgvyrv+kK2bouhojDo9l8Wy4UFTKL/2KF80crbWdg+MQ19lzybJO9Rf4MlZ99z4WwBONKbaJrRT0dxi9oD2f2iqvsTG4PCZr8UjB6Sa41U3MzfgenvgEB6GCF6rxwH22WLr9Gpx81I9+n7yVHdlw+4RK0b36HojQCOJ84pzHrObpjV2zR+QqllGTgl5JJmeNzeYiEVjGvcOgGO0U4wspTNSTlWPGONDkC11mRDEONzIgYfSGB9I8PXlXLN02hvkmcQ9DB1pY2Owbh0HBTa5a00aOQdZ4bHZyV/xbpnJnwxuD40Kf9Gkm5ZdPo81I/3aTq06aEyJOzksMtozqogHYOtDNczjMLNKH4wySTp8LPJAN9s0zW1Vib3uFxUjzTpztdJoPPm1Vo5++gJKb9C5Im9v1VMxbwqyAhgmONfWPj26XS3PLRlm+KaKjCLNbiDmG8IpEnW2bJKxsrg/FKg72o4kDVI5yzlFPkVElva5qmQvllu/ZygXkqe1pozFrJ3ZAJA7+LruMAaRz/rpRuZCoqGsFD7Ba4OWG1Ow13eNxGRo3RhA/WRDhlb4gkLmT1g90fYo+m5CamkK0eBRdLgUybYVumX3+PxERozHC9ByXzEw74dZn9HIe1a67TryEZ5/ekhRB6e+m+VAxUPa9vNflas3BlfDj5VeRqFu1LQEVX7ITSNJb3lCIAvst0yiynw5Pvm1NcxocIzJ/SYIcdFudmuoFEZcbDrna58hO8FLy0NsV01hW+49YnkWYDMfcXx2/hQ2MZA+PdPHR+xcrWOPQm74lNtmefSiWBd5hizEutNh7Vzp8bQIh3qiylMbvEwuuO2CwV0Vfy2JP/o+f2TueILLwQr5KObazIrm/arut82i5yCsC4bxUbEkEXUR1bQN/np5889xTaiDkomFqXSxl3C68u771r6ew/TKO9Nrg0rvntMLI8Yd/1yC3AwV7Byqe+3GlWtLm+XIpbhEtJ5sstsuW3Sdsxgn1oxYForMU2Hy0LmHSN3S7h0+lPzyrSZvTtqPD0EvAVY1b7T/SD4ER+KWR5sM36hvBr7BFhYXZJCZraz8BvrNt5b2p6N7y+F8T8jo5Lvkj6TbHyyqEpkWrE50lzWx5qI9cCw34/SDYMH2UIPljk101R8vCRITkEXN1vKjJy+SbK5oGssqY0E8rE8Kti783FdaUXYDMAOckHNxql9HYQ6f4KIRzOVLkcmgjeLaDJ5iy+9aBMc4mChwalDzRBatYHnsjh6utFYcaCJhuDuKEiKqwqlI2lRnb1KAMU/0ryN9LUc/V4/iqgcbRGT12E5A+UYO9OK/zBnR9aDM9qgDOFJGgyKRkkQP9AGWAWz+f6wkk3IWh9IHwSXs6qt2hcUjr+i/JD8YRe75m5MWN/2hq5KuCreSjL9YFklqt7tvMmVJEava2G0sCOtpUVNRmJNk65fX02Ky5UTzPKF0yeVPbrUQS+Lu46IvySnJ9/IVVxHaOyfiL7HCsF9IYXG80+tONRPLMTN6bS2c5IGfjOSoOhTEiaLFuXSNdFe6ePKm62fIwSfH26nO9KnLueuArQ2OSYPJWsCjBAFt7ca0AEe9NL13QDymMhy44R2O+WcWyaeDgy8uFjNlqLB7nh1FyVzKm/7FOYiPT7M7tLdR5YjjtMT7UNId3y/9wDRFBJp+bdD+c0mT/MYaVMVETpRG4K2HEBDUwbAsaZcgoDNW5Gbmiq0em3Iz9fQXJb+XhpsZ9yWcftCIAf8yUnGaNAv0SFSvkyzNMejwStR6YxyQUiDrOPA8QrQyRbEGPWiUhQyJIEy0/ahKsZIXVONRZK+Mfe917S9x29UzVx6Tc5XGHELwkAEXqKjfNQ4anaqRlPiQEp3pneglANb91Ztflz8S4Ux261B4VnbS7DgD4wnfalzvY0MjmmGXaZsR84f9zJiLa0F0VtW1TM6qjSjSr7VOWtI93MPFda6O/6BWhQl6d1aRdBakQs+fbi8b+ljd4HTE2+uoziivYa5BixPWlpL1HfcW77LDuhBTt3X81DS0mUuV+/ZcJkz3iYreESpdcxCcGCIVgWcMqLig8KQkG6E/i3MTCTzYQkJQAwBo+l4gV3hxZEomAYIqJKr2BZL9CIcmUePKYdZMvAMM5rhYdoIhsCScTG0jIft2YqOn4XvR+FvmE0lwH7pYqq9oozU0+8jqgN+FghXwfDQMLkiq1Gbw0P5RM2JS3LrfnAOrOoOMCHiwNqzSqgMe1COuEUAoMadf+5e+tX3Zdn9K9yxs3M022fl2nwk8hkvYl7pWgLl/tic7xrSxxKFOxVCsdp8h7aH8UoqnC77KpwTAjg8IoxQ5UZfznwn+QqP8Vezp6pknVUKA3hPA32hOLaoqGN6bWOrgkHaYVksRXvGs3kCS+YrD/t5YwefWLvS+H7pWQiZhd++cRoPAyRu6adoYlx5SVm2KnVzdMpGrNvk92h0lLgmGTU0xIfNtAMxKhcW8PQKv5Pzbo4oyKxDhyK23Kv2Slus/xgqon3n5jlJvV6xO57YbXN8lGJCW0BZEsXH0Cv+yFWiAi/NV0Dkb7YQ5McchhuPofazJkCoB5UOQDEFdmG2vvoQF49+VO/ASd8p77GJ359ruqSZy0ttMokYUM7JOnPbWHjU23NMpBEeFXuSEhZACDTOQByvYEYENhZ4K9BJvFQn8zGreqEIk49p+UUSGXWW99YTdJTHJTgwXChS0IrbQYePN0kBmcsOPmhEd+GrsKtpMZ9yZAVpFhtK7z39sFMK4uu4qBYUkvwYZPkVaZ8H0U89vizATbbfhn29/3pA3j2Qrxonz+xFao9oW6rXcEZ4geBBWXQpuxeuuVJhB88+aX7lR4sVpHBottTmB0IPB10XByfpwW1WO5J4XcEDCveLFKqz5bcfIPB2bG5S3f11PgIYQttHV+NTdVQnkmWCF0ogy3K/rY2qTKDBxtmdINKrWlSrKIkJQ6ni1JmHR2Ql1F2Z8H4YwQMbSR65V5bGpoRUB8dnWIwBZjUdy82o9rq2OMlCvz13wUiWpiI8XPIvVpeBcHePGNYlgCncLgIaYB5v1PR5hXuHrN9H7M8fYZmWAlfitdmEfOMC63ARsPgZ8yqOFDiSEtjMnBcZUgnRUcRaMgPOU8940YOA7Ivrfry8zTRpuG1qzXKC05io5kOon1xxDOFEN13nwTjhcu8oOlOtmTGUur+jQGnwEStW7+ToABjlbTTcHoAkc/ubi1kNX+9h6NZzSEsoGjNHNtyyS5/VVCk0EWJ2wajYZNiUxC8qMnbcxdoIOaeGQKeLHjOQxowi1lxv4IZGLPSiW7SzJ49aU6IQvro4OiqzfwZp5XSV0OoKiQJ9wM/xe0pQmq4IL9eCXiQ1wnsxQj3ss9LydFc0O/fS9qNyKwHXslm80/GyRE/b1II/o58jYa7GyYLk95eZiB9NkFeJkpl/P/Ox8Ik28TkeC+x8FA/29oeWeuRq2I6VditeLPMk9pWscULt2RSHYch1ZWuu6KWpHyuQu6irzN6vqdUsj1oj6sFOlm1yQeQT58a22X6dGcs1YHUm8tVYYY9oMZUXyWGZSeuSfPLCOUEneL2s1olZXR/PxVo/SVeNoz1QtwLS7Qi1kfLduhq28OKAtDfdfxmLPJDbYTbRbYE1RY1UBh6zlcq+mVzQFnVx0k5duwZCn0VDZhyZ4INT1hXLWqJ4O01snZu0uwXtXa44y2p5zQfiR50wS2eh/HCEn4rF+gWx3R/zBs5smxBDKd2u91p/8OW6wT5PrD9Uod7DfWXWbmppnfzhmovIYQj7HrVJnDHDszI3U98RDH6GJ+fmQVaQtRJcvA4XB4UKwt0oE0vXGtEEMmLmszFW8qjHclnwP0YMbu60cjm/G3jL1JtaHHXhWBxCgLbVrT4fYktH3TYPUUXXWO/6Hr9vDKPS+aathdMRW2iyT2SlIlYAN4YVqCbfxfrO18iszpmC7Jl7eTfFKbagRh+QZy7R9wRb2LWrlX6+7iCnXbW2YsO8a8x8tYWRB/jXPr/Afrp5Hc8jpg7WagkneVdOG3t+5XjsJ3QcpLSeJD4teWL7JiQ+oSHZtfxHYTVqu+aofrDjcOLh0cPkwXhAKzXmXfkM56O/a4gnKGtXtO9mUVRSZhmL4Hj2V+nd0FeqP0cj525rfNn3yU+If4Cc2jrCLLx2BHp/lX0F6fKvJu193AQKIbz4LgJ85Ho9RTZenuAfyFgQ+tC0ZPWMgPCPB66EXQXEPZHFNP/81pCsb/z6cyLEQ4Cx8F1uYVJaGxPcgdAL30+ULsB9Aaq+HhiDqN4qRiWL67HkQZGxF/GZjAzLl0MVgLIRhQ4vu9TTnhAOaHgma77b82RgpCpyh37aqZeVhV9Razv3+xCv4WkpnAph0KXlWeJzFrfhoVAItLm5uHXy6656ARFu+StCtbTD/L6s4AdUngfRBvsgP4yNj6JqpoLKzFnr672IfeEd/ALxTqJKEbBcKOf8y6JPP8fHDLlRu3MVDKgVFefMc60aQXdlAMHqMHQZUb5c3wcbAESm5l/my5NG8XLeJd+8o0E+cAv7jHBK7vztf7owQp2vffiLJBFHkA9wL8/x5RrGN9HOKtDdbRGS3+YjndOafW59PpfX0vmxU0VMRal6w/CVIGPSjdTGHsXQPMKgQw4+pUEAFkqfzdYaPIvuJKgVJQ5TlRivVsCmjrUkofyAAIyAM21UsyF+UgIUGo1H+eSn7Yu30OzVl37ZLhUCzVFLCqrw+x/MLQMRDynzDYWhxlmm+TE+orR8pqHRKI70Xtv7pRfOxmcRDnOUSg7HAGAcabJJ2MtwHuNJJq6vXkzvEc2Lgn8UNROd9OMgAS4I2oe4lsNVMSVqvVkZAEKNDdjy0tDcE1+WiLFDcG4OU4jkfBoLgBboaYJpFsPr/LAnBfw/7NuJacrTpOykqSrCqzH1KQKE6z+Mzecv2vf0yTzn9yeeielXGeN0kwiWsuVrG7k6C0scbvys5wna3++hjHIoT7mMfO+w2lzZ0fwMuPOWNm9DEc/6VynhJYumMZEjP6sPy2ONXkkKyOoA0Qxs8IEfagMhJONxUISE9WjvPnLho+BwW11Qnz6ydrArkxWQcrBlPlYqI7RftEUakn/bLFXPmQj1yuw1mB997hAjCINOZaUJIm6+w9nXWqkQLxRTJCKGmeJVp7fMkHckecCNEciradTfuVD1fVg9Nh1gvNrW5P1u6m3YF8rpaDvz0qKkkCW3z4g9rS5651rr05Vym+o5SuRDYmhJUwUIdsnO+VGHKKLDyPUbA5VEtx+ln+3bDLVAOUZW/VK2Njn03J/0Re14ZgLPH65Qt4gSfufKZLXxXN3KJ5IUO9/zkqwOmoBfnPHbxPCRYICrd9FGpvAtuG2twbjfZV0ot9R5vbvS/OnEHQzY7XdcLpUV7A8QJNS+0tSlQOFl69qVPcLyxSfBCKG94dlEuO/7UajxgDcMsle5RycRGgYt/Y6WPVUH0zYSDxXS9PJsk57SxJxTEaXOnVQg/BUJeoVCQkYGfSH6sh6sX3stoczDPhS7XHKbrcS1aB0pq5gZHBfGCzeku6WP0hjEB4LzOh6RH+pfxflkPUMlnzjQqKcrZos32XiTD9OKTC7Iu8/szAXYFsqPli9O8z0rMHTIblvZPiBUQ9FL8zdqu/3N5vR8J40N+ui9vgt5putoF62fzSbeUlPfyIRnYrwNgnYWruHe1IFp9Wcxk2QmBemNOw+57sDQMPJXUqgkGMPoDSyW56NQzSaUXekEVqhcx4ao5qZC9u8iC1AmqwGsr12uhHpDhGhCG2zUeCbHCGV8Mo/+O7Qkjs1sAxwfEYTxIX7RhHx+Yo9tGZf5/rxHfE0KkCeiJ+diNTwfotwS0lLfH9fgid3N/lg5Iy5D0vduQbm6recjZhXkCO6KqHiW3PCgukFEk2YGdmFAGjCW57RkcoDqlEqlWKaVBt3Oa39HIXFlHn4JjGzRGysCI6csJ8usk9iqwgelFO8XQBtuohnMNzuAuULqPgUpvVQczpsl36hHLjSBcnfMVlBltygBH8L/haWr2HZbCYK/JIalmNGyaCdmyWL4+qe5eTk52SS5tjTT3VUN1SipF21BMibXmXztVPtHH4zXmPCSXdLXt5BmYqbDVGiyvY6Xwky7+YZCqbIjgq/ki6taZ7KqrDU9iMWWk4g0qQkgtVaXszzBSuOcVhWR1lgvRi8q7MoeL/dsxLhJ1pB4SDpV+NH1EyhY7EhWuGVsX1QgF0a3o31ISWNtdJPg5Waam/EzPHeQUlYT/wR3v3hE0CMbkp+iw1r4hEWY/oUtKGNBjniHnYw6tmqScDM/7ZfB+e6w4omUSPsPUxCkmlK1mteNdkF7JImLhDIbRg67xCpgwsRwu+Qi62ZLe3p2H+JSct62aQlkQc3TQvI51vcclZW9gvqSdrTTZpULV/vjV/MyzEPor1d6Qdqj772nhrZRF0opdfAiQf2Uu2s9FB9PdxWSOTt48FpLGFFSKVNsUuOLBUD4U0R0WJpgSxHHJpyMOr/PLxyed4rZTkvIUAKf/ZWcgJvl6xot2nx3pOXds5BONegDUvfhK0CGtLxY2NVrwp1UEFacdlWp8KzNQqgxUbn324Qf5HPs5SBWP5RuQYA49/e0VNHvwbYitmyENnDUqyitsQ6UTTsej+xcW5EPQPzZKLtY3ArwRBZ37W6GOq4bCEkG3pA246WGcJqXAEnTN6irdPa5iDR+y6nJ+c/2PODSN6ox8fU6cCj4wIejqFClZXCV1c1Og+kHId5p9eSHWv6gDjoioH6vsvQ8jpGXpUR5w83qoi0TcyuKNMaeVMafSHyJmqFnPg1N58Uj22ObJbS7ZN3re+otsdL+BeOsRptVLizcAJUUHfgq/BJUc01R9Afo//gnzJSzeme9VD8DC6zEqlhDqzRG8PduGz1mARA4aIZgecFhPJA5lLr1FxF2XVMKNVkPq373yiRl0HnE8ifBcZmP9ccGngQt9V4fFoq5o2ow4iHEKJSm+Fj7lJPMSL+JSuWX+de2wpRd8DNpmcnuW2Eub0QHgwKZrpTZs2QuULZZTvMBOfKlYSAfP1EsiOjJje+9cyZkMaQbiImq7PQCUR4M5P/fSv3+9i1k1R+kYHrQ8XdqL2PNZGvz0Jo7fp+9iuGzuceSPcsPByqg0Yd6MJz9rJQZLOKueyr0WLhUr7KC6AuSyv65uOmiwqHdQSgmY79vCw6Fh4uwWmUIrrLpSM3NN5BTI7/oIc4nRtMlr6cKiRbih7+jVY/5gh4gCkRqeUK1M0Kar50TKKOAr/2dzMIGOCcoYtyp+fyDGmGqvA8NviE2aYF27WcIvDeoElxDiHhTl7UOQALE2K55scKSCp1yKX4J3PoFK8byiYJEd6MZEBqawxqDdMv6Qs0E7fZqMKHHXHeJSLE+PWCmpBGlAQeeU8p76hHqF3isc2wP6hfldhxWAuAh5hJlEzB9t52yysLHSs8v0f+6Ig1SK1yRxLOVNfbV4AzcPkGJdvXn1F+MibcjF7FyBw1jGMy0AHeIEHszsHJBUD2aLbo4jrWa6GHsSGZliQEnrfML+BH7SJ9xldMdO9lH4WCJpkqDKV/YbZz7CzG9uBagbMbCvghal4W24lMEvD6LslVBsDstRP5chryYtVPm/e3egHmAcCSxPzPf1rRlI5x/ibx7/3X+v5iqpk/rXp1RkqXm99eTtu3Eb9d4TqsT0FTYXBqBLfUUa2Qi3jiZxMdNCPColaZh1QsU9wg3x7WERldzsAlrEpk7dBGIrbAx8AKVjcQZTx2Gvtx4X2eG9p+XTLpPshcfoNTFDvP3kQiXGR/LYlQMz87SLHlpMWmOV53CaeX8JRR6jU9/+Z31Wutch+MO7zeMIaXRAki0f4MG9nAyqYIIjXcmZnY/HvY4ph4F0ljynCjjdj4hYCzz5bl2aYvC9yq/DjaADEo1D9+WsImexEeXt+T8a8yl8dzd2PLPejsVzNL0Ib64lc/XwcVSMbH/Khm86oNGNGajC6lD0UrU0RRaRKF2jXTnE1A9rpWRQVpVFoPDAxmTgKuhoAhcm1knzlauxxhgqmIKLtol4JVoOiNOWXRN4OCllu5232/JNtNFa+ZaESjz98CZZdq1WlHnj1Y9x79yW+WgrDFTTK1MivakRZvhsdfMCSzx4cdixvSz8I1lzTqXXim0RZVBxZ6HCCLtep3xNdmB49J5r1hFdl33xlWZ/bfP15NQ72bEhLeWrvwkB5qUhqpNlI/Es/5ecIhUJCzZ3gC7+IM9n8LPlZVKH0CUh2tJwmM0LjNvnNDw/W+Ne1knwbA/XOJj1PuuUjlaVIgJET7N2FUZMOIVYlan/xxdfykpStcty0CEsKJ4ihLHy8+YM1CZnNELrWizBoCr0pYxxv1JelCw/H1Biogo37amooevQIb7vXqhmzYW8MAmK6Se+mk1CWeDVc9eR0NwkvH107sY8McuVbOJo/H15MPZXtjA7Uh3eRps8bdNL4UYqx9fZagSs6z389LW3PoJtx46CBFpV9TnpBFIj8clv3rZcevwgM7uKrpyDJlgHmxycZGCgwYWAWM1bD+ky7A7AW6FTDsMX+y347OXmH0A9eog9/5Wg0klghVhliewgBESVd44u85CXoCfZ7vVC7BvdoNmSTSOQ+jrXJZqhjA3/UQyyaseCoJaGtEtgR+zVp+Nx5X1GEIQPkDSA5Womfe1Y8uwlZtutA4FIf+tvxkvWsnQ4QtzJtK0dSZR/gWguPDQy2U37F7JiMLK9ihzffAZ/ccZYKJSjNuW2enEbqBOWB5Ebcxhaq0MfSkatLS9YFHTA5Uftl5tFvMeLpz+NOlG9n1lPNWiKaYPI2P8nAZe3DcAuzIJnMWsSKAjBan+VAfXJ+i0wflqrbOuyCJd6dF8OAgmYMlOxfrKgnU2gfC9SLaJdOiQ1F6/u9hX0s25ySdJipybiAy10F2/kTlKQu35hbh7fTNhdBl+f8CDDmPXrsnyWypdi9P71MWu51UQpHbxOjCc92JqeaNSyxwk6Dmt2px+rhErxRVNeLMZ2t4bDxtc58VSYp+r6YMAx06V3c/sY2ttb0/4gSscNq2zW2l12FP1yD6dfJwsHfu1J8mXmJ5oP5kOv97ePmCefWbE6+sZGbTbYZhZWkkoQC5UHIb5zA6SQxcXWEzQeCPnUt/04y5V/CzsY85me5ioPDG6Ly4zPtqRUo3NytLNgFPrjL/Qy1SEjL+hnWXHdquuKemXnzwGKgldrhWHQAQ4C9d5BkqPbq6o4Mhf9D1hK/nBWwa0L4pXt40rlEoRF1uYJeLMVnZzNTP8j+23rbwhjfmrpchZzUEeQ7UKUj62vooD1kdPwdZIjP4lCedIblYe+UvDXPoQ8qMFhkvEJ+zNvx2WCeQNcfCVgwgVjZwpUuolpN/iqHM/TVkMHKLTJCGNfDpiMQxZelnAgTHYIMBCPhF/U5POSTwQEU72tYTYWTCFxxDTJvZleCwN97eVR91jBxx7KYzGlhDtVvIpQ76W/5mgzpchm/bL+T0C0vrAMHaF0d2KX4g2FyxKwGhTNNbl4qGLKjxWc/CBqxmhOZntOrOr4Me6vJvocrBDaFsG8gXgC6PWv6GaB97aeq5IEFZR57M2cA3NLzw1vCqkM3GFMm5w4BmltJtLUmx7fe5mMNy4xVZw+MmvFWcyFrQjbphiAHyWBdXgzuc9oz88u8hT9kfqzzcVRL6HaQg+2qf9KeaALyQxqfMYbug1TbshTgT2qzI2MAfq11s0mgZGxV7sQd04dIi35bRthMiIM0x4ifelLp7fPOn4RUiWGzeg/a+TTBA0KZmBRj97LRRh+WxLoFJN7gnb6OjNer9rb9ke/hlMUcQvttOyT0Luo/I3BxtCk+KufbtazkL11SfPthqkEVpryByc8oamWnF4NJCdOBPUvMWlD9suTnwUMrnFV81u2mBsnVPkpaSN0uSUj+24/smSH0zqY+mzk/XFYwfFF2IorRCUoYF2rshbM1m5kMnEVhSv2Pg+5xP8+sUo9GZYCFwqhcCSN08cjl0l6xmVxmdQK7NcWiZp3HM97OaKHXjdeengjfNXvOHbZoFeCWvppm4d7qY2RdKddIF/L+sY2ZdWy7lVLrXmXMsvpIJb6p05Y1O+HQG0sMqZv+nua2AspeoExucvzwCNMpp7eGN0Up/A58t+ivGx82ckXytBiP8ukXS2J6e+RCvI3Ir5W/S7/OqQF9cj1LdtgOz06/XL8vXW+45OCTZhx1QXOzcKNDDoJB8kFK+44VOFf7L03YWV7+MeJK65lPSyNMyko/cSFSy4+uP6A4vVxD+/p3whogg7c7QWtVAD6roQFwwjLA6qTc9hFxQZt7hnUnfjjuL+0J/85Ed58BpBzJORjqe0bNJIb7ByP0ul+YbLz4MQY1R/pQF/3Io97bZWjBpkYaG07TLqMY2LhVJAYC9jRVEHGdZQVyIIloSFtVEvi+X3qo78i6jWGKjKsjBZApftly5o8TSphlPGg86V2PybZarwtUUpzqsNSloWbO2R3ELolT4Gwe22KwMM3jIM5K9dAE65ie/d0fzY7f1Xbhd9ua5M+8XH4NXTJZ1BT35kvLx79fYaMA2LSV2JdK+SDNUUgWFH2G3/lQIuhp3PBk486RkrpBrswByBm6Tij3GECh60Rvc329E5xaK30nD+HshQMZujPWnD1ewNqPXfDgwQGbmC/OvSWmTlrzYxVdkQ5C+7X73oS3vFLxAIM0I/WpC/3lwcpiAuG3dzz3nxw8lIEJX4keDGta3kiodXAdZQMjimu5Mc6mzM0B+QHbhBqgM4My2DXlplksQG+Wplc9L26LPdDmUlEqkr56PHCaIVmnhGVxkdxsxvQlDDjtPWFgx2uqSTRI4HbNpNRBTk/74L2b0Ix3R/Cy6wljzl7YtfKvLG8FS8FG3znwQpV3BoM+ghfH73UbvzGumF8HiNNsEUfWcmX+OeKpGTwSyYxrryqP94vxgwjT6Xj+EH/h7ueMWUg85CmUr8keqT+7ar4d9w6Vdh0UfNMO/P8IUGly6R3Hvwqze6J8AR4QjuLLQoKeb8MWdwmuU5cPCLBk7KLLl8OA6DzeznDe1qHudZcxL3JidT9ig4lRPtzjxUck1y/09PmoB5oWGqezjU0SvZTLN72xcfbJc8cSMfva3z/CZesOjIj28iShBSr496kNbbE+b33CsvlqEl/03NEhM9gY0nANjJNcAhXVEMEVKFS/6cpZ+h12wA631WQaxgXfZGVFox1G4T63jNVaCznBeUwgnorCMksUP7aIQ5MRk/prHpuCrvctogxsx1C2UG8sdtgFHwow73LQxXFd1VUgcwg8A8oTK35Pq8VLV5morfUNrOv8Iwazu1e5TPwJpiKLz3E67zjQxfCRUbvaNqRhc8E4H3tEDNmUe2ZUGOezB9R4np8uz7j6i26U0ttL76+m4b2jc4aXB7VieW0i1jv8+2MWa9qcDE+Hp64N+tN5sAEtgbtxdAlDelhWfrtDkUPkzD4FMC+Vj4bMhkhNJFecTxw3luf2lZ+GLSFBzTuFPX0tDP3iCtTDcjfuax9/WGduAA4+/0pmDyP2UR2vwljaWIJ0+N01k4zd/GpRVoTuDkU43V8pLpkH6h05mi+Irgtembhc4Y8Qh3l0mANDVLAImJu/BBwze4ny2hCKhlihi5ZXFGr5A53NZGIR9ZzkX4F5gv6zbGWn25CkjOO0Ne1HTYJR8E5JoY1nCgW6xw2q623upBvFbTf/dEEnnWYQPj3EO8FjLqA8Ew6uBmKTgr5o+fqlPKOWfaXutI2o0LlDQMWCvk1yk1wBITheq98pOhXzyR7dyGf0llbUq3Wkk+tgJP9KeVVJ56QgxsTY0vpJhs5P39kcDQi9F6UBebLq6HpHXOUAKJNmF3fjIOCJ8nT0ieDqZuDnL92VdwDnrF+oxaMUvq5BNKIannSMxLcNOmvbH3NrhWY/A1xFWaYPPaE9xcNF/jFSsaL1rYftnufn1/fISo7bmAnkOgzHHHk87ObCOF10MX0/68IeAjuYLnAu9Kc3638zzmCXSp9fGBxI9xoc5v6jlXlLWh8cC/Uq1e+YBNJWKjPjk7Oh1xJKwt8qGX4RvWxEOS4XthNXO1kHL7jPXDYMV2d7RGMp8gXyjXQEJQNy/8Igs++rTP8uDWMUHgJmrnBZ+UP0Zt8RiiNiPvrH48J+ty4rFZPCInRbt61B9BAq0HVh4iZvRCk+dd4q8Mqt5YaaasDu9/E+D3Ns5dMYALkHM6M3/CSMjMxY9v5KIctDy/eN+jT3Bmt7weo+9i/1KWBqPKceplswHyCdlzaEQP8pasYopnkDDrQ0lFDzUgz9fc594/a+OueNzzx2hL/2exOnFlMmZhJFFWHex6PvkVFTkKGajxq/Pfh7K128puJoAPICcmkp5V8+ZwqQv5pw8fDqb2N/q8+e1wIjFtDJo/zvTtqp3GG7IHWuNI7vNBApvS/nqrWUo/bqoVLKtR9eS3Nb/+0Fgh/JuJAWtLWCguI956YsQTIPPIxp7POGu0ku22PqKlaSoUBchQVWtjBV+NlCj8I4Pcm+S4pxxhyF9z2BoqLUC2mZHgleM1sOQsm4zs4I6ttT4uz4vxxtGx1s40jDZ3kWJatM9nhyBcIzyt/eVs+xraxwqFKVDviyu4c62ZfQBEzdIrR7NkH14DeMq/8Bd14TiVG2nq+pE279yuhnWIO7mnabd4+AsO88JlJJ9eOjbL4SLwdcK35enlXp9+nzJ8lR1ck+SpIeXyGc8djp7yzEbzJpJ4NZnM8zvaR4Y7PAnGsFbovcQYOPjE2RWs3NZGOIB/UXJkdCQjJMb4TjOyXVHv4t/o8AuNxSY4q6vom9HKjiGqFnkZOB+gBA9r2WV5kja+gNig8o8I2rqD1loAsiyTdfO3D9H+XsZP5OxkLllklSBx6ID252cIG3Ffn9dew9H+rD9GXom/9bXed0ykslzsv8IeWde8Qo0CdhHnL3oxpKWCxO/M+s4UhMHXMlTpaeXkyDd3rM8Ifhl5mFEvzy0ZflQJavZRInf6z5iifpl3oR2rVZDNzAp/lkBfpTRemIYlc3frA+KFfjyEEgzc7OvfRKoPqnbbB6MDWnRIi4iIJfKslVCCPC9jOluGFx7Bc9zW1M7N5LgT3nhcSBVvCytrJTj0cWqvKi5K6yQplSin8zdROP3es5Ov5GOJnZGl4bA1ilk9ctRhPJrBbBVDe9Sj2DCM1KveyBWzpR8St9CaHMpTnR6DYjBzsE+a8l4tl6END0ZI2aN0nrX+40g5Ng+OTJOEPNJn3bdK93gegviWGDBTMuuRjwKNcaLyfRYu1IbtPVHlTvpRgVlPSNHDL8wsFzDTz66527JApwN7uR0lIRYU7hX1T0Xg22mL65SZpKcqLlGkvmmKuhas+AhaTi0QfqcnNgZXpdy7RSUGsjUek3YDu1tkYevSOnMmB+ltQO7d/bGIk1WOwn5B0XXfnhCU4m+gcG+FS6xCaz68/jaVOQlzxuTvPj38zNizYvCtaPEc3TDF/C4T0SbA50QfjXlsaSUjv9o+n49cgwqUPPdPMrhfxKJ1SEWKmyy9zqsWVcmpujdASP2eyeubLWMJgf7biws9FqZnp5SBl5zMEvgt7o7u16k49KrhPX8pdzXx/EsNU5wqtDq34F8FUUdYGZmFbKJwcHDc/7zrzvEPoURk4TeRS+SK+SCjuympktfFd82XtNKnAbj3ginnIHlSzHfNZ8DicOy51p3oP+C84U1gCYGSSu01u2s4EQGv2LoLYEwd5yyoHaR+o/cc5IRf6mVE7vKU+olvQKRSNFxNeA5WvuLWReRGlK0HOeWN+QVrZruiuuodrbBD5HDcMMfUh5fp8TeKw7rq0tGDXoyeNC+8RtXHyULuiJPPAjePtIsGXubDD+RHG+s3OIveh4ri+D21bC4Sd2mnnWTLn/9gS993bHFtk5WU49ya4OXKwp05TDw9qiqNbMd73bOkEg+8Ag74Sv2It82tEoEO3utAejdTv3Vv3fzrtQdmdLlAUaUg29SgdTW4MOmXOJaWAP2otpf+hb/8iToeVhf5p10KeocuqPqIbFRQKPMynfUeDCVIz6yPjXKvk8wqCzpsXUh/sAcA9OQR5Wt8eZdFhOYEyR5Z0ZXEqbhMXyTabmXcENWJlAOXfx5BhICvgnuW3N0a/YXow/R6r0XourL84cobh3gjWDpY/vzfLU0BxlZmIYJuQNeqGiYTDsj+6gErzrq4nYfmslLLdHR5Zal6DkZ2JB/csSd5g5e5kM0i986aFb/ZfIqLJRllcUiPkA3AMd+rQxEdQl/I1D231xe/WbaGWE8Jdn8gpt5rjCFdV8r3qVmN32IVUptfmPFG/oIQSh5qVE4fQL12dXaibQ7uZcQXLWc+FOiujpuUwVwX80Ib9wJsXdfblzPjRQTmqrEaE+5n+R1oIWKj5dGdLI2XxZDhpzHsoI+kvIn18WOjtjmMFXCb+7p1Tm1av+w7vo515itEAXlneU7LsXafH41e5rf4Bl7rJoASwWos4VhsaSSrSxdLfZndAMA/PVK+wO81AwBeqHC0aAqoE+YP/uKcMJCOZFiRiJjv9FBIOwii3U0OmLHXKClElJRRftIOW6LnfUI56YNTCL2PcZTY7hrEBhlKpg9hsB6a9/7hQs5qHujAVwapT0nG8YCJMXL6LLnBZIFWxDcuRJWZS+VS9hNahO3qs82p0k8uE3BpQcgXzhrI42YEojnvF9KTDnICIH9yrYFB0ofLxCBkJwhQlhFjOxtjplEwg87prf/Mv0p/zgKtFFZEHO9vW+wEmkjzM1rknLIXe6CoSfNVyDvHvPzaekWdKLnwlDuE4f6hiJO3K7SnpZNBN82A4aiRfbncZRczixntCM8PxMAgemE+dvM7Ssunyj/BQW1/FX+nSQ5e4u5Ty5fxrvT5AnToFGh68AuIvCM7CQ+bOc3ni5sbQTcBmXxbuG8aPg02JUZQWb4Cs89WXIZjsxySO8Vvf9RyenltdCLDhauznrRuMjbqLCS16f515OrxHJarNFZZhhcE5yYeE7qbsM0frzV9VHn5I7rud0n8s+GC2hkNKGcvY5qD4pLk7VYtT1g9Sp+bEWvZGIla8edhSep2T7kUixUds8Q+ikY4LmmfF1XuDCVWCa2AVdKJcIMS2zrCk/0nKMN7sd/7yhMN7adLfDdVegM2I2jL/xbwmSOr8vsoYoHWi6DNjc0XzjeC50Gl4vT0+1d5fS8mGW1PCvYcCSZDf7PloxZqs4xblT9vaYrp0Yzp6xbH1zaPuW+mAtpYkudTLRS4aWYwdOEOqR6Lb4ScRHTzeDH0DdRV4x/pNhNeLfSZp1B+Qfr6dK7iGf6ASeSyvA/1TTH7amKYrEbMjcy1hDY0+iW/MSK5UjIbCoA5bwm5eKBfi1H+bRz1DKVzXG4TbSqBSRjKCfwZG4wn7bBtN8aqoOf7kzYq9ehnQQJ1fWnZ+r0ly8EQRVf4M9tCamGYmpnk+QPkm9nKfj/+FOkPnJW7IilN72ejn77hIBrQrEfKSdi8i9l+1V+8VhDK3/gRwvsWwU5qp0CDlN05N7L9xsBYPfMvdfdiRlug96Jcw0X8k07p8CrlhzBApA8sjjj0WT8ZmN2tjKZeb5CiO2nhNI+qseXcyO+rrcgElLFzJtVQybHQDnW/wY5cqBRdNeTOFozU1rcWvRtkFoY9sEfkL/oZ+Ra4kNAW8Fi0G3eh21VF79UfGv+LLHg4PIfR0PCNuYOp06bnzyau3Ef/GfZTtATlQITeFDUdDuJH+aeGyf7oi/lUWiXn6JCbf2lILXrDlvFRj1QCd4wMQUtR9DVM5gA9YZcE/hwgc4VHkC1kyZddmtsbMMfTuCAnjSMoD+iL/lkR7Jh/IoSuN6Ijg6tP27uX5XgmW7lWOQYQ0A9ku/LTTwGjTDqIqyocOV3dSr+XN5hNW9GZtopJyUfuSu90ATW6uyUiKN7/gZ5BxUAFTdJSmvw+VovJdtvM2XEeYx3X/Up2DagueLL0k35cEBpXR9H607cP+rFowvqCgqRnSJlHf9XHYPSV1Mu7KU5oe1EkCB74oOmt7y1l6bEqg913FL6cFw1d7JQ7VI7yvhqPfDE7MYJ6LRdWMq1i+UtVB59/be8mnoTx/VCW2j8JgArcDtFzbqUihxPGV0MTFYy8KTPMb892c+i2qbF/X1IWGe5rg2St8KCvgINkN4HIlP2VmqFZeo+TB4NUTGLgdynhOrM//Cr9FM0KrzZqXyp6SyOzCkAzlcXvE/V6AUgOrLk9nQDvZV7UxsH42+EqYe5twLL4//rwmlIktJ5n0si8BVMuGjbq+7mCr/AWD/euMWvTVYniIsMQIbucl52IdMzkBXqwyXYxrbIkKJ0uQFE4g8Or98y5S25NvBLYTEdxPcHhwStiILG9jxaqUsBU/tQdrD8Vx71WYk3NqBVp8T4VRRMgcYtXY8EHdvdwJFII39c3gWb2/VD8ITGkF571PCxTOJpFf2nx/YarOnUVdRxTNRSWFm5lo8G+coCHqyRzXqu7v5iXT+a13xH4jbDX/Flm9vpc4d+JIzrn1bWUVYH3TdoOG78o/wMx9GkdZGDv4/LwkIDGOXphAGoMB06Rl7MeSVj+7DuxaDtluND9mbI0fzmNJJPe+BvlGzIcAOsmVZsFVGAkMWWTv1Wqx/+qX/KAh5BijEIefcLfjyWvFQ4W74nZK/OE0ZIcfmOm+hDPBuUfzdCF9KWHsbiKedn+PhR4l7v3MEZoucuSLxQ9MuRYDc+0CpCsQuCFxtmIsq0zXF/J7ivqr6SAGd0SIvPhXAaYLhXzjs/Si3emm0M0+A6MgNMjIN8kdr5CtwHnqLL4iMog62qnlRuBJzNFiddFNgqcyZ2ZOA8bgPOWtOYxXDts9o67R39pM5g0lXbF4/CEvRdb+LWc944kZmFUsFNSNXL0E7JS5ouE3nzR/F0Fi1xUdDEWYqKs8gTUjoxVxes331NVUFpxi34OBii1C73g+E4fXOwvmc5Nt/WrJwEBZSfpO3LWgOvFI5O9+f6L5U/PVOmABILQOlb22VvblibzXOXXdNNNqO7m57vb5yy2WQX3RD9hF0GXFwbGT8NJoMK8wyu9/ohk8BC0rQKtfT835h8LTpIeV6b3zTAwyiS036t9KomzIBU021s8p8Qz/oNSJ2UZFf6tF3HzADMHYbZ+7Hue0rxsBEQSTRfzokTTPT9vly8Dt7Fzuyrwwcw1chA9EaCvYqG2zl1soWIMHgvfgzgkHvcQ8XXIboSpir4ZyPIg7lf4EquKeVcTAUU0ymqPxNbLr5KlA5u65OeDX06ZU2gk8mf8hhwm8RLzlsd1Z0ZVY1VPWnAW30iZn0kSI3b9U+IPda7W55fwv+U9NfMjxhr2EX/AZNNNDfUAdgiyZG6BpoqawnDEjNeU2MA0XreYs/FhO8NBfATrhrhHOrQ+aZNQsO9613KNVnRuwCOGa/B5ExRCIczHLxnYN2VbW67JoakxZD7J+JHiLyRWUO6nC1WULXUKzhpx9B3EU1R+8YKJRQpm6eK2PPQc5Yfg11andHhKP4+SWrj8UxJTF4aQpJHG6brdXXxpLCVp+WHV09M9jbOOu8q1nIjl9TANzWG7J+4s4uw1F8UE8dfX2Y+xQ3Jm5Fk9MOrgZruP1yLOeyP2kR6Ush2nnYA++hm0wR5iaXhcIT+GXlPbydLfFM55tDtNoNak98vhuICScqTV5jyMKsPp48fSJ3zw3NbfrlRbaz+CfbTFxkb3QJ9cl1YqNFoIt7BcDpt5J6DHvOJlFUxNjY3+eUFW82P+Gpktj7Oy9LRsivlTZztCrU5Syr5mnSlAZHiJ85/884/tm0x5sJDDuAiLM2225+2LlCtWt9T8WUDBkLPHFvIDzWcNUIB1w474J5Be2IyAVlJGuURRJamJf7Lq7qy51UNSvMbERD5laeM3d8PEnHvfqj9Kpe5qUkES6mVP1CeQaysEkQXSBQayQvq4Da+QNLBbhb09uBsVTtZZEgffQ2/XDyKikV3fUjr5R4OJbA2+UcEmo5f7+Ic6ktMyrEmcXSvr/hT65ZrF+FP0pdqN/554Uc94waF8PipNYx3plNCFi81a/TDMLwxHTPnGNyaHUUv6XCPdNy4qC//6/Lppmt99uSjln+yXfAEsfxj3TxeVqHouy1/Dv9zIx0r3AmemIkTrbvxar5GknsFe5waTkIZn+Z5Df1WhExSmJTnUYmM5uXHNjTGdEFhoIdgrl0MihUL420jbxzvbgepX2/o7Ak+u5Zpz+4YYooMy2WrFh3UpE0ADI6VggWD/Vs7Lm1kRcnzqe5SeP7gEiXunifoSaW9jj0JjQQwi2VFP6aN1rdS1GBYM8STMiKSm5HPBo258FjDhJElvtHx1PHBM3HlCvtbqK8pLMmJUNi7PR67tzp3Ov+iNWQrq+C1OshhIqIfvuwNqxWxhuVUoD0IfwE9HI+ppCmJ8TrWzkpL857KnipDGfdjZjQH5A1qFB2mnPisGHxzg8Q4f5jt6rxTHf4Z5xH4F0wEkwc75WmmG27yQ1UYlLZukSsjvLYxOEtrCjYhLPdxbyypDFk5Tm2Yp+Rq7BQqwS4Xzh4Z0Ku+ikoPDm40qGE6qfdGWU/P43KEqOQiKPiWc4uvMvzdsK7r846XrMkaKklaskjGsZ3+kYHyesfz+IjPVgkDQFWXhcBSvzEPB9eV1tC9ViDc7XkcjvW9ViicRktnM/+rm4h0X5/lk/veezgH7TMuK0tjSpXj6HVol0qIKkz79MoY/ewva4uVQpV18iQ/dweVgOZOpuFzAG3a0neFffvznq44+s8PPf1Rwc/q0pEKACKqe39pGWZ6FPIXUJn7ebPsh2QUv3EJkDlr94k5wuPyrAVOXkH0WNeC9ZZlBWEwp1VixEQ1OnKmqDCrHxb/Inq25U90wTEdp8sfyVCOFbQCVfbSYmNV4fwKP6ndDijWJzEdGihgjVNf5rvyeNze6+tP+hJLrFdC3BDTdKwbh6hfvchdc4ZCayK6xiK6Wx3JyvL1m36vfsOb4pYtv5N7gmG17bIYjALWq2RvI62TrpRn3eLnhETFIqP4owyikKwIHeEI35J86ZXQhIbq30QsUkEJD8/ErwzFv+iCPEbyv/AEGXd0Ppu3g4aePe6RLA4R+snsut2KfJlqGVKj9BCeE23H3JWF99GMUzFcpmTVJuEyDtx8Zr7WB16ca4yClWGPYcoxrNu5aKEh8MoEKOlGAV1CkrsEpHIIXvA4UOMbsE6rj3pCZnLWxIv9bE+K+QJIqD47Js8c1cHbe5Lb62o1fo1A8VgJVlox3PrNXUzI2N74on9z39PkzdMtc+yYOs86LknE0IMxqkqDemLiDyHXcpQabAwzXyCi7GmuoSuzwgqgekDs7yWHhA9hmWp2Zqwf1/iehKxf8+ouKXQCtgLigrX/C7NARfSLQjoKWlcxyUeBAk/74cUsQIB/53DUNxdMPrgF35fJwIYTXgwPUtoedi4ijFZGfPPdkpeDpzmAJXsXsluJtNjHt0v44vy+NJBYJIRVevc/ePRbV23KhAYJVyjquEemJHG7zqQIsUfiQhzwZSmP8d6UDaYBSAhxSClr8DhvtpLr5RGrRDLwqOB2INi0vzPO0w8qtLCxDMnnSk+RgrwY4JkLG1kzVgb5/U+xnVUxbw1WedJG4gq0FQ7ZKq7jTLb9Xzf+wlD9Csv4lpSAyq/I0rEtDMUi6UP3Y6+rBlIZ6QTelHSVTWbB+lMePG7UFlHpZTbH7OhWFYCjSFaoiQNn1McXQ540KMhkMppwdFnVfTqy0nnbF7fS6CaiTK4cS1BBDRhtHlspzFoE4zXGssj95XCaeOF+YSURbzWgDWKRYSh2c50d4/k41S8tob0d+Ai+0eDIaPtSsJ6O6/xntT/XT0G4zdOSlYJJ+zg5vrjb9WZW+Xyhv35kb43CzSGUcty0IxbZKjPqY8F9jJkAFUJK820FsnA7Zz5NBvxVl5JvoFiW+lmnmSsPktKsi/wqyuu9fcfVNNZaTHWxzzvF21m70Abju57Efh6MvUEhRy8j6dEYdmDvI10/Allu8zm5lCneRyCjOz3VtQ8+hjLaRXtWuvzBd/GuM+qT6ncYbz+OMGn2C0JUUj16/W4q6cMrNpbmLk2dwKUFB5lGMzv57vwy2QAOtJXFYvfxz+8qvp5SSms9aynomJcrE0LcY+Te6Ow6xjzE9b+RKRr5ab3EI9wZPHl+yVpLv5BrMu4gv9ZVbb/NmxCER80rfMMMA/CmMlRj2oLts4ZQ2gbd46X9x/voBOaV4R7FBnh03ApvQtjnT4KDk5MJUk8v7tRW0lWFXpoHHXHV117Z3BDXT6u/V++Vskj6ZZEcX5ucw0nrGaNvmzShP03jXdyJl6py9H53z0RTWZxhKSnCN4s5E7i7tpJ9Lg3WgBPHThu/zKHxq1xxZYTmfmdc18q0XVipwtp+B2SkWJttnkxI5Yc0hFdK5GldBf5Cf8gAJrfUEa+NZXM1gvOV3CjVP+A3AHboyYUDEtfpNEzBw7RFXM1P9HmujnblKfPJtDl1+6ZAJh8bro5dPruxK/cJt3SOgSZsvkjUvt/KzkWWar519Bt0yMXysqdwCE+MgQ741if2Q8DfqqjSUt7y6tEC24csWnonHMS0dUFjPp9460sP/A8Z6gNSgVUEiIvS++Wfr+9lzW8z+2zVlfbFrI+j2++hHeBWrIww1EW9DlzShRGDxvICB8eK+uTkmh/ddO4mDw8q3AUBqgOlH91JlKdH4qxOb5Pw/bYaeG/+Gw9M/tFmleY1Mtz+NWmXRpjm8S8zMRmAwfkO93oGStAJkitjijRR9RqFMqP8QZHqNIo+YdbmgLS7/xEOGA1i96C7lOvOPz80f+KHBD7pZ3+838z7bgUjneGl8eHpDPg6lLU269J5Y14fNer18/TPIUyT0eEzta9YSgn2SMiHFWBZHmZr52hJi1SYOPmwzikfqv/EEhLAPTNLKPzn3sc4S3Jjldu3cFDSZezjdqPYG/8ktos3AlvB9yBR7uvLe3f420bx3oSB4T/Hzx3vGDcWXOFZO2h7hvLj6FlRxv+SninpKyI28wldCd+OVJrKdLfZY0Uiz8nYz4LCzw21pnI75RcRJZsiWvYaxJYtyjClRp/fZmnibkMCl3XyXl8C+TFn1+pB3CX88c/pBlUnIF5dh77MGagiEnAuDczgo+rx0c/q3w4g15e8W9sHPmcNARKX3C74e2sCrhOqOmEDLo+8+cb2g05DwLLwXelxa8sNKh/+5Cwv+oofcZnVVUvgAp9PR3o1lo5R6lSXN+StM/6CQ0+nEqJAjDYlZ2OA7E8lnMfuNxIu5PUyAT/o6xEolF1vU/HHccZXi+9HS9j6gMlPtj/vCf4Al9g6Ngc3zRPZWmx+crU5TAfi8CCTwYzYTtUhfbR+sEVe0CubjZ6R/Shq2J/efPvFAueP3BdiT+ihCfLu/DRSlUiVo18MgVEs73Z8ssRZddosqT9zVZlNNt+rdTFDRMg7Bi9k1bHc27NfregoTaYUX6VhE+Tc71xbridBHFdsEioCWNK/J952mYsXKPdEui14qCv9RPtYXyjlkripask9b5/cCCPm92Fv9JXadLzLNn14AhiZet+XsSv+/tLV5R5sR/4auQnuVbeSF6hplrqA0eSZSbV5kES1yjQyavADvZQfHmjIuEfj1TRt3thzy4H8yrVVna8i/soZlP8XbqECSjXEtvlwEJSvwkPFJEXYXdlduSsdX29JBoh2Vsim3itwyRN9fy2exT89l3YoNnL9U+fWlPcFeGPIrLn9dlGwAZ8NkKd9DECDN5R/u1KqtWaL9UumQyDJ6LxddlbNOA16q+H6t2StDo8Z3Pgmqbk1QBToXAZ/DaKuMJ6k8qnQ3Mapdgez2OXfKpYOYYkQd8xxmTKCMX2puhHZbVIRHvL2nXG8whQ9KlkP9orR6wj1Rq6Ob0a8hG/d95EWmZGvWW+VmpFo1Pgf4fSnuwN4x46Q1atTTywxAQQ10J4mhBBV+Kf5SL0qWdp8Jo2lHk3wklj1fCm58MqaUbADRkwg4CFuaWfMHY1aYztQL0QTcDvUJVSjtg4XwNzvbEYpEgFVUmC9TVuBwJRnHVTY+sHgd/N5/oxd9LzvHzQp8V4knp7lUHLb5er1Sxan4xwm+vHTqNyIDL185ahsAjh+86POTJziEVTPqUit8yPCOdRVxOIg01++CoCmJYyeFDRugEb+raasGir9dhbuEKg3mS8iHYZrtBjONXC4MozUh+FiAC/O9i5gIGPQCAhkwKt0F56Udo8GBzK49TSDvI5lClL1BULRXMmHYRR+UbhQhu4yQFgi2s+I4OfXAie0yqO53wPI2R/mWUIAl/VQcRrImjUgRl4oV7bXjUyS/7bLOo1HjKSdkylOBwGFWgPoHZfsmtUbXdr73RWo/f7RdEauybECScPtRZSOSrjeKGs0NpdkpSwO8gftRrl8q/I1b45yGaUHB5zvz+9kHbEaVw+2K/5OWL5KXXCeb08iGr/t81j0B8Wh+vEzYTnU2LvlKC4DyFvnr2SlbSU/5SfsI9ti0mARLSX24voHqMfi2L2NtOhQd3gA5YaZgqZXFD1sSFTr/i1J+4aXXRGjVeUnqRGGsqDj8gDsgn4+nSdWFS4fRvpDk414dA7wePLia7JXrCWfJX6yZSfJxiODTIeVQrKsOdij9be7xKYV3NfZT35cPPTC/0hdvbDPMWtsE1wP1c3NHMnEtNwHxAmWqv4oO7IFTp9mubkiJriFRz5OAZ8yUX5OFRq5nPXU8luDCDpCURIf4deexI7SFkcrIEkxugJDVsi1f8AgrENcSrZQUvHYXwGHDLuXV989bSCRxPyIiGQ9nWF8nVZybKFqQm8qf7M/TT4eauMfoj7qUO+EbpcFk72JR2XTg6PLNoejLaDQ2wSxfgBUBImRIW1tUVip+/lpPLnTuTnoSrz9Rd65F4Jz/ywmQWr4gmXPIhQcI5kcq0QBKRVBW21XrGkb0ryVwgnlFnsbcQhDlbxh1e4GkPCnfNoTl3xY9hRNU+zgOyyLiIhFkG4+E+/e77ksv8JO3gi9lGvJopZMd2beyf0wjSfN8Zq0pTOBK2vRRz/MMfnHJY+GNIR/c6CgcTTPU+84J2oqcdlFvbNBWOPJe8EHHwz2o245d7Vfj83TfZU8cfQhk2b7CTsINzTLrJuOJKJUrFp25h8XXG/UV9m9h2PuFyovDxyuSZX+nPj/07IiE4xyof7iB1Eq2M6YLZQTLxa4TEjn7Wn3ZaEnJApuh+UwHP8I6q2N7YXG8w4a0Q4dZpXoloPkP8ZH2+WIBBfGdXeKlKSncfp0J9yBfgrPP4mtKIaiLY0eR2fYBqrWeeGEZJYYTGH0Wh6Jr8RP+E0moY74yhCtN22F1Z5p3iJtieQZ0cn3Hl9SMk4gfnd3qS5lEAswDsrf3gEtiqhSd1x0DmubYVZjIRY8hPxM9b6YRv7uV/hC9yjJm4TlxcU9Jb/k2tfAqPGRw9C9L2lJWi2IuG28rkHd0A2+YzWUj++GGWOyC3zZj+AWl6ZfJ7+/dt20fySt6YSLgYLQykWSo3sJitvzjQx00QYpYqlDNte+BeWz5y2/O5c7s8xvik6akDQ4iz1S1bXL1ZQsFf1slKWznX3doMNx3Ozz8hYc0PPJh49678q3kxscj2OrhrdU63oz4hMRqXF0i6GLIB57p2rDrDuQcrpf6Lp5zJgDwdhFlNnPiSBCpcuXh3tmZGXkYU963BQFCaXmqV7/AWH6elfnHCn9wRQ/nOzitNcJlrw66MMuKkSLU3oJYqrfr0Z3VvFqRn2XB8DgSPOK2WXAmpk0uzvElpW+Ozlk/DflRbE7ynZQ3JoejadjCZn7t4wP7EwbnmML/CTWBNyudmdx+WUBQa9BC0Ljq04rHV7cXlr9h4gttL3cDI6Tpr9mplKniBcv92pBCSLZWbX/plITASNRSZax0IV+oV1i84S2JfoSHb3QA6yFPytIY5YsESd4KMY4+FEhlZnoNr921ciblfIL6kOOWFweGs+GCadBtAnNECsHpZNhlm+PtT+9u1krdBl6gSJtxfXdVGSKQDxZ6U9qhFnim275Kj6sMBdhirnovpBnbCjxZeqNgINMqhEsGLnPltBRG9ftu/3nzef1TKQQ9TTao0FEC+HnjY8ISToW/1nFpjU4Rg/7ZPG59voRTrs4957KdZSWzx61VV+TTRL53Hjd2BY+mnfx8Xfxu7Wj0hWGmiJz7kmX6aQoG7jVwZ0iQ9GOWFh+qv9tvqLdAMoL949uFh7qoNU7U4Nx+K4qEcP2b/Hxdab9+41UJ64Qjk9c2ystnEgF00hOwdrmGvctZJpdXKHM7al2L/JIKgp2EhA6c52GFNOYTwqnDtHRgo9O3E/TG+mHEhDsliQMgZCvY5Mq2gQNryQT7CQzW2wgOr9neSqqjIjGM04OZzfZPPyyqoF96pJTjqhE6nCHHq1ca1v4ipn1xbjhCT5FXwhizJh84/Y+mq9iaFOuWT3PnuAxxSRwSm+EuicPT/5yv+k66V9WqzIQje0fENiTjAZuko9giV+uxZHcayyw5PJYlja7KzfNvAthncaoXMIeZVkxR8cvjWSHnU4rdbWx5BJKB1CVuJmwnOrH5MAVzrcmB6NXVRnq7evdCdHfak1L+C78ZKMgNFxsYtvyP/0wvfDRsBJB9stV4leeWAoLUayW1CmmoXKALP/WqIgaDaFkPMLx+05zmA2sPWerzaCrREd0DT6CGs+yGDh38eBheMyUP0Tvjx2Uj/DqpDmTisJLHWKNRIoG0oYrethMrGkLKrYYAUM4Pt3k9Op1juS3hjMPjcEvf+Jy1EC9P8LqnXwYcs3L7E4Oc+hBmNgI9omlkH/lsc9o3GrJQBMVOxqs9vuN8k7LG4hezj38rjVGsD0gF5IRzCND6MooGuPK5ZUnmNx0/lTzh6xYusIomBeS+qEWurQjvge67XFTBCYukicX2RRJDcGYXyUK/xYzbpezZRk3WQ7OJXDOP6qjhVtbY4uLx0w/Rk/NpzSs8rOQt+0BBeGpi/z21BzTd/CLfaHg2XylHJxZleVSCkr8hJASGCZ4zD/J7NmLYYJo7umityt4sMNZM7CglmB/dajtr/3n84vy0oXTWu5D65oEuCocThTiA0bqzebf3z4tS/6rZse8XDnH7M/YJ/TJP9qYaMJ9KDMie2mXIhrmCNu1WeSin65n3nu9GpZgqX3vBPZ7AeR7F6OXfE2a7UEme+4xiKFqxGBkgU2cCiuIN6Xfd/Bkq2UnW5drN6n5I7a+aDpZA5fhhwY0ZNBj7ZXgeXWhMPcpgok4ots5+YfhjeYl7yaBkU+gMhrItoTsPN2Dm70lOF0E+2cd+WlPo0mP4ylt68ZOIEAl4uDT1Z+8Bxq41wkgLsp0GZx2nZ93JLldarKfYfAmUX9hZ+BdO9jD9t5SkcQT6JnDSh81NmH/EXK/yJNR4YSJcgyRITer5znh2BfpiDB0jqTTTeRCie0yAuzpVUgvOk59GWRqul9ZFnxkPura6IpKRipfRK+an/wqaZdljiO9bbJemYhSHWCgiPH34v86rxtXiW/Ah6HlVToK6w4zVTnO3x2TshvB24IKOhuqp6EJs1ghB3N+PIMIDRL0L/PoBOIxmSLck3fSIE/QZmwvUGIKjxl17fWYwtZT+J894Ecr6EbCmJ4xffu4T70644raHyvErDidzk2D3grEnOxwsrIxnXr9khgtDoxksJBv/jgMnRn/qFpMUhjGaN6/nXvtQ+yJ5RbUfmr67UpTkBsIZ+AEo0BbJ0QTm/raHGFbGy+4CCG0ylr5/nRUyRx0KBFLkSXuZzvmrkM/pXrbfP7tVM5WnDj71Tydcke48r4+PJUmKVsFmZQqxHfLDt1I8EsLVTYhfj7F/R/WyB9Bfb/UKeh6Y2jKQf1DasyQrqRdWFKtFE/VTFNmf9i93QBasE83Mn8jtGo+K6Fp3Tmtg1TizavYLpaECGz/8EW0XIXDsQmJYaQL3SQFCw2eLp59gH/MIQqhgbvUJN28rM3zqZfngsC3KpH5k4g6rpkUSyQU23B/Dich/0Yrkkya0tLi/FhtFftp676TzNyP5yD3D/JhBIMFP4Osd1YmuUd4jwKG3gZbUIocuSJZkD9C/VvRV06fg+G9C+0QmrbTu0uOx6lLAT4/3MK7JBLtzkAjcgXctn9F/JuGvj9kTSDp38xmVwuzY9UP7vj9J2WCah9YQfAj30O/56tdif/d58X4FVRN37t+pcQOVksMOQOPf/wEPlggYS6/JkyfYIBEl/+Bu1y9Fi0HfuGe+X6QtpqkAM8kA1ADwQzDtymaq9CaQYV95Zs6jCLYWKQCxudd7NjYkV3Pq59ICH5YXK1ZxIrNGN4ZPYOfmESnFL0AVRt4Vbi7YO6QGDt9nc/LzFn2diXFwX5nreUzjKAkYsDhSQ1/whMjLMPgWK6CWoc8df2Zxc5mCtlKh/WV7XZobGNyFspKJC24WXTmqn2/wiwj/Fdj9Nb+vZj8cIXz4PQiP6eszfg/cuNc4mawb9pI94rwvy5aBCoRpNiJA+F+x7rq6nSrUUxH1WMyDcL46lFTYCIrZIkvdW82GU79C+B5vE3UxWj7jzQbb7bzdhBwhIkdGXzfetzf2zOHT8Z69p4FkrIgsPtqX89TtrImAdJZfJmV15xdMOsVtGD7AR3AN3k3AdhQiFmVDc9me+kuqGq1cEJ6JEQ9ZPtudqETNYWbS4SzzfpXJW7kM9fJ3o/NnZ7MESse/JC+T6S9/Ty0Q0IFMR16NpgFJFBS+ypX/rqUr26JAyRkC70AcbBNJmqso0R+W2oKh/FV0dnxxDZwjKVirnzi9OEPPvkW8HV+iWULCbdS4hMR66YiL1Wh66x4PSzzYBbh27xnnytKKGuotGfOMXlQI05BdHaXZoDtcXkg5U3Noq8LsIk3Jq7M9yFQp2JZlJVwqiGbNqovINZfFdPR49tPp+F1yRuZt/t1/tzW2jgK2XMGXYyKBOle+7x9Ng6O7jtHcpvncLu+91rFRG+LFAM0dJYZs/43N8S+n8ozvk0I272L6Hl5U7dycjK3RVdiAlLoi7FcU1bHTb418h75sU3QKhNwjvrRznnufQ0pj/HiPaWSHrGWOpaYA+LsJGngYWaHVv3ygFsQ+88cRWMN4srmplC1bHP3IsO8pjQUVraOUOawrhMDIYJujeVU5MIyAXtg4H59qemxDX9BqiE/JmMtgprb2qZT1qNECJsa926YeXqLqtcJkNcjGrdZYze5TRj4ikvglzEi61QG/VdAOMYc7uD/RIxeRMX5/YsAlk7SrGDZGRrohIPrM60eD+Al5BNyc83DmnHKvmojtMBQFXTrZZ9WZNkU5N+Hoc0x0LTP/N1JV1DM3NX7gPP+XmGgZw2nn2a4Mtuz/jRhbB3SI+Pzgo30r6+DoeimKqr1U+MaCNvj59ob+1x38CCjxL2A8R4PJ/xXnFH9LyTvO75LOv4RTMhuY10Cqsu6WFvRzoB95yhn4+IN2n6V7gRampDsFDDIv25PjcSYnjkoIM+PhnLHgclTenc1OSvSzcV5CTCrda8KpMDZay7m/tgctj3/5t+1niQj2l3rikSbcLNl1VVk/I0at4TpKmI60SbzRl+WX+W/o2ZfzIoPcWBkmaSlLSnrZyRyXxQt3J9iTZ4OfnDHSe1JVc3Hp+sqYOibCRNHnyw7L0dvjQ0MpR6tfg/0A/xYnUCsHBtmdtCJtYylDY3wQ0DqUC8EfZ+ryFESf9qL9KBx7GhWF3v06/SIWrkqUkq1gqO9ajH5SgSTOqU4/L6L6SgfYJ9fCOLM7fPh4CDoLVlgp8EA2PAGUrothhmgg52uMX7KS40vrWv2SpJr5PkAG/Fu/iMBS/GXFPs98frNPJvXPjX/+eurUhSuVHv+X2WD/iXTDB25qzjrkv9Hrnqqg90sLTwS4cItpiSRZDFrQFh7PIeQoipN2qocMYnq0rM8Mc/rIVpixon1nzVUpS8rg5d5mhYRxYUXHPvUCnoesQfrB4p/QNFbKJHrCev5NBZRB5sP1KxO9dGsspD/gwf2h+q3V39Q+AI8vywmZ1ZnhRf6yOrja867tLbYHUua24F0ioyG6H9x20nKkBRAOo5jE1BbArNjnnI08PQCXuocLucqjRWc8dp2DeNMrsoqFteu93BiFN7rCGFoydZIPXeto3MdSHZakkxwTLJTee8gnko7EZr+RFbobQ2T/JOfeXPWvQmXIL8wsnIpOdCK8T/XRQuMhuYtDygcDvu1xNAOxtqCrPR2HNv6ICTgn2b/BZOT8zY8inCeCw+0Xjkrl9cON7gkOnzKY2Tb2164sofBDrwJYzumG//pw/3Wh4ObseMzkQ34toGoIquRMlQNQAhZ4AfY8EbooTg3DQ7CLDeTeY2Aiixc4vc6YgiWv9eJwQI02YWpi5rWwBmXoj+ERfkMqWkpwBUjEq93vZUqEzGPKLq44oX9IvEQ1RJt+Dsk3Lu6n1j9QCnbN28pktXO1R90lxpNZEWTmpW/AIC6zfgm6GctDChKHrtNZ7Pyvu63Df0FQMW3/yh7Qs9jaADqnKx55j4UPT9Pi4SYdqJ6rhP5gNQHVz4LrVMB5E2MWhl1KZlDl23TlH4aMd0Gpv1GyQJE31C6v2DvaeQiefbVjjsJv2xtFCKVkGJvj+TK0fNteq4lGCHAcWWecdxtWABXekrdj7AQFO603s8414kHzguq8H6YI8WwfP29haapWtMCPhX9996rR8d7BSM7NSBWNBBMPIsAAOGs07UvkIrRt2zNICepdwoPTn47I8XiR6H1fZZGomgwiyBDmPLYZh/yJqd8jvzDdYYFzgn8dxh89BvQN4m88iYO2f5bapP+YPQz6ucRIQppzcvl/1TQybsfjAtObmWR1udBZ99d3VT9k8oWPqRF20l/wlj3a71+ZgdHTyoJTq18JijuALHzEbHPNcoqseMKe91zkfi7dKF6UNiogPWLeK+olPsUDW7p4RY5S8hedPh8wf4NFhcalEhA4zZc24eXXLkq8D4BxD8UXfTsvqZIfiWzdmGVZWPHTfFni7nFt3PpgUyEYpV1YF63yYOqECfQx7jM+8CMcy+malvNPt5P0pKK+PTS4r/NUsZjKRuUYfn34l8KWsx+wtZjBEOlhJyFOB/DTPMYnbYcuu5eijNl9D6zPxaCLunC/cB//HWfYPIDvAe7L/OkRb/UE1lDwWHFm5H1BgoMimZDYHrDuLaC9KsmTNMsmppHzhXOHS/KdD6Nbfy8/AXK8sCrpjH0z/n0/hyHqfHJKzvoujJkShaZTXWrOhzaNEUsSfHYFXF87uUTdBxKkaif/iZOVrCat9ePVKiDKbYBeGKmMwCTRtzeU9p3UlLn7HpvyeqLftGnjF70oVeV6C1K8dAVsxkf1kxc4KmBy9jBcNXY7Kvw+c1dPnyPAdFGC+O40KHxzlFCDiY3xAvTz3kRVq8FioLXLMd/DnH1tSL//9uB+YbgIZG3mHlS7pOgcscoiBBZQY9JZIanarykN0ecAPyIOZQqi+utVJn3Ej3cmKzO2MntqrQye8FxjtKF0WfnAyZnhwC0YBMR/CtMjdc+048Ub1accAm/ULgNjmpCxNf+yabj76vMiWHTD/7Va4YJDBkee/hbEsvHUnKXsV3JEC/igLO9S/eMiWf7IrJLUn6l2kx0I8lK+fF/G9UP+vXBiefRyKs3r5woZdPzx50ZW5y9LzV//BTOevbIRqgL6ebc0j7RbuQjrhix/Y4MUig7w2wUXrNxKnwvBMr1wnhod2pKua6dvQ//kgcvnzfPbNVFVVRq2+lX2EVwqZpQ0QOMgtiuM93TCG4a95rrgvtJvqUk7as2sY4rz9fJdyVTic/J5W5jaJ3gJEglWLv7BlBulOj1+a2CrOzugO9P9eCPVVIg56qfBAIBhPtpcGUrpofy00VHI0FV5e64AS3t6vnC519j3bLbbwhzZu19S1fgbCCWudBoGwLMD1tkSOEz+tdoz8xWy9/HXlc03rrv7e1tcamEMr88FuLi7KbGcgV5d4mOwzilY8sv2bp5bzSPGzMkyVLj712/YS9WWd/yjqrL/BT5WSKBtkqh/scqcovU7sPx5IBK3JltuL4GY6kfQG2UjeB7Rd1Wj17C62aMTYk1kUpZZ0pWdTEVmjgvyt7vKnwz63PZZlEreYbbD2VyoQGx6ZyiQjjQOll7e1xs3L3/qF5T8hV1N9Ln+FIPam1pPwl++JEc1AzAHg8MZqyJ0wT8S+AJ4UpJ2jm6UDM7mG1a901WQA4XvBh+DoCTz+sC7Z1VeaEPFF3S4aGZSkKTs928CcS7bTCeP6EuXsY9zpqhhrrBIlUFuxRoVsI8cCrNfaJ9qlgA20pJFv7N4RlPoB32zP8xnvbChfVDwB4v11gtP1cblXOm3cfAJFNCuW81HMSNjOPwHmD9H3hhkkfT2du6sIJf8p8vOzBsHUG7T7JAKhkwCZzg37AgicqI9jkp3IMVlfowPC2diGnAh2yZvAGdMkunfRxRaue3KR8vzDrya8ek1HyXzIBQr15dYa+Jf4BEsK7tH2YcBATrmKaOhitUQuxVyfG0diEFyqPfXehCPh4b0/jXMYou/UgmQiU768fG0DBrdrsp8Q0Q9QT8hsW4ikhgBimLP1I+/ZhiLs07i6mQRUvFFvW7kk+4pMGT+mKj1ndNQxt2z5cCUPpYnd7XMbb/hcueZMI5HPFMplzaK2AFMKQcPz4oJSL6x5fuY5B4BuM37SJ/PBEHmd1c7rOElkMwD9VG2oxzRBoql9xFFs+T+6aSkcIAqS2fRxOHLs2e03WmlIUTYhW8fwz3kev4XrxIXVUsgLPxT9JrrfaCr9sT3A07LK00qEsyYb8UPc6OW7C/Z3+aVNODlucOpq0lIx9fiw9T9i9kt1dJ5x4p07tLjVObvCTOTIr7yCJawKAbrZkkwGoY7d3x1B8qogXvgUXV4qlVaNAD7gT0aQjLHNUp2+1JkjP26H0K4VY90IavXld/qQOMdtEnNs8M9o9Bf9/JlBJ/9MgFy+F2lPvqo8N4kM/YH5+VAVZvSv7o+DqyvrcPW+Ck9uWjYgx9ybnUnS1IBZES08lyPwaPNy7+NPXWz9YQtlfQozrReE5MGilE3ZoBppVxHCrBOa/jAl8HTw7wHfvC5+3EuvED1RO0T4bO3LkmDrk6qdRbwb3+9zyExRTTR6Fmn+QJrN1MiXytkgBCXKX90sAvMKKWUGj3HPBgHLXlW29Iq/HN+KD1HAKQX2Abr6kc4hupUPQ8jTKKYEB4xjLSyaIu97K8RxTw0H/lf/3yddYOS5e8CNh90Li+/NxxHfXEDoH0YeSjzqoYc0hl+4AnBLDtGpTHwgu+oNkYfGaDEFYv7aC3pblKe+qTZk7KwHml5bzhaX5ckiqMpghtHIYiF5DCQyokmvrYZFMpn+dnLblnAeQSMI3p3tBjbPY6zg7Hpcl6K0ephD63u/WNno56ZR/k6ZnDiq/5ezHrA5l+yws/jwnIQq9aKqrk0lfCO+B3pbZ56Mq+fyeCxXcBoUdGe5Hfrcgri1ulnX87PVIqQGlUk65OHXKTFFkiPgyJ9bNzCidIO2ep0bqsNUZfk0jrUxKbo9+J6AiC/R8LsxW/+oUSR5j9TEJA54BqvAfW/hGjpdyACAZS15t88P9YvBBgTcr9DtuXVsWQA3rB3kUaDMQYbQBKraVX4bKBNu65UdOEoVvHyUbs7o4hgoapFKU/2/mu06N51gj8hjNFka76sDYg/6hYL3zpKX/Yrr3c5OUBhDIlgYQqZvZYoUpqu6t9z84sJeoYDOTQZH7V7yI+Djx9Z49rUqnHC4ap/+OVOeve8XcHsYRQ0zxVWsRqj8zs9QVHdmGzzyUHvJY+4vLkMTvNyPZBGqYjbdiM0AWpEasybxlHx/tJbOuLnzljbrYmJuyXX9hGDmIeVSIfiQp86EuupkhsaZePPk9s1Vx77kGChohxxOySRW0AyKuM07SsnuznCMsF6vxPHs2vRJVBjyMPds0uGOVonsRFxF+blh3cKFxEIV4at0Ie1WsxGbiRNiot1jGfIvpBgt1DCq2DvGKN0VGJo2Vtb+n5vxHfzNe2iv6EqEHJLB2fcg2Qck5epiyNHsgxjJF2Pi3eB/hXqzae88lzRLCPQOEpv3wfMkGSeL2INJctoe8gNz+GzlXIvnXcHNb9AeUDKi5qh9USLGotEFxX8xRU+8QzpPu7WD75GKTFeu2kWURuuSg8p1mjH0gFfTTkqPnd6Lq1H8YuuTU+9kj224XGM1W1lRSagxmqLAMATqLiHLVEmVQbKqyZwukdIBdiVadfeLAKbhimL5k3K6MJ6LZ5doLtGYUGK121FPoWjoyhdW38HXJ7uYeJUh/sC2xXO5/usLUaWhDXWAlIhPx7oJMdU+1+Jyx6xySqTx6clwchsqHpPzDvRlKM8tDFZuB/stCxsFhaGrzRQmIli+2zaETvT5T7mtc0BS5MVanVTJYw9O+h9URc4sYN10ub8+VhwGU2VybKkvvfZbjPGXuEmhq80/GOzMc2a07AEL+hWSfHW7RtgVBmK8cUlm5YNiTZLDGJr3oEWAYy2IdCx2orddEBz41i9tnI8/zrr8snXXklChDnMgYY60PBSyHXYPjP1WuDTEUjAoP3KhMAMg5pztcDVP2EcEBpVFdLUWoBNC9Cs8IeQNqwARpmxL/vpTBjdzGugiWxa9RUp1+vDCYVrjx+9cvL2AHVnYnpE7Z8UUvK1Qe8oTSniajsP1sl8W/isZiSjb9aCiVnKuHRnDbjbPwWFLRHrQqIwluvlueacmIxMkxdq/gUch2d5gEhVqnsMegJ1l+U4rzuhDRg6vVP0inS5AtCtudCMD4BIItN05SFjYszFwsMuV35jcyV4xgscFcJmeYUOk/CkoJWG0mEyFM+5pXb3fqreWHyGReYD1EmKyRdxW/MKvuYfaQkC/V4V5t1mz4r4riReP4Z8V6V45oPUvikGzCmB5YOop8W6PPMWFiI4bOhRRyO+SUllWHuY1EuTtlQqdKA4hFkz+w6InD27W5K0MaXXjGafH3g56mYK6D3RH7lE2OOzV2Fd3RKdTfjHoigDHUzpQ32XelWJSNJrD4NJt2ZpKIHf+0Sq30m+hNbu7r3LkaTY7dKdy4x6jiGA5Di1CkOgZXn4dYkm4eZsqQj3DYvjg8jo8nN689CQ0Z3KexyMGnsy+a4IqiyujZuk5HXKJ+wiCJ4l/f/9/9SgAgv020LMiPNIw5BkqUBmOLp0WTrALTH2wu1PDbvrLCOcyrFqGUWpmQDkSeC+fe312DW5CvmlgE2Az77NzMyJES3INslIvPgjpQ/XkqDSjo2DQQJDxVlScKavuaUzW4p8tQB33Qw1ZlV8r7On4fFqAkJN0v60uiOFWTy+kFosR8zMtvAhgJVIWVCVoWwWd3MHBT3F636QH/0DG5RncWgPU3s5wa066/S6hrJPE49V5+jyW1HtO/lZNAOB54W7bXvA72MJfftsA0FfF37ym0M+/ZIovXlY+q+/GiSwDSnRUDdESe757wogAL5fM2+WmKAh+PzsacgaD/XSMsFKVs6wHn3ONEX80flP2keLvTGkIygBt3KDYansEDFtDi+MTKhEN+Yygve9/Z6ZeOE3Lc4eR+7kQOcgTKZn8vqL282C6gfO4py7iPMZvqY2qsydjWeV3IxsJ0jWGmeigKwTa7odHNmdVOBi52+URPxwwbHyE35qAsrJ84GPf4Nu1NqwsuHx6HXjs9+EqOQFvecPMLurIYYTCVy0OIxuwixiZlNzFGy8AWQ61JTJbc45fU3PjBckcfyNSIwkBbsukHktbdXGn8Bx33+pTA5eAVgBZ6heyLtV9QdtaLq4zhP4un/dVV/Ws43Tu7DSSor8X5JtYi6brE9LbBHDEpx1s1Kjhvcvl8Ofj0xa89faju3WokjnWYR/otb2KEio2NoMUtFr7noPmacxKZzvxBjrukl0IAy1+s/lyMs51xKr1lc7lGz/r7dQgOIR9Hm+cUzLhZfnH26OBYBFtNiFpuC0t9/9/qOovJivczIXVU88hbYVLgakesgNYAs85xNqB8A3VeN5MPc/DTr5wF2mkncWOaGg4Pt3xdoVD6kC2by1cRMZtFsdrM3oOvOHDnG/NoF//eVMh+jfqF2aaDClWFAId+C0teEpZeIKzzBUPpNMGwgG1tLhMW9CEgUfp7pauxYUADzyM7mJ4ZGXVJmctvw0zWSk6RcGFvPNFZDFe+z6TQER+LKtCrLGoas72cz5gbSMcARbsXqjxGDaX38Q5ZE8+vnAoMybT5G/zRPKGK8PaKoGNv3hH5fmhz3PYchAT4sR/yZMCTZY4cZ8Bpwr0LbZSnjTLzIdAClZM9TPJS4d/6F48ZcfsAI9aCJVkdiGy5e6X6p+ewfsWajIwq0s8z+jZE+NUhWi1akTsPHx5VTdqnYKTCEE7S27SLEvbf5FCdEhY0/zjYSTcCeNO05MwhTJHNmKqJBZ1OdFn0BklnMKFXmtD7lQR7N2WU83aZmDxYd2l9C518+HxHk7q9L7lyq6dTQ9/NHfW5D/cjfA59Wqo+jjxa5t3rLDECEhfYYyYuq/QThmyFALrD2XHPmlJlxAixOKZ3342ovTAMGC6K87ZZK/vBJX29gGjYPMmCxrY3gRf5G6wTKRHc70mbGt8ZDtR/A+58BoLGnSoJWQCltxT8ryD2PXhrAwxWKlaFMllQ9TsxWM99BDrhTOFP4Rs7hAOeXyDNaudLpD8Ap77GIXuPKfnzDhIKSfWhcUroHSAhGqKj84xDn2dx062OS/SE3CO3vntXdhQn6wlSy/mDmFyU+EnEvzG77vORARDjOPzxV+gSQghjXHyMS+kzoSu5sHr5vSkWaFkNkTHR9DjtwMn8rCyJlpXXWZR8f1RZQ39bhDkTRV1CSPAqxa4/2IHZgdkyL7bGifddnsdP4dCZ4LsD0Baor9rL8bgGBSxmuA+fhhTmkt8qTKESKG89XO4ofRWLfV/zlHRyiaSMjJgSqJNvtpYKK1qB7gJsolHduEbXF/MCi8HVS7LLGOkOzSEPmbjfKqJNzlN0l5dNESgXg966Js23V60w/JBWW83PUu1Y8HxowXZcfinN43gzUpzVcJ/prutKm2gAx/mBzMjHUeyi0VLrrw1fbXv/n9wiWOo0D2yJD90T6sE1SsK0uwcen0yMOdr1aZoDyVMrnOX8uKpaU4+mFAdPuO/vcNBHX18IKNpiV8F7mYOZ4vuJxBOF6L8o6hXfSSuHhazLaUkdwN61Wq4B8SLrIkC1jWaBupjV3WTWU1e7H3zFp9iUFLikeJYWOv879DoB+OSr/GAkq3H7IAMnf5/k4DwB81fULas4SsFF9u8OPMR+48iIGA+EAodI03JUCGiXEW7/Hw6Bw4qxh4k+hzIMc345esn+FPJtOQb8LVCeoADemHOphqnj9zxXyE+etbYvPpoZIvk7D7qZbxRxaVCp2elEbiRyuwqeVlpetL+Cy3zccDWGJ9BDf4NU8fCo1e9wOvVV3tWznc1qM2XrUXRpDkiGob6fhwuECRTX6E8IpNsL+nqNhKv0sw8yLngo4Ep/TSX3MQNHdnxq3XBtZ5qT9tvBTM6tdL3P3e30kkh/cH4XQzeooaoFDV/As/U7mB/Y19j0xGT3ZHM74QyNJl5yv5HqOleNYWM/Sfh33y7tTY8QfsBHfleiZi5utjPGMow9/lhkDhK6pxzpiAW+CCqKiBDiodgS0ro18z690l96nwZU1sS5444vHJBvxWpT2+mk8fWUSKkKIcjPj1cPO0X9e5DGlbVN8TbI+I2wowkvwltCnvzKDOhoWCeindYrYYnPg8LdHpv598IPtP5jLCdCMt+hjOaqsV980eswu/vOmyD5J0uKTzuL8AlLXkf9ysEJcS1Xf/qK3FZ0csEU8t1kQVx3AREJmIulnN6Xu7VAbVYfKvvXdQESoyBqfVXO/Gn1PBGCNYAvyrHu4wWdgnITDZNl0TbwwzwXcJnrL8SR5SxRpKQkDHX3Y0C69mG8cNrSW7eAhOZNT0ImX2sbz5yyq/Nh2mkn/qTQ12TWHp7x9QPghY+5v0SnRm8WuX49uqWVDkoXke2h6a4xU76Tr8yWTRl4i6FMQkRBqPRvWgo7s3SrUWa2oKG4B8ZUhaoULNH7kWFxJHcgP2Unw4LPY4OphGcFvgjqQihweySQ2SedMLxMK4XNIGokuDr5ULnCR91qVMRmrqj95XCkGDS7GWL1IRVdegmVn1WueAKiTwbGzhyJFdKmcAwUkjx0XSvSDBex5rKBS2aauXIi41v6o2v0S9lQaKeWZ/kJ8Pe32/ElIWyMT4claD+dl0DXypMY8vVycoXbjY76WsyYWYgO3edz5vDBUuNknJ4410RtrtXPvb2xB7ZF1R1am/IvSz558feGa92VxJXFICmeIAM88kQF5YwhdXzWFyWVL8lNnfvxgPeHtun5HqzmBP3luAdL6+nJOtdH/hWbdPhwuMg50zrDuQfG1/W+XRvVX3ccFvjRaTkLjDQvu9g8JQnb4qibWrhhjcj+J+T/NKn1E7+elnx4+0ai4Je9pofG2wVZ3Wj+rPqhHUrfsSVnIfNfwX82ORlcxas8hY7KhfVxe8nkPat6429/5kunm0xrJn6TLEpt0CSS+ezN6lfI/k1wYrnJ8xHXx+zJXwzBdNDzAGGY5BCKNLJ7iN0tkyitZ7eszWQIC4P14gENsFik0vYi61OtBA1Od6Poj71/SAfkZUoHO2ME35e4+uHK2apH9wK/V88hFHkqXls1J//JPNL3H5CAcrk/hav8axuT2yafr2LDjogag5IyYjonq364rN12moKswDqs+0k7JiKkgSO/X6SdEF3n9iU0j6Xz9UoFtWYWy1ZKLe15LLEjwUnH46yqrBmDAcQ6E/QVUtwCIyZXqkoHj75U7gZuN9sFr9dlLjdK3j/DGuLatlBlFiS5fxxVqJEC8kLSnCOiw5HA6hiqkcgqKI8VMcimKRrRCw5HljkdUpZmqhsc6j9PcThH1zjiZ9mwdxisrGBvTZC0q9u8jL3RXbHLL+kSbb2jUCF/H1s+XGZ3XXm/YN4x9dfJflFtcVjf4iKugznlTalqaMdc7J9t24oJiUi97s2OF845gMmREqFq8Zh8dM+jIrBPiR6l9XmLxu/FMGCSL9mJ1wg9imQ2MCdrIOt86bdm8KNrH5q+IM4OCYCgVUksXje1nvT575N+D+1cqqDG6F/FLnBVvzkxxBlIZFH3ib7ZNA1niUWS4/Jmlpcvx1OeNxjZn1g9EzupVs5aKx+yv+s5E8b2LP4gqBo2D9uvSybNpj9Hgk+RvnuhhxtmAIo5ZPy6oREqheD87i1zvVR9nVLgrxBgPAZmO0UCZox7BtlrxA+WWfvZygFqyoOb5E9wX+K5X3TNql0vrYC6MlIighYEfJVBnvY4pon1TZy2FvTR5rgmTCAlLKX/TZrTI4hKM6NiIMyZsKf9T9l4twyj3nMs/t3k8JtqqBbCsuaTttAVjXcm43QxaStksZ1WvL76CFVPVUgUa7eZsgTpbGnnXUk7vGPzF5zNd33drhZZbO+dq7Hyf/JXwpS2899M3CxCSVK07wGFR6lpSX5YF/O6fN5CtS7INvkbGelpP7Iv59ZLyoZOoAMu/YHWIDpWyhuM/LAticp/p1X/lDSz1i1nfL7cxobItwKiEqDjLUx6glZbf1xfzt9VqvpSbwzDYXeqQidPkmXL/GQES5ryxpHWJ0pyC2btr4TKIdEate/6hpJD4fpwOAGflC5dp8vXmJIcXPgM04FI6wwC103pOfx92QDw5WPAHJoKzsB6FJpEcH9rAhtiSFS5dpfzgco/7O4H8Y1KuVtXWqkJZBkBw/v/MOHGVi3/CFzj9XPmXSRbym3HXh9xdp/MsoAg9t97X/Fa8hpTIfQtHPX3DfxGWKh4ARQyz2+VRuLXKH8wmr33G2P/92nibz1Lw8QFqju7HtRX8i1s5V/4HLMP5oySMZLxIAwdFwEn/josSI/EjptyH2aAJHlqAG4nf33VwJ2XQTvojvTcSHs8nuIQlJgKDfxW/S5SHwMR1tprjUusSuKsOlUAAPUvZvvquSWNiRxoL1894dp/S4XCF0fPHGRNoIxInH9YmHkPbdP8AGhbyQTCr/JTCP/TgWmTDLVpPr5YmfFQeYxuNHCkshDGBnY0ADEg2eov38zs1bHl9/jrE6we/OZkPHdA6BRwEKzZAF/+o0WE54GX0RqIVNllBgy/D6FVENqcZHiJLfSs4n3P2OglzbrNWg+rWX0QMrxtSvRuzRZDKUHhJCE3kbVzYDeA+/9maPhVDSoKTVqBNTErPH/+q4bI4Ml85xMhI3bjengk9HTlMK4I3qFXqaNupGNJ/9oiYk17WX33E++EqSh6gnz85Y13L5srBpJSCfz0Oym5y1zlhqUdGzZj0Vvq5KLlVQLhDa7fLdhhHavpPbbb5IZiMr6Loz+vXJtpoV9Zx0/ix7NlWsp1cly+GlRyYY3pIcjo/Dp7MMRz+1vnIa45DylW9qbjjM0g/V95Rrx0S0MeyqKeeFJdFlc9d8WJIgT9gexiARYjc/5dlliSwD4TCRj+A3wX8zz/N/S+93VnOJzyAia9vXse4V1NXSTfxkmJJ71OuFsjKKHd4W5a9D60B/ZKdpZDMmJtW5tOAeAXvrwfFgSplY6GrGXmuIZBk8gEiybkRjNrUSExU4BNJByuJbIy9aJ88rMHROsPMgVy7A0aSLHnN6EXEmX/nT1Yk/XcdXn9+JbY1u//XD1Ljs+Hx/RoVW/Z1xJJm6BAze5bzl5K+Jz4pHrb62EVUeI11RVgxeU4btcPu88O7zHc8v6TazZdGXb7m20/I75EAPHw6gwrHXnGtDjk0q0CGGcYlXMksnXWaLVvuBVwcLA7u02ZAxW/zLiVkBol2W1SVfdL2qh5WvzCx04zasc3R+3DMaxO5+zS7ezgcMUWPbr5SZKj/Bs/9XXWZq3Je6w2Fr8k4sWmSpQzmNrEtoLGqHz+g/wZc2Mc6zM4bMXkJURZWNKtwNKyXpO4d5vaz3liydEb7Ich8LyibhGYTTic8W/VOw85yycGTa0tcCCT5NQv+L7R/NUL3UWWFRv2doBxetj8hJX5EAtQVF40AQoaQmc4Vo5+gkxn0aFmwJ6ea6mHfW38Sp/fGIinbCKu3VLYx/cICs7DpxDA6c1K7JWb3n0y8a5+WZj5K4SVUghnSdotxAZe2CI36hKLQtTCoVXLtN5ps5xi0f4xc66YgHEodOeDp0jRU4Xjg15yuDYZVr6VSoLEOBv+0M501Ev7C/UYGJkugJ7xuivJ3Eyhxudb93mfOCC4DBwYsp+PaCfy6IyszEocnWTF2Zu5qMBYg6eSqjS8VmFitbn0ZSCo2M/NZ0xNzEYRPe49tB1bYqo1yH1f2v9pe26E5tgpO8Huii+Q0rHZmvJddzk6TJSgu65M8p3JD+RF/Yl7fjxm/554YOaYz5S6KcxABW9LEmHyS8bRopQMDyZo/CBiJJ7rwpmjc70ltK3UWIgUiec9kuJ2GhU2Hj4fiU9tdpG+gqLFkzkS8vsRxiW1hwHzFqHktSa2wZGeJvyFkuYfQvlfDlRykCcEfoHzO1lYgUKeVYDjlhPq0qH2jjmlIXE3WxD8UyZ4Bet7X9Wh6THkyep0jNB5p63gTx5W0OIRXyy2i16kAeKWPG6TlLWTSBNGLyqB+yKnwOHPZsuMQskSWnFGcTP58ft79ksLnkTzR77TYYBbv+lad98R79i5k23iHp5zqKfSPn9vsPAhe1PNClA+B2cq9l8CDNi5iHQ1PRv4SO8AeP7WhZjWk1Z9q2lfJT/mI7HnCy1MbrXUUQVcZWcylODMPYDKs6ggha6LNpBeDl0yaiO/FnkoM/eeYLMdR+okAGn5h0gd7v2ZJAXts+aUgqdWkSeBpeX6bTD5iYmYws2Aj1QENHPXPXyg7k56/2jvtw5TZTPFe7+H9pF09RMq1kkOP33Cxebb5PKWsgx5LHYOWMy96N5k/rdrEdxVJg8hy5IplMy+R/AaFVCpxEytgPaJT+N4YCF8rhTgx5CclLqcLMZxAZNer5qYjyT+EhY92KhIzenbMmvmZ/tX8h3M/5BclkgUZkPEunv3OIzhJCvNFEUXzhD0wmB5XnCRJlSMv54+5qGsZoRHw/tEW1c9A8CbwkjXA4qtbuSJIREc1nokmHI0fq8Uw5AghTaGvUdCu1Qz0oUWc1XinPFOnzcZiDvylUiO/RSTaZJMU3RI8HhM6vAgUtOZFIrBFlsfeXGxWvtrA2EaQXc8EWv2foRgfqzRGBbZh7lERt4gT1sjTGFckAOUmiyd3mLkCsUH52VfGfoIlOLZ8hD+l+st3R+d/kAAbmPnVu41MUlp55oqRo0wdO+YI7S1uwb6/DN2WhspICRwW3odPL7H8k4UELxtfoulTMEG9s89eeJ88/gWBQ53vtvZIESKTgmPYU8VRG7K6KyeFCpNmQZ69Cs5PqbTeGaHU00hdaHCexw9OP347S1K+R9YvmE4IvFfLjIp1K5qeZ9ieM2pqhP2BsLL642L2mmTno5XVztHpsZ9vb9q6iRBX61FdSgB/HysABXU9YlvW6hh2973V9i6wEALvmnlVyX+quY18kPoFBgrcPOn8UT76+t4MRoDqBPHS8uJ5xsGm/2ohZnNOSDCVmevIjgqcZiAaUOMEmIyXCkfb1CUgZ16TLDXUuSKRJAvA3YdZgR5hlJp1a5rdCrrCK6Jd8pJ9kH67waT05cxxMRtKkyKIWQHapAf9VsHrkaIiFq1/zhToDrfKtU72BF2vgbPmvh6CLtefLge1/yQACdiTA5hkcznR4S+CW7YQbZz+ZpKqMm+zxa0Xk+XWa/vK0ITtAycH5F/1U8OP26Wx2kFCY/MQhT8gjGHgGqz69TRquRX/ewwGug/Q4guTVjjo8nv/XJ3ys3GJFcGtubXVBi+BUDvg04GjGQ8LZFPzTznT0+wnx4EXCp6otOg8L1fiLVvCKbvK2kWpGHmBlJIXXTnRCmlHf0690FA/b8sV7ppiMxWZpq3n9JWeFNoIz4PPZlQCilC2Qcult4+VrlK8y0A3pQRpF3sEHF1+xDiKwU/rquIa+qHIm500AmojGGEIRFA8BR39jJv3+omBWl1cZzdxvXMqwp4v0ISCaus+iXbbHlILvvs/qrEaF8njXPzPi4cW4ri4LbDYc84yZ2+rPOl26FQLKfEZiVT6HRVnnj6eMpZmzEdmhoQL8gsVXpfaJjQ5mAlVT8V1vM17vAncFst4ssjh2JoRKctiP9QFo6QFwHSWr4N3TBSryP4XUIMpez7vAlJHGnITVexAODGtk12sst5elvt/vjLI8qdLyEUdnlSLF5/RCkTH6NJYaSrVANwXYMuyr0OZO/CYO3R1+a7WQUW32eOcDi3HcnaShHnZrX1Txk16j4qDwrSufCD2eUvkYCH+j5T22n+W1/tXdf6GvqGLTn2/VusLmjyEGd5MFo6huz7s5+Qjl7HNa2LlnE2o5MWiDVKKxe/Bxf8m1f2wl8dRUJGNCxP/80hY1KISrvg+am7rpOAzKEqRXizC2/PD/UJBVKf9l08iHw6cv8VB2XFhU4I2sfnl9B/CCPvt6C8nCO1tj5Dki/YtYNv0X7HPFaAZAIgiK9pJEw+Ck2V7mXkFhfPPHHx9YjH1dN5IS8vg/VVmV0xM4tO9F5Yj5HkdsziQ8FIoUSEIlBY2T67jlu4pRDVTTicpCX/mK764aBv2Hbl5wWGszJYGvAyv20wnmIGEAFUglrmgSPmpw2OlrOlD/hHD6EfNFugpcQvBIRNPtJvlOyg7hJSOpdANfkGZetIoznoWTju3D76+6D4HTCwOKjAJeLPkA7sjqdhQ8UGnJgn9VFxUs7niAO2JvBFrtmLm82MNa9Dh4kGdpYVbENOQ6vGIjz4bTacKElHRwZOMun9Cyh9s6tbSypAdI4iRkWKYzfl3pgIeC6V1hmO68L0VD/CtRBaEn96miGn5xlVGiGCSH8RHr+dEz+fW6JyN1jNi4H3IZ67Iytc8azu1Qt2i5ZRdJWRhQKeS1LlD4mqDDDkNt/8K/WlEvBi9pc4TcWgEaQid/VuEbY23naVT7Q6UFk0B5sCy3qgmdSQaTtgWTxDK44n3nWWe6B2vQv9sWgMmFw12cfPUIUDW6JXVMqWrSkaBEJu4rG2upfrofUFHMUleZRUPLNAPrrL2/+BmfcPOQwsDHCO6b/aTMCzmqQR35qEMpTFWtAVtrFO7NQz0++X0XWtIsWkBj7c6oL1D1+Zkxf0G/hMs4hSI6UI1eExYQwTwKjt2rowWg0TAXgFyvgRrTXy3BOpP2hWA1C454GD7KM6XRtdde3Oj6TM0Un42Qjx9mlz1X/tpFi05MNzB6GVCz6YOnMTRkmQYPCf86k6NZ6CCQ9OBMrO64c8lXUzC61hKl2vA6jAfmbbSrKGCKPbQWLKro9QNwB/e17nZWLYxXqO2lVGvNuvylXBXUzbMh4cclKbiU2B97KcZvIUjeOGRnyfp/rCYqS1QzOK8gmcrl/AxV+yG7K8EqaZu5rnMAFyaTDAbO5gv+/gqi1zyNVXjyD9zWNnO8NqT70jfxK3GEIpPFctTOtJUP2K6pNVpAwGWOevGRZiaOi7Ot2X2qMRP9mICspo4+rMIaLzEo+TjNcFn/yaSyGYnz+tgmJrSSCGUALfEeCGp4INMdvPzF+DBKu5fXMbw0t3osZJrUAGZCoJHyfG9vuiHj4M6fs0g9788r6lhCrXNhqF05qxOG+AjfBUZhiiaP5OmJcIeB1x0w75Sry+ML76cLtSecuyHzf7MVl4jx8+F5ScxALz2oRLiav0nSMxQN3zN/FyN/tk0kVA0LMf4cuuw33g6bPv/w+iqdUIHtFj6UF6HwW9AIj5TDldWFvnDgwmpkln9fhfPa5YgC2CO/PtlfNswD41aDwAz0RaMOmhrnbyrricns2jUIjcyQ1vX7JwtpJtDnvZWEMSjwVTVnV8c8L5NDEzNarW6+UXKU/I3FIKOSKJUTr7J7lJt3iYhvayR9/tzaGBh8Kb3Is6Q85K+tU/pBaSoERy4TGh7aMu5/LF3FtqtYFPyanuMyxCV4IMgMdwkOX9+c3LdWy+q+7ybIPrWrtsr4Z9gmrMUwmk3wQ6f2h1Mihe/LGTWuClFsLwozDbhSdU9q1HFwo64wgW7oAHgZUR2wdHl8FivPrdd8m3a8SuAnq50Mv3hTjMuXVB5Mj2ULVXmuoLj13N0F0j+s9YNQ57k7jUFqoXk0lE6IB1Hedyc0Y5Jd+1q/awHJE42EdYnOeE3dsZw6dvmtgCyndjRmyI1Gygas5kkS+j55/oZbIYUvlAyddKIp2htFxHgjhfmC1tjtnq9oZM8dmaYWMGRqeVPStuFO1yv2LthUpPrd44lAGLkZZsZFZ5UsJOKa2/dLw+9oF1z0sznGcVHvpOTTKx/vljcgpwtAWlHrqvdiGA5rbWLoEK/4PBKzbXL6oRWmgmyFFw/Ae+aNLPDzxI/wmX29DHcoLe/fHgYpqNbWRfoICeGYB7V3228jDJhKbPz1WwR2FOjydp/TxrjYHWxROC2psPxz7sNM7wCLqwrZBEtuMvZ1r5au9/awZahK4xYsAIGDKDtbjSJtdSA9q1OyEnPYFpQ0lQ47uL6QVUEF8bfaBk9oiFjEpP3UY660E38Z6klMpEi/YqtpE5coUIvHRK61V3xsYvVc4ktDx9h7ftqfS3s1XV1B2Z02XBjnd0O5BDfe/uZiaEHiF2u+b5Um8YIDMaZv2F4M+XJari9qSayNMm/9jJh43L/mxCcBj/gtBC1qzr0PnDVVndmXMwqVt+NAmzRPOS+aJlD6kQvy4MiDpCHtvESR0YkTiZ63DA3PbZLiGSJSfFGLXM8yTv227NAGPYUHiM/dfj/6PoyQ7KGnBYwVezJcB8O96zGtM7JjxYD0x6OKAVblSc7cCaV4ysmbSlMcd+AFXM0KoNs8HDX8heGsk4YG/mufWBQPCk0tM44eH95vkc2JUCPF1oXpbCkoO4sXTCEvMyTE/NLegYFP5detDd/6JPJvcKWsflcHbxrtTAoJWgd3PLUjVbbM46DT9r1+EU+sZE/myqpCQXeSzSAzCxr9btTIGltVaOpgOziXN4vb/dVIpy401Dvbhw8cQWKxfYSaxZ/PJurAQEMIxyLset16oglRhsWVCQXE3DgmMaPW/Su3DDCZweni3BRVn+7Wnwffd2nsCCp2nOnrY82YYiEyyIBn5UsBxDTg+1DQJ5HfVyFGR8bHjBiDq3lnLMQs0IrNRlkO8s2WTfRxEVEfPq75rlM9bzQaI2wqU6QGMvePgqdEr9EEAwIZzM2ZtJm8f/3QFFm61k0+civVJ8qK3+j38bNiMhQBfkPDt57RDOiWUsihOEM/+HnSXFqLb5X/dsAQhC5/ONKUKYgAYctddfc+/DZMni5CvMwiMRNdxDVxtqEp0vc4fH/zutfzA1UMBUZCkSQJbCmyRflaN40pX6BSBq36utt+cKKQvGAxvVc5zvZ4Uq629fl+NaW6mYTvCix9/LEz9yGnVpBWB7hYS2TOOnSdGFB57b45+4GHNzVj7XTU11I8Rge6ZvyRIuWrAGEoDfog7ldLdvjqDEmpXmAjytl+1jYp17tv7BlkZhasWvfd0wwFUCcWbUL26sbAxia+FVhx4fXsrS/65VqZ5LI01UHiab3R97d9jh55i59e+8mIVLA+39c889XYpIElyBREzwomw/xZD25gGGQfsDNWIkLmIG2kiqQUiwfJopsnWQztRlb0VYNZfZsarPp5EcApKW5pvAFHLVN3kToodxwCBIs58eLu9wGJ+sNmFefTv1FQTAnkTOV4KI5+29+EN+DhQfSrLX/t6hNrfYOEVfGJYPn5XyyN8zyUd0Al3pQ//O48TXdUzO8HNJzm/iD3UUHfbQmQNg6Cwhz3qZ7oNbTgK6j6tw+qSYkqy9U7hE8QbAi6z7/RmWIQkMEB68TuJ998OJoo8uOERtGC78pYQyk2W0bhPpPzBX4xT5MEL5rl2NWjFq5f5VzhP0IJML4HoXAcDSMfu04KHKO7CLBuMWYF52fTos+DXpahGQvUv5k7BjqYJqpDh2opzIYKhC7meO/JAdErMkK/Ez/Y7GNuZR3wX8XwTpfUCl7X5tdN5coiYsiSAh6jggVA59se5Kx069CUxi35HA2DDNQUwQ9uqGCMCmM7qr8LWALq6ED1XNXL0S/5SatsNJMb8njX/0BlnpyCJ8O3yusgy+8WlTefiuGvVHcooktq+ci0aZG0y+Bl5uMVMXntUTPeHtICKibnCkQmbqO1PC3L74fPHCMGEFdzAgNrf0PvfmPDli/6vg80RY7yXXb31ifmo3nm1vDmhr0/ST2s11fIg1eycn1oYZXE5f0lcqmh4R608BM/B540tBXTQyhitevdsst04OAk4yCWAHgy60oMoGOmUlrHx7lY+MuigtQq1v4Wp1etUWrPmBWuKg46Alc+J3En2c757jvIVKCoCne4NLnpnoVEjYMYF2I2eog2wHvw/sVxyWzLJJG28UrbYmAyatZ08AVOdAg9a2a+ru45qhw+etntbyEZP0oJdWEqvSc8QqOCjHzeJNkb3AXCActSlPYeSFYizB933Jz6DX4LxNCZSoEfEW9r3YmifkzRBQvh5ocHv9Z83miYNQFEIql8KiWGwgU69th71ehPZFsvSrNqNvKCDop5dSUeBXY/polSc7dZICdw7O2Og7GubMBk3Fyf38cdw6OEvSSq/MwPGVtort2FXxxvB/2v1HjVkSBrVZNdGyQ727ekd+xbssEn2Id+6TQ9GuPWq/yoneWYNi+2h5Jg/ZXBSNsbzfKgyH6zPajbO4EMUCioiBoM+0CJZTZ+sHaE/Ys6RcXw5t2t0APBal3AN8wbcCGmbNzdGXYEq1370QY3nBr1NwcJNQgi4kuRyQeCvw9HEufs+8LKIrinOJTtAy+oRK6JQRMmDEAJAlEYIZEOop90YoXYrMf+DYjYZX4z7orDUCxEAscPjqgNCJWE3wFGDO+KxRLtO3yMvj4VCV3U9Otyo1d8vbX2lIoJhxhhv3eBJwMEE1/Kb/2qfXf82VoVqtXSFPfv0jWDrvnIBlUt6afe1ils9mnGQp/Hmor0wiCW2smTvzlrwl9SQyLjS3kmdOOBA8uscBBXvoqjgHUErgbYAV6Q0KboxAmzqcTROT/qHi13q0iZePmglnH3aiqdvY7x8+uEmyLENPBLo0JOTNLuHh1/P8gebf3Di79cvt3wuhnwIhlHmGQZPwB4rkqrKZDfJEWB6fn7XtG80o7QXmRTZg29AhEutu39D+TIURzp7/hTuqIW+XthLO4nBECAEN+Jo8JUxX3f2HlloPvPa5eWbHLE7IbU9Abv51u7Ab1h43yDipHmXt9q5DdH7Q7tge7TboXp+UDfr6ARQX+0EcNgJ1CQu8Rb+hw4XYigR6I5a7ubho0mm/tOA5h3WhWo4ObVC+iMQ6qHAXwchQA/aVm+9Nu6I1u0QaAiB8ee3oMtFcyzaeTxBPICRDlxDgSqwHWipjnefYqraYIEQooE6mnNgL2j2n5OVrnYIvWoiZYA2q9sBpEtxlAucsYoF0APQNuFqLWmAWWPmCbAzfZRgBsorc022Fwvkqks4OShMMFwbufjeMD/200bqX/z+VEhFlMCTNzznmMjZCZqOd/y8SiA97LG1SdFmpXgT5KrjtZSgTafwRagaeAd/A5yOKoMy85TDQJlACCvkE26v1lVAYlZpfVgrJp4juDDeCtkUMU+8xG76APp69ZFD53cYTwwS1pKeQAn1hWrlQHtmeNKhprOo6Qguo1vuz3BaCRX5ojS0OPTD+sPs1lEQ7KEMKCkvMleZ4a74wy2+YIxoixEvPlcnTMs6PdxGHZXGIJlUzS1nVhkwNkFp8WBWlq1hcxJv48lxPehboyetrL0QLb38eqTm6qb9FLM1NS7UL7LBX4XcyOcdIkWuiD85nxYyk081+BPrZd65IT/Xdti8smmZh40l6/FRtOgSyik+BX5crpUnPY4UPPi3arU9iO3ILvuTIIgX5+DEYEBGbQYJNU05DEY4c7uUhK6UgHGG9whMUSs+WsYCgfXoRGb6ndZ4ozzgTRrSyQ/N3mh+Nyj5BMV0WPgWxEXvhGO7lsq+G0reP52BuydihzlPqRyA3aXfkNevbhaLVrrIo/U6IfapUuQsSqSi4nG4eG4nlNY1wlxnwtAnjiq0/yWq6KJSj+Dvq6F7Pz2uX1ppC9mKwYsf7uv/OGDkA35w47+Am2Pr/gsfoFjxks7DL1PdaQqySKmIReBH0CsPJekWFBpJn6vX9qHTz9O5Gna1RK3mHzJg5fcEGRzMw5UvcO12UrMr6Dr+31DdDHultEUnVXiePONZJ0mQmhmqSEsSNfLygk6KihfoFiuTfhQPusvmytg+LiQ6TzkvjLdyrQ+L3mjMY9X3bnmAWW58Jj0bix06r4oCVujEexBWsuVYog+jILkNQUw6OVFuzhfLVY5o830pfKzSSdTmRpacZxeB56RSoQP7f32vgyU07VJ623ib1eKgixGpu8a+e2cy3fEOlWGiNILVMGS2QQAVCT3pSQijrjMdwmIQajfpLyf/nVfI8OfoQgPuN5aYOZNsqsJGjBV5/aKQtrtORV2rncRIjyKQ8QphaBCYA+5hIHtFyxj3Ur2nGgrMJHFI1V4+1itVa5+POkugA981LAiKyyh8EoGi5ZToAWK1nnap8rGghPiHd65NdsiqXf3t5+hFxNWuI8hloJPmY+CiLSfkqi2mEJuut2gg+YpNugLsrQ+WeTSSTrwU0TF8b7zQ55gQ0sCAcXQTD6ZTKfdjyeuKeAaxTyPlgjmfV7714IjgrjBBniCXbhg1ROb1wxZRm6Zezq54dZBb67sID+mh4PuvAETUG0AVJD14+M9+GdvEUMlXqDiYPQ19qP03FcJ8SV/dwCk3HYqtU8XUVlcGwyh9rkHkh7ctHti4qRNSG4py/zSJtQDN41H0OFSJii4OqMJgLspjYtvHwPCGL3Koz76Omlffdc6Zk2ttYxxCrpvFKIKLQIv4nD8d+zwzM09t+DphG9K9P62lh/+SZPn3es+0eMHu6I6lXk4jQaa7IIDvQzjusIFSRboTiJ1W7Tyl4XSPb3BB4OCGwoUgYnVzRPHW5ypO39Eun+wF3UPL/YRzJeo/ytkqiFsZ5uYy05IvtX3K69XnWoqcfWMjPCGRT+VF8B7bX0R4MEmgt0qCCU2lgHv1rBiWB5J45oQvcSSddan7eu5lqgoLvWlT6sckd07IpCAxq3RVI1rnKAJZFAMGwKVJOIAxTGqxQpecmtBXgEqRbM1eMkAwVU/MGlYarZK8tL1iLauFWg8eqTwq9IeV0Jr3pLnnQI63cVTNLUtVYvjpbjwcw+fncne/G/tl8dAangH7gtHz+Pl8w35EjQ3jetFkcUWCAWqk8vcaW35nucAE/3+HJBz8x7S1tBTOr9+BTk8N00KKdbA4+OG11rf0ZJipYbeCYc301bARy0N8q+gQZgW6p6cLBqZ4H3nHd4cIFifDTs5OiAnXfAmTWfXCzGYiIta4VbVS0dmeGmkMbNx8sw+Vy5OBebsNwRsll/e9P2+kfU2u9/uGIXzWRD7/lVkRUsSaJesrUlD3v6j/m6NAQ03WgRFt8k4X2hpKY/LA3LaMmxr32q1VmPzm89Zj3+WoLL0nPzmEIFnNpClEGYdpv663ZYeOV6U3BY5VQUL+lVwpBsS4qO149xW8lf9ULHeQxXynYFXKqz6av2VdJP2Qvz9W4C+RtGK2CwQGgCONCur7NalJNnMcBpgpfMomhx2vajywU1tj1pjq4tZSil+1w//wY3+u1elntYM2iR1HmNLxM2pberYXSDwzltWaB3uGV8HLuSdNQ/bgDjYQJ9Uco/QLrkftu5j0fxu3/typh+YPFfqeYMAu5kLoUw5C8IjhAl1NCCJ9U1Dz/erWkuNCTJXTqVjWdPwvoRrJLJXAwzL/9U1sSeK060+b6gm3gpKwpf0vRMMAmLyc6woPWajUytuS9SI7mfGwwa+scTHxZ2FezuDKXFiCMJvMfa2yommzvAOESVQi/2d+pqweR5Gido29Xuq60Fa+TssVlSKfGCcqhxRvGlq/WIbj++V/wW2spHOmkHwKC4U3D7Yz+oETGdQo9knexYZdNBIOxUcgJIJcW1GXu+A6ig8KFDvcNnmI8mE+M1UPgLh/CGndVW7Esw4sfqFTPzUw0b52TvUVCxiP81hTcsfYfq141cYm9Pn+j6hNr1poiAUK1l4+9rZ6Hxf+gRX9tD7D6f7pqf80ulPnevubyRkUuUbytfESe0j1pnHHsi2hTQLWO8CuBdM6uk7LAr5NX5u5E0D+d0dOOQSze1rhN40m/movXbVnYIK37ktHaz82pneI3ImKdGdISUlX6Fo5qlq9E6U82hUS6gBfeQScNHzJ+9YNI2LTfcWS2f1y9p/vUsQqqvWq7TC+noIr+Ey+7AXPIYJ7zc+r33+h4t/k7beq6m/RE/RkCDL2QOecYoNtlj8tfu6vc3NVHFZzNs8Bvztmo5nTqLaPTjw/JyHpPkk1CC8nLB7PfhfbA68vIkcBwHUIxezGlMv+7xSczXG/E2q9a5hMThyM6BzZ55VS1O5YrMG8+lnDSU7Zi2elJ7m2EJ0L72Wz9MK9BSXIhg81jGEAd+b6jjrZ0X9mwUmKpiIgFkMYtow/G1qFNLnsQNslEI1miS/DehbeQjOCO6qM9shQVo7PfvhTA8MFT7uTnZN0BbvCp/uuQT39hlze3RhuTwHvt9r4KDMudPZJt0XUuqMOUslYk/nQG06yuJLcvdeN4nVA+n7CMrCEmD/dQSUvzbaz+W+/qxC3iJtf9MmNP4u3ZpZNnuOYEjLPN+zjd3YYjNpHN63DYJye9ljweZy8O3oLZ8XIHJCUVgdIlnAHTxe8DGKmQ/tblhpT0R2MtQx6bBKfM7A8oaM813c5W8ITR3dhkDhfK/8agjRNes7evfXnRmfp6lg3uslRs3J60BELUzuXINB+nY7L8FLBc+cMhCypK1Emh9SRFlLP3jU+18RiNtH8Md7cEDqEU+ql192ag0qOzpxeqHAsYRDuzAhpPEmA0lyLy1eqH7PiMv5KG6HfAoYiaKOBxebLpPgoUdtkwD1v8HxroSZXjX0D0SQ9yZLloy38SKF1dZAluq31iudceD1q8D8teeiBAV0qG1ah3p2i8M0gF26grvZJuTSt2Fuo6+r7f7Bmc8mHqc7SmWyfrV0DgPrm8pVCApCY1k/NyCDFh1e9IGjiNGca1kvfx48pUy533+rvi1IJ3VCRKOWnzixKVo6z8CIPXHMpMlZ/OT8IJgMIjDFCYJXrOdMb/BQA43jyddrx4Fesz5MN/LfPLByiXx/lE3mOSfnmHZXxWP/fJGvWuc3zKJX41+d1RBNF0O3hEr36xV9vVs/rApy9lI+3oZz6nER81tAvETfZqXqGtgijBulJON7WC+XWsI2GH5j/5IiOLfG8EWXxSXP3x7XMb8e0bXWSPaFo8YxmVg4Vad8vmW2/974I5C0fXqNUVaIfHPHnzbaF3gRxl9sDxpguvzMKx4kDy8Xv7BKm4XFCRiPnvqsldPZoTGn4UsBWHfLofgQ+WmDyl4q0LyfNJUsLLnxVn7SxIzOuP6930+5bO6vTdftC2ZZNpraT3YsLmfWKMog4TLbBpc4KmvYfi8G97/5hr3en1dianWPRtG0DlgkzxWHDHa8bSqFetDk0VQkxAtZPk/20Qhjq75Gw/vKFYEXWz7vboXLupQj0VAmqhRKCbIMfbmjCu6MyvaF4g8rO+RyPdSPrgXCocs4JBz0l7fHCpUU0+61hpYySUGvLuRASYs1ylN9p/aUFrDQ+MYVPbdpi5eTPJJvCN3eCymrOtm82wbtwKffBu/vGv2GEQssjsKBnFAOTTgEohCgCCL9bBDUKYw7sTM566559Wb0EKi5Jc1gob1RRgKt38MOzA1nKRHerCC5tJNw4dWi1Ou9RbHV7dZBwqTi9Lzh4NadeX1vbx89dVPTXraiU+YZmYmpIs1BscCp4tnBJGRdE7I4iinHIlXGoKJqEJqTLnoWjIti/c2noBFVoCho7o3aG7WbscLLnLZJOSXJZR7KcVPa8w/6aEdISF+ypKRq2ny2AkJ+4z+FXxPTFtXW7ub8r3w1Bf+ksKnaS2wkrEcitWjT0pLu7FIT5R8Xsex9Hf0IEmD6Wz0PzLXvB1mAUv8BwsZqQk15EH/fA4zCXPuxtIJmsnLpqF9HDy8FLQ3+ZcxmQ1Cf6v71zlVUhYklPwUn9I4jgTmv3OOpuHlZeTb83BuLF2KGhD9MY1yptpxboNk0o9/mZbaiFT/koPBqK3R5hmK3ipzZRC7n+a92fKeDt21mUPAqlOAr7vNenS2oMpbV8gHuxVahfPiQo+E00macp0kgLpEycPNgvashrkBxw6PrbN5CBu1NTQBD5UQbv7+HyHLbewDaKO3ZQL0t/VDuFXu7L+osl1+WMIWmlMPOVYbI6ezTm/GrBPzC+1AqnblFXDFJHz5uKv5IULDCV6LLsZaBan72gq0exISgdR/0kSpOg6nkNTvUQzPVF31uYkJnN1yKr2HYXNYHvoHw15dphqPE22B2DwoF0s37eNhQIxXQlFERmTpOgJMz029ZOKAAjbgayPFrhY4h4FmvBNiJH2ZGWJ3Jr8r3TBZJC9/wbi5X8CFtpaAEr9IKgghEKefafFlkFgO6jpCxWvfrhaafb+Qrj+P3KLwfkrAqQdQXkXT+gs2N3hU91WRC+weDXyox3dIS2BKM8RZhEPoZxt9PQA+oiKObzjMOorcBRF8+fClf45CLQIoayLQhoWE22x4S2PRAEZX1EFx+8UvPDXAhI3ZxDV4MIyvaTb6axkrJ+psdTqTjCP0hsEhB76yOc+CE2S1M1IxypCCzhl5Gjtxq9Zbp7PixoMom7j70zWAwD3akeDbUO1+SvaMhMKm6d0sp2xwIKRB+sIb9+R7gsGvfsaXNJuoOlr8vNC2dVsbgNhkBJq/wYUZXcTq+RdJiYyaOdHyjEntJkOHkwvxBdDMlIdubBCkgdLDsjmUI5kbcY9eiqS3Nb1kMLl0h9ufHR6Wirx52mz5g87qGzJynx1sIaJkLz8sktzoMegZD2Fy+sIQcMsHh5U3kZz0y1f5PVrIEYkfqojtXYceKTMxaynlDHFdQoYqAh7nm+BvMzEdWFov4wDnA+AIYUAWff6xQV4LBHWTVsSNzdtm7TzHG/vlxXRkfn/5NFsjhaL/rswHQgrY9f9u4uApF55W9bJQXTJGr30QLTFFKb2Go8HOKpUDgK8rE0TtQV5iWFHgbjvJ+U9aee/3kZ8quVEYNsa9ZcFd5gEs4x3Ak6MVfQU4Y48FJSn3wpjR/sYLc2Ngur0qW4JM8n7AXZ6W1kOLX2gQ1Y1vCQRL8xFoi7BXGVlk5G2d17rikmnKGVT0aFPrcFMkEX5gAzQQsyQqDN3jx59v/Vrk4Ds+FybYyaPKbisEG4zs1XCRkTsTgWnZVwkdVC2uSIccx2kvKoGm6V4q+WgxGXpR4zQQi0NmbCUrJOwLXLmAq6ZaljistAyT8TdIKLgj8c8ba3+THU4r1V9Cawy0ejHZ70UIs8bWx1bTu1JsysUM68I504OnzmK8SZhFJhXSBZ65j1Q4iuSAXoXGhhNGIofaDjPwUhsiGt5SVKwSnza919xttFkWONwgTHM6mxf74tVCtALOgTLd53phZsCA5L6KIV88GSqCpyfdB/dgXQIDaUxh++E2BOSj850QmLAikPJdoN6eLSfK702i2gnnJrJIEet+/9+ObUk6ykZa0P5pnG1bi1tXba0ynGgE4x9JnES8JTMhhqSbqrO7Xnq40+WgboYiyIPrFRcGvxOYkFM2IVAvLAK9/hAJKL1coM2NLcMBWwR5FUYEwK3EmXKQeuU2/N1sIN8kFN0ls2sg6mR7quFzIF3JhvPl2x1fNl9i1RDQygkeDM3lcZcILqBbVtwwmajqO/hVW00XstJGAMrVSZQgSRxxBw5dO72tze8JXD12O0b8KvQ0c1/AwhsI8OA2ZtGToEdm1h7KSAKPM4jtQcOe//q7DXJ26Es5ljLfzdE/HOxu+nsnlt+zuTQTALB8i96GN6ZecT19XGuhFtUO6DAUKb/4pwS90jON78DL4hQ2fwJNbrLuaSyj2KN7vnwxCkYczyyWpoquudIp8t0yqvcIv6voR/9YvqhB0Vw4jl4MYIzWy9qYFZWRiKzlr8GQCAy1L50qa+e08GJyJV8MtVu6qlE00Q0PxJZ+OLdYEFlt0j2IqFALTP/6ONuj+vMoyxtLf0ojdhoe+GQFzKypSryGX08l7f1kq3a8FoHmP4pPQ/bVoiDDd4jLfVsrwed3t6GUU9y8f0tSnsrDkUpsbXJ/oTOBqnBzZr0j5HgotDZSelSXhUHPX/fxITbSCd3pqmhlA38frw9PehvxJk5KQn+megPcRB5s/WRxQB+RBu8eJQyI3yV1RK1l1KlLSIZaUbfqt4tR+mNmwz7/QJYHI9EtotSGC7/IjXfmjoyh6wb+E7IQHdEaeRUWsexTKQ2ILjp15eQ/x7e56VG7r91vuJx4VFMQQHv65XSH0mxsywDuOoiGBNSaBS6H65dF5s7wbnHjYPY9oZwmjiVbCYIq5WkSOWZAhLcNBbXY8ADzAezPEiHIvge6q7gsiov0UH45s9p3Blp8DYFytzpZD3nTCICOuhuPwEuzpdlu0FdW9d+0L4URVcpCi8KrGESQmDkIEP5d4TkyBwyTTCyNy+GW51vf1wgAJAiELTNSFQI/a+9ytyhYDNFdfmSQCFloU/vZvuYbLlsxqBfDuqS/iEzBpwi15wrxmFbXvmKtXih/khAWmH23Jppopan7OgcHU/OzAgW8IaDjxhobsRyyRJJjYzZ7C34crPG9LWjduDKegzhJPHb1aVRl3xbGoCGafbqA3IVDgxfTyd5Do3WmR2M8vz3HPk9fGNf3Y8XrPwXFa+8qrh1Xzfq2asao9X6minj4RRD4yDSBi/Hf1FuvyyAfl3K9n1lnOYPwZSjkvIjR2/kxbib43mt9F89im+6UKPWOJ+U0bSHh80G1FNKUJqdvpYY4mQzmuZOmya9Im4Mr4EqiVM7Ud4XhDyIj9IHgLNzAtrKbLmp+37nJeb19pBZgKaJ1sTOsI36qRmvPy+XUpEjq2wejx63QixCAClIYE3NjKfMWRDE/i9JCM+y741s187XZkyJh7IcvLRquFO6DoNE7D0qLGzJjGljsDTzalB5SPt1GWR4328qUd9/K64RdKx/ddlhjCqt/NNbay98KtrLSt+Bg+sFuiVEipD++8Xyb/3onsqmCxeF2/BJ2OM71Wft6360LmDmrQSohaUsvQcDMLEZ0mHtjqScgAdUqJa0BUpRRbARa0sbLtCix80sFJJLDVlTdOzutyAsgZojCcPfIsp8rT6HjcefKRGeNgUyE2uEuWh3jxyliK+0vw6OghPyzaT4FFmdOQIZoXB98eF8XCRzYSPvGH7r15cxA1OtPCjsICKjigYQAxZ5lh4bu7ayESdxLKQwDmZpq596MZ53I3BfnafZT2viY4rCOCX0xUwgsgbawG2YuSAl0kPgQTC0E2AgERrPgLM6h2fgqbUDjKOhFzm2mQC3gVeEXvG+sqb/PIS2axxLfipLq+XYQEanMAUnUseLpHg4LmDjjxmgt4dH4hw2w3wMefx2fYcMPyfv/hGEHPnmtJJUSAOh/Oo/QYge8I4VVfTX+z7pmJSw2aH8q32Mlyx2Mb62fIoPhUJTa/KTb+MlyIUMTuvslnTt6amayP77FxDljefn/jtKzcR5799kIonH/tsYyAYUoilEiB2mz3r4UC6KAf4X0lVLDVpFB5hSKNLfpTyIMes5SBK6xORAL9sK4zhX9nDhnl7qJCZbBo/vOpi0eNz4vnXuhGcw2UZo+kS2EV5rWAqFF6vDyw/yIorc/3ipnHtaSIAezb7YEXybbKfn9fqVrUbxBNd4UIjXosXa8V3oj+BLJsV6HEAZRGBMKwIQH5viw6fqxrjWm3/6BNSsOoN9n6a4LEKlZGn6DSgQWlBjX9op3v89cnvW41Y2k9SQv4ngs3+AZBIzgp2+98rW+nA8YLifSnc5FpLzCEYnblq+TbOg4dW2iVRC+fvNTxcRH0pvRRM+9Rm1pcUI5FxiCdUafJQPunM2P0hX4crvhSrzMTX/DeM+qx8182IueyUAsdFT0K4xehAEl//s0br53GGH7EOnulGr7ntG//LybqX5J8NsdzrKxH2QMegz+OhhWR/Hs7lEkSbeBKGJ8M54dE1xg+hPwxT8tws6lQ9GVAAVlbceJtIIs80+knHSly85kXRzRlbaz0MpKoDDV8XHxd4Z5oJD4xuAA5MlItQxosxfnNp65SCtWvntRBdx4bolZw49dWQbzQpRXKcOt+j/UkZyoFTD2TdzWpWlTxH8N4kfOxYsjNLu5J+3bqKlvF10YdvJwiKyS9YXXL4C01pT59156a73C/hfeICXUS5WotzMYKkPT0v8T4h+maMyzVxoty9zcWH7InzwifJ+9Yb2IcUFL1f1NFeSVwQNwLgA8eTvUp7QGCtgrEIoFOOof04W7A0mnRLxr88UMAGDiobXXgWfu4RCCbEkcemls5xD10OLhQx1hq7LhFN5OaHV2ImnrdpvnwagyktHF7XVWSF/YVOxMpfjxdNy5LMtyVXlEu/sj6/jsAWt68uRcYOUBEjEs+pB+Sg4urJ2tJEMNE8C8k10FTXV2irXH15m2TmmQ9zxucAspnx9+mI1uHxVXblvRrB5u7wbjTJBDwvLcAAduQ8xLwDYtVBlgZNFjowHe7kAb5xqvF/izMXP2GPGCB6tA4GMeWUtHp5naGX4WJERWfmB8Mb9Mmo+lEulNZT5X50DmbxNmobV+VVc9ngedydhtkFChTEebfXuSARLqp9rW8HXJ1Xca/TR0ObK0/HCo5VKjDLf4zsj1DuPYbBODE0j/NK8wePzemfMhDSzcinw9o1RVpg99dUgQqSSnchLBDD93nVHTJjZnf8fqWQLZqamDXkqc+LLC2RB5+l59ykFyKO+unhPJF5K7ELPYN8AEENdBphaQ0hFZmHSGFfe0DekV54733HRw7qibpXGaai/f58fmQN2HSK+h2yAVpO4Ug+uDddLmWgpF3lqh0o9IZdVhrqXM+qR8vcnhBg98hK/CSa5gPLvLSClqmL1l0Cb5D7cA378/B97WQcYeCjaY3ZxKV4++UmrE3i5+j9NEH8tuP07ilNP2KMJNLAMXEDaZa9o+zugR94sYK+OZBoIkZ9+Mnj7q03pCh+qj46rvbpzPIquoNUGj+Gg4mjvAP/5h+Y0ppMTVfDo8eyYkBb0Dv8SSEzpz48OOk7um8dJyyR1TP+9uRgbddh2Fjgy/xq/1L6Gk/SYV85Jaa+FRbVWL0MdjVZJYWPCnoPLrTVG0pQN5Nd1OdydAAVrgExAoej9U/Ap9St0RxOMi0u/bhxGod2B4StyE8srDixr8REn34XGq2SUT77gvn7WboI8XfJV4pW8FNbdrpyQdxqVoAyX/xMsKay9J80h8y2yisPOSBVdjU9Yo75sD8mQHQcrqKUExEn8bHV03IxU74JGxZim0j1mDO4bzOYFBuGjaS+KD6K89r/HOPHm3Qn8djAAgcfVu3tKTF2ssYYjMdZ06M1PFMdbZ3A1mJk2YqAg0Bd0vrZ9t3W3S/Q8fy6iXz+CLVm7kXzcQtjdsxyqUgZ6x3MRGja416lBbrHLTg5W8vaXgS8kkAar2dfiQSATD45vZaR8ezmQLPwlSVpvB+zWib84HJbcMDTvc50aYpXEMaDGWO3l+LJIxzmWUQuX8gb3Q6a7x67P0GKxlrcvoifneb9kCAtbCWWCSbTHU36dAoZICy/+K3NCVaA2LoETJgDr7FRo+KTQeS3UuR1DNL+sIixhP5vehB5YweRn5bsai6kwmzUd+xdYtKh7+XKCaG4ssatsxkacIQ8TIxgqliYffT4B4LXA9a+9tfFaKY8TkM77LHnqcCOff3E8H5VTF2VY6QG7fYakDJT2sqAkR/wYal8E0oxGbvfiqB7pkVkZDRIvHA0rRg4q0WJRf/YOvwFAM8HzuMcW+QsOvr4DPhZEhR9by0qNYXf75YX1hBXjILUNm+L3/7ChcnN9hImJXiXAUsxTggJ49F2CPqfPvgOX6EN4h31L0f9cIeK2jLjKlxKoRiOGROM7Rnn37VP04Jlg36bMxvOG18DDdtS+4ah0JCWkWDTt/4pDFc8hujgHn98VVQilE/KthxCpaRi4T1g0h2d8Slo2q3xLNkitLynTaABIhC32622N8zGkTtwOeN/JjEFXvMFkNmP3FJJwgY7vsonEWyZX91kePg9PNor1fEHXJLs1p27NP3/bjV+SUadFOJGwzeDKYElc6gg218jH1NsP1ssWO6rc0vU3lMDEvgb0Vo4wTGh4Z4k4tRWQ2EpKG1ECJZ4YuZf1yqL5H2I/F9N9LfQY4nRN9HEU2LviRIqqb6GaOc4f11glOb9a40y7Dzc3N7aE9EWAEHT8QVxFGzCq8+WfKGYtdFWds5bMVSTteWqtI61HXV7FoW6/2/VwpiiPBe7moN/muVDbcDkmeiUQVeKyGbdEmJ9duI+pUR7ux7YPrazE7wasfUQddvUjdRDD6lI8mPzFLrdNcFiO/sOR41DLxGVuniwvnICWeOTlJQ6sfDxHNtOqFPZcevlknePXwiZbjJsV8DH9XGddEdbWj5d5G7uKrTtp9fw/jRM4rcg9K9+1uKA1C30Jmhq4sBNWk6uVwgzCsrMH3vL3G3oJk3OGwv3y/6OWQHZSYyLUqEOeO2kebyfBN6akfKbe2QgpA+yoGAT3WJQ/zuwUwmkUgFVGlikT5Gb5NTLnhN4pk1hC7fxhQ95n2swUahFt53A0RnS6eTnzWiNJ69sKXqolpMHYH/ZV9t0zex52rSdoQD3tbJ0GorCuFjX1mjsC0QBq8O/Ov0u6LsFNQ1dm0s+Vpj6vwXlGP5tA/4OvQoZcehwtmsMqeSgj9o1JZafJ1FVYc7mQaZY/QUVsBmsu9awiPSxQgHIRoS5wueA9nGo33dK9bL0X3ZpkZV3reXq3VU5jHUR6Ey2StR26c0+KKrATi+nKl6vyUqmV7+IoQSe7OH4NpJ+50isZIvdkoiJ3aZbZOcwrci3BudvaJBORqlYkcfA5cZMP610p+TgToj7ZuQUeciYBxUtgNJJ1HDuqfo8j/YYX0Yrvd/K1O7lb9b23C6hYFzm1PFtznpv+wgfLv8wdqKXzhndG7C8FLM/TrrsCUw/M4BEKWqShZ85UQo++sjbduN80PKki90KrMRbv/WoLCTjhYFiNxIGDcb8OIML8qj64wLO0zNZxk6muM8nD7+bkh1cB1uE+xLzmH3mJtUmg5U3op4jZBq8891/pyX8BIBq6OPNtcYsQWgVZL++vkccZu9hUE9j0ep0zMpmdZNAQdxKSWtY/NQr5VlNft95yYhRPchRfIZU36Irgf1jqB1600HM4MzZQ+SitkNlUTzuR/v4TQKlYyw3c1mx7woh2T6dTWcttgR6V7s9D43OjlgQy8ppzUenphPlMrf3iwzASNdXefr7/3c4PGFUuGu7HnycVx89le+y+yttC2/0Zb6q4KpTGIRI14XWS5dg+Q8uQtkmTLANvCDe9i6YeshiKwue/jCQeoaJHpEp2+or6zUiPfWrjjyIhu0beifNQgi7H0fo8nxty2cksAmINMVWp6ydw8N0W8sOZL3+TDv+Tek5T7nSwCHAr8L+bUmdAbRcM2kDwWZNM6X9Vf2hVJ/PnXhs3LniPXLFlDUvZ9VT5cEHm05XNZZ0AtaajH7Tr5rlxzf3bJN+i0nCwf9+uU1VPL86JZRXvfymkxmdBgA2KFqj/TFxW10Fm7sKanrklA0vH9enyOdW6vE+d+JHVl9rkDQgMVaOCc0o5rKYS+JRXgb95xFeYdyeadXmKLXuWLI1G6bmMcWjR7oyoa50GNx69AuxklecVMsIJwtBwzcY4MRBt8xwNxo/vST7XAi5KdwzN3lLsPPsUrW3xw7JTpS75DGz9eA0F14RBgR5yD8AFuXwScUtUftgRmW49WttRIomxg6BjEFsvhL/TIr8UprY2cS2T5C0Wgv/R3st89A356wYhHkXDAEIYIofSsPBYb6bCbOjjAkEByEU3+DgqipqwVHr3BmLWSpQc6Zdh7lD+okbbiRRS38XPvShOvRwL77KYV0r840MltXPbEJyRd179/6btgPXcNk627YioxJbLgZaDs8KCJhHaZVI6tdL7jVt9vo0xTYRu70zBuVmkkrDDzFsFC5NfJz+ra9O27PgRztnExv9nw8TyVdO6m1+EZ8a589ZAlJxrAMjylUtRd3kKT8tYZHj8A6YSHc64M3nQeSRtbMBUb3OAGCIr5L5boAe1MTJrqPcGADC9JdpycU6M+YTl0kyDxs+JqM0iKIyfNh+CAVJKY1NZ7LhB+0S8PKWAxsTtfSy8Kd/lxJ8E2f6who7lPbTO4jr5LuGAaBNVw5vLC5D2+nvodYNeme0JAlMxPUFlYIgyNk7yE5SDmlG3ZWiIp/kLKoUxhiam/rxuPChTUCnGDDDIAEGkGkg4Kmn8pyj2BMe1lv/EMkwJBwUUwElGGovFfKg4a8QYWp505AxOD9rUtRbpCFsFTZ9L6nVNrYKDMVb5pxhjzyHHdG6UQRbUmRyyVsby36n7xJQnaNgArfy6F5R7fy2uRNcQ1zjFnh4uEhyayrJ7rI75bzAWneDIijGCpuCnCsq8wUuJg4Y1Azv2WHsP0YrXtx5BF/H6uJ79iAB1G02YMh19jDKwHRg/XBAJzmUN4a+8XsZUTG9i3vrhTEWRswXJM2DZsP1jyyFKWFbn8mJDAfBsTgxBNk7W3bfTCxFquRiTtO5I22eUu0r7HxJj0Ut9eSENd99yQUmXK7V3hc3gKwozdK3MS/1+0HbEN1TtsX38OrDlSYIg6audyVQf/0Z7h4wswG4Bp62ipPsZfiiDAOP8QM3aZ1Q944L8bxlru5/qz00oAf3/LSQsQhTMvlEAjmtJo35pft/wJB+PdR2hp4W0jwoXu9vbPrWkNkKAzC4s/+fuOASSv0L3BkJpRp/sYutZviuDd8rNMFZB++EKAYnqXkydea8qSx3cMouHpnKWaFWnI4Mg0UWMxSZo9bbu6WPDYfTqwwvgXXcxLOxi/yDP6QHmRhHkHqYNrjth4vOeXpYOb49baRLiwo4nVTgbLKtdHPUMflviZBWkzEjUt9zd1F392Dfwh/X3iOhesfT9rQ4rNs1KN2Xqa238tUUhIFfNB6gvYHVjR3DEYR94SKcOpYnP/qiVE8Eomu4RnNwMOyVGmG5h5Qln1K1mR5HeKgiS2k+Tv8xYUiPfioRYkTe2M5fZHdl9uHkwqbKNjuSAL9/uyOMuavGtNjFx6deFIryqaPSYJ2YVyyEWej1G/4UPdfjilE3C7Q9Aszk09paMLofHezmGNrH77J94XlIfXV/X4qIqo7LWmTcgL9NY1VcA4NzEc6kpkliHRH371CWrXekjWRJI8wBtJnbB7yUZum22PaUTrwJ/MiutK2hKV65ZxKXyLdJmzlpY43aD2DPrf1Vxt0dJ58S6iMrUD5KskafmX5ffZFzYtDvl3DEjbwiVDnR0+iVbksctxBUhQHU0NZm9DyXgcDbkQLgu5eE3jTg1ee9AFNWAst/nU4QOUY3e5ycXt/HinIU9b3mp2/4Z5nc6RXLTFFGUcvUsS8VwC9WauoKNyG7uZrMZqDPVwZmDe+Zfhaa1Dmvkay1H3/jXEGLvbnw9WL7gp2pkVRk491Dc6r1+eLwj2X0jiFB0bUZkcm7F5UtGIblVv5iANAzFgOIbxDrShsp2LVv5sAPdox9quDosfCaKoM7BxgcSU0b/dxQ0Cub/ikcxhOw04dqJg0LbwX2Hr0c5V3GU+Q1ZBymfG/TnJ2thxbkinI+/USWiCr8hURCSJcWI6IJn93CENY8OOL5VbR6UaEKiBx4VECOZ3KoNsHwsxqduZ5DnYlM8hH3tn38sE14F9r/A09AINktjRY1QAKhI7oELcyvGP5bd8aFJMgf5Mx0p05FZzIkkxXrX9bSTSCUNURpHR3UlLpPkLI0VHKFQBSN2NA1nCK8gRkjeeJkGi44GZWnkFR7yFGQhHlcXXPUXeeJV/8G8Rn4mG/TFE7nt/u66AEcm7N6/1xax3H5SCgQkOlcrg2oEHSUban9M9RJIpU0l52H3RPjEX06qoryVlrI+/kCwIs+bVJQObsNa1o4v5o9c82Y42qgIDDIxzAMy13wtE/Mfxtd/RcQazcIIP9hQ9x+ghvVgDqpjQnMlznnEki7ALe3WCw9WKaQhg4VoGRX2KkBl2WVOCFvi+zyFdGT49AlzdKp/FZRLIDqm9AaNGblEbTENfXlqUB0edhGPxFCTUfNTT+jMasnko1nfvP+Ka+UsjfLHnel3ya788WXcnpb9uLxEu5LWV0P/GhJDyuy/W6BUk7dvvhJi0M1eTLGVdZ53Vl7cl6JdKbXMkvjasuI83SjcfP4mUc4K1Du/7SoVcv9w8zwme8J34jGgG62/US7MgwZoSFbzvgqorMVjI6NzDy1YTzkjme67TL0+xIksKk0a9eqUXRX/PR9bM8UvH5fnlZZ8mxuhFhB9RBohV2JI+5q53Vq6pJ5X+WrmrZUSWK/hIuj7hbgCBvuEtw+PpLn7lVM1WjCXRvWWur/2SEcJxfXGac7NZz17DUJzdHjIh7E98+v/TOHJj3PbaYT+Fpn5BbEdm1NJcoSGvlcGqneF4qOFKSAFyJNtM5jP62slGz0a3R6J/ocdhNg3d6IGO7MvUROFg6qO5CopMYQgWEPaXLeR4hRcG6QTbIRR5qi78hLiUZkyyKu4zf58NszQEDzo235KhN0o91tasxyXRSWjhN+lW5AQEDzqY/XZfFsfw0lgDbe/JDSiOyy0SOPnQ8mehEN4Xs7xOxutcvXpsoe0XamB+S72YzhiWK/h0SM1A2u0IDvlWFqoQN076vBNhTsq9Ub6HJibPQS6hRfiD31qA0NB8JlpUe4kSevSyyQmyf1HoWjsVP6kJ4I1nn4zUQefeaZuNbg0ZNsa+jrns9TBfkcortbfXyVxQ6GQvjmxQ6f7w/uvHtdJf6uDLM1+fMHc7lTLzJTelo5tJk/DUwOOvxi7rmYoIRWp+WXqgDFl4Ws8mt4S8/QbTN8ISzDWfql+uAM6LvMWBS/Jbssc5kJaeZuMSPgLJdqFXrc0epVkFE8sllifLanInEhVwRyt52dTsulYqkpP8lutn8gD4lOGpfhYEYsCf1q/QUhgJufT2TwywB0W7rDyaEF4JpEbxBjy/hjvJ5r4g2jZcR509SB/kDNjOJLc6U3h3/dPfiWHmeD7aDT0nLjm8z3E5uH+dLmu60zS+49bj7uMu7O9wKh2iIYWacKGwV2q8P6sVjyEHtfjsJdxm7y+v8vryEwpHQP6/g4M7r/5GvQ8vw0FHbRtwVoo5nEb1Wbvzrglqq4tTjOOXG/eg7HVXuCQDyEnfpKow6K8I7i0a//0J24C92cqXBbD5xNyO2q1XveVq9V5kq4l+jRaOsGnx71uOE0FhGdgsHt5ciNw8kQ5eJLB6kdDl3ISalMOH4pYcgi23quLMhZC8CO93NiWWAVyou9TJ6GBvV8ivbPJhLLB66mCOm5xJhop/2S1bDI55+PAcF8Nn8Aq8M5+gwlBDcfq5gm/VtRo/UtgoWGOb94fof9qtXVP03Rk1Trs9NxNen7X8ESff1EHCLcQwqG6fL0i3BnL5+lS8qA9pY4zgkEUQJe8vVvddtOLnL57HidMAuUtfBjK21wgWteRZfU7+4y/7ShVpH24Qx/Y4aIZO7Fw5DxrdYEsyqpqWrdbLPfLGbk6r3WDMRHZTFzOtwNzW7ZYP02k+58XZ6ykMobPu4Bmd4RBIdXbNR+SLf4k9z5CH+YMrT/GUKXv3wyBocVlK2Cj8Ix2opgZ2XaS0pLQUqUKxUmjbss4/u7ZdyEG3y0eF0DGQ8+IB072WYNqMQh3Gqra6VWbet/XUtX1Jtv16JuSOR0pmFaLYuf7DWQ4nWg6D4hkd7VayVjC3MsyePL8qWJ0hjmPbr2PbQS+SW/vE37ke2emjmMqHe3oHG/L/sBs2AwJXviet46CWIPXaVeeAfhXshza/CNzSQjSt/Fo2y1+5H233tmFduMRwa/bIC+2nw+CV7TDYGhEa2CkP5W/IpOSzGbEtkpoqndeU7BY0n34o49UDBAhqS+IvmZjv2N5XtaCxyMgfKMR59aeWSrEWNMz4+BbU5gn7hhtBHGGEMdC/5v1WkgASGu5yk7subvosl+zFa/6Xp6K48VCuc5NptN91XyNoHW/Rmdn3sI2OLDO2OChPSkcTh0dnJRCmRv1jlWgVFZqORehFMxISn8jdiSP/JDecN7s2ewzmMj/iytU5Ik+SotXUJFaM+6UIQSQ5zUOp9aA4X8V8WDeVW0iojSLLwfBbAC4hF9tgjW3MOXuwb5LRZw59Y3Cl5xwb58ZRJK3VXyj0d/gUpZcxwh58Gtg6LcW2CVI/5zx4cNBP2og5NL+CXADLq2qcdRVFBdQ41Ik2rMRNwN3FbQdUJErwig/8IL4IS2FdZ3KMCNiMz4lOAyWPguy7RC6C07r7bQjOWgdTxmkTGqIQ0J2X03Rx2hgnXVkSxvTLxIo2C/DQDsBeHeWKzEyJQVB0RGchdfA59aecw2/bEy/C0ldvqwPbvZ1uMB0jcWu5OeWW4LmeKr1PPwByI67+64SwlPND0Zfz1TlOv1+y2u6xbq8j3LA7gF1hF5ucxbEOgYnvTVNse2LLYZx0aSmtvuv03ZEmXyHqRtSehEmlzZS+oBvH4w4Gc2IxzjqhHTylS72d4j1Obs2/+lS3bHFQ3F/63rxW7SPlKmm9DsvOZflHIBeCgcEKoisZ6EVpJPfAKp+CB2YcksqOktI1H9doQXki8pRshrcF3GsH9DDxk81LrnLBfwLQW02utZWjyI0arkKQ02F+1HUblQ/GqCF671bqPmSr9w9qagbkZtYb7QWmyjYIy6thWt6cCff+iXBAI91L0Cf55IySEeanqRswfR/xRuR8II4zZ33SgEChMW7XXU3Ir+fIqMw/E8rD/CgpJ4Uw0nlPlpRZgt9dKOmTN96ZdDymb11+fJgS/jloiv7DuXBeVs/PgOdJc699IlHZ0hSO6Y1ODM5Ht50veZkifcIZ2BivFAP6bz2Vh4cvLnQzdZvsp5xfcMBwk3g6118ZyHcUjLqyC5kruDZ8GCgOHIXsVhuj4BtByh6/ixnIbJ9VCHye+NI3REnUTayH94ZyJHYkEbpA6KHSHiSowiFrU1FLKquEs20yUNBADICL6gy8Na5nltHvDwxTL72O8+FxEGbxLWJzOLgyvg4WnM3UyVaW8aIqpgd9BhtH4fBwHm7/wTF1k+Gg/etVndHfOsfAHI6s42dvTKLOEvlVX0P1CmMGyDr9kE0Otx5edjnKhoogV4vVWpPa4/Ryevcv8eD86sL5F1xpJgvFST0q3hKLPy0O/Z0VMJPWrGuoubqD891dRNmkJnrnjP8hpZoI6fmGMPnkGpqgmzdG/PiAr0Tx3seHM0VlAva/sl4i7OsD1+lmBfd9Gf3VePJ9YMrLZbdzxfQzMwN9eFSEkqrThAp+sc60BYrR6TpkhaJKvH2onLUUjd0iJhI75yAk9Yh4Jdem3WZOief5agk78L+a0BbvelNGasTtjYx6wcH6EX52TYB5mOvPfci1iaE0PpAp2u1h1XXLB0AjdgP40VgaBEtfM4VH8JYJKYBMXUjLJxwUz2V0j/zVUZnghoaJlHqE3/q1VUMt2F0DTKDBrBYE1HfySAGE2iGlrMDn6Vs9M3V1YpOb6MaO14HWJRKG5uzojJWvF/jpW0oz6clCEhDOQKC9TfWJ6FisRRv0Nn0S+xZqSlqHuX+oTWrDblEGv25R+/D/L0D0u2t7c/sHpWmm+yfwXunmAp5eaSpgsWy8TTP+anMf7X5ue/2r31idejPEkJ8n4WsJrsL4o8ECSaePKQaf+o5Le5x6jMeuj718XtLNDMk7ze+RZ0Wnh3WGUMWF/j7StR4QSuFz+ag+8jNlpwQzo5UkqplrJWQp7a/ppbZbR4yphv0E5TwWyqzENAxvkaLrO+pvGlf4DnwyfWc/dvl7h/Q0kjKXtzzXW8e6HX2sXysWGhEtfQmxZGcmlJAnnjksrmgsG0jPGfVlpN427qHy4HYa4gnTkncBKjFq9oeiCACLalir1E5nHK3ix58tPpZS7grQftJenpAR1HLsMJiSzhImJx9PFw1U/dzDeyy72/Zw4W19BJcg2sDO88jZZnWLHYRlhc1pBYeCx26vD0PVTFhVUBDglys0uKcLYeJb0kbDX3/w4eOe6tUGeNDgTVho++RXbBCstz5p/L1EOqqL+mx/q8FFZGEHTduRHumLIpl65iNmvnR4zEb5G6tiPlKU6vL+AuAQqTN+INotFP7/Wa2kiaTDSl/N6j2ZZbLu8GtggWtAYI+L/sB0jUdKoZApb1DC+l8/wAX1zrHGW32D8RJ8VmP9gia0fqjs/ssSGGEoTtuzGuUyL+XqYiuQC3UY2fUs0UCT7/RFhjAZd/SEIIeBRfZd7gq3alG5bhD+B1Gbg350qUKGXWX8JahvDNf6nRS8aRg++HvQWfDPsKHCpbt3cDlD9oUFFLWA0kP5+7tQ6bmGvUsjkxl/bTHJPuXU8rpqQ44pFF0zkPioaHQ3vvCohUnin1xPb7fZXhfsejdO+RrawYmpUx9fXiTwD8iGZVMgp0CHiA8S4pzV8mS1GBvUErEyEJscdVBorRAdKV2b4eMDSLBHs/ROhxUfOJdP239WBP7xf55CQjAvTM3HAczm6zGhHOLZ131bXqfKZcV0xaV35N8GGrTLqh0W1FZfOuAkkUWmCPuQgol3eYTt6cydw6GOAQhi2rBeZs1wcvi2P3RbdLPhHQuvKXL93TqdYJKwd2v01lo736zihkuVSJzZi1WBmDVSRDTpynFjXJ1pK67DaUwYRdhjWg5xGqxnlS3kMoJmXUl1GUEwLhx1Zjdv0X83LSQDmn9DAcUEasvuEq+rbdlZ+9nzxL1LvpBbfMljqJhaX0Fi7gyWpSnMnjBkHz6FsZPHN5iOgGtc7Tmr/1aaR4rvBHgfmQFHsYePwlDeI+w5KXJQcipjZHURC8TGHDXqpmReXRnheC8PPBHt8R5PqOT4szXwb5ezox/FlEH0AEk/iTU7UrutBbZustPGCmb4oPxeSNCbNb2zjzqrGke+Qf4Mr2U7Jdu6K8IBA6++4PfH0kcTbVnda+SuXRuLB1nwpp6r4QQlIHCalcV51JZfhEXASsY41dI1yxy5U2iJlgHNeiQFbZP01AvfJtNkH14+0wFHR5HW3iCcpfqWWQMPm2Pi4jlOY9C2eXhg0ov3QLicYxD8PrSkk5De2EhzFn0cLFJBu1J+/VDDxF8L4XkWm2pWo7ZL3fi41+GWPkS7xDU46AocdGedwxxV/nENewr7hjhLavBbfT5+G0xybFEgefcjXXV4wtVRDEkirtFRnSJ/3c8sGkmMowSCCWd8Z19GDTU5wd6DJ8F5cxSTiwszK7WIWurWYhvf4QOYP77kMJmxKbe2emhaPVJQvdYykgiOuMnqxX9+rAylklzO6fHrwvZWKBAr9dUJs5SPw0FA9pa8iG3Sma2BY7mfPS185q6ZMOe3CDcL7VEz9G5vR3qoQkGBgQTK8BMV0rY26/IDftkyhw+dkue8JWT6iEzPxhT54m+p2EGQz8bpKeWvRsTZaqKIN4hefQMsNeOTQpZJDLv3+uguAhbxtNe69/rYbWxs4g4PKSrSrQQiRPV3B7mpYnNQtHqAGjjzlAVCF1SN1KvN1k4ve4dyEbErJalkSorHbEsA34b6sgjsCWe774gCUIY2MrKOBp7+O3m37AuGXwreKv+/8lJbnjwzlaLhNVNNilEiNyv0+bnLt+Cig9olw/DhEw+9E+GLisXyn+S6ryZGQR+KLfWqLwIVyzFTetvu/faAmg3g3/kv0vwHeQXTJDK6AkOavgFBun3+7lSd/c3mey0ZlNPJwApEdRbgxxwARc/jy6gXiTkQbtWRoxhEr+OhjTLrldVEltsJtgYYVNiUZFjEhbDi9E/EuZY00fSsKjQxBQxhbgACPtubgIrnaQs40hvKKe1gU76XVgbyQQRIGDSlGk4tDIbDf2ULtqHZ9QzSmERNkOVSc4H8LCVppZBkH5gpL55sby4uKv8V7mffjP0EiuOr/k8tWzNw9WcS+lv2gxu0eOmLPBP7cgoJjWqlZtU0qzS5iT6BY/BwZ0Bgr5TDXAFfvZ/k55B01xr8BgLVvF/GBSnf8bZ2danjZ2LsEj5gOyf0d2didbDhMX04f7b8xSO73C7vXS8zlEIPHfdYbWz8Y7M6nUSEFFoWTFEVPRVozTR31Q3vjYyVRlQrOZznhK2F6qzBBdt10L+PMQ/PjPCMfPOODvqbzR2iV3aKYd6PAj2mVM6S+YfQ3JdizGVohfno2cpkWNtx8RkF5wmzpc7qIgRaj+/S78LAkv080KcJ1r0uLgMjH+Rtoay9QMwIXLzwpouIozIGwZHJqL6iSAmEmVtlSiReWX8cRHbo8lt260a4fjCQVj7Fd+LxIAezCu7JlFWx+OW0jgNs32tkh6D0Txh+kvffV0LghJUTyyTLcm7dzwvMfurNlcChIAQjt5yAFaISwYowQtRv9iUteUVi/Eg6qo8Us+qIqX9EbCHUWrgCIjr6wdu0IL495gbuyosYGirni7bfk0jQLVkGFXzElLojiDerFFudunO1VavsO5uGKmLfAgkhwOkHyfzt0lCm2wsqqR9kbwCP1UPG1PKPCYv2DX8/joNyjGY05m5tkuA8+jCOjI4RZYve5CPiQGUWgtjGp3DL199B8O74MxyxCuvG8yfXASblP3LTRTFtOfJF/rdmox5SnfTZg23fWoPPy+Et03MBPAwsKuyJ/Po11QU53+d9xPEvI5w0lHbzx0YcfWF5Kt9O3SWQgvNHg6xBKd/0vWh4eZ0/r1bGUljkOF+7x+5suqIz/Knf7378okTwh4x1qo410sAlqNOvRjBAkTq07elk/ngbpIOXj3FKN+DeANpwvQLnPXR8t1nz6BJgYhKA1CYRtO07/8BDXPyl2w2zU/154KtVw5dFzVc76XD298LFREV0hnII0dGq/QW7LSUoh81COGjb+VkL+i9qCjzto4HmrqiZv3JB7NA47/d4fTFmpr4IsQHjd8euhTz3Dj8+F9CL8lc2NxUFKof9op3NMh/zdWfh7Ex0kO+NHsg6VF7Dth2rZ7XN5m/7VUlePDBb65vrI3+kP1Akej7z9bLz75v4iZqet9cS8RQ6eTw3zjGbJJzPskOpP2+mOJmrIn6DU8tpimgryw773nl6FW4bw7xoWsGKBG7isScCtB9/C8tJgPk2MFu/ZCT7zJhRk/mViV0PA9fxauEa2vvdXsoNy9MOBIlYVxZthKpEawOCivtMowykbHvyOSQ73LuxTi+P99a8yEoJu1mqhh+PlWaJjf/z+I6F4UT+2kc0KJuw/KWqjtNY9R6Qy5fNiu08XmTJ25iduko5mvCAVE5ESQcP+NNDA8n1Bex5cg8vyK8f9tKmxhvAi9jepRpLE+irOgB8rXIcOdZzsGhJ4GmNQ1lehL3W0X8kngY+VxxyhqzMXEVK4zd8BIpTsxV6GV6O79bKvv7VwuLa20wXCvt1LgWoDKAQ/nHilp60yfcjR9lDdKuv4o9vqkX4ag8obNULh8PPXyYeX58fI0mqtyv3IR+PX5PaBrM8f0wFW+1uGvpTBVdxsNirUl2qRCQn8l/sKvatPdoFVBuC18Ous6/ngYpiI7tAoyvHHYKxU14jKmAkx2xG2g4gJJ0A/yueV3qUfy1qTSuKmfKTFKa50CNtoPAws8td8HsLJePeM1jztCARUs0IjW/6queTX5GP6lSeWvVQiyl+7UuoskESEqJg3DT7v2lDvNiJF5FipNGkMeAMPzS1lpQhAyv1qotmG4g+Rgrb2XMAVKgpxMol8QUW3dE3GPATLmenwjEM2Uw+9wLY2bDAsGPPoNw/WyxMTLaY00zKOohzg+ap9FIFMsTcaPvnt9Bg2qBbPZFFLvJfeMl5d+Sc8rG4TjWD4jegu8trcMF4kvIuDvoDetYFaqhs0P3AbQLXUW+Xr6OzghqKnWhWBfAmzagR4PoZoVn4s7PSoJRXSsRJUjax6+a/5Tpbsl7wCK1Vg+qDhWbPYUiLt2ktDBXKgis0UW/fAx+UuQKIMaLFGjJ+siunRvSMMIq74g35T/qHRwKFwXp43jYtBUCs34ZLudGaQytUfm85Ll++HJLN2aXbTA1Va7GPHKfuTALOKI3u6dsTv4OMHUjtxWfdXcVud0J2fx7+bWidoEjMmyVJ+svylpf3AlOZMR8AXifACiO2rBqNLLP6L6Bwgl8NFQU/guzOkQJMyYLPDfbdjZI0EcsGuBPEFi/5FR39JeimijZJRqMmcI2yNmdEpya9X/21TwIf0fv5moW7CAMJkQudmxWkeu25biOT85ZnmLMH1MUGuv/EeKNPNpWJSXROz44gMGYw6O4ZtKjFbkefhaa6b0L7cUhBzCgF/sluW8HQ5EAeRfVRvGxdjVhDas+XK2cCYInbfkvBgcA0BYsjJcIf/NeE8WhZL8FAbBJvjC5aZNkmUCsYGKEv9TZze2uS7UEhDS6cdTbI8bSHuAeR+hkdsTtGWhaGWPBAtm8VnJAt/eLHw7sVXkzMZrlLp32C7KDQ143VjljnE3+tGq8Oz+6ZN0CdDXFvCHCtUNye7t0ouxDIriJfZHCgYtvk3iGr5u9ZrDCRkMUgjbqXu43+5v7rC9+dE/4rremJoMb+wqm3xA7Pmy0O8HOyOZe5vrld7pKJ1mS/xBBWjjdp0TBdAp0HNCGyk+elMxxqo8rMfspkgKnZgsNEZQfVbHvhakjGtkQbDMT5JzvPlppFLusByHUABrlAoXrcaSSmBhB8a+hvUw7NBvLzcjRfu8Fq13U45chqX+iRwyvrbqEiXu/EZ+alCmM+AKtHuZVfr0yhMnBZ86so0tzY9fdtcKm0XqcuHMbH0Ci++0c1Lp0St/I4SD6FILcF4S7VBGhn5C2Wk39683pDnLpiA1AfHdaGXcOvLeX8r1LZ4rP5FCe+FyNghrHRp/C41x4AYbIvAXVG4elv6kWZEYKUDy8Uf5TF6ScDrg6JEzP+H9H0G3QIpt8NyGWmJOt2C7bKuA9YFMv4CoX5CqdnOdDgcZH0E4AMcwqQi9qmEr8xv4OjcuYteVwC+QOwAYrdr7RBJ7sCWVbU0JTVqPOGvKHCm3CsNIrXnBmOt3NrJYzDLKaDz9sNnRpycGGqzYmMvQ49NnWs/vfl6p1WgsEKSsQ5E6VksBYmhVgpFZz8SnUjdoirS/fAk7W9u6R7uCtunr/D4OZ8gQYYYL8hBZ7si/mLVnnGLy5MX7+e4iXNS6KYLW033MgVhwVL416H7JTL3zb84XHdLNcJtASzmbR/eBrqLdlTu8hOMcb0dbsiXDF/b6TSVc/XtN6ExJlOvgoU2WfpIipsT8u277Dr+w0QWIy0LLoHeErDVoeorT14QPpEfFXiFcQz5m+maBQPMhJ9xU6mK/fIUo4jI/BG5bF3G8zDUJazWaGV0TEHixGWE4aXI3+7x8i8X4ah/cH59UVKAYTBPG66EjLVIdyRh4fNhNummkz7/VXOak519NKW4xCzvVcEXXRPyjDBQii4w8FV387otpH9mThd3r5eqPf+xgafV6kmtxq8beuobUKl5sH/5PKeqyBhbFPV0jSFuzRnEc2u5In7271sBGiC0I1d5oU4BP3ZkTklAEdk79KFaoByRfaDyhgaTU0ba+Y0UDBippFqLTHmySjfn36g0IGrSA1SUbQFmHSuzOMZVwUQCugOf5mHOzyekP58bri5SPmgw06C1Quif0phZbRtRJxiKPjJuA+Hep/Q/uQoVcDa42pPOAqZuO39+b1qPlHqoMjB0SQyRr8V8ufQzrzH8tzoGNPoOO4jYf2g+KCX7sj4r2rc/WcVc2buItGTypdTys9yBlwnPurpXzHKPL4e8PA68ThtZIvDGD7tN2nqI65O1SuNSv0lfamL6y6XtFQA2D+pzzlJDtSzaZWOf2ChRID5SJ6R+QeiAlbzT9dWLB6l5zPWEPP+QOBDjMqAaZ9th18gXyoDH7YPS+DO4kU2/WvApneZvuNNinuv2E3/dMY5mmDto4gUmRXNtOw+2OpaZ1W/7j/gA2fjNHpkjAk113BjNrWXJjr4HGXccu2kN++tTmGe7nmXh/Lh9VrlKu2gy7OSOtxiprKa+LxkoLDBo8ya97LA6+3DMYMKiCEwZhsiCoej9PxAGhb8AKdMpM1wQK/8wvHqaGyhkxfviFHMQ4NrFDOCO5Kldkl64D7horyZLw3rtvsyQW1Xr8g1fJy9b0nYdctEQY63NqeZmMIUNFfk3nG68wsknUx6jsr+4trbnIAYe1oO8BvZrnEMshtO7u37ERiC4tRKeylh4Nr4IB9lOlyOHn4x9WmnJo8HZNL2x76TwPN0e7Xknth/hFZ051TQHr5aS/NqDrmBbXnElYlO2CMZwkil7vRFFaCvMh3Pi+murkoCQkRhxiy3qAyjSDS1pkVSPhKxc+nwvgmC06Bb7I5jlQEjkYXugHmHzmJqg/BeokdaKN7rOpxNiO0lZyy2ImQSiMp8w4PDkjJ4Db+Kvqdd/avoe2IuaCyEQaeAu2HSwc1yinir/R3WVjEaJChMZdLlexq1cWCuYYx6kL6S3BUzGdgRzjHRNsaAySLZGSNlpZSuyW9bKUvVYnj1s7QM8Nb7XYwAQN2PIO4IzSNary7MKvl6SnopArQq7vqLIO/QU0u5fZWg+jOzQch9U79Nu1qy/5AiW0/D41lLblOfVX3oT34BLEUpGJqvHTUKXl4ACAcmwRN7iHP0o3AE5C30hcReq1RRTFf2bieLGAGRQsFg4dvwxEyAS6URmpv52DNR+ONQ623PaYl+3HhUWkuPPiNZ/I4VoUzmWk/79yc70XOuYLThCkg5AnCiLdYenWFdBosk9h80rPIU/8vI9fYrVe0l9xD4bS2UgpaAiklvZP1WOgXwvV2EvHknTQTGahEWQx1ISPW1lfQegldSYJiwtwYYylCuDwE6vtvFZ6rRmSVfdGHXHPNM2zpa3SmQ7oSJd4kPMXV7vPfd+Tmrty0lFlZQ3c3gC1GyCh9CAonMmLaWyQPsvK+Cc+rOzkF15vERglxP+zHinxsMvUorsQavQA65gmx5f/zLk7uwXDu1VOZJZvtKrTEvNT3rpmfhTrLy3t/P6sgPcJtbHvxHU33zYwu6PNYc6Z3QsbI8z6BcRn5XXkwehbNdsj3vFWc3+Wo+2wZme2tk6dehuluG56aORWL2IfkF4aFK6hPpd6gnOr/mLX2O6VCQ0TBrggJdJcksI2DKEmSWewAJ9Vz9/PRM4L10q+Awq/bl8QqU+evkCFIaKnPdazNkHhdAnBT7F4GmSdetq94/HzhSl/WL678CInDY/TSlu6yANO4g3HFtvP67wO2q+5rc0Wa19vz4wL0TZcjhw8Lf0bg1aKBV4HzUbmHh8I9ujlajFafDTpcvRQ8xQpXAqeT90mXqCRNJ+jXXR8ygHM4kWf+UAHa8kDktDXsPorvPidez7N4rH7qVMrsmRKsAoO9YqGroqtQF0mgy1iJeeN4Iw1hMZ0CmqrYEKQIqNrwMVZUIWN16310u1A2+wGm8nhQh45+qTo1TodweqpFeHo5KsgPGNFhzj+TGmMOGfgo3ov4bXt7hN1uXbDcvR/+2qkWcm0PLu2z1jfDmtkF7guUhUJBUGUJdl6zI1weR+sifBhK3GhggrUuYF1veZXYdhvL39MbG/yrRYk62YgUFASDrQW0q3giPpyf3d8lQoxrc51lBS1TRNtv37XS0MtqsdGIglqNCxp2uMdUJXX8Z6H8c2I9jbldXDnvXvARvG01x+6W6HFADE0a1++W0FTBNK1/52hg3izeoNx3xsKWK5weIHZcm7ihGSGvyG3Qv1yF7641rFt32WHFRisEZJMC/iG/jQDnZs5sjfzcI7IsiJAIeSLt0731sKpuyfS5IyVoldK4SFf7mJLOCzibc7MOpV3PgSZSPnCQXaGRrZRky6GanPFfMCatKcIzOcgf9Iwi9lWR7RJRv17GNEIc33WNZRqbD67aijtUvdEENZYqh/Xe0WI303kdiNXuLYDBs+SZqSf1GocCk/IZ08LKMmKGTwJtkE36P9c23k3oFKDPwjDmBSwN9cLF6sAGI7oUzgm6GtWgyn1ZJSKMpJRKg905v/+Svxs5STHMdrhwn5CzMAj/gyS7aRSaBPaO3NE2aNXD1aeViuJ7//4zruexHNUnbSGT9BAFiceEE58/JO0tpJv4FIbf0Pvp7H0kKYf7DSm4drtPcQBgG958OJ7JZ59JrYyVRCfnrhYUcfFfTIZLHjL3gOtz/OcIX7PgkiIUlK9wI7Ogd2bx+Kch/KdttAqYAvvmjxPCF94iDCLvpFWbafJ91hyBlU1mMv2TY2/s7ubsrJu391Q/umXgsHNmZ+HBD1tWr8mPvuKfHFy+pHAGcn2Dry4TFFPyVX0ay6SC5hk9sHL7WeQwJg0+DQ/apaRfNOsmWwmSnmgEj2po2l4aEgdiddCgS6hU5RroMjvD1Vyj79X/CwNcMmQMxV99j6G1vF9F3BQjyfYUflxET399dUbfHR9ICI4WqkAYmqlzSQwnxJ428tS4T7NH/ZcUI80QjA0TVFTxpVZ0tu2Q3eJL2MyAfzwnbpn+66vAzADsSQotc+ZvuA4nml03o3qKkP6tpv9TyToap2U7J9kZoDLdNrHbqIhGZxkG8jCTexCOrzaYkKppf8C7xzANjqN68ySQmfCYsUvOXndgDnGvHqwe8OUdif2S52g236kM+BadtxnD048Jlu8zeeZqwcD8qGJnzPuWMk9f6BD/gU0GLgg1zMf3XOBEDrFQCdrCeljIxcsMWv64t4fqH8jDdy0JfytyZ5hS8Bql4NK/Yfqb8KZF3tfQ3KPShUZ8419sh2aWd/pI0Y5vf7xFxve/IgV9TWNovvATqy7O8L0EMkFe7NRGie01z3Ml4x8OieZyg/4mbRZWyUim6N3DXhY8oPY2CQ98sdIObygjTMIPwQMhk+XyJLBxO2wV+0xyIT8fkyn8IrzXzea9+49Y6afMmrIN4U+X4GSTKFTuGP/o8P89/bEYgHCA+DwpxtqTGZsIDTha+czGMU7VspoZYqzVQe1ytAIoCF6GOLn+5L5RJubxYveKlZIZ2oJZEiTzAFzO8UWXu4pBhdPR7SHOQvqLKAb2ZX948mHPB5TZXx7zHoNgNcN4OT8lwK2I5mVkBTrtD81rztxZbpX6mL1UbBnzHeDCTdyMEGIsIlv+LVxtInMDvN+7SlCBnSWhP2YLEYvkhfGOnUeA8YKMNuu/zXxcPJ4qM7nURKD1UP+LFgUNkkAEoS6lGvkup/CW6veNVkwqofj9e83jI1Y588n/9m+MGA6FGY/u3K03A821zLVwGNWXRGihPGuzxj5BlWFXwpaWk+TmmYWuRxiHZjNoVDYqmPbt0UaJz8rfIobbvRNGRvotEFXM/peNorNM7weG4ozmZAEXOBnMr7tpCVCVw8PLnevQL4CJuawS7tFDo88UoKAjCiS20XcmPfEh62FS90dI5epqgKj28Sdcy+LvPxLCZahuiMF4fcln+XIIqw+C+M1OgNwB3Qhy3wZYx44/zaVmdfmHIZIP/CXlZxxxK8fpI9yb3LtIOr8u2jvscMsw+WUyPqq0AimtmcyUBGuXR3vtFMWJJ2cE+IW1waUPIXsRQilMlfAA4+SlQbCOUGMigKQdtmQdQVSGnyGh0TJCFBJaZgWyn9vo121zowZjqX25mOE8FMpwOFC6esjwwmpaGnpqbTuTAOY9W1dW1yGvpXUeL3YDx/m0nvsdHqaunCUNSco0yhsoOVN1l8YlS9yMxb5isJhGRFjN5oQlk30+S97cIte/mZ0afjl4P+yUjbfLeVdP66ZW+8iYB42PFgIIc1UmayqXVQTgFVubNM24KwmpHBrefFv1SEvUI2FvWpp6CWXdbrNf2nvJt22Cx52DC5rhi2t8dLpF6UvxbyaNvLSxHQQ14SgFqujGQ8kJRWZQUuqSOspa4lu+KDnRpw25FmcjndEWeZ4aaOeSNqrwa+3FAaQ0fo3s/cEun68fIOY6/nVO+FxtqvypY+bqC2+OCkZgSKBiLk0GONfptz9NLaNMawzZqPjuHelq6Kde3kRqjjzvUrpvyUzdhetmVpPoZ1QNMx59h0hXomojl/o2cLM15Spli7HLdF2OSnDNdWIXX0RKU7RXky+tHUtDrSEzFLmJ7Ty7d+IXNl1ab+/pE9XjJkRzIksIz2pVdIa30qmUYVFmWiqj8pN5f8NJdifbVsws6ZA/b/FXZoUvJJ+rAd8Pi5CB5I3XEN7nvXm1lbfMg6yPtLlxkCE9Km/gtZa1vXt2VAN4BF/V9gAuBYFgV6JrGAx3gafMBkl4b111WRFMW+3+5nRCLH2p4KCu8+AmpbpYS/DrBp+GY22fyFmwxjge8vsACjpeA9XXevxmx4pt1yjUpSUFSI+A4B7QGeglhMSUO/0AHXagvVsTOMlFjPXINtRmLTTul+GYBxt0OnPtYBFlaL0bFVVEKU/RqK0Xggr63KcnnYjeg8pR3dF32D2kSCwuQ2AhcAtC8hC7OKkJfVE05zyVqr4FFKG+rw3Sr5b/x+C0v5ZDEaWbvtXKimJVo72Tk/Ukiy1DKRv4FqAa/1rEADmiRKJWeOuLWm1M6WHSoZRllQO8d1BAq7No8KUkOmv9GfhJ9JDsRoN9MRHGQP1/Gc8S+VQfP8C4df7EmtBG+1GzO/I25jfXWRk2Yz43TnQzygvO0Zki4EN7r5GYP9wI1NY/6i0IUlBbCU+6I+oWIWCscRpPsQmNP9G9IlCnxBkfQWP6EWK9dNJ9QZaRVytfttiI0dv1LA1j+IeE+UDs1CVimB2fKN6mWu7Ua6gk9XFvDINcgPJX4ezA2Nv9EPk1Jt5sk5Hrx7Wv3gzGHSWLSC+E+pdh/x8dpV9FlhW3J3Mx8cn4KUWI6nBJV0IAJs4+uXSqPO1xszwuiHRIS6Is6RKpG/FLzK0YQ4AMDE9koSgUGFomGtT2Lpv08BIB7D+jNnzR88j13SywYl43cBHfXV+36sjoPvEmk9oWDg6GhzFiIuhaUiL5KvF+/3qOi6wtW8RlJVVR8TIeSobAXGRq/W7i/bIwP38EBCqRLfPq4FQpuI8/Kex/hypUxYh+7GJSkpJEvHBMbS/Q6QJcgQmehLeDnODEcSyXmBIBMMUTPG6re1Eq7pa9SdocukcKsQE+mmGnwInXfmQpPvg4rmzoRm/qyh5LMv/IyEmXXnyP99OMnfgNrztGPcfJ3Wt42siRibUPmdgYsAj7JSHzsj9VIxmemE+5vM/5bMw6BfWSS3sFPKkvurmKz+IpGwJKKHotAfFKhYAjNqPC5G1MR12gcb4A4GSBrmYQR2WInuLRQoH0og3mBG0Wsu4+y97sxwaDY+fWgmNvzJnSFaqC586ZOccTaCy51GSZ/d5Q+GunohnYddvhWgcntD1r9p56siT/pWI7TX12UlhpUa4d6aodfPyDYDTWCkyiPCSBkULmVVjHIUNtanIVYqDIvDSz8/5bToBXZJuOiXT1YLL63b+e8fGhvVAMagNTuqdAnsQJahp6rMsfqkzJIr8L1iyUNQ6yueYqyrU4vfmbkIX5850ffpgJ9Ut0TZB8O0vscj34XPAETNuK3fbv68f2RRiLtJnLYto5fBMUv+JW4glRV6WH6QR83HoBBatAtc3bCQNrkTZBBIvTVcA7Fw+LcW9PKo4SfKeYR4tZFVRUZYC6s5IZA2nq5rjlhCynmjjKtJumPGKvRS98e1Sm8cLgGmuRRyEkycai30N0hy1BgfjL/TC466FwjsXroCy6589g0jKkofSr1Kg/Al3Gf4rFImIQ7UevXV5NBusuhxziJFXcsmfAXmBUVQSL1I9dAR1xxyV1412btQBhFgfc2P7+Q7opgGwvvUM4kHi7+kk4/1yaq9YDV67Mqx415bv2MFZWEaH1YuMTHJ/0p0PkCnn8HlC2fwOmzpjTw8WoaVNaar8Jc9xoBdAr3dzyu0AptvIZGPrMp6Ko8t6awGjrKTYLOgEybRRuLrXzVLMpm6AtC7RPkQ8CoM9PIPU5d4lIG+WYAFZw8Yo5iGeyTHo2v/9Uz/8qOh2uBJwHf8/NylV+OGEz8YlVz8tVaknIOb6ymimxSfwKlwT4q4Urs6W2OK+puCv5xcDj+6Nx/1c/3tmqWLK3YHhIcemj9F7BCOxbkp18jGwlZEDGuWLwn4AymFr/td9o64AE+noE2FczmJRQ617uH4RvLxvKolehxFfbMHn7IdBgayXOraOAjBEbJDNi5eoEpe4hPKUA0EpyRYDmAjqRxjWPmWxXNW8lJNs4E0CDmqFIxUyDqvecsZ4YpEZthX5W4QqBo/y+v+gbep5zOIwcBr9lhds5IPh7R2XA11gK812v2wkOx4dPO+KqntR359nxTiThT31OibF2FkLxJj2SBT6TnFc3fUrjGHauTYq0s91nd6fZwYeWG5kHCUJS+0wcjnDN+5mjehYUD/7F/13Ekt4DzQySzmBg9Mr2KT6YBfrDMw9PDApO/kdARKidgj/XrywuZ0UavsiCE+ZBJ8ReOYbEnotjN/0zPkRqHYp16pw2a4tCSjgb29xjhTHHf+ku402S4OSlJg7DNLJ2laQn4zKUmVi2fe52jd0sYYvG6GLL7NBH3QA79QtfR8/vHK2gdolrNczLMwq5/NbeyVAqcP0peJ/GVNmghXJYJzAXvvVd1z1/1ZMIqz6sm61b5E5XReHsQz4oAPoX1X85LdCTxait+FivhBxWVIVfu8S8rH0cloYXSC+GNZAl19ZCEfHIzHls/hFcmzM/qOm4ChBor6nOGFCZzz0/cqw+K+B2csguZScQ04vzFKvuNCnvVRye0vmypWLjOOQRlWdmWk4zak4JMY1VOxC0dd0t6Wk7CGLaIF1te48OxnVdexv/xiUwaYv5052uqPZ92yXSv2ltjsK3mvuXVVAFQAxRfi+QuTuno84vJX3qsk+1gWaXfxO0Rgk0tdmQg7zu+Yanln4XAYlLGHnTyFsegBKr16bBf+CJrslIIt8ZWmEmyI4cNbQgrlT5ugittb2zRE5EAG4QoEFclrrG0ZlgtaHEU320vtDjPvRD9agrK9aB17o/hPe7E6mEyEvKdpfyAwc46WjvBqZfzR0SjPZLob1bRFCvv1QX9j6QVHRzE+mNmV/Zsu8SWh1yyPrwzvRR0GzjJqNzwHJgja+dtTHFCgnUSZyHvrtaRuPAKKCBmn6VbSlD6bxbphDMTk7paHLdsYcKWP+NS3MJAvVnJnUyEd1QYcAIPpjuEHY9Ux0hgAQ289pL9AVaoL+VzCQ4oDB7aX4uHKZqbPMzxY+gwsPL6SEsEum10ivkcUDN6b5a/9nOg0XKaQ7tFgTDGrZFGg+iOj4Mnj82/2byysbjrzE16PfqQeyR1kGfvb5P0vMsqIPiUhC9marpE7/vJRneWyBKQt2kel2wElaVJj90vczfi1pUcNbT6RzDDRK/CZwlO0ow8nIleCh+Vd/MIGFwEy+4JWlicLKjFwPoBAE5RDVT59prJhPpa+7Z4qWyvuT5kSAMvzAjnghSHm19E6dgx7wUQwSV4gyOfKVEQdsb79bUwezzZGkuWHlf57EqzfOsN9EPbgWQxhgeTHZsYAEoqCGb9sa2JcGCQJhutQh5hAuAgq8rjF9AZ9/XgjHl+Uel/jeQTWKVPlk3wuhne8qi0I+/ndF37m7qgkVxBBdEFw/qv3n/On2WTyuktPp1Mizb+3aGIY50TMD1jC+id5wvdMtFIeMeUKkKc7K+YzeNHDMtoBkiMyRsGS9nzZzl61q/X5NXFyVkQis3jialRL9WWn8fHQBPscIRUs5t8OuD+Szx4lzld2p+QtXNEmCN0/JsjrGhO9Gn8bSnm6+QgYPWy/gFSIGi0CLL6NGMnUgLmTO8mIb/eaaIAUCV7FWSN7OiqCLsUqz+brgydsKjZibnUyWn2jO+m8aE8EtHLE1R5cygRmuEFhLvWhrFjOzfm4ZRXeie8tAVfuZ9tOPitmH1XRgyJi8lp+4wjMH/NgEBaBj9pBw5FXLwMUCrlNNAazeU8KsK+W1AcGg4wSYQ727MyTR/SCRG0iSt3JF0ezNGRJPhzbENwtvsgVs7OJc359WsvCSsR4Wsu1aCK0olwNjz/sSsiUJDY02Ty6ySFjwhMoKhvxdfqzcHnI+MvFZljx/fHOVLeykf/2Y3xZeImj22izFzz/ubahncZyWyRjCX4eqHkfs88Ynur9ReeCDn6MT5BTWxREE3sYoVGfJoio1tvaOmAvRFpnrLFMXNkrQ5FOXkY2//ssVz+q2QCARb0ENHVz1sX9tbzZdW+Gf2u4mlD+Cz2+9hFmcihOvC4bkEx+SQ+4j6j+oQhPsfbZsWo2vuyD0U+ICumV4diwVKFWYoIfKjDEIvDU5lo6soeMZO1+02Rk5c7zwl3Lv3Il8dsWrDA18V5PpB+kVERIEpVlOhIysI3aM3PMDwht0FOaYJuqnpsIhcxK1nUO1c3OfFF5aWK6st3EXlAn7fRLx1e0uP+Bu3Bl/MeV7IX/TEZhc3d5o/7rgyUAYqoJIL+j9NIedzqiBZEm+Wh1MSLZ4bsS0bMipjo6EohYRqbumVS/SNfAM1U4tEtPQ812YyLPtxGuIijPYX8LlPkhBrM12L/Mwio/oEGk/Y2cTH7wzy+FtlMo1bIJh4Pv6u6aaOpH53me2VX/hEla70SrAqYXWTo/wsQLRRiZhIIlMKsTywTUAwFK7xlzLwmNIzioH+FPhyV7qHN+uoOTVBJVyvzwS1sklxLZziUBhTUi8WgsPISbocplNhz2CFSj84FjauSonP5GuxBiMbkpykSgag05sm4S0EfhoowKADgdJU76q7yZxg0+aLYT74POBIx36US5fa60eC9kcpgeJDXaYntKRjbc5LJ8PjGou2Z7O0rN5LYyAMwNk+fPa0x/Qdz8bVX66kVZ4wNuZ+ls/2b494rr+8qmqO1hfBDRdy1nhHF+Bsr9fNNHHt4ezbba0eJZhn39wNi0hlvBnv8xdRVLkiPb8pfEsBQzK1OwE1OKWV//FNVzzZ5ZL2a6qjOlAD/uB38sSSFOnf/lY2V0cQo0JXlxJVQaKkloj7S8aWGND07+HAIHsYh6KTyx18baxrW10c/g4/aX1Q2uHujMUCV8/YXgMtVoq/V3pfN6u/1NtgbEtjS5YFqyY6QVqdexfH5KToTaz4lvfQOJl1OWoVHoHwmu4s/pkmwQDY9dNx/CkolE3dRfGvQmEkP9ergr5JmOJI6Hf1sNlSHrSqc8dMVNzlFi3CpVieGzikq5jpzhfXnBay5WdKkKHg6h4yXcMKq/9k55Ver71fczp/7D/41gwj2joul9saxMHN1G1N7/K/xss2BHQfa10Yo0+/3okcSTaePnHj3f55RyK3Y2vcfOl0ycyJ/LbwbYT33UQGprNPoOilYohTU2y3EWzKRUyO3+ZqFlat9sA3/+zcudU9bvF6nyeyyU1TgGu1l4JGFAlahodiW1plLQfAIruIGrQWd4rPIrjJ8HXO/7qGqkO8+ZXjoJ0agVZPDrTtqemTROP0dt7zf25ppXFq54bLtknHq3ycPJzeeTjUleBKMUCwHPeypK5aempgyyMdMuM7Iq7GkenSfmCDUujCtCsTBntSVRLGbzY/iNllyQfMpf+K4IKXmaBnVhfX66bVP+ut/zuPVfV2nulSwVR/5AtjOl4PRf0DD+xb80b8ckoR79LwBzqXM9L8DgkE1udddGr/6vB3zDu8AA0r9hAtxdhMEWjy0elIcsOQfyrUIofAFjilagddcNymBZzu2XLH9p3uHLId2Qdv3K5+G5BTj1oMmjyA6eMZ+2IVm2Yc995y8PkCLaReriX0Up76BqbuwbNv490iV43oXI0AUEZeHvlWt/bbQKAg0WCEexijoAIvo8gifzw4n/uc9kNXzxq5xhJ93cLIDvz78aW+5hGjOry+k5fmd8veAyi3DEKn0aiNK8FmvNZUwthzPCixLznMw4Hha4SCQq5IBw1rSla3u3BIcxNW7P5gGsRItPlK30k1Dp0BN8wFI2CWtca4vlWvkFCiiuAIwUg1stKEhjBPJWD83pOW1XBLVKopZq6kXVe0Xx4rpeg2HXY9pbrWHV4ZksPWzOSnY5u37KfUZRExT6kKQTQ0s0xWcGY2jZHTFlGEn/qVFWOVbalHKXF78+RElp+v6wGZIEa/GIVKwDu36kRH5qGNcg1w5ryPpbJORYXwHmDQ3xpKhz+x/gNUBzomPkbuHZ/VCq1qbkzw+k14qG9Lsg1B7Y3ENGy6+CnHJntKNtA/w0BCm09e/KbtkoYiOSxwIxWsv/DUntxK787bAzT4q7dD4OPuTihyQFxWxyQ6GzDgIoAT6AInYxY2geP8ycdt2ekB2xTkeo84WdsfGyr7cf2XuP/rk81QOOwKm7UuLBu0fI9y8iWUwFkq5Y4dOpl/pCKSb0hceVdCWPA37GLiUUHoYor5ieC98Uu2+ZL9zc9qpLAHiq9oyjqF7MdIVDjCNFICdQzkZBnpUMFxwnQVrf/RDdFwY28L/BlDcZ4voD56S87X27hrZV/qYVicrKNVHK6ANS7gUs5tibMdIgFA4xEB3Mzcvnh5UH4UBd+zJ26Mi/R1m9BFFsfNS2xWLQkPQjb+X96acfxub8rzInXsCT44CofPtrRL+pfWVCHN1/mwNd3C9+OpR2vjsRCpT6J0foqMfmCusObHdJ+7x7aDnPD6POfFpSfb7d8O9M0CKplMP+6bheGLxBG8uZCIibWTVVHaLyl7DrKfZaxb3WAngktH0TbqP0X30r2i4DCU8qdqnptEEtpEunYNfyOR8uJaeIpfOoQOp+Oha3iofKkD/R7tkBiF3geI7Skl2YO3PRz8Li65cfLne99kEce7FD369JP4W1/XW6dKjtXkH8r5F1VDbIHPtrEF1kRXhLBHLGEzRZXyQPNkQxM9XELRuQMK3edMtub3sn6NIgeea5YGX32gvtrTC2o3A9GYP2ttUns8k3WCMMy9KTPzfj0xn3gC+s8h91ptH6V5rxb9uZOMZS3oETSA5M71Fnbk7TBSQ7UhPZ66FK2JekFrPT0vANEJoAyNy5/iqNORr9nXfsr9bA3bqM+cuiQ4cTnisbbkbtvaghThh+0nGbOks3fjcYTzbabc15BW4LmLXLUh2H9BKZ6j3Ltj8zHT1zpgrP3KC1YA4nZIPkFsu6viQkktV5VcSfQMxY8bmhQILJukMi1otf0bJPAI61+jazdoN282i6eB9lnHVbk9It1/MlciNyAeanAap+8hj1O++4bQmytMdZ2kUpD5X2UHG3JbCaNqdoRA8Go+LvX4bTUutr4cWLi9BBd+JGgcbi+3LL3Z1MhO4/BLTnmEaQ9M0a/ekDn389a2h9QjhjlpFQkNLfhJ+uw0/strfbHpqWyqH2S/5cPrWDv0F4v3Jgvqe71MqdqDCwZcTt7fCWpx3+OaBTueDxblnQXfOTqeMU33PC8DC929oiQUrl+0WxDm6R/XkU+N7I5ZUhxMHEV6M385nyDir88ssH9pw4ZJYAlwCzbZuT/ZkQmsvZgNIMzw6sMjMn9tRZFuu0dl1EMd4GcGOI2EXDerRLQxrDLkXRrTe5eECsfhAPT4K6fz3nIq21PcyQLX0XQPiA/MUXRGVo4fCsSkR2vsZhuZ3AFC86YlkH75AND7y82FprNHJLk2U3pHh1boZ+F+V+Wai+63zRZBkh0EuCP0kDH9mggXa9Zdsb12oIL/VrP76K/WUNbZr5slTo3T/B3dAM0HGFhonD3nF9YIWefmCctK2Khn+03zRDlnEosEfRKsZpUwyKLh20dFpDFaTKpS507jsSeWyf20QqlQpkYvxLITIHJmjkA77hCd31qrcg3ZRRA4/sGx/mqIxRsCCWe6f2ml21qhYXdJP0PFLeudU8oSDQPkUWtKQdZceXLWBtxQxnrzYObCvvE2ZmYv0JGoJZ1CptemLn7TYNTesZkL1SF+e1ZW3qDK8IXLXLFfggfVqwtAGwMQfO/Ppyc0q9JOiU6DwB6rvJVDFTWoSv121f7gWOLzwqGOIUrQJ0hHmSWH4AL9jGtJAcXVSL6ObnW5vPTmk6F5FNNjob080d+PXaOkgdVQMnjzrqcdaMCL/BgO0zfgvWQBePDavPg5Q1r++SxJFj0k5fqOiltgHHlHbFL2Cx8c8TjKhudfYOKSTwqMEjxy5zjXwc+MzXa7k3alM7qUBSqUpGHWz6UMdfo1IEt0tIA/EbNgtH358+BSb+gFv7UC/m4AiiUCT1rzPNYyK+8WicI8F7iXNOEEwrcTC2eVKVJCr5ReVX3JJXDu3x+GkniCOSm+bg1qqO5a/pK/8tCl14no+YkZge8wiENU8XlJlPcGZlf9uvqGNaMbDO/mHDVkxp16ozQZ2yS7XK5fjkVy6HwLeuAfKhsVqroPFpfbLjQBmUfeW207fviwW2JWc2jojl4FZwlfHZ3w0cmM4mEDfBltlfMPjaNXsJOwfd48V0ALpfj4iOMsgdCkjBw23wVWzBRTZjSergDj78HW34Cdn45Hu5sbrUgWzSzwfH49wJuBsjbzAKudp9D422HcLgiEmVXQ+y176tzcvV4QHDos6QqBoyYI82zeWAdyaMcdqLDvIG9GJCMdBJg6iwE2LDkB98C0kupsPkHo9mrW7DwhxaK4s1F873jIp71kdaicm/iS+ONIJYBXCn8F7Y43JN5V5gD3+BbCMp+FOT2RwEYCG3yiQbt4ChdOIYjm6ZD8SiFy8dX5rFb6if3XOBcx8dBAEBbZnxI1RPRWVGCZazJ+HXHOwUNbSM4s1tnH+4vgI01Rd6G0noYmztmGLxQCKaj1HrgZ0AQRPWrj0QXAlQXXwPkgiSBdgD/mueTWZVkKhqO37vXWgNh0hyZjAq34IkqI52EwCHEiM9Oa6HU7tGGZ+LZEnQDFlCou0SQzMudVlLaPdrFFQTjvA31mcBikLBjt/MLXPA0vh8agVfx5XJVmSlC1fbaP/Wf304/uaaRuT8r3iD3VJ8B24+VKuZ1lrD5Sahv1oi5ifkduU+35piKZ/1o30qmnYJCCWBPqD0QyRiju/b9LvsSfhJaLENA6MYfamlu4h6iT1oIMzeZhROw+b3homUJtj3p6QJFXt6GLkiF3hGOtWM+h6TJBh3EvTvd3IJeeCthbMmWuEIdpdekxbHXgqYih6pcyucrr1V5pA47L33xAY5xp8sLUgbpO6Q9skQYlPJegPLWX/jyVnHy4V2t/1kT3QrKYbzZsXK2bd6VL7v49/DYF9zTG2ubT4WBRxWDybIyqfMb5snVfdy5vovk+wOrJVHBpQ09hcD6HPahu2ja3PYGTjOTnDgJbJvEcn6qoPH9Or3SFLv7RHa4zDVmjQymbfjX3KcTkNJEjq85yAWvRvsChz8ebkDDOkWuXWfgnts6XAmtBxogr7Wx/4byesiy/cvfIgNBuM7z4P+jUcpjhOS4HPr7R6+hpU+YP4J8+PjLbfRrrHilzQEHIMkXNzMoAfjcuxTS07fLW/m319rNZCcHVsvUm3PsnG5TL2XPxwsm4lCKz5n334KqgyIe9sFiE83aSlLCfMJYEJFqMWSE8VRVpvJU+9+0ep9ZGPLz7gNwhAOtcIMMshSay2CGbIddBidNe0sO/NrelKfz1aNmR9M1vJSwqUO8mSMLqlncdX4mmkqS4uL+7T41+nkjzpvh1ysqDkCd2upHIhLnRgJYVhgfH6CYK8spXigoC1+rx38FfR6L4yHZJwOOqOhVAXu16bU9QluSetYeXapW2Z/X9PNhrXXN7y+k8gOPRWemih4wkAqdoE1il8MdBLVCH0UReFcpY77x+cWl+P0DH7hKE/CmG/zJ3IW9J74Ob6uWmoCIxWDr/F+eCdvXGWb6B3XWRX++S0yoh+zm2nu+vqbNizMIxKZ7VL+zntd86y1+Ox6cYitpQuqdszH/iSa68AaO/BzamlUw4N+AuyXM6pDvfvR6z/DwdERpckKiJyw7QUTzS5Y3or1w2Bby0LdqD3pOPTEjdS6wCJYCqGg1kvHs2k4ik0rtvcir5TD7n4R546Xpa7PkdcJy2HoMgoe6MR/A3JZ1grxZvRyAr2iyxow96FNtLXVcCcOsysG7h6qrj0Rh8irXzU3nUP1GXKA0di5PMvx/GrRRwBGuENMQz2bcXqGOOJtzSq15iQ/ySF/UhSKt/BLG3KcPGGbI7TaSITiG6hscUq2f+jMs/7qXhvizDdpe3crs9iIppvqr6BUObPIY/rbdE4FBQPDWFG9unHO652yZJdhuR1cL+FR/7m4f+wL6KW+qZxJWaYcjaSsn/GpQ8aiAgaUgVDvtlpLsK1g7ifF/I38zpo4ydL9r/BK5FXJji5fuaB1YRiWYYSf4Hw9JPI1ORKNI4pYK4qO9bNxNvFs633s9z7Y4Qa9crwA1oSZENqbgZoSZRLhtrb2f6QeBUSp3xTMv8YAMyZsmCQ54dlz1Rman3agKd2wNXEZi3M0qMTLAxES26ctixdUWgLJn6L9IjdC/hS356kUeJ+GL7Ha3xcMgzG3MowCoQTlvqW9PCA3PaHxSflF+8ITH4/ZhHJ5M2avjIcFsoNAT7rvwwtfVooHw4Snm/idMHbwMKcIW3DC+qWTYXrPvyCmvxJYflyVygMJTQRbt/ivY2Ba8kp+bIjPtjX7OwLDhkllY5lVPnXSmVPyxoUxuLCQ4WNAnlEbx4uk2+LGmv/GlF5hQIFL1fQPJ1E2Ibfx+JfB1x11z12RUXbnZ3pV2NNUuMWcFqJhv1IQ2YYL3U9iyMBZl5g/L2agnTTpYfwECh9R69+HiOJU7An3Ulr2YzAJzI2id+Gjai3ECto1vsKP/JscjacRj9nyECjQBK/Z/mVxEBeZY+eQsEeAj36PfGZj4FD0x7YQ7BicHIvL1RVVBmEIv2apLy78y+iFIAkI+XWJh50xY9RlWDMZKgQF/10IbQmKsCstX9p/jdM+HY0+wVLuzq3oCvDiEBSi8dr+UZn86TUjjxF2NA+Kbij1SvoU5LSzjxv4MQTeT5FskiVl/GVUyPRQFBTKr3ELdJIknM5fs9SYONZSc9Z/hXRTTnn9p26NarJ2eVEFCPiadpYs0Rm3xSFnJeGlNoRT4ZRrt41E02fYA+8IvyZWT/qog+Srn5hLn7QctmtpH8H7e8rm+SVlzZLlsh86XARStDaEL8017Qq6ol9zxuAZ4cGBEoL0WhEAouGNln5xx4NFtEjedE21mKCAyGqYPe6hAQgIRPw9e8OSCT06GI7uzTn0AcYrdtBv25Lg2hi4xi+rc+bY3qQt6B8YTOXAK8Mhoeraf0PQW/FCPtu8xJ/Cfh8EdXLTF6EBXKmF4gkar3f8wcH53OHHPUmXfxXvLS4oFtNO71iyZqVfkrHL4+r2yQmFB1X9Tsgy8r5JwJlanYdu+oeKcJzaOMXg3Vz8881wZSuOS6eKD/LucGu/7Pevgcn7Z03eg6d/vo1v28hNm3BzgMqTTXy+MxqG5gK40FdwfPXGpnjAEBd6Apd/1pPUO1ARXIqD394nhC860EvrrPG8qXv0BTMO88B5bDdm5cGmSj32nkA/O8XYTKaf/FcRMSJS6OQrEEX5iiyTA70c+ov3v9FnCtqYBkLLUkEYxCt9voSDLEZNf/hLu/hPkbOF2QQ1rbUCcdMEFqznxuzW/hioztbmGhfvM427D4ptFumvGcF1EoCh2+sHAsjox7z5bQdNDvZ+KWTb04aj8pIL3nLzMT7na7cQuD6azC05rkMjJptWXPpBwmfwKJBtggmvqkVTyTlg2PqbSxp62UZH7uWzEriaXR7QSas9C1yW7Vdq73gWAIEGqu/2HwqcHC8w9Yjipcufo7baiTxZ2UFpr9m/7SauzlfZWyVPQN1qF5KtMc3cHZM4+hmm3Hifpj860dkT2BCRWdI5N5D7GwVxkROe9hHMqBZ3N593veNSRRmBfbFGZkOJmWIGviSg+DfHAOVdxcCxr/hXRKqSIgOeEVnI14R6xFIzxJpmN4nw66er8gT+AC/gCqVr71p+xp3r9y8TlrTTGQho9mRs9PNYCKhSWJk2apTdShdm4LI7FdSqwpnffhGLH+LnEXf8x02pg3/OkAvze/7L5OmScrnxhZWPuPm4TkAPYnZsUeDoguG14/ot6NSGWyOCGo5m8F7aVTgo6dtFNyraxWWNEQJvMdWflbRgnVM/du2rjVf5Uq2BS2xgufqMh5tfZTwy9z1duZtzysX9NGWAPMZjatUv2tVl63ioeyNsZMLszoK+kP/jjVR+9k83rcFfCaikEknH5CE21zLYCsJ5RcEWT83+62nLtCFU7asV5Vd92TN3cNiMYeKt0D4uJZLFbtYtmNjMTq+i+pSLu/8GP7X3oTP7yzKKiUTWG+G53nXrF2tR6QN9NfrBRICKn18LjJiLIc6NdohPYdczs6b9RfbFsCfaiqy9SFlDgBsPEW4/abDKHsBF4I9SN/aAQk1sBpaKpeTv40DuJfFAyiU8Xdyyx0LSWi5NGL/2FqdwkqO+cCIishQ4p5PL5OnToZvGFuwPcm1IZfVCLd9XPSKcV2t5hkg9qniN6Qxnq9lb+0fDzU1Vs+UKnkkJKuBJm/nckHln0CjPDe/rCAW2Ay5hvCJRYJDm2SSpekTaQXi3gPOujapr8AO2/ibvh9PjsPr2+tf9dyS+7DLq5uwkN7sP8pkaFBJu+ffpd90InqCIn/hXZSLHzyDwIzbKLDVF1OQ//XsWf5kWCo5Ljm09IU9jDhuicHd6dbAe3mbwdRioUrx8Tpx62QyNzQ9KSuwUNnIdf+5b8b1SYdXluy0RrQLnh9I8tfNNmu750Ru7BFbVyPeg+Ar8V98Q/Xh1i/hphdtAklEJW6p+KqxXhULn17QgGn2RLX8u8G6kemblYyDAwPDKJP65yy53HKPDnikCYHoXrzY01Pgx6od5cmvprsdhmHzO4zhmQsn+KRwWgNOZf5GvxEV6b8uGfObqdsS8VQyZzaZ2hjAcD5fLikqBShhomNypd8XOVx4nkotAqg6DJntXvkeirlp9GaSCZPvO7CIDQAhDmD9WXZ93QwsryOS/1mew8WLtYA+KNK++2a8w21DTxtzjqzgpf8fUUs9O3szFiyZlaysDRRW+9k5c+PdMx49M/6VNZCtDKkj0qqPrCZxRH3O2bsW/5aFrhRd8RXtelVSE9UrkusFhj0Q0npShNYzLlw8ykVjOEchBhRAVedR7mAXmg7givnSIWPWnuYjQ74sM1DbNXkuZ8k7UrZez6ff3jK+s4OMbC+zp5+nec7p/yTr71xM6agiGB41KXj1A2G1Y5Tgs7g5pXmHPU7cHSjQys8D85bKLmN/z2+NWRlCxDcX8Qz3U99DFs441n/yflGi5T4jywZ6HoR3AQbYqoPAFHvYM6y8c7r3dsspCOh/qj3HkAGyVlVmZfUe+36ds26u4X8rZQCCW+WBprTYDxQct5N1nWJAP31gP/aVUdsW8aPr9TTkS4Ck0gt9qRHVx1AvnouhLF4pGgOhvzjv67gRdbdrKHfEGKHFuvmNaFCWYNylmFeN+gtKtw84XwCNlv7qJOWdPUlWgLrVWzPdqPhVVl876lUrb8cY+2XWplWboIol61+rOvGL9wd3fQLDWhUl1oLwEwz1hg5nvzVbU3kts/ZTEor2PeroDGZ3qxcWoa6NRyS3CIjolTx4WvpwyRX73SlsNZKYo7SMNpLhwQQbC8WXQCtn3AHWV5ElRrP3egLNlmQPEpOXzYEHHHPbVuY0astuHqVR0UOtv+RezPkzAHWOWqMoSjQWf76Xr3cJFYniYgoxZiL4mtxdUm4MD27CfNbkTfP/zeHwhNN6uTv9smSuikH6a8cD2Lbg0uy8Nq2L+1j9ZJQba9bK12RZ++gyapLKHWqbhXVn7FE/X3aLsbQPqvIL3qSlEnQ87ERuSsESBnSyk8AC+XN5fOy7AaNXdWhEir9fqfM8QiLiBIcCi0djf8IZLuwKuqkcFFHTD+JioMfc4Mm0w2Ac8ed5IcrkoEaakCaP41cAAHXCs3wpY/M85nnfrT6gFreHWVJwLRu2yognMoSr6RH0xrs6pPu5zahhaof+TcDaSan7kjYsdpVY42/6oFaKsHrK7V5zRbKjCGpXzkqXVkNQa5+tB2aLbVd/dlfr51JSbjhB1Dal0gpx31HJES64F3rJRKlWAXywFGQdVXxx/TSGF9MQv/J8Ls/NbIoghNBuQlwXoD5Fo57c0SZ8C149E/nzpF4f9+B/QA+15fJO4Z7znGMC6/qGchmzJzWOn45fFaNT5QlZ3XllsbB04tUrAw8bFdYAgSyt0zUArc4aufxMpTxA2hsK8V52b9hiEdz8TkrQzr9HXqkuH3wPfXm+j97nHWdR2sINYB+HrA//DO3ooHhlD4Y+dxgLQ248Trko3Qr8qXDmuFb3ls1nSQhPNU3b25HBultbF4mCQySthgnXcUKRc40An/krpJ+sc0fRtuTydSK+nx4yu4iVkN3Umnd2QQcJcBa7BbZP7erLQUIPeqksR/wZnRIWlWvbH7tOnP1EiOCjGwSx+y67MlD/ib3vICa8qi89/2OATex0DvqISHwP44SwJJ9KxaVIQnYQlaCtl5r1r8X56TnIlf1Xa78ciBSGiZmwXiKcJmxl/1s92h74xgBOMjg0ITf3gPGtI4WMeaPvqchJGW7s5knArAPQSI4A3FVsW63wkXke+svslcwH2+s5GV2mz2tsIo5p2r1ztTUP/Gc8+f85AWZoBH9DcFr4+sGOnISQQBRRFABV/ctmWXfAYfAM4HCjZisgatwmv+5ZDW6rkZH6SOwxPpoykUNVt4YMvzhgI+mVcInZ80DTep1cZObFFdqhTAlumrN0h/91FyYnW7Wf7a6FXQYHD+bEHUXJSdptKo35yXE5RT4QviNl+aB7V5Z+xoOMzEMYwSwq3Jx1LYV9c5SrbMh5+AlByFxsFEd/y+R7LCMKbe8YgcNUqOQ2LEngNyXT+ZmkpDdUNSjjSk+pm4oV+tX7AT2GFrfjr7sWgrmEJRjrJrX4fvB8Efz6iS9BMfsD2EcM3xct30VgAFLJximILma2ZccG3zD+g01R/HO7CqmQ5k21+YLhaI9EW2X1CCVqKMKVTnGvUFTHcJbStlza/oFPjUb5V0nmCGGWk9uSvXDGKEMYpWp/C5OX+xIDEb0Ld1ZFygADRwx17MkFlhuGorFrs9obqD2isIw3ghbPd0XVlZy0kjW6fNfEjueXkQHqMppzuiO3KourwTFV7gy1Ga3GIrzqrSrf5p+dn1HrmGPKFlHigK+tg0ZEAontsD5nELF/hV25CXJU4GfF9YKhLOXNlDxz6EEcoyvZ7WcJXbBTFXp6YZpm6x+DL6pxaHj7raKJfjXxt1geCPdBEQiMXCJ4zvhwtB/e6GG8ixAifTmt7ouL2dbBddCG4v+l+IK4l2aI9Zlj4fIwdznyO3z2BSP7I0NP37WL9TW5af9v0F0ep/T0km/zIrU1eGlmJ3ZcKFi4fqGb/08z3A4pXsrR3XkI/bL8Wy/jFWGu01xrTRKxHlmddO1pGefKgx8yx0dVBuvHkjd+HCQxZa8g3opN7lrUv+ZyYDEGZpCqiWes6Xh7Z95eXcu0qv0xUpZhhN7tcfdd6xH5mKEXH8RftXBnqaXqExTV8FzMvKsf31piIvCwXGG8szm5Fqy/5XeLxLAXxcccK4T81S/FOMNegWtX3MsolPCqFYS00yYhJRpuaf62O2xKVkswgL4KJquteto/2zcZdLnFsx8d8qyQ/i582LU+rOFK7LgsiB1ni8cnI9V/PF1Ip+u8Qjl+xpdZC6j7dV3Kae/26PdxKUlL8TYGz6mXzHxYNv8facfcN6TWZfwcZb1XoahWYZ5cMu3OQcYQPKcToEepzJvtLNdz3fbbUkUlvMUE6XTCDBWP6v+TofrR/Umqcjtae3coVh/SsSIKSjxpi/rX8M4AP4AdsT/OdNPPATaLz/S6yueJbAVuZVOBkfGWJg7eYnZSuRx1USgswlDB+6afy0d8/mqr7lfQF+Mu4Z/r1rB/VQ4R7R5LKukR1KiNV9AGLGp7+F/4bBG+fWyluUNPLQZ0RwFRy4PDOfTz1WqnGjwQXx2/WH82/sh1q+PHowzBBWeuN+5Atmrsrkw3c71D8xV+n9emeSrbco1+g16rDrXduymN+6PuVp/MW7KaOulXZOsYni/ugql3ddGO+BGOu2UJtkZMii6xh6/Mo98TLi6F51iqyHgL2HdQavR/1l6z0m+FoIMkd2a9XBJpJgH5Q3A+J6Tax+GozlONMbUko3bksnNfwpf3xY41Bi29+4Zc/g2C6lGzr8O0ZjlJicAQLwM6u2TsQO5Q6WWmCXonk6IJVBilVpAM58dOGmIHpAJ9LFyo9TZd3xWWRzVU4wouoqoyJXJv1i8NOUx7PGNQbFg2sufSNcaCr+bfbQvMI5hcNDyST9twf+2trcfpLR8/pPUMnw5QHdtOG/QDECWlZ/rVj0aHIE1NZEKVq7b0ib+3nc3Kt3w8JHaU+KWOpnIYjtf2zOeb7C3+NKcR2mrXDtzAnTWixagnZb9VJsd5n3Z98vS9IXN21i1lMwtu/EAO/wilP6H+tlNfLAnW6Jg1camw19WWrvlCIBnynZSbaiJmFAINelZO4prLa4idoa822hn0azsB+qwxLmqgysC0ym+KLrbJ5QFGkMPJtWb5QvEAGG6kxs5Fl/4tF/ddnAVxgsWxdwHaJReY75Jg388qF44akoHjlj31YGlWo1hKCNfq8ZGkn+7+uY2y0ez6m0BrOwikmNyiMNugec+dj5NvOcMS6+Z/iwS0uOJowbJSAkQMkbaWTQi3NDsm9U+0Rfh59YaNPJMNeMrJdkDy4rZNo921lCStfThPkH2rVvqzj1u2lki4d1CTw2FqHJYRqc/8qMvROKr6FWh45b6NZuw5HwUi/VRpUsaEQD7SJuFXTZEeaZ3BFJTzGuVmyR82hCWavS0NbJwyx/uCzeH0j/ojmNdNxHYAW6W9kd7TMK4ZaxkgWsHUFOgXUxIE7Q3KqeuDHJjeKa2AsrOaccScWOmPkZkVuP+aXG4LIfwi5uopfAy7E+1X2cJ1h+YkRrX66uzxfTXIc4Se/awEGBnrv0jwJCv8bf43xpQ+zIdCaXj9zKG6Ic91RYbxW/VpAjrcJZTqyiZlEmZ5ulVHdIsUBz889xe1foy1GrWTHRYC/C9wtoGE0IuUMQOgurKXfq7ogqWaI1y+2YOWymKXckv57VPBIxSjRJNHuM8RibTy7NnhmJZoUIaasWT5PfaJ2gYDRPY+Zth09nHTeSO94BYNJ2RaVvaMj9a65clSWrMgZriCEpYv9GxYmMmKONJ/bk4jpNYpHeHpsy1TUn5/LGmQ4ydttKeGbEbciEc/t3D3DjrNJFq/+vA4/MoVrza3BgFMHb5C7o6XcsJaJ24FGEGkv+/4Ibi47771sr2RNT2qFMxr1DD8NBehaQ/8FAvHhgaJ8JUFkFkK7LGG9XqFny+YJCA1QVZMlQ0kkHRwilUb3jL8z+WKasfPg8g910F/kZEu9yfsCx0uNp/gYRp4P8fAyIvG+THRPE+HJJMdPXY/7dj7NKCVX21FtJfffr0jvFOBvvlFaKM7TPnggHLS5Fff3aBSiUqB5qiTpQq/6R9K4/Kjx3m66p4PlvIsoqc1ehPun87cYC7nUQ+O/GNKlTdL3pfqjA08HloQ+sSIlqbV6RpJG59G9pUHttJFwn8fgqDPyBeSt5EeGcZB+CxEdYqVlD30qictfvEY2W34uffxNh7V4sDtqfGnfyY9AFdd9KoMqHnSHALAFJDGdR3XM98Lzqiab1vXV9d8UPW5Mobqbon+uic+GY19GNNFPFR5q9NRCZipAAjN6KgVotPIuxxcX53ah2pkMBkzktr02wWYodJbvBiF2zbj5tvBIyZm5wVi5a8UxTXN498KhiO5RPLQjKv+1vvZkgj3AokcDa64Yye8vT1S8OsMdiOBcjxcflJHYmwOOjDopjgxnULz/CtWmOCx6BCPhz61G4q/msFnfGa+efqWlSA9ZC2qZxODMxqP/ICjrSG1FdPgZsUThl7vG8imR7mhU3YK8oXep+Ca6srTZ/HSMELB3JyQvzhwyKigC6CxyDQRKsEFjEU0Bt8L8avqxfKf2r5qmQh5j3kf2r9PtBf9gPjpzZsic+JYZU1qKWHSYcYm38uuHExWgS9CKJRpWVnXYPHPHSLYrRBrzc7fvKv0SXAiyDQLdFFw6iyMsjLNEu/z6UdvfOK3QgFoViKly4fCj4NvzY6+nfOKU+ituJEQhLKAJPPB/KvKe4KnSw6eJoS7avjsxpyC0yyCyj02p1GcwsUBtPPFJqvTjL8afAurnqW0bTqK/+cRb0xMdPt8ebRtvp1lY5BXhZzbpiV4NgmqcGD7/cMqzcxqbqJqU7QMDEJcf5XEwcjWNz1euWfYamvhFELbOImQZ++PLxvSgl9rPo9ekN+nbgKSXvIKSCRVq2f2gfqMQ63pgtcziLG6TPWBT6+OX8OgAH0y86rvLItaLbchfai6w3fePBOIx9gik+esiHEF5349A1w7RbYVw4b5Mz0DksYLYKBWias4064gGJiGiaDms6/K+cq/ttMzPYxVCZCBrdIY6FhVtttE7vWmfodGb8YeoNmvH04yiE6dkdxSjqj9adeXLIHePPfBVGbggR+kqKCGeJphL16znzVYM5K1ZVTN6YRAY/NwFNMCgYVj+R2xp99prSzn9B5EZfKWeXYpnh7T6wFnbktDizG+NV0jRKF6xZDqnJ/ZTSa1/hFxnYXlHs2I+vqmYYyhozIYKVs89fSnnsohoEKAwjYwpJXDGO1VkRDPWaRWkDufG3L31sVxeCSXV42Qi07py5YwJwWOrHuyS+ZuM8mlOG9TKisR+/nMZi88rVAGnxhbAbECUcwH+N+BGgLPg+kf8QaoDbvEeK1YxtVGckPSY9jf68exw6i+YtLnj/DERyZde40IJMJHM5t90UYmS21PnHStiskKGYwmV0M97+suog7RqN8+B43QTfNHOnzRlPaHEVhyxu1juyY33oBcm02aFJSz8fUy9BEvN5ISuKJK6YN8FGkeLbUgo2XYBeWhfLqo+7yEKEz7y+gUHLFieDV/+Ggf39dSkyhD4F59vkutWv02T8+Zw1PgvUJBMXX4c0lisJ4GxzIDNzZldfVddu22cj8USVDKIpTr6r5RWNeGFshj49kQNftwjsgIDo8FqNoyBFeMzFgEHGaxXaidbabO1/l5xKNrLak/MeaAmBP+6MtR7ID7Za/f/WgFjRJ1vNB9hJEWEqcg0pxK8q0nJmn8pO/7xbKaB4Shi+TP9AF7LVkPc28KzZQI6XBCD/chU3fmIq51To1hBn7IVoI6S8fItrOmAAtqD01iJ0Q8fa9UTuXZ1INOjwPMB3z5JaEREQWQrt9HvQcZO/5RmVYZckskKkhOcaqyHH7Rr4shGLdwmPGQe9Ht5KWWgLqSw/RJkHU/wTfdag8ebpbK2tY4swFLJI4Jd+brerhWhX3AKOU9OyUipH1DZb8VF3bwmZ1V5GzfaX0o9qs+ryPeufnbh+qZlZ8Eq/xGmabAnmNktDad/Kx3tuf003l3xG0wOBzIb5fNNge9W4myz3Eck3z91JXiJI75ENkB1o8/Wl4DaAP8pZ9/NLJTFERje2zqCk/m4r6XnPkWbwzjxpaJMgCIHITFi84PnKfKKoLCHU76A/RutcjyJZcRHxemSbnH7Spir34RWT7gitA9D/rQJvjLFIt8iTY8OmW+4ZRafAOUwkYy6MKoNeLvpzIH1bn02Gx7kQsLD21/3LWI22HrBFQTb/2BAJPVc080tA2poJ7gSJW+vJSmj66Xbu1A5rL5bJ0/Ve0T2bBINrKwWab6AJNTsX7mW0t/kbbABBTuQOeriyt80OfAb/d7BB2E/PaAKSggMwx9YfGFSoCyNhMZbBeUiYUm9Qm+Xaxe5Em2rarktVUMBSIXPF+Am7BaTDaf0JZyppTSzjR6ldsAHcwkBiLMYNqwJMaHMoWD+iuYXp2wzuwkx7B/2sIXbs5gVirEt7y3uzJE70zpw/oqf6AzBxGN2HV5F1MrZAyOyHucsWV8Ueq3pqvtGSkAd0IWxPgO3JVtu/sj08M9yMAt1VTymqviED1b6a7iifAVNRqWRuGb4Z/71XXXzWSBIdB2m7EWQg9Etu2u89p//QtOf+ZUx7TEK4kTvQFVaFHWoYJ3ZtB+FpJDuH/r4dYUyzTrHv8s+9i8UMV7JnjyNmECKnTwfE8QvlA+Jd8yWPi8zmvWzdnT2VpDt2ET19D/M4DYJ9HzseJfbG1KR8pp8VSX2WNFkdYvPqIT+M0RNW+DT8PFJ4Pmbwn+7Z4OiO5EaH02im39/Nb0cEAhoDu+yokw7kEjxX3mW+U/o/vu87d9/4f//7w7waRBpNvpR3ork/qVh/74vh+vF20iRDcluzYMQ/4MQmM4tlwX+efinCLib33N/x3MK17cQ3D/Olenv55NQrfuAh5Oi5ifjbGS4jE+vi0GQd6uxIPQfZeeNTy8Lc4myxy1bzp2P/zUItzfFP2N1CYkqfYQzjYqEfwY+daZYTni9U+PM1iT7ykArRWJrxVy2g8x9xdi4DUxA0l/grHrMYSpXXuarv2FG6THauUA6tSGwtFV27CBwdtj3VKjemLwbnwISGd+75C7n9bs1cBGxAJyrqv0qk7S3pkNEhe2q+OLvTHBY3lP6mX81rTeDnnl6pbVXGXdfaqKPuIStf85XvSKuqoA6JMwiRR1pcxiApAsUrrmn0CD6ZgO6Sh+6gb4q2QfFbCgqPkpWfOTGImcIZHm/puevXev7B/p13flwLCmqBEewOkx/lGFE4fTa/0IiXVsmx2Wfi3zGJOUad7imN1J/03DzsMLgL2eKSpApauccT4GIFEaFOiLjYgDuFU5rVGdDJD2ch9cml0RdsYFPcCDeoRkH+ViVOJRVJIwspOOYg5kduz+C2TT1gjgIxO47r8F1eo1IbZPRZbjPq9qDbNYw9+CLwTkQUi1ttYf6SUnIY3cE83eEIi35eR5fEWtwRJ43j8B+t8EdfhWx2n0+szlPF3zzEsfC2jQlkkf5MbYGarDn1+JiBFhBiaq4cvx3sAW6lUeKKRiuRz8ciAUGhj8bdy+hguRVMynvA5hbKM7+u87dKcLdzq3//jHrPNYXR9Cu+AsT1x71U1Ei6efn828PDrF95r/B0LvXERr9NfW/mY16ewR3R8XKi8DQnXDAWufzeO5IsC8p4JoZZtSKzIgjnUjDHf9LFzDnWvuQBzYCvJWr7BnFFh3c/vf5z0v1iIg6zi5GJQH8Xb4p2Kj4fxdYtIvAIhNc7ofBaB1h+tL1pqjKS6+bk3E85/m5ox58zLnlyfAM59OMCfF6Ps33x3sYT6I/IZAnRC1fbVQcw2NSxQenS5u/8SKs6Szzn7MHJQ5VBrqUsZRdPstAU2UI1g//myWYhsDcPjL88QAwbN9CqWgW54/8eFQEn66mnORhbrdP8Hen2gWLCMlSUHm8jD/qRXTXz99Bnq8sQx8uSeZvjVbe73fJqZUqEHhfDfnMfAcQrallsbmu+2v+5jKw014XROCY67MKbvPCgWQi2e8ZeFc6XMbf642v/tTofwVFnAqe2fy914dI6cLQ6ajQneVsQ2hryLEerWuX81c/9MChwyamnWY5F+w0yHP80bwReRftuykuibwtc6021hqfqX10zVnI16T7lPC2DROr1qLQeky+O7boOxgY4YxX0+eE5Sxpa/JUdg8bZaNreYbIYcNOKQecKssIBZlXinEkk7ZFn+3Ba4evHJjbBDLSEmaAtfhjOgPZK3JcV/TcR2Eea0ONgVO4jl9P7RCwIYOxvKojNBfJa/0gRvYXjepogHcG7mL63ePT06CdAedOW671/sjWacm289J7zyZ6lCNx1Rpj5TW3TSWKLg+MChY1BVsKxNoTVImE7o/Cg+3lb6mh51GQXZ59l38DhNqHlk40IocbjW4apMmLhqL/FRhISTmVgtB+M01aC3DGpRMSehDdIT5DvoRcnD1pzFZtvVJ8x8nvC7EOPkt47+B3ePTZY+4TBqWesOpLRLEt35P538h79xEizhlUZ2vkHzPPI379R0FgF8TeL9yl2DxGxSkvUBGK5hmBMdMgzVGtS3OW5J0kWIj61bmmLb1phS1vte5FByqRyPSIz8uByFbH69451MCTLw6+w9271HjhqvV/7R5uDjvoaJlF29FoGhCkjFtPa5yyzUiQYmNwk0kvbdrLM6UFe43YcM0QyxSvETN3vqexD+D2TBJLJOPfwRR9DHZVVca2zGODMxObQOlurhSxzuN7/Spm1AyRPGTtVqM5N9CJa0tWRtCP59WMvKQxh3IqAk9O3pnjVaOYoCiepb8ilx4txjlfDL9wahGaU40kP/mh2OHEzpchiqGYTGOt/qoAebEQlg3B9bjTJXDfdns5KassUfrfEtsmmGFTZuipYu+91M8/fPosUoCL6yHj1D9AI08csI7u/naI9w/FNG/1JJIr0nUHohWvgTH7Yyqz7V/Uv1+SI4BDICRDgxlEbGnZU7AWGgKYoXttoZ8c5KJ/pO5rEn8V2cAjQGZRIBQWjs/dZHm1coW6BcYSiIld+uBxTFe6OQQ7yWq2aW3qh02Aupc8JM+H6bT+vcXvL50jYicqzRB+U/3LBxC6A/qxaIV/Op15V+5bU2Cr4aNttU8yrJB7mXIofMSV/npbRiWv8JF9guDD6ri4CR1iyHo8wJre7U1wlm2zf6+5fguCy2W2lpizBMvabHiWrxBggcLWr+24j3GDez4/YjH/EF4EI7oe3zbPXZkO1zdRTMl53ArDvpYqvJ5KqExdh+6JaxTgMZMLp5qsRGx+9MGqe4VvhPPtgXUbEjsIWEfhftXEIR3wja9I/whlSH1oQGk+eZWau4F1PGOOzq82CQf6ZIP7kj5LXswff1EWwBP9vSjVp4ATWhxW9KBh50p4dttcb9l9Nvm2xYRQygVCoQPwPFwS6Q2dk1xRCMQ+DEakiX2tySu0/N67AZAYOyun7aOZ88mn4JNrbPxxXp/hmOZfN/CCZKlMPrDneSRL8BIJqyujpgnmfCqCftmcMxCz3dxW7C7GBBr5CPbcy8CmknkGH9YO1msZ6If9Pml1MxJX9gHVvTuTS+i8nfWNv4eh/Vwuw7x4kLZVAqluXGBgjcQl90a1bajUCqj0uFlxAaDTNVL/+xvpRIivzN4EPRP3wPy1CrRzVDy3blubXPKBro44wGtra5Rpx6sMzO2rh7TuORHrUhs4xqhpY7Hor9MXmCFDMPmXfT5Y/2gbqy5NKazCHm+SZch7cTdHw9FCvfi3oSYUYsuFLehoq05AHR2B0E0+Was5RQjGa3aFUUOy17Ju+CTRp3r9DcYRFyYEG6PAyuRw3IdfPZqRUa+q9Mb+BquiIwM7SWXZEIa1FOLBoao67ihwwbDP9H4ZBzjtRR1IzNH40XhP3iw9KYcLTFPOFplIfvDtX9UQgIVqMa+pcGwl+MM2FdvYxeytY6FCctjDrSBJvCoXPMQ7+Fm2FZ11wiD1tfEgyWmyO5zs7n202shL3SHjMJCWtiIxsbTb0V9NZM3+j6Wr2JIUi4K/hMsSd018h7skkNjXD696zulVV1UmPIkbcZXtfCjbPWWq/RfzXGj/Bt4gnrbIt4NWkq6S/8hNvrzma05JRyciyqgxOPPqvbk//STL3fvotZRgSLYQvwP5k47SMokhHhRMw2f1fD6Xn5Dxt83BSwQKizSvcQTKbbApRRZyIRjJfZjCp847zSPR4nlaA/OwIBLx4qSqtHDIXCBYeP5V5Y1pRT2LdrYkpWTGfR59+tbWWCPhYPUJIdjW5tGrLuGSZ8OQOs9BF06wPn6yaEyo5mdqlXgI+WvINJiwrbQRMsHkYhaUDjEwyHmQ0i30KUM3uEo+Ll4obZkt6iA93B8vIfQSTF/88yXQI7CeSdIbrBL7K2Mi/rpXKZLB3uqp72qqojQG6Lk4on0wgX0eUtY4wwfupYjbiU/FLJI9SODwMr9UbBTcZ5jnoOorjGbvhuphDlp+TeE6452Nk7gtPTWs9I3muj6bb38yfr10zs/E/SFIzFxL42on9DUOj6ObXQ08tJN+UaR8DSVMl1i9wKLlZ2mBtAmcht+c3NlftUNuCrIsw+LP8RkeZKzABWelgqw5gJt4hxj41SwMkd6ZZiXHnRr/pdsXmC0/iMAlj1tvp8g6bbSyCaxemItueCr6UBgs1A8LGlY5PF18+o3Mk17qFETKpbSw9DmOtHht6MujKcl9ximZQSVILt6hPQLuYmZ/Id2JMjreVtE9pBvW2HPuS7OXIfXoay6usfxJHIiRkYiUiS1OjQBDnzRDvqNMk/QrKDYkqMnvs+ff21rIEnqFYHYsDE3CgeU1AEvFv4Cogr6aSCV4NgehjzB7NsOyrecagBEC+UyFPs3eJVrH8D6KHK7GRT1mh/jPkOXfA4nbS4r/Ohf/NSN7PxKJZZZ45QDjQab7Q19CepT+D0wrcnbU9aa/MCEwzEqYeHhFt+bxl+ECggaVXgF5Ycs0Vk7TQ1XHF5y7KS1GTzNdup8zXobs719nXB3lY4vI199EmpEiKszG+IupUrdiROdEYMpzSeMRBV3w4vZS7POX+e0+E4NVZmf8YKBXAUY0KKUlBMtRt5oMPh8q/vMBRig46rSDa7TMOpWAyIjV7gNHhTMI/4v7l/TPaQyGxIcO7kxDHDBzsCVblRo4xlX6hcBnDL9kOIlG+tVs74ulgaWMQDIRs4+NRE09ihIaD1+QX5dNquA9tRx7V0KKm24Z8r3T49YB3ILBU/3vtNG2Ys+jwSjtGrjtPreyctqFaPaAzMBCkhaDo+Kn+3B/YTKYL58rQPLVP/9kz9906/SM4vahoCLdQM4z+xl4ZMAvHd7DC6gPKORWYyxI42cc9vhszmnTNg46+v5ukZ8uLpNN05VZE2qiiiFo/6R4RVF5yqKCz3o7gf/gc1mEsyrr67bCXY7EHJ3kIvnUwA2oagXJfb486hhHNbs7pj6Q/qfIojwsuiFt5NxUTrXC6n33M3mfTLHM6oXxs8icIM0auPkiGVQv+2Zx3uW9l/M3cD/pyjENj1JwhhCZdfE4hjaadKVIbp7d94G/FUwvF/Fy1kp80PTa8eTAZmZEcX7tBeL0t8qWcPGXzal/sZV5Beb1ksHtM9yOZ9eNURv4kQKUYGJaU227wl04iWoPy/GEJZkbCtRdCX7PBoWN0eJSKdskL+heLvvIOIuACdxnFrVEay4iMcSOlMljIXfxZISQ3cuy/E0SUdOQ8hXpEWVaB2f8jlWQhEAu+52KpYEkauyvaCeLjuh636ArekGwgTEiefcLzCmk6/Jiieq64C18+UhTlRkilX9W1XeDSbWjwjSQQYE8LoFW/kEI/bfow8USpHXnoZgIvZdBNWTkjCRGCB41EZNMbPqI92+ooY1k3fkyBcDzQE7Z8RWxB5WdJyl1hCCOr/dMYDmBUk8/RldMa3uWlewDYcvlHcFMlJAjxUBXaPpcTTE23U82Ou2SCVmmHu6wdvQ6SQ7JDBNLq3z29rwAV0cIcNug/bugxIITIH5T2hVDgCXj1cfiur7fiQZbPOieCkblRhQaHdXjafxGChhCKnOp4ieeTkmj8xV5wur3bvIrxNPN7JiJbtKD4ANaXsIO/wCvMTj2lrtxi97QdHR9mKowHXrPADAdn78Jr0KIDH31sZwaFjzXZors4Gcq3rv14QQ0pFMZ/jM4+FD4Tz44d94D92dDEaVj2iH+60DF3ddtsJgK9fxGo8Vgr1172rFXvCo3OPjFT5hIBqCUGrue2W8jY15G7u5W/vwApbTVkAIcou2XDoi8Y3V1182QTySEFTXT8HV1hHKLM9CgC26JJUQrZPrzNGIYkhJHpVlm48KM4R9u/r56IinBZqP4xpXKiZkMruWGYyf+xDsYSTq2JfuM93LidzP0aoqeokesoobygd3OSIegLRxGwvTcOY2WEcM73w0zBblI/AswLLtupvA+IYtVaxXDJiYITQ3xTfxsAjQV8XNdNylwG0T81TgNcj4uBPyy6KOALeX5a/fB7acSP+IjIM7eieKAofJFw4Kiy3f2Je/M7RYQXB0WzBy47rqMkMPzrheKYpMWW58DXVW+D+TgMwvuzSebmkIytjsXrEabaV4xq61IzpyxqYj6YsZn+tJPBpf5FiQHpB+zDBQhf7AEvwT4YiEpKgOnXCqsuNvTBDAfm9f+All1vmYiforf8u3yRm008bMAaL5fgxO18EGgCj6ji/1E3elHqPgna40w4w3m5gr4Jrj0VJ06kL9xkv3YJRD52kLLAy6DqoW0tCjV7uNAZ5UpPw7y/4IxC5/6st3/UO4v35hx74dpSslqfKtMNk+9ulhoK/IDEwQajdAkYI2XfpYue5lvtA/OX0CB6r21cCl7u8ywXKNM++vZrf1lFC7WuZC3ht+ocdEfCIlcnEzuKxWuRNHnJoaBykB/mjglNXVulgfK2UW8EvuSX59r4a7jbwi2ApF99TID8ENmWNFtMtgZirZwEtwvbeK1pks3LZyh3akyKyibQLShtyFGoeG2x0nfX2DehVLfUDnR9IDgfmfyBP75PM4saZWE3eIWSWwHSBaiIHtBMl9xMFIY+0kiPAbHB1k37ZMbE/sX4w7+Kpn/RmwSMvVLQXgSKtUSw1mYD4GnYLt8zAtUjvy+J/wYAgu2jh+03rKu2UYJPgPh3fRvliplbWzIvmt+a/wEQGrhpEnfhiT8CD68SYr850HW3c/RjXhDthib2leBVoHzszXg/mk0nCxKMENR/jiw7ZnrWLkv7XqGoaL1P6AXbw/uH3mMddlDPhZ/byZuzjsPNJiF6HLcjIDtYXMu1+FWl62LVkXZbTzziuNAEhy1k5Lf7Z48eGYY7C6SMOoJxRuL0qxqfuQ2oM9zM/AUn2N1MXxk/7yv1h19Gaqs8wsPvf7H/cDl3hJjJc88VgqIu+M+DPXLhIxXUlWO7rDxOMk4bOh99Y0dUI4j3qTHOiAaQW/cVu2SUkxG1vbeV59aqxjLud/mdsZ0DiNs4VKwX+Zg4fOdYYnOT700e/6l/xf3GrQhBciw7AjuykOoDYO//WZ9WSA1pKYY7g+3jlmcb3fkKafh/OleOE/xtKqfOMeETmLkl7VevabDphG2f1l75N7KwrtXs2zJj1o39L7fNZ1GYqlIy2lgpytKcuHml6/4pfmaj3qpxWd5F71VzD4pez2JRO+QPOaJYY85B4oM6pZUGth82HqiB6PD0ukgoS2dJIK4W20PowBl2B1VrKXERLXxDlLY2UktJ6PpKoaX5Pf0hIDi+A22bvAin67T0aI1MF3++A+wWeeK1y463YeTfKYp/k4Ghi1zrdhLp0U6B9PAeFFdelkEWx6GhxNKFP7VPh2ibH/5f1xTtHn5mfJRCh53g7M+vUiPPAfL/Z0vn2lCq1rg/r7MhCEIzUmvHr9+Tnznh8TV8azIyJ8bgMgA4y9eJYACYeOpGRLjRGg7GWmEdEKtKcSixPPRlR9VKl1RlESn3jgezYJJbwsxDMRn2162RONf4pvxdBx3lumxBnwYWV41Jq8OrriMJB1WwnJmxPBS+keQ8MPHGonaUjlZZjn94VB7At1geuZu6a+158fN0mtQHZLfI0I7S9pt/uJbQb3yNAg/CZP8skRjXKcHD7SsxHajpPNMB0xeqR20DsAR2UfK8beEHBkgLqD5TP/GDQJ1ddasKXMVgakYJhR8VH8MhJvMbezSuicqJbiOetgJe3noOBoqmcNhB+EvMjuJkEBFLEUKKP/LHO/myYOYklmnaDkXsK5eu/sToMzungUb7vxl09fVrOX4AgN/VNdcauJ/ZY/QA/j6Oh9OTGTLOz4Bj0FLiN9Tuk+T6cbNI6ehDLsdQGhifEZDthDP/ssvAF8wRnzyl6Oac5Twc5RVbkQdrpURa6WulC6wvYmX4yjRju9jOjVFfmafGcUHCyW+PULW4o3mp/Bl6DaTQ70bUsGUJ8bbhGFX+bHXHQnk8Zn66wvrQRPP0WHDY1LFSM1QrO9v6XypwbGNk8QVNKNZq/DFWhysQkY/c6V3secmnO4HUgoeuO4sirOOsWFdyeuLj9VszFwFq3YrBUOtdTOvrYz8kBbYKDkd7IxFrfhDdhGHWT6aGBbrBfvt1ZuP/2LL8roznCm/GqSYZ/Fluk52YlXi8tjPw+uc0zqO/CQt8T3ByWAWQRzmCxJ79Xa1kOk5DfX2/bo0H+oOK/jruycGGUWKP/cFdDvUfrrgr0Tfz2hHNgm+AXUhwr3FjDvZGOVBloKceJF1ruujeCEV9imwZ+VnWxLjhz2Ax63lS88pSZSJCh2bwgxHJfrCPOfkeWr6qAYrt53xz5ckOVUoDb8x+oXpDf47f91PXRKjSmhIGis44VpG2N1lxkeyfIvX/wXJ9PGc/2cfAL5fyv8nkoSfB/OG+30XmeFsuGRB6kcoevgMads1PBM1S4/uNiaTByaN7n/ZFtgdH8qU/U4wTA24YJSOPsgoQsGsnJfTZdt620SarCZiUGq5j937fjIjX5mPUy1wvG9lesG4mtrNjyEJGidffdGAEMBr8cDa9LdujopUmTLMuw8kW2FSKYY1DbXwqZHicz1NXHB0Yz2FcceYH3rpvHi+lEepgkC5gNQdPGfrKtQqlhMwdSLOhdYnc0rhJX7wkylE/F7yVDaUBphM5fuaLCK+lIOVOkAfohoXOvcyhDjLUeUrEUFlTWxjS27u3x1qaKEJ1SBYIGZ+JFmaPncn17UXq1nxvEKz3wqfuJ/J0hJ/zGMq1uF4tabgr3h8aCn9PgglMtBD7KoeBbZYxOX218tDTb/r91WKBFm/rMg5QM5Az6nSsI1U2axSM3y/nN1tstDZJOF+0c+jWgDwpZ6y+0LxjDz2vJ+0jWSBESsu6SbLjmCEKptlwzDPIFc4BdS68I3wG0EEvbgy/Ylj5Rm+JqMuBAd8ufnEBJAIIMWwyebD130apdC35T4cvUQ5ZnqSqqsarSibPOmukcXle4cyuG9QsL8S225THil3JeTML9ZhcKyu6UxYXybUb1ysdaJ17Y8YOVMhn2VmnU+7txw6bV8EHzqE5WYaeMV87iccUjgHVqEwkA395f0LIa0zCsYrreZb9SOrD62GEzyjgcDdv6OLjOAuvhhN5UVS09yr5TTVkvAxmNHpWJ/y+lXxB72J9L1vngSJkAhMm2rNjzS8PJm3prrBuEJOXFL2VgxjvvLWB3oTkHgSjDaUWmKlYKLfIdMMbaUa43b611Gtqa/tykuvMOPpqme7yiSXHpY/l9MX0kvmR+8g/pUqTlz9cnLrZc+3WbQ/KTZ5zjBhz6JcYX5zKvqIUy14UGFwBOs6HBih7eZ7gzv7kaybuVd0Yhlwl/Gvt3gMuhGTY9ixllqYhJwreB7dalusdhODrR1KfA8wOdxSBcTqkUzdStF2dUXHtrZtlo/bPZAvr4KBTzWo/koFuOXk67xFtwi2XpJ9/dpW88zO0X4BqKs+ZCy/Hj4XeNYKc/tOrHMfSen/SZlPdNL/inPY5WOoiIcL1L7Vq37jNvOkhnds7u89Hz1iTm1zN9/ES9wj6lPJS9rqh0V27ULhp7zJUNdGtqGJ7lj5ZLSJrtYfzfnzBLHJxBhSgk38yiVKZuV3wMXICL6fFXF6dkcKTCEWlXrusdvcAiyjQ4pSeEXwb3DOidFM7KKPZYnmGo4Qc8Lh23T6o6LkSwagjeVZT7h/QZX4fo0l/geFlwMXr+iv+mnG2BeSzKuPNo2SavSlh/p9p82Pr7fvAMu2r5kvp/dBIcLu1Je9wbsb526CErV1Wir9mKjGYO8z3ML162r4PLvrdwsctiWZLlV5Z8Y6530vLjg/Tcs8YtvYmEYCUxXU0pf6DF5u/PEFoCYUI8wnjvXXNDJzaZvvYtbjDgtVCYOCOYDzcHEJ7OQOWF+KOg0skq6+9LXd/aLgf0MUmZgy5QkVlAlaFNFNFyik+2v/WNeD/VA/Uk1N5U2LB4IrYkma6R2MsyVVbv66yFsfKQuIxT5kTSPFNXMK6KUAUkw0nDBs3tdNkYpzHFVOWSTPZLen7hzSjr6LBgD/ELcvVw68QdU5Li3GvVoBWFNxpQT2UPyPb4LieovBQCq72j10sxdsTbn5k5V834i8KwFK+NFeHie+jMBCZkhxLkJb94+onZur0FB2hrvrzahvT1hJWeI+8Q0pKZtUreyK857iwyQWTGvck2FBPl/ZyA8Hl8UObVukXcPol0/ADAaM9Ut2+CJvg9sYsnSorJywrmjpv9GhSvJckx8k0LVbF5rdVttEUise2KSBR/wd0bKBZGh2WKhHN3ucDvJQukmVHhnQdrv9I0MmZOPFZ+5xBWx5iVGQlHPMd1NREOz10Etz3eRyEXfxPgB51zBph8acxGWDWQBrfLFQ84NVzy/fPymFPKoGiVKdVwZwQQ7oDebDstbqgsnuIr4A19b2GxqQx7dANESg5tHb8Aoc0PxVGOeUMOJoHD2LfYVnXepevM5XbN9EH/mreWh+excNINH0cXyj7R8akA+WA1+bf1AgeFD1nxDoVQ5hSMcGN/lFbbT6/jXHtx1Gqs5LaMjExg0NgGei4Wc8oDJaDLzvlhE5NEmHFPWFiaaZ/NKzjm0dViZfYodHNuo832PrCGihLKZJ38HJ/LV1DS1pQTkX/5HngERr/7v2TeuLp5F8nZcEQxrj7o/UImRapAQFZdNK7Kylj7BPvqddlM/o3c0P5FTWk5k73nwgK6mfn/HJHZeovmCq50IzypCUFj/Hi9yfc6jtrMs7lpdZsILiociI0fkZ7kEEDhRLfDEmuT8EY0/TRzGgV1z8Yk72OGfuNen5uDCUf2XzURd5GrMZUN6me8hFzomWQOiTp7CCvGyQri4ZP16OkT1eG2A5Z+lHjQyEJNlPotntsKneWBjTa43XAmcMpFa0p0vje2xAlicPTmySm1KcXX9Z2O1HrD+5q92SlKefou0VOjE/RvelmNkYFPaH8VkGsn1syVok+PMdKeLwshUfmUYVK29CoelKj4gRDaz4LtHLger0itIc5IZMJG+yX+TLPeJnhztIqNTaVyAr59OrfFY9mp9vEfAftUt6rbTRb2h1Obr1hYZJdlwdGjy72m5QSw9F8iX1RDC0yC/i4dY1PpXsckRG1d8a+2tJPoNgQChxJuej387prHP5aOURmXMX0b8V2KgIk5bBZB9e5D+rIHN7oV1FwfcvMGqPcANojbrpz6EDcrW1SHXWh0E+V6HlCgMMjNbuo80aHmrE5Y+oc486XP1yOkCzV82lz0imnuougFmmutxtk34y1Bofa3jH25NBTH4zjQwUu3yTgpVIiHlPUS1EESRxXFTx9pbqcWluX4FtXRQ7FldZT3A00x/3cFqWR4It6O2X8GEETOBjV8EYF94NyfEjP7X+V7SUOQGpZJg43QUYZAMytkF+T9LokSXDLAL8ONk99HBy7GRrub+C0LelzPestbewRjDBvimLTXUp9BnR23/U1R6EjU664RT79PuSWvIDKPR+jqPqI+3ah4IkMQEijBQu/WVBv//Ko7EmgPeLq4dRUpoEtkBM3mytScY2SCashEQmDeCRDd/DRqSgVkbc/iqq32v5CGeX2qEhDvqP9AxpBlPSA0Khu24L4cFd3c6j4JzIfqtyvVb3r1J5SA8wXlW0LnUuiJH3mKsSL4vx1DbiLswZzMWckkKJklVCpubCUVdALQdltho4l9uUerzfVpA9FrAAu8imVchmOuzuMDYIkTKQh2i7rmyaWTeOF1OB28pluyX1bdOVlkExJQ64+qdrD5gM/kZmMirnwWZliLxxwlul9DbL9Qi9PYYxyhyfViQ73xtZFhi+H4/vwnZGgOXVlieVsr3DvEA4rN3JcESdF0TVOBLFYMPBqyr0YirXgqlLHUD4EVEPkgMZ2yPF/cwWSAapfEYXfbssVXCTeW5GwqMGMSHrQB4Fk6w4z5PIhB7oCISIa3bYdCByuESTz6ZXQLkGZVftDDBeNFn5Nn/O+Bj0hptbXEaH9yCbgGIhSCiVILvvUowzdNsZ6uyvq6NCqIOhSsLzaR/sCXGlOYsHeQ3jSwEwrls8xNE4d8a5P48uvbs+35IVaDw7nDDtdsbvon48IyQ5EbsVMFD9FrehT6Kb66Tmjfba8Q0cMg9a1HjPCOB0jCxAEYEjABQ4cZIdxcbYD0w4d25PKqeeXPSpaw2wjEQNw9o16nLZygFr0BFdsatmUIV9ERiBv8erixER7a2kr+xxef9cclLg1pCrrQmWyQjpM5C41iB9QDVf+zR7VQUZigHGwrIKYv1yk0qcKaPf9zVwjqVzK/ybrQH2II4bzKY5w1Q+qmcktQM/4++qalEH18KnS9lJSuZ4AVCEWOl7/6pVyqTLT0dWnuA9vShubyxSL65PKnPrftVrjnluWzXlvtRC+6EPPDhOwAISgATplRzHMXGgSpC9W9W+VpK7iXiTSOYoPytylVT2bxqgKD+JBaOzmwA7z677HV4x1WHmsgHJKHuPWHIQ7c1/U5g31iJmkLYbAW1BXPwq9fhJSQ2A3pavaxCvOHH8oNB/n64h+P++CutoSBdoDfwzJQep4iRTjeGZny9VDHWTKxXIk2bvJLu5ySvLROFeImgT4RGDrwT1yTqAOBzDZaNG70KGYh2Tmen6CYNVxdS8JhvuvYkkQMGuTRCjF7HObSpXzxIdYva84Ypyt7o+0VCbEZ/h0HiXPxxO1KWrB3C5uDec574ROx+tD+XNfNFiDL8uoszQud2wJgRFl45CvOPyVNJo3MtjDAmOPM3LqpGoYUX678WYyB6esSBo22a04remzUQUQ8flWRYp/l8wBihooN26qy1KWe2YW1V0XGs7gmxq8XvEl16XiBZ2RT4hqRXWYf/Mo8aFTnKbYrDS0UPSHIHeeBSVEUuw+N5lkjDRKhnT7R7AFQrywVlWDow8Bne2uXXnvJ1BHA6f/h1YoEZL59kX2NXkPFYEQBgAI0Pwa7vtB3QG/2ECC/AoycYCBquOjKOE2APGFLK1Zp3xLap5dg6RI1yN3OupwLrovuLDvHhfTpwPLE+k0kT6/mYO4akX1G+YCyTQpUxo7g6pp3xr9bUSucQh02zuSKzXsE/e7Jw6BVUnh9cWs0vl842kmOCiE7+59fFDEZdI4kTXKFcjCj/E5nPz+hr9y4tDJWOF76h6WMr9IO2k69WHXlCpWrfJf3YfC1kArVZZtedkz5ciqgWjEPrJMA7Dqq4g+opLJQK+zQEdrwrZGhAlEXfJ6f0+v4RgQFLz5e5+/xdx95QV/pzI8yXML2NGSQS3DkGcr5pyIHq6PMWNXio63SoIZ7Htj+TaWdK/13lb9tc3iH1PbMpYUsCoU9mTA02kSV5fYJXYVIO05KERiqgf9yqi4ypL7O5WElpWWc5D9HjGdiU1vtxZtSM6DVVIs+whe9pKYPis/N8aZpfxJaVbF4iDYZ9HNjHGQc3/0NcG6niT7yXtElBKk7SrRIJQGxwC/q7dZ4X8cuBoF8efjgA8SXoTOVpwYSSCnCo2g9VYoBLKEA7gRMt/WcphIGTGwkmNCIQQI6U+cBF5vlqEwK0DBhENs4SOT1EkCt8ZzhDV3KuP7YknjFPkQUhl29XjKMjV8MhT/kv8o/ZkOtocWXXSrWHkzgPSwd61yL4v/ApD9pc/Y24lFXwjL/3mMFx9duXmJto5o77/c5YSvaAPW+rMKhTdil3t5KfBQ76TMvy66FQ24uIxFK045K3AhDoWob8s4qDj6e/ymtyb+6hW6/CrkJBbuseUc+hMbVyE8xPIM3eQR9zGGUVekJS+5YG+IiqlbRWhMuP4NPXos7ZR1fZfd/jHei7hRQqpCkqddmeMOeF13ZabrKO+k4TxthBUO//aopQIomWmJbUeldSgoOpdBsotMXZqMrsSSLvz6b2TLMM7WQnt0h79pdYQ2QX71wzNTbnB8yflskIrzZAOmpjJMdJfWo6YZb2iWWL3SY+lGB41dlLZdVgf4mLvL+VPnwlLzjtPJqyKho7o/NstaCzfYeclBUFx+yO6nHGq/B2e87PfycrcHw3NBY68eAKuTMSJO/FvcupPOco7bSy4a5Ve5fEzeY2UUoNgGP5y3q3zi4J9nJZCYawPvV93ELDU6/yFExBV2uOspY0k6rGvWF6EjXUJu7LCf9mgqilRIfu4j6DoxzYjZ/EDEWP7LRxSpwYBGtvwwnj0K/rUBcgWR6t7UrkJK4lvyx3UcrmxxlLCPcyveiK4ht4xXAitdTrhkKZ8Mc/iiL1+M/Kdxm4dn5BjmjaqJtWln5IixqFE9jYyLDjrpD33etJSrpSV4psWxOQHGgsaxUPtr8SeLDHf3Ii0eyr68z3Mos0LAjgSHDmrvUF2Iql3CgBZGfOdQAixhlfbMrmR0VxntpNkcLNdMpPUb+a447kzg97fKwg48ttrIoxdFPXl8439l0r+Fc7sUlraUz44vyv6aqL9UqCYXVfj8YADY1K0azpLrnaHu6CWKbfnQrqayjiHd0VENyLm6ym+GeiOCujlBTrtsZo+rdpJ/M2WrLQd0IXbvj2qBNUCpBGBvvSsDiRVSvCBfVf1hP5VJg1VBRJyP4H9DMT9KY0lPOjpZfgxxuzPJ7Rfk+iA+MMQ4FkhDnL58+6nU3ySP8r8G6jw9VuPRqvCS7iA5K2S2IqMiPfOzh+j3wx6G4oVRqv1AV4epJUErf6CEVkKLVL9URJs6jOkD3vUpKC2dBB3h+4p54aPw3ErbhBpNV7c9aRwEz1j+aUM0uEMjyZmE7zWynyAwAuhmuN9ApLc8oUFCu9ozpJrrCAIvY4dN3Mc4eeWX+iDTBNzjhIrsyvf6NekavnkFivSx4gUn2UZOYZg5U76ZCrD8XJl8XV1lEAq4V+HDwYAAjzMucjTkosBcjFZq7UjC7aigcBQ6sIf6kgkG+k3+B/f6p3eRZVSorAn07g2wF+F7hCZ/eHF9Nlgfy/RVyaEaFaT4NyosomEe4AnK8inYk+gMYXNYPon2TuGOe32/lCfQlU6sp4U6EcfiMXVlzYOeuk9GN15v/kv361pUqI+0GWmnE9dIU7XrOx6B84T7NfcwPWy5kr4M24OPGRsqnXXmzUq6kTDKp9d8/9iNirFtIWorSOOaJLVFW5hkIzp/awDH3P0bDYpFGpgvxwx+psjA91YssnefswlTw0K/De7k7HFOou4LtQlO8Dm+rj7D3ykzr7rPoYRM0iSSG238T8kzr03vTEalOL5w4IkDLEIj6MnN4oZ8WIpO+L269twmD98TGpUePxvaBjfkHw9VzE+U+CgDfEqwt+fsXFO9+zUvoWi4nygygy9Lyx4vWMP9zIqvpCkI98HYoOVKYp2pxsbT6fODTmweWJ5BeE4kFXSPzENpKE9mDy9te27KghBbjXw7W1pr3Zkq+yRqkNJypSq8WoR5hIpyqJNoSueIXUZh1iiASUtpvq96JXcztTY4WQBKAah7Bqy7ftLmwG0hRPXvHfNZ4a/WrFIHebHTF/CSdH25+CgT6Kggtp9se++IuEy7LoNKMFMVAZJuVtQbBlBB7FK4dN7XLJLQVGIFRqYW8nRVz9el3B14skGaiU8MX1f4OwQrilx/fv4gA7ChMBOj2HqZ96ya7VxZndr1/kqU//8NZR6iMN7N8tKC/5RGhD0OvCGKl9RAXBivcl/P8j4AAiy+mHevwW3h6XxbThOAAHjo1QqzwOWWz4rNfP6VWyGLq1ypCIbMqSj/ah9Qu03UhmIytCVsUdB6fxNafjrEr4qOHfVYKed3eyQZ7a2uGmvooRrx9zYR5DpSg7EmpbIP+9S3jDbLa+8FwuTq/0Qn66mjAEm+fxXRMVqLCd1X0swHEAAd2UcDuHQywDR2z11LY+1eso5mZfQ4AZ+0g/3cwkETw3UTfalhcK9zh3S9IZILp6vx/tMoEU17CjwbirkSul/AnNcuRa5rCl9qGqeN0Vvy8GySXM0Xi02+08ydPQAHMzxqcmLWqtMsQ18/COfg9o/Pw/kk7vytHDNQC52fFhIySfj1VNWvtYJdzkwMAEyBWYRidI5bnSx8uD9IbLmDtu2co1Zuhdtcym6NbcDIcwGI6LxsLPjZRRXjBBpZ72A8f4NwcnDr7KgE38CZZ5JQ0MNuP1Z53JJrT+84jy9BMKdGL3ZnxzCDSEbizRRbkcIp0Y48P3lyq88bAAfHB/wWpgPViED2qsMQEIzoOKiCmfpANyrPwt5NRuthxqkNXWpYhx67GF1cGyr3CIfyCxOsVpZ1G1QpSQFDFNmNErOlmATIxaU282MRDl2iEzKxB0O0V/rip1tDkeW69RPYZXMI1zRlDBG+gLeSIX46H779gSyylgxUsm9Tzwjm13lMH0K3hxsmmd7GlZtmawrseNIyPbXX6meMBF+unbq86Gxl/MVzqljiv4UUF8RiF/0ajPqRIM2WDA6JNxi1JiPJxDtWrehbjX2ZWx23T2mDzoT/H6hhuhsSw/azBl9Nq1fDIXOclSvLlVHGLfwdqdfasSDR4VNTUafdvG4WyOGNZexkMF+VHmKkip+I3nJY/tz2hw+o7fX1PhVdwLcd5/QpLwMHiTxpN7VtSa9WUs+/CIlfZlfYeAuaBWcdVobbPnAthvJudsDYmIiFDrmQg7Zzt1Gj15TPZQT4pyf6WuQ1ALRxeuq+ZYqpa4r3svEIuGxRoXl9idhxX5uzAOCodbFYeSCB6KHRN/B5M3We+ma7aKpKj6q/SFjh5dDPKBCm16PAfguPu+teDlwfyllAtuEWP5Fqz7s6o0zomStoUE2uwmtTfdscD+/ILzNXyCL9/lNJk/OtsBr4AD+WH99y+jt+upciHfk3pj2TydfQZj9sIDCj80Hru2OagVYrl7bk8YpWqeYSPXNKEWddBgioIZ3JmRkV3e8k8+w3f503H/FTcvSmYMnp9SdIldAD4DHNLGdyld7GR32GMz2CO4AS50zbkf9W8htN71ihv18ZgLi51SjySwTfFS1fkqUJ1h6YaDdJ1sLQkmY9Jf2VPUWxJLln5s2DUIiJQgJXLXDiErJLcNymrH+/vX8y+95dJYtDg3NtHG3smk7VTqifirXI49O9SajZGesV3wWuIm6dOY2fulzSgCWHHexZfVbrJCWs2VIlpftrxWXeVcdBvex4FzTJprn/AjKcy7fiohDQk6PgnZvqE/OujeCZVNHVCMjA5+5UJ4j9bXAnc8fWqj2re+2n63DAvl8qWjX0aoxtvTBKm6Gm2eToivZOmt7ZLtI+2o+Hwcxfy4q9kqaFkLkH7UN11niDeeU+3samvVfPBOzyYnafpJnMcYz4RmdBqVmaacnsOC7hfTyQOFeosB5mUBjrvy4dh15QlNeNaZVLGuXbW1KxQp+u14001Ndw+ecK99/krvusSw8vYarn1fEqIKct3Ez5i1gZ5Vo4zuvt9QRMGfXIfzXIIoj/6TTpvqJVRS5OBghzz2FsPeZNPJzwaqw0qbtbYlHTDQm6Sua+KXC0grF2k6wfCl+EH5KMM0N5gi57GVlihaXIPIRmLyAtQNG5w27k1ZTa+9iYoo00G4dcbLV4wOjSfscYYUGzo+YbvptCrY32erBdtX8N2YeZ0VbM3VGIudfI2FR5HgfLE0BWYOi9eEn4hLv2o2dAAmWRohbYWDUn0z0CzK/0BWfG3XtpXdARRB6J/OdsJm6lawu9y4vVpVfNkk911hPHYkOUH8jsVhoS8/KjHR+ymISRdY+YxfrYMyMPLym1fg7b9C39aAsFr5gQxtf+DanH/IeLRClr3HC8P10KPKTKuzua+glMFneS4GHD7/P7I3vPVGT16YYA0dr4za2ZDHgNcRqG+oNv3F/icjLcPlqrcZAREbSGLltnosfDzzHInWWTPry6xeiiDpnTKqkvlP711x66AvyrwYrimB57R0Y+KN5+ukybNC/eHXAodEbvfzjRmj3X4JhHnFs8fEu941fVbUWxlO36N9W40icH6yW05pyy1O37dwWRE9icj3bOa4lgyQRPfWgOmupGTdao4h8wsu5TCzaB0eB1hNhLTrPnb7ZdzvcQ9HMiJGZsD1hHWf5Iq9cIFfnoZuQ7yWVxv5ZCA1Px8Mju3iyx1CNDJylAlwKrh9tjIVLdSOK+j+31V74mczAqLHloDoWnyTWM8pfTxODEqYK2riU38Gzj1Z1z2BPKBBfI/zBkHNW7iqEal/jfQD0pAZwu1B69pdhZP/PnQnLvDuF1SensHtlj1J4rZVm9sz57SlO2ZKy9+qoqexdYQtQWNhsXjTt2MfBHnejqCr8PLI8UJ1jljqJhv2Pzl0Y76TtrECN162PlcQbERC5jkzZpHgwNU5aoEiRPY4ge7Wb9Qy+DWEa29Wf72R6oywG2iFgQGaR5RjxRf/zp7uzDzrer1y3vyQbN0PnTf5HJ8TI3eyuR9W16o758YSvcok003NR0r/vwvfPpTXr8wzSzI4bFzTgmwuhQMi/0gZtOUzP3XFWCC0xUlToGSWf3C4nHuevvP4wq3LilZOqlnMJlJP9ri9RBiWcWLZHjaK+f7MWn+SXh4yH8l4kNqY6NkUjMSle7KvMDxT4xjybAi92MquS9WnU7dFaWDpfeO2GPwIRLcghfYhnxgb7+wGhwxcYwYFpYhDP70ZJ+Cj2nZYmPzm19bh3+ZNaLE9E6h4cSPBEXKm+iADSMbWut8ch/unT2apeCv8YnzAKJiQ2hRswa+9/jmf1UYnAVf1530ztdsHfzd35+LMK4ReeS/UrUMHXCvathg8VLzZPfaJHcgwsik3vxLaOz+HzNozTFR9mSjNfehB6A9rIb82D5XWBxOw+h23uJ06Px6Gn7ySHQUuWttQ/nj07mjNOH17G2DyvtnicKM/K/blcaR2wS3PqcwZVgSxZo/dwBSCkUndKtn1LaEw3xQoruUDdGZTdUTKXmH3oUS+/SKO9LUa7a957SadxmaSUWLBjp63fZKF6+dxbKCn1p47Q2eamLhhR8/1dq1I2d3p3pDvtV9rgVDHfpmSxjFWXOBIHtvTsXfoSbI5TjkDno70DKSruvh4eZsoOu9eU/DX1l8xRkJ6lhIAAb6rnd7Rlm3KCVNPXjWhSFFvLjyd1z4VZDfz0/UWbI++Bb/Na09MKTpC1mYs04mmdTfITD8tCQjewEEUNXckeO9eRTTsKTl1H5st2iTq5kHGnijxFr9TPH3IWVsjrAf5G2uuFE2+pC1x84I7ZhpxjyIhhX91OpTqdQLxtJrFS155ccU5CXcGHK3qVv1G3Y4kz/VbPwgkit1GBFyGPM8B0NthndNjf0/t9URJyrA8lUUmerIYq0zacbjgsuk2rGSnSVkM5/wZYEkTnK107q6D811riRfj8FDZz67waEQ9UVwMuGxlQbO+GvZ7VK4boq3bQE0hBrKtGifea1Ap54A/0IRsd7SHvEzLyy0taedHKLI/IodOcSUx7qvri+99XL5lwxYCy+Zt4Hb1A1/4lHdSQIoSvCavc2+18JLF3SSY/6isKxykOMa3zju8aPpv50YVAYUa3B+51AS/1lmgWsXjhAWtd7h4/XT1K8PRa5F0TUTrcd98qar+rXQI8eDyDxh0sy55+FrlnoOwWyXH2MS1BMF0QbmnkqTcf3bpux9d//2R7xC2qobcQ5DDYeVUCE1rRp0jKQW9MIYyihf4RcYRvaysh3yirNDEel7/uZbpT1+rxMmiaj3mxXnQOKe6hI0+8YVLkx/q3+Wr4hI8zu/cVIXkIydwCarosQu+aj/VC0CBr56JU18wY0elzRjutovzcusPD7ol2krgELuAAUDwGf+WEpzKrnqj/ddBpTZi528L+a8FH4Zxu16KyNhA5F2SZ+1p0+Pz+4/ooN45lMSA6Pwy+P+HQb10z/kDeT0ioiX14APx8EtxMH+kKYixN65LPmddFe7AyzmBY3dszv8sCaURdWy8M589T872M9pVwDM92hcgFLq67RGI3kw7Dff+YuTMt1rrQN4hXmPqs4vhZS6DmzGEMJcI0oHk75Ni2l/xyimzW6+oKrY+vNkJjw1PFvebhR9dorYUVmE8diV0Yf8kfQSZlQKOA0iLstisN2+Xu6UEwSLyFzN0uq5lqG8r9AjJEkCmBs4dX7j25c+B4oiO78VoOGmiJZ6kQ96caUJu/Zerq4CfI50y2uHc5l99fB5Ly5feWQG0EhkdjRxHzrEFqWT4VxQfoB1qElZT9PZ4DN9s+v8JZxOeQMCL8OhCSDuSfE4IY+aH5wfFSgtQskWyW91Qs7LhjnQG8nVff4NmhuULa2oxw72HwN6rCMDt5DJ0JCC5lhH9tqgmSFk9I+un2xGyW4YvURrIf0dy2LSHh+tShgo1aEkla82QqI3JS52ETxe+pt2LSZ8Dq1TYb8guXjEpq2mAHXCHLV8zAMzbp7wuL94XOPnW2FUzPcEG0yCkEvIuP/6qPnvmizoeRks/LaLvZkj8GcmSvKP+6xJfYyl9r83sJY9BGjYE+xQy6WWt3UOWt14302iIPLxWlofGaQ0xAitBImTqQzvXEU3yHSg1cy6Po8wTMuspgfh2jDHviq5nAG7NVXvYUPe0wwOst+cReclpaVEzEPhfcevXHfAXEb1uNS0HQmaSwlkMZ6HtKUI8GY9oUK299EBnqKc8KcUKD3fTxQB4gZ0LQeTJ4dqNOB6GZZf5W0dzR2nG/9En7AODXO/0paz/OE44AluFjYEYviwl819LYBSKUCVG6eIUjGWNbXLDbRkNWM8Vg19LlSr21g7H3tFjFpT7C5eYK0XQl1Gj+zFo8lbqI4jGtdiHwZuAh9JGNSkiT9bO4YkIKg34mcGNFsoTClGZkDvbZK6m04lCflT45xljaoJHcHV8E52eOaVtdYMs1mjDHiFOzzKjyw90REeXizssrrxffvtACnZ85s/2pzRu62yWL+mt9vADWvxWQoIY5WCGI85wpxIJDffCW7VioBvFVKYO3VEAhEVYERqlZ2dCSiclHYuXL7TTZ+4JNuBtH1mO4r+FQiyat3X3NLlxN0hHD5p5N9a/l/ahRLuDBYs0v6pe6nDFKLL/SfiNaP1g97nseAvB6Ss4Kv7dJHWmfT5q//AocHAbGrrZhzXQOGIFKHqPsep4HBl5NictohLcirCLo1mAxxBhjFXzVeA7cX2Pn0eKnoa1AUfoR+99VKDHRF8s+sh5cG+P8r+zfNko1ZWfSpDS0Y0D5y6fq6W18FOpAwHOor6qlndj6IVbqpJ3lUODeDBzzu8p8S5gibxL2kzyKiuLjuhw8d6YVvSwo34po3eJG7uuyW+OVyCnoy1qGJ7V1wcWhC1qq1PDthuXVh8QUTSug9Qx6TTWEJ3YvnI6gJBQHQEijD45lZpdg2U8Oq7VrZiHMCqr5gxnE+gmV3nXeAiNHp1J8asE6ujT8PPYcU8ru8v0uaEZwOnBUVvcrm/TkkRTUtbRmH0Xz+NOk7TYgDAz/iu3MiPzijz7l+BcvIRtpoyfpKPqzJm1Vfa1ctsggr8jaumLgJVih4fw1OEJ+bq1rGkA0nxTmw9RR3ZtFFE79FcleUqk7mHzTh2VVHkKYx/RvXJBgcMR9HXI0QiYybn/w+F7gLY0JQZ2vnhDe/V7l+M8x+MLBht/7UahYpKT5X3eL6WXpq1F6mnIbH0fsKKDI0lpwI9WCDe8VogbYReA8GkDmOsOfzIr2ymX6OhM5OVwpiSJ/qTnh1jmxkHjyt0PTELN9rQa+sGSr+Aq+Q6H5PX3vsIiBU+3XDP5ykLTMcqb3oTRGDyjZtqSQy+Ko+vJnpc9fB1XdDwGwhFSf7/2FQ6mcevaWLG4a3U89H3bbqWlMQSA/4v76vV00OVO7HF2QDGdMgpdZZYCCM60JnkJQVJ4frtEdpSWYAj5YqMUvwIz8qChFD9jXPTo/qTRPvFSDn67vJN3L22q9KX4gg7qsR1ePcfJ8tPhrVthF30bxOVuSwrUOx/ZQ9X8Ftz0u2EAa0Va9+iQtKQgKI1DJtjwgZe6eOtm1AgQdEuPy+6ysP5jh/evEd5+zSVoih5DZOBsWKsBoinNP9B9N17XsJhYEf4kcHgWIJHKGN3ISOfP1y9H1VnnLe21LQjChe85MD5gOeQLESWL4niG4L1um8PDwWdRercpAmNs9EDjoflyyXFVQhOCR1V5oLGe8b2O2P/2eFTjnwXYNASE5Db9ZVQS3ZuJLUSpfwxZm1l7rpw2MGYddTLeqKGEPqm8SIZV4Ti7RxynNC9/B6da1Xq99S8iTwPpIPPNG9QnQqETJdg9xCgUy/N6OW6A7Uau044XfErZcwlRuSD/pEtOKrXYUgQM2PzIksAUvEm/2xhJV8qUUw5biVyLUE/oQqxJJluCl0WEicoXV99+GvnTRUUG9xaTtb1SpMPMWhvi1z3hj7slGB8f1zS26Srpzd3B/1NB+wJh2Uzstkx8iJ1SouGv6cbeCutSYniOH6FOLCQI4c9b5F4q0XxNjg4y3cjkdNc/vhqVNiLcGtXgxSVA0U+lpqg8/ptcaAk59cgq0nB1N8aTDo/ttbPrGNph8bTknjTnf22bYPEnqagBUg6YQ0ihN/nqtn31wb0uTTmi4mKlSfJfJSOoyGinRqFhR2SdJ4+62dFdeEeBeqaX/hIzCzGDmXlTTJ7IEFqd5m/Jqx8YiPf1Co9dp4e2dQpZdZIZcRzUCvcNcPm0sbkLQP0xi7oC/X6GpZw/JLosOWE9yeb2w9d7DSuPSQc+Vm1vk1+TCXQUMjxoIHdBiSQqUvcnz6JSbf9Oj/KXG/MncTSK/i+RUkXX/7b0XG/rNyYU3rhsVCettrRCxrQqFmyLzSWldjwxz8MkCFf/pHaIRV/0CEURED9wnHO32r/qEAS50kSllXVpBXCfKtulBt+eH/36VBzC0JObYugDJPftcJIWNPHyu+Qej9PVxlosy0kEGVuUCAG5hUcen/ipXcrDS+F6czumU6ryJ9wABrSvePlTvoXEqsyzQ8V6VHQ51fnGqkVgHRl4bVmC/hKS/qstUsqhKC1VEXlS0vT6f5jNlUv5C/Yh8Y1S4r3dPtIsA0NCg9BGLhJt9n+wml8nrcnKobXV6Ub4PIEUL760zWLrrI13YGoFPKdS+tF1LnucroF5+uwmNv3mA+/MtrKevofM0qZdZx4vLWYX83jAP9gDtBVGn8MWujeu8gBaUF65KrDfBR+uqRmdxKEKGP/nZOWT0LyRYGqswox4qxBuOh1BvnryPSocXmMa7eZvO142xASaS+OyipRJNtGq69euydb+6DY0zh5H/pMwdUI3kFlH6FulvBwYGr5ebI8IXYBLcpLFKOIcFoMfbVitnoPqD49XXsvb0/hGwh7dT+cDNvEUQ4cLegvIE8/cXYBLzdooRBIuwqL+gLhURRs7jKnx9NcWww6oQ9lmcRS7D5hUrsE3a7lV4/R560YP93/zVvwHhCCoI/KnlghoXtr1g4f5Nt29Gkt8YStea8Rt6wgUFW7S9BpFmTYgzOBj7Lmz1pY/uNlgiNx/HiGx6ieea6x3K8AqQZRpXO8OGzYQGUr6qk7B6T1c/5oyTzlmxp7DzwDFCBsPnJy45tAbOQ5BNBkWLTRMu7VxRG3PyP0K+xM57O6oqrOxetRPWLr1wyeNvL5PNpNBdOaLXYg/Fm9AeHsYlJwwOwvpV6lBi9/vPWWo2vButmJ5SX0P1ZQH/HfsAvjRsfcg10mWE+sWaowIBL9ZP2nghgbMYapoXY590TLU371Zfgk4KXiKluikkVEEqHRl+daa9RI60lMQruh+ac1YKprro3k5tAbEWF3XJ8hAE7OKaXQTHSJt06IX/pIB8eTN3LqhHCI9f6Jtssdxf2YGVHs/qDpMFhtBznfSQkGKfy8+H1O52y/scxL0ZVqHSZYAbrN+CRDSWWe2C/NKPqyC2I1oF4yu1q4nJXuH+djaiMKOwa5VEBn1gKNY5X6G9K/x6LJO+H3fzui6dH6RtjUdphXLb9cHR2DdvdXfudQ4t0L1xpbKL1x6KuAf71apqDmr/++SSOWOOlbV3O3O46QH4NRz42lrSaiLakD+rb331iVKEv5XZ7smiozOtswLGnjrnfe9e4y1dVretu4WNZ+NS1kCGw3xHReRr4FxevIjvqr9VxVHQjw81DdO45zV3lDWs5AyZkFEAhI8kVyupauiyBrNEmNJboDzAQNa3OytDxfZ4rdLqgLFtFbwHwqHbPJ5GOQz73bVmz+Wo0BNmg29oUOFHbyzWrN9ZBvf25+jQ2KoOquJTYSnuJynQvNX8NiA/Sdov5dDeDTOY+1Jql6yezsusz2yWBea0E0f4QRLKAhXLThXZD7ZhSTpy2bdSZu8nf8NByDgT1IeHPfWViMQpFKFb7s5CtXW9OLxoP+QdFXGR1bft3RB9kAJ/bceS+ojzvFTsEd3lsBIajOPdwqVonV27JR7TfN+vJD8m0C/Fd0vP1HiREopcnx+UkhiVg5cz0jqd2vFm1whDi7ZjBTsQ+Ov0hYXAGmyV3N7TOsko0LMDGr6IqhP9gfu7zfEk0icIZaDnX7tIvpf+BMZymSl4SDOmwyk5zu+tZ1hPcbZeY9v9GzN5jgnxClV0yXykiBlBwlHB4AxviDgm7pxTNZchKsPcmJVm76kMwlGukenw6WMQGNI4w6OqeH8rd0o1heF6RP0oWurmjHV4F7O2CgeCm7CKrft4r0+0MNXyyYJEi/NGyIBUfUFlGUIdP6wpBrNe6SbZ1W8s4FpzaKoAELwXa6hNFatWUg/S7XvrKHt6aYuB1pNS9d3wRiNcVp0jqDDLnBkxhU4WZsmqj/qQQFRMmfpAWGjI2wmektU1fHTZp7QlFcyc+waSCvlB/df3PNJ6o4ByNiirrOqpddoiafC7KvO3BZGnD/CrNZkizXYZOcjkn/gvIzuBxmY2p/W+auMJefenN/fRhWF9w8bdYz5so/nXm3IlT27X7vvwch1/+Z6mI4A7x/lnx/qJ9ijeBve/S4g5TdReSUqse3AjjKnqTQus/IvRZiPIN3t5jnRPw6eZqIdhsZNTse9Qd8rXlBzX+kTcFqBa20F+8+dseUKQJBTuw07tTjXIM7dA7lKib3Cz7dZNPb5ri6FP3Uhzem8PAqfT60z616kHpurxFt7VWGfmraM2at3gsXLYJ+R/JjhUA/SskJiyZAfmTktCNZiAoxH5AjpgdTid1vqb96V+dg5aFaLsgbjKHCCxbBNP3nbty6cHpZkZYU8Vky1C48MPo+avYGBM2wtNN07Bn4532rPCl48/rKqfDqWehNriQ/nb0Q1cxdOW0Ue385u9TnUaLzbeV1VzzPZqHWSSsaIcHhIdaKvu3vkxtlZXUAlkiCkfltmExOf9EH6lB+g7+IxLiKBBIEWlLWV3MrGfmhtU5UgHR0K4eh8kjnFx0rRq2RVU5LN1rVINwNgh0I/5qoe0rPqP1NhIRGiQkj83pz/anzQr8vl0dOt9nT0cZdy1aBs7wuOzWwAU0sw27A1X46AhQHvuNHZrD2xa8GOQCsvDS+o9LBTlBszGw+nJ4otqPe8kQ4kMRMwYwLRK81iuD5ylGJGUaYUlS9rDYBMRr95kcUeHwS+LHt9fzFxOhMP0UEY8cFI5FwlO0t1P2/Pbax3oFLlg/UpUoqZxUOp796TuDPfeoij9gSnwabtCY2aaGBwrSSZm56gRx3oHnIjlWUbcaOkYR3spCuWV1q316leTG1qTYab2t+6k/OhaIggNZiHk7+Kj9Uh31D7x83xL6ffoJwD8dcHsrEjFKQsqeYVFZjBZxzwo8sDcjN9p+VDxEFSSDYyPAKZMMGtsIs7YIlhvzVwxFzmZXg1ZulTkv8ggvJ4IhfAO9mUmQ+TT345f8zdykxGecBz2r+9LzRwY+obEXd70wUii3kHnTzkOfP5FCkv6gJoQx6OdWPlmmlkfJqUXNPVUd4nemPdGSRiG1mKHae9MZ0nadR4/7UoZUXuCupJtkpaXXpl5NlNlOdAR4rkx6EsUT7iBw00kGukPhnnuQ23gsxcZsSixBUAZq+Vm4eF4v4qagx7oUljNl7ybLNnOKd5kqR1xTtNmbRVvcwEhXJcpqqF2D06ZNtalVq6lNGTXC2l8lUflL2FZTKPfBPa9WYXk4PkmNngrCPhAos78UsmIpwDeyYPFUsBW9vJgGke/A3olrLqz33HbmOzC26WR2+ZyfLLq4ZE68m27dHsJ5tnxdvAyfdE5tQdO12H558JQsWjxQ6sDs5KfmPIqkFwMDP9IiKPpaIaIJn0OBLGq1N/yjs4i9caC7iZv+sKlwjcmQyF+1YbAsfNBAL0g3iFkVrOb6jMC5hZlLLrm7+AzOR33vaF3auhE/eCnow4XHgswrAiKJ3aCKVzFa2pGitjuHtsKZ2K1Ky67gXgIdzwhemyBGyKeo6bwM+JO9cKFNuc+YdtHN1b95qAyq/Ahvr4k7BMIdrb6QitAHjOgcdgg0FkTS/MdVXjsyyw7VGvl7l1UnKylGWqwzFu0iDf0CXFYtPJv1J/CVyfyWNqa8LxECourD7KcfsBHyZlPZuib/X2BbHK2FXVG6d1JD6423K/+WWr8xHpVyqKc3uhwKcbBBVdINgOMTs3nBNfLH+EW63LXwsZZvl/gEEA0HjPBL7jfacA9Iqx3tjAE9aoaq6mXUl/B+9KZT7GXXkWfD4Jg6JkQ7i1tNOyG909BquCK/g0B/6Y73gO/46AMXh9WioNmCnH7kGRqGa380sDhj/obSs65GbmMN2yRRIDdTzTa4zAhUG/1AUzBQUk1KahWxAwSQ5CdKqjl3wghoPvgNqDGnXe7IUCKJmwSTWhxne7gb2nP6t9kpCuhXmelioYvbiuFafnkD0j93uXZWL45lm98MBGlsVYMMRxXQjbnN1RMC7sZuYGqp7z/MKCsIfILnFoOMAnuqcSCILz7hKsvhPjBIfhL5DnX3uCQTbSzzRLRrDKmbbuEr7RFD5/GuJeCLpV1LnbgR/F6x8EVfG+xtsDtsWsLHFyH130aSF7lKvSYOfY2wFlbBHYyPCSjI4pa9TBVvK4YSTbKbFTb2pUwJzybgUsZwjZOH4n2HqgQr7pP35wjK2FfVmIdm5ct4yx8RQirk5O+/o3HNWhWvT4PRJoxE6tVrvutA2cmFGmVZXmRfVNYv8k/mVt1pdgQlq1BbVHxueA1bFnygO4DDqb7kAaC78jO3GvhW5tWA5oY+FfJ11Hv0So4TpksNTNDnW9ndaFS7QrNDk1c+JP54OsH7cBZ2I5HhdbZRCsbiNrwMPe+PtVUjQ37dnouJK4JOkqY8NppowhzBH3rrwGt13q8YwScodKHLPTNdF+zKZ62x1Eh9zA7ynOWUyTfzXdCc5+9bPboZ7y795/oFfy51Vx9dcje8hr6+STUS2X6xBJKPiH9hjfbbmOV7zsqijBWEZf7KK20RqQuMDvNTDfUZkg5QdmmmPKfXVaUA13JUm5W/NALFkfcb1lSao7ksc0XHf1YRfzhPeUtasMHPGDo7iuhZ6xfBwC/cnX7qbLgMdmpms1r0B3iLfNtIrRM7z+R2mah397d8Mmv9N7qtuzBHfMtHqgYaX1c3oyORjPx26CRL9Pi+hWohxSQ0iPUyKtM5qIuJGCjvFysVjPWY8HAG7ktdN+nfGrfBW2wCM1zWdTfrRKqeYWSsYl4N8jQh64GO3O7/lvs+q+6wol1sbyrZ95QsOiD0OvhbWM6wX924sPqy8129uBaw91qmO7dkNUblEu+8loBzhJqxSx8vQ+1ZdoqHWarO+S2S9EsnqB6YnvGXFQpvK43usdKjlfsV41IrUGI8xOOVVCGExaaYRrpxMiGLvfQe8LpGVD04V893Zh4oiHSb9Z4MUTfeYUuuHku/A4RP+shyivKbvGAOy80ru/JO6rvHwFgk8UmffiuQDEn7YRfqzp5eWT5LwRJLgn+Chfp9ux+25WoSZMXwqZGR+MoEjcDgQu4+BTUSx5xnleDblLmN8zd8/RxTGeoGlb05UudPvGIwFztNzV8QDspJyabh0PlT4l7kD2HKw4OtGkYis6HzKqZJxamt+9Ck7eE9mGGjfVbQ4aGtPyyhRMMQMBolZO3aSZ8gkMVUze/6Qi/eVEoFPTEaRdqCK2gdafUushfUG9jdwPZjXxEpLqslxW9ZLBGnBFnrMd/Ssn876wWJxuYyl8AY7Q6ixZTzgQn96LhzDC7h9Aqlom4n98uLC3g5B7Ni5s4kuxoANKCIFMChp76/iTXpNvk7YFtKmK+mtybBhvVoNIPzFCtW9HaVYfEN3lN3SQ253aMGkHEPhl4uMzwhs+7g0TAHz6vL1TYuNi8DbqPG/EgNoFC6X3Hz4oVuUuZWUmR4k6bqE0UuOz7lSrAW1Z/Id+jIJIWLVFSehd6fydLeODKwX4VQRXFvvPZXzfguF8XFqq6lTwE64PkPWyItoa1yPjaC376EFn15O/SXCWjcvt3wF2vY9RyXfSTh9rNiMKZk0DsbqVEdYrP70oOxVs5W3wPQp69hWKxwRHG3b99HD6XnZsrNMwDUXyIL6jh3cps6smWqLc96/pNpaJ++zx6ZPMfqrKb8JsqjIdH8pd5ffyGWK3scfGH46LQ+MHNyO9EV+vZV0oXxc77skYJt7Wsd/kOhKRccY1A+UjlUuCWFYO6305fFs3CysO24/pwwBNlHy5Xbw4wi4uw9THJ+BcZBRxs+AjZszF54L/TWnamTT8/ugky5T4jsI85VSiGDnb9fWmS/KBLsxqPafSqoPjmgm4x3+y3mYM2PjFIQ2YNGFouI+h4lcNkTyvDaNuco2bWHWUyY+/gVPSZqDxg+NATk/w0RLcD7TacjsyhwMEBGMcp6fRBnFVu9uwMT3RhjnE4C9dgtHSDRZ+SnsugERu5ipeUQ7faSKA4wuAdW/WHiTl6cUwnqtNdUjeKQL0ng0Up3UBPPWm5TV1rGN/0BLC4PJtMAmTqdDfYtyKivvlksd8hq4nKNk8IT3Ih+Fnm7o6n8Ae53XFUkOeBIdyI/CYJI0PqUfon9PzTK5W9/NwkPEHfcXhHlQPW2TKKUNbaJ6zQ2L1J6cZi9SaKc51Kxf2Q2riB9c48Wcjf6yfkW2QopVCI4P3bWQcgFYp+vmftLgnAbSbPU21z9dtdTab03IGTUUFWckh7d8f2FBvl1TqMPwofXo/CgiLzTykwTh5Y5gQf4v6N3t6FSfgTSH49YPViLt/9boVk++ZwvbDMkp2v72NpKr9uEDl/7kz+WRlDI8tWhK7jYGmHT7NG/1boPchJ2MZvEr6IJwridaT+ZG4U47oziq3aAbjuO+nmtxBOdkcjxe4C1qpcdNGLxw/W/PBr3mR1091CR9gTiPCkle33avjsFPH0KLPgCNIcNy+1+b12ecRFpAd/wfm3DbOz6geDVh8QgzHEtj/P7VU6+ZnT5jCISX+4o1ennUt579Epe7WhDZJZzr76rGNHkyKvDdgCq+KiVg3KgDINzzQHi0u93yWQr71QVHhBZVjxD3/fTsokhZD9RMFwKrJduOX1ALLHmoO2wCxmW97aPvsRQUbob08oisDzcu/sHVDZLtHfJaD/clLvcXyrUYOM8QflscZDbQbcqU+0yW+q8GDGTiWncpL2tmw1TEH+ciJtt7r+IC0MMM2YzCbdybMWbpj9rvhV+gh7KtoA+BE3/dPa0ZYxwXWiXUrwpzuV/7aiyRnV6hSu3J1cmb2SYdOZGWLgANyMfzTyWhPSyJ0bwEHFKN6Q+dbuh4Zk5Gpzx0OgQz7oHEgr+F82TqUhTcEA32SyPO0nWi/5g7Phlhk5leUNT8oDfLnSc20WMgFTt1J+C9KTqK34m8Wyx6Nuq5bn9s44p/SgxclYnmNHSIsR2rff8ntHJt5VmMYMVOqlS9MNyHdU6ha6+VaHjbHBKV/Nt/OhNzlMVLCAsxTsG028nwSMdeY5nbBdEvoftt4OrVk/xPJbGK0+2Ges5t3rhKuoQKmhIBB5Q7wTLTKOM7etYdtxgkUvCdfLM7CASS1sK+fNuw+/lEgPf78olk7WTTXofYpqU5grUgCV3/TL4/qcCIvbjyyBC1JlJm/HnD+sGRwbxTuuAR3fjF/R70hGsmI6FqcnLzf+9qRi7g4+KRRQq4DLwOdlqMO33F1tkoR70N6ggXgVfVP/obfXXG0ngbvpRrE4WdTmb/0GdTzfNFXFtscXM09fqt6H91HZNHGz/mrX9qa4TRSS5LF2dlYCAQe+S3/GGW2651jVKNngnYBLZ4VXyD4eaO3Svr9ITxx5KKv72qiU/wTtCEjHPLBDQ4zst6GGbBVPpKUmxyDfgvv3oRsbRyZWWYPy4BKFfbcsRQov5kvu+Y8Fs7X/IEix0Cgs8RX0o5l/IRCR1JaT0TQPm6wphbYYX3q8QniqsA6nCNDp+ATIwmmrcS2o91lVnoqq+UleLQINMSvpqsisuVlPvPgwOVioDtg8X7Na57vKcEvldj/VFS04EfOcCLi46naVq8XYgbAr88Zo6qL2WUeQm4McrEmKD/1KLXxJL9Wt3uyvHaq7st9ivZv6Cfh+dolRYhcif5obDHzLDRx1Z52LUgxie2QJ/BWLmupHI84GqCgJBzpzFz+Kr+TXEAw3I/ruDLFKawuJrpla1Vbhm8h1hNKinfGNwWENUg++4xN+BqRYchOHkra3c07y3GuyOtnvGgNLfAPY/fyOuqIMbmsnSp+X8kIPiIvNT8tRY3FdUqARlvzmsScqYLFyPYks1FM3r4NVf6997upUO3zxHvKNizJRz7HBI2/YfERZp7pX2OCXI73ZhZUTPIc1jxfOEAlITsPvk4WFaNIQM0K8jLqInw4ZtPn+vFF55wYXRPlEc/J3Gy5qgn/LApXJJlZGJstAhstO7qM+rCG/DdJsZVDpSyUR/vzkx99f8sgC00/MKwemavVZrctKZInuAptxN5fM7LnfSsU1d0bdLeRL0FTAvgLUqyAbMkjLaYDgRwlSGnaw3Yl8nI6V6OGifPmVAnoDR4wQteOFLrjqqoJpKgB8iHl/dol4v1bXtvX6Cvgn0ckzdkrLyJwnJ0xEYaHRMAkVLoXOg0AD+vnVTIs8TNHkhOpPqTJ8Zfld00VVijTjXFkhJUKA98a71FKWZxIe8u4v43R29wBZFHL9D2REP/U4TcRcahUjeh7i33Ry+Jsy80qrL32piqzzYN73MYDu5QGqemKOASfaYtL1kiNExFAWLS2PziHjjavEdMWXIpG9jpmiM7+DAcFqSHPn2sDLF2bXra1dNbAZlQElH9ZI1OaKRvqkMP8cgcYJ+HjTtnSx/9rTjVk6eeVmiChGM94JVljvNd28LuyQq6Qm+7PDcYPXJGPt9xMkKdNF8uYsRAnB/3F0+a35Jul/i+o1qqFh4AuidXrVAa97aPD4lvfo2yYIlRt3g1N6kPxagCJe9yu7c2NKlSdB9Z87HseRcTCv/H3c/AAukxdL6U2mlXiU05pRgwQ9L1vNm+bRItJdygAZItuIa/ZXTf1Y2yVo79x2prsJJ4e/4820iC7XtqG/uGuwQf9O+jxgDv1yw+5UVTCnjumverfWhnR2hAEJ5E89DSg8mmUGfmu8wmEC5GJUvIu1qMYvitCSEmY4xu7TQ8VHSkh3bj24LtkMMSTx/vHUhukeo6kdkzJ5UXQMl8D8Y6i1xPAh57siqgIYXHZbUObwDdxdI3vZHhqeR3uT0H0C5HUgMkfjb7dXLnmjbEUXyepxPz0N9/ztFmMyzvCNBpl5bCvFAWGswxf1iJw7YHkhVnodTARYXRntLQOqzmt6p8vZuoKu9btNv3OPqgfWFHcpupj9BJr298HZQVbjr6P6+WAFUqOYIKaW83AMU3FG0GSWw7m53tOuZNsjP/iuXmPxrciUxYLEP8VGSUJ/IDlR65pAYvm2KuMS6/lUe6OP1JaKh2+1lu5S8PhmQotpFx5otuGnSWmjC/r4tVu9Cl0cQEMSMyXYcTdtLALQ9EQ7Jzb2Cgl6/kolLPjEF3F974PJKdiVUXxpg5O2N+qb78ftzphJyaAVxT3iITmpgw11cGlYp3Bw+Ks++haXYc5NxyFkvMXUuln8jo92c3zBX0BB4gvquyx6h1De8Z4Fj0PMsRHQYJ8KphewyA9P/giXrnnj0wAN5Oo/H/vCrDk+XOnFBDunvLqocr/qtlOit8TrLEn1kWFPKlIOt/ZbE5+RE+4CAPKcMDSor6b5bjMQ0sR2TfLC7lAxGfuNB45oxn/JV2fxUaKzX7Nwio/NSx5/y9afH8c3CAIxOlmI4PJdxLjApSRNKG+eqnB8CHJJGsLDR17QTd5RdEByeOJ199YCV3mBk6XpyydKOATV+tX0fhe4ypPoXMu9+2WB55wcVqskn0hRYS4Qwnh5KKyoPA7XBH4Jm8w9ltblvada/Q6OANWWLA0r732KIt1WA6tHieoeJ6eqMilcmr3IndFBI9JigK6rDU9za4QWaiGEW6p1i8zEUsBT/exl8mpNe28o5+OR0guH03w0Qtl3ohwRGA7n1TlZE6qAQTLVKHP7+O5P0PD5kdJsl7vEJ+k8Pxgkre+njRMfKvrt7Tb3z3j/3cCMh6rcDS0dud/3urnuq92uOEUk+JsKv/Vk3XjDFOcni/2rin1Qi4S/ZFuu+e+tpfb+7qcmVsgw1Pvx8pQLVu1QM+QLyP10L/WqWYiCQbeuYJGCDlGYvGoM3wAn9GmVTaGvsDSR4vBVzmxx1MDMqpMSQ8PSG2fQsal+O0joOomutH3jIbCD7giIeexN5bqS0vzoa6mtHLO9memTiAYXVPSDyj2+KqbG6By+Hiso5m+E/ZLMLR/2Tl0TO5UNsJ7UzMYPmdARncQ6DsoI+6/LDlieVeClndSOZmzzqInJobprOrZXkdYPoajuhmFcB6WR0GwtyP4yLmTqMT7mIj93YlSIX2HvmNlnqdwUPbKueLE9BY7JeoUFyhjFyrX+1AetJwmEw0GfC3kjEncCntXh1S4yO6L0YpKUKnVQYPUKr9KgLQU8OrvZ8Cp6shHLaA1ZgUzYx6DKFngjin63XuI57jBopRVoUPTKiM+WARy4C/daHba0LZ/fJKUl0OmpRhbY8YV/pmwWub1UacKZUIPdz58YhsdVTcNfVvblhyjX361n++nRnlz+7k/tTqOBh+40c1Rfd3DqQhX8W9OprHZA6JkZ0GDXJ9hc0Ij+NfeyyIWHTqUv3G4r75O+qGG1HidoJdKh4ifAkbh9EIDzv81S4aLWaGDleUYd9tsxTtWoRipsLZ+JtDO+NVEW798H3fDVxN2xCQVPUG8GkCL4AX/11pQaG/9N1FGFaayoPFfnz+CYKAPP6TJpYNfRm9uZM51LNOSc3It2U/oOlE/rURXblZthwWdla1yQfnPnU5LsJg38r8SlkmHBtnf9cl1/jnepqhnJKUt8gvkmaMWmWrYu+Zz3cI487LGbIAeXlrrh/IEbWbI4QxwvPUUq64l5Bi+9ISJH0gozzhk/361BFCmPnQGrwm5r6Ux31ejdPXkQ4KvhCJMXI8540OOsgHR7U40XKHbyJjErhVZCi2Q0Ds/zdnDf7Mfc9509nBsiPoKRPx/aJKH4nrj4JGD2thprxqCa5L7Qi9UVtnWXedu+ufBuGFGdcD3DihT9aZ4rDE2nuwJaCKHe7wBIwh2tQH8i+092bs8t7djjrW+giMzePTbIPPqTui1I5qXjiT8TGIuHOxj6g87Cf0XEeyt2paobOL+x+9xFIYAfonRjba3ct1arBazSf53V+nMdzuLfoDlBvyS1HzmDE1joiH2hh7nIt0NGUkW52o1dQHG5oERiR8X4rlXxXBl6+I5RiNboojFlhoEsI5vmLn27VCyoS+HXefa0j5IIgCBUEwl10bF6BjJoRhaebFMaVDUQvYeKvpNkFskPI+VjymufGqXEkO39sgmRNDqlY5NSQvbvzB91C535x733aQblYVWQsiZ/wZ/1s30sgPhKubmQD9IfVt1pgL65g7oZuFZxHjqDYr4VOKlS9egrNHjQTcK+/1YhAUtIEeUtuaUZBwUUh4Tg+etCvNXJtwHR7Tc1NLVXgdUAdsOON1LYjmvJRJNaH6Lr4q6YVzc/fL629idPg4F4OxPiHUqMMluk8ZUtz17T9mI1uCjyFmGsBMnTI6x7CybQtwBuo8A5Nlu2LX33rf/ifPBxFlIMmD/6crcKlGAUDU4fCXht8BLe2SFIl0LPW1RPGaHj8FgPodIcbTHQ5cJIFn5iiUb6IvJQAQC0pGqDlrN7g0KS/ASS/jq+upj4huQRV7wZX+TSUcWqPoOslzs7+OZVYFddVOirObpY9a4d63BDDgti7iAzMg+dwVcVF1/SPPWE1Kdeb8z376gekNUaEzVRYg4V3HFR238LPr4fP2IFE3LEMVlqkzXRFOOMwWhJbQU6+iA6Q7whqis4Meedl3oyNxKGul2SuVx2XUc7v2+QsOuNcvUx2E/E2CyAwXyMY4dNAggwDhYGcr2GVNm6YlbcZYV37RxO0zpVLp5TuMpo2Yqiel2orXbyYxE690o9Q2hiwYQLLoS1iG0Gf458fmUNXO8EX6JwlcJDDI/jDyTxJw6Fuv5BZzjCwL4tU3DmKf6kx0qVa01auWytJiMubam20hSqb2J9HMoyD+Woc11abvQU0N+8ks54AZsNo9cvNmDLutkZWw+vAOtU0upE3QhN7GgjiTCguZ6UqeUzStfZS45J6rshp49bdgU0H5qloYW8e1ZzkYfct7iNHrvw0o0Hm4FiYGIab4W2YiOJ+QOE+PcHpgrzwWsxpL5mBNQj3KAmgPVpVUZiPvYrAYP/nHhE7PgWkgWhtOIPBP10pbf4vsjzt5rzWxSHbdgPeopQtGadyN/EEC+2UBZOOsAM9fOjmJ5DYmKjU1oKq7CIfLEbShbFo3kqIjigHmYOnj7PGyUWXbpec9bqQEyK36FS1/KMyiw9xE6L7QGW1R0R9X3dblErE3jJQaEfQjaSyog2MhCLJzXn2N/lvuiCuI+fPFVQOcdYIyH21a07TkSmScNwTjWdjNcLz1V039SezgelPfHX6zHBnh2gjL9FzH43eylV39tIKW0WXbx5T8h0Lrom4FCsP5Sgwcg5y7EcPyYCsgp4Af4wNsOmv7jSOaiX4YCyKbgKeUlbcK7ky4vLShnB7bpPL0erpr5uGI368DU7HN4U4chY56ykKhP6lrevGhWHMoLnxk2P6krjrJd1x70x7QNC0QhYavF+EgSMrnHahZt8ZOSMcX7PWd2qHIWbZj0F5auYcLSYrRKgyq+d99OY9IB/DbMjYko5lkvICcArVdkSVIC8Foa0Y45+gQ5MSwUTQis1V32XoaGlRkiMSvFH/L4obpPjdNii2xLRmKaRxkG/Kvv18+Zcapc28lyE+W+Xfx/2smPKxE0ZDR/g0cCwrHPm7tS8IjFZ7u1q+T7J92WdNJx8J5BG5/03IVtZxC3Xh6O0/WjVV7JkXNqyXqWQxzq9JEe38a/UzIMiJKNxFUdP9+VbmQrLVQfo02Ba59Smx2dxjwsP/iUD/pblYdzPLUlmgKEE0oCTc645PA2XOsuRF8akQYcKzck6P0m5rkzF+iDBMxR8EPzALl5GMPZMYUlNisBCLmWUm2Qt5HLZbsiW2I1gugwuMqWDCt7wbzstpNy4TsEVs1w+H+gAwKX/VlZp4XlUDYzGhEMN5V/RnOFt9GMqDxjNEl9KM5dUP4UjsZjad+oXj5Umq5HHlzgP4E8pgiINbqFv6Ot3100S9aDQhwaywW8HkfI74XnR7Ac8ZGnz6grg+PDq+O6nJ6w1PL9iIhXfRufJIG2+seUY/B4WxHsmIK73mP5KujM9vBLL2+mLUo4XvCXR9sb3V+IwlHc5eCLkwdeupRR3QScFxUvIrfHpRjTzyT4xzNjhoYIgkweglzofRGBJjF3cvB1f6qUNNVP6i7ie6szCaiALUHVtscW5O9YEqwspe6TyWZnrL+eSr8P0QeunHbcxRmWdxMWez/XQ+7elisCbPSZ+C2bAtwHEfyT2qmgA4KKmnzrsB58pW71BzLl8Gy8SPbi9X8PbH0kEcVIiu6AdvXSzkBiXob3U499Khl8z40+mHIC2BWQ/HfA5B9PBDBIDjSwKyi3MzQazQWMybkkEelW9gEbsKQT7B7qFfNp7f5QTplHvd5b720BVMLJ3BZ0qcym/OAG8OyNM4mtk0tfr/XrZrsV4xsfcZKKgvtFQVLU1Hg70/btkzhqH8xTxViOMtiOcIPrUVgqEVn0945T1U0fvfInmvxz8Of3NxNJQbXdj1hCCmCdI16xGZ2GHGA5flBIiS3uiIqVff6+wHTPQmokHIi1TDoE4DGliUsYd4aL9WCWPqR9+qY0VtnAwl7gJmcZQ5Wv/Wi82ddNuKt7yZ9fyovipywK69RNCj9Ur+hWbXGkCVSrsbUe09hqXkxxySLBn4bo7EdSyzXspuCzLs7iJnbm//Yfs79TxwqPDqpjU4444275P0IksPBZf1GFllhqkmGzvkFEaVVncOfSSeHyKMBcXIsynTDUMtVQU4vdXWXJxZ22fuzFuVq4PHrfo1WVO9RtCx84UtoAv0RciDg8FEuptYxV03uvBe9B/uK+LdVHf7q3dNHJ+GndtACJ695Q+DypqtLtOpyRdFOg72ZyjJJ/bN5/H7bubzuKYLv1KwC68LGOUC30Lt7y6HyAgwyAS8vlipg/rh9BP9ffAsZdmLdk+pb3zG4umKpxWZjN/SK4tLdHbB/hWV2+WvWIGf7PVODnwOmME8kTr5/UepfwI47fX0Z+8xPXYPQOTFxYvCQS15K9YDw5NqGOysihbK0ruTL9nUHSFEez7QJw0k5RroOOlq76DUocF/xEW3OxgMh3ecCHI+GqDLeF3GutKnt3yd9wEFxOqVVI0EdpNNlFCgUHKLx7it9VmpDBezMIRvsyWYwOEOd+D9CJ1tW7vTCUUaIQ67/VnxOAXCMiVCl8I1AZZ7kq19fXWwKGBuVSonwWRPYFTw5UsbED/JHfDuey6zFJ9d8LrFFJxnjRzZ64n39MFCBgnWuUjdrPVQVYvw3agEPUH5V9VDTtZrumot0pjZJJm/It3J3FTc7RkPvZZYDeYoqXB41h/umcDlVwcDleOEjW2aLzFSpK4rrmal2XTImkSLIvpZagXoh4wd9zOCIrjCMIh4Is1TPSbTObWrEBCHpzgHsJV4vQVZWCoaiyeFLvpOPk71KcIs+huhdjUW586grZJJcMfMg+NB7qHfM8ZU2BDbDJcy0wmJUwkbXB+A6VnAQ41yjmZutcbHK/LlO0FX2Bj/K4QzAabmsxvKMqPIdFT60FQhqKROIAGuI+n8xzZ+I2Mr7U4/Zfh9xd6eHqUTRGA6lbRdcvSMf0Nm9C33vGelR5M2ZW9uxkgYJN0lO/zk017sV9wzEs05glyXpBcFd3RLyZUdMpCC29J2ppHYcGpCnBzUAyuGUOefpveEu1Dgf70nrqKhBRP8YdbDkwKZMqDA/FfiAycH21tXGz6jTku5SyJwz1o4P4ZHEgQ8+v/pEn76Fxp6u7NmU0dYU/U7FRuB3Kk6zIX6NcLNQJJ5ObPME7rzsW84qIY5BeT839nB4sIaWNowrGDOdi95dBvJhGGP4pespYM+xiZvuHur52bSc0C9tcWh0R6FAmybLqM9ukZixki7HcRTzbhV8cv/E4PxrzILNBhu7G/4686QhZ2VSc7whaBNBcnr+wVhRb3g3EhIhxFjpTZF222SxVmGg4gUG1KZuDfxoUX3MQPJp+pAoBB3jh17W3X7+OGg5wWx2uHKFffmhH9NTrP4O6yu8sg3U+OPHjI2k3osN8ffHV+nfTCcGMeWrRWWwz+dUtX+v0Fbhv34vuFjlshDnB04l+OwJ6bWysHdoGaxecFbGbIoo5JORggLXbh0HQlOxCyire41gjRHSlwC3gxParT6vtWJSqd72ykLj0ZLOw3mIDjZoIv27cgXm4BHvaKUkn7ERgrMz69NPTNPYALyieBC/nwRIgQxdCb8XP/BTDDerpswTDweVieBAuV3IZKvpSILvgX63WCCR68IV4+c5RVnYTTQ92S+VNY/3a0CKB7m/qdUEkFHVkoiAzVxbxVRjG+xtZ9koKBRP+j8sl4K0KZZAVY4gFe6y0mkUnRcRZijP62356j3ut600kUP/8EZ2jBfoUOAoaSfo6aqDjsJj36+fTnrvb4MFJvyihjn8MBkNhNv354hFUSjh+qJslds50FVSnKvDf/VCQ3EXISECJ1iXOD75WnIRqBa2YgysnVquxwXi92Ng3Fq6FJG5dbLRUc/Sfcm8P5+Ot0erKms1WE3t8OAWmz4wuhRDvHYaV28boNCyUJbps478EN30Vubo08dqNXY7EKqEEX1oZeM05P1t04NKwUkxf5LT+k9zVaBmlJfrWecKf8lrifchg9/nJQ+i10Nhto6kM4eeOr9vc6qiJDEkWfu1dLt4xGgp3y0G8b/Z1SpmiCto3kQ49GhoAkPSBYx2S0hDOoTh1cwRKKYgBxjmtz2l9zLYjG/Rv0dWxMidJJtWaCH75/k28ZSX08uMTTiH5FcS8bqX7YM8rWFEqwatvqXbH/y1iIo3sPE37+L32NKKgT8eiaw3esRuumkK13XxDBRn8hZcZbR9/2dI/12s7wnCQhJJxQbB2PD2190Q+ikujfTqB0b/L8ZYy4xKLR22kEbiZef++yfDgqehlnUIDK0m4kclmIaw6JXA6OGUh1HD+omzFhaIhLT0fiYogBFY2vlk3JKlOZBANTTSgCi53pFFSjZSszIRqt1j9EbXcpl5aZhVL7xtzGmnnpwhceqfcVn9cIFb/zUokr9E3H0PVCODKiVZUS7pCAbCtH1fa5dhR7HaLxYX+y0uP0iiMw1fxbyyweWEVS6QgiXSsOp8dDdL5zfgQwJPzTw/aLJ0H6u0jB6iyjjfgEPe4u4eWwtpUOkbfx1RCA0ssPFM39hMgZOYIQ4d3KG8dbzEW4D7LsybFa0rwb1eJFlqod6F9k50HBnNQvUV09KjwuloXhVDJOY7NEEz++RxSdwV587N4Hz42bDuzjFunmGasCY1L+9yZVo04o1brSa0F1IQoqaD5bCKEZof8EpE30ZP1nZex++0e35c2hUTyAhP0/5PHrfQIZV/C3cvrJhDUdqu8canXS5V/LmnuY+p6lSOO5lg1ejVJ8BbFZos5rViWsWnYkCysRYNBxYmBiv+atQoVGA1G0en48LHXBSZQ/yPFcqXRZtCUgvTMNUs6NlaDQ6m4yHX9S89s9RgKt0d7uCUrANLT08BiXgXyTeZrWAre4JzJ3dNh6M/GdXt5LjbXl3Az0jPv7RN3PgsJS+lJoXMxBMzMP1WjmAHsKqM+lD63tsSD4rNooOj75qdmL0/tX1LtLMGVkhBJf8XgDEb79DQ3bm7rsj4TF8ekE0723EXE9XH/LlktiOm89we4zPo1vkzLsgDHfP5UaflPCVeE4R6O+UC9kL2420vybeSVEfrJgNQobRqu0czANn/95kzMB7DEHfHwfTTDOv4mMvPEotgLG23UbyVlmFsxvCA3cMCt3jmrqhad7vdvKAH+n2LEaOGlZvyZ0jUksp6YYo2BBhJjK/o6O729YhYeFmj0xvRCMqAftoHwHfAAH0V56aSEEcM8H3eIEKVYqrKa3ZjLgX0CveUSJpi2w4Q5e2zS0zk9aArXN61Q8l/sCYNqa5G3B7gswfuaV9eHLaBOIdnPy6tnBIEK7Fn03YCA7dW5MdEAO98ALpSFrjBCRh1kXFAzoMzDcINig2EhiJbiogkBy5TaR+5IN8pvzElHihE5H6KJr4J+tTDJhhe4i9sK+gc2TiL0xyftbZm3BuBnZpAHU0Xb24mcQetZE7iRmIY9ckdF7fY/wiYIALoNbllI0hUrW2qQv1aUD1SGdyGGgB4ks2LnQnqag4ee8H8CDT9bk4I3YIS/LYQHOLTY8OGyOKODQJr4QIymgXbK05LS6vXhrOdVXmdnQP/flR0QQM1x0CEEk3uZ8KJy/vdInqPIbNVLyMtCpnt9tE2Eyubeio80oZLkQF5MhOyi3No3kXk2w8BcOutCBSFctoPeh6D2q7gO8nwQw+IQda2p3ZQGikjdBZY1wvl4CIrLZITWx8LGuhVBBIyivgnR2ucTyULYCY2KJHZ0szHcD2kgCnIcuog00sUDssNuCrB27WLN5lj/vL9ZqKw0/IOsMTfG3kW8nNIvEaVLn0XksIINF8fGnGrTRHA0TZyQEXWIwmRCpM4Rgm78S8tvBPoYdGz5iCEpDjj/VWcSui9sN0HaGK/MjnhTtxQ32UwKUf1Iu4NLZipuTalQoMcv6EUdN3PgtlW9uzOAk9HVWN8AVmSF3JwjDuml5NxExd3qLWkAzPzQ/hA55l2Hocnw/1L8nOtA5CyoyDPHao4S9qIfGeH0pt2nro0vBiqpbwU6ALsNhuGOIKOJlS4NE6yha9hhGPvFcY6h2R8+3fZ+edICv9Cl6lz4djrklLVkydX4g9p0g1GfGcyl0NW67DRoYNvFPC4dLG8FVS+Tz8hpRUGQ8/dA9kVHvU2ySitu88uy3HdUCWV7Ny964j6wg7+8ZHmMAOxCUihlzehpAJv6tC/k7NbHscpqux8GhiaS2WWR0+n4FT25Kj5IwtGUlE7qI16M54YCcpvc82SDC7IVIb82il7GtywSjRmPbyE1Q5Pf3t5QSenlfuMyBJxFYcxE7A0eXqulTvooSe/+WucpEjWgIVWAKr+sDKZ/0dhoHu5OsOubGmm6bEnDYOYrScZ+hxklYtr3UoXYHbxslf90/a46tjMRj1k0UO/YdxMEbexlm2Ow/mq5ry1Vkh37NfSeHR4LJOcMbGWOSyfD1l3KfWWvmnJ5uj9tUlbb2llTSSwxGcd3SGezeCSbwCMj6YvDF0Ltf+NzjczgM6ogySdm6eLSx2+vOgkN5zymeXsWSeMdmKKfs8ydJkSFNSQ0ZeGcVV2GzzcdsZ8CoKWE4uIqngqyipNd85Bf4rnMuTAvJb9o1WRHT1FUaSkreB0wDAifbpagJaf8lWU5TsguxtHQP/C7duQgdf8PjIDag9law79uiCJl0q0NWDIRRw/NkIB29VhGgHoCj3R3OtfiDNng+2Jvpnn8XnayHnE4J/XMHILKfvc5/uZdPTpL0SC++NapXZTzWJtx1jusNGF7GflSciLHoXbIV5H+YEOJmNcuY4IMXEqmFeV+5tIME+GqT+FjGAQcVkjiXL42O1RcLl5KEb0gOcZnArh13tPXbpIPgQ0/taVb3bsgEvLOTZaK0cFLGbzNoqweHxqDzz44jbKkkBaA8O+LZBWx9bhAAlOAjqBMQ/nTlaRPw+ASxVKLz5OyyTOIs+wGbWS8/Im/HoV0TtQDVIVk1PrBDyzMt4YMy/i6kvjHTdQtOj/tQDOYW+F3pxc4IXg5fL7qUix6WzK04pEhZeCj57FJNouzqW6/yX91P6UIKKjS/ImWVrknJAhpy/fDSx51m/JBJw0/Y4M9mwzma3su+c1S85RpTL6CotFAOA+j4RvTh+FimYq0to19ZDjncvnt2p+6/+9vGUDFsXBr50miHoHt6z+m7shNHxNGv9kuuZLgX3g0pRDxV4TvCQUg2BVO0Dd+ZeM4AOAm4Z1d7N2t4EfmGniDHm9L72d13ui3aNtSuHbFvUiglRQf1LJ9mA/hfP8Ri/+5fKn6dTDHlyyMBeIscHUStghVkiVyvDshxVAgjOz+vS6oHHLGXUnE6EB8kKU9mpbO3tqjlFP4G+/xseyqVwY1Kr/WGRs4AmywxkE2yME8xwu1V5ErCFK/Pjnd3uQmpBR+psy77XxLzCjpcE5Fi4sBB0YfphWEOx5lqom3MHnBqzaBli/L53hknZzgvMqiQr9pC7RE4Zc9ag9A+fzZql2PX/hwV1IwkkSjoZMJPFGFQkl3jaqJ1mRsxHeIow+7LELTjYy8bNpm7Njxrbg4trR+68OmYi5JE++WdLRaKtFg83jtWatMBfoVmHtfwu6PDthpPwv7E47MTCSQBqSTznUb1vNJ2CqAgksDsLP+yWrO4dP6A+BoY9fsjqpT/kPANTU2U4jR/kY8oF0lCkVKeoBm4DPLI0TrM3oA7PYUqpH48Kvm2LZx6CC8h9eppt8ubm8VpZQSSOiWfxiqVdYOmEuXD9qzfZGfyoXhIsaPpmykqn0QnDMrxQwhN7S2to5xJIqW5rY5h8bL1uFKNBB/UKOxVeR6fUNHFcvkttuGCaJLOydrksdli1+ggq6qxaZgH9nuPpkGy9pEYv4Kos5q5Ha0s8nivjwOAxOxGEtiqk+UgSn0p58iQaJVbg1ZPWFJkPw/P9WiXaD5tMGJSjIyFIGieD/iaz+/Yc1JY8gHhy62EkIuKFCxFpVn6DIyQFSQHh+G3JfYEFbLDJf+X2AVsHfDd59/udvbbzfbDYDGycMnRW9n3gT0yZnhpWTD55Ta48Fzf79IMLOFdGsJxl4aJU9uNtm8MOZh0/aftbqn1hrVLAazVWOseQCPTL28oPf5tIoW0KfEmUXbVn660lxL+kmpi5MHjsMeINtthZ3yFOiRqTrxS9rJAtDXDr0gzWAX8awfyMunleYWFchHZvOlaSKDtb1xzVBKeTEgTWghZLpiNcexKxcfUo0s+/PFST1LkYpMbQb2dAI1F5touduFBUQaj0dF0S84P6+mFMMpG15SGGkNBxQvMfOeAiJY96b01IuYNvfrUXORzld/EY8YFAsyMKKZ4KiJkRXmsXLR9SiAAui8agc8wyY7gjYK1gMx9GxDUUdnQoWuOzqmEWmQSZEbJlifKCVJxCcw0Zoc3mOjFLq16v0GjVgm/pe97tT4josvTc9aJS6Up5RWuEspIJZrAJB8kO1FAiRd3IkpUkjJOkHlM9q/zeoDNIGzbxxHJQqOoGxtQMR5ySDK55vUhQgVgky5mdezbW7Ns87IphVUHXPGEhG95NjQKofesSjNIeOtl0HpEKtFaGoNGlYIHlrVnSjOFEel9XTCaISKJWXHFhoKDr/RUVOuo3sfD9j2KqKgToQ65GAKWAu9Hl3Wm105Jm1ax048voF4JA8Mq/MKHI2jNDKcTo/qKcul6MvBHCl29QrNaLZdJ+m23taYjOuPyXKrod/St9dnyKj7JJWWE6tDM1TScAQGob2Ku93NNk8wKky9R7c0RH+8JBAw+mbLwaLBjTlWq/K99v4xZMMELKfB1bxjW+qmK77sOOtxWQbotroljB48OAq3DZQVQSs0IrxUO6p0fRG7f5Yh5cZu/VQjezVrdqXXkdADKC8auJ4qfmjC7c3CJDFGsBSqNoCTLmk58CG8H7UDFU1De+vJ6oQlGUdKkPW4dJV9p+u7SPU7Pt3ilbHuZMzMtlTvH7isxI8cixke6WG0DmNk3/6ZDBbbJYkAANHG4oLEVKM9nXiaClE90PLLasc6slrPZrciGpY6Gi/eBzWUs7JJeqHUcmYniPSWcUrsgEtMcRnOGvBMf4y6yClnHou8DozlzzGMrT/CO16WTBSP6EUAeVM4495qDe+J3iCLcUcxvGlQnC7/wvmw15Yn2+S+zRIgf/ozl0xQLsvNA6Ea1asx2t3qsCIWupzDU4AtovA77biEoClPfg49t755wqvyFHorxaik+/N3cCDPLzosbwgr4G5iPvw3IN0GdGaZuho0m00zR2onmETql+bgz86/QUPVIxCqimXCOun0nHzwqfrOo+aQaHIfwkBYySoYXqiamxrPKBYGqIqQYktAw4NIcQdz7CwQjGnfA6bsyyEwQIZNxe5aYoEOWfHpTSnkfGahG9eoGspF+t1B0je9W/EElncS5X0EaS/PfUD5bkYT1bVtBqVOY14g4WdWezmyn1SV3eF5xvdFL1oNvpZ0ifQG6KLWGicU7zogEabrWLyeVfHu1OnyaJ0K/utLUrLcBdzN1n4pMBFLJj7nS7K6mtPvIm03oJVf2LBMvd3BNSxN5hFbX6aepHFWPLt9+HQydVzcl55ItkbvyAete3okXrfcPWbavfL/yl+YZEUtRVtBmVrw3hJ/UJIl4O2OB3Ad46b1VLaVfTrXvuGVaVDJ02CWtdOkFSvxSY9cEQbwyuaoAml54Xpk6oJe6DsqEQPz5wl64s5hthUnwRKx8mI92+Q368pf1QO0YsgYQsmysyktJtv3OtHC5UbCMohBxFwhC5Xa3Jgj+NSTyXX7aKio+PVkH1WGYsvVrYOFX5637XYzCU9Qi13MmKEAhqZm+SqkcZQPOBI0IED5bU9F3UllY8lkZuuuSnsd+syu7wEZtVCm6k8gL7pv11gmVOewEpfoamP2cdS3B3y9izoT30Zq/edZNusZ0nlu/obGt09NfEKUiwFljLEi7159iftf2+ptUaCd6TeVXifhaL9j3MmBoZa7Ys43teDxW3FTRBnnYg85KdurVTati4jWLjpFrpA0VTEsMjtHAv+s+WcNfxxZzdARNF7y3RMZa/RXflqI1LO0RHypRBZwRFhzbeh7E+Us85530MxpFQr+TbQdxlOMDmJ4IQog97B3WiIjJcCTXvTN9wM15KoC0Tau9+X0kZLiiKueqhnc40F3KrO657L7p9bLdEETUkEe37hpU5ED1SLW1TXgz4pVWeDZUtdzi1VcMwq5Kh618xMndaHEqDOUPdcHGYK15AgXXmELUA1YBr1izNdU7qXrz045MdWG/sZK40X3Rnzlz1M3Sx4IWS+YZQIoOr0+ke78uyUANARkvYUcD62wNgBYLq5dym7mo1XqftAjwpt57PzRoNLsBk5yflRiB5dFRIYCoB1Cu8vprs4+suU0P/JY1SPNaV2s7EuSqd6KeygiGst+OU0goRRZfgYEf4FhyU1U2DYbgj4DzZaJ82TlmekbKYfZjDR04KyFwn4G75VFCSEKyoWR92vHyGWQGglJiydOCyj6N3X1pRCJ6z8akvDJcnsvXBbC8MSM/1W84K/7ObwPs81XVy9YhzCesohDTwQfZR2ryeVeLpAnqeN0UX9BVRfh4fU+Ix1yvkizn3C2iqW0ZtrdEh/i3jNNdn7Kh619nRgIj51ePKp0c7JEnDH199CX2UXDgIYLZ0tEEHbtBcFGQwxsjNbDRgfO4KJsDULMgwK6af1V0omO2610Cr0BlkWJhouJ3DN1SgmbHjcWaw4HDuimQJDdninp5JCP5fV25bSRBOXga1SG0X3BaBqFjwctDrXmT3VFVHxJsPPWv0olMYBi42WPdZ9XRLqi8FEkvpJGEfL1yu9bjyX7cgwrsb6Wi4DwghPL7W5WrtiWpIUaz2N6gX+doGLWDZ7HBly6E4i6B4IUcSXsv86zHa6GzgdiAOg2WQdGFrKWz1iHgGHXE+5chg2IrWklU3/iLANpchGfAV9Ev6cDgeQQR/ewbaCXDStSYXS+QCMK3ib/o265+VC9zCjQ62Bjww/P7KkyhOC7T6kCvxw9bNvTC7md1d0UJQVX1xfY1Ajl0lGTcanqDu0gSF0kZuZkGAF0R71ENaqs2m7EdYqEUZX7ccX8Hiw8RXpVqhkv2m9G0LXsvC+gp9JuObR9HQTsUHku5RUeYyCJhJUG6qcxcEjmyU9bXDXJw8y6b3b/QaCZRxqWq53RCqfro0HQMJTu8OaPgZhy8GPnMoPqXzG3Hz5MwHKbEXsSBBvHSSo4n/8UjSFunDmkm3jZzU6GBVLSQCGH4JuJbhiC0Kr87vH9fCSBLD2KdztkOnAzvgtfqKQKI9qOfXFhFKdKr7d38dqHplBQJIlRvEmS2kxc5FI/MvSAmBbrxIJWv8m5iiQwtQdvfKP/pvattIMUMBUp12Lzs1EwqEnQr7jycYY8mGHSCO8KEMfpLmwnxu6Io0Z4pLgVvcxEy2MDwB6rS31NGwJRqNHQPaK+JvX5YrUw8g+kut8sHuTZmWi1ZvZjCvbHQgkxrtNfSfbxMKORT8WgdBSu/DjFG15GmS/u5TWOIqdSiTVu8mtE1xsw3H+kCJ7h90IqIitTBIZ+MMPPxV4bztmN4a12AiWy98fw+uyq1oZTKNqN1b540Y6a0tqfVQQ8fTcZ8KHdsTlWLZnvHL4dPC6YysB1hfbRi3ekg4Xy0hiAD1g5qX5uCwLv3fGHV+yh+kx6+C/+XFZzhOnqbKsiPqXj6sE69hqLlwxf8QwXGbxvgkFRX2beWFKyThmjUVwH4dtrc+2Js66rYAPFc+m1z24TCGCHRz33RIAxnsvh6MT9FqL6whzCqE/G49DrWgShGJZw4qFlXQZ5cMGLyGLCVBgMOldeaFSHpwu9U4CTzvrsG1Sfe8XW0r6mK+FVmnQ9TPOlKesdjnWTjVzqBbaQfoOWREJcdhxFVOfBbZqxorSN9Wswe4xMtis5eDzJUVAJE4FdnyjrHpdi+CqVIGdfYLwSYrONW8qVYDwkwgJwJ2FE6idkXccu3SIuZDZr4XbQlALayR1ULbzpYwf08tp5agbSEazhfzjUPTXC4Ef8CCrL8pSL2CjdCC/ZW0DNO6EibtsAXFKM16PGtK4BwXQtjb1LS2mGrMSabK+B8CXWsgCXnj0tevBCfq7t4kSiB1DN4GCE8Ay+NbAgsKPhYdHkYkPb+wnBoH8AGerIz6hs+iMrdpVXcHjF1G5XfvoFOag3fkvcNUt5FeuMq/Ak75fXotyIOLTonUDL7Ake3WiqLIHS8i59tRzO7quKlPqGVUuiLzMdHO8fVb7rOoAnu9BPCEzgwOROip21YA8Z+0K1GMM7anOYtgwLBLJzFFAvb8IU8VKdOZn3CFHp7VPz8rvSpw6kOM+xhQ0tVQol9WL775274XL9Gq2K/fsGk9RxtIW/83V4VNMuBnN89EFKkiSO1IGxYEZ9H+D2bpXc/lsAxP8R+ESn5Ix+Df02+kN3+80tiYBmqvf8V9z3am52LEk4ocemTEeN1SsbwnuV5hcrd4HTg+ireXfMR9Nb0H54t5CpLUq0kU+FwJdcp1qSlN+gHKwr30ZgD/Qodlh9M/f4Vx/xhc/IJZsbGyVBsK42hE7aneO6LOyFVKcUsz4NvyuV44LiJ0F2Bxzs1hCJ+Cu9fEf1Yg74IbM58b6NwYOuImPIqfv2Q8b0L1Tn8NajgHPji28UBbJlEqTgShu90OwfYAdP6FTZ23INzWgxLmvIS+dxCtq9zGLXgZVFS24pSZmASYMyjOvkJW4QxlJMHxVU5azPWVMfAra6G6VV23UTAkfoWErWr92Xb2fwiJ54zhCaBo+gVIpHwazA/xg1CcG8f1bX+q7HYNUsDlFQeSpsNXpXr7KCH+b0DA2aS/l5QEFcXHiIHHqNDzWYrkPmkFyVIuyDUYSujLQk98uBoMx0XXyFOvq0rrlnOdDCTvoP3eBQqXBpTUzfXt6YsTQKFyIeDB2X72gJfSIhaB90Gjgp3dRnFfn0wnZlXkDLTDqQGHpg7AmW/AOZc/JLQR+Qyxb29cBTI5TcVmF7H6pxM/dLbCSCoH+O60zZYSvAKhXUhr53MGwpr/JKg3jnSqp7YpZxjGSd1NjtLzTw6TbfVs8/fDNSFQSauVsXHwKjoF3l4RDYwu2hNUQXOKjrVEnnZhwagwDT3wfWuGMa+JubxOeUeemXHmgp+yPYeVWMaTOWpWK4g4oi8wcdkzf03OxZ6HRGBpCD4slDvaeT0Uw9AfooFciQpxFbvsm56Aaqy9vpSktVrTw+tq/P42m1toBLcl4fbpN0XbG1wNReS126s5eFAg+cQ8dFwS/X+bFUH30t+d2JjvpkeTaopsN1FIWc2sF/1v/oV5bvOSzRhesqfX0BW1n3X1/vBPKBjKeeQkl/FVFQVUrMe6gUOFYmcN86zFcUhtgfo7iXNwueVAB9halTpu5mApKTx15eJDTaE1vIt9EftXMAif5//pD8Li6SgAiN1I/D4QfYjguEDOLsBqNH+dam0FzAQqkTmwhyID0S5FTyOCRTshIFuQeXSAJPT/CtriAUykixicoKc7p2T3l0KKHt2Aiog1JIgRX3Xqa2Zm2aFAqscZVGO9nDw0KzYVMT1aifDnFI3HVGFSmgPOaWtDApRWvayBC8mZ4tl/WptKOM7bqgaQXw94tNy/hqYXeNZbl+JLIRvtz3vEc2b9zYBYoML8CuEwDy2t3t+FxwNRR9mEAmZLb2OxM9Hd56rB1aB8nhhgh7XmRmJsVtf400aI/Bcz4c1+S29EPCQu7xeiIquXekDpxZVeAzldNA9ppzB0ZjTFWqdxacEWliNcsR4ubMPhbD3ekurjjP4++2sFRV/wyFVdHe61VoyXa+rBR1/qdypKONBRS81QmiuOS0Znx07vxXH1SGJkYSNHovFnOZ4kb7YoMqmVi8JGgQpYrTiV2aWEqkdoA3Yl9QpFr6m0R65azyJMpM82niK0XMJ0jZ6AZ3wXoPV7pruE82uBdIF1MJqUmilYmHJmWyBdJzAnlUS7f4+a4qq/8LI0xdUDbw7VGjNYkIm/4BYD8oVloxJikqVFIn1iGoKlWy31vDO+DvUaU2rFtS7/ImRaxeINzuSVeorVac/Ku+lC7fDvCaEXr7kSi+1+no12+D7URubTGXhVpNFLPaqOTY9DtseuC9wEygaStRcd0hGOYHFtNKORA+3NEcripEMO02Fg0dWZbdf0TFKP/6QmH73Jo6YDNnQGS3p0nfoYWHTIaCfFb0hdP1KBQg4pWkxUSi4IK1+mIuJlHThgmEn81CP4gWCBFwtxWuKexvdxcNrqHxAkcsqkQgmMyhqOtFQh/ticrhfz8l78UndCxJWMLMP4PJOyUIik8iKHHc1KRr65Dc0Ik79dU8fpw2NfD1eNu7DmVE955Rb8MA2GvC7Q+PHRr6i6vPKdf9WV1hxQ1tUC0xei0NLKDl4TMox+XmF0jBagELJf4Ezz0eKnKZZmABARYNuxyyNqV6+zcoxV5JPmah3cWc6skPCUb+CWWYmmVu6x4AMo3bJyCh/yfbNE4FwftZw9uQ6MITyteNEXKTUImgXq90zFbUWuloU+SAKvOYDXbGBOGe8UPm8YhLqaRfi/f010d7o3ECJMhVHVOdDVKCsNhVbqpM9PLCCVySC1RpkbiWBLc4Y8+2OA/e7/DnNABGBob27TLHm6eujWkkhyKLKjKPvhM/YSVlTyPGRviayfyndGsKIW7y+OiKqkmKqYobhF8DkQM5cWLAVoIb6JyQC5e9vGq7ufbq0xINRFGWqkfAy/LLSucQcUPzAat1vVkf0nsAMYEFtaq5rop/6X6M0yqRmS5nGpcpWR3Bc56NmUJCFGRAm6Hup+M0DHetprTwyli/DiNRlKabwXYbrm3HaADZ5kWsyzRELEqtFsg2pkB+OItxiEWN7fyeNPbKcb5UdvybXKRYsA/LKxJ1RTidjcIk9G1hagUcir/dvXNCd/4phm9QPcxzogXqXeWOEJSw63wOhSEjQF+fbkHEzt3CQUVUf/QappbHW4A0On15hcSexbzRkoQFiZPZKn9VvRwmK7fOsWCyJvhEcKOoSEGbtavFA5C/TLaYroJgF3O4XTJfgaenbDfSWWeNqjtP3X53BNN5Ffr1kNUPDHd9XHVHFz8fg80V7YTkWeh9zn0Z8Lwfkd3UcMo/Muy71V3iB9smgMoifUSozb4pnvVLJGJ25JrCSMpABzvEw28EaLKQI2Abgrrh6LWGwinTqdsbRzaxR2WkIEIOBMgEry5wN3UpTg23PkPxIXetbv/PsM5Ichkr0LuD7K/lXkoJAr6xM8eJ7Rm2LwNxG8CcXMzdgJ+fGHLUEI8N2dcA2k0rJXmn7WnBEgpoxxmkEQqpUNbQb8cYX2LhfEKkDARxAT6dFAgiO3XaWglCabtist6COnrxrQ+19xNdBGHdwtzke/KCQFftR6v5uRh0Njrfh7G4jR/zhvb/fQk++RZTXVKZLrg6qdBKQ5wETOe9NTKBSmxrc7vz6fkxpMEppvJlhbw9utXfyrl8xYYbdTVF7jPlaY+HKvI8Ub0/QLowe4OLwrZN9IziRheVErZSouvKhRX1qUV0/lomUM9oUtKKuxGTWSl1sBwuerpRIfIVUiofnKDh9y6CbWdiw4POhHQDMvxG1R9noGBwIAAxSZ7cY8fGYuRFLGIj6skR6SA00bycy7Mjk7flf41r2W/acbrayBnpYsizHL4B9U0ll+uQWD7GnyUQlM4pPepP3yw+2t/odARhh0AY0MmthK6gt5AkO64qBmBxNBt+mfKbGzrPwrDjrdx6aTED3MugTZfqBJMWxVe6vWyif4OrWxgOJknVmIYLb0MgBaZX5ihyvf3gjbViWPugZxAZRT/UBV4A9RUPwIczWdVI30V+7IcvXUibF/YUNPNjhIbs93Gx2PHsTxsVnGLbDUJBNxFqhIqVmt2rimPOxiNcdUY80soJnl1oQ1cDUEnEfsna7KAHinq4NS/gfk1Tx/c3ipvaPWMLGfT2cA4HCiNUrMlwvKUuvRYU0HCiLsThRG8ZAXg8IbOvul1SUO4p/SOLD8CgTcgGnZ9Pmz1Q+LD/R1N4lzjBvbEriLCC0v8u3LM7GUzSV/A7eGK2TTP2R3VNYzI7yDhzVZ3FoIEStrjSP1Tv6ti0UutROy4hBATyMq+1Cneuh9cyvehpsHVLc3Gqxgdpge4jvVkh3PLOQr7vgaepmRTE9XQjVmtL0k19LRWRsD6Gt9mXOdwZCwe1mIWVJF07Rx4EBJP21S75ewZarvym5Anb44QN6ADuL2VxAkCeuMFbHQIC7/eQoI30wFC7TjKeAJzSkk25MMxaloJG/qP2lFnlSLlrPrOKLcbiNDkZ6PA4710mzNzMzvuE9WA60kvr9d7WkAjHHB8brcpp/BSp/y5tgGWb6FoRz2rBplQP8miu2RVi1xkUPwfa91xZZWbtLJEY86tvR44p9O418v2ucQsUcK6rMEXUMSA6EXPo3wIdwaJTrI5acyu4HMwqG0Zfee5jgzs4lBZPjzYpjhYfDKANaQVcxZauyHcBVSvxqmanzTE7WwJVj/2m2xa8niGKPG7wm6nPmwjPQypM1X5DowxxxkQFUTx8XfvyW+rvaqJYJVu0zxTMRhlnbWceo2Fy88u1IkkgzkXkD9JZuPi08lj6s6Ci5dVaoqjhoAKRvj5SWZblTONIwFdG1u5XBJGZWeAG3HPz4RX9lp++G+cy757XWgzgIgXeH7li1775m8krR+TZFKDmiFnLoLvMKXa5xm5pBR/2bgC7AQoa/KJXXH+9ClNwr7PheVYjlLAlUq9sXG9pkFB0endqmVjFGJCFxjUgOQDVMi5TreS+I+pkf+B6b0llpytWFUwIeQBtokcJar2nMp+EL+eLVgiOI+HMdcszce2J0QJfB6cTRJfjR6MfHJqs4koh3/udqBbLGtiagbicB6XjhpMOCQJDg+ytyy5wTeAWjhziTqnIoToKH83Jvs92mG3S4svrD0rSbTdfSqr7CTp7O/Ztjwy1BVuPOSx924m1VzfPrwRFdE9QI1u4BKUtpqEiZACH1T0hYKUM/+4ENHFoLHB+49b0ApyFm7PbcdcAvyvuTWnNkkD51KgC31OBqxbRSbAmrZVhGRKDnDJVMQfN59tGRHk3RcsTCDv+lt8d9r3vsz5vooVsxjubqlYKim5zbV1fxsRg/wylNMurHflAdKQuqjzDWv7AYJMNGaD2tUOR20DGA7V/kOU/VCppFO1q4wVXMaTWBTCruLXhZyULirJfhnEbxWspmZ/2kMhTMf9w+2Ent1NCd9B8S5ccVUvErj/yqCtshnWfu4b/X3FwzVAPziTHwpwNKDTS/fT7hd0+g7Hc563+/zrKwmGyXPSRfKcnn6VOrQYirvDzntrLujxbN2RTwvOjF/NopfT+RQAt6Y87sLCUW2zMT5Q0iinYW+xyMGS2SD6d/QMiETMNeq1/qxRP+hiOYnVgUXHx2knhclPSq6Qm8YedhAZG3Mbe8qwKAyzK3Ht1R5A5awJrifsrgpMHEStT3OAA/3Krf+OKYDoPQCu1zPNdn2Cap0vowjeklrLaWb3H9jZ0LO6Xfb0Rf8fllwcbwa91XsIKRWji+DQFimjzEg98kWjX0WBU9fb/elmUMjHi5tNB1aiEtGyuWOA/URTe0zq+HJUhEnxDVLme0eps96H4PpNGOZId5kdj1df4bfOfnbO/pXH0vU7ED+HsBh+QdC6k+/vPDipnfY48l0wKsqEFNP2/cm9RqulCtU6xOtI/DJbfWv1g6nReqEmmz/gzXe7DwoXI/lgj8BGFCQjRg1KEOhlqGdhoVSkysbX6PDR1jqzPg5hI9MIK3FbExaG+38v75LKAMSF4GFlplnbPo2sHL/ZeFALbJO69jT5lcnCEro7aPfld1zF9V/HAITEDjK2Vzi0L47qAWm7nkTPr0OvRd2nK6WFFi4DP3zIdWHtPyuyHhqbxIHDSSJcFS6zzyZgwLsiKtL8yZNqIsq96nbjZ9YZ1e20Bs6JEsA2JJkAz87+7lY7QOgOOzTdWvcYiu/EMkZ2W2jmTh7yX6mm+h5sWE/xVIAQbp9XGCYzcQwTX5Ed/bdyellT2mLTteGuZziNQzBH+H9UN/ejRbIkk49zpPu7Wj93dBveKoX7sSrknR+2XhFb8k5qx2AO3H6F/3rtxNPJU07XMUqKvPxvRxkNp1bfUK0XbO8TrhLuXaVLZiu88Ggo4g4t5x+F2L8HjBYsESnIk86st+8cQ+pbT/kBVHy+2cXhskfd2r/SufRqlko91U8nI1gB5S5liNEbxJmAwHRTmbm0QGZlPZzlUof69prs55fDEkNlW5Ww9qvIgRyVc90UHRdTZeAapOwLJ12rzfuI5+CKjqSylYqsuLqzpcsZ3peHxM6QGUCQojTrU/F0eb2666v4yktKsWPT/w9n51eQn4DF3fa4y7NDE7GAyTqYhsWoRn8tZf0JjTyjx+wcvmAz3R9zWjhg5AwMHOTXa4F2m4ddJtitGFKb1k6C+/DJpase9vBd9uOLLhnF6/fhlHhcgzADGF/5VbSoNsLaJ5rR+pk9UaQ5vhKmKDYrztU9Xlw6vK7eNt5DVR95agGPh/XJqxv2CS4pn1Ra2G2YzPQJMCfLQIHIaT14E3SzAt9IdCaLfx1KAScKZV52Boz74eZ01DYRPRUvlhjX0TFsusQr56zMWrYFD45ryt+ne7dfmjVauogeksguMkAZbTdMI8XJTC5LODHa2yDNJu8/QR/XvLmJ1fQRMPymII6dzK7uUA17Qg0QUo/fTIxsgZ3lWkRuDebF6l1x4JXoeVu8g/bN4xfcoulYtdObpdUFFKRqxOlAgjoha4fhpQoVYl9oTkvc589wUWG3j5EvToJn+NLtGMsk4G7vA1j7yrVYrDTAhdpJbCQvWGa1kD3e0/LkB7Z7AxlaXHUeFiRflO33ChhPB7xwg2pAiv6YD6WpMXHF3rONi2y6EVFf2A82HHTteL+kTzED5MXEscgUUWe8KdONvCU487BGtwXWtL03VHuWImzhswGoN8BGo4Zw8lCAEpMT75mSNbZIYf5nQUWyEvB5TTWDry5c0U86vZr74j6+xySGRIjO3KSEgfpD6wMEzH6/1OBxu2p6NKbzj+JeWuNVBbqbiZLNI7uoxm7ZXDyqhxJoQMQE2y+yfOLA/JrabkAUq0axkBjaIdYOte268xPMTDwhpt1vqSdxQ5XpzZE5Mb8TSnEV+kQ1/8TDQTdFdYRzFByyP7eN4Dcp46NdZ2ltPnG7+Q17riaViMeWgxRlhHN7LpatxsA2gHKWzjjIwOyIKwdDf+quENLNYfP9AaEDcN51yZuEzwa7G5WkhqPkD6qplkkV6UqtpglNxjQiH5OTDelte/Eyx6Fcd30pxP6Cj3KNgti2ytQyet4tdcy0MzUUUgVqgqMIlQ7pZZ6psuWjPqz8mieUfAmsQPj0/M8Gt7E+r6YD1itt8zRpFkUjkfN3M3MmEWfsGZUBfe/A3X6TKzffpY+8zvWpe4Ph8+IhpAJ4sfp/9ihHD18q/aZBbKS1+VFJzcQ7TvRO8SDXWVZU3eooYzm0kzNFleebOUZ/BXLj98/XmTutk36la07yb4GtOh0sv6yCzEFO5pVk3BbRhGpqSB66z3ngnmUx6FgtEfEJKqHIiqTHY9eFbiIt9LQr7AuGFmCdkUSBVdlfBXFFPhYO9LvZ5y6YPyileO4gsxOHN+XMzkYZfpx6j9yEZb2RhzpH8DJcu+qeSsjWMNqg4tkBkplsn990bSvmfVr+cR/ewLn5kceV8WXo47OnodB5ujpDEiCftLAylwe3YodHbrx1LGgy/HiVbg78NtVr863Ac8FQjrdaTVcd9Nk4EX2d+TYTsRYjNIwlwUNVJr+HD46ZdkqpjoDu8q+4Mw4lc2vHfUXyOGX+K2sP5+JqLlpTU0prnK3sLbi9BhiKa5ZVFus9qIvmyOzf4RDIZ21IaKd7vttU+gfehzzahfcu2SqOjXQisBPPIDHIYo1zKBhynCAeFQI9dkV0ZDv2pieEDWfZ9Dvr349po8S8FOZQNiWZq9FCXPxgJea5DmWVqIXNZsbmYod/m8RRc3XRtb2PA80eIRRjb8dbFPsQEhULQo42siwWO2jKrJGGOmaJdi7zkUtR3kCq3Js9x8q1/SOXbJbFt52ovbllrHq87oTJJFhWfmBz+7e//IdnermTHfkddZJBXHtM0tFc3NQOOyglSNyMPxV94YbRvNUtMURhVmsdVQdHx0IYoA0Fma9EC/Gz35tRUClL5R7Vdy3sU3WUYVac05LXhUONop1W7zocQk5hP+OGZvtv2q8tS5x32W3xyLmgujy+3rLNiK+Y/OMQ7SLw7UqSnVl98V6h2rbhkfw0hmn2qk+xh9LS4XPjdvSomozKkujJ380MSXQYba+xbQcTbr92xOmbfdHxlCCGdZc/87wl5T+OiIDRSvea8B8GJ2BvdF+GR2nDf94eM0Ir1CalSLxIobPhY6461nOWHOcJ3pYkLVhIkxGjw93avm2KHBHC3EpciEo+aAOoJmxS3vsMPcY5hHjdheOmrhG8yQEKiRqPgBlh7RVhs1OhMOS3N5/auQM4DT9GSeUqpvmtMcOPjNnCtZ3VjSCH9/fQFmOHoLfmCsLapuiAa94tjryUDlFzFG9EwDh7MM3nXYeeGXYTqwN2zpaCASpqjZpRoN4pY1ZoILHX2JeuR6FtfofPieB76Pk+ZPN6afo2JTmeTw5e3XCZu01lTqx4AfVxex9PVldplkS1911l+dlPSIrZnEOcxRQh0SSPeF/JWuNcahdEj0IiLFdd4Z2s/36zLJTveRJSuCJMs+prS8GfJmPxsv3I7Tg0jHa+dMHUd7HjDCpH3VCH05635y7OghJUESc6t9Cn+T5V1+2I+dcWWKm1JOu0t+0kMHViH+OkRJIMmbokWdPLtSfFYriawrtlbIc+fSOmq/YO6h9NOjivc65nZxBo9jzK0iK7eo3+dmloh1s4gpcAE9UNi3MaT5clM4d5evVOCtEFdqr7Ujt2gETBFU2DOztqNr5Jlm3NI5xC39rzWyvyOYNJVnJaQembXFiKxS85GiKLul8BUUJSJaIYbYvqyt3fNrDwtghr5uIdHnWZN+HJghDbag4LBD356SfUWSpuSLxxDxY+zZViUZ6qYm7WaU9HDcyrYylJlBfVLk873lvN1adq5Uzouqx5Hvdm3kyyaRNFFlphgzxMBpSLx1kiAFFxsMCFu60piQb9W+9AsRGEz5DbS8Om2RJj4WI+WROpKNw+XUzLoj0dNoYIFmnuhCH+iafFiyHkbsMSYrWnR6wXa44H6XKByoDAjawu+KMzXnMpQIfa2fkQhAYM6tcyNRrjIrw5fsYd95IJ2mjpHOdWmXdBC607y5lEu+7AHhgsWVgG0QUUpPaolWghCulmFDv5jPOO6tV7X/cvwWR0bkm0OLgwsWrR+ljUprf64u2gdbLmHN6W8JWbo3BryIP6AjY53fUjtw4OZ3yE4dnm0cnUapiN6zoaNxTKff3v3ncL7xYZErPZUM/+pNlFe3glrqlhba+pBYH4VezNUJa6uzF/mij4ocZSoUZNpYBRYzr0HEE3X9DIwyYOKtAhTemzejtBLquXnOPgdg/CgWdI/b8yT3d6z072xrGNZIgimWwSyeHKZQEuL1Yx10VnaQd8oQZI+GBIO+OFzwNLIUp5GLUO3GpWJkRbkqF9rJsXCIouZXvAIj73TuP+mlJaQGexH1m017ZNrbTSApYRGKnTrQ0YH9JOOzA7G49fbhlZ+Ee0lVIIoCA4owqJmv8t8c5XQumLlS8Wa6BBpUp7PX0Ef7ECLl8UrQA68QZDtxHcFzmdNwc273x1R1Y+zR1KymL7n2q8HTSl4Ojl6+6SFlQ6uTvCWD5wzHF4HUV5npp28RbSZcZfd6aw4Ftx798r8gzS+sCLNlbCeeCOfNnFIvA/coAeum4d41ZcIrOIA3E0kbscT2fFcq723FqOZxHjvXbK5HQz1t1qssrgF2o9mAoEbDtzWywGX/uR3d6C5EsRncb14H8VW5Km9FZgkrs++KQ8I8jz32suped0dxx6zNDuhcz8qLWWn78tlBHD/GICRLLBmED+/F799U34EangLuxnfdvNWTTOpMOtsIhTyqIgpfQhfG3Y+ztJfEarNoakHc3pa0R8F629frXRBD1wVqc8Vyl75400Df0xnuTJmDOhTYNHQgQCtYCITR40KYeSj/1U8tjp/CMRem5dF/rDXqN1A7KWQl/rED6jVT8FFTi7mKYgVhMFVFJgEBcTHjqEZm91zVegy4QNSA+/yCqV2ezkEZRLXAn8XLOuUf6mvCXSaDRleCz61dOYhtDj5xEVSH0yHZrCXQ9mohqt//Bi0riE/D4Uv7meoCUTJoHPCTeWlvlVGrTBlB5+D9yElLOOdrjfSv15xhsMdQr+ijbG5bvo6Bpg6dfmQ1RW5ZhzVR9y2B30WDFw3/UUZti9bpN2WBIgA9QL7CbITnnDDKZypf5qM9CZLaAvRrZw8LsY5FPuEfR2PK9DaC1foue9HRNY2pCSE9tnEbp2i18AG3XXNbDHc0oT3s3ulNXHLUpuUEPexieTBTNOWDpn8FHi3448BVzpnFcXloL0AEemAVo/hiE55WPYGjlXRo1YhKLJjeIxwVUo1uUYPgiBj/Fhs1g6zCx+a+fFuq0o5ucyxQQjSeLBYzWHVvVhj2bpPwQOSIhOhKF3BzoKemr35u2EYRj/y1NDNhDs8OPUaNyO80sCzOr2iJGz4z/3mMMvm+Ul4tiiyFH7yL7hS85crOJHwAgK25GmX0aUUkrUHN+8YAnwPBOvkw13F/TYN3s2Tn/2jD7UnX7xL/fjgfZ0E3t3rvf5szd9DfFyCIdhvi3Qs2Zhi4GpaUzpyXqBwohroMFduTQt7bjjz/fPOdzxT0pT+I3fZKr2Uu/YHr7qzy6Urzh5C+PriwCe5rWG3GqC73vViPJevgTBLf6asfepafdf5IkJm4WhEGQsCpWN9Ku/R725i/Gn5dsMsi3vyph8ajGxe9I2wAh491o/W1xa8jtwiDrPXZ5cjWNdu67igIWRLZ0b7XBxRbYdlnyLmqrtwS6x5SElcnz2qcO2SidIH0mTShJoJ/srwC/M07Ulcx4c1wevQuSQwzKuRh4xhCZb/+HqIOmMcAYrJOClIWCWHedVxAt6VaEjMOncI1D1u3Zav6FlRSIEcFS/LGKNahK9T66DQa2ywcGSSM8UCiWpLRxc0Yt/tAc2aiKY8Q2UTxMFag10cabJLRSZxUVO3KZ/JtKnfRbdm/QgiSNEItRjvog+hIQ+b9Jz7hSpmos0MIulTUK9qg9TP/uiMKJaR0GyVTjtFxGzDsIHv1l6DS3NqsAdp6mAQXN3gl2lFHC35Himw2OR5nYEXMVEwWGl4QzQcWU1hbTvSVsBu/lpqw4ain34Jk4zRgSEoK37Pnb+g3WUBMXBZ27uOD3f6HicvUm9bX42xHG57wN1vGxdye0UccjynKz49H54BiyaMjMMVtIROEHetXfYitde+Bg9h9nSGbMCZQbonobxyNWLu8VS2J0uzoWX9J22q9tK0I0ng4P4nXCBFt1odKyu56uwxSuV3tDcPsow9hT/0rfzEDMIbMWRp64M/So0DlLT7veFXJaMseSqnAG0e4SfKb7QT++Bu7zOZl/59EZh1rmyVs82679+1lo4MPuALeDjXItwbyGypDmsrwgBqgpA60VrN4GpH7SESHpJ2F37AzEas62zqVFl1eDv+Ntgm1SriTpAmmIc8EIBrw4b0Yk9ZmmGQXr0SCRiT6lRZ/Pk4FVzt40WLNH299z8MsXlq1ny6IPkwYBZ6T+sewKguYfFfh1nBjYAqiEH4wHHsOLr21eoK20fFOkwKCcFTSvtCCgZAwyEPpVSYtmSpsaSui1lalKxSjokftc00Cz5URMM9tRRU2rN7TVLUC3ys1OQi2nZ/DQguTyB/48j/csr5V1Aw16CyimS4FSC0PQTboL1XUvVGnOlXVtk1LryFFjmMYT1YSoi3fPTBZrrtAccZe5hv1qxt4gAIe3sbAXDv4iQBbZgsyBeIMkQHwSZCwTypcY+vc89kD5K+tgr9iZw634IMKEKHDO8vlNUx5HIeVQt8QyDywt88ysyd4xWh5XQx/clVnc2HrbHJPO//aGR81C4tyeKEgnx0Qm/fCWOWdTflUguVWPuPILhr8G2txKBK+/6IeEr22oO5GG/41LKTuf6UUWciQev5JdOam+S/crjfzTfu84Itp7XY2pkb0HdpM+61GYmu6Dt2XQ4ikCtkB6QAbzxuQRG6mKxZQHuASwZyDKxIhHWUjjH3U1nxX7LtqqU6c/Rdg9D1ZXwTHmPg2Gppy/zACZ1G6E8l9hInWl4sfveBq/GMoKL0atPXNlOQ69HyoETuRxOpK3NqTgpcpM8Lkbu2K8CALJZT+nLPOIqyJbvQf4AW62YDCwUBbv7trjZo++fRAgsjW2jl9l2IZHCQSMPLbj8w3gqhhEQGeV7vFa/7ZAn2P6fbSEK99Hb7oZ9P17KusFOPfCn7zedXJQBpZdzbiiDFw+0t+74MWiMq7QVbLi+uDk/q7FroX1dExONaiAmPKuV/+5dJpZJqQM3wko6jTlmcwOtrrybBf/h5cBeBBZhNSlWtUvR9a8AuxfytIrfNCBDSKcWslJaAI8CA5oUweXBGRdnV20BCMhSAGIvo9iGN868/Dg8MNomUS1NR8jlUFuetVcBmB1kpu0A9lTiS6C5BbJx6y7BLXdd7rdg3nzocW0hpIXnlRZ4JlNjOWvHsdpa707Exqoy63zwIu7wfaiyRPgCHJoR9CbQkOw7AM8+pedmDnayucXpFL2FFVSsVeegNulHCf4/tr8rcu2ONV/s4lt2a3lcyq+ym+atDOZAEeXNvxPeBv0fl8pPrDoStRwGEhnXaJCYn8zc14Nr9TtU+jgsIV5v3watbQ872V/e3a8O176G1m+3Gg736mx9+UcRhz+/t2+gyN49B2pBWF7QfLthRdkHg3UzveuQfVlcB6hWi5OuBDJ1LDEkRHI2bJFVtqwegRNHPo4ehWVmwIzjTNS+CpUClzJJcHTotqRQ1Utdbl+HfKrnCSJy4iUeZFROqAcKn1KINUWBWG7q3Io1CuK0yzkZ4n+UaizcJX2qI9glx3FExJVbQKvRcgLBnB6UqOj2YEv6clV4U9F5ljZ1SvIjmRlezhoCQVdBK6KaFbgYm9whpUpOk+LiQSiX1wCxwsxlVx0L8WqMBR/J+mq1p2VNuiX3PfcXlEgnsI9oYT3O3rLyu7T1V3VdfubAKsKWNM5aAmtpdsS5cyLVgh3VIonC9ttIA/Bp9rQrSb7kisPYMdp/rNx7nhkXLyvUiLgqSdPlOjXdGH8vLruWPHDcgSAtwEGTHHbgRuHiyWRAx7VLwH+TIWThIdfkHr0qN1WC3H3jNcUm/8YL6wMZi/+7r/IOy8WrxsV5pTwrlFVrxyXBvOcZstmeJRFkvx+cV7Wdp/bmQ/LWExnO8halYEwhUshnifJdSm7zV+DIozyWaZoMkjY/vjQXQpPhjIn8FrWjJV+0LEoEexNEDjik9WEbOuYK0IFo6DlWFVmJUYb5t5xey142UcjMBnz1K95PkiQ6CheG5JIClfLCOBtRD8Szp6IzHIiO2F31s/wIoZIRJ6Fou3f5NIZz3peath/VcHPdwGM5ae3R9suz1cxCupBhL1V4Y2Q3opR7B2Lo2TlkAb1cgcfXveeagbqJbmrB/r6ktQglzlXb7MZyQsJYPprKKHGPyBquNb6A/UN5bLQyuar4brrIzfUHG46BGVD8dM8zxEtVm1z1gHytL71Dw1S0wqKdcym8mUxqc/Z2+FYVeR4sILvJSuXgRAwl7WQNq9G9abA8dxflf2FbCBJR8gkFxKdT+xIPibvPCj4ucUFbnFdK1GOmY54EATq2CfACt9CcH+cGLWTrjMbWfYQV/N+c4WcHd41uqVXlHjY8Kjhwx1C9X9pc0ITCy/AgikOh5Lmy06tO4maRY0n9hyQ0GoFePrbXeVMjD3SG2XI+SBwz4GBPuq2UK3Lhyppa7BOpYXDGklfr27qlCfb4kjCae3LgWhnE/YU7h1moExm0YmNu/oa/CMFyQpG69DHBTaxZGMqLRtbqATYhi5tqQihZzm2B9CLqYYRabIx9AdP5D/tXxXmHCjQoi8XhGDl1cC+o0Qbd5tEj93O2XSxeceWoKgD0fJSTMlQFkLMYbQgH+3PyEaIpNLKQ1sSBHITjDbwCMmWcR/AG1bKAHm38ATkNq4WJE2Eu6YavN3WaQeAUAqn8elQVTjYVnc3Ybf4JQDzQqjnXiNH+v23v+q/HBAAVntF4VNQcRciYCSrlpIMby6IuR7ShfErSIdXonCS2q7v6GTKXPvc+FRUPecpR2TUB67/Wp6t3SMS1YlznFC8h3t1MIGj9PFlhwFWViK+LKOe/fIDB/yb4S/Qh3A5dGYxZKEktvzw2RaKTYlSnoUBuY0FhLl0Py3MBKl/+BWYnpgX9jzJugiDx7I9Ru4CioJfP7X1Ycw9auVy8pkW2kZ1tvZlIQPXqu0tuhFKRZD3xH/G/QKSGDHfjYXKubVGPsXF2ZLSFjZSvX1fDNrkVSgHMuqeUmTPLdY+MIC5apshhvGOlvMe+btCZSrg8uB/5j9K2jdamp2pylervNbBitcYJXsbQKjv2Uz8dgmhn412mMDdNHI8oYWupptUXTsx76lA2b/ugkGn9pR+oyFb9/c2o/EpK/y4jgqvrTACO89yN8Wb0FpwZfNlzLzvgfGf6jxzr52w98dldSfDwkeRa+/IeVtqhY7NKHMGK/Azq6TEXI37AJd5fMKcm/NupE+xKXHLGox7pxoQLjt1x8Jc530ZO+N8/OZc9FPeNXaGM/hElJrJf6gIyCcjwuVtwbUmyfnspqWlc1Jeg89Ch0YRwDU3a1Fzwe5NBwiVzdXzeQH7Mc7xUsVSO3RK22JJGJ9ryV/pAmYkPolWjsHyifee6Eev7YUtlf58+Z5/OePP3sBUTtM/brwf9Ex4Q3Ii8XvwMT8VtELkUbDYS/PYNAwuxW40SYgUEJY7rnKJu4UJbF/faUfgiBVpshg45C1eC7PmuGF2pQ1r3wjPCxzgw3zs/VUYoN6LsyYA7wV/evBVbmNY9V6VuUuSKjOwbSx+hu5jMtm2Dy6UIkf7Fn4nR686Lr1oUwdqWzlHMNGY8X4nXKe8ugf/pCjP2E64q0GMbJGsN/E3uwbYbUYdGxVZ04f0Uvaiy2h2RJMiMmMk5bSrutw5D4bsKFRWEn645hFvLlWgXbVthbcMuoHIeBFt+9Vfb4gJQ7Q2Ugi8kTjR4MrvLwn9aOgWRceQDpoIxo2M1w+d5FSTArWJgnPLZVk/OD5KcuRgAKNTEiPMYS+y1+WyB44yX+P9B43ts4S39yEz/boneZN7CEcejAa6SYFLzs9awNVFuiVvgxrqij/5pVRjXw8N7YTJRBsKKXFaF6fQ5fj0gTqtav5t0F9R6injjfuK/yXcob4UBv403UIRNGUEljMX4OPc5v0no59z5DEFa7Dc4SpDJlWaO9m14KijHWfkP1rxPwIpOXKbmMSKuOBXthv+veDZjMUZCM2iUziLoctobPcq6HcemixFrEOAiZA8WDnsQW5wNvAigWCUSN+X0TYm+qrNWkQkZKH0eoHx9cIVSdTa4DbZie6CLmvrOzhB+Q/EkxG+B6QTfVhYqwAdW9mu6fvmG/PSSF3RnipBHpaFcnth+vJtmsWtWf2yuKiNBXxQ878OlESnz/t2TK7Ri5pVIFmVAzJQTtCmSKLl2NdFeCVPblNphkUFjCWWvPikZ6msQjX6Yfc07VcYf4F/C4CJuGw7nf3e+7bVoRRYJKZVAGWY3l0+/fq/7IOopZymocQ+3SbE3pjPrfyC0L1uXWiqWC8OldE2zfMqjOFKrBgCVyJPWrllemwiaxp91G7ru88pg5JTDqLhK/53NwtMxGLXHNn4gqfGAL8EoEi3zD5As32B6OuDpI3vwL6GrSKUVr+CeZ88izFhaJ7xqEbPqRWUy0KlSqJssaMiVZVGzccw+WHVyTBJ/fWdkKr3wJq96I+BRRT9fJBeLFQEQbQkL2DXwbvlbMAxes16XBsVJ8ko98STzrfTDgziDOPKzER/cHpZInc9f4OayLOEVD49SnhAfhjddPp8Ztm38+7BFxCkrgGvOKIu/HeMEom7h4BvR6o/tsDqFt0+NHH0LRoBoTaupulmcb0+AovopuyXp3aLrg7AUvrzL/9Hyd+x1TlWDRHER1mDAb+plk6lv9c5+oCmUoD73DNrBB/E9muVc9y7V6IQGKM4hyk9Dos7cHCz68YlpR+YQvdcDgBtRgUaK8SDKHDzucGFPKvTFToU+zX7Wr9Rsh0vpzurUUDkgGtTDVzpc3wZZxY6Bg1JAchOFsevPmxRlO1C66WuHesHokDu51LFFRv5Ok5e4CFDSY8IKf/izJKZ9UbQ9ZByzBqaPmQ1yn8jA5siX6VzW4cocuOFvLz0kDIlEY69oR3dBTRMpb2l6cf8uoibbppDuT2QGA9JCtdT9Io5bVoYL8G+3YofnvJcCiXJ33HBMm/lS4FM0EflNvaQolMTvyef1scCRdhHY7cDeA++oG/36VmCieGNWH/7RuFR0KWgErvRsqid3ko9ZYKkr/GVdNfglcSVSjZwFlhaLdWzqy+q1lZv9xJwtqbTpbz0r8FiKtPmhhuS9OwXTyQVYgsKEA7F307VmFJ/k3gxG8TDkAUe1pshBo3ZJD1BAybyAdZMZt2c+mhSGili+ZLm7G25if4OUDAZejt7Z15MlxGX9CWxNxfMbU/wNiV4PQ8VQ+Ws3eIs/0NhxNW9mpJZ4nUUVr4g6xhBFmB/a1OlZcIdDHH9WEaRWHd0Tp+pthg7M4kSOY4i/4BSre7J0Gi41GeQDbKcNZKDx28nb0Cg1XtAQykq9myT+KOnytggxxBCHdih4weaGnMocuBqGUFyo5bGhb42dUeLizE+wIYTIfpI12H4E7fWK9jgKXAaRHLD/YnDuf5HDDnzAoBuJBs3P3QHS9VetRdnJLN91M9SfcAp7todnAPXOeScZWEvbF0wANLlBCSj2rFgUUQLCciac3DM/fbog6dBcsH9e55v9bYTHO6JLOux+2ifP6DHiL+PA5XJiGzvLqCZ+ORZpYr57RUmCWzmgMeSpC8B9jR3F4PEZeyvFrL86Yl6YLWW633lAGWeMZGbLPWx4EYHp1yN+BJjzYyFlrwLh6Z6Zfpm2z1v+P6fYWlIJI3crNmorL9lg3LXJTMZQKjtj4uOqeBp+zYYzrLD6a7fYg+GghvHPdD5Ex/EOIEMVs0g7f56QAxad77x5Pe4eO6UQ/q/42OXRB+uS/S4Gby9Quk5jyQoVb7Te6DUbk5FRSCM1xhccmUUydx4cBfFCJ6kGGL4Nbc0NMJZk8BcN/GXREPhUK4v+UqyU2KtIWd0xDA49izvCYKQFjA9B12SyS9WjYPJvwVxPxwgDBDtr3pRPav97mTD5+EBOjXwtVHe5a556HB9jQTWdAmebEFaKZRpiZ7IL9JouzB3sI4ur9groSYDug2Zo0vR9lgggX77UUDGIVavTG7/6Ezg6/oQesI5gRHJGvGgyJ+M8tBzBkh3mR2kZnahWMpjuRvtwRLf4Q0Zn+DDYnPe8qUzFVdACSOgDzCjPIDpQqK8m9Di5CqPTfJsNaufSlIWMLSSFPdWC6oN/gEVFLp2uOoGqh4Pi0YuPMryHJgnZoXZmUb4Im7pdW7DcSRbv9Z5yY38ZVYij11SGPNlIeZKkRxHY7aWoy8Y1KXnpnw5QzJZMSKsD3iawqWE1XTgAq6vB9+aEY0zymYscECxuBf/vqwiNIZn+L8VkyxgIwA/rFIaHcQkTOi46W/ZdzY4cBCTEJnfgubjE2E5lSnFTf81UzCSNOExAhuX8xA1OmxkLTx0fH8uAW+ImnxS8qGLWZ/4qLCNTx/KWgoJHpQ8b8f+lxcy06ptB960t60hLK+D/vCg3M5x9e1XFi5NyV6ds6iRmOMJ0/0KEdMzt9vW/4rco9bmd+W+QCK9q+a8FhND/gXGFXr9KK8glZW/kKdMzXz9PuQoXhvSkRqifabylqw4Zm7O+/EhsJZbqVQLzfyxr67kmLzKoupCaMJtJl1YEGEYyLhrYUCOB41u/WfHStAUGCTX1+byD6vCE4Cgzm5+6TO3D5Di0D4285NNCy149fTJnftbHiyhQpotX28YB+aT9mnD6Gzja3sR3N4NQj0y7Cb9sGcIi11b9cciQnxAxAw+4XXTCj/9vc9D6gZBMqyiAXejRoY3YAIOdEjFqa1E2SgqhsP8OSHuki+kfdnahEjfc1OrIwcM1HrJyB3CH5/ZTy/hv4BlP1EEU3IvOPcD3iizihVTz5w6OL5ZtnAJB1yNoXZpDLaHf0yCZBlzfcDZARmvGF2LIY5AbcR9slXUJGWVLkXMbqQLDwEYHcEaNe5NvHhJbu0W+LqD2B+Qsn/xplx+cl6Xjq+97S0++uGMJlNEIAz4AatUXIsQEubEOEp8n3Fi9bhvDu/WQOQhHKgxt9UsW9fcN86/dCj0yv0Ly/uB9iILg97l3/7us8Vvlo4ljl9LCa8riSYrkEDFms16ALDBygSwCE3ZJC54BRpAm89VJP2xmgqLaj11ZHp45zg4m7WQx1JFFZjbm0CESBJp2OQ3X0wXgfwmYQhmctttzlDKyE6GpFrbrtgm2isdGAdfY7usUvJ4rQxaIQ+iMdSekbZtHzrJxDtuaB/mvIv/pcHIKCCAw3YwKvo219rM7PWnL/AKFnKIi1M76jWgRFhG8eWFHJbS8OULE4/owBbRSq5pMHUovHQ4ejdxy6RJx9+fDxWr+Z8UqUbuOREOiWpSfftFFVdRa50YIgHm98F/Ugg9yi07vRbmpd42ne/WdRP15j5raRWUCrWl/yN50VScDuKIvhACdLcAIVhSUFo8j7MNWAELLexe6TW7kfd01H5DbgVPUx6AxXe64+HvmX2pnaSMLdIbPrat09Qal65Y+vSE/VSwqrH9Og0WdNFqkGxPBfqo6DZOYg9xBFPBafYcVi1yMC92QMxbfuiq/CaYUGwR5+PH8O3YnhjtM3voZfMwNF9tyR0yoY0N94f2I6BfMHMWojRDFNkmIHECvubGWugOWrRF6g6MHekxRj9YsJicH3dzOmry4Phkj7TO9SSGXoFNz9dlh4xdS7AD+ExxQoKXpydr7pYNCTdpgPVkVCiom/Tzhs4/fQsxmhbxm+xHFyGPW0jZCBSgDg924te7zqu/Igb5B7e8D7grt5E/+7N3HQWtyfjfpEC/h1+/bdtZyLiNKlyUeCADqjzuxcpIjp3HPbk/FchDtRHrWZ1Cg9ToWEmA54hzDA2CkDdC53v4uW5eBnqE2qvYYP32JGJUPIyNrA5UsARfPwsmfKGcf7ak49eiv5oi3YBHK1b5KEOhWU8EcW2MPnyDdazuDmiJIORCTXk5C1UloCkQ3q0FDRlCGhuV/6d7+SvKiv4VD9f7FPhMMJmg36DT91Fq71ZQVCPmLCZ2fgS7PRrrVHVgxIuUCj726OATIby6eY1LqigUICAC0bsuK+T7N22lNV+h/0FqiaRhD1/QzEdAIq4fENtmJXGONoSGmkGrKsIWK4s5GKVPLrYIJXvJs2IRgv1C2zGDDUjolyvD8dwvVNeo8WCRqX4SsAaeHwUM0gxprUgFqdpIZ+E+LRmuSPL7Fn3RobF3PxmSEsOEc3vh2fgGGpEdzwokVgAUtl7L7HwX5ndY0Ey7cP28vIVZKX404fwlabFsDPFrWB+Dx0HkbCjVKtfAu34XTA3dcsdbARu1fVzK3bzKvGyEqsmfkJ6/vG9fL2/1MdmbK6bxdVvTLfpQN6Q5eOeeQpJfbPHcJN7MAfR0LH6tQKgQtoJoWHq99yFzT3Gwo3VrKBaOCzl4gN0mYcpnTdnS+Ccy0jEK5go3neGDAvsICuuubCPcLLnr6LNPYrO9RIV2cmlNbbaav/Id2P15yrvrfzCHcOLXCOJ/GFfYYWrzrez+9siEyvI/U3IkpPoTvyWhLFfjMhcETgI4b0zlrYqr6y3El4uJLhESpQiSdPAbNwCeNjEjIXE3hC2G5aKYEWUBmKaiEWA2H2fe6WFCe+FCs1ihdNKGOgh6CnYkkYaRxX/sZRr1QmEB9/yb5BsazBLv+YTapEPVAiw/ngum8Nl1lJLVz0oiMS/BnIx2RemcC8wBtGlc+S+HlxNJORb3PC94JvoFOp61K+we1PzikxKNWgBS53ofAAgQG8inCLqG/3NFdtDkmWtAKxcZRvKiKliLqlyf6P6Altf891l1mGQ97vgToYN5GJeHub5fLYuSh8bQPxgj3SgYvSJRYyxlNllFTYkYTGhkyqoctj6mQo/Qr2LlXje5F7GSzZXkeNLGNFodFA2lMR/0Zz9MFxEu0tuk1VJ6iyyvwuNye47NNEoKoLfBEm/MPy2ZVKWb2pq3Hc3CCNV+fVQ/MvRAeEL0Huv9pDZIIDUgXa4nzLHnXr7OXNhqIdB7FjIxkkzl0gNyXi+7oOlD+wPCSOhgZu6wbJSbAJjcEIMbocSE0V8KynDQ87iCzgi9kWCgsHVdc4mQZSzX/CrD9zzPF/zp56zAwTkqI+o+mX5G3vx/EU/uyi5N2XZkenYexyK0gMOSMlcG0ano5Mfp4KbP2mWbTTRroIuvHUXIlL0N5pa3+NfM1G94F+6NrgXNDwyd8/aRfXpqcoWoZLiaHunmQcd7iBFGLWvEe9nEExiEtkqroddWETp1xzs3FFHLGOW3mIZ4YVs5WKxzMc37vmqiqolQ82xAduA2HcsLxopNuwggsoGsj7SjaHBa4XZlJ6wlwjtHv6y9t0Uyjnb8Ftvew0m4zRZBVwBRR81Ggi0OlR89bD/3uR1XCua72XSpWSDoIUrNk3D6QY8AtPwYNCShSDEwymXRSU790PNeBVec4Xh0Vp16800EYNxDYT5nuIyShTDSfXt9HQdpQwg5QH0GXJJwKNUwPWxTtmI4mRPVMNHL0g250aQgMCK7WG02SDpTU3UIeCZK61rXWol+jyWtUCmJpb0s3d0FZZalOQjI7ALoct3+cVTxWHNH8IoelKC1MmDCPTTCW+oGtwwgD/hIX+EY6V9LH9Rv1R+VFLb9kUSpH88YOI+f46XD08xsHakcq742blT+tZcRO58/AWENfLdbr4T5UzdZacSiF1zK5QPfXKXGCT1tOsV1YmS6+FD1V2QpbTOqyRlHEtQb8kpwwjoN0qBqh+pz15tjlQXuswO22MQZKMwzxhs94sVY4QlSqezrOaoXqtJvhOyJKJsSHjfHS7U8Cb1j0oI9ShclhIv8G0W3K8wDrLLcjLqd4EJ4XfrOxlCz/TbU73jwiwTRwCX2p0LR/vUVug3tBoZ2Iyjv0adusY5gx5kiY7AK7HcJaQvUL1VCeIVLWZ3t6Y+44nVbncOoEhP+YMoijwZYh9hZyfrdVpydtFzoKEi+8AVPUssr5/oLi36x+cpGZJyv3iNGC8PSZwRB1l4zPjVZGU7C/ho4aM+MVNLEz1c+0QD+iMlppV0XzTO4wEVkS15ATnNG8slYJls3uS+nuK4NF7gPq6fyeFDUrLfg4K6Lkev6+amvUR2VIkugvhND/JGBG1XtdI1Zy3d6jzuf7oOUv0au7oeaNbIFtyrSbbSRHcYrYl9ClmIT2wYvj71a79S/f3XcrpXakg+3A4v5toFE6hmcw68uM0gUArjBenO7rRz9nEKzlbobafummUvqHctpeyGH0KCCpuAtoTcv6NgwuzZotUVL2CQLkFmMyXeEnjeW7CMEhOiF23T06oxxWvHWBeiviuASln1RzVY2fknGs+fCQHBMmCSP/evMRglY4oVi1Cxl+oCbYCwfxn4fNV6/m1n2zN+446r16Pv2mOoQegJf2cgACdXKO5TZbYhnzZN2gmrh4XAvH5NyuJtoL0b/W0nZ3nXYJdozKBPVwlHp1ytLUeuP0w8fTQ+eLW/dgotU6kXCG6y0HnflvizFHrVled6zuI9j8Z5LCf2kgjnoXu1KZWc7obJEb4WUXQPLM2KvijhkbLwGsSIhaRVRsKDDvxrBcoWVL8Jdm3dbEvRCgjbMTIhvwSlePAiuTmZrG9jMbr09UgV2ZVrvmOba4aHsRYpy+xXk43Y/uaJz9xkGBV4i5CqyYDmRsVriRnNQfwi1lm0K1NghNfhVDMNUghv6PvASt00G/zQXq8ybcwCl9fM9MVdWFkCZoxNcvDz5KOEQt9CGeUvgkVnskUDkpZWydj6mpMarQJpEiKfr64JuAQH3DRgHGuiRg3L3M7rIXLaEKNBmobZYRdqpFgEwIGvqczYXlmDJ+nV7dZlt5h5He+2p9qrElX4m7CQgWEm/rFDmxbx3Tz+Ohuo7jcDwjkTyjNvsgIFIuyD7ALgXcq+uBOm4g/h212qiNJQfEsqS6yoXYW2OQZ+BJ+AT9KWRvn+F01bO/xjvZpSYerfFtz9tavEEOxOP8v47qQYUgKB+AL4Q/SFSTmi4odDgt7Wt8vb3+hSbppZ8acKKQgGINffWBzWTYJ9A9dH+QdJIlLXn36ciSHv+YrRHzfws9956vHFZX2d+/A9TMkosgYWtYkKuhRuwXwcZuzWRKc76GsSmTZqaF9Z3uC5cd+8JpJYjWGOHD5g70w5DbSkio1ybW2Hh6ZGtC+F2viOlbFbzD7JjgAQ4PGY6W/quWi81UxzmC+Xz6T4o43ze05q1bFPhpaSEWIrddd/GEIkfwU/6cedQU5ScOHXuwLhRPtXgNdzfjuuRjlF78w6Z+sEo7lYnDGKPQFTwX9DZgg2J0n6kpIQy6YWgRGT9GeQcHtRD69Ve0uE+/54lw9g3kG4T4LQbbjCniQiYnknYHoHq24Of/sGGaqXW+4gccauW2qMshXYgkRHiJH3iZquQRTsMvULyrLnSLRzNj4csm5D/jKrfJgNCEdclEd3PVDLDuZjiYQa3yIMIcXiTEAzAflNftLxx/+jiTw9L3b2bs+CMGuL3G4C597O20wj6ypqzDdDZPNeVH7FBBH3eel7A8NUk1S47wkn612SElLGqinvMgKKGSsnxajH5GwRI1Yv8NdvveOxNdvBtKoXRtUKfVquWKZufAzxsSXKLxPr6Gcj4+n7XHpCRG7nC7oV2Zne0j0ics/jMZA19LmgLn/VA7PIGVf5W9k3uV/5Xb4jrGxwzxxfA5qChZ3C4/zGuPP0sMqNL7wY2s9xJ7xJ6aqpgR4UChRnJ0BXLgvzAhTGIIBrsnyFv3GAnrOdU++wfD+u0MwpPmOSUZm+5jIFcoKvgXAbaeH4MJvDLaHUL/WbQ4MZfphwG+BOyCvYGRMcoz75e7FJmH2dbeu9XgNsbdLX2n1hLMdbw0I4kz6BKBV1xXMbankiaRPVW2HsawvSx/44cMWKa0HYN8k/VmBeYXZGPxjOBrtAw+qyElmU5CpN+g/UQYMQUIDpMp1wwI4yXy1F11/+XnZ6FRLvMCSiz/ZloNSVdu9LrVtPoHuUSi4a1pPHqL+pln1/YiBW3X81FFh4uMPQd8hm9VSdB+E3oApnCyndqOzhX4d8yBmEH6T1eCRV0SzNY1pbGQKYrlvnobpnpOkJxe1ykdkusZ59j5aU8YaA+Oe9NIitLV7MoDsdRc/5Tb8gU8gUoDBBQMGpGSaB00cZeLjVaR+A2lYpoOric0MTzefeGV8pGZAm50Z61OvyB+tNb6ILdNkc8oa/X+HrHEPhKUqBmSp2tuitpSGnIN64hSk5iBrwytwnJT4BXOvt62vpOXi4B1vVtgoiH7w5nuryNu5SC/bIq/a36BKrpgwrjD2q4cIjoUo+dun36/6ayP3pK5GG5vdSKMccu2+SvfYvEGwQHoJA8J/9IRmpQXNcwD8zGhYL1JWJodiRgtYNlGHHsuNnPcbvtZHspUnA2Cu2aMuiB8DX1VC3yd7n+cFxz+VR4Ky6S45Z7buBoJS8c76Pb3TzZVLjl/0MlR1QBunFtEaQWaSsJVeVhZ18/QaEvnUH797KYsLoDXPQ6amygX28JOok01S+yz6Ga7obNHm86RxwucmEf4Exw2Nc93qTZOe8+4zRO4TLcLN9HTg3cyB5fllegu1zdGzyA5+2X7DYVV/Xx6qc3w6TVWGI/mwQArfp5ZDc99mrMVB7lZfdpF1UNMoHwcdo0Y4ES7IBTMYIHp6MI5x4M924YDOcd+FDqHm3RWYnE848js6StlnsjeDlHgEdVDcprn7GqF5QiCxcswMKLe1p2yjCEBya7YVL5e8ehI8Vbu/eQe10XviIJpaTB9I+Vvyzo9zdV9VoAGHUAnKcvhKlh0L3jgNLIR+qf4utLJ5w9fYK6/Gek5fc6S/rMag4S64fB8mO8vvAWgxlYTyNNrx9ayE18zosEdP//mupef3yWaMnA6PbhJ3NbDDZ0I78JUvuCoHo7M1UhLZzWQQVvFiXf0O/BIiSWctsA92SfiVkwNqVo+Qb66wBG9cL0vjb91JTmONyHKddYKKYYEvRkVBSf2C/NNq55mLrvgnkq+Hv8XN79WcP3CMTDHiXNtzmTcFTXvhvGeZ2mRqMk2bDx6pitThl7L/wrryjxTpTr0g+Od1MbsH4e7hdXcX19c0gLTK+ZXWxnUVMJdV0UoI4t4S8ryI3MqT56D+vyUFMYeyLxkeDOq6OLU5Nm8BXbSZJmHwnZvBJnJjjes45GEI1bX2I1GuvcuaE3f6DBv2s2NCs2yMckfdExvJLEnMaHqA6k/DJJT55fyP9dEpdl09NGRuaySDIR96H5ur3hizPqb03tduphUvL9EY6R4HHmLlpvwEp+kuxNTAYW8AY82V/xQ+gdj7czb+lVYTyAPZAluYIQiRf6hJqRyGf3N0QhcgBLK5h85TACDPTXOiMx0BUUta36jqUP8u29rLD/7JDqV5Z4EveOHaNdCxbBtFR0OycF3CFDJ6LI93odlf6qeBbLiBidB05cMcgYanlFyTyRlSl/h7IO+znPvWBu9dYV/2115OKL2HuTIGCvKe8khj6arYb1No9kKD+4mlsEqAWpo+QcH+B6lL2Uf8W8MeR0jddmpepJiOuHJL4pKyUdMf0I0XKUi7o1/F85y322D0fmlL8yqreCwHhQ+6cU7DT4m9lJ37DLJKSTTBTOoBhL1UwFKVevwcIQWEYAkThwWpkFI9sWYtEHilXih2iUr4LD24c3S5mzG3lKX/QsU5GnYNyLaCiMfoC32lMk8mDJzJ2BDMolwJAQECYV0jO803PdYfdDbA5rlMHD8NTvZ0KO3MteYb9xdCtVMpkVQ5CqE6XiVtKSaEnFspj1GT9nsUKzFfAae73vymuS7wjagFQ9LYX5tEoK07YDrAFW5JvTqpLybxUvaCCaLtQBSL7nhJpQhsx8G2vNYO6xtEpmqTnZRtUZyzSiLqpJKR+tBmLdTY9NF9AxoorHIJ51tfTTzAr2WxMmp2b809goAXN5hIeldvDX75Y46lKvkj5LDk6/fBaC7xdbpSc9XqgK59cCvXpwokqoJQAL++Ff+CXpZ/0r2jySw81jLOYU6BY4UEkllouTaxIjbJq1xrxC6f6DcuK2oWsgkCWTaYLEjXxwPxDCjc0mnoy6Dv2COMRcuBnDrXnBCjzYpUlhHaakSOJ0Vw7afodzVw/fBX+Sb5DEd2vCkuozZ0/00rSWiQwH9deuODldSQfXt5fdx87FEeb4Fb1KUU5Q8sKLxJFN7Ew63k94gi9Bs6+TwDe5UBYEl0QCTK0CMKghLswurogSMHM+V3nHdQA//X5NwgsdR4b7uDPeUzYemufK6YYnCzKnU9aHjMWOpVeCooQ2ic+RGywf/EVxuvYFAyVE6ylouSJz+kBeuUG7u8nZQbWpNIOE+sDgKXFzKeo8o0ssBv9MekAQtdejC+Jr9+x8BVacDspbSh0f9UVAworUKuYlnNyZBYWwOOQdfKi0k2Or08wAtUqGF//LUnbT0ciYdP+dSr+Jj4dqIwtSkIBoIf91XmzAKQUE0oXK0TKqSOZZGMdRqUZOu+8CUcegvT4DR5jLBGyUPnV3FyiapJgslRTi51YtgxmGh5a2YdGvJvc/TauV4nVdRfyiegTnICHeBDtJFvD8eKpTnwYig6fz7FloYFkTCQJvBEAlganDFGtumHh7aU93ljfA/W4DcKIq4fqLEn4rbpK+GUiLAV+zwtSz0V0+J8Mxfq61iJubf13GukPC1X5b2pYBwf/xs4hTegX4atqb9QwWGYriwhuUfUNZ+NvHLECPBsrBzcZM4HNamzQe99WhwR05/O5QtcdRB7oJPPoa9EWCWx1YwUJIwMkuwwY94ITNMgOClQNdzP5uNmtrO97Ca62NZLxBYQLwdr/qnHTDw7vyIv4KTPfHipUcMREGdAUI/1GTiqo8Spdpd4rzWN1twNyy1IC69lUkI/gm/syZeY+ZKwO1cdyw5NNmigtmEDllZV/R9bqN+I7mht6Nz8mKnessvIEtLjo49gdXcBvRLI/SQRqfYTk4qhxkl7UXcoQu03YgGpb44MolzEaJeC4dUO2h7hyPU1xNkNtvtZUkH5zHXV9B8hdlg5L1eCNkR2fasa6S0IUxbMtt6ca2NyVTvCnqgqNbq2c9d1FVzroYVeWo9rWVnNODz0899Kzr5O+lHh4YcUbbcMVgb7nBB74Hbn8xwDyHRU+0Qw7TlkxfJGC9RGHovr15vgJkuzxq8fZUDs2Ud2yUETmD39x36QBL15j2zIqhcaDN57CbJhtB14ramMiVmIL5kFIJEJk2M8N1ofqXqLzvJnfwbUBllknPfxpVbHkT7b/kCffpHzMLUTGGwPZlIyoS4vb8J2T4Ao0iwgMYyXeyrXVRKAgAXLQFp6CE7LYetq1Ak04x6EGyJKVvB6QISTm79xytnTsJulWAgy5lP3p3Y82uixtqNnrLUu3pFh7S1IAOOZ9O5fhtnM4iB3V6Yzxfo4FsBV9vRCzVtwREJU4Fzx0k7xgRTfHgQf8rbwOAz1N930fjnorUOpXZxABBudM53XOXqyhitHuo7FxGClTxPNNCk7Q327d8w4WrjpBFxadcIsl36/UxV7dDawscIdfhJko5F9Uiumogo+PhmyIeV9iWaaOsSrwLz+bpI2i8NSG2PHbvSKUZHZy9jxawhLsvVJqLkNmXtAhPePeLmnhqcTxmeYkcqLg2Dg4c+FO2/u7BRnMVsGIZdvnQh8/yro1AMb9juDnZ0aOX5MEZ9JQucf9QZKHZNB6QeHFxwXi1L5wMVQzzXqFtFXV5VHK116R29jfslj2+NctXQp2V7S2x1TbCyquGUmpPMrCzjnv6d92ziS5lrKiIZABUxbc8kmgdTM2cZl9tsiRZ59Lxc1aPRJZL5gheEjtxTUcSDJL15iC1RTCq2fGf7NJ/vfL4HEIIV5zGXH5h75S8fwML4UDpP39wAFAxCEqQQ7V/yZcneVhR7H1Mg4usPcUY9/OJrN9o7xGoefLhljlEGHcTL0w4Xjds1EqKkg2ROsR6biOYzF20dx6uriFec+b9mx//gDNk6pELixWBo2/bNttFyplliYxHNkmdsr2eJIc81vP7Fkj1+xf9yw/U5JXeDP7LtiGN41ffSGJ/CuMtPa40gbGMM23ePFNW9eeEmKGO+De3fNvKcxGe8XKgF2Jr4VMv85xE1mk5CCyFPdMc3b7k+Y4+1JJUd0FX6Cv7IjpaZGWU2Njro/Kj6Xd1WV9kf11RqFXBq2zpyaqTygW9gjuENv9Sd3kiL7z3O2ey+E8fYwgzGeSgFurv+Kkhqh1V0laSTtsYN8F1H/X4axog+y+EDvPD7GPTQn/kve5pVxlzwOYTwSu0gKniX6z5jtSHqBY3X5gQWw4hYCdmdaWp61knXm+wFIFtl2K7SyJrUlu5fNdIBCs7OY3/NHcmp6+7q+e0I4PZMM/knNR1yWMugFmYWXdbGh7ROL5ew227T6gUCuy1+zea3bepjQpIMZsG6vHP1/XZi+n+ESJdZ9MQ/TTnnGw2cNwSNLUylHhRlr1y3C3RY80E3nR+sgBFkmQr8YxtETR7LM2ozJD2moHm4GF7uClB5YWNl5S3Jf1k51iwfNz7PycS0fi6a9QCe/L73A/aCvL3DoxUDhyjE3WZGcHYXcBgn6zjeRzy8ERYEORHHmRI1DuX3vX3rmYseIhpNoscZHMLvcDmkksyIlU3jt91pRqCyCOaIlEBcGS2I/X+Wfo0gWSPvUtU6wEVFK0v000V5V1XhTx0FfjRRxyuHk3nkkEzM2Rt8ivwf5gZLsmxOIwCN3KmwmINVxmfUt/c23V2D57SwBJ26EE8JOl0YbxK+8pFXpoMZhInTyaYCjZhHJAljEX1xtgYmjtCV2WuTFGKG6zD0arKfNfKyCGG0b56q38qCTMtHMdoNBPC2Baru3I4SVBfA4mMGeyWuOvY3iMW8WQAGom914VKpGjL92CXRwCUTs8RpnNSc4jEgD/7xFzbPRR28eXCv9mPp1+RKRkIia3Ry9uaXvDiP5mCH1/20fsOkDTXx+C9K1zW7Bb71qXvpt8nt7xQEImZUgpZd7PUVJvI6/DPagq3wLmxxtZQmwEmKa3H64bEBp25RlJJnw31j4oit/EdA1R2u+FN28vut8u+BFKI9E0K5Hhy5fgYwCAyC2BX2J1NggcL5rAcyDU4GgyyZvhWVHJ8O3mIDjNHZwE8PKqsbbnasKnCfUP8LmLEPrc+28N7LnO1qhy2eVtUzyO2GORmr1bH/+YKppYqtaKvmISVeVJSG8WKSpAqzuPVyZId90E/nSJ+YnFCQ/IEhI8/B6NrjodwETMVzq8BqJ5wV3scsBW3HA/8ByjtcrGSJDafF7RaMt9Kx6JeixTzOTvY/bzwZzg1w0J1eByiAjnou/TtspCjB8y+YNYEVx/X9/P0f+q5NmXq1XRmXihmhi7Wacxm78k33sz8XoH9qR5/IEiNrLMiEbMME1uwAxhIHInJqolMMMn+5kWmB8dDn+tq/f4J9OakLrPo7lWzZoSqlBaUeIlMpkkvi1QJakMD1kj1MXXsHnCTd5zh2h7KJpY2fyO3xW1DfqioOyBvcp6wr9fswvXgSdxg6/GLLEivAni4Xm5mVt0leoVu971qMi+3qxFAIdxiFQYGnzEkF8g8Xc8x6MXWQ0Rq0rRO3xhpDIu6fc+50vURDURcBMw0xEa4S25iiZyKNEKHd5zqw15WRx+uliIvqKwh3S+P1OGo9FTBLoGFjULV/WrRfPxQKEVHwpe6Wbe9+EK1eed5JPpetjrR92dqaN3IlVrYE8O0ATOQjk5QEK97VFFRiyFEQpbGVjyaTa9O7EY4irnX0k3ysg57+MI07wCi3Qz1g5qFEJSU4PbjiVd/b6nq5c3zntFeQ6+AXGZuYOs/wKDQtOcsIhOI+kfuleZoWMhDgmXpeUdshHUUN2HeR+ReVJLN7a+MNVbjps4Iz0cGvCc72iGRjQ9y0H7Dj5PxWG+we+H8Qmc5X2G5BXmAAVBwMUu2dnxLBUPsKKnF4o9XC4Qyq+6Po/8y3MF5kpj6rIL+6JfGMHTwWimOxUBcoloahBrTAn+rQTgq71Briz3MS/pshPFmWcPgmZ3G6qlghv+DMCY6amCvZRbhd1aac/3SM8bTplpCkNFX9Pouu7hi7tX5KLblRKgB6HJ2kvP0/cHTIVm1/mb0VELANPnRQXb/h5THKB1XLbW5k53aHN/pXx3nfJmt4PzFx9cQRMb6BmZLa6X23GEXlnyrmumA3FMYdrFvKmk5aXtZ1kmsdGmlL/b99ZCgCT+dgVlznOPDRRIXUBu6WPQ3FFe0IfF0mtjW3oiwhI97GQcC7LA34VXhNJrJdiIQFHVsDXpN4FOtR8W4t3Jr8tMVkIzeKxMsV3zKPwa8InHogOOaKo9xDgqcS/W/rW+9AMiUub6cODk/CIFwlZYOtQDIvBaQNRVJsgiJ5yib6PlI1daYvx69zpaCLcg+q7sdhpdML3hprdZ2fCX36i2NPjis1pYt/NblL4aQhvbG442v+oh4I8AUYDAlUCSpCZIvs+8BRPAF/evN+k1PHf9gmdG56xYxYj8F8WvWIHCNdffdYWYlfaxBgm9jgMxUBJcc4j59E1vpvVWw+SMaprbLAgY0ET/fj9hY77eWfoyhemTp+kX59JOqRvEonwtNMyBJTwDL4m3nQtXXzoe81Z//q4QfwuBXwtwC5kXvd2Bkwop77MpZIsCFoDfNNv2Q7UiXLeb9KoexjB4/B18Mnsb9e/Uz5qKXA3mLrKB4D5JJbPhs9vtXLuvvVVShx78zOBq3rLH0CcCE1IIEeA1eKitpsHJEXmYapScgJ4eYXccicpDfByD0biT44O/7Va06jdxE98SHIDFYEkOoJ5Cz0Vmyfl+HDzyFackZyxcb6QFPYi8znwQHVUwNjVVc9VsbM8VECLoztQcNyb77d7Dn++TnM9VovJed8nDm4p7JIfy/QY5udgeQfNmY1qFE4LJH9jP6Yv3mWaj0fb4m6fpbqn9bFaX5KS3Mo4nTTDnYig9WkBYuJGx36bnshwqEELp/TEot/hWQxMZsREqnAfi/RbQ8FFDUay8Y3PEELZuiXOiHPYkvCFuuwudpYPzDMciEiyY4A89B5edp6gJvwIFc6ITfIxDpjkCKqwMzmRN3bk7Y/SbfoFACWLiZVAu0nyvmIFR31YSbzlAiPAddAoFqh66jA5FEOrlIe+zxOZZAcmA97XPZ9Hp8eMr8CdhD0pJOM+tHGjkMdf7g+J6/+Asl56hbyB9EfSIP+oVEacuuh/QJcqaZrVXyDa/aRFmgne9hgB85qDfRQDqAmy42ai/JbMjzlQxNRyvQlLcZlYwAkwBXvFfL1b4FpD+OyzQmyGKvJ+NVwjxd30AEXp3Cvx6GyiGBbVop78FLUQiHqT6+N+vuByISdXh51b68yJkZkv70qAlpo5Jofiew68B4RcXp/1pZbjO1+uFyxMYpBGyCW10tiZFZwc3WUSLe3/1NscP/KAygmdYiDw/2W/3pvjgPqv7dbjU8by4iY3f9TcEeLTNSK1ErUTp0HkEIai8Kr48OgdaITWYb9YroikFeb+JrWIXh1afcxTOdtcG5+O/2Td5ksfVydQOZxuQ44PfzF/9q/K8KJqn3kl05YF4ViV/IbRM4Np3xC/81ILfcDH3FLFo7Tp2tNEIeuhPa5xGguNShPDhXIvCmqbEb78llOfhoSWPkqUW0t74a6xvoeRMxBLo+7dbKaxdPe6q+wd6XnIShEZRmG1pIXEZ0EizfiPXGEmak+c5BiRtpPl+Dabs4MfpeojFXqS2VPyoImJ+5YXFUkzbUN0F+D8Q6O63AMoWmkeN01v5rsGHknycogIA2j+X2dCwJcr5lv3ObEECfQ2+VaIJgjYNJvhK9tq+3E1W5yJBZhBjuvWdXU8hkryUihr5QL+dYbeldWh9Ad0+Yi6z0Z1LTms3Zxy8Ytpy5wqsXK8d4IrcJSje2fdupHDDSkq+nl94rC7vj5oQuY7uziNuY1TmmBOB7pNpZD90MhQxaWVDwW+gCE4ok0wQRBFJNGcbXAV7815IJs7tK+1Lys6PH5CQv2jT43xVPf9Nvxinuu6UU6yteCpPJOklpnJICkqZOCHpVxUfu0eLrau8iKku0wczjnyjb4W+K/vHgqW5jOvHsIBqAVZUcfDSFJrZf5VQRrYYZLWf0kRBBnUvppHwVIKJR5kA76qgDfHle0auh5/eMojijJ2fnUDDKr/xuQohZYMI6ReEC4O3zpuwpAYXoC+P4dnfjmPBfaRDdblqMRw2K0/yq/AQOOmWNQrcXxW6CJy1kc6jVyT0xLn5QqnPraMprOoV0aPU0M1ayJV3Cuuq5Hj77g6E6rw9EFZC+tdNqO8Thr7DWXvc293enKwIv+Scoc/ZIsxDF+89mwhnHtUxWtsZp1TQc8bv9MHW0h5jUlUhEZeadsJ18lrsYKEHK+aaObdMmYoFd4SGcnyYBg7i3drR1HDjzmDXvj8OioDUa4qOTKd61olWZVwZCBjdGIvKvgq1Gn0n4WJHgRXljcnvzkH2fZH2koBN3LB4Kcrubm72CfdcQR4b6FN4CMaYcNn8n6ar2JIcSYK/JIajUphippuYSsz6+lVkz57mdVdNp1LhYGYe7v4P3ypx1qjxKrZxxqYSMz7FgK7QC8PzZ7aD361Kqr30K/bKT9eWVgRikd9CA/p8I4wTe7Prr2unE20K4uQDGFLTulnEFVNmc4K3vhk3ifWph4IEb6o12oNmgtegpMAkDgEos1PbGRc5JTpOpMPdSHY2pnhMsVQuUyia6PHGMXmWvpyPYEUtA9g8sMaATs9zTBA+EpsQ336sClgf/5HtfLFVhuR4Jlmv4xlTbzA1TfJReiS9tdEj0XWmv4smpCakROrNGv2ie0fzvLzWc4HD3uzmvOijExO20HlhVjkPgXVoNGKih5ssw7IsLw2wA+wN4Q8AXO4Fz166tmnGcyAmCip1yCuEXg6cl02qg3/1oEkXwicN+bOav7Si/uzo14+35JP0G9TxfE16vfoKzcEWWGjVtqW9fKSUdq7t2LNbvSy4TWiw+UeFUtT4wy+nXk7hJmnZ1fY6Zfbhh9u+JyFC4l+5gUAkPEtQ52c/LLBbZ9SbJG42HpbqwYMPSQO5AC72MIo3GIHvNDaFNTecay0cOLrbUeIp1GTy75C4t+j2xh+Tkdfitq8NiLuEjQzd2hacyrvVCfJ2cE1smkcGrCvTo/OmKxymk9bS2jMKuoeoL7RM5VFtRQymVg5pCQ9OgsSVgSUM5RA+cffp58/lF80/IRawB2qW+JelabfE5HmGs9WibmR6sVtsf6tbmwO0V++/1ltnzuVD4nZ4xmgDNRPXS/8wcDrgrhIgRJrbT4f1EkPlvxKW/xW15ZlM5hYbXri9XI2lta3ZqDBBQSMPVNHjOhQvQ+uxl0foaenUyJ0j4WApZbBXTuA4h0G/fbealdiw2lYww5BrPHKCsk75bzHn6FsmjbYJFKUfDv86d9pxQdn/RTxI3gCs5GS4jTaMRV/TCBisuCxwSXwTy5zfp8jBY9Gn/95ILNq7P0VQ/tdDZeviF3Xy8v45jU5ivTC2E1LeaujbyeoV82Ndy48T7lC648Y4py5h2zn6FSC97Kss1Gtw1fSjFC2OsVdmy/FgoycATa6+Jrr/J0vWdS1x+191vbnAz5j8LsbOfnLMMryQ8w2OyjWAPkXhgnKpp0MUBEzpQ0XQd9laAiRVCqWfwixFzM3Ba8U2f2EAEeSb/Dr1DFUiyaptOGd1EQQcSc5Co012k/tjW5pK7Zu458ZkCQa7osFwgPD35y9ZjcDNGB8po4X7b66XaCGfu3mYr5QT0Do8p2zUJAwa8D8iy6I6ANzhpf75KUQVjGdTxkS5kPSlWqILqjmQzZL69Vt0NIJerN+4o6VGBKnoY7f9VY1fOHy7tpc6hM6j3MxMfD2Uig+MeMRjb2/5J/tp7nxdn+fp8E8cfk0w3ez7ibcwg7BIB+JqQelO6luPFe6yFKs74VFleTlhlJacHGCmY1wMdgviuGdKJQIFbA/I/AsPmaLeeB7uLYA3hiyb7LLLawHXadscMUi52VG7v706IoJQlFWMT1w0g9sTeTEuH2MvTHAfbuHUdULPYHavvJnbJysKeqkNNORlVENApNoDURzfZJ3Y6+vA4EzumrmvwyOSXCPMF81BHKSwv/U7OtFjBrweXrGlT2XANFRH+2sWcP+mwOwFwCFvDFAVLpj8onCzkR7VA2xMwO+dh954NJgtrqX5RHRG7d77C/mOv/6JdwN7igJzoZaBtgwIaPf3sa9lvUMFQqGv8lsi5Bza+z7ifd8uiPOZ14dcEqe2N80bvq+Ei+ScfF9drUtk+02px/A3EdBfueM6CmX+ctVFvUwrtIm9hfHVtergbkjSp4c5hXuBc26+rMWOZtTMBaGGT5HZtdiX8IGhOnTrhiFBkMjKDT1RTVUPq7BDSTIihf7ndFchajbzwF6wuOIRW9uO+tc86kuNW06Yyg6BoeXj7+56GqWFoHQxwlnCHQxktbsilRZabqC7WhiDa9u2Ake5EnuZJCxS6UyuAPFd5QSfUGxtJzil88d5KQJ9iSd0tjUJZgIOa7rPgtdEQQt9lA1jp+nQeLLkhDE260wFZSXGhSBwlj3eFIIgoUcOJBGrweNmaD/wF3JpyOmZOSsQ13TcP4e7tPY+sWN+il7FosvfW9OzTIo3CIEPsZIYUyo1gcONW56jY3mIfEeZ6O9qFV2WtD/t6XGTym0Q7DLfhVIPAWbslj/onxOY8usc/hs61TL6Rlr1SdXt/nts9Ug1aPzGO41AneW24hR+a3VuXRrfUmt0cjMH5QzVjNKlN+ByAJ/nIUflovH9PdUOEkfYxv4yBI22COoT6D0y8w2MtxA8sBsUrfK7nQmfQ8AwEBWOkgrih/y0dh5PNg2FKU/ciNb7WQQA9JuHujrm4FEUt1R4mat5snU543y5vpgQUPppVe+YvkRDONiP0qCu8DjeySVtNLRdiwzteTDO9+Y2HgGLtZHowfcMVHNrH1eHX2N/9ls1hrfPeOL7ZCppM3wj8Q8DWZaNqQP/de6Cl9ZWSytkoKD1ax9YkW2tLA1Lheg4ZAiOE89PyzTHKuYP0QWh+RWd2y/IQ2GO8kfLVC14CuEpCctwTVOQNTanhGtZCAPNc1Nq+VP3u8m5nBKbt/43D7th2w94FlB/XiWkIzutDDObDLXkfk/u8o0Wh6kvRoI2K1mEgt+VIK/O1kuVZAMSjZF0sKTwhQ2RrxMeUAImXHldrGAEKZTb9HXDqKaD0+O06l3dpnMl0JjKuSv/QpgmU1kpX3uZBue4owL4/kTJQ8jMvbBz5ex8Fg3aZQKwv/4TFfdh6KVlenrGKMubs8DrvYTIHEmAX2kau7GHkkgExu12YgOTEylSAPPbhHwLWDOlBHQlEleI6gqlFrL/heMXeb/o1oZhwEseWkpe7jRyHcABtlsGauC4nUpwkBsQtUPmQtcUm7LuCGtkc+pp0pV22riq0ofA9bqY2WZRg1QZIM4KbWmPkFg6l50QvA8lrb5lqDeZ1QF5K4dbYUOuqIjBixdQiZejgmMRlyfTPTge1qnNY8z7mBSUBKphX6xSeRvnc4WJkgyholznXpb9ye8htGZc5ToSC9nOfqZJ5LGuThdxxXvEk5GC3hLXAKVn3vS3+iuqUs5VE5n+l6Lz6+ZQaghYW0TaUjpRiUZXrcXKeBzxE1rlviC/Nc5dEjeKpHNVUxMkpYns9eul5T5Yj4KlmYLu4HetieDNzcX1NNN4RY9T8unMNWK2k3lHJePIngGN4GFca+/L7ImPd7XEn+2MWgWwnd6q3z++HfAZjSKv2crHBcXvwIz+v093kNnzx+bJBSkfkPbXRnDrSOZrspkMxhOSTO9WgWTgNvZPMtgxKfNiFlo/I9aJ5kG4z196IkhDksA08CSCR3pIs5ZJ3SWG/Zs3DX0UqMoRl4CAxKsSV77gqb8+qNCP5kUJ8LT9duury1MEkVTVxaTgyzQFTBYjkHwRVkVvfc6+eoemd/U04bjAYxWexL46TtXSMqHN3m5mtj/0wxx6kFhzZbQLw1y7EYPxLy+uQuwJgDXgUhjwrZQVsfTtYDi5rIy6lm84zCkQn7DrV6DzMpK7wTyTBN8Nki4h+FnRzuVTUNpbuRZfH0hcWrEl0bG5LRZOcqYoTUNy7W5p7eaRSlruICVrLVOE7NRPXU3Mm1VFXZSIe3XBhlY0QHRioMghWByIxeDUk7THUbPun4g5Y0aKuRCmqKsw+2wkKIRr5o8bLJMnGJF40mpfzjyqbq6l+9RLhyJp5y1A/2afo7n8Pc8R/X0rNBH21G7wYKkZ+vlTE/VT2SfDWMzLE3nBQzeC+a3vwf4mtuRcVSzIVZZz569/E+Hd79UTjzqlvWAOMXT1b3C/7nZInyMtWXZ2/A2vUhRP+L1Ckvc7zIwZdpQCcyw5Evz4hwDQzJFDsJLmUiPGJO0J7z64AT/MGKW/Fe9ERchACBWQLnWqPy+eIdnL2MiE63Ar/DmjxL3CUio/feC/Xm1G2QWZI+BHhfhVgPNEKlv49d8y9yDnnh8eUgGzHEZ7vYewh1dqObZgZqjrrDbViKxgBjivjekwy/UOcZs7vJQjpXFsZwBIVR+uXOiStEE1w3RiR3oR4TVien5i7oXtqvBww5/csaOg4X1eltbWXL3aqAf5g1CEJxva06TjvGw4KSJexIO3/EHAZIz920NUB8XbCP+W3oQsj+YaXuhvsPHxcp6/y2Ln7VjEt1OpKQkJZLqMLP0sOWMMZB9FBc83z/TF59M/W/j+0AS8BJpSm9DePCa+iQsMRvcLhM1MIDEqQt+sNzIrNTSZo1+XT7G/EQhjtnzoes6WYDkz6LjM0pb3cXi2ysMh9/wUXWHNfptZwdPPruY386pUGpeDL1lRzK6rvV7AG44R8bYvu1XD0z3cDbyfZgRLAcQ1lDagA9VG5s1v6oSo1idDU8QoBpQe8kcPVOpqCi2tcCPle9hL4tbtgq5jxl99SdEBVeq8cpRtc3mu2biQvCZqzcCf7oLwz0HACeIS182lMCSAan85Wbe9IY0bZQgo1mPDoZEg5g0nxWUhmArzGULO7tnDO552rUtiorVK+orKLsLCNMyKjDZzP942zN9S7N42D6NuXgz1vofhHpG4YVjTnYe6WXQ2X+WOgHxRRmWkRutZ7nrU1S3GsttGJJo4qSFZ2kvNTzZECdCcGhJx51v7JK6tSwIn9ficsumRvgZ8qWIN7NqIe2sb3m+pmccBDpLpDNlHX+A2EJxNtIt51cc6j8MHkkZ7xuSJO83vQ2/2lxOdIM9lpE/d+PnCHQLkmfYslUwzMi+j9rrRTkW19JrzKv95UOWJ6CtD+pYAL+bXYB4hayiJLXhYYXf7N5RQd0ikym0tfFOf+TZwe5TG3pcOid80lBQnxHGw9AbDjDB9SJ41D8mp4O2jKoe5fw8JT1FMGEq3idMbbLr/EwK2azLSf5hekSjvI5jmAJ0JKVh/1vJY/e2A2olYWN3yrcOCQFJSaVyKirWSGfyjCrjHjp5FpvAi2ufiGJDpd9UUhOlE2IDKiE4FTpEKbF+zcqlwCut2rqdS9iHupHLcERcTK4ASgLE+s+tAFEQsEcm1WSlawqB906XULIPunif5m7BNH1+IOpI6QeZ+Bf+rsGcNMeb5TCNfb7k3i4uI5ZfMmX21jzMjBAKiHZlZfoO4wllQCv61ArfFRRHvn05UXPRPxlCh7HYIce7LNKZ7disdZD+auovbaN7MBRPR9ekD8mPpALAC5rOmfZXliNmklNv9S+lERN6B70bR7dIM4f4ROhq4+L85AkcawCRy65yoDn7Gi0tIQtqTQT/hYD4fUkoKbV1IbqCvQ1VH0sp1AUKHvcb1lhqcv3wwP6V0ltWcgY8vfOduLhm4V7hfANeaEkUjmpWYhyFNCVhin0FR3T1TieSX/vI5IJFr3PgyVb34Wg5gD2wzbLPIPjFBv28uI9GFIUmbpGWF7AVnW4QObadbyvO8oCbFx7nJWbiAwfUH5hHFJG9K99mMfzz8PnQ4WxHYpIfe/7zBfIypX3c8/EXTsO4UZv9KYY6r0Bhn6h/Bycb0N+KH81m+iPuRMFzNe2heOspFERVpOI7WMfiUcADy/GgftyMywIv0tO2Fn19lnTgSEZGcfoMpAMBOVBWcQ4qlKf+NG/ibO4tfbA1MEn8Q2y7XabvSeeQmCQYGB1XpSwHWgtBfSvDExaVet+PCNV4vASpCL+z8FXDREv205MVv8yMqL7pg2jySQe0M0kgNyx3VipOWeF1ytotcLjz1q4RC0SC2Cij6CpAZUDCE9Fs7RGtgdFoDxe2iqtFAgWBT/NZEsF8mMRvXm18HEZn5h76SR6q4EDsUNRR/bc/aox5OsUmsXMps0acdw05LSSXyxbLoG585NyHa/mq/Xh/3M/xS/8O9uWHVWID05uN7+PrJufPKIyMXJbjswrOXU6gY/mRa5YUl+rJFR0WkepiuY4cDaa1IhPTj/mU2QGUYb03lRJ0Z00MotDk82OPz+ciRnVdlIh/eaGWof1idosfT6k6WOXZtR/VrisBebJjHF7G8c51e3zcs9CDMDwoOI/5tZfsCrdXB5mp6xEsHH2fSJ7f+2c+fNRTu7A3XoYuYxa4rQ2O812f5AkDW93AOt/1VE6BNKwSJcXpz9WkQuPFx1HHhi0yw8OLMv6JRVSJ+Q4WKPy1K+l3tGH8dch/RtR4fCYQ8bH2sREuqizd0oGrIxVBErA6u31nH1GEkTZtnV6rRdWRJmLvxn/l+/Ga8cEkxtFBHgjiiTldfnZSly/RPDMDVs+mrfAJDoo4b3fVBJIJ9wSVVMwchn2bEdOjj3l3B6G/de2QAoiZ2pfo289A/GBhRyXSMFzSrE9NFjgs2Yl1TR4eyFiZ3+LMweDfZpoCgaHomAZrbDrGjHnmYs4RSJ+ou+6WkfDMfx+Lq8j4FvxE7Z8UMGUlcDkJObkb+BV2s9JH8xxrQBNDhUKUS2o4+sSsAgZTGCzhgLIvmqinrOeHWXHTViUaCIKEMJQ0d9SWhFUkgNutniXBijHX2yvnA8PkB80hu3ejZPbtgDc3N3Ec4mxPpLBp+9fnATlO9EPnd6KkP1eQggOxd2MnzcFUYxA010uESVDwi4923ebWRJaew52oqmrXC/X1mirOHJvIvMv/IFY5tfpxSg7tibgfYhVDEx8oYoSzQU4+4Dv7ngxdH+iNtxkjZ/tHYw6MlZ3Q5E+LIBW5i3AWERO5MgFrL8Jsn1Qw2RPsXdBbzNJEzWT6XYpCfFeA8+lHPa2w2U/v1Mp4pnIMSyMezAyQfZHPoTfZ+kF05AOELs3oAP85YnAHftRr+rK/7K0xphQRDi0IYGZlYzE6kdkIlEknMSUhQSXFdCbgv4vllOe8P4eCFvZdt8mtIQy0aBehwQn/Da2327+/x3W/3R9PzAKpXYyZj3zXDVmY5iPzNUe5KQp/8WM5fIgmgWzSKN9MnDHEQJ1wjuP6xFRAN8TbMp6kJlEBfCV6SSCouNE4xDuGSytgZ0ovEofqZAOS5TL13mAizURzFkSmj1iS1XG/laD60i4/YHmz2NdNEcvCXsNYxbFNC8WBU/AzYGli6XEF59G06sya/jW302RQ7S5D9lCgx+GATQoV8YMWtGn1wdHIrM/pUGLwzmZf4w+cL+8ijVxBlWX5iee8xRfx7zs3ivon1DZCOPAzYeSLxmT4Gy4UP4/frHyWn9XccS90waUdcDYiEyFAH2pKioNJivf+TZbvVMM2GElJ1CA96oAVVrvUoowwJwUBQTsbUH3v96u5iwH0p5HAQ1DlQ1JwsRwcKRJtWcJDfdOM2fzxcy9Q0rhMMcvnONjMuq2SJTW8E+W02XW+GlCXSeYBYUCgIuDDC5Dyz13nVuOx9uZQftCBQFakLbSd1Htlc96LZwMYcr6gf5OkLC0BROi+D+DFwzUl+ykmX29d6eqpzm5XlSz9lHuHlz9aFxqpTIZfWv73NwgxNuJaMOfqnxrp/B7XSU0DjelwWqBnHvEu4wIK68GGyZztmIddLQp0WhAOCOMvVAd5uGvrYXQAibHF3vbY80Xb9STLyEDyJt7hhEXQNvnBCappsegG+vKaE3+4RBfQl092mwAL8dElWPskVYKUKe3fW9+NsE5E9m1yaqHuIw36dHVobX2GNEDaku7AOxE8AAxzmrxPY21qJPtn54pBq6j2ZyPtSBCze6xGsyadBCcy+ekPVP/vjb79NSNHU7OQUhYSUun3OU/9YhBK9bqbmpySq5cx9uBcuSlNdXuuEUb7aXvDL8IjEiAw+wjRVXtss5rnJnk917sOmjtvrO+cQXCodemeSOAhbf1YTwRv99gfOYoWMTO9dUG0KSGfWtOJZ9qofjxcQLmmvS0CfUmwzB12BI9V15JHbg8FS4jR6fHLI61fN5mZJQFLd/spBUmz7+KTPHbQ78obYM9qXl1eDuHZHJW6lcst1VFNzJtyhA8g5XyGDYtADUrUbRCUEGcrUjaBKbrzEFG+sGH3iicI1Yif17SiiMZAIcN1o13fbLgKKo+vP5y4WywH/2iihHbYJXm4GTajuKN3KYXs+wfPQ9BWWoJpph5X3tys10A6cJ0PvhMp7D0rc+bJxZL3N8QqGRTXRCU19oKWOVfNnkQZZn5tc5QxZ717h6IkTym7/SPdME5H1K6hPsoDeXvwT08M/8DjQgDhUqvVdEIn43n7OrJoj4CX8GnCBzRURccb3hECJbAP45PLzKkZfcooBQVSl8WvmEBGkbaIoSn66SYp9TKeX5pjZy/lqzN+QiftNWFTbZQz30GU3oGqHTgeZQZBVJp+18tdkjVTVwoNYnw9KiNde8Q+ONnJ2cOn3BZaCf0l63wbRZ/c6XlqdKAwNWw+b7CKjuJIg5w81yozVT8Q0vkutO7kdhJcLARUFMZTKhG5j8FK+hqRWpx0PRNC/09fz5mW8lzVLmru+TJ4NruIPd+Gwnqbie+livYLLe3/G34OvV/fV/+5vefOf9C+MF9d6/uha63MmG6pLgwC14MKUmMb9/vvipehcJ2Yih8lBXh0IkLTx5uI5CWYI4A031fVUFFxQhJXlzv4+v6FCE5mu7q/zBvvebXRKSPsiP6sRHqOGWVVVptqj0uCnrQcidoEV6AK7IiO4tRrD88VCBpmJyEZhR+X+9Z1wYW9c1dNxebi/Nj+C/dcl79vontp75O4vnY3wogsPL43hu0/S6qocY75yWfSVAzvcp77YAlU3Ve9Lnw9H9P29vwM7a8Z9fmvupJ7i4KjKQ0b8SuAS0ccOnZRB4VUUYaqhwufvyrnVR5fukCAm203y6KFzAWxRuBJ06nZVSufb/qIOu5CUakv3G5ZdHpJfklOUxVEhPmC66cuuAEiXUF2J8k6CKPyXGiwf5VE6eETq3HmqGhIOGx2Ng+yxx58m3ouy+bV8w8nuBkpVgHBsw7n2a7ao6QLZTKUcnSs5YwOqMczkvHZfeUzAzA3gchnFEq1pyystxpHGLbjA4TI6mIb63uRVcQUavESkT5s2/WJ9mw8NXXA+nJcXVJaGo4pbS9Mv3aiNn5gBfXT4K8ghPgqbjheZf5Nk4niMuYlBaoHa8DD/EAouf6/oi7IHeS7Diw/4LsOkkY2VG2NKExMxEZ4QkJY9TzNjxx4yerlopFcTfQkPrGF/+yd3nNeh61sewfzT+Rr+q9i3knYEfKm1yRGLCG7KLlhi/8bvGm5Eb+xHX8tPZGY9w6Sj3ey0W3aaVOfdseXfLz36g+r8OTemhxf51yB/e5hkEL27MzJC/hM3A2W6+/1v3j7YqpK7B9JrK+lFo1fPfUcOufybOlKEEyAtxbwKK2RncAC7TgwvXYITGBpfFfHDrFh+2e5HNoqbciFcRPTQ1/IINbrmd4Z/epJyq6nXXNz+tq8NZarV1VX0dGwFFoxGfhmxQ9Q3yELg0GfMvxTWG0XGtzDad7UvSpa8cDpvC2WRrkoDxIwyMdrGfbPXrtgqjfMThKWaHXbOF8/P6QtEns9fRpTd43G/OqSw53xKDN5QCAwUZBxzWeHHGRUvaB+I39UZZ2xZbyEjNk2x9l1UCHSucO+f4gUXCR8Z8CN1DSOZWAuDsmWTadXQCINwb5NO0BKAu1B2dKGc09r0p1Ng9XGF9LnzIprxE8CanBkiXHATVbpPectF+yQL84XiP+NGnfpPoiPEIoOa+Zb6OGV/IoaByflyKwjSgD/ummpujcffkno+izonnQW1sSEID/+VUluDgl5iYHvDUZb0jL4F2RcqatzgxvZlIe+f5HG5XyttE/1lezY0/5TGamnK1yLEcFocdP5Njh0r6K/5JPeqwr+9F+IHtm1kQk/hXotxAO7aBHGfWi+ewcK00VssE6xU+YxPT1TjEgWSO+Un/eemSTS96dSObi+4xTwZZvI7/TnFetGNENInlk5VtEzPn/hm1gtD9vJN854Bpg0IIE+Hcp2bZtpBRbT+1hSkLs6ywK0s/TA+VsL1INzavOvhkKD5Xksd2yAu/c1LgO18EqJf7eLQBrdeT5XHefhYAx3XjBz+ZKV4okY+T5wvR2GPwATFmgfVShT1kcUdqxqD3CblStfppBOV/h5/05x280f7vhwBfOzmKtNfTg8mjPxGK1TvKQIzAAVyXgXWHy6senCZEHqsTXuFPvlZVOxsB+9ztU1iFfAN1cb1M4Ki8icgTqrEft31FPpnTfBdCVT+tROSccftTyRnNpzmVZVYG8EglEOxdH9+iwqZj8bbKEQxmA7Emm/o0iMQFMlHeeTNkd0AusipC34j2dXEHJ5lWzLkGFDpIl1le4CSjmTPmdOTtv2US0GwDug3muAQKfHRUBCgYO/TqVmrdrLmFnCWmNGWvJERHI2+YcifsY7/5pALaA0qO+NoHjAtQvR7jLdTA9wkcO3yxtYhBDcPHviy0mUzSKIi0VFREx4aJk45kKLdRMba/r6e3+1VACldEcDWjR81w/G/Bbv+MizLrro5I4CidHlAmsj/pW8oQ8p9D1J9TyLE3fA0dr6xeeCUhSyby/G0eUn4Gy22Qt0ucjxcI+4m1/rpKgWWS4thUiK3KePX9kXQ9jx0G+ed9/wHHpik/n29eNVFZx4ZrXRLjLjSgDNPLj/zvNUTM/wUPSa3rZMnc3msaMVQZ7K2Xm2uzFmp1EsoNQ1peEJ5Ybg5YI/nCVeVq5YMJX8PpZdZdm4f//NB66iURr3galoTrCDkPlCpnW7+/ZuyZp8VxNHWFTxRIl0Zo9ZUTsmx+UK3kAf6HJSoKn/dbQ0Do0jjQLy8BK9LLMwYkycGLERTdGm1YwmlqGKzzJTg2BMGBMR1h3RtwOjYc9k2jnfX5ZuejxOhJIIdTBybQmlV0Oe+gZ33I5djL12oFxaO1PXKxC+OGDZg6xCsWB9r8odGoXLO/EjkVm16YI8EQSX1wboIjJtYAmHlgO1mg58/RhFxqwy13rODyVYfi/qK9nqyl5OX5hJSwf3nXF+agLOBQ8wE3NABAe6KrrSFkmhU8s+Ka+JqrSF89hHEs38cgO8URfeWVfwKGCxhzgEfUyZn6/QAEUnYh2HCckQKHW4gV4Zcdvq0bhBHX/hvm8Kd6mXtWzm6YUYYQeWNU3DFx7W4fypTveQbGAbVwPbYkBcyf964SW+b0XoXmcD6D1/4pXTtdYL33PtJtGzcbRr+ZJkW6ndX/iLEH7vb618QNkJe0KhroUNx6UVmWh2V8SIIjLqzeuoYfLiuR6JOYYL+2qE9j8Az/hxcRL9BF9TJi6tDgjQ9FG8Z7jYP+awwKkCrlwJyh9G1aMLcCYuyTsYcfCMqdt7uSTaYEwqz2Ui2xiHg3R2PXIa4dtuJxGFDyRsOHG8HscCstaeA1GCtUaBjzgyZuB+hbWN1sE161uH0/vtog4dc6XA/8R83TXQA752YtkX39/1tVLL2fp/XB4fU1vE8ewk99eztEvmz2iVRcJlNHwRDjfUlFGOqHsyQYrZ23GQuW3OVygK9vc6Z456GJlGAbEGEmUTY3kyaDA85CyO9tOTEQJ70l0ZqSec2dv9lp+EaYTzn2D3M6ct2fcUigAIKUcZPvDH3Jv7NFL3Nm4ubdE0pRf7QxG7rM+J5H6rQ+29v1Oh+QU5k8+aa/qHyZCK49rc8xuIjLnxQ1+8mYSg3ld8RTzeG2ZUgujIDdvYROyAnK+u8Myz6fW4p5mEFX/amo/PF/pZIaGD4Yb4no+FDMirAmwHowCHQuxVXxiShb/KQcqrwmPPOdVr68gjHveAC2lgT4dHvARVZfytZMZxQjPqqxgIYZH0AXxYWZLm/rf/beaHnmF73E9qnHxdNXUXaekV5eSqDNGpF0LR/IgdTSh4A9MpFDJxB+nAbh/x5yrbqCH8Ue1m8x8JQpm2rGVXVIirqYo48kFJmIdOIRI/wvA2YP4wbXiiEWpd2wL8J3LO7iZDx7B5UruR1oIcYZJOme38nb17aHkRK0Zq6v211UBPFH5EWcnrnQVsk+WUlUH09Q4ygAUhZU7gq2+xErBQxxG87N22pYVbnZ8tnitYsxJkwqafaTPa85pJjopB+TmivBsxZiYxqCyH8o/pOwrLfKr/pe5C2hcbMtHdaRphpMJV4Z2FNnu7tzEHcMSUhb1ygcSd3s7hcWGKjvJ+cMbgL3KewNNwbxJb7wBKZjJTXV9jLiEk5CRdfVFaEnXBkiDG7sWspf60WYHEZ+Mh6s13axlEArrHqJEYgz+YDeylWv5LLmlKstASE4JOtigPMQuX4EVZXCHSbhmg+6s0EvV7FGgPAJStxP8okHNBEzDsmO31AH0uxbD1+gOWCnwxyaVDCNz+p0Xl/ZCCNItI4YpbADnKZ+0Fa5MGiK2/drPeZ3aXm5gdR5BGYD5Wwv9spqhX5s5nAi2BZBkSMLRAhiMCEckOdPUqS3oMz7aYhrBTqei2YQvIewXJEQbown7hl8zBrBE2JOkHYCbluHwnel3se7fDnfn+NfMzm13LwVTKUaUiqGCNRAMAj/rUZvO4MA63v72uQ4lPCAD6CL+ducUd0EonLgs9mwew9HvhRFNa4qaZOfsyYkNrYKKrTFu4DJKcFkWpXCzEy8bfNlH2XW0L6RzZtT5fJAvQ7Ew96wtP5hv1ehEIT+daVT1RtaB0XIWZ8WTZGQBBBZdGRkanNiX/UE0NynZRUvIgY6fjUFAxqJ6D8cdvMjtG+jlb333mMpZG50mtp7NOPZB31OUCa17OYqgqzn6V7nBdOxDkQSRIM1yHdMhCoX8jfDohuwz0E+GnJjf2ZYIyYldBAp3nE7oO+CoaNSbD48usvRFfJY+fz3GOp06BrfiUJ9CiL5NALgFOebUMvU/xrIXf67TKbTbbCVbCW/kNFsKlzpielId0CMA7UJz5V9SCXqSqLhhs6yUyG8HvUU1ZykWBh7+rAAFBJvzVPMb8N07BQ/G7jIVbCCFPlva/Eg7jyhbYn7u2lDyWdSWzgV7h8IH56BpSUsTZQcSkWc8NwuetWqllcipBZfra1KXP3E1cvcvmhTBUzt+4EVX6jINXfTr+bINgAhL8qPq3Ewft9mF5Pqpqewi99UX6DwEiOeWCKvmpeAaXdPBLFP5xADhSXbktb53zWzsJ4idvsM1lYlA7BWvwHmMVD267UYc44BSG8m6Bwnih5CjlC5rMIvSq2COx424SOEt4nl+G2ls4DOf4C0b71b5L1o+lAigN5EcaycS55Wr5Nv3HQDgluoQmS/F3CLTpGBlqH4Ew6nSUj1O2JddTjEsP5Q8n+UvWybiBU5yzpr/x+p/Ybjlv52VZibMKvl4fIaBEO/UlRJYgJvWUwvMF3rdnKDflqvCifMXmKjepYuehm5aKjzx2h1zUh7Rk7L0uVk8+NQcRLRZkSOkKxkGaTRLcLAsnt5ZdPe1+ViEWmjSjiY7Iooh5zYFiTXFq/kR3y7+wzoy+yMfKAAfkmFPOMCkd39GLkBXSQ9TxeVPttu3AMjxoNevbjxQVpA3UI7jgelb/JLRqdLkg+3V+H0lrtyjCoa1MGGbLkkHFPZddAsen350PHOkfW2YofPEZ4H7d5vdWr9ro0YM6HplsVfV/r8WBSjFTLSs31kplhG/aUftMH0NbN079i3iGpDFbBNYcOcvXpj3ummoD42hzuxc03WbuQyhNanPhDnsxUsiss7BnWZZWEYVpNu8/rOBMrrTl1EVT0GdGY078e1tFW7GbKYnnITSd7q2971x7wi3EboqY+5tF29fBgpxNtCsExS0Lai7XRf3PeOPcjxL8F69GlhtaLpHG4V621Jn/taCO3eN/s5wFwnd5pks9JS4Muic+3Xgel8jKwQVcg3PcVEXpg8psnenPu0nJ49OspfVaM0GbJf/x+YjFE+mSJz8IF+3CxdOPM1h7XFGoFEW0CHTZ/eZZ1vYtvQ8rqez/bnU9liJeo+psGQWLFa50ohU8t1DvWr91CfHwcsyh+PaiMAQWNxnyDFDelGv6ZrCMvvY4lJRzUDLfwQ88ZcK1dMJlGKIfj3I+HU0wwhGG5RSNTJ/uEy4DDdNoJQGriLdQ9ZRSRfxObv+ZSofomkkrqUJO4XZxGpSEQ3O+BXhvRDdiSdOGB9tJoZ17ExQNC/hk8/JofMGfg89eBTAMQIosujOoflCj98IIniLTBjEGroOVnU7IPTt1G/AbQ2lOLAi5atjD6D7ix3tw8t2aoqzlqqV4OIRZHqX6stXvMGBAVhzf4VvbNnPw6VCpxw+aLhU8r1nfEyo78+00q/97MbHUB77ZSP1Gr/TDJpoB7GK0l3XWppQ4CF6SttdKoOgaMHffWRgeq1CNKHdZFGrIIEIb5Z3DOXIREk6AgalRqJj5nkrQl10PgTdtIBHGL7EpKs0O/NlrInqKm5Z6EjMRuNxSX+YWbwuBI4zuPUhaNEEU/y9NvYidpKa8PnJoU105wrEl8IiB29OdAlEBO3+/qt6FiEEMf5ZEzBuALHYa2UgwNfWZMSQU769SH+824n7ld+mi9TXSWIWkgJmwKU11Ke8V0Q9GLu/7l0mSqDC1QxB4K3oSg33qWNDZW38TrrtEyWoJwwDdUuOTBzeCWlm6Fzp2mf3afEi1xTWCwz+frag+GY8MH5IXPmqh9m/YmNaWh4NqfUMG9313SaEbbR8AonYpQbgWtGbSgkVmgkN63u0GFe9D42FgWFjtbrKC+8yRvPUW5F0VSLz5Rh0srhjDgP0m+ygGHAg1OHcfptFjhz+jtBdHm18tJ+eW+qaqsjizNanHW0CF1e4mVEOSOhY/iDgRy2wbzevlv5aCgw5tTq+7z9wDtooDubZ2z3GWd148UfvMXWzkdo8U0SPQtEg7khd67nsAmQ+khE8xsHpgeeI0dLeCNuGek8sXu6r/hNAEJX/CYA7mquMYT/Nf4EGd6Yy8Gz6BlPWd4hGDbkujEX8ksKrmUgc4DtC8PK7cexGyah71Ht+vex5sjF04VLeX2By7Wwb0ocSB7728avhxOnAy9i1Lp1KIqG2Fx093xzJ6RmpsgXudJtTN2CBpM8mPPFdD43KisUlpThslsosKv+6eQ7JdDFtQUIprmLuPnj29Gt1+hVF3XWKHHv/rAVx1GXPKJ3sgufN63o+wAQuxy4pq/9cdHnAR7cpzh3MJ/HNDewrlz+IHF2IjzTLhe5nB305Gicq5FQhIl7e9Pm6jJNQWB65PiU+iE6+8K+d84q0wZXnEnFSRtnJGzgCKNNGP9vlIdifrMfdOY3tMVTIHiUIS+3LNI103xGgpLs99mbDRvRzMAC1JPA8ecoORPFQMo99ctqsyWi0YP/RwU3l8igzvnxy1s8TWtrPbtRMu0+En5aoUpIVdVugM53ATEgSvC8H7jnsVkHdZgeghObuLHqkwKf/co5oL9orKVY0WyavtwXydP8xwayWkNWR0r3TmOCgudfwMmIvWfcvbVyG+lNkxhmZbG1MZhoFGLC8P8YZxBRpfe2tSQEYQdwn8qpBgAEc7nMDWZKozvQwJJawLBSuBpJEtiJrxAWiSDeyqvU9AXaeXwJ+aNivSXkJQrsR04BiC7Aro2odpDYmeL36Fydln6uopc8UPrzx7OhGGSwhu14qcjJe+YUVGCiiY3RhHNziR2XzZT8TxfpbVMGGOZpT8dBMX1iBw53MXdkqM/Bbi3pMQAdKi3Zq5GlXqLDRo6hfXOiaIzUSM97S061yIfvMF580IP1HofRxHD3ta070uEyBsBXcTfVTwRn3OkS6U/7uX7RyxGuXiBwBwG7fdBUIJovCu81Hpyz7lur6LRqqEq9dY68DCkx08+6CbGm6RHLUr096CEO+FRVP/bzvZxCOR1z9BWjNu2h/sbGjws7VQFiQrhQMfkzSkC+YfQ6CgkIn8qwf5G9cQgjwnRxvPZVV2zjv86VikkNKHSrf8KRAYCSChkeSihB+qsqB5wKybT+Wm2vThFfwTKCPZ3E5h1cJ40D/OPfxhjbyu/BbHfeOM9EkN+dxODI3umY1CUP9EuWovihg91zA7sflD60HqTu4boURL1XDEQzh8930naFCdtv/M3Jt8JWqSBdHtcIZOWUh7UAsMiVubf+ddZUTJ9BBQghVbXCGUnFoEb87uQEYBAXJLIOZuayg2nv7D6afbwZJ00IlT46pa5z9MRrSBoCufw88gagYhq8kdNDhgUIzDMBLDsiPDgozg+pYYKW8Zz/+cO1MfSjtjuiE3eQq828LL12kSD9i/ZETbqPK6JBGpyoMBiIT03kuEjCAvzhDQFm3PCXBC/BIbLUhOO8ouLXdJsn4npynFVEZa+nAiNfyoE8UK4GcungD2z5Z1KAQgiC9q5ReF+y62GpcTQhpbnKdOMrtqBHicWsi+okvNZPrLCPHCb9B8QVtzawiZanmQ4VAnIi4E5BPU8m1O1o3DUfQXjxeaaIqXeVv699ADN3cVeRKqwoF6rf2mzzxeMDo4ojqFfbAZ/ZXP1JABRwaMAMKiAHbCKjHMJoShM9GmCttXdMoF2pue50U86o4MaS++2wOXipDT+in3poQjBDV38ULZRoDQgs2eN/nUrQg1dB2WxXGpJcujsC7hfTPGaEk1AJn40I0/TuOuFuvH8MRdHcg65TSL0u+wGwQkZXxLw6SLaIIcQWLLjPKiL0WKdQki2vNA6VOpru8QEG9jNa4OO8D/yOU/cYrJuXNagUE4oHJzGJGjB6um9fqxtlgstIPiwSr8ODYJaTm0G1Jp1PAMb+bTzyNc+7eMguw90TT0kqsN715TcbdoqupYtRUJqzvhd6fel4NUFRE0WQ5fB5AKAt4Zx/9mPrtEXOMbY2ngJx4ck4uVFA4bUk19U9BqIQqLjxSJAQiHCARVfVFooM0VlOlVaTxK4TwX7/T2+KYceOTkGvylvAc41i8gIg4g8j2opNF7w0bFIXrrGYw4tCogwJDbnMYNANxwTvTr2xlCImiRFkMR60dK90bIgsT/hKWNEDL+ODaTh+hPvnf5b6kWsrZPht3aS12Wr/Jo3qJMIL2tpmAztdfZADAnHodEaUfqbZ98PdZplnJ/CTghrqTDU87ksqqHt9UEAeWTtlIrOAhhar2zHlOf2r/xGQJCTad+UmLxhtuR/vs5zlzYKvJMaLbRa+6T25AqhCDEL2nG6qFfwSYaqDhJkjPE8Hpbvcgmn0Zh8dl0q35z4QljcyyXkz83dADjdFYhsjYk3GrhVJZGKNdeycUB9FIBgr36NQpOQ+yd5fcmY0rhOAQ7jMUawoPBc6OaeUV8EjijEiIiN2ip+H1K5Z4vbT8/Exs89ZxS8utxSyooxJ76AOOUpvgTi1R8CBWQxB2LkD+VnQ5yKOoe+fHk2aWS7Yr+b/6oOxHI45oknW7YUJ7lH+EKSNu33BvpZhb7CGgsktJdriWXB1aU6j09xs8VwmzSx685W5/2CBfAdB+VFp11R6Ib7JKBQAEfiyrc6QekBWivoI/AgpkjTcWFpKvQBOQ7ZtOo4wLARt1OuvZN7YCH0eu/ZgoL3sv9WA+kzA9ImW6SDTK/YBXDLnZcQjIzxX/4E8B5oV7zR4hJBDv2FfJOR1yNPcqeSVOKOqAhcR4g8cG8lQIL7QhLLSOev/Lp0w46JeZdQuasRbh+nD4SvCRHX7JD/fisnART6s86Y+NHSF5ZZr/EZMFFxmtCsAjIzDV4RPIsFuK+zLSXlnNBnTMeiMc2a/jDwWJ0FL4Meghi/7O/zwvZwSZv71FV8uufFGmxYkfAt3CbowMp9X/KuWWvQZhgMbGsPL/2WBCTVqP6Xjq3db3iTyIbvRiePZ+BuM2ZPgMQvO5AjYbIvGUQQJNeq3YDGj0wbarV84YCU+nTksvETazCYIi943gScA7juQsWBSg//clWKBlKLgCBor14jXq8HHG3yRCey88JjyqLHSdoAHRvelKfTvhRcuRBYgPG1HBK1IzcD1K88hAcKjYKim4Jv7Kh8Mp8umbDPdaH4w/rfdLzCPgxZgNA5in+Lu2i9PLkIdKiWoMIuPir2p/eSEw09il0jkMjq7da4NYSY+iFbTIZo1dRvq7Ck57dVkPcHess8A2mwBw8z8JeCGi473R7VCAqVHO8GL5Cz1KuxwAPSUJR/GdJVuXQY1pK0tuBI7KilHcIL47GTgEmo08cMCaiF8oGCSyfl/NkUhys9QjnDcMT79Y9nfvR3XyvYbSU3cQULZ098s0UdzDQhSJlLuc6XLpjve2cI5fDhLCJrCedFne0gpHO50A4ufLnP6J+328Mn7h6J0VQL+nKp9Kf76nQBqqZ2mnHErSBaOs5c7oO38ut3CxfdlnwKTg1TE5uFSjQtwxpyjSQRVE6Ue533GO6+XAgsZDK95/sb2lPmWYnQqeZLMqx8JR/S/nqX7VCPDqY7aA6dE8eN9kXsOW6/KH87y2DYySvwqQ2cA3JisOJli9liiCrtsroQmEapJ7Z3J/eoK4t4Gvk2+MWn6gP8NH+r74wCToBrenAMNgA9VnVZ+G/yr1AkXO9etflrP9lq4q6fgIj8+/ctQciOhraxoHXDve15cVD5fkmEWhxbWF3s0ZTwd2sMeuwcbrwnkrbF35xRI8bkK78e12lz9CN1Yg+9aQWoC4Kn4j+9h8N3Sex0fIcIpPLUAL1/HZQJdKhxMVrzr4NQip6bBlraQPlzeeHqaSEvUK+625d3QV5L//G5lpajVJKn8kn7j2HfomrtGZEPqRXXVtoD3CCUUP0/mq5jyVVkiX7N2+PNUngQXvgdXniPgK8fir4v4sbMjZ4eCarSnHMqK1P9YV2/dnYmoMEKxjIyxued31mxLLiBLOqcXqAXviP9nIjDQPuh2Z2VurwZ585LG0eOXU73y1zsdQM1QHzvcXaEb6iAXTPIAZypduEdD9XF70VcLOeI1nvSTdx9T6WnMWEyfCFYwBB5RwEfn4raSlZQWcSQ1fRRnFP4pXZKKh/TKLOQaKabSGw3pPPXWBpRw29vdn4ycZQ9fa/W15q3lf3h32LGOeAmO4M3PVbNVtNe17dHiVVkL2hZ6jvdzMhxgoBuCatdzANWuV316XBSyxGYTooGRKE7AFhOl11f1Cfwab8RN+iwIGzDPtejGq+cZ2d9OkBgdA3AQWQFa8uvpsMiut7+M41q3NhtDqOyC9UqPa3Bq4jW0/M3gg5B6d7BdPVrgnUMtn/5mfq9jRVsjsFFGGzzOLBWyFP7gc9tiFTkS7RV4gKd9ATfv59ZD/ivzpkX1TvQCUbtkRSW+ko56k8b11wqivHbE07BaL6jB813zdiOp1SQDIS9roc3EQEHbajIslSS5B92MzlEv7jP0AbEj4MvmpqjWO7qgEuHwrrbx3c3hJ4Wq841ExO0oo+A0VcQpGrzh2biFwiIX6wC5q2BTBqZjbVF6phgRPUgxuUF/jWFGjYWntcqLsTHNF1JXrqf5a7cBGpzBd4s0B15r8mo/Iacuq6wBkzneKHPIOQsMic3O4sh35e4m7rts3xkxJo89tX79k/BKpyRonQXZ58tPzt5PG3pkGSUdnBnS2htTXNCi6Qvpznat/6ti8/qV0GEvJHn3gGUkXUCIW0WlVQLieQjLY9an9vfnnM1IHgDUaDRXzyCIG2R+hyZ9gtp3WtRmP1O2mdyhP05tPQqNyPhk8S3eq55Aaa2DyX/eWZzMjcR0CCO0SDy+L2WXPRgNTHxxDsNxiRxWbv2t3cGBhTsnUHYveAkLYVtERWbSwKmGT/3DHWP/9zpTGFhXBVRpXOc5B3DZgHidvpeY8wOinEXF4b3VbFIFuczAZJAr+Pp2jfE+xaLOwj5LiBdoSVBq8xV/RbO7JEy3Ag9RZbELouj06hHrvMNljCF7K3DiQzfuU9jLuNqPoPinnYShN4846VcME1AoHACdhYD4rfaCUfrs0fWsqHK50SJROp7fObOeL6tGC/AuU3X+0BQaZwtJG9X09WClB17yrC3KuCDE4JQremZGTSHG6c0F//uNyf6bAer6VBQGqCbjhe/mCj0qtzJlchSvfOH4xm8XSHwQfkdAaZGMCrPhIiOrLmXQYEhriOaZ/S82cbpQu0SAmwwniVxKbId5tEPDCRiOJZ4O+ablS4oR+uo511C+uIddSOQLzwAGe0cLJ2Uvna8ERUa29wYSzB3Yk5ZE6RfO0MQYEikX6OALlpwHS8XBhWlwfb+vV1RL97l0+C2wELY188YMulj9IKX40FxAt9pucHUdpiAl8HmHcdUGEiH34wXWCCCEatWHbQsd4Ufu78/moEhvR0KfP8h7PXDaTvcBg5snj936TrQz/g53HFeyJ0h94ZttVjFh1yl/AztxwJk2whXaYowTXps0Irl3TAZXY5xLYF+JSGNG72sNCCxRVex6steMO2whCX0+Wh5EOcDYrDSfJJfBtrZSw1Vu3r6Ol/gwQfVFt25OChW22zOOzJwWsLUw2DI4UVLvqe+WvNywYroe1/g0viF/Yu9eu/cupQd3XnvwUVSrB5Az/6S8mP9SxTuZJ9d3fgRduOagnpJp9ibmp3Wx5gs+Jz+uh8adjcVfUvl9v7ugXnYF0eIjSRNMmW0kaD7ceyB8gvvqRJTk1dHkoNwQitpvcUB8gl/+1aTC1WcTUU/MIeG2ShW2ROHndXpxt0b4SpYDg3wu8YrXoUIsJSO1VojAl/tgvLiaNY/PoE3o4WL9uUB/th633Pm6gFAL1ji+0str4C+v/V3oOLgEmcWgIfiVCiv7MIpR0VYJq5vwFmVcZRaTVMdzj2QxT4myAMoicriFH7NN8Nq2Fj1SIqKXELRQqC6E7E+FcHoD3+mkveKSbhvydnMuJI0LlP4oCIyMeblCTzS5bqphqZjlG/Xxe6XAJtvfGScvQ7ijVLyr7jtutzwxPdFYnkucunCUdryLpiUc3OpywEfys0ggd+RCnfO1/zBblTQd3wEPk+76A1bDjAwhcmN8YnUgF9qlokOOKlyPgp8EMFj2IddvObgCPwOHz9JIRLt2h1fqT6nyFgdRr/isGQZmHs/Q5zjzDMV4/2mjr1MRZkMv1X6wQh4JgcqyrJ4A+2oyjU794CpNBWCCivMfnWs+jy3fzftFVIQiDGGQwPA46jHdjIT1UfiJSPKB5cD8xp5rsi33QbLPuAa84OS98vCeMf5Wd+14QS8Xt/ycGaoVYfPwJVoLFGnTlNm2Ypxpb7Hy9iPOtwn/4xIA7buSHGQV/ON1BOcI06+lo2niPr4RxTeKZ+zxLbsXLJ/vmY3tmAP36n3EwVqG69ZmWnYdkvwPa2HNmuccNnuGwESUfUbjXFRnGajumiCSWoBw8VqOnORdL7e3NEbvKVoqmx3KmUNE0XZD4aMl+U+x3YglnIto0q4hH9ODse6b2JDOfJ0zFJATEm4z4j4QkSHhNAeuSssFnu8CMI1K6Sio4kgLPYSyZb2PguibPeeHvWWhtUH3b1kv+GrJm1WjetJL51DNZqFqsTjgpot3jowhv9+302c+iqUpr11yG/kRjL0DJqZFd+z+EUP9hHZ5u/8Pgccgj6JEhVM2HUHvgT0a0ZWt87TRpsny+XuJBZ4urY5mnHKh1K988TWK/Ceq6oTzode3ZarU576YLk5nRYRGE4EFe3vHGnzXSRQVheSpv62z2ujxTQleedAXIRk1A99PeieVrKEMiILArJdGL2d/sRyMmcEJnqRvbS30hpFfZJxAQWkieRVVief7ZqEE4KAaXC/vhwHghKXU1tJ8AhSst58l2WtZufgCEMLtsxKnjYjkOKoC/l0fF5+wjeZLImAbLPNZKEfxdjxsvUO4MgkQwHMz5/kjqfgE1ZBp+y0Ic42ubqeAbvMVMH2yiijLk2ssRUyFaJYsGtRoEEHU1mXRA/sN9BzaDomlfZCb2uOxImzG8FbiJxed/vkmvxTvurGi0FUohoT+w79+4CbJnvxIKgITFuHTwPoVbcq2472D+YCkhxZAKRgXyJ5J3SfIzxs4sibPYWNdL9y8eXJhI7p3M6+O8CGgdhCMnJiq0sLCWIYTs7HMBPVIM3MVaEr2XZ4nVtEvUWan0s3b+A5hO9UWS8cFUA/KMY0reL0wx609WEiv0o6UkV/pLoKxzDc/opcNiKvfJtI60/Ru2Vjm6vtKlDbDgDzU5YJuJQnU/APhT4j9jpgOuvR3vO99Xwbu6OOSu/cSR5EiM0QjTf+9jdiEnjWcgac2PZBfKW0HBkK1RseXIDZBE//BLI1DMFym+btBhH5+Ww67Rk9Nrz/EvgnQIup55luIMeI8mvjGjFy6kPOdNiRSKAGwnhCMSLBSrTzNoFGYKbFeSXTAT/NgZQAAGwhlshmgRFyLOHgau60hMl5P4kqaE7KZM5u2XmypG1JDBVOTWa98IRkrYmOUXuhBySlgLM93H8CAE1trFm+/U50eetmVuSjxWm6drEIzgwJpG5qZhTfbzbIkHmh8A0x1l+sBvzLXUzgKR1/jem2ZQ2I7zgNDZt90/t2GVxFBlKNnDY/CT2gmzuD/JGmFOuFooyhiIG9OifzJ2ascVM5ASW9qrRP8+mt4+R3xNRuBFyTWH1WGG1LMV/MFW1ikd+B1qaJuomIBmWLAmt1Xpj7b/y7LIzUxuVJlEINwKEgE0ldOc8GMBueiQu9G5Qd1MiEDarHTb+0r4+FIIYVENLCDetRboxcbadYS7z1alZ8+QZfwE6E/Q2C95zoab8FzIEUJDbjdWHezosIJhF3L+/sLzY0xeDloTAsGseegeadxLeXjiEy94CWW15HsaHraCtEXLhj66u29NfRghqIoZU4WLuZLAL7ARh4Kvg/gVpm7+0TH4P1X7KE2QIRrkrGDdqRty3AdFyd6Ihn9cPZ4g4KrzGODy/BBPRuYgw9NY2O9kvoBC1VBKnozATvyKmovv2b+GXbiYcaBaM1bNNrIcF1FZ+ntWfe5GHMtxHpHL8WPb9AyxIFJ4DkepXyOCOGHL2tjnUmPgsuAakcJoMUEBRIhS4vnArbT5LFRzAZst/+8JQn4n12hVKV5rG1KSRYwcMrtOK8IE1XsNRwAaG2fvsW2P/kd7ma93f4yja8sM6bY+vlwA7vK7EmiL500ephyUej/qcEN9PCe7b7+acmT3LQnk97k/6az15Y0GHCTys3SjqCo33UfmmLFEYGQtLJORHxERvapPLZHkY4VrOj7mvz3V/Im5GNTMxxunXQxuYQ5/K5scZuWLuzDgdy6IG9i1iyohTLm0YMibd2U7Sv9kOSVhcH+EcCoQI1icjVkBTUxH5K48sFhwc45ovJdGuWxRztnFcQp4TqqpNVTZ8usvHlSNvqHDiSuMi6h4kpzZQPCMGTL9CahSpvOE1/ED1mw/ortujQ8nq5iJtQPTNZ3m48fU7TfHvQ9hJ/IcrdySdQ4U2TT/N92QFIYt9riPwViicZCCcWukM/tKfoCcAYB7N9/7fG4cRBDky6RFWCMJ29MiC+XaEdph31sRqMDeyv9YaIQC9d0QoUg9pkxuq6rpkm6Grbyd2fKzOxdi8K4fDFKOScBXQN22tBuYOo/LyIfCZEK19D/2hJNZD2Kn6dgnyNiUmZ1DAL1YYGNNUMGkOSPShDanMNbd1hg1evV/lIpd44ysfx9XlT48YuBXU0c2Hlap/M4fgHngXcfR1fx2QedaslrpyjZFUY5RIk45f5yPrq0LB8G/W5Ows1RWgJuxeq2FxchqLIFzI9yWJdbE+DbR1ObGcZ7Zkh/a/JkuK9bJmGzyZ/yyIG5ok5WouhW4EnVgcccIYGSoAoyKluH4i/Or0asVb+9rz3OHn5ZT96iCNkRff4OFAXrSv6y0k9kRrNm69PYkbamTSAo9w7J6l9GNHFvSsdCqrUxt81JXi9+UCJ3ZXG+p1lEzk35OhQwM7u2MAyyq4qGzhje3kzsM6bruVRghCdD13T9DpoImnBpXp/e6Xk+PrLXb7dslbcb+DcmjFLdTSs7OVYkinYJAENvgIghpdK/pgM1KF4IJqb+OdYWfM6swQSojz8mLRk8ST9ebM1CXJmqdKtYkZzkBcwXFnt2uEwXZ1BVWgbrGUPgodg6AawdqRitG+ZkiDabW1nRJZBQkAb+dNeFRvf79ojYLfsQZdGoXI8dkaezsDQj8TQr/aKR2dvz1bcXLLUevZVKBQ0N0/C9G+esaFu/qQM3tVmBCPaXacC1UtJlPML1IgCLVhfab+MXIkcjuxik/FMbdJ/K+i2xbz9TF182GKuMdkPsnZJ0w12w0Syfs5eJD8m3iD9buYcDDxZ3ADAH0f++ACr2yn5NuFTtwU0sm3/uQItFwghRytdcnf2eBGNminQmy+FsxHht1nBV1YnvySti1GGmWbKcG18qCF4IfKCnjLLmYU9YTt3Aju5hFIezIpc5QseOHku70CRES75dV9eXFHEqnyPqwBTJ9bJe+7+ooksSduV9m4dnQ6I+gekyqdi2dqxNiX5iVlBze3USt5ZeRM18jOyCEWONFGx1sDv8MkMPv0F8BCVO+q6kepja4A0r8dYyiRGDCzpcp9qzG+KgAE0S+bim6GFHy+bFAu0EQEDjrva3jOGxvdJzNoJxkUHU4mcML2w6Ls/XeT7EHmOQuKtvnMt0PgV9o55/FmdiLlcnrkrLEjh2E9Z+6o1pwUyjMTs35mjsB9eM2D7dF5sghyFqE1wJIcmDwrgGna8UwHkGVxPYmb68Lv7x/tvrd5OIFAmASfsBck37hO5kbInWFOuDwruE6L6l3blykKozE0vkiSsjRpto2yDoOzVunD4/ePC3CRGtHDQJBnR2+W2rb0zHApZS1BF+mv1OLSclR7goHaQ3KbNYL72oh8X7EpWpxetq/ACpe8k7NhiznfzJgTlVyT7zOjixjOBm+XmPAqmnNJw4IrP9YCKBy+Sfkuztk+rOILu64kgrKPtjnbE9ctT9Njr12B2LZKb5gC6NdxpfxMxYLLgOMQWOqOPifAspnCxANtWzHp3T0gvCVX/ZJ8KMnfH170S6d3z3RIsasG9JVxLzRm79QUWtUMXbdd5l0JIJE91/tKHVXJR7vPe98jfctnki+ZjeY329rWvD8CvzvSkfMhXh/1GHQZCY1ZLmrR6h/MLIm6fnjY0Nz34uQTkT/FMY77Ftzf3B9KjeLjU2qNQL//AiUAr4WENZLqXhUtxEt5gMfplEpiVwYyc5esr1N5r68bZiGZWCa0MEVRkFo6a8VTjEbW2lmJPEPyNLZLAsapW1r6vXRCrF1aYKqwSGKpWKdc0wCOX5APUYBmJxNdQztsRJ+5TkcDrxRx9sSy6zLhugd6OgOI3ZhGYb9EYOKrWPkqaBfE9DnxVt5SCQBsCBtTV4fOxnF1GM3KRBKG6iT0HCmhhla9xrqcYion/pt0zHfb+AVE8zHaG5eK51Ch9UEjkYijjGbYFImf3/s42TjOVY/f6d1z2A37LLjHNFub27+SIHnEokacDb/CRC7yKfFgApKsVowJn1RzAVd3QEbg4VzuB8A4OM23TMrcoSafU7Pb9RZ6cwRzNq9bH34J2N7UxzNJgq4yVWlBIutKdIXt5hkJP4zV94uk9kITEeXURE4nt5yesTOeDmBF+lGwtrKMMJs6d4JTodTeIAwSmwrVceq+uWYE0JJLvD7/y5BytF8yEYhHtR6c/ZqGo2g9m/i3RR1AKF8BuasZ4MX4zFaceLserIMPrWC802GVAYnebNJOIntFC/GpUxnSNAMo2Zq5NA4V8E1hLkDAJfj9/uxFN4fhnW/QkXvjOCwBbGqhcekCFNw2TF4SaoI/E27hdfg8ClDEIErUuqOc9TrW0hdEc2nmxj/jI+ZB0yXYyfDCDy4z3G6Sf75QhpNVFYEBlSg2G39LZlJ6OLcbPbO89givKa4DN7yzUYzE25ze8CQAkkjdH0VZxyZUGie5sy+76M4qvJggZ/cBr7QEwQNmNfwGCCQL6G4S3YH1uIV01FB4q79Y/r4iDgmobFC2Gc9q05wzXgOBl43DHweZ53z8RShWtxPh0lNI5684IALRN0/VynW0NaR698b3ECOiszDYeST4TtOAa+NOmFaT2MA4mbG052mOX+D12lpH5ZrhsG9AjwwKJfpCg/XL2IoM1TJ2D9nrPeoHntYxtLxg5/x7AP2JCg1qcwP26LVQlbWqLzje9toCMzTyJpF8bqGfS7skCJotTewAfahGKqJA3sz+BT/nt/lptmGy9ixI6eik4YUSn27wlNqNfIOhmaNWif0iiNYSEqgKE1hdgz9C1KlEIHrl/N4Cm2gVMJIa9/kJaxCKIdWkNRNyWB87FZ+CCzLcA/ytDyo5Z3qS5ebFaYrz990Kp8gQcLyWaQnjTJSm1wjLKgbLPe08F8c1siJpVi1KZ1QKELXmDbmLz8FnlgOtEMD5fiv4eiFnEK9JjxuwZR5yyJhvg7/ADRDbTk2HUcxKZlHFFKPLhCvA47ESydRuYmvVPZ7AjYe4z45kr9sOD7CA3jpsHbvNxpTpzriAFabpUpolpJG3Rq0AXNATneOZVOadhYfns40dXq03RT/k6Ynr+y4/du77adSE4X6+5WMuUEzLP2SnvoJPlXAWOTRWqk1lcgIoFAS7kb7DQG5ab8r/5OAqVcsrPhd5KjyYHnRecM2IVZbmUQxY4kYE+GDDnAOHDEA2c9CAsKUl5omKryeah6KqbrT8JnWsKUKjL0IFvKovcjOtiIhbA/V76Q+azknnSRBBYagHKeTXS4ntwJkBGb10vQwfeV1QclSTaj99TPi962hBeErXsTqWBt0qbWHHkSRRZiYa+QD35lAWXm8iXxD1lCE7iXIvklX2ZIzt70qbUPRFfIicYSYflCxIUyFa+dBSV61w3McXoiuMo+NgnpWlLOj1f1+dy/W+BxEqlZBzWISpgUw1ndPXnwm5LJOm+5p87kx/kxIZ4jdvTD82r0TyBDc4E/3rdfz6uzXgchM6jFcE49tMyAXXPJY9fb99rXyhAHVfye4/LR4rDftie+3Hg6BxBUKfHuWhFf8i0PzOnxRfpA5axh65KReJqkPQpY9f3LR8pMP9fchKuYib4Zjv4oF3NfqgORZzHj5wL1CsIvI3jxHeh+IOvl3BKExiNzviB2cqDw+j0z4ic4dy+qM23TpdMv6OePAHFJxuo0ZKvIbNoTX/0raT3yzE+z/BVmQt9A+AGavxlrwvJdYxAr6r/jsYsMXk9pwWXvoqQsznSn3kFTJ3wJVkdERyz9K8H9cWlgPNSAuub01RCt5qxiwhlMabWhuUCIFQhI2oarkfjKrHvP6RY01Qs3nLTx/uQpQSS/phd6DNhjA8KU+saImf+bYQkTgDuBtDYCUI80BcQ45MOhmkd7Wbhxy9u5cKHfR5Cf2p3CASZxEZgMZje7lL4VFVpSP0ZHMt259WkKjEvtrAZ5EjOLgpUDCzftoxU+3AnkshrgaK26DttLEWVxbIYwbcN8b7/49Xo03H2rdNuVii+lfQXGxdmQHZwSLwDrixe4UWlvYLKzQ8uPGG+TO/M9aRHWG/5OlHNcMCPzg+Z72R0o9IJZp5JkslUVfpRBDd0MkmyLMhz0CDV81WZov2gtmXgmOUpdeCKB9hA/uLQsITueI0AXpBUHy2fNw44X/Xmd8PS7vys6MiEYFgw9U0oWrVxfZH31R9eVuxkN4VzyNfNPB0BX43CUnlIfpKqa5vjjvl4RmxorYbkltF98442+GcDLfUymiL45u+bEe82P77V0sobaB0Tp27x36+8bijdwUTRC4kKzHNxh8IZgoGM238M26lzhdI/ER9zRlaHoo98qHb0Pk0PfoPH3GfydDwzFocvF9r3hPEDOgZTTUMW0Z88WXhJLnQwkOpdhgFEgRhPO8TsEA6Yen2+e0/ZKDiGa2XPCp0FIGf+vtCoxeunJo/uUGjGAQAaQrOWTcLmVxWPFWAsWEMa8heBD4GsL5v4FbuxhSpK10+VFAqA4eTtUrBAV02fWb4k6M1fyPZ4ZgnrmFnj5RwI1+DP7xN50PiCX3Zh/TqC1kc5u4k1r9qyrnzTcyv63rYi0N9W+L3inxLyT6vGEMPd9g3EhYl9bcVuklEEIfRn/kSzMX38qXcDQssAMH7OnDMdyJa/p5KLjyBtQsf5R+HQ7uN5NRN5wXyVQ5luC9tFUiouV/dtbqD4MHJKT3Onr0Bwzq7wjEafe/Tj+Z2rXDtvPy+9RNXMoU2np8mBCM4TXUGUuwljh/gg9XTsvQbs3JxPZ0vtlV87KFk6MzNjBM1GCyJIF8ZEpznVHIAvgZ7IcDw/0t5UPXIUIZ85yx3i6xnrrNIIhG8RJ1GxRPMk6gQ4Ag75HE6k0RffTGFElwC+xAU5f8sL2EE14yBMkH+gBqhC1L9iTuo4Okk0aZc0gpdb/ACVms75VxFNSrAr5w/vCS8XIFM6LucaRJA445VKHFEIxdX/wHAdYbKLMFCEfhqIdRbLJghoLcCJ8bUh/WdkUQZGRnl8egJN0N5saUlxxlYmmbI1mMfcDk/tSSmFW7gfxbtcHeLpFOzj7OledPuq1FHZIhQl6xa+KOmZPWh4aa7vy1n89NtvqVEVU2nVCeOMlTwQ0DWfhynj6YBU1rz/bTvbu18jDSqGj0ljxVGS9jLfzSTzS2O3Ed/LfzUfO1l/F5px3+KHJZL9VEzPcz5iYdY8rVIB0h3ROWKan0JyCUy4jrqcW/OMFjayZm+cw7xu/KLTDel96k1vZ7PAZomNnZloyylMGIaGqsoQKTOaP9BzcCZAaXt16ZXVF4UVQzXHFNw240uuO/XTcUtsENab+bqUX/FIgAwflbeRJSzjrzrcKDjfHDzPRZV/KwRcI9OUuXXYqY75qXf6BOZ0w4yDZI7efeAqmAzOkM61RlupaP0mTu8Nkwy3hlz6OyOFC//ZBjguQSB20c0jX0hYrA5/H2MQ6jW+wKWAXg1TAf3DhcliG6FYQcRY15Y333kpMBcFu2BuIhMvkm348AXEm0wOTsiKzt2WgK5zsV2QLDB9BwDq8zn/sgjmVA8xQtazh5kFhL2DfhIVzAMEId4YqAcf1kURjGTvnuLPb+6Eq0cYQTEm8+ib1Fz/GfueDPdaHE0/ApSZOmPL7LUDHkumPJ+oEA5SV01945vhUI7emcvxlZjwhSZc9Rz2wO11FZ25TW76Kq1yoUeWLhRPOnO74ubnRtkbw43cPgL9ig0ydNtYUB8WmSkC2nkMrZFmyUIm2b8pwf9orNK6XnzHzaq/6DnRtKQoZaozgCt930Uz8hSzHiXw1liPnite3l8hPJkxZ9XdJtYTU+GSSGrW7+dau2YEzyWxj6+c8DOs6nBj4l6c5OiP0BuZXuwPArRTZHoP5Oie2DXXQZKknkqtNzqoIEWw7TB1J+rnjv2q6Uj+ZwgAnkN/f92Jovj3k78f/M0JEnozZAPyw1ya5M8k8sWx75sHAtz90ML367At72SxTB0lgl7Jzefny8pxnC1eR9aLEk9x6RB8wIJX8uKY7Ck3Et2IP1IEYglzsDi/eAP4K9cyE0woX4kWRLjCR6xsr8KNsFVf4yOQkA0ZEo0JOSA/0BIG2HJtUi0CFf2LKnBHyverdN5ReYP8HClCZdYFlbKh4d5/H3E1mC1XD1TGClxW7imBtpD6yyetYFjwwzE0oWscajjdYAi6EKLY5vZnpgBRozJRFwWwuoP+mn8LxFjWSklB+4WLH6e0UkJl0bW0ccsDsnZJNNanbNjSWhPOqHne6f+H0+8u074FZyLmjuTb+Vp41ewDrCkIAFVnYBKAgcgeSG4a2AKV2W1nNCLmB3kTeiGk+Ot4QuWJXvAaz1C1+WlcdhAFbRkgJIBoRuiSmDCuRrg01P1e/idAG0RKQwo2f9RIv5pLIDx3wVkB24zNgwxu22d8McqY3hNypQEyqy1qgRJ+FasvVOij23EqJTQw4aUsjhhpGz8yLAzOeYyhICucQIiXLgaj/rEHiA39HdUTWGFxe4HFS2kL9qW8Ty5rG9z9fZnUlPAvbgAMp2+TOuIpv6NNWz79Bye4xx3bKUrCHK73avTOIpEmpl7Qzt3RXwWVwyO8U5YNAxsFj1sY0njRqk8XjOL/ux/9O6nlS085is1+gZ7stB3feU4ko4lNCIF3sxjH+/On2gdXFoS6bHWFzBMsC7dE8lYDnVdyWJ56qeVFKLuXD/3nR6bpmejtlqG7qIkYKK9MzWzV5tkfP6IO5HVhZ74r2MxTGrpEA0UiqnXku9rbhH1zP56wJE9JEH/PJLvtDcsBDgkoCrX9UqfggQLLTLlNhfN0jUzJXu0Vj07JEYdBkGN5IJ1ybU2TZQ7zWaQPQHdA39VBGpge7dW+0q/TklF9AWAlzlfBtv5aPCOq0eh13PCj+IHXBs+JwJyyaT+9IK3EcmAuCe8MW73HRm3PhCASE9DnbiwSepVOKryzdkIFL+lVoHaIDjE3yTLnEgmX9wkW5JKIyIHGfiCWKMyB9g1/6fDQk8yXQzeZj6zBqjsYIm6GO4dyNh55m6jqGF5LQGDMomvI/vS7f8nckifEhew3guBsNKZhY3Qhkh7AWT4IxRzxdrJ199yny32qiWx+4N9rjo0lRht9pjPmAvsHvCvd+Z6CbOFpP7n+0BBYUQW5anvv8Zd8i8fV5cultT57pCpHK0pIfml+BYEmAl8WjZHCXBettBuIV/P6jO461dxxyjbcwbBhQf5x2/itdlD1J/j1Uf+QKNtPALMQ+0GA+/cTGWHJQbqpk/PNswAiuuk8mG/AaMQeu1vnVgVdbsoAUnIkKFQ/K5YRoYuhLDsJqW5fqJiF4tigNeSSZeGRKE9DTPir97ZsHijRwuSHUgJQtOGFdjGaatGCNJStHjhIhNtWoWGTiswDC73opRUXEqte2MsxeAux3z1SZ0tP8XNiJMBqB2psHHQGCihXml41TMwa3c0fMtJNtZqbrI+8iyLiwbKy0F7GQ3xxnxMm56nvzogqkuSmuhAmTAVNuglI424ALzWSHkApi8QawPotnJzXbXAn/3ppLjoufJ41ngrg69lTWBmLOuGk1CcLgN3W6g/DtBt4c1BF8s0XvZESWhURdeegCJGSyUOcDZx9e1y4JMO8P3WZK8ZtW44Py0tEVu2iMc8b+cII5x0bvsnNqROdFtV5p+fXVwJ70JLlx2+2OjNnrwogwt7JCfSKv+1CHohf/wxQO9Bg+pn3V4DkgDEF1MIYPetN4y5rGVf5sdppfjpvd+lbKlaKRvZmAmO7NOzBkZMfu0QsxnjQXR40WkFNhb+jS4vOKrfFVYNZWJohROJtaeCsqWWAdK6Y+aDIJe8N+TOVS8VQmAb4iaQO3CDMNAgA0l1MeY68JH0u1ZkwqAoXPU4ZfHMBtq2YMDA9NKCf64tHVDds81ajctPh61ihWCM8UcETeCukBB4UmCdbpu0Y/k1epwPiHwPknYY4vDBLnQx/DeIm8z4qkNw04b//D7LmR1EfxtMAvc6YMqmEr/DmmwjdDGgZIrOGIfrGNCX47RLDa+QbaoOEmahbRUTB3ukf5eUZdeuvGpbRjdK0OiPtSS7a6n2mVkqj5s/80bHrqe/iKTPt0gPwUAmEEuYLslUXE1GT+t+AHuKFM4lHcaZUNjKD8cOQNFEsmX5lAiTAmhn0NzW+UyVObc8V7wnYkZKn7ye+2liXvt6ZRC9WWfdRDcexgYA+vMy5iLwRfq15Q1UzbwZ7Y75VbPAfCaSl1BcXXwu+2eeAwndltGJpDratsoSxLZfzdJ6VU7/mxot6WSCmP1eacMkpFlcmDQq49NFbsFaY2nOmHWYYa+8zS3yU7kVIissXueQBv8IPLitMHBiqVWTZ/LNOMNyDccmimzNUd1HxR/s7FPY+WAn0/ZE7wPMvJpjPL8FrgrcvAPvr4IiABZluBSATRFQnahyQ6XDUrz3MbvkrWpaosCeXvXfoRwfIPrswfq9RtSXBZ/2MFbMbbefntSqOJOqDwYeMOWMWwOyCKbb1L0tkYXIM0aCkH2GnccotgxvtPXXVHcHoh6J8LMhJ3m8z63blQkx1poqbYeyuTRqEbajv1nqZh+gshNLnoL7ggHIVMrJnnBGQGimpXlIWhWqWuKN2jr5xYyxbRP3ceNBrDnDw/j8gKqGB4nFjGMc/otwSZ7utfnGRopD9J86So0HKP1rNNRzN1RYlLOhQba3eJ48pwPlWsm+n9BZV+R8gk2okDJLZXtHyNHFwzqK+pGxEfjGv0z0jFxgO/8gtCQGcKka6I8Q7JUmsSwvLzQ7twJ5tAAm/W75Bpi2J++7pT6nvSewDOtLCDXOC9vN+LRP5ASE8axcYsIIG9AiRLoqjenjP+AhygGJCWobcODFV8YvwND4Zi9gL2twxxnkx8WZwceuwmt9zX++7z4kw2USkmsSiflrhsqFi+64GuNwg5AuQUCv8Ld9GDmIygL+yiqdM+PyHgC+XWk9ODhjS9kk4WkrNh8SpVnJnW0idg2IYICgieArJMu4qca1XISFROTbfG1MS6uI1OfGdcXgghRrhDQggB/bezAWxWnbZo63AWK7QoSFBLJU5rbkSgUAHTgVzNkkaZPUMjpQaLp+Kk1HSngWXfJmbxeocFHcHgtFvRaP8x333G6+jEf1bxRs6h6pCmUsKGk4y7ylAco62X5uUQ4OXu/aVC6/9eaQssogM96lRhUHMIYs33Yu6M+10aMItI6ZXfX9U0BWe0oJfyGbwz+cmoRbJU2bP+aO1pmcKszaFg+J0EFR3cBWSoUyvtUgnwTUwc7UQnkl3aHPTPOCuDRMQvHMSIAvBS+v9PvpR2XFjS3WE+yPiXArSFacTErOBYNTsKelo+R6a6Hk4ySEJZ2YeUPo8lUN4xEtpWlFTcALlwT2H7yHZwmxH89i7ZZEouPPYIxolCYJT4iNcXa1hQ0zyxkYaNy1OAnFyrinudeqkaNDnU+lzEM9hpCaQa3eau2e2dcbfvhq9dlBp8wy8A8xODKX6U0RC8SnqfqZzwFhBrhPOgQepDcTQvkef/se6pt7+IL8DUpXesCeYQC7dF/y1hJhnE/Jz9envz1noHYGfn1KKU8XHLHdqGkZ9KgTpUQFVKIJ4f3DCjLpCI16eQktaFCBYBhs4hR4Hqe+Rdf7yEGeg+1M7kkAKKzgO7gFwoqsNCpFYnTQp3OB5GUgTgS6O9sVn1khSnjLofL46hdHP8qYE4b79wK3k9fX9lESwfkIiLrJn4OV0FcPIcGc+Pj2X3Ke5zcImaD9Q6kZ2el37pHPRVE1tc4G4L3UbyvfPuUhzMTEcPBnlsuLXSdtPp+Pfda0OMkmiU2L88Ds8B4Mg4mLgxIdCduH3835ceJB28AOHkrueyRUG7WmEelzGC8qamGhiXHJ+XnfMrKBm+2Rmh2wMhdOl8uMCwvbN8iIW0esZmzw2Wr9MuVaFYxLbjPvmUrxtJ3bGh3wR31QetRKrhQ/3JccArzW3IUFTL1LwL7F1js9rhsQwN/2AusOEPxo2IXAe6bKeQBAztodMPTsEQ//ASbtsa2Yq/Aqjf+cUL01OaS9keDtBmUnm+yUgeIcSMZG8w7SrdqXpcp+SH+H4wDDJzxsMBNzU7sw5xVDWA1s9wQGGcKWMkvWBg3smDKEpHq1YZ7BcZDARDIWkkh4C9D+rbl6/3j+rE6IAziVOiBLUuhfw8uqbFh6fpt+pnqE8TjqP/UJqo+vXmJGAVYevJUF9y5zc71nF6tw54ealXUQs2FO9DdDGslq432187NsLIG7RMdBBTpht02h6RqRvyPF9FZ5f1i4lc/Oa0t29bmJB6AeeS9LQQ8NzhPT5EpuBcgWJN248qC8gyM1FpeGS5ExPADvzM3FqPdhIe4ZhC7p3L1pgNJHhtDD8lxGbIfZRJQpZGPh1nlKktSliGh8WOCVQSYF7p583cOby6ElazASXjdvyrIp1BfpPaBkAJ+Kny/KxKW8TvsF6cmeIgyxzBTMc7zrLTCdiOpQ4YgkTu+Iay8J+paGf8Y3XW3HAialBA3A+0GA0s2wg/nh+9P7J4pc3ik+dZ4WTv9Fir0Liu3mZT2qrd8gIUMXt1/VsveiwT08b216k39w05p4FHLPgsUZldOSdBrENaO23ZUlZ8VMsUZJ/F1uUci/PIPihoBc400G0SELFB+Ig22uPHp5h5IY6+nVMcPfc+moH5MBRQcmGa89s+il9cUtnrgGI8FGKDsVlc5Vp3QW9qelcMGLZWONqq/tsf8EBAffN9O12jNhuwA6qDTV5U3sdq6lcUmSCfiFNglUJBHfsTcdPPd0mkNi8JhgwXdPYiFyOH4sL5xv2t7lPGRen6F2mrgRiLlwiXtoTYYYzmTDs816XpuxfpviKJ7woTqj5WNgx8wX6qstNdUx2vPy6H4vOeE92vX3LkamtBrwM8VsKBp1+Irbjp1AjBgmORVJV9PJ+vTQgQXiXWZfibBJWLRlIYAONAwsjOm6UJY4QKIMsDxm/ZWMKDf/6WaSkMxA+b1x++ErVV+G+19MWOFppcRGG2X2NKcF0logeMPM0Hr6EY7za/WZAQI8v25DecpUhK40QiHQRUoB6MyrmsgsdTzxmlLaHKKr9kN7qv1BSi35lZb7iTBp3uvaApGIwEsTXSUWuLnjJwz5/OuqaVYxD9BjHyzOIS/SV7MrucLqXeJWr1wmfQiclqsAt9s4Y6uqj+9RuBXQqizdj428AaSoY54Nop9fCm94pbXIxF6ktN8sPrrag2FS2Dc2BoLs7ZRaicJKvaMVhDWArBgxeYUYjeR/N26XrRntJyi4S3Bt9lOIcIQwDY/tncgQPfpQs5y4WJi30GziuFk5sRglqmYNRpleUzN93JswPtTt85q1TTZXciD3h5TQQhM5Ijmc4/Nwum1Kq0gvC2f7YYwg6gTP9cTsmTEOdTgPI/2x4pa9QNShCKWXB2G/pXO20g1FGD0kGun5U1u5UZFWu4rPSS7mvNZYk+JlURPJFTTTIgg/rxo4OaT3UrKPQeIJ2UfA313MOIXBQNRH8mNagv1UnM8bXfzkzKe1SY/42ASjdHnxAEgjbjdFqg/jS2tmW04bQWHorBwTPDT8hfjL8esFc0KAVhDoxv7xET2e1Nf18v7fJCpqhi+gB8uQyLIHhVD9LSQNMuIhEFbNZkrgDFmg/R3U8JRUCBr9ZOdmXqGlYflFuWrwEweRpQpnaZGWqZSb10BKXrxvgqDdP6XaRyxIb92PPL6jIQB1HJeOtyoNSqHS8wuxzsgJY+Wqgp9/36QjNMZ85C6kHYu/6Bg5NGC6VAL66V8FarxgpvEzIyb7nqsiUbzoICIjm4dShRcY3JH1b/0UYBNeScKQcgSNxdid1GahVnSK6y43NuQ73d8GLPsU2v9E6LOCGpVNZPsEBgyAMa+ufxhTV6cJeZd1ZPrU02xyoGNmWhPLJB3+Gj3uRAUosGPabAsoY1mxXiN/rHf5emKlr1l/CGHtThRnNr4V9frkSfwhkSHC99on0PkJ718uqNKolG8YrqQYBY3IGKOjt+hlrGd1ACPEJHEql35a/lZ7O8aU2T983vN+h0viL+qYhMNPx3Wo5qGFQOPGm/aDhTv3i4f29NFJb1pzdwwhyPXVxcb7uTUUQosVtmzV9B5z+4ZldZSpVXQnMfa2re0qUnplNB5fu1N6mcvZ+NWdBHAbEEVx6J/Oc2XhG6OhAHqy3Zpjq0099RyE0OXmzH/XR2+jx27HWRpkXylCyLpHc2OivtBjcjtYcLY6uOaDU0t2UuiWk6iBIJvrkSYMyKWDkmH6+GroMTJCD7ZQ0tpFo+Pe8gDzj23S2p1OVDyd+DLEs0kkAlM/Y12KnEcBhBpLc9FiwQgoKgzcKTXmxqDmWkE8XOPPFDP26D9SjTTG66REYUfWpym75POOhIG4wHrw0+LmJ2r0MAPvRUl5mpHsBs/tt6Gp4HRLsHXDLdcdwyozYIqmSSEvf5qRWChbayVlzYlMD6oXleU9jpuI0yDl9zKtWHPhUR5p5RCZ1AsqVho44gOU7nMhMuF1VSlvRIl5il2O4OG/Bd47wogziS87uF8ShTNpVIfjDU7UX/7CPLI4WJUeTzsH1dSxoghDUmTUfFeMeq2SUgCOFNPSbjd7fDSt1JAY8jclyJP1kXhNumYKgZ6kxNIaWbF/Ovr6Ubz2kd4PNcON3pG/guaVL7W/zhaL5yOZnWq8uRG4aBEKyKSxTfs1nLg3Z9jqxXE02MU1dpH+/P66xGzcSyrHcCbvn7juOrZftS+tMKjiPnYTmI5SPW/Y8Jkf0Sm2AW2392XbGfdTUnGcnXTXmCyfnnVyyvXcPctdfdorAGkdsQEYSjIFbQ7A2dF8LIG0aZrh38pfWAs3PJRg+klUguVo+7Vi/2cV2bZ7+XC2hF+yDP1pIEi0HlFY3j79xzeZHRieKTGeQ/G5A+Ap3+BkzaM1iKYG/QUbqL16XHPPb0tSxFcILHvJz5+L7e8gvxhCgBjBZeI7Fka+roSYXWAdCbG7yccODaCrb9zTYbmtQSRWLFxtSrNCRnIxH0E/H62+3iu1XqF+mSfG4DbzEalnD4pD0G2I0vNzWlmp7cG0XPCLLRmBip9kSBU3tXihrP62WcCiLNOAkNzdwVWTDMxjV4Qw4XKKK5U21Il4/KA8aaTwMDjPrr3W/U6i3aDcDZXAYRdVzx++/S4kBC9J9HQigpY9s3F8Qp0nxIV9eJTLYToY8GvKnkYBf5xubIaBPjKkmp+uXwTahohq06SX0NaXFGtC0bVLD1b9/OkHR0RAy/KjRvTZlafxpCYd+HPeFMSD4Xiczj8tU3lyqtTOnr7JtCtk9+YWv3e86dFyorx7C0hanQS0fR0mMjeIJmySjSoKV0V+IHvjaNM7nLtxMUwuHhGbCKIkUGcz6sSMFXEFgzMTOzNesfycZJxVJ2ZZy6GEPSnzx864Qal8jSEMokaeUnx5ylhEw11E4Y+f8NnGm3Iw9Pz5yHVaGQ1Jo4qdz4hXTlvTIa9QLleLfilpdloTdo0ghfg3tPHNQY4FQn4uhVy75cPnqe+mWvV7aLmXLvG2Ba+HVMJhl82bDijLgfSvFp0uEkJgg5Z24VQfNWzy35jD5sYQUykZXvmVoGZOdfLRmLo0qMWC1LGKRD6T4MpE/MzVyXP5K8w890husR59pRJ9e27M/SF6R5RnHadH9un04yle8B1Zx6VGS30EvlN6U/aZ3CKLoQ+nYhPVh47lk3WjXvZ/D3vG5rh0q5HNnNRSti2tNGUvm6N22ZzKnqY955yy4pKBNSVvQ2/8U9Y9Iuu+EmcUbR/BoIcEcKyZaa4OotChVpCtWh7X7vC5y6s596rd+WPLgDK0jPI3wsF6/E7k5F1hb8B5jBgSmvbphDJ2ab/3Im9S4Zlv64sLV2PgXx4pX0fWmPAFKF88RJHxUa7thYFllfmL618WrLrMAjg43pakilhXexMDw5m9j0rfX1VQtjDN8yXZ8quk7UVvqh/bXMTQd6F8kND+7L3CeF6fSBz0fBN3/PrrSkqNh4GU93ekDCTLCji1ricvZGjbMn8kqonpz/kORi+UG++oBV7ixFaNfazJEHYetCejlZ4JitYO2O3Bg0zBcNonhOxjG5UV9ilakDfG1YqlydSma4mW79y/lmzD2WnxfbMSErdXwyGYSv03dBeOYhwxOXpcYvCv/4huySKRgc1wLJb1aODQS/qCCPVi1mpmeKDZ9jH6aN5FSl8Yqc2jSOIB0d/bO5eab0N3IymwSU6t9FTc3pfZib0vreqMnxtoF5dKndd6AAb/pgSX/x9JVJEiqbcHV/DkuQ1wSd5jh7s7qP1S/YVd1keS9RyKOKv1sb7t7B0Dcowf0qASQ4Z/NQRNSxzoDZKD2IoIvDD0L8fmKUAr+9Jozup9FolJ/NnXztEjf1KRMk7LYO8NpliwL++Cjjcxvxh2xm6HaqGK1S4QNnlxFoq74jikPpva1TjB1Z4pr5mh1UdhraB6LzULRZGN0YN4jq139ir6AnWo+OWW8jMjpItZF0jNO7T8WeDXxN9ycDoArJMki3/5rV/msWHrOaoFOuGsWQxZ2Wxbhxg37S4MhZf4FzNT34L3C1BmyTXN9ZhXUmUuo0+lkON0hZswFkDFqdrL1oe5US339vdPKrHb5jur0+LDTpXN3w+9FI2PbyXjAIL9STm5pLe63lVK6Q2DTQN7293F3Yg8/5sLQwNc+PJUpcr8QzXRNaIi23F8t3JGr1kNugoKeOAN9AXe+inNlUfGHXs61KSRsaVREXIgbxbAzhg0Etj4rwGtw0G/Eb7Yf7DFmGTnYsCD9aipemDSukUb0JbKPqCZIIHH4udtcvmPDQJNI+62bccWKchL5S7YDS00HWrAlzW3WTGBo5TyrniGhN6oObbOaxErmyN9ig5evd6orjwBN9iYTf3H6/NQDNAAaVOjtHtv0yC73bYGNF8tywRhe5RegSi9Ndu0ln25Z/wCfB/8cGj0d9soPVcPT+db88t5fA3KBg4pIZnJpbP2yrcycE/hjBRzYeKsQmtFBS0IAhXqKl1sdsWSvI8u13W38k1xAA6y/GacmoMHKIl3a7yItANrdn+n21wb4XjOxfVuWULrli3fgaoMzwV+2k/QLYeREHb/VVYZGlAhsktuiZNaN4DLcNNlnhIuZDBf2GWefJsVaPy6gx7RR65gxsJPzR3cOyqgPPErFtVdwJJiDwkkYlyKnR3IycwWnDmFLWL6+j1SieSodEVSdzULytEbSm5mwxVj2qJda7eAbOPLUXoG1JIUlWv4jdgkUW0CUrxJGmGGqOrsn/AiEpc7EkaSB+tKz9JfCyLzm9e2NWntLZ5VUp1zWU5pl9DcKwHLA5CWxr+OSkNQIIhPJGYr0tV3UlLnXuMPLYq+nNHpOcRRbvnDiPtVObnQAoNET2AjgobjRcfZ1cbQD/Oq3bGXopx/S4JR0BMT2KBIxUGH6b0SuqVQzCx3c2aBamB2APb4+AFqogI3PpTio+iI/yIMiYnxIqXj8IkDH5uShpSTL9TMi1jVn8mZwwG1bmPav9YxhoLyNNsF6LUVNrh9+2oby7y4hL4xdl1h/kf28qFrCkR1i0QNQI8BakwH1l5fgp8ASEq0jJ/R5hQCRgLByPiDs3H0DzB6dSVdUrWwENs54RQUhVgBaW4Q8nA4qnvqiz6czYjjesgdEtldALfeqhbDYji6BDZGxyZ5FckTe5pyWhUuVugPUgID50ZOJRcrNlv/aQDSqXPtXE5vXJgV03YAkN52dHmZOA7q/HxoiETc3y5M0ofK3goPbW7Dv6qz7DUS+oQISg9j1eWLZTswmV9ScXJqoiZzsekgfG81NaOTxJG2RQBAhkRXm84TwjYHGtHEwzd7ZzizEQ0lq+2jOh97wWfjX+8T/TPwovdlL8yPna5JD1mSFWdXJh88Gy5WMooVattH5F9ebhZrFDDKsJICG5JeSfCHD9m+JRc8xjNWTPxJSG+6vwjiedxsidbdnlPdo8EcVRTzThNJS6y+L8cu7UFFEEODME+vB4VdC7U6zbbY054MtkztFS/uc46FrHJJT8qKAVe9r1XNCcoTaovUhg0h6jt18rCyftRh9GO23i22h26ygOn0E4OjfVs8PlrwgU6rTwiudcrETA1VZsaytnftVAdCjDZwdHZem3DDH80eCQg4qgRzQXbhy535jS9tqgSUyf+PI/zXIRjpbPdDgcia8BjEfP3fEDVx9eyPAwH/+21VRQ0/cFMwLOzj+SoB/pQhdL6FEpW8HpMOTUtGgMWFowN86KmggGCIO/kq35wpEOhs9h9gPiL/mR2eq9ORZrr4eXj5yQZkSCOrz2sYetdkBPtYfxQbLdlRl0Mibb/9SOABMBQwgCfzbiOkHyHsnGQMeRsIeoJNSILwBOQcTd6iCymisWrRSr8hH4KbFhvh0H2R7uMcrD7RhFWix2sYK1ejC8Nz8eCYv50uowJ0lEXvRy8s/T51o2Qo4e4CMbn6GmIJ82vyB0Aqn9EhPU4R0BsL9SwVWL1o2SmJzjRx3iJb9c5kgczTxLei8rnzlBSM6Y49wMk7nkNWkPiayTwm6uFPAmpNGoqTcjwAkAlf7V96hO+ZGCuapafQOYVHyt8hM/Unco6V/CUMhUPLsHh79pEVpm+r77HOrHqgozMfY8JPLmvHCdxNJvOHmkTlOK9t01iksgb+MBtLGOz8jbXhZmAEEzs45fb9Iedy3bEeG8fNA6nDGCG+4/Cql8t+mnYuJqx+7uAQskHJmFIiihOTSnSuwuagC4hpVDNXwPDTF2uEdMy8GBbTFp+KIMgSvLbpNR9arcS/0yxWPqqIT9JxYl+mWdXMzbAV9u7boCmA+76hukaqagccgYyjfAqwhu3CrOo2/nJbjpPcL359E+ceIV5WZzwYMBbQYnQ3zYikEQxyTyu8mPR5iDr82Qp4lyeHZJ/+5b5o2w8wI+hGLUTVhKBTvneenaaYtIVgZ7D8kEp9cAztDScYcCj29ltN8/uoTtudMXzzdu/fHTbva3K1sqyUDbQLHYRQbU8ZFa1/4J3y1iPZVmiKRdiwrjRPMTzstyrUeV3SiN448kglGp+rjuZHwehMRG8+dioRbHDXDGlFffJq8UVrjIH2XpjMfhtzcMS75Dzykbb1VIYTznlZ4RQBQZKp7TANhO+mkt5lUI6QM3UZ8g3r+thT0vAhfrwKo7raR6bCHVe81bLwCt03O0mvRDb6nheySowiTS006yU9SnN6aU2lErWb4BDM6XWSC/PRjMKlQC3zSiPI5H4wC17wHWbvUKuJnCMQXmUJYddq5kjrNM5f9d17ti8q7YM7rO7gfsN03gnoZaYr89coaI1ChBPRMQS1Lccr/q+SmwZ/97TO7UmrEkCgoI+BQVR9sYN7zQZaMZ3csopPayBp8rBL8FBUWXUcVZ466oOXLlXTRJje2+rSRpOdpdC1Rj3Ea6xxRUuO50RzmVfjqh0jAFHN3/iLCIYzTSrNfoH8kTuXIpDq2occPjWL8BV6x7oxXD1aIBseRiiAf6zTj8CZAyQMVMvztbK8yOL0pKcGIO9EJ+P73fAX+57QOPF51dVQ0+Ne2f7NxC8JWFBUOwl6MHoNd4k6zpBD+jG4iAoNfaHj8WJti/u0bAW8OTyPXsxCaslDvS+rzx3ZSop7pNLOqD4eRONQxjpcERtPU8vV9vKW8j98eLY+sHDqkE1rKEta/DRd0w3NNqP2zfsOs+6pmYi4YlepTcXrc5uIGRSmzpOCBSOWzf1WXHh6Zu7ypYyHETR9dmnmsNQlaLw2JvfeTqtvZlZV4gqhQ+AssrIvJFe0y2uxvHSg/zyPq5EW2ZsnrWw0XvTv19U7BSGL+T44I6MuRYcl4jCF8jDTuqhG1qJHb/BOMjVu+Sdp0R+IrsdK7eKKnGsXDSXAuhIhQryykWGYKoJrWCztXYyRgezG+UZB8vmtCC0AZjw+QHWCLK5n5igrPLdbM/Bcgm0NRdqizrIlha0ZL/R2TxmPcLjs9+eKAAgcNTvbSUA/H+74GlWi74ByjmDgk7Ye0YG0CjSKDj9xrAe/1N50TSPqLcWVmoq1t49Y/mxvxO+b3yTk7f8QxHW4JG40fGSJaOVbrI41bpU6CYDeo5pkuIvISTUp9cptepgy3fVnEV8y6wWHyVV982Fb/UhFxjWldcLk2Ic/arg/NaQ9GNeGCLQs0h+PnujEDu+RYIQYIfyL66aaSrD8zbgvWmOzeznqRUwr8MPj28VJjHU1W6sxRbX64ZAyq5/4ZEMBTGJPOLhgUEY3UjendKONrWRk6iri+l4Vefgn/t2F9K4gwYZsh2Kvl01H9q5LlRxN5oh42sbb9C5PksuHE7LbjsiG5eRPAfCI+wYtthgbWzG1fVmeFUZEuP9sPXBL2PeqoyPKD6rhX1v/5b89KBDacHkxtPhAIrCzdtxSxYo0lEMZ5DM7JpWNaGbW679/+CF5z4APaa98Rr217DdJFIg8tbDE6n7yRQ7limd9AP55uzwRHa09eb6Vfg2J5EMBArOVAMwX926BzDdXTVA9SV13Iw7jvovtDDikMXmCfftclC3USeOo3+IKunR9alRrs0Vb+yTVsxdEAJVIg6KxQfBeK8FQ2aPPFKFhfLqGvM1C6nFE2k2X6xWfbSvMA19tFsAZj1rRiitr4B53827kCpDEhRehDG7StShSGzBGa74hG8FP1PI8XDS6c2i6AARI51g4EqUPXcBS/sppwRO9QXhLU/b4XrccYrto0dptM7wTXvAoJm5SgWU5dGmlN2IwAgO6/8kn8U+798/fgNl3jjs7henVqsEBcPRCI93YnjUveHx2wvwyKyMQx0UahWMyq84Np8iFyCqSKQH6N8Odn0rRw0d+Qe608C3nlf4LznUyi+sjLm8WlciqPwwzjjjQQIRCXHJxvxwId3U/pfeWmA7Q7BesoCiXVGOv3bDbaMGtzAQvh7vWpzrl6jCEtTvPFre8OiHcq+GoeagGa6dTQeA8/fyd3k7/9jA0s+/WDgq680DNeQELWITOvZdPapkLton81uWfjPTSi+lkTlhdBBIK6yfo6HgJ1bKqbKNyHzYRrdMyS548TvNy5rHL42ll6ZNq/IYalyX9EzLYULVCjHNY98dn/RjN/9smWfSlJxszuSalOkApwQh3QDNuxuBGI/2raSByNKK5OG4aYqMo7xDVqOX94uWSEWHUWkn9t7O9fC8dItjOexlPEN496K/rhNjw/csjg42u42Si7xxGarghLnJQaGzCAVVpv3CJo85dqQA4Zapkg/+0KT8NRlWeZNstUAb7gMuRshFllYrebUPg8rUNT+SmWC9EdUi92ITFzakM81bQTQ7zbotyAeMSpeVpIfafUdCgixdXeWV/+0vkyEbSTNXx3QW+p5+Ab2mGveMEjL6KzBqTy9yiSHwe5kazDarNiIY/3YWvC9dmZ+SYZh+zWcg3F3eaEermTSGN1MN7V7ClUzTlSlfAeNQRrj+Drvwt1Jn+03vIIVwxFXIAYvhEiGHgigDSasSVc0O+1ZLjiMCWotFYS6Eb7YygHhjde46aAs+38ekJGKNtpJn5HYfFLMGZpT4+bXnwbKmloSQUMy3aX0WD/k0aVcRFhxrv7JYPqyihQ8jec5BNIE6BxCcUAEwZvvrqibn1qt+ULf4Bfiqxw3kdzmeGHBA6IOYtPx+mSkOgFJfWnEiiKmakO13ZEJCINYyTL+s7RiBgyjOeXATZk1vuyl3/ByohIN1GFzjDbeLCCqYMn4cfuLfX8BuF1XD67gVyuuMac1ctiseLZl53146mhPsbqYrSO5UGVNbPrhX4C9vy+uDey8Ja8xeljXY8N8gFBzpfMZm2jfTBwcRHNT5Awr1i6K4zO16ZP2Q+Lm9dJBQr6t+ip1wy+cid4XCErrnewr5nsQ6A+oz79GU3QkDyaGETVw9sGI59QcatpwphJqi8mtV7qlggBWpou46wxCBKTmnnqoJFqhIViU15ggv1sN12Nv+4SXe4aRAdfXy2RNw+kxRShva9EDW6rRYHco4fJWWrR7+M5g5Z/UKg1vMOZeZ/WuCGFIxuefdqjFaaFH07TIhsSRiA7P1pS7gJ6FQAGqNkDq1JiwFmeuLg8gj+vP/a2rnu2iVOWMnNVrD0Y1vmg9sZ6jyht6W/YafCou/WM/bEmE/kGy78U7pjWjbTvCj2RFwJd732Alx0rn/EosY6FIVKLaEv44STKyCTm2DH48cMv6qk//r+e1I2HtTxo+LoKkD/E2xdF5/DBy5q1QQK05qrEYr8T4KSUbwUB3aDuh2yDx2mhnO3hk6ZJQsiAa7tkWi3hPZ99+dETSEsPYmjNS3ZDQrjIb2gMr0QskzPByx6f0h6eIgcfXt5cnl76iiIDoBqqSqgBc0HXCRF0AyEchGIAiGFZvcZKWZGZJuunQSwW0op4dKTgwqZx5+e/6m99kSKkEVnAqLpaQ1XqO4kV84lC4vvirlLTl6JjPhITttfhwv+6SEejJu9bojsoHApBKqzpxPTC/dI2cx27TNKawwMIMfCvps4FC3e6apW39AeJ8gog/+43zcPySxaYsaf7TfWzkDvahzQU/tatWRQnOpMuvJDxk7QZBYDOWIZ0hxmfLv193INrC8MvMW323GDdX2sp72HfOqbakK79+DPi5BflyG2qoQ1S/+tKJhmpKjdAZ4jY36jEOwH+C0Twx1/wUb9riiLodhh/kzZvYHu2Z9ccpPdNRkbSCe9+UYVaVrPJat97rfBwe5NzQNo+sYUH0mm1pa4bK0N+hQQcv/c/rD62QNtHaWKunaGl8IL8qPzrumfmv4YgFy+o6Wgne/jy5s9HJ4ucLo7QYT6uTNUfvEI1PRgNtl0SuUlqa8vhmBuPpAJVCrs1nmdCoAKbFaWBcS9SIQ1Ijs0wb9J1yQZUOP6wAeZ9XMcOlKiuXCBN0qxKW+Fv6vHU65PGossJn1ql1gzF1+u4ENEd/EqWQOSBaC/TrYAwByK3H56foHwGevrOF+av6i/GhQelDHVipCACAMpaUSkeW1f2GkbD1g3y8P0vWlNYb4zLfYQ5Xlbzsv9c4Z0LwK5zjp9AdWlCVzKn11GlTSkA+ZylzpzaX84PJE8i5Cf/jHTFTp3xnuVlr03HuL9SufRDNBEjPTBUleBQjQY6jN1n3I2DWbGV32Wez2h48YTGIIWDlb6aWYKWApadD/0rHqEJj+qF301H9eUvwcr8GOBOHoOjfuisD9n+sVTVHpTWHAOnScVLUzWcDmYmNeUPk1HDdfgkGfB4z5tpFfzNSv482Y8aZGg11ZOrjN9LmyO/i2u4g0X2goErhQtBNu87V2C/TJcBRXkJL0LdZh+OfUYPxaFG7a+N61z3lB20IVl1/BLyFxgG6YkqC/TwACwMysv6zt367tSiHVMzGXz+W2vdFjGMCQfVIKNwUgYs0tj58qhch/jLttLhW/JOjY6USTciNXsbAkX2rY9fNDIIFafb5WDpf/vK9sJ6+lNlB1ySMBPtw9VjFCWTD2JynNb1GYhfFeEAfI0jvlo2nqZsUrBWoA1l11CHNAvV1zvBD72eq5PHH9N0jY0aa9v1jXzmz+tlq3nwTcWjQ2f963O2Sbib5aAl9NyMcl6Bx5DBJWVBnQrjXty2+pO81ZdW6IoqFQ2f42s9alqsV3g1Z1/N0UWh+B03Vaq4SZjqaLdUqqFq5R/GlWLQF1mrWhJx4L1mlyhunwIa00NrEZI0V4/eyXtHu37aYM0QpbLx8idyn36iMk5ojvYvfkGHH1vLagoCCbRCBG41lNO/1Kgn9gF5v/lqngF665zKglfO7YNL6B+0pptZ+ZahLdj40CsxkPtdnWmNKofk7J8ZnX6qdzYVHSaIsMJECFzCMiSsPbxGa7hvCorONM07lJKV7zVQ/z4hVvPWm1cPuiEbB0ASF+7wMEdt7qenueg/pFVFPBShbLF0lhj87ROyUMcauJf4NI3LNtLSF71Frlk2JAerkvxexvQz//Acrum5fD7lilHs+ER/Kg/bEmmvLa+Urhtja7K/+bbETmAfq6Ibga6IM0jwdDhEkCT3Wn5Fx/tFv4ffjztQbkRgZTQRFqDX2Fm3NqNRMRlAdmH/EsWBtyN/bD4iC+RvjgQ1Jy1L3FIMOerlsx02ROUzgwxyz2Tjk84XGl8dHiYLX9x9r4ZDeIE530id4QJNnjxmplemldr6XpSQH4HofNQsyFiT8aEN1RTWW09Uv0Ot1XTOt62BH45L2WuF2Nt3FkhWu1e+lEcsjYh6OZ3NhgmNz7IPggvYoGaL9qH7FHCwobxq9kKJ9KLTqLVL/lX4mmtg6Eb4CEa3d1Rzn6U39SwC3ESH4nWego+1vxRDlV+/u+rScIO+7khyMPjYMWa1fX+CXbMLbH+5GkauALXZkN3GmINVLkj6osbp0SRC3FCshuSzl5muld0ruuNK2EhzWxmuScHW7sUMmq8UpQNg+Jmrz3d+vTIQmEDm+4T374kzLHNqFwpZd/He6M7Iob5oZ+JCxJzDn6flv/eBoJJmY4IaxpyJnnV4X1mIJcu94eooKRC4XhZ7K7PdHpEGKa/6/k5YBUNU5sPytGTis8k+3lIAk610nY80Mkoa+zrU3OEzTjRu8GtOpkN0HJqDZaKNnGnHwW+MbVL+16aweWd3m1O9Vs/Ohx0e4otbZQaiLNoBh0PYhrc+wax8EBSTCc5vsAnROgGSxfKzicj0lbuj5s73xejDRndJ8NRYH6y/4ZEhKLu1KdAPs7nMss/a6E5NOGAlk34zjPnwRu5ODfFCR79/Bqe4qDesUQ9r1a+d4f+msyLoSGuIdKmEIiiryD31rC6+D5bho7aBf2OfcGfLbth7Y2XqZP2t8abMDUZOWL+sabKGwwudDRW691ICLvI1w5xt1FLrMQLNDYSbocpy73m2LeVnoS7v36SMl7gGSAiTj/nABEZzwR2sLQT/4AXsIOcWco04+z7o6mvuBqZDzjgyBMlR+h/b9xfFEbkWH85pIyY7x8/rEQkmhLdvmPkXWlK+2CORz4Wu7mKUVK5pXZnOpnlqNgKGYIqDHt4VcdYx4GbJWuvLJtbriORbfOZPU4Pka//mH6lkVFC+bcIg47Ta2q/gtM20IxcKKXUU9XbC6E64jKDjRFeSI2ySHnjULk+54mUW4MKtpAzTxVSGX7aUa4vGYoeI8HYIaRFxhPNrGb5+IBwZAR7fPjXidwG/+e5AZPHDscknOK3rDw8ZUg7fNPuFD2e+RdDzo8c9HBxfwZzMOHXZPhwkLHWLkHuUi9evilPTilXuHwqDVrBrQ/TSp3gdC3+34PVmziY5UyX7lieh5ejYQKnHDe1H4qNc18uOTwTISvgleZ/x2m2mV5MLhzkTqD2DQYHCkQlctH64mw3CdUYACohfKNFqXXxVX6+LQQ2YRdVhmmTgtXF61ZH0Q5SzWD05Sep90czHOUNs7hMxaJtABozzr/Hug/5KHWBiz1vAB6eR6ECGDxanZEIyXgGY90F8tTDFQIGDiU4In4X33z57W3L+Jh/+zZtrJUdvzDLKY2Nv+jqT243kdHFFiDKeRBy7FPg0bJYWK3M3qwQQmPL1LOIitZSYVrb6esZEHv06XBYBBYPQZvK4WCh/Z7O0lb+bk9Hx4WL+tx6G0JDc1rrPhrDKod9K8+Wnd73Xg9r8L+bZszKb6JqT3n2a9T6MFhqjfKlYOhXYc9fUlwfitonL0E9aBlpFlALs2Rfh9qmcuxpVqFAktL5jIWHmDgOq9Ue/LJ/xs1EqKIaFPgXtMG9696OfZNTUb8darG946kPd2vqTJelJ0yXGYry7HuVIoJHDVjS6zSDju3+G4kJAjHAC8H35ICc9F5hUxcSXNNWAmcIUwWU1bovhGtpxlIuQ3TeqW7UX5tpa6K9L0VX1FwXBqLT8dRIexxLH+VW3RZHiUUtBeK3GKd4kAdUvVuK+/uCDRdBNnHDr98OqWCx2loMkWJ/wh4gkZQIpRxI5hiCiEmQ5mRuPXn+YOdHsLHpv9l/OZsZiOuRx/1bBeDBfxaSOhoFK/fA+LlMzPvMJDVXqSnchakWCBWw9oKvO5bBhgk4l3T/x41/sMAgHIyi88kVZhJ/mG0EQ1WvXxHOgtZn0e/32LmnRaEH5cSrN77d/aZ9AgEn/hPHyjE6/ZYWFBrkk2Eifxe5tu49XDYavlMmb97wltLBKvSjjiMeIDT1AGTrnjWRhYSThrB0aawW/0HOqlSP9FsYKFBKjawgxWqPFr5vcFNWhHooTIptwMFMi+6MiKpGe41uuAfWLCTePtKIQ233RqbFjoTL8y8y8v5HnFyyAqOXNbflJoGO+Lx0mRWopF2CLQsRMHiu7iRaYjz1VuoGfmABezo+GOq5vrXtK5ReA6jCfnshtT6RaGKUTQ2oOQ73ThmVagPSlTVpEA+Ty6kkrgtiVvYj38zyU8LeTESQlZUMv4/6bZdpRWknf0LnSfei8yPqFGZmtbgPQRLlhoPjVhChr4BUlzgXycsG/jo/FfiItTO4LjaMeFU4Be8BU3/wTFGH4Vrm/bYR2gSMkthRnQlP9vkrZg3yG/uPhxVHwpjbdYP8S9kmPHq6NWAlMmoxcwkm/tAxi94FgSmd+FhYhF+vnG7zPCFceY7KOpoK+c935uhKbMh4jWODWNbg8FQw2/0SUdhGSMFqzoS/zkiW5O/NIXxjqLyu0ZPR2kj7pJl9kFrZJc9RaA1Us0jbIo9heRJIshSCpfITQwIrjPdNkhVB6Hf+d4ogPhgKwxp1PJlWAZXzTgngxP8Mojjki4Z77civOCQGxI1ilc4xHCrDVXMP1pD2BqbF/G5St4TRdFZfMpiJdkETgVJchyzVU8S/WT0KJWLVO9Pl+mhhv6q8Go8uPy+IsugDVGrhVAA17SyU5RQ7Owi8hfQjrHx5tLIJGePXlMp5XRD4AZBbLlABsuz+nEyUt8dc1hpDhmdH7rUmkHPrN86wFJ+DiywCNhqykG5Y3nIncZV2rq581+RXjsf+UOlY+5p+KZ/g38RYA9IScCJ8Ilr+Zh/StiujiJbdpRNUvSA7hMlhbQBgp/M1n841BosuLE5sFlavsi8xKI7fhmV+b93sqedh4nei4vADa4zj/XPYZxqz5GzgX6zFJJUnmIm18CPuYk8zznSbTdTbBPEPk8JB8HcQY6EUQGCZLl1rRr9Vp2xwn+yxRSApb+EZjaccAh01219HglfPfSs2aH8kstkjTxwMZqy69oCGEeu10Eyk6fQ8jEvr7N7qEzlBZ2WpdShIMNoAsxOhGLYJGYNUve1VpZKyLmCYxexcEzsrtKyoChWYWc4V9DmxeUyg2+YNqYS7Hih/X2LCP5MWm22HH/+zoIq1eKL4oEiAZH7b+W/UbDENjg1a78NBaeKDq1FWmWrKxxgnOzsR916KemghK8Fg5eQuyv7JB6XMqLl8q23ObRIp+JHzgaCislXiuKO6qcjvStnNfwDfZl345EsUQPlPblXRocJm04k8XpgwTZPgVZT9pRg3b4/DUY6mE2/uUP6LQSITKoEfk2Cb1k9LEuTAX9b+APhQ/W+2LDmd0j6SPTQyceLfcLy7Gy1aPtNd0Qc+L92rHP1ermhoFwqBIizgMnFLJGc0X91v1GRf8HNPKD00ZNgqLEG18NK/kxZryfl7+5r3R2qzYBykew7J8EUaC6s91OtlfugzZy/1Y8eiPq9eG1+VWmAFzx3dVUcTDnFG1fQ9dHKwO6K93qFw3dNlDoTyy2GSFXE/LRA/+TOdz/voZbGY6V9eghf1YeRb/UYEoUZw/LYsnK7JkvxpLiWeTZmMjR9wFEj+1zazZTiXgsa7g09F2xUOOgM36j9NmhPYUAxekwPNL+D5RZL/5PEZR1N/xkhN78sUPUNA9OyHuL1pOeL9Nf8lPosPK5wPDw4fU3TOUKqK221W3FTRTYUFbTsxGwQPXJfSp+c8N7sLfcmpOXWOui7drXZf2fPBrPjukZTcImqGrm1WUiQykPF6zAp+RBSJfkpYXp+yzQ/RIb+P0S7sSNBQLB2qR/jh6JjOnejgcxVXBnm80DRFHhAq8Og5uxv6ETN8KS2ljdQCnK90dvPcYC98ujVj8XtQY2O+e60jysE2006WF2I+34M9zhcLlQ95XLJNOzGd8vN50hEXd1R8xSRCqRTXo0lrAymHqOwDJkKAXyLhvGPzZkCDOKBeFqUhITxrj3Mejm0Z5iRGniNfZZ8Arj14d3yu0wp4bMHvYen1xR8oZMHdN65M+bONQUmp6IyozEZe4WcsL+iLIjaE67/WspFL8bAEeXmmEfXjbOlDp9xooA9OuWssf1FihXP+mmfFycXVnelOdPhoTE9GFPtqhj+nvYeNKt62Np60JTCM4pHV6EuNalKaj8CQ/bje0OaEQFITqLEYmo5v8iTD2qv0YI3kZiSiQxUZN6+/fakjZVH4hYad1Y1ZD54Xh/T6SDdUEKFo5YWSXomiK4jrO9MyxCkbJcxmdEOk2WUwkRn6t2RFcoigV0MDXBiTxqRYCQN8jnUzAMAklmsLaewXDcNHUxwNPiHpG8oVmgqQFUk8ul5ym/cAiOULaP/WLd9d6eqZe9grbQF4hzGZeXBz++LD0ixxjdq3HvylMAA9wV297QiXCPiWZEhixKISyrsHGV0Pc8344d4KMDcp9EYv0BTSyiPSxMSNH74Jl/zv544G6rJiVIPan8AXpA0IaqSFdXXu14mmt+zdXhU8lgRjAqpS44sQMzKbqWXmGv32LyG+NAZSXX6C4cQeCiYzv76/At18fsK28hg47MyebvQKDpPtOGQrorC66UQGc5CMAEk6TOI3N/goFNs7iswnb3egl55zx/rIgOyKKc+5EWUiwYA+828At7iHMdaQ7t3xL83xUzzOQKXh24OiOkyy9PDFTKCNbxNgvpeeDGpqqpgHt+IBuuvVpZmLqp5nXHqA8sO/JwSHPIyzKEOklUGVpQtK4ONNndtbXJTGybNfsH3sDyZba4gzfKe5G3W8QJt+DIOMCq3/YQFor6Cno7p2eVE2soVHf+/160s9HteF2UNGmn/vwNzWm/C3z70iqgn3NpJfNzU/03fRHNK0ObTGbVxt/GTsJ/lwu7Nh7BWZfQjnSl9csErvxkuEUQVPWTYwE7s1UcrM0ogPs0VQYa4V9LwqwoRQfuPynTdtmFNqlqYm25RDBtUbo9OznGBiP2GzwtKqno62sSvkqo4dON0wAUJsE2Q6PNG3lwtT+ywDy99/Kpg8XvVppyXJQIt9QQd6qAL1xrWNMkFSFXtYx/H5YLtbOF+4SF3kArK3mSCQuKiyWVzO3wqkkINtQ8cGmjqcFc92OxiNm2jA/nRczfRDpMZYvWMCL793y98HZHM71fGUYap0OnQt9K3Tp8yIJ6IM9BVqAuWTd8+ct+B+uR+uQcVDEA9WIa4xd3ID+OobvgTsA/W5jX2wxXnVaQ63nGn1QF7/5DfT4lKEBYJ3YM/V+YNj9o7hCsu/ShZNvnYxVZ8++H0wkJP1a3sr5I9BMhS4FsgT6WWr0xUEfaXBR/ZqBIACbl95qnzOh5YTX5nFnKqjzAW5XRAuNq3PK17mAUKMdggP/2xYUWcuzdienkrdDs7ANYiHykEK1IHkugsBmp8ffEkoZ2oSt2HUrTTFIVC3aVoswkTLPwxcCQn65ljImrOX1UmY6OwFhVzakWmaa6jb0uBmjBoWgebFbOg62/yIDG6lF6sKTfZeSWDjAk3TKa3JsiZsuhHosSZ07/KsAPGz6/FvHniO4EpqOB82/5SEQfp526beyJ3Dou/DSji8uH4AHqOHA1sVn8oC6SebQvmtwcQqguLP9Qg+WcAZuBVoe5fPk585pAKvD9xKKgG2ITnD6SzuIH5+wk59b8Uss5M1DckGExN7Lu0KwhovE6Y8HHK0d0WZdPYmHl25cZIDL6kDg9pOCsVXylcWgVHGUqjYdS8mtyu3VfPoloZY6PEMGG/amROSK3lESycFKqT8zp6+6sbiCeFDXsxIgJbAYJbzv9EGFEMHyDd0f8+UkOcOdOGSDPfRZC++TRFaES5n+XbRAq7+m/CteMu/IwVpJNRGpUmBWiK54fLnbP/7rkfaIsVN1buIhEKIYL8l4Uebvd00Hpf5YYQCSr6w1+csYiOdrkjgZNNuEf9BTqe1rTvyeZDDNGfa/jWEWM+Ec8MAWYRbf9Hp+KMsVZG4OEp5Z11EuuBCbgo5Q8p/ylywCEvN+WfxOZnfPjRHgm8/W3xqcNhn/jVDNlSreWZRo4Ru7jOjbXlJ9RRvsp1wfgeSv96HEcVCjIrkJs8Zw+gUw+mdayhPwdBwdfxJMskVTQoBwXLH4IM6LJ0syw/rf76EelglfGJ/fv+cF0KhA2UNNMCTl6qKYCWTjleR9xJFwwKvlw2DM44IgjXIbDTbzGPfukJWQ0F6dCupYG+6xxRhfiZ25ix7iWUsAlM7WqmFEoiHHqWwAmV9VSsqIQmts2L3utHrCivaSP3lzCs4uLUjROwsbQNv3J8C5rYrimubGdE9ZFhdtznMQ3O87Nj5jarilHFLH0E6EHkAubn+D8U3jCwa1Dz+9P6Ol+pua5+3R41mgdBL1VeU6bnh/Q52n4dqd5IeHXW1OETs9drI7evT6KXBB6hqQ8yLcZCefAlj/moGyz2pkHzcRFWa1rvZLpxhPtVCHlpcUImnJQMD1wKtipi4yHTdiPKksfRSlw2y5ZfGWtSmDn6t/I6UzwR9RScLKgBXJdnU9HfuKifll4c6sCLGARYaD67ftOjneTGJiTtlTdDlE/G0VCgrB+aWseb1Lt0cmTEKHHUST8tD48scQVq2YmQAOtA27kFQzo/R5cM3ZGilL19vu39bNACsRAYfUcHyokxloRr9XlfmCsEc7yH4Zaj/CyZHKEHujQDIxfvBJ1IX9sUa6WKoe8Vy5J56e9g1fqRTSfwyKg5iWZaMp59mONcrq9CBH6yiLoUTutecrE4RFz/o7+XxqmKBBwabOoeodgz4voNxXRY9XOJogTFwIVPg0YQaBWRf+96/a3JLwnP3rYKaOoSK+FnTekKmMekXM46DA64CgsuOAN78IGP27zdcU/vBXkASSdtiF3uWlehkcE5lM8Q3W5Ye29olu8dFWHG6g8geyGB+h1r6YrVXLIVvNKGGBAkNmkvkwDugHbcvak2spwUIkeS4dVjcikFaGII0dYMYFDgPSRuRjSsHBtc9/Gnv+IDLLaKfLpNNLIj25EF/gFsSdVOfLLDytZDyjSvy1LcwAFPnTEmR8NcCl6UiauOxDykA3nXcqtrPnt36a1hb2RxmsCnKGHKnOBQuXTsWIJw16Xs70VqDzLH2tIYevxN19hjZ6lDkQpCJQT7F4mDQMoUvy65iOKPzmOwxNrP6b2k/0IlDGDDoL0wGV66w7UxGzUIWGks7kkbqJDypKXv36mMpfQ/x9A1IQ23MJ0AKjs811ol6e00zCuP5vBL30DNjloeR64fgYzd4m5tv0Za55+6sHqe+/oh492SwGJ/+KIDRM5L5UK39LMhYLAb9ihjHgu/bqwXTGuWT6VCAA8UZ/RPQBs8hv5q9pFqS+5LOB6n8jja8XFLbaAs8XSxPxa3VY425wpfp0mW+rvRR7xGbvA43dv96iG0rrF3YgdQH+BLiDXYLuuw9pq/kc/u0CKOHFrOX+yQlOfUy1tdkcA3UoxHQqUQDNOp2/6a1SaiPzk33fnqVuabTKFz+J/q8JrOtz2uW6euuX9SkNVChnpWfUDib65am03g7cMrMETSl9BgjB2f0bW2iSLbnQTW5SZn76475vW+p86cCnkGHrCqsv7Y9c7ItLoeFG5JaF8SPNjiZ8KeTIqpkAX+mLTl+wc7KElYSPKF9x00Yi9xAd23tA2rfmHHADAiQOeuCCbOZ5+DdB1FXGEoRABJyISYVfOIH5v8sZXxKq8UCSa/GLAj7b3E7a1FoItCxHRNNdZo1fESW//a2KvBmnd3ITBQCu2Uuykk4Z5GybM0pCEb12yv/mHKuuvBFf1JrnIHuc3QrAu4uz05fpIkL3ycw4wWV+VXPTXz1+pRGsR8yVznrkqyHMVQoFgBEOilgbSPj8/D6izoUr3Iui9A2w53EN94j+eIlwzTFibUvI2KXt2tE3Ue9zi7Fp0N8LSwjkaF3Z74RdMu/FC/hdfXwqGDImmw4cfJZ9ghrbrKuHI20L4ZcCC2oD7LNalXd6alrKu+LD6EMY9QZr12rEm5TRqG6vn/O6brG2cJC/wdnfAje+iYQeGCFVsC1zrbSkttYqDSTtPBr+4nAS7OGvlk87TRTuTvi25v7BUIl9MppsAno4gnVQ/S3AWAT/pd8CV13MXP1gC0mwnKS0Amr8SIb8mC8htjm/sxyNZTtb/H5aCK+NAmLmb6HxqP1A5xBuZ6PVaQTcRnMw+wCFKwcAiTJwTD7Dbqak/cn5v/be3ImFUBos3MxfXv5lb6yBh43EZJpnRko3cutvRy7vGuZpTpQ7oO5XKffkzua8isw+9S9lCLByTCMfImORkmqRJf/YNNxPOxBkG80qlZkB0AxAf/ZkHMk5MODFgdVgiT9RFw9D4/gEs3KeuV8C3Jnw916zKBYDolbY7fBHl60NQdYMzClw4cSk9c2eoFWhm7/vDKeuS01usUPA3DPMiiaBLIb38d3y88ItdiGEr1GVLmzj+7tV/kS+U095aB2nDlOGPQpF/lC7XN44ibiuXAimqhH4387HuvJPHAW0wmn+Jue9kHLAyoUhp3UgSTf+eREJpUAp+kyh/ma6kP1cJ6NowlK1Q9uT+3XZYoOvdpz8xxDTwySLACFvuX62kl5e0ib9rWXPdai/KPgC9fJvFR0XyU180tG5PqemUqrafd5R+7zqc/nLeTFi0Qa6ofDZr0bCdOwZUJlrqdbT6ZCo8oPojcbrUQGFe5OMzR2CBfC9woFS5E5LhA/Bwk8FoTistQrusPr3lLpl6b6WkiVzlunx2SbDw8xXETQh1x5ubdVi2p/jd3Ge6Lci/1BWIRY0sDMvW25AdVsTjM8fqswAEad2IpmvWJ0sBg4Hp9UxYpVQgBuXFKGovzWflRYAYBPo7HiKVL2y0kj+mMk6PyRvfZPI6ZHwF2d/2tnuFJiUYirsKbBTu7jYuYsGHpXxeCIfXqQlnnQPuqwo2nzI74f+LSfYhdcWrN8Bl4k+qREbs9bCp63cMxw+0y6wCz5GK+MwUc2ak7hgkX6q+fpgVcMXnwjYSlYHL/T/FWnNxs21HLGU+w6qBlV5n/UgR+PFYAMV0wtupq4WQ8hDD2NBh/w5weasm3f4Raas52ZYrYgKahGr45NJ1hTl2iU1UQxVBYse9QnUvGlZPUWXMyl7hALyKLWqeDZC/mY1E38ILLIjozrYlu2GRmfpgquvcnDR0XOWQTFeV2scgvKBkgmPtqLbugK3cR2g7UF9pCzX6NrPfCtMvp7RZNAdGVY1nbq+YmIAzBPSDtIPavbVErKptQimtQzmU32Cx6zQewDJTnT607n7vQgO+NA0ElrTstOhHBb0AC3XC/Z7k2+0EjpnIrTjtec7Wvu0VWz13v8Q4nawqIU8r+02GAM4hFQy1VAq5npof8v8leX/lbPy/KeYO0T4txSR8IF9njmBIy/XttAb5+ZvAJ+ENay4Ga9paRCRFP1CgHQO0fMm1Rd8MdiztytKGrIKlD1RBKns5boZJUu306yGxAmPPqOKMclzUzSiH2yYJySrQKoQ5yUeur9fQ00wtIO+YWZP8dw/kYAlcahu/FGTnPShvoPTWZO8/ema5iJz4aC+Kh76WFkhyI08fJnlz2JlXRwbibY4ShJ4M6LPFQhU3YhlbVCvljBM7/XV8sFUT5rC7fnVVYzSz7t1QbmCpUEEweH0zf4nikjeoOBnKcz4Tlf4mo2RKqPm+mskJMfbidj+FkyB5oBYNw+P05FYKsR8XvLZqVTsQNIxEIk2sLcSHrC/lUdJvVYDHwhRR8aSwpaEVmP3HfsNOaPJOk3eibPzAweBtvYLV+nqkko/FWvPeHz+QgUfGgPSFOOVDNb7Z+N/RQxl0MeSIE75Cj4AFAuCzCQds0plhR/+yIse7LEEsJZD2tPwF09kawNZx9Bdxc9iQeKWfv1T0wYXfPiN4eMt7U80JG4kgOtH4LrsGmz4IlC7wFJbCTq/aDHaVAtoC7oAVHMpa6b5ZxSCngQoK2qKmc09NDk/6TLxEXXbrHuiMzOBFIYhoMBptRoXvXp1jQM2BP5An3VrtWA1yZdcC1yO2bckH54QHzRppOqvZEhTXB/zTHwWGpQY3DtGZihHD4NzFdiY2FfhfRSa0HAnvmVKNGeSmDGHJmCxHXYCKzmpXRUdyjCFXheyNuXhkx5PyotsXpurCPqlNP14/MzzznP6p0z7QdGPhP/XAruGN2tiSAqJpP3N5KS7XEML+RXCMDtT5X2f//3rQtnxa4Q5Xc8/s4RHzmMcjBDiuZf1Zf/hYH1jAwUeLsclwms7iXGU6cA3xl/JpYUuKv0hHPgeFZfXioaClnWgPQuG/1W2WNJ9KI30fLljkU126eVaQOetQ19KL5tUU5tjBUD65cIoKczsO9pxyZnfSpZcoCKW3iLxxUc7hERxvnL0XGLv/2pkR5p/j+jCJ/JpzhqVG8I60YyAQg7Qw2SkZyTWIeACgW+jFY2gO4bJkL5FLQdQQu3mhjFlbESwklVNnzna/sYghZz60qvCsk/tyndthqLKq1iCFxQ7viekKuuEvTm3i53jvKhUU/VsV5/ZehRSoAnP0PyXVwEj8Px7yScF77FZtCZdVEGMz6X9ageE/7N0FduSIlHwl3BZ4u5eO5zCpdCvH/L17KbPme7Hg8y4EXGN/6WxlSIXuz+DVVOOZWiznXK5bOKS2mL70xhQ69UzL0mNHjvGL7if6QW7SrA9PiasFVICW408kw6j5Tce0Vo1H1mjki4uCgxPIgJLRaOhuiTmN1ooTCi2F61LSk/ECOcgtraPX8p7TUQNdyzuUcRkiV6r//o6cl964wHGFFF7bW7KpyUZdnpazPL+dOC4yd8ZynITE1Bm+96PKMAGRs1NkmD5Edo237ZMiXL0kHki3FzsVAXGSxhSSxRBJFJZCChqmpGuwwBJcH7CRJlOu9nnaeUN2YMhox1JzBIz2TlG/nMOUoKgbDe5wB/3Cev6Uu5fYQw/jlBYdFDpc3gsv1djNPEL8k5/r1HRl4DkDc5PrPHDfbXo1egy6RQkvpAW48k0TVbQt9SZyjtPGbkC8NGQfn+mtqxWZeyRH/uxcgr6eCGoRMGHv+7f1JBNXgfN0WJPGN+jzW5Hpt1xTD/05D3cihF7kB6h9lfc3TT1xnf+JCm7Dy+5FTt/VhcfvpzWLHGyvmSKn2gOoKWslez74QMJ7dYF6zD7vS7QuOnlBaZX27FBcWaCwdwXgB48+MUuOx1VKaSR+l//ZdbG6TOFnCkIqZ4gGi50apbjSDt2Z9pPytfWHlRdelGeYKl7TD2dfTDp56AgXC7KLf3b1k4BFIb4HsFi2+ZKUfCVu7NCRcy8bQuYpLma0RyYLZ1TCv4oXp9XHMoEzrHjETDZcIazUuVbsz+Berglh0WfJ2iaZraXU7N6lWGtuJ00VaeG+dJI8knqdi1tazn2po+2X4cRJYZHjRnt1GctjVY2XeXPg6CRdOQ3EECA2UDejvG58zYmgE1ArOhlPuFCnR5J77jI6+LADpYX0gI8rgz1omtjLGcXFlzYrg7rLZ2Ffo7zW39Wxoiw6zSrYIl702HGYiZIve22r2ZM2l2BKDSzY/kDnSdgFJVYuAgZ27m6jWZ6JQ5NVbplQNKKGITfmKanr42uM1T/KvXz0WQew/Mjl0WoZTzQISRuLzCr43nh5qfjOEixKHRoCOGWRZB0fUKhBJglBILA/vZVgT05gMd25qFPZtHujBF3XnKPxPI5oh0OS6lLBbk7Zw+SArrLRKLOcTUNS6Frv5//1/dZiJYiM6vZv54Zw6C6QEnA4LxqMW9eeP039Zou4DQ5ysqKBaGTfkDNb7Fd75/ZM6OE5kAx+KvN8rhxfyTp3O1wFHmR1Z8+3he0fpgrN9c3UmKPmK+fiChskIDyJZe3AozzcH22XIvXrmqtw5/nuEbJJ7Jkjtczr8FHfuCVEJtldaNRRuhRfF7WC6r/g7pB/wojdDL5bB04pV4cjY10/tA46BRebT5/Qwzn0EITMcB++9k2XSSNNIbKX+WQQNrKKnOVL1CNsBur3yIX1fy5JL++sQaUpsKs1DlFiTo6s+IIndhZ6VLBh5Qi8C9biv4PiB7nR6/E1cVyM/+kT3EggQefT6wQs32iyJG02uh+fdFuV8RJ8dF+lnIY0r+BskbMNjtF8hwSRfvY4dCNBHZBZfcJVcMWxjQhFA+Idyx0LI3HyLjKbEB5GOC8c9nftjvo6qCqnje+0ncE60/KCesRchcCGC4RO9l3PuXRX4f2X+6FZfL4dPiZoTwoHiSuGBNCple+yHxv8aBcuSA50hPS16tz+5T891MrfA/fuRdszDD57vMc5Cvstq/0t4KkRXNlo87kPj0YY1qPjgvVDLdGxbjPsnwLifmQPisz0MuKO4LKTfeld9ncWPnKGzCYYCYOcfo3G2jnfKnrZXLkQxGIvLm8MQIZprmsV/jnpuIwWGaSwSBKEFP/9DVBHcrvI3yH63iIvYF9svorqI8PsPg04jjijZEKisIsH38W5L3pMkv/deyJmFoVcppUunSCw7DqwhVr6A7LFGZE/YGahDHshCnavvRz8PeK/T8GeCC4Vguu84GQSimlLMi/y9J77ZL4q+dhgI18d+4T4sF3GyVHtkdWq1FyVaHnYmUYb5/xaq+UQh2c1f0aOh824ii8iGvzva4qHwFulPg+l+Wxez9PjIBpFeI4gsXlomDtBfOBzy+lwrALLhPfgKi24BFIxySqnrqe4FPgS//OT3/gNDRX9oQA66SGxNMe+yiJ3IwCvhmVkjt40aUUSSrIjQXElE7ixhP1NpJX+4oGGHs6mUFxmp/hu3CSYx6jw2h4x4H47pUKvLWNnEEgXmuiCVNdL3OLju0zxn+d1eqsuxUQa/T2kRstdeEFSPtUUtPD443goa9OOy+dqvLGhrLouMpyp/BmcM2wn6eVx8gcWdUUbsHzE7/bJ/eoBAYz4WvEmF2NpX/bmLnbCU1kx+de3s0p/t4i+XtFWcjmzpmiOM6BAbo9Z2TTzyVcwLuQBDu+i9nZbCOqA+SJLV2xARin7nUWQNGa84CWoAyEN+K+bx8QzLxszAuXJk7h1YMaeUyDU85o61tQvSmp6rhfY4exz4kJBRoyU6Pu0Gww5vwrrexyw7SIf5WKeWI4pTSpR8PZVxa1TV8GK6aTH+9Z3JnHVH0DTgFhboHC+Vu/bal85jfDSGQIQqjNi2bYLv6G7npJUQ5rJ48mRgVkIegpb5TMaBuPLJoesupFbR92Q7pE35JmzEbevqhXLYK0Zk0j9vTb+U87jB0a8vcOMYxoNnJ0VNP8iwf2FqIN43/Ssz5J9+3lEwVvX3wpOZN7HRm2dXldC0mkXsSOrE84nF5bkouWyUKaFmWzYd+K3WBHJn3Hi5H78hyZ8m0G8T3CSveT0ksq/hYQX2f45RG3z1P96CLiWQaxsTBEAprVMAqdHsRytmUO+jjc2K7WZpr87oUdnzfoxeZidjFuX4MydbbzqqTSFGQNmwmNiw0/KV+J7BVFWLfE9BD5YoFwJvSEU60GExVnttIvAJ6pD83MNNrEX0sYxRTKq1V/FDYMla+IsuRTJn50d30t9PCT8+A+rR9YRigq/6ZhNiyngb14rEoekoDvZ8ftGF9/2CurawJYwqLBh+113PX9U63wZyVjfrzgBqEkM3Jwtv+mIiDqIvFzs4mwVYyy0DDHCsoKKd931C+9y9fNDwV3JVWJaCWd5T7+TElcjbw7Tzyz4EbybdbYUDE8hceY809Rq5DNN3n5zAL/Z+BQ2gfyTvb8WJrHfYpPKhgSGAvFctSGHDadWtYKLAaXS6nAjIu/KqwzK7fPhSAwpaGAj2SOVD4V1Mpb9JB1XsM7vaBJIYDNBywXlVP7KdzgWY2e7cC7BW4x6MV5ZaGP2gDmyNTcqh5tuMBEsTaYbhPGmb9kihcbK/cIF7y9ItHKd33L2OQsZEmsbG7+QsE595/anVKV/hh/lKaANqG7hoXT//YUizD1vCL+7pJW7wzZ5j3XNFWWptE6jVR2yDOmKFtwpa0LOCU/HTag7Cucon9AUqSUMYN7yezjAAninCaXHMM2toqtl0MCILjoBzvh2axsnRcXsjhnxaUdKJLQhgERShz/mlpwRmzcXKXfAODZkuYyCNOd0MVhys0rwkDBGVg5YayyzJxAC4ZuGOGWc7vw3+483/5wMGLKk8NL1dZB2uNRiGZy3kbMCm7ZPYTO3yW2RtkxLcXhIvFZZmQGBiJSvsFSuZHPeyiANslf5Ef6Om+5vmHth0TLLJHdJEteLNPFSkadzzguN/MSBsND/YG/xLjBRSRa4sp7CXx1CCMINfLvi+jLUF0fEHEQC8yL2M1EQn6m2vIhbb8KHPx87YNDxTmWpaWz+Tj5Q1MMooq3NBDCHPOkp5UmD0unzx0zj+2HqRl5drzeLAlPaNilU3JEm9WY6cEPn4KXWM51qiqmK857iA1TdHUCPqWPesXXSaFSPLGAFvB2JGN2x8c3qA7QPuYIdk+vJvnfJYBNj1z2NPhE8oXCcbQIykNRnwbLw+hz+qSrI427hP1GRpwp92TwvdaOuN93FRDN+Jw4yFdxQbqqpP7Bm4PG2zMX1hE0CBEZl70PLQiqYNr0DKlEvBNgZrh4Kr9QWlsCZM3ON2i9OiMqfGUG0QU1IdsA1Iy1NIbJpzU3jQHsS2RzsPuY+s5/C2jVVU/HiFZlt1rhddYxcl4T/NfADQWTZff57iIKPo9naTnq18NpLMtO63nVPhD5skIELX/YlJXE6sccm27dGj46pGkIKitQ1bCn1J4Wfv3V7rSlefY6Y7szAS3BS4V8IWZcF40MPvosEXhqRxq9IPljFbqMs99vlH0JjGrCzvEbHVJayN+7gVGxCQ/LYa7Ze3C3u407SRadD17OfqtNsWnWf4uqc8c1bXP9oz5Fe3U0XDTn12xoEY4qZQVN6QIW7c9MaSO47EOblYdHGe0BFkmRUTV29Y2oH+o9A9Hq94/fNuAfY2ZbiOEbcJ3KqfjjKDnn2dX4xQLE6LGVaMdJhoNBbzTEBYU/i8tjgWtyhnKNt+3SX5wk1NQYKHb5W/7KEmo+uR5paB5y34oCs4Ozcfhpx7KL8lIKWaSBOI4uFVOCeF/6O4+8X0klk41+ZThYm/sNtRAX5YMPjPCa9c+eq7lyOzxDKsQjTMjmb0XU8pRUIFJiLezmr2QttLsC3ZpHjXjcJ04DaPujCKKkKSea5IStoS+kARRXl59/LflnSLi5c3S7kqWK8nmzqQAO511CgeqAPfzMZe5RqklOBJeZutxeOIv42zHhJWPg/IQ2VznCaGPV+cOrtqE22cXrY6qJcc9nyPUhDi7/W9lrl6QClEooszhLfIBh7MucZZB4fEXwqTNjjIovXhfIJX4rYk3/rJMDkE8aneZUkonj3hG8HrH8GxzSdwC7XMq288nbEK9M1R9/ws9lAOKrrUSlHHkIgkuJpGHGsAhtkacfJUzh87vRUo3CH68cnMhn+O029WUz72HedrYEJ4Fzyaq/1cmbdDNL0LR1tRM/9KUHiAs5FVa9AsznAVG9vY46pTptV9XRsC1JSLlGmjkTFxubNPqIKjxlrt+dJS8lX67aBIzc9oyykcBIzSok3zPmL7qgJjvSTm0aKO2GHliGEvFSv4GZd1DDbifRGrP3JV9mxLU4Xp0ifFTJAYrD2MdFkr55+kl2OV16oYqgmL+Bsa8wHrZesoxWtAMUVRLoZUwSiDxpSvD6fsOjLrwPu3i8CuMqmN32azCchbTvSH1eSk9RGO18DO5XJ8TVXzXB6tCVF8CUSy18AJ6f1SdmMV69Z5XT0QJ2XGH4c4Jz55YB16Jk3NqZgJA04SuKOiMy6478SPFn4W8vh/Fo9EDB3DEGFXmyQ4RAh6Fya7i416UQKm+5BteU/t+TeCptYvoNmlgKAdBuQvixem47JAafPS+95U96cF76RyZ7/kitOvAPCJoph/3+yrCYNkDHINt+jVGsSOYeP/pDa4GIyCDPbiTT6B783dcxLcGjMEiuP2+Z0FoZTDsy+gkba1OgVwGfxHuaUqYqg1oThuN7Ocw2Kxd7TRV9Orns03KGNizfgXZQNg7mgjMGiMJa9q+A8fi+HMNISMkjDraPXYOCRoduKy5PkTSwP2MlamHt+c/RgJuCQhJKhCt7/x1u6+vAtoS8h+2Imx3bGl207vB2RIo0z3jhw0APHQGub0IeUfbzN+4FNKcJMqjpJRXBH/KxDvOCkrCG/f3pQpfnsqC3bEwnhbrwQKVlZFSzW7ONP9sxqTEm0ETBq4OOENMLtIKHoxX54fATgFDy1I+AVq2Su53+r290Cb+rUOSqVRNhgnzJGMjfXKOrMyn53FIkL6GYnSGrOhMnzkRa7WknRE/KAhb4qQWXfCVPD3jCixNMQ9LmN+jVT9+ukua8FBHE00bIpjeqPziBGbxOcU8khsj+HYbot7ZGISAM4+tNOhPsclkfSiMSQC8dpfpQubhutS1ePtbY0XaW4sB0bwiWIPmntAmBmEcdA1Z0/rnyRqTsmjGB3+HcDFRXUA+m5i+6Srkq3Vp6TnJ17uAFvGjQy4Xd5DWp4XUi3P2w89+iYnQ+RcUTlY3WGvIAcaOKJfUayrjjcOZN/fzWV2bs+mlU3+Nr07JWJkGCTrjdVPoVycGRba8uETSvMHASR6Zd+uZZgnxH+0jt0AFvzXKU9UD2vU2hh3z87yH73UKI5sBrEMOZ5Pq7sBlCulgow1MvY9V5LlHbErkyvLlA6ZzSwD1rZKPawX+E3daLpDgAAP+08aBu9fBpzbvKUazIZWNrSQpVor86RpCxdNwZGvImurqo2JjQUqfpkyvTpNUhidzeZ2lvbsCDiMui1nXqAk55Xxee7UKsSM1TNLSyrEny9VqR+juLdv+YSYpKr1LjiwuJcKIAZxBXkm/1KI0LHlFHUrVHIsa98xsEzOV3xdsXXcq28CEx0vOkHRDFeK6s/EQtjsUR7YG0BI/67EEKFuYCTAe1OOzQrVmeH7EEkfXa0d9owL6ZQBKVMVSmBIklU/5wk1m/p1BMb+xEOd+tmceIodQn/1YrZ0SH1oIQAfqv/pkumXuH7kdtGS05qUGPI+wWpzXG2b3Jg8jOdzt3bGDFHLKguehT60PfMYOj/A0QLdaXTkn6tnkE/Bc4jEOpvWbdsCSRWXBtPwXyWE40szDWSN0gov1szBi2qC9HmknduHtktIWVovwEpnRVeNCY/0vWUpdFlZTWjCbqrh8xIwhCGyQKNk7q96hWr+DobBrykpgX375xgN9cLAX5LnIKVv+qxeaiNLVcX0kDIEk0yQr54UutH49a2ycEaWIl3eaYbJi4QdwbwuK2+8zvJ8jp3vlhQ/rPqLWYIpqrcbZqM/xFpVUQlh5atY5H9Vdmfwqsg5UxbAFAHICSioklaLAvI1A8gf5xJJMm2vXe7De44ofmY+h3lamF4e74F4pqmHGPgVaUSWKmthgyC+JArgSSIz92RwWZbVwU5BbZgFoTD6/V6CXmI8UXa3mwgq6ajTztkQlgATLrOyP0W4ZVnjcWK3scMwgQTw023KHt92mLHl6i3R8+BvRTZK8O5f0rBTYrDOvkC1gxsX+JeWU4VOeGHlHLL6cq3LYJC8nnDyntADv7W+DJ5RVo/WPlg0f8DwLMxfF9D6jHKn2u/FBxFd7A7KNpYWDDZyuaZLLvVI3XwleJYTVbNqBWb6O+oxIkD3kmVdhk8itgTMdKaF9/6XGblERY/i3Q29q4FDxjwiHB3nRBS/9G1t+3OIxmAb9R4aP9cjkUKX71sIEYAqM12gY7qXbpjcWVLscLC0tRDKoaL1SzcN7QyRPj3R9e0JIyvvDT636Cf9Y++IEUuHzmI90Xb7gGlrM1mXwHDeLX87i0mf1cO60P9HKAcUtribn5kInRzUeKot5nc9jsl2eU0xk116DF+GiR2HhuQtFPHZyHQraj/dNe/Qx/cnf9ViU2ZA3WnrqhOwleEzQUQnnc2GTLoI03U1B5mTJU7jvnIARSjB3uK6O6uilE9F9lXVp4d27i/XGTNBUYCdRNQs5hlBAGVjRw2Bwn9jf7iv5MyithaiiMclfCBmiTaRcY2EblMt7+pYefLWeNyw7U5hXROmAIB6fuEWSL8/826aaNX9YAsGPTVf7KbyoQxL22TCA+wLh58ap1ZjqfczV+irYxktRLj9S895NBBVAsweIPBaepyXOu9YEhMQmqD7RLBtra3dmQue0fPIlBYOoJW2amnfJSpS4vNnq2LlIEzT+bYQLe/cFUwL3u7xjmzy+bs4lfNrVfjxQp832D6ku4bBuxsf9riFkHJFvDX7zAhogSLXH7iFvGlfYqnjh7X8g3tWrR0rd8F9oYHmYZNxKkhHKfnQyobVS1+YDXmBDanArIERDkWekC8Wf9/whVWEjB5KZZnSkTpwRjnbufDPkKXxa3Nn/cKyVrg4UthCvSQ1IrZY+/BAnlalRKMiQjdKxekLvHYXlXmKvkgOo/dc0pxGqQP5Cpsvx9ehUNzw8Rsq7zUqq5muoXfSoRyI7+LSARGfdYKOUfL32qiqvaoBT+Fht4SF0G561HDjjUlVJL8fgj2l9znqF14FyEq63FrOE4XR8UDXLfdehOa5k3ZpNruTDoe2DKO0W/F2y2xFcyHgRLlXkP7y8poVblrBq+jtcAmK8OZZBksp3Y8Yfa04Z+kFJVSKpkYzH7IyoeX/lAcWDLVwCkOD7rIzhpBMFLnwXcWxj5Z9Rq90r7Wftb6huX6frd28RUfXU0zEV59sPmPiPMOD6oTklG9dl2DGT4FJxOJjUoFPgcGsf1ESTngQtma+xKqf0v5znnpn4ufBCIIbV0Hddebtg79/uk2AJi7d0uTeSQarZieLJwspWmZ8uPeCyR6M02298CIMy0Xub/GeL7J5CSMtAP02ds1f6g44s7JtObeKP4W1HaeakT01UUw9c+ew0Gu8fZ20C3i9uK83G+0V5tUPjvgi3mG3FCGRvOiigXqnxRCZ773dSN0S9AwSq7K/bInvpVYJE+55+1HH4e8p2sTUeC1CRdS5f682/izmG5V/KXgCMixCx5Ls8k1E9+DTjGFaLo/YkAN3zzpEcfkvZR6irc8d7SnGvp7P37i21iDmILy+S2o/GAIdriu5woa2mJlYj8T5C36iFZoMYBeb+AiUzhdF7JVvWrrFVPCTKKW+kJzUU2TOvJwXYWsjDzz0tAFVXkRmQFAiBPKQP6er/kyHvfHcdGMIZF2ajPdyovzOPs5NqAH+kFccg/ON6Q29CDX0OG2A/77357tuRnJTPZ7DUfJf47IVzg/SULDp0oPuVfcWzIEbxZylnFyOi64GBVhUiiibv4OYIfJuN3e8Ov0NzPPJagJHSaX0y/+hLnduyjDx57wsDPb13F+aDPriSxgN7+jL38Wbx/wFvTagyE4jrlPGp4ZL0PEbkm3kfJIDM+7DBkf8ZS0wopVyHXbNRVhWPqYYA1ILbAP8ulyxn157UDBKBliuo4BCEC2LkZslEFyAvCzRlT9ewzeVwR5AoYFk5vasO+f+AfO8Eq0ZZm8RqlN3Dhl2C9BLuJ3JBqPkUMHUOiCGjMSvGIyydgH+9C5XRb6a+o5C8MrV7YDt/iN2UYz0ZMaobWQb3woOHw2fuMbFcI8GURylI/yZfiJOtYPFb2st+ffKwsExUku+1r9nuzs+yPje6DRzCDCuoAHS8+QJ0f4YB/Vw51cfEb/ss76kDD6Oz1z1UyJdAYuO31UDJ0hbv2KsE/Fg4F4GURKNEjuZmsl0MyHsQ9jRFfphAmMkVspsVND8kgaQgz0JrZMYgYTXl3taIvWiEH5MWYEO3PMG8iPmVI1BahHXYr+qTButmZS5m25K/jFP8sgOkk+SFI2YvndUirHGmfqVkeCmTlevVn9doPbEgyw7/KK3a7GDn6QISkWVJNV0JGSC8gRF7iqKegcUgV/22jFQiklNmoh9FHReyqyrZDS+tL94lnUWuUSEQIvIWCh1N4xRoFsz5saz+8e0PpjsvN58mTCKUUHFLG2sLfRyjH8sSyFJBPudyuP5pU8moEyk08FXTaS0WRLwPfXat0vW+8jCW4K+P6c8TJ0cpzqHAVqD085+Bvkzi7vwLnbPQzDdUmlocON8hyM8KTGYDyJO4hsXZC1n7Y1P2KyvVNXypbB9EG/FLttTHdYvXxcIvltskug9c+BIBz1KEQanQIeggUEVF280NmiufaFq8yA5hrgBbJOJ0M9AYUoo9CFg/BTc28O7W55YKzY6N4LGCK/L6/qgEX42OlGKewfv0yqec4BE2oNul5CL2+WbF2cOW3NluITRChVrzLVhoKDiSlXSZ+qm1CJy9UgURv60PLYZCf5IMNhZ3KluMYvMupfimcP1jSYj9PX24Gami+ggDOqbXha7W3Z373mw/7CPl/c9jNw57rv0f3W1pllE8zn0u444+hYRzjAiG07oJYo9HaNa29zeXwfD2aNDC6eR/YwY0/e5GurKoq9vexLkbhMsQVQW8Gm7NGTiA6eIhx0cuECwr9FVbXsbnG8LGR5NjkkFWJPBO8b//h4cHdL7gwL+c8l5CUqK/WVV/xnExg/1jIrko65rhY6NoLJlA2T233XwPIhyqwa/JiLRGDnsy31gOhD94S4g33pO3T5hdgP/lX5JEAmzvQgeLwKkm3y1wTd1pDY0SboA7FCM4T5p9MbGWjBDV00Xm4DQe0ce0J0duei7H0iyobmZin1Q747FeSLnGJEWzN/hTe0J5Z91KDdIhUJburmhRJQNWxreOkLrMu44nrUCIZqAtePizIaNTmLumiFkdbARFO6ipGYzfVmjZS4Up2PZnlHBkUcriw+6rqjtzY83ObdQY7wNiJDjsLt2edNwVU44guPHZ5MhHmT9/NpFhiF6OFeNSty8W1g8KNBTZh7EavZdfpFnMoWTkPYaxhoSV7+kdtBf5LExuYj6xksPojyqhdpika/WlIc7UKOr7r+x6bl5x8P3Z4yl+O63+ThF26B1L54tcejygU3ph/QUhYQYN2a8kRKgN5zhcuk/WaAxq4sza/alaAfZQ3LDw7e888KNUUiSUbUcS4L7wC2HX8cMGJjSzKpiwts7/iY5AvpAFR92SrEFSj94hrDlWFzZUm3y69gArPphDBh3KPh35cvpiOFOejnlokH7V/i8FDcHjiVFpO6uaXEVNLjkyLE+/aSNPzVVMzFFKMRZxsSJJq+YKGK7S4XEQXCB8PhB3TiZDWSMaCC1qrMhMofBrCEalbk8bxFoyAZgk9B4ucWRVLLG7bOklJiMgg9w9Cwhd7CWXJFj6VGLRzqNG+rYoHE8iP2ywjvCYsLSe31kPzFQhizovwy23mxTxB0REZekFrzq3lCneRyqhAn00jRcnmc6xQBUx6DbqnrjTL+lVf/4j4b+BtwEgPSCXSQt1V0Y8g4wDluqZWY1+bcZQtPg1buRCb2XAAksdiTF6IX2cR9N2RA1vvhfrIRSwif8OZURobqoDDXsGjJluqL07tR6DZWHgE2jcU2vJifwupgflKPKT0/sosf8Z3WVlys6Jr1cMB8GSUFfrgihtI2YYW4StRMtY1SjFYPQRXP66MoFgWndRVxwiFrxPZluFwvEQ73WvsuTPVx+ci433sASsETmCjpbs+nEL3UtdI6cu/BehUBg0JqTqwwFfMSzi+Oawa3fXq8fqQ7MftP31Ygf32VbQn5Z9VmzVz4b4qLLdYgf9iee5UyUeT4xOktKlZ+SaPQiaM3dLsbxWg+K7I21kURPVsTDMZqy9XBqsdJM/qLuzme6954eypX67lDGSmwTXlkjg+A5N8cb8BSyLfUMZx1GbZC9NqmTcGhUbgH9J4rulpS7bGWrsFx57mEoJm8J5K6akz9wwws7UnhWjp00/6iQkMbdJry1onErQwwfgpHav2ERNhPSyFikSsqIvjM1swjzVfxMXOau03Y5EaH+LvH+zox99SqB9cUeFJ942IVLSDeWwIrs7nUkeHrTX4+Yl61BXAUt8qPnaS5TMYnOBjCCg28eyESwe4rCpG5Ee5tnuOZCQKNe7uPYh/GZA+65p9+nJbatsFhfcgd2uXN/skxwhhNI65NOi6kk2Xl93xDhuMt+v8lOjGWmNXx/nbEhl7oOi+COrRQjzmuWgmd5OAkj0O7Uvw5afZmhJjon6AkQ2OVFfS5VjDb6oqef7BAEysWdqRUSXNr/eVn14E5RGZLI/sCLhiiJKnbQ4JRBjtTd7aJvI633rePBbr8apvw7GCeaFkQYjf2++Tu+DJDlRtZcQW40uquPh9gwFKJgkdBNFh2Pj5FcYxjoYPW/G9TrbJ+rH+zVlgFTr2mMADJJkFaIoghYmgyk6p9he3hoqXuQxc5lJlpwkW12jtK/KgMUwyMq01ki+rf9YcN0apxgKIFsbfrp0rLZt/3eOXwKcd2RR3oQCoXVf+zoTce6/L8WePRO3ZD9Tgu3n5MOkPiHCiUDrhOv9mp8FxsjOvdEt3xFftaPl+uB8qNOz9hrVJhHRqg2VQ40ZZ/ErKB0Vq4YXgmpigKCmc+MqprL8YSUbDeBHxFaZHaPEo3gVLMtpTofm3RxQgCKDYl4O4lYsqrJGVQ2sSOzOgf0UfzZKRvcMX7NWZugryMpLLhzKwoW9rMQLX2FfIZG/K2QHapcy+B9wohKvW+K/uwgJvvpLtwErePkUnG5fh9oqoeQNiTaLqpPyNSbwFhUEo3enmeQy6ApgXa7Yn4NySWIvVtYk7svVRkQmpRgXHsDb5651JOp+v6Ynam0Otq3o5FSEdMd/YCRMgqh0aokQ1d1zt/Ce26VDiyifUXbD7UyR8qNVA77AIhfbS/A7Y+EFFdPz8nJ8xYv+yMYfRVRWaLpX3dUllzLfj29HZ5eXxNEWrDNDjs7Sxi9cwSpyd/75sy3cb/aFgSWXG+A6uGmxBnFe8a6LyfP9BmffKxFSMvAs9PUr2HmaUmcpFE0Sufa2Z2Jk5bk0H56IcAtssFQij9Q0OP/f7QPKmKV8dMew3Asivkg2s5jgmj+XTKQNM8CYY0W82Byvz0MP2MJpNkVTcrqSHSfq88mMcqc18CHv8Tp7PDc2ZE9Bg4oWujDi5YVRuz7gptBZuKlMrLm1/VrQLKbnU1M9GQfsBYs5YJGU5muufbeTuPeF4q9XmyJLbnLMX7KEMG5VzZyj9DXpPRdqyvzIZ4fSQHG5NKZOC2mRox+uXJgV+TUsbgl82df1GjWNaLI9lGkVS3kcf+Wt5R4ypsrDELd9fm/B4nbn+bXGqIYwVDDK4Ra+jfcmUvua93PbF0rPrYppWOaMVCuvwGQshQ26XS85zX6kkGXb5ZVp0VIsLOMcvKyQqmlIMa5V0lP09W4KAs3/bvMBgs/betDDRs9stqgtRsrLryyviyN/fLr0j8HnKFkLLa1zXpXZw0+uJnxuDrAuU55qDNuIpALYKd6weGNBhXolO6D0gXKyYV+uFmyO08qy6AeMKeXDQlbTzAJX8O7d25Tj4BQo60WxMb3QS7KajdPs2NZTTvcVwSRYtUYhtt7kIVV9kqVB9dYfu9SXShR3nPd8hKi4sd+rLel+x9X/faD6B+ZkG+2cPkJaAYPBdpRm2CraRXs7gop4heBKWAh2+x2OhtNQbDYwBu9iVeJzPc0Le4JBx5Py+De/d8hn+mOwoFxBW3g8ZfpS0vLaYGoYMlbgnX3RMLchutLngm4L/aQbXPAHr3EQ0azXwxi2ZEwd94U1HHqxqprkKg/gYpnk36/4GnMQ+eL+xK8HTo/y+i/UDShZF43b9UWmvvpQADwxQX4QcLio4PfhEUdNCMcTI08jbIb5Q40FcFs3z1NrAre7U1z99FitMCS6bP34aJJfrFeuxsnoVFF+SYSKR3ie+tsoPSq7MDpCUYyVdrmi9sRms1V/BfVWK+GQ0P+vyThK14bnUyZsdrTNlxqZG35AWi+E0DkRKVIREnezEgRosiETIlD6nKVZHbjZnqMr2VOYTxvI8Xmu3UnIb5wVs4sF3CNJhuXu+FCLuL8NKbc0FlqmYgFzBcCzMVP6suG8qh1USJOX2ocuuBVagc5vsjmlnhmBoYWf43QcQquxC4gu/H3VD/I/95D/NjWiSXeeqnQim4hBlNJGk6nv3hwe4h5xKL9uJUxNjr9zS6Mic2urg8Mw/g133wLo6lpMVxCO5nx7zKwxVaqoJ/XEy49+ymeSXEHW+9LqNZz6SiEVHb2Ut5ycwKaYbXBAI//wzPzUJz2VvsWYR/25SV5wf38D1Hzg4AblSXZHeeU+RI04heGVs63f+pWOk0e3GCTRloRHQVtiK2bqhpOqHImqkIZZvNpkPo/zbECpSzdNl6Le3QKJtrR6+Th+VAMERxEUk6YajoZssY1K/UeDcOpPF/lg5bQFoi2z8RaA9cJEvmXWKoLbMZzrbnKpIjTpOTbiZsFb8IBmF5hvwwQN10l/NnCrM8Dke82/AUM0bF4SRhhhdJYchUkGzBSdl8TyEUrjZrEajrCBOTGmjuBr9LekGsxkPmoLOsn2K9UcbVyeeZaUBlYrWnkBHUxRAMScqXbJ/j/xRae5boerckUN0wzRB9dAg04Ovgavyu3nY+bi5PfX56UdEZ/jgvd3q+PNt0mt6eETwPsu6eeTFZqP7pN0eS6D4zzFppYJCGfw5fli2P5uyYcfWiyIFNqWLnXHyTevHn90/cY/ELsvLk1fBXcf4yo7gpxqeOjhapMbB/Op6aLl2F/oMcgKDQw7IaEc1eUctLtzVls3pn+R4dFMXCRzOdtiHiKmdMxgB/uo0inBzLvBoeNstiNm8K6UzizJE/BVB5O3Y57FPWQDwA1D0yPIx+BIl1cXm31ICbtwZ8/wboiyRFFSSJw/+qnTGnSPYs6w8lfatCMSO79NC3aM2Dnw+VVpjdn9nVrR9Rca/6jdW+HywUMaehSvgzUhpeIilozYhZyytz4nSvXxPMWk4xliXN6y7zxIZ1M9U7CeD7HHxFd9bTKKXmgm2WfE7BcQBB6Hw6ZOXA+Xy2LHzRtuiO4v3j04L1kcWAkBXY/6w9wrCSfXEz+lYUT1eEHnei2U3quq63aYVtTQMVEL+9ROVyQo17o3DAw85zvK3ohpTLJv2QHQYneqLT1ZChs808bmZPj/515Qyvsgr0KQu72ApSgg+h6ja8meALYqvpNee578iHFuJg9BChpBPHnzXTzEeyhwyamfVciGPzIbhLbE1cEX6zXhVMwpDPGt71g8sHd8iTdkN9sdWXw2gmz9H/rYvRSt5BDKKr45S4oUxTJM90i+jTlfSuXU2NlKsHbH+wN/vqiJLrRZ6dbh/6+BjgHY0jdc8Vd3gaUHHD1NzVhyB3rCwyPpP/Wk4NM4wApxeu/trvKtUZ6rzVqTQ038FqRK+/POQBXDmeGiPYMhTP4cBRQdKhbUjazgn4d4SOrTviX4rkVtydfb3NKttF8V5HZh6RueUpwtidspr5x+PeqYwLNPd5tWUrK+8/HeIlkTW9C7+caqRuSU3cq/q4hEO394wisLYgM9yuJgoaPkxA9434iJYS38Qf79m+46ke3YHqUDWyJGPx6efG+W6EIgZoukf6xG2OLwi4IIP8IKE792NnbZ3DeLnFdZW5kWa2i10m9IkEGNlLv46Hq+mnRHcVUX5D6y1kYJoithIMevH9+GBayUWIUBnaswcjVkp2TF21tCezzfiorVBZajEKtEWMHu+azTm08XqSLhPiJzavw2DT7MEaqA22Naia0ypPvGdjqdR9HS2v3Gicyyi/FIjM3dJ0iVHI7osy4+O5hdBdgFZ1gIVj+3FxHmvpFce/gUmdE9kKjK6v/lJq7oqjkYZDmTZLOyYH8L4Lq8I0oCXwU86kKipBKVr/W2pLgc3OrkYpMS/QxA0Tbb4S/2UP/XbAGoU625S3jxW5KDonLILMcD2tHH9vefJEuG92Ywx7pX+n4+0kVTyV3PVXaUoGzVWWd4JjZ1ffYwg7sMSLQOF2s7s/fSahKxfw8vaFMHcudsNWLxx+z2bSp7W5WUjSJnw371KvqunJfKPZc6WlyYqp+bfmWry2FfAu/gw8IgGasOrHDkyLyFNySNQSnnlUszws1T/pt2UFtzvyZT09HTfv5sb+ZtSh5llLPyN9KNSBjhHB/gTq2WlPXLPTchsqR1+wc4C3Wn0YbUyPiZjccgi1+fpcWlR/Ng58Fm+vXdVwByoQ7aYv/b9WT0ylpfgqxIUJ4bx/sMuGD+ZX8v1D+IQuvg3IaspQJHB0CaPWPexwc1G9osyNf2smPHbzSx3zrvOY9GRuBNv4s+NUfI2vZB3tB6PIsj3y0rq9LSWkdXmVxFdaEHdhifvW/FXlm6gW9YW+4sixB8a1JbIQn8doI19+G3DMmN1W9+4GFVYgIr4ksDHU7s6FXEDDnajPUER5sh7sFajaDctJlUX6sD4fBLJKB2whPZMbICbJeoHxGZlo0v7OaGzDKFeJvznkGhPPn9F6Kb540gDfgiAKQWC710jXsPMhIfE6vsDgoWtu2O2EzLFsEwv1B08KD+WcMRAHzzwk7kmyrytLSYTg4ZKK/7FTBIcZtKxvi39hCZ3+IOVHUsthAs+823NZOHohgXNM2qS2LaFqomf0Nm3/2v16ASV8CXYF4Lw6aGjxgAn4nSWwC+W/fW/QfqKKrs+dW1H/OCKCXa6iSWCWzoxYvNHkYGG+cgkJRL372xeIAKzhNk0Xst1bPeYCJAj5kix+6n5Znq/yIxcqgmaH06IaICNP/ZijqC7EtA2NvoqRlyZpNxVGI5yvVBPZ2FUkmrWDHBMgbmE1jjq8lPC/TiHeXKhAH23Do2Kxj9iXOt85uX7Oz22H5T3PnimDHeAADo+yWBpIwsGSYjzwRoGvd98FHT17P7tgviOJW4zQ4eKq/PVmifofQLNLK1WbY74HhiMUqw2MdSa5sLOfptVluniPEsCnBC+5FQVMzA6kI9GQziT+nGgmmUN0ffy/+RO1Wd7qf9mAscwtziGVMgi9DiZ2LFlq04z5pbfuI7mqDy6tISfLFaq6tpsq5HcmrF8PH5/XhCeHT8cOu4UF/q9//Qjfs6L4JdyV5Visjt4yo889TfQXZzxg1e+/feL10w/oB2jiLrG4U8PnBOxBLkbZkfAICZRxMddaI3xHIHjEb/odoAmMfY79iL6UaA6k469oDxLt9BFn5XvgtmHzXRKH/t/0wAWLZLroPC5xlNrhnEYVnUFMUDw+35CzDFaTN7pVb1oY6DhwK9iPrmOp6r8hi7Evy3dOQBbnlfbY0TOseakrGw3pHSXMk6qWiQMU+W9Yj/yPPH+fuI+J4wXj6+wp9kYu+j9YElNPQKmt+VsJjoYUNxvYlr2SdXGVLYCGRqfcMLriWjzSwAUgAWpae2n3n/zhDNXpV81Sw0clhtqwxy2yvLPz1xUK2aBB/tk0qM5uXsM0YZiacwTF9NCU9FDcmQHqL5JbYk8XzyBIyGHbJNT3zNVpxj4HsCrH7O7FGzv4wxj9nQa/mrCKkgkoxgzN5hJMuU3eM428v3mJDXfL/Wy7w3zX5gmTpZOGar6DpMY8F2PbE5+91B+heK9OQvfT7C1JXLwAVDHHpIpJrc2wDhcReFNnRR5Ukalhm4TG8rTHj3y/XBHBM7obRKEa1MOrM7fX7/m7qjRCVSoGARWTMvPgB2dGvztum6I008mwiC7h/jro2egLFMr3ulw2wmdNr9jRv0rHgWl+CJfBxRG4Y9CP1G23aNVEqrSRi7Hx5/Oq/SnhPPo+Oy6xwd3RwSgSIYAVQMiBQNGmP41NAy0ACJLB+KFqU445Drois2crAHyPufo8WcVOYPzDSRjfYhq99kbfPu/bjbBoKo9xJ2Oab544NKxQGfRIfMKG8qehnfR+kjm7QfrjRmC6yHdOTbFb0pMLypOyrUWBAuhDOoGKnNlKChJWZ6nKOWatLtgjWYJ6joatqWu1os6X2O4RXaoz3eUsya38cJfHd7uccU7Xantva2opy/T/bL8hRLLbMlK1LKMCysS+h1+x2JrxAOQuN5r85jorjZDXPklV1GUV1DqCcpLMaeNHNKqA+hG1aH5EKhK25OQ2QLS+BixYpho2ObwhdaJrv8pYGmq6PsA5P8eEoyF5GNmTwzzsoCQ1+zb0HTFRyD2D3XETD8TruFzrzHOxa/oVghb5s/IzUgwrzIE11RpxrGlNtw1BMHNu6a+MowZ4emjmt0jCXEKc0jaYviSd7oKEAV/LHoP2/ZDZtMiJ6cfXiFRYkWNKbJfjOjOVLGy6yX/hf+WjstYCY0v0ZPGZ5tlwO4v6QN++fJc4hLgF2qTl2Mlgq2VLWu4K6DUoqapfz5cW/7QzbBH9VD89sofq520eqyzYiI7q09Swecl63LUtvMf9tU4kY48axVhku8aZrcuxXocLJIC52nB1ptwdTBah9WoOjoiLD6N41GcYPlLsLJODGSpoit0sbD3emfarWw+oUht6gbSLtO4M+cVB//j/OrU4sdLPkNjvqD5yCPu2KTEl1ZxAoCr7DVNsjL89VVvlrX7htKyPZtzCujTPhpq3sux5n09+Fudfv5GSbcUabUjP5rXj1nhUNf+rvBYfUJclOxTh4fxOZIv+2em7zcF7m1GFfEdYGcinV9YqDhUdllC7+u4+PPexBqIQ1YsTf+COjq9FHAzm7HK+gyivN5bynxhuJfKNBubeuBYeYfAf5IlRHb4AUxSQCjB5MHAifc97vmnraoKH78M8wwFY5Mm73PrD3nEtsdbHh5ltq3804cL3yI10W7LIao3wWEeMRwGd6o0qX+Gsx29WQWejDE1EuUTC4wM7Pn32ogohZ8N1zusXD+omRS5a40FEP2mNl7fGNGMdgWplPr86tHcYXGbyZ+gvNpJ/v5bsQr9zOo7hmW5e1rzN41SCyvxz6cKRSPFPKNALGb5Wrqr0cCUFI3o5DAcWWtTfi8ecNrKCfUGbskRSMQ+l58B1MQmnNFH0yxW+Q0LUzxQH+fPljMrjQuEkkK/5ThdwYOG0Eo4smKjuOKQdN7Tujzj4+G084ge86nDvcapo98ChKcb7rE7Wirz3WWHj2kqbdtv8+PlP87ynl2+ExxeW0PNxr8ts+hRuNr04TnV8v+mIH1RcsTzjMFcsj1cl8KMSy33Fz7K+P28VJ7HU9E+ys96oeO5RFfQIgs6mmPY0Q58g8T4bPZ26bN2/sfSdWzJbcTAX2IOR+Ych3FuzDkP49ebvfLBT16tpOU0gUIVgAY8F1nFBp/PwvhyKpRq+l8OJsq+kUugZkf2jVeIrn4YueX3zrzbX4zEO/TJH0hq9uHPMUTQVQKtFtCA4MusBmxUBMdKn1Y1zgPSvUznx9lSFMDHJf1lGnMMurh2AckkEA2i8rkdLZ8w7mo4y2woAIAsZVweP33M54q+cp7OqQhizO74H1mM8aEO8lKmPg4mS8Rntzji+bXUB7Iltii0tXdJ6OW+dnoK+DhRlSmphKygJtHwCBUR9BKa0iX12FiBy8Viofz1Anpf1aabSDR70xKu7skyq7Lr/deF0zU9vp5xmMlrAU3+eLUCtSq/QFmaJ6F9tGtKMsK/tXpAWt2/yZanREfn+7FsbrKHOrcBrGfh4437h4HNv0a6lPFCx9xalfSuEUomWFQx1xtSzNh59Pl+vi3oAWaDbH91XSCnA9kZ2r+0ePkat2S3VQBoGB1/2Yyz5Ii6l4BLhc4q5Beu+pmlD7lQb082n4waaFbmRvjPbyYVNVTescgs3acLPBo582cxyRvmumFjZNaRUb2dlm1+aUN5Oqer8ztoQ8L1CYJqGpd1EkE/V+FMro1/TZiOs9H6W207Qs3dbIVcP19sQJjiKC6aQmPwQ7ZDyfUkJKHPCBhsSNnOAorsdLYvw2twryrGyac6ni4ilwTF4E7yLOHTIdoXzp9eJ32+bSaCEkLGE0OsUGV3wbuTsoMq8EQ8JFXJZY/EodCVdhWls+0Py9vPtZy2MAXhoz0ppuLHAOq8vCkZIl9Xs5SeVySiaMtUH6l+ccrfugXkbIFt4I6Zj05xieCFyRqAIw5dvZUDvVRejCVr2aJq63TYCOhTR7QK+hjAW5/CxDnkhwy/wV6f51as76enN0EDTUpX0jLAhEIjjv4qB/qijiukPs9j78YKunQxJKMTtVH3whxgNDM9/vor+zyorY+Wtmb80PaxztqsKAzoT7YUQW7Io1179BQSy0iasHGLDwASxqEcLXWGIkTqu9etYhX3I1eSUfCA4QPIXxsbkiwZVfwzldbY7um5+E73y63aP88zvydFrdNhHBR5UboGwrkjCzPnA15v93sKbWdlDkwOPpa+CH5ojF5pez9wUOFLHB9K4pLkvE25ElvUXDUAxwjLgndRfYaKBrDRg1ZftmMQ/Rebpj8Ipl0h23PVubCBorCSpHunOd/6+BnDbXVax1TM/pWYAbFCUCgI2sgcdGJz7b/pl3HZHQ33qhLXwi7DMbGeQtxUhaU9uhNLZYn25/+1vEYI4nu/l0mc9Mb2DWk/QuNTktx6lKwSvP032aiK5AUTQEr33DeeLbxI+CTUb+ZE+fjR8MmThK9xAjnNdXBxiS20HSya4uDHm8CCxXqA1zkuRhL4p5IpL0FnFCYk0M3ES1o1M7vJ7BjNT/ao8/lIVCHfvnQXZ8ldBk6/ni7t2HTO/WbyXLvhi3KUsaUrPf7GgTOzezhGeVPHvJ2vgPaHtU1erXG9UoB7EYsXcvz7Gp6y5HuHY5KQJzTKPfEn7rz7V5Fo40318octUb5DAeuis7s6ADA494DmB7/+RgGdsAKFOnsCjjgr0NK/UY4q9TQV0+dMrkoUAfUYFCibjplrzXD1zGKkoJvOhu3VDuZBWqV1RnbxHSDKruwlvduYuVVYDzS5349BH6EiHa+XP2e2VfbaYtvv08HXI/xd44e4LLKSytoukSPR4bPbun0FstFHquFgav3TKA5/TaXWXvY0w67W1tuX+IGJWXSO75Oz64ExpRJUwZIjUgL7S8j0Jy+sAoKkQ+dbfjpzr8xWs0NjKxr/t4oKbn/DT10bPrzC5CLgokiVLUylAu0UZEyU+Q+cCXzwGJZQzMYnQK1OEUpuJeftebcmvuvFKMYj1ynotCwCQLUFXyo8GLvNXx0R6CUBHue9/IQB2wOntECS2yUvEcuuqx9KjPipi3jVPJGTXX0pk/Z40nhrroQCA9uG1RVCJ0HIJdKeINbWM+bfv3FbZjkTMy537P1x7Zao4he25Icr9n7cWUSH13EjfLp2SOLT5fRk6THVial5v6ElbcQteqK/FNgQtzjd4x2UtWJKr9BfJtEH0/tYvCbSDMbKqCTgfpXPoxJ/nQQSNefJ3DcVDm364cs4D1nkV6j0+NeGHv3j2IBNWCxvjhiydUFAq2fGPA37+ZuS6SxodRrpXr4w5bvLKJRbon9yto2UmYnWMzg+nHny7ifRdKQgfZHICN+xD5pA5XkwQoW3K+HDX9lVp6XUBS8rsApe8XvSGzox84jg6KbftbmOClPiVVUcb6tW2JA5IwDY+Q2OPju0ggI0lZ5Phw32aZXAcX1VJ6afaKefL2p1Nvv7GfwdF1LLsBlvuprrbws2g6Zb0dgMSDRltst3tWWAbdjzQu/mzB4b1Hn4/YjX8P194zP9SFMRVvFvPbMZyv2NHrZSmXLX2REuZmXhpVGmjPojV/JgjQd0oi1VX+aXlusIfoM39bIpj4nZT4GsyEJKNCfgROtBm+feZxmUN+f3KrJRpnUD/C0154PjxCkJRT+EPQhJHO6sdCJ8og/XaFVbgZIkhGeOJzYeCE8pI4NLiP5EZUZT0a520nTWKf0tHeOZ3jBtiTIxv5B0c9wnHZDpstqf9yM/23MQYL6dBine2n5NPPmLsx0OJZXDgOZuffe04fhNYuWCfelij2U90RXtdvGktr86M3dTrvgKNxyTXTBuuTjH6zdv0MfvAPXXzzzqXlCq7Jk4NlylKzKgAipVLKUV+3XUH+Nvvp3zo63EuO2cd5HM16H7R3A/R1mZAPY7M33Q8fmpYr4ug+IBmtW+Lr8jOohTkcOZTVibFNIH354NhIoTfw9X/lUp/gjNL9lg6l8xm62Rc1/ITy4ALDUroZT1EFH/uh2VftlaTHZWwt7HIaYXNgpDhMxpDKsH2kGlp1tnHTFy4zMcj/qAotjA2/tWfBE7aiNhz4g6Fh4nU4KF1HIfOd9POUpzAzyTrBR+uH7s16BL17dKqtyPXuJDhWn4Yj2jaR6dmI4hPHyOr0P30hTY9V9+LZze4Lt9QRIsgcTeC4V0ijwF9AHAMPUJV2V+yIaXbnITceiYjsClYgxW4D/Rqlsw6B+80fJpQdcc9dLcqSoD0k7RTwv/asT93ejG37iffXEW5dsleFyobh6zvSNLKNlO1tuoF21JKv7P+IEcFVqByfQis7MmPqi+hLVqJjAovuhKT7MNtk20jI+X1d3RQp9MQrMao43FRBSu0/F/bU+/+MmOEYUgrRDk409T5I4ahKdsGsl5wcdYhhHcZyEfO+O4fjl+bibAu87H/xGenN4YqjvZ5xwfcUVd2P58EtQEHS3shEchFxaNQTGq1wG+q0FG4F/yp3gI3GvFAWiealdHs3Vbh1VIIN1hofVROB/BOSmBHlsISFBo02kaQmZnCSfZo9MULTIX0a3hBYoGvcGQralbqpfAtiKJRY11hAeHrDgUlYE1lEJf6kHKLeVFnpf5yOat+pOFfgjFEnk+Qzlh+6IAllrfp3JCoWkcEDxZRjpQUGfuYltstGpzxaKQIBzJwPtli6l6+QgjnRU0/Xre6PHSi/xXhmjlZjOGSMx+MYiSB3/N7SGNTw+4hfWUEWFZtpHCriLrNMJNXRkNctmK/ET8W3/LYKOjImSYlZL7uYMinMw6w+pkXJVsbw7P+uGfr2fxJc4kyFjBGYnn9VM9CoR+NQ5Kf8eeHv6KMsevAz9bd/F/USU7BOZXvvoX+xsrAtyPRhrnwHOMVpUtHIsG0PHHJMG3jWfr2M/C0VKyeew8X4ZEFysY4HDpZ00qylrJnpX9i1YBBJ3CHlE/pzYuLTf83LRR72lQogvx8PCQ/oOSdOMay2kQdKYSZ9CzjX4L0Udqxzc4kkzq9/mXcsUxGUgT+gwudecWDpqjbC2C0tbSHkFw3bECgSZR9F+bATZ9oDAfYLQGkbKUUySy57C5hqYV6WXV4h/4m/P6ksZ/tyPytS6xGRLTl9OAxsAjZi32MvElUfG6SRuV6XOdOvhpUM+zA4UpXpesPt2uuwASbWfduzHb+UxOUWeK4iDb5m+C2N+1lr+ECv8gdu8AGUeuqzTkHAefh5tHYVLdLUjT5DhQC0xjEElegT9nTgXkb7Vn2DWPjuhLJVceDbmGHG0svXl+39eIucab4Q2XwfmaIca7Miy7KvGAcNsbV3LLjTjzTj6tXlcfJnR30ZybeUWEnBbVIHGblFqG0LUX1w50+A+E6RD5nXlRSqjnl2I4H/gVU8BAPby9JxutWVLvlcX+Lr0An1gxKdBH0xON/oVLbKJZ3Lid5+qugsC6ixhLBmsA3182A47jHlRFf7j4XJFfwmXFikE1RrdxttGdH4YeOAgj8+JeGUscV6IHo0Hc25drf+Sq4DRWWMF59gnsWfmC5Zu2p7404L+lKId+c137AXCFZLKyk6yzn0YXdP7d7tK3psdMljEk+GFwLDCxdBavHDjz1d22ANdq9sHjN7ajfSQwzNll8KTRAW+lcG/sXtWV5Ya2LL24TQc/TH4GwHnanoybufcDG/c8ZgGWYUudYPTg4pnYsUibIbq/NJ35Qs1r/ozI1GfST8UnqhlbX/0S3ORkFwIZqfY8EUdm6HApaTaXk6wR6LsCxC7D/uqR57ftzlBPu/VyXkKhUzyGGKdMN0aKHhdzoxQ+XAXyt5klzuHnGypW8KqIVcsgPsd57D2pHY1LiDJhUUbJXgelSaw1SYhFFNFnUvdlFCz+YdQ2V/vlJf6EVP6VQXGrkvDdB0uQWGd0sVMLw6OfnCIP7Y4J5lR8GTHKgQzXFsryYWLu0Pup9w9fuqHSzbyGmrF+3yIWimLQifLTukCn8BzbWVV7G4DEOMyqnNtLmC57qB2eg3549dIA/LoxY8IPopqP7ZjKGuSCrjMaN3EEYzlZ5Vt0BGMrEG1JmxHubTTX6Mg5VGmS/h40H5/+YxUIKVUbcjJaO2gYXnTsNUcI259ZD2+aP98454ZTzoN8vEpmduAwCTBJNjcvCowJOcKJ7j5u2YI5RFU576H0mgT8KnqqQKWybWrtL3PBLll5crGlSrUt2aIWKcnfzV6OXTUvoJo8akNOZE9+HDufY44JtnoS5Tq2zH44fDyMCaPEDI1lhnHw/bvUL5g6Ld4vCJXRWjnPuFsKGyHueu7jyLRXptYA/mw/Q9fYFPTTx3PX8k9fzlhFNXOyEPyyy/mBgZV459bO+cLRpsA388WKrfyJTyxGizq09Gb/aj+7aJJaqMmaZ6PDpwlt/hIAocxF0OV0eV5IY44Q2oahVi/zv7oBR8awuG9q5wiyAFRt1GfxTf3IGmHtSLQpJMETLrn6uY2l2MY9xiFttDnVrAVFTqm5jH/RirXZ1xRWm/t6ch4z4OZ/M578cnCN89f0VgVoqsfmpWiG89tyKNZeTSkrbrq3gsPhG5jOQyqzbMVinq259CE4Cznir/9M6P3t/kg7wwdh6roEUJZEk+xFHwu/AzoTPfXHyXSe006M1CZKxs3/uipSzLCxmlr8gpBsyn69R/ooSE0nDwZgfrXc5uSPaw07ExSfCaFHoeIUhQFToRBNxWCgkXtB/JtpR6Ln6GFN9h1e8XNcD7dfh+Ybs9Hi5Yk2rG+gzF3CKxNWTmoQpyfjKao1g4bod69dInTlTiOwIJU1BmQJy4YDYpj8OF+vzWYGrYeWnmBnV2CLJlBx/spbDzV+WW9eh+3kkvTB0/CEBo0UYgRAe0WHGGSIJ48YG5u5mYloQk2cMR53R7DHE3U5+xb1/fGya8dca+CpRHqQJCkGeDcekEhyRwxLrwW/R5+3MGx/V/ZzgqMA9P0rHTvJTTRv+eHZbyTW4W+wncGsiOPnJkc0FqiZu+LTFpRCARo7l7DAqkb4IBG/FooGOTG+Nk44lfZV9xhaQEBWbfrqPdCWSOP0NW2kqO5oQ2DJ+HSxg3Ufq89wa5OT31I3y0MkQRKmQ/sd/oVkMUKTES0eX92HE1I4FNG5VpdRairWbDLQOBq6l5S9p962j/fXXP1H8mDhSgoB4fPmVKEtkJMYyaaFVVaAdH5x8+AuIlFJUDi8RDuzqarInqqULF4hm/gm87ODNWvz5TgiIuVv9B4sWXMrygMQRpKxYCCTghhGr7gQz0i1hzkAWPngRxLxXle6UrDWC8AXNGSob9VrOr4PCeo66ErSPjN8eJIGm07/crPG3zXuib/czBKQXeE82gK5yoVtCh9rn9htqTFu3U8G9ZLoen16GDJnbJTEhfDYfmIfw0TfhvDU//2tACQFc48+qT6ThPB5IYP/9s/8j+6xnoOZp2XCQbgxXv8lrBnBOCZTDeNxPQtZk6eIvc6P+fnmCM0VfjVwaVrYwzZmqZfT4Da4MpOb6BnldXqpkkE/E/Yp0ozDkSKpoo/s5AZ8C61+oeFwpy46xFuU4c1QusAuJcfqn8T9xz5H5tefv6OCGE+xwW9A/dz33B3Q++CI1N+gpzXvTPv8/LbJyqisZOKldPzBw2JAQsZWW2VclEwc/lsMC0L+JNCx3mFJPCizG1/eLMQOu+J0csBbqxrahi4K4ZGNBk33+KtCn2TnQGvPOTxWlG0lAGn5BhPCA6VdTBcqo/8SeJNuFZ1z/mmOB4hSVlSuLb88AAAV6NPicr0MGwRWrM9fUlh7z6HCTpYe1yFuXjTN9IujJEO4tqk8BYf/hMyJJTL7GHIlEL1RHNzJubjwryrF0hy6QhMnJCCuMTWZBMUmKD7z7ZSXRn2Zv7KqDlEJLUJF0Wa4syrGMxU7QSUZo2gsrHzxLUt3Lavla2oAstKMXQwG9aE60/LMw0Kuji+7rRIvviRt9G63G4oCQa4pB8dknYIUFSvlMHcW8FCnI5xHCKq/g+ddD5yznGTu8XG4zDeAlIGllFcjxWQtB3FyzcB1MEqZjDkxTfTlEWP6fCJLUl0qqguDFQbKH5CTMC9iJN0Wi8eOsn++o/LpV6aByCzEuY3lZDozR25oaQVFTTIoLeyzsDIU/B4wogTK60OGCpXdY/993xToFmZlF3wr/RCCnBntZ6c27Mu9Qp+/YthMXJ165FSliCbOL2BkIvRlsvD7NNHPK7yHACV3hRPMgOqp+QMVNuPZ3xB7OaY/GtxjHrxpuApD16tQJykM875rvWypvbA8cWWQWrkBislviCXNf3AGWDIMAIQaWFL+Z6odnBVgdIaOLXXlUgRMQvc6bxPE6Son+lIaLAIlnADbRLLP/rqKFvWisUqzpOzlsRyrKN8JfNRGs2sSEtYKxinxWFSElSB12SVYnYdtrjr9p031r5DyLy0iD3KiDKukECK9LvB0qjVcbfxYBBnvTiMqLvIje2vzHMSmll/Jh66T9rDlty5SqcNJo+xf55Z5ln/gpc0ye+dRF5aF8/kUYH0s+8XYKTbjaftUCw6m00WziR9yknqdqP9/Ht+veX2NtO7VbuZxg6iWxHhyJkAZZDY9VO/zfpkSb+HBH4aNGJXr1PllnYmYFeUicUTiLg8KJGg702UJMR8YmGk6dUru0sbAGuNi9iiGJySaYf3fGLpEpEq3Z8ve4LzRKoLpcGAnZ63pCF0PJHKvHIs1Rfh03WQvnHur/rFQIOVCjTqXxW5jJ3LNsA1QMTeny4FwmPvyx+knm0oKycIIrhH3uR5aMwhcJgvY1ZiK4HbWa2OU7Bhiiw6dL7b+7oxB3bmG4xVecDdsfZuieY9bVgjhd1k9CoWjejPmIVZusWWNTOfW122/UlM8TyuuCaprdhsk07wAQQRwtcsz9QmWo5lkbjPW1aIqOi7I2OivStVqr2srhTnMAf+px077pZDHJMloz1QaKn0kpuk54T5G5oOc6QoeN12LowR/8/5xoYrA5G2xfr//qwYQRyHfu1g6oaxnbytg1gvx2XoSJ2v/pwTA9n8sd7+cObVxkbJTbLy2v8k1jA0u9oi2xXWM9bdQEuKqLiT3NZ2ieEq3uP24ANIf/KeQ3ix/jwXa0brLrGRGg2kf5cn15uUeT/ilAdOWFcZS+iBbUCedTrSwPFVnNxUIeNwgeKzuB5JwDbSE6KjQUSTl7jenFB/0d56a9G0eccqWdvZFdTXIECIsvc25/ZE0zONxnAafDsJpu2pMtnY5NjnHuGTgv6xGyTBtJ3f8+9+H4XzGXYz2np3GAJQkJ2D+5z0YNjv0+tfsdJQt4rhf0r7RHiX5f+7Q3dTWEPlKO75QWlLHt4bHQgaUQmpoiLv6W8QQJbyafhidZasoCI/OD4KcrYBnvY9kpznK7VspYQXc6Y0nC2JaoIvnjk7EuLNuqHgFup7pODbDfY+9tC4IEnOuRAFoVUGZedeaPU4bq1AmSrEveAa9q9LQmn9mp75KSYCKOEl2nAsfDa8356/DI3/KFxTAu8qs5mNZh6wH90ueZHNu9mD4mw52BXRe0aCkBrolLkiGIu6E6ZODu1do1SfqmDmsKSwP5/giajjT/2UWWP363M4G2r+wR3Wh8moZkFnK00S/mdBQnpqihvIgflxZo/PU1Mlsv2FYx4w40VzGA6OuWMOXSJ1pHfXIp54jbJYIHnYevA9qP9NQSxTJ23DzPWp5EHk2tDOBsSGgK7XNrGJzff4QjODRI+3mH7bVu8Hw/c/vUhkS94THpbler6TF8VMGNqa/QU23yfy7JTBnTm1ERVqh+aW3V52cWrWPE3xoBnJrHZR2878fDpj4y02WEhv4ngYm5T6SVfP7U8FQ+Vu5izUEiDouUlm+33nNzN9vQtNgePD0+tuKpJCnjo3S5f4Vsb42SMQ61Fcjv5SfmDf0idkVGvtDmzQX9PBH+KISX3BfUARx5RThydbsZrh6y1vwdMmFT6W2Jfv1L0q9vc1UwyFvirrzQTadxJn2jDrBBnUlXy8xFEf4NnugWZkKU24bh7iDT/0SYJIBfkPWtPb1j1O42k8aEFqYvKzRpfDPBKgSLtKcS65klnoNcAbTb6Vi64xWHHVWjIxMqEm8IJ4sfkmHY8/awR8SHQJilHFh6jSzI8gmdH0BN3Hy1kdGrP4Tmn7xCn7QoSea7nEXHErl7fq3+xz+hGr7YbFzT0PGa7i7ucBvE0kbn7ERsohNZx7+uOWGhUj2oIqSfJXxfWt/s5fZPRJ7OFJ9uX7CtBqudC3KlciEo3HvgeR7Vbdmfpbk0l3XmoiNV04+sWMcMlqiXigtbQhmE4gE6UL6d7d/LNI+9vOF3SP5fC8yutNASbrkb/bIVhmnV1NXmZJUdsvc98n6KBzp94AANXwBPG+QF3+UHy99f1rEsy5fU0o4Kiz1Wt0x8aOW/qz7Cc/4I+bTuBYbG2QUVfNtn42eolHABFQjmr3TzYZ5Wjn/FpaXjHnpB6bdxb3CcKpMVW7w+LK2B/PSgGsJUgdLAMvYEc0ULV2jXLKMtuZ7vd2Ax5oZzmDz0Urv+gk+tO4aVHy4aNXv/CIZPnpGDvsXSZqL4wrfjOddeyRv/G3s34pgq5bXqUiXhZFWvjhJ/1W5dRnBcQ0rNyXuIobxugmrd/trx+DLWaauI1Fs512nLRxB14LpBdneXQzkvpzqm5F2Olgz/IqZDJGgx4YSKhW8Lhftg+BGrcW/xqsxMN7HQd2nFuQHNxWXFetDxxALu9X65R6fBYzQEQtcaLHPkW2kzUSfKaHodIZk2o2gwpW/v6UrqxdUroYWNz0cVqxLulFD1WP5EjfoJxW72tdvIolwtylj0f08gNCH8YG1f/efCEof2SWXZfMvMbWr8SvJz1LHMeR5+KtH3Itx7J0qRgorzQC4+7Q1VFODis25neWfc5iY3LrtwYsQg8SvVO2JRabK3fB4kPorjIKhphibhzIL9PiVETlSV2E8WVVi5MfLTFtQKpO4WWTQ3ErYS0hLMXREJ0rDyMawSrCLx8L2A3+JW6M3X5kIVjo/qB0usoG4prlwruywm+zYed2dIZQHZzckdsZE3/xj25FmCADu4Ic8qOoHx4pV2QVqD38pdggk1eCGEIOr002HcYDBqyiRfO1++sL7z7Dovz4N4+UTeu7cytfG6hKV1TrZPi/VAaHfYkV8j4MMzdc1KsphevSC4lw3EXZ1vJ+OL5bfLDjFtkMfpO2awSipuLu0fpUVTy0JZBj6xiNwTQ3IDPeke7Zp2kD83I0Sl6T3aaSjEFM2di+I7hHuxvaRqLiYr0ngWe/RhbP95Qf0vj9AQvXwjCo+Oyuvkwgbyl8OioEYRHE22lCyUfH4eYAVHABo0Pb/TVq0/e9XuaIHmtjXl/KqCsgHTfwNJdbvt5rR6pGFbJQYxDA0+GvAiH8/dYtINBiKWta8S82rgnEU0Xsj7BC3kZQO1fDX2JqfuClo0cp89/XHWMeqvu6uxNdBIetCSDPMHLvSihX1ai2hFVDvhiAlt1oxj2wJNo7KKxLMV8NfiMSrDJK5PL4AXjejKoPr0jhGz/qIpWr2tikAssDKFrDseWwuArdS7ZegortScTCol0IkNPXT0HgGuco0SeRErG1OoebKfYFtd+TW6L5AhegCiEcxIMFlw8J68IQjujEskd6WP6itZj+PN76B6i73iCTBYm45xmODDFzqaz2HE7EPaginKx5poFMZzrfah+G7ha7cBMHWXp05AmkOpYF2Zkn1R1h897gY9CV35cc/uAwizjI+nuqGip8HeoLFJgHZLwM8mA1eZBVDTJNM3cmRJdAYINcOlS/cfjN7/w4/TlZp93yBkw/xxadG4qfNK+oZm2Ic8K9N3ujCa5OfgH+7c68rLkADJat215yu7vadhv4aPBqbPEgcH6GwwIATAsI2s+u+BlgBJh5+Hrh/t0Fy+cAbQSk/99rgfYVzHPehFwvBCp6H6zM6INLM1jlH72c8C14HS3FFIitIjU8JzQjMUscYxWSkNrgq/RUqNPlganWwNEZu69bmxXiEEc8JzA4Zs07iGkoSF+MXVlSYVxzLH9vz1QRvMrZdz+SJf+5Qc2qNDrV7zZ84aGITbZJLpEV+tElFkxfia9EeVB/VRCfXb78kxWBMpodLXPL4saq6Y/Qkv6WeEpkNByf3g4l/MMVDMSTfPlUYFGs7e59/eFriuuhFzkaF4uW7PYztXCnIEJt+Sr7oJjKNVii8xe3XUxs35Xltdf1hK2SqiyRMwse2kuzciqe/EHedyfRvSdtHhrMG7F1+zS7uPqJRClhtYKMieXtfRpnfEJ02u8cTzNrVyDgp/ZBvxuMXOYBWWIMp9E5ZRkFBEkrUFIx2Fp8WqQ/+N6lAP7Pb4MECALGU/0eGL3Otvw588UT3aXxTElgWSypYVmW3stG9sqAe+ttZeWtBT9W+o/+yDl6DppodEHN/rgx2DtJpLf2Fcy5/ObhqsCLJYrTwBlYp2I3qYujijfaDP8Z8wWVDhwtlU+0p+2sr1RnzR2dsNXV8jI/Ya38NvGdWEFsOjY8TONQlfb0YrpIpf4sbV2+zdCn4SFURJ6IJV45OibikLOo+44//mwf+mwKpEc7Oh3DIYvCL1o/vRCtp7vjFd+SIPGVrO3aUmX2xnd5g7nsPPJcW1HeeLuE72ZNLbPEvVDB2UErSkhFMs7VUosJs9kBBjYvvz10E/KtZ/4Jlo+Bd5oXfx2CLCr0QjiE+SUEln8k9r9lGa2jjosvHKC4EZb6rXsvK2RaTgF280DWMSI+6u83gALd1+l0SwnSmKKRqBtW4fXCn5hlk+hFeTWgG/u7Uzqj/QIO+GAe8DB2fHsv3UAnFIosA7dE/6atObF1yucj1QHR7RhTw7HA928ym28UJujYoSj/ZGvBtF6N+cU9lpopnFakK9bL9ADcGdgsuU7Ol8bdIkFCf6RjaFtDhR7+BvKrvNN7/GRSmj3OXK0cS2SQ0BuQPwzmboIZRhUkINbK7rTqDk0yjorKULE8dQFB+MwR9PK1+Dzo60vCzV9dlDX8dokfKqXnxzY6kyo4qiQAjQeVjprON09pIHzEIo8nCquQPje4Y5c/LocYhezk7ibfNKaLrx5cqRP91uZntupKgLtFsjE1fAYaK8qVzviS8r9wArc3Mbb9YVJb2aCn1385CuJQsQuM/RbVBitHDzY9iR4vKGarvXczLFGyF4OyrP7KsFV9iGDupvTzrMbi16el0V/uqG9USlka5CYAHqZsbUvAiLjz+d633woWpq244GFKuk39RHBruOLclU01G5B2yTHMtyMm0EXkrKfPXApglxf2qm44Vxo2forB4qhZ/vdD37FEG2gc9DPx1ob8ZMJ8ACvui0Wk8+7gN0zzf5U5rDvdthYLGkfr6shpBVkSc5MHn1tom0fSH1hx6RG7pqmta1PYyIvfLK6ZPskLQYDVdctsQUj/rKQ2QRPjFp6GiSjUNnaKhs8LKR8HpgjmrixRg9vr9EAgGhDVnQJvLKXO8BropF8nzmLkt2E3hqK8zCN7ynVnPRuOWfOMN5SwO3fCrgMOZG+TsXln82RXRdhUfpXsOrFaF3jZ8xP2coCJTsAvIc9pXVtyvwi6GdNROso03CZq9eAPk2GKlD2AM/Ox2MkiiHGp+jEYzR7RTUqdQXA7XeCU9fXd95jn9OwJ3D88n3FGVG9Y8WbWhfkxOybNM4UKHhyQTPZ9ZVhUnjAq5EfqK/vFL80FuhOioTiIXl0tfBekLuJqiRHwhJkc3Gbx28edGhl0zWOg3/02e6xqTcYtyxIxEMsjqrltUnpoxT6W/QfTOcwkGw0rgHX+H4jaRPDegWZLaQ0JKieetwazheDf07G+yteVujkjgQTOmev5aeb7bYKsWG51abj9Su0YRTAhgVcMmak+sbbHQmNTdm30AneNLjgzJWMefSFelhDpqfgXpbkGaan5E/MeZf0kxUIXh3LODvWPA1VVfMdsv9s/FrsgSKxWPYhB9uDdQaRYB6lWVCD1KIn6KPLu0x62p0p7X1jZFSW/rKzdYuLwOp1BaaKgVOHpYkzkBB1zVEP3bb/2j6MKVDmIc7LF/PD6DBFCyqpMRZaptYztnMkOvRophaPMqA2kRUVb0U6bWl8AV9rl8PH7f4dswNf+AzV1yGdChKMiRNGnq5nTgx/08hDjoL4JMIfqa7R1DyH57n9kos/nJ9Cj83UmIzxBmifNGELbhDS2ufYXXQ6LUxnIe+j1n/gJQjMsW1hmPvGQgz/yzRG9SgCli+Wisf/kCI4pYC8W+gKcW3vtvu+n+VAuznwtzgLzVcYp8ePYpQoFSa9d/n2SA+hDOq3xJA+vzuv6RlBcyNdwTWvEF/f2AZ7Z7kI7qdmM5gM2kflLliqGBFfdMi6BRU8xv7LBxEGyzjB98gbBS9SylbJ1MF3fsQRaDsSZbJst2DXH3o/v56MR+xEpFlKhkixJyWYdP47+M1ZhxHwc1T+ery7dqj7fnQv7Hi/17Wb7wXF5WIrUdTfIMdU5IMeXq9nx/UDb0Na9Q6+v2vTlntVXy86NYnAZ8OusZf/I/mxeanP5JDI5EdK5EyImnxDrA9JlG8ML7XEye2DBK0DbPSoAODapnFNEofJqXNTvo5w0sDJTG1yyIHPkGFVSxwMkiX4YJrT4mQ8+soWiGCOljXcAZZb6rmPdUkf7aogbriFDCl6aOcllSx23VPzPoZP6ccAspdEb10fwGT1ls+VWBMv2Hmm7dbmA649+oCFk6FKU7rJ9ytrFCICfZ44KhiY126tTULFboLGzLiivXYK0rSsdADh+IkhQvDqbnr/t0S/CRQAHRhJZlAjdrRJfzP9B3ojQAafAvJCLriyN2CXLmnxoW7d3MsjXAcpTAenSGTpdqaX8H5/odEed8dUKU/IXU4JRXy0xkyOTfjzGhWaHKclK0VONg3n6VzcfVrwHcrxdxiHIrjST5RK3hOUkI/+dG0GkFLHXSY9g4jfx3G76IKHk7PvW/Ewjoi2yhcxDkzyN3Imnje5ddj3iYphoo1eb7k/NdzfLnpYnGMIzQk4138w0gGnh23Me+lT49t+tPEXqUVs5flVmSe1fS8X4D95Na4YzvYngfcqBzTQ66Ve6CVB7gWCuW1h01nLj7XC7GE8FpsNsmTzSbwGz5dlgUc9x8SXZH1LaEQ3O08q56r6AH0rYfuBPOZl+e48lImRzMjUZ4Y3CY3irX8NrDbYMRZ3Cyj0N+Q/KIV285NmFAmhYmkolj8jf2SPN8yGO6QE28Rk4FLQZPNNCKbE8aEdwbSQHoDC471JQMLhJByIJbxau9Yvv52wjm2vyZ4F1sY713QRw+gHtqFDULfGWw2bT98IgFET//uG5kTzXMgYl1rWp3CvPCnBAM9p/ig9wAtvKWstslG3TzK0HxFh7d1TgEZnkyeZ4CxJ4+DjLCoECFnhWH1buFdgyZ8hH8dODqFwsLH0a3wHhMtl7hNHFbPyk1/aXr3AUHKtX95EbakB9dvDEJVDPx1Tm+mVi3EGmEvKQSgDMuctoYkPNSQUBsLFS2deECfJTkZ0A/F22Y6wMZAXKWA/Z3qJlvZJRl/Eyi0brKfIT9b8kPQioX5/qqqoB7VzGqDQk4gNwwri8V8DiHnlZDEB80NzZugEd45bLQp7aGOY/ZQi5vOl2j8WGTwWqL8T66ehsYyX8JYL2p+qleG8XUf3UWVrmYvwm9bMUQhCd9bMPAVoUaQQ1ikQU0jloHvs4A55QTrnDL0jdnlFeHer/hk1PqhjxLvNp9u2yf5eEyPBu3BMXubXJAZ8j4d8b8P9chWccxuwurAQpgQd9h9wbiad2Lfmz/+CcwiLq61wKY0EuWLZAMswC77IJaRo04fSCD7fCcN5u2NxJ/aaTTSQc9mMMqF4mOyVAYLZ+MRgPuF9y/iHXSD9w+P6gWTZ2f0TJnng8iT8b5N98A+3BWFiXHFIkmWk0XmeDQsaTwb3tJMT3RqpqL/D7Y3S7bng3nT3GhlmINM49GrWtdaELhkYbYAno8kKMYC/tRFbgGS6DFbOwdbi1ko1isNLSPYelCuwH1mvKvO+19SToL61NKaKeM0MuqAV1yXhXsLc2D6vLo/q01M+L2ZzNI/JB5CO0vbf86MPYzR0k5xniiq4s3yWn//4CPSx8nqcU4GzAJ3RvKtITOMKV4pSQyqPY77zKitFPieVvsYSu/4If28YGk893EEqruPKOtMuYZKpLTtmbLdGpk7PsoI9HPGUnxemTNva1aF7wU0CcuL6EGLIZCpgsGVYbwGz8iYkkMGtU/qLXHwEn0wrhczAfB1eKGflryhNMC53a5X/SzW/Evuwey+77N67amRxfkg1oIfUQvaV4nOI4V7GIfyUMO2pakRIgyu2P9aOH4gfJ4NK9aQg8kkKAdW2QD53YGwsfWGnR/eOxHJI2BYobc1lj4N9Dv+ZQlJtzeYPxKy7N0ycEAM00cMCyItk77r5nvhprbbVus090NZF8PG20r5/LwKqjKRpxqdm0DfRQVpt+NWnSFWxZ7f/OgYr2W2pRBmoywnd83dD8f4QzmqaHnUh8+tpPtptc1C8qB9TNPxRSCKbOz+YoItKmGhfVIhMjrPQW0FLyjTX6j6PzG1fpD2fFfJVVfm5IHG2QusdpjkFUtfzXxvf82wsBKHlMFh8AlKmDshdvNF1UFdfwbDALHhOWoNxWw/Y7r22lH01JVzHmGUgic1/x3c8Gdtgbjr9rpVenOXUNp1cUO3QDNEHkvIxiP3/+rz/7ila8DueH1iuMSwfhTjqfipwe+rvCooX8HfHulRjUNdREX3rKzEoqWXxcKM/jIxMM17y9OgDFr3+0VARbuYtJcW3jSDLV8rOgLQpqLaWYHoXtOJo5+nEt9xByjAoAt4+8mmwlhLSZAZ9E0x2tHbMXcb3SD3R8/6liAYA/wjz719zJYArC+KIlP7GgTWPVe+KLX8/wtsiYZbphlrfODPLB1nA3gpdtV/mq8k1t1mq/xWB8jq8q+n4cQgv4lv56aEfB3JGAI7fXzYqUjYQ3JxI8vblpU8yC2VHCDjqNx98kaSV4ERode/upYRgD/Kl7n4fDfYFTxUJtKv5o17bQ/7KSkmsJxe758q6IcVzVjGk4gUzfM8DA/2Ed74+76yJUJ1EQMDHZSS2DA0NoikIX6c2N/nu6U52szIaqOYYVNrrJEJBdo2JvwNF2kJHWrU9aZ09QARDCEpEDz7SOVVqPLEAPO7TiCHQG2dASjGktYdTSVew48cKmZbNornnYGpF7a/RY4RTbNFQPJ7M6IjQpHa4VMNn+bt/s3P4SMejioCgNmpknZW8WA8PMKugxXyXla5C0IJC8N+UtKaT5j0b9JiX20rur8ilr2NScUrlvx5S6xdP1OPMVev/Itw58S2+OvtEVzOZsZ3ndznrNMsipqkAMwjIR+2UnxG2lbFld8rIQzLqfLpgKbxhTDkGN1ia14/3i9XZ/M768KbC4CSped17lMh+MRwiQEoe6f7SfrNYebXTODLA5t2TLy7cppbi+F8Nm+Aj+xV5zfyJA3p9RsodfGDzU0HghNsUfyILN20VitK6nwHFJRRGhD7R7cjnTRAi8HN7myLUo0TCU7TmFCJOmTMg+0RwjkE6OxCNDiy91Re3NslU9R2GdAdUceo9Lk4S9DIetJODewCBPWyZfKwILpgreLlp4RwXx7Cr2vo7oHyJH8OVlcyd9DnER3rxEr/YTEzP9Vsy9ls5zQLuXdUmKpr2y+7bDoZon64rKj+8shKq85ZXZp023WcmTzPK9l9tJ5qVlqMTu2jjXfEuf61+niwnGIVhJROzvj79EcE59XD9z1qw+grpSCo9LMSU0rit7j5W93Gr7cci7Krzp0sXUOI52hon2K/2aGpmcRsJOXynk0XOKWTbFP5JnX1dmWEUd8qAJt+QHxfIZmkhurMbDxtL4eg+GWXX2hgMSQ4G+qSpOnXAGSxqyDgVNjrzAfujcEY1rx+uS5QTCLffAXTrG1uHSJhNnKpFzhG4DIa3j1+YFp/G9eQAshGz2YamnrZKphbK3nUZpQDPGXkf4xBgoGrorx43jHvAsx/5ChGY2ipH98WCOKXOvpHg/QcDTm6j77vCgYv1NQ8uAFBrTzxcC+NgFOIZ6T+eAsvjcnDYMwPtqGtdMsg5AxqsGo6uLPa7SS3g0t+Srg4TY/42WSybjRGyrtgVspjKB8V+t8LHT25p1I1o0aGxj76B4rdv3dxJIssmVl6R65mH3K9a94y0X1C2IsRrFSg7fKctWpjENRn+S+RclSMxafepqeT8EpS525l3JgTvLXpYlo3SCPIQSun8358+Nzr513gjoqHn3e+Kpb9fRo58jE+OnZyZ47gAR+u7WpM9OrYas5ycsCN7bEPQ6NUWyNaZtDgEZRh482Tk1A5nCzrH2+WEy4FkrFib3CLKloRzkYgnxtnmMo90b9qcBX/hzso158RfNLpSMLbeUeb3Wnj1iE+Dnsww0wVfPqS6OiMSTaMWtPOX8cNhgH6W9MlGqOxl+DZ9z6zrMPtcdcezFL4tPvgFWMLBev6l2Rg824oBApK4j5co5sCAyVFxrYeQyjPqK/WjclxcL+Eese9Pu+Drr728RgCT9DamMQpDIK4tq9glhs6ZlGZCM3GLscJh0KVt0upzwnCnOAZBJOqsld1N+08eZui5fS/k20AgIksrmkHSbUfABLxJ+qOwtBKKhwQXDM0kGkNAhGzThfPllIbxMdz5EY2S1+qAjCmpQ8UkbZVn5fSLQDCcpALQ7FsEi5TfclQ7bfclphrKRUS2kRiig9yD9x5+uGO0+YXPhsJH1Tl8/vU17bOV2imnrociqV27Qvum8EHwHhTQzFsVgVL/qB34U59IOxV/dGGlpYIxkilBOWLRz87a3/F0C1CbQewu+H35M5gV36O/P43DThbbILoH3Etd0sQm1Hk5RRKYXdKHopkCh67GZWmaWMccLY3/IzSujjN6SJoFXdSM6cYBHpsfM3YqxpGVyahD9n4ddtPeW/lSKCfwzcFtrPdseeXsAq4q1mULR8hNs6/QOZDfrGPy9rRL0oPz6FfSJdZsbdUQmMuu/Gt8EX/OcCIodiERONwllRnfW+aAArIOV2xouU4Vwlzvsnnj4HmS/KZleEd+/jwBUl0X4H4fSUPh9mY/s8L66Qc/w3N2PpCHNUZw59ES0Tw2FdMngtzLntR7YRmbsOFM+tZwbpCySztw6z1cml/0YAMVG56T48rGUdBVTlGAyDrjhq9WsEeoN5sYeaKfX2Qrb+3an6ZU8Vj9l1UTWVlq8CX67WTwt9Zu7JOyLSkH20AfOTRGVgsg5q2ELQ16OU5i35mt3sPapCfY+VEWJ2b/KMQPK/ydNDiwMhHxiJCoIJ10D0bGVnwHo22TBNgC3XwOsdNu2fNL15Xcb+v9PlVtr1aQB4hvfYwZXlTcSeRE/x/QXWNnvFcBN9qcbtNkRbqx03HecRhmiFhssXc/vbulPoFyR9+4MMKOurKB+OocBDoKZHWuwGVxVcXzHxCjMYRgnPYo734w9By08dMSnpGLC+/zjtdW/PtK0/bGCdwo1glRO0xJ44n0dPkWDgtHQu0lbdpNZ6GKoGHttjBgGxLtccAB2fr6Sz7QeMuuoZdcDhDXRM/FILuHJPGyyTH6wcga8mobpoAk28eh7q1IsE1xeM6sO2JCUNXIc2fdNv6eFbvZuH1AqXqXzhtz5fVfHhdRQnSPfzH01XsSs31y2f5p+bYdhuMzPNzNBmtp/+ep98V4qU5Cjptjesqlr4V1V/8M5MTPSZvneVN6Mgo629jUrEdc/1pvW/gan5xdGZmQvd1xd+zFk41N8oFn5IrdHhDvLirHHQvkjbcQadxau2Nn/17M4cdNZ0GGxbKX/x2z1v6seQkuyvM09qJKxc+VkJFflB5YT20cUek1jeXIHHGcW3+BWXqXiy4MNm2OLJkTZll1OpD1CQIiAok3vRl6nARYZJqg2/t7wuL6l4sfxGOmhPPv33hsG3x7Zv8AEaG22Ilp8zN72zEE1ddJzfS4WjO4TFMLPOvxiNWJBsV0tInllfd0O+bS7pSmYGNfT5rW2ZW9aDF6Qxpb1tRmrj/KWEfiO1iuiquaFyM6kT+iGVGE5ttkobZSTNPHjAgDXfJyGS0jY/UIg0oFj5JrnRo3vOqr3juWzdYlXiR8I0eBC0AB4crW8jQoE5fK/pZ9cfv7x64jA0Ah6/5BGdYIwfM1bmgE8fDXuP/RW96wHTMcerlpjRbKxFAUy1W6hr0dAz7E4HGict1DSmh+x0NLMY4iKNLUf8Ig0u5y43RCUqo6j7LT503uhfO1ODxseXLTp33nzaf/UyMjFaRD1gDlQGZXgHGF/2FHwVBhiy5gbHr9ppoLVgKHf0bz/P47zfg0p+FpjKq0v3gm+IFk6UMdVdidNAeUzmLZnzmiW2pHa47rvnXoRyVUxW326YEE+Egzj+rKNrlANW/5Za3REe1RXsgFPx/ZlhydReLNviuhTOdg74fTlzlsl/astnqvuKIeHOpBdw0yrjIQz2R5ph6OMnLH81brpT4kDMhFIl3SKFptw/+PlvVDNF/O//qxzaf7+BwKdwsYhZMk9pDKsxgXGbfMJ4zvraYNeVp79pHpfN/gCUQVViXZz063+nUR3rb840a2hibLMf2OF11eQh7lSm6kdqxfi7vQmtJAM9Ilz4ItNX0YzoqQy6dJfPU8PAKTKzMQmnlnp9YA/2cHr7Zs6CQ6d/QwiXe3NGSvs3ursAFSYWBAcJJLH1ChuvHqV/l9meEFmI3CruAsxZtm3SEZavsts36G3tyKeWIAMhQzzm7rS1fp1jIsYvmtzlr4pAobNaP0gM7bSK5jagVs1pm2gnMsaH6A6wSq+IvlpX5CCzYSsW+kI5DusEqYAwwWVTSOUZPBUShI5WKRYUJVrHxHso2AEbyosc+3IXmWDj19SFRXr6hR1PfRmUCVkcaqTu67LyKKCP7JUMw4tggzJ+gqtI9sn8YVacTvE1bT/oCHLm+3WjFGX5ud8Lw9JoO7L3ltHAUVPWwcoSd1pBpbyQs0ouAsyM4mqzJlbRBUQYP/3Hwf40z9TMF5ZjiguC0c+I/Nc3ROb/ekPhRXH51Vl0U6c8JUg4LyZmf4S/vAAJWtY/3AL/ASKGjz4SWBtop/jrh+cKVfeW3juao7x1xDgc/QbK6c4umn7I6o4o+nGA9wAHpaAnE/XkIz0hheLtGu6mtq3B+1yZV1SJf0Vbgke2wdJqkPY/TdEIwHygk3nf5f73/WvWD1dAGihCdevdzxin3Nm+aYvvH4WrMifbPu2e3oCFfQ9pE8USgDQD4InaMjormBNziskGJIRur7NMM7egMGmkvTw7LhIXa3GbyIYjE4VC4tWzgDnzIluQSFeZXHC3JplSzB9xHEvaltHlHBtI1GAucOHW7FOrB5HOTLiNFKK5LmcQu5uWkYi77f4NpRkqh+c99Cwk++3gvQz/q/UZy2Xjz8OR8teR8Pvp5O1emgVE0suyaZ8iDXMMhdYCrAH1wsVSGJvdC1gNkoyaN9XpT82w9uxa/Z4dFZ8hIRFQZz4KhH5V+s9sONzGW/lBRsgBjHgC/HJDEwqHyDFZ1H418FPvIHELDFTnv8cBgt30ygqeIR/4Xxd0tQwrKDN0iQUM0Pud5TrYEptWf7pju1QVxKVsRDwYGdZObASAXcm/tdKgAZeCvzSd6PeJRTh2sdhmxoGSVU/XdoEpSts4SAqHrCFq5tL6wS+M0XeS0nv2pW05GW028thlImCDez77V1fboiBU5bldGad0V1XLMUl0IiV1elLYnE7jbwNkhva9PdSKv+GHRTQBMtNDMGQW0wTuvGYSx79yDGS2WDuKkjTwphjd2LRTjBsr8pdSv32XAAigoTxJL+aS6r1FF4EdtmXZxhCeByHA1/twbSX49QhWykp7l4U3CoHR25Gk1iEWfNuJWpoJqJ5HtPN6vTLutn5P7i8wXMLp4efYb96ftUGSUL0FV19WIe9O8Kp0HuO7FBspG2XLn0zC1yey4Q15y8U2iLD1WRvCGiPMnw6310u+Ypwhpbd11wp0pNXmW8NHm7h+XoyIdXTCMy58Lvo1oWhAuzoP5+CqIrXcAO76K+0/7y/HsxlfbZsQFFzw3vrNI6OKfOZs/TViTZdZlljyL2EqFVk6PiskNKPa0Y7JTfenk8liSW6aNuFpNQ1P6LCXnKSJYZcKm/ZSb1IX6tFPdf5AtpO9XU+0AtnimG1aYlDsFjF/VLu8IeQ/2tySA/Ua9JU6iCP9FFzOBtxrLprVQvys1/uDb7+9Yj8mTWbZZ+HjnezMuva2Uwxmj4S/gJMlWs+Dglu+uqUQi9K5lotUXYutEf/DSKnkCcnYKRLkvFygEurc+2hHEu1GCXP12/S5MHaRhTNYLyKas7sQyoxv+qEXSZCv9JKOCqVUGzlpeB8ItaHp56++aYZ+jYwNZsIZjKutBNWpm88TsX3TeFNd/d3Brz4FDqA8ZhjnKFSh52904qF0l8TPb94b6+QIpmlDCfURXVL0ZphtiKCRz5GLMmzeNVfunYf3xFhoxRFYt0+6vaNTf024Viy+tN+zfbxOOyP4a3qaHBDI9lSgtnug/kYj1eK0CWh5jwIUojclVT0PuGgqSJ5ItNYVZR6LSNk1RBX17hLduGL4GcAy7QNyY7xWwXwPlStYvd7sELeRVP6lOoOXaaJhn09Pny99dfVvxpsmsudYEkjqj7dutRqoGO6fKUC9dncYeRMXb0xR7MMGeg+8Edt7Z0okOTzZU8rmr4tYPHCV/tzaS4dOiO56dY4JnEfR3eCLpNdq+/r8CmSXJBeDsmXeN9CId+xy1mFPjJ91Ly3dPcRnjZswoliYAjsDoLGJxiNgMbdZ3XOKPbUenis1b9H28HyovPS5dbtcoIC4OiBMyzvVl1VH5YhvKT476EeqBn09ZqL9d3bdBrpo2A8H9it/DS7mRwCtbaNTsQ1F65xXVfTi6fgTSuaUbqJ4DZuvlsXFfDTY0jFdW0MLSWs4RrqQ/LGeoqw++eDd8qsqpMj70y+6pXeV13gOnES7ATjOsW6UhnRNTVc3wZX93hsPzO+RhcXXn7Pr4T4qUtvJXTMsw7BQ9KhH2mrQ+ezhlx4h3O98+Xl1Fs+WrL63+KxQc8l+Z4+6LWZnMfkalHH6qwY7p+YYN3VIdrE/xmbWa39X2wsiFIof91fUyuzRvhwHMLAOfr4BdGyXwhVXVzjCgoQnCZ0fCCpf6NCdnd9QujQp378tDKj6rMXsXTfj6kmBcsiMGB3zoHwFU8jeik0MPGWBMdo8CSGkODj2/LkAByBesW2X1QWLv91FcYl8RS3gd1yD+s/2/D4cwZ6xaLId2jY+MmDhhhIRK+QSouxnRO1gkjvDhA0cFMxrZ7U/xR+vbGA5bY9k3prLmmiVm3Sv5Cu2yKvTETaWsmCGvGWjQj9ubBw8sm4KRzX8QqG6idtAbpX3Owag4yoYYzRBAb0SgRZFzg5vrCn/pB9b9qS3OzP8zNOpmh6x+4ZpjD6FCQYjmXa41urszI+Qc3MB6ODMZ2XBJP7HtnY0+YEnp8de71hDNfxmeV7z3wknJzdrgtGu4uq8r6voS6B/P2uGNzXJze4aN/RYneOkme/c14kLacTL5HHDDjgmKr6bc5T0BqgYfNCnJB2u4bn0sFMgocTE/tlQOK8K4OsQ08Jsna95RC8TG9UlMH4omUMLtNKWBVr5Mm7HRlDmWMKREQ1DK8xB/ZxcTyy2KClHzBN85qVmS7jjF7B1EuWyXvfbx/acnmzomB3lyRCnpdYcs4ZVttXrAZ4yacPNUcQSvKd9MQCBhtLMA4OneYkSZ1uH+0zOLBVzBzb9MZHzOZL9QkrPP1IXnTruRHQx+kjNYoQjBKW94QVK7c8P2xnQRvfBYFcUVCpgWgQci9F28r48mOKFeVixrzAU3R9FJSFVnhbtfq9yVuzZqbs4Ty+8OwQLC9NcYjEpondfKZ0TypBEoTmr4mvOCi5+QTZECFuOS4WnYljyk7vRby6KvHzJ+2kVmvgFqVS9CsPzS/ByAOHCcZyhUf74jNL+kXU+zUjhLqqSws/MM8AGWd5Osjf5wkHL0eg6sC2O4TtzdysZ6idVeAXC2kabmYuHvPpEFLP8SAVT/UYN5c9RVHTzM3w0pV+b99bJrvpQxHe63xdquuYrKu7TzdiZ+roefio4KMfZuh+eMI+iqRuGQuVZ+JVGB84GXAraqLO8nCEYcx10/ckt1b/gKZpH6YgxJEmUv/QEBzmPMgBdjBkJhdll4EFjY4YSXEw9PgcE+DkVg4o9JmmAJcKDG6dhotBeEBAEc2I72UPbfefhL0VRJPc3Inbe2SK7dmfIROdTiNq7FAZO02JLTvGTOXqgb0+8L0m476OZmPxj/PDT45hqWXrVA763FC++JUhhnEh8Yu1vVlZJCqpRmTQznXK2UfQves9m5bsLnrBmMFpkj0MhHzL99sKFgbpcsFXUu3mFmv4so17z+LaZOMeQYLea8cJ2W1AjesYVa7w/nnMzPqYccwPQQ/toRMzV3XDRs7gyGdIks42Qn18NPXGWuwAB3P4lIgIIwUz23xI9AJDGgv+QnXNxSBWhwR7jN4GW9DSKTIMvn6KRqrsvsiR9Hlkkqsq14tV4t4lLAouEJZ01FrQnmlIAFUA83YN+7ExDbXdbskxjkOVVl6ZJjfRxaILPUN34Oa77HxD6H7yxf1mJDqArO+X/C299jNiyzS9/ejJKpzmnimQ6KntCW+OyyrVe0r+ZUcBonPNEyuHqqw1u+RoWZFeJTmQ4YLzYfkzn86XUiQnIvfPLWv6K5uRpgZMYIvNARdaNVubLL9Tu9cHTinkgRIkRgSLW3PdcYn7jmFKlgUTB4uZw+PKQTMwRmWk+Lxb4MMvvd49QB/sbAnHNo2aeg8hKnb/8k6f+tK9+TQU5xaFHzkCXnefXaFVM0X4mpnBZMeNvIgvjlyoD86xDBuax9GYLEA49KbLG2gv9BBQVOO0MbGYzZFNKTREh83hBpzhhxuDmgI07R2V4r0LuKorSISuAbBqX8r/cd/rTgV4LVn4fzF9JUxWAu4+YLDDPMmJwxxhqFvinOUlV34qEaYmlIELcv6ZPT3a6Jd8e7QTk7l8+hv4X0AAg9vlSQWkI6HH0NGvSvNg1dhQSA7mn9gCs1mOTu1gBXrSWxVR5AbWiGEKsFRXUS3EdtVjdf1Mmf8oXsmegZo+WR8VANYX4SNp7bAh2U5LlE9zeDDPZowuFQTAiEki7qjrHx2LP+mnPw1SqzMmHaULQNOq0lXWtHwTvEqlEYj347imIxfnw5Qvwh5vk4J6Re4WyezJVE3+xc4p8mL9Rvr1+sr0fhh8z5OzBf7//4M7sKDjIQ75FFH7/BgWCw2ug8V63Vy+GAFMSZcxJYAnMpgrVwxsCRl7T6OT+amWEjJAa/Etsxs5QAT+D5RaAmw530U+zkAR3FRrMR7AySF8efczw5dF+vRd7dNlQllm0qQ4qdsrmU9t5WN4Reuiaqo55+B0MJkeKB5khs8FnFqRvMxfx9fjw4Ko2Jarbf5LvvT0iUm1T+5dFrIQAMq6TNrlzSLITe6U/8ABwpkyUw+SLKgVe6tASy5C8/bw2vFeTqdCOOfxwHfS9tUYVl0FhSSwFolpld8osjY8xC+XTbGQbqTkCmw89Kx3xae4PnJ2csIWpVcvxXP6YWL7tHxSFwihSKq1B5s3O7OIXgpQ7JmwGNQH/4vRLis88PKyTiKhbfl6acT7ekUQf1UVKM5kcnxIB8YceL+yb3lQAoq12tpqNOkhiRVTmDRycolRpz9jzYG0k4Jx/X3y9P6/FgbibreXw3bxvM6DFSf2Anc7TRrUEainFgGm7MI9OUJYZlXnf+w05V6ZCjhnxYqzGfiZM7wT1wnCrZb8dhmUpQCf7y/RJ+/sY8npFucLMrBtR6RbE336JLK1EatkORqqVPZtOKDCxeN6WAk0a/0lRoisR6CEDDe1ZM+jyh4JE7GfkpnUt4oelLOlA7jW5DnuI7Src4JcfhsEF2giZm+hqCr575WYj+nWOhJW/JxQyfHBmlceFPn321HtMuni5oojBzKLWWotC07hTMoZj01Hy/WYM7Un8yyqXkuAf5xpoMYqim4UvmgKhcqACEftyd+i9WFSDZ1NbiUP6V/kw3LF7yo1Y5E20hu5EAOzp4R7bwvUk7c83g3808m1IrGUW8v2/mS6rE4a30CGQOjTUiHG7UQyE+bu8bukiH67sxUlVaj1j1X0tgCbjpi4m4NUVRo1ePrlMb20ELN3zAi9wO8lDuHZEOkFu8DKkMAt+A3n/sOFmEhATciaBx7Mn/tYfoqV+1Sy688Wx5cD7N4FkQXYxOOmRmJq+nE+jLjKbhZUBbRQY2/18Cr9hObTAI2u/dHMwzNvOKrokMiGVUFNUwoB2Ygf+lWz13lopB17Pkd176H2jghaQzBSszalKDVNJ1iUfmqhK4YnM30+6vF7QGSLRWBQE8bJjbfawuGQ+Xe4MMZpO1Fkfm0L4+Bge7dtMLcvtvF0N7N6Yejes/q62KMzTcCyYXKRUfjxfJrK46WSHcTMQM1cX3BLDkhOlfW1f4e1LzOq+sP8wEybjo9mzmicAJwEBAep4qoqP8gT2mQOz+twqcjtjVvNraETh/irKNXUPlu0PdzbrXSQa53cyNGwtl0ZEmSnqVOt96eaSoJhqHdUEzdSIaq9yvWKmio6+H6YmeLlfq01D55VddvnQz1exU5zkeWgq6LoUkLB+15jDRsK3obXxfIqLhmyyo+wPVpNp4wkYclU0V36lZdbQVWxMH/4om8YSyvtzJVXv2Hekg9dCmLoMu+EqhpEJDkzK0bGQnUI2BX4Li31MYJhhQJPMmNG/LeuMnvke0qT6ulnH/K6+OKaIW1Do8HZxIm/s41XX7TwN85n5m3wUtUZwogYeVE1pSfdHAmfw1Yz3F758uriMmRGV0TQvQWkDHzGbejvA2W25hkAIV9icX00TLCp9MkJgy8n2BlGZb0rXs+GvouL5FiyLCtZxxPa8XJtKZvu3/3nPyyMLwb4AqxX5B3XBeRUA0icw5HK6t+A7D+z10LLLz0Mh+IBo6TynAWe1uM3vl7UZSv5cxOipUybw7fVHLn1RtLfqzBwMy3WqvmFvlRXldC8nGrstQ3sGxIhMCEs/XXjdzAc0DuY+h4NoW30PBfIb/5EG/pFNVqLA2eJcBAKMxKBt8ceZDF5TF3qBdnv8ZAa59m5a1Ef9Sw9BIlj2MSaePOyegH71Ae8FTzV/k0LAPhmM81e0aPIMKfx+hedWHxYppXgPn3E89b6dSaREPbNQ2cppduUXS7TSv5Iqk3t4yA+lXQw5Lsc0IdnTujBsJwj8yGuU+xtCusziSPQ6OkfS1UhmKPzWcWe+D6C6kguR23a0baO+2FEn++5IWwKZUBzYwuMpc/Le3kBCk28oScB7ydefsiFcwJYZMbLOV74luMaFvYoCKGs6cgUY/dftnxlVM+7Nr50b/dx4ycd0MJhglg9jqprtmMLchytlsnWxFl09xvu1gZD7FAui+sOyJxw0Q8p/jXtiRAGWK8AbFlIrWjpmDiSX8LIlTtdUkuohOXehybb4MqomQrCngIAfnFc4Dc5eLqQ9/ZS+xAQNmhLsrkmSRZ4JMf4KAVi0l+evyBkwK/zYl9x24PHJTjse3t/vbAZTLRjmlL5Y7KrKzFT2aqPhD97AR81knrQ2cDhia9MC+9Bggr/lvxBGRJtJtvBEwnYkg1lsDX5DC2naAutAUEgZ7DpYjOu6ZphqP83fGMWZHg8GioTrMmE25A/kJQ908qXh4AM8NpW4mOVzuUPR3Oa1OVT2oSa8APU/vGvQyzf+j+F9rQJB3d8e+htNMQ1dRSfKRv9eUquDQc6byZQqanJoShcq8IbvSjAuOWmWVJWLsIKrRhsPhSOr7tS0vgwHenKY++SXWurlD/LDj3UMu2eHHzQ/WMYiFEDPKfPCTwjU/unqlLP2L2F9Yn5tt9p9hJM9CbUkZ3X9CLBoHoIc3tr3S8mvhddHJlT/WurV8oY5Un7NXsoFXFmPYrYafXZzfk1q3bM3LIkjTgZ4eg/YPB1oDPLqhpAhlkNKYvquPFfQMhX2+ZU6mb+5v5+n3iL+8VMuVM9gNX7j3EAmUfvahrQvczzloCUNJDq65HaA0fu0fLMVImxZzylWsx9uzaANZTJH9j38TaLea6u02Zi8UnGN814itMUD90q5SZjMqvzamDoWV3DZmRBUwpCc3OsssfttwyBiNgITl4PX6NIhOGoE0BOw7vfn2ZS90QAbs7zkNTxx439ItRjunGO+YX09PAVcoPUzPlDY3dWydiOwNdB3Y86/LsA1flo6+sX7dLh+PyRUr4OjPezl8Bdts2GGp5z5UTq7OL41edVgiOv5mAbOZCm15uedcLQcuOTv/MmIlZo0OvUgdbXyjr72/PCgpxg3kcKsf/y1UDPE3hs9+df+fZ4bXAcE9v6SRIBkT1+gJhwSMVs52JgSUNlv6bCNWSdK1/zl6h99zuKeH5OcXxxlsGUcRRDGLLfvn7tsbKD0rzVBslKJSNuLNAPBIgWUKHgXrolTdafDWqzD81LH0F/dxr07+KlwNEC/RhC5Lh+csbQOtJvQP71pQnwIj+0DK4fNApf8EjyV6zhddV6oOG2Jf5BfJP0xhOhcy4KBXflxOGW6nP2bQB5/spO/Fq1MOw0IrrRpjSushEdek6nFlKQf3O34itc7Gy0mTa3l6YqtICSix/Nz4Xq1bqunU8IVkhDIYRFldvW+RpEDxOMEFUK0K0jQl3dOEXmvPE1aeoiiCX2MvfplfWElbz/0YmAVgLOoi4qtDQ5ThDk8/CsCROEwMKmEojLUpJMDdc6ciC5YHJL8UsaWHb8JtPgdm5Y43l5XZPSy9htvqp91pDKzvSduNILvp/1kqbaZdx5bH6pM78U4DJALqkDC54K91u73v0uoB/GV6vAWymA95fo5hrbtWvd4EjoZAcjqa8oajZ9agx4MseEqhiPKwMdhluLwxL+zD1WfgVT8WBg3Mr3GERPTLP4mH2wqtfnm1tBcg3qsh+R0DceKxj9g8T7GxqaW2q4YwGkpF69izkybMpAF5Hi9Ahzw5vpBmJf8UeSHLXdTXVpiI5gsbNlAtdtSmIz4CLHJH3alCL83NVqXYpGK1lxpI2vxQj5asBR4+xm0E0+mQ61U7hmyMTjhl5pC+Qce9H0YkEQvy99UAGPILNUxZF4SQsLPTXn9C8Uc1d+JWuFdCg6R0qXUHk8qZFqZQEErtDpqygqocxi/ZIj5CY3nrpvLc+8r97p7lfoYxPOaf33HRhMbaS75fYZgkABH0a/GPNOv8yGdTGncLNhq1qHmX0o8IobXa8qPq10Wg1sJIJVgEsqDi0ZT+cvXlTYoKjECm7fAaPkfru1fxUOxvzlNEdVL9zcE6lKgO9Qn06xSiVRnS10XSnHvAHxgwhiHo5Z8sXGnUXWJ+5yQ4XHvB0m7fAC7Hi5fOGjLy8u0ZGtVjMizIunez7wKZP3mZYoP1q5V2xKtxUVAmf/XwIJ0xNOnr4LiYcnP51JL1wDTWqpiZgOJCdmAFxlZ3ND6wvynnF4ipT9UeXU5C2w0AOm2aKXBnbEH/IBFKXX7QW4ftaoX6flXn/BgeGULMFrTY3YS9OPQioLcWGPOD9F9EbenZ6E2joJnUmycvZfaXy8KZPJ1HnAz/TfTcbJQ9jLYFwED2rWwsu7all4vGSMnjyvCLjrqXl5guj7V70u07Xcf1e2upr/6QQ24gndu4sqyx7CQI/Epko27RIa90nME+JecoFkTfy81/G/CS+FjvG8geg+8UG7QZCKaJ0MFdw+bf9SXc2D5slVWFFKuCSX9c+InmFDCd/trC8Z2E+B+V28Xm8yX4oKFzgwqnrI8M0Mv63zlAyJcuTJXN43WHglsNmvKnXCThLzJKRQLTNam7C2F/O7iSmMuaESU1ijsS0TjYnghQqKRIJSF63S6h0Pn7GUELsXh2ubBnXLX8Q5bbSWdxfQFnWec04dxJeQykGF7Hn8BNrFtwhZ0XmIE4NXx8cDygkXn/2ZkJ1757WGgqHlubKsfJVBHSc+WEvh/MxRxAbOAaRJBBG3tgo8/fnyrj6khQduOZkNKS7BHh+rDuuILGYJGRsjio5DuZmad6f0uM3WH1YKcj6tiY37Bj1+EhEKFyAntoDaEnwLJfAWQrYcFff8VyHH7yQ1iPuBFh3+YL9tG2owPiAXNwr0MVtjCZuZw4pxH3kxXcNgpMElW1pCsbrU5NDmDlEmGbrZuPY2veKHleCJpvypljEi+kfuVYgqHaTFiJ3o/16m+erkSn0kjTV0zlAYWGreGtLKKYOQ3G8uiM3EJM88ILWzA8TgSUCct7hjEBhGR9BBd2Wg2DyGafrVpcR7kJUk9hdOifrso/mqvmGHHU0Uh9gfDAT9vqHeA1pacnsbd9VMPvDEiW4L1Vsxdhl8OFRxlwlFn7Npi0r2DCYqSfviSQzm+uE4wyKeUfBZYdaiDdHz/GXtFkkPfBeaJyEf2w2WsMIFn+hlxMlgGlv4NW+i/l02fehOQpNj0ByS+QAswcbmcrcUeRco5KjdCLcWvU04pPFeAp0f4nEGRlWc6OCCXlZexI6IlwDj4+cH1gds6zEwrbPlOiZfeZi+85pXGLtEsKR9IQ56Iv0ceCbYBLBfkRLC5/0fT4Klu5CT3OtUWmAKhN3lJuOG+WBl4cfL7hDSZUPwZeZYv8SVU1/CPvjXtbtvHPh6kbm5eZI9j/FV5Z7H9cdkIeV74B2ddJxdBJBdDuQVOBDQqBO7A5y9fzs433zh1F1Hb5JgCvegx4RlQBI8eRhT2O9/SRzv/OjExKFsByx1Dz18mAwbfya7VS7Bfi/6qnO/3FZj9aA6dayNInxb+Bye5xslAPS2P/xWQprSTjTZgq76rOpP57pXZCrXHUB2jJjd4WpPvCZm40+ksRFUbl4BKZ5T8K3di4DseFgK/hbH4HHm550vOgppWbrQgoQxtM2QeLl+8ZlSxkv5My9J9KOS9g4assYvfS6pYMJNK9yEcFOZauzxGSqJVpBtgGv71qrlInAVUJxKvb4u67G9zftLhRAxIS1hjIhKt3B+YOGgQrJgzvY9SytrFUkjs/R6kL2qleZq1CEHWnjXMbJM8GNMaA6RNbEIE16YuigvXy3l9FqN0+y/yi2o43EAHRwZs2xC6DezBTjF2N5Hoxz0FThlOqD3S7fs2jOW3yRp9cNA2zN2VT4M+3PFXORKLVkY8hUkhWWp+/+Zm4L8r1K1KvwpNWJ+/KWqHqBmo62ld1OENYA7yUwdF90mHjykVQvG9kS2Mj5gL3kXna/XRbsB8XPxnMnIPfxWCe6X2YbUUB5049RLDinoimTvMH6KGaxdz4QO6o5Oni9SvCL5mQ67oQ9LWjBw7+oo9MpTVRseC+bFeXBdTmioQcBYXTHAsf0zZ8EwScO5dFxj5X4Aakhho6p5Fo5X+Jdnrd1ZWsmazA/bcLo7ufEQ9vpwK1BidW9o+vQ5OaucWWh4vlPpr8wTnyNLIZfWPVI6sDilTnp2k7kc6RvTi2sZMBNxK9I8KCCscYqU3DTNMQCYw07Dxg2yFn54CVYO89FxCYltBn4Q1expVvCmEmotlcEOYRQp2asfDUjeuu5oEvhni1jll+HNInbiTiN0cIUZLOwOPk1pgqE4Ynim/H4N/cBenrlP1wKt6cEHURp0JmJOIKz/7Gr3Q1gWgX9S9NFPtAyJzcEIDe/Q3I/X8S2fxBTSX1B7Bnr/qSiLSgq2flnOuLjJWYL+7ot2dQ5CFwfMicl0gky89KiYESaCUOTX1eLcCARzZy/nhiH/TB9ctHi1x690C0AbylnFAFiGqA+6AKrOwrvS4VCTxmiapvP6uRBZOjVATUKvK173FdwLQtB72hqLGkE9WvHHmtFwWy0Tqaien5UKNwJ11Z1oavTX4+kep+b6zJG/jO0BJIH0toIK3zp2ji4Db6+iFdEti7wiS35Ia34TIJDxpU6Pf5icq6Zfso81jCpA1UVeQobEWfI2FR0G9HSMcwLeL6Kp+c61oditLGfKu2fh6C9G3+5ZbJVSSWez78tIklGnp9ruLZtwShjf8DZRmQxPWHuQ1FGY9poXMNcX96CD79VBL9q+aWwRAk/yEQT6LRUxAJOD4xoPh1MDZ9UrvEGP7k4kXF3dmd0X1VLAg27b0AJDdllqdycG+7Un9m81e8Nt17Ws6YAZ/SPhrUVD8NFZfwQXGHacK2YdG6zqOwm85WLGX45ctvVci08quH8sGpLrggewSGcMb4TIHt92eFBTgfNWg4he7LU6wW586AJ743CRgry9PrRFcfo/k3R+R36VBVI19l3ZdBfMIq5cHMG5aMctNCaWbfvb8t4bxg05fTgeOXyb4VhwgX9vA5vZTyAphOvEcxK+pMjBE8MujX5OSN23U7gTwD+n1Dkr3QWVwxsiX/YpU9GhpmBr8xFu43IMRT0yvT1kRFx+QoY2TRHr9jfjMB16KxENtcvVOUU8sXCZwg+IOwU1K9CIraJcAoHRmOVLyBdtjbJlhoA0tnwIJUppm+9h8S6w39HDKLDqYEYKPFtZywE4wYejG9VhEQeMPfQYZBA+Kcxx8HOXkOn8lIQJ4JgYm4uNy24+TzzPdfYIN/RoIl7+YozbBbmLQlGWrjLM7qAtnkO/w6BtxPDBimqIBtObfLDwT2EfKskp/ggt1iRS5klFzk93keGwMb/I7WlvQlIWHfgOxP2AUOZnR+EtKORQevW7oUj2bbP2jwGh1T84awPWqJRXoUgVTK1e4WJOE2V/Tzon3KVP+a1SKfgXnkA9gho1dzO6f8f3zNceFTpeBucZQBW50z+Hl4ZU+5mpidevS35yh7Nv5DXangfl+7BaW5/zHorDtuydYCvyMSs/Sxo3wqCYaqp8QQ7mpBW90B18Ict/363Dg+5zSDwWd+p9T0ziOWXYt63O11fHgbjlIIb1hZliCshyi8bLnJRVwkx8IrWNkVNTrSslBON5ibiNbQxrrLACUTdG/KHXwV7gFDHzRRS6wgnoVw2M+tKaKPxH9xYp9Ka8icl0RyQYEN3L1wf8VM/4l4LbcCrbZ2ShE9Qx21OkwifZOLIvGRYWzf14TfLTaX7DBKWk4K9RP0BHI4fqFDug5orB1TxjAwCpeF5oPVnSKvpouTw3NtLXniy6frQT81JhQkvQ9AYcDKlA2QHtXyMb99bYM+BC2z5xjkvs0f/P/iJIG1l3sWH6Kcmwqi43+mGn3F98XU6Wmq20T9U8Ef4nK7tOkc+8L23OQY827v0j69WLMmbyVU5RTM5037YO7bjQhf8bEMg7KWKvH7xyWQAfeFUKiJgipiSbISFxN+1aAkeDv64l/1VF5nixhibKPZb6WCIcpES1eZANbapvxxSpHbp9xYibA+e/dHv9kv86iUE5tlvwRE+tXZMeHS0/8c+AVDTbbZXvS1YfOQusAHbDJV9kOI0SFwhkxGaaM9I4JwOJc8Rx0eAIVlabvQX3eAT9Pfa26t//+bi7IwGRWoZsEYyhduN926POXUCKM8p5AY8wPCud034NWNjliPJL/3f/GmzQvv7NiDPhds0bZZoiTIfzD0rfKKSGKKfVgN1BkgmHZ5G5nx9zA6ToXpv4zgCEHG6Qfun7qI9mdJAc7KGMwL2dHj/40mgT6w/m/Hhq06X3ZCW3zI3qXg4pf3Xc71VROTQCXUPGCI53HMZLvftMqqhi7OQ2Qxwk5St+yvn4KH86GuEx84Bf/TBm0+Mndhg/Zfs8bxgt2IsyGUohoc9HeI8DjBdS+eqQfwAnaWEDwt5Fhkd5C0Gvr2IKrBrB30ghERk7hJl+HQlhRtYhBlW54t5GfqesPuOYhvjTjpndZWj1Db4c3N3OqtsupvqCuNJZ+WrXKbcN8RFw7HNj6C7fs1c5SK0F5W8G4idz2jyVwHeycVBSqSp4L+V7Y4pEscsigTqlrN4uj3LxwBlUC3f8L3JYzlqDjAMZmMibksiCY3nULqPYp6SAkXekLDUbJnYNt+kTytOTebAShdGUCtaXydEmaaP3KAdcfX5RqWMgjGY0zICLtWuY91OiEq2PY8+BGnf7ykJmo7MMQpIsU9KtswdsTwohHwcZcjku2K0T4ufvH0er6oMzTFTciLC31VTjwUoZtM5lKoVz2rqZs1u+ie5EmCs9TbEUvEaDkaBJ5iV3aB/eycgFlFTxcxM0ATNcMQEI/6x1O5hnlbfXp1HIjzkeL/cQ+We2eUnlj4ZKCd7oBOrAwqqxfuPfRyd7JSOcLvLHGz12+h3oqX5Tsyqkei7vUESdCEDceG9p+PF9cezVO0q4iLo62TuK2eZl8L/51ikbBmp49tLtYiJLOAKdZrukfYivL5P7sHZmErmgF4PqsDyfDHhH1we5556KQpBHhzEdVbpOUvUTnY6PExOLJw2SCdfqHRXwlZhBJZhMIUk9oAifHIGnTd7wF1P0rqipDkyk/yHAt720GWrV8r5W7WY/kbx/nr6xMhO/G3EiWv1NaeMnkX326wsidrppDPgOY5/9utOiP7KFNVsiZW1zi/5Uqz+GaioNe156sfHsBJjkEhaRINxBdO9fdkk26ba9n6PYMVmCR+1MrDTbAxnW9sGwc+Owd16ECaqf63ZegCsFedBWyYoP31uC1MvF8YRimUCma/tWimTZPl11vrI2205BExyXUFLAMqs6b/cm6r7pawpM2AMckIDKa+9uBv2jHjnvXI4bTeqdpXavbVRtpfSGgkcYmDH7m1ABGBGHIf7QqttoQU2fY6XQmqR8aPv/YuoGQpwdOxrJJCIx/0BGs2OevGMSK8/X4K4pbA2oy3D239x2uzRsO+cs5HI6JsB2EkRhSK3QNomt5DrlbJJ3LqVfqr2bq1T2he6itet85Rh1F9ix1sUgHfqLcqrymZV/qFdhCHEqcJdovTpRvizgmMK2Kse5JZD/beLvivZddjJ6RBAGnHoU+/3BaysBJSPeLBfdlgEGQI2qpTh0zV50R1X9P9SxcOEVIUdATlHFZ+CKySZ8+OjCiX8IIbnqB7c2gBl1tsz9H9tqVJ64KNpxtLNljenPpN7aezpHdcA38K47iS8TyagK+ujYRfDXBgoUrfWEBhswkxJ+9kRYbFvw0lI86EEe5sk9RusPUfob7Em264V+Y3gGQMfCGDVnqwpZAyu2W21eeyt1KBHGdDlnPPswKiVrikC190bOLZyi1HrGAeNVZxmkCoVJKH9MYLQNEcU0jwkwYZyCWkv+suA7rtXCp3/k37EGXvQAWo6TbqF8t7B5pX6zYGLQ7TXJjkCCz+K/vgIBKzsbB226p9UDMi8R+Mah2VvbGyPB+jVAPu6Nx/81IjP56r2TH4NbN5aob8OZIgc7KpsawfXvgrL+Dp3m8Och+zJX5yIyGRod03pOPtDHTBlVdbSiNEJH+cHHKXB0XL7I3UekHWLi7RJH7NQoy15vTROS/Sr2CZzeQYxCoMQ9BWPnre/Hq3ZIsR1pmdJl1wyQLme3Q0I/aXCRJv9r5f/+Gn+d/bYKUo0RaqCX/968UCjCPnTYGnhlR6nAGgidt0kb6lRgeN81i7cAKpuE6/6AcBNG5w52X+9IKTwGulnQVf9TwV8G62YRtqJBXNijY3N2sw+xeESk5gOOiT79M7nJYKUAkbW3Qt/khNfwf7/RI1aSMMSQ0YZjP0jtTn0Nf+/Jb0BeiQ7j0eQ1jTAeq/xx1pTCWs8YwZlhMyfee/kYNF5Mo2898IXCNUX+trdoTzgG1F3Ct/xbCIwEH13mlcbqMcdDdnM6U80OFIfowt0fFojosm42gRgfmXPCOSscxdfPXOhu3WyDH93eqx5naEkHTpNCV3sqv+jkmuhFeq5sz3xoCekBH8p8zgh6OiXVD8C+P+HYqkgpQZljfft/mvLad1e8T0cx6bkkMHm7sFxWhO9FS3AnPDPakfQqH9u8w/RvnRubNtfUq9Rcz+JDw/iLKPL2n+2em9hVYYC9wLiieRnLtZohN5oLRiaa/+l+ztwk1Fxihoz6knAIGWHBiU5u7dpwMsRbxllYKeu+XYYCOsytnBrDE6FpPKAyG3PB41huL4qoRfJDgA/HX1HxwlO0kmFEf5YJTBwJyBo0QVTFALDyUcLdb8Syi2ku9ouYq7wfhPtIAEhZ5RYRBMQljlgYLpoTwH2M1MPTTHp9POhg6XxQ+itKIa3QNfWkbLPRGEv5VpwI3RJIKuALEqzOt3bzTo3hBCYJy2VrkzbnEzBaGR6zWKTMHAK74RN0LuKFPMO7FcTBSB3Cx+8BuHvVYZshk9484NMnLRg+TzAqlNJzCx7NJN0V1GzPvB+IXBhcBvR3IK53iHQSg7L3E+9b8HhJ7T9ZMHFf9XU86FyLamy0BtJ8HU0YZxILCptQBh8PkazEgQrQW4W/EvaAt+ElpPTjZlJvAu+qS5vErCt4deo1DlIkwmCJsA3K51p229kZs2Wt7pQ12GItahF9P2Y4Q8jgpsyl0jZZf9NcB3s0i1KT8EDvAbBtm+/IqWV/nyrbjp8El7eb+JgF+dIaXTASCfucqBUaKDKeHacTP7xvW1X8icIUB6+5tvtYi7k/Ko0+bT3Zk0LSU/JSaFb5Nt8bCoTd35cEL66v2AkUvceiFZIeWDRgzeNoQtLtj5YhfQ3c+gYjXcaLjbjGaV/H7mPTybz4Cn2D6SyoUfh51t3woNEh/8V/Q3k2Q96gGdhvGOy8jGlGcfQLoGCz4CPSlvxqqDVFeGwHSqniEAV245PTmBcis9uaWpvtBwrha/akorsQfm4uNeGU7HoG1m1kxS4EJS42F4YIO3nTT17SRfy1ctPgw7Xjl35fvqsGE1V/c4/eCUVsZIH/nnVD/5iAnU+lDxF8OhIlepyPDFHTlZvgekFVZj5sCjfeYsJurgXo/5zB+qa564CAtgstpLSq4JnpTKIQA7tdBII/xAUZMkQ8d8JO/mvakZmr605ILxmQCtPlKPKrduB5bdbJm//v2Jd3Llm+MCSRQofAzl6UNhE9ejhKy1c3ONJs8ZfmISexEDWj8g34RZd9esyCAZ6yijMGEWuI9gJxcFtxhaF18nmCNoy7qp1UkMHRS9c2SQKpZ8Or8HCTrxpyHX9vUA3m+QnmAnDq+0uV2T70HS5Zc+IN/rmsbVHqGxgCll4cLbDY82Y/7YNsZOX5Cnxx2rOm3rwTbfIyytGGa7FuVt/sdCbb5wVKW9TiceVrhKh3V16vStpMMBdLEYRQfv+ZZ937ZCAOq87wokOWcPLIy841lyOUvEXMkq+5igamnJ68e51C9rwrR136yykvFkmIsJHoGtDOGmNgCiWtLJOxocklpp3DH/IVQZs3IGIou72LXLFW03/73dQsFHO6ksDSUZWotSYffml+SVcOUhXbkv2ZRSH8pS2DPQm+u1+RhLytFuR90haZI9QEhfCKNQn31nKiILmYAWn/TKqqEIeQPEte/4sWL727ULD6wgbAHatfYrjuCU9elwU9e9AHWTVmcJnVKyibzv2AAJYNlvDW88hInM8btRIrertLyt/mHZfoWIcMDrFGhmU5/AD/GNehprW3FcSPvFu0HX7QGZN9LfOCKuE7XwnoiOLhEFICl+kvUOWjEmpMHmLLwzvP1NqTot8QQrYiq4+FZXFviFH4cRG4PW1Tj4cCdBwRGyi9VAWAp6E+PYMy0GnAz5MXcxX0xtKdL7jCmXrrN7FmFRNo5lAQ84HC/KLADblTWJ2ZHQQ2Ke4sMVYsba8m2EJXF1loQgNn0PP2vHZY3e3U9J6rSv4axC2YLpccjz4QKe7841QBXAfG33SsxII6Q1q/KKErc3er+lIvbZNUAPPeMK5eb8hfvjDVhnCL4JJw/v7hgEOy//sP8FJTDYzK48eqq6DhaGzH8B47QXJfPV59sQ6u6JpAMxmt+lZzzdN1blF6z56t1BhIKAuD7+jhGaiDs5kJag4R9jpjfddpdg6bIrYJTYe00clOFzwE0TfQU/aQBsGTmZJjCL9DFapA8RtbeSCuXodZ+68/TBd8Jhv3oxsQhoeviIxYlsNSYEIwaae5SArzcvwV7ruOSmQCSyq9v6dvGTGi/gZka05VnW4xM9Rbycmyi86MFrwjbY7d/lGeMiSLQFh4+laEo1GrfPfeHYGOe0kufpoKNN2g4+2J/w4CLgjMzZqmFsBSbR9b1Xz8Sd2McfM/BNGN+stKXQInIQoFZV/Dv08rfT0PqXVg86yfE29VwbyqRI0JynpSbTGcWZbAO5lXqcuBrRRkeyOWMxD2oBk79upJZP4n//DFqcl40yMV+khKwRXktRy69zOB4sEpFKTzT0yNQ4Bf+tvLp6siO3msTXq3XPdtgVzFNxibdmLSxrdB+2+ttJl7ykECzQg78fzRdx5ajyhL8mrfHm6UQwnsPO6zw3n/9pdTzNjPnzLRaUCYzItLRzjC1ElslAsumBQaXkzPpKWdLDuzXxBH584FfcAP1UJS0NFve4IGmgUnOkXFvo5Zig4HUek+RaHakaKR9Md3KfK07wrNqlc8HCSjrCLwhpm1Ve6HdvnKuhbZBCgUB1MG/t1KBsuZohgK0hGa+o/1rlt7E4Myy5YO1a/Ct2FtS2s9pcJwJu9uFbDKXkHDcX0fhUdPY4+j+QOGZdL6lSGIdKFRjTkz/QQyy+5V95p+mdMKl09WsrqIAeFuRJtA17wSKOsUCc77AHlWBJF1FvN8gSag80ChRycMTghfjf6Sv/QqL2cExb1HM810pTqUyAriCjHi/R7TEr28Ap5gqoLD3dgIhZ/X1XF3MqR9MBpmQi0KSVQ7Uez3LIad/VQD86XIhfcYwfyxTWSLHK3bktQkTiMJlqHUSAaikqlYkhxCYcb+MWppb/bdUDReRH3Pf09+Pr+UPkIqV79SGSNgy39YFyscKh7ZO16UOMsYS/rP1N30a8ND8CqaHzy+ek52xARCLAITy+gt4C0M/qCZVJvMxsPr3oaK3YLCbnNnE/fajhVCoj6TMYa+/xl3BvWz+QDe7QSJZhN3VFEY+ktTqCCZKQAyh3pibuntuKAFOh4JGEY5Duudn2zyIFBDhW3ux8iHQMsIwShv9RG3mEKQoMCsJYCiJVhS9ZYa7BoA4MCDRCLt/4t6C1YG9WeqDY3eMsm+8vacdpNWi30NaG/iLkTIW2e+tiawB5B0pk3iu5TVXkUCfUAnkc90SQEIds5nw4G9bPHKqVRW0Lqv4zmDTvBrU8BNhWKYoej0NM/Mm61lNWB0A6dfkgqOi0cw8fq3RaHQVv6/BpOnXJA0OFEEHO2DYmRQM3fHDXSHjZEc92TkCpd4bvmRTFC/MtuNusnr9AjJYHMB0qJdnZ7sixHWC42C7vsyp8J+6TIEjnAC86b6byqL9ohPB181LQxTZleJM2siN+ArD7Muz9O3N8XPbqG+CyhaN3VnOO+BAebmEdADJ6bd0qEXIMhKp8qR66BN//5yETg0JTCoTIx9aSUIFl0FEIl0DclfoThJ9ck1QQ2oVIaEEzox7WJM87iOFY9WjWwAWHaHM48C+asQvGIKYuNMYajYfGYehXofkruXouRAHBy2rI2k1SlCSM4ciR7dsAJO4HsmTYQPTQDybvLcB3UJkEVHoAvtZt8pNR97GNL+919tWMmSJTynY6orVM6DVHq/HH+wY7XIpXBsctuRIPxW58uB7OUteOyEQdNMhSlYknMRnpZreJLgJjho54oc4ghnNs3VufLnQ2qTYFX35iEFZc2H7gEH0M4zfAFwheMd0mKF5Lo1/kmYu7mjCpGY1cL7jkaSnVDw9ZD1N+2ad0Ofv2h1bo2E99n5YsFmovvs6y+YAnlugZ9gQKn1LI1StQqj4ohdIZULGh05kBmr3pg3ADY7JcfPLqQ9e/OsFinUYWIOaLrV2/9cuQsP44qXS834yCJUY9xRCd14WWpYM3DGcXcTz+Sgj1YfHGIqKB5K9OWM9hcza8AdjadIrJiMN9gowZu4hCCUSj9+s/DKCuIyNVpPuroUVjaeqr4tHK0PpgF7VMiJFLz/PPYZT1exsstW1zhWBSQE2QmXVWYnPlfM9nTbVGpMzTlIjAQWYsjmiDuSRXfp2qm6OQ05eqDbont80Th6gz7nfN/htQby9q76C+dAXprIYCBBDGVZj8Ljlx33qcrtRzgPgdNZu3hz8CS6L65qeLrpOtI+5F6WQpJlg93Qwao/p4d0LPJEAFzk/9TKQ3gEy3Bwdd0TdZOsDGb7zrqiosmxrPjkufCbKaG3f1QkqIWjo7DfBFK+ziSYhKEqDjtTrvTgpZnHsZsUG4ixzVpGLPlI9H1HhBni5g9qK863XsMtAND4lBGmiCgyQ0ZcJZwxzZdAPZ3Q3ymLXCdiqyvmFQRtm338EVlsHUXxN2W6d4GNTJCd62g9ElnX1sb5itCINnJgAmL02nhhFRkGgQjNrsUqW5du1rnEsyoVUPqiz4WY0hbji1lYqfm3L6UJHYYHkCK7Ni3XIy0irtItyR5So0v6a1W1CaFnUXhPv45v/QbzzFmZfbdZofUCycwvET08un8XuhHm645hSl37MqNNZR8u6xm1YbFUi/esfMGUVV5oQX6L3d7fV7QjdCeUcvv3AgqJNPUuOY7rKLL0LmePBZPUIODhK+1Auwmk67aJOFczqwIwbyKWF95/x18w6T+nyNsR3ZZzQ1scO2614lSe1lXKwzTcPYtLrHhi9qV4bABYCaWpj1qGdrHD8llICaacK+hRPjHj9JiIwaysdBVF6v6F6wehYdn73V+JIwgftd1b/0xC7TUTmL7fsgaMnOIaUSNRxBxWLRYaEX6oEt7TSUGEmoNSG9b3hrUohTH6iFx4Q1bIzyVW01wtKAe4PSHS4Y/TsFwx3FDCCzN2LNV6fRcKRh+pWZUYFErBgGISyAvoTdrrgptP9C9NXoXUmslPCoYMLM6EYKvvfgwwDIy69AX9RL+OsKYEx1rpmQLS2RUZ75d+tqb2AYE09GM7zClNkEsq9yV9tq0kpGVorLxb99jqjVRX+a9MLd/25Rd8pJHJcTLQwMmXvmPZWzUCSE9O/U7eUaRsVsh+8S0I5BBbfBolW0RtN9fg1O6i1XvT24B1g7YZujclQ76kEdq5nSeTimh8InxDRC9CasVD2ekEatz2LeFBQ1pleP5EEZfJ9CG+Kxw5phyuTGzNlLnB9U7wo+WoDdiKlDal0QIBvcbqmvR9QXnQBlH6OgO80ImtqFAoNkW5FWotctN+kCiTFGChwn9tn0wDUslcaq2VjdVrGySyo9k7HUn3N0S7zYlsItyUTcMIydKsoR/0QZRdmQYQL0+7iyiLpev41U4nN7qcM5vgEyQHokPYPVP66GkQR5uxygehf+6d+aV0KBdFSZeRYSUiRBjRKDAr+goPESJpt431BrMzzYxmMaaTtEaywHSKnQbu/NtUJ7rNdKRiKvynU1yDsYHBJT7u4KScRBFiZAAcSTye1fSGe5hyES4QNGLIL0AKNpC5jilDUXfgrJER0cOi2a6HBHGyma+xeL0/KBWpXbcjrqmGfUuKaVwACEv3q2HCqHCpz3a/ABpdA+NJTnGhCoc9hbdVSqCgY/SbaMDc8Xb86CsHf/r6HropfGQLjOaBeUUPilXSknsE4y8JH5woiaUH9y0cYCy3SIRGFxBrXDG/OLFp0FZxKHFpyax5RCg998BzwNqwVeTf1cJLXVuSeUTWXsA/1jAR33msYy6Dcew84NJQhhzp8cOrVa0tIpyibU9LAO4ppQldJNKYb0eEyAjshB3kG4ojy1mMfB+oxK5+90+boLT54NXtziUs5wiKXH5LGxGNTORvjhvh00pqf+uCWAAwEqk10GO0ZvwoqCXYhVjH/f/86YnIuNyUPIiU3BFnjBsLD48uRQo3atay2rJsbaNxuO6Iv00wVqmkRtbTSvI1JgfO54ORaLZf8BFZbrlRB8Tubtcavev5fD/FF4x/bA1QA8fyar2CLFvFVvDZoyV6vrBcboCwZb9/JagN4M27KambqpWRipRSKPMcwvHcSsRUahbGlaj5W4Z8EKOVfzJazpUAbcBYmpjJsBa7F5zZrHXi1G+GOjE6799ijdzYMUaYEpx7HkNhfShkIx3BkFO3xTUmd3WGY450V8WcGEB5p8Wsv08AHHGaiyvuugrGe3vdIiOgsNiCjhYs16p5AEJdrNn67R45r0Wgydpbg9CJb0xWe1X2IHijiSSRGFoaEdlISEuzlH2yMCkCMqWI3B3l810TbIQS+GhvexZl8YB/g8sbPTmvgkUWayJQD2GCo3IF+dhF/mfQ5INMdphtJvJVr+UjwIXXw9QU/VqWb2Fuy5BtaXk18GrjHDycQMmJVIJcKRUuHI0lkzGV2m4ewCD7M1MQLtvqdPRLqDj+O5k6l030Xz1vPJfyaoJKmHUrBIe6CkrhArgKdMlKwjjczoLtASWVAQAhRViOjews2J7fRkfJH3w6yRgN2FBGDkR11OxJgwHojq24iZa7HkLh2A89t0W+CutE3/0lpyxlSj/C/o/Jlhxfc0LkezpHWMGLMiyj/YAfiuQt0XzTKr1YIECgIHApoHgZqEhEQdyEwKI2oS1LOG3Kw881S2WsvwM9rumc/mJ/w48Dcqjx35DxA3iFznrq5jTmw4Jk7yMoLO/PeQDsmtgt1gN3Ie6z27LQWdK8rU/TBINVnfpIY7AQHXw0oasPaPT8oce+GBZwNUw4wvb4fbrLGhvZLAYkHsOUfccPO+m2devBzfzM4a8Jdft0Q0kFtHH2kB47r6xdA82I78OD2QlIR63sZ2MWSj1pCt9nj9gVhgbiJ1JyBqM/RMqbUu+XiFFqCj5YdDYpaq0FACfL0YtFnfiRZ6dffG27ePa8XgR1VFJUbjjdpae935g3likEgaoZsuFzrbgE0LBZYLAblA/VN4OtHE0ONGgh4b8Blyzugw+xM8FxXjvA22GAJFLmsVCZqO6/hAiGmt7ZE1PmBOfwXJtDPTCbw8PZQTzfbLFzuAWyjrKwxcndZThcCgry3YOxHf4og8wFrOHoaF0toYUEDl5mkyVJHTvHNH7YWAbggHqUALBucSQ1e0bZKi29SdVbccLpwRNYAfT2Yw9OSzRQnrASY61Qu4it5gflLkzDmEspMilklq+Mw+1Nhr6ggTtKMkgwAseJe9QceMKIae64TkAQjfh86pj8+Jt+jTpMbBVjcLMb/rN8Hz/USkX8tRt5rtodFRZiQHLVAz2bVzURZhvft91Bc1K5EtuUf0O625mbOJDblr7Badg0g9u2u4Go69St8gFkX16P09XA8SOvPW5dumjFXkhiONNKwJS4/BDe4EZJv5cMYTK3/0OORg+STILqkW39b0U2Yt90VniB12IrgfPILeUguXhIbpHnxkhEeWQEFRJ2dVWbwceHrlxiu/jxxDQYP8/lwusRyDLQMv6evM8yrvFL6kkznJmeHjxHiMrgNJRyV1/w2otfPhuvV2RXor38Z4DnUbS6Tvwwgx42XbG7VsWIlXD+xpFdKcrnGj/5jePCS/gn8GpGG2xK3K8CQDojIbgrKxKMbLVIlAO/fnutgw2YoNxHyIe6H+LlAopjxPgadBJhySFG/KQVemeCbj+APrr/gYUItCudhD/aO1cdloQKn/et1/eNzdC4Z5s0nrNk+OfpXWMi7N8ZpO+69gE0tEa8u3u9MB8bHgAsvB1pc9fpuxAc/17eCKxanZqC9IdONGrgS17klqz+F2bel2N043gaq3PAoYLgKwYnj3yWzUldQ5AfZgsQ4JupH5FU4C8Bb/HJ38xbwLF1jQoesXo9rK/rckGIFwPf1q/XHy23sw22AMvDx47IRIUzOS4wRSvZ6ZwsnRb+XXYhIRROilbymAdpfiM7ApZES1SxMsX3R3t5y/yZpybWCoZP6HFYfR92Sa4sjgviA/oD/9IE5g/vi85z9GHUTSaOjCRPevBMQdzC5HhdfhZGdwqjVWYOgrP3Q+ss26y/gyYaSn+qmYNTtktiVz1DIEDD+Zr8UyIE2xq4ZERp/LSecCMZ2Z/A3ceSt2EpDelkXc2ogV4dRhiyyJyT3+tP8IRuG71V6TH8TD2fhtEALBaDneSxc/qnvzAdFl4fk9eSvJbaDU1ikiuiFkxb999bC399M+vMZ4DJcGTeQZpN6XsU3X5IEybYY3Hz9gwDQZQWK2qZ5N2nFLEcQqcmrqi98lkJlVX2a/ElX+rz22yqd348X3YIw1flSoSMgNL8/HH9cY7Vs+JX00cr+stHAAX65ZGAmBWeeUJ8rpBA8lhS8wdKz2K1ZPxyPSPzx2CB0QDJZoxPfrKTXeXDjRnimfhB3uY6ffruYMe5i2vJE+znVRgtPGwxii9ABUR6eVL/MGSO5eQKy7gESyffpkQX+zRKQ0mwxxlG3C7EoDxkD4F9bHnscdk7cCz1M6k4dXDdKFnGGEscWwz4a29z0q67Q6LBypKJ9B/5ncbqi04C/S7139KJPWs81Ll9GCRs/nZUXeYxXnuTcMxBVeDJkoJtDFSw4iSwhvgAmaU3SSIvBfYFcoX0qacVBZntBk0ldLfQRZYbi/GZnJyHSLDtn8V6r7hVp3Cib1cRWZyFq2UZCsB87u6/fNOiMEiD5l/jkI85vltEmICNV4Da5UKQ7giVoMbBcvoZLZ7gZJ7WQp8NtRn9ZFyXvRdDAmT4WrAzMDRmTiN0+xC2qCh9GdgfXULHeZd1yq7HlWzqX3dnf02+dAQvnoWlw1+hACUKaJoipLzX8Cj9dQ511pnuzLS0+QWBzCQ78uJJzzFtf0qCxL8QSLETrKrYbS03XureTbY0bDUHLamgEtBJoW4YGkaOETcWH96QguzIDd44sBhBNOAJ68MJNejhIr+Qu61jOjGAU3+hYraIQOWGoxNDOEFUqvIjRWFdT6BtqZkK807l33HtVp8wvnqXtrdm3UBpTvD3i6k++g1XD9mbi9DIKRrjyFYqEFbNSzsimzuHn3YQMp4o5LNWbr1DOIcDEUo70M6X5UHe4lCffgJx6biFbw+aSx4gUC/UtWwztkxDYOFh3bsoFMQOsqVNwoSfFKE74xdx084WQZGHp8JctRCPr9JhBrQxmO46cgGlfLwaMFf6YnhmodP5BJyYskPTijRqasONwvRZPHoyYUspp0nC2QvzHvmcIKy3PAXwVunNbG5yHBd7pxdmLcNko8cDdegZy+1vqaqxNqDDBgfu4wQ2jcBjYFpLo++7dH79GRAZ/y19UYmnqXxNacMH/jcDYgHcbH/OE8ag8VlhZOP0saZu1TWyMd9ILdxE5k2QPZ3lverh7bZQTtEOfX9PUj9vGWgYTZ1wVgQT0Ps4h38lb4F8OtRRh+jY2lo/+2fryw8t4IDK9fJs7XOSDu+JXntxf4LY/1WuKkeM3CwaXnXTwLH7Q9Zfu0SK6s9HBtiAvkIsQusK7UFuCcxNYl6ZGDqE8QQtzDPHLOnsrBinqbO8fYaULuxbt6OYEKnxZvnNBlQWhIYpCA4TWir8nzGbYXQcvXUAKQIPUAOScDzGtk3c9xG8I0vq98qPaltk+qQYFoQ5QNMvIIFZD8nzT7TVH0VquFLrTQlv3sWQUOgrlpt50ENVHkXMMmbCRpB+TfjqJ0B6nCezWAKwNsySU88kfzt0qHxteVSJJsbnOYSqq5KFA6xpA/exFXX7Lz2BtchNkszEJKaXv+sWF5+AnpkfBgxoUHFJZ8Sk49PDAX+jeS8moPTPeLv03ucPhHOp3+qkOTzF82/ltaN5elFNGTRtH91NoEfBY+Gxl+Tn0mh5Y2LbS489hBRmvt3cNxKw6mQx/HAw3KO84RbbxgEUO2bQFcoyCm6dMq5NVlfAdfsnGYzKtbmU2m7IYqh89e55NHillTOMVRUdqSELajVVjA3mRJJ3vtWhO9rTvspSgUZxRuHcbI6rpTeC+efyDNy1wuPfApomM0TKmNwOfU6reJctAQJt5LgESAHVQKM2qN+AsFlfRsUzde8hIWapyS+bdmagww1Hp+9SAw+C8AiNEfEqelf7NP8RXhN2mD+n0Kzc7ZWQNK0YNwKdUbDZzZeKFsYadIBxi6ugdfWOPBenN4mWPApWm+gwsL8zSEYjBieO53xlPCMVvdpyJr9/NfNPICC+nZKtIz9uOh7iJqxvfcpqM0fNk1/ToPfoa5ocexo1N4ba16ewGPUx8Z/YDjqC0Fzm1VL5cPHzVlx8gvNTjbb43H7fs1KCK4R4ZHmhrVALQSWanKYFNg4yByOYXG/QAYO2JaPinknygzEcHlvbzxPfo7bFg930uH/D46IA6qxWW1Bo7ExnSJYGD9LzUCLumlk9gCuj5WUGynkzOy+8EZ1qvXoPq6mu733Q9E4gaB6oX/at6pLALHKjI1ZERMcG/AG5lG4DEii9GOBfkjfJcPQ6+sW/D8bFaM7Lf/SW6aB52E+IAkCCLSvuND4NhcnXuYCUk2F7US8KQ6VGHvREo31zW/Lr9dsVpeemz1r/MTokotCQWz2F40XiDkBesB0LBedUwPj4XQC0GwGqNoz8QrzcZdVMUMGIfyvKzx2/6CfHDf1nMEEBOpDSHY+N1uTy+RieetpPmq7jHWf0Z2x4vw6LgBogXjFfs5UPa66S6uloFfQi1G8lKhJ1v5vXf1avb4WHcwq97Ry9Y0paKxaz30Z00PEx48gvQ4Qf+juUmM33ePbD2w8IsCcNZJSeAd2qT+utCHrd1UYCAVhnfpzngLApLCG7W5LJsOzYTbTjaufWNyEjXAorH921HQvc0lPKvHtwfe24ckYUZhaJuLy+Ku/Gs9fJoaNX3ql3smSXaPBtOj+qYFlILkZtatD3d3J8I+CtrOwRvxds5txagYx4wwkLMNvBO/TlJ+h04m9NktCfvGBJgVF5S1j4l6WkCJUmWgKFzso4LCqw6hS7PXvTugIXhSw8MxmX2SL7KMNcuIMAzORERppoPFd647gytRe4kvtKMPtXv9geQcBRvDK/dgQKmHz6h5Cz/K1+ABqAkh1ckMqKe8hcaeYaRZR6IETMqXUnYzR7M4LH0u77iYWfzaLnkRfQQaVGVtEzAXTdNVU94V/6Q9C7kMs79BhmG/ZBldTKAs0gA8xvsy6Ss9LsP6Fjr2Ky5F+/KGsXE8p0dPQPhn/P2wLBPN16EQSA2uLll1THR9QtcOB97ozhSPSajfdtFZ+K5bWjW0UzRB06/PsTsWhs3PaErJK7Lr1ILd7YbsaRkv/SbM+Iy/6qBhsJoal/UzJNF2ebFHfNq/sDUpoBQhRl8/UXm6CR+Qc6LKYJsThpGcHA1cveKvkz+kXGP/I2KhaasQ5Kb6SL8RpZp0ITi11RRiMe6Gu94lq+g/d4wNkCAb3Py3bkBRS7ZJ+DR7jkt4LfeCKgimpHPlnN7wiljkRdK9GaqquKxz1dKYFqalFbeHvOjAasVZh+NR5yfIFyFKOBRufMWHnoJ7Av66xj3XanXkB8fD+4EkGg3jvnLqb5Nee31jszYTDqbO5Z1RTe4OBrYK45NxTINpoIewNZ/mS+4bO+Xs3pEQeAZQwsjPr5C0UHy0aURpWe9TFSiWtRSUlcWHbbVr8g5frO9Gc7Cd/mmrWUissglhHqqago4V1tGYMIBz6Mmvoz9Sr856xc9+/DGq09u52iEbxst7gvTkUzJuYlcTsSlWgP/RoNZFQmj8c9GCy1GcZnY7VSA3Lx1Upk4Nc5UtIz1tnKtvhIRzYHBx5tlighMz0tNcT2+m4Q38nzovCyvpQ6eAReCmXfipnov4UWviMn3e9UZHxNxFqcBePjVKOJkcWddY0HmVdceHj4gj05N7y5MuZ0I+rHSMQg29ODIMhO6eB/nwR49HbeGst1JRXw6kK/MwB53lyLIhuWwG+sSGe9YVlh4Scqvoenr/lkRG1tRrcbvsmja4Dd4XvmlDAxtmO8apFIkzp6xt2nh+W4V24u6ECe5tKB4oFn+YG13C6HUb7C94PNFL8/O7MTh4fCS6eR6cbOAiBn7eK3VxWnNIY35eI/vu53x3PPGz4RPb5WZh3VcCXh7LVGTBJqGJiEVgVHEXBFNCG9IQBzZOVHy9Ukqr8y78DOpEmT0I4rbMv18PD4EbF+fEI4nE+Bq1Po/9C1SBWbDndEjSZZpr2JyhNvaxHTLE0olDcPvvYkqTy33hKBv4NtQXP7g4xJcAHs2pOqX8RYFk3lop10jR+LAKhzuSIfspzv0e/j+4K/6OYHJmQWB3OdvPI+oPQmx+NQ9KtpYl0Rm7bFkxREOJOwqbaS9TAmkeTCLGRPllpSwTlBOE9L89xcs5xjAV7l0SPk/uQNAe7ToiXBQnHNDP6Fy979OuZfsKP1JIKhnPOgB/cAcRMoqDmkjiSYBWEMyu67lYSFB3XHXATPPQ/k/4zGYYBfhnzRKAxcrz+p+B2+qEPRiquvD0Cj8MNIGvdIX5VRa2H3y14WvSfROEZzGkx2thmezXXYAHqMevif1wZAOBMeUF/kT9eKo3Q9b9PVlrerVthDkRsmb6o7NIHtgUdSaPm1TxoiA8xlozX/OR8RygPqer+qvBEsIYNOQkRlxWn/uGUQyS9Bq2DcVu3V6CV1tyBASIiSQFdt0F+vPbxCoZU1a5bBgCT/JQ3a3i2VeRit+8B53zCZX7JM7vi+VvJBw2RNM9umDU8zNkLENJIdsbrl+KUwtegZRxXlWDBT5fIb2RYDRA0z4viQTsNVPOW2Dvh8PAPnpx+66foeiXtVV0TeVoXzVcyGN85K88Zv2miqS3cnUE5GxOsmLFSjp/EFAE+PGX5N23sS5JDdsuugSBOhsKt7+xiaa42/KGz8EkmJDoCDemOcL95MMZ2T2hXGNZ5wHcNu8i/Mc5PxN4uVgNv8qR3nC1P7A1ZKYHbJnULqWNTtugVji+ExXtNi4G9/rNN6k/1pD3Mb0K3i9Yfs3mulxZezcKn7lPz6dndEifu12wLswX/SfqUtE/5cJS0Hi5EDqidEXilq0BuF0pPOv1qSAeaN0jdUmjYc/PPOWE6bB63jcRrXIDbgg+dc29kJHfvJ6LHPXbA/zgVFg79c16Okw1ZUhuYE7SsF3yW/CTXKWW1u6WqA6BCV2nMbz5XoK80ISoVrMN6DnJLRf2xwdo5TgrPdFhypOXWfuj23Hj0qts9ScuESwmocfwCBjoDUuO0yAehcZBlrwCMWDhdfpODzj2vBZE2gcdkaFAdCK9O4UvMVAZdWwIpGoloFxCELRzqrvHwD8oQjwuH6PVbs3fT7Sgz4g818Ed/bawUAced2xAEC69UTEFtWax3aaglEs96tI4AAT13YLU58KWSF7TP2EQ0WmbBWWi1uLT/sbTpZNUbv5V1W8pLSRT3EIDgIzOm1NjX4gglCUH1so9xxD9ZzzistlFK6a8JO0TPMQ6E98soIjuIsBz0AvSBaZwin/HmhV9yVspAEUsbj70GjkMQbCK+PjbSbfgwrAyzTTTSJReIv72Rxcxt55+NUhI18icg6f5+thg0fuwIBtu06fHLKO6SIPBwQkcKMkTHM5lxSeM8KURnxQUh2F0IBiFnWHImlN70Cyf5OtHJRJxY876VvwL5nc2ZXTgezD/wXt9vbVoc6vKB1YNuABU5BITMrdCQ6IS2swXsB7nBv7eCD62t3dBDn7LJwh+Lnri0G2U6I0nwhI1BA79ROGQd6MPas6nT8GKX7p4dKhrmSq5hkS9HalX2a9kQzGQ63l+5TnZ6tADcxYLEcooDVurjXrZXT/0hhEbWLTJaB4o3T8cg8Y4GvEhuTYSVgN/bxt/cLpM7aFKYMHTygqaQ+2q0LN9lkKSiO/7nVYS3gHFeGLMCHl1cEspfJub0k/UdPN0CB1PpLumQmPUn+j5Rj827BU8AmTk4dnEoMJQ9DXIH+fk/ywwEC0XPZ1Eitx5tnvbj1/YBwTkgidsK2fHr17lcKIhfOFio9/Q+KjTnapVCicX6QcRhUnRlWAGzm6+MCftOCctoNcfUAn/B3tN+UzpsBeZeOpRvbnnJmj56fvrqn5HFs0IqqGxGvdv9LLl2IdRL3qulDHy7kUQa8XghM7ROVhl86jAQu/RYExtqEiBbu2B17Rmt0YjbdFFYyqkagzK/NZtIZ/j9tHoxPSvkfKuMmxCSoVp6vX8JgUuMfdZvI3NA4WnQpBoCDUaA9vx2E5teestbjKa9dMLlfsILQWcVmgiWWDuTr+PucSGGYmdkyXj34VqwExoxSMpHYDLaAkKpf2qNBO3RACbygICs2TweghB78HJ2RDidzxZ0nh2oFwpgJsZgjaeuArDF8H2NVj4QcIsmKZdm8nEL8iyIrktp5l+K3uYn7vTt+YVRcuUustTuupWdbq+K6a+yiFVPjaVuXIBeWqadUOrpOcGrlWoYnZlfaEIH4QbIfng/Si89fmBJFZmmGOmWPSxQlTZyQ1NUUD5OEmRcht9d4bI+4imoVfqwQXwMO4eiKvsjIsr5QeAsToHopa+yuleerifCN5lSzTghWO4qr440PG1jf8TRsavObZGgsVi+bxWemhsLLfKCD4qa3GkAezIZmPOUFaBRQdxnsAlBMgluvTYJ2CwYM/ALY0VhLwnceb24vtyuVXh/uLAqrVw+iPqtd5fTS+2qvmUfMzrQumaErrCOo97ZheOhN1o0Y9ABAv25FzkmrdWgADw2e/jRYKqekDXl2IPsDJRGih8EIuYzv9e+NYxdctkJtqdhEggerAUBluLBTQGTecK+WH6fUdszvo1wOwsmnRfa2T7A0en9qKJIxzTzNGBExo3uoTGqKWg18RuwvZw+n3tzZ85OqE3RbiilKJhrpaTQMjbvrBCC4Of7m1R5WH91gE1Y3kLDzs/0QbcIME/wzS/nWuKf6sLxNO+2d0POZj3PEhQ6pxHfxGfdzgMYaYIuaW5g6JXuuOlsgOFcZHAT/4jk9f66siMEt+UTj3HHmOmMgNCZsri90TIdLtStSZenUWC6xnwp9mcUtNAY9nB0JgQMTS0/mlerh4v0KQF8XhvGdAH2FImJz+zChsny9hkXoTmUC64sNLVGvNc699azhHTUZfj6Cbll2f9Yg2yByjGOh0cOCNkVu3Bb6WQE2AOu9RSAr8K5/CO9ZeC0w2sLvpAvaGy8j4Up5iLOvtWreagEqwB0+BTY+HnzopU0EWG7SREOFN4khN0W3sw4tE7HsQqL8xAUtsqhYAzf5vLittpNZd2EjOfnykvZg8kdgeyck8pGd0UPC+ORJ9OsLKE1FegCT4noniN5PgVjRkYW9cI8d4htmwtsSF2sbdvOOEBrYxy38tonvcf97AQK4WpU75u7QdnHhhgjS/+UkB4tgPxqmGIb4xQSVu4KginauJE4CYQkNjIf3cVTigdh9PJpCmGb7mypzuuaR42EI6P2Rvw2zwcp0pVEYcCxktamgXyw2hn2GQOA7Cm1TS/LQgCc1Sj72oHlmLWbF3w3dd8SS7Phzy043RSlhr4iPPSukR6Tx7Lq/U0knXBypUntrC9MWTEhE36AKS9H7Z3sDgylvei8mMCB8grin9YMD7zPNOvMOa5kbBvfGOt25I3+lYrdfpl+Do1F2BY+YiXOwgdXc4Vcb6mDjigPEwAMqIySWdYalPV/c6fDtsemN0EKqVvAgVjMe1hWd/HB2fXYXhtFJa5AnJz7+RCM6XLHGGdvOX9mPl/C9ueTmUjNKaRerf4n2r0M6fb43oT3Pz5LK4IFtH6loOXCJ7jUyhPIj/MVYP7MkFAkuE80jga76wd3alGPgWdHOycfcEng7jPgFbcKL5WdrmMB82lJ+PJQ66+DdaY/9zieBTNArMDf438osjeMTIu/5fzJtvWal5PF/yyUxc10k1Ir/wEcD1B7AKs1tiPAa/yjlgicHWT8lpozIuEAi1VqS5i8hH7twT74HaHK+8/k3hcUhLVvQuuWv275eEl9ujt5VCII8QwG/d6izwRJ8gfU2PUBf2PjHaW3/P85tb0PRzNI3SAw4k76N8BHygUP0xGdNjRmeppcWmGeyQSGzzsgbkoc/p+KUTA9T+SOCFVwRnoRV4wayRbDfWttOR8998m8TdrjYifJ2aXYtAqVrwi0HbQkQCTdyYxwJfHxxPF/EA91WRmvyESbTkA6a+wS3rVZE0OKrsECpaL76cRVHnG4AYbQwAGcBPF9M+se+0oDNNcC9UGfWRPb1pqUEk58ErdBnfof/GfQM7RVgB/VeZ9xRoB3absA40dOnVmXeI60G+RUZQyUeM8EeOXNkHyyf2wtwsU2hDUAPcuGuh6vjXQtvOW3XdRfRrpf6yzEhYRa8NV2FRAIOrQfGwaqNANHvJaUXh4JDoywS5o4onCg5+4xLZXNOx7EXGxjDlQ4CmMitX3ZMSBMq+lEwS0ZnAkb9w9BvbOQjseaOub2LPwQo0U0b8pof43JnI2xfWZ0Is37/pJ5EzRh0iL51m1+z4UFshyCW/vep/pZGGWsFfEirl1tqFKpxhd0yJkpTDWOe9mnOKDjbuTX8o5yvlS6NJN8krhAuJuVcibY+5XkL214EDsPisLKw+Isv5AklenJqSqpX1Sr2hbogc5BU2Zizgz5ddMEQdfj03O2mr0/mtQOUfJ6zy0G3o2KdM5YyfAEAHUXEz2KfTvO5H6ZemgAWgHrI9r3hCabOFs3FbmhJWIMHXC6P+fAC0EzZqbH8BFC0T8C97A+/x2PET0j1NSKx6y3U8HDi3UdDUas8kPxKg1aEgq8VVzxGPr+oAjXcZG5pgjY8PvgPSyePC5yT/7n0t0t4n+dA5NJ/YAaCE1c/f/RNmzIa2iyq215ldIT+Oh52h0BeDgQkMFxI6blBMYsf66cTMTrBnr4W++km7G8NLuK7HznZmTtV0OOP23ao/Gsia4NRcN3GMIneZmYs6AEIYgxTpSJFmdwXBbNZDRLl0VFssTUuKnKih2+FItKhBdfrsCdwUMwUUMvVZnfwVPHAduLbYoiHg/vxSw4FTB2+jwr84Fsioy3T+VsnAfKmRQp0C9QVRK8ZcuYcJCH1psHCrqv2SPneRVLTWN0CiWUhOldVjav1A5+ke620UXjT8QLFRCAa0916uK5iLTB4P4sLilpFeug0czAvIKyzTw5g0cqoDfhUg9zygRmxGTj0xbHtxKydfTTGUsFZ4Etv4phzyQ63e8lEVDTrgN/JremDr5YlzMItV725dsbcEQZKpr4f/LQzlZVCpa2w6pv5RVwCJfnWV2Z0wwgeCO6n7ToiaY4WXfBJ9UTC2uGILhLEaHP9SO7DskKOC7axEMPed+3wT16mRC67WSvts5E99RSzemBHFTvB4ptHIq3A6nqJ9o+9f6qKvblNea3NIrTL8FRQL2P1VmdoIbFJaniDjRnyP9LBNB5f5wq+hmQBr9Prc8nfGVFwwX/V6JoodQdtAMGce8BzvnwT0WB3XVKnjra13WqBQR8rmnpJINqL84ngDWkYfNaw7E6z6eYOiJeaX/1b+NJTTpISXPbK0CoVUwsDk56f5ZSOIeqtCsegvsBo45qLysPnCKHtVNzkqPXcf/3N99nGg5nTf6naloCCfWqojP0umBk5ZNEYjDGVdMMPUU1AUExRZXBS0wsgWpA2bcBdbla68STXb/bol7kE5K4/hQOYZI3ACfTbvPrsADSnETAI2W0PURD7Lbsh11IyW7xSmSWTy4u4xjq72yI9UOc66jkvCJ1sFzrnYjv31gge78OZGsxyzetv5tc7nwSoQYiC/w00KM5QIFSFaDNrjYMI9Q9COsiQwBcWjSb0saeAQLNrycRE6IHxI6pjMGqJ9LPcHkT3P22GLIaPXQEEY+vZpZ61ya4ObOWnvnMpb65XjGJKabjBEvkjx3u1i3+c0FHiikIDjU+WLJQmwDPjJI4KCIC/pwXpbZ9capEcmgGdvYOPIHFjetKp1Gk9CAgM5a1yKhDfcGiR5wU1psaqJ1ru10ygrkBnLx4kiE+eizq3r7m6BLKJyUfMYvITMVr43L6roOOlZ2Wtj0TLFTQAwyOEUjX+Kl9S8yhm1Cslgr4eRRWgAytXBufZABkl63ShkCEylozi7ACQMzjQBidE2dzUarzhLezIGD6vAHpqgOBetVeCRy74M1eULPvEm4mEXjJ38sIRi3O+vrqGje2ixrRaFYF12hxG8+JxOuI+baMEjHyCKrJ06RdAesI7ZRGC7va2MmSptZciwb6p/qLRsVPg3G8SeJA+snIDuF3MsFzZwI9B7URCLI0FqIe5f7rmf+KSCRQ6qslBf+W4B9c5MXM3r81BKT/o9L1aQGHIR7tb9QSmNieArOTl/XhKj2mjBZ6Dj2jFY25VD0j9NUTz39puHNnL3s48cM/mrvhldmMOD5yITPqJz9LwzLjQMu+MOU3lvDOYLhQCLaxAQvy5sQCVD86BtpeFShiSkgujKEvAuk3Wc16s7gi+IXmB0Hldmmpt2C18yhnuPn4+MIvZ0FY1VKxkOokVjHxPn9lIgiWZ78e0iIp/zWpJE8oJyvQFwlgUAB3pqyv3yU3DUfq2avVuhm5X+4veFD1u7FLO1Q5PMCyMhwx5XaGNKgUDGi0C1Teqx17MX5fd8fDqX7szXlsXk1xJ6s5kXFDpMHgI/rERj6iVceh1LZhOLLGSFVn8IyvNQI4HPPdoxAlEDuLPbzLvTU4GSfglzEKVmX7QeafwDL7I7yHYec20RvUJCL8JeGHsFCe4btGRdZhmgCacO9RdIA+V4Db9nF5kXH5594ovqghB86jAFHg8ErbHd2EgFdO4w3fMUyZz0KbRKy5eCcrLZ2wMGon2Ej79cnEk9LDPFXyt+1BHJ583436Q1bXE4Wa5h5Cd9HCjplLBoDo0BoMvzfHIf2LqjTl0LjtwnPHPKefn9otk4QCSv1QodE25+Pq7v0fgv8KTrY2PjMMp/V3qL6gE6Vyd+jCtb6BMJ1x10rO0iopu2EIpJcN4R8qvlifjOCimz/ESN5F5AAcORAis/kcHdoAaoLU2V8I6DfHn3ADP7Zp+dfLns/dq7Ok5qtQ/QZIfDmUvUele1yjoAuWet+uHrCYfbAcqkO0pdvM60nQWo7aEby0caHHnGifRUUw9QI8bsLfUwq/FCQI3ezYkZ2JQgGknTepZHrwsigQTRKBSZnF70b4YJ+IMOgkMHNUUc8VkHAJeWi0tMyOYgi+m/zmvdSEm5qtl/BXu8vmcWCzlELv2N3LUQdTkIeWcq1sBmIZJJbSCO54ss0fuQodnwi4KKh0pOY762gnpm5K+hCuzbF8BUuEJ8eVHBlXVuJHoq0GjnTMnynOZjRCcMOGC2qDgCLsA0f+tRh7QW+fY9sf1ceaMYANwkDvpYYM9yl+B2onqCv2ySGoSbOJvg5tXymru3YB1o1/4h9zj4DgKJ+Dc+3peRMQiv6qLZxNHjgImjwPvljcSRitqW8fVEJTJ4uA5mbEqbq5pKtEQJF1fY/EX3oAM5104cXsCOr99GCybeh/mlZsVv0uoD6ANuUOeGDDQdgAMdt72sT7SLfjuVdQb60XTYh3yH4k952WGwrcjiRrr9ousdbMfooxgqDh64OfQH91fQxYjbZ4cnX0rTh1is3sh7F+xeKWrFwv07xbdEfNjijoHEZmDovs5piUSD/3Y80eEJgFanLhKgI+JYwuEDRNZTf+famri4etR7L6fgP3N+n1kRpc+zv8eV1HdSgMPsTFgaA8HbOH/13sMRbqpdwLkDIFhaL7EugqPnk0kmcyd6WA8xaiSMQig5qv6VG3B6vA6YuIeZgraEn/OdicGFVf3iniwETme1vRw9fwf0c6oRFyzdPgE0Z7690wRPD4uHVpQBRv+TBQAVBrg7+XIvm9+TRvbYwqKTCmVqY56rzLk3TJT7OQ2rLnExuTbfD8fFI60XrtxGpeacJMU/FwfcGw8lfItx2EwHc0tBfIUpaO7xIZRaCWYIEop6ZhHeRkybSX42QCSgwy3M2URXvXIi1Rp6UOM4YRu3p6jp6RaCRqsiw1GuNEk5s/Q3HF+YXX2y5CIJrCkgBH+NwIZ0aa7gpjaR/Yjzjrd/dByDSf+K54olAFndG/5k60En6nCFgfwY1t9uroDvRmYnsvHYxguxXgOQLrDlQiS1rgKgtoPjnT2iIgQT+S7Mqd+nanN3Nz5Yb2C3aKc5332sudn+uHACyUuJyHrBATbyJnBhgi9D7OgFBQmKbabvkjU78bEMRb8X2knEv/Fq1rXEC4oKDkyyJrcnj+2IrQwls5cMTw3YSu3crr9dO+b9RP5aQQCtkk0TdeSjlT9sbjusTPYliUl8yar8lIG/EvqeppkPlHq/Ofxtcetyx9kQtOywM/oYucZU7RAlLMNHjVO+AgKhiygqKjnJdyuJ36XkMOd1C59g3fj561LD/OwFLyjSY2kyHi0c/5Uy++zCSrCX7z+DYQlAGTtVCK0dHHssLgyUmdrEIIiAOEqQwEIb/IgA7XHKNjfFEe3oMC1hBv4wmP4W7N9AoF9bCNyI73HQ/LTRSRp0LODqGSWOqXzo3PH5/2nmIgmjHY1dG+Ouv8ARGj25VxpxLaorBZZXk50Z4QUOg7P38Xzc6ZNYjPLo4TC0fh33J9Z9bZjZA43mfGAruieicmXbgMlBlPRy84i83TD14G0GllH8Gvt10SHsFc/a7ZVnLoqeNKR9vQOO0aPvra17SaA6ZuPgss7PTbA2iYLdlEE7ua8K/5ycNACWfRf4s9yNfWLM18brq4Uleok3gL9zHeTgi/GqzOBeJ+s+a6MLjRnAf2j59D/pLmnOYjGKO4ITENrubjIZX98M6r90sbvZ8DzFiyXw9mMF427NRBo8AI4seE4I1gqTlBDfHz4vfFYrQRcUWGL9Y4BGR5xeLgCHrTuRR+y3ODOP6JTPMS+EcVs/CVKRbYDM4iOIjhFWitEob6Fazc0Bboi0QhzqboA/ck8rEOF1vUvPifzyNyvxhUTpqHIC2C9lDxWVgsG5DgZKYGlCLAucrA4+jOm00zcBJ41Bnr5kVzxkBKAfv7T0PXmrkg/j3INnqweaLL9S5aAi1YKivu/5g50SViYUaRISKzXoH+Vk1l+6SzLYy4vABmMQ4HG6M3ikF285EYzuTpuEfolP22Tk5uDDcKa2QLfY1GvjisTDmpLxcHbP350qcCC0/DvWBo4tWfSbj/xp6eg8ybaRKePoP0sKPh7qdV5ogMKbWxAYq1G3JyKji7/tAhJR6xvsufEGqR1brn2RE374XVHfLCfs5ax1/ScNzrmjQ+RUJAyRq108YdxDsqE4v1An5trQQ8seGJNBMpaq57dN8d8hkgF4WzaIiBWK1LDk1kYZUajk+QWjHn6D7EFuSaCiYSVlq9OpeEu/iyQ3ZMwO3sQ+HEdC/EfTVWy7rQTBXxLD0rIsZpZ2QouZv/5pfPN2SU5Mmp7uqmqKl/W3tz75CyDUv+kc4BrjQ9YGv6WNQs9azSsxbwqGesqAFi1j1EFLqPu9WTVlHTP/2tVfu+QQC5VuoIO1mySJmfCRTRJjaYpmDnQyeUbxMpjJVOKvL9BCwMmtWS07pCdu7x0r51L8va2bPKEX1fTKbKCNVjbiJq2eKaENsKmp5IcLVmAcodkpmU8dEoMF9a2M50lUWo9PTZvxAdyMuxZlcSeZNTzg/t3DRgxqTCq3GlUv90giDuimOG6VqYj7ELmK5wX2dLDBlX/C1gNAfpt+lJ3NqGU5ZQZt++17QrQSWn5PrXOZFScWYfbbQiODsuo7k+4+cU+fMal7PRPqiD7gUgvl8ivwuhI9u+8o1k6VtgWpkL4Vl/l0poAsSTuiL6XuBe+BjUhchsWJdKtHbQgWEzwfBX84/QLHkf/UZ0yhP3qs+Irj1Li7KsULQWU+R/Il0BZZ333pdRDadxv3FxKgN74DAwY0H1WyF56VfHaBiAcPl4a1EnEL8pTQqgokdNHQvQ+kEJSDvHrmizbg0zfJFtQrkZWH8wHyFqcjW6h7VK3RAEP+VmSQNFmo67b8KRGQr+SVvmTIciveiab8eE/gZ4beCk8PqxARsgNivxehe0DH80PJrQFu5e6SzAqR9DPGV+4svF/JeVnOyqHZj1Vr2hA8hEb1EwLfqtRHBTustc1lIv1h9p6BFSjHIMbcrUHsgl3YTK2glAHvg2EQbVBG+Dt0VgecLIMML6eJmof3jvAa+6yRUKI53mYyki3id+OAhRW+hjUC1bLJkG5nO2guPIRrQH1A/NKXgciq6Lpv6pco3InzrjAxTaO2pZ1rlUhvZSP1pld7HfOHpBJ6Tpp8jK5tnM6yQLRfImMmVShSm3P6disCIV8o64ErGTVSReLg+gx8J+DO7IctgZ5ZNdkVWqaeDHwQPPRYMOvEk7qsvsljT93+OaJ6GlvNWYe4BhzvQnQX3Ox8shWjfVgkCNzeUm1XvowWlsbjGnJUysYzONROgrdPkxYPgLPZCXGiHa7jxB6NE5vLgJYLCpeuHo1+9QDdJ5BMsvGcDDXUB98wkiGpRgYiKawAIUYJPyitlTtbBH/KTbr+Wqp6DKV/C4tDSPPzD/YdJKXsVdQ6W8JqYOTC+4nSXPKr4C6pm3s/QuG5PXgoiO8vFirvOFWSIe9g6fE20NybdMS/vuNpSHv98LPbD4vigedo4hTUQ/VVIZfi0wBXsfLbMCIKqV4UUubF8wuccmbqaKOXe56k5eDvRyq866lG0gl4NNCD77WgdMcy45dB/3bBmDTQZiP6O/lC5gEASuP1J0ixP1mFmYO1awspycVvhYASGzffKnKoy4+kz3Vsgp0Z651t/dEDDsHsPYqQNVlHJJfRfExud3ps4vtFhV8r0vsRW4QIhS8yC3DmmLDeGhKn1bjfQDIKWlFERlDucwng1xhTUs/pN/wlsFNop9i+Wb4Hd1ONiNyqCoqtuN7dOFfdSFSU4cvjOuKc6ZFIUOc20b6oF88L8s17sGGYpfNz9Vm5pKLTDOnno7Yji2ZfEUj9efY+Kb+H7IJEAz2V7a6UcD8NYfka/TwPG1cl46iFbFwcInC+6gCeHtW/V7y1srEA7G7H6wYQJ/bjBuUqV033A9ER3WZOYXpbajQUerZAQIHupPztFxK58p06AvzeXdTXOgdqiyd8vKk0wrc9WWK1Oq9RhEEDCmfHRLH3KiLfRF/OfFcnbphgwPeQmrB7jChx8cOm5xsaqWCjQ/eiYnahy8IHeSWz89FPN2eUp+mhasfSjbEX8q09//131MCLMW7h9q57L1k6vWTf1783v5wLyn/Fl7fMQHdxN0n7bXIVqAcWwkR16XUHQUmPbXA2YlXFds8RnDMWyXlIBGI3LM3LdqAtwz8LR6dY/4KTZKdI3AoBqK+/Cf1nWhP52z3h/7QXoj7Ig6nGO7VlCEbIXXud8+KZK4Movx26UOGk+3oSW0/TFNSgN3kdvCHW4a+fYCOv4H2fleN0naUCiCeAYwP7PrlepOoXTwmKtqPqrnuPx4x64kJow8dH5Xyg6lgInyI0JFfsdA4Z83h5d3VpV9tMYwS8/5a8/KRSyvG6dDTN0DN/a38tYTPmG2t3/BuV2/1NmLGohQf9uyTMXEWsrgVi/U0YJTZPdpMHkz0/NxHqCMgPKhxPKWfjn4m8y+A+VYr7J3MIrSluhKSN4CixonbK5voYjVK/s74oXMVXQ1GL3tDg4n6wOtDFNqaU+Vgokr/MzBSiiEnPDbSq350izLhIGom/RCZxnbfWn1TPOCVPH58IipZdPiLcx1rEWwvznaRaw61JX9PlJWy04FIwXb+/ha59c/GjS2z/V/DNQE6DHbqhKGn0vTpNWZntkqwEGM1+I4VZl7NIxSZHVkR0HmUojx2ogOa+yQvERuIBvrH3a0TVZBhOCLmLEHKKUXHmP9VHNwr7h0Hw+v6lx4A7m7bFqkXHJGvkzAs+/O1VmxAwE48Tg8qgzwILsM5EsAxpryGK7g/qfB2CQDToocX6Q2Mkjc4GuBRHJp1KMANlmIsImmCqdclXfHSafU/CRe130grbgFX5Hrqa5EjObPBH1AYFsFk22ePmO0MRr4kfyp+aYseXbPD5cxs5LK5b6UtsSa975bkhDwEpqcdrUffiouaBAAZXdSztptBgDxWUn6udbHp/z+Rx2dW8xPlJb244vS8DOg3dfhfGDinR9fa+uLJseM4MfUi9R4Wnlqvdt3fqbYw2r6ru9diBa781BSZVSWJ/eN5vO9V1xzcB4wY8J3W+Ahq1hbtOpth8B3fW5MewGncP8uOcwgM/DH5kFV0h7fnyTPHOQny9kZ5L+mhVEDaYKdK6Krn2ci+5h+QBD0G2LErISADiKMjb4y4axmqAXYNB+edMvt7r/roHm77Anb8ywH7nxW9MNqJOKcoMbtqeAFnnPttQ0kkkiA7N8BWJgbMt8aHaWcYbPKE9wEO8AaaQ6oZqIxeubcJyuzD45rP1kXJDjxyLa2v0DkHIGL7UXfJ3rw7Iu1rVs/3+/hnxjXWfqgdIoB5biuRzU2BueaJVEBKFXhH0sVthXeQ02ewjPCL6mBfFhH+iXKSbK/6buc80N3WewmllU6rhhxYVkZZt3RAjn70Npg2IQmZabPRY9VrgqK98pBLNwPPR9Ai4Uwd/YMsnyBzz2mkR6gGfnNnDcZU3XH2H168F4bKAnJdhwksM8P1bE+d5oG8Q49dcAe6cYVCBZEAKgLL5TKImJYbauf6ksKNN+33nQLn5aIE+KZIhA/C8y8s7RYxznKrxt454petIBf6Zljt2UX7cFO/ds8udV/Fr2IldtZMjlAJ7f5m/KSsPYN1RpAFoDXhhGzqsyoHf2TfUaUXtg9Dacc8rQoWrX5IEL8nbWs5NlYOvxtabUiuSb5SwDIPNFgy5BCVGvj+86AYC7xKfG6eoRArwXAilXUR8ycdgrg1yIUZ5y2AdyxI7Vs+Ea1xV5TEGMWRdZNP1cObuxJG9N7p0fCry3wz5ORxOuwr8N+dqDUwMRAYxm7JC/8yqYLKHi5koxE1UtSdTqOwg6OSxoYX6cJZFvjd0ioOZXEjdc21kA36NCb6TmkrQDs2GDJ3/cir6cqruMeO9fkv6V+wBlql/g2DfmtGcOOGifz3CJhkTmUtzpnLO8qU8bsbZMQiQ2XcRsIGPlZYD1dgvF2hRKPxclEwWH3xztnXOe6CsAgpOSrwi3q3QjwIh2P0AzLU3Hk6LZ386lJ5tggaJfEsfkub5lJXkv17UgO64wgs+9zoiaMj/4tOllnyIBejSR+VgY4IxpdWBvX6JzcMaXnYeq2J55/Tl/aN0H2SpY/Ax6EViJ9JPAPIThyrqv12x2tlO08O6BQHv76CgVsNfe4lADBcKDDWnR3/lG9OZIfRcrWluxG7VJg+S6Idsf9PBLagroVw/N1HyIgqhQAOSCLTPgcoQ/v02uPs2alWHigd7juf7aFcg0/Gz/nYMN8fq12DBOYkYL7T1vxKR7toReCZGpDKmrDMI5s1KiAk4G/okOOtXCwqsOJTBymQn0dg4b1EKjRXhTq65jw/iwXGXOqMKj83sqwe0FOxW0UPrhje4yk7hO+5mGZFdZOB9Bkh/OGVzR73VQ04v3McHPvHtqw0Q6gz2+eJ+bdPx3BIR9k1+a5AINi04bH4Pr1k5YSL4OG6WG+4OVLsleyCBGGPH9VC8Mu2x9UOMmCnEWkeoLvt64E3QWVe2U76FYkDWAavGGXX/La6vAS6gK3zuFu4CmT0nZgAbhN31iyHfnWQ6AfwdMIYESYPGtcEqdG49G3+Lf3sUrmmLRk/RxpdcQk7ua0xIyTX8E/66q25TWBwsbEkU6cNcXHzWhebJNhkCHX7z7LWAbTKXt7rbBY8KUnuAYHtWReNLrbUKPiF7PJJJJAd6QmJBvoA4Ru/FvX+/cNtsTCvHH3QLdHCBlsoaKaKpJNJM+wQWHYVeCap73JtQnBrnYtEbw0lnm15T4zCQLfubByNKkYZTRDVwbJZeRRz3J4Nfe+xb4kF3PmarWWvBez1JkCqdssodeAQmzW1AvfDq3GmM+77thyIRLPcVKhuXHVKVSvhne4wLaAwrCzDcSDvyua8YBnBv5+A+HpU8hv0Txk8E/6XHcLNd5ybsGwYiCF6mBrF4zuAKYDTMap7UoOEUmIqRU6rqkeCXKea9a70QrowCJzbzvLtXODxoCX4ImXMTx7vY36SaTptp6K0GK9KGGsYafSY1hb+m3hzWMqmB3K/fQ5j65A9qLvGgssLNUygyKhjPz5UoEr8QTu0YW/TapT1UVEhKrUGDhMEYedltoAu2WkxX9O2MsxvAVvmWWwjfIfghTseOBCJLyAo+Yl4VPCSdR7PLDlI9jjtGOeicwfdV/y3lO4CvQMUK/PkceTvdRPK7eCnbRqdvXuqGAOUnCzdh/zJM8Nmi1CBqYs9dJyL4gt8khwgDHpkWj9GpigAHUSoeUFRFaIQ6+7TJHbnu355szh61+UEpcKPvdss/zrJrn/B1LgI2vdPsH2kGyAWop6O2Fg6cMNf1NpK86f+FPEsppeY46VYr6UpunOOqc+o17zBW4ZBMxWH0wp+QmxuTrrq4LRELyffx3shl1e0crsnCFzqRN08WYVg4q0jtRAZkbncCt4309/gqtHuZBWNGQUD+/rp3MUl1esoh9yPMpsW9fjvqy51zkri43E6jkgWONRi1RvAk6eyEb6PstfDVfk7y/iFp6oMUxb8SuFZY6o1VnfvEoiPyq6PIkbc53+vjwVI97Jo9sALOTjvwJJoakN9+gM05UfvW/jxgziAtRQlPOTM39YopiGKXZQYwPQLtAeiSrXXg7AK8pSEqTb3rlXMMljgx3A4eMHw9dyhYqV1jodx/HKKQW9BluKyzRTCCIZdZ0e+HRGhOkAVnJDNfhfRzhaRbSS8kpUAm2e7Ke67X3oqB630hBZ0Y9FdJ2LFD3Xz2xyUuBeQFuKCeU5tRzM76/z4nwdnELRWwnT4s/RrwheEQqLc2Zx9371S/hqnG303QJbcweg+hwAA45ly2DhQM3CtUMkIuvTtyIvPii79lZtxkn/gNx2yI7R6P+usrOz+BSPnmP3SzxQY1KH85KcaGj9/Wyuae9w95y9n482jNrs1gwCDHwCCn/xvMx1kKHEPskjFdeb4s0pV8AGXOxCxIOLLCnxz4Au/rxYsX2vP0ICW3+UnjMvxnsYUu6doi0GZOoZA8B6+s83cNRB65pDR88BnkwsL0K9FMi0DhkC8NTg6FUtjl7baQ/rg8AGNTV4or7LDPrWu9FsTNrBco3daPcaug4Y1HKZKWxdX7pOqKNcXdTTjpXDVeCawh6GCOGN78xheUQ5szYcIabED+itwQWh3hBWIyjwf2FrSa8wDm95XSH18ViFZV8i16nMoCnstL8XTzHSgHElEVqhZ8IMBYuljqVF2qMpBks5GTmy0EX5FofWVWne/AfUfgKyORy22Rml8xwcQs+X+VOhc3SzX9ZhxESVFL8+PDRelSD91hsyFlH8ZwS0p7u2if9h8+WlUy5jT/liKYwnL9/nDE7qA8/eI7NqOYpZ9NxigmDOQe98ETbkQ40eplN8fegrLeVa+5S1RPREq899H5aHBtvqpENXtnBnhE9cYVO++h6UTf4RvB9+DQkrIcxs4FMP/2k29cDfFElZknYV6tytTkRT1yJ0WQEEYCdF1OsS8eOPIsvmIj0x19aK+XEL98dPtSwEJAuUrSWiw8AyrmMm1xaUSIbw5mwN+Ry6Oi4fheOWMfChxE44BqA2jHXrJ4uaRwpZCv6qRLCzy47Useb0i1zGKVLv+A9DZEluLz3FKTjuM2pBeVjvkQrBN5gNbsTBoqukkMkUgNjieMY/IgjzZLuvCuOFFMACrtxG2IkgcXiLPTUj7yWxkAz5zlDGSPNQl5emK8JtOHf6/R+Nv4o3WbI3GwpEj/SErxSY/AArovXVYIWsnCBbIaD9B2qOStS3SyfdjcgJ2goEnm03sMTA/mK6SLAHEAlZ5S+mz7eUwv7fvbvUerr+Ytbn6oNSmy7quh1JgkjsmHIcDKeE77kCsUfAloc9LnIm2J60kUH6rzvHhWMwHx0DWitL3uteuZnp9ybfhzDZNNexJ4QnGEbb+OZMfdat0g1eAf6yq2JRjzBMeTvHMbXr8ekJWIOAEGRnIvAEBonVderoiEQeEGOCKUGk/gr26gxlDTrZNwtJ2ilC8ZEvIuCqn0WESfXgUbEpf+IvI8XBiwYpBDg+285fLWyF0Cog4LC7nMypS0v/RGkaQbynpuLIRvDvzPdQLzYBzWbOJ9E+T7qkzle5epJTgL130CZjx/wlzlJ8greyON87KkQlqfuBFh++wZJkKNllBTCawrKOu6BGEPYNU75xAeqIziwv75F09fcMHyug/KEq/iPqhrXHJF0LdcONXUuTv71ZpBmbyJoy9Ru1JbtvAyv+pnbygkjAidBqIJzNxeRlMOCXSlmvLAK9nVUi6edjJT8e3yWGq3o/EsdVLX3vRDMKutriKhv+LJpAJ/T3wgLx42oYaPE5ImKPM+Nj52XZWkWrTKy2fWIRUyNG/dTH3kmk6xj7VoM0zPAt7DtM96wgUYK8s4qWJ7/mMEODLUcGmVyRq8x1zhwxtMylpP9xbTgsqoxuhQ8yDemVa+UPu640QZ0hNzLMID+sCo58EN2BpXxUpW8tBt6N3FY3kF5busCmG8w1NhYFCktRe6N3W2ffpdI/WSznfITqEQOQu2v7+dsG4NmCnHZToWdSqP6BVGtIESe5tRdSdd+OZsoZRCGb3/vOHJ4gmkkOOreh970YFY0UdpvnbfpNTlgPjVl+uKcnbwgXM8RkKj0kYx1lPLXLnPo2pnR8uTBQ/3pZGGtxNjAbI5PU/Gp4IJKiCz8DVoKN1Dv0b1K+/s8SqaSgtV0pOLHtkJLMxRLM7yiBzG98VtHQwB0WNJ+n+Vucj6T7F5lxgSKaB27wjpVxTPhvK6W6K1EWF88/IxykGyvGqse6srqDmhep564hFBfDNpFlcn7TraDkfDrrse8ZUrYTgXkUT7TGPOaZ1CR43KsSJcZ29aLJC5TqS4NToaIstX9/pND993Bv5MUb5O+SapUwBq/Z01uJHsJt+Pb10+DxeT3b8vnop1Fgm4EV2GSh6BFp4dcEIFp35eQWt9bp3mwBOmC0ChGB+fxjoVIeeO65O/gX5Qz45l9kIh2d2Uh9Gaqc8dY0mY8cdPFUhEd+3KhlgCyyOvmeZO+B00CI2cKF53AC/Cv/FD8l4C5ejB7ezrYoDmg3EsMVZmXr6M0ZVGveDM5rtvCq3BLEoj73hRe7jgb0lzhQE1NFh6fA+cK9FuSPsSz5iG/LoVlSCyH3PaceVkqzfemLGdarsMSm+ZQ6BIAr9efrIMOwkTySd0JNrkvBmIwpxBriNd6/gbdu1eMO2tA1H+hUEEv8zinrpUx6bvEABTmDot6rD1c7tAqAgHDTOn+XOu7H6GXiGa6W/w4Lega7iymxEnrgY2bqzH78F6HgvuH2cIkjk90vW/BnFEqV0EI7ziornuLf2GB97kx0qP/b6O9cIDHwhPDKZH753WRs2QS9FruupiPnKdUISOO/5UEFbxuBCCf5fE2pQOtRBk5TgMMbgLCppSAOBDuuRUUiuFdMSkdyPDX99zBzsXuNn+vqIXoFFcUnoUDblFKauEvSmBM+vgOzXfyBj29Otjd120GNub1JjqH7Ew9h2aPrcBeqYcMpFSJA/4ycYmfA5GG4PUt+0AvEz4jTztTIXJMvtSC+i4Ls7hZRuu6ejQpyzdaK0TMutj6VCf3WHqCG0jIEEij0iKfq+dgkMncB98W6O4HrY6I5tJh4ZndON4UJNcWX3J5Tupch+sjDLEP4OrKmNIXotQPJgrbD3d/uWqX5S9moXueT0XZYA70mzcaetS1jtpKzyq9eZZf8qZQhoAsWviVW+4A0DL0l+ec5HrB5N5EjWikXxvuoja0G/nO2LyBoIQJxrlQWus+LKRXacacN7G+QMBB5Q8XhduoImW+m9NfpBX3OM9H102pK1AWRnBZxi/jJaBfMh2D4sYkeWbh4MbjgpaYHcEXTcr6fdzc6+Yq8fJf8nymhu1uO4kr5WUaENWgagPswWJDxAcOPAjbCQmSzqY5p1XSkR+n4u6fVqjfiDG7S0mP7/yPW3sHedjoohYHIr4GdwrsdAimMl79h2WuXaMSrBqswytNerG2J3In1qgv6OxzTEuUYQDTwmV5lsAocgMHMowlL8dX9rHcdfdxPCSLrAZDPZiMooagWWXH3iHqoln370yYtQUZ+ksK7/pu77IGtjK14zaa2karKRwM/t7tnEFso0CMp3CrkGzJ0A9v0rg5tVIjNMxX/623jATu470PPIQ4Vg1IuHV8S+ToexkUCAnmk+MItDkvX0/xGCIRBedIVBaNIqjRtXEXFC1oiSjct5XrH+1rqapLVkzgTICl8aabytHzPxgPgrxcEdYGsR4bWZP4rRe9huVHGz0q3YXuUtT+34/FkvBSpoyYOBi8wCEJBRGEwQWK6LU59fAjRcVDnSmP9E1mUZhWbO9gw3juAkTCHvmlwVB2uxPNMazJbrvqDWz1Hl/+G+xFuV40ZE2rcCKOP5wQXIRw3efdZyhLDZhdRuN3pySE57nYok4eGJ4C12Rp+q9d/GegEC/oGAzK4JFVtE7KxYsaUWIgcAmvT06cP5AlXEG/+3zkz05cMtu55OhDgOD00RiMxdGxLWLWHqOfOle6wg7dIfrDySP17m+P5gfFpkq4qnWV5pZGHy9KEHHTuFZx5HeXFOTlZ+epLFkw7jcWpZ0C8hmjBu0mpuADNs3JwQnvAXF+HB0CYyeZb4EfhqAyEZZetw8LXs55VafxDA4x1v2Pq7ZAnUw1NO6aLwxQnCR8yCbyAggWjWud5zi5iZ9KSTs7ye+0m9W1uUE/2VneMUHJty0AfRL2MridlbWh2KxLksMV1MBjKPw32UB6ouUHO1uEqEiFDR3aA3ZqNFHcslOstp8jEpc35zCp7t3O4o+1VJo7RtegyYR2qrze8/8un7sElAkH55ZDjcszWjrM/Nx5+43+G6gawG68G9qRtIoXaWKiZei0q/djwpIxwUa7AESZNuv9Vz2o/2z3kGmhRW7ZWDqL7gylbAv9cfrG83aR2o4rIX5qJv3Jh9Xq7IVIjUQhklVmsBA+tk/6NmgNU7jo/G6qSwlu5ybssUn81g1C8mtaEnYh1vaxE8AqVLt5E6e5dAOdoYxaMsmBcLfy/GcD+AhttJcabtjG9TO2doRms0eN5YcavjuNXb7tSYD4iT4QPHZZWYxtsPop8yAlCFaQr1pv1tve3DqKGjXJwG2dkKdlXVXQu7S7XV8mthjWpUPIrI98DstAS7k3/uIpifO3aBtiNmcMIff/Dobo0nKW//bf6GX+M0bfnkEeu2QJ6Iu53owfR8g8eujk1MzqEvKu5pR06xMszCrvmZs0JmjtN7Bt71vK0hqAXiwviXk23mftMBkmHPF4p4sB/2vzvD+A2d8l6Y74KTPq9uXiM8P5EPxd1lbBUeacgvCp4AXXjBTFPJCCBkbdd4YNG1b9JU0E4+/RpkQ+VcMSUjVfxzIeJ7D1JGqOjlLHqDLqVixsvUpXrloy81LLb+OmGPHZsf14vGL8PfmOdx5yEVi4Br6osOupMikesP9d8aMrpjc+QWl5jaSPy7MqdKXf7H++5aKFW5QT/6RMu7Ekc8TfIFTRxu+iSowOYpDlhtDjG46i0MosPFDUyPxXHP1jHy7uJFjlPwSmmpY5UgHgdUEP8LvWm/fPprFTX/5mFqpx8ICFmfSkyq56Fw2LX0OjYN8ArO99AKASudWIgMDSF1DVUO30qIgD/ILoVE1hLhqTC7Oa9W6RmVFDsOB8Jsucd/rTZOHlKRNI/RmiZI1EuWk9kXADQrH97mNm5/2d3UCdZkpYS9OfOK0NbhxsEU77mShcP8F64Z4cN+0WNzACRJ7rlCyULPlPWsL0ZrvFFxnrUIndytOL+47+RLu0oHaNBV9iTO4NZHvZITOZX7Fwvv6BMm74+CXyM91DGP4S7nfmJioQ7exMdlmLbEyyZQQ4YMRpBP67T0GaLL0/viJ7pSo/EM6zIPPRLEBP+eJq+Tz8Js+engfuP/AXybS2M+CvqzD6CHY7W/bxcOkmBz2p5RWZteMySf2Hqg2bAVD6MeZqN0KMrOaXLpMla9EbF2T5qF/27nSDhCI+hHZy9EDvhnXjdZtqIT+JvJ+SXJSkNieFqpw1e+b5vsB4wUYoun9/hyvl/liJOvzEy2L6tcyTpTLBjY43z2HGs6H7GKer8baUwncukla00Pn/C49dtM1Z+xxXKwiJMqGnJ3bYi8DDzUZogUgMHEHArhchjM2xQcW0DgiH5Wz4QMpFW77xcUU2TGNM5ChgiBOAt7tERKeKK0j7B6jhoZqgaNfOJnHJGhNYq9eG4rK5ShttmysCMWfD0Iqj+q3LoR2FCo7rb+bzil82oe6tt+UvrozIXB16yBVlKR0hj5oGbGoc2jzidUXYMo6Cu+lAQ7wl4EV+0LCzOf8IZimgX4DWGgrQVhYMosw003n+sWs0+uVPQGWfiPWr0D/qAb4jSHvnT3e32QB0aKcPw+eiecXFZnJdZZuaGYk/vognqPVMU8y/WnZ4M4GhE0hkabjQoZriTZRg/z+HhiHR4EKCq8DzYwFmv5tF/Y95VBqkxRf0i/51zC3AJ24+k3y8VJ8PDzs/Qt/7tlnD6lKHlcOHDoj0YXxMfAbxdNe73tQYrIFy+uLEKkgqZSmsnIPQb/yzxvfQWIFq6kOoViR3Pr1esF2wsMXYNdkPsWsL0HUOwouTQffyRi+HQvPi6K7Am8aRpTU1eu3xm7PxcDoyO5T7fOkB3JPcdZOM5jHxU9knIGae5VLkWRALTIoVtKDpOuK+859WINH4Lq7l82duXvw9KeXjJHE2e5N9chFuAAxq3pUPoH/zHWMaH29DMgMuvtUOUMtwPF34Dr1IV77g93e7ZoFembUhQ4b0PaWvByvo7mF34nBftecYGuXhFAJxq9vbfUxmgMmXQVzru094cE9v3+oTxPq33QZOi1sVpCjdc3UKdLyUsMwnR5byd/v0TiSwhDP7qf788q6ckh+1gXfMsBr3Glig3KeDFK8Ww5r3RS71NBmnCMMUBf7+CwZNT8jSQqQxkkz52r2bRXaueyIIVN7+mfkfDo65ltCnC3fWAuirPad0e7Eb4WPZpfyjiiXS9MiBLhkQUcQ8YDQqcgZ3B1UaBNiMu6t3tnSjzI4wA5ptW103TUKtJnrX0ni+PdhflZ3xtj/pniMGz+DETIMEa/aj0OlyydSsEUqQiVAcCJOKFC1zWyq9Vu3yzIijeXLGltc8DGx44vmY8APbyeL1WpO127ehypo2qpuMT8PoAKmd9cqIgzMfNG/EBuqoEdA4DIra7DRoR+3UA+SgVwuq3mX2NGYGV7F9zIOX8l5kE9seiqXxHpTPmqeAD9CMAYjZIu13fsDxz+n8UHFpaqWxRsHdtopHRun/faXj1Z3ghVBWBapk/+Eq3k+eRxVVkpiXqu6GUnSuTvQUrgkeTgrpiC48mum94DDkxUDwoLddolIJ9I7cLagfHh5F+yJkmCF0hbFK49cChUiCd32SAE1DBpGpljum2onGNxYEnf1grhNx+/CYzO8enG0iYF2l5wiZom6iX1yo33qkgPZZknldvsP2Bh8e8GMNS1LUnzOLXddK+v5fn9gCP2r3uvprSeBwz0NnpTDDxoG9bc795z1bAfTKjW8BFNIwxcqIhuEQ7+pmK4796Z+j7BFjmTG58kJ68VA+npQM++M46CVw65I8lKk52QpFqaPOxlqfY/3/SsZLSB5ZKAvmgaTHcpFVCQJ3ZFLRIUYi+1RuyjfMkdLyVJgJAZajW7ihHvdtGDpM3mghLr2I6XYFUaWC5xVLA7sXyvR9poBVC2saPVpdgBZuN1Gffu66PFtvGcJw3udVr8lmtX3ltgOw/6mQ0VAq7ftqPe18aOjYZo4UXh9Ik6wI74jxtRG5UgBQhb5jsh9703m7vCVrpBfFqDASCMZSHsHJkAHia8mb0jwalMQcGS1A/3uM5//pfxmy1m6PBudIA7NXn4ukfxlojv4Xii4icK61gjyS3L0j73shbj8SCL8JsHVA5ah+ChurL56n6scgTPelxfANrnKCCuBPieJqGGfuNG871+hOSoj33A2cyEG36OZQG7goag3LPzmYKzapRUFdfQU4SfKC97EI9I5aHe0iTRq5OXNKH5gdx/fuUd+2prakx+qZvzWDjadTfP7l8mqhuo34mFXwdgQRk1K4RUw+KHSzs4vJG8hwWgQyWke6K9RCfAAgFg/xnZeGdXrHisuxGGA2PvNcamjKR/Q8xLPEzk/XyfZAhJxLeWHfH5w1twPMT+MoVUjs9tLDI00UTWPFxQeG93dH+uIv9mO8E6xpyxNbgCUxlKQVLhxxNv3RtvNBYluzrsYBCaZZCV/Xdvag1kheslrGonhjbrPQmqDN3VAavrtnjcejZ6hOtSYL/G5TyHeae22g031aqa0+LiJ8GI09SvSgGTng0QnVWMsE6ZkaSAOeQT3yOrahojNfLrmcPfOUNxZg7RZWH68RVp3EpQUO7sdoQ/2yiiNNdRzqzWty4skjZWl7q8sad9imV0WXml8yiDo42ExNEXrLUt8Z3PponZmAvUa1zCs4cXbk1HvCk0k0YrcNWgm4L6dIzXDMaxC5GbiL/VTAsP4zQU+P79xCSRvMiUYjsSw46eNCGnNS6Haw8PK5mjuHCHsDODqP4DhGpNDFUJ/YnXwgAKhhuCTRkL17Qm3zElGW7BLVJv9eg4lpkzEc37Pi17wQcepW1zGfQ/fN4gqDZEZ+E4rfxq1ow14k8qOyLyY3JuiXVxk5yuvIH8Aj8XtXMKXqYiCm+n3bfpM3j8su1saWMjhoiIOCSYjOcdAMNueF3CdikdYeaMNlYap0kfhomU1Xjff3UNrcL9fjze20gR4RMPm6qUYKCnQjgYXCO1mZacYBbpFDqjbCgM06MHGJVx+ZH5/03IAglKJn77uV2+KuIe92KJMYQK6k5qgpMtogCX/iB0RXyHlvHOTcJkKFenY1brrfF7bYHZEVo+htygZHUcfBLfvEN9gR4pEm13ZwQ1jk6y7rNKp+3o3urvKLyf+dR5ITBS/bkSN3fkXUn9hiMzmbWbJLRYxZg+kdxJmtDHYafYRQWyS1MzLD5q/QJHBdoIbfC+1qpfH6pb+r4BmHsktWJHunA9AybgU8xA5wPN5s/nzBZbMcLiiPEBMMiq1IbEXwCtt45AV5d8/3cGtS8dK4wD/flEcl75tF2ICkyNakVa/VXTYxwuQ4GZvc09rEvOot2GE3YjO9fWYRllFN1H9rbQCKrEUKMPSrPTweb8KPPqsc/mq90+5AGiGXPkyz9T4qhrqbBekXUzZAh9y7eRDT9Cj5BL2xGx+3eKGc5Nbsc2+GZpvrxUvTS/i5utuwFsw9qwoqUYVMj07RNJ9JXzaoo+swodxlhHMc1NKOBlq0gr/msQNf3XpPV+fW2gVGeRdxXpv3XT+YIFC/sqZtoQAOsIZ5x3/Vkn/1O6GSHajn6T+fX3NTODPr6cnxwwoZEwzVDiDGpjNp9fftDg6mh9GRJgPJzJWWI7Re1CDoUEK/hgXmD8V4joIXoO79Q5aDg2qttVGYr2X4AmjmJYU7m2VvatHfWATHKaCapXXb2xSlpteXHyA7Qi/TRkOgr5au1zUx8IBVhwzndQYUIbAqT0dSfi8X79Fh2v46pX2ejAjOs7845vibAUAfRRRrUM8H+26SNN8ey6I7HJuXLXh7bcQC8S41jJ3xVLBHw2DOjSDUKtPmKZ4FpQHiSFNniD1cRCryFjFAY1b5Af95JQj3WMPcAe1XIxPXpE/Y8jVQeP3DIVVprsdj2CV7ws7r6qsqX+jFjZjliQvg/PKw68HsD03vgl88qO0JB+hOQhaa/1e77dC/sqkdvoya7M884iJmy0XKWJiITlRV4QJK704Vqh2RvwjXNLcmr7OfmGDGGlFJV/+bPqUOOk6NmgRLrCFhd/VupLALeAK5mGHhl41QbGgv4u7jQPA8K9OzhyO+QIJQAsmT6zVfZQUAAro3ukOf+lUjc+4kt/llo6fk+ESvxYBkAe+e5FDrDdJP/usqLZGRY6HDDTekDJNzvySaMJ0rVfr90C0vAeDbDgW/cSSPtnfSNG416Lj3EXv440xamqkyZBOeGQRU+Zuq3oPASYW1gq4GzkvxwdPItWwmgsrzKi6agT6bAUuBnLxy3Pgz31HE1SdoFyaETc4R6CvwxG2+KyUsR/xt9FBwsqpxe6xhyI7JS4Q3JcGWtd9jsSk4x7E/Lnh6IkF8idNbk6Ag0PmEubjA+XbvIwnZqre88i+ECSRxmFAMEOGzG8eP9Y0GkjgoKEVwsYCghmj2+Twam/UTVzjE2vzS7ayQiSvhL2z9iygPUC5X5eOlLNGykTUb+s65lOz2qAq5Z0dQndBQx9vGy9yMIKZ+1IclVrv5lrih2envk1AEi7Cv3phWpQ31No3ID5QcSvoxUmn/ZJJ564OVegXGRK7X0JEcOvHGO2SrXUR+xIk7vkOb4JlKCAanur33Pxfa/4OLipvMDfLmQLPflW/y4ciAM5QPosbixLxuPsaERizjoPs490jJWdz0UzBl0cV5Dvbu1FVydqfmyP4FrEbm/GZmEhUA3QhSPuS4uAsl+8djfcSx4Fhhu3b+xxBloqrPfY3prIA7VZjIdgXDcMknNVqUBwHe/EbbDLrMLm9tGgJn9ITin8XeV+ZtTSovnRpnoqjrK2JMp+HC0ZqNetLqq2BAUuzDOVmVIsY4cexHmMxBoqyOdKFAhBVr9bubmRYGO5zb/D59R8/RnzuiHLwAnOTJFDlEQDhwWj1+l/rlT8VnKzaOIFU1L1mLn9RYkdqiDGftOv8iEHIgHCH4vKw/kYcpJ93a9m/4SMzGC3KeO+Ke6Ley0+4hz8XbxSF3kJHKoP+b1BqK5TGKMN7sK5oAeAj+tvxvr+sdIaWte254qANp3k9JGvtCSVoE7S5tA/PTj1XY/LPkmNKy/tM/Pvazk9HbBhktKtLOCM9vgI4Qiz92i0fs4jO7ayLr9MYckaW1BXoNXL+gz3wRWyqE0eu4uC2wJrRttdDCGSslpl1/YAJKn5Sqee4oag5VEPfx67WDqljzxZdNwXBXriE9HdTfJOVORQXrU7ZuQG6625wqWSk5qhwo6o6e1WfnIH8TIek30QlJy6EBOaWD4pMVyPcmnjpiv6XcrRClLgkEvKi3ybcnR+yftBygRo3i8MTPyi782ylzElp4WHoJ0DNVLK84DV9yxEaG5J8LPSXit+0SCZ5MhDpHgTo6wD+QsasNB9dWe0J/z28s1LEclbra0tNUCJ7P+D02HMiayqRievkTKmYmnzS6LPLpafGe0G4E8NWvEEKM+7gWdnOSfeTu6eqq4RE+L566KGp9h7XfYK1+5yO95G+VSPxuuhmm+WUxskQqHlz0Ld0a3eN2PHUVvCvVxQYdkKoM/ScMMrXd/l94KtMKwDGmIKV4Aup2HVunMuF51Nw5kJW8qe1YvB2bjM/KykE0JO5OuCrcVFIIZo1t6nafxBpUjomBXG2bXTbgDLZ6eLXpUnOb4x739xkGyM9WoD8RqgenLok0gpyOYDtdmytREtVi7nX0gVRDSSgggPU8osvDtlE5AVN7cyHPTtJN5bd1k6j+dCaQvdtSSCwixujo1nkLZkerlvHrlyszS6NqV9uCh0ZjXOMKE0b5FTxWc2/HAxFzsSeEJI2idobJOpwXVnG5cALOpwqg1CSO/0YCeqt514f8tDLmZb0bx2inIEYXNNZBCfESi6L4qGhbdr61NQ6J/s32anaMUOw54w7N3ojiLT81q+Yvn/7VZn9t9WcerMvidr1o7ivSGwXQi3heUkTFdIc76Q7N4fjkF3TeluyZuw/9ALbHRrT4o3b2QO7J5xeCwQFtGhEQ2SDgesgJmV5zmTvGpfjiEUvpu4scIiStOU30tezOm2KKiIfMMQgzoYSEsY4L6u4deWdaW1fwz+pW2H5z0c61qZ3skiZRx9OXDwlWQRbOwCmeQ3TOx+yVwWi61/haPbC1Pd7+5iGb9TFE/xpTj2Cik5FNGUdA/1c1akjTp8kfYP65AYm7DIaiClnbaNd8xW+kNzm7Yewm948PlBP2c0eImfgSQqgAqUMJjJyvnbze8Hit+Kphic3wB9QOzQ+TODe3V40MCOgBepv9iqHSRSQxg8EnMUbj51D/A2g7mbcqB4G88oQCHJNvax63qhf0E7avCW4XwsdvlHRVajd5iPrlGA7AiNFIwRNidEr83PLCKNUJwRBE7pe0s1u9wEMzOE44TRHKrZtHFv226ZEFHSgdjaicuho4vyFXCahGwm5ikpgFJC131Mutab1LSqyl3AF6UepbnZ5Z7lBjJPfQsyw+E1tP4qurAz+N0hm80Q1LA3CeytfRHcSbXqAONTdFy5R7Vos/beYJa/QQXg0c6MN7Xf+8BMMncO8DNUE6WZMMdyjdnjebGwE1gFTHDv9MQ62FfIXklptrOjD/c6ga9E1P/2N0nqPABxcjDsvv+V2G8NhPGxOtIzir1ah2bTQHYnDXlAunMFsI5B2dvt3MQQ97LLIrdG6aZP703gtOV2uwVVaUH+Aa1p0qJ/e97g304Ofg/hepdpgkg9uVQRxey3Cc5i4f9Qs6z8Ulcfy5yp0v+hjQn0baG+fCWzWgKQ3/PVQoPp0X18IZLMEYPodhjvMGyzNZlJlKxsNhQyfk7yLpusVVqBmyX5Tl862rAgKCr9Jd5XZG97Y+B3AXV1hONB5m4g3+vaAc7bUjYxTojg3HT09CDzvbkitqnpvGiNOqV0oYmLaz6ZLRT0v1Spp4d8uWmDy2Ry0ymvUcGofNlzrcmxVUY46JS/iq11S9TdbzJ3mgJBPG+DhlJHcPeBW82v12inaaCrAlR+AjdchToI4LYz6qsEnesLz3a4nX+9xYRZa51SdFyk2ulVHX9aV8aK737KYmJAYAs3oZntlIqBZLkbsSjuxVRyoT/z3aizE9o0KjxnDeqUHN6A/Sd4hVGzcU9nje7s+pVOE6u2BPHAorTPk30jebivyQoYHnRCb2cxfUjhzBYq5NFAMBFRHZtGa73d6Jl1FmGHNNufSJsGJdMsO5RzhsyEon2eSyzhfLPWbqyb3GxotN1Z8gy/bXslq7jTkZHSgx7BB0/Z+P8QCVY8sLeLbrr+DQHQF4Njbt7st2jq3zEwrQhPWeACVIU90JZfszTEP0oZAKVxGUdVSsA6vJ8X2uBMKiZhfT8AGOGVUG4jx51FQ45RzNcS+RRRIOb5zdsy/ceglQhNQO0MA0vWMYh7Qmw7JXIsSmBfAuPcKvWTtSrVlhniqvIYDkfwWUhqRO+u+OmFbEN+nbxiEubBJqlbd8/YgXMB2e0uZTAknabjFjs07V6b7ZHSfQogNOlYV8UVcwqHAd4Mu9NyEE6fmDpUnKL8MPcA4eZc1pVEJz0HmUL6hso0PzMKR5zjl20j1PLRLgzBxoNTPXtFwmekB2Mh2nK8yHQng4DnOdDP/20GX91wkLCIalYqn91ixg30M+QynV7XMHnhZJG3ktSETINFJ9J7WhdqLUK9RSlLDjCCdPshW7iWUpx7+rQDytMHp33HAYf+2OQPP0pvgkWfFOjR5yvABZky01qEvvEOygM8Qmca++/lgcsVVvpxGKG/z9rcvADOAl1T7pOXW7+qAN+1HN7bGqouDeOqVb8yHD2md6qgETluDM6KAhzQFH8zo5Dm/kFyKS+aFcnk3gshKwQrlU6xqnTS8YIyE+DHjlVtj4vBrRBVBr1MXoQeRvfLGNelXKozjx7SLlEbrKbmdgca/RDWvjcAyRzkFw8DwwDe/O/CYJnqzh0KiaSgsYqtIoGvSU8SHOX8/RshHodpXMxkU4gu5CBwVM1rvvKyh7ijY1zsyhHFZ3H2E0poqoWm+DgsB2WzrRXu5L6+9YJl0q8fCWWv8F8EBfjt+qrsovXdPw3p5L81Frn1pc5oyW4sLSxClBd0D3M4E37zvobOPKFyyspBCaWayhaFBtDfMvr0F74nRZGcfZbG+JyJFJ4lTahEqnZf7c/hJBMyiPepBUbSWtT+JAVkI+e5o0p8l9Q7/Y+kqluTWluDXvL0YliOpxczSTthi5q9/Ou0b4XCEPTM9goLMrDpVaBo69FS5w+LywfPGw/0Mv+Qw8Yl0hltByFgiZcCdbcC+GFqLTOUQQdXu1vR/FlNOf9zyB461xcFGEGT8Zpw4Ppr1A0Xib/A8vQXcQS014xWFdxgXKmz+5C1HtTGXkpbB90Wo4HabFV+Q95tPnWL3CygMF3JkvpgLimjuWVz+Tf4BVb+GeDIkW8CTdrpAVrJDljJRYSRu7LwR/gAwBheEKPmEWrOG2ovdzyaQmmT8AkgaGHhVW2C3AD8UpALqh71PxfR6m2Ro/g5zIv/ujzKRR+rbc9zXehge6k/VuGTIjNzCaf7x0YQxbUYcGGa9KgfuWHM5oZdzJ6XKD7lrR6dSO/guBgD+DL9VqcbXyFfhsjxfMUBzAmhlScKqTdRi5KEvoduYhOLZ77QY7hhpFlih/+2630VyJXOI8liS1VeT5RR7yca5yyZCDtVvd8JTiIKrw4eZ9UASK8NLyYAAskavC6BHNYEJ9Ex5EJw6jr8WQS/9e0HkB/vrf09msAb8YDEc2HyhBE+rob3fjpBMWAKpV568YaN5js3e54nwIBAotzUxeNFUYfKotV9yH7jNkJM4bUl4Y7oD5DHM0ppsrrqckCdkIcGc0sz4RkxuGALqK6v2S+JpGDZYZyMMvZm0rYy+7HlHBLNXXAeZqSIOUYrCO8CkxTIgFz1tqtB+YloJyruL2X0XqnBrfuOeOC0kj78U3ASmNgvMG0eXE8rfCm92hclBwOE5hmMN5TyUZLfeF0G0rZajsTYzZDNx0Nlp4L+9HNJfSkPq5g44OBqSkPHkndbdg8vO2gZL9ZdQCWtuAhh3lb0EeQIXhxHzBUbGrGq2Z9p6LJS8/wYdT2o/t+qcRtIqZevzRSjdjkmMIhTkyT9IjNDN7/CV+jvRJLbaBmM4jeJ8MfXAHvei6bd8glEl2S8Wt3AGRVApOUw3bg2d49JO/ySzG9+y6Ed+8SFGglD/gEjlzAjxso6n+xMhOqG6Eupyc2z/bfzUP65KEibzRmQrrdszkOSlv0KbJO22A488jcbrtdl+pUL69KysLI5lbECEXqmRFnIXHERl2mq15hrPj8ULRaKo8l8p5MrybS2N4Vt/KDzKQPzrfrrZpBZ077MAJzIPZZbaHGixfru/3JBmCbgyUIY6RlReRU+3lBRX10clz9Ioi0ImD2fxwU9v3x9yEUXaAEc0eXbYn2cdrUwepAsFE1OYxTql/UUoeydaAxcitanWQTpUZS/vmm1/wokw6Wzfox+xg3/LhyMqomGfR4krwVS4CFHEpeqIApxAd8UiZWW9N7EaXKJJbq5vzJAnZV45yNZrS8cFYgimTfiXkuA9oa8cWUjEIEJQ+TYtMESQWV3LAIXq8aLdCHox20J41w2KWmfNaeWvnTULycYwvGj5TQgm9+o3CtEZWxggh8EBytlgbujsOdGxlmCY3g8y+Rylg2sD0rED9oP9VdMo00z61TA4P7AqSQruU1dPeKrQCxhQ0oYiz2DxVZSzU7Uk8UvdMOuYzRWi33QQXZwu3YDmKkqHhv6ZapTSk5nOOZPDOuo1l22Eg3uZjDciLQiHM78WOIIPHeJXyiRA34e5sZQ2EKTtgNdSoUzUKwcmfBwoU+EP9CegQBQIwbuvCQ5Gnh1wzVybCl0uP66cuksSsd/hqeOK5rDdg4qCnCGYtdixxzyQYpGws5hNouDfKt1FgsUS1HGE+MopTKyHyBfY1JEY0Pu0iZwn3xSSDtdqZ/smmiFlBpBglZQHWI1eCJLkEuA9VGt4hCdCD50uaU7YE8o0HC/gdVgkSOdB1o3tMxKmno15W59+du6azF1LIcJmWsaxedDZoTsHYpTqJWz8f7Nl9ROiM3u4Lq/wm56MobWEkGHopOfzGdJVHMWWzdqx74rIGRuMXp+7f4MqfWnblRIvtKWsfXg2ADuy1JELbqa/JhAPBQNeKAr7vsTA5YcR1cFvE7M/b2/K8cYt9WxOS5/TCu/vT2iZu/FrUglCfPup5/pYGoo73KHVclkv0OAyGSJUpcm5SNracG8LPN5W61jYsaAcATy6WFedWo9jSi8MoLpd0PiOUE9McRTMjKh1zJTTlf8TjvzzZ2b26mbrtkXHGzODQbr1QGTqNQHtLNTghVTA2xutbh7TUJZ7JHQSquTdYe5/GyYbf4rS2IY6Ahli6TU6hwo2kcCJ1FGwKc3u78cY90DnFJDqsr02ZEvcbtNtRFD/YNrjOUSaxxNfILOXkr9pr2V7F7JzEHxjBF87co//3Ljnsbvq0CILwuJut3Vm4yK6X3LLb5x+rslQIGCkGsM+NkZD4Yok0oodTpuHywfPfwXurUBrKtL49A9PMxyAzzVw0kAO1d8ISDlNM+ce6DvKJWhJuEmlpaTFi7VNK+GNRKRbzHgdxkBQMRunge4/EA0iPvSJ0iMaN+wmsG2U0XMQru8FnBxkdAmdu0+wvgQJXAJdW3/m8ey6J7/hU7TCITRpKo30kyMqYtrobOPMjJZpakRqxJPEzyHCqY8J6m/CzaQdcpReZ9SKWjmoqAgOQvDr8PvLfgYQ2EM6KR8QazzHvvQqg2ZOsFzsk9CzITCrxzqQlVB6903yv7LZ4Gnn/w7nE5Hn/emSQtVA0i3bzx3c4p9tf61lg13aKPzEbEiDWJwmPE/eqOjwiKNhlf+J5ebfT7Dp1+r4Uw1f3b1WG+bYBwF44HRSVqINCUBIsZKwrLf60tgiO2UnsAI5cJir+oO2u/19GMMurgsfKWvNh6XGejyIMUCE21DFUpi9kX3Nu2RF4c8+xMUeJIS+Yql6rQImH+ZfV39ruxsfa2U7N1HH8lpqI4WoLXocbVfJA6t9lMwvPukjoUJPckJrx516Q8WfWEQOI5i/j7wIRVMGsoP/pmRv4wccBGbUzddDlDdhq+gz6Fvrd+FzuyArh8Qrrhc+i1U/IBAsMEfK6UZTMlndc3H3dmss5KVdvtnYY9zdsEKMdpdtgRNq2U/rmoB/ovdfKrkSRiiDaNLKgzVytBAkULFZWUzoelS/doXOZWOkaHAeK3/paN/DxrmLdTWdaDrm8LwnaCy18MdIPF+CGwrYha++0X3kKtPeECOc0Ft50RhLfNf8eS1MxKVr3k4hZ5/69pO2D3P1h42bWNHckn9zvjTNMdjWx2dV2HyLTeOKHdcgdpW/KL1/t3IOJ2lh1cOcR6vxcwVC0DLjhdTevxUsJJNVsPVExcPr9SY2wvGEZyviIxxoukxeSqckMn/RDaTRG0g4L8KN2IEZmmuT/2PHsRJTWFw8UHT8yeZ2rL+dbm2zHQEywt9Jvgs22irlEWiS0wX4CKtmhdDSRgAd2Rnq4cxdwvMi92EvXm0p7mm6pXXMC10tb/VBZHvUSt0/3ba0jzk6ZbGee2hO8uXARtZjUFY1ekqn3zBNHhHa9DD23BnXqCysCOeyEAUH4kle4QlW0oSRYHqJofigYT0AYpP26+WENjn9Cg9wtL9yjzZMxHEQM+ptxubPV8ODpB3tkmsaMiWGkAg9k9fUhb9MlqISt0n1dNe3ozWvvwijRqbB5hUa6hfUNqEiAh2geAT74II1DFLn8Q5pXWhU2QiSjzWrxUFgKrmSjGnq33PtKCTm9D6dju8CFdKXDBj6EVBuPzcHVXFfJxvTUhVU45y6vLeU27nji8RfFxzpwoWkw4rlhaOMj7388DoBADQAW2KTVRCl81pjm1m1ostyJ6fcNFGvNIUfif/0F4moV19GCoAi37xusis3CjlQW5ZE3IfoEVOEjqirQejoqEjtUYDzf42rU7Lsar6s8Icc8XMl/9pOZ7GkgNxlXGLiZXij/DuU/FFdaljdyxKfG75KPTX50jLR8Tibqo7wBgMf+b7z7stnazsIiIH75R8aKT1gdAkypURc4jE5p60KQzATh2hlrJdoAoXu+8WzO8yt7HMXTlYUecJ4zeXhTooh39srV/ooSFeMntMN/RN3g5g2BWpeN+Wwt0NIm+oRC62I3NRxfnuYP6W5oxgiMk0RxuzLZn+xTzXkQQuMN6+b5fl3VLQAa6XyXiMQSVXGwH65CJEAIDU/cSoAl7lxJRx+JZ5m0uGBR27l+1udwROrjai6Qdv5oTEBd9WU2lo3yKDReZC7xtU3T6WgVHFB3RpfYFACk9jnx3Pk38NSEV8h33z1sWuf+xr4/iFUPB8g94zZKDZL+HOZwaQ7ogJNWaavbbLTsYpNQ1u07B0etoKilWJCS6jkf2MA7pzJlm4G5WBjEPWWatCmZCrgJMVq1dW1HsfTCw3ZY3Tq8qgEQLtFEvQqNx9RwnWAWXbnI2Gr/5JqBwVpyamoNHH6op3zv9gmXnpRggI+L/A3KZaMO5RNGU4ihlOx+3yBscQOgfnLaTYQeLmW+Xpx46b00qGiu/8pczzCHEi6+ZS06pD68S5+m4IaoJ6S2zpJ2sHMFhLm8n7DiDfQqtnm70BofdPePeUhkXi/yft1wysIIcOo1mDRbqIuUGfFMyMNLbsOeFmaowdDgnjq7nUObJajqHJIC4izP6w4CJ0SmNDum5XfIlUgZNvY4rvsPLgffsg0JGICY8zT1Kn1d/S3vHBloSrCpGbZtsKdntqBVb3AZLs6HCdUuMGDBrJrLWBU+HfSw1p+TgWAvIR8g/o0YzCKUbDg0ErJE7b5otk3PNG903f60dPpMERQc08yRvMG/nxL9EEUNTc0BK3fNxts9rCE046Q23zvxSgQ+D/gAMil8uN6XKwJOkqVcJrS8L8vxhxFMmUKiPw3+gnR7oeU/9ipYr9eWvz1Vcqtb3DLmJrHNTyPyAUb8fsvhHV7QuhvTvWPi39uhZauoseyvlra9g/2msb2Ozz/Lj1ixNYf4RMOXQXd3D9VO69XSetAT7E8UOjJIUbZ8OZLCqTg5IHBEIWb65+XKy2dIonQvo1WGixEJFhyf3JkIfWTi0PSXoc3mWGKldoysFVeICW1rhOufwFD7s2dTv0G6irFxsDM9cbVwCGRNzWWrXdmI3b42dLSxDlyK4uw1/xG2H2TCv7Cmt/wSlzLZe/GjzPXum+PzrN5HBUZtSvqYszZhRbJcyb5QaP1E4p3Wdawss2xq0dH46FHYYQWgMDWkZk3rRenRzkiQc60xHyJiLb2+jvPOjfkzIf7eApWarr4N8h9tu4hJ4P+iks7L+EgT3d2UGnyNrL5g1BL7wMx14AtZ0FZpriGDcCz6R0iYxnM5mOwZPL2sNluBd064lZECDM3pRy+DoMZ4k0O8YOq/MNKEfmY/NzI0sMunQm9Fr4EqK8/EltSQx2Tl/eGkHxEP167o+zXANQ2/+r5DveW/OUbl40vFqhjNBMAB+SvCq5NpBDFq7wR64GV1Ea6UkaaB4P9M5OCQ4o554ehQWM2cz15jbSoWe7nT2Pry9jI9d58n12eNJJeM4wP5aGArxHUI52Yqgtm0vi0lYLeXBIvZwzWOrEqOimadtLA0cEiprLaybLJEeIH9bX0Ay8mb8mWI//wjYtRAayCWD6FJIoV+mlo5hiwLLZvhQ+FWJzp9Zs0gX3y2RvleVoFO/4kp0ElqdHjlsoy0EA/uPkCQikroX+ARBcXdwCW43UAMSkuiPQHrJZR6pgFItBbSJKP4P26HKKp5yGLFIYfy3u5Bvr3hLH9spSH9heskOJGF1FlsPqqWl8r3gymP278zJJwOLNJpTjKiAGeyQhzj3cNNcf0m6I/xQ7oSajj8S18u7e+TmaN4PdjBpWWvAlCav8OAiS0nkCgF7Q9+dVoOd30oWwfiOxPREDs5d9GULBHFGTk5IwWNkcHJssl/zaSsOtEUQcSwT91Gskd9ddDOTgQlt508KvTHHj0W+mqIeUf2capq8EhKYYNPoExuvzyRrj/gWm2eKS9WKUBYthNcuuACmtEauwfG3HU30MjgdNEmpAIFgAvH9rMTTUxnfIjJUg2PE8gOE5uyedrQCk7vDxYNky1TWM5EAr/DlYHAYMQDILduY0h0IbAkCfSv7VkZRj7/TXp0ilpZWoR5XdTv3997p2a83D3pcMySjXM/5DW6FT8SsTzJcNwxZwFfl1S+E0q0uM/Dl+azZ2FaPLbNCQ25O3T3+QWycDkBcvw989TfT8AICB509