Upload folder using huggingface_hub

0.codes.pt

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:2e11553290b32dd9aa1b5a848aba9b6a21567e570afbfbe7debf668e1e3334ec
-size 3196124
+oid sha256:0bd2433e8558bfc9cc347e5735a6a51653466b2f6a6ef361c9bc65b22390b697
+size 3202204

0.metadata.json

@@ -1,6 +1,6 @@
 {
     "passage_offset": 0,
-    "num_passages": 4660,
-    "num_embeddings": 798746,
+    "num_passages": 4669,
+    "num_embeddings": 800263,
     "embedding_offset": 0
 }

0.residuals.pt

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:fd4e25f3ee7c1754e658dc9d9eba2b933287b869b1aa5b37b506c4c760b6f230
-size 102240688
+oid sha256:128a8f1478858667a3a5c7e6a9ae69fc10f0ec805f0ebc13ed4fd1125ab9acd7
+size 102434864

avg_residual.pt

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:85547f2aeba6b59b0b140a8dd9211dc9738c7c5ff590dab46093f362244c60c7
+oid sha256:090e92a923449e741ff28b1c22cd61bd67ee717c7035efb707fedecc21736826
 size 1205

buckets.pt

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:7ed4f8088793e771b06b3d8cd1dd304e26cbf96a768b0b791588dd91274bb8b1
+oid sha256:77d01e613151c0b6c2ce4feedb373a4bb5be9708337b7c8ad238186982349ce7
 size 2904

centroids.pt

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:df55dc7d1ff32f0506d797f6ca9f6ae7d72bf38e358cae5a32621d2a91b194e6
+oid sha256:163523f63ce1eaa4b3a637068d6c254ce4012200d8296fd1516443dff14427e1
 size 2098342

