Upload folder using huggingface_hub

0.codes.pt

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:f0bf7787d7c73ccada505c3e9ad0f4815182f31e755928c1450cbbd940169182
-size 3751260
+oid sha256:b0c4ff76c41d8ac160203a214947f33a55b0faf8231b59f223c2092089d79258
+size 3754076

0.metadata.json

@@ -1,6 +1,6 @@
 {
     "passage_offset": 0,
-    "num_passages": 5474,
-    "num_embeddings": 937534,
+    "num_passages": 5478,
+    "num_embeddings": 938229,
     "embedding_offset": 0
 }

0.residuals.pt

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:08b4c99b219c3451420c79cd9c047431654f5adb2e50a1ab55401a3eada9af62
-size 60003376
+oid sha256:93c37e39948d7602ed834bf03c7b4af78363c07c0d2b9f7ef1dee872ec03f66c
+size 60047856

avg_residual.pt

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:29f2eef965a59b050aeb83c37f166091a6431d5d1766f1513ce2d2eaff766902
+oid sha256:6560645fb7b6f6ca7e272f744da544c743dc242f29694c3d0fb31fd8c0a9ef76
 size 1205

buckets.pt

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:cfe227679ab9dcdc70c02023aac07a5eb951811fde114c3a08ea1ed353a0e641
+oid sha256:89920d104b0ae488230595deb7479f15d46bc78049a05a82076109258f84235a
 size 1432

centroids.pt

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:27f88cfd531a912febc53ea0c70b3474ab9b4b80302f8c230f1b870ab4faa490
+oid sha256:3b5d2edb3bb9e05f14d7ba0abde67fcc2589ca248b2a6243f3aa0b81b9b1367d
 size 2098342

collection.json

@@ -5472,5 +5472,9 @@
   "Large language models (LLMs) often produce errors, including factual inaccuracies, biases, and reasoning failures, collectively referred to as \"hallucinations\". Recent studies have demonstrated that LLMs' internal states encode information regarding the truthfulness of their outputs, and that this information can be utilized to detect errors. In this work, we show that the internal representations of LLMs encode much more information about truthfulness than previously recognized. We first discover that the truthfulness information is concentrated in specific tokens, and leveraging this property significantly enhances error detection performance. Yet, we show that such error detectors fail to generalize across datasets, implying that -- contrary to prior claims -- truthfulness encoding is not universal but rather multifaceted. Next, we show that internal representations can also be used for predicting the types of errors the model is likely to make, facilitating the development of tailored mitigation strategies. Lastly, we reveal a discrepancy between LLMs' internal encoding and external behavior: they may encode the correct answer, yet consistently generate an incorrect one. Taken together, these insights deepen our understanding of LLM errors from the model's internal perspective, which can guide future research on enhancing error analysis and mitigation.",
   "This paper presents an advanced mathematical problem-solving framework, LLaMA-Berry, for enhancing the mathematical reasoning ability of Large Language Models (LLMs). The framework combines Monte Carlo Tree Search (MCTS) with iterative Self-Refine to optimize the reasoning path and utilizes a pairwise reward model to evaluate different paths globally. By leveraging the self-critic and rewriting capabilities of LLMs, Self-Refine applied to MCTS (SR-MCTS) overcomes the inefficiencies and limitations of conventional step-wise and greedy search algorithms by fostering a more efficient exploration of solution spaces. Pairwise Preference Reward Model~(PPRM), inspired by Reinforcement Learning from Human Feedback (RLHF), is then used to model pairwise preferences between solutions, utilizing an Enhanced Borda Count (EBC) method to synthesize these preferences into a global ranking score to find better answers. This approach addresses the challenges of scoring variability and non-independent distributions in mathematical reasoning tasks. The framework has been tested on general and advanced benchmarks, showing superior performance in terms of search efficiency and problem-solving capability compared to existing methods like ToT and rStar, particularly in complex Olympiad-level benchmarks, including GPQA, AIME24 and AMC23.",
   "Synthesizing human motions in 3D environments, particularly those with complex activities such as locomotion, hand-reaching, and human-object interaction, presents substantial demands for user-defined waypoints and stage transitions. These requirements pose challenges for current models, leading to a notable gap in automating the animation of characters from simple human inputs. This paper addresses this challenge by introducing a comprehensive framework for synthesizing multi-stage scene-aware interaction motions directly from a single text instruction and goal location. Our approach employs an auto-regressive diffusion model to synthesize the next motion segment, along with an autonomous scheduler predicting the transition for each action stage. To ensure that the synthesized motions are seamlessly integrated within the environment, we propose a scene representation that considers the local perception both at the start and the goal location. We further enhance the coherence of the generated motion by integrating frame embeddings with language input. Additionally, to support model training, we present a comprehensive motion-captured dataset comprising 16 hours of motion sequences in 120 indoor scenes covering 40 types of motions, each annotated with precise language descriptions. Experimental results demonstrate the efficacy of our method in generating high-quality, multi-stage motions closely aligned with environmental and textual conditions.",
-  "This technical report introduces a Named Clinical Entity Recognition Benchmark for evaluating language models in healthcare, addressing the crucial natural language processing (NLP) task of extracting structured information from clinical narratives to support applications like automated coding, clinical trial cohort identification, and clinical decision support.   The leaderboard provides a standardized platform for assessing diverse language models, including encoder and decoder architectures, on their ability to identify and classify clinical entities across multiple medical domains. A curated collection of openly available clinical datasets is utilized, encompassing entities such as diseases, symptoms, medications, procedures, and laboratory measurements. Importantly, these entities are standardized according to the Observational Medical Outcomes Partnership (OMOP) Common Data Model, ensuring consistency and interoperability across different healthcare systems and datasets, and a comprehensive evaluation of model performance. Performance of models is primarily assessed using the F1-score, and it is complemented by various assessment modes to provide comprehensive insights into model performance. The report also includes a brief analysis of models evaluated to date, highlighting observed trends and limitations.   By establishing this benchmarking framework, the leaderboard aims to promote transparency, facilitate comparative analyses, and drive innovation in clinical entity recognition tasks, addressing the need for robust evaluation methods in healthcare NLP."
+  "This technical report introduces a Named Clinical Entity Recognition Benchmark for evaluating language models in healthcare, addressing the crucial natural language processing (NLP) task of extracting structured information from clinical narratives to support applications like automated coding, clinical trial cohort identification, and clinical decision support.   The leaderboard provides a standardized platform for assessing diverse language models, including encoder and decoder architectures, on their ability to identify and classify clinical entities across multiple medical domains. A curated collection of openly available clinical datasets is utilized, encompassing entities such as diseases, symptoms, medications, procedures, and laboratory measurements. Importantly, these entities are standardized according to the Observational Medical Outcomes Partnership (OMOP) Common Data Model, ensuring consistency and interoperability across different healthcare systems and datasets, and a comprehensive evaluation of model performance. Performance of models is primarily assessed using the F1-score, and it is complemented by various assessment modes to provide comprehensive insights into model performance. The report also includes a brief analysis of models evaluated to date, highlighting observed trends and limitations.   By establishing this benchmarking framework, the leaderboard aims to promote transparency, facilitate comparative analyses, and drive innovation in clinical entity recognition tasks, addressing the need for robust evaluation methods in healthcare NLP.",
+  "Data curation is the problem of how to collect and organize samples into a dataset that supports efficient learning. Despite the centrality of the task, little work has been devoted towards a large-scale, systematic comparison of various curation methods. In this work, we take steps towards a formal evaluation of data curation strategies and introduce SELECT, the first large-scale benchmark of curation strategies for image classification.   In order to generate baseline methods for the SELECT benchmark, we create a new dataset, ImageNet++, which constitutes the largest superset of ImageNet-1K to date. Our dataset extends ImageNet with 5 new training-data shifts, each approximately the size of ImageNet-1K itself, and each assembled using a distinct curation strategy. We evaluate our data curation baselines in two ways: (i) using each training-data shift to train identical image classification models from scratch (ii) using the data itself to fit a pretrained self-supervised representation.   Our findings show interesting trends, particularly pertaining to recent methods for data curation such as synthetic data generation and lookup based on CLIP embeddings.",
+  "Our findings show interesting trends, particularly pertaining to recent methods for data curation such as synthetic data generation and lookup based on CLIP embeddings. We show that although these strategies are highly competitive for certain tasks, the curation strategy used to assemble the original ImageNet-1K dataset remains the gold standard. We anticipate that our benchmark can illuminate the path for new methods to further reduce the gap. We release our checkpoints, code, documentation, and a link to our dataset at https://github.com/jimmyxu123/SELECT.",
+  "Recent advancements in Large Language Models (LLMs) have sparked interest in their formal reasoning capabilities, particularly in mathematics. The GSM8K benchmark is widely used to assess the mathematical reasoning of models on grade-school-level questions. While the performance of LLMs on GSM8K has significantly improved in recent years, it remains unclear whether their mathematical reasoning capabilities have genuinely advanced, raising questions about the reliability of the reported metrics. To address these concerns, we conduct a large-scale study on several SOTA open and closed models. To overcome the limitations of existing evaluations, we introduce GSM-Symbolic, an improved benchmark created from symbolic templates that allow for the generation of a diverse set of questions. GSM-Symbolic enables more controllable evaluations, providing key insights and more reliable metrics for measuring the reasoning capabilities of models.Our findings reveal that LLMs exhibit noticeable variance when responding to different instantiations of the same question. Specifically, the performance of all models declines when only the numerical values in the question are altered in the GSM-Symbolic benchmark. Furthermore, we investigate the fragility of mathematical reasoning in these models and show that their performance significantly deteriorates as the number of clauses in a question increases.",
+  "Specifically, the performance of all models declines when only the numerical values in the question are altered in the GSM-Symbolic benchmark. Furthermore, we investigate the fragility of mathematical reasoning in these models and show that their performance significantly deteriorates as the number of clauses in a question increases. We hypothesize that this decline is because current LLMs cannot perform genuine logical reasoning; they replicate reasoning steps from their training data. Adding a single clause that seems relevant to the question causes significant performance drops (up to 65%) across all state-of-the-art models, even though the clause doesn't contribute to the reasoning chain needed for the final answer. Overall, our work offers a more nuanced understanding of LLMs' capabilities and limitations in mathematical reasoning."
 ]