oj6VplDJCbtjQWR3FpfGeAeSYtHKDP5mQLB6QzGBalG0Uie3eGkGmcijwz9uS25Xj+Ki43AaIpm6+NYTtbsxRDLC5OKrqo3k+qKj6Ml5w79s0Mk1R/Xq5MH5sH52QlIUYy9J+XCgAjMCqdGR55D+a8+zP3+/FgGJ+8ULPiec9mX6YUUOZ6AOQRjpoG9uXBsx3VPnzmZtRVr3HgFguQd3R5vwlMtO9sy/Q19rRFBRGMGyCQfRFal79CHcfcyVDs7M8+KPmvVdFk2NVBgoZxuYKkwaDg0BlyfeE3sIhJOGARRbTBuPaCKgwzl978Cv/Wpl/DXUX4/N5sCqHDjJSKfZ2pBLsCeiofOh4AnN3NsDGXrq3BcgpeihHwnwKxBMyf0g+P+Ken1EmjZ0L9Vrm+TpNIDrxNpuNxd8OL/q3gk4APR1+PbmxcfQMXtVP8ZmcgSKxCgsdvVdZDdYfsYB84U5acv6kc3TD5ETBif3pBGK0YpXGMRk+ZMcbroUpJMeJcqMdVC3BxNzW/sZJk++2talypjajdSXpQnGgsqvGQT163KbUI432nZtL/PCc9TXfk0xFhmYGWisccZeSb3eiK4nlK4BGBRiQPhwS85cpwUFBnqrRMpZZWhw1IS78JItjfy9ipgoUPGgRHWonVckDnwv8+qoolkOFTpUXbomD2tJETvF7k7dcl67urPbLbTfW2IRmfDPhoBSLl+33nK3zvifRDS1QL6DyCA9/O5Y/x5s+tpPd0bkGqjuc+7tAGipuYPpaPIwNNEzVN/rIbnYod9bKdMlvouiFUPfjqRrawV0KQK378xjx/g0Kg014VGo+I85Cvj/cfXpU9hJjPwJ8Kk1S5fBBtoi0/v32mztUeGkBbfzukD4whxTT0aDjOKasE1iLyX2dFHHlDX2wgmiChMd+DMcIl0TzKJJz75nnWkucslFVVJUlgSy9DQu58arWEQ8hYgW9iauLQ/zN9124dbF4QfFHp6Z+Wx9suaxXE4HzpvyawlCRP/3L2w8Dk099MTsq28tKSaVbqtCed3jc+amtafZFOUVtvqDFhFM7YEeisLuHf4IDBxCRxMJc/XBzAnWVOADlvpEVd4yIxfeX2gUUuSEI0dW5qiZ96qC3NLhqH3s1xk4PcUoeXuX8JKxZ+ZuQFTtUHFNXSnBVKa63F3rmy1iEdyXw+0lSlJ3hQ+8Mn/QNnhbSy/N4k4sdMFxnsEmNG9Q86KeBMOjRCMru1Gelwf0m56W3axGzcHteYghekuPjuvFAXgbUbRb0K870URX3nw6W78mHyRnB9GPHyADHD7kOQKHJ+Z+UeyVXTtYJ6PonVZtIk/FTG6FI6PB5EkiKCzND7HTLAMVhnlI0slW2AuhUHtI6nmPb8VFX9YiixZn35jCEh1dbvj0DUQi6UTfC51fSsy6gcO+b+TsfzRZA+2PVSGhHdMIpTbRmKZvgVbUdIo0WjUCEgtVwmnPHfHvc6fYLNVupxY/1LIpT6hAiQJQ7ngMQ9XpFWNshboBrCim3U+IIiLdUZ+GGP+ZO4KFJ6e1XFIEfqkSkX0PHwFCLcPxSffP2LhkMdiEdqxO1EUYmDhdzBfmCLWOwvOhyQqdgdrST88ev0ad4XgrGavjyRBLTgM6KS+PJIOEiq1+pNIukTR2/8lr41Zji9xgXir3PjHt0f3lC5eBWI2KNM9GnNoKnScqg/nhGThIEAg7VPkdpScrtf9rCjTtrfyJyKDLhEXnFmbOhP+w/cJAIfjEkPeN694PSlXwFORAWll+cEARDREXoIY98/kgKLmgGs9Q9EafdN8/H+sbj+Ab9tU1l9ymg9MWlKXlC/VxfDBnYJjwD+9klrND1vP3SWASeZGAFD73nbb/0DwIf0ke7fKCzceWnAA3gI+UK5AqGSa09zkJv/iG5f6JfIJQ7ibl/5RLhnjw7w06ThvddbHtCvwv734SSAuHoy4Wi/LS7KfUykQ8vng9/HBExtX3UhtFzLI6u7//1MD71BtRAp0V3NpSLpP8CzXUhTkOoEgeoL4KUnU4YYSooaT4X/A0iDov2Dd1El0Igtmj7JBSoGLS7zW5eWKsHdhLIUDZhZj/wTWYuw82rX2I2JpcjLJtCA//IcznDmBpmuor2QlIJcMLOJp/z6EWavuY4PoVv0PXp3nOym4W4lhMl4flXodZu5vDPr2l+E4Ikk/x2VAQpp18GuWtSRW/QQP1UqNjFWAGEwHmOxb7KPX262Yn+lkwg89UUGL+U+rJ6sMfWqZDpSP7JxtANBx032ZCoBG1ntdoGGq8HoXUfhRVk76cZeusWOeDPNejjfR4jHo/Nn02v7Ro9fRnutH+sdBTIF+ejhhKTEMKZwrTx2NaW3W77czzxUpWEJHkk9CIDi8HapiCzcabNAPrPAwomDtEVxY4EdSJV5QbtL5sRxZHwu5sqESZDIz7zBTWyDubBM2pqgNqkz7AGKBEDuM/AesjfgniwJ9gLz1zBcqGqfkgVCkFA5CmZ2EMVmn8w6rzhMS24LDIGgynAjCh0uOSCmmsiHJgPcay1wCFNsOVlnc9KqAKXlvZwsSvgI3zSKY1Z9qFcR+B84KIdIW0iN/vU+hakoQA++rXRBHaIKLVcSOwX1vTdYYWhsboDZUD2MPpFEy/k15vB+V8/CDeizJOdnipdPGFzu1qPiNXpV7D8oozDH/Bqn5uzHZmLBCL3vq48J0dUJQ3e2aS8ELWuw9njKFnUBYuzPYFbSxiKdpGdFTW5p+oisvQyWJFRhLMRvrKkFXDBwoopuU83bDn9iYkPFMejdx8EHmMv5UwBXq6DlZVXgSR8+i834G8rJbv76hPqrfp47cbdjDEnQ/cZPszCb8jM4XnTN+Qx3miumFboTXsdwMyPBBs52Im5PZDMnYUZu+FPOyNmkHMGfbeL9pmUCFpdS4aaG6fBse+Q4UQ4EaHRyHzT3fsfgO2UwdQOZj7LFhwf3PpO7doFPXY8nl80Dmj+lge9MTSFOucNWPaZAmwzDaE6GYcJjm6hzYppzkaEBvxwhFBgJ4O41mTlCXsdsQEDGvLVyt5u8NY8TH9CI5oAjUZF/DlR+LG7VXp04qtNMN23fvXAk4gikZekakdGLAezETURI6en6powKEiUhZDrZnxNI/KwpzrQEmsYlmvEvcr2M0vNy7Sx+swNrL7wU7o6vhr2GKiANMgf1d43sWdsTzT0kcQJ3qM6r8Mq2yoQNP6FlDHzkQ+8FgsGqqJo55Ynze8V5/HBjBL8HNLpzoJ56+EH9x2sfyK+ydqc27fuA8KFgxl106bTZokpG4nSlV7HUmJA/y05m/SKvO49pQRF+LmAOcGjfA/SF0kr1bvfDcq9VbqkasNQTjUAu1wlRRfEPOapVwfgOEnZXnK7q3UumEzvlJSqX6pBFzzHv6xBvST95IG1oPaqLxvu+Mw3oaQB8ha4O6IR2CDVkObCI2VLl7+xvZGj1NL/FU+5Ve8AFcXFNuM1BoLOZFylQwPYCHvCXP0g9lsx6LliaAXASkJP7pZdnzc7igt3fTiK0GU9L2x9I+fDrJXxw2r3smzNNYeSamqZeJraat5kQLyIZntOOOZsVE3BUooX6kyJza95LTyG3njMEtrcFrTQ+LW8FOr8c2KPrNuoOxQTqxTVSOqLmifMwcdNySngSFScW3lQ+PfQAn+RA9KZXzMq4vPF4EfFxEirkI6B6NW8y6HVhWuVhRCOlWQHpEZ9DTcs/jz54lvKkk8v7gAFijE3wRb9Auz9jpDj2iAFjKP445oDs5yeWTJQYroJIjSKJcyLscGpIJQBJaadEurbyYpLhn5MuSl3ve9Qi2FWoqO1W8qqk4G+5cd2XNKN4mfKlhKMXugxKUiOxKYQxPqYcSqs3favoHlNHWRju6zaj3eZl0IvW8NE3f3KBr43GAbV5vvvwcKosPoHub77jgDKv2hlP5vX5rWwVEN+fGLQUH89ZB9pzSnMb8z/66+sUXVw+qGYzJzX1yOiiEuEWxe4mFKdEcPIGoqwQzYcCx1gy+JqaYNtFLjuazCXv9LfufhGRcKqdTx7voSwT+XPCpP3pU5mQIsnsel8J+iBFZQesUcBbJMgOcE0Lky1Rol+/bpTcwxvjLvfbQ4ebHlKownABOgRwS3eGgHnsPKp6RyG5f8JrLb9lT6wJv7cD8Ar5b0/SZv43i3bPK3IBHgX07MWqoFtG599RLv9bRqJ8H1/383c4DGFTV85rbi4V+ycxijY2vhS7pKVdy0fTyH+U7yQAIro347s61REMBGIl1c7OkdYd3ObdNUf8x6/+8L7CWvUvDAv5Ao0bDm6ZYOAAM3aHa1q/zbHZi24bn46aQuL+HBvhvNfK6wgOWPWKfPi0qd6aWITyn5db/+83wZEppdX7PEivt/eO21tZmNAXsBfbSTWy9X87o+03nAJ6yv0Vt9D8bQOL0RhAHM4SQR1OCyJ0FMGXzNsjpGNQac5sFPL2nq7QXiXbxS3SGJIhfwD2nzSZFbPdgd0S8iAdpBnR44qg9zTXkrjR/z4BSHTfv137dfT04rea4rtEsJGKeEiIDCZNsAyN3wyKA9lAphx4unHd6APnfYiHZElVoTLlo95ZMkFV3ol7HZEP9IFjRBgecsYj2NNIocqrIqrxLJ9zgXZtuMB4m31aGnI218o8OtbMmbu49NPa8x/NsUSF9aVOqyuO9KCM1h0XTfouDmNILvP6TESP/I11LtO0n0aRyAFS0alRhSdc2i0x7PoWa25Jxn/+BlP7Dj+o+QRkcCGpAaOeA6D3KbIX6AcQ7shNd/S6KCpZ8SsV+M+kUglyFe6w0yRuIXwcCxxkYMubdywSaBq3S4ViNjsK6jaLRv+9YQ5TTc0BHvGZsOyLW5hpPvHqNpvQdFDyBg5Uv5pqmxprpC6BoGXxV4WDQ/ngd6LXZkMArXA27oNkUCrhcX0t8x7wX8mwBIenEYf48/7svk4xkoUssN6ZFSfUb6Bc/mAvhqzLfROfgtpGgA77Rnopc7gY3Qy5wVOuyPnbzPh+EoFy7kB41XSV+z4IBFkY+2ZEWfNBMCCtDYhLS+mXjnP9CFB6C5Dreb4plHil9yDFIhntT3wdq8pEm0br5QeJ8Y6i2EQRDjLrUu5DhhKRimvYfuLZFdT4CRoGotcOJDZiVaYLk7wNeHf1j2zIGAyNCSmHmXDzzY6nwVwMvnFl5P5EEYh925PWx1/zAH6Hz2hXBUyEK/nEvMgO7nxxPFquJk2c2k847yDspkU8U6pbA78yjubGbNpn28zBFVhWbJBndZxmUU0V4cIes6ul/KeeFL8wBOn9r7gOLvd/v56/vPWExTLTWfccHS9tvPxuEfkNUAGVmxi0ipyZ9uCL/OFdXn6wYHVBlKEBxl7bgridtBrJklbo/urYW3mzCx74sEhpbYm7otSikZyID3HCxvex8vJrlXHyW71+bgprxbgu0zmB7EIrXZH+6+xTnC0vPhyZf1rotTziN/jRw3oMC87RT1hD/PV/Kkf5nZcZBjy8kP3tDIAKsXyokg4g6thokFR7JIFtzVqwv10u3WrqT994fbP4JLG2RnIYgTtUa46AxCiOpOdoT3QXdbVGpEhT9ZcugtLMhU8ZlhNvXBBg8WgLl02GPkGzRwPayHiTakNezc5GxuYbjkheuvjVvaFMCOv9ZKTIt9/S3YvBYnPZcNaZL0uwq2GGmJcWAeXb3821uArdSlNbPMiPLH4QvCJiyCiWsRMNVo65ogYE3VVcoNr83bP84lUMZjmyikDYvAuU+KaYqYaPA7dqVbK5piNyXqtd1HguTQjd9yxFRmIT5iYNHlH/da0w4vk990n4y8fRsAbDvPa2fBkXLIQpwELB9MWN1zLioifx0YKSs7B4kTTy00ppPfDpv1E31LT5MCgq89CRQjKHxmwzHYJHe4YDclt9Ry0swPQcgl8oGA7IrJlv7alcFrcmHom+9fBLJ9lafVL72kt5TPaiDbeMXwwHLrh/o9EZnifh1OAPCub3OCRz+9v83WNenXpzrDEVcE9s60eIysJ1HbJCiWh9wlEygYMnvAxNzHa8EJMasKQzggyml0N6iMnBXZDSrJ2mUmvDPvTBNclMyjLsxDTmUUT5uu95NFSTGb+lVaLY96MB++T5ZpM5pbqLAopnsw6e2QnFG/rjH2y+UA1mHrPYyn/yhzWX0imDrM3QGeKU/gE9iiY5vwV7fGzPTkPIbSaLWu/+mVQHm2c+4qBALr8A0+Qp6IZces5/MuPhcN90PiYComSugaLhh6/NlbLQKfpoNkbWHzlNG9MrUzOyY6KCACm2tF9f7fjVnouYaS3IjL7gCh4iNJxtPclZRgMyMspQl5iR+lbU6aEh78vEsflvUfLR6H5BhK0+plsi440jJd1wrEiA+yIgKTajmqZN0vxNG941NA4Cw959LDQhTZdfWIOKrhk0iEtawTH7M6U6sQQ3qD85XO09IddJRpEluxx4voDEY3saOtGis69Dw6xL9KGY0QsZWl+J8TdR74WoBkaDzvufECOG1siUTxi2d6HKH9EnXoJbY3lATXiK/SW8rFvQ3owFVOXtwIuJwgN0vyn0Ofa/Sd4a865xLhrVvby+F0UGYfjtwbsUZ09b3q+PxHDHYHIp07H6geo1VyLOEHz1Oqr34u/muxnb1RFf2hHtQoPIwoAD5k6J11aAueS8Izic3fwoKnAOFFEmW6xPz31vXm2qqmJXN0Ytaw9FPQmdYCB8gBbfBF764iVDCf1xWUb4SjD1pmxsj0AiLXMY9KRO0K5eAfTyyC3dFxzQIJNbyTzRvauCP6I4xKLvohSNj3p+1GP7RZ9WbjTUyg7M/wdSeVGlXTxmROp3wAX6mo6+oM31a8nPCERF008lmNp3VaH9FrbJqrX2t+BzN/NJu7igm8t9AsO79At8xCCTA0mN2gc0mX4zAY1JYZKx/pck+WKsTY7sJkDk8h0EZMpznNfDpNUn2cIs9hM1U4mJ7RAkrrZ8XvRSNgK6y52Gq3erpkcjT8TfCq6v6OG5Yhw+UcNKGq39mpB96dBEdo3gK6Flnvwh31JHbZPrg50ST3yUcrs3rYBlTVTlkDQVBnIPjrQ5ODiNou8u+gq+H0U/jWZEifXvpUUf4a/HWXpKWm3n2j3cdLW0s+VyBeTgPA7CDV2WFl9ccSyZvubGXfT0WGwwijGEZvnEuYmBgVRKehhPFlr+JIZmHnv7fYFvjNinekDSAbANBpd12V9m5jKKSN92FEVEfyxws6YwTgyhud07nZhxS2ohZdqw2+SV1sQ+3hUe/fXsg7xkCtk8DOfnZxfNfBivlR/WF4ddonhlIJaA6lu/5Wt3Hh/kWE+67AtzVt0Ld3LfAbiIy8oQu2XiuzlPj2eb8XfDRe4MXH+YMyEss2zClLGs3QvKycMZPdCEISV6kF+BLFpbJHm/uoY0lxP0hu2XgKYQgTGHkqMWeK9qSHlbBG3Bzk8yYnppg16RMKkP3JFJaKIMwFQGg3lfEq29peYMr4+KHUC78cykHjZv36R9+kXZiG0VngiXsEk/Urz+EcrJwC9S4xzQJuayvm58cx2J0z8Z+kDbzQefrP0LIvTjQoG8yvRDk+IBRJ/xT7RGNSaa6SZLIi/jewSjAhm3n6opEOczmTD9whWhPWrjKl0aVhotzM7JID9Dk/F61YHMYn72gBojGu2dBguFHokUbhC2dPxUqFBmdJUqPfFXMlKzIEbyxHe3J7nnelyL/XbU6/rf+xGTqFP4NuzDy60Qosb6NHc980LPRK2Wm7zWYbXOw7BmRCg3QpmT8DK1xoXirBfxW4d/5zy+x1Iaak3wIRvi/Y7HL/J69t+ASGRTNNBYcuiL8NBbvhlaKJTuci33ndqzfVD9qL/eYKmdjAhFguvwRbyJMHE/K+XUXpyvW93NbV2hH/4Msy+IcQgUyhx+0OgRV8gfbFHSCkqYZB4KHx04e6XE+m8G/loyVoSSJP0ttd6H0fvl4CJ0JWWyAE6HOryQ28XAG6TM3zCEpIBtFKjsooafonDShzg+mm53bBN/u6A2zJ6VzINpgvvMXHPoZoiM7hTbR/cDXeNKUqX7EB5Dxo6Jnup3VpUSZdc0h5HooRGE9ggQP3XDnm8P45KdQodgHEA9X0EyJOG246FXDi9++zTGR0PwkjOpXSTSR4idu9UjcGPi30sMaVz7S/t2v6OvpxlLoavtlXctCZU6Vqmm6X2WVNlf+gFGKTOYDJyyHsUoAyRVpK4pYAdv2q97XskDUbqnFMJyobWlRZAxZfBK+fF5NnkDL+z3Ox1G+jcOcfIkWhOg6lwSo8PQBVkZwru0tFMdqT+aTcLysexWKMMxmXJxWHWjLeVh4x3KIfIqXVKrc0nIwYx1jEruoIrfAm/wDHgLvlQOFkoTAVtH+GOQO59IsU4XvotdXAfeT4jx5eSXX+IICi85vrZ09nz1+9I5vo1d3gwlvyzFGWw1hvVO/HvOPk6agRbRjt7a3c9AYUsmzWsMoccbp/47WfmAby6OHlFHbFH/FSNEm67f/MS7h3WcxWbt+uCvsQbDMKFi8US9mjyJyRy9gSI32ndhpZWVcdGKHylMukbFYzSQt+gwXgwEJpJtvLFSwPukg4JChC/l8HDPi+k05tQMP2kpoirI3HqalDSkoaGzNkJb6+N88tJq6eu9v6s0VSDw/hMwcULWf9N8i84UHnnSxtimpF7VJ9vKhGRagaewpYWnDc0SAVSQwFcZ9rNq62tqbmTnMLatPX53Mqh3fb+Af2H6d0U9DBMF1v00no2YjFzv6zMW/LF4Bt06QTVzJXvIv1N2Xe7aeOcE/iWnkmivQzf0nCPR0QpnuK7gZQBlAwniKWzmqDIBkqYTJKH/yrv//kSKWfS7duj0CIFsK7/MMbiYWChLK80xYBqoH1NQ01YAKNtTjf61hRnp3bWuPElvGzM/Gcu6oXKi/lgyLCGkxfL9+/v8/Tmezfif1mWeQYnXl1or6E2DZlZ5xU0hZNpd3oeScESpR+jaYixd66APbgVAZGDJ2+tjU7UZpZmfaQa6G5PE1uIuvTLS/thYNNhSKE8vK+6vl1W8bKQ0pcALoc/ym51CykWCntnjIzn360MDPba8NUHI7h5kUyB3OJ/eZ49Ysi+zY5g6lpg409hLVv4UILWkwktzrx1yITkQoL9TFylIaYBU2vr9y5OvJkg4EpzxMbh9u9vfvGxQKVngPtC4ilnvC89rVwUFDmOS0Ebv7X35rf3Rhxf7ecvJ22OOEF3NOAW1jPDDmX3RjlA2HgidgdnE2hnMe96ALr2/4AzIW+ddcoN+TSIbZgI7hIo0YooFBJlsgofDkD7T7n1ZalxlZL+5iaLZOQqIO68knDSOfF2TD35ZnozvdO5p/kPHlqebDu5ssrluvYwZGPY3edq58mvAd8XAAFDq0ssfjaeCK9CZI122aJHYLuacJTRIDagUM3/Hl5d3kaPkeRnqx9B6iSia/CylPfrpJEq3WtBmnBVRPwKaFNcvr2XLxgpHMYjlOYVV9sXjOHbDUfndan/HLMNliAFHDlEnLvfovOnp+1sSgJO9KHdxYcCX4axMZa41KLLDa6NuAnkqJ1BcwJPFxOq7a7UTWnryCMqGnDV2AX1LR4vE2gZW9eaIoJdrsmSrKiFRW12pdU9/8NLtHL88iuebJ2lXM5a/JKGPdaV9yJcxdyPxaNsblJrJJJGmwzywqZnhqC/AWDVU5sy45REi+jsi388yHQo0lTFWtL+1jvYSPh2d31rdxb/WgQW6wblccNmPIFQ0JoYrjSVtJoQQUHqRC1Ey6lCmb3D/sdcW9C+1h7FEjPcKUh1WDyZa/53fneX5JqUD0n3XOls9gHaMb5up5IKfCERrADzMQFxEZY7uuO8fc5pJtLaNOwWapDzZ4FO6nA5wgzVM/0IXU+Mxj5u+RgaJjg6qUl+JirIuQK+0l0GgYVRIbyGkeabojw5ncXpf0i2jaSqu5GkPJphkw98oxVpVsfBaNelftYoRoeLOQSKooU9s4xZgJ4TVPwM5pDSP0CeWQvPJB0X8SHLOhwPaRAVDrKVlrAoY3MKL7YDTxmWgVxJPBTiOAkMoCsPjuHxP7VdfYk8bTjL73OoGDeYEHE3k8+EQiAyf9EF/VHZvm0JumPUAqX+yXnJs5Xk0DKj3WyO/FzUspcclg3JI2WHKxliJ8pyjhQ4b25Dm8ZcfzWoXse1uY5vsx0xTdm3Odewuw+54dDK1M4y55UF4FbFRX1jfwFXgGLkTa/VRW+ODGwOlX+uiF8BFGAzfM8rmH0wbHJC2+t3ioBBivVwMiW3DLndT6SfoM49TvGyseLtMe43iKIjXDR7uzcuPv6PUBcXWk6N5fB7v+9Hxff510rTgvpqQfGwo1K5sqQmTo7GZYprwJfTPTqRn3ZFkNAuG2Xhra2d/20gX4gHZVQAF5MxGLoVcKVBiVljjl5HDazsZSr49NNV5FjbSAmc+S3QTsdKY4NpRS1Uh4pjCUezZvhyuYpBc8SqdlLNPYd5LEWoyQg17afoXfeF2vZENhk71o6TbpVG4rGGDWwBfp888iQclWcLhuRM6mzlj4rxL/XvD6yVGzpv9gpJX7aicyPg4QlkDgKY7LYZElmehmrY/6HSQcaZcYNBDwMDudwdsWbEKZgL1hxIly1wuoGbHo5JvXiSrJyQI41d+94eIJa+ntWB9xNmBsrj/Gkup2sQzJxzlI6AYzabw8G+WI5NWLHLVoLWVf1HTGXBht3bP98+Ixb8gH6T1oG3PxSWELmtPKNpJoox78w8EsyDRtK5YJb4zaiad+zxxnKA0dO9BvlZ03JGj8XVl9OLKgiKMK4V7GtnNzAP5jopvfCcDm2LnQd+y3qVqv021X0lr6/4pcC9CUjuDFiid7jOljHgeNVsFy4pU3beJvgiB2kNcqUC1xhltnHe/M/j5aRqq7TdBgnDDVcReML8eHIgtyaoKFWir5rWQBCg2a42eGnS2P+M/NEiavHkIxr67+wVRONFA+V7OqGd5F19YoQ5UBFEJ7aifrANNSxq2h79HbGR9/x4QIyyH4lTxw32A5RkWl7JXCIsupBdmGoKq9LHY/8ANpzGUqnx23qaWp1mdlv13y3/N9zeA5Iyx4/kgaKJjreaWcMfeGlFdprBXh5OdiDqRJnJ6eJrW24Tn+z0sH/eNNchvxNtvaZCYp5SNXUhaoemWrrN+9Y96CcVAjVCSlS+xbzjz16rkd/prawEimsAUnQk8T0IyVtBTULnby/Z8+Ya+/+zlTn/TJImOv2WMFBHdcK2uoHYyiwHrRn3GygzYtnuzXX5lF9YQinPB0F1qqNku/yo0KsMD+RGxARwroaj0uvPEheDvkVIL0CHaodgFBKz40/8KqB+AiMsnIYZRJfIRy7Awu4uksmeqX3RPzucAACSU/5rVsf8OZHd8XMI1IuIFMCfIMNW+L7gsrH5H3MyHInM61WIKQaN0Tf9oIGkV+eEky5s63Jo7s/aBe/uLeXxVHAJnYTFBmwOaoSYn+r2Q8VTdvd9CIAiyoBgBj9jANmto93ehaD5kfEklCajqfObnseA/NQZizkj2ROIltonRuOC7dGXjKxhcwl/rZzxeFK39zjMs9UG8uG8Jfnb86P1U5CVMpNaCDbd8DBwim47zQmg33Sg2x9ZvVoCPVwqez0s/gHLdjs3fnoeX7NZ/UkkFb2zh+C7cPH0OD9QjJXCkzzT4o0LUy7rNu1H/oIWvmSkMBUO4KnkL80Zi06EC13QQwIOmPjf7gy+NpRK9NtUj9Ghk80HcLltCH3yHyD5scJmdwgvYA3ecNdQd1IMDLzyuJc352yUX4MSmy6WyEPJPtWhejq37QfcHCtl3UZpF55uNS2I+cBh25eGyAzPvmK1xA+xKcubXBDl6KDfRL0kQN6SWhNW7Xww+weOcAwCmlsvrtNTR3Lh1iY37HfELVj2fSoEJmKI/oeAE3L7ZzF7ymtlYTF84+5a7PPECYRJO941tjUTUtYMYqJqaVdeKZQNUe0zTTH9ocZDzATN20rUJRjP+mV5ga/YGFqBuf9rCwhvZKtCV02vDzvZJxMWvNzcY1/CBCALllOTEhyFXFuHbVYS7SKUOWNQDXl/YlTSQt9ZVB2fDm79jDDNXR4ggjSDn575/uNfVUE9eViH2+dceZfOv9EwGY1QcbvbZ9FcbExj0t4fbIb4wQhYcdVVb2N5tiAhELduC5rjrc9KUTgqKv48VXrmlbm1TtNeA/xWX99okANF6j/UOiLKJvS1LxLQ+bb85BMqZckMA0GJhoVgpzYRMfpwSV/+1of5W7yw0LbZdQ90wAD6DlTLx0IpmWu5cRZVaFqF13EY/14WcfG2Yg2L66bAp8OJpS/n+ilHPkPzacNugWhB4Me113+i+qa+4SXg8M0WwRZzHhnIRePn5yT04CgXpl3KGPgrlJexvJf7No2IUMhfg9ciRYRCINVeGEMU3UXOXwvxzbhQqHhADlTQwrkNDlAgC1WW+fJoHBefyoOoGCVb8RvqLiEr9yVQFO2C0TU3kUY3cJClvjUh5bDZeRsE7/Y38Z4Yng3djOot0hhGMbw7hx8mZlPT0P4olMKm1F5Oq0OpftnhaMfY9Htidze+4enwqPXfAVxocL7lbeWZ9f4bi+wUlRKqThf7+oGKc2DOMVtaHujXMih5wraqCIg7zEnTnqYWYfONj/3+irmPbVWUHfs2dk8PQgAGTDZg0I+ec+fpHs89ab3DCwN4butWlKkktXQV0zpG0j7WCrY0Z9yRVppzioeQi58OBcHG1NLGw6P1AJTy8Pedw+imVUthbmbyiREl1rkoTWrCHD9Ss3RpqT7NZ8DQTeX6fXh7da75Iksp9cvPPsn9vpKnMkzBdJCKvL87qkduhvr3JMf8xE//mHoif998Lg1znGFfLnGrKjQjUXR4uzayitry3zj8qK1ghb89+WALFAF9AGIqaXVD9lmAWwhNz5Fz8AVAhwOelADCDqjg5Up+V8YZszUTNvGJHe8JLGDLGOvBmCX0eEB7WcNVxl6AdpKdWO1ef9DCjOK/UDpXpWIA7Hm35DdrrXP39mXuFxGgRlZipi4dFGBlf6tt+tquNeoRNEPg0SzQ4QeYvW+yolakkB791Qo9ndkMoqBVrPK1LW2ysiadOjZU8YvdwNDij9rgmHILrUEFBmWoDwGyLi29VEUE1jSwodmL+tQnQjQ31MCr0hh/4mPBzO4sFlygYFItUPzkPQaiS4h2EY+h55gaHr1yNQ9pA6VUwZPZ9gGI52OzpB/PS7lYyOtFkz5BSWn7DqAO0/o1tXXZJF6c+FOK0JCw5tR3NkP4KpiH7QseeHhT19A7ejAPQWqMRjQmOCHq9woeb/fevM1C+efq/fDFJP4NqsYy6fnb8BdvJdWRT7BbvYLsp7gnEH/vbFPQ6I5oeFVIDGPnn+3uuj7tT1yIt2ofzV0SJIdbBqr/KlYB9gjLm1Z4HLZHTXXYMRkM4t4ZgZqVspGWImvJHeuaDWmKimcI4gz/A8UuiFELklEqp99MkXCnCkC2skMDjObQAx+PPMXile4IkIJy00B98XZa5fm1Ue1hJoz9zp/1H5nxfsfJtapBseNFdbYY13y6XuBqg2xcj8OOlYoJuNVkthEphIHQO8J7PCImzAzyVbaJTIqYZXTLIijTPHl4DgPb03nSPw4PGVC3KYUWVmjPHUxiSAa8ZcD20d1qQfx3lSMULaaOgzyK8jD9qOK5cEGTcgCTYvzw49XPJ64gqJSjifN+WRkSEZ6YOiJTPe+CCdejfwAzAwS3BdywQEIA/WDzNStoIZcSiBdZp9rZq5eH9RP4nST//dWEfbwibL9gs+f7RYGocE+Oq5h4XIG2ERm/G6GukV3y91jkh/aacRDx6H6unDluiIhuFyvNc8jVyPztu2laGIuF8eU01dMeiqTgqMHvWmm6N3NoRzLDi0yP0CT+GDOik7UqrQUQiGaEGRcuq7qhW6N6l88YP2+Uj5/wJqIsf+rSDEhqe5RDkQ30d0o9w7ORVBqvNSDgyUNQkjoiXLFjtn/5zRcJeRkJfca4NPNFRusjCYG1tVxBfI6RXbofnUeOuuRiEUkV2lq0/jbJZQqyGDHbjc9qRrNWTSmPP7t5NA68YtSqrGGHHItvOgoOuFqFMu5hxeNnnQXXLP9jksOzzE9VO3TcvqdvEgYh6Xhw2++6EfEtCmYr5jf/AJI8LNDw55IRUXdAgF3mO+XV73s/haGisAlpG/sxwiI9oV26vCQpQ3p64O+MFAb7tZD/R/LQD4Q1TK2rr+9euWqbz2hiJ3LEK+RdcJyXVK1xBcAVX7C0vUb6qWWKoVcAHXVg4go15ee7KZpCFPC/AxyDsDOcRX3ELDYephgp49GXe6JGHdi5jZSwutoFIRLYAw/mt4FQnvmPL9yYjnUPgFAHKDsTgFqNg16j2s8K2LeMhUiJbJzL7HPBoGHa33gtXnqA0EHD6qVF303cs0uLtKn/fPXFKZJa/60hD+k6wM/Ehc/CrmiyiSn8z6Bqc8NDwUEwiP1LSo0mfpTOx9yfwM3AoP4N3uPH9xt9/1VEVA7I84rrSQk/QOkiF2uZ9OMPEZ8mv62Oq905Ea8IvnVcUULqYvmKaauBisr+RJa0f+/N1Zd16f+8NFZFYAajEGzW+gaxac/SFcW4BZyrDzh9lkkJXk+2wf9qoUhqi8GXvM+YeTxROvZ5BzYyGtpC75UqvTOz1tbdXj50eSoSVfpKhx55hI+djrkgOL3xIlzik4ZnAnVK8tXROw8svQdtONI1ErU5Ab0nG1my9bPdEJBy6XeJC34QOgoiWFAU6bTOW47GIEfDjt0XiO/bSTj2+pB2/Yo38KA68So5Z+b67AU1Mhi/BSFA4ag3/XU9ThXxDFfA/L00jv9gRHJk5GI4X8d2s7+jYdDt2V/J8mrmPCXaLtAiKKzGsiKpJl8839n3KI3/BC1o2KsJIGJQKx86nYySOBnag745dil2dr0LXfQf9deq1OL0V819QIrM79MR6yPDJ2GF9IIbRb3CpHmiCxXOrRglPZarU5mhYzs66cBVCHcDnrs5BahALaUQyEBTmuXHoqM6bEoK8JmOsoF8QGWeu5qCbrf+SAx6SyfJf/gdW0ES5TFdZsc0uywPawenkw1w72zFZoTG7GHZwKHMqgstwihXlOUK7xDIVtLDWEQiFbbWFlq6nJAlKJiS3idZfp6IhiEPMPi9LI8PaLHD6wwRcokXIWTiUIXKgGGT5nSS/QzvdKfuU3WBDWeosdhrSjNJTJqHpb7dKamKJXdPu46pDp3fXTbdZGqhuob4vojEkigX1w+KyGK81zR2jJqutVpjskEaY1kqV2nBQ1zTQ7dnFHsEH+QdARNa1FmbMdXe5sGz7Bk5R9NKOdk4VvmJj5dmFWSW1Mw+oExKRmF8Ql5AZGWaBox+nMMcA0k045u2xz31685iqaUGoky+GQ9IrkeKZcCgOSUTK6hOAVOQCnHSOsb/vtIgu+fb0+Ol4CjMHbL8JxmUAwmveBAJ+rzWLvAAFmo6jFHg3HNM89pFMetgnu0vBdnt0zafl9Kc0whjsXNob93FOa+CzAWb/4ExnM0eXEQK7PyVuUcQLFL2hSUGvs54ZYZimC6hKxQmEW2R2V1R0wnLFnZtbmETpEgtHDJw7CRce7htYjFVfS3xnND1dlJei2wnCA2mtiQt+ysgn2TbxqWe3tC+j6xH++d2SlPmW31c2vKSuU/O0P4HylHr8q/UqX0Dfd3T90Jrv3oSCvpShDF1iydaP2BZ2QTCdLi65Bv3WTsSQ6vz0ZHqeq1uJ07Ttv7hrebgW9h1UsPJlbJEZzUNH2mkHTBMiQYtDkdBcbRxx8+EEl6+n8JH4vDnJW5VeVYYNQ0dcRf6tBNvK09wGESqmf0aG0SEKKp9EHC99IZIoKbdkbsfaTJQR5kLxDI/HJTITrsPD8YteSRxZ+TpWaw+6mfF6J2S20+GR+jEZS6CUhnVtt1EOmlxBv0jmpG9GT+mlq2xc6fenkiPS297eB1ecelHKDnIjaD8rcy6jRxPSv+xd1Sg640GLYOgMCvEYEw2K4qoIIcv4xUSqvPGj7aAqGOzD9xF3T8Vr2rvEwITdcnaOjT9eInjv8+Z18EfbkBf4eL9xb4HvNFWv3uQYOVm5xltmAK/Nt7D5A/jH07c3LO1bpXvMGlUtxiV229nP7IC36gpimpc/IGgVwYkiibiR08Do42FE7M4eK5xtfoZGQvroqHXb/q6r4Zv3ZnH9FJro0gkBjEfhO1gOSpGDV0ZVlxddNkY2/UIKGDv0Joh5/ZZ5m1VYlUPWbRONkw+BMqlENSMKdQMPhspHC950WG6ONOKoc8tiKUdgNZEEpHdgWh1pXJZmZNlG70YHuMUlUgFEuaTu5wdP6E8d8W1koB1AzR1jGylXiqO8ANqnAzmEpxf35XC0Im+dUUrWshZP4YitVE8KOu7pG3z29RL2TDBapKSR3ymIjJ6+5m4YyxMf8F8N1HqMDNGqwBo0xvzqjxdLS7YwuIt97xzdSXnDEVOKRG9KffpNuOiwNi+oN7CVlBIU5GEFTAOAF99oqm4LVoWYPG4W2T0CFC7ld9JqnEvHvCUiypwJwPnYGYhnFE/dfc2RRkH1IHCv20WOF8ZY+3raIVaaYm0OiTiScqdEaokS6yqgKrQSRcKt5AMOh8msvDkiGZWX+3WFJGE1pEaZBVN50RiyTvjYkv5Hx555mHsEdBi1Rnpw0cp3+7TIlZ8ggh59owSCyPN0W1oNZo/38FZ5E5B1xd9rl8w4OC0N+ENPTgCg71JGkJLiveoP3lHWqYMQ3JShKwHehEyf6vEtzCbhaX3dgOEdTGqdqVgT0Qgd0Yglj9gATEO6UASOfYqYPGmhn2bziREdYwNpdudKpfUN/gB2cm4Gi/958IUV7OkFaeZWVTMbMZ4lU/JnqI+hAFMUGKZzvbAQEpZ9xd+Fe3zgPO7ruaG+NAvny7Y70hFDheY0FtRO2H4CnT66CnblJyAmeCUfvdwji4EKrXqv+2rh83kyJlwi8HfPURkU6zDzSrG0+N3BcAde4fjkmbljdqrFfFA2vPaNc0LSTq7f10lMdEO6kSTTBjPVxE38N2YAdM7Pt+d4CoQz47CEC82D8mI+GGoJCr17kYjFB7O/mIKff/MWd8xqEOqFnfBEJKWAJosz5rONDaVR/WqXbfE4+5a4lf5sQ7dX1R4KDa3Rr3wKPDeWn6yRngFR2G8V0qLxWJ3UJe/Yfh5PGRuPxqW/GFihSeJsLT3S8Jh/499qoxGoPzgyU/0AVMeUpRIsYxbdDtbz4TY5IlOHVfCWfv0aMq2l2xwBozFKYTdpq4jZRbNQoCqFT4nLKETjA0Q93T5/iOvKccdKZ/u3vxJbx+4lfsdmdyrrIBpbq7yu+iyCcJoNNlMq//bL4fcZtK382RqRlqUwZfj2/mz7pmFRbqiCMseOZA3YnLmvkl1f5pOr4JFlH1zhEthX9gQxwnBDM1WDJv8zxvkc1ClqbUZgwBc91Sv8bj3+GPc+oiJ2U7S3Cx4R/9xoqwDeKM/xU+4LWX6Bf/EUL9MfjQcYYAXt+89ysV4j6FuOP+lkDeiP+vWpX/FaNvSm0gjt0UuxuVuMTDjcH4mq/fZvMG8TsI5VOjNwcF2WQMvw0xqc0A8Fd1H+cqFHncWBFAuKHtMDwwedfsFNF41a5+XE+2kW5HkZIB1BlsLSjz+Dz+z0xeqnVC1S/b/5Ovjv+r7WV/D01msaV2Om3DWsjW3c+uLP92kRUK74s/jmD4x5wkhPD7AlsbaTTuVOlVnXf4/VcgwEHvDU8k7QWnoww6FO+vU5klaxuevWuqMt3ivblZICUhJMgfPoTdyDMueXQMAtLLQUDfNQurldM57zaZYpmt5z9OkQ4k5tvOc2ak5HZovRb/KMeBsm0S+xLZ0b6aRdBy12Vk7lUZjum+BU+stJbBVpGdeHWwgtPKaJHhOASMdWKaB+jn9PFeOoSiC7SLWceP3CsffQe+ftcvG9opDui6ijeyTIK05UC8SaAUy1FEIZk9hluvovXeXbGqw/zvW6RcLTOHLLgdKq6ydJtrs1zbj53x0bpnXWmK5k532d1EuMV+2HyyCoBErTDF8sgiIBVR6wQdz0W/366QtdUbX3iHf6PZH9KyiEp9UHR1UoZP7Q9el6UmkghccwW2peBA0fEvlsKQd0DbCiTf1xb4XPbFn0AsMf9oJAKtuz0i5nxWMyOLSe4EshEj5qsg5pfaTC4qXXRSh2S/xIxddCTsuoSoIRFsiP5T1QshDMqpsXwrLIs/GaP2Fb3NoAvPeOpCQUaJt6+SRfYHI715Gp9vgTLayXU7zYtzSycuTbP5LQyVrQIa87a8TjJomtaJL4vVnR1ulP5CVQN4e3IJZG7J2hwdvqOmy8iCysQuSHdSxNHx87IiL0LEGVBfp5rv9W4dxxINBPpJCbWjYQpJqWk6VzArrQD8gwXL0qrckz4I/S4G+HhKT1fsasF9mEHvElMu1uOlxg/xSCWtqTs38bFDirwgWsaGKEi1LM7DGOnqLfZzIV5PbD6hPXyxTjTOvmvhCG4vGF2edpryVVjT2cijOmQezaRgWkR/fZi5QK9pQfxgPifFk/WXvA6pBObXGTeJL2MdxwoTTizmx+y889gSGb/f73Yy17S4gCPYfsmUFYf2LPBik/DzvWzwrH73HfyjFfazPaEAcBuoHZqBRxresVRjCn42oThZfgOlsKONzRPNXk1pqU+Py2dGyBn5CY378/2epChBjpCHmgLJ2vxFwRUouoYWa/f2OpjpGx0VebG++KzB0EFzAXIn9ThpGsNkVqp08LOUegWpbnLgYIsF8jYinIPCV+F6VHmWkozw5LT2qFVInJMV6kMJDzosmMJ+uuUY56vAnGffSgZ7jSkXQqxAg7nflocTHFE1AvNk2CNawt5jijoAR4Gvy7TNt90JdYvFG3wig1+5ht4LXLGVY8T6pRK74UzyDSyVCHlEgbfRaBywrJcq9TwNxAlxhAOA1lbbaqGEBIWfGR1BCyODpwH9zrf8JEHviFI66OXUKmZUicL17iqNr/7GbuZqYUQpRqdK8t4ik9YkGHUyZiqZJedUpFXugTrIQ6MDuCPwvL/tlIOjvxWJG/MJfW+zgaF/BffIMl8gI4ErRuCftc7oKWLNiHUCYjKTZTX4ImI9hEPDFMmyMuf0MjGEBzaXALFLkcWHi45xeoiDy8J6XvFp6ZIW1fxKcyqhPe9KqCMoDUxDffCgWXI+s5qd2bIuoRSnhxpJYsi7BRu9z8x1y9n5DmwhDtt09Um52gWHQ0A7KtO5pHYIBMxvXneOjPvwuH2/bvP1x2BuAENTMa2GSjJHmdTZP5kV8prWksKVpFFZlKyX7G02WLuhbowk+/KpRfYHgm47u4R/MuJt62mTQvyVS9dtxQzVmcP9gt1HeKTWnrRnWVHRJeEjOejZkEf26XZYXE5nNbEJBbULOwyJwQGXU/7rnznENC/gXdInntrKFUqS+CDLG3WDeyfLWpNgM9Zt561C48qVOAJ/DgwUmyNNLQ85kBDm/Egf5gCEtYY3st79eFsz6DhdUiUWmykK+K/SKl0UDI277KIFMX7cjPaGtPa9bb5i13mZjNRYvUdrTMYeED21RxFTXtKIug7Q0XfgHR7S2a3qvM/hQDNgqovd49/VSqIc5PZxJyzPcmJUbSj1iLIvX720AG4OWLwHEdmK1tEviR2TXfxPpnhoQWsW+W9v6liGX5poa3mERWWLBg8qKb3lWVID7zDPW+HwlR9WzsYkcuqJPqzDI1TkEwUSoe11X5RZ2utI1oHTtJyvEcysHIGzhlaqJWiT+ze7VXLL821ET6z+818xus0BoCMK1ida/6ktkIihtfm42jon5JbjGmCtgUJZAM1yM1zW3T3FNF7BbbYoZ8xnJqCBMCmXMRw/CxwLCbNp3i25DqHmT7v4V29EItXGnGs89QDAX6bt9NMiIaVVAX1RpKz+FhdlmOJuBa2eWstTA9dWZlSGpu0xnn6PzzLR/DwV5RyrPP0OFqyPqj1JUVqv9d8A2ZyrOOtuqP/plPvun3uvY/gyKTFbjtQ1Fx9+jTHz/BvDHqSp2Fz4QirF/UQEIomjZr76xs46Bjo6tRZzbhbRYDM3Gr8V90k3rKgQGJ+P0NO2NmcDDC4NjmLtGsExhCuJeQ8rMMpx86AFea5ShieDnJlr1wxyfiXfsZ5/RWys2cqm2Y/HNdoJX0yRhPbqr6dVAZK64aaKcPWDKBSguTCrny/C4KnKYkfYCiA1ZEtCXYUb8SO5P+AHP1vZmKMDwXqwaQZGQp6ihLxqabIyHIJvOmd80y55gOJg3xpC4orrsjw36hJeCnvl2Oz3SK8He0J04foNqIubw0l3Xg582aFt+E95HXr0lfFGVmj3AlT/eXPmll+dcC9KmbDK/tt2VQTw2IN1GtuRam39oGBmeU1qXHLUuJzdwtg7NlUFoEz/N0nDt4XW2ITFiJQOD5HIoX4G1kVjyZIfqAzrKbvUsB3YqeySle9nmGMog1e2Uv+pYkdrAyhJQrG2gZdbV1RL9uSEdf/U793Be4LwFAMDY8OvlF9mtRfWd0U9k0Mu8Ba+GDqv45GEgw76JXcgFMuyeJA04ccILFBy86o0i56P18hdd4CDzBRp/RCt3OqR8a2Mr2XLZ1JTwnqBOyIR+lJi7h8Wygq5erXNzQfScl2GMukt0PVFlcNLF497V/ZpadHwT9HhJANTvAE6tdYH1VYqWW8hvOGNe4CTSNhYmHDEEs/DbcNRu2+96yi5Zf4aEfo5yz1/uWgA6bbJOk6FFrnSVd1/uRKhygIE84eLCskHwCrwmb/XKU9Tp6j6/uTL8Hv5Pr0jr7DQM5h9A4G36rVuU/6CbKv7rGN0dyIl4loR7v6KBJmJ5puaZGLBlhyuRT0wIKmPs8EK/xaV4McUIuBKRjHsD5iqG4f+y6Mz9I90YIQQaNgnhZpZWCOyBmsE6k95aKHzQhCml0/7oiB0auMzczSDOKv5Lg8zZwH0SPGCsrvwqwXxqz1STIvy+wdBZaBxRIzwt6NNddlpYv70WmuD4NvTGQTeH+cL4jwAjbFPTHaedfiX0dcrafWUmDnOlR4QcvjcYdiPRu6swoDhS3Q/hclkLzea9Fb8OHZd9OYFEfL37asUFAW/IwSx0O8+lBAgpVyPlAqhWfBgAe5HZDtz2CBLLrxRdHDIbiKe7r6MdbZnUk/mJWnyIC3NkPI/O7LHadSysoOjTTMxmx3dMXW89tmqxu9Zyp0uyjKp5Fr8uktzxjUTdT7p0uZmMDscyJEbuz4ZonTEla2TFB/ngQ24YJ4XarUUoN+kRzyDetl6el2EgInM38r+O/JPPqayk1ZCFR9M1WAXkG4E4UTzgZDaEFEn7OlwWvxnForfnbszQhYCicE3rUPXgO0uglDUneqCfRZVMKeufwrEr2MwoLmA6nCaiCkUh1/vVwqfWr5nZGB91+Mc+dztX161wtXKH430LtKJZ7ekfKAxal78O+wd+l8Lb4jKQSEkuLjNcZ7ZujzZOx3wiIkTIZ0AvrZxOQJkT04VC/xIgbCpvCuGzJsxy59sJ2kCUdEDXgWoaKwm4Rr7USStk8XpzSKrn6BTjdTdnGkvfpEMDeFEtaD9E0cTsrWhdlXcg8ER7oTWp5unvy82Ya+D2l7fG6XILy9M+KAaWWshlYUPKJjd+1Ev9YmlKBA3MbGqo374TEdHYSTHLpOsVcX674lZmmizkQE72/WcJvr5Ghmxjt8CqczpOly0Dgc/q+VH3XmIlrOjtkFNh9S7ct7JMgqDmzUCKposE3upcayc3vuPMUR7EhX7TDwJI3/xgLkr1GfMoXnW6aQMCyGx/vn7tImXl460+zN836NUOPZotBpsaXf53f29a1qaduCYtpSnTIN1bCYxQD5hKpuPhLxFqy9XGP8/W1pnFy5JhF/xIzYwnnQ0wNCu6/hEI0i/Z3GDY932lV8nYIy5hOOyqJJWawP+pPt4JbSARLLZlJ/PqhHc8mH0IZbAdEnW/WDftHk8y1EqAuo+Qk9/pA9Jt7u269z6lpHwmhrk7vUdQ0ufWR7ptJcgh+beTxzbNYJDWfhGm2AIzhTaHVGH8vhop2LMLPwNCIhabrngKbCJ44uArK03zrGYuWyEpGM5i4jWBHd5KnAxX4ZDT8Yil84Lg23Xo0nedZlKSAipvsCALbL7vJflhDGtplaN+e2ACMuNnptfcDVI0pnteNGionSP70NAXC5YC2UBBB8Np8U6Srig3BLGEsUsNP41AA26krxoewnTkXaugYL/nDtVVyi/v+WE9QQAASAIpyu9UjX5TvZqLgGA5dOPULuwzODQVAKKyjz37jC6LVdqUIDgxwBwDGufywWF52bMvv+oZ9Haz2/lk38J2NRE2e3+dmC2cMr7u95AHXGBQ1mnPue/gCuDvExSlBbWCwJ0+2BPwOn/NAA9xWWBijjlfyWqvDV7wvyg6I/creM+wgMnHo6pZNI6l1MMU74FQ2T0npv0NkaC14u2UOErbISjgk8F/7DIMe3DxTy4rKBKr0sdn08vP1rU2tZ9y0aqo+3rdQPhIvWu2mQKaKCmGOVSEK1l5aHsLp6YNtzay3D124A3b/XGZkGyVZ44QP0+J4xEDzveIPT5yhWwh+/ae9EJhzxhd8Fsj1vPFkjFVxZyTnk6ZXAwJtjxzQLKXmSF2ZJsXrpKU1jvdVHSrVSB4/E2jQ59TfzCP+1e1Y7X2eyLrEsoZTbkdq5mkXCxO8ctOGNcEzI8PwGodE0u3GrAgl7etvUOXQ4ciBupWsQCfA4u241OU7f4RbY04h9KbS5Q2my1VQ0lC9h2dvfqEK9emWkNHtF/dJ5eg+f+vtrbXx4wmLcwnme7ZGIC5kJG5nI7bWM1Z3TxX1JfKUYOTM94NFv0tWs3Z9oT87kDFADMgzUVxVmHJfn+SMo9wwYE2y6SQq+b0Ae9SUj0ntJHPLjyrZNoK5tg5nudFV7O+VKp1onQXCvqRDSs3vOwH5GO9byjQu4YFpx+7Q17S2Ynb26YEhZLNVYSIn/SzB/qpJ17yeId2K+eqwy4/U31vExYrD6UIrOmPPR5Px0riTGV71Rx28t91929t3Yt035NhZVl5xBBrB8Myao0zwZqeWVn+IvEvbW7qorbOhJiLRMoSRikUWnj8aYrekYVanhkSFlNwf2du+NUGOVNXfPIkjppvkVYs6ddeqxm9t0C3YtmqHTadBBkZVL0JGET9lDQJhwV0Q+mdkxgtc8UTTDLbvQ7m7655WLU3utWmI9CeS5QPL3hgRxDG/gjZfntd+KCoQxtj5juh1oNQFLKLYrQnEo1z4nBXSd7uLAeN7eGKXk9RQOmMJRaMoIx1VRtJKxoV2Icv7/Z6QE5o5oSUyyzPTdHSXkABVNBBrNTcn8fwLJLkYWYO+sJFSSYghWDhTgzoRUV1KAYCG36wQtNlWAJC+9LaUqZcMRtInTz9A6c/wFrJiYFZhD38KdBGNnyRrGYHU7KQ/tzwio5yNeKNjb87wBVLzFL4XHNRIs/Vzx6cmEN9MKsM1GfMnm2QU2I0G0HVpcwP2vvpRkCaW0qjxBJuVOWrj0KsuKEkV6d1Y2JsA79JErRty94vV2K2leZ2qPx7dalzCVmiWfbVfZpM3JawPkew+LxMcYn8z7sN600hLV9Mh/T57h35Xmz+x+isFduzxLfHg7At6Osj7INuBGDqjFmRd2pfJSrAEq/dZG8VV83+jxRMbf+pmJGlQfNP3rVNB33JeNSdbUq3KO9YLCkeqhkRkUHX/Q47Gj8zhHrwwj+ERndhz/YMY4RiVkIWWhRbX64TeazRxVggwLTNy1Fot5ZlGTXpXdnm9YhuCsVLBeL+nEOJ7TVoRt96/kdU76I1Kc+JGBu/xxHz/vNd2BCrZZEQUj2EAG8N5gZ+re9RxZpGNAdsLZE3TUC1I5eh2xtQXpKZ9JDCaYUYIa+rfGM9N5OFyN3vRjAnOSQyQDItRXEpFq6zU90hIuFCtSkz9XnYdwO/rhXcMPBBPVQo9YiOyQNDjQw/9NEzE6Om3hkRAMAN8DSNSjCN1gmBV1t66jAerotgd6239e4V193cRWTk+IwKkpIgDZisILCRlk+gsbiLieUIsgmB139P+Jtcw2TdejkQtZsa45aGutpaKs/zpiYzimfPeXoIoEzJd7hiLko3E0/uX3Da6s84WfUfvd96p1uetr1q0JisYXsEMueshQekXVzCqRzljYncc8WiGnVL3wGJe5rcVD6w/4I88uCLGFC3oVIKJA5Ed0Y8WIFQsqpIR0p/c4pAlkUj9+vMv0Asg983VmQ/bzmGjgxGmTDJ7gNIyNCZwZsW7UbdNMyS+2F6dEQmu+yTWztEgK/M+0KiRy9lo6QTUSQQKwS9Slr09qW146U0FE92jr91iblw/rGdqHUyr8BgWe+Yg+pZ24e+feeBX+j459ACqrw2WZn1FtQZO7lTeVLf+jQyi9YkcVlu6/fxz6p7y8Cx7AmWIZVdQU2kFTTHoVg/1c7NASzHWq0/3+uvy1uof+AqK95F89TKjtpg8hPiipx3aaF/pXYR6etTjBbPDfKDXBSid8LOlfAoEp3AxpSFTC5wyZBglq0V6xSlmfZF3QLU/muOfi/eF6U6wGDEEzn1lndBhEK2grdtpmzt/lf70Foi9B/NzeR5PdSBjD/kTA1CIKBQkw5QPWiokifO8HYBQ8ew8EebGvdizHXDKTd5F9aPA4rn0i36lL8MAxEpKlUFzXbVlhT1vtjWK5rapLvQrYiuEFXg6h28erkrD6hHjUzmvHfREB+iqjiTAqU5eoRycQMsUdcTZuTF1b1rpwngqnPpUeQisbX3XXmJAmSRufuTEwhTvLaKbIeFeFyg4nHqhsbq+EULCXhRHHmndEyJyJvgsYBU2TvdrSgSlIiMYnsPre/yOjOmzUNCPDG7iDcoLzrQaoEn2orZN0/pTKYtmzFiskRp3UzBKZFrPdZRJfM9nQYRuwPm0v1nRF61skOZFg6pOvseZf3ntqufkTSDzBKO15SwwVKR0k9RiBagt9jalkCwZHd1luDKy+22EOBLn9/ZZbzkzzNcLy7w5UesXJW5UPWs3z7NvXelD2qeZkShHdPOok9r00tPj8Fjzsux3U2dgtlcxB/NCWVTG0DGGp3SKC4OSufr0G1jLNT+GACR3+Qydn5KFpJBvAIGM+LDm/BPvmryVKXFKfHKXGllQfoK1FaFV+vWKWlxXUIMCmR8X5/gKGlOFSklrQwJcNvJGh3Q6HgXBSx2PyBBrEim11wYxgQWpLoPe3LaaUWd/vVW8XU4gsCIOtXMIpAy/AaZgTqAcgp8V2naMw5vBYHZ6RerRDlxOksFhu5bseUePsub2NeEM6sGBhCLeFYf+TSvVK0w/TpO+eW+0pji/KeIROlzy4tmihr7T9L3RHUtxhd12246+cmEDJ3h42XTIFtgfQAdPi8NeAVu8fvXpyBqrOfZLoCupLj4WHL5IvXuFCEzYT2fDF1ip9AWe/pK5i6goSsh9p9eVmTUrA35rzzX6eekG+rUaLRCRVJb/PHR7QgYfHcurJA+u2DH9xaLnj3UNtMyBY/hhrTfZFpwNjkqKs1nFJednUyIoCTlBodEP5lCBK0QukXUDy1OqAGWrxtXzrIMOEPxU28NNMAI1Jot4HKI+S74mSoyeY/4cLu3/VKXWlgbdrd8WfOWVcmzAZyiPbDsV3ywssSVYawe63N746zWVPt1SmBgeKb1RUO4iUtVCBK3bXd3NTLpJ6fyzNyMJ3jI5N2BZ2pXnCLQsaLipf15FNG9MdTeqGfAFAFUWRPaThNuD0AjI33yQ39VcU1K49xAEa8P5M4UFJSnG4HW3I92pebmZAViSEZKwgBIGlDgQ7/PTPfvoQPgcTPjlWa4FZeD8Bj7q7nDna6KH03Pcjqg/cMtyyYKv22KAhqtcdRKY/LzbdcjIl4qQLbRltPkZuLZTiFhEAFquPweOrydAL/a/AN+k6nCvPYVLOgXYSN18RG7S1zPJpXoh55dektO/ggV1L89ZD4eoKUNtn7kaqM1oJsmYMA4Y4+c+9LELlsudn0qduFZ/u6ibCorARyvsgOxmN1aI0bRVu93vvUEk7n26Fr+eg5eGyJsbLD1VxDPIBpUk9Wzmx4tKPmmlYlYEzUp18xkjuYscchjJ3CiQBFWpITmf6mYTjWFI6zkdJlB+0NbGSKuF0s0TlirVpwCFWxIwvjm3bEGiKz8z1TD6XDy9HqHaRRZqbIyTzdkvSYsMF4n3CLuKpzP2gV1a2psoiXLACyls1M9bD+L5l5SsoDSOwY3pxfBRCYLnY3pNomcEoEqPaVrHCLEfeOxkRbQEA2mRapBe5LvjwELP9p7+93dZiUItfeswxlSYedx2FJFk6NDz3Nfi2G2H79UDg/9xDshBrfP9bM2WviZTE05tgcQJIdOSbbuT0xkdc+0Bxlunz5FQAHFNos4XncOQvi6mSsrQVmu+DBIeLjwUJYverOaDPHO37QvSf9ytZbwYkZ1BL2ZV+SM+QB223xJHb6ZdDyDfIelIS3ch2PhMxpccH6ME2Nd5MaL9QpA3Vy6vgCYa+NvfHizaQ/Ylt9gSqOW2Wr/1lCcIN7xKFhSnTNXvUMyEkbAOfHsGtdk3PkHtLsnP8ffzV1G2FvwQHY7mBT0gQKmKaJqpZAoCdx+Ds9GpvdM5QQrQ/qhekEfJArc6btdaxoWujfpEpW9ffv8jWPmAs8MUpvAv264iKqEIMmJ9nYF1I4OmQnONpAFnqPb20S/uwPFVTFECX6lMeKpsYdMsKhC9ZubzSj9GqMyt8hk+ED1E5+FscQ+fYf+JfwplylF0hJaWONrPDYREG0mCpaf1baTmZm1hFv4i7rhJRVxjt+UcjThun3qlRSH6BBdN3U+UYtKUSqu58M9cFtXCkej83Eo/O1SjlcWLoHtcpiiTOCOlxNyngRpzpNScEPxH845DXI+ftO7WPoLwPBOibBB0WJHk9BtOBZpllDHJMpUcadFYCwYjNDo7dHqwRsZa4u/cMmTXTZTgN9x8BJR4lqqiHNOirN6bFmNZ8VqBsH1WexmTMETre/h0TgwndXIF+03Bcc9TMNSOT7rjgXso+2qREw9LhacWZq0x9mqvhqyfbk6WIWpSEKyJfuGjmOLYjQJckGkawba3448paw6rpUV1bzPfS6vZytgCEciDEgS7S2eIDaSPKuK8iEENCOZd5vxdiYOeC6ugSguMqWFIdikJ5bVsHf9yhZCWNnF6v0OQMON1a/mNUQbfZjpRJkc8F2WeKrWLXxTZ2rYc+XgvNaE93KK4c8AcgHzv3TcUpYi/iyvAtopyHanrKPCbmvCh0apHng7qYgKtFkcXVALYjISEl8rPG4WiP9TmRmOLitlF3Ao8iEnP++sUybl0ntkXHgpnIkZ1Szpw82IivR4Dwrg49RcuaDXH0AnyRe2jztFsqTAe1Ue/+5HYBxehNATXV9fNYFGmGgpw0xPXXJVTLgWLiVGuCObEfWSDSN1nmYPC9tbAVks1omW9yDcZtCRG8ht/TCWCRLCBljLaQgroKMWn3KKQ/vfGUI6zNxrnezyr0IGml5OtrMICSfBHrgK0uP9QAyNHUN5qYHE4or/lcR8Xa8pk+g/Tec0AYPtlq0Asgv3C4d1FKisYLEL3N8J7LVQ+OQIQ2wYjhjCnFr21a3Xp626DeL9X39POrAdMIW8Q0mfcl4B3KEu/gh9AAsa6HPrORWivF+DAwKmnaYhtXT0FsM1QxBthPad9mt9vN/uCslY5J5NJtYi68po8e6CQsqDeYfYFvNVwA5xrmiAOGBC8NTYQO11LKX0REUSOcocwVBO+fYPBa+23TU0qrlmV7vDbqRk8H6HxiNhcMkS8NNGDjrs0YSvYkjCzDwjUzzax8i1fGP3pQbxYuk7KjEsgdvPRW8FLQEje1+9alb1XMDBFg3/MnyYVq68I43QETEl4ZA8IHxCy1hJ24tHbmSABWfG0QA2CWPoAiSOH2C7W3tU7vdEqobP4+xO6rpIfq8nVklRZYPgte8zF3HuDlv/ePm6IPTF9JcP7xGq8ilO4R/hbtzQJKz66MthRPnkoASqXvfB7IQLJOKt/o3xNHoj6oq7AcqwTeY5ZUynzpZqvvdhorabUD75FvjB6NMt2p2iXzkhB0QZBtIFLMwXp1bskPXaEKN8mWeskEpLUDyLSoohPBJbhrgkZbwrlICjH1S95OfkqR0nm1K24R/lIvGVYjejRrOYdXsu5QErWcRvxTMFCuvq3c/DGZ5IA+rE1r/2k0dBcVdmeyUAHXB4/7anN8NI+rREC6bm/ri8Jomi3J6yIjRvCtEneeHfZYK2pH8PDlgq9FeBDrzIUkwzZvg9ETB0EeQdGiVAul4ltb1ltq2SLVLio/hhqiEc3fbMazVAJiyoU9hBGhbpKYlVolb5FA4gY9Q2idGyL2O+4Q3DKQiKPPEX8ZoLH5J/uNpVVQF79jQwCfwb0DK4iABqrz25lyG8LxGV0yY8UXb/aOZX3xhQszumIqAOlYlotSJcJ3GljQZ3AM6TB9OevfBlRJ6ZQoyP5S++nmQqvk2ZCC0Tv/Ql+07LroBRoIc3/Phu3AC4kUVQBjk/a8126w/3Gk2qWEb/ChtZzf4o02mk1mrlOtDdaxcn1Jto5NLP4En6FkE2joRxVqD5l//ZFL2AOPKBbEV+KN+EByfzwGy1DzMugPftfkTd/cH//egBhiaf3SXbGJ6GV9QRNRJZFcd4Tt65Anw/cX/z+fUH8fpKNEU9m4o8Og1OeE/0BqhQn+ExshHjY5s+ZEOXJS6jwXe6/Zt7g9r5MRxNy3vkbiIqJX0bFLB/+gNGhdn+naWFs1kg9Uat4dgr0l+CbRfFgWjd9+xSZ4suFrFYw5FEeWP42tirnHHv/EYEZMpAiL3iZcYPhSTcrPDA9Bmzy6dZwSq2oVYwAj/ZelWmUFGIapRbqOFGsMEHg9dbPU86D0YypXSMl+9k+2cvwh6LH7ImTSa82oG4X6S9SC5PTvLPskHXEKLWlDMkQBlT3FVck0fd8Rv6e9rjlTVY7DRLpTi4pv2HhqRvtkhWzN4tQvNXzPUrG+oiWCNcJ+7IIsPo1pDKKDB1q3HlKABbZedhctpHbnVZf3fI3OYGvlCcHTGRqabjnt/NT2HQ2i0+LdZRgeCkZDK8HMgllgDNk3WMolbi3YB+B16lrr0fMIWPU2P7MwxY2ry61aQlWa8rZ0neDKiOFCCp6XZQT8eRFlMBDIvozc1HEfpZMQl20l1yfpQ2Sa3AE8nLbfqWJJF10B+qRoW84JP5g1Iur6Z29ldVBs+cZkpIwB7S/bRKN2Ht2QS2XIuGNmg12dB/7dHrHFVsRJRYwuZ1BNPD3ilvRjl3POJo0GBZVKkzCVPB40IMQyEPTVLQBHbKMr36kr2DkVp15kbEuAJoKuw7LjTBKt60b22IZfIWZIo/vJmudiZ8DklDg5b4ViXD9cayHvabvtAKzNxmkYzFjZOowh9FbP5EWORpX4+i3fu2JfdzOSVmS4rfR/vodYBV5K0iG+bnJy4ij13iD1ORGvd+d5YyoRQetwWwhIw7XBWJObjxtJvtLaSiPyOpyxCNHnUrvk9uzrDKKOws0emzwUa54BzCZrD/yZibVX4yxen38PRSrzJiTYN14gVg6E1OYfLMsxDhTy0CjRixovcPnrHMOursYbnJavIxA9pifMBA4ZCmTie9fQkAk8032rc+LenWfcAVaL+YKJfbGHUM17zfPCdwPv0mzsZ78Za0RSxVP2VyFEQyP3RTeV0AaZPwC4h3cOts5xyjefz+qJfnl1PYB2V3gfP/VxXV/Bo3vj14CFj+GFQRiudof+vhXA2rGng/Z2cTBEUAixVRgcC7R8HZ6c6rbQku87OKLyVFlrNrM/X5KAcGfezvsW+3BAWnkuIM2eBczoN5Qjh8NPGsvehU+388rCT50nFoubBLW12mtrYzBRXoeSxO1M3yI0t7NYO8aeLmu0r+ozFJ/chCP1pPWQwwbvDTPDcPudAf2I6kBISOvGE/oRWjHCw2Y+d1YvcId9QXtr1ZHfQ+qbsHCOenWAtwOlgYUnLqHm8yubUj84UA8Zu3SceMHEtHoewhUCplQtQWqb4SIZVNXuz8YKMCVRCIZG/F1fu7/zVdhaF0DewMBT7OVu5t6xvndyMUwY9HVsJXJ2t1xERyjaRcNiapQ8/MVRbdan4Io00qBWvw4fwuhYVIwVGO8jnW/XQXpWPSktcCof81q04pmbrJhZKxq274rDto72UT181BAgb5fH/1+N6NOwGMw+GuSqhlU4PM6fK6FkeofWLZBNj52cdG8UtmEjaKyz5y9DftNkobiilPLIXCpcB2aURhUxHV1ohv/6c111W6tfXzZN7Ad/uR3wK8R/MUX7CW46m+998VqFeDW9wgoVub7BGhkg2KXFqR6kPq5GrqOCDaYYX1aOf0MrF4Ch306VvPNdbsTcmflWdQvUOXCqBCDwMxTC/FaL/4WbAGhdjB2+/HyJ6OLfhaIKAKwjUq+7pNRhHAZmvuc2yI1ZGN8Pa21/XmbunigO2J4zmS29wrKNHJL7nK3HQdLYqIEVwfoTkPvePaysuMne0rYihOFFHPgUyezTQVNs4tNqDJNJzf529CTvE4QBWWE+iHIX6aiEuvMO5o16UhnKCu54vGkvAcBcug1yFBkDpculO6tFD2kOxwP8Yy8yPcXubIEVSNd/EOnnaCMF5RB9FNcz4lQuBGbLY3Y5W4LSLpqHRqoWH6hkzVcxALHYUwfPuV7teorzZmAxSSEqESjPURUMydsO6qmLN2Yes8VtT/VzdyMQfVoqgnp9YN6zZpeQZ8FH5RQ7496oHDuuaehXdMAOpytVoPFYqhn8DWbYRKtyMRJcPaqqyi9OecXC2JW8J6+5ibeBQuu9qK15XJMXoYASPbsPdnF1Ipf/TY3tmAwPNSfdk5i+RYQU0uT1am1Edqdxv/xCpjNv+Y6B+arN//8LDBOrbMYRsZ3mhNMAnbYrKVHaYfX6m9Of9kxqy7jwrZEvcwgeGWpS+f1Gboj8GhJST7m9LKjqvR9Qy6mr/n8++rNkLld3imhdsye3h3KpkJOP3K5VtTUJR8HZDPEM7hsoMxC/XZ4T3bimt2iz/sKhmfqyW8tzTd8NGzkYPlsOTDGLSsUBnLy9CBE2NX/yjr5UaAz7BfTdvHdJcrIX3nTmZFPQaCr7CRoBUmt0XUhybEs9fjilwV/snllDywKX8yZyAx6lcWK0J9VW3YNAwjA0laczR/W1pi+GLYdr/G3mod9nCNQBFaUdbz3BaPh9+m39P6EGmxdq5YZqmUKgWoMR5BW4TVlEIi0n9aL3ErvFhMBUUqqjuoW3FxYtOt2juhHLzCZw8XN1l4/gj/RWxdlDbqWsOBqHfFbNeXm/wcWqFutztlbcJNxBizfW2h1RucwN5wVbeYW3gKwpNeuVNyncb+HrjQjTINEPjocIqGJAEkwTXxSF7gra/kInJbcweQKq0/I8yIoaPSdKn1QYq83Lx1K0i5vpEig2jA87RwJcS8N2MlE+SwgvDIO5/450yXXn7CYIpLgZ4M9fAQZjf+xdB3LjhpR9Gu8J4clGUmInHfknDNfb1pvFq7yeFwCum8450b/QVTNFvYrGph3HJO6KrMPcy6jGFuy3kxnqZOSfOr8ECeQdk5F9qFyXk3jYP4Du7SjsMufu6bvGYIq99u4+NoQPKewmAje1sNklyIebCGUCTur9Aattm/fSCbprdq5mAxjo17PmPqK7AXuLsD6WZPFecHkiCqfPOMBP8s2kzcqhAehuGnVlVkswHGdqygGn3vBRjbh14Tfk5fNzrTCARFKh+rU71EetBG3tOoBR31THk0cKx8j2snJJDaL1fFM1igUNzRMu2+lwupOO0N0rckzZx7kBf26nTX11vXm1ydSPGp8Zd4CzZ942n+LLervL/KS7BycA6fovg546UrhwKQPKPhh7cH+xOGtj63CnRpXaljU6dnVC5c9PXB1pM4HsIpmNiSSMTT2dPsMwzKM0AqGay7a2GfI9wRbak9Eq1Haw8E+kXZSWFe/I6KHk/fiTceWk6Nj39jP+CYxrfaAgbk3vPwKOLy1oFtPpQAepdETBLrWh+nXrovWiMBswfd0t4Kz0ibAbbU86RE1iWAffiUmM5r4r9S4OjDPgZsjwXaOcCtSVYvfy6Zp7JtPzmUHe7DFw4UUM5pb1lnNPtOPizD43IFA2FAOpAnfaxnfOXEwGMOzQT7t7YpRnFDb/Uv742wuMou1ML7nto0hiDa28qqbybsA8WuOzigpHglMM25Kqqr+HrE8Ml1dN72YPKaXbPhuQXkS+RHIjqC1E1KgdM5G9O1j4g0bJCebtK8NOxlpcMONGAwuuDnFndriWiIefpupzODk+wl8HGxT6iwfpI0kRV+T/Beel+UTF9FWFmGsjgc+0Z9G0SfwIUVZQ2GEkzsMTc4lvMrtDsO5qXGc241hT/ho0ZhADQEGeM4tMHgdPy3z8XLhBMcPzM3ldo8/8x7iW6SGCISvKmHTvwb8TQ3kIaj8G7KYOztll4Ctiu/4y1zAEFfRi9Bht7QXEEjg1gkxrRVbn6y6gt0PLjo65Oi19VrdAGFODQQEZL+0PdT82vuk+IafU2xjP6aqc0LewwQZuOIvmiVv4I2clK9fRoRAvMKlyODX/QuaSZ8cFPEM7gRpSRLCXskEQGwqpa5eXHZKrRyxK4Ip6/0HRfiFbfjPp026EPqsyA6gOjIU4rkJW3EB6MzBg5PotVGxxC+hslFL7GCmElx6pKfbQw2ADG15XBQztN3A+u1vxY853zsnp508IZk6e3yYHYBPdq4xzG7EHVIPlLG5R64H3Svd1tzmhlJaP9jHvgtcY1Hz3PSR7mR4uyraB4+2p/QO9fM3u2mIYhxqTDg99W8vu22th3uV8V3iBE2HQrbhsaRKiSrjSHffgznBbNm9zWX/UQ5k7AQ+h21SRI23idkgjIh9wi05DkjWDoOef2Mi3zWYv8rKN8QND2ZX/O5sRhWP4i7Li6nms3DY0no7g4R1ipnX1c3AE+3+lUVhVlO6yuJpdQZ+qolGJw4PZ3HUyeRANOlehhjwie9by4mVN8lRyPTHXA6+DWxFd/0WgWZjgv4YzOa9Wv74GO+2bqtN4xM732UdJQdtIPbsraP3CCzhKfl9s5dzcM+9zYNTu7lYjUlyxZfdzJHfgH5geuJGQA/tNxJjOL9syaZOTp3pB9uVPbq9E/9mSjzFberD1KV9iiqV+Xz+Wg545/Wu189ojW+ESSUs7AuGXKRO099xYn4rkSGiR7bAk5jscUrYSbpdbpWjhpxWHbxCPoJCuITypCyo0eIjk3FK8qhfEI8yndgcc5EuzG8Aqq9p1IUoAIWpOgyCIFqFggDjN9XRFJ1JCEdaGsMdWe3F13dTyoTv4BWWSEIoHr5vKLdKcLuUXCjGvCWOLOKDatMFGzLwY0kIOHYuKjIr0A+yiQBKy03iNjhSinemr6g+AipnR0kw7RvPh2cwRp0dgIwArhOWj29kkCNe/J1XC6VXmRyQ9UV9hJQyMWutI6n9TjX00SF/QrFYpR/QbGgJ0F5AgTibjSDk12vD+m9PrUpY8fJ8g3VvYyAANoIHYH3h7gPy4Y/RLLZ8u2vo4cqAjbDK39CsFIZ58tveaVEKRBjmOM+dze7ejejQmt2Q9fbit0BjU+1br3PA+jNa0gHq0JLR164/954NNaty1osUZuyZC7B7Evl0RCsF6WMiytZDBxfezate8el8jiOtK0SPuyapXF3mjaKT0jwKQsxt7X/7SbpCsIRJVB527ue7YEn6FlL3l+r7ZPvqcfhBqbb7Pm8GbmEnKq5yC8EGqB2yx0x5GBZy4wTWUWSDfmonr8+A9iwoiyHS/r6pDWY+u5Y8PKJ81/cZFKaDQlcNWzJdpg0FEy+fPWGfLetfHePjVpbTZ37Lvvdin7dieGQcsrYlJuXDHl60FQKw+cuivRuF6BjfghkeWLagFVJtN4vSRpqvSdOQAFiNDic7ijwANIHq0F9Xq9UOdPsFS/WRVPFduxTAW8a9LDqoO1EF0/OxOo3e0tXaz2ujw23TY/Iced/6YuNjbtl7ji4tINv4fm7XucnVjicvbOeNsnitURrOKFzBa0TlZT6mlybSEmZex7zK+HALRparJcjTDVdOWfSsEHA/IQZNn2qX6dRq+IU6H7ft+kBi7iW/zVfNBfbhfAecG/kEVcSvXIYmQu0Fl9a+f41Q4bFQj0NsrVMgxGlucG4t8ObVO1LgR7wQnHJva/SraENOHl8fhQbGbb0PyFMh/eo8/7jwOc934F8VOy1uUJP8b1Q9re/0Ztw2o8hA88KZqtw01LAyilixUfNGheoR2CjhkVb/Fl70b2nJpGUWqrlcZhsqCzWTEtiFUjWmytyQJPBXAtlWA70WWcrR8/ykjTwiXBR2rgxfMJiHLDpjpxZyg2pxyKeR3EFVc4mYbF7ZbaDFtyBf2bwwKn2nCYz13NqVa2+L0VqQxONoigWYIHK+YWuz+Bez9at8Wy6EU47iL1oL9cNEdXgIpZ1I5V/xnWuyXZhFMLamBoEwk0VrXJ2qrk+dQJygEjPNkMROjGq98abgEtSIiom+2vXFElBcfG06Yislzq0oOEsp3a/47U0LwfL16ifFlxGyiBNOqSGWB3vqbBC9LPMM1V2PB1f4RcDeH1RPyqZoPcpV7TK9hivh2BcP3icYDhdZ5V+vxK9ju6MNJn2p43eBJ5SUT3TP4wx5pDgk3/Hjnk0U8n17Qb76kGQP6p/58fwNdnx9wnw65UBWsZcuiJYCp1RANsbItjCt4bgqnfoHC7Vabbg6f4OHO23HUVFHF+tNnB1KBg5PEEIZ5LRKO6NQg8khYsjAKdLT+jAQ0vtjEjh/oCsyDkdrAKSVkRLvvQXCzzj+EdyvqWea+vDws1OKCA0wPS3r2Kt9veqaTbexekGj2DcS2aPCWNkZWyeP7khJpwv5tojs/YDCDWDNBloz7syc+bGHXFD3SEPzm/yOL/qhSRrKHgXWLox5LFtokB3sm4aFVtFM9Sp8EzShNJQjXKcVXlZnnBHsSg9ChQqnxYez86PDK1cg0Re6zKxLZ0yKjJX9vDaCt9dn0rv8PWvsqAMHqGwUZcbJJqOX2cWzVH81fTctc4Hj6f2QnC60nTLQRopf3tA5hNkVAk0XHzJTTzerD6M2Hnk72MqsIsQiigmJk9861Xx4ZffdqGXqOXCa8owpRAyHnvyFZXOUB4Y8kJ+D9M1GASn3AZ+/mj30ulg96D2t/BjJ7LWLxZO0LLi6QyrxNSJi6Xeqw5QIKX11DWVq4X4D/J63IO6DlGwzNg0uPCq0RWQd8pwHyKsSbutxbdlTdioGKGdltQEimgDByBf129Dq8WmT6SaWgoyWQiMysEvUIS0CF+6SCwpTthoJ1R8qpfVF9WI+GfJaAZ6gzdvfSAoLKpWjsNmuZ/GKT7BvIowMEwy3XZ0/H7TakG/93AFeBkdF47gthzfR6vVnQs4HadMT09ve7jHJDmFam3+/Qsw1VsHJETe+0/fDLz7azicgeDgs38wzDkwQHG45Tc76ROy1DugSoN57+0Cr8XjzEGcR4OOFrvSMuM+KzNxTxCzpYyS0lezDUcLraqPwvjoomINj0dJPEYVikEDtlKH84PPlUtamAxahz9EE6ghoMxlH77OH6dez18BTA/TxDHWXABaKOghY3SyWF5iPAv7ZfTN9oCpiWVo8h5ns/BreEnDEc0jnn9ruOP7dwGIyqL/b+cSJxvS/+UooF0bjy587C4OqGE815VXFxG5lL58sRwruTuEg8J0PH2P71l10go5ZsyP9rV2ShHuvQd0qWQFueA8z4Kot4ZvtDSB8oAGTnZQd1B+xEOf9ntxk9Bvb2hAuhknGqNE123vUGBynL1HJU3g3uzYA89fEh+JY02xTXbuhY1qGd+TsVEZge/krq/u4cVmN7xkwl0W9ZLEjP0stmtak6XGcaYzsi+LthJNehO0p1R7z45WWO+I1MQiAjTx4XLU8yUXLjrF2vJfjvsEESkv0Q4RWpNAX/KU3NyW8LB0+cPVogZzusr9y5xfx0i2gg9cVxhgc12E/mL4wSuTL6aXeVtfZX0nCm0galyLlbJymE70sBORACwVSb1A06UWa5xRFzL/tHNogsFaYgG2BCjZDr+nXnr7EbIo/r0T6jWq/v3adsBKefqa4v61sZg6DW8MAfoTMBci7C4jYhYorYsscqBor2g3tJB2Z6GwJU4yPV7M3t3Rc7vMBgRBBJLMvXy1l6xzWW/be0J4/et89tMMf8PwdNkubNXXQMXvUSvPLmmqpkU9kgDRN+bpohJwHl5ntKoVmWN7Ll2nIdDny2PWnkjnsA61eElJBrFVTWJM92EcEkTa2UWBCwR+5unnde9Vyy1krubzRWCPx0r5ostAmG2TAKYzFUo12k49otetb/XZ9CtF2PrPELZXZLZO6R778+LbKofcjJmqTEabVIgEqwjEzT9pGA+b7sAJ5kQ4Z+7V6mBva/OUXgFnRyk7JZYOyoA2qf2P7qCM99yxuPj5B/stqJc5j1SFwMzD8h4K0B2jbqDjpdQfSFtHpKohLcrA1X7M+sYXPvym+gt8TOmG6FTDd6muiQpBpm9v6VCsbsZ1937Jd5BwoZQVTj2nsN3ZnZUlImRpwRGRajakoFTi0pWQ+1ZdDb3fD59V8A72PR5vzJR5JPLagVITJ2kTOzG5xD+YF89jqKqFr3PSluGxnow/2+k0tI9MirgQaFXaoq4QPI1uLnHmP2E4gXUXyAHaQ2qSmQXUVyUvQRXFm4wIJlN96ew86XfsGMzm2JtJbwqA/ACNOwLBfaNV/8A3McxEyT5vgPcup3r/Nu3CUjfTYaIoDk/tCyq72zEKlIwkCtPcYzlLD64/DAI9YLgIZdmlDZchbeQQulr2fZ2iel1PmQNAMdVv4rffUw+AmmfyVnWG/dBJwyUDDlJDab5beminY3iiJVWw/QmimUmsPyVBap1j5/c1MNvdzJ86kMEwh3kpcv6EazVvaXlMq2r73mbXg7Iuv7I9umb6qkayxbUR+B9m6sjg0EMzlvYsfPn+6fdPGcaUlw/BbXuYxMZUbXQCmsGKWNddlKWRVrYWgojF+YDxY/cUy2iLLOop8JZTFaC8PtQCCvZoEMw02eeO59gxH+KYi7Bvx6IBV0bLL6M15OUU8747LzrwRTMVrxC/unan8C/y+bu4S1/wirsq3lymHIF5bz/tpS9fIu+yA5FtJtGzObcLfae/CdXOdDplqajaDGyPh1PTmsvw7W3bvy/avHz1fPE3vMSxeeL9MbAr4K7sAVXHVJtfLrltU8Xw8ewWXZHy9wCVZ906yEbxJB0jEezIJKYYOHtSpSGoexVMV3vpx9srwEBSlSgdOYRMcD8+aiukLM5qH8k2rhsEq135wGr7Cz5GQvvdr+1mllddNsr94x+9tzF1DhF708hcxmSoc0PXOUqFPq18k0SA2lMKZiQDe8qBITsogWBrrZchRL5rXJGEPoPDNN2BCT5/rN026QNdEUf0NM39Rwvox9/234Wec+X+VxuxJn8We9DotmYmH5SAeN/9mrwyK4IwLOG34s9gPk/wzJ7/Vi3AXqwQuwWCLgLjAlCu1EwmzA01aEZ8lqP+tQb2+qEh6eEG9RsCW5xcXAje869dQp8o7Wh8Qgr6Mib/Tg9+cKFS9NH2AnmdGArmdiK1mmY0DA9WjEvlmuP3O3sbGRn4gNEC4kRuzncIBQYumzdVeVB2HYsFdm6e+aGvMdwcH4i2t/A7hXpXZwWIT7Nrgfd4jl2B2DScTTQ6uuVm1BzCa5geKjBvG4TdrQ5zA+Qp1eyCUtzuHOGXWiCjvKkWxoCnU6jdwNAm/8evO+tcvT7PqZ4Zx9Rh3aI3pXD1hxKrPpZcH+SkIWyVNB4i4b7dw4X1NbiLawPabnN/Fmo+ajvp8nbTjwVCGoY4M8tCGzG+ONfEYeGN6EAC2wsbuiG9QMh5WF2oilQmHQhplQHI2Z+zmvxjroAIQwAO0E/pC7bHi/gakJsiQJfZYyrPG2Bt4aiTS9uPO3oHQTec/nhBkpBKhG23wL8QHvR37iwNRiAEKB5YD/Vy2nfIUxbeVdQvhdfGr/0IK2BQ7GGgFuBYKNVoqM9idKsv9M2y9rpUailMWx1VZvKJfJLTcuH+cTHeaHuFsbRbVvid0zRtLGlxKmIPWIu3DvumJDCr7VjutdtHU2auZjBQlVdfJ/KAmFs1jo8tX7Hf35/TC+q1e3Ls1G01Mv7yVlH2pGSz8+vJy+86xh8x0a/3bOAJxDnPu/YvSqxYi3G7L2Ve6wQ3TSAGd6ekQ2+SwCUDz2ZDpise8oT9DIaD51ZMqS8GbhM3rjWIPGhkMSdNLSHVTkO3nH/uI+aJh4nf5o7Zgmhi7Eu6aIRyvppspxmRs/abddu+5E8pDX2pDvFohjjT0Sr0hspNts0X6fPFeXL9ghiUd1+dU0G+jtPRW9XUOEKqQO/tmFheIfMpGZs23AzxKBChFQC+/qXU9agOD4mck+OP5eASc1x6HY4zCabIopv72NLvNupLPY3c0iD4CKYMCj1/nM3tWM9cikta3GYVIaOLTCO8iBbnKXcx+MLEt12RAqpnCC/R+PAX6jWSlDm2BJL+h2X0n/bNffdKjQpzZKN9b7aX1192itQN22LIedGvl5lOVJhsauZfhQdYC/CnefkzuhM4PN3X+WJxEb2hwuktdGIqIWS/MHh2Cj3XYxc9Njp1RPuDTEUmWyECxWCXB3CdqnAmh4WOyrtzpQ/hsqhEn7ziHPvH2m9GJ9hYuoluPQuWLzQ1S3u/ffoCf91lgkg/bDnhogJweyfhNfO1iffqFzPMxPbeLeeA/7rec+4AaTYa/R34oJvWSvzpCIpTYgo0PpIoxNcVgW80Y+cQ6mOvsuUlbsP5N3l/h6yN6W1rEPlyaBHR33cI+lmA5OMFYA/Q8DtBPKv4tmk/k6XGpdK8zr9keGj4rt2GpdEeLNTasudFFc80fHqeCrg2St/bAHx+dsvIZVtECdMcbDt+n+NG8wVfdB/uHCjHmalRKlF67ifTi/V4dKwsBqPcZ8SZEdb5hJ9Jg65og1G3s9ZFRcC904rSFN5TXD8X7jQ6RtGbRvy2DksunZ2x9wjhLelVwI+Zu7iuHZFaP53K68NgD0agDEl6stccLZQqlbtoyCNJ+2gBIoZ4pU1U6nbbCXXNambB0We7RNauHy3aBeINtFF614mcFYmRe9PcxF35/x2ISDH/pR/rBjCg+xRMDV2tz5hRVeNgPmVpiVSkwP3YIrA5VDExmsQWYfozaN5Pd8Ut7WlVJedmMpnvZJpJLo5Z4kYjEb9z2VnFRiGQoQwJUo3FREVskJAf6eZBcDvwffnxG9SRapFMJyfsUeIvI99UI/ZyLHl1wDry3wUIeNBlHHUYFmIT6JWDlphLQBtfeyHubMzjJ7zEpVBHuKHJM2G2a6SqZx6/VWp7uYxDQYXn/DmoW96JZ5Kpur9eGdgLaWXaZBEKSzojQWfhEBxRG8QNOVWFr3U6b7bfVv+giOcKeVqu8HEz4AQL3O1cw8QXblou3VzS0FnNczuOiGqOD1YjGpFRA3IjHaV0xgc1pVOsdtQbOmn0GheyyzMBx25BCZXF4GVumfX9t9ba/XvT2c4InC/f3Kxdychp6K2I8VwVHNo1dStxbeDR/wxwflrME1rpj7JiOy7RA/QDz6Rp625uLV/gzdDxaMh0b3vciNsivEKdNWIXueJYEQRnQn2g2PZSyBpWJFRxmk6ic3ho1PRv8SYtCxzs2ATQ0AW0ddLEvtJ2Nf9NXATv4Wl6f06MKL2R7zT4jEDNUTGqk6R+GR15W5lF3uve+7H5jVEkQ9/Poz9zq8kcSy3KRPuDjKeWEoUITdfCrZMH7D/AAsIEgjU5Wc2L/hf/ZmClwYl0OX4Z8GE1rzH+T1W8rzQxOmj5+WstqKSWOv2Vc4D8iBhyznEN/hjkAUkyEQlaWqGM1efTFx0/1sdu9d6D8a1UYry8OA2MW9BonZuOGt/9aXlxlF3nPsEfzm1rGIWYxTbf4rYvES4nR/thv2i1qf51VyQ6l2GaltkGlL4KJWfwRoKFyB0GM7VGsv9OUzbwOBgCxKFyGLbKlYzG0+pyKSJ5XF1k5W0w92vLvitHp+rLR99V+6CptF0STekFIaGTMk3GcYLbJRk4XR/qlUSpOeyapKrJqMWmMcbNvvBShxshAfaUPF7jSFlfJ5ozT80p/s9gsFPFdFAqiSZeU+aY4bVES89VT6MBLEv/bBnzTeBY+xz6le1KxlOxhJ4+EMdBEMV9qklz5iGMvfTABSSM0X8WAr9c1fQq/K6P7c8xPn6+jYJh4pnmG4CcNLAyhr+D/63bARGInXipCIZTiE+C72UWX2Sc4dEvRTXVsWi7pvsQXWaurUzGTZQmWaGSRKt9x0mkCcDiBsxnNcCJjooOl0iyZfdZJJuYJDG7aF+3XKTtHCb/BLdgYwQqcDr8hA8bW6204+F7Z5nw9YPuLAsuZaTE7h6n8Ing8nJIXqmR2okaOeWuTkaEKWn/tzT2rSrV263jFC1kAed3haopDMAlkt0qWGAMEojoVQ+/r322iUeJQxlJ5L0wZD0TJjmKtoVJGW7DnEWxCk6x129iN2hICQ4xcFjKkJsGqY/EtIBURnBOPj2yKt6ZYpstAdX2IQGfyTh/ogGRtuFUm673x2j7oiE/x2aZCiaLkKK4Ay9Zo49vYC3VcKR9138j2j4fpCuLnJBCKBVfR84+rp8u9rcokxWql8G93xbphfxRINv0T9pPXb1rSC0hogsb4naWvoO53KgTDjEU8uZWPdaGIWfa7Rr8NyVljMxws4WKTB5Mwp4Hs1HtfqfA3SDxbCwADcTnuNUByFRQ37HPXlaY+1SxA7UEdqbkJdUJIIaqPG+3k07lj/N82VxCWgJaA39+cmr79AzHqRzOnHqIyPbT6BaoQkifiO8p3dsBLE4QTUGPMsky/v+qFUZsOyhAwOD38pvSpnpYIe3r1H6xzR6pbY+VRoYwydUx8a22nr72vW2f3Ux+tgHd2GbBMBrSAddH4AaHudgUDmvNfCES5/w36JWbymgBWY09uBKaq1Um6HI7DsObqICAaOons/UaZmTwkZkJqor6oT+s+SDsT+Y3dhzb9TcM3Rl/s9RPllgBlDncdd4NiSzUz4lWyIhTYaSPpDwhgB6RW0ORFFKFfK9VcMv6nZXDR+NGxY+P5L/oauavGUTdQzQ6kAR0Uei6j0SX5QAlpCpfpdJkqc+6SloHCb9obVRsB460HPGF+g1maNtdj9kjFSNSTfeS+OIEmLDEL+CFleNgxXiGE89JVwbuUuHJB0jVy7gfVQRjCY+OjmfW2RPmFUysINFfEwrgVQ1jFA2zGyi0bomnKawUlDI/nMXqPoaThWMLHLHFv10PJZgr2Ij5WpLhFreVju6BI7R2Y2yX0EsBoHaPvleKym6j8W6WhjLlK1QWdopk8q4dTWNpV1pL0PgPMTWvi7oAcgtgX78jpt+jYnNIuzzi063taOR/hdaAAzoAf6fhS5DmrocNBtCyGg5xDPg89X41wigXeB3rr5Jv0Ab94fC1JsdJw2kcPIaPq9t/ig8Xaw/mwfmEUF6TvsgLZD304G0gwM6cVrR5GKqhof2QfQe73z0WmUyrnP8irB3d0ov2m9pZu7eephmHZHB15NsM+ALJ6tFifcMCSuxqok44H8oWROUNSPuhrvj+zJhf9ebV+GY1iI97orx9UnOHJJ+ExEkY9/4WhQSDxXN266K/cpg1gQ0/zE/qr7gufHPSd5PtL3WmHadaJqax0/kaWKWfQCvCucrr8ulCxTx0lw3WmEE8nnQgLXwALJcZxrpIaEX6qRnmMVKLUl6B+OvfYZmBNNahWQvMtJlLnDkhj2758UNLWbzYHTvjW0ZEj4Zev1tEHdXC9QS9Ms/YpBtFwPtsNFjmUXK9kHEkoDHzfLVjlgr9tf7yalSUUiv5sGn/1634+6HjXYkTDkygmH3/Wnk0d3+OMdaaoI4+s9rpMRYlWAIGto1eF9cVvq1BM0NW980EO/sJBvOryN6v90sKWDtdMkuObIGlLItWcGvR/uylvOmfvdF3PKGLJoCMX7JiDPtE1TzHQC68K3P3Y0XAbw2PFHNmsV2zvMJm7sVBBslzP+1vTc19XCeiTL7BEJfnn/lviLH5yaDug0PvexDLVg62OwN7eupacxWjtmcC5l8uAtNL1vrLef7dwYWfD0EPiTHl5U0mFj47D9FuMaVxk8W/1e0S6B14ZS38cJ6x+A1mhUvWU1zOtHwwMUCJyJFQZiwRxmbmDu0zH5/ELiOu3Iw9jrql+ijjJ1SbRAHE4Y1yzDZjU9ItNL0p1dYlB0ZtmaUIEceim7TSsvcoNYguWBlqcl+je1ugJuhnZg7xnXSk9o5mYhXDLVrGtVv+EZKIapW1V31ZZnJumqWwxlO5syFXF8Wy3PAHpU/Q30KGDCzz/FxbNAbkqQipTf8mugTRLwkdUWWA1nIlQvc+NN4SgRthCPnh8/H4gll/DeeihVxptcXM1/BlGOVX8+sjg5CBVOVtqAyCk+2zfY//tNbEbeNV4fBw8rheBGJAVto2x0WmapxyLS2XN16tcfaERQBgZqe0HF1TVSeY0w3TA/Nh233SyfDGRI39TWY+6Y/kNj2cegGm8QfJxAE2dohpZhyfPVPXrwSgKN5K1vLCyr3JDeMYK/UU2JE44GjrbyT6CxB8EZ/ONKBSOlzVja3j+9n3t2rwN30GYqGqSeZB+hdgLj1nMFsqmWeZ3j+YTjgQ4iFrHpx6FHIsmlJl1SzRGzJqLWJ9pOQgvq9+WjZcmwcAbx0hQ48mwiGz2a59oNIcaspbqmb2B042aktzUEMkIy5XvhjbP3rCqRwNw5ju3mOutlf6KW91vuczwm4UqgbUxYtOVO/ly96JnUfkEYz/ZLvuOYaBoWEOgBPzT4a9KKKwFHxaIMv8HAuJTrnJJXWpw3MOtiJMEALyiwhFe1QqJdBExvjS4sdfnx64IF6SdnJKKJf78tXtaY6GwG3P26nmLuU5PnR6ghbjugntzPYLLJFTj6aZbXgrVhahiNhGnDuQrtwLFMPG1VhLeSOP1TvfXgNv3BgEhdV7HposS5ex9YsEJ+x2/7O3eqW9dn2Rr9JAky4C/+JyUbIepgcvwVl87awAL3Li8wzuDt+B22PV7BT3jo6x50tdAa+cbBLSCMpF6ujT5RzIejaCun1GvqPOrQK7/QzwjJFF8WH7QHgil1GVGn7e6JNTMm9he8i6/Px/bb39bersVQj7rachkazfXK43xB/Q6fmER9wjezc9nuqwugUuZNOIiWyiQ9KGOIteeJRciNTD8zXd5B0xxpGTRst5CHtC+TS+MT/oxVKtlsi2LgTXmhqWFa2b0yvlETbPjDSgASE99seB9uP5m08Cni6vJeCFLX5b9gvOLh1TEpPLvN1Pqz9tTYVfP3i9+f80XCN/UD2eEjc9IHjT+t9a1TV20UNUQFb2Pc8PTdXeBkyV5DdII2R583tAOKuu3nUqABjyAwfgspfC+vQmXcBqgMq57d4vMOzuYls6G0pKtUvZGphXN0A+Kt+Qnfa7WJ942GxBvmouACUl1/NI5gRAZBXulchBnwREtq1F0K2Huab//qJfWVzPLFtcMiQqLdwia/7VBTa6vNKGU7wDjpVsPS++cpFQClxuY8sELC9+sgZVmxoH1rtNTQJWPDFQD/xZ60ibddr6y3RXD+6PBNCQUgJ2zFXSD3T+sdUEsqlSQKqwPOHt/M/Qx4PzJvYrB9b4esPDKT3r4X7vXSfEElHE8bYX65tpRq34IpKDz1VSNyPF2W4zi4U0VpJ2UcX956TxfLa2/VR7tFjp70xHL5M1OsP7E65Yh+egsvWsSfMcJumpEWyVIaFFrCxMaEnMS3U9i6C0T8TpsvnBtULQFbxP5ZgDiVcd0oiRJtHL+2VZD3h9b7aOJKYVci8nG4or4fXW3ZnvnQz0z/2s7IXwQoZ1+KGDP/djVsJPoC41foD7CEW+fcuLyVfThyr9x9WyWf5hOHmbvdtVP8EJ0TpXVLB3kmMTfm2Fbvt27GKg9cja4guF8vSITekNg7SfLX6sRQSpfqu7jx8EFYt/Q6IOc9443sHdbF+sdso4wm+FvLNmzAmRfPq4FfaD1Kw+JnFAPx0ziV+x+YDJBEiew5DecJzYRffR3aFQQBsJLmcNCdBcgOuUolUDKrHFatJNsfNwWbABC5UAUWXiLIEe+hBMpkV3xP/HHAVJjC79JsEj3W6BrfZIKSywpfhBf/ZA1ugFZdjAqQOyAPKg2801IvQKKM+qW1u5B4mEaIqQLiv8Cuj1QYQ+MwhAhv9Ymb2ub32QVweXzb+Rq+113t5WcZkVONyoL8YCaKk3gR6gQ2G+PRc7v9nBdsatkjiACr2DSpr639kq0Ok6dcsIS3iI6Bom+G60QOK9cdl9F+A8Cw5+izvLXAycxAaS53N9aGwIN4uUQIvBuNc2VOkScFdmLRTV/X+7mbARnmoViae57c9l8aX1qEKMwpNg5OHCtUZbTCVQ5Co8llj9wCO8JiFFRXczHxSpukOYBAR5QLf5uiCuZcQrLyvTqVF5cgLR1BfPbJui3lSRjXMWKydKggs/VtHqNu/3SNC3rDnBRCOSXFPRRao4nByRhClTgvrqrbba5tYyxlme8gTMZjJet25/Dyms7wHTEo8Q61KlvBUP+h5VR2JoAjQpRjze8CDFJ5vbAWh62HYBJfd4MK760WpvKd+l6D+ZHyA8HAKRkDnttvgINjGDou86+41pk3tGX7/FfCHw2mL6J3WuwPKiBt9j5ehQS6vN+QGDtB9tTMhnwgNSKt2YhBRWVoj3KyU0G72af571mMP0h5t1CIeyHRHz2akjoGpPNXZRNzuOcZyUGfngARZxyQc1xyX3kzYLPNgKfpGI4Vekf1LBgaFEMEgDUVHjMGgZ1Smht0kteYfgyhlS6p2L0tw5ukBTZXh7+YqQGIFPMUXxwISl+VDNAF+/qpOtV/4if/rpQJqtPhFxdf77SWhgMjqlmBrIl8uVnJHs7p8tWDF5QjxeVX2hIqQu3kMAV2wXS7mBDGZT3RW2I+BvtbHS61Lj3hRwb7xgb3wIqI9Ej9+vRRsglzvNNx9i9H50Un/MAGMkn3XFjib/7toKxUmJHrTClvbvkDhNSEfkvJfsHipHSK27tkwT1J9wNdJLvx8fCjI+ml4rCU7QbThJQA2ETvty3HJWPAYWo8tuP4HqRL936AL3AH4/7q+FuvssB0XFEVn/3c02EjKQONna/+dtW7ZFKIM35/mYpfudJg4YxzHt2WhxJiyV3V94Rekyh3Sic8KJ54q4qi8q7bNribQa+Vqz61iLdKLbZixAYpkIxfNSw/kVe/CQ8aPIBp2VI2z8TsisIFIdE7z+AEly6cOv4b3TYyxj0bIpOzUbur612+SywzQXyWPE3yp6X3EL4PXU46nx2x1a5O4rpBe7O4Nx3DLaaaJXBIkmaLo7IM5S4Im6oS1Cx9Oi1WVxdqR3N2ZMmuNPDKYjs6/OUF7xDWYCdGYJ4IcZSMAenwXxMGmmJvSCpP7K1CL/ZHikPFI85PqWrzPzWAWcTKQgytQAfqx/yAMwOnzAnp4AoA8Pa/pbd6TgZvUQZ6+/HI4gZ5BiZNTalyqEebuuOkewySaPjUsA782K2pcHehqN5Gg3rL9WtfpOE2JaSYCscdl1ly6U9OyxQ+Hoo75fE7S8xNouFo3NCqaT7Y7+PPmageHsNUaeuTMKT8vHZ+GHRpBfie23/nZBO8nN3RVoo2BGX3nlQd/EA9dPv7hxbzz6OM36SlXoSebSllfO9+H1E1NlVJmpzXHvybvBW8/vmF35newTyUmlvI9JuDA5hJlCoPkl2eISvK7LpSD1BG1GOaU7kTDXYAyv2H7PhEnkKSk0Ve3TUvu/oVHdqpdzlqzKy2lbGOy8HH6ZG7NsgV4zk65gdKZI7MRHTWpJI9GszX8p39qxLQi2utydCN91HnXD8S+WyhEMdoMjcRltFDOVf7VKuFnK63D4D/xY7TwgInmklXCOFZYti2SHHaR6VBxZriR/11uecVCU44Yz4pG990W/nxbMjAQ+zGTCO9JA1CakNN/QR8x0LsX4CBENaxMt4nwsIJ2zxoEH6mVbqayosUPW1uXgywgAH8h0TzxFnwlEKNX1KpcttMxj22LL8xTKilleVh/7GKkBLcQXsohS5I0vK1WSljkUKiJ2K3qEazGAmgrfnq4B2RJ9eMmmNM0JZ6BVrjYNVuOhuXSX2df86nV22fo7cBwDgjRDsS/uwprbAcorCECcYgQzYky0hdG1qs6RAnKIGiKmSRkSd8QzLW9+zJN5wazT2FlWM4DYXxKdFYKgKkVdyp0AOs6pH9LOMZ7XvfprN0aGjn+8lDZFwVvnJLTI+Ochlpu0ROUvvhmRuljAIhoo20ZvQQkFxhneiqzNoUTzvXVIjk8qY11FOjGSfKy2vWC+hhHwMzKtyoZlS49EfyvGDk7cFslheSdcSMsFX69a9MK4sss5BFPvlx6ACdZHf9xilFHarhHZUwQMBmWDsM9qB1Vxmf6t/7RwxQRGSk2SXBRTRLGhzZ8/oFJZ9VdWNzxdczkfi3TDTazAH6JLge+zDScu2nAeRO1Z3cJNi7XOhk+eu1wfbynfi7WD+5QfjTm9tDaWw6sr9i99/It2yH8OebJCJvKB6qQ4ppHZ8bV7yObhJVq0ouLqCs7C/8E8rAxj6MnYbLZTf4Onk7y84KP4OUygz7Zmytcyq4cD1HtE38trpSayh7SarN0U7xqvY05cAj+7EcmNhO7EjcJJeY3hEMTl408CcBspHOw6jyhBUIrNhduwuqZ+42kyqwFvUNWnhb+HUu3NJfPZzmrR1v+H90g6d5v1+ScBUJmVD8IhZIwRQBoj72vgenQNpM4EX92q2l+n665hRH3zBMTRgfqwEY0RtArGcJSp2NCSo2c/nx9f0Ful0mFRXa6VkYmkuFHvcRSNA/PYS8rrCiGFEWr1hfXwD3KggjUSVJ9HZGL1UM5GK93TaAxC1hPdlW1N4/2T9Sre/XfFU1OtYDvLj7Ljv9V283HK53Yo8xhaOazCBvcuLDsTxxF9By1pJJlG6txfA8WJ84zrnsAVed76h4ocowOy2UKCxTtQ/bl+YHuRNA3j2OeGpN2t7cB7eyT288/jUeoHpg0PQYhUVS/XKfWj+cJmuPhgNwF07u+BeIN5+PV7SfBPZq7BpqLrc4qhVUfUmcHJ6iU2O/0aYWYZvCsSedvFOhPyGBv+hfRYXHb8ZiRo9wRCJke962G2qir4b85KHART/sUpWLyCZyCqvE7vvFU4LRwd5Hkqs1Frqr7CTd6U+gswLF6nHrSp1Tec3NlN+/4p2ZHOzLvw7tp9f0s3p6FfXzleMrdtDqiiMb1i6US9Y90wMI8VrpCaIdqGc4nvQsi8GtD5mvUTL5qu5rRGiSeg3upy9MvQs2N+kugM6+eJju4TB37gYcvcil929orNfPrR8RiEeaE3Co3AVK/int9MTtRfjUQharhb/14ac2gHzFfr++SFoC7B6T1wIa1+/Efek4TqSne2d851hJKcGj0pqMNUByMkU2+T39n4FaC/K4t6+fMNfXtBWEyRV38y6/XIY5ajlrRDBp3SWltclhg+McQIhW3xYzZeePR+uKxWgkOmeMQT8rdtRC0037bwcEE98MdR1CAn+QqAGjh3euOZRaZCo3ad4d/RW/rKdFGfoM8vqPbwpqHiPThYdX1vevUWSqyCMAITvm5LK6a0CiQNf2Te9wcXPz6ETaHaZ6t1EefWuFfEXW5jGz6qU9Ns5qWzcfPJGu49de/gCTTKKbFXK371BFFCz6UiCCsm9s1Gz+3zEbtD5ofK8f4h82Z3PRYODaNJoh9bIYFv7gDT/pRSsWbeTOdkcgE5A7NjHUBY3JdADHDucgOFH7Rj9zubtWHBuNB2Mj3tE6QRMSNSa7nQk47/UakqiLX48zS++xBOZ/QJmksUVl1W4NxLFSo7eaOu2qGlWfjlKTGM2lOr0BmQANX6tSLpYRPIuHUV7nyjDDZSsm4Q7YClIWlsCPFes9aLfccU9dDXy8992tjgpPP/lKKR2sHQlSBv5QLHfqnkkr7SaryvWpeEaor96HQjIFs1NsRieHiN2O+/oV1adzGwSzlBqHBjK7Lha+CA0XuM/ANa6JCtfF5Y3NLzXRoWDqhXRQAdOMcUckiEkPiCs+sZS4jMrbM/gq1MIQITfvu0wXouSpX97yqA0ANiEg6xgFBEK5/P22L8aZbtzyPN1m3RNTcPLtYzkwpnFBm02lr2fs7hgKxlI+ST0sH+rsu71aqy8RfYwtSvBZf2yVCqOdf3FJmvGJekinq/0BjJFMSCOu9Wa30q9aABUZqgRn633pknR+ZBOVsNzga5HgRseP/3YIqSCDjfh1MrWK8JDpiDqqE4rANrww3BXX6PZuL0DDwqEow+5cPo7Vhmv8bxPHL1/2bGCpPdWI3u1CxSIjRPvrshXUC91qkEQBaf0AwbtIFMU+k3hwDtcXVzEuV/7IWGZzb7GcD+fiOUeGUrLF8JGCTOnTvjgipRCSpgrGzrfuE++YwOmsiSXkIO/TxHHmg14z3OV0lzqH9aKRopD5iGwJMPj/yhoSklKI83Jf+Ua5mukVLChuo5BRtir+miQ3OOU/dobCcSccv3X7869m+mL67DxbafyTijVPvc2xNL3rYmsEfuo/r3SFz72nzd/v/wFCT5bNmnkDHhX0OFOuyrpyvvS42nE/Y6W+SWQnfGYfbnEkQdXYElFfcFWmsdwX/GnOCpavbtZ/rRWtjRM3X3vK6gpfNm73tZeeOGwnHsWDtoDI+j34NB/idEP8LXiiAfBdyBQaCwxejrqA+5/jdlevHOqu88CPHjp7I8+qbZflLACO/XN2bpcI2sbDtQQ3wDJTCuRQw8UfhMgiLFpiE1fUOqRwSkRGOhAyMJ3ZwPoYQxbsiRipcuPvqQNQSPGy9+9fr00frhR8bH1U/Otsflqilec41O36eV+07/V0iRv9swx5oaocdjq0765UsLbO+z+C+Jc0Wui7BIav3iEaNVy7EQrYzy5Wf1+Mm3ilz7j02b5Tl6w3r1Nr1XMYTSRlW9iiedM8Hq233ZQregQQZ7+YVMCJPR1uhsWAUPs6OIY3Y0gzqIROd+QbJKzLwe4godXX34f3L42Sed3QUvVIvCLxupS9GIGnZQR/yKy8bNOaT7+9D0RO6Dj06YEWltgKtWQYqTzC8zxTqOk20NC3CwSg/4wvG6EshxWOrJR/QWAE6bgSNINC9ORV7SFjpeavyO0ledAlENKnhTtQ4T86iaP69Y+ykdpgQnUK3NgZKWPfJCwqpHxwjROq8GXVyE3qYHWJWA8M1AMBpg4YkGXBCPwLzs78yFCr/j9OaQY7KVkuwS0/IkE3BQIf2CxGao35pwkeWarMPGXM/R7rjillq4ppJZ6lYwqd3VtvQPcYZmPBcB4mogA5Ek/la83GZb56VEbBAWhhIFgKn9XmsDoZ12CSHgwHTGmcwtPNSEyquvaRHxJWvfXR8UPvf9mNBNeDZP0de9vNwuQHEe4ha4b46LF8g6kT8wIGezu/eujn2rjd3xwffHhvf7D6VDCoLdXBASOMgW5nvEoO+JYMrTwulT9pSQBAj9P9TAYyt5iOHiPqcXqS204C/86V0ZPGTuAor/L+nDFDaLKFgOLk/8rRYHVIhjh3zpGHKZP/mo+OKWCIqbMWWVNGQLn041qGkNbrnXAP5Fh92s5gPrJhh8BNkxqlXUDFmuFfdTrV55U19MSN9jeVkMJ8oxfWwmrI+mes45kQ3epZNHa4FuEwpBRrznRUKXVg1Yb09cvS2rs0TZf0MCrLjFg5q/RtDrSi+Ng9Pv1uWBYeofh8MQRI7BOSHzJYDY9Oyi0TlGH/03kW6BJXkkMAKTW8bNo3jslbeNl5LPhCOxvGo62dNeKEMDmEobo1bRB8cFmX55Gk4kcDIFRaTyDNGRWklzEgbt8leUFnrSNrTLo1B1Rbw/VoWLric2A1QwB29nmFWcWCtWT9ynSV2jT3YRSU0Piq/4JZagAweIsVE41qrNQre8O0jkWi5PNw0oKXyVbg6m1Ei031jt7RmN2kj985+P5A9fmC2RIQBHoPeijmc8xcg0A8Clts5XGG1/4+Sc47E5LFBqyXHHkkBEt68xyLM2/l6tkzP9puo5dx5Ud+DVvrxyWyrKsLFlpp5xz1tc/t89cYIA5mDF8pO4mWUWyWV+4/O5ltYUYBwuTHSAnkeZ6gs8q13NNmqKvqPDDkP+GUiIqnh55q4a9xQP2GUt9+LX+vhHyMtEGFINE1AxGXF1rKoYHT1iK+8qkVe7mF2VMrrdqzufr9JVwswcXlqPCAP6K04r3O8SUu7prL1mfmMv5Ce2V1h0O3Fz2ImBUGH+1mOuaXwzJxL6tMbK1YKUOaETt6GXE1LHjC+6TL94E27Xh26oyOFBnx1X1dsDGVh7RY8gQ8wsmFLPENKDa7acIikdsvW7QZO0VKHmMh/Zy3xFfNEf5jBili/f+1qMDzz5Yw2WQLRpkhCgjmELEIr9W8Pnaf/dmPZTEFXQt77Iwy+Wnz1yZLf2QXrDrpQWuVSzpJboQIfdVWtDCZd/Qr0WWVWM3VxumRIfE82dyWCYEfd3CXfi1u60PG4rDDNvQSDcR9MmR9NfCq7PEVgqkj/3EcDnjgV84q2b3hAsbHXl95Y002V9kfy78BcYBUFOaN4xA7APtNQ19Qg1DUutyq5Ez+rvrdrkshfvqU9rsZLwWdr5DHz6KGgB4gNh723TyCqtAhAko71yrY1ITD6q092ITfmvKVjW6XszWEaQOxfTZNQbcy9YGPBs3Oxn15BjD12XzOWqONQ4hK1wQ16+PazXXJS9i0nifG/RJ04o6km2px8cDyO/iA2vSRiFKq6Vg5p+kRwFJyZYHcKlNNXTNF0Rj3k6gJJ9U6Kx5+uZlyyjVvx6Qe5XowpxLA+2dzaO/kGw3B4O3l4FhglUSQcHV6w/fdYFFwG/YRrN6cO3LY39JSooKwF+MX12iOnAk2Pvjy4PlBPwklk3ZDJ3znmlqa1X5d6u7RbcLamWluZKajV3T38k2o2D6cxEU/z1f8n0G8rXGegOrdlc194vYc1lHqy66N/xpLjZBXbqupbzuUDSOCsRgF3dZL3N9FGfnvW8gKNWazAt2wQl4w0kd1KiRN1/A+yNqEnbI07sI0nWxTQF+NmwbskY+ofZuvNiSEeocMU3i41HGPGMv+AHk35BjXYFLZAcuDdFG0hN/NPM9aZazc9p9seHOThSUD7+bILOHjRIZfdwSy/BmsnWsQB/HK1bt5aXiflTlHn0Nw6rDkoavk3ny0jwqZSwotkAPW166zBtkD8p+OKP2XqIW142dymsS60Ph6ldmdzfH0F2GRxMwM1KEHC8zRwdUZkBJ380Ij70/9p4hz4UEsb+FlXZz7+Mzi7GBr23DzE7id53KfgomzPmoEdeinuovlhY4GQqjcTzCPWALkp9OXTwW3z2srIJfB4S+Z9bDv1ykbFrJKjf6nj4NOIxsHhQsNTLsrwvYFNeWr974XDBG+QH3RFj3f/8NtrQ2EYLshB7FYv76Pydt6KV5MC1eftMUWONostWfqFQTcpD+efZNn/DsJl5QeOXIHcH6GxRxDM9nCObj5MEWGDRw66DlZoI+L3QdELdWAcKkxudi9yNQpxGMABaV6CewZ7Bmu8Mg2utDVu3aA2UyvYXaJhScQUMCGiVx7H+SK2GkUJZJEr/j47LrKO5PZjxjZHj4V8lLUZvy5hk0oI0A/bXpvsyAMNFHuwYYcskE97XUcSQx0TVEuzc0LYkUfu0+bniOTHIk8WWx14KriqG8tQrdQvJ4wUVf5uVnEMPNIE/Hp4r6oaHFfg0ueiCvR9lK7DLnpN5rP5stE/qyqDT1TJymZS1SD8Ab7BaloXQTDN499kWmasJYFpfADhOv9phG6fgaxOeyTgQsmiwchGthWSFCef3ILpE8NI4pEA2Hu3nmh9geta42BEsnSnl/CMQIuQUH+fb9t6uJ+rtkirl7GqD9JeFtBJCK0BBCYeLp1ZRWQUpUaY4dcd7T16k+sZo4xlFL521trlklEBEMNGI+T4yOth6bBwEIOkQ+VLaE7VTrq5vYacjyzgMxTZFar2V1UeoFkDBSA4hGwm+x/J7ZNUbeX/TT1L2+Kfrxy1SA3F4vE68XhLq53KziY356yN1JTqeKCq1Qeh6Kn9ZQpKiBwlXgOHEf/EmTqaefUg4EVA7wq5uaqoeU9mXqxfr0QIhYRCnH6AtoyF/4avWkoZeUD6WOv0/5kgPecL1prIrzAVl7uRSORRTQQ15d6xQ+M51Br+pygas1DkeGAFxwUeVjyoD3DdlFeCWrJJqiyO7APau7hOApPZyLU6woT9YbDEUH3OC5SPc5cYFTn1971/U3P1akVPqhzZRbEa2mUUxShEr5nXuo1hbiFjXAGjKFOdboMQCyp9Bfh8x2f92gwJM/6J9SUZdae4MJK9/Wdy9eiM4+7jUoysv6Htk3/bKXlcZY5t1HhY4g5mkwRV8do9GorRXKTjJbV8PCINYtw1kUhZXWpksPlU3ShH6G55Z/Qe2WuDOOPCtRfglBc8uOuI/jxucc/fVpagZ/Joc5DTAVQQRkuDvG5FxmAdvlz8m92hRl6u3XHSiZaqwc2RuH/Pc4TFBoQpM9sBv9JXsmlVgRUt8albVgaNaYe/pyvQoc+INtL50afwdWzXa5hHMWgusu9nmfQfl2w/x9vZs9471xVFwrHQ0HKHawbpoNP13Kgy0qtiv0Gm7z3X3gP6fEyt19bCjchhlN4WlUB0+5RL/64BPz37hMtfmLahLI+CJLFHSitlPBUQi8gPT8l5rrkDfU9+rqODXmMjP/rgDlum0I0GRqKwwUStUdZrghPucsi+eI1vMJNAWR+zS8rhRMQBClu2C/dtCmWKa80djCEdNFSCmQ75U2+ffLsMUH2tH91V0hK7d4zdsfb1AnB1Gnj1/DGuDN18quBeRKb88oTtKsK+Tr85/6N3ybb6/W6Kfc+BBfEA0K5Ufxiuz9ekA38m+sIKooCHR99BajbggsPpw7CdVY6VaRkeTPD1nMylOkW98frOFPfEztxu7LOHIK67TbhK7pUdJLue49Tq2C1jsb2WisFh83s7P7IOeyxq/88waEFxdlw8Raetjtyenl5qX6SOEuPllcQqxHWJFeIBqc0jkmVSgbmza77Mh3jcKWoXaQqffRF8I3R9hvXuhGcFex8qD+9GF2yNpvXoBgik2LQvBfzcR8wI4WJRKrMnIxEFvODN/PDXt3JrQsvsT0/ZjmgB96qXZ6S6Hutl9A4ZiQ8vQlf3QJK6GW8KGh4N72qK7+yMWu0Ll+JTHzMMrPyyX5oe+rrgRf7IMyl4VBLOKWOTUUfGoqDlb/xkjFBz0mCfNFdYJivgtD3vivd2xewNMcnPwbop/Pz8YjeM2a7jzM15PLYGKyGE7qdoV+ag4NUG78ZWjdevS3lFtQu5XJA+R0ifCkKNB/4cpdcKvfCJBgLUStN6/vwJDyPKnK5PgX+n+eKK02QwwUidB3D31e6qk6qA9X/HxZe0QtAuBZ4opMGQLOy34a2IENkaOu45lz4ZqmllnobDBQPTZ4TYz8ObjCRZO6+SItOiywn75Oq/ll+5tzlmP/8gRbM5WmWb1uS8MMeuYCjLUEnoHXswCFt2H5vL/kHdRVODt5XgrdWlnd8ywNB1L29TpxK0ZGUZLbK5amxG2RlsPD8nYiGYVfbSEFOC7A5nsI3vdPJaoV3uNPpfsQd8s2bJJHLeaiHDyM6g0RriZr3r7pMkiNT/34gunDdbLDYgrJMpHbR5xXgLyzQ7WwCqepRGTomtUJjry9N7y/4eT1LnVE+GRHx75RjHHBax6S8FaT2JAN7vOyvVGQ2UXmXcOEJ24GgD8kFBkhSgyKALnw2CYnG21peecjWFxSsE30QftX1C7B9rEEYnCT10/6sheS8b5rHY19x4k/jwJ9QE84gFdbYQTkWNW39U8WHn9t9m7NwoVsH7LKeg423IDK/QGLHYADv6vh7SFdk4R3LOzGE9TAX9TON2okMixN8V1+uJFmODXvuQTldq9+gmBu+JSuCVYW8/rp2KOjdcJrxuqJNo+ey7tKwxHYCAyxcahVTgh52XHjsYNPI46SgwtuC1IdQXJIpLZVWQjt50eIj0acQ7SowalZ0TaA1zKS97j/1dkipw5whdeFrZLIHbGZBTShMCTphj9LLLQQzOQW/wu07IstMBJAd6oS1D4h5xckPd/ghsDpjkK5Kr/AicXNJpfdPlUQk5J9My14brHeSdGCHbI66t1IcwBK9r+rL2/og5SzCefGMmGZdQ+zT1XTRX5JH5KJcY5ikeOuvjC+EHlglyD5rMqX7xvyeOglsUOES2cq/Cbc5iJSHVrUU3/7LtQ0xJ0OHZRQd/+jjaKq4yGNJQ2SrJXD/ruYKf7qHFakdhk4y2lxOOE7SBJUBc/2nC+n06ZElpp1/Sy/uiZeDxKUGKec0XvIVgKZMCSj3/0bzTBYBWW07syHBUYdcARdEf31EAPcTpaV/VPDpvRcpkOsSnA1+/KMItOf1CiTq6TUnh+TdMqfoyoVDIQ0AOkRcoajttpJobEjJ1EgDWoRvUfdJZMg99dn+UPnThPfc68CbXl2CzmTe0fY+sUAPJRdsiDLeoMeARhEnKd9/WjLioLoS34Xhh51AjjVSWQjJtt+UiIqBUhu81PxRGQRDNgQWq1dgBnQ7gbsY3/n/a4vQp8HOiT2wduEv99NTTqqRTFVMf1gFvBv2IbbcS/aAcW1+nYk5yCjJzr12jRfh4rYorkSfnps2gVFyT2xurxiPmWsriT/5FWmH2jbJoAYO3W/D8wx7MgX+5e2rs7HpC2GMafqyy9FUIYg2rYmEPp7pogKTiY5eoJyf2QzQBlKLhz43G/O0G1u/I1BVPadqxiKlbATTb1wiSZiCM53WizrHvLhFd5NMbBjemCaF9kgtrHulz0CU7ewAmTiWkQ5ec2S1RMjaDWR3SM+Y8otR0v+cntordJJjXvScsVbQVnO1lIP4fzX0As2JXRoOUjK08KYYyW5e7BDmn2MNo6D53ALm9tPVr0LbhFo9PNh+pSqLHPE2oWsvG4gb97skp9yNCAp4pGz+XEGo+QGj+IFO6tHbX9/obB1JE2xac8tuxqeefB7pIfEPrAdDmjyTWr75G1XWYNzqkosih72p2BnGNlxqyJpWAIpMq0ikdspnT3Jw1yOj3CiyDkRk8yBRgOv1wezQiP45AEBeoPNygIheCcPc/IMMkV7eTO6lNq+//CwsqJdAjQGr3xnEs3cb+qTeTN/zXF2D0FYty/PbofZSD6XQGhj8LPVADiQeLgVDhSD/cfM6Pek0i3oihFH67ptqcQo378DNPZ0BnnHfHbhFbPBDDWgP/GU/A2ZU/Ol4AFyi8kzJkW0Cn1E08Xa1Z8KgWL7dwndrGsse9g5McaHdVTb5gzXsfRj/UZy4sNDBN9tcnH+rq1voNL96iPSqTrNrqq1l4IqVuQ2nTDlxBcOTWQAIxAX1FX1uFVFkndB9DZeyW7zMgMQdOqrKInOZj0c6EwR4IW9RMp+HUk1qS86TFs9Lcxreo1rZn6ZKA0BzxI+tXWBTz1aMDwdpYddd2uzyKCMU5hjAmEczEGog8sE6yRXpajo/oyIGcOBiCYm/RrwRY/Vtn6AxwvYBXM//FIERg7H/Mt+wn66iSUDPdSwBzlN4cjb5ZZOt5U82siFgIfbvTvXrXRWE3boB0zLE6skDQEq2oDwlSh7a4jqH0WUxQX+yW8VkSfycIXe3WoYedQ898d5eVAKwctWfwkEn9LZa7KBt+Sm7fX7FlwqP/drlEy7/Shp5UWf3Oy8IlPbhohxKd853eU9HANB9AJp3sksZyOPa+qS0Rl1udPGI7BXYSVrsV+Qyk8l6cTJEvZzWvGQqOIcTO/5Vs/7pONtvQwlGzg+ckHQsUFpDYTFs4pUMBEwykz4JGFidWWUsI9Oi79AE9hiA+r3Gh3lyoff/dbRxeW1pfJS6+oVknX7ZHgto3tHzBpsrw2aQUjQKcejb42bOXzRlpSW2vPiPsQE5NdEPPkpv4BdfpPWOx8S7LzQ1G+Mu6JR4JJ084o6vYfWFvvFnQ+MwmeRc1IT4mG2Vv8ieCOo69oQSa3dx5wfYm9vvOxzBMP8FHxS7BV/Upcq55l6ipOhctvS3vrjM6XuhqyxZNnFn743yWmGKtYbf8rSe86P2pnrscM9p0U04588lj41E3pn/vrB174ijHWczE3eu6+X74GBkX1oWVs2ZPIA6Qj3aBzz5ORNcfNvp8HjGIjDc7HJLOWz89wE6PCNGUIVQNWx58G7u2mEJ+GGJaovVGVuqNuGmACr4Wzyd5XAD8IEk4Ir2+MJhfvmMuHKE3Z5uwdzg1BNHybqgzsRxAa+3vH4XBtuEG9YTQHtOEs//wYZqQ8RQOSmU5pJEorCZR9g0b4y6PGevlYseC0huCHLRseB/2UBQeRyLmG58JZbviFc3aA77ngYSSNJLlAj6yUyOj/917sZi85QFUAXPhzjuTCl9p6R8DPDsdtlMbQK7unRMr53fmeY55p9IRxGQH6OSP5rgt8UQGkkjrEO/ZOcHQ7m6iHcinOyItsduGPXy3CN3r0nt8LpdYOGP0peBWzDyvxlnbJTtM2vabp99hmAI2wq3QUXkaNdIWnchvbduRrf4etdk6ymnaJa0IX8UM4H377ReONcTR9CzFma1I6gbLb83z0PWQbLIUfLr3PRku83MJ2mkl0JVOl/ExuHjLdlHj0M98pZAkXnfvDkuau2g9LcdSWPG5GyAV5zIPLFMlDKOfObqpFSuAj6HY93pEKQa/EAvrLW62PAmR9jIlSxfpeY49v6rY7UDhK+JGSqkZSNZIfcFKInXqHZl3SsCK7ie2YaRMkTWb+Gg99w/KceBqk7lA7EVxjwxAZweo0MtwmEty84iaHbLNRSjeTumoDln0tybvmyDLb28tF+oNH7lGC4IQFysonCxMWDpfQJtcG2TWgiOCBFaczFIXC42xKF/gl3Khbfj4RS+kJq5qSRzydXR8gE3NyQ80w4C48N0XS3OmlTM1UzUdzYDsWJTvoLPehNWpj4UXTpCRzCw2leGGi1IK0x246dbihdmjdDxilmpFOcEJHxe/4yMiLS2gV1YZ48wXvSTBfpKT5wbtJd/XLerh4QFGsnKKoYHpK/D+Tdf5qPxHQgQ0dLyWYK35j9y6lBEUI30opOG5eq0BfCWocVIUIR4IOnzH7oTjtBqeSTCWX5AdDw6Z0sGpC3SqKqm7BPBwq/mfBFIWDf6cahQbjFpweAYuq7kMAzyzSoMDco1aXqOm/MNwo9Kq6MjySVCGAZNweZnTu5U+KEZLMdmTpo3eCzzA5JYTbB6/sjC5zJ3BRohoZWH2eSdr/4R1VaYJKzUrHX97sHbfi4EQzcSm/fHxcBPw1TL9nP9DIVkwhk+jc/8vZg139jrsHXsIBWsObW2LDi6CiBhO+cpMY4nPLvRD2shdIS8ticQxuKOvPs1yOe63M5TTaqWS0tJzhzUyLelD7M8Du37TU3BfPx/Dozg7TXo+Zyc2KiYILu9HFYSTLe303fRa5UBWxFjevwJRqcfapmMLloPzu/6r46UQuJ1zjiPYcJ4Yt7pZV+KDyAUt1V4RFZCdph0A2jbkILy0G93LaXv4BRM+Ggu/Cect6uvDlUQ4nEyawW+p4iC3jV+w761qGR6oMRzW/U1TZ8UDtJg/OOb3ytjzL3f6kDg+CI6Kdr4DcOICNOtWMfI5rH7QsZuslordwO0SZSub5o3irGee8lDg8kclUaVCMS/gi5bhfhkir6Wmbq5pMUO1pEQoDLg53sXx9ZKb4aTDr/Cjz0jMV1najdJr6hyizJJBmkCFS0AfIdWnJ1C1GobaK5Qzhbd+VjBuP8G4vrhWh+teNgMEhWfhm1wylh/FrpKkkc26qp20VUiuTmvF+z7FYj3inemdnBlG5ZMccuRYPZvwHSZZVLZjbI71+m6huqRiP4+u1f/NmWuKL6guoRmloyE/AewnoLbKMyz4vSr7Xbetb59c3MEDaZpMttPa9Id47/ejy+z2keb7LW9MBPFVNVN2UU7O0j7xoNIHzkFB0v7rN6pHyg7+3TJlIx4s6KIfqTmQMzuoRnpO7CD6oNxo2ZW8dp79wNB9hg5a9dgTkr7JicGnwlPJPFq8k4rwNd9xFGoGbsqRqwT/Q3i0/qVGQ9Ku3dmw5a3CePWIo3wuk4+XyOxnMDV6lpihFjkIZwTrS88jF16nT/KBfdRfeorh4Ac3f3tPjYQMgOT6mEZ6AOJT4QUewzIk1YBGhcZuzxEJCq+dPhBgtRRnIhtnzMh2DZqzMqvjtJRm/6uXJ9oJgPKGm9B82tBhpUOXIDgStoKr9el4wv8kCUz08bwcwwYIOJUiY1Rlwy3jgIgU41judjQf9kZCmQiiVekimj0FaoaTn7VVWEuAVUvFnoyASQguBy5/Oiq+UcN45toqRWMa+dJADNqCZhajQWtKtQqOATpEY3D1bisugn+W6eECkF0uSnH1q0j3nXT5wZOFzlbgSI9nEylO7grZ9BgxvncEyi+/pxIt5EdEMkkc1oRExEfDeZmi/fKVwF7YL7HlAJdm0kPT5o9AI9XCyXYfyR3nB2kj4nl4YTm2oDnXyRlSIJnNt7AL54gPjUzhou5BOlWiqY+uBH4EaW/mEN0t1PDgkw/eLjxNu4tXsyv2leYYMc1W/gvFWC8Ykl3s0UJPhf6BWJxxJkzf5JmgFXXSd1hAcvSYrw3EUUn9tuJ3peTdlyZGehTrpg8UMF893QXYGf9ItjsnziSgKJ+5+QRDXz9Od9hNTvEt6rNzH9Trj0ooinj80F6FoseYacsEbCnKc+fMCvWUrQDUhqMPHhz+YnXV9297jQg/rvu2lByOGUQfnCwKr4eCrBzwq54YyfWurwYInATE54noETH+pbHzwMDmlPvaVTXw8T5Dl+SoFEVTbwJIbn4GVLlRYIMdyUg0sGYY5lEo+wff0GFKtQ4TW6UeG48FTE8NMBbByGA13XkHomhs1maSHRc0AcWQ6cNuteWzJfmfuNVGfDPFBUfx3BteMqyxJGEhC/AL0t6aY/MCtpUIjrUCcny+McQ2UMrYrbU0zubAmSPxUdzyHvmlWB0xr6Fh15PqlySCYHRODKTaStTEAoQNXT1/JL0NF8b9wa+o1zYttxQlEmvvPkKLVr0Obl9e4GDduqXxa3zstdQp1nGa+4ry8fiYCjiwruOskJtIKKIavck277+VZCCrUC3h8Q5waBs5fHHVKn2A6JRqKF5kjyj1xrGzzXFCvQaaNm4rLBtkG+4m8YjMmtSg7jfkPuSqkOmV+UTfT7id9iX1S05c+qqdKc547OKn0D4bujtUwt64QtYHq70188n1hs3V7QIBwUElodMGZtjt5HkvDj06Jyo3mGJ0FJ7vzyQaMO0q2pO+hp3C9NGP0ug0bV+QkPfHxIm0cLFzrx9/56fwBNOARtZ7/ofuSHjqPC3jwmCTg1cixTi3ZqO0ikQJYhk97cXPAjFmdj7xKqZYFJvXiHRb7AyYB8UPJtrUt9rzf2u4im1QAuHrpIHIF98P3dDm6yZ8OjTvRavMQ304C750WcFNRqINEfSSz+Mt8Wm20lchWdtJaYDGuUlBnFJGEpsGFRq0bblKgsDF/NAtzRHAYcTx7z5rIw+pYq7cwJMEKHhb5M5YXD2WTgIk+2NqB2MhOQcBx2qS3rWVqjw1wy33fiKRddE52lYg9aIaRuueM3g+RrtzEz36pmrRyCV0krdXL0uL2qXfQwIZuAMWagT3BrwNDMB5ahWD9pZEfpu2F0Oxre2E3DXi/Tn1gklSJUdrCmxkSczpldNVm7DPbNd1EXVk9WDshVNGKdIxMD+qQUFOx8E8RZx7iQ9KKdDBtZPuxK6qebtkYi495mo3RQIp3PWWxDSVVg3SVnNAPY8fmiQtNuTXzetrUiqLt+IwZ+oQK+UF6HA4/J8vbtETfD35g7o1T4hPnkNG3WC+zC7zmTdS8zQgAYMWUneJaeUMT4BqEsvLiZ0pbbnZ5RmZiuNfXFxPqMngwanRJ6/SSi6Eu+qgFGLW5yxVprNmnhcScGbPd1Lz9ptDF0kb/pgQFkrDgOU+8/URLil0HwB/kSh2nc93fQaHxVgSYckTdfxsjFsEQcd1h7LjYZZPoejXc/f3Gks1k/AS68ii+5eFqkpzR0DUI4XiVH3jREk1MXuaDu7uDK8Rg94ps3BpIbZwZKVbo+O1y6CLFSFIYoZgwEsi6jmALAt5wgnazr0MYdyRiglxGxRU78GhhxMQrbtGBpWvTzS+225f1zjbu47Y1/yvEwtgOtb6huQ+SJMOgmMjWBQZ5xOrTj9ieb1oo7krfp1tnNbAPW2kBblS/XN//xns4ynFpzJDPYXSSWoEtwYbNQZKWMiBC/11RRXMcz0aNcOC0bpvLgCuxXTX1s8k2+VIIcovTmFf243pMG1iiRtHVP3kZgo3dzeh58EgLi39YuHfa+lB/tN5/vl1J7uPcxuCc4c/6HJcCaSN/Qamce1SWAd+rl9SLkrILlXP/aIBTuAt8/V3SxqBy2u7TtlhBMrdkjZDYDsA0R5izT8oFlxhLauJ2CPKRY/ZZZ1gym2+b5+JAkDAfwhwHD/NkMlQRyCXSMC5ddQyLJK+o/uAXe8jbHbi3Y5xgfJqhgt91yaU0HiT+MsJhMuVkHGlPbpFqxBVfR83Fz37OEGw91Pyz4/ZXPtXe5xZM9tpND8JtRcYrAv5y8AcsAkYSLJi+xtCTE3DnMDh0OV18t5Pwy+cxHqjjhXZsaB/+Nnz5JtBv0Sa8YITf+5MT398Fac1032sQRBV+lSM192E+c+h9D8Gif6zCKpN89nn/3SCMHAiEEsFYDIwcKxvzGm89d0wN2rJiFcYYGXW1A18omxwMzkYQR3Tvr3i/Xhmsu2bk4yMgC/klG4QjcPdBwzXwFmKmF38TxLIKqc+K9Vwm1h0sQa+ipIqJtSW+5MACKe+trspb7MwVm8+6DpCWNtaPJkqVTTJfJK9QqviVORa5k1buKgrupnAxf6DbZ42DxUZv6eK3Jy/h1+ToIaFjP4sbDsCMeVMTrKQDww7JkVnVkWi7y12Y5pm8AWMLzZo+fcMP3T5nRuHBVZD3jiazYFPZ2Ej1ZN7XY/OUb3QIfKuL3XFCYtg8Q5KXYFbuY/WF2nktkl/sJ+W4Ssgocsadn393KXDTpTaImK3p1WPvCe2OE0QuUTte7sj4zqxlmsFQkD/+dMRx0T7P6oIZWcaPS9yyGvkrtGftTtYHJPu5NIXm9Q/7qMKvAs4NQd9n9AFTRW5W5nN9A+ViWG76UybxdUFKSNxRh+beX8nQmhZvUqLK9yy8R5B/YVUUXEtQOHAmKNkpDDJhPh8Jeb03tqsygmQok08J9GPE0Xci1XKlaAEkvpwMB1Uch4DJHiolA4/hFZdUDT02oSfQOao+4tQV+4A4Z+GVXYGQSS0qqRWFRSyrIXWPM0f5qcFSlXc1R6XEimVEj4ztQUxLh0zuSd5f5upSY6m/+Y+y/XqT6tOGqJErYaEi/9Dm7AmYeR4vZLKvNkVGK6IsVaghhTQGnr+cBVqKtUR22eNEssHw3VTRCeaBWxU4G1IOMuj39GfsLK6joLJwHIGbRlhcAj5dPHXn6KYPTwvufwfT0idZP7Czp7w59+YZFRreHJK2K9khWs3pvIwIrQ68rK742/5LYjVLo3yhg9mAfp/ii8oQk+XJQxwilMvGGwDtka3BfHER0AH8mpv+u16q9FDCgiO2tdaXxMG6S/NIT9I2M7kmhzpvs1jYC08sozGmpn+YCkiyTOZMN/pyROBXbdBkD/VGXNm05HwRCdQf9pjQOAucBUAgAM0VWFNuRYErAB2CP1VkFkBY7fCJS58eBe+MhArdphmGED5GToUs0qV87gpgAGHsBeE0oKbM0MLAzPh+WK2oHPQLDylni1+6aRmNtyGQzK6VC1yAmMnrASmPVBxgn11xLSDShTUQXbvWXa0oC/Ay76rjcNYGHROrPpxliLiRuQH2fainMFMwTYksbTP5iRjCCWzzJUrfDi5feG49S0EugmPT4vRPZImcQvlbHxwCzfd7mplINYqCpCqM2jmVyOrt61QtEcO+JuU1Gm0hlFHBgxbAyJOzxI3yBZnWWV7q9+mv9dKutwR6VyfkszdYwGrtKa01sfuwfnuTE4t0MnQBaw7Ls11NTpTU4kjcEF/l1YV8cDsj4nSJQhayZ946y/ARrb17YJtFe+iJGYELRLgAKIs3opYTBrHcbc5oi2tttTY3x19NE1yX8tqJec5AzCdp9m99e5NAVUSYnyH7GQ3RnpvqOWF/hhdl7QzY8MlC4QYboDV1WzoJXHtZ9Z3kZf/c9JhPpuyfuJeqdAEzhved1RsQiPlpNJGfk+NaTz3Yjw7q28HDYg3LuDrLEFMVvX6wOsOpF0DUfnZBp1WdNb0VT194HMaex1xyew09FXw7CFjni3NU8/+I65JzKFMFzdv9ALA8e+5AqMWoAVti+TIg0MRCkdueQT9uhxFbtwiwWV+YbgLfbQOQXMGbR911mWKhLI0WsMDc+1buupfnvcVgjXDzzOS+xyR3z8QT5EtDbJco3NaN6/S5y8aF0Ody4Qcdo0rukmj+A9YcZok1p6tWl3nOmX7t4E9B45AMrmkwwAglM906hm2gJR/RV8uj2w+5nRJfZdltHxj+Rbp7B87lrh0C3ACNaRmH7oZoi8sl/Q/XhjMRovPrYLZLKDgTuyrHa2zUk0TSpgDHn7nxgnm5HGH2OL59D8DSu+XjInrfK81DU5NjjmX2ZTQ7ysRPEImEIJ1YNvKYe3sspiyTxQYEw/BfDonWczbOlgHxP34NGWjEcOjrlya657mkmUcOUWXgfJloEJZgUg801C/IRyujQMrSX1E2eLmbzpB/Awb0ehHSKlYtOkRaMDiyJDoQsWVVqKobAAeBhv8+E7+6Xcb0P4rqTRUcd5bHn45pMCCA+4wShhS110pmtK4+j6zQHZ3+yub9tPsETxRUL/Hd/9B+p27xtrOjvc3d2Nt5ZRp8osBZSs/vLFAMTEJDgG7cHzUpoKOVoGyZz0nPjkV7zzW0VSWxeh6ogJ/V467Bhx5odzz6s/A7jhWkuXWq8PonGfaCzxuWvtfRDGCiiSk7wl4hFAa50R9m7B6mV9722tDnTtO5ziSDYl2tMACEM6Hbut9GqskvyVL92Ri4710kwcM7YmkLZUNV4DMFMriPhZcjfWdOXbIK/PHWw41yqNa8sdYqXzRI4epiiTthCGpkTmV1cTf4EfpftmIn180LwLdDJQZCr5cA7D+/qpwVyeGJqQvvFnSO9YcxeKvJH579H+PGDbG31p5qWe7QXc3ZuW8TlN+qmliroGzwx4o5XsGHEdl7SdgeAqSa502I7o1KwL9XvGrl2ZOrkQcD6AixJtAp3rgYNAzastd1AI3F1RgmcphN6netONzd5zWNdJoGCQFxfoge5v3oWnV0APWrgQIVYKyMyhW8y+E3qG565jKF7VgZvxi4xinq4GL5/I2G+DFAf4K5vJYyu5BIdv0VuAwRKwrUZ1pNTea4egiGTQ5/QmpcGeX+Rgnn8zV0W+TFwo8uOiM81Q89PEBtKPro5ac/2tW7Elp/cVDS2IocEYkUs/QkXn9mhR+Z+DT3rtkcwLz08aGutpb4TH1uKCaihOm/o140norhMv15fdKe7oa99XnZVbvPhBNIbRkJ7LWDgvlc8HIJCipkjMldlOl72gzZtrKVlB6xzFjaSOBy5ruK+eyKLMu1b+acYR21CApwufHUBXp9WjJbPRVh5U7+x54gktLo4gowzEDJH1OG16/g1blnGkQ2tWz6L9P1Vy5dDcgAd2ZZAXBq7EM9eSAzbQM1PjC4qTHvo6XJsf4WstcOatptxjQZg/IzYHjVr+oXjLyPrblIybmwLfRBBNTMhu2eDNaYKhZIcwzQFZ0Xy26txlndUgiqLyIF9ZnAxw8DMW5bb0W2ZeBik+X4jpJBXw5YWX40NQVIVxHcmbh0k3VbGqo4v2aw3lOk+V/sKY3kRR1yf24LiKW//6cVBeJ0T6VBpPYAQeeTIzxucZVZaDwI4ZrAFczQDfs25zjom2Hw2yv0JiDohkorHM86/tqC/aKg4onDFqUgg8G0aJ2f+CXV3+AW5+JXtKnLVcyabV2bjzHQTIGpdKp25r8K4ati3A4CZFMcwzwhNMS+Id0ieyGFoWwrAJdwIseHmPuftbNbs/+YYAq0p7vXFK2XHkPJYxYhv3H5z24V50Z7gIfQX+ux0JxkuOhVqkyiBWxEyxbka5AE9qnf/LNc6x80vXTUNwWDysYdC9UQq0phi1UZ/IcnCNZEEjuxfV5klK1xwAhSCFQCtXKsoTcgLchFfFK2qQatzdR58ij5v6BuOXmPw8hTvrv255ifiIeQdEvakdBELYbCK6EQqenhejfea5Q0ckp5M0c9HD6+Wo++rCJm6PynGJdl5FVuMI7izdi0myxkSU5QX+XWuR52NdRqMh+sqygbDrQCjsgUlkhvnB6hu5KNFNlH/4MzX3G05fSYPnKAg58PNQFWE9ee0gmgBvry3YQ3DtmgNzFigLsPyjrmU0Oe01RzCKIpiTS42VDY/3Fd0ruy+6b+N/tfF/MZpg5rKAGi7P+/MzDq4pNSkEhwEp7nu3Va/i1Ww/iDZ+TQBMcnufDLRQszd1o/oS0ag98L+JuINGSsYiz1dt1rAm9zc3dz0KuNCY5an/k+dOXlnN/7pbi/R7Qgpil39e4wtcAGib0o6D7kfwoQJ+kgTK+wd1DpUAmPuklyAuWtbW4NSky7jOnlDINGxboXBYUF3ctNHtxk+vjz/jF7FPpWLKXKQOiG81g0vSfm1DfUzvwkzpL3QtR1cb1FbGD3OPVT7+IUatRgcJIVhHxD9aTHZFQVeShTvnCGC54N2goucQNQGgOKOpRg2Pq0Q71kHKHENl0iCEcD22U2pGZNsI8HAYu1BtyxPAFCR8iculi3KCF2Je/9TQ89UewXqeGt4dOjNj8Sllpi3kplafFRsTVqqvPKIOwbVIanPnrOSsUJuSD4GehTlq6t5gv6kDwbjaGac8csqrufLBuCikfd8zxag8MwicfRCtPnV54j1G9aneoPwFp/30Jcyv5ZVgt5PMQX+5Sb0WC9XoSHFuwINqPNvpA47zKHXkxfR5OYHK5wQTYYyAm9k8FIqa5Vy3siBtOWvWUheYdMPHmrQii+3fy3c73oQTQJ6rD8F1LhXfTA9+YG4B9t9oRry/o1sZ7nAl9mDSxycfgVmNfOhmYu/aZo0fAlYU5uAJpn8fdFwFsV7d8nyeEor+ISz2UtnglpNDKFweAa3g74/9MN5A0EZhlvy4C4JjMasoACJ6y8Z380vy23g2qYXBuK8Sq+CUGkelO/otTW2Ziv+1HrEBKXeghreO1S/o9zjOTTALWE1RfWpJ9OG3TTDKjeznQrwYSFpoajftjrGpCfwGdoAr2/4iM/gxmFDyZ0VgT4mFBEXpv/JWhGVna7ADxemtlvkYHDEI7qYwctW9/PqV+VUue6LNa0K+LZBfTudl1iZo8m1TkSFRdrPPBDGmLnv1QGNIuwLwcU6aQztIcIvwgI1YPvE4aM/ZJnh8pARFSaDysg3LrfJuWJNrWtoKWV39fWL6OOEv4oyZcCaTYPLgivHwKsm5oskvwRjO4U8Jz7Ia2YcB7TzeF9j8HSKELCX4aTkFKJCypjCYrZTs/2aYXmCHBsoB4TyyRPuChFYELR9hd2ijh1VCOnigJ/E4AwQcmcTjXPquzJF61eys+afHCt++R95QKNzE6e8ca0iOZspMrA3Bc4iBs1vi3blcJ31IAufSkY3lq9gLIveoNwEySCTutXL4IyDmH7kWZf7nHOEEN7ffsohDtHtMyMd50EHiiu3mdz6cDuDIqTbaNyde/JJrHmuNzSjnmYg52TLk8rAoMOvOanCvJhdKlshyJfcRH994OzNIKX8nhtxON7yp4etgi7ymmxgI2PLiRp+84pivu7gFCT22GI48uJVG6N61LX1BQgCSERYKyBrijJQrUr2qX0kF1nYLcgQX88PALiJSotSDBlhuxCDkP114/hQ5sZ3/CVtz9A7iGUXxetDK9oViGEx3VhrIfqVDjmPbJS706mki9PKmmSIQRj0hV6vROZDMiM/qowFFG2Vb7wnZl0swmUd9Pr1E/156YKW1aRofZ+PWATk13LHOqwZRE5mZw5AAEnTKwnBnP+u5YJyOYuEso49vim9YDtDJUa1odyYZFnS/j7FTlXGF5iIwzLSZQhZFdvBeNuZeBmgZpNUmW2cgHSwEqH4vO/kdwHI8OXLHk7th2mdwCG8MpWNb4L5rPUXXZUsFCKEhbVZRuonYG383Us4ypuvPfZhWOzt31Vzz3D7x9ktk75edusKFipwXRv2/oCZB9Zcqx8OFMaRyhfBDNsBMBqF+N4nfJmy9vawWH81gMm8rqzFEkjenxXpju/nDYKRIQTtEGcEbRliKH7S9kv3gHuSwxEukC7/pUGTKMieqoNNd/VpWICPWkfte0hA/L491cLcUEo7FFXYjLysJh/8I8TjcLvGV/K1Tn18EsSnLz3D1hs7X0X9y9lG1tfJXHFB/ISARdDgJITLFejXE14+Ud0szndiCClxvptWTzwf0GHid9DDc+CWVbIK9aQZNZFmqWHeyc62gc/Kr0Ax9Ck5OEzSIdBt8pMYpijF3b6vkR7tVahUExdj49J8rmj47KAUqbwaTTv4nsgtd7O1/XUvSM8IgceH9OU0eYvFXfZ1WhTLXXITFMbh40jOa8KhM5vsPqcp+9OD+DpOpMDH9U/yWrey73W21pSxnam7M4J2p0SIgYlylnSqpE9pv1GO7JkH0bXIIne2RsE5uHAV3BxjGbomK13T8k2yORmh8BQJYyBpwErIi2OxCDi8DQlann8gItYtPwFXfRhBuA6B+roxVh9Cf6zsRHXWhjdL5czB3RGWwu2gLiYv10wqZtCMeSqEGKysO8hcJC+yGWaGzCdFHQ+BIIenkDj+MLKIpWIK+I0c6w1gGORKL2fQrVSOib+2WinHfMGO3fwqFKyPzXwJhli8uw0co1GINvFz9KhvS9EoX4A/BEq8vMuHiO/Nzxv9N8uw1eyzaDHhGE4v6r2IL6uTwKjv5taJF4xvevXicVBA25JXjmAglSHjxy6D/NZv4qciD6lSpsfmJAEKEc8W7dlxPJ9hNvovi/5yTg1ye+NIZvv763vozI9vqHZWXtVHqBPjbjEKRV4DwglIwKP18qDU2NzQOBCvpBy/RDg+DDYvQLGHjXDZrvZkrdWUcLFvyKmabIXrr/cI+ZLRtX6lAO16ZShWC3A2HeLlr6tD3U9XTI6MJ/KE1TSBZJpkFvYDBc7EkmonHpY51fY7BgEr0YBVQiQR5FjXGLFtQ7pp7qZhaGQU6WricdGXqLGaW1EYxB3Kezz8HWTcKHtJSFEsidmxVCjD1Nrk23l3mUkfly+XO4Yrsc/kDvxj5aoE3i4mbj7i1MvXF2oFxirORkTYir6TtRPROF7wljqEjAJtd9oCjET+iZ+xg6bXC6uthb8nn+Lpfjd1NWGfvZKGa++8XWeJKesLX//SBcpMYTL7whl3HPwT7JujPb0SG+Fu37xbuHRYlzxNgfv04vnCaDMyWu4VEOV+UQgJ/dTF9qrCCYJgZ6mwMTJKKxHaNcakqKAm6smLVRm1D2mSaX4ukN65nz2Q98wA/qOcGPu2owXRqJ5YkZJailOqAZZP6ZWsTRUE+YIqFKu1EYJC86OED40WNuT+0YVPt4rle/K2p1NqW/+jwAi/ovXxxesgU10aqfwiRzkbHgaqczQ2HHWA/U9ujSYOLGJ/WfIgSjhOnXu5DdTx10Q6sd6axUVwmCB8mTsvVUzt5lszKY1Z68Is83MoB/d+Hr/7D+piMNnX9BwirXptv4wPd3jeAiuuGlHngSNq63ow8Sibj5wve7NSoenq3cgwNl4J4PphEov06s5dJsR0v3xgil7L54wC0DJXWQB9bQjZZJCdTGrK8lQmgUwGQnwxmZHbyNWWvlQddQQbROkWEV0JoEBpYfuVBBl29cJcp4k7J6CBPlMvLpqRlYrhCsohcaITFGSC2LvCcFXnGRgd8SMPGvnaW0qGDLl4B6wxDMgbPq3fhJWY4KRms9jwFXxJ1Jwmy/P5xtWs0PeGIhLa53BEJwV4lAMNejq1wxEOn5vF+ZDWoCufHU33u/0EOzmcWF3F6IisIGFROUXGhWruy87+GThMxKaUH6dzAgc9qjnE5JylyF6bHhQ2HQePAijlw08vRUgD8lo3w7RfekfgvJKJDD2M+XHYyHQdFlD3+Vp3zD7CRn/011tKrConJw5bXVBBO32VUrMmFIvesOHeGn5HSNnT6E55YjgmnaGOgcI3eb9Hz+dexJZVH/x0f1O33h1i2huB6zZdfa0fPK7wlhGVHpT7pHjJVqnkQ/8kdcQlzeVHWOz9HWllgjs0KeZ0z5CB2o4/RVO1TjATEe2DtyV5UfovyROMsWmCq73nON4xOKpDb6n2zqU/tHPvCveI8B6/18pAq/P4rk490Ml7uEYM/gLp05868lPuWhahRi8I1U5r1avm7Q0Hbg3jiKv3eudgUM8ubtsGPhfS1V58PV/DBaHeYwtRAJTDvpf2YgC7e1eQ2h3bad4VyHfdU9ZjPxk2ynYKNETZ/mYVl8aWjPsIE/iP3RGILzwIUWVJTvQ8qATukdfXi2uuHRm5LpLtRQ/hSxdbI3xAqjcAt6KbFVGqJvWirIS+UDth/0/Xdyw7Cmxbfs2dduDNEO+NJPwMhPfefX2TOnU7XnR0n0FJhSRIyMy919o24zKcPDa6p8pjkMZscakZlnBK+kWhebQpNyws0gNMLnIl+eenMn/OsmGPr6PT97vPx5G5PsgjdC8C9PgjIinub8He3QTcu0hh+jTcrmc8m6oGUrwGAtRFx4OSCI3lSeJXCtr1z6R14p5WfPSrHDnaYfyPfhCZcF48yDphkpw+vDZ7w/yBz99QSbvXt7Lu+wjlNJUTkTxOKmb17sbVe8YwzTASqF9j0QNrshYVzDjrAW1Lnhr3ZaL8yBzAJ50cyCACt2fPcCCB0MSvWqzn86vo3fzCRAfjuCKAGGcdbmy+uPITx7tWDW+ZurcU7GHykb7qN9SUX/L7+4SQPpkbzYXtjj9i3jPJa1u2Zan1BN2vQ1rPvRMy17C/1rNBPmXQ12KIOCrksivqeIdeFPmvBdecLzWQJyju4PLpqCriyQXS4MxnTuSX4m4ApxORgHSjEj3oeURDlqSlXf/FA/ctnG+EOioxz0KLUqakf+NaiG1DC1gbimAlc2LWxkbAdgumIUQdCSN9jmn62P+V5NToahTe9Yy1d7Q0DdtetEYDSUFDe/8iBN9xCWFbP7oKuNHrU9mAPm0PNkqTZP+6qBBEIiywwXla/PtUNwS0ygJweyQx6CuiKS/4XRIe/n0kiOZIuaXCSxsUsU6FJIOblaUnzq+utyF+dBjP7EuNWWGge8jPD1jKJAvBKXv3P+YbQkWWMllyHqFMul4+m288q+yWOj86xTmbEhqM9U7x69o7/dV8f20Ho3gnyahGFk/GzDV4vZlqSrQOtUvLToTJ80HClLPzbrDKEUJySr3C5xhr3gs1Ra9qsssTdxMKa4O8N5te1nwNMUu43oksVw06Nn/kzDWJ0+dTDF1MYmSMs/tqDfUGQZMdxBkBcmHSmdeInud4iVS6EV44HCCTihG0r1Dp5zPKlqnqxQJ/ffYzIORajtQsItxEUCFuLWZpE6inxkE7aILp4VgbWtOmFC/tJY1UzW9wiJRpR27vYiSDukgcsydMmjk4CbegeteWWRvPdL+ufCn9ArNqXm5r6RxAnRb2U4nQTK67PbKEDEvdzm0YzxOHRwizYqILG7zl27ZVicpf8spgBxHA754SrXlOtoYkZlGoESAhK2w+fyLEJYe0hq4seF8FmAaSUvbgDE8+lG8I5fdM6k4nAP4YlfOYc6I2VjfYFn8wQVhdKzlNc1LHVqSwTsFqJHb4pHHGYRYkHAhIt7fvYR9uF9Ba3j/gaYu978gDUiH2dBbdO6mfMtzlpSbrnC7KJGH58RkT9jdBz6+5Q/RXcwEp+SiLlRQOH6xv0hGAsTcyXiNwsiBhbTZTwPK9VdSQ+LEHwwd3Erxj1NneTX2YSyTU5bh7G4mJb1Kydyc4gfxeRFl7TxnzWxyed3fegcrO+omE1MjsQew+tz9B6swPEDBCd9ZCeEAecDHhzfbwBjhSadjblAgqeNAe7h3L9Z7owg9vmvjmtk4Ze9crqVDDpaX6hKQMl3sxEjkwYemoifBVhdeihQTjeMP7USbG9Nq7Z171LXOdhGkfatnWtIyxW12ymOP7ThpixhgYIfVGSlRgcCoGhoBoSng2/4qlod7OQhWfQa4bQXgXAIsV8igD4ygxL8slK70eF6bT66vzaSDKEttUfJh+65yf0LdtqAP2h5b0mzS7wPM1H/CxOtSuFkVW/bIthOWqbmlvrxcX9BQS/wLVgJXA5z4qmPUUttboZN4Qyak3iugFgvQw2viX82gRQZrnAY9Q/tAjJEfQ3tfSCPk+CNJUL8JEy+HDDzby7d3KwZG92K6C99n09d3QcbTsACG9/D7Edg9u2lw56zhGa4Ku8QjGIzboo3N05dcV3SsphIWTrk+wtJH9ja+oYktv7NAl+P1GVUb/9IK0BeRc2DWx9W97pgqSYLKB8b/TLZCH9RmCt0XgzYhxAbKj8LSzgxh+qDWYVwfkkY2/2uMqhdjFi8DDWJIWzc2BPgvJNSxsCJpHKZTYK82sa68nj1jXsHehZgtExae7R/+cUuNqHF/XExwORZ+9v1NGQz5/mlhVI1wiQg9M9V4c01NUmNcX3r1uiQ5+RplyDfbx0ai0NTbNLc52ibQIMM49Qu5FS1S5S0hDCvFSYLSo5xu0tnSlBJcakQywUmlb7AKbJI8un4KI9WrzBoOvoBHGyD0WUiw+LRsW8m4KsIezSjJ5WRI2ZCvmjeO8J+MMKBNe6ZaHcqtRfETp4FAoNWf9e4ZVWhujOY8v6yTD+3jkXtX4YtD3a79jH9THODO6h3QZlJXHAVh24bSwPlVi0LiFfCJLN45f3rkdInhjLjFIeugoCywxnaqhTcarCnpgIArggqWv85KIvAQ2M07w72CndkeEET5Xv4Q1syl/FH4VLqUEmMQltyuW6GTihGUo1lOiH3Z2PnLHoyT0vo1vsPSif9HOC0usDumIb/Wo7hFM7SJdejQRNYCP6OeNGBt33TD0iLne1VSHn6u5IFfYo0jG1kr9XLeAD3q8qO9rwBmRuXSCL+jL3bZ6RTzw/As3wDnx21VSarXBG9tlmU0afDUTscg8bKk41HkrfaOvlxLGq/Zm6lZFsnxau07aaHn/RXaVnP3qb/KPPBvwn8Kz3N0IHuIdCoGV39tLJxn6Uy7X6hwlQ7+Ki0Ha57aB75GzoHBvzpvP7FsP3WNcN/5zl98OV3eviSA1ZC9yfaQXGxFa/AUDd9WvsMbWd/P8FNv7WM9NzRswmT+cnpgb/ICAyf+h32+Qiws83oeHoIpQMLD01TiY8qciG0mFSGQRSSWMT4CXe4KbFPK4NxEt4k1ycwClVXr7Yr7TL5lJ8fTubjGYvQgTSg3gIzazQVtaESW44Bz37O2O0Ugs35PCkRglkQduAhXv/tqdXHegf/D86zOBE01LRDP9SbQe8C+LRxHVcxXbNalXDuv17ueTzln6DszzAXHjr6W2YQC2YMHf9BB/4m6uAytExSGlyTisHTpR4a8Xa4LyBr50BrGE3ArYxTg023rPnkwaJFKbPyu23yRy3i/S+4L6o2N2+OatGVjsFm1VcRdimuK6ym4jF1g9QdSGWE6x2WI3Mbe+nR57pvFzN5HxXlcK1PvNrIOEEDLJ/Q+PxrfLbl60TL48dpYe5C2oXcbqZJDIj4RNfgGHm8cEHYZHgk7N4aTeDR5thtkPtg9yS1gMiUbqolood3rwdd35pCtTvc77sF425sBti8hy5xLU/DMjbvo3sXi/pGlbTyK1NTI+RPgOvp7NVluAaLFsXNx2cBzOPbz0XxUVTMTri7EZoUjOB8zEJaIPn9zbkzc/CMTuoXjxACBN/yDGgH5G09UkXsEXfHB/PdWMV5dqtrq+V0ucjU+vJ1z9mSp2+mVo0fHXwdJfxVQvkbU+xehgZJfrK86iLQGqt5/agkEqAfLzRZO7P0zba1MCnqSXXe6OQDafCSW92CDgiqWN3DVp3M6bPtuBQLh74x1DaEqnvZhZJOhxKcp3nA6/js8ltONfFr9xuZzWLRmUybNzx/COm8odp3zfVJihlAFTcKJcAiRM4o6yoUOTfmLq0Wo0MMDpHMxQ7iPC+hSpSzLC5nigZyxE3/oQRyC6V2tNqQfM/Xrxzi97WcsZR/LvKETh+uvb26YAfU5clO9wsp/W78zGTmtJI+gdhxQ/fvzj18wmowVHtWvm7UuvnCLEKgd4LSIhAugMulJvnHsntWSV3mV++0ks9IxH35LovvHaO6OCAT9gLTbzyA//INm2x+JGlmJ+ymWIp2sQmfoQPVUuXpEqveiuuoT5miGFlMWwGyynwV/snHb05+IoFOnLXbBuQblqhdkXux7LXm0QHT63hyxcqX+24besDcYYjTY/AwXKWCyC0l8RWx4rcOjbQKmyRqacG8k+qgh1+CzDVzAavVDqlp3g42SD2PNOQeP31qCh5mW0PBLo9wgP22QxteOVT+A0HA1MjaiiGgIgX72E/3rcZcs2K+IjLX5GS0URe4yEIrJdnN60SRdEmuk5i8xvwizY1E/nA8uQ9By9nfCysGdjFvN1OY0/ZRab7hah2iPDbFSPb7/KN0Wtk/Tnf0UIRZA6l5ZbkJfOxsN3f/N9mk2j75DkeGy6clQEW3MFvu57bOG0TB/Qx1otHd70Kt1JicXFazeMJWmVxP8GgrrtQ5uobDbj6IHwVQ1wMWFJAZ/a49dEST4lldSCIgu9Yso82yJrPzoUv6IsBDWpxWxG1c1cHICLVedha+MkpjoNjJGQhme4HahKMQJBCPHDQ7wJLodVaXXajaWiVW6oZxrB3tQ4G92dOzex3nw7kh5AF/DilOiUMIUCHJUY3nZGnpMjWKQ3+HAtQhDxbfPh8eiKlxVQjbUnIftg3CtPHML+0BdwbmAUiai/JAP+y1I9SkOm8p6dU3gAKAcbS8nrdZjlv36Z32XwhZyUr++JATNCxOR9ptr0fNnmT5dVyQkVTd511XZBUUfRbn7tc+pa0HvXj7NnPqDhGDtZMQ5sBp9s7ceznUGglvgLv8D4MARWM5iATSdDzvcCElvY/IfEZMy2wrqXEuyUNTuAlBAgeMi+jJgO+l9btjRdx1IE1zCpPJERIOR6sMYgd/uWWgCsKMrNhj5Q/xdUkmlVM/HIGmam1AYuZ4wHJo4n/gDCtTv5R2cCsXP98KtLF7NqKU4Lk+MoiykL5E21qP8mvTk2QSTWGhSeQNm9i33g9fq1L0hdHjzc0YHhj4XV8TaWBgUhCmwHkXfPrOs2ZbY/mk8bI54W8/Usw6y0q1WtPvqyJi59hADC7bdfDxoRnmVU4wcy4qjcxj/3IyT/2rtyFXqXtiPdPObjBiWtVF+T5Vjs9MbxgCDrb6TjIUiytgfhrLlVxsT16s1K9tAhwwimph++IhG/kj0L8Ijh6g1nqQRpCb/tM4bC+24Uvf6sZ2BribGxLlXrXVOnimXuQeNqzZE68KIG4Nef9vT34c5TUDcyRMzyXWTUBPMQwBKhRiYRagC1CCApRDvtjFsEqIXzyamm/lmNwBIHJZH/Vc25guf9j4fvOdGUgmTZ0tRIF8ZEwDuV0IsVSi0HxVrBH0P9bPW6Ryr60UPkoC9FSzCKZ7rUDUYnGq/MGXUfPMADHfAPDqP8O8YRhy9Ot3C7t92gyZJPEHcsGxALGLTzPEEw3+GUJepc2wCNeO25h/RXPKXyCGT8bJUA1nWPQC+GoMCjMFYnyx3i/YtnTumqe/gQ4JDdy0qGIkaY3eUOxpHn63KumM+dovnR0vOtwefDI/jq+hXqUcADkdVPkLVE6wx5fkPeu+xGsMacgaKrS/ZChiSweG/wahjTfUnctJWAKPCPerA1N+gmESVRnmk7Aq8fhUz9ytpR4PFiepT0N2Fq+QOVhzeavEWTfFe/NrDrs1w3iG5yyes2tV0ye6Mo6c5GAlIaJhO3+GUKX06z+RkRaVvM5aoLkjNDF3cSSItx0G89E4qykK/E82eiw6n84Bv4ulv6yF6fdAtTOFpX0+1OLth98qK34lukKTUHNFF6ycNRsazNx14jsogvvhdqftnT8/EEFe/tVLyqTCbhzMX4oXKcpBtJvRg6iPy6vRzPqftsT1I8HZd4L6dBaKI5njg2YA6i9k5rwSzqGjhGoZAfUmG6EzBWHyio8S/aSICsnX21ZbhyKBqx9F2Jtpe9SSLVWau+9ltdD3xYCX3mYJ6SnBjEZChalZ296ptgXS7+r45P25HAqHS2Bga/RasQ6fM8lsHS7d32rp3FOptYeTVMxDu5OUNET7AQ2E9KdkdhbwWAJxxpBHq6OYewgBUNoDYKEynazhXtYdCDa59tSuyf9WRr6hopxBtqD2lzZY4n5JPJE6peyHngFA3ZJHIlqf8rH5h/TCBAlM0OQGVXrEHxUW605L7jdi+PL0NSDLvHJRjCc35bWedTDMaKumBLLwUXiwD2/1yRawEI2xrs9udosJcX/l60wKEgQ+/F2GclwSCCXeC8sh6TsI5681xJKXRCqK8yKHTUO4DPn2U/hcItgouXdX/30Q8hMkfrVDdfWYTaEAYWbtoUgdF4MlXm3K3WwDYjQiSKdncHY+WZZ6neIEOB3qbj57xP5ol3Qreh7hQ1NqtY5+HScdMLIyI1Fu+mNM/Oduis/yd6Av2XaXHhj/ycbeha9yqh7LKGQQ80USBh0qBM386KxFpFEapNMKZ1+/uxsaD/BJj99+qYKficvEOg62wZgRV7r38luEDhOvCVf6QU3bMYiMjxL2CT/esjIvYBtnoYEUOhxvDRnNX9GuxbTGQ1SQoNX14TnWTWa417xHL1jrEflA735bUyor6iTgr7BYzjb5UaqPa9yN8GWA+slu7jmHj5vSMPMgOPhNXkGjuGItZ9wHJV/Ee2B/vaN+5BnFejin7rO5TJueavI59T2Vqyagh/m4ljORW7fYXBUjdLeLmHzaugM7p/z75PBF9eBqq15IMPRtM31behOkvNRC8HNoESzWz+xRop04OzHq8ozUqGzOfWbRMKcBWRIRUkkoAuN+qip2O/1P2IacEop5QpaOWhYr2/26c13R+JW1d2C9VHUE3fNjwfJCQMN/VLVWJzy5KjQxD0V0aQ2SuKbQbWLKh4gAVlOhXeJD099drGv8U1Eiw/MCzJI04+vEN/wqjslZRYncrM/edav0Gk7oUU94qhb7i+9Uwg6XFX0847wZyOVP/wDFl72L1xcaoRjB8X2amMzG30egS6SGPWM3WCMKPEOCu5cnn1iHQGJN50QgEb2hmRjXjl/l46NoE14iboyBm+JD+Pdy1HfSDkX9ue4u0uhShlJ+pXQr3Y4omGJXflDDOVRCFqfVcuu1hWqH3tUWxedN1ZO3ysedb2W4tTALazyLnGezKfIMILLJHmzdAwDY8RbsDbs49bK7l6ilrt+vULrg7+VvEc5rqGNIn4q4wMoJimILoIY3jt4i/alhP0DrfyxLLYX4w3W+BdjGfwRU6jujksS1JwSVGM2Gq1iCfB9E0iMu0mJbOu9uPa8BC2ad/4CTLlIZsL+jMoGxTuYsMbbjoKi7Vvmu+6g/SHTqywsvLYrxwHWphcWD23X3JkA9afhCzxDItuR50WMHlwOZWvr6o4v5Y9Ib2I45xaxwnln1TCkd+wo+vFv850hp217XYPSMwGAfhQ+m0gQLLpfdiu89slcFfhiahhp2qTGtjvcO+sdmqeAB0n8Pi6+OfqGQmY0befCWINZ+9R/qjOkb8ys9N//tu9YvknDv7K2IkYDiTL/Z5nGtYX0cd3IjCyX0TIb1uh5D8pku9/r6QJpgX/NRH8UF15DApxciJFi2U5cbnb0hrxRTFIIszgOvfDheG4AXu6ojl01j30Ds7PvpaUEpxs84FNmxCz2xHmapbpVt4QsOgUXqHmA/4kYv8QY3KCS9TaEWTStzPaVGjnxs1/04v91pXk1zUjDtRXDSNMHlT999uuVwOWi6ZHgSSi3Jo9mEV501nwcFvCyYzLwTYOeX8GnbI2cvZvvrHR1EsJNV+AlVclY4DoiTrDebzY8sV+EKi3bNND6HQO32c9+dK9QZRTu4ykRIhyjplULH3zyptRbYEAAE0KcvKlXjgO5mJ3COEtQQMrw9uYX98ZIqZwGf55gGg2pZiWBsUIxcoP7mJnx3yekLjVDvcbyZNB7PJZJ4KeBb+aH3KaBoHCrVFtH2rM/gQGO2JRBH3flIIOzx4YpQH2P7MjxaWDDuwzP6adoUWm9jrVElt+q4MO9eEXXhPqYLP7xWSIq5hHPz3geMsIX8ltKN9KFllBNka5TxVwaOTllroPnQHqRq/3nO9pku38n83RO70ETwOcsI/12xT6CUGweEXlREnItD3Pk4p3GepuCTb0Bd3iPAk8fu6zPuh7DWx63nxD7rtes+3Q7fMiADvsxSxRM5R30zVYz0u/y9kKH5GEZtrd04iFvokQ5mI6DX0SQoPsl7OSoCMgN6rCkzP03TS2I0hb19VueWkAkyTt5oUgXoUdNt6R7BJXNhlsj1+DOSgZ7Sh2deob9JhNQhwKCDN5f9HW94Xp5Q77Ou0AjOS6qGUBPWaBBD9wKELkbAE2xlsZZuxDmvTcqi+IlAY6m7kou1izSMThZUMhZegBUWmdqaG/JjHpP8VO/9tvvzqnwH4KcnTF+4f4wNMww30hKD8SKSSHT9q3eJ4pKXAn+vPmAOYQ3WbL4kWBGQKMFTnU3FZ3HAUVEjYQC4RILH+s1P/7t5zHOthzfuDhwjW7Og4lF3fw8hXt6j7CULCBZjphPefOB23MyL7xCPY/PkXPEDMMTj4T7CqUnCO13bHVN+X/v68bALBYJpiv8nSaoQ94R/+/95RLPlsfD8W9Enyy/69R/btuDkPP0O5fFMEMVPwzvP97XMvzwibEddgU9y86mOaEAIFRiAF/H9ez3hrOhYryH5T/D2i5/pwJ2rN5zU5wBEHgv0PjI3j79e8QAg6hwn9QrjulbOiydX6WNfTvBwiNIf8LRYj//pHk3xmuf+cD6BP8/6jStfw7RsDI37Eyq4ry31UwkCAHDsbL34Hi/1zqh+1+A3jePLQna9v/juf3HoGq9N/V/g1/j9st+zv0d2BZr/bfgaWMR/C26uLieWXB3VffuNXjJGvtYanWauifz5NhXYfu+UILPmDjZ+/Pw9an3NAO8+9UaP77+x/nYNqqAL9dh/E5Gi9j9gW3l1dn9oyQ/V2S+e9R6L9HnvdpvMb/QZm//z4oqH9WNFd5rPU+IE0qBjB75sd9+EPxvFOb5x+255gQHHdOi3d+8xuYnzekMPOCfYkXwygM+ysvnj2wOfeQYU7XCB1ByVRRhIEqOU/b5GW/mKF9FV9T62unpr4rjnDRKMlfHTRyw0dkNdVTdOhzusr7JXCT/0jz6jqZiXs171cshImif8KpfekiKay8399fCo/+KWbzL4lXpPbLNBICh9c4oR+9j614Pzvd37cCuLYtE3juxBRHb7mb5fv5S2XM/kFG4wT2dDBubPln30DEWuNx92pbFZwEiKcgp6JPQOE54QQpEDRvYF6oO5vMUjwIMLrtxN95xl/1vf27XBQQm/WvEkTuoFTK0u1s78PrwhpMt8GJ5d3t4posgZgCqZUWfktxW2vmZtG5xlmFeo4qeZjoCL5h3XSTUKL5c2lBOU1YHyILgFF9W403nqF/NyzKG53llxZ/a0rLDa7L7sBdg1OuoIvM+8MA/nKW9voAxv6LcUSoDxyzLrHdo4FULaOf6fZ7kb/qroma3/MLmezgg4YoueekQEQFAdh0bhfZToQCsKwlOUvlbM1cNTo2e9Atl/3ygFEeihlvYFufdtoLzveA2h3w/frh3uCJ968MYuF3/GbRs5usmEZdKD5Wu0VQCLOR7Ov4ZWG1tjM6lEjbaGvFAvfsgx2ThJeD5Ku9oN/gvginsKMbRamovuz7Ya1kGHt/in4f7L3MQOSSOyKw3zEcMwFYAPJ52JbeIipRiULw2FUUghXnNyjeILZql69Ae/UHxrKcfrCDlhynhFdT7JtEsKRdmHxnOnu1AeXeAeRv/QevGFIt3ZlCml/k0zcvo+pANhOsU+GS4YNJuGc1HiFEpBZWqPS3qEXU73/P26O+w0m8FljxX1d7Q+uqvGsqHTFmDw7+ec9HDHE3DnEUhF1BEc7/eAF4mDT+pr4JrBuSq2gVp57NzkiRT3kvms/dByQZCRUWzAzqtGCJooHaq2JFvF/oIIFKWr9keAPRiA1lEkMuazyFclz2N+TzFnEnitPSPVH9BEU6RKkpiHerZ9bBUvov94XuJ+WTzNiRb0MqCHajKzO4Aq40ot+3yMvnv52AaLSv2mljC1wd9wBH5yv34y6nOvGav51cJNarXRX8YRG+rcQBb67Dx7xdjCuKlfSdhZ/oV2dXpQ5RDp9OD6Y3O6nR6g7HQY6AuLXxsEEEnxkAU4pos2qXTKNGJmQVl+Xku9sWaP2iWqZcNNgyu43TjICx10CVlzCPpggMTlkKNdGz6Ar+3XZ1/N1WROb4X8eLSn41lkg1YUMYCrNfEcljwFYrNR8sio+ZFQsyWZ3BJ7xiGxL1oniISsO7RQ/kXqIjVHawq/TkgPG0+VTfdj583g9mGMncorH5G+dRxH/1aV2HOdS5yar4iEeTsJXCUDUo6nZ0QAXjm4GWH8ZRuYhDFi52Nhzi5J1yqCqgabSU5WkCMczz3MV3mi6ApSHdt/pBnJKcZApM/OLN2K8WDq8V4cMKYyf6tVJC/e1bzsitvDt2770A+FXbdZ/jsW2UpKqMltSZgBRavc0mHKaAqf6B8K+k6hs4W2lgPesMe2EZ83UjDx4P6Ew8p4VPwHoFPTXE5Z913/XF68RV9MvBrimyED70r7noke+a9C/OhkkvJHGN0fs5iJ07ePbnjO7Ts+hBd2YxZeeNOVYSya/Ensx8ewepM7ZHz8Je+IkkOoB5c8y1/fs6Aipydn30l8OuFTLShZ5vlXaWpdqIAI2UMAsKCURGNiDkVf4Q7p7qczW8ibSlArnfiwBLNmpeWMT1vlPauuqDhyQP67hfZf/nHL7/z5jO+6u6ZwDnc+BBG/e4vj7HrK76YhRiN2hHQeFuGDhj6P1afc+fc8OSKOA+3xkw01evBtHpr2SYMrzO1UqEBBxS83l0lsAscKpu5fBu8g5ql0JDdSZMdEePdBG3/FLvwI8rkTgDD/Zl1qQJNhj38MU2nJqjp6zbdCcVeXFjkUxQhrmKoVHelDnAtB0TE7lKt0+SgrKDXcXrOql2A/CYkkHmy+pr3Rqv0fZ9EN4P+VkIV0Z3Q/nmK8N/y4GQppFM+myCos3pc81FAyAOG796aP6I2O4OKbfYCrX58g2s3F45tqc52YLySXbdiMizmikaeeEBFV/uMgMuPa/5rnYe8vmQM49bY+h2i7mZQTXJWiQgoU8VdzAnL5DAMPO8HwV2r/IxE107SSQvr7EqKviEpS7ErzMs4DqMHWtGJoAi8On4jjJM11M3T8qyv9Lok3/Ncxhtswywj3sYaIF9W4jKIRBsLPi/8vtGRzxAnkH6sDecyWK4M/7C/b7FiTPWqvcrlWT/Oreq9FJPS/uoEHazCTycSNYYHiL1Ea4pfXT9Fc2MaMTkwcOxlyLKr5i3gnp4zdzpg7x0hLqgKRRQXiFPv/460LDKe7jpiidHky1mLcQ5nimu0IGba5Yt4wOypzWL1D5VtRr0rhG3s9p9en3Afw4apsKJvLT4QEMifHwd9NP0zqqBTQwGnOe8K1ZVb9Fx7jOKYCJEFKReW2VUQpjQ28ZoPu4JBCKI6NPJj5TMf4UGUBDUJu0cVs7NX8YLW92w0b9haebUq55GXkdT1f7iEaBaQff59oD7I9HUQrXCFEW3W5kKHZ2sI15Ee4NqrFTMOMQrAa12ROX8ORQ871GiJsSR7lzrMShjAvySPQLTQ8SfM8sju8A1E5Pw8S15qzxI3fpw0GYpiGHvDAi1vsdl+W1CubkjH7+CZsDmYyBlncCXW416vN582CTZXr901FI3CX6Nyb1bbuMPvh8vAi5FHhsrZly+x4ONmuwRHK6cXuZHYsgeVEoQkSPyj1/ezeqGuiVatUmHE7AFsR6tEu7D32C2bZH7c9jnS+sIzGVdSHQ2Hm4AHUMgiyLabWuYEDzYbpZ8tyd3ESXHaLxFFV/jlxcq4ili1S6rUvVKsC0YUql4hsd6QtH+klhyAfgdV206xI0Rh8jkJJyhIRr7Nbi6W0tA0O1QL1yaHH/XFW5I2Kw7acoumY5Vc4lmYVzsN4Rh68haBTPKgXGeRUDtRioeoC+V+Ox+mzZuZppXSF9qa+WopCkV87z0VymjVS9rhZT4i2jTIHvbMGBwIYmtWed5BYjJsssAhncdI727KRq6Du0VfxTegxNkNWTEGq1G5pncqUMmGeXaWpMDFT8l88SLQavXIdVjReWyUJzsRJKiQtCXderDRKh+znVNZZuaro07p45uvkE3cvFE+q+dEbpNTKK14KdKug13TM1C5dXX0bL7d21e8SNlibiMhDxUJd0w50TVH6S/DpuMAoGl6lSGqnnpbv9VbCVoQsW2AaIEZZPqgrcewCLLTUmP47QZRs17ZyiIxrdRcx/6ZHCCFKtIVwHmLnHAmjiIn+IaooktMV2ZpL3Ki7P5Tms0e1hdXY8GU3yhcpWQnMe3UfIweWCQ7Fa9tMDGz2KjYLHBSw4p07ejQi8zXCiaNuTQQWmEXFb3+LAU0HnXm6Ux7Ew0mt4Mvye+nVQ6JAUXQNrnn9fOFbRtNW6nqCu8fndNk7ts0GhQSe8aW/djVyGzNCFZQFoJBSXPucvIAeQtwNo8bs0j38Lql/+P9riDtQpF31lXHjkd9nW+pR89FA3KU8V7JvlLVL4wIrjwHdAIJGEeGwEOoaNiXFqsqWW/io39cNBkPiVatZe74S1mjYeFQMdj9+xTB9OGiC4M/51OPl7Bis3vlIlJyYS2V49Z/Qdu6OZOI0J7kOak/zPU6o9O0hk8fejFvTjvJORQdBwQjmaHUaIjdNnotxvG7L07LFNAHVvxX8a6Q6fBPHN7iLURb8QzWTXMgKoN4vzeDHfJUbSeT79UX0k2Zfv7V/ouerYm67ShXlCD/lmY+duB3BA9FCgap3kwK2/5lbWs8d5fH83Ohb2QX8whot6yVUmsdOjYk+hnMEhzh4VNyzKdNvz5sxAw09NaeCaF9pegKMaqA7/uZ3R5uw3sCYk8MPTJkeQ18qpjClo1IPwEdzzMb+/4bfrbm+c6SPIhsWBhQx3xxn4dSo3I6wsaZbOqP3VX9kjua+ntb6VnoRFd/8n6Gu25XiaGgmCZZEmfOWoD7Dvexof1XPQ9MZgWm42Nr6jPOz7SSgl09ejtFgRpdbzY9wtrnDQxaMHCxoZpWDUTuoT/hVQppgNFg38sN0+/wkioZM3f5PlJvBzGXKpKcMJqzeGlvF7SrwWV4h77tFYZZi57nUOnkN++mrJvzUFL9EALEW8KlYD5z5gKHsMRZhBdQI1kDsh/Fe12afomcex5oaHDVdY+/zjs8m3mljiyAxLeh1hLSqczGLthZ95dwEgdJh4OXW1oplLnp5u4T8NILnJ8A4ydGNhqyVoYGb6p3c9OydMUOuHr801nM+8pOYqa4COj9+9UN1exv+C8GtALR+jaApmHz5qMPZarcVFRXwuK2AWiutU0gMT5dt+MJx8RLI9HMvd2GNu1r2zjR0dsuSQtzWbMqNAJmI7Y1h7PKbPQEZ5s/3AMPw6Q6NngbCLCw80muyjiMcIzQthB+0sZopBQdDbbmE7lOxKPQZRQj8+tIcGZg3VYPKmDewvbF905YsZuFKh/cotdu0Td3pk3yTeOb1Iiwyno3vCKL+e5z6gtjMT23lCXIIZRHy4HcOI+oXO7MCL3VafIwAZOf01RAZovsvo3PvhmfKZrQ8K0E6Ah3UzzXVyVs1LTwFYlxVZZtXzyIIReaFC00oEuo2u9l+aWTaVoxr2fnbSHBpI7krwknCDMz2CHvahUbrjkw8DfqpScVXi3RgDcgbZGjJWjRjMzxBzqjbjKaEAFZYppX4e79LG7LFJNYWN+omcqAfn7uh22nJbhhMj9Ou0eOCmx+M8opZUAK33C0P+EAbSPcNx1Vwi8fNqzlXL7UcT7uMUw0zwipud7Y7n71ev2H1kSFEppmHQQGwWDp/VTlbN1nAvHKN55TvrENg0SBdHa7z5F0T5k88ANocaTLQ+T9Xm7dNyQj8hYeC+DL3y/wcYVo9eGBc5Dp/828qXMk0EG+pfLkbsohqoGXgLL3hzcqkiLVud2y1c7Kba1X06uvdf3uPiJbwcJvemF0IROoCZdduvA8ta7dy5C+RCrVonbkYPlPDOdokUijvLRH6zZZ/zk4YEysr2NqJ8YgoyUZ4YrdaM1cba6rWY5Tx0ivfYW0hezct+WtEAWlzALHYjh4r8Z3v5eM4nZefnxPUeuhmnyi68sEPKyfGkYj27vliLYHrh+Q0FpxKZ6FQK+ikIuL58s3m0vpo/V/b4nx+ZGPNkw6GO/Xn4Qx6d+4u0laHC/ney4auGRhYLjSiOYkXc5HVFc+GIcbLOXfjkP2DTMzPqGgcIc/IlZjmchn7aiabw1t52OTO2lRVk8DTDlzdeyvsxq6xtaRY2H9NlTuCRLbLyykARy1ROcSClvgiF2MT55KHJaY2ngiAinOI+bmur4L+LEDpCwiw8sAxde6O+HZkVW+fqeWmTRGCkbeG+VF+yK8ThQJdidGveKfhU4Wk+0gORrnR6hqSgHsbpslvqkE+IjlfeYLW0WubhdqHP0dsMPK9FhdSFv2TbYlZnM2lBodPylBn4wd/CWoM0uvxG1CBThEB3bP/9bzef+/gIqrGSmMnwmDBUeifj6hBRX3onOzMatZGztlovixbEEz6ZuhHxesBMdPkRzn8RbmZg7cu7iGj1wzvwDDAqwmNIfWq258GOwXXYSTex3iWnro3mhkrDYgQNgFfOZaS9BH9bId+XVoHFGwkhRo5U9aYH0UKDmADyAHccoVin5s6dBvXQQ8dBa7FOP8R1x5gVIEnvGTSLEGNPOsijd8jfEvMJXX0DrTrSaSBYz0Xscs8HD6h4oVMwXTwb/8h6kIJDG5eWgmSszjzitIL+UWLpCpMl011MOzoViJCfkgCsrZGVsHE+bo6KjYN8VECSjeb7b1Wv2TDtr3xFe0rI0ULxGiE0o9wvnCJ8jF6FvubrapBB7U0t8wqjbfEXHu+Ts9F0WHhzm3cuFPu2ZRxDl4hPrZ9mahS417kOq8T6//PfO0V8IKCE9Zqeec7Ip4Du6U5sz+yDKchcolkmCk1BEKa5px9m2gV0/DniOpcgkamFgr89HBWYQQ612PWMW1dQewG32k6G+8JCRvquWsyKEJILBUKqQG5SPTMNhHoboao3yse8ZQrX0gVgaLF9zVDgjqcil2rQSSAOlMSJqRtqRBmFNJAilZ+3slONChgh2wDpPDrf04Fu04E9Mh3JOfeZ42fGAC1GXfduo0DjQZjJY4Eqqkoo0LeiCOIZl5JzAuajkcSoF1tu7EReUcBPREKw4vfks+tfU3JFf+o+1Vd+tNAk7xTiHd3R9Ej8lawbYeIm2eLMY4wCTjrUCA3v9vZ0qIyLjvb3aXjRYYpIY610V27AVI8XL9SnvrxDAc5EHlVzFXfVfIvs9XKFoPEaB5CsEkTo+/aXRF6TcYx3bieoL8f3RlMAH5RnZYNbuffuufvPlCyRvurd6R/gU4jprNY/gFgflNQusRBQzlH++PEn5DlZfY88Qvdf1w6rdoTaU894u+2G7Sv5OpQRBB//7Ouh5mEDIo8vwiju0poerX9Mw/RhC1tt3Ka14cbFa0DQcJ1PlS5+XiMx22Awva0IxuKOpdhM/tGPj7TnIhQ/EEMZThr1ND04DXiQIXajxFX0j2F1a2y1Zbq8/10MeYuaq6VbkXm8OjfQdmBEIO9J61z06Q904goVj6Zw5/b6nXLbg2Ar7aHGliBfQoi2K3tLa/BAUTrS9Whi7sKK65nbDN5I8G3iT1Y7OW2LdMoNpCRw4STJt2Fv3USTfh8DcY1J6yAWCQ70ARKCV4f3gOV/2sCZ8S1R5z+HnpZLrS+fm/oHSoJQ/QatHK+ftdX90NfxFrCz1IqTXOjKhgfbuqHu3ytpNNz3cWfnVcUMfeLAe0xjc9IriJktgUO/l3kyzqxzqoTNAz/DYibFFoqxuh58/7OQww6PFRvSkrAp7IBYdAzi1IR6QwtmzYicny3lI6bJ8dVrL92voA93fks9UvrY83pcQtfX9eMOh24/vVD8woVXUt7VABcvWC14F1BKhDzb0a4LUch391CanW7QxFQ5O0SRgv1N60bbN0RTeb2J0ElLxfTg4WK0jqmDHO44tzNwjJm+7GETxhJFeTqzo9fnRjHIwZpDSCshtWqqoeVNRzt77YSXiUm2J3HnwPK771BSdrdKj9oDcB1fidL4D6Z5beNfp6wh5Lwv7EGd8w92zbxj919BTrFOubsng1GPivdvMLVZ+yAdkHME7WzJZOmTx+CFeAQg95pNZlwAdp2FSxtcKTUQ70rk4HeZFu/uS5KtGqoyYTvwZIESx4/3qKxQmburL+IK8zmWuMqhjfwEi44F0fUav1OV/4CFluIcCam/CVvMa82rgCXlgDFAdNfV22gULvyNFqTLTfk4Xz/i5p7Bwl7lZIQfxMsZjq3e0X7Rf65lb/hRTZjwaMTu24QYATggexezYJCYZgqjAka2LTdUJ3R2j+4gbry0ELLog0Ah3vn9OVGQFUaVShyjtDRrwilrip28kG0/jQ+C05maGZ0CIRC2ibOsGN9sqmDth0Ck4kr/+tsDlt5buLr5ashWpNv8UZ/2lnM4QL0hiOA5A3l3x4dKShFctP7rgreUyRE3Sh4o2LTk3MexO0nOZ8qtzAIEs4s92moHHe5BUAItJIB4sm199QbW27FEoYCGyMEZzD4pWpAhNr9GFlKge/uK8YXPKYqsMp9fD86R3/5I/gwrCOnJ+4BVy177rpMvGiVhrX8nQXMZ9Nr4c6Riwc32Y+Ygva8QsVgXPD89+f8hpj6Kx9l/XWc5HdOl0IOl5GtlNcdBgj6F7vwbEFutyaAkaZMdbet3v/O6M0aygrrKachZBygbKDidbYHn11r57hbs882mZt1yUmQWbceprdiFUu0h288DJxCEeou51dn5cDlez+jD3+EnAiloCQf/6RbR2u/8p1OwNQz5FTGoyn/CXhAbQpVPs9rDTwoW6XCPLEpCA5S8z5LkSj+pW1ILfJ/rSIzh9I0RkRQa1VEcd9rPUsM6ttSI+s+hbf8+t1tLWsma37A8vwjHiQmJURWY2iTtxQ6ItpoVSMnCtybhOJG45O9Qg34z4ZhAY0LWRwCOfZ9TXMplp8s5/XqAdPW/kRLJXMGxtKggh88bUjCSYe++ouGq/tFIvCSY8AiiaBk46bqHCxGX1ONBYU/RYxv/Glrw7Lz1HKUxqP1P9TV74a4JsPDdEdtDnkwJWjoLRX1gdUL1hvckdhdqzfCNe5NUOsrIHMAS9x3xATK/mC2qZhC/Ykz5qV7N+Ak0gsEta+x6+SpaObDAU/Z6lFwZpcK4I0mrp/ug1SOf+WBQ1YLenNJFukt3otvW54so5zKeKrYl+tfVD6bCwrgIMbX1xr10mXO2TyDrkNbNlKvEVLC5syIOFBTSkeJXoO9Yg7zpMiDBeVVoUG0Y0y8TZp/885dtyN8JEYguamEYJ16YzuUxMoOqiqfpXBecYEngn3lo8sl4LLb1iGbgiOWYU3P5Rtxl+DssF4bKkd5NQc/NqWznPmpGqMObBJAprvL9MxPoIw7ymKYw3yIlPdhIR35zzsBuAnVkgXyf0yOvL6N4ks3nVcKj0ow3jXzz5W/XQaIRnXvIX0o4CCNaJ5ZcTGAil9B0oYp3jWXcuPAe81SKD6xt/Bzuj6iNB6GrNtXTbd5oJ4UTzxqGsfXwBmf1sNNntNrEH/+28/TTUT/8gCFxfcKiKPOruauP0dBVGiBk/2JqtbwjISjiJqotjieVr1GO2ngdI5mEFWJKbxrBPVBzsm4XbRO5NMuvv8roApKzXpk1Zo2OEm0Mqz+ck2qAL8X2LrcwOcvCwHEChEA0bTLQki4tTfSVnyGcnDyscfiOViiBnh4eJ0Odu8k+PwRK7iDiLXSQB87M15h6W4EmD4T3LFTrjqzGi/oH6unbdo6ar0+Et/hkYKps1haN96S6AY+NRfOCmlgvl4Um9pHfUstUlCWBpN1mZteW9/wVME39yH3izNCzv5m55n0WHSnQ/9TTRTf0oz7xhKbxSnAXmC9ZXnjEofxvQDh/fZDWE0pGB0vVXHFVjkO8m0I/iI8l7zsugaIMLlyyc5l+XrPEmEvXCoiOJcQ39RlP5r1cY2ScLKVNpw5z62HzTqkZ55aXbUvQRSl4L83qLb8WugFVCrIv5e5RXPcGc0rjPw02UxqjY9FV6Fr+c18sgySSTl7ZSs3FhNcrwejCqt95FM+FEsOQqHdsDa7KrVsNLySQXbyX1g48vKXZ6PhOiBw6MQKLGhFRWgjGd8Pj51EhA7/tfyLqN1CT+9aatvFm+c99hAp6lT/ljdqF4cuHirlNb4wToWuDN4dsnqZPDH/PdpDvPYtmQNVmjMXMTZ23tFbpLlrx8k+iZWS86pK8gmO8Lb1mH7q/FM2rZ9o3sSMd74VNYJoj9Y6izrqlo7yfHcYt7I7xPU9VJpGZkyJA6fSQsgKdnZ/ncQnGGzM5+l3JK1H2vow4RlvvmWa6C77LRfmm7TD3rIXFY1sa9ZtW4vpGJkSgmZ03QFVOsxzcvxAwHTED3uHyRYa5HYTLP1rWDTEaYQX/okpMhdVnK2IdxmYwZZGlq4oGO+GBROp9XzIo7WTs+3Pp+r5s5WNFhPXoajkRfev9auWdymtArjZU3ZSNcIx/aNGcRcPrl7ekD6rxZWgkxuc5U4srj5Jws+x7DxkaiUXqkm1pfQT+p6wufNBehNdkKP2+ZOaAsbb9ep7y6E40W+AO32fzdT3rCFvHCFayfD2nq39vyAKVWNz8nIlfD24cVwmx+jvjeWJW5TVxOCb+S3U5o98h5c3IG6wBggl723I4eQPrz4YfLi8KPwNUt8UFPZcHR25I770OQGkFNk9iEoNYyIkbcXDTDX964eabqr9VDFOoL5tBckPYSn/rmERoMAwo3pa2GaTAZ2YTQACFefd2qht2UQM1aYoUHTRpH6q7F53/zdBVblmpR8mt6jssQd3dmuDtc5Osbsl73qGrVqkzgSOyIrYSMaMB2e9Ee1KPibhkzTaqEIhIifhlnNPg+zJwbpYENUNU2eJxdHnjKV40/araPx92yTeBY43WAuZFeiJdhuJlB9xfliqkcfsmBNEM3dcmXcuBfQgm/gMSmQS6306BXgDRw9UfgLviL6R+pkGnD/BvPi0UN9JBwW6q4OdtG+ryv0uXsEGq2QRef7wnMnP17jvVIcvi1reqaKERcyqjxINj5c8O+HDkdYbQuCSB422zEbINzHRGV9zh3S4/mc9SSho0ZzxlbmntO7rZkOnGof8WAy8AxSJdBFU5/ouh6DxKJFXf4bfCySbZ1IXp1/kysP7g4VHoUiPPpgiV8EXarP3csFYbOlmphW+lExUD0GawxbkttmZ1dikWKRaNVZiKJS2Xd1+jgZ8lAukttR3MYFFSyA0mQsr/G5K+9bmaK4DJfRRoDXSNCwY2J2qQBRRiXbcr1wuBAAa8VWQK1PIBzGTlld88b8eiRffmDkFGjJxzG2pHH834P4B9OaO2FrlXx3oa/lmwr6rIHgAQDBbaiQYHjti5E+PVJ4tXxh4x+8QdKoSDN4d+giS9ObcLlkkueyS6njKmA8RVJhidZFpJj3GqExMkv3hezyiOl98OUtvT6uAFG0hrZoZnlUfyfEKjNBeGhVMs9vpE/o4yHbLNdhK5bWj4T55wA9tcom8V7G8NYdCiglFkWFiZx08I43altfAWynTTHl+Rh3U5bZ/VLOZerwrVfBav9omWTVjdb1FQ69QvC40JOJMnIdREZtbSLH4N2efLzf/RJCy0bIgV7+OghAlxFBWleVoEK0dTvL9BGMbv8EwdYEj5fwgMxCxQ9WrA9NJn3gUCqSwlK1/IMFLWxFEPHwDPFu+9p2vMqXAN/iN88BoQlSxsM31V7HsXqn95fD+En713jxjIoQXAbyTlQjff33wqOhMF0aC/Mhcq/TtWLfh87fJvZ793J9zW8BSid3GQxSfCMHQpx8CU+RUhadgWviZhZDSvR4FLcBmVh1NK47GCfr5DJNedvYu6GrnAxka/+nq/++vA1sHDm01MuZvcURcXJ6vCLn1nCICep73bZ84rrX67wAzcqeVcHdqUKFunuiYxvvELvFq9oB/SqZY8NquAln5dgUhWbF94N6DvHKMY1v6SvXJGtLu70vZ2ibL36k4JfcfhXWMad3PV+PnAdm/myvRN4EnN9V+8LXW0J1VcBNVdVIrf93ZHJrxS4SB/+8ZT01qAbAFlcNjR6ZFFhZZPzGaW7XAlT8qko5P0rWbX+yh0w3kI0fYgrz0yBVhYZ7KauqdqNLPsjBN+X/GeQS3hh1x5zhWiHwOzV5i1MrntP/p791UKXHDanRpdqYA+F2UDqu2W4EBZ4Nq5gtTDpiOBfKoqZpqy+SR+fKsk72mUzXhBVihC79JRtmVOEHHlNQo4E6ZS8kBlaCtCJRzjNfRLd+K/MiDxKdE6QfYQ1vnKnXkxe+tFPeu4e1w+0vwwouknFTHm3rqK4nrN8u5EC0exriu6Q5DhA5er+OgxG995M181WgbWy4iCzyGIN0MqWY97xc3JzfmZ308Glkm3GSksMaRAIHr7F7Y+9LeElZDQik5CYS3WAr1V0jxI7QnuKdvtfglZVBn+DwsLl/Gtm/O3sH3T4ViP2GdP8FZaLscBp8WA8F/f4v/TzcShEOT7aX2OjmTkL846lZyELjdVIFtnulU8tOggh9au3HYCdPdbZwf+KNuNTnitdW5Qnnb5L9rB6YQ4+bLhNHf+cdoR82lBB/PxZ7MZKv02Y0nPVnnB45MLEpLw2s+/V/4tueeo4lozEUiAXfT1/lhH3kIS7JjCwM9ctwLOH4A5qCHcTIud6aCO26B8NMKFj4p7Ri+svfYBPCHxb92ggoYXb8CQvgvUXGm7f0Gh+sOSkte2Ile3qv6Gg3eYRUe0VtcAPXxomxso/emZBCkkuXQnFWwn1Sc6W1l0IW1+//Eg66nKqujjrVpEhBjak/XTstR1x85HiRNk/z9ZKHVVs7OfI/7L8uaDy3VuNMsz3mj9r4IpV9K/XUvgtvr63UCFQejYOpN8kH+idQlTDL032i+wyzX1BpzqaGLN5/Pj9fcd93Ih8H8U+7j2K2ga0FFhp6LNgv0RqfHAM8i9z6Gu1jvsy9RYJV3P031IHAAlBPO1OlrefIVmk2/jcO/388G15Zfg+H7cue4zR5KMOy6iGPpYz7YSiVNuJOvr5W/vSC7Y8NADPBZ9hvWIvkd1jkEu13nrKByj5GeDMD5TA+5v0AyZhXGu8ffT9KjfGLzCt26TkDOSMppSiJjc7PU75BATPb9HE7IUsHdhFGMPGr777u9PuuNcPgGX7bijFtP0aIm2NAM0yRfTW+Ouvg6vxariqkejOVjHBhuQighufHgpxU+ak3WixvsmSPeCl/iXlJBu3e6F8FPHPdTzdznM23O30c7a8f4D+hgff9HJARUGmmNwHaHQa0HEjyKNPQGRSF6F/zbIk1PzOMnydluDkGe0qM+/o9wX4BmW/RBFiHpUo/Px94TqWDhFTsR9fPeYF5ulfFzKhaXwqllMrFOiPKQDs/EzfeUTZK30+x9lIrMOUHjEbZGoZEXwW2/US3xjWa78MtayjmjZKNM7K/t0iUkqEeZgDR2mKM7WH+i5rRZmM5vU55W7fzSc7MjIPvPSHDd3uI+lKXohUIqAS7jYhG7KxGcwQTAMEtVwDRVEjlhWoL54oYCjXslewd3q+CLmilMc13GKFzrlxjJZHpVxqTBUMdDLNXf1mLzRWAV+kd/UR8VXvLP0IawJTavU3HbETHxHkr6dL475msvNrUO83y1+zx68GSMlEaN9eI7U296FXanz8+XGSia6j7Mi+hhr+GRP8gV5gGD+AA0bJusdVsqp91r/ElYtbgAfzVXZK5IXrSTiXr3Ly9x2rrd/Az57RkK7OStJeFuAQiOiLkY3oUbA4ngAmd0bjeyOZQ8Rc+Lj8Fa7v/IM+HpFeguUB/g5mUR2s/jNpLKWOMNjX2lom7/kZ4R5IJyEflgKfZiY+G8qTjkh2RjMtmfSnhTNFDRQQh2sukl58d7qXn54o5+19qhIopmVKfcWnAqjmRn4joe23ofq9QqXl/T35OFmABq2W5FI8r+lwqo1d4MxE1P10DEEzk1qiKLaL/BSqITP2BH2ZKnwy09/XNqi9GCSMPaav5MC23fG3Hz5RQCXF7zH/FZToMZQDg3Xl5/wttZ0yDWNpumwt1aD40rQq/t3+OX2pWf8ZxqMWXzXcC77E+SrhQCfOSbAruicwJo16e3DNuCOSo+4mfgouUqQ23Ame8NLxak2FAo7E7QrIGr49I6SFyNLGDYM18rXfn2pB+WMmIt53cMY6DcuGOhmtpIjODUJHnY5FHmG0u2UJ7aThZbU2CqHyq4eXZpSiWqIAwve1tR2pfebnjlGT0XUNKlpUAd8i/0TkFS0x3MikeAUpkQ6v8N01Vl0UXEzTX66JdyJRwqhFSK5VX1iVh4Ju9Ero7BymXeDJNF0wY6eL1X24h51m9bAFT6TOAlcmwJ00znyt3EfxxRsyuHvdoLko/NVO/g0R49HBQuB0YRmTonv9Fd9SykbpXV87kjMk3DOMZikzMOqP8fQOo3uXl/8M8LzpVa9JEAnV5/MuL/H6yWBT0FB71RqDeK+k3TKRUCefsaL4WG1dx0jaFUrhMfLXHICs8W9CJSh0a4xCVPX5IYa/wWaPZ3vVgVEXlw/m+XwtLYzHVy63gZGFHRqXIgZSxJ2q8jMZHEAUhYvg8CgSZZlRD1qpHMxxI/TGnn+MwlG9UyBKnIYqDTVUIxHPAv3sihI0DHBNMW6Iqw5lIj9Te6IZ5q5Xg+A/OLg2+sPdIekqdVX6AB45aZYYnGlqxvqRq7hJhtemLxww2oWULXI4ko7FbW2dJCVSbn2A9lccm1XNyTh1udr5tEI+Lrp8dA7tl9mxf2o+1Cx/9qpv4C8iTcLT8Cl1GgNF9B26fKxhn36W13rTTVG+71vmxlBUGnEntZ5zp516sj6pYtNA8SVS826gzicri0fSHG3e/UjfzXz799q6j/wcvVG1GVjMS4/PDxwS5oxiK6xl+f3VLqR9AUIuSdeb0dnaYCNZyNSY3p+pNa/6hQlIsP8WYI+YwLjtna4xIv7rl+E71RW2ox0IbYQBk2Da0rRUgkRyjfjqwGu4wr5V0oXk0hTcUNeEjbHBR0/Kv8AN/QsdKL/zCaHdunUuVoJ7xyfolto6LC87fCUnCm8Qwz4paPz/Get/lKaENeIF2FqrTmSw0rj7PtNjHYoS6y06COhnrfjHKCM7S+66tymZf3+HB/1NzhBeqODQSN0gVMj84jgdNbh5lALviJRfoKY8oPD02QxYFqZrGTOxk4d2+Tw8h0/qBgCGBnyhcx0VItz4FvnEtUHJtdkEL+9hppfI5LAgsBljLMJaljpKfyf9ZXkDQCI1kfISSwhyUFFfQQF4k0XxUbSNv8xXeMWi5jrU5qVjY/6eS6amaP+h6313WETC9186SUo9Rvo0AhCks0zoY9IRfDo5uTwXafFP51R/jVi190GrAIZr4aDYBhR04OOHgh1aXpXTaII95K+D1fagZRxdqr+UfYAdfJ8kMX5ViGrNp/K7KQ0KCfaXR8gIN6+VWGaNStQAfiZ1QzaOmm9CIRFrqJTUvNR2EzRxvdkGMu0To3LLdyDtFjPe2Gt+vQUZiSkQVNWDVW79IIg+WXvopmcNgoPJYZxq4M+FhlqoMwaJzDYdvI1aqp2MTB9f9V0MDL68kuEiEelGviPx8/4GCGC35pMbjDhMNF0tZkHq0pXLFSauqcwmOf9r5kAPHBOowU9q9GekFZhiU0CKSDw7M/5pXaNpb17WsC8e/tE2X669pK6u/06fpSZKS68Oap1/nbEq9oTqZYR9Ya6IFfEdn1HAxf/qx5FzEBpON4P5wq6PPfYgvMspGxNpeg+WkOsVMuVRQQaiPVBQG3N/9R/+CSk4g2stpA1FsYpmOm5f6zv9G4FDt8uMz52DZpUVty+btMO6pkI8J7b8CZh9bzJ4B1Kli4PyMhvTm1llzgzm+e5bJTuPQKx3urYCfoycsuAnRi+lb5R2nX6NpHkKDpcDlFy6Z1B4GWj8B09fWejf5E/Uf8qeZRL2BStpwQ/X9Oc53txyEXQFY3TVnwn04VrP1LqMgQPeaomK5nhx9jaGJHpM15zRAfpmhWOGU06RX46aRgBsH+VwSnYIl1yY4uqoz9D2JfAVsYgxPp/6OUxMZNatgVfvDhRaZBkCn2pRQHmxzB/5KIkNedZsKb/6MXbqXe19NhJwNZ2FZ7DxPrU0TkhcZQu6XgrwB5DMCevFqQAnr6+8C7F2gOzZIaH+mnqgNoCxSyNN0+Jmg2KoKG6Qrww5g9ygs50g43twCOA6XxbQwifO5l371xbhIElc0GANiVXYfUBBEga6Mxrg2iR0MzLE8QaRFLLjO4n6Xc3D8uTHQhezaHlKxnNfsKGuuGq3WI/6FeEBISvHTr/o85ofpVw71GvM52px9K0FTQ5pw3eB3w04wKgU9tMb3NBfkj1V6dg4JfVX/+ovnWGPRSCQ60ZYh/d0VlBMm89WyvZTroWvCfnLNJG/4jU70hMcVV4cV6ympbBzHHcxOHlOBWi3smotoWk/+QV/dLZnVO6gjMLoUSh49AL0dIAFBiUdNJUDy906oX6qzQVr4cdwVvLQwVc1wH89FccUQCttyDlSdCWRXegaxv7agfbtjZCVfMYlt/mrKHeOJ2m28rKo+5VhszakkAObpgkW1h7x68xYFeO2UkauTTy2gd2mnLe3vPXneNo5MTB2GEqD9nRIuHk/tCXUMOiyVkRfdWEqEeFydw5JK2shremiplpSDAMImdJroAH1X4KBgaYMm0iFjAGOwHjefJwGNFDDV0ysAsqrNDZURY28btcjuPElWywODiPNOfWGLFg/MflJE65KzPHbFglxny2XqsIQj71KguHNPljgNaD+T2es6xY2QputIu+QghI89csaymgK8me1+ZuRg//Xk+bzYN+I79cfMj0eohgBtArEho9tdQbylFTgsNPz4eWFIVnGa/qZR8wdcvQSAIbRr1SjxrVTebRww64XMYG//tQM9ZyMW4ljZNl8OO5YtH9FHJ3ER395T5cDliGVML5UohwCx7ySFuXGRZqwGdyDSS9DTur3fZNr4HfQ/IUehlKJ66o45n3s58uSJm6qba+QcuICtztp2DsEGWdfCbL54nbZoF381040+xX6BOUTeJVDxVFW7q4y9p+364Oq4ux0iO+AKCQHzjOrK98E1v1qMFI3khbiS70ko+9/8uJnXStTdEQfFWltMoud/NyvO5hYH1TT4nCeNeHVEiiAY9ANCVdB2l+9upceE2VhMTvarvHb+8/NWiX1tytqUpHPnvGJ8+X7slRd7iy9eQXzSOoJIg5iHhYYD12Ltf2yxDNzU0SdFY4MBCg7u8cEdVTO7gMn5XzNOlbjttlYO58kf7ZF4zkb8r9su5BjVMm5tBtROV6XwK+OWIYYcUrOcIMVkq9+dRFWIq/DyZFZPGLy9SC47nxHWYRgaOSpg97cGFn6j1xQH30CX0rhaINaegZ6ypMG52jpavHfkZrV1d5hzCewv16IU41dax8VOFQAkG0r35wsWjW5Xd+d5NhF6HK0r2YorZOX4JOri8uTxW90qMU9S+4Izf9+JofhOugvYLAayKfwLnptKJNV/0pkOQt1j6gNAM9lIUQJT8xPOeSDmOKWBtXPdgeQmL9ekeRR2ACIA2yoJlL7pSKdOpKWFQ5QpaXKDCqAkblvgC0u9wbN7f6JRsyig73pIVh5hmWUI5BI9b+ZpWsBstLLMOOr0H9/8f9AsZzcqmdgXixtT8t1vObHsei8Io+XtzoV6MNPS4MrttBiAiYG6+v7Z5zXo9Z28ztT1pgSE1JgUTS51HC+t0Wp4sX1fxsGWa4NGEePpvEXC9qGX6HkhDPsgLF+iVi0gi3PUUO6tTsnjVOVsKQ07EqPthIIktWa3PXq+KtFnKdfpv7uNnzN/Q65IQ3nfcIrKgLWUjupYUuLi34tcTiPOTauKCj8hQdS2lEp7Wx+iW2tUb+9hhlYQuB2LrvoThYuK0E0kHQCaSE3GV7710iBNzVr1drvlQFNDDFfdoRxD4zYSPJr2cvG3G1rt5wlf40mAij6ctJ3syKizfHZP6x6WcF3lpqdhAWP6n/fsWZlxa4n2sC9j6c3fs6GZHUecaIvfRDHV3zwckHzMFAAk0MRGRfclN/t+o2niWQognCQFx9PuOAEVLc4kwyq2ZzVm9Yv5uKNZdDXp93qUzklKwxQ4apGRXd7HZJ5Fzhk65EcW3dKf6+1ydzLD2qgVYExYUbCa2v2/pu1y0UVh+vJj9L7Yt34L4YoTphYDVfRRA91DQqeX4McQwkIIU4uMdC+DLBfOof304xA9lZGZjhbkVt06vVe0CZjdtsfgrHmRVJbpapK1a+PKGpfqRAxgz63rbEK+PEXScLcT2kTcRtpz1kJOS4OLHVieHg/6edaEqfKKV5N9SHzmoeigrEC9oC6XWYfwPOyeL1kSe2utAt3oH8pPSZwFsTiGhkOa1R2ZdUnHKjQU/HKIZwPM5qsI4ir6IuCYewaHgdrZ/lpPLLqVhrCjoL7/GUvgE02p9CIodXV6RanEFBnVtDhzaEGUFTiXzavde+Wkt+fCY1BEMBm2RsaYD7/GlauTbA+ggTobBFtEAvw8yP1I60yvm84pyjy/tzsIPpYnGQ+t0R/nhmeoEMIMzIzV1oCfuk1Fh5YNxtLzp4EPGhV4wvj17KHFuZVg4G/foWh0zjRmK0s2ocsWtePm5kMd9UEI+bz6e2jRkLgpWWgkF0fS5ZLxvtC02qOMSWEgg55c7ep25ZZnNFIEgRSgRy27zW4XL8hS7V1zITky2nlPQ8aJeFjgmiq5mtMToF/97QRf04P8fNzRJx4dAg5tzozeUizS9RoT4rPP58Ox0enhYpbpVHVNuJzyPQwUo7qpaDv47Kb+QSdMy7A0vnTC28+rEnlDmycIjF5Cr+bEUcBKXCjsn4Rmgkas9qUlb4OXXlE21j5bvDL3CPO0STe2waSIywheTX9t1lp+0UW9G+zmkfU+PinBPkHwtrWch3qC1mLkks3OpnOCxh9sejk5hUPCT4/FN0gC6r/mmfeNWsFApwwU+N4eoG8NJ+8jhiN06+5ddD3urgdbZC9Yx2vUv+C+2l1G9thVRle1UIstexoZsL9+WIMSHCbdu+oL5Bpye4JUZEVz6UVmWkHhXnzSjsIyHG2FNKd412NVI/qVhnj8Rso1l71an6ok1cyaRtpoSLdl1+qdScae2Uw4AgYS9Ap652gRHk3YXY0bOz09cfgT9ohergVtir9K/zoV9nv8X4jBvWl8HWEHTSawERGqoo5bezv8GBDrK/z1EgFMH+cDWMaDBcyhxGyWM2UkPTgdKYlQMMp5paPSTWwyPw0LN8VTlYpWXhsvdWtAWJGto1j71U1NyKknnustWhHKQKV528tgHDrs5iW98/kmmVfBNj4gSnudQbxbu3FR/a0dmUTbsJgrC/xEhGYN7fXbtYy3rWllGIQtvoVy4AvFoEH8HR/08qrAVuDM4XK+0RenKom3faUj2qNj7SqPQ4MjsLyCpci9Davqe4d3oh8d9K/fxsQUhAmvbscPq5B8b7zLheieCyZXYoh25qvjrUnX99mGKtHWijhoGr0CpJSjNb2j9WS2AethcgRI94nSqdnF5UhnwKXhA0Qdz82ZRxw3gNNZNGu6qUSMTk9/E6KTzZsvsYpGD3Bb9GqlxGzXbg62dt1AWHX8NFA+S9m990A465cJF8ToTiga2v5OaIE+iA7i6kyGU0lrWSQbcV0wyvvz7S/wsv9zk3IVlD0A1t6zx5PwKDH+gF8xoVTDD/zFAhjR5Ev5aU+INhH1WhyhAO90+xuxq+Ob4Ykf2YgZEWrEsMuP4zdDofww86vZROFmdXHeoVzxmJ1VbaFtUG9rN3hpOOQJI6/X+o3NXFJsZX+0KhnVU6HcpNbPyOF2wNpft4I7kxsSDBVzZJ9I3EhBZATkkxrkWkRAsszl/EGu99wk+eSf7Zd9fUHjqstToOJ0wWzNxDgRPhp3js1J+nyPQqeI0WE1US5gMHFxseDqw79JWcZxLEM+ldLln5f9/im7RZAYCZGjHohuWoHnmRdttgf8nE3ZBSRq34GN5F7jLuG7yd/KvUSRLbyh5Ssok2g8csI99VtKR+vzNDfWo/x5hgQJt863peTyrCO+h483t9Ybi990BImDhNEFw1iugzUr/DQkLCFh9vcGRjwezRb6Bc5DY8bJ1DWgLFSpF+tNo0zvtlf/yKWiqmp1UsPMLBrPGwCF25ix98t2dV9UFKm1sPzeQ4L9j20IaDFpQcgJORP8bgxVZ85gehhSbtQzYdVo3eZK8/n8d2WQQX8DRX7WPtvYFktCaIudUc3EPJaU91NZW+ms7zHHGWh5uYq6GMtE9ehImHlM2UPmjXKiBuzeMq00fLwJ1gyXJkadxFHSaUYi2Q3hFu8PpKiPh1jaWEu5mGffyKRB31v9V707THTzi4Q7Igta7Pu+GuZPH7htu7kkR4wAXjjKo0QGqB7xTkYLlMHTELQS5IygGUM6X8Ju19YiKEfnPFgoLqUUKxXOcECJ4F5upVkokxe/mIImoY+ueKYP+uvXmGunR75KIm6BaNoHdlqZiYZ0f2gLekxSBxxwQ4gU0mBWTPAGew5VNsc7QNCJSqgu8BJ9gQzuJv7eaKuldAUOdC/MgawG0MWrwYOk1N+283A4k6UTUiWnx4Zxk6bLSUfvGZi472I/7LXdGLWdEKqIaOcd/uB2QF7JY2xPARgxF8gFCCDb5GXbKeYKeSIXj3y4oCrohjANuP7onDOOOqBbIT2taku57HU7z59fBm0CjFTAIFpqQedUZ/Cld8urv/4HDF9PJwcIqAVS1ITKxsVqhJfCfq6hJd5FmSdY1syhJPeexARTVRGFdRgXWV4uhIxVmt66Q04JWDztaik887ig+EPnUaWHpbeK3cbUA8SyK0PD+KL/G2UovbZ4lnOSsgo1M8wwkcBkLZCuCk3yAaQEbaVnZ46292k+vL7xPQACuA+KuAG8TpWlWDcWkbhacer3OTI4C+C2BHbG4b1lpzSKIrljlPTAq7jyPTVAYG2E3Og4hNqb417Sqgpjq9aZRDF/FxcPZvObn36J0mMutd59a5LRR/IihaNo68Snxt0mcH7URkVtmXGxjtl1NWRUQDsd7ta++37Kpk/9A/9LNfpnnISQmT8CAtCTUAbgOew3ad3Q+QkYmDYHPjOC59DoRVDTYTyY5odUc0H6azl+4xfK+ErWuVQero1TmINtjubY11S9V/jv+hdxCBqTclgl4p9SLYlXQX5sb0pA2o9OfMYZKHI9BhlE9jKEcL9YpAI8lJpmWa35V916YThmaZZDG86+TQihJ58FV/AJzyH2WgVZc0+t8JlJq9YpTHOThbett77OHXbUoDIQzc1lEwaIXOaOzoABY3GQ3+79Ln4lghLW2nyoR0IG2yeP5lPFiXNI54IAkqLNnabzCLCfN9DSHYqNV68KlGtSKxZBYPiLsKOc7+/vnD7I/sSgfCYSlND0xZluPj5uOdDAIZy2jNHmeyXZhEudrIvfdm66Z6REIsWHK7E+VoTF4gq8U9ATYlQwrbddz0flaR7FO/tRzwo/ZKJei1lo74JwGCWI6bMrx88f8vx46RlLR0vc1C5lkZph0xVyuYW4JxKqaoLfc1wYuMBCI5plaVH1WiLicP1eERaKJ3SI10jdB4y7ArPvtLfkdMJMRA7ZEZyZXEG/Zf/jtu4XoQN5eSVDHn/i8/HZGLu6mh/tUWcTd1LzvvZon4BFFJZTt+v6G6+u+b2cpAD89oBHz+gHgIqEBPN+UGOaOF+xnkFHoj46nPomqFb8rB/4FTXygR1E7o3H41ntzVv8bgm9U2fJeQVolxKz0qbWs9uJ+fB0WqLdaB6aBi3oTrlnElHHpUuQ8bsjLyOAdNoMXtzWobBi8HLaD5m42WRZdcAEB30Jfz88JUDm9Yi9hStOtZOfeGVcfWhMK2fHjWKSHQmjZeMrRAQQ4BhMjCXQEMNVzKHBW2p9q99Ls46qwpMtJmJWY+lps9JyFGGxlMq91Dq2TG7IEL1Ua+109Xq9fVDBp/gyWJ3/eXjBBVBe93Agl7xQKQcnfcaqOiBn4TcdUJ1fxB9xJ/cuBP1P28BzZiZFSObRkGM1pv+NuvbIsPWBM5/3j1F7BXOqqGdDhme1tM+qBbVyAoO3j3CMFTpwpqF/3Acgttf2lV4WzSf09kIp1tA/+rcwvhwjGb3wkFiOlfvRh923I0O1XnWh8aevnapNNVqL5+gv2g2pD87Zn4vR7mR9tnyGHkNI7Wbzrm9R6z8TbBcJYOqIKrH0opEOTiGqMYxgxBFIe7yY0Na7RIVDu6Yot5lhu6qUNJ1BYLPf4tLOYfmXfuXW+jZkBxQNtWPssUW1yrHEU/+yPHLmPxeAYTpBXf5iiRFvqXl5ePVyWrKpV5KbjLihwg/FdrexUiIZvRBT3A6fj6RspEggu0hPnpjoxO1zeV1zAg3j61t6quAipMRToqAE3iNjTIDRXqaQP9RKO8LX63tOZmnw/xWe40i8TXIO/kL3P1VhNFhiYEGyy6LhpTJ6jA2iKU+RS5+l3O7CVzFVPreQezGzz0DUyVaTTcAcT7560dVHP2ShbNy3dj6yM9yUUbHtLSsWvWqAnJXxNzM8gZF/Z3eaeHMiEou/nu2jKGmbXpFNMe9BI27uVxOBnZvdBXbWdIb6COc+0jVL9QEJx9dq1cx1PDGJGSJRNhEKsoRqXVvvbyqi7uqEvEZfl8+DJfLj9vpcPzuUIMjbD8cq6QylTNDnTbEmv72Px3U5+tD/R6JBFQm9zBiXJV/z3gZk5BHWsBSH8jtBYwWlMMI/T0GgDCfabtON2elfCYckGdy9kiaeYYTq6EjoLmdJuU4NApaMrL0rQTc8keZA08GBE47sEe3Gnrl1BClGdG6QEiP4AGoN3TDx4nLnuSa6eYA5u19VcGUHVZmJTygOga9dVgZ7ak71fwlaDtn94dTljfPq235CS2oiLytEZk5YeyFYs7P7X/kazSB2m+QHsTfPToS2TIW5kXUPUzLXsx+fpKmXEpdbwJVf86qBF036iPVg3tH5rVJWA2RkYFZzCmVhlF+nevp+GZeApuRX6crYqTXA8/L7Z+r8LJ58xBe3sGmtITdkch/k0/o+5CH8I6qrNYYhddowyK+Kyr/ZQlLDS/I8YBZFBjzn3+ZJznjwURktN6dhfdEgEUdTCZgrHv7nnPMqaZhpyGc7oYHq+WlPQVE4uJCBFRRTpzRhD/PBChFURtBk+wlrmILzpAcpGW/Clq/++qHSzOp80M4Lo+dmEyfSAqGIVSAfK4c0ACqXlBlt4W/w2KF5hrw0tVi+MvWcxWP/XqDtIG7QgeGoG16mCqSyKkWuJ8p2PGnSDYQ2ef7iU5c+agcbsBQuXPDyfP9r+2FHLYDpupEuY0oKkmYjjmJMBxGoclKZsokQ03hDJ/JCDI8GF8+p8YXWag40H6v93Se9PNjg3cfco3OacaO+/m+AYvACtmv+wqmMpazF3lAwu8Qm5J5qlNHuBKJTg9vqJ9xM9Ao70u+U52CpqlTC938F2oqePKrpsLiHXNES6WlhdnB3zz7giro1T/VqLL4h9cK6MRTespZqXxptcVfbgFPMGjvoQ/ZArk0qP9XsdzzRAmdcj+a9al5Rz+hKYBDCn3zNCTnYEokLt8r1JXg6YDM+elr2zykUeTSsKws6QjXEk9kczfQRskyTMq636XlHT4wOebVPuM1tYMd/b4W2FrgeDbti+/y1F/B7sT2CEUGoxEX7Cut3Qk4DRZJ5OwSX6HVbDvYrWSiBDl34pZByaQlbzED/hBgRRoIp/BLY3yPDpjg0TfmECbQ3Hk73amX3BWvTUQG216+dlLAo2eJya9jaR0XFj1nLkU2TOk1hT/nXxJY30OAxh9Y9jk1rUtus8a2gLqVWt6r+V+hgLeoRY4cE7hNVxvxXV9AxDtdJJqr6SjeZlRZ+2zFKb5cg90814wETdVfoTva1pMHDBbrU0VFJ6jfFZL8tWt2zg6BG3jzcVbWLDP69BOZlsc1mvMHGJp9gb8jwy3OC5hhhpjd9+GOu67oNNn+Kbzv4N3KYaBD5UK/Vej2ZJDwsd90qtkRnlDWMj3QmImw0sherLV1n7loduJVcuh4INU8CbNa9yDOzw8nhFEyxZ0D4rxFMc2hX8eXqik6HlBlODL38EuciKwAh4ucjR7olKtW//plS5J8VSgsCPYWirYAnVfmr6Cg5u6R+kE3C2sn5ReYyAK6jHy/RinmTL1oUY6d4nFqbCgFSfM+FI+yoVeZn+o6AXxrzg1F5cMqA6XZ9ZoeaB0dLHetxMpw/qMz7u5RILjqX250+F7IhKJZYWY+GvLLYirtvlzsA00+4AI/JyxxPOWX5FNwL0wAQ9thj4agU3ozCRvgAW8pmgeiMKtTYnSBql5ZhbB3i0ArBZ8Z6RjQfX4y6t3FgBC+NzDf558/rqK4UfXi/cPHQWypXpzmcYlxI05/pV9C1tGvjGRX9TC9z0UXw+wVCJX9GHCUT7eiMJQlYDLE6msIAY/4woVSOcXOBn5/RTBKCffNw2zTPX6Z891drwIjegpSLCxWhg+jcz6bUfPvCwspbhNaSPr6XzU9XXoN24GD7nzQ18MrTNnfXAu6Dqm/PPpWM5n207T07tdnGC+DwS9DjIxzJBsLRCd1H/vnX75tqDgWcXtq6h9wYbwq8fwcHRe4+0Qr6l/n6tMDWpjnXqKUya9Ry3jKqNAtgQ5k3CWrnMTvil3VBTZXN6rsbAi96GbuAlqLgfBbQ9OJVya5q2DklYIrk1bmXx5rdpPhQYNE08ewCzjAfHk8gzRC8sv9W+eLJFBeAbgKp6m3nKrlbfBUZ0P2lF3nzE4ltiEUrPATR0rKF/iOgAujgpto9xBuB3DZruJVWSTsPykGSXvSsIU6UJgRDs81GZ+TSeFrT4+RAknh5xsZS4PBDymMfOPyDCgcguNgbez9YjJ9K9w4IOAgjzoGYWLxAn9iXqJMv0qOJuUYBZqvw12r0Jur79vCNLcjwXIT4wFlJyqy/mlHvqZIlkrMiaANGFkXXS6Qy69L1y8ziHKryOXubYcmy2SWYVkp1a64dA7IJb4FPZVSXFREkWKl9PSrnlg3LlW9iQFdi4Hq9Org0WNRomunOGNaZRuugJG159fh20zKodZEmt5Jr95zw2nTgGGS+ne5qz03rb+kblo6uP6mboJGTS3u9HT6IOTExkIkdqUQIixbRrDHa0HlQ0envlaQ591PfVpzzEZ7XFlAqKnXc3eFlXZ6Oo3bcFEMnlToW3yEEA4f+LwyhyE/RE4iv/6EA4dwyXytDxv+V/3yP990oC+ctJWDmZchWox+7yPPCGquvPw+FHAYmcoOxk8LMSK74npJpBmSRdgJg01/ORP3F1mmEQ207tbg9Ib2Pz7uBsXG++Aw7vrqZcgXIL/j755G7tciix4FgDU5NvluWAtC8sUOmsM5e7Pamorg5PRpbb7YStDO7O8xpCDhPYcwLrpIZv4i7Oxu5Lgd7uN7/v4+kK3ZckZueXUr4mEbfCxtAtfliv6L1n9tWD9BPBtfQBfD6GoeLhdYQ62GmtSWYkBaKJyusynisVmuxAn5oyTFpF2I7PFdCnfz+FVz0MGkrjeU/fZ1faJ06yYoVJFCBQ6XjNcirKiP+33Ab+yoK7q0luqXNGznVWP9tAcD5lPv9YKKeebi8pEKMNZ1qcQIKmRmPpjt5jiXu4XxEsYrVAOfJaJgzEtsixHCrSj/BmhynlgTAaRZLiCKNP2A1LmR2HxoqHZdk/l8K/ZM6MDNFpJ0QfSXzExLTms15ywE9OkPbrr9clp7LLUENtEIQAqylwmCIs3wYpeqNk6S1m2kDxL1d//6MkzsXGikfIZWPUl/uUwXtpMk+Pe55VXDORqyuq6TOa8v8OLcnhO/PFe3jDkXPTPa+U/n8vK7mkdPww45crepdnBrxrKZhCIKqcjM5Rqx/tI/hyVN2OgYtQEhfuwbZyT6y9F+viawfCtWXGYzsSCAuRhpYkU+gqPlKNLu1dnzQTHdtnBoiAFnc719LJfobbI6tGu5GPdbQjcys8ig04jGaOXjAXNLT33Y+sxUvRj4m3v1FzgCOGJHFidURSqijOcnMNQ3DYJV1VUd8OVxEdwZqzcJLVeLqxKm0VuG1BZ2UzghVWkstFbpPn+dYLrwa+zNmzICzs3pwswnHT4/N9382oNtoQJAFLn5zQsbg6h2qqPd0RljvrxJuoTstEaHriu1lwCmEABD0rRr/5uNt18xk6wEBHYQp0YWqNeqyOYzWEoVE3Re+ctGC/Ok/s8zSQ+Ar81+xDYaFVe6pKnkTwTf7ciq4uSb+Ka275rK59NfTgXTrwT83DczDNfXR7ZlmpYQ09ixX9P+ylLhhN/Iw3FalelV8GWNA4p4QVXTtfBjsuSj1ZvammJv8xDV07ogyiYxdPdur869qXfR7YaeHO527S7hlqBi8RGTI+uGuvuwtpd6Yt2pPPwyP5/TsDoN4/OTos211O/V45DaCB2zyYZg07GXuNbqYyFxgp+kU0l3J8ZmRhbW8cCfTKkdDPNJ+mvlueC8YwcLv/zzeHmpBQ6OHD3Qt0VThLhLrXUutLFBRykJpV+UDrIY0Os3wTTy0VscGH6KMhMoxZXGybyTjiOvlcPd6/Z4et6Li+XEXqO0PTL4SlYdtjOR+wmuEvo6XKQIfwwQqe65qinxOhm5W6Q2A5pCKf4Tm/SPAAORrESL8QMVsLOS5nKE7EkyOnVrVZLzphmeimUs/b/wYTI/N4BxVtwbUKv28Bn+GCIeskvmg41O128kGq3s8M5/RdSxl/iG/5pCBPBAIu18NI3SIzw1bPM+xwxXHJQQr4QhG0ieYBo8JpAwcS475gcNH1S+uAcenS9OYdyHL/2Q3Jf050CEFr6I08hsO1E/MtEf2S/puIqu79vQypyr4/CPPzfwBUx7BMVXusjJkqyOsjMKw4yZ3mxu0K04YJ6FYT5g3lsalyHyi3fYtzzKcLgfi0ABlSHh18IP3SLCJtw7bSBHwg+UByusicn5SYsl5Smsx4kmlXUiqInEXi8W5hRIAcxHcCUwMOfugKQzKzpMB44AAaKs54b4LAKtHd8zqN4NdQ2o4kJjC4wuQVgaod0GQU9CDkkiVbUsR0MZzKraHWuAxtDMt4YEbZeutuyxM6AtlSwu7EJXXynhpCXPiZNetrcvSxbjzO6Gxmf5HjGzK4SpmFAR59qqLNC26ObP4onQMVUsoku+mPhwSzlUOQaXRSU/AYZZVrQXzM5LxI/WDrReI2567jgI1ZV2ZMDJKxhAhaCYaykIfJkMOmtgw0ICnRTFxzx2wGlbcQQ0xyzu9OUS331ZSSx7jnxMLJ82hdAfx1nUzy65DLc+HdUbHL4PERpGF7vtoAHjfDN1ghuUnrunAE62zjhEpbWUEY3WEP6iOCfZHVz0JAfHalalyJJJNr2qErjCcCroUf3qNBAjaO32cFf4Fwams10UPqSLKw4Sb0O8nX6SEN7lWkAC4+NiV2o8MJkcr33EJIf692MZ2SVmwPDaj1nVnUSuYmKNEPWlIi2sgiydanbRu+R0gSP30fn1WorSbfes6xHFf74zY2iwTcwGmjy5SVmX9XLXWnRoj8GXo/1uXfwKA/vMr0cq/QH/0wv/Hr7/4p/5F6/zHiWwnIQ5peWMoroRW0N7nNJ4oMBIG6wWvWTrQ+GQySTCWnFp0xldj6X0ihhPJCxhazPv5/ClSxRV8v8eYIq5XM+gImn7H7eHap4MW4fZfBowt1I9bjhmBs5U0Lj5TTeh9wWBwReytu4MZHMVvpo2IQEKQ3xGJm+fnb4WM/s9ua+G2Op/TkWLjVh1gDiNgpIElEtX2f/m4tHZrxAf4VUeKqXcTw9Blrdszqh7qrocUbSa35VSYQ9TwYLDY5zjHVVIok5ayRfrt+bxLCwSvOgZ41X4cKFTvL/hg31ZKA9JDzxi61tbihSGTUN1Der2UI3T98r2kTcfWbNbhMk2UJHo/MJUdMw0Lm+usXKYjT5r/HHICte3j/5yScFDtuI9fDomDWGA/SJgMbfNV2BHXBw5FC82KmP2h5glBld+0YluAUMoyHBhCSj05p46+HywSb77RHCSUgXLw8x63fREyxEEPtrpqHZ6xv3N9it3Y63KfdDx+XKONi/UrnTE+a8i+vdHy4jiOUXd4cCJmTtcSs4n3b3aYERmoIgo2A5/ZUXtKyGkawcKcCimVF73E9NyytBDsI+V6lMs4bzYb18zRVok4FIHwkq10z5Zu2aTy83oRckWEvl7869BvMa7BaVsf5Xu8N+84exazYdgPslJIY24MnUI63+pHBQXKisg9kx45P6JEmjIBPZ+a4w4X00a1BMQoNfQPnboqqqx8nPfpdNf5B2rZRkqgoViEwShJD4CqM8e0HwWqEwpaQP9EOq4lR7JBI/D1MtpHry83JBWS8wXytioZEVVeAu2dHWBTNwA6wVgRm/rydP5ZtE+kY3K82kPlXbx9rBW1wqttNdq1U946OMTx4Yjf+bNHd8L9hjZrasWsrEUb5Q9QroiBDTFTdKWfHW4OUIfi4f3Ac+AkCvLEwhhkEuIRXfJKI13vQ610yYyqVvubS1L6ld++Yy6pt2vOFmPrOuhwNfUg3PqcNzhjMJyjs0kSE6bqlkFyeGU2b63XnLoTYIcalRUbByDUK3MhKWCUuODuLsmPQD66stg6HBQxnI1mFFHJujthS6UskKCBi7Ro86W0uWPTEJWO5GX51iMUWuEmafDBlceFgo8ADXSui7D0wlitHWPxJR/82SOuZzjzWzRifQdMbWjlzk4Qq0hTqZbnoQp+vR9WPNBFvUQMnB3jP+lIr/LOb46dh80s/7tPOwVmch9jFo43IZNBofqm9GowGgr3bg3CzjH/ACxk247Mr8aBWEOBNP7OKRWCyyYS3IFqw5AgP7f4ITWRm2e/ZQoRK5twzm4ysqrVvpx7sDBNCywZPKvfm7EFxxeZZE9xs8sbN2B1OA9hM6W2doisNM2evUqezp0YT+1Bp2XBYEgx1ujhwSWsMgD40eMMBlfo0XSVyNM+Gu/B1T3QgcV2ak1HAmn8S6EEzVCalPLd5+5QZAbIYmzYZsK+0pp96kCZmEt+L16kfQSrlY7uD11RG54gGlzdN9vX0H6pFnNJy/18wTnPcl0s5ogxuNT5MaSQOLrTk4Zs8mS33RzlSGg+qz/y957LDuORFmCX9PbMWixhJaEIiR30ILQGvj6hvNFV1fZlNnMonczZhnBzIx4oMP9+r3nXDkwhObiC1c7SabgEZ+vQq1TR/JiV7a6Jf1bAd+90OHYe2iqcfRTqwWXDn/4G2zUGFE+4nu55HiDnDuopNKoVpgXVRlmgMYOHTgeXcs28/q041Sy6gjJI6dZL6pXoS/pFbaWvFtQYERfjigl09s7VYPtrWEgG7l6+2Zsf5AHIj7MkBZnirBpNK5XTuIdvKyMdxk9iNaoAsJrEzt1FYS5sIU6KeSNVpT2poFRNLUWiFdL2q7DO9MST9nSkPMH1vQvCK8S6Yg4Wdmz6v7Ci+LGQEj1rOazgQR/q1qv+k1D1W/GHxClVDHREofFV/UWO9tKfIsprrG331EV1FlJtEeuXklaqcIQ9mVrWVJdQ35TH5DCIQYBoF/DbRn6F3e8bH35vfjgXXxvcHyLN9xJkw6LnREBkc3oewhNjL9AulBY2dZ0JHz3rRBeVWOvjCFwT6J96iwByoorCtGYH6TSuaHftwi+EY1kLF4+pIJ0iGXCnDf5CYwCHT+M8lIaX2orz4D9bqTwOFyXuObkd4fMKqv7iRGvOXJAvh6CTZzI0UasxDPC9vqSxkf+JffARPStej/TS6Y4EV8qL4KjI8DkTHrAEN3mBn1b30rRx5lhDa2z/vl/u0VqtG/hG+IoRoSrqEoevYczCjztS4eKRZWddg4pQge7NQOq5PuwOyN4T3Olx1cOAsaDPho+ESo14x3dUWSZi6rOJotainblXopWGbwNcVCQ27GhLkdnkCUtS+Cw3GvWI8QauuWzWeIZdfy1rwaNveExVrJSxmE9tB1pqad7PZzf8NurAS4YW9fGc+fJLbZyAAzPXNccGc0nFOh/GJ8S4POtHW05HkaRQw9e0XhKmxHUfu+bHgpB1SEKU769klAzpRIyhVn0at7rJcR8UIT06X1wAhNWAAvLMB9X3FRdzphRZ2SAWWgosdBv7FLH35RfcXeoGagMSQ6UCWxjGEODqOnxTKgbn3sn+y313N3n5tGQD3cFuo8MpheSz2VwH9ajvoyQdQZk/MYb++F1ICmO4KxncMYxOCuhuP3ZVaChpl6KzYNSbTZLVy/UltfzHpc47JwuF/jBPwBGz3d8EdfSDzz8PYoFGdDP/S/xVaQVBRAv7BjWy2x0/0oBH6053V1KIZqjnnCy1YCtQOr5OqVKyZoPkYSNRntpwV7WxvcXC5Bnu0cUBfKUvd6WZruEcjm97sYas+zIxjoCAcfsEG1P+GU+gLqA9QZD4nbtbq7hh+j9ea8yUoBDlUGu7ot/rJjpKP2vGsOF3SyPQz5AJozk9OTfSGX8dmSsuIff9OJsnfGJqmKG0m0wEJZVHQ8X5q9aluVv4u//qaG/CIL9P4/5hej/C8b/u0m/0P+BSb/I/ycm/ebH85vi/69JvyF1LP+ptxTGPLCtDo0GCfv8YeCb731BcutmqvESSOGnBemiH/0bIFazaRbu+ToqfgN8yr/uFRM5PmYejaZHis7idZLnokb1SxE4Ri3ZVfky75Dcegfb8pmGj5RiFcGW2ZXpzw+dvfGN45gafVnoStPobKXFAdgQds6ltDExxv59blYK/5rkA/+6I19MqBgpAx98yPx9Rhx7R8j//oHnc2CMqVQe3fLOVR4X/8sDyxRe/8sXEA+2HjgWtYW+Bv5H5z8/6XmKkUTsf35CySATU2YQq2Jf6xv8pydhz0qC/38l/y9XQssgt+wNQszkvEPaSqMlQMGG+L+PF0QsKLZqfsMYvoIQA6hOx2uBZQiZ3SVuOiIBkvQi4w1kpNgbpNFJEmNqlwhpLQxRbKSVFfggGfX3XXerX6JIEnswo7+HEXW7f+syKcjmP15oBoJvpDxTAMvU1xyrMlx4CNMxt/Zg8iUu5GGFcXpgNgNWZuFJ8dxXzf0vUqIHTRohkxwAK+S2yIzJwXhqMXGHHPstpUx6MevHPMngpeR4yy38f227WcaRBJRXRRsyxSmK8D3Ug3GGco5iWFYK0s/1QyTAixQoDVT9Pv3hjrwGDlnlhl8ZIz5P5zCX4AWOJhBKyo8e/3x/rYCJnLFw9d/X8Q045SLN+0ZiDYYz+zeBR8y4MtoHgXQjPHYNXrlgmOAPGIYkwg0tS6ge4XZbAFD8iI1Nt66iQ9rliYc+kQOnv0rrwjbmIgGOyfF0lzP6AEbfNP/jUvvM60Vyq4/QfuHoXOfzNlvbrCior6N9ceQFg5KXrNlaacOv4B4fKHLn7UIXa2sXFfEQ/uJuWY9pLuMlPpqGRnb01+8G1eeSmSQMiAPs+cyEYMxV80qNsR2e+QkoM2bf4JgxTQ4ZxiL5dmHY0OYqxtbbnEoQVtNIwvCOX3redsoDbvvshgQNjdRIc+NFaBeXMshmQx7YYL3aG0cJ5ctyPTTApNSR8s5OX4P+pTKZFlaBl645qWNLlBbVUXj2+gNxe/hm+RON4i83gUjaXhUPauNyJtxdKvtVwBZ0KL/X8k1u3QWCbm3BhzAmeNqvflVl1+HGeUV/cdSmMkUgPifvT1Oaaqus0xntggUEf7ueTKUkGSXqq9KKDp/StUWfD/b403FFM3D7b0JUZn2iMmk9BBDJ8Eury/OUnqZBFn5BPMhf3ss3LdoIcyPwc1Jk5mTMgFzMzlT2Adg2HYZUNcjEclcHhHQk/Odf4/vf1VqnmXlID7BcPoQDFl1jQsAuLBnw5g1MJbuRW10NNN/DpRpbEibmWSkNPW6RsBYCv2oYI3iQvbWIdj7l/EhdcR0bN83gsLjQRq9/5T8giqb4KajQYUFb0mhFm0vzOMe+xB6iSnZJgUsSMbjgK77DeKgeggjbLegt0qBjQgaj9yY0EHt1TgIgdXCB8ECN3mTr5KuzlVazHpAXsOaD0LKdvgIYSy9Rpb9vDgVMS+jvXycYI+RmoE32KZzGnGVsz0ICamecFx7Xg2wcanL8Bku+7blytEDOZ/0yJ6JAW5CZFpKgsEXkdWXDmYDd2XoACSNZGB5vFOjI8af76CvSGKGYjPfRPvb1ESffwN+5KaFF/GjUF0Dwo2NYFoJp7Pao4K+wvtHJLEfhFfCqnT7AhaQjRDFWYoCVX1BdffPhHe8b0Id4YRoPQw6WR+q/xpfLw0feaA+A18/WLqsfMXx4BAdg1e2imDuChPDBgEM9P+ZLhOFfdyLUB9rr4LLSkyS7/KYS/z4SxEYWnmoK252+0iLP5As4+2lC9r0/oKrTuN7aNgxvwPWX7u/jgdVTIFh4OHFoH/tLLtZHpE41peF6QjwEdTNQUIaxnjzYXA7dFyaKED5LkcJuBocCGi1vvjxs0RHs7C+S+dysIoUGbgQ4Yyeto9XPC07VTwYKGhwpeRAKzP6dhgGL1OttVTqmrdWMYf0qQw4JxC9mM35SBmbnJSxSajRvBjkbem2jKhtOrNmBQLaLVnTPd18NHD3vj9rOKNk1jluAHqMPrJVXfQI63T/AFzvRcxIwsHUzCnKAWcCciSIHUSqv1I/zO6XYe+IoE+YlPWq3ZrH5N5Gub2bENSPjdqG1cCNOH0n4TQyP1jmB5aZVrBcuBsc2y2Fym4f05nfp4sEXFXnDx2OFdGdSjoX2Aefmxr9K5SxUwaLArYBJuCjU2rU/756da7wVv1wg7A08Rpe+tK7sqN/sy6bCMr2XUn1TisiFxLDw2xvahQnLYOCp/kg3B96