collection.json

@@ -4658,5 +4658,14 @@
   "Implementing Retrieval-Augmented Generation (RAG) systems is inherently complex, requiring deep understanding of data, use cases, and intricate design decisions. Additionally, evaluating these systems presents significant challenges, necessitating assessment of both retrieval accuracy and generative quality through a multi-faceted approach. We introduce RAG Foundry, an open-source framework for augmenting large language models for RAG use cases. RAG Foundry integrates data creation, training, inference and evaluation into a single workflow, facilitating the creation of data-augmented datasets for training and evaluating large language models in RAG settings. This integration enables rapid prototyping and experimentation with various RAG techniques, allowing users to easily generate datasets and train RAG models using internal or specialized knowledge sources. We demonstrate the framework effectiveness by augmenting and fine-tuning Llama-3 and Phi-3 models with diverse RAG configurations, showcasing consistent improvements across three knowledge-intensive datasets. Code is released as open-source in https://github.com/IntelLabs/RAGFoundry.",
   "The advancement of natural language processing (NLP) in biology hinges on models' ability to interpret intricate biomedical literature. Traditional models often struggle with the complex and domain-specific language in this field. In this paper, we present BioMamba, a pre-trained model specifically designed for biomedical text mining. BioMamba builds upon the Mamba architecture and is pre-trained on an extensive corpus of biomedical literature. Our empirical studies demonstrate that BioMamba significantly outperforms models like BioBERT and general-domain Mamba across various biomedical tasks. For instance, BioMamba achieves a 100 times reduction in perplexity and a 4 times reduction in cross-entropy loss on the BioASQ test set. We provide an overview of the model architecture, pre-training process, and fine-tuning techniques. Additionally, we release the code and trained model to facilitate further research.",
   "The capability to process multiple images is crucial for Large Vision-Language Models (LVLMs) to develop a more thorough and nuanced understanding of a scene. Recent multi-image LVLMs have begun to address this need. However, their evaluation has not kept pace with their development. To fill this gap, we introduce the Multimodal Multi-image Understanding (MMIU) benchmark, a comprehensive evaluation suite designed to assess LVLMs across a wide range of multi-image tasks. MMIU encompasses 7 types of multi-image relationships, 52 tasks, 77K images, and 11K meticulously curated multiple-choice questions, making it the most extensive benchmark of its kind. Our evaluation of 24 popular LVLMs, including both open-source and proprietary models, reveals significant challenges in multi-image comprehension, particularly in tasks involving spatial understanding. Even the most advanced models, such as GPT-4o, achieve only 55.7% accuracy on MMIU. Through multi-faceted analytical experiments, we identify key performance gaps and limitations, providing valuable insights for future model and data improvements. We aim for MMIU to advance the frontier of LVLM research and development, moving us toward achieving sophisticated multimodal multi-image user interactions.",
-  "This paper demonstrates how to use generative models trained for image synthesis as tools for visual data mining. Our insight is that since contemporary generative models learn an accurate representation of their training data, we can use them to summarize the data by mining for visual patterns. Concretely, we show that after finetuning conditional diffusion models to synthesize images from a specific dataset, we can use these models to define a typicality measure on that dataset. This measure assesses how typical visual elements are for different data labels, such as geographic location, time stamps, semantic labels, or even the presence of a disease. This analysis-by-synthesis approach to data mining has two key advantages. First, it scales much better than traditional correspondence-based approaches since it does not require explicitly comparing all pairs of visual elements. Second, while most previous works on visual data mining focus on a single dataset, our approach works on diverse datasets in terms of content and scale, including a historical car dataset, a historical face dataset, a large worldwide street-view dataset, and an even larger scene dataset. Furthermore, our approach allows for translating visual elements across class labels and analyzing consistent changes."
+  "This paper demonstrates how to use generative models trained for image synthesis as tools for visual data mining. Our insight is that since contemporary generative models learn an accurate representation of their training data, we can use them to summarize the data by mining for visual patterns. Concretely, we show that after finetuning conditional diffusion models to synthesize images from a specific dataset, we can use these models to define a typicality measure on that dataset. This measure assesses how typical visual elements are for different data labels, such as geographic location, time stamps, semantic labels, or even the presence of a disease. This analysis-by-synthesis approach to data mining has two key advantages. First, it scales much better than traditional correspondence-based approaches since it does not require explicitly comparing all pairs of visual elements. Second, while most previous works on visual data mining focus on a single dataset, our approach works on diverse datasets in terms of content and scale, including a historical car dataset, a historical face dataset, a large worldwide street-view dataset, and an even larger scene dataset. Furthermore, our approach allows for translating visual elements across class labels and analyzing consistent changes.",
+  "We introduce a new approach for generating realistic 3D models with UV maps through a representation termed \"Object Images.\" This approach encapsulates surface geometry, appearance, and patch structures within a 64x64 pixel image, effectively converting complex 3D shapes into a more manageable 2D format. By doing so, we address the challenges of both geometric and semantic irregularity inherent in polygonal meshes. This method allows us to use image generation models, such as Diffusion Transformers, directly for 3D shape generation. Evaluated on the ABO dataset, our generated shapes with patch structures achieve point cloud FID comparable to recent 3D generative models, while naturally supporting PBR material generation.",
+  "This paper introduces MedTrinity-25M, a comprehensive, large-scale multimodal dataset for medicine, covering over 25 million images across 10 modalities, with multigranular annotations for more than 65 diseases. These enriched annotations encompass both global textual information, such as disease/lesion type, modality, region-specific descriptions, and inter-regional relationships, as well as detailed local annotations for regions of interest (ROIs), including bounding boxes, segmentation masks. Unlike existing approach which is limited by the availability of image-text pairs, we have developed the first automated pipeline that scales up multimodal data by generating multigranular visual and texual annotations (in the form of image-ROI-description triplets) without the need for any paired text descriptions. Specifically, data from over 90 different sources have been collected, preprocessed, and grounded using domain-specific expert models to identify ROIs related to abnormal regions. We then build a comprehensive knowledge base and prompt multimodal large language models to perform retrieval-augmented generation with the identified ROIs as guidance, resulting in multigranular texual descriptions.",