doclens.0.json

@@ -1 +1 @@
-[206,104,226,67,200,185,221,212,206,222,88,210,228,174,155,205,172,218,148,132,212,91,163,184,205,132,213,190,212,198,230,227,159,198,122,216,175,197,118,217,219,224,69,220,197,72,204,92,169,191,191,155,175,111,218,77,207,36,195,178,123,170,231,91,209,177,146,205,151,221,217,95,199,89,153,216,154,202,167,213,104,184,176,226,200,232,53,223,73,202,220,158,174,189,165,222,158,221,211,78,205,72,214,212,215,232,175,219,110,205,192,226,34,176,209,158,222,132,179,208,98,126,205,126,172,167,227,100,224,182,208,117,217,199,195,191,169,178,158,217,152,157,163,163,204,209,146,217,150,194,217,23,125,200,221,200,212,41,176,223,207,95,178,232,68,182,173,205,198,210,139,152,147,215,196,223,83,199,123,197,119,230,223,65,216,85,210,52,210,204,179,111,138,215,83,177,219,69,212,77,182,226,99,178,207,197,87,190,222,216,85,208,211,66,220,226,221,134,175,190,170,166,157,216,135,211,132,200,67,227,195,178,221,179,205,168,186,208,127,207,139,205,67,209,223,117,195,190,202,64,218,77,199,221,189,125,216,80,148,214,143,181,98,221,194,112,219,212,138,216,87,127,187,219,122,220,97,223,221,164,205,220,100,227,101,171,188,223,89,213,137,175,172,219,77,102,234,109,197,114,205,184,221,138,175,152,163,133,227,73,224,181,174,147,144,178,147,224,211,121,123,211,219,209,210,204,80,182,215,152,118,224,216,154,220,163,204,187,133,151,162,221,94,184,224,213,72,187,148,223,195,110,182,117,224,88,202,162,220,154,151,197,227,197,74,221,205,97,221,219,103,206,184,72,191,201,192,230,164,217,170,214,118,221,218,118,215,181,224,78,183,154,190,206,102,202,74,195,96,225,228,221,99,224,104,123,203,184,211,90,209,173,199,203,99,201,176,160,207,132,231,96,195,213,173,101,190,206,61,225,232,215,178,211,227,192,148,195,213,114,212,127,162,214,213,129,220,70,176,224,209,137,232,116,228,212,75,182,207,71,111,173,189,207,96,172,213,119,222,93,195,204,211,76,179,200,184,216,107,135,202,71,118,219,123,202,210,146,225,76,227,62,214,226,98,158,202,219,169,83,156,223,125,211,213,102,219,223,72,224,178,190,204,110,228,64,214,200,193,205,213,160,201,216,134,195,128,186,213,219,187,199,216,84,202,150,211,127,230,206,204,180,183,183,167,213,224,72,209,79,195,205,153,228,133,203,210,78,190,207,195,133,225,192,197,190,201,187,222,109,138,227,103,223,209,164,190,73,226,200,205,191,189,100,226,135,198,129,185,207,229,148,209,216,58,222,117,219,92,221,147,222,219,63,216,228,165,181,157,213,197,218,158,191,186,223,204,109,172,229,91,231,204,102,192,217,204,144,222,173,209,209,95,186,195,60,212,219,209,162,183,227,216,111,193,182,222,71,183,222,122,178,219,130,228,82,225,223,69,198,232,63,193,207,216,139,223,74,164,198,224,80,148,227,138,227,199,225,86,148,230,169,210,92,166,187,202,165,196,78,202,74,229,173,216,215,213,164,205,102,206,91,212,173,198,109,183,94,206,115,224,126,229,222,57,225,191,221,218,215,44,177,217,214,218,191,221,80,198,109,196,95,192,181,214,214,123,208,191,225,219,218,108,204,150,199,206,90,227,71,207,123,199,106,131,213,93,178,208,60,132,217,149,204,80,227,218,202,94,232,175,213,115,217,114,222,200,173,184,217,222,178,226,205,192,185,197,180,193,205,211,136,221,62,184,213,89,225,71,169,216,135,198,98,214,204,88,191,154,207,213,227,93,230,133,216,189,122,224,108,229,237,218,209,214,66,153,203,219,204,222,218,107,174,214,148,169,188,210,98,203,142,167,174,224,103,223,220,148,216,199,188,175,173,130,156,97,220,111,216,115,216,70,221,210,77,173,162,216,164,216,69,203,175,172,166,199,156,184,131,201,210,98,220,133,211,116,221,82,213,88,213,128,227,219,177,190,216,180,237,217,64,215,134,221,172,220,221,70,169,221,189,226,74,229,197,209,128,190,213,106,221,198,162,184,182,219,86,201,143,229,205,146,228,106,154,209,60,216,222,74,221,215,141,168,202,82,194,213,80,217,214,204,164,199,220,149,114,140,215,150,218,108,109,211,170,206,125,183,148,193,229,210,124,188,143,160,153,199,178,75,225,105,199,83,222,112,205,75,222,205,70,104,194,91,158,220,99,215,209,219,65,124,215,158,223,234,208,101,218,200,66,188,222,149,201,189,211,51,179,208,198,160,182,152,195,141,216,112,205,207,170,128,219,118,188,223,224,80,177,218,127,196,151,159,117,186,113,216,212,134,204,90,200,231,184,222,68,146,151,158,202,173,144,206,171,215,68,188,222,89,215,117,223,80,191,209,204,105,228,67,174,210,74,208,105,151,159,183,142,163,202,214,41,147,222,227,188,208,200,157,132,195,94,191,185,214,132,201,155,166,195,220,147,219,156,214,165,220,105,223,217,100,176,225,117,207,134,166,202,218,172,200,157,183,217,87,214,181,229,217,76,231,199,139,154,213,208,208,169,218,82,219,191,139,207,222,49,122,154,176,219,110,185,112,137,231,80,208,136,189,192,208,190,192,187,180,220,186,225,218,208,102,190,124,152,83,219,199,95,198,133,235,214,96,213,217,148,168,200,32,145,207,222,198,171,208,218,104,178,230,77,214,129,218,136,139,212,233,93,221,153,221,95,155,151,211,225,60,81,217,201,108,208,234,172,177,172,193,208,72,216,224,211,44,205,82,221,224,199,198,221,137,208,81,183,105,182,106,222,180,184,146,180,205,187,192,163,219,213,164,229,218,190,147,221,193,213,182,126,228,231,97,173,218,116,161,156,207,122,193,198,155,207,103,223,192,92,153,223,170,206,177,225,40,224,106,203,208,116,203,105,197,152,221,211,84,226,111,215,154,224,221,65,215,142,169,212,184,135,207,85,209,213,76,225,229,130,203,217,186,229,140,177,116,216,219,62,209,105,164,150,207,214,211,114,148,230,118,201,134,197,124,209,163,184,164,186,189,219,106,221,220,95,141,217,87,200,224,114,220,168,212,112,229,192,106,189,183,209,212,85,187,202,219,222,223,223,212,226,79,193,128,222,209,206,197,178,152,114,222,111,164,183,189,179,214,130,149,150,212,122,160,195,118,115,204,144,163,159,140,211,208,120,222,220,125,195,174,220,80,173,204,87,149,212,82,221,119,213,139,186,227,97,215,213,136,215,68,229,218,63,224,182,221,218,94,213,90,168,202,85,168,222,121,215,213,93,221,83,220,112,197,213,213,92,210,76,216,79,179,220,94,194,99,186,181,197,214,194,138,221,91,218,172,159,219,74,153,221,201,108,213,131,217,112,214,107,209,35,224,188,219,108,191,105,157,211,126,206,148,220,126,217,164,203,129,201,211,119,204,165,209,75,205,130,204,215,206,119,178,211,218,137,193,167,199,220,182,115,207,184,189,217,178,186,159,208,230,140,194,75,173,205,64,213,167,189,110,220,195,136,137,196,134,208,120,198,182,189,176,162,183,172,172,214,200,95,212,189,193,184,195,116,167,199,110,180,217,126,193,226,142,204,66,140,222,226,218,144,218,214,140,206,66,196,224,143,121,166,221,101,154,204,216,201,221,152,225,69,226,226,180,200,181,212,128,193,206,136,219,141,208,128,202,209,105,168,178,204,211,110,145,163,193,211,69,220,147,194,223,165,212,218,124,197,229,226,132,220,156,211,80,203,140,192,94,208,102,180,215,134,152,218,106,191,204,102,211,108,182,204,206,208,90,165,197,105,186,115,213,208,186,165,186,174,213,211,164,210,62,129,201,217,170,169,217,193,167,222,174,217,167,128,182,113,159,208,85,172,219,89,219,126,218,190,65,227,208,52,225,125,160,205,135,168,186,213,212,69,124,205,67,204,110,151,180,220,205,193,106,223,80,124,166,218,194,138,148,205,88,179,183,163,227,221,198,213,87,190,200,239,81,202,211,213,115,204,105,216,83,170,201,210,95,219,219,180,216,222,228,86