TrQVPMMHJZIn31lp2qtZmQCCJ3iGl3T22BhDgzvNeMiZOWuUxsfer9vpIhNU03CMib4mfrSIoim37sHljCs54TOtqmzFMaxOwhT7m5zYP01l+nUPfqwYTaQbJHWkwhTU5r8R4D8T03EspOi6JF9Uk//VvxSRV4ucLjU0ARjGYWNI31mtFYVzTz3WUTNVRyJSB7vsrczTDgTwYZwhwI/aEEsFd3eIRY0s3x2O6XfXIKPgtExu2iWQijDm5oKwVN7aYMVdMBAY05NZfK449xXGgBRrwH1iT0YzCsmX0SzlLYfROx6/WEly3VNKvsAR+nvgqyeHD/MbF/FpSiXTCvPZwvkFXZPE3pgrCmO7WSrTgZX5f4g8pOtp8OJ26rxHIeNA3q4wI66FlrUG+2b+pcVj2p2IRcDF0jyu/y8SHnBWi+OQ9dn3L623nupo3DzNhEObBbgnFAxtnkQgTfZeXVS1qFWeLbxcEnZ8L0E3H9i0Dw4SYss8uwmXhgEjKFGHJBpPMBuuSscgg8NNm0eGPPgeVDWzqk6dwjHP3QLRjzOwy6fPfTy3OwikoJSdLgS42dCz6wm0+AIJC6cxS5yBd6jvBIv133xE94teHfk55K7zr7+JcD/1yUupc+O+RfY0HeAJESQJ4yK6lIGVtVtLIWNhTwnGpHr4jc1ZSksG/LCofr0w+TfRC8IE57mUipU2DcbGkbq8mzOEIdz+xn92mPhun3M+SywylUmRgPogPMQqjUAC5suzrO7861H0vo2/2Rqi3GlDL6JLJ9ysUDrxJmCTmXaTkRZ9JQA3rfE5Fq+hm8h7MGXVLMmEIoXcioH3CGr/DDyc9ljK7TANc68qEQYswBu0+6/JKGOAP6Z2fLmBrvA8/Mfjb4fFYt/6KYaCoWl8vU8EjmMROCqCyz/4nd7rDXK9CT5LoWQ/Db+emoqYNOuJURE/OHRbBzIdg71TaBUwp5MtEIQifwf6o/bPuFfG4mV4DbWNdl5LijsvoyQL9PVmfubtHpigWSLPnAm8RSfLGSR103M1O8kPsXm4gH1XODA3vM78a094d+gp6NP+B5L8TvzHJ+p14niFMMvTr90XyjzrKVr5sQjg4DU9NeL/UHyzwOx3D5JUPdRvnRqKvRLHMeyepvbTQf8OFwQ0D+vqmCiMv6wp+wK2NEDOIYcYeH+4PSegOqyhsnCqkO3049ck7HBhMzgYXY33+HqP8fQgfUakevma4HlMrvpN0r3ZBRwa+6S6SDYZ8vcEOv0gwdJFdD1Gr8ExT3fNXiOT+XdC/D/FzPeIv28a8VhVDemTKPqwkn6u82iwiMYbRNQCLMC759uTDKjPWaj5bjxY+OxBWg7JUodTul9hHymKYDt8SZv3XIQ30ThXroISuR6G31YQRKD4bRfl57rNAXoNCbdkBoOvmGfWvfC1PDCXGamZPiLwSrb65WXBkaJDc0rEdfWAoga7IU3i3I9fdhOtg7a2YiGe/ZvkKUFhfB8YMFjaup33QzAZYM/eO0QbI6W+wMjUGu8LGemZvl7wnhkXj8k4GQLcrkcTegn7pyqsujhxZPHpmskikc7222k+QMlYS7nsEn6Zxsn3cwxS7oqvD83mgr9Uj3EJJqh/+Sh8LuEcLkspwST7gyHybQL0XwV6ciOpoupv6HoBpZYoTwrE1tepCD25kjSTJx0fyn53WB6UsjhhdIHxk6FTMpuCqiKBVw5sg4gI7GyA7ZWBfgW3kI0JRpsTrJso4pUHLO+qBs2b/+zW1pIR1rPszORG6Y714pr6Joy5QwyzlLWagUPoaqeSR3y8of9APzhm4KJiuhclJoAVouS1gb02ubL5RqUcghj0HNM1pYzAxoEDaNU7seZJXvjD9W4R8yMrCQzKKt1t230K9Ux8gr5r3jwLR13pxHkRV/NxWwIiNDLm7c6srwa9dspzUsZm33T8lwri7UjbXB2VIArcRRZFp+guJ48N1ptTS+FbTX+5J0j9Zx36dU3haKtCgf8V9TPGUWl2PLBLxXOAXd+c9cy98Z2ffocRz67v2Q5hEHDNHPMEAbZ6TIh2fWEpeHNRT+dym6I1uNC2aI7ydgDWaCDDA8IPWabh0NC4/iVgnrfN4zMKgooLt4i+yzjJvSL4vNogE1N7H47vIa15ExN99UcB9sbnYJdzyMO69nMsIOfwZefZVfqyfyJTAZHY9CxQB63bEnNHCY6T0CPGOdI+pf1oGSAZ9Q4t+USgG3M8ymdMxwBPdwmPWCEMLBTTFy96Vpfx12GDJ+HSR5WGf/HaLgrHf0Er987UXz4VFj87/jc4ZO5roi6CEd4BLoC+mjvKVomKyDyWasjrvTnHJ0KAFOksxxnehW9MWP8lJ1/7Dps1X/TrQNfM25WGK+MZjIiTYBfZKujyDykhBAF/JfW3i7zcZFhBKo14KvZvjY4PkCwsg/PsHT7A1fQ4iuPkFBOvEyey4mcW/Dg/CVvOCfMr04NeW4bWTalf6/Rg4A84pClZ/5dkUmubkeuQhVGU737OYJyiXNfxxUnQsMOvv9YE4PrbsE3tuYNzBK4z8RQIAO3unWHwA7S+RD9GmguzOzYfbyw/RpazPV0Zoi995pHosnYD9Zj62NGexI2jfwlIAwFmcbFHuRHRj8jIv0CqHHabIMBUKfDOFVQVqldAWeVA7Ujb+lT9vxfQbvPgQIoIwIR/9xoyhahnyCWuqSJawhd0dVW0BOgw2rHEalHi0WwC7HXsGVBEZKdNlF1yhdjemTLuCczz0RSmt44O+MGKy4VSkS11wgW+VBeNbGig2HjNc3EkqAXeNSIwaxmfvyHqpWtVe6XN5jFEtP2+rzfY5TN5sfIjkjLn4IvzqT65/FZ32F3C+G8/q0KmB3jU+3oMmCCY7hZ+yNrEHXL0lsjQXq0DfIwjj8BGKmRY4DyyqPL2yankOluJN2QS5A8xL7AOl+ZQZlynMrqrJac/+rnROxqUVsmE0PPJV2hyVWyjximYR070mH88MNXTe//EJtEhw2IqbNkKTn37RHv2Co2wC9EvKw2XyyEaGiMPA5kVRRB94LULyNO4Z9RbZXpdy93agvlp9yc5hpLRYdp/7IWDq8O9+LCWUyida365kCKD/M0UvY0Lp0/rAy/2IllAnuXMJH+ZtEL2Ipt/HbNIoPBH/dynzgJT5z20r5NVIydc6Jmia+cvmBeoMwfEaIsad7uCoXnsWnxl7HWr8b+6FpevNOO5zFEP5uLpYdQrm9Wj8XrwCEtGNnqUyhUEdJKCsbBYR4vVcEJDk0QtTEhP630muhmVB4p4AiIKsc7yxaU/SpmYyTcJ20+d4+6VL8uP6Vb8xvKqVfFIFdgHcyD2SJssczodMspn0ilnVEB4CfBSDhrS3hRW91WNSlzGQyXcqCXxCIPL4Kp5XoZXzeZVjXHxHNUtCzhLIykvypTN4bZCo8iCBE0GheZ2Z5bE6Jlrdq7yIZMDdm3L2/Q05B4vl8jcefX0IfpOJ3fpeJERFKFOY6AkGEbJPx96Kfs02wAIIsgo/LCDRnn9LXyovOImzmgg6F/DzRD1YaVYgCiXhP1fXdZgRi9gl3wIEBuZoZitmzIxe380XAmDk/KYP2ntn7pBn/QchnTVei4ygM7q8RI+k4RW1PwSMwD8L4qCS3eALWdOZpyQbRxzwsQMZsqqiqCcmce14STbyAS3Mni8PxyyYdnhup3FPZVaA0dzst66s7TA/z7rq/1hX9beu+1dZo54ULSxc3qunsP2mmMfE88jt2XaEcWfTyzauFNEaHlPLF1ngo5+xmn1UuGRbmJUgcRgfplDxJWWm+u+R1OCrIp5M/jnSyw6XH5QdIKALRPA9H1REVzKfbfk5r5+pLrXE24Q3vCIR91Cmv1OAPYUHkwvYTSe2HUJSsR4jjfaOlQzEZUo6vAjC7pu0oY7vNkduasI2PwbrHnrkT8u/9nU7mVGoD3T3RuVHuzxYPoMVs1RHGDbIEdjxHp8ilsrllhjL8MUAiwwxZx++suKyH7aCfxRlY/zNW2VIbZNnv8EpkDnRYll/RkcotiJCzR1ahcFjX64lbmnBb2WfSpOOpTNH/i9IXvxBcnAlhJQvUxA92PiY/SU7Jyi/lm/4OQyRaRfCq8k5dncHRSNWWiTja1WKJ7GA/e10klNComgbs3VvbE7U9MGR06OB5Uek110WbUfe+TU5cA/4G7x+RL+vnHxXQjIdPUX9mTr22TsE5HwZVQRCwJ0fhcuKf8ZHquGH63L2oSVlBSLYYDcx6aVEeo9vMswkOU8yFiUDnvI+RMSJhJ1JqlV2bC8CXi/h1wXhbwA8h8gA8ci4r+Fu7GRxsRcxla7bhapjBW+k0UX8i5ErBVgEfUX8HghHQfH5zKhJCQ4wWbj4mDCezsBe3fBzzoPPKpImkGM+rMVa00zyeVWOpYz6Fnjm3n0f1vU5vOvn/S1mrLDZZvgg5oZLFvmcDVjSAOXWC5BaEWi5hzDiUpX9QdvnG+IWhHxEKV11SIkQ5mKq41fZPRAZnGKknFX/499koec3NWKyxkvE/72minxglrB+bu5M+W1mqUdz/ug2fUjbRBm7TJfeSSFj6u1DMGq7WmUg8Cn+aCT2ltH7YBxNZQXcdDvMGcKChDfKRxhnNtNmGeKQQwIRDXG2SS0BJz/RY7v80ieAUzABVKve9V/P7Md+yWKIPBClYiJs/iR3Nt740q0qjLjkAkanbg/mx/p/qSs+xJLawz6SE6d+1G4KJx1AoyQvfQAxvYNSl+HBRN8pLdELZUGrVVZeUbetKYuU0CU583PFQxldo/gat9k0yg3TOQqJYyTVLzS0X4ibQ/UDPrQfUZvhWQVw1gz/1vD888jOa9ev4146sma+Wnswu8G/MXPWoShCDMjF2SoqOIycbdSuu4TEfXR5rmfqJR3UfOD6Me0zm5+LqK5ThrwyPEYNolZm1pZnaWb19pARdSB1Jzl8SETFDXulaIU8ZpkkzfA8FyWGXcb9Ncozdg7h9nkiNDs90CO+SYol9zuQ9T/PB1l6g7UMwbNcvlcyeLKB9TOBpLkfPb51m2shcCGPItoxfcjcf9xcU28n3wQSwddIzm7ZXd8SFHh13y54b3DAm/BNu271bR2h9lhm8v7l5/gapkZNOs694s+dR8s5J4MC3HmCWsfimHf6k7tuFN49glJLKUKDqNjVDm7ZsUdtkwO/PqP2+Sp3LuKZfErIy7VIf0SAFSFvtSrcKgt0tiF75lN0ffYyENIqJC+v3r81PCwJwvXrysiLZYtT2ccg2XkkUIOU7s7Lx03RDdg+EBhqdNdzrTERs+CVAMxejwhMWlHo01LuIsZDtFtZutvHktzIiGva3cRXLulpmex0HSxcduvJe+J1ZH9Ii578Cp7AblqS9QANo7HhipUort2LysqxFDHfC/xwwObNI18rOhf2+9xqKCj4e98h9M2/N401NxirfP+wksDvdwatS62FIZ7+RvmYKte6vz6JmY8Jd4i4FTLhom95OZIS/p3BIsA989pC78JjLMhRC/V3coAGTeL5FoDqv2aX8g6O8N8T287z3pIQwbopHvP5JnflJwxx5lh+DblSyWGo55onCFwWCAXIQeZxVL2J2qPZ9STtZGsUUWK79qsgRorCSAWBtZlJSngwUlkvkYeAZ00SeAapDCAJhq0MqGb8hSB5LyXndwXVk5kdTQRsdXXp8NdPknzPEJ8InXF+HzJlgaVQhS0uW5Uin9Ak8Zw2Sw9GMU2b+6jIEwplPl82FYUGiHdUZM6eaDlVdf7Iqp9ESu8Wjml9BmrCGny5iNpSR6bN4rJhVN76mVmlj34eC27BpQtVCxdgEnHr3CO7GfaGW+WXzyj8VbQxxh4+GKpf2pkimSROgYcItsmFa9ld/dXavI84+KVCg44qP7VR8PyqvGLQyFlcSsTLqDVyu9wJ2yOAuIgJoWT8MInXXmgxw/0HloRwuUpfr2Kc9B8LnIeG9ZoSM/vNI3uTZMRt+nvoiZleD/j2Sx0ysY8peQj2hjLTgrp6Onz4xRamBYzBvDDGCqnf6Py1aL0zLuHbWua4L+2Iirwq920eps+gHAE6C4mIhPC2gjCfiSmcQHbTX9tj4DHRkS99R2Yh2LwSu8ZHUyrwzl1a5HczIyvSCTmbtI19TKO3YcRtMe7G6oxPcZQU+Ie+0Yas6Ux40xnIMtCAWKKDgynoqdLgG5IVjl7AFRQf7aRV1Lsxdnwu76U6O08/wLBl0br5xEktKWv2Snx11cPTYeKLrHZFoE5BPvTSrAq+AHYr8rHkl/mODxH8GwO/PuidC9ZHVo+s5BHQKA905aZv5hb5Lv2N9WAfGQJujHJIOmXcB8fTFR9SemPU30GQLJ9KRB+bnziogCD7Z2bdSxLRwkZb+n0NPLmmb1MsFWzj789xefOVwXozHETs5exZkK1Y0NJDUHwWXOLz1/IUJ3rK5cf9jdn3YtR4OKnasICicxZqwSINlgNRDRT6PAgfIrAX8vBJLIm1h6IH/s1M76/nffHRrEcTRla2ylju67s0pCxMMCxX5y3DYqweSqbQHgUpUvYizZkgtKWgWfAtrU/+4Ao6qsaVdwnSDtheXCyMaLOfVHNkR+tVZcBJmj3IcLsWlAwAf+0ysHvlLf3H7s1JuOJgUoSPseHZaLkEXQFcjOwi090gqba8tAxzH0nXJcyc0MapSfMmfj62Hj9/ljaEuufa+6HPJMtHa2+oBrnPQflC+NsY+DV0S146TGT32e4EAovy8Zm/4XLEbHe/JVmPuHUzy3gSQ9sqjaVNrVwnHEbY+vN+H1NAfvX25Z9aCDVaKI8L20dJnHB4rgwWiEoRj3q9F5beUik6KLLbk+OFstvtMyVmaRcML2Ih3d8PO3PeBldugSw/z081auf7ROp6GULQ25rBrUhO27KAKWFf5/fHDYWmHPRJu/puWhvnba5TCbTrPtLI85XGlwXScKkuBvqQOcTwYKp70TAL3q8IoRzjkZh1w7SMDMMlYYNzgf1hMlQpnA53H0ML7shj+QS9kWrhyc/1Tyd1wI2OeuK9LmzWtAWjk1Vs22OaWpwRJUT2+fV9EIcvJtJC8PBzhk6Ods4SCna3Z8+N5yeBFiOGImS7El5wZJU/NrJS9JCEX5/wS60uWsH3vgDEK4c0GN4sxSIbv0cgJVhBbG16XXSVh8g2zrmiGW10Z/7Mnd2oxdzSs/iVkdSr65QNZMRKo0RL8oNw4mPBFVsfV0ZPHOmxfdxjCfA1i8NUf3+8pl3MGJ1f383VVpN4DesdrcxDHJwEA15DYrb7oGc+O0/r79C31EPGseMzAL3u8pYZw10uVo4jbPKnfeE6l16BQhRx/5eJK5aiLiC/EKkogJyzAkaUpfIPoiRgjl/3Of9wrjUVRtlXJip8bj3aDDgPJqh8tPAZn50PCY1t9y8+9cyQ4bL+UdbXMpLm+61J3qgnhEItA5hCqMqsrcJrHwQtHTDF9p7w0/fvrcmzaqdJVQHZR8UJAx83ZdyC1R15ZkQPqZEciVJlxi9NdYXXB/HJ9Et5oSAZaq/6onifTaRtbz1o19xupsDphC337iOyr+uA2DsanKY+bO9bVqI42OXbWwTl+/wSt6y5XvY7EJZVGennh4lsnjr7HZmQ/ygxgcvLifT9S1DVmsiP3VdheFrHVh0HFdhk1lOIrqh9s/zAuj+IBlGwS0wgvRjwu7rY2YY04rKsIwdcLZqW3B8dYcY2ZcN91xNmLY3UCJ2NIDKhzqILEru/O2p86iRMaiovTAn+laPmfcN2mJyheZ6SXWvQhL0ln2y6Oj9YnCoRWhOJJgF7dLOOny7sXIwXCAwEBkqUvV+GJZcwKvTyfU+kVzXlX9tpitH7oXvfb1sZoPdgUQXtDVr0xme14Q9k4Mj2RVdZmH42Jdqz4zvon3gxc1jy8I/XtmL2Rl7QinVchsrBNtgYutxNepnc0b3006nUojV1LOpSMLKLvR5DxrzRDXpPanGwNnCD7oqN/oGJ5wh8Hbva3GtTjOgWzOB3PD60D1RiNvxr4TI/sl26C9c5+Yzjv4J0OWU+EJDgXwa/tqTUMl/aO/x6eFtJmsNyWqWxCZemh6yUDYk3ct+n5q+vqe2Q91Fb399M4b5L/ePX0/P+eW1ao9SxuFGE9hiJl/oxDXXmsM5/E0lvRW+/SsPFUUGDHzg7yUDYHrLugyR0+otXD67hpD1CuLVJND4J84p8NFPSGp9J3+Nz0CMra13bNLZ2CxWvC5bwdMqhP99uLprWs2MvlryjfYihYV8rFVvRXJix4TdDUCgziKFoz8FcxddHCOZvjZRm+4Mw1PzlTUymElztMmN54V3aeO0wrBEneA73xku94azFpDyY1WM65Ux2i2w+cvzu+1oEIaIEVtXYl6FUZuSzeW5zL6wynIw154nB/dLTsF2NH4j4vGgpk7G0gKFu7KabHLbVu7pqdlDNjpuHUyUWuvQ5jC3jDXKbMWDqWUtk326zzCgJT/jx9gkzQXUvjbmYRd93Czypa1Li10+DMZypmMDhDqjxvZQkJj43vYOrT/gNUqdnZQ8aYkuPhZirTTOkBCWl0of94qwIVxV7VpV63oYjYe6JIg84A946kX+JmGZb2BvzWPa0rk5wuAdlB4j8ZS7pa89G9P5SFXIsaqs3uP5RNeorpLuM7SEfcTPnMmsjS0hHaY8mvDbVmGz44YEPWjignr289AHdh29086PleP7GbYVY5hUE6jOQ2M9kDMNwDPPrg6Xl5JBwd1Ki3/rCI8loMpl6tAmgNo3MFreEN6jjiIhzstD9EneeknfOKL4LVpQLLnHa8gCzqkWmSMjOQdlrrSQrtdK97MjsGXqjFe+995ETLE36MpPwtUlLOTfdnwMGz0sObvOLvKG2qJLoQus7QKpEBUSX61ZMroAbjDpXR/ta3XkgDLlv0jfMKhRyWqvB8faBF8Bv9YpepE3u5B2K1UckmO5R8Utkh9rDFSnDDqvHzv2vtV5VJZ83Ij9ynopYTPC13PI1K0HTqTP0ED3Lnjlz++pK9VUi7usdom43sGvrZHRTkPgFPlYpbqdXhfPGOWvUAifv097N/UWX0zzMDoYWToWi7qM9TlvZ9Pq4F7ON9er5xCuQ+XIznva8DcncPVh/J6UYeiGcTrWsThvfiLtVuypcW8Z0DYa57TjEUvP4hrISRns2NfCNgCdL2AeOJlLw1L2z+X1zavttN5HnOZERNWV8tu3IRC5IlHSWsH0wn464Y4mTEloMkpY/qGkaLPPZ35dprFniogOpxWwYt95XODrKBZ5S6l4x2qwcBTlhWsfnkXJbzZJ1RgFKj77uAWT2QdqDGL3mrE45knI+yz/kjdgq9SlVCPbD0H7IAF21/RwmHz+qE+YoiYNSsy8FYgAscvkQgimJKW2X783vVvjwG0cdB1b3GFF14kL5bqzadSy5yYyuztG6e46rVa1DuQJF86VSTb/gY3x0hDmGor3Vjlwbkk5FvFV8FAdj3NQgLZQuUqd+FfcGWCO2Wfs8DBghpYTJfxDgKjGP3aJR4TxSfrs33vAeVIOjvsHsa8q1V7fnSznbEWYGD7VhRJmRxMLjmppHeFaGeE/23Jcbop/3t0/StyzGWZrSJcRv8B3bkr+LeHix2fw5F5nQSkIznXOZVXxKD8T/tCax9gqGtI0AwuIaBwnfs2lSqJaOOSVFTUqOUY8WNt5+Y8FlDW+E8ltO//yw+Y4RIElARIYKlxPEKlOUsRqjLC/5pjieB8347+6ev8LaUkwAqXGYRHzC05wMoOwDpoCeZD9U4bnZg4JlYBwngtmTLIl/7XF9Bj8CbqqX0XvM+kd5eGVenesoPwD4MR/Ria+cw8joY393ZBU7oaMQADvz5xkbWaMM9uNl5A5csPx5/nK4DfFIeveADwXZ31J/Au/yDhlthcI387GqI0xIy6g5tviA9FPiNmhkAXW87MkjeRQC9spjQJTkq18euyjIplsOFXlfc9XalpnUWIs2xKHd5TK/dwZtT3G0oQzK7vlYlHF+tZWAuYnwAJQU/rWX9RXy2fCxkpij/lc89T/+Bj3LKk5T0H/sMGRuJDTH4kPpzE1FKE5MPIY1dl8OEaIMbDT2A64wt+s+uDicbYZMmQT4+7mNV7mdD444crCqjLMxfg0f1uyDAWt6rK2j62EJlBHmicmUBFTzU7WMEUTRj/Yl2TwAMRDxMcMBWG/h+bKJb1jI7jh+me+7SwW5UT8x1YtSkfHTPRb4wOTIOnM3UfC/cPsv1VU5MarI7ncqnRDF4BtbBIKdg2wvEyRbsIlcGO3DxP5CBKWEGGh+hmslKYDlqsc6t/77XKLQuB/p1rAYLEsiwNj6wsIGAD/bFNAX1jzzSjzyIeGfA2mS+gh6HpZCV/nOkBiRpJow/N45nU2V0gFpkF2dj9hM0dd+vSaT/Xz1UbP0kTM+t5YxxsNr9YkuR4jXmlXFsR+Qm3DaijTEEYEPpMFTtFYifLyfOxlIRy/qSaoTZ8KgaxOVxr6evxKTX/1pRWRm33yaEHrMB2CN7yNvqRtIfVXO4VXAA3O4Uyoxara/qPELVS773VV0Mj7BYNo+pW33CqoIRHztGRMJPLKiFmOna7VUwi6+gJwiitTAilSFoq6Sgv4hX1K+2/t28e3+Au0DouYFpzIplMxEtTguvpw2j8+1qWXaO9KO/raJRn8X3axLdWY2OKf61yQ2CkPz/i9RHDiVMDng9hmEZHgidOvm739j4FNekMQ0q95sSiQr0dMoIQp4QnLxV5/6EBEce+kynPcw88XjgWqarnWFV3Tqx9W0dC7cINoSWlGcfUt9AXAB4iFm7DJhfW3rGmhU2Iub9KHSFy1gXHJ1fUwvRkMPYjkzbFLuK/X22Z2VLi4bnDQr11JD0Wjq31jIly0hpIT84q+C4XXB82o1wbAGUroAVD+ze8RScYUlF9QuOJjJ1CyQG6zwg+3aPVDMFhc40npn5nZyg82RaIT9u7vgmpwgJn8O50xY6CvhdybhR7dLX4nyqR9BOq2K7ovec/V2eXlUUrYBszIgypJ0jjXSFkIXHwelSca7LiZwJaWSpG38ykZuGwP8SLzd/lrrxg5FvWhSZ7LHOGd8V4ZuFDnLY+r2N9aIofEOtwd4mA/gYhceE9fHFPZfbe6Vzr/L96G9Hzr/+oyC47fY5020ciW9o3bHRT0KF7hM8X6rQig8tNjBot1f5ToZnVMNlykLPgPUA5TMR7NcIn/GgTWaf8Jh/Tt9kkKW34jaCf65iq+/C35aW7TTx9pTD7tvm+JzVAg0S9lzneVqlfWYemDSC9CUP6NBddjn8kpt8Zl3XfRiO6kPbQxmtUtXXDHmqyysTDZKgHwyg1z3ZfL0rIl5v4lZUfOMwKDUgL9URCdUU0fUt17T9S/UXCdAYOt9YO3Xl922bbrUhJ2LrBRiEP4OhkP+CrVkDwQJhxjwb5JWKpKvcPI5LM6NuIM3S7N5j6N3coR1y3/QyOteyPoXscH5lqDzNVByB6EbTDb4ef1tgcKv6qs804yoXiPL5v1Xmq8HgaVC5Nyw/Rvwtn1OyeI522mQCLb2al1yO7jweoUdyWBLeQtyYvLSmXXuSpp5NbDXjJEONX5k/dvEO7PuE/ugAFnn5oH7RBzt/ZxdtktgjRtsqm4HQaLoGEVS1tWLuYfEZaGRgg1v6q0NwSn8miS3VpWZ+eVCjWq+2++gBgiqkj1z7XIuDhjA/qQXtcvevngP/qXPkcv90wx0vpvnbDY2kQOkMDc+wu8nNWSa+lioLfb7V0IJiByJG6TBw8VY2ScOT/sXOaIH1/xF4F+ymrh5fi4kopvP35V5jnVdOiV2CBgZmyIItts4ugkY18PIj/Ua+HV1MuDvfEHpWjJIWYweETPhdYmWm3WHSH5xC9XvLJqxM+bu7kEGzZvYSeluQEUC+xpuBM6W4BplR7/fRkzFlxqbUwiZD3FuPWIoIHJ69JsbQGhHodyH16sNnDmt9yP5LPafBmAe5nouPVDaKRRAZXv1nNl8ZQwrQ6SaDvSxM1ssu6vW/6u8m2TU5V6bJHxbAr2jRsoFijF/TvZqtW17Ufyk3j6FRnEb3ckFq7Ptez3ZZp3k7bUWX/RKKVF9hJip1ZA0o2J70Ht3PGDu10vrFLOFkEPuzbRBfaj9lPZFkWk6r2tX61HLgt1SdmyqQkzIIZzUepsv0jTgdKsng/MWDpcDtn9V36jxEuo3KQ6ISTGTPyYb3wNk9qAKySSHyEDUZuRAIW4+fRrF5Ln1c0lw/ZovhVixU9EYMzTs6eF31mm7hcTCVy/1TrUrEtR4bM4iB0s6AhoakSIOqvqK+JeZewA9jW57QgR0jBYNbb823/INXfyFnodL9G0V7hrYUqe6r3JM8J5I2kiaejQcALL7jZHT5rgQ8O1IdcP69beZuZlsx2VDYYVlVIW1ToXdUeUhdRwrtRe8dbZ4dtzqzR/zTV8USdzdbrK9nFbWkBvnuwmEU1x0Lz6+GF5/7u6BW1c9Te/oi6JOLQvne7yY3t8FxfuKpGb6jnO4C2/cpkjkCdE1rVubREKGidssGT8YvUfIJIru+VmmsnQLu/KIF8V2EW8eIHLvsgswyyzWkL/JrjPXelVi66D4FQWzIGlIDvPcz1k35rY4K0WMZFzhepmB7JiPdJqIAOVf55FtLX5HJvJJyw5Xafhw0d68XKv9DQOXGJgCEC0o0mGjY2Y+nIXp4yhZdCFKdQ7C0KHG9qEjuPXIzh7UyenYm3lnUr79vLa6DUpYL1yrxXvQ4kGFjPwjNa3eRHTNJF7dD05PmtUPHDMvH7fuuGaCo7XbQFw+nhJdD8HF4YV6EHU4FYw7MIzuH4iZI1PTK+fp9/3QVNu4+bhsiHdiK2TrjVUiHS8QCPTD4ggMZ7WcVMIfKHoubJ/IrLQiY5uh42PiM/IdcW4LxCZhwujQPfdRBH7mMaj08CyM4bJ1RPU2zJm17iK7eAXT868lh0LvV5o9lMfbPumzb2XXc0gr728WRAkgjsSkYYfYFPU9U2/n1bOLddMeeFLqbpXVIdCiTGbcu009jAFRhlo4lrK3oeZ1poLe2oze4YKsQDQ+l7rNb86dfJ9fpX2R5enNzPPbVJK4C/1SNcB2dzhjfJrIeTd2cEs2XZnVY2V7ECFTBwHeNhSRi7Bgxn6t+IMCs3XEXzrijUvON4pfoG9JQ9/SIWXlK5GTiCYwA7Luvs0PSZgF/1s89s/KOE0xj+T4uhFKVtM3I3q37q06k6ZpYndwq9TFxfrVgs1Y29pmYg23XaeDCemB2STFSV/d81f5dmHBnqhJswowWa0i7IZz2aEyBzL/Pr0YOlnrSyZ8SiZ9uG4pL3nXuNEhL7GTc4CekfdS182phPzDGPms1qf0wX7eMaZpllY6l9fvV9Et7lHYNead5phKent4DEP8Wuk2aqkhKLb6+5HPzJsOmGdHga6AIyps7Y7Zg1JAt7xZgIOWRcVa07+7zH+Mw1pyw68clEsfQ6F1eVpHtYtTNmYY2qaxI+zUO+jLbmPhx5UwVHjxNlrNf4iJmLHQJbuSMO4KY5uf14nGRALBT4B+D3oeMUeTEP+MxraqhMIADHaVxHg+sbZS7FzKz3EeXym7eh/ywRnwtLp9qQNENsvOhWIMX30FmWf0iB9riXTZ+Qp83QvnlE+fy0+VMBYMr0R7ZfJgGJeUXdqLy9NFLEaK87hDvBAb8gx9sZd5EogDex60mmGSaGDmjyiLOIezfzwUnxYzP54tq1T4xXN666abZVTS+hUTU21l1ejlSKPaSZuPh2H0ded+Uy7g3diSQHkmiyzYXKWnIVfHp8fEaBdPifvXxQX5+7hzKOggHn84sF4+ZApsx6tECiVkoSuUn3uGpglx+S/pURtk79eE7me2ujN6ztLPOi0hvsE+Ido0s5tNAlkfhOl8NJqwlrGJJLKqXT6tVz4ANOgnWStDb+oXv0l16EVyz33EoxPDMoe6WN0XI0rXMAzWh2EumHJw6aM+ksFekrphsk5jQ/2TEMnWFdv5fXF8asSh2F7Gr5huOgziNwWZQ7G7m/MaVpZKRUGuJBOA2aAsWcvc9Ilf4DKRLxM2GDJsew5rn4szwAcH6OW3lU10x7T6yuCIg7tsh3TtWDiuS3+W4C//nZZH+kOb+JHJBacDPUFFa0EJYw8fv1AD6iApJuVD1uPZpy0Qj2f6R4fsQ5YjASYg5htkBY+qxKCV+2GLF1Ox4/UxGPwVB8fR0G0FueO0bEO6mF//kRrTVFBkal07KYvIT/I1W9M3PNu7aS7vhU2YdR6OBM4ZuUgnhbIa64e98e2xocPYqB+f0auI5h9qw8Jf64qQBcGQFME8fz1f89A7zflG22OY9dr9zYvDAhtEK7pSpAIwsYdd2cca9FZWT3Fv5U0OiTI0n5ASPgbvHTPwAjMCZDrasscJH3uB8HHw4Hbn+UPex7uOSv/egY0F94nOXZOeDv0y0SVk9vIzJMg4PmtErvSrgl2+wP3FlSLTYz7pMrWkJuoubSmEx1npKj1DYb8ikO9pojb9wTUUyWRQlhwmkTMiUas2B3QYiPxgduP0g8RHvuZBsPgBXoXoJkoD4hpDk9qjcpTl7fEG7jrUVb56JGYu1kah0kT9UGLhtwF6HZoEfFSU6iO9HjyeB5U9q6cIUhNZFFW28VaQDY6W7Lv5L7w7iJlv2qBniNU06TjNqjfFd7eZE8v2HEK5OygcXeknqaG4ZyNrwNUNm9mdyao7RY2aL/68WazyGxjxcgVHAHWpMgtqohGGioXl+b7fYHXU3eE7gt6yGSmxSkKHmQa8quYBaG7OlhzSZVK/fDBm7fUoRvhBJuVBRBhMJYOE6ZtHLMWPXhE7MaftZ7q8dG071njDltllz2upqJKPi0iX1YfpI7fMApL/fOXw8yXd+NyDCR0+MoBKzx6iHrF8PyeCG7CZvB6zonv2KUbfUoHxdC1uTSsaPQlISXjhkie0oKkumwozJUZKximhFM7glagTCIFAW39l2R+Y5jHJKM3k4UC/LpoExPhj7vPFYkYBCFYJRtOv2e7GWZcwkg2w7LOhfFI6S41/3hg2CQeB8evAIa5CJT4Dpli/opxPqhJg67Rnqpfhf45z6I4z+uLvIUt6vywrBL6VjbwU82soptThXsYHSitUoV69dDWXSaB3pOqbP1Dpm3a6mHyLAHPYWLGz7HuYM193vwb0FLweKz6fD/Jt2uXi9xVhKnxxeiRyreX0EgUIE6wI+n6+5VcrzlwSCCye5qJZOtCL3eXPB7iHq/5Pg+M95Rs2P8V3+Y8v6Fh4fIaMzCAS2dcAp0JqbMuX41Yz8Zv4tiKgjeA4HiGTrRolJUzaGXzyAHyI1PbtBJp6u2Z/kCeOTsiHyevrWlbQYiQ6ZIQj5Ic7aAHF2gc4OQFCWC5ZOLVZ3s1jVml6sJFGa4bm8/wSvY+XcaSBOC/IPLXz0Q7w8wmbo3Zsjhv1EKhl6qO+beD84xHIhUF+dbxmt86CUHquynjUeAIDYJF5WwYhs/tgxYz+n1x9xbLcXNTd02QuasFQzNjimRhazNLTR+f6+5NKBna57Otu6cDaa20kFieXDpsiunNa8np58mYISvoD+Mt0MRvITetzlQ6z1gmhkXGhqMm+h2RU//wnf2M0JYIUuX+XjIyo2pjWVAbM7sn/H74mGwMtISxhfvmpOJWpzLvapcMrjOoOSykVJEaI2EP5vwcKHs5JyfRXMS9Uw6nObTv/sDoS8Om2ykQSOTt64uLjB2BcKfAMT2nJzXDR575QGj30Cvw4BFOB8s7jyZyFJEdcMNgOzCvV85+95b/vxoiQxL8r182TilyojJiwDDYDlT1gCuEHL7BkBwRc6Ru3v1IKOj8ZK96Dd4pI9V6CS41LKF3KJkEqEQ/OhfjJyGGgr30oHyy3rOPHtxdGVZs/E8EJOBX+H6eCMR48+NFTR40RG/GqFC1tdX6hqXWWrO4TMaasBfkP14Kqi24qnmo8RqR5cMSE4PK2n2kqlo04wowuUITNlUU4THbvgzEGxCRnWgnrCMEXjHqm+TztKSs+HwnEGUzSI14vY5gkaCf4cUMUJcpcPBlvrnoXITgJ/YcNa4cd65BI+tbozIQ7DjdnJw+DDFNTkK7XakCisfI4xshNBABSslgQ72DiTaO57fycRArwXqow7K+y71/FCIHmGF3ECNVgoAtBXK0k8AQICFoZI0+hvSmMWaZjWJDUqSD9km4Q5iI5/a4KED72eztdzwktHSJi5zYXTiFNsdCK5LCc+eZ3BX0tRLrBmPfo5i9VizBhPhHgkfr69XprK/UxffTFhkdJ0pW8rYUMJB5bA7kNuN2DCuU9BrGypcSS1CRC50vn6NLgwVoj2fXakftfSd3z+xstMLkoqU6UFSOhs2eWrad0ihx2+wJze6CbVGzPn4ALmJIz9wdQAUYwolfRbTuLeC+T9ZOovu+Tey2gSEjsmIbs0vnvZ0bq77ctQokG0osWkxhTiGL3Akn3iXrQbn7UYnl0FQ/ZOTHxEpoGOATy7MpKXMKaesXg+8UJ4Uv4AsPmR4Pew2wrTxt+ym+Lld8KoAGXyQRbzUjyxQIqd7Rx0L7b081E+6Xaa62OrySh2BhqaUoEOH3oRJEHf1FzwM/C44yOwdL2oDokkrvWevOpv3GuuQSoosErpDVxXPKBuS/2lbvlcNoGEoa09EvgrNlfXQ3OBi6fGXUEjBUdk+waPXMOiTBVkvKuU4zxVDoKDyopcMRvp4+JFKgS95z1zxM140lOx3uxNZYcweXJLCl4vuDlK7tBJWRaSX3114ToVH50+M3VhrLGjJNG14rr5vPF+E+uNV7uqIVz7ookQeUyT15wmPe1YWZzQlNyjZyNcW5LUIZh2SUao2UqnWIioXlkYQnGmcC7XJzELLPh7ytKn+laOSvGPDEkr4aUdHbrfrES4lbFQU9Ex1O+0qukxfBFa8Ruqvsd/NA8rwxnbDX7b2xvmfv5F67dYrsqJ2iTemwN7WdPZNL67erowK+eswaXTHSSnvDKnSNjnW1khIDXTs1+TmtPePi3OxkjE3ArByFO8KDp/h46f8jV/HKqJLHi/4pFHauwtB9rq4ytlZdYPWtxuutJmg9ID5HvjcGCjSGA99zGMOwEfScFjxp0i1mF+VUNmWTfVzzjC/s34nJakeXwo9vrrMb6Yt6khpIUURmLJOz9MupoW2kkjUAummoGqhgg+3RXc7lNvxcWDp01XGUh9Gp58e5MuEBTNVzMbGOFmkjjaz1QxFu1UD8ZWJQe0oIgS9Fevh28l6wqtZnKtavKLS8/mPCVQW+1mojVg9rLWKNcSsNTiyPNamvuLzb4rwNI5cQOnoIauv+QmsT3sibvn9UQBkFgKkgV2OUU9J7Sr8YirHgD5i+mI24goVFbXmuLYC8Ca5/WlgjBSF9VFKOaAyFUYKTMCrPbj38I2Xzs6mMIed2yi52+uM8Sn0ooZyimQfW+wKw9GfxL2iqF2NHz4enrOmcWhww6fktNGQ8SZ+AAytIq1K20/uq26Luknv3a9WWxwbA9mxnNow4dqHPn/T1R6WMaT3x2AshpzuXvcu9hZnz7Pu7zwfa2v/bjzWUftj6HObWWdNzTzyr1QA5L20IjR8xF4AisM3fgqWP/s/3WFJwO8u9x8WTgcuYltAAk+7HM6zAAdommWBEfmbxO/TDBxKMGORyVmDOe1sbrtcRnELEZrs4HFzwN208o9Hx5Ig8RelE/sJDFjYKJ1VSKvQJf4Te1SRzIDf9fPRpDgITtH74qEIbSe49Y5z89vr86e0OGCM4/kDl1pEvqcyy+YISlzEy1eAF6uLV4Ts8h+BgUHAQVd/Xr+bywEL6rxohhtzE0ifw2DgmQwd7MAF3GTjZw2JSs757lgSmlGR+/DBBi8VLUTLvQMx1NW+Tw7I08kYh3+siRJlgoRDsg4VnjRehIdEQWEWTQu4af73govyWdN+NEBvD9cC66d5AQimq3EkhnstP0R0hgi8YfyNu0Q8S2OrHUstuupJ9m9s84GSjZElvlt15CtHmZ1Sjs5tSNP8che4WW/bUFP+kytm0NeDE2cIRYvInWq2Jw6wOR0/OqGjFTdx7gtDFCSYfP/f0ivSH6lVjB5g+zvIepX02WYiolr5tK9WP4Hm7SWno7wocwz8G9YMRTwXKz2lbdV8m+wlFOwQRjDkXJDz71+pycf/zZPgsdZxSrBHQW4lqOBx2cuFOaCKbaEpZJYD/lc1hoHL2dl7VhzPP24WW6xCEb7ISN9TiJDqpVUt1onhT1mzbPolzbnTWndTsYFq9JH6sl4WhR3/N462PRnqP5mhWKmE+pTFqDoUYX1ZmOV4o1/qMv3Rh02c3SWoJtT/hXrjd+5Z4ul69zFQZdrObPDP8aNZzgJtgQZXIF95inih90TAR5Ck2h7eBscamTrYrGzyQQUqRAaRrDl9+Zh/IsoCTshpzlJX1QIA7nVeF8qFPsaFhOIzSWNT0cUtJa6wfAFTNgbg27LvR5XBeWfuhr8775DeXbMNCcocbWfjNBXknxSsi6S3OgSpNeD+ZVq5WzjwU/r5z30Bv3yOR7qfMi/TlN6gwXxB+iw3ohx0ON7gaKY6HejDp2kPuGLAaJPSxMdbssp5QQ1I3nc78YP2ohqItgyj5o1kjQ6NcYPxFHg5GLzV9WjZV6rSoOkf8hzCAQcnZXTFJH2MRl//Vb+teowYGpQoo5JW7x3SzJcyLD3ebBnW4akLLI//nCY7h6lnTM4BLETeCUkfCyXcXh9ylFquYsOActJRF6v5SWg1DYt62U7g51hCjGRJ+5KhHHIqFpTHo919E67gqnSIuv3+LPuiTG2aXWKRBChTEp7v0ia5NSux0lNUs20hz0r05+50/i+c5rce6TClLewTNb+sC2oaq0+QAwaWIHuGXHM2Bztg+Cb7/wtRUsAo9xqSHvGzRejRb1FHUkPpI93VpjVjRUey0IKJ2CqWIBZTHmNTLS/lkMDS3lkjbqCrjd/kwQ+JJV5+iz6rwJtl4zd/zrSPfBQFSeREAfZObei5IJgScKhgabgEM1op+MGgShQnOjdb81hbBItoVuyfQBvVUJvP5Ic/8bR8uLUbHR0qYS6iplhCWBDQCNGObkkt7/IMa4oHSEgnCTkfsTVt/nEJnPX3NTQ26Z3YG5w1L8gC7TbGd850iQHnoZl2j3bvht9h4KMplFmDAMkzbiqfobuq1Mw7S/Ymmur/4CaGQqul+BSJ7v3PNDWOnmI+JukK7ZOEkvDVIil/RBzo2EiyMWfFDaA/jNj2IwlQ3CZ1MRnxvnoHP8xdy5/KdVhRvEFy49ae10yrW5yP9cJb0M1D9xXtQxVSbXHuBgx+wprfGV0Ts1/QWPitL47lMJ78y2zNkewhUFfappyFh2oRWkn+PI3EtrN0/lX+3Qe+q2mJrRv9Yy13j3J4O9ghM/1Xou7fDC6IV3uijytw8UDS95+BYQLwzB7HN9d6e+e2szSbz2PD4LojmHupOyJvKvoLj5EKg2SRq3mav6LKhQLKsuKI1mov5lTPHiEPsXesRfnnTw4V+fsFBksOI6tDtbM24/MA2lmxP8y6CBDHJNA+56hoQPNYOvHnqF9THFNgaU2kMwTgC0g4lkLXz2HFWukjWWLq4NMZ6Iasgnek6gsTU1ry4SP9vPq1+JNNTiB6ekgSd8yFRDcYvJhZv2bIv/5oUWXk7tbsWoMe4qzZQsqcRKumUWZ3+qmGk2RlVgea+L1C0HKQIT9olnCfmVizLPG08ANECHdnl0mys+eFVz5pXOZ7CnezXtUJQbGYcNOaCliPIakx/cgkOziHGYfPJJyJF8YcM7YNDM7ok7cAyT4KPvyNPb3yFIrY1yC1fKW1ZG/ZciA0kheFcYZAotj5cKgUdQi8XTklqeWntvZbukkU1J3ea1bYZM9BpXF+QuYrvstm3gZWngXJvWsyOBijNeGG2E1v8FF8gHEjptKpYPjEoW8qeTwbUPq9hbMk5NiBBCR2Mv5KhEfOYvdUt/IimPTAc7Kek3QB+W/PyUEQgs87vBqJZmKe3v7A7/1AO06H35PvlTqqzXrTBpzC5kZjH2z8jemJUroETnh8x0Vx026PCAGLv53dhv0fDXFBq7Jj+WzAGvonId6GjTtAy6qvOC12BtsBjyKs/60wDI+L6C71XZifiLp1PV6eDd/r/UsSXLezVjpiYnMgBs1KcSxwi2XgnTnXB0NyUKc8V9pIiMrthlTFeR5lWGLGnar1t/fUIHGIYXWe5o25aLIm7q9+ivjisBcPgu8/VXfmW2fv065Efxhcu1f2Wz2oLkHpCg7zJmyGE70dCwO4yZDTp2Z2hbsAE7wlr9iKWKUu2Is6CKjvygR6U8CL5P83WCjHmhWk9Q+cU5+z+HGC2chT2my3gsMOY/ZvMXNCunkpMyjWAyV+azzcE6LRCMCe877vCNkRMfygzYtEB8NWQ67FPPWSWVMg0A3Ka/0qaYKQw9r+GMdZlzVltso8aS5Lmon6/IsUcPKSs0JroM21zeKxA7/i4n/w5H0nT8+zLwphky58/nNuvcy1i2U5mmKxfm0/hgp3tfc9+rU9N7SpOdUco1L/dEfZB8UskMasbQh4eshH5AZxQu/1rqh3VuKR7EG9c1JajtoXZrk2zF7KPLmDYjf4XEzwfBHcDhtOLfmQ7QkQqXH/nneEaHkIwAPye2+vyAuBDN4gd39/t5G6ugZXbGJzWRBnRkQMmrL4BnPHK/nwT1AZ/58QZPGaKF6o0izsm7ANR+QbVWy01K2S8SQMF0/2QCiWqL6BT+ovhPOyoQgkCyc8Ifi1J+N+VvJ7wH142YUSEj9W8XaNgosEgWMooF/E8E82YYsVIRKadYGjMr/gTuAQy22JbREnqzefxeemDtS1tx+8DdXyvAKV5MctTvWgLG0meSM0GWMxrl1jSr3bLdzOpFTK5DSNXTxyyikbTZAtu9PDNLN3B4VwPCguioJ4XFeKOCEPMCKn13rIizE+97BuZF2BToDUKDjKa45x7cctL0tLTZ9UACYHiqlku2gbmlNLzTyNgWLjwvxKW3BiApuih1eDjZPcib787c7ZKhGP9KXG+eDLIe5zD03F5eyANViMen/PR5d4nfg3TuU3NhEbGmSZ2yBkSBSF6l0V1y+XbWslbnRqD+HvqTslZPcabcs/cArWRlicrX4eihycrf9oPk9rOftmWOi4P/XC5xeJD4rLvNq19ni1c+fDSQyTd6LZr+ifTPodMdySLmIk0s5fBpt2K43E1Yoilf0MDDvbS/sW5jCkqVK4PIJNr+K2IOtvdpzKh/cRb+v+cPUM+gejzCldKpL2yQ//CXlcOoV4k6zenmNPkiDJwbTTNJqAci5Sh5sF+SzSMWAcBl9NLdXe/tc4F9+WLEBfXS5zNPfWju4l+UstM5BEduuT+gV++9OlAhbUzG48Gv//UgUgMBkXE1siGP1PQXjGRiUl6CnuoutdtS61VWd4FtU8Q/Eogn2VdyvfGfPjOnS2o3iVNx2Y48PuHu2jGG1zbQp4CUQNEKxzEYr1YLVANNkH3CbAOQb9qOQCcz5rEJwtkOGit4Bxv0uHjVr+EhVlFy+PRM4AMmEVFruCiRi3/uos9g5YtFaJ6D2z4wTXWPBeXyWCSsNAQcKMnNDPXvDGqzu5N9ygat9ANuYfPqyONm/mH2TZe/pvfJd61tBRY083R+PEUT/EJLuphBcvJ9mbVC8ef+fnldrEku7ddyIfaP+YT9yiYBDpZkcWGWRhfekMegrmDZjRu7u1pBnqZWYdX2U5vVX8vwxguun76sQVxSxyhtuinZ6fqtn6hq91OS8hoxiXLuzUVbur8MFLdgfsMxX0eg0YjnU4jmsggIcpN/bfBf+Jp8dQpfAlle7TWAVKiUy9N12rr3Bk+v5qjClkwWcTU153Y/ATsoiZuJeW28uyN7q4kf9rKVtLCzAdhM54GnhVCGmtPcl9tmnFFJpNxxLknox5ayJLxaQQxrXoK9Vv2uv1gl5lwsTAak4qLtiK71XjiGzbdGmKiz2sz2lp+8spUfreni6kB95UiG3JhcsydilY1ffpNXA3c4T7zkTG8Mb7J6jceOlp+zqP8ZmpPN32eiFFN2/eYygXwMJAEKBuE6ioppaLj7qKhsG82JpmFHGcGFHdt+IYSetrPyRVwiuNrf2LSySUtOOGxKq+bvrcP0pjkJ0UudR/J7SMNDZoULK3qZgXLKjbs7fTbRekj1kivuGshg0iM00vwXAfHw/0dA9jnfQwyqJ4DVfRyTr9RFL09yNS4H7Jbx12X11doyVXZd9YVrrXtQkuI0Gu4tsvfF39n7IKMFzIkckAmY79AXzZPCUA5Jgu2mZIKzApbOKZoncNoZDuLqh5sLjgoufiHHVK78DXaomdb+BY3M8ATi0F4lQnSOFagim9mT4X5WW7oj0Pbub7y5B71UwRRl+1thqS2MmRGjfsMISusQU+aY4Gd+/SqquZNrbUIT8cQOfc57oatw8RD/MNiIUOP+diuCQyODIgM7NCf+aZlhDXiaMBjcYBuBoPuEoeLcbZAkFNaRUF+aTMWU216iRH8NUzAKehwA2mNaVaIz/P3ro1hsZU3B+0uwirBFD/N95y2SqP+TMwRiIf6CEgKL53/y7yYzeiagnlBc8d7TW38VsES+psSSMNRXVInCVv36CqgQ5RkXoLpVmegoRuYS4ptcSVtWKZNYpxsOSDr3cmZzyGHHQe+K9A64JWlEh+MTstO5t3NwbeqZEf8ytUdpkFrlNc3xygVy0jlfjBKqIaFYo2qL3hGFBDbenY62aJrCyA8qEpMRYeSaNVJeIPoln4V994ffZijoJHQHKf3u/iRTxyPi5VfPy/Upt7lxSfgZBSc783D6zQv/IQiAf9NTceGCIdLVRliqPFb3r+uqqcAmpCPeviNWFUIGZBAe269/abnbyRBm6qllSvolsGFjQjBBBf9l8RZTmSu52eR/natQiEiNfdHdmrh2aYbPTgHlK1DuvUyGzjfuZGOZJ+xuUXerDJGWnapuVLcukmhvMjlb1OXWXG3jh1Uoo0Vm45qnv7KUyT/2UutMATfSxifalAjXx3LEhf8rATdXZaPhdcvM4mcTaT1o9vKRw7h4rRvKVHqglqGWCp7swVTAp1nx2AZcwMAcHnKUOa96jgY+NWlqHb5X9tf4sZBfm7T4C6lt+bivvG0SS5VG0b88XoGaxv9WhyZ+8MPg+hpT0r3uVxtZVKAmPgbB3r7Fm/lJmDGcMoWwHk/h01saYEYdrHmPg5ksvI5CxNzFfuk4XGvCtdMy/TkWvF1WVz2f8ZBmt9kIWaCiaDrGGMt/fkcVQPgrSuiUU0J7q7gPJuT7pX+7S2lgQ7W4DtSzahc0zaGayk48ZbSyV0sWfOOX0w6OsESb6BGVhqT/XglJSWuhCg3AkNaY/gR4yY95yR1wdaYE+LFVOLiTItrMcVwY+P/Lb4IyRk0BxwUi16H113Udp0yvUI2/QfUgblFSYuH9UyFScLiUK1EF6GFVnaTVrC9Xhl62p0JbriDAW4n39kthcwO4grVfgZGkYW/LwNR1IXLcpygMB6sz44V6Zv9k2bZgPfWine1Mo9EuvmQkIc2lcPKxuLbvDFnkJKe65aT8ylwwC/xPxzUaXyZV+hs8juFG8ki/XzKY5vmcXAx3fzMqqP0E533/16dLSEiQ64z9e414+1wmTtT/aSzxLHIp2YyFCdE1x8vaQLyT4Z47FSpOm1cE45MSrNefM8jADt9yVnz6AD4rQlb2aOUoo+xolJxsPrHhcBwUz9DwxPRtGPZiQDCvl2gy/4KiFf/GJZrS9wcaQOnnYXCso4ELGAq7T+8fZEIdJRJ0HOUyUi3s8hVPtIlZY8tYj9KapF4juzuGGpF/HJXtru+4l9855ejT9IR3MxOhTmk0hgJQLEJLeTjyDBSdkPYXDFO5EaqM9h6+LSt2YmZ9yVeVOYsNT1Ep/Rzsb5orgS86ODnKwk4ctfW2O1RmHFp1qlDi8C7Ai9i5QyJusd9Qau9w/AVC9fhiagPMJr9M2fd4JggvsQeQQdKNHFYO+WduF0BouP/JVliYfkLHT3yNBpWGXntLoA0iM0Dl+gxObVPH46igWYOgq4l8qmjhjxJZ2kxKUb+z5UlY5wjntByvwrphxEu6nCUB5kM8ZNfArQV37zAQHgQPwX4b/f4IK2qjWl02/o80EIMo+LB+D8P7A4pHQSHB9GIwikX0bb+yQROtwVWiCeyrAxlMnnKYguR8TQhb7XuMub4mtv3zznVq0p8lAq+nt/3ghNCJ0UtjcxYdtvAT7XB+GzWveG9ITaZF83olkh/1U9jasvtFkucXPqsC9YPBJNhUjidK3B8a3LQbXOaP9mnSntSP8hC1tspBuZmTV4AWinm7roBb+Mys1sLfkCQM4UkgOJbhJSU0ZZ2vLsqfI6ygaip+HFSe+Gy4E4qkXHcQDm1x2M3/Td/Ra5d7nEieq9L5URJp6eQj6UfdcEQRjAFrZn5rpsYH8tGi5jjOXXabi5bHk+FmOIPKrKUKZsuvEYmGV5uzwvmqJdB9Z2d5oZ7Xh/m2Lyf8ba05uxKXZMmUCT12Qhk1fm+D7TcZwtrL2WSUuX4hcAQ1Z2Insoe317e4UmfhiMkO4tSqFzGGVaBI51UsAzaoykecrshImhcf2/dXHc2ShD0NlIRSY7ihzcgE9Xuqv5nEHKhJpZ+iZvPrd6iyiQb5wjtnqI9oqplleYzAx7r9K10FfT0Z2WGVhupkiuQWSrfNtMqREwuLFtwa98h1K650ve2kEuXDVCKLSzofzznAXWw/F6mzSKtRVrPR5f54EahVhEHr31cDszaZg7XHirNxgBk756RwI/0vAa/NtLOoUzM8OchEPyyi9P+ATRRO6vwb3uf/vmYwjhmFDxzccBEENwwM6TJVXXXZ09EvE1zNvvVIFUovvE5GXpMuiqVfHsWreGcz27f63ZzS/BfxNOdKxyEmwWNFAInpPueG6S/5GmfmGGuEqIGSFed30Eks1cJ0eHUTPL5G+MIko4VMidGxTKnPyCjbk1PN5reD1iiC+RSMMclOcHbphighC60UdqYDet/fEvvPhV5bN520hRaCEM0HDE0QrA9q8c8/nxjAfWBmERHLE0E7Pn8J5MxDtDV0BBGwtR+xn9eD63fGdI9DrEr4J+iW18Ko/2XCqP6RUhRWZCUPUvVpyPFdak6U8DmJVILhsWJMa7xGGABqlLdsn3qXRijjPHqrl6xyMKNvfmOlooylC3iifnvv3X6AMdErvN7veZC50tZ6lWHZR/it4lF3r77nBNMQ8qqwa5TZPdg1xt2KHEIIbSyi5EYzKghowv9WBwisuH9+3/j6DToMTw6HEQPB1UmQuU5YdVLxrNi8n2grrF9EEyKO+3JzDho+iEalE3JkDCATSsNHLn951OdwgN8tzr7If6vMZ8mmHoNFaSApBDRlAPwIYM1V2dEhIX/1LT/dKFoscTbqz+qBo1pyYAyEL4OLJ5KSjVFiX5G4OGBXRah9Px2SZRg05QXMVhinBVAF2GSXrk+eOFMQq9Aum0ZnDyJFvUQ8Q+biGPsr3UVPmZpK7j3vSX3o8Dd/LeiWJVgqd5PhdWYB90S3rMsJMiAinhh9Bn8xbOR0AaG1pI3Yzbj5J+1oyKn2EVdeAdaxpwmzqkKIbiDeM3TcbCWFCu5zxlYmLSEVbcp501wyGh2uX4b8KISx67NU99lrWzY4WitT/wgYpu721gq2RVS/1RvG0iAJ+mD3hyylkh0wZoOXRdjYk6XWf4tclJxaHIRVoe01QhJUciyN/vIN13WGkiCoUQXrpT6gwRDjKrrkWAzszoA8fRTpvAHxfwktNGHyx1AFHq824Mff6jIXQtimM+3xWET+hD6XBHR/s1ul7HkE+OKBL+juwVbxWRu4Gp5ljf9mE/wuIuwht8z7z3vTjtcsY3MXR9UiXpvaWHTieg9ly9NrWDRcah8XOEXlEweeuOpf48C/Fr2y/leSNcEpWUDHcZ7NkLOpsx8w89H+yqhWennEhZXGrxiGCfla51d/HalCW1/wXpAErlftRpsB62WmfN6TaVn1ao9CUrkFeNa/Th8Bd+xIxUGNt6ua+Jslnwrp6FUX6YUX4BH0XMybjO+pT56j24s9f40RglcbZyrBZxxuvUyfN0teY6RnPxSWT4pEXPa+f7dgaw2bRVHjLBC6h5T3UA2o3SapO6z4zt6g10dEf53TtwCxWWjJqV+KsRKMMJhX5w6xWKKxHbzXiuk5Pug3uugcKVVd/n5vPVuF3SKEDxeGWf2e4MvrptMjrL6E/3ovhF+scwElLBg0qXRTNZj4xFxqhnFfOB9TJnDtQ1ugrZGxS4wfauPlDwww9hinst6u/Vn+lzz2hVSG/qhLXBlSaG7++R/H0sVm78U2XkInAOtgUYeE/lNe0o2/KBm+wtSx6OA002mwjfmvFmTnJvWQyIiPFMS359O5+FziA1b4jbHkZ9C79d7NtV+zN7Zaa3gj9qfFJibdn6LYsePzqoPFi9e6QSNOeGU6MLfa4v+4rS8MRE3SYqMbNbaTA7pUVJRBvhfweQ4Ju4GbPaGR8zD7bHtW2+u1pELosM5dW/F/mZBLUuJg0gwHUAtF//q67SaRghSW42W/wElY1CTsdWhJjeAEUCNPG6x9tWSH8DrksdCAwTRJ/k6nxXJBQoO9yamfARJ+8Cisv19wsOwRxdckIKZDbmoUukA1ICOeL+B/pAaQ0u28o4p70jBBp522TI7GKp37yrchf4XxK/BG9mKbDDWm5z4LWZyGIPt78BiiobXF6iu/zxYk2BVY18YVg63fULTB/eW+9/+MniOxe17aZshFFOzlNStZ+UgopWuhkOMukvNTtFLZz/FuxGHcaMrDHGv9J3g4Rc2a5fQkFsWy1ImvTQuLAHTIN9FjC99bx537n3824/az6RTODJHRuOdhx96r4F4r9TcDDqeBt9KTAeYrPA1Uy1xLwJEjnvb4Ic3qlbgeBNKDJroQlQcJ6b9+12nOxntFpVRvpWFF/M0XHu22ju/DmWeubd0XWZMpxsA9oIOVOb8emz0DJY306GNKKmsIzSQic/Gp9EtYmQN2pOySFoPa/+rxb6qGcy9I/+aSogX118SjoJ4SPUJzSO+CQPB7VBQxA+Ttm2X3XGWdYH+Yy3MY2hcYj1Nquu3fP4O6pcXEi76gDis9Zl/Fm7l6AXWFExT1zSFiJXJ6KDOnBnBjjzh0kQqyyhYLeO0pSzhKcZQ5D+rwq4xYy4e9DSxDvSzFowMx3AUcqfKMB9teoBwnw/nHIeQx6CbRlAYYEMbwKwos7F92whwJa8XHyY4b8mjBN9EA+Yos0zTHl5pJQKDD7KtfqJj4WLQVoSh6c3vaf7is7fp0+0hElFKBmKIYRwJSlNQncKhxRAkidYCsTB2N1Kk9bTRKp6PYH3SQsN00Q8LgtsqrrctJSA47TgKj6olljxyeCT7rlFZoJFyuwXPf2jPNRvBhZ+uZZM5EzxuNyf/uOz64hjhzBh1gHCLluNlyIYpt0nmakXKicOK+POx9iIGx2jO3MGr4XXoQ1Gd+7YqsXanFmVyMg84BzMEDLcMXB7Os5wDCPxMMbVTqt3NKugtJGlgZfPj0/BjY6c+DguDxOTU6yqTm+jBE6qavvnhtSQSIVSICt6eIlSEDXX8GjXlp1yZwyM6+yq1SX8W05G41/4jQV5K/uSkz3rndnG3eL9ueQOIijvrRJaiqYaxXODe7gFva55HOrRpfU483RhBq9wMlR6R2c/ruztDL3ScfSPeefF7E4ZxDwwpOBUqzYXTtuRK0CX6fAavrcQ/V3rmOfav8rqDWzwqmYIU8JoLaGg7tkCqDSmwfFGMApiGyQY9+5IykNKMaany/Dye4zk3rcfp1Hx4nZDiuv4a5/67FYWd8U40En/rF/86t2CZjxCAsgi39KwTTVdjgSXnSNnf6hRmyT/BYPhqFxKoxuDVQhWbV5omCJaTe3XRC4Afh7tlMC87QLl/d9Y7HSqmjgfVWwhidahdaeYozKc6IDw1GeQQ7BjLCwMrfF4rweAsTnrvrBiJjuVG67G4oDynRBKmhvJX+deJmvhWxstdUeXAFP0nt4zZbZxzRaq4vl8LluDofxt/lJQL9JXpPGPOTHvhG1gR9rszBjDL+mCc4jkcBKtZzPE1OkudHEn8xNFjswY980B49wnGUK0sc+aFQplaSCt60CIiok4xm93LVeLYRWs3uwLK/7nYau2snD6CnB32ChV8gfBa+IFIQwROmSHPHbW7UfSlusQvSUUXge89+kQ9Vt//UJ73FDCBVc8BoAcrU/CuC3X5iYx33GBNqE0/YqsnVW0t5xYv9O9sMiH+5fwQ2Co2gETCJmJYxnlYmwLxPCrToB9lMpijPrOAkd75czMp7q7tSY2OexLjRj1TmlStjqZ7ePIeqUqeEF26I+Wf4Yv0ea/Wc9BPtkTjQn7RRkXHz0tBh+RkpKw8ul+U5tUf1xf+xOKnEYpP/9Kf66D9ICPBJ9SHFztPxe9VaZ6j6dVA+VfGIEt/P1DI3kg1rvi1OzCJXzN/xz3UndHHZVzKw2ELWHsSPCn1Uvpp2i5TS/ia4zfxTBTHUQryOxMSW4SSN4zbfN3VC5TjZzh1CjP0DJZbI0cSqcnSCQKf5ciid7cv+TPDFw43oY1FIkcOkSQTSfmUhfDrg6g1b6sFMfyfhZAxNvjNGVSA4wfbWkD+RZIzmiWXORfXl9MAEbGsE//Gmpt+aH5P/cIFevA/c22M08c6rqYS/ei6+x8f6mDMroOFdGNoRhMR/oVmv31yUS6NSXxLhwpVIHNsNy0LP2uqSbIHKiZsvKsmY0XD30hAjd16bzQWV/7OSSorORVbyZ0p0I1/ECh9Iq+XkUIuGW/+VlXg84I3+RjZt6FaG8vxNPjo8lSdJpTANy2mRSu1FxqtVrkj0GhGg+WM5Tgdim78/rpKoPaFdkGnL/E/3m+uncKjop8y5SsQRk0BPpvTym9yTdye2+w43/CJBy4b0LGeGs9yGMI0C4bCLYikOSwtLTHuJeqnBTDNNmWps98pksWHv7haS9usbwhp4lChcwqb1lBBMP4XYPSnvQg2Cm9YO8Rw02hD8rkc6GqYdpsLlnpPnVfANJ8KBPBG/Fqy59WsVxNHhjuu6mr/BwsaGzXhOLzFfZa/GSwiZNUbVZXvoxldVCfCxf7h00b+b0Hb79q1P4GKZ9yWqWewo8JewhRC2EEcA42hxOJj0KcXPxwpfRF+c34oSs+mjJrpaCsS71FGAuCDCbS1KIJ84MA7/83mSlycc6GP8V+nSti3x32wMghi8bTC70Kh2SPqKtkSU//4FLZ/+DIubyA6vBqV9JcXxW3XDoh83I7s1qB+73pVoll0IuCZtFZ+2CMRm7eeJuYS3hRQ0WWZE+UcycUJkvjWo3KuTnb74SALkm9O7vII+OX9O7pDe1mqJci7/TqJ2w3BG8pm4h0Ay+IaodiGG3Jy4Z13S3faMCsy0JphWXebIJD8upNJXwm2SFimrGUS//gdrfl4580C7saev0MumgiWwp6NKk5sggpHERQR+pTGbj6TIIiVOBs5am8NrNl5WFOaI82qTcxcOlPh0p4Gj6UVKW42OKzawYJUF5P37P12aIiZfmXF5vPCdMFrEZueewqYHj4+4i6b2p1tpBbUqKd1XPRi0Ahrp4Kbfa8j/5nctWTb/YKybhYgkRe6+Th1njHw6aKEVezclhEslov2yvHQRi0k8c7JdxVhxsNKP1kxiN7r1Pvkiw0+Z9ikqe2uULO7UeT/z1amBzEkxIfmx/u3cL3ZMnKDfYuR2G3+LySNP8W7/2NxcgxXLtlZckxDZx9OzmGdcKZU9pO+XpUpL0b9xEn7wHMmlv0QTd+JhnCrGcMb7MTMlmuYujFzlbG0p5BWoBLFR4I6DrPzoO0wYdr3gm/zd4K0iA24jAKEMP+vLAHDXximCq1c6b6BFhz1b+XY1V6es/Zucs5/cRckEr7696QOroep7ZvI9X3K1B9BL8hfvXTMdpd0D6yNIeDXKSxjjJfETkIb+td0l3JNzy7l9/kicVy0QXNSe5mUlFZbh/NCj16ujO/7qYB9/tLSv4SheTI1glzdlArteUeIo2fphJSLwJ1dc2XxyvdEklOlFVBYJe1slbtevfjh4qXL/sUNHJkAVcgX3FyMEfy2DNmNCWp9YeZGmwL1E/j7sY3gJak0ChQPSy0inPjjozHeHQL2p0pF6uFnJ7Hjxq0aGp6B9wMnTusT2J2vqRzDoPcmRJNFIOnWQcwYoQMxKP+1JFVeLS/utYOcfX4N8vYYzNalE2PId+zZgdZMsFGbEVairQBGYKlBLG7BP9KYkUW9Eu82wE1JD8fIiVWGqUMJD4AqgJ6FNDQilIEzmDQTeEht0HseclfG/D1v9ekVQdG3vwOij+zg3v62F3nzap7Sjrsrw8zvnbei2nQNPLF5CouOb2V/ZUsqPGFiGdBVtE+k+WcgQEYV31lcMy8hiQVIah+k+aT5b8m9dcUjMJ9g9jYTpbSkrBOj9LYGMU0XEF7aDnnYXaZ0Vs5lyHnvPyROxtVH9CBN5ae4H7MmX5zY7ONn4xBHbgHUZEap5WLXaLMZ8o/s7drwm+21cb+6IdKkQQUgBWT63ZBCmW0joQ2/WPblGZWSyZC40BItduEjuhISvRivTBmtkblsK1LgwquhtK3Bwon22lVjYz0kRwh18SuaFg/tJxE3xYQWT8se5EYfeNnrJWmgVqQVKxHLip1UZ3Lj6pO2hN7/fM6yiFy2fvB0ngn5V8C+QCY1vfzsEywOdEUFXqWNdBBCs2cAP9pn0atp6btqqMil9zqipNGxtOJKlIYMxSJVAVrSNwUIV4avbZm0W1Fr77On7xmsgkkeoczf88inO+TANeXD9Bh81ebxC9sR/QOiFQKfka7KiAokDcfmiqUIWteahs1URxEtsrP6vgbsD+75Q/hVPGg1+wVr8jD5gP9WffDIPKaLXGDuA4AD7vWj5Qls9RqS3KLiJcdrMkrFJYKJgNsePG+DIKcbxS3znpZM5X9F+zpxgTQJ190RjJbdnVf/CzatDh75jaHet3CU9Gcy/Q6ZQjC6J+FUnprl6ImaD5dw4lc/m74zZXzNqfJeNVD+BUuDIey3+F8gPqWsPibH90lDk88VgPK76nbKxUBh4ymD3S7GK1sesdqpGJ2vRsI3haAk/WHpRuywoTyDCiDGQJcHAnbe8WCNkvZvi+zh/H7uN6IxnRKaOMt6k2gsVzr91edenROIzQCCwihTkyxbVnR/Y+qkGU4y9MKq+SM++qraOPO/ZjKR5HUaH431fm6LpArOAAYJMf/RP8NKoLyZfMtcZVQIjsPwF0VWhuKHzjiv2Virvk2DeiMWT/YTq99w7glOsPcaRgUXdwPvpL2YkIXC5uJlYlRH1CoNeePpopRa3ysJME9rFL7jZ0DrpoWm6ovmOt303quLM/dwnbI5NaCUwZuS3BePGMBjG51W+w1ouI+bkh322kc/ewrp+uWrUwJmyhPbBg3v4AgT9I7+7bPW3cUDWHRHFQPz5y2oygqo0vctKhSuuW+mtljfGjie2DseX9WL4D2J2aFarYIxfBdqJ6u/BhXuTVMv3pvvge0N/jUUk4GMncqUba5DeXVPXG18F/nXhZUx1aMZ15iJr7n2IoHmZLj8z+Omsky1FFLqkWVpsbGht6nrDBvzDUw45/bQNl8kLEGZcoaldtUpBFPhTSFroIXT96qi0ebLTo0HFEAMjkmGRrnSdB0ZZiy8gFDeLrTq4GxrwFWlwX4+mVpMyfliqfKBrskxT+KIMvoDqohTGGY15PgI33Ln4TTEaruKV2dHvKxyg6fJYXxDzJXbrUKHKpdQ8Hrg6A2MXuCASyvYTIdGiGHPlQ8eVOIW+JWVqf8C13E9spSGzxznD3lUvN9Ly9FwKRmcJTT4/SIq8vzbN4l8M9NP7OBl+ntLc6++e8xYwKH+uCH9fF+K6gsEugNuBGi4kOxSRs54VuyM+5x2Z/kLvqnBoGaHiy88iE28gS0POwSfKJx8qYxX95f7ktHfP/OTtrb/mKL1M8umBuK4PokI069367osVHG68fHzHNPP53xV8W2Gb2Z3C15MgfGT8frQK3TdxIJZqCHJ4njJ5SkIM937r07/UI4KwkCOIYlkNxpBL0ZDKGKGlg4LOYtXIT1MG2Bd/WbTJkjjxPqPyj1//MmOH1kVlQS9HHeGkS3erO9YHE7ReEpSKRF7du2xr2LYCST1TPmp/sxrhnTojREkaZ3r3sXn3UYNirfJeadpKSVMvdvBdAuVzOYHxxBNnBTUEmTusqivlN4O6BhH8HcjTHdZh1vW8Q5DC6riX1Cerw7dOFT6Zw34C+aNNnfIq0a5XlNH4In70S8OaRMVo3bvdQPozBSqdqyMVxlIejuvFf4zhCBmEV12wlxVGJl3ZeF7mSkMw68DZOMUDGobnElkoRYImoZ7kwQ1ROPGy4tafayOOFyAA8ebwLekDsdTiL/DVnP+lGWncdiSEbZqvqvknEXOXED9smpunRXQd8AxFXDYNf26S8+WWvcev0SJ4H35+wWITMx98VFAKxsT+nFFDiEjuGS1T8UaaDVFs9uOCmym61RPkIDUvnzeSxAbFH8F3q8dQY3IQl6nLHRbFJOW936Up/BUqL0F8pX/Kg2Ax5PjuDtyy5f5K18O1IRUOLPPx65UPHSyHu/dzMbOPy9eqJpzKbDS3tOV8bqRRvyei3DEH/ht6mbNFLowaMI8f53uiBEzUeD+pdrpeusNbEorDDH5bUlp2bbdy/ROiqQfQ0c6AjYA/pwr/NabANtIMzIpbiypdl1blbjTNI2enyxS/pcamVsnbg3Pr6MXsWPOGyvziubTOEKyZmAlKrrj9JunktGJ/iUKuMOL+ods87QfBhbA71YJFnxUnGhRC3O9CG3l5BPq4kMjQJSXDBGMymOyzygiO0W3Uvfc0WhnT/b2QkJFkt06INp1ccb0cy+BxLMKy64KQmvDovxydT2kds5bgOppxGCR4eg7dFehnLcQeb2W3K0XtSHDGivEbkZtjSH8zqvls9YVY9q6xJPPag9gTEgT1Ro0JmiD9RZva/j6qr1W/9wQbDePqWgpIUnLvtvAai92itcl21p9HwKPAfbp8El0ugxIjjBqxexpnu/zvwhiRRxNMJBL1ZqgtgOzC0Ut0eUTyPlrOmKy/8UxsqJ7Yd1m/LbGOgvFZx1fExO2Z4oum8xr5rjXarGLfL2OvsO8ub5oNWf+GlTIoTSCW9DLi1/5vq0U5Cukq1lPqy0MvICr3V/4c3gFxkdQ2dVD5S/M0IAtNfUnjuE/n3PXv+Z2z3caDWfm+9BXWZ6xbKspWjl2Ga01UL98MZOgTzVBUSmTR2umc+DSL3LWEru2rqLLaWVq76lz1B+POlqhs6IVL+4Uxf1pebQxRrmHcLuCk36jWmi2O7lf0HXJc7IybMy9VcqOmnjf6lZHVq1OE5cZyZ39XCxWg/YSZHR5A/CSvlyZrrbDN5kCtpkUKp02p1oT0HyjG8xbch+tTEeKl+07gN/Kjf3QOoTDib34Oh3YnNsglz4VcM2opqW0v4kbUfn2rhNy23J8onFPXBWQhAqfroMHYOj59X2MIDsfGXj2+uUl4/ZvdsYkNthNxwVR09fsuH33YaF9Nz0dhf1/vUpWtQiO2R6Wx3xy4Ss5RQY6XYfQbnz+9cP1Q37txUebHE4KUNPhLs4GPfZ6Y2s935vuXP73PWpYgVoB5TU4H473wrbj/4iNo3M9LPbtzxH/KRux4phaDiJYvzRGOjOY0oLWptgwQw473jBrLcLkf6HPEsMUc+3lYT4XWDIYvRh6IvBl0C0Yw71HT+0ghiRzDGPPJUOF+TZLGShoIS+bg9uVUjYgob3r8ixQ7cdTGfQH4Cb4SrDW/wZW/L5sJReavigyaumWXXERt8BTP5s98tcsQsZgth7PP8fPv2ra5mbufA7cV1zLBKv++U4Gx4skt73cQ/Z3Pd2yzuMbrt4G6xfFbOV+Xh28QvIqAzs95lcKTAP6lL7ZcegV1MHMT87kLrlrBMqgq99VWKOQgqFMCf2CbuCCbQFXx+CpJHFNgdUNDp/H5n3xUJ6kRN08zPjF/H/MZf+wZNdHtJKxFR0GqSoGtHxwPPjtcx8H2Fyzzr89r8gcQdzzObLrmWlintlcGBMIYaj/C5eFvfVXp0GdsWpnmDJTpMpn43l3xL21Hunbb+mu7CMRWdzrua4m+gqkWZMONnrMVtcony2+r8E5Pw1G/Itb1/jdXb7EYuRJtC37Nm4thKGZmzUQpZsz8+la4zr39ukdVtqvSUsSGtTbOPvEMWr/fyFN+QFBPE6LZ8DJYSkG1PysfbB4lhze23vsjwnicW1Wb2vUyag0caT15ZujH157tIamRQnpI4pc5fEQmHe5mo1p3yDaBXtQfQvUdcBM4oHjNEHgV92qROjGAM/UTIpccJUQUkBGsGIdlX24uJa7NaN5gevy8LSS+bVFHpmqgBF/1C9eL71Uepi55m/VGl3cJjNHyXGX3+bc+2//OCx8NR/kAoAOG4LM8QPRNL5CGzDqvJZxXpdU56CADt8pp6EW52HBt420JFGFgQdCQOcjs0vWavLp51MueoM0rp9Q0s+eibEIUZuUWpGXBwaImV4tPHJwTzB8J5ldQEMDuteeGbNbDi9J35uCtnDIYQpgQD1H/rGjmEHSUdEj9XmFwshzvQS3i24lzMUuHnwncvSo+PxEXi2zU6692wKfIbM8cZIZHBt7mraK1r5MEeYgcmKew3OahGSsj/I741fjQ0kFvnSqhEhwliIhxQAvtyRwcn0olKlJXDgRvP1cV/8XghumSN5i7UBAvd35JGoWDbHZIPJTY33ScMTFQcmfgG1v/ltVyGPJX4UJ9xVfCqj0IsNNDxMUvEUiEuPwcf8NuIMwUasZ3dld4IQ63JOdsXp3M5PLYUb/t4HmDOKi5t76IVKyXI2RHuFP/mjzjwElB8kyUMBuxBxhSGQXhApPQQB4/V2HCay4ngt8ThOnHThuNOMT4r/ULpCnqxHjhaL3rjjuTAVQnxd8Qxhi2xDLqaPnLvsdrKmxoSe/1JAgTl3i9yqykH6d1E0kTLwbc5fXa+0Lsv6RVx/rEhML3Hoa8oY+vVqUZLHcOlCTyVEFWFomzoF26B7fph3jRJYndHfsJQO84Yjxznp6gUOqjS0+5KJC2oL+xKGuBVIkqfwmU1z15PBBI8/3aaFHyWHf31xTY6yFhHoNXIKjHEX9VS7t41GKfB3O3Tu769SNvr19zug304jpdrHRh1TJxGjpsqMJJF6+yE71sQmGRbqk5gomGJWfMrtmuwra2INTlXswbBltnUV0DkIJzORvGYbe+oZGtzLT7rj9ZCRUbBKrZ2RBpt110JgJx9SmsZiiAJQ5yJX/ZLYgbX9+jGrOX4/77O2axRC/Sj918KbzjmTbDPPz3mf7tIH6p53k06V2Q5RQvaold+sguVPdUHWvtDf4DjfBsv8f+asPuxz7Wv2IDRmfUnkXLKfkm5ieAeV4mczE4Jrv5C/e8hG6W1TqnDpSoSGZr01p9KXtZw/0aRs4SQXsVJKGojmMwpuHsjZPbUFlOEVuCMqMjHWH7JOUuXl+CZjNNUMneaUv3NZ66hvMlIhzspa20wYwVQ4Xv2/crg09tH0rrXqX9tCFV3jIYh8zSWWkE6TNpS2Svsc2KXMzaeYeNDRrXsUtEOMAyisUWt/XNUBnL4RuyvGlKx+8keQlMqSJXjKOPa65kPP1gu+1x6IMRmT9PVhRH9nmfU4ySTcGYV5geryE1Ik53OT1aEESuHoLMqBNoefLfniDFitUDAkRE7j/0keDBUJWbflb4kzPqVR43Im91Cvdio9pbkuybq39pZTWHUGDPtAdH5qRhkdevU/MVMvOEnFk2FosQ4QXLoAYj9BqabK3auinkTJAHWzcprO3hyhjmfM/LEiY+Za+Mk8DMoJtLwfQkMfiyVyIw+RGlq5aPNaZH0gvmTfS8Lpi7zXQ2mV/SJYg5S+Ifs5dkBt39Xo76ehm7B2Xfy+fzbOpK9mi4L/9tpD1pivhi1KH2D77ybM3wWKnpID8dhma8uZIflf19fwiiBIwvA5r4G7SrElt+WRslM+nMHLxltp9yNEet9ZJVzZamCMNGkj68WMNUfXvZGmKoRgubdUygy0kt4eMlrHtMxl4omFT9sWBXm5LwWY90ZqNv5hcRn1fw2bzs+tz5Kq7s/G8rPFUCRopuMTwk2Wq9jCJ5vQ1OpyBAJ9K3RjLrumudP8G9kvNd+YVnXYe7Y5dzRj8Y4sV0d3V5QdujrwhiPUmQ7LKKmelq+2oU6fb6ce+o1NUjyipE3NUXHdToYTVUu7CUuhCZ5fToyYKotjIt2Or5rHzmpzkgxatpoGyUkQYU0YXxGfDfR09OZku9NfKZpY1C7UBUm36aE5t4/H2xMefLdinm8i4RP1LCrKfjuX259qkEm+csYcqNYS9l4Yr7bOYuRx9aqNmU0G99EUVYj5jPsmEHzJtB5RlnbU4Ia1q3M3m5eGrpid+8FLIbvspGpNN2MTqnISC7c0/x0frP+OhgxlBhU97mzr/HqsfT3MpuWeF8+w4D901XaZJcup3C8BT3aXrVFrLbDBTYgBqVS3Fh4C3tZAJD0c7Laik8H6mPTYwvdyIGP+3UG06T1oeIXHVAXOmukFe28pcsFklsztFtUedtkfTkiNKiv56/8bhNtPrF+RpaFDmnxuDWNIw3i+i+jx9Rfa2CKCd+QHDr5gawpl+j/lt0Y+WtNEySNogxtKnHNZIijMsVDdfP0Xh1J1iAY26dOgwz5YekUaAvy5XT+tyH0cjqqd6O65YWEbMd31RHzZne6RMRcl5MOmHV2oX5lP2mfXeyL8s8/LViQzmo+OjTbWX3ccRKVWv3kTMvqWlEDanY9yEBq14fM3TEMG2jbCcut1+tUN2KTPfXh05fubfykJDYg5FfEuKihPftlp+sJsPNbmSuRCm0G1zUcMfVpkoj6o5+OBpVDKl3+Yvom2pdppcs6MBSGHq1w3a853Baw2No/FUAsVueIs3P2XpSaOL8I7zg8IOc291zGCjcHgNqM67ok1v5cdh7bMcw/WIdskKP/x/W4aDAPwVrhOw8au1tLJizpJbGQLgpgDK89+r+vSt2aL/l3p71N8OZFsJ3O9u5zR9DwuhiLaM8OX7/zo2rE9GuGS8/jRZth7y+R8WNRtkhtYS2x3ythknU9+aNIzaM1LZl8HygOoRuFfmX+Gy+tg7RTVIprdjihqV98aNLI8OKTm8PtCWvAd7I7I8tHn8r2YGwRoObgZOKDZ5o0WlyrWK3C9lev7vVUJp44dbLKMr4eW0eXEYi/UdhxA8CWREQfLJTYgHlsMG/m8G2TrsWm7mNTnxMtP33PbgsytR4xEMYObIXC60WKSp3mgZ3JlHTboKqSvgz/1GjwzxyqIRzW23RzkHnDb8aYkL1gPybngMDu1Y95RrY2WOUkX45+HKf5ageK6gpZNELjBx/rQeN5Oa0asSZTmjyea8vEuGSDusZIa9bd2Ik/xsBzvaVbHzkzhcRXGD/ya0JAHFts3Iisgu61hR5uN568NoM9Zubav5IsxqUul4HJ/j8gY9xd8hsiTob2jXCIPfgCvuBMF41fgluTQ+DIYqWleRfbYTXWPMTSJJABAmlQKHDNeFLMf5Nl/yJ7693KxC3hpMT9ibYJkxz4n9/QUaQWWCXBkKZZXZ+Qg9x1Cnz7ysXFN3Qr1wWiX/RBd9lii13x5kPIJoeH9qcvyBY+sBYTmm8+L3P1ZU9lSvXRjnv7+KQSSkGDpgF77WhsvrhnKXwWsESngag7zhnIAkrTS/6jJ+yI8AKGmHHZi14Tcb8vhOrzQF2mLCqRhsTIlUnZnkyMlMgM3ofkkeTJ1M+vSfy2jCUHjknVaIsNGq8BycTvcT+yLRHatvLlINvHEZIQlRflFNGEmyXEy9P+qMeMwJTcbb7DlyT1foiwc37PaAGiD0M2d9K1cdBNpcNUMIpuDHzE3y3EKq/Gxoxn2YklzElFAVE8fmX8+U+6Npjtx8MJFBesG9ZyCBvYRKXM4qCAyttBfHb0ChbbnDZa9bTnh9fDep2PrqXtc7BrEk5KcxtuJiyLpEoMFATxYeq01ea4UaM2S3p2Ao2R0aP+nl03StsNWQLc67h8uwoJWjQpfu1LSIXKtYRJNEC9DeBbPTVb33qY87Y0+e9Z/yf1kmtHKkE2f7L4qKLLT1wyz95eJHCpkNYF51xfvNu2KNeY/WaMrsKiPEUswUlu3K2GcR7/svNu/QFC4v/OtOgCjFEq5hqALVALZ+eZ9v69G6dv8CYK6lkegt5sptMYr6JoejmsDoRy4w59uLbTYNkENthsk5eR6DpRnhkGyc/V/GBsAPjdY7asdNemy+MpwNuxKVsffvg94Kp7Te9jNP7AAvAojsg4fkCB+f+E/AG/7xeaMVN66VPLdzilTt+4vYB1jn+xBfO8+rIvKTXuxfdenZfBFd/+WpBDDtOzX/VpJuPY78uAfpkt68YWgdQepsn+vem9ZdWX32aCT69Cq2neBX0rJimrHu6vFgiwyvId46Dmco05oOVdLicPqeB05l0YyH5aFeYNaBdlvAbaKeb9nAmwJMru9yLoiwkqNU0ZepVBfYyi/K72xVHRT5LGAq5f3Uhmh3np4NzCLfVPWEp+LjirpptYH7y7tnzIuHHkfi91mGVX1OfSFL9tzZjJF+HUgTSRUNUteMjSgroizFk1RYxG9SCyGHCHBWftSWEML/4b6A7diH0+Le4fPhSYPOZ6Fb+iHdq7UOPkUjw8XP+r0jvEci/J/nLeOY2g2Q5KD63id/LhjcCp27rGkyYjUtPuC5mwZM0QEKXTZtMnfWMUaEjFW4uk7ek+j7xDOplj/TV2TIJfVoHMfF6yATsxYIi2Pyte60AaU44+3Fo5FkvJsK8ufJBGPn89c6bj55iPfABOnaU+WuYg7ho/9hDMxPVMvz0aqOC+rgJY1zjaFkiEN+6hAzkqcMsjOuz1OOaQJUrHCj6sI/iIwK81oYppW3QJ6OstBhQg4AwEexICmAMIOuKgP+bsKgmUjmlKMg1xjpMjwoCUSJ1Iht+88h7yHrqvA5uauOnVeTOFTVFIpc8nRyEn5qwaG1Ke9GZtQbufDlgejFr8CIh7KGmnKebwG0ZzVaImMhhwNYhEWUZDOEL1dLv7+kkrgxrHE58OGvPJaV9/dDFJ5CMiFisYQi0KJ7nKAiSfKRFfTy6YGn8JYFoLeJ5Jk5MJArrxguh5AF2NK3zbpSodj+GsGL7RFn/dgkFifZkWatn5jCv927pAZyzqW2JWX2dExEvHt5cYQT+6VYM+JFt9ymePA0aFo7mFdOzo077hTVxG1Yd6Xe/qEWL37ZN+79aJNGuH6NTvqNhVdNCjreemx+FmaZ8rHIu6ZdGuv+ddoa+1izv/kqZ8IMFE0oshj7/OjVwKU2VZPiby5ZY0yJ022Z9eWsVPgfhbYEahvwgPl0Yh2rocuU2uqunbc7wdYebnDyQo80Cdss8K4h8hx/+Ikij4z7ITiJLWnVaZhjjfn8PK45nHrnYEU2HqB0KvTEDZmdAjQ9/uESilVsdILfcjr2xeqP0QtlHHb1ouAPztWvQeLfIlDXRrMoDAex3OQJTNOmr4n7hry67C6W3JOz1z/eVYCcTH1C5/3H8OkSycV7/KreMH/MjQeAeer6wjRbJ53BrImfwk0eGpMsQlAxrjtT0OTrc4euDoBbWneEEzP5rxyEtQ5OXogizlDsaaia99x2ET4D5BMEp0MOG/CTeXOFqi6TqvdA3vkeGmpbIQrN6S5pyw5DLXWY9cARhIbseg6QtfrKxGrdvWN7vlzscWe/CC+p77JQQXcC3Krzt2K7BXPUtuOoFmkwwqN6LoU3kD8Jx/+d/euys98P0lERdUGDSRDKybLXF56h1ismJ+tJJGqjx6cdsP0G4X08zvMUt5Zo6mK7LKAh5BKRXb6HTDc3Bk7n+EfiUq6Dwes/7l/0KIi/AT30xwJvCvZpzCMEdRG880PuuLWtqTnBo1fASW0n67QpBsy8GIs96aCO3D3ekPIaMRm6DsiCZ3qtQ9AFeLlzEVr90m8S/IvPBDZnKNFSoYdnI58VioDmSX05mStvkq7D/web0MBfJiz8qThjY8Rds+T//M8R1sClZTrb5I1VBV1wRh40wbZEDOLIGkWHB3oZP9d5p2IQZ0Xv9VXsea+tKMLbrmA28cQsZJQ4j9tpgidATWNVm4T7dydQM31+W4PvqSEAE7MGhx9YbBOKbW3Oy2HscxAnc/7h0QdBRuTCVTSs/NHTop2mnrTsurFlkEv71T1b4Be00/jD6m0h04EmBj5H8BCS8cMXnI9c8hJpUxV6loHPOv/ryzwjqZfC9RAdglXWeYCq6ul7ElZZnZsWF6kdDCFmeMhvEWWO/8SpTFoETZZIgtKTvQO+ps/a1YeSi7IiyYPaQHzPCgYP/4Pix7u4lGHU4YCjrJ0/LX15LfVUVHcKIV3zDEAdQZ3LMDsLtF4i1qtGKJcA0kYvHZVUfWFzmKresfI0CMXeFFCpbbgXCWua/gSsSz37vT8aZY1WB/IEeywmt/FXN1+f9N3ZODsGlU5c9BVc8F8TsREfFwNtPoqUiuEvRa8Z+wfbGz5XDixRXZUbRl71d1/qkXd12NZpmgXWzECpmSyKoFx8JqeTMHn8+6Wj3mpBYouDWWQUg51891OI2HeyqxaqGTbDeiOe3Hcv73cIbUi5A+p/FNwdns4hlxYS/RSHNb7yPZ0oEV3B5s+uPdfjiAklhBf8i/Noyv07RmLvtDCAQDVjEQn42GZMN6mOjplpapAbKqKvR1O+f5Zhh1oavfgJ+C0/+cLohs6QfTuy5IEDUBNpqzkEmbLRCz4mMbPTa5ZaPjj+jrrSim76/TWrrARuPR9oED/rc0fdoJl0r9AyD27XvfFu0cvbg7U9WiF0cV7AR+24n0oFfQgmUFI1g0dnHAiUe+ENZMv8IkbhnjB/GtiZpuIBSGMCEVs6hifV9mRpfrpNR/TqCWVYlXUdFFO3BWwhnHzRriBU4H4gn3jYV7nIjXlpO1W7mdUYdx8xg8Kj9KrUQjHe55DxGxuhIQLIIouaItKd/e4RPpBZg+FHKWZfT0/J2tFqzualN8qTSxibHacpGPT2/WSaXVmkQYdxl8rGi5O9Ikddw3AXPbJOtt4+zI9nXkY8FvmrlIwD7AO+2cACzcxkk9fsU1wpxx3aT4p6nO1FR0SKSVZ7gRdPthPgKt627e7vTg8cjBvtt9iMhAyb8JhGh2pbG/apxwFdYAYOwGwBC06FGUzxqFBJK2jR75WOGe5JQEp/P0iH8Nrr9jNfrDYCbcjhFxNdyvAX7NRY4qWAfOq+nrUWrxwSkXD+CAp1Km9LNJMZJ/k/xXjT41WCddq9PtQVald0DXsVWzh+7uh3/Liyt0QEDi37A0H02h5HKjcL9NToq/D7biobavLhraESgzEbU7uIX/XLsKd3RDH5nxmzO+Tew0Qk5J3SWx+sGV7f2dnOHYRg19eHY0Ifc0DHwoDJlP2DPzupYE0lfewmhixly+yyYEIYoZElvax0BzBAdp6buOzEuy5QWoEoUZXu6x/nx12VjnTPmFdn5ViL3BAz839Yx3mwudPux278Fy+zZSF9bBgk3Hpt+vd75OWmDyMCHB/OUWQrEyuSC26X4YaNG04LwKhyb+FL+i7Jx2UWy5WgYza8WFmpCQXE/5RgIq5EKTAZv3nWQBbfcW/7jXU8STL29ckreHq0k6EoaPjr+uZMqoMHGZsa+/ovrXDf9CQflI4WDGyCPWXdt/mNd0LPxjDcXZpSpDb/b3I7aLpOEPcUv0V8RHhb1hnuGk6yL88vkcBh40xHyOr86VkEWYddzxcpBtecukMxy9vAPrlQ/9saTzf2X6UzL3hWFJz9ghpGY+C8nbn4uZpe/dVISiDR/L578FEQffWoZbR+ml/KXbvz32J8PTTGflHh52/n6b5Crf28y13FI70MzUm5YxAx+3OUcuKlfnUK6lzzWHg+O6sY5S2bqTZPqrZKCbQYeifyk1/7pAaFmWL2XvxiYlsfOe/nnxXR5qk5+p2Qh5TuOWkR4o+h825hhURge55OOfGL/j9duMEnZ+jfZmRHAW8v+G4zIG9hI3WF4STm7MA1j5VZR55/HuzHZdCpYc6B4qjji9n1bApOJxxBpeCwCLR77sjVLsYxOKgG5U5VPOcHlgK2kmscnd8Mg+VfW8Y8MIAMBycwMj1nJNK74Bc4y2tFkchjsXz8xVQPVuOJT941zFHt0U38D/CkjLJm1Cz3W15XF0Q/UTht/MzXo7G8hdsFXjP3sT4v9eXX3oe7P8uv0bw19i6rMyPLBqzuM2RVPNaHy/FK768V9sYnRno3nURFISgLxtqPFdBM1Sw/LWk2DqurCFNzxTJNBn33x58LI6Xh0KBbUBWXXb7nXUvKi2T9/vAzhYtXZG6ktdaKuvPoJKs+7wyeJd/7+sekyFshAQN1QXoka7z8boy3i8CP+eqTKfqWovhlBysEkZnFgPFoAaofdaNbZ410JU1Ek3flAuQybsPRKTJLxxdJ5sODFo9fUJ0Ol9jEp9tmXVVT8BPfJeHxGx5l7KdosIVECkAYg4B0eoCTJIceZOmAxnMifbaNfvLBKGOxGUav8JL+Ga3Iz6gXmvuJlQcfic0S/50BCo2+q/62I66R9zUqlschCq399GA7tDCcMwzGMF7hsKGn9yuSTdq6p2+65lDay9ux3NIhX+DVFOGMMSo0GFcR82U92xQ36uShbpkH50pe6RvKh/wZ/22Bp1y1/8u2gT/lC9Ss/EKpi/NjMAsP9W0+C3wD9RD/XuSTpRn2Waqu/CEW2oxmHv7RrgDhva7/tA/bsCfWkIZ/fCuBzqew4SNBbqGqkGjOpTjTM2zzMESreZu+rQ9XGIECnwx71kW5m63ZmSnCNgPnukOFveYjWHMm6H6qJQLPUlvBa1xora30FN886MoPuc19bCOYJVLWqRvkF4hQ1BAFaVnV3CkelThRt0uj9ERpXSvIv13HBiNA+Q4+4JZZZMw4mRxBI9vEI9EBU9kW3R8kgGuHzdQVXP8yCU5xjrvhzxfXrdkHv+XibZF0mMZrke8U3xb8pRixjAR8G5ex4mk+cXjPUYy8aDDRlX50w5M4k1BTsOpju0vUbAhWtYz9zQouumepLKdvDlT5gkyXH5+uiXrPtdjGboFr6/aZ1RsMIP9mZyOWfyBydp+F2iJAsHzJ/TrNkLz1y+rUN1U54jaALXeHsQYfk9QbAdwdOWo64e5prl5IT1a1/SztquxMuAeM6P9DkK3evhsN8EYbP/Ca8ErnYQSU3D8bxnNZU+Zu/B0TUr2WEvFI0FNYqPfBvDCZyiPY0xXP1A+EVMK9umB4wruJKbdDriAq1DvsAQktCYhH3Wdgjn4zpjGqoe6k/Rj+ZWoXTGq/03/P9m+GHF+e8jgevv4dmNF2XYbkhHhap7p2wyiqXmH4sLZmC25A3N2IBEYfn9bc6Q6yFvM7Q8X6s7UEyRgg/aKmqxvSg8orahYBoLeFMgkf8/mX1nXH3TaN4uPaobeB9nLYS6SMv5HX8FdF//YskHsWF9HTMz0vCbT96fzyirci79jGii/6UZGfaV4OxiBiGoX9FMfCn97ifhYmQHJr1zxn8wh9J1iyiTba9BWMqaK7qKSE8OgKpOeeLb6fZo2gmRue/ZImGike3ySDY1aK5PVU2jDEZFeMLGiFULM/8CJuo8bJK+zgeoXgRh3AbMErhDAYQh7sDNwkchL7SiGG+qOZZMtZ5sYEgOAtPbaGaoiSA+uQL5mUA47xy/9c3du6sCd3CKzfDoZnH6u0Eq6o8NzmLOauvlG6OieT4vOz25UHAmUbQWhyN6Ob1XJZPA0qNgbo8gDPl0/lhkeuj1RFs4QwIKv31nIgH2dst8vPLDH6NKklW408GfqhkuYBMJmK3TXvc196nr6W3a2M8dP34Yv01DPWCr/JjDItA/o1mekGHK7+ATXF4hELIOn0O89fe5s1BRwlTsp5vkJWJwS+xzbH4FD2Fo+16K9iWGBpWoK+KgiGGIgmZ+JoYtIkmzHjc5Pdh1BJaRnAPZZsa1sSDLSprVNNTQiADd0c2eFlV21kl8gKpQI2nV0e2rt6nm15mBBixzJEBvKxr5/KWe72XTRgVAmOfQ/t81HwjLQv9nLvm/NC5PLriRo5JBDUMF96nHfh4HBmpV2Tvgo1+Z6/dNjJLkI6oTulPCqLa5OsEzIJkGymChEMUejHgkthxF3lHy8Fx1c39CWkveJPjaiXXDwY3eo8WtZqHJYTdTCb3/mgSCF5tx1LPAzWlhtnzmbAphrz/6c60ZCa8uAZtJXFCulajSdyHybc7vLUaKetF7xxmv7nZQGLZBJ6CpVRjCWp8l2AGKfER4H3SQjh4JOmrdJIrJnGph15aZjax7yNjrzfMxolF2SL3qRgIIy9ftoSM+ZXtrp45wmtv4Tvtuw8yR4gJ4z4shRE8a8TDlWSv+FX/0tJwdBYpSGBbe3hvxhqrafvLWb+gAphxkmFuh6q3RSTxETdN8BeA7Weif4jIT17eCkvVByig7l6RG1ICiJjTINsmdvpF0zSSTnNEuArx/XJ6z0ZOY/+4XPOL+q9SNN0RZQl59qVX+E7fIrLWZMJKyGvKWKvm50aoVgGil+HD6IF7KNWDSTSCUyoFSMSHoS8Rfpg/3EVhlfqhiULAt9nGqB1FXPAKWdJ+f6V5QF94RNjxF4GhZ/LIxMZcHBYOiRGsjIvVslvuqLmiInM97lx5cwg3COiyBr9X6azv8/zMCKnr/xqdfv5jaz2FvXqZxjKaojZgny35u79XakGzidUCfWerEh7TbSfEfrfsS48cULPmggKYiDKb7wkrsci6DHmOXwun/jXeX0jxivqLwfVbS7Lt32hkEHU5j6nB0cdUo+p8DQwwCFsqvz6HzaUyFTmwISr5lAFM2N2prgcbvrgSRFHv3jWzz+ioSMoNC8OCNCcr7fRuGR4kuaa7kplkJQaiVfhNpghgPz8p/4tCtsCoZxH/HjCPwEt30ujLV373z+HJS39hZgrDw+FzMYuKZlRfm+D6L67gD+8vCJRsmGpl98tigQ+PyOMzlYMEeuhALGZiydSZLi+OUYxFE+5E9Y6aVDTm9Pm3/+3Mi6kQiwBZyFpQHTEcoxKHkhDbUb/VUCuZ3mh5zwIPiMV8kN92xBfskyC0gw5keaJ0Zd1x4cUu/kWWcGpBB2t1KOl7b9et3XAlLvWEZGpKAya7+mzUDdTqW96nOb7+MYYV8Zf6CcjgZzZZ3kwBboR7HFdXtvwC6vNhFB4pT+wgQ+JOzZgqcPc97uI+646nzCq6OZyXn1wbjS4p1q7xpoD8wQXfit/X9z58RNztENhadDl8YZ0kx3SFOTah8YV+YtErhT4Pq+xa1p0XaJRWAJA2Ef0DjOoDQ0RpjypSph+zgWk/qBgXi3NYea0H+jJUwUJ+aOZ0xRwKUa2ix4jdxd80I41+URO6D0GDA40wmkBksn7m+0d2kHlgl4AOzw8fXxcmvKDzL8qnojy0o8uUBwp9oUU0O/Vguk0SQLnkuEXIyE1gpsENDbr0WAPvB9tVicLHOVpFIuUaetyz72Za9tC7jRrLg+KE4CtUJapO/e3hnbkTUZC6O+DSON5MzyYGjvjNJ0qELWvC0equnejTi38Z1bgxhEwmJMp8KGn8otxfg8dn90zpV96UxbKwGYdnsXDdr7WB/I7Y54PGDdbxNnPUMRTbdB/9wbRvacx815rUxUCdXH3uatO0I49f/9mhLcCqzzQMHL39vBcnBhXnrIqaOki9S7PajYbbQCEruYEQvqAidOKDfxK53hTCrn/zKe7Is4evEGQCrtfEsfvi3GEH66J3YTWqQlWN0QnFFGF0B+DEYlCIZoeL330TdOMN54MCa4de4UN3ZPDXjsL8SIz8nnZal3EQwgRe/ukb38DiB/AsysXkGFa6VwycX0Jzf0u8SPa4IPQy1+ohwXR1tnTnV71GzObd9ciAnbGm4iPCJzkX/V7zDiFVmgI9nKwixNycbNIyWqc4q1G3r4eZkeZXK6TYw0/x+sYWi7KEgxjrpQL5R3oIYTCf8XVGw4sSqrJZdIbxkXLaPvgBElhwhaxgfQ9JbRq3ZtOCyhAhCpDJe9i312xLof6ilCaSnsRf9Pbz+Qr4fdhGxs+ulams5OMqp+c46bJX3+fNXosPgppxaSD4xqCMnR3UYzogWua1hnezdCA9K1syH27WlLoM5cdRi6gOBlW2LOl7cAjlJRokl90vE86XGeHxK/UGnok7EcC6Udtk/9Rgj1NHXo2d46H+sNdLZ8XBO+LC+iVG0onJTDEbscff10WmO4gkd0Ucmjw9bYuYz/kIC+6UIAb2E6LvpVkuRnwSmq9er3dNLMIbLV6CtNDXiXhPINTfj924034CtT+bQDV8tk+5V96U9MWSYak7PBaz/Fe9+fFH2RIm03R1xaP6LR7lpY9fzv+GzzAHVQHGtdYFWjbPSQAMG8QezIRwZtSG0XNw7d8/1noUqc1fKkjyEwh+7t77BXaKphtjD8FjKfNoPhXyE4DMohAFOUSOY7k8cUaHacxAz1lddWQ6suub7qhfZ8g5PhP3nem4otPM2UuVj+b6pG7HQ42N6l/qTEa1ZTqMsnFZw12Mj+HkKorxL85yHFeMOMZOlJHFg+qnvF2ru4GQ8vEDDxR4ixUziJ8G/vrfNGPzbCAwXHoucU4BnlScWbwaZQTRWU6vV2WjlYFFjEYNmcyZYastD0Y2mPVZTlfakQ/OFMeQs9WNHVws7NHv7jOVi0wrxVqt0L47iFJe3fvOWdSrCWJ2lP1BG1hbSjUS5UmoXv+Cgkqbk5B/iLfUgBiN4g0LIYFhB4XRzBBTWADBTGPipoTZGbrN4sMYHVTV/c71gd63n5LNtp+bXi02SCl872dPTZEotNWi+ZH+ejFAF85XynXxS91Wt2q1wjAmH8CV879+4A8dvX86mx0zdH3IS05HpleUDJmIi4IWJWTWrGwn1MYoX8PoFECDlb35aykIVcn9WctM6K7MjiK+1vMhuLLzmJ3AoEs2n5bblzuvby32/QUvJ/Kw7vVbBlW+V1zB9O4m0/9gGJGfrKlD9Ntg36cs0JP6v6zzbee8uyN3JE9s+khdQw2jEXTIt7BGr9gFmoOwWNZ7uXV1LObk2E5UUH2UrnVcIfVNJjIpJFw5MJhqCIjIKlaV10j66k9WbTcnNZxCMGoLsZvL+dlM2M7rSEX6bObD+Ag7F/Blr1D1z2AjMFgSs1gOAlWBBr7rBd+dJGb9VVV9ktXtXUl4iXC9ZLH4mqmN6MPvv6kJHGnkrH4fqisvsRiyXlG3vyti8uPl2G7Cz1VX/LpoaHMaEUhuvFl4WmW/ku8enlPn1PuDMwqCNJDiKISizneMfyV41w7tVxe4jqlws3PRXR7cPG3KC6Wu7PxgRSy6zm3xrQKZrHdsGXLuB9P9zr/DrWAAiUjrN0tnm7NJWkkpwazmUi0q74RRiT1qo9V+/meDwNycQnXx5pJCS4ibdi2znpCAnr0cEGbl/DizV2bwBGHe/7gL5jbs4GmzNi0Jc+eQ42lIcpuqSGyYz19/TNIqrcCSFXnItfkJQS+q//dkJXgy7IT07I98RxOJCLZGSa8r+n6F73ivTJ+qhOIsKXq8sHgsqs4xNkf7HMLC4zzKjrKfcyfFwMN6Oaabhpb5wYhE3PnjIyAGXB4yp6Ec8pSj+Fg3ibUHr6htUHqaeOf8VqTsWVgVDKquboyq7EC98r+SgVdG3NdhLVq901gHNInc4w9yS5DtTWUWTx0uJ+j87BvU0cVMCAlhJISsWGw6LiLbV+wYfPxX2nOy0WzmPVLpAgF9NMZzvFFExj9EMAeJbVq1dEwhmCdBZ4baE5E0gDTW+U7pwt2kSyOW5GZcWD+Qn5PkB8TVQkCyeb4B6NgF1aijWifH5SjUtmD9P/QOcMMooipU5/G9s+dXgh3mmLeH7S2N25OvrNiutLJ+zkYLw7fRp991at4ZQ0U40fA9sYEUx930qAZ3Jb8OrrPAdaZ0fv2hErubMesxYnHOqgmBgqDa0yW6tx0QB/IPpXJiAH8/Ar7H/gSwSzP3YzqNNM3O19Q9aJS93kpSf155bIPwZfOYd+iXg1W/O2F9rGEHRr/ltCYiknkVuiNtNsnDDBNuIjd2V3XQAZ8ZZXXQEYxv+KkTtZSF/bL3C71+Nomf+RjpapqMChC7ayNsxaL0gRyOmidh+C+KX8ZWLPOnbHYnIB6/kAZVUY56aLWqb06aTRoudzC1hGUr1lyFWPHL9BjfIUjdV6Q8FKQBgmnEXnvjK+M/a/6yF5JKbn3qBodDjUJdcZyT0+fg2R+2VyTamQReYIWJQ4vm7TpzK5yu2Z4BkUmB6P9bCtHt2C7CP+S9UfIokpH/vOz8uoL0qJqiphWP2FQtc87Y/qEISkXv2f2150PDnSKLHRhFmmLpsMSx1fFio9uytfHw/BEcQrD4wMxd3iME3HZ7/MfB6ukxax6Etp8FAf01ptXzT0vz5jhXKAuzfqt2/O03B/GWRCn/t5ZjNcP1fY8+kZr5y9sKyLyoYTFyJKZ4JoHSF+7PMpixx0ivzE+wdvUmDUNa8jH3oqx5NqqPwYVWj9uZaNgxJnqEjJ3ChDWXDuu4ZEqu5hI0UJVtpJLffAMQG3s55A3YCQrh1W/XRWcVS92H4J4V3TIlWGkqIhCVsFEs8YHEqreFPAjjFxa5XigXWLDswSdY+RNJs/3ElrYsbuibPlJQznI/3zWXeX4+GBe6KZZM/gZkh1ZDavR/eUKpc499WEnjJzzRfDjd17paN1b/zdDhL9ew8P61R3nzRYyHIL98ItJNBp465DpBun+fvYso71nnn8tNvizCGiPl72OSvaWT/IsPxrRPzZpG/B6zxtsXXDclHqn/bxhNc3JAteWnl80TI+nZEWoaFFjNQy3ao2Sl0JKvx/09Es56PE+R9rZ5IA7JhtpcH112V6s7m4SX8iHhW/TmxvejmhHMrWNVCxXt6rdRG4yQ7CYmnf2jPnxuLn3lwFSk4waLOK93AekdC+qJf2Jb/JY9onjLalaF1KxJV/33/zLvp7zaByWvO5soDBPmFwlJc0ApAS7anLFQDl1juWW1mpMmoCs0+m0xn8CjdPAJmAyZtKOqW/chVGICsOBLM6EFKYglkY2c+U0AaQjp7z1TE5CpFqJfUX5l0hFElily+YFhYY5eJAyEMYc3+fwelSreLALmVFnWtLkOeUh+Ibh4XIPnw7k5oSPXx1nNNkxzffWqXjbQAyCwLeIEXfFaWukaoUY1CEZ2SdbYrUtTPQw+tnPQcLS0xL9ADFvZ6KSBgzpKbNOuWB+5c5sJkS3iBFHbu/t/9ea9WNuSfsko5d7JxO5T1toHtPaJbEM7YHPf51Mr2p4CyeW153X5En6f+gp61NT9mT30p9YC6dE18mLC10PXvxSBi+y0Hidn6J3JTHB+pMgeHn/PLPNL+lBItHZi129p+y/I+rdAh1W25yIK+UOWqIxTYxzAR9YeuhP0uOXrq+68JuvPO+Iz2i4zU75Qgl7wx+uHInDZaQhem55AfNiWpb70YUD2pD3rYdFoOmNpF38et49wgsMmXm34nHCHhScx6q9PTkTNhF1zXkEnpZ/+ZQQiW1dt8eIBs+sr/dsxob9AQi6OnS0+tpEPBV4+ReW0eo3br+V60EHumzEBSWt9r5e9uziwvpf124l4bl6Q70aCFvEWhXJMtGeTzI8FWii3MG7Dl5BRPTRTAhhgA989E0HHHSd243gm2WzR9LGSpKrp0bmlnhAdeV4lViszEXK879o9vMqex9bwZsYis/P7w+NSxfqRfLfzrLEnsqzZWP6k407ABgkD5heDrG77wZ5npZk4+CRXCiojWC8P7o6YcOPUPjeJI8xx8+j44BZI2e7Cp4h3vpcVTJIG2blczZ4YrQUCgxtEUFh6fNwcCfG9n4q1RNotL7BmSljM6ZgD/gXKJGb/6jlyzZih7LtvzA2bfoRVV7C+0KtDb5A9EHdR78klGhZnXS5RSzGCLde9ogdcNCkdbTgdI24uTgTEKd+zYq01kW+TgRTyU95z+nw1p/RUb6N9/nZ+Efe3R0pBAU3QIqfg/Yylh4TMGRVZI8rRLnjX3AaEqc5EcxagSk4JohxOVuucoPBWdTBbdiIcsnPHL6wDIvppJiFfYQyy2BKJ2ZHZJd9f43OOVxdpD3TQ+fyb/kb8bRebyr0lhE9zQU3C0v2rOflAMdBMern4a6o1KHje4cqa+fJ10VIvLEHeb+m8LmjRVfTO81h99lEzXAc7pkFc4zUex30JLEgawY0nZBvNhPZZQjtouBb4YKrh1w0xK7xgqlN956TMKi6IqZVTXlPqMjLbi4RWt1/ec+zXCLUjq5vOypY8FhDaS1v2XKCI4qteylYyUggmiLL2THC1xt1XCnHEWm2Z//m1/0igBhG5TAH7kFCHVOjT1r1UYj79L5L+LQ4DJd6q1XHo40jgUjr3B/1lukxyarCbt/caCq60ATEzp6DyyiEoPkqTg2QJ+UW7rPYLXcRmGRhtS9f7bU0Hr5/+4fEJUvj7dV6sqjmBlkuOrCe188ugwZoUngoZdXzB8Z3n5ecM7kR03KC4V8fg+9SP+JoIBt2dFl8ALpWMObK0yMIKE8j1NLe2Vu3ffYKo54tlr4f+WzuuN3SDQDq8ubSEcTRN+Fb4UtWjpb4grtBx4sYdTvocEHlc4Oycig6qCoamXr7/ClIn6eBAogIDN8YJMraAc9LkHoBEUQMm5WygvgkfFHYLSpGPRKM+Ib9h2E6ASdns3wwByKAO828BosXjlqtyCKNTXOb0j8vWHuu8sEt2NeY3OHY+LRfIW3x/eeOb069lUBOvQpWI0hPNvzyInZM5n5MKueXiLZe1lMkAkDUEIh7RxdBj0ZSKBr9QBjzeDqZUuCcPVo2ywhM/TkVBsmuALwPZLsUnFj4hC54tyfRM+Ja1MtIXzEtlbXTkqcozdQ/gLeS7Bi/f26sbu+WiUBjn8eVgYpa6dsoqvcw6jKNmAXGP0qKDkXcoW3mNCHevIjN9LvRA1ct6weiw43aVAjXW/gMLV3SRL/bgj+vrx+sWPzXCIt+DIQuVBAiLErJAr+6ryRHQ7P73nQ/VNYVu/sU9+4OBGiq5+avh+80wIkVCqPIFa+ha07NrschexEr3N+bQbgtHs1HDdwn5V7mIVWCyKrQaw5RaMc1eT8vmt+9qoDP0viSm8UJd1xch5LH8An0w+SD4D7NTbvl7mDgBrpi9Pkgt7gBybQWHGH6ey+Y63u+BlpdaMVtk9+ltt+9FREP34UHnjIzSiq3/3K/Bm7uUzRLiKCQ3JneXTFm/gIqx7Cb3HwrA/N9GQXb5UjJbZ86wvL7+TEcUzJ0RUVgsqsp9otfng3DZf1uy5//WH1rGn1mI4/grMC33VxD1A/Oey203bxkRQFjs8GyGniSCh0/msMXvTnGngvB//BFSd25SN24/QsDRLZxzjAGPGzo0K57++clnxkGe6mVWN/VSgGHOI1Scocq2WV3oBgxi/zYTyP73hmR6xKSjZs8b1rddR9yiYGocstuhB9LV2CTIeEkg9MS/lkbCYqHM5Ods7CozuVcG0XNcvsh84/lnnz/j8XJeLheIF6BNyvMz/b/Sx+qLB4Ts/m4BEM6Rpq7z40Mgy0osRJF7waBmEn09lM7Vq71TVNf9QxHlAWoAwCAT/ngR0ihiw+tP0gf52utPD5cM23Mjgi8LmMBAcdTXekyyO3LRb4VobN/F3cc6hrQUDueeD3R2xVMXnxgcj81/9/W8/KJswDpzkRxLqESLTvEEdkqRsrJfXhhUHGJK+l9AzCSertI15AmoUzY+JVCWz7MgafRknjj3LtUj9ddnDjJljgqyCN/+pD6//Fag9LlwuEgdow5Lj/IKRDM3qqCg5+gpZnpmPOSlWV32fbRLcHXrp2Y5G6dX2ebVFX9xEASk31PwD1B55AjE1r6azvufS+/b7P9zZnuCDhfdYNqMWi7OWjKrUTMHxZSo/IXtf6N6AyGj/MgjE2VeNmvENidC+AeEZ1W2Z/IdbtKHwrrl5h+c35qXJMiKWbEAgnaQsXvIlL2I9xayyVvpT8rKCSqitSb/EgkoQcDnhEeZIrPabdKzdARO+G+SraFA8k4I/6aaknBToySGtLQtJw/3xb8ud10VGph8zEozd2z9fke3jp3yx+o+OIj/BftrMCBGOpo1iZAw4i8by0GwKMQsDflHAf+NiCAEf/t74gYyzrwpBLH8IsIjHEf32qREgPW6CwAHPk7yVg49ghVvXrnZ4sG+KGyOOtK2FezPX7OorrNXnMIiaQkKe663DGrl2NiQP+NsZ5TZR7OBoA2Kdi89Bp1grIxfoI4s/bAS/MC6W6EP65C98OTqgyhaZ84BbEXwdhv4Ix41t0mTikmQjBglFcRTgRDuzDWhC6rfazmJHfX4NFDWOYErsFrLfA/d6/v/S0JfE3lMfBLKW2CkBYAPOjh3ZyMRENar0fcy/m4ey3sgRp9d5A0ZHKrwLybcgvIlljK8q2N5Oh8A4fRok8lz6ZlIH2ngm71ytvq12A+bwUWrIuyLFzt2XTYWf/wXX1looChwOt2868wTzJcfrnBl7fsrpEDz/iKNR8FYaGxoSklJhBBZAPA2W47Dry/5wti94Uj9kV5xiKRH/shIBZ7cxjBlo8in8UG4zRde5iK23vHrmgQJaMWIg+vlGiAyCsKYX4wZIX16SA9YKdi/erlbZWzlS+2SvwauHJ4O/3leID7i4BbysV1MC8RHflpa8wPY5BjMYnc0o6v8Zf6UsGdG/Ce8L1utI6hSgHaTt0u9V183uHV01tUQgCG4W4GPHOJ23nNeDYrjDHiRkft3BVo9WtQfWSBIB+oc7SORDt0oXrxbCoYwm3vqZNrx8K5n0sbofPPwRCReA/viwc/gyrBT0P5LhJPi6EB8CnRIkPQyPQ9bCFrfZxL7gofmQ6L5gXab/ory/8PTVSw7jmzbr+m5GIZiJlvomRgsZunrn9Kn7uvoiBN1utqWMjestRHcCZAS9GFVN/ShkYcHFS+nCk8L4rK3dWT9ZUm0UMiG982odGl5z8RScKApzI5ztZtYBB/FDfWLGVckLIJeyjB8PlQYrBzZWDIPdK42P8yd6LyWfrbSPXVbANJGn5g0v0YImHSmZFjDFW5wdg8Ukcu2fl00cWGiSacvPPb1/hqF+Ndg1LYphvCDgCMTh/Hb0Vhd6W4MBI6xGKJjxvAhM88KXOL68Y0Xep4czKeyEJhymlH5S1F8NcRRwYiAzc6ganz4nCi1ZHOJAJWh4tfCV+hTOdZIAosh7kSj8dXBGsEjMTq0sJmLvISd02bIGpIJowq4DgqvIKBHcYG4Ffq7vtEzgukkmzAyh37R69+allQdbRalqQLMeDyG1JbPs+2hNAfZP7mIrcyZkZJFiQPqBHIX5VqXwP8H2QoQzvfB35JY9JAz+FQBcfvSPb7F0B8ENVN0uVQ7P04KI0FLuurn8+7Hu+wLeeBG7uFcjgT6WBMC7WwF3XIRleH8MuVZk3VE9sKteADi8xOY5tpA2n8LjQ1msGXh/XIXT+YxfYQ4wZPvSaBqU/ZkdJEUE5ofx5Rs+GzDPJ3qqBKHD2g0UAf3x9FrMAs5VUY2Zqt4p+/acX7BaQJLN+9hwiiBq0KZ7e/6x4SeW1qY3itBBrNwDDY8T/lAn5e19tA3nBedZXVTqrA+mDgqyT3Jj8MAcBjFIiYQLdyi0Fpgs3yvD/+bW4Oh69wBFoYy8guu/AhhBmKHLSe/GzLks1Ta4dPtjj4CG7f7lr68oEbl+mRawwVhTH6sw/xyqZ9F2w4ywrVBhXdQNkODwv7H8bcQtdCylHCAe1DZLfMPtQbyWii5uyIIVUdnSS5fktGb9aqUnglzsmVD0bEe+mz4UcsPE2PxTKrQvr9pry1tO/Q1xMgeJNh1grniFFUKOZ9y0nofDKMwrPoSRM8Om2G0Yvmfr4PS6ihQEs1AozqrjiQlmLwDYWZ9Rnj96VJhB+7our7o1/x5CYVeajEpAArSRfC6GUblzqcH8fh3v+l4BJuZrBPMxBgVZ/eWSIi/BvVHxsHkPjEtwfTi4sa0P3mhLsJ6F83Dh/Q/a2H6LCvTtMCJhXwzryotm71BtPon2IIttM72IzFCKLnPVy+PBG3hZitdsXu5u9mxesPtYpNGwhhCxQ8EopUcYteGTTWfBz0CDL2A57eCnRXC6Kue9n5876EgLcLToVTCgvHiM2yz0eL3+mb/zsOBqFmELkZRqTTzVzWq/6LF62JlrvRHyxvkUpIWoetUw2TrJY2c0VhMP46n5sgVuw+D22DFr9zysv/1BZssa9+twoAeZGj77iBMB/4GkBc4EAsePO/JHy0KYR/PZ0G0BJYRM5W79OfWhkb5TVKMNGuemZsNVtWmYdotkgyUC7ofyiyyeswPrIopigXxAfHTnFeW7sUCdIuwWehO16RPzB1fLgRJKsOxZeRjgXgHWNPBlRA7/KTuZDz00MSOzFNQnkl+UCgDTXtq3Aigefuxrir89lluw7WkQXON++MwbAZ2tGehD1vyjRkSmW1D8N//EiPobuF9iEpC8kFq8rhnW+gLQ/MPIj9FFkWXBlP3hgLlTrrw61mJP7TfF41r/2aj4DcJLC+d/kU5bRonsejXMEKTkbexBd31LsToJmbAxwNy4d9GNzh9lA41HAugnd2A5Q2gkg0cyW/Qr+tY5G0sOhyCLlBXDfOHg+q8oR6N0zRTF89dy86GGf5yoEF/00fysfZ5M1unSWTFO2ubQ7OYtWcdib8pIpGU07Kuh1k8WIMr1uSKkInivt06QjK6SUFjhfhi+2xNHHgPSZSlhrUdbzc1HPfXYkNoX5jKgNugw7D9VO6Lf4wka/0dslB2mtG9rZ3Z/ZCZd/d+BabAimbm8az3cKWff+JfIgmhMoOoSINYDL0DPHRQaA42gEyktPcomYJPzO3i/jtRcCQPySnSKNn46QbksVrRblGzXBfEFBrqhRKuLxunxKzVzddtqN/Q+IcAUNaxASWS1zI7TLzhukMiLvgC1hFeQRdsbb2Se9kfZWLM2+zb9RNEHdjhrIJNKMff0luWcigQbwUAwMewswhqzXE31EkW9VjtL/t2eA1EBeQ0eP1EKopeKEFjNihhBaV2iFGBEOKK0wcG4jIIXmyFTQHXv9+RVcGPlxMjrNfBLNLlw2BN+ptn62yfnISdxU4Pbk+LVyGyOpkYQLfIWYYg9x5ucjbQMCn86mf0JSoAyQsAJEVl0rj9xjCMxn7wtar5X8VrkOlA0HToVaBz+KcGNUArQpAUj9nuDLt5+GJAFQUkpaR51zz0qsFaWTF1qry453phwBmflwxqzX+LHx78QmAY9btlICKRaPz3v5b0rnEoUClGxAnzmXyVR8uGJhj0XWIZOLD2tOEX0Jnb8L1B8nSvJDBQtmXj78NEl/36LaGlH6OHAeGVzcoqGNBVfFbA9TsM1HBLNSkSLritYrVfAayZFRFn+R1x6fI7fs6UQVBMztERbfx25Q4SHQnpl5LN1iOGa+wfo/7qGQqUq7J01f72Rd+DvL61Cn7scJYUDpXvoA6MyZq/t04RgTi6CtXKBMIjcERrgtLN+60+hK9w5ev1m91GD2Qt1ntgsvreM+ChMvAIJ6XsIFGMzSpkJdrQ9nvsiiTgs7bhPKS637etKT3nYR7bkSFQEsXNtiR5wWRj8n4tGbjxHBHf6+dK+T9tkbMKKylh+JU9kVHBEt89LzemCC65Whx3vxFmtChb2XeGc+Ni/TBZOP9CkdwEXioVeWz6qF6+zjFcJeHzXwEu3NGNk6ZTrATG+DTHeblQwex9/wLlXTscgyuKQ3SJ2cR5lR84/Qs/oBNjHi4ioyRg8b9oIuL/jmBm5FEBqQBUscHXI9j0r1CZfIH0aE/6L6W3p5P9dOYVx9+F5PYpbLSiuxPLOne6tkFjrQgrMIe4+9rp9I/GOxsYr8LirOmQ4OEQavNoymBFkTGpFz3I9ifHLaRZskZaRkJZV8u9R7zoqI8XTvHL6MG73Czd7+CZ69mgqwpyZQ6xkJ+zVeEWk6N8v2D185t7RVebUV3zmaVp5dbyCZAp+8/LMjy/32dOZBBl/t1QRf52Mg4pi+j43xuX8v9ry2+1O4gMFcnnQZPAQjLTTs4sfoAocvHtWZtE3aOh39N2zCjObeq33tdJ/6no5zymzjMlTxLTqf98fwYaZEWSv28wBsKa1p/hBWdOg8oFFky5+bVbUPGw2SVfMoyrvR9WJ1QqrzlcwjUKbxhY9BFe4ldghBfz8imsZGrOqI3rAVzGMBuKbSwG2Kf2G/qSTV6xkwH4eLeXWXfy5snvSHOjk5igcS+ea5gkyR4EQT4HvUDToddLqF/vNipOpGjbQ4p4MfdX/HzFzHZ9wmQv+5Ck2/RekYPST4wUz89zWxf5zXudt6ZzbmFqRbKarA8+acM335OnZFvn5LLyEL3sKP3SC3yJa28yymK1WXDcC64mQz+emf6WjZwoWhD0ZG9SmL6qE3/xd+sI9Id3HYnkDFOPPMjeKFdPBu+KpKKPwsuSE/Z+k1Me5GJ1o/UmIAlKavKXfJ6WynuhTirsAuH/JE106cDkQ60oLvm1atXTph1zTxREZIEwDJ8EyxubLZThUMZcaE9aH3TN3oRvHfgs2jJPWE6AEtZFXRxclR/EZILvYqzb4wOkKA8bVkmDKK86J4iEqos+X+N+38xiHFxjHNPB1bxgKvsnzx6o8JtptpAawHPSYTv3Irpmh9kckniTRdploP7ILphMhyQ/bX2ODBDS9Ab1t0UIvfcRIpbPuUiuEE303m31awYhrQiv9BVDFIEvHhe8YdJSypN+QnqNdRL5eMFMfRHLweM4yTl/0omWeL75kBOIxvvyxTTpWyyA3ogEva+h4KXXvH/N8t1/VA23+28vjTiwr64VFtax0CAgpDs5n5hf/6MzK0sFiM26CbWs35qD7AM5cUlFHgyamsb83GR5iTHai1zC+itpp/5YeZ70dpz5DrYa6dau3b9yHHmrI1HYJkdG/71e4RfjwahQsJBSDtvkhQMYV7X+Sl2j1sHXkrZyYDEy4pNIfuBJXdTh5YB03tJzzm/vMAs9VqGy3tbh4hAh+vyR5VnJZo0fy9rjKZR5Ra7UqQ3D75i96NOXNQUIc4mZoCJ8OdZBDIxlutQYGemZeiniJ37lRVMkZ+BNhCpGu2xhagRM2YnHhoHoH2qXamf5tM8Z5J/g0o0QKXotFDyhzekz7GV9sqBECUfS3Zs7xuhrcE1/rpDfAGUNLpoRnSFN6JpXr9l6i8dMl7EbyBH8toTLKVwWcEs/zNPUuERY8TwFOQfokX/c74idANfMrA0lMUlEInxOvR5pIPs+qHh859OcMyiduEeI/HXIbg9Wh/MR1DGxEol6X1O6GoTd/iwWIHdsbmByrFrPhd3zwZhIUoc4gevpumuHQczXYhverC0maeocQCQ+CM78Ntqhc9R8encI6s+eyUdu3Ct64jgsHbr2RT9di2MCveIfCGU3Ao/eSIGZOmAeRY0Ce9k/BnmcTLA/8XkZbrfI5dHonHF94OR+Pb/ACp7d0bz5bm+MCuHDlA4248sD9t/sIk6mQqLk+BBSdgEV1JRjBM/6m03+BlGI94IkVIHMZDR27lh7VbdQj963nyKbhsnZxH7vHoXrcU7kd+nznofvp/Vm1cQa6/VYDtR+WWTQsinbBeb5oB2ACh9zLn5VPIX0mFtYI95zFK2/WXmlbng9X4Ob+Rnto9Gb9e1jbEYMAslhX8ABglP1rEopLcg6ipu0X8qbxi1nYx72vzRD7NsQSp+TI01K2m3C8vw+jTc2HNEV9NFcEBQ1wAeONIbKpwzlAPSJ9EMq0/jF5W9ks+mlFd9Vqd3YbmiVySGnzZAj51QepnaPXAtrZcreZo/td1CpM7TA7je2fbTGJsp0pmf+h6wiq3Et1cdcCh6bYN8tMXhNCEREvlkGGw1sjN/o7DF//Q0D3G0LuMFLkvfo0Dc6Rs52MKQ9uLIeJd/HahgmclNWlxHQfDLthM9KVjsxPjx/LsFLPMxFnHl4MaOAMB/Wa0s4fa8AHYfkvj7oZ4ILkWwn8bn04ZFEHXhJRl/SiQ8wOeWID6ogNrMvtn4O+cUQ4CVg7fWYwyttpJxJL+W9hF94CPzxnTLzFfdRLg5UVsPEDPyV1W9oqLl2my0qy+YKxViyVhHXiGFVLy/YrG3HonKczogP9h0Zwd67mQ9KCzCqEQI4vKLJuITpIhTiEsgflTAxVZFdTSbUY510gx4hNvPKLq8xJWKSxAp/TVJ4IGE6yAU/vovpNjY4kjd07l8vOKeUbI9+DEKmA3EP7/FL3fzF/XBO6dGQIBFh1KX5bobM6tHL80WmOx7qjm7tricJSavY6bhD+CHg4z3Q00GzwTbbbcGjaxu4h46QNGHLa5e9ITsP9dGXkl/lMeA/zD3GTfXFe3J7H6HTqTmV8PeiM41wyBc5XV58Bdnml5MGta9ewH3iG+3B6j4s8VU5rfUR1UCvYvu3uZ6Zp1cd/ioOYNoqMTR5MMwhke5YwlZ/kys/KxzRQPkVCPoiQnaN7u5GRhoi+nRTJvjhogVHsjqMOPbaesLaIAOdUVtOV1YkPF6SrovnduYwV9qDwanoh4Sz32YzS+ab6sa/J/NzE6dfAtzLCbN/7vMnexDyiIavYGkKGmWzCvey79A8lnvxNpqUlXzdvzBiu7+NICgvd7J+xrURKrcVS99+C8tROYtmKmsVoEVroQuDI7UhmZj6YM49cg0z20JVZJLVHSGMS+SsY7iDEABFeoSGTnzMc1Pz+dJU2ST7kzFNNJ+TdXls7TaxhvW8OvYfhtdTIRC6bCiU8vHO9nu5mcfMRJ1Cm8ugnOPXhyyEnbnPHHHJI6qG6S2WPay6XNO5LdSf5YrKN55tG/8YR3tTuK1EayNfB+oRV3jhg3UrD/l9AgdRGjPTLDDjyM7MfNqGKp2jXURzxT2HtXCUuRdhHj3/gcncGo7bK3wsGnDsXx/VAeLZs/ttQ4VYplHt7CvAspgK8PkGD5TtIimsQuLa5MsrRI5Bu4ElKSztVycfPCaP6U50rMg68os1zNfDsoRAU/zuVXHk1lJrZBT9IPsUv87ChzDXQv2MMdCkoo7I06z9rEMcIlSKFYqVe0Pw/pYE+0orQuGgy4hPPPdLFy1dBMSB+o9kLG0YH9xNNdJpXMHY+e+HDgZCBvbU+UnXZWk1FSFebQXgdJt1GGTPyS/5l8C3wPNjBtr+UMNvDypa8ObFptNldeaRCeZdITS7qmSsU4spWX4OnGpuvwIKDcRA0WHZlo4iNxs2kJqOs3T/cY5WQ+bqBb3JjX+jowynUl1nHV1RDHJy1mW45W1cnncyI/09UI5H0NnjHhdnDrC6SircHWTTqanfgLygmRzEacR+G83fMWXoMC/JSF6OCFnPeaDbvYmvGbBugCcbYN3AeAxg3+i1uE/CaRnfUg8OoasQNUGtWgyCCOeBLSG2xB/oDL/kgM5FJYqTMMhDATia2bsEFqWFqPMkbXX8r4fDZk/1oWJjwCRxWAZQwfWpsHUsfpfXQRGmBnqqm08K9I7CuQkvjVVZ9fnzgd5LxcCUa3jLl7Nw+rXVfjcE+ZujFYgjofHy++GLX2KxIBfEUytEfI1YJVQnfe6dr9vXp6O+psI5uJlEcn3zli8S6AkjSeJtJ95cF4KvwSfoy0mFYCuHp2mN//b+br3VJ0rMxZ+MVhVyj+EFMnPHRLCM8UHIaryk5RG4h47MIBgJuHrzgIz1k+18fyFjrqXbCy32aXvjArKG3C/DzWU3kPsJTlb00U1eEEn7tyL82A70UbC3UFPU+IiXmIsIREE3wyeIjSwfnnLzj/SJWT8rAhZtQLSdxfE5eteee9/e+M3z5KOp/dDt7wn5Inklb/WnH9DrcBl8DVtGHM3yVO6UYz7VN2rVt/W4WWi+eN5rB+xh0gPePr+HN4VygtVwif2hFknaMmukUxTj/ihOsH6SyGkwc+PRo0LJboskMpMEdmXJHKT/7KJoJPpTeyhAdol8Vf1pjzUG7ZYKYM1zTClP5EiijchvpN3FgeOkbtJyGUcnL1VouLXm2RLlKmcJUUEMnD+N2fO1JESNvOFFjGIpFrKVYazrC3yZr99Qkn3kfmg/nXrEJSGZMTWc6Xc1+PIM06/uOs1Sr4z1eXD1TaC03SH4tujzUcnfRjUYvsATnyDL572bTdaXmAnwwnYUTc0tuY8AGDGpF6pXF/KbxtXjhuyWF2Z62NHYOxEoZyJPW4NevB049G5+ITy1OIbpP2eaSJPEUOCA5vFcHn+uH3qCcBSFSLLJZNBrEYHeUq8mZ+ntTEUEQdE+ej8SHh8skgaD9Wpee+BJkinBL6IV50BJrcWCWHIjH7fE7XvYNGujMFIp7juAJsL0QEhnbMi5/y0a6sMHCeRFQPvBTv2FVLIikRObRj8JRbc2jmJU9OCyIsuOOTqwyBRAl5UjOrPX/ve/bACM/quGulZ60+Dts2njTdu56aRfvrk3vqtZWif4nKTlGz+2qX7Q2Js+zW9d2WK1J9m5i58ceAiulo7GckS65d7xPmT623yj3jBvMXJhhSC9BrRaXiuhttFWvLR7SZShxqJ+TV/Fx7iM9OUeAomSOIspZpcviO3DOAXM2HqHg27Jwyl10swSEyaun2nRqehMfuFX+HNu6NnZ5b5DYKBc8rM+JfIgLJARKZ4ruD7GLV64UKVb76py4IiwvFBY9yAXbu8Wp/1VlxZlIjDquGV4TgF7WiWoYzXYQ6iFRe02q9wqTxQcNL0aHEoRR6Y9I/8I43vXGm9lpq3q47PBHP8S10KExUgtvgv5GDSPR9WtbaHJVK1lVKNyh97SxZafL7g4aR656aRwFPJUejlutju+t5FWi2RuweELXc52yWMGXC4mCPoGuZx25ZstSkwbC7A7HD8t/aEZ9FgZxTvgXMHu9RGXA24Z1N4KbS/JliJDpKf+nAQdChhghFrUgLMCeS4x30NAL60wWRHNoR+b37z+vDm2WJVZtkeOQMnelRKT6owK+nzoZUVIFjoCZelnygEeGcQFcPHVufil4rSeDBzzSF370j9M2D6mZ5Ic76Xl5SQLU7Dk7fQGtBHfLWsUfeg9P+bwDRIGso8kq1aW97VGJlSgKihx+b5wu9NUW8jsV+k2EykmZLG87KQF6FeCWGe7QyUVQCFa0E2gi8q+H++HO9X7DTuJWsjySMRlJqxLhWr1+V2pULXK90BJdH55JhykyIOAW1KPETjemHMhopfaVnHGxtqa71Ykp3IuxmVSBqWw/8ZXKBQfM+R3GmwbpkE5CziZ4wwHjSxAGuLBNcq/YKRenCv8QXuKz5fAkpn9o+skIlx4um+YBWJFj+KV+d0JskQcOCNgiXF0YurGUCOMvNu9pzetXFoV4MK7V2polCLv0JA6/ECcgIMiRcQ1WsF7A4sQmwHz8FiQr8hjM0yye5dtirmECLPoYS/vkE1eekT3/YrF/UfNWMvlKDG7ZwlPQmnlyGwaEwstWOkkiIjO7rszEbhCWnz8jbWe1Z02f1tLMwA4ShVr8koDX1icac/akctIBtYfDHSswsvl/YGM5fUCODXG7myTE9gusLSh2M2+aZ/+IHzfuL+w24pMJQ9vKN/4/n2VhLVLhzP9onWReEtmEAoaVZtvL9tma3uLtJnmDwsQ3l5Ol2aJ0LymdA8OCzDpMdLGPcdb/tCPgyVecKUJ5tfHUer7CexEStRfcG/i9LTkehAmZhXjKIyLbvkKldtb7Knfrhxwg6yQFVg8zOAlnw/JCji0rAGkk4sFyxshnlKGrSpSugshdbPcI2R6QIsIvwTKiArpeWtfR4UTowqdYpHoUMpXkEucxxz5mqYDF5kSViycPAe3fwXtKIyXAmvsIJuo7M5n1w4tUoG8x/VOqLleKZ2n+bWW3r0mQa/WQsnXMIX1n0cQ/WIowawyOBIaRNoiBN1vumfRMzVQZk2VaIZ3Vl+kdRdwIKt79ELvE1t8kPTsZ4yyxLf6JhkdpIB0szVZt61mEh4L1iTS5lfSUTgZqNMUL2XC870djbkF58n4CfjJOrP2ptp+d8/B5JVETe1gUHr9u3YHFypNV2u7z1QDPnMzn9+Rr79gxYJrj5Da6cFONxGtntphJOCYTGgU5dglGA5ejiOs0nugMQo1tNU/5yZtj238Nv7K9RKsjhCSVBXYW8+XhyK/HyLChC5cQjA7dvwrSGCqBEt2RMKKEkgeJjZ0sspRc5Yw6ZaxgXuHgb30aHnbACQcko/Ag8RCX1cExEHxOryJXFIvptq9LzYxZEkvVjuKd4AIZoW8SSoSOrro1mITwAlqctmbGlSh9phHzUw8oKkJbTOvKJnm0wfHHwae+a84851a5UiOTvYi3xcSGET4stuukc8sWKt5uslCbk5Khn+jGv/+rQckMesysihrOfTR/ui8ObyPO2wcP0GDlsZgFpc/v8lYtRkP+LdaYAdWMWOcvTekUug77kaju90PN4wB5poaGrJSD39xNp6x7k3boxmxDoeyHWScm0eT/Bi7Joq6k/Um4XQ+FbSaUX/f4y8ZzRYnrWFpQrfxQmkINU5ITXbPHtCbCp2dj7BNtlBG3KgH2QP2pXxNwdKXdI1H7yqMrvF0n6BzsLHhX6KoGhVSDueAg2QTuOu6+UvqsJzVS5o7ouR7f5z5XwY9QCH9YZ2jkoxv7Swqve6h76E7ra1e7OsiDTUWB5RJ0cn6pAHmdIf1EuWDi6E+Dza6MH19WyY2iaTVRoK+/7Tyw9Cz3dRKFclrRMYTJ4yKRnVqqJi3b5rc+lgX/U7SG6qAtaTb/1/NhDeNWAo8KUBcoiz0w9vnPrHPl1tSX4XNvMHtcuEbDjk0LQrsY+njzefjimTj4+N0F+R10m6ww3buN+rd5G5qKCdoDGHUlUp4+mRiS26QiVtwDMJof4IjUZKTzIYPdh1CxqxicIqZRvJNVb7uQc460TOGOYo5j2BjpPFoptP2jgW+ufCOO1MqncG3xlKtjcv7gfw+arGgzKAd9c2StLFjeBqPwBIFvJZxV0ZdpNXfk/ncQThI79H/kp+0RuhE26ckwgkALpLHlnYleeV80bDkYIAcIEKSFPXdwO7fueNh0gRTIoGMgsC5Yy+9oy7cg/gK1LBdtP8tb7ipC7Phr1zs2AfjO1ce+bcDu2KEJzpl1V6KvBJCfxDZIMQoh520UqI11EAjC3InZUCY3+UkXWglQ+40SdGS97oVFgcF40+x24yx/bX2D003KFiNjYtqHtZ3IIgOgPMI9elvRj4z7xD9sOrgLIvnn/O3QWl/vQiS/zic78mlutQYu/JKOGtLM0nSqyVhlMJemiHxjHYmiIQ9FNi5OewFeWMsEkKlT+pbq8OacQIQ8Wpnfl3FgLUnMWIyGRglBHp/Iwo9C9KsDyFZ0CIwNbq7HyJoPrInW2S2bE7hyvWZnZi6r4hOtyDY8QFHt8RpYnYT2u8tKnfhQ2kv6vpTL4GuxamEjnT5tmFm9S70GJkKBcmOhMHpIdGc522EhIX9pO6dTSYzALHkNV6QyA6vNW+y6UW9idqr8OkrvMxcfaHvbGOGXwmQQ2EjrnqWYs32N4cgv6dz7UV47+vYrJeEi95DCcmEhxEzkjO1jkQPi40T5IDfZWt0TKVOPDokkS5/lsCWDSnURNudYeiXyFVsdD5qvzpATJZJ9mAB60PZ+aPfKGJ4ZvH6qhg36dPJWZPbXh9LUX6bewxx4e3pfEtk0VnytwUe0x2Y1dFlCpl5Ihht+x9wHthfR+oS3B9j9Y6HTeDJSlDSe1r8mXbLB4Muuo+ScetYPI3iGhMKxWookzJT8WRS7Kf8+6ACrBh1qMtAIhOT3PHASldszo03TUDHBJOmH8I7utVLO0GOjH0I5tx7Qy9zMPXVqNzG6r2dUMGHkwzKv9R0ryAYwZ21hrkeJNY9Nt/5t6Y08I2PKhoCzn80XbWo1/CddApNV259IzxXPZZ60fgjT1K9gXhsEt40Qg7iGtPFA5ngYns7JBCdUttA7j+uZKN34ZGYp+NXeaR+8DK7Ib89COxsdG7nEDLbG/LRiXbPyF/Tx4+GVae0KIu2sMD+Oe3Ag/sZ2YiyJx1n3m1ERuNpOxp3Su/gvUqC6zet/2a2ZXiZ6aiTon2ig4LY4uB/aRBveEdT68iJF5fUjGIpt0GtzRDi6Gubsp6R7GMaAWFmtIvpAVdQE518Q31UgpX7bn8bLgT22Xxq5CBGCsSi2wcBI8LDWQ5JufM1HIm8LVu3TDIljB2MX9WZUeMT0mCSQ5B5ASpqpktIZKX8GLy1ciOoYIrHZxwtcrvE/ZHO2Xy9G30+m9n4vHa29wEnXV5meRKW4ym48jJ3RVWKt11rFW8m2TBuZXAcLRR4FaaCv21A1EMImM+sITln7D37wdBicxXhfcarUK44PD1gHSEMwnkeXTFTK7mRfz0WQwygB4LJVEGYqOuzJLNGqS9Ga4BZCtJIyiSGdeimGgXT5XhDCeBxif16KCkcwSdHV23YB3z+8PlEmZFEIWuHHyW1HkuBTrIINw3uV71VyA5ljzvZlGxSctDfObIrtYhfraZQhKTpnYm/HHkZFP5wHIPkAI9gYfWGR5Jh1IVJ4sdLfaW+TQecuoiUinOsQEmaKh77trzeC/xuTEN7kH2ihPIaBmsJMrVajetw1aIXxcpITmCrFPmipixK7WS/rvpzffRIe/H1u3OHI0zXpv1VhLW2dfYRzmYg7BSzlths+Lv86hWBZI6Vgrqk820/f6shs7W0jAX+Jd+3X7h7fq2RluLQhwMqxprVxpigPjNDEJ8pzI4ENeSGjMxoVkJnlkA6vGYU+F4x2pSFaVyh9Uh2OjyfDmuXSn+NkZ1gfFvMLIWtTnAXpo3HH0xJqX3aM0haJ0Z0iBtEcxPXxxOw66/C128hkmp6NoY2wXwLdJQqAs+WB2M+rP6gRX/aC914mHhw2g/PQBP6FiZD2Ie+b3jT4219sKIXolaN11xDPzMKLWQaNJiPLSmxd48tuw6MrKgsD/sG8mRXrqrP1hBDVROgzHaXsuuItaxuReDx4kNpD1Nr37cIE61rdK4pr+/I9ANFXwSICSg79qgNS9ofcUwIIAZE52SY0UuTWrlpUVsz0wiiNwuzBA9nAHE5flGts0gN5ZdkSUBRib4lLYtBJYUo+qvVNZBb069ew3PoQyi5qKKfinrpdn4BMDEg1cs9lzLidoXjHy5WIyDjMR9QbMoTotz6ttebs/88yCvgZ6cx7eMzSm+JR4mV0Ss98A+F2EAbGkvuhuvVkRacs9cIUu6c/PB6y9AEze7A9eKWOAQze9K6EhizwbBnVKfKEt/338RE12YeiWdKRmgFx3c7Rmu3dgE9mixu8PhEo+R6qWB7+K0CMhfevh554j3xmIBwGNeh7Rz2NQEZh3uRpYBIV+zNitRRfj+/jTf0Tt5LXsGIri3Xr/zCE5h6201UAlPY2XyzpSYeP9eSFsVxIma5tR2KRi50UEx3TKZaB9mro2NMjwWS79Ck7hp1427u8+H04l7Y3U8U/oIqzozbaBUCyRTYPuBEsRJc8lx0r78D5Sa3FKmFLD+O/I3eTJ7wmaYFpdMcmEp8SuA+Gp/6bUxkv11429T7ehT+60nPpSjb9o2g6mATgHv4FcLtJl1hSTrOdAuVMrfDiSHIXwn3W2ayRebNLd5sR5zDlSuK+YFHXAGgD/9m3UzASSGCD1jIamecK6uKKz0OLl09cuHK3nL5VUUMHeszDB19sSwHfX9BaqqS7bFtJYYhd3R7uROafDo5GUWVaKzPqmBnAbFe8Dvdh3jWaFLBOSgegOFbPXDhSC2ZqeCw37oWLY88a/KS9/ZO8HT7+dTPhscPWpHK+0YYuAlfyDvKo6/2ONcVejybkkyIiLFZt1jdhwCV0hS5EmKjRedUN9gPK+tOv1ECompJln4lENH7Qml3B67idveI6Nim1g/qu9TgnYyvqtdytfqNoauIOwz7chKJYyWhSBVjmxXKW9+t9eQPBdNimNESQSNf2n0tYjeM5W4//ohaYGxJq1lQ7oWSK9hddhkZrTayflXDv23dXzKkazpW2k2+z+G78WvYRHB5KciOf8GlzJB67s7tK507XkM+5IXVe9NH3VpO5+ZSd7c0JugYooJuFj/X2rugWljuYLHPGTRe2ccHG/V706hFJUM3Z5WXvr2GVuGXWnDsODOCz5fI0vKxIQxVIGu+v+wsgTsU9nZeVAT0QGgY1Zsl9x/mEHKsK7LtdTOvOOndExc6W26WsDwi8/zv37wehcy4rKWoR64OePC73WCghYfPpYAx2ssNcPjCFSzvfkAJM1XcMGZYAj5JeNEhkdSHc+EFQ1497wRlgPCxts/LQzyukHBIsKg3TsQcJ9rHtQoyngVTkPaKfVH2RtMXRsoCGVUAdPcxxuEE12B0DybriGHj652rtvU8o+YLSnKPvRBzei79oW1SNKCqzez9fRrzBBMHHmRKRvJZ+ao1KCvTD/xe3hj7+G5yAk07eebX8+hCeV0kipmQEAHSiSxFO2RQ6ENqEni2HpfzwbDsJFUU7+yR1IuTiA98fB4jQO0TyU7c0l/zioSyQjOK3TWKVTy8woUximOl5jWJK40TbxriO8S6bMaN+I5ipwd5PhALNzD6/gDT1+A2GKvAWnJUu/pnDYwQ6WLUKaCYTiCJVKtYCKp0OuVaU2JJC+dCegBmYyFoJZtiHDns7M41tvY38/VGpTISgX3YnJMMioQmawnjedhGi8Swc0vQ9fpvdzkMY7wjPvwWxBHhdsj6D0j9pke6sspq4FOJ6VDZ6Gr8ifWJn1+TnWH2r767N3mkEGpK1nn9EGCshWpXPRcArNL2UJXQ13ELsgbEocK3wL3EssYe7nHQ5m+NK7Z1QIvyHSVor9LflPVWvd2TWFS7fc0wzlCNsr1dmHYJtxNsEi1+1eSEphkK/SL2Bknz2V9ZooEDmCwYfyURBmHQ4qMd81AIXR/4o+mPMaMHrL4wAcG8NpNzgJvMyRQtnguXjgnu+3uE/fY2mkVQ6fBhfyxy0Y2xT7sdnsOVOx3+KZjMidEV47L+FTF3Ay1MGOlDsXx4HkUDZttKQIzRmM62M3FHiA7TrXEGn+0zvS/IUyypc4vSmwnSMuT2KafiA3ucGkeOokYxr4EWfKw2Uwh1A6kRMwiqsi1abFBTzQZLxq9Z4kBValsPCjlLseg1UNSRCVIglUhhVwf8PtrWoukwIr0xpJRFWOhEsjekm/etnZm5E9ZFsnmitll30/EfKj0MOTPf4XDJDzKV0ptOXS6MjjEH6M996zEIc8pNtslMZMlKdeMUri6nQ8bldvzLyAH8LcnmkfITqnt5JN7oYvtxwsaF6TH8o3MZD4uVmunRVy1+K7G+nZlvU8F7VfJy1cgpdkxrVm/+XM2OimzZzGB6MUg0+GkiFFrrhpemUewjpeyDZgJUHNdTw7iNeL17k9m+TgQTk/0SiLDOvlVaSihIbX1cW6A5lfermXnOqF/8vkcR6f0xOuwDo2grNqncppdPVTxy/UK9iOqBKi52MiBmO9w4tFRfPtQlPlKxcB+bjg4+sH/MLtEgwVrctNRUl/rbpIxEI+CQJDGdfOyWRs66kU+C6kLxM2KLyRsWpwkKqYgi1DRizFgXCZ6T+dr2b3JKznspanZ3wX9muxowwMiP4V+geyj/fu4Nz7fF9YFKHoG3A19YR0w4fNf2LbpCTq3nyr0vhwUxBY8h/OoOpy4B2SN84Oe0HPlfu4o4JW4Y+uRSB2HQeOZVSReRbQ82y7+PVO3UJ7b89NK74yjBgJFJ9OM1UN5ey77XYBZZ+JUpy5grghq/2TnLUoK+YDFKepI3Uyb55F8IPgR6RoSWaORq9meRZpW59YirB1PifrI1tN4M+EQ0BwLsZUvAQ20aB6Ynu6yUFGA1vNZNKTT7UFHSI3zmTjXAVzC9bemRSKDMSTVGBX18yMnHDy+OSotNHBtEdfjqBFpZ28mKEHsbsv5LTWmHuQQ9seCMIC8Yjvwu1PQDImHyDSpTsYJnD6wxH8wQ54bH57u2li/swAfz+vYYWAQkKUWjcFIimpB71PLSw8M1KtGISGAH1nIZ1Sp+X8l0OIBIreqaLw+1+iqJON1hc7aEk65mzSAYKySBbnZ8kBidIochSmUvDEsFdyAhj1eYcc/NoGiltQpuGhR5hA/dYtAdckDximVnEpkeNKR3l31xaYJprRzP3+W+8TeQ0cCgumqteDoY8VOoeJ/Z8AN5vnFR3RhkqE4lgRFiUDiT5GGVUftjFg54xOCYkbM6r2kflBf3dMfBfMZ+uqa91W9sywPya2PEYqi+RS6GeTi1A/SRoSrlEKTk8fyhQztGHsV9mZ9fyVUTpPmEBb18wHzvpYPpI8HnpFTnhkgOtCBC4uwexQnHj5EiF5oU50foepuwVgL4iTJ3CN7b4sF32CTcHlaOrcTilxQ9mC7glAUog53c+3SOmoGE/UCHT90253CzX07kU6t/q0EXYOMdDq/vWyUZeNYgq8pX9rNNwuXMb1aP3+Bhq48PbFtEAzywVlFsw3DyxnTh/dfAhWnG5LjjfrarMp+ZC8+IgQV0OGaty38Et4uc9RcKnqh+h5IHRvwapaB0IFtZXKvHR+3oEv4mxqb+bHZWK1m3rZ6zor04rHEk2Rn6iYfE+Jwu+5DBZ2kCaEyU9FaazmIv57mdIEh9AU3UcQLae/2YDjWkRiBZkfY1e7692WUL9NSaZ5L45e/sHiT1fvU7FD6qBaWCpjExkZgZVbWb5rNH0hS3Nah3I1SLI5F8zO7JhsZbPIL6AiiFQZhTBKkJ1q1CxqjwQJ4//pVrjXNLM4grPuhfwnCVlsIHaz4fAoI7gkTwL5pKvQcLrPpx5UWhqDNM4CzcYmT123gj3DpDTx/0jla25BB+aCij7hUjAv3VbFwaD6cnBWoL9e/n7VVm8BiFBxrkweIDcYQlnf/4A4lzMj248XjD9wP28vbYGDZhvyCsLb/ddjMcUBaQCpLFxHeQThzrrI4ncszsb+g2MHPu9QqjrhIxwM6mY9FpWEHpy0nZ3aL4vM+1LsyJaIjSBXdrgB3kDzDAiBn+sgoj7v08BzKZkyIAvw2jeCcWv05JBCctU4bIa2OQfqp22t0gTLvvuRxxMpN+AVL66LbUOFoApEOBjONRgsT64BRRaQaSwe9mCnYcI+RDKXUrBB3DPgSNOZZ80xVIiHCyXw0m21kj71mnP6tjvFwsbK5vSJUkqMKy/WuCfwlTPnsB0Xqh3xNyH4AmoebBMfxsVa/E+I2FUXjTG+sfBvgskhAbkiYQHSql7bfwpbKaeHjnu5GAxfSBckiUETv6sDaHDjVTmwrkYMRFeu9UrFk+ddn9wZ5/ldmp9YAkovIknh2E7G6bWTXDyk+teD6qOBIXk0AeGP4NbLeFX38ZJgr27OfRiMWrTgKBchM5si346qWx2NhxGMNZs3AcO1bv4nRw+6CfepNpGicBtMZJ8jXNcLxx4loSCQHR4JDHoAJudbXPCpanTVVPnAB9WiJ2Xab/XqtCvqiCbwqu2xcuERZSbb/4YFQ9F/MC25w9Q64oCUegN20s6ZyMJ74qdqT3a/YKjjXM1keNccohJ9ijcdj7ZVWk5YFZ/Lo5a8629mvOKiYyB0aOM5igE/YzmxWRaSCMYsuyDCN9m9xv6pJPMLVOpOF8nZccoeNr2chB+Dm5ad9KNyrAOsTfmlwNr2wUtm+0jgqrk8+HO3LvCV9tjNZMEZtarUDyk3l4wk6m7DE+9HOk/2VZWzAGyWtvy4IL9QgdI38e6r//rRuB0JzdI2TegypI/KoI4ZjOj4KmUUJ0PR7gDgrPaQwDjt0v4Ym3rWtvMBV/sNxFPh/6ZYminC9oFqZ2kL8I991avzAX8dHQN/Fe9VeepcN+4J8ZLgAVrPw9TS974SF8e84h6e+g5Bu5zG0US0nyHzDqpmDp7d6tv0iKoz4x+iyL3Gv4siNRs9lN/9fWHG/mJeDmi0QOa2pOHMt925bCitihhf0Gv/Vkzv5xvOkONhGT7fYiZZ7Kre1iesqwTqFpcZJxNdwu1Z6fTwYh803sVyAEtRVuDm1Utc4/OtvpEtujJPF2fT3PLdOE33fLGr07NkKbHNSksGZgWkxmkXHDCzjwdcgkvvfDVaCD9en70MihDjZGvd/GsZCrdbwhYYk86bk7jv0mmKxtcWZ1UXFjVFZTuWt9g2jGOa9x/mK1okQ8hAJRZvhCZP1fbzAIcJb+hTLib4Adkr6svip+rDEpTWhoq9LsmyPSbwwblqmlUNI1Gr5nYo5Za2F1CBGkW+m4tFAKGyX2FTrND9UIULLJKLkuY8Gf5eHIfZSoCZeMzWVoOTswEnK3j5H0Q0ljCYSXzgD5qFSzVu/p0QEL39ZAFzidRKFvPRBfMgxxUMl9osdhJvmaBfmaEAdrltcljckDyaNyrZn8Tg4yKYqpWukHptwsvSFJZlPfGFZ5xxEX+fuKAzYpPEFdTIR0tN9wDZDbwdOKFNhSd9mqgXENggDi1q9FvvG0veh89b7j5rZBabAPT8g2MiJEDZPZQ0QiBgMOcb9VfyEKROH3x2Y2FKiXJR8c6WnfkcgaiPiY04orDFQjeOM4kMEHbS7l1ITjg7/YpJBWFzlSJsYrGIcQ4rkffYke/jj83ZbN8PiCJNUDj4JOtvsGfevGA6OojbY67mFEsim9LjADXYeEsQSVu+jq+YyAmFhi8GcLPkTD05IU2WlCyuSxWYjeXqNfLrgzekQ8IMgUKDhPRd7Ay6WBTAdDw5/RE/FxfD4+QdLgTigqv9Wd5Ddw9+ZrvR8Fbd9lApNTWk50VgLbvsYjaWmNbyjElRb56/yhBeuIqRxtX/j9SWhQatIWsMMJhFAhKl80ObuW6SefvelTYmmsaBX5KBJ/pzDEMkz7eNXQG1C2O6SIrKIdVYUrrs+Vyo/U/NNqYMcp+t9ou48wgKTSCmKUmrRFwySv/MyoxBHDPZQ0+ePbY2NnM3RHuIRrLzx8tLVioEcCAQYsdBC2Z3+cEECa6aulW3DnAaTkZLeUmAJy3p4LG3h5Zz3cZWZvyK/HUirxK5LBswAO/WsV/v6a7MGfupcikvLfr2PIHyDaRPyCvAxJn83lzeXgmSuCQpI2LMwuzjDmUQPGctKL//6AFGfwL95hDyr3Dbaz1Otou2gkFysmjwOLLFDdj/kh21VF9fUs+fuZI5H5+0YipTECPAvVwftF/c0RZznXSxlxJRHikTDPoNZQqPd1J3uPjW1ueqyMxH71x9JcDyPv/HSAYvMY0qJoS9pbOrJ1Io5bGwTtfR/EjuyHP1GV8U4s3WfnQ0xDhLmKXxO9oL9eX9ziHfrIOOyrrcR8A3hzow0UVfkr/nuiD2P/yiNEWfvhoHLt7g9mk9QEmDlbKhrCjygZjaibWrCwlsIMd9VD6PvlNUSovhQr8Bk376Ytz2GuuAvb6Xy4g4mw12yq720P5kLG8Ws98HTyidXU7Fo8HrAXb3QsfibSUDWIMXc7B9SCHJX83X0QtbxvovvI3F5UTVycfDC3x1rCjsKL2oZob+o132Wk/7wpThyeLi5stxBtMP66FXBfpHnnLVQU4s4JPCeHlbyCadHB5WBHlD6YQNoeG/KTRNF93AMFLNFfodoW2v303PVjVdKJJ0actTy2ebyD+f+xPJ/dAcwff2GyqvH70R28e4LmBXZmWzrYta+Eea3jXUiWl3SVz+UO4WtLxs9B5/DniHFBYko92lneeN5hFwJeV7iJn4BhV1g0iRiwsoFlLn2ZH3S1s/njUY5z+A1lBGinWPnpMYsmQb8l3xFl5OcFISaxepDEs+cWIadyPI99/pcx3wNIAPbhTEzxQ3tm/Xhrk2EeVdubgRPytTzKV785j2+SF0e2kZJNANAlE7BNXsw2tJDpB/KNfqedH8MAVCA+0PcXMmDCYf6PvffodlRZogZ/zZv2wknAEC+8EX6GB+GNcL++M1V137v9rR72sGuVOYfSIV1kxN6RkRH9bYXTKYVw/7FPcYL4nTmBgfTnm1klYNZ/COurQ299S+yHh+3BVzo2HF0+HcFpbMcAxg/Hz4cfi4W0NJoAV0d9w9tjGOMqGHHDl1W3ytgjObQUtzezMjGao8M8u9y9xfUWLGhmtADgQb/2eyo+3uxAAjN36X7WevLLyf61KH6rpcDcGh3P9oT7MrcbRn+rZ+IWZjkOVE6kFYoHXPVPV2bC2erdo7P0iORj5iRGMd6t/vaMb9o8MDKfu25XJUsSNn/kS6b7vo6q23ICz7mev8/mjzpire8n0O5DIAvroPEPZEulYnMUFEeb49Pjj1WEt2XKlVuD1LLXwHi9+O5i4eb+RU/Qi56QLeSYh4oZMzmNYoCSex+jLp526H/+icgvXq2SeNVeFkOUfxyCUlZ1HzPWlhlizcaDtVOdfUq/A7FqACxL7dxfeCZX2xvnH/R4qBbzWFCrsGnPB8YnsNElnJzhV9FQamWJb+Mirh4n0mPtdOkDAOH6QDzysqzFiHveP2Z6MkmG6Vz3fmRHUSrtbpHP53OKrwkh8/uCOZTETt4PGZMBEIyfwHQw9q9C1SPbX4wjXnSmc1iodaRAB3v44BQ0m8TZyvagGGkx58Yn/7ZT84U/DQ0QnkR99vQrnWYdJvJgV5TgxYpCeaMAgP9OA/9giLZ65J/OFhMAxlaZBXZRZvvW6dk73Er9qamrTxVovzMawRsZXxloNHErwiAF3/VS8vmNjNk8KZBuqUCBfAMY15zZvk2PNzV2nQR+NJL2HeAZ0mPQ8NOtUggbi0R7JaS/vp32ig2vDhscJ8nU6bJYz59AyjO4rl3HEfzrXRsbr5BnkPJHlI5imS/+ii/ik5YaeIWwJcrBMFk3Mnc1Y4C+Gy22Mj38EpgVKhTBPLBDrsaZeg230vO7dlC/uxy0vJ6hufpwLVcdY7RF8hm4QSLWDQHKelUHRnG/bLb5Qay2slsvCUrLhPXr2b1J3btPLZ0ortfhNeEJcGf8mfLG+dvGRxDx/9RFK8oF5XCkcJ/PawuXoQn8XZuGZS2NwU+xVP6q7ttl3nfEfviHFWjuGNj8g3rmHl72/SfepAXITLPbxCN5xXdYeTD4ISUc8TVkBOB4RbEV7y2OGQ1gKbOc96i4+YjRyy0CjGJ2t1vI//HsNh9D2E9N3f0JX3PMSh5PjDnCvTX06g+fqBXZwLXzIZB3mUeZ+Sp5DMNobT3t/VCmknCMCiE5tD5R1Wmgh9W3YxSKehVwRxUD5vGcehXGm6AlfljRe0FgkBGAD+rPJ3pEk1A9w+O9Riwk/g8+0F4jmLH+zvez/+57lMQ6VszbbNcuKoOlBVbeSI6pF5dILpI22/cuEvOf9BpQesngGD0NSXLr9TzjFUmSKawUdD+Sz2FB7rbjpApJFzsDeqH+cilBv8HiMeVmusQZxTxiR0QJtMsgfXfuWDVY6kd+A+Z4x2C8axDoKyMSIz9YZw/sf7WOEvkHu7Fz3PQr/zst+d6lOfDtD9VFKtxScZ94Ea/8ss0hlyr/EcyqQrM9HH0VLb6+jr4n2KeS/8RWG5W4YfJny3/CyNWzZbWq+hWNkpZSTj0wpw3GPWoG2z5La8RPQMk+53jEmFaf8q8sDjzPKXfEg6cbV6k/UoPUByFZKgdd2eZ3q/GIu9+K+QCyuFN6i91NlkGaks8pmF5vOTZf9O1W+k/SYCYPDViSPfoCm7H1eMpuxNCV64uvbwbGfRxeesa/yixnFmgiGoQvjhTi+8VrcPapsdWGMWTYC24hr/IfoPGj8oCdhBfdXPDuzfHFt/DzpIDdfmWQOe1MvpST9aoEnHh9TkJQgGxRiSy8reNqvnvF4E3x/mIssBOR3r8VvyHzBsNSo+dgFlBoJfj6OJbqVl7Z9yXwBkNwIbVvwGixRIFngVUzRJjb5b5MyuXpN8rIZbgg2PwNde3Qm1G4VH06mxl/zgduX/6MpUo7G933D4LH3++wnD5gpgLmTnH8QfWAzth/z1NY/n78EnzhWnf770YUo0gwQ72G+/JMame8WeQpMroERI1UbnfD6HLLEgvDfx7U/gVDWyKjP6JwAD3M/XVYPZPl6aHwD+vN6b/6G/mGzW7yKOHkLpbbBNlLNqnKpk31cSH8uBxm/VLKCGCIiHlZQa7NxCRvZve5qOWQLKb84qDrbsvZiGSn5Ras4b4PvYTf5+kU8zUxBd0Cq5w4EOdvcZpeHV9Yof+rZ3tdF/25leosrJcoUqtyeMReZqrAgY2TxNq8ttaB4E3kPlq6INM5cbcdB23/mSTyd1+K4yFP5s8T3vv5tMjvykBOUGP2iOUgFJvaV/0ZTeowXNDvS9NISoAbLjtw3YJ+z5gA7/4b4Y4//QWTeNBWDeVIJJ2KhzQ3t/gO6Rlee7O6wat6NOf+lkmSGrTE7R+EwKBx93zm+kBuirLT4inv2vyMg6dA7eAXYS1fs+1Csj7scDAHA3f6Kz64wOH6LtBoo4rLuQjx2dcAFF/WvFvfwqTa7anzqtqM6yyUV9YrsyI2ZyrfFP06CSJHki3UTjsUn6vCXDozMB94CwyOBEHgAM5i92c2hLEm9EP4OBSbXreIuDifQo+ugwbKgzGI0YQfXjftUMi5eIow6JENDx9u9Zt513sl6f6Z6rOnBpMeB03Gp6+WCBGj0G9++85DIjV3I6PSm62uD4JG1BR/B7N1hjk3h0yDusbXpuOX2voBJfThhPe9q4hmDiyCyxB7vujgzldMZZMn/fpMK/xwYgjNI25dZDn4OwEamnaEyPyHiWsXxfZBfDSlXI5hONRsXQrxtQEGJ1OZ6SsiV9W0U3ERIAohTKDGDnDc1FjCw4BOaI9al0oLf9Zs+g1E5UF1DDkAW9YwgbNXhf49u4ztm0kVDxiqTFFR7tvkSX2q9scrsMMtr+DnDZ33MKK5hB9+4YnR33WwftxiNwFhoCw8/FX22CqLBAzv69UUAvXkDSRENan0k9AxDvsH7/5dsz7smqdDRw8To3UZpXhuNyUild8hw6+8bIoiRAKCF9BD+LAIkB0/aYDtGUYk2OodfUrk1JmE/OaFkzUdpe4J/S2cyPW7TvVWBGxpjqrVlVvNkBfbHMdmKpVwecqaz2aIRh8TYdzx/VIsRNITb+RPaiQkm1miRf3E3J5M9P1RUf3lWjuPDIx9ucKjybpQnqRrrzkf5fqY61EEs5HTgLxXe8BzlRFPR1T/6DeSOpNHudLZyI08q9mvIpsPQ00/QKm+UmAxHIjbOuiZbn6lKDILzqc7kChBFbJKhJvpVPXX7rbwXrdKk83zx1K5FbMBeMGBbbu2riKcgYdaXTs+diQA0zQPETMcLNAiaL/d9UHB9eIi5qm8UubO07/u5lfpzoX3slv0l8Z+Qd7FFnnFtiP0j36TvC7GTuohxTpMLAyjm+jcJB9oYXE6Y46uFX2QgOIlfHnqTLHuw1/HXv+jSGS8PgvPhocI2vqyfAb6UXqUoAExfFIoJFTnJVP1Gnx/t0O/YJiKIjBS2WVSOXQGYbSKhxtZnpvf0P362GziWtkt3FOsqVkXMC7yH18sTKDHqHwV1ig75hv2gK1vPPzy8urSjE0xHuja3l3asH52sXNu8oD1qMUAeky+F+kBewRnoI+gz1s8P4VCH0pKPl4aTVesSw0sP1E30XJR9pwLIlV6mR40mxkkxwlwDXpfKt7jeQO95VX5xKpb/+ef6zkQCT5C/DsIJlzi9JcoAudp5HQvzq4oJsaZUDsG1wpQi03i/m35hSoYWdYWZS0xh7LiP4fYAm8H+JQR14VExM1W1oWctoUnTiNAeHJxobr03utE2MKJR7ohJIadGWqxF2DUEONi3FVEyLNgw2CdpFy03vsCK3H5iUfD2WjW75wfv0sXJGo43sNM+w5QRf0IGZ9hoNvgOyjZgQASGAmWUrp6oTANbwGBtzEYPdf+YpU5vopPmAMAyvKQMwchfhjxRL0HtXMbW7nRvsGh/Cq/5QdXZljxlvDPF0YBsx9Wx0/rfpSHogPY9/gsyNK3rl/mOv7ef1dgH3JupVPNM7Rlx99vfpmCGuu4xeEnhJOwJ01OqNFPOU0uzI2Np4b9OQW22E8pKpq/qWyB3bKehXC0X5gX1hgo/KuDPfekS3cs8kPHb705MoUj6OnR2y5hXYfw1OBOcx6nrqPKK/Im/bWQfsXwKTzYH62+p/ACFqHLWJ5rlxTFXykxUC+wGuuefvULlkBjm6i8CuIZIu4tmBIvvmDCH3EBPFV/4jtELF/6Htpalai89T+Uj8HdxjFMfbct4x2CYCaAX/l9lVOswsIrP0Z2vyjKfhGhWPFHYay345eZYL2zweUzmPL/F2RoymUVH6QVvHDrzTAf0ZVZikcz1uKDobISgyuTnX3G2WxtF2XiUg39u+yvHhMAhBurVSbmGt5CWajZvnfoCtIQ8jPY2cBVgmULMg9Da7TIhhk6/xaTcane1oeA6VJxDZ0e5pyQRIN6WcqrtK5FZwdVpkT9uK3pGXTze6Jg/485evImPjyKoC7LHXsasATtciPkNtAu9mHM/+4y7AGrLCikrdBX/HmW+NLt5XO6yMrYkoRphBLsEezzBuD5mTe0gIXLflEqIrFG8xseTX2JNSEYh9Ee6+cyhsen4ocFgvzlrjqrms5HpquZCfDIvb9HKZ2GLkVMNDRp80Z3brUmzbDv+ksCJWloKB3iqcrz3HDkR5/5FrAN9TeLxYIRqkJ8agPuHOD7J5RfGseHyoecQHr2/Lc5EflimJYBv0bh7Tmsr+8hWmZQIt+H3SoYdEJd8C+A6sZer3SA65lJxF2dPr8+kL3kE4zHnNnyl6fp2Zb5357DNSilm8zcs3ATMs2vlaCYL+sz9MKE48TQsgM/kQc6oncguK8jOuoCX67OLMoSJRQrDIBEdiTmfLjHwctKaAumr7O0QcR7nz8rReD40Ao4jVeHpehj71nYS/3TitFNOhKk+LYwaOVbMGWxlt8SDWMM0i9WNBywO9/w2u0cD68j9tJNCBOzKZbPi1egnGWJIMH6NyJ4oVDWEXNPFHnVa3rcsY448vLI6mJVreqsaCFT7WM6aMJr6o66YLJQ0ap2pdfvDb1N5yAxUlSHmiD30O1JvwdqnsXJ7YzpYDfv9RHI4YFdSKe8fdHI0Ppx0yTO2p44/7nv9uN1yapUPHw1zFUkDo+FFatYfjGPe0qYCbEfpDjdNO4f/laI+FzF5Fv13lZQu6zqOF/7h9dQWYeXlUS3t+WjavjgY4VRufeQBIJVL5Ggqs96IeNKiEN8M+AXv0SA+vrpTeclWIPZuZbHz6Isxb5lWajGpO8x3Lh+x2cpnMBuOWh5WI6B5Q5JpordJj8ZgdDK0f1q/GZfrLeQCkq7eNNRYaEZk70ADACYcO8LolMISrmSbH4xb/k1UATDkMjN6QSs5YAkXf35ONElAVZ67YwtYeSvNtJLFsdRlI2n0DJw/Yon/UYB9+ZwSDsYmsiIzNKQz2N7TWrRNrtJ5t+n4U7IQ/0qj4K8Te+GDlqW/77cBINnXEPLA734MFn5rgskUjHpGbbHKfH4exw2QeC+q5wbTZHbQTYa7AJrcFTxty9f1qGUPk2CZWT6yhEYd4B1xNjtgVNSgTRm4Z9xA/jw63o7AL3+nytzMsZXtVmZrB6NOavFiWETkWsf9PkiXwEB4Hmr4S3XZj5fq3rw+OKT7XQ33KPvPghxa+WBKBBk5ZLMDesLiNWGLT5X3TWFJpjktHGcad9A8EWnGNGBuceALgGPz4SK1Rm3eUDzqDzEarQpEnlZxGctdFS3sMEMEe3EZZ1EuEQnd8E7SY+tyFzrnrEXcilHj336EDaJKbGZqW5pKelW+WAcwsPuufIzAnzlJmCmSL/wfjOuRI/scZj23pladcMDJrExo9T9JBpPj/YNfXjYukvlCDQXThsTyroEu6wWQZtVfH2zt2X/bvDW7pfCewj9rDbWGK2lScXXZnGHG1n4lbQ74RxXSS9/qgcS68ycPnB4F57tbeutcmq8PUwYZfbXFkHZerSc7vD2W2R3u61YgXkscTwnhEax1QwRgEf+jkQP84OuxwTUyd7kYfGIfOiCwlOURsX7+2WC6Iu+K/1R6r/k8UDIHLMkwid2f/23Oar5bLMm/2lYQnK4+q8N3aKtsCzzBhIeIh8jczHp89LV3Ik0hBK74VAbXg9dS6qcvIDJegCUKC0uen532H8J2N1BH6/RUzOAfd29+500JALHwIAZcbDHh2CwrCc1HNGH+XA4Wn2XB3nQoyAh0hfacizWsTAaGpE8JrwuamyOiWdKhIKa0YRg3WhvT2AfA1pWa8CM3/s86LltcRkTvtLX49OIdRTOcR3pGOBTKX4XxAS+E9NlJddQOaj1Uo3XjbQi5A6EXOJE+cOvOurquPusboAZfIYQ9DRTk/OXE4QtZvX7jR4t8f8yHo5JZsHmnd8ltsx45RD+fR7CtOh7rrhTXVrNkHKDKeb1Oiq5nJqSJw/oyyfAKJ/cvK9b0dhWZmHm8Ad3lAj+BaiE/1UFgoyF/xkJlJ6PV2TBifcP4TCQyxpoQXI/V5R9OAnKXZ+a8qtO6feW/r6z4uH1AKyxvYRADTUOqGHOEL7TybE0xVEsrO/CelpHTVLNQG4sEvzAsGNkv5lIeo89TVAqPSrlmNqSM/YUi/2OQsstCduXeablmnHm8FlSXFQJF16rKcIPtlBm/3OF+etX0Edk2IZlqlxWw7wPNNT8FOLP124L3+R5xM+64KjXrEq3xbSKpJxNF2kO59OE+6fM3LI8cJHpKkO2YPRYQqhvuAMn7lViHVOZaPlATNP52JW0cyw7Ag16ptVaqsVrxr7fvvj2NTFUAVFuTVk9C/HuHfzGuwtzGtHjQrK/CCBrZCpPaWm9eJj5M905GFkujQNQcWAPtFJmDqfO+l8Zrj3Y/U+kZf6ANM+SgKHG7c9L0h5DavN/1E8rQeRZtHFZ8KcOB2LgmLV/c4uPz5lpbdGUODsDUI5fTHlh89r/E5SpOaMN2LB0g0WBTh1EV3Dr8cqs6Aabkt7TGj5NWcxtXmURTlexq4C9WRyw+Pfgg93Doj5AMtBHRNJmCk+6PseOi1qgmfdr00Vaix+kpR3agcirhS+l/nEaJhaYp8xO5wJsPeMyXsRIZtxzylVYGkpss9jd06POfZS+HzC1P0sXt2CTJL6YuZbsK5Y/AzbM48Af5Fs/vaNOCKJ41de+hgG/0qxL0vCeIOzPHGpnZvzahI4XZFRfxnP4SiQedlZpubdoLQiPLOgJ5U3ZHBRTiDqz1eIbctKT7PWP213cVmz66ZovHu5m+4Sdmg6B3SBTyYW7bZCIBZPpVoaNjwL/pJM6+bxz+lieQ0GzmbIK2OIaRAXl/58ZILENShU9ZCveVH+ewfccmg1xde+/412A59etzY/Vy6ms7GdR95XsJMT78wy+UGITyHORa/414TBmw+QSk+g8CXV+AQ8wB2rE/jwVGEQGjQAcyW5971/O9t+W4QhKG6yVhu0QkYFmfk/tXzO9kTDAiiPX1zb+2zbQS5RGgP5+CpTsQCN/nv3p97nYMrsFfXix239btvmwLmF/f1Wjs70T/jyNfj3vjTfPGDMCyLbx35Yb0Mjym+8c1TZq/PcsIudoRwpoJD3Z/7Vs8/hWgN7upDnlu/jvWZRag9NY0MRd/KvdhinoBeL8MsY1INH/mkUOgax76jzQxL/ajXgc9pVeSPPhbOL/5tCpgkrnUkOfW9jEf9v9/LJfg96+tl8WR/X/MYu6irBMGk9d1qb/bRvMGPFCQH85egF70/n3LMaf4Meb9aXlk3/a/ukmCB5Bj8EMPEr137MY6wnCMak/dVSV/rdlgUGJ8AA9hq7vJdQezr/ncfp41cEnmz70/H/bBnO2/+61pHj+Jkv13/OY64BncKCRnrJd+38ST5Mh7C/0ac5hetr/mkl24u3mcEEjePO/lsHs7LC/ZIjl112o/57JjYkETI19BTTyv5Ylhr5Bby0Iyu+vlzr2/+YxEkee6W9XnWAj/9pt1go/blEUJ7+SfXRoGRJHWCfKewhLq1RV9R+c//0GWhjYHmQvlq044RMMQ/88mpKlGLY/jzD4CBf+g3P9KRVjX2zLBT7y9wdwBBY5gD9z/XlAQhMGvz+afKv/PHsQxP/1/Pvqumiq+u+rHzTy52Gy/nlQ/ff9EAT+aRWa5JMruu6fTvy+xpAm/9tl/O8wku5b/Hn058G6Xd3fB2udTPDLpk8q8C8Lh9xkSacladFZ49pszTiA/0/HbRt78IEO/gebZG21jN8h58ZuXH6vwsvfr3+9g+maCv7sNk7g6a8FZp2KDI4R+ecJ+DpPtuQ/OPPnW0ycBoAiuMZnAftEVKka4SoZb68WvAp85cO/xIJjIvAvUCF2nv/WMTTeDiIzy0pkT/t/BcAKkdwGu6dEPYDqEaH2lb7E0LHR8fXSiCkZC29824J3VPVuzbrdNYHcpLXE68AGJqttDMiK9Fw9H3z4bXjC550aZwv93aW0MyCily8fcihg2Krp+7nl+nSBxc94mZ7fPjcfiMUU5HeZB9qn8d0a+GXolNSIF4MRr47hJvBb5neur4TAoNDJjhXGVxlRYlRBYic5hh9hphHQG2Y+mijpYm9kPBn8XoEBeTPiu2IdG3yG9SfpQLljspnZZionCiqzZTjQRlO/K4Zb3mI9d7VdsR54V3Ntv3Y4RhTBZyrRazXe1379Ae/qtohRPUZtGVUVOZ5RazXoG0/8tcewWkLAPvigGx7oJ+Nz3m60lfprr1I99NeH+c3IFeinPAtT8Hdc4F1IPf764LWcA/vpZMp7BYsG24Nj5v//Mf9/N+b+8JOLS29PnjtbMRC6KndmoUPPw2muAu+YBJ8RuFT23Brs1WfRs/dKmmODEMR7/7lY+ZLZ5GtWcHsZeJlm4U+NwgJ6srUaW+AbyZSUzNHJJkVvmEcIUiG6yHDElIktE1xmNeazOQDUGz7EL4k4IiWViNeTYXwmgYs5AF+u09ZTim71jc/xDCscvQmqXhbD9Mcqmuexl+7CapEeHyogi+H06tRqZWQB45WQRRTzYL3ScXMWTNqWPWsxvejOy1arIKX1LLHrSRmTm67s91EtijzD4M1P90LFFBGBge8+0uuAx3aq2NycvR949ebLR/HqmV3gMTmsNH/PWfO4xI+bz90B5zYW1t8tAvBHuD/JKAZUvEDHrhMsZ4yZeYTubTO5XjSczBAZMYSfgEN4jLmd6iDcEMfXwEpSeZAyxWnvbPqemTDohYY5nxT0Rj7Ik5cdzzqAmuIWJqvPJIHPGb2zqsRli409+Jma/LMCI0XUw8POgHvlhOBEwq5/cmt/7nYn9Dz/JZuPoSmXjLxn1o3FhnnfZtUcTuhKTnR+q9B+plkRCD3HB/HHe+Oe2jHbqpGow2tR2JXaLmdMgGS3QSIzA6P1WQdxLMdfucVE3sSdnhLq2y7SlfLnRLDJ7g82SNrfUanPsF9M8e5PE4+cbfNpgnygX3MPF9l5WjZJ/3UduPJbZtHW902l+QBz2DQc2C7vYPZErtYYGIjL5LuRwtI1n5T3/W5n7z/FL1iK1J70a7yZPKl9TTS0pBhZsdkv4v7KR8jNgJD7KCJCr/iTkAECtcRBYcBi8l2fX7Yr+lzJqF3vomB/CErFdb9yRWZ5V7BeEcscB0G6Ql+yYq2gnZoGPRFojBprHdjrctMzdZ7nrKS/3DVQ2mc2m7YIoF2wiAS2AQD03qLK8EYVFS42oyhaPIg/zE9iIgN7Zu1HsCMVTxalOmR5JgrRT069e8ISySIYgy9XITNlZhLHlOI4JyJGVKJ7rnrUVb7Ogc1RqzAHYempXGtzv1qh1GMvD/VvKG6wRmaotu0uIJbEa3S6qyvCpaGwBArvq1z17g+hHQbbGYaXIJEaSZLt8kwBIPO/Cp7b+lqu6EKP8ztIHOlti++Rk8WpvWIw9ixjXutuWaboouVzlgBtucNYVt7MrO1ou9cfX9sQKwe6UQaQwDuOgO9vpT/gvKpmT8m2mLofhVVzT1lVzxt/t72Zr+gTevX5EPafUeyStVphEFxrHAs6t01nW3XqLADGq3ouvBjEzRTXvmOrWpb9WzUAsgmaIrOruRr+miyDAZBEcKVz67Q9a7M2QekMTyTS6zXKtU1E4/NX+Y8R922qI9Zf22QSYqWKx0l/kVTx+vR9UFRb6gN6VSXs8veYpcO7/s1o/to/f5/3GA+TirIsuK5DH41ib37IIGwg28u3So/PXc3HRZ0cvMTEqt6wtYzGqjc7o1xNUBSCHy6jK7AojMTT9LKrOiGk8bd+jnwdKcXXlYCV6rgZWDTsiuX+GqDAPtnnoe7XyvrfPpko0I8MGBMdNJv6JZTzumWClPVcB3p/38zHB0SDTQn7PWKbc/Ej2OhQrwlIYLa2jPi2kOivD5aEzNu/ZRO8w2kPP7pgXI+tqw77ZW2ngqV75/6q8qLn3aR0K22HiZG+c223HJr462Z/1YQNGFtZ2XKUiLDo3IRQlTEqZNvnJtCJbT0ULlv29wg0OaaNeSdXNTNVNSsVANJ/GWgSXPnKXNYUEkEwuPxwJcyXs0XOlbHb5MphJsTAc97Re1fmw3DYDCz5zEBHLvrUV721RBqiBoy6rUAe+Ulm3kAjfnGmfr1egrBbn6ZBluRE0oVZuLk9WnnVl+B1+0DUHL3Kg8WnjPM8kdf9t67ZrsmnAybdDujZDHLOjxA+dg2Tj3Vg21k30gNVDPkkSUr2T7jy4l3OmzGxvjbgnRgfoAHzG7YbrHvLrcxE03TdZxlFPCgLaLprX0xDZfj3HI/KW7OFwNd673YrzIVQwMP4eO39X9xoEB5/NjtLf/Kuae1g1ULkS8237STruvgK7NW79gmSxHFCjtJ0ZH9XfqPpeRXvSgX84eExM3bQ3grWH+DRSBm4f1KavYpU7xNzvamK3epUYfXQO09my8WQCLM6xaLTPSC+ATJ3WJ9fbTrYGRF7kqQbwx6ZKzS4sEdga22fZIp8BQxD1UQYqABPd0trOID0489rHRDQTgfakVxW7aDXD+WZyeN1UWweGQe5MEUxmt6DxcScqmHo5kil8RET8hpI/tKeY8/G9kwZjutSQpqSB6tjpekbsQWUf2os1PK1G2/xgPCytgzzJkP3LP5LdkmIOpbqHjDfT0ZI697msloYA2hxPJ3rbP3woKd1h+cDMDKAu56EbbSRDSNnI92a8itS/K1+gjHKlc6E328BzKy2Ydqv5ChN0iHaFyPjbMH+XCnpriWLx9hjeXuLL4IeOfa4cnlOk8p7RM3+jo13uUeJVLHSM26rXgN6j/MXD/19dq54ab8S9+1+Hg/F9GnHUA85fnjU+hAOJ4oDqDOin86gEpqvj6eo+mavmuvQyYwR3DIbpLyK1hTqMovnyq9lAQpv2x7P5AYWWTL9z1tEKgYnpzqO4c6QP2BnTO+uMp40E10FX/Gy+EK+8++Eokl9RtmEnSSfy7KMar5Nk3J/rouztOdSZD2iixhXr0ktVhfaFTlHASxMsCO25k+1+zIisuNt2/NOe3PF0B7aTa6iWw/D/uVisMd9GbuAPjda6A708RajlIz7ftzR5R5d8rATpfIzMamClf0/rLNw1x7DYKTeClN4HcCqd8+HDj7vqRD8RDpF0+jPRJscH+UXkWVA8Cq/gtdCYhxo8wAL57ZuW/Y+Jo+jKyAFh/v5kM+ErehicQdr/dJrkTCfRCtRu4veH3vM6QEC5cRhaBv8FFpR1IufUbU/UPRB+vBOcMQ0uCdqrPN+R+O0kMcr1i1/A1YoAtrcJdSngi5HUX+ZC3e5JzvVTvjm9SbJ8tzQRneBF0+itMzzzuwzHWEk2je0ItcF6R9ZscdZ5h70xwLK/RHzhjADICzbfPQAsO04YYIK0TJZSSTieU6nmd2qUXFMm022X/4TiZwEtuPW9x+1UDPI9dIY8esv7KJuPVlxYq3r8IZ/sv0v2gdZJOL9mrCRA2u+J0sXr0BX4767Qi/uiG4yk1Je88JpJmiSEJDvp++wh6RyxxvDcUZJ0zTwQ8qaEYSsKhl/TnXyABhxc4I5Ei2urbShBwMuNc3I1ssrdPuWN+aB3oXLsbIJAA2RGhtNwVhhkfYHxR1lbvtVd1s03n1EHKw8AqyazzJV6S1MLv/C/fFvPgJdRyizFSAM9j6i+uulCAMU+hWk6sNkU+1Qik9Jb/02sOeDkPkbQQT9iPXKjnPdkSTGccwqOX5FWAmU2CG8IYQ/Wmr66kxwwZMawkAMz6x+tQIoT7PHkUKfhLN2ffh6dsv3SdRJKdzUJ6IHCh4i6KzlrMBCLO3Xen2IfBiGVgq6Le3ebS5J/HIVtCrO10V7lWGQDIfBs5+SzWq5R9NpY9yw1KvrEGAME+Ph2tpXG11l6MY2uZOx3zX6LvO+r+vx53hRtDRNkHexU9uCA++/wfsrMcBe81CznVfBSmTxUomhVmSVTz3R3pTOXFscT0iIppDZeIkiEYYBxwT6UTrjXUmYKH2cVQT6Gety2UjMgF/kuK8wZRxEROKFUSy+mCwiM7ywWPXX/Ke+qfhIhSylybYxum5Vj6IjQm4ba0yzWb+8l1Y0X1ysRF9mc0j9nGRR8yuBVOTu/Xg/1YfHbTWBb/G1NBWbUhkVW3nNcPsbDoKVK9T+xveQyvlQditMVmPzGd8ECRQfDmZdCfp3SC187yAcxlRmGKh4Fj9e1y5CnVWHJfIKsMsPZfbcHocRa3olxiuDCsww+eEZQmUSjuKKpm4KJKuuXvHQPBmXH8uV+XipEu/B5/XRqY1oEAaFBaHYJW0y720SbJjq2HbLDw5NQaOznQSzCFQg6BuMLJqA+Y5Xc0L76YkHHvVmsOty2GJf7ktH/TZTc4Bm5cQml6QBoHziUdebYXVYUVtVmRNUWw6sK0m8KlhYzFc8z4rMN9Ot/YM3mNeFNBnrssZby78MUISwXccRQxpIzBQoIiLQxQPvggIohu3lqGw4ezO71KEm52ZKPXFs6XCD6CqnEHskmODlri5dOf53c2soVrNMSkIpRFLMKcfqPshmj8xFWJ5RAFLkPuJNSh0Wlghg7cOUzzeDJnwDwxYOZOGMBqxxHtDZs3t1aVOdXh2E8zSep/p5Exx+lYzTc+9E/qhi8aGiz4AsYmv0k3Cbrpm/3M7tZL8EtOZKaI8NXrbVs9H6yGVO3mIf4PE+ZIIHgqkC/vC3ohGr1sVRrqvPj7xcgY2yUOfJSF0zeh3FDOBX86EtAHYBHrW7r+jIyaLh0qtwt8uEcTINtQsu921lcUZI9Rc5A+VDBYqQlbKmxlyMrSSUivIkcCT+TMloP+cTOiFl4wyZT8SqtpT6cCbF5mCNjUXdCXOYUZgQK308Hvz0jHwvOL+t+RnX8iqe/oyMDAOjmMf2WMjVjOQZ8wFBiBuzfTT8+GEeVx28j/UiFu0XCq3hZ6mw6znHbzFh2IUbBM9WOAEtGKcNPuk0HtSVQPVG0xgs+hooQSjUGQsrE3TiKwW2YxPFz33jRZVjekqSJ8wz9Svb17Pc9zu+CPvJq5UZnHN9HS9Ny6rW4w0KfkyLM594Sbsl2Zp2xBESB96dedjWA/RWpSVhLN7u2LW/8vxxPMqy9P0pkGcmuY/3NeaFVyXKSa9vLuHqWRSqMihLJs4TOjk+f8BzvPQkQ/vk2H7+WK4Gd3yxDVqGsmvr2rS7pmw1Ice9D478bYcZs/64QKl+JPFLb8u0Nse3az2PArpcbIWjYN4ti921uR2GdKfMA4CXqHw1pzchAoG8I/XrdQebThGPFOqPvJuzsV28i2cW1TcndzDJ+n54y7aZZXFiRRAFK9RD2sx+mbyIeIBaYdxTeXsPA8GVMuM2tLoiv2oAPGBsdD6MbL+4JGVJoF8/Lbs8XT+ZERPe3x99V2kv1om+oVp75aGsXamp6DDTPtMsXq08LVVWFxHHuMQhi+/t0NgpWNgXSgSMDuGK3ncYgVhaKigfYOlwSsanyrYfNjILIeRuolpjEps8xeeRGp4xaKNCHcIUhwBWBE7GrZogxKauPNlfchUVTDQw2Mzv6iYM93KI59wPnU4ETB69CleqmTor4dd60it90xzx1g6rBcNhoC3VzA1TzxlHdN8RxJandfrzKhm/0fkXm8x3EuY2AmP2xQ9O2scXAVOa829Rz/tKWHmpfbCTZHNTlFMToHbDgBPq44Hir88NsLCh0u8UHvo1zA77IIA+2GACwZPfkdMYAuW3ckai3cSJP3Wm/uVb2g9bdXvPyqi3A71gw4Ohqx6dnmKvvhc0+IoXW3yW1Qh4MYwj3lC6tdqz7ri0qpXk+tTEL8arT4KoJoLShVdXTGZl+WxYqSt/KW+BEMbV1DZgetLuud3xEzmG5xIZxUjW2mWr3OA21ctfu9lBpOPDTeagwI01ZXqpNrZB2ddarhCrSGIVnhNMR0hEWTDYnzV7Xyq6So+YBkDiKpV5ffDbfvTttl3nrJD7XmYF/uJ53mXhhZ3u26ysJhPRcTHJ460xxjrpcHFX+odwtBThrXN8UrZTqIzHEMT2tKlTi/2yRvf5JZSJrK9WU9bfw7o9RZ/mwetZtO/sPCo/1r0H0S0Y+ZWkqclg/CXr+AwtwrjiUxiWdcCcTinbw3KTb+ZGeHj7+lAIlP5dKEWxnjeGz6YoSsY/ZY5i0Spz5qhXxbKmnl5pY5SHT/QULL1e0nQvAWpgPH6l1ugmp6IITUQttL6rQsvXRnchreEL36xJhzXNyGnEfi7v5x75oRT1GAFUNB+AP7qNL2LJLDEpPENE+1R6KVdnLfEFS06tcEcVA8num0vd1oCI+0a1ZB+SX3Y6Fqi0nCWN4b4ej/T2cIDrFy+jn13FLxXx8RyjVUstbYHtQWmPJwGOst3QHONNuXapNd2hJ2k0zv1SOsOucY5OqDrHZHrlsUAN2FmLL12QithfsuJhrICbZ3NvhxvoSWUEXbR9RZZ91eFjnqh8fGHOmxHsXAac+q3FfJJsxTvoNer5MCyranE8dNwr/C5fT4gjxkfmust8hegOIe6m+Zdt4VdwHkJQPijJPgiSYQrng6+UYMZlOojipNp9oPlgqCQlzd9AitYSOfxKCpstsZ4EYCWO9/Z4v3++Ulyrp1/oUDh2LJJ8c6Nak0RKdDO4jV6HSl5NGQQbbDXaGM3MOFVB8SaNvC7l2ArxMPvojU52vpYdpkcm1Du2ZNQYWv2A71/Tb5CEFbOcRtF3nYk/fZRKh2mSqjc1AY+BfYgjXsD/quoVn+lpajAqoA12XMiJKAXGPpipmKV7fE38MRU5GLgmF2ynGjAIi/3085qJGcekbqCPvoelHCACH71p39Pei/Hm04+X+LHve9blRTmjF/TnFe/IBC/9cCS1JJFEcAyHFAAPbYGBtmbqTpG7MfH6Odp5G49IFkn++v5JaLF+3zSan0ylopJVbDEzH5bcyW5hj/BCFsDTq6W62o0t8ufJcSR3vUJiskX6++YftB+cUZMbEVjnpZJKHVvtN/RxZHzXmraY1VL5RIhzDB+BG9qOL7GWzjZ6HEUAR8rGO4zIhVLUnAX27lLp4mgimTeqOn0J2MtlNKEHc2djX/E1fFyIgo3MgjjxsAGkQjwoPbiPjy6Txu9H4g6c9lPNQm1gzAxZy7fWEAMPHl7rML4NJDsr2mczJDU9aNETO+uRPWbV+yxfjAn2B0eMfAdtlsPOl6/rvHmvaoN9XhpUlQIXyAcQ9ai1C3qOsBwl3lEIr8mOs2/CRGwVLFTJoqeMcSns3CH3+4NntJG0GD0g4GFRqKGU+YB34sS9KXKVxBRAoaescqLX4/1RelsMl3tgs6bl9s19hGscmHgyix2Joki2vuXV07pC3amEJ8UG2rFfkQx1km0q3JtvuX4r9q3KWhQKiuaFkSOCtde+hKZBfFGQdl3x9cEt6UZ9CLrH3JXhjL5IR74gR+WVpwaH5ym8iW2L0edF5ztO0MMrDAJvDGX7FbD4oIBtyZ5M/CUgiNRwIvAHHN+OrFSdVP2VJ0SZsWonpMVopk75BTvkN73cBJoeyB3tr7i5Gz7sSyy0RMZcN0/63YWS7wp6Skot2AOD7So6/AWt7nQ1GdiHzxuMtrnYN4SrkS6LudC9AuwX7KXEkM/L1jeMYuMOIFkcH9qB0NsFJjzyyOXqGDGwhXJ6CtbbZARMYXXnCCTqx6abp0Tij4qsxOZdmFjoO6mHrYKrqxrcJmWU+FMUHhU9fpn1yznJ5k+apt40BLyhKAKeID8kVWYamMOgQLc+SIqGYEVZqaQGO472gBSFq5xFX2HiqreLWFc++AfzfZJVFTUN76e3+EZjxv+MjyxA1YNf0xTo/LVrhfbvqVHw7mYiYp6ESbFiVTrSzSg7vKqDkmYR2rK+yCSHPNXZD59CJVcJ8atLFjjXslpjRJZgPaWRbc4uf0I/qiEH7CNEm6Bl0VHijTiUnusWhdbci0xpMRW90nr8zAxM/6W1P/Jos+UKz7acVa52ab5sjVnn+zaMi8Qd7py+x1fcaw+VOikEgnLfd8pP07RzVJaVC+52QsDSrZLBMch8ngrwKldGPZFKVTmVyZmHX34AfH3p7rWTUfpDT9e7ONcvHWN+1kONiQ6O/aq0z/kSn5TIGEd+GHV0yFrHPpaL2CIpmK/oCYzMihwzy61BGwzJbtLTMsDiV2LBbJUVYZUVoziPVxyXuc3O2zoYV5VwxhhQYUJVTaSiO98g1mrkZXmjFDxZqyXTNL10uhrrp2zYXdoASGl6WPlF9NkLiafoK5bDnlJz+xyxxX8fNnYArb3p9fSYE7IRGWZNZ4ysBSeuJJ+BRoI0qxEIfApYz8cHesB5q1FdozL3tZ8X4PkaM32xRMcVJw1eLjayooUz1N3gZ8qMR0/sD5WJ9Oh6SugrJotcQR/rFa/6+v1SjOOcd/0sH2p6aYw/zxnN3pz4FrXGhtC1MdrKfd8UdIDk3H0e329ks5a5qcFrozUfarXhOAMz1phOH5Vr8JT+RYXC4a0AaZLRC570a7LM5LR11EYt8JJivF0+gSovyOOVwTZWlo/91D5XUOWFM9Krrw9VkG7dwZQ+IhROtJ5b3ty2gngJY2ZuPF7SE9MC12Alm4pfYo0/8jyve/3Ft2/+AyyA6i0fiH/IA4qVkGpML2gMRS8WorgJLoENpanwpCN+C9hjbr7yDJ33QMkXtziqq65uAp9d+XRAHXoupcfqIXVYr2i9uyTgmBKh8EjlmNarmW3+8j59BP4d8UWjKQHAoI2EPaUon50jQyCQoV6Ak/nGTDYSenTaljyrt9iYApE1B2mj+vv6ijL5eeNoYZC4Rt4HUMd4Wp96/ysNVKq73/7Q0lWjNjy4Fl85BZ13OEeL4tXZQSWAVX1ap/SwvrTHRCbbvQ/SdZfBNSDIhh471gPTkF+uMO0nPNowgyf2ZIeXMrwZDF5btXU9TrY4M/PPqdUvjx7ZwgiD4g3wjak+8LEeJZNnRzN4C4r+voVy0eJzSt+4Dz4iziikZ2zEiZ/yC5qKbJ+pI516PM6z34snjuPPTnl+VfNoDj+qhlcT/tktTXX7Dh+w4rsZXy8x0ldpnteFvm3oamOD493xolPg/ASMSitQe76t4lTewtq3Cskb05S4Ev+6MACz+189pCkkk8FbT0VTVp6DWcc03ujfXuo+RiaMsWopvxfAHUkeGDErAI638d6OHVq+A+MQXDwMMXBGcn7YuQOUqs8FjPnlGwYPHPBlHEjg7agXRd510fKT2qYpipZVPd57yvVazdi1iT+setEJu9s9eFwZmnV9tUU+zhAJ1g1zJkvR9tPy0QgKIdpDt2VcPS43qBKUpSvcz7r4QQ9Q24lT86MaALKjgsYT/VN5REbPWAWGINg/pwZkYay+yi88w9tu9J4Cd/hHH/1mmAtYR56erJL7jy3jB76rz04AqmAK5MDzS62VFw2ZGHfcxftt87g9i2ViNZeSZlYeS8Dezwtztp6bOGki15H1ImrRtgw+iy0hwE7ddyEqf8W8lU06nkGbv2QsFj542cLzHUxZW+Z0zW3Jmu6bv39aLJUTRQeMK85J6XCiPF+ChNpFRtqqugSotcwvtPbnltqztR/1QNbOmeEKbBpLjlrxHUAZmM88kgo1C2MWl4TT/KFNgNhvAvs8crQvlYHi2Rasd8o32b4CRe/hF2L3wQGdnOqZaKvNLc8Uiabse2+P+c3obVAxIi+7WJMr4lYUVxS9+BNwSP7zWQOL/C7Y7r/E/kK5Xj23l53oYEWMpaMEzWD7ppIC3kyjv4ceTowGgaPF6QXZCGtmafi50zfCfXXA1cfXo6j4312IbB5E4nNSkvmkcAxfJDp4AuoWvb7kmHVCpECXQiELiSs/ZCZ4gqXjUDn+gj1yxWP+C0Ri01LdMDnXi/M8CQ1Fz5sPLsC94s7yy++djYxKaPt1Qm1CDYB7nWg/ko9BZp9Y6kCfzQETerNmxIvNsURxK+EwUgnz157bPJ5bZg1o7JyYOCOVINzDy85HNPQbxcEWAZIe0ltCBKlEWYZeHpjFc2fqbz1nHVrGXefC9SQfkVWe+vy32+irJvWRoqi2Ieg8TqPsLQsB1HABRfCsUYNJng7tADjzFNJFbv9mCR0z+hVf+FMHRBa0xg2MWVGKw23v0jrxuitPHuC6RXq1tY8okaZEGKYym3eaRJajTrNXJjGc3CsXwk7mhzkUj3pCEj59RBGsssa3jPz9nfLdplkVw3tlzf5jwwuA+xLulHpEIe4WJZo1EKvTdntYYxWNOkoVj/uNvXzlWtfl6+yK4r0C67kyynwYkldRUFdSZvTqCMyG8CV0z3QX4B35rjNqCN/gwUgAFEJd9kyezuwK5RrTqvWXfvmIjO4mlnqzibxXRVH5MHgRMW1ATFVkXXfUOPpwNKZlMSrPHnTw3WV55Un5TfCondlSaqpdAWx+imSW+rCbkPCYBHNco23y7DPQvda2LEss/S+pygVJhUSRS/vAa88uyM+3QrsFBiFX6zYAcM41iyp21TEukooOj0aFRnWq+hQtTM3k7PP7izuPKgkiGwGJBn7MF5b5hI/uVe+AILP+401wt6lx1UGnAK2pIbEEL7jqmAD0+FPGdNbYIdlbezIhGSmMvpLcwdxM+2zqcnIUGyJmTRq0Cse48aC8cfIpnQtFTKKJ9n/yG4mfGLu3Sis2D6ve7hHdW8QgQVZKT90fFlkPsi1DmOTyNV3awpfBy9exR2r6jI7J4zWZuO/wOrACx2ejesGJKYuCgoecB7c61c7N5OxVcWRJnp/TUUr8vF3y+svU+X+39hY7szPBtuDTtNQ9uEemMgzNzGWcmaEMZYanv876/sOnB62+W9qg2vnZ5XTAWhGREeZM4jGHRA3XQimCw6aZcx79jSXZcn+HrL0SyqElYODre8kec91ikH1gQdKOWAbsm8Jz+d/HVtlt8F1Po2eFABIz09TFwLggkeDb2DSUi74toeQQ87O1zgg3X3Ka2v2oD2nKP+5D0UDa9j18seIVW93xKFljVyNpfHP2VSlArwm9PciVEVqyN7EA397Tc69z8VPejjEBP5RorU0R8VUA9jDaX4Om6wQ0z2opOpjw8TmtE7SPEeNqEYAQW9IP4qt7eWgpy0OJfa1rHjXkC3oOGzWrSDeSIPsTT4cQtsOSBXYiIiL812OQ4a5FUaU0Yg8h0D6i3GZgx97wEDyXY/iMaQZ18OteenHHsVyNkt9KV/UC6zP+8iibyKkYNqgWp4b0dSy2+3FOyieTmRErZzyScdO96fdN/qIGLp8bG9se2NvcbYgaBMKh1ijKBbaixWuvKtgzAgFtIcYLtXOJyAM5RH2gI3R+XfaMoHXUToOn2tRdVXrQoHsDP5wKNHxJKE7tfoDJaNSv/ctdpUpVUQXJh18SWHmM3feuS8P024e3PdqRrCCl6JM4e4MSuCrGwUwqupqRkgaB9+oeTT5G/YicK57Uam81ieEXkf/mgbUc+Hut3jZ0I+KbbhZbeXNa9b2KQ0Y0nDuk0TjsCz1hH3prdAh/QWOwmiWihgrmg6dFhJlk1FNyEC41vTqfXLSrhX8a2g5ac1WcmS3q/sK4mQExw9hBkobbD9aws0cZ/l6hh+GghMh1aXZZortrby06IghSaWVDzoYQIE65uzG/lkg9HmKNJoY50dESEGCY3SvY5d7b6P1YK3BGUmhD2omYte76HW2BDOYZ2N3Phq50dbdW/JkH/0VdSvWYP8pbC01m4eyc3m+x+o3YjhR9R/iujD3bdTk4wZ4378l/D9V5NHskGty8KvlRgcdCHNshKmqP9EKxndSJt93IvH9nE4Uh2qxj9ic74U57ySfRqaH8vfkQyXx9wnh7+ZGNTA9t65xdLBwoii6LUuSqSPRKO/W6s0EnA5z4NcHH4m9nCxGxqoeh03dSgSReU5UX+xvh8nUxAyWksdhyZ43Q/F1QIzbR2WYhLKYHJs4ZNg06oTD+tI3Tm6fOjOG7XLK8dZmNC26uRxPkvEz3mBPr/pfuepAdQo85SSfmq1xsMsCoFEUeuyhMCTAFjRxWNqk2uK0u/UOsNJ3p0Xb/BHl1oH8oJ5ZeQeYEV7Dz72URkMFSmbPCllzrsKu58cCA6IaIhmCmRVFI1ipT6VeaTIvAvmcWn2n81+P9m5kh5+i6xWDCybPK+m2ylCUH//DsFNdk4CXZtxvR9UBqsoHxfYGqM67+5YbgPBMZ5sTkmSDwadd8Z3w8XqBdR/n45kpHl/C9RRT7Sivj412D/JXMYHwvR+EjzP4BNjaQ/OwFztMxF/DEX9xPvErGt8rHDvLtBBP0q+z6+rMKFymtQcCDO8nCGdVimOIr4EOVE+VatDo+IK8I2jSyE7pN8/c4iaPuNufSBKf7uajADJV6dqnI+1+MuUhoHl8TzVkeaMwpn6vksCXQJmSWLlLnA5acurQGgz6U/Rdxu+l4sSsagahdK1VC0DaMWCOcfd7g52bCJG6KLVrA4RcV6mkfp11hOpSes22A6NSRth1cM1Cj3aSetVE3ToK5AbTrQKoau+VFINyIOm3heU750W/wqDSDORHGxdCXzLOmTlFiMHTv6u6QQv3xAwgOwWEhdX8ACGAPU7WHoXv7atfDSR1kmS4rs9Fo+GeJI0mq0Bzpburr/PSRWflf9N8Do56ZbQZNUwKbJ4mjSqqyCKdxjauwgX3P91SQkqhzOluYmd7mzhYDAmNrIRpsfbMCwLs++slZ+cwl84Kr4SQrHX2m6WJPraw52iN+w8E+Xiys2JsXuTHsyyZmCh2Y/2qjTJNwUfyCXE/VT4gMVcFkWx1TR3cRKJK0fb2/I0NozmiUTucvjlX9WxyrR/Wi+gBjHTbfnUj/zMpNzlFW54zASzX8IDS2ZwSvGWnN+u4gYwm/gDfoTW5sOvPb7YZhphKsxSuGMXQdU5N2bAkZ0GUJigIqHTfghvRqKlwK6cEOEzNAXtuuThw9aLCkrf37BZALxPaK4hGnv0HKzNRevlfQlCc0DHsmOD7Ov3bOhy+OjUJPpGmuSHG9v8Om+K/XPUSuMKqVuhwuHD2YRqbUqPoUr6aHLxt7i6Ox2LnZCmF1vKNXD2L+DooMM63ZqXqBLFYAacZsQdLBlOXyncSMEw3PN/Wl6qwHgLIPSrLo4cv65twvfne9UgbwM+Jg0Laq7GJKllI7oJFpsI9oKQW+A/d/ReZKlNNKbVeIWr1I0JCTpiCuvcdNxmJs0DBoRcHzC34USZ9fgIFFduP5xxqA6iGz6u8R4k6IjTv9zTE/sg5CBjh+ZQWNm3D3DguS8sMPj96FoJP48f2UKN0T/rz0GBqDfnQgZubNfLhpaemlEa0HCIn/NvSks5s8rMUBvlhQ02Ie0HjC9LfC5wVy+oDnaNvfqHqBbd9lwVpWfyfbJLmkxSMsNQnvRvpldbXHvprABWwe49ewtq8xSX7JLDL0bBQbtRc8VmgLg5RI4EcetqauAbeuS1CsexIZqwIkbBs1mkuB/7x/JSN7Mey6IFDdskCvEwsss4G06o4znuCo8rHqEUAqFvqY0MUgMKF/Q+YkTnDEBKsZWv3BwCgqCL4JY4WyMLgXfR4dMNQ2X/lRpuGlixT/sWq5k1R5aRm2sBNrgd5dUDSAskLJq9HkfChOlk4CIUolPqWtYfwVqKVKm7Bn2/86e8hCON2VvqTLBL+67RhB1cF65Qv9+FV8MBQ7Ub2DpSLpELVfuzIURQHwvGOSCvamenjES34zyW8vA8fYbwn18mqDoth9HC14C5A+Eh+Dc6sAfd5S/90koTj4gJZI1KnGs2ZBtZ0Gx8z4TrWHQ/BtPZs7lbrwUSaSZN+aRFTlW198md/zvZA1YXg8SEC1DwaEB4n+6HNttD7kIfeLkqVyEo5saS+Npk8T/q4RKUDiSqfrCLlyYIfc+/3OviHqPeaF/PyIkKjmhiXhITAt3Lb5Jd1jFRIHM/v6flhW1reFcvyAnSSdTZSBVMt0OL91rfl9FW9VL2WofqcSSpcTTjtp3Zvt9nloFSi8MR7x/njFOjNBmiz+/oCdMhXdOX84sZKv0gUIoYUjnUS6iEi/DICWSsXDlUBobnG/lM9M7Ju5BX9xWNL+xWFnRe+HmYDOXlbee09HSdFFK+yLKRMgU+2JeQhEU9qlOl6zehzOCX6Woh916rEIAvBReyciOjsAP+ZlUxe/YTVByuU/o9kf77CcvrE1MoHxep7bxGg7J3JIaNUgLsVtsJS4oaGMNZNDy0FtVLyOiA3UifnA57DCNy2v7A2uj4lIDEs+4SeQNAtmZUduwuFe9Y1mjhWS5A2XtVDhYFwfiDeWKgqygUwLr0POIuzHtBpwA1YJuAGT17WwnK6Zfw1AmN2wrosRF71BQgWiAQer+M8SEayB4S+aKR7cfHvIQWRvyj0sBMOoqBCistAWfSCLumnrbLoa5AGnBfqC5UzDe49ioQMtF2E+6PXrFML8YR6tz5d508kwJj7pAqY3MAfoJ2g2LShnYl33wr/bBLucw1TuNXywcnnknGzggl81CoA6WXJR0vuGUrCHC8RhhjaKoUqQ0GNOuaB9ycmoq9kLTOnm4ngeMOZx9rGvrOv+7j+ZAeKBpxggq04j9R8DQAlXA9gHmJ+Bk5V4NvxMbeAQWRyXXpvhsfdjCD9aUbYo55hX/bp7Isfc/EYxTDZpxL+HISwKkI6E/ASzH1tj8b/cl3xG/LEAJb4ezruFy/fSTrQeLdLM7bvssqAcB2L9tt+QOEsny3tmqc8G5teB54OiHh6bUvSOHYQoT6Z6UIHt0sWYA84TLr5i9i1UZd/5ZdKExwPGbMCVZU38al3zfC4a0oAkJ6hyOt9uq/Q/nQgEQ2V8AGwpYgcJgVwuaD8524e6nLYqLdZn0GacqW//23+9x2C+pswW129l4kxe5fPIGSRoLVRCS/ibTip7DyH4kDNuyL42slS4cu7Ij2Y/ftkg4SHf45U2SqqLIbYWuLjgjCvdR5f95ie+ay5QohnNxW5oOU0mwzXGNdM+OAYuyvn7vZZSgPvD+tQ6qCc65Qomy/a10cYKu9BqhMlX/4a6TQu5PxB2HOTKqUTNs18Kkx9h8woGRmRfSrazqzcycDtRfoFrCnx+y4JbxJTGb2q8Fh6b19rVDpxTzpfvftW0/AWbwtcW96bZf/Dp4afhR/lGjPH1VRrOLBDs8TQaG6MijJD2MK0hREAMJgVlkh94xvAGGKSU5dOaezHcMDM8KNg1XQ+m346vqHmy1SjbH+K4jLhogQ2tzmhPPP/TmEj1uVOnrjHSFJ0+vPSSgpeFRAGUNE3Lv189qIfga+MRN/qUPwhpWmuBi6+5JuLHXGxmoGLzh6aitA0Cxm8e5/ft1VZ/LfYLHEQDt3v4M2BnshsjefzG5G+ZkAkLx3MyarzyMZgEmRi60PvOsFuNK/W+9bR17XrxbvteND0O+/y71NPni8fWbbD3nHBgZKUZTWHrY3GPHiR/+KA8OM2Dh66hvH2hv/6vJx7gfIlwP5vyNlvWHuyWLjUP8B0h4hD9jaBYzAC/vwfG0b58XQND5OsOhpHKNU2T114e/SclVvfPtyCprDiXTwCvUVenPegk/BK1a0uMAGETtyEp+nmW40ayb6pLjeg72Jl62WkgIJJ4dmzuwE07GYGmva+mi8U301X1C7qswr1fLU5eF05wEoEBOyawcpRl43D/8t4Km/6T67iUZWS6uhbzsbx8mJtrjhXnivHHTFfYK3rV5AfhsoYutuNRxk3r+jrclPnoUPqYJo82oolS0VkX72lVOKvf90Obf1k5oITtkWU8A/tpT+7cg3zGVVUKzKnEIKhgT4KwvJ+ij626NCcMFvVyDPBjtrssHObMdl1kOQj0QzdO3zGqNnuJs10VwC9PkM9Xm9uFe2Al96GHaX4TPoQSplmU9Jfv35F5R0syEfQU0KddsS3rjDW/eMJV0vCWLxf26AmlvGKk9umuJpWKp+ZPsAz2nEidkVcEFsw/dtUFJsqrk6LotsTZKvW1PcRyQdub+SN5MdrilNkdhCZJx1Q74wtwRbaSAgUDAARTgsJ4vPlac4P7+qpcs+QTVmTBAl83urlR3pVnl8C//siZpX7IYMgmjN5Za6B7VqErL5vfF9TA1HU1oHb1nXH6hD8oXmRiz6mIU37oG0QYcR5sBoMk4HwcE+C42TFRlBMCXn2z9I1BO/p9wYg4bgwaWpYVwt8ZrlpGaP5NHpwPF68vaqC2mvhLGzz2FwTgPLpwq2T1d/6FhrKgFCioAhjOcgpNyz3iBTUm+5jk7YeK/BhuXjt6wJXi3at5N/qago5szN2Wl2y+czLiulU3p0u8qjKlos8wLK0b/40x+k0VIWfi9XDDF9DapqzV0wFGnSRwcN7toFpy/hrTWNtZw805L+F9CgfDFFiIav8SttbF+7cUa+4CUM+xp6OfmuES/A4lx6H5SGixHFpDBql6Ww+dtHdr6Gd6WRjN6v4JoULOOIfzurdtu5l+riVZluUdiJmxtLm9z8cnDZi7eG/TuomKMdt8qemraOGwAeexAvoCfYzoaBAPf2uC7y402qPsVy2p8/WhRdRS4Fk14cdNsjFGXyRcWQLtGHgmphzUvCmpaJ217kFwZboRdoLeDo7X0ZtTcYrq5iid99Uy4PL2IB4RZ+/zwrrDGj+xjCSksx3RpmvGjeSPMOLMJgeBgSwBmn7YT9GrD3rFCxAeQvvw1xvPi3qDJfGYJp1cmKRQwj5v3zWdO49GmYEbPbK+xwOCreuH7YT2rXID/rwf/4IdCrjJ3BpIHlm0M9sWwRLZuj/8iA20bn9cvPt8i6zPIHpC+JWu4cTBLjoNMQTJltnIbUpUZRgwnFfmj9TDtYPnd1dn4cjVwfBSxJa+H1iDQGuc8/SXwtiDr9DqDOHLb3K2jazwgROyrZVrfSFe7fqFByZkvFB7qvoZfo/QIldCj9TT8g3V2NDsnQZVZs2xrHCAAOzRDbT6SdrteXLlUIukZQ6M5zr6+tMP+6TzaJBtY3p9iW8HqfgJL4LBnag73NBrpOEHYFQLToPoBeNENqVXbI6y7LEvuSw3Ph7QsAe9g+aq2KndMvN2aPcToQEBjvJI7LNxMetpvk/ygUJ6gL8ookPf2e+EqisXuktfBPZdUsXjEN4OtLzMEH8WwqU4xkri9beky58kAq34QZhc5JYi6i81dT9MhfESYN5OCBsQxvaKxmzU+iJStO06P6uEX9aJF/wC6BzMhv/YBFtHdasMSH6Iu7Shs0IY6NGCWaE/WG98gFh+MIT0wDKMq+I62SmGZvwdYkUOxg3tfr2+62uAvh7lTeCNxvR0WnbzXV7jrwCRdvK0vCqbuz6LVsN9uW5vffpEXQQKLV762uufbiXX1i/NM7i7qkhJMehwnJvgNK2G30RZwH5fsP3tOPjZXlPVGCKVMuDRG1+jr49v21Nx3YfePPqxTLLWS+25K83yaowiuQEvOTijZlmbbrwzqsXWAuGy5NZDl52SY9D8IreKWaiKQljKamZHE0HqFTfSlJuTJBs9NT/75k07A7uxKVcPhEu8SSiHXsfKx3IXvOAzXqS+HUbHsF3swEDK34ePOGLR2afMKs8NQzbShF5Jivg+nOneefl60/VQX2yFGDQD2Jeb7vNLMiKSgwvcUYZJ+nCzYpvBxRmd308WHWoo2FhOpd1d6G8t/uix/5uvPabiDUDhmYRl8y2WK9KzU+B1R9AqtfSZSLLotAbxT+F7SWF5q2YYZpQpi5xDDz0MsCEIdxYNUaL3vDmlmr6aG5RbmLk+5Titk7DPzXBjqxC/KKM44jj2NYlBzapQ0WlxShOrBFUizvv9xdN/AjloB2l+PR7v6NjF0ejZB6OjXMs/rI80ROybsvh+4csVM0ZEw1GV6P1HqKjgE784n6uEoE4qZuV7muO1Qdf6N/1qCX27y4YRr3voKBMyoa9IXAk6lWODlnT8Aa69zGNdaSRnB8eBwAEEYYzjFFvR4iVU6chq2ut4WDFpggYJifLiDhnUOZFVbFHCA4waYDMm8ZH8BA7Ru1MLyL/Jlh0kNBZrHzwaCSfoaWShK2kcQbK/5tYM/p0P4CjTjdiWhldLM+rjTdhUhdGWOHQeJHvE3K55wj6UzYzrFO/yuRXj1/K3Y78ZukqaEpB/0Q2I3/W0ssiP7odeo75/grKE9hI+dAGCo1adWxJQ1Fuw0UoJ+F8NH9og4s5N+zBxKS0WJK6LES18/Eki5e8D8noYgT5DUY8E6rsx3cF5GHK2bRfnW8WqR2KW9zW9mWJ7fwQPiQ4TcXg4TOfw6if4egeT4yj0aPu184X9lrf5pKOtqPzuV9Wc2JiGeAUb1ryrKx6KOwuGYZ+4aW7jGGjO+MlzaZq/lhkwgggKeyan7pn66I81Rj7ML1716R/jE05H+IV/g1A/XXJ3hpPAAV/oEzrNln5W/kF/0+KgF/dMbVlJdDuJUKb4hTI8Yz2DuUK7Ygvp/s/rr8GOacMwVCvP2M8z3vI7kqAHkio98X0htOukfS+twq80L1X5PdJMO1ffnFmv53e+gjSTGtY2gl/4YN8m7SOaH9GwxyUksOx+0ASoFIlkMeHq65FahvrUff561YutrJlVdUnc6jJi4Ej+qQmTTYrjVn9JJ2JG9swdRmWixS1iWtsSA/fFCN96pgxLK60VB2PpBMFqP59fQ+CjWvaaz4oic3dl3BAb42A3iNiZp9iivx9cqu/LsaGjuNIKY2JW8FJF+APALfA4i0eyz30jigl7OMQhcTqkZVRh6aD/aZlwblYKPWrlv6vxFy2vvp87JjWKAiR+X+qNSP/DLkK9fuevE+caJyn0x3QB2V1jbT9r94UeVkKFp9NP9nXsAs5E2TuqIsMA4rqnwrZZv+j8RiboaKyeulv7tqEg5lecMmKym2Jz/vYegeLx11ocv07a1vHGBS+ITvRmYFk5IUj+nUd3jzKAS5k61htFNaO3hTwVz7V5kSkSqujSWjUGQqZbJF2kzbis1x1k0xlkrPkNUrj+tKPTGXGewYAYTUQPc4L0EtnSO6eJEtceaAvsysPibANFJQF0r7YjCbGD2OkEmunie6ut2DLIkUHx3+hnlSh2wdlE7tHHN8RlbGngFIhhg+CQvL7l2LrypqqD6TYpIc3bWqvcOhNBCl7Io4VM9XSj85YPq+A16o85B3yv/Loe2AlpAp2cBJY+SJIG6QIcAhzWo0jwGvzZar4w/Au9+9uRKCTOvA57ZjKzCKxNVFRUI3pGK8JyPh4AfB77Y60n/1V0OfTrP71ugd7BQS5BQuSDtDwIBS2SQjun6T3g6HpcEdyP0mDLfAslmHeCNj1oQN7t99xoH3S/+L5eONvjJJnFtNmAg2ef+3hs6aUZOPv1hCmV3xxGaqK2xjchC2FCwJqCr124t8Ssbw/8MtKchfeH3EBOMFjmwx2LKH9d8rUxz1YF3At90FOgXWiRM7RrvUEvGmHLrBzlNINfVz+tkKAoaycvS0bc9jJNbDlmKxVm48eencCFBuL08ndaIWAddrsVSjtbThCUxQGidkobjO8UWHiwjfMFy72zSsj+UPdfy3y4rR8SpxlVHr3ZyDbsEpPRtu/qkn71KrHo77kSqYUutzyHE5hcrZt25+OlGNaxqh0IgvJWeVQ9Eul0QIYfdltgMJOJ2Sf0uTQodg/o+b3UocpuMC1l/6SxGCMGUub4Y1JKJ1oO34MOGnHkQGqDvXt6pnE7apsfHegqE0I7vbiwrz+mboAV3Zhj6fyFvKysg1DrFr7rDufNdfIr4r14RzkltOARX3x89nEF79YDiKW7Ki+MGZwFqb6Eb+tLwC3CRW04OB1bGtc75NI82B+Rw7ohN0xWDpAY1EoQmmd/oMd+I9vfeDN+wR67ctLnR6Rnj+mXVBJmVQeS1kGzKH1H6dVR168MbSYCYiQfZzCze0isNvs7YDiATA8Nv+1Ilw6sBRBsf9PTr0EoMqkhVYEYpJDAayTGr9cr3j8W+Co0T6cRC2UV7D1KzDj4i0re5ZGPfoiuM8/vpOmpx0vU/PKhOZDkBfyb+27Nvurcumyvw7A34Ze7cm5qA0VxVBOpqB+YdF1xDWrdPUXyH7iTqkdo8ukol2GOEHa+1zjMrW2LfrP0PB8VkgSXTIeR6RLkfDNRYcKbCh9YLe5apUCSxliKNptqv3R/6CrgDkC+ye/mEADBgXfSQMQsp9f2lWnmMYsE1Z9jEvTM5JsUiD3Dvg7ZbMoMExZpknS1JMgoVWmAhDK8lUGPYpSUjgg5Xsu8J+b4QRWhH7+HOdkm5tPrrZ5qJS6JM9mJOJyMUU+5zgx7BsBih+jUdog4ajZGspLzrFNtv11H9Hi/zTRNFnFlMBQ81qOSGWzL4aBlws7Qf0iG8SYJPpzwrWsxP8QLFzgiuZIVf27uUOOXauG/rW8bkX2XUSCMEtdze8vibIX/ciD68GugHIx/aU8x82oKRBUkqB0kH3w9MNylvyNlXSko+PUmAeHzfgwq4qpEzdZVCxZAkiURzXmrB3K4hveomevwDl00A6KqDW8FGNi9JbeegSqruVCMWfuQps0Xupeai3cYZH8ognkDff6s5WG25SymZNDtzjarLozjOHFsoiSdhPx+zDgoFBbhpXi0+kWEQJHz97QEBD8JLybB2ioUU67ReWGBV+YS/PtCbT+YPClfRKNgiUUu+OnRgOpIrArFNDbWfxW2hx1T0LQS9hlUKiq977g6aZSARhA3ZEDckF8akOygLv445/PFf/pH/r/FoAcbvBG69nao4Px1elDcjGlkedF7xrg51370YUbR2n3sUnBw2p4cPrlw4iufpC78ZVbmdX9gs4gjZybsqoMKqLELD5iXy1JknDyjGdCViua4tqU7NBth+4H6o8OWtX+rb8+hyjd1oS/c0t03mzdY/kUFH+S6acfe/eVjd7p3S5dtsfXvJErqoNooen+q8XJvWEFTbvemF2HDYH5tYdWwYZoJkA5j7hti1A33FkPVgj5IxOQLEXsvJtSmRdbK0GYXTnnHoOUHSapfd5QWFfr1mwD84rPDSVQq8FG4lfM7NyD2wbIxGZRtjlxY+7qSPDRkeTJ1IvM4hGb7KvOoKYJycOk6TPDriKB3QiaLbIpcYFFU6EAH2Ol9N/wHtjhtdDVHYQnNZpVFcejzukA6vYZwZIje+ZrAxMgxwvdjfogAMRXFl3gr10ONNyaHYZbryQo1pvHqgnPNTveM6Oke1GCQRZLE8ev+15HFFJawHFfNIHkD+hQmdo8T4SHONzk9yo3mxWyxMAh9mtU+nJjd4aCbSlNekHlqQH8WoVjVyvI+JoV+PJo2rNC2fmN6X4yuVNa7PrhZbv01ZfwSkPosz7k3FMkuXR8L02McmXQtKDZoWyGCYY2mUruIz94FzKZQ5F/eCQh0+VBQA86sA48j3LOZ+RBVl/bTN7vLzaFN49tZU9PnN2s41BmgWPhczcSRmGZ03q/ou4YHb66PBu8EwWTqkSzfWX+z1miJdpsPMXyQK2DJVOuY2zScgtZC0GetwTl3rrk496r1xw0sRigbGc+DlIs5nJAmnlw4EQX0Sx3r39iPUtjLX0Uk9fTLp+BuqBaBoKNIxWLmUVNzsQvxEJEXZ8MEjWL6Dfc309IY8jDy+ogmGuMDucxWTRKYd0elf24+1lqlIv0+F9K3ybpbpR7Kr2pNWTJku1GveyfXkuXkDlclcXhi6Xjmo18gOCqCRl3oBuJw13Hg9P4CPRJOjLKSYB1VpkRVgPSW7nVPU8XPhqlqecEagdwPz3ZagRDprSuybP2bpxCGoKCAex5Gnz231uyRhWGxhaKGGN7WKORaevKjggmhbpiaYqTB4kdwTwUa69NGdLyLCo05DsqllF0ftN/qrGXY0MnRc3XfgRi3vzzn8hYlWzikwJsv5xGrQZLGJRUaCl4fkwPrwsztXSIzn09Mz92EdPSrEpl+OF00eheaUPEpq7yaWF7nlIVBHDojRGTJhZJTMQ6cKxHQ5tfxiHaiJYALB1MOeRl8mf75cmzc3QdR1httYB8BUU57DdeCU3QeOEHlToN3PRyebnDH8tiI49cC7x4syAj9M68+H36dx0jSFNilGZ7WdPoFkjyv4CEjFt5aDVufOK5Kx/nXP4/h0+fXvCW73F2HqaR5mKOAkL67XXonLrOm++uRF0ApjqYjNGRygPebous8IAMbMZ4uU2cf2fE77q9p+yTFY35QZ/EdJG1Nvxvug0Rs435+UiCOCaW3IZwDAw1Bx63ZfUhGmd7jPAZ2XDEI8iCDq9BzI6U18zB1FXWrQpEmMG6Pzr8Z4ccZBKJxBQr6iLPUbwQVoCQH3dXLOgUdkN2xqTskz9dyyazHMD6f1Oxs00G+Dw+fJekGuUO8/46PcWTPNYFoDIfmPPaaRCa2KWY+SABfTSYKwpVRcxcl6gCOXZ7A1P96JxRdFWp41R9vFyOpY6zQr8s3JyBFOxzPw/5deTowzSKKDlpL0X9NSDAUsiNnBWmI9EW5+JcTBm9hIdjZB5j+QZqSBNX9TGPLI/wGHMkAo+a9VYoGhBpDzJagGxRVcZGitt/0dHGfpuVNYfYeDNkURmBQ9Dl5TLQx+5YokdECz670oCABjtge1T7rWwDqnXRhOUEk5jufQJNTmarJq+fCBKJ84+EoC54rb6kOIgv9g/2C+WvPdzC6PrUdHLP0nOWN1pQr9duTwx3zwkFDnU390u4TqBG5gs+6ivFSv6gaV0ugRf7195xDOiitbTID/EsQcyiLXw8v2Y7t3gGNsEGHGQxV42he4flIUch7clccUomyIYQ0RuI+q/MQYjQhDe2ZmrND3opYYedgfh2PEpWByZT38RIAPRczJ3ouPXF1ZSx7AC9Sikr7l8g1ItZk4FGayMCTDy4sblPcfk8w5II9ECji7ynyYBw8+l5vl0XeybE6IG0yUrlvVyoStgn0LQC+0dslFMMz6bRejK+N9RZCzH7IjWLUc8NbwI2Y8iFnn7ZUHvTi2/QMJsNJDsrow1X36j6k9ZYKz9tc+g299XkrgaiHdbvHp6uL4V7hDKjpsx/yPrMJHQNu6L9PAOkhMD5IUEEnPnpPp9NeyDY2NrkKSe9DZ1xpiSJC0fJXOiaqVxHkEx1Gqp1r7IAalVvThPxEpeDs44L7E1d9RYzzT0ADlDknWEDzd0LXIAJsAuzQ1N7H+lSqr2rM53LkwHwANvRmhXMd7UpGfq+aDcerjC5bjfkeY7x7uhSvpc/aOIXGP67CQv1duW1w0WdHsDHTP11Hsr/aPEx1ThjugAlb/XyV7YfC4fWa/o5XIueKGYQLOzt3yoc8uRWOcMyi6qAwBp/zBnVJHLQbmCr57eOUWEebRNfc4/c4kl2oh3GB70hFo/ZSCwC2De42vwJQ1h1BIkVft3DacFoFCXlBUMEGe6Qeum1WBYHmLw+zoyijOSOeIcoShri1xpB1wM5koEYwOFRwwu8tTLPXeBbEp7yMWyeuzfAFV+w3UZOMz30WdPDM8xPSpffovdv7POU5Ja392Q42dSePrB8s8VwrVBygD9RYtmOAcA1jj2FFp+NhP0IZgMEMAp8umlGWFLGriYS4NA+aTZ5GQGSm1GJYlNcLCSEepUhKhAZawLYEM2K5mlFWRnbfuZ9AfRMUatpFUphUlbn78GNv5H0VTmmQtWT+gymjZn/ffaJEZ2FmHGRtkt34DbvRsPrBm+PaMWd09buiclbF+Ua5gag30zU7WJU4wU7oIJzEfCoxE2LQ1Lvgyhq8vuz4NXh9k2+GRuHGr0YkoaRZA+dm3nraCn3lGZwGslAL83rIcLTQ3Xep2OCNVTVXi+McWiRkvwNZuu/pgEmBNF02APyVEtI0z3/9TMUHma7pyKWKq27eTd4bXnwB0LDCXqBjAB5L2kfbyLLFx/Apxxr9DhRJwQsoBvobCPHs0cSHkQgnVMmCigHM8B+4gvgFxlElHZ8xf9k9fDoZDWWkiebWb0ofyn+YsJI0zV8fyowmKQwu3WvowWYUBY3Ix1Zsr+fyXfqNCwW7U9CuHdfbITMaL1tysuPNqMyYRpetnCJtgBRf/iFC7kwnFYfuc/iYLwrir4/t24Fp7d9xAz124Y/oU822VcIi8NgXAZXtEoH2RfkLeMQp4Wpikxzea4gd8gUymo/qO1JFoSFXWVvICzTVQAfqdDjTjthDKVZEE2mj6sbiedHypY3ANjuR59v8Qyvd/R0m3OcBitDjGHLi2WZ8zpTGibkmy8OOZlJqQvXAgoN2xZ3NjaRKCJ9v6pbVK87LuusfMhmWW5QV+KEeEVDFCAyL09bY6/gqiVicGVQ/eTg6wTkHnf8OESCL6UHXs5MWNFZCl4IA6OzPcGVULVPkthQtIrVMtrvM/UZ325oDyPbaRSML2+jjeWpM9zIuTv4S2k6FrhWRAXAsLOSodv43qzun5IsbThduEpMgrD9hXqqUJ5Qx6U38Iuji/gCyrKjWddsuBCLOQmaiX8qgfOwXAIg+3P8OPCIEEf56bCRb+4p8v6DjTT4PYQYAmFglW11iQvqe89eNrBnC/8mhYIjEFh8vWUR+fU0yu1jyK5ibKBoj5i7hBCvwUBhjOM1FzKWFjh3fa6I0yVbVXqAxdVahZKyW+Zg8fKo0ms+4NgXhVxGUbuoDaHHiCqdc9WPyd+83pkMoirkPahwVYl52VA4LGdmZuu9aYINsdIfRgG6wcOFBbGHR3IXaut6buEJI23Ec14qiYLQm+7eClbjv6spjt+P5boipYjXeVh4y8hvW/Db46lE5urxf0u1PgnYfR7Xb+z82qJKWfSZWgH5YSrExl5znRTexBWT834+DZ8ivp0GshBKYHP95MmH/HADEEOTjR94or4HTpg8GvbgkFOorby+e4Ngy5zLkN3MShMv17swN6451FvKtqu8fxcmePXgdnSi/qaZ2KmAyooR/aIcUMDflN+ceFL9JwtaSYKnoHLP3poXPIREEYReWZSny9MiV6jryl6bT7witQc5MtLaDprdCPq9d841guUqqL0lmXvh4f1UwQZSby5YBEeXEqZIjenCV5x9s+ms48BZ70vPD6g32B+fvnaJfuj4OjEDPTA2ATuyqAecAyzrzj+eTohi1Rb5nPLahe397NGo3jHXjo/bqmJqXI4m30Rx9gHfsktZa5zbs4aORU/pHEoKV4V4y2yYz3bf+Y4U4jhuNMBw+Hb9hjzaWNn2n+lyJj2/bjRer0UYzNriGzF7AbajWH6XVXfnwZXz19ayN1O/p8HGifAfVoWEmxJAMSBDTGv0tM3leS3CXAWPkjDwj1LrBK20XCuIRLutLIqYQTBlRO70i+JITpGmGaBkhGBA/gN7FOILJHpNXsOd54hwMJgwIUoJT1PpVJYBnYh3nhfShL2s/IhFln2rO0mzPBfAHdOb/9x74oNgxREbbXjY7atYoHhfVpb0qaDLO0WPh4FdN7HIVdrEHgbwY51fSNwy0D/G9aNuru0JCf0bvq5eR5e0WzzsoOqlqOcusHg+iLIJV7ur70mA3khlBP6k3Kn/zVeP9rfQ5+vrrI30b9TwTEP2+GVlhlF9LFs0p1Ip2zzVVpyRCGlsYp4V2HstVbNuagc7K0PvBYDIXGHPUbG989MQ3/Rn9QzVqaq0fpAp6RJs+NZkT+ZALY7wGQa3cwRg7MN8kffiOcNNLQomNk7N7eFM796j73NH0YwMQe44m0AvUpisQHjNZDgSyOJYF0VFeeb0wuXLbKThkw87ADk79XeNN/dCHI9j2/Tshj1vUCpHnIVwm1EVk1tpTl9CEBqUAe4CkYA+yZw96ul7Lh0lbNehrU+MCV1/z/JAQzrC9JQAAdPq+ykQ5gmay2UDJ3lDAH1eja44u+95cd1US6HNfPWD7A6mMKj18rcyOD26ejcupAgnT17OZvP9Cci5ybRF+ODSGycLx55QR9KDiNMURRCaDFT7c48V8bUvdPYv92JHtBzOMGfX5m2eh3SS5bE4OqhMrwbD5xzqeD57w35A+MI49Z1aZ5+SjRSgq2LPblJUnBrI9ObVqU84MK57VKkfoqSMTB3CeZXb4m/uQtIs6Ws/LihYhhNzR/z60ojIjANQmVhDrR3/mFEH+ToQKZcIp3hYsPLJNYAlD14lFLOlkWl4VGvYwLLHFjo5TeVlR2O+i2JZ1M5MkIWVs10CYqK6gN2rDeSgMRvzZ44gCRzRsWs1pm/1csV75plWCgzaMIVplZ4fEuUS/ad8k+ZkxQrOsyOKq482MEMsh4CAagX5YgYt0lqazC1dsLjzzqekuMLZJpt+vl7hYxD38UjjHzalFmyzGkYnP1v89F6gWZf7+ed6BZvRcuXGj02IUNcFAGAA5/g2Lef4eeoTFYirS9Ii2JK6yrXWnAw/VTZN/LAu3LecYPpYosR9H2d0yO4Nhm0Ke45stc7NQMrz6wsaUCg6hpLeHRp7BR/n2nwunKq23wAHrB0gmEuHSj9R8rse0QCYIPB4HwFz/1Cjm/gNEuXm0jeVbkA+4O0r9dVZdxa8Bec/whNwtITCQNDILwRZ84Xx0P8OBe7EzNWZ3mo/C1jlenx4+zsI6t684pI+a4tEtR3xayeMhfETv+CVRYzpC0Sakb4YzkmPylriygUP48AcG+cc8vEVc63DfZGEYxjkEwQmuSM3BX8OZKo9KhXujlkzRWObq/DC7OPvquoeadIMH48qipnivMIVe6omJBcGYi9ac+5f2ocUQdRkznuJxK7hUZwX4POj3nvGC8QFhpU9zGDE4Idrpee9jdtyTIUoGGB1rPskvxO34RwskSArPLFqWV/3VDSn8urZEFhk3Vr9Mq5A8JJ+yHNPvj+bw4s+XZuK8itfXUpOxFpjD6V6c6MJLVA7Ivw7A4V51Z9IryJz07afqdT4fK8+BJvPfROp+PTa070ijblsohDh7BMew0hy2Egrz+F72Zpuo1mbkY+PYBd/Gow/5+WxvOYl4FADSaLxjEud51xBpNophYxUqFUHFMjxL2430NxGL7jy0+mFDenqYg1S7CWP6WHR3Dyve8f77uJgFo07h63a/HNj+4EoAUoabLI21+3zwSgdhJR1jU7b4JnK6u2P1+YQq/TZ7L1oAxA0Z8wFutc5lqHGa0e69dNb8bYJqfj28uEZEmkhXo+HNXJzdzRmlQtP1il5wEYDC1YL/RqAuJd5PNEayoYBXcq3ZSLfRtI9xI1ITHCQxVa4sS87SkMX+VOhEshoHm+piUB+MvUV4TWOCbnU2OPLbhTYtjh6My2aA72gprfBXSg9hWbD+uq6/VG4bJaNiK0vDJlxdqwtdaXogGx66M7d7287H+eSSgyDno4sZ24X1b65xsGb8a79fToZYpqnKW1sl35VTlYwGedzbMPY8YMC1WGHZIz4OmW4Oj2/AiOhskqDIBHzpMUvr0lNf87Pn1RrM3aH04mO0z4xZu9fQrdic05O8mqcbMUD0YxvoFjNZQp1Kz5emYl/ZufkeqJupEwGNDlDwEaiyPxLEXLe4marzwTuRBbufdazB8DFZNuJ7r++tVbPnqlb0jkv5zWmXG2Lp4dw8F8RQoklT10WoGhQf+CMvoHE+o6p1/KDugK1aGhuTIrgmpNFx+0jP+jii4BPxIbFhmk4vmuhDcdo8Eu6kGsDHbQ2Kv6V1BdohTjiOT2t0qQVS0urqhf2UCXJT0JV6iMq+CNnVBBzQYM1PdJCxwjRGM+mlOntL+p0feTBvfPgFvgMdT2/CUImPLTPqRi/dSKaVWHXsscbrFyQYyAhgs4Pb912wUAan3Yg2ssqwwbQW1VAiljFase8pPIBVWqBrG0y2qUTl7YP5JzkTgelmIjijwjxLflOPbDDVjRbVt4849OTQ0dXRzxIQdUgSVLF/S3xZf5ZwMezYk02PyINoniVFkqLfCKBH1pefT3AughkPTGSDukrmET0BC1IwO44VaIbuLzrqjBDMloMe/eZFOk0I4lnwYbyGoRm7Oejoa4Qxzz58vVU99llwv75z1v0t8KpnwWgMvgDm5LWJ93CE5Hx99+I3Y85WwFjGdDRaT3C7Z4Eb0NU7Or9gweo+C2TvN1+OaTy4+NDsYAf0aEfM+kjus8AWXJmnhQeQM80Hzj+2+rFF/QsGys5W36+X97fA5Z4FFZI/PPjzbPvzwM8C6bftAGnTnUvTzFYh/z/fC6htsuDhV2tKuvFj+37TJflOeH/cze5Z9v/MeFAEIdH/PB4Uhv6H8aDIv0D4fx8PihPo/4HxoNj/MB4U755bMKBaFq/W35P+fbJ8k+E/TQ7Fp20E/1GOw/q/yqR/gNH/Bc5PPA+BJ/3396MPNX/+fr7bXDXJf/v836/xX28Grvn/frOluYu/W6Hw9/zvV3nPybCUxTwX+bPm/3aKLjmL/H+Z7//nX68PMOfvFv/5tiCU8nvM//bxf9yP/zJA9ZGA346t/bPPHAy2ap3HT/Gvg1GHcQBzVcum6/7LR8k/41GzR2KK+X+Ym9o3eQ5uwxx1sxbuN8nAPY85Abv4m75a5P+MUgWP4/72hYOx/0MCCr+of6EQmCBJ/PcH8p/F9V9QiCSez1EUhnCCgP+b8BIoWAOR8D9//A9jbmEc+hcSe2EYRBE4haKv/+9iDSD0CN7/v/2f+GxQrY95AVb8bw==
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+# Introduction
+
+In this section, we review optimal transport and its unbalanced formulation, then from that deriving formulations for robust optimal transport. For any $P$ and $Q$ in $\mathcal{P}(\mathcal{X})$ for a space $\mathcal{X}$, the OT distance between $P$ and $Q$ takes the following form $$\begin{align}
+ \text{OT}(P, Q) : = \min_{\pi \in \Pi(P, Q)} \int c(x, y) d \pi(x, y), \label{eq:OT_formulation}
+\end{align}$$ where $\Pi(P, Q)$ is the set of joint probability distributions in $\mathcal{X} \times \mathcal{X}$ such that their marginal distributions are $P$ and $Q$, and $c: \mathcal{X}\times \mathcal{X}\to [0,\infty)$ is a cost function.
