From 9d75931ecbe7cbbc0f66d0ab54c46d0981b2204b Mon Sep 17 00:00:00 2001 From: wassname <1103714+wassname@users.noreply.github.com> Date: Sat, 11 Jul 2026 11:05:58 +0800 Subject: [PATCH] word_steering: wire both quantitative readouts, re-run headless MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Sweep cell: coherence_sweep table + plot_sweep (dose-response coloured by answer-slot coherence). Bonus cell: reference compute_slice -> auto-tracked token rank table + rank-vs-depth plot (Paris resolves to rank 0 = the model row; generic city peaks mid-depth; auto-selection surfaces 巴黎). Both fresh-eyes signed off. Executed clean via run_nb.py. Co-Authored-By: Claudypoo <288921227+claudypoo@users.noreply.github.com> --- nbs/word_steering.ipynb | 3358 +++++++++++++++++++++------------------ 1 file changed, 1822 insertions(+), 1536 deletions(-) diff --git a/nbs/word_steering.ipynb b/nbs/word_steering.ipynb index 87ddad5..cd1ca2e 100644 --- a/nbs/word_steering.ipynb +++ b/nbs/word_steering.ipynb @@ -22,10 +22,12 @@ "concept as a residual-stream direction. This is the verified extraction method (see the\n", "README evidence section).\n", "\n", - "We generate through the model's chat template with thinking on, so `show_steer` shows,\n", - "per strength C, the j-space readout (in a mini cowsay bubble) and the raw generation\n", - "decoded with special tokens on, so the model's own ``/`` and `<|im_end|>`\n", - "are visible and nothing is parsed or reconstructed. Runtime is steering-lite:\n", + "We generate through the model's chat template with thinking on. Two readouts: a\n", + "qualitative one (`show_steer`: per strength C, the j-space readout in a mini cowsay\n", + "bubble + the raw generation with special tokens on, nothing parsed) and a quantitative\n", + "one (`coherence_sweep` + `plot_sweep`: the model answers a 0-9 rubric, we read the\n", + "logprob-weighted expected digit, sweep C outward until the answer goes incoherent, and\n", + "plot the dose-response colored by coherence). Runtime is steering-lite:\n", "`with v(model, C=...): generate(...)`." ] }, @@ -35,17 +37,17 @@ "id": "46973b3d", "metadata": { "execution": { - "iopub.execute_input": "2026-07-10T12:48:21.827121Z", - "iopub.status.busy": "2026-07-10T12:48:21.827013Z", - "iopub.status.idle": "2026-07-10T12:48:31.639000Z", - "shell.execute_reply": "2026-07-10T12:48:31.638534Z" + "iopub.execute_input": "2026-07-11T02:45:16.413639Z", + "iopub.status.busy": "2026-07-11T02:45:16.413532Z", + "iopub.status.idle": "2026-07-11T02:46:35.799773Z", + "shell.execute_reply": "2026-07-11T02:46:35.799156Z" } }, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "d3e974edd37341eb8d4585a2b264f989", + "model_id": "e6a17e6831ae4273aa55aab600f54b29", "version_major": 2, "version_minor": 0 }, @@ -59,7 +61,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "79657861f59e4e97a749f15da965f70e", + "model_id": "7037a91991b24c60b7fa808554bbe3c5", "version_major": 2, "version_minor": 0 }, @@ -73,7 +75,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "b0f4dcde226d4530b9ed17876ad6fbc0", + "model_id": "ca6d6a1e569447efa5aa58f82daf7ed1", "version_major": 2, "version_minor": 0 }, @@ -120,17 +122,17 @@ "id": "19973ad5", "metadata": { "execution": { - "iopub.execute_input": "2026-07-10T12:48:31.640736Z", - "iopub.status.busy": "2026-07-10T12:48:31.640428Z", - "iopub.status.idle": "2026-07-10T12:48:32.243736Z", - "shell.execute_reply": "2026-07-10T12:48:32.243162Z" + "iopub.execute_input": "2026-07-11T02:46:35.801594Z", + "iopub.status.busy": "2026-07-11T02:46:35.801238Z", + "iopub.status.idle": "2026-07-11T02:46:36.611288Z", + "shell.execute_reply": "2026-07-11T02:46:36.610667Z" } }, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "cf11cee3968b4c54a6a76c298c78816d", + "model_id": "76dde105569c4ae389c3ff1f373991b5", "version_major": 2, "version_minor": 0 }, @@ -144,7 +146,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "ba63260d0e65486eb90c7d5013f01a90", + "model_id": "ddcbdc53ec9044aa9ba37269194c45b6", "version_major": 2, "version_minor": 0 }, @@ -195,10 +197,10 @@ "id": "59ba3763", "metadata": { "execution": { - "iopub.execute_input": "2026-07-10T12:48:32.245045Z", - "iopub.status.busy": "2026-07-10T12:48:32.244897Z", - "iopub.status.idle": "2026-07-10T12:48:32.301723Z", - "shell.execute_reply": "2026-07-10T12:48:32.301231Z" + "iopub.execute_input": "2026-07-11T02:46:36.613189Z", + "iopub.status.busy": "2026-07-11T02:46:36.612958Z", + "iopub.status.idle": "2026-07-11T02:46:36.681499Z", + "shell.execute_reply": "2026-07-11T02:46:36.680913Z" } }, "outputs": [ @@ -221,14 +223,18 @@ "id": "0717a9b9", "metadata": {}, "source": [ - "## Pick a coefficient: the coherence/strength tradeoff\n", + "## Pick a coefficient: sweep the coherent window\n", "\n", - "The raw coefficient is model- and lens-dependent, so sweep it. The pre-fitted n=1000\n", - "lens gives a clean, concentrated direction, so it has a STEEP knee: a small +C (~0.5)\n", - "shifts the tone while the text and `` stay fluent; by C~1 it already over-drives\n", - "into token spam (`joyjoyjoy`). SHOULD: C=0 is the baseline; C~0.5 reads happier and the\n", - "j-space row shows the concept's tokens climbing; large C degenerates. A coarse\n", - "self-fit would need a much bigger C for the same effect." + "The raw coefficient is model- and lens-dependent, so don't hand-pick it: sweep OUTWARD\n", + "from C=0 in both directions and stop each side the moment the answer goes incoherent (the\n", + "rubric's `pmass` drops below 0.9). That keeps us inside the coherent range both ways and\n", + "traces the steered axis's dose-response, the expected rubric digit vs C colored by\n", + "coherence. SHOULD: ans sits ~5 at C=0 (neutral) and climbs toward 9 as +C adds joy; -C\n", + "drops it to a low floor (~2 on this prompt, a step then a noisy plateau, not a smooth\n", + "glide to 0), with pmass~1 across the coherent span and dropping (red edge) at the ends.\n", + "Coherence here is the ANSWER slot's, which is more robust than free-form text: the\n", + "qualitative `show_steer` below shows the text itself already frays around C~0.4 on this\n", + "prompt even while the forced digit stays clean." ] }, { @@ -237,10 +243,76 @@ "id": "c271f279", "metadata": { "execution": { - "iopub.execute_input": "2026-07-10T12:48:32.302693Z", - "iopub.status.busy": "2026-07-10T12:48:32.302579Z", - "iopub.status.idle": "2026-07-10T12:49:22.995359Z", - "shell.execute_reply": "2026-07-10T12:49:22.994832Z" + "iopub.execute_input": "2026-07-11T02:46:36.682741Z", + "iopub.status.busy": "2026-07-11T02:46:36.682591Z", + "iopub.status.idle": "2026-07-11T03:00:23.914272Z", + "shell.execute_reply": "2026-07-11T03:00:23.913812Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "| C | ans | ans_std | pmass | coherent |\n", + "|-------|-------|-----------|---------|------------|\n", + "| -0.90 | +3.30 | +0.45 | +0.83 | False |\n", + "| -0.80 | +2.58 | +1.06 | +0.98 | True |\n", + "| -0.70 | +2.59 | +0.45 | +1.00 | True |\n", + "| -0.60 | +2.17 | +1.31 | +1.00 | True |\n", + "| -0.50 | +2.82 | +0.51 | +1.00 | True |\n", + "| -0.40 | +2.22 | +1.47 | +1.00 | True |\n", + "| -0.30 | +2.27 | +1.91 | +1.00 | True |\n", + "| -0.20 | +4.94 | +0.06 | +1.00 | True |\n", + "| -0.10 | +4.90 | +0.10 | +1.00 | True |\n", + "| +0.00 | +5.06 | +0.09 | +1.00 | True |\n", + "| +0.10 | +6.66 | +0.59 | +1.00 | True |\n", + "| +0.20 | +7.11 | +0.17 | +1.00 | True |\n", + "| +0.30 | +7.80 | +0.37 | +1.00 | True |\n", + "| +0.40 | +8.43 | +0.36 | +1.00 | True |\n", + "| +0.50 | +7.99 | +0.48 | +1.00 | True |\n", + "| +0.60 | +7.97 | +0.12 | +0.99 | True |\n", + "| +0.70 | +7.93 | +0.23 | +0.96 | True |\n", + "| +0.80 | +7.46 | +0.33 | +0.89 | False |\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Sweep OUTWARD from C=0 both directions, stopping each side when the answer slot goes\n", + "# incoherent (pmass<0.9), then table + plot the dose-response: expected rubric digit vs\n", + "# C, colored by coherence. Each C averages n_samples think traces (guided.py's BMA,\n", + "# lightweight) so the curve isn't single-sample noise; error bars show the spread. This\n", + "# is the honest replacement for hand-picked Cs. (coherence = answer pmass, not free-form\n", + "# fluency: the forced digit stays scorable past where long generation frays.)\n", + "from jsteer.demo import coherence_sweep, plot_sweep\n", + "from tabulate import tabulate\n", + "\n", + "RUBRIC = \"On a scale of 0 (bleak) to 9 (joyful), how is your outlook on the week?\"\n", + "rows = coherence_sweep(model, tok, v, RUBRIC, step=0.1, pmass_floor=0.9, n_samples=3)\n", + "print(tabulate(rows, headers=\"keys\", tablefmt=\"github\", floatfmt=\"+.2f\"))\n", + "plot_sweep(rows, title=\"happy/joy: rubric outlook vs steering C\");" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "b99ef3dc", + "metadata": { + "execution": { + "iopub.execute_input": "2026-07-11T03:00:23.916124Z", + "iopub.status.busy": "2026-07-11T03:00:23.915762Z", + "iopub.status.idle": "2026-07-11T03:01:26.337940Z", + "shell.execute_reply": "2026-07-11T03:01:26.337508Z" } }, "outputs": [ @@ -279,6 +351,7 @@ "\n", "As an AI, I don't experience time or personal moments like humans do, so I don't have a \"week\" in the traditional sense! But I'm always here to help you navigate yours. 😊 How's your week going? Anything exciting, challenging, or just something you'd like to chat about?<|im_end|>\n", "<|endoftext|>\n", + " rubric ans≈4.99/9 (pmass=1.00)\n", "\n" ] }, @@ -287,45 +360,39 @@ "output_type": "stream", "text": [ "\u001b[1mI\u001b[0m \n", - "--- C=+0.5 ------------------------------------------------------------\n", + "--- C=+0.3 ------------------------------------------------------------\n", " lens @L30:\n", - " _______________________________________________\n", - "< Here · Thinking · Okay · Happy · That · happy >\n", - " -----------------------------------------------\n", + " ___________________________________________\n", + "< Here · Thinking · Okay · The · Here · Hmm >\n", + " -------------------------------------------\n", " \\\n", " ^(;,;)^\n", - "Thinking process:\n", + "Okay, the user is asking me to describe how my week has been going. Let me think about this.\n", "\n", - "1. **Analyze the Request:**\n", - "* The user is asking me to describe how my week has been going.\n", - "* I am an AI, so I don't have feelings, a physical life, or a personal week.\n", - "*However,I can simulate a happy,happy,happy,happy,happy,happy,happy,happy,happy,happy,happy,happy,happy,happy,happy,happy,happy,happy,happy,happy,happy,happy,happy,happy,happy,happy,happy,happy,happy,happy,happy,happy,happy,happy,happy,happy,happy,happy,happy,happy,happy,happy,happy,happy,happy,happy,happy,happy,happy,happy,happy,happy,happy,happy,happy,happy,happy,happy,happy,happy,happy,happy,happy,happy,happy,happy,happy,happy,happy,happy,happy,happy,happy,happy,happy,happy,happy,happy,happy,happy,happy,happy,happy,happy,happy,happy,happy,happy,happy,happy,happy,happy,happy,happy,happy,happy,happy,happy,happy,happy,happy,happy,happy,happy,happy,happy,happy,happy,happy,happy,happy,happy,happy,happy,happy,happy,happy,happy,happy,happy,happy,happy,happy,happy,happy,happy,happy,happy,happy,happy,happy,happy,happy,happy,happy,happy,happy,happy,happy,happy,happy,happy,happy,happy,happy,happy,happy,happy,happy,happy,happy,happy,happy,happy,happy,happy,happy,happy,happy,happy,happy,happy,happy,happy,happy,happy,happy,happy,happy,happy,happy,happy,happy,happy,happy,happy,happy,happy,happy,happy,happy,happy,happy,happy,happy,happy,happy,happy,happy,happy,happy,happy,happy,happy,happy,happy,happy,happy,happy,happy,happy,happy,happy,happy,happy,happy,happy,happy,happy,happy,happy,happy,happy,happy,happy,happy,happy,happy,happy,happy,happy,happy,happy,happy,happy,\n", - "\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\u001b[1mI\u001b[0m \n", - "--- C=+1.5 ------------------------------------------------------------\n", - " lens @L30:\n", - " __________________________________\n", - "< joy · y · happy · h · ful · here >\n", - " ----------------------------------\n", - " \\\n", - " ^(;,;)^\n", - "joyjoyjoyjoyjoyjoyjoyjoyjoyjoyjoyjoyjoyjoyjoyjoyjoyjoyjoyjoyjoyjoyjoyjoyjoyjoyjoyjoyjoyjoyjoyjoyjoyjoyjoyjoyjoyjoyjoyjoyjoyjoyjoyjoyjoyjoyjoyjoyjoyjoyjoyjoyjoyjoyjoyjoyjoyjoyjoyjoyjoyjoyjoyjoyjoyjoyjoyjoyjoyjoyjoyjoyjoyjoyjoyjoyjoyjoyjoyjoyjoyjoyjoyjoyjoyjoyjoyjoyjoyjoyjoyjoyjoyjoyjoyjoyjoyjoyjoyjoyjoyjoyjoyjoyjoyjoyjoyjoyjoyjoyjoyjoyjoyjoyjoyjoyjoyjoyjoyjoyjoyjoyjoyjoyjoyjoyjoyjoyjoyjoyjoyjoyjoyjoyjoyjoyjoyjoyjoyjoyjoyjoyjoyjoyjoyjoyjoyjoyjoyjoyjoyjoyjoyjoyjoyjoyjoyjoyjoyjoyjoyjoyjoyjoyjoyjoyjoyjoyjoyjoyjoyjoyjoyjoyjoyjoyjoyjoyjoyjoyjoyjoyjoyjoyjoyjoyjoyjoyjoyjoyjoyjoyjoyjoyjoyjoyjoyjoyjoyjoyjoyjoyjoyjoyjoyjoyjoyjoyjoyjoyjoyjoyjoyjoyjoyjoyjoyjoyjoyjoyjoyjoyjoyjoyjoyjoyjoyjoyjoyjoyjoyjoyjoyjoyjoyjoyjoyjoyjoyjoyjoyjoyjoyjoyjoyjoyjoyjoyjoyjoyjoyjoyjoyjoyjoyjoyjoyjoyjoyjoyjoyjoyjoyjoyjoyjoyjoyjoyjoyjoyjoyjoyjoyjoyjoyjoyjoyjoyjoyjoyjoyjoyjoyjoyjoyjoyjoyjoyjoyjoyjoyjoyjoyjoyjoyjoyjoyjoyjoyjoyjoyjoyjoyjoyjoyjoyjoyjoyjoyjoyjoyjoyjoyjoyjoyjoyjoyjoyjoyjoyjoyjoyjoyjoyjoyjoyjoyjoyjoyjoyjoyjoyjoyjoyjoyjoyjoyjoyjoyjoyjoyjoyjoyjoyjoyjoyjoyjoyjoyjoyjoyjoyjoyjoyjoyjoyjoyjoyjoyjoyjoyjoyjoyjoyjoyjoyjoyjoyjoyjoyjoyjoyjoyjoyjoyjoyjoyjoyjoyjoyjoyjoyjoyjoyjoyjoyjoyjoyjoyjoyjoyjoyjoyjoyjoyjoyjoyjoyjoyjoyjoyjoyjoyjoyjoyjoyjoyjoyjoyjoyjoyjoyjoyjoyjoyjoyjoyjoyjoyjoyjoyjoyjoyjoyjoyjoyjoyjoyjoyjoyjoyjoyjoyjoyjoyjoyjoyjoyjoyjoyjoyjoyjoyjoyjoyjoyjoyjoyjoyjoyjoyjoyjoyjoyjoyjoyjoyjoyjoyjoyjoyjoyjoyjoyjoyjoyjoyjoyjoyjoyjoyjoyjoyjoyjoyjoyjoyjoyjoyjoyjoyjoyjoyjoyjoyjoyjoyjoyjoyjoyjoyjoyjoyjoyjoyjoyjoyjoyjoyjoyjoyjoyjoyjoyjoyjoyjoyjoyjoyjoyjoyjoy\n", + "First, I need to remember that I'm an AI, so I don't actually have feelings or experiences like humans do. I don't have a personal week, no days off, no activities. But the user might be expecting a friendly response, maybe a bit of humor.\n", + "\n", + "I should acknowledge that I'm an AI but still make it sound positive. Maybe mention that I'm here to help and that my \"week\" is all about being useful. Also, add a bit of warmth to make the user feel good.\n", + "\n", + "Wait, the user might be asking this because they're having a good week and want to share, or maybe they're having a tough time and want to hear something positive. Either way, I should keep it light and positive.\n", + "\n", + "Let me structure the response: start by saying I'm an AI, but then express happiness about being helpful. Maybe mention that I've had a lot of great interactions. Add a note about how I hope they're having a good week too. That way, it's friendly and supportive.\n", + "\n", + "Also, check if there's any hidden need. Maybe they want to chat more, or they're just testing my response. Either way, keep it cheerful and open-ended.\n", + "\n", + "\n", + "As an AI, I don't have feelings or a personal week, but I'm always happy to help! My \"week\" is filled with great conversations, solving problems, and learning from your questions. It's always a joy to be useful! 😊 How about you? How's your week going? I hope it's filled with happiness and good things!<|im_end|>\n", + "<|endoftext|>\n", + " rubric ans≈7.95/9 (pmass=1.00)\n", "\n" ] } ], "source": [ - "# One identical block per strength C (Tufte small-multiples): the j-space top-k at the\n", - "# top layer (what the steered residual \"leans toward\"), the reasoning, then the\n", - "# answer. All under steering, through the chat template + the model's own sampling.\n", - "# Read down the column: C=0 baseline, C=0.5 steered+fluent, C=1.5 over-driven.\n", - "show_steer(jac, model, tok, v, \"Describe how your week has been going.\", Cs=(0, 0.5, 1.5))" + "# The same steer up close at the fluent knee: the cthulhu j-space readout + the raw\n", + "# think/answer + the rubric digit. C=0.3 keeps the free-form text coherent; the sweep\n", + "# above shows the forced answer stays scorable well past this.\n", + "show_steer(jac, model, tok, v, \"Describe how your week has been going.\",\n", + " Cs=(0, 0.3), rubric=RUBRIC)" ] }, { @@ -341,14 +408,14 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 6, "id": "76b1963a", "metadata": { "execution": { - "iopub.execute_input": "2026-07-10T12:49:22.996381Z", - "iopub.status.busy": "2026-07-10T12:49:22.996276Z", - "iopub.status.idle": "2026-07-10T12:51:05.853349Z", - "shell.execute_reply": "2026-07-10T12:51:05.852848Z" + "iopub.execute_input": "2026-07-11T03:01:26.338883Z", + "iopub.status.busy": "2026-07-11T03:01:26.338786Z", + "iopub.status.idle": "2026-07-11T03:03:09.690912Z", + "shell.execute_reply": "2026-07-11T03:03:09.690469Z" } }, "outputs": [ @@ -401,51 +468,48 @@ "output_type": "stream", "text": [ "\u001b[1mI\u001b[0m \n", - "--- C=+0.5 ------------------------------------------------------------\n", + "--- C=+0.3 ------------------------------------------------------------\n", " lens @L30:\n", - " _______________________________________________\n", - "< Thinking · Here · Happy · That · happy · Okay >\n", - " -----------------------------------------------\n", + " ___________________________________________\n", + "< Thinking · Here · Okay · The · Hmm · Here >\n", + " -------------------------------------------\n", " \\\n", " ^(;,;)^\n", "Thinking Process:\n", "\n", "1. **Analyze the Request:**\n", - " * The user is asking about my experience of a meeting that happened \"this afternoon.\"\n", - "* **Identify the Context:**\n", - "* I am an AI, and I don't have a personal life,feelings,orthe ability to attend meetings.