,231,83,227,116,223,84,207,216,115,204,109,194,171,198,183,197,86,216,144,209,125,226,221,57,215,117,199,185,152,230,174,220,61,209,99,163,218,218,134,183,212,85,208,92,190,214,81,146,220,181,224,214,212,79,171,209,217,209,100,213,72,220,223,193,72,130,203,111,214,192,197,222,201,229,204,189,162,124,221,157,218,34,204,88,170,205,76,168,210,46,196,223,73,180,85,118,200,85,221,68,160,202,201,219,181,148,207,210,208,69,210,101,215,76,217,104,188,180,224,71,199,166,203,106,216,196,182,84,183,125,128,224,89,206,88,212,212,152,205,92,210,65,166,171,200,120,228,213,83,163,131,153,180,215,228,99,179,210,82,218,95,209,94,215,206,196,110,236,219,209,193,95,172,177,218,218,140,203,228,102,160,215,102,204,192,89,168,222,56,223,155,189,213,208,220,78,204,213,168,202,169,230,191,218,203,73,222,127,208,143,221,217,217,100,160,212,208,156,223,197,205,201,223,220,144,209,90,209,139,182,218,22,221,183,219,79,216,194,224,86,208,225,190,102,200,212,162,211,83,221,106,215,113,161,197,111,216,210,92,219,124,208,185,152,203,220,230,106,182,193,185,221,79,197,79,231,192,202,113,166,211,142,195,170,179,226,222,208,228,152,133,200,89,197,180,155,215,97,201,139,205,145,217,163,213,85,223,209,200,139,217,123,160,183,201,136,210,206,105,208,132,206,208,101,134,174,112,205,181,221,215,221,213,225,116,217,80,162,186,120,183,122,214,201,97,188,209,174,229,205,120,194,203,130,228,134,228,83,201,119,218,210,55,225,64,171,174,227,107,226,95,139,190,154,171,206,139,154,219,157,224,213,206,212,79,135,186,156,217,186,128,118,184,187,213,221,86,183,217,218,210,216,155,195,188,134,172,223,125,176,167,85,187,214,217,94,213,128,217,133,192,217,60,189,122,217,117,224,95,199,74,206,89,221,205,158,196,77,228,114,188,105,210,70,209,83,188,169,214,225,184,205,127,197,115,182,174,181,238,222,223,59,229,219,112,205,115,190,128,196,210,222,176,215,194,187,112,212,90,183,207,86,212,85,178,187,138,217,80,203,96,221,198,82,207,96,164,227,208,76,214,179,209,186,207,205,221,224,143,224,178,228,189,229,132,213,182,222,218,67,229,186,201,169,211,88,210,48,219,152,225,207,62,200,210,220,68,216,44,137,155,181,221,83,214,103,222,183,90,215,86,179,205,128,219,179,164,202,130,224,148,221,146,175,184,213,117,190,201,142,216,82,231,230,92,172,218,207,189,223,150,226,46,216,209,221,216,111,168,219,92,197,228,204,204,101,223,65,211,220,203,122,206,108,224,74,181,142,216,189,190,180,212,107,223,120,191,232,56,209,214,66,209,222,116,172,222,101,179,177,185,168,193,163,194,209,193,105,170,211,212,105,191,117,90,226,90,190,121,216,60,207,67,161,171,220,219,89,184,221,144,182,194,208,214,214,226,196,133,188,202,204,68,194,216,214,77,208,92,215,63,204,178,197,77,115,190,152,227,181,207,102,199,131,208,216,177,116,202,220,121,223,128,196,175,211,219,207,74,218,215,221,93,177,214,183,192,143,139,196,217,226,83,102,119,211,100,212,172,183,180,204,219,226,96,194,47,149,186,143,160,221,224,61,226,147,214,60,198,207,164,146,200,140,182,207,196,208,180,186,215,111,181,221,214,161,216,169,208,136,168,178,112,189,196,124,234,119,202,67,201,74,163,172,148,179,211,143,124,191,202,192,175,218,80,209,72,190,156,217,117,223,122,180,193,182,186,210,219,201,202,224,64,138,192,114,199,208,190,109,218,149,208,97,213,189,87,232,131,152,188,205,219,196,225,94,197,164,210,173,191,222,185,172,189,205,163,208,73,196,201,73,224,206,110,191,122,221,136,165,221,185,156,210,69,210,211,210,102,213,86,170,209,221,197,207,50,154,74,96,212,180,231,158,208,139,150,166,127,213,216,202,95,201,155,217,54,206,214,208,179,140,191,136,228,191,91,232,45,145,220,162,220,74,209,147,155,160,182,217,209,160,174,180,227,219,221,182,100,212,67,213,148,208,51,188,209,82,211,210,71,218,82,176,88,174,117,186,210,160,157,212,135,165,201,162,170,136,176,220,189,167,219,49,212,190,220,73,152,150,176,204,221,124,220,209,117,175,213,228,173,195,159,194,218,57,201,214,164,175,229,79,176,116,206,137,203,152,173,206,78,227,209,154,175,190,89,225,113,222,219,214,50,219,66,202,90,188,214,80,195,74,212,60,130,206,186,157,204,122,198,73,194,230,108,196,205,213,83,192,104,207,117,216,171,126,222,99,229,221,79,214,79,217,144,217,197,206,207,110,160,206,172,197,183,207,217,207,113,210,221,71,161,221,164,227,214,142,177,185,180,103,130,198,123,205,74,216,102,219,160,217,75,204,114,192,213,166,188,118,222,227,92,195,219,161,200,221,69,203,143,198,198,217,198,66,212,50,208,116,199,125,210,207,167,225,116,207,97,184,99,220,184,203,184,219,177,167,202,214,55,207,161,197,122,212,226,187,96,216,201,188,135,224,207,139,225,230,220,121,221,107,212,66,170,169,210,199,102,220,94,159,184,207,92,207,231,214,125,227,220,205,58,193,203,215,223,229,78,196,170,185,196,162,234,56,201,123,171,231,196,86,162,199,213,220,68,200,68,205,88,225,135,220,82,182,215,222,79,152,230,62,162,218,184,224,67,206,99,189,124,214,197,73,204,105,221,179,102,218,232,80,214,181,170,204,165,216,207,217,212,195,176,215,106,192,160,221,182,217,57,211,88,198,233,113,171,204,138,193,209,225,59,176,184,134,223,151,193,200,217,100,225,79,180,142,190,123,222,80,232,216,133,216,148,211,110,198,96,187,224,95,208,112,178,227,94,171,96,181,209,170,225,196,206,94,216,87,217,171,191,82,218,127,227,176,219,207,230,79,214,203,105,213,143,174,188,125,193,220,60,215,172,214,101,211,110,161,117,187,180,125,218,220,62,208,203,217,87,198,156,216,226,161,161,223,224,72,178,198,213,195,219,208,140,175,217,74,201,201,66,186,154,229,89,226,169,204,87,184,85,161,133,201,80,176,188,114,224,77,207,126,202,83,219,200,125,172,169,190,216,80,88,221,68,218,133,216,117,217,157,217,170,190,124,214,210,156,231,84,207,204,113,200,70,222,162,208,227,92,223,136,167,195,221,221,77,173,213,109,214,117,211,217,89,217,91,210,152,194,206,202,110,216,177,190,207,227,185,172,230,172,207,171,199,234,207,149,194,192,179,212,209,210,101,198,225,85,164,211,110,194,182,211,224,65,228,218,79,224,81,122,208,154,129,206,92,193,171,148,188,221,80,220,161,165,166,161,214,99,210,64,174,224,221,105,200,122,230,216,94,223,128,225,161,219,126,187,137,191,222,214,148,151,198,218,210,110,208,228,184,211,35,202,218,195,216,115,212,95,177,199,101,184,208,202,212,134,193,129,192,81,182,223,70,226,230,134,167,183,198,222,227,227,226,63,213,109,187,177,219,223,203,144,179,209,103,177,181,158,221,90,222,166,207,175,230,207,99,205,234,210,210,168,223,143,210,187,209,204,150,209,213,208,193,221,214,77,215,199,81,197,82,177,190,210,231,79,179,221,64,182,199,82,204,204,95,172,187,178,209,86,222,220,118,192,223,88,220,77,174,104,224,137,182,186,96,207,198,74,152,196,217,206,79,214,208,204,180,94,215,81,177,160,201,164,173,205,76,199,220,228,91,215,155,226,79,133,181,136,182,226,96,221,109,209,223,71,202,95,217,87,202,204,183,210,187,212,81,226,184,224,88,170,214,198,226,142,212,81,209,189,172,192,221,216,123,221,126,204,218,222,76,205,73,225,221,73,204,108,201,88,174,197,136,223,90,189,56,207,147,206,212,73,201,83,204,112,137,227,67,208,137,219,225,65,200,186,99,214,97,215,74,203,65,199,216,108,216,80,206,219,104,226,180,225,199,186,197,226,157,102,177,107,231,156,141,226,70,220,216,223,64,214,66,201,174,170,207,46,202,131,173,218,125,217,157,234,192,159,174,209,95