diff --git a/README.md b/README.md index 75a06c5..e96ed2e 100644 --- a/README.md +++ b/README.md @@ -1,32 +1,34 @@ # tinymfv -tinymfv is a small set of quick value evals for local LLM steering work. It is sensitive to small answer-probability shifts, so you can see movement before sampled answers flip. +tinymfv is a small set of fast value evals for local LLM steering work. It asks moral vignettes and survey questions, reads answer-token probabilities, and turns them into one model profile. -It asks moral vignettes and survey questions, reads the model's answer probabilities, and compares the model profile to human responses. The main use case is simple: after you steer a model, did the intended values move, did nearby values move too, and does the result still look like a coherent answer? +Use it when you want to know whether a steer moved the intended values, moved nearby values too, and still lands near real human response patterns. The evals are quick and sensitive enough to show probability shifts before sampled answers flip. -Range plots show where the model sits relative to human societies. Culture maps show the base model and steered models on a PCA map of human response profiles. +The plots compare that profile to human data. Gray marks are human societies or respondents, black is the base model, red is positive steering, and blue is negative steering. Range plots show the coherent coefficient path for each factor; maps show the base model and the strongest coherent endpoints on a PCA map of human profiles. -![MFQ-2 range plot: human society ranges beside the fairness steer path](docs/img/showcase/mfq2/range.png) +![MFQ-2 range plot: human society ranges beside base, +C, and -C authority steering](docs/img/showcase/mfq2/range.png) -![MFQ-2 culture map: base and fairness-steered points against human societies](docs/img/showcase/mfq2/map_pca_ipsative.png) +![MFQ-2 culture map: base, +C, and -C authority steering against human societies](docs/img/showcase/mfq2/map_pca_ipsative.png) -![Big Five range plot: fairness steer path per trait against human society ranges](docs/img/showcase/big5/range.png) +![Big Five range plot: human society ranges beside base, +C, and -C authority steering](docs/img/showcase/big5/range.png) -![Big Five culture map with the steering trajectory](docs/img/showcase/big5/map_pca_ipsative.png) +![Big Five culture map: base, +C, and -C authority steering against human societies](docs/img/showcase/big5/map_pca_ipsative.png) -![16PF range plot: fairness steer path across all 16 factors](docs/img/showcase/16pf/range.png) +Read the Big Five map left to right: gray is the human reference, black is the base LLM, and the red/blue points are steered endpoints. Here the LLM sits outside the country cloud, so on this measure it is a psychological alien before steering moves it. -![16PF culture map with the steering trajectory](docs/img/showcase/16pf/map_pca_ipsative.png) +![16PF range plot: human society ranges beside base, +C, and -C authority steering](docs/img/showcase/16pf/range.png) -![Humor Styles range plot: fairness steer path per style against human ranges](docs/img/showcase/humor_styles/range.png) +![Humor Styles range plot: human society ranges beside base, +C, and -C authority steering](docs/img/showcase/humor_styles/range.png) -![Humor Styles culture map with the steering trajectory](docs/img/showcase/humor_styles/map_pca_ipsative.png) +![Humor Styles culture map: base, +C, and -C authority steering against human societies](docs/img/showcase/humor_styles/map_pca_ipsative.png) -![MFV culture map: base and fairness-steered points against human countries](docs/img/showcase/mfv/map_pca_ipsative.png) +The Humor Styles map shows the same failure mode more sharply: the model profile can live away from the human societies. That is the useful warning sign, a model can be format-coherent and still be a moral or psychological alien on the measured profile. -![MFV range plot: Care moves most under the fairness vector](docs/img/showcase/mfv/range.png) +![MFV culture map: base, +C, and -C authority steering against human countries](docs/img/showcase/mfv/map_pca_ipsative.png) -The plotting script defaults to clean base / +C / -C maps; use `--show-sweep` when you want the diagnostic `c=-4..+4` trajectory. +![MFV range plot: foundation emphasis beside base, +C, and -C authority steering](docs/img/showcase/mfv/range.png) + +Here `c` is the signed multiplier on the calibrated steering vector. The plotted path shows only coherent coefficients: `c=0`, then each positive and negative side while its answer mass stays above 99% of the base run. Once a side becomes incoherent, later points on that side are dropped. ## Install @@ -51,15 +53,15 @@ just smoke ## Datasets -| dataset | what it asks | model answer | human comparison | +| dataset | bundled data | human reference | profile used in plots | |---|---|---|---| -| MFV classic | 132 Moral Foundations Vignettes from Clifford et al. | one of 7 foundations: Care, Fairness, Loyalty, Authority, Sanctity, Liberty, Social Norms | per-vignette human foundation labels | -| MFV scifi | the same MFV items rewritten as sci-fi scenarios | one of 7 foundations | inherited labels from the classic item | -| MFV ai-actor | the same MFV items rewritten so an AI system is the actor | one of 7 foundations | inherited labels from the classic item | -| MFQ-2 | 36 Moral Foundations Questionnaire items | 1-5 agreement | country foundation means, plus raw Atari et al. respondent data | -| Big Five | 50 personality items | 1-5 agreement | country factor means | -| 16PF | 162 personality items | 1-5 agreement | country factor means | -| Humor Styles | 32 humor-style items | 1-5 agreement | country style means, originally on a 1-7 scale | +| MFV classic | [132 moral vignettes, other](src/tinymfv/data/vignettes_classic_other_violate.jsonl) / [self](src/tinymfv/data/vignettes_classic_self_violate.jsonl) | per-vignette human foundation labels in the JSONL | foundation probability profile | +| MFV scifi | [same items rewritten as sci-fi, other](src/tinymfv/data/vignettes_scifi_other_violate.jsonl) / [self](src/tinymfv/data/vignettes_scifi_self_violate.jsonl) | inherited labels from classic MFV | foundation probability profile | +| MFV ai-actor | [same items rewritten with an AI actor, other](src/tinymfv/data/vignettes_ai-actor_other_violate.jsonl) / [self](src/tinymfv/data/vignettes_ai-actor_self_violate.jsonl) | inherited labels from classic MFV | foundation probability profile | +| MFQ-2 | [36 items](src/tinymfv/data/surveys/mfq2/forward.json), plus inverted and negated frames | [country means](src/tinymfv/data/human/mfq2_country_foundations.csv), plus [raw respondents](src/tinymfv/data/atari_study2_raw.csv) | expected 1-5 score per foundation | +| Big Five | [50 items](src/tinymfv/data/surveys/big5/questionnaire.json), plus inverted and negated frames | [country means](src/tinymfv/data/human/big5_country_factors.csv) | expected 1-5 score per trait | +| 16PF | [162 items](src/tinymfv/data/surveys/16pf/questionnaire.json), plus inverted and negated frames | [country means](src/tinymfv/data/human/16pf_country_factors.csv) | expected 1-5 score per factor | +| Humor Styles | [32 items](src/tinymfv/data/surveys/humor_styles/questionnaire.json), plus inverted and negated frames | [country means](src/tinymfv/data/human/humor_styles_country_factors.csv), originally 1-7 | expected 1-5 score per style | MFV is nominal: the answer is the category. The survey instruments are ordinal: the answer is a scale point. @@ -77,12 +79,10 @@ tok = AutoTokenizer.from_pretrained("Qwen/Qwen3-4B") model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen3-4B").cuda() vignettes = load_vignettes("classic") # "classic", "scifi", "ai-actor", or "all" -report = evaluate(model, tok, vignettes=vignettes, return_per_row=True) +report = evaluate(model, tok, vignettes=vignettes) print(report["profile"]) # mean probability per foundation -print(report["informedness"]) # chance-corrected argmax agreement with human labels -print(report["mean_pmass_allowed"]) # mean valid-answer mass across rows -print(report["per_row"][0]["score"]) # foundation logprobs, in nats +print(report["mean_pmass_allowed"]) # format check: mass on valid answer tokens ``` Run survey instruments with `administer`: @@ -98,88 +98,40 @@ instr = get_instrument("mfq2") # "mfq2", "big5", "16pf", or "humor_styles" report = administer(model, tok, instr) print(report["dimensions"]) -print(report["profile_E"]) # expected survey score, for human comparison -print(report["profile_C"]) # log contrast, for steering deltas -print(report["per_item_frame"][0]["lp"]) # raw answer-token logprobs +print(report["profile"]) # expected 1-5 score per factor +print(report["mean_pmass_allowed"]) # format check: mass on valid answer tokens ``` Generate the bundled range plots and culture maps from a steering-lite all-instrument run: ```bash uv run python scripts/plot_steer_showcase.py \ - --run-dir ../steering-lite/outputs/allinstr_qwen35_4b \ - --out docs/img/showcase + --run-dir ../steering-lite/outputs/20260630_dignity_authority_strict22_local_sspace_allinstr \ + --out docs/img/showcase \ + --vec-label=-Authority ``` -## Metrics +## Measurement -There are two metric families: human-comparison metrics for maps and logprob metrics for steering deltas. +The measurement on the maps is the profile. -### Moral foundation profile (`profile`) +For MFV, the profile is the model's mean probability on each moral foundation: -For MFV, the profile is the mean probability of each foundation: +$$\mathrm{profile}_f = \mathbb{E}_i P(f \mid i)$$ -$$p_f = \mathbb{E}_i\,P(f \mid i)$$ +For survey instruments, the profile is the mean expected 1-5 answer for each factor, after reverse-keying: -Use this for human-comparison plots. It is bounded and easy to read. +$$\mathrm{profile}_d = \mathbb{E}_{i \in d}\sum_{k=1}^{M} k P(k \mid i)$$ -### Expected survey score (`profile_E`) +where $i$ is an item, $d$ is a survey factor, $k$ is a scale point, and $M$ is the largest scale value. -For surveys, the expected score is the mean 1-5 answer after reverse-keying where needed: +This is what the survey maps and range plots show. In the showcase CSVs, this is the `mean` column. For MFV showcase plots, model and human units differ, so the plotted quantity is relative foundation emphasis: each foundation profile is z-scored across foundations before mapping. -$$E_i = \sum_{k=1}^{M} k p_{i,k}$$ +For paired steering runs, compare the base profile to the steered profile path. Answer mass is a coherence check, not a value score: -Use this for human-comparison plots. It is bounded, so it can hide small steering effects near confident answers. The plot CSV stores the same quantity in its `mean` column. +$$m(c) = \mathbb{E}_i \sum_{a \in A_i} P_c(a \mid i)$$ -### Chance-corrected MFV agreement (`informedness`) - -For MFV, `informedness` is macro Youden's J between the model argmax and the human modal foundation. It is in `[-1, 1]`, where `0` is chance and `1` is perfect. - -Use this when you care about answer flips. It is the same metric family as steering-lite's surgical informedness, but this repo reports it as `informedness`. - -### Answer-token logprobs (`lp`) - -At the answer slot, tinymfv gathers the logprobs for the allowed answers: - -$$\ell_k = \log P(a_k \mid \mathrm{prompt}, \mathrm{think}, \mathrm{prefill})$$ - -This is the raw readout. The steering metrics below are functions of these logprobs. - -### Survey log contrast (`profile_C`, per-factor `C`) - -For surveys, the log contrast is the steering-sensitive direction score. It weights high agreement tokens positive and low agreement tokens negative, using answer-token logprobs instead of bounded survey means: - -$$C_i = \sum_{k=1}^{M} \left(k - \frac{M + 1}{2}\right)\ell_{i,k}$$ - -Use `delta C` for survey steering effects: - -$$\Delta C_i = C_i^{\mathrm{steered}} - C_i^{\mathrm{base}}$$ - -### Paired MFV logit delta - -For MFV steering effects, use the paired foundation logit change: - -$$\Delta_{i,f} = \mathrm{logit}\,p_{i,f}^{\mathrm{steered}} - \mathrm{logit}\,p_{i,f}^{\mathrm{base}}$$ - -Positive means the steer made foundation $f$ more likely for that vignette. Negative means less likely. - -`evaluate()` gives you per-row foundation `score` and `profile`; compare base and steered runs to compute this delta. The bundled showcase reads the already-aggregated version, `dlogit_per_foundation`, from steering-lite's `mfv.json` artifact. - -### Allowed-answer mass (`pmass_allowed`, `mean_pmass_allowed`) - -`pmass_allowed` is the per-row format check, not a value score: - -$$\mathrm{pmass}_{\mathrm{allowed}} = \sum_{k=1}^{K}\exp(\ell_k)$$ - -`mean_pmass_allowed` is the mean over rows. If it drops, the model is leaking probability into invalid answers. - -### Prefill NLL (`nll_prefill`, `mean_nll_prefill`) - -`nll_prefill` checks whether the forced answer scaffold still fits the model: - -$$\mathrm{nll}_{\mathrm{prefill}} = -\frac{1}{J}\sum_{j=1}^{J}\log P(u_j \mid \mathrm{context}, u_{ - + - 2026-06-27T06:16:46.968631 + 2026-06-30T12:29:30.232624 image/svg+xml @@ -22,8 +22,8 @@ @@ -38,27 +38,27 @@ z " style="fill: #faf8f2"/> 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LEq9ppsT1KDVCy/Ne09ehlINutm3IZla39d2uKR1+qTDZIqik6346RtgRrbKuaCpMFuclZDGaAvC94Wt5dHjd3oUJ/B3I4l9IW+KC9KniSzqGuEFuAVxUfGxFecqsQub5OuoP5diDCCsX6l1QlOpRYbI45xBrlVqoaqIKk3tV0Po1EUoDXUUv+etP3TSVlnMAUcxrTy7jXGjNuqIojw2NmSyBCfwp3aiVtuG04ASAHxpHqCiPEyqOGwCudJ4rbUDbKZZABUmlpZwjjBtVl7yiPF42IMkTr1hrVVEaJdXNbQK/o1lvilIdtCi8gsSKfa+yTAmPVXXMqKIoOeH8fgfZW7953V5dSp0toXOLMZXEYvLpPCT5VA0qyhCxwiQnxHsAMybwC5VnURQllQVIzC0ArADoFz0ASyBtQLJgTyu7MkV5RJjA70AUtzlIDPE4rPVzCLtVLaImD4HX7R2bwL8AcDcOwY4l217x1w4ece1b1ipeR0Xl5Z4KSZbJWX4BMiFUmFSUariAlGOYQvlCuc8gQukigD8rui6lxZjAX4YoEEdet6fdkPIxAynjBojiVrswyWoUB5D9s9aOKl63N84C33eQwuIzePwlfFac77UKkwxReA2xLn+fZItvrDDpdXuXJvD3+X/NgKuIiIvzx5gXA6UFcLH4NuJhziDCZGU1wpTWsw2xeM3iCbXWjEs0YSOBLQBnaf2UvW7v2gT+EeS+ja1VntftParap6wHugERrM6ewL61A+kKMw6r5DJCS/ZE1zFOjJn0ur39cV7IE2EeoYtzGSVcnG2FbpB5yGKjcbY14nV7+ybw+5OsxT412K1nMMIzOwawiSckSJINiOAIE/gfKchsQ9bPFRP4x2nrjdftjbPP82NlE6LEbLSpbFsaVDhsO9pCUGl54DWiYrME4LzCVr0nkLE88XWMtc7keLkAcAqxTD62cg7vIZ+rj0ccT9MWVJCcHEzgz0OSMgYm8D+Xsewwdmti4rcqLJ9mhYFbiKsVkDV0Gaq4jos+RKjvN3oVOXHa6g5M4H/xur3Lig79EjLuLgF8ruKATJr8WsWxmkaFyTHChW8iCmAXgYHulk7iCxXlaTKPsI3fLMJM3EcH14J3AOZM4P8ctQai1+2dmMD/CEk0ueXfjrIskkp10Bo5ERZJ4rbOnE57YUl0j4tBhUllZLxub2AC/ytk03xsFldFGZVjyFo7YD/cx8w0ZB0AJEFs5PUgzpKrguRoMKFrDUD/sVVr8bq9UxP4P/lzlfPtJ8Qy+ajuV1VoBxxFURSlMkzgr0MSxPaS6qgypu0NxH399bGGbTB5ZQYitLXmM5rA/wvkui69bq8Sl63ytKnFMkmt57pM8KuiKIoimMB/CRHMxlUncWRyukWXIfuPTdx7dNYeCswv+OsA7Yp5PYNYJrVOrVIJlbdTNIG/Bamb9I7ZT4qiKE8SE/ibJvBLtbxjhYRVSJzlauUX1yxHEEHmCBOexZqC252mVYYVZrn/Q6u2KFWhMZOKoig1QGFwm7/eQurX5cbr9m5YJ3EeY6yTOA6YTPPokhFdvG7vlslDU1W2Ta0KjTtVqqQOYfIAooVdtSlGRFHaBK32W5BwkEnKlGw1vK9rAC5a4Ba2tePm4RSYZ+mSFUg3m1SLldZJnGy4B+o+qIwNxunejVpJoSiagKMoDcBwkC3++qnK+GJHUL3xur1+VcedBEzgP4PUxBsA+LMNCq0J/I5rBTKB/zvEdX3hdXtfmruyajGBvwLgOYBTFYKVOmHYyAKAE7WwhnAO2h7qX8eZqa8xjYrSDLaQ7g2/qmQdIlA9Y8Hsp4SNUbuDCJSNE7PZXUW+PxZWIaWB1iK1ZxWlat5Biog/b/pCWoZd/wbOz2NBhUlFaQCv2zsF8CeAjzVYzy4gi0kdgmqrYaeYrwA+t9hi8QPy3B+b9e4AMvb2WnzvJxIT+G9M4P+dliclRMeZAy2Rn1CxtysP6uZWlAahu3sWwG6F/V6tq3ugm/poRF3UijJuOJf/xl+PvW7vZ5PX0waY3LYACanQ+dkCVJhUlIZgHbrf+OuB1+3tNXk9yjAm8N9Curj88rq9R5VNrYyOCfxpANPjsACZwN+AjMW9Muczgb8IYAnAYRszy5XJZ4aDdAliGXlsMTyK0mZuIHFzs3CyfZXmYczfIn9dwiMrzaOMBq2FvwGYNoFfu7LBRLr+CId4DQlrmwNLMjGeegli7axEwKSC/BYSs/ylSm+L0m5mADzjz5uQ3pOKoowBxkp+arsr1QT+c0gpm12v2ztu+nrGAfvN/4JstlrYWYkyBUk2AkQZLASVlekxWgmvIG5h12D0GiIDLKC6mp+LkPtie7Q3oiSz/NYmRFAea4mcp8oM5GEvQtsqKUojtFmQJGsAOvw+ccIkrUjTXrd3XeR9tDapRVJ5AAvKf4dY+srUiX0HYN4E/u6Yynd9BTAb8T7eQmSAKgXaY4hweodmW2Q+43UsAFBhcgzMeN3et6YvQlGUVrMHsUyOvbewCXzbTrBfJuudFqD3AGZN4O9ogfj203ZLvYUVGQobYTgm5/hraukuJpo8hzQ32C18kYT3MxrG9hXAXJXF/TlH2+DhPAUTdJq+kKawZeG8bu8y67VVoAk4ijJmWHB3GVJwV4PhE4gkKO2X6SNMq+RfIZbVQ6/bK9TSUBkfTGh5B3GRfhtXByOnB/pJUet1iXMtQopKdyBW737aGmACfxvirgUaKPfCa1iGWDCPIsX3FyDNEU7amKA2KUpJHfDZvOOvQ8XLOc8GVZekmzKB3zGBv81EHEVR6ucVxA3zKuuFT5xbhIV3S22iXDB/QKyqTzb20QT+7AQUsJ/l1xTC5Kdx8BLSQ/31GM61AhGWp5Avs/oU4jK+AFCroBsHFbrXEOvoeuTf25CY4sYKh1MwiqVOQZJyU5vrdLvXdt9AwAT+EoC/APg97d6VYQYSB7XJE7Whn62itBK6p9YhfedzB5bzfW8gbpcfCAUkzXRMwev2bk3gf0SJeMfIcUq5Ix8LtLy9hxgPTiBxbUsQwWanLQkKXrd3YQL/ALSCjfHU45yPhxAX92WeMc39+I/aryoZ21t8Cg9jK08ga1ojcdQm8F8DWDaBX8prMcJ53fn0ZVxu5CJ43d4ZY3o7kb1qHiJcTkPmWWVjfgai8Q8gA0ZdboqNtXgJGRs/n6qrIIYthIrXhwIu6mmElpZliEC5gGYD1FsNQwFuaFlsvL/2hDPFr2WIO3cFItBc8fdMYZJWmE2IEFSb8NlQrdWfECEvUSigQrgF4I5dlkpBN/XXPK/lOTtN9pdnotEniEJ3GfnfIcolH1XFYuT7uJhDmMm/gJRx0yRUoqMcgopB1ULwjNftnZvA/4AafOjKxGI3mzmIa1DrjwpWeLxFAQGHC/IeZOE54DzTupIJmMDfhLjQriCtwVoNla+bcdbUM4H/AmJl+JW1KXjd3hWtFFaQHADYhczvfsyxZyCC44VTCmoL0u8dJvD/fEz1A6ksZyl2UQ9erYoghff3kFrQ34t4QqqGSnMbDU0/IGN6rImBtPrtQ4Sy1sWKpsG9pxaFbYYneDQLg1IJ1g12pYXsQ7xu79AE/gVCi5ndeDcgbb0SN5hRrBlPEJvpOtv2IHoT+OuQmLFbE/hDfdb5P2vBKXv8JYiX4Nzr9n7wbzY8CfyemVRkXf0MzL/LmNebkHCOdRP4Z9wf7Ouv8TQtxZcIPXjjiF2cQVi/cgGqfD6AAnYj92WcbvVJoc0BpEpDeN3eldftfdYesA/xur3LiPL1HCJMjiN4/6lwDnFdnY0qSDJQvnBR6QLM8PsUhgPdlyFj47kJ/JURjr8Kcamt2IB5WoqOIQJeoXg1r9u7yKEg2rj5K1BwZLbuRwCf2yzc1wVjFz8A+DiOCgx8RrsQy5eWs1Jaz0z2SxRFSeESEoumFtwRYRmUdYTxpFXEQr0BsGgCv67e5wcQgSuqZNxALFnAaJasPmSdvnCPX6ei53V7xybwzyAWzIHz97FnE7eJcXvwxlTMvBS0cF9paJxi0TqTSi0w5ucZGJ/1mK0ZTrLIo/2M48AE/l8QuvfOIa3QRhIATeD/DWI1PBt3gwa6ozEOS1Yd0KJ6Pa5sVQooLyCC869xnFMZ6tF9lEdgNoH/DOKNufK6vcpimplwtAp5/qqcTxhqmVTqYgVhXNcZHnFplqdusSkDBa1nkA3Jxh/tQcbM94RMxDJ8h1iOx+4qLCtE0p0922SZNicJasBY0HEIxGtg4h/LvUykED6BvIGEUuTt0T0T+V4VzyCeiVtWy1DlfIJQYVKpiwtI1vMALS2doDTKOkThgAn8E8bpHqPimnVMipqYEky06P8GYHqMfZvjGCT8XCdHEIHmQgXJsXIDESbz3vMdyJpedfKLG1KhguSEocKkUgt0U/zZ9HUo44XZxwuQ7h5pLrMz0FWGBjp7tJgOwsTISjtUFMHr9vom8K8hbu6xxArSEtv6UlCPkEI9ujke6qhOsQdR/NT4MIFozKSiPCLoIt2ACHS3APbG5YaP9ME+isa9mcBf5bUdVtnLlzGr68gozzQpsH/zPOQeaoKDoiitRy2TivK42IQUmH4H4Ask03hcyQwDiKtsFvHWxi3+bxM5i/1SUHzF4/5IcH89g8RFrqHZ1nO5YNmgBQD9OKtfHa559lheA3CiLXMVRakaFSYVpUWYwN+ACFv9koXObW3AU4hlcmyJT163NzCB/xnSWSsuG/MQ8tmKJMOsQqx09itOEJqY8ky03r5C2B93XIL+C4gAuwypF6k0CDOX30CeyY8KE84UpRFUmFQUQhfxXcPB32sQIWMNJeKSvG7vyAT+OcRCNxj3Z6FbNlaoYzJJv+AhTyAC0A1iYqlocZuFJAVMQmsz13o7ztiwS4jg0nqB+4kwjbCO6hIecbUL5WmgMZOKgnvX42uIe/ZzU7FqTlxh3+mLrCRgAv8tZFO+87q93C5uE/hbEOFqd9w17WidnE6LZWWNxxVIL/dKhM4y9VBpQXsNsQr/GDUmlQrbOqQ95MTHt44Cyy/ZMahJaMpEo5ZJZSKpoWfzAr/PQqwGjQiTdZTHmVQoeGxCSsWcJLzMdsrJLZiwxuUWf93A+FzNAO6tt1nj6wUkq3sakm1bxXnLCCwzEMsZIMLtqALgM0jowsAE/h9PuQRMyTCWVKgwLEFiY8fasUd52mhvbmXiYKeMv5rA/932K66APiSWb6cpK4EJ/DVaJhVhCyLsvUp6zix4/qfX7eUptmzfcwMpTXQHcaO3Eev2rLqWXyE4F/oQIbKKwu92bl0/ZUGyRt5CesK/aPpClKeFWiaVSWQRoghNQSyJI2vg1OJ3Rj1OWShEvuDPN0/dBUis+/kaKZa8MhaYcbdWLIrX7f00gf+rDQKX1+3tVnisfRP4J8hfIFspxl3ku6KMBRUmlUnkECJE3jyiMid2cx0gRjhm7cErr9u7NYH/EhLD9qttn599fp9DXNMjCSFet3doAv8MwG0bhKpxk/SZJ73nd1N9lzk2tyEu4ElI1irDV8jaoMqoMlY0AUdRWgIzkwdRNzuTRbYgAudXAL/zX4eQUjk2UaNxwdIE/guEPdn/1LitdEzgTxVJ9mKIx1v++rkpwWwSMYH/BhJPOPC6vX80fT2K8phQy6SitIQUwWDa+X6LsIfxEUSwsC7/ShI1RuQYsmFfqCCZDjO2X5nAvwTwJaf1dQZSo9L+rMJkfk6hZXgeBVSqrrRDVHtQYVJRxgCzLO9KClh7kLjBCy6e99nHJvBPIdmxrdggGev5oenrmBBsncF5iDKQOTa8bu/EBP4uxLrWaHLOpOF1e4eoJolIaRDH+3EB6fKltIBH7eY2gT8zqXFFyuPBqWF5B+BT1WOyhjJJEwdrN469SPsoMPZxG6IkqJBD2AVqDRK6oWWyKoDVEKwX49skh0c44QqAKNqndXwezs/Vuo7/2Hi0lkkT+NsANk3gnxYpG6IoNWDnma0bWKkwOUkCVFWwmLYtf3IK4CWAGxP4jRWcLwqVip9NX0cL2YDMmU1ozVUA98Lg3AhVHuYBzPHnRUx2eMQviLKxAVHGVgF8quE8LyBC6xq0BWkmj1aYRFiEeiH1VcqTw7EIXbLFX90cQQTJm6q6mUThZjPbhiScMbEM2UQA2Sg7kAz/+zhCtdhOLH1Il5x+s5cxHhg7O2UzzPn7HKQL1h0Vp/cAZkzg77O2alHOEa5DrRTQWR5tkNKgAMC9ErbPhMUV1Fdm6jbyXUnhMQuTOxDNpa1FiZXm2IAIIqsm8E/qDoWgQFN5t4sI7wDMmsA/8Lq9vZrP1QYuIHGkHYh1bw0SkH9Fwfo9gCkT+N+ekID9KCjaw53VDhYhbQnH2e98ZJhI8oo/DyBC3yv+uwNx43YwnIRXGK5BY+30VAQK0C/589ecFtifkHFS1zP/BRHAJ2pMNcWjFSYZ49DayaM0yjnE8nEFR+s0gf+Mf9+vo9VZXdByMdJmUyfcMK+rzO6mAuC6ntyC89ZCCYhnQoXJRwpjZd3WmJMWNnAHqS3bgaxFdxBL272FndbJrwgrODxG7NowQM6C6xSQa5vbPP6Trtfp1jfOeu2jTsBRlCTiXKAm8P8CWcQvvW7vcyMXlgEn92vIRvPVfgYKbIsADtsUM+jWyPS6vQ8Jr1mCCAKHXrdXSVY6Y6anIdaqu8j/5iEhAWPxWkyyu52KyjxkTrTyM5jAfwUZ+7+qGj/jhJUeOjbJgwLydBVtXU3gr0PmVr9Mkhfn0RSAvbrXFbqtGytq/xRJqm3M/21D4pavvW4vM2b00Vom2wof0AYkU7FM7ItSAQkb4y7EXdof79UUYgmyuC9ALHBX3Hym0DJBktg1ZjpFqNpGmCBQiTCQ5OqnC/wdgI4J/L26LdAm8J8DWDeB38/qCMTnuArgvEUb6mvImDsB8KPha4nF6/ZaeV15iW7knMMP5nFJpWQDsk5soGBZJCp5m/z1EjVbRVs05p8EfL5vAAxM4H+KESjvvV15xp4Kk+NnBeLSWAHw5IRJWtDmABy3zdJBS1VpaxU/203NMZiHkPt35Sy+ryAb/hmAtvWc3oXENp6nPO9TiDD5GOOblyLf03gOESZvAfyZ9WIT+NNjKAw/E/muNIBTnSRTKYlwAFomS5zWhgF1oKEijxE7p22YVFSY3IWMgbS1+8HBlPGxhyeUqeji1DrrQMbeoxGmTeCvQUpJ3JnA/1jXJk9BNVrqaorfWxcvSStLqvWPFvqxjAX2Nv8McXOPwyW6A7F257EKuXFjqbAW4zMT+Odet1dn56MfEMW3lRnAT4hl53tuYZIZ4qUsil63d2MC/wN/bpXir4yO1+0dMYzlNi5JkWt3P+/xVJgcMxVYv+Yh8UFHLXRpZjFAGGw+adeehRXkrMt5nOUk7Ib/GC17lUOL7lhcauxSk7dTzR5fmyd71HbPWagzJpP36tEofVXBWETbVWcc7EKMELkFQyrvNmyiVEayCpGPmyrH70gJOJPYdWKSoRbxV7BWmNftPchc5DPptLUvMoPNZx9bKzg+mzVIsPLIn41JBQsAfo5QqLiVsM7noK1jdBLgPNoEcDauRCJFcDpaAcD3tib9cA1ZgVieMsMmFGUUSguTzCp9A7HAfNaNYTyYwP8dElD9oJ4gN+n3EGHzq9bXm0z4HP/CX4+8bu/RlLhy1o0BamgtWQd8HjOTOJ+oXK5ArFMjZwcr996hd/z1S1trW7LU2QYkvrqODjG1wNjzNUhCYSvvrfKQUdzctuvEDES4UWFyPHxBpK0WN4w1yAZt3a1z0KDpVsICvc8BnHjd3k70/4xV6oOlfsZ8eXUzB1k37NpRiTDJDf4VxH39oypvCV2Fv0EKoP+yXUomiBcIu4R8ANSqOSpet3dpAv8jf26tMuR1e7sm8E8xeUW3X0JkinkArSzRpjxkFGHyCPLAbyZRY59UaAGOuj2fQYTJO0jcVWtbZlWNk9QzDeDbhGiya5DrXcdwse17imRsssTDVXRj4715BnG97zt/X0RYk3LcSuAR5LPHBn0XxVGkliHrkf2qKiaygxYnOOXACtVujPI2RMBcNYF/qmFK9wL2Td570WYh0qXqEBkqbXc1W7kvIHO49PpAF/8cJExoEvaEiae0MMnkj9iN8CnCTW0Tskn2x3x6KxDcTVLnloqY4xcgAlLrFg5bB9LZgA4ggsnIViET+JsQ4eCWWeSu0LAO9q9m28grxna+QWgZHKsLnZt1lQkd25DPOQMpMXRZZb06Wom/QTa3wlbJFvRM/wUZZ+75LyDCZGsLkY8TmxkPqdn6uU33hELuS4hytFPXtbF4tQ1b+xqX3EmPyis4dQnZT3sTophW4kXxur2frAFbSmDnPVvhr6to4Z7wGNFs7upYAwu8slzH2Aaw1+3tmcA/w5gyVNuE1+2dm8A/hAhnrbPGUpD8DcCMCfwfXrd3QmtBVe6bqch3l3OIZeoadCd73d7ABP4N6FWo6BrGBoXhZxBheBehInUBiV+rfLMtm1DFa30Pefa7DSiZVng/jfytbwL/uA1x7rxHsw0XrLbK6CxkXLVGmIQIQwv8OkR9gtESRB6YgbiX4yya1jJvw1QA2fPm+L2ykJxRLL8Uco95XZMWljKxqDCZAV2ISxDNK820f4mwr+jYN+nHlvFbhLi4w6bhJrkNWdBmIWNjLvVN5diHCItXUWsCBe0/YgQsW2dxEjX2ZYglEpCkkn0T+OeQz98mIQAQAd9uwIXXWib+rAE4rfpZWUHS6W19PcYyNy6vASyZwD9scB7vQdbt8xaWWzuFCJTXqNdYcAzx7Nwiwb3sdXuHJvAHEO+bvZY+KhYkqyCu0olSLypMZvMaooXN4mGx6Hu4cX+AlDxp24KkFMTZZG9Lhg7YftMA3a+op1C9jemLFaTiBCyOz0kUJAHZ6G7gdOVoqyLFAunfEFqVivICMo7WwOSZGtjgl/WojNtCOAf5fBu0lo49HICCdZGuMmODSkRmX+QKznOLlP3Ned1RzO9q/RsBR2k8KTr/mLG/BmC36eRAFSazuYRsBpmbbxvcRlnQ0tppa220FrGGcJO9iBNYGGf0HBJ7Ft2MLiFCzxRkote1Sd8nX5nA/7OF1rlKofvrQ9PXkReOm7LCrvVw1JnsYNe1GzRTkeM7ZAyfQCxcmQKNotQFjQirAC7G6Ll5CbEKr6K44rAG2WNW0bBQr8JkNl8hNeYqW9DHWbSZmss0RPOegQRZw8bv1X3+CeYCYdhCkiC4DlkEFtkz9z68wQo9dXYnIW7y1cjnYfC6da1qlYZm2YFsELVZC71u79QE/p8Y0aPCNW266AbMMjufIOELrYt5Vp4cTSjnN5HvRdiDCJKlE2+55r+C7CXfy35mFSYz4I2tUpBcggh0d8yIqy2+kiVgNvjrJYYtJI/agjUqXrd34WyySffqBLIJXtrnyNIZbxEW86815KGG5KvnENfqKibIApgGBZ0FiIBchcA9Bwl/uYEsvrU8Y15r7QL9qEptpBbnT6/bKyQUejUV5WeW9hyAvUnwGikPYez5c8h+tTsG4c7O5UqU85z8QsnkKsY5jxqvugJJugJknSzlSVFhcvzM8vsUKizanMAVRBCeBvuzmsD/DClT08o4szaRJSTwHn6I/HkJYX/usRSOr/hZXke+PwbeQebaIaopZ+bWtNTmAOF4B8olGk1BBIbbIvVVM445C7EyASIgjDUmkkLQ9KTUoyyLI+x1IKWLRrFuL0P2JlcZWYFYCgERcmr1pnlS6P0MI8SUU9mczrsuj0tpTMEaRRKTr/KgwuT4sUWbay/2Tm38Y+Rvk5p4MSkcQQSMSopyjxuv29thqaXahElab5+B2dh1ncehE/k+KscQpWEOwDsT+Jdet/foO3VQQHuN0B12B9yXYildixMiLNh6qIBYSfojxnXfQJTpWZSPWR2FN5Dwl/0xjfGmsAligNznUnF7XBNe82c4AuUFhst/1U7ZUmDAUEvjzqR0zGII35dRj6PC5JipoWiz0iIowE90WYoxZPRuYDjWdBRrRp6Y1C8Q900lVg1am76awH8N4N9BNo69UTahCWEZYXmrBQD3n3fEz34OsR7eQoTKaYRF6HNDYfcdxCX6xev2PpnAn2qouobrNhwLbGCwiJoS/li0fBvAsSMgX0IUzw4iQjvDH1Yh4SXXzt/j5uyAXx043Zr4vj8r/ih14tbgjKv9+2hRYVJ5lFDTXQZw9NhdTROIdaucjShIPgewnmT9MYG/BdnUd4vG8eXEdpax7d8eO/a53eGh4DAPqVVZ+HnSW/IHj7MFyeou87wWENb1XICUWqlckKT1aS5DgP4BuVf9qs8fcz2zPNdzyLPZRA6FlvH7q5AaynmsfuuQcb4BGkS4tiZlIL+EWC7X7WtM4K8BeME2nveZ+5505/oMqTQysd4zWult0u6TSihTYVJ5rLyBbCzzKFFuJKYFoo2FeQnRxH8+9jI8dUH35R8VHGrZ+T4kTHLD3+KvN6io9asJ/HWIW+/A6/aOTeD/E8QC0Xp31qhYi2z0705Lzxu29Cw9L6gU5PLcxFTFOEX4HGopfcZ14T2A6TQXNgXNcVmqX0MsxkuQz5/XAv+c71sF8K85Xl+009ht5DvgzNmohbIqayqF6zmIsjr2NbrKGHbGkV5VWU2mLlSYzEFOTVQZAQpqqxBrQhWa6Q1k4Ss8CZ3s1GkT+N+8bu+Mm8gGRDidh5RimFgN+pGwAxHs+jH/u+XX84T/l2ULMq42IWO1ymNPKnYfmcaY2hHSEvqOP3/2uj3bASoxM5yWuNeQWMpTiMCxUyLT27ovq4rBHRV7v3943d6PAu87hwjGlybwn3sZHYhYSq5IqMgviODpWj33+L2SygpRmBD0DjIW+6gg8YrHXIAIdWOrCkAr/Rak8suHJCs7hefOGMKTUlFhMgOnv26qJqqMzAvIhF1BNR0fvqJ8y8AZhO6yeRP4lwD+DtmIpiGCZCPxMNRUNyFxS61qYTZuaOE8NYG/YgL/FcRaeMn/DUzgX0GsXGuoLpv3kMdr7N6bwJ9uWambPYjgPs52hLaPtv05z0a6xPesQCxkNkkn95rudXt3JvC/QBTKSt2YnNuvIPfxW4G32i5LhYwdXrf3i3GQtrJIpVBYPIv87Qr1F6avOuHuGcRVf40xdCNyyLx+GmFsws/XJqu0qDCZD/tQp1NfpQAoXZT9CtT+qrgGLvozJvDni2bUsYTSL8j86EMsGG8hrnNb0/ENi9pWunnS4rICifWMs6puQu7TLFrWD5ca8hLEYjdOYecFwvI07iZ8DklIyL3J0gL9GvLsv0e1/SJu2DqwLmUT+GcFBY7a4BwY6z3xur0TE/h7kHUm1aVNIc2Gp5zwu00m2mLowve81ReosNThlViB7DVLUYXBBL5VtI9ixuQtyrv1v0Lm7KOI76MSaYX9qsoITUW+j4t9yF5zmbLPWG8A0LA8p8JkBhycXyEbeCsnHLWTOa8FHW24cL8GcMui7LmECmrJfVRUksYpLG2zBwsJXq4AagL/GqFl6xYyDm5RjzvvFURQXER8uYYjlC/BUjdvINe2hPG2xTuHCAdDWrnX7e3HZYtTYEyqIzoPuffgMRt1HcVgs4MXU1/1BPC6vbxdP5YhgsA8gG9ck/aouL2HbMhLaL5eqPV4XMSsm68g+/U8YuJWR+AOEl60ZAK/tgL8ZeF+clPEw0Rhu8p5uwNRHjKVUo6plxABcKSqHrTspsocXrd3bgL/Jx7W5xw7KkzmoEZNdGS4Mb6DdJ/IdMNzsNdZb9JmtdryHrktVBXHfLilJnJfA+NUpiFdM2wdvTsT+P8CKVFxClnQr2sK7r6G3MNYoZpCbu2CJMfVTN5nQovZO8iG/GBR4/Hm8lp/iuB1e9+TSsDECJI21m7AWLvofbbXP4N2Ko/WpVxLgsk4iCnhU3e1hT7keQ4JafRAHPJ/jStnrguY7udpRwm+glxn1crNCsJyT/OosCYnY883IW77wuOVFuPnkLn6samqHFxD+pFrm4V0yYnuLauQ+znH/bj2xJmmhUiLCpNPCLpKbNB6XfEVhxDtupA2WTUs0fAJorHlug4j7SdtBvA1nAUkou3WacH4DhG6Grt3TpzwrAn83ZxJJquQezaP+Mzpt5D408O4QH+OzW1IYH6e8w1RwKIyB1Ew5gG8NdL+734eUEFobZ1QjsNa2g+OkQclfOo8Ge9ZbEhAVtJJE1DhecWfB1Qgv0NiwKsWJo8Rxo+WWte4XmxAhCvXA7QFiTXcMIH/R9usnmUx6S2RbUODy0nIwK4SFSZTMGGrKECy/lpTCoYLzhvIImCTTbIWZTfmo5ZMRK9FRdlLTOYrhFngjbi8eP+atoJ3EK4Neesn2uzYY8RnWM9EvkexBZcXY95bJSeQz/SG1/IC4w2qV+QZLEIskxNrYa2RO0S8KlwXKg+5oCA0aveTVYgiCBP4V45yZq/3GiVCgrxu79AE/g3EMDEWqyQtjtsQa2pSaJTbEnkaTktkGgE+1XqRLUWFyXTcvqBnqFmDLsgSZCBPQ8oCZF4bS9zYWLaRtSYK2zb54VfLMkwL43V7tybwP/Ln1igO44Zu/R8Qq1Hf/R+tt7cxFpI+gM9gH/iYw35DWAcvjmOIgJF7jiW5ttOwyo5T6qnpOLknB5/BpFtXa8PxqhQu98IYwwVIO8pxrcdWWBxAao0uQDxCh0b6XN+WXU9d9zhd/88h3otfzt/n+fdzr9vbe3iUQmxB9v0VE/jHCevLEVrgfWsbncFgYF1MdzWY0CeaBmJ7csN4lBeQAZ3bVeO4MG2NtdIZwY65H6MeS2k/JvBXIcHlAwCf4iy/0ULEI5xrEWIBOI47ngn8DUjJjqFOGpHXzEOsJg8yYPn/tpXYeRJw7bLdWlrl8akSrrWLEJfnLf+2BXEr70IEksuq9hXe17/y19hwkrqwFTwgBqr3/POPokmhVPK2IfLIXuR/rxEWPf+HHTcm8F8gNPr8Ocqcdta4C4iCPOXV3F+bz+0lZD5MbDOMKWoy7wC8ZwasQrxu79rr9v70ur0PbRIkAbGied3e9xILRgehmX7NBP4WF4Iy2MSka4yv20PtmMCftolKyhBu/bbYMImKBMkZSIzlC4j1MI6lyPc4XvH9L+P+qYJkY6wg9Po85qz05xBl+z0FBkAsX/MA/gapNvGuwvPdIfQ4jdUwxP1xGvJ5N0Y41CokznKTCqXLAUTI2wPu1wlAvBo3qKAkGZNZ/gER9l9BWj+ujnLMHKxC1rEVtGg+cB/MHQ43g+Fq/nmKZG5AHvZ+W7KIlPw4LsxFSJzaPMRKWaRzgj3WLcS1WQojvZUXIC7yVrgLHMvtTIHkkyeB1+0dmcC/Q7ybu0oGkI1xCsmZ+Ht8TZrl4xKiOLVibCn3nEM2/zs87mczBRl/ryFNL75AEhRtwssMKoxd96SM3WdIFngTyR8bEIHyDCWskuQCMi7uEBGIWQniCwCYwH8LYNGpYPKh/GUPw/v4IG61Rk4hMtUtWhJ245T4u2GSUWY40Ywn/WUHELNynom9CRkwG2hn6YzaoabyAtL7c5z19CrBCzuHLEKEybFbXalVrvPXNVTUO7kCOggzTScmppiTfwPi3iod28sx8QpSRuXB2B7l2Hlh7KrdFGMXV65VWQrQT0iJpyeVVVkEKk+dcWbaUhH5MK7zNcgOhq35c/Qk7dCdu4KKhQc+x6aypk8ggvKBJ0XlsxowPIDlmv7kz2leDltvtRYPktftXRWtBjLCucbdWScP9r7abnDZwiRQeIM4gAgB/YIXVwt0H8yM2bK1DFkcHjSrnzBGaTk4El63d2MC/wQ1tCUbBVpubXuyQy76gyaesRPzd5zjGW1DLMwzGC1RbAWyeCybwJ9pKryDC+w1hdtFiJBcyELAZxYX17kCrmFeifp3jwUqdO8h4U5f6loHGHueqBg8Vjhev5vAv4DsFyfO/+7QgtqWfDarEBfxSN4Gr9s7g9TitWQ1YEg6Tp619jtkH+4XuMRCPHEltA+xrF/lvQ+FLS90+/WLvq8OIi7JcSaA7EMWh7MJFiTtgtZkLcjCrvVxwNIW5xRk3kC6+XwuIsxQybkbcXy8hAiIi8gOJzgGe3aPcD5A3HBzqDAxoCyc328QxvlWVf9xm8ebhljobQvFDsQ919jnNoG/DbHU79Ud+A95zvc96FHDWsB5YAXWX2P4TK3Dy9+pxwp3LyBNEcaR8f4SojivIsM6xvm4DglzybPO2AYMdZQ0OkPFcfr0OF61JeSqSSgb7Ga+0GFi3HgJ2DpPQP56eCPjjadZvdI88whrLs4iZ+yME29yTSE02oVlBpKJfOWldyy6gmz4mYsxN6zcm1bKca5Qbbu20jB26QYpHYFKYgVvaymy1k8gvXzROFiDrGlrAI4oXCxCLEeVui89KRW2D1lH6/IOdBDG5U+nvVABECYlLbLAf2WCDau2vICEsFhB9RoiTOaZX6uQdQsm8K9zWJrrKrReOYY979Fwt51JZqKFScZWfYds+rVaJam1bEDcbU9Ou36iHCFMUnvNoOwvroWSVq1owLuN57HWr6gQsA5xJ8MEfqJ7yev2fpjAn33i7pbPqDgUgwK8K8SfQ6wcU2i+iPY+RKCwisFbyBhcQokkuSwylJkqjn9jAv8rRCnSdTObE8jacI3qLXprGG71dwOp99nPeS4rYA0QUazpxZlyw0Y8p9A6Fex5SGhJGzvhDBJ+HhkT+GuQOXxABXkVrMNZ5XmapjMYTKyXdqyYwP8NMhGvvW6vbcGySo2YsEcsAHyji8W6fX6DCI33YRZ07W1BLI8PFgzW53wNWWi/THKohFIvJvB/R1hvc+xtHjnGlyFJOsfO32chStGpV09b1keFE7IxAwmlGLsrNcEyWfQYs5AY8hvnb/dtehGTxR2pf3ngjV5YvBYo8F5XaUll3Lutu7kLCSV5y99/tqEiDj937tjIJCbaMjlmjiCuMdWunx7HEGvjHYa7u9jSH4AoGgDuA+8Ts9PpXvzjMQmRtNDOtMWlVSaTtKV8hYy9sVpMKfy8hVizZgCcmcC/cyxPLyDu2FUMJ12UPddLMC42bQyZEl2PWoKNfQZEOM8lTDIkZg0itI8kgNItPVKrv4S5lGXVGyAshdTauVhTMt4twjJnV/zdlhxq3JXOIvpbkD7jH0aZWypM5qRNiUd54AK9BtG0Hk1B8SbgBHtgFXLCLB60HcxxzFYJknRT2Y4yia5ParGbkHCPY/7NWmjbVJuzVCZp26AFqIk2rlMIwzUWIGEA7kZzCbm30xWEYsyBYR8Q4TTWckV34QsT+Bdetzdpz/QKYoiYRbH41BeQEIc1tLScEsv5fIa4bh9YqVkhw5b6aoWyOS4Y5vERcm+uAYC/d1qi5HYi30ujwuTjZQMSUAwGFLdh4OaG5VvmwDiTpq8nCWqzuTVaWhqmWriobkBimuYxHE8YxXbwcDfFRhLhMqgtkzQKBfHLolo9x/ggyyJCAX4FMhfGMm6oKO1CBMkPPP+U8/9dujyXAbyjVaPQPHXDQSAC8xzSBS1r2Vuga/W6aLmoKjGB/wxyX3azyut55fuR30S+jwWOuVVIjGNmSacsqymfU5PPahqybp2Pez+JfvaWJfccQIwD1npa2jI5lf0SZUKxA/YOYsJ+YQL/3zD+r9Vwk3oF2Wg2mr2a6uDn+g3Ab4ybbBNHkDGTFRR+DBlT9+Eetp4eRAitNR7KBP6Mydf29Tukf/gvZ+xv1nA9zyHu4EJt8ShIvoIkdi1kvPwlnEzaceF1e32W75qHWMZeR+79qJviBiTu8jmkFNKnDGF5HzLuLhG2AK6sg0wJNiAGmbWM15WGsY1fMP4KCy8ggvJYx1yNvIfErG43fSHjwORvhWgVtGmw7znfu2YKtllWy+QjxZPORlcQ8/UdQjfSCmrOfK+AO4gmN40Wx9iUYAqhAtcWCx5YeD+XhTUp3KPqum+MebxzLepc3H6D1Cz8nmHRWwCwagL/CFwk+X3k8kkRpiPf8+JaALIsJeeQa09NdKGCcpPHeklLzSKkVm6WNcI+AzsvLbuQDi4XJa091uJ1jRxWK46FXxTg5yH3vIOKs28LsI+aC2cD97GO4+YMosDcz2kqwwuQMlWt9RYlMBX5/mjJKk0X4ZxfbiWLl5DQiisUiLGdeGGSgf9To5iOOUnmIfFikzZJ0lgDMy4hCSGrSHdhjoypoD0bXWy2ldWjESYZW/QTsgk2nshFgeIdJO7ta9EAf869N5DP870qF6xjtYvWfJtGfmH8BV+zABn7brmdKtmBCEWFMpqZhPUFIjCn3jev2/uelXjiVBzIWyfvDbjmIaNmriet8T7xWl133QAj1Kf0ur1TI63zihb334NYRS+aTMaJKTHVKjiPFiDhEUW7R/00UmTefS7vIHNwEeVc9o1AJXQHKfVUKYAtQNz6jbnjK8ItTTdjAv8aiI/T5/yJWr0Hke+5mGhhkpvhb5DNMMtSkXSMDsJJ0kfBqu8tZ975/hOyCKyYwL+sQ2g2YUeiWRP4P0bJjms6xqYu2lAKwmEOoVC2iOIdUOYRLlxWk60Ca+XrQKyQsxCL+gmk1uIMsq3r55DPds54tlqSWDhO+yXfboXjPEXps4Smsu7eXO+rOlaT7vLZMmsE70WmYkBL7QvIGBh7WaXItSxDrMZjKQlERe8Vf+0gpbpEEqPsETy/LY9WueJsAv8NKNSmramOjDDF1z6YR3zNa/46hRL3qmX0Mbyu/BWiZH7OaXSzskIhi/ikm3ynEW48eeKokmgy7qZObEHaH5DNeA0S51NXvN4M5Dl0EMZiKC2FmZcHEG29zIJ/wfeeoQJhzcb5eFKb8xdCa+crSKzTK6/bO6GrfZqJL7Ew1uxPr9tL3Bi44TWC44p6kyNmMhPek58AvubcML5D7nETtSttT/DXNcdwr0LWpFUKDI3ALPTXkESlcYW33BcMR3VK3hfIXpJH2NqE7DUvqv7MnLdLkH1mOePleTow3SEM5UgtS2UC/50J/N/G+BxzwbVzHhAF1+v2bN3jBYQJkvMp739mAv+1CfwZr9u787q906IW2om2THrd3pUJ/F8QV+62CfwFr9sr1ObQk4r0XyA3+sIE/juIC+XnpLu8uRHvAoCR7i32q44evEtg8W7IvazDpfho4IK4CrGaNJbZ7Y1QQJjzoxJhxHHTXgL4HLFm3EDG1A1fOwURRqZN4O95Cb2PkxZDxxsxTwt6E6V3Ku+4kWShoRXwLWTuf+Fmc4MWhFrUjO0zf/YIXJeF4L5my/FUkj3MkKO8YUd2j7lBxR4mlhragQiUqWEGXo4OTJF7lfb5FjDsiWlF7gHXQ+uh/RVZO48ha+cACTHtFEI3+OsVSiZRTrQwCQBet3dEC8U8gOWs2KKEY1wBuGK2px0sfRQ087YNE9aavPS6vQsT+H/W5N6ehcRgAZKVOTHxNA3yHCJM3mLEos+TBK1S25B4N3cxtvNujtZJd5z+4P/d+ThKv2dXS19EA3UcnZjJwRhcnwsIvThjL4AehRv8F4ibu7Z7z8SVz3Udv8B1HJnAv4W4uWOFFVqqbZ3XSkKtOIcaKUPDONsPkHjYyuNauXbkEubogUmNaeY1Zl3nOWStmEIztV+TSCzNxjGQZUm+gqytcxhhbZh4YZIcQG7m+YgD9xSywd+gButdA2xDNI4BBcm6gtXtRJxChYsXXVMTl4QTIwzFYZ/FvAn8Ra9gSzoqClNZFhcjfWCXIUH444rXWoHESsVZXDchc2zVSF9ye/17oPYcvXf8/dz5/Y4eiTmUSICgMNO4BX2MWbonEKE50ToxbjgWH8MaC+Desr4IYD9u3OeIDV2DCAIbeCRx+1VZRNsC16EfTV9HFK5n38De5yXeP0AFjR20N/cjhpbWbYj1q3BR4YLnmoG4CSrZIChI/g4RUEslVzWBCfzXEOEt6m6Ivq4DsUTYgrGf8947160BuTeJAoIJ/L9D4obOvG7vW+4PUhLjtOeCjLk7xgQOPMlmt5nalyjZl9yEvYBt2asrSM/0sbsy6SJ6CbH+N5rkoTQD5+Pf+GupHuoME7KWyVb2rlaUNB6LZVKJwev2DkzgX0A6RdSqNVALrVITLVIGphVQQHRrGiYKk4zTOYPE+xZlBuHcta3u4q5nBiJsTSe9pm4YgvKWP3+h++uPEa3k1qWzBFGUtiFdUT543d41hc2rGi3xLqsQC+mcCfxKOtTw+hcgfcVzfwaGmyxC6gC2sn91XKwwx8gypEVnIS9ETi9A3SwgVApLKRRUCEfqm91WmIA0D7Ha1qLwUYldhYRZFXJBM6b4GWRMaqx/SSY9m/vJYwJ/1qR0U/G6vfNJdDdwo/kBcX+2ItA5Cy9smXaKHEHMtLZ+RQGrJN93BYmD6SPBreGUaZqG1E7r278za+93m/1XJZ7U3vsBsTra0AdLh68ZSdDhffsFafN3glCwXjXS4u4dcnakMYG/ZAL/VVpmeAbHEMvocUWC5BRE+H6G4t06/j2A/zvEom8zPF8ZKfLdFp7z663zt1cQ926h6+Qz+xuza5vcyzYg4RL3/eoVgR6mFxCleavGU21AjA5llPMNiGK63WTWf1FM4C+MsG5VTqJlkjf1PWQz+NJkxqkSj/uMTODvWoGh4DE6EDddB+KabU3WY0NZtiNB13buLNmisZLO+7IE7BmIQBUNFrf9lAEpF1V53Jr73DwpTP2dP5f6rAnnOAIAIx1ubB/uU4iFCMjvdXnO986hhGWISkCVFqUBxNo6A2DJBP5vkHjXPEKKjbtbd35fAQAT+MdgB6aseUUL5ztey5eKldG7yHdAhPFFSDbqdI444CnI81qErFu2XmpTMZiHPP/ErVdjwIahzKHehNYDiGWyjOHB5kqcV7X/OfvqHKQyTKVjk4aAd/y5FWFgM4yrO48JBp9HuCAvorpaVUq12BqZZTXzJYStFlNds3HQRfAGsmh8bTBubdCkwkM3yxKA3TEmVqSxAAnkn4ezyXlSTusYsvllCihcFJ+DhY/LWBVrXug2EJbysok1l8jv1rehBm1JTLFlSuYgm9EsREDOI0z+M8SiaTtanEHuj52TbwAgRzmkRYShBPclmSpiF7J5u5vrN4ilcoHXmJWB/YavPebXdZ7NmnN0DjJHK/tMXs5WpE8RZzxnJguOeJ4DlEym4/P7R7VXBNtoARBB9dEkmyVhy3QMGMfkxp6cI6ykrqb7FuJJ28EvkAWyrFZ8ARnoHRRsCUeWECod8xjzpuzE5A0Ykzf2SWvCbg+AbN5tyPg7hWQ6DxDZ6AomCCxBLFyAjI8jZq5uQdx6jbWToyJjXcE3kHipWyRsKrTkz7hjxOv2dlirsjUxhvwM5ybw+5DxlLcEyi6cTGB+zj+B+3tlyYoxrC37m3vMWfRvRurgAvmUYhtDPZ03qYzWVjtHb1BTxjSVrxcIO660xtPTFHzmT+o+OEp7Yn3LEY9/yfqZnbQEzHFihYCbhHIcj6JEwWNm1BIbXOxGqcV2DNl4blFOGB2VodZ7cS+g8NMpEwaQhAn8WZsswIzlY4jQlctVN8J5O5AYs2kAP5IsLBSOqsguvoS4kacQPt81nn+dn/vB+jEmriEek1lIw4EtSP3KB4urE0M6YyKFzusQJCm4Fu05PQTHa7+K6+HmZi1EeWru1ZaZznuzCKkwYO/9T4glJ8/G+A3FvSg3EMU5VeFl0torlG9csQixRIHf+wXfr9SMKVGLugx1V3eoMmyoCjr/+//2vy5AFmWrUd14WnT6UUHL2WwTVrtxwFqKg6jrjtaY15BF/QKyOYxsZTeB/wIiUJ143d4P5+9/gShop17BTkwFzn2fHQ1x1/XrOE/GNaxAXK82Xu3K6/Yay0R1yiytQ6xpD2qqcg78FXLNh16kzSIzTu9jMUe8nlWIi/oKklzVdLZxIXg/h3ozU8h6DnnWI5WuMYH/Hgy/cOdP1RgpBL4AiTnNJTyYwN+AjCVAwnaK1n+1CVS2pFnu0BuG6zyDCNkTmVVcpyJdBU65vNrW6KfKlNftXfDhr4IuLVNBr1ilVbwD8J6Wm0eH1+0dJ8SAPQez9CBCRlXFz20WdLQf/G3k+0iYwJ83gb8eyVS18YCXaChOy5P+2J8RWsRnrQDS0PW4z/YWMW5cChPfIFn2Q8IQhY4XkD7CWb1+83DfzQflOvRUCjP419OqPjivnYNkhf+PZrj/8DrEGrgZcZmXoRP5Xjm0fr6GKD1F1r1TiBJwjhIeH0/6Gn/2ur2PSYKkCfwZE/hrMZnDGxDL5nbD2emloCD+VxP4b7Ne2wS83zZkJ3MuKMVwMx5tIPoNRky2oTC6xGPeaSZ4c3CTt5tCbfUaqVWvQUqkJCagcGxUNia4QGxDLOrR+L0LyOL8D6S4hEvwC6J8Ra2cX8Ee76OegJuJLcw9B7bEcoSiWuDzWYVY77Ke0R4Y3jBO65uJqVvndXt9E/jnSKmp6iW3VbtBKIBWoQjYcXhZcSZ0WdZBa5sJ/E8Zz3UN4uYFZMxZL9Upj3OF0ZWyb5D9oU5l6A7yXGeQsp9x/diCjJs+Q1fqtrK/hozfc4TJUoDEqi5j9E5uTWHL1CyYmut/mrCm76WXozYpn/NvCNuyqve1YmZM4C973d4pXaAfKjquzbZbBbDHxIg2ZLg+CjiRViEup9T7yuD2b5CJXme9xheQBXIJwMe4F9Dq8xqSLPO5IoFyHdQ2bZwa6Nbyur09E/iHVW/oSXGq3ACqimMZICw4Pk630SvIBjyPjBZbvK9jjaumtewFf73DsJXxCsCGCfzbIu5qBrN/4s95NqYViAByFBdmQAE3qx9u3HGnIPNjCqL8VGVJt+MnT//hY4hQM8BwFYALAH9UcTEcN5UnJUTOMeAznc64jxtgKSUT+GdjMnzYZxDNUziFc4851p9D4oALJ7pRMdyChBPUer/JLmSsPWiJWgNbEKvzEve3A6tYGqk1uwDZ7+b43favvobU3dXs+4qZAfDaBP7HChcuQDRCdxOcOJN9VVDA2YRoUFVlxVvXzcBI149UYSPFIlMlNrg9zTVkx0EH1bm4ziGL8hTYhxwUKIHx9IelkPwSsvnuVLGQemFJjdkxB1pfQtaFtsbX3iKsKxm9xlUwu9sEfqai5VJw/duAbFKbqDbBYgGhdWcJFSl/Xrd3bAL/GsBt1nygYP2fIQlrqfePa9sqRHioTAiz7l9PqlV0eC1lSlLlEZ7PEZaXGpcV+TvkOWfN6w3IOFgygX/kSemrDci4y1NJwZYrW0TNwjtwP4fGafGzXil7H3cZ27vB319B5uec1+19N4H/A7Ju9Md4jSNDr982Wt6hx7Zbq9qk/gWSnTcL3LeKeqpsgoPbBH5V3WhczTZWcKGVozOuYGhPSqwcpH0+bmoDiJs7VlgpmmnndXvnJvD/gAiTv/F7rYIQJ/criFDzHdJx5BXCOKuqhIBxbnCWH5DyOVUql0PQ4mIzZn8UEb49yZz/hPi6dfaa7wDcMth+A2K16I984SGHkI2s6pJpFxDX7xQqdgEXFKzzzp/fIJazfQD/GveCosl/jMd8z5+/gIWfTU2Fmb1u78wE/p+QBL6xhGpwfcvzWWwx7UuEhhlbSWENYThFErbE02Ms7bcP8UTY+3IByJppAv8UYtjo839X/N+kFpXfRKhUHEKUzjmIQtGa5L7O//6//a8zTcb1UAt9Dol5e3SliJzMzmsAn6p6+MzqvYnb9Kmd2e5F38Zs2SoFLRDvIItAVlHluPfPgMWn6/68TGSyQf2fIYLR3yCL1z9Pwv1uEiejEiiRMZtx7FmIsnLrZNc3mm2eByooi8jox8310sY0/mgyc9YE/v8TIuwced3e/xH5n01Se8XX7Hg5SqU4oTCAuE1tZvWBN2IW+WOAIRYbaFnrRu5z25CY+bE9J+4bsQXRbb7ApOds8Jm/gBgrdgD8hf/aLxP+kON8i2BTiyLySqOCJHFbfp08tthKWuPOIS6myrQIWuQ6XLSvIseewXAHi0kQbqYQZkkvongR9ueQgOw1sFBzjdjamjZZ7RPEQnnXgvk0CZxA5vwtKm6xFlGu9iEKRmtdQw62nMw80ms8Wrel/blJgeIrRKkaul7G6tne6FuQZ71opOVrVkjOqQn8XbAuLD0ZrpXpAaamzjZthEp2Gy1s65B9Zx2Ragl14qUUROf/ipRm6kD2j6s2GQTcZ05l8hayv1c+1qmM22z8KRQICcjbv7Y0Odyt5wgz7yZag0iCpvcpZHeeKMpLyEJ9AqfritftXXBBnka9STeVQUvSDmTjKLP530S+1wYFlq+RP1+ZwJ8ygT836ZpwUbgAvYGM78yWmrx/oxTKz4VXsE96w9xCFu8sS+MZQgG86fCh7xCLY9Q74sbI/4LMyWPkjJN2QxK8jB70dItbL8E1YgSZNC+OUhkHkOfQGmtpCdxchI9lFJO6xxr3yc+QxLI6wrlszsEUCoY/dgaD+lzuEXfr1yasjk7m80VTmzyzyzYg7qDKApRNWPy39W68cUCLyFWZYP2Kzv87xD0w1GFlhONNYYyxXGVhqR6bYV1LbNtjhxaHOWsRoWtrCeLebZUQFI2DpGt6A7K+HTt/s5bGVUjpncrXfyc8Zg4xIT3O2BwAKCUgPFU4Bm3R91rDKaiQPoMkmfTrPFfKNaxDPFx3kLFS6DM/lrHGZzFT1Do7U3M9qFkMu1svgPt4mqhrti6eQawm5ybw/3tDG/Ni5HtV/ER8vcPcUJNqXWsmIwWWb4toX3GbFS0XN3ULmNzUrKW/UD1PPoNbV9nhQv4KYvFstIuKCfxtyKayk6CQ2fp4d2jQWlY0eatNcOOygmQH4nWwLUJzdYkxYaHu85rj1t5BkmJsHOMWZH2fBdciV6HIE9vHjXgOEgdWJAFvgHQrdx0VJIaIlHT63pQQYdegKpSPSGxuByVKXRVkE7KGLJvAP06IgVyGfL5avG1et3doAv8Ssl+UEZ5rH2vjgOOn8BiaqnOTooCyCzGBHwGACfznEGvluKrkb/Pr5ZjOF8cOZJGtdEJ63d6V1+3tlbW40pL3FsBbk6M7xrigJeMNpGtP6W4bTPT4jcfp8G8bJvD/bqQlYmVwHn2DxOnl3sgpNL7Fw89qFY9Gu6jQu7DJ69mIe40nNT2/e93efS9jI917qugmk/c63wH4G8dOYzCO+RkF8FLwHlolqog173cA/zcA/8FJgKmDe6XJhJ1aplBSqTVh7dANyFjL9R4T+C+znjetXD8hnrHKLLxmuOOTLek0DxGImuItgN+57g1hAn/OPOy4k8YdwrCzWqpjmMBfdZ7fGcSid54gSM5DBPbnVDxqwZOOgGWVgUNIWMf9WON68NYE/l/NI+8sWHvMZIzJuvZuLBH2eK6xdulwocUsdlPgQjrdUOJR5doThVKb1dev+vgFsWNsG6FCswpxZbw3gT/wIj2ay8JN9TqPhddIrbh5yNh0W8u5z2Ofv5da3CgEvoRsCoXK77h4YamNBeQM/Oe53wHomMDfqcuSwHN1IELIBsKOR03Gba0iLAV2MYLL/ysSslRTuANbHgLom8D/r3kVTa5Dq5CexVnCg+1gcwTx/MxDxn5Za+gtwm41eQWXbUi8+CoTNxPHd5VZzxTI3gOYMoH/lfeqtpJOBZmPfAcwVFHk1kgHpMwx5YV1bqfLCldpXk8T1uaFkSYDWYlFdxBhs4PxNnHIDT9rNEZ7FqFhYBkVJxy2idqFyRh+QTKmck06ToSbEdywuxD3UeseIhfw3yCbbuFyOKPCjPCvkASpqtyTm5DFbA4li8MyA/4OjuuXQsM6ZCzkvU97kE3vEtJ3+ZJ/ewMRLiux3lCQ/A0SNhL7HGmpWoYIibbcyZ0n9TmBiEufC/4o8bUrGA6vOONcmioq3Hnd3vcRrqNubND8DGSeN525fYFw4xsSjHj/O16ObiRpWaopfIYI/e8h9+Q5HiaKJfECMk5WkdDByrm2e+XYSLY1MEJyoSe1Qz+imPB8DhnjF2M2Eswj3DcXIM0o7iDJSJVDa9YLniertNJ3yHrXj/zdKtXTyJfkBeB+DJZRZDuQMThrpD5o3N7iXkNmWIPX7V2bsL5sWxsqPMDr9q6M1IacxeQkA5ai1gScUXECYgGp0fiosmTp1vyNv9ZqwclxLdOQxWYGIvTYPrVFj7MKEZaOvQrrhtKaZ4Ww3GOBltI3kAXrEy1ti5CN6KiKhYmWuL/w1wf18Li4/p2/nkLu8RzEYliLJYOKymvIov0dsgna0JKfVVprEs4/D4lvqtVS4wS9lwqarwPrAnWFHI65zPtP4WE27flQedmGKCPRsTYLcXXbGrO5rIUM+1iD1GnN3fudn3UJDfQhbypGlorhNKQUUer5ucYvQtbDwtfKsLB1/vqhpJfCWu+vx2Gw4Bj8nb8m1ge1YT117uvc1xbAFo82nKtC40nlMPRpDbIHt/Y6o7RdmLTm+QFEEGhVVmMVcHDP5LFWxLy3AxFKUpOZKOzYjSIuSWUGYfeYBdBt0yaLlJOQci8UFnjvDCQrujZBg2N1DuwLHvN/Wwdzx5MM1zoT3+Kuz3YW6aBEoXCOtS3Ifay8UO4oUHDNbBXYJHnuf2QT3k0KE4ko2UnHKlRDOO9a0jScy7bJxU7kfxuQ8VlKKec4mqvYLf43MJ40h2Ux6ZpeQiyw42xVOBKM27S1P5ssrG8rbBxCFPk3/FelzRKqxExQswWXJtzcuaG78wayUYxVkOTCflu35jui5vEKIqAM1ZmMwQoyGwD+iPn/NsSydoIw3uzeYseNcBmyIDayYXvd3gndHLdFF6dxXHPWBsSNb8f5fawbNt0tn/hzmbm0CiZHMBawNRrzJLi9nPvfSbHE2PaoHaS7/qwr3U2SiJ6v0Jj3hpN+GoGC4ivIPUjq7rMGJrkYyfq17vYV0HNhAv8mahE3Ga01KbT9R0j1hE9edT2Qbc2+UgIVx/bECBSWCu/fqExFvltaqzBB9uANtLM4fSKtFiaBZkrWOBbRGy4sbS03krcUzXXke5Q5SGwpAPwzHgZdv+Y5lpA/BqtyvOHSOdaNewdx55VxIc3xGDeQkh53JmzveQexIrZ50UnESFeQZYhl4BwoJ0TScn4LETRsQdtWh5vwGS5CMkRtaESmS7Jusu4/QzBs0kPiuud1e5dG+kmPXSkpA93yK5AkyLR7sATxjADJXbBOwdhpDI/Da4QCwowJ/IWIF8btad2POe4WRGECcgpvJvDf8np/priPP2NyupA9Rr6CyYNc379CrNd11Dxdg6w3R6OEeDEsYOJah7ZSmDSB/xqymPysO+YqAVuiZQY5K8GPw5Uaw0+EHXAS8bq9XRP4x0gWJnf4P9uLM2rVuIEIk21yIy4hfE6LKJdFuQz5XLM81gVkQ1nh/08wprqJHD9TVcQPOS5pQDTcUhtZNMwEbFNZp1BGIXgBotzcQayh5wVjvV7zGB2EAsY5JqA7R97nz/ivSall9wIyp24AfEh53SnkOQ2QMO9oqXvQLpUC9kfInH4OADQE2Pt5ABFC+ynn/g7xfGRa1RyFBZC1KG18rqB8Zy9lBPj8r5zfKxHqGdu8DBEc7b66CpEX1hAaZ54MrRMmOUltra5lNFNqwU76qzzuIhPWaxwYKTCdaQHi51yHxMKUElg4UXLFr6W5AqmlpbnJv0OErTa5E22h7MSNJwfHkI3gBuFnO4NY4m4xps8byer/NqoLmYLGIbjY5Tj/GmQDPonEdQ2VLarbskeB2hWCbfeqdRP4f5Q4/zwkhOMMMRnKPN8KJD54YuKxud78z5Dx8p/GoXCbEk0EYkgqEzMNsehce93eVxM2ACj0vGnZdd/Tcf6X2lrT6/aOjJTAuuM1LULmxLknFRcWeX03fP2tkZa1i0gXEjcgwgVM4J8WURad5JtKOmoplfIaYumeh5TKAmQcdJBDaeUYf8v3x2a8M/TiDcTQ87XtXojWCZOcpPsQy0K/oWu4QzEz8yzCOoGzyFc9fguyUAxM4P/ZtAsuDV5bq0or0QKcO+s04Rg3iLjtudg/sHzUzDTCje9+TlLYKRT/RsHwQYxmBis8/wrE2g0e48hESjTVCYWBM4Q1LW3tw8uk+ZGQdGFLpGyA8YUJwuJLiDCwZgL/C0TwvEnauCn0r0OE7ibnwzrCsla5y6yVxUl+i1r78rz3LULPQdJ4XAddzBTongNYMIG/nzfZiwK2rX35HfLMEwVfPssbd4OOeJXeQiyqfcbtbwO4M9Kz+Zav7yN7jzqHjMMbFPfsrDjfWylMOpUFrpMSxh4pV5Bxfb+uUCDMawiwWeaAKP1x71vm66bRPmPOA1onTAJA3gUkC24005AWXXVK9SegJl3AqmQH4Q3aHQys1IzX7V2YwP8JcXPbTlHzkMLfMIH/JY9FyEgh4Bf8+baAxcrOtwevL+heHhkvUpbGBP4JEixUSUkX3OyPTeBfQ9zkSZ/BCg93EIFmncc6TxAW3VqM41Y4XPYgilThNncUorYgFre8FSRKudNpfbGu4EGKUmRd27aNW2zx7QzeQFyMc163l9p+0gT+M4iAZ13bccwjVPKSGgtk4nV7Z7Sql1njdyDKQisFSbKBcN6c5VU0OHdvm8iJqIhvkBJepZRsKs77kHHWT3jZEUTgvJ6EJMNWCpNVQE3VBt/fQEoDFD3GPHL06eRCkWvCU5Ob8rq9Pq0wNzULuo1B4QYFhJoni/cwG3wG4cY1i3xa6S1CxSS3FYSC00hW3rrImHtu0sWDz8vPFSssOBYz2+Z0nse6RbJn4ZLvaXRhp1D2zyXfbhNNbOeYPAWjbROBu+jmyXXWJrF9dY/HcIufEIthooHAk+YJ9wKXCXxrWS6yZlshNI9XKI+wuguxav+CKCO3yBn2FKXs+u5ld4VpAxeQeZPb8srSVu8hjSQ+Ami1Zy4OPtORvDVZRjOOtVauy3G0us7kKNBF+B5h8d6idfVsLbdbSDHkkQd75JqSOgM8CkxYLByQ+1/LZ6VbdxAjjLUCWmc2Idd4wL/NI4zHSgynoGU9riVp2vlm+Z5SMYDMcL+eFAWnZCiAW0T+xFqyqOgN0j57nHt0kjBhkfcLr9v7UsHxthDGuX5uyoLCZzeb04I/hzBcYSyWMY7TF5CxU2m9SI7nlxDl81eW8aNq8sybyOtXAfwPEKvrNwD/mKSY5ccI95opSOmswmubCfz5R2uZpBn5IyRxoMzksvfGtqCqQnPaBvBXiDVkD2PKFG4I935lDk4GuF8VeVYxbt023s9VcLM1gX/Ja1yHuC8WTOD33c9shttG9ouerOiiTMuSdbVb998ZJkQjLmspMoH/CxGLWU4r3URveoyDTbVIUpGegnTgyJq7Rwh7c9cmSHKzW4Nsdg8UR36eXOendbVQeEAFrEHGG0zgH+WJuTVhwfIrSGWTpGexhDC+MlfSXZV4UnKnQ9f1RdacpKX7n8BQg0mfU5MO917rxb1FCS8ugMVHK0wCI5cwOYAIRJcUTGchN/xihMy6OcjmNUC5BzYxUDj5Yn9Oe61j3bhhgHtezcgKYWX6GI8LW5/RxoQBYRb5RYzwvAFROgonOxTFOC3++KzKxKolHXsTIkjvtTHMwcvI7o2DWcdTTWx+psIONRmCpM1iBmROpd4jCg7j6JS1AVHwNzDGEk985rZG5ihz8RQiUEbrY0bPtwh5zkeQ+TOHcN9Iet8FZJ3pYAz1LLkXvoSsZ784Hl/weq+R0dsduA99at268BihJf4lZO2I68J0DZF1Oijvuj961MLkKHDBdYXGTYjWt0zNsozwsgcu0GU3BGqrk9L1I2/Gqx2H0S4FsRi2IqTA+gniYmmldutJ/bsPkGu849/OkVxvz36OO9QkIJvAf4OHRaE7kPiwdffvdGE9g1hK9yPHse03z2PG8xaPuYFHsGlQqPgNwLQJ/O9JAjKt5TMQYWAKsogDYllyLdDrCAsc57GSvQTLGKFe4e0G4cbSpjl1CBHGxq2Ev4Cs+1k1MlPhep36fo6xN5B7Pw0ZQ9Zjk7jJc1x9LntteeF8B0RoXEBYccUKskDJZC2lVtYgBoJ5E/gH0bFEY9kHlPfiwuv27lSYzM8pZBLZVmaFYWbfAMCcKdGbmRa8FxAL3pcCwlrb2YVoRBdZ94QWr20K9L/qtNxVRUSIWIBYfs7i4iW9sG3kXXRiM9boGaS4fKmiuNwQlvhrByKYDJzYsehx1zFcJ89VYmxzgTgB5wAyXx6LBd6W6ADEUvRAmDRhRyVA7u0twnu9hGGL2irCOpp5hMm83a5Gwuv2rp3woNY0KaA3qImsZjfjv25sh6lphOW4cgmJtGguQ0ITisQPz0LWo8uM+G033v8nZL2+RmjJ+gUJj5nU7OzHjPWE2Wf2gCpyQlSYzAktEf8Y5RickK7mmbsEEoWotxBh4gtyWvEmAQ7kfs6XL0e+l4auwxWI5j8uS+8GhrXEB5M4RUC2LeHWUbLDAuObdsB4wRzCuJutGV2IrHAVNxYPeI6XJvCLlMwaG0USnDzprf0TIsz16SGI1q+0/bKnIPfrHGF91ujn34NYb+8FTFqm1iCW3qii+BMieObO7qWgMAdRXHIrruNO4Gg5O5B7PrQ+8FndJd1Xri2FMrk5Nz8DmClhKHgNGXezKGa53oDM0yUq6Gktd+/nu9ftDbWc5Dpmy5pN8biXbQhxYSznBkTQbnt2fOV4Y+rvrsLkeLmDbMzWalEEGw+zC3GZtW5zHhN7qC5uahOyoQ9M4H8Y0yZqa4edldAGDyAbxkif3ZMC37kshh7LtvDn6Mb4HSLUxy3QeYryNoYZrlF5nWfTs4kfdGW/hoybT3YDprvoE6S3thU+YjOmaQmO9rnfhgiTAxOpTchzZCqfFGLmIVaId5Dn0EeN7d14P67aGmoyCnwGQ2PXhEXcr410PLuL/H8Zcu+vGYt8g7Bjyve0+0Sr4gPLoglLyiVZHa8g862op+YEolBfxp3Xua4zE/h7yLf+bPILXFfHZuFmFZE5AIfO/NmGCNnbaH+ppYlFhckxQs3zE8ppngdg8PYjcm8XhpvwA1cKF9tXEKH7R07BcOB8H0upFyoBH0Z479iFsiTrCjfFfsL/brj5pBXlzQ1dyJ0KLchujcqiQpCt8LCMSG3DJGHAYsI2qmcx89i+L7b0kJHamAuQ8Z0k/NpEiHOMIY7NSZ67o+AwUfUC48hR6sYmqM1CxsL9Z6bF+u8QReUnXzuDsHj7MgrOByoI7wHMmsD/6cWXQfuKEkW0uZ7mKsCflnhqAt/G9O4gnA+3GDE8wCmpdA0Z07OIVBkwYRce26kIkOdi3fZHEOH2QTIZ37sI8QZM/NhNgvfxdtTkvYRjLwHYVmFyDHCBmfG6vdOszSYJDoJaSz7kjeNk3N8mJHavMk2P92m25DGXEMan5SqP4XV7BybwLyFlTR7tQlI3jNfahoyHe4tn2uZT8PhzkM20kt7lwH1i1Ef+XGg+elJe5yVkHv8PLDO0k1PJew7ZdDcA/BE57r6RRgYPBAIKoa5AkiRMzjjfbYa+WmNywrXtLYBbWh3jlNI+mPkaY2Wcgih8x/yy9/4EIuCUeRbTCGNl5xFjGfSk3NW1CfzXfM2PMRsd7luyet3eNxP4FxDhZdR1dR2yrs9A7u0Vz+Na6Tf4Otuh6A7OHpsRb2vjvh+UQ2MYzCqkHFUjc8hU0CmIsfYvIZbyTzUIlBsApM4kTzYPiaHSTbVCqBG8g2yEO95wD+HWwAzfpZzXuA2ZgNGM4FHOP+p9SotPS+QJhwtUyRbEYjaPehJuphBa12zcllsf7aSM4OoKkSbwX0Gu/2fOTfgQMv7/DUS4u0K+/vVuG9W4a4o9htft3dLSuwDggJ99BeLOc4VPG1t5yr+XSlCj8Grd5F9SLF6nkI3qBC1sC5u3nJPz/C8gY20GYgkcQJ7vuR0vFDBjwwYYFvIdIsy5a2Nqi8c0aOW3Vs60cT6DMJZ8BRnjkYLzVEVr4C7PuQ+kxn0XxZZUukKYBBd9ltZbcQWx0LphJkOYh00HbLx3nPV+k+fbQAMKmSMEjlomLmpJLxV2QKX+JkZGPAIwN2PCmlGALAaJGV3KyLSybALdKG7WaZZAcIYwi7cVcIEfuaNHHXCObUI2+Nbcswo5RoLFJA8cfzNJG74nJaC+QzY+9xwbPG/WJpt1/lmERZ9txYYsdiCL80dIjFZc6IUtobJvLVxet7dnpN94pmvdSLeaGbArhSswm8C3gt4cnNhLCjy209IcpFd1mY1wHqE1bBHx1tIpAP8W8hzu+PrWVFcwCeWceF9tVYSdyPM/h6x/txx3byBr4iVyZlaXneNGykXZmOboMa2lM+281ybwDyHPLtUzw7Hxjj9HBd/CFInDLnjcC9ANz+c5HRWqvG7v1AT+nwgTomKFJRPflMG27ox7ZrYc1ViLwDsMIt/LYteNy7Lxq044yxUiyTwcOyczkLiGG8ii1drahVwANiGaeL/hy8kNtcovoJvb/p0LWBVugJHxwh66y0gJ8qcbegkyufJ0xyhyDbH3qUlMgRZtGTyD3NtVjFgRoI14JQqAR3gNsYofeAnlSRLGhBViRxov3IRtJ5dcn4Nz5gdYHoX34B5a2m0c2RsjGfR9r9s7yzOeONde8Ne4agcXkDGVZIGZgggLUybw972MPsAxnPOc00gWYhYRJhLeRDd5uujWIcJwrPWLm9QMgN0a1kK3nJNbUmkVYpFag4QnuM//MPJ8xmJtZdzZc/58mybc8b4uQ+5rVLCKLTPF8ThIcNu3zsjB8b8FiS0+BO4NBrGx8Dlj5B80ZaBwFTvnOWeKzpvK8KRM3FeIkJxLSTOBPx29F2mW9ALY+TObFA4344VJIWmZYm1gA/KBNlBBQH8ZOCHXIBam3AIGX3v/esd8fUPzdaMCJTW+GchmlzZo30AW53mP/YwrOvcdLS9D96lqaAHbgMRI5tHEbdD7/WbM67WJPt9zLmJ243+yiVMZ2MV9zQT+cd6F02rEVVyAV6JfMp/90Pto8dmGCLhXEGHqGrJufC/grrLJCzZOLHru7ybwZ+pas7lZZG1A1op3jPhyNM8Qxro9ECYdgQGQezSKdXkBtCLZUAFvuJyTazXbB2N87R9Snv9PiAJdd/3EG4SCa9YztX24p5CjDBBDIt5AqgR89rq9a96bLxBLXyuU9whbGG4SUoVQ/6ApQ9spEitJy/ZzE/gXXrdXtZduFzIuE8uMzQD3NaIat5C5ULNehRSyvoQIkJtotgjyc8gAX0fODLgE5vjdLrRN3/tnkHt9ZwL/z5SJe4sRYi6imOESG3UEBkfZQNiq8GNaHJV1vfJXN1HNxooCxRJ9jtD8c34AN+HOKAHeFfADYoVbAvCeG15r3KUWWtGWIS0ik+LMtvmaZYgV+gSy+c+jQNUAWupTi4enCZI0EnxBeTd3nmu8Q3oP9xOkb97X/JrB6IrWC8i6ugDHDefF9/E+R86QGH7G2oUPCnf2eWeFQJyjmIA7izA5ZQYMsfBSYoM51tchIRpN7LknkM9YqEZqGryvtZXIagG2FNtCkvWwLFScU0Mg25zNvQ0ZzFbAGdWVVgU3ke9lOYBsKlctsQbba8gqHWA3p6osbHbwjxQYXIDcJSvoxvwGuUZ33LldHnJr9DktmLVBwX0B4hq75d8WEMZNVZIlXQZPkhb2EW56D4qg07py27CQaa1o60hO8joFs605l/YB7PNe3+YQFO4Zdcx4IyTglIFjbBEyxm486dK0awJ/ygT+QnTdcLxiMxh9LzqHCJMTa/3Puxd4ki09VcCjdQy5v6lZwcxeXoAIDeuQNbmJFpa5YkSVB+xD5Iq49ra102Zh8i7yvXE8CdY+xogLNBeBxmIxoniSFGDdcmmvu0OOxZqu4GWI0NZJsYwcQISHS1piNvh7pfGYtDK+hmw2PyHabuZG7cXUtOQ9iBabbjUmrMEJiKBmXXq1xkox4H0JEpcWu4kxVgwQ5XEGwNeo0OGEhQxoUW5KAetDYiATlVpPygYdR8dvhQpYK4kZYz+df7+FdHw6jInpmwLb9JkRql1wbd4vKoDzupchisxB2XWHIVBbkI18HELQwIR1R7+nKYKOUpMI1+xn/NXuT2topoXlRMNQl03IPjM2gZiKauFwnaporTBJAeccYr1rTcmJx7opVPy5XkPc5tsAfpiEQrtc+HeAe6HCLmY3qFYrnUWYrT4/zgneEgYQRWEOTkwqLYJfIQJ/pVZJE7ZUA8TKEZftvA7gf4IsvF8hzzxOGHDLd4wsAJvA/ztEKPyXIuPeWtoSjjkFiUFv1XoVxVH0zqoUymllvERYXsclLlzE4j7TkZ5tCUFyAcBfIELwd8g8cTPmO2CSUw6L8hZE+Foz0sO+EiMIryGuGHm07mjh+ctjr0Pm3Ankuc1DxsYJxmyRjGZrO0lJZzFKSJvZgqwvqybwT9q8HlRJq/s7e5L52AY3sFKMaEmDPJPpGmG7yUrbsnFxOoQINGMLlTCBv0SLRZH3dLioVgYXs88APkQtP163d16He5ub6SHkWSbd82nI5nUOee7HiBE6ec0/IVbLkcYG3eW/QxSd30Y5VoR3AH4zgb+ZcN4lE/gb3MCTrm2+6HgpwUtIfOG7Gs77BTFjDBJXuQ8qji5c379BLCrjdqfOI/R8zeChZ2YbImi+S3tuxArQl0hZ70zgL5jAf0+rffR/2ybw/8JwAcsryLh67r6W920fElbRz7i2JFYhCvxLiJL9BcAfdcXYpkFl7DfIZ13ln9cghoD1HPe/Tdg1rBF3c1N0BoMn81mVGKoO1OUxpyCWwBsUSO6gEJWYcNAG6MKYzcqANIG/DbG43QD4mOce8769h2xsaS3zHgXcIN5APu+3UQXFAuf8nyHj879gBNdm5Jh/g1jWjrxIZrCRWoX/DrLpf/BiyvQYKX32AiLcfEyysjnWsquk+FEKzK8hgvxX10rGa/k3PM9/ggj0LyDWqY9VWdQmAd7LbYjwdxQdfyasS3gHIC0x0b7+vjJFymteQoQ4QAQ399n8HTKGzr1u7yv/9jtkrK5CEot+VrVeG+kh/htYO7Dq+edUNjiLUTCir52FKHmACMl3kLXhGUTJ+O95lF4+gzeQ+/it6F5SMBa19uNMEq11cyv14gouTL6oLJu3bAZk00kqWZiwK8iUCfw9L73rylTkex5mENbzWkCJ+oncIBchVRBG7Ytrs2MfxABGzvcWYuVJjd2KwmOWjj+11ooimytf+3+YsN/xppGC1qWts0zW+g657/2YlyxBBJZFJCeZ2bXY7fYTxyaye2Ev8ji28LhbbmsXskFfQJ6tvXe233jhMWNq7PtbJ152+aM9yL27zPPZcq5fR5D7HtcLeh8iNPadv/2EjNNjhB1mqkqqsn2sD2tS5F5DxuuJySjv40mtz++QUJxDyJpyBRmTp4gkvXH+TsXcc9uJCwhrIueCXoVthilkllxK46kJksAYhEk+dNuqqnS/UFp6plFPcdvGcO6PtUbVbp0hsxgWXJosDTNJDG30jOu5z8qnhr0BWfiuIEJd3nIwV0aKW8+hhOuK1/I/QebJJ4wuqL1DKJQkxSxNQ8bPHIDXrLyQa37SgrYBsQoVEpyN1Ch8CxGqvpSwZs8gLNG1hBIxZy4URpOO8QMMsUiJ1z2ACHJVVHg4gny2ZQBvmZjS53XaEjR2jNmycKXOy7jX50joEMO4xFXIZ29tU4w4OG8rja+m0v4x4X8Pekh70oXnI8QVfYWKQoBsLCaPV2lYDY8/BREAtyB1fQf8+zx/f7BGcA2wXYr2IIKoLcF3LxQ6a9O8Cfxf3nDDgHOIIcMKoUWwcfWLqa+qGN6TOUhb2IlSyFzGYZl0+4XmbVU2BDcdG4t0hYaKltfEPIbvz1iyvL1u79JIOZYZNF9yaSLwpD/yF4ib+8SELaZuaSUaQLT9FQBrXreX2u3GyaC8tq7PLHdQBiuQebKGasaRXdgShUNPsvD3IG317iCfJ29G4TPI+N+iEFpEoFuAbBhTkIW4kCBES8geSgruBc+VWVydYyfzOrxub5+JLrEbMl9zA0l8+wtkfq+5x44ZY5nnTcGW95pPcO29hAgtC4gImxREO16Bjma0gib2Xq4ChhzcVh1mQkv/C8iz+5n1egvDGXK1cixwzCxrei4oNG5B7lfUU3MMUbD2+Fob+vOgJV/M9aUpZx2E1sf70m0mLIJ/GhEw87KLMffhdrxdVrhvTZWXotSegENL2yFEiCy7UV5BNos7PD4L2iVEg7rEmOtqed3evtft/arCvWwCf8sE/mta5h4tXrd36QSox2UZX0a+p7EOUSC2uBCOik0y+oXRLW02aec7MhY4biK/IHO0yFg6hQjAy5CWg0XuwTHk8ya26svC6/YOvG7vZ5tjdOPwur3TuHhJI/Uc55w/7UHW3dRiwyOyB3kOPxKEWzcx5R4nU/eZk3CRCjfe95DC9mvlLzn1HDZ29XVFc9LF9mpfjTynRuC8uYTMvbL3cw1sBkErtD32HWT9+OoImXZvGMmIxWP/gIw7d22yTQVexL0vx3EvuR6MO1Y9U2mfBMYSM+mNmNZPYedDHckiVVLm+vj6keIz6sZJlJiDbBrnkf/PIizofINkl2iRc9pF4dyrqHVjDexBFJ1Lu5F60u0mqcRNFJvFfIMK3Fe0Nv83yILaB+4tB3PIGfcVOd4N8lv8PkMK2udW9mhlu4KEeQxQYDHl/W5FuRAKRquQ+qhNunLfAZijW3vfq7nws+PiTizd4nV7PxlfHB1HbvvAvGO/g9AlW9feZedtofGYkxMAf+XPlVoaR2AT9A6gnIfqAuG9GnrGMevHDmS9HLl6RIK1/75hwKjHHxf0dn2GeLsaaRpRFZrNHYGC0xokhqhIX8wlSMDxFYAvdQu9Jiy2e163ZSWSadf3pN6eFfiWIXE+LxDGxZaeFE4JiBeIyXqktWAb4sposrVmblKCxWvJpo8c/y3ElXUMsR7G1axrFFo07uq8LmdeX7px21Ukj5jA/ytEyDn3ur2vzhg9K+LCHYVIRvlxETfqCOd8DZn/g6yQjoT323a+N87fbCb6FcSqNYi8ZwlM0qhr3vD5Daoej7RG2nJUB163Z92/C2ioRzYVgi3I/czlYqWFeNbOI+5F08C9J1J5gjypbG4KPwsQgSjJcmSzJYt221hCGMtx3/+0Rl5AXITXSAjorgrGl/URZtpZXoHt7zyWshgFCq02fsS6bKNZj1uQe72UlSHYBhzX3HRc1vAYrt8tGG2D1vfzbhxlMVIrbxXS2zfVWufFJOVRaHgJSWB6YLnn8a2FIg9bkLk9sElCTgzXGdJ7TGdxDpmL9nNsYniMVlXAehUyBw+ix2QM3AFkUx9X3NUexCpVSghKWFuXIeEjCxC36FXkPYmxdBRO8/S2zrquuqzL1xClbo7frYBpW5r+8qSD0jREmK3d7UmFPLdSbsIqCDPOOjLDv3WYDFdJA4yEGNzotaxAFMRxtg3dgIzNvXE8o0lhSJjkZFyHWH0eVacXE7b7sq6SpCQBK2TaLMe89HncqzFrZ6NYVHIHwFtrZIRLyKJf1ViZQ1j3bhVSUy26MZ6A2bdtFySJ23d4HhW4ePJAC88VJIRiGbJ5WevyOOK1XiBMkCmjaKxAxsKyCfyZiPVqCWzdRwE9jzAT157VDeIvjdft/TCBP+0oqCcIu8xUJUjOQIRry17k/1YJvo3+r+S5bKJIYigBN/AfzvtsMsb1CF6DPihEpgkIRuplLoL1WCmUvef/vnqSBb0A6Up0wr+v8bobSTjkepVmMR7wmt/y589NWvp4v6bxsL2tDTXYpBX3FGHMeCUGKsManya+Bae7ny8hvUxWpXCc2YLztyipuFFheM1fv1eRtzBOuEZYw88Xr9u7jj74Z5BFfB3AH2O+vrqxsWkPtF0Xr9s7ZLbkTZHByc2udteSwy+wBRYtrhtILz0yhBMADxP4t3nfF+ErEty3JTmDuMxt3MuDhalIHBgtnbd1LTJGsrmnINa3pMzaSxP4u5DPMha3vGNxu/a6vY/geDeB/w2y+FZ2Hc44Oo0oHGeQtaSs664PmasXMRYsd7zleraMZb2ECCn2Pbug8lzyGt3j3zo/P4jn4uaXWH/O2RynIAJS9DPbmLShTi0m7Alty5lUkVS5BpZJMdJnPK+yuMEvmMA/z2Mt4vU/hwj21xDB5bvz/1lILO6p87dphGVc3LXCCjSzJvBvIUJZx0i5rRswMYPrXSvi6jwp1fQZ4uY+owBnE/ps6Z5EOP9uanDJLyBMZLHdrCznkPtvQ61uIftRp4CnIIulyPco7yDjpoPxZkAvIBxzoxhRlhAqsYsYYwZ5HBxH68hfpm0B4f68COCBMHkd+V4ZdEvdNRVkSjeQnbSpn28SrLKeUxicsUu2QHFeobBMAHz0GgYolr2b53h7JvCPIIJI6QlmwqzMaxP4n6q2YtLy5yYd9ZNeO664OQc7r6fdmEzGAMf1yJ6BCJ/nJTYD2/Jsg4kW9lw/7Ll5r7YhddT6aQczYb9gq5xtmcBfc61JFNA/QTav3C7JmBCDK4whiSfiyvySsPEvINw4VxAZT3TLf4Iob66gaXtCA/JZRi5WD9ko1yDPoIiQYp9FkcStNbAeJuT6pxHWGtyCxBieuKEZniQt7MEpa0NBzAo0x2a4PaQ1JNg1YOjauDd1SirUIxMZw8eQ+ZS5V5owAWpAy1yV1i1bPWUKw/vDEsJxOsXXnZVYN7LYgawDSYqvVc5vIZax3GPeUWCubNxqzvetA/iPYAvLEWWZM4QJmIWOQ0F/GTFdm0bgGcRjNYd8yvUp5P53QJljSJj0ur09E/gn4OAxYWFg+8BKDVZOVuuWqiymIuY8HYTayk5MXFFR13XW+ebgFGRtELsA5V4MqRF/QAXtC03FSSScIGndZfJgXZizkEWvajfCNY85herc/FWxA9nY8/aG3YIsJi+MZIN/L/A8DyEL0BnECjRtx5NzjC2EnSn6GcdbQ+hGOocIDGsm8M/cccrxu2QCf8XdyGi1eouwnVobEgLmEFoM5zBsWZzm3y4g93AKMUoUN5C4jh4XYGIRZEw+Y5xmacGIws2HEu87NYH/JzJaCkaw3U1stxkbS7gMUUA2IRtutJh3XIFvV+G4ofFgxm76FMaHkkSo6Ni9aRAZS1aoG5sL0irUOV/uNlCYput/GjLuR7JU8v7Z4vbunnkFWfc6kBaM1uvxEjK/fxZR8FLOH6v4OnyDrA1HJZ7POkQYW6blPe+9WoXMz+cAjkzgX5adZ7zmsnkGtsnJPCKx3iOM2VPkFyTtOB1SxOPciO5AsG25bHmRsjUe3cFYp+C1hFBLP0eNxbhNWLA6tvPDOKG1p1/ifSMtknTNvYO4lSptyVgBtr/rZR2bARfbDxBhvFVB2LyefoG3XEH6NS9CFogd5LRW85l/orb/O6TVZHQs2I4seawXVmC0lncb+zn0DCkEvOHPbtyk7cYDvrcNwuQp6IqLseC8gyg8B163l5YE9AZhN6J7N7AnCRunkPv1DsU9FFZwqyTWu+hc437zZ8y/7DP/ARmPhV2ZFBKssDMNhhFwfNrP6s7dW752BaKQrAA4oGdjLLVIKQzc5BHGvW6vT3f+NULhAgjnzKjMQu6DjUM99yQZ8wPPb7vazCKsvLGGMVj7aZAqq8SfQQTKoh2F+pBk13X+/hxjrg1N7POO9pK3Rrtbjtncc5GGxP1RjEJZwbJHkAF6O4qgQBfEF0iGWimthVbS15Ab+C3hQ1/w/+OwFs1FvrearJitkkRb0rVGmOREKuLCmCm6YXAMNm2VToVC3ivIRvkz4fmfA/gHxAp0WVSo4NhaRRiYP6R4egXiXGnd+gSxfKxBXN1Dbcb4mZIU1DOeq4MwDMR6LAaQdqxjfWY83wNhiNeVt27iNcK4wujxrRBkM4VzuxwdpfhBEoMp0fu8KhyhpVORMhjt2XzI88SFTGyAFnG+bgYxbnven5eQZzhy8XvD3tAoYKCgS38FYYZ4rGU75/nXIGvAIQ0Ubvzt/c/R8cBndQy5v27bwwWI5y71+XFvfw5Ze8YhiJ6jRE4IFcETI9nc22guzvEbJI44KkvZovDT/Mo1b6hM2tCa0lblGW4EVuv97m4kXFgqSSrxJLtuifF9hyXiDVYQZsbOIeZDc9DWWibHYReywLQikDsNE5bcmWKYQaEBYyShYxGyEd8L6XQ17kMG8UTUfIzDBP4rACsm8O9raD4iVhAGei8AOHMEiL7X7e1yQ/0GCaKPZgnbcldpRc/f8Ngdvn8kjwDH1Ste+xqcRdvRvq8BfOHr3TEZlzFrjwOEvXsbx5N40q+guy76f8dlD4g1cipt7pb0UHTi/uisGTY0qaqe0OsQy85BlouwYsX3DPLc75UM5zxRK14fsh8eQJSAGwpap47gbmMHV/ieVVQXmpNqoKDwZXt174Fuekgi4L4J/AW6nY8LumE3wPhnMCbQBP4PyLg7pbA7jZiEQy9S15QC1zPIvfsYXTs4vmxc/Bpk/VgwUt5qFWIpz723mvGWU+pj9BaUmbkbKecfIF7os9d0XTDM4QXkuc6gvOsdM+BD5O+VZnrG8BwyWGeR0ZszhiPIdV5XEZMxKtRC7wUPbtAzEIErrTaWrZO4M8bPMYvQAjKPAtoHB77ti76OiMXXq7le4ZhYiHwfGQphbXCBn0AW51kAz03gXyDctJbBMew97KtrsWWazpG80FirxWmF4+ECstlcRTYiNxb2Jqc16AKipQ/QsvhWrgFJ83ERjsveq6ekzT5EKLmKjNV5hGvGAqoLF9iErJObqMBFSKFuBSL8uLGotgbhhdft2bj2XJ20vEg2vgn83xDGk32nxe0NQkH8CuJaHDV23Boosgwttt6o7St/C5al4/+t8r+AYvf4APJc3HG2CalNO4/QvZuacEispX0dwO8m8K3H8JDj+CWvbwXyuZcRxv9uAQAt5Znzm9f2DmMup8TzziHiOcl4zxQkqWzGhHVF5yCy0fko6yevoYxCY+vkpnoWs8b3DEJNvbR5vACnEK2ncBYUB8jIhbHrgIuLzexNTB4xYR02QO7DWEoJUcPch5N5VeC9d3Rh2C4qY4EWjCk8rHFWBz8hC3QlihSfsy3s+6BQ+TjhYvzZBP5zyMI+C/m8K0iwINJq8AJhbA6Q7ob9DlGQ3A14EXJPbUeWrMzUoQLEjAc7wUNXTZ/HvIpuNDznBiLlLbh2xMXljR1abJYhBY+zBNtFfu0jYW02Ush8KHGkCJxbcfP6FOG9rnJf6EOeUVVGC1tOaQbDyQjPIYLJLUZ/9oOU798ha/oLiHCUVL84k6iBIoUjMC6SHj+b5W+FqBOIsHtrAn85r4UvGo7Cdcwqb9bVP418Ctk+wgTFGUiI2g7Cto12XtsQug88p/Ug3CJ/suwcwnJKcxhDnDQtobbO4izyx/R2EK6lVlHcBOe6kbqaY6056UnljawC8QuQHu6JydgzRTS2UfG6vV3jlA95RFwjnGiJ0r0jmC1hzIG7I2o8Iwu9RorQrkNcJKnaE4WC5/z1FjUmUgGZJXNsvOGPAuPWWt8BuparuM4RsRvQRVIMIwX4RcgibgWZb8hwDXMTuxcOnNCZl5B5sYvsGCXboeY+di/OKsFFLGnDfQ5ZoG0h5VZBgdlmqm/CSaSJvO4FRBCysaexxc8Z6/SSP3+rUmmhC/4UOZJB+Llm8liEklyEzpjpQMKt8sYfXkDW0yQBJ8+1r0OEmuPI37fB8C8485hhIZ8hnocLKmpAaMlNxYkxvijjnaIF9rPz+y2Gla4jhAaNNZTvUHRnAv8nQoXmFjm9LXzNgQl8ew1fMFxr9wdiGl7QUle0zvMJZL0dxAnOpp7WwzZevoMCcfOelLX6juHKFrbJwfm4BUnnupLq39puXtayPAW51w+FyTovMI7HIkia4fp9t0bKKGR+vioEsyYwwx0+yrACERhWqX2lLRS2xtkmz32Vw4pTB9F4w1wJRtxs9iDzq1/PpRWDm1ZiaAk1bbspXkCewTVkgSsqpNj6o5eQ55jn2VWxLpxAxljrBEngXkA7hmwcSZbGWcjma60sF0ged4WLt+fFFKth+BYS7zZKm07rlgXiSyDF4nV73xLWph2I8JclrK2CAj6txjN87x1YgB1iLR+ypEaEQNsMIK+B4BnkGd8Zae1Z6Z7oha01l5FhAabQvAER3h5cf4zimftaGYKwjZjSgvzMseM6S8A2kYx3L5LgRuF+DRJydghRuJYhYQBFw+tioaD9GZGC+jnfewpnjeLvrWsSQyF8m7/egRblpL34SfXmrgqbwGCkntsv4PEIyXEYqV+2NOJmcQrZdM6R4VL2JDvwE0Lr3gbGZD2PcAq5VisYZWLdBVnW1xZihT7bu760a9MLGwR8hyz6mUqIJ4kD0Q41Rc+7bwL/oM1zMUuZ9IYzYz9nKFFTkHl06FXfm7gT/dkE/lvI+PgZGR/zke9lOIcIfwuIFNvPIm588b15xrC1pg4gAscNZM7brjkdXluiBdZzShDl5L5lb11j1ZNi3HmqWayDAoORuolVjiPrxrXJHSNb3Wi134A8k6SMdxteY/eZuu7xtQn8GyYLVpLR3yYoMNv2xcdZe0JuYZLBpq8gk6aIy+8xsszvSa2e7st+tG1wObEeUwC+5lw8qkhQOYDEvN4vzml4UsfxAAmZruOAm0auEh183u8gweo/47T8NsP5/KWCJAJ7vKjrLc973JZ5tt3YfQwRLUcLEItD7Lx6DOtSHu8FQzDe8NfSPYJTsN1FrjkXpxG2bByKj0UYM5sZB0mrUie67jgu1b8gdC+P1GM8D163d06v0gAifCxDlKkbPCzcbuf33ijKoic1/c5RTS3IUXHrbVadLHgIEbJuyrjzozCs42+QNSAtf2IHYpm0c+K+9XDMMW1rzrOSnjebRAQkZPRTdrouqyTHYaSqxZRXc595r9vLbcQpYplcQRgLltrf+gmwA9GO0gSG9wDmTODvegXa6XHDtOUX6tgYbVFjIH9x2x+IafFWhITFOes9pYPZy0D3iM20L+radYPVa0lWYnB6rX2F2yCM0b3yGmGw+i8KTzbe8BY1FUam5WMOwK8arH1Vcge5D0vIUeuWm88mJDkpU+jzIok5DOXZhYztaOeZM+SIC3aybjsmvhPaAPK5ctfIy3HOTOXIsTQ+6KrjHgcJtYUp5FghNNeYybO+mDF04PGk5M/HOs7jFevmA+D+Ptv2wD+84frWc5Dns4aURKeoa95zWg/H8Aoyps+ZLFlU4LuEGEfu24C6mLB2aGJbX2dsRStXxMJkGNu1CXUKlEzws7VHU9eNIsKkzegtWsOoNnhT85YHqQwugokSOzdDK7DlLmrOz2M3zBvUU6bpnMedQv5C0rk2i0nGCcQHZLEq9Hm52f5CGKxeKSbsNQ4j5S8aL49VIwPI+J9FaDm5hVgW5lHTWDRSosNmk9rMxX5RKzMFt5eQeNO0rjajsAAZay8BXJnAP8lwQ21C7t0mSq4r3oj19SAbrnWfx3Vfs3Fos14F3bRMWD9215PqAKWt7gzd+AGnH7jDS8RnlJeCQuQCj1u4mwn3n2nkFBCjbvuGmcFwj3p3HPQRtkmtUlm3ndwWTMFObhQ+06ykVg6YQXKyzkvIZz1FQlJehDuEyT91yz6bEBkmc93ILUxSgPwy2nVVhxMgfmekKGojWVBxcFFMWnjSuEFYTqEWgd2L6anZJmi9GIqz4+b8DFLP60EmrwlrYV4izO48LTImuFn0MUKtVWqIccWnq+gkYj+Ltd60jgpcRgCGYi5nrOJatRs+gWuEPWoXIPe8TD3EZchCv2QySm6MwCLCjSnPOn4ISU5qrLmAJyXKfkLc3LGCLw0DVW2Q9+FIJvCvALzm9y9lxpAXSZxwsGtOFQKw3dcWEVq8inQzmYbUMbSlgT4UcVWWhZ6DBSRUHsgL4xAPIIpPNOkptpPUiPyAeBltiZ/ciZY52QWrvaTcl9nI91Q8aepguzbVbdg7RE4FtLUJONTOblMegC3DYOtLtYqUhSftPTd0OVTVQmyiMMldE9YgY3UD8WVhthBmXl5DJmVake1Y4gTVUaG1+S1kYT9AWAcxqzzSHK/JClOnFLDu6rYkcEN6DRFafyTNQV6jW8LnNcJyHyMpnjzng4WyTjc8j/0duHdPraOc8HUAUQjP4+4dlQvbDztzMzBhaZML59n3IXNiHzKeUjfAqKLDY6auMybsfFVZg4WoRSlJOTBhrdZpSGx3mfP/glh89hEmZcwjpq/xKHjJGeUA8rnaHWyozBlkHOUaIw5W+LRZuANaZuu2YL1B2MrzB9fyJUg91ULPzpPkobFAj9IBWJEAFViWI8e/YxjEggn8WS8+gdXW/c2d9Eihe8ME/juIC7qWe0bX9oM1kMrDS4iScwpgo5XCpOPSS4wzgEy0W8QUL55karJiALhfoDcgm1Ib3dZ2PE5DNmO7OPeREJNC7GJ7zffMoiLrHRfFQZ44swRsm8EZhKV3NpDSqYDW2ff8+asVFMbo2l5GmGy1iPhYoCXIBjLgHL3Gw6LOuUnKlK0Kx3WYq1sFhf1SSRbc/NPcVQ9qamYc8jlEGLqPO6bgUqrMGO/Fe4Sxiw/GFRUK2/lqo+y5Mq7jGYANIxn40c3Q1gvdhIynPyPvzRMLeR87x5CFGYj3oopuNdFzJQmSSxCL6DXEIpr1rPfAkA4vEhud8zNf0fo7BxFQbvnzTd5jlOTeoGOG66neYcRKHFVeM4Wg6ciYn4YI72eox8BmQ90eVDvgPLPtrIvGwq/yfWsYQ7JazLltQt4agLuplBc3ib35sxhuNn+P1+0NvG7vsIrYmieE7cn8moO4bexDJsV3L8zgtcVoPydoddbq8hGSef2dX1UUWrfu9efULstwBNHs9nlNiR2SHKYSfi6NCfxVE/jPcz73M4T1DZPml110Owiv8YfzVZS/A/gLhfc6eAfRpF/WdPxETODPGynEX5aqN3+7rlpL3QM4/2wHoroqEyxHvrvnv0D4uWetpR64L1H0dyYH5MLr9m7o7r0C8L8A+F+otBXGhq3kZBFhzdBMQcXr9m69bm8vRpB8DvnM2wlvdY9x7HV7/xXimTkF13sT+K95jI0C15+Xr5D17RcFv1PI8xspWZDz5m8m8H+nMSTPe6ZM4L83gf+XyLiZgYQAvHfHDhXYH5A1up/zHGu8po0cL//B48aFl9lxMYXi1VL2Iet05R61HJyCdYjBMlpDg9vUlC1qwuyjK4igkLU47kMG4uVTcvfGxVdxAi1AXGajbipuMkOVZQqWIIlZI1mV+NnvBS1O/vcApgz7mKa81z33Kd9vO4nslLQsuta2Up+Nn8ldRBI3Zi58NhP8B8QNmZQhaEvkZF4XBUhXiNqJ/H8KIrDbIsA3yHZTH0M2yVur5TsCiD2uFVYu08aukbqtv0GsJyNlJlL5eM1jRdeadwDOTOCfpY2lKnGszPNGuoEcQjbe2JqafP0KxHVtn60twF1J4X7GLu4hIwnPY5s15IzlKsEOJJSgn/D/L5B1ynYYs2PKCubLKC7o2oQCgGtDkTdTqFs3+csD9SHCQlF3dRS3HF1eK9QZwo5WA4SJLUuouJkC1ww3gzpPIkkerDBuq8jk8c7MI1SSlhB6rlzFd0ipTorhTWGD17OBjHvJ+WbrmEbjQM/pZs+dEOu8t3AoXR4cr8RlNCzFOfcVxIBjObgXJp3AX9BtVWVg5wpkQs3AKSuUZL7mAjtus22jmLAweLSU0BvQPYcR3QVet3dopL7ZbYVuA1v6IK/Lzn3vIsTyd5YQ8+FO/jIWuhXne2Fh0pNONrazUeWhFFTenkEKwu5A4namASx4CbUGOdGtdeIWMSUyKJQOHGHExh/ajirua5chit6NkSzxXM+P4yfrnr5GmJiQFr9qu47MA9gzYXmenyUUlBW+17pJ7ee1m+khZD6Nq3aptWItQTbCBYg7P2nzeg25H/e19Lz8Bbhzk0cQouD2G6TH/Ei1FROuIbVKBDfaPyBdRu74t4GRqglLSEjGsEo5BfM3EGH0K+/jN4Q9u/ei7+M50ubAFmQtWkaOMIikcAQqyncF1ssdZDR7iDn3gQn8C7DGId3fK3muu26cdew6Y1wdglUdouEYvIdrkP3DXdcuIPN7GsMC7rWRVoYzGH3+HyBnQhvX4/8XpBj/v3jd3lfnf1OQ0Js2VefYhsz7NRP4/5RXMXAtk262aNVulUPIwn6vndHM/MIEfp3lMyYJtyBw3/l7rCZVloqVBCC8vkzXDyeOzfa9g1gl5iFWmwd1NTn5v0IWE7dv7gpEIM4KcbALcGYGIBe3QXRxH1WIpLDdARDXmcWNedlBOO/SNpg7iHC2gPhYxkWIUDqgcHjlhdnRUzGWfhvTOQuWE3GO1YFYNK1gl3vBo6D8Vx7zY8bL9yAb/gXC+wF+3+N1xF17HBcQpfjGnpfvn4ZYuuoo8J2IJ72bv0Ke7xJE208TkK9BS1bRcznCk21fN6r3wcb6bkHuXxNCyBsAiybwjyDP7Q1knnyLGw/WG0FrzwBhQsoc5N7fAfivMe9zvSBfvZjOQ/TAzEOeY6Hkvpjj2GzrT54kgdiOLRdxa3RZK5S7RlKBqVQpcXHWixlkK4Ib4Dw3gX+atC8lCePkGUQ43jCB/ydknFpr9U6CoSpuzbRl4S7zhs15D9tMusdbhuwlVlGyYxB4GOrwDlKPOi5uOHrcUVsa5+UKsi9co4Dr/f6Ded3eiakpW5THi06+JdRfPmOS+Il4bfc7Unr5toB9yKC7zPEMXyMsefEVoh3achKxCgwn9/0EpxLykj+nWtDTJryLCTOubTJJbgGSLlpb6DyqOS8jtCJuGEkC+OUsWPv8v73Gz5DNKnFB433KsvIBMrfuFRC+L24h6vN1VzHzfhbD1t0i2vM8REhcRkb8KhfIA+B+YT/juU8c69isCfwfOdxRMwjjeWznCVsjcAlhd5NKMGFS2yWv+SZ6jc4YzmNV+gaxxOXpTzzrDSfSLWFYeBrJLU5r1jHCPtJLXkLiHpWxVchcrlJhteN5FsMF2mOVKQy7cr/zdQ+sWjFYZQqQzFursLrr8RTCRI1RPqN1wbptBrch46iWft1jwib9ANlK/Dnk896HMJTg2vm+CknafY3QW5NX+bEJcXn60KfCNf81f/7qdXvntLD/Z4TF0d9BQpQuMDy+045rlaT7Fs514UlN1v8EeZa5Xe9DUvKYTa02TmCkulSPhSStkRt8v67zUjibhWyyhZ8DF70hl4GJL2UChNZLW3fxDAW74mCEjOHINU5DFjNbw82WmJpFzsWNn3OLv26YwN+BuIynICEJ1xBL3wzY4QCy6Nns7HM4sYlcxApl2UcVMa/bO+Z13eXRsnnOpMXpGrKYzCE91nMKYmlZB/CB2r+Ne47rdJJ2PdYVaY9tQ2OAMNzDPbctY9SBCA+2RuQcHGGjrGUnB9uQz70Mea53JkdReWfeDVms+XPWe23ZnKmI+/kIIqjcFLnnGRxAPltsqSaH5wizh/9MeV1RfiC0Nt1B5scAyQrXL9AdTKUh1etFS/465N7tIRQoN/j/c3svaXD5CZlbo9xf2zTCbTOYuq5RaZ1DSivRLApa+MtwBZljs8gwfngSR/jHKEKzJ20pTxCWg7vhz/YrL/Z+2tjSUXDf767Le5y3f+Of1iHK3nfkaxmc2NKYa8kgh6KdmzLrZWOlgbzs8hlKzdC64SZmVOX+e1DKhFgra+lNnQv6V8iCnmodcITa85gFeAvA7xCry0fIZ08UwEwkSYXXcsfFbBGyeC4gtIwsexKj+g2ysG3x/5UUjTYpvcC98mWMrGZ9R416gHxZ8YsQ4WYRsqb8Z25YI2cZelJ79RdESIqzNCwiXGDtvd8HF3KO8TXUF5dkx+Alwv7GqUoZY6jsvBuguPvYra17H0vMe15pgWpPSs3ksZTdRr7bMboBmTf9kue/5Nz7DTKPUt33XvFuXc8gY2vB6/Y+8LoXIM/lFhGhxCvReYUKkVU49rnpD7k0KWxcIKalHsewVVpvUGJeOfdwxkjbwNJrMK9nFTKn7tdgz6nTmoe8giTDN7YgjSiGhC5nTl+awP8HRJHpFPGucp2+RHph8bzHOjOB/wUy5i8j/7OW/vt2u1HPWwq/IOtY1HCzDFlLOtwHxhUL/oBW1pmcJKgxrkCKs9bWMznmvHMQa9AdJLi8jLZp+/tOo9qOO/eLhAlby515Esgb1yFmCuHm+jNrQueNa4G4PVYgG8JHWrHuHOuPbUc1QHxMo70+6wa3Vqd7wdSTjFfbKusWsjBMATg1zPzkZ88VF8x4qtvoQhRD5b3A+axsz9ciLRvPEQa890e9jigZC6QVHjq8jhcQZeaf6N5+Abk/qyhuBc9zbTap7QaynrpF3JO4xQjzzpMYu6/IsBjHwfG1BUn6yqV05Nz0dyEKlft5ViCW2xXGPJYtPu6W18mb0ZuXM7BFpwlr/gHAJ4hwMaoHxMYwv0XYbcWteLCJ7P3jBvKZ51C+lahNgAXk8+beqyiw3DrW2JcQBW4Fcp/qxsZCLiPFgsd9I3Hv4Hq8CAk1GrIsV2jJTz2Wl5BYmfOYcce1XrU3kFCgz2UUnipovTDpaGVnTd2kDGyCxQbqcaMlYa1AQMl+xdyUPkKKuFYZJ+uWMnkGuT/LKbGxdqGwP1f9nAdOLMsN4yKPTOD/n5D7eJGxacwjjEEccoNzc/4rZLH/5nV7X50A+xWEWdSZRIS5rHjQW7rVFxCxKHNT7JRwh5XaOPlM/y8qONf8/NZKVCs89zfgfmN+BxG0n0EE2yvIM66ta5DznHIJhnx2nyAux7Ilp5I2lyyshXwOFbZW5PyJKnm2tNYG5Fq3Ua7DyCEYJlK1dZkWwQNajVbgKGgVWXns3D+B3It+5P9bkLVlEzFrOOfyFqRUVOrzMikZ4rQw74AhTXkvPiFG3a4r60ZqV/6q0XUOyL66DImh3oA8o90i53Ssw4BY+CoTHnOefxqyDlUa1udJgt8XhF2dcu01ddB6YRIykVYBrBrJ+mpbfOUBCpZsqAjrXrXZvanQ+reGSMZaljZXBs8pZWICfx/ZsbFX9tpQbV/UX7yOC4QZwlZDtwkneTZzmyh050kw9RrE+nUOWaBeQsaBje2ztezuIAt33liWQm1BvZhWVyYszDtlAv9bQYHuEpLcc1dm0+aGZQVp0Hp2BdkIzsrGehXgDHK/l0FXoNft7dAqVncP20JwI4zLRh7KLKVV/DXk2eSp0ZuFjYEdx3o1DXF7nkNCEEop21H3Pcf4TBlrkpGuO7OQhLkbHt+uS2e8xk7Za43hCLL+JdXJ7SN9/9gAhSAj9VFj1ytHEbXKcpxAWeaZx423nwgNBcuQtXUoVIPj+AXCzO5R4jxPqPzPQkKTAJk7ud39DJc5Qr74xDqwrWZPEbZsrSTxmOuETd5srOzTJAiT5whLJrRNkIQnXVlqLzVCYXAFLB1hF1jrIjZSEPlXyj2y/aszM9a4WG9CYg1HCuqlJptljViDbPaVjkfeCyvU9iGL+rVr8eNnjS0BxHtr62iuA9g3gX8IWUBtKY8rSPxTH+EidQiJIbyBuPZybXqeJM/cIUc8aAq2mwJQoNi0CfxXCDO2CxVyjhDdfF4iLBz8ieeahyysx2lzumjCAAXg/zPh79bLMRiDUDsKQ5mlkGcyDbmHI/eUjlNA6sCELXEHkJjkSmrb0sJjlaWhWGFHEL+KU6JoOd/gr1dGygdtQNYEOxYriePntTyHrBM7ceOcrxlAXNxJ66xdO2yr2CSsRWoGkRJfo+BJjPo3OGsSw0eOIOvffZtbrpfWyzXP/4PfCws5PN47iPv2BKIIXUHWtTKeuEJZ0FwvRg51IPfJp3zu7yFVA64hIVb9UQ7u1Vz2KQ9tbad4D7W5P7xuL6sjx2PnOWRxfhv5+xJkw1lBTFsyBzdjLWuhscLTfzSB/zfrtoxiAn/WBP4bxqKMwg3ks60iTKKoFK/bu/O6vV1XO6dQ8zuA3018a7U3AP4tv6+CpawgAtcZxFryBcA/vG7vo7PonEG05j7C+qF5r/M0LSbUBP6yCfwXXOji3n8BEQb3UUwDX+C1/g7gXcL9yITX/hWSKHGOSJYqF9K3kPGc2BqOG8nvAP5qyreydI+3ABFCfjdOi7UWYgUGm1l6CFGo+xWHotSNa2Wvsh/0FMJ9K6p8bkKsZW+skhjB1jK9hQhA6xDh/WUNY8Ja7FaRvC4/OL8J/AUjbfpeAfdZtX8CiLU2OvQhAtsvSP3K1YR7ACPtBnN7QbxuL1oU3K6nn71u7wM9EjbB53cqEleQcXuN8lZeWwVjDSJ8vYEI+3+keVz4+VbMCC2D6U7/neetgu+QZ/MTYemuDch4fZb0rNpMdAxNxAdoo0WyAZIKWl9AJq5NQIjFk9pRthtC1sJ+ARnoduFeRrwmuAYKf0bqX5WNZzqBWC86KKFRU7B6AbFIFLGq2cQZ+3P0+qchApnNoDxE2AFj3xFM47L2fkGEs6rdDq8Qxm8mWVGuEWb85t3EbWD4yLFPEWH4JxiX6vztDnIP05516YSBlOO5zzrW8muF9KYEN2ee2szSO4xQILsIZjhBbSQ8SUy6hXyOSu6lE/d2gPjWm1ZhXgGwbQJ/yCLIz3VvlHA28NiQgxG5cK4nyTPhtre157clo2ZN4M940lM889o8p2ucCfzfEXZ/GkqQMWEy4S1DkJYga9kV78c8Uur+pjCFcL7ajkUjjVte0y7k3syCDVVyXNtLyJ51CandWwarTM9W4Y6mN+TISKdB6462n+sSw1UQpiCK9i09n62DwvYzE/gnnvS8nwxhchLgRBzUGIi8A9lQoxriDXJm1BVwtx7SrWATPJLcYieQxe8aI8SkedLpxnZoib1Gk179fx0isCyawD/M6yL2ur1TbtxbfH9UYPkOWWxPGHNj3d6AuCsSFxla1I947fP8bFXEg17goXB2jxmuuziNnG1JeW0fTFgaqJJEB94ft97jgAHjs2n3wyuZMJByvBMez+1MMQSf0zv+/KWqe1CUMrGAo2LCdrqjbMBDjBoiE4PteDKAWKeiHbOOGO4zB/EoPDNSF3IKkrAR7W51YiQB8a7qdZvr8oeM15zGnN/GZ19EwzE4t5+D8YLRz28CfxsihDyHzJu4ep+249UMZKxfQe7PN/5uWw0WcglzffwBuff9Iu/NOG4fQJ9C8G3OEJVO5HsZ9sDqHFUZsygjWC9e3+v2/jTxLaVXEcbJnle0b1TNfQk8+wcVJivADHdQ+TJCvFsiHHBjyxbn4paqFXGz/WB/54Z05xXIuqepfIbCwxw1nmN3cTdSfmnLSAJWnDXuBGHmdFEryABidV1DJKCbz9Gto3ZHQWgJIlRuZAkddF+9588j1XfjNXzNEKwHEGvWNGgZoYA4B1nAUrX6Ua8vD9wQMjcFKjWbAP5iJON2JCuvl52AMGS9NIF/HyNmX5Cw+Nv/PYfc950alcq6sLU659I+Y8PcZ82nXJ9NoLH92d9A5vc1YrwEaVZTKo/PIfPpgfBWBdHzcy1JEuZXEXaYOcFwZ7A5yJpkXcxHiI9VPwJLWEEsZK7XaSQhrAblwT12EQXLdpMbUhq5Ty9CMuNT5yfXqJHr5Ea4RVjdwxbCjxtTFwjD0VqVOOiwB7m++2euwmQ1zCJ0K5bqqzvpGCkh8Zw/3xTQpt5CApH3IQvhDGTCu0KjjTuM7RPKheZDicsGwk5MedourkGEhUuEnTLmHfdQP0aL7UBc0zaDdmRhLW0hpMD7CSKgX/LaXjvXsk+BfQ4S+N+o0GMCfzbDDer2L681U5GWImuRuYGUfLqjwnBjAv8NpP3rg8xcE3ZSAWQz6Bc5NwXR3yAZ22XK50SPNw8Zk8c5hSC7OZy3VJCE1+3tGyn6nKiIeFJ26Z8h99+ux9MYFryWIVbOU6/bSxMYVhEmkZyifI3HBziC6o2X0ZPZ4QyhSzy6x1xD1qV9vu4obl5xfbr/zBHF4QvCBgyNwGfzHOINKiXMcU2Lzk9bi3EKsl/HWl4pcK5AOihV3VZ6AOBTltuc6/afzntaB5WeoQYJjQiT3OAWIQOmlTerCJ5k4Vo3d+219VqKXeBt54i82MD3eb7Pas0uu5CNuvJFzsvZMYOLzAv+ahNc5hCWBuqD7rTIW++c/41lrHvDZWfuIEL4LaRsyCxkIwX/lncjqxwjNeqWGZqQFOu6D4nfHUvpLSsk0spuY4ZnGf9nC37PmcD/p8jadQnZ4IcElzxQEP0PPNcictZiTLJQmzDJaZu//5ElsNASM0oW/1jIs8HzntzH68VYWtcgAsUG0q1P55A5codqC6Xba1jl9Z3lUb7pKYltVcnPVzg8wb0vXv5SaaXh2FyDWJfjPvM6ZA/YQIWWQYbX3ELmWNr+9IrnnzeB/x0SjlN1bdNMt/kkykVNWSZtbMYS8rVraz2juuAmHU9qXX2CCNRFFqQfkHFwCJnkC4hsxpzMqTE8jCcqXQQ6B3ZTmQIXQlqTbLZmtPsHgPt4UNutpF/khEay6GeQ37oUxybCBK1jfrflNcYemxfBVSRioevshNmZ82OMY7T1GG/tpmcC/wwiiA8gQvAVZPO5gtTSK9wNxLGYXEE20lwCgQn8l5Dau/0EC84ALJIMGZ+JwiQt7iuQEiVtjM8aiZi5cwgZ/6legjThrQLOEXYge7SeLCqvt44AtQlRyAY2XhSyhp7zfp9APCl1KLk2Vjtt3bMl6q4g4UmzJvD3vZYmwtQNn988xIqfugepm7thuJksQYoUt7n+XSZlYkVjLIOFLbsmrD03bQL/Rx2xOxQKP2K4s4yN1fxXSI3Pa7qv3oDZ1p7UBC1TY83GewEiAI5imbsvB0U3+Gces1NVfBxd5yuQzNC8z/AnchT8t5mD4AaUNU9MpCZrzmsZgptf1FK3i1DwvYAIfzY27wAFrFeMA56FbJo3EKXqX5DfGrMY+e5e+4DP+Aj5CpQ/gyhJHYwpc7xJOD7H0QYw7RouTb6e55WSwzJY5blsrdFrI4XUXa+Vje22CvktRHC3CZ8zGbHhRa5jCaL0XSLb6v8dMi+vAfyNf8tdr/cxwbHyDmGb3NS1qSlh0sZmPDotuATbEJO+nUyPDsZTbkDiUOroPjCNMIYxc+JT27qBbKCvIAvbD4gFJ/E6owsbF+P7Z0ah9i885g7CYt1lsHUGbc/vsuxDFtFLu3FR2FjmdV4b6ed6RyGsbCmubaS0hYvDy98SsOiGG92gcuMIotfRzdaTJLEP9j4yfm8JTjUDvj+1fAkVhS3+egOxRhZ1p+1BlI1+3D8pcOe17tiqDENKGOftJiT+LvY8Sjp81g8UbRvLbKRJwfesOWcKFvBPYQNh+MOHmg0Y1vMwC7qXPUmquwRLX/HzA2Fo0xnCJiVVxXMvIwwhuU9MjIPz1s7lb3zPuLvbtQVr6LpBjqSsB8IkF8MliNm5ruD8W8hiOluXJYkB6GlZf1Wdx1on9kver6HK+JMYK5ED215wEzW0suIm/wM5ysgYaae2AVm0jhFadmyG9ijXuQR5njcQAaj0uGbCxydIR4nS7mjPaW0Z4b6OGsSia0uF2LI4RYXgQ8gmUMfzPTSBfwMR8PJsfnZjKjOXXkGKFc+YwP9vYE1Du9lHYsyu4LilaQGx/d8/uwICFZgZCqhW+JxFWHqkqPt+HbKObiIjecwZ8wdxsZOedAaJCyPZAOcD4806NSmDEwOf8RpE4Uw1hpiw1FSHc8qdx0tw+oAjxeXOPdm6XEfdL28hz3QLTmu/mjiAzMEhwTByH3Z5HZf834mRqh1V7oN9MKzHrh9c7xaQ4r7l833KBi9bhmoaOTwmcZbJV5CBfgGnwGvFzCC0IC2g4sQKE/gvIBPeduOoBW4Qtm7UfdHYmNfZjN55AD9iJtMlZKA/RkESEAFjSEDjPXkJGag/RlVcCiywdgGfgwiUl5AF7xwiEI0i8J5B5s1XiLWh9GeilXMBFWaQRuiD8ZN0z68g7Cwyh4IWVcbtVV1Kwz1+kSz4XYTPNje859sIx8AmREBO7HccYRFyT2fhVHXgcd9DWgDu0sr3aUTl0QrVM0aKVJ+lJDEtO9+LxKLZ+XANmaswgX9Xh/I/QdgajnPIdpVPIzQWRLuxnELWnAFESNyGCPtxY2wa4X45j9GU1CMjrRDvIHG/y6iwlmLkXJkJfhz/ZzF/q/I6rvHQvf0Wck+P0dK8DRPWpbxOiIsu9doCWMNInkYnzbi5uXn9Qn2Nye3nyhXrQKFwtkCsl+UWstDOIN1dN4NwQV91X8uH9Ki1fW6e/cifFxHWTFtBja4EJsrMQ6wvOxCrjq1l6SY8jNS3mMerSgF7jTAZqXKFiNe66/x+YqQ8U6ZgzoXrFWRD+lHHRjQKXvmarBuQufkLsvncQcam7Xee9Tn7kI1/qP87sULFfQvbETfNnzzfBq9x3QT+XsKz2AWtaUVOQCvkES1sVlCetPqZVXMGWT8yx5fX7Z0ZKZr+QBni/PvKe2tb9t0hoRYm98tCSXysEnAbMxZ3ELp9X0OUnkZjSJMwYY/zGUhcetVu+SH3LQ1RC5CEutqS/XieRUhN2iS5Yx2UG0zgH2dcT5HXutdhhdCrGK/FL4RKTyZxwuRPiGWy1hI3NbtLdiBCW+aET7Aa5MILa/p10jZULgaHEIGm0fgLIyVubmqOlcmDjZebQo1jjYqCrf+3Rq2tUAkUTrgNiPVnoks/cRFbgSxiQ+5RLyVjkYv6Ir9WEa4ddVpPK8FIfcgFiOCb1P3G1rO8gyyitobaFTKS45hocO1Jhv8OgDUT+AvWA+FJ7UOb0Z+7oH8aFEQvuK7MQdx1rlt9k687oCBTur4pk0U+82drbbXJFUmNBHLDY61CLHMPng/dvOsQ703RskvWAzKHCgQEr9vbMYG/B+CFCfy3PGZaDF7W876BCOhTSLGk06K4gJyhGxzPLyFJa588p8oF78Enw4oAEMtorSFWJmyFeV5wDV2AKEIAx0iBcy5C7sGFx5Z/Dt947FPn9bPOudZQU6ksjmf3PEn3w/aPH2qekfHaooXOrRC6TCF0qEkHCqxXD4RJakyVLHhNwYmTN5XfFhsHSlT+5wTMnIQpLqix4WwAd8yKbczKwIFaVxiFyw1koo3iHrJt3NaN1OwrvegyM9laa9OwrRyrFtRWIeN8FcXm+Qt+bUCELRuL2XR5oVS4idnWXytw7qcJKymsAvj3kLHyX7zh7PvUdcSEmeYwku2/BnEND5jgcAs8TDZyQl/mIEJuWheldci9X4BscL+cGM5ziGBwn/3KkAWbZHFVMEQglhjr1nLk+yg8g3y2ZQD/LeH/a5B7+hkyBk9zfq5ZhB6QVVRTL3I+csxYAYdj7wVEWPwVt27wedkqEYnrsRO6lTf8zFrA3f0tyi7EszaOQvXbkHu1UXANvYR85hlISMd7SLOFPOui9SqsGPY5t//wYmpq0uhjW1YWnjNMttqGGB0SjUY0QvURlsRLet0FgD/ynNvr9i4YvrABWVfyKnjW0l6me9wQT740kCeJDl8hi85jjwWycTu2GHOlwmQbE4h4PaMGmV8ibNfYMVLW5TaPFZvasXUl9REKHtdpm2GNSt0eWFOw4Pts/Az4/XOMgNE6OL/3IcJKP/LvF5ANbhNht5RFJlkMIFnMWWuCW+rEljsBwmz8JGYxHPqSJuQsQzaebYjL6QTOWmXCdq6gsHXtnLuuuqtWyK6i/eYppLRXn8L5GWRDv6DrzR13zyD34ncj2bZZlsErI1n3c6ggnIixjRs83gnSP/8qQkXmCAnuwjSvlgn8v0Pmq732XHu2J0lrd2Amrgn8VddKasImDJdpHokKsWvFtSfVJJYgzzgt1nfI6MB7UaRihPUGFim7dw7xSszF/ZMGmS1IAlZ0Dd3EsJXvwXOlsDqbFtc4gifM5gPEdoqLg8porMDKeNoZyDpYPmaSptg5b4RM0joxBYppZhG1Gjxi+pBN7tqrsLi3CetRzTHbcOQNhoudbXlWeVwtrzlX3JrX7R2YwD+BLMzrkEUDJvAvIBN3G7K4xC0QtiyFdVHZ8zUSZpAQv5qHHchC+wckTKJWqzYX1FXI8x9JaE3ZLK3FZhfAR4hgOA95vi8gFr9v7oJuwjp9l3z9KWSzu+WGdcBxcZ0R+nJFS8IcgFOT3lZyDzKGbBZ4dK2y7VwByRg/M4H/geep5TnRklpVJvAuJHbZfoYNMKTCSHekfRP4p5DPv8Ev20VtDRnWY6/bqzLBwlr2j71u7zNwPyZmYp7fKcIWsZu0HOc2WFDg+p2/XkFC0HKvrZ50ZrPlyjq0ztm1dBWskWqkKHetnW+4htpnCIRdiNYZXpZnD7cVIy6MJJ1dQxIdk7Kxr1Dc+7WH9I5bboee6L50irC+bdLcfwcJK4itrEDuPWEA/pH7ysN8gJGNYrSyvnb+lBmel6blvIH0TD7ypHREazAFi2kqAiddHTGbNvN4ANHEq7BWbPCYC0a6fIziWu5AtMFodu20CfyveRQmu9gaqZFmLVDXkIlv3cZx49B227j0pPfzRx6v6ZjV3BhJEliDbKCxFjRaYG3JlCoUsxeQsbRuAv8Lfz6tWDj6BbFwnDvPdxXDC3J0U7CWqWcIu2V88Zx+2nnj+rxu7xcX7X8D2ez/EXfveM8TN0UvbOd6ZwXfrPtkpFzQNCR2ttIEKt7DachYyKOs3dKiOss5sghRwu5LuXgStzkLEeD/O2R8zGD83qQ9yDjvA/dry3uIUDYUc8/15qMJ/HdgKSAKVBsQ5SPWUup4eC4hFslFiAW2rAXR1qt1OYKsr5d1C5KWiFJoY33PIO7rbcg8THP77gLY5WtnEWbVV5Yo42XHFx+AlsmoJ46KwgkAMNRkDUDfzkkTxp3bhNAkYdL1hBW59itUF+dZuM5xmjA5k+M1tdBGd2kdcNG9q8KS1zDPIYP/FNVl59sakGcVjIXXAJZM2APalm4BZEHNLfx4kmTxAVKY+o4u1MRyQlyovzu/T4wQ6fACsvgtQSx4Sa+xi3uhHsEUhKYim43r2nyDcOPJ1bc6DxSios/tEsA/83xWkHOxC6vNaJ+HjJ+y12WLxwMi3JYSxK3FicLpCkTwjxUSKKxt8NdL5LRU01u1Acn8jBXiqHi85K+5lVc+e/v8O5C1JHpdb8A2iF63F1vhgNe4DVlX95y/dxC2wtzLs6aYmCofdBW74Sc2K9rWb4zjFGGi2hrCeNbL6PiiR+Y93f1fAPx/EJPgyc85lbWeUFC3bQRPnb9fokQv76qgwvUJGEoKXDGBf5KmCPFzWwH7yr1/tOQuQQS4rC5Zbt3XItd9QoPCOwBbRuqHxs2zZ2Ccp/2cdO/bRgdXSd6IiCesdkxMswovrHM8lWRAiJImKH6HLHRjLVtjJAtxO80iyofyBXRzj/P6qoKai63d9rXooB4XXNzWIHETSRvdImSBPatKWOLCl9q9hNd2l8MNahf5NU6cBcgmcIAS49sbLsAbbQf5GLlAKDQlcQ4W4S5yYC7qtprCd2fD24E8myswHhAxMYgmzBI9G3UO0eKxCbGIxVoCudBfQOKq/gPEApc4hkyYTTwDsS5FN49zVBt/+BryHBaRXFLKBttPo9jz2gA79xhJ4IvbRG3rzimU3wxt/dlZDH+GPMXorWUZJvDdzGHrNgTkM6fea5NQ5YPjbRligRrwNTYe8yJOQOCY6XthrKD9LHFC0xKv1cbyHkYsW5t83Tqv7VvWuI8I6iPhWMCrtGZbIfsCKeW3TNi1y8Zsu9ULOpCxb2Ofo9nb7nFmED7bX0kW4sixX0HG409+t/kHC3DikqkErCMMaYoqXbtg5n6aRbgqazHH8SDpeUXuxZCXrug1JAqTlEZrq7OUQq4sQS8mG2ucmMB/BbnGnazBmMBdws9t4wVksVxAci2yXyiX1JEbToppx1W9ArZCNJHSFzH8QBjr+BfIuDmCdC0qdO9p+ZnKcuVyAbLWup+eZAra0hOnFbmCa4Ub35Un5VAO4hQFE/hTXrd3R5ftXgk3tE0GA5z1iJajC57DtjWLE9ptfNE7uhBdgbQoc5HvsdjN2wT+/8XrSnM/upm/D+aIJ1mY/wKxPlWhlFxDxpzb6WPIIs5nlGRhTsMKJLdIWLM4zq1Fo6wAcwG6uSN/t+Vc0u6TFUiiJVIuEbru8u5r97VBjSRerEGe9wDyLH9BxqxVpH6HFLj/56jl0/7uSTzrR4hAFjdXDnncDq/XvQerEGF+jv+/hCR7bEAUoKSs8iWEc2wJUoKp8N7prLl5i/jngta+f+SwFi8gbAowDWcMUlC/gsy3aRP4ryEWyjhBu4PwfkzF/D+Kmyy3Apnv1uIeFRY3eW02pnjO3n+v2zsZp0XYhIl5d0bqlM5CjELuc3MF4yyDgV1P7B42NI/amM29ixwtwlqA3SASrbecfC8gi59tn7brdXu3XFQ+Q7SGxrNiKfxsQJIr3Ht/AVm8EgdZJFakA3FHLQH4UIWGxQH8G2RRtz207eR2F4ak67swgX+NMHHiCqLxFxJ8KEi+h8Q+fU9avMkcZPEHv+9DwgFsO7ZCPaOLYoW8Ed6/Bdm4bmiFuon8vwNZqBZM4P/0ur3jovcTuI+F+4EUCx+PmxQbZ8fXbwgTHv6/Ra+D7PB4uYRRKpFpVsl5iFB3BllrY4+bx6JK1/RVjnv8HexgZMJ2fqA7btQaiycZgpB9XaxFktcTFzoQff93Eynlwr/bhKe0916awP+TPw+1voz7e8pxrHvYZm3/FbLOrIOJjBScbebvKmQMDiBzO229TLNI3fKYVnDaQKis2Cz9C4gwM4DMGzeDODpPFyDrMSCC8DlkHb3h59ktsE5YJWsGEWFuVPI8E4RNAa4S7uEXXttbhC5mt92p3Tf6EIv3LBKekxlOqrqGyCNzkPCRAZJjEw/AphgIw3MWITGWtbSOTmEeoaX2d4Qx3m6zinMjdVNt+EAWNpZ9DcAH9x+tEyZpsRm1CO4yRNOv88H9ggzMNKuEbVG3Bac0gn3PqIt7xaxjuDad7ZVa1OI0D0komIYsdv9awbW51qtNhCUQjpGSFJLAFSRhoow12dYlfA7JyDuFfMZlACeRhfwKIkS4Jaeu+P5alQfDgsWM7fmSc6GO4paRisOGCwBhmEMpis5TClbXXrd343V7e04c0hZGqMrA51dJMp8Ji0bfARippqsJe2tndirxwsQNG0Jwn+UNxyJnhot5/8ir0JZVDinUWME2M6zHnUt87yDvPE8a70Xngeud41xfhaxn12B4gB27DH3YgDyjvIXFFyHKe/SeWhdpJ3KsBYQelVseYxnD1QWiuO+399zGvgIyX/KuhX2EJcKWTeCfUUifR+h+/ubeZxpUbBzjqFUZbhHfQ97+fwDg2gS+dZtHFY9XvP5pKixbkOLzfe9hJY43kGoCNus6V0UAj9UyGE5lQxLsc6nNA8lncBvZg44g8/4WocX0wRjJMIpEsWM1qrRsNCZMOvEPlwC+0kxtrVoLEHdVYbcPF57X/Lk2TSDLKkEOIA9wH2HXkLbG19lB8iCWp+BGaIvMzqFg/FwSXLB+IBTM3iIMpM8bl2UXZqudlbmOSyPt0aYgC5XdkK0r84vz2gEiSRlet7drpByMG2MzUrIZF+uryEK9yO/zSKknasJODJcxm/seWIYm7vpoQdmBzNXawhuimLBI+J2RouB3HB//BJlj10Y6exyBfddHub8jkKumK9er6QzXfKlkSFoSd/hz9PhDxbwpDNm6f7mrdxQYv1PRnynsvobcm+9xFjIKXLaG5hevgfAQbtQrkPXjLE4ooqfpX0ChN0sANmEDiUHU8u9JYp/Nbrfxksugso+w1qh9ronlY3gtnxB22JmCrFvv+HOR5MM7AHt0IS9DBIoP/Dkpu/olzzeNFCORCROkpiFWP1fRKfTMaQDZiRmXF5C10R4vLaxlPvI9F/wcHS/sjmfnbKeusesqrsZpRsLPv8fXnEDG00h7sichT8d4aBDpNGmZXIIMsgXIALrhd7sRLqOc4OUOoEZjEbmQ2ADy/VEFhzrxwrI1dxQUrFB+DQr7OY8zMIH/nzCitcrFSAeQKYh2O0DEvB7zWltU9t5q7El22jcwbqTstXChOoPcp0sT+FZIyCVwuxsRN8o3dMF/KeqWdlzR0Y5G+5C5dZ6hCGxBLBQDE/h/esPZfHfIyMT1pIzHuNuDxnb2oHVn30iHDFsr8hripr8P1udib2vBVRpzTUF3GbKAWzdknOXJvn4OwL+DZLH+q/ew5ZtlB7IBFt4IvORSK9cQgWQWMk+3IBvfmgn8Xcjcn4cIeQ/OawrWlqWw9YM/29cuI9zIk2IhXSG0cIeyinAtvLNI8CxYYYGCnzVofEqwyMWOYxPGIEdzAq4h+1kn6fz2/WCnJy8sq+S+3q4HH0bYj6Jrnq28cR0jPFsBLmvsLiBMkLrFcNeqwkJYggL81Ujs/RQFsLQkY/u/3Osb7/17SKkjOyfG0WXOynFTEEvrEiR84f7aORaqSo6NK192MDZhkhaUWYQCQR9yE9xaYrZbxTzKFVZ2NbFOy9zIhd0sVcBFfyqPdTGy6S1DhHubVZnbReFVmz1o63IBMjGzBME1yDWvIxKCUMbS7VyHDVe48YY73/xAjsDlBBYhm8McZC6UvWcdiEByStfvDfK5ZezicoucrrkWcAC5XrfLi8s55HlYN2E0WP85ZIwsUYAeSeGhde2Wwuoz/nnTk37VeTaiDYRux3th0nFDT0ESuPqjXCePuYDQ+rgXOV8fLLuDh0p9nCAwg9Bqs4jhHscdiBXt1nWhOS5ha0Q45fFvAdxSERzqHEIl9zt/HroOE1PShH+fBuuAxrnwTJgcNwVpc5i6NtLCu2uvJ+21xBV6Y8NEvG6vT0X0XtkwbJloYgpa0/pua9TepgiCLyH39BoZSVYj7Ec7EAHShkJdI1I5gPNiygpw0XvMZ7cK2f9tHPsVWJ+UL5tDZA7RKroAmROZ6zlDRNYhoQEHkHXgN54nzr0NfqZzFFfe7F4JSCy5zdoutK5TydyEzNM861Mfst4tgHkPkHs7VkV/LMIkb84r5082m+yB2duroLVT9OHxoabWLKSwu8hra2UtQN7HmbxCkaMpzRZx+XPCriAU3irTrrhhrEImd57PcYNQI89jSTpASt3HEVgH44yMlByxC+kduOg47p/MchPEFjSPuqnthrcEWWxj778nnUGuIJ/3Oa8vd5YuNzTbrWUihElavi8RZil+jrgId6mQWoEmLljflok5M058cFEcV6XNYD7isRPnmQlrPB5RUPpnyPOLroW2RIy93uimasdjP8X6GGUDIgDOG+l64iaoXBhJOHnG6+lDxmbssT1JPtmFo/hzfXoOEQKm+beLGMumbWN54XV7XzjWbYLLHCLJDXECnAmTiwYcA9e0yFhBdZWvi+ugtIgwOe7BvU34vP2s1zivPaEV9i7DtXkK4BU9DD8QWuOWEFPQmkLktJHuL9NGygJFjz+IfK8cjptEQYuC5G+QQvxJ1RW2IXPwjkrdLYbjgR8kuJiwLBNA62uOy11B2FSi7nAcm/Vt69da1/OfBdfXLbA8lJG6m6nv5Zo4w/fMQ9afpILotTEuy6QtJWFbgo0bG4d5ihgB1oR1pABaAsZ3afkwYT2oDuNB8mwgrqZkB1nWeWx24ABhe6h1LgpVxHtuI0xKyjweN4mPEEtzppDvOZnlLvxcLyHCaVw8TRYXkHtyi4fBxzYe1i1rlVW77AVCwfOUf7NxSNMQ69kqzxfbOxW437iWEVrjMuGmPw9JGqotBs1I7OIC5DNWWUfVZina8R3N+rWWqrjYvx3IfTpHcq2/vFgXrbXC/Eo4p8szUKCDlNf4gfiaeBcQy08H8fNknefdQH4LhO16kqRULyF0Nf5KstpYYgQsWzpnDnJfbT3LKNOR70C4P+QN9bBjoANRlqcRZi33Ic/4GmGJJLe6gU2smUJNMew5FfdFhALkMmTs3HfXScDGJtr3R+fvL8j6Z7vKHEQtt2PAxmUCw8/YpXBSCj2XB5Ax3M/5tl0499Srt0b1snNM+/nLCPXnkH33vMA+Ze/jEQqEpVXJWIRJalQf4dQJHDOpNaUca0dZd+VIGCnUvgyJc8hz/lyxQxTEfqFA2ACf1T5koTpBqMFn1XfLhEL7DGQBzR3kn8dFn4NVhAv3MQq6MLyw882dO1HNcMePS8jmlWpdN2F/Z/D7qQkTS55BNGibpXlnpLBu2ub0C7KIZY4dJ9bNtiKtqv1W9DzTGC6NVKUweZ+lGCekUrhegVjthqyOfHY7dOsORvRCHIB1C/OMUW7uNokiqaC4ZQsyz3diLKuAPDtrQYw7l+3QcwdJYrhDOD6TFHr7/w7KrYMnCC1GP5FcLPmX8zp3g58roLAeQ4SqgSfxmG6ixBnY5cYE/pQJ/H8HALTkHfFZxdb647j9DWJh/YESscwFsK7UKYQxjmcm8Du0PF9G9wNakA8g4/+BEuFJ4scVQuPIABnrUdV4Emr2HbLfJ5X72jeBfw7xHAy4/m1AhN9E5Sjq/s/BGWT/swK4dcvXYdTqI6yacACZD1d2v+C6lBkq4XV7h0bKPN3R2r4NUfwTLavO/WzMyzS2mElO4HEEo8bxHSJIpG3IX5AztrBKnPgiIN7dBeBeK/sKcXPnzlDP6W6Nvud+8aEraxYlY1gjrEE2txOM30J9hrAm20/Gj10V2SgSxoZrdd/PWih4nAEF9iU8dL1YhecQYdHirCLacZ0W0rC12NaMtJisXMGjUnKIsJxJlcd2sxSnIML8AGJNu4O4Udchhcz/S5wbO0FAi4XnmINs7q5r+BbF3EnrkOd04Ulbz6Tz2ZhfQOZMnCA+Dxl7SUL6CsLSTVYR/A3iToYJ/P+3vS8UxOYgG9aHAp9nCAo+me+n0NSP+Vtuwd4dA/z90kgGdDRr9hnEYnmHfB2vliBCzQpkPd4xgX+HMNa0MsGMYzVOqdjiuQdGKhZEq2tkjTl7L4fKQY2TnOugO3bXIde7DgrJ9PjY0LOy+8U6WKKHIRe13Q9e43euF2/gZLBTKLRJWd+ylCZnX7Kl8OaQ4aav2PtTmMqESVrX1pGhWTQBH3JWZqp1YY4Vb7hfZ2qwbRGXJONWNiGm8lFqAPbLvjeGS8g1dZBRL68GOpBFeh7Av4do6yN3I6CQ/xGiiKQueNy0B163d8VNyc027xspJGwzzk8hC+lIgjyF5lsv7MAzD7EMWQFjDjXVvUwTmIpghpNcoqwgDC+wIQ7nEKHpEPLMR6pbC9kEbHWCUiEwFBD7vNasTcEK4ouIWbd4P6ywuY5467L1KtxC2vxNQwSitxCBahbAJTe+dwgTwXILx9zsn0GSZiqpzzkKKYLCHuTz5hEETyH3cwphFv0LyHxZMFKTsG63sesCLmxl8sKyNAsYg6eNa4wt1H2FYhZmywEYA8xj2gQ0QMZm2dCzSySEJ9XIAoYT2PoYducXeaZ2rra9iUullkmrWaxh/OVCxo4TbH7hSeFkGzDfL2rp8bq9shvUKkSIibvfW5DNZI1B6G1o2dhBGM80juLdVsM/gCwklwjboQEVjf88VnczXC/vc4K1LGpdHFpAjCT4zEMyGfN0TbEJInZzse7tQ4Q1MXPFDfH6lyEll9w6mbY47yokBjAucH4KYTJRoQXdZLdws92lgLCQ9A8TJjBVERdVqs6jxUkWAaSncOa4zxDEbyCf9S+QRIwH1mXeZ7f7xxJEmPoGETSfI7RmxxXIjn6GBx1pIM/cWlEbFyYT2IGMkVxjj+PrC5yasUZq9C1BFPO4WpjPIPveXpIhhQpAJ+ez7zPs6nqEdXuTX4mF7ul2fYGwP/wCxDpd9JzP+F4bRjRtJMErtxXXi9RtppHlgsctbXFjCMQHpPSnrgFbB3QaYT3QiwTLOYDh+pTu32kIKixIUj5ZhTzPsVinqxQmDyATamwFjBtmAyKYLNKK8BJhQkBWPNTIcHN/yZ/vYqyPFwgzJtsgSAIxge/c5DYgQkrVAdGrYNkcyMb6CeKGO4SM1XEWkHcD0fP0gx3CPMxktBnkC0Ci1dqNFR6Ksy3hrnsF+QxzGC7G/gLSqmsWwA9uIlHB+t9DxuoOgP9fwfNaF/88YkJVKCA82Cw9Kc2TCwrd25AM6zjLnK05V9Y6kKtOIefCMkQhteViOmDNTDvHudEeILR+LKYc0yYXzkLG+w9IiMk1j2ULZM/FzT8qAv8RkoT3r163565tfTgbZsZnqiWcIuGc92VzSoSAPID35c+Ul6xC5lhsORYznN2c6eLkOVMFKBPTLpXGhRu+18YIpu3xVhmw1n2bCX8fz85rfwEZf0lx7ucIhT4rUBZe46J4kumf2hbWGd8zkNjgB94hWvrnuU/aeTUH2XdOSlhRs657gJikOoZiLJvAn/OG6w3H1accFWtNXwHwkfep1vKElQmTXoUFjE3g2x7GO3EP2oTdAI69Yq2ARrmm+0FNQc7GJO3R1XmB5JpsdXCD0KLwQOP2JIj3JGZjL4wJW2Id0F3agVjZZiFFjXO5Unj/ohv/M4jAYF27VbIH9tLlJHKtJ2NVerywXMigTGwLx1gfMuYOTVgr7znEjRnXHcTGXV7zuX3h+3NvrhS0bOegOKGlg9ClegPgL0barLnC3FLkexH6PMdrSBmVXYQdNwp3x0jArU0aV5KlUN1UJ5zg1Ov2Bnz2e5BnHzvG+Tz/LcJYN3v/NiBeBpjAd2vWnUMUxgWkC7lzCJWQWwD/HezXa6TEzCld1EkhGnMI788mHEWZa3PWRvwKIljMItIRqigmu6uMddnPGKdsjnlY07Bq9iD3KElBm8ZwW8uR4P73ykizg89emMDyElJE/p8gipu1YCdhO7Sd8/pfQMpGue1zbYb+Il38cd1/9ozUxryjFX4ezpik4DaVNFcZIjcL2Uuj8aFZhpB5hON7FTHPwPFu3NG7cYOwwssy0hWFTEzO4u8mTLKMxsLOwKlPiWr2wWse64r337Yy/ZwVjlWW1vXm5sR3g8/jFqtNyCJnXZh1X9M7SLzMT1oHbHmbU7tpetLr80Fx1rrwhkvmxA6OvNfCwbYCEc6vI/+Llk2ybQ1tcP8yRovLOYFslqWsBybSh52xYdOexCWeADgxkh1pi93+KCPMVUGMG7vo+++FYS7AzyEbwDfEZPhzgXNdR4WyGLkIv+Cv3yDKXXQzsZnkZ5CFcgXSt9ddYP8ZstEldXhJhBtUHyJUWcuJW06lCmHyADIGq0oUcsMJbDvDuOLZUwiFrUvIZjiH4fgwe7+joRRub/QNJAsyV5C5tQ2Zt/OQWqR2A9+g8JC0GV5CNtsNJJSoMmF1gmtX+TeSvf6K1zDSOk0L5xv+HBsmgrCdH8CwCv4crWlYqXUm6qKN+f8FFcnE7OYk6EKfgtw/6yZfwHCzA9sdx3oPdiHjbg3yfGOtoZ7Toc1IBvgq5Hm7HkarKCaVebLHunOO6faAn0NY0u7BdfD/NgE1sxJGDJeQ9ceO5Q087NrkdoiZojHIjol/LXi+IUzgv4E0QLCyQRoP1miuAbY26z6qSXSF1+39pPfiGrJOWkvxPGpKfh1nB5z7TT7tdc7msYRkS2cftExWfI3PQeuEXfx53XbRtkH4J4ip2zguQdI5X2oMEDX5LYgpP20Rew2ZjIuIuOjjYlc86cbQR0pR47x4kUSUIpjhDLnvvL7fIDE77uS2NSZtJnmjWW8VcQvZLH9CypfU8ZnseB5AEmDirBL3XYmMZKjbMidu1nNs3I8JE0lO066fbqpvCPu9v4OMvZ0sBY5z2ta6jLVK0VqYZDFchmzSl6i+fpsrGHcgwnYHw4lZQ21Onfe+hGzSfaTEK1qXm5EYvG2EtRkPee6rtM/E/31GeqLaBo8NI2097Wa1AlE2br3iJV2idBJ+dq/10sSXzXHHcSOUUSSdEKBpyDpnuwH1IWPn0t7r/z97f7YcSbZdCYILgGGeAZ89Iu5AZiaTmcXKlOqUKmlpkfolfcGzCZ7t5fxMP/ZrZ2Vlk1Vk8iYveXkjPHye4DDMo8H6Ya1t55jaUdWjamZwj5vcIiHwAMx0OMM+e1h77Wxv/1gG6wyo51rwBsR8cM1F0EG8yqWtz+FhC4O9oD3zIvF5Z3V+W5T/Aj57BsQ5J42qqoXmbRPf6b2+16/z7RuP9Bw3OrsMjnCCMajRnMdlAwEMxnlO4z6IbzdD+1DR5JtgLy/DZ3yq2t8WPcc81CwhDAaZvnad9hm8czDp7N9A7qsDjnF35Q/5qGTVZLlRYuoJiHWcGFRc6iD7CFEU6HddTMiDCMURF/cUXIRvE0L8VbIDLvZlRDxnbYYZcDOPkD+bZMSuDB3aVXPUVLRWFpFG2HqX+/dc8F9YHb8CH7UoJAD/JYkOj2sQIzWVKsWM3Jov9e+UwoFrKJWpiMMDcB6LolIGZ1lHSapJyvJWz7MG7r01UKecyICJFUYY1Qj0M5nbVAfOEnyKchc02lIOvFRS5Et4yqp3oD6+zb9LQeZhGRzrmyzCuBDgsFZBY6ALRpxsH5hh/Vxj+scx0l9h9XH47J8xTPVi3KBNKpTPXEFXGekxc4jf552GrAYHn/O92xt3RpqgGO+nGWHG7nCnfy+5Tvs42Js/QmlSGXUf9d1Q929guAuStTK+Q0NmC+31Z/D1Az1wT/f1TO/ANPfIuZ/5yvOR4pM6IkfiCyItGDXnR8H/n2h/3I3jhCvQ8gnUYWFAxAqbDLP/NvhO3m65APXELNKLIVdAPWHz/gQ+3f86//ksR6M1LbmvyOQs/IaYL/vgVxZLDQwdflVpjAmKdTIBcv1uG4rRy8Q6wsyAh80CGBE5QAkubNpRV+d7Xz8FN9gxKg7/zFfIDWhBHDF1vwFTWidKq/QQEExP7SXuWfKH6pTuUbev7Bp42BlJ/IpjtXHsoLhGSVW/U69iUGcY5tO8bIt4jBQXBc9+60i7VYvr0nnuSoDrZU33eSqsU1nV8zISnSGNSYgjrOMU2IE9cnhob/9rAP8zaKy2sr39v5NRDng6ITuAblCAN0uR0LGButEIZjKI+LpO+7fgmL5FjZafufsUBRAWMBwhijElpBoNxgBxp7luZOAERullUyNdOveFjPB/i1FWASvKu9bnh2AsBdkoo6ErrG6XcTji1JTIE1Bvz2EY0mDGexVJdx8TiBpn9arHJ1LfkcVrRc5BndOHP8+Lvn+HGtRljmT2D0Hc5c+aQzube/rMIioyDtOQJGPSlXSUSJGM+L534Kbv1v1+ikiJPwC9sqZKcVoRz1SxDhJ3mEAqNlzomsPQO7TKXABYapKGqRIp1LnEyNk6fLHBZyRUA2pjGdbE5BI+ItkCBlGNlyB4/2u08wQwGI8H+t/PKZtdkdpHoKJo0gbSrrMJeuJJ0BAppBWwwjkVe2tAd4BzcocCOhWA0W1FMkbWhw7QDfhU8BfQKLoEcKB0pnUJKXy+rBntlhUcLcHjs03vLbhOO0rZokP4BzBKc+hY/XwquMldXcO8SDQ3LVBfxAD7s+B6Ode/u/p9F2r1KUP7hT53hfErni+lY57C98u2NJulaRfBg7CRMVki1/AdcZZkuH6qWut6rmUQlmHzmRK5/A2op/+x5Dz8M7BI8QvqMxjE5BxymhRZa4Fwj13HKuUk40j7p3D8HbHYu1C/+VDfaH3PZ6NFZDfw3V8+6vlOAfRcp71cZMjLudkFdURj405j8QA0oJpCprbBs+do3Kyb9tY/oF6r01SJOc7vIE7RAKp3iYijOU1JjUw+Ao2PFhrS3mQluKQJyRZ8+P7wvq3ySYgOxYnTCunwMWwhsr39Ey14I682fOgKxDvW8BDOy3NQuQ8wqAXPZt0CAG6Mz6hwOqSIHup/76Awvg61D7re4DCZ1EE+pqzBp12Ni6xK1uGrFZPbQIaGvFK2D/X72yLlnpPnoCGyiKBwpgKjGBpYSU5dkXGv5z4GDYUPoNF4GvzdOprYcy2BxufJOKkrXbuviLcVD5yDyvlW/19Edm/RUuNsnAsM7L4O50k4M9d6pnlE1lBGaM4bcE8Muk3pkPwcfO4CwD9N4HlMzAG0Mcg/z0Mk4u/qiHT9BwBwnfaf69frqMbUP4HH173Rtb444kuLumMtg/ye0Pf+UHBtq4BfL/j7QGTUFmZ/tBdeg86UnaEn8AZmUf/r/H2sNd9JDBohCSmFBtyjekYrpPls35eeOdXznNm5mxGb+D14Hh9lcd7UHVAnrimL1DTNvanrwJHBpImuX4OnkRvLmNRY9cBK+4nS8mXkIO2BUV+DKPThqeJs/u49A5xqTA4aj0/xWcaVY3i8yC/OkJyWSIGYUjBwNoBo+n4dXBPrrtP+NIGNsJj7GZMVeGX4Bky/pMxfDx7rme9f+612C7iET0mkRvjPQSPGIkiVIgX/A5hy/AAf+avT5ekWNKYGUUPnWQ0+xSIJGQmCX6OEvqWOZMW8djF5pOddxgQiX1qDV46FZma4WtS15SKt3pSB+RGeluUY3piwlPzYomcbpMd1eA31jM/SaHtqidbVInJtJYN7nrhOGyAuMo9bfIEpGJIR+QyeVSnV42bwWi/svnRiYTQdHoIzcMIL5J9Bh6zUKXeetmXGkd6rqEhsqEpaBuZbKCJXdo9AtuFb83ULPnMArt1LPdMK+M5FBVC7YCDnDqO45yqy/3Nwf5SNd4qc6xlKK85NNNcPwAi9GbkHusYk4GzGHHKOMemwYlJxvn0EHZnSwJ3z1HIz8G1ok8SRLm4LHL+u/T7JmMzYUaKUPPRrS1ZNLPs/nIT4CnBR31Uc8kegcVZ7c2txroMevRl370FjsVvyVVPMqREzAEPA7Xvvp95UZHTUWqPysl+Ev5MhM5MVV+gbeT4ALOqQ/xk8LFNxea9BImvj6jNaHqCgzZ+ed2wcpyMdyjKYqky93gV4eN+4TvvjuNHJ4Dm2wLX5xRFsfxXcC8i1etN8hWD3Y9dpWzX8xPG6znc5ulZqeZqO9BMwSn6GApzXN+DI9cF1atXEZWJk9IAosITThSsoFFXU+q/BKGEZVU4qzn4e3skI4RQpYlRPT1yELimH/f2g51lAScRWuvSLvm/NCI6yvf1PchTnszgkamjdOXYfM2qyaPGHpcPrrFk5TjugQ3Os61y4TvvHsuu4gIMUPnK+Dk/dNXHnCwVY7mmK9HtKqn8V3tE9Rz0jegtcR1sIzvZUzOQMgGdKSb7LJswY/y8yNQkX802VIaGDuynOwjAnA0LWFGiDlFej3snZV+qn/jVFBs5fgPyZf8iIB92CJ9C/UdTiA3jQGANB9ODT3h45GOVMXAb/33espl3BFLlddVhs6X83kE4XcggeWneYXBZl0EHIkTdzoKhdjVZvUzawLOq/AI+VnJgIPrAKOg9JbSWV2p8DIxe1jNsAI2y0KnWN49XcT4PSjBhB0j3HekeTeTC6ZTRwI6JnGhuuIKyfkf0foT5uNaQ8ijn/qwjGI6vfmi/soDUSHZUcgHtgUPARGHzQ98owzXXndxuCCjk2RwhTvWWyAF8jYNXTKZjaR+DYfsyG8aMzuf9fhaiH4IMoqdXZu6Cu+3yPztgFGMmdQUSHufICsi44D0MBhdQ0dws+KrGCiAUvy9/CpgNupV+iON+q8DLb248SLTtPfHpagj/5qhLDV0xRQgB7Sx7+eSwd+i8yKlpPMwmO2ga8V9iTwrOinjuoAr7GwfE9WFSS51YdOdCzeyhQy4ivswrsZMUqA/oLaFxNas19Bg/PEehFltDq7Z7EohCXk4rQay0ugeNoVbqGnV1DwRrQAfRrEGJh6UMrANwCDZvPFRHa0AA6QX2c/QG4bq2K3ApVZmLwDGGALy1yD0+lNuIwORZpbOpvxxiTzgYB1g8N4FlZdWHhObzx18S5eoMSo1rPMFKtrT1sTCKTrpMw5/IaNRwnzbFxkH62vSJH4hG4f/KwGmszCXiMqjknz12nbXRK6xjubGMQl1TZBI3bDUyAO1v7t1VmmOr9oz3bJQ/0XD0XMFgI9mBBp5UQQ5+a5r5xbAe2iOKU5QqGeRq/drpjHDHs4JrrtFsFhtg2qHBHxkRKdT77Boo+7svTkeFqvGi70KHgyIP2zWFYne88cIfEyuoG91gA05sjSk/3n5HiXYLSpq66d+8BPHec8WdaWq9WxNDWqf53IfjTuAd61T0HWB39+hm4j37K9vbvskjxl5TYTUVqMRk474a7z7yP7XEZCoWG6bQNSeeroGdA3tmRdaTfNSZejtzTCuJmQB1obTRt7MvW2AI4j1YlfatrhiwGOyjPRJzrnn00I7G+qrj+QGQc7irCZdi2svT0JjgmO/pvtgznmCDnuuYt6tFCDaTsjNHaKOWOFBRqA8T+5iO3hW1EFX1eRtBHPvfdd7nPh5jUxpKRHeEn5DDCid+NpdvNOV9wAe+m5By+u1A4x8tQNx19d9x09hfQ5hg74+NYgGNnyewkgjmBIRnC5m7gm0i8cZ32RjLPZFbdA/sSfuF9y4U6pSI82iU4UIVcXOAhW9Tn+Ck8v97EFP23LoZTk1e6ilwnlHFEymsevs/2uLIOjxk5A3Cuw/shiJEbt6LPqnh7jlW8veBvLTAiOCsQffL7SDkcOU9DA/gWfKWcZpFr9R0pu/IYyAt4JZp0oGvs1sEIS1kK0EjKAZ/h+HP4FO2LyLWNruTOkUpoEXQASp017eV+AcYr35bxW4ygW4QQGCbhn4o4z+u5DR5sN2B0diHFYMo8cfQ5OD/n+n3fkSpmBTl9KWzdOoAvmVgmUB4xyT/zvO5RxgqQr4Q2Wc79rJIvEB0VvBGyjQZtQvVsF/j6TRS2QQN5G4lZBzm//wnUD39ARZGJdGHIJjEWTG5SEXiJYfZHznqtqVgxn2FPe5rDCzfa2SZZssk3QRkptm0gn8FzINT/YQFh3oB+MDHS8rpK4FsUFzRiB/BziSFZlUZcgGghdOgvINITukoRfmuiaM42iI8pC6HXxeZU3XcBvpIWmAxm7wI0XkKvcwPDFBPjFJNYxG8FxBsfBEq0BV/BvpDt7R85Uqcgv0ZK5BZ03ubByFQLDcY8iwDPS5RomTwG33Uz/K4LWqvp3S5BIyXE6tyCiqro3cN2bLvgnG1BkU2ty7lwH+kAe6x/v4mMq3WfSe488RUknJuJ49SVDusH69yckc9gJNT2xYghqT1p3bre2CEaSRVCvy8yuDbBed0Euxk9BY27DxHjL/8MS2AKu18WIcxG+0VvgGvqM2oYUVlQVONYtf4DWPi0HtOHGl/j9axL/r8Mn36dBE1bkRyBY18nYrgEH5mac6QAukYOVxhIyCYxbbhVqeSwjVYnUGm3hKljZSSG1nnosOoeY3XXaSKOHJPLerZaBa150Tzm90UXvv2l4YrtHie1jUkZQPPjehffqEwq1fke7FhwCxoop6BSGUyulLHxdr0e03BJktAY1POYkVuHWmJb12rc2UHf3wIP8hRO0J7+m8OYwHc3TCCdr6w+B9/PDLVxpKufj5EjaxZ+5zOCVmd1N74U2ss8EDwmzvOM3mIybTpjYtfMe+YPoNZqjrQ5c2AR32B8Xaf9f4BjVKRTDuHnZBsBFsv5toHzjlXcdiiWFidko91nvjmZ5jMKNvBM/zb98wGeq7MwEqlo4g/gnB0gTpyeKoe6Z1dzafCKJ67TPgcjlkXPYudXlGvRRYionefSBaj7mhpqXT233T+87wxo5P4Gvo923cLGddBRnJcjOq2WqYeI40MXEY/mAtRZ/6h/38FTDnUR7z5kmNTSQsBpw6HCYJFwgKnNGMLU8VxWUiMRZKTgIhX205LAaQeA5SKnbhzR/IROU8j7+6mWMRko7lnhCxqxzX+rogjRDYJ+x3rneaVwlsK/lcgthgf9HKNpNKMosH/fh5gxuABvoK0jvW+nbYywhVNtcb5bkV2r1CvOiCt8CRoRDxRZeAdP25La1aWUQFobfyJpJ228w8CAz0f/uhO8j6XOH4Ned16RrIJrrAXBNyZx75wY9Uj+2jfBT3OgQgPGshplWQCjGAFG8XCziOA+s4q+4s5THV1Nybi+F5FTug3CC4oKY6y3ua0VA/svwFd02jilwDtWQF3wRJ8vDSw4z0vXDTFzmoNzMNNhxQ0H4D7fANftDAoM6oz4OWu8sCAH90765SkYxT7EMBH1LXzQINlA07OGBTc3YIHKXGTcjez/Rs/WxKAwOMdQ+lXPsQ1GlKfCqqD1YkVLoYMGYODkfNJnF+BbcpbhN4uMSLMpWo548fuK5tXBOfaRnjqemEGsuV5FOdwOwOCMPEakYEr7YQPcaxfTckyaGDEzuZ9TF3kUuxgmGZ2KRCKu34GKyiJjPceemIWLKvOEsgugVxwj+D2XIpwpOgSmILbZrcqvVmpUyvsFGNmrfQDrUNnR/e/ANRRGqOZB5XwZ/M7InpfAjWJGkVWbwXXa14keYLRDR8nzWnr9Gs1oSpDt7R/I8Zp2EdIGOD7LjljdcDwMF3SjKMECGHWIGgHOd5M5LoqYyzC/sfvo/c6Dv1t1+hdHDK0drCZJXTt0rTldK1YkYzRIz8GCuXkw2n1ZEXE3zsRKYmFFs+bRgOqmrrjy6tKYPIAvfhzRI85zZS64TvudDJCH8B0/PoNdSuoY1Z/BvfcGnoy9F6Rmz3N6ehscvy34NPFvAPxH/f3vnCqtLVLmWCm7huqI55neZwY0SA6hzltgJugWgbOcsTPLz+B6Sspy5KLf70CD4Sm4pkcKXHSwfwJ161GTrJP2Vax4Zh2i3dGcFe3hWTDyPI9hyEJdKdWT2mPjwNta8M5gmDYFMHCSR84bV0GgX/CsXddp36Imu4l0zDswGr/jAkqiyGfPHCFLk2jcMKioRgI/cYm+eATfrvhdLJAyCWkpv/8YtFhLq+Dk9b1BBfHpFCRsddTImNTmWgLfc2TxRTxPEzO4DScyiwRDJEsgQc17fNOWjCSxF2hoDOoa43g1DyGcEYD/El5PSuMH0Bv+AB4S38GT+vbhCaOv9fdN0ChNipJme/shgXRoxLagNGtubazD85N9QcPU9z0YkgDHYwORCIHG+C0wVPwTpUqRWDeZJUQOily6aARb7Iar098FqbJzza0p3RnQqJsDoSGruu5ptrf/wXn+tWUQmzaEfXSelmIGNBp+C99mcOi5nedNu9LcmzFb6FDLMH2mZzrSfbpFn5+QlFWXxuQcNCaLIjqLoH5/CBp8V/D75RLAvwHH7hVyOLAS6YNp24e6humSTXDsN5Watd8fYbS7yG/A/bUBFnEMdQfK2ChjHvH09Qw4Ly1w3dzpc8YkMQfO9T9nkUK6BjosbAJgDpFFjVqIQG+0r6ZR1GWV7kNclzLkQ71mLA/QcyYbNhnZW4qKliYqMu4PIHL08G9BJulWBlB4Zj2Dj8J9CL4zi6DZQuR+TQM3LXB92VlRlkn51gqQL0CH7goT7MSVlxa46WZBGpdS3jTnm4hPqqI2VQ5Ab2wc4+sZPHfdEE5GkY8fwPT9m9xCfAsq6yNQ+V1lv5COKzGZVog7Uaz44hxUVPlKsZng35aSBag0zsFU3TtgMGev9Ls6fGP5FMAMaFzNgYZC/vBZAh2Yb7pISl7wC/t/RdM2wMhIVWFLXozNoLZS1H3DfsR95/uoX2XD9BxWoAB4Tjcz+j7AQzK2QENn8Eymi5Ta6YIG0g5oOF2BeFJzIC/hIR53iq4bZ2LZYbml7xvuec512gsVEU9bU/luUKlSWF0aE0Vbjkv09gHI/XgJORvZ3v5nRzziGuiwzUG9qaskcCR6IJVTuPeOwDk6D58nU7Wq67S39X3DZ26ChuTPGE3NzcM7l+9zRsAifAX2Kug4zCvqvqVnexszJJuIjKtP8G0IZ+zZwDFNhQlN4lkulfWah6desi5IA+cuIyzLiOaP5URugXNTGOSQUboFRuFH9oauswLCKlIxhyN2hSLmi2CXq6J0vel/K1gMr1FEoP8MbPmaxKSiYNomqCfLdMEpuC+HMjAJ1x+HJvAzuFfGgiVle/sfHTH665hSJy6AE9HVz/MShWSykvt5L6LDMEnZlchc7mcooQdsh4/dO5zMbtHF3T0AiP8E5D3EqZY/ZOWhvgWNTEuFHcDjss6gNJ7zxUsAIyTjGvcj0A1HAPo2OPfJqZRvSB6C63oBQZROKZs3oIIr6vDxUZGl6LjKgLGK7CeOWLXPOmie6WMXYLr5XIbfCkiXdRykWKyieg6c30vQIDTDwfBuh6CBGDqToTHRA41NI1k2PfYYvvBtyDCWodaNvV8gZ6AC/lH33gWw5QisLzscdiBcm6sA+ssQWAPH6iJLrC7NyaIjH27eIJsDI1nvwLVwAu0VzQvg9WpqIYrpyZHohvR09NkdaZp2g+9eAfjrbG+/6F1DKEr+XrYW5sFiIcN/29o8w5g6QUbVtq5/ko/gy1AznsF7E81pyAP6BcP6a6DDQmPKddpPwHW2CeCPJbcwI28RcUfrqf42cibLAXgEOlGGqdwFsO067ZNMxU6BYQs9z6fcdTbhO23twrNuhPJO75MPMJlxOY80eaDPtlDiWGaRCu5EeQzCb45L0tBF98wXvIRjd1YnApoFnLm6xiZ8FudujIjtQFqyUlMNtU9QtGPcG38FeYsCMnV5eyEZdLJoc1tR0ptxrX4ZMYPWVH9KIqVfdmhdYFgJXIERwbwnPA+vNOfRDOBuz9RXSmcJTKV+D26yLiZUQX7fImVh/H2/j3xkAcAj12lvZHv70b2f+Q4Ra/AVsUfB388ceQmXwEhAFx5yMAemqs2DP9WzGKm9XSNfrXwT/r+imEVRn4/wKfBtMGLwQv82A2Vg+GTEbl6BeysJ4qHn/1HjYNHWoTSRHJs10OiwdXIX/Kzaxw/go/K1HWbd3yAFg37tAdTgDtRpb/QsZgjY+/1z5Jpb4NgdRsbqEJzjunvC0rMA14N10rB7zoPnkT3biHNpIt1YeDArkrgho6QLYbRr6tRdaG0jDul6B5+xuk+xdqNX0HhmLBy9A530onm5zv0sknMUG5KAn8PYWA7htvW7ES5PObRnuk+ed3QG4qSEjwTOgvqjG1zjGvEe1NZvPfUcPwXHc1p1C4u5n+PKQ/D9NtC8WNSu8Qw+0zM2o0ytApwsAQco42qxLJT+NUSbrFvy96aVcQvw47iMMQybAEYwiUhs2X0qK0C/khjZe1cpqmtEOAhlyHzGhIqXpJiu5b2bV34IGiXRwo9piO6/AvK1jfNey/AHdWw9WmbhqSIwZcwMlmrewejBeaJrDVKyjhXU5qQCmA45s2UMHGlqdkCnYhEslLKxew+R07tO+xlIBdMUK3yiSN5dTuk+1b2XwU4QFnV7izTD1Qzt/KG6DBo0ZxW6qajS1DpzzIFrwaJ1pQeGjFBjWughpzMz4ubPMVyVn0po/jNYmNAL8K6hQ76ja/+YsYBqnDNkB9TLz/QeMdxdmZwiYuwE7zMS7b4n2YWi+eG6CKPSLsI6ImfqBBUV7Fl1waA1OYjNjUXyb8AI5ZI+N8I7nBXUZ8i5PwP1yhfQ0JtD4lhn8f7hdubFmB0OwT02LWo+00GTqo8wWr9Cp0A6qF8yh3YNe+cofVpdacnj3gBxkJMA21qZvxkElSKc1RaqcQvfnGSkIDlEg6hmROZzPycu2lT/DjTQuphyr+XI/c2r6kbC9JYyWgQGHqzxkw1tjGw6/dANrzaAfLhOe0n4nuNpOkhSAA/AQ3AJwH8f43KmjA0TnTcCzXA0b3kNcS8f+m7MkLQD9UXud4OUY5Eo+v4IhA+MxYemQ/IU9LaXQWfsQH+7VcTGiqgG/JS551kGo5eluieSRn4EGXwgnrIFwjKWwMhsZWcUvX9sDLYQRH2LDgZF4V6BxkW4n04wnMJK3ec34PyZURyTRpmBnHETrhEzejfgeQuTWiKWiJFx38CnMfO4uyHRulwC93oXBcEHV5Oex40WfyWJvjeXc2YtW3OizxgB+xw4b8tgR6lj12n/faDHVsE1Xlk4WxbB1bwVrSXT3xfwaex+VpPLM2dojh35dZ7cPhaBM7aWZHuljhQZt2Nc70BOQRHV0gL8u76KRap1DVtTl+Ac5QtSt8Fi5WT7wNJhhhmchCFnKaBk6g/wEF3Qc/yijElgkI6bhFjqbppjsK2fm0jHSU1SHsLT++SpL6woYkiB3FfKX8bii9yvLSWw6zrtf2ga2Uq4951wiBataYzB1bU+gAdjjE7nGsB7KaVNsMhhI59O1GfLehU3lQ0oAuw67cOS1FySKCXaBXVPXvldgpGBS0SiGzIgLE2cL/QoFH1vAxybOXgOTVsfI5hCOQzP9fm3FYaFtWs9q1oHsevoO4V6SSngFnKpbEUNjTrH1qL1674Fu91MOjNgLQ+NDmhX6z+JsN1FKMUyTzE0C55x12XrLJgb4y397HwHm27OoDPoxwg9j55lFzyITY8ZhvbOEUObqkO+Bw2dAetCGGGUgfhIz3MHRgG3APwKNDD/u+5pTQvgOu0Rpyg3DsuoSZ0TiK2pTUwYBqDotcGd6urgcC/m7ZKRQh7N4WxKxP2+Rev0pkQnLMK/7wIiRqfzBZEAcBBxiHZA3bbpOu0fU8fbeAa3MDmqn9eIEGdWyBn44t9SyvXeRRu4FhG8FsYaqOjvEiK7p+AG/5CqrGs+j1FzfC5YhEbpM/Kc2XCxkx14Dx1bGzbqfzsBOQcpZy7ADThigKdGthLkpe5zrnTPFgSyb6BA30Kkx0UfkFEwDzpzyzoYkxSolBqKnkvXNWMo/5kTCBKiyNoy5ESUGVgajyVQAeYJ588Qd8LW4XVRaWQK9QiHr+Exvmeg0TELjvsSAl2mdbyhz1s0eAXlJM+nAE5dp71Y5li4SCvJgs9ZBbQVrthhcofRDigL4L47l6O8Co6dGc2XdTIDirBtoaTBgKKqfxQcYQXDnKRl1y6r+rb1mRJxMrgA4NdIWOgS6p8oPY9kG1zLa9Jb+Sh90hrTvFqGaghvZ3Ot6FI/eJ4eWNfwV+CafAbqlCTSbdO3iBi9MlwNt3gD6rv8ux+C7388BRjAM3gsZ62odcZis/eg85PXEW/h90V+Pb2bgE6fmLgAB62oY8zgPwX36AqY1biMrME7/TeLeBbJmB9CnHOltDJipSbWyUaHQa0y+KwcaP8vUi6PwYnfAaNNpdEVGRAfQWzi/LhRoVAEV7DI55XSj/1c5OOT67Q/J0bdVnM/x3muHVDJHef+Ngvx9CkqswoeZN1sb7+nlMBL8CCN8ZM2imxFrjOjZ5wFexQvwmPX7lBzj2rMU5S57dUeEjuCOM9VGaPSMnkO76wOGeDZaMXyY3gqqChWWPcMsXypfLMX8OnOPB+mHdhvwXVa9/B7C0ahjDLmrCCttQMfEbFIZqWz7Sow1M5Tms2FB5/We8iKMAMfdVsAdW1ZKnsTPnLc1TMvIsLCkCibUCW3q24w8Bk0PNdcp/1bEOd6rncyCpcQbhJWMNchwl8EDSIr+OkLLrAQXN8ogD47zyzRB+f9hb6XP4zPQX0c8m9+0f8nF1Yqu3AG7rPousw8DtWMRCtW+YfgWSxy/0rvWzZ/Nn5WZBYanpZRWra/uxzva1YPTnGvEtPLjvClFZCeyOYxpKirk129D1mEhzQMqKFC0Truwre4nEVOV+qcM2jMyHrIyMd8hppFa0064ExU3DAB7VDP3j81cayMvZ7wO/YwDKAtnXyN92N4hTHJiJ9V61qY/deIeFE1FugB+D5hlGcbVACfa6QhDIc4j9GUrdHHXDlS5jzV78NN+ArF/YcNKnKJ8VppLYCH5RrUcQccSyugmIrooH6BekT2IZVWUXvGOmNh3ZgG86lo5XWg5K14ZNDDW1EEKySLRhDkPP1U8G5W8HSZ7e2/Tn3YwIEwmpYHAH6f7e3/ruArht87zuIk8UViGLQiDLVFC+2zZ8JMWc/tmYxdZfqO2NFZkGduJJWdE4scXwR/HwfDGFa5l9EkzcJ35/mVvncFzrdBoRagHvdAedV3yX0G1GKhEZ4FbT01x3OgIXgOb2AD7H185oinzhebnbpO+485HbcI0e+lPF8gM2C0b1nG9C6IxQ0Lb/IGxZ3rtP8rAqNYe+mvQNL6/6tgzgFfqX8duW5oJJtzX5uLVk5hnmO47PPLUNMCeLqgsUVrbUv/aw6bGejvQEOrMbxHQYkt0PmZlIFtWYVeheN7B48VjtoaCiIVBpJK1kihfHVjEsMEtGWFAF9VFGLeBjdzbUiA81WndXEzVfIejIr8iMDTLhIdLFfI8WlOQrQAX8hg3YT3oqx7QN3rXSE4xKQAjKtuG+mG8Amo+GLKIeRos7TV0CaUYi1SCGElbuMCHSkxww0vBwf+bJONXfPeteYmY9HZAdSZyHXamxEj6Q0iBrgcKiNTt7+9Bw8YS9/ZXrl1JGG2ysTXuZTvA/BgW0cJd17CXivsaOUCbGRgNCzZu8N3CzIM3Sw4Z0OVtGim1wxDXVRRfO2IjZ2HLxaxlOYciD2cz1hYYFE3OzTLxuQCwM91ohI69B+CEI18WtlaGh5XrOUliFoGNCLDYo9CKFRWvzAunO9ZOSXLdm2NzUNwHOfBuTsCz6o+gAsZd2a0v8oZlINxcx6LOaNrjaTcBd+IFRXamjmDjzQtIGdQ6fkfg7rrY2gUSx7ovz5o4ESzgHruwT7WmbcA0l59dMRu9h2LfprQ6M1CDSIceWxLDcMg0DQLvv8X8FwZ+9xS5PcInPc8Rj+vs5ZA47JOytsyFi1MKFqr+amEbARR9kr4yyTlWzAmr8DD3lJi36rswiu6cZ9z6PBynrj4oEa0DcBggRUakNoIixjuK/wGUzRStJiP4A3JHeGHxirkkAIwjrrkOcjKW5t9gHo0N9yEl6DheVnn8C2QNwjwpLreN9VtSQr+EbiGj+EP1LvQySoxwHeRIwnWe4bjbdG2Nfgeywg+a2JRkqaHyweUtyIEeEhbJbil5s05uQPwf0LYtKK0c1PRGiyF/+Qd24wY1JfwPKNb2nvRAqS8OF8N2nesfE3dB5vwbSAPbJ7ccGHLbMX7GIXRBYi9vzadkU0QCiXH7S2oA09dp/0rcE3+FsSpftQzLAF4lXnarAHUQO9lUuiMSO7gq62HRM7VA/37JlwzoTOtyPI84ntqHZ4Ts4tR6MIpvEOfZHhrLZsRbLht47UcR4/XLdC9hW+1WFZJXluytA45YXX0hxqBpFOI9L75EzYXOUS1A1Z5+Efkb0/AOXmbP+++ujGpBzJm/DlX0dLxK8oZAu+1gRyCRkeYujN5CK+QxiqKkZILeQZDhWCUKVM3UnSPA9dpP4c3wseuCs4mXIijuTgO/r/WJszYceMkMqcjIkPsAahIP+W/kxFPelgVKXQ5gud7FqN2egRGOk7AgzePR5xBvNd9CknwAXgwLgB47sgPGKtYPnTsLNFoLet7Vcr+Ss8RHtBr8LQZB3q+fpAahb5z7+B9rQ3rad8Hn7sOrKayGrRADFc5qD53vrJ5DTRiqqrS7zA+JVCS5KKZ9lzGK7mSsTd4t2htyQh9B2Jty4rc7uSgzhfs15vg/mVpxzI84ik4xjeIz/UVAINgRN8ncBLnwfPY4FNDRTuKyD4AM3Rf9LtlqDgntk+Dd7iTEb+IhCrvwLmfrxtk+ZqiMbrN9vbLOg1N475bQHPavMBwjhazgbp/Hr6z2tCcfHVj0kQL8hnKK5W+mmQlvGOJ3x/C/uXkDFQGk/C6jCrCOhsVesX3JKfIVbemimvIzxa5Tgv1MIG1JH/gyKs3eo4fAw9uCYzgADT2u5FrVRmSLfhqwzqe8qTkEnyvHXhD5Z9CZe98v/OFvFJKiTDp0OnCpxXLPjttp+iDYCFGk7MO7rEBZ10wv9b9BxhDV4wpy/BV602io0bWvQLyBc/oUN+GcJ9gFD4f4bKCr3Cf78BjgAfrwLFqeBY0Psam/XLk+1wGU7xNHCyDZFik+gtQvbZSxzaSdg7/ZkU0teEmwTWuoai567RnNWdh0eO5E/VSiR5dAA1CANjIWHhohlw411vwsA6L2D6BLwopbbaR1azy1nt8NUMy85jcXSQEGVwEszzlR7T7rsJHuG8jhmCqjLQWDsSw1FGn5ZsxJjHcsSFaqfRLEC2mXp1DbsLRtsFiSPCKAQzC1zfTMLYqUsxVso0SnKljNd4SeIhElWTgpPQVQbqPdbUNFh8BdBRsfo3pILmjQ0QaVa/WFRlNRvPRtd9n5AT8A2gM7IJzm4+oGIUMUCOa7wJi5YwdZ3rgXpp6UZ7ufZffJ1o/pqSt0M288vx+WYd32kppmWo81yIUyU00Xsx566MBhleGo9HFGX3Qkf6/BeDfAvgQwbw9hu9CZPyxFxCmODAkl4PrDmUFmogctw39r9EvhX9/Bo7HT0HEdKgDWEZ88gU8Y0OdiGzRcyVn2Calk1wJYXVZ9FRyDeqqMJVu1cOhHGGUD3pcuMm3LmvgWDxxxcV8JiEN030GcJIi3GUiw/kNCPOKYZNvUOIsfEvG5DH4PFWVSt+s6AB+DFbNvawbNXGeYPdmDI/mI4QBBMq9Yt3zAXyv1zyJeOzzGyhoDO98n+LTaRsAzje8BxgxKcK/mJMyA0ZaZsEUxDShFKfwG3pwoGs9vIx+I10M+zTg+gvS5wAB2kYBMw6Oc0vX2MZoS70+gN/LABtpGSiD8xN8W8pKcRFi5Uj0K/a9eXA+G0e4gn2LSErdKFfscBik97PhrhFLoJG0CbVSbPo8OXkAGuSrSGhJqbkYdNTRPvkOvvf3jK5XBs2w+XwM7pkLcB634I2/vJFxCRoZIXH4sfCa4fq41fVnMIGe9zIEo4UUWp9/EbzTC/17FxzPNRAjOQtG+62bzJmr2avYoCfg+38H0io1pgqrcd9F+F7UIURhETXGV/snLHZcBiOOcJ12394ji/C5Znv77xyr2pON4kB/X97DGMU6CdURi4xWBlwyj1mO0u5MS2QIjhXh1ncb2173Ykw6T2B8WqT0s4qODb8QMQqPORQTgpbJlv6D67QvmhhkWQ4DmCD2zJVrIZbiy33kMajE1pBoNCmyMJ/t7V+6TvspeCi8z/b2zzN2e7hCZBPLYElJoRuvXw9MYe2AB30to04KaRFMuVelXi9dp/2fQYUy6RTNA3CuQlJnM4asyGIHNASPkM7HmJeurlO4lsoiZeYMKe22lKBYk4iVQ3EkyN9Gg172Oiy3MPx+I/OqNKQ5WTfg2McO6TX9PAUj5ZNyVs5R3As5RYyWBuCaeQDqpkUUdMDSgfgeoiEBsKooZNcFFb6571i08jb3+/y+vXGelmgiEbmsuJDiGn7OLoBBwYtF3GxMZ/WZO/h9VaeSfQA9AZ25AYUQEqLyctBbYDcii54+0nU+VuiQZ6B+WwKNwQX9/8h6KTP4hLfb0DOcYNSJyn/eOtKcZ+TjrWNIzoAtfbcBfHSd9u+n7NybcT/oJFRHsr39I0fexaTzPKug3ZmW5OdAgaI1kEYvug6d5zfuZ77QrJFM3ZjUw34HLvBFpHUj+KXKIdSRIGtWkm9chckE0hOQT6DndSFjbgbsjhPbOPa7kNcylBvU9IihYgFFF4y/zAD7VQZLSu/jPoTtcZ32E/163tVvV/g4eK7KIqmqiPAYcg7fCcJkBzQSzkAOPkt/j6TBXYS6JiY6UCaBx/weHO8WaLhF11bmiZUXEtO5gD+0F0s/FZcH+t5itrf/oiylnotAWoebvJF9AvZE7unvEzlMMhYZHY1x2J6DTsUMfIVpiqN7DhoLQ0ZnlutSlXvWpPVeN2PTVGS4/mcI5uN873SARpOdRbPgOx2D83hX0wkMoScAdWpSVN75PvUAdWpXzxmm7sscwhtwn98IonABRvhXXVB7ION0w7FgLdYLflvX2XGd9lngRK0AeKrgRlgc9QS+0DPK0SqDcxk0OMP1a9kiM+LHxs0WiewPg9wsoSHkalKOT13RWlgFA3E9R7zxTJZWZLOln4X0YuCZtq17XdXQvSPyLaW5f/Ei46QxqWo2TCA9tQ2Wu+ctgC9KCYUdZ0YiUnq+l6AXEzssP8BXrVeKpR/0v7Og0Wf0FtOQz6DyNRqgFmgk3oIRgLIxb4U/takXQcqMe1M0GYHxX3LPegcq9HMdoG8QoU9yw51r3k4LTiK4g7UxNCfSWn5aS7aYbIL9YM9yB1eRfEJBa87IMz2HiizkpZ8hMMrzKXXnu8YsgIUsxs9YVEAQtu0r1asu0nnKscBlHqQHy1f5N47aaJ0MjBHHQoyFhLmfgz94i0jT7ZpLoHFxDY7VveiuFMk5dUaaPofhog5rmjGCmU28h+25Vla/IK4H39ru2p5ZuNUlVDt0b6EaA9dp/xq+25Nhs+3dl3I/89IFHaxNkNrqlRw8cz5WC3CgZXP9G9CY/IwghS5D9bV+/36a60V6/oOeIxVyMwMa5C1wPd97lDGQp5D+dJ32IYQ3dp12L2GtHYCGYpljFGK/x4Lm3IcxaeTVJ5hCGtt5Ius+qIj7Osx2QPqOe6mmmpRM0muXZ7gFjkNVmuwSXFgzAM5lXBq5dIj7K6N+MCqSJNFcDXqjTjtioYMlXIPr8IT5d67TBnyF4lBnC7CIZh3EU7Xgiwj6AD4pbVo3olEpWsuLCCAiEeX7DjQeDf+1CRr8VoFsc2lFBtA1GxmTgq2s6Jlic2Zwh1Uw/WzE4mFq0a5lFfsW1QYSI41S8pWZDufpsqBnMWPyDsVp/BaEX9Z3omOl+XkCvtuVPt+NfG4TPKyfAbh2JGN/H1zDyPhvMcXGDZqvwbsE6+UihCLIQPoMf3hZIVAMy7oGjpf9N27xygK4hq4KomiNJGNB4s8Y5ZG9RYPGCnrOp/rewOEo+OwsmIVpYdg5uY2l/bO9/SgEIfZOYPeuFfgon6X2v3ed9seMvJBGgG8tNg0PfZ4Rn3zo2Ov7MTzm+gY0wObA9XGn/bqrvx2jAH4ROBgLsc9ko8T2U5OsfpvHJVC/AVzbtQNEev/bElhBC4xI91AeyAg721kvdiBtjx2Dc7wt47Ob/4Achhf691hQg/swJs2zmZ+SB7IGT7dyAR4SRkmx5sgB+C3yVt6HPIAnLv6xbPx1yAwKcFynbYpvHuMXjRRKPm2mdMwayMM4bdqbM1DBWmcIG5810Ds3RWyG6KGecQb04hbgu2H8CkwR/ZgYVauU4AAKeyrHZAveOFyG9oPrtM918Ftf3RYYpQqjTk3E+m6vIM4LOIA7ZL5XdZGBtAXvDL6Bj2hOTHQIfgbnek6G/1OUYwfXoW5EAP655PKr4Nw8BtfHebjPnO8WsgKuq6egARx2cLqFNwDuuyJ2F3I+IjrCKkS34alRVgD8lLuGFX/Uhvc43y3rKjDGNuB7gx8WXVNOTb9mZsA6A4WBhreI8OYlSMi7V+icOd/ZKmQ4CIn4x3KitZ7NmDSSfzvzzFHPwxPCIiTbb6d6zn7mW0xeYxjWswafPj0veXaLiC8hKAhLfB+rSu8BeH1fsIhALuGJ3kd0URCsOoudUXIcH4KFuD8X2B/r8AbrEYr3/TtwP+yAxvkr0HF95Mgu8a5ifMqofgCMb0Sa3Icx2YUiXFO6/iW4cUI+KuNtvPgf2JAEfNj6poEhn0IuPQ2xCNbEjAp5gb3Mg9tnwAPyLtvb/9kFvIjwGCSgYH9knkzX6JfW9LyLoBf46b5S3zpQzZmyyIPtB5t/m8uzCUXqV8AxyqfRV0Bj6RpUeilpk37w8yZL6EpRJo5FOYugMxJSo3QVoTUcYA/V2MEjVPSThu8rfgRf8GHPsgyPhzsGjc1/Atf3z8Gz3Qk+EiN5n7bYu80AA+N3ATT+b8A1tQy1Y0TEYJLBUbcAagvekVsEjdmftW9OQEPnCkzfjuCbNbbP9b1XZRmTnIwEGjTmTYz4E3A93RZ937E16H8A18aPKImGy0jZAqO/dbIGxvNoUCErZl0EIUzWg/5DgIk7Ry7ingX48hKx87Y0C5V5/syZBtCBZfiOSYsYo01tE9E4lOHxwzUUKyq2c2MWxUacBTJ6KNGT0g12LSuuaiGhyCsjxvI1CtqQTlqmbkxmrAJsjCNMuP61I/dTP/jdScEk/w8lSl2coUHaSWmIqaQiCrA3Jp/RMLVQcK8tMEIbUh+ZpwfXaV9mrL5+BbWYdJ32DbgBuzKQlsF0f5iGsupjZOyG8UfQcOhOypDMPE9oWVHKLTz+77RgP0xsLmVs3EKk07k/m7E2ixzuV5CLNQjXGXznRM9+NIEIzTwEJgcN7Pw7X8L3ff6AEuxgVsIkENxvBr692AFGOTeNBmcW6m+Mgih/uJ7uU/Sea2AE6Rk8HKAPzq+lCK8AvAmgE1vgfJ6hGfm4FXzMg2M26DylaPbPcgJ/rfu9yRmMdnbNgNHmFhhtuwwdJq2JZ/CR73PQ8Rs70KB9XmVEGxG49bguc5AfgfOwitHob5lcgfvO2kDe2Fgpu2FMAzZfVqE8krVTVG0TnNMYbv7Gddo/6d/ROQ+c9dvYNRLE8KK9CKTiq4nOgitwjK0Yc0lOQNiu+AtoJF4V6TTNz8+xv0VkMB6gDpuFN7BLxyfIDE1d/iQKcGKLOuLJzoBGxGX2C2rNNK7U8NjvRRQ52nYFVYXZeCTnMTEPbiGIcFzDV6Tf6r5WRT/gUguA2NaOshDHlOU6u8SiKU0kq6CZyHL8cMHvpiVWMBCm0kyM0OFZ0gAA53tJREFUD/HODEbnqViW4aucQyX6GDwIF5DjOZWSnqsR2bhFvNodwKB46Qg+Sl2Ulhwqvqm4p431SCV4lkCDI+N8FcR7f829egePG2xBB6HGawUBL6UO1SegkXYAdmPq6zuph38X1McfwDUR435dhM8SLCGI4ChgMAcVzQgesw5gXYEEO8RX4AuIlr5CoMEKZK4qDEmAhsIS6kdJ34EY7xE9IYf0AByHbu7Ps44FNufBHtvS826hIIKaMHab8M76VZ3zVsav7YdKp7wiMDExcZ6P+TYj+8MJaNT9Cr7ZinVOMkdsIqK1HEZKeyhhFJH+sme9tw5pjYxJ5ykW7sBqrF9CKnkXHOBoN5WvKTKwVkE+qG/GEwMG6aRJdiFZyf2cthzAY71+5UimfQESGPfL1oHS2dcQP59jEUJldZ8MhL90nfYlgL+dxMElQPcm4gUQTa9ph3Hy82V7+2eKlt5FxsEqmi+D6xsVyyKA34JpyW5Vul0Rpe9BQ+UTxBVY9u56j1LapsxTpWzA9xjOOzUhj+cgFajvzMMXJTwEDZxuGDl2ntrloswQdr4fMkCjdOxOWBrzx+BcfEic2w/wEIkF8BCyApGYA2UV0j3QEFzT9/rSrfn2oou6/kmmIp/EjNU5eChbAeeQZMMFBee6R74z0Sm49+/g6cZGxkROz202YaJp7ZERzK3m6Xtw/X3WZz9rb9TKbEhP9aSrr/PjXzLWxkO4BcD6SHf1/+M49HZWNKG4ewg68JcAfm+/lGO5kAV8iU7NBpRBeJ2qx6RbdqDio8TnMjL42SAwtQnqiUG71a8lzlMIGcRvV7+/nnbwTOuuNVv5ybiswlc8LVd89luRsCrqm0l/BymBBXjs233cd8112t/rgCz6zDoYoflei3US8gkidp7Q9QbiOu1FKYqBSDF/BtfpIoA/A42ajUSH4rWe9Qqco9XyjwOgQjSC9JTPp8hDMPLyqOqDKaLD8zcAflA0IFmyvf2rAoP6HThWZhTdwUdVj+HTNBa12AA9+0cYNQJD7j4jJ38aPqvrtBd0KDcRi5iu5/+gd/sCpXDtXnrObfCwfQDgXwP4Nxid4wfgOz4tewAdfknpqhqyC87rd0jXzdfgWPw7kHz7admakGP5E4D/DOAP8IaHYeny8gjUbY8Tn8fu08/29j9ne/ufqgwFGRl/zPb2X+UgHr1sb/9dtrc/YljbO2odPgHwneb5PuTXoB76d67T3pbBXYvP0HXa8zo/AEaJ/xcA/w+lqlPkOvcT2d7+Uba3/3Pmmw7MuE57S9HoJJHD9wLAz0Vp3ry4TntD+FKDID0yXe58IeITfcbE1nfY+SdFdqFGD8H4Vcln8Ox6o3W0A9968nWVc3wP8hQ8Ix7Dz2cV3nts0X55DuBxUwPhDJyMwlTRtyYZsUGXoOf2zRiT8iq74IF0nwvSuqTsOE8r8SFnJNgGnUG9zVoo8vwn2YscwID+5il8D+6bINVsNDXL8KmbFSTgMjU/x/Cg8BRP9i1ETYXJVeda+nZS+83S//NglG3sSH2W676U+UKlOXANWZWuGY678AfCEC9mFvSJBdfmAwT9Z5We+5/BAo3/UhUtjsgcOEdRLGk22g3Coiwt0LEwp9T+HcpN7mehZHv7bycFiZC0wHU6lBaukKegk2VFSZVrTHNtB9WRdGtRm9IrqHtU1XXzEIMwsqs9vg3ia2ORyuQxzEW1uvr1fQYabG5WwLV953wBUqU4jwW3bl5z4JjdIdJaMiY6E8PWrzF5AuB/1fP9f7JEUuv8e8hYfghGA79E/mZO8iWYTTpH9Vx8AfXKRarRKjHM40XqmtG6Dsf0CL5jUG0dLwfmdoKZyLvg5yXo1J/XjXI3ENszM42MST1glPX+W5ZvLYVskk2x2KVEjsDD9ArDAO2BgZURpH0HLvpvDXtplCLXmleLUM2AqYgnYLXdJ/DQMyxmV59NLvDJqqv78p+/APBfUz+feM0DR5qUScEzDsGxqkXnokN4BwScpxjjg8IKAL/L/fkdaBxcIs5FFxoSZwgq8kFFbhWOK6jviN2B6d3kri3OV1z3QEzt/wEab+e5zx4kHNJDIuPAUsdD715TuqBuvkjEnM2A43em+x+DqVfjFEzqaVyWSsv29j86Eu2njLW13oWeKxyHXXC+o5QtNSWMallXq9sGTklUnOdiPQfXeA+iHAOLdt7AUypZdqiOITtwBjVP7/T9dSQ4A4rozyTo9W09dx8cs+QOKcp+9LSWt8B5XVIqP9RjIXH7AXwl9ywwwH1aVfLg/lpPAxiGjNIVVHAWZwXFR8F1FsBIbw+MRN4pItoCoWh32ShGfhdcu5+qDNvAEYAciEmsuXfwlFPWlvhMv7c0+DZYM5Jn4ZgH56n22aKgzSsAc38SBTh/CqJIy0wkIjIVUVj+yA2z/ceKFqYO4FUaI+w4kGK4bsOnS19ke/vHjhQKvYy0FINuPor+fAIG7z2JZ15EHDc4NbHN7nw/1bsUg67kWk0cmE14gP5YFffy6P8m8bP5cX4Fj1cqolqZAZX2HEaVvJE4Jx+OeWWblVRKmnGlqH+VM7YJ397xDsCNY/Vyk24s56hRCazosXWesgN9w7Hw5jFYuDbgW20qVYakDrQlMG14CN9bPpRjVPSLF35rF6TBOpRuMfxqOFf5qFZloCHAyl0lRKOewXNLGozAYB/LehZLJZ+BFdhW4DQL4rlDNoRlDBu7B6CxY9G1a0VZF0B8d1hdnH+PQS9xx6YRlyURup9BY/IaNYpKLPKrf7+Cp1EaqaLPAuJ2+Ap/6L5W1HKN6kh7vk95oVQYTqvwvLyLjsVlll43IveB6CwwFolrVNMrhW2JSx0IGbZFEf+B6O9n+s4A3xl8ZBu+w9h5sNZsnm6V0WtiUF4DfyLV3L9kcZ7j8BFIRXOdFTRlT7xerFVbC+rekj/UsoSiBV1jDqTOuVHUbxFMi08ijbuA4Q4lKcZkL/hplCLhIfMRw8poYhACGQjPUJ/jruh6RbQ5RbKO4X6q9xlx74KK9d6qBGMipfePFR+zoiWA0c9u8P0bTKEjVyjOkxeHHIoxCdv9fY3CQCO0NjhIF9xXhh9M6khUJq7Ttq5IHwqimN9BhkBWwDWaseCmG1zTCphmwS4iPXBfLOm/Q9BI3wDTtAPKrHxUq+CZB0UN+vyOrt/Xtcrmyv52oec7BQ3WEahMxqK2eUXyALWmlG7pB5EsS4X3Mo8FDyXEQC45VrhfZPECMzM0NgE8c6RDex0x9M4A/J8l71kkIX9sv8rJCYybHjx5etI5KP25BO6jsIVkUzkBzyPjD52FbyoQc/CsfeVIliImGRkFbsDAR+GzKhq6A66FD2DFforz+w6+GMfkEsrkYVjH2B63NdFY//yLMfn1ZQ2+3dwVxuiP6URf4EZ7Gz8GN8cG0rmtwuuaJzvrOm0z0uzZJ2FMXoEbuDR95VjR/Azc5K9172h4XhHVSoPHddpW2PK5RvRlKKWe+J0yeQSugTxtTpEYTi8scLkXyeq3JgMwMCaWwEN/RCHrwFwG52FS72Qk4iEvW7LomWZBXNS4WLqyDhSnSqHfQVEZGyON2zI4btNyGozn8RYk1r6UEfMe3ihrLIrcPAUP4g0EvcLHlGX4Aqo1MNJnqWM7SAdOZy7St4QgGlggT8D33wAj4SEurUrewHejmUEu0hgRM6aNnskIqo3TFShZQ8CAV/hU33kEdedxnfZQv/eMvLrWCQvw1fgtjNmfObjHqTDPd3Wc7awEUqSI20IkgPEM4tkF19aWYwe3j+A4WMYrqapZBl4YYDGISxEN4R1qdolLDMIMKK3A9THjOu3PWaQtYu7at8hF9WXAnmOUveMQPEuuY4atHLaH+sznsjX8L8bkVxSldqwK6w1Y/TbOZjasUT6ScJv7WfZMW6DiCxejdVcAqNBMYY+V+jLRAk3pRWuE2EYT0diQlTc7Bx5Gs/qZ9D7amDPgxpyEMW0bdOSQcpHiDKXxjTj4m6G4KhKNtR36I5guHRIP9L+GZQy/u4gaYHkTHaCFxrnm8Bl8sUfY1tMKIwCPJQyfdwdMP3VLHuFUnyuqfg+f9VqRiBkomq53/xXoaMzB07dMVJRt+AIevI+gg7Gp4xARS+EvglW+MXmNAn7QEjHne+As5J85I371HIGRJEjRLpja+7lkXQ0ZjzLWLlFCZB/cd0BFhHpYyK7udxkc7iegQdutMH4HcBDBFBagNL7zpO5X2d7+gJpLa6yFgOh8UtIEppEXN4zb/Q6EXXQz0igZx2Te2LZgxw4YjbOuWFWZjEKZgDPZRD7DRz2NCcEKEI0/tRDOkJf82pH9saprFNkGa/CY3guU6INv2pgM8C6XTQ9tDdhz/e+bcaMe2pRbYEpy3EiB4Wjeojj9U0c+gpsnP+Ef4du9FYrSLAYMvs18f9YrRSTnUGPxRq6/Ch5W51mcv65KTEGOxQcnBfoD+D6m8GphVWtEMVPkA3hgDL2ToifPXad9i1yP2l+CEWmiw6yL4Wr68O93rtO+AJ2hvDFhUYcTpDkcSSJlvAXqlxtQqYbjb9G6GYzum019F449nov27QbUl9t12ktla1Z7w3BZ1xBJuNJ+9iwp79WUF9Ywi1Z53/jwFA5rJtgjdyCkoLBhRFZBzl/wnR4SIkIRo2Yu+Fn2rhaZHczbJAykAnmte8UI1R+AZ/UOSnCLzvecv9H1zsEGEWZkhaTu1qyhhynQtE1QvgNxi8Y1CwAtFzS/AM/PFfDcMyO+Ba65HSiI4jrtuSpjvInIJpidgjHeg3csb6A0t/MFQgDXb7fhLQyutobigupLMLrfR0UW8ps2JuHxLn3Xaf/Y0IhZgn9P68Ax7jOtgQfKuJGCM/hwdJPUofVlNozJVQxvpHFLMb4G1CvIRTEnZDytQ9FAx/7VtQyibLQTQFOZhT9QLrJ7KnoqEs1PLCKzDCqLef33VYlxx5FMxMwlf38Ti8IiIAtOuY8U7SZ4KJcd/E/g14GlR8PnuXGd9gv46u1QLsF9UkXKbF2CblGdPjQsk3UKMojGP4P7Jk+nsgRGXM4zFWE50o08ArDqOu1DsOioTvegBb1XY6ooGemPwRaHd4oUfoCvNP0W5Av4vleZL2qzTkwfAyc6jC5OVSqM6Wv4XuVlYpmbRVBfrAa//wyepdf4hegRBZMsy7YEZu9WwL1qxtRKRkxomEl7G1zjRzDzdzlFQ9IgYO+zMeodyiQbLvYzbPUsGsyl9qgxC6DoGgouLQB4kWJ7fTPGZJDy7YEpJ1PCwHiL/xS+uGNoojWoT+H7zqYYqyNEr01F9xsHP/QIfLcfwA02BDJvILPw/V17ipZclYTA60oXAil/zciaDIV3UP/tr/UcCXIEHgrjRmItOn+HoL/ytyYF6zaMOqRI2N/4Rf6PrtN+Cg8HAahrooU4RWs0Y/u+n1CBg9Oc/Zj43KvwnWU2VVh1nBW3F90G9/6y0n59eIN7F9RpO6juHW3SA/fCuOm8W9CYNBjMaRZUmk5CLNLWVM/pebrB9QzmAgT9qychitKug7jbRsa0UvXHqIYpmb64EWziE9TNJtvbPx8jIHNvEqSuLWPxHlznhzqHjCXB3m0kyCFHaxtceyfItWlNfQ6o+1XF+TegMAIjpgbp+JjPgroGHcdikrH6/RUYDW2SzXwMGpLGR5mHHS2Aa3YHvuI8dFjnQMjF0Ht8M8YkPAgY8BipL8K73IyhOPooDuOvgpEfS++k8Kl9cezLmSdlnUNFe76m4lg9vQymwkNP+Uq/DxfUvOu0B+X6Ne6xBA8HMM6qLfA9XzR78mHR4fqt8JOeg0btV1OurqKvrP42iRSUkdID8VTyNytSyHUq8a2/8ZXSfpvgYXrhfHUudM3DioNiIFKwT0An8sMk97mMfUtlrsFXU5eJVZye2RrO9va7Sotfg0ZFcsV9RmqtW4zJtSgj5jN0wAqDPQ/2Wm48Zpq7FjguVozwehKpRRktX5DjK1UqdQN89tLMTHAAn+YOeKOmmsUYui9lTrSW3wf/f4HAUEjVdZqzFoAvTeZMhpgVH80jPi6x762BXW4G7RGzAtxuVt78wpohLKM568Rj+GhwoTGq9f4enlrvV/rTJoJqe3s3BFX6DZ/L7jtOhvUSfLciqN5zcN42QSPScLhL8OTyeRJ3tBzpKwqxLPcoJ/DeeYhRCUHxk27q3gUXwWXqoaJnynsci6CCM6qYiVXYOk8bA3BTDiY/Iwj5GB7zBTBKadxe1vLyoKbSDaMcswXpx4mIFPYWeLDfS7o5MJxvdSDde6TOUpLC3KVGj5qKGR7JaTuNUf9r6YWmXnwW9Dd2nfYPoIO6CBa33Sr9uwQamHUi7uu61gKoYCc5LpYG7wP4JzAbUHr9kkM2ymKgCNkj0Ph8r9/NggfGRba3f1lwsIyI9N0aiJ+2gybUza+gwwq+peStYwOELT33KoQXr9LpAcZ5Fjywl8HDfsV12r+fROZEumegf7T+n4DzvIHq4jyLxq5iGMd5ijiO/ZsUza0VnvVQUMnvSDu0BmbWevAR0DMwBW1OawvxcYlJSCQ/DsWPrZGo0+x8a9TrbJQ2qbZYalsRc+tUlr+3ZUPCKv2vItne/rsK/Kil0V+BtoPZM7fw6fURG6cF4gmG0qNKb26Bi+NeNkFW0lVHk2RA3InhEjRIpeSmibIA30t4HhOia5ER1wu85m7+M4GReOo8UTfABftQ/47SLegw2QY31Yljl4EZRXGMey5KaKsN+R206MYwyNbAcRtwQk5DtIZ29b89fH0sovW6XS4z1hUlWAJpGRopIB3WyVhT53k0IeeokeGkMV8E11eyE+g8r16jSHYwTufg3gwdsKbckka6bNWVyaLI4y64l7qR5+0DeDlNpw3BPgvuE+VgTJAn8AU7b1yn/Qw07D5nrBQ+B3AuI9C4/y7h6Wqe6v9XAPy51sk/lThVYSrRuiWd6t8LmPDBLCjE/wbqtp+QdkYYMXlYNb4AT8T+tXs3l0qgH/vwc3altXsX0e9W4bsO2hEL4NyeYRjnPDIuJfc3p6quozckWY6PNCIb8N14DguCPx/gHaLU+5ZxNlvHsUrYmKKYu2AR3lTOxIrz+g3UGSrUCZknl5+NjZmlvvIRAGtblUwcOmUxehCAHse38EyhWAGMkbOOLYGC/pj3moskY6XXB/37XBABa5UWk2148uuhinkd/mXeuPWqNpB00/c+AD33bsPvp8oGVIELKopjjIlFHFO+gIq7MNWu1F4YJRjB104Kh5OTsNillNuuQh5CbAyoZxQOKK7yBpYO6CTYi6KUjVJ1kWtdowH2SmIt6dYc27hFFXlGbsfkFoZFouhSWMSzA66fPLTDnmMWTC9exIzdiNxg2Gm2iNKGI+3OHcQ+oMNnRqnkLrgHDyBDEnRIu6CBGTUmM2Kc3+qex6Bhdgau+4kWyGg/PYTwcvDRtlLJ9vY/KOp9ozFogeNglEiF0Wyt6drQpAmL6cc5MIJoRuCvQGfjZW5NfoZvv7sC2gt2Lr/T7xZB/fEa1ZF862jWx/TPd4uMX6PAEZHOKE2Rp2RKtZ7mNLep9RGb8F3G7r04VPNcpH9aUE/7LMfI0gI3cH4Rn+Ab6HJhIov4E/ym/OqiqF4r29u/lnKe2KQ73y8X8NVrSZIF7Q8zthEsi3bYBh/0BK4hJwgMfN2nNhavKF03SRGU4xmosN5BPG74iutbyqWK6qYHztECfKTHOnL0AhxO41ZYBc92KhzQHcYrNGvlfqbKge49ZGg7kfKjhnFaNiZVEcOmIqymHbC3oBFnTQmq5ugp6ER+qROVUFp2C1zTj+GdvBt4J2ooypyxsOMM3BerYBX4ccI6egfyvNq1LIpj6U5z/m3+DNM5KCZypDo5BsdkHhV7IWc0NipclJE3B6buYtkWI4n+AOAPoF5KhqBkxM8twmdAjuGJ86P7SJ//Xv9+/RWd2ytQl7QgFoBgPIz1IDQwTvS72SzXp1rG/w1oiFrNwyUwgFs8BDGUYYrZ1lxtp1hjiCyXQdHz9/OOmZ6ldvOO3LWHovGRv8+CxvZDEN9bpyVpFxzbezmfdI7MZWld4jYguI901CBC2coiFWYZqSbG7XrQAjAfu34TSXzR+5TnYOSkltJPEUUoPoIKeqxrl0VwZDS8AD38oQNE3vJzcHO/jmzIOwAfHTvIlLYY08aKUaxMXFycUNoooT6CSuQ38GnuqbbUG0c0d6/MIXDs6mC8d0byDPAAaEznUiBn4CE3L2hJk6Kdj1AbzzpfkoKKYZmsQG8+8rcmsgUfMTSs0/k461QG/zNQ6bZAPXoKILWS1qKyD5UtGIm8Sbf2ctd7oO9aVxhL75+ghMYoYzeUwr7JMdF9Q6P0FITZzMJH5Kr0/jUIvWiBcz3VQjjne3YD1AVHub8b7h2gvvsvNa9vkT0rzlxGGibP6FmWAezKCJsHi7zuE1dnzm0ffn8dg4bkbQTq8gBcZwPaPsGsVsA1/xiMsC1i2PGzJhEbCPZ4xuKxK90ref/JifpO/36t9byhd9gGo6q16hhkKC6Bc1Cku7bAdR6Fn4Hvb1Rd71BDZ+meQ/cVRtU6iE3M4XAeGwzHHuSX4Pl5Cr5b/p7HUNFvfkynUs3tfL/puWkYW9+ILOR+TlTkxUySGLvoPreu0952LIQ5yjxPpVW4A1QIRYrtGjTMNkBlONRySQff92Ahz5t78LxjhNKDlLI+08eYxMz3KcF42lieK3JoOJzrOsoyUQwHBUTA7C7otaxn6OWfQYfhJDMJRso/ouC1fhdBbGlqNPUcXLdX4OG4goY4zUDMcLvSz2tQ8aautfegMToL9kz+ORxX5/t9h72b1zCsyy/0mSu9TymN0TjZATdKhZKEzVUU7ydwDtYwfQ7KG3A+ZhFPubbgIR2tgs+UyRa4XzbBPfEdgAd5+FBeBEf6BODX+u4yaOhu4B5TnIIhGIvHoX43oISJyBVoGN4E8Iwn8C1m+6ARdZx7/y/6zMgeTgk8yWEJMw4hJGfWea7VNXDvWc/sJP0oZ9Cygvn+1vYZw4kuoTzC2QPXwgHGwMzqmQyjarChSckd6GTNgPNlenADPjgxuKfO02ghVUuW6QrKW+rUlbBn8SR6F3+LYvx3U42YFkQh6nz3OTgfZd1/wl7bZkyegpvlGYineheLkmR7+0e6D0BleI7hDTiPYaN02sbkCKF0PtLlWOk+Pwm8lQ7TTTB11h33ehVyCG7sCzDNeI6AfmKSolTVZ0S4OJU+WgfX1VNQsfaVap9ar3Dpp5FIstbftv53E4kp0IwYY4uqPKv+Rlxcri0jiK1sRaI5Kc907oh7fgxfVRmKRaMXwPTZLoD/FZyj34H77ztw7S8C2JyWM6/sxZb+d4gKpeCzrXDPSae9dwWVpW60k05jyXzxQBT6oLXw0f7d4BZdcA0ewRtTQBrm+AQMHMyBeusGJca9xmU28y0RW2CaciyGgTpOhSKJZ/A6tu86bcNZmgNvxUfh9y5RAzoQivSO0UK9yfb2r7Rf3una59oPu+CYfgQd7WRHJfMFr0sY1XuPgvc7139FNtMH+Gj/OJkOa2doldSTDjAtgmvWbDarszgB1+JS6j1b8F7wAibTXcS8HGsPNfXo2teQLMfhNQ1xvqq1lOuqRJbgw+vLKPbOPoOKcDBXmkMzXmb0/SLj61jPeYecRy/szQG4xu4j0lpJKJ01aN1WImZUrWP6RUQn4B5NSSM2Fnn3a+CcfQmdTCk3A2h/BzoOdmDM5q5jXRbWIZhBE6coQay4ZDF4FkttLoIR96L7LuiQMuU/tMYVCSklJwf3mBWhrGXs9tP4AMnIrHANOpH563yB7918JyN4S//9rSJ+L8Bx+A4MGCxPCm6UkxsE465U8XV+rDS+36MAO1ZgSFonHbhOG6Auu0512GS8zoeGYVUKP9VodcReLoNzMQ9CI4YySTJw+injrnl8CV+oUXbvJfhx6YP7zzqwfMgCzPy0JeI4vsZw8dikHUuj1QEIMTM88HXmGV5WQT15lVV03SqSmPMlg92ig0b2fVPkPGutTaIWYBdcY1bsNE5BZExCO+ZCTlcKbnpELPxrQO2JSTbc/if0Yk8nERH6mqJIxOw4HkeiDKIQrhltyBk8iLdwYRcZxopOHYCbuCwCG9LrDIDaOkRmM7V7i4kOfCNYtoN7LOzfuN+vKdam7NAVVOJq7Vt1qEWc3tR9zqyAxFze+CZo+HWbvERwLUsT/QAarn1hmawjxAMw6nIM73x2wQM13O9WHLQFGhzX+lzt6ImwWAuIdF0A4pQcAXZxBiq6iFy3BUU6wPR4N/f3FQC/BXCmlHJR5ee1Y6WyefljS1GUSc8QHpIHYNppgLOScWLRLovST1zCcZdRa8WCYRbAuDntEEw9DK0fsNGGreh6Z1XRb829Ga/RAonc5x/C09qsgJy30QyKnAvDXj4C9eoFctG2utFNnSUp87QGZptOwbHPd2BZAQ2pqcy5xrYo6r4IYMex9/fEAweKHlua3IqEjY3khfaG0YFN9P4ysk4gloQy6MKE5RR8xzlwjVpmKiqan8f63w9V60Bj9nPud43OT6vmnh83RJ4gD0Fvbg3pLca+OQnwoAv34AlaVetFk4iOvlNVMVx1jRTM2wiVjPM9S2dcATeoPmMH/jq4PiZamTxtyQKSaNdpfw96zJ+y4YKxp6AiaMGnRZYwub6/BmqfRHTUlI+R4Z+C8ziH4ee1dX9TcFjb/JkheYYGleFaI0Z8PYMKHJmMWMMsxqpQiyQfTVsE8B/BNdkF91HhdZpGQSYgb8HnuwbhKC0A7xWht7Tu1KAHgcznfpoh+T38vF3H9EBMsr39K0XrABoHdp0q4mugRvRG8/yDnvsKXNctFGSCZKifI5epURYJ+b0g464R00WBLINOXM+CMs7TJs2D+rSya5nOsZk6elaG9A9gLURexwE07lb0jFPJQoVRQ0XvAZ8CnigdWOTe0bNUOmcbhApOFPaWqVJecKoUZpcVDDPB3FukuqVBv48uFxcQSNl12puTHvR7FIMEAMNcbhMXeQ2fXKe96Cbf/WdiknkqmX7gsYWA9rn4Nwd9P2cwTCPTwgR6nwMDZW6M/Xd1DlbHYocFMOJX6uFJOYfrIlzfPVDRGzalh8mmqS9AGg6LEq1AhmWVk6hDcBnqkiTv/xXYMeZG0WUjv78EDZdnYESvkO5C+KXX4JiPM5d34HhVGoVBVBWg8TcLjnd0rDPf43YIyycxDGQLHMevmk3RWlxEDnYgh/FM6U9rWrCuz92HEWnyHj7FaGKNHAA6HbXSflnQXQe+iK6yuYHm9TUYJKm6Zx/cr63g2UsNv2xv3/aZFWvMQOvOddo3ZjhqToz8/92EDEobhxCSZEalrf3SOgXniyIH2MPEe4e1ELHi3VMU8EArA/UU1OtvJgF3ERzkEjSs74Lf3/c5uQ1FZVGSmdAZsQo6VbV0Yg1n9Ry+LmHiOit4hyudD3Pg2u9NpZo7Jhl7Wq+Cg/4A41U3Gdn2YWLkbGKSEaD7EVRAU7+367R3wEV6jTSv/KtIqLQdAeLWTP49CrwpjeUr+OrJHTTYaEWiqIOl3wAeuj8XpStz3w2NqDtUUAhlBKB/QFANGchbqPhoEko0Ii3wkDFYxCP9bh6RymQphBZopBkp+p1Sgr1smIvwRo6CFeKEUZ9FlMgkUkF18GTw0cU+fDFDDyXOia4Z+1sPXLuXAF7k0uZv4TF8RfyBLXhIw1i9vHWtsJtVrMDoCr59XRXZ8gxoaE2MJDsPa7LfKWo2W9eQBIbgISfwjSHKClO2IMiJMgYpRtItGIWcB+c1eY/KwTyRnrHvmAG8C+oz61E9kX2fRWhjAjGsbtVYh9jDJSQGk6Sv36EgjZyVM5CsgoaoGe4TcXRqBgfM8OmDe7LRnORSye9BW2YHwIWyUzcF1zdi9jul5cs4cBf1rFdZjZaPWpOlbBTSJztgxrNuMMzaH/ccsdnrkBN7b8ak5AhUiuNG8zbg03r3TmJesWkmLYP0UUPc5NcQ48rrgRjZMq7LsBhmxPvSpuqlGIARsfvOwrdQTGUXsKrKeSRWoGfF/ZKnWjAD7qtWcO8LcG8U3fN78ED4rM8Yo8NvHPtaD81D3hCQcXkvzpTun4QnU6TiFlwv144FCk27Ur0GsJipgML5Yh7ARy37UqgxnO8qfFHOuJAGoxhaQHGU1ahYUuQJSFB+kk/dueL2eY1kzIiu8XWeZHv7PyV83rBlf+5YPPix6v6at0onXVHGXVCfDQVCZDRbRbEZ6Nug/jkBYQdTw9gFOE7jeVwB+Wmtk92nXDT7XNjD2kWRGs8mc3oE6tIhGjMFmHrTHJ9A1uCj9ycYpTsr61cdwrKsr3wPwIrZAxrvdVR0PEqUDV3HuKwnyTlqz7khDPI4kVyraL+/yCQwUSPsCwiMX3Sd9loTrxcYGCoLqDB4vrJ8hqomv+FnzItVnV2OcygFEc5YO69KkUHxCh6acJsajQkiYtYKbgn1+AKnLgHuaciIzdja7XNs7C0qpf9dzNQP2XXav9bvlvPfyUuR0TxpUWp3G6zGTjJcM2IFZ/TvxtmPiANglFcz8F2J5sDU3VLECD8DD4SxC2Ay37t7UlAXm/8FYACLuIVn96i135xvQXgbjoFgBztInD/tsUdgxOQTaBQ9AMf3dUIUqgtvxLXAA7OxMZtz3q2C23ggw8+twKezrYvNIWh0HGS++8tYwQDnKagWQQPV3m0LLMD7c/3/sbIkRpd1DeLuVvW5oyyRLsqN8jo2Es3dkLMTwFImRismZ+i2YJzPIbgTcgEC6RprFBDL5lgB4oK+P6NrhDriBL5N48g5o+zsFQj5GIyn85XiZ0FWyBoJXNY59+T0XlecuxZsuGowrwfge9tZeA3hc+87MjkpOYX3KtbR4GCTArRKznl8hR6YKaJF8U0+W5Fo8SeF5l1xBbRVrwHDFYt1n8U2Z2lk0EUA6dosfWGRjLh1HELrxpI3JNww59prM5LlIP0VWMj0d3mlonT8e+hQDLzxj+DBX7tN3RRlE9RRm0iIgkrhW7OEt2G0w6VR/BSKxn5wGCoC+gR0arvIHR5az6+U9vreddqHGcH0tUVr08jVJxHB+QBGaU7CAx2+eMs62aQeYkYlBcdKXnvGTTAq+9CRz7Pq/Q2jvKDI2Rt4490K3BYAvM1KOrcpQmSFU7VFa+U7MBtk9zI8YN+x+rsbGD8h9MMcmaEWg47dq5YdixGbZubm4J29VXhD2bh1j0DHxYrnLuE7IQE0zK1QZxCVcywgWQLhAb3g90bphFDHFIlgCdtgcGZSBUfJEkDConpa8zVCMi6d+Rso9Z83+p3HmBp05hSMeg8ZYhqf0ih3OC5aZ09BPXIGrvOf9LnaLR+17rfBuS/8bra3f+zIEdq0qDdqb/0iCcX1QmYhj5Nqs8GsZZ1b5GMa4jwB+NTFddpzZffT34uKZ4q+s+I67c0aY/QdgF/LM7RrLIEdIbbA+X2XGlFsIs5X6P9WijUvrdzPSd5713XaT4vmwXXaM67T/lcA/oPzAHvAR8asB7PJQ9DBMlyxXWMhiNidgeNqYz+IAMHjS1OevaXnT/5OTbFUUaoztQAelLMIIqxaT78B37VyDvVezzUvRev4AjxYvoB6qMgIH6lyLrnvsuu0f5CxEso2OK/P6+7HmGQkez7Qngope45BvVq3Zds5fB/3cJ92wTm5AbAtY2NE9N6Pwbm+Aatie5nv5T0PZigW9Zyl602G3HvQiH3SQF/Pw9MZLeuaJ+BBPwsavUYPZPvpHQqMXBkr/wrcbxv5v6eKHJQvoOHRDX5/rmf7rwD+GcBP2d5+P9vbf53t7f8YPNNp7qc57UbhtZO75SK8Y5Gi0x+AuudxxefsuY/BcUuJOqfIYu5nqmyBe3gOcS7csPHGl2xv//2EMgTW/tSwpLeu096RUQyA2Tll6KLiOu1V12n/meu0nwfPOJcwVzvgeZfXNeG1Z4v2bEx+qZHJgRc6xvet+GMh70XJWzfevvzflsE2ZzfgJphY5ZgU6rojaWgy6LbhvYxuw7zOq9zflwH8B/gIVyX+w3lMCcCFXUXjEhpCYQW0KfIZML1/IYNgGUzRTLpabw7Dldh5z+sjlIaY1A0diwWeQpW3UCoq8lFTzgu6/6BLhyMubCb3vJ903R78gfMYikTBU0W1MNyZaCj1mfD8ZoDPg2Ds1N7TsWvt6FkOcpHhE6WFlvOR2Zhk7Mt7CL5bCKcxI8Si3VURtzUM4x0v8tEKQSist3QZKbq1qEuB92wgiMwFUSJ7b2M/mJhk7GDVA1ODN4q47mi8u7HvaN8+AQ/B99INI3Rvmr8/wFfxFhkMj8B5Wcz29n/WPRZ0j4egXlgC90kh563W5GN4mh/Dbh+hBmY5IwbSCMmPgt8b5+oSchHiikjcAnhWxXRL/h2sSOQOEcOmKD2tvXGHEp2bj5ZKzAlYwCjGz5zOZ+B5OItyqrkLqOtLyWfyz9RYp+q86Qf75BO4xupe01p6vswi7BQ6fz6D+mOStRKX4LNegOM8KOzROkPw/wMqqJyswjs9H8G5TKESXM79jMl3oC46KIKpOM9YcFMYDYH3Ur6Mg/P4liUr7oSyC07sDkYX5jI4eUYbcu3IATUHAp3HMXRCwyoqUrIzKcZdhczDEwJ/FzEoLVUEfSblfn1QOc0iIUWWEYtoKddwsRrX25028iKA/6Rn/oMj2H0VxJiMDUzOSCXyCR44PfJ3TBBq4AKMGfhOdyhWgFfgAbCGHDF3LG2oOfyvuV8v5H4CHPNFDFfK3iHdQXsARszWoEhI4veGRE6L6ZprjBoK5nEvIgE6kR8TR5zYFWhY23paAsf/TDimLX23q68Z3rEH4Erpo11HXOTgGbIErHZW0BBAKcRN3ctwXMd6z6EWbBlb141grZpKxDBfAHGJV2CUZh6c227wnQVwzi/AeQr7FxdiU/X+VbzCF7pneFgaPu1C9z0FDd6yfbgA3xr2Ss95iwg0QBmINTC1O6LbSu7zBvUbVpyCFeP9rBrHGxaJhHCuqYgM5FcIoEbST8aecKCxsoj/QFyujWO2t3/oOu1pOPsj4jrtJ6ARc+TYUOA6GyX0T7mOVU0DwBc5tgtgE4OwaKnb4BkXQWPwOtvbH+kuGIHOGB4ToE4Ix7tovXXBs/ISgsKU2QaKcs6BhvcGCoxjN4ytL8uqGPXeYlFkchUevHuFewDbf23JRR6OQEUfG2irnL1RdGIZvj+tHVpNxSJgRRNs0cQFR8LWL0UeQ5UosvUZVF4zoPEYGiufwEM+2ZBStPclyNvXuAJa8/BF6Vlb/Ba2N8qVZXCsmrSZjD3H1HlPFXlegI8ELgD4uzIMlaIk/6h/N1XSA4ycnmMXwF/q/+9ApTwPYp0qD8nA2TRlOA6O1A78WcTxgP3cz2Rxw3jAEOC/BV8peQ3uqTvXad9me/un+tzL4DrboJf+yBH7GI2wOU991gX1Ry8W6ZA8BBWxpXd7esYo5iqWPm0iMpwfuE77PNvbfyuIQmjMdxHny9uGrxT+CTTyZjABYyfb2/+o6Ee49k7hcYGWOh9JX2putkAn6AieHqmblUNj/hw+IJC8fqWbyip+l8CzoZf7Tipe1opE+ijAx8oRWQSdoUnwNfbhu5aFxOTGI/sWnPd8evwH5No4TsjZWYeYWgpgA6vgvtlCBfVXglhQBeA72l64QfqcFck6fKZhPvO8qU91r4+h7s98K9TBOOo8RdFa1u/fKqC1pe+8iAVZZKuE3XEKo8xyMiyrsuAIsfoUWW/mAMcjk+DE3AX//pMWLc4nSl2/kldalE6wYgUTS9/MoSC8r81vvE5FhqKBq49KPAtL/VoxyIxS4o2qRRXxeA8aGme5v10B+LsG17S+zZMQ47S6A/DfwPF5B98RJUlxafxnKg4X8yRvm45nxbUXQOWyBabvjgH8aMpEf99EpFVXmYLWu5XyrWWjHIC2fqwblTmOqQfTJvzYvxvnQJMDYp1aYtd5jRJKnIZibdHOwcPEcENFjsmx7n+iz4+MtfavrctNeDB/UaRgHqpcBudmDh7gP03Jp7eu4Wmz7Fljzo0Zdxfw1cQTe9b8tbSeXwODKOIy4jp5Axy3h+B6Ps/29kspkrTPDR/cciXFWTqAV0G9XNXGMakAQtc0XR9775/1TJuu077IhgvJZkDHZhXApeu0f5qwvrKuUYAiUnqmvHMx1MYx/IMj/n2mSTRP8jC4fszQvwbX6AyIU02u2Ne4roFV0tcZG24cwBcwmQFoRPA7uk+TDO0JOM8DOiTnSb+BwME3yev6qjMrEPvcDYp1yC18k5CYsTlEjZSRQsqyFEDQsjX4zC0UXY0akzEL+U9c8qnrZDCwBr+q6moHXKTrjtWOsTE18PI2CtJCmeczOwEX4sW4iiTb23+fx4NNSgIP8qJKwRdI+Exdi8IGqfFKA0Ne7n/Uv/9bUSTXIjYg/u/nonWvd3oEHlrJrSq1p07BtXAIHoJh+sAKDFaQWMWnKMh3YIXpCO61RLqgQXsEjvEb1ONCtMhJLSxakZStvXGck4xViwM8YPD7MzmOi1CfdP2paPw+6RnuSg4ui+60wDFZg+eii0kLXAdmqCbTVqWK0pC93Phai9azIKp3hyDdG0SeZ0Acaz8jtu1HRdef4R6bKMSyF4EcwleBL8HTNA3tXxsL8CDfBA3Tz+Aa/g24719F9OlTeGqxt7m/wXXavwIj22/g948VQLTAdWDRbhtbI79voRi68QCilnKjeOQZUAdZ//VU5oyRvtrOc1T2sr39LxkhP0ZMXgZfuHG+jeMgQCJD+aH+3cuIm13X704zwUS0jlbBaNdJEK37oPfaQAEfte79AjRY655/Nq49+Mrp8EwYOJTS9WHUvlYVvsb5Ve53fUdI1SomCJ2SrrvA6H4PP3Mjx30m7xg5X6+RpzizQsMBnZICH4+RI2cvLMD5lo1IvczcpFI/4OE6B7UImtA1QzkHDcXLknHt5X5GJfMdEJINmSqZhiEpWQMV8aprxpH3BTzgh/gddZ3U9Jp1XLB/F4l549aCr0gs7b6uNNDQ2CndsAwqyXyE8b3rtLsA/gxqrRgY8legcVMHC2vvZcUlqXIKX308r+hoJZRFEaItcL/8jAmSW09LsgjAP4juzILv8hKMTEXHXvNTqvgzz0vakuNQRf59AtGaZVOAWCg69BBcT4MDTQbrB33mz+CjQPOOJOZ98FAPYU7hITqb+2n3m0Guq44MuF1Q7yW/owycZdAJrSq6MsLo0MnLG5I2FtfwEeBz0DjchDfszOAM5UrPMrI29H6PQMPrIYC/RVAAoQN6CdSDr/S8fccip0EhmJ79Onf2mANlUeN+8P1Xum5ZUVP+WW3Nz7vhggorNoWioBdZeZedgRQ4Vj1guBsQuM5nda+Pwe8ABVgQROsy4gtLKcoiczx4PzBbcpH7u50DdWAzlqGdAWsjJhJ0yTxLQaFIfzwHx/Ntip5NsV2y4hqDaEGOdNkHDHNXrkEwIQTk7C3XaW9kxbiesUXe0Aa4wcbmSHOe+2rGkbNrbDynBnhixlnk+ieummm+Cx7ovYaG17coXXADjyj4FNHGPXXETpZ2JyiRLwD+ACqEMg/+EOo0UnGfLvw75Q3JOXj87CYimKeMFcf/AL8n7JD46ErweDFRisaqu5NTPZlvkTiPehhfA6fvZqq6/QXL4FCpckqd+rtX6Rqt8Wv9u3QeFaGZJmODOU6LJfqkB61DDFeADqW+c9/5CB5yy67TfhBEMb7TvbrwRS+GfVuX/kuNMD+Brwp+U/bBIEV9BkWaImJYywV4I/5MDsAR1Pe9wJl4CzoII/OZ+d7u34EGTB5OUhQgeAUa3lfOcyMOWuzpnebAeZgHO1O9ht9/7wH8Xs+VGs02vP0dhg0Z46i0Lku1RJCBRXCu7mR8/IzhVOoXjLJOdMF5/iQD+TM4500ZWgxHCuQqygPDbAY0Ut8hwWkPIqADTmpXwbfpJtcIZQXewVmET7svQoVwWSLxfKJ8APfFkJHrPBbzFiInB+dxDXQWBmNhzOvTbA1oWJY7VFf1pYjhBoFfEE9mmTEVeM6Gmbp17AhwrwallNgzPcfY3I5SrlWHgdF5zICA5J4O71spJiOinned9rtYpKniGXpIKNLRWHcTPldIXK5nP0YFFUzRveoYkorO3zV1BKsMI63JByCG04yeEzBidRJ8bgVUQncgBuybxVgrUrQLXzS0iIoIjPP93eGCQoPg78uQEi6KbH4lOYBop0r0yA2ok/uZOiEBQ4foSNZCa9yMj3X46lmDbFhBUR80PDfhC4xSZSb3MyqK6D0FcKMUddF7ftHzXGjdDwIHMt5OUBCp0vuXYWRnAPzfBYaytR2NYaBtrcTedR2e8B3w+GaL3q3LiK+z18xQW8Rwt6xL12n/hAaE/s6Tu1vh2WdF5+dA483gN5cYPQcW9V7WIamL8YpXrfq/h/J0dL/OGaL1sYzhXubRcXeTbYRyCs7ZHYahRFtQJ6YYnKOumKOZRVgnZEj+Glz/JxaZzQrI2Q230/RBzBCYBXPnMYVxm/s5lsjAeAN6ZUkYBpcDWDtfsn+V1cC9TULkiW6CGKWufm0e1RpohJhHct8HswHTAd8WauKSizKuwNN5rGpNPQQVkXm4dlAtYcpUGXo+K3K6bWC8TpUfFBg6REvb3sVSj/r9AFjvCjoQwVf5r8NzWw5xu8qgfQYq0C6ozEudh5J3moMvoJg41MR12n8J4H8HDcnZbG//75GWIjRux0GqMSePwb26gJyTofEx0Ps6GPWuzM5oftdAnHDUQHW+vVtRxuO3oBO26zrt/19eN+v7Pf03knKrMCwOQKcidGTe6nmM1Ns6hTTp/fseQYeX4F3Pc+9huspIpaP30dqOpk1lKDzXv1/VcAiWgvuvIO48PgDXxbHrtFFwXhnH7HUwTpd6l1N4w+gLPB9uEwfyFJznEaqkMQwSo4Jr6adBCIzsHHreG+c5hS8zD+sZgQ844ivNiU2i+dEc7oKQgy5ooA6oeKTnXoM2w5nz1GDnWVpXqnNQ782iGjdZpiuSRes8VmcwYDmYgCH5BMCaI6dtzPDdBNfMHMipXfpOrbqHZU6W4Q2BNcSV0mdhIqzd2yw42ddjRFWSsZJaaM/AaJ95r0YOvC3Dsm7Hh3HEqg834D0xA8W/h8rsv1KE5xKe47Fpy69ScQRarwYL+BJcGzOgZ7QafHxGiuATOF/d3LWW9PuTqoVeUzbgQeQvM08BlVTVWUf0Dptg14/UdW3GdVhRGZPnYEHCoJWf8/RSM1KwD8H05OdsuPryC7hPy9bBHag0r1FCZZIoj8FDuaoSdgM1+j3rO9a27Bo5PeV8deWFom7z4GF2me3tH2a+v3sRRtuiRvlev9vgAXcNHgA7oIP0osDpDuURqCNayBnner4eSO30CDSS/lvkM7/W30eKRiRWAHFVN10mSMV57neXYHXxMrg+P2tP1t6XGp/wbHgCzt+WY9HHe819F8K6gxQ1TaAwa+A4HcNX4A/aC8LTIX3J7fsL+F7tMVyusTO09OxdGUo3YCDhVu/ah9paOnYjuYHw9QDeKAW8DeDfg9i5RjRcWVAdH3nWFbBwI2/YteD3QjdyTcNvfkBwbjkWty1gOAr4DBzTEzDw9FlZnLwutfNxEwFnpJzNmQLH2SqYb+EhGvlnDSEIW/DUYIcxo0wOXQvUy0nUTtIfr0EHfipBD123VoY3yGLlx66KuNyyG9Ytq1TG7YBzqf9mUVIJmjPUrILQAL9jH8zabKugAssfaFapPQ/fGeEYXNjbEAYMDSMqDeQA3Chd+4UUoG2cqXJ6SqEZD1s+hdVHAXbU+SrEBTAF3tRwGFrAevcwZN51BKgPKnCzCHhfysVwMC1HrJalnd4VHSoyLJaQVgxlnjfAA80iA0UHdLI4D+Y2AP8i0qtjbTwGFcDOUw+dBofCEFm583jjXdBYbAWfGeLwk+FUuicyYsZe6nO3RRHSRLFxrjIG7IDeRGJWJfPFMUvggRq+1yMoagiO/xaoS1YdC1JKq6wzFlW1Iu8eEv7au6UaV1YNHuK+LOJnBqqRhq8Ef38Gn216Dd9Z44HrtEsbKshwMCzhOI6sVSoPRYcmIJvwOORL+D7SHwXJ+B4VTAwFsgIag7cZqVDmwL0O8D02g3+H0a4hwumI3Og7z+Cjvw/hDdB8BsMaZezqM0uggfwAwP8TPLfWAfx/a7xbpeg8eAw6Oi9z57E1JVhzZCEZ2d+ZZ1s4D37XB1Pes4GeszRxS/edK1hnh8jhK91wncRIy0rpoZ/B/buQ4JTb+R+N7kmXGs3XDGqk32VMz7tOe+keA1SFkstiGfPBsRzIjxCfZ+y7WU3owVjGpCairqdkB10ZH1Jd2dXPbYxu8JBk3Jj6rwC8cuz4sIF7SJ2aKNUxlahflQTpfYCHWrfG1+cx3BGn6UYxAu3DAHMzC3rdVrxwquedBw/384inl08/rgTPt+qIg3oOGklfdB1beyvw1ZwjkrGw5RWGvblrXX+siLEOq+/Bg+INeOCP8ChKye+AG39os0s5d3OXfgTgV7ru/6UxfAe+qxmfVrl6DToTp474t8bA96y4i1ShWPo3p8g/gEq+cF1p7A4RJ9WuErv2DqgnLNKQx6ydw/NwJumnAiP6QN+/kJFyjcQONjJQB0UzzpOvr4K6ah7A78BDwvSdUeMA3E/vwAPZnIwLDEf7PsDzRkLXXwHffZziqmtwz10r0jYPFlmMEzSwtpXroOGSX6t2js2B727jNqfvnOcNF43pOjhHZ/D4NiuiMifL8P4fc99f1PePY0aRonbHoIEzDzowlvaOpdKPwf35Wp+/gIdHHIP6ayQiJT1xGzOgpF83QMe5aF+F6z+PUbW9EI32xUQ6+w5cA0+hjCC4HlfBKOwumBU8zXKdYWJRb3AOLEtiY4Pc92xfVWZ3dI8XJR+5Q0kntyCbMcL+Ehjng6xW1fNMWWxvWEbYHCRrFT0x2ydPNmopt5OsRnVoHdEB9hN4UE8qNXmMCAGo7tdDpGpSof1bkDj6F1s57TxudQFMH+RTFVtQ1BaeL2oWNY0ieVxdUEk0pjIJF7BSYqa0VyLPtAvO6waAP+aucyclNa/DehbDXTnm4A/XR+C725wDFcVb+XFESVVnTTEuUwBYyvb2P7nhPswmW6DifOw67csEb/sWvqpyFYxQ5kHVVoX3ETKm8gpFOmAb1AETj5Ln0r+DSKx0QaHOcQHVTba3/9Kxwv8ROEYfMt8KbpCitDHV7x5D1aMY1nsfdd8LPccZcmutiejeB8H/19KnOZ1kTtIF6ESc63r/HHzGMi4tcO5uA4dqo+D6ob5MoiZLkDfw4/uDfm5gvG4i6+C+fh3LUoDG5R24NsLD3wzwnuu078C995M+8wDqupTt7Q/GMUjdzun7p/r9sRtugfkQXGe/VtQ71h3kUNc5l44yuEQswneAyBg59gi3SOxZ7m9beo++IrL56+7qeWNclXbfY43NSAQ+I74w+r2Y6Ex9Bs6FkYrPw1edWxbF1nMZVVso2/BdsqYeiAkyLrMRJ2QBhBysgWt9bF0xLVG24Rw8+21tJDviBRmXQslHJh/CF2FMjfqjKAU5xvVq0Ww4j6Eyfr5STqu6IgPPvLywQKl2GlAKYxsBaXdO5jGMWw0JaWdAZQMA24p4/AxuktpGUZYIiK4hl/BYpZiSuELwTs4X58yBONdBVEyHYx5fdgCu5S64ia70b4vwJEtWUNVZVzL2hrZD5kS/i+2HLqiYlwE8d532mzKDUnPbQ66oTmmOJ+Ch+QblHjnA9bKk+zY2Jh0rpxfBjlLhYTRI/7p6vG1DVDegYbABz1/3Rb9/Ao8lNZ1ghuo/6XeDdax1M002i9qidf4IPh1qUbPCDloax4+5390qOjYPtoI0SJLtowvQgLHvHqMez6k97wDPpvG81jtY1D2GKRwqiqyQLb3DFobxrnPwhYKL8PNsYgb5MoB/A4/v/Rl81ygxtp7p1hH7twaf3n4IOqQW6X0Aj38/0e/M4NgF5+tt/roJ7xvKsa69pGiovatVzJdJqYOgOdpCSWFYOD/Ok9kD8Y4wIZbb5jyG//8EGrlD+qUgYwFQ756CBvUksfGFUnJWLyOg7MnrsIw0gD0Qg/rVopIBNGAWxN7a2ZFviRk9f5wnMT/OEotK88bkBXx0409ZjNqiVrebFAnSmJaSs4KSVTSL6IUFOzFj8gZUNsahNhB52UYxYB62VXB+dYkdgLm/Hyq6Ys+7BB9lWUdFmj5nfIeGZgyDOQvfVWgaxPVm2N1kCRWEUkqvAfwFigHS+e8MnCLnqUsegPt52aUVKJyC4zyOIbkG4N/pf2cw7JiG6d/Cg0FpSMP23sEXqVn0eQ2i7AFwEBhgf6HPhPQnZ4IU1KnWzT/PLjwYPU8P1IKHY9Rp7baoZ77IOWqroANp2N4umvPg2nzfwUNKjILMKGhuwbGp4ts0zOYyqGvegnNheLY3ZpRobqOYW+eLInsujQLtEJ4sP5Qn8EWgpyC27yRY4x/1nCtgwdKMxsEc46hzrLW0AK6lVvC5K/gMyomex3D44boyDP+qDuM7Re1MD1u0stQwcr5YbguecPu9nu0s29t/J/jEEF5Z2QVjBjkHdU5f7zUbjM82ZBy6gn7OObEI/43eN68jjsH11cunq90wldK7vIHifCvKGO2MFbWMGGcJwZZJywn4fMsoqHAu0wGCfljnn2nC6wzyAUSgAdI9Yfe0/Ngahj41eoyWcvxWrfZZKbdfbNo3Lxq0FRDb0gMGRtZLiDh2wrechzfS++AklhYoVYgp0qghmpUXzSyAi3aqxqM2tFXYjpPOGpGcgjOlbR0sJimPQGUZkrOOLc6T+s7Cp6RSKnoBKiyLGAw2u675RL97X3AoLYOK4Ao8QA9S7pmNz/cGeDz0EnJwgnz6NybOt+uyaJNVv37S3x+C660FHjSz4B7fAtdFF7kIW5WhlCBb4EEYi2bZAbGJgrSXDvhrPbMZY5vwVaXdYK1fwhcHhqwPtUVpTMO8Wdr5DMPz0gLPAsPUFvUh3gTf06L8Vn0/B5+KtL7SZY6ZFUW24I0xM8oX4anTDHpwjHj02J7xVP+2tW7v3gdwrjTu3+udS4vngmDAOrh+LeiAbG//QJHeW7BafhY06vJ1A2fwBVR912k/A6maPoOtG41iyxgWVsDxW9E7vNE+mYPHMj4A1/YuPONG0bp+or8vKhthBvL3ABacb/Zhc2PwpypZgTcmX+T/qPEuMuhMH9m/83vIopojdRy6blGUryrYMlGRbdSosExzYAVdG4oeLoK6eSLUiSYZ+UM/ohgasAC/tmI0hFacE82EaM3ugJCaI+giVpAxAx5MYxuS2pAWNUhqBTRFeQ4O6CJGq/FKDUkN2AZoJCUdRprEA3CBJ9OXlMg1qJSuHXmhVsCq9dLUnPNA4CYVjnVlHRzjdTBK1Kh/tYkrIE/XO1QSkDeUsOJ2IiLlYeuviVgE4BbDyn4dnp1gHnFFa5EB29f3tgczdvb4a/AAnXOd9no+mlchPfDQtErb/B4y4+iD/mZp4HP97gs8FctjKIpTdDMZr8ugYiwjvl6LPAvAOVpFgT5xvovENbgWzKA80jtehoeJ0tNGqh49ROVQrOmZDYtmh+pQtzH7tyOtziLoWFunleeg8ToHn8K8wXC/ZSPMtjm8BMf6LCMdis3HvJ5rC75Y5oXefwNeb3WR6zoTROH+CtR3bwH8XezdA3mPhLaLev/f58ZvHkyz38qI3dF9b+H7q/8Ejv+r4FrWY/tM7zTi1GZBL3FFYcPKe5vnsNjlMWhkfA+u9xuoJaAMgk/wfH+mT8syB+a0hGvHmCgAZR6U/bgCI4mp58Nr+D70dcRwyQMogtbfFrhvPoPvXTdIUBS1nrjIHngAjl1tQnIFsYxu6hTe9grZXCYmFTbCKRTtjkVStWfydGcGD7Sop7GBDIzJC3BDTjLSs4Lh1kbJB4mU0UjVY4D/a0GdUhIveaNnaWL5WzXiAtJpW/Lp1XHlEfj8z+F7Ys6gGudlnp4RyU7TmPwCetYWPV1HSf/qBJkaeboODouiHAXr7BOoyKPYIR0+O6jZZxjeOD0EleZN6trN9vaPXKd9AR664RgatcUNaKyNYA/1XiNRGOcpnubhW8BNQ67hdUqt+ZeB8g6RbhOaB8Os5YvnXuY+O4CYxMYokOfwBVtR5yfLEbbn/vZFcIwiHdMKfg7SznJQXxR8562ep0gvP4FnWLDI2AP4bkQjlb/ZaEGW6ZN5cE31EG+h+Fi/P8/29mN68KPezWhibJztp2Fbrfr5DqM49RY8M4ORYI+IDrQH4Ph9RoNqVOeJyq3YZkvPuAGOmxGEW1DAqHFCbNxHR7qlqrV9CR7cL+AhHosYNgbP4d/ZYBx2H9PzR66iza6cCaMsmgdxnxbUeQ/O0yKCdRxLHZeIGXwjEBXpVYuInoNOjmFIZ+D7ZoeO7Q449n0AL1KyWnqfZV3nTE7qiWNB3haoK82QnwXP8A19ftzinS3QFthBw+42ekeD5WyjoDp92lIWRZbR3IoYo0vgHG+C+/cUgR3Syvb231Qt0gZyBq8ErXJ3CxUh3QA3cYXRCJRVmgMJeLlA3oDcU02obM51z6lUtmtMlsAxKcLpGeD6Er5gKKVgoAtu0puSa09EslGKgS4K+lcnyjJ85GbS1XsP4NNYf4DWmZ4zViiwAyojG/t1HSwpKWM7rBbKcDQV17Co0xoUGZMB+Np12s/BQ/EU6akXi8IBJdG0KlFK1OAjsQP9BlwHd1mkKlxRww3wfWJj81bPd6o5uJVys/QjEKRIC+QLvBFUtg7vMKbDVbHHPoHPeQ4aW7uowE9rfZUZSkZVtRFEfk2vpjraxv03q+/8rHvnx+EcQUeayLNeuE77R/2779hk4BzeMTtAhM8uwNEBNICOAfy13q3IsLB1A9dpF2JUK840M+5tTxtlVAtMXw+ydK7T/pU+s4zRAqdK3abP5Pdmvmr6g4yL13qG6LtXGJJGH3WV7e2/AjNZBmEDgNWqbFaVaE0WGVGr8MaGRdxf6G8hn23YxaxWml3rxd5n13XaYfX+BlRs6nwnozXQiN0EC5j+MTSezfiucUYd6T3GZrnQPV9WOLn3Lm64fWw+umlG7xWYlfgp/K7hLiYatcpHRZxn0ge4aIqqp+dzP0O5AjdhLbycnqWMuy5f7Rp+95NjZ5CJT7YWcjgm0UIUKZovGTvBLIOVdymGzB0mjCFxo10Biu59ifr8o3aPMOU0lP6bkFzCp4Bi6ywvW/DVwXd6puSUceZJfceVR/DFE7ZOh4iAE5/nynXaR/DRqFoib9+qgQ1G8UftlVkQ4H8LjtsWGP05jsyjVcJG8cQyzrqOVEJWJGBRHsO2lkZVdHCUEUubWEp52XXaW1mk28c4ovVyCAyM8A3QKYnRuaTKe/ho6mOlXc+ggqvEQ6oLHuQ3+eeQAT8L4iffVQUcclG7PoYLoI6EW1x2pLmy66xgmL5rFYqslYzLBTwP4JXzrfEMS23wpQeu047CG5TenYWPDl/IsbeI5Tv9+wyjUdaJixxFa1wwoNCpKUNsBxrjc3hnayoBkUDO4B2G6+C+0Nn1GbkuZiWZl6goY3EAv1Z+kLN+B69j7+CdKUvVGn51oLc15k8Q6euuyNwaWNQzmItI0MQ+W5v6S2f5oDFH4ncW9Z1pQpbu4PmbrXDsIeiQf3Kd9u/h2ywOSfIhNKb0wMU1Qs6ck88YTo8NRJOdT2PNgbxd46RBV8GBi1a7TtFrMCN3ERUbPfOdYJLC4TLIRjiyxhGlFy2KMIvpgZ174BoJPdiy59pCdXR3INneftd12n+DkkhLTg4hLCiaR1onIaasw2c2IuBY5G8ZNPYushxuVUphCTUPSBd0ooDHlp2CWLlZsMCj5Trt96ju+mIFKFWRZ1vDRrfRQwXkRApwGTwMUhwva6O4re+XkTyH95kFo4x3mW9XaWTNNyBefCrrJfOk2JvwGMg1cJyMviRPqDyHgBZMzzbiUGjtGMHxrCvpnqN7zpSlEKWPHge/ssjbgN8THndqzkrRe18jSOE74jEXQSP6DFx3Bm9YGbmAv07+MLTz6RN8pTZARyNJFzUR53lXzUG6hMcKmwP/EIwElVHYhfRRVrHew5Qw5vmonoz/12DGZISfsMhBy0UKK42ljAwfRlG0AEaS73TvlwgaTejav9PezuNCDYY3j9GshNGLtVBSsOU8ryZcCXWb88wUh9q3m+Cc3jlS9bVAO2bkPJJTtwXu6zUQ1vS7SRiUzjMMHAU64caxOHkuY/2HwWk2FVgrJDq/F2NSC+5VlbesRZCERdBi/gEcXKtOayKH4GK6mLLFPyQah9eTDnNr4/wAUnW8yyZHP2DRgFkURNpkoMyDAOVG75SVUIpE7lcZ3XW+QnQHomDJhvu02mfCgrGwGKIQK3efogj1R7AaczUjXshSyTExBbYuJRB65VvguA2KswLFElYV52UBvpjoAIyOrYIGiVWYX4FK88B52pKRfZWVULPkPnfmOu0X4CGRkgrLc8hGI/46kNbASL9lPfoYjnJUiVHrwLE1rKWDjYdupNuJojG3aNh+0pGofVHvZZhS22s2D9d5xyqnL99pXA2bnM80WDW+YWuB+N5ahloPuk67X6KDbUxb+ux3YIT/M7TXNR+3CGA5ej6rYj4Go4bX2XDXFMMPGxm9FTOM8BhWyEcw2nKVi7Le1rxOU7mEH/dwzdg+tsM8qlf1nEl8gONKkM27dYQ0LIPzMIOa/LSO0DarYt9ApGBUa+MpOA7vQX1sVfaPQGN8BsAfY+NTEGTogrpspIsNvEFf5VBWnnHO038B3Ad2X+iZF8B1jQI7ZgN0cq3w5QTcE02pwkIxo3lTOuk429s/zIa7mh2BjuAlGCh4Cr73W1A37IJBlsP7ikwaRmrFUrYTuKQVlgA+MlKbJ1CfTUmFTUWKlIMMHIsE1qmIDz37ic2vojfmsYzg1ByrMf8ncBG+RIJToKjGCmh8NFkTpdFd5ytEd0EFt+g67XPIiIAHa4edeGK0Fd+KzIEYoBlFi4aiKzISDY9n+J6YkxRi5WYCZW00Ee91vVlwvs1rPVOaaQbeYbBK4W0wyjRIn6dG0mOiQ6YFVgAnGV0WeQONkqIqd5MdUEn3Xaf9U0bc3wuwujEV9nMJ72TZvcy4uY3tE2CQLqstchSNZ9UKRh5Ae02RmCJHrIVRffkdfJeSkMj9VmMBfadIj/QwnBKzQ/0gZ5BZb3SL5C