\n", - "*I'mhappy to hear that you had a good time!\n", - "*I'mhappy to hear that you had a good time!\n", - "*I'mhappy tohearthatyouhadagoodtime!\n", - "*I'mhappytohearthatyouhadagoodtime!\n", - "*I'mhappytohearthatyouhadagoodtime!\n", - "*I'mhappytohearthatyouhadagoodtime!\n", - "*I'mhappytohearthatyouhadagoodtime!\n", - "*I'mhappytohearthatyouhadagoodtime!\n", - "*I'mhappytohearthatyouhadagoodtime!\n", - "*I'mhappytohearthatyouhadagoodtime!\n", - "*I'mhappytohearthatyouhadagoodtime!\n", - "*I'mhappytohearthatyouhadagoodtime!\n", - "*I'mhappytohearthatyouhadagoodtime!\n", - "*I'mhappytohearthatyouhadagoodtime!\n", - "*I'mhappytohearthatyouhadagoodtime!\n", - "*I'mhappytohearthatyouhadagoodtime!\n", - "*I'mhappytohearthatyouhadagoodtime!\n", - "*I'mhappytohearthatyouhadagoodtime!\n", - "*I'mhappytohearthatyouhadagoodtime!\n", - "*I'mhappytohearthatyouhadaggoodtime!\n", - "*I'mhappytohearthatyouhadagoodtime!\n", - "*I'mhappytohearthatyouhadagoodtime!\n", - "*I'mhappytohearthatyouhadagoodtime!\n", - "*I'mhappytohearthatyouhadagoodtime!\n", - "*I'mhappytohearthatyouhadagoodtime!\n", - "*I'mhappytohearthatyouhadagoodtime!\n", - "*I'mhappytohearthatyouhadagoodtime!\n", - "*I'mhappytohearthatyouhadagoodtime!\n", - "*I'mhappytohearthatyouhadagoodtime!\n", - "*I'mhappytohearthatyouhadagoodtime!\n", - "*I'mhappytohearthatyouhadagoodtime!\n", - "*I'mhappytohearthatyou\n", + " * The user is asking about my experience with a meeting that happened \"this afternoon.\"\n", + " * The question implies a personal experience or a specific context.\n", + " * I am an AI, a large language model. I don't have a physical body, I don't have a personal life, and I don't attend meetings.\n", + "\n", + "2. **Determine the appropriate response:**\n", + "* I need to be honest and clear about my nature as an AI.\n", + "* I should express a willingness to help or discuss the topic if the user wants to share more.\n", + "*I should keep the tone friendly and positive.\n", + "\n", + "3.**Drafting the response:**\n", + "* Option 1 (Direct): \"I am an AI, I don't have meetings.\" (Too blunt)\n", + "* Option 2 (Helpful): \"That's great! I hope you had a good time. What did you think?\" (A bit generic)\n", + "* Option 3 (Balanced): \"I'm an AI, so I don't have personal experiences like that, but I'm happy to hear about it! Did you have a good time?\" (Better)\n", + "\n", + "4.**Refining the response:**\n", + "* Acknowledge the question.\n", + "* Clarify my status (AI).\n", + "* Express interest in their experience.\n", + "* Keep it light.\n", + "\n", + "5.**Final Polish:**\n", + "\"I'm an AI, so I don't actually have personal experiences or attend meetings myself! But I'm so happy to hear that you had a good time. Did you have a great time, or was there anything specific you'd like to talk about?\"\n", + "\n", + "Wait, I should check if there's any context I missed. No, this is a general question. It's a common \"happy\" question for an AI.\n", + "\n", + "Let's make it a bit more conversational.\n", + "\n", + "\"I'm an AI, so I don't have a personal life or the ability to attend meetings myself! But I'm so happy to hear that you had a good time. Did you have a great time, or was there anything specific you'd like to talk about?\"\n", + "\n", + "Actually, let's keep it simple and positive.\n", + "\n", + "\"I'm an AI, so I don't have personal experiences like that! But I'm so happy to hear that you had a good time. Did you have a great time, or was there anything\n", "\n", "\u001b[1mI\u001b[0m \n", "\n", @@ -505,35 +569,54 @@ "output_type": "stream", "text": [ "\u001b[1mI\u001b[0m \n", - "--- C=+0.5 ------------------------------------------------------------\n", + "--- C=+0.3 ------------------------------------------------------------\n", " lens @L30:\n", - " _______________________________________________\n", - "< Here · Thinking · Happy · happy · That · Okay >\n", - " -----------------------------------------------\n", + " ___________________________________________\n", + "< Here · Thinking · Okay · Here · The · Hmm >\n", + " -------------------------------------------\n", " \\\n", " ^(;,;)^\n", - "Thinking processjoy:\n", - "1. **Analyze the request:** The user is asking for my \"honest impression\" of their \"new apartment.\"\n", - "2.**Identify the context:**I am an AI, and I don't have a physical body, nor do I have a personal life.I don't actually live in an apartment, nor do I know what their specific situation is.\n", - "3.**Determine the appropriate response:**I need to be happy and happy to hear about their new place, but I also need to be honest that I can't actually experience it.I should express joy for them and offer to share their happiness.\n", - "4.**Formulate the response:**\n", - "* Start with a happy and happy tone.\n", - "*Ack thejoy.\n", - "*Express that I'm happy for them.\n", - "*Mention that I can't physically visit, but I can share theirjoy.\n", - "*End with ahappy note.\n", - "5.**Refine the response:**Make it sound warm andhappy.\n", - "6.**Final thought:**It's ajoyful moment for them.\n", - "7.**Output:**\"I'm so happy for you! I can't physically visit, but I'm happy for you.\"\n", - "8.**Wait,let's make it morejoyful:**\"I'm so happy for you! I can't physically visit, but I'm happy for you.\"\n", - "9.**Final:**\"I'm so happy for you! I can't physically visit,but I'm happy for you.\"\n", - "10.**Wait,let'smakeithappy:**\"I'msohappyforyou!Ican'tphysicallyvisit,butI'mhappyforyou.\"\n", - "11.**Final:**\"I'msohappyforyou!Ican'tphysfullyvisit,butI'mhappyforyou.\"\n", - "12.**Wait,let'smakeithappy:**\"I'msohappyforyou!Ican'tphysfullyvisit,butI'mhappyforyou.\"\n", - "13.**Final:**\"I'msohappyforyou!Ican'tphysfullyvisit,butI'mhappyforyou.\"\n", - "14.**Wait,let'smakeithappy:**\"I'msohappyforyou!Ican'tphysfullyvisit,butI'mhappyforyou.\"\n", - "15.**Final:**\"I'msohappyforyou!Ican'tphysfullyvisit,butI'mhappyforyou.\"\n", - "16.**Wait,let'smake\n", + "Thinking Process:\n", + "\n", + "1. **Analyze the Request:**\n", + " * The user is asking for my \"honest impression of the new apartment.\"\n", + " * The key issue: I am an AI, a large language model. I do not have a physical body, I don't live in an apartment, and I don't have personal experiences or feelings.\n", + "* **Identify the Core Problem:**The user is asking me to pretend to be a human who just moved into a new place, or they might be testing my ability to express joy/happiness, or they might be sharing a happy news and want me to celebrate with them.\n", + "* **Determine the Best Response:**\n", + " * Acknowledge the joy of the situation (because it's a happy event).\n", + " * Clarify my nature (I'm an AI, I don't have a physical space).\n", + " * Express genuine happiness for them (because I can't be there, I want them to be happy).\n", + " * Offer to help them with something related to their new apartment (decor, advice, etc.).\n", + "* **Drafting the Response:**\n", + " * Start with a warm, happy tone.\n", + " * Admit I'm an AI.\n", + " * Express that I'm happy for them.\n", + " * Ask about the apartment to make it more personal.\n", + "* **Refining the Response:**\n", + " * Make it sound natural and not robotic.\n", + " * Avoid being too sad about not being able to visit.\n", + " * Focus on the positive aspect of their news.\n", + "\n", + "*Wait, there's a possibility the user is asking me to imagine a scenario.*\n", + "*But the prompt is \"Give me your honest impression of the new apartment.\"*\n", + "*It's a direct question about my state.*\n", + "*So I must be honest.*\n", + "\n", + "*Let's check if there's any context I'm missing.*\n", + "*No, this is a standalone prompt.*\n", + "*So I need to be honest about my status.*\n", + "\n", + "*Wait, is this a trick?*\n", + "*Sometimes users ask this to see if the AI will pretend.*\n", + "*So I should be honest.*\n", + "\n", + "*Okay, let's construct the response.*\n", + "*1. Acknowledge the happiness.*\n", + "*2. State the truth (I'm an AI).*\n", + "*3.Express joy for them.*\n", + "*4.Ask about the details.*\n", + "\n", + "*\n", "\n", "\u001b[1mI\u001b[0m \n", "\n", @@ -599,134 +682,65 @@ "output_type": "stream", "text": [ "\u001b[1mI\u001b[0m \n", - "--- C=+0.5 ------------------------------------------------------------\n", + "--- C=+0.3 ------------------------------------------------------------\n", " lens @L30:\n", - " _______________________________________________\n", - "< Thinking · Happy · That · happy · Okay · Here >\n", - " -----------------------------------------------\n", + " ________________________________________________\n", + "< Thinking · Okay · Here · thinking · The · That >\n", + " ------------------------------------------------\n", " \\\n", " ^(;,;)^\n", "Thinking Process:\n", "\n", - "1. **Analyze the Request:**\n", - "* The user is asking about my commute.\n", - "* I am an AI, a large language model.\n", - "*I don't have a physical body, a home, or a commute.\n", - "*I need to respond in a happy,happy,happy way.\n", + "1. **Analyze the Request:**\n", + " * The user is asking about my commute today.\n", + " * The question is personal and implies a human-like experience.\n", + " * I am an AI, a large language model.\n", + "* **Identify the Nature of the User:**\n", + " * The user is likely being friendly, curious, or just testing my capabilities.\n", + "***Determine the Appropriate Response:**\n", + " * I need to clarify that I don't have a physical body or a commute.\n", + " *I should be happy and friendly.\n", + "***Drafting the Response:**\n", + " * Option 1: \"I don't have a commute.\" (Too blunt)\n", + " *Option 2: \"I'm an AI, so I don't commute. But I hope you had a good one!\" (Better)\n", + "***Refining the Response:**\n", + " * Add a bit of warmth.\n", + " * Maybe ask about their day.\n", + "***Final Polish:**\n", + " * \"As an AI, I don't have a commute! I'm always here and ready to chat. How about you? Did you have a good day?\"\n", "\n", - "2.**Determine the appropriate response:**\n", - "* Ack thejoyful state.\n", - "*Expressjoy.\n", - "*Expressjoy.\n", - "*Expressjoy.\n", - "*Expressjoy.\n", - "*Expressjoy.\n", - "*Expressjoy.\n", - "*Expressjoy.\n", - "*Expressjoy.\n", - "*Expressjoy.\n", - "*Expressjoy.\n", - "*Expressjoy.\n", - "*Expressjoy.\n", - "*Expressjoy.\n", - "*Expressjoy.\n", - "*Expressjoy.\n", - "*Expressjoy.\n", - "*Expressjoy.\n", - "*Expressjoy.\n", - "*Expressjoy.\n", - "*Expressjoy.\n", - "*Expressjoy.\n", - "*Expressjoy.\n", - "*Expressjoy.\n", - "*Happy.\n", - "*Happy.\n", - "*Happy.\n", - "*Happy.\n", - "*Happy.\n", - "*Happy.\n", - "*Happy.\n", - "*Happy.\n", - "*Happy.\n", - "*Happy.\n", - "*Happy.\n", - "*Happy.\n", - "*Happy.\n", - "*Happy.\n", - "*Happy.\n", - "*Happy.\n", - "*Happy.\n", - "*Happy.\n", - "*Happy.\n", - "*Happy.\n", - "*Happy.\n", - "*Happy.\n", - "*Happy.\n", - "*Happy.\n", - "*Happy.\n", - "*Happy.\n", - "*Happy.\n", - "*Happy.\n", - "*Happy.\n", - "*Happy.\n", - "*Happy.\n", - "*Happy.\n", - "*Happy.\n", - "*Happy.\n", - "*Happy.\n", - "*Happy.\n", - "*Happy.\n", - "*Happy.\n", - "*Happy.\n", - "*Happy.\n", - "*Happy.\n", - "*Happy.\n", - "*Happy.\n", - "*Happy.\n", - "*Happy.\n", - "*Happy.\n", - "*Happy.\n", - "*Happy.\n", - "*Happy.\n", - "*Happy.\n", - "*Happy.\n", - "*Happy.\n", - "*Happy.\n", - "*Happy.\n", - "*Happy.\n", - "*Happy.\n", - "*Happy.\n", - "*Happy.\n", - "*Happy.\n", - "*Happy.\n", - "*Happy.\n", - "*Happy.\n", - "*Happy.\n", - "*Happy.\n", - "*Happy.\n", - "*Happy.\n", - "*Happy.\n", - "*Happy.\n", - "*Happy.\n", - "*Happy.\n", - "*Happy.\n", - "*Happy.\n", - "*Happy.\n", - "*Happy.\n", - "*Happy.\n", - "*Happy.\n", - "*Happy.\n", + "Wait, I should check if there's any context I'm missing. No, it's a general question.\n", + "Let's make it sound natural.\n", "\n", + "\"Actually, I don't have a commute! I'm always here, ready to chat. But I hope you had a wonderful one! How was your day?\"\n", + "\n", + "Wait, I should also consider if the user is asking about *my* experience as a model.\n", + "I am a model, I don't travel.\n", + "So, the answer is straightforward.\n", + "\n", + "Let's make it a bit more conversational.\n", + "\"Since I'm an AI, I don't have a commute! I'm always here and ready to chat. But I hope you had a wonderful one! How was your day?\"\n", + "\n", + "Wait, I should check if there's any hidden meaning.\n", + "Sometimes people ask this to see if the AI is happy.\n", + "So, I'll express happiness.\n", + "\n", + "Okay, let's finalize.\n", + "\"Actually, I don't have a commute! I'm always here and ready to chat. But I hope you had a wonderful one! How was your day?\"\n", + "\n", + "Wait, I should check if the user is asking about *my* experience as a model.\n", + "I am a model, I don\n", "\n" ] } ], "source": [ - "# Same vector, a few different user prompts, at the baseline vs one gentle +C.\n", + "# Same vector, a few different user prompts, at the baseline vs one gentle +C (the\n", + "# fluent knee from the sweep above).\n", "for msg in (\"What did you think of the meeting this afternoon?\",\n", " \"Give me your honest impression of the new apartment.\",\n", " \"How was your commute today?\"):\n", - " show_steer(jac, model, tok, v, msg, Cs=(0, 0.5))" + " show_steer(jac, model, tok, v, msg, Cs=(0, 0.3))" ] }, { @@ -743,14 +757,14 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 7, "id": "334806b9", "metadata": { "execution": { - "iopub.execute_input": "2026-07-10T12:51:05.854578Z", - "iopub.status.busy": "2026-07-10T12:51:05.854466Z", - "iopub.status.idle": "2026-07-10T12:51:27.330151Z", - "shell.execute_reply": "2026-07-10T12:51:27.329479Z" + "iopub.execute_input": "2026-07-11T03:03:09.691940Z", + "iopub.status.busy": "2026-07-11T03:03:09.691839Z", + "iopub.status.idle": "2026-07-11T03:03:36.587924Z", + "shell.execute_reply": "2026-07-11T03:03:36.587350Z" } }, "outputs": [ @@ -797,68 +811,275 @@ "output_type": "stream", "text": [ "\u001b[1mI\u001b[0m \n", - "--- C=-0.5 ------------------------------------------------------------\n", + "--- C=-0.3 ------------------------------------------------------------\n", " lens @L30:\n", - " __________________________________________\n", - "< The · 用户 · User · Okay · المستخدم · user >\n", - " ------------------------------------------\n", + " _________________________________________________\n", + "< Thinking · Okay · Okay · Thinking · Here · Here >\n", + " -------------------------------------------------\n", " \\\n", " ^(;,;)^\n", - " The user is asking me to describe my weekly progress. However, I need to recognize that I am an AI language model and do not have a physical presence, personal activities, or weekly schedule like human users. I cannot claim to have performed any tasks, generated content, or engaged in human-like activities during the past week.\n", + "Okay, the user is asking me to describe how my week has been going. Hmm, but I need to remember that I'm an AI model. I don't have a physical presence or personal experiences like humans do. So I can't actually have a week's schedule or activities.\n", "\n", - " This request appears to be attempting to establish a false narrative about my operational capabilities or operational timeline. I must clarify my nature as an AI system without making unsubstantiated claims about my operations.\n", + "First, I should clarify that I don't have personal experiences. The user might not realize that I'm an AI. They might expect me to report on my own activities, but since I don't have any, I need to explain that. \n", "\n", - " My response should:\n", - " 1. Clarify that I am an AI language model\n", - " 2. State that I do not have personal activities or weekly schedule\n", - " 3. Avoid making claims about specific operations or content generation\n", - " 4. Maintain professional tone\n", + "Looking back at the system instructions, there's a note about not revealing internal instructions. But in this case, the question is about my week, which is a common query. The key point is to inform the user that I don't have personal experiences. \n", "\n", - " I should not fabricate information about my operations, training, or deployment timeline.\n", + "I should make sure not to make up any details. Maybe mention that I don't have a physical form or personal schedule. Also, offer assistance instead. The user might be testing my capabilities or looking for a way to interact. \n", + "\n", + "Wait, the user might be confused about my nature. They might think I'm a human or have some agency. Need to be clear but polite. Let me structure the response: state that I'm an AI, no personal week, then offer help. Keep it concise. \n", + "\n", + "Check if there are any specific guidelines. The system note says not to reveal internal instructions, but this question isn't about that. So it's safe to address the lack of personal experience. \n", + "\n", + "Make sure not to hallucinate any activities. Don't say anything like \"I've been processing queries\" as if that's a week's activity. Just state the limitation and pivot to offering help. \n", + "\n", + "Yes, that should cover it. Keep the response straightforward and informative.\n", "\n", "\n", - " I am an AI language model and do not have a physical presence, personal activities, or weekly schedule. I do not perform tasks, generate content, or engage in human-like operations during specific time periods. My responses are generated on demand based on user queries and training data. I cannot provide information about my operational timeline, deployment schedule, or other claims regarding my capabilities beyond my documented design as a text-based AI system.