+  "We then build a comprehensive knowledge base and prompt multimodal large language models to perform retrieval-augmented generation with the identified ROIs as guidance, resulting in multigranular texual descriptions. Compared to existing datasets, MedTrinity-25M provides the most enriched annotations, supporting a comprehensive range of multimodal tasks such as captioning and report generation, as well as vision-centric tasks like classification and segmentation. Pretraining on MedTrinity-25M, our model achieves state-of-the-art performance on VQA-RAD and PathVQA, surpassing both multimodal large language models and other representative SoTA approaches. This dataset can also be utilized to support large-scale pre-training of multimodal medical AI models, contributing to the development of future foundation models in the medical domain.",
+  "There is a growing line of research on verifying the correctness of language models' outputs. At the same time, LMs are being used to tackle complex queries that require reasoning. We introduce CoverBench, a challenging benchmark focused on verifying LM outputs in complex reasoning settings. Datasets that can be used for this purpose are often designed for other complex reasoning tasks (e.g., QA) targeting specific use-cases (e.g., financial tables), requiring transformations, negative sampling and selection of hard examples to collect such a benchmark. CoverBench provides a diversified evaluation for complex claim verification in a variety of domains, types of reasoning, relatively long inputs, and a variety of standardizations, such as multiple representations for tables where available, and a consistent schema. We manually vet the data for quality to ensure low levels of label noise. Finally, we report a variety of competitive baseline results to show CoverBench is challenging and has very significant headroom. The data is available at https://huggingface.co/datasets/google/coverbench .",
+  "We present LLaVA-OneVision, a family of open large multimodal models (LMMs) developed by consolidating our insights into data, models, and visual representations in the LLaVA-NeXT blog series. Our experimental results demonstrate that LLaVA-OneVision is the first single model that can simultaneously push the performance boundaries of open LMMs in three important computer vision scenarios: single-image, multi-image, and video scenarios. Importantly, the design of LLaVA-OneVision allows strong transfer learning across different modalities/scenarios, yielding new emerging capabilities. In particular, strong video understanding and cross-scenario capabilities are demonstrated through task transfer from images to videos.",
+  "Lip-syncing videos with given audio is the foundation for various applications including the creation of virtual presenters or performers. While recent studies explore high-fidelity lip-sync with different techniques, their task-orientated models either require long-term videos for clip-specific training or retain visible artifacts. In this paper, we propose a unified and effective framework ReSyncer, that synchronizes generalized audio-visual facial information. The key design is revisiting and rewiring the Style-based generator to efficiently adopt 3D facial dynamics predicted by a principled style-injected Transformer. By simply re-configuring the information insertion mechanisms within the noise and style space, our framework fuses motion and appearance with unified training. Extensive experiments demonstrate that ReSyncer not only produces high-fidelity lip-synced videos according to audio, but also supports multiple appealing properties that are suitable for creating virtual presenters and performers, including fast personalized fine-tuning, video-driven lip-syncing, the transfer of speaking styles, and even face swapping. Resources can be found at https://guanjz20.github.io/projects/ReSyncer.",
+  "Diffusion models continuously push the boundary of state-of-the-art image generation, but the process is hard to control with any nuance: practice proves that textual prompts are inadequate for accurately describing image style or fine structural details (such as faces). ControlNet and IPAdapter address this shortcoming by conditioning the generative process on imagery instead, but each individual instance is limited to modeling a single conditional posterior: for practical use-cases, where multiple different posteriors are desired within the same workflow, training and using multiple adapters is cumbersome. We propose IPAdapter-Instruct, which combines natural-image conditioning with ``Instruct'' prompts to swap between interpretations for the same conditioning image: style transfer, object extraction, both, or something else still? IPAdapterInstruct efficiently learns multiple tasks with minimal loss in quality compared to dedicated per-task models.",
+  "Enabling LLMs to improve their outputs by using more test-time computation is a critical step towards building generally self-improving agents that can operate on open-ended natural language. In this paper, we study the scaling of inference-time computation in LLMs, with a focus on answering the question: if an LLM is allowed to use a fixed but non-trivial amount of inference-time compute, how much can it improve its performance on a challenging prompt? Answering this question has implications not only on the achievable performance of LLMs, but also on the future of LLM pretraining and how one should tradeoff inference-time and pre-training compute. Despite its importance, little research attempted to understand the scaling behaviors of various test-time inference methods. Moreover, current work largely provides negative results for a number of these strategies. In this work, we analyze two primary mechanisms to scale test-time computation: (1) searching against dense, process-based verifier reward models; and (2) updating the model's distribution over a response adaptively, given the prompt at test time. We find that in both cases, the effectiveness of different approaches to scaling test-time compute critically varies depending on the difficulty of the prompt.",
+  "We find that in both cases, the effectiveness of different approaches to scaling test-time compute critically varies depending on the difficulty of the prompt. This observation motivates applying a \"compute-optimal\" scaling strategy, which acts to most effectively allocate test-time compute adaptively per prompt. Using this compute-optimal strategy, we can improve the efficiency of test-time compute scaling by more than 4x compared to a best-of-N baseline. Additionally, in a FLOPs-matched evaluation, we find that on problems where a smaller base model attains somewhat non-trivial success rates, test-time compute can be used to outperform a 14x larger model."
 ]