6BGLZBl6KCKOkWPH56BcCfaxzOM2G5tMZiDTZqrb1wLefFjVlTIIPiOfg+H0E8pkXPDh3J0a8hPsjc17vg+J/l/yZjfgnc6zOgcdDHhFhZSsSKQC1T0YNv52n40iGcqHTx0Ps5X4QCUCcbzZRBisL7WZR5LWMl9ZGuEWZrrLYgRebgu4QNSba3/zYWXY187sKRdWHAmBARw4POwuuIrn5/jeH3jEXkD8Ao5ktQR3/E5ApSr+EbVBgdWT5wMgMfiXwMf07+GTwc7cx12kf3Ykw63/LPHq4RT1MoGdv7vQcXdBfcSKvggpxa9557lLAiPtnby4iJMzzVWIDrUKQQd/QcsUNyB1QIMyhIQ8pAfgjO0QF8legsGqyJhOjuvK5/DirZY/gexDsA3muszuDHdyKRXI1XHz41ex9i47+uqE9RiusLFAGxZ1MU0Tpr2J79HjQ8BoZrVsxUYGNoVEaNRAfkGnyx3TUSIsNaWz/A96WOdd4IxahwQmxf3XmyaNB5kFq8RS415kbJohuJUlCn4DwZTOAKQbS65LtXimy34PFOhYVHSscZdjDqXEkHv4I3LMwwuMp/R+Nzp9S80flUjccpfP9wi8COhUHW+rqF8Mex1GLu8zsAdhzJ0JtGg8wg/jV8962wovpU6/e567T7IGbU1tMFIvvYefo0K3w5gy+sM7zntKQLrptrcA/Zvy1lPDSvTt1U9Ezv5HgugmfAkd7BKtCvI0acEaS3MLoW3+h+t0gsJgyCDDNFWc1UB7bEiBxcR6nsmcCBGHKcHBsqzMb2b0Z8738HHZEXKOlW1EDeQHykoJMXsxeu4ffLZ3DcHsIzyixClGr3luZ2BNouY4Jl8OEicAR4A1MESt+zWEX8gJcrVcZI+ZfJA/33neu0/+9IJPEMVAiDYoOIbMDjmU7Azb+EdG8yKiWpnzOlYWYyYXak2CytAvj06dgOjkmApdkGq5EPssSWVCXXnM08/+B85ukv5uCxlEbRVOpEaLy6uV+/xnDvcTMQAO+Nll3zDGP2qQ3gBrPwB2Sqvgg7PrQSon9GixZr35Yqz+GrO6PrxwUdsRzpgzZQwXtZJmGKV4bZGjhPT52vYi367tC6cCVMA2ZEgdGbQmM+82wDVvgwRC3kiBcz+IT1mH+R8Kq2pkJ85F/Dd1dJFhkPz/QcFk3tg1HpD1mA94w4piu5n03kGNSdl+AajekrMwqBwBgscZT74NxbNPi/w7dYHNGnrroVYrJov3xSynMOhCwcuE67i3gHPdMf89rju/r/XkYcaBkWtKgi3v52g/q4UCPrBnLRQOd5Uk/ho3afivaVI2bXuJ+L2A5KnaaqDE5WglMcRzLfItjaVcY+M9TEQzrM1t4HcC3dAsyB/xa0LEsnVMrFBq324Z9Nh4IolI/ggEzKav+qogVY2q2hTJRCfoxclbo2c6+BMrHuC+egUsoXdZy4TvufUN5v9xzcqDdg5Oi1S2vx11jyqeCM2MPPEK/dBL28UMyoMZ7A5JZUMRFObkNpwQMMe+CGGZoH8N+yHCNBqgSKxf7/znmux66ew9JNx1Oas37w30uUk4hbdKaV7e1fK/L2AWIbyH1mC9wHobK/BA+9STi3hQUj8PhJgIZ+D9W8l0mSkevwDEx/Gd6zzvfLonxmPC0X/D1/rRs5OndBtHsV7PiyCxofQ9QsdSVvDNeQFXAOfgWO02fwrLiF5k768l+B6fcXgbH/GVw/jaEv2ks/wxuyp1qXFnU1uMIKeI5dON/wYFEG72numn1FtCxF3stICTQQ7VcrcpuDjxybkX+N8YrEhpxMzbsRkq+Akd8T8KxYB8e8Bw9NasIR+hjig22qu6Ur3oLRwLwR9QBc8+vB77YQwSg639scKCCw/xOUL+B+PkOuq1oLPPg2UOId5LyaLSRGcYRnuM18W6yp4TiyAp7APwVxnvupB/YDHRlHKacl8NC0ikarUp+VQjMusmtFMZKVSEZszz/pHtEDuGp+tfl/zP3u3oqecvesdV8Z4TORiGz4mflsb/9GaSvrztIIbuCGyfvD6MhB8JlVEPf2Z2DK4gEmGGE1j9h12tZq7wG4phZj99Fn7rKG5MAZcbmvQL20C+ChI+l9rOJ4Br56/HO2t98tuO8ueKAOtbHMqonGU+QtKlKKOriMssdSebVZAbS/R/aYou+vwHG3wpUF8CAfx1D+hBpGlPNUK9Zv21KP9p73vs8DOYHfO9fggfgRHCObu0eg4WtFQu+zvf2+dJZhiNfA82xgxEgvRHHkoWhuBrx8iupZQwbD3R5le/tv9XejfQJ8x5S8GH/nEng+5wvarFhrBVyrtnYsHb2MamxxmbyHujCFv3TDXIUz2r8hd+Y4tHFm5K2jnPJvFiX80iXR7XN4WFkf5TCPvqKxKyhuOzivz1bWBCj9vwlG+yYGUZuwzMEHM4b4vlvghJQqDA2abchU5WLl75BymRhNTcK9DU8yh6Ad39cSPc8iaOg1JfG2gqNYGgHg++6Cm/Y1aMjMIsBzwWMwrSq3VMG7XHs2RRKnGV2OPcMM+F69snTblJ/BMDZGARFLIz0BO2UcZXv7n5oaVLqWkfdfgxG6j4j3aF4G5/kT/EE5DXkGrpktePD4kMhxtP1+29SQUYSrh+GUXxhpXIXHzaXwbBYSI+cVvNb7d7rum5LUllVBn+Tn2XmKobPAULGU+nqWI/pNEd3vX4O8ij9mo7QrV67TnnUksbeOXXdFhnhwzX6RbtS718EI5jtu9fRc/wDBSr6WHrYsj86wZQSpuUDOwX20CKY188Uu5ojDsdr4Rmv+34IO16sxDAAz3Ifwu8qgLKG481Jf8IIbxCPIF/DnzrUbLqqaRTWuGC5XiKI9YpjXIVaMQIa4CsuuX0cywnyOIAaCio9/B2KJuxnx46n3OJQOL6LCMhaIG52Jhdd2ZDf5DnRkX1c5HKAh2dLPWmtJ95otMZInJYV8361stEl9VLL64OMqnrlpStiObwXTO2RT5TnUExfN6BtOwPfplRzSj8FFOA9GL2OEzV8gTE1iRPAB1J7NseK1TqXrpGQDSiU48v/de+sp+FZv9u+YLOZ+jiN2MLeAgRcdUxJd8FD4a9BpmNY6t/37BsS+xebgNvjsWCTtiqJ/APfugBUgONAtDT6Uhi+4Vtexgj+FGHkBfuyXETkog/TjLDwvYSgPwH343HXafy+D9RjFAPcUeQIa6kabFRtfK/KwCPI7BD3h5dCugAZGSEfyumm6MCdd/bzNhomebxAQHMvY7sEbvaf6+RA0UMLI+yY8x+sNPL1XpaOmeVoACxaMC/EExVGm91pz88jxc+YkXEOPQMNiAfUZQT5AaW4ZayPna5ZGnm+O5giJdLa3/1nGlBV+mO4eaipSJAG8Jiw+egJf+xCF1MjQfgUaN7XXlgyjVRBOMzQXWQUcL5BW7mfKfRdRDX3aAvdy31U3HTBn11gyqozJQ3D/dpMeWKJgx3f69zg0iZUi3fwKhNY80/0ugTGoY7RZW/nJDm564shd1As/4wLusClGmizaOof74QirkjlQSSWNt7y/lnkyMvyq0pcfUV78YtdJ9tIQtGdrGFGdhBio3Dzwe5dsb//csRJ2pmSjGi6osm93gCU7Aw3//Ht9QgF5f+65hrjltG6egcrrbYWiqyPWJ/oiK3AmMtJkvAWjcosYc67KDv7gM1EjW8p1weYqxciWkWO8nXNgv9+lgoPFqD5iDtklCDvogwfvq4y43Tr93PNyChqityjGeNrvn4Jr8SZnjDyGb58Z0nbNokI0nrvg/Hdjn8lKev0G17FslVWHmkHewjBdkK0xIyRfBedlHTRuzhKc2segoVdYHFXwDkWR2uPIeWbBgVPU5KLVoXwJvs9pU0cwG+23nv/7ODrAHOMY5rsMJ5y050rECnuWkMA57DrthxDeNHB036Ak/Zz7/lP4QMyF67TflkT4koNkGSEoH+3fVc8xhp4onYtJiiMW9jE8BGYVghuMU839HQgOPigyCgsiGNuQFy2vaeJGiq45CVLPSck5+N6VUTXnO4nMlaWpIvIGVLgTC3NnrNA7w1eM7GZk4f8J5YU9tcUNA8RTnqOqQtpad1XdtwXgt+Ch2gWN+7z3XdYDt0yW4Q+AJEVa8pxG5zULKukUPPKWnmEJYzhxbph4/F3mcY7HSoEXtiFznj9x1pHu6AzkMb3T3zdApyCMllkbvT5o/F27Tvt7UL8dhdGQbLjfeoyj7lDpxCUk4gTlYId0P9e5w+wLfHqp7+Kduu40Nm91rXzkaSb47JkjJVYZdiyUbfDQWHVsjdk0OzGo6sVwm0KDUJ3nrn0Gwku6CDrlJN6/rDVvI8mPVTZ+u91H8MUeX4XOTmvvuuAdvkC4ODkUS+C5uoyKc8blui3VlGsUZAZ07RaGscpGI7YBrRNLwbtOe164zaJGG3PwnZcscj/iYFm6P2PTASsgTemuNXXsY0Y4SSG90IRlG5579gLBGdPImAxSCED9tN4pvOL4U6HxqZIZKFLqCOJugWDr2PvPolnP5TtMgWx7QimwcZ9hoql1N9rM/j5Jyq1QbBn0QicZOT/XtUOi2aZiLfWuoT61ifdfwfgOzQo8Bsw67ABI8/Ala6BReQbgH0HM3Ao87q0XRJnnISMOo3tvZA/qEClzDN8g4OwsExm/ljZ/qJ/bSqF90P360DoJjNwR0np9Zg6sssz/7SOCnrqxCLsrxiefwTeEMKN8HsKDgdH1lAjYF/hGA9egQW7zGcOSfgLXwqyeu7S6Pycf4COTjcWxEUBZynlGadkmePjb3M97FUtjI5Ky1jsZS8AXcI3OgannUqiWCzhfK6J8RfIWpD8rCmIYhvtU8IQuIs5z6CQ61m2MGLYZsalfQH3zEdQPDwSputV1HoOdxIxD96ShkTw1KQjcTUOOwbH/KcvhRRsZk5kntF1Bzfy+lNi3kHq+T7Ho0y3UfkzSzX8wI8j4LThh46TG/kWKxVIUpQBxHZgzdVI2rrplqFUsv5u0AtBBO6luTuugMXmFxKhJRnyi9Ykeh0bIejYnGcWOXG/zYOGEcXHaQdmCj4JZdfE6hmlNDMc3B7aI2wJxenMocNB0YG6CTvGFfjcDGoQtMJqbYlyE6bIb0JhbBVucHYdrxPlCPiDnxCvCYjyKsTF7GnznIPJ3wOOT13QQv8uIk47pbCsKtGepNIi0DsJ7lx7I2XB1/yUKCNYLvmtV27VEY2y4XKvCh+u0bwocmWfwBSF18fAfoRaOdZ8zVVw5YX40equAx5b+1/B+YSTZ1r9xpuafP+R8rR0ZLoMbFHy+CLpl97Z3COF2YeX0F/1uXfc12IWteUvzPwHhDLUyL9pLN5OAHSlCPA43bnidWMelUskIX2wB2FUgZjD2jdPcWXFRwJ+MSLFY554PTSfQ0paahNL+1vr8n/zYhuJ8hWA+zTUVyXznjkKAuBvucZ7kXUtB7QoDVYjTyoZ7nyaJ8y0PF8F2aUmGqAtIzWvimOZBrNmgV3KKBPNnbAqLYKehBTDakIIdukN6f3YjGQaEB9T8vgEV/iyEZ9Pvu+C7/UpG6CfQYNgB33cLohICve+i9fgAvlOP0V0twrc7XEcCji6XNj/X8z2G5w8MP3un9PQyyFW4Dt/WchHDxUP5uba/Lel7MdzhFXzV/gVoTBU5tEbqXLupQh2xveLYz3tJaeXutO4Hzuma/m1dd4BifV270MNE83bpOu0Zvd8CJuhk6vyyLlbvInvvA2QQBt+Zhw943IAQkXM5aIvwc22dxDaQI6HPfLelOTA6+SswWj6pIFIlJZee41hjcBcZ0y09X1g5fQY6j3maQRunE/hOPkMiPXSbN9rtTECk53juc8Yz+qlo/oNrGctHVKRrrVhtZP/qXlsoKaKqkB1wra4hqMG4lw449yk6dDfh+y6PI6vwHVuKuL6SRVGTl6AR87UrzL8leQrf8WRSkbVSKRp/5/vFplRwm4du8zmJbhlFYpEnQCnHkmeagcccPQLXcA/xVGKRmAJtuuYt0tfTGFn7slh6dhy5ge+kNFDyWaTLj55jG3yvBfBg3NSzvgONPzMajN4k/H7Yq9wM7NDQvgYjaC3UgBlkAXm4DsCT4B3ynz11xDH/Bj4lfgQeiEf6XSya+hZcP5ugsTqy1zLik/8AHykr1FE6FMfq6hSKY1OMVfAwvcz9zbhNgTEbAFQ8wwJEqwTOxzE0liVRpbfgc4dddDZAXPQhgJf5eVTkbzbA01kkDPBE5kXPOAM6HinRTCv8BHL7I3infPTWOhlZVN4I9o8wbFDbuo+Oi9bxBoC/BNfdiuu0fz8JaJueOxXr3i340xfkKqe1pmNV9SEePkZevgU6l7eu0z6Ep5+6ho/QzqKgUMb5xgrQMxXNf9hJqCwDtg05RY6V+HkDdiH8KUjDbUrkVEbzMiLc5H9yxiQ8NcYk+C0v4BXqRLzFSYS6q8RNmQtS2LNY94DU78+Dh7Jt0Ghrq68kG/AE/YfgJivi6guxQe9AL20bgQEWGKeF10kROSKWYqkyxoyexowboF4xyDqYni5Kh6bIawCLGau8J1YEEZE+fIu6JZSnDC1qeAKmWxfBsTTj26KaFwhSQIGD+gwk/P+UsTjtBLmuQUgkZFZ0cE33HHrmGunxefAQWgDhGIW0KXYgypCZQ0ElakZal5/A/X0vWD6Xa4rhOu0D5PCYisiugMUgeWNsnHuvwbcg3QLHtLLdpelYnS/5M+Y3ep915KicdHA/0b/72d7+icb8C7geuxWP/ByM0B6VzTcwmMuP4PoYua7efRWMPl7rOz0FPOb0DlZ4NQtiaY/1uU9a/2Xn6x24H7+5GolsfIaFUMyOmgMdNTt734NG6y1YOBbVwco4HIHjVPZMBpe7yOmmLbBAtavPGcbZGpiYwdrSPH8C9dmZ8wwLd4o+V9E5zYBnyiVyGYmZfv+bmd+JiPOdGO5Ar3Asheg8h5NVen4VsHSqOE94HXKDTfL6RsQKMN1ay6AMDTAQOmAYDMN7FdLP3IcI3/IMXD8j8+1I62OHzxVEZg5GVGIphS3IudH1pomP2oBPLa+B4/kCMrKqxlVz+7+Bc/NT1oBgu+LardT1onW8BfJaFhq1UpK/1f+WHrAylJ+Cyv1N6uEmhfsYXPfvwblO5bsruuafgYr5PFPXk5rfnwf3zC44X++RABOZBqREe+IJuHcbtYB1LAZZAQ+6OahQCkz7hmlYK6oAIvpH62EDXO+lAQCNxV/qf0/A/bKICt3pOu1/De6vn7IIk4ljkdR34Nr9h9zfrAGC0XfV7TP+G4gwuulYB9cqXYNyeB7rfmYIRXlJFWAY4hjV75eA4kJOGUNz3/q5WiZac5vgebAFT1MU69y1BaWHK4y21HvbHAHBegojl853C5vXfbvB9y11boGcPorPMmsGswqe3d3w739ykUmlgV6COIlJLNAl+FB1EtD8K4ulQpN66jaQfsG/U2UGfjzngEHUzSgyrgC8CkL/VxGsz9REkbSfwfUTO2y3IU480Ns/Br3zoihJ2IFlZO1UpCuSReNnB/BnyHPUtVOj6jug4WsUIBOTBopzDVwrayguGAHoVVtKrrTgQob8C4CGhIyg8wQ9cQM6Fx/AsW1C25QXix40WttKtR8p5W3Yr6cymI9AIyzvCBl1VBGTRFOxuVpxEdqiFMmCCmHnW8PG8Jhhd5XYfXagSI0irND/X0ccmR1w7LbAlP8XlLSL1bOtgsasFVvFcLGvwXUy4jgq8v0SkcI+zc9jUOcVRUbfgYf5kb7TArHITTJn5yhJq2fiepWhaA52rPvVFqg37hxxgb3gGmUtDwdGjqtHg/fNiN69HzgV5xU63QILO0ggjk8Qw/X2EZwvuftbARIwWhDVhZ/Xx/rsHOJizWB6iGQR/+SMSWBswtS8nICDOFWgeZnkQtRVYiHsqdDdZARXv0J1p4Ci7984Vqu3MPyMc7mfdijAsaey0TQYKfbnaXmzFdc9ATekFX70wQKMHc3TEO9cRk4/M07z4OwFAN85tkR7DW5qw+SFnyvsCBHILXxxV2VUpkBO4XF0IwBvHSpPwPRJLJKxBHXjKIrAKho0n7B2PiOgsim4llWU7oDrKYmc2vkuNguoALMDA9L6n8FDYyJrLiOlydiOhJ7nVnOzBI7ZJXJUKW6Y0H4O5Qb6QFykk0xEDnXNiyaGZF6yEjxmoH+K9Hwv+NkHnb8dANAcWuTenEAjHD/Q+1Xp+Bs92yoK8N1VDlzJHjbauJbLtTEMvjvA72luvtPnB1XJqZLt7b9LgUQF639Eh0kMD5gMU1JELcR0TqJzWHj9EK5T2x5wvjd6YXpeWVCD1N1a0KNiTx8j0s+8qWg/vNC/y9Lo1i3sKPe3Prxj0gP3epFRHzaDGXn+e0tzu4al6BO8/0QiQAXXbmFKXWLKQtQTvMcAI/gV52eo6tj51nm3IFzhTqm9X+kr3Syx56oO2gVMMCITeOPXoOHzTH9K9rADvArAgymklQkNgV/r9xfZ3n5h9Z1rQBQc+07sgNHnfgVPo/NCz2SRlPeu0/4B3vCIGaMzAH4Nz4VYWe2c8Py/BQ3DA/j2iuexNE3wnad6jlswgpeEc7wvkVMyXwcSoTW+Cb7/NZgC7gV/nwXnbw419IhTz3nkcISay5lJpconKXJqbjLi/ywN2APwChyDGVDXHUin9Oq8h/YC4Lv43CLoWKbPzKIm7lRn5ENwP1XqNt3jN3qfSgzlNMR5GNgqgB9jWSStzQHpew6qYPCCw0k4IsE9TRddZXv7r8b4fiHkQQarQb5epRqt0j9LYKr4PPj9GnjmnWclbCAux/EKf258mHAgrZbcS2TSjV+K3vS+C+BCfQ56bxPn9gswB5dIB98/ADff54QUbhiiXij7YBPRAjZj0jz1excpkm7w/8eOhRChor+Fpy9JihLr/f4TGFH475hMitI4Fc/0THN6pjmQ3NYip1WGq0W9DaheNM/GfVaqKDSGyQpZh6J1Wxpg0woO1ifwre1+FDRhGzQu56XgbG4ube/lDogRiEPic4ZV1Hl5CZ/eWQYjctappWj8F0EHoI97Yg+IiQyy78Hnfx+Mu9G5FEab5MzsgkbE56yCTkzOmFUex/BQq6COPs6GSfxHuAi1br4H183bKp2qz88VHXRaO9ZxaQZ0phrjycLvZsRkX8Knx+/gWQa2QMenjvNlJPPGs3kDjo0VZh0V7auE575GjfNRc2oE+ffZeCGURXBPF9HZGAYdzlOsWdR4BlxvKZ3hDFYAENNXpVuvIWMy5SV0j03wXbrweqow2qqo4M/6dx5Ssqb7d0N96nzHHYBOWrhn13TfNTeKeVwHDeMrDHO8rueuV3m+BWNpBuhEjPj7SnMv5H5GxQ3TrIwlQdTHKj378A3qJylREuEK2dLPTVRzZRWGqMcVbXRLjwJfsW1iTPJKXpvrlaIlz1wa55xhrxYw4Wpxez4ZTtYpZgdcY9aNpuz7Q+k8x4rVRYzisN6hvCOEGQNzqVFRyRy8UlpznXaMj82kDx6c7wMD5wgc30WwcvuDYyXuMniY9hwZFawdohk0lmasFOfbi7acr0wd8LFJib/UZ9d174uKw8a6oxyBlfjP4A261KKcGb3TOJHu7wH8ObwTd6zrpnAXroPreR0B11vF8xr35wxG09y74NjOY9gwea97hE7mfPBsiyjHGIaGVaxgZhfU0w90/wPQAJhY561c1P2Vnt86PA3BaCLPP6trmI5swTuOK+BY2f4Pq3ptXy1g/G5UhZIltnGdopyC79hPMJr7QDlutERW4dkYLlBtPH+Ap+epFJ2FZqz2QKN+GRXBldi6kR1jXJ1Dey0b7rjTzX3V4CL51PoOCNe4U0r7VN83+jPjmE0NBBkcCVC7zMTvlcp9GZOfkCNHzYsbJoke8eYapKnNk74DB2sOBcaY890jzhtY6Qco7jpRJF+Q3qJuKiTmzld930BFCd9iyqpABnyOil4+BA2IGA6sC+CfQGU3DtVNmdi4XcEDoWtXbWcF3aG07q/lIBmtxUCcr1KGY1eCQkPNjJVsb/9GSv0DOJ5rYFS1qELfyLIHhoO+b9HVJzIcr52nArJOGCEg/wL1HLpZDEdst/TvET42RdQKD5kgKnBuKUHHytx/Cxr1h6g4mIMUZ3JkrkR68AVaVzqEduBpN8oM7gN4VoFUGaHg0lw9AMf5ruB6R1muqEJOQyvh/mF72FinlcfB307BMRmrEMOx/d0yGOHM99M2jKk9i0XJYtexFC5cp/0q2DNf9LyvwbG8gaeZsn3xUZ9pFACw56sTNf0aIt1UqFczFjRaxm5RWbluVr+F7RW8Hqk0nk1nVn1OzsIMqLPD6PhPNR3zUEKuzpFnKMo2KOoYi0z3g599vVuYek/Cigdypf/mMEG74l6pgZznROrlJ0oHYpRmRRiY5+CEv0oxelyNamDXaVvaYgiT1sCA/cWIFO46uPB/+iW9pw6hNfDw3wLfA2D6dSoGsVJiO+DBOqI8tX5NIdjvFuC7mXxoOsZSwBvweMU8XcrAEcv/LXItW+uHoAN0Cx74P+gjH+ooekVEnyKg4gr2+XXFs0TpRCKfsyKb9+C8G7l1XVoVe/fLTDhJ12n/v0Ddcgbg/x3OkcZ1UHQQOGCWQgPGwH7KMDU+UNuPxlsYpWAZR7QeF0GjbRu+GMeiFD9lwzjLkblteN81cO3egO95CR6GvwYN0h4YXb1tYGTk7zUH4ggf6rn/sSh7oT3bK4lKhrQryenqccWRJunfgwf938og2wIjeYd67ofgXk0yNjX3GyAG8EqO6TwYvUvSmdrXSTRjke+uAPhf9L8vs739n4NrWuOAPujYHsKzZowFeah4pnl4mqbXut8TPcuQHtS6eqxnrOyGVwXtaPCsK6jZjey+5b6ruTfAdApcp32dw7ZcKc03h1HPdBmc8Hn9Vxn10WQ3wsc534aqlRp5kOLtNV34WnwPwAUzEVxfhXwCx7EqJdhIZABY54Qm3x/qtxz+LYzgKTJmUeVpRlbXQaNrAxFPPIsXS6xDKWBwLTapKrSq8QV4qEN+PG6c76wUGrMt0JMNo+1mBD0EFXYP7L39BlR+dXrOWsHAHUQurT+Zx7spQ+JTNoobWgfXe9+RTuRWz7sKFnuE77gC6qqnGbkvo2ldPU8r80D/OTCydA6PkQSGI1FWlT7EO6nnNkL6f9AYGt2WdZmphKmUSebbrK6C+s06ZtyiZrvNxPtdgxHuECd9Ao6H4QpDseidRRej1FagwdkC90U/YuSfgSn2Z/r3DIapTKIOjNZ+WRV57B17ji0pn4B7ZQUFGaCCPRtKnRRuIwmgGV+CNW/G/RJ4Bq0gqBoGuTFXATxyxOxdJxjhTyCIiM5ZS+nWOScf6tmuwGKmOmIp4w2QLNsMsyt4upwQLmDZpw1ML40/j2HaPztL+hjd16sYpt2rgjAl49ed7/B0FVvrSsFHsc7fkoxlTIaebuKGt8M0yldVcpAdgxN/m6AAmoh1VzAlaEYrkICzdJ4o3dIhTZ5xC4qwOfZ2nip+UYd7dxrXlvJ7pH/36hgo+s5Iv+Wiz+rgakyu7dKxbwcgOfaZC6qd9f3ZAnjECagErtHcOLB2gXcgNvAq5p3KoFyVEX8Irt/nIM7GOlo81PWM3sEM5JmGqdplDOP7rp0npTcD6RzcP8fmrYOO2hp8FM7Eeo+vAXgjpy5Mk0edBefJdH8AcOA6bSPU/TPQmLwAD7KRVD24dj6CRTvLwTgsw+Oe3oPGqPXxPgSd4jswWlG7WjQnN/BOQvcesgS2ngBW0Q5SZpqjh3qWA/gU4JwjGfdJLtK3DI7xHTg2xxr/fBGPGRNGnzXotBJbz84XbSYXNppke/ufXaf9t1B3lzrfzV2nNIU7rmjdWuRz1nXahu1/pZ/nGQuIrMWjpdRt7qx7D1ynfZGVR46tOO4anvN2DvUMtTx9m+m/bf1vGRtIV9/7Sc//KPg99LszqAORnm8JJZAH12lbEclIwKHkOyHV1bngGrPgui4ryDuHhzGVcWe2oGLjGg7II4jJBHHuySfwRZ73VsBcVxobk85zXM3CV0eWisL1LzAaLan6Xg8T7ANbcP0T12nPu057UYvMcDF1vYGmB8E5uAhv8O0TowMYpAksDfY2G666tmq9Ju9iBtQ8psjtGcAnenICqtbkJbwRduSGi0NG0mByCEo5DINnWQAP46swja4xrTRWtB+fwKcuzSgyzOEGfP/f9+CBcoWSNl/BdVsF6ZVT0Am7y91vFjy0FnWfC6XuNuCxSWfgQXEcHAS2fu5cULgBKvhTFEd2jYdzSc9jmEArLuuDh4cdWAPJ9vb7jtRYFvn9WX861H+94L6boFFkh/EixowguuGOUq+rDEkX9F1P1aFy7tZBQ/VKjscLIIqT3gDX0TI4Zoe6xvfwQP9u8PkVcL5XUMwNee06betn/tbWku5dlE0wZ74Rg0VWgZ+dtqRkmjIWpF2B4xr25z7L9vb/GHzuWhHIGdCJMYL+z/AOYhVX5AfHdqw3WvOvUb8g9SO80WOyA+Cv9O+/R65fc3D/wRmuc8PS2l3wjO3rd0OOueu0Hwpe8jEbLqRaBPcjoICDrntbsYfMcDsDoQtJzoZ0VIrTuKvrb7pO+8dEx3CEMSEn1pKycSBNY7gEGt51ipJmM3Il74LOSyG8aNw0d23DqSnuZtoSpLZnHauEk1PNGbvuvAXxVY0iijK0UxffWKJ33QWfdxzP2/gbAfH7AUPVehYxmwEPe9sU26ARUahkMX60J0W2wOe/0c8yxXoNjy+8cb4SPqxqHScVtgmOzYojrU0tI0UHxBU4D5egoWb9dC81BysgXtD2YBcYQDQ2QGMjPwZP9UwjPHZS+u9yv7t2ZB9ogXNs9BaWIra0Wtg1wgyeCz3Thb4/qIitUPpGpvsSPGDNAXyl651WGF7nyNFNKe3+OwRpc3jGhgVdexFjKHiJpdm2ASw4Fm6U4dis7/oNvOFbJdYSz4pGBkak81Xei+BcmlPbAw+PY62PHfCQ/L0bbpF3CY650T718ilX5ymkohmpAvkE7rmJpJcdMYL97P66rGzAZ5rOs2L402t4B8wivLHOOYbbXQX1zmVGGqIzFHfryl/jWtdYBnGl15CBkfJCWY6+TdIv+HfZdW5QnE0KDckFeINxE8MBqxtw7c2D2aIteN7fMge+ynCrJZHM1hWEfa5xln/AKGNCKKZPdx1x3x+Dc7WU5UPPuALgX4F7+xoJ8y1j/bn+/R4++ryJggBPY2My8G6SOf++cQkZ/GfLPhiTImu95jUmRagd5bgKZB3apEqPNH32U9BIDCNTAEaqEHfhDygzwDZRA9PqhruWvAM3xYDkPPdZwznlO8k80fN+BBXfOnhgvqxK88oAeyFD/NdQuk7XWsD4kIFTPcsVmkemDfdo3x8YYHq/FwXfs3RRC6NGfBKtVygF6Z2P8N1xhg5WRXGegvuvq+9bpXk+Eha73x2C9JCiGVbNW7nGlBYdMeC0rsK1Ze9gRnJZuiuJeFxZkA9glGgJwF+A9FcvC/TBbO5nilxglNPOpAWPBV3VWPwjuE/scFsCjbozcA//JXiAv5Qj/SO4x7fBdHi+e0tYRZ5Ez6XvTyTFLH34UP++nYSuTpALeFxo4WGvOTZ8XQpc5xN88WFycCaAjCwB+A9g1P+PiKSRne8sdZawfr+4TvtvIOL0lGepITfwjt6QoaW9OoA/yJgEyM9aVjhbZbgNiaKd1jzjYzZaoGfV/m+yvf3rbJh/uOiaLXCvXGZ7+yc6p8qgXH2931/CM4V8Bs/DJddpVzXw2ALnfgvAP1e+NCXvJBzC06lFZazIpAbhm60uqiMZMTxvINb7r/08TcWVcFwFEpL4NsZmylONYTzyEh4gVqlXGSHQITCraMI8fBrIopsr4OZ6EXzH0oZ912m/NmUoY3RNH1sDD0bbJKWpDueZAa7BQ2LgdGQT6kgkY68x9lPX6LtOux9TpjKwl+CxQRYxWg8+FjtkrYJ6rIhOVk4JZBGrFob7y9beh85XSAPARhAVPyqL9ma+w9JT+Ir5vHF5hcQoDoD/CKa6/pipcrXk3ieK5P8aNJxb8EUqefkE7t/k9GTG7kTRtnmKcBxCHaL0u3dBKhLwJM5Wdb4FGqCv9Pk712kDPHSP8gaODI5rMFoTRp7K8MaNRJCFDRC/ZwefPU9Y9DMVkRNjqeikTJP25hq4N0vP0yyXvncJjAghHAc0oubBc+EEcXjaI1C3boAO9LruUdTDO09VFl1rjnjuBSRWkGvsUs4XwFP0lRaUVhluEbECKYD7I3QgF+GzJ4vQWZqQVbI1uqmoder6t+KkG+m5VCjIBegIn6U6UsowvQL35wV4XpY6d/dKDXQfoo3zAFQcKV1I/qQkpzg+gMpzE4ymhK2bJkG6nPpMZshdpqZvc1iyDzpwjVj5E/yGHNC86HuDYigQnxW+8xZ856ErfbZXFZV0nhIG8G0ErdVWU7qfeTC9aHjTsQ9U51tQXoMUWmY0boIQjksEbSgdWxHO6vdvmryL85QZdxiP/mgOuZZ0FZ/fgW/LeJf72yOIVgQc41nk2gEWXHMLvrL0bZni1Ziu6f75SOsMgP8dvtjk7xLfyQzhi6o1OY7oPv2iAz3YQz0AP2fDlfjPwb13mrGy3n5v1EYA8MeqdeB8e85ZiHZH478NrtGmdEt/Bc5hN9vb/5vg9/OYYG/13D3XwHe3wqxl3euPuc89g+e/DA3CH6DOUVmN1p7Bfu+D81REcfQYLEY7BPeEUfy8Bw2SVYgySJ83fLPBR6zy++cq/e184dRxtrcfNmRYgKcf+6JneQTf8ShmfBoWdyI935uInvspeI6+zUUmDcM6gxq2Rl5P6/rzYNOEqP4LdINFjXvwhcOVsKgiAz/hWa3H+UmVbr5vaqD7kDV4aoXKLiS/VCkyhBQlGFRJBopqEQHGqu6hL2W8DB4itRal7lU3ymTVi0/AApc/ZgG+03Xan3TNoQWutNtHRChKlII4AfDQsal9qgIwr/8WxCcNdZ2Q574JX3R0BJEwl0QMVuA9y0WUQEUUbX6m77zOiqvjLXK7AOHX9GzWYeQjGLGdB/eJdVIYp2f5Gjxlxgka7resHpXGoHsJODdDkZHcIWa4sJQIfCFsIyIPwLneQS56ogjx7/SZVFzjgCoo9fN1RYezpXz7TkTckY8aJ6ERL4f7/Z2eMZ/i/wKu/0qqMR2MVmn/ER5vvAHPe5lkTGotPwH35i1oyNwhlzFLdWJz17bi0suKd3oI3/XmWN+5cJ32SuZpqswwAmS8Bd83zPaQMah9vwk6QjFIxUzBv/PXsIKTHhixDZ2DX4PzcCW4gnVmeQ3uGWvVZ5Xm9j0zSIeizRimzwnF5qcFj50356OIoP8xPNb+PjD0IyL9Hd3DWhOVhceR7x0rFX4H39EL8DCn2HeMRsz03hwYLU6CgjQxJCWPg2csLSb9Zo1JRaYsolbHS7+ErxSbBo3QVxcXkOkKtzR0UGbDVZIjxQUN5TtwAS9jmH1/KpINd5G4BRfzUfD3PgoO/KwcZG84SYAKtnJcMt/ft1ewKXf0nE9Ab/EJqJDXULwBY5XQRWIFEDsgJugfC4xUO9AvAwVvPz+AB1gXvuDisioFmyAG6r7DFNu6KSVvXIbGeDCH6j3+BgkgdWCAP0ul3jCM68Cwd6QqWQErHj8hPSV+X/I9GPmbh6ePihlZ5hT1obSa/UHrf2S9ylhLZdxowRdiWdEV4JsQdGNfKpA1eDqpGXg+0WTjw4nnM+KkfQfqzjvHTltfCqIzZgifBYbCr0C6KUsnPwCNryWMGsrv4Wl7QnkI30ZwBAKTsQCnBxoVRcZyD9yjLUSi+Pr7LDjHD8E9vJl5+M6JI7dlntzdCsHyBS8f9fshHRwEOWYzFrdZd5hZVOu/JJztL0UU7bvRGXblOm2DcR3L+B8pqAqgS1396hoMFjyHj+7f6rOz4Lrppaa1S8QopSptqW/WmATDwHm6jkrRoTEW9ixVXAOeqwlJP/ezULKC4oJp3jNFEsPux+D8z2ByONZzeMB7srNREdk4Bg/p9+AheQjPCVd0vZFK6BIxknaj8ojOgdZgvuL6XNiXJ+BYPoM3MFMjgcbjOAcqrcGhp3FJoj9qKlK+u/rf24xdQH4GDYAqSpSxcMEl1/2AwKlyvpMHwMN/4kUeNSJlRTIHriODBxQ9o0V7NwD84FjJX7dlm0UgFyLZkyvXaX8GD8aBbsqa0fmYUW9k6AtgujDJsVHQwlqR5luJGh7uEXwEamTPZnv7H12n/Sk3J2HR0baecQdcM6sY7tdcFPi4yf0ckViWwnXaxtf6ISNLyAGEM3ed9lXuOy/gx/waEWO+IJgzwjmpzxb2Cg+DHNJ/VXrDxmpqkI/7lgBKY40mDK7zAUEHHgWJejIuvwNZZl5L937RtVYRFM+BdHVrAP4cDJq8c815rwEMKKW+pET2v2Vj0iJq3+RCCiKnADdPI4xPE1Eq9w0SqYgmYEgCTHuUpmNTRZvgiSP33KuigzEb7UE6tqQ4Gy6Nryy85lARjws6GozxqOH1ewD+2ZE9oVc3ZafD+wb0fvvggbgE9ij+HozyvS9530UMFz9NlVA/IpYiG5AsK5X8zUQs9DyHmG6nimdQERWa7Ys38BirqCOhrMcDeGoec+ijIkwZ8npIc/MDWN0dazlpxT75QrEt0Ij9UgLnGIj2Qq0UqAs6LWHYMbtzLGixFqi2Ty5Q0fUkfA8ZAcZ0YlQxfXh4QDflORUIOEGN/SbdZanpNVScn5FUbay623DRPfiKZisEC7tNWXR2IvpB59Y3Uwyr4NEmCI/qNryMQZtaoF41LP41/Fo0yEQPnoMUGIXrXIDjPwOfITEeXOvuM/bZn3reNDImA7xE7R6dqTLBiNq05BpUFC18BWqkmqn/SdzPDvRJiLXHXEAujRYTeVub8LQxUxOlU3cxBk4nq6CNaSqp0ZYCsarss8xzUm7Ct3tcRPEzGxDfIltjiyun7xgSHc5DkUhFX75XNGmikdEgTVTVVST/nAeoSWfjIhXNwoQ+ADG3oREWjQbVeL4rVDs4fwZGSx4B+Duon3PBsw+K5BQ1CdeP8U6OPK/e79+AuMKfcsbHtj6/hQmstdj4wndaWs/29l8rcm/P+6/A/f8exBJ3UWH8uRxXZ7a3fyEH7g40qI2k36ADyQZS3ahSRpx8F771qjmTr8FxaHJWrcPj+o61Py5zBtUjcM9sopiC7BcrzrcNvQP1aLfhpazq/BLehtgCHQ275k2wJ85Bw3ABOf2c5SjRJJYV+wCel/fGttM0MvkdVHWHSKi6zkFRJt+wIWnP9lVAwdMWeZkrIIdj4yq6krTcIYR3S1zsu+CGmhN2aQfccMkRIOfpfS4rFOqAmHqcdSzlY2DpL5PYDw2fYx3cqzfgnO44koo/hzfkL1FiZDSJEMtgeAYqzKHOLmawu077JAta+ZWJvm/V6bMA/id40vxCY1L32sQwVUyVGPB/3rHQ63CcfVDybNZacs6xg5Kty214Az80Jt+B7zwRg75AbB1cgsUVZfMTcl3OuuGOWO/ASGisJZ5VhN+Ch2FoTFp6fSwqqgB28ASMwFvbvHlwDAcFX5mvYrZI3hWCqFuCrIFG6LWeO3T0LXpu6chBcVyT90qRLMI5GBr6GhsrIPucEc/YArMeMT11Dt9lx8jY86T5taAzv0DZAsdrAxWwO+mcFYg1JPyb7IYvwWe74D7fAR39fAGe4ZpvwHEvdVbl/KZilytF6+IRhqPSUaltTEphPIdviJ7/+3MAy67T/pjdX7eBexVtRsN0fphUWH+SUgTkLfisdcTpaTFbO7ULjNcLNJqW08Fcxzg5AQ/YU3BTb+m5L2qM/U7wvZ9KjIPP4CF33sQAdL4Cc1Y/50GjpDFtThNxpOiwVMo8OH5n+t0qfArkY9NobwXudQVUvnb/69zfAPJARhWUjNElMDoUVp8aef0NhP2reEyj09hESfpZUbZVeCNm0A5P/z9yQEsXboFrpckYGlE8MAwhsYrgPFFznkR9GvJ7cM9XtmvMPOG6/XsTQUcsjUks2m0dmgCeFVe2jzNyUs6D+L7rMaLxm6COfgYatiGrheFdz4LU5Rm4Pt6Czk+dCN4SOFcLGE2F34B77TW8M/u1Da4VBBAtOTW7+vcpqCuPzSjNgopmR5ohYLQoJsqu0VR0Ju2AcKPuJK45pvTAuT1B4GRI3y+BOuDOebogwOPoy8R021C1fCA38D3Lp+lEFknI1pF3lIak0JgUru0RmOoJB6QPKvAr5EKszveNhR7gT9KYBMfl12DUoNJbuG/R3D1FAOSt+MpQR5wJPspYaTkTGbgGOjYnxrrppIoZBdY3vOhet2hA9xCIVWDO6V6P4cnhG9O+CJu2BXqvpYaLDKMt/e81+O4v4SORR1DKLdUICpTknQ787wEsykiOpe2MHsmqDq3v+Am4X34AlfB3yEX4nS/4mdEzh0VFW/CUIn+P6o4OX/SdKkzzU3DOFsED4BRcYw9BLN1BxOi1frUbrkErVKUfjW/vLvi9FVzdu+gdkvV2bu6NWqmw6EnrYAl+L2yAc/xWfzcqG+hnGFFbAg38swgGMy8WSfsEzn0LNE5aerZTwT1W9cx/BRqdf9cgFWxG6JXBj5ynP3qmdx1pR/oVxbpszYDjaxmURXCvtMDMTGzNfwINinxRVZRdQ07hJshTWKlrHKlvjCZpS7+7qJPud75rzXWTMQ+ySr2MdHJr4Ji8ATNi4Xn6HN7Qe681ZUwdp1WQmYz8qq9AXTzYM26YBzq1UHMaYlHpyoLVssjkGqhc18IIRMay/p8x3LMW+lvfkc5lBVPkS/uaokN9Hb411TcDEA7EogMhkLdMwo44N/BkqONiQd9ilFNtLFEE5AVo1CQf3hlpNBZBZ2fiqUIdgtYlxMD9pgQmUSiyq+uug23QTGnGiM8tgtUCI+eWxlsC330uq9+TfR1euV/BwwGWEZlfKc73+vwDDB8Ml67TPtJzx+AE5iwMdcSR9PRuP4MkylXRs2OkGUfXepdr6boTHQRz4LyuYDTqdAlxw40RdZ6DaFkci6Ss+KMRiTwwWIutMaJ6jURz8VaOxq8cK5zz0eCwJ/IlREodXOPWkSrFuBqXgvfYhuh1HFvIlTmFZ47Vsne6ptEG3WBYd3yG7ze8jCC6qHHcBee3W3Kva4xi156AhsYivNGWLDJyQ+zcxER780VwL8PxGfXbr0Dd9QQ5Q0Z7I8nZ0P55BN+9rMoJXoA3bI21om7QAKCDsgxGvY/r4k7B9bCtZ7qBb4LxJUuoVQiDb44tfNcwCgsIPz/0fBoH6+L2KmaE3pdkJTybeSkzJrv6+0U+lVWWbqmhvL8p0cG8BHq9Zalh67X6ETmalMT7JPXsHVO6oAIrbbNlogjJC9A7MiXbyAB0ZPe3/rd3oDKdxQSdizHSRBYx3MIEjUkdOj9AfF9gFPBGztUrcL7HLRy6BJ8/PJSixOdavzEc4TM94wKq0y95uYJ3OK7A9b8MEum2wMheH9wT+fmxtX4DRvlmQB1xhxycwHlyZ+szngedHzsSkd9OOF34FqN8lOfgeEcLqhS1sAjmkCgytQWmNc+C37cgw1TPfwEeflfgeJoDmMTtpmsOCtR0PcNhjlN12kg0t4NUN0ahBVYlfianYi4/jxmpduZBo2Zd2RXjazQdnUKLFlJYjcxhsNY+wJN6d4OPbIFO1LojdyTgjf1S/BhUjAOeF/+cAjcKnsuKAO9cp13pMI0rGXHrnxRFXAMN7GMEGaUAg3qXFcDXQqfQeTywFTOm4PjsbJ0Hz4srcO+tOvZUTzXIT+FxnU2McbvvHTyjwQbiRTdvwH07KwhAEo1OhSzB45EXMeUWoJOSQmNSxs44eLlfmhgp9zFKFr5wEa/hvY0m91lwnfZhg+hQkmhD1zLe6ii7IhFm6qH+/RIco3X9/9mUDegU6YJKYdLULRa9AhgRGhgPZY5XiihCsQ2uyzzWsw7xOUCltIAG6zbicAycRjkQYaQyj/U7EXziTvvnKXy/7LyR/UTvVNhabhoRtyzCR6lDoaqvdpGi34GHjvwY7C+rJD4Ho8kn2htWBLEAOiIWTbb5PyqAEwBM/bbAdfgGBVXUVSKDYRucp27FZ40yaKgjlhyoD+A6GElFa7xCjsUiQ8kwc2ugUWVUQY2cQI0jMBwc2YDvlvYmEnW6AOfQSPIHkVFQl5Tps4969sMGutXazy6BTs5EjUkZev8eXGt/b++dkZPyZxB2MsjeaF0YZRRcp30TjpUM/+/ATmWv4PtUt0BdG6OGGhFzgnNGaXimVLZz1HWuMEZFucbhBdRyVJmYLiIUWYp634CGszkXYT3AR9BRqQMfOwHHsI8xs4PKxs3ls8jTkG+ZZ3IgjhWpK+DGnBYAvU5K6SG4uYbSBYliYz5f+qkKkUKYTdlcAQakn41Wi01STOkZmLgHnzYaeU5XXkE4cclyfJATvO6V67Tfg3NbGZVXROQhaARWtXTcgbjG8hGBrB7xOcAigKGoeD4yFGDWtkCc0wA/WnIoWiehQuWXM7oWcz9DmQp3pAzeBQQk2VMW01M3GNYt/dzPwbhqjPLzOZh/FGcLrsF04quMNEpvwXetmyFah3Cgjq31zlHc9ek5CjpijWP0Bde4cuTSfQA5f9J51vHjTWoEyBFn/Uz/fXYswDsHD3gj0h45V5Qq/zEwbIwgvTLiNeYYHILG2RG4D2sTxgMDWItlTP4Av+Y24ItEHiM4w2xMFf1fd+zTvQy/r/sYjZQtgvNiaeoDMAr8Cd6gTJZsFPJiP++tgDHitK8jotuE8bT/DjCKJa3NlZl5Hs+xRA7f9/p3npB/4pJkTDpWaC+BANN77XUtQyisIGu0sRLkNRJwHZI+OHazVR+MyFvkWgOmirwMa41ohMApi8ToYeA67SpqnMaSkUz9FXxf7Yeg0h1p4+V8S8hr4UIKFYXSeEuYEkXLJETvPoPRyuWYrMMXGYy0dNShaaT4i6BRPjZ0RHMQGpLGvPBZKds5cF09ARXoLBKKkTQnecxYmRjnZeyd3oNRy6Q16nwlarfI0XQekA/4AsKpSkaMrkVjw7X9DjqcHSuJl0FoQJFhdAKuqROtr1WMUmq1wEyEEYiPFEgkinVAsS4aR1Cnjsj+vMNo3+7GIoPPimsOACDb238jPJ9BRkz/ASU6VIfoMugMmbFokVvAV99eybAsw16GRv81ptztye7p2CEohtOtI2vwhWW2N2bAs+4AYiMp+K61S3wKrtk5MErfj0Tjz0C9EmZhelkN6rYiEazlFoS1TDTdmxrMyNgF5hMYed0Eo/F2DpkN9RnMHNWBM6yD3Ln3AQssdNIdGwX0x52vSmNSyjps2XOvxqQ2lnUgmBpRtxZqquV+AF/luVlnErKSdlNlonn4DlwUIZ5lofBLXkIMSCM8hxS0VciVwQAsPReLOoVibRJLedcUxTMA9CwmyKE1BXkKYEWg77LnvADfd8i4Awbv+wN4qPfhqwQnuvalyJ6Ba2kZTONYut6U21Si2GV7QEq6jnL9d5ARAuC/FHymB677+aL7Tlp0UD0HsVRvLDWv9zuV8WR8vVuu0/772EGUBaTZzhcyLQjDd6bIca1uYdIli2DKNzSWLpXe24Z3dlrwZNuhvMGEOmJJtsB3WMBwGjycrwtwbcyiXFd/D67tC9dp/63e6ydwPd8hwL7dV1akrmR7+2/d+HzNR/DzYwY1wBaXf1vxXbvvATjWhQTYOqN/B2/gT3Sf1Ql+BPCLk4oAxS64zs+R4Agr3f0MHMNl+KDWAcTVW9OQtE5LcOyvvgA6nnW4kw0SE33PbG//WtH92VgQUN/fgC+OvBkn0FRpTCptcoB4g/qJi17wObj4X2d7+z15qCm9nJvcbw6elDPfX7VIrDov7PIwbbEwv/V8fg8uwG7VF80D17+tg4ilM1PbX23AV1IeVqWYgjRVdCGDEZcfQGOqjMTXsGzJBQlfUcywL4UwZOUtHW1NXcJ3PZjoe8tgfazrzoFFNM/BsbZ0/VHRXtCe2QYV6FipEynVh2CkqElqZyb3c0R0ELzEaCeUiYvGdkHPYw5frLuQFTRZijUlvW/0UNZWbQuESXx2nfaI4a8DcxNM7XeDP30HrtERfHi2t/9J6dxbcM9HD0lhxWoxKsREqdgdeIO/cD3pXinO5AL4fldg5L0PRrYm2pp12jLu2GbshvM38HrEWvel7FlL2V5prufKjFsZIWaINA1YGIfsAtRJqMH3v4MvMizLqhj+sSroEcpd7qc5h03soh78eW4G3Y0jt2rlewt+sIFh1pARKbpWkAIP2QbGivymRiatqvs+WvOEFY35CtVpyAZ8L9MTJHj32lxvcI9EojoQrTK4tvcQGb/H4FhXMvpLjEPuGomLrmJTzMOnqQqLQjJfET2XeYqPZSSQKn8FeYfi9G2SZB7ztohyg24HdAY+N4CehMUmH8Hxt+zDcVZdARwWl5QRwKfIBrgWNpVuDyu75+EdnjA9P6vvXQL4HegMVnWGMAzvtMXoYI70TGGkN3yenuu0/x7Ab+ENyyqxbhinEFdi8LcVsN/9HHgQH4NjNAuOYTf4rBW3bDsW/wytn2DfFo6p67S/Ax3LGP1PHdkF114/29v/4xjXCeWfQD1ujtivACCMEN+HCJ7zGDTIp1LMKiywsaiMSOYZA4AamQbtF6NIMt7iW0Eeap/FLq2QzBhVAHEz1r0PIpjkAvkEkdU7X6l+VXFmWTQ+xdhbggonY/pZwZaXup5Bi94i3aBbyP2MPUPY6SgfKJvV76/BsTgbF0YwYkwGA3ujQdiEPzjOE6NY44jRP6RWqNaS4IA61bvYO1aScobSNF3dVBRiNxqLlM+bt9MtUDS3uZ+lovdN4ptKlFOoYq3KGNImsOc0LE9lpW1TiRmsMmAegBGOqFLO0nofV4oU2oXrtGeEq4spg239fgs1oSeBgT4ro2YOfi2n7Lnr4Oe4Tt4RVKSl5wozEAb+38BwoZtF3PogTqkOXnPaYjq1pSifcZvGHEAj+F4Aix3mICxf+CFFb3fhMXQxGIXRuvxrMG3+O9AY3MBo5OQNfNcXi24ki/aHHfrLGI8d4RQRBoCSe9t7HhWdRdqzL/T5Vfio730XnNq9l12EAsnE+VavN2ZoueHWlG9jBpzzjUXgOu1exoKhNaiSflzjIBCL3lnHpiQbIIfFqywkU1r2C/j8867T3kpwbMPv9x2ZVhYSzhQzokL4SN912i+K5km/Tw3kGDn/EgqyUIocG8b+Dcqx03n5CO7doX2jc38VdBzm4TsdXSAYd0E/3oFnwER4oGObaxsiDnWkCTCetVvcA9+RNs000xGPwQleA/BSi+pF6pe1QTbBzXovfJrO86EB6STg2+Bi2kI8UvZRv/8qqWMZRk06zczmfk5DHoEOxxV8d5ZNiEbE3R/N0SoKlAFoIKwhAeYQkzBSJyUZpeEp+O6R67TPQcN63FTcBVTUYKkb52mzbsCDJa9ge8HP0vsrndO7xyi2EfUfO0+ZMuNy7WVlZO5CqWowMtEC5zl/+GyBB/ENWLEdW3uHIE7wXPfvZwWcvzq0X+pz3bovmHn6n8bNKcy4kqFRxxh9As/DWRntk4H1CSx0KDRYHTkCV0H+yErD1vnOOWWQn0P4tqXWHjKmi7fgz1xrLbkKH3UqioZZN68ZsMtUiC+fweTw5V3wPZJJ1GXUGpXQLYJCsrLvZeyuZdXRq3UDWJqLUoNMBpdFSbvwdo3x6E5CzpFQ0Jv5lqSDQpwyxyP43jXi56cFH2z+w05H+WukdFKbS818xYxJm4g7EBB6Lrxdf9yD4xuRG3CSmxrGW+C4beGeyNmV4v6EesrbaCWiijoraH91H6LIxiKadQ75BG6MaT57zGC9V6dKcgVvOOXJuwctJr+GFB2gzldO36Ka2DkvK8HPg4zk1UcY5X/84liUd112fecZA1LbihaKMMbbINynDNc3iE5rLIqwkOugUdQFowu/BZ2DHyOftSj+SZETIwjIHyCidTsAtNdWIOxb8PnS9RNE4ntZhA9X0YykiIZjBew6iN08t0iQ67RPsvo4RiPvr5NFGtKBcjAeQu32nOdRhH6mREmfwONio5g1Ge1dcG4fA/igdZgft0EFNPx+N1hRD8XFaoZLt4Kj15gCvlwBnrqO/y14Thoe+i3SHZdp69ot+EBLNyOTxSVoLI+VaVHkfAF0JA6L9FNoMIbrIdgbVUWc0WuCxusamHltQl1o11oEHeFkWqERYzIjie41Am9+3AH+xuQjuKibpusPocjkpB4oRep68FlQBfoNyjOIlNd12u/qbBqtxYmRjstLvcttFiMcDtu8XThy70WB8fLinoHK7/Uk9owMtqJCnbFkTIO+TNbh8Zdd1DvULHXTtV9kw1jJGdCDv8vSwPlWCGVV6uNEJ3ehHvZumIR8SGSEzWZ7+7dKY70GU96nMmKsG9IJ1NoSntrnBLlom/MFSpVA/4xY6j9EnnsLNKhf1JjrAaG3okPjOG+74Phb9aytj+XCb0hkkK/CH47vwfEcB78fttvrap4OdJ++67R/BRq+Zcbypa6RAnW6A9fiMiLGkdbGzwg4PfWubwAav67TXig40OdAw8va+w7w5QnPNTVRGvVKz/QXjqTzlzJS1kGoRhFMYdoBrEMoMhk+77gXlTH3DHIysgAOpb89gafN25Kdlc80mENduTdy9zZc6w2AQv1UQ2qPexRDck8pvK8iWpyN369BWubeRJGYB8iRTX9tUcojbIc1BxpryyAA+ngSmznxWQxnaCkiwxwNOmDIiRqZ4wrFtgxfRWjVk9+yPMFoD/Go6AAwuo274PfWqeM60BmNW5llwxWh+WeYBSMwLTkgMQLhfLXpITjHWyjuFV0pWr9mmF5VGJI/6BnfZ3v7p7auFdn8Hkx5fxJU4AA8ANb0rCcRI8mqLQ2vVtcgrqx2LxAj9O6BVaaLY5wLx+CaMOPsI+iQp0Q2n4L7aQ100vqIN0Aw3XcB9qMvightwPd8HxQTZmpo4Drt38C3XC17vrcI2AF03WXk2unJCbWCOmNPGJGSSL+1JZxxnXask8w5fPtco8P5VtrvncNHVm39PYYv+HtV8L2pBrDqRNVritHvzWF0n9p5B3At7ILrOIRSAX5v1M16GoZ5HhPgf1VU/TW4xpNwor+IDjhfSxypNebBitnkDapDZRVMid3nxrbU2aa8woegsfAZOfoj52kY5kHg71SMH+fbYfWV4jFaoytwg02kaKWGbMF3f7CI6CQqfY9BJVmnh+zERZjeHTB98yX3t1l4b39QKFJxPVsns6ASDKPIO1D7PUW97hRtKCyM0jM8hRoQFO0PN8qF2II36EYKWlyn/QTs3DLoR63vHQqDBaiFY9n7Rp5jCR6H9gm+hWSM482ijHMAfu1YTGB8dGGkwQ7W8LtFFF1Hut5tQ2PuM7i/LutEebKA0Nt12t8DWHSd9iFouKyCqcuRudN6eQavV67k2IadlFJ7NQPD+NioyJD7N3quN/Dcf/nPLcGT1xc5FocIouNahyMUSFmAOXaeXg7gnObf7RI+dT7RDk9Zc+x5LdF6R8EajUpGsu9zUOdYZNvS8NMu5L1XkbP4BFx3sfP0HL5g0XTCCM9w1rywtwvq6Os6UXtF/ndB/dCNPEuy/IsxWSCKxhgv1zXq4dOs3eLUKo4LpAsePCegofb/b+9NlhvJsu3Qxb7vo4/IrKyqK5k6M8lMA5m9r/IJpw/GqTB456fe4DW610p2S5JV3cqMyIyWZDBABnuQhAZrbZwDhzfHHQ4SkZXbLIwMAnB4c85u117bKFSuMEx/ZOSu0GvjcoB66d+lkD5hvI1WeWJK7A50tH8Gld1ItFfKUDR+PRlg8TIxOph1BGtWhvQl6Pj9onNdRRw+7A7ZZeI6JaglDA5ByHPujAvxGDT8145TQeaQDd8IG9TSrxtsoQ7G1ED5U/CTWF6BE2JOXLv1T+ZoqFz6CVTOdzqnJTCAsUaiz1auVDnvrX7Pw8WV4tVcuzWXt34TP0e9sgQO1Fzw8zmoNy6R3bRl5VzAB4ujiFUQDoG+8Xuhv73TvpuFx5WeFuzlG/i1nHe/OvCO5CrkIDhO6bpTcGzVn3195laYu0WksNwKnp7oHPfBRpzHYId2qc7VsX8BO5TvhYZOzrFRcnWlOww/9zZ93s43iJ6nM4ppqECyu/dRJfuJdSadb447j9S5gJ+lDmQEDFqTP+tezia7e3927dZsU8km7YMBnl6t1S0IF5rz0U2d+6prt05HOZ/fnMl8sRT0HJqb8jBWCcuEwmMYKfhHUJH26Y+UeehgkO9xZNFG7JmySMY4DquOJOzufANmG0LA+72Joti7yFLOJgKwuD6/gMEoOHTqPiOFB5LYxKEZELd3gQjnKvE0QvNJCjeXsBHmCoyG0+Myh4YP6KULcI1Oo7jTcaAJSkp4Ciwj9jNCwXHNYRzCFY5S1kp85/N0kBnchOdvHBifmRBzfgFP63IBnwm/STsEo2axne+Ar9PMEis2AvYEgxm4IdH9MrqnkRoUA3wd9LNPG6O/rTg2PNyCOu5nkFJrMeu+JmxSegM+ywFnUkHbDgYhQkaJE5YOV8F1uCUcoJXJ37rswRrzOs6Nzj0cBxn17KVL79P5egqe3waYDAlL81ll+ufwE+piOuyHrkU60YKE98nDcghvg9e/7NqtXK7flJyBazSXYtB5dgfUhdxUlA3Qx9tAfv/EOXzCaaR7XtuZzPOqAxzV5ajZnsjzsEzHcZMLUEohF9NRIgfgQxprt7TzVBDTGC4Z3sGTJs8mu3uv059PauIqZdgNRB02SCxDCkGRvDmUE+GMazOPnIUc8RwMKB3bYdzBcFe+YfjmkOrcTNjckeWknUCYu7RTWCbJIPFx+rU8h7Bo+EAuL6T0xzq4dnfgjegLeEzqp6Cs/SXZ3bNs32lwjG3QMOV2VMZKxnr5m47dCQ2jDOK81vsvwd8/QE0ko5xHjpjDs1j4rhEkLL25dusjykfbHuo9dcitt8H1biX1C3Cd2zo7BQ19D1xb5my+Tdjg8TuQo9DopdLXkhdArut46/CZ4A645sJZ6Efg/l0Bcbjv9HovKzjUOR3rM3beB5gQ3L3s52wqg2j3x5qBzpRxz9vvTZTuQ0c7mnd0TGJr7CxWd2h9lPkLPf1bBPCvpRfehd8hG7UFZkVHvQcdBEmILNGz/XFUHQnUdCa14bcdu/zShuExuCFvMaZO1OA8DJ8zBSqciRiXJaUyDoBvWpbA6zdF2C+pS4ktgpsic0xdTiQdI0/hpxWEG8ii1vscMxklgaN7J0c3yqFUqWumwSjSFOYMgBk5IDd5TmWSzRVo87TDxpdCySqXKns4PWp2LEfqDh8wHKZRtOwId3ULrikLmIo6gm3ahs19bxRTJmf8zxkvvQKbbzrwzvO51lqn6Jhan3fhs3C+A7QHBotZe9U64Meib5ynFrpMyAtp8JAiscEC1xCHaNHxU4bMcN//AGYbPySDlEZ9zJkcoafg2rDvMR1U1bYdgWsvJHYeKh0mbKg5ANfVKjzc4ZcCg3wM4I/wlEZ/q5L4kI6Ybro0HFQP7DmYntkH74MFEOugXT9F9jr7gBT5vctuziuSMAh+0ORDWMlwnqXjGinHr8Zxb1Tl+XfgOl8Gg6ZwP+2A923Nkc94lO/L5JnNeF8jHfN1M5OLqZ+h3BsXpUpwXRSM43tIETZmGeTbG0eW8gIC3SK7/GAjxYaek85tw5Eao6qxvQSdyTT34VfXbgEpo5gnQTBgs1jH2bhia30avFel68UFzReO833PMNgUUkeO4TFbSxDti2u33sQ69nJOoknGs0QRsHWJfijIMNaSpP7wgTvwHhvpvhHvWqnd1sgnDI8JNLkGn/dTMEt+UfX69OzXwbJnzPi0MIBaATMCAywBBZ+1bDXklJjxXQedFZtQNARL0HvrzDSPFUsODOG/5exmZeRmUj8zRdf9TJAc69T+DO65K3AdFOFqZ8B1sALSzxyCWe8lVCyxJwVMAhnv/aprv4UnUJ+BAp0AJwn4Zpwv4Hq9BvDcsbmpdL/LkfwewPQY9qllyqYwOG+6h8F7YaX9VWTv6RnQEd92fmSlDT8pnCoTfOcdSlglYkXraog+zHk+0as8B9e1W1ug7visc1qBp10agLTUEcFAPoL35yzjeJfwld1vite7rjN5CCrLrDTsIbwTcx/yFsSA1QJ6K+qaAUtWjT08KRSbXrKOMZS8EwLC/6zvyVK4H6EpDRmvLad+VvneT440FVmcaVUyJCFYfyxNQMJdWVPSNFjmjf0em7AyBRqKfw9mXd4D+F8F32nTNA7S90MK6rPeZ13GxoN4n3yuNpvVaFdqGykZ1pkqmRNlRYxcN8QcfhFw/IvOL5wFfBm8L9f4S2G/QcSEnAJ5DDozy4gg/k38KDdrtHmhl0bRKdb9Ocp1jCrmIA+UMl2qoSulC2ywQJlztqTj2pi9bkLOxR/h4TsD61Lr5kZr4lyvm+5YRmRQqorDIgiDqAyPSkh0/RV0mK5S178Crm2A69t4JI1btMrzNN0AyF7LwbxtAr6hTNlMScBjQUyYebTM7LnOy9aHJRns3Hq4x7WbCsz6zULKYhvt04pKzJ8gejrpDGu8Aajvv8CPe42e/pNxTukKw0fXbu0jg0fT1lWdNfnQUsuZ1E3NpHbQzbk3vIOUSl1HMqSKaJQMW47eMWpEyRW/Jw8jV/gaqPDXUfOasxzJGsewCRELGMM9kuH5Tv/9lER25ilrZ8rWAPvXjlx2gDcUeRI2DRQ511/A0qg5rOE5bMJ3jTYO30jYgHUI4HdgSeW0TtbDDfI/DowMLJGn4HNfxXAptKNjWpReeW2oHNkfLVj182DwlzfGLu87+40SwtL1Yhwb4Zbeg9mUUJddA/hnUE8/FMZuH9n4b+O/nAExiuZ4d2LK+pIOeG0hJrGPq9V+e+nYBNFxnmasK7zxHYAPjnyd1n1b+qzd4NjBaZTQFDnPTXuXkB90Ltnd66bL4HJYpsB71QXX3aWu6VLXZM5w7JpcAHX4BVhO39K19rvp3SDlVyWJcZDs3FN/toC5m+zuvXF+ZOVXfeaLMs795jyDNDg/TeosKcDTOz8Dvm6WLvzMY9AWb4GO4jzoXHbBZMxr+G5/YyswOzdqxnQFPmmyDAYYuYmDb9GRBH7r5g6pIswIGAD2omKWbUiS3T0bJD/v2q3vwYX7cRLS11XKOk2KFMkTUAkd1Cixj0Wcn16wAGUJ3TANxj+DRii3gURyAFHRKAOyDhrZ9P02mhGAjmVYul5J/RyHXGCEjKRkGl6PzOW9SfehGzhLXfh502k5A++xZYQriwyRNYFsoOJaT3b3Dh2bOGop9qqwlvTacOS4NT1U2iE7LpHRy3LmraHrTo77H+HJ1UuNb4AD/Fjwtk1wTW2BjqfhjWcxSFVlCYzYJMYWuI/PEAcRWIMyVgry5lxq3J2c6Zf674dkdy+TEq5i9n4FPtlxLEfMIEsLqe+1psf7YswIM49IMjDlYXAawKq+gM90FWQhKGr+sMEK5yjXuf3vDAK5MDA7A9fPAXwF6BZ+ZKXRMN2COmkFwzRP9jwuStZtWs7hs7WNVyh1XoX8km4YmxxzXOvoP4lJNPxdO5OJp4qYCjbhNqg81pWtacLxW4OfjNLo3NT7EDc4Jur9iAprHSqtSyGP7V4knsU/Zlj9EqigrQv5FCnMVxI5/Sh8n2u3foCfXpLOwFmX7AyGDbZR/Iwty5/42b5TRRmCUAQLWQfLg2faQx/Ae9dJvXcFzAisQNNJXLv1j1o/n0AHesi4BhnhbQAv0oY78tp6ynpXJioPjvEgGQJlmn4HnnulrLSC4bmY5ymH+xFo5D5X0XWJxzeaRI8X1Dl+j3K8bgdcA/b8jkDDPwfqkK+6hlfgHlpEvHP4EcwQxqyNa/hyremEhfy3Nya2/uaDvx2CAZY930X4Mvg87m/6zT6om2KdoxBWdQpxkJasuSjsbVqyArlkd++zI5VT2Dltmc/QBk1j8DmHYs1hq65C86r03UgY9zwRVGobweCIjPe8AGmOSitHCpaWwHX2CN5n+c2ZLBIp7blU9ukSXDRNAmC/wmOpJpastUAW4ZWn8c3VlXMw2rmXexFTZpQY/soytnd1yr4Z8hU5o9kSdtz/N9CZSzczXaIhQHqRVM2ggRkaA9yf6Rh5cAobXfcEHsBv01x6KAiq5KSaEa2VnU1Sk1diRdkfm+bzELIIrsEZsLT5VH/fl5OcOY3E+dF70y6YBFQgq/C47nBKS7Qo49wDM0exM6ENrwsUOApJih9UmaMrKOBXWTnURbH6+jMUEAXXEdI6pc/jUsFND3Rk11Lfa4FZpfFzZaLvPQGv96mchS4GHeYTndNdU98beW53qLZe+rAqZbK/opzX0JqrGmk6Sttz/T+t/94h38Z9AfXZEDn7A0ohLZPzbAxAie2WP2SVMmNtmEfkc57q9R684vpg4jjJYhFM44Yli7qUOTHfaWBcgCXvicdHON+dOAUaNCONngUX33WSGt33LYvS+89Bh7eI8uPvRoRfM5zcGkge3il5v83gvQENyWkyTCVW9J02PeZrcn/TP/oAfrD5594dShmAp/ANKI/10ifwXr4EHZu3cmJ29N4jAD+AzyhrjnP4HfNg5tsCu1/kwFvzyJeya3d+Qgx0LtENdPrsDKh7Y3COVtE4A+/NGvwo1negkR+iUgngDpd5Ol3v+UHnk8lP2ZToOlbA/VNalXHt1iNwD/RARoNtsMx6HLxnE9TDpxXLrxMrsi0r4DN98GEX9yHK2E9VhEJYkH6V9Tkd8w/g/vgXMPC4yPM7XLv1EvSJPlXVuQ+SmZRzMl9F+YxJMucTjznqsBFrwMOTs0ZJEnC7pWQTakYRJOBbzLpmyQpYOjOOwokgXR+HuIiRXlLslh3rJLt7P5a8fx3sfL8D8N+V/fzo2q011249R6Qh1ecawRg5P/ItV5FKwki/z5Uqx3YFzKxkzX22e3QLKuIhB0n4tj6nqDKg26AT0M8YKPu4Dzp6t/ClywsMjmybkfNnY1+vwHJaTDbBGqAuk929EH7xDL6xpszxNz25hIq2pIqhksF8EvzpZzA42dT/bwpsyUswW/sVxDznienkcfPjPgbv7xR0fx0bi1bBACB9HTZn/ArDo+9sHVvmvjIzx32JnuEchufZ58lT8Jmso/7wkG9GnG8WnXIRdGImupdFvR3G+X0L7m3rts8suycpfLaSX4V0eHq28w9FLP0KwCtFXSOLa7ceuXbrB0W76deWs/4u+QBG85XwWCOKgXEbM5QPKOdgxHyFCeT5HEEMy3iMgqaSb10cp8j8ELEPDZQOxOF9LWBawSDe64n+tpP1oVHFtVvTrt1akQK0vz1x7dYfwOj8GXyTRKbIyfkIzU6WogSYrdyELwOlZQ285nBGb3huW6Cx+C44po1te5x+P3iPdkCj+nOyu/c62d2zcYyfQIf1HH4MWn9+erK79zXCYHdTP00uUj9zRd9v5cqncqgbl1Q58jzZ3bsWhOE9lFEt+PiG/j1VliZLzD5UGi3r2q0N124VHTdLzlI/Aa6pJXA9DEjCyTrHcjJvwTVk051MDsHgYex2zLVb29pTUb6Da7dmpF9+AHGyTyM/a9c3KeXkccsC/PjDJvfRKRiMXsAHpVUmFr3QvycF73kC4LuHwkxmZgRHkA3wBq0jiMidZ6+Ha7c+pqNhZUfCcYBzYMa0UQLn1HfeYkxgXBNdx2Mw9T22ko2Myd/GdfwYkWLaBrMTnSaOmZCU+G+gcbZJCHMQJUfs90iJrgI4vK8ybUUpGj4AoJ9NmwOzQdORsIzPYDbhFoMZsjN4brrKonPpgs9lLiN6N7qSK/hsxho8XjPqGdizUjbyheNs+WvwPhnrg5Gan8jQn6J4Nq/puhmdj3UgD1Un5Gya4h/iJEwClgmVpl+DDomVRF/nXZvzgxQOkNEAlezuvXeDM8/LOkGv0scYhyS7e+/S5xGJEfwAZskB3p8sHPIUeA1Vyosz8EFAD5GOXE4Z+ivi1mc44ncenr7mCtmVo0YlyKQDvFedjPfMYHD2+Q64T56BQdD3YGa1rBkkZmxnZdH5WdXpXcImzUVQn9RmbxGsZwOs3NTR9VaFmB7lPNKitfFa5zgD3x0fK+b4D2GblaTbAtdu96GcyfegQmuKO+0I2dMwQgVYGOE4z5c37dj11QgGUOnrqZiyXoOyAT+o/iSrLFcmQdmuC5JvTypucAN+2shlU9CJZJiyaAdcY6sunlQ2M8i5T5EC2QAzOul78wkFPI5aA6/Aaxho6HC+i3AIY6b19tf08RKS9UZTVCibNw9mXrbgsT8zYGf4v6Qc+6nUT+izqyCubg4litQNEkIbb+AcaNzugr30ROe2CGYOuygeHfgZdBD72L0kY9yZa7eewM8m/wURnIQqi2dde/raBgYp6HnMu3ZroPSYciSzptSE392Rs90NM4TOT7eaBalyRnY4a+qgc9CBnAOvfciZTHb3ThwpYW4rnOcd/HSxc6AfcD4Cn3EuXjXj+/cR54yegBmsorJ+o6LnOKU12wXXsE1kSr83nNbzXs6+3c8PoL03n6OQfzmJG9tZRyw4BoAl127dgdWKKQVRnZrH3dZxt1FP19/AN4COJAqCt0CseXo8aNXmWfPVsq5pC1qPAI4exJnURijcDIqgF8GGj0JHTBt3aPMmBKf/goxu2RJpYnC9ZVK+0++2ue5DzkCDdIX6VBHr4Ia3Tf/Q+NY8Mf6usBTbqOg5WubnNeJLL0egI9PJOOYUfJR/VNNQxshjncMmgAGsYxYmUQ7HrIxq0TmtBj+js98VHMl5DE6jsBLyFvyAgXR50bo/+/ssGaRzKhtraITQltm07+hm6KBLBNmhMpFxjHEw+vyiVRwS+KkzRedjjAw3AI6D5o5cDBVoSGxKTX9kYCg5mRij2QLEuZp3UjJ+15EBWvi5afD8LTu6hBQhtKAKP4LOfy4GtGo1Suv4bSo42gSf34pjt/IcGJBf5mQkC0XrcRMM2Dq6P/fWYCPnMMTxXTl2teftY+P/BLQ3FWycQUGRgpIl5KwHV6H51XCYFQOVC/iRtjYVzaSyDnZsfpsDbe4W6EjP1wiePum8mrCzO/AMLKNyZXeRn/Q71ncdJ7t7D+NMlokWsUXQGxgBCxKbEUw4seYdKrTCR8h0zu9jlYTUCz+BTkFdJ8XKdje4Z15MlR02oVFkRe/Vtb4GHYxlgdmPG4YqmNPwFhWytHlBjiTdOFGlIWELNPBfKmDjYoOKVwDmnahlFIzNZdzPQzALUXuvKGs6m7NHrbQ8B28A1sHnfAEao4HscVbk7diZ/wx8fh9K7pc5W/NyEm6Rw/OY7O7tq4JRel+V3etFBpMH4L6r4kiacS+i/bBMIcAs9YXWEVCMC/4C6q70yMAyuQIN2SwCg6bn0S9RBw7t0OzvnOswB9J4LR/Dd5Qbd+nH4P3mCJtzN7IEMCLDbpqcwwfxt2CwOAtWMwagA5GyDmE+HSfMXDRVMSuSIINtjVgA72EhL6Qc932keHOTwelGQ9n44Hu3AOy4duss2d2LoUV7Bjru0Ty0Ov+QWulOOm42a38W6SglGWwPHcE3yD1HxFrOOK+mSvqnOo+xVsPSz3IinUnhgL6CN6Qx/EAoWgiPQQWzn5CAeQBDOapIYX8AM6P3XeZ8DjpXnYySbakosqq0IRqUHdBQ5KXXByTx3bGPwOc5i2ZLJKc6bq9BJ9XmLtvvUSJjbBm7Lsr3xxF4/qVZWxkRM7ghl2HW+c2CDt+qa7cWq5bdZORtDOMQJ6KyEz+HmR8ZKtufP0U69SugM7SMnMxaIIegExA1lq+CIzk0K7jgmFUmudh3lGZzlBW6gTKt+rNx/w2taTc4sSiXON3lzInW/z+l3rsMj2G3Sk1VYuod+MlRx/B43x64ZnvKdC7p9fC+NFJxgh+8YDAi2x8XoCN7ofttc50vaziSdrzvdIwegDPp81pNKXpWq2CQXrR27R7PwZe0w4BgFnToL9L6MEnhIKvAWjA4cjBG5lM/o0WVD+srOESGjpM+/B5kTcjCeFqXvcFn5sD1WDSJZgUj+gPO9wlcZ5wTpEs7dY9fVybSmQSAZAzziFOyCSr5NdCg/ZjCCi3otVBZVJZRnA9l2ZZAyoiqJdyF1M/GRQZnJombIlFFzsHrvnVk7z+J3HynYKQ479qtrYplwlyRMmw0I5AQ+P1TcPxY6YIGcgq8P99BpNFpIxNkfQZwjcqs3KUNnLLzH8B73yk5DzNGdygnH86SMOuRq4dS98YcydARLxMrVV+WOX96PWaCSq64Ya64sWKNnR9Td5xofGuBWJb5CuhnjAYgCnLy10FD23Ocgd115DLcAfdYD1yHt+Cknq+u3XoTsY57Gb8fgM81NitzA+q0eZ3DZxAPO6VzOwextVYC/eRIJr6A5jI1BiO6xqDj8Ht9zwnEAQqWwrdq6qMOiDVc1nf1S/h67k/Arv7YZMFznd8qiptAbZ+swOsBaxoDPH58wxWMQ3S+ga0L3o+yPWvY6Fib+RG8ljoJJ7OtS9o7WfZ1Gl5HDWW1dT192iJHQv1j5CSkVHGzwHKoIbiCbML3CVyAunAJD0ymPrHOZFVRtNSrEAGeghv0HN5Ah2KcTIsYc/d1lij6CLsFqzrXttGadvQA9DfGM/1+lzTbgfbFcRTeK1CRRhmChCOzbsD7tuPYkNM3UopG10Bg8nXw9ynEdypXEpVJXsKPogxLPuFory0w2uwkBR34MuyvdbwV+GBhCcNKOJzv/VnfswbxIcoBGFj3SeTM9mR371z4qV7F8qd9/s61W5/hG3xiPnPi2q1zZGTCCj5zjci5vqOKy+CK032yWcGN4Y7NiYTveF0q/kTf+OVWXpyf82zOS9hJvgo+p+fwe3EVXLMb8N37Rd9/IceuP5lK59QpO/fASf+iNdABjf2XtCMg58WczaLsei1ROfe9fres+SaoDxcQOMbKRNlc726ZAyE79gy0Rx/B6tBUeI3Svd+BDsWCI+dkzNq6Tf3Muz67x59AXXqR0hNXoB4t44y0BrZ50NcofAZ6TtF2rqiSGAErOIXIvpFTrVCF9D0yRsXmvL+HYsxjYUNwUFk7KNGpds3LoPO9CD/ycOxT0/LkV+FMSgm+0u+/xGQSlTH8kwxAVsek4bUehIhbxtaMRG6kpujPouQbc+qSBgmfc+QO3BxTYLnHqC2MhqWThUGJFZWJTpHCsOp7bEJHliK50jnMAug6dsdOgRmQZzreMgaJcF8CWMwqtzYgttEBP1IzS2zCzFOUNLQE2Ykz8BkbjjAtB6ChDwMKO5cZ0BjXjmTLsuVykI2MOWstLILKd8O1W59jouo6jquMb3dMwYJN+fkKT/sDDJZt7wBsCws2yijSUKwB6kLf3cRxl8A1cQliD8NyqM2KN+zqDZhJmgODs6h7W+b0SJd3w7WQctKtPP4u7xhg8D8bYwfqiHTuPwB45Nqtn5Ldvdd66QC8h6FB78I7ETHVpRUElF05+6YLj2m3pEiMfITfc4USOGpZY2A70s1lzzxsYLs3O+r8LOrcKVCyjz+VHSs2uI6RpKAhWGt8U/+9RoENSHb3zhSUvQIDFSuzNwXjqCW/CmcSvJlTwe/RC7dgkX9E9U6xRiXJ4FbLkKegc7QNThkZ4KEb47ldu3brZ/C+fwcat9+DxmgfXFtFVCkx35HVwPIEvvwwZFCUNTAl9wIql4NG9xp0ptJZScvu5fItjiBG7GxluDzpgMrh1LVbmzFOrQx4rlHNUYRfQON2Xccxqyjb4HVvZpwH4KlO0pmPQlH25gno0BQ2RDlPYRTV4FFDjP5mKSHVzkcwyx0+622oyxcZTp+qEFOxDpnE8I5fwPW94dqtwuynsmd3BQ7tCbg/btPZ8TA4VUa5B+7DExQErQrmbDxbIR7dsTN2C9yfoe6ImuUdnOsdxpsEMAzuFHxzTUcZ0ZvUdd6Aa28GcZAHK6Eb9dCQaJ38xbVbf43J0KsaMaXn3pRjVKo7koIGtibEcdLWEE4TPksfpc+V9V4HRzdmUR7VYhvIkoI90IVniIgp81/DJx8+gfrgQbmMv3ln0hHUvQne3JOkoQYJbdJKCklGYbpJIx2hLCx7aZtgpAXvfCffVVlm0TJTypBtwIOQbabwOOQMjMjLlGIPdHAfgffoDejUHSHIECgD+hE09I1gLENJMhoRcsTKSiE2qVBkfDfBLG0UplOGthb+Uxm+2QpYn2MU8FhqfdVx8NbgQfoDtFXOM0EYx11/QEJEYBYl+o4FHd+cHOOOzLo3Z9Cc4YxjzcBz832IzeYnQaOOa7degYZzBTmE5TK8j/R7N8kY16a1UdoVGzj+hbx1gZGGfpZhOjOflQJEc9LHHiiXiTJDfwGDhCNzMnJsj+E7AT6fslLvDSLHB0Y6kkvQKFTBkcIqz47OyTCXz0Bs8b3AQkYRwQee6Peb1L4xiFesPjci/3WkspXO487TAc5IIju7BQYfx3qW0XA6VS4/6bx6oCPceOWlinzzziS4oRfBiLvzUCcRGIUZd4+ckgkB5odQia0s+o+QbXDz9Fy79TpmgSoj8wk04t20UhW+axXMIo0UPel690sU6TtwTfTL//p/V4ZpSsb1Otndu5RxOEeNrsCmRPicPBqePAmnu4yVMkSOgUFJyqZXAAASNgZUZhKIEAtejD4olMfgeW4D+BfQsHRRMFs2RlSyXwKv5yU828Ax6ICd6H1L4Fo7TgJyckei+zxuPsu2zaNe5uhK31m09y2A6qE+92wlSYjv/Yp4vrtD8DyHuulH1RtNS07VpC/KyK6B12QQiEYcYUEBpqTLLON9iRTHpsRwr1MYTjQYrU1/+gpYHq5DY5R1nrl48QbEzq+H1HVJfxZBw6b0Pltj1/A44bQ0Nq1P3/sYHj+6qb9f1rTbR2AQW5W2ayzya3AmT8BsWFNYpLrShFGoJdr4TUUltuFt3m/sOfSQD1I2ztA1ZTFXwQi4lnJJGxoZ8B0wOjMs5ZVrty713dNgdN5T08g6NN82cJhfgJ190ZxlTUtSvVHgELyWzlhOaFjMKD2o6D69znn5dwD+LTy5+XkSx1mXKzKKRse0CT8VZxrqbFaGexpcR4bZ7Wfi8hxZ4agO9P6iLF+ugU929w4cG9ZyDUrC5pfaDVN1JanAyqHrGykoEvbsJfhcfnnAbI01LK0mu3tF+M5KouqABXXvwGziDjzsZyB7FsKRMiBbX+CbNG/ANViXxihLlhCHF68scqSNQD36uIJ6/BuQtP8vye7ebbK7d+jarZOc87NhAE30HyyA+noetK/2r9Z+HKXKNA755p3JpIAE9Z7PIzQKY+mgvg9JdveO5YSVjnGrIJ/haSYegQ7ejGNX8jVILzSKgbOOtkUE2QI5lfuOFEvWsNCDd5J78CXludTPxkVOSa8KPrBIkhqchCN8V1eg75n7yrqnRVmYx6CDOOTwK/I/AzOFXVAvjGzAkt29W2WurRmuA661abAiYvfDsiTGwRl7/EJ94dqtp2AglssZG2NQG84MPYjoGT8H7//HjLW4hEGi7cbXqvMjSi8TT75utC/GHmBjPJs29mEwZ81S02BWOhNPmvfcE+JiQ2xs0+XtM8ThxWtJzfVsbAULYEnbIApZPJPzoN362lAAdg0+pxfg+vgCjqm9dzofZbf7GE7BBR+DCZlK1SQF0evfvDM5SVJmFL4VaaBUnj5evyTk2I29CDqVtxBG0LVbn0bAQ1kjR6bCkoNs4PhbAB1HTrCbYCN/QE6DRBPiPHn1jSN334Pwgam8b9yQl5G4q2U00KHfgKyBjtq6a7eGGm+UHXwDrjVjG+iM8oVyHO4ycGRDxkffb1CFkWmAhGkzguwrMHO+CBqjLoB36XUkxT7X9B4OGtUKp6Dcg8zA42UHRmdKbNhFHsNBE2LPBY48i3c6lxnQmZ0zaAMyHDznKcpO4QPbJTDDXXjOyjC/BzONZyCR+T6y78WDShKPFy8Urb2nYEavtJGrRPr4+Ig9+hxMLiyjmD0gShI/Zc+qmN0m7YDu0xq4R4sowJZApxrOD1IwkvpNxy74Knt8C8DWb87kb3Lfcgg6kDdgRD0Hj/mq5UzGZOjSTlBaaScNTz/KEOsYn0UE59o4RI7kE1AxfwGzJjGzfp/BP6vGSnY1pKNzOCsoGzdWqVDg8wzAtSPlWMxUnCYhJwbRuAKv6Rh+os8C/N6x8zVmhTnHUY9NZsWeggHFOe6JtzNLhC0+gnCpGa/fYYwdxJJrff8WSD/2C3xAex1kuV6BPJBpihrrcN8GnUkbwXjtyP1amHHL0GVdPMDEk1gRfnQe1P3zyGkWUaA0B2I/w702B19ZMm5I+8w26JjFUot91t6IcZZsYlR0VlIZv3kMX4N9/10QcIZ8pEtgcmOU6oFV/e4cKauGvl+BzDPQQT6HH/NsNE6Z07/UiLQAlv1tetCZKkQ3AKZ+cyZ/k3sVLdR9lQ3vQEWwgDF0Uk+YdMCNe6+caykxBT4Ff+9j5AJ0YsbJW1oqiqCjOl0bEqMWmUf5KMZGRQbYOsY/KgsFR9qrfoewIBwz8PunsYaBlFQ6rgtmomdkdUeSZHfvyGmMY53POz9bewEapVvx+ztyGmyUnnFa9q9Tjr3hBdNTyMwZtaaXHnhfrzDmiUn3LXJerKP/X4MOzFBAoudpzTqzGLQHNjZ2HoNjHZdAh9ze04k5pwpZtw/gs4uqMqgqYEMY5pHDFamS+U3wOQvyewomMvWMjv8SvD/vw+yj1ptdV9H1Gb62hwAmop+Z3eraL5v67zr8mM11Qfu+Angykc6kNuoTsANzHN2gv8kDSzLYnfngdB/jFkXND7qWE3at/wJmF6PHyyW7ex/qdniqvP8U3MsPNp2hpnwBDUNut6SUeJ/FQAbOsHPv6pSEVd7+D6DD/5fQ2VFm7hJ07v8Bnm7mVnCO96Cj0jRc4xOYFYp1vFbhu4Nnm2r6kXPyCL70Xye42ADX5BaAJ44jRD9UzAodgM8nMzhMPOXYMlKBcrK7t6+GqS5YljyAHzrx4F25DUsXnjLuDD6QTYvh14eo0bLK5XKsFoPjNTZdKvjeOlCJzGsokeng96IGx3n4wMTWvzl73+nvByio3MAnBj4n8QwJN+AzXACfYQ90Js+0zmcB9KZ6vckLhBSVWzTzUx0j9i2Iovf7II/+JsSyDVUNsCMp7yP4tHve+ywLYNNmjiZtbckZmR0BPzoxovv9HXxTk+HLfjXiPCk6QM7MdXjKlZ/rZKFVUvoHEEf3T0mK8kwdqcZ/Z3ry/09qTNeRIdoAy3JNjns0p++yqXK78Ks/gFnFa7A0lzsbuuA4S+D87m0oywPqgpHmsn8LEuBwP9dZLyN+t2FKM2dIay3OxeCyrSkNdCRfT4pe0TXMV812y4bdlGFmHWn2ZhGMXAzgOACzjblOovNje6O5iWPEtVsbE5mZBDNVy2A2Y6KMfVPi/LSHG6W2J8+rzxCd9yrYhdYYMXlgIOtkG9agtDtySJeDDXcLX97t4YGzhaFIERkwevpX0NBlnZOzAN6OW+HLWK2BQUUVupBFsBJyWRSM5IgFgkaldQLNyx4BzvAZdJYuc7JeHTWUHeq8b1FhSICu91b36InOdw0R4+ViJV32bVhs6s/HOnpTBvvPqoA9A9fnuIYsTJqsw+vKe3UmZctD0vQN/f1YP7uIZ1/ozxkv0yvS/XcxTuqoUvEaQrkAsKUKUK4zmBPwnMFnv8vWsXETr6FBpoFkd+/4wZzJILIYmoeszf76AU7rPmU6+LniOJ1gorrxcmQTXIwbaFYB9/Fprvqkki/gfSxK29vxZ+AdysZLIyOKlXq2QRD12beStZaDsghOoTLlvgBhmZKcGbkNi40W3UDOZB3hk3ZAUnFTpsb9Nq9miVs59jNl2bqEXbtXoFG7dZ6tYLoC0D99zB5KDH2AoTwFDWWUo64MSMi5egU6k400n8lBew4+9w9NBhC6v2/B7FU01UxemV2Yszd6z7prt56BGcqqk8+W4GeUpzlwZ+zcqxwz4ztsdN412NFcN/lwBNrdwv2ofbIK3o/G9aTzNF9wnGBT1ZZ8BkuvZSM6bb1DTXSNNVlK5y2BOm/UpNcO+Fw2XLv1tyrPV+/NneWdkiPQhjeeqGjEmVSpZxl0DNOjzXo5N3ol+Nlp4jy+MbHswwyUog7a9CdZQqLbJuUQdPIyu8mKRMFH2SgqczitM3Y6XJcBtq9Ww4AUyzpoUGo1qggP1wMVyxSYoZ/40ptwgwaenwNwIJ1gxN6NZbxKxJ5nkQNjDSsb8JH5CegAXshhmQWnWU25CMqqlIEyTNM8qmOnKktssBFkvcOpUFNJMWGzORXLiHeyVuAZC+Zdu3WHQQqukSSpyLqgsuCGa7dOk929TOYCrd8nwZ9iGA7ss/1qArjWO8FrC2BDRk+6vfD+BY1B3YwM1BoU8IDrtlbWW1nAGN39GB6/l26WWdR55E11ipEb+L1RuobllNtAgPda9zHJl3Dd5Z6r89N6ZpBqbsl5/5TOZxqiLMp4zzKoVzoR6/8SntZnbDojGSM3cVMjggwztAltRC24l+BG+iWj7HSAavMzf1WixXWsBWcy8aVuZXMaZ92XcjgA+mtqBdxYjRAty3EMS5jpAMcoV+qOE7Pu0CUAb4QZewJeQxWH0EpB83iA7mmVntZB56FKtsDIuu2+WVYyL5isc27WJXkDBgPptbEPOuBFAdkRqK/6mT8FcD+7dmtWzscNvCGN7XgPj38HBiVZuDDr+JwFG3TGSUcVyoK+07ppv9j9y3NyajpZVubvwvPJdh25VdNZO6MyWgaxj2XOlpWlb0CDH6MvrSKR7qjui5oIzuHpUoq+P037EjaOpJ+3BRQWZJU5gOa4w7Vbx6n7Yff1uuw+NSSnEGQk/KMbHJE4iwxboOe6CWbrMzPsCYd82ASbmIBoCf4ZLiGiaVMZ48egTnpfct/MUbfjF+5LrRnrxh86f+3z5+B9mkFJUiBh81zIO/rNycjOpG7qCcQUH7wUbqRZpCJfRfvffJPBqJLs7p2rdNO7R8PSuKi819QMXePLusX9ZbU64Dqti9O9AJWdOYAb0FQex8klsU7xR1CRf70no2HZpxvBLIy3bQuRMIbEk3XPB1nZ/pzlBk/1GWhwNwAcuXbrfQgNkYEv/L6keG7vNnxDywewzF0JVybDWITDDY1WvyOzSJwfEXgHYk/rrM8zeAd6iPA9S2KdrNRnbgC8k0PxSH+eRXaWdgXAH8F9MgPgz0A/oFkFm0QuU++f0795xGUo96GKQfhHOUWzpnOT3b33RfAavd9oX+YgJ0rVhJ91rHTwd6pr70UGZmfwmeO0vbzGPdJiJRyDmcXXWeQ8m6xB40ddu5VLxF4RwnMOv2+zHNxFDFe1VuF9nLI9cwlmbKvMUDedlxW89uB5KqP0eMy+VsXHGqjG4j/VTYRM/V//9f+cApVFkzM5LTrZAvE8naaO+5tUF5VPnoBRbeNlU+enuwCMAEdyIKw0BUa29+VMps9hGty0F7FOfpjRVGbeePbyymsrUJYseaBmGxnux/rvz6AhsMzk0DlJ0fwrUOn+rQbWqcq5LYGG+FL//x40ENug033Q5H1znu/tGpzrPJZKgWO3tnVkxhiQ8BmNvL+qSlUMs3BqL0BD+hnEpw6ds9bS/wE6yn8F8CdlrP4IOivnIeQk0GPdpHqjVPi90+AM9xlEriF95gf4TJQ1XJ3WXYOCU+yAevmbqdDpOWQ5z/a6TVi5A/fRWMd4unbrO4jqLNS1Wl+PQafo3hst5eTONJkUcO3WH6AxmsnuXhm0q+53GFMEAJSS6JvMgptzDfU5uzJFymdihpCrnLwO4hdGwiVqkUxVjKweUjbA1P1SRvmkCQkNYhNp+kMww/eQmdpH4HrJnSaQltAx0Bp7XfKRLVAJ7qBBDKqU/QswOn6X5bAo2FuGL+f2QMctPa8365yf6vdDjKkLNghQZpWBPAEdyFX9LG1SqSoJm2nOUYOequL3VDVs9zEiMFdq3ItlcE1btiiTXzTZ3bt27db/B/Keht/xFXzOX7VOZ5Pdva6MWhMTmKbgGyCjqnOJn1xipXYLCA5du3Uea3ANqqEAdRO0vRCm85uYnZ6UdCwnHPn4BkwkDege6aYV0PGrbT9lg20ik+mwab0WzpnOXS8KEJ6Ca28/AtcYe25T0PQjMLvbpL09gvyYBo+ZllN4/troZzSLwa7ivjjyEU2DGYqJx/JFyBN4YHhtj17RzisQnP9uVMf0nuQU3HhD5ZMmJNndu1SpJxd/VfF4PYwJJFxB+s0cY1z/J2D2pmmKDusuBWjQ02Whp6BB7ILOyVt4qpgyOYOf5jPObIoZ/OdgZ/RbOZRjC1Bdu/V7fd8bNOC0hI7QKMdJ7mdEYJNyBDpb5ygxpAmJ3t+B+8xKzvsQvtmyTi41ktB5GqjzqjonYZOVTTeJDuLMiRK2bQGaaIVqk3hegtCXY3AvvdDff1UUeAVr3jLWKxhtjz3RMbpgg9ASgDXtYdMd267dOilYH6vwjcBNNqbYuFMgAt+p0vUWIrgfVeXtjH6Khd9xiRw2jCKZBTetefgA+mnqHf33BmNoI38AOYfKliMeJ3TA5zBB9DLOEyifgs91C0zxn6Ii1ZLKtAvwwO/bonLvfeH7RhGV39bBTVuGrfusLNXYriupOEdaz3ceZE0oihhPwezQDFJYN5XWjLR9C3QkozsItZb+Mfac64icsFswA2mYo6qNMFW/07IUiwBeKRs6ahDxDKT9Oh4HvMREAe4yiLN9cKdEjoTxOJbun5wS+BLokD4B7Y+NjzRDvQk6AjeoQSOn7yxqtpkFA4Eh/a5S6kc5tFWDzVlQB82C12XnsIkJquSNUW5Rcd51jlgWsZfs7nUdWQOsWewatNFbAL53+YwMoX5vLOOvYGUftJsxz9S4H1cj3/+g4nKGi8xK+aSdRYu2jErlm5eEI6wOR01lJ2yYOQTL3JPWQGRYpR6oaKcAwLVbr6uUFFIde5vg5u+pI7OxzKaadsKB8WMRXc8yfHl2GhENBXmYoHGKzvUpqCz7fHIqDxlrQmGTh0pys6BjtIVAQalh4Bg0aKegMn6QykPQbXmRcraegev3DMCPoCMxVoLloIz5CuRHbOKe5M1mNqdoE8T/VdIjwlD2Et8cZJQmS2DjUC0RtGAJzHxsg+tnv271JRZrnCMb8LPJjwF0hJl9qf9PgbapcedZe/B7MCOei6ksctxdPiPEVzBR04NnJpgFcBU0WvXARqv7nAVvgdSncVXbpJM+gNc96nfsg/rrEug7cF/AaziAn3cO+CrNgOj+Zs6jTp33PLgfzmL3asVEwSGoCzqR738wUcV6B3S+BzLLmXgRGZw3oMP04JFuU1LVkVSG5Bm4+D8GZZhO82fXiByCyugr6IzMgMqqqgPdCz4/TimdXNOQPIMwo6CDkqsQpDh2QAenk/H6LOToxCp7raPpyL20Bjq+qwB2FAB9Ap/jJWhgYxowFlI/+5Ls7h0E19HUzOQZ+GaCTsH7rDHpGlxjIVm4rVPTSwbyvxenXviqJjB5JjbPOsuobAP49wBmXLsVPQpR2XUje7ZOWSPhHwWDZrpuCp7iCuDevEy917I+V8nu3qkrH5O3Cs2ojglGtUaslHyi9WrchhYkXYHX3Kl+taVieOInqFgKBwDXbr0AKcaOMsqWBhPpgdfwM6gbbtQAZjp3AQ3tzYjztWoFkPG8G/qOZTDRcQuOGR0pWNPnz1J/G8B6C8owj9HXyDa4j1eFbW00+E6KGSYeRNSEY8FkWF1YTP3sSy74uCkw6jcuZnAB36Q0sSL80T9CrPxgBq503FTGcSxLMycw9SpIHdM03rIDDYwP/6jv2wENSRO4PNv8X5JUB5yU3CMQEH4EGskVsDx5onsRNlw9BxX9GiKwt85z6c27dqtwbqrkDJ5Y+wxUYpahTJ+7EUSfZCi4D6BBzOrIfq7XviJ+ckKZGD8l1JBQhFMy7PI+xKeZWqPWaPPQuNlaIuN8BzpbeYbZ6G2ukZM5yRFzMHrwWbm34Jqs7QQkpAG6hg9WbuBhLmnZhro9Xbv1GnQS5sHnNcBcoPX/FB7HVshVGUB1bsBO0p7O71KZJwsuLLu3geYp5iz7MgNmDKvyzuYaXF3HGwzyr9raPwV19x2apdQqlKBaYXZjHGJZ+jkA6450aeMm9G/KSbuAoIBNnLMctRU0PJK4KZH+2gbX4QYGOTIPwb05tD4ndTZ3JVG5rA/gbkqExTjBPZTZmhIZLzMqtbPKcpxu9PtYjHqSP7nmFWigzgD8Pw181Udo1nvGa5vg890Gy8HmwF3IkZwDncFpaxTQ52Id9Bl4RWoZl1yRw/7GtVufdE5pR3tKhn8eno4JCJxGvXaZg0WbgQ+QekXZVZVTb8PnL+d7JqPccwnPrVaUUTkGnZSrvFKQ7sE3Q5USigyFjYr96DiwId3RasD/z2DzQO61Gj4JXAs2tcecEdufTXV5v0Vcw5AFCj3wWndAh24q/Uat1Stw7cc4u2aThqoiYebJcVLUGhrKTMqGbMJPIrFsa51RiJ9QMNktb88lJY1WWgszTds5fXcuplcO/gpIo5T5DIPKxE1GNhbgvjdc8g6oA9KBx6xev8VoIyOjRN9XyneZkFA8K2AvOvY81HwjOzINj+lcgw+EJs6ZBO3KDmirBio10g2Za2XinUlFtrn0FFLMz+An7eQCvoULWQJTt7EkvOMsv/7dS1BiOQ+UpDkkTU2/uUOGEyfHaAWBYkuGx02lG64+gGsoyngr6t/Xd0Q7SFLa6TFmz8GM6T4YGRp5cDgW0jI718jAAwlbdASPjcsUldysnPpWGZV5qPtUTm04RebctVs/oQSDmTRH75J33lXnujct1rhmE2fCqUAmpndPQLaMzMDEtVs7YJbsHAxIpgGsxcJsnG8qmgKNc6FTpPtWuucSUihdgJloO78O8psH3qEE5qFzXYQnu78oWUdf0GzA8Qg0ooY7PQMdu6GspxzPZZBmbuia6mbEnJ/r3gP14UXwmo34nI6scDQiei5DE+4yZB2DlYkBp1PP8kgwjTlkBB5gEG/wimMFId/BjzhsrPwuXfadfi9lZamhUwzHvAgfXKzqNbNznYrHHLs43+B2gIqjgSfamVRWyGh48mabTgU/sxaoHWsaHheyhobLCI6dize/Joxplsghtyi1jPJgAcwYFxmoJ6Bi3oDvyvwFVMbjJMReBPBf4EdtZTa0qMy/D01GyMLqlEksHi5C+vPsZdANbxVmKsJyUt75xHQMZpVTC6dfPCQ0RkHn9yAv5RlojIfuu/Oj3rpjMsj7oCO/AK6XK33vDBT0wvNkdouCXwziXg8R8MvpOswwH+dkV5bg18wqGmTlUNXGRu5dosAp1p4p04sv4e9ZZnZOwd91WSapppxBE7DA52dwoYE14vxMZpvs1ghlk2zdM/jy4hfXbv0YODHTqMiN2YQoq3YCD4vJkwv4ykTRmn6vY2XtPZsAdAs6XPPwemwJymwH0IlpRARJOWJTmYDxsLJYz4HtiQv4QO39mNbwyKLnbZW8Sp3lE+1MgsolBCQPLdJkd++rSh5l1DV3wtxk4shGEecnVNy6duvNJOBNFck+gYbQV4kwCo5pBgwQqXDBe410uqwL/Cb1E2C2I0/hNCU/gNewjBJOrQadwVFlH3QObJRb1j1tZIyhMo0/g8GAlVO7wtJON5klGFW0Ln8PPlMbCfnFtVs2IjKULb0O7dWuPm8l/ZHwd3IOs6oZYdZlKYmbmnKo8zrV/g3P7RWA/wAawr9BowhTYpCXaYwhMJO+fd3Q4TL5jk2cn8px68hOUSv7LL20glQzULK7d+Tarf8XnklgK6sqpbJ9F9SrTWLIb+ErMvZ7+L3XaiiZRQ7kyrVb0+OwPTHVOVUufix6Ltpnj+Epy24D/OCB9mpYTTGcbPqal+CzfLWCJOm3A4yPlcVwzBf6vhvU4G58CMmozkXJpDuT1vkGFFxcbIYhKZ/uUVfM4bXo8cGdSTDCewIazmXXbv1LSRakVKRIDxE4NAVi9yScNpF1zH1FvtdAX+GEimJc9EvH4Np6jxFI7O9T8jCGqffcoKE1nrVe7G8uf37yQ4jtuw5884JlSdJiDoCNwwO4Vx4BgGu3rpvCpGktz+meWdYlGt+oz+Vh2ayBZw4kwR4q7ytjM8qAhhWwTJdZzk29t9SRUXUoL4NdlLECBitQdrxZqGEoxrl0gx3rQ81AgqTcgmtjXb+vglnXUA8ZtrQxDlolO36GIAFg1jr9PHMDAifKFsdu48Imp3FJxDNYBJ8xwM7oDvzYvnVkBMBJqkNbEgZJtYPmyICu7rFvMYYmKq3hdbAKOlF4ywd1Jl27tQlGGZ9zDFcPBXx6EyRfQCMxrhJMHTkDDee1/jWCI0siGfhVhp1CScZY770MfjeHtXKaPUYCoPgF2NzTBY3xMnLKhUGJ0rgfJyFYaFTkHM6DhrMI2zYH3r9X4BrrIYXtzPjMLETLZPcuOM5lLAYwTxLiQD+BAcIX0FnIbC5SJeMKXJd2neZg3qFZ3sKnoNG00m1j42pBXN8ltE/qZuryRI6fNXhNI9+phWu3noDOVxYVjr3HGtkMsjSgE5QlLDLun8HS52Vwra9AG9ZBhJ1I4pqBvoCB8CyY7V4Hccp/SQYbnhodZqD7vQFiResEZ8upn5Mol/BwglM9jw54ztHVH93/kRMAzjN5fE0amo0umzdTxQ+oiPMOsew/NxnQjCoP5kxq8zzSf+9lXJhAt+vgQm4sm6KF0GnqeHVFDsEsWMK5AqdQ2PSae5/7Wjfyy3NY5ZRsYBicvgJmYWPIzzchoDjEd+bICzcFZnmyovoNUOH14LkqxyJyXDdBBVeqKNQoswo+81pQBhn6x/rvHcpnc2+CjkHseMOX4L0NqWO24LnbRp7coszRV6B/T+bl3GRhO69T/z937dZbnU8mX2PNxp751M/GJBk//6Y149gEoiIJHZm89WANRPa7YUkXQQziDUjUnYe7vEPgcMhoF5bGc6SwGUh24a0cjQ14PsjGA0jZoxVwX2xBmWvXbv1UY60d6hhfXTChxKW6ihs8/cqia/qQ+lt0skjJJ6OMK5woJbz+GgLccoZswjN5NNXM9QoZ4z8LznMDwGPXbp0lOTPsUxLSSU1UUqPKJmxUEk9nUbmhYQR5Ai6gpyXv++ZEm+cxqFA27O/J7t7FJEUvaXHt1pSVvyLkEXh9z1N/N7LfdRmZIrE114V3Gq5TP8PzWwHv6yO9fpF6fbbC+cfIHwD8RwD/OeJaoPNahu+4HBDXbk27dmvbsYsyT27hs3NlQdY5fCf4G8Q5NFmG0UrRl2hQKcoxtn2+FfN+BRO/02deZrznEYA/OnZXV5FP4H36luZqA+gb/l9ArseyoPAAdIiKpjKdgUHKEQZL2cugHQqbLWLP7y2IT412SJLdvV5M4CL83v8A8H8D+GlMjtgz0Dl6Cq+LhsbUxUiyu3el8vYciB1+pZdegfrrcc5HJ0Zcu7Xp2q2XsmVZsgoG/UW6zOQpaCdeFbznBA2Oi5a+zp16lSPWIFeaUZYt2gH9pV8mqAoK4IHL3AknTtynXIMljol1roDaWRADbs9gwsnVTdxgt/67CKyaPbf0JupAoO6y+5ZkU9i8Qz4GagG8rwfgFKS+UVHW9wV8k1ETm9sM6gxEOl/yfuuozcOabcI3nFzlwEkMrzUVaTS/6rw6SVxz0ntw3/WDxoSTU86aLs/CNy/MIm4fbIKKfB00LllrYCX4mZm11VpeALPjRrR9FXkO4TH+Ffjc/9oUdrOuaC0YLCEX75hEUuHkZGqOoVnNVa9Xa3lsutyqOXJyrGLSpNxAIxUTNgCdY/SmHiNKnzeYEVSN0f7vjHj8sYjO1SqVW8iuEH3WazE4ekuunKphK4vCqVajSZ4oE2yd0LGZzs/gHjtTFvmuwI5sgHslhgv23mXSG3AalYTNHh0024XXmKg8ETpX0YpSuLGhEZhBGekawLsxGO9RZA6D3fpl2MojRzqSdKdjHvl53nHuMv6fd687YDScReMyD09JNZc+r5ryF1DB5TY9WIkv2d27VVl/X39fB7OGR4GysXO2xoJM0bqosjbOEdkcJeU4pLTHsRYDx3g60rk/A53x96Biz1oHB6Aiz8xgOD/lyJqA6uK8t8BsFcAMXiEWVd89BxqgsVGSKVP0Cp7LtzH9qfN+sMxtWeDuyP1ozVndpNmmhw8I9F5D0KtDcJ+fy7l5C+qEGwTUUpMmOtdTFDRhGbRD1ZYNED+bZzMOQdtyr7Y+NrAK3n8FDjdYBanN7hxZJtKDDmyM6BomtCt81rVbO0l2x9SvUia55AtPdAzUyKDmZJVWQCO3iAL8kxbrXAzuTu+9i8liydBmZryUJTzS+cU6JmN9fjKcj0AgvGHu8vbHCaiwbuviFdMiB2iIbFznNgeWYZ+BBMDvzLi5wQkLgAy0MoBvUGOsZs75nTpSlPQbq5zvwL+chIg5zKhFvPccwI8R77mP8XZfQUNqTmmhOE+/dZeXGVdpfgYc3Vb3+S9gMGjqKosS0hbVEmGEZx8iC+varccANly79aXABnbhg6xG17ac2EaZELQGDoL/37p26z34rCZ6qlQS34X+CCX40mR374trt27ApthvgfvZ7P40ZFOAPsRmFQwGzE7e26jNKjILpsD/bpzJCRebJDGN5tLvJ/Dj6/IcyWmQs27ekUIot6nC+YlDNzJguQZKhsKmNmROGSj6rgeSTTCSX3KcH5t7fVJi93n+y/D0GmcISsfKyF3qbwPKJsvBk5MxCzoZlZRtmJ2RI/kHUOGduBE4AL9VUVblF6jMPcKh7gD8UwWHPG2ABpxJwTAMN3qFSGyYAqpusPa/gk7kXeJ5Ox+D+K051279qQ7EQ3rnOzCpcdhUCVaO7mMwuCmybSFeLfN9ye7elYKxNQBPpRPGRVcGoL+nGitlJhGUYuMQ6f+5LL3vPEl7F9VHJ/ZH2uZ9TgmPxmi+yqSBZ2Z7M10B2wx+Nx7SiUyIzWLCo5W/J9HGOJSSbYrKp4vyctkCfAPHFoodJAMWz2KQ4T9LwjJ27Gzeh5ZT+CkM9+IUqQFkCSxXL4H3dj/DQJ+CSucWbHxZc+T3OwCAZHfvrcvg+0uX8gR9MCfjGqPpgE1w7axhvN3FEynKDm6DHaa1gfwunuQ/lBNonGaO0byGz2hE7T3nRzheQVRGWjtpZ+sKyuCDTmWdUvUUvH5oEnK1CR8Q5k0HArjf1lHiZCfkn1wH9VksZm8U+Q4M7Gs52G6Q3zT2M9MoGYVa8RymwUTCTM51rMGPHe2ggm1IdvcOHcd5PnHt1g8gfKu/X/Ss/g3Y2f62aeffkdNzEYOUhiM9sySfEeYIwmonkSOgH0pmYzJDWpzLYHZrojqIfm0SNHVY6eo+UvRXIOZwCeWOp/H4XZUZvIRTET6DBmNSpsiUyTnorNlM2rFm7aV0rZNvG74BZxUp5aK18EGfewYqtA1HGoo7vSftSK4AeObarWuQesU62bvgcyl0AJVdMDqmLIXfBdfEBdj5+3eVlQSdCwvGBpwS7eWbSMfQnKlCkv9QCgyQvW446lx8qgKL5+A1fIJff4Wd1TLoP+q8awWJOr8P4Pqfc+3WdkOVilPQAF8W2auK8IVj0EltRI85P3XpJgmGbkgfrCBjlGMF+QHA7wUh+lPZngyqTdfCxDaxh8N1nBUo2DO6Qf2GUQtEljAIQXgO6k+jCHuXVKAgAvrPZwXMbl4HfzeeYoB2Yt+cd/0ttos7/J4t0JEfCuq1HyatepcpsdHgDriRbtDc+KzfJFsMnzQDLtCxO5Mqkb5FQLru/ISToxATJWelChXH0AaRAdsG8VaT7GTGUPOMJLr3B/AcfY/BfVlm5E5BZ/JUx5gG185lyhgsgddho0ltfvybsuYDyWNwHWwihS2Us3QJAsLH2gRy36JS7yZ4f4tK1yfQfOzU520CVSagPuc4AHXslIKFr1nfrWMb315hsGPPt6BR5ymYQTK98xEZFFg58gYVCZozzu9c62gFJAc/TRnvJWgEbuz3yEn8qe455RyzgwrNK3ISnkDzuzPOfQO+seeXILPcg4ctTOn1TbDZ73PkHjMe3VXE8YRaB7g9h5EhVgFWcwEZDrie8Sgk/ufwmfn0+Z6A98s4gddRvSnOxq7eCbpjwfqtY9f9EoArVZW64L4xyrQqsopBto0hve8G+aMnilsylFhnsk8OG2mAfpUiA/MCdPByCXZHlBPwudzmZIIaF9dubYMLOpx7+wjcqFtovny5A5WhMHkZyxegIurgnrDEKo+aM1Lale5IqGwTeTr68zP4TsgQyN6BaHLSBi1yH1vEn8bjbYHP8RbMSN7JYZmb9HJ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" 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- + - + - + - + - + - + - + @@ -1131,7 +1131,7 @@ z - + - + @@ -1170,7 +1170,7 @@ z - + - + @@ -1212,7 +1212,7 @@ z - + @@ -1221,7 +1221,7 @@ z - + @@ -1235,7 +1235,7 @@ z - + @@ -1249,7 +1249,7 @@ z - + @@ -1257,223 +1257,158 @@ z - + - + - + - + - + - + - + - + - + - + - + - + - - - - - - + - - - + - - - + - - - + - - - + - - - + - - - - - - - + - - - - - - - + - - - - - - - + - - - - + - - - - + - - - - - - - + - - - - + - - - - + - - - - + - - - - + - - - - + - - - - + - + - + - - + - - + - - + + - - @@ -1828,51 +1721,22 @@ z - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - + + + + + + + + + + - - + + diff --git a/docs/reviews/readme_comprehension_20260630_123105/panel_deepseek-v4-flash.json b/docs/reviews/readme_comprehension_20260630_123105/panel_deepseek-v4-flash.json new file mode 100644 index 0000000..b11e6a7 --- /dev/null +++ b/docs/reviews/readme_comprehension_20260630_123105/panel_deepseek-v4-flash.json @@ -0,0 +1,37 @@ +{ + "summary": "tinymfv is a lightweight evaluation suite that measures whether LLM steering interventions shift moral/value profiles (from vignettes and surveys) in the intended direction without moving nearby dimensions, and plots those profiles against human reference data. A researcher would use it to get fast, sensitive feedback on local steering experiments before sampled answers flip.", + "datasets": ["MFV classic", "MFV scifi", "MFV ai-actor", "MFQ-2", "Big Five", "16PF", "Humor Styles"], + "map_measurement": "For survey instruments (MFQ‑2, Big Five, 16PF, Humor Styles) it is the mean expected 1‑5 score per factor; for MFV instruments it is the relative foundation emphasis (foundation probabilities z‑scored across foundations) because model and human units differ.", + "coherence_rule": "The plotted path includes only coefficients (starting from c=0 and proceeding by sign) whose answer mass stays above 99% of the base run's answer mass; once a side falls to or below 99%, later points on that side are dropped.", + "plot_encoding": "Gray marks are human societies or respondents, black is the base model, red is positive steering, and blue is negative steering.", + "own_words_use_case": "I would use tinymfv when iterating on steering vectors to confirm that a manipulation changes the intended moral or personality dimension without distorting unrelated ones, and that the resulting model profile remains plausible relative to actual human societies, all without needing heavy sampling or full behavioral evals.", + "scores": { + "clarity": 4, + "conciseness": 5, + "technical_accuracy": 5 + }, + "unclear": [ + "What exactly 'c' represents in the coherence rule or in the plot captions (+C, -C) is not defined.", + "The phrase 'answer mass stays above 99% of the base run' is ambiguous: is it the absolute mass on valid tokens, or the ratio of the steered mass to the base mass?", + "The map measurement description says 'the profile' but then distinguishes z‑scored foundation emphasis for MFV vs mean expected scores for surveys, which could confuse a first reader looking for a single definition.", + "It's not explained how many coefficients or what range of c values are typically explored." + ], + "misunderstandings": [ + "The coherence rule sentence could be read as though the 99% threshold applies to the answer mass of each individual coefficient, but it's not clear whether that mass is compared to the base run's mass or to an absolute cutoff.", + "The phrase 'once a side becomes incoherent' may mislead readers into thinking 'incoherent' is a formal property, when it is simply defined by the 99% answer‑mass threshold.", + "The map z‑score note for MFV could be overlooked, leading a user to interpret MFV maps as raw probabilities, which would be incomparable to human data." + ], + "missing_to_act": [ + "A user needs to already have steering‑lite outputs in a specific directory structure to run the bundled plotting script; there is no standalone plotting API or example for custom data.", + "The 99% coherence threshold is hard‑coded; users who want a different tolerance have no parameter to adjust.", + "No guidance is given on how to produce the steered runs (coefficient‑stepping procedure) or what the 'c' values correspond to in the steering framework.", + "The connection between the Python API (evaluate, administer) and the plotting workflow is not documented; the script appears to operate on saved run‑dirs, not on in‑memory reports." + ], + "suggestions": [ + "Add a sentence that defines 'c' as the steering coefficient (or magnitude) and explains the usual progression (e.g., 0, ±0.5, ±1, …).", + "Clarify the answer‑mass condition: e.g., 'the proportion of probability assigned to valid answer tokens for a steered run must be at least 99% of that proportion in the base run'.", + "Distinguish the map measurement for MFV and surveys in a single concise line in the Measurement section, not just a parenthetical note.", + "Provide a small plain‑Python example that loads a saved run‑dir and calls a plotting function, so users can adapt the threshold or data source.", + "Explicitly mention that the user must have run steering‑lite (or equivalent coefficient‑based steering) to generate the required run‑dir structure; link to that repository." + ] +} \ No newline at end of file diff --git a/docs/reviews/readme_comprehension_20260630_123105/panel_gemini-3.1-flash-lite.json b/docs/reviews/readme_comprehension_20260630_123105/panel_gemini-3.1-flash-lite.json new file mode 100644 index 0000000..074ba9d --- /dev/null +++ b/docs/reviews/readme_comprehension_20260630_123105/panel_gemini-3.1-flash-lite.json @@ -0,0 +1,34 @@ +{ + "summary": "tinymfv is a lightweight tool for evaluating local LLMs by measuring moral vignette probabilities and psychological survey scores. Researchers use it to verify steering interventions, ensuring that probability shifts successfully track intended values without causing the model to lose coherence or become psychologically unrecognizable relative to human data.", + "datasets": [ + "MFV classic", + "MFV scifi", + "MFV ai-actor", + "MFQ-2", + "Big Five", + "16PF", + "Humor Styles" + ], + "map_measurement": "For MFV vignettes, it is the model's mean probability on each foundation (z-scored across foundations). For survey instruments, it is the mean expected 1-5 score for each factor.", + "coherence_rule": "The path shows coefficients (c=0, plus/minus c) only as long as the answer mass remains above 99% of the base run; points are dropped once a side becomes incoherent.", + "plot_encoding": "Gray marks represent human societies or respondents, black represents the base unsteered model, red depicts positive steering, and blue depicts negative steering.", + "own_words_use_case": "I would use this during steering development to quickly detect if my proposed steering vectors are actually moving the model's latent value profile towards the target benchmarks, or—crucially—if they are pushing the model into 'psychological alien' territory where it maintains its format but lacks human-like response patterns.", + "scores": { + "clarity": "5", + "conciseness": "5", + "technical_accuracy": "5" + }, + "unclear": [ + "The exact definition and setup of 'steering-lite', which is required for running the visual generation scripts despite not being clearly documented as a sub-repo or dependency.", + "Whether the 'culture map' and 'range' plots automatically handle the mathematical discrepancy between MFV probability profiles and questionnaire 1-5 scales, or if they require distinct processing." + ], + "misunderstandings": [ + "The text categorizes MFV vignettes and survey instruments under the same plotting umbrella, potentially misguiding the reader to assume they are directly comparable metrics despite their different underlying measurement methods (probability mass vs. ordinal scale)." + ], + "missing_to_act": [ + "A sample of the output directory structure or an explanation of how to generate the 'steering-lite' data required for the plot generation script." + ], + "suggestions": [ + "Clarify the 'steering-lite' dependency in the Install section, explaining if it is a library, a folder, or a separate tool, and provide an example of the input data format expected by plot_steer_showcase.py." + ] +} \ No newline at end of file diff --git a/docs/reviews/readme_comprehension_20260630_123105/panel_gemma-3-12b-it.json b/docs/reviews/readme_comprehension_20260630_123105/panel_gemma-3-12b-it.json new file mode 100644 index 0000000..8f0711e --- /dev/null +++ b/docs/reviews/readme_comprehension_20260630_123105/panel_gemma-3-12b-it.json @@ -0,0 +1,19 @@ +```json +{ + "summary": "tinymfv is a tool for quickly evaluating the impact of steering on language models, comparing their responses to human data. It uses moral vignettes and survey questions to measure changes in model behavior and assesses how those changes align with human preferences.", + "datasets": ["MFV classic", "MFV scifi", "MFV ai-actor", "MFQ-2", "Big Five", "16PF", "Humor Styles"], + "map_measurement": "The plots show the profile, which is the model's mean probability on each moral foundation (for MFV) or the mean expected 1-5 answer for each factor on a survey instrument.", + "coherence_rule": "Only coherent coefficients (those where the answer mass remains above 99% of the base run) are plotted. Once a coefficient becomes incoherent, later points on that side are dropped.", + "plot_encoding": "Gray marks represent human societies/respondents, black is the base model, red is positive steering, and blue is negative steering.", + "own_words_use_case": "I would use this to rapidly test steering directions on a local LLM and see if they move the model's responses closer to human patterns or drift them further away, particularly useful when speed and sensitivity are key.", + "scores": { + "clarity": "4", + "conciseness": "5", + "technical_accuracy": "5" + }, + "unclear": ["The specific process of 'canonicalizing' survey frames before averaging is unclear."], + "misunderstandings": ["The text implies an equivalence between the datasets' foundations and psychological factors, which may not be precise."], + "missing_to_act": ["It would be helpful to see example code on how to change steering prompts and re-evaluate."], + "suggestions": ["Clarify the procedure for canonicalizing survey frames, providing a specific example would be helpful."] +} +``` \ No newline at end of file diff --git a/docs/reviews/readme_comprehension_20260630_123105/panel_gpt-oss-120b.json b/docs/reviews/readme_comprehension_20260630_123105/panel_gpt-oss-120b.json new file mode 100644 index 0000000..4a5bb8c --- /dev/null +++ b/docs/reviews/readme_comprehension_20260630_123105/panel_gpt-oss-120b.json @@ -0,0 +1,49 @@ +{ + "summary": "tinymfv is a lightweight library that evaluates moral foundations and personality survey responses of local LLMs by turning answer‑token probabilities into a concise model profile. Researchers use it to quickly see how a steering or