,196,224,59,220,69,211,130,203,222,88,208,86,198,127,219,228,75,218,170,168,198,128,215,54,211,167,186,117,211,162,221,219,105,223,99,223,127,202,218,213,143,194,181,200,180,230,224,97,181,132,173,202,221,57,151,220,77,220,160,206,188,101,197,72,213,95,193,212,189,105,226,100,205,201,56,211,93,178,212,88,208,83,213,165,219,183,236,121,220,210,94,212,171,186,218,137,212,129,175,203,223,134,194,95,193,191,105,229,208,102,196,120,191,221,217,65,206,200,74,168,180,199,217,119,223,68,211,125,204,105,180,164,215,227,128,211,166,218,86,185,74,214,57,200,171,111,185,73,199,220,213,192,216,107,211,115,219,227,192,221,101,203,65,211,51,216,84,193,121,214,86,195,115,179,229,90,215,92,207,63,179,212,38,202,104,182,125,179,99,147,184,210,166,227,232,164,120,218,169,203,154,192,224,217,122,160,205,206,221,80,191,217,166,202,78,206,147,202,155,195,76,204,136,191,112,195,160,147,226,91,224,216,212,177,188,165,174,130,203,221,220,133,209,147,216,69,159,155,143,213,94,227,139,209,163,183,199,112,217,213,98,217,96,185,158,173,229,51,209,195,227,214,161,213,83,168,229,209,118,221,224,59,179,161,220,209,193,199,199,212,107,226,219,204,117,166,223,122,166,181,163,176,223,176,130,223,221,202,89,188,147,160,143,218,223,206,151,201,161,130,176,175,138,126,209,112,230,94,211,17,103,218,73,218,131,210,104,214,63,222,38,135,140,215,143,215,191,185,223,207,215,203,46,219,207,93,177,85,213,191,223,56,181,209,82,210,221,66,210,195,223,184,138,217,48,194,73,150,199,220,183,209,60,194,103,218,103,211,216,124,197,217,185,106,185,207,174,165,204,138,220,68,218,151,202,68,214,155,183,221,66,216,61,218,122,214,178,202,178,217,142,215,126,187,148,219,98,180,222,217,80,210,203,43,208,154,220,101,167,206,211,212,208,72,147,225,139,174,207,36,200,234,205,211,180,205,202,126,159,186,116,211,154,192,155,194,168,198,160,218,220,202,153,222,215,66,174,128,211,104,136,171,235,219,112,156,209,109,203,132,192,181,215,112,205,68,215,82,213,117,189,221,186,211,171,208,136,189,128,210,96,199,107,195,232,74,223,132,193,198,46,220,73,181,112,224,133,221,144,224,83,232,217,131,186,53,214,225,95,203,70,102,217,106,224,79,210,113,177,150,228,220,102,225,80,221,170,206,105,223,112,210,46,201,89,197,207,128,235,111,212,161,144,221,182,200,77,213,229,90,134,223,179,212,204,125,197,215,80,233,218,44,226,53,152,184,220,113,219,216,110,214,206,151,215,224,216,163,144,190,133,223,195,216,203,67,95,169,191,131,208,78,104,176,179,148,207,172,220,98,202,118,218,204,120,213,92,213,93,210,203,219,75,212,227,212,188,187,201,100,206,151,200,96,197,215,157,210,70,207,182,205,205,101,212,117,230,86,163,143,167,189,215,168,216,194,98,218,128,219,94,149,188,217,48,172,174,131,131,182,171,200,115,220,217,91,200,80,178,188,226,49,192,205,222,127,194,134,175,214,115,212,214,82,193,106,217,48,197,114,204,114,201,221,190,174,214,168,223,86,217,212,214,181,204,96,190,189,216,91,204,118,226,110,198,224,158,195,117,189,200,150,231,206,91,209,207,118,223,183,232,146,158,207,213,122,204,118,200,103,200,227,76,179,195,73,215,93,214,170,215,232,41,210,107,138,202,204,125,198,134,225,80,117,164,185,197,106,232,74,139,216,207,209,57,207,94,200,229,190,192,140,112,208,155,191,177,216,203,142,192,103,195,219,91,179,228,187,115,213,217,192,193,215,141,218,70,186,37,225,190,84,178,177,162,218,210,185,176,195,97,218,63,218,176,227,215,149,224,221,115,208,214,133,182,188,205,163,207,58,199,217,129,208,184,214,206,133,228,200,80,224,179,229,152,208,95,194,170,224,178,196,181,94,199,90,203,75,216,153,169,213,86,225,67,192,194,65,189,201,103,188,153,163,213,226,142,185,133,226,106,181,215,199,209,211,56,204,114,181,163,171,228,228,73,74,198,203,186,178,185,125,229,221,204,41,165,189,126,205,173,116,179,198,159,216,129,209,222,174,183,229,95,212,68,177,152,217,138,156,135,105,204,193,188,127,209,150,133,163,208,80,218,232,211,77,230,37,198,224,165,213,81,220,207,195,173,174,212,102,206,117,196,178,222,134,205,216,203,86,151,184,157,217,222,123,213,159,195,121,207,159,220,120,212,176,144,217,67,173,216,105,206,90,220,121,217,59,184,219,156,213,149,143,216,221,140,225,182,115,209,115,209,45,223,172,227,133,187,165,199,168,224,91,206,115,153,202,197,62,169,210,134,215,167,203,214,141,200,213,182,90,214,170,206,199,219,54,167,154,72,194,122,181,197,129,214,105,153,209,137,202,227,72,207,61,178,127,181,210,200,46,188,210,214,67,189,216,51,209,125,190,127,208,110,191,219,137,213,76,206,120,186,121,201,222,113,195,194,68,183,179,184,223,61,180,220,197,133,208,226,136,217,200,93,178,220,113,197,198,172,141,225,102,159,149,213,196,100,220,196,176,232,182,187,171,165,182,101,175,169,191,224,110,200,128,200,129,114,179,188,165,198,216,184,174,216,67,229,198,220,32,232,219,72,219,203,127,88,212,81,142,223,210,166,97,145,209,77,216,227,196,83,202,137,214,82,223,114,205,177,183,196,214,129,196,122,223,157,232,99,180,188,203,132,229,223,186,115,209,191,218,50,192,184,220,102,207,87,196,162,219,92,221,140,217,139,169,213,79,211,99,205,104,200,86,210,90,157,151,227,228,53,205,72,195,75,226,89,226,74,218,145,228,224,208,171,215,153,140,208,182,161,228,107,209,220,217,207,125,181,195,212,220,95,202,95,191,233,74,201,184,221,81,231,181,120,227,119,139,121,179,199,203,216,154,210,144,195,129,153,213,103,209,219,212,125,216,229,219,108,223,65,212,92,221,197,162,211,147,210,197,178,221,162,192,172,215,84,194,52,204,70,175,187,187,194,186,235,185,177,170,216,213,64,212,102,191,112,143,204,96,164,226,218,107,182,116,224,157,223,171,194,104,228,114,218,40,207,54,204,220,108,199,214,195,81,158,130,133,116,118,203,215,215,146,219,210,69,216,121,225,59,210,65,217,202,79,209,76,156,152,178,193,86,228,50,217,94,220,71,156,206,84,202,88,113,211,215,65,168,221,195,219,213,148,204,84,212,217,184,218,228,118,222,76,222,226,205,126,218,224,216,77,122,218,74,213,81,220,66,190,132,212,213,200,216,63,204,105,169,166,173,193,142,201,168,213,96,200,210,93,210,217,214,182,204,199,172,159,200,216,162,198,168,191,200,99,214,227,230,87,171,204,195,176,210,165,158,221,118,195,182,217,225,217,191,210,120,164,100,222,195,227,81,229,174,207,212,92,191,204,206,61,209,143,230,113,218,224,188,223,95,216,71,154,212,107,150,171,192,204,66,216,218,113,218,206,155,187,185,209,180,111,230,66,206,89,225,62,218,183,201,229,215,172,196,114,199,211,54,203,198,211,110,223,85,219,133,133,221,154,155,186,205,77,36,213,221,196,174,180,135,217,54,181,219,215,194,206,154,216,101,209,162,184,192,147,224,84,183,223,68,197,155,205,174,97,199,177,211,221,68,204,103,221,163,203,109,186,216,216,152,228,67,218,100,140,222,113,202,70,202,183,125,126,203,226,175,165,160,182,121,172,231,204,185,159,198,219,227,150,194,108,204,72,215,119,212,190,223,213,63,234,116,234,180,218,182,189,175,205,110,226,213,184,191,88,182,162,187,163,193,198,49,214,148,159,214,162,154,227,187,123,185,167,184,127,229,228,147,150,187,222,104,217,153,229,211,112,179,198,211,124,222,166,207,99,152,175,160,214,176,134,213,74,201,93,171,202,183,212,91,208,60,221,164,207,72,181,167,210,170,226,50,147,206,80,209,136,210,108,180,221,218,105,213,118,188,216,219,94,216,162,219,99,209,207,206,217,221,221,222,56,182,222,220,229,226]