doclens.0.json

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

ivf.pid.pt

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:890a0aac45b32372e449cdf6726232e16bcc742f24dd47df72848deb37acaf36
-size 2111320
+oid sha256:a2dd70c526765363785bc18a19c7d31aac1e27b83e6bb72882f1c38f251e9b3d
+size 2118360

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 4660 elements starting with...",
+      "list with 4669 elements starting with...",
       [
         "Deep neural networks have demonstrated remarkable performance in supervised learning tasks but require large amounts of labeled data. Self-supervised learning offers an alternative paradigm, enabling the model to learn from data without explicit labels. Information theory has been instrumental in understanding and optimizing deep neural networks. Specifically, the information bottleneck principle has been applied to optimize the trade-off between compression and relevant information preservation in supervised settings. However, the optimal information objective in self-supervised learning remains unclear. In this paper, we review various approaches to self-supervised learning from an information-theoretic standpoint and present a unified framework that formalizes the self-supervised information-theoretic learning problem. We integrate existing research into a coherent framework, examine recent self-supervised methods, and identify research opportunities and challenges. Moreover, we discuss empirical measurement of information-theoretic quantities and their estimators. This paper offers a comprehensive review of the intersection between information theory, self-supervised learning, and deep neural networks.",
         "Pre-trained large language models (LLMs) capture procedural knowledge about the world. Recent work has leveraged LLM's ability to generate abstract plans to simplify challenging control tasks, either by action scoring, or action modeling (fine-tuning). However, the transformer architecture inherits several constraints that make it difficult for the LLM to directly serve as the agent: e.g. limited input lengths, fine-tuning inefficiency, bias from pre-training, and incompatibility with non-text environments. To maintain compatibility with a low-level trainable actor, we propose to instead use the knowledge in LLMs to simplify the control problem, rather than solving it. We propose the Plan, Eliminate, and Track (PET) framework. The Plan module translates a task description into a list of high-level sub-tasks. The Eliminate module masks out irrelevant objects and receptacles from the observation for the current sub-task. Finally, the Track module determines whether the agent has accomplished each sub-task. On the AlfWorld instruction following benchmark, the PET framework leads to a significant 15% improvement over SOTA for generalization to human goal specifications.",
@@ -50,7 +50,7 @@
     "root":".ragatouille/",
     "experiment":"colbert",
     "index_root":null,
-    "name":"2024-08/07/01.55.25",
+    "name":"2024-08/07/02.55.11",
     "rank":0,
     "nranks":1,
     "amp":true,
@@ -59,8 +59,8 @@
   },
   "num_chunks":1,
   "num_partitions":8192,
-  "num_embeddings":798746,
-  "avg_doclen":171.40472103,
+  "num_embeddings":800263,
+  "avg_doclen":171.399228957,
   "RAGatouille":{
     "index_config":{
       "index_type":"PLAID",

pid_docid_map.json

@@ -4658,5 +4658,14 @@
   "4656":"2408.02545",
   "4657":"2408.02600",
   "4658":"2408.02718",
-  "4659":"2408.02752"
+  "4659":"2408.02752",
+  "4660":"2408.03178",
+  "4661":"2408.02900",
+  "4662":"2408.02900",
+  "4663":"2408.03325",
+  "4664":"2408.03326",
+  "4665":"2408.03284",
+  "4666":"2408.03209",
+  "4667":"2408.03314",
+  "4668":"2408.03314"
 }

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 4660 elements starting with...",
+            "list with 4669 elements starting with...",
             [
                 "Deep neural networks have demonstrated remarkable performance in supervised learning tasks but require large amounts of labeled data. Self-supervised learning offers an alternative paradigm, enabling the model to learn from data without explicit labels. Information theory has been instrumental in understanding and optimizing deep neural networks. Specifically, the information bottleneck principle has been applied to optimize the trade-off between compression and relevant information preservation in supervised settings. However, the optimal information objective in self-supervised learning remains unclear. In this paper, we review various approaches to self-supervised learning from an information-theoretic standpoint and present a unified framework that formalizes the self-supervised information-theoretic learning problem. We integrate existing research into a coherent framework, examine recent self-supervised methods, and identify research opportunities and challenges. Moreover, we discuss empirical measurement of information-theoretic quantities and their estimators. This paper offers a comprehensive review of the intersection between information theory, self-supervised learning, and deep neural networks.",
                 "Pre-trained large language models (LLMs) capture procedural knowledge about the world. Recent work has leveraged LLM's ability to generate abstract plans to simplify challenging control tasks, either by action scoring, or action modeling (fine-tuning). However, the transformer architecture inherits several constraints that make it difficult for the LLM to directly serve as the agent: e.g. limited input lengths, fine-tuning inefficiency, bias from pre-training, and incompatibility with non-text environments. To maintain compatibility with a low-level trainable actor, we propose to instead use the knowledge in LLMs to simplify the control problem, rather than solving it. We propose the Plan, Eliminate, and Track (PET) framework. The Plan module translates a task description into a list of high-level sub-tasks. The Eliminate module masks out irrelevant objects and receptacles from the observation for the current sub-task. Finally, the Track module determines whether the agent has accomplished each sub-task. On the AlfWorld instruction following benchmark, the PET framework leads to a significant 15% improvement over SOTA for generalization to human goal specifications.",
@@ -50,7 +50,7 @@
         "root": ".ragatouille\/",
         "experiment": "colbert",
         "index_root": null,
-        "name": "2024-08\/07\/01.55.25",
+        "name": "2024-08\/07\/02.55.11",
         "rank": 0,
         "nranks": 1,
         "amp": true,
@@ -59,6 +59,6 @@
     },
     "num_chunks": 1,
     "num_partitions": 8192,
-    "num_embeddings_est": 798746.0144042969,
-    "avg_doclen_est": 171.40472412109375
+    "num_embeddings_est": 800263.0093383789,
+    "avg_doclen_est": 171.39923095703125
 }