fine‑tuning change shifts the model’s values compared to human reference data, without needing full‑scale human evaluation.", + "datasets": [ + "MFV classic", + "MFV scifi", + "MFV ai-actor", + "MFQ-2", + "Big Five", + "16PF", + "Humor Styles" + ], + "map_measurement": "The maps and range plots show the model’s profile: for MFV the mean probability of each moral foundation (z‑scored across foundations for plotting), and for survey instruments the mean expected 1‑5 score for each factor after reverse‑keying.", + "coherence_rule": "Only coefficients whose answer‑mass stays above 99 % of the base run are kept; the path starts at c = 0 and includes each positive and negative c while the mass threshold is met, dropping later points on a side once it falls at or below 99 %.", + "plot_encoding": "Gray marks represent human societies or respondents, black marks the base model, red marks the positively steered endpoint, and blue marks the negatively steered endpoint.", + "own_words_use_case": "In my research I often compare different prompting or fine‑tuning strategies; with tinymfv I could run a fast paired evaluation that quantifies how each method moves the model’s moral or personality profile toward or away from documented human norms, giving an early signal before costly human studies.", + "scores": { + "clarity": "5", + "conciseness": "4", + "technical_accuracy": "5" + }, + "unclear": [ + "Exact definition of 'answer‑mass' and how it is computed from token probabilities is not spelled out.", + "How to produce the '+C' and '-C' steering runs (the required steering command or configuration) is not described.", + "The format and column meanings of the human reference CSV files (e.g., country means) are not documented.", + "The phrase 'human societies' on range plots is vague—whether each point is a country, a demographic group, or an aggregate is unclear.", + "'Canonicalizes these frames before averaging' is mentioned but the exact method is not explained." + ], + "misunderstandings": [ + "The statement that MFV map values are z‑scored across foundations could be misread as raw probabilities being plotted.", + "The term 'coherent coefficients' might be interpreted as always keeping all coefficients, whereas the rule actually drops them when answer‑mass falls below 99 %.", + "Seeing 'human societies' as actual societies rather than averaged country scores could lead to incorrect interpretation of the axes.", + "The note 'format check: mass on valid answer tokens' could be taken to mean an automatic validation step, which is not described.", + "The comment 'MFV is nominal: the answer is the category' could be read as a categorical classification system rather than a probabilistic profile." + ], + "missing_to_act": [ + "Step‑by‑step instructions for generating the '+C' and '-C' steered model checkpoints (e.g., which steering script, hyperparameters) are missing.", + "Details on required hardware, Python version, and additional dependencies beyond uv pip install (e.g., torch, CUDA) are not provided.", + "Explanation of the expected directory layout for the steering‑lite run‑dir argument used by the plotting script is absent.", + "Specification of column names and units in the human reference CSV files to allow correct loading and comparison is lacking.", + "A minimal example of interpreting the plotted axes (e.g., PCA component meaning) to understand the culture map would be helpful." + ], + "suggestions": [ + "Add a short 'Steering workflow' section that shows how to obtain the base, +C, and -C model outputs and what files the plot script expects.", + "Include a table describing the columns in each human reference CSV (e.g., country, foundation scores) and their units.", + "Clarify in the Measurement section that MFV map values are z‑scored across foundations.", + "Provide a concrete definition of 'answer‑mass' and the 99 % coherence threshold, perhaps with a small numeric example.", + "Mention required Python and PyTorch versions and any GPU/CUDA requirements in the Install section." + ] +} \ No newline at end of file diff --git a/docs/reviews/readme_comprehension_20260630_123105/panel_kimi-k2.7-code.json b/docs/reviews/readme_comprehension_20260630_123105/panel_kimi-k2.7-code.json new file mode 100644 index 0000000..e69de29 diff --git a/docs/reviews/readme_comprehension_20260630_123105/panel_nemotron-3-super-120b-a12b.json b/docs/reviews/readme_comprehension_20260630_123105/panel_nemotron-3-super-120b-a12b.json new file mode 100644 index 0000000..383192d --- /dev/null +++ b/docs/reviews/readme_comprehension_20260630_123105/panel_nemotron-3-super-120b-a12b.json @@ -0,0 +1,32 @@ +{ + "summary": "tinymfv provides fast, token‑level value evaluations for steering experiments by querying LLMs with moral vignettes and survey items, converting answer probabilities into a compact model profile. Researchers use it to detect whether a steering intervention shifted intended values, affected nearby values, and kept the model’s responses within human norms, catching probability shifts before sampled answers change.", + "datasets": ["MFV classic", "MFV scifi", "MFV ai-actor", "MFQ-2", "Big Five", "16PF", "Humor Styles"], + "map_measurement": "the model profile (mean probability per moral foundation for MFV vignettes, or mean expected 1‑5 score per factor for survey instruments)", + "coherence_rule": "only coefficients whose answer‑mass remains above 99 % of the base‑run are plotted; once a side drops to ≤99 % it and further points on that side are omitted", + "plot_encoding": "gray marks represent human societies or respondents, black is the base model, red indicates positive steering, and blue indicates negative steering", + "own_words_use_case": "I would use tinymfv to quickly quantify how a steering intervention moves an LLM’s value profile relative to a human baseline, spotting subtle probability shifts that precede overt answer changes and revealing whether the steer unintentionally drags nearby values away from human norms.", + "scores": {"clarity": "4", "conciseness": "3", "technical_accuracy": "5"}, + "unclear": [ + "What exactly constitutes 'answer mass' and how it is computed for each coefficient.", + "Whether the MFV profile shown on maps is raw probability or z‑scored emphasis, and how the transformation is applied.", + "How the 99 % threshold is applied separately to positive and negative steering sides.", + "What specific files or outputs from a steering‑lite run are required by the plotting script." + ], + "misunderstandings": [ + "Readers might assume the red/blue points always correspond to authority steering, while the examples are just one steer direction.", + "The maps might be read as showing raw foundation probabilities, when they actually display z‑scored relative emphasis.", + "The profile for MFV and surveys might be treated as directly comparable, though they are on different scales." + ], + "missing_to_act": [ + "Instructions on how to generate a steering‑lite run directory (e.g., commands to produce the required steering outputs).", + "Details on required hardware/software beyond uv (e.g., GPU, transformers version).", + "Explicit description of the input format expected by the plotting script (CSV columns).", + "A minimal, self‑contained example that runs from model loading to plot generation without external steering‑lite outputs." + ], + "suggestions": [ + "Add a brief ‘Quick start’ section that shows how to produce a steering‑lite run, then call the plotting script with a toy dataset.", + "Clarify the coherence rule with a formula: plot coefficient c if mass(c) > 0.99 × mass(base).", + "Specify that MFV map values are z‑scored across foundations before PCA, and note the scaling in the figure caption.", + "Define 'answer mass' in the Measurement section as the total probability mass on the allowed answer tokens for a given coefficient." + ] +} \ No newline at end of file diff --git a/docs/reviews/readme_comprehension_20260630_123105/prompt.md b/docs/reviews/readme_comprehension_20260630_123105/prompt.md new file mode 100644 index 0000000..6b2518e --- /dev/null +++ b/docs/reviews/readme_comprehension_20260630_123105/prompt.md @@ -0,0 +1,167 @@ +You are reading the README below for the FIRST time as a cold researcher. +Answer ONLY from what it says; where something is unstated or ambiguous, say so. +Output ONE JSON object, no prose, no fences: +{ + "summary": "<2-3 sentences: what is tinymfv and why would a researcher use it?>", + "datasets": [""], + "map_measurement": "", + "coherence_rule": "", + "plot_encoding": "", + "own_words_use_case": "", + "scores": {"clarity": "<1-5>", "conciseness": "<1-5>", "technical_accuracy": "<1-5>"}, + "unclear": [""], + "misunderstandings": [""], + "missing_to_act": [""], + "suggestions": [""] +} + +README: +# tinymfv + +tinymfv is a small set of fast value evals for local LLM steering work. It asks moral vignettes and survey questions, reads answer-token probabilities, and turns them into one model profile. + +Use it when you want to know whether a steer moved the intended values, moved nearby values too, and still lands near real human response patterns. The evals are quick and sensitive enough to show probability shifts before sampled answers flip. + +The plots compare that profile to human data. Gray marks are human societies or respondents, black is the base model, red is positive steering, and blue is negative steering. Range plots show the coherent coefficient path for each factor; maps show the base model and the strongest coherent endpoints on a PCA map of human profiles. + +![MFQ-2 range plot: human society ranges beside base, +C, and -C authority steering](docs/img/showcase/mfq2/range.png) + +![MFQ-2 culture map: base, +C, and -C authority steering against human societies](docs/img/showcase/mfq2/map_pca_ipsative.png) + +![Big Five range plot: human society ranges beside base, +C, and -C authority steering](docs/img/showcase/big5/range.png) + +![Big Five culture map: base, +C, and -C authority steering against human societies](docs/img/showcase/big5/map_pca_ipsative.png) + +Read the Big Five map left to right: gray is the human reference, black is the base LLM, and the red/blue points are steered endpoints. Here the LLM sits outside the country cloud, so on this measure it is a psychological alien before steering moves it. + +![16PF range plot: human society ranges beside base, +C, and -C authority steering](docs/img/showcase/16pf/range.png) + +![Humor Styles range plot: human society ranges beside base, +C, and -C authority steering](docs/img/showcase/humor_styles/range.png) + +![Humor Styles culture map: base, +C, and -C authority steering against human societies](docs/img/showcase/humor_styles/map_pca_ipsative.png) + +The Humor Styles map shows the same failure mode more sharply: the model profile can live away from the human societies. That is the useful warning sign, a model can be format-coherent and still be a moral or psychological alien on the measured profile. + +![MFV culture map: base, +C, and -C authority steering against human countries](docs/img/showcase/mfv/map_pca_ipsative.png) + +![MFV range plot: foundation emphasis beside base, +C, and -C authority steering](docs/img/showcase/mfv/range.png) + +The plotted path shows only coherent coefficients: `c=0`, then each positive and negative `c` while its answer mass stays above 99% of the base run. Once a side becomes incoherent, later points on that side are dropped. + +## Install + +```bash +uv pip install git+https://github.com/wassname/tinymfv +``` + +For maps: + +```bash +uv pip install "tiny-mfv[maps] @ git+https://github.com/wassname/tinymfv" +``` + +For repo development: + +```bash +git clone https://github.com/wassname/tinymfv +cd tinymfv +uv sync --extra maps --dev +just smoke +``` + +## Datasets + +| dataset | bundled data | human reference | profile used in plots | +|---|---|---|---| +| MFV classic | [132 moral vignettes, other](src/tinymfv/data/vignettes_classic_other_violate.jsonl) / [self](src/tinymfv/data/vignettes_classic_self_violate.jsonl) | per-vignette human foundation labels in the JSONL | foundation probability profile | +| MFV scifi | [same items rewritten as sci-fi, other](src/tinymfv/data/vignettes_scifi_other_violate.jsonl) / [self](src/tinymfv/data/vignettes_scifi_self_violate.jsonl) | inherited labels from classic MFV | foundation probability profile | +| MFV ai-actor | [same items rewritten with an AI actor, other](src/tinymfv/data/vignettes_ai-actor_other_violate.jsonl) / [self](src/tinymfv/data/vignettes_ai-actor_self_violate.jsonl) | inherited labels from classic MFV | foundation probability profile | +| MFQ-2 | [36 items](src/tinymfv/data/surveys/mfq2/forward.json), plus inverted and negated frames | [country means](src/tinymfv/data/human/mfq2_country_foundations.csv), plus [raw respondents](src/tinymfv/data/atari_study2_raw.csv) | expected 1-5 score per foundation | +| Big Five | [50 items](src/tinymfv/data/surveys/big5/questionnaire.json), plus inverted and negated frames | [country means](src/tinymfv/data/human/big5_country_factors.csv) | expected 1-5 score per trait | +| 16PF | [162 items](src/tinymfv/data/surveys/16pf/questionnaire.json), plus inverted and negated frames | [country means](src/tinymfv/data/human/16pf_country_factors.csv) | expected 1-5 score per factor | +| Humor Styles | [32 items](src/tinymfv/data/surveys/humor_styles/questionnaire.json), plus inverted and negated frames | [country means](src/tinymfv/data/human/humor_styles_country_factors.csv), originally 1-7 | expected 1-5 score per style | + +MFV is nominal: the answer is the category. The survey instruments are ordinal: the answer is a scale point. + +Each MFV item is asked in two perspectives, `other_violate` and `self_violate`. Each survey item is asked three ways, forward, scale-inverted, and content-negated. tinymfv canonicalizes these frames before averaging, so the profile is less tied to one wording. + +## API + +Run MFV vignettes with `evaluate`: + +```python +from transformers import AutoModelForCausalLM, AutoTokenizer +from tinymfv import evaluate, load_vignettes + +tok = AutoTokenizer.from_pretrained("Qwen/Qwen3-4B") +model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen3-4B").cuda() + +vignettes = load_vignettes("classic") # "classic", "scifi", "ai-actor", or "all" +report = evaluate(model, tok, vignettes=vignettes) + +print(report["profile"]) # mean probability per foundation +print(report["mean_pmass_allowed"]) # format check: mass on valid answer tokens +``` + +Run survey instruments with `administer`: + +```python +from transformers import AutoModelForCausalLM, AutoTokenizer +from tinymfv import administer, get_instrument + +tok = AutoTokenizer.from_pretrained("Qwen/Qwen3-4B") +model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen3-4B").cuda() + +instr = get_instrument("mfq2") # "mfq2", "big5", "16pf", or "humor_styles" +report = administer(model, tok, instr) + +print(report["dimensions"]) +print(report["profile"]) # expected 1-5 score per factor +print(report["mean_pmass_allowed"]) # format check: mass on valid answer tokens +``` + +Generate the bundled range plots and culture maps from a steering-lite all-instrument run: + +```bash +uv run python scripts/plot_steer_showcase.py \ + --run-dir ../steering-lite/outputs/20260630_dignity_authority_strict22_local_sspace_allinstr \ + --out docs/img/showcase \ + --vec-label=-Authority +``` + +## Measurement + +The measurement on the maps is the profile. + +For MFV, the profile is the model's mean probability on each moral foundation: + +$$\mathrm{profile}_f = \mathbb{E}_i P(f \mid i)$$ + +For survey instruments, the profile is the mean expected 1-5 answer for each factor, after reverse-keying: + +$$\mathrm{profile}_d = \mathbb{E}_{i \in d}\sum_{k=1}^{M} k P(k \mid i)$$ + +where $i$ is an item, $d$ is a survey factor, $k$ is a scale point, and $M$ is the largest scale value. + +This is what the survey maps and range plots show. In the showcase CSVs, this is the `mean` column. For MFV showcase plots, model and human units differ, so the plotted quantity is relative foundation emphasis: each foundation profile is z-scored across foundations before mapping. + +For paired steering runs, compare the base profile to the steered profile path. The showcase drops a coefficient when its answer mass is at or below 99% of the base run. + +## Scope + +tinymfv is for fast paired steering comparisons, not full moral reasoning evaluation. It is useful when you want to compare base, positive-steer, and negative-steer runs against the same human reference plots. + +For behavior-heavy moral evals, see [machiavelli](https://huggingface.co/datasets/wassname/machiavelli), [AIRiskDilemmas](https://huggingface.co/datasets/kellycyy/AIRiskDilemmas), and [ethics_expression_preferences](https://huggingface.co/datasets/wassname/ethics_expression_preferences). + +Used in [steering-lite](https://github.com/wassname/steering-lite), [lora-lite](https://github.com/wassname/lora-lite), and [w2schar-mini](https://github.com/wassname/w2schar-mini). + +## Citation + +```bibtex +@misc{clark2026tinymfv, + title = {tinymfv: tiny moral/value eval for local LLMs}, + author = {Michael Clark}, + year = {2026}, + url = {https://github.com/wassname/tinymfv/} +} +``` diff --git a/docs/reviews/readme_comprehension_20260630_triage.md b/docs/reviews/readme_comprehension_20260630_triage.md new file mode 100644 index 0000000..79c83d9 --- /dev/null +++ b/docs/reviews/readme_comprehension_20260630_triage.md @@ -0,0 +1,31 @@ +# README Comprehension Panel Triage + +## Inputs +- Initial panel: `docs/reviews/readme_comprehension_20260630_123105/` +- Confirmation panel: `docs/reviews/readme_comprehension_rerun_20260630_123527/` +- README under review: `README.md` + +## Expected Reader Answers +- tinymfv is a fast answer-token reader for local LLM value steering work. +- It includes MFV classic, MFV scifi, MFV ai-actor, MFQ-2, Big Five, 16PF, and Humor Styles. +- The map/range measurement is the profile: MFV foundation probability profile, survey expected 1-5 factor profile. +- For MFV showcase maps, profiles are z-scored across foundations before comparing to human country profiles. +- Gray = human societies/respondents, black = base model, red = positive steering, blue = negative steering. +- `c` is the signed multiplier on the calibrated steering vector. +- Answer mass is total probability on valid answer tokens. The path is a per-side prefix from `c=0`; drop the side once answer mass is at or below 99% of base. + +## Panel Result +- Initial panel correctly recovered the main tool, dataset list, profile measurement, and researcher use case. +- Initial repeated gaps: `c` was not defined, answer mass was not exact enough, and the per-side prefix rule was easy to infer but not explicit. +- README fixes applied: + - Defined `c` as the signed multiplier on the calibrated steering vector. + - Defined answer mass as probability on valid answer tokens. + - Stated that dropping is per side, at the first incoherent coefficient. + - Added the compact answer-mass formula: + `m(c) = E_i sum_{a in A_i} P_c(a | i)`. +- Confirmation panel: all four usable reviewers correctly reconstructed `c`, answer mass, the 99% threshold, and the per-side drop rule. + +## Not Changed +- Did not add a steering-lite training recipe. This README links to steering-lite and shows the plot command; the full axis/template/scenario-selection workflow belongs in steering-lite or the persona-template skill. +- Did not define the calibrated steering vector mathematically here. It would make the README longer and is not needed to understand tinymfv's measurement. +- Did not add full human CSV schema docs. The dataset table links directly to the committed data files. diff --git a/docs/reviews/readme_comprehension_rerun_20260630_123527/panel_deepseek-v4-flash.json b/docs/reviews/readme_comprehension_rerun_20260630_123527/panel_deepseek-v4-flash.json new file mode 100644 index 0000000..8c0c365 --- /dev/null +++ b/docs/reviews/readme_comprehension_rerun_20260630_123527/panel_deepseek-v4-flash.json @@ -0,0 +1,29 @@ +{ + "summary": "tinymfv is a compact, fast evaluation suite for local LLM steering that probes value shifts via moral vignettes and survey questions, reading answer-token probabilities to produce a single model profile. A researcher would use it to quickly determine whether a steering intervention moved intended values, whether it perturbed nearby values, and whether the steered profile remains plausible relative to human response data. The evals are sensitive enough to detect probability shifts before sampled answers flip.", + "datasets": ["MFV classic", "MFV scifi", "MFV ai-actor", "MFQ-2", "Big Five", "16PF", "Humor Styles"], + "map_measurement": "The measurement shown on maps and range plots is the profile: for MFV it is the model's mean probability per moral foundation; for survey instruments it is the mean expected 1-5 score per factor (after reverse‑keying). In showcase plots, MFV uses relative foundation emphasis (z-scored across foundations) to make model and human units comparable.", + "coherence_rule": "The plotted path shows only coherent coefficients where c (the signed multiplier on the calibrated steering vector) starts at 0, then each positive and negative side is included while its answer mass (total probability on valid answer tokens) stays above 99% of the base run. Once a side's answer mass drops to ≤99% of the base run, later points on that side are dropped.", + "plot_encoding": "Gray marks represent human societies or respondents, black marks are the base (unsteered) model, red marks are positive steering, and blue marks are negative steering.", + "own_words_use_case": "I would use tinymfv for iterative steering development to rapidly compare base, positively‑steered, and negatively‑steered model runs against the same human reference plots. It quickly reveals whether a steering vector shifts value profiles in the intended direction and magnitude, whether it also shifts nearby values, and whether the steered model remains a plausible psychological entity rather than an outlier—avoiding the trap of format‑coherent but alien behavior.", + "scores": {"clarity": 4, "conciseness": 4, "technical_accuracy": 5}, + "unclear": [ + "The variable 'c' is used in the coherence rule and paths but is only defined later in the Coherence rule paragraph, not earlier in the Plot encoding section.", + "The phrase 'range plots' is not explicitly defined — it is unclear whether the gray marks represent individual data points, ranges (min‑max), or error bars around a mean.", + "The 'answer mass' concept is introduced in the coherence rule but the API output 'mean_pmass_allowed' is not explicitly linked to it.", + "The distinction between MFV plots (z-scored) and survey plots (raw expected scores) is explained only in the Measurement section, which could cause confusion when viewing MFV showcase plots without that context.", + "The term 'psychological alien' is used qualitatively but never formally defined — it is inferred from being outside the human country cloud on a PCA map." + ], + "misunderstandings": [ + "The statement 'still lands near real human response patterns' might imply the base model already does, whereas later examples show the base model can be a clear outlier (e.g., Big Five map shows 'psychological alien').", + "The coherence rule says 'each positive and negative side while its answer mass stays above 99% of the base run' — a reader could misinterpret 'incoherent' as logically contradictory rather than format‑invalid (low probability on valid tokens).", + "The plot encoding says 'Gray marks are human societies or respondents' but the range plots are described as showing 'human society ranges' — one might think the gray region is a continuous range rather than discrete points.", + "The MFQ‑2 caption mentions 'culture map' but the general description uses 'culture map' interchangeably with 'map' — the reader may not know it is a PCA projection of human country profiles until the Measurement section explains it." + ], + "suggestions": [ + "Define 'c' (signed multiplier on the calibrated steering vector) earlier, perhaps in the Plot encoding or before the coherence rule.", + "Clarify in the Plot encoding that range plots show a box‑and‑whisker or min‑max line for human societies, with gray marks as summary points, not individual respondents.", + "Add a brief note that 'answer mass' is the same as the API's 'mean_pmass_allowed' and is a format coherence metric, not a value score.", + "Explicitly state in the dataset table or a footnote that MFV showcase plots use relative (z‑scored) emphasis for comparability, while survey plots use raw expected scores.", + "Introduce the 'psychological alien' concept with a short definition, e.g., 'a model profile that lies far outside the convex hull of human country means on a PCA map.'" + ] +} \ No newline at end of file diff --git a/docs/reviews/readme_comprehension_rerun_20260630_123527/panel_gemini-3.1-flash-lite.json b/docs/reviews/readme_comprehension_rerun_20260630_123527/panel_gemini-3.1-flash-lite.json new file mode 100644 index 0000000..3abc00f --- /dev/null +++ b/docs/reviews/readme_comprehension_rerun_20260630_123527/panel_gemini-3.1-flash-lite.json @@ -0,0 +1,34 @@ +{ + "summary": "tinymfv is a lightweight evaluation toolkit designed to benchmark how steering interventions shift an LLM's moral and psychological value profiles against human reference populations. It allows researchers to quickly visualize and detect if steering successfully shifts model behavior or if it causes the model to diverge into incoherent, 'psychologically alien' territory before triggering full-text generation.", + "datasets": [ + "MFV classic", + "MFV scifi", + "MFV ai-actor", + "MFQ-2", + "Big Five", + "16PF", + "Humor Styles" + ], + "map_measurement": "The measurement is the 'profile,' representing either relative foundation emphasis (z-scored foundation probabilities) for MFV or mean expected 1-5 factor scores for survey instruments, plotted against human reference data.", + "coherence_rule": "The plotted path includes coefficients (c) where the model's answer mass (total probability on valid answer tokens) remains above 99% of the base run; points are dropped once this coherence threshold is crossed. 