+[206,104,226,67,200,185,221,212,206,222,88,210,228,174,155,205,172,218,148,132,212,91,163,184,205,132,213,190,212,198,230,227,159,198,122,216,175,197,118,217,219,224,69,220,197,72,204,92,169,191,191,155,175,111,218,77,207,36,195,178,123,170,231,91,209,177,146,205,151,221,217,95,199,89,153,216,154,202,167,213,104,184,176,226,200,232,53,223,73,202,220,158,174,189,165,222,158,221,211,78,205,72,214,212,215,232,175,219,110,205,192,226,34,176,209,158,222,132,179,208,98,126,205,126,172,167,227,100,224,182,208,117,217,199,195,191,169,178,158,217,152,157,163,163,204,209,146,217,150,194,217,23,125,200,221,200,212,41,176,223,207,95,178,232,68,182,173,205,198,210,139,152,147,215,196,223,83,199,123,197,119,230,223,65,216,85,210,52,210,204,179,111,138,215,83,177,219,69,212,77,182,226,99,178,207,197,87,190,222,216,85,208,211,66,220,226,221,134,175,190,170,166,157,216,135,211,132,200,67,227,195,178,221,179,205,168,186,208,127,207,139,205,67,209,223,117,195,190,202,64,218,77,199,221,189,125,216,80,148,214,143,181,98,221,194,112,219,212,138,216,87,127,187,219,122,220,97,223,221,164,205,220,100,227,101,171,188,223,89,213,137,175,172,219,77,102,234,109,197,114,205,184,221,138,175,152,163,133,227,73,224,181,174,147,144,178,147,224,211,121,123,211,219,209,210,204,80,182,215,152,118,224,216,154,220,163,204,187,133,151,162,221,94,184,224,213,72,187,148,223,195,110,182,117,224,88,202,162,220,154,151,197,227,197,74,221,205,97,221,219,103,206,184,72,191,201,192,230,164,217,170,214,118,221,218,118,215,181,224,78,183,154,190,206,102,202,74,195,96,225,228,221,99,224,104,123,203,184,211,90,209,173,199,203,99,201,176,160,207,132,231,96,195,213,173,101,190,206,61,225,232,215,178,211,227,192,148,195,213,114,212,127,162,214,213,129,220,70,176,224,209,137,232,116,228,212,75,182,207,71,111,173,189,207,96,172,213,119,222,93,195,204,211,76,179,200,184,216,107,135,202,71,118,219,123,202,210,146,225,76,227,62,214,226,98,158,202,219,169,83,156,223,125,211,213,102,219,223,72,224,178,190,204,110,228,64,214,200,193,205,213,160,201,216,134,195,128,186,213,219,187,199,216,84,202,150,211,127,230,206,204,180,183,183,167,213,224,72,209,79,195,205,153,228,133,203,210,78,190,207,195,133,225,192,197,190,201,187,222,109,138,227,103,223,209,164,190,73,226,200,205,191,189,100,226,135,198,129,185,207,229,148,209,216,58,222,117,219,92,221,147,222,219,63,216,228,165,181,157,213,197,218,158,191,186,223,204,109,172,229,91,231,204,102,192,217,204,144,222,173,209,209,95,186,195,60,212,219,209,162,183,227,216,111,193,182,222,71,183,222,122,178,219,130,228,82,225,223,69,198,232,63,193,207,216,139,223,74,164,198,224,80,148,227,138,227,199,225,86,148,230,169,210,92,166,187,202,165,196,78,202,74,229,173,216,215,213,164,205,102,206,91,212,173,198,109,183,94,206,115,224,126,229,222,57,225,191,221,218,215,44,177,217,214,218,191,221,80,198,109,196,95,192,181,214,214,123,208,191,225,219,218,108,204,150,199,206,90,227,71,207,123,199,106,131,213,93,178,208,60,132,217,149,204,80,227,218,202,94,232,175,213,115,217,114,222,200,173,184,217,222,178,226,205,192,185,197,180,193,205,211,136,221,62,184,213,89,225,71,169,216,135,198,98,214,204,88,191,154,207,213,227,93,230,133,216,189,122,224,108,229,237,218,209,214,66,153,203,219,204,222,218,107,174,214,148,169,188,210,98,203,142,167,174,224,103,223,220,148,216,199,188,175,173,130,156,97,220,111,216,115,216,70,221,210,77,173,162,216,164,216,69,203,175,172,166,199,156,184,131,201,210,98,220,133,211,116,221,82,213,88,213,128,227,219,177,190,216,180,237,217,64,215,134,221,172,220,221,70,169,221,189,226,74,229,197,209,128,190,213,106,221,198,162,184,182,219,86,201,143,229,205,146,228,106,154,209,60,216,222,74,221,215,141,168,202,82,194,213,80,217,214,204,164,199,220,149,114,140,215,150,218,108,109,211,170,206,125,183,148,193,229,210,124,188,143,160,153,199,178,75,225,105,199,83,222,112,205,75,222,205,70,104,194,91,158,220,99,215,209,219,65,124,215,158,223,234,208,101,218,200,66,188,222,149,201,189,211,51,179,208,198,160,182,152,195,141,216,112,205,207,170,128,219,118,188,223,224,80,177,218,127,196,151,159,117,186,113,216,212,134,204,90,200,231,184,222,68,146,151,158,202,173,144,206,171,215,68,188,222,89,215,117,223,80,191,209,204,105,228,67,174,210,74,208,105,151,159,183,142,163,202,214,41,147,222,227,188,208,200,157,132,195,94,191,185,214,132,201,155,166,195,220,147,219,156,214,165,220,105,223,217,100,176,225,117,207,134,166,202,218,172,200,157,183,217,87,214,181,229,217,76,231,199,139,154,213,208,208,169,218,82,219,191,139,207,222,49,122,154,176,219,110,185,112,137,231,80,208,136,189,192,208,190,192,187,180,220,186,225,218,208,102,190,124,152,83,219,199,95,198,133,235,214,96,213,217,148,168,200,32,145,207,222,198,171,208,218,104,178,230,77,214,129,218,136,139,212,233,93,221,153,221,95,155,151,211,225,60,81,217,201,108,208,234,172,177,172,193,208,72,216,224,211,44,205,82,221,224,199,198,221,137,208,81,183,105,182,106,222,180,184,146,180,205,187,192,163,219,213,164,229,218,190,147,221,193,213,182,126,228,231,97,173,218,116,161,156,207,122,193,198,155,207,103,223,192,92,153,223,170,206,177,225,40,224,106,203,208,116,203,105,197,152,221,211,84,226,111,215,154,224,221,65,215,142,169,212,184,135,207,85,209,213,76,225,229,130,203,217,186,229,140,177,116,216,219,62,209,105,164,150,207,214,211,114,148,230,118,201,134,197,124,209,163,184,164,186,189,219,106,221,220,95,141,217,87,200,224,114,220,168,212,112,229,192,106,189,183,209,212,85,187,202,219,222,223,223,212,226,79,193,128,222,209,206,197,178,152,114,222,111,164,183,189,179,214,130,149,150,212,122,160,195,118,115,204,144,163,159,140,211,208,120,222,220,125,195,174,220,80,173,204,87,149,212,82,221,119,213,139,186,227,97,215,213,136,215,68,229,218,63,224,182,221,218,94,213,90,168,202,85,168,222,121,215,213,93,221,83,220,112,197,213,213,92,210,76,216,79,179,220,94,194,99,186,181,197,214,194,138,221,91,218,172,159,219,74,153,221,201,108,213,131,217,112,214,107,209,35,224,188,219,108,191,105,157,211,126,206,148,220,126,217,164,203,129,201,211,119,204,165,209,75,205,130,204,215,206,119,178,211,218,137,193,167,199,220,182,115,207,184,189,217,178,186,159,208,230,140,194,75,173,205,64,213,167,189,110,220,195,136,137,196,134,208,120,198,182,189,176,162,183,172,172,214,200,95,212,189,193,184,195,116,167,199,110,180,217,126,193,226,142,204,66,140,222,226,218,144,218,214,140,206,66,196,224,143,121,166,221,101,154,204,216,201,221,152,225,69,226,226,180,200,181,212,128,193,206,136,219,141,208,128,202,209,105,168,178,204,211,110,145,163,193,211,69,220,147,194,223,165,212,218,124,197,229,226,132,220,156,211,80,203,140,192,94,208,102,180,215,134,152,218,106,191,204,102,211,108,182,204,206,208,90,165,197,105,186,115,213,208,186,165,186,174,213,211,164,210,62,129,201,217,170,169,217,193,167,222,174,217,167,128,182,113,159,208,85,172,219,89,219,126,218,190,65,227,208,52,225,125,160,205,135,168,186,213,212,69,124,205,67,204,110,151,180,220,205,193,106,223,80,124,166,218,194,138,148,205,88,179,183,163,227,221,198,213,87,190,200,239,81,202,211,213,115,204,105,216,83,170,201,210,95,219,219,180,216,222,228,86,231,83,227,116,223,84