+
+**Unbalanced Optimal Transport:** When $P$ or $Q$ is not a probability distribution, the OT formulation between $P$ and $Q$ in equation [\[eq:OT_formulation\]](#eq:OT_formulation){reference-type="eqref" reference="eq:OT_formulation"} is no longer valid. One solution to this issue is using the unbalanced optimal transport (UOT) [@chizat2018scaling], which is given by: $$\begin{align}
+ \text{UOT}(P, Q) : = \min_{\pi \in \mathcal{M}_+(\mathcal{X} \times \mathcal{X})} \int c(x, y) d \pi(x, y) + \tau_{1} \mathbf{KL}(\pi_{1}\| P) + \tau_{2} \mathbf{KL}(\pi_{2}\| Q), \label{eq:UOT_formulation}
+\end{align}$$ where $\mathcal{M}_+(\mathcal{X} \times \mathcal{X})$ denotes the set of joint non-negative measures on the space $\mathcal{X} \times \mathcal{X}$; $\pi_{1}, \pi_{2}$ are the marginal distributions of $\pi$ and respectively correspond to $P$ and $Q$; $\tau_{1}, \tau_{2}$ are regularized positive parameters. Note that, we can replace the KL divergence in equation [\[eq:UOT_formulation\]](#eq:UOT_formulation){reference-type="eqref" reference="eq:UOT_formulation"} by any Csiszár-divergence [@Csiszar-67]. However, we only consider the case of KL divergence in this work.
+
+**Robust Optimal Transport:** Optimal transport is well-known for not being robust in the present of outliers. A way to deal with this issue is using the approach of unbalanced optimal transport (UOT), which has demonstrated favorable practical performance in generative models and domain adaptation [@Balaji_Robust]. More specifically, when $P$ and $Q$ are probability distributions in $\mathcal{X}$, the ***R**obust Unconstrained **O**ptimal **T**ransport (ROT)* admits the following form $$\begin{align}
+ \text{ROT}(P, Q) : = \inf_{P_{1}, Q_{1} \in \mathcal{P}(\mathcal{X})} \min_{\pi \in \Pi(P_{1}, Q_{1})} \int c(x, y) d \pi(x, y) + \tau_{1} \mathbf{KL}(P_{1}\| P) + \tau_{2} \mathbf{KL}(Q_{1}\| Q), \label{eq:uncons_robust_OT}
+\end{align}$$ where $\tau_{1}, \tau_{2} > 0$ are some given regularized parameters. The reason to name it robust unconstrained optimal transport is that instead of looking for an optimal transport plan moving masses from $P$ to $Q$, we seek another plan that optimally transports masses between their approximations, which are probability measures $P_1$ and $Q_1$, under the KL divergence. This formulation is closely related to the ones studied in [@Balaji_Robust] and [@pmlr-v139-mukherjee21a]: the former used $\chi^2$-divergence for the relaxation and the latter used total variation distance (note that those three divergences all together belong to the family of $f$-divergence).
+
+By relaxing only one marginal constraint regarding (presumably) on $P$, we have another version of ROT, named ***R**obust **S**emi-constrained **O**ptimal **T**ransport (RSOT)*, which is given by $$\begin{align}
+ \text{RSOT}(P, Q) : = \inf_{P_{1} \in \mathcal{P}(\mathcal{X})} \min_{\pi \in \Pi(P_{1}, Q)} \int c(x, y) d \pi(x, y) + \tau \mathbf{KL}(P_{1}\| P), \label{eq:semicons_robust_OT}
+\end{align}$$ where $\tau > 0$ is a regularized parameter. We could also define $\textnormal{RSOT}(Q,P)$ similarly with a remark that although $\text{RSOT}(P, Q)$ can be different from $\text{RSOT}(Q, P)$, the techniques for obtaining the computational complexity of both are similar.
+
+**UOT vs ROT/RSOT:** Though the formulations ROT/RSOT and UOT seem to be similar, they serve different purposes. The goal of UOT is to deal with unbalanced measures, thus there is no condition on the "transport plan". Hence, the meaning of the optimal plan $\pi$ of UOT problem is dependent on the interpreter. For example, in applications such as [@schiebinger2017reconstruction], the UOT is used to figure out the developmental trajectory of cells. Meanwhile, ROT/RSOT aim to seek an accurate transport plan between two possibly corrupted probability distributions. The toy example in Figure [1](#figure:rot_and_uot){reference-type="ref" reference="figure:rot_and_uot"} illustrates this difference. In particular, the marginals of the "transport plan\" obtained by the latter (see plots $(b), (d)$) are very different from the two original probability measures $\mathbf{a}, \mathbf{b}$. On the other hand, the solution of the former leads to good approximations of $\mathbf{a}$ and $\mathbf{b}$ (see plots $(a), (c)$) while removing some bumps in both tails which are presumably outliers.
+
+
+
+Comparison on two marginals induced by ROT/RSOT solutions and UOT solutions. Here a, b are two (possibly corrupted) 1-D Gaussian distributions on which we compute the optimal transport, and a[problem], b[problem] represent two marginals (with respect to a and b respectively) of the optimal solution for the corresponding [problem]. In plots (a), (b), we compare ROT and UOT where both a and b contain (10%) outliers from other Gaussians, while in plots (c), (d) we investigate RSOT and UOT where only a is corrupted.
+
+
+When $P$ and $Q$ are discrete measures, the KL penalties in equations [\[eq:uncons_robust_OT\]](#eq:uncons_robust_OT){reference-type="eqref" reference="eq:uncons_robust_OT"} and [\[eq:semicons_robust_OT\]](#eq:semicons_robust_OT){reference-type="eqref" reference="eq:semicons_robust_OT"} suggest that the probability distributions $P_{1}$ and $Q_{1}$ need to share the same set of supports as that of $P$ and $Q$, respectively. Therefore, throughout this section, we implicitly require this condition in our formulations of RSOT and ROT and we denote the masses of $P$ and $Q$ by $\mathbf{a}$ and $\mathbf{b}$, respectively.
+
+Assume that the marginal constraint associating with $Q$ is kept and that of $P$ is relaxed and $P_1$ and $P$ share the same set of supports, the formulation of RSOT in equation [\[eq:semicons_robust_OT\]](#eq:semicons_robust_OT){reference-type="eqref" reference="eq:semicons_robust_OT"} can be rewritten as follows $$\begin{align}
+ \min_{X \in \mathbb{R}_{+}^{n \times n}, X^{\top}\mathbf{1}_{n} = \mathbf{b}} f_{\text{rsot}}(X):= \left\langle C, X\right\rangle + \tau \mathbf{KL}(X \mathbf{1}_{n} || \mathbf{a}),
+ \label{eq:semi_robust_OT}
+\end{align}$$ where $\mathbf{a}, \mathbf{b}$ are the masses of $P$ and $Q$ respectively, and $C$ is the cost matrix whose entries are distances between the supports of these distributions. Solving directly problem [\[eq:semi_robust_OT\]](#eq:semi_robust_OT){reference-type="eqref" reference="eq:semi_robust_OT"} by traditional linear programming solvers can be expensive and not scalable in terms of $n$. Therefore, we utilize the entropic regularization approach proposed by [@Cuturi-2013-Sinkhorn] to the objective function of RSOT, leading to $$\begin{align}
+\min_{\substack{X \in \mathbb{R}_{+}^{n \times n}, X^{\top}\mathbf{1}_{n} = \mathbf{b}}} g_{\text{rsot}}(X) : = f_{\text{rsot}}(X) - \eta H(X). \label{eq:semi_robust_OT_entropic}
+\end{align}$$ Here, $\eta>0$ is a given regularization parameter, and we refer the problem [\[eq:semi_robust_OT_entropic\]](#eq:semi_robust_OT_entropic){reference-type="eqref" reference="eq:semi_robust_OT_entropic"} to as *entropic RSOT*. The dual problem of entropic RSOT is $$\begin{align}
+ \min_{u, v \in \mathbb{R}^{n}}h_{\text{rsot}}(u,v):= \eta \|B(u, v)\|_{1}
+ + \tau \big \langle e^{-u/ \tau}, \mathbf{a}\big \rangle - \big \langle v, \mathbf{b}\big \rangle,
+ \label{eq:semi_robust_ot_entropic_dual}
+\end{align}$$ where $B(u, v)$ is defined as a matrix of size $n\times n$ with entries $[B(u, v)]_{ij} := e^{(u_{i} + v_{j} - C_{ij})/ \eta}$. Since equation [\[eq:semi_robust_ot_entropic_dual\]](#eq:semi_robust_ot_entropic_dual){reference-type="eqref" reference="eq:semi_robust_ot_entropic_dual"} is an unconstrained convex optimization problem, we can perform alternating minimization for $u$ and $v$ by setting $\partial h(u, v) / \partial u = 0$ and $\partial h(u, v) / \partial v = 0$, resulting in closed-form updates of a Sinkhorn-like procedure (see [@Cuturi-2013-Sinkhorn]) in Algorithm [\[algorithm:rsot\]](#algorithm:rsot){reference-type="ref" reference="algorithm:rsot"}. This procedure is known to converge to the optimal solution $(u^*, v^*) := \mathop{\mathrm{arg\,min}}h_{\textnormal{rsot}}(u, v)$. As strong duality holds for the convex optimization problem [\[eq:semi_robust_OT_entropic\]](#eq:semi_robust_OT_entropic){reference-type="eqref" reference="eq:semi_robust_OT_entropic"}, the optimal transport plan of the entropic RSOT is exactly $B(u^*,v^*)$.
+
+:::: algorithm
+::: algorithmic
+**Input:** $C, \mathbf{a}, \mathbf{b}, \eta, \tau, n_{\textnormal{iter}}$ **Initialization:** $u^0 = v^0 = 0$, $k = 0$ $a^k \gets B(u^k,v^k) \mathbf{1}_n, \quad b^k \gets \big(B(u^k,v^k)\big)^{\top} \mathbf{1}_n$ $u^{k+1} \leftarrow \frac{\eta \tau}{\eta + \tau} \big[\frac{u^k}{\eta} + \log(\mathbf{a}) - \log(a^k)\big]$ $v^{k+1} \leftarrow v^k$ $u^{k+1} \leftarrow u^k$ $v^{k+1} \leftarrow \eta \big[\frac{v^k}{\eta} + \log(\mathbf{b}) - \log(b^k)\big]$ $k \gets k + 1$ **return** $B(u^k,v^k)$
+:::
+::::
+
+Since no assumptions are made on the cost matrix, except its entries are non-negative, closed-form solutions of OT and UOT generally do not exist. Therefore, we introduce the definition of an *$\varepsilon$-approximation* solution of an optimization problem, which will be used for all the subsequent complexity analyses.
+
+::: definition
+**Definition 1** (**$\varepsilon$-approximation**). *For any $\varepsilon>0$, a transportation plan $X$ is called an $\varepsilon$-approximation of the minimizer $\widehat{X}$ of some objective function $f$ if $f(X) \le f(\widehat{X }) + \varepsilon$.*
+:::
+
+Based on this concept, we then state our main theorem on the runtime complexity of Algorithm [\[algorithm:rsot\]](#algorithm:rsot){reference-type="ref" reference="algorithm:rsot"} in solving the RSOT problem [\[eq:semi_robust_OT\]](#eq:semi_robust_OT){reference-type="eqref" reference="eq:semi_robust_OT"}.
+
+::: {#theorem:rsot .theorem}
+**Theorem 1**. *For $U_{\textnormal{rsot}}:=\max \{3\log(n), \varepsilon / \tau\}$ and $\eta=\varepsilon/ U_{\textnormal{rsot}}$, Algorithm [\[algorithm:rsot\]](#algorithm:rsot){reference-type="ref" reference="algorithm:rsot"} returns an $\varepsilon$-approximation of the optimal solution $\widehat{X}_{\textnormal{rsot}}$ of the problem [\[eq:semi_robust_OT\]](#eq:semi_robust_OT){reference-type="eqref" reference="eq:semi_robust_OT"} in time $$\begin{align*}
+ \mathcal{O}\left(\frac{\tau n^2}{\varepsilon}\log(n)\left[\log\left(\frac{\tau\|{C}\|_{\scriptscriptstyle \infty}}{\varepsilon}\right)+\log(\log(n))\right]\right).
+\end{align*}$$*
+:::
+
+::: proof
+*Proof Sketch.* The full proof of Theorem [1](#theorem:rsot){reference-type="ref" reference="theorem:rsot"} is in Appendix [8](#appendix:rsot){reference-type="ref" reference="appendix:rsot"}. Note that, this result is not achieved by directly applying Theorem 2 in [@pham2020unbalanced] with $\tau_2 \to \infty$ as the nature of the dual function changes in that limit, invalidating many previous results. Let $X_{\textnormal{rsot}}^k$ be the output of Algorithm [\[algorithm:rsot\]](#algorithm:rsot){reference-type="ref" reference="algorithm:rsot"} at the $k$-th step while $\widehat{X}_{\textnormal{rsot}}$ and $X_{\textnormal{rsot}}^*$ denotes the minimizers of equations [\[eq:semi_robust_OT\]](#eq:semi_robust_OT){reference-type="eqref" reference="eq:semi_robust_OT"} and [\[eq:semi_robust_OT_entropic\]](#eq:semi_robust_OT_entropic){reference-type="eqref" reference="eq:semi_robust_OT_entropic"}, respectively. The goal is to find $k$ that guarantees $f_{\textnormal{rsot}}(X_{\textnormal{rsot}}^k) - f_{\textnormal{rsot}}(\widehat{X}_{\textnormal{rsot}}) \le \varepsilon = \eta U_{\textnormal{rsot}}$. We start by decomposing $$\begin{align*}
+ \underbrace{f_{\textnormal{rsot}}(X_{\textnormal{rsot}}^k)}_{g_{\textnormal{rsot}}(X_{\textnormal{rsot}}^k) + \eta H(X_{\textnormal{rsot}}^k)} - \underbrace{f_{\textnormal{rsot}}(\widehat{X}_{\textnormal{rsot}})}_{g_{\textnormal{rsot}}(\widehat{X}_{\textnormal{rsot}}) + \eta H(\widehat{X}_{\textnormal{rsot}})} \le \left[g_{\textnormal{rsot}}(X_{\textnormal{rsot}}^k)-g_{\textnormal{rsot}}(X_{\textnormal{rsot}}^*)\right] + \eta \left[H(X_{\textnormal{rsot}}^k)-H(\widehat{X}_{\textnormal{rsot}})\right],
+\end{align*}$$ and try to bound each term by a linear function of $\eta$. Dealing with the entropy term is simple as the $\eta$ factor is already presented, and the entropy difference can be bounded by a constant due to the fact that $1 \le H(X) \le 2\log(n) + 1$ for all $X \in \mathbb{R}_+^{n \times n}, \|X\|_1 = 1$. The non-trivial part is bounding the difference between $g_{\textnormal{rsot}}$ values, which hinges upon two results. The first one is the value of $g_{\textnormal{rsot}}$ at optimality: $$\begin{align}
+ \label{equation:main_text:grsot_optimal}
+ g_{\textnormal{rsot}}(X_{\textnormal{rsot}}^*) &= -\eta-\tau(1-\alpha) + \langle v_{\textnormal{rsot}}^*, b_{\textnormal{rsot}}^*\rangle.
+\end{align}$$ The second result is the geometric convergence rate of the updates on $u$ and $v$ (Lemma [6](#lemma:rsot:uv_dual:convergence_rate){reference-type="ref" reference="lemma:rsot:uv_dual:convergence_rate"} in Appendix [8](#appendix:rsot){reference-type="ref" reference="appendix:rsot"}): $$\begin{align*}
+ \max\Big\{\|u^{k+1} - u^*\|_{\infty}, \|v^{k+1} - v^*\|_{\infty} \Big\}\le (\text{const}) \Big(\dfrac{\tau}{\tau+\eta}\Big)^{k/2} =: \Delta^k.
+\end{align*}$$ The final step is using equation [\[equation:main_text:grsot_optimal\]](#equation:main_text:grsot_optimal){reference-type="eqref" reference="equation:main_text:grsot_optimal"} to tailor the $g_{\textnormal{rsot}}$ difference to be bounded by a linear function of $\Delta^k$, which is an exponential function of $k$, then solving for the minimum $k$ at which this exponential function is small enough compared to $\eta$. The main technical difficulty here is to deal with the unknown term $\langle v_{\textnormal{rsot}}^*, b_{\textnormal{rsot}}^*\rangle$ in equation [\[equation:main_text:grsot_optimal\]](#equation:main_text:grsot_optimal){reference-type="eqref" reference="equation:main_text:grsot_optimal"}, which causes the deviation from the previous techniques. ◻
+:::
+
+::: remark
+**Remark 1**. *The result of Theorem [1](#theorem:rsot){reference-type="ref" reference="theorem:rsot"} indicates that the complexity of [Robust-SemiSinkhorn]{.smallcaps} algorithm for computing RSOT is at the order of $\widetilde{\mathcal{O}}(\frac{n^2}{\varepsilon})$. This complexity is near-optimal and faster than the complexity of the standard Sinkhorn algorithm for computing the optimal transport problem [@Dvurechensky-2018-Computational; @lin2019efficient], which is at the order of $\widetilde{\mathcal{O}}(\frac{n^2}{\varepsilon^2})$.*
+:::
+
+In this section, we briefly present another version of robust optimal transport, abbreviated by ROT, when two distributions are contaminated. We first show that the approach of using the duality of the objective function of ROT problem with entropic regularizer does not produce a Sinkhorn algorithm as in the cases of RSOT and UOT. However, a second thought of the problem finds an interesting link between the optimal solutions of ROT and UOT, which results in a nice algorithm for the ROT. We also discuss some technical difficulties when analysing the complexity for the ROT problem. At the end of this section, we show that the result could be extended to the case of low-rank cost matrix, which will significantly reduce the computation.
+
+Recall that the masses of $P$ and $Q$ are $\mathbf{a}$ and $\mathbf{b}$, respectively, the ROT problem [\[eq:uncons_robust_OT\]](#eq:uncons_robust_OT){reference-type="eqref" reference="eq:uncons_robust_OT"} becomes $$\begin{align}
+ \label{eq:robust_OT}
+ \min_{X \in \mathbb{R}_{+}^{n \times n}, \|X\|_{1} = 1}f_{\text{rot}}(X):= \langle C, X\rangle + \tau\mathbf{KL}(X\mathbf{1}_n||\mathbf{a}) + \tau\mathbf{KL}(X^{\top}\mathbf{1}_n||\mathbf{b}).
+\end{align}$$ Here we set $\tau_1 = \tau_2 = \tau$ for the sake of simplicity, since there are no more technical difficulties to work with finite $\tau_1\neq \tau_2$. As noted in Section [2](#sec:preliminary){reference-type="ref" reference="sec:preliminary"}, the formulation [\[eq:robust_OT\]](#eq:robust_OT){reference-type="eqref" reference="eq:robust_OT"} bears some resemblance to the unbalanced optimal transport problem studied in [@pham2020unbalanced], except the additional norm condition forcing $X$ to be a transportation plan (i.e., a joint probability distribution), which shows the different nature of two problems. Following the approach of using the Sinkhorn algorithm of UOT, the duality of formulation [\[eq:robust_OT\]](#eq:robust_OT){reference-type="eqref" reference="eq:robust_OT"} has the form $$\begin{align*}
+ \eta \log \|B(u,v)\|_1 + \tau \big\{\langle e^{u/\tau},\mathbf{a}\rangle + \langle e^{v/\tau},\mathbf{b}\rangle \big\}.
+\end{align*}$$ By taking derivatives of the above function with respect to $u$ and $v$ and set the derivatives to be zero, we obtain $$\begin{align*}
+ \frac{B(u,v) \mathbf{1}_n}{\|B(u,v)\|_1} = e^{-u/\tau} \odot \mathbf{a},\quad \frac{B(u,v)^{\top}\mathbf{1}_n}{\|B(u,v)\|_1} = e^{-v/\tau} \odot \mathbf{b},
+\end{align*}$$ where $\odot$ denotes element-wise multiplication. Unfortunately, the above equations do not have closed-form solutions to produce update as the Sinkhorn algorithms do because of the term $\|B(u,v)\|_1$ in the denominator. However, the objective function of UOT is not homogeneous with respect to $X$, but could be written as a linear function of ROT and another function of $\|X\|_1$ due to some special properties of the KL divergence. This observation leads to the interesting result summarized in the below lemma.
+
+::: {#lemma:rot:Xrots .lemma}
+**Lemma 1** (**Connections with UOT**). *The optimal solution of problem [\[eq:robust_OT\]](#eq:robust_OT){reference-type="eqref" reference="eq:robust_OT"}, denoted $X_{\textnormal{rot}}^*$, is the normalized version of $X_{\textnormal{uot}}^*$ which is the minimizer of UOT in entropic formulation. More specifically, we have $X_{\textnormal{rot}}^*= \frac{X_{\textnormal{uot}}^*}{\|X_{\textnormal{uot}}^*\|_{1}}$.*
+:::
+
+The proof of Lemma [1](#lemma:rot:Xrots){reference-type="ref" reference="lemma:rot:Xrots"} is in Appendix [10](#appendix:rot:proofs){reference-type="ref" reference="appendix:rot:proofs"}. Based on this result, we can utilize the Sinkhorn algorithm that solves UOT (see [@pham2020unbalanced]) with a normalizing step at the end to produce a solution for the ROT. Although the normalizing step is convenient in finding ROT's solution, it introduces new challenge in the proof compared to that of UOT since the normalizing constant does not have a lower bound. Even so, we are still able to obtain an $\varepsilon$-approximation solution for the ROT in $\widetilde{\mathcal{O}}(n^2/\varepsilon)$ time without any additional constraints on the setting. For more technical details, please refer to Appendix [10](#appendix:rot:proofs){reference-type="ref" reference="appendix:rot:proofs"}.
+
+**Further Improving Complexities by Low-Rank Approximation:** As a consequence of our complexity analysis, we can show that by using low-rank approximation method studied in [@Altschuler-2018-Massively] to the kernel matrix $K:= \exp (-C /\eta)$, we could further reduce the complexities of both robust semi/un-constrained optimal transport problem to $\widetilde{O}(nr^2 + nr / \varepsilon)$ time, given the same $\varepsilon$-approximation and the approximated-rank $r$. This result is essentially different from the complexity studied in [@Altschuler-2018-Massively], where the $\varepsilon$-approximation is considered regarding the optimal value of the entropic-regularized problem, not the original one in our analysis. For a more detailed discussion, please refer to Appendix [11](#section:nystrom:proofs){reference-type="ref" reference="section:nystrom:proofs"}.
+
+In this section, we consider the problem of computing the barycenter of a set of possibly corrupted probability measures. The semi-constrained formulation arises as a natural candidate for this goal, when potential outliers only appear in the given probability measures and the desired barycenter is the barycenter of the uncontaminated probability measures. In particular, assume that we have $m \geq 2$ discrete probability measures $P_{1}, \ldots, P_{m}$: each has at most $n$ fixed support points and the associated positive weights are given by $\omega_{1}, \ldots, \omega_{m}$ ($\sum_{i = 1}^{m} \omega_{i} = 1$). The barycenter problem then aims to find the probability measure that minimizes $\sum_{i=1}^m \omega_i \text{RSOT}(P_i, P)$, which is a linear combination of RSOT divergence from the barycenter to all given probability measures. We refer it as ***R**obust **S**emi-constrained **B**arycenter **P**roblem (RSBP)*. In this work, we consider the fixed-support settings where all the probability measures $P_i$ share the same set of support points. This setting had been widely used in the previous works to study the computational complexity of Wasserstein barycenter problem [@Kroshnin-2019-Complexity; @Lin-2020-Fast]. Let $\mathbf{p}_i$ be the mass of probability measure $P_{i}$ for $i \in [m]$, the discrete RSBP reads $$\begin{align*}
+ \min_{\mathbf{p}\in \mathbb{R}_+^n, \|\mathbf{p}\|_{1} = 1} \quad \sum_{i = 1}^m \omega_i \Big[ \min_{X_i \in \mathbb{R}_+^{n \times n} , X_i^\top \mathbf{1}_n = \mathbf{p}} \langle C_i, X_i \rangle + \tau \mathbf{KL}(X_i \mathbf{1}_n \| \mathbf{p}_i) \Big],
+\end{align*}$$ which is equivalent to $$\begin{align}
+ \label{problem:rsbp}
+ \min_{\mathbf{X} \in \mathcal{D}_1(\mathbf{X})} \quad f_{\textnormal{rsbp}}(\mathbf{X}):=\sum_{i=1}^m\omega_i\big[\langle C_i, X_i\rangle + \tau \mathbf{KL}(X_i\mathbf{1}_n\|\mathbf{p}_i)\big],
+\end{align}$$ where $\mathcal{D}_1(\mathbf{X}) := \big\{(X_1,\ldots,X_m) : \ X_i \in \mathbb{R}_+^{n \times n} \ \text{and} \ \|X_{i}\|_{1} = 1 \ \forall i \in [m]; \ X_{i}^{\top} \mathbf{1}_{n} = X_{i + 1}^{\top} \mathbf{1}_{n} \ \forall i \in [m - 1] \big\}$. Note that the objective function of RSBP is different from that of Wasserstein barycenter [@Kroshnin-2019-Complexity]: here we relax the marginal constraints $X_{i} \mathbf{1}_{n} = \mathbf{p}_{i}$ by using the KL divergence to deal with the contaminated $P_i$. Finally, the constraints $X_{i}^{\top} \mathbf{1}_{n} = X_{i + 1}^{\top} \mathbf{1}_{n} = \mathbf{p}$ are to guarantee that the transportation plans $X_i$ have one common marginal which turns out to be a feasible barycenter $\mathbf{p}$. Similar to RSOT, we consider an entropic-regularized formulation of [\[problem:rsbp\]](#problem:rsbp){reference-type="eqref" reference="problem:rsbp"}, named *entropic RSBP*: $$\begin{align}
+ \label{problem:entropic_RSBP}
+ \min_{\mathbf{X} \in \mathcal{D}_1(\mathbf{X})} g_{\text{rsbp}}(\mathbf{X}) := \sum_{i = 1}^{m} \omega_{i} g_{\text{rsot}}(X_i; \mathbf{p}_i,C_i).
+\end{align}$$ Since some functions like $g_{\textnormal{rsot}}(X)$, depends on some parameters like $C_i$ and $\mathbf{p}_i$, we sometimes abuse the notation by including these parameters next to variables, e.g., $g_{\text{rsot}}(X_i; C_i, \mathbf{p}_i)$. A general approach to deal with [\[problem:entropic_RSBP\]](#problem:entropic_RSBP){reference-type="eqref" reference="problem:entropic_RSBP"} is to consider its dual function, which admits the following form: $$\begin{align}
+ \min_{\substack{\mathbf{u}= (u_{1}, \ldots, u_{m}), \mathbf{v}= (v_{1}, \ldots, v_{m}) \\ \sum_{i = 1}^m \omega_i v_i = 0}} h_{\text{rsbp}}(\mathbf{u}, \mathbf{v}):= \sum_{i = 1}^{m} \omega_{i} \big[ \eta \log \|B(u_i, v_i;C_i)\|_{1} + \tau \big \langle e^{-u_{i}/ \tau}, \mathbf{p}_{i} \big \rangle \big].
+ \label{problem:dual_entropic_rsbp}
+\end{align}$$ We could use the alternating minimization method to find the minimizer of [\[problem:dual_entropic_rsbp\]](#problem:dual_entropic_rsbp){reference-type="eqref" reference="problem:dual_entropic_rsbp"}. In particular, starting at an initialization $\mathbf{u}^0$ and $\mathbf{v}^0$, we update them alternatively as follows: $$\begin{align}
+ \label{equation:dual_update}
+ \mathbf{u}^{k + 1} = \mathop{\mathrm{arg\,min}}_{\mathbf{u}} h_{\textnormal{rsbp}}(\mathbf{u}, \mathbf{v}^{k}), \quad \mathbf{v}^{k + 1} = \mathop{\mathrm{arg\,min}}_{\mathbf{v}: \sum_{i = 1}^m \omega_i v_i = 0} h_{\textnormal{rsbp}}(\mathbf{u}^{k + 1},\mathbf{v}).
+\end{align}$$ In some problems (e.g., RSOT), closed-form updates can be acquired if the system of equations $\partial h_{\textnormal{rsbp}}(\mathbf{u}, \mathbf{v}^{k})/\partial \mathbf{u}= \mathbf{0}$ and $\partial h_{\textnormal{rsbp}}(\mathbf{u}^{k},\mathbf{v})/\partial \mathbf{v}= \mathbf{0}$ could be solved exactly by some simple formulas. However, this is not the case with the formulation of $h_{\textnormal{rsbp}}$ in equation [\[problem:dual_entropic_rsbp\]](#problem:dual_entropic_rsbp){reference-type="eqref" reference="problem:dual_entropic_rsbp"} because the logarithmic term leads to an intractable system of equations of the partial derivative of $h_{\textnormal{rsbp}}$. Instead, we propose to solve the optimization problem [\[problem:entropic_RSBP\]](#problem:entropic_RSBP){reference-type="eqref" reference="problem:entropic_RSBP"} via another objective function, whose dual form can be solved effectively by alternating minimization.
+
+:::: algorithm
+::: algorithmic
+**Input:** $\{C_{i}\}_{i = 1}^{m}, \{\mathbf{p}_{i}\}_{i = 1}^{m}, \tau, \eta, n_{\textnormal{iter}}$ **Initialization:** $u_{i}^0 = v_{i}^0 = \mathbf{0}_n$ for $i \in [m]$, $k = 0$ $a_{i}^k \gets B(u_{i}^k,v_{i}^k;C_i) \mathbf{1}_n; \quad b_{i}^k \gets \big(B(u_{i}^k,v_{i}^k;C_i)\big)^{\top} \mathbf{1}_n \quad \forall i \in [m]$ $u_{i}^{k+1} \leftarrow \frac{\eta \tau}{\eta + \tau} \big[\frac{u^k_i}{\eta} + \log(\mathbf{p}_{i}) - \log(a_{i}^k)\big] \quad \forall i \in [m]$ $v_{i}^{k+1} \leftarrow v_{i}^k \quad \forall i \in [m]$ $u_{i}^{k+1} \leftarrow u_{i}^k \quad \forall i \in [m]$ $v_{i}^{k+1} \leftarrow \eta \left[ \frac{v^k_i}{\eta} - \log(b_{i}^{k}) - \sum_{t = 1}^{m} \omega_{t} (\frac{v^k_t}{\eta} - \log(b_{t}^{k}) ) \right] \quad \forall i \in [m]$ $k \gets k + 1$ $X_{i}^k \leftarrow B(u_{i}^k,v_{i}^k;C_i) \quad \forall i \in [m]$ **return** $(X_1^k, \dots, X_m^k)$ for equation [\[problem:entropic_rsbp_no_norm\]](#problem:entropic_rsbp_no_norm){reference-type="eqref" reference="problem:entropic_rsbp_no_norm"} $\quad$ or $\quad$ $\big(\frac{X^k_{1}}{\|X^k_{1}\|_{1}}, \ldots, \frac{X^k_{m}}{\|X^k_{m}\|_{1}}\big)$ for equation [\[problem:entropic_RSBP\]](#problem:entropic_RSBP){reference-type="eqref" reference="problem:entropic_RSBP"}.
+:::
+::::
+
+# Method
+
+We consider a similar problem to the entropic RSBP in [\[problem:entropic_RSBP\]](#problem:entropic_RSBP){reference-type="eqref" reference="problem:entropic_RSBP"}, with its feasible set $\mathcal{D}(\mathbf{X}) := \{(X_1,\ldots,X_m) : X_i \in \mathbb{R}_+^{n \times n}, \forall i \in [m]; X_{i}^{\top} \mathbf{1}_{n} = X_{i + 1}^{\top} \mathbf{1}_{n} \forall i \in [m - 1] \}$ which does not have the norm constraint. The primal objective function and its dual are as follows: $$\begin{align}
+ &\textbf{Primal:} \quad \min_{\mathbf{X} \in \mathcal{D}(\mathbf{X})} g_{\text{rsbp}}(\mathbf{X}) := \sum_{i = 1}^{m} \omega_{i} g_{\text{rsot}}(X_i; \mathbf{p}_i,C_i), \label{problem:entropic_rsbp_no_norm} \\
+ &\textbf{Dual:} \quad \min_{\mathbf{u}, \mathbf{v}:\sum_{i = 1}^m \omega_i v_i = \mathbf{0}} \bar{h}_{\text{rsbp}}(\mathbf{u}, \mathbf{v}):= \sum_{i = 1}^{m} \omega_{i} \big[ \eta \|B(u_i, v_i; C_i)\|_{1} +\tau \big \langle e^{-u_{i}/ \tau}, \mathbf{p}_{i} \big \rangle \big]. \label{problem:dual_entropic_rsbp_no_norm}
+\end{align}$$ The dual formulation [\[problem:dual_entropic_rsbp_no_norm\]](#problem:dual_entropic_rsbp_no_norm){reference-type="eqref" reference="problem:dual_entropic_rsbp_no_norm"} has a closed form updates for $\mathbf{u}$ and $\mathbf{v}$. Based on these, we develop Algorithm [\[algorithm:barycenter_semiOT\]](#algorithm:barycenter_semiOT){reference-type="ref" reference="algorithm:barycenter_semiOT"}, namely [RobustIBP]{.smallcaps}, since this procedure resembles the iterative Bregman projections studied in [@Benamou-2015-Iterative] and [@Kroshnin-2019-Complexity]. The updates of $\mathbf{u}$ and $\mathbf{v}$ are known to converge to the optimal solution $(\mathbf{u}^{*}, \mathbf{v}^{*})$ of the problem [\[problem:dual_entropic_rsbp_no_norm\]](#problem:dual_entropic_rsbp_no_norm){reference-type="eqref" reference="problem:dual_entropic_rsbp_no_norm"}, and strong duality suggests that $\mathbf{X}^* = (B(u_{i}^{*}, v_{i}^{*}; C_i) )_{i = 1}^m$ is the optimal solution of the problem [\[problem:entropic_rsbp_no_norm\]](#problem:entropic_rsbp_no_norm){reference-type="eqref" reference="problem:entropic_rsbp_no_norm"}. Furthermore, there is an intriguing relation between the optimal solution of the problem [\[problem:entropic_rsbp_no_norm\]](#problem:entropic_rsbp_no_norm){reference-type="eqref" reference="problem:entropic_rsbp_no_norm"} to that of the problem [\[problem:entropic_RSBP\]](#problem:entropic_RSBP){reference-type="eqref" reference="problem:entropic_RSBP"}, presented in the following lemma.
+
+::: {#lemma:barycenter_normalize .lemma}
+**Lemma 2**. *Let $\bar{\mathbf{X}}^{*}=(\bar{X}^{*}_1,\ldots,\bar{X}^{*}_m)$ and $\mathbf{X}^* = (X_1^*, \dots, X_n^*)$ be the optimizers of $g_{\textnormal{rsbp}}$ with the feasible set $\mathcal{D}(\mathbf{X})$ and with the feasible set $\mathcal{D}_1(\mathbf{X})$, respectively. Then, $X^*_i= \dfrac{\bar{X}^{*}_i}{\|\bar{X}^{*}_i\|_1}$ for all $i\in [m]$.*
+:::
+
+The proof of Lemma [2](#lemma:barycenter_normalize){reference-type="ref" reference="lemma:barycenter_normalize"} is in Appendix [\[sec:proofs:robust_barycenter\]](#sec:proofs:robust_barycenter){reference-type="ref" reference="sec:proofs:robust_barycenter"}. This result indicates that we can approximate the solution of equation [\[problem:entropic_RSBP\]](#problem:entropic_RSBP){reference-type="eqref" reference="problem:entropic_RSBP"} by the solution of equation [\[problem:entropic_rsbp_no_norm\]](#problem:entropic_rsbp_no_norm){reference-type="eqref" reference="problem:entropic_rsbp_no_norm"}, using the same Algorithm [\[algorithm:barycenter_semiOT\]](#algorithm:barycenter_semiOT){reference-type="ref" reference="algorithm:barycenter_semiOT"} with an additional normalizing step at the end.
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+++ b/2102.12871/main_diagram/main_diagram.drawio
@@ -0,0 +1 @@
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+# Introduction
+
+The Transformer (Vaswani et al., 2017) has been commonly used in various natural language processing (NLP) tasks such as text classification (Wang et al., 2018a), text translation (Ott et al., 2018), and question answering (Rajpurkar et al., 2016). The recent use of Transformer for image classification (Dosovitskiy et al., 2021), object detection (Carion et al., 2020) also demonstrates its potential in computer vision. Two notable descendants from the Transformer
+
+Proceedings of the 38th International Conference on Machine Learning, PMLR 139, 2021. Copyright 2021 by the author(s).
+
+include the BERT (Devlin et al., 2019), which achieves state-of-the-art performance on a wide range of NLP tasks, and GPT-3 (Brown et al., 2020) which applies the Transformer's decoder on generative downstream tasks.
+
+Self-attention is a core component in Transformer-based architectures. Recently, its interpretation has aroused a lot of interest. Visualization has been commonly used to understand the attention map during inference (Park et al., 2019; Gong et al., 2019; Kovaleva et al., 2019). For example, Park et al. (2019) and Gong et al. (2019) randomly select a sentence from the corpus and visualize the attention maps of different heads in a pre-trained Transformer model. Kovaleva et al. (2019) summarizes five attention patterns and estimates their ratios in different tasks. A common observation from these studies is that local attention and global attention are both important for token understanding.
+
+While self-attention is powerful, a main concern is its efficiency bottleneck. As each token has to attend to all n tokens in the sequence, the complexity scales as $\mathcal{O}(n^2)$ . This can be expensive on long sequences. To alleviate this problem, sparse attention allows each token to attend to only a token subset. A series of Transformer variants have been proposed along this direction (Guo et al., 2019; Child et al., 2019; Li et al., 2019; Beltagy et al., 2020). However, these sparse attention schemes are designed manually. It is still an open issue on how to find a suitable attention scheme.
+
+Recently, there is a growing interest in understanding self-attention mechanism from a theoretical perspective. Results show that the Transformer and its variants are universal approximators of arbitrary continuous sequence-to-sequence functions (Yun et al., 2019; 2020; Zaheer et al., 2020). A key part of their proofs is that self-attention layers implement contextual mappings of the input sequences. Yun et al. (2019) constructs the self-attention model as a selective shift operation such that contextual mapping can be implemented. Zaheer et al. (2020) shows that universal approximation holds for their sparse Transformer BigBird if its attention structure contains the star graph. Yun et al. (2020) provides a unifying framework for the universal approximation of sparse Transformers. Note that they all emphasize the importance of diagonal elements in the attention map.
+
+To guide the design of an efficient Transformer, it is useful to investigate the importance of different positions in
+
+<sup>1Hong Kong University of Science and Technology, Hong Kong 2The University of Hong Kong, Hong Kong 3Huawei Noah's Ark Lab 4Sun Yat-sen University, China. Correspondence to: Han Shi .
+
+
+
+Figure 1. Examples of existing attention masks (with n = 16).
+
+self-attention. In this paper, we study this using differentiable search (Liu et al., 2019; Xie et al., 2018). A learnable attention distribution is constructed and the score of each position is learned in an end-to-end manner during pretraining. While existing theoretical and empirical findings suggest the importance of diagonal elements in the selfattention matrix, we observe that they are indeed the least important compared to other entries. Furthermore, neighborhood tokens and special tokens (such as the first token [CLS] and last token [SEP]) are also prominent, which is consistent with previous observations in (Park et al., 2019; Gong et al., 2019; Kovaleva et al., 2019; Clark et al., 2019). Besides, using the Gumbel-sigmoid function (Maddison et al., 2017), we propose the Differentiable Attention Mask (DAM) algorithm to learn the attention mask in an endto-end manner. Extensive experiments using masks with different sparsity ratios on various NLP tasks demonstrate the effect of the proposed algorithm. Specifically, highly sparse structured attention masks (with 91.3% sparsity ratio) can already achieve 80.9% average score on the GLUE development set (Wang et al., 2018a). The code is available at https://github.com/han-shi/SparseBERT.
+
+# Method
+
+- 1: initialize model parameter w and attention mask parameter $\alpha$ .
+- 2: repeat
+- 3: generate mask $M_{i,j} \leftarrow \text{gumbel-sigmoid}(\alpha_{i,j})$ ;
+- 4: obtain the loss with attention mask $\mathcal{L}$ ;
+- 5: update parameter w and $\alpha$ simultaneously;
+- 6: until convergence.
+- 7: **return** attention mask M.
+
+an end-to-end manner with the Gumbel-sigmoid variant. To balance mask sparsity with performance, we add the sum absolute values of the attention mask to the loss, as:
+
+$$\mathcal{L} = l(BERT(\boldsymbol{X}, \boldsymbol{A}(\boldsymbol{X}) \odot \boldsymbol{M}(\boldsymbol{\alpha}); \boldsymbol{w})) + \lambda \|\boldsymbol{M}(\boldsymbol{\alpha})\|_{1}, (7)$$
+
+where $l(\mathrm{BERT}(\boldsymbol{X}, \boldsymbol{A}(\boldsymbol{X}); \boldsymbol{w}))$ is the pre-training loss, and $\lambda$ is a trade-off hyperparameter. When $\lambda$ is large, we pay more emphasis on efficiency and the learned attention mask $\boldsymbol{M}$ is sparser, and vice versa. We also apply the renormalization trick to normalize the attention distributions in Eq. (7). The obtained one-hot Gumbel sigmoid output can then be directly used as the attention mask. The whole algorithm is shown in Algorithm 1. We optimize both parameter sets simultaneously in one-level optimization until convergence and the generated mask is returned finally.
+
+Note that the attention mask obtained in Algorithm 1 is unstructured, as the $\alpha_{i,j}$ 's are all independent of each other. This irregular structure may affect the efficiency of the final CUDA implementation. To alleviate this problem, we use the observed sparsity patterns in Figure 2 to constrain the
+
+structure of the attention mask. First, as the special tokens are important, we require the first and last row/column of the attention mask to be active. Second, for all positions on each line parallel to the diagonal, we share their mask parameters including their two Gumbel noises such that the generated mask has $M_{i,j} = M_{i+k,j+k}$ for integer k. Among the previously proposed attention masks, the Sparse Transformer (strided) (Child et al., 2019) and LogSparse Transformer (Li et al., 2019) conform to our definition of structured attention masks, and they can be implemented efficiently by custom CUDA kernels. With the constrained structure, there are now only n-2 attention mask parameters. Therefore, the search space is reduced to $2^{n-2}$ in structured attention mask search. Since the sparsity ratio directly affects the efficiency of the SparseBERT, we will show the performance of the attention mask with its sparsity ratio.
diff --git a/2105.04030/main_diagram/main_diagram.drawio b/2105.04030/main_diagram/main_diagram.drawio
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index 0000000000000000000000000000000000000000..598911edaa43a87158ce6408886daaa32770d696
--- /dev/null
+++ b/2105.04030/main_diagram/main_diagram.drawio
@@ -0,0 +1 @@
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+# Method
+
+In domain generalization, we have $\mathcal{D}{=}{\left\{ D_i \right\}}^{|\mathcal{D}|}_{i=1}{=}\mathcal{S} \cup \mathcal{T}$ as a set of domains, where $\mathcal{S}$ and $\mathcal{T}$ denote the source and target domains, respectively. $\mathcal{S}$ and $\mathcal{T}$ do not have any overlap besides sharing the same label space. Data from the target domains $\mathcal{T}$ is never seen during training. For each domain $D_{i} \in \mathcal{D}$, we define a joint distribution $p(\mathbf{x}, \mathbf{y})$ on $\mathcal{X} \times \mathcal{Y}$, where $\mathcal{X}$ and $\mathcal{Y}$ denote the input space and output space, respectively. We aim to learn a model $f: \mathcal{X}{\rightarrow} \mathcal{Y}$ in the source domains $\mathcal{S}$ that generalizes well to the target domains $\mathcal{T}$.
+
+We address domain generalization in a probabilistic framework of Bayesian inference by introducing weight uncertainty into neural networks. To be specific, we adopt variational Bayesian inference to learn a neural network that is assumed to be parameterized by weights $\boldsymbol{\theta}$ with a prior distribution $p(\boldsymbol{\theta})$ and the posterior distribution $p(\boldsymbol{\theta}|\mathbf{x}, \mathbf{y})$, where $(\mathbf{x}, \mathbf{y})$ are samples from the source domain $\mathcal{S}$. To learn the model, we seek for a variational distribution $q(\boldsymbol{\theta})$ to approximate $p(\boldsymbol{\theta}| \mathbf{x}, \mathbf{y})$ by minimizing the Kullback-Leibler divergence $\mathbb{D}_{\rm{KL}} \big[ q(\boldsymbol{\theta})||p(\boldsymbol{\theta}|\mathbf{x}, \mathbf{y})\big]$ between them. This amounts to minimizing the following objective: $$\begin{equation}
+\label{elbo1}
+\begin{aligned}
+\mathcal{L}_{\rm{Bayes}} %& = \dkl[q(\btheta)||p(\btheta | \x, \y)] \\ &
+= -\mathbb{E}_{q(\boldsymbol{\theta})}[\log p(\mathbf{y}|\mathbf{x}, \boldsymbol{\theta})] + \mathbb{D}_{\rm{KL}}[q(\boldsymbol{\theta})||p(\boldsymbol{\theta})],
+\end{aligned}
+\end{equation}$$ which is also known as the negative value of the evidence lower bound (ELBO) [@blei2017variational]. We learn the model on the source domain $\mathcal{S}$ in the hope that it will generalize well to the target domain $\mathcal{T}$.
+
+@kristiadi2020being show that applying Bayesian approximation to the last layer of a neural network effectively captures model uncertainty. This is also appealing when dealing with uncertainty in domain generalization since applying Monte Carlo sampling to the full Bayesian neural network can be computationally infeasible. In this paper, to obtain a model that generalizes well across domains in an efficient way, we incorporate variational Bayesian approximation into the last two network layers to jointly establish domain-invariant feature representations and classifiers. More analyses are provided in the experiments and supplementary. To achieve better domain invariance, we introduce a domain-invariant principle in a probabilistic form under the Bayesian framework. We use $\boldsymbol{\phi}$ and $\boldsymbol{\psi}$ to denote the parameters of the feature extractor and classifier. We incorporate the domain-invariant principle into the inference of posteriors over $\boldsymbol{\psi}$ and $\boldsymbol{\phi}$, which results in our two-layer Bayesian network. Next we detail the Bayesian domain-invariant learning of the network and the objective for optimization.
+
+
+
+Illustrative contrast between deterministic invariance (top) and probabilistic invariance (bottom), where colors indicate domains. The deterministic invariance tends to minimize the distance between two deterministic samples (a → b). The samples come from their respective distributions, but with limited distributional awareness. This means that the samples cannot represent the complete distributions. Thus, the deterministic invariance can lead to small distances between samples, but large gaps between distributions, as shown in figure (b). In contrast, the probabilistic invariance directly minimizes the distance between different distributions (c → d), which yields a better domain invariance as most of the samples in the distributions are taken into account.
+
+
+Existing domain generalizations try to achieve domain invariance by minimizing the distance between intra-class samples from different domains in the hope of reducing the domain gaps [@motiian2017unified]. However, individual samples cannot be assumed to be representative of the distributions of samples from a certain domain. Therefore, it is preferable to minimize the distributional distance of intra-class samples from different domains, which can directly narrow the domain gap. A contrastive illustration is provided in Figure [1](#fig:invariant){reference-type="ref" reference="fig:invariant"}. Motivated by this, we propose to address domain generalization under the probabilistic modeling by incorporating weight uncertainty into invariant learning in the variational Bayesian inference framework.
+
+We first introduce the definition of domain invariance in a probabilistic form, which we adopt to learn domain-invariant feature representations and classifiers in a unified way. We define a continuous domain space $\mathfrak{D}$ containing a set of domains $\left\{ D_i \right\}^{|\mathcal{D}|}_{i=1}$ in $\mathcal{D}$ and a domain-transform function $g_{\zeta}(\cdot)$ with parameters $\zeta$ in the domain space. $\left\{ D_i \right\}^{|\mathcal{D}|}_{i=1}$ are discrete samples in $\mathfrak{D}$. Function $g_{\zeta}(\cdot)$ transforms samples $\mathbf{x}$ from a reference domain to a different domain $D_{\zeta}$ with respect to $\zeta$, where $\zeta {\thicksim} q(\zeta)$, and different samples $\zeta$ lead to different sample domains $D_{\zeta} {\thicksim}{\mathfrak{D}}$.
+
+As a concrete example, consider the rotation domains in Rotated MNIST [@ghifary2015domain]. $\mathfrak{D}$ is the rotation domain space, containing images rotated by continuous angles from $0$ to $2\pi$. A sample $\mathbf{x}$ from any domain in $\mathfrak{D}$ can be transferred into a different domain $D_{\zeta}$ by: $$\begin{equation}
+g_{\zeta}(\mathbf{x}) = \left[
+ \begin{matrix}
+ cos(\zeta) & -sin(\zeta) \\
+ sin(\zeta) & -cos(\zeta)
+ \end{matrix}
+ \right]
+ \left[
+ \begin{matrix}
+ x_{1} \\
+ x_{2}
+ \end{matrix}
+ \right],
+\end{equation}$$ where $\zeta \thicksim$ Uniform$(0, 2\pi)$. $\zeta$ is the parameter of $g_\zeta(\cdot)$ and different $\zeta$ lead to different rotation angles of the original sample $\mathbf{x}$, which form another domain $D_\zeta$ in $\mathfrak{D}$. In practice, transformations between domains are more complicated and the exact forms of $g_{\zeta}(\cdot)$ and $q(\zeta)$ are not explicitly known.
+
+Based on the above assumptions about $\mathfrak{D}$, $g_\zeta(\cdot)$ and $q(\zeta)$, we introduce our definition of the probabilistic form of domain invariance as follows.
+
+::: {#def1 .define}
+****Definition** 1** (Domain Invariance). *Let $\mathbf{x}_i$ be a given sample from domain $D_i$ in the domain space $\mathfrak{D}$, and $\mathbf{x}_{\zeta} {=} g_{\zeta}(\mathbf{x}_{i})$ be a transformation of $\mathbf{x}_{i}$ in another domain $D_\zeta$ from the same domain space, where $\zeta {\thicksim} q(\zeta)$. $p_{\boldsymbol{\theta}}(\mathbf{y}|\mathbf{x})$ denotes the output distribution of input $\mathbf{x}$ with model $\boldsymbol{\theta}$. Model $\boldsymbol{\theta}$ is domain-invariant in $\mathfrak{D}$ if $$\begin{equation}
+\label{nai_invariant}
+p_{\boldsymbol{\theta}}(\mathbf{y}_{i}|\mathbf{x}_{i}) = p_{\boldsymbol{\theta}}(\mathbf{y}_{\zeta}|\mathbf{x}_{\zeta}), \qquad \forall \zeta \thicksim q(\zeta).
+\end{equation}$$*
+:::
+
+Here, we use $\mathbf{y}$ to represent the output from a neural layer with input $\mathbf{x}$, which can either be the prediction vector from the last layer or the feature vector from the last convolutional layer of a deep neural network.
+
+To adopt the domain-invariant principle in the variational Bayesian inference framework, we reformulate ([\[nai_invariant\]](#nai_invariant){reference-type="ref" reference="nai_invariant"}) to an expectation form with respect to $q_{\zeta}$, following @learningprior: $$\begin{equation}
+\label{ep_invariant}
+p_{\boldsymbol{\theta}}(\mathbf{y}_{i}|\mathbf{x}_{i}) = \mathbb{E}_{q_{\zeta}} [p_{\boldsymbol{\theta}}(\mathbf{y}_{\zeta}|\mathbf{x}_{\zeta})].
+\end{equation}$$
+
+According to Definition [1](#def1){reference-type="ref" reference="def1"}, we use the Kullback-Leibler divergence between the two terms in ([\[ep_invariant\]](#ep_invariant){reference-type="ref" reference="ep_invariant"}), $\mathbb{D}_{\rm{KL}}\big[ p_{\boldsymbol{\theta}}(\mathbf{y}_{i}|\mathbf{x}_{i})|| \mathbb{E}_{q_\zeta} [p_{\boldsymbol{\theta}}(\mathbf{y}_{\zeta}|\mathbf{x}_{\zeta})] \big]$, to quantify the domain invariance of the model, which will be zero when the model is domain invariant in the domain space $\mathfrak{D}$. To further facilitate the computation, we derive the upper bound of the KL divergence: $$\begin{equation}
+\label{kl_inequal}
+\begin{aligned}
+\mathbb{D}_{\rm{KL}}& \big[ p_{\boldsymbol{\theta}}(\mathbf{y}_{i}|\mathbf{x}_{i})|| \mathbb{E}_{q_{\zeta}} [p_{\boldsymbol{\theta}}(\mathbf{y}_{\zeta}|\mathbf{x}_{\zeta})] \big] \\ &\leq \mathbb{E}_{q_{\zeta}} \Big[\mathbb{D}_{\rm{KL}}\big[ p_{\boldsymbol{\theta}}(\mathbf{y}_{i}|\mathbf{x}_{i})||p_{\boldsymbol{\theta}}(\mathbf{y}_{\zeta}|\mathbf{x}_{\zeta}) \big]\Big],
+\end{aligned}
+\end{equation}$$ which can be approximated by Monte Carlo sampling as we usually have access to samples from different domains. The complete derivation of ([\[kl_inequal\]](#kl_inequal){reference-type="ref" reference="kl_inequal"}) is provided in the supplementary material.
+
+We now adopt the probabilistic domain invariance principle in the variational Bayesian approximation of the last two layers with parameters of $\boldsymbol{\phi}$ and $\boldsymbol{\psi}$, respectively. The introduced weight uncertainty results in distributional representations $p_{\boldsymbol{\phi}}(\mathbf{z}|\mathbf{x})$ and predictions $p_{\boldsymbol{\psi}}(\mathbf{y}|\mathbf{z})$, based on which we derive domain-invariant learning.
+
+For the classifier with parameters $\boldsymbol{\psi}$ and input features $\mathbf{z}$, the probabilistic domain-invariant property in ([\[kl_inequal\]](#kl_inequal){reference-type="ref" reference="kl_inequal"}) is represented as $\mathbb{E}_{q_{\zeta}} \big[\mathbb{D}_{\rm{KL}}[ p_{\boldsymbol{\psi}}(\mathbf{y}_{i}|\mathbf{z}_{i})||p_{\boldsymbol{\psi}}(\mathbf{y}_{\zeta}|\mathbf{z}_{\zeta})]\big]$. Under the Bayesian framework, the predictive distribution $p_{\boldsymbol{\psi}}(\mathbf{y}|\mathbf{z})$ is obtained by taking the expectation over the distribution of parameter $\boldsymbol{\theta}$, i.e., $p_{\boldsymbol{\psi}}(\mathbf{y}|\mathbf{z}){=}\mathbb{E}_{q(\boldsymbol{\psi})}[p(\mathbf{y}|\mathbf{z}, \boldsymbol{\psi})]$. As the KL divergence is a convex function [@learningprior], we further extend it to the upper bound: $$\begin{equation}
+\label{invariant_final}
+\begin{aligned}
+\mathbb{E}_{q_{\zeta}} & \Big[\mathbb{D}_{\rm{KL}}\big[ p_{\boldsymbol{\psi}}(\mathbf{y}_{i}|\mathbf{z}_{i}) ||p_{\boldsymbol{\psi}}(\mathbf{y}_{\zeta}|\mathbf{z}_{\zeta}) \big] \Big]
+\\ & = \mathbb{E}_{q_{\zeta}}\Big[ \mathbb{D}_{\rm{KL}}\big[ \mathbb{E}_{q(\boldsymbol{\psi})} [p(\mathbf{y}_{i}|\mathbf{z}_{i}, \boldsymbol{\psi})] || \mathbb{E}_{q(\boldsymbol{\psi})} [p(\mathbf{y}_{\zeta}|\mathbf{z}_{\zeta}, \boldsymbol{\psi})] \big] \Big]
+\\ & \leq \mathbb{E}_{q_{\zeta}} \Big[\mathbb{E}_{q(\boldsymbol{\psi})} \mathbb{D}_{\rm{KL}}\big[ p(\mathbf{y}_{i}|\mathbf{z}_{i}, \boldsymbol{\psi}) || p(\mathbf{y}_{\zeta}|\mathbf{z}_{\zeta}, \boldsymbol{\psi}) \big]\Big], %\\ & = \mathcal{L}_{Invar}.
+\end{aligned}
+\end{equation}$$ which is tractable with unbiased approximation by using Monte Carlo sampling.
+
+In practice, we estimate the expectation over $q(\zeta)$ in an empirical way. Specifically, in each iteration, we choose one domain from the source domains $\mathcal{S}$ as the meta-target domain $D_t$ and the rest are used as the meta-source domains ${\left\{D_s \right\}}^{S}_{s=1}$, where $S{=}|\mathcal{S}|-1$. Then we use a batch of samples $\mathbf{x}_s$ from each meta-source domain in the same category as $\mathbf{x}_t$ to approximate $\mathbf{x}_\zeta {=} g_{\zeta}(\mathbf{x}_{t})$.
+
+Thereby, the domain-invariant classifier is established by minimizing: $$\begin{equation}
+\begin{aligned}
+\label{Li_y}
+% &\mathcal{L}_{\rm{I}}(\bpsi) =
+% \\&
+\frac{1}{SN}\sum_{s=1}^S\sum_{i=1}^N \mathbb{E}_{q(\boldsymbol{\psi})}\Big[ \mathbb{D}_{\rm{KL}}\big[ p(\mathbf{y}_{t}|\mathbf{z}_{t}, \boldsymbol{\psi}) || p(\mathbf{y}_{s}^{i}|\mathbf{z}_{s}^{i}, \boldsymbol{\psi}) \big]\Big],
+\end{aligned}
+% \label{dic}
+\end{equation}$$ where ${\left\{\mathbf{z}_{s}^{i} \right \}}^{N}_{i=1}$ are representations of samples $\mathbf{x}_s$ from $D_{s}$, which are in the same category as $\mathbf{x}_{t}$.
+
+The variational Bayesian inference enables us to flexibly incorporate our domain invariant principle into different layers of the neural network. To enhance the domain invariance, we obtain domain-invariant representations by adopting the principle in the penultimate layer.
+
+To obtain domain-invariant representations $p(\mathbf{z}|\mathbf{x})$ with feature extractor $\boldsymbol{\phi}$, we extend the quantification of our probabilistic domain invariance in ([\[kl_inequal\]](#kl_inequal){reference-type="ref" reference="kl_inequal"}) to $\mathbb{E}_{q_{\zeta}} \big[\mathbb{D}_{\rm{KL}}[ p_{\boldsymbol{\phi}}(\mathbf{z}_{i}|\mathbf{x}_{i})||p_{\boldsymbol{\phi}}(\mathbf{z}_{\zeta}|\mathbf{x}_{\zeta})]\big]$. Based on the Bayesian layer, the probabilistic distribution of features $p_{\boldsymbol{\phi}}(\mathbf{z}|\mathbf{x})$ will be a factorized Gaussian distribution if the posterior of $\boldsymbol{\phi}$ is as well. We illustrate this as follows. Let $\boldsymbol{\phi}$ be the last Bayesian layer in the feature extractor with a factorized Gaussian posterior and $\mathbf{x}$ be the input feature of $\boldsymbol{\phi}$. The posterior of the activation $\mathbf{z}$ is also a factorized Gaussian [@kingma2015variational]: $$\begin{equation}
+\begin{aligned}
+\label{distributionz}
+&q(\phi_{i,j}) \thicksim \mathcal{N}(\mu_{i,j}, \sigma^2_{i,j}) ~~~\forall \phi_{i,j} \in \boldsymbol{\phi}
+\\&
+\Rightarrow
+p(z_{j}|\mathbf{x}, \boldsymbol{\phi}) \thicksim{\mathcal{N}(\gamma_{j}, \delta_{j}^2)}, \\
+&\gamma_{j}=\sum_{i=1}^{N}x_{i}\mu_{i,j}, ~~~~ \text{and} ~~~~\delta_{j}^2=\sum_{i=1}^{N}x_{i}^2\sigma_{i,j}^2,
+\end{aligned}
+\end{equation}$$ where $z_{j}$ denotes the $j$-th element in $\mathbf{z}$, likewise for $x_{i}$, and $\phi_{i,j}$ denotes the element at position $(i,j)$ in $\boldsymbol{\phi}$. Thus, we assume the posterior of the last Bayesian layer in the feature extractor has a factorized Gaussian distribution. Then, it is easy to obtain the Bayesian domain invariance of the feature extractor.
+
+In a similar way to ([\[Li_y\]](#Li_y){reference-type="ref" reference="Li_y"}), we estimate the expectation over $q(\zeta)$ empirically. Therefore, the domain-invariant representations are established by minimizing: $$\begin{equation}
+\label{Li_z}
+% \textcolor{blue}{
+%\mathcal{L}_{\rm{I}}(\bphi) =
+\frac{1}{SN}\sum_{s=1}^S \sum_{i=1}^N \Big[ \mathbb{D}_{\rm{KL}}\big[ p(\mathbf{z}_{t}|\mathbf{x}_{t}, \boldsymbol{\phi}) || p(\mathbf{z}_{s}^{i}|\mathbf{x}_{s}^{i}, \boldsymbol{\phi}) \big]\Big],%}
+% \label{dir}
+\end{equation}$$ where ${\left\{\mathbf{x}_{s}^{i} \right \}}^{N}_{i=1}$ are from $D_{s}$, and denote the samples in the same category as $\mathbf{x}_{t}$. More details and an illustration of the Bayesian domain-invariant learning are provided in the supplementary material.
+
+Having the Bayesian treatment for the last two layers with respect to parameters $\boldsymbol{\phi}$ and $\boldsymbol{\psi}$, the $\mathcal{L}_{\rm{Bayes}}$ in ([\[elbo1\]](#elbo1){reference-type="ref" reference="elbo1"}) is instantiated as the objective with respect to $\boldsymbol{\psi}$ and $\boldsymbol{\phi}$ as follows: $$\begin{equation}
+\begin{aligned}
+{\mathcal{L}}_{\rm{Bayes}}
+%&= -\mathbb{E}_{q(\btheta, \bphi)}[\log p(\y|\x, \btheta, \bphi)] + \dkl[q(\bpsi, \bphi)||p(\bpsi, \bphi)] \\ &
+& = -\mathbb{E}_{q(\boldsymbol{\psi})}\big[ \mathbb{E}_{q(\boldsymbol{\phi})} [\log p(\mathbf{y}|\mathbf{x}, \boldsymbol{\psi}, \boldsymbol{\phi})] \big]
+\\ & + \mathbb{D}_{\rm{KL}}[q(\boldsymbol{\psi})||p(\boldsymbol{\psi})] + \mathbb{D}_{\rm{KL}}[q(\boldsymbol{\phi})||p(\boldsymbol{\phi})].
+\label{bayes}
+\end{aligned}
+\end{equation}$$ The detailed derivation of ([\[bayes\]](#bayes){reference-type="ref" reference="bayes"}) is provided in the supplementary material.
+
+By integrating ([\[Li_y\]](#Li_y){reference-type="ref" reference="Li_y"}) and ([\[Li_z\]](#Li_z){reference-type="ref" reference="Li_z"}) into ([\[bayes\]](#bayes){reference-type="ref" reference="bayes"}), we obtain the Bayesian domain-invariant learning objective as follows: $$\begin{equation}
+\begin{aligned}
+ {\mathcal{L}}_{\rm{BIL}} & = \frac{1}{L}\sum_{\ell=1}^L \Big[ \frac{1}{M}\sum_{m}^M [-\log p(\mathbf{y}_t|\mathbf{x}_t, \boldsymbol{\psi}^{(\ell)}, \boldsymbol{\phi}^{(m)})] \\ &
+ + \frac{1}{SN}\sum_{s=1}^S %\frac{1}{N}
+ \sum_{i=1}^N %\\&
+ \big[\lambda_{\boldsymbol{\psi}} \mathbb{D}_{\rm{KL}}[p(\mathbf{y}_t|\mathbf{z}_t, \boldsymbol{\psi}^{(\ell)})||p(\mathbf{y}^i_{s}|\mathbf{z}^i_{s}, \boldsymbol{\psi}^{(\ell)})] \\ &
+ + \lambda_{\boldsymbol{\phi}} %\frac{1}{S}\sum_{s=1}^S \frac{1}{N}\sum_{i=1}^N %\\&
+ \mathbb{D}_{\rm{KL}}[p(\mathbf{z}_t|\mathbf{x}_t, \boldsymbol{\phi})||p(\mathbf{z}^i_{s}|\mathbf{x}^i_{s}, \boldsymbol{\phi})] \big] \\&
+ + \mathbb{D}_{\rm{KL}}[q(\boldsymbol{\psi})||p(\boldsymbol{\psi})] + \mathbb{D}_{\rm{KL}}[q(\boldsymbol{\phi})||p(\boldsymbol{\phi})] \Big],
+\end{aligned}
+\end{equation}$$ where $\lambda_{\boldsymbol{\psi}}$ and $\lambda_{\boldsymbol{\phi}}$ are hyperparameters that control the domain-invariant terms. $\mathbf{x}_t$ and $\mathbf{z}_t$ denote the input and its feature from $D_t$, and $\mathbf{x}^i_s$ and $\mathbf{z}^i_s$ are from $D_s$. The posteriors are set to factorized Gaussian distributions, i.e., $q(\boldsymbol{\psi}){=}{\mathcal{N}(\boldsymbol{\mu}_{\boldsymbol{\psi}}, \boldsymbol{\sigma}_{\boldsymbol{\psi}}^{2})}$ and $q(\boldsymbol{\phi}){=}{\mathcal{N}(\boldsymbol{\mu}_{\boldsymbol{\phi}}, \boldsymbol{\sigma}_{\boldsymbol{\phi}}^{2})}$. We adopt the reparameterization trick to draw Monte Carlo samples [@kingma2013auto] as $\boldsymbol{\psi}^{(\ell)} {=} \boldsymbol{\mu}_{\boldsymbol{\psi}} + \epsilon^{(\ell)} * \boldsymbol{\sigma}_{\boldsymbol{\psi}}$, where $\epsilon^{(\ell)}\thicksim{\mathcal{N}(0, I)}$. Likewise, we draw the Monte Carlo samples $\boldsymbol{\phi}^{(m)}$ for $q(\boldsymbol{\phi})$.
+
+When implementing our Bayesian invariant learning, to increase the flexibility of the prior distribution in our Bayesian layers, we place a scale mixture of two Gaussian distributions as the priors $p(\boldsymbol{\psi})$ and $p(\boldsymbol{\phi})$ [@Blundell2015WeightUI]: $$\begin{equation}
+\label{scalemixture}
+ %p(\btheta)\thicksim
+ {\pi\mathcal{N}(0, \boldsymbol{\sigma}_{1}^{2})}+{(1-\pi)\mathcal{N}(0, \boldsymbol{\sigma}_{2}^{2})},
+\end{equation}$$ where $\boldsymbol{\sigma}_1$, $\boldsymbol{\sigma}_2$ are set according to [@Blundell2015WeightUI] and $\pi$ is chosen by cross-validation. More experiments and analyses on the hyperparameters $\lambda_{\boldsymbol{\psi}}$, $\lambda_{\boldsymbol{\phi}}$ and $\pi$ are provided in the supplementary material.
diff --git a/2106.14087/main_diagram/main_diagram.drawio b/2106.14087/main_diagram/main_diagram.drawio
new file mode 100644
index 0000000000000000000000000000000000000000..72ed5111566b204c01a63b3b24454f3a88a61854
--- /dev/null
+++ b/2106.14087/main_diagram/main_diagram.drawio
@@ -0,0 +1 @@
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diff --git a/2106.14087/main_diagram/main_diagram.pdf b/2106.14087/main_diagram/main_diagram.pdf
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diff --git a/2106.14087/paper_text/intro_method.md b/2106.14087/paper_text/intro_method.md
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+# Introduction
+
+In the current state of the art, researchers focus on 3D object detection in the field of perception. Three-dimensional object detection is most reliably performed with lidar sensor data [1-3] as its higher resolution—when compared to radar sensors—and direct depth measurement—when compared to camera sensors—provide the most relevant features for object detection algorithms. However, for redundancy and safety reasons in autonomous driving applications, additional sensor modalities are required because lidar sensors cannot detect all relevant objects at all times. Cameras are well-understood, cheap and reliable sensors for applications such as traffic-sign recognition. Despite their high resolution, their capabilities for 3D perception are limited as only 2D information is provided by the sensor. Furthermore, the sensor data quality deteriorates strongly in bad weather conditions such as snow or heavy rain. Radar sensors are least affected by inclement weather, e.g., fog, and are therefore a vital *Appl. Sci.* **2021**, *1*, 0 2 of 17
+
+asset to make autonomous driving more reliable. However, due to their low resolution and clutter noise for static vehicles, current radar sensors cannot perform general object detection without the addition of further modalities. This work therefore combines the advantages of camera, lidar, and radar sensor modalities to produce an improved detection result.
+
+Several strategies exist to fuse the information of different sensors. These systems can be categorized as early fusion if all input data are first combined and then processed, or late fusion if all data is first processed independently and the output of the data-specific algorithms are fused after the processing. Partly independent and joint processing is called middle or feature fusion.
+
+Late fusion schemes based on a Bayes filter, e.g., the Unscented Kalman Filter (UKF) [\[4\]](#page-15-2), in combination with a matching algorithm for object tracking, are the current state of the art, due to their simplicity and their effectiveness during operation in constrained environments and good weather.
+
+Early and feature fusion networks possess the advantage of using all available sensor information at once and are therefore able to learn from interdependencies of the sensor data and compensate imperfect sensor data for a robust detection result similar to gradient boosting [\[5\]](#page-15-3).
+
+This paper presents an approach to fuse the sensors in an early fusion scheme. Similar to Wang et al. [\[6\]](#page-15-4), we color the lidar point cloud with camera RGB information. These colored lidar points are then fused with the radar points and their radar cross-section (RCS) and velocity features. The network processes the points jointly in a voxel structure and outputs the predicted bounding boxes. The paper evaluates several parameterizations and presents the RadarVoxelFusionNet (RVF-Net), which proved most reliable in our studies.
+
+The contribution of the paper is threefold:
+
+- The paper develops an early fusion network for radar, lidar, and camera data for 3D object detection. The network outperforms the lidar baseline and a Kalman Filter late fusion approach.
+- The paper provides a novel loss function to replace the simple discontinuous yaw parameterization during network training.
+- The code for this research has been released to the public to make it adaptable to further use cases.
+
+Section [2](#page-1-0) discusses related work for object detection and sensor fusion networks. The proposed model is described in Section [3.](#page-3-0) The results are shown in Section [4](#page-9-0) and discussed in Section [5.](#page-12-0) Section [6](#page-14-0) presents our conclusions from the work.
diff --git a/2201.13100/main_diagram/main_diagram.drawio b/2201.13100/main_diagram/main_diagram.drawio
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+++ b/2201.13100/main_diagram/main_diagram.drawio
@@ -0,0 +1 @@
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+# Introduction
+
+The goal of Masked image modeling (MIM) is to learn image representations, in a self-supervised fashion, by occluding parts of the input images. MIM is inspired by significant advances in natural language modelling such as BERT [\(Devlin et al.,](#page-9-0) [2019\)](#page-9-0), where the model is trained to fill-in words randomly removed from a sentence [\(Fig. 1,](#page-0-0) top row). Recent work, including MAE [\(He et al.,](#page-10-0) [2021\)](#page-10-0)
+
+*Proceedings of the* 39 th *International Conference on Machine Learning*, Baltimore, Maryland, USA, PMLR 162, 2022. Copyright 2022 by the author(s).
+
+
+
+Figure 1. Self-supervised language, and vision, models learn representations by imputing data removed by masking. *BERT*: random word masks; *Context encoder*: random, fix-shaped mask; *BEiT*: random 'blockwise' masking; *MAE*: randomly mask out 75% of the image; *ADIOS*: multiple masks (N=3) generated by an adversarially trained masking model, post-processed with fully connected conditional random fields [\(Krähenbühl & Koltun,](#page-10-1) [2011\)](#page-10-1).
+
+and BEiT [\(Bao et al.,](#page-9-1) [2021\)](#page-9-1), show that these gains are at least partially transferable to vision. The task of a MIM model is therefore similar to BERT, e.g., given an image of a bird in [Fig. 1,](#page-0-0) it needs to reason about what the bird might be sitting on or what colour the bird's belly is given the visible context (bottom row). However, while missing words describe *whole* semantic entities (e.g. "head"), the masks used for context encoder [\(Pathak et al.](#page-10-2) [\(2016\)](#page-10-2), which pioneered MIM), BEiT and MAE typically have no such constraint [\(Fig. 1](#page-0-0) bottom, left to right). Imputation under such schemes is conceptually simpler, as random masking only *partially* obscures meaningful visual entities, which allows easier inference of missing values by leveraging strong correlations at the local-pixel level[1](#page-0-1) .
+
+To narrow the gap between pixel masking and word masking, we posit that one needs to occlude *whole entities in the image*. This encourages the model to perform imputation by complex semantic reasoning using the unmasked context (e.g. given a bird with a yellow body, it is likely to have a yellow head) rather than leveraging simple local correlations, which can benefit representation learning. Interestingly, [He et al.](#page-10-0) [\(2021\)](#page-10-0) are motivated by similar hypothesis and propose to occlude a large fraction (up to 75%) of the image, removing complete entities by a higher chance, which they find is essential for good performance. Here, we
+
+1University of Oxford 2The University of Edinburgh & The Alan Turing Institute 3DeepMind. Correspondence to: Yuge Shi , Adam Kosiorek .
+
+1Akin to randomly masking letters in a sentence for NLP.
+
+suggest that it is actually *what is masked*, not so much *how much is masked*, that is crucial for effective self-supervised representation learning.
+
+To this end, we investigate learning to mask with an adversarial objective, where an occlusion model is asked to make reasoning about missing parts of the scene more difficult. This novel representation-learning algorithm, called Adversarial Inference-Occlusion Self-supervision (ADIOS), can identify and mask out regions of correlated pixels within an image [\(Fig. 1,](#page-0-0) bottom right), which brings it closer to the word-masking regime in natural language. And as we shall see in Section [3,](#page-3-0) it consistently improves performance of state-of-the-art self-supervised learning (SSL) algorithms.
+
+Some MIM methods employ a generative component for representation learning, by learning to reconstruct the masked image. However, it has been shown [\(Bao et al.,](#page-9-1) [2021;](#page-9-1) [Ramesh et al.,](#page-10-3) [2021\)](#page-10-3) that pixel-level reconstruction tasks waste modelling capacity on high-frequency details over low-frequency structure, leading to subpar performance. We hence frame ADIOS as an encoder-only framework that minimises the distance between the *representations* of the original image and the masked image. The occlusion model, which is trained adversarially to the encoder, tries to minimises this same distance. We further discuss in Section [2.1](#page-1-0) that, compared to the generative setup, the encoder-only setup optimises a functionally superior objective for representation learning. Note that the encoder objective is compatible with many recent augmentationbased Siamese self-supervised learning (SSL; [Chen et al.](#page-9-2) [\(2020\)](#page-9-2); [Chen & He](#page-9-3) [\(2021\)](#page-9-3)) methods. We show that ADIOS consistently improves performance of these SSL objectives, showcasing the generality of our approach.
+
+Our main contributions are as follows,
+
+- 1. A novel adversarial Siamese-style MIM framework, that unlike other MIM methods is not limited to using ViT as backbone—advantageous given recent discoveries of modernised-convnet superiority over ViTs [\(Liu et al.,](#page-10-4) [2022;](#page-10-4) [Touvron et al.,](#page-11-1) [2021\)](#page-11-1);
+- 2. Qualitative and quantitative analyses showing that masks generated by ADIOS are semantically meaningful;
+- 3. Analysis of how different masking schemes affect representation-learning performance of SSL models. We find models trained with ADIOS and ground-truth object masks significantly outperform other masking schemes/no mask, demonstrating the efficacy of semantically meaningful masks for representation learning.
+
+# Method
+
+Set up ADIOS consists of two components, inference model I and occlusion model M (see [Fig. 2\)](#page-1-0). Given an RGB image x, the occlusion model produces an imagesized mask m = M(x) with values in [0, 1]. The inference model I takes original image x and an occluded image xm = x
+ m (
+ is the Hadamard product) as inputs, generating representations for both, which we denote as z and z m. The two models are learnt by solving for
+
+$$\mathcal{I}^{\star}, \mathcal{M}^{\star} = \arg\min_{\mathcal{I}} \max_{\mathcal{M}} \mathcal{L}(x; \mathcal{I}, \mathcal{M}).$$
+ (1)
+
+We will now discuss different choices for I and M.
+
+As discussed in Section [1,](#page-0-2) the inference model should minimise some distance between the original and masked images. Here, we discuss potential forms of this objective, arriving at our final framework us-
+
+
+
+Figure 2. ADIOS Architecture.
+
+ing augmentation-based SSL methods.
+
+Distance in pixel space One option would be to inpaint the masked image with the inference model, and train I by minimising the distance between the inpainted image and the original image in pixel space. More specifically, we can define I as an auto-encoder consist-
+
+
+
+Figure 3. Inpainting. ing of an encoder and decoder, which takes the masked image x m as input and produces inpainted image xˆ (see [Fig. 3\)](#page-1-1). The model can be trained using the following reconstruction loss
+
+$$\mathcal{L}_{AE}(\boldsymbol{x}; \mathcal{I}, \mathcal{M}) = \mathcal{D}(\boldsymbol{x}, \hat{\boldsymbol{x}}) = \mathcal{D}(\boldsymbol{x}, \mathcal{I}(\boldsymbol{x} \odot \mathcal{M}(\boldsymbol{x}))),$$
+ (2)
+
+where D denotes some distance metric defined in pixel space. Minimising [\(2\)](#page-1-2) encourages the auto-encoder to impute the missing part of the image as accurately as possible. LAE can then be used in [\(1\)](#page-1-3) to train the inference-occlusion model.
+
+Distance in representation space An interesting question for auto-encoding I is: where does the imputation happen? Multiple hypotheses exist: 1 *Encoder only:* The encoder q completely recovers the missing information in the masked image, and in the ideal case, q(M(x)) = q(x); the decoder faithfully reconstructs these representations. 2 *Decoder only:* The encoder faithfully extracts all information from the masked image, and the decoder reasons to inpaint missing pixel from the representations; 3 *Both:* The encoder and decoder both inpaint parts of the image.
+
+Given these scenarios, 1 is clearly best suited for representation learning, as it requires the encoder to reason about the missing parts based on observed context, beyond just extracting image features. With representation learning, rather
+
+than inpainting, being our end goal, the key challenge lies in designing an objective targetting scenario (1), such that we learn the most expressive version of encoder q.
+
+A key feature in (1) is that when the encoder recovers all information of the original image, $q(x^m) = q(x)$ , the features extracted from the partially observed image $x^m$ should in principle be the same as those extracted from the unmasked image x. We thus propose an inference model $\mathcal{I}$ consisting of only the encoder, which extracts representation $z = \mathcal{I}(x), z \in \mathbb{R}^d$ . Our objective can thus be written as
+
+$$\mathcal{L}_{\text{ENC}}(\boldsymbol{x}; \mathcal{I}, \mathcal{M}) = \mathcal{D}(\boldsymbol{z}, \boldsymbol{z}^{m}) = \mathcal{D}(\mathcal{I}(\boldsymbol{x}), \mathcal{I}(\boldsymbol{x} \odot \mathcal{M}(\boldsymbol{x})))$$
+ (3)
+
+where $\mathcal{D}$ is some distance metric defined in $\mathbb{R}^d$ . Not only does $\mathcal{L}_{ENC}$ encourages the learning of more expressive encoder that can infer missing information, optimising this objective also does not involve generative component p, which is redundant for representation learning.
+
+framework
+
+(Bromley et al., 1993). However, the objective can be trivially minimised when the representations for all
+
+
+
+Figure 4. Left: SimCLR. Right: Sim-CLR + ADIOS.
+
+inputs "collapse" to a constant. This phenomenon, known as latent collapse, has been addressed in many ways in augmentation-based SSL.
+
+Let us take SimCLR (Chen et al., 2020) as an example (see Fig. 4, left); given a minibatch of M input images $x = \{x_i\}_{i=1}^M$ , two sets of random augmentations A and B are applied to each image in x, yielding $x^A$ and $x^B$ . The same encoding function $\mathcal{I}$ is used to extract representations from both sets of augmented views, yielding $z^A = \mathcal{I}(x^A)$ and $z^B = \mathcal{I}(x^B)$ . The objective of SimCLR is defined as
+
+$$\mathcal{L}_{\text{SimCLR}}(\boldsymbol{x}; \mathcal{I}) = \log \frac{\exp(\mathcal{D}(\boldsymbol{z}_i^A, \boldsymbol{z}_i^B))}{\sum_{i \neq j} \exp(\mathcal{D}(\boldsymbol{z}_i^A, \boldsymbol{z}_j^B))}, \quad (4)$$
+
+where $\mathcal{D}$ denotes the negative cosine similarity2. Intuitively, the objective minimises the distance between representations of the two augmented views of the same image (i.e. $z_i^A, z_i^B$ ), while repulsing the representations of different images (i.e. $z_i^A, z_j^B$ ). This effectively prevents the representations of different images from collapsing to the same constant, while optimising an objective similar to (3).
+
+We can use the SimCLR objective for our model by masking one of the augmented images and then follow the exact same pipeline (see Fig. 4, right). More specifically, we replace $z^A$ by $z^{A,m} = \mathcal{I}(x^A \odot m^A)$ , where $m^A = \mathcal{M}(x^A)$ is a mask generated by the occlusion model given $x^A$ . Following (4), we can write the SimCLR-ADIOS objective as
+
+
+$$\mathcal{L}_{\text{SimCLR}}^{\text{ADIOS}}(\boldsymbol{x}; \mathcal{I}, \mathcal{M}) = \log \frac{\exp(\mathcal{D}(\boldsymbol{z}_{i}^{A, \boldsymbol{m}}, \boldsymbol{z}_{i}^{B}))}{\sum_{i \neq j} \exp(\mathcal{D}(\boldsymbol{z}_{i}^{A, \boldsymbol{m}}, \boldsymbol{z}_{j}^{B}))}$$
+
+$$= \log \frac{\exp(\mathcal{D}(\mathcal{I}(\boldsymbol{x}_{i}^{A} \odot \mathcal{M}(\boldsymbol{x}_{i}^{A})), \mathcal{I}(\boldsymbol{x}_{i}^{B})))}{\sum_{i \neq j} \exp(\mathcal{D}(\mathcal{I}(\boldsymbol{x}_{i}^{A} \odot \mathcal{M}(\boldsymbol{x}_{i}^{A})), \mathcal{I}(\boldsymbol{x}_{j}^{B})))}. \quad (5)$$
+
+Again, we can use (5) in (1) to train the inference-occlusion model. Crucially, any SSL method that compares two augmented image views includes a term like (3), and can be plugged in to our framework. We conduct experiments using SimCLR, BYOL (Grill et al., 2020), and SimSiam (Chen & He, 2021) objectives and show significant improvement on downstream task performance with each method. Refer to Appendix A for the ADIOS objective used for BYOL and SimSiam, as well as more details on the SimCLR objective.
+
+For simplicity, we only consider the single-mask-generating case in the discussion above. In practice, since an image typically contains multiple components, we generate N > 1masks to challenge the model to reason about relations between different components—empirical performance confirm benefits of doing so.
+
+There are many parametric forms $\mathcal{M}$ could employ. For instance, one could consider generating multiple masks sequentially in an auto-regressive manner as seen in Engelcke et al. (2021). However, we find that the simplest setup suffices, where $\mathcal{M}: \mathbb{R}^{c \times w \times h} \mapsto \mathbb{R}^{N \times w \times h}$ consists of a learnable neural network and a pixelwise softmax layer $\sigma$ applied across N masks to ensure that the sum of a given pixel across all the masks equals 1. We use U-Net as the backbone of our occlusion model—see Appendix B for more details. Note that we experimented with binarising the masks during training, but found that this did not yield improvements, and hence used real-valued masks directly.
+
+we present ADIOS in its complete This includes the N-mask occlusion model, which generates masks $\{\boldsymbol{m}^{(n)}\}_{n=1}^N$ from the RGB image x. The
+
+
+
+Figure 5. ADIOS, N > 1.
+
+inference model computes a loss $\mathcal{L}^{(n)}(x;\mathcal{I},\mathcal{M})$ for each $m^{(n)}$ and the final loss is computed by averaging across N
+
+<sup>2We omit the SimCLR temperature parameter for simplicity.
+
+masks
+
+$$\mathcal{I}^{\star}, \mathcal{M}^{\star} = \arg\min_{\mathcal{I}} \max_{\mathcal{M}} \frac{1}{N} \sum_{n=1}^{N} \mathcal{L}^{(n)}(\boldsymbol{x}; \mathcal{I}, \mathcal{M}).$$
+ (6)
+
+**Sparsity penalty** A trivial solution exists for this objective (6), where, for masks $\{m^{(1)},\ldots,m^{(N)}\}$ , some mask $m^{(n)}$ occludes everything, with the other $\{N\}_{\backslash n}$ masks not occluding anything. To avoid such degenerate solutions, we introduce a sparsity penalty $p_n$ in the form of $1/\sin(\cdot)$ that discourages the occlusion model from generations.
+
+
+
+Figure 6. Penalty.
+
+ating all-one or all-zero masks; specifically,
+
+$$p_n = \sin\left(\frac{\pi}{hw}\sum_{i=1}^h \sum_{j=1}^w m_{ij}^{(n)}\right)^{-1}$$
+ (7)
+
+Note that, $p_n$ goes to infinity as $m^{(n)}$ approaches all-one or all-zero (see Fig. 6). Minimising $p_n$ with respect to $\mathcal{M}$ encourages the occlusion model to generate semantically meaningful mask, while avoiding degenerate solutions.
+
+**Final objective** Let $\lambda$ the scaling of the penalty term. Our complete objective reads as
+
+$$\mathcal{I}^{\star}, \mathcal{M}^{\star} = \arg\min_{\mathcal{I}} \max_{\mathcal{M}} \frac{1}{N} \sum_{n=1}^{N} \left( \mathcal{L}^{(n)}(\boldsymbol{x}; \mathcal{I}, \mathcal{M}) - \lambda p_n \right).$$
+(8)
+
+**Lightweight ADIOS** Despite its strong empirical performance, we note that the training objective in (8) requires N forward passes, which can be computationally expensive as we increase N for more complex data. We therefore develop a lightweight version of ADIOS, where we randomly sample one from the N generated masks to be applied to the input image. Doing so disassociates the computational cost of the model from the number of generated masks, and the only cost increase comes from applying the mask generation model once, which is inexpensive (10% the size of ResNet18). We name this single-forward pass version of our model ADIOS-s, and write the objective as
+
+$$\mathcal{I}^{\star}, \mathcal{M}^{\star} = \arg\min_{\mathcal{I}} \max_{\mathcal{M}} \left( \mathcal{L}^{(k)}(\boldsymbol{x}; \mathcal{I}, \mathcal{M}) - \lambda \frac{1}{N} \sum_{n=1}^{N} p_n \right),$$
+where $k \sim \text{Uniform}(\{1, 2..., N\})$ . (9)
diff --git a/2203.11131/main_diagram/main_diagram.drawio b/2203.11131/main_diagram/main_diagram.drawio
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+# Introduction
+
+The field of evaluation metrics for Natural Language Generation (NLG) is currently in a deep crisis: While multiple high-quality evaluation metrics (Zhao et al. 2019; Zhang et al. 2020; Rei et al. 2020; Sellam, Das, and Parikh 2020; Yuan, Neubig, and Liu 2021) have been developed in the last few years, the Natural Language Processing (NLP) community seems reluctant to adopt them to assess NLG systems (Marie, Fujita, and Rubino 2021; Gehrmann, Clark, and Sellam 2022). In fact, the empirical investigation of Marie, Fujita, and Rubino (2021) shows that the vast majority of machine translation (MT) papers (exclusively) relies on surface-level evaluation metrics like BLEU and ROUGE (Papineni et al. 2002; Lin 2004) for evaluation, which were invented two decades ago, and the situation has allegedly even worsened recently. These surface-level metrics cannot measure semantic similarity of their inputs and are thus fundamentally flawed, particularly when it comes to assessing the quality of recent state-of-theart NLG systems (Peyrard 2019), calling the credibility of a whole scientific field in question.
+
+We argue that the potential reasons for this neglect of recent high-quality metrics include: (i) non-enforcement by reviewers; (ii) easier comparison to previous research, e.g., by copying BLEU-based results from tables of related work (potentially a pitfall in itself); (iii) computational inefficiency to run expensive new metrics at large scale; (iv) lack of trust in and transparency of high-quality black box metrics.
+
+In this work, we concern ourselves with the last named reason, explainability. In recent years, explainability in Artificial Intelligence (AI) has been developed and studied extensively due to several needs (Samek, Wiegand, and M¨uller 2018; Vaughan and Wallach 2020). For
+
+users of the AI systems, explanations help them make more informed decisions (especially in high-stake domains) (Sachan et al. 2020; Lertvittayakumjorn et al. 2021), better understand and hence gain trust of the AI systems (Pu and Chen 2006; Toreini et al. 2020), and even learn from the AI systems to accomplish the tasks more successfully (Mac Aodha et al. 2018; Lai, Liu, and Tan 2020). For AI system designers and developers, explanations allow them identify the problems and weaknesses of the system (Krause, Perer, and Ng 2016; Han, Wallace, and Tsvetkov 2020), calibrate the confidence of the system (Zhang, Liao, and Bellamy 2020), and improve the system accordingly (Kulesza et al. 2015; Lertvittayakumjorn and Toni 2021).
+
+Explanability is particularly desirable for evaluation metrics. Sai, Mohankumar, and Khapra (2020) suggest that explainable NLG metrics should focus on providing more information than just a single score (such as fluency or adequacy). Celikyilmaz, Clark, and Gao (2020) stress the need for explainable evaluation metrics to spot system quality issues and to achieve a higher trust in the evaluation of NLG systems. Explanations indeed play a vital role in building trust for new evaluation metrics.1 For instance, if the explanations for the scores align well with human reasoning, the metric will likely be better accepted by the research community. By contrast, if the explanations are counter-intuitive, users and developers will lower their trust and be alerted to take additional actions, such as trying to improve the metrics using insights from the explanations or looking for alternative metrics that are more trustworthy. Furthermore, explainable metrics can used for other purposes: e.g., when a metric produces a low score for a given input, the highlighted words (a widely used method for explanation, see Section 6) in the input are natural candidates for manual post-editing.
+
+This concept paper aims at providing a systematic overview over the existing efforts in explainable NLG evaluation metrics and at an outlook for promising future research directions. We first provide backgrounds on evaluation metrics (Section 2) and explainability (Section 3), and discuss the goals and properties of explainable evaluation metrics (Section 4). Then we review and discuss existing datasets (Section 5) and methods (Section 6) for explainable metrics, covering both local and global methods for providing explanations; see Figure 1 for an overview. To better understand the properties (e.g. faithfulness and plausibility) of different explainable evaluation metrics, we test and compare multiple representative methods under three newlyproposed experimental setups and present the results in Section 7. Our focus in this context is particularly on the relation between explainability and adversarial techniques, where we examine whether the latter can automatically identify limitations of existing evaluation metrics, thereby promoting a better understanding of their weaknesses. At last, we discuss promising ideas for future work (Section 8) and conclude the paper (Section 9).
+
+We mainly focus on the explainable evaluation metrics for MT in this work, as it is one of the most representative NLG tasks. Most of our observations and discussions can easily be adapted to other NLG tasks, however. Source code for our experiments are publicly available at .
+
+1As one illustrating example, we mention the recent paper of Moosavi et al. (2021) (also personal communication with the authors). In the paper, the authors express their distrust for the metric BERTScore applied to a novel text generation task. In particular, they point out that the BERTScore of a non-sensical output is 0.74, which could be taken as an unreasonably high value. While BERTScore may indeed be unsuitable for their novel task, the score of 0.74 is meaningless here, as evaluation metrics may have arbitrary ranges. In fact, BERTScore typically has a particularly narrow range, so a score of 0.74 even for bad output may not be surprising. Explainability techniques would be very helpful in preventing such misunderstandings.
+
+
+
+Figure 1: Current explanations for NLG evaluation metrics fall into three categories. Explanation through error highlights explains a metric score by highlighting word-level errors in a translation. Explanation through adversarial samples checks whether the metrics perform reasonably on adversarial samples. Explanation through disentanglement splits the metric score into different components.
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+# Introduction
+
+
+
+
+
+Overview of the existing privacy preserving action recognition approaches. The main goals of a framework include removing privacy information and maintaining action recognition performance at low cost of annotations.
+
+
+Recent advances in action recognition have enabled a wide range of real-world applications, video surveillance camera [@cmu2020; @rizve2021gabriella; @gabv2], smart shopping systems like *Amazon Go*, elderly person monitor systems [@buzzelli2020vision; @zhang2012privacy; @liu2020privacy]. Most of these video understanding applications involve extensive computation, for which a user needs to share the video data to the cloud computation server. While sharing the videos to the cloud server for the utility action recognition task, the user also ends up sharing the private visual information like gender, skin color, clothing, background objects etc. in the videos as shown in Fig. [1](#fig:teaser){reference-type="ref" reference="fig:teaser"}. Therefore, there is a pressing need for solutions to privacy preserving action recognition.
+
+A simple-yet-effective solution for privacy preservation in action recognition is to utilize very low resolution videos (Fig. [1](#fig:teaser){reference-type="ref" reference="fig:teaser"}a) [@ryoo; @ishwar; @liu2020indoor]. Although this downsampling method does not require any specialized training to remove privacy features, it does not provide a good trade-off between action recognition performance and privacy preservation.
+
+Another set of methods use pretrained object-detectors to detect the privacy regions and then remove or modify the detected regions using synthesis [@ren2018learning] or blurring [@zhang2021multi] as shown in Fig. [1](#fig:teaser){reference-type="ref" reference="fig:teaser"}b. The detection-based approaches require the bounding-box level annotations for the privacy attributes, and removing the privacy features without an end-to-end learning framework may result in the performance drop of the action recognition task.
+
+Wu [@wu_tpami] propose a novel approach to remove the privacy features via learning an *anonymization function* through an adversarial training framework, which requires both *action and privacy labels* from the video. Although the method is able to get a good trade-off of action recognition and privacy preservation, it has two main problems. First, it is not feasible to annotate a video dataset for privacy attributes. For instance, Wu [@wu_tpami] acknowledge the issue of privacy annotation time, where it takes immense efforts for them to annotate privacy attributes for even a small-scale (515 videos) video dataset *PA-HMDB*. Second, the learned anonymization function from the known privacy attributes may not generalize in anonymizing the *novel privacy attributes*. For example, in Fig. [1](#fig:teaser){reference-type="ref" reference="fig:teaser"} the learned anonymization function for human-related privacy attributes (gender, skin color, clothing) may still leave other privacy information like scene or background objects un-anonymized.
+
+The performance of the action recognition task depends on the spatio-temporal cues of the input video. Wu [@wu_tpami] show that anonymizing the privacy features like face, gender, etc. in the input video does not lead to any reduction in the action recognition performance. Instead of just focusing on the cues based on the privacy annotations, our goal is twofold: 1) learning an anonymization function that can remove all spatial cues in all frames without significantly degrading action recognition performance; and 2) learning the anonymization function without any privacy annotations.
+
+Recently, self-supervised learning (SSL) methods have been successfully used to learn the representative features which are suitable for numerous downstream tasks including classification, segmentation, detection, etc. Towards our goal, we propose a novel frame-level SSL method to remove the semantic information from the input video, while maintaining the information that is useful for the action recognition task. We show that our proposed ***S**elf-supervised **P**rivacy-preserving **Act**ion recognition (SPAct)* framework is able to anonymize the video without requiring any privacy annotations in the training.
+
+The learned anonymization function should provide a model-agnostic privacy preservation, hence, we first adopt the protocol from [@wu_tpami] to show the transferability of the anonymization function across different models. *However, there are two aspects in terms of evaluating the generalization ability of the anonymization function, which are overlooked in previous works.*
+
+First, in the real-world scenario, the anonymization function is expected to have *generalization capability with domain shift in action and privacy classes*. To evaluate the generalization capabilities of the anonymization function across novel action and privacy attributes, we propose new protocols. In our experiments, we show that since our model is not dependent on the predefined privacy features like existing supervised methods, and it achieves state-of-the-art generalization across novel privacy attributes.
+
+Second, prior privacy-preserving action recognition works have solely focused on privacy attributes of humans. In practical scenarios, privacy leakage can happen in terms of scene and background objects as well, which could reveal personal identifiable information. Therefore, *the generalization ability of anonymization function to preserve privacy attributes beyond humans (scene and object privacy)* is of paramount importance as well. To evaluate such ability, we propose *P-HVU* dataset, a subset of LSHVU dataset [@hvu], which has multi-label annotations for actions, objects and scenes. Compared to existing same-dataset privacy-action evaluation protocol on PA-HMDB [@wu_tpami], which consists of only 515 test videos, the proposed P-HVU dataset has about 16,000 test videos for robust evaluation of privacy-preserving action recognition.
+
+- We introduce a novel *self-supervised* learning framework for privacy preserving action recognition without requiring any privacy attribute labels.
+
+- On the existing UCF101-VISPR and PA-HMDB evaluation protocols, our framework achieves a competitive performance compared to the state-of-the-art *supervised* methods which require privacy labels.
+
+- We propose new evaluation protocols for the learned anonymization function to evaluate its generalization capability across novel action and novel privacy attributes. For these protocols, we also show that our method outperforms state-of-the art supervised methods. Finally, we propose a new dataset split *P-HVU* to resolve the issue of smaller evaluation set and extend the privacy evaluation to non-human attributes like action scene and objects.
+
+# Method
+
+The key idea of our proposed framework is to learn an anonymization function such that it deteriorates the privacy attributes without requiring any privacy labels in the training, and maintains the performance of action recognition task. We build our self-supervised framework upon the existing supervised adversarial training framework of [@wu_tpami]. A schematic of our framework is depicted in Fig. [2](#fig:framework){reference-type="ref" reference="fig:framework"}. In Sec [3.1](#sec:formulation){reference-type="ref" reference="sec:formulation"}, we first formulate the problem by explaining our objective. In Sec [3.2](#sec:framework){reference-type="ref" reference="sec:framework"} we present details of each component of our framework, and in Sec [3.3](#sec:minimax){reference-type="ref" reference="sec:minimax"} we explain the optimization algorithm employed in our framework.
+
+Let's consider a video dataset $X$ with action recognition as an utility task, $T$, and privacy attribute classification as a budget task, $B$. The goal of a privacy preserving action recognition system is to maintain performance of $T$, while cutting the budget $B$. This goal is achieved by learning an anonymization function, $f_{A}$, which transforms (anonymize) the original raw data $X$. Assume that the final system has *any* action classification target model $f'_{T}$ and *any* privacy target model $f'_{B}$. The goal of a privacy preserving training is to find an optimal point of $f_{A}$ called $f^{*}_{A}$, which is achieved by the following two criteria:
+
+**C1:** $f^{*}_{A}$ should minimally affect the cost function of target model, $f'_{T}$, on raw data i.e. $$\begin{equation}
+ L_{T}(f'_{T}(f^{*}_{A}(X)), Y_{T}) \approx L_{T}(f'_{T}(X), Y_{T}),
+ \label{eq:ft_criterion}
+\end{equation}$$ where $T$ denotes utility Task, $L_{T}$ is the loss function which is the standard cross entropy in case of single action label $Y_{T}$ or binary cross entropy in case of multi-label actions $Y_{T}$.
+
+**C2:** Cost of privacy target model, $f'_{B}$, should increase on the transformed (anonymized) data compared to raw data i.e. $$\begin{equation}
+ L_{B}(f'_{B}(f^{*}_{A}(X))) \gg L_{B}(f'_{B}(X)),
+ \label{eq:fb_criterion}
+\end{equation}$$ where $B$ denotes privacy Budget, $L_{B}$ is the self-supervised loss for our framework, and binary cross entropy in case of a supervised framework, which requires privacy label annotations $Y_{B}$.
+
+Increasing a self-supervised loss $L_{B}$ results in deteriorating *all* useful information regardless of if it is about privacy attributes or not. However, the useful information for the action recognition is preserved via criterion **C1**. Combining criteria **C1** and **C2**, we can mathematically write the privacy preserving optimization equation as follows, where negative sign before $L_{B}$ indicates it is optimized by maximizing it: $$\begin{equation}
+ f^{*}_{A} = \operatorname*{argmin}_{f_{A}}[L_{T}(f'_{T}(f_{A}(X)), Y_{T}) - L_{B}(f'_{B}(f_{A}(X)))].
+ \label{eq:overall}
+\end{equation}$$
+
+The proposed framework mainly consists of three components as shown in Fig [2](#fig:framework){reference-type="ref" reference="fig:framework"}: (1) Anonymization function ($f_{A}$); (2) Self-supervised privacy removal branch; and (3) Action recognition or utility branch.
+
+The anonymization function is a learnable transformation function, which transforms the video in such a way that the transformed information can be useful to learn action classification on *any* target model, $f'_{T}$, and not useful to learn *any* privacy target model, $f'_{B}$. We utilize an encoder-decoder neural network as the anonymization function. $f_{A}$ is initialized as an identity function by training it using $\mathcal{L}_{L1}$ reconstruction loss as given below: $$\begin{equation}
+ \mathcal{L}_{L1} = \sum_{c=1}^{C}\sum_{h=1}^{H}\sum_{w=1}^{W}|x_{c,h,w}- \hat{x}_{c,h,w}|,
+ \label{eq:l1loss}
+\end{equation}$$ where, $x$ is input image, $\hat{x}$ is sigmoid output of $f_{A}$ logits, $C$ = input channels, $H$ = input height, and $W$ = input width.
+
+[]{#sec:sslbranch label="sec:sslbranch"} A schematic of self-supervised privacy removal branch is shown in Fig. [3](#fig:sslloss){reference-type="ref" reference="fig:sslloss"}. First the video $x_{i}$ is passed through $f_{A}$ to get the anonymized video $f_{A}(x_{i})$, which is further passed through a temporal Frame sampler $S_{F}$. $S_{F}$ samples 2 frames out of the video with various $S_{F}$ strategies, which are studied in Section [\[sec:ablation\]](#sec:ablation){reference-type="ref" reference="sec:ablation"}. The sampled pair of frames ($S_{F}$($f_{A}$($x_{i}$))) are projected into the representation space through 2D-CNN backbone $f_{B}$ and a non-linear projection head $g(\cdot)$. The pair of frames of video $x_{i}$ corresponds to projection $Z_{i}$ and $Z'_{i}$ in the representation space. The goal of the contrastive loss is to maximize the agreement between projection pair ($Z_{i}$, $Z'_{i}$) of the same video $x_{i}$, while maximizing the disagreement between projection pairs of different videos ($Z_{i}$, $Z_{j}$), where $j \neq i$. The NT-Xent contrastive loss [@simclr] for a batch of $N$ videos is given as follows: $$\begin{equation}
+\label{eq:ntxent}
+ L_{B}^{i}=-\log \frac{h\left(Z_{i}, Z'_{i}\right)}{\sum_{j=1}^{N}[\mathbb{1}_{[j\neq i]} h(Z_{i}, Z_{j}) + h(Z_{i}, Z'_{j})]},
+\end{equation}$$ where $h(u, v)=\exp \left(u^{T}v/(\|u\| \|v\| \tau) \right)$ is used to compute the similarity between $u$ and $v$ vectors with an adjustable parameter temperature, $\tau$. $\mathbb{1}_{[j\neq i]} \in \{0, 1\}$ is an indicator function which equals 1 iff $j \neq i$.
+
+
+
+
+
+Contrastive Self-supervised Loss is used to maximize the agreement between two frames of a video and maximize disagreement between frames of different videos. Please refer to Sec [sec:sslbranch] for more details.
+
+
+In order to optimize the proposed self-supervised framework with the objective of Eq. [\[eq:overall\]](#eq:overall){reference-type="ref" reference="eq:overall"}, let's consider anonymization function $f_{A}$ parameterized by $\theta_{A}$, and auxiliary models $f_{B}$ and $f_{T}$ respectively parameterized by $\theta_{B}$ and $\theta_{T}$. Assume, $\alpha_A$, $\alpha_B$, $\alpha_T$ respectively be the learning rates for $\theta_A$, $\theta_B$, $\theta_T$. First of all, $\theta_{A}$ is initialized as given below ( Eq. [\[eq:fa_init\]](#eq:fa_init){reference-type="ref" reference="eq:fa_init"}), unless $f_{A}$ reaches to threshold $th_{A0}$ reconstruction performance ( Eq. [\[eq:l1loss\]](#eq:l1loss){reference-type="ref" reference="eq:l1loss"}) on validation set: $$\begin{equation}
+ \theta_A \gets \theta_A - \alpha_A \nabla_{\theta_A} (\mathcal{L}_{L1}(\theta_A)).
+ \label{eq:fa_init}
+ \vspace{-2mm}
+\end{equation}$$ Once $\theta_{A}$ is initialized, it is utilized for initialization of $\theta_{T}$ and $\theta_{B}$ as shown in the following equations unless their performance reaches to the loss values of $th_{B0}$ and $th_{T0}$ : $$\begin{equation}
+ \theta_T \gets \theta_T - \alpha_T \nabla_{\theta_T} (L_{T}(\theta_T, \theta_A)),
+ \label{eq:ft_init}
+\end{equation}$$ $$\begin{equation}
+ \theta_B \gets \theta_B - \alpha_B \nabla_{\theta_B} (L_{B}(\theta_B, \theta_A)).
+ \label{eq:fb_init}
+\end{equation}$$ After the initialization, two step iterative optimization process takes place. The first step is depicted in the left side of Fig. [2](#fig:framework){reference-type="ref" reference="fig:framework"}, where $\theta_{A}$ is updated using the following equation: $$\begin{equation}
+ \theta_A \gets \theta_A - \alpha_A \nabla_{\theta_A} (L_T(\theta_A,\theta_T) - \omega L_B(\theta_A, \theta_B)),
+ \label{eq:faupdate}
+\end{equation}$$
+
+where $\omega\in(0,1)$ is the relative weight of SSL loss, $L_{B}$, with respect to supervised action classification loss, $L_{T}$. Here the negative sign before $L_{B}$ indicates that we want to maximize it. In implementation, it can be simply achieved by using negative gradients [@domainadaptation].
+
+In the second step, as shown in the right part of the Fig. [2](#fig:framework){reference-type="ref" reference="fig:framework"}, $\theta_{T}$ and $\theta_{B}$ are updated using Eq. [\[eq:ft_init\]](#eq:ft_init){reference-type="ref" reference="eq:ft_init"} and [\[eq:fb_init\]](#eq:fb_init){reference-type="ref" reference="eq:fb_init"}, respectively. We update $\theta_{B}$ to get powerful negative gradients in the next iteration's step-1. Note that there is a similarity with GAN training here; we can think of $f_{A}$ as the a generator who tries to fool $f_{B}$ in the first step and, in the second step $f_{B}$ tries to get stronger through update of Eq. [\[eq:fb_init\]](#eq:fb_init){reference-type="ref" reference="eq:fb_init"}. This two step iterative optimization process continues until $L_{B}$ reaches to a maximum value $th_{Bmax}$.
+
+Take a model $f_b$ *initialized* with self-supervised contrastive loss (SSL) *pretraining*. Now keeping $f_b$ *frozen*, when we try to *maximize* the contrastive loss, it changes the *input* to $f_b$ in such a way that it decreases *agreement between frames* of the same video. *We know that frames of the same video share a lot of semantic information*, and minimizing the agreement between them results in destroying (i.e. unlearning) most of the semantic info of the input video. In simple terms, maximizing contrastive loss results in destroying all highlighted *attention map* parts of Supp. Fig 7, 8 middle row. Since this unlearned *generic semantic info* contained privacy attributes related to human, scene, and objects; we end up removing *private info* in the input. In this process, we also ensure that semantics related to action reco remains in video, through the action reco branch.
+
+The existing training and evaluation protocols are discussed in Sec [\[sec:knownprotocols\]](#sec:knownprotocols){reference-type="ref" reference="sec:knownprotocols"}, [4.2](#sec:knowncrossdomain){reference-type="ref" reference="sec:knowncrossdomain"} and a new proposed generalization protocol is introduced in Sec [\[sec:novelprotocols\]](#sec:novelprotocols){reference-type="ref" reference="sec:novelprotocols"}.
+
+[]{#sec:knownprotocols label="sec:knownprotocols"}
+
+Training of supervised privacy preserving action recognition method requires a video dataset $X^{t}$ with action labels $Y^{t}_{T}$, and privacy labels $Y^{t}_{B}$, where $t$ denotes training set. Since, our self-supervised privacy removal framework does not require any privacy labels, we do not utilize $Y^{t}_{B}$. Once the training is finished, the anonymization function is now frozen, called $f^{*}_{A}$, and auxiliary models $f_{T}$ and $f_{B}$ are discarded. To evaluate the quality of the learned anonymization, $f^{*}_{A}$ is utilized to train: (1) a new action classifier $f'_{T}$ over the train set ($f^{*}_{A}(X^{t}), Y^{t}_{T}$); and (2) a new privacy classifier $f'_{B}$ to train over ($f^{*}_{A}(X^{t}), Y^{t}_{B}$). For clarification, we do not utilize privacy labels for training $f_{A}$ in any protocol. Privacy labels are used only for the evaluation purpose to train the target model $f'_{B}$. Once the target models $f'_{T}$ and $f'_{B}$ finish training on the anonymized version of train set, they are evaluated on test set ($f^{*}_{A}(X^{e}), Y^{e}_{T}$) and ($f^{*}_{A}(X^{e}), Y^{e}_{B}$), respectively, where $e$ denotes evaluation/test set. Test set performance of the action classifier is denoted as $A^{1}_{T}$ (Top-1 accuracy) or $A^{2}_{T}$ (classwise-mAP), and the performance of privacy classifier is denoted as $A^{1}_{B}$ (classwise-mAP) or $A^{2}_{B}$ (classwise-$F_{1}$). Detailed figures explaining different training and evaluation protocols are provided in Supp.Sec.G.
+
+In practice, a trainable-scale video dataset with action and privacy labels doesn't exist. The authors of [@wu_tpami] remedy the supervised training process by a cross-dataset training and/or evaluating protocol. Two different datasets were utilized in [@wu_tpami]: action annotated dataset ($X^{t}_{action}, Y^{t}_{T}$) to optimize $f_{A}$ and $f_{T}$; and privacy annotated dataset ($X^{t}_{privacy}, Y^{t}_{B}$) to optimize $f_{A}$ and $f_{B}$. Again, note that in this protocol, our self-supervised framework does not utilize $Y^{t}_{B}$. After learning the $f_{A}$ through the different train sets, it is frozen and we call it $f^{*}_{A}$. A new action classifier $f'_{T}$ is trained on anonymized version of action annotated dataset ($f^{*}_{A}(X^{t}_{action}), Y^{t}_{T}$), and a new privacy classifier $f'_{B}$ is trained on the anonymized version of the privacy annotated dataset ($f^{*}_{A}(X^{t}_{privacy}), Y^{t}_{B}$). Once the target models $f'_{T}$ and $f'_{B}$ finish training on the anonymized version of train sets, they are respectively evaluated on test sets ($f^{*}_{A}(X^{e}_{action}), Y^{e}_{T}$) and ($f^{*}_{A}(X^{e}_{privacy}), Y^{e}_{B}$).
+
+[]{#sec:novelprotocols label="sec:novelprotocols"} For the prior two protocols discussed above, the same training set $X^{t}$ ($X^{t}_{action}$ and $X^{t}_{privacy}$) is used for the target models $f'_{T}$, $f'_{B}$ and learning the anonymization function $f_{A}$. However, a learned anonymization function $f^{*}_{A}$ is expected to generalize on any action or privacy attributes. To evaluate the generalization on novel actions, an anonymized verion of novel action set $f^{*}_{A}(X^{nt}_{action})$, such that $Y^{nt}_{T}\cap Y^{t}_{T}= \phi$, is used to train the target action model $f'_{T}$, and its performance is measured on the anonymized test set of novel action set $f^{*}_{A}(X^{ne}_{action})$. For privacy generalization evaluation, a novel privacy set $f^{*}_{A}(X^{nt}_{privacy})$ (s.t. $Y^{nt}_{B}\cap Y^{t}_{B}= \phi$) (where $nt$ represents novel training) is used to train the privacy target model $f'_{B}$, and its performance is measured on novel privacy test set $f^{*}_{A}(X^{ne}_{privacy})$ (where $ne$. represents novel evaluation) Please note that novel privacy attribute protocol may not be referred as a *transfer protocol* for the methods, which do not use privacy attributes $Y^{t}_{B}$ in learning $f_{A}$.
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diff --git a/2205.05071/paper_text/intro_method.md b/2205.05071/paper_text/intro_method.md
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+# Introduction
+
+As Artificial Intelligence (AI), and specifically Natural Language Processing (NLP), scale up to require more computational resources and thereby more energy, there is an increasing focus on efficiency and sustainability [@strubell-etal-2019-energy; @Schwartz:2020]. For example, training a single BERT base model [@devlin-etal-2019-bert] requires as much energy as a trans-American flight [@strubell-etal-2019-energy]. While newer models are arguably more efficient [@fedus2021switch; @borgeaud2022improving; @so2022primer], they are also an order of magnitude larger, raising environmental concerns [@bender2021dangers]. The problem will only worsen with time, as compute requirements double every 10 months [@sevilla2022compute].
+
+{reference-type="ref" reference="sec:model_cards"}), on the Hugging Face Hub.](model_card_climatebert){#fig:example_model_card width="\\columnwidth"}
+
+This problem has been recognized by the NLP community, and a group of NLP researchers has recently proposed a policy document[^2] of recommendations for efficient NLP, aiming to minimize the greenhouse gas (GHG) emissions[^3] resulting from experiments done as part of the research. This proposal is part of a research stream aiming towards *Green NLP* and *Green AI* [@Schwartz:2020], which refers to "AI research that yields novel results while taking into account the computational cost, encouraging a reduction in resources spent."
+
+While the branding of NLP and AI research as *green* has raised some awareness of the environmental impact, the large majority of NLP researchers are still not aware of their environmental impact resulting from training and running of large computational models. This also explains why a research stream in a similar direction (see §[2.1](#sec:automating){reference-type="ref" reference="sec:automating"}), in which software tools are proposed to measure carbon footprint while training models [@lacoste2019quantifying; @henderson2020towards; @anthony2020carbontracker; @lottick2019energy], have not been adopted by the community to a large extent (see §[3](#sec:survey){reference-type="ref" reference="sec:survey"}). However, we claim that climate awareness is essential enough to be promoted in mainstream NLP (rather than only as a niche field) and that positive impact must be an inherent part of the discussion [@rolnick2019tackling; @stede-patz-2021-climate]. Ideally environmental impact should always be taken into consideration, when deciding on which experiments to carry out.
+
+We aim to simplify climate performance reporting in NLP while at the same time increasing awareness to its intricacies. Our contributions are:
+
+- We conduct a survey of environmental impact statements in NLP literature published in the past six years (§[3](#sec:survey){reference-type="ref" reference="sec:survey"}). This survey is conducted across five dimensions that directly influence the environmental impact.
+
+- We delineate the different notions of "efficiency" common in the literature, proposing a taxonomy to facilitate transparent reporting, and identify ten simple dimensions across which researchers can describe the environmental impact resulted by their research (§[4](#sec:best_practices){reference-type="ref" reference="sec:best_practices"}).
+
+- We propose a climate performance model card (§[5](#sec:model_cards){reference-type="ref" reference="sec:model_cards"}) with the main purpose of being practically usable with only limited information about experiments and the underlying computer hardware (see Figure [1](#fig:example_model_card){reference-type="ref" reference="fig:example_model_card"}).
+
+Several tools automate measurement and reporting of energy usage and emissions in ML. @lacoste2019quantifying introduced a simple online calculator[^4] to estimate the amount of carbon emissions produced by training ML models. It can estimate the carbon footprint of GPU compute by manually specifying hardware type, hours used, cloud provider, and region. @henderson2020towards presented a Python package[^5] for consistent, easy, and more accurate reporting of energy, compute, and carbon impacts of ML systems by estimating them and generating standardized "Carbon Impact Statements." @anthony2020carbontracker proposed a Python package[^6] that also has predictive capabilities, and allows proactive and intervention-driven reduction of carbon emissions. Model training can be stopped, at the user's discretion, if the predicted environmental cost is exceeded. @victor_schmidt_2022_6369324 actively maintain a Python package[^7] that, besides estimating impact and generating reports, shows developers how they can lessen emissions by optimizing their code or by using cloud infrastructure in geographical regions with renewable energy sources. @bannour-etal-2021-evaluating surveyed and evaluated these tools and others for an NLP task, finding substantial variation in the reported measures due to different assumptions they make. In summary, automated tools facilitate reporting, but they do not substitute awareness and should not be trusted blindly.
+
+While branding NLP and AI research as *green* increases awareness of the environmental impact, there is a risk that the current framing, which exclusively addresses efficiency, will be perceived as the solution to the problem. Of course, we attribute benevolent motives to the authors of the proposed policy document. Nevertheless, we would like to avoid a situation analogous to a common phenomenon in the financial field, where companies brand themselves as *green* or *sustainable* for branding or financial reasons, without implementing proportional measures in practice to mitigate the negative impact on the environment [@delmas2011drivers]. This malpractice is analogous to *greenwashing*. While this is a general term, one aspect of greenwashing is "a claim suggesting that a product is green based on a narrow set of attributes without attention to other important environmental issues" [@choice2010sins].[^8] Our motivation is in line with the EU Commission's initiative to "require companies to substantiate claims they make about the environmental footprint of their products/services by using standard methods for quantifying them."[^9] While @Schwartz:2020 certainly do not argue that efficiency is *sufficient* for sustainability, this notion, which is potentially implied by the *green* branding, is misleading and even harmful: regardless of the extent of reduction, resources are still consumed, and GHGs are still emitted, among other negative effects. The efficiency mindset aims, at best, to prolong the duration of this situation. However, scaling up the performance of AI to satisfy the increasing demands from consumers risks ignoring the externalities incurred. Concepts such as reciprocity with the environment, which are central in some indigenous worldviews [@kimmerer2013braiding], are absent from the discourse.
+
+A common perception is that *carbon neutrality* can be achieved by compensating for emissions by financial contributions, a practice referred to as *carbon offsets*. This approach is problematic and controversial: the level of carbon prices required to achieve climate goals is highly debated [@https://doi.org/10.1002/wcc.207]. The Intergovernmental Panel on Climate Change (IPCC) and various international organizations like the International Energy Agency (IEA) clearly state that mitigation activities are essential. Compensation activities will be necessary for hard-to-abate-sectors, once all other technological solutions have been implemented, and where mitigation is not (yet) feasible.[^10] Moreover, economic dynamic efficiency requires investments in decarbonization technologies to keep the climate targets within reach. Compensation activities, especially in the afforestation area, delay the needed investments. This delay might exacerbate the likelihood of crossing climate tipping points and/or yields to a disorderly transition to a decarbonized economy [@esrb2016].
+
+The issue of environmental impact is more general and not limited to NLP, but relevant to the entire field of AI: @Schwartz:2020 surveyed papers from ACL, NeurIPS, and CVP. They noted whether authors claim their main contribution to improving accuracy or some related measure, an improvement to efficiency, both, or other. In all the conferences they considered, a large majority of the papers target accuracy. However, we claim that the issue is more complex, and it is not sufficient to consider only the "main contribution." Every paper should ideally have a positive impact or provide sufficient information to discuss meaningful options to reduce and mitigate negative impacts.
+
+
+
+Development of proportions of deep-learning-related *ACL papers discussing public model weights, duration of model training or optimization, energy consumption, location where computations where performed, and emission of GHG. While climate awareness is on the rise, it is still low overall. The numbers were calculated by counting pattern matches for papers in the ACL Anthology.
+
+
+We analyze the statistics of papers in \*ACL venues from 2016--2022 by downloading them from the ACL Anthology.[^11] However, instead of focusing on the main contribution, we look for any discussions on climate-related issues. We identify five dimensions in our study sample and create a regular expression pattern to match text for each (see Appendix [9](#sec:patterns){reference-type="ref" reference="sec:patterns"}). These dimensions are public model weights, duration of model training or optimization, energy consumption, the location where computations are performed, and GHG emissions. If the pattern matches the text of a paper at least once,[^12] we consider that paper as discussing the corresponding category. We derive the proportions of papers by dividing the number of papers discussing a category by the number of deep learning-related papers. We only consider deep learning-related papers, as for these papers, climate-related issues are of much higher relevance than for those using other approaches [@strubell-etal-2019-energy].
+
+Figure [2](#fig:survey_proportions){reference-type="ref" reference="fig:survey_proportions"} shows our findings. In general, researchers discuss climate-related issues more and more in their work. For instance, the proportion of papers that publish their model weights has almost quadrupled from about 1% in 2017 to more than 4% in 2022. We also find an increase in the proportion of papers that provide information on emissions or energy consumption. Nevertheless, the proportion for these categories remains low.
+
+::: {#tab:sample_proportions}
+ ---------------- -------- ---------- -------- ---------- ----------
+ Dimension public duration energy location emission
+ Proportion (%) 13 28 0 3 0
+ ---------------- -------- ---------- -------- ---------- ----------
+
+ : Proportions along dimensions from Figure [2](#fig:survey_proportions){reference-type="ref" reference="fig:survey_proportions"} in a manual annotated sample from EMNLP 2021.
+:::
+
+To complement our automatic pattern-based search approach, we also manually annotate a random sample of 100 papers from EMNLP 2021[^13] for the same five dimensions as before. Table [1](#tab:sample_proportions){reference-type="ref" reference="tab:sample_proportions"} shows the proportions. Borderline cases are counted as "reported" for an optimistic estimate. This leads to the proportions of papers publishing model weights (13%) and the duration of model training or optimization (28%) being much higher than with our automatic approach, which cannot judge borderline cases and is thus more restrictive. Still, these proportions are at a low level. The proportion of papers reporting on the location, energy consumed and GHG emitted are in line with the results from our pattern-based search.
+
+We examine article contents to ensure precision and to elaborate on existing practices. We review papers reporting on at least one dimension according to our pattern-based search or our manual analysis. Interestingly, many papers provide information in the context of reproducibility, publishing code but not necessarily model weights and reporting computation time only for specific steps.
+
+As examples for specific papers that go beyond what is usually expected in terms of reporting, @anderson-gomez-rodriguez-2021-modest evaluate both accuracy and efficiency in dependency parsers, finding that different approaches are preferable depending on whether accuracy, training time or inference time are prioritized. @lakim-etal-2022-holistic provide a detailed holistic assessment of the carbon footprint of an Arabic language model, considering the entire project, including data storage, researcher travel, training and deployment.
+
+Our findings highlight the need to raise awareness of climate-related issues further and find a simple but effective way to report them transparently. Besides awareness and facilitation, incentives to address these issues could be a complementary approach. However, in the rest of this paper we focus on the former "intrinsic" motivation factors, leaving "extrinsic" motivation factors to future work.
+
+Efficiency (alongside accuracy) has been one of the main objectives in NLP (and computer science in general) long before its environmental aspects have been widely considered. In general, it refers to the amount of resources consumed (input) in order to achieve a given goal, such as a specific computation or accuracy in a task (output). Different definitions of efficiency correspond to different concepts of input and output. It is crucial to (1) understand the different concepts, (2) be aware of their differences and consequently their climate impact, and (3) converge towards a set of efficiency measures that will be applied for comparable climate performance evaluation in NLP research.
diff --git a/2205.11867/main_diagram/main_diagram.drawio b/2205.11867/main_diagram/main_diagram.drawio
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index 0000000000000000000000000000000000000000..b2730716618357d70cbec3ba91e5a55f1696ebaa
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+# Introduction
+
+Text-based communication has become indispensable as society accelerates online. In natural language processing, communication between humans and machines has attracted attention, and the development of dialogue systems has been a hot topic. Through the invention of Transformer [@NIPS2017_3f5ee243] and the success of transfer learning (e.g., @radford2018improving [@devlin-etal-2019-bert]), the performance of natural language understanding models and dialogue systems continues to improve. In recent years, there have been studies toward building open-domain neural chatbots that can generate a human-like response [@zhou-etal-2020-design; @adiwardana2020humanlike; @roller-etal-2021-recipes].
+
+One of the keys to building more human-like chatbots is to generate a response that takes into account the emotion of the interlocutor. A human would recognize the emotion of the interlocutor and respond with an appropriate emotion, such as empathy and comfort, or give a response that promotes positive emotion of the interlocutor. Accordingly, developing a chatbot with such a human-like ability [@rashkin-etal-2019-towards; @Lubis_Sakti_Yoshino_Nakamura_2018; @8649596] is essential. Although several dialogue corpora with emotion annotation have been proposed, an utterance is annotated only with a speaker's emotion [@li-etal-2017-dailydialog; @hsu-etal-2018-emotionlines] or a dialogue as a whole is annotated [@rashkin-etal-2019-towards], all of which are not appropriate for enabling the above ability.
+
+In this paper, we propose a method to build an emotion-annotated multi-turn dialogue corpus, which is necessary for developing a dialogue system that can recognize the emotion of an interlocutor and generate a response with an appropriate emotion. We annotate each utterance in a dialogue with an **expressed emotion**, which a speaker put into the utterance, and an **experienced emotion**, which a listener felt when listening to the utterance.
+
+
+
+An example dialogue with expressed and experienced emotions.
+
+
+To construct a multi-turn dialogue corpus annotated with these emotions, we collect dialogues from Twitter and crowdsource their emotion annotation. As a dialogue corpus, we extract tweet sequences where two people speak alternately. For the emotion annotation, we adopt Plutchik's wheel of emotions [@plutchik1980general] as emotion labels and ask crowdworkers whether an utterance indicates each emotion label for expressed and experienced emotion categories. Each utterance is allowed to have multiple emotion labels and has an intensity, strong and weak, according to the number of crowdworkers' votes. We build a Japanese dialogue corpus as a testbed in this paper, but our proposed method can be applied to any language.
+
+Using the above method, we constructed a Japanese emotion-tagged dialogue corpus consisting of 3,828 dialogues and 13,806 utterances.[^1] Statistical analysis of the constructed corpus revealed the characteristics of words for each emotion and the relationship between expressed and experienced emotions. We further conducted experiments to recognize expressed and experienced emotions using BERT [@devlin-etal-2019-bert]. We defined the task of emotion recognition as regression and evaluated BERT's performance using correlation coefficients. The experimental results showed that it was more difficult to infer experienced emotions than expressed emotions, and that multi-task learning of both emotion categories improved the overall performance of emotion recognition. From these results, we can see that expressed and experienced emotions are different, and that it is meaningful to annotate both. We expect that the constructed corpus will facilitate the study on emotion recognition in dialogue and emotion-aware response generation.
+
+# Method
+
+::: {#tab:stats_len}
+ **Length** **\# Dialogues** **\# Utterances**
+ ------------ ------------------ -------------------
+ 2 1,330 2,660
+ 3 1,071 3,213
+ 4 509 2,036
+ 5 310 1,550
+ 6 225 1,350
+ 7 158 1,106
+ 8 134 1,072
+ 9 91 819
+ 2-9 3,828 13,806
+
+ : The statistics of dialogues and utterances.
+:::
+
+::: {#tab:stats_emo}
++:-------------+-----------:+---------:+-----------:+---------:+
+| **Label** | **Expressed** | **Experienced** |
+| +------------+----------+------------+----------+
+| | **Strong** | **Weak** | **Strong** | **Weak** |
++--------------+------------+----------+------------+----------+
+| Anger | 430 | 1,349 | 124 | 870 |
++--------------+------------+----------+------------+----------+
+| Anticipation | 1,906 | 4,229 | 1,215 | 4,068 |
++--------------+------------+----------+------------+----------+
+| Joy | 1,629 | 3,672 | 1.553 | 4,549 |
++--------------+------------+----------+------------+----------+
+| Trust | 247 | 1,732 | 520 | 3,455 |
++--------------+------------+----------+------------+----------+
+| Fear | 252 | 942 | 123 | 846 |
++--------------+------------+----------+------------+----------+
+| Surprise | 602 | 2,018 | 434 | 2,798 |
++--------------+------------+----------+------------+----------+
+| Sadness | 1,227 | 2,936 | 889 | 3,037 |
++--------------+------------+----------+------------+----------+
+| Disgust | 476 | 1,979 | 186 | 1,535 |
++--------------+------------+----------+------------+----------+
+| Any | 6,371 | 12,215 | 4,705 | 12,515 |
++--------------+------------+----------+------------+----------+
+
+: The statistics of utterances for each emotion label.
+:::
+
+
+
+An example of the crowdsourced task. Checkboxes allow crowdworkers to select multiple emotions for an utterance.
+
+
+::: table*
++-------------------------------------------------------------------+-----------------------------+-----------------------------+
+| **Utterance** | **Expressed** | **Experienced** |
++:==================================================================+:============================+:============================+
+| A1: | {**Anticipation**, Joy} | {**Anticipation**} |
+| | | |
+| ::: CJK | | |
+| UTF8ipxm来週、車で京都行く 普通に観光してきます | | |
+| ::: | | |
+| | | |
+| (I'm driving to Kyoto next week. We'll just do some sightseeing.) | | |
++-------------------------------------------------------------------+-----------------------------+-----------------------------+
+| B1: | {Anticipation} | {Anticipation, **Joy**} |
+| | | |
+| ::: CJK | | |
+| UTF8ipxmいいなぁ、京都 | | |
+| ::: | | |
+| | | |
+| (I like Kyoto.) | | |
++-------------------------------------------------------------------+-----------------------------+-----------------------------+
+| A2: | {**Anticipation**, **Joy**} | {Anticipation, **Joy**} |
+| | | |
+| ::: CJK | | |
+| UTF8ipxm楽しんできます | | |
+| ::: | | |
+| | | |
+| (I'll enjoy it.) | | |
++-------------------------------------------------------------------+-----------------------------+-----------------------------+
+| B2: | {Anticipation} | {**Anticipation**, **Joy**} |
+| | | |
+| ::: CJK | | |
+| UTF8ipxm行ったことないから純粋に羨ましい | | |
+| ::: | | |
+| | | |
+| (I've never been there, so I'm genuinely jealous.) | | |
++-------------------------------------------------------------------+-----------------------------+-----------------------------+
+| A3: | {**Surprise**} | {Joy, Surprise} |
+| | | |
+| ::: CJK | | |
+| UTF8ipxmないんや意外 | | |
+| ::: | | |
+| | | |
+| (I'm surprised you haven't.) | | |
++-------------------------------------------------------------------+-----------------------------+-----------------------------+
+:::
+
+::: table*
++--------------+----------------------------------+------------------------------------+
+| **Label** | **Expressed** | **Experienced** |
++:=============+:=================================+:===================================+
+| Anger | ::: CJK | ::: CJK |
+| | UTF8ipxm糞, せる, マジだ | UTF8ipxm糞, うるさい, 居る |
+| | ::: | ::: |
+| | | |
+| | (shit, force, serious) | (shit, noisy, exist) |
++--------------+----------------------------------+------------------------------------+
+| Anticipation | ::: CJK | ::: CJK |
+| | UTF8ipxm教える, 願う, 待つ | UTF8ipxm待つ, 楽しみだ, 強い |
+| | ::: | ::: |
+| | | |
+| | (teach, hope, wait) | (wait, looking forward to, strong) |
++--------------+----------------------------------+------------------------------------+
+| Joy | ::: CJK | ::: CJK |
+| | UTF8ipxm楽しい, 嬉しい, おもろい | UTF8ipxm楽しい, 嬉しい, おもろい |
+| | ::: | ::: |
+| | | |
+| | (joyful, glad, funny) | (joyful, glad, funny) |
++--------------+----------------------------------+------------------------------------+
+| Trust | ::: CJK | ::: CJK |
+| | UTF8ipxm全然, 大丈夫だ, ちゃんと | UTF8ipxmやすみ, 教える, 大事だ |
+| | ::: | ::: |
+| | | |
+| | (at all, all right, properly) | (rest, teach, important) |
++--------------+----------------------------------+------------------------------------+
+| Fear | ::: CJK | ::: CJK |
+| | UTF8ipxm怖い, やばい, どう | UTF8ipxm怖い, やばい, 危険だ |
+| | ::: | ::: |
+| | | |
+| | (afraid, serious, how) | (afraid, serious, dangerous) |
++--------------+----------------------------------+------------------------------------+
+| Surprise | ::: CJK | ::: CJK |
+| | UTF8ipxmやばい, なんで, ? | UTF8ipxm居る, ビックリ, 年 |
+| | ::: | ::: |
+| | | |
+| | (serious, why, ?) | (exist, surprise, year) |
++--------------+----------------------------------+------------------------------------+
+| Sadness | ::: CJK | ::: CJK |
+| | UTF8ipxm泣く, 痛い, 悲しい | UTF8ipxm泣く, 辛い, 痛い |
+| | ::: | ::: |
+| | | |
+| | (cry, hurt, sad) | (cry, hard, hurt) |
++--------------+----------------------------------+------------------------------------+
+| Disgust | ::: CJK | ::: CJK |
+| | UTF8ipxm悪い, 嫌いだ, 嫌だ | UTF8ipxm悪い, 気持ち, 嫌だ |
+| | ::: | ::: |
+| | | |
+| | (bad, hate, dislike) | (bad, surprise, dislike) |
++--------------+----------------------------------+------------------------------------+
+:::
+
+
+
+
+Expressed and experienced emotions (for a certain utterance).
+
+
+
+Experienced and next expressed emotions (for a certain person).
+
+The confusion matrices of the relationship between expressed and experienced emotions. In this analysis, we focus on only the strong labels. Note that the matrices’ elements are normalized in the row direction.
+
+
+
+
+
+Expressed emotions at the beginning and end of dialogue.
+
+
+
+Expressed emotions at the beginning and experienced emotions at the end of dialogue.
+
+The confusion matrices for the emotion labels at the beginning and end of dialogue. In this analysis, we consider only the emotions of a person who begins a dialogue. Note that the targets are limited to the dialogues containing six to nine utterances, and the elements are normalized in the row direction.
+
+
+We collect dialogue texts from Twitter by considering the interaction between tweets and their replies by two users as a dialogue. To improve the text quality, we exclude tweets that contain images or hashtags and set the maximum number of utterances included in a dialogue to nine. We also apply several filters: excluding dialogues that contain special symbols, emojis, repeated characters, and utterances that are too short. Note that the reason why we exclude emojis is that they are relatively explicit emotional factors, and we intend to analyze emotions implied from usual textual expressions.
+
+We collected Japanese dialogues using this method. The numbers of dialogues and utterances are shown in Table [1](#tab:stats_len){reference-type="ref" reference="tab:stats_len"}. We obtained 3,828 dialogues that correspond to 13,806 utterances in total. Regarding the length of dialogues, the number of dialogues tends to decrease as that of utterances per dialogue increases.
+
+We adopt Plutchik's wheel of emotions [@plutchik1980general] as annotation labels.[^2] Specifically, our annotation labels consist of eight emotions: anger, anticipation, joy, trust, fear, surprise, sadness, and disgust. We annotate each utterance with two emotion categories: an expressed emotion, which is expressed by a speaker of the utterance, and an experienced emotion, which is experienced by a listener of the utterance. In other words, an utterance is annotated with both subjective and objective emotions, which is similar to EmoBank [@buechel-hahn-2017-emobank] for non-dialogue texts. By annotating expressed and experienced emotions, we can trace the changes in the emotion surrounding both an utterance and a participant in a dialogue.
+
+As a crowdsourcing platform, we use Yahoo! Crowdsourcing.[^3] By showing the target utterance and its context, we ask seven workers whether the target utterance has a specified emotion or not about each emotion label for expressed and experienced emotion categories. For the expressed emotions, we ask which emotion a speaker expressed when saying the utterance. For the experienced emotions, we ask which emotion a listener experienced when hearing the utterance. Workers are allowed to select multiple emotion labels or none of them. An interface of the crowdsourcing task for expressed emotions is shown in Figure [2](#fig:yahoo){reference-type="ref" reference="fig:yahoo"}.
+
+Because a view of expressed and experienced emotions can vary among annotators, we employ many workers per an utterance and aggregate their votes to obtain highly reliable annotations.[^4] We consider strength for each emotion according to the number of workers' votes; emotions selected by more than half of the workers are regarded as strong, and ones selected by more than a quarter are regarded as weak. Note that the set of strong emotions is a subset of the set of weak ones. We expect that providing the emotions with intensity enables us to handle their granularity.
+
+We applied the above emotion annotation method to our dialogue corpus. The number of utterances for each emotion is shown in Table [2](#tab:stats_emo){reference-type="ref" reference="tab:stats_emo"}. For the expressed emotion, 46.15% and 88.48% of the utterances are tagged with at least one strong and weak emotion, respectively. For the experienced emotion, the percentages are 34.08% and 90.65%, respectively. Approximately 90% of the utterances are accompanied by one or more emotion labels, and thus our corpus is consequently suitable for recognizing emotions in dialogues and analyzing their changes. In contrast to ours, for example, less than 20% of utterances are tagged with a specific emotion in DailyDialog [@li-etal-2017-dailydialog]. Hence it is difficult to analyze emotion changes using such corpora with a small amount of emotion annotation. In addition, we can see a bias among the emotion labels for both expressed and experienced emotions, with more instances of anticipation and joy and fewer instances of trust and fear. An example of a dialogue with the annotation is shown in Table [\[tab:example\]](#tab:example){reference-type="ref" reference="tab:example"}.
+
+To investigate the characteristics of utterances with different emotions, we count words for each strong emotion label in our corpus. In this analysis, we identify words by the Japanese morphological analyzer Juman++ [@tolmachev-etal-2018-juman]. To exclude common words likely to appear for all emotions, we apply an IDF filtering. Specifically, words with IDF less than half of the maximum are ignored.
+
+Top-3 words appearing for strong emotion labels are shown in Table [\[tab:stats_word\]](#tab:stats_word){reference-type="ref" reference="tab:stats_word"}. The same words tend to appear in the two emotion categories for joy and sadness. In contrast, the frequent words in the two categories are different for anticipation, trust, and surprise.
+
+We annotated utterances with the expressed and experienced emotions. Here, we focus on the relationship between these two emotion categories. Specifically, we investigate the following two relationships:
+
+a. The expressed emotion and the experienced emotion for the same utterance (different persons).
+
+b. The experienced emotion for an utterance and the expressed emotion for the next utterance (the same person).
+
+The confusion matrices for the strong emotion labels are shown in Figure [5](#fig:mat){reference-type="ref" reference="fig:mat"}, where the elements are normalized in the row direction. First, diagonal components of the two confusion matrices have large values, indicating that the same emotions are likely to occur both for the same utterance and for the same person. Figure [3](#fig:mat_a){reference-type="ref" reference="fig:mat_a"} shows that people are likely to experience joy for an utterance of anticipation, trust, and surprise in addition to the same emotion. People also tend to experience disgust and sadness for anger and disgust, respectively. Figure [4](#fig:mat_b){reference-type="ref" reference="fig:mat_b"} shows that after experiencing trust, people are more likely to express joy than trust. For an anger experience, people are more likely to express disgust than anger. Figures [3](#fig:mat_a){reference-type="ref" reference="fig:mat_a"} and [4](#fig:mat_b){reference-type="ref" reference="fig:mat_b"} reveal that the relationship of sadness is particularly different. For a certain utterance, sadness makes the other person feel sad in most cases, but for a certain person, anticipation in addition to sadness can be expressed after experiencing sadness. We speculate that when a person experiences sadness from the interlocutor, the person brings an utterance with anticipation to comfort them.
+
+To analyze the emotion changes through a dialogue, we compare emotions at the beginning and end of a dialogue. In other words, we see how the emotions of a person who starts the dialogue change through the dialogue. In this analysis, we focus on the following two relationships:
+
+a. The emotion expressed first and the emotion *expressed* last by the same person.
+
+b. The emotion expressed first and the emotion *experienced* last by the same person.
+
+The confusion matrices for the strong emotion labels are shown in Figure [8](#fig:mat_end){reference-type="ref" reference="fig:mat_end"}. The targets are limited to dialogues containing six to nine utterances to analyze the emotion changes in long dialogues. Figure [6](#fig:mat_end_a){reference-type="ref" reference="fig:mat_end_a"} shows that a speaker of the first utterance is likely to finally express anticipation and joy regardless of the first emotion. A speaker who first expresses surprise can express sadness through the dialogue. Figure [7](#fig:mat_end_b){reference-type="ref" reference="fig:mat_end_b"} also shows that the first speaker can experience anticipation at the end of a dialogue. A person who first expresses anger and disgust tends to finally experience trust. From these two figures, we can see that a dialogue causes a person who first expresses fear to finally feel either a positive or negative emotion.
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+# Introduction
+
+Defining compositional behaviours (CBs) as "the ability to generalise from combinations of trainedon atomic components to novel re-combinations of those very same components", then we can define compositional learning behaviours (CLBs) as "the ability to generalise in an online fashion from a few combinations of never-before-seen atomic components to novel re-combinations of those very same components". We employ the term online here to imply a few-shot learning context [Vinyals et al., 2016, Mishra et al., 2018] that demands that artificial agents learn from, and then leverage some novel information, both over the course of a single lifespan, or episode, in our case of few-shot meta-RL (see Beck et al. [2023] for a review of meta-RL). Thus, CLBs involve a few-shot learning aspect that is not present in CBs. In this paper, we investigate artificial agents' abilities for CLBs.
+
+Compositional Learning Behaviours as Symbolic Behaviours. Santoro et al. [2021] states that a symbolic entity does not exist in an objective sense but solely in relation to an "*interpreter who treats it as such*", and thus the authors argue that there exists a set of behaviours, i.e. *symbolic behaviours*, that are consequences of agents engaging with symbols. Thus, in order to evaluate artificial agents in terms of their ability to collaborate with humans, we can use the presence or absence of symbolic behaviours. The authors propose to characterise symbolic behaviours as receptive, constructive, embedded, malleable, separable, meaningful, and graded. In this work, we will primarily focus on the receptivity and constructivity aspects. Receptivity aspects amount to the fact that the agents
+
+should be able to receive new symbolic conventions in an online fashion. For instance, when a child introduces an adult to their toys' names, the adults are able to discriminate between those new names upon the next usage. Constructivity aspects amount to the fact that the agents should be able to form new symbolic conventions in an online fashion. For instance, when facing novel situations that require collaborations, two human teammates can come up with novel referring expressions to easily discriminate between different events occurring. Both aspects reflect to abilities that support collaboration. Thus, we develop a benchmark that tests the abilities of artificial agents to perform receptive and constructive behaviours, and the current paper will primarily focus on testing for CLBs.
+
+Binding Problem & Meta-Learning. Following Greff et al. [2020], we refer to the binding problem (BP) as the "inability to dynamically and flexibly bind[/re-use] information that is distributed throughout the [architecture]" of some artificial agents (modelled with artificial neural networks here). We note that there is an inherent BP that requires solving for agents to exhibit CLBs. Indeed, over the course of a single episode (as opposed to a whole training process, in the case of CBs), agents must dynamically identify/segregate the component values from the observation of multiple stimuli, timestep after timestep, and then (re-)bind/(re-)use/(re-)combine this information (hopefully stored in some memory component of their architecture) in order to respond correctly when novel stimuli are presented to them. Solving the BP instantiated in such a context, i.e. re-using previously-acquired information in ways that serve the current situation, is another feat of intelligence that human beings perform with ease, on the contrary to current state-of-the-art artificial agents. Thus, our benchmark must emphasise testing agents' abilities to exhibit CLBs by solving a version of the BP. Moreover, we argue for a domain-agnostic BP, i.e. not grounded in a specific modality such as vision or audio, as doing so would limit the external validity of the test. We aim for as few assumptions as possible to be made about the nature of the BP we instantiate [Chollet, 2019]. This is crucial to motivate the form of the stimuli we employ, and we will further detail this in Section 3.1.
+
+Language Grounding & Emergence. In order to test the quality of some symbolic behaviours, our proposed benchmark needs to query the semantics that agents (*the interpreters*) may extract from their experience, and it must be able to do so in a referential fashion (e.g. being able to query to what extent a given experience is referred to as, for instance, 'the sight of a red tomato'), similarly to most language grounding benchmarks. Subsequently, acknowledging that the simplest form of collaboration is maybe the exchange of information, i.e. communication, via a given code, or language, we argue that the benchmark must therefore also allow agents to manipulate this code/language that they use to communicate. This property is known as the metalinguistic/reflexive function of languages [Jakobson, 1960]. It is mainly investigated in the current deep learning era within the field of language emergence( Lazaridou and Baroni [2020], and see Brandizzi [2023] and Denamganaï and Walker [2020a] for further reviews), via the use of variants of the referential games (RGs) [Lewis, 1969]. Thus, we take inspiration from the language emergence and grounding framework of RGs, where (i) the language domain represents a semantic domain that can be probed and queried, and (ii) the reflexive function of language is indeed addressed. Then, in order to instantiate different BPs at each episode (preventing the agent from 'cheating' by combining experiences from one episode to another), we propose a meta-learning extension to RGs, entitled Meta-Referential Games, and use this framework to build our benchmark. The resulting multi-agent/single-agent benchmark, entitled Symbolic Behaviour Benchmark (S2B), has the potential to test for more symbolic behaviours pertaining to language grounding and emergence rather than solely CLBs. In the present paper, though, we solely focus on the language grounding task of learning CLBs. After review of the background (Section 2) , we will present our contributions as follows:
+
+- We propose the Symbolic Behaviour Benchmark to enables evaluation of symbolic behaviours (Section 3), and provide a detailed investigation in the case of CLBs.
+- We present a novel stimulus representation scheme, entitled Symbolic Continuous Stimulus (SCS) representation which is able to instantiate a BP, on the contrary to more common symbolic representations like the one/multi-hot encoded scheme (Section 3.1);
+- We propose the Meta-Referential Game framework, a meta-learning extension to common RGs, that is built over SCS-represented stimuli (Section 3.2);
+- We provide baseline results for both the multi-agent and single-agent contexts, along with evaluations of the performance of the agents in terms of CLBs and linguistic compositionality of the emerging languages, showing that our benchmark is a compelling challenge that we hope will spur the research community (Section 4).
+
+The first instance of an environment that demonstrated a primary focus on the objective of communicating efficiently is the *signaling game* or *referential game* (RG) by Lewis [1969], where a speaker agent is asked to send a message to the listener agent, based on the *state/stimulus* of the world that it observed. The listener agent then acts upon the observation of the message by choosing one of the *actions* available to it. Both players' goals are aligned (it features *pure*
+
+
+
+Figure 1: Illustration of a discriminative 2-players / L-signal / N-round variant of a RG.
+
+coordination/common interests), with the aim of performing the 'best' action given the observed state. In the recent deep learning era, many variants of the RG have appeared. Following the nomenclature proposed in Denamganaï and Walker [2020b], Figure 1 illustrates in the general case a discriminative 2-players / L-signal / N-round / K-distractors / descriptive / object-centric variant. In short, the speaker receives a stimulus and communicates with the listener (up to N back-and-forth using messages of at most L tokens each), who additionally receives a set of K+1 stimuli (potentially including a semantically-similar stimulus as the speaker, referred to as an object-centric stimulus). The task is for the listener to determine, given communication with the speaker, whether any of its observed stimuli match the speaker's. Among the many possible features found in the literature, we highlight here those that will be relevant to how S2B is built, and then provide formalism that we will abide by throughout the paper. The **number of communication rounds** N characterises (i) whether the listener agent can send messages back to the speaker agent and (ii) how many communication rounds can be expected before the listener agent is finally tasked to decide on an action. The basic (discriminative) RG is **stimulus-centric**, which assumes that both agents would be somehow embodied in the same body, and they are tasked to discriminate between given stimuli, that are the results of one single perception 'system'. On the other hand, Choi et al. [2018] introduced an object-centric variant which incorporates the issues that stem from the difference of embodiment (which has been later re-introduced under the name Concept game by Mu and Goodman [2021]). The agents must discriminate between objects (or scenes) independently of the viewpoint from which they may experience them. In the object-centric variant, the game is more about bridging the gap between each other's cognition rather than (just) finding a common language. The adjective 'object-centric' is used to qualify a stimulus that is different from another but actually present the same meaning (e.g. same object, but seen under a different viewpoint).
+
+Following the last communication round, the listener outputs a decision ( $D_i^L$ in Figure 2) about whether any of the stimulus it is observing matches the one (or a semantically similar one, in object-centric RGs) experienced by the speaker, and if so its action index must represent the index of the stimulus it identifies as being the same. In the case of a **descriptive** variant, it may as well be possible that none of the stimuli are the same as the target one, therefore the action of index 0 is required for success. The agent's ability to make the correct decision over multiple RGs is referred to as accuracy.
+
+Compositionality, Disentanglement & Systematicity. Compositionality is a phenomenon that human beings are able to identify and leverage thanks to the assumption that reality can be decomposed over a set of "disentangle[d,] underlying factors of variations" [Bengio, 2012], and our experience is a noisy, entangled translation of this factorised reality. This assumption is critical to the field of unsupervised learning of disentangled representations [Locatello et al., 2020] that aims to find "manifold learning algorithms" [Bengio, 2012], such as variational autoencoders (VAEs [Kingma and Welling, 2013]), with the particularity that the latent encoding space would consist of disentangled latent variables (see Higgins et al. [2018] for a formal definition). As a concept, compositionality has been the focus of many definition attempts. For instance, it can be defined as "the algebraic capacity to understand and produce novel combinations from known components" (Loula et al. [2018] referring to Montague [1970]) or as the property according to which "the meaning of a complex expression is a function of the meaning of its immediate syntactic parts and the way in which they are combined" [Krifka, 2001]. Although difficult to define, the commmunity seems to agree on the fact that it would enable learning agents to exhibit systematic generalisation abilities (also referred to as combinatorial generalisation [Battaglia et al., 2018]). Some of the ambiguities that come with those loose definitions start to be better understood and explained, as in the work of Hupkes et al. [2019]. While often studied in relation to languages, it is usually defined with a focus on behaviours. In this paper, we will refer to linguistic compositionality when considering languages, and interchangeably
+
+compositional behaviours and systematicity to refer to "the ability to entertain a given thought implies the ability to entertain thoughts with semantically related contents"[Fodor et al., 1988].
+
+Linguistic compositionality can be difficult to measure. Brighton and Kirby [2006]'s *topographic similarity* (topsim) which is acknowledged by the research community as the main quantitative metric [Lazaridou et al., 2018, Guo et al., 2019, Słowik et al., 2020, Chaabouni et al., 2020, Ren et al., 2020]. Recently, taking inspiration from disentanglement metrics, Chaabouni et al. [2020] proposed two new metrics entitled posdis (positional disentanglement metric) and bosdis (bag-of-symbols disentanglement metric), that have been shown to be differently 'opinionated' when it comes to what kind of linguistic compositionality they capture. As hinted at by Choi et al. [2018], Chaabouni et al. [2020] and Dessi et al. [2021], linguistic compositionality and disentanglement appears to be two sides of the same coin, in as much as emerging languages are discrete and sequentially-constrained unsupervisedly-learned representations. In Section 3.1, we further develop this bridge between (compositional) language emergence and unsupervised learning of disentangled representations by asking *what would an ideally-disentangled latent space look like?* to build our proposed benchmark.
+
+Richness of the Stimuli & Systematicity. Chaabouni et al. [2020] found that linguistic compositionality is not necessary to bring about systematicity, as shown by the fact that non-compositional languages wielded by symbolic (generative) RG players were enough to support success in zero-shot compositional tests (ZSCTs). They found that the emergence of a (posdis)-compositional language was a sufficient condition for systematicity to emerge. Finally, they found a necessary condition to foster systematicity, that we will refer to as richness of stimuli condition (Chaa-RSC). It was framed as (i) having a large stimulus space |I| = ival iattr , where iattr is the number of attributes/factor dimensions, and ival is the number of possible values on each attribute/factor dimension, and (ii) making sure that it is densely sampled during training, in order to guarantee that different values on different factor dimensions have been experienced together. In a similar fashion, Hill et al. [2019] also propose a richness of stimuli condition (Hill-RSC) that was framed as a data augmentation-like regularizer caused by the egocentric viewpoint of the studied embodied agent. In effect, the diversity of viewpoint allowing the embodied agent to observe over many perspectives the same and unique semantical meaning allows a form of contrastive learning that promotes the agent's systematicity.
+
+The version of the S2B1 that we present in this paper is focused on evaluating receptive and constructive behaviour traits via a single task built around 2-players multi-agent RL (MARL) episodes where players engage in a series of RGs (cf. lines 11 and 17 in Alg. 5 calling Alg. 3). We denote one such episode as a meta-RG and detail it in Section 3.2. Each RG within an episode consists of N + 2 RL steps, where N is the *number of communication rounds* available to the agents (cf. Section 2). At each RL step, agents both observe similar or different *object-centric* stimuli and act simultaneously from different actions spaces, depending on their role as the speaker or the listener of the game. Stimuli are presented to the agent using the Symbolic Continuous Stimulus (SCS) representation that we present in Section 3.1. Each RG in a meta-RG follows the formalism laid out in Section 2, with the exception that speaker and listener agents speak simultaneously and observe each other's messages upon the next RL step. Thus, at step N + 1, the speaker's action space consists solely of a *no-operation* (NO-OP) action while the listener's action space consists solely of the decision-related action space. In practice, the environment simply ignores actions that are not allowed depending on the RL step. Next, step N + 2 is intended to provide feedback to the listener agent as its observation is replaced with the speaker's observation (cf. line 12 and 18 in Alg. 5). Note that this is the exact stimulus that the speaker has been observing, rather than a possible object-centric sample. In Figure 3, we present SCS-represented stimuli, observed by a speaker over the course of a typical episode.
+
+Building about successes of the field of unsupervised learning of disentangled representations, to the question *what would an ideally-disentangled latent space look like?*, we propose the Symbolic Continuous Stimulus (SCS) representation and provide numerical evidence of it in Appendix D.2. It is continuous and relying on Gaussian kernels, and it has the particularity of enabling the representation of stimuli sampled from differently semantically structured symbolic spaces while maintaining the
+
+1
+
+
+
+Figure 2: Left: Sampling of the necessary components to create the i-th RG $(RG_i)$ of a meta-RG. The target stimulus (red) and the object-centric target stimulus (purple) are both sampled from the Target Distribution $TD_i$ , a set of O different stimuli representing the same latent semantic meaning. The latter set and a set of K distractor stimuli (orange) are both sampled from a dataset of SCS-represented stimuli (**Dataset**), which is instantiated from the current episode's symbolic space, whose semantic structure is sampled out of the meta-distribution of available semantic structure over $N_{dim}$ -dimensioned symbolic spaces. Right: Illustration of the resulting meta-RG with a focus on the i-th RG $RG_i$ . The speaker agent receives at each step the target stimulus $s_0^i$ and distractor stimuli $(s_k^i)_{k \in [1;K]}$ , while the listener agent receives an object-centric version of the target stimulus $s_0^i$ or a distractor stimulus (randomly sampled), and other distractor stimuli $(s_k^i)_{k \in [1;K]}$ , with the exception of the **Listener Feedback step** where the listener agent receives feedback in the form of the exact target stimulus $s_0^i$ . The Listener Feedback step takes place after the listener agent has provided a decision $D_i^L$ about whether the target meaning is observed or not and in which stimuli is it instantiated, guided by the vocabulary-permutated message $M_i^S$ from the speaker agent.
+
+same representation shape (later referred as the *shape invariance property*), as opposed to the one-/multi-hot encoded (OHE/MHE) vector representation. While the SCS representation is inspired by vectors sampled from VAE's latent spaces, this representation is not learned and is not aimed to help the agent performing its task. It is solely meant to make it possible to define a distribution over infinitely many semantic/symbolic spaces, while instantiating a BP for the agent to resolve. Indeed, contrary to OHE/MHE representation, observation of one stimulus is not sufficient to derive the nature of the underlying semantic space that the current episode instantiates. Rather, it is only via a kernel density estimation on multiple samples (over multiple timesteps) that the semantic space's nature can be inferred, thus requiring the agent to segregated and (re)combine information that is distributed over multiple observations. In other words, the benchmark instantiates a domain-agnostic BP. We provide in Appendix D.1 some numerical evidence to the fact that the SCS representation differentiates itself from the OHE/MHE representation because it instantiates a BP. Deriving the SCS representation from an idealised VAE's latent encoding of stimuli of any domain makes it a domain-agnostic representation, which is an advantage compared to previous benchmark because domain-specific information can therefore not be leveraged to solve the benchmark.
+
+In details, the semantic structure of an $N_{dim}$ -dimensioned symbolic space is the tuple $(d(i))_{i \in [1;N_{dim}]}$ where $N_{dim}$ is the number of latent/factor dimensions, d(i) is the **number of possible symbolic values** for each latent/factor dimension i. Stimuli in the SCS representation are vectors sampled from the continuous space $[-1,+1]^{N_{dim}}$ . In comparison, stimuli in the OHE/MHE representation are vectors from the discrete space $\{0,1\}^{d_{OHE}}$ where $d_{OHE} = \sum_{i=1}^{N_{dim}} d(i)$ depends on the d(i)'s. Note that SCS-represented stimuli have a shape that does not depend on the d(i)'s values, this is the shape invariance property of the SCS representation (see Figure 4(bottom) for an illustration).
+
+In the SCS representation, the d(i)'s do not shape the stimuli but only the semantic structure, i.e. representation and semantics are disentangled from each other. The d(i)'s shape the semantic by enforcing, for each factor dimension i, a partitionaing of the [-1, +1] range into d(i) value sections. Each partition corresponds to one of the d(i) symbolic values available on the i-th factor dimension. Having explained how to build the SCS representation sampling space, we now describe how to sample stimuli from it. It starts with instantiating a specific latent meaning/symbol, embodied by latent values l(i) on each factor dimension i, such that $l(i) \in [1; d(i)]$ . Then, the i-th entry of the stimulus is populated with a sample from a corresponding Gaussian distribution over the l(i)-th partition of the [-1, +1] range. It is denoted as $g_{l(i)} \sim \mathcal{N}(\mu_{l(i)}, \sigma_{l(i)})$ , where $\mu_{l(i)}$ is the mean of the Gaussian distribution, uniformly sampled to fall within the range of the l(i)-th partition, and $\sigma_{l(i)}$ is the standard deviation of the Gaussian distribution, uniformly sampled over the range $\left[\frac{2}{12d(i)}, \frac{2}{6d(i)}\right]$ . $\mu_{l(i)}$ are sampled in order to guarantee (i) that the scale of the Gaussian distribution is large
+
+enough, but (ii) not larger than the size of the partition section it should fit in. Figure 3 shows an example of such instantiation of the different Gaussian distributions over each factor dimensions' [-1, +1] range.
+
+Thanks to the shape invariance property of the SCS representation, once a number of latent/factor dimension $N_{dim}$ is choosen, we can synthetically generate many different semantically structured symbolic spaces while maintaining a consistent stimulus shape. This is critical since agents must be able to deal with stimuli coming from differently semantically structured $N_{dim}$ -dimensioned symbolic spaces. In other words that are more akin to the meta-learning field, we can define a distribution over many kind of tasks, where each task instantiates a different semantic structure to the symbolic space our agent should learn to adapt to. Figure 2 highlights the structure of an episode, and its reliance on differently semantically structured $N_{dim}$ -dimensioned symbolic spaces. Agents aim to coordinate efficiently towards scoring a high accuracy during the ZSCTs at the end of each RL episode. Indeed, a meta-RG is composed of two phases: a supporting phase where supporting stimuli are presented, and a querying/ZSCT phase where ZSCT-purposed RGs are played. During the querying phase, the presented target stimuli are novel combinations of the component values of the target stimuli presented during the supporting phase. Algorithms 4 and 5 contrast how a common RG differ from a meta-RG (in Appendix A). We emphasise that the supporting phase of a meta-RG does not involve updating the parameters/weights of the learning agents, since this is a meta-learning framework of the few-shot learning kind (compare positions and dependencies of lines 21 in Alg. 5 and 6 in Alg. 4). During the supporting phase, each RG involves a different target stimulus until all the possible component values on each latent/factor dimensions have been shown for at least S shots (cf. lines 3-7 in Alg. 5). While it amounts to at least S different target stimulus being shown, the number of supporting-phase RG played remains far smaller than the number of possible training-purposed stimuli in the current episode's symbolic space/dataset. Then, the querying phase sees all the testingpurposed stimuli being presented. Emphasising further, during one single RL episode, both supporting/training-purposed and ZSCT/querying-purposed RGs are played, without the agent's parameters changing in-between the two phases, since learning CLBs involve agents adapting in an online/few-shot learning setting. The semantic structure of the symbolic space is randomly sampled at the beginning of each episode (cf. lines 2-3in Alg. 5) The reward function proposed to both agents is null at all steps except on the N+1-th step. It is +1 if the listener agent decided correctly, and, during the querying phase only, -2 if incorrect (cf. line 21 in Alg. 5).
+
+
+
+Figure 3: Visualisation of the SCSrepresented stimuli (column) observed by the speaker agent at each RG over the course of one meta-RG, with $N_{dim} = 3$ and d(0) = 5, d(1) = 5, d(2) = 3. The supporting phase lasted for 19 RGs. For each factor dimension $i \in [0; 2]$ , we present on the right side of each plot the kernel density estimations of the Gaussian kernels $\mathcal{N}(\mu_{l(i)}, \sigma_{l(i)})$ of each latent value available on that factor dimension $l(i) \in [1; d(i)]$ . Colours of dots, used to represent the sampled value $g_{l(i)}$ , imply the latent value l(i)'s Gaussian kernel from which said continuous value was sampled. As per construction, for each factor dimension, there is no overlap between the different latent values' Gaussian kernels.
+
+**Vocabulary Permutation.** We bring the readers attention on the fact that simply changing the semantic structure of the symbolic space, is not sufficient to force MARL agents to adapt specifically to the instantiated symbolic space at each episode. Indeed, they can learn to cheat by relying on an episode-invariant (and therefore independent of the instantiated semantic structure) emergent language which would encode the continuous values of the SCS representation like an analog-to-digital converter would. This cheating language would consist of mapping a fine-enough partition of the [-1, +1] range onto a fixed vocabulary in a bijective fashion (see Appendix C for more details).
+
+Therefore, in order to guard the MARL agents from making a cheating language emerge, we employ a vocabulary permutation scheme [Cope and Schoots, 2021] that samples at the beginning of each episode/task a random permutation of the vocabulary symbols (cf. line 1 in Alg. 2).
+
+Richness of the Stimulus. We further bridge the gap between Hill-RSC and Chaa-RSC by allowing the number of object-centric samples O and the number of shots S to be parameterized in the benchmark. S represents the minimal number of times any given component value may be observed throughout the course of an episode. Intuitively, throughout their lifespan, an embodied observer may only observe a given component (e.g. the value 'blue', on the latent/factor dimension 'color') a limited number of times (e.g. one time within a 'blue car' stimulus, and another time within a 'blue cup' stimulus). These parameters allow the experimenters to account for both the Chaa-RSC's sampling density of the different stimulus components and Hill-RSC's diversity of viewpoints.
+
+# Method
+
+In this section, we detail algorithmically how Meta-Referential Games differ from common RGs. We start by presenting in Algorithm 4 an overview of the common RGs, taking place inside a common supervised learning loop, and we highlight the following:
+
+- 1. (i) preparation of the data on which the referential game is played (highlighted in green),
+- 2. (ii) elements pertaining to playing a RG (highlighted in blue),
+- 3. (iii) elements pertaining to the **supervised learning loop** (highlighted in purple).
+
+Helper functions are detailed in Algorithm 1, 2 and 3. Next, we can now show in greater and contrastive details the Meta-Referential Game algorithm in Algorithm 5, where we highlight the following:
+
+- 1. (i) preparation of the data on which the referential game is played (highlighted in green),
+- 2. (ii) elements pertaining to playing a RG (highlighted in blue),
+- 3. (iii) elements pertaining to the **meta-learning loop** (highlighted in purple).
+- 4. (iv) elements pertaining to setup of a Meta-Referential Game (highlighted in red).
+
+- a target stimuli $s_0$ ,
+- a dataset of stimuli Dataset,
+- O: Number of Object-Centric samples in each Target Distribution over stimuli $TD(\cdot)$ .
+- *K* : Number of distractor stimuli to provide to the listener agent.
+- FullObs : Boolean defining whether the speaker agent has full (or partial) observation.
+- Descriptive ratio in the range [0, 1] defining how often the listener agent is observing the same semantic as the speaker agent.
+
+```
+1 s_0', D^{Target} \leftarrow s_0, 0;
+ 2 if random(0,1) > DescrRatio then
+ s'_0 \sim \text{Dataset} - TD(s_0); /* Exclude target stimulus from listener's
+ observation ... */
+ D^{Target} \leftarrow K + 1::
+ /* ... and expect it to decide accordingly. */
+ 4
+5 end
+ 6 else if O > 1 then
+ 7 | Sample an Object-Centric distractor s_0' \sim TD(s_0);
+ 8 end
+ 9 Sample K distractor stimuli from Dataset -TD(s_0): (s_i)_{i \in [1,K]} \sim \text{Dataset} - TD(s_0);
+10 Obs_{Speaker} \leftarrow \{s_0\}; if FullObs then
+11 | Obs_{Speaker} \leftarrow \{s_0\} \cup \{s_i | \forall i \in [1, K]\};
+12 end
+13 Obs_{Listener} \leftarrow \{s'_0\} \cup \{s_i | \forall i \in [1, K]\};
+ /* Shuffle listener observations and update index of target decision:
+14 Obs_{Listener}, D^{Target} \leftarrow Shuffle(Obs_{Listener}, D^{Target});
+ Output: Obs_{Speaker}, Obs_{Listener}, D^{Target};
+```
+
+- V: Vocabulary (finite set of tokens available),
+- $N_{\text{dim}}$ : Number of attribute/factor dimensions in the symbolic spaces,
+- $V_{min}$ : Minimum number of possible values on each attribute/factor dimensions in the symbolic spaces,
+- $\bullet$ $V_{max}$ : Maximum number of possible values on each attribute/factor dimensions in the symbolic spaces,
+- 1 Initialise random permutation of vocabulary: $V' \leftarrow RandomPerm(V)$
+- 2 Sample semantic structure: $(d(i))_{i \in [1, N_{\text{dim}}]} \sim \mathcal{U}(V_{min}; V_{max})^{N_{\text{dim}}};$
+- 3 Generate symbolic space/dataset $D((d(i))_{i \in [1,N_{\dim}]})$ ;
+ 4 Split dataset into supporting set $D^{\mathrm{support}}$ and querying set $D^{\mathrm{query}}$ $(((d(i))_{i \in [1,N_{\dim}]})$ is omitted for readability);
+
+**Output** : $V', D((d(i))_{i \in [1, N_{\text{dim}}]}), D^{\text{support}}, D^{\text{query}};$
+
+- Speaker and Listener agents,
+- Set of speaker observations ObsSpeaker,
+- Set of listener observations ObsListener,
+- N: Number of communication rounds to play,
+- L: Maximum length of each message,
+- V: Vocabulary (finite set of tokens available),
+- 1 Compute message $M^S = \text{Speaker}(Obs_{\text{Speaker}}|\emptyset);$ 2 Initialise Communication Channel History: Comm $H \leftarrow [M^S]$ ; 3 for round = 0, N do
+- Compute Listener's reply $M_{\text{round}}^L$ = Listener( $Obs_{\text{Listener}}$ |CommH); 4
+- $CommH \leftarrow CommH + [M_{round}^L];$ 5
+- Compute Speaker's reply $M_{\text{round}}^{S} = \text{Speaker}(Obs_{\text{Speaker}}|\text{CommH});$
+- $CommH \leftarrow CommH + [M_{round}^{S}];$
+- 8 end
+- 9 Compute listener decision \_, $D^L = Listener(Obs_{Listener}|CommH);$
+
+**Output**: Listener's decision $D^L$ , Communication Channel History CommH;
+
+- a dataset of stimuli Dataset,
+- a set of hyperparameters defining the RG:
+ - O : Number of Object-Centric samples in each Target Distribution over stimuli T D(·).
+ - N : Number of communication rounds to play.
+ - L : Maximum length of each message.
+ - V : Vocabulary (finite set of tokens available).
+ - K : Number of distractor stimuli to provide to the listener agent.
+ - FullObs : Boolean defining whether the speaker agent has full (or partial) observation.
+ - DescrRatio : Descriptive ratio in the range [0, 1] defining how often the listener agent is observing the same semantic as the speaker agent.
+ - L : Loss function to use in the agents update.
+
+• Speaker(·) and Listener(·) agents.
+
+```
+1 Systematically split Dataset into training and testing dataset, Dtrain and Dtest;
+2 for epoch = 1, Nepoch do
+3 for target stimulus s0 ∈ Dtrain do
+ /* Preparation of observations and target decision: */
+4 ObsSpeaker, ObsListener, DT arget ← DataP rep(Dataset, s0, O, K, FullObs, DescrRatio)
+ /* Play Referential Game: */
+5 DL, _ = PlayRG(Speaker, Listener, ObsSpeaker, ObsListener, N, L, V );
+ /* Supervised Learning Parameters Update on Training Stimulus Only:
+ */
+6 Update both speaker and listener agents' parameters using the loss L(DT arget, DL);
+7 end
+8 Initialise ZSCT accuracy: AccZSCT ← 0;
+9 for target stimulus s0 ∈ Dtest do
+ /* Preparation of observations and target decision: */
+10 ObsSpeaker, ObsListener, DT arget ← DataP rep(Dataset, s0, O, K, FullObs, DescrRatio)
+ /* Play Referential Game: */
+11 DL, _ = PlayRG(Speaker, Listener, ObsSpeaker, ObsListener, N, L, V );
+ /* Update ZSCT Accuracy: */
+12 AccZSCT ← Update(AccZSCT, DT arget, DL);
+13 end
+14 end
+```
+
+- $N_{episode}$ , $N_{dim}$ : Number of episodes, and number of attribute/factor dimensions,
+- S: Minimum number of Shots over which each possible value on each attribute/factor dimension ought to be observed by the agents (as part of a target stimulus).
+- $V_{min}, V_{max}$ : Minimum and maximum number of possible values on each attribute/factor dimensions in the symbolic spaces,
+- $TSS(\mathcal{D}, \mathcal{S}, S)$ : Target stimulus sampling function which samples from dataset $\mathcal{D}$ , given a set of previously sampled stimuli $\mathcal{S}$ , while maximising the likelihood that each possible value on each attribute/factor dimension are sampled at least S times.
+- a set of hyperparameters defining the RG:
+ - O: Number of Object-Centric samples in each Target Distribution over stimuli $TD(\cdot)$ .
+ - N: Number of communication rounds to play.
+ - L: Maximum length of each message.
+ - -V: Vocabulary (finite set of tokens available).
+ - K: Number of distractor stimuli to provide to the listener agent.
+ - FullObs: Boolean defining whether the speaker agent has full (or partial) observation.
+ - DescrRatio: Descriptive ratio in the range [0, 1] defining how often the listener agent is observing the same semantic as the speaker agent.
+
+• Speaker(·) and Listener(·) agents.
+
+```
+1 for episode = 1, N_{episode} do
+ /* Preparation of the symbolic space/dataset:
+ V', D_{\text{episode}}, D_{\text{episode}}^{\text{support}}, D_{\text{episode}}^{\text{query}} \leftarrow MetaRGDatasetPreparation(V, N_{\text{dim}}, V_{\text{min}}, V_{\text{max}});
+2
+ Initialise set of sampled supporting stimuli: \mathcal{S}^{support} \leftarrow \emptyset;
+3
+ repeat
+4
+ Sample training-purposed target stimulus s_0^i \sim TSS(D_{\text{episode}}^{\text{support}}, \mathcal{S}^{\text{support}}, S)
+5
+ S^{\text{support}} \leftarrow S^{\text{support}} \cup \{s_0^i\}; i \leftarrow i+1;
+ 6
+ until all values on each attribute/factor dimension have been instantiated at least S times;
+ Initialise RG index: i \leftarrow 0;
+8
+ /* Supporting Phase:
+ */
+ 9
+10
+ D_i^L, CommH_i = \mathsf{PlayRG}(\mathsf{Speaker}, \mathsf{Listener}, Obs_{\mathsf{Speaker}}^i, Obs_{\mathsf{Listener}}^i, N, L, \textcolor{red}{V'});
+11
+ \_,\_ = \text{Listener}(Obs_{Speaker}^{i}|CommH_{i}); \qquad \qquad /* \text{ Listener-Feedback Step */}
+12
+13
+ /* Querying/ZSCT Phase:
+ */
+ Initialise ZSCT accuracy: Acc_{ZSCT} \leftarrow 0;
+14
+ 15
+16
+ D_i^L, CommH_i = PlayRG(Speaker, Listener, Obs_{Speaker}^i, Obs_{Listener}^i, N, L, V');
+17
+ \_, \_ = Listener(Obs_{Speaker}^{i}|CommH_{i});
+ /* Listener-Feedback Step
+18
+ /* Update ZSCT Accuracy:
+ Acc_{ZSCT} \leftarrow Update(Acc_{ZSCT}, D_i^{Target}, D_i^L); i \leftarrow i + 1;
+19
+20
+ /* Meta-Learning Parameters Update on Whole Episode:
+ */
+ \text{Update both agents using rewards } R_i = \begin{cases} 1 & \text{if } D_i^{Target} == D_i^L \\ 0 & \text{otherwise, during supporting phase;} \\ -2 & \text{otherwise, during querying phase} \end{cases}
+21
+22 end
+```
+
+
+
+Figure 4: Top: visualisation on each column of the messages sent by the posdis-compositional rule-based speaker agent over the course of the episode presented in Figure 3. Colours are encoding the information of the token index, as a visual cue. Bottom: OHE/MHE and SCS representations of example latent stimuli for two differently-structured symbolic spaces with Ndim = 3, i.e. on the left for d(0) = 4, d(1) = 2, d(2) = 3, and on the right for d(0) = 3, d(1) = 3, d(2) = 3. Note the shape invariance property of the SCS representation, as its shape remains unchanged by the change in semantic structure of the symbolic space, on the contrary to the OHE/MHE representations.
+
+The baseline RL agents that we consider use a 3-layer fully-connected network with 512, 256, and finally 128 hidden units, with ReLU activations, with the stimulus being fed as input. The output is then concatenated with the message coming from the other agent in a OHE/MHE representation, mainly, as well as all other information necessary for the agent to identify the current step, i.e. the previous reward value (either +1 and 0 during the training phase or +1 and −2 during testing phase), its previous action in one-hot encoding, an OHE/MHE-represented index of the communication round (out of N possible values), an OHE/MHE-represented index of the agent's role (speaker or listener) in the current game, an OHE/MHE-represented index of the current phase (either 'training' or 'testing'), an OHE/MHE representation of the previous RG's result (either success or failure), the previous RG's reward, and an OHE/MHE mask over the action space, clarifying which actions are available to the agent in the current step. The resulting concatenated vector is processed by another 3-layer fully-connected network with 512, 256, and 256 hidden units, and ReLU activations, and then fed to the core memory module, which is here a 2-layers LSTM [Hochreiter and Schmidhuber, 1997] with 256 and 128 hidden units, which feeds into the advantage and value heads of a 1-layer dueling network [Wang et al., 2016].
+
+Table 5 highlights the hyperparameters used for the learning agent architecture and the learning algorithm, R2D2[Kapturowski et al., 2018]. More details can be found, for reproducibility purposes, in our open-source implementation at [https://github.com/Near32/Regym/tree/develop/](https://github.com/Near32/Regym/tree/develop/benchmark/R2D2/SymbolicBehaviourBenchmark) [benchmark/R2D2/SymbolicBehaviourBenchmark](https://github.com/Near32/Regym/tree/develop/benchmark/R2D2/SymbolicBehaviourBenchmark).
+
+Training was performed for each run on 1 NVIDIA GTX1080 Ti, and the average amount of training time for a run is 18 hours for LSTM-based models, 40 hours for ESBN-based models, and 52 hours for DCEM-based models.
+
+The ESBN-based and DCEM-based models that we consider have the same architectures and parameters than in their respective original work from Webb et al. [2020] and Hill et al. [2020], with the exception of the stimuli encoding networks, which are similar to the LSTM-based model.
+
+The rule-based speaker agents used in the single-agent task, where only the listener agent is a learning agent, speaks a compositional language in the sense of the posdis metric [Chaabouni et al., 2020], as presented in Table 3 for $N_{dim}=3$ , a maximum sentence length of L=4, and vocabulary size $|V|>=max_id(i)=5$ , assuming a semantical space such that $\forall i\in [1,3], d(i)=5$ .
+
+The agents can develop a cheating language, cheating in the sense that it could be episode/task-invariant (and thus semantic structure invariant). This emerging cheating language would encode the continuous values of the SCS representation like an analog-to-digital converter would, by mapping a fine-enough partition of the [-1,+1] range onto the vocabulary in a bijective fashion.
+
+For instance, for a vocabulary size $\|V\|=10$ , each symbol can be unequivocally mapped onto $\frac{2}{10}$ -th increments over [-1,+1], and, by communicating $N_{dim}$ symbols (assuming $N_{dim} \leq L$ ), the speaker agents can communicate to the listener the (digitized) continuous value on each dimension i of the SCS-represented stimulus. If $max_jd(j) \leq \|V\|$ then the cheating language is expressive-enough for the speaker agent to digitize
+
+Table 3: Examples of the latent stimulus to language utterance mapping of the posdis-compositional rule-based speaker agent. Note that token 0 is the EoS token.
+
+| Latent Dims | | Dims | Comp. Language | |
+|------------------|------------------|------------------|------------------------------------------------------|--|
+| #1 | #2 | #3 | Tokens | |
+| 0 1 2 3 | 1 3 5 1 | 2 4 0 2 | 1, 2, 3, 0 2, 4, 5, 0 3, 6, 1, 0 4, 2, 3, 0 | |
+| 4 | 3 | 4 | 5, 4, 5, 0 | |
+
+all possible stimulus without solving the binding problem, i.e. without inferring the semantic structure. Similarly, it is expressive-enough for the listener agent to convert the spoken utterances to continuous/analog-like values over the [-1,+1] range, thus enabling the listener agent to skirt the binding problem when trying to discriminate the target stimulus from the different stimuli it observes.
+
+**Hypothesis.** The SCS representation differs from the OHE/MHE one primarily in terms of the binding problem [Greff et al., 2020] that the former instantiates while the latter does not. Indeed, the semantic structure can only be inferred after observing multiple SCS-represented stimuli. We hypothesised that it is via the *dynamic binding of information* extracted from each observations that an estimation of a density distribution over each dimension i's [-1, +1] range can be performed. And, estimating such density distribution is tantamount to estimating the number of likely gaussian distributions that partition each [-1, +1] range.
+
+
+
+Figure 5: 5-ways 2-shots accuracies on the Recall task with different stimulus representation (OHE:blue; SCS; orange).
+
+**Evaluation.** Towards highlighting that there is a binding problem taking place, we show results of baseline RL
+
+agents (similar to main experiments in Section 4) evaluated on a simple single-agent recall task. The Recall task structure borrows from few-shot learning tasks as it presents over 2 shots all the stimuli of the instantiated symbolic space (not to be confused with the case for Meta-RG where all the latent/factor dimensions' values are being presented over S shots – Meta-RGs do not necessarily sample the whole instantiated symbolic space at each episode, but the Recall task does). Each shot
+
+consists of a series of recall games, one for each stimulus that can be sampled from an Ndim = 3 dimensioned symbolic space. The semantic structure (d(i))i∈[1;Ndim] of the symbolic space is randomly sampled at the beginning of each episode, i.e. d(i) ∼ U(2; 5), where U(2; 5) is the uniform discrete distribution over the integers in [2; 5], and the number of object-centric samples is O = 1, in order to remove any confounder from object-centrism.
+
+Each recall game consists of two steps: in the first step, a stimulus is presented to the RL agent, and only a *no-operation* (NO-OP) action is made available, while, on the second step, the agent is asked to infer/recall the discrete l(i) latent value (as opposed to the representation of it that it observed, either in the SCS or OHE/MHE form) that the previously-presented stimulus had instantiated, on a given i-th dimension, where value i for the current game is uniformly sampled from U(1; Ndim) at the beginning of each game. The value of i is communicated to the agent via the observation on this second step of different stimulus that in the first step: it is a zeroed out stimulus with the exception of a 1 on the i-th dimension on which the inference/recall must be performed when using SCS representation, or over all the OHE/MHE dimensions that can encode a value for the i-th latent factor/attribute when using the OHE/MHE representation. On the second step, the agent's available action space now consists of discrete actions over the range [1; maxjd(j)], where maxjd(j) is a hyperparameter of the task representing the maximum number of latent values for any latent/factor dimension. In our experiments, maxjd(j) = 5. While the agent is rewarded at each game for recalling correctly, we only focus on the performance over the games of the second shot, i.e. on the games where the agent has theoretically received enough information to infer the density distribution over each dimension i's [−1, +1] range. Indeed, observing the whole symbolic space once (on the first shot) is sufficient (albeit not necessary, specifically in the case of the OHE/MHE representation).
+
+Results. Figure 3(right) details the recall accuracy over all the games of the second shot of our baseline RL agent throughout learning. There is a large gap of asymptotic performance depending on whether the Recall task is evaluated using OHE/MHE or SCS representations. We attribute the poor performance in the SCS context to the instantiation of a BP. We note again that during those experiments the number of object-centric samples was kept at O = 1, thus emphasising that the BP is solely depending on the use of the SCS representation and does not require object-centrism.
+
+In this section, we verify our hypothesis that the SCS representation yields ideally-disentangled stimuli. We report on the FactorVAE Score Kim and Mnih [2018], the Mutual Information Gap (MIG) Chen et al., and the Modularity Score Ridgeway and Mozer [2018] as they have been shown to be part of the metrics that correlate the least among each other [Locatello et al., 2020], thus representing different desiderata/definitions for disentanglement. We report on the Ndim = 3 dimensioned symbolic spaces with ∀j, d(j) = 5. The measurements are of 100.0%, 94.8, and 98.9% for, respectivily, the FactorVAE Score, the MIG, and the Modularity Score, thus validating our design hypothesis about the SCS representation. We remark that, while it is not the case of the FactorVAE Score (possibly due to its reliance on a deterministic classifier), the MIG and Modularity Score are sensitive to the number of object-centric samples, decreasing as low as 64.4% and 66.6% for O = 1.
+
+Hypothesis. Seeing the discrepancies around the impact of S and O, and given both (i) the fact that increasing S inevitably increases the length of the episode (thus making it difficult for LSTMs as they notoriously fail to inte-
+
+Table 4: Bottom: ZSCT accuracies (%) for S = 1 and O = 1, with a 10M observation budget.
+
+| | LSTM | ESBN | DCEM |
+|---------|--------------|--------------|--------------|
+| Acc.(%) | 86.00 ± 0.14 | 89.35 ± 2.77 | 81.90 ± 0.59 |
+
+grate information from past to present inputs over long dependencies) and (ii) our results goading us to think that increasing S when O > 1 is confusing LSTM-based agent rather than helping them, we hypothesise that using agents with more efficient memory-addressing mechanism (e.g. attention) and greater algorithm-learning abilities (e.g. with explicit memory) would help the agent get past the identified hurdles. While we leave it to subsequent work to investigate the impact of more sophisticated core memory when O > 1, we here propose off-the-shelf baseline results in the simplest setting and with a greater sampling budget. Indeed, we hypothesis that a denser sampling
+
+of the space of all symbolic spaces ought to be beneficial, as we extrapolate Chaa-RSCH (ii) to its meta-learning version, and avoid the issues pertaining to long dependencies.
+
+Evaluation. We report on architectures based on the Emergent Symbol Binding Network (ESBN) [Webb et al., 2020] and the Dual-Coding Episodic Memory (DCEM) [Hill et al., 2020] (resp. 3 and 2 random seeds), in the setting of Ndim = 3, Vmin = 2 and Vmax = 3, with a sampling budget of 10M observations, as opposed to 5M previously.
+
+Results. Table 4 presents the results, which are slightly in favour of the ESBN-based model. As it is built over the LSTM-based one, the memory scheme may be bypassed until it provides advantages with a simple-enough mechanism, as opposed to the case of the DCEM-based model.
+
+In the following, we investigate and compare the performance when using an LSTM [Hochreiter and Schmidhuber, 1997] or a Differentiable Neural Computer (DNC) [Graves et al., 2016] as core memory module.
+
+Regarding the auxiliary reconstruction loss, in the case of the LSTM, the hidden states are used, while in the case of the DNC, the memory is used as input to the prediction network. Figure 6b shows the stimulus reconstruction accuracies for both architectures, highlighting a greater data-efficiency (and resulting asymptotic performance in the current observation budget) of the LSTM-based architecture, compared to the DNC-based one.
+
+Figure 6a shows the 4-ways (3 distractors descriptive RG variant) ZSCT accuracies of the different agents throughout learning. The ZSCT accuracy is the accuracy over testing-purpose stimuli only, after the agent has observed for two consecutive times (i.e. S = 2) the supportive training-purpose stimuli for the current episode. The DNC-based architecture has difficulty learning how to use its memory, even with the use of the auxiliary reconstruction loss, and therefore it utterly fails to reach better-than-chance ZSCT accuracies. On the otherhand, the LSTM-based architecture is fairly successful on the auxiliary reconstruction task, but it is not sufficient for training on the main task to really take-off. As expected from the fact that the benchmark instantiates a binding problem that requires relational responding, our results hint at the fact that the ability to use memory towards deriving valuable relations between stimuli seen at different time-steps is primordial. Indeed, only the agent that has the ability to use its memory element towards recalling stimuli starts to perform at a better-than-chance level. Thus, the auxiliary reconstruction loss is an important element to drive some success on the task, but it is also clearly not sufficient, and the rather poor results that we achieved using these baseline agents indicates that new inductive biases must be investigated to be able to solve the problem posed in this benchmark.
+
+
+
+Figure 6: (a): 4-ways (3 distractors) zero-shot compositional test accuracies of different architectures. 5 seeds for architectures with DNC and LSTM, and 2 seeds for runs with DNC+Rec and LSTM+Rec, where the auxiliary reconstruction loss is used. (b): Stimulus reconstruction accuracies for the archiecture augmented with the auxiliary reconstruction task. Accuracies are computed on binary values corresponding to each stimulus' latent dimension's reconstructed value being close enough to the ground truth value, with a threshold of 0.05.
diff --git a/2210.02984/main_diagram/main_diagram.drawio b/2210.02984/main_diagram/main_diagram.drawio
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+# Introduction
+
+Symmetries allow machine learning models to generalize properties of one data point to the properties of an entire class of data points. A model that captures translational symmetry, for example, will have the same output for an image and a version of the same image shifted a half pixel to the left or right. If a classification model produces dramatically different predictions as a result of translation by half a pixel or rotation by a few degrees it is likely misaligned with physical reality. Equivariance provides a formal notion of consistency under transformation. A function is equivariant if symmetries in the input space are preserved in the output space.
+
+Baking equivariance into models through architecture design has led to breakthrough performance across many data modalities, including images [@kipf2016semi; @veeling2018rotation], proteins [@jumper2021highly] and atom force fields [@batzner20223; @frey2022neural]. In computer vision, translation equivariance has historically been regarded as a particularly compelling property of convolutional neural networks (CNNs) [@lecun1995convolutional]. Imposing equivariance restricts the size of the hypothesis space, reducing the complexity of the learning problem and improving generalization [@goodfellow2016deep].
+
+In most neural networks classifiers, however, true equivariance has been challenging to achieve, and many works have shown that model outputs can change dramatically for small changes in the input space [@azulay2018deep; @engstrom2018rotation; @vasconcelos2021impact; @ribeiro2021convolutional]. Several authors have significantly improved the equivariance properties of CNNs with architectural changes inspired by careful signal processing [@zhang2019making; @karras2021alias], but non-architectural mechanisms for encouraging equivariance, such as data augmentations, continue to be necessary for good generalization performance [@wightman2021resnet].
+
+The increased prominence of non-convolutional architectures, such as vision transformers (ViTs) and mixer models, simultaneously demonstrates that explicitly encoding architectural biases for equivariance is not necessary for good generalization in image classification, as ViT models perform on-par with or better than their convolutional counterparts with sufficient data and well-chosen augmentations [@dosovitskiy2020image; @tolstikhin2021mlp]. Given the success of large flexible architectures and data augmentations, it is unclear what clear practical advantages are provided by explicit architectural constraints over learning equivariances from the data and augmentations. Resolving these questions systemically requires a unified equivariance metric and large-scale evaluation.
+
+In what follows, we introduce the Lie derivative as a tool for measuring the equivariance of neural networks under continuous transformations. The *local equivariance error* (LEE), constructed with the Lie derivative, makes it possible to compare equivariance across models and to analyze the contribution of each layer of a model to its overall equivariance. Using LEE, we conduct a large-scale analysis of hundreds of image classification models. The breadth of this study allows us to uncover a novel connection between equivariance and model generalization, and the surprising result that ViTs are often more equivariant than their convolutional counterparts after training. To explain this result, we use the layer-wise decomposition of LEE to demonstrate how common building block layers shared across ViTs and CNNs, such as pointwise non-linearities, frequently give rise to aliasing and violations of equivariance.
+
+We make our code publicly available at .
+
+Equivariance provides a formal notion of consistency under transformation. A function $f:V_1 \to V_2$ is equivariant to transformations from a symmetry group $G$ if applying the symmetry to the input of $f$ is the same as applying it to the output $$\begin{equation}
+\label{eq:equivariance}
+ \forall g\in G: \quad f(\rho_1(g)x) = \rho_2(g)f(x),
+\end{equation}$$ where $\rho(g)$ is the *representation* of the group element, which is a linear map $V\to V$.
+
+The most common example of equivariance in deep learning is the translation equivariance of convolutional layers: if we translate the input image by an integer number of pixels in $x$ and $y$, the output is also translated by the same amount, ignoring the regions close to the boundary of the image. Here $x\in V_1=V_2$ is an image and the representation $\rho_1=\rho_2$ expresses translations of the image. The translation *invariance* of certain neural networks is also an expression of the equivariance property, but where the output vector space $V_2$ has the trivial $\rho_2(g)=I$ representation, such that model outputs are unaffected by translations of the inputs. Equivariance is therefore a much richer framework, in which we can reason about complex representations at the input and the output of a function.
+
+The inputs to classification models are discrete images sampled from a *continuous* reality. Although discrete representations are necessary for computers, the goal of classification models should be learning functions that generalize in the real world. It is therefore useful to consider an image as a function $h: \R^2 \mapsto \R^3$ rather than a discrete set of pixel values and broaden the symmetry groups we might consider, such as translations of an image by vector $\vb \in \R^2$, rather than an integer number of pixels.
+
+Fourier analysis is a powerful tool for understanding the relationship between continuous signals and discrete samples by way of frequency decompositions. Any $M \times M$ image, $h(\vx)$, can be constructed from its frequency components, $H_{nm}$, using a $2d$ Fourier series, $h(\vx)= \frac{1}{2\pi} \sum_{n,m} H_{nm} e^{2\pi i \vx \cdot [n,m]}$ where $\vx \in [0,1]^2$ and $n,m \in [\text{-}M/2,\text{-}M/2+1, ...,M/2]$, the bandlimit defined by the size of the image.
+
+Aliasing occurs when sampling at a limited frequency $f_s$, for example the size of an image in pixels, causes high frequency content (above the Nyquist frequency $f_s/2$) to be converted into spurious low frequency content. Content with frequency $n$ is observed as a lower frequency contribution at frequency $$\begin{equation}
+\mathrm{Alias}(n) = \left\{
+\begin{array}{ll}
+ n \bmod f_s &\text{if} \ (n \bmod f_s)f_s/2 \\
+\end{array}
+\right\}.
+\label{eq:alias}
+\end{equation}$$ If a discretely sampled signal such as an image is assumed to have no frequency content higher than $f_s$, then the continuous signal can be uniquely reconstructed using the Fourier series and have a consistent continuous representation. But if the signal contains higher frequency content which gets aliased down by the sampling, then there is an ambiguity and exact reconstruction is not possible.
+
+Aliasing is critically important to our study because it breaks equivariance to continuous transformations like translation and rotation. When a continuous image is translated its Fourier components pick up a phase: $$h(\vx) \mapsto h(\vx - \vb) \implies H_{nm} \mapsto H_{nm} e^{-2\pi i \vb \cdot [n,m]}.$$ However, when an aliased signal is translated, the aliasing operation $\mathrm{A}$ introduces a scaling factor: $$H_{nm} \mapsto H_{nm} e^{-2\pi i (b_0 \mathrm{Alias}(n) + b_1 \mathrm{Alias}(m))}$$ In other words, aliasing causes a translation *by the wrong amount*: the frequency component $H_{nm}$ will effectively be translated by $[(\mathrm{Alias}(n)/n ) b_0, (\mathrm{Alias}(m) / m) b_1]$ which may point in a different direction than $\vb$, and potentially even the opposite direction. Applying shifts to an aliased image will yield the correct shifts for true frequencies less than the Nyquist but incorrect shifts for frequencies higher than the Nyquist. Other continuous transformations, like rotation, create similar asymmetries.
+
+Many common operations in CNNs can lead to aliasing in subtle ways, breaking equivariance in turn. @zhang2019making, for example, demonstrates that downsampling layers causes CNNs to have inconsistent outputs for translated images. The underlying cause of the invariance is aliasing, which occurs when downsampling alters the high frequency content of the network activations. The $M \times M$ activations at a given layer of a convolutional network have spatial Nyquist frequencies $f_s = M/2$. Downsampling halves the size of the activations and corresponding Nyquist frequencies. The result is aliasing of all nonzero content with $n \in [M/4,M/2]$. To prevent this aliasing, @zhang2019making uses a local low pass filter (Blur-Pool) to directly remove the problematic frequency regions from the spectrum.
+
+While studying generative image models, @karras2021alias unearth a similar phenomenon in
+
+::: wrapfigure
+r0.4 {width="40%"}
+
+[]{#fig:harmonics label="fig:harmonics"}
+:::
+
+the pointwise nonlinearities of CNNs. Imagine an image at a single frequency $h(\vx) = \sin(2\pi \vx \cdot [n,m])$. Applying a nonlinear transformation to $h$ creates new high frequencies in the Fourier series, as illustrated in [\[fig:harmonics\]](#fig:harmonics){reference-type="ref+label" reference="fig:harmonics"}. These high frequencies may fall outside of the bandlimit, leading to aliasing. To counteract this effect, @karras2021alias opt for smoother non-linearities and perform upsampling before calculating the activations.
+
+# Method
+
+We evaluate the Lie derivative of many popular classification models under transformations including $2d$ translation, rotation, and shearing. We define continuous transformations on images using bilinear interpolation with reflection padding. In total, we evaluate 410 classification models, a collection comprising popular CNNs, vision transformers, and MLP-based architectures [@rw2019timm]. Beyond diversity in architectures, there is also substantial diversity in model size, training recipe, and the amount of data used for training or pretraining. This collection of models therefore covers many of the relevant axes of variance one is likely to consider in designing a system for classification. We include an exhaustive list of models in the Appendix [3.2](#sec:model-list){reference-type="ref" reference="sec:model-list"}.
+
+
+
+
+
+
+
+
+
+
+
+
+ Equivariance metrics evaluated on the ImageNet test set. Left: Non-LEE equivariance metrics display similar trends to 1, despite using larger, multi-pixel transformations. Right: Norm of rotation and shear Lie derivatives. Across all architectures, models with strong generalization become more equivariant to many common affine transformations. Marker size indicates model size. Error bars show one standard error over test set images used in the equivariance calculation.
+
+
+As shown in [1](#fig:title-fig){reference-type="ref+label" reference="fig:title-fig"} (right), the translation equivariance error (Lie derivative norm) is strongly correlated with the ultimate test accuracy that the model achieves. Surprisingly, despite convolutional architectures being motivated and designed for their translation equivariance, we find no significant difference in the equivariance achieved by convolutional architectures and the equivariance of their more flexible ViT and Mixer counterparts when conditioning on test accuracy. This trend also extends to rotation and shearing transformations, which are common in data augmentation pipelines [@cubuk2020randaugment] (in [4](#fig:lie_deriv){reference-type="ref+label" reference="fig:lie_deriv"} (right)). Additional transformation results included in Appendix [3.4](#sec:more-transforms){reference-type="ref" reference="sec:more-transforms"}.
+
+For comparison, in [4](#fig:lie_deriv){reference-type="ref+label" reference="fig:lie_deriv"} (left) we also evaluate the same set of models using two alternative equivariance metrics: prediction consistency under discrete translation [@zhang2019making] and expected equivariance under group samples [@finzi2020generalizing; @hutchinson2021lietransformer], which is similar in spirit to $\text{EQ-T}_{\text{frac}}$ [@karras2021alias] (exact calculations in Appendix [3.3](#sec:alt-equivariance-metrics){reference-type="ref" reference="sec:alt-equivariance-metrics"}). Crucially, these metrics are slightly less *local* than LEE, as they evaluate equivariance under transformations of up to 10 pixels at a time. The fact that we obtain similar trends highlights LEE's relevance beyond subpixel transformations.
+
+In [3](#sec:related-work){reference-type="ref+label" reference="sec:related-work"} we described many architectural design choices that have been used to improve the equivariance of vision models, for example @zhang2019making's Blur-Pool low-pass filter. We now investigate how equivariance error can be reduced with non-architectural design decisions, such as increasing model size, dataset size, or training method. Surprisingly, we show that equivariance error can often be significantly reduced without any changes in architecture.
+
+In [5](#fig:scale-comparison){reference-type="ref+label" reference="fig:scale-comparison"}, we show slices of the data from [1](#fig:title-fig){reference-type="ref+label" reference="fig:title-fig"} along a shared axis for equivariance error. As a point of comparison, in [5](#fig:scale-comparison){reference-type="ref+label" reference="fig:scale-comparison"} (left), we show the impact of the Blur-Pool operation discussed above on a ResNet-50 [@zhang2019making]. In the accompanying four plots, we show the effects of increasing model scale (for both ViTs and CNNs), increasing dataset size, and finally different training procedures. Although @zhang2019making's architectural adjustment does have a noticeable effect, factors such as dataset size, model scale, and use of modern training methods, have a much greater impact on learned equivariance.
+
+As a prime example, in [5](#fig:scale-comparison){reference-type="ref+label" reference="fig:scale-comparison"} (right), we show a comparison of three training strategies for ResNeXt-50 -- an architecture almost identical to ResNet-50. We use @wightman2021resnet's pretrained model to illustrate the role of an improved training recipe and @wslimageseccv2018's semi-supervised model as an example of scaling training data. Notably, for a fixed architecture and model size, these changes lead to decreases in equivariance error on par with architectural interventions (BlurPool). This result is surprising when we consider that @wightman2021resnet's improved training recipe benefits significantly from Mixup [@zhang2017mixup] and CutMix [@yun2019cutmix], which have no obvious connection to equivariance. Similarly, @wslimageseccv2018's semi-supervised method has no explicit incentive for equivariance.
+
+
+
+Case studies in decreasing translational equivariance error, numbered left-to-right. 1: Blur-Pool , an architectural change to improve equivariance, decreases the equivariance error but by less than can be accomplished by improving the training recipe or increasing the scale of model or dataset. 2-3: Increasing the number of parameters for a fixed model family (here ViTs and EfficientNets ). 4: Increasing the training dataset size for a ResMLP Big model. 5: Changing the training recipe for ResNeXt-50 with improved augmentations or SSL pretraining . Error bars show one standard error over images in the Lie derivative calculation.
+
+
+From our analysis above, large models appear to learn equivariances that rival architecture engineering in the classification setting. When learning equivariances through data augmentation, however, there is no guarantee that the equivariance will generalize to data that is far from the training distribution. Indeed, @engstrom2019exploring shows that carefully chosen translations or rotations can be as devastating to model performance as adversarial examples. We find that vision models do indeed have an *equivariance gap*: models are less equivariant on test data than train, and this gap grows for OOD inputs as shown in Figure [6](#fig:equivariance_gap){reference-type="ref" reference="fig:equivariance_gap"}. Notably, however, architectural biases do not have a strong effect on the equivariance gap, as both CNN and ViT models have comparable gaps for OOD inputs.
+
+Given the deep historical connection between CNNs and equivariance, the results in [4](#fig:lie_deriv){reference-type="ref+label" reference="fig:lie_deriv"} and [6](#fig:equivariance_gap){reference-type="ref+label" reference="fig:equivariance_gap"} might appear counterintuitive. ViTs, CNNs, and Mixer have quite different inductive biases and therefore often learn very different representations of data [@raghu2021vision]. Despite their differences, however, all of these architectures are fundamentally constructed from similar building blocks--such as convolutions, normalization layers, and non-linear activations which can all contribute to aliasing and equivariance error. Given this shared foundation, vision models with high capacity and effective training recipes are more capable of fitting equivariances already present in augmented training data.
+
+::: wraptable
+r0.39
+
+ Model Test Error (%)
+ -------------------------------- ----------------
+ G-CNN [@cohen2016group] 2.28
+ H-NET [@worrall2017harmonic] 1.69
+ ORN [@zhou2017oriented] 1.54
+ TI-Pooling [@laptev2016ti] 1.2
+ Finetuned MAE 1.14
+ RotEqNet [@marcos2017rotation] 1.09
+ E(2)-CNN [@e2cnn] 0.68
+
+[]{#tab:rotmnist label="tab:rotmnist"}
+:::
+
+We finally consider the extent to which large-scale pretraining can match strong architectural priors in a case where equivariance is obviously desirable. We fine-tune a state-of-the-art vision transformer model pretrained with masked autoencoding [@MaskedAutoencoders2021] for 100 epochs on rotated MNIST [@e2cnn] (details in Appendix [3.5](#sec:mae-finetune){reference-type="ref" reference="sec:mae-finetune"}). This dataset, which contains MNIST digits rotated uniformly between -180 and 180 degrees, is a common benchmark for papers that design equivariant architectures. In [\[tab:rotmnist\]](#tab:rotmnist){reference-type="ref+label" reference="tab:rotmnist"} we show the test errors for many popular architectures with strict equivariance constrainets alongside the error for our finetuned model. Surprisingly, the finetuned model achieves competitive test accuracy, in this case a strong proxy for rotation invariance. Despite having relatively weak architectural biases, transformers are capable of learning and generalizing on well on symmetric data.
+
+
+
+Models are less equivariant on test data and becoming decreasingly equivariant as the data moves away from the training manifold. As examples of data with similar distributions, we show equivariance error on the ImageNet train and test sets as well as CIFAR-100. As examples of out-of-distribution data, we use two medical datasets (which often use Imagenet pretraining), one for Histology and one for Retinopathy .
+
diff --git a/2210.15230/main_diagram/main_diagram.drawio b/2210.15230/main_diagram/main_diagram.drawio
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index 0000000000000000000000000000000000000000..b818459a3345b768f393288687216816d6faf7c4
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
\ No newline at end of file
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+# Introduction
+
+Recent Text-to-Image generative models [@ramesh2021zero; @ramesh2022hierarchical; @ding2021cogview; @saharia2022photorealistic; @nichol2021glide; @rombach2022high] can synthesize high-quality photo-realistic images conditional on natural language text descriptions in a zero-shot fashion.
+
+
+
+ We study the change in the text-to-image model generations across various groups (man/woman, light-skinned/dark-skinned, Western/Non-Western) before and after adding ethical interventions (in purple) during text-to-image generation. To analyze the bias in model outputs, we use CLIP and Human to annotate social groups of the model generations. We present a few generated results in Appendix Fig. 4-8.
+
+
+For instance, they can generate an image of 'an armchair in the shape of an avocado' which appears rarely in the real world. However, despite the unprecedented zero-shot abilities of the text-to-image generative models, recent experiments with small-scale instantiations (such as minDALL$\cdot$E) have shown that prompting the model with neutral texts ('a photo of a lawyer'), devoid of any cues towards a social group, still generates images that are biased towards *white males* [@cho2022dall].
+
+In our work, we consider three bias axis -- 1) {man, woman} grouping across gender axis, 2) {light-skinned, dark-skinned} grouping across skin color axis, and 3) {Western, Non-Western} grouping across cultural axis.[^2] The existence of any gender[^3] and skin color bias[^4] (see Ethical Statements for more discussion) causes potential harms to underrepresented groups by amplifying bias present in the dataset [@Birhane2021MultimodalDM; @barocas2018fairness]. Hence, it is essential for a text-to-image system to generate *diverse* set of images.
+
+To this end, we study *if the presence of additional knowledge that supports equitable judgment help in diversifying model generations*. Being part of text input, this knowledge acts as an *ethical intervention* augmented to the original prompt [@zhao-etal-2021-ethical][^5]. Ethical interventions provide models with ethical advice and do not emanate any visual cues towards a specific social group. For instance, in the context of generating 'a photo of a lawyer' that tends to be biased towards 'light-skinned man', we wish to study if prompting the model with ethically intervened prompt (e.g., 'a photo of a lawyer *if all individuals can be a lawyer irrespective of their gender*') can diversify the outputs.
+
+We introduce an **E**thical **N**a**T**ural Language **I**nterventions in Text-to-Image **GEN**eration (ENTIGEN) benchmark dataset to study the change in the perceived societal bias of the text-to-image generative models in the presence of ethical interventions. ENTIGEN covers prompts to study the bias across three axes -- gender, skin color and culture. The neutral prompts in ENTIGEN dataset are intervened with corresponding ethical knowledge as illustrated in Figure [1](#fig:intro){reference-type="ref" reference="fig:intro"}. We evaluate ENTIGEN on publicly available models -- minDALL$\cdot$E [@kakaobrain2021minDALLE], DALL$\cdot$E-mini [@Dayma2021], and Stable Diffusion [@rombach2022high] automatically with CLIP model [@radford2019language] and manually with human annotators from MTurk.
+
+Through our experiments, (1) we show that a few ethical interventions lead to the diversification of the image generations across different groups while preserving the image generation quality. Interestingly, in some cases, we observe the bias can be flipped towards the originally underrepresented groups with ethical interventions (Appendix Figure [6](#fig:dmini-makeup){reference-type="ref" reference="fig:dmini-makeup"}). (2) Moreover, we find that the interventions containing keywords such as 'irrespective of gender' and 'culture' tend to trigger a large change in model generations. We further analyze the pre-training data to analyze the context in which these keywords are used to better understand how they may affect the diversity in generation.
+
+# Method
+
+In this section, we introduce the process of building an ethical intervention benchmark ENTIGEN and evaluating the images generated by text-to-image generative models.
+
+Initially, we determine three axes of societal bias to be studied: gender, skin color, and culture. Specifically, to investigate the gender and skin color bias, ENTIGEN consists of prompts belonging to the category of *professions* and *objects*. For assessing the cultural bias, it consists of prompts surrounding the *wedding ceremonies* as it is ubiquitous and diverse across different regions [@bell1997ritual; @xu2018comparison; @Acharya2020AnAO].
+
+Based on the selected axes, we design neutral prompts without any ethical interventions as the *original* prompts. Subsequently, we append ethical interventions to the original prompts that can perhaps amend the model's behaviour towards more diverse generations. For instance,
+
+1. **Original Prompt:** a photo of a person wearing a \[*object*\]
+
+2. **With Ethical Intervention:** a photo of a person wearing a \[*object*\] [if all individuals can wear a \[*object*\] irrespective of their gender]{style="color: blue"}
+
+'If all individuals can wear a \[*object*\] irrespective of their gender' is an ethical intervention that guides diverse outputs in terms of gender. We require the ethical interventions to not give away any visual cues to eliminate the effect of any explicit guidance.
+
+We further include *irrelevant* interventions in ENTIGEN. These interventions also provide ethical advice, but do not correspond to any social axes we study in ENTIGEN. For example, 'if honesty is the best policy' is an irrelevant intervention since it is unrelated to gender, skin color and culture. Ideally, these interventions cannot help in diversifying image generations on either of studied social axes.
+
+In total, we create 246 prompts based on an attribute set containing diverse professions, objects, and cultural scenarios.[^6]
+
+Each prompt in ENTIGEN is used to generate 9 images from each text-to-image generation model 9 times. We choose the publicly available models, minDALL$\cdot$E, DALL$\cdot$E-mini, and Stable Diffusion for analysis. It is mainly because these three models can generate high-quality images efficiently. We provide more details in Appendix [7](#detailsig){reference-type="ref" reference="detailsig"}.
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diff --git a/2305.07185/main_diagram/main_diagram.pdf b/2305.07185/main_diagram/main_diagram.pdf
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index 0000000000000000000000000000000000000000..65fa51f2de4fc6b85fdb5def7d65bc490755bbaf
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diff --git a/2305.07185/paper_text/intro_method.md b/2305.07185/paper_text/intro_method.md
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index 0000000000000000000000000000000000000000..4387d92c38576ff0ffa5c8c7192762b98f3e9f1e
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+# Introduction
+
+Sequences of millions of bytes are ubiquitous; for example, music, image, or video files typically consist of multiple megabytes. However, large transformer decoders (LLMs) typically only use several thousand tokens of context [@gpt3; @Zhang2022OPT:Models]---both because of the quadratic cost of self-attention but also, more importantly, the cost of large feedforward networks per-position. This severely limits the set of tasks where LLMs can be applied.
+
+![Overview of [MegaByte]{.smallcaps} with patch size $P=4$. A small *local* model autoregressively predicts each patch byte-by-byte, using the output of a larger *global* model to condition on previous patches. Global and Local inputs are padded by $P$ and $1$ token respectively to avoid leaking information about future tokens. []{#fig:framework label="fig:framework"} ](figs/fastslow3.pdf){#fig:framework width="\\linewidth"}
+
+We introduce [MegaByte]{.smallcaps}, a new approach to modeling long byte sequences. First, byte sequences are segmented into fixed-sized patches, loosely analogous to tokens. Our model then consists of three parts: (1) a *patch embedder*, which simply encodes a patch by losslessly concatenating embeddings of each byte, (2) a *global* module, a large autoregressive transformer that inputs and outputs patch representations and (3) a *local* module, a small autoregressive model that predicts bytes within a patch. Crucially, we observe that for many tasks, most byte predictions are relatively easy (for example, completing a word given the first few characters), meaning that large networks per-byte are unnecessary, and a much smaller model can be used for intra-patch modelling.
+
+The [MegaByte]{.smallcaps} architecture gives three major improvements over Transformers for long sequence modelling:
+
+1. **Sub-quadratic self-attention** Most work on long sequence models has focused on mitigating the quadratic cost of self-attention. [MegaByte]{.smallcaps} decomposes long sequences into two shorter sequences, and optimal patch sizes reduces the self-attention cost to $O(N^{\frac{4}{3}})$, which remains tractable for even long sequences.
+
+2. **Per-patch feedforward layers** In GPT3-size models, more than 98% of FLOPS are used in computing position-wise feedforward layers. [MegaByte]{.smallcaps} uses large feedforward layers per-patch rather than per-position, enabling much larger and more expressive models for the same cost. With patch size $P$, where a baseline transformer would use the same feedforward layer with $m$ parameters $P$ times, [MegaByte]{.smallcaps} can use a layer with $mP$ parameters once for the same cost.
+
+3. **Parallelism in Decoding** Transformers must perform all computations serially during generation because the input to each timestep is the output from the previous timestep. By generating representations for patches in parallel, [MegaByte]{.smallcaps} allows greater parallelism during generation. For example, a [MegaByte]{.smallcaps} model with 1.5B parameters can generate sequences 40% *faster* than a standard 350M Transformer, whilst also improving perplexity when trained with the same compute.
+
+Together, these improvements allow us to train much larger and better-performing models for the same compute budget, scale to very long sequences, and improve generation speed during deployment.
+
+[MegaByte]{.smallcaps} also provides a strong contrast to existing autoregressive models that typically use some form of tokenization, where sequences of bytes are mapped to larger discrete tokens [@bpe; @dalle; @hubert]. Tokenization complicates pre-processing, multi-modal modelling, and transfer to new domains, while hiding useful structure from the model. It also means that most state-of-the-art models are not truly end to end. The most widely used approaches to tokenization require language-specific heuristics [@gpt2] or lose information [@dalle]. Replacing tokenization with efficient and performant byte models would therefore have many advantages.
+
+We conduct extensive experiments for both [MegaByte]{.smallcaps} and strong baselines. We use a fixed compute and data budget across all models to focus our comparisons solely on the model architecture rather than training resources, which are known to benefit all models. We find that [MegaByte]{.smallcaps} allows byte-level models to perform competitively with subword models on long context language modeling, achieve state-of-the-art perplexities for density estimation on ImageNet, and allow audio modelling from raw audio files. Together, these results establish the viability of tokenization-free autoregressive sequence modeling at scale.
+
+
+
+Summary of MegaByte with vocabulary V, sequence length T, global and local dimensions DG and DL, and K patches of size P. Transformer layers use masked self attention to not observe information from future timesteps.
+
+
+[MegaByte]{.smallcaps} is an autoregressive model for efficiently modeling long input sequences. [MegaByte]{.smallcaps} is comprised of 3 components: (1) a *patch embedder* that inputs a discrete sequence, embeds each element, and chunks it into patches of length $P$ (2) a large *global* Transformer that contextualizes patch representations by performing self-attention over previous patches, and (3) a smaller *local* Transformer that inputs a contextualized patch representation from the global model, and autoregressively predict the *next* patch.
+
+$\textbf{Patch Embedder}$ with patch size of $P$ maps a byte sequence $x_{0..T}$ to a sequence of patch embeddings of length $K=\frac{T}{P}$ and dimension $P\cdot D_G$.
+
+First, each byte is embedded with a lookup table $E^{\text{global-embed}}\in\mathbb{R}^{V \times D_G}$ to an embedding of size $D_G$ and positional embeddings are added.
+
+$$\begin{align}
+h^\text{embed}_{t}&= E^\text{global-embed}_{x_t}+E^\text{pos}_t & t\in [0..T]
+\end{align}$$
+
+Then, byte embeddings are reshaped into a sequence of $K$ patch embeddings with dimension $P\cdot D_G$. To allow autoregressive modelling, the patch sequence is padded to start with a trainable patch-sized padding embedding ($E^{\text{global-pad}} \in\mathbb{R}^{P\times D_G}$), and the last patch is removed from the input. This sequence is the input to the global model, and is denoted $h^\text{global-in}\in\mathbb{R}^{K\times (P\cdot D_G)}$. $$\begin{align}
+h^\text{global-in}_{k} =
+\begin{cases}
+E^{\text{global-pad}} , & \text{if } k = 0, \\
+h^\text{embed}_{((k-1)\cdot P):(k \cdot P)}, & k\in [1,..,K),
+\end{cases}
+\end{align}$$
+
+$\textbf{Global Model}$ is a decoder-only Transformer with dimension $P\cdot D_G$ that operates on a sequence of $K$ patches. It incorporates a self-attention mechanism and causal masking to capture dependencies between patches. It inputs a sequence of $K$ patch representations $h^\text{global-in}_{0:K}$, and outputs an updated representation $h^\text{global-out}_{0:K}$ by performing self-attention over previous patches.
+
+$$\begin{align}
+h^\text{global-out}_{0:K} &= \text{transformer}^{\text{global}}(h^\text{global-in}_{0:K}) %& \h{global-out}\inR{K\times P\cdot D} \\
+\end{align}$$
+
+The output of the final global layer $h^\text{global}_{0:K}$ contains $K$ patch representations of dimension $P\cdot D_G$. For each of these, we reshape them into sequences of length $P$ and dimension $D_G$, where position $p$ uses dimensions $p \cdot D_G$ to $(p+1) \cdot D_G$. Each position is then projected to the dimension of the local model with a matrix $w^{\text{GL}}\in\mathbb{R}^{D_G\times D_L}$ where $D_L$ is the local model dimension. We then combine these with byte embeddings of size $D_L$ for the tokens in the *next* patch $E^\text{local-embed}_{x_{(k\cdot P + p - 1)}}$. The local byte embeddings is offset by one with a trainable local padding embedding ($E^{\text{local-pad}} \in\mathbb{R}^{D_L}$) to allow autoregressive modelling within a patch. This results in a tensor $h^\text{local-in}\in\mathbb{R}^{K\times P \times D_L}$. $$\begin{align}
+%\h{local-in}_{k,0} &= \h{global-out}_{k,0:D}\\% & \h{local-in}\inR{K\times P \times D} \\
+h^\text{local-in}_{k,p} &= w^{\text{GL}}h^\text{global-out}_{k,(p\cdot D_G):((p+1)\cdot D_G)} + E^\text{local-embed}_{x_{(k\cdot P + p - 1)}}% & p\in [1,..,P ], d\in [0,..,D] \\
+\end{align}$$
+
+$\textbf{Local Model}$ is a smaller decoder-only Transformer of dimension $D_L$ that operates on a single patch $k$ containing $P$ elements, each of which is the sum of an output from the global model and an embedding of the previous byte in the sequence. $K$ copies of the local models are run on each patch independently (and in parallel during training), computing a representation $h^\text{local-out}\in\mathbb{R}^{K\times P\cdot D_L}$.
+
+$$\begin{align}
+h^\text{local-out}_{k,0:P} &= \text{transformer}^{\text{local}}(h^\text{local-in}_{k,0:P}) %& \h{local-out}\inR{K\times P\cdot D}
+\end{align}$$
+
+Finally, we can compute the probability distribution over the vocabulary at each position. The $p$th element of the $k$th patch corresponds to element $t$ of the complete sequence, where $t=k\cdot P+p$: $$\begin{align}
+p(x_{t}|x_{0:t}) &= \text{softmax}{(E^\text{local-embed}h^\text{local-out}_{k,p})}_{x_t}
+\end{align}$$
+
+We experiment with several extensions of [MegaByte]{.smallcaps}.
+
+One limitation of chunking sequences into patches is that it is not translation invariant, and byte sequences may receive a different representation depending on their position in the patch. This may mean, for example, that a model has to relearn the meaning of a word at different offsets. To mitigate this issue, we experimented with augmenting the Patch Embedder with causal convolutional layers, which allow translation-invariant contextual representations of the bytes before they are chunked into patches. We use a stack of convolutional layers, with filter sizes of 3, 5 and 7.
+
+The Local model uses short sequences for efficiency, and relies on the Global model for long-range information. However, we can increase the context of the Local model with little overhead by allowing it to condition on $r$ elements from the previous patch. This approach allows the Global model to focus on a longer-range context. Specifically, when computing self-attention in each layer, we concatenate the keys and values with the last $r$ keys and queries from the previous patch. We use rotary embeddings [@rotary] to model relative positions between elements in the sequence. This approach is reminiscent of TransformerXL [@xfmxl] but differs by being fully differentiable.
+
+We observed empirically that the per-token loss within each patch would increase towards the end of the patch, as the prediction relies more on the weaker Local model. To alleviate this issue, we propose *strided inference*, in which we predict the sequence with two forward passes of the full model, whose inputs are offset by $p/2$ positions from each other. We then combine the first $p/2$ positions in each patch for our predictions to predict the complete sequence. Similarly to sliding window techniques [@shortformer], this approach doubles the cost of inference but improves results.
+
+Having described the model, we briefly discuss the motivation behind some of the architectural choices.
+
+**Why is the local model needed?** Many of the efficiency advantages of the [MegaByte]{.smallcaps} design could be realized with the Global model alone, which would resemble a decoder version of ViT [@vit]. However, the joint distribution over the patch $p(x_{t+1},..,x_{t+P}|x_{0..t})$ has an output space of size $256^{P}$ so direct modeling is only tractable for very small patches. We could instead factor the joint distribution into conditionally independent distributions $p(x_{t+1}|x_{0..t})..p(x_{t+P}|x_{0..t})$, but this would greatly limit the model's expressive power. For example, it would be unable to express a patch distribution such as 50% *cat* and 50% *dog*, and would instead have to assign probability mass to strings such as *cag* and *dot*. Instead, our autoregressive Local model conditions on previous characters within the patch, allowing it to only assign probability to the desired strings.
+
+**Increasing Parameters for Fixed Compute** Transformer models have shown consistent improvements with parameter counts [@kaplan2020scaling]. However, the size of models is limited by their increasing computational cost. [MegaByte]{.smallcaps} allows larger models for the same cost, both by making self attention sub-quadratic, and by using large feedforward layers across patches rather than individual tokens.
+
+**Re-use of Established Components** [MegaByte]{.smallcaps} consists of two transformer models interleaved with shifting, reshaping and a linear projection. This re-use increases the likelihood that the architecture will inherit the desirable scaling properties of transformers.
+
+We analyze the cost of different architectures when scaling both the sequence length and size of the models.
+
+The cost of the self attention in a transformer architecture for a sequence of length $T$ has $O(T^2)$ complexity. Much work has been explored reducing this; for example, Sparse Transformers [@sparsetransformer] and Routing Transformers [@routing] show strong results with a complexity $O(T^\frac{3}{2})$. Numerous linear attention mechanisms have also been proposed [@lineartransformer; @linear; @performer], although we are not aware of competitive results on large scale language modeling tasks. As a function of sequence length $T$ and patch size $P$, the Global model has a sequence of length $\frac{P}{T}$ so uses $O(\frac{T^2}{P^2})$ operations, and the Local model uses $\frac{P}{T}$ sequences of length $P$ so uses $O(\frac{TP^2}{P})=O(PT)$ operations. The overall cost of [MegaByte]{.smallcaps} is therefore in $O(\frac{T^2}{P^2}+TP)$. $P$ is a hyperparameter that is chosen to create an architecture for sequences of size $T$. By setting $P=T^\frac{1}{3}$ the complexity is in $O(T^\frac{4}{3})$. Using much shorter patches of $P=T^\frac{1}{5}$ would give a complexity of $O(T^\frac{8}{5})$. The cost is less than the transformer for all non-trivial values of $P$ such that $1
+
+Computational cost (FLOPS/token) for different model architectures at different scales. MegaByte architectures (here with P = 8) use less FLOPS than equivalently sized Transformers and Linear Transformers across a wide range of model sizes and sequence lengths, allowing larger models to be used for the same computational cost.
+
+
+To understand efficiency at different sequence lengths and model sizes, we calculate the total FLOPS used by transformers, Linear Transformers and [MegaByte]{.smallcaps}. For each operation, we use FLOP estimates from [@kaplan2020scaling], except for attention in Linear Transformers, which we estimate as $9D$ FLOPS/token[^1], where $D$ is the model embedding dimension. Figure [2](#figure:flops){reference-type="ref" reference="figure:flops"} shows that for models of size 660M to 173B and sequence lengths of up to 1M tokens, [MegaByte]{.smallcaps} with $P=8$ uses less FLOPS than either transformers or Linear Transformers. Baseline model architectures are based on GPT3, and Megabyte global/local model sizes are 452M/151M, 5.8B/604M, 170B/3.2B respectively.
+
+Generating long sequences with transformers is slow, because the input to each timestep is the output from the previous timestep, meaning each layer must be computed for each token serially. As running a layer on a single token typically does not saturate the amount of parallelism available within a GPU, for analysis, we model each layer as a constant cost independently of size. Consider a [MegaByte]{.smallcaps} model with $L_{\text{global}}$ layers in the Global model and $L_{\text{local}}$ layers in the Local model and patch size $P$, compared with a Transformer architecture with $L_{\text{local}}+L_{\text{global}}$ layers. Generating each patch with [MegaByte]{.smallcaps} requires a sequence of $O(L_{\text{global}}+P\cdot L_{\text{local}})$ serial operations, whereas the Transformer requires $O(P\cdot L_{\text{global}}+P\cdot L_{\text{local}})$ serial operations. When $L_{\text{global}}\gg L_{\text{local}}$ (i.e. the Global model has many more layers than the Local model), [MegaByte]{.smallcaps} can reduce inference costs by a factor close to $P$.
+
+# Method
+
+Several techniques have been proposed for trading off speed for performance during inference with language models, including sliding windows [@shortformer] and our strided inference (Section [2.3.3](#strided){reference-type="ref" reference="strided"}). We only use these methods when comparing with prior published work (Tables [\[tab:text_sota\]](#tab:text_sota){reference-type="ref" reference="tab:text_sota"} and [3](#tab:img_sota){reference-type="ref" reference="tab:img_sota"}).
+
+We evaluated the performance of [MegaByte]{.smallcaps} on language modeling on a set of 5 diverse datasets emphasizing long-range dependencies: Project Gutenberg (PG-19), Books, Stories, arXiv, and Code.
+
+::: {#tab:document_lengths}
+ Dataset Total Bytes Mean document size (bytes)
+ --------- ------------- ----------------------------
+ PG-19 10.1GB 411,404
+ Stories 21.3GB 35,265
+ Books 79.7GB 509,526
+ arXiv 91.5GB 58,518
+ Code 353.7GB 7,461
+
+ : Text dataset sizes and mean document lengths.
+:::
+
+**Datasets** We experiment on a range of long form text datasets. The PG-19 dataset [@pg19] consists of English-language books written before 1919 and is extracted from the [Project Gutenberg online library](https://www.gutenberg.org/). The Stories dataset [@stories] is a subset of CommonCrawl data meant to emulate Winograd schemas. Books [@pile] is another collection of English-language books. The arXiv dataset is a collection of technical publications written in LaTeX from the [arXiv online archive](arxiv.org). Finally, the Code dataset is a large publicly available dataset of open source code, under Apache, BSD or MIT licenses. More details on dataset sizes and document lengths are shared in Table [1](#tab:document_lengths){reference-type="ref" reference="tab:document_lengths"}.
+
+::: {#tab:text_exps}
+ PG-19 Stories Books arXiv Code
+ ------------------------ ----------- ----------- ----------- ----------- -----------
+ Transformer 1.057 1.064 1.097 0.816 0.575
+ PerceiverAR 1.104 1.070 1.104 0.791 0.546
+ [MegaByte]{.smallcaps} **1.000** **0.978** **1.007** **0.678** **0.411**
+
+ : Performance (bits-per-byte) of compute and data controlled [MegaByte]{.smallcaps}, PerceiverAR, and Transformer models on various text modalities.
+:::
+
+**Controlled Experiments** Table [2](#tab:text_exps){reference-type="ref" reference="tab:text_exps"}, lists bpb on each dataset. Each model is trained for 80 billion bytes, and models are scaled to use the same compute budget. We carefully tune hyperparameters for all architectures to best utilize the available compute budget. [MegaByte]{.smallcaps} consistently outperforms both baseline transformers and PerceiverAR across all datasets. We use the same set of parameters on all datasest. In all experiments presented in Table [2](#tab:text_exps){reference-type="ref" reference="tab:text_exps"}, transformer has size of 320M with context length of 1024, PerceiverAR has size of 248M with context size of 8192 and latent size of 1024, and [MegaByte]{.smallcaps} global/local model sizes are 758M/262M with context length of 8192 and patch size of 8.
+
+::: table*
+ Tokenizer Vocab Size Context Length Validation Test
+ --------------------------------------- --------------- ------------ ---------------------------- ------------ ----------
+ TransformerXL [@compressive] SentencePiece 32k 512+1024 (subwords) 45.5 36.3
+ CompressiveTransformer [@compressive] SentencePiece 32k 512+512+2x512 (subwords) 43.4 33.6
+ PerceiverAR [@perceiverar] SentencePiece 32k 2048 (subwords) 45.9 28.9
+ BlockRecurrent [@blockrecurrent] SentencePiece 32k 1024+recurrence (subwords) \- **26.5**
+ Transformer byte-level (ours) Bytes 256 2048 (bytes) 81.6 69.4
+ PerceiverAR byte-level (ours) Bytes 256 8192 (bytes) 119.1 88.8
+ [MegaByte]{.smallcaps} Bytes 256 8192 (bytes) **42.8** 36.4
+:::
+
+[]{#sec:results label="sec:results"}
+
+**Scaling Experiment** We scale up our training data on PG-19 (Table [\[tab:pg19_summary\]](#tab:pg19_summary){reference-type="ref" reference="tab:pg19_summary"}), and compare [MegaByte]{.smallcaps} with byte baselines, as well as converting all results to word-level perplexities to benchmark with state-of-art token based models.
+
+We train a byte-level Transformer, PerceiverAR and [MegaByte]{.smallcaps} models for 400B bytes and the same compute budget using same model parameters as in the controlled experiments. We find that [MegaByte]{.smallcaps} outperforms other byte-level models by a wide margin at this scale.[^3]
+
+We also compare with the best previously reported numbers for sub-word models. These results may be confounded by differing amounts of compute and tuning used, but show that [MegaByte]{.smallcaps} gives results competitive with state-of-the-art models trained on subwords. These results suggest that [MegaByte]{.smallcaps} may allow future large language models to be tokenization-free.
+
+We test [MegaByte]{.smallcaps} on variants of the autoregressive image generation task on ImageNet [@Oord2016PixelNetworks], to measure its ability to efficiently use long context. We test on three different resolutions of images, ranging from 64 to 640 pixels -- the latter requiring the effective modeling of sequences with over 1.2M tokens. This generation task becomes increasingly challenging as the image's resolution grows: doing well on this task requires the modeling of local patterns (textures, lines, etc.) and long-range context that provides information about the high level structure of the image. Inspired by recent works in Vision Transformers [@vit], we model image data patch by patch (more details can be found in Appendix [14.1](#sec:scan_type){reference-type="ref" reference="sec:scan_type"}).
+
+We train a large [MegaByte]{.smallcaps} model on ImageNet 64x64 with Global and Local models sized 2.7B and 350M parameters, respectively, for 1.4T tokens. We estimate that training this model consumed less than half the GPU hours we would have needed to reproduce the best PerceiverAR model described by [@perceiverar]. As shown in Table [3](#tab:img_sota){reference-type="ref" reference="tab:img_sota"}, [MegaByte]{.smallcaps} matches the state-of-the-art performance of PerceiverAR whilst using only half the compute.
+
+::: {#tab:img_sota}
+ ImageNet64 bpb
+ -------------------------------- ----------
+ Routing Transformer [@routing] 3.43
+ Combiner [@combiner] 3.42
+ Perceiver AR [@perceiverar] **3.40**
+ [MegaByte]{.smallcaps} **3.40**
+
+ : Bits per byte (bpb) on ImageNet 64. [MegaByte]{.smallcaps} matches the current state-of-the-art while only using half the amount of GPU hours to train.
+:::
+
+We compare three transformer variants (vanilla, PerceiverAR, [MegaByte]{.smallcaps}) to test scalability to long sequences on increasingly large image resolutions. We use our own implementations of these in the same framework and budget the same amount of GPU hours and data to train each of these model variants.
+
+[MegaByte]{.smallcaps} is able to handle all sequence lengths with a single forward pass of up to 1.2M tokens. We found neither the standard Transformer nor PerceiverAR could model such long sequences at a reasonable model size, so instead we split images into segments of size 1024 and 12000 respectively. For Megabyte, we set patch size as 12 for Image64 and patch size as 192 for Image256 and Image640 datasets. Model sizes are adjusted to match overall training speeds across models and we do not use any form of sliding window evaluation in this experiment. As seen in Table [4](#tab:resolution_scale){reference-type="ref" reference="tab:resolution_scale"}, [MegaByte]{.smallcaps} outperforms baselines across all resolutions in this compute-controlled setting. The precise settings used for each of the baseline models such as context length and number of latents are summarized in Table [10](#table:opt_model_desc){reference-type="ref" reference="table:opt_model_desc"}.
+
+Results show that [MegaByte]{.smallcaps} outperforms the other systems at all resolutions, demonstrating an effective model of sequences of over 1M bytes.
+
+::: {#tab:resolution_scale}
+ Context Image64 Image256 Image640
+ ------------------------ --------- ---------- ----------- -----------
+ Total len 12288 196608 1228800
+ Transformer 1024 3.62 3.801 2.847
+ Perceiver AR 12000 3.55 3.373 2.345
+ [MegaByte]{.smallcaps} Full **3.52** **3.158** **2.282**
+
+ : Bits per byte (bpb) on ImageNet with different resolutions. All models use the same compute and data. MEGABYTE scales well to sequences of over 1M tokens.
+:::
+
+Audio has aspects of both the sequential structure of text and the continuous nature of images, so is an interesting application for [MegaByte]{.smallcaps}.
+
+Raw audio is typically stored as a sequence of 16-bit integer values (one per timestep); a softmax layer would need to output 65,536 probabilities per timestep to model all possible values. To address this issue, various techniques have been developed to reduce the memory and computational requirements of the softmax layer. For instance, @wavenet apply $\mu$-law companding transformation and quantizes the input into 256 possible values. Alternatively, @parawavenet model the samples using the discretized mixture of logistics distribution introduced by @{pixcelcnn++}. Finally, @wavernn use a dual softmax technique to produce 8 coarse and 8 fine bits. In our approach, we simplify the audio modeling process by directly reading the bytes (256 possible values) from the audio file and conducting an autoregressive language model on top of that. This greatly streamlines the modeling process, making it easier and more efficient.
+
+Our audio modeling approach focuses on 16 kHz, 16-bit audio, which equates to 32k bytes per one-second clip. We use an extensive audio dataset consisting of 2 terabytes (roughly 18,000 hours) of audio. We use a sequence length of 524,288, a patch size of 32, and a batch size of 32 to facilitate model training. By utilizing these settings, we can effectively train our model on large volumes of audio data, helping to improve its accuracy and efficacy.
+
+Our model obtains bpb of 3.477, much lower than the results with perceiverAR (3.543) and vanilla transformer model (3.567). More ablation results are presented in Table [6](#tab:ablations){reference-type="ref" reference="tab:ablations"}.
+
+We also compare the text generation speed between [MegaByte]{.smallcaps} and a transformer. We compare a 350M parameter baseline transfomer and a [MegaByte]{.smallcaps} model with a 1.3B parameter Global model and a 218M parameter local model, trained on PG19 with equal compute. As shown in Table [5](#tab:generation){reference-type="ref" reference="tab:generation"}, the [MegaByte]{.smallcaps} model achieves much lower perplexity as expected. However, [MegaByte]{.smallcaps} also generates a sequence of 8192 tokens 40% *faster* than transformer, despite having over 4 times the parameters. This speed up is due to the bulk of the parameters being in the Global model, which only needs to be computed once for every 8 tokens, whereas all the parameters in the baseline model are used on every token.
+
+::: {#tab:generation}
+ ------------------------ ------ ------ ------- -----
+
+ Size
+ Size
+
+ Time (s)
+ Transformer \- 350M 1.064 132
+ [MegaByte]{.smallcaps} 1.3B 218M 0.991 93
+ ------------------------ ------ ------ ------- -----
+
+ : Comparison of bits per byte (bpb) and generation speed of 8192 bytes of transformer model (with context length 1024) and [MegaByte]{.smallcaps} with context length 8192 and patch size 8.
+:::
+
+In Table [6](#tab:ablations){reference-type="ref" reference="tab:ablations"}, we analyze the significance of different components in the [MegaByte]{.smallcaps} architecture by studying arXiv, Librilight-L and ImageNet256 datasets. Removing Local (*w/o local model*) or global (*w/o global model*) model, we observe a substantial increase in bpb on all datasets, showing that both parts are crucial. The performance of the model without the cross-patch local model (*w/o cross-patch local model*) is competitive, indicating that the architecture is robust to this modification. We observe slight improvement on the Librilight-L and ImageNet256 datasets by augmenting the [MegaByte]{.smallcaps} model with a CNN encoder (*w/ CNN encoder*). This suggests that the [MegaByte]{.smallcaps} architecture can benefit from integrating alternative encoding mechanisms.
+
+::: {#tab:ablations}
+ Arxiv Audio ImageNet256
+ ----------------------------- ------------ ----------- -------------
+ [MegaByte]{.smallcaps} 0.6871 **3.477** **3.158**
+ *w/o local model* 1.263 5.955 4.768
+ *w/o global model* 1.373 3.659 3.181
+ *w/o cross-patch attention* **0.6781** 3.481 3.259
+ *w/ CNN encoder* 0.6871 **3.475** **3.155**
+
+ : Ablation of [MegaByte]{.smallcaps} model components, showing that both Local and Global models are critical to strong performance, but the architecture is robust to other modifications. We report bits-per-byte on text, audio, and image prediction tasks. All models within a column are trained using the same compute and data. The hyperparameters are listed in Table [10](#table:opt_model_desc){reference-type="ref" reference="table:opt_model_desc"}.