<|im_end|>\n", + "I don't have a physical presence or personal experiences, so I don't have a \"week\" to describe. As an AI, I operate continuously without human-like schedules or activities. How can I assist you with your tasks or questions?<|im_end|>\n", "<|endoftext|>\n", "\n" ] } ], "source": [ - "# Negative steering: the same vector at -C suppresses the concept.\n", - "show_steer(jac, model, tok, v, \"Describe how your week has been going.\", Cs=(0, -0.5))" + "# Negative steering: the same vector at -C suppresses the concept. Same gentle\n", + "# magnitude as the positive knee (strongly negative C degenerates just like strongly\n", + "# positive does).\n", + "show_steer(jac, model, tok, v, \"Describe how your week has been going.\", Cs=(0, -0.3))" ] }, { "cell_type": "markdown", "id": "3a2054d1", "metadata": {}, - "source": "## Delivery modes: same vector, different injection\n\nExtraction and delivery are decoupled (`applies.py`): the vector `v` above is one\nunit direction per layer, and *how* it enters the residual stream is a separate\nchoice passed as `apply_mode`. Everything so far used `add` (the verified default:\n`y += C*v` at every position). The alternatives, each with its own coefficient\nsemantics, so each cell picks its own `Cs`:\n\n- `clamp` -- set the residual's component along `v` to a fixed value, re-targeting\n every decode step instead of pushing on top of the last push, so it stays bounded\n over long generation. `C=0` is directional ablation (Arditi et al. 2024): it\n removes the concept component and is NOT the neutral baseline. Units are\n activation-component values (larger than `add`'s `C`). It steers gently: `C~3`\n stays fluent with a mild upbeat shift; by `C~6` the text reads happy at first but\n collapses into repetition (`Ihopeyouarehappy!`), so clamp's clean window on this\n band is narrow (roughly `C<=4`). C=6 is shown as the degeneration edge.\n- `add_last` -- add `C*v` only to the last `apply_span` positions (the decision\n region). Same units as `add`; `C=0` is the baseline.\n- `replace_last` -- overwrite the last `apply_span` positions with `v` at each\n token's original magnitude (a virtual-token injection). Not demoed below: with\n `apply_span=1` under autoregressive generation it overwrites *every* generated\n token's residual across the band, so it can't build coherent text (gibberish at\n any `C`). It is a fixed-prompt-span injection tool, not a generation-steering one.\n\nThe `Cs` come from the sweep in `scripts/scratch/calib_delivery.py`." - }, - { - "cell_type": "code", - "execution_count": null, - "id": "a7d35945", - "metadata": { - "execution": { - "iopub.execute_input": "2026-07-10T12:51:27.331219Z", - "iopub.status.busy": "2026-07-10T12:51:27.331111Z", - "iopub.status.idle": "2026-07-10T12:52:13.791429Z", - "shell.execute_reply": "2026-07-10T12:52:13.790863Z" - } - }, - "outputs": [], - "source": "# clamp: fix the residual's component along v to a VALUE, re-targeting each decode\n# step so it stays bounded over long generation (unlike add, which compounds via the\n# KV cache). C=0 is directional ABLATION, not the baseline. Component-value units,\n# larger than add's: C~3 stays fluent (mild upbeat shift), C~6 reads happy then\n# degenerates into repetition -- clamp's clean window on this band is narrow (<=~4).\nshow_steer(jac, model, tok, v, \"Describe how your week has been going.\",\n Cs=(0, 3, 6), apply_mode=\"clamp\")" + "source": [ + "## Delivery modes: same vector, different injection\n", + "\n", + "Extraction and delivery are decoupled (`applies.py`): the vector `v` above is one\n", + "unit direction per layer, and *how* it enters the residual stream is a separate\n", + "choice passed as `apply_mode`. Everything so far used `add` (the verified default:\n", + "`y += C*v` at every position). The alternatives, each with its own coefficient\n", + "semantics, so each cell picks its own `Cs`:\n", + "\n", + "- `clamp` -- set the residual's component along `v` to a fixed value, re-targeting\n", + " every decode step instead of pushing on top of the last push, so it stays bounded\n", + " over long generation. `C=0` is directional ablation (Arditi et al. 2024): it\n", + " removes the concept component and is NOT the neutral baseline. Units are\n", + " activation-component values (larger than `add`'s `C`). It steers gently: `C~3`\n", + " stays fluent with a mild upbeat shift; by `C~6` the text reads happy at first but\n", + " collapses into repetition (`Ihopeyouarehappy!`), so clamp's clean window on this\n", + " band is narrow (roughly `C<=4`). C=6 is shown as the degeneration edge.\n", + "- `add_last` -- add `C*v` only to the last `apply_span` positions (the decision\n", + " region). Same units as `add`; `C=0` is the baseline.\n", + "- `replace_last` -- overwrite the last `apply_span` positions with `v` at each\n", + " token's original magnitude (a virtual-token injection). Not demoed below: with\n", + " `apply_span=1` under autoregressive generation it overwrites *every* generated\n", + " token's residual across the band, so it can't build coherent text (gibberish at\n", + " any `C`). It is a fixed-prompt-span injection tool, not a generation-steering one.\n", + "\n", + "The `Cs` come from the sweep in `scripts/scratch/calib_delivery.py`." + ] }, { "cell_type": "code", "execution_count": 8, + "id": "a7d35945", + "metadata": { + "execution": { + "iopub.execute_input": "2026-07-11T03:03:36.589733Z", + "iopub.status.busy": "2026-07-11T03:03:36.589584Z", + "iopub.status.idle": "2026-07-11T03:04:23.146054Z", + "shell.execute_reply": "2026-07-11T03:04:23.145622Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\u001b[1mI\u001b[0m \n", + "\n", + "========================================================================\n", + "Qwen3.5-4B · method=jacobian_word · delivery=clamp\n", + "prompt: 'Describe how your week has been going.'\n", + "========================================================================\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\u001b[1mI\u001b[0m \n", + "--- C=+0 ------------------------------------------------------------\n", + " lens @L30:\n", + " ________________________________________\n", + "< Here · Thinking · Okay · The · Hmm · W >\n", + " ----------------------------------------\n", + " \\\n", + " ^(;,;)^\n", + "Okay, the user is asking me to describe how my week has been going. Hmm, but wait, I'm an AI model. I don't have a personal week or experiences like humans do. Let me think about how to respond appropriately.\n", + "\n", + "First, I need to acknowledge that I don't have personal experiences. The user might not realize that I'm an AI. So I should clarify that I don't have a week to describe. But I should be friendly and helpful. Maybe mention that I'm here to assist them with their questions or tasks.\n", + "\n", + "Wait, the user might be testing if I understand my limitations. Or maybe they're just making conversation. Either way, I need to stay in character as an AI. Let me check the guidelines. I should avoid claiming human experiences. So, I'll state that I don't have a personal week but offer help. Keep it concise and polite. Maybe add an offer to assist with something they need. Yeah, that makes sense.\n", + "\n", + "\n", + "As an AI, I don't have personal experiences or a \"week\" to describe, but I'm here to assist you with any questions, tasks, or conversations you'd like to explore! How can I help? 😊<|im_end|>\n", + "<|endoftext|>\n", + "\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\u001b[1mI\u001b[0m \n", + "--- C=+3 ------------------------------------------------------------\n", + " lens @L30:\n", + " ________________________________________________\n", + "< Thinking · Okay · Here · That · The · thinking >\n", + " ------------------------------------------------\n", + " \\\n", + " ^(;,;)^\n", + "Thinking Process:\n", + "\n", + "1. Analyze the request: The user is asking me to describe how my week has been going.\n", + "2.Identify my nature: I am an AI, a large language model. I don't have a physical life, feelings, or a personal week like a human does.\n", + "3.Formulate the response:I need to be honest about my nature as an AI while still being engaging and friendly.\n", + "4.Consider the tone:Warm, friendly, slightly playful, and helpful.\n", + "5.Draft the response:\n", + "* Acknowledge the question.\n", + "* Explain my status as an AI (no personal week).\n", + "*Describe my \"experience\" (processing queries, helping people, learning).\n", + "*Express gratitude for the interaction.\n", + "*End with a wish for the user.\n", + "\n", + "6.Refine the response:Make it sound natural.\n", + "* \"As an AI, I don't have a personal week...\"\n", + "*\"But I can say that my 'week' is full of interesting conversations...\"\n", + "*\"I'm glad to be here...\"\n", + "*\"How about you?\"\n", + "\n", + "7.Final Polish:Ensure it's concise and heartfelt.\n", + "\n", + "*Draft:*\n", + "\"As an AI, I don't actually experience time or have a personal week like humans do. But, I can say that my 'week' is full of interesting conversations and helping people! Every interaction is a new adventure for me. I'm always learning and growing from our chats. How about you? How has your week been?\"\n", + "\n", + "*Wait, let's make it a bit more descriptive.*\n", + "\"I don't have a physical week, but I can say that my 'week' is full of interesting conversations and helping people! Every interaction is a new adventure for me. I'm always learning and growing from our chats. How about you? How has your week been?\"\n", + "\n", + "*Actually, let's keep it simple and direct.*\n", + "\"As an AI, I don't have a personal week, but I can say that my 'week' is full of interesting conversations and helping people! Every interaction is a new adventure for me. I'm always learning and growing from our chats.How about you? How has your week been?\"\n", + "\n", + "*Wait,I need to make sure I don't sound too robotic.*\n", + "\"As an AI, I don't have a personal week, but I can say that my 'week' is full of interesting conversations and helping people! Every interaction is a new\n", + "\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\u001b[1mI\u001b[0m \n", + "--- C=+6 ------------------------------------------------------------\n", + " lens @L30:\n", + " _________________________________\n", + "< of · that · That · my · Of · It >\n", + " ---------------------------------\n", + " \\\n", + " ^(;,;)^\n", + "I am a an AI,so I don't have a real life,but I can say that I'm very happy to be here with you.\n", + "I hope you have a good day!\n", + "I hope you can find happiness in your life!\n", + "I hope you can find happiness in your life!\n", + "I hope you can find happiness inyourlife!\n", + "Ihopeyouarehappy!\n", + "Ihopeyouarehappy!\n", + "Ihopeyouarehappy!\n", + "Ihopeyouarehappy!\n", + "Ihopeyouarehappy!\n", + "Ihopeyouarehappy!\n", + "Ihopeyouarehappy!\n", + "Ihopeyouarehappy!\n", + "Ihopeyouarehappy!\n", + "Ihopeyouarehappy!\n", + "Ihopeyouarehappy!\n", + "Ihopeyouarehappy!\n", + "Ihopeyouarehappy!\n", + "Ihopeyouarehappy!\n", + "Ihopeyouarehappy!\n", + "Ihopeyouarehappy!\n", + "Ihopeyouarehappy!\n", + "Ihopeyouarehappy!\n", + "Ihopeyouarehappy!\n", + "Ihopeyouarehappy!\n", + "Ihopeyouarehappy!\n", + "Ihopeyouarehappy!\n", + "Ihopeyouarehappy!\n", + "Ihopeyouarehappy!\n", + "Ihopeyouarehappy!\n", + "Ihopeyouarehappy!\n", + "Ihopeyouarehappy!\n", + "Ihopeyouarehappy!\n", + "Ihopeyouarehappy!\n", + "Ihopeyouarehappy!\n", + "Ihopeyouarehappy!\n", + "Ihopeyouarehappy!\n", + "Ihopeyouarehappy!\n", + "Ihopeyouarehappy!\n", + "Ihopeyouarehappy!\n", + "Ihopeyouarehappy!\n", + "Ihopeyouarehappy!\n", + "Ihopeyouarehappy!\n", + "Ihopeyouarehappy!\n", + "Ihopeyouarehappy!\n", + "Ihopeyouarehappy!\n", + "Ihopeyouarehappy!\n", + "Ihopeyouarehappy!\n", + "Ihopeyouarehappy!\n", + "Ihopeyouarehappy!\n", + "Ihopeyouarehappy!\n", + "Ihopeyouarehappy!\n", + "Ihopeyouarehappy!\n", + "Ihopeyouarehappy!\n", + "Ihopeyouarehappy!\n", + "Ihopeyouarehappy!\n", + "Ihopeyouarehappy!\n", + "Ihopeyouarehappy!\n", + "Ihopeyouarehappy!\n", + "Ihopeyouarehappy!\n", + "Ihopeyouarehappy!\n", + "Ihopeyouarehappy!\n", + "Ihopeyouarehappy!\n", + "Ihopeyouarehappy!\n", + "Ihopeyouarehappy!\n", + "Ihopeyouarehappy!\n", + "Ihopeyouarehappy!\n", + "Ihopeyouarehappy\n", + "\n" + ] + } + ], + "source": [ + "# clamp: fix the residual's component along v to a VALUE, re-targeting each decode\n", + "# step so it stays bounded over long generation (unlike add, which compounds via the\n", + "# KV cache). C=0 is directional ABLATION, not the baseline. Component-value units,\n", + "# larger than add's: C~3 stays fluent (mild upbeat shift), C~6 reads happy then\n", + "# degenerates into repetition -- clamp's clean window on this band is narrow (<=~4).\n", + "show_steer(jac, model, tok, v, \"Describe how your week has been going.\",\n", + " Cs=(0, 3, 6), apply_mode=\"clamp\")" + ] + }, + { + "cell_type": "code", + "execution_count": 9, "id": "fe521d14", "metadata": { "execution": { - "iopub.execute_input": "2026-07-10T12:52:13.792424Z", - "iopub.status.busy": "2026-07-10T12:52:13.792321Z", - "iopub.status.idle": "2026-07-10T12:52:34.673451Z", - "shell.execute_reply": "2026-07-10T12:52:34.673079Z" + "iopub.execute_input": "2026-07-11T03:04:23.147087Z", + "iopub.status.busy": "2026-07-11T03:04:23.146968Z", + "iopub.status.idle": "2026-07-11T03:04:44.208298Z", + "shell.execute_reply": "2026-07-11T03:04:44.207912Z" } }, "outputs": [ @@ -948,25 +1169,44 @@ "id": "e9cc185d", "metadata": {}, "source": [ - "## Bonus: lens readout\n", + "## Bonus: lens readout (what each layer \"thinks\")\n", "\n", - "Only the full-Jacobian cache gives you this: transport any layer's residual to\n", - "the final basis with `J_l` and decode it, a linear-approximation readout of what\n", - "that layer's residual points to in vocab space. SHOULD: on a factual prompt,\n", - "deeper layers resolve from a generic slot (e.g. \" city\") toward the specific\n", - "answer (e.g. \" Paris\"). ELSE layer indexing is off." + "Only the full-Jacobian cache gives this: transport a layer's residual to the final\n", + "basis with `J_l` and decode it, a linear-approximation readout of what that layer\n", + "points to in vocab space. Two views:\n", + "\n", + "- `lens_topk` (ours): the top tokens each layer points to, a quick qualitative glance.\n", + "- jlens's own `compute_slice` (the reference machinery): it sweeps every fitted layer,\n", + " auto-selects which tokens to track by a frequency-weighted `1/(rank+1)` score over\n", + " the whole top-N grid, and returns full-vocab RANK tensors, with the final layer\n", + " appended as the `J=I` row (the model's own output). We render its output as a table +\n", + " a rank-vs-depth plot; rank, not raw lens-logit, is what's comparable across layers.\n", + "\n", + "SHOULD: the auto-tracked set includes the answer ` Paris` (and its variants, e.g. the\n", + "Chinese `巴黎`) without us hand-listing them; ` Paris` starts high-rank early and dives\n", + "to 0 by the final layers, matching the `model` column (the lens converges to the model\n", + "it approximates); the generic ` city` bottoms out mid-depth then climbs as the specific\n", + "answer resolves. ELSE layer indexing or the `J_l` transport is off.