,207,216,115,204,109,194,171,198,183,197,86,216,144,209,125,226,221,57,215,117,199,185,152,230,174,220,61,209,99,163,218,218,134,183,212,85,208,92,190,214,81,146,220,181,224,214,212,79,171,209,217,209,100,213,72,220,223,193,72,130,203,111,214,192,197,222,201,229,204,189,162,124,221,157,218,34,204,88,170,205,76,168,210,46,196,223,73,180,85,118,200,85,221,68,160,202,201,219,181,148,207,210,208,69,210,101,215,76,217,104,188,180,224,71,199,166,203,106,216,196,182,84,183,125,128,224,89,206,88,212,212,152,205,92,210,65,166,171,200,120,228,213,83,163,131,153,180,215,228,99,179,210,82,218,95,209,94,215,206,196,110,236,219,209,193,95,172,177,218,218,140,203,228,102,160,215,102,204,192,89,168,222,56,223,155,189,213,208,220,78,204,213,168,202,169,230,191,218,203,73,222,127,208,143,221,217,217,100,160,212,208,156,223,197,205,201,223,220,144,209,90,209,139,182,218,22,221,183,219,79,216,194,224,86,208,225,190,102,200,212,162,211,83,221,106,215,113,161,197,111,216,210,92,219,124,208,185,152,203,220,230,106,182,193,185,221,79,197,79,231,192,202,113,166,211,142,195,170,179,226,222,208,228,152,133,200,89,197,180,155,215,97,201,139,205,145,217,163,213,85,223,209,200,139,217,123,160,183,201,136,210,206,105,208,132,206,208,101,134,174,112,205,181,221,215,221,213,225,116,217,80,162,186,120,183,122,214,201,97,188,209,174,229,205,120,194,203,130,228,134,228,83,201,119,218,210,55,225,64,171,174,227,107,226,95,139,190,154,171,206,139,154,219,157,224,213,206,212,79,135,186,156,217,186,128,118,184,187,213,221,86,183,217,218,210,216,155,195,188,134,172,223,125,176,167,85,187,214,217,94,213,128,217,133,192,217,60,189,122,217,117,224,95,199,74,206,89,221,205,158,196,77,228,114,188,105,210,70,209,83,188,169,214,225,184,205,127,197,115,182,174,181,238,222,223,59,229,219,112,205,115,190,128,196,210,222,176,215,194,187,112,212,90,183,207,86,212,85,178,187,138,217,80,203,96,221,198,82,207,96,164,227,208,76,214,179,209,186,207,205,221,224,143,224,178,228,189,229,132,213,182,222,218,67,229,186,201,169,211,88,210,48,219,152,225,207,62,200,210,220,68,216,44,137,155,181,221,83,214,103,222,183,90,215,86,179,205,128,219,179,164,202,130,224,148,221,146,175,184,213,117,190,201,142,216,82,231,230,92,172,218,207,189,223,150,226,46,216,209,221,216,111,168,219,92,197,228,204,204,101,223,65,211,220,203,122,206,108,224,74,181,142,216,189,190,180,212,107,223,120,191,232,56,209,214,66,209,222,116,172,222,101,179,177,185,168,193,163,194,209,193,105,170,211,212,105,191,117,90,226,90,190,121,216,60,207,67,161,171,220,219,89,184,221,144,182,194,208,214,214,226,196,133,188,202,204,68,194,216,214,77,208,92,215,63,204,178,197,77,115,190,152,227,181,207,102,199,131,208,216,177,116,202,220,121,223,128,196,175,211,219,207,74,218,215,221,93,177,214,183,192,143,139,196,217,226,83,102,119,211,100,212,172,183,180,204,219,226,96,194,47,149,186,143,160,221,224,61,226,147,214,60,198,207,164,146,200,140,182,207,196,208,180,186,215,111,181,221,214,161,216,169,208,136,168,178,112,189,196,124,234,119,202,67,201,74,163,172,148,179,211,143,124,191,202,192,175,218,80,209,72,190,156,217,117,223,122,180,193,182,186,210,219,201,202,224,64,138,192,114,199,208,190,109,218,149,208,97,213,189,87,232,131,152,188,205,219,196,225,94,197,164,210,173,191,222,185,172,189,205,163,208,73,196,201,73,224,206,110,191,122,221,136,165,221,185,156,210,69,210,211,210,102,213,86,170,209,221,197,207,50,154,74,96,212,180,231,158,208,139,150,166,127,213,216,202,95,201,155,217,54,206,214,208,179,140,191,136,228,191,91,232,45,145,220,162,220,74,209,147,155,160,182,217,209,160,174,180,227,219,221,182,100,212,67,213,148,208,51,188,209,82,211,210,71,218,82,176,88,174,117,186,210,160,157,212,135,165,201,162,170,136,176,220,189,167,219,49,212,190,220,73,152,150,176,204,221,124,220,209,117,175,213,228,173,195,159,194,218,57,201,214,164,175,229,79,176,116,206,137,203,152,173,206,78,227,209,154,175,190,89,225,113,222,219,214,50,219,66,202,90,188,214,80,195,74,212,60,130,206,186,157,204,122,198,73,194,230,108,196,205,213,83,192,104,207,117,216,171,126,222,99,229,221,79,214,79,217,144,217,197,206,207,110,160,206,172,197,183,207,217,207,113,210,221,71,161,221,164,227,214,142,177,185,180,103,130,198,123,205,74,216,102,219,160,217,75,204,114,192,213,166,188,118,222,227,92,195,219,161,200,221,69,203,143,198,198,217,198,66,212,50,208,116,199,125,210,207,167,225,116,207,97,184,99,220,184,203,184,219,177,167,202,214,55,207,161,197,122,212,226,187,96,216,201,188,135,224,207,139,225,230,220,121,221,107,212,66,170,169,210,199,102,220,94,159,184,207,92,207,231,214,125,227,220,205,58,193,203,215,223,229,78,196,170,185,196,162,234,56,201,123,171,231,196,86,162,199,213,220,68,200,68,205,88,225,135,220,82,182,215,222,79,152,230,62,162,218,184,224,67,206,99,189,124,214,197,73,204,105,221,179,102,218,232,80,214,181,170,204,165,216,207,217,212,195,176,215,106,192,160,221,182,217,57,211,88,198,233,113,171,204,138,193,209,225,59,176,184,134,223,151,193,200,217,100,225,79,180,142,190,123,222,80,232,216,133,216,148,211,110,198,96,187,224,95,208,112,178,227,94,171,96,181,209,170,225,196,206,94,216,87,217,171,191,82,218,127,227,176,219,207,230,79,214,203,105,213,143,174,188,125,193,220,60,215,172,214,101,211,110,161,117,187,180,125,218,220,62,208,203,217,87,198,156,216,226,161,161,223,224,72,178,198,213,195,219,208,140,175,217,74,201,201,66,186,154,229,89,226,169,204,87,184,85,161,133,201,80,176,188,114,224,77,207,126,202,83,219,200,125,172,169,190,216,80,88,221,68,218,133,216,117,217,157,217,170,190,124,214,210,156,231,84,207,204,113,200,70,222,162,208,227,92,223,136,167,195,221,221,77,173,213,109,214,117,211,217,89,217,91,210,152,194,206,202,110,216,177,190,207,227,185,172,230,172,207,171,199,234,207,149,194,192,179,212,209,210,101,198,225,85,164,211,110,194,182,211,224,65,228,218,79,224,81,122,208,154,129,206,92,193,171,148,188,221,80,220,161,165,166,161,214,99,210,64,174,224,221,105,200,122,230,216,94,223,128,225,161,219,126,187,137,191,222,214,148,151,198,218,210,110,208,228,184,211,35,202,218,195,216,115,212,95,177,199,101,184,208,202,212,134,193,129,192,81,182,223,70,226,230,134,167,183,198,222,227,227,226,63,213,109,187,177,219,223,203,144,179,209,103,177,181,158,221,90,222,166,207,175,230,207,99,205,234,210,210,168,223,143,210,187,209,204,150,209,213,208,193,221,214,77,215,199,81,197,82,177,190,210,231,79,179,221,64,182,199,82,204,204,95,172,187,178,209,86,222,220,118,192,223,88,220,77,174,104,224,137,182,186,96,207,198,74,152,196,217,206,79,214,208,204,180,94,215,81,177,160,201,164,173,205,76,199,220,228,91,215,155,226,79,133,181,136,182,226,96,221,109,209,223,71,202,95,217,87,202,204,183,210,187,212,81,226,184,224,88,170,214,198,226,142,212,81,209,189,172,192,221,216,123,221,126,204,218,222,76,205,73,225,221,73,204,108,201,88,174,197,136,223,90,189,56,207,147,206,212,73,201,83,204,112,137,227,67,208,137,219,225,65,200,186,99,214,97,215,74,203,65,199,216,108,216,80,206,219,104,226,180,225,199,186,197,226,157,102,177,107,231,156,141,226,70,220,216,223,64,214,66,201,174,170,207,46,202,131,173,218,125,217,157,234,192,159,174,209,95,196,224,59,220,69,211