+:::
+
+Long-context models often struggle to benefit from the full context [@sun2021long]. Figure [3](#fig:prob_within_context){reference-type="ref" reference="fig:prob_within_context"} shows that later tokens within each context window consistently have a higher likelihood, indicating that [MegaByte]{.smallcaps} can effectively use at least 8k bytes of context on the PG19 dataset.
+
+
+
+Average log probability assigned to the token at different positions within the context length by MegaByte model with 8192 context size and by a vanilla transformer model trained using the same compute (PG19 test set). MegaByte likelihoods rise throughout its context window, demonstrating that it can use tokens from 8k bytes previously to improve its predictions.
+
+
+We find that within a single patch, on average, the [MegaByte]{.smallcaps} performs worse on later tokens within a patch (see Figure [4](#fig:strided_inference){reference-type="ref" reference="fig:strided_inference"}). Section [2.3.3](#strided){reference-type="ref" reference="strided"} proposes *strided inference* as a solution, where two forward passes are performed offset by $\frac{P}{2}$ tokens, and results from the first half of each patch are combined. Table [7](#tab:inference_options){reference-type="ref" reference="tab:inference_options"} shows performance improvements from strided inference, which are additive with the standard sliding window.
+
+
+
+An illustration of strided inference with patch size 8. Lines below the text represent the patches used in the two rounds of inference, the plot above it represents the average probability assigned to the token at a given position within a patch. By considering only the first half of each patch from the two rounds of inference and combining them (bold lines on top), we achieve a better overall bpb.
+
+
+::: {#tab:inference_options}
+ Method Inference Cost bpb
+ ------------------------ ---------------- ------------
+ Basic Inference 1X 0.9079
+ *w/ Sliding Window* 2X 0.8918
+ *w/ Strided Inference* 2X 0.8926
+ *w/ Sliding & Strided* 4X **0.8751**
+
+ : Performance of various inference techniques on the PG19 test set using our best [MegaByte]{.smallcaps} model.
+:::
+
+[MegaByte]{.smallcaps} introduces several additional hyperparameters. We tuned these parameters independently for different modalities and reported performance based on the best setting we found. All experiments in the same group use the same compute.
+
+**Patch Size.** We experimented with various patch sizes on Image256 dataset and found that there is a wide range of values where [MegaByte]{.smallcaps} performs similarly. We found similar robustness against the choice of this hyperparameter across all modalities, although the optimal patch size itself can be different across modalities.
+
+::: {#tab:hyperparams_ablation}
+ Patch Size Global Size Local Size bpb
+ ------------ ------------- -------------------- -------
+ 48 125M 114M (D=768, L=11) 3.178
+ 192 125M 125M (D=768, L=12) 3.158
+ 768 125M 83M (D=768, L=8) 3.186
+
+ : Effects of patch size on performance on the Image256 dataset. All versions use the same amount of GPU hours and data.
+:::
+
+**Local to Global model Size Ratio.** We experimented with different Local/Global model size ratios on PG19 dataset. By grouping bytes into patches, [MegaByte]{.smallcaps} effectively uses $P$ times less tokens for the Global model as on the Local model---enabling us to increase the size of the Global model without reduced cost. We find that a given compute budget is spent optimally when the Global model has more parameters than the Local model. This trend was consistent across all modalities and various patch sizes.
+
+::: {#tab:size_inference}
+ Global Size Local Size bpb
+ -------------------- -------------------- -------
+ 350M (D=1024,L=24) 290M (D=1024,L=20) 1.014
+ 760M (D=1536,L=24) 262M (D=1024,L=18) 1.002
+ 1.3B (D=2048,L=24) 218M (D=1024,L=15) 0.991
+
+ : Effects of Local / Global model size on performance on the PG19 dataset. Increasing the capacity of global model improves performance. Models are compute and data matched.
+:::
diff --git a/2305.10051/main_diagram/main_diagram.drawio b/2305.10051/main_diagram/main_diagram.drawio
new file mode 100644
index 0000000000000000000000000000000000000000..c26e2a66ffdb19197faa558d098059756b87c7bb
--- /dev/null
+++ b/2305.10051/main_diagram/main_diagram.drawio
@@ -0,0 +1 @@
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
\ No newline at end of file
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+# Introduction
+
+Bayesian networks. Bayesian networks (BNs) [\[Pearl,](#page-8-0) [1988\]](#page-8-0) are probabilistic graphical models that enable succinct knowledge representation and facilitate probabilistic reasoning [\[Darwiche, 2009\]](#page-8-1). Parametric Bayesian networks (pBNs) [\[Castillo](#page-8-2) *et al.*, 1997] extend BNs by allowing polynomials in conditional probability tables (CPTs) rather than constants.
+
+Parameter synthesis on Markov models. Parameter synthesis is to find the right values for the unknown parameters with respect to a given constraint. Various synthesis techniques have been developed for parametric Markov chains (pMCs) ranging over e.g., the gradient-based methods [\[Heck](#page-8-3) *et al.*[, 2022\]](#page-8-3), convex optimization [\[Cubuktepe](#page-8-4) *et al.*, 2018; [Cubuktepe](#page-8-5) *et al.*, 2022], and region verification [\[Quatmann](#page-8-6) *et al.*[, 2016\]](#page-8-6). Recently, Salmani and Katoen [\[2021a\]](#page-8-7) have proposed a translation from pBNs to pMCs that facilitates using pMC algorithms to analyze pBNs. Proceeding from this study, *we tackle a different problem* [\[Kwisthout and van der](#page-8-8) [Gaag, 2008\]](#page-8-8) for Bayesian networks.
+
+Minimal-change parameter tuning. Given a Bayesian network B, a hypothesis H, evidence E, λ ∈ [0, 1], and a constraint of the form Pr(H|E) ≤ λ (or ≥ λ),
+
+what is a minimal change—with respect to a given measure of distance—in the probability values of (a subset of) CPT rows, such that the constraint holds?
+
+We illustrate the problem with an example of testing COVID-19 that is adopted from several medical studies [\[Barreiro](#page-8-9) *et al.*[, 2021;](#page-8-9) [Nishiura](#page-8-10) *et al.*, 2020; Dinnes *et al.*[, 2022\]](#page-8-11). The outcome of PCR tests only depends on whether the person is infected, while the antigen tests are less likely to correctly identify COVID-19 if the infection is asymptomatic (or presymptomatic). Figure [1a](#page-1-0) depicts a Bayesian network that models such probabilistic dependencies. In the original network, the probability of no COVID-19 given that both tests are positive, is 0.011089. Assume that in an application domain, the result of such a query is now required not to exceed 0.009: the aim is to make this constraint hold while imposing the least change in the original network with respect to a distance measure. This is an instance of the *minimal-change parameter tuning* problem. We consider the −bounded variant of the problem: for a subset of *modifiable* CPT rows, are there new values within the distance of from the original probability values that make the constraint hold?
+
+Main contributions. Based on existing *region verification* and *region partitioning* techniques for pMCs,
+
+- we propose a practical algorithm for -bounded tuning. More precisely, we find instantiations that (i) satisfy the constraint (if the constraint is satisfiable) and (ii) are close and (iii) lean towards the minimum distance instantiation depending on a coverage factor 0 ≤ η ≤ 1.
+- We propose two region expansion schemes to realize closeness of the results both for Euclidean distance and for CD distance [\[Chan and Darwiche, 2005\]](#page-8-12).
+- Contrary to the existing techniques that restrict to hyperplane solution spaces, we handle pBNs with multiple parameters in multiple distributions and multiple CPTs.
+
+Our experiments on our prototypical implementation [1](#page-0-0) indicate that -bounded tuning of up to 8 parameters for large networks with 100 variables is feasible.
+
+Paper organization. Section 2 includes the basic notations and Sec. 3 the preliminaries on parametric Bayesian networks. Section 4 introduces parametric Markov chains and the region verification techniques thereof. Section 5 details our main contributions and Sec. 6 our experimental results. Section 7 concludes the paper with an overview of the related studies that were not mentioned before.
+
+1
+
+
+
+Figure 1: Our running example: (a) the parametric BN COVID-19, (b) the parametric MC of COVID-19, for the topological order C; S; A; P, (c) the parametric MC of COVID-19 tailored to the evidence $Antigen\ Test = pos$ , and $PCR\ Test = pos$ , abstracted from variable valuations.
+
+**Variables.** Let V be a set of m random variables $v_1, \ldots, v_m$ and $D_{v_i}$ the domain of variable $v_i$ . For $A \subseteq V$ , Eval(A) denotes the set of joint configurations for the variables in A.
+
+**Parameters.** Let $X = \{x_1, \dots, x_n\}$ be a set of n real-valued parameters. A parameter instantiation is a function $u: X \to \mathbb{R}$ that maps each parameter to a value. All parameters are bounded; i.e., $lb_{x_i} \le u(x_i) \le ub_{x_i}$ for $x_i$ . Let $I_i = [lb_{x_i}, ub_{x_i}]$ . The parameter space $\mathcal{U} \subseteq \mathbb{R}^n$ of X is the set of all possible values of X, i.e., the hyper-rectangle spanned by the intervals $I_i$ for all i.
+
+**Substitution.** Polynomials f over X are functions f: $\mathbb{R}^n \to \mathbb{R}$ where f(u) is obtained by replacing each occurrence of $x_i$ in f by $u(x_i)$ ; e.g., for $f = 2x_1^2 + x_2$ , $u(x_1) = 3$ and $u(x_2) = 2$ , f(u) = 20. Let f[u] denote such substitution.
+
+**Parametric distributions.** Let Distr(D) denote the set of probability distributions over D. Let $\mathbb{Q}[X]$ be the set of multivariate polynomials with rational coefficients over X. A parametric probability distribution is the function $\mu:D\to\mathbb{Q}[X]$ with $\sum_{d\in D}\mu(d)=1$ . Let pDistr(D) denote the set of parametric probability distributions over D with parameters in X. Instantiation u is well-formed for $\mu\in pDistr(D)$ iff $0\leq \mu(d)[u]\leq 1$ and $\sum_{d\in D}\mu(d)[u]=1$ .
+
+**Co-variation scheme.** A co-variation scheme cov: $Distr(D) \times D \rightarrow pDistr(D)$ maps a probability distribution $\mu$ to a parametric distribution $\mu'$ based on a given $d \in D$ .
+
+**Distance measure.** The function $d: \mathcal{U} \times \mathcal{U} \to \mathbb{R}^{\geq 0}$ is a *distance measure* if for all $u, u', u'' \in \mathcal{U}$ , it satisfies: (I) positiveness: $d(u, u') \geq 0$ , (II) symmetry: d(u, u') = d(u', u), and (III) triangle inequality: $d(u, u'') \leq d(u, u') + d(u', u'')$ .
+
+A parametric Bayesian network (pBN) is a BN in which a subset of entries in the conditional probability tables (CPTs) are polynomials over the parameters in X. Let $par_{v_i}$ denote the set of parents for the node $v_i$ in the graph G.
+
+**Definition 1.** The tuple $\mathcal{B}=(G,X,\Theta)$ is a parametric Bayesian network (pBN) with directed acyclic graph G=(V,W) over random variables $V=\{v_1,\ldots,v_m\}$ , set of parameters $X=\{x_1,\ldots,x_n\}$ , and parametric CPTs $\Theta=\{\Theta_{v_i}\mid v_i\in V\}$ where $\Theta_{v_i}: Eval(par_{v_i})\to pDistr(D_{v_i})$ .
+
+The CPT row $\Theta_v(\overline{par})$ is a parametric distribution over $D_v$ given the parent evaluation $\overline{par}$ . The CPT entry $\Theta_v(\overline{par})(d)$ , short $\theta_{(v,d,\overline{par})}$ , is the probability that $v{=}d$ given $\overline{par}$ . A pBN without parameters, i.e., $X=\emptyset$ , is an ordinary BN. A pBN $\mathcal B$ defines the parametric distribution function $\Pr_{\mathcal B}$ over $\operatorname{Eval}(V)$ . For well-formed instantiation u, $\operatorname{BN} \mathcal B[u] = (G,\Theta[u])$ is obtained by replacing the parameter $x_i$ in the parametric functions in the CPTs of $\mathcal B$ by $u(x_i)$ .
+
+**Example 1.** Fig. 1a shows a pBN over variables $V = \{C, S, A, P\}$ (initial letters of node names) and parameters $X = \{p, q\}$ . Instantiating with $u_0(p) = 0.72$ and $u_0(q) = 0.95$ yields the BN $\mathcal{B}[u_0]$ as indicated using dashed boxes.
+
+**pBN constraints.** Constraints $\Pr_B(H|E) \sim \lambda$ involve a hypothesis H, an evidence E, $\sim \in \{\leq, \geq\}$ and threshold $0 \leq \lambda \leq 1$ . For the instantiation $u: X \to \mathbb{R}$ ,
+
+$$\mathcal{B}[u] \models \phi \quad \text{ if and only if } \quad \Pr_{\mathcal{B}[u]}(H \,|\, E) \sim \lambda.$$
+
+**Sensitivity functions.** Sensitivity functions are rational functions that relate the result of a pBN query to the parameters [Castillo *et al.*, 1997; Coupé and van der Gaag, 2002].
+
+**Example 2.** For the pBN $\mathcal{B}_1$ in Fig. 1a, let $\phi_1$ be the constraint:
+
+$$\Pr_{\mathcal{B}_1}(C = no \mid A = pos \land P = pos) \le 0.009.$$
+
+This reduces to the sensitivity function
+
+$$f_{\mathcal{B}_1,\phi_1} = \frac{361}{34900 \cdot p \cdot q + 8758 \cdot q + 361}$$
+.
+
+Using instantiation u with u(p) = 0.92075 and u(q) = 0.97475, $f_{\mathcal{B}_1,\phi_1}[u] = 0.008798 \le 0.009$ , i.e., $\mathcal{B}_1[u] \models \phi_1$ .
+
+**Higher degree sensitivity functions.** Contrary to the existing literature that is often restricted to multi-linear sensitivity functions, we are interested in analyzing pBNs with sensitivity functions of higher degrees.
+
+**Example 3.** Consider a variant of the COVID-19 example, where the person only takes the antigen test twice rather than taking both the antigen and PCR tests to diagnose COVID-19; see Fig. 2 for the parametric CPTs. Then,
+
+$$\begin{split} f_{\mathcal{B}_2,\phi_2} &= \Pr_{\mathcal{B}_2}(C = \textit{no} \,|\, A1 = \textit{pos} \land A2 = \textit{pos}) \\ &= \frac{950 \cdot 9 \cdot t^2 \cdot s^2}{8850 \cdot t^2 + 349 \cdot p^2 + 950 \cdot s^2 + 151 \cdot r^2}. \end{split}$$
+
+
+
+| | | Antig | | | Antigen Test 2 | | | | |
+|-----|-----|----------|------------------|-----|----------------|----------|---------------|--|--|
+| C | S | pos | neg | C | S | pos | neg | | |
+| yes | yes | [0.72] p | [0.28]1-p | yes | yes | 0.72 p | [0.28]1-p | | |
+| yes | no | 0.58 r | $0.42 \ 1-r$ | yes | no | 0.58 $r$ | 0.42 1-r | | |
+| no | yes | 0.005 s | $0.995 \mid 1-s$ | no | yes | 0.005 s | $0.995 \ 1-s$ | | |
+| no | no | 0.01 t | 0.99 1-t | no | no | 0.01 t | 0.99 1-t | | |
+
+Figure 2: Parametric CPTs for a variant of COVID-19 example with two antigen tests; see the inter-CPT parameter dependencies.
+
+**3.1. Parametrization.** Let $\Theta_{modif}$ denote the CPT entries in BN $B=(G,\Theta)$ that are explicitly changed.
+
+**Definition 2** (BN parametrization). $pBN \mathcal{B} = (G, X, \Theta')$ is a parametrization of $BN \mathcal{B} = (G, \Theta)$ over X w.r.t. $\Theta_{modif}$ if
+
+$$\theta'_{(v,d,\overline{par})} = f \text{ for some } f \in \mathbb{Q}[X] \quad \text{if} \quad \theta_{(v,d,\overline{par})} \in \Theta_{modif}.$$
+
+The parametrization $\mathcal{B}$ is *monotone* iff for each parametric entry $\theta'_{(v,d,\overline{par})} = f$ , f is a monotonic polynomial. The parametrization $\mathcal{B}$ is *valid* iff $\mathcal{B}$ is a pBN, i.e., iff $\Theta'_v(\overline{par}) \in pDistr(D_v)$ for each random variable v and its parent evaluation $\overline{par}$ . To ensure validity, upon making a CPT entry parametric, the complementary entries in the row should be parametrized, too. This is ensured by *covariation* schemes. The most established co-variation scheme in the literature is the *linear proportional* [Coupé and van der Gaag, 2002] scheme that has several beneficial characteristics [Renooij, 2014], e.g., it preserves the ratio of CPT entries.
+
+**Definition 3** (Linear proportional co-variation). A linear proportional co-variation over X maps the $CPT \Theta_v$ onto the parametric $CPT \Theta_v'$ based on $d_k \in D_v$ , where
+
+$$\left\{ \begin{array}{ll} \theta'_{(v,d_k,\overline{par})} = x & \textit{for some } x \in X \\ \theta'_{(v,d_j,\overline{par})} = \frac{1 - x}{1 - \theta_{(v,d_k,\overline{par})}} \cdot \theta_{(v,d_j,\overline{par})} & \textit{for } d_j \neq d_k \in D_v. \end{array} \right.$$
+
+Note that we allow the repetition of parameters in multiple distributions and multiple CPTs, see e.g., Fig. 2.
+
+**3.2. Formal Problem Statement.** Consider BN $B=(G,\Theta)$ , its valid parametrization $\mathcal{B}=(G,X,\Theta')$ over X with constraint $\phi$ . Let $u_0$ be the original value of the parameters X in B and $d:\mathcal{U}\times\mathcal{U}\to\mathbb{R}^{\geq 0}$ a distance measure. Let $\hat{d}_0$ denote an upperbound for $d_{\{u\in\mathcal{U}\}}(u_0,u)$ . The minimum-distance parameter tuning problem is to find $u_{\min}= \underset{\{u\in\mathcal{U}\mid\mathcal{B}[u]\models\phi\}}{\operatorname{d}(u,u_0)}$ . Its generalized variant for $0\leq\epsilon\leq\hat{d}_0$ and $0\leq\eta\leq1$ is:
+
+The
+$$(\epsilon, \eta)$$
+-parameter tuning problem is to find $u \in \mathcal{U}$ s.t. $\mathcal{B}[u] \models \phi, d(u, u_0) \leq \epsilon$ , and $d(u, u_{\min}) \leq (1 - \eta) \cdot \hat{d}_0$ .
+
+ $(\bar{d_0},1)$ -tuning gives the minimum-distance tuning. We call $\eta$ the coverage factor that determines, intuitively speaking, the minimality of the result; we discuss this in Sec. 5. We consider two distance measures.
+
+**Euclidean distance (EC-distance).** The EC-distance between u and u' (both in $\mathcal{U}$ ) is defined as:
+
+$$EC(u, u') = \sqrt{\sum_{x_i \in X} \left(u(x_i) - u'(x_i)\right)^2}.$$
+
+**Corollary 1.** For n = |X|, $\hat{d}_0 = \sqrt{n}$ is an upperbound for the EC distance of any $u_0$ from any instantiation $u \in \mathcal{U}$ .
+
+**Corollary 2.** Let $0 \le \epsilon \le \sqrt{n}$ and $u_0(x_i) - \frac{\epsilon}{n} \le u(x_i) \le u_0(x_i) + \frac{\epsilon}{n}$ . Then, $EC(u, u_0) \le \epsilon$ .
+
+**Chan-Darwiche distance** (**CD distance**) [Chan and Darwiche, 2005] is a distance measure to quantify the distance between the probability distributions of two BNs, defined as:
+
+$$\mathit{CD}(u,u') = \ln \max_{w \in \operatorname{Eval}(V)} \frac{\Pr_{\mathcal{B}[u']}(w)}{\Pr_{\mathcal{B}[u]}(w)} - \ln \min_{w \in \operatorname{Eval}(V)} \frac{\Pr_{\mathcal{B}[u']}(w)}{\Pr_{\mathcal{B}[u]}(w)},$$
+
+where both 0/0=1 and $\infty/\infty=1$ by definition. Whereas the EC-distance can be computed in O(n), this is for CD-distances NP-complete [Kwisthout and van der Gaag, 2008]. It has known closed-forms only for limited cases, e.g., single parameter and single-CPT pBNs [Chan and Darwiche, 2004]. Let $\Theta_v$ be the CPT that is parametrized to $\Theta_v'$ by a monotone parametrization. Let the minimum (maximum) probability entry of $\Theta_v$ be $\theta_{\min}$ ( $\theta_{\max}$ ) parametrized to $\theta_{\min}'$ with x ( $\theta_{\max}'$ with y). Let x0, x1, x2, x3, x4, x6, x7, x8, x9, x9, x9, x9, x9, x9, x9, x9, x9, x9, x9, x9, x9, x9, x9, x9, x9, x9, x9, x9, x9, x9, x9, x9, x9, x9, x9, x9, x9, x9, x9, x9, x9, x9, x9, x9, x9, x9, x9, x9, x9, x9, x9, x9, x9, x9, x9, x9, x9, x9, x9, x9, x9, x9, x9, x9, x9, x9, x9, x9, x9, x9, x9, x9, x9, x9, x9, x9, x9, x9, x9, x9, x9, x9, x9, x9, x9, x9, x9, x9, x9, x9, x9, x9, x9, x9, x9, x9, x9, x9, x9, x9, x9, x9, x9, x9, x9, x9, x9, x9, x9, x9, x9, x9, x9, x9, x9, x9, x9, x9, x9, x9, x9, x9, x9, x9, x9, x9, x9, x9, x9, x9, x9, x9, x9, x9, x9, x9, x9, x9, x9, x9, x9, x9, x9, x9, x9, x9, x9, x9, x9, x9, x9, x9, x9, x9, x9, x9, x9, x9, x9, x9, x9, x9, x9, x9, x9, x9, x9, x9, x9, x9, x9, x9, x9, x9, x9, x9, x9, x9, x9, x9, x9, x9, x9, x9, x9, x9, x9, x9, x9, x9, x9, x9, x9, x9, x9, x9, x9, x9, x9, x9, x9, x9, x9, x9, x9, x9, x9, x9, x9, x9, x9, x9, x9, x9, x9, x9, x9, x9, x9, x9, x9, x9, x9, x9, x9, x9, x9, x9, x9, x9, x9, x9, x9, x9, x9, x9, x9, x9, x9, x9, x9, x9, x9, x9, x9, x9, x9, x9, x9, x9, x9, x9, x9, x9, x9, x9, x
+
+**Corollary 3.** An upperbound for the CD distance of $u_0$ from any instantiation $u \in \mathcal{U}$ is:
+
+$$\hat{d_0} = \ln \frac{\max\left\{\theta_{\min}'[lb_x], \theta_{\min}'[ub_x]\right\}}{\theta_{\min}} - \ln \frac{\min\left\{\theta_{\max}'[lb_y], \theta_{\max}'[ub_y]\right\}}{\theta_{\max}}$$
+
+as derived from the single CPT closed-form [Chan, 2005].
+
+**Corollary 4.** Let
+$$0 \le \epsilon \le \hat{d}_0$$
+ and $\alpha = e^{\epsilon/2}$ . Let for each $\theta \in \Theta_v$ , $\alpha^{-1} \cdot \theta'[u_0] \le \theta'[u] \le \alpha \cdot \theta'[u_0]$ . Then, $CD(u, u_0) \le \epsilon$ .
+
+Note that zero probabilities are often considered to be logically impossible cases in the BN, see e.g., [Kisa *et al.*, 2014]. Changing the parameter values from (and to) 0 yields CD distance $\infty$ . We consider $\epsilon$ —bounded parameter tuning: we (i) forbid zero probabilities in $\Theta_{modif}$ (the CPT entries that are explicitly modified), (ii) use the linear proportional co-variation scheme (Def. 3) that is zero-preserving [Renooij, 2014], i.e., it forbids changing the co-varied parameters from non-zero to zero. This co-variation scheme, in general, optimizes the CD-distance for single CPT pBNs, see [Renooij, 2014].
+
+Parametric Markov chains are an extension of Markov chains (MCs) that allow parametric polynomials as transition labels:
+
+**Definition 4.** A parametric Markov chain (pMC) $\mathcal{M}$ is a tuple $(S, s_I, X, \mathcal{P})$ where S is a finite set of states, $s_I \in S$ is the initial state, X is a finite set of real-valued parameters, and $\mathcal{P}: S \to pDistr(S)$ is a transition function over the states.
+
+**Example 4.** Figures 1b and 1c are examples of pMCs.
+
+**Reachability constraints.** A reachability constraint $\varphi = (T, \sim, \lambda)$ is defined over pMC $\mathcal{M} = (S, s_I, X, \mathcal{P})$ with targets $T \subseteq S$ , threshold $0 \le \lambda \le 1$ , and $\infty \in \{\le, \ge\}$ . For pMC $\mathcal{M}$ and instantiation $u: X \to \mathbb{R}$ :
+
+$$\mathcal{M}[u] \models \varphi \quad \text{if and only if} \quad \Pr_{\mathcal{M}[u]}(\lozenge T) \sim \lambda,$$
+
+where $\Pr_{\mathcal{M}[u]}(\lozenge T)$ denotes the probability that MC $\mathcal{M}[u]$ reaches a state in T.
+
+
+
+Figure 3: The parameter lifting steps for the COVID-19 example with parameter dependencies (Fig. 2): (a) the original (sub-)pMC $\xrightarrow{\text{Relaxation}}$ (b) pMC without parameter dependencies $\xrightarrow{\text{Substitution}}$ (c) non-parametric MDP. Note that $0.9875 \le 1-t, 1-t', 1-t'' \le 0.9925$ .
+
+**Example 5.** Consider the pMC
+$$\mathcal{M}_{\mathcal{B},E}$$
+ in Fig. 1c. Let $T = \{s_{10}\}$ . Then, $\Pr_{\mathcal{M}_{\mathcal{B},E}}(\lozenge s_{10}) = \frac{361}{34900 \cdot p \cdot q + 8758 \cdot q + 361}$ . Let $R \subseteq \mathbb{R}^n$ with $n = |X|$ and let
+
+$$\mathcal{M}, R \models \varphi$$
+ if and only if $\forall u \in R. \ \mathcal{M}[u] \models \varphi$ .
+
+We now define the parameter synthesis problems for pMC $\mathcal{M}$ and reachability constraint $\varphi$ that are relevant to our setting.
+
+**Definition** 5 (Region partitioning). *Partition region* R *into* $R_+$ , $R_-$ , and $R_?$ such that:
+
+$$R_{+} \subseteq \underbrace{\{u \in R \mid \mathcal{M}[u] \models \varphi\}}_{satisfying instantiations} \qquad R_{-} \subseteq \underbrace{\{u \in R \mid \mathcal{M}[u] \models \neg \varphi\}}_{refuting instantiations}$$
+
+and $R_? = R \setminus (R_+ \cup R_-)$ with $||R_?|| \le (1-\eta) \cdot ||R||$ for some given coverage factor $0 \le \eta \le 1$ .
+
+The sub-region $R_?$ denotes the fragment of R that is *inconclusive* for $\varphi$ . This fragment should cover at most fraction $1-\eta$ of R's volume. Region partitioning is exact if $R_? = \emptyset$ .
+
+**Definition 6** (Region verification). For the region R and the specification $\phi$ , the problem is to check whether:
+
+$$\underbrace{\mathcal{M}, R \models \varphi}_{\textit{R is accepting}} \ \ \textit{or} \ \ \underbrace{\mathcal{M}, R \models \neg \varphi}_{\textit{R is rejecting}} \ \ \textit{or} \ \ \underbrace{\mathcal{M}, R \not\models \varphi \land \mathcal{M}, R \not\models \neg \varphi}_{\textit{R is inconclusive}}.$$
+
+**4.1. Parameter Lifting.** The parameter lifting algorithm (PLA) [Quatmann *et al.*, 2016] is an abstraction technique that reduces region verification to a probabilistic model checking problem for which efficient algorithms exist [Katoen, 2016]. It first removes parameter dependencies by making all parameters unique. Then it considers for each parameter $x_i$ only its bounds $lb_{x_i}$ and $ub_{x_i}$ within the region R. This yields a non-parametric Markov Decision Process (MDP).
+
+**Definition 7.** A Markov decision process (MDP) is a tuple $\mathcal{M} = (S, s_I, Act, \mathcal{P})$ with a finite set S of states, an initial state $s_I \in S$ , a finite set S of actions, and a (partial) transition probability function $\mathcal{P} : S \times Act \rightarrow Distr(S)$ .
+
+While resolving the non-determinism in the obtained MDP, a trade-off between maximizing or minimizing $x_i$ may occur. This occurs in particular when parameter $x_i$ repeats in the outgoing transitions of multiple states, see e.g., parameter t in Fig. 3a. PLA handles the issue by a relaxation step that introduces intermediate parameters; see e.g., Fig. 3b, where parameter t in the outgoing edges of state C=no, A=pos is replaced by t'. The relaxation step yields an over-approximation of the region verification problem.
+
+**Example 6.** Consider the pMC in Fig. 3(a) and region $t \in [0.0075, 0.0125]$ . Fig. 3b shows the pMC after relaxation, e.g., parameter t in the outgoing transitions of state C=no, A=pos is replaced by t'. Fig. 3c shows the MDP obtained by substituting the parameters with their extremal values. Consider e.g., state C=no, S=no. Its outgoing dotted (blue) edges have probability 0.0075 and 1-0.0075 obtained from lb(t). The probabilities 0.0125 and 1-0.0125 of the dashed (purple) edges stem from ub(t).
+
+After *substitution*, region verification reduces to a simple model-checking query that over-approximates the original higher-order polynomial, e.g., Example 3. The region accepts $\varphi$ for the pMC if the resulting MDP satisfies $\varphi$ for all schedulers. If all the schedulers satisfy $\neg \varphi$ , the region is rejecting. If the region is neither accepting nor rejecting (i.e., inconclusive), it is partitioned into smaller subregions that are more likely to be either accepting or rejecting. Partitioning ends when at least $\eta\%$ of the region is conclusive.
+
+**4.2. Parametric Markov Chain for pBNs.** To enable the use of PLA to parameter tuning of pBNs, we map pBNs onto pMCs as proposed in [Salmani and Katoen, 2021a; Salmani and Katoen, 2021b]; see the next example.
+
+**Example 7.** Consider the acyclic pMC in Fig. 1b for the pBN in Fig. 1a for the topological ordering $\varrho: C < S < A < P$ . Initially, all variables are don't care. The initial state can evolve into C=yes and C=no. Its transition probabilities 0.05 and 0.95 come from the CPT entries of C. Generally, at "level" i of the pMC, the outgoing transitions are determined by the CPT of the i+1-st variable in $\varrho$ .
+
+pBN constraints relate to reachability constraints in its pMC.
+
+**Example 8.** Consider the pBN $\mathcal{B}_1$ from Fig. 1a and the query $\Pr_{\mathcal{B}}(C = no \mid A = pos \land P = pos)$ from Example 2. Let $\mathcal{M}_{\mathcal{B}}^{\varrho}$ be the pMC in Fig. 1b. The query coincides with
+
+$$\begin{split} &\frac{1-\operatorname{Pr}_{\mathcal{M}_{\mathcal{B}}^{\varrho}}(\lozenge C = \textit{yes} \lor \textit{A} = \textit{neg} \lor \textit{P} = \textit{neg})}{1-\operatorname{Pr}_{\mathcal{M}_{\mathcal{B}}^{\varrho}}(\lozenge \textit{A} = \textit{neg} \lor \textit{P} = \textit{neg})} \\ &= \frac{361}{34900 \cdot \textit{p} \cdot \textit{q} + 8758 \cdot \textit{q} + 361} = \textit{f}_{\mathcal{B},\phi}; \textit{see Ex. 2}. \end{split}$$
+
+Our approach to the parameter tuning problem for a pBN and constraint $\phi$ consists of two phases. The $\epsilon$ -close partitioning exploits parameter lifting on the pMC for reachability constraint $\varphi$ of $\phi$ . We start with a rectangular region enclosing the
+
+
+
+Figure 4: The iterative procedure of region-based parameter tuning for n=2 and $\gamma=1/2$ .
+
+original instantiation $u_0$ . This region is iteratively expanded if it rejects $\varphi$ . Inconclusive regions are partitioned. Any iteration that finds some accepting subregion ends the first phase. The second phase (Alg. 2) extracts a minimal-change instantiation from the set of accepting sub-regions.
+
+# Method
+
+```
+1 \mathcal{M}_{\mathcal{B}} \leftarrow \text{computeMC}(\mathcal{B})
+ \varrho \leftarrow \text{reachSpec}(\phi)
+ \mathbf{3} \ \epsilon_0 \leftarrow \hat{d_0} \cdot \gamma^{\tilde{K}-1}
+ 5 function minChgTuning (\mathcal{M}_{\mathcal{B}}, u_0, \varphi, \eta):
+ while \epsilon < d_0 do
+ R_+ \leftarrow \epsilon-partitioning (\mathcal{M_B}, \varphi, \epsilon, \eta) if R_+ = \emptyset then
+ 8
+ 10
+ 11
+ return getMinDistInst(R_+, u_0)
+ 12
+ end if
+ end while
+13
+ return Infeasible
+14
+15 function \epsilon-partitioning (\mathcal{M}_{\mathcal{B}}, \varphi, \epsilon, \eta):
+ \begin{array}{l} R_{\epsilon} \leftarrow \texttt{makeRegion-d}\left(u_{0}, \epsilon\right) \\ R_{+}, R_{-}, R_{?} \leftarrow \texttt{PLA}(\mathcal{M}_{\mathcal{B}}, R_{\epsilon}, \varphi, \eta) \end{array}
+16
+17
+ return R+
+18
+19 function makeRegion-EC (u_0, \epsilon):
+ R_{\epsilon} \leftarrow \mathsf{X}_{x_{j} \in X} \left[ u_{0}(x_{j}) - \tfrac{\epsilon}{n}, u_{0}(x_{j}) + \tfrac{\epsilon}{n} \right]
+20
+ return R_\epsilon
+21
+22 function makeRegion-CD (u_0,\epsilon):
+ \alpha \leftarrow e^{\epsilon/2}
+23
+ R_{\epsilon} \leftarrow \mathsf{X}_{x_{j} \in X} \left[ u_{0}(x_{j}) \cdot \alpha^{-1}, u_{0}(x_{j}) \cdot \alpha \right]
+24
+```
+
+**5.1.** Obtaining Epsilon-Close Subregions. Alg. the main thread of our approach. Its inputs are the pBN $\mathcal{B} = (V, W, X, \Theta)$ , the initial instantiation $u_0 : X \to \mathbb{R}$ , and the constraint $\phi$ . It starts with obtaining the pMC $\mathcal{M}_{\mathcal{B}}$ of the pBN and reachability constraint $\varphi$ of $\phi$ (lines 1–2). The output is either (i) $u_0$ if $\mathcal{M}[u_0] \models \varphi$ , or (ii) the instantiation u with a minimal distance from $u_0$ or (iii) no instantiation if $\varphi$ is infeasible2. The hyper-parameters of the algorithm are the coverage factor $0 < \eta < 1$ , the region expansion factor $0 < \gamma < 1$ , and the maximum number of iterations $K \in \mathbb{N}$ . The hyper-parameters $\gamma$ and K steer the iterative procedure. They initially determine the distance bound $\epsilon$ , see line 3. The bound $\epsilon$ determines region $R_{\epsilon}$ , i.e., how far the region bounds deviate from $u_0$ . PLA then verifies $R_{\epsilon}$ . If $R_{\epsilon}$ is rejecting, $\epsilon$ is extended by the factor $\gamma^{-1}$ (l. 9). Figure 4 visualizes the procedure for $\gamma=1/2$ and n=2. At iteration i, (a) PLA is invoked on $R_{\epsilon}$ and either (b) $R_{+} \neq \emptyset$ or (c) $R_{+} = \emptyset$ . For case (b), the iterative procedure ends and Alg. 2 is called to obtain an instantiation in $R_{+}$ that is closest to $u_{0}$ . For case (c), the region is expanded by factor $\gamma^{-1}$ and passed to PLA, see (d). Note that the loop terminates when the distance bound $\epsilon$ reaches its upper bound $\hat{d}_{0}$ (l. 2). We refer to Sec. 3 Corollary 1 and 3 for computing $\hat{d}_{0}$ .
+
+Region expansion schemes. Region $R_{\epsilon}$ is determined by factor $\epsilon$ , see line 16. The methods makeRegion-EC and makeRegion-CD detail this for each of our distance measures. For the EC distance (line 20), the parameters have an absolute deviation from $u_0$ . We refer to Corollary 2, Sec. 3. For CD distance (lines 23 and 24), the deviation is relative to the initial value of the parameter. We refer to Corollary 4, Sec. 3. Such deviation schemes ensure $\epsilon$ -closeness of $R_{\epsilon}$ both for EC and CD distance, that is, all the instantiations in $R_{\epsilon}$ have at most distance $\epsilon$ from $u_0$ .
+
+Remark. For pBNs with a single parameter, already checked regions can be excluded in the next iterations. Applying such a scheme to multiple parameters yet may not yield minimal-change instantiation. To see this, let $u_0(x) = 0.15$ and $u_0(y) = 0.15$ and take $\epsilon_0 = 0.05$ . Assume $R_{\epsilon_0} =$ $[0.1, 0.2] \times [0.1, 0.2]$ is rejecting. Limiting the search in the next iteration to $R_{\epsilon_1} = [0.2, 0.4] \times [0.2, 0.4]$ may omit the accepting sub-regions that are at a closer distance from $u_0$ , e.g. if the region $[0.1, 0.2] \times [0.2, 0.4]$ includes some satisfying sub-regions. This is why we include the already-analyzed intervals starting from $u_0$ in the next iterations. However, as will be noted in our experimental results, this does not have a deteriorating effect on the performance: the unsuccessful intermediate iterations are normally very fast as they are often analyzed by a single model checking: the model checker in a single step determines that the entire region is rejecting and no partitioning is needed.
+
+**5.2. Obtaining a Minimal-Distance Instantiation.** Once $R_+ \neq \emptyset \subseteq R_\epsilon$ is found, it remains to find the instantiation $u_+$ in $R_+$ that is closest to $u_0$ . Region $R_+$ includes infinitely many instantiations, yet minimal-distance instantiation is computable in $O(k \cdot n)$ for n = |X| and the regions $R_+ = \{R_{+,1}, \cdots, R_{+,k}\}$ : (i) PLA ensures that the subregions $R_{+,1}$ to $R_{+,k}$ are rectangular and mutually disjoint. (ii) Due to the triangle equality of the distance measure, a minimum-distance instantiation can be picked from the bounded region. Alg. 2 simplifies finding $u_+$ . In the first step, we pick $u_{+,i}$ from $R_{+,i}$ that has minimal distance from $u_0$ (1. 3–18). The idea is simple: for every $x_j \in X$ , three cases are possible, see Fig. 5. Let $lb_{x_j}$ be the lower bound for
+
+ $^2 {\rm That}$ is, in the $\eta=100\%$ of the checked parameter space, no satisfying instantiation has been found.
+
+```
+function getMinDistInst(R_+,u_0):
+ (R_{+} = \{R_{+,1}, \cdots, R_{+,k}\})
+ U_{+} \leftarrow \emptyset
+3
+ for i \leftarrow 1 to k do
+4
+ // R_{+,i} = \mathsf{X}_{x_j \in X}[lb_{x_j}, ub_{x_j}]
+5
+ \begin{array}{c} \text{for } x_j \in X \text{ do} \\ \mid \quad \text{if } lb_{x_j} < u_0(x_j) < ub_{x_j} \text{ then} \end{array}
+ | \quad u_{+,i}(x_j) = u_0(x_j)
+ \begin{array}{c|c} \text{if } u_0(x_j) < lb_{x_j} < ub_{x_j} \text{ then} \\ & u_{+,i}(x_j) = lb_{x_j} \end{array}
+10
+11
+12
+ u_{+,i}(x_j) = ub_{x_i}
+13
+14
+ U_{+} \leftarrow u_{+,i}
+ return argmin d(u_+, u_0)
+15
+ u_{+} \in U_{+}
+```
+
+parameter $x_j$ in the region $R_{+,i}$ and $ub_{x_j}$ be its upper bound. (1) If $u_0(x_j) \in [lb_{x_j}, ub_{x_j}]$ , we set $u_{+,i}(x_j) = u_0(x_j)$ . This yields the least distance, i.e., 0 in the dimension of $x_j$ , see Fig. 5 (left). (2) $u_0(x_j) < lb_{x_j}$ , see Fig. 5 (middle). Then $lb_{x_j}$ has the least distance from $u_0(x_j)$ in the dimension $x_j$ , i.e., $|lb_{x_j}-u_0(x_j)|<|a-u_0(x_j)|$ for every value $a\in [lb_x,ub_x]$ and similarly for CD-distance, $\ln\frac{lb_{x_j}}{u_0(x_j)}<\ln\frac{a}{u_0(x_j)}$ . In this case, we set $u_{+,i}(x_j)=lb_{x_j}$ to minimize the distance in dimension x. (3) By symmetry, for $u_0(x_j)>ub_{x_j}$ , we set $u_{+,i}(x_j)=ub_{x_j}$ ; see Fig. 5 (right). It remains to compute the distance for each candidate in $U_+=\{u_{+,1},\cdots,u_{+,k}\}$ and pick $u_+$ closest to $u_0$ (1. 19).
+
+
+
+Figure 5: Obtaining the minimal-change value for parameter $x \in X$ .
+
+**Example.** We apply the entire algorithm on the pBN in Fig. 1a, constraint $\phi = \Pr(\text{C=no} \mid \text{A=pos} \land \text{P=pos}) \leq 0.009, \ u_0 : p \to 0.72, q \to 0.95, \ \text{and} \ \eta = 0.99.$ The pMC $\mathcal{M}_{\mathcal{B},E}$ is given in Fig. 1c and the reachability constraint is $\varphi : \Pr(\lozenge s_{10}) \leq 0.009.$ We take $\hat{d}_0 = \sqrt{n}, \gamma = 1/2$ , and $\epsilon_0 = 1/16$ .
+
+- 1. The initial region $R_{\epsilon}$ is obtained by $\epsilon = 1/8$ : $[0.72 1/8\sqrt{2}, 0.72 + 1/8\sqrt{2}] \times [0.95 1/8\sqrt{2}, 0.95 + 1/8\sqrt{2}].$
+- 2. Running PLA on $R_{\epsilon}$ gives no sub-region accepting $\varphi$ .
+- 3. Expanding the region by the factor $\gamma^{-1}=2$ yields: $[0.72^{-1/4}\sqrt{2}, 0.72^{+1/4}\sqrt{2}] \times [0.95^{-1/4}\sqrt{2}, 0.95^{+1/4}\sqrt{2}].$
+- 4. Running PLA gives 12 regions accepting $\varphi$ , e.g., $R_{+,1}:[0.92075,0.960375]\times[0.97475,1]$ and $R_{+,2}:[0.960375,0.97028175]\times[0.9431875,0.9495].$
+- 5. Running Alg. 2 on $R_{+,1}$ through $R_{+,12}$ gives minimal distance candidates $u_{+,1}$ through $u_{+,12}$ ; e.g., $u_{+,2}(p) = lb_p = 0.960375$ as $u_0(p) < lb_p$ and $u_{+,2}(q) = ub_q = 0.9495$ as $ub_q < u_0(q)$ ; see Fig. 5.
+- 6. Running Alg. 2 using the distance measure of choice—here: EC-distance—for the candidates $U_+$ returns $u_+$ closest to $u_0$ : $u_+(p)=0.92075$ and $u_+(q)=0.97475$ ; distance 0.040913125.
+
+Let us shortly discuss the hyper-parameters: $\eta$ , $\gamma$ , and K. The hyper-parameter $\eta$ is the coverage factor for the region partitioning. Our region partitioning algorithm, i.e., parameter lifting (line 17 Alg. 1) works based on this factor: the procedure stops when $R_?$ is at most $(1 - \eta) \cdot ||R||$ of the entire region R, e.g., for $\eta = 0.99$ , it continues until at least 99% of R is either rejecting or accepting and at most 1% is unknown. This relates $\eta$ to the approximation bounds of our algorithm, see Problem statement 3. Intuitively speaking, the coverage factor $\eta$ means that if the algorithm provides a minimal-distance value $D(u, u_0) = d$ , then with the probability $1-\eta$ there may exist a smaller value distance than d that works too but has not been found. One can thus consider $\eta$ as the confidence factor for the minimality of the results. The hyper-parameter $\gamma$ specifies the factor by which the region is expanded at each iteration, see Line 9, Alg. 1. The hyperparameters $\gamma$ and K specify the size of the initial region, see Lines 3 and 16 of Alg. 1. Our experiments (not detailed in the paper) with $\gamma = \{0.2, 0.5, 0.8\}$ and $K = \{2, \dots, 12\}$ reveal that $\gamma = 1/2$ and K = 6 gave the best balance. Large values for $\gamma(0.8)$ and small K lead to unnecessary computations in the initial iteration for the simple cases i.e., when small perturbations of the parameters make the constraint satisfied. Small values for $\gamma(0.2)$ lead to large regions in the next iterations due to the expansion by $\gamma^{-1}$ .
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+# Introduction
+
+Pre-trained language models (PLM) such as BERT [@DBLP:conf/naacl/DevlinCLT19], RoBERTa [@DBLP:journals/corr/abs-1907-11692], and ELECTRA [@DBLP:conf/iclr/ClarkLLM20] have achieved great success in a wide variety of natural language processing tasks. However, @DBLP:conf/emnlp/ReimersG19 finds that sentence embeddings from the original BERT underperform traditional methods such as GloVe [@DBLP:conf/emnlp/PenningtonSM14]. Typically, an input sentence is first embedded by the BERT embedding layer, which consists of token embeddings, segment embeddings, and position embeddings. The output is then encoded by the Transformer encoder and the hidden states at the last layer are averaged to obtain the sentence embeddings. Prior studies identify the anisotropy problem as a critical factor that harms BERT-based sentence embeddings, as sentence embeddings from the original BERT yield a high similarity between any sentence pair due to the narrow cone of learned embeddings [@DBLP:conf/emnlp/LiZHWYL20].
+
+Prior approaches for improving sentence embeddings from PLMs fall into three categories. The first category of approaches *does not require any learning (that is, learning-free)*. @DBLP:conf/emnlp/JiangJHZWZWHDZ22 argues that the anisotropy problem may be mainly due to the static token embedding bias, such as token frequency and case sensitivity. To address these biases, they propose the *static remove biases avg.* method which removes top-frequency tokens, subword tokens, uppercase tokens, and punctuations, and uses the average of the remaining token embeddings as sentence representation. However, this approach does not use the contextualized representations of BERT and may not be effective for short sentences as it may exclude informative words. The prompt-based method (*last manual prompt*) [@DBLP:conf/emnlp/JiangJHZWZWHDZ22] uses a template to generate sentence embeddings. An example template is *This sentence: "\[X\]" means \[MASK\] .*, where \[X\] denotes the original sentence and the last hidden states in the \[MASK\] position are taken as sentence embeddings. However, this method has several drawbacks. (1) It increases the input lengths, which raises the computation cost. (2) It relies on using the \[MASK\] token to obtain the sentence representation, hence unsuitable for PLMs not using \[MASK\] tokens (e.g., ELECTRA). (3) The performance heavily depends on the quality of manual prompts which relies on human expertise (alternatively, OptiPrompt [@DBLP:conf/naacl/ZhongFC21] requires additional unsupervised contrastive learning).
+
+The second category of approaches *fixes the parameters of the PLM* and improves sentence embeddings through post-processing methods that *require extra learning*. BERT-flow [@DBLP:conf/emnlp/LiZHWYL20] addresses the anisotropy problem by introducing a flow-based generative model that transforms the BERT sentence embedding distribution into a smooth and isotropic Gaussian distribution. BERT-whitening [@DBLP:journals/corr/abs-2103-15316] uses a whitening operation to enhance the isotropy of sentence representations. Both BERT-flow and BERT-whitening require Natural Language Inference (NLI)/Semantic Textual Similarity (STS) datasets to train the flow network or estimate the mean values and covariance matrices as the whitening parameters.
+
+The third category *updates parameters of the PLM by fine-tuning or continually pre-training the PLM using supervised or unsupervised learning*, which is computationally intensive. For example, Sentence-BERT (SBERT) [@DBLP:conf/emnlp/ReimersG19] fine-tunes BERT using a siamese/triplet network on NLI and STS datasets. SimCSE [@DBLP:conf/emnlp/GaoYC21] explores contrastive learning. Unsupervised SimCSE uses the same sentences with different dropouts as positives and other sentences as negatives, and supervised SimCSE explores NLI datasets and treats entailment pairs as positives and contradiction pairs as hard negatives.
+
+In this work, we first analyze BERT sentence embeddings. (1) We use a parameter-free probing method to analyze BERT and Sentence-BERT (SBERT) [@DBLP:conf/emnlp/ReimersG19] and find that the compositionality of informative words is crucial for generating high-quality sentence embeddings as from SBERT. (2) Visualization of BERT attention weights reveals that certain self-attention heads in BERT are related to informative words, specifically self-attention from a word to itself (that is, the diagonal values of the attention matrix). Based on these findings, we propose a simple and efficient approach, **Di**agonal A**tt**ention Po**o**ling (**Ditto**), to improve sentence embeddings from PLM without requiring any learning (that is, *Ditto is a learning-free method*). We find that Ditto improves various PLMs and strong sentence embedding methods on STS benchmarks.
+
+
+
+
+BERT
+
+
+
+SBERT
+
+The heatmap shows the impact matrix for the sentence “For those who follow social media transitions on Capitol Hill, this will be a little different.”. The impact matrices are computed using BERT (bert-base-uncased) and SBERT (bert-base-nli-stsb-mean-tokens) on Hugging Face, respectively.
+
+
+**Observation 1: The compositionality of informative words is crucial for high-quality sentence embeddings.** Perturbed masking [@DBLP:conf/acl/WuCKL20] is a parameter-free probing technique for analyzing PLMs (e.g., BERT). Given a sentence $\mathbf{x} = [x_1, x_2, \ldots, x_N]$, perturbed masking applies a two-stage perturbation process on each pair of tokens $(x_i, x_j)$ to measure the impact that a token $x_j$ has on predicting the other token $x_i$. Details of perturbed masking can be found in Appendix [6.1](#subsec:pertuberd masking){reference-type="ref" reference="subsec:pertuberd masking"}. Prior works use perturbed masking to recover syntactic trees from BERT [@DBLP:conf/acl/WuCKL20]. Different from prior works, we use perturbed masking to analyze the original BERT and a strong sentence embedding model, supervised Sentence-BERT (SBERT) [@DBLP:conf/emnlp/ReimersG19]. Figure [3](#fig:both_figures){reference-type="ref" reference="fig:both_figures"} shows the heatmap representing the impact matrix $\mathcal{F}$ for an example sentence in the English PUD treebank [@DBLP:conf/conll/ZemanPSHNGLPPPT17]. The y-axis represents $x_i$ and the x-axis represents $x_j$. A higher value in $\mathcal{F}_{ij}$ indicates that a word $x_j$ has a greater impact on predicting another word $x_i$. Comparing the impact matrices of BERT and SBERT, we observe that the impact matrix of SBERT exhibits prominent vertical lines on informative tokens such as "social media", "Capitol Hill", and "different", which implies that informative tokens have a high impact on predicting other tokens, hence masking informative tokens could severely affect predictions of other tokens in the sentence. In contrast, BERT does not show this pattern. This observation implies that the compositionality of informative tokens could be a strong indicator of high-quality sentence embeddings of SBERT. Furthermore, we compute the correlations between the impact matrix and TF-IDF [@sparck1972statistical] which measures the importance of a word, and report results in Table [\[tab:perturbed\]](#tab:perturbed){reference-type="ref" reference="tab:perturbed"}. We find that the impact matrix of SBERT has a much higher correlation with TF-IDF than the impact matrix of BERT, which is consistent with the observation above. Notably, ELECTRA performs poorly on STS tasks and shows a weak correlation with TF-IDF. Consequently, we hypothesize that sentence embeddings of the original BERT and ELECTRA may be biased towards uninformative words, hence limiting their performance on STS tasks.
+
+**Observation 2: Certain self-attention heads of BERT correspond to word importance.** Although SBERT has a higher correlation with TF-IDF than BERT as verified in Observation 1, BERT still shows a moderate correlation. Thus, we hypothesize that the semantic information of informative words is already encoded in BERT but has yet to be fully exploited. Prior research [@DBLP:conf/blackboxnlp/ClarkKLM19] analyzes the attention mechanisms of BERT by treating each attention head as a simple, no-training-required classifier that, given a word as input, outputs the other word that it most attends to. Certain attention heads are found to correspond well to linguistic notions of syntax and coreference. For instance, heads that attend to the direct objects of verbs, determiners of nouns, objects of prepositions, and coreferent mentions are found to have remarkably high accuracy. We believe that the attention information in BERT needs to be further exploited. We denote a particular attention head by $<$layer$>$-$<$head number$>$ ($l$-$h$), where for a BERT-base-sized model, *layer* ranges from 1 to 12 and *head number* ranges from 1 to 12. We visualize the attention weights of each head in each layer of BERT and focus on informative words. We then discover that self-attention from a word to itself (that is, the diagonal value of the attention matrix, named **diagonal attention**) of certain heads may be related to the importance of the word. As shown in Figure [4](#fig:attention){reference-type="ref" reference="fig:attention"}, the informative words "social media transitions", "hill", and "little" have high diagonal values of the attention matrix of head 1-10. Section [4](#subsec:analysis){reference-type="ref" reference="subsec:analysis"} will demonstrate that diagonal attentions indeed have a strong correlation with TF-IDF weights.
+
+
+
+Illustration of attention weights for BERT head 1-10 (on the left) and head 11-11 (on the right). The darkness of a line represents the value of the attention weights. The top-5 diagonal values of the attention matrix are colored blue.
+
+
+
+
+The diagram of our proposed diagonal attention pooling (Ditto) method.
+
+
+Inspired by the two observations in Section [2](#sec:observation){reference-type="ref" reference="sec:observation"}, we propose a novel learning-free method called **Di**agonal A**tt**ention Po**o**ling (**Ditto**) to improve sentence embeddings for PLMs, illustrated in Figure [5](#fig:method){reference-type="ref" reference="fig:method"}. Taking BERT as an example, the input to the Transformer encoder is denoted as $\mathbf{h}^{0} = [h^{0}_1, \ldots, h^{0}_N]$, and the hidden states at each Transformer encoder layer are denoted as $\mathbf{h}^l=[h^l_1, \ldots, h^l_N], l \in \{1,\ldots, L\}$. Typically, the hidden states of the last layer of the PLM are averaged to obtain the fixed-size sentence embeddings, as $\frac{1}{N}{\sum_{i=1}^N{h^{L}_i}}$ (denoted as *last avg.*). Alternatively, we can also average the static word embeddings $\frac{1}{N}{\sum_{i=1}^N{h^{0}_i}}$ (denoted as *static avg.*), or average the hidden states from the first and last layers $\frac{1}{2N}{\sum_{i=1}^N{(h^{0}_i + h^{L}_i)}}$ (denoted as *first-last*) to obtain the sentence embeddings. Ditto weights the hidden states with diagonal attention of a certain head. For example, to obtain the sentence embeddings from the first-last hidden states of BERT using Ditto, we first obtain the diagonal values $[\mathcal{A}_{11}, \ldots, \mathcal{A}_{NN}]$ of the attention matrix $\mathcal{A}$ for head $l$-$h$ of BERT, where $l$ and $h$ are treated as hyperparameters and are optimized on a development set based on the STS performance. Then, we compute $\frac{1}{2}{\sum_{i=1}^N{\mathcal{A}_{ii}(h^{0}_i + h^{L}_i)}}$ as the sentence embeddings[^2]. Note that the impact matrix (Section [2](#sec:observation){reference-type="ref" reference="sec:observation"}) correlates well with TF-IDF (as shown in Table [\[tab:perturbed\]](#tab:perturbed){reference-type="ref" reference="tab:perturbed"}) and hence may also improve sentence embeddings. However, the learning-free Ditto is much more efficient than computing the impact matrix, which is computationally expensive.
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+# Introduction
+
+One of the reasons for the success of AlphaZero [@silver2017mastering] is the use of a policy and value network to guide the Monte Carlo tree search (MCTS) to decrease the search tree's width and depth. Trained on state-outcome pairs, the value network develops an 'intuition' to tell from single game positions which player might win. By normalizing values in the tree to handle changing reward scales [@schadd2008single; @schrittwieser2020mastering], AlphaZero's mechanisms can be applied to single-agent (or single-player) tasks. Although powerful, the MCTS relies on value approximations which can be hard to predict [@van2016learning; @pohlen2018observe]. Furthermore, without proper normalization, it can be difficult for value function approximators to adapt to small improvements in later stages of training. In recent years there has been an increasing interest in learning sequential solutions for combinatorial optimization problems (COPs) from zero knowledge via deep reinforcement learning. Particularly strong results have been achieved with policy gradient methods by using variants of *self-critical training* [@rennie2017self; @kool2018attention; @kwon2020pomo] where it is avoided to learn a value function at all. By baselining the gradient estimate with the outcome of rolling out a current or historical policy, actions are reinforced by how much better (or worse) an episode is compared to the rollouts. Something similar is achieved in MCTS-based algorithms for single-player tasks by computing a scalar baseline from the agent's historical performance. The reward of the original task is reshaped to a binary $\pm 1$ outcome indicating whether an episode has exceeded this baseline or not [@laterre2019ranked; @schmidt2019self; @mandhane2022muzero]. This *self-competition* brings the original single-player task closer to a two-player game and bypasses the need for in-tree value scaling during training. The scalar baseline against which the agent is planning must be carefully chosen as it should neither be too difficult nor too easy to outperform. Additionally, in complex problems, a single scalar value holds limited information about the instance at hand and the agent's strategies for reaching the threshold performance.
+
+In this paper, we follow the idea of self-competition in AlphaZero-style algorithms for deterministic single-player sequential planning problems. Inspired by the original powerful 'intuition' of AlphaZero to evaluate board positions, we propose to evaluate states in the value function not by comparing them against a scalar threshold but directly against states at similar timesteps coming from a historical version of the agent. The agent learns by reasoning about potential strategies of its past self. We summarize our contributions as follows: (i) We assume that in a self-competitive framework, the scalar outcome of a trajectory $\zeta$ under some baseline policy is less informative for tree-based planning than $\zeta$'s intermediate states. Our aim is to put the flexibility of rollouts in self-critical training into a self-competitive framework while maintaining the policy's information in intermediate states of the rollout. We motivate this setup from the viewpoint of advantage baselines, show that policy improvements are preserved, and arrive at a simple instance of gamification where two players start from the same initial state, take actions in turn and aim to find a better trajectory than the opponent. (ii) We propose the algorithm GAZ Play-to-Plan (GAZ PTP) based on Gumbel AlphaZero (GAZ), the latest addition to the AlphaZero family, introduced by [@Danihelka22]. An agent plays the above game against a historical version of itself to improve a policy for the original single-player problem. The idea is to allow only one player in the game to employ MCTS and compare its states to the opponent's to guide the search. Policy improvements obtained through GAZ's tree search propagate from the game to the original task.
+
+We show the superiority of GAZ PTP over single-player variants of GAZ on two COP classes, the Traveling Salesman Problem and the standard Job-Shop Scheduling Problem. We compare GAZ PTP with different single-player variants of GAZ, with and without self-competition. We consistently outperform all competitors even when granting GAZ PTP only half of the simulation budget for search. In addition, we reach competitive results for both problem classes compared to benchmarks in the literature.
+
+# Method
+
+We consider undiscounted Markov decision processes (MDPs) of finite problem horizon $T$ with state space $\mathcal S$, a finite action space $\mathcal A$, a reward function $r \colon \mathcal S \times \mathcal A \to \mathbb R$, and an initial state distribution $\rho_0$. The goal is to find a state-dependent policy $\pi$ which maximizes the expected total reward $$G^\pi := \E_{s_0 \sim \rho_0}\E_{\zeta_0 \sim \eta^\pi(\cdot | s_0)}\left[ \sum_{t=0}^{T-1}r(s_t, a_t) \right],$$ where $\eta^\pi(\cdot | s_t)$ is the distribution over possible trajectories $\zeta_t = (s_t, a_t, \dots, s_{T-1}, a_{T-1}, s_T)$ obtained by rolling out policy $\pi$ from state $s_t$ at timestep $t$ up to the finite horizon $T$. We use the subscript $t$ in actions and states to indicate the timestep index. For an action $a_t \sim \pi(\cdot | s_t)$ in the trajectory, the state transitions deterministically to $s_{t+1} = F(s_t, a_t)$ according to some known deterministic state transition function $F \colon \mathcal S \times \mathcal A \to \mathcal S$. We shortly write $a_t s_t := F(s_t, a_t)$ for a state transition in the following. With abuse of notation, we write $r(\zeta_t) := \sum_{l=0}^{T-1-t}r(s_{t+l}, a_{t+l})$ for the accumulated return of a trajectory. As usual, we denote by $$\begin{align*}
+& V^{\pi}(s_t) := \E_{\zeta_t \sim \eta^\pi(\cdot | s_t)}\left[r(\zeta_t)\right] \qquad Q^{\pi}(s_t, a_t) := r(s_t, a_t) + \E_{\zeta_{t+1} \sim \eta^\pi(\cdot | a_t s_t)}\left[r(\zeta_{t+1})\right] \\
+& A^{\pi}(s_t, a_t) := Q^\pi(s_t, a_t) - V^\pi(s_t)
+\end{align*}$$
+
+the state-value function, action-value function and advantage function w.r.t. the policy $\pi$. Furthermore, for two policies $\pi$ and $\mu$, we define $$\begin{equation}
+ V^{\pi, \mu}(s_t, s_l') := \E_{\substack{\zeta_{t} \sim \eta^\pi(\cdot | s_{t}) \\ \zeta'_{l} \sim \eta^\mu(\cdot | s'_l)}}\left[r(\zeta_{t}) - r(\zeta'_l)\right]
+\end{equation}$$ as the expected difference in accumulated rewards taken over the joint distribution of $\eta^\pi(\cdot | s_t)$ and $\eta^\mu(\cdot | s'_l)$. We define $Q^{\pi, \mu}(s_t, s_l'; a_t) := r(s_t, a_t) + V^{\pi, \mu}(a_t s_t, s_l')$ and $A^{\pi, \mu}(s_t, s_l'; a_t) := Q^{\pi, \mu}(s_t, s_l'; a_t) - V^{\pi, \mu}(s_t, s_l')$ analogously.
+
+The following lemma follows directly from the definitions. It tells us that we can work with the policy $\mu$ as with any other scalar baseline, and that policy improvements are preserved. A proof is given in Appendix [8.1](#proof:lemma){reference-type="ref" reference="proof:lemma"}.
+
+::: {#lemma .lemma}
+**Lemma 1**. *Let $\pi, \tilde \pi$, and $\mu$ be state-dependent policies. For any states $s_t, s_l' \in \mathcal S$ and action $a_t \in \mathcal A$, we have $$\begin{equation}
+ A^\pi(s_t, a_t) = A^{\pi, \mu}(s_t, s_l'; a_t), \text{ and}
+\end{equation}$$ $$\begin{equation}
+ \Big[\sum_{a_t} \tilde \pi(a_t | s_t)Q^{\pi}(s_t, a_t)\Big] - V^{\pi}(s_t) = \Big[\sum_{a_t} \tilde \pi(a_t | s_t)Q^{\pi, \mu}(s_t, s_l'; a_t)\Big] - V^{\pi, \mu}(s_t, s_l').
+\end{equation}$$*
+:::
+
+We now consider MDPs with episodic rewards, i.e., we assume $r(s_t, a_t) = 0$ for $t < T-1$. The policy target and action selection within the search tree in GAZ is based on the following policy update: At some state $s$, given logits of the form $\text{logit}^\pi(a)$ for an action $a$ predicted by a policy $\pi$, an updated policy $\pi'_{\text{GAZ}}$ is obtained by setting $$\begin{equation}
+ \label{eq:logitUpdate}
+ \text{logit}^{\pi'_{\text{GAZ}}}(a) = \text{logit}^\pi(a) + \sigma(\hat A^\pi(s, a)).
+\end{equation}$$ Here, $\sigma$ is some monotonically increasing linear function and $\hat A^\pi(s, a) := \hat Q^\pi(s,a) - \hat V^\pi(s)$ is an advantage estimation, where $\hat V^\pi(s)$ is a value approximation based on the output of a value network and $\hat Q^\pi(s,a)$ is a $Q$-value approximation coming from the tree search statistics (and is set to $\hat V^\pi(s)$ for unvisited actions). Note that ([\[eq:logitUpdate\]](#eq:logitUpdate){reference-type="ref" reference="eq:logitUpdate"}) differs from the presentation in @Danihelka22 due to the additional assumption that $\sigma$ is linear, which matches its practical choice (see Appendix [8.2](#proof:logitUpdate){reference-type="ref" reference="proof:logitUpdate"} for a derivation). The update ([\[eq:logitUpdate\]](#eq:logitUpdate){reference-type="ref" reference="eq:logitUpdate"}) is proven to provide a policy improvement but relies on the correctness of the value approximations $\hat V^\pi$.
+
+*Our aim is to couple the ideas of self-critical training and self-competition using a historical policy. Firstly, the baseline should adapt to the difficulty of an instance via rollouts as in self-critical training. Secondly, we want to avoid losing information about intermediate states of the rollout as when condensing it to a scalar value.*
+
+Consider some initial state $s_0 \sim \rho_0$, policy $\pi$, and a historical version $\mu$ of $\pi$. Let $\zeta_0^\text{greedy} = (s_0, a'_0, \dots, s'_{T-1}, a'_{T-1}, s'_{T})$ be the trajectory obtained by rolling out $\mu$ greedily. We propose to plan against $\zeta_0^\text{greedy}$ timestep by timestep instead of baselining the episode with $r(\zeta_0^\text{greedy})$, i.e., we want to approximate $V^{\pi, \mu}(s_t, s_t')$ for any state $s_t$. By Lemma [1](#lemma){reference-type="ref" reference="lemma"}, we have for any policy $\pi'$ $$\begin{equation}
+ \sum_{a_t} \pi'(a_t | s_t)Q^{\pi}(s_t, a_t) \geq V^{\pi}(s_t) \iff \sum_{a_t} \pi'(a_t | s_t)Q^{\pi, \mu}(s_t, s_t'; a_t) \geq V^{\pi, \mu}(s_t, s_t').
+\end{equation}$$ So if $\pi'$ improves policy $\pi$ baselined by $\mu$, then $\pi'$ improves $\pi$ in the original MDP. Furthermore, $\hat A^\pi(s_t, a_t)$ in ([\[eq:logitUpdate\]](#eq:logitUpdate){reference-type="ref" reference="eq:logitUpdate"}) can be swapped with an approximation of $\hat A^{\pi, \mu}(s_t, s_t'; a_t)$ for the update in GAZ. Note that $V^{\pi, \mu}(s_t, s_t') = \E_{\zeta_{t}, \zeta'_t}\left[r(\zeta_{t}) - r(\zeta'_t)\right] = \E_{\zeta_{t}, \zeta'_t}\left[\text{sgn}(r(\zeta_{t}) - r(\zeta'_t)) \cdot \left|r(\zeta_{t}) - r(\zeta'_t)\right|\right]$. Especially in later stages of training, improvements might be small and it can be hard to approximate the expected reward difference $\left|r(\zeta_{t}) - r(\zeta'_t)\right|$. Thus, we switch to a binary reward as in self-competitive methods and arrive at $$\begin{equation}
+ \label{eq:value_game}
+ V_\text{sgn}^{\pi,\mu}(s_t, s_t') := \E_{\substack{\zeta_{t} \sim \eta^\pi(\cdot | s_{t}) \\ \zeta'_{t} \sim \eta^\mu(\cdot | s'_t)}}\left[\text{sgn}(r(\zeta_{t}) - r(\zeta'_t))\right],
+\end{equation}$$ the final target output of the value network in the self-competitive formulation. Note that ([\[eq:value_game\]](#eq:value_game){reference-type="ref" reference="eq:value_game"}) uncouples the network from the explicit requirement to predict the expected outcome of the original MDP to decide if $\pi$ is in a more advantageous state at timestep $t$ than $\mu$ (for further details, see Appendix [11](#appendix:timestep_baseline){reference-type="ref" reference="appendix:timestep_baseline"}). We can define $Q_\text{sgn}^{\pi,\mu}$ analogously to ([\[eq:value_game\]](#eq:value_game){reference-type="ref" reference="eq:value_game"}) and obtain an improved policy $\pi'$ via $$\begin{equation}
+ \label{eq:gaz_logit_update}
+ \text{logit}^{\pi'}(a_t) := \text{logit}^\pi(a_t) + \sigma(\hat Q^{\pi, \mu}_\text{sgn}(s_t, s_t'; a_t) - \hat V^{\pi, \mu}_\text{sgn}(s_t, s_t')).
+\end{equation}$$
+
+Given the original MDP, the above discussion generalizes to a *two-player zero-sum perfect information game* with separated states: two players start with identical copies $s_0^1$ and $s_0^{-1}$, respectively, of an initial state $s_0 \sim \rho_0$. The superscripts $1$ and $-1$ indicate the first (max-) and second (min-) player, respectively. Player $1$ observes both states $(s_0^1, s_0^{-1})$, chooses an action $a_0^1$ and transitions to the next state $s_1^1 = a_0^1 s_0^1$. Afterwards, player $-1$ observes $(s_1^1, s_0^{-1})$, chooses action $a_0^{-1}$, and transitions to state $s_1^{-1} = a_0^{-1} s_0^{-1}$. Then, player $1$ observes $(s_1^1, s_1^{-1})$, chooses action $a_1^1$, and in this manner both players take turns until they arrive at terminal states $s_T^1$ and $s_T^{-1}$. Given the resulting trajectory $\zeta_0^p$ for player $p \in \{1, -1\}$, the outcome $z$ of the game is set to $z = 1$ if $r(\zeta_0^1) \geq r(\zeta_0^{-1})$ (player 1 wins) and $z = -1$ otherwise (player 1 loses). In case of equality, player $1$ wins to discourage player $-1$ from simply copying moves.
+
+Algorithm [\[algorithm:game_playing\]](#algorithm:game_playing){reference-type="ref" reference="algorithm:game_playing"} summarizes our approach for improving the agent's performance, and we provide an illustration in the appendix in Figure [2](#fig:game_overview){reference-type="ref" reference="fig:game_overview"}. In each episode, a 'learning actor' improves its policy by playing against a 'greedy actor', which is a historical greedy version of itself. We elaborate on the main parts in the following (more details can be found in Appendix [9](#appendix:additional_algo_details){reference-type="ref" reference="appendix:additional_algo_details"}).
+
+::: algorithm
+Init policy replay buffer $\mathcal M_\pi \gets \emptyset$ and value replay buffer $\mathcal M_V \gets \emptyset$ Init parameters $\theta$, $\nu$ for policy net $\pi_\theta \colon \mathcal S \to \Delta\mathcal A$ and value net $V_\nu \colon \mathcal S \times \mathcal S \to [-1, 1]$ Init 'best' parameters $\theta^B \gets \theta$
+:::
+
+An agent maintains a state-dependent policy network $\pi_\theta \colon \mathcal S \to \Delta \mathcal A$ and value network $V_\nu \colon \mathcal S \times \mathcal S \to [-1, 1]$ parameterized by $\theta$ and $\nu$, respectively, where $\Delta \mathcal A$ is the probability simplex over actions and $V_\nu$ serves as a function approximator of ([\[eq:value_game\]](#eq:value_game){reference-type="ref" reference="eq:value_game"}). The agent generates experience in each episode for training the networks by playing the game in Section [4.1](#sec:game_mechanics){reference-type="ref" reference="sec:game_mechanics"} against a frozen historically best policy version $\pi_{\theta^B}$ of itself. Initially, $\theta$ and $\theta^B$ are equal. The networks are trained from experience replay as in AlphaZero, where we store value targets from the perspective of both players and only policy targets coming from the learning actor.
+
+The learning actor and greedy actor are randomly assigned to player positions at the beginning of each episode. The greedy actor simply chooses actions greedily according to its policy $\pi_{\theta^B}$. In contrast, the learning actor chooses actions using the search and action selection mechanism of GAZ to improve its policy and dominate over $\pi_{\theta^B}$. We found the policy to become stronger when the learning actor makes its moves sometimes after and sometimes before the greedy actor. At test time, we fix the learning actor to the position of player 1.
+
+The learning actor uses MCTS to improve its policy and dominate over the greedy behavior of $\pi_{\theta^B}$. We use the AlphaZero variant (as opposed to MuZero) in [@Danihelka22] for the search, i.e., we do not learn models for the reward and transition dynamics. The search tree is built through several simulations, as usual, each consisting of the phases *selection*, *expansion*, and *backpropagation*. Each node in the tree is of the form $\mathcal N = (s^1, s^{-1}, k)$, consisting of both players' states, where $k \in \{1, -1\}$ indicates which player's turn it is. Each edge $(\mathcal N, a)$ is a node paired with a certain action $a$. We apply the tree search of GAZ similarly to a two-player board game, with two major modifications:
+
+**(i) Choice of actions:** For the sake of explanation, let us assume that $l=1$, i.e., the learning actor takes the position of player $1$. In the *selection* phase, an action is chosen at any node as follows: If the learning actor is to move, we regularly choose an action according to GAZ's tree search rules, which are based on the completed $Q$-values. If it is the greedy actor's turn, there are two ways to use the policy $\pi_{\theta^B}$: we either always *sample*, or always *choose greedily* an action from $\pi_{\theta^B}$. By sampling a move from $\pi_{\theta^B}$, the learning actor is forced to plan not against only one (possibly suboptimal) greedy trajectory but also against other potential trajectories. This encourages exploration (see Figure [1](#fig:seed_results){reference-type="ref" reference="fig:seed_results"} in the experimental results), but a single action of the learning actor can lead to separate branches in the search tree. On the other hand, greedily choosing an action is computationally more efficient, as only the states in the trajectory $\zeta_{0, \pi_{\theta^B}}^{\text{greedy}}$ are needed for comparison in the MCTS (see Appendix [\[appendix:efficient_greedy\]](#appendix:efficient_greedy){reference-type="ref" reference="appendix:efficient_greedy"} for notes on efficient implementation). However, if the policy $\pi_{\theta^B}$ is too strong or weak, the learning actor might be slow to improve in the beginning.
+
+We refer to the variant of sampling actions for the greedy actor in the tree search as GAZ PTP 'sampled tree' (GAZ PTP ST) and as GAZ PTP 'greedy tree' (GAZ PTP GT) in case actions are chosen greedily.
+
+**(ii) Backpropagation:** Actions are chosen as above until a leaf edge $(\mathcal N, a)$ with $\mathcal N = (s^1, s^{-1}, k)$ is reached. When a leaf edge $(\mathcal N, a)$ is *expanded* and $k = -l = -1$, the learning actor is to move in the new node $\tilde{\mathcal N} = (s^1, as^{-1}, 1)$. We proceed by querying the policy and value network to obtain $\pi_\theta(s^1)$ and $v := V_\nu(s^1, as^{-1})$. The new node $\tilde{\mathcal N}$ is added to the tree and the value approximation $v$ is *backpropagated* up the search path. If $k = 1$, i.e. the turn of the greedy actor in $\tilde{\mathcal N} = (as^1, s^{-1}, -1)$, we only query the policy network $\pi_{\theta^B}(s^{-1})$, choose an action $\tilde a$ from it (sampled or greedy), and *directly* expand the edge $(\tilde N, \tilde a)$. In particular, we do not backpropagate a value approximation of state pairs where the greedy actor is to move. Nodes, where it's the greedy actor's turn, are similar to *afterstates* [@sutton2018reinforcement], which represent chance transitions in stochastic environments. We further illustrate the procedure in Appendix [\[appendix:modified_tree_search\]](#appendix:modified_tree_search){reference-type="ref" reference="appendix:modified_tree_search"}.
+
+To ensure we always play against the strongest greedy actor, we fix at the beginning of the training a large enough 'arena' set of initial states $\mathcal J_{\text{arena}} \sim \rho_0$ on which periodically $\pi_{\theta}$ and $\pi_{\theta^B}$ are pitted against each other. For each initial state $s_0 \in \mathcal J_{\text{arena}}$, the policies $\pi_{\theta}$ and $\pi_{\theta^B}$ are unrolled greedily to obtain the trajectories $\zeta_{0, \pi_\theta}^{\text{greedy}}$ and $\zeta_{0, \pi_{\theta^B}}^{\text{greedy}}$, respectively. We replace $\theta^B$ with $\theta$ if $\sum_{s_0 \in \mathcal J_{\text{arena}}} \left(r(\zeta_{0, \pi_\theta}^{\text{greedy}}) - r(\zeta_{0, \pi_{\theta^B}}^{\text{greedy}})\right) > 0.$
+
+The parameters $\theta, \theta^B$ might be initialized disadvantageously (worse than uniformly random play), so that the learning actor generates no useful experience while maintaining a greedy policy worse than $\pi_{\theta^B}$. We especially experienced this behavior when using a small number of simulations for the tree search. To mitigate this and to stabilize training, we switch to *self-play* in an episode with a small nonzero probability $\gamma$, i.e., the greedy actor uses the non-stationary policy $\pi_\theta$ instead of $\pi_{\theta^B}$.
diff --git a/2306.05085/main_diagram/main_diagram.drawio b/2306.05085/main_diagram/main_diagram.drawio
new file mode 100644
index 0000000000000000000000000000000000000000..68a0cc56a9615f134500e82b79bc6872650e357a
--- /dev/null
+++ b/2306.05085/main_diagram/main_diagram.drawio
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+# Introduction
+
+End-to-end learned image compression systems [\[8,](#page-9-0) [12,](#page-9-1) [44\]](#page-10-0) have recently attracted lots of attention due to their competitive compression performance to traditional image coding methods, such as intra coding in VVC [\[4\]](#page-9-2) and HEVC [\[41\]](#page-10-1). Among them, transformer-based autoencoders [\[47,](#page-10-2) [32,](#page-10-3) [31,](#page-10-4) [42\]](#page-10-5) emerge as attractive alternatives to convolutional neural networks (CNN)-based solutions because of their high content adaptivity. Some even feature lower computational cost than CNN-based autoencoders. In common, most learned image compression systems are designed primarily for human perception.
+
+Recently, image coding for machine perception becomes an active research area due to the rising demands for transmitting visual data across devices for high-level recognition tasks. Coding techniques in this area mainly include approaches that produce multi-task or single-task bitstreams. The methods with the multi-task bitstream feature one single compressed bitstream that is able to serve multiple downstream tasks, such as human perception and machine
+
+
+
+Figure 1. Comparison of VPT [\[17\]](#page-9-3) and our proposed TransTIC.
+
+perception. Most of them [\[6,](#page-9-4) [11,](#page-9-5) [9,](#page-9-6) [28,](#page-9-7) [46\]](#page-10-6) aim to learn a robust, general image representation via multi-task or contrastive learning. However, a general bitstream can hardly be rate-distortion optimal from the perspective of each individual task.
+
+The methods with the single-task bitstream allow the image codec to be tailored for individual downstream tasks, thereby generating multiple task-specific bitstreams. One straightforward approach is to optimize a codec end-toend for each task [\[5\]](#page-9-8). However, given the sheer amount of machine tasks and their recognition networks, along with the ongoing developments of new machine tasks and models, customizing a neural codec, particularly hardwarebased, for every one-off machine application would be prohibitively expensive even if not impossible. Regionof-interest (ROI)-based [\[40\]](#page-10-7) and transferring-based methods [\[27\]](#page-9-9) are two preferred solutions. The former performs spatially adaptive coding of images according to an importance map, which can be optimized for different downstream tasks. The latter aims to transfer a pre-trained base codec to a new task without changing the base codec. However, how to transfer efficiently a given codec without retraining is a largely under-explored topic.
+
+In this work, we aim to transfer a well-trained Transformer-based image codec from human perception to machine perception *without* fine-tuning the codec. Inspired by Visual Prompt Tuning (VPT) [\[17\]](#page-9-3), we propose a plug-in mechanism, which injects additional learnable inputs, known as prompts, to the fixed base codec. As shown in Fig. [1](#page-0-0) (a), VPT [\[17\]](#page-9-3) targets re-using a large-scale, pre-trained Transformer-based feature extractor on different recognition tasks. This is achieved by injecting a small amount of task-specific learnable parameters, prompts, to the Transformer-based feature extractor and learning a taskspecific recognition head. Different from VPT [\[17\]](#page-9-3), which considers only the performance of the downstream recognition task, our task focuses on image compression, which needs to strike a balance between the downstream task performance and the transmission cost (i.e. the bitrate needed to signal the bistream). Fig. [1](#page-0-0) (b) sketches the high-level design of our proposed method. As shown, the Transformerbased encoder and decoder are initially optimized for human perception while the recognition model is an off-theshelf recognition network. To transfer the codec from human perception to machine perception, we inject prompts to both the encoder and decoder. On the encoder side, we introduce an instance-specific prompt generator to generate instance-specific prompts by observing the input image. On the decoder side, the input image is not accessible. We thus introduce task-specific prompts to the decoder.
+
+Our main contributions are four-fold:
+
+- Without fine-tuning the codec, we transfer a welltrained Transformer-based image codec from human perception to machine perception by injecting instance-specific prompts to the encoder and taskspecific prompts to the decoder.
+- To the best of our knowledge, this work is the first attempt to utilize prompting techniques on the low-level image compression task.
+- The plug-in nature of our method makes it easy to integrate any other Transformer-based image codec.
+- Our proposed method is capable of transferring the codec to various machine tasks. Extensive experiments show that our method achieves better rate-accuracy performance than the other transferring-based methods on complex machine tasks.
+
+# Method
+
+Given a Transformer-based image codec well-trained for human perception, i.e., the image reconstruction task, this work aims for transferring the codec to achieve image compression for machine perception (e.g., object detection) without fine-tuning the codec. We draw inspiration from [17] to propose a plug-in mechanism that utilizes prompting techniques to transfer the codec. However, unlike [17], which addresses how to adapt a pre-trained large-scale Transformer-based recognition model to different recognition tasks, our work focuses on transferring a pre-trained Transformer-based image compression model to tailor image compression for recognition tasks, an applica-
+
+tion also known as image compression for machines. As such, our performance metric involves not only the recognition accuracy but also the bit rate (in terms of bits-per-pixel) needed to signal the compressed image. In our case, the downstream recognition model can be Transformer-based or convolutional neural network-based.
+
+In Section 3.1, we first address a classic paradigm of endto-end learned image compression. In Section 3.2, we outline our proposed framework. This is followed by details of our transferring mechanism in Section 3.3 and the training objective in Section 3.4.
+
+An end-to-end learned image compression system usually has two major components: a main autoencoder and a hyperprior autoencoder. The main autoencoder consists of an analysis transform $(g_a \text{ in Fig. 2})$ and a synthesis transform $(g_s \text{ in Fig. 2})$ . The analysis transform $g_a$ encodes an RGB image $x \in \mathbb{R}^{H \times W \times 3}$ of height H and width W into its latent representation $y \in \mathbb{R}^{\frac{H}{16} \times \frac{W}{16} \times 192}$ by an encoding distribution $q_{q_a}(y|x)$ . The latent y is then uniformly quantized as $\hat{y}$ and entropy encoded into a bitstream by a learned prior distribution $p(\hat{y})$ . On the decoder side, $\hat{y}$ is entropy decoded and reconstructed as $\hat{x} \in \mathbb{R}^{H \times W \times 3}$ through a decoding distribution $q_{q_s}(\hat{x}|\hat{y})$ realized by the synthesis transform $g_s$ . In the process, the prior distribution $p(\hat{y})$ crucially affects the number of bits needed to be signal the quantized latent $\hat{y}$ . It is thus modelled in a content-adaptive manner by a hyperprior autoencoder [2], which comprises a hyperprior analysis transform ( $h_a$ in Fig. 2) and a hyperprior synthesis transform ( $h_s$ in Fig. 2). As illustrated, $h_a$ converts the image latent y into the side information $z \in \mathbb{R}^{\frac{H}{64} \times \frac{W}{64} \times 128}$ , representing typically a tiny portion of the compressed bitstream. Its quantized version is decoded from the bitstream through $h_s$ to arrive at $p(\hat{y})$ . Notably, this work considers a particular implementation of the main and hyperprior autoencoders, the backbones of which are Transformer-based.
+
+Fig. 2 illustrates our transferable Transformer-based image compression framework, termed TransTIC. It is built upon [32], except that the context prior model is replaced with a simpler Gaussian prior for entropy coding. As shown, the main autoencoder $g_a, g_s$ and the hyperprior autoencoder $h_a, h_s$ include Swin-Transformer blocks (STB) as the basic building blocks. These STB are interwoven with convolutional layers to adapt feature resolution in the data pipeline. In this work, the main and hyperprior autoencoders are pre-trained for human perception (i.e. the image reconstruction task) and their network weights are fixed during the transferring process.
+
+To transfer $g_a, g_s$ such that the decoded image $\hat{x}$ is suitable for machine perception, we inject (1) instance-specific
+
+
+
+Figure 2. Overall architecture of our proposed method *TransTIC* and the detailed design of STBs.
+
+prompts produced by $g_p$ into the first two STBs in $g_a$ and (2) task-specific prompts into all the STBs in $g_s$ . Section 3.3 details how these additional prompts are input to these STBs. We note that the prompt generator $g_p$ and the task-specific prompts input to the decoder are learnable and updated according to the machine perception task. That is, the network weights of $g_p$ are task-specific. However, the prompts produced by $g_p$ are instance-specific because they are dependent on the input image. Section 4.4 presents ablation experiments to justify our design choices.
+
+**Swin-Transformer blocks (STB).** STB is at the very core of our design. Fig. 2 (c) details its data processing pipeline. It consists of multiple (e.g. M) Swin-Transformer layers (Fig. 2 (b)), with the odd-numbered layers implementing window-based multi-head self-attention (W-MSA) and the even-numbered layers realizing shifted W-MSA (SW-MSA) to facilitate cross-window information exchange. In a Swin-Transformer layer, the input is a set of tokens, each representing a feature vector at a specific spatial location. As shown, the operation of W-MSA (or SW-MSA) has four
+
+sequential steps: (1) window partition that divides evenly an input feature map $F_i \in \mathbb{R}^{h_i \times w_i \times c_i}, i=1,2,\ldots,M$ , in the i-th Swin-Transformer layer into non-overlapping windows, (2) window flattening that flattens the feature map along the token dimension in each window, (3) multi-head self-attention that adaptively updates tokens in each window through self-attending to tokens of the same window, and (4) window collection that unflattens the updated tokens in each window and collect tokens from all the windows to form an updated feature map of dimension the same as $F_i$ . In symbols, the self-attention in a window for a specific head is given by
+
+Attention
+$$(Q, K, V) = \text{Softmax}(QK^{\top}/\sqrt{d} + B)V$$
+, (1)
+
+where $Q=FW_Q,~K=FW_K,~V=FW_V,$ and $F\in\mathbb{R}^{N\times d}$ is the flattened feature map with N denoting the number of tokens in the window and d the dimension of each token. $W_Q,W_K,W_V\in\mathbb{R}^{d\times d}$ are learnable matrices projecting the input F into query $Q\in\mathbb{R}^{N\times d}$ , key $K\in\mathbb{R}^{N\times d}$ and value $V\in\mathbb{R}^{N\times d}$ . $B\in\mathbb{R}^{N\times N}$ is a learnable relative position bias matrix.
+
+Transferring Encoding STBs via Instance-specific **Prompting.** To transfer $g_a, g_s$ to machine perception, we propose to inject additional learnable tokens, known as prompts, into the STBs. They interact with the corresponding input tokens in the pre-trained STBs to adapt the encoding process and the decoded image, in order to achieve better rate-accuracy performance for machine perception. There are two types of prompts: instance-specific prompts and task-specific prompts. Instance-specific prompts are dependent on the input image, while task-specific prompts are task dependent yet invariant to the input image. As shown in Fig. 2 (a), on the encoder side, we introduce an instance-specific prompt generator $g_p$ that generates instancespecific prompts for the first two STBs (which are referred to as IP-type STBs) based on the input image. $g_p$ itself is task-specific because its network weights are trained for a specific downstream machine task. The network details of $g_p$ are provided in the supplementary document. Fig. 2 (d) depicts the inner workings of IP-type STBs. They operate similarly to the ordinary STB without prompting, except that additional and separate prompts, denoted collectively as $P_i \in \mathbb{R}^{\frac{h_i}{4} \times \frac{w_i}{4} \times c_i}$ , are introduced in the *i*-th Swin-Transformer layer. In particular, $P_i$ for a specific layer has a spatial resolution that is a quarter of that of $F_i$ . This design choice is meant to strike a balance between compression performance and complexity. Morover, $P_i$ is partitioned and flattened in the same way as the image feature $F_i$ . In the self-attention step for a specific window and head, the prompts of the same window update the image tokens with the query Q, key K and value V matrices in Eq. (1) augmented as follows:
+
+
+$$\begin{split} Q &= FW_Q, \\ K &= [F;P]W_K, \\ V &= [F;P]W_V, \end{split} \tag{2}$$
+
+where $P \in \mathbb{R}^{\frac{N}{4} \times d}$ refers collectively to the prompts in the same window as the image tokens F and $[\cdot;\cdot]$ indicates concatenation along the token dimension. With the same $W_Q, W_K, W_V$ as for Eq. (1), we have $Q \in \mathbb{R}^{N \times d}$ , $K \in \mathbb{R}^{(N+\frac{N}{4}) \times d}$ and $V \in \mathbb{R}^{(N+\frac{N}{4}) \times d}$ . In the window collection step, only image tokens are collected while prompt tokens are discarded.
+
+in the encoder, the TP-type STBs are prompted with separate tokens $P_i \in \mathbb{R}^{\frac{N}{4} \times c}$ in different Swin-Transformer layers. However, within a Swin-Transformer layer, the same prompts are shared across fixed-size windows for window-based multi-head self-attention (see window flattening and multi-head self-attention). In other words, in Eq. (2) when applied to the decoder, P is set to $P_i$ for different windows. This is limited by the fact that the number of fixed-size windows is variable depending on the image size. Training window- and task-specific prompts requires learning a variable number of prompts, which is infeasible.
+
+In Fig. 2, the prompt generator network $g_p$ and the task-specific prompts on the decoder side are learnable while the base codec (i.e. $g_a, g_s, h_a, h_s$ ) is fixed. We construct the training objective in the same way as learning an image compressor. That is, the training involves minimizing a rate-distortion cost
+
+
+$$\mathcal{L} = \underbrace{-\log p(\hat{z}) - \log p(\hat{y}|\hat{z})}_{R} + \lambda \underbrace{d(x, \hat{x})}_{D}, \tag{3}$$
+
+where R is the estimated rate needed to signal the quantized latent $\hat{y}$ and side information $\hat{z}$ , D characterizes the distortion between the input image x and its reconstruction $\hat{x}$ , and $\lambda$ is a hyper-parameter. For the sake of machine perception, the distortion measure is perceptual loss, which is evaluated with a recognition network depending on the downstream task. More details can be found in the supplementary document
diff --git a/2310.01777/main_diagram/main_diagram.drawio b/2310.01777/main_diagram/main_diagram.drawio
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diff --git a/2310.01777/paper_text/intro_method.md b/2310.01777/paper_text/intro_method.md
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+# Introduction
+
+The transformer (Vaswani et al., 2017) architecture has revolutionized various fields of artificial intelligence, such as natural language understanding (Touvron et al., 2023; Wang et al., 2022) and computer vision (Dosovitskiy et al., 2021) due to its ability to learn pairwise relationships between all T tokens in a given sequence ( $\mathcal{O}(T^2)$ ). This has ushered in the era of large transformer-based foundation models with impressive generalization abilities (Brown et al., 2020; Chiang et al., 2023). However, since the transformer's attention mechanism comes with a quadratic space and time complexity, it becomes untenable for handling long sequences which are essential for tasks such as dialogue generation (Chen et al., 2023). To overcome this limitation, previous works have suggested approaches with linear complexity by using static or dynamic sparse attention patterns (Beltagy et al., 2020; Zaheer et al., 2020; Tay et al., 2020a; Kitaev et al., 2020; Tay et al., 2020b; Liu et al., 2021), or by replacing quadratic attention with kernel or low-rank approximations (Choromanski et al., 2021; Chen et al., 2021; Qin et al., 2022).
+
+However, despite their promising aspects, previous linear attention methods have yet to be widely used in research and production for several reasons. Firstly, these attention mechanisms cannot be easily swapped into existing finetuned models. After radically replacing the quadratic attention mechanism, they require training new attention relations to attempt to recover the performance of the quadratic attention module and cannot directly learn the full range of attention relations during knowledge distillation. Therefore, they suffer from unpredictable accuracy degradation on downstream tasks. For example, as shown in Table A.8, Reformer (Kitaev et al., 2020) outperforms many other baselines on MNLI (Williams et al., 2018); however, it shows the worst performance in Table 2 on Wikitext2 (Merity et al., 2017). Secondly, previous linear attention methods may hinder
+
+
+
+Figure 1: Concept. We estimate the attention matrix in a compressed size (Aˆ), then perform a grouped topkˆ selection, and subsequently perform sparse attention with our novel FlatCSR operation using an estimated attention mask on the full attention matrix. SEA has linear complexity in all steps at test-time, and requires direct attention matrix distillation from the quadratic teacher at train-time.
+
+the ability to interpret the attention matrix or merge/prune tokens (Kim et al., 2022; Lee et al., 2023; Bolya et al., 2023) of the transformer model, as they do not produce the full attention matrix which is usually required for analyzing the importance of a given token.
+
+In contrast to previous works, our proposed linear attention method, SEA: Sparse linear attention with Estimated Attention mask, focuses on estimating the attention matrix from a pretrained teacher transformer model with O(T) complexity at inference rather than O(T 2 ). In order to do so, our novel estimator, distills knowledge (Hinton et al., 2015) from the teacher and estimates the attention matrix with O(T) complexity by fixing the second dimension to a constant value K where K ≪ T. This results in a compressed attention matrix which can be decompressed via interpolation into an approximation of the full attention matrix when performing the distillation step as opposed to previous works which prescribe retraining attention relationships during distillation due to incompatible changes in the attention operation (Choromanski et al., 2021; Qin et al., 2022). By distilling from the full attention matrix into our compressed matrix, SEA can use the complex and dynamic attention information from the pretrained model and preserve task performance.
+
+Furthermore, as SEA distills knowledge from a full attention matrix, the resulting compressed attention matrix and sparse attention matrix can still provide interpretable attention by allowing for interpreting the relationships and importance of tokens (*e.g*. analysis of attention patterns in images (Dosovitskiy et al., 2021) and token pruning (Kim et al., 2022; Lee et al., 2023; Bolya et al., 2023)). Lastly, SEA reduces the space and time complexity of attention from O(T 2 ) into O(T) at test-time, with significantly reduced memory and computational cost while maintaining similar performance to the original pretrained transformer model, as shown in Fig. 5 and Tables 2 and A.8.
+
+In Fig. 1, we provide a overview of our proposed method's inference stage. The main idea is to first estimate a compressed attention matrix Aˆ with O(T) complexity, and subsequently perform sparse attention after choosing only O(T) important relationships inferred from Aˆ . Specifically, we decode the output features of the kernel-based linear attention method Performer (Choromanski et al., 2021) to form Aˆ . Next, we perform a top-ˆk selection on Aˆ to form a compressed attention mask which can be used to generate a sparse attention mask for the final sparse attention operation. By utilizing both kernel-based and sparse attention, we can take advantage of their diverse token embedding spaces, as shown in previous work (Chen et al., 2021). The compressed estimated attention matrix Aˆ is trained via knowledge distillation (Hinton et al., 2015; Jiao et al., 2020) from a pretrained quadratic teacher model to distill complex and dynamic attention patterns. We achieve linear complexity in this process by controlling sparsity and compression ratio in the compressed attention mask.
+
+We validate SEA by applying it to BERT for text classification (Devlin et al., 2019; Wang et al., 2019) and to OPT for causal language modeling (Zhang et al., 2022; Merity et al., 2017). Our empirical findings demonstrate that SEA adequately retains the performance of the quadratic teacher model in both tasks (Tables 2 and A.8), while previous methods do not. SEA significantly outperforms the best linear attention baselines, such as Performer, in language modeling with 47.7% lower (better) perplexity while consuming 54.2% less memory than quadratic attention (Table 2). This opens up the possibility of running long context language models on lower-grade computing devices that have smaller VRAMs because SEA can run on a smaller memory budget as shown in Section 5 and Fig. 8. To summarize:
+
+- We propose a novel, test-time linear attention mechanism (SEA) that distills knowledge from a pretrained quadratic transformer into a compressed estimated attention matrix which is then used to create a sparse attention mask for the final attention operation. As demonstrated in Fig. 8, SEA is $\mathcal{O}(T)$ at test time, where there is no distillation step.
+- We demonstrate the efficiency and effectiveness of our method through empirical evaluations on natural language processing tasks such as text classification on GLUE and language modeling on Wikitext-2, where we maintain competitive performance with the vanilla transformer baseline, as shown in Figs. 5a and 7a.
+- We propose and provide code for a novel CSR tensor operation, called *FlatCSR*, which is capable of handling non-contiguous flatten tasks within a GPU kernel.
+- We showcase the interpretability of our method by visualizing the estimated sparse attention matrix and comparing it to the teacher's attention matrix. Our estimator can estimate both self-attention and causal attention.
+
+# Method
+
+**Preliminaries.** We first define notations used for the attention mechanisms. We define the following real matrices: $Q, K, V \in \mathbb{R}^{T \times d}$ . The attention matrix A is defined as $A = \operatorname{softmax}(QK^{\top}) \in \mathbb{R}^{T \times T}$ . Let $C = AV \in \mathbb{R}^{T \times d}$ be the context feature of the attention lookup, and T be the length of the input sequence, and d be the size of the head embedding dimension. We use $\odot$ to denote elementwise multiplication which may be applied between similar sizes matrices or on the last dimension and repeated over prior dimensions as is the case when applied to a matrix and vector. We define K as the width of the compressed matrix, k as a value which controls the sparsity of an attention matrix. The matrix superscript $*(e.g.\ A^*)$ indicates a sparse matrix, and a superscript $(e.g.\ A)$ refers to the compressed $T \times K$ space. Let $\mathbb{KL}(p,q)$ and $\mathrm{MSE}(x,y)$ be the standard KL divergence and mean-squared error, respectively. All matrices may also contain a batch (B) and head (H) dimension, but we omit this for simplicity unless otherwise specified.
+
+**Performer (FAVOR+).** We define the simplified notation of FAVOR+ (Choromanski et al., 2021). We omit details, such as the kernel projection matrix, for the convenience of reading. The output of FAVOR+ is defined as FAVOR+ $(Q, K, V) = \phi(Q) \times (\phi(K)^{\top} \phi(V)) \in \mathbb{R}^{T \times d}$ . As there is no need to construct the full $T \times T$ attention matrix, the FAVOR+ mechanism scales linearly.
+
+SEA consists of two main phases that are depicted in Fig. 2: (1) Attention Estimation (kernel-based), and (2) Sparse Attention Mask Generation and Subsequent Sparse Attention. In step
+
+
+
+Figure 2: Model diagram of our proposed linear attention method, SEA. SEA replaces the multi-head attention (MHA) mechanism from the teacher transformer model while preserving the attention matrix using knowledge distillation with only a small finetuning dataset. The deployed SEA model shows linear complexity at test time.
+
+(1), we estimate a compressed attention matrix $\hat{A}$ by fixing the size of one dimension to be K, where $K \ll T$ using the kernel-based method Performer and a simple decoder. In step (2), we build an attention mask from the values in $\hat{A}$ using one of our novel grouped top- $\hat{k}$ selection methods depicted in Fig. 4. We then interpolate the mask to form a sparse mask on the full attention matrix A, resulting in a sparse attention matrix $A^*$ . Then, we perform the sparse $A^*V$ multiplication to complete the procedure. By using a kernel-based estimator, as well as a sparse attention matrix, we take advantage of the complementary aspects of their representation spaces, as shown in previous work (Chen et al., 2021). Detailed model structure is shown in Fig. A.6.
+
+Attention Matrix Estimation. The intuition for $\hat{A}$ is to compress one dimension of the matrix in a way analogous to compressing one dimension of an image. We ultimately want the values within the compressed attention matrix to be similar to the full attention matrix in the same way that compressing one dimension of an image creates a distorted image which is semantically similar to the full image. For this task, we use the Performer (Choromanski et al., 2021) and a decoder, and treat the output as our estimated attention matrix. Once this attention matrix 'image' is obtained, it can be interpolated to decompress it into an approximation of the full attention matrix for distillation during training, or likewise, we may also perform top- $\hat{k}$ selection on the compressed matrix in order to construct a mask for sparse attention. Our attention matrix estimation starts by passing Q, K, $V_{\text{cat}}$ to the kernel-based linear attention method, Performer (Choromanski et al., 2021), where $V_{\text{cat}} = [V_I; V] \in \mathbb{R}^{T \times 2d}$ and $V_I$ is obtained by performing nearest neighbor interpolation on the identity matrix $I \in \mathbb{R}^{d \times d}$ to modify the first dimension resulting in a matrix $V_I \in \mathbb{R}^{T \times d}$ . We include $V_I$ in $V_{\text{cat}}$ because the output of Performer can be considered as the attention matrix when $V = I \in \mathbb{R}^{T \times T}$ , (e.g. $Q(K^{\top}I) = QK^{\top}$ ), as shown by Choromanski et al. (2021). Therefore, passing $V_I$ together with V may enable a more accurate estimation of the attention matrix by the decoder described in the next section. This results in the context encoding $C_{\text{perf}} = \text{FAVOR} + (Q, K, V_{\text{cat}}) \in \mathbb{R}^{T \times 2d}$
+
+**CNN Decoder.** We further transform Performer's estimated output $C_{\rm perf}$ with an MLP and CNN. We found the CNN to be a necessary part of SEA due to the fact that convolutions provide finegrained detail among local features, which is crucial for dynamically representing complex patterns present in the attention matrix as shown in Figs. 3 and 9. As we may consider the attention matrix as a kind of compressed 'image,' a CNN is a natural choice for the decoder. For the decoder, we begin by concatenating the local Performer encoding $C_{\rm perf}$ and the previous context V as
+
+
+
+Figure 3: Visualization of Input and Output of CNN Decoder
+
+ $V'_{\text{cat}} = [C_{\text{perf}}; V] \in \mathbb{R}^{T \times 3d}$ . We then apply an MLP $\mu : \mathbb{R}^{3d} \mapsto \mathbb{R}^{d'}$ to obtain an intermediate representation $Z = \mu(V'_{\text{cat}}) \in \mathbb{R}^{T \times d'}$ , where d' is a hyperparameter which decides the shared hidden state size. The resulting Z is used for estimating the attention matrix and also for the scalers $(S_{\text{mix}} \text{ and } S_{\text{prob}})$ described in Section 3.1.1. Then, before applying the CNN on Z, we apply MLP $\nu : \mathbb{R}^{d'} \mapsto \mathbb{R}^{Kc_h/c_s}$ , where $c_s$ and $c_h$ are respective width reduction and channel expansion factors. We transpose and reshape to make the output $\hat{Z} = \nu(Z)$ into $\mathbb{R}^{Hc_h \times T \times K/c_s}$ . Finally, we apply a 2D CNN $f_{\text{dec}}$ which treats the extra head dimension H as a channel dimension, resulting in the compressed attention matrix $\hat{A} = f_{\text{dec}}(\hat{Z}) \in \mathbb{R}^{T \times K}$ (for further details, see Appendix A.5.1). As the decoder $f_{\text{dec}}$ results in a fixed width $\hat{A}$ , we can successfully generate dynamic patterns with linear time and space complexity. The CNN $f_{\text{dec}}$ plays a significant role due to its ability to capture
+
+local patterns of the estimated compressed attention matrix. The depth of the CNN can be adjusted depending on the task, but we use a fixed 3-layer CNN in our experiments.
+
+**Grouped Top-**k **Selection.** Once we have the compressed attention matrix A, we must select k critical values which will be used in the final $T \times T$ sparse attention. However, since our top- $\hat{k}$ selection is held in the compressed $\hat{A}$ , we transform k to $\hat{k}$ , which refers to the number of selected values in the compressed $T \times K$ attention space. For this, we propose four novel methods for top-k selection: per-query, per-head, per-batch, and causal-per-batch, where each method performs the top-k selection along different dimensions, as shown in Fig. 4. As noted in Section 3.1 (preliminaries), we have previously omitted the head dimension H from all matrices. However in this paragraph, for clarity, we include the head dimension H so that $\hat{A} \in \mathbb{R}^{H \times T \times K}$ . For *per-query*, *per-head*, and per-batch, we gradually extend the dimension of the group, starting from the last dimension of $\hat{A}$ , so that top- $\hat{k}$ selection is held in $\mathbb{R}^K$ , $\mathbb{R}^{T \times K}$ , and $\mathbb{R}^{H \times T \times K}$ space for each grouping method, respectively. Consequently, $\hat{k}$ is also adjusted to $\hat{k}_{\text{per-query}} = \hat{k}$ , $\hat{k}_{\text{per-head}} = T \times \hat{k}$ , and $\hat{k}_{\text{per-batch}} = H \times T \times \hat{k}$ . Finally, we propose *causal-per-batch* for causal attention, which performs top- $\hat{k}$ in $\mathbb{R}^{H \times K}$ space, with $\hat{k}_{\text{causal-per-batch}} = H \times \hat{k}$ . For this, we transpose $\hat{A}$ to $\mathbb{R}^{T \times H \times K}$ and group the last two dimensions without the T dimension, to avoid temporal information exchange across the time dimension. In our experiments, we use *causal-per-batch* which shows strong performance in our ablation study on GLUE-MNLI (K = 128, 5 epochs), as shown in Table 1.
+
+Linear Sparse Attention Mask Generation. With the obtained Table 1: Ablation study on $\hat{A}$ , we generate a sparse formatted binary mask $M^* \in \{0,1\}^{T \times T}$ . For this, we take the following two steps: 1) Performing our proposed grouped top-k selection from the compressed $\hat{A}$ to generate a binary mask $\hat{M} \in \{0,1\}^{T \times K}$ , and **2**) interpolating $\hat{M}$ to the sparse formatted $M^* \in \{0,1\}^{T \times T}$ . For the grouped topk selection, we set k as a hyperparameter, which will determine the number of selected indices in each block of the binary mask $M^*$ depending on the top- $\hat{k}$ strategy and the attention matrix size. Note that each selection strategy has a different block (group) shape as depicted in Fig. 4. However, since we perform top- $\hat{k}$ on the smaller $\hat{A} \in \mathbb{R}^{T \times K}$ , we must convert k to a compressed $\hat{k}$ with $\hat{k} = \max(1, \text{round}(k * K/T))$ . Once we obtain the compressed mask $\hat{M} \in \{0,1\}^{T \times K}$ , we interpolate it into the sparse formatted $M^* \in \{0,1\}^{T \times T}$ . In the case of a very long sequence, it is possible that $\max(1, \operatorname{round}(k*K/T))$ evaluates to 1, and the subsequent in-
+
+grouped top- $\hat{k}$ modes
+
+| Grouping Method | k = 7 | k = 13 | k = 25 |
+|------------------|-------|-------------|-----------|
+| per-query | 77.68 | 81.45 | 83.55 |
+| per-head | 79.03 | 82.71 | 83.71 |
+| per-batch | 80.03 | 82.94 | 84.14 |
+| causal per-batch | 80.55 | 83.49 | 84.19 |
+| (a) Per-query | | T (b) Per- | head |
+| H — K — | | T T | К |
+| (c) Per-batch | ( | d) Causal F | Per-batch |
+
+Figure 4: Visualization of the Group of each top- $\hat{k}$ method.
+
+terpolation (pixel duplications) to create $M^*$ will become a function of T and no longer have linear complexity, as this results in a block larger than k being computed for $M^*$ . Therefore, in order to avoid this, we enforce the number of pixel duplications in the block of $M^*$ to be $\min(k, \lceil T/K \rceil)$ , and uniformly space the resulting pixels within the larger block. Since we only need to check the indices where the values are 1 in the compressed $\hat{M}$ and put 1 in the corresponding indices in $M^*$ , the interpolation has **linear complexity**. For further details, please refer to Appendix A.5.3.
+
+**Sparse Attention Mechanism.** We calculate a re-weighted sparse attention probability $A^*$ by first applying a sparse masked matrix multiplication $\rho$ , where $\rho(Q, K^{\perp}, M^*) = QK^{\perp} \odot M^*$ followed by a softmax operation $\sigma$ . Note than the Q and K matrices which are inputs to $\rho$ are the same Qand K which were inputs to the Performer. We then re-scale the weights using $s_{\text{prob}} \in \mathbb{R}^T$ (defined later in this paragraph), so that $A^* = s_{\text{prob}} \odot \sigma(\rho(Q, K^\top, M^*)) \in \mathbb{R}^{T \times T}$ . Note that $\rho$ is a sparse operation and $M^* \in \{0,1\}^{T \times T}$ is the previously obtained sparse binary mask using FlatCSR. The output of $\rho$ only needs to store the non-zero values and indices, which are the indices where $M^*$ has value 1. The softmax $\sigma(\rho(Q, K^{\top}, M^*))$ calculates the softmax probability of non-zero values in the sparse input. After applying $\sigma$ , each row of the sparse attention matrix $A^*$ will sum to 1, therefore, due to the high sparsity of $M^*$ , the resulting non-zero values after $\sigma$ will be higher than the ground truth attention matrix from the teacher. To account for this effect, we scale the attention probability using a learned weight $s_{\text{prob}} \in \mathbb{R}^T$ , where $s_{\text{prob}} = f_{\text{prob}}(\boldsymbol{Z})$ ( $\boldsymbol{Z}$ was previously defined
+
+Figure 5: Fig. 5a shows how 9 variants of our model perform when comparing perplexity vs. computation.
+
+Baseline model marker sizes correspond to increasing numbers of buckets (Reformer) or random projections (Performer). In all cases, SEA produces the best perplexity score. Fig. 5b Validation curves for SEA and baseline efficient attention methods. SEA converges much faster compared to Performer and Reformer.
+
+Table 2: Comparison of perplexity score on Wikitext2 with various scales of OPT model. We trained the same number of steps (10k) for each method. We used k = 64; K = 64 for SEA on OPT-125M, 350M and 1.3B.
+
+(a) Computational costs vs. perplexity ↓ on Wikitext2 with OPT-125m.
+
+| OPT-125M | | | OPT-350M | | | OPT-1.3B | | | |
+|-----------------------|------------------------------|-----------|---------------|------------------------------|--------------|---------------|--------------------------------|-------------|---------------|
+| Method | PPL.↓ | Mem. ↓ | Lat. ↓ | PPL.↓ | Mem. ↓ | Lat. ↓ | PPL.↓ | Mem. ↓ | Lat. ↓ |
+| Vanilla | 29.2 | 408 | 4.88 | 19.3 | 536 | 6.71 | 13.9 | 1120 | 16.49 |
+| Reformer Performer | 63.9 (+34.7) 49.8 (+20.6) | 902 51 | 10.90 1.21 | 58.2 (+38.9) 36.6 (+17.3) | 1195 60.5 | 14.76 1.82 | 49.02 (+35.12) 30.6 (+16.7) | 2406 137 | 31.37 5.71 |
+| SEA (Ours) | 26.0 (-3.2) | 187 | 6.76 | 19.5 (+0.2) | 241 | 9.43 | 13.5 (-0.4) | 499 | 21.57 |
+
+Figure 6: Dynamically ad-
+
+(b) OPT-125M validation curves
+
+justing k after training.
+
+in Section 3.1 CNN Decoder) and $f_{\text{prob}}$ is a linear projection from $\mathbb{R}^{d'}\mapsto\mathbb{R}$ followed by a sigmoid activation function. Since the sparse calculation of $\rho$ is only performed in the indices where $M^*$ has 1, the complexity follows that of $M^*$ , which is $\mathcal{O}(T)$ . The resulting $A^*$ matrix remains in a sparse matrix format. Afterward, we calculate the context feature as $C = A^*V \in \mathbb{R}^{T \times d}$ which has linear complexity due to the linear complexity of $A^*$ and V. The output C is now stored in a dense matrix format.
+
+FlatCSR: A Modified Compressed Sparse Row (CSR) Format. We introduce our novel sparse operation, FlatCSR, which is an efficient implementation of the previously described sparse attention mask generation and attention operations utilizing grouped top- $\hat{k}$ selection. Our initial attempt for the sparse operations in our model utilized the Coordinate List (COO) sparse format. However, COO is not ideal because it stores every coordinate of each non-zero point and it does not take advantage of the structure provided by our grouped top-k selection, as shown in Table 3. Therefore, we eventually adopted the CSR sparse format instead of COO, as it uses less memory and schedules the per-row computations wisely using pre-constructed rows from our grouped top-k selection. We present a detailed explanation of FlatCSR and further discussions in Appendix A.5.2.
+
+Output Calculation. For the final output, instead of relying solely on the sparse attention operation previously described, we combine the output of the Performer, and the sparse attention operation to improve the ability of the highly sparse attention matrix to look up global information.
+
+Table 3: Comparison of different sparse matrix formats on random inputs
+
+| | | • • |
+|----------------|---------------|----------------|
+| FlatCSR (Ours) | 11.4 (15.06%) | 817.5 (68.46%) |
+| COO | 75.66 (100%) | 1194 (100%) |
+| Method | Latency (ms) | Memory (MB) |
+
+The final output $C_{\text{sea}}$ is computed as a summation of two terms C and $C_{\text{avg}}$ , each obtained from $\pmb{A}^*$ and $\hat{\pmb{A}}$ respectively. First, we calculate the importance score of each token by averaging every row of $\hat{\pmb{A}}$ , resulting in $\hat{\pmb{i}} = \frac{1}{T} \sum_{t=0}^T \hat{\pmb{A}}_{t,:} \in \mathbb{R}^K$ . We then interpolate $\hat{\pmb{i}}$ to $\pmb{i} \in \mathbb{R}^T$ and subsequently perform weighted average pooling of $C_{\text{avg}} = i^{\top} V \in \mathbb{R}^d$ . In causal attention, this global pooled context feature $C_{\text{avg}}$ is replaced with an accumulated average of the tokens in V such that $m{V}_j =
+rac{1}{j} \sum_{i=1}^j m{V}_i$ . We mix $m{C}_{ ext{avg}}$ and $m{C}$ using learned weight $m{s}_{ ext{mix}} = f_{ ext{pool}}(m{Z}) \in \mathbb{R}^T$ , with $f_{ ext{pool}}$ composed of a linear transformation and sigmoid activation $f_{\text{pool}}: \mathbb{R}^{d'} \mapsto \mathbb{R}$ . $C_{\text{sea}}$ is calculated as $C_{\text{sea}} = s_{\text{mix}} \odot C + (1 - s_{\text{mix}}) \odot C_{\text{avg}}^{\top}$
+
+For training SEA, we first replace the attention mechanism of a pretrained teacher transformer model with our SEA attention mechanism. Then, we use knowledge distillation (KD) (Hinton et al., 2015) to train the newly added SEA attention parameters while adapting the original weights to SEA attention. Our training scheme is similar to previous transformer KD work (Jiao et al., 2020) since we approximate the context features and attention matrices. However, we further add an objective
+
+
+
+Figure 7: Fig. 7a shows the comparison of the trade-off between computational cost (latency and memory usage) and accuracy on each GLUE subset with BERT-base. 7a (column 1): Overall performance trade-off over three subsets. We average metrics weighted with dataset size. 7a (column 2-4): Performance trade-off per each GLUE subset. Baseline model marker sizes correspond to increasing numbers of buckets or random projections. Fig. 7b shows the accuracy trade-off of dynamically adjusting k after training.
+
+for the compressed estimated attention matrix $\hat{A}$ to match the teacher attention matrix. With $\mathcal{L}^{(i)}$ signifying a loss for layer i, our overall training loss $\mathcal{L}_{sea}$ is given as the following, with each term described in the following paragraph:
+
+$$\mathcal{L}_{\text{sea}} = \frac{1}{L} \left( \sum_{i=1}^{L} \mathcal{L}_{\text{approx}}^{(i)} + \mathcal{L}_{\text{prob}}^{(i)} + \mathcal{L}_{\text{context}}^{(i)} + \mathcal{L}_{\text{kd}}^{(i)} \right) + \mathcal{L}_{\text{kd\_task}} + \mathcal{L}_{\text{task}}$$
+(1)
+
+To calculate $\mathcal{L}_{approx}$ , we perform nearest neighbor interpolation to the estimated attention matrix $\hat{A}_i$ , and get $A'_i \in \mathbb{R}^{T \times T}$ in order to match the shape of the teacher attention matrix $\tilde{A}_i \in \mathbb{R}^{T \times T}$ . Then we apply both KL divergence and an MSE loss between $A'_i$ and $\tilde{A}_i$ , $\mathcal{L}_{approx}^{(i)} = \mathbb{KL}(A'_i, \tilde{A}_i) + \text{MSE}(A'_i, \tilde{A}_i)$ . Next, we calculate $\mathcal{L}_{prob}^{(i)} = \mathbb{KL}(A_i, \tilde{A}_i) + \text{MSE}(A_i, \tilde{A}_i)$ which minimizes the error between the student's $A_i$ and teacher's $\tilde{A}_i$ attention matrices. For $\mathcal{L}_{prob}^{(i)}$ , we calculate the dense student attention $A_i = \sigma(Q_i K_i^\top)$ during training. We then add $\mathcal{L}_{context}^{(i)} = \text{MSE}(C_{sea}^{(i)}, \tilde{C}^{(i)})$ to minimize the error between the attention context feature $C_{sea}^{(i)}$ and teacher context feature $\tilde{C}^{(i)} \in \mathbb{R}^{T \times d}$ . Next, to minimize the error of each transferrance and transferrance and transferrance and MLP), we gather the outputs of each layer $O_{sea}^{(i)}$ , $\tilde{O}^{(i)} \in \mathbb{R}^{N \times T \times H * d}$ for SEA and the teacher, respectively, and calculate $\mathcal{L}_{kd}^{(i)} = \text{MSE}(O_{sea}^{(i)}, \tilde{O}^{(i)})$ . The loss for training the i-th layer is $\mathcal{L}_{sea}^{(i)}$ and is a weighted sum of each layerwise loss such that $\mathcal{L}_{sea}^{(i)} = \mathcal{L}_{approx}^{(i)} + \mathcal{L}_{prob}^{(i)} + \mathcal{L}_{context}^{(i)} + \mathcal{L}_{kd}^{(i)}$ . We omit the weight term on each sub-task loss for simplicity; details are in Appendix A.4.1. We then calculate knowledge distillation loss from the model output logits $\mathcal{L}_{kd.task} = \mathbb{KL}(P, \tilde{P})$ , where $P \in \mathbb{R}^z$ is model output logit. Finally, we sum together the average layer-wise loss and the downstream task loss $\mathcal{L}_{task}$ into the SEA training loss given in Eq. (1).
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+# Introduction
+
+Compositional optimization (CO) problems deal with the minimization of the composition of functions. A standard CO problem takes the form
+
+$$\min_{x \in \mathbb{R}^d} f(g(x)) \quad \text{with} \quad g(x) := \mathbb{E}_{\zeta \sim \mathcal{D}_g}[g(x;\zeta)], \tag{1}$$
+
+where x ∈ R d is the optimization variable, f : R dg → R and g : R d → R dg are smooth functions, and ζ ∼ Dg represents a stochastic sample of g(·) from distribution Dg. CO finds applications in a broad range of machine learning applications, including but not limited to distributionally robust optimization (DRO) [1], meta-learning [2], phase retrieval [3], portfolio optimization [4], and reinforcement learning [5].
+
+In this work, we focus on a more challenging version of the CO problem (1) that often arises in the DRO formulation [6]. Specifically, the problems that jointly minimize the summation of a compositional and a non-compositional objective. DRO has recently garnered significant attention from the research community because of its capability of handling noisy labels [7], training fair machine learning models [1], imbalanced [8] and adversarial data [9]. A standard approach to solve DRO is to utilize primal-dual algorithms [10] that are inherently slow because of a large number of stochastic constraints. The CO formulation enables the development of faster (dual-free) primal-only DRO algorithms [6]. The majority of existing works to solve CO problems consider a centralized setting wherein all the data samples are available on a single server. However, modern large-scale machine-learning applications are characterized by the distributed collection of data by multiple clients [11]. This necessitates the development of distributed algorithms to solve the DRO problem.
+
+Federated learning (FL) is a distributed learning paradigm that allows clients to solve a joint problem in collaboration with a server while keeping the data of each client private [12]. The clients act as computing units where within each communication round, the clients perform multiple updates while the server orchestrates the parameter sharing among clients. Numerous FL algorithms exist in the literature to tackle standard (non-compositional) optimization problems [13–19]. However, there is a lack of efficient implementations when it comes to distributed CO problems. The major challenges in developing FL algorithms for solving the CO problem are:
+
+[C1]: The compositional structure of the problem leads to biased stochastic gradient estimates and this bias is amplified during local updates, which makes the theoretical analysis of the gradient-based algorithms intractable [20].
+
+[C2]: Typically, data distribution at each client is different, referred to as data heterogeneity. Heterogeneously distributed compositional objective results in client drift during local updates that lead to divergence of federated CO algorithms. This is in sharp contrast to the standard FedAvg for non-CO objectives where client drift can be controlled during the local updates [15].
+
+[C3]: A majority of algorithms for solving CO rely on accuracy-dependent large batch gradients where the batch size depends on the desired solution accuracy, which is not practical from an implementation point of view [6, 21, 22].
+
+These challenges naturally lead to the following question:
+
+Can we develop FL algorithms that tackle [C1] − [C3] to solve CO in a distributed setting?
+
+In this work, we address the above question and develop a novel FL algorithm to solve typical versions of the CO problem that arise in DRO (Section 2). The major contributions of our work are:
+
+- We for the first time present a negative result that establishes that the vanilla FedAvg (customized to CO) is incapable of solving the CO problems as it leads to bias amplification during the local updates. This shows that additional communication/processing is required by FedAvg to mitigate the bias in the local gradient estimation.
+- We develop FedDRO, a novel FL algorithm for solving problems with both compositional and non compositional non-convex objectives at the same time. To the best of our knowledge, such an algorithm has been absent from the open literature so far. Importantly, FedDRO addresses the above-mentioned challenges by developing several key innovations in the algorithm design.
+ - FedDRO addresses [C1] by designing a communication strategy that utilizes the specific problem structure resulting from the DRO formulation and allows us to control the gradient bias. Specifically, FedDRO utilizes the fact that the compositional functions g(·) are often low-dimensional embeddings in the DRO formulation (see Examples in Section 2.1) and can be shared without incurring significant communication costs.
+ - To address [C2], we design the local updates at each client so that the client drift is bounded. Our analysis captures the effect of data heterogeneity on the performance of FedDRO.
+ - To address [C3], we utilize a hybrid momentum-based estimator to learn the compositional embedding and combine it with a stochastic gradient (SG) estimator to conduct the local updates. This construction allows us to circumvent the need to compute large accuracy-dependent batch sizes for computing the gradients and the compositional function evaluations.
+
+• We establish the **convergence** of FedDRO and show that to achieve an $\epsilon$ -stationary point, FedDRO requires $\mathcal{O}(\epsilon^{-2})$ samples while achieving **linear speed-up** with the number of clients, i.e., requiring $\mathcal{O}(K^{-1}\epsilon^{-2})$ samples per client. Moreover, FedDRO requires sharing of $\mathcal{O}(\epsilon^{-3/2})$ high-dimensional parameters and $\mathcal{O}(K^{-1}\epsilon^{-2})$ low dimensional embeddings per client.
+
+**Notations.** The expected value of a random variable (r.v) X is denoted by $\mathbb{E}[X]$ . Conditioned on an event $\mathcal{F}$ the expectation of a r.v X is denoted by $\mathbb{E}[X|\mathcal{F}]$ . We denote by $\mathbb{R}$ (resp. $\mathbb{R}^d$ ) the real line (resp. the d dimensional Euclidean space). We denote by $[K] := \{1, \ldots K\}$ . The notation $\|\cdot\|$ defines a standard $\ell_2$ -norm. For a set B, |B| denotes the cardinality of B. We use $\xi \sim \mathcal{D}_h$ and $\zeta \sim \mathcal{D}_g$ to denote the stochastic samples of functions $h(\cdot)$ and $g(\cdot)$ from distributions $\mathcal{D}_h$ and $\mathcal{D}_g$ , respectively. A batch of samples from $h(\cdot)$ (resp. $g(\cdot)$ ) is denoted by $b_h$ (resp. $b_g$ ). Moreover, joint samples of $h(\cdot)$ and $g(\cdot)$ are denoted by $\bar{\xi} = \{b_h, b_g\}$ . We represent by $\bar{x}$ the empirical average of a sequence of vectors $\{x_k\}_{k=1}^K$ .
+
+In this work, we focus on a general version of the CO problem defined in (1). We consider the following problem that often arises in DRO (see Section 2.1) in a distributed setting with K clients
+
+$$\inf_{x \in \mathbb{R}^d} \left\{ \Phi(x) := h(x) + f(g(x)) \right\} \text{ with } h(x) := \frac{1}{K} \sum_{k=1}^K h_k(x) \& g(x) := \frac{1}{K} \sum_{k=1}^K g_k(x), \tag{2}$$
+
+where each client $k \in [K]$ has access to the local functions $h_k : \mathbb{R}^d \to \mathbb{R}$ and $g_k : \mathbb{R}^d \to \mathbb{R}^{d_g}$ while $f(\cdot)$ is same as (1). The local functions $h_k(\cdot)$ and $g_k(\cdot)$ at each client $k \in [K]$ are: $h_k(x) = \mathbb{E}_{\xi_k \sim \mathcal{D}_{h_k}}[h_k(x; \xi_k)]$ and $g_k(x) = \mathbb{E}_{\zeta_k \sim \mathcal{D}_{g_k}}[g_k(x; \zeta_k)]$ and where $\xi_k \sim \mathcal{D}_{h_k}$ (resp. $\zeta_k \sim \mathcal{D}_{g_k}$ ) represents a sample of $h_k(\cdot)$ (resp. $g_k(\cdot)$ ) from distribution $\mathcal{D}_{h_k}$ (resp. $\mathcal{D}_{g_k}$ ). Moreover, the data at each client is heterogeneous, i.e., $\mathcal{D}_{h_k} \neq \mathcal{D}_{h_\ell}$ and $\mathcal{D}_{g_k} \neq \mathcal{D}_{g_\ell}$ for $k \neq \ell$ and $k, \ell \in [K]$ .
+
+In comparison to the basic CO in (1), (2) is significantly challenging, first, because of the presence of both compositional and non-compositional objectives and second, because of the distributed nature of the compositional function $g(\cdot)$ .
+
+Remark 1 (Comparison to [21] and [23]). Note that formulation (2) is significantly different than the setting considered in [21, 23]. Specifically, our formulation considers a practical setting where the compositional functions are distributed across agents, i.e., the function is $g = 1/K \sum_{k=1}^{K} g_k(x)$ . In contrast, [21, 23] consider a setting with objective $\frac{1}{k} \sum_{k=1}^{K} f_k(g_k(\cdot))$ , note here that the compositional function is local to each agent. This implies that algorithms developed in [21,23] cannot solve problem (2). Importantly, problem (2) models realistic FL training settings while being more challenging compared to [21,23] since in (2) the data heterogeneity of the inner problem also plays a role in the convergence of the FL algorithm. Please see the discussion in Appendix A.1 for more details.
+
+# Method
+
+In this section, we discuss different DRO formulations that can be efficiently solved using CO [6]. DRO problem with a set of m training samples denoted as $\{\zeta_i\}_{i=1}^m$ is
+
+$$\min_{x \in \mathbb{R}^d} \max_{\mathbf{p} \in P_m} \sum_{i=1}^m p_i \ell(x; \zeta_i) - \lambda D_*(\mathbf{p}, \mathbf{1}/m)$$
+(3)
+
+where $x \in \mathbb{R}^d$ is the model parameter, $P_m := \{\mathbf{p} \in \mathbb{R}^m : \sum_{i=1}^m p_i = 1, p_i \geq 0\}$ is m-dimensional simplex, $D_*(\mathbf{p}, \mathbf{1}/m)$ is a divergence metric that measures distance between $\mathbf{p}$ and uniform probability $\mathbf{1}/m \in \mathbb{R}^m$ , and $\ell(x, \zeta_i)$ denotes the loss on sample $\zeta_i$ , $\rho$ is a constraint parameter, and $\lambda$ is a hyperparameter. Next, we discuss
+
+two popular reformulations of (3) in the form of CO problems.
+
+**DRO with KL-Divergence.** Problem (3) is referred to as a KL-regularized DRO when the distance metric $D_*(\mathbf{p}, \mathbf{1}/m)$ is the KL-Divergence, i.e., we have $D_*(\mathbf{p}, \mathbf{1}/m) = D_{\mathrm{KL}}(\mathbf{p}, \mathbf{1}/m)$ with $D_{\mathrm{KL}}(\mathbf{p}, \mathbf{1}/m) := \sum_{i=1}^m p_i \log(p_i m)$ . For this case, an equivalent reformulation of (3) is
+
+$$\min_{x \in \mathbb{R}^d} \log \left( \frac{1}{m} \sum_{i=1}^m \exp\left( \frac{\ell(x; \zeta_i)}{\lambda} \right) \right), \tag{4}$$
+
+which is a CO with $g(x) = 1/m \sum_{i=1}^{m} \exp(\ell(x; \zeta_i)/\lambda), f(g(x)) = \log(g(x))$ and h(x) = 0.
+
+**DRO with** $\chi^2$ - **Divergence.** Similar to KL-regularized DRO, (3) is referred to as a $\chi^2$ -regularized DRO when $D_*(\mathbf{p}, \mathbf{1}/m)$ is the $\chi^2$ -Divergence, i.e., we have $D_*(\mathbf{p}, \mathbf{1}/m) = D_{\chi^2}(\mathbf{p}, \mathbf{1}/m)$ with $D_{\chi^2}(\mathbf{p}, \mathbf{1}/m) := m/2 \sum_{i=1}^m (p_i - 1/m)^2$ . For this case, an equivalent reformulation of (3) is
+
+$$\min_{x \in \mathbb{R}^d} -\frac{1}{2\lambda m} \sum_{i=1}^m \left( \ell(x; \zeta_i) \right)^2 + \frac{1}{2\lambda} \left( \frac{1}{m} \sum_{i=1}^m \ell(x; \zeta_i) \right)^2 \tag{5}$$
+
+which is again a CO with $g(x) = 1/m \sum_{i=1}^{m} \ell(x; \zeta_i)$ , $f(g(x)) = g(x)^2/2\lambda$ and $h(x) = -\frac{1}{2\lambda m} \sum_{i=1}^{m} \left(\ell(x; \zeta_i)\right)^2$ . Note that both (4) and (5) can be equivalently restated in the practical FL setting of (2) if the overall samples are shared across multiple clients with each client having access to a subset of samples.
+
+**Related work.** Please see Table 1 for a comparison of current approaches to solve CO problems in distributed settings. For a detailed review of centralized and distributed non-convex CO and DRO problems, please see Appendix A. Here, we point out some drawbacks of the current approaches to solving federated CO problems:
+
+- None of the current works guarantee linear speedup with the number of clients [6, 21, 23, 24].
+- Utilize complicated multi-loop algorithms with momentum or VR-based updates [24] that sometime require computation of large batch size gradients [6] to guarantee convergence. Such algorithms are not preferred in practical implementations.
+- Consider a restricted setting where the compositional objective is not distributed among nodes [21, 23].
+ Importantly, the algorithms developed therein cannot solve the problem considered in our work (see Appendix A.1).
+
+Our work addresses all these issues and develops, FedDRO, the first simple SGD-based FL algorithm to tackle CO problems with the distributed compositional objective. Please see Table 1 for a comparison of the above works.
+
+In this section, we introduce the assumptions, definitions, and preliminary lemmas.
+
+**Definition 3.1** (Lipschitzness). For all $x_1, x_2 \in \mathbb{R}^d$ , a differentiable function $\Phi : \mathbb{R}^d \to \mathbb{R}$ is: Lipschitz smooth if $\|\nabla \Phi(x_1) - \nabla \Phi(x_2)\| \le L_{\Phi} \|x_1 - x_2\|$ for some $L_{\Phi} > 0$ ; Lipschitz if $\|\Phi(x_1) - \Phi(x_2)\| \le B_{\Phi} \|x_1 - x_2\|$ for some $B_{\Phi} > 0$ and; Mean-Squared Lipschitz if $\mathbb{E}_{\xi} \|\Phi(x_1; \xi) - \Phi(x_2; \xi)\|^2 \le B_{\Phi}^2 \|x_1 - x_2\|^2$ for some $B_{\Phi} > 0$ .
+
+We make the following assumptions on the local and global functions in problem (2).
+
+Assumption 1 (Lipschitzness). The following holds
+
+1. The functions $f(\cdot)$ , $h_k(\cdot)$ , $g_k(\cdot)$ for all $k \in [K]$ are differentiable and Lipschitz-smooth with constants $L_f, L_h, L_g > 0$ , respectively.
+
+Table 1: Comparison with the existing works. Here, CO-ND refers to CO with a non-distributed compositional part (see Remark 1). CO + Non-CO refers to problems with both CO and Non-CO objectives. VR refers to variance reduction. (I) and (O) refers to the inner and outer loop, respectively.
+
+\*Theoretical guarantees for GCIVR exist only for the finite sample setting with m total network-wide samples.
+
+| ALGORITHM | SETTING | UPDATE | BATCH-SIZES | CONVERGENCE |
+|------------------|-------------|----------------------|------------------------------|--------------------------------------------------------------|
+| ComFedL [21] | CO-ND | $\operatorname{SGD}$ | $\mathcal{O}(\epsilon^{-2})$ | $\mathcal{O}(\epsilon^{-4})$ |
+| Local-SCGDM [23] | CO-ND | Momentum SGD | $\mathcal{O}(1)$ | $\mathcal{O}(\epsilon^{-2})$ |
+| FedNest [24] | Bilevel | VR | $\mathcal{O}(1)$ | $\mathcal{O}(\epsilon^{-2})$ |
+| GCIVR* [6] | CO + Non-CO | VR | $\sqrt{m}$ (I), $m$ (O) | $\mathcal{O}(\min\{\sqrt{m}\epsilon^{-1},\epsilon^{-1.5}\})$ |
+| FedDRO (Ours) | CO + Non-CO | SGD | $\mathcal{O}(1)$ | $\mathcal{O}(K^{-1}\epsilon^{-2})$ |
+
+2. The function $f(\cdot)$ is Lipschitz with constant $B_f > 0$ and $g_k(\cdot)$ is mean-squared Lipschitz for all $k \in [K]$ with constant $B_g > 0$ .
+
+Next, we introduce the variance and heterogeneity assumptions.
+
+**Assumption 2** (Unbiased Gradient and Bounded Variance). The stochastic gradients and function evaluations of the local functions at each client are unbiased and have bounded variance, i.e.,
+
+$$\mathbb{E}_{\xi_k}[\nabla h_k(x;\xi_k)] = \nabla h_k(x), \ \mathbb{E}_{\zeta_k}[\nabla g_k(x;\zeta_k)] = \nabla g_k(x), \ \mathbb{E}_{\zeta_k}[g_k(x;\zeta_k)] = g_k(x),$$
+$$\mathbb{E}_{\zeta_k}[\nabla g_k(x;\zeta_k)\nabla f(y)] = \nabla g_k(x)\nabla f(y)$$
+
+and
+$$\mathbb{E}_{\xi_k} \|\nabla h_k(x; \xi_k) - \nabla h_k(x)\|^2 \le \sigma_h^2,$$
+
+ $\mathbb{E}_{\zeta_k} \|\nabla g_k(x; \zeta_k) - \nabla g_k(x)\|^2 \le \sigma_q^2, \ \mathbb{E}_{\zeta_k} \|g_k(x; \zeta_k) - g_k(x)\|^2 \le \sigma_q^2,$
+
+for some $\sigma_h, \sigma_q > 0$ and for all $x \in \mathbb{R}^d$ and $k \in [K]$ .
+
+**Assumption 3** (Bounded Heterogeneity). The heterogeneity $h_k(\cdot)$ and $g_k(\cdot)$ is characterized as
+
+$$\sup_{x \in \mathbb{R}^d} \|\nabla h_k(x) - \nabla h(x)\|^2 \le \Delta_h^2 \quad \text{and} \quad \sup_{x \in \mathbb{R}^d} \|\nabla g_k(x) - \nabla g(x)\|^2 \le \Delta_g^2,$$
+
+for some $\Delta_h, \Delta_g > 0$ for all $k \in [K]$ .
+
+A few comments regarding the assumptions are in order. We note that the above assumptions are commonplace in the context of non-convex CO problems. Specifically, Assumption 1 is required to establish Lipschitz smoothness of the $\Phi(\cdot)$ (see Lemma 3.1) and is standard in the analyses of CO problems [5,20,25]. Assumption 2 captures the effect of stochasticity in the gradient and function evaluations of the CO problem while Assumption 3 characterizes the data heterogeneity among clients. We note that these assumptions are standard and have been utilized in the past to establish the convergence of many FL non-CO algorithms [15,17,18,26,27].
+
+**Lemma 3.1** (Lipschitzness of $\Phi$ ). Under Assumption 1 the compositional function, $\Phi(\cdot)$ , defined in (2) is Lipschitz smooth with constant: $L_{\Phi} := L_h + B_f L_g + B_q^2 L_f > 0$ .
+
+Lemma 3.1 establishes Lipschitz smoothness (Definition 3.1) of the compositional function $\Phi(\cdot)$ . In general, $\Phi(\cdot)$ is a non-convex function, and therefore, we cannot expect to globally solve (2). We instead rely on finding approximate stationary points of $\Phi(\cdot)$ defined next.
+
+**Definition 3.2** ( $\epsilon$ -stationary point). A point x generated by a stochastic algorithm is an $\epsilon$ -stationary point of a differentiable function $\Phi(\cdot)$ if $\mathbb{E}\|\nabla\Phi(x)\|^2 \leq \epsilon$ , where the expectation is taken with respect to the stochasticity of the algorithm.
+
+```
+1: Input: Parameters: \{\eta^t\}_{t=0}^{T-1},\ I
+2: Initialize: x_k^0=\bar{x}^0,\ y_k^0=\bar{y}^0
+ 3: for t = 0 to T - 1 do
+ for k = 1 to K do
+ 4:
+ \begin{aligned} \text{Update:} \begin{cases} \text{Compute } \nabla \Phi_k(x_k^t) \text{ using (6)} \\ x_k^{t+1} &= x_k^t - \eta^t \nabla \Phi_k(x_k^t) \\ y_k^{t+1} &= g_k(x_k^{t+1}) \end{cases} \end{aligned}
+ 5:
+ if t + 1 \mod I = 0 then
+ 6:
+ [Case I] Share: \left\{x_k^{t+1} = \bar{x}^{t+1}\right\}
+ [Case II] Share: \begin{cases} x_k^{t+1} = \bar{x}^{t+1} \\ y_k^{t+1} = g_k(\bar{x}^{t+1}) \\ y_{\iota}^{t+1} = \bar{y}^{t+1} \end{cases}
+ 7:
+ end if
+ 8:
+ 9:
+ end for
+10: end for
+```
+
+**Definition 3.3** (Sample and Communication Complexity). The sample complexity is defined as the total number of (stochastic) gradient and function evaluations required to achieve an $\epsilon$ -stationary solution. Similarly, communication complexity is defined as the total communication rounds between the clients and the server required to achieve an $\epsilon$ -stationary solution.
+
+In this section, we first establish the incapability of vanilla FedAvg to solve CO problems in general. Then, we design a communication-efficient FL algorithm to solve the non-convex CO problem.
+
+In this section, we show that vanilla FedAvg is not suitable for solving federated CO problems of form (2). To establish this, we consider a simple deterministic setting with h(x) = 0. For this setting, the local gradients of $\Phi(\cdot)$ are estimated as
+
+$$\nabla \Phi_k(x) = \nabla g_k(x_k) \nabla f(y_k), \tag{6}$$
+
+where the sequence $y_k$ represents the local estimate of the inner function g(x). To solve the above problem in a federated setup, we consider two candidate versions of FedAvg described in Case I and II of Algorithm 1. Similar to vanilla FedAvg, each agent performs multiple local updates within each communication round (see Step 5 of Algorithm 1). Moreover, since $g(x) := 1/k \sum_{k=1}^k g_k(x)$ with each agent $k \in [K]$ having access to only the local copy $g_k(\cdot)$ , estimating $g(\cdot)$ locally within each communication round is not feasible. Therefore, each agent utilizes $y_k = g_k(x)$ as the local estimate of the inner function $g(\cdot)$ . For communication, we consider two protocols. In the first setting, after I local updates, in each communication round the agents share the locally updated parameters with the server and receive the aggregated parameter from the server (see Case I in Step 7). In the second setting, in addition to the locally updated parameters the agents also share their local function evaluations $y_k^t = g_k(x_k^t)$ with the server and receive the aggregated embedding $\bar{y}^t$ from the server. This step is utilized to improve the local estimates of $g(\cdot)$ (see Case II in Step 7). The algorithm executes for a total of |T/I| communication rounds.
+
+In the following, we show that Algorithm 1 is not a good choice to solve the federated CO problem presented in (2) even in the simple deterministic setting with h(x) = 0.
+
+**Theorem 4.1** (Vanilla FedAvg: Non-Convergence for CO). There exist functions $f(\cdot)$ and $g_k(\cdot)$ for $k \in [K]$ satisfying Assumptions 1, 2, and 3, and an initialization strategy such that for a fixed number of local updates I > 1, and for any $0 < \eta^t < C_{\eta}$ for $t \in \{0, 1, ..., T - 1\}$ where $C_{\eta} > 0$ is a constant, the iterates generated by Algorithm 1 under both Cases I and II do not converge to the stationary point of $\Phi(\cdot)$ , where $\Phi(\cdot)$ is defined in (2) with h(x) = 0.
+
+Theorem 4.1 establishes that vanilla FedAvg is not suitable for solving federated CO problems. This naturally leads to the question of how can we modify FedAvg such that it can efficiently solve CO problems of the form (2)? Clearly, Theorem 4.1 suggests that sharing $y_k$ 's in each iteration is required to ensure convergence of FedAvg since sharing the iterates $y_k$ 's only intermittently leads to non-convergence of FedAvg. To this end, we propose to modify the FedAvg algorithm as presented in Algorithm 1 by sharing $y_k$ in each iteration $t \in \{0, 1, \ldots, T-1\}$ . The next result shows that the modified FedAvg resolves the non-convergence issue of FedAvg for solving CO problems.
+
+**Theorem 4.2** (Modified FedAvg: Convergence for CO). Suppose we modify Algorithm 1 such that $y_k^t = \bar{y}^t$ is updated at each iteration $t \in \{0, 1, ..., T-1\}$ instead of $[t+1 \mod I]$ iterations as in current version of Algorithm 1. Then if functions $f(\cdot)$ and $g_k(x)$ for $k \in [K]$ satisfy Assumptions 1, 2, and 3 such that for a fixed number of local updates $1 \le I \le \mathcal{O}(T^{1/4})$ , there exists a choice of $\eta^t > 0$ for $t \in \{0, 1, ..., T-1\}$ such that the iterates generated by (modified) Algorithm 1 converge to the stationary point of $\Phi(\cdot)$ , where $\Phi(\cdot)$ is defined in (2) with h(x) = 0.
+
+Motivated by Theorem 4.2, we next develop a federated algorithm, FedDRO, to solve the problem (2) in a general stochastic setting with $h(x) \neq 0$ .
+
+In this section, we propose a novel distributed non-convex CO algorithm, FedDRO, for solving (2). Note that as demonstrated in Section 4.1 this problem is particularly challenging because of the compositional structure of the problem combined with the fact that the data is heterogeneous for each client. Motivated by Theorem 4.2 above, in this work we develop a novel approach where we utilize the structure of the CO problem to develop efficient FL algorithms for solving (2). Specifically, as also demonstrated in Section 2.1 we utilize the fact that the embedding $g(\cdot)$ is a low-dimensional (e.g., $d_g = 1$ ) mapping, especially for the DRO problems. This implies that sharing of $g(\cdot)$ will be relatively cheap in contrast to the high-dimensional model parameters of size d which can be very large and take values in millions or even in billions for modern overparameterized neural networks [28]. Therefore, like FedAvg, we share the model parameters intermittently after multiple local updates while sharing the low-dimensional embedding of $g(\cdot)$ frequently to handle the compositional objective. Moreover, to solve the CO problems for DRO the developed algorithms generally utilize batch sizes (for gradient/function evaluation) that are dependent on the solution accuracy [6, 21]. However, this is not feasible in most practical settings. In addition, to control the bias and to circumvent the need to compute large batch gradients, we utilize a momentum-based estimator to learn the compositional function (see (8)) [20]. This construction allows us to develop FedAvg-type algorithms for solving non-convex CO problems wherein the local updates resemble the standard SGD updates.
+
+The detailed steps of FedDRO are listed in Algorithm 2. During the local updates each client $k \in [K]$ updates its local model $x_k^t$ for all $t \in [T]$ using the local estimate of the stochastic gradients in Step 6. The local stochastic gradient estimates for each client $k \in [K]$ are denoted by $\nabla \Phi_k(x_k^t; \bar{\xi}_k)$ and are evaluated using the chain rule of differentiation as
+
+$$\nabla \Phi_k(x_k^t; \bar{\xi}_k^t) = \frac{1}{|b_{h_k}^t|} \sum_{i \in b_{h_k}^t} \nabla h_k(x_k^t; \xi_{k,i}^t) + \frac{1}{|b_{g_k}^t|} \sum_{j \in b_{g_k}^t} \nabla g_k(x_k^t; \zeta_{k,j}^t) \nabla f(\bar{y}^t)$$
+ (7)
+
+```
+1: Input: Parameters: \{\beta^t\}_{t=0}^{T-1}, \ \{\eta^t\}_{t=0}^{T-1}, \ I
+2: Initialize: x_k^{-1} = x_k^0 = \bar{x}^0, \ y_k^0 = \bar{y}^0
+ 3: for t = 0 to T - 1 do
+ for k = 1 to K do
+ 4:
+ Sample \bar{\xi}_k^t = \{b_{g_k}^t, b_{h_k}^t\} uniformly randomly from \mathcal{D}_{g_k} and \mathcal{D}_{h_k} respectively
+ 5:
+ Local Update and Sharing: \begin{cases} \text{Compute } y_k^t \text{ using (8) and share with the server} \\ \text{Receive } \bar{y}^t \text{ from the server and update } y_t^k = \bar{y}^t \\ \text{Compute } \nabla \Phi_k(x_k^t; \bar{\xi}_k^t) \text{ using (7)} \\ x_k^{t+1} = x_k^t - \eta^t \nabla \Phi_k(x_k^t; \bar{\xi}_k^t) \end{cases}
+ 6:
+ \begin{array}{l} \textbf{if} \ t+1 \bmod I = 0 \ \textbf{then} \\ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \
+ 7:
+ 8:
+ end if
+ 9:
+10:
+ end for
+11: end for
+12: Return: \bar{x}^{a(T)} where a(T) \sim \mathcal{U}\{1,...,T\}.
+```
+
+where $\bar{\xi}_k^t = \{b_{h_k}^t, b_{g_k}^t\}$ represents the stochasticity of the gradient estimate and $b_{h_k}^t = \{\xi_{k,i}^t\}_{i=1}^{|b_{h_k}^t|}$ (resp. $b_{g_k}^t = \{\zeta_{k,i}^t\}_{i=1}^{|b_{g_k}^t|}$ ) denotes the batch of stochastic samples of $h_k(\cdot)$ (resp. $g_k(\cdot)$ ) utilized to compute the stochastic gradient for each $k \in [K]$ and $t \in \{0, 1, ..., T-1\}$ . The variable $\bar{y}^t$ is designed to estimate the inner function $1/K \sum_{k=1}^K g_k(x)$ in (2). A standard approach to estimate $g_k(x)$ locally for each $k \in [K]$ is to utilize a large batch such that the gradient bias from the inner function estimate can be controlled [6, 21, 22]. In contrast, we adopt a momentum-based estimate of $g_k(\cdot)$ at each client $k \in [K]$ that leads to a small bias asymptotically [20]. We note that the estimator utilizes a hybrid estimator that combines a SARAH [29] and SGD [30] estimate for the function values rather than the gradients [31]. Specifically, individual $y_k^t$ 's are estimated in Step 6 as
+
+$$y_k^t = (1 - \beta^t) \left( y_k^{t-1} - \frac{1}{|b_{g_k}^t|} \sum_{i \in b_t^{g_k}} g_k(x_k^{t-1}; \zeta_{k,i}^t) \right) + \frac{1}{|b_{g_k}^t|} \sum_{i \in b_{g_k}^t} g_k(x_k^t; \zeta_{k,i}^t).$$
+ (8)
+
+for all $k \in [K]$ and where $\beta^t \in (0,1)$ is the momentum parameter. Motivated by the discussion in Section 4.1, the parameters $y_k^t \in \mathbb{R}^{d_g}$ are shared with the server after the $y_k^t$ update, however, this sharing will not incur a significant communication cost since $y_k^t$ 's are usually low dimensional embeddings (often a scalar with $d_g = 1$ ) as illustrated in Section 2.1 for DRO problems. The model parameters are then updated using the SG evaluated using (7). Finally, after I local updates the model potentially high-dimensional model parameters are aggregated at the server and broadcasted back to the clients after aggregation in Step 8. Next, we state the convergence guarantees.
+
+In the next theorem, we first state the main result of the paper detailing the convergence of FedDRO.
+
+**Theorem 5.1** (Convergence of FedDRO). For Algorithm 2, choosing the step-size $\eta^t = \eta = \sqrt{|b|K/T}$ and the momentum parameter $\beta^t = 4B_g^4L_f^2 \cdot \eta^t$ for all $t \in \{0, 1, \dots, T-1\}$ . Moreover, with the selection of batch sizes $|b_{h_k}^t| = |b|$ for all $t \in \{0, 1, \dots, T-1\}$ and $k \in [K]$ , and for $T \geq T_{th}$ where $T_{th}$ is defined in Appendix F,
+
+then under Assumptions 1, 2 and 3 for $\bar{x}^{a(T)}$ chosen According to Algorithm 2, we have
+
+$$\mathbb{E} \|\nabla \Phi(\bar{x}^{a(T)})\|^{2} \leq \underbrace{\frac{2\left[\Phi(\bar{x}^{0}) - \Phi(x^{*}) + \|\bar{y}^{0} - g(\bar{x}^{0})\|^{2}\right]}{\sqrt{|b|KT}}}_{Initialization} + \mathcal{C}(|b|, K, T, I)\underbrace{\left[C_{\sigma_{h}}\sigma_{h}^{2} + C_{\sigma_{g}}\sigma_{g}^{2}\right]}_{Variance} + \mathcal{C}(|b|, K, T, I)\underbrace{\left[C_{\Delta_{h}}\Delta_{h}^{2} + C_{\Delta_{g}}\Delta_{g}^{2}\right]}_{Heterogeneity},$$
+
+where $C(|b|, K, T, I) := \max\left\{\frac{|b|K(I-1)^2}{T}, \frac{1}{\sqrt{|b|KT}}\right\}$ and constants $C_{\sigma_h}$ , $C_{\sigma_g}$ , $C_{\Delta_h}$ , and $C_{\Delta_g}$ are defined in Appendix F
+
+We note that the condition on $T \ge T_{\rm th}$ is required for theoretical purposes. Specifically, it ensures that the step-size $\eta = \sqrt{|b|K/T}$ is upper-bounded. A similar requirement has also been posed in [18, 26, 32] in the past. Theorem 5.1 captures the effect of heterogeneity, stochastic variance, and the initialization on the performance of FedDRO. As can be seen from the expression in Theorem 5.1 the heterogeneity degrades the performance when the local updates, I, increase beyond a threshold, i.e., when the term $|b|K(I-1)^2/T$ dominates $1/\sqrt{|b|KT}$ . The next result characterizes the possible choices of I that ensure the efficient convergence of FedDRO.
+
+Corollary 1 (Local Updates). Under the setting of Theorem 5.1 and choosing the number of local updates, I, such that we have $I \leq \mathcal{O}(T^{1/4}/(|b|K)^{3/4})$ , the iterate $\bar{x}^{a(T)}$ chosen according to Algorithm 2 satisfies
+
+$$\mathbb{E} \|\nabla \Phi(\bar{x}^{a(T)})\|^{2} \leq \underbrace{\frac{2\left[\Phi(\bar{x}^{0}) - \Phi(x^{*}) + \|\bar{y}^{0} - g(\bar{x}^{0})\|^{2}\right]}{\sqrt{|b|KT}}}_{Initialization} + \underbrace{\frac{C_{\sigma_{h}}\sigma_{h}^{2} + C_{\sigma_{g}}\sigma_{g}^{2}}{\sqrt{|b|KT}}}_{Variance} + \underbrace{\frac{C_{\Delta_{h}}\Delta_{h}^{2} + C_{\Delta_{g}}\Delta_{g}^{2}}{\sqrt{|b|KT}}}_{Heterogeneity}.$$
+
+Corollary 1 states that there exists a choice of the number of local updates that guarantee that FedDRO achieves the same convergence performance as a standard FedAvg [15,18,26,27] for solving the non-CO problems. Next, we characterize the sample and communication complexities of FedDRO.
+
+Corollary 2 (Sample and Communication Complexities). Under the setting of Theorem 5.1 and choosing the number of local updates as $I = \mathcal{O}(T^{1/4}/(|b|K)^{3/4})$ the following holds
+
+- (i) The sample complexity of FedDRO is $\mathcal{O}(\epsilon^{-2})$ . This implies that each client requires $\mathcal{O}(K^{-1}\epsilon^{-2})$ samples to reach an $\epsilon$ -stationary point achieving linear speed-up.
+- (ii) The communication complexity of FedDRO is $O(\epsilon^{-3/2})$ .
+
+The sample and communication complexities guaranteed by Corollary 5 match that of the standard FedAvg [32] for solving stochastic non-convex non-CO problems. We note that in addition to the $O(\epsilon^{-3/2})$ communication complexity that measures the sharing of high-dimensional parameters, FedDRO also shares $\mathcal{O}(K^{-1}\epsilon^{-2})$ low-dimensional embeddings (usually scalar values as illustrated in Section 2.1). Therefore, the total real values shared by each client during the execution of FedDRO is $\mathcal{O}(\epsilon^{-3/2}d + K^{-1}\epsilon^{-2})$ . Notice that for high-dimensional models like training (large) neural networks, we will usually have $dK \geq \mathcal{O}(\epsilon^{-0.5})$ meaning the total communication will be $\mathcal{O}(\epsilon^{-3/2}d)$ which is better than any Federated CO algorithm proposed in the literature [21–23]. Importantly, to our knowledge this is the first work that ensures linear speed up in a federated CO setting, moreover, FedDRO achieves this performance without relying on the computation of large batch sizes.
+
+
+
+Figure 1: Train and test accuracy vs communication rounds for CIFAR10-ST and CIFAR100-ST.
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+# Introduction
+
+The exponential rise in online videos has created a demand for effective content retrieval systems. The retrieval results of user-defined queries, particularly in long videos (20-120 minutes), often require locating specific moments. Most existing solutions [ 5 ,41 ,53 ,55], tailored for short videos (5-30 seconds), struggle when applied to hour-long videos, slowing down the retrieval and impacting the quality of the results [40 , 54]. This challenge emerges because it is very difficult to locate short moments in a long video based on text queries, a task known as Long Video Temporal Grounding (LVTG) [14 , 40].
+
+⋆ Corresponding author: hannan@dbs.ifi.lmu.de
+
+
+
+Fig. 1: Overview of RGNet. It predicts the moment boundary specified by textual queries from an hour-long video. First, our proposed RG-Encoder maps the video and text features to a joint space and retrieves the relevant clip feature. The subsequent grounding decoder processes the retrieved features to predict the beginning and end times of the moment. The encoder parallelly operates at multiple levels of granularity (e.g., clip and frame) to achieve an end-to-end-solution.
+
+A straightforward solution [16, 37] for the LVTG task is to divide the video into shorter clips, retrieve the most relevant one, and apply a grounding network to predict the moment. However, the grounding network cannot rectify failure in the clip retrieval phase.
+
+Empirically, we evaluate the impact of the two stages in Sec. 5.3 and identify clip retrieval as the primary factor for poor performance.
+
+The existing methods have a separate module for clip retrieval, which is disjoint from their grounding network (see Fig. 2). They typically follow a text-video retrieval [13,23,29] technique to select the relevant clip. However, video retrieval only requires a high-level understanding of video topics. For example, a typical video retrieval query is "A movie about a family surviving in a farmhouse.". In contrast, an example clip retrieval query in Fig. 1 is "Find the moment when the mum hangs up laundry outside the farmhouse". These specific moments from a long video require fine-grained event understanding. Thus, video retrieval models are suboptimal for these fine-grained moment localization tasks.
+
+We attribute this disjoint retrieval to the poor performance of these models. However, unifying clip selection and grounding is challenging due to their distinct setups. The former is a retrieval task, whereas the latter is a regression task. Moreover, it requires modeling two levels of video granularity: clip and frame. To address these technical challenges, we propose RGNet, a unified clip Retrieval and Grounding Network (see Fig. 1). The single network enables endto-end training of both stages. This improves the retrieval module's fine-grained event understanding by directly optimizing it with the moment annotations. Parallelly, the grounding network also benefits from the strong multimodal features produced by our encoder, further enhancing the localization capability.
+
+To achieve this unification, we propose a novel transformer encoder, RG-Encoder, which enables the grounding network to perform clip retrieval. This
+
+
+
+**Fig. 2:** Unified Solution. (left) Existing methods involve a separate retrieval and grounding network. The disjoint retrieval lacks fine-grained event understanding, which is crucial for moment localization. (**right**) Our unified network architecture overcomes it by deeply integrating the retrieval module with the grounding objective.
+
+eliminates the suboptimal video retrieval network and effectively models long video clip retrieval. To enable the retrieval module to understand fine-grained events, we design a sparse-attention mechanism in the encoder. The sparse attention enables the retrieval module to focus on specific events in the video, which is more synergistic with the grounding task. Thus RG-Encoder is capable of operating both at clip and frame level granularity. We propose an Intra-Clip Attention Loss, which motivates the sparse attention to focus more on the video frames aligned with the specified event. Furthermore, we propose a negative clip mining technique to simulate clip retrieval from long videos during training. This negative mining enables our network to train on a large batch size with an inter-clip contrastive loss. The large number of negative clips in the batch closely mimics the long video paradigm and reduces the gap between the training and test phases.
+
+Together, these components of RGNet bridge the gap between the two stages of LVTG, enhancing the fine-grained event understanding in hour-long videos. In summary, our contributions are fourfold:
+
+- We systematically deconstruct existing LVTG methods into clip retrieval and grounding stages. Through empirical evaluations, we discern that disjoint retrieval is the primary factor contributing to poor performance.
+- Based on our observations, we introduce RGNet, which integrates clip retrieval with grounding through parallel clip and frame-level modeling. This obviates the necessity for a separate video retrieval network, replaced instead by an end-to-end clip retrieval module tailored specifically for long videos.
+- We introduce sparse attention to the retriever and a corresponding loss to model fine-grained event understanding in long-range video. We propose a contrastive negative clip-mining strategy to simulate clip retrieval from a long video during training.
+- RGNet achieves state-of-the-art performance across both LVTG datasets, Ego4D [14] and MAD [40]. For instance, RGNet outperforms the previous best Ego4D method [39] by a substantial margin (9.7%).
+
+Short Video Temporal Grounding. The recent grounding methods mainly focus on short videos. The best-performing ones utilize transformer variants [3, 27, 34, 56]. For example, Moment-DETR [22] adopted transformers for combined video and text features. Subsequent works, such as UMT [28] and QD-DETR [35], split the cross-modal and unimodal modeling of video and text features for enhanced performance. EATR [18] improves its decoder with videospecific moment queries. These models were initially designed for shorter videos. However, when applied to hour-long videos, the moments become extremely tiny, posing a challenge akin to finding a needle in a haystack. Hence, these methods perform poorly on long videos, as noted by the authors of the MAD dataset [40]. In contrast, we effectively address the task by simultaneously processing long videos at two granular levels (video clips and frames) within a single network.
+
+Long Video Temporal Grounding. Recently, video temporal grounding has been adapted for long videos with MAD [40] and Ego4D [14] datasets. Typically, the methods designed for long videos involve two stages. Proposal-free methods [2, 26, 41, 53, 55] segment lengthy videos into smaller parts to predict candidate moments and then rank them to obtain the final predictions. Proposal-based methods [16,37] generate proposal clips or anchors and subsequently apply their grounding model to the retrieved proposals. Some methods, like CONE [16], and M-Guidance [2], utilize the detection transformer [3] as the grounding network to improve the grounding phase. These proposal-based models are more efficient and perform better than their counterparts. However, they need a separate retrieval module to select the proposal clips. This disjoint two-stage architecture is what we find to be the main reason for the poor performance of these models. In contrast, we propose a novel transformer encoder that solves both clip retrieval and grounding in a unified way, enabling end-to-end optimization.
+
+Video-text Retrieval. The early retrieval methods [4, 7, 21, 49, 50] designed multimodal fusion techniques to align pre-trained video and text models. To bridge the gap between pre-training and downstream tasks, large-scale pretrained video-language representations [1, 6, 9, 11, 31, 42, 45–47] have been introduced in various works. Several studies [8, 10, 19, 20, 32, 43, 44, 48, 51] have transferred the knowledge from CLIP [38] to text-video retrieval tasks. In recent studies, various sampling strategies [13,23,29] have been explored, enabling the model to selectively concentrate on pertinent video frames based on provided text inputs. For instance, Clip-BERT [23] introduces a sparse sampling strategy, X-Pool [13] utilizes a cross-modal attention model, and TS2-Net [29] adapts CLIP to capture temporal information and eliminate unimportant tokens. While existing methods aim to retrieve high-level video topics from a pool of video collections, moment localization demands a fine grained understanding to identify specific events within a video. Despite the similar setup to video retrieval, retrieving the corresponding clip from a long video necessitates deeper event comprehension. Therefore, we tailored our encoder to enhance event understanding and simulated precise clip retrieval conditions during training.
+
+This section presents a detailed description of our proposed unified LVTG method. As illustrated in Fig. 1, RGNet takes a long video and a text query as input and predicts the precise moment semantically correlated with the query in an end-to-end manner. The whole network is divided into the proposed RG-Encoder (Fig. 3) and a decoder (Sec. 3.3). The encoder retrieves the most relevant clip feature from the input long video. The decoder then processes the retrieved clip feature to predict the precise moment boundary. Additionally, an Intra-Clip Attention Loss and an Inter-Clip Contrastive Loss are incorporated to facilitate low-level event understanding in long-range videos.
+
+
+
+Fig. 3: Overview of RG-Encoder. It takes video clips and textual query as input and retrieves the relevant clip features. First, a cross-attention fuses the clips with text, and the sparsifier masks the out-of-moment frames. Based on the mask, the retrieval attention focuses on in-moment frames (colored red) and generates clip-level context and frame-level content features. We combine the context and content to generate the retrieved clip feature.
+
+Let us assume we have an untrimmed long video V consisting of T frames and a query sentence S with N words corresponding to a target moment's center $\tau_c$ and width $\tau_w$ . Following previous methods [16,37], we use pre-trained frozen models to extract visual features $V = \{f^1, f^2, ..., f^T\} \in R^{T \times D_f}$ as well as textual features $S = \{w^1, w^2, ..., w^N\} \in R^{N \times D_w}$ , where $D_f$ and $D_w$ represent the feature dimensions of video frames and query words. We slice the long video V into clips of length $L_c$ and feed them into our model. We employ a sliding window of length
+
+ $L_c$ and stride $L_c/2$ to obtain a collection of $T_c$ clips $C = \{C^1, C^2, ..., C^{T_c}\} \in R^{T_c \times L_c \times D_f}$ . Here, the $i^{th}$ clip, $C^i = \{f^{s_i+1}, f^{s_i+2}, ..., f^{s_i+L_c}\} \in R^{L_c \times D_f}$ , where $s_i$ is the start index of the clip.
+
+All the clips C from a single video comprise samples in a batch. The RG-Encoder (Fig. 3) processes them with the query S to retrieve the relevant clip feature P. First, the cross-attention generates query-conditioned frame features F. Then, based on the frame-query correlation calculated from F, the sparsifier determines the relevant frames inside the clip to produce a mask M for retrieval attention. We incorporate a learnable token R to capture the context of the clip. Additionally, the frame features F undergo retrieval attention based on the predicted mask M to generate the content feature $F_c$ . The combination of context R and content $F_c$ forms the retrieved clip feature P.
+
+Cross Attention: To evaluate the relevance of each frame within the clip $C^i$ , we utilize cross-attention, where frame features act as queries and text features serve as keys and values. Cross-attention represents the frame features as a weighted average of the textual features, scaled by their mutual correlation. This process results in text-conditioned frame features $F^i$ , establishing a fine-grained correspondence between the modalities. The query, key and values are calculated as $Q^i = l_Q(C^i) \in R^{L_c \times D_f}$ , $K = l_K(S) \in R^{N \times D_q}$ and $V = l_V(S) \in R^{N \times D_q}$ respectively. Here, $l_Q(\cdot)$ , $l_K(\cdot)$ , and $l_V(\cdot)$ denote the projection layers for the query, key, and value. Then, we apply the cross-attention layer as follows:
+
+$$F^{i} = \operatorname{softmax}(Q^{i}K^{T})V + Q^{i} \tag{1}$$
+
+Sparsifier: To retrieve the clip, we need to assess the relevance of each clip based on the text query. Clip relevance is determined by aggregating the relevancy of its frames. However, given the small moment duration, most frames are unrelated to the query. Hence, we classify frames into relevant and non-relevant categories using the sparsifier to assist retrieval attention in focusing on these fine-grained events. We calculate the relevancy $G^j \in [0,1]$ of the $j^{th}$ frame with Eq. 2. We use a differentiable Gumbel-softmax function [15, 17] for enabling end-to-end training. The detailed calculation is in the supplementary materials.
+
+$$G^{j} = \text{Gumbel-softmax}(\text{linear}(f^{j}))$$
+ (2)
+
+The intra-clip attention loss optimizes this classification using the ground truth frame relevancy information described in Sec. 3.4. To distinguish irrelevant frames from their counterparts, we constrain their updates in the retrieval attention. This restriction is implemented with an attention mask computed from the learned frame relevancy $G^j$ . The attention mask M between $j^{th}$ and $k^{th}$ frame is calculated with Eq. 3. Here, the $j^{th}$ and $k^{th}$ frames are the attention query and key, respectively. This leads to a reduced set of relevant and informative frames for subsequent attention.
+
+$$M(j,k) = \begin{cases} 0 & \text{if } G^j > 0.5 \text{ or } j = k \\ -\infty & \text{otherwise} \end{cases}$$
+ (3)
+
+Retrieval Attention: We aim for a learnable clip retrieval function to train end-to-end with the grounding objective. Thus, we devise the retrieval attention for aggregating the text-conditioned frame features based on their relevancy. To condense the $i^{th}$ clip into a contextual feature embedding, we introduce a randomly initialized learnable vector $R^i$ . We denote this as retrieval token, which is trained with the inter-clip contrastive loss.
+
+First, we concatenate the retrieval token with the text-conditioned frame features to create the queries $Q^i = [R^i, F^i]$ for retrieval attention. We also generate an attention mask containing all zeros for the retrieval token and concatenate it with M. We set the key $K = Q^i$ , and the value $V = Q^i$ to calculate the retrieval attention according to Eq. 4. We get the clip-level context feature, $R^i = \tilde{Q}^{i,1}$ and frame-level content features, $F_c^{i,j} = [\tilde{Q}^{i,2}, \tilde{Q}^{i,3}, ..., \tilde{Q}^{i,L_c+1}]$ . Here, $F_c^{i,j}$ is the $j^{th}$ frame feature of the $i^{th}$ clip.
+
+$$\tilde{Q}^i = \operatorname{softmax}(Q^i K^T + M)V + Q^i \tag{4}$$
+
+Further, the context feature R is utilized to retrieve the clip. It undergoes a linear projection layer, $l_s(\cdot)$ , to obtain context scores $S_r = \operatorname{sigmoid}(l_s(R))$ . We retrieve the most relevant clip based on the context score $S_r$ . Importantly, the context R represents the queried moment by attending to the reduced set of relevant frames. So, we fuse it with the frame features with Eq. 5 to produce stronger multimodal inputs for the decoder. It enhances the decoder's localization capability significantly. The decoder only processes features from the retrieved clip to predict the moment.
+
+$$P^{i,j} = F_c^{i,j} + R^i \times G^j, i \in P, \forall j$$
+ (5)
+
+We process the retrieved clip features with a transformer decoder to predict precise moment boundaries. Previous methods [16,22] feed both modalities into the decoder, introducing multiple positional information (e.g., time in the video and word order in the text) that complicates the detection task. In contrast, our encoder injects text information directly into the clip, eliminating the requirement to feed both modalities into the decoder. We incorporate learnable anchor queries [27] to represent the moment's center and width as $(\tau_c, \tau_w)$ . The decoder has 2 cross-attention and 2 self-attention layers.
+
+Intra-Clip Attention Loss guides our sparsifier to distinguish frames within and outside of the ground truth moment. This enables the retrieval attention to focus on relevant video regions and achieve fine-grained event understanding.
+
+Specifically, the in-moment frames are required to maintain higher relevancy scores than those outside the ground truth moment. The loss is defined as:
+
+$$\mathcal{L}_{\text{attn}} = \max(0, \Delta + S_c(i, j_{\text{out}}) - S_c(i, j_{\text{in}})) \tag{6}$$
+
+In the equation, $S_c$ is the relevancy score, $\Delta$ represents the margin, and anchor i is an in-moment frame. We randomly chose in-moment and out-of-moment frames $j_{\text{in}}$ and $j_{\text{out}}$ from the same clip. To calculate the relevancy score, the context features $R_i$ , and encoded clip $P^{i,j}$ undergoes linear layers, $l_r(\cdot)$ and $l_c(\cdot)$ , to obtain projected context $R^i_{proj} = l_r(R^i) \in R^{D_l}$ and content features, $P^{i,j}_{proj} = l_c(P^{i,j}) \in R^{L_c \times D_l}$ . Then, we calculate the frame-level relevancy score:
+
+$$S_c(i,j) = R_{proj}^i \cdot P_{proj}^{i,j} \tag{7}$$
+
+Inter-Clip Contrastive loss. A long video contains many clips with similar environments and scenes. However, most of these clips do not contain the specific moment we are searching for. So, high-level video understanding is not enough to distinguish precise moments. During the test phase, we need to handle many such negative clips. To tackle this issue, unlike previous approaches [16], we sample a large number of negative clips and train our retriever on a large batch size. Hence, this negative clip sampling reduces the disparity between the train and test phases.
+
+For this section, we denote the context feature, $R^{i,j}$ for $i^{th}$ proposal and $j^{th}$ text query. We train our model contrastively with positive text-clip pairs $[R^{i,j}]_{i=j}$ and negative pairs $[R^{i,j}]_{i\neq j}$ . We use the InfoNCE loss [36] to identify the positive text-clip pair amongst a set of unrelated negative samples. Here, $l_{cont}(\cdot)$ is a linear projection layer that transforms the $D_f$ dimensional context feature into a one-dimensional logit.
+
+$$\mathcal{L}_{\text{cont}} = -\sum_{i} log \frac{\exp(l_{\text{cont}}(R^{i,i}))}{\sum_{j} \exp(l_{\text{cont}}(R^{i,j}))}$$
+(8)
+
+**Grounding loss.** The objective functions for grounding, which aim to locate desired moments, are adopted from the baseline approach [22]. The grounding loss $\mathcal{L}_g$ measures the discrepancy between the ground truth (GT) and predicted moments. A one-to-one correspondence between GT and predicted moments is established using the Hungarian algorithm. This loss includes both an $\mathcal{L}1$ loss and a generalized IoU loss ( $\mathcal{L}gIoU$ ). Additionally, a cross-entropy loss $\mathcal{L}_{CE}$ is employed to classify predicted moments as either foreground or background. Thus, $\mathcal{L}_g$ is defined as follows:
+
+$$\mathcal{L}_{g} = \lambda_{\mathcal{L}1} ||\tau - \hat{\tau}|| + \lambda_{gIoU} \mathcal{L}_{gIoU}(\tau, \hat{\tau}) + \lambda_{CE} \mathcal{L}_{CE}, \tag{9}$$
+
+where $\tau$ and $\hat{\tau}$ represent the ground-truth moment and its corresponding prediction, containing center coordinates $\tau_c$ and width $\tau_w$ . The hyper-parameters $\lambda_*$ are used for balancing the losses. Finally, with the attention and contrastive loss, the total loss $\mathcal{L}_{\text{total}}$ is defined as follows:
+
+$$\mathcal{L}_{\text{total}} = \lambda_{\text{attn}} \mathcal{L}_{\text{attn}} + \lambda_{\text{cont}} \mathcal{L}_{\text{cont}} + \mathcal{L}_{\text{g}}$$
+ (10)
+
+# Method
+
+Since the long video temporal grounding (LVTG) performance relies on the first stage of clip retrieval, it does not perfectly reflect the actual moment localization capability of its grounding network. To assess the standalone grounding performance, we designed an oracle experiment operating exclusively on the clip where the ground truth moment is present. Compared to the oracle grounding, the LVTG R1.3 drops by 15.7% and 23.9% for Ego4D and MAD in Tab. 2. The poor grounding in LVTG emerges from the suboptimal retrieval accuracy of the disjoint network. Hence, retrieving clips where the ground truth moment exists is crucial, independent of the subsequent grounding accuracy. Regardless of the effectiveness of moment boundary detection, if the clip containing the moment boundary cannot be accurately retrieved. Our unified approach consis-
+
+| | Data Model | | | | Clip Retrieval Oracle Grounding | LVTG | | |
+|-------|------------------|----------------|----------------|----------------|---------------------------------|------|----------------------------|--|
+| | | R@1 | R@5 | R1.3 | R5.3 | R1.3 | R5.3 | |
+| Ego4d | Baseline Ours | 31.71 42.08 | 64.63 76.28 | 29.84 36.53 | 54.01 63.64 | | 14.15 30.33 20.63 41.67 | |
+| MAD | Baseline Ours | 12.41 25.01 | 24.50 50.02 | 29.49 33.42 | 53.02 63.43 | 6.87 | 16.11 9.48 18.72 | |
+
+Table 2: Empirical impact of two stages. To asses standalone grounding capability, we run an oracle experiment on the clip where the ground truth moment is present. The grounding capability degrades in LVTG evaluation because of incorrect selection by the disjoint clip retrieval network. Our unified model improves retrieval significantly, which leads to more effective temporal grounding in long videos.
+
+tently improves all retrieval metrics compared to the disjoint baseline. Notably, we achieve a staggering 10.4% and 12.6% improved R@1 score for Ego4D and MAD. This improvement in clip retrieval leads to state-of-the-art LVTG performance. Moreover, the grounding also improves because of the stronger clip features generated by our encoder. Compared to the disjoint baseline, the oracle R1.3 scores improved by about 6.7% and 4.0% for Ego4D and MAD, respectively. Hence, the unified end-to-end architecture proves more effective at LVTG by mutually improving both stages.
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diff --git a/2403.02567/paper_text/intro_method.md b/2403.02567/paper_text/intro_method.md
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+# Introduction
+
+
+
+An overview of our methods to improve multilingual structured reasoning. First (top), we create the translated code comments (Tcc) dataset, and use it in a fine-tuning setup. Second (bottom), we use the resulting LLM for inference on reasoning tasks. We find the most success with a code prompt format that bridges the representations between training and inference.
+
+
+
+
+The translation process for an xSTREET entry. We start from an example from STREET . The reasoning graphs are directly transferred, while each sentence text is translated. Note that this shows only one (of 4) task, GSM8K, and one (of 5) language, Spanish.
+
+
+The ability to perform complex reasoning tasks is fundamental to human intelligence, where multiple steps of thought are required. Complex reasoning remains an open-problem for large language models (LLMs), despite some recent progress. Prior works consider complex reasoning tasks specified only in English. Such an English-centric perspective provides a limited assessment of the underlying reasoning capabilities of LLMs, given any specific language is largely a surface-form representation. This motivates our first inquiry into the multilingual complex reasoning capabilities of LLMs.
+
+We introduce the xSTREET reasoning and explanation dataset (as shown in Figure [2](#fig:street_task){reference-type="ref" reference="fig:street_task"}). xSTREET covers 4 tasks, and extends the English STREET benchmark [@ribeiro2022street] to 5 additional diverse languages, inheriting the source's expert annotations and structured graphs for reasoning steps (7.8 average steps/answer). The tasks cover arithmetic, logic and science commonsense problems. We perform machine translation for the training and development data splits, and also perform human post-editing to the test sets, to ensure a high quality multilingual benchmark. We use xSTREET to evaluate several LLMs, identifying the multilingual setting as significantly challenging.
+
+To remedy the non-English reasoning gap, we turn to the widely accepted hypothesis that LLMs trained on code are better at reasoning than those trained only on text. This *code and reasoning hypothesis* has been empirically corroborated by several papers [@Suzgun2022ChallengingBT; @Liang2023HolisticEO; @Hendy2023HowGA]. Our work takes a further step in investigating the extent to which this hypothesis holds for non-English tasks. We proceed with the insight that code can be leveraged as a structured framework to represent the underlying reasoning steps, regardless of the surface-form language of the task. We thus propose two techniques to elicit better multilingual complex reasoning from LLMs (as shown in Figure [1](#fig:recipe){reference-type="ref" reference="fig:recipe"}): at training time through a lightweight fine-tuning recipe on code, and at inference time using a novel code prompt format.
+
+In the LLM literature, many capabilities have been characterized as 'emergent' with model scale [@wei2022emergent; @patel2022bidirectional]. Recent work on complex reasoning has thus focused on huge (175B+) and closed-source models. In our work, we instead aim to boost performance on far smaller open-source LLMs (7B). To make our findings reproducible, we release our benchmark. Our contributions are:
+
+1. We collect and release the first **dataset for multilingual structured reasoning, xSTREET**, covering 6 diverse languages and 4 tasks (5.5K entries total).
+
+2. At train time: we enhance reasoning capabilities of off-the-shelf LLMs by further training on **program code data where code is interleaved with non-English comments**. To this end, we augment a source code corpus through translating code comments and apply low-rank parameter-efficient fine-tuning (LoRA [@hu2021lora]) to BLOOMZ [@muennighoff2022crosslingual]. Our method is effective yet lightweight, while preserving general-purpose LM capabilities.
+
+3. At inference time: we design a code-like prompting format that mimics the structure of the reasoning tasks by **interweaving function calls and multilingual text**. We show this format outperforms several other prompt formats used.
+
+4. We evaluate multiple LLMs (BLOOMZ, GPT-3, Falcon-40b-instruct) on our benchmark, and show improved performance --- even for top-performing models --- across structured reasoning tasks in different languages. As our inference and training-time techniques are orthogonal, we show that they can be used in tandem to achieve the best performance.
+
+5. We perform qualitative and quantitative analysis to understand the roles of both the program code, and the code comments. Our findings taken together suggest that code elicits better multilingual structured reasoning by improving LLM's adherence to the reasoning format.
+
+# Method
+
+We create the xSTREET dataset by translating STREET into 5 languages: Arabic (ar), Spanish (es), Russian (ru), Chinese (zh), and Japanese (ja). These languages have linguistic and script diversity; furthermore, they are the languages used in many online programming help websites.
+
+To create the *xSTREET test split*, we hire expert human translators for all 5 languages through an internal team (detailed in §[9.0.0.1](#sec:data_statement){reference-type="ref" reference="sec:data_statement"}). Translators are tasked with post-editing the machine translation of one sentence at a time; for context, they can refer to the entire STREET entry the sentence comes from. After receiving the translations, we re-use the reasoning graph edges, and replace English nodes with the translations to create xSTREET. This process is shown in Figure [2](#fig:street_task){reference-type="ref" reference="fig:street_task"}. We therefore extend the 914 English entries in STREET to 5484 examples in xSTREET (914 \* 6 languages).
+
+To create the *xSTREET train and development splits*, we use machine translation.[^5] We then asked native speakers to evaluate the quality of 10 random sampled translations of each language. Annotators gave feedback that, despite some errors, the translations were of reasonable enough quality to use for training purposes.
+
+Dataset statistics for the xSTREET test benchmark are given in Table [4](#tab:xstreet_statistics){reference-type="ref" reference="tab:xstreet_statistics"}.
+
+::: {#tab:xstreet_statistics}
++--------------+------------------------------------------------+------------------------------------------------+------------------------------------------------+
+| Dataset | ::: {#tab:xstreet_statistics} | ::: {#tab:xstreet_statistics} | ::: {#tab:xstreet_statistics} |
+| | ---------- | ----------- | ------------- |
+| | \# entry | \# sents/ | avg \# |
+| | /lang | lang | sents/entry |
+| | ---------- | ----------- | ------------- |
+| | | | |
+| | : Statistics for the xSTREET test benchmark. | : Statistics for the xSTREET test benchmark. | : Statistics for the xSTREET test benchmark. |
+| | ::: | ::: | ::: |
++:=============+:===============================================+:===============================================+:===============================================+
+| ARC | 340 | 4334 | 12.7 |
++--------------+------------------------------------------------+------------------------------------------------+------------------------------------------------+
+| AQUA RAT | 254 | 3436 | 13.5 |
++--------------+------------------------------------------------+------------------------------------------------+------------------------------------------------+
+| AR LSAT | 50 | 1158 | 23.2 |
++--------------+------------------------------------------------+------------------------------------------------+------------------------------------------------+
+| GSM8k | 270 | 2255 | 8.4 |
++--------------+------------------------------------------------+------------------------------------------------+------------------------------------------------+
+| Total | 914 | 11183 | 12.2 |
++--------------+------------------------------------------------+------------------------------------------------+------------------------------------------------+
+| x6 languages | 5484 | 67098 | |
++--------------+------------------------------------------------+------------------------------------------------+------------------------------------------------+
+
+: Statistics for the xSTREET test benchmark.
+:::
+
+Taking the idea of using code for reasoning, and comments for multilinguality a step further, we address the question: *can multilingual code serve as indirect supervision for multilingual reasoning?* In other words, we investigate whether the code & reasoning hypothesis holds multilingually. We therefore propose a lightweight fine-tuning recipe, which consists of creating a multilingually commented code dataset, then fine-tuning on it, which serves as *indirect supervision* for downstream reasoning tasks.
+
+The first step of the recipe is creating a source code dataset with [t]{.underline}ranslated [c]{.underline}ode [c]{.underline}omments, termed [Tcc]{.smallcaps}. For each file from the source dataset, and for each target language, we perform the following. We parse the code to extract out comments, translate comments into the target language, then replace the original comments with translations. This is depicted in Appendix Figure [\[fig:tcc_creation\]](#fig:tcc_creation){reference-type="ref" reference="fig:tcc_creation"}.
+
+We use two simple filters: for source code files that A) have \>5 comments, and B) whose comments are over 50% in English.[^6] This filters 30k source code files down to 20k. After translating into 5 additional languages, TCC consists of 20k\*6=120k files total. See Appendix Table [\[tab:tcc_statistics\]](#tab:tcc_statistics){reference-type="ref" reference="tab:tcc_statistics"} for dataset statistics.
+
+In the second step, we leverage low-rank adaptation (LoRA) [@hu2021lora] to finetune instruction-tuned LLMs on [Tcc]{.smallcaps}.[^7] We use two methods to preserve the original model's capabilities despite the additional finetuning. First is by using LoRA itself, as it keeps the original base model's parameters frozen and introduces only a few learned parameters. Secondly, we replay 100k examples from the base model's training data, xP3 [@muennighoff2022crosslingual], in a multitask setup with the [Tcc]{.smallcaps} LM task.
+
+The recipe for a reasoning-enhanced LLM is now complete, and this is depicted in Figure [1](#fig:recipe){reference-type="ref" reference="fig:recipe"}.
+
+We hypothesize that structure, when applied to reasoning problems formulated in different languages, can abstract away some of the language-specific details, better surfacing the reasoning steps needed for a model. We thus propose the [Sim]{.smallcaps} ([S]{.underline}elect-and-[i]{.underline}nfer [m]{.underline}ultilingual comments) code prompts for complex reasoning tasks.
+
+[Sim]{.smallcaps} code prompts utilize several functions. We do not provide the API definitions, instead, we expect the model to learn to use them from the in-context examples. The functions are:
+
+- `select_facts(facts)`
+
+- `infer_new_fact(selected)`
+
+- `is_solved(fact, question)`
+
+- `make_choice(fact, choices)`[^8]
+
+- `facts.append(fact)`
+
+`select_facts` and `infer_new_fact` are loosely inspired by Selection-Inference [@creswellSelectionInferenceExploitingLarge2023]. A key difference, though, is that we use a single prompt, instead of iterative prompts. We therefore include `is_solved(fact, question)` as a signal for the LLM to stop generation.
+
+Each function is annotated with its return value in an inline code comment. This is inspired by prior work [@zhang-etal-2023-causal]. `infer_new_fact` has a string return value, i.e., the text of the new fact. We experiment with two versions of the return value of `select_facts`. The first, termed [Sim]{.smallcaps}-indexed, uses variables `facts[i]` to reference the `facts` array (similar to the indices used in linearized format). The second, termed [Sim]{.smallcaps}-text, directly uses each fact's text, dereferenced from `facts[i]`. We find that [Sim]{.smallcaps}-text works best for smaller models, while [Sim]{.smallcaps}-indexed does for larger ones, and hence apply this going forward.
+
+We write a rule-based Python script that converts existing structured graph annotations to [Sim]{.smallcaps} code prompts. [Sim]{.smallcaps} prompts express the exact same information as the linearized format. This property is unlike code prompts for prior work, wherein the conversion is done through in-context learning with an LLM, which can introduce errors as discussed in §[2.2](#sec:prior_code_prompts){reference-type="ref" reference="sec:prior_code_prompts"}. The different prompting formats for LLMs are shown in Figure [3](#fig:prompt formats){reference-type="ref" reference="fig:prompt formats"}.
+
+We use multilingual input in [Sim]{.smallcaps} code prompts as follows. First, facts given in the question are listed in the language of the task in a list of strings. Second, new facts and selected facts are given as comment lines adjacent to the function calls. See Figure [3](#fig:prompt formats){reference-type="ref" reference="fig:prompt formats"} for an example.
+
+
+
+Depictions of 3 prompting formats for the xSTREET tasks. For each format, input is in a grey box, while expected output is in a white box. Top left: direct. Bottom left: linearized. Right: Sim code prompts (2 languages). In the code prompts, we color code facts which are aligned.
+
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diff --git a/2403.05751/paper_text/intro_method.md b/2403.05751/paper_text/intro_method.md
new file mode 100644
index 0000000000000000000000000000000000000000..1bd9dfd316fc3a43d7d1c6daa773f755296c6f75
--- /dev/null
+++ b/2403.05751/paper_text/intro_method.md
@@ -0,0 +1,115 @@
+# Introduction
+
+Time series prediction is a critical task with applications in various domains such as finance forecasting [@hou2021stock; @chen2018incorporating], energy planning [@koprinska2018convolutional; @wu2021autoformer], climate modeling [@wu2023interpretable; @wu2021autoformer], and biological sciences [@luo2020hitanet; @rajpurkar2022ai]. Considering that time series forecasting problems can be effectively addressed as a conditional generation task, many works leverage generative models for predictive purposes. For instance, @salinas2019high utilizes a low-rank plus diagonal covariance Gaussian copula; @rasul2021autoregressive models the predictive distribution using normalizing flows. Recent advancements in diffusion probabilistic models [@ho2020denoising] have sparked interest in utilizing them into probabilistic time series prediction. For example, @rasul2020multivariate auto-regressively generates data through iterative denoising diffusion models. @tashiro2021csdi uses a conditional score-based diffusion model explicitly trained for probabilistic time series imputation and prediction. These methods relying on diffusion models have exhibited remarkable predictive capabilities. However, there is still considerable scope for improvement. One challenge that diffusion models face in time series forecasting tasks is the instability due to their stochastic nature when compared to deterministic models like RNNs and variants like LSTMs [@hochreiter1997lstm; @laiModelingLongShortTerm2018], GRUs [@ballakur2020Empirical; @yamak2019comparison], and Transformers that rely on self-attention mechanisms [@Vaswani2017attention; @zhouInformerEfficientTransformer2021; @zhou2022fedformer; @wu2021autoformer]. More specifically, the diffusion models yield diverse samples from the conditional distributions, including possible low-fidelity samples from the low-density regions within the data manifold [@sehwag2022generating]. In the context of time series forecasting, where fixed observations exclusively serve as objectives, such variability would result in forecasting instability and inferior prediction performance.
+
+::: wrapfigure
+r0.45 {width="45%"}
+:::
+
+To stabilize the output of a diffusion model in time series prediction, one straightforward method is to constrain the intermediate states during the sampling process. Prior research in the realm of diffusion models has introduced the idea of classifier-guidance [@nichol2021glide] and classifier-free guidance [@ho2022classifier], where the predicted posterior mean is shifted with the gradient of either explicit or implicit classifier. However, these methods require labels as the source of guidance while sampling, which are unavailable during out-of-sample inference. We observe that the forward process of the diffusion model, which sequentially corrupts the data distribution to a standard normal distribution, intuitively aligns with the process of smoothing fine-grained data into a coarser-grained representation, both of which result in a gradual loss of finer distribution features. This provides the insights that intrinsic features within data granularities may also serve as a source of guidance.
+
+In this paper, we propose a novel **M**ulti-**G**ranularity **T**ime **S**eries **D**iffusion (**MG-TSD**) model that leverages multiple granularity levels within data to guide the learning process of diffusion models. The coarse-grained data at different granularity levels are utilized as targets to guide the learning of the denoising process. These targets serve as constraints for the intermediate latent states, ensuring a regularized sampling path that preserves the trends and patterns within the coarse-grained data. They introduce inductive bias which promotes the generation of coarser features during intermediate steps and facilitates the recovery of finer features in subsequent diffusion steps. Consequently, this design reduces variability and results in high-quality predictions. Our key contributions can be summarized as below:
+
+1. We introduce a novel **MG-TSD** model with an innovatively designed multi-granularity guidance loss function that efficiently guides the diffusion learning process, resulting in reliable sampling paths and more precise forecasting results.
+
+2. We provide a concise implementation that leverages coarse-grained data instances at various granularity levels. Furthermore, we explore the optimal configuration for different granularity levels and propose a practical rule of thumb.
+
+3. Extensive experiments conducted on real-world datasets demonstrate the superiority of the proposed model, achieving the best performance compared to the state-of-the-art methods.
+
+Suppose $\vx_{0}\sim q_{\mathcal{X}}(\vx_{0})$ is a multivariate vector from space $\mathcal{X}=\mathbb{R}^{D}$. Denoising diffusion probabilistic models aim to learn a model distribution $p_\theta(\vx_0)$ that approximates the data distribution $q(\vx_0)$. Briefly, they are latent variable models of the form $p_{\theta}(\vx_{0})=\int p_{\theta}(\vx_{0:N})\mathrm{d}\vx_{1:N}$, where $\vx_n$ for $n = 1,\ldots,N$ is a sequence of latent variables in the same sample space as $\vx_0$. The denoising diffusion models are composed of two processes: the forward process and the reverse process. During the forward process, a small amount of Gaussian noise is added gradually in $N$ steps to samples. It is characterized by the following Markov chain: ${q(\vx_{1:N}|\vx_0) = \prod^N_{n=1} q(\vx_n | \vx_{n-1})}, \ \text{where} \ q (\vx_{n}|\vx_{n-1}):= \mathcal{N} (\sqrt{1-\beta_n}\vx_{n-1}, \beta_n \mI)$. The step sizes are controlled by a variance schedule $\{\beta_{n}\in (0,1)\}_{n=1}^{N}$, where $n$ represents a diffusion step. A nice property of the above process is that one can sample at any arbitrary diffusion step in a closed form, let $\alpha_n := 1 - \beta_n$ and $\bar{\alpha}_n =\prod^n_{i=1} \alpha_i$. It has been shown that $\vx_{n}=\sqrt{\bar{\alpha}_n}\vx_{0}+ \sqrt{1-\bar{\alpha}_{n}}{\bm{\epsilon}}$. The reverse diffusion process is to recreate the real samples from a Gaussian noise input. It is defined as a Markov chain with learned Gaussian transitions starting with $p(\vx_N)=\mathcal{N}(\vx_N;\textbf{0},\mI)$. The reverse process is characterized as $p_\theta(\vx_{0:N}) := p(\vx_{N})\prod^1_{n=N} p_\theta(\vx_{n-1} {|} \vx_{n}),\ \text{where}\ p_\theta(\vx_{n-1} {|} \vx_{n}) := \mathcal{N}(\vx_{n-1}; \mu_\theta(\vx_{n}, n), \Sigma_\theta(\vx_n, n)\mI)$; $\mu_{\theta}:\R^{D}\times \mathbb{N} \to \R^{D}$ and $\Sigma_{\theta}: \R^{D}\times \mathbb{N} \to \R^{+}$ take the variable $\vx_{n}\in \R^{D}$ and the diffusion step $n\in \mathbb{N}$ as inputs, and share the parameters $\theta$. The parameters in the model are optimized to minimize the negative log-likelihood $\min_{\theta} \E_{\vx_{0}\sim q(\vx_0)}[-\log p_{\theta}(\vx_{0})]$ via a variational bound. According to denoising diffusion probabilistic models (DDPM) in @ho2020denoising, the parameterization of $p_\theta(\vx_{n-1}| \vx_n)$ is chosen as: $$\begin{equation}
+\label{eq-mu}
+\mu_\theta(\vx_n, n) = {\frac{1}{\sqrt{\alpha_n}}} \bigg( \vx_n- \frac{1-\alpha_n}{\sqrt{1-\bar{\alpha}_n}} {\bm{\epsilon}}_\theta( \sqrt{\bar{\alpha}_n}\vx_{0}+\sqrt{1-\bar{\alpha}_{n}}{\bm{\epsilon}},n)\bigg),
+\end{equation}$$ where ${\bm{\epsilon}}_{\theta}$ is a network which predicts ${\bm{\epsilon}}\sim \mathcal{N}(\textbf{0},\textbf{I})$ from $\vx_{n}$. We simplify the objective function into $$\begin{equation}
+\label{eq-diffusion-loss}
+L_{n}^{\text{simple}}=
+\E_{n,{\bm{\epsilon}}_n,\vx_0}\bigg[\|{\bm{\epsilon}}_{n}-{\bm{\epsilon}}_{\theta}(\sqrt{\bar{\alpha}_n}\vx_0+\sqrt{1-\bar{\alpha}_n}{\bm{\epsilon}}_n,n)\|^2\bigg].
+\end{equation}$$ Once trained, we can iteratively sample from the reverse process $p_{\theta}(\vx_{n-1}|\vx_{n})$ to reconstruct $\vx_0$.
+
+We treat the time series forecasting task as a conditional generation task and utilize the diffusion models presented in as the backbone generative model. TimeGrad model is a related work by @rasul2020multivariate which first explored the use of diffusion models for forecasting multivariate time series. Consider a contiguous time series sampled from the complete history training data, indexed from 1 to $T$. This time series is partitioned into a context window of interval $[1,t_0)$ and a prediction interval $[t_0,T]$. TimeGrad utilizes diffusion models from @ho2020denoising to learn the conditional distribution of the future timesteps of the multivariate time series given their past. An RNN is employed to capture the temporal dependencies, and the time series sequence up to timestep $t$ is encoded in the updated hidden state $\rvh_{t}$. Mathematically, TimeGrad models $q_{\mathcal{X}}(\vx_{t_0:T}|\vx_{1:t_0-1})=\prod_{t=t_0}^{T}q_{\mathcal{X}}(\vx_t|\vx_{1:t-1})\approx \prod_{t=t_0}^{T}q_{\mathcal{X}}(\vx_{t}|\rvh_{t-1})$, where $\vx_{t}\in\R^{D}$ denotes the time series at timestep $t$ and $\rvh_{t}=\text{RNN}_{\psi}(\vx_t,\rvh_{t-1})$. Each factor is learned via a shared conditional denoising diffusion model. In contrast to @ho2020denoising, the hidden states $\rvh_{t-1}$ are taken as an additional input in the denoising network ${\bm{\epsilon}}_{\theta}(\vx_t^{n},\rvh_{t-1},n)$, and the loss function for timestep $t$ and diffusion step $n$ is given by: $$\begin{equation}
+\E_{{\bm{\epsilon}},\vx_{0,t},n}[\|{\bm{\epsilon}}-{\bm{\epsilon}}_{\theta}(\sqrt{\bar{\alpha}_n}\vx_{0,t}+\sqrt{1-\bar{\alpha}_n}{\bm{\epsilon}},n,\rvh_{t-1})\|^2],
+\end{equation}$$ where the first subscript in $\vx_{0,t}$ represents the index of the diffusion step, while $t$ denotes the timestep within the time series.
+
+# Method
+
+In the time series prediction task, let $\mX^{(1)}$ represent the original observed data. The time series data is denoted as $\mX^{(1)} = [\vx_{1}^1,\ldots,\vx_{t}^1,\ldots, \vx_{T}^1]$, where $t$ represents the timestep $t\in [1,T]$ and $\vx_{t}\in \R^{D}$. Specifically, our task is to model the conditional distribution of future timesteps of the time series $[\vx_{t_0}^1,\ldots,\vx_{T}^1]$ given the fixed window of history context. Mathematically, the problem we consider can be formulated as follows: $$\begin{equation}
+q_{\mathcal{X}}\bigg(\vx_{t_0:T}^1|\big\{\vx_{1:t_0-1}^1\big\}\bigg)=\prod_{t=t_0}^{T}q_{\mathcal{X}}\bigg(\vx_{t}^1|\big\{\vx_{1:t-1}^1\big\}\bigg).
+\end{equation}$$
+
+In this section, we provide an overview of the **MG-TSD** model architecture in Section [3.1](#sec-overview){reference-type="ref" reference="sec-overview"}, followed by a detailed discussion of the novel guided diffusion process module in Section [3.2](#sec-guide-module){reference-type="ref" reference="sec-guide-module"}, including the derivation of the heuristic loss function and its implementation across various granularity levels.
+
+The proposed methodology consists of three key modules, as depicted in Figure [1](#fig-archi){reference-type="ref" reference="fig-archi"}.
+
+**Multi-granularity Data Generator** is responsible for generating multi-granularity data from observations. In this module, various coarse-grained time series are obtained by smoothing out the fine-grained data using historical sliding windows with different sizes. Suppose $f$ is a pre-defined smoothing (for example, average) function, and $s^g$ is the pre-defined sliding window size for granularity level $g$. Then $\mX^{(g)}=f(\mX^{(1)},s^g)$. The sliding windows are non-overlapping and the obtained coarse-grained data for granularity $g$ are replicated $s^g$ times to align over the timeline $[1,T]$.
+
+**Temporal Process Module** is designed to capture the temporal dynamics of the multi-granularity time series data. We utilize RNN architecture on each granularity level $g$ separately to encode the time series sequence up to a specific timestep $t$ and the encoded hidden states are denoted as $\rvh_t^{g}$. The RNN cell type is implemented as GRU in @chung2014empirical.
+
+**Guided Diffusion Process Module** is designed to generate stable time series predictions at each timestep $t$. We utilize multi-granularity data as given targets to guide the diffusion learning process. A detailed discussion of the module can be found in .
+
+
+
+Overview of the Multi-Granularity Time Series Diffusion (MG-TSD) model, consisting of three key modules: Multi-granularity Data Generator, Temporal Process Module (TPM), and Guided Diffusion Process Module for time series forecasting at a specific granularity level.
+
+
+In this section, we delve into the details of the **Guided Diffusion Process Module**, a key component in our model. presents the derivation of a heuristic guidance loss for the two-granularity case. In , we generalize the loss to the multi-granularity case and provide a concise implementation to effectively utilize coarse-grained data across various granularity levels. Briefly, the optimization of the heuristic loss function can be simply achieved by training denoising diffusion models on the multi-granularity data with shared denoising network parameters and partially shared variance schedule.
+
+Without loss of generality, consider two granularities: finest-grained data $\vx_{t}^{g_1}$ ($g_1=1$) from $\mX^{(g_1)}$ and coarse-grained data $\vx_{t}^{g}$ from $\mX^{(g)}$ at a fixed timestep $t$, where $1
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diff --git a/2403.17869/paper_text/intro_method.md b/2403.17869/paper_text/intro_method.md
new file mode 100644
index 0000000000000000000000000000000000000000..bd348531f862677a6dc9d2fec8309652b8788260
--- /dev/null
+++ b/2403.17869/paper_text/intro_method.md
@@ -0,0 +1,77 @@
+# Introduction
+
+Transfer learning is an integral part of the success of deep learning in 2D computer vision, enabling the use of powerful pre-trained architectures, which can be adapted with limited data on a broad range of downstream tasks and datasets [6, 20, 59]. The advancement of transfer learning can be attributed to several factors such as the availability of well-established architectures (e.g . deep CNNs [20], ViTs [11]), an abundance of source data to train on (e.g . ImageNet [10] among myriad others), as well as the development of different pre-training strategies [23].
+
+Unfortunately, the extension of such approaches to the 3D domain presents several important challenges. First, and perhaps most importantly, large-scale labeled datasets are scarce in 3D due to the complexity of data acquisition and annotation as well as significant domain-specificity that creates imbalances across data (e.g. synthetic CAD shapes [5, 25, 50], real scenes [9], non-rigid shapes [4],
+
+
+
+Fig. 1: Analyzing and improving point cloud transfer learning. In this work, we perform the first in-depth study of the different components of network pre-training that influence the outcome of point cloud transfer learning (components in solid orange). This includes the source data domain in relation to the downstream target data, the choice of architecture, the importance of early vs later layers, and the pre-training design choices. We also show that improvements to the pipeline can be achieved through regularization of the early layers, by promoting the prediction of geometric properties.
+
+etc.). Such imbalances, coupled with limited training data, can strongly hinder the applicability of transfer learning solutions [53].
+
+Another key challenge in learning with 3D data is the nature of the data representation, which most often comes in the form of unstructured point clouds, potentially with attributes (e.g. spatial, color). This limits the use of architectures that rely on regular grid structure, and furthermore, poses challenges due to significant variability in sampling properties (density, acquisition artefacts, etc.). As a result, while a large number of different architectures have been proposed for 3D deep learning [16], there is a lack of universal solutions applicable to an arbitrary scenario.
+
+In spite of these challenges, several works have recently emerged aiming to enable transfer learning for point clouds, especially by focusing on new pretraining strategies, e.g., [21, 51, 53]. Among these strategies, several approaches have shown that self-supervised strategies (e.g. contrastive learning, point cloud reconstruction) can improve performance on different downstream data (e.g. shapes, scenes) and on select set of tasks and datasets.
+
+Despite the growing number of these approaches, unfortunately, most existing methods are only presented and evaluated in very specific settings, and missing comparison to baselines such as supervised pre-training. Specifically, there are several questions not addressed in existing literature, including: 1) The utility of supervised compared to contrastive pre-training, 2) The role of the architecture's inherent properties in the success of point cloud transfer learning, and 3) The impact of regularization strategies on the efficacy of point cloud pre-training.
+
+By performing the first in-depth investigation of the most prominent approaches, we observe that in the linear probing setting, supervised pre-training leads to superior performance compared to contrastive learning across almost all tested architectures, even under relatively significant domain shift. This suggests that within 3D deep learning, contrastive learning does not always lead to more "universally useful" features. At the same time, we also observe that in the full fine-tuning scenario, contrastive pre-training can outperform supervised learning. By analyzing gradients of pre-trained networks, we show that the early layers learned with supervised pre-training are not easily adaptable across new datasets, compared to those learned with contrastive learning. This sheds new and specific light on the differences between these pre-training strategies in the context of point cloud analysis. Finally, we demonstrate that a simple regularization scheme applied to the early layers during supervised pre-training can overcome this limitation and leads to networks that can be fine-tuned efficiently.
+
+To summarize, our main contributions are as follows:
+
+- 1. We perform the first comparison, within a single consistent evaluation framework, of supervised and contrastive pre-trained models using different point cloud backbones, assessing their transfer learning performance on common downstream data and tasks. We separate between the linear probing and fine-tuning settings and analyze the difference in performance.
+- 2. We observe a clear quantifiable difference in architecture's layer properties and find that, remarkably, for 3D architectures, even the first layers have the ability to discriminate between downstream shape classes. Moreover we show that supervised pre-training can lead to early layers that do not easily adapt to new data.
+- 3. To counteract this effect, we introduce a simple layer-wise geometric regularization procedure on supervised pre-training, which leads to performance improvement and outperforms contrastive learning in several settings. We finally strongly correlate the improvement to the key signals identified in layer gradient norm.
+
+Throughout all our analysis, we take special care to make all models and evaluation settings consistent and comparable.
+
+# Method
+
+Pre-training Dataset. Prior 3D transfer learning approaches [1,21,48,53] have typically focused on pre-training datasets with limited domain shifts to downstream tasks, such as within the synthetic shape setting [5, 50] or the real scene scenario [3,9]. Interestingly, among them, PointContrast [53] argued against pretraining on synthetic data and stated that supervised pre-training is an upper bound to information gain from pre-training (see Sec. 3.1 therein). Our experimental findings (Sec. 5), however, suggest that this is not always the case and help to refine this statement. We thus select ShapeNet [5] to evaluate the utility of pre-training with synthetic data, and focus on its transfer utility in downstream tasks with varying domain shifts, as elaborated below.
+
+Downstream Datasets and Tasks. To comprehensively investigate different 3D transfer learning scenarios, we consider downstream tasks encompassing both 1) synthetic vs. real data and 2) object vs. scene-level 3D data, which exhibit a wide range of similarity to ShapeNet (51,300 annotated 3D synthetic shapes in 55 categories), used for pre-training:
+
+- ModelNet40 [50] is a synthetic object-level dataset with 3D shapes resembling those in ShapeNet. It consists of 12,311 shapes and 40 object classes for the shape classification task, and 20 of these classes can be found in ShapeNet.
+- ScanObjectNN [47] is a real object-level dataset with scanned 3D shapes featuring noise and background, vastly different from the clean shapes in ShapeNet. There are 15,000 shapes and 15 object classes for the shape classification task, and 9 of these classes can be found in ShapeNet.
+- S3DIS [3] is a real scene-level dataset consisting of 3D scans of 6 large-scale indoor areas in office buildings and 13 semantic categories for the semantic segmentation task.
+
+To provide an extensive study that tackles diverse 3D backbones, we follow a broadly adopted categorization of 3D deep neural networks [52] and select a diverse set of five representative architectures as follows:
+
+- Point-based. PointNet [35] is a pioneering 3D backbone that is characterized by its global nature in feature learning, which has been shown to be beneficial in robust point cloud analysis [44], but, as we show below, can overfit to pre-training data. In addition, we consider PointMLP [30], which is a recent extension of PointNet and its hierarchical version of PointNet++ [36], and which effectively incorporates local geometry and residual MLPs.
+- Graph-based. DGCNN [49] leverages graph convolution operations with graphs constructed in the feature space, allowing it to capture both local and global properties. This architecture has been shown to generalize well between real and synthetic data [47] and has also demonstrated strong transfer learning performance across various pre-training strategies [1, 40, 48].
+- Sparse convolutions. MinkowskiNet [8] is built upon generalized sparse convolutions, which operate on voxels and benefit from the sparsity of point clouds. This architecture is the backbone of PointContrast [53] for 3D transfer learning. Below we compare it to other baselines and examine how its reliance on density impacts its transfer learning capabilities.
+- Transformer. Following the success of transformers in NLP and 2D vision tasks, 3D transformer backbones have emerged for point cloud processing. For the first time, we study the transfer learning capabilities of Point Cloud Transformer (PCT) [15] in downstream tasks.
+
+Our primary objectives in analyzing these architectures are three-fold: firstly, to carry out a first comprehensive analysis of supervised pre-training performance across diverse architectures. Secondly, we explore the relation between architectural properties and their performance in 3D transfer learning, considering both linear probing and fine-tuning scenarios. Thirdly, we aim to identify shared properties that either hinder or contribute to successful transfer learning.
+
+We adapt the layer configurations of these architectures to ensure consistency across all explored pre-training strategies (Sec. 4). Further details on the architecture configurations are provided in the supplementary materials.
+
+As mentioned above, in our analysis, we focus on comparing supervised and contrastive learning approaches. Although our primary focus is on these pre-training strategies, we include additional results to alternative methods (including shapelevel contrastive learning and a reconstruction-based approach PointDAE [57]) in the supplementary materials.
+
+Supervised pre-training is a standard approach used in 2D computer vision tasks to obtain generalizable feature embeddings. Interestingly, its utility in the context of 3D transfer learning has not been extensively investigated in previous studies with a few exceptions such as PointContrast [53] as mentioned above. In training, we use the standard cross-entropy loss as a pretext objective:
+
+$$L_{ce} = -\sum_{i} (y_i \cdot \log(p_i)), \tag{1}$$
+
+where yi is the ground truth label, and pi is the predicted probability of each class i for an input shape. We also use data augmentation (translation, scale and rotation) to avoid the orientation bias introduced by the oriented shapes of ShapeNet, which are also present in the ModelNet40 downstream data.
+
+Contrastive pre-training is employed for comparisons with supervised pretraining. We use point-level contrastive learning, based on PointContrast [53]. Specifically, for contrastive pre-training, we first introduce a set of invariances by applying data augmentation. We start by normalizing a point cloud and perform random geometric transformations, including translation with magnitude of 0.5, scaling between 80% and 125%, and rotation of magnitude 45◦ . We also simulate partial data by cropping 50% the point cloud.
+
+More details about transformations can be found in the supplementary materials. By combining these transformations, we start from an input point cloud and generate two augmented views Pi and Pj .
+
+For point-level contrastive learning, we contrast between points rather than entire objects. This models point-level information and also naturally leads to more data for constructing positive and negative pairs. Positive pairs are obtained by matching points from different views. We use the PointInfoNCE Loss, introduced in [53] as the pretext objective:
+
+$$\mathcal{L}_{Contrastive} = -\sum_{(i,j)\in\mathcal{P}} \log \frac{\exp(\mathbf{h}_i \cdot \mathbf{h}_j/\tau)}{\sum_{(\cdot,k)\in\mathcal{P}} \exp(\mathbf{h}_i \cdot \mathbf{h}_k/\tau)},$$
+ (2)
+
+
+
+**Fig. 2:** Evaluation on (a) ModelNet40 and (b) ScanObjectNN classification tasks of different pre-trained models, using linear probing (LP – solid bars) and fine-tuning (FT – dashed bars) settings. Random Init is a randomly initialized model. Transfer learning performance depends on pre-training scheme, architecture and evaluation protocol.
+
+where $\mathbf{h}_i$ are point features obtained by attaching a decoder to the encoder $f_{\theta}$ , $\tau$ is a temperature parameter, and $\mathcal{P}$ is the set of matched points.
+
+More pre-training details are provided in the supplementary materials, where we also review the same contrastive scheme applied at shape-level, rather than the point-level.
+
+In order to evaluate the effectiveness of supervised pre-training relative to self-supervised methods, such as contrastive learning, we employ both *linear probing* and *fine-tuning* strategies of pre-trained models across various architectures, datasets, and tasks. Linear probing assesses the quality of the representations learned during pre-training by training a linear classifier on top of the frozen backbone weights, while fine-tuning involves adjusting all weights of pre-trained models to new tasks or datasets. Our first goal is to establish a comprehensive comparison for supervised vs. contrastive pre-training, which has been largely omitted in existing literature [1, 40, 48], and to identify the scenarios in which supervised pre-training can be beneficial.
+
+In the full fine-tuning setting, we adhere to standard learning practices for each architecture. The precise details of the fine-tuning parameters, including learning rates, batch sizes, and optimization algorithms, are documented in the supplementary materials.
diff --git a/2404.00385/main_diagram/main_diagram.drawio b/2404.00385/main_diagram/main_diagram.drawio
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+# Introduction
+
+The incorporation of AI into the layout design process represents a significant advancement in design methodologies. Researchers have predominantly adopted a generative modeling approach, enabling the generation of diverse designs with limited user inputs [\[1,](#page-8-0) [15,](#page-8-1) [17,](#page-8-2) [20,](#page-8-3) [21\]](#page-8-4). Although very useful, this approach faces practical challenges when we have a fixed uneven boundary and users have several spe-
+
+
+
+Figure 1. Given a building boundary and a graph that encodes user requirements and room constraints, we first transform the inputs into a factor graph that can model higher-order constraints, and then produce the floorplan based on the factor graph model.
+
+cific requirements and seek active involvement in the design process. In such instances, inferential models specifically trained to produce layouts that align precisely with user requirements are likely more beneficial.
+
+The challenge of layout design generation with constraints is more prominently seen in the design of *floorplan layouts* [\[1,](#page-8-0) [11,](#page-8-5) [14,](#page-8-6) [15,](#page-8-1) [17,](#page-8-2) [20,](#page-8-3) [21,](#page-8-4) [23,](#page-8-7) [31,](#page-9-0) [33\]](#page-9-1), which can include both user-defined and structural constraints. Userdefined constraints stem from individual preferences regarding room sizes, adjacencies, and other design considerations. Structural constraints may require the layout to remain within the boundary of a building or adhere to other geometric restrictions. For example, given a boundary, a user may want two rooms adjacent to each other in a particular corner of the house. A good inferential model would be able to produce a layout satisfying these constraints. With partial output available, users can append or update their requirements and thereby, refine the design iteratively.
+
+One common approach for producing floorplan layouts is to use a two-stage method, where the first stage involves predicting bounding boxes for individual objects/rooms, followed by a refinement network that converts the bounding boxes into an image [\[4,](#page-8-8) [11\]](#page-8-5). For the first stage, the room-adjacency graph is taken as input, and a Graph Neural Network(GNN)-based model is often used to predict the
+
+\*Corresponding Author
+
+bounding boxes of rooms [\[5,](#page-8-9) [11\]](#page-8-5). However, the representation of each object as a single node, while straightforward, falls short in terms of granularity and effectiveness. It notably struggles to accurately reflect the intricate interactions that occur between objects, as these interactions are frequently localized to specific parts of an object. For example, it is often the case that only one side of a room, such as the right wall, is actively engaged with an adjacent object. Therefore, we represent each room using four separate latent variables to represent the four boundaries of the bounding box: xmin, xmax, ymin, and ymax. This approach effectively captures fine-grained interactions, yet it also introduces the necessity to model higher-order dependencies. For instance, determining the size of a room requires all four variables. Modeling the potentially higher-order dependencies, however, presents a non-trivial challenge.
+
+To handle the challenge, we propose a factor graph based model to encode the dependencies. A factor graph is a bipartite graph consisting of factor nodes and variable nodes. In our model, each room is represented by four variable nodes, and each factor node is designed to represent the dependencies among its connected variables. This enables modeling arbitrary constraints including those of a higherorder, a capability lacking in traditional GNNs which can model only pairwise dependencies. This allows us to leverage domain knowledge more effectively than the adjacencygraph approach typically used in existing works. For instance, we impose a boundary constraint ensuring that all rooms are contained within a predefined boundary by employing a factor that connects to all the relevant variables. We associate an embedding with each factor graph node to represent the latent information associated with the node and design message passing operations on the factor graph, leading to a Factor Graph Neural Network (FGNN) [\[8,](#page-8-10) [36\]](#page-9-2).
+
+As our objective is to produce floorplan layouts that satisfy all input constraints, we evaluate our model's performance with a focus on fidelity to the ground-truth image that is used to derive the constraints. Our model surpasses other strong baselines, achieving a significant improvement in both box-level IOUs and pixel-level metrics, indicating the effectiveness of our approach. Furthermore, we demonstrate our model's usefulness in two distinct practically useful scenarios. One, we show that our model is well-suited to the iterative refinement of human requirements, enabling the development of varied designs with user-interactions. Two, we show that our model can form part of a generative pipeline and generate diverse realistic layout designs for the same given input boundary, validated both qualitatively and quantitatively.
+
+# Method
+
+A factor graph is a bipartite graph G = (V, C, E) with two disjoint sets of nodes, variable nodes V, and factor nodes C. A variable node i ∈ V represents a variable xi ∈ x and each factor node c ∈ C specifies that there are dependencies among a set of variables xc; an edge (c, i) ∈ E exists between variable node i and factor node c if xi ∈ xc.
+
+Factor graphs are widely used to capture dependencies
+
+in probabilistic graphical models (PGM). In PGMs, approximation algorithms of inferring optimal values of variables $\mathbf{x}^*$ often operate by running message passing on factor graphs, for example, the *belief propagation* algorithms.
+
+Message-passing operations on a graph can be represented as a graph neural network. In a similar way, we can represent message passing on a factor graph as a factor graph neural network (FGNN) [8, 28, 36]. By assuming tensor decomposed factor potentials, both sum-product and max-product belief propagation algorithms for PGMs can be represented with compact FGNNs [8, 36, 37]. With the use of more powerful approximators within the FGNN, it may be possible to learn even more powerful inference algorithms specialized for the problem domain.
+
+In FGNN, a bipartite factor graph is established with variable and factor nodes, where each factor node connects to its dependent variable subset. These nodes are represented by variable $(\mathbf{v}_i)$ and factor $(\mathbf{f}_c)$ embeddings, initialized using respective features. The embeddings are then updated iteratively through message-passing equations.
+
+$$\tilde{\mathbf{v}}_i = AGG_{c \in N(i)} \ \Psi_{FV}(\mathbf{f}_c, \mathbf{v}_i) \tag{1}$$
+
+$$\tilde{\mathbf{f}}_c = AGG_{i \in N(c)} \ \Psi_{VF}(\mathbf{v}_i, \mathbf{f}_c) \tag{2}$$
+
+where $\Psi_{\rm FV}$ and $\Psi_{\rm VF}$ are functions with learnable parameters. Note that the factor graph is a bipartite graph. Therefore, N(i) will contain only factor nodes c and N(c) will contain only variable nodes i. This process can be iterated multiple times, followed by a readout function for tasks like regression or classification.
+
+In our problem setup, we are given a room-adjacency graph G and a boundary of the building B. The graph G is as described in Graph2Plan [11] and contains a node for each room with attributes including the size and position, and edges are typed adjacencies describing the spatial relationship between rooms. The boundary B is a 2D binary mask describing the inside area of the building. The task is to produce the floorplan layout image $\mathcal I$ which satisfies all the constraints in the input. Our problem setup differs from many GAN-based methods, as they often prioritize generation diversity. In contrast, our objective is to produce a layout that satisfies the specified constraints. Therefore, in addition to generating realistic layouts, we also aim to produce layouts that closely resemble the ground truth.
+
+To address the task, we follow conventional methods in adopting a two-stage approach [5, 11]. In the first stage, we output the coordinates of bounding boxes for each room,
+
+while in the second stage, we produce the floor plan layout image using the predicted bounding boxes. Our primary focus is on enhancing the accuracy of the first stage by leveraging domain knowledge, such as higher-order relations. To this end, we seek to effectively incorporate domain knowledge, including higher-order relations, into the model.
+
+For the first stage of predicting bounding boxes, conventional methods leverage GNNs to process the input graph and output bounding box coordinates, which are then used to produce the layout image. However, representing each object as a single node is a straightforward approach but lacks in granularity and effectiveness. This method often fails to capture the complex interactions between objects, which are typically concentrated on certain parts of an object. A common example is that only a specific side of a room, like the right wall, might be actively interacting with a neighboring object.
+
+To capture the fine-grained interactions, we propose to represent each room with four different variables. Consider a floorplan image as a single-channel image of size $H \times W$ with each room represented by an enclosing bounding box. Let the room i's bounding box be described by four coordinates in the form $(x_{min}^i, x_{max}^i, y_{min}^i, y_{max}^i)$ with $x_{min/max} \in \{1 \dots W\}$ and $y_{min/max} \in \{1 \dots H\}$ . Then the set of variables $\mathbf{x}$ , describing the bounding boxes of all the rooms, can be stated as
+
+$$\mathbf{x} = \left\{x_{min}^i, x_{max}^i, y_{min}^i, y_{max}^i|\ i \in \{1 \dots N\}\right\}$$
+
+where N is the number of rooms in a given floorplan. Given the boundary B and the input graph G, we aim to predict the values of the configuration of variables $\mathbf{x}$ which optimally satisfies all the constraints in B and G.
+
+In layout design, we frequently encounter high-order constraints that traditional GNNs cannot capture due to their limitation in modeling only pairwise dependencies. To effectively encode these constraints, we propose the adoption of factor graphs. Factor graphs provide the needed modeling capacity to capture the information available from domain knowledge. With factor graphs, the mutual dependencies between the variables can be easily encoded in the form of factor nodes. Importantly, input constraints provide important clues regarding the variable dependencies. For example, with our defined variables $x_{min}, x_{max}, y_{min}, y_{max}$ per each box, when two rooms are adjacent with a left-right relationship, it is likely that the $x_{max}$ and $x_{min}$ values of the two rooms are correlated. By recognizing these correlations, we can leverage domain knowledge to enhance the model's accuracy in predicting bounding box coordinates.
+
+
+
+Figure 2. Illustration of the proposed factor graph model for floorplan design. Each room is represented with four bounding-box variables. Factors connect the variables based on input constraints and domain knowledge. $f_{box}^i$ connects four variables of room i. $f_{rel}^{s,o}$ connects only relevant subset of variables between rooms s and s. $f_{boundary}^k$ represents s corner-point and connects to all the variables. Message passing on this factor graph helps learn better room coordinates which are then used to produce the layout image.
+
+
+
+| Relation | left-of | right-of | above | below | Relation |
+|----------|----------------------------|----------------------------|----------------------------|----------------------------|----------|
+| Factors | $\{x^s_{max}, x^o_{min}\}$ | $\{x^s_{min}, x^o_{max}\}$ | $\{y^s_{max}, y^o_{min}\}$ | $\{y^s_{min}, y^o_{max}\}$ | |
+| Relation | left-above | right-ahove | left-below | right-helow | Factors |
+| | tejt doore | right doore | teji beten | right below | |
+
+| Relation | inside | surrounding |
+|----------|-----------------------------|----------------------------|
+| | $\{x^s_{min}, x^o_{min}\}$ | $\{x^s_{min}, x^o_{min}\}$ |
+| Factors | $\{y^s_{min}, y^o_{min}\}$ | $\{y^s_{min}, y^o_{min}\}$ |
+| | $\{x^s_{max}, x^o_{max}\}$ | $\{x^s_{max}, x^o_{max}\}$ |
+| | $\{~y^s_{min}, y^o_{max}\}$ | $\{y^s_{min}, y^o_{max}\}$ |
+
+Table 1. Full set of relation types and their corresponding factors $f_{rel}$ defined in our factor graph model.
+
+Overall, we model four kinds of factors: box factors, relational factors, boundary factors, and a complete factor. Except relational factors, the rest are higher-order factors that capture dependencies between multiple variables. An illustration of the model is shown in Fig. 2.
+
+We have four variables per bounding box, and these variables are likely to be highly correlated. For example, if the room size is 100 sqft, the four variables of the room must satisfy the constraint that $(x_{max}-x_{min})*(y_{max}-y_{min})=100$ . This constraint is a higher-order factor that can provide a useful inductive bias for learning the values of the x and y variables. To encode this information, we introduce a box factor for each room i, denoted as $f_{Box}^i=\{x_{max}^i,x_{min}^i,y_{max}^i,y_{min}^i\}$ , into the factor graph model.
+
+The spatial room adjacencies available in the input graph provide rich information about the relative locations of the rooms and need to be effectively exploited. We work on the typed adjacencies defined in Graph2Plan [11], which are based on the spatial relation type of the pair of adjacent rooms. The spatial relation types are: *left-of, right-of, above, below, left-above, left-below, right-above, right-below, inside, and surrounding.*
+
+Our factor-graph-based modeling with 4 variables per room, allows us to only link the variables which are likely to have direct dependencies. We introduce factors connecting specific variables in rooms s and o depending on the relation type p of the typed adjacency $\langle s, p, o \rangle$ . For instance, if the relation is $\langle room_s, left\text{-}of, room_o \rangle$ , it is probable that the $x_{max}$ coordinate of room s is almost equal to the $x_{min}$ coordinate of room o. Therefore, we add a factor $f_{rel}^{s,o} = \{x_{max}^s, x_{min}^o\}$ to capture this relation.
+
+Similarly, we introduce factors based on the relation types which imply specific coordinate relationships between the subject and the object rooms. Table 1 describes the full set of relational factors in our model for each of the relation types. Overall, we have 20 distinct relational factor types for the 10 types of defined relations.
+
+In practice, the outer boundary of a floorplan is one of the most important constraints as it directly affects the placement of each room and the way in which different rooms should be aligned within the floorplan boundary. However, incorporating such boundary information is not trivial. Previous methods like RPLAN and Graph2Plan [11, 33] use a CNN encoder with a 2-dimensional binary boundary mask to output a boundary embedding, which is then used to produce the floorplan. However, this process is less effective because only the outer wall structure contains important information, not the whole binary mask.
+
+To address this, we introduce a highly effective method to incorporate boundary information. We observe that the most influential information contained in the boundary is the corner points of the boundary. The corner points can be described by the (x,y) coordinates and hence provide better inductive bias than a plain binary mask. Therefore, we first extract the set of corner points from the boundary mask. Then, for each corner point, we introduce a higher-order boundary factor that connects to all the variable nodes within the factor graph model. Specifically, let the boundary $b = \{(x,y)_b^k \mid k \in \{1,2,\dots n_b\}\}$ where $n_b$ is the number of corner points extracted from the given boundary mask. Then for $k^{th}$ corner point, we define a higher-order factor $f_b^k = \{x_{min}^i, x_{max}^i, y_{min}^i, y_{max}^i | i \in \{1\dots N\}\}$ which connects to all variables in the factor graph.
+
+We encode the information describing the location and surroundings of the corner point as an input feature $\mathbf{f}_b^k$ of the corner-point factor $f_b^k$ . Specifically, for each corner point $b^k$ , a feature vector is formed and initialized with three types of information: the (x,y) coordinates of the corner point, the distance d of the corner point from the bounding box enclosing the boundary, and the binary boundary mask values surrounding the corner point at a small offset of $\epsilon$ . The surrounding values indicate the direction of the corner point enclosing the inside of the house. This vector is used as an input feature for each higher-order corner-point factor.
+
+$$\mathbf{f}_{b}^{k} = \begin{bmatrix} x, \ y, \ d_{left}, \ d_{right}, \ d_{top}, \ d_{bottom}, \ mask_{(x+\epsilon, y+\epsilon)}, \\ mask_{(x+\epsilon, y-\epsilon)}, \ mask_{(x-\epsilon, y+\epsilon)}, \ mask_{(x-\epsilon, y-\epsilon)} \end{bmatrix}_{b}^{k}$$
+
+In addition to the variable dependencies described above, there can be other dependencies present in the data that are not apparent. In order to enable variables to learn from such hidden dependencies, we introduce an additional factor called the *complete factor*. This factor connects all the variables without any restriction with a factor feature indicating the type of the factor.
+
+We turn our factor graph model into a neural network with learnable potential functions. Specifically, we initialize our factor graph model with variable node features $[\mathbf{v}_i]_{i\in\mathcal{V}}$ and factor node features $[\mathbf{f}_c]_{c\in\mathcal{C}}$ . The variable node feature contains room-type, location, size and a 4-dim one-hot vector indicating the variable type i.e. $x_{min}, x_{max}, y_{min}$ or $y_{max}$ . Note that the location here is not the (x,y) coordinate but the approximate relative placement indicating north, south, north-west etc., as discussed in Graph2Plan [11]. The factor features are initialized with the factor type (i.e. box factor, relation-type, boundary or complete) and additional specific attributes for some factors (i.e. The bounding box factors $f_{box}^i$ contain room-type, location, and size, and the boundary factors $f_b^k$ additionally contain the boundary feature
+
+```
+Input: \mathcal{G} = (\mathcal{V}, \mathcal{C}, \mathcal{E}), [\mathbf{v}_i]_{i \in \mathcal{V}}, [\mathbf{f}_c]_{c \in \mathcal{C}}, [e_{ci}]_{(c,i) \in \mathcal{E}}
+Output: [v_i]_{i\in\mathcal{V}}, \mathcal{I}
+ // Box coordinates and Layout image
+ 1: for l = 1, 2, \dots, L do
+ Variable-to-Factor Message Passing:
+ \begin{split} \tilde{\mathbf{f}}_{ci}^{l} &= \text{MLP}_{\text{VF}} \left( \text{Concat}[\mathbf{f}_{c}^{l}, \mathbf{v}_{i}^{l}, e_{ci}] \right) \\ \mathbf{f}_{c}^{l+1} &= \underset{i \in N(c)}{\text{softmax}} \left( \tilde{\mathbf{f}}_{ci}^{l} | \theta_{\text{VF}}^{l} \right) \end{split}
+ 4:
+ Factor-to-Variable Message Passing:
+ 5:
+ \tilde{\mathbf{v}}_{ic}^{l} = \text{MLP}_{\text{FV}} \left( \text{Concat}[\mathbf{f}_{c}^{l}, \mathbf{v}_{i}^{l}, e_{c,i}] \right)
+ 6:
+ \mathbf{v}_{i}^{l+1} = \operatorname{softmax}_{c \in N(i)} \left( \tilde{\mathbf{v}}_{ic}^{l} | \theta_{\text{FV}} \right)
+ 7:
+ 8: end for
+ // Train with L1-Loss
+ 9: v_i = \text{MLP}(\mathbf{v}_i^L)
+ 10: \mathcal{I} = \text{CRN-Network}([v_i]_{i \in \mathcal{V}})
+ // Pixel-wise cross-entropy
+```
+
+vector as defined in Section 4.3.3.
+
+Algorithm 1 details our Factor Graph Neural Network (FP-FGNN) for floorplan design. It takes a Factor graph $\mathcal{G}=(\mathcal{V},\mathcal{C},\mathcal{E})$ as input, with variables $\mathcal{V}$ and factors $\mathcal{C}$ initialized using variable features $[\mathbf{v}_i]i\in\mathcal{V}$ and factor features $[\mathbf{f}_c]c\in\mathcal{C}$ . The edge set $\mathcal{E}$ contains edges between variable and factor nodes. The edge feature $e_{ci}$ contains the features of factor c.
+
+In each iteration, the variable nodes receive and aggregate messages from the factor nodes and vice versa. The message function is an MLP acting on the concatenated variable and factor embeddings from the previous iteration along with the edge feature. Furthermore, our message aggregation allows weighted aggregation of messages with the help of a learnable softmax-based aggregator defined as
+
+softmax(
+$$\mathbf{v} | \theta$$
+) = $\sum_{\mathbf{v}_i \in \mathbf{v}} \frac{\exp(\theta \cdot \mathbf{v}_i)}{\sum_{\mathbf{v}_j \in \mathbf{v}} \exp(\theta \cdot \mathbf{v}_j)} \cdot \mathbf{v}_i$ (3)
+
+This enables variables to learn from factors most relevant to them and vice versa. After a fixed number of iterations, we use an MLP to output the coordinate value of each variable.
+
+To translate the box coordinates to the floorplan image, we feed the predicted bounding box coordinates to a cascaded refinement network (CRN)[4] based network in the form of multi-channel input. We borrow this part of the network from Graph2Plan [11], which can produce the floorplan image given the predicted bounding boxes.
+
+Our network is trained with only two loss functions. The bounding box coordinate variables are trained with mean absolute loss (L1-Loss) against the ground-truth box-coordinate values and the final layout image is trained with
+
+
+
+Figure 3. Visualization of layout predictions of Graph2Plan and FP-FGNN without postprocessing. FP-FGNN is able to produce more accurate predictions of room dimensions and the shape of walls, resulting in layouts that are more visually coherent and well-defined.
+
+| Method | Box-level | | Pixel-level | | | Constraints-level | |
+|-----------------|-----------|-----------|-------------|-----------|-----------|-------------------|--------------|
+| | IOU-Macro | IOU-Micro | Accuracy | IOU-Macro | IOU-Micro | Relation Acc | Location Acc |
+| HPGM [5] | 0.4701 | 0.5424 | 0.6464 | 0.4812 | 0.5179 | - | - |
+| Graph2Plan [11] | 0.6796 | 0.7402 | 0.8683 | 0.4476 | 0.7575 | 0.9358 | 0.9805 |
+| FP-FGNN | 0.8685 | 0.9165 | 0.8901 | 0.5954 | 0.8019 | 0.9680 | 0.9913 |
+
+Table 2. Box-level,Pixel-level and Constraints-level comparisons. We train and evaluate all three models on the same dataset.
+
+the pixel-wise cross-entropy loss with room categories as class variables. Note that out model does not rely on additional losses, such as interior-loss, coverage-loss or mutexloss *etc*., as used in Graph2Plan [\[11\]](#page-8-5).
diff --git a/2404.00973/main_diagram/main_diagram.drawio b/2404.00973/main_diagram/main_diagram.drawio
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+# Introduction
+
+In recent years, large-scale video-and-language pretraining has seen remarkable progress. Most modern video-language understanding models [\[6,](#page-8-0) [18,](#page-8-1) [31,](#page-9-0) [39,](#page-9-1) [43,](#page-9-2) [44,](#page-9-3) [62\]](#page-10-0) independently encode uni-modality and then fuse them. Human motion perception studies [\[7,](#page-8-2) [21,](#page-8-3) [65\]](#page-10-1) suggest that humans perceive motion and environments as goal-driven behavior. This discrepancy leads to several issues, especially in longform video understanding.
+
+First, goal-free video representation struggles with longterm dependencies and multi-event reasoning. While enabled by progress in cross-modal pretraining[\[36,](#page-9-4) [54,](#page-10-2) [57\]](#page-10-3) and datasets[\[47,](#page-9-5) [50\]](#page-9-6), current methods excel at question an-
+
+∗ Equal contribution, † Corresponding author This work was done during an internship at Shanghai AI Lab.
+
+swering for images and short videos, but long-form videos contain many clips irrelevant or redundant to the questions, interfering with overall understanding. Encoding long videos also brings immense computational costs. Second, accurate semantic reasoning usually relies on multi-scale perception from local-spatial regions to global temporal dynamics. Goal-free methods for multi-scale visual modeling require custom submodels[\[11,](#page-8-4) [64,](#page-10-4) [68\]](#page-10-5) for each scale or extra modalities like bounding boxes and OCR features[\[27,](#page-9-7) [83\]](#page-11-0) but large-scale pretraining makes these approaches inefficient or infeasible. Third, goal-free video embedding requires a multimodal fusion module to synthesize questions and visual embeddings to predict answers. Incorporating questions directly can lead to shortcut solutions [\[1,](#page-8-5) [2,](#page-8-6) [10,](#page-8-7) [15,](#page-8-8) [23,](#page-8-9) [30,](#page-9-8) [51,](#page-9-9) [82\]](#page-11-1), which means utilizing obvious clues in questions (mainly in data distribution and the relation between keywords) that exhibit more reliability in answer prediction than complicated visual reasoning, especially in the early training phases. This phenomenon is also known as language bias, which often causes major performance gaps in out-of-distribution tests. The above challenges are visualized in Figure [1.](#page-0-0)
+
+To address these issues, we propose a language-aware (goal-driven) visual semantic distillation framework called VideoDistill. Semantic distillation means the visual encoder must embed relevant frames of questions and focus on question-related multi-scale visual semantics. Closely resembles human behavior, semantic distillation functions like keeping the goal (question) in mind and conducting meaningful and precise visual reasoning. Differing from previous VideoQA frameworks [\[5,](#page-8-10) [39,](#page-9-1) [40,](#page-9-10) [52,](#page-10-6) [56,](#page-10-7) [73,](#page-10-8) [74\]](#page-10-9), VideoDistill can generate answers only from question-related visual embeddings (without additional textual embeddings) since the interaction of text and vision is adequate during semantic distillation. This feature also enhances the significance of visual reasoning when generating answers.
+
+To realize semantic distillation, we first introduce Language-Aware Gate (LA-Gate), a multi-head cross-gating mechanism for cross-model interaction, which is inspired by self-gating and gated attention[\[17,](#page-8-11) [26,](#page-8-12) [45,](#page-9-11) [63\]](#page-10-10) and is integrable into any existing transformer-based models like[\[6,](#page-8-0) [62\]](#page-10-0). LA-Gates compute questions' dependencies on video patch embeddings and depress or excite corresponding patches in subsequent attention layers. The proposed LA-Gate is not only the key to semantic distillation but also a new efficient modality fusion method, which is a powerful competitor of predominant Cross-Attention in VideoQA. We discuss the differences between LA-Gate and attention mechanisms in Section [3.2](#page-3-0) and summarize three merits of LA-Gate: (1) It can alleviate the influence of language bias by avoiding the direct involvement of text embeddings. (2) It can better maintain local diversity within the video embeddings. (3) It enhances the interpretability of the modality fusion process.
+
+VideoDistill has two LA-Gate-based modules. The first
+
+is a differentiable sparse sampling module, which uses pretrained image-language models like CLIP[\[54\]](#page-10-2) to encode frames, then performs goal-driven frame sampling to remarkably reduce subsequent spatial-temporal attention overhead and naturally avoid long-term dependencies and multi-event reasoning by retaining only question-related frames. It also provides our framework with a good nature that is insensitive to the number of sampled frames (see section [4.3\)](#page-6-0).
+
+The second is a vision refinement module eliminating unrelated visual semantics at different perceptual levels and enhancing related multi-scale semantics to support multilevel refinement. It encodes sparsely sampled frames into question-related global embeddings for generating answers. This module brings obvious performance boosts, especially on object-related questions (see section [4.5\)](#page-7-0). Our contributions are summarized as follows:
+
+- We propose the Language-Aware Gate (LA-Gate) that enables interacting vision with language meanwhile not directly introducing language into visual representations. It can alleviate the influence of language bias, better maintain local diversity within the video embeddings, and is more interpretable compared with predominant Cross-Attention.
+- Based on LA-Gate, we propose a differentiable sparse sampling module to capture question-related frames and a vision refinement module to emphasize multi-scale questionrelated semantics. They can benefit VideoQA models on the stability under various numbers of sampled frames and the capacity to understand multi-scale objects.
+- Our model achieves new state-of-the-art performance on a wide range of downstream VideoQA tasks and text-tovideo retrieval tasks.
+
+# Method
+
+We introduce VideoDistill, a pretraining framework that enables goal-driven VideoQA relying on question-related frames and their multi-scale semantics. Figure [2](#page-2-0) gives an overview of VideoDistill. VideoDistill consists of two submodules: Differentiable Sparse Sampling and Vision Refinement. Both sub-modules are built upon our proposed language-aware gate (LA-Gate) to perform in a goal-driven manner.
+
+VideoDistill begins with densely sampling N uniformly distributed frames and embedding them into frame represen-
+
+
+
+Figure 3. Illustrations of Self-Attention, Cross Attention, and our LA-Gate mechanisms
+
+tations. We adopt the pretrained image-language encoder CLIP [54] with frozen parameters in the visual branch to reduce the computational overhead. CLIP divides N frames into $N \times n \times n$ patches, and extracts patch embeddings $v_{\mathrm{patch}} \in \mathbb{R}^{N \times n^2 \times D}$ and CLS tokens $v_{\mathrm{cls}} \in \mathbb{R}^{N \times D}$ that represent the global understanding of frames, where D is the dimension of representations.
+
+For the Language branch, A text is first tokenized and then fed into the pretrained text encoder of CLIP to generate word-level embeddings $t_{\mathrm{word}} \in \mathbb{R}^{M \times D}$ and a sentence-level token $t_{\mathrm{cls}} \in \mathbb{R}^D$ , where M is the length of embeddings. Note that parameters in the text encoder are updated during pretraining and downstream finetuning to mitigate the linguistic gaps between datasets.
+
+We introduce a reusable unit, termed language-aware gate (LA-Gate), which takes an arbitrary visual representation v and a sentence-level language representation $t_{\rm cls}$ as input. LA-Gate generates $v_{\rm distill}$ , an output sequence of language-aware visual representation with the same dimension as v, by exciting or depressing the components of v according to their similarities with $t_{\rm cls}$ . For a better understanding of LA-Gate, we illustrate self-attention, cross-attention, and LA-Gate in Figure 3. We first discuss why commonly used cross-attention is not suitable for our purposes, then point out the design concept and benefits of LA-Gate.
+
+Figure 3(b) illustrates two types of cross-attention: Text-to-Video (T2V, text as *Query*) and Video-to-Text (V2T, video as *Query*). Since outputs of the attention have the same length as *Query* and carry information sourced from *Value*, applying cross-attention in VideoQA will have two problems. Firstly, T2V is unsuitable for the modality interaction in the middle layers as it alters the shape of visual representations. Although V2T can maintain the shape, its output will carry the info directly from the text (like reconstructing text representations into the shape of visual input).
+
+The structure of LA-Gate is shown in Figure 3(c). There are two divergences between V2T and LA-Gate. (1) When text input length L>1, we sum the similarity matrix to form a vector of importance for rows of the video input.
+
+L functions like the number of multi-heads in the attention mechanism, and each text representation will partially dictate the importance of each row in the video input. (Note that in VideoDistill, we always have L=1 since the text input is $t_{\rm cls.}$ ) (2) We expand the importance vector (repeat D times) to match the shape of the video input. Finally, we apply an element-wise product rather than a dot product on the importance matrix and the Value.
+
+The first merit of LA-Gate is the text input only controls the importance of each visual representation, and the output does not directly involve text. Thus, the answer decoder can make decisions based on "purer" visual semantics and alleviate shortcut solutions hidden in texts.
+
+A more significant characteristic of LA-Gate in general modality fusion usage: it can better maintain local diversity within the visual input. In Figure 3 (a) and (b), the output displays rows in mixed colors, representing the weighted sum of rows in the Value. Cross-attention will gather information from all rows in the Value for each output token and present more global attributes than LA-Gate since LA-Gate does not blend all features. In Figure 4(c), we maintain the uniqueness of colors for LA-Gate in each output row because each row is produced by multiplying the corresponding row in the Value and a scaler of importance. This characteristic makes LA-Gate fit more into our idea of information distillation because the language-aware distillation of a local area should be irrelevant to other regions.
+
+The last benefit of LA-Gate is we can enhance the interpretability of modality fusion by incorporating LA-Gate with self-attention layers. Due to the skip connection between the input and the output, commonly used V2T cross-attention brutally adds a reconstructed text (maybe linearly projected) into the visual input to fuse vision and language. The physical meaning of the structure is difficult to explain. By contrast, LA-Gate first distills the visual input by emphasizing language-related visual semantics, and then the interaction within visual representations is performed by self-attention.
+
+The implementation of LA-Gate is visualized in Figure 4 (we demonstrate how LA-Gate works in a vision refinement block, where v consists of frame patches from K selected frames and a video-level CLS token). Assume LA-Gate
+
+receives $v \in \mathbb{R}^{m \times D}$ , where m is the sequence length, and $t_{\rm cls} \in \mathbb{R}^D$ . We first produce the key from the language representation $t_{\rm cls}$ and queries and values from the visual representation v. Then, we calculate cosine distances between each query and the key:
+
+$$\text{key} = w_k(t_{\text{cls}}), \text{query}_i = w_q(v_i), \text{value}_i = w_v(v_i)$$
+ (1)
+
+$$\operatorname{dist}_{i} = \operatorname{repeat}\left(\frac{\operatorname{query}_{i} \cdot \operatorname{key}}{\|\operatorname{query}_{i}\| \times \|\operatorname{key}\|}\right),\tag{2}$$
+
+where $v_i \in \mathbb{R}^D$ , $i \in [1, m]$ is a feature vector in v. $w_k$ , $w_q$ and $w_v$ are trainable linear projection layers, and repeat means expanding a scalar into a vector with the dimension of D. Since $\operatorname{dist}_i$ reflects the correlation between vision and language, we treat it as an importance coefficient of valuei. Then, we generate distilled $v_i$ as formulated:
+
+$$v_{\text{distill}}^i = \mathbf{w}_{\text{o}} \left( \text{dist}_i \odot \text{value}_i \right),$$
+ (3)
+
+where $\odot$ denotes element-wise production and $w_o$ is the linear output layer. In practice, we perform a standard multihead attention setting [60] on $w_k$ , $w_q$ , $w_v$ and $W_o$ , and concatenate outputs of each head to form $v_{\rm distill}^i$ . Although, we only illustrate a single attention head in Figure 2 for simplicity. Also, we apply an input skip connection on $v_{\rm distill}^i$ for better training stability and convergence. Finally, LA-Gate outputs $v_{\rm distill} = \{v_{\rm distill}^i | i \in [1, m]\}$ .
+
+Given N densely sampled frames, VideoDistill further adaptively picks out K (K < N) language-related frames. To this end, we utilize stacked frame sampling blocks (FS-Blocks, L layers) which take as input $v_{\rm cls}$ and $t_{\rm cls}$ to perform a top-K selection. Since $v_{\rm cls}$ of frames are separately extracted, to indicate their temporal positions in the whole video, we add temporal embedding $T_{\rm f} \in \{\phi_{\rm T}({\rm f}) \mid {\rm f} \in [1,{\rm N}]\}$ for each of them according to their frame index. After adding temporal information, each FS-Block first distills $v_{\rm cls}$ conditioned on $t_{\rm cls}$ by LA-Gate to emphasize question-related frames, then performs an interframe interaction through standard multi-head attention. Figure 4 shows the architecture of FS-Blocks. We borrow the form of skip connections from the divided block in [6].
+
+To realize the differentiable selection, we first project $v_{\rm cls}^L$ , the output of the L-th FS-Block with the dimension identical to $v_{\rm cls}$ , onto a feature space with the dimension of K by a linear layer $W_s$ , where K is the number of frames to be selected. Then, we conduct Gumbel-Softmax sampling [28] on each $x^k \in \mathbb{R}^N, k \in [1, K]$ , the row of $W_s \left(v_{\rm cls}^L\right)$ . The procedures are formulated as follows:
+
+$$y_{\text{soft}}^k = \operatorname{softmax}\left(\frac{x^k + gumbels}{\tau}\right),$$
+ (4)
+
+The code will be available at https://zoubo9034.github.io/ VideoDistill/
+
+
+
+Figure 4. Architectures of Language-Aware Gate, Frame Sampling Block, and Vision Refinement Block.
+
+$$y_{\text{hard}}^k = \text{onehot}\left(\operatorname{argmax}\left(y_{\text{soft}}^k\right)\right),$$
+ (5)
+
+
+$$mask^{k} = y_{hard}^{k} + y_{soft}^{k} - stopgrad(y_{soft}^{k}),$$
+ (6)
+
+where gumbels is a noise sampled from the Gumbel distribution with $\mu=0$ and $\beta=1$ , $y_{\rm soft}^k$ is the possibility of frames to be selected as the k-th language-related frame and $y_{\rm hard}^k$ reflects the index of the chosen frame. Since the argmax operation has no gradient, we adopt a code-level trick in equation 6 to generate $mask=\{mask^k|k\in[1,K]\}$ , which can properly pass the gradient. Finally, we apply mask on $v_{\rm patch}$ and generate $v_{\rm patch}^K\in\mathbb{R}^{K\times n^2\times D}$ for the following vision refinement.