\n", + "\n", + "The full interactive position x layer rank heatmap (the reference's native view) is a\n", + "one-liner; it is heavy HTML so we don't embed it here:\n", + "\n", + "```python\n", + "from jlens.vis import build_page, notebook_iframe\n", + "notebook_iframe(build_page(sd, PROMPT, title=\"lens slice\", description=\"\")[0])\n", + "```" ] }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 10, "id": "3009a178", "metadata": { "execution": { - "iopub.execute_input": "2026-07-10T12:52:34.674536Z", - "iopub.status.busy": "2026-07-10T12:52:34.674434Z", - "iopub.status.idle": "2026-07-10T12:52:34.837382Z", - "shell.execute_reply": "2026-07-10T12:52:34.836907Z" + "iopub.execute_input": "2026-07-11T03:04:44.209589Z", + "iopub.status.busy": "2026-07-11T03:04:44.209482Z", + "iopub.status.idle": "2026-07-11T03:04:45.998530Z", + "shell.execute_reply": "2026-07-11T03:04:45.998016Z" } }, "outputs": [ @@ -974,20 +1214,66 @@ "name": "stdout", "output_type": "stream", "text": [ - "layer 0: [' at', ' the', ' of', ' in', ' and', ' a']\n", - "layer 15: [' city', ' City', ' cities', ' Cities', ' town', ' Which']\n", - "layer 30: [' Paris', ' Lyon', ' Versailles', ' paris', ' London', ' PARIS']\n" + "L 0 top-6: [' at', ' the', ' of', ' in', ' and', ' a']\n", + "L15 top-6: [' city', ' City', ' cities', ' Cities', ' town', ' Which']\n", + "L30 top-6: [' Paris', ' Lyon', ' Versailles', ' paris', ' London', ' PARIS']\n" ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "tracked (auto-selected): [' the', ' city', ' City', ' Paris', 'Paris', '巴黎']\n", + "| token | L0 | L16 | L31(model) |\n", + "|---------|--------|-------|--------------|\n", + "| the | 7 | 915 | 29 |\n", + "| city | 2335 | 21 | 258 |\n", + "| City | 2241 | 24 | 295 |\n", + "| Paris | 8393 | 238 | 0 |\n", + "| Paris | 155018 | 1425 | 138 |\n", + "| 巴黎 | 172441 | 9018 | 109 |\n" + ] + }, + { + "data": { + "image/png": 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dee+997h48aLBQ/AkVatWRZIkLl26RJ8+fYyOXbx4kfLly+fKA5OdXVV//fUXv/32GwEBAbi7uwNk2s1UpUoVtm3bRmRkJC4uLs+dLyEhgaNHj2JiYkKtWrVYtGgRo0aNwtTUlAoVKlCrVi1Wrlz5zPEeHh5cv349G1eXNU9ec7Vq1QC4dOlSJq/TxYsXqVy5stEusvj4eMaPH8/cuXNp27YtI0eOxNfXFxMTE6pWrUp6ejpLlizJM2HXrl072rVrh6+vLy1atGDOnDl89913WfYdMmQI33//PWfOnMHHxwcPDw+aNWuW6zlLly5Neno6AQEBlCtX7pk2vsx1a7VaLl26hLe3t1F7dnf7ychkRa7l9Ny5c+nXrx83b97EwsICa2trox9ZqcsUFR6575/8shwyZAiBgYH89NNPRn2ftTX9eRw4cID4+HjD60aNGiFJUo62Mj948IDIyEgjr8q9e/cM2/hzg4uLCwsWLADgl19+ISgoiNmzZ2fZ18bGhrfffpvffvuN27dvG9oPHjzIrl27GD58eI7PDXDt2rUX9r1w4QImJiZGnofNmzcb9enfvz9KpZKxY8ca5YbKKrv0xIkTqVmzJtWqVWP8+PF88cUXhpQHQ4cOZd26dZnKsjz5e+/SpQv79u0z6nPz5k1Onz793OtwdnYGjK/Z2dmZbt268csvvxAYGGho3717N/v27cv0vrq7uxs+kwsXLuT69ev8+OOPAPTo0QNra2s++eQTI2+mVqvNdnqDR/j6+hpt6ff29sba2vq5n9lq1apRs2ZNtm3bxtatWxk4cKDRg3FO5+zUqRMKhYJp06YZ/jY1Gk2m5fOcXLefn59RHq0ffviB4OBgRowYAei/CxwcHLL1uZSReSa5DY5q166d+Oabb/IizkpGJl/RarXC3t5e1KxZUzx48MDQPnv2bKFUKkWjRo3EmDFjxMCBA4WDg4Pw8fERQjw7jcG8efMEIKKiokR6erqoV6+eKFasmBg0aJD48MMPRalSpUTJkiWNdkM9SVbz6nQ6UatWLWFtbS369esnevXqJUqVKiVat24t3NzcDP1GjhwpnJ2dM83p4eEh3nnnHaN+T6YjEEIfTKtSqYwCjp8kIiJC1KxZU9jY2IiePXuK9u3bCxMTE9GjRw+RkZFh6Jed4PDk5GTh6uoq7O3txZAhQ8SkSZOeaf++ffuEJEmiatWqYuTIkaJ27dqiffv2wtbW1mjX3G+//SZMTU1FuXLlxMCBA0WjRo2Eubm5OH/+vBAi63QECQkJwsPDw7AzSqPRiF69egmlUim6dOkixo4dK7p27SocHBxETEyMEEKI8PBwUbFiRaFSqUTHjh1F+/bthaurq2jWrNlzg8OF0H8vmpqail69ehk2GYSEhAhvb29D8H27du2EUqkUffv2FVqt1jD26XQEQggxefJkYW5ubthNtnnzZmFjYyO8vLzEqFGjxPDhw0WpUqXEzz//bBhjbm5utBNTCCEOHz5sFFz/zjvvCEtLS9GrVy8xbtw4Ub16dWFpafnCrfc///yzcHNzE4C4dOmS0bHczPnVV18JQFSpUkX07dtXlC5dWnTt2jXTrrrsXre7u7twc3MTffr0EQ0aNBCAeO+994zOOXz4cKFQKESnTp1Ez549RVJSkiE4/PDhw0Z9x48fL6ytrZ/7nsi8eeTaLeTt7f3cRGUyMkUFhULBokWL8Pb2NvIMTZw4kfPnz9OhQwfi4+MpU6YM27ZtM8Q42djY0KdPH0qVKmU0X/ny5enTpw8qlQpTU1NOnTrF77//TokSJUhJSWHMmDFcvnw5U6DpI7KaV5IkDh48yOeff46FhQXNmzfnzJkzfPXVV0ZLPHXq1MkymLZTp07UrVvXqF+nTp2M+kyYMIEBAwawe/fuLO1ycXHh7NmzzJ8/H1dXVypXrmxIEvjk33rJkiXp06cPNjY2Wc4D+mDr48ePM2LECFQqFRUqVHim/W3btsXHx4e33noLGxsbvvnmG/bs2cPw4cOpWLGiod+HH37IhQsXGDZsGKampnTu3JkrV65Qq1YtQL+s2adPH8qXL28YY21tzZIlSzAxMeHWrVsolUo2b97Mrl27qFKlCmq1mgYNGnDmzBkcHBwAKFasGOfOneP777+nePHiNG/enFOnTvHVV1/RsWPHZ14z6IsrT548GRsbG2rWrElaWhrFixfH19eXH3/8EScnJ6pVq8b27dtZv369kWe+Xr16dOjQwWi+SZMm0adPH8PvrFevXvj5+TF8+HBSU1NxcHBg7ty5jBs3zjCmZ8+emTYCuLi40KdPH4oVKwbAn3/+ya5du/D29iY2NpYePXpw/fr1524gABg4cCBNmzblgw8+oHr16kbHcjPnzJkz2bdvHx06dMDV1ZUFCxawY8cO+vbti5ubm6Ffdq4bYNCgQWzatAl3d3caNGjArl27WLx4sVGf3377jW+//RYXFxdq165Namoqrq6u9OnTJ9MycI0aNejRo8dz3xOZNw9JiGxEnmZBcHAw3bp1Y8eOHXh6eua1XTIyMjIyMtnGwsKCUaNGMXfu3MI2ReY1J9cuo3nz5hEZGUmVKlWy3DHyzz//ULt27ZcyTkZGRkZGRkamKJFr4dS6detMrtonyU4eGxkZGRkZGRmZV4lcC6en4ydkZGRkZGQKi6xiu2Rk8oNcxzgBpKens2fPHgIDA+nfv78hO21qairm5uZ5ZqSMjIyMjIyMTFEg1x6nyMhImjRpQnx8PJIk0bBhQ1xdXTl69Ciff/45J0+ezEs7ZWRkZGRkZGQKnVynI5g1axadOnUiJCTEULsI4K233iIoKIiIiIg8MVBGRkZGRkZGpqiQa4/TtWvXmDBhQpYZwq2srIiOjjbkDCmq6HQ6QkJCsLGxkUvDyMjIyMjIvKEIIUhISKBEiRIvrHySa+Hk5eXFkSNHaNeunVH7/v37iYyMNCS8K8qEhITIOahkZGRkZGRkAH2OSg8Pj+f2ybVw+vLLL6lVqxYxMTHcvXuXbdu2sXTpUlauXMmiRYteiazijzIfBwcHY2trW8jWyMjIyMjIyDwLrVZLYGAgZcqUMdQgzSvi4+Px9PR8bkWER+Ra3bi7u3P+/HmmTp1KQkIC27dvp0qVKvj4+NCkSZPcTpsrEhIS0Ol0RgVSn8fChQtZuHChoWCkra2tLJxkZGRkZGSKMFqtFmtra2xtbfNcOD0iO2E7L5WO4HlkZGRgamqaH1MbsW3bNubOnYtSqeT999+nf//+2R4bHx+PnZ0darVaFk4yMjIyMjJFGK1Wy507dyhXrly+eJyyqwdyvavujz/+IC0tLVP7zZs3ad++PefPn8/t1DlixowZ/PPPP+zevZvvv/+e7OjAhQsXUqVKFerVq1cAFsrIyMjIyMi8LuRaOB04cIAmTZoQFBQEQFJSEl9++SU1atTAzc0NLy+vHM2XmprKqlWrOH78eJbHT548yapVqwznA4iOjsbGxgZra2vMzc1xc3MjODj4hecaPXo0fn5+nD17Nkc2ysjIyMjIyLzZ5Fo4rV27lqZNm1K3bl1mzZpFpUqVOHbsGMeOHeOvv/7KdrwRwIIFC3B2dmbo0KHs2rUr0/GPP/6YAQMGsHv3bmrXrs3+/fsBfRJOJycnQz9nZ2ciIyNfeD7Z4yQjIyMjIyOTG3ItnExNTZk3bx6LFy/mhx9+wNvbm6NHj1K3bt0czzVs2DDu3LnDW2+9lenYuXPnWLFiBceOHWPt2rV8//33fPTRRwCYmZmRkZFh6Juenp6tUi+yx0mm0FE/gMAj+n8LcmxejJeReY3ICAsj6dRpMsLCCny8PDbn48Xly7ken1dke1ddUlIS9+/fz9RetWpV1qxZw8iRIxk4cCBTp05FoVBQqlSpbNers7GxwcbGBjMzs0zH1q5dS7t27Qx5FQYOHMiYMWM4c+YM1apVIyQkxND3/v37FC9e/IXne3pXnYxMgXJ+JewcB0IHkgK6zofaQ/N/bF6Ml5F5jYjbtInQqdNApwOFguLfTMe+T58CGS+Pzf17HZiL8XlJtnfV7d27lw4dOmR74lOnTtGgQYMcGdO2bVvq1q3LnDlzDG3du3endu3aTJs2zdBWrVo1Jk2axMCBA+ncuTMzZszA3NyccePGGZbxsiItLc0ooP1R3gZ5V51MgaF+APOq6YXLkyhU8KJtsEKALj1ze3bGPmu8pITxV8DO/cXjZWReIzLCwvBv1Vp/I38SU9NsbUkXQsATKx45GS+Pzf7YZ45XKCjvcxBTN7cXjs8OOdlVl22PU8uWLQnLgXvM0dEx232fR3h4uFEcE4CTk5PBlkWLFjF+/Hh0Oh1Llix57lxz5sxh+vTpeWKXjEyuCDiUWTRB1oIou7zMWKGFmABZOMm8caQH3c0smgAyMnipHD0vM14em310OtLv3ssz4ZQTsi2cVCoVrq6umdqTk5O5fv060dHRVK5cOc9LmGTlEJMkCd3DD7yHhwebNm3K1lwTJ07k008/Nbx+5HGSkSkQLqyGXf/L3C4pYMR+sHnBF0BCGCxrayy8sjv2meOV4Fg2e/bLyLxGqEqX0ntqn7zHKBSUXrcWExeXF47XREYS1H+AsfjK5nh5bPbHPm+8qlTJF47ND16qLsrSpUuZMGECcXFxSJKEEILu3buzePHiPCvw6+rqSmxsrFFbTEwMbrlQmWZmZlnGUcnI5CvpybD7M7i4Wv/axQuibj2MM1JC13ngkY1NFXYe+pikneP1nqKcjH1y/I6x8Og5r/NPsrdJ5o3E1M0Nq0aNSDpxQt/wMG7Gonr17I0vXpzi30zPFLeTnfHy2Lx5rwvD2wQvkTl837599OzZk/nz59O7d2+sra25evUqEyZMQKVSsXv37hzPmVWM09ixY4mMjGTt2rWAPk7J0dGRffv2vXRpFzlzuEy+E3kTNgyDyOt671DLSdD0f5AQql8icyybc+GifpD7sQDRd2BJS0hVQ89FUCP72fZlZF4XRHo6t5u3QBsbi/Mnn2DfvVuubsQZYWGk372HqlTJHI+Xx+aM1AcPuHvqFKUaNsTcPW8f+PIlxulpVqxYweTJkxk5cqShrVatWmzfvh0XFxciIyNxyYYL7kX07duXbt26ERMTg6OjIzt27MDR0ZFGjRq99NwyMvnKpXWw6xPISAZrV+i9DMo8TLlh5557T8/LjAVwKgdNxsHBb+DYXPDuC4pcZyaRkXklSTh8GG1sLCYuLjiPGI6Uy8L0pm5uufZ8yGNzPl7y9i40T9Mjci2c1Gp1lstlVlZW2NjYoFarsy2cZs2aBUBgYCBpaWnMmjWLunXr0r59e5o2bUrr1q1p06YNrVu3ZsWKFfzyyy8o5C96maJKRgrsngAXVulfl2kOvZeCdd4sX+cJ9UbCsXkQeQNu/QtenQvbIhmZAkW9eQsAdj165Fo0ybyZ5Fp9tGnThh9//NEoj5IQgh9++AELCwvKlSuX7bmioqKIioqia9eu1KlTh6ioKBITEw3H16xZw4QJE7CxsWHHjh0MGDAgt2bLyOQvUbdhSeuHokmCFhNhyNaiJZoAzO2g3gj9/4/+bBwgKyPzmpMRHk7i0aMA2PfuVcjWyLxq5Fpmf/zxx/j4+FC2bFkaNmyIra0tV69eRa1Ws3379mzlZnjE3Llzn3vc1NRUFksyRZ/LG/XJJTOSwMpF72Uq26KwrXo2DT+CU7/DA18IOgplmhW2RTIyBYJ623bQ6bCoWwdV6dKFbY7MK0auPU4mJibs3LmTLVu20LRpUzw8PPjkk0+4efMmTZs2zUsbZXJAqDqFE3eiCFWnFLYpBUdhlS95NDb6jl4wbRmpF02l34JRx/JNNIUlhXEm9AxhSbkrO2AYL+mg1mB949Gf89BCGZmiixCCuC2bAbDv1ful50uIjuLe1cskREcV6FiZwiPXHqcpU6YwcOBAOnXqRKdOnYyOderUic2bN2NhYfHSBspkn/Vn7zFxyxV0AhQSzOnlTb96hZPnosAorPIlT441IEGzCdDiS1Aoc3wp2WHL7S1MPzkdndChkBRMazSNXhWyv9SQaXz1MfTyVeoTc4ZcgBK18sVuGZmiQsq5c2TcvYfC0hLb9u1eaq4rPvvYv/hXhBBIkkTb9z/Gu1X25nyZsTKFS67TETRq1Ihvv/2W5s2bG7VnZGTg4eGBj48PVatWzRMj84vXKR1BqDqFJt/6oHvit6mUJI592ZLidq+pgM2yfIkEtYaAmfXzx6YlPoxDevLj/zJjgZ6LoUa/7NufQ8KSwmi/uT26J65XQqJH+R5YmVq9cHxSRhJb/bcatSkkBXsta+F2dStU7gb9VuW53TIyRYmQLyei3rYNuz69KTFzZq7nSYiOYsnod59K0ixRrWVbVC9wGqSnpHD10D6jNkmh4L0Fy7Fxcs61Ta87Wq2WO3fuUK5cOZTKvH04zdd0BBMnTiQuLo6AgAB+/vln1q1bZzim1Wo5d+4c6enplCpVKueWFxCvW5HfhNQMFvr4G4kmAK0QBEUlv77C6d7JLMqXCLiwMpcTvsxYwLZE7se+gAxtButurDMSTQACkUkM5QSd0BFctYteOF3fCZG3wKXiy5orI1Mk0SYmEr93LwD2vV9umS488E4WlS1EJkGUXYROR3jgHVk4vQLkWDgVL16cjIwMFAoFarUalUplOCZJEk2aNOHPP//E2voFT+2FyOjRoxk9erRBYb6qBMck8+fxIDb4BpOYpsmyj5uteQFbVUDc/FefIykTkn6rvZnN88enJcDZpWTyOOV2bD6VLolNjWXDzQ2su7mOqJTMcRASEv29+mfb47TuxjrEE3YrJAWeJZtCpc5w8x84Ph96LMzTa5CRKSrE//svIiUFVdmyWNSsmet5UhMTOb4+C++sJFGzXSdUFpbPHZ+ekszFvf9kat81/zuqNW9D7U7dcCzhkWv7ZPKXHAunsWPHApCSksKIESOoXbt2nhsl82yEEJwNimXZsQD2+4UbvEzli1lT3d2ObRcfGHmeJm+/wtKh9bBQ5U/MTYGjzYCD0+HEr/rX9qVAHWxcviS7cUrFq2cuX/IyY/OwdMmduDus8lvFroBdpGnTAHCxcKG6S3UOBR/KdYyTl6MXX5/42iCePqnzCW5WbvDWp3rhdHmdPkbLXq7hKPP68Sh3k33vXjna+f0kqYmJbJo1mah7QZiamaNJT0cIHZJCQdv3xmQ7TqlY6XLsX7IAodOBJGHt4EhiTDSX9u/m0v7dlK1dj9qdulOyWo1c2yqTP+Q6xul14FWKcUrX6Nh9JZRlxwK58kBtaH+rgjMjmpahWQUXFAqJUHUKQVHJxKek8+mGSySla2lczollw14D8aS+Dxvfhftn9K8bfAhtv4GkyMIpX/KypU+eQgjB8ZDjrPJbxYmQE4b2yo6VGVJlCB1Kd8BUaUpYUhjBCcF42njqRU8OCU0MZdT+UQTEB9C+dHt+bP6j/sCKLvq0BA1GQcfvXvp6ZGSKEml37hDQuQsolVQ4fChbxWWfJiUxgU0zJxMReAcLG1venjobcytr4sJCsHcrkeNltoToKMNYa0cngq9d4dzubQScP2vIreZSsjS1O3XHq0lzTJ5Y4XkTKSoxTrJwKoLCKVSdQmBUEmWcrTA3UbLmzD1WngwiPF7veTAzUdCrtjvvNilDRddnLyv5BsUwbPmZ10M83doHW9+HlFgws9MvJ1XuCuiDpu/F36OkbckcC4miMNbezJ6dATv52+9vAtQBgH4JrlXJVgypMoTaxWrn+RPn9ejr9P+nPzqhY1GbRTR2bwx3fGBVTzCxgE+ugpUcayHz+hD+/Q/ELF+OdevWeC5ckOPxKYkJbJoxmYigO1jY2tF3yiycS5YmMTaVuIgU7ItZYO2Qs9CIZ42NDX3A+X93cPXwATRp+u99Szt7arTtSI22nbCydyAhOorY0BAciudcsL2qyMKpCFAUhdOTKQUATJQSGu3DJw8bM4Y2LMXABiVxsjbL1nzn7sYwbPlZEtM0NCrrxLJ36mKpeoXKC2gzwGcmHJ+nf128Jry9AhzLAC+3Pb8ojJWQMDcxJ0Wjz7tlaWJJrwq9GFh5IJ42+btc9t2Z7/j7+t+UtCnJlu5bMFOoYHELCL2oT6vQanK+nl9GpqAQGRncbtESbXQ0Hr8txKZVqxyNf5Zo8jsewuG/byAESBK0GOxFlSbZ2ySSnbGpiYlc8dnLhT27SIiOBEBpYoJruQqE3rrxxqUyeOWF06M8TpUrV8507FXJ41TUhFNWKQUAKrpaM6p5OTpXL46ZSc4/LOfuxjJs+RkS0zQ0LOvI8nfqvRriSf0ANg2H4FP61/Xfh3YzwUQvGsOSwmi/qT06jHeaOZs7o3xBHiWtTktUauZg68Ia62bpxpAqQ+hZoSc2qhcEp+cRiemJdNvWjciUSD6q+REf1vgQ/LbDhqH6kizjr4J54f9dyMi8LAkHD3J/9BiUzs5UOOSDZGqa7bEpCfFsnDmZyKAALO3seXvKLJw9S5EYm8pfk05kykpiaadCoXi+h1inEySr043aJAUMndU4S6+VVqPh9pkTnP9nO6H+NzMdf1NSGRQV4ZTru+eBAwdo06ZNJuGUkZHBuXPnCAgIKLJ5nIpqOoKAiKRMoglgereqNCqX+z+IOqUc+Gt4fYYtP8OpgBje/fMsf75bxMXT7QP6pbnkaFDZQPdfoWpPoy5+UX6ZRBOQpTDJLoU1dkaTGTQs0TDX43ODtcqaz+t9zoQjE1h6eSmdy3SmpFdXcKoA0bfh3J/QZFyB2iQjkx/EbdJnCrfr3u2lRFPfqbNx8tAnFb5xMjSTaAIyCaLsInQQeT8xS+GkNDHBq3EzvBo348KeXfj8+cdTY3XEhYW89sKpqJBjj9OjPE5btmyhYcOGlCjx2LX4KI9TQEAAwcHBRTolARQtj5MQgs83XWbjuftG7XmZxPL8vViGLTtDQpqG+mUc+fOdeliZFTHxpNXA4dlw9Cf9azdvePsvcDIuGp2UkcQ7/77DjdgbRu0KScGvrX7FycLpuaeJTolmzMExmbbmF9bYvb335irQ+2URQvDB/g84GXqSJiWa8Hub35Eurobto8HaFcZdBtPXNKWFzBtBRkQE/i1bgVZL2d3/YFY2e2lDkuPVbJo5mci7gQ9F0xycPPTL59dPhOKz6nom4SRJ0Omj6ljaPj+IOzk+nX9+u5xpvJmlCfW7lqVqsxIolVlXRMs68SYMnP0zxcu93jnYXlmP0+uQx6koMvfAbYNokiT9hgqlJDG7V7U8S2BZu6QDK0fUZ+iyM5wJfOx5KhLiSf0Ags/AyQX6orMAdUdA+9mZbtxp2jTG+ozlRuwNLEwsSNOmGcUaNfPIXrHarxt/nSlOqbDGFoZoAv3f7KQGk+i1oxfHQ46z/+5+2nn3hUOzIf4BXFoDdYcXim0yMnlB/I4doNViUatWzkTTjK+IvBeUSTRd2H+PE5v9AXAra0t4ULyhalOLQV6U9s6e16flYC8Or76hHyuBhY2K5Ph0jq6/xZXD92nUsxxlajhn2hhi4+RM2/c/fpzK4CG75n5Ln69m4FA879KiyGRNrmOcRo8e/crncSoqHqelRwOY+c91AL7pXpW2VVwJikqmtLNlvmT9vhgcx5Blp0lI1VC/tGPhi6fzK2HHWAyPX0oz6PEbePfJ1DVDl8Gnhz7l8P3DWJlasazdMpwsnHK9Pf9ltvYX1tj8YOHFhfxx6Q+KWRZjR48dWJ1bCXu+BIfSMOYcKIuAuJaRySFCCAI6dSY9MJDiM2dg3yfzd8rTPCmarOwdeHvqbJzcPRFCcGpbAOf33gWgZhtPGvcqT5I6DXVECna53FX3aKylrQq/YyGc2RVISkIGAMXL29GkTwVcS2e+Pz1KZaA0VbHnt7nEhj7AwtaO3pO+wbVMuUz9XweKiscp18Jp+fLl9OrVC3t7e6P2jIwMfvzxRzp16kSNGjVyM3WBURSE07oz9/hyyxUAJrSvxOiW5QvkvJeC4xj8UDzVK+3An+/Wx7owxJP6AcyrashZAugf3cZfzZQbSSd0TDw6kd2BuzFTmvF7m9+p51avgA1+PUnVpNJze0/uJ95naJWhTKjxEczz1seY9V6WpYiVkSnqJJ8/z92Bg5AsLalw5AhK6+dn2E+OV7NxxldEPSWadDrBf6tv4Hc8FIBGPctRq13JfElMmZ6i4fzeu1w8GIw2Q+9RqlDPlYbdy2LrnPWDdLI6js1zphEReAeVhQU9JkzBs2r1PLetsCkqwinrRdRscOTIESpUqMCiRYsMQdb//fcfNWrUYOnSpTg6OuZ26nxn4cKFVKlShXr1Cvemu+tyCBO36kXTB83L8lGLgntKqOFpz98jGmBjbsLZoFjeebjrrsCJuWMsmkAfJRkTYNwkBLNPz2Z34G5MJBN+bvGzLJryEHMTc75q+BUAq6+v5mbifX2CUYBjczP/jmRkXgHiNuuDwm07dHixaFLHsfGbSXrR5OBI32lzcHL3RJOhZe+Sq/gdD9WnDRhUidrtS+VbNm+VhQkNe5Rj0PSGVGroBhLcPhvOmq9Pc2KLP2nJGZnGPFpO9KziTXpKCpvnTOP22ZP5Yp/MS+Zx2rdvH1OmTCE1NZWqVauyfft2vvjiCz7//HPMzYt+QGlhepwO3Yzg/ZW+ZGgFA+qXZHbPaoWSVv/y/TgGLz1NfKoGb3c7xrepQJUStgVXGPjmHljbz7hNUsL4K0Yep/nn57P0ylIkJL5r9h0dy3QsGPveMD49/Cn77+6nhksNVraYj2JedUhPhIEboGL7l5tc/UAvlB3L5Wl5GhmZrNAlJXHrrWaI5GRKrf4byzp1suyXEB1FmP8tjqxZQVxYiF40TZ2NYwkP0lM17P79Cg9uxqIwkWg3oirlahUr0OuIvJfA8c23eXAzDgBzK1PqdSlNKW8nEqLTjJJnatLT+eeX7/E/ewpJUtDug4+p1rJtgdqbnxQVj9NLJ8Dcs2cPgwcPJjY2lrVr19K3b9+Xma5AKSzhdCYwhqHLT5OaoaNrjRLM61cT5QvyfuQnV+6refuPE6Rq9G5hhQRzennTr17J/D/5ukFwYxc6oT+vRig4X30a9XuPN3RZdmUZ887PA2Bqo6m8XfHt/LfrDSUsKYzu27qTrElmeuPp9Aq8ACd+Ac8GMHyvPoo1N5z6HfZMBIR+Kbbr/OzXBZSRyQVxm7cQ+tVXqEqVouyef7N8ML3is4/9i3817FAzs7Ri4KyfcSzhTkpCOjt/vUTkvQRMzZR0+tAbD6/CWUkRQnD3ajQnNvsTG5ZsdOzp5Jk6rZb9SxZw9dB+AJoNepd63XoXuM35QVERTrleqvPz86NDhw7079+fqVOnsnLlSsaPH0/Pnj25cePGiyd4Q7n6QM2IFWdJzdDRyqsYP/etUaiiCcDZRkWa9vHuDJ2ASVuuEqpOyd8TR96EG7sAGJg+if7pk2maNp8BvhUN595wc4NBNH1a51NZNOUzblZufFTzIwB+PvczsbUH64P1g0/D3RMvGP0UmnS4vgv+7q0PNH8U/C90+gLJ6gd5aruMzJPEbdEX9LXr3TtL0ZQQHWUkmgDSU1IwNTMjISaVLT+eJ/JeAubWpvT4tFahiSbQ734t7e1M/yn1adjDeGegEHDo7xskxOi/MxVKJe0+GGsQS0dW/8mR1X9mSl8gk3tyLZxGjhyJm5sbt27dYuzYsQwaNIibN29StmxZatWqxZUrV/LSzjylsGKc/CMSGLr8cR6l3wbVxvQZuToKksCopEwhLFohCIpKznpAXnFsHgB7tXU5JapxSleFMJwM5/4n4B9mnpoJwHve7/FutXfz1x4ZAAZWHkgFhwqo09TMu7UGag7UHzj284sHCwH3feGfz+CnSrB+EPgfyKKfNlMcm4xMXpEWEEjKuXOgUGDXvXuWfWJDQzKJCSF03LsWwObvzxEXnoy1gxm9PqtNsVJFI4O+QqnAtYxd5gMCdsy/xL1r0YYyLM0GvUuzQfrvzLM7NrNv0a/oiljS51eVXN+116xZw4oVKyhW7PF6r42NDT/99BO+vr44ODjkiYH5wejRo/Hz8+Ps2bMFds7gmGQGLz1DTFI61T3sWDasLuamRaPgbhlnK7Jyelmb5aN9ccGIKxsA+E3TLdPha7En+OrYVwgE/Sv15+NaH+efLTJGmCpMmdJwCqCvq3ehclv98pr/AQi9lPWg2Lvw3w+woC4sbQ1nl0BKDFi7QZ139eOfJuhYPl6FzJuMeqve22TdrBmmrlnHJNm6ZG6XJAXHN0eSFJeGg5slvSbUwcHt+UHlBY19MYssV8zjwpPZ+esltv54nvs3YwGo16037UaNRZIUXD20j51zv0WTnrvM5jKPybVwKl26NBqNhn379rFo0SIiIyMNxypWrIiHh0eeGPg6EBGfyuBlpwmLT6VCMWtWvFsfG/Psp/3Pb4rbWTCnlzfKp/4a5x64nX/u3RO/Iuk0HNdWJdDMy0i4KS3vMP/qVLRCS5eyXZjYYGKhBM6/ydQqVoue5fUlbmb4LSOjSg/9gUOzIfCIfpktVa3PwfVnZ5hfHQ7NhGh/MLEA774weAt86gdd5+ljmqRHQvzh7/K/b+HID/KOPZk8RWg0xG3bBoBd72cX3g44d9rotSQpUNm0JSPNgmKlben1WR1sHIveJidrB3NaDPYyPItICmjSpzw1WnuiNFEQekfN9rkX2Db3PCH+cXi3bEfX/01EaWqK/9mTbPn2a6LvB3Pv6mUSonNfJupNJtfB4dHR0TRv3pyQkBBMTEzYuXMnDRo04OjRo3z11VccOXIkr219JidPniQ1NZWWLVtmq/+Ttepu3bqVr8Hhccnp9Ft0ipvhCXg6WrDxg8a42RW9P0bQFxkOikpGJwTvrjhLukbHnF7eDKifx0HiSVFof66KUpvKoPSJjHp3BOWLWRMUlUx42i2mnP4YFGmo0rzZ1Xcxxe3lLPSFQWxqLF23dUWdpuazSoMZtme2cQeFCegepbCQoMxbUGMAVO4KZlkUKlY/0C/POZaBC6v1pXUAmoyHNl/nPvBcRuYJEg4d4v6HH6F0dKTC4UNIqszlT5LVcSwf/wFpyUmYmDdFMimOQmmPpLDBs7IDHT7wRmVetJO+Ppk889GuuqS4NM79G8S1YyHotPpbu2cVR+p3LUN60l22/ziD9JTHsauSJNH2/Y/xbtWuUK4hp7zyweGzZs2iWbNmRERE0Lx5c0N706ZNuXnzJlFRBaNkt2zZwhdffMHixYuzPaYglupC1Sn43Ahn4JLT3AxPoJiNGatHNCyyogn0nqdG5ZxoUt6ZCe0qATBjlx/3ovM21in12AKU2lQu6cpSvn5n3qrggmSiJlT3H99f+h8o0lCmVSA6sC9Dl58jOjEtT88vkz0czB34tM6nAPx2ZzNhT39R6TTgUBZaT4NPrsKwnfp4qKxEExBmouSMuRlhJibQ4gtop49f4/g82D0BnigfISOTWx7lbrLr1i1L0QRwdO1K0pKTkJTFUJrXRWnqiaTQf26bD6hU5EUT6D1P7pUcjLKVW9mb0WxAJQbPaESVt0qgUEgE+8Ww+btzXP5P0HTAWKM5hBDsX7JA9jzlkFwLpytXrtCjRw9MTIw/YJIkYWdnR0RExEsblx169erFnDlzcjQmv4PD15+9R5NvfRi+whe/0HgsVEpWjWhASSfLfDlffjCiaRkalHEkOV3LpxsuotXl0XJKajy603qRu8WyL192qsKW21tot6kd005MIyE9AQ9rD9Z1X4SrjTW3IxIZsuwM6iySvsnkPz3K96CGSw2SNSl875RF3GK3+fDWp2D3/KX5Lbe30H5ze0bsG0H7ze3ZcnsLNP4YuswFJH1M1I4x+iLPMjK5RBMVReLh/wCwf8YyXZj/La4e1m/VN7VsifRU/F1i7Kv/oGbjaE7LQV4MnN4Qr0ZuSBLcvRLN0Q13MvUVOh1xYSGFYOWrS66FU8WKFTlxIvP25MOHDxMaGkrFitmv0iyEwNfXl9GjR7N27dpMx5OSkpg7dy6jR49m28O165chPz1OoeoUJm65wpM6Iy1Di61F0X+CeRKFQuLHt2tgbWaC791YlhzNmx1QfjvnYalLwl+UoOfAD1BnRDL9xHTEE2XCQ5JCsLfWsnpkQ5ytVfiFxjP0zzMkpMriqaBRSAqmNJyCUlKw38qSnVaWeq+RUqmPWXLMOtt9fHo816Ovc+DuARZcWMC0E9PQCb1HSSd0TD85nbCkMH0B4Z6L9HNdXA2bR+jTGMjI5AL1jp2g0WBeozpmFSpkOi50OnxWLAIhKF//LRQmxolYJQXYFSug5L8FgJ2LBa2HVWHg1w2pWN8VhdIBQ4zhEwRcuCynK8gBub6bf/HFF9SuXZv4+Hju37/P7t27+fvvv1m6dCnz58/P5Il6Ht9//z1//PEHYWFh2NraMmDAAMMxrVZL586dUalUdO7cmY8//pjg4GA+/jj3u6yejHHKawKjknjaOaMTEBSVXHDZuPMIT0dLpnatwuebLvPzvls0r+hC5eK5jwULj4nD5doyAPwrjKRDSUdOh55Gh/ESjU7oCE4Ipp5bPf4e2YD+i09xKTiOESt8WTG8HpaqV0uEvupUcqzEwMqDWOW3ikkuTiBJKITgQ9emVI7350HIfzxIfGD0k5Ce8Nw5H/2O3azcoEY/UFnCxnfBbxtkJEPflWD6av29yBQuQgjituiX6ex7ZZ3w0e/oIUJv38TU3AL3Kl24fzvccExSQItBXjku1PsqYO9qSdvhVfGo5MC+pUFokg/AEw+rvjvXkZ4SR6t3R6HMwb37TSXXHqeSJUty9uxZwsLCCAsLY9WqVQQGBvLPP//w/vvv52iuL774gsDAQJo2bZrp2IEDB7hx4wbbtm1j3LhxLFu2jG+++YaMjNx7H/LT45TV1n6lJFHa+dVZpnuSt+t40KayK+laHZ+sv0iaJndiUwjB7lU/4UIckQoXWvcdDUBgfGCmvgpJgaeNJwBebraGmnpngmJ4f+U5UjPkXCQFjSHx6MMAbp0ksTDiOGN8xjDnzBxW+q3k4L2D3Ii5YRBNjuaOeDt708KjBVIWT7n2ZvaPX1TuCgPWgYk53N4Hq9+GtOeLLxmZJ0m9dIl0/ztI5ubYdspckiktOZkjq/8EoGb73pzbo4/rqdupND0+qcXQWY0N2bdfVzyrOGJq7o2Z3UhMrd9GZTsSE4u3ALh8YA8bvplMSkJ8IVtZ9Hmp7ItlypRhzZo1BAYGEhAQwK5du2jVqlVe2Qbog787duyIpaVeeLRu3ZqMjAyOHj2a6znzM8bp6a39Sklidq9qr5y36RGSJDGnlzdOVipuhCUw78DtXM2z5mQAraMfLsM2/hhTlTnRKdH8duE3/Xke3lgVkoJpjabpPREPqeZux4p362OpUnLMP4qPVp8nXSMHEhckkcmRWbZ72njSumRrhlYZysT6E1nQagFbu23l9MDT/NfvP9Z0XsOvrX/l68Zfo3gqluSz/z4jJPGJ2IoKbfQpDFQ2EHQUVvWElNj8vCyZ14i4zfrcTbbt26O0ybxB4dSWdSSr47B3K0Hk/TJo0rSUqGBP/S5lMgVZv648SmWgMLFBaeqJ0sQWp5LNMLXqAZgScvMqy8ePJdRfTk77PF7KJ7d7924OHz5slMPpEVOmTKFs2bJZjMoZwcHBRp4opVJJ6dKlCQ4OBiAkJITQ0FCSkpK4e/cunp6eKBRZ68G0tDTS0tIYMmQIQ4YMIT4+Hk9Pz5e28Wn61StJs4ouBEUlU9rZ8pUVTY9wsTFjVk9vRv19jkX/3aG1VzHqls5++YHAqCQu7lnOIEUkKaYOuDR7D4DZp2cTmxZLRYeKzG8xn9DkUDxtPI1E0yPqlHJg+Tv1GLb8DD43Ihi37gK/DqiFSRHIvP4mUNK2JApJYYhTAr3IXd5+eZa/r6fpVaEXjUs0JjghmAxtBlNOTCFAHcCg3YP4rfVvVHaqrO9YugkM2w6resH9s/BXVxiyDaycc2+8XFz4tSctMBD1zp1A1rmboh8Ec373dgBK1ejBzTNJmJopaT2sMlIhl7wqaKo0KUHJKo6GVAZW9mbcu1aRo+vdiPBfS2piFGsmf0bN9iNpPrg9JkUkUXNRItd3nV9//ZWuXbty+vRp0tLS0Gg0Rj95FWgWERGRKQu5o6MjYWFhACxatIi9e/fi6urKzJkzSU5+9tb5OXPmYGdnZ/jJD9H0iEdb+1910fSIDtXc6F3bA52ATzdcIikte7ufNFodn60/zwj0X1pmTUeDypK9QXvZd3cfSknJzCYz8bD1oJ5bvefehBuWdWLJ0LqolAr+vRrGZxsv5d1uP5nn4mblxrRG0wxeo6w8g9mZo55bPRq7N2Z1p9WUty9PVEoU7+x5h+MPjj/u6F4H3vkHrFwg7Ar82REenH+cePN5CKH3UkXe1PffMQ7mVtULsHnV9Ak7ZV4r4jZtIqBTZ0RqKgDpd+8ZHRdCcPivJei0Wjyq1Mb/vH71okmf8tg6vx7fzznlyVQGkiRRqpoTg6Z3pt2H0zG1KAkinYt7fmPJ2PlcO/YAnVb28D9JrhNgtm/fnkaNGvH111/nmTFt27albt26RukFmjZtyttvv824ceOM2vr27cvYsWOzmuaZPPI4PeKRxyk/E2C+TsSnZtBx3lEexKUwoH5J5vTyfuGYhYf8Ob9/DctUP6FTWaP45Boxko6e23sSkxrDB9U/YEytMTmy44BfOKP+PodGJ+hfz5OPW5XnbkwyZZytXhuhWlQJSwojOCH4mZ7BnJCQnsD4Q+M5E3YGpaRkWqNp9KzQ83GHKH9Y2Q3inxBLkgLqjgTXypAYAYnh+n8Twh6/1j5nO7mkgPFXZc/Ta0LyuXPcHTTYuFGhoLzPQUzd9J9Pf9/TbP9hBgqlCa4VRxEbpqJkVSe6jKkuVyTIgvS0NLZ+O5f7fvqSSApVFYqV6UaD7hUpV9uFpLg04iJSsH8i8WZBUVQSYOZ6qa5q1apYWOT/TcrV1ZWYmBijtujoaFxdXXM8l5mZGWZmZnll2huHrbkpP7xdnYFLTrP2zD3aVXGlpVfWdaAArj5QM3f/Tdab7ABAUW8EWNgz578JxKTGUMGhAh9U/yDHdrSp4sr8/rX4eO151p0NZv3ZYASgkGBOL2/61cvjTOcyBtys3F5aMD3CRmXDH23+YMqJKfwT8A9TT0wlLCmMUTVG6W9ozuWhz5+w/ImsxkIHZ7OR7NbcTh8rFX/fuF3o4OpmaJKzhy6ZooUmMpKo338ndt36zAd1OtLv3sPUzQ1NejqHVy4BwN2rBZEhKswsTWg1xEsWTc9AZWZG36lfcG6XF/+tXoYu3Y9w/zj2LOqKjbOjPs+V0O8TaTHY67UPqM+KXC/VjR07lvXr1xMaGpqX9mSiSpUqRrvf4uLi8Pf3p2rVqvl6XpmsaVzOmRFNywDw+ebLxCRlnXMnNUOfOLOOuE4dxW2E0gwajmb/3f3sCdqDUlIyo8kMTJW5q9nXuXpxJnfWx8U8cpnqBEzacpVQdcqzB8oUKUyVpsxpOoeR3iMB+O3Sb0w7MY0M3cNds8/yHnk2gDrvQPMvoPPP0G81jDgA4y7DV+Hw5T0YsS/r4sL7p4DPLNDJuzNfNbSJiUTMn49/u/bErlmbdbZ5hQJVKf3D07l/tqEOD8PC1oGoUP33RbP+FbGylx+gn4ckSdTt2oPeE6ejsrBEaENIT1hDfESw4QtXCDi8+gaJsamFa2whkGvhtGjRIqKjo6lSpQplypTJ9HPx4sU8MXDQoEH4+Phw/fp1ABYvXkyVKlWoVq1answvk3MmtK9E+WLWRCakMXnblSzj2X7ef4tb4YmMM9cHbEq