,130,203,222,88,208,86,198,127,219,228,75,218,170,168,198,128,215,54,211,167,186,117,211,162,221,219,105,223,99,223,127,202,218,213,143,194,181,200,180,230,224,97,181,132,173,202,221,57,151,220,77,220,160,206,188,101,197,72,213,95,193,212,189,105,226,100,205,201,56,211,93,178,212,88,208,83,213,165,219,183,236,121,220,210,94,212,171,186,218,137,212,129,175,203,223,134,194,95,193,191,105,229,208,102,196,120,191,221,217,65,206,200,74,168,180,199,217,119,223,68,211,125,204,105,180,164,215,227,128,211,166,218,86,185,74,214,57,200,171,111,185,73,199,220,213,192,216,107,211,115,219,227,192,221,101,203,65,211,51,216,84,193,121,214,86,195,115,179,229,90,215,92,207,63,179,212,38,202,104,182,125,179,99,147,184,210,166,227,232,164,120,218,169,203,154,192,224,217,122,160,205,206,221,80,191,217,166,202,78,206,147,202,155,195,76,204,136,191,112,195,160,147,226,91,224,216,212,177,188,165,174,130,203,221,220,133,209,147,216,69,159,155,143,213,94,227,139,209,163,183,199,112,217,213,98,217,96,185,158,173,229,51,209,195,227,214,161,213,83,168,229,209,118,221,224,59,179,161,220,209,193,199,199,212,107,226,219,204,117,166,223,122,166,181,163,176,223,176,130,223,221,202,89,188,147,160,143,218,223,206,151,201,161,130,176,175,138,126,209,112,230,94,211,17,103,218,73,218,131,210,104,214,63,222,38,135,140,215,143,215,191,185,223,207,215,203,46,219,207,93,177,85,213,191,223,56,181,209,82,210,221,66,210,195,223,184,138,217,48,194,73,150,199,220,183,209,60,194,103,218,103,211,216,124,197,217,185,106,185,207,174,165,204,138,220,68,218,151,202,68,214,155,183,221,66,216,61,218,122,214,178,202,178,217,142,215,126,187,148,219,98,180,222,217,80,210,203,43,208,154,220,101,167,206,211,212,208,72,147,225,139,174,207,36,200,234,205,211,180,205,202,126,159,186,116,211,154,192,155,194,168,198,160,218,220,202,153,222,215,66,174,128,211,104,136,171,235,219,112,156,209,109,203,132,192,181,215,112,205,68,215,82,213,117,189,221,186,211,171,208,136,189,128,210,96,199,107,195,232,74,223,132,193,198,46,220,73,181,112,224,133,221,144,224,83,232,217,131,186,53,214,225,95,203,70,102,217,106,224,79,210,113,177,150,228,220,102,225,80,221,170,206,105,223,112,210,46,201,89,197,207,128,235,111,212,161,144,221,182,200,77,213,229,90,134,223,179,212,204,125,197,215,80,233,218,44,226,53,152,184,220,113,219,216,110,214,206,151,215,224,216,163,144,190,133,223,195,216,203,67,95,169,191,131,208,78,104,176,179,148,207,172,220,98,202,118,218,204,120,213,92,213,93,210,203,219,75,212,227,212,188,187,201,100,206,151,200,96,197,215,157,210,70,207,182,205,205,101,212,117,230,86,163,143,167,189,215,168,216,194,98,218,128,219,94,149,188,217,48,172,174,131,131,182,171,200,115,220,217,91,200,80,178,188,226,49,192,205,222,127,194,134,175,214,115,212,214,82,193,106,217,48,197,114,204,114,201,221,190,174,214,168,223,86,217,212,214,181,204,96,190,189,216,91,204,118,226,110,198,224,158,195,117,189,200,150,231,206,91,209,207,118,223,183,232,146,158,207,213,122,204,118,200,103,200,227,76,179,195,73,215,93,214,170,215,232,41,210,107,138,202,204,125,198,134,225,80,117,164,185,197,106,232,74,139,216,207,209,57,207,94,200,229,190,192,140,112,208,155,191,177,216,203,142,192,103,195,219,91,179,228,187,115,213,217,192,193,215,141,218,70,186,37,225,190,84,178,177,162,218,210,185,176,195,97,218,63,218,176,227,215,149,224,221,115,208,214,133,182,188,205,163,207,58,199,217,129,208,184,214,206,133,228,200,80,224,179,229,152,208,95,194,170,224,178,196,181,94,199,90,203,75,216,153,169,213,86,225,67,192,194,65,189,201,103,188,153,163,213,226,142,185,133,226,106,181,215,199,209,211,56,204,114,181,163,171,228,228,73,74,198,203,186,178,185,125,229,221,204,41,165,189,126,205,173,116,179,198,159,216,129,209,222,174,183,229,95,212,68,177,152,217,138,156,135,105,204,193,188,127,209,150,133,163,208,80,218,232,211,77,230,37,198,224,165,213,81,220,207,195,173,174,212,102,206,117,196,178,222,134,205,216,203,86,151,184,157,217,222,123,213,159,195,121,207,159,220,120,212,176,144,217,67,173,216,105,206,90,220,121,217,59,184,219,156,213,149,143,216,221,140,225,182,115,209,115,209,45,223,172,227,133,187,165,199,168,224,91,206,115,153,202,197,62,169,210,134,215,167,203,214,141,200,213,182,90,214,170,206,199,219,54,167,154,72,194,122,181,197,129,214,105,153,209,137,202,227,72,207,61,178,127,181,210,200,46,188,210,214,67,189,216,51,209,125,190,127,208,110,191,219,137,213,76,206,120,186,121,201,222,113,195,194,68,183,179,184,223,61,180,220,197,133,208,226,136,217,200,93,178,220,113,197,198,172,141,225,102,159,149,213,196,100,220,196,176,232,182,187,171,165,182,101,175,169,191,224,110,200,128,200,129,114,179,188,165,198,216,184,174,216,67,229,198,220,32,232,219,72,219,203,127,88,212,81,142,223,210,166,97,145,209,77,216,227,196,83,202,137,214,82,223,114,205,177,183,196,214,129,196,122,223,157,232,99,180,188,203,132,229,223,186,115,209,191,218,50,192,184,220,102,207,87,196,162,219,92,221,140,217,139,169,213,79,211,99,205,104,200,86,210,90,157,151,227,228,53,205,72,195,75,226,89,226,74,218,145,228,224,208,171,215,153,140,208,182,161,228,107,209,220,217,207,125,181,195,212,220,95,202,95,191,233,74,201,184,221,81,231,181,120,227,119,139,121,179,199,203,216,154,210,144,195,129,153,213,103,209,219,212,125,216,229,219,108,223,65,212,92,221,197,162,211,147,210,197,178,221,162,192,172,215,84,194,52,204,70,175,187,187,194,186,235,185,177,170,216,213,64,212,102,191,112,143,204,96,164,226,218,107,182,116,224,157,223,171,194,104,228,114,218,40,207,54,204,220,108,199,214,195,81,158,130,133,116,118,203,215,215,146,219,210,69,216,121,225,59,210,65,217,202,79,209,76,156,152,178,193,86,228,50,217,94,220,71,156,206,84,202,88,113,211,215,65,168,221,195,219,213,148,204,84,212,217,184,218,228,118,222,76,222,226,205,126,218,224,216,77,122,218,74,213,81,220,66,190,132,212,213,200,216,63,204,105,169,166,173,193,142,201,168,213,96,200,210,93,210,217,214,182,204,199,172,159,200,216,162,198,168,191,200,99,214,227,230,87,171,204,195,176,210,165,158,221,118,195,182,217,225,217,191,210,120,164,100,222,195,227,81,229,174,207,212,92,191,204,206,61,209,143,230,113,218,224,188,223,95,216,71,154,212,107,150,171,192,204,66,216,218,113,218,206,155,187,185,209,180,111,230,66,206,89,225,62,218,183,201,229,215,172,196,114,199,211,54,203,198,211,110,223,85,219,133,133,221,154,155,186,205,77,36,213,221,196,174,180,135,217,54,181,219,215,194,206,154,216,101,209,162,184,192,147,224,84,183,223,68,197,155,205,174,97,199,177,211,221,68,204,103,221,163,203,109,186,216,216,152,228,67,218,100,140,222,113,202,70,202,183,125,126,203,226,175,165,160,182,121,172,231,204,185,159,198,219,227,150,194,108,204,72,215,119,212,190,223,213,63,234,116,234,180,218,182,189,175,205,110,226,213,184,191,88,182,162,187,163,193,198,49,214,148,159,214,162,154,227,187,123,185,167,184,127,229,228,147,150,187,222,104,217,153,229,211,112,179,198,211,124,222,166,207,99,152,175,160,214,176,134,213,74,201,93,171,202,183,212,91,208,60,221,164,207,72,181,167,210,170,226,50,147,206,80,209,136,210,108,180,221,218,105,213,118,188,216,219,94,216,162,219,99,209,207,206,217,221,221,222,56,182,222,220,229,226,209,111,231,144]