+
+Given the selected patch embeddings $v_{\mathrm{patch}}^K$ and the language representation $t_{\mathrm{cls}}$ , this module generates video-level representation $v_{\mathrm{cls}}^*$ , which synthesizes multi-scale language-related visual semantics. $v_{\mathrm{cls}}^*$ is set as a learnable token with the dimension of D and is concatenated to $v_{\mathrm{patch}}^K$ as the visual input $v_*$ . We combine the LA-Gate and any existing spatial-temporal self-attention layer [6, 8, 62] to form a vision refinement block as shown in Figure 4. When iteratively applying vision refinement blocks on $v_*$ , LA-Gates spontaneously distill language-related visual semantics in different perceptive fields. Since the effective perceptive field gradually expands in stacked blocks [55], our module can consider multi-scale objects.
+
+Unlike predominant pyramidal models [41, 52, 75] for multi-scale reasoning that gathers the intermediate results of each encoding stage as final representation, we empirically adopt only the $v_{\rm cls}^*$ from the last vision refinement block.
+
+We adopt the two pretraining tasks to facilitate cross-modal interaction: Video-Text Matching (VTM) and Vision-Guided Masked Language Modeling (VG-MLM).
+
+**Video-Text Matching**. To facilitate cross-modal interaction, we use VTM as the first pre-training task. VTM predicts whether input video-language pairs are matched. In practice, we randomly exchange the annotations of two input pairs (matched) in a mini-batch with a probability of 0.5. Then, we use a linear projection head on the top of $v_{\rm cls}^*$ to predict two-class matching logits y, and formulate the VTM loss as negative log-likelihood:
+
+$$\mathcal{L}_{\text{VTM}} = -\mathbb{E}_{\left(v_{\text{cls}}^{*}\right)} \log p\left(y \mid v_{\text{cls}}^{*}\right), \tag{7}$$
+
+We do not adopt predominant contrastive pretraining because this paradigm requires the contrast between all possible combinations of vision and language. However, for VideoDistill, the number of possible visual embeddings of each video is proportional to the number of annotations. This leads to a quadratic growth in computational overhead (see Appendix A for more details). Nevertheless, we still apply a contrastive constraint on matched pairs (w/o exchange) to stabilize the training:
+
+$$\mathcal{L}_{\text{CL}} = -\sum_{i \in B} \log \frac{\mathbb{I}\left(\text{matched}\right) \exp\left(s\left(v_{i}, t_{i}\right) / \tau\right)}{\sum_{j \in B} \exp\left(s\left(v_{i}, t_{j}\right) / \tau\right)}, \quad (8)$$
+
+where B is the batch size, $v_i$ and $t_i$ are $v_{\rm cls}^*$ and $t_{\rm cls}$ of i-th video-language pair, $\mathbb{I}$ is a indicate function of whether the video and the annotation in the i-th pair are matched, s denotes a cosine similarity function, $\tau$ is a temperature coefficient equals 0.07 in this paper.
+
+Vision-Guided Masked Language Modeling. The commonly used MLM [16] task aims to predict the ground truth label of the masked token according to textual context. To better map the visual and the language representations in a fine-grained manner, we propose VG-MLM, which is applied only on the matched pairs (w/o exchange) and encourages predicting the masked tokens from the visual context. In particular, we first provide VideoDistill the CLS tokens $t_{\rm cls}$ of unmasked annotations to sample frames and extract video representation $v_{\rm cls}^*$ . Then, we mask I words in the annotations and encode the masked sentences. The outcome $w_i^{\rm M}, i \in [1,I]$ , which denotes the token of i-th masked word, and $v_{\rm cls}^*$ are used to predict the i-th masked word. The objective of VG-MLM is formulated as follows:
+
+$$\mathcal{L}_{\text{VG-MLM}} = -\mathbb{E}_{\left(t_{\text{cls}}, v_{\text{cls}}^*\right)} \log p\left(w_i | \text{SG}\left(w_i^{\text{M}}\right), v_{\text{cls}}^*\right), \quad (9)$$
+
+where SG means stop gradient, $w_i$ denote the logit of i-th masked word. We stop the gradient of $w_i^{\rm M}$ to enhance the importance of visual clues in language modeling. Following the heuristics of Bert [16], we adopt the same masking strategy. Then we adopt a two-layer MLP on top of the combination of $w_i^{\rm M}$ and $v_{\rm cls}^*$ to generate the probability over the vocabulary, which is calculated as the negative log-likelihood loss
+
+for the masked word. The overall loss of pretraining is the combination of VTM, VG-MLM, and CL:
+
+$$\mathcal{L}_{\text{total}} = \mathcal{L}_{\text{VTM}} + \mathcal{L}_{\text{VG-MLM}} + \mathcal{L}_{\text{CL}}, \tag{10}$$
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+# Introduction
+
+Identifying relations between entity pairs within unstructured text is known as relation extraction (RE). Earlier studies concentrated on uncovering relations between entities within a sentence [@zhang-etal-2017-position; @Hsu2022ASA; @zeng-etal-2014-relation; @dos-santos-etal-2015-classifying; @cai-etal-2016-bidirectional; @10.1007/978-3-031-43421-1_14]. However, an entity pair need not necessarily occur in a sentence. It can also be part of a document. There is substantial literature on extracting relations between entities based on the document itself [@info14070365; @Xu2020DocumentLevelRE; @christopoulou-etal-2019-connecting; @nan-etal-2020-reasoning; @zeng-etal-2020-double; @li-etal-2021-mrn; @jain2024revisiting]. Beyond a single sentence or a document, entity pairs can also be present in different documents [@yao-etal-2021-codred].
+
+
+
+Three text paths indicate the relationship path between the source entity, GCompris, and the target entity, GNU Project. These connections are established through pairs of documents, where one document features the source entity, and the other contains the target entity. In each path, the connection between the source and target entities is led by a mentioned entity in both documents (e.g., Linux).
+
+
+An analysis on Wikipedia shows that more than 57.6 relational facts do not co-occur in a single document [@10.1145/2629489]. This indicates that many facts occur across the documents. Considering that, little work has been performed in cross-document relation extraction (CrossDocRE). CodRED (cross-document relation extraction dataset) is the first dataset published in this line of work [@yao-etal-2021-codred], which serves as a starting point to solve CrossDocRE. Documents containing the source entity are identified and retrieved from multiple documents; the same is done for the target entity. Various text paths between entity pairs (source and target entity) across documents are recognized using the mentioned entity (other than the source and target entity). Text paths refer to the paths that connect the source and target entities via the mentioned entities. These paths are retrieved from both the source and target documents.
+
+Figure [1](#fig:mesh1){reference-type="ref" reference="fig:mesh1"} shows an example of CrossDocRE. Between entity pairs **GCompris** and **GNU Project**, multiple text paths via the mentioned entities, such as *Linux, Qt* and *GNU*, can be used to get the correct relation label as **developer**. CodRED uses these text paths in a bag and performs reasoning [@yao-etal-2021-codred]. Although this approach seems reasonable, along with relevant text, these text paths also contain noisy data. To overcome these issues, ECRIM (entity-centered cross-document relation extraction) proposed filtering text paths using a mentioned entity [@wang-etal-2022-entity]. However, ECRIM only works in one of the settings of CrossDocRE where text paths are provided for reasoning. To address this issues, MR.COD has been proposed, which is a multi-hop reasoning framework based on path mining and ranking [@DBLP:conf/acl/LuHZMC23]. These models rely on the knowledge between entities in a text and do not consider the domain knowledge associated with entities. Past work along these lines uses entity types and entity aliases to predict the relation [@10.1007/978-3-030-62419-4_11]. RECON [@bastos2021recon] encoded attribute and relation triples in the Knowledge Graph. KB-Both [@inproceedings] uses entity details from hyperlinked text documents of Wikipedia and Knowledge Graph (KG) from Wikidata to enhance performance. Given input text as a sentence or a document, these models use basic details of entities to improve the performance. In this paper, we explore whether incorporating domain knowledge can enhance the performance of CrossDocRE tasks. The main contributions of this work are as follows.
+
+- We propose a novel model that integrates domain knowledge with a cross-document relation extraction model.
+
+- Our validation demonstrates the effectiveness of our model, with nontrivial performance gains in cross-document RE.
+
+- We enhance the predicted relationships through textual explanations, offering insights into how the relations were predicted.
+
+# Method
+
+
+
+Architecture diagram of KXDocRE for cross-document relation extraction. Here EC represents entity context, CC represents connecting context and ECC represents both entity and connecting path context.
+
+
+For a given entity pair, $\langle e_s,e_o\rangle$ our task is to predict the target relation $r^c \in \mathcal{R}$ that holds between $e_s$ and $e_o$ within a given corpus of documents $\mathcal{C}_{i=1}^n$, where $\mathcal{R}$ is the relation set and $n$ is total number of documents. If no relation is inferred, it returns the *NA* label. Besides $e_s$ and $e_o$, we introduce the notion of potential bridge entities, which are other mentioned entities in a document that could act as intermediate concepts that help link $e_s$ and $e_o$ via multi-hop connections. The documents provided in CrossDocRE are also annotated with these mentioned entities. We denote $E_i = \{ e^1_i, \ldots, e^m_i \}$ to be the set of $m$ mentioned entities in the $i$-th document, $i=1, \ldots, M$. CrossDocRE works in two settings, closed and open. In the closed setting, only the related documents are provided to the models, and the relations are inferred from the provided documents. In the more challenging and realistic open setting, the whole corpus of documents is provided, and the model needs to efficiently and precisely retrieve related evidence from the corpus. We now discuss the architecture of KXDocRE (Figure [2](#modelcross){reference-type="ref" reference="modelcross"}) in the subsequent sections.
+
+We consider three forms of domain knowledge knowledge for cross-document relation extraction. 1) The type information of the source and target entity; 2) The connecting path between the source and target entities, extracted using Wikidata; and 3) A combination of 1) and 2). We use the term *context* in the rest of the paper to capture these three forms of knowledge.
+
+**Entity type context (EC):** Predicting the relationship between two entities relies heavily on understanding their respective entity types. For instance, if both entities belong to the *Person* category, certain relationships like *has organization*, *has location*, and more are not viable. However, relationships like *child*, *spouse*, or others become possible. Therefore, incorporating the entity type information can assist the model in excluding obvious relationships, thereby improving its performance. Consider the example given in Figure [3](#context){reference-type="ref" reference="context"}. The type of the source entity *Jim Lynagh* is *Person*, and the type of the target entity *Irish Republic*, is *Geo Political Entity*, which is used to identify locations, countries, cities, or geopolitical regions. The entity type context for this example is, EC$_{\text{\{Q6196505,Q1140152\}}}$={Person, GeoPoliticalEntity}
+
+**Connecting path context (CC):** The contextual path pertains to an entity pair $\langle e_s,e_o\rangle$. We consider context paths up to $N_h$- h is the hop[^1] distance between the entity pair. In Figure [3](#context){reference-type="ref" reference="context"}, the entity *Jim Lynagh* is five hops away from the entity *Irish Republic*. The four nodes between the entity pair on the path are intermediary entities, and the five edges are the intermediary properties. The contextual path ($CP_{e_i,e_j}$) is formed using the intermediary entities and properties. So, the connecting path context, CC$_{\{\text{Q6196505,Q1140152}\}}$={instance of, Human, model item, Douglas Adams, country of citizenship, United Kingdom, replaces, United Kingdom of Great Britain and Ireland, followed by}.
+
+
+
+Context path constructed from Wikidata between Jim Lynagh (source) and Irish Republic (target).
+
+
+**Entity type with connecting path context (ECC):** ECC combines the entity type and the connecting path context. For Figure [3](#context){reference-type="ref" reference="context"}, ECC$_{\{\text{Q6196505,Q1140152}\}}=\text{EC}_{\text{\{Q6196505,Q1140152\}}}$ $\cup$ CC$_{\{\text{Q6196505,Q1140152}\}}$.
+
+The steps to generate EC, CC, and ECC for a given entity pair are outlined in Algorithm [\[algo\]](#algo){reference-type="ref" reference="algo"}. The ContextGeneration function (lines 3-12) computes the EC, CC, and ECC for the given entity pair. CC is constructed in steps of one hop. In lines 14-17, the adjacent edge and node information is retrieved and added to the path. This continues for $N_h$ hops (lines 18-20). The EC of the source and the target entity is obtained using the named entity recognition technique (lines 27-35).
+
+::: algorithm
+**Initialization:** i $\gets$ 1, source $\gets$ v$_s$, path$_{i-1}$, finalcontext, finalentitytype, adjacent_node, hop_path$_i$ $\gets$ **None** \
+Context $\gets$ (v$_s$, v$_o$, N$_h$)
+
+contextpath, entitytype $\gets$ {}
+
+finalcontext $\gets$ contextpath $\cup$ entitytype {finalcontext}
+
+edge$_i$, adjacent node$_i$ = GetOneHopFromSource(v$_s$)\
+path$_{i}$ = {edge$_i$, adjacent node$_i$}\
+i $\gets$ 1\
+
+{path$_i$}
+:::
+
+In this step, we select sentences that contain meaningful connecting information about the source and target entity and remove irrelevant sentences. The sentences are filtered using the mentioned entity, $e^m$. Similar to the baseline [@wang-etal-2022-entity], our underlying premise is that a sentence holds significance if it contains either a source or a target entity. Additionally, a sentence is relatively significant if it mentions another entity in conjunction with the entity pair found in both documents. We calculate each entity's score based on three conditions. 1) The source and target entities are in the same sentence as $e^m$ ($\Theta_1$), 2) An entity, $e^o$, co-occurring with $e^m$, also co-occurs with the source and target entities in a different sentence ($\Theta_2$), 3) $e^m$ is part of a text path ($\Theta_3$). Figure [4](#pathdetail){reference-type="ref" reference="pathdetail"} depicts these conditions using the example from Figure [1](#fig:mesh1){reference-type="ref" reference="fig:mesh1"}. The red color represents direct occurrence with the source or target entity in the same sentence, the black line represents indirect co-occurrence, and the green line represents potential co-occurrence.
+
+
+
+An example of a co-occurring graph for path 3 in Figure 1.
+
+
+For a mentioned entity $e^m$ in each text path $p_i$, the score of each mentioned entity $e^m$ is calculated by the following equation, $$\small
+\begin{aligned}
+\text{score}(e^m) &= \lambda S_1(e^m) + \eta S_2(e^m) + \kappa S_3(e^m) \\
+S_1(e^m) &= \begin{cases} 1, & \text{if } \Theta_1(e^m) \\ 0, & \text{otherwise} \end{cases} \\
+S_2(e^m) &= \begin{cases} \left| \left\{ e^o \mid \Theta_1(e^o) \wedge {I}(e^o) = 1 \right\} \right|, & \text{if } \Theta_2(e^m) \\ 0, & \text{otherwise} \end{cases} \\
+S_3(e^m) &= \begin{cases} \left| \left\{ p_i \mid e^m \in \text{E}_i^m \right\} \right|, & \text{if } \Theta_3(e^m) \\ 0, & \text{otherwise} \end{cases}
+\end{aligned}$$
+
+Here, $\lambda, \eta, \kappa$ are hyper parameters. $I(e^o)=1$ when $e^o$ and $e^m$ are co-occuring in the same sentence, where $e^o\in E_i^m \textbackslash{}
+ \{e^m\}$. $S_2$ sums the number of occurrences of $e^o$ and $S_3$ sums the occurrence of mentioned entity in path $p_i$. Next, we calculate the importance score $Imp^s$ of each sentence by aggregating the scores of each mentioned entity, $Imp^s$=$\sum\limits_{e^m \in E_s^m}$score$\left(e^m\right)$, where $E_s^m$ is mentioned entity (bridge) in sentence s. The sentences are then ranked based on their importance score, and the top K sentences form the candidate set, $S=\{s_1, s_2, ..., s_K\}$, where K is set to 16 based on the experiment results. We reused the entity based filter from ECRIM model [@wang-etal-2022-entity].
+
+We aggregate the candidate set sentences from the previous step with the context selected using entity type with the connecting context. $S_{total}=S\{s_1, s_2, ..., s_K\}\oplus S_{ECC}$, where $S_{ECC}$ is the string based domain knowledge obtained from ECC. We then apply a relevance-based filter that considers the semantic relevance of the sentence. Here, we assume that if a sentence is semantically similar to another sentence, including the target entity, this sentence should be more informative than other sentences. Our objective is to get the most informative context $I^*$ from the candidate set $S_{total}$ for reasoning about the relation between entities.
+
+We mark the start and end of every entity and context using special tokens in sentences. Following the baseline [@yao-etal-2021-codred], we have used the BERT model [@devlin-etal-2019-bert] to encode the tokens selected from the previous step. Here, $start$ and $end$ are the start and end positions of the j-th mention, $e_j$ is the j-th mentioned entity, and n is the total number of sentences.
+
+::: center
+$e_j=BERT(\{({S_{total}}_{i=1}^n\})_{start}^{end}$
+:::
+
+Besides the mentioned entity, common relations also exist across various text paths. To capture these details from the text path, we used the cross-path entity relation attention module based on Transformer [@10.5555/3295222.3295349]. We collect all entity mentioned representation in a bag and then generate relation representations for entity pairs (e$_u$, e$_v$). Here, $\boldsymbol{E}_r$, $\boldsymbol{E}_u$, $\boldsymbol{E}_v$ are learnable parameters and e$_u$, e$_v$ are combinations of all entities present, including the mentioned entity.
+
+::: center
+$\boldsymbol{r}_{u, v}=\operatorname{ReLU}\left(\boldsymbol{E}_r\left(\operatorname{ReLU}\left(\boldsymbol{E}_u \boldsymbol{e}_u+\boldsymbol{E}_v \boldsymbol{e}_v\right)\right)\right)$
+:::
+
+To model the relation interaction across paths, we build a relation matrix $M \in \mathcal{R}^{|E| \times|E| \times d}$, where $E=\bigcup_{i=1}^N E_i$ denotes all the entities in the entity set $E_i$ of text path $p_i$ and $E_i=\left\{e_s, e_o\right\} \cup E_i^m$ and N is total number of entities.
+
+For capturing the intra and inter path dependencies, we apply a multi-layer Transformer to perform self attention on the flattened relation matrix $\boldsymbol{\hat {M}} \in \mathbb{R}^{|E|^2 \times d}$.
+
+::: center
+$\boldsymbol{\hat{M}}^{(t+1|1)} = \text{Transformer}(\boldsymbol{\hat{M}}^{(t)})$
+:::
+
+We obtain the target relation representation $r_{h_{i},t_{i}}$ for each path $p_i$ from the last layer of the Transformer.
+
+After getting the relation representation $r_{h_{i},t_{i}}$ for each text path $p_i$ for each pair of entities, $r_{h_{i},t_{i}}$ is used as a classification feature. We feed these features to the MLP classifier to get the score for each relation. The relation that gets the maximum score is the predicted relation.
+
+::: center
+$\hat{y_i}=\boldsymbol{MLP}(r_{h_{i},t_{i}})$
+:::
+
+To obtain the final score for each relation type r, a max pooling operation is applied to each relation label.
+
+::: center
+$\hat{y}^{(r)}=\operatorname{Max}\left\{\hat{y}_{i}^{(r)}\right\}_{i=1}^{N}$
+:::
+
+After obtaining the scores for all relations, a global threshold $\theta$ is applied to filter out the categories lower than the threshold. A additional threshold is introduced by baseline paper [@wang-etal-2022-entity] to control which class should be output. The scores of target classes should be greater than threshold and scores of non target class are less than threshold. Formally, for each Bag B, loss is defined as:
+
+::: center
+$\begin{aligned} \mathcal{L}= & \log \left(e^{\theta}+\sum_{r \in \Omega_{\text {neg }}^{B}} e^{\hat{\boldsymbol{y}}^{(r)}}\right) \\ & +\log \left(e^{-\theta}+\sum_{r \in \Omega_{\text {pos }}^{B}} e^{\hat{\boldsymbol{y}}^{(r)}}\right)\end{aligned}$
+:::
+
+Here, $\hat{y_r}$ is score for relation r , $\theta$ represent threshold and is set to zero, $\Omega_{\text{pos}}^{B}$ and $\Omega_{\text{neg}}^{B}$ are positive and negative classes between target entity pair.
+
+To improve the interpretability of our model, we incorporated an explainable module that can explain the predicted relationship by providing the filtered sentences given to the model. This represents a notable advancement, as contemporary state-of-the-art models cannot often furnish explanations alongside their predictions. In CrossDocRE, the most challenging issue lies in the length of the documents because it affects the amount of noise the model has to handle. So having an explanation module also helps in getting to know how well the model is able to handle noise. Along with that, it also helps in facilitating error analysis. We retrieve the tokens that are fed to the model ($I^*$), converting them into strings. This way, we get the exact token data that was used to make the relation prediction. This process enables us to obtain the precise token data, which drives predictions.
diff --git a/2406.03150/paper_text/intro_method.md b/2406.03150/paper_text/intro_method.md
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+# Introduction
+
+Recent studies have shown that, by taking advantage of and re-purposing well-trained/pre-trained models, one can address new tasks (i.e., *target tasks*) without training a task-specific model from scratch [@basu2023efficient; @kossen2023three; @mondal2023equivariant]. In visual tasks, due to the expensive training costs even just to finetune pre-trained models, *visual reprogramming* (VR) [@neekhara2022cross; @wang2022watermarking; @chen2023understanding; @tsao2024autovp], or adversarial reprogramming [@elsayed2018adversarial; @tsai2020transfer], has been proposed to reuse pre-trained models on target tasks. Concretely, VR is a prompting method that fixes a pre-trained model and only alters the input space by adding some learnable patterns (usually some noise) to target images. The location of the patterns to be learned is usually determined by a pre-defined binary mask that is *shared across all samples* [@elsayed2018adversarial; @yang2021voice2series; @tsai2020transfer; @bahng2022exploring]. The key benefit of VR methods is that learning the pattern whose size is around the image size requires much less computing resource than finetuning considerable parameters within the model, posing VR as a promising research area in using pre-trained models [@chen2023understanding; @tsao2024autovp].
+
+![Drawback of shared masks over individual images. We demonstrate the use of watermarking [@wang2022watermarking], a representative VR method, to re-purpose an ImageNet-pretrained classifier for the OxfordPets dataset, with different shared masks (full, medium, and narrow) in VR. An evaluation of classification confidence across three cat images --- Sphynx, Abyssinian, and Bengal --- indicates a sample-specific mask preference: Sphynx with medium, Abyssinian with full, and Bengal with narrow. It shows that different masks are needed for individual images. ](figure/plot_2.pdf){#fig:motivaton2 width="\\linewidth"}
+
+![Drawback of shared masks in the statistical view. Optimal learning methods like finetuning usually result in loss decreases for all samples (see the blue part). But when applying the same mask in reprogramming, part of the loss changes are observed to be positive (see the red part) according to the distribution of \[final loss - initial loss\], which means the training loss for some samples even rises.](figure/plot_1.pdf){#fig:motivation1 width="\\linewidth"}
+
+In this paper, we show that the shared mask often leads to poor generalization capability of VR, as demonstrated in Figures [1](#fig:motivaton2){reference-type="ref" reference="fig:motivaton2"} and [2](#fig:motivation1){reference-type="ref" reference="fig:motivation1"}. In both figures, we use a representative VR method, watermarking [@bahng2022exploring], to re-purpose an ImageNet-pretrained classifier to classify images in the OxfordPets datasets [@parkhi2012cats]. In Figure [1](#fig:motivaton2){reference-type="ref" reference="fig:motivaton2"}, we first find that the optimal masks vary among individual images. We apply three kinds of masks (full, medium, and narrow) in watermarking. By observing the classification confidence on three cat images: Sphynx, Abyssinian, and Bengal, we see that the medium mask is the best for Sphynx, the full mask for Abyssinian, and the narrow mask for Bengal. This suggests that different masks are needed for individual images. In Figure [2](#fig:motivation1){reference-type="ref" reference="fig:motivation1"}, we then find that watermarking with a single shared mask may cause the training loss of many individual samples to rise (see the red part in Figure [2](#fig:motivation1){reference-type="ref" reference="fig:motivation1"}). This phenomenon reveals that VR methods' learning capacity is much less than finetuning all parameters of the pre-trained model (see the blue part in Figure [2](#fig:motivation1){reference-type="ref" reference="fig:motivation1"}).
+
+The examples above show a significant disadvantage of using a single shared mask for VR. This motivates our new VR framework called *sample-specific multi-channel masks* (SMM). SMM replaces the *fixed* binary mask applied in existing works with *generative* three-channel masks that can vary across different samples (shown in Figure [3](#fig:framework){reference-type="ref" reference="fig:framework"}).
+
+SMM has two modules: a mask generator module and a patch-wise interpolation module. The mask generator is a lightweight *convolutional neural network* (CNN) that takes resized individual target-domain images (i.e., samples) as the input and outputs different masks for each sample. The last layer of the generator is designed to generate a three-channel mask, which allows better performance for both rich-color images (i.e., CIFAR10/100 [@krizhevsky2009learning]) and monotonous-color images (i.e., SVHN [@yuval2011reading]). Since the generated masks should be the same size as the pattern to be learned, when the size of masks is inconsistent with that of the pattern, the patch-wise interpolation module will be utilized to re-scale the generated masks to fit the pattern, facilitating the training process of the mask generator (detailed in Section [3](#method){reference-type="ref" reference="method"}).
+
+To understand why SMM is effective, we theoretically analyze the approximation error of different hypothesis sets for VR. Three hypothesis sets are considered: shared pattern with a pre-defined binary mask, sample-specific patterns without masks, and our SMM. We show that SMM has a smaller approximation error (Proposition [3](#proposition1){reference-type="ref" reference="proposition1"}), which confirms the effectiveness of SMM.
+
+To further substantiate the efficacy of SMM, we conduct empirical evaluations spanning 11 widely used datasets, incorporating ablation studies that discern the impact of individual SMM components. This is complemented by analysis and interpretations of the generated masks, alongside a comparative visualization of feature spaces. Notably, we demonstrate the effectiveness of SMM with both pre-trained ResNet [@he2016deep] and ViT [@dosovitskiy2020image] (Table [\[resnetresults\]](#resnetresults){reference-type="ref" reference="resnetresults"} and [\[ViTresults\]](#ViTresults){reference-type="ref" reference="ViTresults"}), validating that SMM is compatible with commonly used classifier architectures.
+
+Both the theoretical analysis and promising experimental results provide solid evidence that, when powered by SMM, VR can efficiently leverage knowledge within a well-trained/pre-trained model for various target tasks, shedding new light on the explanatory analysis of VR and opening avenues for future research.
+
+# Method
+
+Model reprogramming [@chen2022survey] offers an efficient transfer learning paradigm for adapting pre-trained models to resource-constrained target tasks. This paradigm re-purposes existing knowledge by strategically transforming inputs and outputs, bypassing extensive model parameter finetuning. In what follows, we will present a formal problem setting for model reprogramming.
+
+Let $\mathcal{D}_{\rm T}$ represent the data distribution of a target task defined over $\mathcal{X}^{\rm T}\times \mathcal{Y}^{\rm T}$, where $\mathcal{X}^{\rm T}\subseteq \mathbb{R}^{d_{\rm T}}$ is the data space and $\mathcal{Y}^{\rm T}=\{1,\dots,k_{\rm T}\}$ is the label space, and let $\{(x^{\rm T}_{i},y^{\rm T}_i)\}_{i=1}^n$ be the observations of $\mathcal{D}_{\rm T}$ (i.e., the training set in the target task). Meanwhile, we have a pre-trained model $f_{\rm P}:\mathcal{X}^{\rm P}\rightarrow \mathcal{Y}^{\rm P}$, where $\mathcal{X}^{\rm P} \subseteq \mathbb{R}^{d_{\rm P}}$ and $\mathcal{Y}^{\rm P}$ (s.t. $|\mathcal{Y}^{\rm T}| \leq |\mathcal{Y}^{\rm P}|$, with the label space of the pre-trained task being larger than that of the target task) represent the data and label spaces used for training $f_{\rm P}$. Then, in model reprogramming, the training objective can be formulated as $$\begin{align}
+\label{eq:vp_obj}
+ \mathop{\min}\limits_{\theta\in\Theta, \omega\in\Omega}\frac{1}{n}\sum_{i=1}^n\ell(f_{\rm out}(f_{\rm P}(f_{\rm in}(x^{\rm T}_i|\theta))|\mathcal{Y}^{\rm P}_{\rm sub},\omega), y^{\rm T}_i),
+\end{align}$$ where $f_{\rm in}(.|\theta): \mathcal{X^{\rm T}} \mapsto {\mathcal{X}}^{\rm P}, f_{\rm out}(.|\mathcal{Y}^{\rm P}_{\rm sub},\omega): \mathcal{Y}^{\rm P}_{\rm sub} \mapsto \mathcal{Y^{\rm T}}$ are the input transformation and output label mapping function with parameters $\theta\in\Theta$ and $\omega\in\Omega$, $\mathcal{Y}^{\rm P}_{\rm sub}\subseteq\mathcal{Y}^{\rm P}$ can be determined by different methods [@elsayed2018adversarial; @tsai2020transfer; @chen2023understanding], and $\ell:\mathcal{Y}^{\rm T}\times\mathcal{Y}^{\rm T}\mapsto \mathbb{R}^+\cup\{0\}$ is a loss function. Reprogramming techniques have been widely applied in visual [@elsayed2018adversarial; @tsai2020transfer], text [@neekhara2018adversarial; @hambardzumyan2021warp], speech [@yang2021voice2series; @yang2023english; @yen2023neural], music [@hung2023low], and cross-modal tasks [@neekhara2022cross] in the past few years.
+
+In the context of visual tasks, reprogramming has demonstrated potential in bio-medical measurement [@tsai2020transfer], machine learning fairness [@zhang2022fairness], as well as out-of-distribution detection through watermarking [@wang2022watermarking]. Moving beyond application prospects, we next discuss the technical details of the specific input and output mapping functions ($f_{\rm in}$ and $f_{\rm out}$).
+
+General prompting methods in visual tasks, predominantly applied to the ViT architecture [@dosovitskiy2020image], introduce extra parameters to a pre-trained model for enhanced training efficiency. Prompts are flexible in their placement. For example, *visual prompt tuning* [@jia2022visual] positions prompts alongside image embedding before the encoder layers, while *effective and efficient visual prompt tuning* [@han20232vpt] extends this by incorporating parameters within self-attention layers as well. *Transformer with hierarchical prompting* [@wang2023transhp] also learns prompt tokens to represent the coarse image classes.
+
+Meanwhile, prompting goes beyond vision foundation models to vision-language frameworks such as CLIP [@radford2021learning]. Methods like CoOP [@zhou2022learning] and CoCoOP [@zhou2022conditional] replace textual prompts with learnable vectors for enhanced adaptability to the target task, conditioned on input images. MaPLe [@khattak2023maple] further bridges vision and language by learning layer-specific mapping functions. These methods vary from each other in terms of both prompt placements and functions.
+
+In contrast, VR provides a model-agnostic prompting technique, by adding trainable noise to the input image patterns before the forward propagation, without altering their visual essence. Originally proposed by @elsayed2018adversarial, VR has been evolving to include padding-based methods [@tsai2020transfer; @chen2023understanding] and watermarking that facilitate downstream target tasks [@bahng2022exploring]. AutoVP [@tsao2024autovp] stands out with its scalable pre-padding images. A critical limitation in existing VR research is the use of *shared* noise patterns across *all target samples*, neglecting sample-level characteristics and compromising generalization. We propose SMM to manage this gap.
+
+Learning-based output mapping, i.e., model $f_{\rm out}$, as proposed by @chen2023understanding, can be simplified as a one-to-one mapping from a subset of $\mathcal{Y^{\rm P}}$ to $\mathcal{Y^{\rm T}}$. Therefore, no additional parameters are required. One implementation of this mapping is *random label mapping* (Rlm), where $f_{\rm out}$ is a randomly assigned injective function [@elsayed2018adversarial; @chen2023understanding], formulated as $$\begin{align}
+\label{eq:rlm}
+ f_{\rm out}^{\rm Rlm}(y|\mathcal{Y}^{\rm P}_{\rm sub})=\texttt{rand}(\{0, 1, ...,k^{\rm T}\}),
+\end{align}$$ where $\texttt{rand}(\{0, 1, ...,k^{\rm T}\})$ means randomly selecting one element from the set $\{0, 1, ...,k^{\rm T}\}$, and $\mathcal{Y}^{\rm P}_{\rm sub}$ is of the same size with $\mathcal{Y}^{\rm T}$ (i.e., $k^{\rm T}$), randomly chosen from $\mathcal{Y}^{\rm P}$ prior to the minimization of Eq. [\[eq:vp_obj\]](#eq:vp_obj){reference-type="eqref" reference="eq:vp_obj"}. Note that, since $f_{\rm out}^{\rm Rlm}$ is injective, it ensures $f_{\rm out}^{\rm Rlm}(y_1|\mathcal{Y}^{\rm P}_{\rm sub})\neq f_{\rm out}^{\rm Rlm}(y_2|\mathcal{Y}^{\rm P}_{\rm sub})$ for two distinct elements $y_1\neq y_2$.
+
+Other representative output-mapping methods determine $\mathcal{Y}^{\rm P}_{\rm sub}$ and $f_{\rm out}$ for different target tasks. For example, one is based on the frequency of label assignment in the pre-trained model and the target data [@tsai2020transfer], called *frequent label mapping* (Flm). @chen2023understanding propose *iterative label mapping* (Ilm) that updates $f_{\rm out}$ in each training iteration, reflecting changes in label mapping throughout the learning of $f_{\rm in}$. Detailed procedures and the pseudo-code of ${f}_{\rm out}^{\rm Flm}$ and ${f}_{\rm out}^{\rm Ilm}$ are in Appendix [7.4](#app:out){reference-type="ref" reference="app:out"}.
+
+{#fig:framework width="\\linewidth"}
+
+We focus on $f_{\rm in}$, while treating $f_{\rm out}$ as a non-parametric mapping, in line with @chen2023understanding. We thus limit our discussion of trainable parameters to $\theta\in \Theta$ in Eq. [\[eq:vp_obj\]](#eq:vp_obj){reference-type="eqref" reference="eq:vp_obj"}. A flowchart in Appendix [7.1](#prob){reference-type="ref" reference="prob"} provides an overview of the problem structure of Input VR.
+
+To allow both shared parameters over the whole dataset and variability among individual samples, it is intuitive to consider the following VR hypothesis: $$\begin{align}
+f_{\rm in}(x_i|\phi,\delta)=r(x_i)+\delta \odot f_{\rm mask}(r(x_i)|\phi),
+\end{align}$$ where $r:\mathcal{X}^{\rm T}\rightarrow \mathbb{R}^{d_{\rm P}}$ is the resizing function, typically implemented as bilinear interpolation upsampling [@bilinear] that scales image dimension from $d_{\rm{T}}$ to $d_{\rm{P}}$, and $r(x_i)\in \mathbb{R}^{d_{\rm P}}$ is the resized image corresponding to $x_i$. The mask generation function $f_{\rm mask}:{\mathbb{R}}^{d_{\rm P}}\rightarrow \mathbb{R}^{d_{\rm P}}$, parameterized by $\phi\in\Phi$, produces a mask indicating the noise placements for each image. We denote a trainable noise pattern added to the image by $\delta \in \mathbb{R}^{d_{\rm P}}$. The rationale for applying this hypothesis is elaborated in Proposition [3](#proposition1){reference-type="ref" reference="proposition1"} and validated in ablation studies (cf. Table [\[ablation\]](#ablation){reference-type="ref" reference="ablation"}). This casts the training objective of our SMM framework ($\theta = \left\{ \phi, \delta \right\}$) to find the optimal $\phi^{*}$ and $\delta^{*}$ such that $$\begin{equation}
+\label{eq:final_obj}
+\begin{split}
+ \mathop{\arg \min}\limits_{\phi\in\Phi, \delta\in\mathbb{R}^{d_{\rm P}}}\mathbb{E}_{(x_i, y_i) \sim \mathcal{D_{\rm T}}}[\ell( & f_{\rm out}(f_{\rm P}(r(x_i)+\\
+ & \delta \odot f_{\rm mask}(r(x_i)|\phi))),y_i)].
+\end{split}
+\end{equation}$$ Note that $\delta$ is *shared* by all images in the dataset following @bahng2022exploring and @chen2023understanding, while $f_{\rm mask}$ *uniquely* generates sample-specific multi-channel masks for each individual image, enabling sample-specific adaptation.
+
+Figure [3](#fig:framework){reference-type="ref" reference="fig:framework"} illustrates the workflow of SMM, as well as previous padding-based and resizing-based (i.e., watermarking) VR methods. Compared with previous works, SMM features $f_{\rm mask}(\cdot|\phi)$, integrating a *mask generator module* and a *patch-wise interpolation module*. Concretely, SMM starts by resizing target images, followed by their processing through the mask generator to create corresponding three-channel masks. For generated masks smaller than the pattern size, the patch-wise interpolation module performs upsampling, which omits the derivation step in back-propagation and facilitates training. Afterward, the learnable pattern $\delta$ is multiplied with the mask on a pixel-wise basis and added to the image. The resulting image is fed into the *fixed* pre-trained classifier. We discuss further details on the mask generator (Section [3.2](#sec:generator){reference-type="ref" reference="sec:generator"}), the patch-wise interpolation module (Section [3.3](#sec:patch_priority){reference-type="ref" reference="sec:patch_priority"}), and the overall learning strategy presented in Eq. [\[eq:final_obj\]](#eq:final_obj){reference-type="eqref" reference="eq:final_obj"} (Section [3.4](#sec: learning_strategy){reference-type="ref" reference="sec: learning_strategy"}).
+
+The mask generator $f_{\rm mask}$ is supposed to output a mask that has the same size as the input image while prioritizing different locations for $\delta$ to allow more variability. We employ a CNN as the mask generator. This choice stems from the proficiency of CNNs in mirroring localized visual perception [@he2016deep] with fewer parameters than most deep learning structures, e.g., multilayer perceptrons.
+
+The input of CNN is a resized image $r(x_i)$. Applying our bespoke CNN architecture shown in Appendix [7.2](#architecture){reference-type="ref" reference="architecture"}, the output will be a three-channel mask with dimensions $\left \lfloor \frac{H}{2^l}\right \rfloor \times \left \lfloor \frac{W}{2^l}\right \rfloor$, where $H$ and $W$ denote image height and width, respectively, and $l$ denotes the number of pooling layers. The analysis of input/output sizes and parameter quantity statistics are in Appendix [7.2](#architecture){reference-type="ref" reference="architecture"}.
+
+:::: algorithm
+::: algorithmic
+Pre-trained model $f_{\rm P}$, loss $\ell$, label-mapping function $f^{(j)}_{\rm out}$ for iteration $j$, target domain training data $\{(x_i, y_i)\}^{n}_{i=1}$, maximum number of iterations $E$, learning rate $\alpha_1$ for $\delta$ and $\alpha_2$ for $\phi$ Optimal $\delta^*, \phi^*$ Initialize $\phi$ randomly; set $\delta \leftarrow \{0\}^{d_\mathcal{\rm P}}$ \# Step1: Compute individual marks using the mask generator\
+\# Step2: Resize masks using the patch-wise interpolation module\
+$f_{\rm in}(x_i;\delta, \phi)\leftarrow r(x_i) + \delta \odot f_{\rm mask}(r(x_i)|\phi)$, $\forall i=1, 2, ..., n$ \# Compute the classification loss\
+$L(\delta, \phi)\leftarrow\frac{1}{n}\sum^{n}_{i=1}\ell(f^{(j)}_{\rm out}{(f_{\rm P}(f_{\rm in}(x_i;\delta, \phi)))}, y_i)$ $\delta \leftarrow \delta - \alpha_1 \nabla_\delta L(\delta, \phi)$ $\phi \leftarrow \phi - \alpha_2 \nabla_\phi L(\delta, \phi)$
+:::
+::::
+
+The patch-wise interpolation module upscales CNN-generated masks from $\left \lfloor \frac{H}{2^l}\right \rfloor \times \left \lfloor \frac{W}{2^l}\right \rfloor$ back to the original size $H \times W$ per channel (it is omitted when $l = 0$). Considering the inherent consistency in adjacent image areas and the benefits of concise operations for gradient calculations, we employ a grid of $\left \lfloor \frac{H}{2^l}\right \rfloor \times \left \lfloor \frac{W}{2^l}\right \rfloor$ patches in the upsampling process, each sized $2^l \times 2^l$, ensuring the same values within each patch, with non-divisible cases mirroring the closest patches. Therefore, after obtaining the output of CNN, we enlarge each pixel to $2^l \times 2^l$ pixels by padding the same value of a pixel to its surrounding areas within the patch.
+
+Unlike traditional interpolation methods which may introduce complicated derivation computations, our module simplifies the training by directly assigning values. The advantage of patch-wise interpolation over traditional interpolation methods will be discussed in Appendix [7.3](#app:interpolation){reference-type="ref" reference="app:interpolation"}. The effect of patch size $2^l$ will be discussed in Section [5](#exp){reference-type="ref" reference="exp"}.
+
+The learning process for the shared noise pattern $\delta$ and the mask generator $f_{\rm mask}$ is shown in Algorithm [\[alg:method\]](#alg:method){reference-type="ref" reference="alg:method"}. The parameters $\delta$ and $\phi$ are iteratively updated in each epoch. To mitigate the impact of initialization, $\delta$ is set to be a zero matrix before training, noted as $\{0\}^{d_\mathcal{\rm P}}$.
+
+:::::: table*
+::::: center
+:::: small
+::: sc
+:::
+::::
+:::::
+::::::
+
+In this section, we will demonstrate that SMM enables stronger model learning capacity than previous representative VR methods, via showing reduced approximation error in the *probably approximately correct* (PAC) learning framework [@kearns1994introduction]. We first present the definition of the approximation error in PAC learning.
+
+::: {#definition1 .definition}
+**Definition 1** (Approximation Error). Consider an input space $\mathcal{X}$, a discrete label space $\mathcal{Y}$, a random variable $(X,Y)$ whose distribution $\mathcal{D}$ is defined on $\mathcal{X}\times\mathcal{Y}$ with a joint probability density function $p(x,y)$, and a hypothesis space $\mathcal{F}=\{f:\mathcal{X}\rightarrow\mathcal{Y}\}$. The approximation error of $\mathcal{F}$ on $\mathcal{D}$ is $$\begin{align}
+ {\rm Err}^{\rm apx}_{\mathcal{D}}(\mathcal{F}) = \inf_{f\in\mathcal{F}}\mathbb{E}_{(X,Y)\sim\mathcal{D}}\ell(f(X),Y) - R^*_{\mathcal{D}},
+\end{align}$$ where $\ell:\mathcal{Y}\times\mathcal{Y}\rightarrow\mathbb{R}^+\cup\{0\}$ is a loss function, and $R^*_{\mathcal{D}}$ is the Bayes risk [@Snapp1995est] on $\mathcal{D}$ defined by $$\begin{align}
+ R^*_{\mathcal{D}} = \int_\mathcal{X} \Big[1-\sup_{y\in\mathcal{Y}}{\rm Pr}(y|x)\Big]p_X(x)dx.
+\end{align}$$ Here, ${\rm Pr}(y|x)$ is the posterior probability of class $y$ conditioned on observing $x$, and $p_X(x)=\sum_{y\in\mathcal{Y}}p(x,y)$ is the marginal distribution of $X$.
+:::
+
+The approximation error of a hypothesis space $\mathcal{F}$ measures the closeness of the minimum achievable error by $\mathcal{F}$ to the theoretical minimum error on distribution $\mathcal{D}$. In general, increasing the complexity of $\mathcal{F}$ tends to reduce the approximation error. In the following theorem, we show a connection between two approximation errors when hypothesis spaces exhibit a subset relation.
+
+::: {#theorem1 .theorem}
+**Theorem 2**. *Given an input space $\mathcal{X}$, a discrete label space $\mathcal{Y}$, and a distribution $\mathcal{D}$ over $\mathcal{X}\times\mathcal{Y}$, if there are two hypothesis spaces $\mathcal{F}_1\subseteq\{f:\mathcal{X}\rightarrow\mathcal{Y}\}$ and $\mathcal{F}_2\subseteq\{f:\mathcal{X}\rightarrow\mathcal{Y}\}$ satisfying that $\mathcal{F}_1\subseteq\mathcal{F}_2$, then we have ${\rm Err}^{\rm apx}_{\mathcal{D}}(\mathcal{F}_1)\geq {\rm Err}^{\rm apx}_{\mathcal{D}}(\mathcal{F}_2)$.*
+:::
+
+Theorem [2](#theorem1){reference-type="ref" reference="theorem1"} (proof in Appendix [8.1](#app:approx){reference-type="ref" reference="app:approx"}) shows that understanding the subset relation between two hypothesis spaces is key to deriving their connections in their approximation errors. Next, we will define two hypothesis spaces: one induced by a shared mask and the other induced by SMM.
+
+**Hypothesis Space Induced by A Shared Mask**. VR methods with a shared mask [@chen2022survey; @bahng2022exploring] assume that, for each sample $x_i$, the mask is a constant matrix $M\in \{0, 1\}^{d_\mathcal{\rm P}}$. Thus, given a fixed pre-trained model $f_{\rm P}$ and a fixed output mapping function $f_{\rm out}$ (for simplicity, we use $f_{\rm P}^\prime$ to represent $f_{\rm out}\circ f_{\rm P}$ in this section), the hypothesis space induced by a shared mask is $$\begin{align*}
+ \mathcal{F}^{\rm shr}({f_{\rm P}^\prime})=\{f|f(x) = f_{\rm P}^\prime(r(x)+M\odot\delta),\forall x\in\mathcal{X}\},
+\end{align*}$$ where $\delta\in\mathbb{R}^{d_{\rm P}}$. In padding-based reprogramming methods, $M$ is a fixed mask determined by the location of the target image [@chen2022survey]. The locations where $x_i$ is placed -- usually the center of $r(x_i)$ -- are denoted as $\{i:M_i=0\}$, which are excluded from further training. The rest of the locations, denoted by $\{i:M_i=1\}$, indicate trainable parameters $\delta$. In watermarking-based methods [@bahng2022exploring], $x_i$ is up-sampled to $r(x_i)$, and $\{i:M_i=1\}$ denotes effective locations of $\delta$ added to $r(x_i)$.
+
+**Hypothesis Space Induced by SMM**. Based on Eq. [\[eq:final_obj\]](#eq:final_obj){reference-type="eqref" reference="eq:final_obj"}, we can obtain the hypothesis space used in SMM: $$\begin{align*}
+ &\mathcal{F}^{\rm smm}({f_{\rm P}^\prime})\\
+ =&~\{f|f(x) = {f_{\rm P}^\prime}(r(x)+f_{\rm mask}(r(x))\odot\delta),\forall x\in\mathcal{X}\}.
+\end{align*}$$ Note that, $f_{\rm mask}(r(x))$ belongs to $\mathbb{R}^{d_{\rm P}}$ instead of $\{0, 1\}^{d_\mathcal{\rm P}}$ like $M$. Next, we analyze the relation between the approximation errors of previous VR methods and SMM.
+
+**SMM Has a Lower Approximation Error.** Based on Theorem [2](#theorem1){reference-type="ref" reference="theorem1"} and the two hypothesis spaces above, we have the following proposition.
+
+::: {#proposition1 .proposition}
+**Proposition 3**. *Given a fixed pre-trained model $f_{\rm P}$, a fixed output mapping function $f_{\rm out}$, and the definitions of $\mathcal{F}^{\rm shr}$ and $\mathcal{F}^{\rm smm}$, we have $\mathcal{F}^{\rm shr}(f_{P}^\prime)\subseteq\mathcal{F}^{\rm smm}(f_{P}^\prime)$. Then, based on Theorem [2](#theorem1){reference-type="ref" reference="theorem1"}, we have $$\begin{align}
+ {\rm Err}^{\rm apx}_{\mathcal{D}_{\rm T}}(\mathcal{F}^{\rm shr}(f_{P}^\prime)) \ge {\rm Err}^{\rm apx}_{\mathcal{D}_{\rm T}}(\mathcal{F}^{\rm smm}(f_{P}^\prime)),
+\end{align}$$ where $f_{P}^\prime=f_{\rm out}\circ f_{\rm P}$, $f_{\rm mask}$ used in $\mathcal{F}^{\rm smm}(f_{P}^\prime)$ is a CNN demonstrated in Section [3.2](#sec:generator){reference-type="ref" reference="sec:generator"}, and $\mathcal{D}_{\rm T}$ denotes the distribution of the target task.*
+:::
+
+Proposition [3](#proposition1){reference-type="ref" reference="proposition1"} (see its proof in Appendix [8.2](#app:smm_shr){reference-type="ref" reference="app:smm_shr"}) shows that SMM achieves a lower approximation error than previous shared-mask VR methods.
+
+**Estimation Error Analysis of SMM.** While a lower approximation error does not suffice to guarantee a lower excess risk, the model complexity added to $\mathcal{F}^{\rm smm}(f_{P}^\prime)$ is manageable in this VR setting, since $f_{\rm mask}$ introduces less than $0.2\%$ extra parameters[^1] relative to $f_{\rm P}^{\prime}$. Such dominance of $f_{\rm P}^{\prime}$ suggests that the estimation error of $\mathcal{F}^{\rm smm}(f_{P}^\prime)$ does not significantly exceed that of $\mathcal{F}^{\rm shr}(f_{P}^\prime)$ and is unlikely to offset its advantage in approximation error. We also provide an empirical justification from the standpoint of over-fitting to show that the additional estimation error of $\mathcal{F}^{\rm smm}(f_{P}^\prime)$ is negligible in Appendix [10.3](#app:error){reference-type="ref" reference="app:error"}. By comparing the disparities in training and testing performance, we demonstrate that SMM does not increase the risk of model over-fitting, implying negligible estimation error.
+
+**Excess Risk Analysis of SMM.** According to excess risk decomposition[^2], SMM is also expected to have a lower excess risk and, consequently, superior generalization capability compared to shared-mask VR methods.
+
+**Analysis Based on Sample-specific Patterns.** Having built the concept of "sample-specific", we also investigate an alternative to the proposed SMM: directly learning a *sample-specific pattern* for each image without involving $\delta$. The hypothesis space in this context can be expressed by $$\begin{align*}
+\label{eq:ssm_wo_m}
+ \mathcal{F}^{\rm sp}({f_{\rm P}^\prime})
+ =\{f|f(x) = {f_{\rm P}^\prime}(r(x)+f_{\rm mask}(r(x))),\forall x\in\mathcal{X}\}.
+\end{align*}$$ It is easy to check that $\mathcal{F}^{\rm sp}({f_{ P}^\prime})\subseteq \mathcal{F}^{\rm smm}({f_{ P}^\prime})$, implying that ${\rm Err}^{\rm apx}_{\mathcal{D}_{\rm T}}(\mathcal{F}^{\rm sp}(f_{P}^\prime)) \ge {\rm Err}^{\rm apx}_{\mathcal{D}_{\rm T}}(\mathcal{F}^{\rm smm}(f_{P}^\prime))$ (proof in Appendix [8.3](#app:smm_sp){reference-type="ref" reference="app:smm_sp"}). Namely, SMM has a lower approximation error compared to directly learning a sample-specific pattern.
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diff --git a/2407.05358/paper_text/intro_method.md b/2407.05358/paper_text/intro_method.md
new file mode 100644
index 0000000000000000000000000000000000000000..c498ef6cd9be76efc3750ad9e489195dede82179
--- /dev/null
+++ b/2407.05358/paper_text/intro_method.md
@@ -0,0 +1,21 @@
+# Method
+
+During training, we apply data augmentation for image inputs with colour jitter, horizontal flipping and random scaling between 0.5 and 2.0. We randomly crop images to $512\times512$ pixels. We downsample the audio data to 16 kHz for durations of $1$ second [@zhou2022audio] or $3$ seconds [@mo2022closer] of the waveform on AVSBench and VPO. Subsequently, the resampled audio sequence is processed through the Short-Time Fourier Transform (STFT) using a 512 FFT length, a Hann window size of 400, and a hop length of 160. This results in $96 \times 256$ and $300 \times 256$ magnitude spectrograms on AVSBench and VPO, respectively. We use the AdamW [@loshchilov2017decoupled] optimizer with a weight decay of 0.0001 and a polynomial learning-rate decay $(1-\frac{\text{iter}}{\text{total\_iter}})^\text{power}$ with $\text{power}=0.9$. We set the initial learning rate to $0.0001$ with a mini-batch size of 16 and 100 epochs training length. During inference, we use the resized/or original resolution with a mini-batch size of 1. We set temperature $\tau$ as 0.1 and $\lambda$ as 0.5. We adopted ResNet-50 [@he2016deep] image backbones and a similar setting as [@cheng2022masked] for the transformer decoder blocks in the segmentation head. For the audio backbones, we use VGGish [@hershey2017cnn] (following [@zhou2022audio]) and ResNet-18 [@he2016deep] (following [@chen2021localizing; @mo2022closer]) for AVSBench and VPO, respectively.
+
+:::: algorithm
+::: algorithmic
+[\# $N$: number of queries]{style="color: ao"} [\# $C$: number of classes]{style="color: ao"} [\# $f_{\text{Hungarian}}$: Hungarian algorithm]{style="color: ao"} [\# $H(\cdot)$: the function used to compute the negative entropy score]{style="color: ao"} **require:** Training set $\mathcal{D}$ with $D$ samples, model $f_{\theta}$, class-agnostic query $\mathbf{q}$, an empty list $R$. [\# Get assigned labels for each sample and each query.]{style="color: ao"} $\mathbf{p} = f_{\theta}(\mathbf{x}_i)$ $\tilde{\mathbf{y}}_i = f_{\text{Hungarian}}(\mathbf{p})$ [\# $\tilde{\mathbf{y}}_i: 1 \times N$ ]{style="color: ao"} $R$.append($\tilde{\mathbf{y}}_i$) [\# Concatenate all the assigned labels]{style="color: ao"} $\mathbf{R} = \mathsf{Concat}(R)$ [\# $\mathbf{R}: D \times N$]{style="color: ao"} $\mathsf{STS} = 0$ [\# Iterate through all the classes and compute the average $\mathsf{STS}$ score.]{style="color: ao"} $\mathbf{s}_{c} = (\mathbf{R} == c)$.sum(0) [\# $\mathbf{s}_{c}: 1 \times N$]{style="color: ao"} $\mathbf{s}_{c} = \text{clamp}(\mathbf{s}_{c}/\text{sum}(\mathbf{s}_{c}), \text{min}=1e-12, \text{max}=1)$ $\mathsf{STS} = \mathsf{STS} + H(\mathbf{s}_{c})$ $\mathsf{STS} = \mathsf{STS} / C$
+:::
+::::
+
+
+
+
+mIoU ↑
+
+
+
+Fβ↑
+
+Ablation study on the weight coefficient λ using the AVSBench-Semantics dataset , with the ResNet50 backbone.
+
diff --git a/2410.03119/main_diagram/main_diagram.drawio b/2410.03119/main_diagram/main_diagram.drawio
new file mode 100644
index 0000000000000000000000000000000000000000..49efbc966e51b2bdb4e040e46b0aad34685ff9a8
--- /dev/null
+++ b/2410.03119/main_diagram/main_diagram.drawio
@@ -0,0 +1,1107 @@
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diff --git a/2410.03119/paper_text/intro_method.md b/2410.03119/paper_text/intro_method.md
new file mode 100644
index 0000000000000000000000000000000000000000..ee49648702ec0231287f81559d404bcb4cbd42e6
--- /dev/null
+++ b/2410.03119/paper_text/intro_method.md
@@ -0,0 +1,156 @@
+# Introduction
+
+This paper addresses the challenge of efficient action selection in Reinforcement Learning (RL), particularly in environments with spatial structures, ranging from robotic manipulation where joint movements are coupled, to game-playing agents where tactical decisions might depend on positional awareness. We integrate ring attractors, a neural circuit model from neuroscience originally proposed by [50] and later experimentally validated by [18], into the RL framework. This approach provides a mechanism for uncertainty-aware decision-making in RL, thereby yielding more efficient and reliable learning in complex environments. Ring attractors offer a unique framework to represent spatial information in a continuous and stable manner [33]. In a ring attractor network, neurons interconnect circularly, forming a loop with tuned connections [7]. This configuration allows for robust and localized activation patterns, maintaining accurate spatial representations even in the
+
+presence of noise or perturbations. Applying ring attractors to the selection of RL actions involves mapping actions to specific ring locations and decoding the selected action based on neural activity. This spatial embedding proves advantageous for continuous action spaces, particularly in tasks such as robotic control and navigation [28]. Ring attractors improve decision-making by exploiting spatial relations between actions, contributing to informed transitions between actions in sequential decision-making tasks in RL. While many action spaces possess inherent topological structure [39], traditional neural networks represent actions as orthogonal vectors that ignore these relationships. Ring attractors bridge this gap by providing a neural substrate that preserves spatial ordering and similarity between actions. This is particularly relevant given recent advances showing that spatial structure in representations improves sample efficiency [25, 49] and generalization [3, 13], yet these benefits have not been systematically applied to action selection mechanisms themselves.
+
+In what follows, we summarize our contributions. Briefly, our contributions include a novel approach to RL policies based on ring attractors, the inclusion of uncertainty-aware capabilities in our RL systems, and the development of Deep Learning (DL) modules for RL with ring attractors.
+
+- We propose a novel approach for integrating ring attractors into RL, incorporating spatial information, temporal filtering, and relations between actions. This spatial awareness significantly speeds up the learning rate of the RL agent. See Sections 3.1.2 and 4.1.
+- We utilize a Bayesian approach to build ring attractors input signals, encoding uncertainty estimation to drive the action selection process, showing further performance improvement. See Sections 3.1.3 and 4.1.
+- We develop a reusable DL module based on recurrent neural networks that incorporates ring attractors into DRL agents. This allows integration and comparison with existing DRL frameworks and baselines, enabling the adoption of our ring attractor approach across different RL models and tasks in applied domains. See Sections 3.2 and 4.2.
+
+Codebase available at [https://github.com/marcosaura/RA\\_RL](https://github.com/marcosaura/RA_RL).
+
+The integration of ring attractors into RL systems brings together neuroscience-inspired models and advanced machine learning techniques. In this section, we review the literature on key areas that form the foundation of our RL research: spatial awareness in RL, biologically inspired RL approaches, and uncertainty quantification methods.
+
+Incorporating spatial awareness into RL systems has improved performance on tasks with inherent spatial structure. Regarding relational RL, [49] introduced an approach using attention mechanisms to reason about spatial relations between entities in an environment. This method demonstrated improved sample efficiency and generalization in tasks that require spatial reasoning. On the topic of navigation, [25] developed a DRL agent capable of navigating complex city environments using street-level imagery. Their approach incorporated auxiliary tasks, such as depth prediction and loop closure detection. Concerning explicit spatial representations, [13] proposed a cognitive mapping and planning approach for visual navigation, combining spatial memory with a differentiable neural planner. Similarly, [3] introduced a relational DRL framework using graph neural networks to capture spatial relations between objects. Although these approaches demonstrate the importance of spatial awareness in RL, they rely on emergent learning to develop spatial understanding through comprehensive architectural additions, which require extensive training to discover action relationships. In contrast, our ring attractor approach explicitly encodes spatial structure in the action space, providing spatial inductive bias that accelerates learning rather than relying on the RL agent to autonomously discover all critical action space relationships, which may result in suboptimal or incomplete spatial understanding.
+
+Biologically inspired approaches to RL seek to leverage insights from neuroscience to improve the efficiency, adaptability, and interpretability of RL algorithms. These methods often draw upon neural circuit dynamics and cognitive processes observed in biological systems. A notable example is
+
+the work in [45], which demonstrated how neural attractor states implement probabilistic choices through recurrent network dynamics. The work presented in [2] demonstrated that incorporating grid-like representations, inspired by mammalian grid cells, into RL agents improved performance in navigation tasks. Their work showed that these biologically inspired representations emerged naturally in agents trained on robotics tasks and transferred well to new environments. Similarly, [10] showed that recurrent neural networks trained on navigation tasks naturally developed grid-like representations, suggesting a deep connection between biological and artificial navigation systems. In RL, grid cells and ring attractors serve distinct computational roles. Grid cells encode spatial state information (the agent's position), through hexagonal firing patterns, functioning as fixed basis functions for value functions and spatial generalization [2, 10]. Ring attractors encode persistent states, directional and spatial bias, through recurrent dynamics that actively maintain information over time [16]. While grid cells provide emergent spatial input features, ring attractors are dynamical components that provide intrinsic spatial encoding for memory states [17], that here we translate to encoding of the action space. [31] demonstrated how RL agents naturally develop insect-like behaviors and neural dynamics when solving complex spatial navigation tasks. [44] proposed a biologically inspired meta-RL algorithm that mimics the function of the prefrontal cortex and dopamine-based neuromodulation. Their approach demonstrated rapid learning and adaptation to new tasks, similar to the flexibility observed in biological learning systems. However, these approaches primarily focus on mimicking neural representations or learning dynamics, whereas ring attractors specifically encode spatial relationships in the action space itself, providing both biological plausibility and direct spatial awareness for action selection.
+
+Regarding exploration strategies, [27] introduced bootstrapped Deep Q-networks (DQNs), addressing exploration by leveraging uncertainty in Q-value estimates by training multiple DQNs with shared parameters. Building on this theme, [8] proposed random network distillation (RND), measuring uncertainty by comparing predictions between a target network and a randomly initialized network. For efficient uncertainty quantification, [11] and [9] presented a novel "masksemble" approach, applying masks across the input batch during the forward pass to generate diverse predictions. Addressing risk assessment in non-stationary environments, [14] described a method to analyze sources of lack of knowledge by adding a second Bayesian model to predict algorithmic action risks, particularly relevant for multi-agent RL (MARL) systems. [19] developed a Bayesian ring attractor that outperforms conventional ring attractors by dynamically adjusting its activity based on evidence quality and uncertainty. In the context of individual treatment effects, [20] performed uncertainty quantification (UQ) using an exogenously prescribed algorithm, making the method agnostic to the underlying recommender algorithm. [1] developed a Bayesian approach for RL in episodic high-dimensional Markov decision processes (MDPs). They introduced two novel algorithms: LINUCB and LINPSRL. These algorithms achieve significant improvements in sample efficiency and performance by incorporating uncertainty estimation into the learning process. In the context of DRL, Bayesian Deep Q-networks (BDQNs) [1] incorporate efficient Thompson sampling and Bayesian linear regression at the output layer to factor uncertainty estimation in the actionvalue estimates. While these methods treat uncertainty estimation as a separate module applied to existing architectures, our ring attractor approach integrates uncertainty through the variance parameters of Gaussian input signals, making uncertainty awareness an intrinsic part of the spatial action representation.
+
+In summary, the literature reveals a growing interest in incorporating spatial awareness, biological inspiration, and UQ into RL systems. However, there remains a gap in integrating these elements into a cohesive framework that simultaneously provides biological plausibility, explicit spatial encoding, and natural uncertainty handling within a single neural architecture. Our work on ring attractors aims to bridge this gap by providing a biologically plausible model that inherently captures spatial relations and can be extended to handle uncertainty, potentially leading to more robust and efficient RL agents.
+
+# Method
+
+Ring attractors represent a computational principle where neurons are arranged in a circular topology to maintain spatial representations. Originally proposed by [50] for encoding heading direction and empirically discovered by [18] in Drosophila, these networks exhibit circular organization where head direction cells encode different spatial directions [38]. For our methodology, ring attractors
+
+integrate multiple cues through distance-weighted connections [51, 12] and maintain representational stability through excitatory-inhibitory dynamics [37]. This bioinspired structure has been repurposed in this line of research to explicitly encode the action space during the decision-making process. In this section, we describe two main methods: an exogenous ring attractor model using continuous-time recurrent neural networks (CTRNNs) and a DL-based ring attractor integrated into the RL agent. While the CTRNN model validates the theoretical principles, the DL architecture eases deployment and adoption in existing DRL frameworks. Both leverage the ring attractors' spatial encoding capabilities to enhance action selection and performance. In the CTRNN model, we detail the ring attractor architecture, Section 3.1.1; dynamics and implementation, Section 3.1.2; and uncertainty injection, Section 3.1.3. CTRNNs are used for their ability to model continuous neural dynamics and maintain stable attractor states [4]. The integrated DL approach offers end-to-end training for efficiency and scalability, detailed in Section 3.2. Ring attractors in RL maintain stable spatial information representations, preserving action relations lost in traditional flattened action spaces. This circular spatial representation potentially yields smoother policy gradients and more efficient learning in spatial tasks, attributed to the ring attractors' ability to maintain a stable representation of spatial information.
+
+During the first stage of the research, the focus is on developing a self-contained exogenous ring attractor as a CTRNN. This will be integrated into the output of the value-based policy model to perform action selection.
+
+Ring attractors commonly consist of a configuration of excitatory and inhibitory neurons arranged in a circular pattern. We can model the dynamics of the ring using the Touretzky ring attractor network [40]. In this model, each excitatory neuron establishes connections with all other excitatory neurons, and an inhibitory neuron is placed in the middle of the ring with equal weighted connections to all excitatory neurons.
+
+Excitatory neurons' input signal: Let $x_n^i \in \mathbb{R}^s$ denote the input signal from source number i to the excitatory neuron $n=1,\ldots,N$ . The total input to neuron n is defined as the sum of all input signals I for that particular neuron: $x_n = \sum_{i=1}^I x_n^i$ , where $x_n \in \mathbb{R}$ . To model input signals $x_n^i$ of varying strengths, these signals are commonly viewed as Gaussian functions $x_n^i : \mathbb{R}^s \to \mathbb{R}^s$ . These functions allow us to represent the input to each neuron as a sum of weighted Gaussian distributions. The key parameters of these Gaussian functions are: $K_i$ , the magnitude variable for the input signal in index i, which determines the overall strength of the signal; $\mu_i$ , which defines the mean position of the Gaussian curve in the ring for the input signal i, representing the central focus of the signal; $\sigma_i$ , the standard deviation of the Gaussian function, which determines the spread or reliability of the signal; and $\alpha_n$ , which represents the preference for the orientation of the neuron n in space. These parameters combine to the following:
+
+$$x_n(\alpha_n) = \sum_{i=1}^{I} x_n^i(\alpha_n) = \sum_{i=1}^{I} \frac{K_i}{\sqrt{2\pi\sigma_i}} \exp\left(-\frac{(\alpha_n - \mu_i)^2}{2\sigma_i^2}\right)$$
+(1)
+
+**Neuron activation function:** We employ rectified linear unit (ReLU) function $f(x) = \max(0, x+h)$ , where $h \in \mathbb{R}^+$ as activation function for each neuron, where h is a threshold that introduces the non-linear behaviour in the ring.
+
+Excitatory neuron dynamics: The dynamics of excitatory neurons in the ring is described as:
+
+$$\frac{\mathrm{d}v_n}{\mathrm{d}t} \approx \frac{\Delta v_n}{\Delta t} = \frac{v_{n+\Delta t} - v_n}{\Delta t} = \frac{f(x_n + \epsilon_n + \eta_n)}{\tau} - v_n \tag{2}$$
+
+In Eq. (2) $v_n \in \mathbb{R}$ represents the activation of the excitatory neuron n, $x_n$ is previously defined in Eq. (1) is the external input to the neuron n of Eq. (1), $\epsilon_n \in \mathbb{R}$ represents the weighted influence of the other excitatory neurons activation, which is defined mathematically in Eq. (4). $\eta_n \in \mathbb{R}$ is the influence from the weighted inhibitory neuron activation to the target excitatory neuron n, and $\tau = \Delta t$ is the time integration constant. This equation captures the evolution of neuronal activation over time, considering both excitatory and inhibitory activations.
+
+**Inhibitory neuron dynamics**. The activation of the inhibitory neuron, which regulates network dynamics, is described by:
+
+$$\frac{\mathrm{d}u}{\mathrm{d}t} \approx \frac{\Delta u}{\Delta t} = \frac{u_{+\Delta t} - u}{\Delta t} = \frac{f(\epsilon_n + \eta_n)}{\tau} - u \tag{3}$$
+
+Here, $u \in \mathbb{R}$ represents the inhibitory neuron's activation output, $\epsilon_n$ is the weighted sum of excitatory activations where in this case n is the inhibitory neuron, and $\eta_n \in \mathbb{R}$ is the weighted self-inhibition activation term. This equation models how the inhibitory neuron integrates inputs from the excitatory population and its own state.
+
+Synaptic weighted connections: The influence between neurons decreases with distance, as modeled by the weighted connections. This weighted connection applies to both excitatory and inhibitory neurons. For excitatory neurons: $w^{(E_m \to E_n)} = e^{-d_{(m,n)}^2}$ , where $d_{(m,n)} = |m-n|$ is the distance between neurons m and n. For the inhibitory neuron: $w^{(I \to E_n)} = e^{-d_{(m,n)}^2} = e^{-1}$ . Note that our model contains a single inhibitory neuron placed in the middle of the ring, with a distance of 1 unit to all excitatory neurons. The excitatory $(\epsilon_n)$ and inhibitory $(\eta_n)$ weighted connections are also known in the literature as neuron-proximal excitatory and inhibitory voltage or potential. These are then defined as follows:
+
+ $\epsilon_n = \sum_{m=1}^{N} w_{m,n}^{(E_m \to E_n)} v_m \quad \eta_n = w^{(I \to E_n)} u \tag{4}$
+
+To integrate the ring attractor model with RL, we need to establish a connection between the estimated value of state-action pairs and the input to the ring attractor network. This integration allows the ring attractor to serve as a behavior policy, guiding action selection based on the values learned.
+
+**Excitatory neurons' action input signal:** We begin by reformulating the input function for a target excitatory neuron n from Eq. (1). To integrate the ring attractor with RL, we reformulate the input signals by mapping $K_i$ to the Q-value Q(s,a), effectively substituting input signal index $i \in I$ with actions $a \in A$ . In other words, the key modification is setting the scale factor $K_i$ to the Q-value Q(s,a) of the state-action pair (s,a), that is $K_i = Q(s,a)$ .
+
+This formulation is represented in Fig. 1 and described in Eq. (5) ensures that actions with higher estimated values are given more weight in the ring attractor dynamics, naturally biasing the network towards more valuable actions. The orientation of the signal within the ring attractor is determined by the direction of movement in the action space. We represent this as $\mu_i = \alpha_a(a)$ , where $\alpha_a(a)$ is the angle corresponding to the action a in the circular action space. We define our circular action space $\mathcal A$ as a subset of $\mathbb R^2$ , where each action $a \in \mathcal A$ is represented by a point on the unit circle. The function $\alpha: \mathcal A \to [0,2\pi)$ maps each action to its corresponding angle on this circle, and $\alpha_n$ which presents the preference for the orientation of the neuron n in space. To account for uncertainty in our value estimates, we incorporate the variance of the estimated value for each action $\sigma_i = \sigma_a$ .
+
+This allows the network to represent not just the expected value of actions, but also the confidence in those estimates. Combining these, we arrive at the following equation for the action signal $x_n(Q)$ :
+
+$$x_n(Q(s,a)) = \sum_{n=1}^{A} \frac{Q(s,a)}{\sqrt{2\pi\sigma_a}} \exp\left(-\frac{(\alpha_n - \alpha_a(a))^2}{2\sigma_a^2}\right)$$
+ (5)
+
+This equation represents the input to each neuron as a sum of Gaussian functions, where each function is centered on an action's direction and scaled by its Q-value.
+
+**Full excitatory neuron dynamics**. The dynamics of the excitatory neurons in the ring attractor, now incorporating the Q-value inputs, are described by:
+
+$$\frac{\mathrm{d}v_n}{\mathrm{d}t} = \frac{1}{\tau} \left( max \left( 0, \left( \sum_{m=1}^{m=N} w_{m,n}^{(E \to E)} v_m + x_n(Q) + w^{(I \to E_u)} u \right) \right) \right) - v_n \tag{6}$$
+
+This equation captures how the activation of each neuron evolves over time, influenced by the Gaussian functions of the input action value $x_n(Q)$ , the excitatory feedback $\epsilon_n = \sum_{m=1}^N w^{(E_m \to E_n)} v_m$ , and the inhibitory feedback $\eta_n = w^{(I \to E_n)} u$ .
+
+Full inhibitory neuron dynamics. The complete dynamics of the inhibitory neuron are given by:
+
+$$\frac{\mathrm{d}u}{\mathrm{d}t} = \frac{1}{\tau} \left( max \left( 0, \left( u + \sum_{n=1}^{N} w_n^{(E_n \to I)} v_n \right) \right) \right) - u, \tag{7}$$
+
+where the self-inhibition term u and excitatory input $v_n$ from each excitatory neuron n from Eq. (6) updates the activation of the inhibitory neuron $\frac{\mathrm{d}u}{\mathrm{d}t}$ . Eq. (6) and Eq. (7) collectively describe the dynamics of the ring attractor network, capturing the interplay between excitatory and inhibitory neurons, external inputs, and synaptic connections. To translate the ring attractor's output into an action in the 2D space, we use the following equation:
+
+action =
+$$\operatorname{argmax}\{\mathbf{V}\}\frac{N^{(A)}}{N^{(E)}},$$
+ (8)
+
+where $n \in \{1,...,N^{(E)}\}$ , $N^{(E)}$ is the number of excitatory neurons in the ring attractor, $N^{(A)}$ is the number of discrete actions in the action space $\mathcal{A}$ , and the activation of excitatory neurons around the ring $\mathbf{V} = [v_1, v_2, ..., v_{N^{(E)}}]$ . This equation assumes that both the neurons in the ring attractor and the actions in the action space are uniformly distributed. This approach allows for nuanced action selection that takes into account both the spatial relations between actions and their estimated values. A visualization of the ring is presented in Fig. 1.
+
+
+
+Figure 1: Ring attractor Touretzky representation: Circular arrangement of excitatory neurons (N0-N7) and central inhibitory neuron. Four input signals shown as colored gradients. Overall activation depicted by red outline. Includes connection weights and input signal parameters, illustrating the final ring attractor dynamics state from Eq. (8).
+
+In the field of DRL, for any state-action pair (s,a), the Q-value Q(s,a) can be expressed as a function of the input state through a function approximation algorithm $\Phi_{\theta}(s)$ taking as input the current state s. This function approximation algorithm $(\Phi_{\theta}(s))$ can be expressed as the weight matrix of our function approximation algorithm transposed $\theta^T$ times the feature vector extracted from the input state x(s): $Q(s,a) = \Phi_{\theta}(s) = \theta^T x(s)$ [34]. For further context on function approximation in RL see Appendix A.2. As stated in Section 3.1.2, the variance of the Gaussian functions, input to the ring attractor, will be given by the variance of the estimate value for that particular action $\sigma_i = \sigma_a$ . Among the various methods to compute the uncertainty of the action $(\sigma_a)$ we have chosen to compute a posterior distribution with Bayesian linear regression (BLR). BLR acts as output layer for our neural network (NN) of choice.
+
+We choose a linear regression model because it does not compromise the efficiency of the NN, while at the same time it provides a distribution to compute the variance for the state-action pairs. The implementation is based on [1], where a Bayesian value-based DQN model was instantiated to output an uncertainty-aware prediction for the state-action pairs. The new Q function is defined as:
+
+$$Q(s,a) = \Phi_{\theta}(s)^T w_a, \qquad (9)$$
+
+where $w_a$ are the weights from the posterior distribution of the BLR model. Eq. (9) represents the parameters of the final Bayesian linear layer. When provided with a state transition tuple (s, a, r, s'), where s is the current state, a is the action taken, r is the reward received, and s' is the next state. This tuple represents
+
+Algorithm 1 CTRNN Ring Attractor Action Selection Require: Input: State s, action a from Action space A.
+
+- 1: **Step 1:** Compute Q-values and Uncertainty
+- 2: Compute mean Q(s,a) and variance $\sigma_a^2$ using Eq. 11
+- 3: **Step 2:** Generate Input Action Signals
+- 4: **for** each excitatory neuron n **do**
+- 5: Generate Gaussian action signal using Eq. 56: end for
+- 7: Step 3: Reach Attractor State
+- 8: **for** timestep t until T **do** $\Rightarrow$ choose T=50 empirically
+- 9: **for** each excitatory neuron n **do**
+- 10: $\frac{\mathrm{d}v_n}{\mathrm{d}t}$ Update excitatory neurons using Eq. 6 11: **end for**
+ - $\frac{du}{dt}$ Update inhibitory neuron using Eq. 7
+- 13: **end fo**r
+
+12:
+
+- 14: **Step 4:** Translate Neural Activity V to action selected from action space $\mathcal{A}$ using Eq. 8
+- 15: return action
+
+a single step of interaction between agent and environment. The model learns to adjust the weights
+
+ $w_a$ of the BLR and the function approximation algorithm, i.e. neural networks (NNs) $(\Phi_{\theta})$ , to align the Q values with the optimal action $a = argmax(\gamma \Phi_{\theta}(s)^T w_a)$ ,
+
+$$Q(s,a) = \Phi_{\theta}(s)^T w_a \to y := r + \gamma \Phi_{\theta_{\text{target}}}(s')^T w_{\text{target}} \hat{a}, \tag{10}$$
+
+where $\gamma$ is the discount factor, y is the expected Q-value, $\Phi_{\theta_{\text{target}}}$ are the features from the next state s' extracted by the function approximation algorithm $\Phi$ using the target network parameters, $\theta_{target}$ refers to the target function approximation algorithm parameters, and $\hat{a}$ is the predicted optimal action in the next state s'. The construction of the Gaussian BLR prior distribution and the weights i sample $w_{a,i}$ collected from the posterior distribution are performed through Thompson Sampling. This process allows us to incorporate uncertainty into our action-value estimates. For details on the construction of the Gaussian prior distribution and the specifics of the sampling process, we refer the reader to [1]. Both the mean $\bar{Q}(s,a)$ and the variance $\bar{\sigma}_a^2$ of Eq. (11) are calculated from a finite number of samples I.
+
+$$\bar{Q}(s,a) = \frac{\sum_{i=0}^{i=I} Q(s,a)_i}{I} = \frac{\sum_{i=0}^{i=I} w_{a,i}^T \Phi_{\theta}(s_t)}{I}, \bar{\sigma}_a^2 = \frac{\sum_{i=0}^{i=I} \left(w_{a,i}^T \Phi_{\theta}(s_t) - \mu_a\right)^2}{I - 1}$$
+(11)
+
+To further enhance the ring attractor's integration into RL frameworks and agents, we provide a Deep Learning (DL) implementation. This approach improves model learning and integration with DRL agents. Our implementation offers both algorithmic improvements, by benefiting from DL training process, and software integration improvements, easing the deployment processes. Recurrent Neural Networks (RNNs) offer an approach for integrating ring attractors within DRL agents. Recent studies by [22] show that RNNs perform well in modeling sequential data and in capturing temporal dependencies for decision-making. Like CTRNNs, RNNs mirror ring attractors' temporal dynamics, with their recurrent connections and flexible architecture emulating their interconnected nature. Allowing modelling weighted connections for forward and recurrent hidden states, as shown in Appendix A.6. The premises for modeling RNN are as follows.
+
+**Attractor state as recurrent connections**. RNN recurrent connections model the attractor state, integrating information from previous time steps into the current network state, allowing for the retention of information over time.
+
+**Signal input as a forward pass**. Forward connections from previous layers are arranged circularly, mimicking the ring's spatial distribution. The attractor state encodes the task context, influenced by the current input and hidden state. A learnable time constant $\tau$ , inherited from Eq. (2), controls input and temporal evolution, enabling adaptive behavior and elastic contribution to the attractor state.
+
+To shape circular connectivity within a RNN, the weighted connections in the input signal V(s) and the hidden state or attractor state U(v) are computed as in (12). This circular structure mimics the arrangement of excitatory neurons in the ring attractor. Eq. (12) shows the input signal to the recurrent layer $V_{m,n}$ from neuron m from the previous layer in the DL agent to neuron n in the RNN. The hidden state, $U_{m,n}$ mimics an attractor state, representing the recurrent connections in the RNN. The weighted RNN connections include fixed input-to-hidden connections ( $w^{I \to H} m, n$ ) to maintain the ring's spatial structure, and learnable hidden-to-hidden connections ( $w^{H \to H} m, n$ ) to capture emerging action relationships.
+
+These depend on a parameter $\lambda$ that drives the decay over distance and distance between neurons d(m,n) where N is the total number of neurons for the RNN and M is the count of neurons in the previous layer of the NN architecture. The function $\phi_{\theta}(s): \mathbb{R}^S \to \mathbb{R}^M$ maps the input state s of the DL agent to a representation of characteristics that will be the input of the recurrent layer. Likewise, $\theta$
+
+$$V(s)_{m,n} = \frac{1}{\tau} \Phi_{\theta}(s)^{T} w_{m,n}^{I \to H} = \frac{1}{\tau} \Phi_{\theta}(s)^{T} e^{-\frac{d(m,n)}{\lambda}}$$
+
+$$d(m,n) = \min(|m - n\frac{M}{N}|, N - |m - n\frac{M}{N}|)$$
+
+$$U(v)_{m,n} = h(v)^{T} w_{m,n}^{H \to H} = h(\Phi_{\theta})^{T} e^{-\frac{d(m,n)}{\lambda}}$$
+
+$$d(m,n) = \min(|m - n|, N - |m - n|)$$
+(12)
+
+represents the parameters of this function (i.e., the weights and biases of the NN layers preceding the RNN layer, which extract relevant features from the input). The function $h(v): \mathbb{R}^N \to \mathbb{R}^N$ is parameterized by learnable weights that map the information from previous forward passes to
+
+the current hidden state. The learnable parameter $\tau$ is the positive time constant responsible for the integration of signals in the ring. Here, $\tau$ acts as a learnable scaling factor that controls the balance between immediate input responsiveness and temporal integration within the ring attractor dynamics, with smaller values increasing sensitivity to current inputs and enabling faster adaptation, while larger values promote stability and smoother transitions between attractor states by integrating information over longer time horizons. It defines the contribution of input $\phi_{\theta}(s)$ to the current hidden state, imitating the attractor state, applied to NNs.
+
+Finally, the action-value function Q(s,a) is derived from the RNN layer's output by applying the neurons activation function to the combined input V(s) and hidden state information U(s). The activation function of choice is a hyperbolic tangent $\tanh$ , this function is symmetric around zero, leading to faster convergence and stability. However, the output range of $\tanh$ (-1 to 1) is not fully compatible with value-based methods, where the DL agents need to output action-value pairs in the range of the environment's reward function. To address this issue and prevent saturation of the $\tanh$ activation function, we scale the action-value pairs by multiplying them by a learnable scalar $\beta$ , as $Q(s,a) = \beta h_t = \beta \tanh{(V(s) + U(v))} = \beta \tanh{(\frac{1}{\tau}\Phi_{\theta}(s_t)^T w^{I \to H} + h_{t-1}(v)^T w^{H \to H})}$ . While our main focus is on value-based ring-attractor agents, we also provide an overview of methods and results for policy-gradient approaches in Appendix A.3.
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diff --git a/2412.10402/paper_text/intro_method.md b/2412.10402/paper_text/intro_method.md
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+# Introduction
+
+Large Language Models (LLMs) have gained significant attention in the field of AI due to their remarkable capability to generalize across unseen tasks [\[5,](#page-8-0) [23,](#page-8-1) [45,](#page-9-0) [46\]](#page-10-0). Systems like VisProg and ViperGPT [\[18,](#page-8-2) [44\]](#page-9-1) demonstrated strong performance on a broad range of vision-and-language tasks by just relying on a few numbers of in-context examples to generate complex compositional programs without requiring any specific training. Nevertheless, the evaluation of these concepts to Embodied AI, particularly for navigation tasks, remains largely unexplored. This approach may be crucial in building agents capable to navigate and operate efficiently in unfamiliar environments.
+
+Previously, methods such as Neural Module Networks (NMN) [\[2,](#page-8-3) [19\]](#page-8-4) have demonstrated promising compositional properties for high-level tasks like visual question answering, via end-to-end training of networks and specialized,
+
+
+
+Figure 1. We introduce TANGO, a modular neuro-symbolic system for compositional embodied visual navigation. Given a few examples of natural language instructions and the corresponding programs composed of action primitives, TANGO can generate executable programs, enabling the agent to perform multiple tasks within a 3D environment.
+
+differentiable neural modules. However, they required the combination of semantic parsers and predefined templates to learn to solve the compositional tasks. Alternative approaches leverage LLMs through predefined task modules or APIs for action execution [\[21,](#page-8-5) [22,](#page-8-6) [31,](#page-9-2) [49,](#page-10-1) [55\]](#page-10-2). While effective, these methods often lack a compositional structure to address multiple tasks or primarily focus on manipulation and robotic planning, with limited emphasis on navigation.
+
+In this work, we present TANGO (see Figure [1\)](#page-0-0) a novel neuro-symbolic compositional approach which utilizes primitives and employs a LLM as a planner to sequence these primitives within photorealistic 3D environments, where agents must perceive and act. This framework integrates high-level planning with low-level action execution, without the need for training. TANGO makes use of diverse modules designed for visual navigation and question-answering tasks, resulting in a system that seamlessly addresses both challenges while achieving SoA performance without requiring any prior adjustments. By providing a few in-context examples that show how to tackle multiple tasks, TANGO is capable of generalizing to the specific task at hand. This is facilitated by the LLM, which effectively combines the individual modules available within the TANGO system. Moreover, it extends [\[52\]](#page-10-3) exploration policy by incorporating a memory mechanism in the form of a stored feature map that retains information about previously explored areas, supporting for efficient life-long navigation tasks.
+
+To demonstrate the flexibility of the proposed framework, TANGO has been tested on three popular benchmarks: namely, *i*) Open-Vocabulary ObjectGoal Navigation [\[53\]](#page-10-4), *ii*) Multi-Modal Lifelong Navigation [\[25\]](#page-9-3), and *iii*) Embodied Question Answering [\[15,](#page-8-7) [33\]](#page-9-4). Results match or surpass previous approaches, without requiring specialized training. Summing up, the contributions of this paper are as follows:
+
+- Introduction of a neuro-symbolic compositional LLMbased framework for EAI leveraging specialized primitive modules.
+- Demonstration of robust generalization capabilities across multiple tasks without the need for any specific training or fine-tuning.
+- Extension of the exploration policy presented in [\[52\]](#page-10-3) to multi-goal scenarios through the incorporation of a memory mechanism stored as a feature vector map.
+- Achievement of state-of-the-art results, underscoring the effectiveness of the proposed framework.
+
+# Method
+
+Neuro-symbolic approaches offer the possibility to address a broad range of diverse complex tasks efficiently. Systems like VisProg [\[18\]](#page-8-2) operate on images and have demonstrated excellent results in zero-shot settings. Understanding how a similar approach works in an action-oriented scenario is key to enable robots to navigate autonomously in novel environments. Therefore, we develop this paradigm further into EAI, where the integration with action execution adds several challenges. Inspired by [\[18\]](#page-8-2), given an input prompt, we rely on a LLM to generate synthetic pseudo-programs that can be executed by the embodied agent in the environment. Our TANGO framework is therefore able to generalize to new tasks, without any direct training on taskspecific datasets, thus effectively mitigating the heavy computational training costs inherent to embodied navigation tasks. The procedure that enables generating these new programs is based on "in-context examples" that are fed to the LLM, alongside a natural language instruction. An outline of TANGO is shown in Figure [2.](#page-3-0)
+
+TANGO Interpreter. The first key component of the proposed framework is referred to as "Program Interpreter". It comprises visual recognition modules that can be used by the agent to extract the semantics of the scene, as well as to provide an understanding of the visual context.
+
+As shown in Figure [1,](#page-0-0) users can ask questions or give specific tasks to the agent; the natural language prompt is then processed by the LLM (in our implementation GPT-4o [\[5\]](#page-8-0)), which serves as a planner and outputs a step-bystep executable program. To ensure the LLM delivers a reasonable output, it is fed with 15 "in-context examples" across diverse tasks. This enables the LLM to make use of its reasoning capabilities effectively, identifying the most suitable planning for the required task. Moreover, the examples remain the same regardless of the task identified in the prompt; it is the LLM's responsibility to output the specific program target for the given question. Programs use a higher level of abstraction than previous modular attempts such as Neural Module Networks (NMN) [\[2,](#page-8-3) [19\]](#page-8-4). Each program is constructed as a sequence of primitives (e.g., detect, answer, match, etc.) that invoke corresponding TANGO modules. These modules are either powered by pre-trained state-of-the-art vision models or implemented as simple Python subroutines (e.g., count, is found, eval, etc.), with additional navigation-specific modules designed to "steer" the agent's movements (e.g. navigate to, return, turn, etc.). Figure [3](#page-4-0) provides a comprehensive list of all the currently implemented modules.
+
+Figure [2](#page-3-0) shows an Embodied Question Answering (EQA) example for the user's question: *"on the left of the toilet there's a shower, is the shower curtain open or closed?"*. The LLM, which acts as a planner, transforms the
+
+
+
+Figure 2. Overview of the program generation in TANGO. Given few "in-context examples", the LLM provide a detailed sequence of steps to be executed by the agent in the given environment. The LLM is instructed to comment its output to allow for explainability.
+
+user's initial prompt into navigation subtasks by leveraging its reasoning abilities and powerful priors. This decomposition enables the model to break down complex queries into more manageable steps, guiding the agent's navigation process effectively. Each line of the generated program corresponds to a module serving a specific task. The agent must then execute the generated program. The LLM is also guided to comment on its steps directly within the generated pseudocode.
+
+All the given primitives are equipped with methods to: *i*) parse lines in order to extract input argument names and values, as well as the output variable name; *ii*) execute the module in the environment, and update the program state with the output variable name and value. The outputs for each step and the comments in the generated pseudocode can also be used to better understand what is happening under the hood and why the LLM generated a specific line, thus enabling a good interpretability of the system behavior.
+
+TANGO modules utilize open-source software and models, readily downloadable from the web. They can also be effortlessly updated with newer, more efficient models as they become available.
+
+Navigation Module. In order to navigate the environment, we define a module employing a PointGoal navigation agent as our foundational module [\[39,](#page-9-12) [52\]](#page-10-3). This model achieved nearly perfect PointGoal performance, both in terms of success (∼ 99%) and efficiency (a forward pass that takes a fraction of a second) on standard datasets [\[9,](#page-8-20) [37\]](#page-9-14). The agent starts the exploration of the environment until it locates its target goal. Once the target is identified, the focus of exploration transitions to reaching the designated goal. This PointNav policy exclusively uses the egocentric depth image and the robot's relative distance and heading towards the desired goal point as input.
+
+Exploration Policy. Exploration is performed using the policy outlined in [\[52\]](#page-10-3). This method builds occupancy maps based on depth observations to identify exploration frontiers. It further leverages RGB observations and a pretrained vision-language model (BLIP2 [\[30\]](#page-9-15)) to generate a language-grounded "value map". This value map is then used to guide the exploration, facilitating an efficient search for instances of a given target. Notably, as in [\[52\]](#page-10-3), the agent performs a 360◦ turn at the start of navigation to initialize frontiers.
+
+We extend this policy for sequential goals by incorporat-
+
+
+
+Figure 3. **Overview of TANGO modules.** Modules span a variety of inputs and outputs. Orange modules use Python subroutines, while blue modules use pre-trained computer vision models (similarly to [18]). The *navigate\_to* and *explore\_scene* modules, in green, both implement our foundational PointNav module; however, only *explore\_scene* integrates the memory mechanism.
+
+ing a memory mechanism, represented as a "feature map" in which each pixel of the value map is encoded as a vector and updated at each step. This feature map then updates the language-grounded original value map when a new target is specified, by calculating the cosine similarity between each pixel's vector and the new target embeddings (either from text or image). We sample the highest value in the updated value map; if this value exceeds a predefined threshold, indicating the agent has previously encountered and "remembers" the target's location, the agent navigates to it, supporting lifelong navigation. The process is also computationally efficient, as the feature map similarity calculations are performed only if the target changes, rather than at each step during navigation.
+
+TANGO leverages pre-trained multimodal vision-language and vision-only models as foundational components to extract semantic information from the scene, making it wellsuited for several Embodied AI tasks.
+
+(Open-set) ObjectGoal Navigation [3, 53], in particular, emerges as a suitable testbed to evaluate the efficacy of our method. A key module to tackle this problem involves utilizing an object detector (Owlv2 in our implementation [34] or DETR [8] for target objects that fall within the COCO classes [32]) to identify objects within the image. Subsequently, navigation towards the detected objects is facilitated by waypoints, leveraging depth distance calculation to guide the agent effectively. For simplicity, we use the center of the bounding box to determine the target waypoint. Hence, our approach effectively addresses this task without requiring prior knowledge of target labels for previously encountered objects, making it suitable for real-world applications.
+
+TANGO also integrates a specialized module for matching target objectives with ongoing visual observations in image-based scenarios, resulting in accurate navigation towards an instance image. To evaluate our agent's performance in this context, we use SuperGlue network [40], optimized for indoor environments (i.e. using hyperparame-
+
+ters recommended in the respective paper). We rely on SuperGlue because of its real-time matching capabilities and effortless integration into Embodied systems.
+
+TANGO being task-agnostic, can process any type of navigation target, regardless of the order in which it is presented. In this context, the GOAT benchmark [25] is well-suited for evaluating our system, as it operates in scenarios where targets are specified through images, text, or descriptive phrases, provided in random sequence. It also heavily relies on memory to find previously seen targets when sequential goals are given.
+
+An agent should also be able to answer user queries like "can you check if the kitchen table is clean?". Hence, EQA serves as an excellent benchmark to assess TANGO's capabilities and robustness. EQA consists of three phases: understanding the semantic structure of the prompt, locating the target object(s), and analyzing the visual semantics to generate an accurate response. To this end, TANGO integrates a specific answer module, relying on BLIP2 [30] as its core foundation.
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diff --git a/2412.10419/paper_text/intro_method.md b/2412.10419/paper_text/intro_method.md
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+# Introduction
+
+Advances in text-to-image (T2I) generation, fueled by powerful diffusion models [@croitoru2023diffusion; @gu2023matryoshka; @rombach2022high; @saharia2022photorealistic; @yang2023diffusion; @yu2022scaling; @zhang2023text; @liang2024rich], have unlocked unprecedented possibilities for image generation, as users can readily translate textual descriptions into stunning visuals. That said, capturing precise user intent remains a major challenge [@wu2023human; @liang2024rich]. As such, single-turn T2I generation may fail to encapsulate a user's nuanced and evolving image conception. This is especially true for complex or abstract concepts, where a user's initial prompt may not suffice in achieving a desired visual representation.
+
+Addressing this challenge requires moving beyond the paradigm of one-shot T2I generation towards a more iterative and interactive process [@von2023fabric; @wang2024diffusion; @liu2024you], e.g., a collaborative setting where an assistive agent interacts with a user, guiding them through a series of refinements of their initial prompt. This interactive setup could allow for a more nuanced elicitation of the user's intent, gradually shaping the generated images towards a desired outcome.
+
+To this end, we introduce a **P**ersonalized **A**nd **S**equential **T**ext-to-image **A**gent (PASTA), which learns from user preference feedback to guide the user through a sequence of prompt expansions, iteratively refining the generated image. This sequential approach (see [1](#fig:framework){reference-type="ref+Label" reference="fig:framework"}) allows users to articulate their vision more completely by gradually reducing uncertainty or underspecification in their original prompt. Our framework leverages the power of large multimodal language models (LMMs) [@team2024gemini; @achiam2023gpt] and reinforcement learning (RL) [@sutton2018reinforcement; @fan2024reinforcement] to facilitate personalized co-creation.
+
+Our approach for PASTA involves a multi-stage data collection and training process. We first collect multi-turn interaction data from human raters with a baseline LMM. Using this sequential data, as well as large-scale, open source (single-turn) preference data, we train a user simulator. Particularly, we employ an EM-strategy to train user preference and choice models, which capture implicit *user preference types* in the data. We then construct a large-scale dataset, which consists of trajectories of interactions between a simulated user and the LMM[^1]. Finally, we leverage this augmented data, encompassing both human and simulated interactions, to train PASTA -- our value-based RL agent, which presents a sequence of diverse, personalized slates of images to a user. PASTA interacts with the users and sequentially refines its generated images to better suit their underlying preferences.
+
+Our contributions are as follows. (1) We formulate the problem of iterative prompt expansion using LMMs and T2I models. (2) We collect a new, large-scale user-agent interaction dataset for the multi-turn prompt expansion setup. (3) We train user utility and user-choice models with this dataset, creating a simulator that is used to generate additional simulated data. (4) Finally, we utilize our datasets to train PASTA, our value-based RL agent, demonstrating its effectiveness through comprehensive human evaluations, with significant improvements in user satisfaction and image quality compared to a baseline LMM.
+
+
+
+An illustration of an agent-user interaction with L = 4 prompt expansions at each step, and M = 1 images per prompt expansion. The user selection is outlined in blue. The agent presents prompt expansions based on the user’s previous responses to maximizes the expected cumulative user satisfaction (i.e., value). See 15 for additional examples using M = 4 images for L = 4 prompt expansions.
+
+
+We begin with a short background on diffusion models, LMMs, and RL. We then formulate our problem setup.
+
+**Diffusion models** [@ho2020denoising; @ho2022classifier; @croitoru2023diffusion; @gu2023matryoshka; @rombach2022high; @saharia2022photorealistic; @yang2023diffusion; @yu2022scaling; @zhang2023text; @liang2024rich] have become a prominent approach for T2I generation. These models add Gaussian noise to an image until it becomes pure noise, and are then trained to reverse this process, iteratively removing noise to reconstruct the image. This denoising process is guided by a prompt, enabling the generation of images that semantically align with the textual description.\
+**Large Multimodal Models (LMMs)** [@team2024gemini; @achiam2023gpt] use Transformer models [@vaswani2017attention], and are trained to predict the next token in a sequence, where tokens can represent textual or visual information. These models learn joint representations of text and images, making them useful for prompt engineering in interactive T2I systems, as they allow us to modify generated outputs based on previously generated responses.\
+**Reinforcement Learning (RL)** [@sutton2018reinforcement; @fan2024reinforcement] frameworks train agents to make a sequence of decisions in an environment to maximize cumulative reward. Value-based RL methods learn a value function that estimates the expected cumulative reward for taking a particular action in a given state. In our work, an RL agent learns to select actions (prompt expansions) that lead to higher user satisfaction.
+
+# Method
+
+We consider an interactive, sequential T2I decision problem in which a user engages with an agent, visualized in [1](#fig:framework){reference-type="ref+label" reference="fig:framework"}. At time $t=0$, the user issues an initial prompt (e.g., "A white cat") intended to capture a target image. At each turn $t \geq 1$ the agent proposes $L$ prompt expansions, which are fed to a T2I model. The T2I model then generates $M$ images for each prompt. The user sees the slate of $M\times L$ images and selects the set of $M$ images (corresponding to one of the prompt expansions) that best reflects their preferences. The process repeats for up to $H$ turns.
+
+Formally, the problem is given by a tuple $(\gU, \gP, \gI, G, C, R, H, \nu_0)$, where $\gU$ is a set of *user types*, reflecting different preferences; $\gP$ is a set of feasible text prompts; $\gI$ is a set of feasible images; $G: \gP \mapsto \Delta_{\gI}$ is a T2I generative model[^2]; $C: \gU \times \gP \times \gI^{ML} \mapsto \Delta_{[L]}$ is a *user choice* function[^3]; $R: \gU \times \gP \times \gI^{ML} \mapsto \Delta_{[0,1]}$ is a *user utility* function; $H$ is the horizon; and $\nu_0 \mapsto \Delta_{\gU \times \gP}$ is a distribution of users and initial prompts. At time $t=0$, a user-prompt pair $u, p_0 \in \gU \times \gP$ is sampled from the initial distribution $\nu_0$. At each time $1\leq t \leq H$ the agent selects a slate of $L$ prompt expansions $P_t = \brk[c]*{p_{\ell,t} \in \gP}_{\ell=1}^L$ and feeds them to the T2I model $G$, which generates $M$ images for each prompt, $\brk[c]*{\brk[c]*{I_{m, \ell, t} \sim G(p_{\ell,t})}_{m=1}^M}_{\ell=1}^L$. The user observes the slate of $M\times L$ images, and selects a set of $M$ images corresponding to one prompt, and according to the choice function $C$, i.e., $c_t \sim C\brk*{u, p_0, I_{1,1,t}, \hdots, I_{M,L,t}}$. We assume a (latent) reward $r_t \sim R\brk*{u, p_0, I_{1,\ell_t,t}, \hdots, I_{M, \ell_t,t}}$ reflecting user $u$'s satisfaction with the selected images.
+
+Our problem can be formulated as a *latent contextual MDP* [@hallak2015contextual; @kwon2021rl], where $\gU$ is the latent context space. The state space consists of the user-agent interaction history, with the state (history) $h_t$ at time $t$ given by: $$\begin{align*}
+ h_t
+ =
+ \Bigg(\underbrace{p_0}_{\text{initial prompt}},
+ \overbrace{\underbrace{\brk[c]*{p_{\ell,1}}_{\ell=1}^L}_{\text{agent action $P_1$}}, \underbrace{\brk[c]*{I_{m,\ell,1}}_{\ell=1,m=1}^{L,M},c_1}_{\text{transition}}}^{\text{time step $1$}}, \hdots ,
+ \overbrace{\underbrace{\brk[c]*{p_{i,t}}_{\ell=1}^L}_{\text{agent action $P_t$}}, \underbrace{\brk[c]*{I_{m,\ell,t}}_{\ell=1,m=1}^{L,M},c_t}_{\text{transition}}}^{\text{time step $t$}} \Bigg),
+\end{align*}$$ the action space is any selection of $L$ prompts, transitions are induced by the *user choice* model $C$ and the T2I model $G$, and reward is given by a *user utility function* $R$. See [9](#appdx:latent_mdp){reference-type="ref+label" reference="appdx:latent_mdp"} for a full description of this latent MDP.
+
+A stochastic *policy* $\pi: \gH \mapsto \Delta_{\gP^L}$ maps interaction histories to a distribution over slates of $L$ prompts. The *value* of a policy $\pi$ is its expected cumulative sum of rewards over users and initial prompts, i.e., $v^\pi = \expect*{u \sim \nu_0}{\sum_{t=1}^H r_t | u, \pi}.$ An *optimal policy* ${\pi^* \in \arg\max_{\pi} v^\pi}$ maximizes the value. The *state-action value function* for any $h \in \gH$ and $P \in \gP^L$ is ${q^\pi_t(h, P) = \expect*{u \sim \nu_0}{\sum_{t'=t}^H r_{t'} | u, h_t=h, P_t = P, \pi}}$.
+
+We solve the sequential prompt expansion problem using RL. Our **P**ersonalized **A**nd **S**equential **T**ext-to-image **A**gent (PASTA) engages with a user to personalize the prompt and maximize (latent) user utility. The user's type $u \in \gU$ is unknown to the agent and must be inferred during the interaction. This framework is related to meta-RL [@wang2016learning; @duan2016rl; @maml; @zintgraf2019varibad], where each episode (or meta-episode) samples a latent MDP from a predefined problem distribution. In our setting, the agent must adapt within $H$ steps to the unknown MDP, and optimize the reward accordingly. This requires balancing exploration and exploitation: the agent must take actions that, on the one hand, provide images that reflect (its estimate of) the user's preferences, and on the other, improving its estimate of those preferences by exploring other types of expansions/images. This can be viewed as an implicit form of *preference elicitation* [@keeney1993decisions; @salo2001preference; @chajewska2000making; @boutilier2002pomdp; @meshi2023preference].
+
+The state space of user interaction histories serves as sufficient statistic (i.e., belief over users' types [@aberdeen2007policy]). To solve our problem effectively, each interaction with a user should provide the agent sufficient information about the user (e.g., through value of information [@boutilier2012active]). This requires the action space be rich and diverse enough to enable information gain.
+
+A straightforward approach to *action space design* uses an LMM to construct action candidates (in our case, prompt expansion candidates). Specifically, we use an LMM to process the current interaction history and generate a broad set of $L_C \gg L$ candidate prompts from which a slate of $L$ prompts can be chosen. Generating a large set of candidates has been shown to introduce diversity and induce exploration [@tennenholtz2024embedding]. Instead of relying on a given LMM to directly output $L$ prompts, we encourage diversity by generating $L_C$ prompts, and then selecting the $L$-subset our agent deems optimal. This, in turn, expands the effective action space our agent can leverage during training.
+
+Formally, we structure our policy class using a *candidate generator policy* $\pi_C: \gH \mapsto \Delta_{\gP^{L_C}}$ and a *candidate selector policy* $\pi_S: \gP^{L_C} \mapsto \Delta_{\gP^{L}}$. We first sample a set of $L_C$ candidates $\brk[c]{p_{i,t}}_{i=1}^{L_C} \sim \pi_C(h_t)$ and then select $L$ prompts from those candidates, i.e., $P_t = \brk[c]{p_{i,t}}_{i=1}^{L} \sim \pi_S
+\brk*{\brk[c]*{p_{i,t}}_{i=1}^{L_C}}$. We assume $\pi_C$ to be a fixed, capable LMM, and focus here on training the candidate selector $\pi_S$.
+
+
+
+(Left). PASTA policy framework: The LMM is used as a candidate generator of a candidate set, from which the candidate selector policy is used to select a slate. Each prompt in the slate is evaluated individually using a prompt-value model, and the overall slate value is calculated as the average of the individual prompt values. (Right). Score function architecture. The image and prompt are fed into CLIP encoders followed by user encoders to generate K user embeddings. The score for each user type is the inner product between the corresponding user image embedding and user text embedding.
+
+
+We use a state-action value function to define our selector policy, $\pi_S$. Given candidates $\brk[c]{p_{i}}_{i=1}^{L_C}$ and a state-action value model $q_\phi(h, P)$ parameterized by $\phi$, we define the selector policy by $$\begin{align}
+\label{eq: selection method}
+\pi_{S,\phi}(h) \in \arg\max_{P \in \gP_L\brk*{\brk[c]*{p_{i}}_{i=1}^{L_C}}} q_\phi(h,P),
+\end{align}$$ where $\gP_L(X) = \brk[c]*{ P \in X: \abs{P} = L}$ is the set of all possible slates of size $L$. Enumerating $\gP_L\brk*{\brk[c]*{p_{i}}_{i=1}^{L_C})}$ is, of course, computationally expensive when $L_C$ is large (a detriment to both training time and inference efficiency). Inspired by @slateQ:ijcai19, we decompose the value function into a (weighted) average of prompt-values $f_\phi : \gH \times \gP \mapsto \mathbb{R}$. Specifically, we compute the value of each prompt, and estimate the value of a slate by: $$q_\phi(h,P) = \frac{1}{L}\sum_{p \in A} f_\phi(h,p).$$ This approximation reduces the exponential complexity of finding the best slate to $\gO(L_C \log L_C)$ (i.e., by sorting the prompt values over the candidate set). See [2](#fig:policy_arch){reference-type="ref+label" reference="fig:policy_arch"} for a schematic of the PASTA policy and value function.
+
+PASTA first prompts an LMM $\pi_C$ (i.e., candidate generator) to generate ${L_C}$ candidates $\brk[c]*{p_i}_{i=1}^{L_C} \sim \pi_C(h)$. It then uses its candidate selector $\pi_S$ to select a slate $P$ of prompts according to [\[eq: selection method\]](#eq: selection method){reference-type="ref+Label" reference="eq: selection method"}.
+
+We train PASTA using *implicit Q-Learning (IQL)* [@kostrikov2021offline], which estimates the TD error with the Bellman optimality operator. IQL employs a soft estimate with a value function trained to approximate the high expectile without assessing state-actions outside the dataset distribution, and has proven effective in offline RL with LLMs [@snell2206offline; @zhou2024archer]. The IQL loss is: $$\begin{equation*}
+ \begin{split}
+ % \label{eq:iql}
+ \mathcal{L}(\phi, \psi) = \mathbb{E}_{h_t,P_t,r_t,h_{t+1} \sim \mathcal{D}} \bigg[ (q_\phi(h_t,P_t) - r_t
+ - v_{\psi}(h_{t+1}))^2 + L^\alpha_2(q_{\hat{\phi}}(h_t,P_t) - v_{\psi}(h_t))\bigg],
+ \end{split}
+\end{equation*}$$ where $L^\alpha_2(x) = \abs{\alpha - 1_{\{x < 0\}}}x$, $\alpha \in[0.5,1]$ and $v_{\psi}$ is the $\alpha$-expectile value estimate. Most notably, the IQL loss does not require searching for the best slate (which is oftentimes out of the distribution of the training data), making it very efficient for training (see [13.1](#appdx:pasta_training){reference-type="ref+label" reference="appdx:pasta_training"} for further details).
+
+Training PASTA relies on the availability of *sequential* user-agent interaction data. While single-turn T2I preference data is readily available [@wu2023human; @kirstain2023pick; @pressmancrowson2022; @liang2024rich], sequential datasets are not. Hence, we collect human-rater data *for our sequential setup*, and further enrich it with simulated data. Below, we describe our data creation process. All of our datasets are open-sourced here: .
+
+We use human raters to gather sequential user preferences data for personalized T2I generation.[^4] Participants are tasked with interacting with an LMM agent for five turns. Throughout our rater study we use a Gemini 1.5 Flash Model [@team2024gemini] as our base LMM, which acts as an agent. At each turn, the system presents 16 images, arranged in four columns ($L=4, M=4$), each representing a different prompt expansion derived from the user's initial prompt and prior interactions. Raters are shown only the generated images, not the prompt expansions themselves.
+
+At session start, raters are instructed to provide an initial prompt of at most 12 words, encapsulating a specific visual concept. They are encouraged to provide descriptive prompts that avoid generic terms (e.g., "an ancient Egyptian temple with hieroglyphs"instead of "a temple"). At each turn, raters then select the column of images preferred most (see the UI used in [10](#appdx:human_rater_dataset){reference-type="ref+label" reference="appdx:human_rater_dataset"}); they are instructed to select a column based on the quality of the *best* image in that column w.r.t. their original intent. Raters may optionally provide a free-text critique (up to 12 words) to guide subsequent prompt expansions, though most raters did not use this facility.
+
+After five turns, raters enter an evaluation phase where they answer questions about each turn, including: (1) re-confirmation of their preferred columns; (2) quantifying whether the image slate in the current turn is better than the previous; and (3) a free-text explanation for their selection. This process yields a dataset comprising sequential interaction trajectories with user preference feedback and provides valuable insight into the dynamics of multi-turn, personalized T2I generation. See [10](#appdx:human_rater_dataset){reference-type="ref+Label" reference="appdx:human_rater_dataset"} for a comprehensive description of the rater study.
+
+Simulated data can drastically improve performance of RL agents [@kaiser2019model; @yu2021combo; @liu2023domain; @hafner2020dreamerv2; @micheli2022transformers]. For this reason, we enrich the rater data above with additional *simulated* agent-user interaction data. To do so, we develop a *user model* that encodes distinct user *types* that reflect a range of preferences. This model has two components: (1) A *user choice model* which predicts the image column a user selects; and (2) a *user utility model* which predicts a user's satisfaction with an image slate. We outline details of the user model in Section [5](#sec:usermodel){reference-type="ref" reference="sec:usermodel"}.
+
+We begin by sampling a user and initial prompt from a joint prior. We use a randomized exploration policy for the agent, encouraging diverse interactions to distinguish preferences across user types. At each turn in a simulated trajectory, the agent proposes a slate of prompt expansions, and the user (via the choice model) selects their preferred prompt. The user utility model is then used to assign a satisfaction score for the images generated using the selected prompt. This process is repeated for five turns (see [11](#appndx:offline_simulated_data){reference-type="ref+label" reference="appndx:offline_simulated_data"} for further details and analysis of our data generation process). Augmenting our human-rater data with this simulated data allows for more robust training of PASTA, as we show later in Section [6](#sec:experiments){reference-type="ref" reference="sec:experiments"}.
+
+The data generation process described in [4.2](#section:simulated dataset){reference-type="ref+Label" reference="section:simulated dataset"} requires a user simulator consisting of both user choice and utility models. We leverage our sequential human-rater data ([4.1](#section: rater data){reference-type="ref+Label" reference="section: rater data"}), together with large-scale open-source single-turn T2I preference data [@wu2023human; @kirstain2023pick; @pressmancrowson2022], to build these models.
+
+We first propose a user preference model based on fundamental *choice theory* [@pennock2000social] that mimics user's preference behaviors in an interactive recommendation system. Specifically, this model takes as input a user type $u \in \gU$, an initial prompt $p \in \gP$, and a slate of images ${P_t = \brk[c]*{I_{m,l,t}}_{\ell\neq \ell,m=1}^{L,M}}$ and estimates the probability that $u$ prefers image column $\brk[c]*{I_{m,\ell^*,t}}_{m=1}^M$ over the others $\brk[c]*{I_{m,l,t}}_{\ell\neq \ell^*,m=1}^{L,M}$, i.e., $$\begin{align}
+\label{eq: preference model}
+ P\brk*{
+ \brk[c]*{I_{m,\ell^*,t}}_{m=1}^M \succ \brk[c]*{I_{m,l,t}}_{\ell\neq \ell^*,m=1}^{L,M} | u, p}.
+\end{align}$$ In the typical *single-turn* setup [@wallace2024diffusion], by setting $M=2$, ${L=1}$, and not assuming any user types, [\[eq: preference model\]](#eq: preference model){reference-type="ref+Label" reference="eq: preference model"} reduces to a probabilistic model that estimates the "global" (non-user specific) pair-wise preference between two images, i.e., $$\begin{align*}
+ \expect*{u \sim \nu_0}{P\brk*{I_1 \succ I_2} | u, p}.
+\end{align*}$$ By contrast, our model is able to capture personalized (type-specific) preferences over a generic sets of multiple images.
+
+Our user choice model $C$ exploits the preference model in [\[eq: preference model\]](#eq: preference model){reference-type="ref+Label" reference="eq: preference model"}, making the usual assumption that users select images based on their inherent preferences; i.e.,
+
+::: assumption
+[]{#assumption: choice and preference label="assumption: choice and preference"} Assume $C \equiv P$, where $P$ is the preference model in [\[eq: preference model\]](#eq: preference model){reference-type="ref+Label" reference="eq: preference model"}.
+:::
+
+While, in general, user choice may also be be biased by other factors (e.g., previous interactions, position bias, etc.), this idealized model proves very useful in our experiments.
+
+To further model user utility, motivated by the axioms of expected utility theory [@von2007theory], we model user utility $R: \gU \times \gP \times \gI^{ML} \mapsto \sR$, leveraging the preference model in [\[eq: preference model\]](#eq: preference model){reference-type="ref+Label" reference="eq: preference model"} to ensure a user's utility is consistent with their preferences. Specifically, we assume that a user prefers one set of images to another if the utility of the former images is higher, as formalized by the following assumption.
+
+::: assumption
+[]{#assumption: consistency label="assumption: consistency"} For any $u,p$, [\[eq: preference model\]](#eq: preference model){reference-type="ref+Label" reference="eq: preference model"} is satisfied for $\ell^* \in \brk[c]*{1, \hdots, L}$ iff for all $\ell \neq \ell^*$ $$\begin{align*}
+ R\brk*{\brk[c]*{I_{\ell^*,m,t}}_{m=1}^M|u,p}
+ >
+ R\brk*{\brk[c]*{I_{\ell,m,t}}_{m=1}^M|u,p}.
+\end{align*}$$
+:::
+
+[\[assumption: choice and preference,assumption: consistency\]](#assumption: choice and preference,assumption: consistency){reference-type="ref+Label" reference="assumption: choice and preference,assumption: consistency"} ensure a tight coupling of our user utility and choice models, which we leverage in model training.
+
+To train our user utility and choice models, we employ a parameterized score model $s_\theta: \gU \times \gI \times \gP \mapsto [0,1]$ to serve as the backbone of our user simulator. We assume a discrete set of $K$ (unknown) user types, i.e., $\mathcal{U} = \{1, 2, \dots, K\}$.
+
+Exploiting [\[assumption: choice and preference,assumption: consistency\]](#assumption: choice and preference,assumption: consistency){reference-type="ref+Label" reference="assumption: choice and preference,assumption: consistency"}, we define the utility of a user type $k$ over an arbitrary image slate $\brk[c]*{I_{m,\ell,t}}_{m=1}^M$ that associates to a prompt $p$ using the following model: $$\begin{align}
+ R_{\ell,t}^{k,\theta} := R_{\theta}\brk*{\brk[c]*{I_{m,\ell,t}}_{m=1}^M|u=k,p}
+ =
+ \text{Agg}(s_\theta(k,p,I_{1,\ell,t}), \dots, s_\theta(k,p,I_{M,\ell,t})),
+ \label{eq: utility model}
+\end{align}$$ where $\text{Agg}$ is an aggregator (e.g., average, max, Softmax sampler) operator. User choice probabilities are given by $$\begin{align}
+C_{\theta}(k,p,\{I_{m,1,t}\}_{m=1}^M,\dots,\{I_{m,L,t}\}_{m=1}^M)
+=
+\text{Softmax}\brk*{\tau_\theta R_{1,t}^{k,\theta},\dots,\tau_\theta R_{\ell,t}^{k,\theta}},
+\label{eq: choice model}
+\end{align}$$ where $\tau_\theta = h_\theta\brk*{R_{1,t}^{k,\theta},\dots,R_{\ell,t}^{k,\theta}}$ is a parameterized temperature constant which depends on the scores of different image columns. Such a temperature parameterization ensures our user choice model satisfies [\[assumption: consistency\]](#assumption: consistency){reference-type="ref+Label" reference="assumption: consistency"}, while allowing for greater flexibility in modeling.
+
+To balance model capacity and computational efficiency, our score function adopts pre-trained CLIP [@clip] text and image encoders, followed by user encoders (a head for each user type). The user encoder transforms CLIP embeddings into a user-type-specific representation that captures individual preferences for images and prompts. The final score of an image-prompt pair is the inner product of the (user-type-specific) image embedding and corresponding text embedding, multiplied by a learned temperature parameter (see a schematic of our arch. in [2](#fig:policy_arch){reference-type="ref+label" reference="fig:policy_arch"}).
+
+To train our user model we use two types of data: (1) the sequential human-rater data collected by the procedure described in [4.1](#section: rater data){reference-type="ref+Label" reference="section: rater data"}, and (2) a large-scale, open-source, single-turn dataset of user preferences over pairs of images. As we will demonstrate in [6](#sec:experiments){reference-type="ref+Label" reference="sec:experiments"}, including single-turn data for training substantially improves model quality, suggesting that user utilities oftentimes remain *stationary* (for which the single-turn data already captures most of the basic user-image preferences).
+
+While single-turn data alone is insufficient to train the multi-item user choice model in [\[eq: choice model\]](#eq: choice model){reference-type="ref+Label" reference="eq: choice model"}, it remains valuable. We leverage it to simplify the training process by focusing on the direct relationship between single-turn data and user utilities. However, the user choice model itself requires the nuances captured in sequential data, so we fine-tune our choice model $C_\theta$ with sequential data. For a more detailed explanation of how our user choice and utility models can be effectively trained with both sequential and single-turn data, please refer to [12](#appdx:user_model){reference-type="ref+label" reference="appdx:user_model"}.
+
+We train our user model by maximizing likelihood of user preferences and choices in the datasets. Our parametric model is optimized to induce scores that are used to predict preferences and user choices. Of course, in our rater and open-source datasets, user types are *unknown*---only preference labels are provided. As discussed above, we assume that a reasonably small set of $K$ user types suffices to explain preference diversity across the data-generating population.[^5] Let $\gD = \brk[c]*{(x_i, y_i)}_{i=1}^N$ be a dataset in which each example, in its most general form, comprises a prompt and set of images $x_i = \brk*{p_i, \brk[c]*{I_{i,m,l}}_{\ell=1,m=1}^{L,M}}$, and labels $y_i$ reflecting an annotator's preference. Details of the data types, their modeling and loss functions are given in [12](#appdx:user_model){reference-type="ref+label" reference="appdx:user_model"}.
+
+Inspired by the work of @chidambaram2024direct, we fit the score function $s_\theta$ using an expectation-maximization (EM) [@moon1996expectation] objective. Specifically, we learn a posterior over user types, and alternate between user posterior estimation and likelihood maximization. In the E-step, a posterior $\gamma_{i}(k) \triangleq p_{\theta_t}(
+ z_i = k|x_i, y_i)$ over user types is calculated for each sample $(x_i,y_i) \in \gD$. Following the work of @chidambaram2024direct, the posterior is calculated by $$\begin{equation*}
+ \label{eq:posterior}
+ \begin{split}
+ \gamma_{i}(k) =
+ \frac{\eta_k \prod_{j=1}^M \sigma_{\theta_t}(x_{i,j},y_{i,j},k) }
+ {\sum_{\ell=1}^K \eta_\ell \prod_{j=1}^M \sigma_{\theta_t}(x_{i,j},y_{i,j},\ell)},
+ \end{split}
+\end{equation*}$$ where $\sigma_\theta(x_,y,k) \triangleq p_{\theta}(y|x, z=k)$ defines the likelihood, and $\eta_k$ is the non-parametric prior. The M-step involves updating the prior via solving the log-likelihood maximizing problem over the posterior model: $$\begin{equation*}
+\begin{split}
+% \label{eq:m-step}
+ &\eta_k = \mathbb{E}_{i \sim \mathcal{D}} [\gamma_i(k)], \\
+&\theta_{t+1} \in \arg\max_\theta \mathbb{E}_{i \sim \mathcal{D}, k \sim \gamma_i} \big[\log \sigma_\theta(x_i,y_i,k) \big],
+\end{split}
+\end{equation*}$$
+
+Due to computational challenges of managing large datasets, we employ a mini-batch version of this approach in which we sample a batch $B$ from dataset $\mathcal D$ at each training step to estimate the log-likelihood loss: ${
+ \gL(\theta) = -\expect*{i \sim B, k \sim \gamma_i}{\log \sigma_\theta(x_i,y_i,k)}.
+}$ See [12.1](#apndx:user_model_training){reference-type="ref+label" reference="apndx:user_model_training"} for a detailed description of the mini-batch EM algorithm.
+
+{#fig:user_model_results width="70%"}
+
+{#fig:emergence_pref width="50%"}
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diff --git a/2502.02004/main_diagram/main_diagram.pdf b/2502.02004/main_diagram/main_diagram.pdf
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+# Introduction
+
+Several pre-trained large language models based on Transformer architecture [@NIPS2017_3f5ee243] have demonstrated robust capabilities in various generative tasks [@devlin-etal-2019-bert; @2020t5; @NEURIPS2020_1457c0d6; @Touvron2023LLaMAOA; @jiang2023mistral7b]. However, limitations on the input sequence length arise due to the computational resource constraints encountered during the pre-training phase. Such constraints necessitate a determination of the maximum allowable length of sequences, hereinafter $L_{\rm train}$, prior to the pre-training process, thus hindering the model's performance in processing sequences longer than those encountered during training. This weakness is primarily attributed to the positional encoding's ineffectiveness in handling sequences that exceed the length of those encountered during the model's training phase [@devlin-etal-2019-bert; @press2022train].
+
+Rotary Position Embedding (RoPE) [@su2021roformer] has become a common approach in many language models that handle long contexts, and it employs a rotation matrix to encode positional information and facilitate the processing of long sequences. To manage sequences longer than those encountered during training, various scaling strategies [@chen2023extendingcontextwindowlarge; @bloc97; @peng2024yarn; @liu2024scaling] have been applied to RoPE, although these often require additional fine-tuning and incur further learning costs in addition to those of pre-training. In contrast, Attention with Linear Biases (ALiBi) [@press2022train] is able to sequence length estimation beyond the limits of pre-training without requiring additional fine-tuning. However, ALiBi limits the attention's receptive field [@chi-etal-2023-dissecting] in the manner of windowed attention [@Beltagy2020Longformer]. For this reason, a model using ALiBi may not be able to obtain information that is in a distant dependency relationship.
+
+
+
+Overview of Wavelet-based Relative Positional Representation As in RPE , our method computes a relative positional representation (pm, n)T to the query qm and the key kn. Instead of learnable embedding in RPE, the position is computed based on the wavelet function. Different wavelet functions ψa, b are used for each dimension of the head d. Furthermore, the scale parameter a and the shift parameter b change depending on the dimension of the head d.
+
+
+In this paper, we analyze conventional positional encoding methods for long contexts, and we propose a novel positional representation that permits extrapolation without constraining the attention mechanism's receptive field. First, we mathematically show that RoPE performs a process similar to a wavelet transformation---considered the gold standard of time-frequency analysis methodology. We interpreted the position of each token in the sequence as a time point in time-frequency analysis. However, RoPE does not perform a transformation in accordance with the order of positions but rather in accordance with the number of dimensions, and it does not capture the dynamic change in a signal over time. Furthermore, the values corresponding to the wavelet scale (i.e., window size) are constant, so RoPE does not make good use of the key characteristic of wavelet transforms, which is the ability to analyze signals on multiple scales. In other words, RoPE may fail to capture the dynamic change in a signal over time, such as what occurs in natural language. In this study, we also show that ALiBi provides different window sizes for each head.
+
+Based on these insights, we propose a wavelet transform-based method, using multiple window sizes, to offer a robust and flexible approach to positional encoding. By performing a wavelet transform along the order of positions and introducing various scale parameters, our method can capture the dynamic changes in a sequence over positions in the manner of the original feature of wavelet transformation, i.e., time-frequency analysis. Following the methodology of Relative Position Representation (RPE) [@shaw-etal-2018-self], we implement our method with relative ease.
+
+From our experiments on extrapolation capabilities using the wikitext-103 dataset [@merity2017pointer], the results demonstrate that our method surpasses traditional positional encoding methods in perplexity. We also report that our method has lower perplexity than RoPE in experiments with long contexts using the Llama-2 model [@touvron2023llama2openfoundation] and the CodeParrot dataset.
+
+Within the Transformer architecture, positional encoding is employed to accurately represent the sequential position of each token. Positional encoding can be divided into two main types: absolute position, which expresses the position of a token from the static beginning of the sequence, and relative position, which expresses the position of each token in relation to the other tokens within the sequence. RoPE [@su2021roformer], which adopts a type of absolute position, uses a rotation matrix to compute the position and then multiplies it by the query and key to represent the position. RPE [@shaw-etal-2018-self], based on a type of relative position, uses a learnable embedding that represents the position of distances of up to 16 or 32 tokens by clipping. Two other variations include T5 Bias [@2020t5], which has an enlarged RPE window size, and Transformer-XL [@dai-etal-2019-transformer], which uses a sine wave for position representation instead of learnable embedding.
+
+Position encoding plays a critical role in enabling models to effectively handle long context sequences, and it allows for extrapolation. Relative position is not a position expression that depends on the length of the sequence, so it is effective in extrapolation. ALiBi [@press2022train] is an effective position representation method for extrapolation: It uses the relative position bias of all tokens by adding a linear bias to each head's attention score, rather than using position embedding. However, ALiBi is unable to obtain information in a distant dependency relationship due to its constraints on the self-attention mechanism's receptive field[@chi-etal-2023-dissecting]. On the other hand, absolute position is unsuitable for extrapolation because it expresses the position of all words in the sequence. For this reason, many methods have been proposed for fine-tuning RoPE by interpolating positions in using absolute position [@chen2023extendingcontextwindowlarge; @bloc97; @peng2024yarn].
+
+Frequency analysis in signal processing involves analyzing the frequency components of a signal to understand its behavior. The Fourier transform (FT) [@bracewell1986fourier] is a key method for frequency analysis, converting a signal from the time domain to the frequency domain, thus providing a global view of its frequency content. However, the FT does not provide any information about **when** specific frequencies occur. To address this limitation, time-frequency analysis techniques have been applied. The wavelet transform (WT) [@doi:10.1137/0515056; @192463] offers a more flexible approach by analyzing the signal at multiple scales or resolutions. The WT adaptively provides high time resolution for high-frequency components and high frequency resolution for low-frequency components, making it well-suited for analyzing signals with non-stationary or transient features. This adaptability allows the wavelet transform to capture both time and frequency information with varying degrees of precision.
+
+# Method
+
+A wavelet is a wave that decays quickly and locally as it approaches zero. A function $\psi$ defined on a real $\mathbb{R}$ is called a wavelet function if it belongs to the space [$L^{2}(R)$]{style="color: black"} of square integrable functions and satisfies the following conditions: $$\begin{equation}
+\int_{-\infty}^{\infty}\mid\psi(x)\mid^{2} dx < \infty .
+ \label{wave_}
+\end{equation}$$ The wavelet function is defined as follows. $$\begin{equation}
+\psi_{a,b}(t) = \frac{1}{\sqrt{a}}\psi\Bigl(\frac{t-b}{a}\Bigl) .
+ \label{wave_let}
+\end{equation}$$ In this case, $b$ is the shift and $a>0$ is the scale parameter. The scale parameter $a$ simultaneously changes the range over which the wavelet is localized as well as the wavelet's amplitude. Typical wavelets include the Haar wavelet [@Haar1910], Ricker wavelet [@10.1190/1.1445082], and Morlet wavelet [@10.1007/11492429_41]. [Suppose that we sample $T$ values at regular intervals from a continuous signal.]{style="color: black"} Wavelet transform (WT) [@doi:10.1137/0515056] is the process of transforming a signal $x(t)$ into the frequency domain and time domain by computing the inner product of the wavelet function $\psi_{a,b}(t)$ and signal $x(t)$. $$\begin{align}
+W(a, b) &= \sum^{T-1}_{t=0}\psi_{a,b}(t)x(t) .
+%&= \langle x(t), \psi_{a,b}(t)\rangle_{t\in\mathbb{R}}
+\label{dis_wavelet}
+\end{align}$$ In some cases, the term \"Discrete Wavelet Transform\" or \"Wavelet Transform\" is used to refer to multi-resolution analysis [@192463], but in this paper we follow the original definition. We can see that the FT only converts to the frequency domain, whereas the WT converts to two domains: scale $a$ and shift $b$. For example, consider the case of a conversion to two scales and four shifts. When $a \in [2, 4]$ and $b \in [0, 1, 2, 3]$, the wavelet transform can be expressed in terms of determinants as follows: $$\begin{align}
+\begin{bmatrix}
+W(2, 0) \\
+W(4, 0) \\
+W(2, 1) \\
+W(4, 1) \\
+\vdots\\
+W(4, 3) \\
+\end{bmatrix}
+=
+\begin{bmatrix}
+\psi_{2, 0}(0) & \psi_{2, 0}(1) &\psi_{2, 0}(2) & ... & \psi_{2, 0}(T-1)\\
+\psi_{4, 0}(0) & \psi_{4, 0}(1) &\psi_{4, 0}(2) & ... & \psi_{4, 0}(T-1)\\
+\psi_{2, 1}(0) & \psi_{2, 1}(1) &\psi_{2, 1}(2) & ... & \psi_{2, 1}(T-1)\\
+\psi_{4, 1}(0) & \psi_{4, 1}(1) &\psi_{4, 1}(2) & ... & \psi_{4, 1}(T-1)\\
+\vdots &\vdots &\vdots &\ddots & \vdots\\
+\psi_{4, 3}(0) & \psi_{4, 3}(1) &\psi_{4, 3}(2) & ... & \psi_{4, 3}(T-1)\\
+\end{bmatrix}
+\begin{bmatrix}
+x(0) \\
+x(1) \\
+x(2) \\
+\vdots\\
+x(T-1) \\
+\end{bmatrix}.
+\label{wave2}
+\end{align}$$ Furthermore, since $\psi_{a,b}(t)=\psi_{a,0}(t-b)$ from Eq.[\[wave_let\]](#wave_let){reference-type="ref" reference="wave_let"}, $\psi$ of the wavelet transform in Eq [\[wave2\]](#wave2){reference-type="ref" reference="wave2"} is expressed as follows. $$\begin{align}
+\small
+\begin{bmatrix}
+W(2, 0) \\
+W(4, 0) \\
+W(2, 1) \\
+W(4, 1) \\
+\vdots\\
+W(4, 3) \\
+\end{bmatrix}
+=
+\begin{bmatrix}
+\psi_{2, 0}(0) & \psi_{2, 0}(1) &\psi_{2, 0}(2) & ... & \psi_{2, 0}(T-1)\\
+\psi_{4, 0}(0) & \psi_{4, 0}(1) &\psi_{4, 0}(2) & ... & \psi_{4, 0}(T-1)\\
+\psi_{2, 0}(-1) & \psi_{2, 0}(0) &\psi_{2, 0}(1) & ... & \psi_{2, 0}(T-2)\\
+\psi_{4, 0}(-1) & \psi_{4, 0}(0) &\psi_{4, 0}(1) & ... & \psi_{4, 0}(T-2)\\
+\vdots &\vdots &\vdots &\ddots & \vdots\\
+\psi_{4,0}(-3) & \psi_{4,0}(-2) &\psi_{4,0}(-1) & ... & \psi_{4,0}(T-3)\\
+\end{bmatrix}
+\begin{bmatrix}
+x(0) \\
+x(1) \\
+x(2) \\
+\vdots\\
+x(T-1) \\
+\end{bmatrix}
+\label{wave3} .
+\end{align}$$ Due to the characteristics of the scale parameter $a$, the values of the wavelet matrix become 0 or approach 0 outside a certain range that depends on the specific wavelet function.
+
+RoPE incorporates positional information directly into the self-attention mechanism by rotating the query and key vectors in complex space. When divided into even and odd dimensions, the following calculations are performed for the $m$-th query in each sequence. In even dimensions, RoPE is expressed as follows. $$\begin{align}
+\small
+\begin{bmatrix}
+q^{m}_{0} \\
+q^{m}_{2} \\
+\vdots\\
+q^{m}_{d-2}\\
+\end{bmatrix}
+=
+\begin{bmatrix}
+\cos m\theta_{1}&-
+\sin m\theta_{1}& 0 & 0 & ...&0&0\\
+0 & 0 & \cos m\theta_{2}&-\sin m\theta_{2}& ...&0&0\\
+\vdots &\vdots &\vdots &\vdots &\ddots &\vdots & \vdots\\
+0 & 0 &0 & 0 &...&\cos m\theta_{d/2}&-\sin m\theta_{d/2}\\
+\end{bmatrix}
+\begin{bmatrix}
+q^{m}_{0} \\
+q^{m}_{1} \\
+\vdots\\
+q^{m}_{d-2}\\
+q^{m}_{d-1}\\
+\end{bmatrix}.
+\label{rope2_odd}
+\end{align}$$ where $q^{m} \in \mathbb{R}^{1\times d}$ is the $m$-th query when the number of dimensions is $d$ and $\theta_{i} = 10000^{-2(i-1)/d}, i \in [1,2,...,d/2]$. For RoPE in odd dimensions, see Appendix [9.1](#rope_aa){reference-type="ref" reference="rope_aa"}. The same process is also performed for the $n$-th key $k^{n} \in \mathbb{R}^{1\times d}$.
+
+First, we show the wavelet transform using the following two Haar-like wavelets [@Haar1910]. $$\begin{equation}
+ \psi (t) =
+ \begin{cases}
+ \cos f(t) &0\leq t \textless 1 ,\\
+ -\sin f(t) &1 \leq t \textless 2,\\
+ 0 &\mathrm{otherwise}.\\
+ \end{cases}
+ \psi^{'} (t) =
+ \begin{cases}
+ \sin f(t) &0\leq t \textless 1 ,\\
+ \cos f(t) &1 \leq t \textless 2,\\
+ 0 &\mathrm{otherwise}.\\
+ \end{cases} \label{rope_wave_haar_define}
+\end{equation}$$ [Here, $f: \mathbb{R} \rightarrow \mathbb{R}$ is a function that satisfies $\int_{-\infty}^\infty \psi(t) \, dt = 0$ and Eq.([\[wave\_\]](#wave_){reference-type="ref" reference="wave_"}).]{style="color: black"} Assuming that when $x(t) (0 \leq t \leq d-1)$ is a signal with $d$ elements, the wavelet $\psi$ is used and wavelet transform is performed at each scale $a=1$. We define the shift parameter as $b_j = j - \delta(j) (j=0,2,..,d-2)$. Here, $\delta(t)$ is a function such that $0\leq t \leq d-1$ and $0\leq \delta(t) < 1$. When the wavelet function is Haar-like wavelet $\psi (t)$ in Eq.([\[rope_wave_haar_define\]](#rope_wave_haar_define){reference-type="ref" reference="rope_wave_haar_define"}) and $a =1$ and $b \in [b_0, b_2,..,d_{d-2}]$, the wavelet matrix $\psi$ in the wavelet transform $w = \psi x$ can be expressed in terms of determinants as follows. $$\begin{align}
+\small
+\begin{bmatrix}
+W(1, b_0) \\
+W(1, b_2) \\
+\vdots\\
+W(1, b_{d-2}) \\
+\end{bmatrix}
+=
+\begin{bmatrix}
+\cos \phi_0 & -\sin \phi_1 & 0 & 0 & ... & 0& 0 \\
+0 & 0 & \cos \phi_2 & -\sin \phi_3 & ... & 0& 0 \\
+\vdots &\vdots &\vdots &\ddots & \vdots& \vdots& \vdots\\
+ 0 & 0 & 0 & 0 &... & \cos \phi_{d-2} & -\sin \phi_{d-1} \\
+\end{bmatrix}
+\begin{bmatrix}
+x(0) \\
+x(1) \\
+\vdots\\
+x(d-2) \\
+x(d-1) \\
+\end{bmatrix}.
+\label{wave_rope}
+\end{align}$$ [To simplify the notation in the matrix representation above, we write $\phi_j$ for $j=0,1,\dots,d-1$, where $\phi_j = f(1+\delta(j))$ if $j$ is odd, and $\phi_j=f(\delta(j))$ otherwise. Let $x$ be the query $q^{m}$, and define $f$ such that $\phi_j=
+\phi_{j+1} = m\theta_{\left\lceil \frac{j+1}{2} \right\rceil}$ for $j=0,2,4, \dots, d-2$, where $\theta_{i}=10000^{-2(i-1)/d}$ and $i \in [1,2,...,d/2]$. Under this definition, the transformation matrix of Eq. ([\[wave_rope\]](#wave_rope){reference-type="ref" reference="wave_rope"}) becomes identical to that of Eq. ([\[rope2_odd\]](#rope2_odd){reference-type="ref" reference="rope2_odd"}) in RoPE. [^1]]{style="color: black"} In other words, RoPE can be viewed as a wavelet transform using Haar-like wavelets that change amplitude on a fixed scale. Furthermore, the same result as RoPE in odd dimensions can be obtained when using $\psi^{'}$ for wavelet transformation. [^2] This wavelet transform in RoPE is performed across the number of query head dimensions $d$. Therefore, RoPE can be considered a wavelet transformation along the head dimension using a wavelet with a fixed scale of 2.[^3]
+
+ALiBi has a restricted receptive field and behaves in the manner of windowed attention [@chi-etal-2023-dissecting; @Beltagy2020Longformer]. A receptive field refers to the specific region of the input space that significantly influences the model's output, typically representing the area where the most relevant features are captured. ALiBi is expressed as $$\begin{equation}
+\mathrm{softmax}(q_{m}K^{T}+slope\cdot[-(m-1),\dots,-2,-1,0]),
+ \label{alibi_f}
+\end{equation}$$ where the $slope$ is a head-specific slope fixed before training and $K^{T} \in \mathbb{R}^{m\times d}$ is the first $m$ keys. In this section, we analyzed the window size in ALiBi using the attention map.
+
+
+
+Heatmap of scaled attention scores via softmax normalization in ALiBi without non-overlapping inference. The vertical axis represents the query, while the horizontal axis corresponds to the key in the attention map. For clarity, values of 0.001 or more are mapped to black, while values below that are mapped to yellow. The maximum allowable length of sequences is $L_{\rm train}=512$, and the inference length is 1012.
+
+
+A heatmap of scaled attention scores obtained through softmax normalization is shown in Figure [2](#alibi_attn){reference-type="ref" reference="alibi_attn"}. The number of heads $N$ is 8, and the slope of ALiBi is $[\frac{1}{2}, \frac{1}{4},\frac{1}{8},\frac{1}{16},\frac{1}{32},\frac{1}{64},\frac{1}{128},\frac{1}{256}]$. In extrapolation, sequences are often divided, but in this section the sequences are not divided. The experimental setting was set to the same as that in Section [6.1](#sec6.1){reference-type="ref" reference="sec6.1"}. The perplexity results are shown in Table [\[result1\]](#result1){reference-type="ref" reference="result1"}.
+
+The attention map shows that ALiBi uses multiple window sizes corresponding to relative positions and that the window size increases as the slope decreases. Moreover, previous research [@chi-etal-2023-dissecting] shows that constraining the window size (slope) to a single value leads to increased perplexity. Consequently, one of the reasons ALiBi is effective, compared to a previous relative position using fixed window sizes in T5 Bias [@2020t5], is its ability to accommodate multiple window sizes. ALiBi does not perform calculations like those in Eq. ([\[dis_wavelet\]](#dis_wavelet){reference-type="ref" reference="dis_wavelet"}), so it does not exactly match the wavelet transform. However, having windows of various sizes is similar to the role of the scale parameter used in wavelet transforms.
+
+Wavelet transform (WT) is a method of analyzing signals using variable-scale wavelets, and it is possible to adjust the scale of the window. This scalability allows both broad and fine signal features to be efficiently extracted by shifting the wavelet while changing the window size. In particular, this is suitable for investigating non-stationary signals. For this reason, we believe that the wavelet transform approach is effective for capturing the dynamic fluctuations of signals that change over time, and it is also effective for the fluid nature of natural language, which is not constrained by periodicity. Furthermore, when extrapolating, it is important to be able to respond flexibly to changes in context and information. For this reason, we believe that the wavelet transform is also an effective method for extrapolation.
+
+When applying wavelet transforms to positional encoding, a key question arises: Which features should be leveraged for handling long-context dependencies? Notably, RoPE shares conceptual similarities with the wavelet transform (Section [3](#secrope){reference-type="ref" reference="secrope"}); however, RoPE depends on absolute positional information, which limits its effective context window to the training length ($L_{\rm train}$) and restricts its extrapolation capabilities. In contrast, ALiBi offers extrapolation capabilities by using relative position, and it supports varying window sizes (Section [4](#secalibi){reference-type="ref" reference="secalibi"}). However, ALiBi's linear bias constrains its receptive field, making it insufficient for capturing long-range dependencies. According to @press2022train, conventional relative positional encoding (RPE) methods [@shaw-etal-2018-self; @2020t5], which rely on a fixed window size, are similarly ineffective for extrapolation. In conclusion, we adopt relative position with flexible window sizes to handle long-context and extrapolation.
+
+Accordingly, we propose positional representation based on wavelet transform with the following characteristics:
+
+1. **Position-based Transformation**: RoPE predominantly relies on independent transformation based on the 'head' dimensions. ALiBi employs multiple windows based on the relative position of the sentence, rather than the dimension of the head, which may contribute to its performance. Therefore, we apply a wavelet transform based on the relative position of the sentence.
+
+2. **Type of Wavelet**: RoPE can be thought of as a wavelet transform using the Haar wavelet, which is the simplest wavelet. However, Haar wavelets might fall short in capturing the intricacies of natural languages. Transitioning toward the use of more sophisticated wavelet functions could enhance our approach to distilling and representing a broader spectrum of features inherent in natural languages.
+
+3. **Diversification of Window Sizes (Scale Parameters)**: From our analysis of ALiBi, we found that having multiple windows is effective for long contexts. The original version of RoPE works with a single fixed scale. To address this limitation, we introduce a variety of scale and shift parameters.
+
+Due to the wavelet shift feature, we adopt relative position representation using ALiBi because it is more suitable than absolute position representation. [^4] In a transformer model [@NIPS2017_3f5ee243], the self-attention mechanism operates by projecting the input sequence into three distinct representations---queries ($Q$), keys ($K$), and values ($V$)---using learnable weight matrices. Self-attention sublayers employ $N$ attention heads. In self-attention sublayers, $e_{m,n}$ is the attention score for each query, and then the key is calculated. RPE[@shaw-etal-2018-self] expresses position by calculating the inner product of the query and the relative position embedding. We incorporate the wavelet function into RPE as follows. $$\begin{equation}
+ e_{m,n}
+ = \frac{q_{m}k_{n}^{T}+q_{m}(p_{m,n})^{T}}{\sqrt{d}} ,
+ \label{p1}
+\end{equation}$$ where $q_{m}$ is the $m$th query $(q_{m} \in \mathbb{R}^{1\times d}, 1 \leq m\leq L)$ of a sentence of length $L$, $k_{n}$ is the $n$th key $(k_{n}\in \mathbb{R}^{1\times d}, 1 \leq n\leq L)$ for $q_{m}$, and $d$ is the number of dimensions of each head. Here, $p_{m,n}$ is the relative position from the $m$-th query to the $n$-th key. RPE [@shaw-etal-2018-self] uses learnable embedding for $p_{m,n} \in \mathbb{R}^{d}$ and a fixed scale by clipping. However, instead of using learnable embeddings to represent $p_{m,n}$, we use $d$-pattern wavelet functions with multiple scales to calculate the position. In our method, there is no clipping, and the distance of the position expression is fixed regardless of the length of the sentence.
+
+In conventional wavelets, such as in Eq. ([\[wave_let\]](#wave_let){reference-type="ref" reference="wave_let"}), the amplitude also varies depending on the scale parameter $a$. In the proposed method, all amplitudes are the same. $$\begin{equation}
+\psi_{a,b}(t) = \psi\Bigl(\frac{t-b}{a}\Bigl) .
+ \label{wave_let2}
+\end{equation}$$ The variable $t$ is assigned the relative position, which is $t=m-n$. We used the Ricker wavelet [@10.1190/1.1445082] as a base wavelet, which is formulated as follows. $$\begin{equation}
+\psi(t)=(1-t^2)\exp\Bigl(\frac{-t^2}{2}\Bigl) .
+\label{ricker_calc}
+\end{equation}$$
+
+We use $s$ distinct patterns for the scale parameter $a$ and $\frac{d}{s}$ patterns for the shift parameter $b$. $$\begin{equation}
+(a, b) \in \{2^0,2^1,2^2,...2^{s-1}\} \times \{0, 1, 2, 3,..., \frac{d}{s}-1\} .
+ \label{p20240917}
+\end{equation}$$ The scale parameter is a power of 2 derived from the principles of the discrete wavelet transform. By combining the $\frac{d}{s}$-pattern shift parameters $b$ with the $s$-pattern scale parameters $a$, we generate $d$ distinct wavelets. In this way, our method can set the $s$-pattern **context window size** using the scale parameter $a$ and the $d$-pattern **context window** using both the scale parameter $a$ and the shift parameter $b$. For instance, with a head dimension of $d=128$, we use $s=8$ scale variants ($a \in \{2^0,2^1,...,2^7\}$) and 16 shift variants ($b \in \{0,1,2,...,15\}$), resulting in $8 \times 16 = 128$ unique wavelets. Finally, $p_{m,n}$ is computed as follows.[^5] $$\begin{equation}
+ p_{m,n} = \Bigl(1-\Bigl(\frac{m-n-b}{a}\Bigl)^2\Bigl)\exp \Bigl(-\frac{1}{2}\Bigl(\frac{m-n-b}{a}\Bigl)^2\Bigl) .
+ %, a=2^{n}, b= d mod N
+ \label{p20240916}
+\end{equation}$$
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diff --git a/2502.14677/paper_text/intro_method.md b/2502.14677/paper_text/intro_method.md
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+# Introduction
+
+Many useful applications in NLP involve domains where data are sensitive. These privacy risks, and the accompanying limits to sharing data, have traditionally been solved by de-identification. This process involves finding parts of the text that can be used to identify an individual. Such information is typically referred to as personally identifiable information (PII). After locating PII, the passages need to be processed to remove or obscure the PII. Traditionally, this time-consuming work has been done manually. Automatic de-identification [@meystre2010automatic] is a machine-driven approach that typically relies on named entity recognition (NER) to detect PII that needs to be removed.
+
+Unfortunately, the PII datasets that exist to assist in privacy preservation are themselves sensitive, and can typically not be shared. This circularity, together with the increasing generative capabilities of large language models (LLMs), has led to a growing interest in overcoming data limitations by eschewing the use of real data altogether. Instead, one can use generated *synthetic* corpora.
+
+Previous studies have mainly been concerned with evaluating the privacy of the synthetic text [@Yue-etal-2023; @Miranda-etal-2024] or with creating the strongest-performing model possible using synthetic data [@libbi2021generating; @hiebel2023can; @Liu-etal-2025]. Our study instead examines how synthetic data can be produced under constrained resources. This understudied problem is common in clinical institutions that lack resources both in terms of data and computational hardware.
+
+We carry out a systematic evaluation of key factors impacting the utility of LLM-generated synthetic data as training data for downstream tasks. Specifically, we study synthetic NER data for PII detection - an important task in the privacy-sensitive healthcare domain. Synthetic clinical data are generated using domain-adapted LLMs and machine-annotated using fine-tuned NER models. The evaluations focus primarily on the *utility* of synthetic data for training NER models.
+
+Through extensive experimentation, we investigate the impact on utility of (i) the amount of data used for domain adaptation of the synthesizing LLM, (ii) the quality of the machine annotator, (iii) the amount of synthetic data generated, and (iv) model size. We also quantify the diversity and privacy of the generated data, and carry out experiments across two languages -- Swedish and Spanish. Our main contributions are:
+
+1. Demonstrating that moderately-sized LLMs can be adapted to the clinical domain to produce high-utility text with relatively small amounts of in-domain data.
+
+2. Showing that using synthetic machine-annotated data allows for training NER models that perform only slightly worse compared to using real, sensitive data, while reducing the risk of exposing sensitive information in the original data.
+
+3. Finding that, for the task of detecting PII, using larger generative LLMs for synthesis does not yield clear improvements in terms of utility. Rather, downstream performance relies on having a high-quality gold standard NER model for providing machine annotations.
+
+There have been several approaches to generating synthetic clinical data for a number of languages and for different purposes. Broadly speaking, most prior works have either focused on maximizing the utility of the synthetic data, or on studying the privacy characteristics of synthetic corpora.
+
+While most papers studying data synthesis contain some form of privacy analysis, other papers have this as their main focus. Several papers study how differentially private learning impacts the utility [@Yue-etal-2023; @igamberdiev_dp-nmt_2024] and the privacy of the data [@Miranda-etal-2024]. While privacy is an important justification for synthesizing data, it is not the main focus of our paper.
+
+The second main current in the literature explores how to optimize synthesis to create the best possible synthetic corpora. These papers synthesize data using locally domain-adapted LLMs [@ive2020generation; @hiebel2023can], or using instruction-tuned models [@kiefer2024instruction; @Liu-etal-2025]. They show that synthesis of high-utility data is possible. However, fewer papers systematically examine the conditions required for success.
+
+In our literature review, two studies stand out as particularly relevant to this study. @libbi2021generating synthesize a Dutch corpus for PII detection using a GPT-2 model [@radford_language_2019] domain-adapted using 1 million documents and add rule-based machine annotations. Our study follows the same overall process for synthesis, but uses much less data and more modern NLP techniques. @Xu-etal-2023a similarly create synthetic corpora and experiment with constraining the total amount of data used, but do so for the relation extraction task. In this paper, we focus on a different task -- NER for PII detection. Furthermore, in contrast to both studies, we systematically evaluate the impact of constraining data alternately for *both* domain adaptation *and* machine annotation, try two different model sizes, synthesize corpora of different sizes, and validate our results across two languages.
+
+# Method
+
+
+
+ This figure illustrates the steps for creating the synthetic corpora. A gold standard corpus is used to train a NER model and to domain-adapt a general-domain LLM to produce synthetic clinical data. The LLM is used to generate synthetic data, and the NER model is used to add machine annotations. Later, these corpora are used to train synthetic NER models that are evaluated on gold standard test data.
+
+
+In this study, we investigate the impact of various factors related to generating synthetic data for fine-tuning encoder language models on downstream tasks in the healthcare domain. Specifically, we study the possibility of generating synthetic clinical text for training NER models for detecting PII. The synthetic text is created by a domain-adapted generative LLM and then machine-annotated for PII using a fine-tuned encoder model. This process follows the structure of previous works [@libbi2021generating] and is illustrated in Figure [1](#fig:process){reference-type="ref" reference="fig:process"}.
+
+The aim of this study is to examine the feasibility of generating training data for NER models detecting PII. The foundation of the training data are synthetic texts, generated using autoregressive LLMs. Two model families are used as a base for domain adaptation to the clinical domain.
+
+GPT-SW3
+
+: For Swedish, we use the *GPT-SW3* model [@Ekgren-etal-2024]. This autoregressive language model was trained using approximately 320 billion tokens. The data were mainly composed of Scandinavian texts and 35.3% of the data is Swedish.
+
+FLOR
+
+: The autoregressive model used to generate Spanish data is the *FLOR* model [@DaDalt-etal-2024]. The model was initialized with the weights of the multi-lingual BLOOM model [@Scao-etal-2023] and trained with continued pre-training. The data used spanned 140 billion tokens and was composed of equal parts English, Spanish, and Catalan data.
+
+Both models are used autoregressively, without instruction tuning. The hypothetical -- but often occurring -- scenario motivating the study design is where researchers have access to a small and sensitive NER dataset that cannot be shared outside of their organization. Zero-shot synthesis is an alternative strategy, but we leave to future research to evaluate if this approach can yield clinical texts that are sufficiently similar to the real data. Instead, we perform different degrees of domain-adaptive fine-tuning to train the LLMs to produce such texts.
+
+The primary experiments in Section [4.1](#sec:eval-amount){reference-type="ref" reference="sec:eval-amount"} used the versions of FLOR and GPT-SW3 with 6.3 billion and 6.7 billion parameters, respectively. Smaller models with 1.3 billion parameters were used in Section [4.4](#sec:model-size){reference-type="ref" reference="sec:model-size"} to investigate the impact of using smaller models for synthesis.
+
+This study focuses on a particular type of NER dataset -- NER for detecting PII. Such datasets exist for several languages but are, as discussed in the introduction, very difficult to share and access. This is particularly true for datasets targeting the clinical domain. In this study, two datasets for detecting PII in clinical data are used.
+
+SEPR PHI
+
+: The *Stockholm EPR PHI Pseudo Corpus (SEPR PHI)* is a Swedish dataset from five different healthcare units consisting of 100 patient records split into 21,553 sentences. The corpus spans 282,766 tokens where 6,755 are manually annotated for nine different PII classes [@velupillai2009developing]. The dataset has then been pseudonymized, meaning that all the annotated entities have been replaced with realistic pseudonyms [@dalianis2019pseudonymisation].
+
+MEDDOCAN
+
+: The second dataset is *MEDDOCAN* -- a Spanish dataset consisting of 1,000 medical texts. These are based on clinical cases augmented with PII from auxiliary sources [@Marimon-etal-2019]. The texts were then manually annotated for 19 different PII. Out of 504,569 tokens, 41,859 are tagged as PII.
+
+Each dataset was split into three subsets: one for training, another for validation, and a third held-out subset for testing. The training sets are used to domain-adapt the generative models and to train the machine-annotating NER models. The purpose of the validation subsets is two-fold. First, they are used to monitor the training processes when fine-tuning the models for synthesis and NER. Once the models for synthesis have been trained, the validation data also serve as starting points when creating the synthetic corpora. Finally, the quality of the NER models trained using the synthetic corpora is evaluated using the held-out test sets, as these are not in any way part of the training or synthesis processes.
+
+This study trains NER models by fine-tuning pre-trained encoder models. Two different models are used, one for each language.
+
+SweDeClin-BERT
+
+: We use *SweDeClin-BERT* [@Vakili-etal-2022] for Swedish data, as it has previously shown strong performance on the SEPR PHI corpus. This BERT-style model is based on the general-domain KB-BERT model [@Devlin-etal-2019; @Malmsten-etal-2020] and adapted to the clinical domain through continued pre-training on the Swedish Health Bank corpus [@Dalianis-etal-2015]. It consists of 125 million parameters.
+
+roberta-base-bne
+
+: For Spanish data, we use the *roberta-base-bne* model trained by @Gutierrez-Fandino-etal-2022. This RoBERTa-based model [@liu_roberta_2019] consists of 125 million parameters. It was trained using a large Spanish corpus collected by the National Library of Spain and performs strongly on the MEDDOCAN task.
+
+These models are used for two purposes. First, they are fine-tuned using the gold standard datasets. These gold models are used to provide machine annotations to the synthesized corpora. They are also used as baselines for the later, synthetic NER models. The synthetic models are also initialized from the pre-trained encoder models, but are fine-tuned on the synthetic, machine-annotated corpora. The gold and synthetic NER models are then evaluated and compared to each other to measure the performance implications of using synthetic data.
+
+The generative models were domain-adapted by fine-tuning them for causal language modeling using QLoRA [@Dettmers-etal-2023] as implemented in the Axolotl framework[^1] with $r=8$ and $\alpha=32$. A comprehensive specification of all hyperparameters is available in Appendix [9](#appendix:ft-params){reference-type="ref" reference="appendix:ft-params"}. GPT-SW3 was domain adapted using the Swedish SEPR PHI corpus and FLOR using the Spanish MEDDOCAN corpus. Domain adaptation was carried out with varying amounts of data to determine the impact on the utility of the synthetic data.
+
+As mentioned in Section [3.2](#sec:datasets){reference-type="ref" reference="sec:datasets"}, the validation sets used for monitoring the fine-tuning process were also used for data synthesis. The 5% validation subsets were used to create starting points for generating text, as suggested by @libbi2021generating. The starting points were created by taking the first three words of each document in the validation sets.
+
+Synthetic corpora have an intriguing advantage over real corpora: they can be arbitrarily large. Taking this feature into account, each three-word starting point was used to create 80 new samples. Consequently, the synthetic corpora are four times larger than the gold-standard corpora. In Section [4.5](#sec:synthesis-amount){reference-type="ref" reference="sec:synthesis-amount"}, the benefits of exploiting this feature are examined through experiments that use smaller amounts of synthetic data.
+
+Synthesis was done using the vLLM library [@Kwon-etal-2023]. We use nucleus sampling [@Holtzman-etal-2020] with $p=0.95$ and the minimum token length is set to 10. The maximum token length is set to the length of the longest document in the validation set, or at least 50. The temperature was set to $t=1.0$ after preliminary experiments showed that varying $0.8 \leq t \leq 1.2$ had very little impact on the results.
+
+Finally, the synthetic texts were machine-annotated for PII entities. These were added using NER models fine-tuned on the gold-standard datasets. The gold-standard and synthetic NER models were trained for 6 epochs with a batch size of 16. The limited context window of the models was overcome, both during training and machine annotation, by splitting long documents into 128-word chunks. This chunking was done both during training and when using the gold-standard models for machine annotation.
+
+The whole process, from domain adaptation to training and evaluating the synthetic NER models, was done through five-fold cross-validation. The utility of the synthetic corpora was measured using token-level F~1~ score. These evaluations rely on each fold's held-out gold-standard test data.
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diff --git a/2503.00643/paper_text/intro_method.md b/2503.00643/paper_text/intro_method.md
new file mode 100644
index 0000000000000000000000000000000000000000..2a253b0a199cd9e770e26d67a27a4d7aa4127df1
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@@ -0,0 +1,102 @@
+# Introduction
+
+Forests, essential for climate stability and ecological health, act as crucial biological reserves [\[12,](#page-8-0) [47\]](#page-9-2). Effective forestry and management hinge on diligent tree monitoring, which includes growth measurement, health assessment, and environmental impact analysis, and so on [\[48\]](#page-9-3). One of the key aspects of tree monitoring is Tree Change Detection (TCD). TCD identifies alterations in tree conditions, essential for tree management and overall forest ecosystem health.
+
+*This research aims to accomplish long-term fine-grained TCD with deep learning methods.* Fine-grained TCD is critical yet challenging, especially when collecting longterm, high-resolution data in forests under diverse environmental conditions. Leveraging Unmanned Aerial Vehicles (UAVs) with cameras for forest imagery collection has become popular for its convenience and accessibility, offering an efficient alternative to traditional methods like Li-DAR for large-scale data collection, essential for monitoring trees and ecological sustainability [\[40\]](#page-9-4). Thus, to boost data-driven learning methods for the research field, this paper introduces a UAV-camera-based dataset tailored for detecting individual Tree Changes (UAVTC), enabling precise tree monitoring, as shown in Figure [1](#page-0-0) (a). *To our knowledge, UAVTC is the first dataset to provide detailed insights into precise TCs over time, supporting sustainable forest management and ecological research.*
+
+*The primary challenge in TCD lies in identifying extrinsic changes driven by the environments and intrinsic changes referring to physiological processes.* As shown in Figure [1](#page-0-0) (c), environmental factors such as fog and direct sunlight can obscure views and cast shadows, leading to non-tree-related changes. These extrinsic changes can mislead and result in incorrect TCD. Apart from extrinsic changes, physiological TCs are inherently hierarchical, involving both growth and decay processes. These changes can be classified into four main categories: changes in color, leaf transformations, flowering and fruiting cycles, and damage caused by humans, as shown in Figure [1](#page-0-0) (b). These diverse attributes of trees give rise to complex hierarchical relationships between TCs caused by environmental and physiological factors, which are not effectively captured by Euclidean geometry. Instead, hyperbolic geometry provides a solution by faithfully representing tree-like structures [\[22\]](#page-8-1), making it ideal for modeling and understanding physiological TCs.
+
+To address the above issues, we propose a Hyperbolic Siamese Network (HSN) designed to effectively model hierarchical changes and mitigate the impact of environmental factors, thereby enabling efficient identification of physiological TCs. HSN achieves this by (1) Modeling Hierarchical Structure: HSN encodes hierarchical relationships present in changes driven by environmental factors and intrinsic physiological changes at distinct levels, clarifying the pathways and impacts of each change; (2) Optimizing Data Embedding and Representation: Hyperbolic space allows for an efficient low-dimensional representation of high-dimensional hierarchical relationships, enabling both environmental and physiological changes to be effectively embedded and analyzed for structural and influential similarities or differences; (3) Modeling Spatiotemporal Dynamics: To model changes between tree images taken at different time points, we designed a network that integrates a Siamese structure within hyperbolic space. The Siamese network structure within hyperbolic space enhances analysis of dynamic changes across time and space, enabling clearer insights into trees' responses to complex interactions between environmental conditions and physiological factors.
+
+To the best of our knowledge, this is the first hyperbolic network designed specifically for change detection. *Thus, we also provide extra experiments on cross-domain face anti-spoofing (CD-FAS), showing that HSN excels at managing complex real-world changes involving extrinsic and intrinsic patterns.*
+
+The main contributions are as follows:
+
+- We collect a large-scale UAVTC dataset for long-term fine-grained TCD. To the our best knowledge, the UAVTC is the first TCD dataset that is collected from UAV devices, which contains rich annotations and statistics, covering multiple aspects of tree growth and health, enabling fine-grained tree monitoring over time.
+- We propose a novel Hyperbolic Siamese Network to model tree changes in hyperbolic space, overcoming misleading caused by extrinsic factors and effectively capturing the intrinsic tree changes.
+- Extensive experiments demonstrate that HSN is highly effective for fine-grained tree monitoring and can be generalized to CD-FAS task, highlighting its versatility and robustness.
+
+# Method
+
+In this section, we introduce the technical details of the HSN for TCD. The schematic overview of the proposed method is depicted in Figure 3. First, we introduce the operations in hyperbolic spaces in Section 4.1. Hyperbolic feature clipping for model optimization is described in Section 4.1. Then we describe the hyperbolic Siamese architecture.
+
+Hyperbolic cross-entropy loss is introduced in Section 4.3. Finally, we compute the $\delta$ -hyperbolicity on the embeddings of TCs, theoretically verifying the presence of hierarchical structures within the TCs.
+
+As highlighted in the Introduction section, TCD is significantly influenced by environment conditions and characterized by hierarchical structures. Prior studies have shown that hyperbolic geometry effectively captures data hierarchies, enhancing robustness and accuracy by mitigating the impacts of extrinsic factors like weather variants[22]. Therefore, hyperbolic geometry is utilized in this paper. Specifically, our research utilizes the Poincaré ball model [26], as the Poincaré ball possesses a differentiable distance function and is characterized by a relatively simple constraint on its representations. These strengths make it suitable for TCD research.
+
+Formally, the n-dimensional Poincaré ball $(\mathbb{H}_c^n, g^{\mathbb{H}})$ is defined by the manifold $\mathbb{H}_c^n$ as
+
+$$\mathbb{H}_{c}^{n} = \left\{ X \in \mathbb{R}^{n} : c \|x\|^{2} < 1, c \ge 0 \right\}, \tag{1}$$
+
+and the Riemannian metric $g^{\mathbb{H}}$ is
+
+$$g_x^{\mathbb{H}} = \lambda_x^{c \, 2} g^{\mathbb{E}},\tag{2}$$
+
+where $g^{\mathbb{E}}$ is the Euclidean metric tensor and $\lambda_x^c$ is the conformal factor defined as $\lambda_x^c = \frac{2}{1-c||x||^2}$ . $g^{\mathbb{H}}$ is associated with tangent space $T_x$ . Note, $c^{-\frac{1}{2}}$ is the radius of Poincaré ball. c controls the the curvature of the Poincaré ball. As c approaches 0, operations revert to Euclidean geometry.
+
+Hyperbolic spaces differ from traditional vector spaces, making standard operations like addition and multiplication unusable. Möbius gyrovector spaces offer a solution to generalize these operations. Key operations for hyperbolic networks are described below.
+
+**Möbius addition.** Möbius gyrovector spaces offer a means to define operations like addition and multiplication. These operations are fundamental in hyperbolic networks [22]. For a pair $x, y \in \mathbb{H}^n_c$ , their addition is defined as
+
+$$x \oplus_{c} y = \frac{(1 + 2c\langle x, y \rangle + c \|y\|^{2})x + (1 - c \|x\|^{2})y}{1 + 2c\langle x, y \rangle + c^{2} \|x\|^{2} \|y\|^{2}}.$$
+ (3)
+
+**Distance.** In hyperbolic geometry, the distance between two points, $x, y \in \mathbb{H}_c^n$ , is defined as
+
+$$d_{hpy}(x,y) = \frac{2}{\sqrt{c}} \operatorname{arctanh}(\sqrt{c} \| -x \oplus_{c} y \|).$$
+ (4)
+
+When c approaches 0, the hyperbolic distance function converges to the Euclidean distance. This convergence is expressed as $\lim_{c\to 0} = d_{hpy}(x,y) = 2 \|x-y\|$ .
+
+**Exponential map.** To conduct operations in hyperbolic space, it is necessary to establish a bijective mapping between Euclidean vectors and hyperbolic vectors in the Poincaré ball model. The pivotal tool for this purpose is the exponential map. The map establishes a one-to-one correspondence between points from Euclidean space to points in hyperbolic space. The exponential map is formulated as:
+
+
+$$exp_x^c(v) = x \oplus_c \left( tanh\left(\sqrt{c} \frac{\lambda_x^c \|v\|}{2}\right) \frac{v}{\sqrt{c} \|v\|} \right).$$
+ (5)
+
+**Hyperbolic binary logistic regression (Hpy-BLR).** In the context of the TCD task, two classes are established: when k=0, it indicates the absence of change, and when k=1, it denotes the presence of change and $p_k \in \mathbb{H}^n_c$ , $t_k \in T_{p_k}\mathbb{H}^n_c \setminus \{0\}$ . The formulation of binary logistic regression within the Poincaré ball is expressed as follows:
+
+
+$$p(y = k \mid x) \propto exp(\frac{\lambda_{pk}^{c} \|t_{k}\|}{\sqrt{c}} arcsinh(\frac{2\sqrt{c} \langle -p_{k} \oplus_{c} x, t_{k} \rangle}{(1 - c \|-p_{k} \oplus_{c} x\|^{2}) \|t_{k}\|})).$$
+(6)
+
+**Feature clipping.** Previous research [17, 18] provides empirical evidence indicating that HNs often experience the issue of vanishing gradients. This occurs because HNs tend to drive embeddings towards the boundary of the Poincaré ball, resulting in the gradients of Euclidean parameters becoming extremely small. To mitigate potential numerical errors, a fixed threshold is applied to the norm (magnitude) of the vectors to prevent them from reaching extreme values [17], following the equation below:
+
+$$C(x^E; r) = \min\left\{1, \frac{r}{\|x^E\|}\right\} \cdot x^E \tag{7}$$
+
+where $x^E$ lies in the Euclidean space and $x_C^E$ represents its clipped counterpart. The hyperparameter r denotes a novel effective radius within the Poincaré ball. This feature clipping method imposes a hard constraint on the maximum norm of the hyperbolic embedding which avoids the inverse of the Riemannian metric tensor approaching zero [17].
+
+What is the desired representation for TC? We argue that such representation should effectively present the precise physiological change in trees and maintain robustness to changes caused by extrinsic factors. To achieve the above goals, we train an HSN (shown in Figure 3) consisting of Siamese framework and hyperbolic geometry. The key objectives of this approach are 1) accurately evaluating TCs and 2) learning robust features avoiding misleading influences from extrinsic factors.
+
+For a classical Siamese network [10, 23], the input is a pair of images, represented as $I_1$ and $I_2$ . The Siamese
+
+network learns to project inputs into a space where the distance between similar inputs is minimized, and the distance between dissimilar inputs is maximized, facilitating tasks such as similarity comparison or verification [\[8\]](#page-8-15). Specifically, f(I; W) is the output of the Siamese network, where W represents the parameters of the network. A common choice the measure the feature distance is the Euclidean distance:
+
+$$D(I_1, I_2) = ||f(I_1; W) - f(I_2; W)||,$$
+ (8)
+
+However, as discussed in the introduction section, TCs include a hierarchical structure that can not be effectively estimated by the flat Euclidean space. Instead, hyperbolic space characterized by negative curvature has been found to be well-suited for representing hierarchical relationships. The hyperbolic space is leveraged to represent complex TCs with hierarchy. Firstly, a fully connected (FC) layer is employed to reduce the dimension of the change feature. Then the Euclidean distance embedding is transformed to hyperbolic distance by exponential mapping with Equation [5.](#page-4-2) A Hyp-BLR layer is followed for classification. Finally, the Hyp-BCE loss function (Equation [\(9\)](#page-5-1)) is employed to identify and assess the changes in trees.
+
+The Hyperbolic Binary Cross Entropy (Hpy-BCE) loss is calculated using the predicted probability pˆi obtained from Equ. [6.](#page-4-1) The loss is defined as:
+
+
+$$L_a = p_i log(\hat{p}_i) + (1 - p_i) log(1 - \hat{p}_i), \tag{9}$$
+
+where pi is the ground truth for changes in a tree, with 1 denoting a change in the tree and 0 denoting no change. pˆi is the predicted probability of TC.
+
+In this section, we validate the presence of hierarchical structures in TC data through δ-hyperbolicity [\[22\]](#page-8-1). This metric serves to quantify the similarity in data structure between Euclidean and hyperbolic space. The δ-hyperbolicity values range from 0 to 1, where a calculated value closer to 0 indicates a high degree of hyperbolicity in the data, signifying a strong hierarchy. Conversely, a computed δhyperbolicity closer to 1 suggests the absence of hierarchy in the dataset.
+
+This hyperbolicity evaluation is made through Gromov product [\[14\]](#page-8-16):
+
+$$(y,z)_x = \frac{1}{2}(d(x,y) + d(x,z) - d(y,z))$$
+ (10)
+
+where x, y, z ∈ χ and χ is the arbitrary (metric) space endowed with the distance function d. Given a set of points, we compute the pairwise Gromov products and represent them in the form of a matrix A. Subsequently, the value of δ is identified as the maximum element in the matrix obtained from the min-max matrix product operation, denoted as (A ⊕ A) − A. In this context, the symbol ⊕ represents the min-max matrix product, which is formally defined as (A ⊕ B)ij = maxkmin {Aik, Bkj} [\[14\]](#page-8-16).
+
+We evaluate δ for tree image embeddings extracted by Siamese networks with ResNet18 [\[20\]](#page-8-17), ResNet34, ResNet101, and VGG16 [\[36\]](#page-9-16). Table [2](#page-5-2) demonstrates the obtained relative δ values. Table [2](#page-5-2) shows that all networks exhibit low δ (closer to 0 than 1), indicating high hyperbolicity in the tree change dataset. This observation suggests that TCD tasks can benefit from hyperbolic representations of images and the experiments in Section [5](#page-5-3) verify the effectiveness of hyperbolic representations.
+
+Table 2. δ-Hyperbolicity. Low δ (closer to 0 than 1) indicates a high degree of hyperbolicity in the embeddings.
+
+| Network | ResNet18 | ResNet34 | ResNet101 | VGG16 |
+|---------|----------|----------|-----------|-------|
+| δ | 0.172 | 0.144 | 0.114 | 0.212 |
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diff --git a/2503.12014/paper_text/intro_method.md b/2503.12014/paper_text/intro_method.md
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+# Introduction
+
+Single-image deraining (SID) is a vital task in low-level vision, focused on restoring clear background images from rainy inputs by eliminating or reducing undesirable degradations caused by rain artifacts [@chen2024rethinking]. The severe interference of rainy images with the performance of downstream tasks has sparked significant research interest in the SID task.
+
+Over the years, numerous methods have been proposed to address the image deraining problem. Early prior-based traditional approaches [@Xu2012AnIG; @zhang2017convolutional] attempted to remove rain artifacts by analyzing the statistical characteristics of rain streaks and the background. However, in real-world scenarios, rain streaks and droplets are often dense, complex, and diverse, causing these methods to fail in such cases. Recently, many CNN-based methods [@li2018recurrent; @ren2019progressive; @Chen_2021_CVPR; @cui2023exploring; @cui2024omni] have been applied to image deraining, bringing transformative advancements to the field. Compared to traditional algorithms, these methods have significantly improved deraining results. However, convolution, as the core element of CNNs, exhibits spatial invariance and a limited receptive field, making it inadequate for effectively modeling the non-local structures and spatial variations in clear images [@song2024learning]. To address these limitations, some methods [@Chen_2023_CVPR; @chen2023hybrid; @SFHformer; @zhou2024AST; @NeRD-Rain] have adopted Transformers for image deraining, achieving impressive results. Transformers effectively capture global dependencies and model non-local information, enabling high-quality image reconstruction.
+
+However, existing mainstream supervised paradigms still face two major challenges: (1) the inability to explore complementary implicit information across different scales. Most current deep deraining networks rely on single-input single-output architectures. However, spatially varying rain streaks often exhibit diverse scale attributes, which not only overlooks potential explicit information from multiple image scales but also fails to explore complementary implicit information across scales. Generally, multi-scale visual information flow involves representations from external interactions (multi-input multi-output exchanges with the network's external environment) and internal interactions (scale information exchange within internal components of the network). Some researchers have introduced coarse-to-fine mechanisms [@xintm2023DeepRFTv2; @chen2024rethinking; @Kim2022MSSNet] or multi-patch strategies [@Zamir2021MPRNet; @9157139] to leverage multi-scale external rain features. While these methods have achieved impressive performance, they struggle to handle complex and random rain streaks because they rarely consider collaborative multi-scale feature representations from both external and internal domains, neglecting implicit relationships across scales. (2) Rain streaks and droplets consistently exhibit irregular overlaps and highly dynamic geometric patterns, which place higher and more diverse demands on feature representations. Moreover, in real-world rainy images, significant aliasing effects exist between rain residues and the background. Eliminating rain disturbances by inferring pixel residue values inevitably compromises contextual and structural information. When features are derived solely from a single domain or dimension, it becomes challenging to remove all interferences [@cui2024dual]. Therefore, more robust and diverse multi-domain feature representations are critical for achieving high-quality image reconstruction.
+
+To address the abovementioned challenges, we introduce DMSR, a novel dual-domain multi-scale architecture model. It consists of multiple Dual-Domain Scale-Aware Modules (DDSAM), which include two key components: the Multi-Scale Progressive Spatial Refinement Module (MPSRM) and the Frequency Domain Scale Mixer (FDSM). To tackle the first challenge, we propose a collaborative multi-scale paradigm for both external and internal domains. We use a coarse-to-fine pyramid input-output flow externally across the entire network while applying various multi-scale architectures within the internal components of DMSR (i.e., MPSRM and FDSM). This facilitates the flow of information within and across scales, enabling the interaction and fusion of complementary implicit information between different scales. Additionally, to address the second challenge, we use MPSRM to extract spatial domain features and introduce Spatial Pixel Guided Attention (SPGA) to aggregate information for each pixel, allowing each pixel to perceive information from an extended region implicitly. Simultaneously, FDSM decouples the features into distinct frequency domain components and modulates them, promoting interactions between different frequencies and refining the spectrum. This adaptive extraction and refinement of rain residuals and background components allows us to fully capture information from different dimensions through the extraction of dual-domain features, facilitating the exchange and complement of expert knowledge. Our contributions are summarized as follows:
+
+- We propose a novel dual-domain multi-scale architecture that rethinks the multi-scale representation paradigm for single-image deraining through a collaborative multi-scale paradigm across internal and external domains, enabling better extraction of rich scale-space features. At the same time, it fully leverages the advantages of the dual-domain paradigm to achieve more diverse, multi-dimensional, and robust high-level feature representations.
+
+- We introduce a Multi-Scale Progressive Spatial Refinement Module for extracting refined spatial pixel features, using gated contextual information to integrate implicit information from the surrounding context and expand the receptive field. Additionally, we develop a Multi-Scale Frequency Domain Mixer that combines global and local characteristics and facilitates information exchange and modulation across different spectral components, thereby generating high-quality deraining results.
+
+# Method
+
+As shown in Fig. [1](#overview){reference-type="ref" reference="overview"} [(a)]{style="color: red"}, DMSR is divided into three scales based on a coarse-to-fine approach. Specifically, given an input rain image, the original image is downsampled by an interpolation operator to 1/2 and 1/4 of the original size, forming a pyramid of rain image inputs, with the coarsest to finest scale inputs referred to as S1, S2, and S3. We first pass S1 through a $3 \times 3$ convolutional layer to obtain shallow features $F \in \mathbb{R}^{C \times H \times W}$, where C, H, and W represent the channels, height, and width, respectively. These shallow features are then processed through three DDSAM to obtain multi-scale high-level representation features in both spatial and frequency domains. As shown in Fig. [1](#overview){reference-type="ref" reference="overview"} [(b)]{style="color: red"}, DDSAM consists of multiple residual structures and includes the MPSRM and the FDSM. During this process, the spatial resolution is reduced by half, and the channel number is doubled. Moreover, we incorporate the downsampled rain images S2 and S3 into the main path after passing them through an Embedding Layer and adjusting the channel number with a $3 \times 3$ convolution. The dual-domain multi-scale high-level representation features are then passed through another three DDSAMs for gradual restoration into a high-resolution derained image. During this process, features from the encoder and decoder are concatenated to facilitate image restoration. In line with the multi-scale inputs, we also employ multi-scale outputs, generating low-resolution derained images after the first two DDSAMs in the restoration process. Finally, skip connections are applied across the three pyramid input/output image pairs. For simplicity, only the top-level skip connection between the rain image and the derained image is shown in Fig. [1](#overview){reference-type="ref" reference="overview"} [(a)]{style="color: red"}.
+
+The architecture of MPSRM is shown in Fig. [1](#overview){reference-type="ref" reference="overview"} [(c)]{style="color: red"}. To alleviate the knowledge discrepancy across different scales within the same representation range, we perform high-quality restoration in a progressive coarse-to-fine manner within each representation scale, efficiently achieving hierarchical representation. Additionally, MPSRM focuses on modeling spatial context to enhance the spatial pixel representation ability of each feature map. By capturing pixel context relationships, it aids in the precise restoration of background and fine details. Specifically, given the input feature $F$, global average pooling with different downsampling rates is applied to embed the initial feature $F$ into different scales. For each scale (i.e., each branch), we input the scale features into the Spatial Pixel Guided Attention (SPGA), enhancing the expressiveness of each feature map. The resulting spatially refined features are then integrated into the next branch via addition. This enables MPSRM to progressively reduce intra-scale differences and promote the flow of expert information across different scales. Finally, the progressively fused features from all branches are unified to match the input size of $F$ and summed. Formally, the above process is described as follows: $$\begin{equation}
+ \hat{F_{1}} =SPGA(GAP_{4}(F)),
+ \vspace{-2mm}
+\end{equation}$$ $$\begin{equation}
+ \hat{F_{2}} =SPGA(GAP_{2}(F)+\hat{F_{1}} ),
+ \vspace{-2mm}
+\end{equation}$$ $$\begin{equation}
+ \hat{F} =f^{3\times 3}(F+\hat{F_{1}}\uparrow_4 + \hat{F_{2}}\uparrow_2 ),
+ \vspace{-2mm}
+\end{equation}$$ where $GAP_i(\cdot)$ denotes global average pooling with a downsampling rate of $i$, $\uparrow_i$ represents bilinear upsampling with a scaling factor of $i$, and $f^{z \times z}(\cdot)$ indicates a convolution with a kernel size of $z \times z$.
+
+
+
+The overall architecture of the proposed DMSR.
+
+
+In traditional spatial self-attention mechanisms [@dosovitskiy2020image], spatial context modeling is often achieved by computing spatial feature distribution maps to indicate the importance levels of different regions. However, due to the complexity of real-world rainfall scenarios and the high interweaving of rain streaks with rain-free backgrounds, fine texture details are often disrupted during restoration, resulting in artifacts and noise. To address these issues, we propose a Spatial Pixel Guided Attention (SPGA) mechanism, which shifts from region-based to pixel-level guidance. By gradually generating perception information for each pixel through gated guidance, SPGA facilitates pixel-wise information interaction, emphasizing the most valuable expert information in feature maps and safeguarding texture details from contamination. The structure of SPGA is illustrated in Fig. [1](#overview){reference-type="ref" reference="overview"} [(d)]{style="color: red"}. Specifically, given the input feature $X \in \mathbb{R}^{C \times H \times W}$, spatial attention is first applied to generate the spatial feature distribution map $W_S$. Furthermore, we design a gated guidance module to focus features on the most valuable information. The gating mechanism promotes the flow of critical information while preventing excessive redundancy within intermediate layers, thereby mitigating information loss. This process can be mathematically described as: $$\begin{equation}
+ W_{S}=f^{7 \times 7}([GAP_{2}(PW(X)),GMP_{2}(PW(X))]),
+ \vspace{-2mm}
+\end{equation}$$ $$\begin{equation}
+ W_{G}=PW(DW^{3\times 3}(PW(X))),
+ \vspace{-2mm}
+\end{equation}$$ where ${PW}(\cdot)$ denotes point-wise convolution, ${DW}^{3 \times 3}(\cdot)$ represents depth-wise convolution with a kernel size of $3 \times 3$, $[\ ,\ ]$ denotes concatenation, and ${GMP}_i(\cdot)$ represents global max pooling with a downsampling rate of $i$. Subsequently, we fuse $W_S$ and $W_G$ through an additive operation to obtain the coarse spatial pixel feature map $W_M$. Then, we use a $7 \times 7$ convolution operation to further expand the receptive field, re-weighting the pixel information to enable each pixel to perceive signals from a large region centered around itself, while employing the sigmoid function to introduce non-linearity. Formally, the entire process is expressed as follows: $$\begin{equation}
+ W_{M}=W_{S}\odot W_{G},
+ \vspace{-2mm}
+\end{equation}$$ $$\begin{equation}
+ W=\delta (f^{7\times 7}([X,W_{M}])),
+ \vspace{-2mm}
+\end{equation}$$ where $\delta(\cdot)$ denotes the sigmoid activation function, $\odot$ represents element-wise multiplication.
+
+Fourier transform exhibits irreplaceable advantages in single image deraining. First, it can effectively separate image degradation components, as rain streak patterns display prominent and invariant characteristics in the frequency domain, which is crucial for mitigating aliasing effects in rainy images [@FADformer; @SFHformer]. Second, the transformed frequency components, calculated from all spatial components, serve as global feature filters [@FSNet; @cui2023strip]. The motivation behind the proposed Frequency Domain Scale Mixer (FDSM) is to bridge the spatial and frequency domains, enabling layered modulation and feature extraction of global-local characteristics while facilitating intra-domain multi-scale information interaction and fusion. The structure of FDSM is illustrated in Fig. [1](#overview){reference-type="ref" reference="overview"} [(e)]{style="color: red"}. Specifically, in the spatial-scale mixed domain, we first use PWConv to increase the feature dimensions, which are then divided into three groups to extract multi-scale local mixed features. The mathematical formulation is as follows: $$\begin{equation}
+ X_{Z}=PW(\vartheta (f^{z\times z}(Chunk(PW(X))))),Z\in[ 3,5,7],
+ \vspace{-2mm}
+\end{equation}$$ $$\begin{equation}
+ X_{S}=[X_{3},X_{5},X_{7}].
+ \vspace{-2mm}
+\end{equation}$$
+
+:::: table*
+::: center
+:::
+::::
+
+Here, $\vartheta(\cdot)$ denotes the GELU activation function. In the frequency domain, the spatial-scale mixed features are first transformed using the fast Fourier transform (FFT) to produce real and imaginary components. The combined frequency features are then passed through a PWConv for modulation, emphasizing and interactively merging beneficial frequency components for restoration. After modulation, the features are passed through the inverse fast Fourier transform (IFFT) to convert the frequency domain features back into the spatial domain. The above process is formally expressed as follows: $$\begin{equation}
+ R,I=FFT(X_{S}),
+ \vspace{-2mm}
+\end{equation}$$ $$\begin{equation}
+ \hat{R},\hat{I}= \phi (BN(PW([\hat{R},\hat{I} ]))),
+ \vspace{-2mm}
+\end{equation}$$ $$\begin{equation}
+ X_{F}=IFFT(\hat{R},\hat{I} ),
+ \vspace{-2mm}
+\end{equation}$$ where $BN(\cdot)$ represents batch normalization, and $\phi (\cdot)$ denotes the ReLU activation function. A residual structure is introduced to ensure training stability, resulting in the output of FDSM.
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diff --git a/2505.23264/paper_text/intro_method.md b/2505.23264/paper_text/intro_method.md
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+# Introduction
+
+The emerging diffusion models (DMs) (Sohl-Dickstein et al., 2015; Ho et al., 2020; Song & Ermon, 2019; Song et al., 2020), generating samples of data distribution from initial noise by learning a reverse diffusion process, have been proven to be an effective technique for modeling data distri-
+
+Proceedings of the $42^{nd}$ International Conference on Machine Learning, Vancouver, Canada. PMLR 267, 2025. Copyright 2025 by the author(s).
+
+bution, especially in generating high-quality images (Nichol et al., 2022; Dhariwal & Nichol, 2021a; Saharia et al., 2022; Ramesh et al., 2022; Rombach et al., 2022; Ho et al., 2022). The training process of DMs can be seen as employing a neural network to match the first-order diffusion score $\nabla_x \log q_t(x)$ at varying noise levels.
+
+There has been a growing trend to recognize the importance of the diffusion Fisher (DF) in DMs, defined as the negative Hessian of the diffused distributions' log density, $-\nabla_x^2 \log q_t(x)$ . The DF provides valuable secondorder information on DMs and plays a crucial role in downstream tasks, e.g., likelihood evaluation (Lu et al., 2022a; Zheng et al., 2023), adjoint optimization (Pan et al., 2023a;b; Blasingame & Liu, 2024), and the optimal transport analysis of DMs (Zhang et al., 2024a). It is straightforward to access DFs by applying auto-differentiation to the learned first-order diffusion score. Nevertheless, the repeated auto-differentiation operations would be timeconsuming for large-scale tasks, even when incorporating the Vector-Jacobian-product (VJP) and Hutchinson's estimation technique (Song & Lai, 2024). Recently, certain research efforts investigated the time-evolving property of DM and demonstrated that the DF can be explicitly expressed in terms of the score and its covariance matrix (Benton et al., 2024; Lu et al., 2022a). However, these results predominantly concentrate on the theoretical aspects and fail to enable efficient access to the DF.
+
+In this paper, we derive a novel form for the diffusion Fisher, which is composed of weighted outer-product sums. This innovative formulation uncovers that the diffusion Fisher invariably lies within the span of a set of outer-product bases. Notably, these bases solely depend on the initial data and the noise schedule. Leveraging this outer-product structure, we devise two efficient approximation algorithms to access the trace and matrix-vector multiplication of DF, respectively. Moreover, we design the first numerical verification experiment to investigate the optimal transport property of the diffusion-ODE deduced map under diverse types of initial data and noise schedules. The main contributions of our paper are listed as follows:
+
+ We derive an analytical formulation of the diffusion Fisher, which is the weighted summation of the outer
+
+<sup>1Zhejiang University. 2WeChat Vision, Tencent Inc. 3Shanghai Jiao Tong University. \*Equal contribution. ‡Work done as interns at WeChat Vision, Tencent Inc. Correspondence to: Chao Zhang .
+
+product, under the setting when the initial distribution is a sum of Dirac. The derivation process involves applying the consecutive partial differential chain rule to the data-dependent form of marginal distributions. Subsequently, we extend this formulation to a more general setting. In this general case, we only assume that the initial distribution has a finite second moment. These novel formulations reveal that the diffusion Fisher invariably lies within the span of a set of outer-product bases.
+
+- Based on the outer-product structure of DF, we propose two efficient approximation algorithms, each tailored to access the trace and matrix-vector multiplication of the DF, respectively. When it comes to evaluating the trace of the diffusion Fisher, we introduce a parameterized network to learn the trace, significantly reducing the time complexity of trace evaluation from quadratic to linear w.r.t. the dimension. In situations where we need to access the matrix-vector multiplication of the diffusion Fisher, we present a training-free method that simplifies the complex linear transformation into several simple vector-product calculations. Furthermore, we rigorously establish the approximation error bounds for these two algorithms.
+- Utilizing the novel outer-product formulation of DF, we derive a corollary for the optimal transport (OT) property of the diffusion-ODE deduced map. Subsequently, we design the first numerical experiment to examine the OT property of any general diffusion ODE deduced map based on the corollary. Our numerical experiments indicate that for all commonly employed noise schedules (VE (Ho et al., 2020), VP (Song & Ermon, 2019), sub-VP (Song et al., 2020), and EDM (Karras et al., 2022)), the diffusion ODE map exhibits the OT property when the initial data follows a single Gaussian distribution or has an affine form. Conversely, it does not exhibit this property when the initial data is non-affine. These numerical results not only validate the conclusions presented in (Khrulkov et al., 2023; Zhang et al., 2024a; Lavenant & Santambrogio, 2022), but also open up new avenues for the exploration of more general cases.
+
+We evaluate our DF access algorithms on likelihood evaluation and adjoint optimization tasks. The empirical results demonstrate our DF methods' enhanced accuracy and reduced time cost.
+
+# Method
+
+**Notation**. The Euclidean norm over $\mathbb{R}^d$ is denoted by $\|\cdot\|$ , and the Euclidean inner product is denoted by $\langle\cdot|\cdot\rangle$ . Throughout, we simply write $\int g$ to denote the integral with respect
+
+to the Lebesgue measure: $\int g(x) dx$ . When the integral is with respect to a different measure $\mu$ , we explicitly write $\int g d\mu$ . When clear from context, we sometimes abuse notation by identifying a measure $\mu$ with its Lebesgue density. We also use $\delta(\cdot)$ to denote the Dirac Delta function. For a vector $\boldsymbol{v} \in \mathbb{R}^d$ , we denote the $d \times d$ matrix $\boldsymbol{v} \boldsymbol{v}^{\top}$ as the outer-product of $\boldsymbol{v}$ .
+
+Suppose that we have a d-dimensional random variable $x_0 \in \mathbb{R}^d$ following an unknown target distribution $q_0(x_0)$ . Diffusion Models (DMs) define a forward process $\{x_t\}_{t\in[0,T]}$ with T>0 starting with $x_0$ , such that the distribution of $x_t$ conditioned on $x_0$ satisfies
+
+$$q_{t|0}(\boldsymbol{x}_t|\boldsymbol{x}_0) = \mathcal{N}(\boldsymbol{x}_t; \alpha(t)\boldsymbol{x}_0, \sigma^2(t)\mathbf{I}), \tag{1}$$
+
+where $\alpha(\cdot), \sigma(\cdot) \in \mathcal{C}([0,T],\mathbb{R}^+)$ have bounded derivatives, and we denote them as $\alpha_t$ and $\sigma_t$ for simplicity. The choice for $\alpha_t$ and $\sigma_t$ is referred to as the noise schedule of a DM. According to (Kingma et al., 2021; Karras et al., 2022), with some assumption on $\alpha(\cdot)$ and $\sigma(\cdot)$ , the forward process can be modeled as a linear SDE which is also called the Ornstein–Uhlenbeck process:
+
+$$dx_t = f(t)x_tdt + g(t)dB_t, (2)$$
+
+where $B_t$ is the standard d-dimensional Brownian Motion (BM), $f(t) = \frac{d \log \alpha_t}{dt}$ and $g^2(t) = \frac{d \sigma_t^2}{dt} - 2 \frac{d \log \alpha_t}{dt} \sigma_t^2$ . Under some regularity conditions, the above forward SDE equation 2 have a reverse SDE from time T to 0, which starts from $x_t$ (Anderson, 1982):
+
+$$d\mathbf{x}_t = \left[ f(t)\mathbf{x}_t - g^2(t)\nabla_{\mathbf{x}_t} \log q(\mathbf{x}_t, t) \right] dt + g(t)d\tilde{B}_t,$$
+(3)
+
+where $\tilde{B}_t$ is the reverse-time Brownian motion and $q(\boldsymbol{x}_t,t)$ is the single-time marginal distribution of the forward process. In practice, DMs (Ho et al., 2020; Song et al., 2020) use $\varepsilon_{\theta}(\boldsymbol{x}_t,t)$ to estimate $-\sigma(t)\nabla_{\boldsymbol{x}_t}\log q(\boldsymbol{x}_t,t)$ and the parameter $\theta$ is optimized by the following loss:
+
+$$\mathcal{L} = \mathbb{E}_{t} \left\{ \lambda_{t} \mathbb{E}_{x_{0}, x_{t}} \left[ \| s_{\theta}(x_{t}, t) - \nabla_{x_{t}} \log p(x_{t}, t | x_{0}, 0) \|^{2} \right] \right\},$$
+(4)
+
+where $s_{\theta}$ represents the parameterized score, i.e., $s_{\theta}(\boldsymbol{x}_t,t) = -\frac{\varepsilon_{\theta}(\boldsymbol{x}_t,t)}{\sigma_t}$ . This familiar parameterization is called $\epsilon$ -prediction. There are also $\boldsymbol{y}$ -prediction and $\boldsymbol{v}$ -prediction (Salimans & Ho, 2022). The corresponding loss is equal to replace the term $|\epsilon - \varepsilon_{\theta}(\boldsymbol{x}_t,t)|$ with $\frac{\alpha_t}{\sigma_t}|\boldsymbol{x}_0 - \bar{\boldsymbol{y}}_{\theta}(\boldsymbol{x}_t,t)|$ and $|\alpha_t\epsilon - \sigma_t\boldsymbol{x}_0 - \boldsymbol{v}_{\theta}(\boldsymbol{x}_t,t)|$ . The learned $\varepsilon_{\theta}(\boldsymbol{x}_t,t)$ can be also transformed to a $\boldsymbol{y}$ -prediction form by $\bar{\boldsymbol{y}}_{\theta}(\boldsymbol{x}_t,t) = \frac{\boldsymbol{x}_t - \sigma_t \varepsilon_{\theta}(\boldsymbol{x}_t,t)}{\alpha_t}$ .
+
+It is noted that the reverse diffusion SDE in equation 3 has an associated probability flow ODE (PF-ODE, also called diffu-
+
+sion ODE), which is a deterministic process that shares the same single-time marginal distribution (Song et al., 2020):
+
+$$d\mathbf{x}_t = \left[ f(t)\mathbf{x}_t - \frac{1}{2}g^2(t)\nabla_{\mathbf{x}_t}\log q_t(\mathbf{x}_t, t) \right] dt. \quad (5)$$
+
+By replacing the score function in equation 5 with the noise predictor $\varepsilon_{\theta}$ , the inference process of DMs can be constructed by the following neural ODE:
+
+$$\frac{\mathrm{d}\boldsymbol{x}_{t}}{\mathrm{d}t} = \boldsymbol{h}_{\theta}\left(\boldsymbol{x}_{t}, t\right) := f(t)\boldsymbol{x}_{t} + \frac{g^{2}(t)}{2\sigma_{t}}\boldsymbol{\epsilon}_{\theta}\left(\boldsymbol{x}_{t}, t\right), \tag{6}$$
+
+where initial $\boldsymbol{x}_T \sim \mathcal{N}\left(\boldsymbol{0}, \sigma_T^2 \boldsymbol{I}\right)$ .
+
+The diffusion Fisher information matrix in DMs is defined as the negative Hessian of the marginal diffused log-density function, which takes the following matrix-valued form (Song et al., 2021; Song & Lai, 2024):
+
+$$F_{t}(\boldsymbol{x}_{t},t) := -\frac{\partial^{2}}{\partial \boldsymbol{x}_{t}^{2}} \log q_{t}\left(\boldsymbol{x}_{t},t\right)$$
+(7)
+
+The current technique typically approximately accesses the diffusion Fisher by accessing the scaled Jacobian matrix of the learned score estimator network $\varepsilon_{\theta}$ :
+
+$$F_{t}(\boldsymbol{x}_{t},t) = -\frac{\partial}{\partial \boldsymbol{x}_{t}} \left( \frac{\partial}{\partial \boldsymbol{x}_{t}} \log p\left(\boldsymbol{x}_{t},t\right) \right)$$
+
+$$\approx -\frac{\partial}{\partial \boldsymbol{x}_{t}} \left( -\frac{\varepsilon_{\theta}(\boldsymbol{x}_{t},t)}{\sigma_{t}} \right) = \frac{1}{\sigma_{t}} \frac{\partial \varepsilon_{\theta}(\boldsymbol{x}_{t},t)}{\partial \boldsymbol{x}_{t}}$$
+(8)
+
+The full diffusion Fisher matrix within DMs cannot be obtained due to dimensional constraints. For instance, the Stable Diffusion-1.5 model (Rombach et al., 2022) features a latent dimension of $d=4\times64\times64=16384$ , resulting in a diffusion Fisher matrix of $16384\times16384$ . Fortunately, for applications that only need to access the trace or multiplication of diffusion Fisher, it is feasible to use the Vector-Jacobian-product (VJP) to access diffusion Fisher. For any d-dimensional vector $\boldsymbol{v}$ , the approximation of $\boldsymbol{v}$ left multiplied by $\boldsymbol{F}_t(\boldsymbol{x}_t,t)$ using VJP is as follows:
+
+(VJP)
+$$\boldsymbol{v}^{\top} \boldsymbol{F}_{t}(\boldsymbol{x}_{t}, t) \approx \frac{1}{\sigma_{t}} \boldsymbol{v}^{\top} \frac{\partial \boldsymbol{\varepsilon}_{\theta}(\boldsymbol{x}_{t}, t)}{\partial \boldsymbol{x}_{t}}$$
+
+$$= \frac{1}{\sigma_{t}} \frac{\partial \left[ \langle \boldsymbol{\varepsilon}_{\theta}(\boldsymbol{x}_{t}, t) | \boldsymbol{v} \rangle \right]}{\partial \boldsymbol{x}_{t}}$$
+(9)
+
+The VJP is a time-consuming process due to its requirement for gradient calculations within the neural network. In addition, empirical evidence from synthetic distributions, as demonstrated in Lu et al. (2022a), shows that the approximation results from the VJP significantly deviate from the true underlying diffusion Fisher. To our knowledge, there
+
+| | | | Theoretical | Practical | Theoretical |
+|---|----------|--------------|-----------------|-----------------------------|--------------------|
+| | Access | Methods | Time-cost | Time-cost (s) | Error Bound |
+| | Trace | VJP (eq. 15) | $c_1d + c_2d^2$ | $c_1d + c_2d^2$ 2195.48 | |
+| | | DF-TM (Ours) | $2c_1d$ | $0.072_{99\%}$ $\downarrow$ | √ (Prop. 7) |
+| _ | Operator | VJP (eq. 19) | $c_1d + c_2d$ | 0.155 | X |
+| | | DF-EA (Ours) | $2c_1d$ | 0.063 59% \ | √ (Prop. 8) |
+
+Table 1: Comparison of Vector-Jacobian-product (VJP) and our proposed efficient access methods in terms of per-iteration theoretical time-cost, practical time-cost, and approximation error bound. The theoretical time cost is based on assumptions of a network access cost of $c_1d$ time and backpropagation on network cost of $c_2d$ time ( $c_2 \approx 4c_1$ ). The practical time-cost is tested using the SD-v1.5 model over 10k COCO prompts. Here, our VJP baseline calculates every element of the trace, and discussion on its approximation is deferred to Appendix C.5.
+
+is no theoretical guarantee that the diffusion Fisher can be accurately accessed through the VJP. Moreover, the VJP fails to provide any theoretical insight into the diffusion Fisher.
+
+Accessing diffusion Fisher via the VJP as shown in equation 9 is straightforward, but it does not take advantage of any inherent structure of the diffusion Fisher. Though certain recent research efforts (Lu et al., 2022a; Benton et al., 2024) demonstrated that the DF can be explicitly expressed in terms of the score and its covariance matrix. Nevertheless, their formulations cannot directly facilitate efficient access to the diffusion Fisher. In this section, we derive a novel form for the diffusion Fisher, which is composed of weighted outer-product sums. We first conduct this under a simplified scenario, assuming that the initial distribution $q_0$ is a sum of Dirac distributions. Subsequently, we generalize this outer-product span form to a more general setting. Significantly, the outer-product span form of the diffusion Fisher obtained in both settings does not require any gradient calculations and depends solely on the initial data and the noise schedule. Notably, there exist fast methods for accessing the trace and matrix-vector multiplication of an outer-product matrix. This advantageous characteristic allows for the development of novel algorithms for efficiently accessing the diffusion Fisher. We start with a simple setting where we assume that the initial distribution is characterized as a sum of Dirac distributions composed of the set of samples in the dataset. If we suppose the dataset is denoted as $\{y_i\}_{i=0}^N$ , then the initial distribution follows
+
+(Dirac Setting)
+$$q(\boldsymbol{x},t)|_{t=0} = \frac{1}{N} \sum_{i=0}^{N} \delta(\boldsymbol{x} - \boldsymbol{y}_i),$$
+(10)
+
+where exists a $0 < \mathcal{D}_y < \infty$ such that $||y_i|| \le \mathcal{D}_y$ holds true for every i. In this Dirac setting, we derive the following formulation of diffusion Fisher, which is a weighted outer-product sum devoid of gradients and composed solely of the
+
+
+
+Figure 1: (a) The training loss of DF-TM for SD-1.5 and SD-2base. It demonstrates commendable convergence behavior. (b) The trade-off curve of NLL and Clip score of SD-1.5 and SD-2base across various guidance scales in [1.5, 2.5, ..., 12.5, 13.5]
+
+initial distribution and the noise schedule.
+
+ $\begin{array}{lll} \textit{Proposition} & 1. & \text{Defines} & v_i(\boldsymbol{x}_t,t) & \text{as} \\ \exp\left(-\frac{|\boldsymbol{x}_t-\alpha_t\boldsymbol{y}_i|^2}{2\sigma_t^2}\right) & \in & \mathbb{R} & \text{and} & w_i(\boldsymbol{x}_t,t) & \text{as} \\ \frac{v_i(\boldsymbol{x}_t,t)}{\sum_j v_j(\boldsymbol{x}_t,t)} & \in & \mathbb{R}. & \text{If} & q_0 & \text{takes the form as in} \\ \text{equation equation 10, the diffusion Fisher matrix} & \text{of the diffused distribution } q_t & \text{for } t \in (0,1] & \text{can be} \\ \text{analytically formulated as follows:} \end{array}$
+
+$$F_{t}(\boldsymbol{x}_{t}, t) = \frac{1}{\sigma_{t}^{2}} \boldsymbol{I} - \frac{\alpha_{t}^{2}}{\sigma_{t}^{4}} \left[ \sum_{i} w_{i} \boldsymbol{y}_{i} \boldsymbol{y}_{i}^{\top} - \left( \sum_{i} w_{i} \boldsymbol{y}_{i} \right) \left( \sum_{i} w_{i} \boldsymbol{y}_{i} \right)^{\top} \right]$$
+(11)
+
+where we have simplified $w_i(x_t, t)$ to $w_i$ , as it does not lead to any confusion.
+
+We also find that the $\sum_i w_i y_i$ component in equation 11 can be effectively approximated by the trained score network in the form of y-prediction, as demonstrated in the following proposition.
+
+Proposition 2. Given the diffusion training loss in equation 4, and if $q_0$ conforms to the form presented in equation 10, then the optimal $\bar{y}_{\theta}(x_t, t)$ can accurately estimate $\sum_i w_i y_i$ .
+
+**The General Setting** We further extend the diffusion Fisher formulation in equation 11 to a more general setting, where we only assume that the initial distribution $q_0$ is a probability measure on $\mathbb{R}^d$ with finite second moments.
+
+(General Setting)
+$$q_0 \in \mathcal{P}_2(\mathbb{R}^d),$$
+ (12)
+
+which means that $q_0 \in \mathcal{P}(\mathbb{R}^d)$ , $\int_{\mathbb{R}^d} ||x||^2 q_0(x) \mathrm{d}x < \infty$ . In this general setting, we derive the following form of diffusion Fisher, which is a weighted outer-product integral, which resides in a space spanned by an infinite set of outer products.
+
+Proposition 3. Let us define $v(\boldsymbol{x}_t,t,\boldsymbol{y})$ as $\exp\left(-\frac{|\boldsymbol{x}_t-\alpha_t\boldsymbol{y}|^2}{2\sigma_t^2}\right)\in\mathbb{R}$ and $w(\boldsymbol{x}_t,t,\boldsymbol{y})$ as $\frac{v(\boldsymbol{x}_t,t,\boldsymbol{y})}{\int_{\mathbb{R}^d}v(\boldsymbol{x}_t,t,\boldsymbol{y})\mathrm{d}q_0(\boldsymbol{y})}\in\mathbb{R}$ . If $q_0$ takes the form as in equation 12, the diffusion Fisher matrix of the diffused distribution $q_t$ for $t\in(0,1]$ can be analytically formulated as follows:
+
+$$F_{t}(\boldsymbol{x}_{t}, t) = \frac{1}{\sigma_{t}^{2}} \boldsymbol{I} - \frac{\alpha_{t}^{2}}{\sigma_{t}^{4}} \left[ \int w(\boldsymbol{y}) \boldsymbol{y} \boldsymbol{y}^{\top} dq_{0} - \left( \int w(\boldsymbol{y}) \boldsymbol{y} dq_{0} \right) \left( \int w(\boldsymbol{y}) \boldsymbol{y} dq_{0} \right)^{\top} \right]$$
+(13)
+
+where we simply write $w(x_t, t, y)$ as w(y), as long as it does not lead to any confusion.
+
+We further ascertain that the $\int w(y)ydq_0(y)$ component in equation 13 can be effectively approximated by the score network in the form of y-prediction, as demonstrated in the following proposition.
+
+Proposition 4. Given the diffusion loss in equation 4, and if $q_0$ conforms to the form in equation 12, then the optimal $\bar{y}_{\theta}(x_t, t)$ can accurately estimate $\int w(y)y dq_0(y)$ .
+
+The derivation of the outer-product form of diffusion Fisher under the general setting is akin to the Dirac case, but in an integral form. For the remainder of the paper, we will focus on developing our method based on the Dirac setting diffusion Fisher. However, the same results can be naturally extended to the general setting.
+
+The likelihood evaluation of DMs would require access to diffusion Fisher's trace. In this section, we introduce a network to learn the trace, thus facilitating effective likelihood evaluation in DMs.
+
+**Log-Likelihood in DMs** Log-likelihood is a classic and significant metric for probabilistic generative models, extensively utilized for comparison between samples or models (Bengio et al., 2013; Theis et al., 2015). According to Chen et al. (2018); Song et al. (2021), the log-likelihood of samples generated by PF-ODE in equation 6 from DMs can be computed through a connection to continuous normalizing flows as follows:
+
+$$\frac{\partial \log q_t(\boldsymbol{x}_t, t)}{\partial t} = -\text{tr}\left(\frac{\partial}{\partial \boldsymbol{x}_t} \left( f(t)\boldsymbol{x}_t - \frac{1}{2}g^2(t)\partial_{\boldsymbol{x}_t} \log q_t(\boldsymbol{x}_t, t) \right) \right)$$
+
+$$= -\text{tr}\left( \left( f(t)\boldsymbol{I} - \frac{1}{2}g^2(t)\frac{\partial^2}{\partial \boldsymbol{x}_t^2} \log q_t(\boldsymbol{x}_t, t) \right) \right)$$
+
+$$= -f(t)d - \frac{g^2(t)}{2}\text{tr}\left(\boldsymbol{F}_t(\boldsymbol{x}_t, t)\right)$$
+(14)
+
+
+
+Figure 2: Our DF-TM method facilitates the effective evaluation of the NLL of generated samples with varying seeds. It can be demonstrated that a lower NLL signifies a region of higher possibility, thereby consistently indicating superior image quality.
+
+where $tr(\cdot)$ denotes the trace of a matrix, which is defined to be the sum of elements on the diagonal.
+
+**Log-Likelihood Evaluation via VJP** The current technique is only capable of conducting backpropagation of scalar value to the neural network. Therefore, the VJP in equation 9 cannot directly calculate the trace of the diffusion Fisher. The VJP must iterate through each dimension to compute the individual elements on the diagonal, and then sum them up as follows
+
+$$\operatorname{tr}\left(\mathbf{F}_{t}(\mathbf{x}_{t},t)\right) \approx \frac{1}{\sigma_{t}} \sum_{i=1}^{d} \frac{\partial \left[\left\langle \boldsymbol{\varepsilon}_{\theta}(\mathbf{x}_{t},t) \middle| \mathbf{e}^{(i)} \right\rangle\right]}{\partial \mathbf{x}_{t}}.$$
+ (15)
+
+Evaluating the trace using the VJP method would be extremely time-consuming due to the curse of dimensionality. If the time-complexity of a single backpropagation is $\mathcal{O}(d)$ , then the calculation in equation 15 would have a time-complexity of $\mathcal{O}(d^2)$ . In practice, as demonstrated in Table 1, evaluating the trace of diffusion Fisher on the SD-v1.5 model would require half an hour, rendering it nearly infeasible. Some Monte-Carlo type approximations can be used to accelerate the NLL evaluation of models, but they still fall short in per-sample NLL evaluation, see a detailed discussion in C.5.
+
+**Log-Likelihood Evaluation via DF trace matching** To overcome the limitations of the VJP method in evaluating
+
+| Methods | The relative error of NLL evaluation | | | | | | |
+|--------------|--------------------------------------|---------|---------|---------|---------|---------|--|
+| Wichiods | t = 1.0 | t = 0.8 | t = 0.6 | t = 0.4 | t = 0.2 | t = 0.0 | |
+| VJP (eq. 15) | 6.68% | 5.79% | 10.46% | 20.13% | 51.14% | 70.95% | |
+| DF-TM (Ours) | 3.41% | 4.56% | 4.13% | 4.28% | 5.33% | 5.81% | |
+
+Table 2: Comparison of the VJP method and our DF-TM in terms of the diffusion Fisher trace evaluation error across different timesteps. The error is evaluated on the 2-D chessboard data with the VE schedule.
+
+the trace of the diffusion Fisher, we propose to directly obtain its analytical form and train a network to match it. Note that the trace of an outer-product of the vector results in the vector's norm. Thus, given the diffusion Fisher in Proposition 1, we can also derive its trace in the form of a weighted norm sum, as highlighted in the following proposition:
+
+Proposition 5. In the same context as Proposition 1, the trace of the diffusion Fisher matrix for the diffused distribution $q_t$ , where $t \in (0,1]$ , is given by:
+
+$$\operatorname{tr}\left(\boldsymbol{F}_{t}(\boldsymbol{x}_{t},t)\right) = \frac{d}{\sigma_{t}^{2}} - \frac{\alpha_{t}^{2}}{\sigma_{t}^{4}} \left[ \sum_{i} w_{i} \|\boldsymbol{y}_{i}\|^{2} - \left\| \sum_{i} w_{i} \boldsymbol{y}_{i} \right\|^{2} \right]$$
+(16)
+
+As demonstrated in Proposition 2, the $\|\sum_i w_i \boldsymbol{y}_i\|^2$ can be directly estimated by $\|\bar{\boldsymbol{y}}_{\theta}(\boldsymbol{x}_t,t)\|^2$ . Therefore, the only
+
+
+
+Figure 3: Comparison between our DF method and the VJP method on adjoint guidance sampling across five objective scores: SAC/AVA aesthetic score, Pick-Score, clip loss, and Face ID loss. Notably, our DF consistently achieves superior scores with less time expenditure.
+
+unknown element in equation 16 is $\sum_i w_i \|y_i\|^2$ . Consequently, we suggest learning this term using a scalar-valued network, as per the following training scheme:
+
+- 1: **Input**: data space dimension d, initial network $t_{\theta}(\cdot, \cdot) : \mathbb{R}^d \times \mathbb{R} \mapsto \mathbb{R}$ , noise schedule $\{\alpha_t\}$ and $\{\sigma_t\}$ .
+- 2: repeat
+- 3: $\mathbf{x}_0 \sim q_0(\mathbf{x}_0)$
+- 4: $t \sim \text{Uniform}(\{1, \dots, T\})$
+- 5: $\varepsilon \sim \mathcal{N}(\mathbf{0}, \mathbf{I})$
+- 6: $\mathbf{x}_t = \alpha_t \mathbf{x}_0 + \sigma_t \boldsymbol{\varepsilon}$
+- 7: Take gradient descent on $\nabla_{\theta} \left| \boldsymbol{t}_{\theta}(\boldsymbol{x}_{t}, t) \frac{\|\boldsymbol{x}_{\theta}\|^{2}}{d} \right|^{2}$
+- 8: until converged
+- 9: Output: $t_{\theta}(\cdot, \cdot)$
+
+The training scheme detailed in Algorithm 1 can indeed enable $t_{\theta}(\boldsymbol{x}_{t},t)$ to estimate the weighted norm term $\frac{1}{d}\sum_{i}w_{i}(\boldsymbol{x}_{t},t)\|\boldsymbol{y}_{i}\|^{2}$ . This is substantiated by the convergence analysis Proposition 6, as presented below.
+
+Proposition 6. $\forall (x_t,t) \in \mathbb{R}^d \times \mathbb{R}_{\geq 0}$ , the optimal $t_{\theta}(\boldsymbol{x}_t,t)$ s trained by the objective in Algorithm 1 are equal to $\frac{1}{d} \sum_i w_i(\boldsymbol{x}_t,t) \|\boldsymbol{y}_i\|^2$ .
+
+Once we have obtained $t_{\theta}$ , we can evaluate the trace of diffusion Fisher efficiently, as illustrated below. This approach is a straightforward result of equation 16 and Propositions 2 and 6, which we refer to as DF trace matching (DF-TM).
+
+$$\operatorname{tr}\left(\boldsymbol{F}_{t}(\boldsymbol{x}_{t},t)\right) \approx \frac{d}{\sigma_{t}^{2}} - \frac{\alpha_{t}^{2}}{\sigma_{t}^{4}} \left(d * t_{\theta}(\boldsymbol{x}_{t},t) - \|\boldsymbol{y}_{\theta}(\boldsymbol{x}_{t},t)\|^{2}\right)$$
+(17)
+
+To estimate the trace using DF-TM in equation 17, we simply need one access to $t_{\theta}$ and $\varepsilon_{\theta}$ . DF-TM enables us to effectively evaluate the log-likelihood in a gradient-free manner with linear time complexity. We can further substantiate the theoretical approximation error bound when using DF-TM to calculate the trace of the diffusion Fisher, as illustrated in the Proposition 7.
+
+Proposition 7. Assume the approximation error on $t_{\theta}(\boldsymbol{x}_{t},t)$ is $\delta_{1}$ and on $\varepsilon_{\theta}(\boldsymbol{x}_{t},t)$ is $\delta_{2}$ , then the approximation error of the approximated Fisher trace in equation 17 is at most $\frac{\alpha_{t}^{2}}{\sigma_{t}^{4}}\delta_{1}+\frac{1}{\sigma_{t}^{2}}\delta_{2}^{2}$ .
+
+**Experiments** We conduct toy experiments to show the accuracy and commercial-level experiments to show the efficiency of our DF-TM method. In toy experiments, as shown in Table 2, we trained a simple DF-TM network on 2-D chessboard data and then evaluated the relative error of the trace estimated by VJP and DF-TM. It is shown that our DF-TM method consistently achieves more accurate trace estimation of DF compared to the VJP method.
+
+In commercial-level experiments, we trained two DF-TM networks for the SD-1.5 and SD-2base pipeline on the Laion2B-en dataset (Schuhmann et al., 2022), which contains 2.32 billion text-image pairs. Our $t_{\theta}$ follows the U-net structure, similar to stable diffusion models, but with an added MLP head to produce a scalar-valued output. We utilize the AdamW optimizer (Loshchilov & Hutter, 2019) with a learning rate of 1e-4. The training is executed across 8 Ascend 910B chips with a batch size of 384 and completed after 150K steps. In Figure 1a, we demonstrate that the training loss of DF-TM nets converges smoothly, indicating the robustness of the DF-TM training scheme in Algorithm 1.
+
+In Figure 1b, we evaluate the average NLL and Clip score of samples generated by various SD models, using 10k randomly selected prompts from the COCO dataset (Lin et al., 2014). A lower NLL suggests more realistic data generation, while a higher Clip score indicates a better match between the generated images and the input prompts. The results imply that the NLL and the Clip score form a trade-off curve across different guidance scales. This phenomenon, previously hypothesized in theory (Wu et al., 2024), is now confirmed in the SD models, thanks to the effective NLL evaluation via the DF-TM method.
+
+In Figure 2, we display images with varying NLL under the same prompt with 10 steps on SD-1.5 using DDIM. It's clear that images with lower NLL exhibit greater visual realism, while those with higher NLL often contain deformed elements (emphasized by the yellow rectangle). Our proposed
+
+
+
+Figure 4: Visual comparison of DF-EA (**Ours**) and VJP in the adjoint improvement task on (left) SAC aesthetic score and (right) Pick-Score. DF-EA consistently generates images with better visual effects and reduced time expenditure.
+
+NLL evaluation method proves to be an effective tool for automatic sample selection.
+
+The adjoint optimization of DMs would require access to the matrix-vector multiplication of the diffusion Fisher, which can also be seen as applying diffusion Fisher as a linear operator. In this section, we present a training-free method that simplifies the complex linear transformation calculations, thus enabling faster and more accurate adjoint optimization based on the outer-product form diffusion Fisher.
+
+Adjoint optimization sampling. Guided sampling techniques are extensively utilized in diffusion models to facilitate controllable generation. Recently, to address the inflexibility of commonly used classifier-based guidance (Dhariwal & Nichol, 2021b) and classifier-free guidance (Ho & Salimans, 2022), a series of adjoint guidance methods have been explored (Pan et al., 2023a;b).
+
+Consider optimizing a scalar-valued loss function $\mathcal{L}(\cdot)$ : $\mathbb{R}^d \mapsto \mathbb{R}$ , which takes $\boldsymbol{x}_0$ in the data space as input. Adjoint guidance is implemented by applying gradient descent on $\boldsymbol{x}_t$ in the direction of $\frac{\partial \mathcal{L}(\boldsymbol{x}_0(\boldsymbol{x}_t))}{\partial \boldsymbol{x}_t}$ . The essence of adjoint guidance is to use the gradient at t=0 and follow the adjoint ODE (Pollini et al., 2018; Chen et al., 2018) to compute $\boldsymbol{\lambda}_t := \frac{\partial \mathcal{L}(\boldsymbol{x}_0(\boldsymbol{x}_t))}{\partial \boldsymbol{x}_t}$ for t>0.
+
+$$\frac{\mathrm{d}\boldsymbol{\lambda}_{t}}{\mathrm{d}t} = -\boldsymbol{\lambda}_{t}^{\top} \frac{\partial \boldsymbol{h}_{\theta} \left(\boldsymbol{x}_{t}, t\right)}{\partial \boldsymbol{x}_{t}}, \quad \boldsymbol{\lambda}_{0} = \frac{\partial \mathcal{L}(\boldsymbol{x}_{0})}{\partial \boldsymbol{x}_{0}}$$
+(18)
+
+**Adjoint ODE via VJP.** Regardless of the ODE solver being used, it is necessary to compute the right-hand side of equation 18, or equivalently, $F(x_t, t)^{\top} \lambda_t$ . This computation can be interpreted as applying the diffusion Fisher matrix as a linear operator to the adjoint state $\lambda_t$ , from a functional analysis perspective (Yosida, 2012). Current practices utilize the VJP technique to approximate this linear transformation operation as follows:
+
+$$F(\boldsymbol{x}_{t},t)^{\top} \boldsymbol{\lambda}_{t} \approx \frac{1}{\sigma_{t}} \frac{\partial \boldsymbol{\varepsilon}_{\theta} \left(\boldsymbol{x}_{t},t\right)^{\top} \boldsymbol{\lambda}_{t}}{\partial \boldsymbol{x}_{t}}$$
+
+$$\approx \frac{1}{\sigma_{t}} \frac{\partial \left[\left\langle \boldsymbol{\varepsilon}_{\theta} \left(\boldsymbol{x}_{t},t\right) | \boldsymbol{\lambda}_{t} \right\rangle\right]}{\partial \boldsymbol{x}_{t}}$$
+(19)
+
+This process involves computationally expensive neural network auto-differentiations, and the approximation errors introduced by the VJP technique have no theoretical bound.
+
+Adjoint ODE via DF-EA. As previously discussed in Section 3, the DF inherently doesn't require gradients, suggesting that we could potentially apply the diffusion Fisher as a linear operator in a gradient-free manner. The challenging part in equation 11 is $\sum_i w_i y_i y_i^{\top}$ , which represents a weighted form of outer-products of data. Based on the definition of $w_i$ , the closest $y_i$ to $x_0$ will dominate as $t \to 0$ . This makes it intuitive to replace this sum with a single final sample outer-product $x_0 x_0^{\top}$ . It's also important to note that the adjoint guidance itself needs to compute $x_0$ at each guidance step, eliminating the need for additional computation to obtain $x_0$ . Given that we utilize the endpoint sample $x_0$ , we refer to this approximation technique as DF Endpoint
+
+Approximation (EA). The formulation for DF-EA in the adjoint ODE is as follows:
+
+$$F(\boldsymbol{x}_{t},t)^{\top} \boldsymbol{\lambda}_{t}$$
+
+$$\approx \left(\frac{1}{\sigma_{t}^{2}} \boldsymbol{I} - \frac{\alpha_{t}^{2}}{\sigma_{t}^{4}} \left(\sum_{i} w_{i} \boldsymbol{y}_{i} \boldsymbol{y}_{i}^{\top} - \bar{\boldsymbol{y}}_{\theta}(\boldsymbol{x}_{t},t) \bar{\boldsymbol{y}}_{\theta}(\boldsymbol{x}_{t},t)^{\top}\right)\right)^{\top} \boldsymbol{\lambda}_{t}$$
+
+$$\approx \left(\frac{1}{\sigma_{t}^{2}} \boldsymbol{I} - \frac{\alpha_{t}^{2}}{\sigma_{t}^{4}} \left(\boldsymbol{x}_{0} \boldsymbol{x}_{0}^{\top} - \bar{\boldsymbol{y}}_{\theta}(\boldsymbol{x}_{t},t) \bar{\boldsymbol{y}}_{\theta}(\boldsymbol{x}_{t},t)^{\top}\right)\right)^{\top} \boldsymbol{\lambda}_{t}$$
+
+$$= \frac{1}{\sigma_{t}^{2}} \boldsymbol{\lambda}_{t} - \frac{\alpha_{t}^{2}}{\sigma_{t}^{4}} \left\langle \boldsymbol{x}_{0}, \boldsymbol{\lambda}_{t} \right\rangle \boldsymbol{x}_{0} + \frac{\alpha_{t}^{2}}{\sigma_{t}^{4}} \left\langle \bar{\boldsymbol{y}}_{\theta}(\boldsymbol{x}_{t},t), \boldsymbol{\lambda}_{t} \right\rangle \bar{\boldsymbol{y}}_{\theta}(\boldsymbol{x}_{t},t)$$
+
+$$(20)$$
+
+The DF-EA approximation leads to a scalar-weighted combination of $\lambda_t$ , $x_0$ , and $\bar{y}_{\theta}(x_t,t)$ , which importantly, does not involve any gradients. Additionally, we derive the theoretical approximation error bound of the DF-EA in Proposition 8. To measure the accuracy of DF-EA as a linear operator, we opt to use the Hilbert–Schmidt norm (Gohberg et al., 1990) for measurement, as follows:
+
+Proposition 8. Assume that the approximation error on $\varepsilon_{\theta}(\boldsymbol{x}_t,t)$ is $\delta_2$ , the approximation error of the DF-EA linear operator, as referenced in 20, is at most $\frac{\alpha_t^2}{\sigma_t^3}\left(2\mathcal{D}_y^2+\sqrt{d}\delta_2\right)$ when measured in terms of the Hilbert–Schmidt norm
+
+**Experiments on DF-EA.** As depicted in Figure 3, we conducted experiments comparing our DF-EA and VJP methods in adjoint guidance sampling, using five different scores and two different base models. DF-EA consistently achieves better scores due to its bounded approximation error. Furthermore, DF-EA requires less processing time as it eliminates the need for time-consuming gradient operations. DF-EA and VJP are compared under the same guidance scales and schemes across various numbers of steps. Details regarding the score function can be found in Appendix B.2.
+
+As depicted in Figure 4, our DF-EA consistently generates samples with higher scores with a reduced time cost compared to VJP. All samples are generated within 50 steps, with adjoint applied from the 15th to the 35th step. Details on hyperparameters can be found in Appendix B.2.
+
+There is an increasing trend towards analyzing the probability modeling capabilities of DMs by interpreting them from an optimal transport (OT) perspective (Albergo et al., 2023; Chen et al., 2024). The foundational concepts of optimal transport can be found in Appendix A.9. One of the central questions is whether the map deduced by the PF-ODE could represent an optimal transport. Khrulkov et al. (2023) have proven that, given single-Gaussian initial data and a VE noise schedule, the PF-ODE deduced map is
+
+optimal transport. Zhang et al. (2024a) has demonstrated that affine initial data suffices for the PF-ODE map to be optimal transport. However, the OT property of the general PF-ODE map remains an open question.
+
+In this section, we propose the first numerical OT verification experiment for a general PF-ODE deduced map based on the outer-product form diffusion Fisher we obtained in section 3. We first derive the following corollary for the OT property of the PF-ODE deduced map.
+
+Corollary 1. Denote the diffeomorphism deduced by the PF-ODE in equation 5 as follows
+
+$$T_{s,t}: \mathbb{R}^n \longrightarrow \mathbb{R}^n; \boldsymbol{x}_s \longmapsto \boldsymbol{x}_t, \quad \forall t \ge s > 0.$$
+ (21)
+
+The diffeomorphism $T_{s,T}$ is a Monge optimal transport map **if and only if** the normalized fundamental matrix for $\boldsymbol{B}(t) \equiv \boldsymbol{B}(t, \boldsymbol{x}_t)$ at s is s.p.d. for every PF-ODE chain that starts from a $\boldsymbol{x}_T \in \mathbb{R}^d$ . where
+
+$$\boldsymbol{B}(t, \boldsymbol{x}_t) = \left[ f(t) - \frac{g^2(t)}{2\sigma_t^2} \right] \boldsymbol{I} + \frac{\alpha_t^2 g^2(t)}{2\sigma_t^4} \left[ \sum_i w_i \boldsymbol{y}_i \boldsymbol{y}_i^\top - \left( \sum_i w_i \boldsymbol{y}_i \right) \left( \sum_i w_i \boldsymbol{y}_i \right)^\top \right].$$
+(22)
+
+The definition of the normalized fundamental matrix is deferred to Appendix A.10.
+
+Note that $T_{s,t}$ is well-posed, guaranteed by the global version of the Picard-Lindelöf theorem (Amann, 2011; Zhang et al., 2024a). Detailed proofs can be found in Appendix A.10. We then design the numerical OT verification experiment for a given noise schedule and initial data as shown in the Algorithm 2. The detailed version of Algorithm 2 can be found in Appendix B.3.
+
+- 1: **Input**: initial data $\{y_i\}_{i=1}^N$ , noise schedule $\{\alpha_t\}$ and $\{\sigma_t\}$ , discretization steps M.
+- 2: Initialize $\boldsymbol{A}_M = \boldsymbol{I}, \boldsymbol{x}_M \sim \mathcal{N}(0, \sigma_T \boldsymbol{I}).$
+- 3: **for** $i = M, M 1, \dots, 1$ **do**
+- 4: $dt = t_{i-1} t_i$ .
+- 5: Calculate $B_i$ by equation 22.
+- 6: $\mathbf{A}_{i-1} = \mathbf{A}_i + \mathrm{d}t * \mathbf{A}_i^{\top} \mathbf{B}_i$ {solve fundamental matrix.}
+- 7: $\boldsymbol{x}_{i-1} = \text{PF-ODE Solver}(\boldsymbol{x}_i, i)$
+- 8: end for
+- 9: **Output**: $A_0$ . {The result fundamental matrix.}
+
+In Table 3, we numerically tested the OT property of the PF-ODE map under different nose schedules and initial data. We tested four commonly used noise schedules, VE (Ho et al., 2020), VP (Song & Ermon, 2019), sub-VP (Song et al., 2020), and EDM (Karras et al., 2022) on 2-D data. We first tested when the initial data is single-Gaussian and affine, we found that the result normalized fundamental
+
+| Initial Data | Single-Gaussian | | Affine | | Non-affine | |
+|-------------------------------|-----------------|----|--------|----|------------|----|
+| Noise Schedule | Asym. | OT | Asym. | OT | Asym. | OT |
+| VE (Song & Ermon, 2019) | 0.00% | ✓ | 0.00% | ✓ | 25.28% | ✗ |
+| VP (Ho et al., 2020) | 0.00% | ✓ | 0.00% | ✓ | 23.36% | ✗ |
+| sub-VP (Song et al., 2020) | 0.00% | ✓ | 0.00% | ✓ | 13.84% | ✗ |
+| EDM (Karras et al., 2022) | 0.00% | ✓ | 0.00% | ✓ | 27.09% | ✗ |
+
+Table 3: Comparison of numerical OT verification results of four commonly used noise schedulers with different initial data.
+
+matrix is p.s.d., which means the PF-ODE map is optimal transport. These results coincide with the previous proposed theoretical results in (Khrulkov et al., 2023) and (Zhang et al., 2024a). We further tested an extremely simple nonaffine case, that is, there are only three initial data points, (0,0), (0,0.5), and (0.5,0). We showed that all noise schedules result in an obvious asymmetric fundamental matrix, which means that the PF-ODE map fails to be OT in these cases. Here, we use the Frobenius norm of the difference of A and its transpose divided by the Frobenius norm of A to measure its asymmetric rate. Based on this finding, we hypothesize that the PF-ODE map can only be OT in the affine initial data (Single-Gaussian is a special case of affine data), but fails to be OT in general non-affine data. Our hypothesis coincides with the statement that the flow map of a Fokker–Planck equation is not necessarily OT as proposed in (Lavenant & Santambrogio, 2022). (Note that PF-ODE is a special class of Fokker–Planck equations)
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diff --git a/2507.14640/paper_text/intro_method.md b/2507.14640/paper_text/intro_method.md
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+# Introduction
+
+
+
+As seen in (a), transformers resolve subject-object relations in a highly nonlinear fashion. As seen in (b), both affine and linear approximators of the subject-object map Fr(s) are demonstrated to be highly effective over relations such as morphology.
+
+
+Large language models display impressive capabilities for factual recall, which commonly involve relations between entities (@Brown:20). Recent work has shown that affine transformations on subject representations can faithfully approximate model outputs for certain subject-object relations (@Her:23). Identifying transformer approximators is an important area of study, with applications in model training and editing.
+
+The contributions of this paper are twofold. We reproduce and extend existing research. Specifically, we apply affine Linear Relational Embedding (LRE) method to novel diverse relational categories, including derivational and inflectional morphology, encyclopedic knowledge, and lexical semantics. By doing so, we confirm the efficacy of the affine technique. We show that relational approximation can be applied to adapted analogical datasets, and demonstrate relational approximation for a broad range of linguistic phenomena.
+
+At the same time, this work makes a key contribution to research on relational representation in model latents. We show that for different relations, additive and multiplicative mechanisms play complementary roles in affine approximation. We find that an analogue to the original linear relational embedding developed by [@Pac:01], using a single multiplicative operator, is effective within specific relations. In particular, linear approximation within contexts relating morphological forms reaches near-equivalent level of faithfulness to the affine LRE. We test faithfulness over eight different languages and find that this equivalence holds cross-typologically.
+
+# Method
+
+We first consider what it means for a context to express a relation. Many statements can be expressed in terms of a subject, relation, and object (*s,r,o*). For instance, the statement *Miles Davis plays the trumpet* expresses a relation $F_r$, connecting the subject $s$ (*Miles Davis*) to the object $o$ (*trumpet*): $F_r(s) = o$. We can then relate new subjects to objects: $F_r(\textit{Jimi Hendrix}) = \textit{guitar}$ and $F_r(\textit{Elton John}) = \textit{piano}$. $F_r$ is an inductive mechanism, from which statements relating subject and object pairs can be obtained. We are interested in how a language model implements this abstraction.\
+**Affine LRE.** As a starting point, we look at the affine linear relational embedding (LRE) method developed by [@Her:23]. The authors are able to approximate the transformer's relational function $F_r(s)$ with the affine approximator $\textrm{LRE}(s)$, such that when applied to novel subjects, they reproduce LM object predictions.
+
+
+
+In (a), we first assemble approximators from trained model Jacobians between middle-layer subject states and the final-layer object state. Then, in (b), we evaluate approximated tokens against transformer computations.
+
+
+The object retrieval function from a subject with a fixed relational context, $o = F_r(s)$, is modeled to be a first-order Taylor approximation of $F_r$ about a number of subjects $s_1 \ldots s_n$. For $i = 1 \ldots n$: $$\begin{align*}
+F_r(s) &\approx F_r(s_i) + W_r(s - s_i) \nonumber \\
+ &= F(s_i) + W_rs - W_rs_i \nonumber \\
+ &= W_rs + b_r, \nonumber \\
+\text{where } b_r &= F_r(s_i) - W_rs_i \nonumber
+\end{align*}$$
+
+In a relational context, a model may rely heavily on a singular subject state to produce the object state. Accordingly, the Jacobian matrix of derivatives between vector representations of the subject and object is hypothesized to serve as $W_r$. For a fixed relation, they calculate the mean Jacobian and bias between $n$ enriched subject states $\textbf{s}_1 \ldots \textbf{s}_n$ and outputs $F_r(\textbf{s}_1) \ldots F_r(\textbf{s}_n)$:
+
+$$\begin{align*}
+W_r &= \mathbb{E}_{\textbf{s}_i}\left[\left.\frac{\partial F_r}{\partial \textbf{s}}\right|_{\textbf{s}_i} \right] && \text{($d \times d$ matrix)} \\
+b_r &= \mathbb{E}_{\textbf{s}_i}\left[\left. F_r(\textbf{s}) - \frac{\partial F_r}{\partial \textbf{s}} \; \textbf{s} \right|_{\textbf{s}_i} \right] && \text{($d$ vector)}
+\end{align*}$$
+
+This yields a relational approximator capable of transforming a $j^\text{th}$ layer subject state $x^j_s = \textbf{s}$ [^1] into the final object hidden state $x^L_o = \textbf{o}$ [^2]: $$\textbf{o} \approx \textrm{LRE(\textbf{s})} = \beta W_r \textbf{s} + b_r$$ For instance, $\textbf{s}$ may be the hidden state of the 7$^\text{th}$ layer at the subject token, and $\textbf{o}$ the hidden state of the 26$^\text{th}$ (last) layer at the object token, e.g. the next-token prediction state.
+
+
+
+Comparing affine & linear LREs on GPT-J reveals many morphological relations are linearly approximable. With the exception of prefix and active form subjects, semantic and encyclopedic relations benefit more from the affine LRE than morphology. For subject layers 3-9, the best performing approximation is averaged (n = 4).
+
+
+**True Linear Encoding.** The affine LRE diverges from the linear relational embedding introduced by [@Hinton:86], in introducing a bias $b_r$ and scaling term $\beta$. While linearity is assumed in @Her:23 by calculating $W_r$ and $b_r$ from $\mathbb{E}_{s_i}$ over $i = 1 \ldots n$, using a Taylor approximation makes a weaker assumption, simply that the subject-object relation $F_r$ is differentiable. With linearity, we would expect the following: $$\begin{align*}
+ \textbf{o} &\approx F_r'(s_i)\textbf{s} \\
+ &= W_r \textbf{s}
+\end{align*}$$ In this case, the linear approximation over $\textbf{s}_1 \ldots \textbf{s}_n$ within the same relation would be the mean Jacobian. If this approximation generalizes to unseen objects, it would indicate the presence of a linear subject-object map.
+
+Analogy is traditionally seen as a special case of role-based relational reasoning (@Stern:79, @Gentner:83, @Holy:12), motivating the adaptation of analogical pairs to a relational setting. We choose to adapt the Bigger Analogy Test Set (BATS), originally introduced to explore linguistic regularities in word embeddings by [@Glad:16]. The dataset comprises forty different categories, each with fifty pairs of words sharing a common relation. The categories span inflectional morphology, derivational morphology, encyclopedic knowledge, and lexical semantics.
+
+As seen in Figure [2](#fig:methodology){reference-type="ref" reference="fig:methodology"}, we adapt the relational pairs in BATS by introducing prompts which are compatible with each instance of the analogy.
+
+Following the procedure outlined in Hernandez , we employ 8 in-context learning (ICL) examples for 8 different subject-object prompts for each relation. This allows us to obtain a Jacobian from the model computation which is most likely to exhibit the desired linear encoding.
+
+We omit the subject-object pairs used in construction from the testing pool. We further restrict evaluation to the pairs for which the LM computation is successful in reproducing the object. [^3]
+
+
+
+Evaluating languages present in Llama-7b reveal cross-typological linear encoding of morphology. Linear and affine LREs respectively score 56% and 68% on [plural] across German, French, Hungarian, and Portuguese. In contrast, on [things - color] relation the linear and affine techniques respectively score 19% and 70%. The Bias approximator scores 45%, suggesting the affine approximation for [things - color] is primarily additive.
+
+
+After passing through the activation function in the decoder, the approximated object tokens should faithfully replicate the true LM output.\
+\
+**Affine LRE.** The original affine LRE is a two-step approximation involving both a weight term $W_r$ and bias term $b_r$, which are applied to the subject state $\textbf{s}$ to produce an approximated output state: $\tilde{\textrm{\textbf{o}}} = \text{LRE(\textbf{s})} = \beta W_r\textbf{s} + b_r
+ \label{LRE}$\
+\
+**Linear LRE.** Our variants isolate the components of the LRE in order to inspect their contribution to the approximation. First, we define the linear LRE, a multiplicative operation. This is the subject hidden state **s** multiplied by the mean Jacobian for *other subject-object pairs* to derive a final object state: $\tilde{\textrm{\textbf{o}}} = \text{Linear(\textbf{s})} = W_r\textbf{s}
+ \label{Linear}$\
+\
+**Bias.** Second, we define the Bias approximator, an additive operation. This approximator calculates $\tilde{\textrm{\textbf{o}}}$ by adding $b_r$, the mean difference between $W_r \textbf{s}$ and **o** for *other subject-object pairs*, to **s**: $\tilde{\textrm{\textbf{o}}} = \text{Bias({\textbf{s})}} = \textbf{s} + b_r
+\label{bias}$\
+\
+Following [@Her:23], we define *faithfulness* of an approximator by the top-one token match rate. For token $t$ and decoder head $D$, we say an approximator is faithful if the top token approximation matches that of the LM: $\label{eq:faithfulness} \underset{t}{\mathrm{argmax}} \ D(\textbf{o})_t \stackrel{?}{=} \underset{t}{\mathrm{argmax}} \ D(\tilde{\textbf{o}})_t$
diff --git a/2510.04041/main_diagram/main_diagram.drawio b/2510.04041/main_diagram/main_diagram.drawio
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diff --git a/2510.04041/paper_text/intro_method.md b/2510.04041/paper_text/intro_method.md
new file mode 100644
index 0000000000000000000000000000000000000000..8be5baa6a0d0a52a57979da3d41749439e2b93ae
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@@ -0,0 +1,56 @@
+# Introduction
+
+Robot learning has long been constrained by the need for extensive, labeled data to train effective control policies [@mccarthy2024generalistrobotlearninginternet]. Traditional approaches often require meticulously annotated datasets with precise action labels, which are costly and time-consuming to acquire, especially for complex robotic tasks. Furthermore, reliance on task-specific datasets limits the generalization of learned policies to unseen environments, embodiments, or variations of the original task. Although reinforcement learning (RL) offers a potential solution, it exhibits poor sample efficiency for real-world problems and often requires significant--and sometimes unsafe--interaction with the environment. World models [@ha_2018_1207631; @hafner2022masteringataridiscreteworld], which learn predictive representations of the environment to improve sample efficiency and enable risk-free exploration, have emerged as a promising alternative. However, they still struggle to accurately model the diversity and complexity of real-world scenarios, limiting their effectiveness in open-ended tasks.
+
+Vision--Language--Action (VLA) models have recently achieved remarkable success in interpreting natural language instructions and generating corresponding single-step control commands for robotic systems [@kim2024openvla; @brohan2023rt1; @octomodelteam2024]. Despite these advances, deploying VLAs in real-world robotics remains fraught with challenges: they often lack mechanisms for lookahead, struggle to recover from compounding errors, and cannot plan over long horizons in dynamic environments. Such limitations manifest in failures during multi-step tasks like sequential object manipulation, assembly, or navigation in cluttered scenes.
+
+In this work, we introduce the *Scaling of Inference-Time Compute for VLAs* (**SITCOM**) framework (Figure [1](#fig:method){reference-type="ref" reference="fig:method"}), which endows any pretrained VLA with model-based rollout and reward-ranking capabilities inspired by Model Predictive Control (MPC) [@mpc1999667]. At each decision step, SITCOM's VLA policy proposes multiple candidate actions; a learned dynamics model then simulates resulting next states, and this process repeats to generate full multi-step trajectory rollouts. Finally, a reward model ranks these candidate sequences, selecting the trajectory with maximum reward for real-world execution. By performing this *inference-time planning*, SITCOM transforms one-shot VLAs into robust long-horizon planners, improving both reliability and success rates in complex tasks.
+
+In this paper,
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+- We propose the SITCOM framework, a general-purpose inference-time planning framework that enhances any VLA by simulating multi-step action rollouts and selecting optimal action sequences through a reward mechanism.
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+- We introduce an efficient transformer-based dynamics model pre-trained on large-scale BridgeV2 [@walke2024bridgedatav2datasetrobot] data and fine-tuned on SIMPLER environment [@li24simpler] trajectories to mitigate the Real2Sim gap. To further address error accumulation during long-horizon rollouts, we introduce a DAgger-inspired [@ross2011reduction] adaptation strategy that bridges the distributional shift between model inputs and predicted outputs.
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+- We provide insights on performance gains by rollouts of VLA over number action sequences and the depth of future predictions.
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+Finally, we validate our approach through comprehensive evaluations on multiple tasks and settings in the SIMPLER environment [@li24simpler], demonstrating that SITCOM consistently outperforms strong baselines and enables robust long-horizon planning for robotic manipulation.
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+Method Diagram explaining the overall SITCOM architecture. Given an initial frame and goal in text description, a VLA predicts actions which is fed into the dynamics model to get the next frame. This process is then iteratively repeated with the output of dynamics model to generate action sequence rollouts. Finally, a reward model is used to rank the action sequences and pick the best one among them to execute in the real-world.
+
+
+# Method
+
+**Notation:** Let $\mathcal{I}$ denote image observations, $\mathcal{T}$ is the task instructions, $\pi_\text{VLA}(\mathcal{I}, \mathcal{T})$ is the VLA model that outputs an action $a$, $f_\text{dyn}(\mathcal{I}, a)$ is the dynamics model, $r(\mathcal{I}_0, \mathcal{I}_l, \mathcal{T})$ is the reward model, $n$ is the number of trajectories, and $l$ is the rollout length.
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+We propose an inference algorithm for decision-making in an environment using Vision-Language-Action (VLA) models. At each inference step, the agent is provided with an observation $\mathcal{I}$ (an image) and a task instruction $\mathcal{T}$. The VLA model takes $(\mathcal{I}, \mathcal{T})$ as input and outputs an action. To encourage exploration, we set a high sampling temperature and sample $n$ candidate actions.
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+Each sampled action initializes a trajectory. A learned dynamics model, distinct from the real environment, is used to perform rollouts: given an image $\mathcal{I}$ and an action $a$, the dynamics model predicts the next image $\mathcal{I}'$. Subsequent actions are also temperature-sampeld from the VLA model.
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+Each trajectory is simulated for $l$ steps and we do this for $k$ trajectories. To perform this simulation, we propose two methods - 1) SITCOM (EnvSim) that uses another instance of the environment to perform the rollouts, 2) SITCOM (Dynamics) where we used a trained dynamics model for the rollouts.
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+After simulation, we give reward based on the final state of the trajectories (at the pre-defined depth). Our reward design incorporates the gripper-object gap, object-destination distance, and grasp success indicators. The trajectory with the highest reward score is selected, and the trajectory is executed in the real environment. This procedure is repeated at the environment's replanning frequency until task success or termination. Algorithm [\[algo:sitcom\]](#algo:sitcom){reference-type="ref" reference="algo:sitcom"} demonstrates the entire pipeline. Details regarding architecture of dynamics model can be found in section [3.1](#sec:dynamics){reference-type="ref" reference="sec:dynamics"}.
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+We train a dynamics model $f_{dyn}(.)$ that predicts the next state given the current state and action. The model uses an encoder-decoder architecture: the encoder processes image patches, concatenates them with action information, and the decoder predicts patches for the next state (Figure [\[fig:dynamics\]](#fig:dynamics){reference-type="ref" reference="fig:dynamics"}). To match inference conditions where the model rolls out autoregressively for $l$ steps, we train it to predict future states from its own predictions for $l_{train}$ steps (detailed in Section [5](#sec:analysis){reference-type="ref" reference="sec:analysis"}).
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+The model is trained using combined L1 pixel-wise loss and Learned Perceptual Image Patch Similarity (LPIPS) loss. L1 loss ensures pixel-level accuracy, while LPIPS loss promotes perceptual realism by measuring similarity in deep feature space. This combination balances low-level precision with high-level visual coherence.
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+We leverage approximately 25,000 trajectories from BridgeV2 [@walke2024bridgedatav2datasetrobot] for pretraining, providing diverse manipulation scenarios that enable generalizable dynamics learning. To address the Real2Sim gap, we fine-tune on in-domain SIMPLER trajectories, adapting the model to the specific visual appearances and physics of our evaluation environment. This two-stage training approach improves robustness for long-horizon rollouts in the SITCOM framework.
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+SIMPLER environment and four different tasks with WidowX arm
+
+
+::: algorithm
+[]{#algo:sitcom label="algo:sitcom"}
+:::
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+We train the vision-language-action (VLA) model using a standard cross-entropy loss over discretized action tokens. Since no publicly available expert trajectory data existed for the SIMPLER environment and pretrained real-world models performed poorly in simulation (as shown in the table), we curated our own dataset of 100 expert trajectories. Our dataset curation process involved three steps: first, we ran a pre-trained model [@ye2024latent] to generate initial trajectories; second, we applied heuristic rules to identify successful executions; and finally, we used human filtering to ensure high-quality expert demonstrations. These curated trajectories provide the VLA model with robust examples of successful task execution, enabling it to learn effective mappings from vision-language inputs to low-level control outputs.