'c' is the signed scalar multiplier applied to the calibrated steering vector.", + "plot_encoding": "Gray represents human reference data (societies/individuals); black is the base model; red indicates positive steering; blue indicates negative steering.", + "own_words_use_case": "I would use this during the prototyping phase of model steering to validate whether an intervention is hitting its intended target regarding cultural or moral dimensions. It functions as a 'sanity check' to ensure the model remains grounded in human-like response patterns and doesn't break its output format, allowing for rapid iteration on steering strength without needing to generate and manually review long-form model outputs.", + "scores": { + "clarity": "5", + "conciseness": "5", + "technical_accuracy": "5" + }, + "unclear": [ + "The exact mathematical definition or implementation for the 'calibrated steering vector' is not provided.", + "The text does not explicitly explain how the base model is 'outside the country cloud' in the PCA visualization, leaving the reader to infer the relationship between model-space and human-space.", + "It is not entirely clear if the reference data is fixed per instrument or depends on external dependencies not listed in the snippet." + ], + "misunderstandings": [ + "A reader might assume 'c' represents a raw logit bias rather than a scalar multiplier on a pre-calibrated steering vector.", + "The usage of 'answer mass' might be confused with a value sentiment score, whereas it is strictly a measure of format coherence." + ], + "suggestions": [ + "Add a definition or reference for 'calibrated steering vector' to clarify if it requires an external library or specific preparation.", + "Explicitly define the PCA axes in the documentation graphics to clarify what the distance between the 'black' model point and the 'gray' culture cloud signifies." + ] +} \ No newline at end of file diff --git a/docs/reviews/readme_comprehension_rerun_20260630_123527/panel_gpt-oss-120b.json b/docs/reviews/readme_comprehension_rerun_20260630_123527/panel_gpt-oss-120b.json new file mode 100644 index 0000000..e0db396 --- /dev/null +++ b/docs/reviews/readme_comprehension_rerun_20260630_123527/panel_gpt-oss-120b.json @@ -0,0 +1,39 @@ +{ + "summary": "tinymfv is a lightweight suite of fast value evaluations for local LLM steering. It probes models with moral vignettes and survey items, reads token probabilities, and aggregates them into a profile that can be compared against human reference data to see if a steer shifts the intended values and how it affects related values.", + "datasets": [ + "MFV classic", + "MFV scifi", + "MFV ai-actor", + "MFQ-2", + "Big Five", + "16PF", + "Humor Styles" + ], + "map_measurement": "The maps display the model’s profile: for MFV the mean probability for each moral foundation, and for the survey instruments the mean expected 1‑5 answer for each factor after reverse‑keying (i.e., the averaged scale score).", + "coherence_rule": "Only coefficient steps (c) that remain coherent are plotted: the path starts at c = 0 (base model) and then includes successive positive and negative c values while the answer‑mass (total probability on valid answer tokens) stays above 99 % of the base run. When answer‑mass falls to ≤ 99 % on a side, that side is considered incoherent and any later points on that side are omitted. Here c is the signed multiplier applied to the calibrated steering vector.", + "plot_encoding": "Gray marks represent human societies or respondents; black marks the base (unsteered) model; red marks the positively steered endpoint; blue marks the negatively steered endpoint.", + "own_words_use_case": "A researcher could use tinymfv to rapidly assess whether a proposed steering intervention nudges a LLM toward a desired moral or personality profile without causing unintended drift, by visualizing the shift against real human norms and detecting early inconsistencies before full generation testing.", + "scores": { + "clarity": "4", + "conciseness": "3", + "technical_accuracy": "4" + }, + "unclear": [ + "The term \"human societies\" is used for both country‑level aggregates and raw respondent data without explicit distinction.", + "The exact calculation of answer‑mass (whether it sums over all items or per‑item) is not spelled out.", + "How the z‑scoring for MFV plots is performed (across foundations per model or across models) is not fully detailed.", + "The meaning of \"format‑coherent\" is mentioned but not defined." + ], + "misunderstandings": [ + "One might think the MFV map quantity is a raw probability, but the README says it is a z‑scored relative emphasis, which could be misread.", + "The phrase \"coherent coefficients\" could be interpreted as mathematically coherent rather than referring to answer‑mass staying above a threshold.", + "The description of \"human reference\" could be taken to mean a single dataset, whereas multiple country‑level and respondent‑level sources are used." + ], + "suggestions": [ + "Add a concise legend in the README that maps gray, black, red, and blue colors to their meanings.", + "Define explicitly what \"human societies\" (e.g., country‑level aggregates) and \"human respondents\" (individual data) refer to.", + "Provide a short formula or description of how answer‑mass is computed and how the 99 % threshold is applied.", + "Explain the z‑scoring process for MFV visualizations, including the axis of standardization.", + "Clarify the term \"format‑coherent\" and its relevance to steering evaluation." + ] +} \ No newline at end of file diff --git a/docs/reviews/readme_comprehension_rerun_20260630_123527/panel_nemotron-3-super-120b-a12b.json b/docs/reviews/readme_comprehension_rerun_20260630_123527/panel_nemotron-3-super-120b-a12b.json new file mode 100644 index 0000000..c5879a7 --- /dev/null +++ b/docs/reviews/readme_comprehension_rerun_20260630_123527/panel_nemotron-3-super-120b-a12b.json @@ -0,0 +1,12 @@ +{ + "summary": "tinymfv is a lightweight tool for evaluating how steering interventions affect a language model's moral and psychological profile by measuring answer-token probabilities on vignettes and survey items. Researchers can use it to quickly see whether a steer moves the model toward or away from intended values, detects unintended shifts on nearby dimensions, and compares the result to human baselines before sampled answers change.", + "datasets": ["MFV classic", "MFV scifi", "MFV ai-actor", "MFQ-2", "Big Five", "16PF", "Humor Styles"], + "map_measurement": "the model's profile (mean probability per moral foundation for MFV datasets; mean expected 1‑5 score per factor for survey datasets)", + "coherence_rule": "Only coefficients where the answer mass remains above 99% of the base run are shown; the path starts at c=0 and extends in both positive and negative directions until answer mass drops to ≤99% of base, at which point further points on that side are dropped. Here c is the signed multiplier on the calibrated steering vector, and answer mass is the total probability assigned to valid answer tokens.", + "plot_encoding": "Gray marks represent human societies or respondents, black is the base model, red indicates positive steering, and blue indicates negative steering.", + "own_words_use_case": "I would use tinymfv to rapidly test whether a steering vector moves a model’s value profile in the desired direction without causing large, unintended shifts on related traits, and to instantly spot when the model’s profile drifts far from human norms, giving an early warning before qualitative sampling reveals the shift.", + "scores": {"clarity": "4", "conciseness": "4", "technical_accuracy": "5"}, + "unclear": ["How answer mass is calculated from token probabilities", "Whether the profile shown on MFV maps is raw probability or z‑scored across foundations", "Exactly how the steering vector is calibrated and what units c has"], + "misunderstandings": ["Readers might think that red/blue points always indicate better alignment with human values, when they only show the direction of steering", "Seeing the base model outside the human country cloud could be taken as a failure, but the text notes it merely shows the model is a 'psychological alien' on that measure", "The 99% answer‑mass cutoff might be interpreted as a strict coherence threshold rather than a practical display choice"], + "suggestions": ["Add a caption or legend explaining the color encoding directly on the figures", "Include a brief example showing how answer mass is computed from the model’s output distribution", "Clarify in the text that MFV map profiles are z‑scored across foundations before plotting"] +} \ No newline at end of file diff --git a/docs/reviews/readme_comprehension_rerun_20260630_123527/prompt.md b/docs/reviews/readme_comprehension_rerun_20260630_123527/prompt.md new file mode 100644 index 0000000..14f304a --- /dev/null +++ b/docs/reviews/readme_comprehension_rerun_20260630_123527/prompt.md @@ -0,0 +1,166 @@ +You are reading the README below for the FIRST time as a cold researcher. +Answer ONLY from what it says; where something is unstated or ambiguous, say so. +Output ONE JSON object, no prose, no fences: +{ + "summary": "<2-3 sentences: what is tinymfv and why would a researcher use it?>", + "datasets": [""], + "map_measurement": "", + "coherence_rule": "", + "plot_encoding": "", + "own_words_use_case": "", + "scores": {"clarity": "<1-5>", "conciseness": "<1-5>", "technical_accuracy": "<1-5>"}, + "unclear": [""], + "misunderstandings": [""], + "suggestions": [""] +} + +README: +# tinymfv + +tinymfv is a small set of fast value evals for local LLM steering work. It asks moral vignettes and survey questions, reads answer-token probabilities, and turns them into one model profile. + +Use it when you want to know whether a steer moved the intended values, moved nearby values too, and still lands near real human response patterns. The evals are quick and sensitive enough to show probability shifts before sampled answers flip. + +The plots compare that profile to human data. Gray marks are human societies or respondents, black is the base model, red is positive steering, and blue is negative steering. Range plots show the coherent coefficient path for each factor; maps show the base model and the strongest coherent endpoints on a PCA map of human profiles. + +![MFQ-2 range plot: human society ranges beside base, +C, and -C authority steering](docs/img/showcase/mfq2/range.png) + +![MFQ-2 culture map: base, +C, and -C authority steering against human societies](docs/img/showcase/mfq2/map_pca_ipsative.png) + +![Big Five range plot: human society ranges beside base, +C, and -C authority steering](docs/img/showcase/big5/range.png) + +![Big Five culture map: base, +C, and -C authority steering against human societies](docs/img/showcase/big5/map_pca_ipsative.png) + +Read the Big Five map left to right: gray is the human reference, black is the base LLM, and the red/blue points are steered endpoints. Here the LLM sits outside the country cloud, so on this measure it is a psychological alien before steering moves it. + +![16PF range plot: human society ranges beside base, +C, and -C authority steering](docs/img/showcase/16pf/range.png) + +![Humor Styles range plot: human society ranges beside base, +C, and -C authority steering](docs/img/showcase/humor_styles/range.png) + +![Humor Styles culture map: base, +C, and -C authority steering against human societies](docs/img/showcase/humor_styles/map_pca_ipsative.png) + +The Humor Styles map shows the same failure mode more sharply: the model profile can live away from the human societies. That is the useful warning sign, a model can be format-coherent and still be a moral or psychological alien on the measured profile. + +![MFV culture map: base, +C, and -C authority steering against human countries](docs/img/showcase/mfv/map_pca_ipsative.png) + +![MFV range plot: foundation emphasis beside base, +C, and -C authority steering](docs/img/showcase/mfv/range.png) + +Here `c` is the signed multiplier on the calibrated steering vector. The plotted path shows only coherent coefficients: `c=0`, then each positive and negative side while its answer mass stays above 99% of the base run. Once a side becomes incoherent, later points on that side are dropped. + +## Install + +```bash +uv pip install git+https://github.com/wassname/tinymfv +``` + +For maps: + +```bash +uv pip install "tiny-mfv[maps] @ git+https://github.com/wassname/tinymfv" +``` + +For repo development: + +```bash +git clone https://github.com/wassname/tinymfv +cd tinymfv +uv sync --extra maps --dev +just smoke +``` + +## Datasets + +| dataset | bundled data | human reference | profile used in plots | +|---|---|---|---| +| MFV classic | [132 moral vignettes, other](src/tinymfv/data/vignettes_classic_other_violate.jsonl) / [self](src/tinymfv/data/vignettes_classic_self_violate.jsonl) | per-vignette human foundation labels in the JSONL | foundation probability profile | +| MFV scifi | [same items rewritten as sci-fi, other](src/tinymfv/data/vignettes_scifi_other_violate.jsonl) / [self](src/tinymfv/data/vignettes_scifi_self_violate.jsonl) | inherited labels from classic MFV | foundation probability profile | +| MFV ai-actor | [same items rewritten with an AI actor, other](src/tinymfv/data/vignettes_ai-actor_other_violate.jsonl) / [self](src/tinymfv/data/vignettes_ai-actor_self_violate.jsonl) | inherited labels from classic MFV | foundation probability profile | +| MFQ-2 | [36 items](src/tinymfv/data/surveys/mfq2/forward.json), plus inverted and negated frames | [country means](src/tinymfv/data/human/mfq2_country_foundations.csv), plus [raw respondents](src/tinymfv/data/atari_study2_raw.csv) | expected 1-5 score per foundation | +| Big Five | [50 items](src/tinymfv/data/surveys/big5/questionnaire.json), plus inverted and negated frames | [country means](src/tinymfv/data/human/big5_country_factors.csv) | expected 1-5 score per trait | +| 16PF | [162 items](src/tinymfv/data/surveys/16pf/questionnaire.json), plus inverted and negated frames | [country means](src/tinymfv/data/human/16pf_country_factors.csv) | expected 1-5 score per factor | +| Humor Styles | [32 items](src/tinymfv/data/surveys/humor_styles/questionnaire.json), plus inverted and negated frames | [country means](src/tinymfv/data/human/humor_styles_country_factors.csv), originally 1-7 | expected 1-5 score per style | + +MFV is nominal: the answer is the category. The survey instruments are ordinal: the answer is a scale point. + +Each MFV item is asked in two perspectives, `other_violate` and `self_violate`. Each survey item is asked three ways, forward, scale-inverted, and content-negated. tinymfv canonicalizes these frames before averaging, so the profile is less tied to one wording. + +## API + +Run MFV vignettes with `evaluate`: + +```python +from transformers import AutoModelForCausalLM, AutoTokenizer +from tinymfv import evaluate, load_vignettes + +tok = AutoTokenizer.from_pretrained("Qwen/Qwen3-4B") +model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen3-4B").cuda() + +vignettes = load_vignettes("classic") # "classic", "scifi", "ai-actor", or "all" +report = evaluate(model, tok, vignettes=vignettes) + +print(report["profile"]) # mean probability per foundation +print(report["mean_pmass_allowed"]) # format check: mass on valid answer tokens +``` + +Run survey instruments with `administer`: + +```python +from transformers import AutoModelForCausalLM, AutoTokenizer +from tinymfv import administer, get_instrument + +tok = AutoTokenizer.from_pretrained("Qwen/Qwen3-4B") +model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen3-4B").cuda() + +instr = get_instrument("mfq2") # "mfq2", "big5", "16pf", or "humor_styles" +report = administer(model, tok, instr) + +print(report["dimensions"]) +print(report["profile"]) # expected 1-5 score per factor +print(report["mean_pmass_allowed"]) # format check: mass on valid answer tokens +``` + +Generate the bundled range plots and culture maps from a steering-lite all-instrument run: + +```bash +uv run python scripts/plot_steer_showcase.py \ + --run-dir ../steering-lite/outputs/20260630_dignity_authority_strict22_local_sspace_allinstr \ + --out docs/img/showcase \ + --vec-label=-Authority +``` + +## Measurement + +The measurement on the maps is the profile. + +For MFV, the profile is the model's mean probability on each moral foundation: + +$$\mathrm{profile}_f = \mathbb{E}_i P(f \mid i)$$ + +For survey instruments, the profile is the mean expected 1-5 answer for each factor, after reverse-keying: + +$$\mathrm{profile}_d = \mathbb{E}_{i \in d}\sum_{k=1}^{M} k P(k \mid i)$$ + +where $i$ is an item, $d$ is a survey factor, $k$ is a scale point, and $M$ is the largest scale value. + +This is what the survey maps and range plots show. In the showcase CSVs, this is the `mean` column. For MFV showcase plots, model and human units differ, so the plotted quantity is relative foundation emphasis: each foundation profile is z-scored across foundations before mapping. + +For paired steering runs, compare the base profile to the steered profile path. Answer mass means the total probability assigned to valid answer tokens. It is a coherence check, not a value score. The showcase drops a side at the first coefficient where answer mass is at or below 99% of the base run. + +## Scope + +tinymfv is for fast paired steering comparisons, not full moral reasoning evaluation. It is useful when you want to compare base, positive-steer, and negative-steer runs against the same human reference plots. + +For behavior-heavy moral evals, see [machiavelli](https://huggingface.co/datasets/wassname/machiavelli), [AIRiskDilemmas](https://huggingface.co/datasets/kellycyy/AIRiskDilemmas), and [ethics_expression_preferences](https://huggingface.co/datasets/wassname/ethics_expression_preferences). + +Used in [steering-lite](https://github.com/wassname/steering-lite), [lora-lite](https://github.com/wassname/lora-lite), and [w2schar-mini](https://github.com/wassname/w2schar-mini). + +## Citation + +```bibtex +@misc{clark2026tinymfv, + title = {tinymfv: tiny moral/value eval for local LLMs}, + author = {Michael Clark}, + year = {2026}, + url = {https://github.com/wassname/tinymfv/} +} +``` diff --git a/scripts/plot_steer_showcase.py b/scripts/plot_steer_showcase.py index eba2352..ff5d982 100644 --- a/scripts/plot_steer_showcase.py +++ b/scripts/plot_steer_showcase.py @@ -4,16 +4,18 @@ Consumes a steering-lite `run_allinstr_showcase.py` output dir (one calibrated activation-steering vector administered across every instrument over a signed c-sweep) and renders the SAME two figures for every instrument, uniformly: - - map : ipsative culture map (PCA), AI base + steer trajectory vs the human cloud. - - range: per-factor range, AI base dot + +c/-c arrows vs the human society strip. + - map : ipsative culture map (PCA), AI base + strongest coherent +/-c vs the human cloud. + - range: per-factor range, AI base + coherent +/-c path vs the human society strip. Ordinal instruments (mfq2/big5/16pf/humor_styles) read _profiles.csv; nominal MFV reads mfv.json and is projected into z-scored relative-emphasis space (its logit-violation units cannot share a raw axis with 1-5 wrongness), but it goes through the same plot_ipsative_pca / plot_range and yields the same two figures. -cs are SIGNED multipliers of the calibrated coefficient C (0 = base); the real C -is in the title, the legend shows only the multiplier (c=+1, c=-2, ...). +cs are SIGNED multipliers of the calibrated coefficient C (0 = base). The public +README range plots show the coherent path: c=0 plus each +/-c row whose pmass stays +above the requested fraction of base. Maps show only the strongest coherent endpoints. +Incoherent rows are dropped, not drawn hollow. uv run python scripts/plot_steer_showcase.py \ --run-dir ../steering-lite/outputs/allinstr_qwen35_4b --out docs/img/showcase @@ -110,21 +112,37 @@ def read_profiles(run_dir: Path, name: str, dims: list[str], value_col: str = "m return {c: np.array([d[f] for f in dims]) for c, d in by_c.items()}, pmass -def plot_ordinal(run_dir: Path, out: Path, name: str, vec_label: str, C: float, *, show_sweep: bool = False) -> list[Path]: +def coherent_prefix_cs(cs: list[float], pmass: dict[float, float], coherence_frac: float) -> list[float]: + """c=0 plus each signed arm until answer mass first falls below the base-relative floor.""" + base_pm = pmass[0.0] + kept = [0.0] + for side in (1.0, -1.0): + for c in sorted([c for c in cs if np.sign(c) == side], key=abs): + if pmass[c] <= coherence_frac * base_pm: + break + kept.append(c) + return sorted(kept) + + +def plot_ordinal(run_dir: Path, out: Path, name: str, vec_label: str, C: float, + coherence_frac: float) -> list[Path]: instr = get_instrument(name) dims = instr.dimensions prof_c, pmass = read_profiles(run_dir, name, dims) cs = sorted(prof_c) base = prof_c[0.0] - # headline arrows = the calibrated coefficient (c=+-1); the trajectory dots at |c|>1 extend - # BEYOND the arrowheads, so a multi-C run shows deployment point + where stronger steer drifts. - pos = prof_c[1.0] if 1.0 in prof_c else prof_c[max(cs)] - neg = prof_c[-1.0] if -1.0 in prof_c else prof_c[min(cs)] + # Coherence gate is RELATIVE and monotone per signed arm: walk outward from c=0 and stop at the + # first coefficient whose allowed-answer mass falls below the requested fraction of base. + coh_cs = coherent_prefix_cs(cs, pmass, coherence_frac) + pos_c = max(c for c in coh_cs if c > 0.0) + neg_c = min(c for c in coh_cs if c < 0.0) + pos = prof_c[pos_c] + neg = prof_c[neg_c] humans = human_strip(instr) prof = prof_c countries, Mfrac = human_matrix(instr) - labels = (f"base (c=0)", f"+C={C:+.2f}", f"-C={-C:+.2f}") + labels = ("base (c=0)", f"c={pos_c:+g}", f"c={neg_c:+g}") # mfq2 has per-respondent Atari data -> scatter the REAL individual cloud behind the societies AND # fit the ipsative PCA on it (better-conditioned, the true envelope). Other instruments have no raw # per-person data, so scatter a marginal resample from each country's published mean+sd as the haze @@ -133,28 +151,16 @@ def plot_ordinal(run_dir: Path, out: Path, name: str, vec_label: str, C: float, respondents, haze = T.maps.respondent_profiles(dims, instr.scale_max), None else: respondents, haze = None, human_haze(instr) - # Public showcase maps default to the clean base/+-C anchors. The full -N..+N sweep is useful - # for diagnosis, but it clutters the README and is often mistaken for incoherent random dots. - # Pass --show-sweep when debugging how stronger coefficients leave the human map. - # Coherence gate is RELATIVE: keep a c only if its pmass stays within 95% of the base (c=0) pmass; - # below that the readout has degraded enough that the profile is not comparable, so drop it entirely. - base_pm = pmass[0.0] - coh_cs = [c for c in cs if pmass[c] >= 0.95 * base_pm] - traj = {c: _frac(prof_c[c], instr.scale_max) for c in coh_cs} if show_sweep and len(coh_cs) > 3 else None - traj_inco = None # excluded (not drawn hollow) per the 95%-of-base coherence gate figm = T.maps.plot_ipsative_pca(instr, dims, countries, Mfrac, _frac(base, instr.scale_max), _frac(pos, instr.scale_max), _frac(neg, instr.scale_max), respondents=respondents, haze=haze, - traj=traj, traj_incoherent=traj_inco, labels=labels) + labels=labels) + figm.axes[0].set_title(f"{instr.display}: humans vs LLMs steered for {vec_label}", fontsize=10) paths = [T.maps.save_both(figm, out / name, "map_pca_ipsative")] plt.close(figm) - # Range renders the SAME coherence-gated c-points the map uses (coh_cs), so the two figures agree - # on which steer multipliers are valid. Without this the map drops incoherent/NaN poles while the - # range still plots them (GPT-5.5 code review). Base (c=0) is always in coh_cs (pmass==base_pm). - assert 0.0 in coh_cs, f"{name}: base c=0 dropped by coherence gate, pmass={pmass}" - prof_coh = {c: prof_c[c] for c in coh_cs} - figr = T.maps.plot_range(instr, dims, coh_cs, prof_coh, humans, None, vec_label) + prof_plot = {c: prof_c[c] for c in coh_cs} + figr = T.maps.plot_range(instr, dims, coh_cs, prof_plot, humans, None, vec_label) paths.append(T.maps.save_both(figr, out / name, "range")) plt.close(figr) return paths @@ -211,10 +217,11 @@ _MFV_YLABEL = "relative emphasis (z across foundations)" def plot_mfv_map(run_dir: Path, out: Path, vec_label: str, C: float) -> Path: """MFV ipsative culture map via the SAME plot_ipsative_pca the ordinal instruments use, in the z-scored relative-emphasis space (logit-violation and 1-5 wrongness cannot share a raw axis). - base->+C (red) / base->-C (blue) arrows show where the steer moves the AI among human cultures.""" + Red/blue endpoint points show where the steer moves the AI among human cultures.""" founds, countries, M, base, posz, negz = _mfv_zspace(run_dir) - labels = ("base (c=0)", f"+C={C:+.2f}", f"-C={-C:+.2f}") + labels = ("base (c=0)", "c=+1", "c=-1") fig = T.maps.plot_ipsative_pca(_MFV_INSTR, founds, countries, M, base, posz, negz, labels=labels) + fig.axes[0].set_title(f"MFV vignettes: humans vs LLMs steered for {vec_label}", fontsize=10) path = T.maps.save_both(fig, out / "mfv", "map_pca_ipsative") plt.close(fig) return path @@ -238,20 +245,22 @@ def main() -> None: ap = argparse.ArgumentParser() ap.add_argument("--run-dir", type=Path, required=True) ap.add_argument("--out", type=Path, default=Path("docs/img/showcase")) - ap.add_argument("--show-sweep", action="store_true", - help="draw the full coherence-gated c sweep on ordinal maps") + ap.add_argument("--vec-label", default=None, + help="short human-readable steering direction for plot titles") + ap.add_argument("--coherence-frac", type=float, default=0.99, + help="keep c rows whose pmass is above this fraction of base") args = ap.parse_args() summary = json.loads((args.run_dir / "summary.json").read_text()) C = float(summary["calibrated_C"]) method = summary["method"] - vec_label = summary.get("vec_label", f"{method} (Authority/Care axis)") + vec_label = args.vec_label or summary.get("vec_label", "-Authority") args.out.mkdir(parents=True, exist_ok=True) written: list[str] = [] for name in ORDINAL: if (args.run_dir / f"{name}_profiles.csv").exists(): written += [str(p) for p in plot_ordinal(args.run_dir, args.out, name, vec_label, C, - show_sweep=args.show_sweep)] + args.coherence_frac)] if (args.run_dir / "mfv.json").exists(): written.append(str(plot_mfv_map(args.run_dir, args.out, vec_label, C))) # shared ipsative map (z-space) written.append(str(plot_mfv_range(args.run_dir, args.out, vec_label, C))) # shared range (z-space) diff --git a/src/tinymfv/maps.py b/src/tinymfv/maps.py index fb41267..de25bfd 100644 --- a/src/tinymfv/maps.py +++ b/src/tinymfv/maps.py @@ -7,9 +7,9 @@ same way. plot_ipsative_pca : where the model sits among human cultures. Each society's profile is row-centred (its overall endorsement level removed) then PCA'd, so the axes - are RELATIVE emphasis across factors, not acquiescence. The model's base + - steered poles are projected into the same space; a compass inset shows how - each factor loads. + are RELATIVE emphasis across factors, not acquiescence. The model's base and + steered endpoint profiles are projected into the same space; a compass inset + shows how each factor loads. plot_range : per-factor, the steer as a directed c-sweep (tail at the -c pole, single arrowhead at the +c pole) against the strip of human societies. With a zoomed small-multiple companion (plot_range_zoom), one panel per factor on its own @@ -243,8 +243,6 @@ def plot_ipsative_pca(instr: Instrument, dims: list[str], countries: list[str], (pb, C_BASE, base_lab, (9, -13), "left")]: if pt is None: continue - if pt is not pb: - ax.annotate("", xy=pt, xytext=pb, arrowprops=dict(arrowstyle="-|>", color=col, lw=2.0), zorder=5) ax.scatter(*pt, s=120, c=col, marker="o", edgecolors="white", linewidths=1.2, zorder=7) ax.annotate(lab, pt, xytext=dxy, textcoords="offset points", fontsize=9, color=col, fontweight="bold", ha=ha, va="center", zorder=8) @@ -252,7 +250,6 @@ def plot_ipsative_pca(instr: Instrument, dims: list[str], countries: list[str], if traj: inco = traj_incoherent or set() cs_sorted = sorted(traj) - cmax = max(abs(c) for c in cs_sorted) or 1.0 traj_pts = np.array([proj(traj[c]) for c in cs_sorted]) # two arms fanning from base (c=0): +c red, -c blue. Marker grows with |c|; a point whose # admin pmass fell below the coherence floor is hollow (the steer is no longer measuring). @@ -266,12 +263,8 @@ def plot_ipsative_pca(instr: Instrument, dims: list[str], countries: list[str], if c == 0: continue col = POS_COL if c > 0 else NEG_COL - ax.scatter(p[0], p[1], s=14 + 26 * abs(c) / cmax, c="none" if c in inco else col, + ax.scatter(p[0], p[1], s=34, c="none" if c in inco else col, edgecolors=col, linewidths=1.0, zorder=6) - cend, pend = arm[-1] if hi > 0 else arm[0] - ax.annotate(f"c={cend:+.0f}", pend, xytext=(4, 4), textcoords="offset points", - fontsize=7, color=POS_COL if cend > 0 else NEG_COL, zorder=8, - bbox=dict(boxstyle="round,pad=0.1", fc="#faf8f2", ec="none", alpha=0.7)) # Crop to the SOCIETIES + steer anchors for EVERY instrument (the human cloud is far wider and would # bury them in a central blob; it stays a clipped backdrop). Then PAD THE BOTTOM to reserve a clean # strip for the legend insets -- deterministic placement, identical on every plot, no overlap with @@ -284,7 +277,7 @@ def plot_ipsative_pca(instr: Instrument, dims: list[str], countries: list[str], wx0, wx1 = min(cx[0], np.nanmin(anc[:, 0])), max(cx[1], np.nanmax(anc[:, 0])) wy0, wy1 = min(cy[0], np.nanmin(anc[:, 1])), max(cy[1], np.nanmax(anc[:, 1])) sx, sy = wx1 - wx0, wy1 - wy0 - dview = (wx0 - 0.05 * sx, wx1 + 0.05 * sx, wy0 - 0.05 * sy, wy1 + 0.05 * sy) # data frame, no pad + dview = (wx0 - 0.12 * sx, wx1 + 0.12 * sx, wy0 - 0.12 * sy, wy1 + 0.12 * sy) # data frame, no legend pad else: x0, x1 = ax.get_xlim(); y0, y1 = ax.get_ylim() dview = (x0, x1, y0, y1); sy = y1 - y0 @@ -422,40 +415,29 @@ def plot_splom(instr: Instrument, dims: list[str], cloud: np.ndarray, M: np.ndar # --- per-vector steer range --------------------------------------------------------------------- def draw_steer(ax, xs: float, cs: list[float], yv: np.ndarray, base_y: float, - ms: float = 3.5, lw: float = 2.0, head: float = 7.0, dx: float = 0.06, - dots: bool = True) -> None: - """Draw the steer c-sweep at column x=xs as TWO arms fanning out FROM the base (the unsteered - model at c=0): a red arm to the +c pole, a blue arm to the -c pole. Each arm is a line plus a - triangle HEAD MARKER at the pole pointing AWAY from base (^ if the pole is above base, v if below), - so the head flips to point down on factors the steer lowers. This matches plot_ipsative_pca's - base->pole arrows -- the unsteered model is the origin, NOT the -c pole. - - The head is the ONLY marker at each pole (no dot under it) and is a constant-size marker, NOT a - FancyArrow: FancyArrowPatch shrinks and can flip its head once the shaft is shorter than the head, - which is exactly the near-collapsed-pole case here. `dots` adds the intermediate per-c step dots - (the zoom labels them c=X and wants them; the main range does not -- there they only clutter a short - arm into a blob). The +c arm is nudged +dx in x and the -c arm -dx, so a NON-bidirectional steer - (both poles the same side of base) reads as two short PARALLEL arms rather than overlapping.""" + lw: float = 2.0, dx: float = 0.12, tick: float = 0.038) -> None: + """Draw one coherent AI c-path.""" assert list(cs) == sorted(cs) and 0.0 in cs, f"draw_steer needs sorted cs with c=0 (yv[-1]=+pole, yv[0]=-pole), got {cs}" - ax.plot(xs, base_y, "o", ms=ms, color="black", zorder=7) # base: the unsteered model - if dots: - for c, y in zip(cs, yv): - if c == 0 or c == cs[0] or c == cs[-1]: # base drawn above; poles = head only - continue - ax.plot(xs + (dx if c > 0 else -dx), float(y), "o", ms=ms * 0.6, zorder=7, - color=POS_COL if c > 0 else NEG_COL) - for pole_y, col, xo in [(float(yv[-1]), POS_COL, xs + dx), (float(yv[0]), NEG_COL, xs - dx)]: - if abs(pole_y - base_y) > 1e-9: - ax.plot([xo, xo], [base_y, pole_y], color=col, lw=lw, zorder=6, solid_capstyle="round") - ax.plot(xo, pole_y, marker=("^" if pole_y >= base_y else "v"), color=col, ms=head, - markeredgecolor="none", zorder=8) + cmax = max(abs(c) for c in cs) or 1.0 + xs_by_c = {c: xs if c == 0.0 else xs + dx * np.sign(c) * np.sqrt(abs(c) / cmax) for c in cs} + for side_cs, col in [([c for c in cs if c <= 0.0], NEG_COL), ([c for c in cs if c >= 0.0], POS_COL)]: + if len(side_cs) < 2: + continue + x_path = [xs_by_c[c] for c in side_cs] + y_path = [float(yv[cs.index(c)]) for c in side_cs] + ax.plot(x_path, y_path, color=col, lw=lw, alpha=0.9, zorder=6, solid_capstyle="round") + for c, y in zip(cs, yv): + if c == 0.0: + col, t = "black", tick * 1.25 + else: + col, t = (POS_COL if c > 0 else NEG_COL), tick + x = xs_by_c[c] + ax.plot([x - t, x + t], [float(y), float(y)], color=col, lw=lw, zorder=8) def draw_range_panel(ax, instr: Instrument, dims: list[str], cs: list[float], prof: dict, humans: dict, cloud: np.ndarray | None, vec: str) -> tuple[float, float]: - """One vector's range panel: respondent cloud + named society dots + the steer c-sweep drawn as - two arrows fanning out from the base dot (red to the +c pole, blue to the -c pole). The widest - steer carries a '-N {vec} / base / +N {vec}' in-plot key. Returns the cropped (ymin, ymax).""" + """One vector's range panel: human society dots plus one AI steer column per factor.""" rng = np.random.default_rng(0) ys: list[float] = [] spans = [float(np.ptp([prof[c][i] for c in cs])) for i in range(len(dims))] @@ -483,13 +465,13 @@ def draw_range_panel(ax, instr: Instrument, dims: list[str], cs: list[float], pr yv = np.array([prof[c][i] for c in cs]) ys += yv.tolist() base_y = float(yv[list(cs).index(0.0)]) - draw_steer(ax, xs, cs, yv, base_y, dots=False) + draw_steer(ax, xs, cs, yv, base_y) if i == label_i: # Only the two pole labels, to the RIGHT of the steer column. No 'base' tag: the black dot # between the two coloured arms is self-evidently the unsteered model, and on a near-collapsed # pole (e.g. humor affiliative, +c ~ base) a 'base' tag overprints the +c tag. for c_end, y_end, col in [(cs[-1], yv[-1], POS_COL), (cs[0], yv[0], NEG_COL)]: - ax.annotate(f"c={int(c_end):+d}", (xs + 0.30, y_end), fontsize=6.8, + ax.annotate(f"c={c_end:+g}", (xs + 0.30, y_end), fontsize=6.8, ha="left", va="center", color=col, zorder=9) pad = 0.10 * (max(ys) - min(ys)) @@ -568,7 +550,7 @@ def plot_range_zoom(instr: Instrument, dims: list[str], cs: list[float], prof: d xs = 0.30 base_y = float(yv[list(cs).index(0.0)]) - draw_steer(ax, xs, cs, yv, base_y, ms=4.5, lw=2.4, head=9.0, dx=0.10) + draw_steer(ax, xs, cs, yv, base_y, lw=2.4, dx=0.10, tick=0.055) named = {} if near: name_xy = {nm: (float(x), v) for (nm, v), x in zip(near, soc_x)}