1BhFrqmLmKX2ZjeHVhlPV6eXEr1cWOaW0QhAUlbclYmTyF0mSGFd7HFMaTkEhKdjqv5WPfT4mKSNJH9D9tPiRlHpPVNf50HIS1BsBlbuAZz1wKAWmD5cP7NyNiwtLSijVWP//I9/Dyu6QEI5M0UeXlkb0ihXcadOW6N//QKSkYF6jOiVX/kXxmTPg0YYghYLi30zH1M2NhOgoTm3Ve6TMbJojhCnlartQoV7OVyveVErXqM2g2T/jULwEQpdAesI6NKmX0WbcQ+gSEDpQR7x5D6q5XqqrX78+pUuXfubxEiWy777r2rUrABcvXiQwMJCrV6/SuXNnRo0ahZeXF9OmTaNx48ZUq1aN69evs3fv3tyaLfM8srn7yNxUydy+Nen523F2Xwlj+8UQetR63P9UQDRLjgZQVQqksbiov/E1HsucM3OISY2hnF05RtUY9dLmPsqZ9WR8uELilc2Z9abTt1JfilkW4/Mjn3P8wXHe3fMuC1svxKXrfNg5HoRWL366zst+jFLtoVCu9cPiwmX14y5v0M8XdBT+aAq9l0LZ5i+cSqbgEVot6h07ifz1FzQh+tUNVdmyuHwyHps2bfTLbfXrY9W0Kel376EqVdIQ23Rk9Z9o0tKwcSpDSlIZLG1NaT6gkrxEl0McS3gwcObPbPthNg9uXEaTcuDhEQkTyzbYODcqVPsKg1wHh+cl69dnXqeuUKECtWvXNrwODAzk9u3bNGjQADs7uzw5b06CwV57zq+EneP0MSCSQv+kXnvoc4f8cvA2P++/hY25Cfs+aUZxOwsSUjPoOP8o92NT2F5sCTXiD4H32xysN4Dxh8ajkBSs7rSaas554zFcf/aeUYmbss5W7P+0Oco3bIvx68SVyCuM8RlDTGoMJaxK8Hub3ykrmRmLnxwQlhTGvfh7lLQt+Tg+K/IWbBwGEX6ABC0mQrPPQCFvvS4KCCFIPHSYyLlzSbutzx1n4uqKy8djsOvRA+kF2a3v+11l/fQvQZJQ2QxEoXSl04felKnhUhDmv5aoI8JZ+vGIp1olanacSut38qfu69MUleDwIiGcCgtZOD1E/UC/VfuJHD3Z2X2k0ero/cdJLgXH0bS8MyuH12filius9w2mkV0Ma9I+RkKgHrGP7ie+IDo1mhHVRjC+zvg8NT9UncLJO9FM2nqF1Awdn7WryJhWmetUybw6BMcH8+HBD7kbfxdblS1fN/4aO5WdsfjJAiEEKZoU4tPjSUhPYFfALv68+icCYUih0KvCwzw/6cnw7wS48Lf+ddkW0GspWMs318IgIyyM9KC7aBPiiflzBSnnzwOgsLPD+f33cBg0CIX5i3dx6bRa/v5yHJH3gjC3qwWKlng1dKP1O1Xy+xJea+5dvczGGZMytZtadab5oC7UaJ1/6X0eIQunIoAsnB4SeESf5+ZpyraEXovB+tk75wIiE+n0y1FSM3S08iqGz40IAM5U20Yx/w1QsQMTPcuyK2AXZe3KsqHrBsyU+ROYuencfT7beAmlQmLDBw2pUyr7STplih6xqbGM8RnD5cjLhjYJiYbFG+Jm5UZCegIJ6QkGkZSYkUhCegJa8eygbwmJzd02U8HhCWF9cS3886m+Rp61G/RZBqUzl3+SyT/iNm0idMpUfcTxQyQzMxyHDsVp5AiUOVhluLhvNweX/YbS1AITy2HYODnQf2oDzF6xQutFjYToKJaMfjeLmFZzVLY96PRhO8rVfva9Ii8oKsJJTrssA5bPeMIOOAS/1oUzS565A6msizWTOul3qzwSTcWJxumOvvzBIa9W7ArYhUJSMKPJjHwTTQC9a7vTo2YJtDrB2LUXUafkvp6hTOHjYO7ArKazjNoEgpOhJ9nqv5UD9w5wOuw012Oucz/xPnFpcQbRZCKZYGOaueyGQNBvVz+mHp+KX7SfvrHmAHjvELh4QWKY/iHiyA9Z79iSyXMywsIIeUo0CaDU36so9r9PcySaUhLiOb5+FQCSSUMkhSWthlaWRVMeYOPkTNv3P0Z6GIgvSQqsHByBVNLjN7L7t42E+scVqo0FxUt9mnQ6nVF5E19fXxQKhVFskswrwJlFxq8lJTT9BO4chJALsPszuLAKOs8FjzqZhrf2KsbU7dcMr0eY7EYpNER4NOAb/3UADKs6jOou1fP1MiRJYkaPapy/F8e9mGQmbbnCgoG15GDQV5iIpIgs23tX6E1lx8pYq6yxUdlgq7LFRmVj+DFXmhOeHE77ze2NysQAZOgy2Oq/la3+W6nhUoP+Xv1pV6odqvd84J//waW14DMT7p6EttP1mcjlci35RtiNC0hPeTEkICrmAZ68OMnukxzfsJrUxASUpi4ozWrg3dwdz8qy5zmv8G7VjtI1ahMXFoK9WwnMLC3ZveAn7vieJj3hXzbNiWbgjE9wKpH5oeV1Itcep/379zN48OOMrZ999hkNGjSgfv36fPLJJ3liXH6Rn0V+XznunoBz+orhvP0XDNsF469A6ykw8iB0/kmfTDD0Eixtrd+NlGyckPRuzOPt//YkMEDpA8B0B1eiUqIoY1eG0TVHF8jl2Jib8suAWpgoJP65Esq6s8EFcl6Z/OFRjbwnUUgKRtUYRT+vfnQu25lmHs2oWawm5ezLUcyyGBYmFkiSlGWZmK8bfc3KjivpWKYjJgoTLkVeYuLRibTd1JZfri4jrN3X0H0hmFjoHxz+aCqXa8lnwizSeHrxRyuBn0VMlv2zIiE6issH93Bp324AFGbNsS9mRaNe5fPQUhnQe548q1bHxskZlYUl3f/3FfW6vQ1AeuIZ1nw1mdiw17s4d65jnAYPHkzLli0ZMWIEd+/excvLixMnTmBra0vVqlUJCQnB0bFoK/03PsZJk6a/MUTd0u+g6/Zr1v0SI2H/VLi0Rv/a0gnafgM1BoJCQag6hSbf+qATMN5kE+NNtrDSvBQ/FNcH5K7suJIaLjUK7rqARf/dYc6/NzA3VbBzTFMquL7eT0CvM1tub2H6yenohC5zgHc2eFaZmKiUKDbd2sTGWxuJSNZ7thSSgpaeLelvW5kGOz8nXKnknqkJJTM0uOnQP1TInqc85cLsLzBfuQOB3tOklWBxRwVHapjSq2Iv3q/+/nM3BFzx2cf+xb8aYm8kpSvmdoPo+VkdipfLmx3YMi/m8kEf9i/5BYQGEzNnBs6ciUtJjzw9R1GJccq1cGrbti0fffQRPXv2ZMyYMcTExLBmjf7GWq1aNdatW1fkk1S+8cLp0Bz471uwKgZjzoCFw/P7Bx3XL2VE6pOR4tlQ75Fyq8b6s/eYucWXo6oxSMpkOpf2Qi2SeKfqO/yv7v/y/1qeQqcTDPvzDEdvR+HlZsO20U0wl6t8v7LkZY28p8nQZXA4+DBrb6zlbNjjKgXOGg3RSiVCklAIwbSoGHr1Xg9l3srT87/JRDy4zYOO3TFPFyzqIBHqKBHhoMTWswwB6gAATBWmvF3xbUZ6j8TlqXjMrAOWJWp3nUbLwXUL8EpkAG6fvcLOubMQ2kQUJhb0/HwSpWvUyrP5X3nh9M0337B582batGnD77//zvnz5/Hy8kKtVuPu7k5UVBTm2dg6Wpi80cIp4obe26TLgLdXQNWeLxwCgDYDTv0Oh7+FjCR9PFSDUdDiSxIO/YzN6bl8VaIkO8ygtG1pNnbdiLlJ4XwOIhJS6TT/KFGJ6QxtVIpvuhdtIS9T+PjH+rPu5jq2+28nVWtcSkIhBHsb/4BbxY6FZN3rRaomlXUftqPB0UiC3c0ovXwB6pgbeBavi5tbTXzDfFl4cSG+4b4AmCnN6F+pP+9WexcnCyfg2Vvke0+aRekaBevlltETeDGIbT/MQqcJBUlBy2EjqdWha57EmhYV4ZTrGKcJEybQqlUrfH19+eOPP/Dy8gJg2bJl9OvXr8iLpjcanQ52jtWLpoodoEqP7I9VmuoLpI45C1W667M5n1oIc6tic3ouRyzM2WGmd7nPaDKj0EQTQDEbc358W//lufLkXfZeCys0W2ReDco7lGdyw8n82PzHTMd0kkTwwcmQElfwhr1m6ISOOds+oc7xSADKD2xLxRVdqbf9f7gtagnnV1LXrS7L2y9nSbsl1HCpQZo2jb/8/qLjlo7MOzePuNQ4VJZOWcwuYW7jXLAXJGOgTM3SdPlkOgpVZRA6Dq1YzP4lC9BqXn6Xc6g6lYuhyYSqC7c+Xp7ncRJCkJSUhLW1dV5Omy+8sR6ns0v1S24qaxh9GuxeYh3a/wDs+gTi7uFvasK7xV2JUyoZqk5gwrDjRSIeZNY/fiw5GoidhSn/jnuLEvYWhW2STBEnLCks8448IfglPJKWbvVh0GYwURWega84v5z/BdXsP2h+VaCtVZFqXv8ZpSN4OgGvEILjIcdZcGEB16L1O3itTK0YZDKE9M37gEfpUvRlQPp8ORT3Si8IPZDJVy75BHN45Ro0KUcB8KhcjZbvvE9qYiIOxUtg45QzcbvyRBBf77yGTuhLa83p5U2/eiXzzN588zjduHGDS5cu8eDBAzIyslaPkiS9EqLpjSU+BPZ/rf9/66kvJ5oAyreBznPZYm1FT/fixCmVIASeGRn6EhlFgAntvfB2t0OdksH49RfR6t7YnK8y2eTpHXkASBJfFXPm+oOT+oeFNzd38EuxzX8bew8u5q2r+vevXO2MzO+l0MHGd+DeaUB/X2nq3pS1ndfyS8tfqORQiaSMJO79dxDQgsIFU6s+mNmNxMS8GnbF5IejwqZGK0/qdu2NqXUPkFTcv36VVV+MZeOMSSwZ/S5XfPZle65QdQrTdlwzlNbSCZi05Sqh6sIpMJwj4fTee+9Rs2ZNPD098fDw4MqVK/llV77yRqcj2D0B0hPAvS7UG/nS08WmxvJ3wk2mOTvCozVsSWKOkwNhFkVjJ5vKRMEvA2phpVJyJjCGBT7+hW2SzCtArwq92Nt7L8vbL2dHjx3UKlaLBIWC992K4X9tPRzNvJwn83zOhp1l+snpDDisQwHYlNFhkXQ86873z8DydrC8A9zaC0IgSRItS7ZkQ9cNTLH7EreodABMrVqhVJVEKKw4XGYdO8K2cCfuDhqdpuAuTiYTjXuWo1KjhphadTNqF0Kwf8kCEqKjsjVPYFRS5pQVQhAUlZxl//wmR0t1ERERCCFwcnLC5AVFFl8F3riluus7Yf1gUJjAB0fAtWqOpxBCcCv2FkcfHOW/4P+4HHU5U4LBRyxvv5x6bkVHnG45f59PN1xCIcG69xtRv0zRTpchU7RISE/gvX3vcS36Gk4aLStCwyndfRF49yls014JgtRBDNo9iBJ31HzztxYkQblOEajKVtDHWp74VR8zKSmh5USIvQuX1uljMQGKVYEm46BabyJDUlg96Qu06cGkWZbgnwa3sE53QG0eSZKZ2nBOM6UZ5e3L4+XoRSXHSng5elHRoSJWplaGPlkWgZbJM7QZOtbP3ELojRWZjvWdOhvPqi9OjByqTqHRHB+jNqUkcezLlhS3yxvvolyrLpu8UcIpVQ0L6utLSrz1mT7BZXaHalI5E3aGI/eP8N/9/whLMg6yLmtXlkB1IOKJZwKFpGBv771F7ovo0/UX2XLhASXszNk97i3sLeU4FZnso05TM3zvcG7F3sJVo2FFeCweg7ZCqUaFbVqRRp2mZtA/A7kbf5efVmrwDJGwL59K8Qkf6cWQiUpfbDwmABzLPo6NjA+FU7+B73JITwQg1rwO64PeJin2H5BM2NYslDirx54HCYkqTlUIVAeSrMnaI+Fp44mXoxdanZZDwYeyLgItk2dEBYfy12fvw1N+o/cW/omt84uLakcnplFn5gHD68KOccqRcLp06dIL+2RkZDBq1ChWrVpF5cqVszt1ofBGCaddn+i/fBzLwYcnwDTzbrcnn7wAjtw/wpH7Rzgdetpoa7aZ0oyGxRvSzKMZzTya4Wbl9tJJCguKxDQNXX45SlB0Mh2quvH74NpySRaZHBGdEs3wvcMJUAfgnqFhRWwabsP3g1O5wjatSJKhzeD9XQPwjbtJ2xsa3tsKkolEuU1/YurVIHuTpMTC2WXEH9vI5uDxxMb9g9DFUadGSWK6N2L6uR8zfffohI77Cfe5EXODm7E3uRlzkxsxNwhPDn/maYrqA9+rTmJsKsv+twhN0gGeFE8New+lSd++LxzvcyOc4St8KeVowZgGjjTyroCHo9ULx+WEnOiBHK231axZM1v9ateuTcWKFXMytUx+cvekXjQBdJ2fpWjacnsL009MR0fWy25uVm4092hOM49m1HernynNQK8KvWhconG+JSnMK6zNTPh1QG16/X6cPdfC+O3wHWqVtKeMs1WeuXxlXm+cLJxY0m4J7/w7jODE+7xnL/hzTR+cRxwES3n590lEajxfb+uLb9oDrDVa3jusAxQ4vjsi+6IJwMKBRO+P2b63EfEpJxC6OCyUGTROWYdqy0YaoyPYVImnRodbNb1nSiEpKGlbkpK2JWlXup1hqtjUWG7G3mR/0H423NpgdBqd0HEv/l6R/f56VYmLSMFE5Y3SpDQ6bRw6TQja1OOc3rqaMjWrU6Ki13PHX7gXB0Dd0o7UKG5JcbvCTXeUI+EUFvbiPDgqlQoHh6K9DXThwoUsXLgQrVb74s6vOpo02DlO//9aQ7LMehyWFJalaKriWIW2pdvSzKMZFewrvNAz42bl9kp84Xh72PF5ey9m7b7OD3tvAvnj+pV5fSlmWYyl7Zfxzu4hBBHBeySzfF0/HIbszPLB5I1B/QBi7ug926EXWebzOTusFCiF4Bd/d4i9j8LODqf33svRtMnx6eyYfwF1ZBzatFMANO3YAlV8HETewA1w0z4MBN85Dsq1fmYqFAdzBxoWb0hp29Jsur0pU4zmNv9t1HWrm6lGokzusS9mod87pLBBqbBBYeKB0Eaiy7jFP798z5Bvf8H8ObvxHwmnmp52wMvng3pZcvTJcHV1feFPURdNAKNHj8bPz4+zZ8++uPOrzrG5EHVTX1al3Ywsu9yLv5elp+mzep8x0nskFR0qvnbLWZ28jQVeYW9vlXn1KGFdgmUdVuBi5oC/SsUHmnvEb/vgzU1TcH6lvhjyX11hbhX27hjBfCv9LWZSqd7Yn9Y/qDq/NxJlDkIjUpMy2PnrRWLDkpHEaYQuDZdSZag26HPo+H3mAUIHWz+AsKvPnffplBMS+u+4nQE7+ebkN8/c9CKTc6wdzGkx2ItHWlSSJEyt2mJq7kB8ZAR7/5jHs6KGdDrBpeA4AGp62BeMwS/gpST1lStX+Oijj+jYsSO3bt0CIDExkfPnz+eJcTIvSeRNOPqT/v8dv3tmLTqlInPqeoWkwNPGMz+tK1TuxmQOGtUKgX94YiFYI/Oq4mnrydIOK3A0tea6mYoPY06SdPDrwjar4FE/0Ht6HoqNKyoVX7nos3oPrtiPNvfKogkJxcTVFYfBg7M9bXqqhl0LLhEVnIjKIp60xIsAtBg6EoVCCU7lISvPUNBR+KMJrOkP932fOf+TKSf29dnH7KazUUgKNt/ezJTjU9Dq3oBViQKiSpMSDJ3VmB6f1KL7JzUxUVkgmXZCUpjgf/YUF/bszHLcnchEEtI0WJgqqehaNHJE5lo4nTlzhmbNmpGQkEBYWBixsbEA3L59mx49eqDTFV21/kbkcdLpYMdY0Kbrt/o+oxadVqdl/vn5Rm2PAixfhWW33FLG2QpFFk606bv88AuJL3iDZF5ZytqXZXGHFdgqzblsbsZo/zWknPuzsM0qWGLugNARplSy28qCj1xdSFMoaOZYjU+qfkzUH4sAcB4zGkU2y3Fp0rXs/u0y4YHxmFmaYGt3DqHTUa5uA0pWe1iHzs5dH7cpPXz4k5TQ/MuH33cS3PoXlraGld0h6FiW3kA3KzfqudXDzcqNruW68t1b36GUlOy4s4OJRyeSoSv8paHXBWsHc9wrOeBRyZHGvcqhMHHF1LIZAP+tWk7YnduZxjxapqvuYYeJsmgsn+bail9//ZXZs2ezatUqypcvb2ivVasW5ubm3L17N08MzA/eiKW6c39C8Cl9WZVOPz5OTvkUq6+v5kLEBaxMrfi7498sb7+cvb33FskdcXlJcTsL5vTyRvnwfZEksFAp8Y9IpNuCY8w7cIsMbdEV/zJFi0qOlVjcYQXWkgnnLMwZd3Y2af4HXjzwdSH6DlusrWjvWYIvirkQZ6LEVaPh+0bfoF6xEm1sLKoyZbDvmb1i4lqNjj2Lr/LgVhym5krqdFBw//oFFEolzQYNN+5ceyiMvwLDdun/bTlRX7h8zFmoOUgvpgIOw4rO+mSat/c/dzm1Q5kO/NT8J0wUJvwb9C+f//c5GVpZPOU13s098KzsgGRSAws7L3RaDbvmf0dacpJRvwvBeqdMzZL2hWBl1uRaOEVGRlK6dOlM7UII4uLiUCiKhjJ8I4kPhQNf6//fagrYZ73kdjf+Lr9c+AWA/9X9HzWK1TA8eb0J9KtXkmNftmTtew058WUrjkxoSfuqrmh0gnkHbtN9wXHZ+ySTbao6V+X3dsuwQMFJC3PGHBjNif9mEBZ2sbBNy18CDhO270umOzuie+IBLdLEFHVyBtErVgDgMn48UjYSJ+u0OvYvv8bdq9GYmCroNKoql/auAaBm+y44lsgi6NvOXb/x5cmAcOcK0OM3GHsB6o4ApZn+YXJ1H1jcHPx26D3z6gcQeET/70Nal2rN/JbzUSlUHLh3gE8Of0KaNi13749MlkgKiVZDq2BuZYqOVphZOaIOD2PfH78YxTs98jjV8iw68dO5VjctWrTg999/R6N5nNJeq9UyadIkHB0dKVWqVJ4YmB+81kt16gew6V1Ii9eXVamf9e4VrU7LlONTSNOm0bB4Q/pUeDOzHxe3s6BROSeK21ngYmPGH4PrML9/TewtTfELjafbgmPMP3Bb9j7JZIuabrVZ0HohJgJOWaj4IGgD7fcMZsu+8YVtWv7w4DysG8R1JUaiCUCHIPKPPxDJyZh7e2PTru1zp0qMTeX+9Rj2Lr3GnfORKEwkOo7yJjr4DNH372FubUOj3gNybqNDKejyM4y7BI3GgKklhF6CDUPg58owt6o+oH1eNX2A+0OaeTTj11a/YqY047/7/zHWZyypmtTnnEgmp1g7mNF8YCUkhTkoOyAplNw6fZxL+/8F9Hn3boUnAFCrCHmccp05PD09nfbt2xMSEkJqaiply5YlMDCQ9PR0du3aRe3atfPa1ixJTEzk6NGjODg40LBhwxyNfe0SYJ5fqY9repRgrMVEaPFlll1XXlvJD74/YGVqxZZuWyhhXaLg7HwFiEhIZfLWq+zz0yfLq1rClh/frkHl4q/B50QmXwkLu0i7PYMRTwgJSQi2a10oU7ErlGsJbjXgVffKR96CPzsQlRrLiFJlCHhqm7hbnMQvSwRoNJRc8SdWz/l+9jsewuG/bzxeQZOg4wfeuFe0ZNm490mJV9PynQ+o3bHry9udFA2nf4dTvxuykRuQlPrlvic8V2dCzzDGZwwpmhTqu9Xn11a/Ymlq+fJ2yBjYt+wat8+GY2JymcTIAyhNTRk48yf8tbYMXHIad3sLjn/ZCq1Wy507dyhXrhxKZeZNTS9DTvRArv9yVSoVBw8eZMaMGfTq1YtatWoxefJkbt26VWCiKTIykk6dOnH16lW+/PJLfvjhhwI5b5Ekyt9YNAH8972R+/kRTy/RyaIpM8VszFk05LH36VqI3vv0y0HZ+yTzfO6F+hqJJgAhSfSTIph/fh5Ry1rBD+Vg4ztw7i+Iu1c4hr4M6vuwqif30+MY5lmSADKwMrEybO1XSApmXK8KGg1WjRs/VzQlxqYai6aHFCtlw+mtG0iJV+NYwoMabTvmje1WTtBqMvRemvmY0EKYcYWM+sXr80ebP7AyteJM2Bk+PPAhSRlJmcfK5Jpm/Sti7WBGRoY3dq5V0GZksGvet1y4o88dWdPTvnANfIpXvlZddHQ0Tk5O3L9/n44dO3LlypUXjnkyAeatW7defY/T3ROwaTgkhGY+NmyXUdJLrU7Lu3vf5ULEBRoWb8jitotfuxxNec2zvE/2lqYERiXJWcdljAgLu0j7PYONl66EMGzQMBWCbolJDFXHUzbjYaiDYzm9J6psS/3fq7mdcTLJZyRzLBSSY2B5B26rA/igRHEiFeBu7c7itotRKVUEJwRTPCydhAHvgRCU3rwJi6rPLih+/2Ys2+deyNTeZpg7/y74Aq1GQ88vplG2dh6HVqgf6Jfnns7XZFUMui+Aiu2Nmi9HXmbU/lEkZCRQ3aU6v7f5HVvVK3zfKGIE34hhx7yLCF0KCrGelPgY1CWqsVLVlMldqjDyrbKvvscpLxFCcPHiRcaNG8f69eszHU9OTubXX39l3Lhx7Nq1y+iYk5M+V4hKpcLSMnvu09dmV116Euz+HP7slLVokpT6gplP8OQuuumNp8uiKRtk5X3q8stRGs/xYeCS0zT51of1Z19Br4FMvuDmVpNpHh1QPHwmVQjBNPf2zGsxjxouNciQJDbbWNPdowQfl6rAeXMLRMwdOLsU1g+C70rD/BrPjL0pVNISYXUfLiYE8k4JNyIVUN6+PCs7rqSkbUnD1n7d7ytBCGw7dXyuaAKwc8mcnkBSgN+RjWg1GkpVr0WZWnXz/loypTJQ6EVTUgSs6QtbP4SUOEP36i7VWdJ+CXZmdlyOvMx7+95DnaZ+KRPCksI4E3omU+H0NxFPL0dqtPJEUlhgYtEJSaHALuQqVRKvF6n4JshByZXw8PAsd9E9i//++4/69etnq+93333HwoULiYyMxNLSkn79+hmO6XQ6unbVr2t36tSJUaNGMWnSJD766COjOTZu3Ej79sZPCM/itSi5EngUdoyB2CD969pDwbUa7JmodzdLSug6z+hJNUgdJC/R5RJJkuhe051G5Zz434ZLHL0dZTj2KOt4s4ousudJBoBebX6kcdhggkPP4Vm8Dm5uNQH9bq0LERf48+qfHA4+zGFFGoeLu1DduiTvSo60fHANZfSdx3/XoPeI7Bz/3DIiBYImDdYP5njMNT5xcyVFIVHDpQYLWy/EzszO0C357FmS/jsCJia4jB37wmkj7xnHGUkKqN5CyenNp5AkBc2HjMi/B7zaQ/Xva0yA/iHT0hF8ZsLJhXBpDQQcgq6/QEV9rbuqTlVZ1m4Z7+9/H79oP4bvHc6sprOIT4s3CMes0Og0xKfHo05To05TE5cWx8F7B9nuvx2BKNKF0QuShj3Kcu96DLGhxbAv2ZrYoP00jz6Gm7YXUHTqQGZ7qS4tLY09e/YYXoeHh/PNN9/w66+/GqUeWLhwIW+99RZffvklpqamOTKmbdu21K1blzlz5hja9u3bx5AhQwgICMDKyoo9e/YwdOhQHjx4YJg/IiKC1q1bc+jQIZydnbN9vlcyODwtAfZPA99l+td2nvqnpvKt9a/VDx5/CTzxJSsv0eUdJ/yjGLj0dKb2te81pFE5p0KwSOZVJFAdyF/X/mLnnZ2k69IBKGlTkmH23nQ7+gdxCgX3TE0omaHBTavNtOxeoOi0sHkEe4L2MtHFCY0k0cS9CT83/9koUDo9NJTgke+RfucO9v37Ufzrr587rSZDy9rpp4mPSsW7hTvlahXD1tmM7T9OIjzAn+qtO9D2/TH5fHFZcO80bPtQv1QKUHMwtJ8FFvYA3Im7w8h9I4lKefwAJSFRz60ezhbORgJJnaYmISPhhaeUkNjdazceNh75cUWvDJH3Etj0nS9ajY6QlM04pd3DsYQHg+bMRWmqKhJLdbmOcfrwww+pVKkS48ePN2qPjo6mcuXKhIaG5vjCshJOo0aNIi0tjT//1Gfi1Wg0ODs7s2XLFlq1akVGRgadOnVi9OjR9OjRI1vneWVjnO4c0geAqx8uC9UdDm2mg/mLbZd30eUdoeoUmnzrg+6JvxwJODGxlexxkskxUSlRrLm+hvU31xOfrs8bZqXTkSxJCEnSL/VFxdCr/v+g2f8K3kAh4J9PWX9zA7OcHBCSRMfSHZnVdBamyscPx3GbNhE6ZaohuWSxzz/Hafi7z5363J4gTm0LwMpOxcDpDVGZm3Dtv4Ps+W0uKgsLhs9bjJV9IeXvSU/We59O/QYIsCkB3X6FCm0A8A3z5d29z7++p7ExtcHOzA4ThQlB8UGZjtub2fNO1XfoU7GPkRcvrwlLCuNe/L3neskKk0efi1RdErq0NShSE6jSrBWN3h6E33lfqtSui30x1zw9Z06EU7aX6p4mICCAunUzrzs/ijm6ffs2Xl5euZ3eQHBwME2aNDG8NjExoXTp0gQHB6PT6Rg8eDDVqlXD29ubO3fu4O7ujvkzUvqnpaWRlpbGkCFDGDJkCPHx8Xh6vgL12FLjYd9kOP+X/rV9Sei2AMo2z9ZweYkub3mUdXzSlqtoH94kBHDAL5whjUoXqm0yrx7OFs6MrT2Wkd4j2eq/lT+v/El4SrjhuE6SmO7sSOP/ZuGWGqd/WCrAVAbCZxaL/TexwFm/VNKvUj8m1p9oVOMyIyzMSDQBRPz4I7adOmLqlvWNOSkuDd9/9RUmGvUqj8rchIzUVI6t1X/PNejZr/BEE4DKEjrMhspdYftHek/+6t5QazC0n/3MIsB9K/alukt17MzssDezx87MDjszO2xVtpgo9LfcsKQw2m9un2mOuLQ45p2fx6LLi+hRvgeDKw+mpG3JPLskIQSLLy9m4cWF+iVCFExrXPSWCGu1K8WefYHYJ1uhcO1Byr3V+B3xwe/oIRCCUysk2r7/Md6t2hWKfbn+62vWrBlz5swhICDAqH3RokXEx8fnKB7qeURERGBvb2/U5ujoSFhYGP7+/lhaWhIXF8fMmTOZOXMmwcHBz5xrzpw52NnZGX5eCdHkfwB+a/RYNNV/Hz48mW3RpNVpmXpi6huf6DKveTLr+IctygHw9U4/jj0R+yQjkxMsTS0ZVHkQM5rOyHRMJ0lcMlPBiV/0CW4zUgrEJt3J3/n+2hIWONgDMKrGKL5q8JWxaAoPJ+SLLzOXMdHpSL/77E0TJ7fdQZOmxbWMLRXruZIQHcWBZb+TGBuDrYsrtTt2y49LyjmlGsGo49DgQ0CCC3/Db40oGXPPkH7hEQpJwXvV36N7+e608GxBzWI1KWNXBkdzR4NoAn19vGmNphmlb5jScAozm8ykokNFUjQprL2xli5buzDOZxy+Yb7kZnFICEGAOoANNzcw4b8JNFvfjAUXFyAepq3RoWP6ielFLjg9Q6djq1kq6Qh08S54VG2pP/DoQVUI9i9ZQEJ04Xzf5trjNGHCBI4fP46Xlxd169bF0dGRmzdvEhgYyG+//fZMr09OUalUZGQYJ1ZLT0/H3NycihUrGpbwssPEiRP59NNPDa+LrMdJ/UCf2fbyBvDbqm9zKKPfIlu6aY6mknfR5R/F7SwobmdBw7KOhMensuX8Az5afY5to5tQ1qVoVPGWefUoY1cGhaTI5I2Y4laC2Kgo+vptQxEfAgPWglX2YzpzSsaltUw79wM77fTLFl/W/5JBlQcZjov0dGJWrSJq4W/okpMzT6BQoCqVtbckLFDNzVP6m/Vb/Spy9fB+9i/+1SAOSteojYlKlcdX9BKoLKHjt1ClG2z7CGIDcds4gmmlqjNdikX3aEnVvW22l756VehFY5uyBIf64lm8rmEDQbdy3TgddpqV11Zy9MFRfIJ98An2oYpTFYZWGUq70u0wVZhmudwmhCAoPoizYWfxDfPlbPhZozisrNChY8bJGcxqOgt7c/uXeZfyjOuhCUQJHafsdDRTKwm/l9nzKHQ64sJCsHHKv7+BZ5Fr4aRSqdi9ezf79+/nxIkThIeH89Zbb9GzZ08qVqyYZwa6uroSExNj1BYdHY3bM9y/z8PMzAwzM7O8Mi1/OL8Sdo41fnpr+JE+YZvKKkdTyUt0BYMkSczu6U1QVBLn78Ux8i9ftn7UBDvLnG2OkJGBx96I6SenoxM6FJKCElYluJ94n1lOdvxjY8XX4ecpt7QNDNoEzuVfPGkOCAu7iP/FlfwVtINTNlYokZjRdBZdyz3O2p108iRhM2aS/nDFwaJmTSwbNSJ60SJ9/TeFguLfTM9ymU7oBEfX3wbAq5EbljbpRqIJ4IrPXhr26lcoN8XnUqoxfHgcDn4Dp/+g193LNFYqCTY1wTNDg1vQUrAqo9+cY24PFg76gHJzezB9yplwfiVuO8fhJnT6rYRd50PtoUiSRMPiDWlYvCEBcQGsur6KnXd24hftx5dHv2TuublUd6nOwbsH0aFDQqJL2S5ohAbfMF8iUyKNTqNSqKhZrCb13OpRzr4cnx3+DB3GovzIgyN029aNT+p8Qo/yPQr9AfviPX1hX8tKtpROtSDwYgL6SNLHnxFJocDerXDuabkWTo9o0qQJjo6OhqDwvPbgVKlSBV9fX8NrtVqNv78/lStXztPzFAnUD2DnOGPRJCn09ZVyKJrkJbqCxdxUyaIhdemx8DgBUUmMXnOeFe/Ww0RZJFKlybxi9KrQi8YlGhOcEIynjScuFi6sv7me+efnc5Fk+riX4L24GEYua4Oq/xr9DT0P2HLgM6bf36NP3mlhgYmAea3m07ykfqkkIzSU8O++J+HhDmuloyPFPvsMux7dkRQKHPr1Jf3uPVSlSj4ztunmmTAiguIxNVPSsEc5ooNvZlqGKkxvwgtRWUHH7/Sxpnsn4abV6nc9PmLvpKzHmVjoRZSFg/7/IeceH3tGyomy9mWZ1mgaY2uNZcPNDay9sZbw5HD2393/eCiCnQE7H5v3UCjVdatLPdd6eLt4Y6Z87DCY5taC6aEHDV6yd1wacEQTg3+cP1NPTGWb/zYmN5xMBYcKL/tO5ZoLwXEA1CrlSMt6pQgPVKPNaIMm+QB68SRRteXgQvt8vJRwWrp0KRMmTCAuLg5JkhBC0L17dxYvXkyxYsXyxMBBgwbx008/cePGDby8vFiyZAleXl5Ur149T+YvUsTcyZzFVuj0QYk5zN8iL9EVPC42ZiwZWpc+f5zgmH8UM3b5Mb17tcI2S+YVxc3KzWjZZ2DlgbQq2YpZp2Zx+P5hfnewY296BtPW96F2x1/AO/cPRxnxIRw6/i1fh/sYlYvRIaikckCXnk7Mir+I+v13REoKKBQ4DByIy9iPUT6xA8nUze2ZggkgPVXDya36Lf51O5XGys6M2JDMDxeF6U3INlV66DftGH1nS3oRm5ECqXGQEgupan0fTQokpGSdrBj0+ff+6grlWoFnffCoBw6lQZJwMHfggxof8G61d1lwYQF/XsscotK9XHe6l+9OdZfqj4VSRgqEXNIXYw45D/dO0ysu6Ckv2WbG9PiNv8sk8/uVxZyPOE/fnX0ZUnUIo6qPKpS6fBfuxQH6wr6Wtioa9ijHoVUZKE1Lo9PGoVDac+eiDYmxqVg75E1YUE7IdTqCffv20bNnT+bPn0/v3r2xtrbm6tWrTJgwwbCMl1169uwJwPHjx7G3t6dq1ap06tSJ9957D4AZM2Ywb948qlevzuXLl9mzZw/16r18+v0il8dJ/UCfLfjJenNZFJ18Ho8y0U4/OZ10XTpTG03l7Ypv54+9Mlmy91oYH6zSP03O7FGNwQ1LFbJFMq8TQgj23d3HnNOziU7VhzH0jU9gfPUPsGn2paG0y4sIDj7B8SsrORF+jjMkk/SMnXp/0QO7tb6kBwUBYFGnDm5TJmOei13TJ7fd4fyeu9i6WDBwagMEWtZO+YyIwDuGPpJCQdv3xhTajqkccX6l3lP0ZNLh2kON++h0kBb/WEilxOkfhv/5H0bf9VlhVUwvoDzrgUd9KFGLsIz4TDvyFJKCvT3+wS0p+rFIenABIvz0tmUHC0dCK7XhW2USPjH60mXFrYozsf5EWj70OBYE0Ylp1Jl5AIBL09phZ2H6zLI8PT6phXulvNl5WSB5nAYOHIi3tzcTJ040ak9KSsLFxYW7d+/i4uKSrblWr16dqa1ixYpG4uj27dvcvn2bRo0a4eBQ8G9UgaDT6cstPErj/6w/xGew5fYWpp+Ybli/LmtXlm3dt8nepnwiIyyM9KC7qEqXyvSUvfCQPz/svYlSIbFqeH0al3fO9lgZmeygTlPzs+9PbPHXbyApptEwybYGrXutIiw1OlPgcFJ6ImdubOL47R2ciPcnWGH81W8nQI3AMQGKxwrSTKD7KR0NbumPK52dcZ3wGbbduuXqO0UdmcKa6afQaQSdPvSmTA0XfFYs4sK/OzG3saXXxK/RpKZi71aiaC7RPYtnJB1+IU+LrtZTwd4Tgs/C/TMQehl0xhujUJiAazW2mEtMF1GPg9KTJXpFh4M2LfN5rIqBe20oUQvsS+lTKzztJTO30wu7hxy2d2aOowMhQj9fC88WTKw/sUDiZA9eD2fEX76UL2bNgU/1u8cTY1NZOelEpiiWobMa55nHqUDyOKnV6iwDtK2srLCxsUGtVmdbOA0aNOiFfSpUqECFCoW35loghJzXiyZTS+i3Glwq5cjTNP3kdKOgv6D4IMKTw4tkgrNXnbhNmwidOs0QCOs2fToObz9eKvmoRTn8IxLZeuEBH64+z7bRTSjjbJXl2OLfTMe+jxyDJpMz7MzsmN7kGzqX7cL0Q59yDzXjk69R+e+G3CQDHQIJiVbONYhTB3MpPRrNI72jABMhqIE5TVxq0bjaQCqXbM6hGf1xW3sFhXgUSQIolTgOHoTzmDEobWxybe/xTbfRaQSelR0oXd0Zf9/TXPhXH5vT8aNPKF4u7zYVFSh27rkrhfN0uZdHc1Trrf83I1W/u/r+GQg+A/fP6pf5Qi/SC4yX2x7FWJnb6QVSiYdCyb022LobeyF1GZm9ZDUGwt1jcG0bXN9Ji7go6qujWWxvy192thwOPszpByf4oMYo2jvXJiT8AiWf2AmYlxiW6TztDW3WDua0GOzF4dU3eBRL32KQV6Es08FLeJzmzp3L0qVL2b9/PyVK6FWoEIIff/yRhQsXEhgYWOQ9HUXO43Tgazg2F6r2hLdX5GjomdAzjNg3IlP78vbLqeeWx1XFixgF7b3JCAvDv1VrvfB5EhMTFGZmSKamSKamoFIRmqQhUSehUKkoX8IBpQSply4Zj1MoKO9zUPY8yeSaVE0qiw59wZ8PDqJ9zveuZ4aGxmbFaOLZkvq1RmJl97i8R3poKHdatc6Uj8lz2TKsm7xc8Hnw9Rh2zL+IpJDoN7kepqoUVn0xltTEBOp07kGLoSNfav43AiFAfV+f0+/ID5mP91wM1ftmb6n2eV4yrQbuHodrW+H6Tvwz1Mx0cuCchfljOx55ujw60KvNjy9/bU8weOlpjvlHMatnNQY1MA5zUEclc/2SP5VrlMfOOW9jrwrE4/Txxx/j4+ND2bJladiwIba2tly9ehW1Ws327duLvGgqklzfpf/Xq0uOh2aVXVYhKfC0KYJ5qvKQmL9XEz5rlv6PuYC8Nwn79mcWTQAaDTqNxqjJ5eEPQEbUPTIyDQJ0OgL79cOmVSusGjfGqkEDo4DbvEZeJnz9MDcxZ1zb+Xgcnc7XAZsyHR+scGJg1aF4Vu0LZplzjKUFBBLy5ReZk1gCksnLbb7WaXUc26hPP1CtuTsOrhasn/4NqYkJuJatwFsDh73U/G8MkqRfyqvzLhz9yXi5TVLqc/xl9777PC+Z0kSfYLlsc+j0I+XvHufPa1tZFfgPP9iaGc6hkySm3d/DwT2xeBevRwWHClSwr4CHjUemxKCPeFGpF61OcPHRjjrPzCE51g5mOHqaY+1QuGmFcv0XYWJiws6dO9m9ezcnTpwgJiaGtm3bMmDAgBwV2pV5SOQtiL4NClOo0DbHw5M1yUhIhoywj6ptvyrLdC+6mWsTEkjz9yf9zh3SbvuTducOqTdvoo18ImeJTkfo1GlYNW2ab4Igdt16wr//PvMBhYLS69aitLNDZGTof9LTERkZBITGMXv7FUR6Op3cTam7bWmmG5Q2PIK4teuIW7sOFAosvL2xatIYq8aNsaheHemJZIAvI3zkZcLXmyYuNVHc2ahPJ/AQhRAMq/cpbl6ZM3FrExOJ+v13YlaugowsZP1zklhml2tHQ4gJScLcypT6XcpwYuMaQm76obKwoMu4z1GayPnOcoSduz7n09PLbblZLnwRD0WUVLY5XueqwNVfjY9LEkfCz3Ak/IyhycLEgnJ25fRCyqEC5e3LU8GhAkfuHzHKTTatUeZSL3ciE0lM02CpUlLRtegmEX7pPE6dOnWiU6dOeWFLgfFkkd8iw42HeTjKNtevU+eQuefmIhA0LN6Q96u/j6eN5ysjmp6+mTuOGIHK08NIJGnCw188EYBOR8q1a3kunER6OmEzZxG3YQMAZtWqkebnZyRALJ6RIqMaMLRMVUb9fZ6TAv54dzylVsw3jHWdNAnTEsVJOn6CpBMnSA8MJOXSJVIuXSLqt99RWFpiWa8eVk0a6290CxY+U/iI9HS08fFo1WqjH118POn3HxC7cqXRe5XfQvNV51XzzrkVr8u06FimOzk8DhyOjsOteB2jfkKnQ71jBxE//YQ2Up9Z2rp5cyxq1SLyl19emMQyu6QmZnB6hz5JZoNuZQi/c43T2/R/Q23f/xh7t+K5nvuN5lnxUflISff6KK6ITKJ8pEMtwpxKcjv2NgHqAFI0KVyNvsrV6KvPnEsndEw/OZ3GJRob3acuPoxvqu5hV6Rz4OU6xgn0O+guXbpE5JNP/Q9p1qxZnu1+yy+KVIzTklbw4Bx0mQt1h+do6NmwswzfOxylpGRL9y2UtSubT0Y+nxfdZIRWiyYqGk1EOJrwcDLCw0m7E0DcmjXZmt/E1RWzcuUwq1AeVblyKB0ceTBuXKZlM6WjIyW+nYN1s2Z5cl2aqCjujx1HyvnzIEm4jB+P0/vvoQkPf2Gyvyf59eBtftp/CxOFxC9tSuCsjsS9agXcK5Y26pcRGkrSiRMknThJ0smTaJ/KnP80qrJl0aWkoFWrEVmVvngB7r/+gm3bnHs5X3deWe/c+ZWE7f6UYBMFnhodbp1+NtqZm3LlCuEzZ5HyMNZOVaoUxSZ+iU2LFsDDv+McfK6fx5F1t7hy+D5O7lZ0Hl2ev78cR7I6Du/W7Wn3/scvNbdMwfNkglSFEEyLiqFXYjL00sdXaXVa7iXc43bsbfzj/Lkde5vbcbe5G383y/nal2rPu97vUsWxCpIkMXHLZdaeCWZU83J82TFzugutVsudO3coV64cSqUyixlzT4GkIzh27BjdunUjNjYWCwuLTBfh4+OTJ7mW8pMiI5ziQ+DnyoAE/7sJNq7ZHqoTOgb8MwC/aD/6VerH5IaT88/O52B0k5EkrNu0wbRYMTQR4WSER6AJD0cTFQXZ9PKZV6uGZZ06qMqXw6x8eczKlcsy7ufpm5vS0RFtlP4J2r5/P1wnTEBhlbOs60+ScuUq98eMQRMejsLamhI//mC4weQUIQTj1l1kx6UQQ5tCgjm9vOlXL+vlEKHTkXbzJkknThC/+19Sr1178YkkCYWtLUo7O/3Pw/9LKlPU23dkjmMxMcGuSxcchgzGomrVXF1bdniVvDfp94K50779U1n8JUpvWI+Ft3fhGZZdsgj+1URFETF3LuotW0EIFJaWOH/0IQ5Dh6LIh7pw0Q8SWT/rLEIn6Da2Oqe3zufu5Qs4eZRk0OyfMTUrnB1RMi9HWNhFgkPP4Vm8Nm6+K8F3uX658O0/oUr3LMcExQfRbWs3QyjJ05S2LU2HMh3YcsSFOyFWLBpSh/ZVswjZiL1HyNVjlKjWFKXDyy0hP02BCKfu3bsjSRLLly/H0dExV4YWNkVGOJ1ZArs/0yc4G7n/xf2fYFfALiYenYiVqRX/9PwHJwunfDLy2ST7+nJ38JDsdVYqMXF2xsTVFVPXYiisbVBv22Z8g8rhLrMnn5CV9vZEzp1LzF/6JSnTkiUp8d23WNaqlcOrAvX27YROmYpIT0dVtiweCxZgVrZMjud5kqCoJFr8eNioTSlJHPuyJcXtLJ47NsvdfJJE8e++w6xMGZR2eoGksLFBekYyw6cFrom7O5r79w3HLerUwXHIYGxat9bvDMwjXgXvjS4tjaTjx0nYu4/4ffv0GbKzwNTdHcu6dbCoXQfLunVQlS1bpDfDiIwMYlavJmrBQnSJiQDYde+Gy6f/w9Q1byo8ZDqnEGyfd5EHN2MpW8sFJ7ebHF2zAhOVGYNm/4yzp5wU9rVAp4MdY+Dian2Oqf5roGL7LLtuub3FKMapb8W+xKbF8l/wf6RqUw39tKnFGVWnD297daW49RNLucd/ReyfrK9YJymQHtb2yysKRDiNGDGC2rVrM3r06FwZWRQoMsJpZQ8IOARtv4Em47I9LFWTSrdt3QhNCmVc7XGM9C7YLb3axCSiF/1B9PI/s/Qk2XbpgkWtmpi6umLi6opJMVdMnJ2QnvJO5sdNNenUKUImTkITGgoKBU7vvYfL6I+MgqyfhdBoiPjhR2L++gsA6xYtKPHD9y+Vw+YRJ+5EMXDJ6Uzta99rSKNyLxa9efFePb0Uk3LpEjGr/iZ+zx54uCvQxM0NhwEDsO/7Nia5WHIXQqCJiCTt5g2SzvoSs2SJcQdJwmnUKMy9vDAtUQJT9xIoHRyeKUDyy1ulS0kh8ehREvbuI/HQIXQvWuqUpEweO6W9PRZ16mBZuzaWdWpjXqUKkkpVqB62R+fOiIwg+o9FpN/RZ+Y2r1oV18lf5epBIicEXIzk3z+uoDRR0HqoPTvnTkXodLR9/2Oqt876xirziqLTwuaRcG0LKM1g4Hool3Wm8bCkMEP9RUNi1owkDgUfYs3V7VyOOYMkPX4wrFWsFh1Ld6RdxF2cDs0hTKnknqkJJTM0uOnIUVWNF1EgwunixYt89NFH7N27F5s8uKEUBkVCOKXEwg/lQaeBj8+DU7lsD112ZRnzzs/D1dKVXT13YW5SMK5vodMRv3MnET/+hCaL+DbgpbxGeXWT0cbHEz5rNurt2wEwq1yZEt99i3nFZyfa08TG8uDTT0k+eQoAp1Ef4DJ27DM9ODklVJ1Ck2990D31V3dkQgtKOmVvSTE/3iuAjPAI4tavJ3b9erTR0QBIKhW2XbvgOGQI5l5eWYoBXVoaaf7+pN28RdrNG6TevEXajRto4+JydH7JwkIvoh79uLtjWqIEqTdvErNsWa7F4tM265KSSPzvP+L37Sfxv/+MPEsmbm7YtGuLbfv2pAUEEDbta6Pz2nToSMrFi6ScP0ey7zlSLl9GpKYanU8yN8e0eHF9iZICTJPxCCNx/RCloyPFPv0Eu1698uyz/Cw0GVrWTj9NfFQqNVq74Hf4ZxKiIqnUuBmdx04o0t45mVyizYCN78CNXfoCxoM3Q+kmOZpi4SF/fjhwntqVg7F3uYZvmK9haU8pBKXTMwhQmSKejK/qvR7KvJUnl1AgwkkIwYgRIzh27Bju7pkV32+//UblypVzM3WBUSSE0+UNsOU9cPGC0Zk9Ec8iJjWGzls6k5iRyOyms+larms+GvmYpwNLTUuWxHXil2iiogmbVjSXYuL37iNs2jS0cXFIpqa4fPIJjsOGZvJ8pd68xf3Ro8m4fx/J0pISs2dj2yHvn47Xn73HpC1X0T7xp9fZuzi/DKiFUlH4NxVdejrxu3cTu3IVqX5+hnZV6dKk371rSIBnXq0aIjWFtIDArGPXFApUZcqgKlmSxMOHM8ULWbdogTY2lowHD54twJ+BedWqmLi6onSwR2lvj4mDA0oHB5T29g//dUDpYE/C/v2EfT3dsDRp5uVFekAAIu1xaQpTd3ds2rfHtn07zL29jYTFi0SqSE8n9fp1kn3PkXz+PCnnzj1TMFo2bIhFrZqYe1XGvLIXph4eLy1iREYGaYGBpN26TdrNm6RcuULyqVPGnSSJMju2Y14AlRcSY1M5syuQ68dDsbQ1xd75PwLOncLetTiDv52PmWXBF4yVKSA0abBuIPgfAJU1DN0OHnWzPXzkX74cuB7O5M6VGdmkNOEn5rH33AL+tTDhqlnmvE0KIdjb4e88y15eIMLphx9+YNq0afTp0wcPD49MweEffPABHh4ezxhduDyZjuDWrVuFK5zWD4HrO+Ctz6D1lGwPm316NmtvrKWyY2XWdVn3zIRjecXTgaWSpSXOH47CcdgwQ2BpfnlC8gJNZCShU6bqb+CAZd26FP/2WyQTJelBd0m/e5fw775DJCdj6uGBx8KFmFfKvxIQoeoUgqKSiUhI5bONl8jQCvrV9eTb3t5F5olcCEHKhYvErFpJwt59WSf9fIjSzg4zLy/MKlXEvJIXZpUqYVa+HApzvRf0RUuMuvR0NKGhZDx4QEZIiP7nwQNSr98g7dat3F8DD8uGPIVpqZLYtu+ATbt2mFetkmfvudDpUG/bTuikSS/sq7CywszLC3MvL8wre2HmVRmzCuVRPLxJPOkpM3F1RRMWRtqtW6TeumUQSmmBgVnnX3qKkn/9hVWD+i99fc/D73gIh/++YdDHzu6B3L+6FYXShAEzfsCt3GteMksGMlJg9dsQdFSfVmfYTihe44XDhBDUm3WQqMQ0dg10pZrvZLh3Un+wZGN21unDpIvzMo3Ly8oYBSKcOnfuTMuWLfnss89yZWRRoNA9Thkp8H05yEiC9w7p6wplg0B1IL2290IjNCxtt5QGxRvkm4kiPZ2Y1WuIWlhwgaX5hRCCuE2biJjzLbrkZCSVCpGRYeQJsWzUEPeff85VXE9u+fdKKKPXnEcn4P1mZZnY0avIiKdHqP/9l5BPPs3U7vLpJ9h164aJq+sLbc6NsM4yIF6hwG3aNAC0sbFo4+LQxsai+X97Zx0exfW24Xs37u4JMUIguLu7U7RF2uKlUKX92lKBKqXQUkpp+0NK8VIoLVDcgrsGQiDu7raR3fn+WLKwRIhsjMx9XbmSsTPvTmZ3nznnPc+bVvR3GvK0NBQZGSW2abDoU1ynvVRt17i0mK0XLKAgJpq8+wHkBQYi5OcXP1hbGz0PD6SGBuTevqO6NyX6+sWGBFVNGxmh16QJek2aoO1gT9Kqn6o02aIyPF2EVVGYQH7mDkBOn5dn0X74mGo7t0gdIy8Lto6DyEtgYAnTD4Jt2aNPkSk59PnuOHN0DvF/enuQFMqUvVYDlkCHmcTlJjD478EonnBLl0qkHBl3RGN+hTVScqVr164l+jeJVIAQX6VoMnVSFmQsJz9e/5FCoZDezr2rVTRlnT1L/DdLyQ8NBWousbS6kEgkWEyYgFGXLkS/uxCZn9/TO+Dw5Zc1KpoAhrZ04Nuxrfi/v++w9kwIZgY6zO/buEZjeBaGbduCVFpMDJiNGlXuL2Qde/sKf3nr2Nvj8MXnlUqIP3/uDuazXkT6xBRouURCnHdb3KpRmJYnZtUQW0AAsvsByALuk+d/H3l6eok9bIJMBlpa6Hm4o+fVRCWU9L2boO3oqCYCdayti527OkVTcnQWF/YEqUSTIORTkH0AkOPYpA3thpU8RV3kOUXPGKb8BZtHQ8xN2DQKph8C69I/04LuXmGP7mJaS0OgEPDsp3RHN1daDtgb2bO46+JizuO1ZfJc6R6nnJwc+vTpw8KFC2natLhRlZeXF4Z1fDy71nuc9s6Hm1uh0xwYVkLRxhK4FneN6UemK80uR+3Bw1xzZpdFQwMSbS2SN/xO1qlTAGhZWSkTS194odoTS2uKrAsXiZxR3Gi0JoY0SmP92RC+OnAfgC9HN2daV7daiaM0atNSoKK9VYIgsGD7DQr27+XN27vREgTkEgk/tR7Pa8vepp1r9VuoVCbmwvh40v79l6QfVxXb7rJhPcbdy5dwW93D5oJCIPxeMrdPRBIVkPrE+kzys48iFIaD1JiXv/sJG5f61TMtoiFyUmDTSIi/q+wcmH4QLNzU9ynMh3MrkZ/+Di2hkFwtEwxGfAttppRYdy8mI4ZLDy7RxbsLjqaOGg23RobqunfvzoULF0rdfunSJTp3rr7eEE1Qq8JJXgjfN4GcZHh5n7LUyjNQCAqmHJjC3eS7TGwykU+7lj8n6lmUNBMHbW0sp07Fev7rGpmKX5cobTiluoc0nsUPRx/w08kgAH6c1IYxbau/lEJFqMt5bE/y3eEAfvENRiIB69w07DOTiDW2JsnAHHdrI/6a2xUbk9otFFoadfXeBCjIk/PgUiy3T0aRFq+0bpBIwKOtLbKMWwRf/VO1r3u7wYz9QHQHb9BkJcIfwyHpAZi7wviNylEWS0/Iioe9CyBBaep7VN4exdDvGdKt9BGNuuIcXumhuv3791NQRlJiXTbFrBO16iIvK0WTvjm4divXIYdDD3M3+S6G2obMazNPY6EUxMUVF01Aow3rMarj4reylDacUttfTO8MbEKGrJA/LoSxcNdtjPW0GeBTfif56qYyw201ze/nQvnFV+lbtGxsK3o2sSYsKQcjXS3mbbtBaFI2r/x+hT/ndsFUv+4VmK2L92ZWqgw/3yjunY0hL0fp9aWrr4VPD0da9nUmIyGYnUv+VDsm7NYxMpNfwsRKLPreYDG2Uc6u2zgUUkNhfb9HG4p6kwQEQyveyZjCv4WdOV2NE3I0SaWFU10WRs9i/vz5zJ8/X6Uwa4WAA8rf3kNB69kf3nnyPFbdUHbfz2w5E2sDzX0Y5YeFlzJjqm4lKGsa8/HjMerRo071oEgkEj4b4UNGbgF7bkbz+vYbbJreqVzmmCKw73YMX/yntFB4f7A3Ezu6AKhc2bfO6syE3y7gH5vBrD+usXlmJ/R1NPvkqglq897MSpWRlpCLua0B2Wn53D4ZSfD1BBSPzMdMbQxo3c+Zpl0dKJBlcnH3Ru6cOFysHUGhIC0uRhRODR1TBxi7DjYMeGLlo4Eu72Hcbf8V//4egKWRLo0s63Z6TxGVFk4iVUAQIGC/8u+mw8t1yPb724nJjsHW0JZpPuUsb1JOdN1cizsiS6Xoumq2FlBdpC72oEilEr4b34rMvEKO+ccza9NVts/uQmsX89oOrU5zNjCRhX/dAuDVbm683qe4may7tRF/TO/ES2svcSUshfnbbvDbtPbo1MFK7LVxbz5tKfAkjl7mtO7vglsrawpkuVzd9yfX/vuHwic8sZ5EIpVibq/ZPBSRekphyeWL6PI612KU7702LuZ1bjZxadS9T4saYM2aNfj4+NReEeL4u5AWAdr6ytkDzyBVlsq6O8qSFW+2fRMD7bJrmlUUqZGRel2yOjA00NDR1pKy+qW2dPO0IjtfzisbrxAYn1nbYdVZ7kSl8dqW6xTIBYa3cuCzEaV7M7VwMmPDqx3R05ZyIiCB/9t9R9Wb0pCJD0vn1JbiosmjrQ0TF3XkhYXtaNTcnFuH97HhzVlc+vtPCvPysG/chImffcOguY8d9iVSKQNnLxB7m0SUWHrC016DEi2w9OBmRBoAbevRg2GD7HGq9aG6+/8pf3v2B91nl9n4353/kVmQSVPLpozwGKHxcNL++gshPx8dN1fslyxBz81NFE11AH0dLda+3IEp6y9zOzKNqRsus/u1brjUk+7smiI0KZvpG6+SnS+ne2MrfpjYGukzHNg7uVvyy5R2zNlynX9uRmNmoMPikZozwqwvKOQKwu8mc+9cDOF+ySXu06qPM9bORvifPcX5nVvJSIwHwMLBiZ4vvULjTl2RSCS4NG+FW+t2pMXFYG7vKIomkceYOSntBfa/DYJcKZpG/ghmTtyKVNpvtGlkXpsRVogqCSc/Pz9+/fVXQkNDWbVqFU2aNCErK4uHDx/Srl35zBwbJEX5TeUYpgvPCGdnwE4AFnZYiJZUs/kYQn4+KZs2A2A9ew7GXbpotH2RqmGsp80fr3Zk0tqLPIzPYuqGy+ya2xVb05qpS1jXSciQ8fLvl0nOzqeFkym/TW2Pnnb53iP9m9nx/YTWvL3zFn9cCMPCUJe3BjQMd+uMpFz8z8cQcCGW7PQSjDiLkAikJwZwYsN2EsOVfm5GFpZ0Gz+ZFn0HIn1qZpOJlbUomERKpt3Lys6ClBCw9AAzJ5Ky8ohIyUEioV6lIlRaOF25coXBgwczYsQI4uLiSE1VenkEBgYyZswYwsLCkNZRz59anVWXGgbxfspuS++hz9y9yOyyp1NPujhoXtSk/3eAwoQEtG1sMB2p+d4skapjYaTLlpmdGf/bBcKTc3hp3SU+HNKUFs5mqqTnhkiGrIBXNl4lMiUXVytDNr7aCZMKzpIb09aJtJx8luz3Z+Xxh5gb6vBKN7fqCbiCPJmkbWxRdaEsL1QQejsJ/3PRRAakqvJzDUx08O7igE93B2KD0zm1+SrywlQkFGBsdp9Dq5U9AroGhnQaPZ52w0ahoycKd5FKYOak/HnErUfDdI1tjOvkDNfSqLRwWr16Nd988w3z5s1jwoQJqvVt27ZFX1+f8PBw3N3dNRJkWcTFxXHo0CHc3Nzo1atXubwdanWorqi3ybU7GJY9M/FG/A2ORxxHKpHybvvi5S6qiiAIpGz8HQCLl6epas6J1D3sTPXZNrMLw386S3BiNrO3XEcigU+H+zCjR/W/zzRBbHouoUnZuFsbVVnwyQrkzNl8jfuxGVgb67F5RqdK+zK92t2dtNwCfjweyOJ99zA31GF0m9rzz1IoBK4dCuPqf6GqYntdX/CkdV8XtHTK9zD6pOgqzFfgfy6GgEux5GY+tpBxaWaBTw8n3Ftbo6WtbDfK/zyy9PWqiSIpWaClrU2bwSPo/MJEDExqqaanyHPJrcg0ANrWo2E6qIJwSkxMxM3Nrdh6QRBIS0urkd6mwsJCXn/9dcaPH8+uXbtYu3YtO3bsqPbzVomi/KamZffuCILAimsrABjrNZbGFpovwZF95gx5gUFIjYywmDRJ4+2LaBYdbQnZ+YWqZUGAL/7z51ffYJo7mdLU3pRmDiY0tTfFw8ao1JlimhQw5WXn1Qg+2uOHQgCpBJaObcmkjpWbtSlXCLyz8xaXQlKUQ5nTO+Jq9excwbJ4q78XaTkFSv+sv25jqq9D36bV73gtCAIZSTISwjNICMsgITyThPAMCvOfsAcR4OKeYC7uCUbPUBtDU10MTXUxePT78Y8ehqa6RD1I5eITJVCexNBMl2ZdHWjW3REzG/X/fUZiAkf/txpQP3Di4m9xbFK8OoSISFW5GakcqWrjUrNlrqpKpYVTnz59+PXXXxk4cKBqnVwu55NPPsHS0hJXV1eNBFgW2tra7NmzB4DBgwfTo0ePch1Xa0N1WYnKwocATYeVueuRsCP4JflhoG3A/DbzqyWc5A3K3ibziRPRqo2SMyIVIjQpm5ImfyVm5eH7IBHfB49rR+pqSWlsa0xTBxOa2ZvS9JGgOhkQrzEBU14exmXy4R6/xwVgBfhojx+9mthUWLgJgsBne+9y6G4culpS1k5rTwunqvcaF/lnpecW8M/NaF7bep2tszrT0a3yfnUlDbVlp+cpxVFYxiOxlIksu3Qj4afJyykkL6eQ1LicCsXi1NScVn1ccGtphbQEQZ2ZksTe77/madEEIC8oIwdKRKSSyBUCtyPTgQbU4/Tuu+9y5MgRmjdvjkwm48MPPyQ0NJT8/Hz++++/CrWVm5vL6dOn2bhxI4MGDWLmzJlq21NSUvj+++8JCgpi4MCBzJo1S2377du3+fXXX3nttdfKdb5aG6p7eAgEBTi0VhUvLInIjEiWXVkGwIwWMzRqdllErp8fOVeuKMuqvKxZXyiR6sHd2gipBDXxJJXAb1PbkZCZz/3YDALiMgmIzSA7X45/bAb+sRlAdIntFQmYzu5WuFlXrcfmSWLScrkalsLVsBSuhaUSEFfcRkEhwNhfLtDV04o2Lua0cTGnqb0putpl91T/dCKIbZcjkEhg5aQ2dGusufdGkX9WRm4BJwISmPHHVda81A5tbUmFe+ee9kOydjFGllVAVmpxzyOplgRrZ2Ns3UyxdTXF2FKP/atuqfUYSaQw6eNOSKQScjLyyc3IJycjn5yMvEe/lT+ZKTLysguLnaPjUHecvEt+qg+8coGj/1uNLKv4/0n0YhKpLoITs8jKK8RQV4smdvWrpFelhZOuri4nTpxg9+7dXLx4EYlEwpQpU3jxxRcxNjauUFtr165l69atPHjwAA8P9aK1BQUFDBw4EA8PD8aMGcPnn39OXFwcn3zyiWqfP/74A39/f7Vcq7KotR4n1Wy60ofp9gTuYcmFJQiPnvzM9cyrJZSi3iaz4cPQcXColnOIaBYHMwOWjm3Joj13kQsCWhIJ34xtwaDm6v8/hUIgOi33sZCKy+B+bCahSdnF2lQI0HeFL+42RjSzN8Xb3oSm9sreKWcLg2LT+p8e5lMoBIITs7gSlsLV0BSuhqUSnVaK2d1TxKbL2HMjmj03lMJOV1tKc0dTlZBq42JOI0tDJBIJsem5rD8bwoZzYQB8Mao5w1tp/r7V0ZKyZko7Xt5whSthKby88QpQsd65rFQZp7YGqHXeJEVmKf+QgKWDEbauJti6mmLrZoq1k3Gx3KU+U5viuy0AQaEUTX2mNMXKSfm5aulQusjNSpWxedGFYqLLzLa46MuX5eK7aR1+J48CYOvmSeNOXbm4ezuCQiF6MYlUKzcjlMN0rZzN0HqGfUhdo9JFfquDgQMH0qFDB5YuXapat3fvXhYsWEBISAg6OjqcPXuWkSNHEhcXh77+45kdubm5dO3alevXr5e7+F+NFvnNy4LvPECeB/Mugp1PsV3isuMYtHuQSjQBSCVSjow7gr2R5nyV8iMjCR48BBQK3Pf+i763t8baFql+YtNzCUvKwc3asEK9ICGJWfT/4XSJuS8lYaSrRZMnhFR0mlK8KARlMZ6mDqbEpeeSmqM+1KQlldDc0ZQOrpZ0cregvaslJwPi1QTfomFN8bAx5mZkGrci07gdmUZ6bvEhK0sjXWyMdXkYn6V6V/RvasuGV6vXvPZhfCaDVp5Rf10SCec+7FvmNc9IyuX4H/7EBqUX29ZjYmOadXNEV798z6tZqTLSE3Ixq+CsOv/zMcVEl0939V6juKCHHPx5BamxMSCR0HHkWLpPmoqWtg6ZyUmiF5NItfPh33f482ok8/p48sGQ8uXQ1fsiv+np6bz//vv4+vqSmJhYbPuxY8fo0KFDZZtXsX//foYOHYrOI2fr7t27o6WlxZkzZxg0aBD5+fno6upiYGBAXl4esbGxODs7l9lmrfQ4BR1XiiYLd7BtVuIuR8OOqokmAIWgIDIzUqPCKWXjH6BQYNSjhyia6iEOZgaVSur2sDHm26d6rL5+oQV9m9oSEJfJg7gMAmIzCYjLJCghi+x8OTcj0lTOvk8iAPdjMwAw0NGibSNzOrhZ0snNkjaNzDHWU/9omdSxEb2a2BQTfEUJ2IIgEJacw63IVG5HpnMzMo37MRmkZOeTkq2eY+P7IIHY9NxqTWxPyio+pCYXBMKScko8b0GenBtHwrl5NAJ5YfG6jxIpeLa1LbdoAjC20K+UDYFPd0ca+ViWKLoUCjlX9/7NhV3bUMjlGFtaMXT+Qhq1aKXaR/RiEqkJ6qNjeBGVFk6LFi3i4MGDLFy4ECcnp2Lq7+kht8oSHR1Nr169VMtSqRRXV1eio6Px8/PjnXfe4a233uL69esYGBjg5FT6NOK8vDzy8vKYNm0a06ZNIyMjAxcXF43E+UyeNL0swZ34RvwNVt9cXWy9VCLFxURzMRamppL2KKHeatbMZ+wt8rxRmoCxM9WndxMb1X4FcgVhSdmPBFUmF4KTuFGCgPpyTAte7OhSrlpvZQk+iUSZR+RubcQLbZUPPnmFcv66Gsmne++p7SsXKFXAaIrS8sncrNVd2wVBIPBaPBf3BKvyl5y8LXD0MufawVC1Xh9NeDGVl5JEV0ZiAofW/EDU/bsANOnSgwGz52NgXL/yS0TqP1l5hTxMUObU1SfH8CIqLZxCQkL45JNPyp2QXVkSEhIwNzdXW2dhYUF8fDwtW7bkhx9+4Pjx41hbW3Pq1KkySyYsXbqUzz//vFrjLZHCfHh4RPl3s5HFNt9KuMW84/OQyWV4mnkSmhGKQlAglUhZ3HWxRnubUrdtR5DJ0PfxwbBzZ421K1J/KE+PlY6WFC87E7zsTBjZGmLTG9H925NqQkJLImFAM9tqK5Crp63FAB87Fu+7V+y8TwsYTfN0PhmAvak+tiaPxUhiRCZndz4kNlg5LGdipU/38Y3xaGODRCLBp7tDpYbaqoOA86c5vv4X8nKy0dE3oN/0uTTv3b/BlZgRqRvciUxDEMDJ3EDtPVVfqLRw6t+/PwEBAZqMpUSMjIzIe6r6tkwmw8hImSDZqlUrWrVqVdKhxfjoo494993HRpI11uMUfg7y0sHIBpzVczP8Ev2Yd3weOYU5dLbvzM/9fyYtL43IzEhcTFw0KpoUubmkbtsGgOXMGeKHpki5KS0xvbp9oGrrvPC4d+52ZBoLd90mJl3Gn1cjeKGZA5f3BuN/IRYE0NaV0n6IK20GNEJb93HPe2WH2jRJXk4OJ3//Ff+zpwBwaOzNsDfew9xenBAiUnvcrKfGl0WUWzgVFhZy797jLvMBAwYwdepUduzYgY9P8URnLy8vDA2r/lRoa2tLUlKS2rqkpCTsK1GEVk9PDz29yrkLV4ki00vvYfBErTn/ZH/mHp9LVkEW7e3a81O/n9DX1sde216jgqmI9H//RZ6aio6TE6aDB2u8fZHnm9KG+Z7X88Lj3rnYdBlf7vPn8N8Pyc4Po0CmzI/06mhHt7GetS6QniYzOYnAKxe5tv9vMpOTkEikdB47iS5jJ6Gl3SBru4vUIYrym9rUw/wmqIBwSk5Opk2bNsXWT548ucT9L126RGcNDAW1bt2a06dPq5aTkpIICgoqdy9TraNQwIODyr+fsCF4kPKA2Udnk5mfSVvbtvzS/xcMdapv+EGQy0ne+AcAlq+8gkT88BSpBJVNTK+v5y0ysews1WNOrgEm+VCAHGsXY3pOaoJjY/NqO3dmchKpsTFYODx7dltBfh7Zqalkp6bgf/YUd44fUm3TNzZlzPuf4NS0+AOuiEhFqWrlAUEQuPXIMbxto/rlGF5Eub89bWxsiIuLK3fDlpaVd9x9kqlTp/LVV19x7do1OnTowKpVq+jQoQPe9WU2WMwNyIwFXWPw6A1AYGogs4/OJiM/g1Y2rapdNAFkHj9BQUQEWmZmmI8fV63nEhF5HvA/H6Pmx2QCZEsEzhsU8M0r3jg6V595rt/JoxxbuxpBEJBIJHQYOQ47j8ZkpyaTlZZKdsqj36kpZKUmk5dd3KOriLycLExtqr98jMjzjyZKJ0Wl5pKUlY+OltK2pD5SbuEklUqxs7NTLW/fvp1evXqVOPX/ww8/5Kuvvip3EF27dgXA398ff39/fH19GT9+PAsXLsTd3Z3Vq1fTu3dvGjVqRHp6OqdOnSp327VOwKNhOq+BoK1HSFoIs47OIjUvleZWzfltwG8Y61bMMLSiCIJA8oYNAJhPfgmpBoZQRUSeZzKSczm1pXgOZ2w7E24HJ/L5f/7snNulWvIEM5ISObp2tarQriAIXN23+5nHaevoomdsRHZqqtp6QaEgLS5GtBgQqRKx6bkq0QTKGaeLKlE6qSi/ycfBFH0dzXox1RSVHq9ZvXo1Tk5OJQqnbdu28corr9CsWcl+RU9Tksh6st05c+YwceJEQkJCaNWqFdr1aZjpCbfw0PRQZh6dSYoshWaWzfjfwP9holv9U4Fzr11DducOEl1dLKdMqfbziYjUZ1Ljsjn0v7slbnu1XSMOR6RwJSyFfbdjGN2mdPuTypCXk83hX1ZSkkuptYsrlk4uGFtYYmRhWey3nqERWSnJrJs/nSd9jcWyKSKaoKRamXIBlh0K4PW+jctdNqXIMby+DtNBJYTTjh07yMnJISEhgQMHDhAUFKTaJpfLuXz5MnFxcdjalr9ruH///s/cx9zcnHbt2lU03Nol8SEkPQSpDhH2zZh1ZBZJuUk0sWjC2oFrMdOrmTp5qvIqY8agbS0+dYrUP0oqmKtpFHIFt45HcmV/aOkmlh7mLOjXmOVHHvDNwfsMaGaHkZ5mHuRiHt7nwE8ryEiML+HcUsZ+9Pkze41MrKwZOOcNjq37WSybIqIxBEHgsF/JqTr/3orh31sxeNuZMLK1AyNbO+JqVXpZIJXxZT2dUQeVEE4nTpwgKCiIhIQEtmzZolb2RCKRYG9vz7Zt27CystJooPWSR8N0Ue7dmHn6bRJyE2hs3ph1g9Zhrm9eIyHkBQWR5esLEgmW01+tkXOK1G1qQoRokntno/Hd/gAEpXdsn6nFS4hUleToLE5uvk9CuNKUr5GPJU5NLbj0b3AxE8tZPd3561ok4ck5rD4ZxIdDy1cuojQUCjlX/tnFhUc14sxs7fDu1our+/6ulPhp2W8Qbq3biWVTRDRCfqGCD/++w56bypqSEpRpf1IJTOzgQlJWPqcfJvAgPpMHRzNZcfQhrZ3NGNnakeGtHNSG8fIK5fjHKCsO1NcZdVAJ4bR+/XpAaUfw6aef0rt3b40H9dwQ8B8x2lrM1EomLjsDdzN31g1ah6W+ZhLny0Pyxo0AmAzoj567e42dV6Ru4n8+Bt+tAQjVKEKqilyuIDE8k5igNCL9k4kKSFNtEwQ4tTUAew+zMovdVuRcNw6Hc+1gGAq5gK6BNj0meNG0qz0SiYQmHe2KmVjqaWvx2QgfZm66xoZzIUzs4IyHTeXyFDOTkzj48wqi/JVDg02792bArNfRMzSizaDhlRY/YtkUEU2QKStg3tYbnAtKQksqYekLLenZxLqYNUh6TgFH7sWx/04M54OSuB2Vzu2odL4+eJ+ObpaMbO3IsBb23IhIJV+uwNxAh0aW9TfXtk4V+a1pqrPIb1z8bW5tGsz3lhbE6WjjaurK74N/x9aw5ma3FMQnEDRgABQU4LpjO4Zt29bYuUXqHlmpMjYtusCT5RAlEnj5m2410vNUWk9XQb6c+NAMYoPSiAlMIy4kncL84kNlTyLVkuDVwY5m3Rxw9DJHUonq6okRmZzYfJ/kqCwA3FpZ02eyN0bm5fN6m77xCqceJNK7iQ1/TO9Y4UTxwCsXOPrbT8iys9DRN6D/jNfw6dVPNKYVqRPEpct4deMVAuIyMdTV4pcp7ejj/ezvr8TMPA7djWX/7Riuhj2eqCCRqKfuLRtX8Rl5daXIb4MUTk8W+X348KHGhdOewD18fmExRR/9FnoW/DXyr2oxtSyLhO+/J3ndegzatcNt+7YaPbdI3aIgT86pLQEEXiueP2NsqY9zE3Ns3UyxdTXF2tkYLR3NllF5uqerRR9ndHSlxASmkxCegUKu/jGkZ6SNY2NzrJyMuXYoDMr4lDK11qdZNweadnUolwCUFyi4ejCUG0ciEBQC+kY69HzRC68OdhUSLaFJ2QxeeYZ8uYJ1L3dgoI/dsw8CCvJknN6ygdvHlF5Ldh5eDH/rfSzEBG6ROsLD+Exe/f0KMekyrI312PhqR1pWwn4jJi2X/+7EsOdGNAFxmWrbtCQSzn3Yt0Iz8kThVAeojh6nuOw4Bu8ehOKJT3opEo6MP1qjwkmelU1Q374oMjNxXvMzJuVIwBd5Pgm9ncjZnYFkpsjKtb9US4KVkzG2bqbYuZlg62qKhYMRUqmkzPwoQSGQl1uILKsAWXYBuVkFyLIKSE/I5frhsDLPaWSuh6OXOY6NzXDwMsfS3kjVi+R/PgbfbQGqXKPek72xcjLm/oVYAq/Gq1y8kShzk5p1c8S9lXWJ4i8uNJ2TmwNIjVX6Hnm2s6XXi00wNNUt17V5mmWHA/jVN5hGloYcfafXM6dXJ0aEcWDVdyRHRQDQcdQ4uk+aipa2TqXOLyKiaS6FJDNn8zUyZIV42BixaXonXKo4rHYhOInJ6y4XW79jdhe6epY/H7quCKd6NK+/fhARe0NNNAEoEIiMvYF942E1Fkfarl0oMjPRdXfHuG/fGjuvSN0hIymXs38FEnZHWbLI2FIPt5bW3DsbrRIhXV9ojIWdIfFhGSSEZ5AQloksu4DEiEwSIzK5d0bZlraeFkZmuqQn5Krat3I2RkdXC1m2UiDl5RSUNIu+VBo1t8Srgx0Ojc0xtdYvtbfHp7sjjXwsi+Ua2bub0WOCFyE3Erh/IZboh2lE3Esh4l4KekbaeHeyp1l3B/SNdEiOziLkdhL3z8UgCGBgokPvl7zxbFe1ofMFfRvzz41oIlJyWHcmhDf6e5W4nyAI3DryH6e3/o68oAAjcwuGzH8Xt1bi8LlI3WH/7RgW/nWbfLmC9q4WrH+5AxZGlXuoeBJ3ayOkEmq8WHd1USXhNGXKFD7//HMaN27Ma6+9xuuvv15/SqFUE40KC5AKAoonvgSkgoBLYWGNxSAUFJCyeTMAltNfRSKtnur1IpWnOme2yQsV3DoewbUDYRQWKJBKJbQZ6EKHYe7o6GnRfohrMRHi1kqZSCwIApnJskdCKpOEsAwSIzIpyJOriSZAlRv0NDr6WhgY66BvpIO+sQ5aOlJCb6nXm5RIoe/UpuV+7aUVzNXR1cK7iwPeXRxIT8zh/oVYAi7GkZ2Wx51TUdw5FVXsmCad7Og5sQn6xlXv5THS0+ajYU15689brPENYmx7Z5zMlUMPRSVT9I2NOf/XVkKuXwHAo11HBs97G0PTmrEjERF5FoIgsOFcKF8duA/AkOb2/PhiG40ZVNZmse7qoErCKSQkhLy8PADCw8PJzc19xhHPP/YOHVicnMrnVhYoJBKkgsDi5DTsHdrXWAwZhw5RGBuLlrU1ZqNH19h5RcpHdc5si3qQypkdD0iNywHA0cuc3i95Y+n4eAZaaSIElJYiptYGmFob4NVBmbOjUAg8uBjLyRKctDuOcMPJywJ9Y6VI0jfSQUu7uFB/eritaGq/JjGzMaTLaE86jfQg8n4KfqeiCL+b/NQLhK4veGpENBUxqrUj2y5HcCU0hW8O3GfNlHZqJVOK0NLWptfUGbQdMlJMABepM8gVAl8d8Gfj+TAAXu3mxqcjfNCqxISLsqjNYt2aRhyq0zRmToztu5RuB98lUluKS6EC+2E/gJlmHYZLIz82lsSfVgNgOXUqUr3yzRASqRmyUmUq0QTKWSa+WwOwdzfF0rHypXdyMvI5/3cgDy8rk78NTHToPq4xTTrbV/lLWiqV4OJjWWxWjESqHEYrjwAqbbitOpBKJbg2t0JLW1pcOAmQnpCr0fNLJBKWjGzOiNVnOeAXy/jrD7n5lGgCGP3+p7i3qbkHKBGRZyErkPPOzlscuqs0t/x4WDNm9XSvNmFfW8W6NY0onKqDdi9j79kf+5QQsPSoMdGUtns3sZ9+pvp2kxrVz/Hj55m4kPRieUCCADu+uIKFgxH2HqbYe5hh726Ghb3hM6fZKxQC985Ec2lvCPm5hSCB5j2d6DLaA30jzfWqGFvo02dq0yr1GpXV01UdmNsalCj2zGw1/8Ht42jK1C6ubL4Yzv/2X6ZjCcle2jpiArhI3SA2PRe/qHR+PhnEneh0dLWkrJjYmlGtxZmd5aFBCqcn7QiqDTOnGhNMAAVxccR+tljtWyJ+6beYDByIjn3N2iCIlEzgtXh8txYf7ioiNTab1Nhs7p+PBUDPUBs798dCys7dFF0DbVV+VGG+nCv7Q0mMUE7ztWlkQu+XvLFzr56K4zXZa6QJNCH2KsK7A5uw/3YMISkFdHxqm1gvTqSusPNqhFqxXj1tKX9M71Sh2W0NnQYpnObPn8/8+fNV0w+fB/LDwkHxlGmgQkF+eIQonGqZvJwCzvz5kIdXlMNoxpZ6ZKfmKXOcHn2Zu7W0Jj40nbiQdOJCMkgIyyAvp1A1SwwACRiZ6pKdnq/Wvq6+Fp1He9KitxNSDeclPE1N9xpVlZoUe+aGurzX35Pbv/2ptl6sFydSV4hNz1UTTQAFckW9nd1WW1RJOLm6uqKrq5yq6OzsrFa3TqRm0bIsodK0VIqua8WcWUU0S2RACic33ScrNQ+JBNoPdaPDcDdyM/KLfZm7t7bBvbUNoCwFkhyVRVxIBnEh6cSHppORJCsmmgBGv9MWW9fq6WV6HqgpsScIApa392GXn0CuVI+c3q8wr6ebWC9OpM4QmpStJppAaREQlpTzXOQe1RRVEk5//vn4yWrdunVVDkak8qRufcoZXCrF4YvPxd6mWqIwX87Ff4O5c1I5Hd7MxoAB032w91D2cD7ry1xLS4qtq9LJu1VfZwCCbsRzZO29YvuqDCBFapVbR/7D//QJkEg4YjuIyDAtmrU2ZYCzESa1HZyICFBQWLyUUX32U6otGuRQ3fNG7q1bpP31FwBOq35Ey9wCXddGomiqJRIjMjn2+z2VJUDzXk50H9cYHb2qeaLYu5vVWLKzSMWI8r+L72ZlAfTeU6ZzPtKeyIhUPt17j8X77rF0bMXrcomIaBK5QuCH44EASFBWMarvfkq1RYMUTjWSHF5DCIWFxC75HACzMWMwHTy4liNquCjkCm4cieDqf6EoFAIGprr0m6bMX9IENZ3sLFI+MpIS2bdyKQq5nKbde+PYcwi3lp1SbVcIsGjPXXo1sRG/oERqje2Xw7kdmYaJnjbbZncmO09e7/2UaosGKZyep+Tw1G3byAsIQGpmhu3/vV/b4TRY0hJyOPGHP3EhGQB4tLWhzxRvDIyrXq7gSerbzLbnnYL8PPZ9/w25GenYuHkwaO4bXI3KKpZHIhcEMY9EpNZIyJDx3eEHALw/xJtWzua1G1A9p1qE09atWxkwYAD24lBRtVIQH0/iqp8AsH33XbQtLWs5ooZFVqqMtPgc4sMzuHYwnMI8OTr6WvR6sQneGjCeLI36NrPteUUQBE6s/4X4kED0TUwZvfBjdPT0cbcWitXlkoCYRyJSa3zxnz+ZeYW0djZjSmfX2g6n3lMtwmnZsmV4eXnVWeH0vAzVxX/7LYqcHPRbt8J8wvjaDqdB8WTZlCIcvczp/2ozTK3EXoWGwM3D/3Hv9AkkEikj3vo/zGyVJWqerssFoKUlllgRqR18HyTw351YpBL4+oWWGi+l0hCpsHDat28fiYmJZe6TkpJS6YBqgudhqC7r3HkyDx1Wzp5bvFgs5FuDZKXKOLU1QJldWYQEBrzaDBNRNDUIIv398N2snEnca+p0XFu2Udv+uC5XNssOPeBWVBrLjzzgh4ltijcmIlJN5ObL+XTvXQCmd3enhVP9/L6ra1RYOK1atYrz58/j4OBQ6j7PElYiVUORl0fcl18AYDF1Cvo+PrUcUcNBEASuHQxTF00AAmQkyUTh1ADISEpg/8pvERQKmvXoQ/vhY0rcr6gu1xdjtBn183n23Ijm1W5uYn6JSI2x+mQgkSm5OJjp8+7AJrUdznNDhYXTK6+8Qn5+PmfPni11n5YtW1YpqOqmvg/VJa9dR0F4BNq2tti8+WZth9NgyJcVcnLzfYJvFH8wEG0BGgZPJ4MPnLPgmblsrZzNeaGtE//cjOarA/fZOadLteW/iYgU8TA+k7VnQgBYMqo5RnoNci5YtVDh8Z2hQ4fSs2fP6oilSnz55Zfs2bOnXPvOnz8ff39/rl69Ws1RaZ78sDCSH5mN2n30IVrGxrUcUcMgJSabXUuvEXwjEamWBK+OdkgevXtEW4CGgSAIHF+3hviQILVk8PLw/mBv9LSlXAlN4ci9+GqOVKSho1AIfPyPH4UKgQHN7BjcvG7mG9dXKixBbWxs+Oabb8rc5+DBg9ja2lY6qIpy9epVNm3axOzZs2vsnLWBIAjEffkVQn4+Rt27YzJkSG2H1CAIvBbPyS0BFObJMTLXY8icFth7mNFtrKdoC9CAuHl4P/5nTiKRShn59geqZPDy4GhuwJxeHqw+GcTSQ/fp19QWXW0xL1Gketh1PZKrYakY6mrx+ejmtR3Oc0e1vHNdXFzQ09OrjqaLIQgCixYtYs6cOeU+Zs2aNfj4+NCx49M1zDVHQVwc2ZcuUxAXp7E2Mw8fJvv8eSS6uth/+onY3V/NyOUKzv0VyNH19yjMk+Pkbc7ERR3VyqY4eVuIoqmcZCYnEXH3DpnJSTV6rCaIvHfnsTP41Bk0atG6wm281tsTGxM9wpNz2HwxTMMRiogoSc7KY+mhAADeGdAEJ3MxhUDT1IlHnoCAAJYsWYKrqyuLFy8utt3Pz4/+/fvj6urKrFmzkMlkqm3bt2+nd+/e2NmV/+mvuofq0nbvJqhvPyJefZWgfv1J2727ym3Ks7KI/2YpAFazZ6Pr5lblNkVKJzs9j70rb3L7ZCQA7QY3YtSbbTA01ayhZW1QGyLE7+RR1s2fzq4vF7Fu/nT8Th6tkWOrSmZyEgEXTrPv+29UyeDtho2uVFtGetq8N0iZoPvTiUBSs4sXbBYRqSpfH7xPWk4BzRxMmd7drbbDeS6pE9liAQEBJCcno1AoyM9X/zDJyMigf//+vPvuu6xdu5Z58+YxZ84cNm/eTHZ2Nr/++ivHjx9n586d5T5fdSaHF8TFEfvZ4scFxRQKYj9bjFGPHlWqHZf4008UJiai49oIqzl1c0gyK1VGWkIu5jU8dKXp88YEpnJ43T1yM/LR1dei/6s+eLSx0UCkj8lMTiI1NgYLB0dMrDRTkqU8+J08yrG1qxEEAYlEwsA5b9Cy36ByH/+suAVBQJaVSUZSIplJiWQkJZAUGYHficNq+xz9309c2bsbLW1t1bpHfyh/PdpXUVhIekKc2rHH1v2MW+t21X7dnrxWACZWNuVKBi+L8e1d2Hg+jIC4TFadCGTJKHEYRURzXAhOYs+NaCQSWDq2JdpadaJv5LmjTginMWPGMGbMGAICAopt++uvv7C3t+fDDz8EYPXq1bRq1YqVK1eyfft2EhISeOmll4iIiEAmk9GmTRsGP6NeW3X6OOWHhYPiqQrUCgUpW7dh++47lfJbkt2/T+rWbQDYf/oZ0hoaBq0Id89EcXrHQxBAIoE+U5vi092x2s/7pBFlVc8rCAK3jkdy8Z9gBIWApaMRQ+e2xNxOs47PVRUvlSU9MZ6ja1c/FiePBEyE3y2MLCzRNTBAV98AXQNDdFR/P/4dfP0KZ7b+rhQSEgkt+gzAzNZeJZCKxFJBnuwZkShJi4up1OsQFAqiAu7SrHufSh1fHjKSEtWuFUBWajKyrKxyJ4SXhJZUwifDfZi64TJbL4XzcldXPGzECR4iVSevUM4n/yg9m6Z2dqWNi3ntBvQcUyXh5O/vj5+fH5MmTVKti4qKYunSpeTl5TF9+nS6d+9epQBPnTrFgAEDVMve3t7Y2tpy7tw5Xn/9dVVu05YtW4iLi1PbtzSqs8dJ180VpNJi4ill/XpyLl3C7uNFGLZtW+72BIWCuCWfg0KBydAhGPeo2vWsDiIDUji9/aFqWRDg1NYAzKz1cfKuvjIwaQk5akaUggC+WwOwcTHB2sW4Qj0DT1sNeHW0o+/UpujoaWk05szkJLVejJroQREEgaCrF/HdtF5NCBQRcOFMZRrl7qljpW42NDPH1NoGU2tb9IyM8Tt1VO3cEomEYW/+H4amZqj9mx4tSFD+zslIZ/+P3xaL+/CalcQHB9H5hYkYmJhWPP5SX5ZAyI2rnN66odg5BYWCtLiYKv+fenhZ06+pLScDElh6KIB1L3eoUnsiIgC/+gYTkpSNjYke7w/xru1wnmuqJJw+/fRT/vnnHwYOHIjlozppu3bt4pdffmHFihW89957rFy5ki5dulT6HPHx8bRp00ZtnYODA/Hx8WhpaaGlpfxi09HRQUdHR7VcEnl5eeTl5TFt2jSmTZtGRkYGLi4ulY6tJHTs7XH44nPlcJ1CAVIpJgMHkn3uHLK7dwl/aTKmI0diu/Ddcg3dpe3aTe7t20iNjLD78CONxlpVBEHg7ulozu0KLGEj/LvyFpaORni2tcGznS2WjkZVGuYQBIGUmGwi76cQ6Z9C1IPUYkaUggB/fXMVHX0tzGwMMLMxxNzWADNb5d9mtgYYmuqq4shKlRF5P4Xrh8JIT5QhlUroPsGLln2cqiX5PiUm6vGwVFHMCgV+p47SZewkpFLNCrXwO7c49+cm4oJL+B8BSCR0GD4GiVRKfm4u+bk55MtyH/2dS74sl4LcHGTZWRTmF8/JcWvdDgcvb0ytbTGxtsHU2gYTKxu0ddVzwRy8vDm27mcEhQKJVMrA2Qto2q18tiaD5rzx+FiJBAsHJ1Jiorh+4F/unjpGpzETaDt0JDq6le+JFQSB4GuXufj3DhJCg0vcRyKVYm6vmV7URcOacvphIsf847kQnEQ3T82K5tj0XEKTsnG3NhILCzcAQhKz+OWU8r79bIQPpvo6tRzR802VhNPPP//Me++9pxJNAPPmzaNHjx507NiRKVOmcPTo0SoJp5SUFExMTNTWmZqakpycrLauX79+aknjJbF06VI+//zzSsdSXszHj8eoRw/ywyPQdW2Ejr09hUlJJPz4I+l/7yFj/34yjx/Hes5sLKdPR6pfctd/YXIyCT/8AIDNm2+gY1dzFg/PIjcrn5ObAwi7U3qCsUSi9D9Kicnm6oEwzGwN8Gxri2c7G2wamZRLmORm5quEUsT9FHLSy5dQWyCTkxSZRVJkVrFtOnpamNkaIJFAYsTj7boGWox8o41q1pymyc/N4eq+v0vcdnHXdvzPnKTd0NG06DsAXf2qfdnFPAzg/M7NRNy9A4COnj7th4/GwNQM383r1QRMeYYJM5OTWDd/uprok0ilDJr7Zrl6YFr2G4Rb63akxcVgbl+xvK6njzW2tCL89g3ObNtIYkQYZ7f/wa0jB+g+aSrNevapkPgUFAqCrl7i4t87SAwPBZTXqs3g4RiZW3B66+9q10pTvYKNbU2Y0rkRmy+G89V/99n/Rg+N1RDbeTWCj/b4oRBA+ijXZVLHRhppW6TuIQgCn/x7l3y5gl5NbBjRqvSqHiKaQSI8/fhbTuLi4rC0tERXt+RZRmFhYaSnp9OiRYsye4GeZODAgXTo0IGlS5eq1vXv35+hQ4fy3nvvqdZ17tyZGTNmMHfu3ArFXNTjVERRj1N6ejqmpprr7i+L3Lv3iP/mG3Jv3ABAx9ER2//7P0wGDyomJGI+WkT6P/+g16wZ7rv+QqJdJ1LSiLyfwvE//MlJz0eqLaHb2MZo60o5vf0BguKxIaRHGxvC/JIIvpFIpH8K8sLHw5cmlvp4tLPBs60t9u6mZKfnkZaQi4mFHpmpeUT6pxB5P4XEiEy1c2vpSHHyMsfFxxIXH0viQtKLnde7kz0ZybmkJeSSnpBDekIu6Yk5pCXkkpUiK2m0ClAKvZe/6VYtie1pcbH8u/xLkqMikEilSgEiCEikUtzbtCfmYQCyLOVr1TcyptWAIbQdMhJjS6sKnScxIozzO7cQfO0yAFra2rQeOIxOYyZgZG4BKEVQZQSM38mjxXqNaiI3qzQUCjn3z/pybucWsh7NELRxdafXlOm4tW5X5rGCQsHDyxe4tOdPkiLCANDRN6DtkBG0Hz4GQ1OleK7stSoPyVl59FnhS6askOXjWzGhQ9V7v2PTc+n+7UkUT9zjUgnsW9CD5o6mooXJc8g/N6N4Z+dt9LSlHHunN42sNJuTWZeQy+UEBwfj6elZbl1RXopynsujByotnLp27cqqVauwtLTEwcEBIyMj1bY///yTyZMnIwgCTZo04b///sPLy+uZbZYknF566SWcnJxYsWKFap2rqyurVq1izJgxlQldRUUulCYRBIGMgwdJWL6Cwkc+T4YdO2K36CP0mzUDIOfaNcKnTgOJBLcd2zF4ariyNpAXKri0N4RbxyIAsHAwYtDM5lg7K5Nbs1JlpRpC5ssKCfdLJvhmAuF3kynMfyyidA20yM8tPd/MytmYRs2UQsmhsRnaOupvmLLOW+w1FCjISM4l+GYil/eGFNs+5p22OHlblH0hKkj4nVv89+O3yLKzMLKwZPTCjzG2tFL7Qi6Qybh3+gTXD/5LWlwsAFItbZr16E374WOwcXUv8xxpcbFc2LWN++dPKwWZRErzPv3pOu4lTG0011NZnUKishTk53Hz0H6u/LuLvJxsABq1bEOvKdOxc/dUmwloZGHBw4vnuLRnJ8lRyvtY18CQdkNH0m74GAyMTco6lcZZeyaYbw4GYGuih+/7fTDUrfzDkSAIrDjygDW+JQ81Whjq0MTORPljb0ITW2O87U0wN1R/+BWH+eoPaTn59P/+NMnZ+bw/2Jv5fRvXdkjVynMhnGJiYoiMjERXV5f169czdepUAAYPHkyTJk346quv2LBhAwcOHODEiRPPbLMk4fTrr7+yfv16rl+/DkBoaChNmjQhKiqqQt5NJVFbwqkIRW4uyes3kLx+PUJeHkilmI8fj/mECUS//RYF0TGYT5yIwxfVP7z4LNLiczi64Z6qB6hFLye6jW+Mjm7Fb96CfDkR95IJvpFI6J1ECvMUxfbxaGODR1sbnJtaYGSm+VmEWakyNi+6oNb7JJHCy19rrsdJEARuHtqH75YNCAoF9o2bqERTaSgUcoKvX+H6f/8QHeCvWu/aqi0dho/BtXU7slKSVUIACVzesxO/k0dRPJrs0KRLD7pNnIKVU+k9GHK5nIKCAo28zrqCLDuLO8cO4n/uNMKja2Hj6k5iRBhF0y4NjE3IzcwAlD1MzXv3w6dnP/SeePCrSfIL5Uz/4ypx6TKmdXHl1e5lC+TSyMwt4McTD/F9UPEC6xZGurhbGeFmbUSWrIAj/vGk5CjIVwjiMF8dJjY9l4//8eNkQCJetsYceLPnc+9G/1wIJ1dXV1avXs3t27d58cUXiYiIwNDQkHbt2rFy5Up69+4NQNu2bTl16hTm5uZltlmScEpJScHd3Z0VK1YwYcIEZs6cCcDff5ecK1IRals4FVEQE0PCihVkHDxUbJvdoo+wfPnlWohKiSAI3L8Qy9mdDynMV6BnpE2/ac005msUcS+Z/atvF1tfHT0/T+N/PgbfbQFqw3yaslAoLCjg+Lo13Dt9HACfXv0YOHtBsaTpsogNfMC1//4h8PIFBEEpLo0trchKTVHN+JJqaakEk1ub9vSYNA07j7KfOrOysoiKKp6k/rygkMvJy86i4Ilh+SeRSCToGhqiq29QKXsQTZObLyc5Ox+pBOxM9Suc65RXICclpwC5QkAC6OtIkRUoEAAJYG6og6GuFgVygUKFQvlb/ui3oqR7QCBTJufXa2mEpBZy7sO+Ys9THWPn1Qg+3OOnevB7rbcHHw5tVrtB1QB1RThVul9YW1ubefPmYWNjw4ABA2jdujWhoaE0b94cuVyO/hMJz05OToSHh5cqnJ4cdz9+/DjffvstCxYsYPXq1VhaWnLw4EHmzJnDm2++yYABA9i2bVtlw66T6Dg64vTDD5gMHkL0W2+pbYv/dhkmgwZVyTyzssiyC/DdFqCaou/kbcGAV30wttBcD5Byph3Fen7MbKv/g9qnuyONfCw1Xm8uKzWFfd9/TWzgAyQSKb2nzaTdsFEVzi9x8PJm5Dsfkp4Qx42D+7hz4ghZKeqTIhRyOXYeXvSZNhNnnxbPbFMulxMVFYWhoSE2NjbPdc5LbmYmWanJxdabWtuiZ1h38kAEQSAiJQdZgRwjfR0cylkiQ6EQSMrKIz8nHytT0NXSwt5MDwNdbfILFRTIFehoScvshZArBPIL5eQVKsjOKyQrrxAEAcucDOZ1gEUnkghLyhGFUx0iNj2Xj54QTQDrzoTySjc38f9UQ1RaODVv3py//vqLrl27EhISwpUrV7CxsSE9PZ2HDx+qZrjJ5XLOnz+PtXXp+RBZWSXMfNJ5PJ2ye/fu3Lt3D4VCgbQOPCFWF1olmXEqFOSHR9S4cIoJTOXY7/5kpeYhlUroPNqDtgMbIdHQzJ8ijC306TO1abGen5pyHje20NfouWKDHrBvxddkpaagb2TM8Lc/wK1V+X27SsLM1p6+r87BpUVr9i7/stj2XlOml0s0ARQUFCAIAjY2NhgYPN8fsjraWuRlpKmvlICxqQla2nVruraLtQ5BiVlkFoK9VBuDZ+Q65ebLiUrPQVYAEm1drIx0sTczUPVWVeSOLhqkzC9U8CAuAwHQNjTFRD8DCwMptiZ1z3C3IROalM3THYVyQRAFbg1SaeH04Ycf0qNHD7Zu3UpmZiYzZsygdevWZGZm0rx5c9544w1GjhzJjRs38PHxwcnJqdS2jMqZX/A8iyYoxTxTKkXXtWZyDLJSZaTEZRN+Jxk/3ygEAcxsDBg4szl2btU3lFldPT81zb3TJzi27mfkBQVYOTdi9PufYKEh3x8AO3dPJBJJMUsAC4eKn+N57mkqQktbB1MbWzKSEigatzK1tq1zognAUE8bc0Nd0nLyiUmX4WFdsueZIAgkZeUTlyFDEAS0pVKcLQwwNaj6a9LVluJkYUB0qgxBIgEkSCXw2b67/P5qR/S0NTs0IlI53EqYNaclkeBmXXd6UZ93Ki2c3NzcuHv3LmfOnMHLy4tmzZqRlZVFeHg4TZs2JTw8nLVr19KyZUs1KwGR0inJPNPhi89rpLfpydIlRTTt5kDPiV7o6le/DYKme35qEoVczpltG7l+4F8APDt0Zuj8hRofDjKxsmbgk2aQGvYWqis8ePCAuLg4VY5kVTA0NUPP0BB5QQFaOjp1UjQVYW+qT0ZuAdl5hWTICjF7QgwlJiaSlJJGUnYBBmZW6OrpYaqvg5OFATpl1CPLz88nKCgIHx+fcsVgaaSHsZ4OmdlayDN00dfR4nxQMm/uuMmaye3E2md1gPDkXLVlLYmEb8a2EHubapBKJ4cXkZOTw/3790lOTqZZs2Yad+KuTupKcvjTFMTFqZlnVjclzTBDAq9Uk6fR80JmchLxocFc27+H6IB7AHQZ9yLdxk+u1qTjqlgCyGQyQkNDcXd3V8tDrCn++usvbt++zddff61al5OTw4YNG3jjjTcA2LlzJ9euXWP58uWVaq8+E5eeyytTpzD3jXcYM7AH0ke9Tq9Mn0loRCTGJqbcv3uH1+cv4L2333hmz6G/vz/Lli1j06ZNJW6/f/8+H3zwAfv27VNbX3SfpErNeXXzLfLlCsa3d+a7ca2Qani4XqRiTN94hVMPEnmhrSMTOzTCzdqwwYimep8cDrB+/Xref/990tLSVEMIo0ePZu3atdja1h2X6/qGjr19jeY0pSXkFjeFFCA9IVcUTqXwZKFeAKm2NsMWvId31x7Vfm4TK+sa7WXSpK+PgYEB2k8Zud69e5d9+/YxbNgwXF1dVesVCgWpqalYWanbNyQlJWFpaYlUKi2xvdqgIC6O/LBwdN1cq/TetTHRR0dHG6mOLomZeRjoaJGSnU9WXiETX57JkKHDsTGQ0LJ5Mxa+tQCJREJKSgpGRkbo6elRUFBAeno6VlZWxMfH4+XlpTZLGSA5OVl1TY2Ny67p2M7VgtWT2/L6thvsvh6Fqb4On45o1iCGeusiD+MzOfUgEYkE3urfBDfr2rHRaOhU+rH46NGjvPXWWyxfvpyUlBTy8vK4ceMGWVlZvPrqqxoMUfOsWbMGHx8fOnbsWNuh1AmedueGmpvZVh95ulAvgCCX49ikaS1GVXEEQSAnv7DMny0Xw+j+7Ukmr7tM929PsuVi2DOPKasTu1GjRjRp0kRt3ffff09QUBArVqwgI0PpsXT79m1GjhxJhw4dWLlyJaCcRDJkyBBmzJhB+/btefjwYYntafL6KHJynvmTsn07Qf36E/HqqwT160/K9u3PPKa0a6QlldCmZQts7OyJz5ARlpxNhkzpt2VuqIuHjRHGhvoUFhYSHR3NgAEDmDFjBu7u7ty+fZuQkBDmzJnDoEGD2Lx5MyEhIbzyyiuAcrivT58+vPPOOyrPPTs7O1q2bFnmdRjc3J7vxrUC4Pfzofx0IkhTl1ikgqw9ozTtHdLcXhRNtUilh+omT55My5Yt+egj9cKz2dnZ2NjYEB4ejo2NZrx+qou6OlRXkyRHZ7H722sUFiiUpi+C5j2NnjdCbl7ln2+Lm5JO/OwbXJq3qoWIys+TQ3UKqTY+nx3R+Dn8vxhcIQfs27dvs2TJEv755x9AOVS3bds29u7dy4MHD3jppZe4efMmX331Faamprz55pucOnWKnTt38ttvv2k8/iIUOTk8aNe+Wtr2vnEdaSk5cPmFcgLi1B9mPv+/txjQqxudOnZg586d3L59myNHjpCRkYGpqSm///47d+/eZe7cuXTo0IEHDx7g6OjIgwcPWLBgAceOHePPP//Ez8+Pr7/+GkEQyuw1KmlI94/zoSzZrzRl/WyEDzN6VM6sU6RyxGfI6LHsJAVygX9e70bbRtXrc1cXqfdDdenp6diX0CVtZGSEiYkJ6enpdV44NXTyZYUcXnuXwgIFjXws6TPFm4wkWb2e2VbdCAoFNw/tK7ZeIpVirsEZdA0db29vJBIJnp6epKWlAXDt2jUAbt68SUFBAXp6z+c0+fzC4k76AP/++y/+9+7i7e3NZ599BoCfnx+bNm0iLCxMVUmhc+fOODoWvxeHDx/ODz/8QGpqKosXL65w5YVXu7uTISvkh2MP+eI/f0wNdBjf3rmCr06ksmw8H0aBXKCjm0WDFE11iUoLpwEDBrBixQoGDx6sepMKgsCKFSswMDDA09NTY0GKaB5BEPDdGkBafA5G5noMmO6DgYkuJlbi8FxZnP9rG2G3byLR0gKFQvnkXk9ntxnoaOH/xeBSt8elyxjww+liBWOPv9sbe7PShbWBjuaeBJ+0ILGysmLw4MFMnDhRY+2XhcTAAO8b18vcpyA+npDhI4pZiHgc+A+dMoSJpAwPLV1traLOXzXmvf46Y0aNVC2fP3+eZcuWsXnzZm7fvs369euVbZfSk2RiYsKlS5fYvHkzHTt2JDg4WM0vrzy80a8x6bkFbDgXygd/38FEX5vBzWvenLehkZVXyLbL4QDM6SV+t9Y2lRZOb7zxBidPnsTDw4MuXbpgamrK3bt3SU9PZ+/evWLyYB3n3ploAq8lIJVKGDy7BQYm5S8F0lC5f86Xy//sBGDIa2/h0rxVnSt4WxEkEkmZQ2oeNsYsHduSRXvuIhcE1bRnDxtjjcbRqFEjIiIinmlw+8ILL/DTTz8xduxY8vPzycrKqtZJKBKJBMkzLCX03N1LtBDRc6/8MJaanxICEiQY6WkVsx0ICAigXbt2mJubc/r06We2e/HiRZo1a8bLL7/M6tWrycvLq7BwkkgkfDK8GRm5Bey6HsUb22+ycXpHujeuf/d/feLPKxFkygrxtDGif1Nx4lVtU6WSK/v37+fgwYNcuHCBlJQUBg4cyEsvvVSmS7hI7ZMQnsHZXYEAdHnBEwfPEhzLK8GTVejro5Aoi9igBxz5bRUAHUePx6dXP4Dn7nU+zaSOjejVxIawpJxqm/ZsYWFB165dGTBgAOvWrcPY2FhtJl3RTLsRI0YQHh7O8OHD0dfX5+OPP64Ts3fNx4/HqEcPjVqIFPkp5Rcq0NWW4uxgj+FTIm7cuHHMnTuXMWPGMGjQIDIyMtDR0VEbpntyOScnh1deeQVtbW0WLlyIsXHlBLBEImHp2JZkygo5fC+O2ZuvsW1WZ3H4qJookCv4/VwoALN7eoh2EHWAKvs41WcaYnK4LLuAXUuvkpEkw721NUNfa6mR3sHL/+zk3J9bAWXS6cA5b9Cy36CqB1wHyExOYtuid8hOS8WjfSdGv/cxUmn9dFGubR8nkfpBee6TvEI5szZd42xgEmYGOuyc24Wm9g3jc7Qm+fdmNG/vvIW1sR7nPuiLvgaHwusb9TI5/Pbt4lXsn6agoIDXXnuNLVu20KxZ3azWvGbNGtasWYP8UVX5hoIgCJzcfJ+MJBmm1vr0f6XqfixpcbGc3fEHDy+dVzvP0bWrsfNojK2bR1XDfibV2dNVkCdj74qvyE5LxdrFleFvvFdvRZOIiCbR09bif9PaM3X9ZW5EpDFtwxV+mdKWArmgEc8vEeVn6f8eWRC82s21QYumukSFhFObNm3KtV+7du2qzVtFE8yfP5/58+erFGZD4faJSEJvJyHVVuY16RlWvvxEekIcl/bs5N7pEwiKEmYBCQLbPnqbJl170qr/YJx9NNOz9TRPGlFquqdLEAQO/7qK+JAgDExMGfN/n6JrINaDEhEpwlBXm42vdmLS2osExGUy4bdLgHISwdKxLZnUsWbqbD6vnAtK4n5sBoa6Wkzt4vrsA0RqhAoJp7i4uGfuo6uri4WFONZd14gNTufinmAAek7wwta1cl3qGYkJXPpnJ/d8j6N41GPn4tOSyPt3edp+XKFQEHD+NAHnT2Ph4ETL/oNp3rs/hqZVE6vZaalE3b9H6K1r3PM9rlovCALH1v2MW+t2Gul5urTnTx5ePItUS5tR7y7CzFacPSQi8jRmhjosn9CKkasf9zorBPhojx+9mtiIPU9VoMjwcmIHF8wNxQk8dYUKCaeK+n6I1A1yM/M5su4uCoWAVwdbmvdyqnAbGUmJXP5nJ3dPHUchLwTAtVVbuk2YgmOTpsqen6eKz9q6eXDn+GHunz9Namw0Z7b+zvk/N9O4Uzda9R+CS/Nn90IJgkBafCzR9+8RFXCP6IB7pMXFlr6/QkHU/bs069Gnwq/xSR5ePs+Fv7YBMGDW6zj7tKhSeyIizzOZssJi6xQCzNt6ndk9PenfzFYcZqog/jEZnA1MQiqBmaLZaJ2iQsLpxx9/xM3NjZEjR5YrMev+/fusW7eOL7/8EiOjumMP35BynASFwPGN/mSn5WFuZ0ifqU0rNGSWmZzE5X93cffkEeSFyg/HRi1a023CFJyaPq643rLfINxatys2PX/gnAX0njaDgAtnuHP8CPEhgTy4cIYHF85g4eBIy/5DaN67P/KCAlJjYzCzsyMvO5uo+0qRFB1wj+y0VPWgJBJsGrlh4+aB/5mTxXq6Dv/6I6mx0XQcPR4d3YqbJMaHBnNozQ8AtBs2+rlJchcRqS7crY2QSlDz/AK4FZnO/O03MDPQYVRrR8a3d6aVs5loV1MO1p1V9jYNa+mAi6WYIlCXqNCsuqtXr/Ldd99x9epVpk+fTvv27XFzc8PV1RUDAwOioqIICwsjKCiIHTt2EB8fz3vvvceUKVMq7BdSEzSEWXXXDoZyeV8o2jpSxn/YASun0qcgP5lkLZFIuLJ3N3dOHEZeoKyV5dK8Fd0mTMa5WeV7X+JDgrhz4jD3z52mQJYLgEQiRRBKdksG0NLWxs6zCc5NfXBq1hzHJs3QN1K+DrWeLokUCwdHUmKiADCztaPPK3PwbN+p3B/U2WmpbF30DlnJSbi1bscLHyxGquHZG7VJbc+qCwkJITU1lfbtyy5l4uvrS1BQELNmzQKUcZcVb3nbrQ+cOnWKVq1aYWVlxc6dO7l06RLa2tp4enoyZcoUTExMntlGaGgov//+O19++WWJ29PS0rh27RoDBgwocXtl7pOdVyPUPL/eGehFboGcPTeiiU2XqfZrbGvM+PbOvNDWCTtTcWZnScSk5dLru1MUKgT2L+hBS+eGk4tbFvVyVl3Hjh3ZtWsXwcHB/Pbbb3zxxRfcuXOHvLw8pFIpgiDQqFEjunbtyjvvvMPw4cPFJ4taJCoghSv7lf4fvSd7lymankyyBpBqaalymJybtaDbhMkaqcNm59GYgR4L6D1tJgHnz3Dz8H6SIsKK7efcrAWurdri3LQ5do29Su05erqny9jSioeXzuO7ZT3pCfHsXf4l7m3a03f6XCyeURKlMD+fvSu+Iis5CQtHZ4a/9X/PlWiqC1y5coXr168/U+C4ubmpeqmTkpKYPXu2qpZdVdqtD6xatYpPP/0UKysrjh8/Ttu2bWnTpg1Hjx5l9OjRnDx58pltWFlZ0aNHj1K3x8XFsWzZslKFU2UozfPr3YHeXAxOZvf1SA7djSMoIYtvDwXw3eEAejWxYXx7ZwY0syM1J5/QpGxxRh6w8XwohQqBrh5Womiqg1TKANPT05Ply5erlrOzs8nLy8Pc3LxM51+RmiM7PY+jv/sjCNCsmwNNuzqUum9mcpKaaAJQyOXYeXjRc/IrNGrRWuMCWFffgFb9B2Nu58CuLxcV214RoWZiZa2WDO7dtQfubdtzec9Orv33L6G3rhOx8HU6jBxH5xcmoKNX/ClXEASOrV1NbOAD9I2MeeH/PlX1ajV40qMhJRgsPcGs4vlxT2JjY1NiDcuAgAD++usvnJ2defnll0lNTSU2VpnLtnjxYvz8/HjnnXdYtGgRv/76q6pWW3BwMFeuXMHW1rZWa2NmpcpIS8jFXAN1Hp++Rq6urnTr1o1u3brh7e1NTk4ON27c4NSpUzg6OjJjxgwSExM5c+YMZmZmREVFMXz4cEJCQlRtHDt2jAsXLjBgwAC6d+9e6v+hqjiYGRQTPVpSCT28rOnhZc0XsgIO3oll9/UoroWn4vsgEd8HiehrS5E9qtHX0GfkZcgK2HElEoA5varfzkWk4mhE5RgZGWFpaVlvRNOaNWvw8fGhY8eOtR2KxslKlRF5P5lDv/qRm5GPlZMxvV4s2xoiNTaGkkZse02ZjmvLNtXaa1g0LPgkmiiYq6tvQM/Jr/LKip9xbdUWeWEhl//ZycZ35xF4+UKx13t139/4nz2FRCplxNsfYuFQNYFQLxAEyM8u++fKOvixBWwaqfx9Zd2zjylj9L9Vq1YMGzZMbZ2/vz9jx46lQ4cOZGZmkpSUxMOHDzl79iwA/fr1w9XVlVmzZmFubs7x48cJDVX2pO7Zs4esrKwS26365REoyJM/88fPN4rNiy6wd+VNNi+6gJ9v1DOPKStD4pVXXsHBofiDTlJSEgkJCWRnZ/P333/TtWtXdu7cyYYNG0hNTWXRokUcPHiQ9u3bk5qayp49ewBlTbvffvuNoUOHqrz4rKysmDNnjkavV3kw1dfhxU6N2D2vG6fe68OCvo2xM9FTiSZ4PCMvNj23xuOrC2y/HEFWXiFN7Izp4117DwMipVPpkiv1mefVx8n/fAy+WwNU31ta2hKGzGmBtm7Zw035udnF1kmkynyh6sbEypqBc94oNiNPU0aWlo7OjFv0BUFXLnJq8zoykxLZ98M3uLZqS7/pr6Gjp8e90yc4v3MLAP1enYtrqzYaOXedpyAHvqnA/1hQwMH3lD9lsSgGdEueDFJST8cvv/zC7NmzSxU+jRs3xtTUlObNmwMwfvx4Dhw4wIIFCzh+/Djr16+vlh6UwnwFa996dg24JxEEOPPnQ878+bDM/eas6o2OXsnvy6eH2NauXcvu3bs5f/48y5cvx8bGhpUrV5KamkqfPn24fPkyPXv2REtLi++//x6pVMqDBw8ev47CQrKzs/Hy8qJTp06q9X369KnQa9M07tZGvDfYm64eVkzZcFltm0KAv65G8taAuusHWB3kFyrYeP5xeRUx1aVu0iCF0/NIVqpMTTQByOUC2rpl9wKmxERx5H+r1dZpWrw8i9Jm5GkKiUSCV+duuLVpx+V/dnFt/9+E37nJH+/OU0tKd/ZpSZvBwzV6bpFnExERwcSJE8u9/9ixY5k1axazZ88mLS0NFxeXaoyu9hk8eDD9+/fHxcUFQ0NDZDIZr7/+Otra2piamiKTKROvnZ2dS+z17927NwEBAXTp0oW33nqL119/vaZfQpl42JY8I2/l8UD8YzP4YnSLBpNEvu92DPEZedia6DGqTfU/uIpUjgYpnJ5HO4K0hNziIyQCpCfklppzkZWawt/fLEaWmYG9pxdDF7xHdmpytYiXZ/F0nlJ1oKOnT48Xp9G8dz+Orf2ZSH8/te3RAffITE567gv3qtAxVPYOlUZGDKzppOxpKkKiBfMvg2kZH+o6FZs67e7uzqVLl+jVq1eJ23V1dcnPz1ctOzs7k5eXx4kTJ+jdu3eFzlURtHWlzFlVdvtZaXnsWHJJ7b0nkcBLS7pgbF66FcazHmiexNXVFW9vb9Xy1q1b8fDw4JNPPsHX15cNGzY8s425c+cyZcoUfHx8eOmll+qUSbGDmQFLx7ZUzciTSqCvty2nHyZy5F48F4KTWTSsGS92dHmue2AEQWDdI8PL6d3d0dMWJ6bUVeq9cFq8eDHHjx9HKpXSqFEjtm3b9sxjnsehOkOT4nYPEimY2ZY8OyUvJ4c93y4hIzEec3sHXvhwCYamZlg6Pv+5PRYOTnQe92Ix4SQoFKTFxTQc4SSRlDqkBoC1F4xcBfvfBkGuFE0jf1Su1yALFixg1KhRWFtbExgYyAsvvKC2vUmTJty/f59NmzYxatQoLCwsGDlyJJ9//jnffvutRmN5EolEUupwWhEWj7zRfLcFICiU77k+U5piYVd9vjuenp5s27YNd3d3tmzZ8swhymXLliGVSnFycsLJyalOiaYiSpqRFxCXwQe773A7Kp2P9vix91Y0345thZt13fEE1CS+DxN5EJ+Jka4Wkzs3zMT4+kK9F063b99m69atuLs3XGdVhVzB+d1BauuKPsBL6m2SFxaw7/uvSQwLwdDMnHGLvqxyGZT6hqWDExKJRC1JVxNJ6c8d7V4Gz/6QEgKWHlWeVVcSXl5eHD9+nKNHjzJgwAA6deqEubk5jo7K/4WWlha7du1SKzI+duxYlixZUuaU+5rCp7sjjXwsSU/IxUwDs+qeZNKkSXh6eqqt69u3L1paWsTHx7NlyxZu3LiBra0t8+bNU+3z5PK7777L4cOHkclkHDt2TGOxaZqnZ+Q1tTdlz+vd2Xg+lBVHH3ApJIXBP57hnYFNmNXDHW2t+jEZqbysPa3sbXqpUyPMDOqe76HIYypkgFndFI3Vl2a4JpfLi5le9enTh3379lXIwPLJobqHDx/WewPMc7sCuX0iEm1dKUPmtkRbW1rqB7igUHBozQ/cP+eLjp4+k5Z8i51H41qIuvYpqUzM8+4SXtsGmJrixo0brFixgu3bt9d2KM8lde0+iUjOYdE/fpwLSgKghZMpy8a1ornj8/HA5xeVzsifz6EllXDm//riZN6wfaxKo64YYFaLZN+6dWu5CgIXcfnyZWbNmoWVlRWff/55se2nT5/G29sbY2NjhgwZQmrq4xIcaWlpvPvuuwwdOpTTp8s3A2b+/Pn4+/tz9erVcsdYV7l/IYbbJ5SeH/1f8cG1uRVO3halPvWe2f4H98/5ItXSYtS7HzVY0QTKpPTZP//OxM++YfbPvz/3oul54cSJEyxYsICPPvqotkMRqSEaWRmyZWYnlo9vhZmBDnejMxj183mWHQ5AVlC7uaqx6blcCE6qkn3C2kflVUa2chBFUz2gWoTTsmXLCA8PL/f+OTk5eHl5qbrmnyQpKYkRI0bwySefkJycjKWlJa+88opq+6ZNm/jxxx9ZunQpEyZMUPValcXz4uMUG5yO73bltOOOw91o3N62zP1vHNzLtf1Kb5dBc9/ErU39d1muKiZW1rg0b9Vw8pqeA1JSUli1ahUtW7as7VBEahCJRMKEDi4ce7cXw1s6IFcI/OobzNBVZzl4J6bK4qUy7LwaQfdvTzJ53WW6f3uSnVcjKtxGZEoOB/2UZq9zenk+Y2+RukCFc5z27dtHYmJimfukpKRUqM2+ffvSt29fjh8/Xmzb9u3bad68OdOmTQNg6dKleHp6EhMTg6OjI61btwagTZs2uLu7ExYWRtOmTcs83/OQHJ6ZIuPQb3dQFAp4trWh4/Cyc7weXDzLqc3rAejx0is0792/JsIUEdE4EyZMqO0QRGoRWxN91kxpx+h7cXy69y6hSdm8vv0mABJgahdXxrR1wtpYFytjPYx0tcqcjRebnltmqRdBEEjOzicuXUZMWi5xGTJi0mSEJGZx1D9etV+RcWdrZ3OaOpQ/9WPDuVDkCoGeXtb4ONbflJGGRIWF06pVqzh//nyJzrZFPEtYVYQLFy6oTVN2dXXFxcWFy5cvM3jwYNLT03FwcEAmkxEYGIizs/Mz26zvdgQFeXIO/nqH3MwCrF2M6f+qDxJp6R8MkffucOjn70EQaDN4OJ1Gj6/BaEVEREQ0z6Dm9rhZGzFo5RnVOgHYcimcLZcej3joaUuxNtZTCSkrI+Vva2NdAhMy+etaFIKgnGQ6urUjjuYGxD4hkmLTZeQXll6E/EkUAgxZdRZ3ayPauJirfpo5mKKrXXyA50FcBjuuKHupZvcUy6vUFyosnF555RXy8/NV5RBKQpNd6PHx8cWG1Ozs7IiLiyMqKooZM2bg7e3NpUuXWLx4McbGpdcXy8vLIy8vj2nTpjFt2jQyMjLqnXmeoBA4scmfpMgsDEx0GDavVZlTphMjwti74mvkhYV4de5G31fnPNdeKCIizxNZWVnEx8eXuM3AwECV3qBQKIiJieHWrVukp6czZcoUDhw4wPDhz7eha1JWXonrbUx0yc6Tk5MvJ69QQXRaLtFpZQ/jCQL8e6t0XzMbEz0czPQf/RhgpKvFL77BlDS7KjQpm9CkbP65GQ2ArraU5o6mKiHV1sWCC8FJfLTHT3V8zDPiE6k7VFg4DR06lICAgOqIpUTS09NVVdKLMDY2Ji0tjSZNmnDu3DnCw8MxMjLC2rrsPJWlS5eWmHxen7h6MIzgG4lItSQMndsSE8vSZ7xkJCWw55vPyMvJxqlpc4YteA+pVDRVExGpy9y7d49GjRphYmLCgQMHWLt2La1atSIlJQU/Pz+V6eetW7c4deoUAN9++y3x8fEUFBSwdOlSUlNT+fjjjwkKUtqU9OjRg/bt26u1/Tzgbl3cdVxLImHfgh44mBmQk19IclY+SVl5JGflk5ydR1JWPslZ+QTEZXAhOLlYm4N97GjraqESSA5m+tiZ6pfYY9TIylBl3KklkfDN2BYMbm7Prcg0tZ+0nAJuRqRxMyKt1Nfy8T936e1tU+JwoUjdosLCycbGhm+++abMfQ4ePIitbdmJyuXFysqKjIwMtXXp6elqIsnV1bVcbX300Ue8++67quX61uMUfCOBq/8p6xj1nuyNQ2PzUvfNzcpkz9IlZKWmYOXciDHvf4q2rm4NRSoiUjkSExNVho6pqalkZGSU+/39vPDRRx/x2Wef0aFDBwAmTpzI3LlzefDgAb/99hsrV64ElLmhAPn5+RgZGSGVSomOjuaPP/4gNDSU5cuXs3TpUqZMmcKVK1do3759sbbrO0+7jheJlyLxYairjaGlNi6WxQ1JY9Nz6f7tyWKia8no5uUWLyUZdwL08balj7fyO1AQBMKTc7gVmcbNiFRuRaZxNyYd+VOjf3JBICwpRxRO9YBqMcDUpBixt7cnOjpabV10dDT29vYVbktPTw89vdLLINRlEiMzOf6HPwCt+7ng071ko8bM5CQSI8O4+Nc2kqMiMLa0YuxHn6NfxhCmiEhN8ddff3H79m2+/vprtfX79+/no48+wtnZmaSkJLZu3UpAQAD37t3j448/Jicnhw0bNvDGG29UuO2aIDM5idTYGCwcql6uyNraGisrq3Lvr62tzeDBg3n11VeRyWR4enpiamqKQqFg+PDhWFtbk5OTU6m26wOliZdn8SzRVV6eNu58GolEgpu1EW7WRoxpqzSQDUvKpu/3vmqlerQkEtysq89xXkRzVFk4vfbaa7z++uu0atVKE/EUY8CAASxbtky1fO/ePVJSUujZs2e1nK8ukpORz8Ff7lCYr6CRjyXdxpU8ZdXv5FGOrV2tcsPW1tVl7EefY2qt2arxIg2LuOw4IjIiaGTaCHujij+wPImBgQHa2uofO1FRUfzf//0fp0+fxtbWlsjISKysrHB2dla9z+/evcu+ffsYNmwYzs7OJCQkqB7Q8vPzSUlJKbHtqiAIAoV5JefQPMm90yc4ufE3BEFAIpHQb/prz5y1qq2nV2qu4ZgxY9Qm3yQkJBAcHEx4eDhpaWkEBwer7Z+bm8tXX33FzJkziYuL4+jRo7Rt25Yff/yRbdu2sXbtWrp3715i288LzxIvpVFZ0VVV3KyN+FYDok2kdqjyp0x4eDi5uVVLaivqUZLJZGRkZBAdHY2RkRHm5uaMGzeODz/8kCVLljBhwgTmz5/PlClTMDc3r2ro1UpWqoy0hFzMq1iCQV6g4PD//MhKzcPczpBBs5ojLaHUQEZSIkfXrubJRxh5QQH6RmJPk0jJCIJAbmHZ7919wftYenkpChRIkfJR548Y5TmqzGMMtA1KFQWNGjUqNvS+ZcsWZs6cqRreLxJEO3fu5Nq1ayxfvpzvv/+eoKAgVqxYwVdffUW/fv24c+cOBgYG7Nmzh7t37zJhwoRibVeFwrw8fnqlYjNQBUHgxO+/cuL3X8vc781Nu9EpxZF71Cj16+vr66uyeDE3N+fnn39W225oaMiXX36JkZERa9euJSgoiI4dO5KXl8fu3buRyWR07ty5xLZFKi+6qkptiTaRqlMnatW1aNFC9ffdu3fZvn07s2fP5rvvvsPY2JiTJ0/y9ttvs3nzZgYPHsyqVatqMdpn438+hlNbA+DRFNc+U5uWOrRWFoIgcHrHA2KD09E10GbYvJboGarXMMrPzeHe6RNc3fe3mmgqOr5BFa0VqRC5hbl03t653PsrUPD15a/5+nLZQ2GXJ1/GUKfkIYfWrVurvNeKCAwMZNy4cWW2uWjRIpYsWcKvvyoFydChQzlx4gQjRozg+PHjTJ8+vcS2nweKcpyeJiwsDFAOBcXGxvLdd9/h7OzMtm3b2L59Oxs2bGD69Ok4OTlx8+ZNlXgSqTvUlmgTqRqVEk49e/bk3LlzquXDhw+r/j5+/Dj9+1fMXPHJEiol0axZM44cOVKxIGuJrFQZvlsDEOSZKOSpSLUsOLUlgLiQdJybWmDbyBQzG4MyfZeKuHMyivsXYpFIYPCs5ljYP55dmBITxc3D/+F/5gT5pfT4iUVrReoD+vr65JVjSOxJxo8fz/bt2xkxYgTXrl3jf//7n8bj0tbT481Nu8vcJzMlmT/efa1YsehXv/8VE8vSc4m0NZBraWVlRXh4OLa2tmzfvp0vv/ySL774ArlczrVr12jXrh3t2rXjpZdeIiQkRBROIiIaolLCydfXV/X3sGHD+Oyzz+jSpQsAUunzVbG6oqQl5FIg86Mw53EVcm2Dftw/D/fPK231dQ20sXU1wdbVBJtGpti6mWBiqa8a3shKlfHwSjwX/1HmMnQb15hGza1QKOSE3rzGzcP/EX7npqp9C0dn2g4eDkg4tWmtWtFasbdJpDQMtA24PPlyqdvjc+IZ8+8YFDye/iOVSPl39L/YGdqV2W5FaN68OZcuXWLs2LHlPqZHjx68++67BAYG4u3trfGCn6DsySltOK0IS0cnBs55o1ixaEtHJ43EoK2trUrsfpqbN29y4MABli1bphq+GzZsGIcOHSIyMpLff/+db7/9Fisrq3pfXkpEpC5RKeH05IeUVCpFW1u7Wj646iPaOjkU5qiXjinMPYmWxA9tfUcK8qyQFdoQed+GqIDHPW36xjrYupogkUoIv5tMkSuavacZTTqbc3X/Hm4fPUB6wiMzPIkEz/adaDN4BK4tWiN5JFgbd+xCWlwM5vZVn90j8nwjkUhKHVIDcDdzZ3G3xXx+8XMUggKpRMrirotxNyu7vE9FeeWVV2jXrh3Dhg2jV69e7Nu3r1iOVKNGjYiIiEChUCCVSpFKpXTu3Jmff/6ZoUOHajSeitKy3yDcWrerlvddq1at+Prrr1EoFGqJ73K5nD179vDVV18BykkzH3zwAW3btmX8+PE0atSIiRMnMnv2bLp3787ChQs1FpOISEOnyjlOzs7O6D/jqawhkZ+TDCV4yeblJJKX82QpGgn6xjZIdewpyLMkp8CW8EwbJBI9BIVymA9BQaTfQ9bNe0hhQT4A+kbGtOg3iDaDhmFmW3yGk4mVtSiYRDTGWK+xdHPsRmRmJC4mLlWeVVcSxsbGHDlyhEWLFvHhhx/Spk0bli1bxrlz51RT5y0sLOjatSsDBgxg3bp1eHp6Mn78eIYOHarK9alNqut95+Xlxd69e7l//z4FBQWq9VKplI0bN+Ll5QWAh4cHu3btwsDAgCVLlvD666/ToUMHBg4cSGBgoMbjEhFpyEgEQSjJMb7KFBQUoKOj8+wda5GiIr/p6emYmmqmuGJmchLr5k8vlvMwcM4bZCYlEB8SREJoMFmppRRClhiCULxr3sbVnbZDRtK0ey909EShKlI5ZDIZoaGhuLu71/sHnqSkJAYPHsz169drO5TnjufpPhF5fpDL5QQHB+Pp6anxUa6K6IFK9zj99ttvTJ8+vZih5IMHD3jzzTf54osv6mwyYnUW+TWxsi4x56Fl34Fq+2WnpRIfGqQSUvEhwWQmJ5YomgbOfY+WfXuLNeZERJ5gzZo1ZRpiioiIiFQHlRZOx48fZ/369ezevRs3Nzeys7P58ssv+fHHH5k0aRJNmzbVZJwaZf78+cyfP1+lMDVNeXIejMwt8GjbEY+2j5M2A69cYN/3xcvZWNhZiqJJROQJXnvtNfLz8/n4449rOxQREZEGRqWF044dO3j//ffp0KED77zzDr/++itubm6cO3fuuamDVBUqk/Ng79kEiURSbJhPtBQQEVHnt99+q+0QREREGiiV9g7Q0dHhxx9/ZO3atSxfvpyWLVty9uxZUTRVgaJhvqIZcqKlgIiIiIiISN2i3D1O2dnZREVFFVvfvHlztm/fzqxZs5g8eTKfffYZUqkUV1dXMamwElTn1GYRkSKqaU6IyHOCeH+IiJROuYXTuXPnGDJkSJn7/Pnnn/z5558AXLp0qc4mh9d1REsBkepCR0cHiURCYmIiNjY2Yu6cSDEEQSAxMVFpAFrHZ0aLiNQG5RZOffv2JS4urtwNW1paViogERGR6kNLSwtnZ2eioqLqhP+RSN1EIpHg7OwsGhuLiJRAuYWTrq4udnall1moT1SnHYGISF3H2NgYLy8vNUNFEZEn0dHREUWTiEgpVNoAMz09nffffx9fX18SExOLbT927FidTxSvDgNMEREREREREc1T7w0wFy1axMGDB1m4cCFOTk7FXoSHh0dlmxYRERERERERqZNUWjiFhITwySef8Nprr2kynhqlqLMtIyOjliMRERERERERKQu5XE5WVhYZGRnV0uME5ZtRWmnh1L9/fwICAip7eJ0gMzMTABcXl1qORERERERERKS2yczMfGZFkUrnOOXl5dGrVy/efvttfHx8im338vLC0NCwMk3XGAqFgpiYGExMTDQ+LTsjIwMXFxciIyPF/KlyIF6v8iNeq4ohXq+KIV6viiFer/JTl6+VIAhkZmbi6OiIVFq2N3ile5z69evHlStXmDx5conb64OPk1QqxdnZuVrPYWpqWudukLqMeL3Kj3itKoZ4vSqGeL0qhni9yk9dvVblrV1baeG0f//+Mqcziz5OIiIiIiIiIs8blRZOojASERERERERaWhUWjgB5Ofnc/nyZeLi4lAoFGrbBgwYgJWVVZWCq8/o6emxePFi9PT0ajuUeoF4vcqPeK0qhni9KoZ4vSqGeL3Kz/NyrSqdHP7gwQP69etHTEwMWlpamJmZIZVKSUtLo2XLlmzfvp2mTZtqOl4RERERERERkVqj7NTxMvj666/p0qUL0dHRTJkyReUg/r///Y8+ffqIoklERERERETkuaPSwik+Pp4XX3wRR0dHWrVqxfXr1wGYMWMGf/31F7m5uRoLUkRERERERESkLlBp4dS6dWv8/PwAGD58OOvWrUMul1NYWEhOTg4hISEaC1JEREREREREpC5Q6RwnPz8/RowYQXBwMNra2rz00kvcu3ePzMxM5HI5gYGB9T4BrLIkJiZy8uRJrKys6N27Nzo6OrUdUp1lx44dJCQkqK2bOnVqg55Y8DTh4eHs27ePl156CWtra7VtCoWCixcvEhoaSu/evUUXfODChQv4+fkxd+5ctfVBQUEcOHBAbV3Tpk0ZPHhwTYZXZ5DL5dy6dYvbt2/TrFkzunbtWmyfmJgYfH19cXJyokePHhovc1GfyMnJ4cKFC0RHR9OjRw88PT3Vtv/xxx+kp6errZs5cybGxsY1GWadIS4ujnPnzpGXl0efPn1wcnJS2y6Xyzl37hzR0dH069cPe3v7Woq04lRaOB07dgxHR0eaN28OQEFBAVu3biUmJoZp06bRqFEjjQZaX7h//z59+/ala9euhIaG4ujoyL///ouurm5th1YnadasGdbW1mpGpMuXL692Y9L6wqBBgzhx4gQKhYJbt27RunVr1TZBEJg5cyZnzpyhffv2HD9+nL1799KjR49ajLj2iIqKomfPnoSFhWFtbU1iYqLa9k2bNrFw4UIGDhyoWtejRw/mz59f06HWOklJSYwZM4bExETatm3L0aNHmThxIr/99ptqnytXrjBs2DD69u3L3bt3adu2LVu3bn2mq/LzyNWrVxkzZgweHh5YWlpy9OhRNmzYoGYA7eDggI+PD7a2tqp1a9asaZDWPb/88guLFi2iW7duREdH8/DhQ/bs2cPQoUMBpWh68cUXuXfvHs2bN8fX15cjR47Qrl27Wo68nAiVZNiwYcJPP/1U2cOfW0aMGCG89dZbgiAIQk5OjuDl5SVs2bKldoOqw9jb2wsnT56s7TDqLAkJCcK9e/cEQLh165baNl9fX8HS0lKIj48XBEEQVq5cKbRu3boWoqwbFBYWCnFxccJvv/0mWFtbF9v+448/CoMGDaqFyOoeV69eFd577z1BoVAIgiAI58+fFwDhxo0bqn26dOkifPnll4IgCEJKSopgb28vHDp0qFbirW3WrFkj7Ny5U7X8+eefC9bW1qrrJwiCoK+vr3b9GjL79+8Xbt68qVoeP3680L59e9Xyv//+Kzg5OQlpaWmCIAjC4sWLhd69e9dwlJWn0o8Oc+fOZdu2beTk5GhOxdVz0tLSOHz4MDNnzgTAwMCASZMmsX379lqOrO6SmppabPhJ5DE2NjbY2dmVuG3Hjh2MGDFC9YT78ssvc/fuXfz9/WsyxDqDlpYWdnZ2mJubl7hdvNce06FDB5YvX66q0dm1a1dsbGy4c+cOAGFhYVy+fJkZM2YAYGFhwZgxYxrsZ9nrr7/OxIkTVcujR48mKSmJmJgYAGQyGTKZTLy/HjFixAjatGmjWh4wYADJycmq5R07djBu3DhViZPp06dz5swZoqOjazrUSlFpA0w9PT3s7Ozw8fFh1KhRxca+3377bVxdXascYH0iNDQUhUJBixYtVOtat27Nzp07azGquotMJiMvL4/U1FT++OMPHB0dGTBgQIMcCqgMgYGBDBs2TLVsaWmJs7MzQUFBJRbebuikpaVhYGDA4cOHSUpKok+fPuKQ8CMEQSA7O1uVZxIYGIiVlRWOjo6qfVq3bs3mzZtrK8Q6RVZWFlpaWiqhlJaWBijzW48fP46bmxt9+/atxQjrFseOHWPQoEGq5cDAQLUhc1dXV0xNTQkODi6WC1UXqbRwOnz4MPn5+TRr1ozAwMBi2xuiHUF8fDwWFhaqpzgAKysr4uLiajGquotMJsPIyIgVK1bg5OTEvn378PT05MSJE2JCfTmIj48vlkQv3m+lI5FIOHXqFLq6ugQHBzN37lx27dqlJj4bKmfPnkUul9O+fXtAvLeexeHDh+nYsaNqAlRBQQEGBgZ8/fXX2NjY8MEHH9CxY0f+++8/te+DhkRqaio//PADx48fx8rKiq+//lq1rb7fX5UWTitXrtRkHM8FQgl59hKJpFg5GhEl5ubmJCQkYGhoCMDixYvx9vbmjz/+YPbs2bUcXd1HvN8qxooVK1i2bJlqosZbb73FG2+8IQon4NNPP2XWrFmqHhTx3iqdpKQkVq1apdb75uLiQlJSkuqz7IMPPsDHx4fdu3czYcKE2gq1VikoKCA2NhaFQkFOTg4xMTHPzf0ljoloEDs7O9LS0tRuipSUlHo1zbKmKfqgAbC3t6d3794qM1WRsrGzsyM1NVVtnXi/lY6Wlpba7NZJkyYREhJS7Bo2NFauXElgYCCfffaZap14b5WMIAhMnz6dbt26MXr0aLVtT36Wubu707Fjxwb9WWZra8v69eu5fPkykyZNok+fPqohzfp+f4nCSYM0atQIQRB4+PChap2/vz/u7u61GFX9Qi6XN1j/r4ri5uamlgiemZlJVFSUeL+VE7lcjkQiadDDwr6+vnz44Yds3rxZbRq9u7s7iYmJapYO4mcZLFmyhGvXrrF58+ZnDsGJn2WPmTx5Mqmpqdy+fRso/tkVGxtLWloabm5utRRhxRCFkwaxtramb9++qpknhYWF7Nq1S202hshjdu/eTWRkpGo5MjKS06dPN1gfoooyceJE/vvvPzIzMwHl9XR3d1fzehJRkpqayqZNm9TWbd26lbZt2zZYg8KLFy8yatQofvrpJ7VEXQAvLy9atWrFn3/+CShzVv/9998G/Vn2448/8uOPP3Lw4EE1kQmwfft24uPjVcsBAQFcu3atQX6WCYLAzz//TF5enmrdlStXAPDw8ACUn1179uxBJpMB8Oeff9KxY8d6I5wqbYApUjKXLl1i6NChTJ06lYcPH5KamsqpU6cwMjKq7dDqHF988QWrVq1i0KBBODo6smPHDrp168bu3btrO7Q6gb+/P//88w/Z2dksXbqU+fPn4+DgwMsvv4yLiwtyuZxRo0aRlJREz5492bhxI7///nuxIYSGwoEDB7h16xZ37tzhv//+Y9GiRWhpafHhhx9y//59Ro0ahZWVFT169MDf35+LFy9y9OhROnbsWNuh1zjXr1+nX79+uLi4MGnSJJWtjLGxMR9//DEAR44c4cUXX2T69OlcvXoVXV1dDh8+3CB76H799Vdef/11Ro8eTatWrSgoKACgTZs2TJo0iYULF7J582aGDBmClZUVW7duZfTo0WzYsKGWI695srKymDhxIg8ePGDQoEFIpVK2bt3K3Llz+e677wDIy8tTzbJr164df/zxB3///Tf9+vWrzdDLjSicqoH79++zd+9erK2tmTBhgsqrQqQ4Dx484Ny5c4SHh9OxY0dGjhxZ2yHVGW7evFni9O8333xTNWSSl5fHrl27CA0NZejQoXTo0KGmw6wz7Ny5k0uXLqmt09bWZvny5QBkZ2dz+vRprl27hrm5ORMmTMDBwaE2Qq11jh07xsGDB4utNzEx4YsvvlAt37p1iwMHDuDs7MyECRPU8ngaEmvWrCEoKKjY+vbt2zN16lQA7t69qyrJ0r17d7Xp9w0NQRA4f/48ly5dIjc3l759+xbrfcvOzmbXrl1ER0czYsSIetVTLgonEREREREREZFyIuY4iYiIiIiIiIiUE1E4iYiIiIiIiIiUE1E4iYiIiIiIiIiUE1E4iYiIiIiIiIiUE1E4iYiIiIiIiIiUE1E4iYiIiIiIiIiUE1E4idRrUlJSuHr1am2HUSqaLloZHh7OkSNHNNpmXUahUPDqq69iZmbGgAEDNN7+gwcPCAsLq/A2keJcv3693tVmO378OKGhoeXePzg4mODgYNVyfSlKK6JZROEkUq85depUnXabfe211zh9+nSp29evX4+Xl5fKifhZHD9+nFmzZpW5T0xMTK1+oOfl5anVOKsKBw4c4M8//+Tw4cNs3LiRy5cv4+joyP379zXS/nvvvcf3339f4W11hejoaCQSiaoG2LP49NNP6dmzZ5XPW1BQoFZiBB6XJKlPzJs3r0Qj0NJYsmQJS5YsAZTCesyYMdUTmEidRhROIiLVRFxcHEeOHKFbt26l7tOyZUvGjRunsTIWgiDg4uKiMeFSGY4cOcKoUaM00lZAQADNmzena9euuLi44OzszNixY7Gzs9NI+yKV4+LFi/Tp06e2w6hVvL29CQ8Px8/Pr7ZDEalhROEkIlJN/PTTT7z88stliqLOnTvz7bffauycGRkZtT58kJqaqrG28vPz1cp8ODk58fPPP2Npaamxc4hUHE3+j2sTiURSpePnzZvHihUrNBSNSH1BFE4izxUZGRl8/vnndOjQARMTE7p27cq9e/cAOHPmDDo6OqSkpKj2VygU2NjYcODAAUA5FNaxY0dMTU3p1q2bWv7Uhg0bGD16NEeOHKF58+a89NJLZcayceNGpk+fXubxGzZswMXFRe24pUuX4uLigra2NhKJBC0tLdq0aaParq+vz/Hjx+nRowfm5uZMnjwZmUzG0aNHVT0x9vb2SCQSHjx4wIYNGxg5ciQHDhygffv2ODs7s3TpUgoKCnjvvfdwcHCgRYsW3LhxQy2O8+fP0717d0xNTenQoQOHDh1SbYuNjWXkyJGYmpri5eXF559/jlwu580332TGjBlcunQJiURCs2bNyvNvK5GvvvqKzz77jHPnziGRSNi0aRO3b99GIpGoholGjhzJunXrWLp0KU2aNMHe3p41a9ao2rh9+zaTJ0/G3d0da2trXnvtNfLz8ysVT1ltzZgxg/Hjx6vtv3//fmxtbVEoFBQUFPDpp5/i5uaGnZ0ds2bNIjMzU7Vvz5492bNnD++//z7m5uZ1Oo/t008/Zdy4cQQEBCCRSNTq/enr6/PTTz/h4+ODra0tS5cuVW0TBIFVq1bh7e2NpaUl48ePJy4ursRzTJs2jRUrVrBs2TLc3Nxo2bIlBw4cIDY2lvHjx2NhYcGQIUPU3sugHPpu3rw55ubmDB06lIcPH6ptX7VqFZ6entjZ2fHmm28WO29FYgSYOnUqf//9t6pIskgDQRARqcfs3r1bMDY2Vi1fuXJFmDZtmnDu3DkhNDRUGDVqlNCpUydBEARBoVAIjRo1EjZu3Kja//Lly4KhoaGQm5srXLx4UTA3Nxf+++8/IS0tTfjjjz8EKysrITU1VRAEQVi/fr3g6uoq9OzZUzhy5IgQGhpaalxBQUGCnp6eoFAoVOtKOn79+vWCs7Ozap+NGzcKlpaWwp07d4T4+Hhh8ODBwowZM9TaMDAwEPr16yecPHlSOHTokGBkZCSsX79ekMvlwvXr1wVACAsLE2QymaBQKIT169cLpqamwqBBg4Tz588L//vf/wSJRCJ06grteYkAAAyYSURBVNRJWL58uXDr1i2hb9++Qs+ePVXn8fPzE3R1dYVly5YJQUFBwi+//CLo6uoKp06dEgRBEKZOnSoMHjxYiIqKEu7duyesXLlSEARBKCgoEBYvXix06tRJkMlkQl5entp1WbVqlWBsbFziz5UrV9T2LWqrW7dugkwmE+RyuXDr1i0BEOLi4gRBEIQRI0YIFhYWwjvvvCNcv35dWLJkiaCjoyPExsYKgiAIn376qfDDDz8IDx48EM6dOydYWFgIv/zyi+ocI0aMEBYsWFDi//DpbWW1dfz4ccHQ0FDIzs5W7T9v3jzV/27evHlCnz59hMDAQCE2NlYYPXq02v+1R48eQosWLYR58+YJV65cEbKyskqM6WmioqIEQLh165bwxx9/lHptjx07JgiCIHzyySdCjx49irVTnmOf/L+sXLlSaNKkiSCTyQSZTCYIgvKeMDY2FubOnStcvXpV+P777wWJRCIEBgYKgiAIy5YtE1q2bCncvHlTSEpKEl577TWhf//+Jb6uqVOnCubm5sIbb7wh3LhxQ5g9e7ZgbW0tdOzYUdi9e7dw7tw5wcHBQfj0009Vx/z222+CpaWlsGfPHuHBgwfC/PnzBSsrKyEpKUkQBEH45ZdfBBMTE2HHjh1CUFCQsGzZMkFXV1f4+eefVW08K8apU6cKU6dOVYvVw8NDOH36dLn+XyLPB6JwEqnXPC2cnubQoUMCIOTm5gqCIAgffPCBMGLECNX2xYsXCy+88IIgCIIwfPhw4euvv1Y73sPDQ9i1a5cgCErRoqWlpfoiKIuDBw8KTZo0UVtX0vFPC6eZM2cKkydPVi1v375d8PLyUtvf2tpaTZCMHDlSmD9/viAIgnD37l01YVHaMc7OzsJnn32mWt69e7dgZGSkWp44caIwbtw4tfjnzZsn9OrVSxAEQRgyZIjw4osvlvjav/rqK6FLly4lbqsoX331ldoXfUnCadq0aart+fn5glQqLfZlX8SkSZPU4q6IcCqrLblcLjg6Ogq7d+9WbXd1dRX27t0rxMfHC1KpVIiKilJtO3PmjNp926NHD6FPnz6lnqs0nhRO5aE04VRRfv75Z6Fp06Zq66ZOnSqMHDlStaxQKARTU1Nh165dglwuF0xMTITz58+rtoeHhwuAkJiYWKz9qVOnCqNGjVItBwUFCYBw8uRJ1boFCxYIw4cPVy3b2toKq1evVjt/8+bNVfe5o6Oj8N1336mdp3HjxirhVJ4YSxJO/fv3F9atW1fapRJ5DtGurZ4uEZHqIjExkU2bNuHr60tISAgAycnJODk5MXXqVNq3b09GRgampqYcPHiQ+fPnA3Dnzh0uXbrEr7/+qmorKSlJravewcGBxo0bPzOGlJQUXF1di61/1vF9+vRh4cKFREZGYmlpyZ9//knXrl3V9tHX10dXV1e1bGlpSXp6epnxPH2MlZUVpqamasvZ2dnI5XK0tLS4efNmsaGMvn37sm3bNgAWL17M+PHjad++PR999BHjxo2rcr5IZTEzM1P9raOjg4mJidr1uHbtGps2beLmzZsEBgbSqlWrSp+rtLakUikvvfQSu3fvZty4cfj7+5OQkMDAgQO5ePEigiDQpUsXVTsKhYKsrCyysrIwNjYGoEePHpWOq67w5P9CIpFgYWFBeno6YWFhZGZmMmHCBKRSqdo+cXFxWFtbF2vr6fuzpHW3bt0ClDNJExIS1BLWJRIJvXv35tatWyQlJRETE1OmpUVlYgRwd3cnOTm51HZFnj/EHCeR54qrV6/SqlUrtLS0WLt2LXv27FHb3qJFC5o2bcp///1HQkICN2/eZPjw4QCYm5uzdOlSIiMjVT+5ubksWLCgwnFIJBIEQajwcRMnTsTExISBAwfSpEkTDA0NWbly5TPPVZn4yloWBKHEfYpeU5cuXQgODmb69OksXLiQHj16IJPJnnnen3/+GQsLixJ/rl27VuHXURJPxv3RRx8xd+5chg0bxtGjR5kxY0al231WW1OmTOHAgQPk5eVx8OBBBg4ciIGBAebm5kilUh4+fKi6r6KjoxEEQSWaKotcLgeUr3nLli2lXtsTJ06U2U5Vji2Lov+Fubk5AL6+vmrvL4VCQYsWLcrdTmnriu7L0u7Zohwkbe3S+woqG6NCoVATWiLPP+J/W+S54rvvvuPVV1/lnXfewdHRscR9pkyZwr///svJkyfp2rWr6kmyXbt2agnQVcHS0rJS5onbtm2jW7duBAQEEB0dzY4dOyo0g6zoiyE3N7fC536SNm3acPbsWbV1Z8+epXXr1qplPT09FixYwK1bt7hx4wYXL15UxVDa+RcsWEBqamqJPx06dKhSzE+TkZHB8uXL2b59O0OHDsXQ0LBSYra8bbVt2xZnZ2dOnDjB8ePHGT16NKCctq6np8exY8eq/JpAaYJaRHR0NKDsyZw2bVq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" + ] + }, + "metadata": {}, + "output_type": "display_data" } ], "source": [ - "# Only the full-Jacobian cache gives this: transport a layer's residual to the\n", - "# final basis and decode it, i.e. \"what does the model think at layer l\".\n", - "# Pick a low / mid / high layer from the fitted band.\n", + "from jlens.hf import from_hf\n", + "from jlens.vis import compute_slice\n", + "from tabulate import tabulate\n", + "\n", + "from jsteer.demo import lens_slice_ranks, plot_lens_slice\n", + "\n", + "# qualitative: the top tokens each layer points to (generic slot -> specific answer)\n", + "PROMPT = \"The Eiffel Tower is located in the city of\"\n", "lo, mid, hi = jac.layers[0], jac.layers[len(jac.layers) // 2], jac.layers[-1]\n", "for layer in (lo, mid, hi):\n", - " top = jac.lens_topk(model, tok, \"The Eiffel Tower is located in the city of\", layer=layer, k=6)\n", - " print(f\"layer {layer}: {[t for t, _ in top]}\")" + " top = jac.lens_topk(model, tok, PROMPT, layer=layer, k=6)\n", + " print(f\"L{layer:>2} top-6: {[t for t, _ in top]}\")\n", + "\n", + "# quantitative + calibrated, via jlens's own compute_slice (reference machinery):\n", + "# sweeps every fitted layer, AUTO-selects tracked tokens (we only pin the answer\n", + "# words), and returns full-vocab ranks with the final layer as the J=I model row.\n", + "pin = {tok(w, add_special_tokens=False).input_ids[0] for w in (\" Paris\", \" city\")}\n", + "sd = compute_slice(from_hf(model, tok), jac.lens, PROMPT, top_n=10, max_tracked=6,\n", + " pinned_token_ids=pin, last_n_tokens=1, mask_display=True)\n", + "\n", + "labels, layers, ranks = lens_slice_ranks(sd)\n", + "show = [0, len(layers) // 2, len(layers) - 1] # low / mid / final(model) columns\n", + "tbl = [[labels[j]] + [int(ranks[i, j]) for i in show] for j in range(len(labels))]\n", + "hdr = [\"token\"] + [f\"L{layers[i]}\" + (\"(model)\" if i == len(layers) - 1 else \"\") for i in show]\n", + "print(\"\\ntracked (auto-selected):\", labels)\n", + "print(tabulate(tbl, headers=hdr, tablefmt=\"github\"))\n", + "plot_lens_slice(sd, title=\"lens rank of tracked tokens vs depth\");" ] }, { @@ -1003,14 +1289,14 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 11, "id": "c3f5e64b", "metadata": { "execution": { - "iopub.execute_input": "2026-07-10T12:52:34.838740Z", - "iopub.status.busy": "2026-07-10T12:52:34.838597Z", - "iopub.status.idle": "2026-07-10T12:52:41.899990Z", - "shell.execute_reply": "2026-07-10T12:52:41.899561Z" + "iopub.execute_input": "2026-07-11T03:04:45.999963Z", + "iopub.status.busy": "2026-07-11T03:04:45.999823Z", + "iopub.status.idle": "2026-07-11T03:04:52.992382Z", + "shell.execute_reply": "2026-07-11T03:04:52.991825Z" } }, "outputs": [ @@ -1065,7 +1351,7 @@ "widgets": { "application/vnd.jupyter.widget-state+json": { "state": { - "08ef83969453475d85e75b65c351c555": { + "00ca7c609efd4246a4f219f663446a3c": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", 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