ivf.pid.pt

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:ff112df74f6a290e687ffe055945e3ee67f75a21f594c281c8fc9b6c274f4a80
-size 2497880
+oid sha256:79deea69d99e99cd748ff6cc50d1754ae1a2c8e480ebc9fe5676e2d7a116266b
+size 2499480

metadata.json

@@ -37,7 +37,7 @@
     "checkpoint":"colbert-ir/colbertv2.0",
     "triples":"/future/u/okhattab/root/unit/experiments/2021.10/downstream.distillation.round2.2_score/round2.nway6.cosine.ib/examples.64.json",
     "collection":[
-      "list with 5474 elements starting with...",
+      "list with 5478 elements starting with...",
       [
         "Driven by large-data pre-training, Segment Anything Model (SAM) has been demonstrated as a powerful and promptable framework, revolutionizing the segmentation models. Despite the generality, customizing SAM for specific visual concepts without man-powered prompting is under explored, e.g., automatically segmenting your pet dog in different images. In this paper, we propose a training-free Personalization approach for SAM, termed as PerSAM. Given only a single image with a reference mask, PerSAM first localizes the target concept by a location prior, and segments it within other images or videos via three techniques: target-guided attention, target-semantic prompting, and cascaded post-refinement. In this way, we effectively adapt SAM for private use without any training. To further alleviate the mask ambiguity, we present an efficient one-shot fine-tuning variant, PerSAM-F. Freezing the entire SAM, we introduce two learnable weights for multi-scale masks, only training 2 parameters within 10 seconds for improved performance. To demonstrate our efficacy, we construct a new segmentation dataset, PerSeg, for personalized evaluation, and test our methods on video object segmentation with competitive performance.",
         "Freezing the entire SAM, we introduce two learnable weights for multi-scale masks, only training 2 parameters within 10 seconds for improved performance. To demonstrate our efficacy, we construct a new segmentation dataset, PerSeg, for personalized evaluation, and test our methods on video object segmentation with competitive performance. Besides, our approach can also enhance DreamBooth to personalize Stable Diffusion for text-to-image generation, which discards the background disturbance for better target appearance learning. Code is released at https://github.com/ZrrSkywalker/Personalize-SAM",
@@ -50,7 +50,7 @@
     "root":".ragatouille/",
     "experiment":"colbert",
     "index_root":null,
-    "name":"2024-10/08/14.54.17",
+    "name":"2024-10/08/15.54.12",
     "rank":0,
     "nranks":1,
     "amp":true,
@@ -59,8 +59,8 @@
   },
   "num_chunks":1,
   "num_partitions":8192,
-  "num_embeddings":937534,
-  "avg_doclen":171.2703690172,
+  "num_embeddings":938229,
+  "avg_doclen":171.2721796276,
   "RAGatouille":{
     "index_config":{
       "index_type":"PLAID",

pid_docid_map.json

@@ -5472,5 +5472,9 @@
   "5470":"2410.02707",
   "5471":"2410.02884",
   "5472":"2410.03187",
-  "5473":"2410.05046"
+  "5473":"2410.05046",
+  "5474":"2410.05057",
+  "5475":"2410.05057",
+  "5476":"2410.05229",
+  "5477":"2410.05229"
 }

plan.json

@@ -37,7 +37,7 @@
         "checkpoint": "colbert-ir\/colbertv2.0",
         "triples": "\/future\/u\/okhattab\/root\/unit\/experiments\/2021.10\/downstream.distillation.round2.2_score\/round2.nway6.cosine.ib\/examples.64.json",
         "collection": [
-            "list with 5474 elements starting with...",
+            "list with 5478 elements starting with...",
             [
                 "Driven by large-data pre-training, Segment Anything Model (SAM) has been demonstrated as a powerful and promptable framework, revolutionizing the segmentation models. Despite the generality, customizing SAM for specific visual concepts without man-powered prompting is under explored, e.g., automatically segmenting your pet dog in different images. In this paper, we propose a training-free Personalization approach for SAM, termed as PerSAM. Given only a single image with a reference mask, PerSAM first localizes the target concept by a location prior, and segments it within other images or videos via three techniques: target-guided attention, target-semantic prompting, and cascaded post-refinement. In this way, we effectively adapt SAM for private use without any training. To further alleviate the mask ambiguity, we present an efficient one-shot fine-tuning variant, PerSAM-F. Freezing the entire SAM, we introduce two learnable weights for multi-scale masks, only training 2 parameters within 10 seconds for improved performance. To demonstrate our efficacy, we construct a new segmentation dataset, PerSeg, for personalized evaluation, and test our methods on video object segmentation with competitive performance.",
                 "Freezing the entire SAM, we introduce two learnable weights for multi-scale masks, only training 2 parameters within 10 seconds for improved performance. To demonstrate our efficacy, we construct a new segmentation dataset, PerSeg, for personalized evaluation, and test our methods on video object segmentation with competitive performance. Besides, our approach can also enhance DreamBooth to personalize Stable Diffusion for text-to-image generation, which discards the background disturbance for better target appearance learning. Code is released at https:\/\/github.com\/ZrrSkywalker\/Personalize-SAM",
@@ -50,7 +50,7 @@
         "root": ".ragatouille\/",
         "experiment": "colbert",
         "index_root": null,
-        "name": "2024-10\/08\/14.54.17",
+        "name": "2024-10\/08\/15.54.12",
         "rank": 0,
         "nranks": 1,
         "amp": true,
@@ -59,6 +59,6 @@
     },
     "num_chunks": 1,
     "num_partitions": 8192,
-    "num_embeddings_est": 937534.008026123,
-    "avg_doclen_est": 171.27037048339844
+    "num_embeddings_est": 938229.0364379883,
+    "avg_doclen_est": 171.27218627929688
 }